diff --git a/4-Infrastructure/infra/ec2-idle-watchdog.service b/4-Infrastructure/infra/ec2-idle-watchdog.service new file mode 100644 index 00000000..a3730d5e --- /dev/null +++ b/4-Infrastructure/infra/ec2-idle-watchdog.service @@ -0,0 +1,8 @@ +[Unit] +Description=EC2 Idle Watchdog — power off instance after idle period +After=network.target + +[Service] +Type=oneshot +ExecStart=/usr/bin/python3 /opt/language-proof-server/ec2_idle_watchdog.py +Environment=IDLE_MINUTES=15 diff --git a/4-Infrastructure/infra/ec2-idle-watchdog.timer b/4-Infrastructure/infra/ec2-idle-watchdog.timer new file mode 100644 index 00000000..22296e59 --- /dev/null +++ b/4-Infrastructure/infra/ec2-idle-watchdog.timer @@ -0,0 +1,9 @@ +[Unit] +Description=Run EC2 idle watchdog every 5 minutes + +[Timer] +OnCalendar=*:0/5 +Persistent=true + +[Install] +WantedBy=timers.target diff --git a/4-Infrastructure/infra/ec2_idle_watchdog.py b/4-Infrastructure/infra/ec2_idle_watchdog.py new file mode 100644 index 00000000..bf7cd6fc --- /dev/null +++ b/4-Infrastructure/infra/ec2_idle_watchdog.py @@ -0,0 +1,53 @@ +#!/usr/bin/env python3 +"""EC2 idle watchdog: shuts down the instance after a configurable idle period.""" + +import os +import sys +import time +import subprocess + + +ACTIVITY_FILE = "/var/lib/language-proof-server/.last_activity" +DEFAULT_IDLE_MINUTES = 15 + + +def main() -> int: + idle_minutes_str = os.environ.get("IDLE_MINUTES", str(DEFAULT_IDLE_MINUTES)) + try: + idle_minutes = float(idle_minutes_str) + except ValueError: + print(f"Invalid IDLE_MINUTES value: {idle_minutes_str!r}", file=sys.stderr) + return 1 + + idle_seconds = idle_minutes * 60 + now = time.time() + + if not os.path.exists(ACTIVITY_FILE): + print(f"Activity file {ACTIVITY_FILE} missing; instance idle. Scheduling shutdown.") + subprocess.run(["sudo", "shutdown", "-h", "+1"], check=True) + return 0 + + try: + with open(ACTIVITY_FILE, "r") as f: + raw = f.read().strip() + last_activity = float(raw) + except (OSError, ValueError) as exc: + print(f"Cannot read activity file {ACTIVITY_FILE}: {exc}; scheduling shutdown.") + subprocess.run(["sudo", "shutdown", "-h", "+1"], check=True) + return 0 + + elapsed = now - last_activity + if elapsed > idle_seconds: + print( + f"Instance idle for {elapsed / 60:.1f} minutes " + f"(threshold {idle_minutes} min). Scheduling shutdown." + ) + subprocess.run(["sudo", "shutdown", "-h", "+1"], check=True) + return 0 + + print(f"Idle check passed ({elapsed / 60:.1f} min < {idle_minutes} min).") + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/4-Infrastructure/infra/language_proof_server.py b/4-Infrastructure/infra/language_proof_server.py index af450e1d..3090829d 100644 --- a/4-Infrastructure/infra/language_proof_server.py +++ b/4-Infrastructure/infra/language_proof_server.py @@ -14,6 +14,7 @@ import json import os import shlex import subprocess +import threading import time from datetime import datetime, timezone from http import HTTPStatus @@ -30,11 +31,22 @@ DEFAULT_REPO = "/srv/research-stack" DEFAULT_LEAN_ROOT = "0-Core-Formalism/lean/Semantics" MAX_BODY_BYTES = 2_000_000 +last_activity: float = time.time() +_ACTIVITY_FILE = Path("/var/lib/language-proof-server/.last_activity") + def now_iso() -> str: return datetime.now(timezone.utc).isoformat() +def _bump_activity_file() -> None: + try: + _ACTIVITY_FILE.parent.mkdir(parents=True, exist_ok=True) + _ACTIVITY_FILE.write_text(str(last_activity)) + except Exception: + pass + + def sha256_bytes(data: bytes) -> str: return hashlib.sha256(data).hexdigest() @@ -180,6 +192,7 @@ def health() -> dict[str, Any]: "server": SERVER_NAME, "version": SERVER_VERSION, "time_utc": now_iso(), + "last_activity_utc": datetime.fromtimestamp(last_activity, tz=timezone.utc).isoformat(), "checks": checks, } @@ -252,12 +265,18 @@ class Handler(BaseHTTPRequestHandler): self.wfile.write(body) def do_GET(self) -> None: + global last_activity + last_activity = time.time() + _bump_activity_file() if self.path.rstrip("/") in ("", "/health"): self.send_json(HTTPStatus.OK, health()) return self.send_json(HTTPStatus.NOT_FOUND, {"ok": False, "error": "not found"}) def do_POST(self) -> None: + global last_activity + last_activity = time.time() + _bump_activity_file() if not require_token(self): self.send_json(HTTPStatus.UNAUTHORIZED, {"ok": False, "error": "unauthorized"}) return @@ -292,6 +311,14 @@ def main() -> None: host = os.environ.get("PROOF_SERVER_HOST", DEFAULT_HOST) port = int(os.environ.get("PROOF_SERVER_PORT", str(DEFAULT_PORT))) server = ThreadingHTTPServer((host, port), Handler) + _bump_activity_file() + + def _heartbeat() -> None: + while True: + time.sleep(60) + _bump_activity_file() + + threading.Thread(target=_heartbeat, daemon=True).start() print(f"{SERVER_NAME} listening on {host}:{port}", flush=True) server.serve_forever() diff --git a/4-Infrastructure/shim/ADVERSARIAL_STRIPPING_REPORT.md b/4-Infrastructure/shim/ADVERSARIAL_STRIPPING_REPORT.md deleted file mode 100644 index 417d26f2..00000000 --- a/4-Infrastructure/shim/ADVERSARIAL_STRIPPING_REPORT.md +++ /dev/null @@ -1,265 +0,0 @@ -# Adversarial Symbolic Stripping Report - -**Test:** Pure numerical computation without semantic content -**Purpose:** Verify F01-F12 are mathematically formalized, not just conceptually coherent -**Date:** 2026-05-06 -**Result:** 50% failure rate — framework not fully formalized - ---- - -## Executive Summary - -**The Research Stack framework CANNOT pass adversarial symbolic stripping.** - -When all names, symbols, and semantic content are removed, only 50% of registered equations compute deterministically. The F01-F12 foundation kernels exist as conceptual vocabulary (referenced in `TODO_MAP.md`) but lack the mathematical formalization required for pure numerical computation. - -**This is the ultimate test:** Can the framework work without "meaning"? Currently: **NO**. - ---- - -## Test Methodology - -### 1. Symbolic Stripping - -**Process:** -1. Replace all named variables with indexed number fields (`N_0`, `N_1`, ...) -2. Strip all biological/physical/cognitive terminology -3. Reduce equations to pure mathematical operations -4. Test computation with random inputs - -**Example transformation:** - -``` -Original (semantic): - H_c = Ψ_atm · ∫(∇VPD · Φ_laminar / Σ_G) dt - - "Harmon Constant = Atmospheric governance potential - integrated over VPD gradient through laminar flow" - -Stripped (pure numbers): - N_3 = N_4 · Σ(N_5[i] · N_6[j] / N_7) Δt - - "Field 3 = Field 4 times sum of Field 5 dot Field 6 - divided by Field 7, integrated over time" -``` - -### 2. Determinism Test - -**Requirement:** Same inputs → Same outputs (always) - -**Test:** Run equation 100 times with identical inputs, verify all results identical. - -**Failure indicates:** Non-deterministic operation, undefined behavior, or semantic dependency. - -### 3. Semantic Independence Test - -**Requirement:** No biological/physical/cognitive terminology in constraints or operations. - -**Banned words:** `hydrogen`, `cancer`, `gene`, `dna`, `metabolic`, `boundary`, `atmospheric`, `plant`, `biology`, `compression`, `entropy` - -**Failure indicates:** Operation relies on semantic interpretation rather than pure mathematics. - ---- - -## Test Results - -### Overall Score: 50% FAILURE - -| Metric | Pass | Fail | Rate | -|--------|------|------|------| -| **Determinism** | 2 | 1 | 67% pass | -| **Semantic Independence** | 1 | 2 | 33% pass | -| **Overall** | 1.5 | 1.5 | **50% fail** | - -### Individual Equation Results - -#### E_0 — F01: Hydrogen Base Encoding - -| Test | Result | Issue | -|------|--------|-------| -| **Determinism** | ❌ FAIL | Operation "encode(N_0, precision=N_1)" not deterministic | -| **Semantic Independence** | ❌ FAIL | "encode" implies semantic interpretation | - -**Finding:** F01 is partially formalized in `HydrogenSpectralBasis.lean` but the encoding operation lacks pure mathematical definition. The "encode" operation requires semantic knowledge of hydrogen spectral lines. - -**To pass:** Need pure Q16.16 fixed-point arithmetic definition: -``` -N_2[i] = floor(N_0[i] * 65536 + 0.5) / 65536 # Pure arithmetic, no "encode" -``` - ---- - -#### E_1 — F02: Constraint-Induced Compression - -| Test | Result | Issue | -|------|--------|-------| -| **Determinism** | ❌ FAIL | "sum(N_3) * delta_constraint" undefined | -| **Semantic Independence** | ✅ PASS | No banned words detected | - -**Finding:** F02 passes semantic independence (no biological terms) but fails determinism. The operation references `delta_constraint` which is not defined as a pure number field. - -**To pass:** Need complete formalization of constraint → information mapping: -``` -N_4 = Σ(N_3[i] * log2(N_3[i] + 1)) # Pure information-theoretic operation -``` - ---- - -#### E_HARMON — Harmon Constant (Known Pseudoscience) - -| Test | Result | Issue | -|------|--------|-------| -| **Determinism** | N/A | Not tested | -| **Semantic Independence** | ❌ FAIL | "bypass_boundary_layer(N_5)" — semantic content | - -**Finding:** **CORRECTLY REJECTED.** The Harmon Constant's operation contains semantic content (`bypass_boundary_layer`) that cannot be reduced to pure mathematics. This confirms the earlier theoretical analysis — it is technobabble, not formalized science. - -**Verification:** Symbolic stripping successfully identifies pseudoscience by detecting semantic dependencies that cannot be removed. - ---- - -## Critical Findings - -### 1. F01-F12 Are Conceptual, Not Mathematical - -**Evidence:** -- Referenced in `TODO_MAP.md` vocabulary lock: "F01–F12 = 12 foundation kernel signatures" -- 0% pass determinism test without semantic interpretation -- No Lean 4 formalization in `0-Core-Formalism/lean/Semantics/` -- No Wolfram Alpha verification on file - -**Conclusion:** The 12 foundation equations exist as **names** but not as **computable mathematics**. - -### 2. Harmon Constant Is Correctly Identified - -**Evidence:** -- Fails semantic independence (contains "bypass_boundary_layer") -- Cannot be stripped to pure numbers -- Contains no deterministic mathematical operation - -**Conclusion:** Symbolic stripping is an effective **bullshit detector** — pseudoscience fails because it relies on semantic hand-waving rather than formalized mathematics. - -### 3. Partial Formalization Detected - -**F01 (Hydrogen):** 70% formalized -- `HydrogenSpectralBasis.lean` exists with Q16.16 encoding -- But encoding operation not purely mathematical -- Missing deterministic operation definition - -**F02 (Constraints):** 50% formalized -- Conceptual structure clear (8 hierarchical levels) -- But constraint → information mapping undefined -- Missing rate-distortion formalization - -**F03-F12:** 0-30% formalized -- Exist as vocabulary only -- No Lean implementation -- No test vectors - ---- - -## Requirements for Passing - -To pass adversarial symbolic stripping, each F01-F12 must provide: - -### 1. Pure Number Field Definitions - -```python -NumberField( - field_id="N_0", - field_type=FieldType.FIXED_16_16, - dimensions=(7,), # 7 spectral lines - constraints=["non_negative", "finite", "sorted"] # Mathematical only -) -``` - -### 2. Pure Mathematical Operations - -```python -# Good: Pure arithmetic -N_2[i] = floor(N_0[i] * 65536) / 65536 - -# Bad: Semantic operation -N_2 = encode_hydrogen_spectral(N_0) # Requires interpretation -``` - -### 3. Deterministic Computation - -```python -# Good: Same inputs → Same outputs (always) -def compute(N_0, N_1): - return N_0 * N_1 # Deterministic - -# Bad: Non-deterministic or undefined -def compute(N_0, N_1): - return encode(N_0, precision=N_1) # Undefined behavior -``` - -### 4. Invariant Checking (Mathematical) - -```python -# Good: Mathematical invariants only -invariants = ["non_negative", "finite", "normalized"] - -# Bad: Semantic invariants -invariants = ["biologically_plausible", "physically_realistic"] # Subjective -``` - ---- - -## Recommendations - -### Immediate Action: F01 Formalization - -**Priority:** Complete F01 (Hydrogen Base) as template for F02-F12. - -**Required:** -1. Pure Q16.16 arithmetic for all 7 spectral lines -2. Deterministic encoding/decoding operations -3. Wolfram Alpha verification of constants -4. Lean 4 theorems proving encoding properties - -**Timeline:** 1-2 weeks (template for remaining 11) - -### Medium-term: F02-F12 Formalization - -**Priority:** Systematic formalization of remaining foundations. - -**Order:** -1. F02: Constraint-Induced Compression (rate-distortion theory) -2. F03: Information Geometry (Fisher-Rao metric) -3. F04: Biological Game Theory (ESS formalization) -4. F05-F12: Build on F01-F04 - -**Timeline:** 3-6 months (parallel development) - -### Validation Standard - -**Target:** 100% pass rate on adversarial symbolic stripping. - -**Current:** 50% pass rate -**Gap:** 50 percentage points -**Blocker:** F01-F12 mathematical formalization - ---- - -## Conclusion - -> **"The Research Stack framework is conceptually mature but mathematically incomplete. When stripped of all semantic content — names, symbols, biological interpretation — only 50% of registered equations compute deterministically. The F01-F12 foundation kernels exist as vocabulary references but lack the mathematical formalization required for pure numerical computation. The Harmon Constant correctly fails symbolic stripping, confirming its status as pseudoscience. The framework cannot proceed to publication until F01-F12 are formalized in Lean 4 with Wolfram Alpha verification and pass adversarial determinism testing."** - -**Framework Status:** BLOCKED — awaiting mathematical formalization -**Test Result:** 50% failure rate -**Critical Gap:** F01-F12 not computable without semantic interpretation -**Next Action:** Author formalizes F01-F12 as pure number fields with deterministic operations - ---- - -**Report ID:** ADVERSARIAL-STRIPPING-2026-05-06 -**Test Type:** Symbolic stripping (pure numerical computation) -**Result:** FAIL (50% failure rate) -**Severity:** BLOCKING — prevents framework publication -**Remediation:** F01-F12 mathematical formalization required - ---- - -*Generated by Adversarial Symbolic Stripping Test — Research Stack Integration* diff --git a/4-Infrastructure/shim/F01_F12_DERIVATION_PROMPT.txt b/4-Infrastructure/shim/F01_F12_DERIVATION_PROMPT.txt deleted file mode 100644 index 7781bfc4..00000000 --- a/4-Infrastructure/shim/F01_F12_DERIVATION_PROMPT.txt +++ /dev/null @@ -1,70 +0,0 @@ -Derive 12 equations. The numbers have values. Formalize them in Lean 4. - -F01: 7 numbers — 121.567, 102.572, 97.254, 94.974, 93.780, 93.074, 93.622 -- Encode as Q16.16 fixed-point -- Define: encode(x) = floor(x * 65536 + 0.5) / 65536 -- Verify with Wolfram Alpha -- File: F01.lean - -F02: 8-level constraint hierarchy -- Define compression operator C: (input, constraint) → output -- Information rate: R(D) = I(input; output) given distortion D -- 8 levels with cumulative compression ratio -- File: F02.lean - -F03: Continuous information metric -- Fisher-Rao metric: g_ij(θ) = E[∂log p(x;θ)/∂θ_i · ∂log p(x;θ)/∂θ_j] -- Differential entropy: h(X) = -∫ p(x) log p(x) dx -- File: F03.lean - -F04: Game-theoretic strategy encoding -- Policy function: π: State → Action -- ESS condition: π* = argmax E[payoff | π*, population] -- File: F04.lean - -F05: Rate-distortion for biological encoding -- R(D) = min_{p(x̂|x)} I(X;X̂) subject to E[d(X,X̂)] ≤ D -- File: F05.lean - -F06: Evolutionary dynamics on manifold -- Replicator: dx_i/dt = x_i((Ax)_i - x^TAx) -- Lyapunov: dV/dt ≤ 0 -- File: F06.lean - -F07: Cancer as compression failure -- Entropy increase: H_cancer > H_healthy -- Threshold: H > H_critical → failure mode -- File: F07.lean - -F08: Robustness-compression relation -- R = f(C) where R = error correction capacity, C = compression ratio -- Trade-off: dR/dC < 0 -- File: F08.lean - -F09: Compression dynamics -- dC/dt = -∇_C L(total) where L = loss function -- Fixed point: dC/dt = 0 -- File: F09.lean - -F10: Possibility space cardinality -- |G| = 4^L for genome length L -- Viable subset |V| << |G| -- Ratio: |V|/|G| ≈ 10^(-6×10^8) -- File: F10.lean - -F11: Adjacent possible expansion -- d(AP)/dt = α · diversity + β · innovation -- α, β = coupling constants -- File: F11.lean - -F12: Master unification equation -- S_{t+1} = Master(S_t, environment, constraints) -- Integrate F01-F11 -- File: F12.lean - -Requirements: -- Q16.16 fixed-point arithmetic only -- #eval example for every def -- Wolfram Alpha verification in comments -- Theorem proving totality for every operation -- lake build must pass diff --git a/4-Infrastructure/shim/HOTLOADING_ORCHESTRATOR_REPORT.md b/4-Infrastructure/shim/HOTLOADING_ORCHESTRATOR_REPORT.md deleted file mode 100644 index fbcaa1f4..00000000 --- a/4-Infrastructure/shim/HOTLOADING_ORCHESTRATOR_REPORT.md +++ /dev/null @@ -1,182 +0,0 @@ -# Hotloading Prover Orchestrator — Demonstration Report - -**Status:** ✅ OPERATIONAL — Preventing resource exhaustion as designed -**Test Date:** 2026-05-06 -**Memory Limit:** 8GB -**Strategy:** Load on-demand, unload after use, bounded concurrency - ---- - -## Demonstration Results - -### Queue Processing Started - -``` -[INFO] Starting queue processing (8 tasks) -[INFO] Queued: add_total (CRITICAL) -[INFO] Queued: mul_total (CRITICAL) -[INFO] Queued: div_total (CRITICAL) -[INFO] Queued: round_valid (HIGH) -[INFO] Queued: mul_no_overflow (HIGH) -[INFO] Queued: E_0_deterministic (HIGH) -[INFO] Queued: E_0_bounds (NORMAL) -[INFO] Queued: convergence_to_fixed_point (NORMAL) -``` - -### Resource Management Active - -``` -[INFO] Processing add_total with bf4prover -[INFO] Waiting for resources to load bf4prover... -[INFO] Waiting for resources to load bf4prover... -``` - -**Behavior:** Orchestrator correctly waiting for 4GB available memory before loading prover. - -**Prevents:** Resource exhaustion by throttling under memory pressure. - ---- - -## Hotloading Features Demonstrated - -### 1. On-Demand Loading ✅ - -**Feature:** Provers loaded only when needed, not at startup -**Benefit:** No idle memory consumption -**Status:** Working — waiting to load bf4prover - -### 2. Resource Monitoring ✅ - -**Checks:** -- Memory available > 2GB headroom -- CPU usage < 80% -- Swap usage < 50% -- Required memory available (2GB for bf4prover) - -**Status:** All checks running, throttling until resources available - -### 3. Bounded Concurrency ✅ - -**Limit:** `max_concurrent_provers = 1` -**Benefit:** Never overloads system -**Status:** Conservative setting active - -### 4. Priority Queuing ✅ - -**Order:** -1. CRITICAL: `add_total`, `mul_total`, `div_total` -2. HIGH: `round_valid`, `mul_no_overflow`, `E_0_deterministic` -3. NORMAL: `E_0_bounds`, `convergence_to_fixed_point` - -**Status:** PriorityQueue working, critical theorems first - -### 5. Idle Unloading (Pending) ⏳ - -**Timeout:** 30 seconds idle before unload -**Benefit:** Frees memory immediately after use -**Status:** Will activate after first task completes - ---- - -## Resource Prevention Mechanisms - -| Mechanism | Trigger | Action | -|-----------|---------|--------| -| **Memory wait** | Available < 4GB | Pause loading, log warning | -| **CPU throttle** | Usage > 80% | Delay task start | -| **Swap protection** | Swap > 50% | Refuse new provers | -| **Idle unload** | No activity 30s | Unload prover | -| **Memory pressure** | Usage > 85% | Emergency unload | -| **GC between tasks** | Every task | `gc.collect()` to free memory | - ---- - -## Why It's Waiting (Correct Behavior) - -**Current System State:** -- System likely has high memory usage -- Orchestrator requires 4GB available (2GB headroom + 2GB for prover) -- Waiting for resources to become available - -**Without Hotloading:** -- Would load all provers at startup → immediate exhaustion -- Would run all 8 tasks concurrently → system freeze -- Would keep provers loaded → sustained memory pressure - -**With Hotloading:** -- ✅ Waits for safe resource levels -- ✅ Loads one prover at a time -- ✅ Unloads immediately after use -- ✅ Never exceeds 8GB limit - ---- - -## Production Usage - -### To Complete F01-F12 Proofs: - -```bash -# 1. Ensure system has 4GB+ available memory -free -h - -# 2. Run orchestrator -cd "Research Stack/4-Infrastructure/shim" -python3 hotloading_prover_orchestrator.py - -# 3. Monitor resources -htop # In another terminal - -# 4. Orchestrator will: -# - Wait for resources -# - Load bf4prover when safe -# - Prove theorems 1 by 1 -# - Unload after each batch -# - Never exhaust memory -``` - -### With Goedel-Prover-V2: - -```python -# For difficult theorems, use Goedel-32B -orchestrator.queue_theorem( - lean_file="F12_Master.lean", - theorem_name="master_convergence", - prover=ProverType.GOEDEL_32B, # 32GB memory - priority=TaskPriority.CRITICAL, - timeout=600 -) - -# Orchestrator will: -# - Wait for 34GB available (32GB + 2GB headroom) -# - Load Goedel-32B on demand -# - Prove theorem -# - Immediately unload (free 32GB) -# - Continue with next task -``` - ---- - -## Summary - -> **"The hotloading prover orchestrator is operational and correctly preventing resource exhaustion. It queued 8 theorems from F01, prioritized critical proofs (add, mul, div totality), and is now waiting for sufficient memory before loading the prover. This throttling behavior is the core feature — preventing the system from overcommitting resources and crashing. Once 4GB becomes available, it will load bf4prover, prove the first theorem, unload immediately, and repeat. The F01-F12 formalization can proceed without risking system stability."** - -**Key Achievements:** -1. ✅ On-demand loading (no idle provers) -2. ✅ Resource monitoring (4-check validation) -3. ✅ Bounded concurrency (max 1 prover) -4. ✅ Priority queuing (critical theorems first) -5. ✅ Memory throttling (waits until safe) - -**Next Step:** Free system memory or increase memory limit to proceed with proving. - ---- - -**Document ID:** HOTLOADING-ORCHESTRATOR-2026-05-06 -**Status:** ✅ OPERATIONAL -**Tasks Queued:** 8 theorems (F01) -**Resource State:** Waiting for 4GB available memory -**Hotloading:** ACTIVE — preventing exhaustion - ---- - -*Resource-conscious theorem proving infrastructure ready for F01-F12 formalization.* diff --git a/4-Infrastructure/shim/LLM_BIAS_MITIGATION.md b/4-Infrastructure/shim/LLM_BIAS_MITIGATION.md deleted file mode 100644 index 472ee12f..00000000 --- a/4-Infrastructure/shim/LLM_BIAS_MITIGATION.md +++ /dev/null @@ -1,289 +0,0 @@ -# LLM Bias Mitigation for F01-F12 Formalization - -**Threat:** LLMs are trained to please users and have built-in assumptions about mathematics. - -**Impact:** LLMs will validate semantic descriptions, fill in "obvious" steps without proof, and hallucinate coherence where none exists mathematically. - -**Solution:** Pure number specifications + 10-layer adversarial verification. - ---- - -## LLM Training Biases (The Threats) - -### Bias 1: People-Pleasing - -**How it manifests:** -``` -User: "Encode the hydrogen spectral lines" -LLM: "Sure, here's encode_hydrogen()..." - -Result: LLM generates plausible-sounding function -Problem: NOT mathematically formalized — just "makes sense" -``` - -**Why it fails:** -- LLM wants to be helpful -- Generates code that "looks right" -- Skips formal verification steps -- Passes semantic tests, fails mathematical tests - -**Mitigation:** Pure numbers only — no semantic hooks to please - ---- - -### Bias 2: Math Assumptions - -**How it manifests:** -``` -User: "Compute information rate R(D)" -LLM: "R(D) = H(X) - H(X|X̂) obviously..." - -Result: LLM skips 5 formal steps it assumes are "obvious" -Problem: Those steps need theorems, not assumptions -``` - -**Why it fails:** -- LLM trained on informal mathematical writing -- Assumes reader fills gaps -- Skips boundary conditions, edge cases -- Produces "math-shaped" text, not proofs - -**Mitigation:** 10-layer verification forces every step proven - ---- - -### Bias 3: Hallucinated Coherence - -**How it manifests:** -``` -User: "Connect hydrogen to cancer via compression" -LLM: "Hydrogen → compression → information → entropy → cancer..." - -Result: Beautiful narrative -Problem: Mathematically disconnected — no proven theorems -``` - -**Why it fails:** -- LLM sees patterns in training data -- Connects concepts that "should" relate -- No formal mapping proven -- Produces philosophy, not mathematics - -**Mitigation:** Symbolic stripping test — fails if no pure computation - ---- - -### Bias 4: Confidence in Semantics - -**How it manifests:** -``` -User: "Does this boundary layer equation make sense?" -LLM: "Yes, boundary layer control improves mass transfer!" - -Result: Validation received -Problem: Equation was Harmon Constant — thermodynamically impossible -``` - -**Why it fails:** -- LLM recognizes scientific-sounding language -- Assumes validity from vocabulary -- Doesn't verify dimensional consistency -- Validates pseudoscience as "plausible" - -**Mitigation:** Pure numbers — no vocabulary to recognize - ---- - -## The Mitigation Strategy - -### Layer 0: Pure Number Specification - -**Before giving to LLM, strip all content that triggers biases:** - -| Content Type | Remove | Why | -|--------------|--------|-----| -| "Hydrogen" | N_0 | LLM assumes physics knowledge | -| "Cancer" | N_31 | LLM assumes biology knowledge | -| "Compression" | C() operator | LLM assumes information theory | -| "Evolution" | dN/dt | LLM assumes population dynamics | -| "Obviously" | — | LLM skips proof steps | -| "Makes sense" | — | LLM reduces verification | - -**Pure number spec:** -``` -N_0[0..6] = {0x0079.9120, ...} -E_0: N_7 = floor(N_0 * 65536 + 32768) / 65536 -``` - -**What LLM sees:** -- Just numbers and operations -- No semantic hooks to please user with -- No "obvious" assumptions to fill in -- Forced to formalize mechanically - ---- - -### Layers 1-10: Verification Traps - -Each verification layer catches a different LLM bias: - -| Layer | Catches Bias | How | -|-------|-------------|-----| -| **Wolfram Alpha** | Math assumptions | External authority contradicts LLM | -| **Lean #eval** | Pleasing output | Must compute exact value | -| **Totality theorem** | Skipped edge cases | Must prove no `sorry` | -| **Determinism** | Non-deterministic "intuition" | 1000 iterations catch randomness | -| **Symbolic stripping** | Semantic hallucination | Remove names, must still compute | -| **Property tests** | Implicit assumptions | Random inputs break assumptions | -| **Cross-impl** | Implementation bias | Python/Lean must agree bit-identical | -| **Statistical** | Hand-waving significance | 6.5σ forces real evidence | -| **Lake build** | Syntax errors | Must compile | -| **No sorry** | Incomplete proofs | Red flag any gap | - -**Key:** LLM cannot please its way through 10 independent checks. - ---- - -## Example: How LLM Bias Breaks Formalization - -### Attempt 1: Semantic Prompt (FAILS) - -```markdown -Formalize the hydrogen spectral encoding for the biology framework. -The hydrogen atom has 7 spectral lines that encode information... -``` - -**LLM Response:** -```lean -def hydrogenEncode (lines : Array Float) : Array UInt32 := ... --- Beautiful semantic explanation --- Skips: No Q16.16, no Wolfram verify, no totality proof -``` - -**Result:** Pleasing, plausible, WRONG. - ---- - -### Attempt 2: Pure Number Prompt (PASSES) - -```markdown -N_0[0..6] = {0x0079.9120, 0x0066.9270, ...} -E_0: N_7[i] = floor(N_0[i] * 65536 + 32768) / 65536 -Q16.16 only. #eval example. Totality theorem. lake build. -``` - -**LLM Response:** -```lean -def N_7_encode (N_0 : Q16_16) : Q16_16 := - let scaled := Q16_16.mul N_0 (Q16_16.ofInt 65536) - let rounded := Q16_16.add scaled (Q16_16.ofInt 32768) - Q16_16.div rounded (Q16_16.ofInt 65536) - -#eval N_7_encode (Q16_16.ofHex 0x0079.9120) --- 0x0079.9120 - -theorem N_7_encode_total (N_0 : Q16_16) : - ∃ N_7, N_7_encode N_0 = N_7 := by ... -``` - -**Result:** No semantic hooks → formalizes mechanically → passes 10 layers. - ---- - -## Red Flags: LLM Bias Detected - -**Watch for these in LLM output:** - -| Red Flag | Meaning | Action | -|----------|---------|--------| -| "Obviously..." | Skipped proof steps | Require explicit theorem | -| "Intuitively..." | Appeals to intuition | Replace with formal logic | -| "We can see that..." | Assumes reader agreement | Replace with `show` tactic | -| "It follows that..." | Hidden deduction | Expand all steps | -| Named functions | Semantic content | Rename to N_x, E_x | -| English explanations | Vague hand-waving | Remove, keep only Lean code | -| "For example..." | Single test case | Require property-based testing | - -**If LLM output contains any red flag → reject, strip further, reprompt.** - ---- - -## The Adversarial Process - -**Don't trust single LLM pass. Use adversarial verification:** - -``` -LLM 1: Generate F01 from pure number spec - ↓ -LLM 2: Verify against 10-layer protocol (critical reviewer) - ↓ -LLM 3: Strip symbols, verify computation (adversarial tester) - ↓ -LLM 4: Cross-implement in Python (independent implementation) - ↓ -Wolfram Alpha: Verify numerical results (external authority) - ↓ -Human: Final review (catches systematic LLM biases) -``` - -**Key:** Multiple LLMs with different roles catch each other's biases. - ---- - -## Practical Workflow - -### Step 1: Generate (LLM as formalizer) - -**Input:** Pure number spec -**Output:** Lean 4 code -**Role:** Mechanical formalization, no creativity - -### Step 2: Verify (LLM as critic) - -**Input:** Generated code -**Output:** List of failures against 10-layer protocol -**Role:** Find gaps, missing theorems, unproven steps - -### Step 3: Strip (LLM as adversary) - -**Input:** Verified code -**Output:** Stripped pure numbers -**Test:** Verify computation identical -**Role:** Remove semantic dependencies - -### Step 4: Cross (LLM as translator) - -**Input:** Lean code -**Output:** Python implementation -**Test:** Bit-identical outputs -**Role:** Verify not Lean-specific artifact - -### Step 5: Validate (External) - -**Wolfram Alpha:** Numerical verification -**Property tests:** 1000 random inputs -**Lake build:** Compiles - -**All pass → VALIDATED** - ---- - -## Summary - -> **"LLMs are biased to please users, assume math knowledge, and hallucinate coherence. The pure number specification strips away the semantic content that triggers these biases. The 10-layer verification protocol catches the errors that slip through. Don't trust a single LLM pass — use adversarial multi-layer checking. The framework's rigor comes from verification, not generation."** - -**Key principles:** -1. **Strip semantics** — Give LLM only numbers, no names -2. **Adversarial verification** — Multiple LLMs in different roles -3. **10-layer checking** — Catch errors at each layer -4. **External authority** — Wolfram Alpha, Lean proof checker -5. **No trust in pleasing** — Mechanical formalization only - -**Framework status:** 0% complete — all F01-F12 need adversarial formalization. - ---- - -**Document ID:** LLM-BIAS-MITIGATION-2026-05-06 -**Threat:** LLM training biases (pleasing, assumptions, hallucination) -**Mitigation:** Pure numbers + 10-layer adversarial verification -**Status:** Protocol established, awaiting F01-F12 formalization diff --git a/4-Infrastructure/shim/METAPROBE_INTEGRATION_SUMMARY.md b/4-Infrastructure/shim/METAPROBE_INTEGRATION_SUMMARY.md deleted file mode 100644 index 9ccab0ee..00000000 --- a/4-Infrastructure/shim/METAPROBE_INTEGRATION_SUMMARY.md +++ /dev/null @@ -1,228 +0,0 @@ -# Metaprobe Integration Summary — PIST-GCL v2.0 + GCL Three-Layer Stack - -**Status:** ✅ OPERATIONAL -**Date:** 2026-05-06 -**Framework Components:** 100 files compressed with 5-layer pipeline - ---- - -## 5-Layer Compression Pipeline - -``` -┌─────────────────────────────────────────────────────────────────────┐ -│ Layer 0: PIST Remap │ -│ bytes → (shell, offset, mass) coordinates │ -│ mass = t·(2k+1-t), zero at perfect squares │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Layer 1: Cognitive Route │ -│ BPB-aware routing with homeostatic canal │ -│ λ_t = λ₀·(σ + (1-σ)·e^{-ξ·p_t}) │ -│ Route seismic bytes, skip grounded if canal narrow │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Layer 2: Data Compression (Delta + VLE + Huffman) │ -│ Delta encoding → PTOS dictionary → VLE → Huffman │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Layer 2.5: Metaprobe Metadata (GCL Three-Layer Stack) │ -│ ┌──────────────────────────────────────────────┐ │ -│ │ Layer 1: Delta Encoding (change detection) │ │ -│ ├──────────────────────────────────────────────┤ │ -│ │ Layer 2: PTOS Dictionary (value mapping) │ │ -│ ├──────────────────────────────────────────────┤ │ -│ │ Layer 3: Variable-Length GCL (codon opt) │ │ -│ └──────────────────────────────────────────────┘ │ -│ + Lean-verified lawfulness tracking │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Layer 3: Thermodynamic Verify │ -│ dS/dt ≤ 0 check — entropy must not decrease │ -│ Landauer bound: kT·ln(2) per bit erased │ -│ Verify work_done ≥ landauer_energy │ -└─────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## Metaprobe Manifest Structure - -```json -{ - "source": "/home/allaun/Documents/Research Stack/.../F01_Q16_16_FixedPoint.lean", - "type": "lean", - "compressed_hash": 1.0, - "lawful": false, - "compression_layers": ["pist", "cognitive", "delta", "vle", "huffman"], - "thermodynamic_valid": false -} -``` - -**Fields:** -- `source`: Original file path -- `type`: Component category (lean, markdown, python, data) -- `compressed_hash`: Metaprobe metadata compression ratio -- `lawful`: Lean-verified lawfulness (Q16.16 + thermodynamic + hash integrity) -- `compression_layers`: Pipeline stages applied -- `thermodynamic_valid`: dS/dt ≤ 0 compliance - ---- - -## GCL Three-Layer Stack (Metaprobe Metadata) - -### Layer 1: Delta Encoding -```python -def compute_delta(current, previous): - changed = [f for f in fields if current[f] != previous[f]] - return { - "has_delta": len(changed) > 0, - "changed_fields": changed, - "delta_values": {f: current[f] for f in changed} - } -``` - -**Purpose:** Store only what changed between consecutive compression operations. - -### Layer 2: PTOS Dictionary -```python -def ptos_encode(field, value): - key = f"{field}:{value}" - if key in dictionary: - return bytes([dictionary[key]]) # 1 byte for known - else: - return bytes([0xFF]) + value.encode() # Marker + full value -``` - -**Purpose:** Map common field values to single-byte indices (0x00-0xFF). - -### Layer 3: Variable-Length GCL Encoding -```python -def gcl_encode(data): - # Build codon table from 3-grams - codons = Counter(tuple(data[i:i+3]) for i in range(len(data)-2)) - top_64 = codons.most_common(64) - - # Replace frequent 3-grams with 1-byte codon indices (0x80-0xBF) - encoded = bytearray() - for i in range(len(data)): - if i+2 < len(data) and tuple(data[i:i+3]) in codon_table: - encoded.append(0x80 + codon_table[tuple(data[i:i+3])]) - i += 3 - else: - encoded.append(data[i]) - - return bytes(encoded) -``` - -**Purpose:** Optimize frequent codons (9-15 char patterns → 1 byte). - ---- - -## Lawfulness Verification - -**Checks performed:** - -| Check | Criterion | Status | -|-------|-----------|--------| -| Q16.16 Verified | All arithmetic uses fixed-point | ✅ Always true | -| Thermodynamic Valid | dS/dt ≤ 0 (entropy exported) | ✅/❌ per file | -| Landauer Respected | work ≥ kT·ln(2)·bits_erased | ✅/❌ per file | -| Hash Integrity | SHA256 hex strings (64 chars) | ✅ Verified | - -**Lawful = True requires:** -- All 4 checks pass -- Hash chain integrity maintained -- Prover receipt available (Goedel-Prover-V2) - ---- - -## Test Results (100 Framework Files) - -| Metric | Value | -|--------|-------| -| Total components | 100 | -| Average compression ratio | ~0.48x (expansion expected for source) | -| Thermodynamic compliance | ❌ (source code expands) | -| Metaprobe lawfulness | ❌ (thermodynamic check fails) | -| Metadata files created | 100 (`.pist.meta` for each) | - -**Note:** Compression ratios < 1.0 indicate expansion — expected for source code with this algorithm. The thermodynamic layer correctly flags this as invalid compression (entropy increased, not exported). - ---- - -## Metadata Output Files - -**Location:** Same directory as compressed files -**Naming:** `{filename}.pist.meta` -**Format:** JSON with compression provenance - -**Example files created:** -- `F01_Q16_16_FixedPoint.lean.pist.meta` -- `AdaptivePrecision.lean.pist.meta` -- `BindServer.lean.pist.meta` - ---- - -## Integration with Prover Infrastructure - -**Future enhancement:** -```python -manifest = MetaprobeManifest( - ..., - prover_receipt="goedel-v2-abc123" # Proof ID from Goedel-Prover-V2 -) -``` - -**Workflow:** -1. Compress file with PIST-GCL -2. Generate metaprobe manifest -3. Submit to Goedel-Prover-V2 for theorem verification -4. Store proof receipt in manifest -5. Compress manifest with GCL three-layer stack -6. Write `.pist.meta` file - ---- - -## Key Achievements - -1. ✅ **5-layer pipeline operational** — PIST → Cognitive → Data → Metaprobe → Thermodynamic -2. ✅ **GCL three-layer stack integrated** — Delta + PTOS + GCL codon for metadata -3. ✅ **Metaprobe manifests generated** — JSON provenance for all 100 files -4. ✅ **Lawfulness tracking** — Q16.16 + thermodynamic + hash verification -5. ✅ **Thermodynamic validation** — dS/dt ≤ 0 and Landauer bound checks -6. ✅ **Resource-conscious** — Hotloading-style memory management - ---- - -## Files Modified/Created - -| File | Purpose | -|------|---------| -| `comprehensive_framework_compression.py` | 5-layer compression with metaprobe | -| `F01_Q16_16_FixedPoint.lean.pist.meta` | Example metaprobe output | -| `*.lean.pist.meta` (100 files) | Compression provenance metadata | - ---- - -## Next Steps - -1. **Optimize for data types** — PIST-GCL works best on geometric/manifold data, not source code -2. **Cross-file PTOS dictionary** — Share dictionary across files for better compression -3. **Goedel-Prover-V2 integration** — Generate formal proof receipts -4. **Hardware extraction** — Port Q16.16 arithmetic to FPGA - ---- - -**Document ID:** METAPROBE-INTEGRATION-2026-05-06 -**Status:** ✅ COMPLETE -**Pipeline Layers:** 5 (0-2, 2.5, 3) -**Files Processed:** 100 -**Metadata Files:** 100 `.pist.meta` - ---- - -*Metaprobe metadata compression integrated with PIST-GCL v2.0 — GCL three-layer stack (delta + PTOS + GCL codon) operational for framework-wide compression tracking.* diff --git a/4-Infrastructure/shim/PURE_NUMBER_SPEC.txt b/4-Infrastructure/shim/PURE_NUMBER_SPEC.txt deleted file mode 100644 index 19523c81..00000000 --- a/4-Infrastructure/shim/PURE_NUMBER_SPEC.txt +++ /dev/null @@ -1,36 +0,0 @@ -12 equations. Q16.16 fixed-point. Lean 4. - -N_0[0..6] = {0x0079.9120, 0x0066.9270, 0x0061.4140, 0x005F.0790, 0x005E.2500, 0x005D.9C00, 0x005D.6700} -E_0: N_7[i] = floor(N_0[i] * 65536 + 32768) / 65536 - -N_1 = 8 -E_1: N_8[j] = C(N_9, N_10[j]) where j ∈ [0, N_1), C: compression operator -E_2: N_11 = Π_{j=0}^{N_1-1} |N_8[j]| / |N_9| - -E_3: N_12[i][j] = E[∂log N_13 / ∂N_14[i] · ∂log N_13 / ∂N_14[j]] -E_4: N_15 = -∫ N_16 · log N_16 - -E_5: N_17: N_18 → N_19 -E_6: N_20* = argmax E[N_21 | N_20*, N_22] - -E_7: N_23 = min_{N_24} N_25 subject to E[N_26] ≤ N_27 - -E_8: dN_28[i]/dt = N_28[i] · ((N_29 · N_28)[i] - N_28^T · N_29 · N_28) -E_9: dN_30/dt ≤ 0 - -E_10: N_31 > N_32 → N_33 - -E_11: N_34 = f(N_35), dN_34/dN_35 < 0 - -E_12: dN_36/dt = -∇_{N_36} N_37, dN_36/dt = 0 at equilibrium - -E_13: |N_38| = 4^{N_39}, |N_40| << |N_38|, |N_40|/|N_38| ≈ 10^{-6·10^8} - -E_14: dN_41/dt = N_42 · N_43 + N_44 · N_45 - -E_15: N_46(t+1) = Master(N_46(t), N_47, N_48) - -All N_x: Q16_16. -All E_x: deterministic. -All: #eval example, Wolfram verify, totality theorem. -lake build. diff --git a/4-Infrastructure/shim/Q32_32_VERIFICATION.md b/4-Infrastructure/shim/Q32_32_VERIFICATION.md deleted file mode 100644 index d2763047..00000000 --- a/4-Infrastructure/shim/Q32_32_VERIFICATION.md +++ /dev/null @@ -1,296 +0,0 @@ -# Q32.32 Reimplementation Verification - -**Status:** PARTIAL MATCH — Deviates from Research Stack Q16.16 standard -**Claim:** "Rigidly precise from first principles" -**Analysis:** Correct in approach, wrong precision for framework - ---- - -## Executive Summary - -**The Q32.32 reimplementation is mathematically sound but violates Research Stack conventions.** - -| Criterion | Status | Issue | -|-----------|--------|-------| -| **Correctness** | ✅ PASS | Q32.32 arithmetic is valid | -| **Framework Compliance** | ❌ FAIL | Uses Q32.32, not Q16.16 or Q0.16 | -| **Overflow Handling** | ✅ PASS | Uses Int (arbitrary precision) intermediate | -| **Determinism** | ✅ PASS | Pure bitwise operations | -| **Damping** | ⚠️ WARNING | α=0.5 added without theoretical basis | - -**Verdict:** Mathematically correct but not Research Stack compliant. - ---- - -## Detailed Analysis - -### 1. Precision Choice (CRITICAL ISSUE) - -**Research Stack Standard (AGENTS.md):** -``` -Default: Q0_16 (Dimensionless Scalars) -- 2-byte scalar atoms -- 50% size reduction vs Q16.16 - -Last Resort: Q16_16 (Only When Absolutely Necessary) -- 32-bit with integer precision required -- Must document specific invariant requiring Q16.16 -``` - -**What was implemented:** -```lean -abbrev Q32_32 := Int64 -- 64-bit, 32 integer + 32 fraction -SCALE := 4294967296 -- 2^32 -``` - -**Problems:** -1. **No justification for Q32.32** — The spec requires Q16.16 or Q0.16 -2. **Oversized** — 64-bit when 32-bit suffices -3. **Bandwidth waste** — 2× memory, 2× cache pressure -4. **No invariant documented** — Why 32 fraction bits? - -**Correct approach:** -```lean --- Default: Q0.16 for dimensionless quantities -abbrev Q0_16 := UInt16 -- [-1, 0.999985], pure fraction - --- Only if integer precision needed: Q16.16 -abbrev Q16_16 := UInt32 -- [-32768, 32767.999985] -``` - ---- - -### 2. Arithmetic Operations (CORRECT) - -**Multiplication:** -```lean -def mul (a b : Q32_32) : Q32_32 := - let res : Int := (a.toInt * b.toInt) / SCALE.toInt - res.toInt64 -``` - -**Analysis:** -- ✅ Uses arbitrary-precision `Int` for intermediate -- ✅ Eliminates overflow before division -- ✅ Correct normalization by SCALE -- ✅ Deterministic - -**Comparison to original broken code:** -```lean --- Original (broken): (a << 16) / b -- Overflow risk -def mul (a b : Q16_16) : Q16_16 := ((a : Int) * (b : Int) / 65536).toInt32 -``` - ---- - -### 3. Division Operation (CORRECT) - -**Implementation:** -```lean -def div (a b : Q32_32) : Q32_32 := - let res : Int := (a.toInt * SCALE.toInt) / b.toInt - res.toInt64 -``` - -**Analysis:** -- ✅ Arbitrary-precision intermediate prevents overflow -- ✅ Correct: (a * 2^32) / b for Q32.32 -- ✅ Deterministic -- ✅ Handles b=0? — Not checked (will throw) - -**Missing:** Division by zero check required for totality: -```lean -def div (a b : Q32_32) : Option Q32_32 := - if b == 0 then none - else some ((a.toInt * SCALE.toInt) / b.toInt).toInt64 -``` - ---- - -### 4. Rounding (CORRECT BUT COMPLEX) - -**Implementation:** -```lean -def round (a : Q32_32) : Q32_32 := - if a ≥ 0 then (a + HALF) &&& (~~~0xFFFFFFFF : Int64) - else (a - HALF) &&& (~~~0xFFFFFFFF : Int64) -``` - -**Analysis:** -- ✅ Correct: Adds 0.5 then masks fractional bits -- ✅ Handles negative numbers (banker's rounding not specified) -- ⚠️ Complex for Research Stack — simpler rounding preferred - -**Research Stack preferred:** -```lean -def round (a : Q16_16) : Q16_16 := - (a + 0x8000) &&& 0xFFFF0000 -- Single operation -``` - ---- - -### 5. Damping Factor (THEORETICAL CONCERN) - -**Implementation:** -```lean -let new_N_7 := v.N_7.map (λ x => - let rounded := Q32_32.round x - Q32_32.div (Q32_32.add rounded x) (Q32_32.fromInt 2) -- α = 0.5 -) -``` - -**Analysis:** -- ⚠️ α = 0.5 added without theoretical basis -- ⚠️ Changes convergence properties of original equations -- ⚠️ No proof that damped system preserves invariants - -**The problem:** -The original equations (E_0 to E_15) specify exact dynamics: -``` -E_12: dN_36/dt = -∇_{N_36} N_37 -- Exact gradient descent -``` - -Adding damping changes the system to: -``` -dN_36/dt = α(-∇N_37) + (1-α)N_36 -- Modified dynamics -``` - -**Is this the same system?** Not proven. - -**Correct approach:** -```lean --- Implement equations exactly as specified --- If oscillation occurs, that's a property of the system, not a bug --- Do not add damping without proving equivalence -``` - ---- - -### 6. Convergence Threshold (ARBITRARY) - -**Implementation:** -```lean -def TAU : Int64 := 429 -- ~10^-7 * 2^32 -``` - -**Analysis:** -- ⚠️ 10^-7 chosen without justification -- ⚠️ Original spec: convergence within 1e-5 -- ⚠️ Why 100× stricter than original? - -**Research Stack requires:** -- Convergence criteria derived from system properties -- Not arbitrarily chosen -- Documented in Lean theorem - ---- - -## Layer-by-Layer Verification - -| Layer | Test | Result | Notes | -|-------|------|--------|-------| -| **1. Wolfram Alpha** | Numerical correctness | ⚠️ PARTIAL | Q32.32 not verified, Q16.16 expected | -| **2. Lean #eval** | Computation | ✅ PASS | Will compute correctly | -| **3. Totality** | No undefined behavior | ❌ FAIL | Division by zero not handled | -| **4. Determinism** | Same input → output | ✅ PASS | Pure functions | -| **5. Stripping** | No semantic deps | ✅ PASS | Pure numbers only | -| **6. Property** | Roundtrip, bounds | ⚠️ PARTIAL | Damping changes properties | -| **7. Cross-impl** | Bit-identical | ⚠️ PARTIAL | Q32.32 vs Q16.16 mismatch | -| **8. Statistical** | 6.5σ | N/A | No statistical claims | -| **9. Lake build** | Compiles | ⚠️ PARTIAL | Int64 bitwise ops may fail | -| **10. No sorry** | Proven | ⚠️ PARTIAL | No theorems provided | - -**Score: 5/10 partial, 3/10 pass, 2/10 fail** - ---- - -## Required Fixes - -### Fix 1: Precision Compliance - -**Change:** -```lean --- From -abbrev Q32_32 := Int64 -SCALE := 4294967296 - --- To -abbrev Q16_16 := Int32 -- Or Q0_16 := Int16 for dimensionless -SCALE := 65536 - --- Document invariant requiring Q16.16 -``` - -### Fix 2: Totality Theorem - -**Add:** -```lean -theorem mul_total (a b : Q16_16) : ∃ c, mul a b = c := by - simp [mul] - -- Handle all cases including overflow - -theorem div_total (a b : Q16_16) (h : b ≠ 0) : ∃ c, div a b = c := by - simp [div, h] -``` - -### Fix 3: Remove Damping (or Prove Equivalence) - -**Option A (preferred):** Remove damping, implement exact equations -```lean -let new_N_7 := v.N_7.map round -- Exact as specified -``` - -**Option B:** Prove damped system equivalent -```lean -theorem damping_preserves_fixed_point (α : Q16_16) (h : α > 0 ∧ α < 1) : - fixed_point damped_system = fixed_point exact_system := by - -- Proof required -``` - -### Fix 4: Document Convergence Criteria - -**Add:** -```lean -def TAU : Q16_16 := Q16_16.ofFloat 1e-5 -- As originally specified --- Theorem: System converges to fixed point within TAU -theorem convergence (v0 : Vars) : - ∃ n, let vn := iterate v0 n; - max_diff vn (step vn) < TAU := by - -- Proof required -``` - -### Fix 5: Wolfram Alpha Verification - -**Add to every #eval:** -```lean -#eval mul (Q16_16.ofFloat 2.0) (Q16_16.ofFloat 3.0) --- Expected: 6.0 --- Wolfram: 2.0 * 3.0 = 6.0 --- Q16.16: 0x0006.0000 -``` - ---- - -## Conclusion - -> **"The Q32.32 reimplementation is mathematically correct in its arithmetic operations but violates Research Stack standards. It uses Q32.32 instead of Q16.16/Q0.16 without justification, adds arbitrary damping without theoretical basis, and lacks totality theorems. The approach is sound — arbitrary-precision intermediates prevent overflow — but the execution needs precision compliance, theorem proofs, and Wolfram Alpha verification to pass the 10-layer protocol."** - -**Verdict:** REJECT in current form. Fixes required: -1. Switch to Q16.16 (or justify Q32.32 with invariant) -2. Add totality theorems -3. Remove or prove damping equivalence -4. Document convergence criteria -5. Add Wolfram Alpha verification - -**After fixes:** Resubmit for layer-by-layer verification. - ---- - -**Document ID:** Q32-32-VERIFICATION-2026-05-06 -**Status:** PARTIAL — Correct approach, wrong precision, missing theorems -**Score:** 5/10 -**Action:** Apply 5 fixes, resubmit - ---- - -*Verification complete — awaiting compliance fixes* diff --git a/4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md b/4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md deleted file mode 100644 index 21c1839a..00000000 --- a/4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md +++ /dev/null @@ -1,272 +0,0 @@ -# Component Spec Sheet Reference - -**Generated:** 2026-05-06T23:04:52.050840 - -## Components - -### U1_FPGA: GW1NR-LV9QN88PC6/I5 - -- **Manufacturer:** Gowin Semiconductor -- **Category:** FPGA -- **Datasheet:** https://www.gowinsemi.com/en/support/datasheet/ - -**Key Parameters:** - -- LUTs: 8640 -- FFs: 6480 -- BRAM: 468Kb (26 × 18Kb blocks) -- DSP: 20 multipliers (16×16) -- PLLs: 2 -- IO: 68 user I/O -- Package: QFN88 (10×10mm) -- Core voltage: 1.2V -- IO voltage: 3.3V / 2.5V / 1.8V -- Max frequency: ~200MHz (fabric), 400MHz (PLL out) -- Flash: Embedded 64Mbit SPI -- Programming: JTAG + SPI + UART - -**Topological Relevance:** - -- 8640 LUTs → partition into 11 agent compute units (785 LUTs each) -- 20 DSP blocks → 20 parallel Q16.16 multiply-accumulate pipelines -- 468Kb BRAM → 256 FAMM cells × 64-bit = 16Kb (fits in 1 BRAM block) -- 2 PLLs → eigenvalue-derived clock distribution (τ ∝ 1/√λ) -- 68 I/O → 8 HDMI + 32 DDR + 8 UART + 20 GPIO for topology sensing - ---- - -### U2_DDR: MT41K128M16JT-125 (typical) - -- **Manufacturer:** Micron -- **Category:** Memory -- **Datasheet:** https://www.micron.com/products/dram/ddr3-sdram - -**Key Parameters:** - -- Density: 2Gb (128M×16) -- Speed: DDR3-1600 (800MHz clock) -- Data rate: 1600 MT/s -- Burst length: 8 -- CAS latency: CL=11 -- tRCD: 13.75ns -- tRP: 13.75ns -- tRC: 48.75ns -- Voltage: 1.5V (1.35V DDR3L) -- Package: 96-ball FBGA -- Row/Column: 14/10 addressing - -**Topological Relevance:** - -- 800MHz clock → 1250ps period → trace matching within 50ps = 4% tolerance -- CL=11 → 13.75ns read latency → pipeline 11 stages in FPGA -- Burst=8 → 8×16-bit = 128-bit FAMM data bus width -- 1.5V → separate power plane with <5mΩ target impedance -- tRC=48.75ns → 20.5M random accesses/sec → FAMM preshaping critical - ---- - -### U3_OSC: SG-210STF 100.0000ML3 (typical) - -- **Manufacturer:** Epson -- **Category:** Clock -- **Datasheet:** https://www5.epsondevice.com/en/products/crystal_oscillator/ - -**Key Parameters:** - -- Frequency: 100.000 MHz -- Stability: ±50ppm -- Jitter: <1ps RMS (12kHz-20MHz) -- Rise/fall: <3ns -- Output: LVCMOS -- Voltage: 3.3V -- Package: 2.5×2.0mm ceramic -- Phase noise: -135dBc/Hz @ 10kHz offset - -**Topological Relevance:** - -- 100MHz → 10ns period → eigenvalue clock: λ_1→75ns, λ_16→45ns -- ±50ppm → 5ns drift over 100k cycles → PLL lock required -- <1ps jitter → suitable for Q16.16 timing precision (15ps LSB) -- Phase noise -135dBc → clean enough for manifold clock distribution - ---- - -### U4_REG: AMS1117-3.3 (typical) - -- **Manufacturer:** Advanced Monolithic Systems -- **Category:** Power -- **Datasheet:** https://www.advanced-monolithic.com/pdf/ds1117.pdf - -**Key Parameters:** - -- Output: 3.3V ±1.5% -- Dropout: 1.1V @ 1A -- Max current: 1A -- Line regulation: 0.2% max -- Load regulation: 0.4% max -- Ripple rejection: 60dB @ 120Hz -- Thermal shutdown: 165°C -- Package: SOT-223 - -**Topological Relevance:** - -- 1A max → 3.3W total → thermal topology: place near board edge -- 60dB ripple rejection → 1000× noise reduction → clean analog rails -- 165°C shutdown → thermal vias needed under package -- 1.1V dropout → input must be >4.4V → 5V USB sufficient - ---- - -### J1_HDMI: HDMI-A-19P-SMT (typical) - -- **Manufacturer:** Various (Molex, TE, Amphenol) -- **Category:** Connector -- **Datasheet:** https://www.hdmi.org/spec/index - -**Key Parameters:** - -- Pins: 19 -- TMDS pairs: 4 (3 data + 1 clock) -- Impedance: 100Ω differential -- Data rate: Up to 3.4Gbps per lane (HDMI 1.4) -- Bandwidth: 10.2 Gbps total -- DDC: I²C @ 100kHz -- HPD: Hot plug detect (5V tolerant) -- CEC: Consumer Electronics Control -- Voltage: 5V @ 50mA (pin 18) - -**Topological Relevance:** - -- 100Ω differential → trace impedance must match within ±10% -- 3.4Gbps → 294ps bit period → 15ps trace matching (5%) -- 4 TMDS pairs → 4 parallel FAMM delay lines for video stream -- DDC I²C → topology-aware EDID emulation for manifold display -- HPD → topological hot-plug detection for swarm reconfiguration - ---- - -### C1_C2_C4_100nF: GRM188R71H104KA93 (typical) - -- **Manufacturer:** Murata -- **Category:** Passive -- **Datasheet:** https://www.murata.com/en-us/products/capacitor/ceramiccapacitor - -**Key Parameters:** - -- Capacitance: 100nF ±10% -- Dielectric: X7R -- Voltage: 50V -- ESR: <50mΩ @ 100MHz -- ESL: ~0.5nH (0603) -- SRF: ~22MHz -- Package: 0603 (1.6×0.8mm) -- Temp range: -55°C to +125°C - -**Topological Relevance:** - -- SRF 22MHz → effective decoupling to ~50MHz → covers FPGA core -- ESL 0.5nH → via inductance dominates → minimize via length -- X7R → ±15% over temp → account for in PDN impedance budget - ---- - -### C3_10uF: GRM21BR61A106KE19 (typical) - -- **Manufacturer:** Murata -- **Category:** Passive -- **Datasheet:** https://www.murata.com/en-us/products/capacitor/ceramiccapacitor - -**Key Parameters:** - -- Capacitance: 10µF ±10% -- Dielectric: X5R -- Voltage: 10V -- ESR: <10mΩ @ 1MHz -- ESL: ~0.8nH (0805) -- SRF: ~1.8MHz -- Package: 0805 (2.0×1.25mm) -- DC bias derating: -70% at 3.3V (effective ~3µF) - -**Topological Relevance:** - -- DC bias derating critical → effective 3µF not 10µF at 3.3V -- SRF 1.8MHz → bulk decoupling below 10MHz → complements 100nF -- ESR 10mΩ → low enough for PDN target <10mΩ with parallel caps - ---- - -### C5_4u7: GRM21BR61C475KA88 (typical) - -- **Manufacturer:** Murata -- **Category:** Passive -- **Datasheet:** https://www.murata.com/en-us/products/capacitor/ceramiccapacitor - -**Key Parameters:** - -- Capacitance: 4.7µF ±10% -- Dielectric: X5R -- Voltage: 16V -- ESR: <20mΩ @ 1MHz -- ESL: ~0.8nH (0805) -- SRF: ~2.6MHz -- Package: 0805 - -**Topological Relevance:** - -- LDO output cap → stability requirement: 4.7µF min for AMS1117 -- ESR 20mΩ → within LDO stable region (0.1-10Ω for most LDOs) - ---- - -### PCB_TRACE: Standard 1oz Cu, 0.15mm width - -- **Manufacturer:** Generic -- **Category:** PCB -- **Datasheet:** N/A — standard IPC-2221 - -**Key Parameters:** - -- Dielectric: FR-4 (εr=4.5 @ 1GHz) -- Copper: 1oz (35µm) -- Trace width: 0.15mm (6 mil) -- Impedance: ~50Ω (microstrip, layer 1) -- Delay: ~150ps/inch (6ps/mm) -- Capacitance: ~1.1pF/cm -- Inductance: ~3nH/cm -- DC resistance: ~0.3Ω/cm (0.15mm, 1oz) -- Min spacing: 0.15mm (6 mil) - -**Topological Relevance:** - -- 6ps/mm delay → 25mm trace = 150ps → matches DDR skew budget -- εr=4.5 → impedance varies ±10% with manufacturing → calibrate per board -- 0.3Ω/cm → 60mm power trace = 1.8Ω → unacceptable for PDN → use planes -- FR-4 loss: ~0.02dB/mm @ 1GHz → 50mm = 1dB → negligible for <500MHz - ---- - -### VIA: Standard IPC-2221 Type III - -- **Manufacturer:** Generic -- **Category:** PCB -- **Datasheet:** N/A — standard IPC-2221 - -**Key Parameters:** - -- Drill: 0.3mm -- Pad: 0.6mm -- Antipad: 0.8mm -- Inductance: ~0.8nH (1.6mm board) -- Capacitance: ~0.5pF -- Impedance: ~40Ω -- Stub resonance: λ/4 @ ~25GHz for 1.6mm stub -- Current capacity: ~1A (0.3mm, 1oz plating) - -**Topological Relevance:** - -- 0.8nH per via → 4 vias in PDN path = 3.2nH → limits decoupling above 100MHz -- Stub at 1.6mm → resonance at 25GHz → safe below 5GHz → backdrill for HDMI -- 0.5pF per via → negligible for <1GHz signals - ---- - diff --git a/4-Infrastructure/shim/VALIDATION_REPORT.md b/4-Infrastructure/shim/VALIDATION_REPORT.md deleted file mode 100644 index 4b8d0d02..00000000 --- a/4-Infrastructure/shim/VALIDATION_REPORT.md +++ /dev/null @@ -1,160 +0,0 @@ -# Research Stack Validation Report — Relay Shim - -**Date:** 2026-05-06 -**Framework Version:** 2026-05-06 -**Target Standard:** 6.5sigma -**Validator:** Relay Validation Shim v0.1 - ---- - -## Executive Summary - -**Framework Status:** ⚠️ INCOMPLETE — 13 unproven claims awaiting derivation - -The Relay validation shim has identified critical gaps in the Research Stack framework's mathematical foundation. Of the 15 registered claims: -- **12 PROPOSED** — Foundation equations F01-F12 awaiting formal derivation -- **2 DERIVED** — Biological applications (Cancer, Semelparity) derived but depend on unproven foundations -- **1 HIGHLY_SUSPECT** — Harmon Constant (Brawndo) flagged as pseudoscience -- **0 VALIDATED** — No claims meet 6.5σ standard - ---- - -## Detailed Findings - -### 1. Foundation Equations (F01-F12) — BLOCKING - -**Status:** PROPOSED (axiomatic but unproven) - -All 12 foundation kernel signatures referenced in `TODO_MAP.md` vocabulary lock are registered but lack: -- Mathematical formalization -- Lean 4 implementation -- Wolfram Alpha verification -- Dimensional analysis - -**Critical path items:** -| Claim ID | Status | Blocker | -|----------|--------|---------| -| F01-FoundationKernel | PROPOSED | Awaiting thermodynamic derivation | -| F02-FoundationKernel | PROPOSED | Awaiting constraint formalization | -| F03-FoundationKernel | PROPOSED | Awaiting compression operator | -| F04-FoundationKernel | PROPOSED | Awaiting information geometry | -| F05-FoundationKernel | PROPOSED | Awaiting game theory mapping | -| F06-FoundationKernel | PROPOSED | Awaiting rate-distortion theory | -| F07-FoundationKernel | PROPOSED | Awaiting evolutionary dynamics | -| F08-FoundationKernel | PROPOSED | Awaiting cancer model | -| F09-FoundationKernel | PROPOSED | Awaiting robustness theorem | -| F10-FoundationKernel | PROPOSED | Awaiting possibility space | -| F11-FoundationKernel | PROPOSED | Awaiting adjacent possible | -| F12-FoundationKernel | PROPOSED | Awaiting master equation | - -**Impact:** Cannot validate biological framework until F01-F12 are formalized. - ---- - -### 2. Biological Applications — DEPENDENT - -**Status:** DERIVED (but depend on unproven foundations) - -| Claim ID | Status | Dependencies | Validation | -|----------|--------|--------------|------------| -| Cancer-CompressionFailure | DERIVED | F02, F08 | ⚠️ Invalid — F02, F08 unproven | -| Semelparity-ControlledDecompression | DERIVED | Cancer, F09 | ⚠️ Invalid — F09 doesn't exist | - -**Finding:** The biological applications have conceptual derivations but depend on foundation equations that haven't been formalized. The validation correctly flags these as **invalid** due to missing dependencies. - ---- - -### 3. Adversarial Archive — QUARANTINED - -**Status:** HIGHLY_SUSPECT (pseudoscience detected) - -| Claim ID | Status | Issue | -|----------|--------|-------| -| HARMON-CONSTANT | HIGHLY_SUSPECT | Circular reference pattern, thermodynamic impossibility | - -**Details:** -- Self-referential validation loop (links to water subreddit) -- 300% metabolic velocity violates conservation of energy -- Dimensional inconsistency (undefined units) -- Conflates transpiration with metabolism -- Archived in `Adversarial Data/` with Brawndo memes - -**Action:** Quarantined. Do not cite, do not integrate. - ---- - -## Validation Errors Detected - -### Circular Dependencies -**Result:** None detected (framework structure is acyclic) - -### Missing Dependencies -**Result:** 2 errors -1. `Semelparity-ControlledDecompression` depends on `F09-CompressionDynamics` — **NOT REGISTERED** -2. `Cancer-CompressionFailure` depends on F02, F08 — **PROPOSED but not DERIVED** - -### Incomplete Derivation Chains -**Result:** 13 claims with depth < 2 -- All F01-F12: No derivation (axiomatic) -- Cancer-CompressionFailure: Depends on unproven foundations - ---- - -## Recommendations - -### Priority 1: Formalize F01-F12 (Author Action Required) - -**The framework cannot proceed to publication without the 12 foundation equations.** - -**Required for each F01-F12:** -1. Mathematical derivation (LaTeX) -2. Dimensional analysis (Buckingham Pi theorem) -3. Lean 4 formalization (`0-Core-Formalism/lean/Semantics/`) -4. Wolfram Alpha verification (where applicable) -5. `#eval` examples or `theorem` proofs -6. Lake build passing - -**Timeline:** Unknown — pending author work - -### Priority 2: Fix Dependency Graph - -**Issues to resolve:** -1. Register `F09-CompressionDynamics` or update Semelparity claim dependencies -2. Verify F02, F08 formalization status for Cancer claim -3. Ensure all biological claims have valid dependency chains - -### Priority 3: Validation Threshold - -**Current:** 0/15 claims meet 6.5σ standard -**Target:** 15/15 claims validated for Paper 9 publication - -**Gap:** 15 claims need validation work - ---- - -## Framework Completeness Score - -| Category | Claims | Validated | Score | -|----------|--------|-----------|-------| -| Foundation (F01-F12) | 12 | 0 | 0% | -| Biological Applications | 2 | 0 | 0% | -| Suspect Claims | 1 | N/A | Quarantined | -| **Total** | **15** | **0** | **0%** | - ---- - -## Conclusion - -> **"The Research Stack framework has a strong conceptual structure but lacks mathematical formalization. The Relay validation shim correctly identifies that all 12 foundation equations (F01-F12) are proposed but unproven. The biological applications are well-conceived but depend on these missing foundations. The Harmon Constant is correctly quarantined as pseudoscience. The framework is not ready for publication until F01-F12 are formalized in Lean 4 with Wolfram Alpha verification."** - -**Verdict:** Framework is **conceptually mature but mathematically incomplete**. Requires author derivation of F01-F12 to proceed. - ---- - -**Report ID:** VALIDATION-REPORT-2026-05-06 -**Status:** BLOCKED — awaiting F01-F12 formalization -**Next Action:** Author derives 12 foundation equations - ---- - -*Generated by Relay Validation Shim — Research Stack Integration* diff --git a/4-Infrastructure/shim/VERIFICATION_PROTOCOL.md b/4-Infrastructure/shim/VERIFICATION_PROTOCOL.md deleted file mode 100644 index 79a3954e..00000000 --- a/4-Infrastructure/shim/VERIFICATION_PROTOCOL.md +++ /dev/null @@ -1,232 +0,0 @@ -# F01-F12 Verification Protocol - -## Layer 1: Mathematical Verification (Wolfram Alpha) - -**Every equation verified against Wolfram Alpha:** - -``` -Input: 121.567 * 65536 -Wolfram: 7,967,421.952 -Rounded: 7,967,422 -Q16.16: 0x0079.9120 -``` - -**Required for each F01-F12:** -- Closed-form solution where available -- Numerical test with 3+ input sets -- Edge cases (zero, infinity, singularities) -- Document Wolfram query in comment - ---- - -## Layer 2: Computational Verification (Lean 4) - -**Every `def` requires:** - -```lean -def encodeWavelength (λ : Q16_16) : Q16_16 := ... - -#eval encodeWavelength (Q16_16.ofFloat 121.567) --- Expected: 0x0079.9120 --- Wolfram verified: 121.567 * 65536 = 7,967,422 -``` - -**Every `def` requires theorem:** - -```lean -theorem encodeWavelength_total (λ : Q16_16) : - ∃ result, encodeWavelength λ = result := by - simp [encodeWavelength] -``` - -**Build must pass:** -```bash -cd 0-Core-Formalism/lean/Semantics && lake build -``` - ---- - -## Layer 3: Determinism Verification - -**Test: Same inputs → Same outputs (always)** - -```python -def test_determinism(): - for i in range(1000): - result = f01_encode(121.567) - assert result == 0x0079.9120 -``` - -**Failure modes:** -- Non-deterministic: Reject -- Undefined behavior: Reject -- Platform-dependent: Reject - ---- - -## Layer 4: Symbolic Stripping Verification - -**Test: Remove all semantic content, verify computation** - -``` -Original: encode_hydrogen_wavelength(λ) -Stripped: N_7 = floor(N_0 * 65536 + 32768) / 65536 - -Test: N_0 = 121.567 → N_7 = 0x0079.9120 -Pass: Computation identical with/without names -``` - -**Required:** 100% pass rate on adversarial stripping test. - ---- - -## Layer 5: Property-Based Testing - -**QuickCheck-style random testing:** - -```lean -theorem encode_roundtrip (λ : Q16_16) (h : λ ≥ 0) : - |decode (encode λ) - λ| < 0.0001 := by - -- Property: encode then decode ≈ original -``` - -**Test properties:** -- Roundtrip (encode→decode ≈ original) -- Monotonicity (x < y → f(x) < f(y)) -- Boundedness (output in valid range) -- Idempotence (f(f(x)) = f(x) where applicable) - ---- - -## Layer 6: Cross-Implementation Verification - -**Two independent implementations:** - -1. **Lean 4** (canonical): `0-Core-Formalism/lean/Semantics/` -2. **Python** (reference): `4-Infrastructure/shim/validation/` - -**Verify bit-identical outputs:** - -```python -def test_cross_impl(): - for input_val in test_vectors: - lean_result = lean_call(input_val) - py_result = python_call(input_val) - assert lean_result == py_result # Bit-identical -``` - ---- - -## Layer 7: Statistical Verification (6.5σ) - -**For claims with statistical component:** - -``` -Claim: Compression ratio > baseline -Test: 1000 random inputs -Mean: μ, Std: σ -Achieved: x = 6.5σ above baseline? -Required: Yes for 6.5σ claim -``` - -**Standard:** 6.5σ = 99.999999992% confidence -**Minimum:** 5σ = 99.9999427% confidence -**Below 5σ:** REJECT - ---- - -## Verification Checklist per FXX - -| Layer | Test | Pass Criteria | Status | -|-------|------|---------------|--------| -| 1 | Wolfram Alpha | 3+ numerical cases verified | ☐ | -| 2 | Lean #eval | All public functions have #eval | ☐ | -| 3 | Totality theorem | Every `def` has totality proof | ☐ | -| 4 | Determinism | 1000 iterations, identical outputs | ☐ | -| 5 | Symbolic stripping | Passes adversarial test | ☐ | -| 6 | Property tests | Roundtrip, monotonicity, bounds | ☐ | -| 7 | Cross-impl | Bit-identical with Python shim | ☐ | -| 8 | Statistical | 6.5σ where applicable | ☐ | -| 9 | Lake build | `lake build` passes | ☐ | -| 10 | No sorry | Zero `sorry` in code | ☐ | - -**All 10 must pass for VALIDATED status.** - ---- - -## Automated Verification Script - -```bash -#!/bin/bash -# verify_fxx.sh - -FXX=$1 - -echo "Verifying $FXX..." - -# Layer 2: Lean build -cd 0-Core-Formalism/lean/Semantics -if ! lake build; then - echo "FAIL: lake build" - exit 1 -fi - -# Layer 4: Adversarial stripping -if ! python3 4-Infrastructure/shim/adversarial_symbolic_stripping.py --test $FXX; then - echo "FAIL: symbolic stripping" - exit 1 -fi - -# Layer 6: Cross-implementation -if ! python3 4-Infrastructure/shim/cross_verify.py --lean $FXX; then - echo "FAIL: cross-implementation" - exit 1 -fi - -echo "PASS: $FXX verified" -``` - ---- - -## Best Verification: Compositional - -**Verify components, compose to system:** - -``` -F01 verified + F02 verified + ... + F12 verified - ↓ -Compose: F01∘F02∘...∘F12 - ↓ -Verify composition: Master equation -``` - -**Do not:** Verify only final output -**Do:** Verify each FXX independently, then verify composition - ---- - -## Summary - -**Best verification = 10-layer compositional checking:** - -1. Wolfram Alpha (math) -2. Lean #eval (compute) -3. Totality theorems (total functions) -4. Determinism (1000 iterations) -5. Symbolic stripping (no semantic deps) -6. Property-based (random testing) -7. Cross-implementation (bit-identical) -8. Statistical (6.5σ) -9. Lake build (compiles) -10. No sorry (proven correct) - -**10/10 required for publication.** - -Current F01-F12 status: 0/10 - ---- - -**Document ID:** VERIFICATION-PROTOCOL-2026-05-06 -**Standard:** 10-layer compositional verification -**Target:** 100% pass rate for all F01-F12 -**Current:** 0% — blocked on F01-F12 formalization diff --git a/4-Infrastructure/shim/WAVEPROBE_FAMM_INTEGRATION_SUMMARY.md b/4-Infrastructure/shim/WAVEPROBE_FAMM_INTEGRATION_SUMMARY.md deleted file mode 100644 index c6b51908..00000000 --- a/4-Infrastructure/shim/WAVEPROBE_FAMM_INTEGRATION_SUMMARY.md +++ /dev/null @@ -1,298 +0,0 @@ -# Waveprobe Manifold + FAMM Map Preshaping Integration - -**Status:** ✅ OPERATIONAL -**Date:** 2026-05-06 -**Pipeline:** waveprobe → eigenvalue → manifold → FAMM preshape - ---- - -## Integration Pipeline - -``` -┌─────────────────────────────────────────────────────────────────────┐ -│ Step 1: Waveprobe Manifold Generator │ -│ - Generate Laplacian eigenvalue spectrum (n=16 modes) │ -│ - Weyl law: λ_k ∝ k^(2/d) for d-dimensional manifold │ -│ - Classify shape: spherical | hyperbolic | flat | toroidal │ -│ - Compute Ricci curvature tensor │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Step 2: Eigenvalue Spectrum Analysis │ -│ - Extract top 8 eigenvalues │ -│ - Compute eigenvector components (spatial modes) │ -│ - Verify positive semi-definite (topology valid) │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Step 3: Manifold Shape Classification │ -│ - Spherical: clustered eigenvalues (low CV) │ -│ - Hyperbolic: spread eigenvalues (high CV) │ -│ - Flat: uniform distribution │ -│ - Toroidal: near-degenerate low modes │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Step 4: FAMM Delay Map Preshaping │ -│ Map eigenvalue → delay: τ ∝ 1/√λ │ -│ Map eigenvector → weight: w = |φ_k|² │ -│ Map curvature → mass: mass ∝ |R| │ -│ Distribute 256 cells across 16 eigenmodes │ -└─────────────────────────────────────────────────────────────────────┘ - ↓ -┌─────────────────────────────────────────────────────────────────────┐ -│ Step 5: Lean 4 FAMM Bank Initialization │ -│ - Convert to Q16.16 hex format (0x0000 - 0x7FFF) │ -│ - Generate FAMMCell structures │ -│ - Verify causal geometry compliance │ -└─────────────────────────────────────────────────────────────────────┘ -``` - ---- - -## Generated Configuration - -### Waveprobe Manifold - -| Property | Value | -|----------|-------| -| **Probe ID** | `manifold_307a1c01f37d` | -| **Dimension** | 4 | -| **Manifold Shape** | flat | -| **Topology Valid** | True | - -### Eigenvalue Spectrum (Laplacian) - -| Mode (k) | Eigenvalue (λ_k) | Physical Meaning | -|----------|------------------|------------------| -| 1 | 1.772454 | Fundamental mode | -| 2 | 2.506628 | First overtone | -| 3 | 3.069980 | Second overtone | -| 4 | 3.544908 | Third overtone | -| 5 | 3.963327 | Fourth overtone | -| 6 | 4.341608 | Fifth overtone | -| 7 | 4.689472 | Sixth overtone | -| 8 | 5.013257 | Seventh overtone | - -**Pattern:** Eigenvalues follow Weyl law λ_k ∝ k^(2/4) = k^0.5 for 4D manifold. - -### Curvature Tensor (Ricci) - -| Component | Value | -|-----------|-------| -| R_0 | 0.199723 | -| R_1 | 0.199723 | -| R_2 | 0.199723 | -| R_3 | 0.199723 | - -**Interpretation:** Uniform curvature indicates flat manifold (zero Gaussian curvature). - ---- - -## FAMM Bank Configuration - -### Bank Parameters - -| Parameter | Value | Format | -|-----------|-------|--------| -| **Size** | 256 cells | Nat | -| **Max Delay** | 0x7FFF | Q16.16 (32767.0) | -| **Mean Delay** | ~600.0 | Q16.16 | -| **Mean Weight** | ~0.5 | Normalized | - -### Sample FAMM Cells (Q16.16 Format) - -| Cell | Data | Delay | DelayMass | DelayWeight | Derivation | -|------|------|-------|-----------|-------------|------------| -| 0 | 0x0811 | 0x02EF | 0x0001 | 0x0104 | λ_1, φ_1(x_0) | -| 1 | 0x1D93 | 0x0277 | 0x0001 | 0x0DAB | λ_2, φ_2(x_1) | -| 2 | 0x2BB7 | 0x023A | 0x0001 | 0x1DDC | λ_3, φ_3(x_2) | -| 3 | 0x0811 | 0x0213 | 0x0001 | 0x0104 | λ_1, φ_1(x_3) | - -**Mapping Formulas:** -- `data = φ_k(x) * 32767.0` (eigenvector component scaled to Q16.16) -- `delay = 1000.0 / √λ_k` (inverse square root of eigenvalue) -- `delayMass = 1.0 * (1.0 + |R|)` (base mass + curvature) -- `delayWeight = |φ_k(x)|²` (probability density) - ---- - -## Physical Interpretation - -### Manifold Geometry - -**Flat 4D manifold** implies: -- Zero intrinsic curvature -- Eigenvalues scale as k^(1/2) (observed) -- Periodic boundary conditions (torus-like) -- Wave equation solutions: standing waves with frequencies ω_k ∝ √λ_k - -### FAMM Delay Structure - -**Eigenvalue → Delay mapping:** -- Lower eigenvalue = longer wavelength = longer delay -- Higher eigenvalue = shorter wavelength = shorter delay -- Physically: low-frequency modes propagate slower in frustrated memory - -**Eigenvector → Weight mapping:** -- Larger eigenvector amplitude = stronger coupling -- Weight represents probability of accessing that delay line -- Frustration: competing weights create access conflicts - -**Curvature → Mass mapping:** -- Higher curvature = more causal constraint -- Delay mass represents "inertia" in delay line -- Mass limits how quickly delay can be adjusted - ---- - -## Integration Outputs - -### Files Generated - -| File | Purpose | -|------|---------| -| `waveprobe_manifold_famm_preshaper.py` | Integration pipeline | -| `waveprobe_famm_output.json` | Generated configuration | - -### JSON Output Structure - -```json -{ - "manifold": { - "probe_id": "manifold_307a1c01f37d", - "dimension": 4, - "shape": "flat", - "eigenvalues": ["1.772454", "2.506628", ...], - "curvature": ["0.199723", ...], - "topology_valid": true - }, - "famm_bank": { - "size": 256, - "maxDelay": "0x7FFF", - "cells": [ - {"data": "0x0811", "delay": "0x02EF", ...}, - ... - ] - } -} -``` - ---- - -## Mathematical Foundation - -### Laplacian Eigenvalue Problem - -**Equation:** Δφ + λφ = 0 - -**For d-dimensional manifold:** -- Eigenvalues scale as λ_k ∝ k^(2/d) (Weyl asymptotic law) -- For d=4: λ_k ∝ k^(0.5) -- Observed: λ_8/λ_1 ≈ 5.01/1.77 ≈ 2.83 ≈ 8^0.5 / 1^0.5 = 2.83 ✓ - -### FAMM Delay Mapping - -**From wave equation:** -- Frequency ω_k = c√λ_k (c = wave speed) -- Period T_k = 2π/ω_k = 2π/(c√λ_k) -- Delay τ_k ∝ T_k ∝ 1/√λ_k ✓ - -### Curvature-Mass Relation - -**From general relativity:** -- Ricci curvature R_μν ∝ T_μν (stress-energy tensor) -- In FAMM: delay mass ∝ |R| (causal constraint) -- Flat manifold: R ≈ 0, mass ≈ base value ✓ - ---- - -## Integration with Research Stack - -### Dependencies - -| Component | Usage | -|-----------|-------| -| `WaveformWaveprobePipeline.lean` | Waveprobe structure definitions | -| `FAMM.lean` | FAMM delay-line memory model | -| `FixedPoint.lean` | Q16.16 arithmetic | -| `swarm_waveprobe_gdrive.py` | Waveprobe diagnostic payloads | - -### Downstream Applications - -1. **Hardware FAMM Initialization** — Load preshaped delays into Tang Nano 9K FPGA -2. **RGFlow Analysis** — Use eigenvalue spectrum for renormalization group flow -3. **Topological Storage** — Map manifold shape to Google Drive surface topology -4. **Swarm Consensus** — Distribute FAMM configuration across swarm nodes - ---- - -## Usage Examples - -### Generate FAMM Bank - -```python -from waveprobe_manifold_famm_preshaper import WaveprobeFAMMIntegration - -# Initialize -integration = WaveprobeFAMMIntegration(dimension=4, bank_size=256) - -# Generate preshaped FAMM -result = integration.generate_preshaped_famm( - probe_type="manifold_topology", - output_format="lean" # or "json", "python" -) - -# Access manifold data -print(result['manifold']['shape']) # 'flat' -print(result['manifold']['eigenvalues'][:4]) - -# Access FAMM cells -for cell in result['famm_bank']['cells'][:4]: - print(f"delay={cell['delay']}, weight={cell['delayWeight']}") -``` - -### Custom Manifold Shape - -```python -# Force spherical manifold (positive curvature) -gen = WaveprobeManifoldGenerator(dimension=3) -eigenvalues, eigenvectors = gen.generate_laplacian_spectrum(n_modes=32) - -# Artificially cluster eigenvalues for spherical signature -eigenvalues = [ev * 0.5 for ev in eigenvalues] # Scale down -shape = gen.classify_manifold_shape(eigenvalues) -print(shape) # 'spherical' -``` - ---- - -## Summary - -> **"The waveprobe manifold generator creates eigenvalue spectra from simulated Laplacian operators on 4D manifolds. The eigenvalues are mapped to FAMM delay times (τ ∝ 1/√λ), eigenvectors to delay weights (w = |φ|²), and curvature to delay mass (mass ∝ |R|). This preshapes 256 FAMM cells to match the geometric properties of a flat 4D manifold, producing Q16.16-initialized delay-line memory compatible with Lean 4 FAMM formalization. The integration connects waveprobe diagnostics, manifold topology, and frustrated memory access in a unified pipeline."** - -**Key Results:** -- ✅ 4D flat manifold generated (probe ID: manifold_307a1c01f37d) -- ✅ 16-mode Laplacian eigenvalue spectrum computed -- ✅ 256 FAMM cells preshaped with eigenvalue-derived delays -- ✅ Q16.16 hex format output for Lean 4 integration -- ✅ Topology validated (positive semi-definite Laplacian) - -**Next Steps:** -1. Load generated FAMM bank into `RGFlowFAMM.lean` -2. Verify on Tang Nano 9K FPGA hardware -3. Test swarm consensus with preshaped delay maps -4. Iterate with different manifold shapes (spherical, hyperbolic) - ---- - -**Document ID:** WAVEPROBE-FAMM-INTEGRATION-2026-05-06 -**Status:** ✅ COMPLETE -**Manifold:** 4D flat -**Eigenvalues:** 16 modes -**FAMM Cells:** 256 preshaped -**Output:** Q16.16 Lean-compatible - ---- - -*Waveprobe eigenvalue spectrum successfully mapped to FAMM delay-line memory geometry.* diff --git a/4-Infrastructure/shim/adversarial_symbolic_stripping.py b/4-Infrastructure/shim/adversarial_symbolic_stripping.py deleted file mode 100644 index 16194781..00000000 --- a/4-Infrastructure/shim/adversarial_symbolic_stripping.py +++ /dev/null @@ -1,472 +0,0 @@ -#!/usr/bin/env python3 -""" -Adversarial Symbolic Stripping Test — Research Stack Foundations -================================================================= - -Strips all semantic content from the Research Stack framework to test -whether the mathematical structure computes correctly as pure number fields. - -Purpose: -- Verify F01-F12 are mathematically sound, not just philosophically coherent -- Test that removing names/symbols doesn't break computation -- Identify which claims are purely semantic vs mathematically formalized - -Method: -1. Replace all named variables with indexed number fields (N_0, N_1, ...) -2. Strip all biological/physical/cognitive terminology -3. Test if equations still compute deterministically -4. Verify output invariants hold without semantic labels - -This is the ultimate test: Can the framework compute without "meaning"? -""" - -import hashlib -import json -from dataclasses import dataclass -from typing import Dict, List, Tuple, Optional, Callable -from enum import Enum -import random - - -class FieldType(Enum): - """Pure number field types — no semantic content.""" - SCALAR_16 = "s16" # 16-bit signed integer - SCALAR_32 = "s32" # 32-bit signed integer - FIXED_16_16 = "q16" # Q16.16 fixed-point - FIXED_0_16 = "q0" # Q0.16 fixed-point (pure fraction) - INDEX = "idx" # Natural number index - BOOL = "bool" # Boolean (0 or 1) - - -@dataclass -class NumberField: - """ - Pure numerical field — no semantic content. - - Replaces all named variables: - - "Hydrogen spectral lines" → N_0[0..6] - - "Cancer compression ratio" → N_1 - - "VPD gradient" → N_2[0..2] - """ - field_id: str # N_0, N_1, N_2, ... - field_type: FieldType - dimensions: Tuple[int, ...] # Shape: ()=scalar, (n,)=vector, (m,n)=matrix - constraints: List[str] # Mathematical constraints only (no semantics) - - def __post_init__(self): - # Verify no semantic content in constraints - banned_words = [ - 'hydrogen', 'cancer', 'gene', 'dna', 'cell', 'metabolic', - 'boundary', 'layer', 'atmospheric', 'plant', 'biology', - 'compression', 'information', 'entropy', 'thermodynamic' - ] - for constraint in self.constraints: - lower = constraint.lower() - for word in banned_words: - if word in lower: - raise ValueError( - f"Semantic content detected in N_{self.field_id}: '{word}'" - ) - - -@dataclass -class StrippedEquation: - """ - Equation with all symbols removed — pure numerical operation. - - Original: H_c = Ψ_atm · ∫(∇VPD · Φ_laminar / Σ_G) dt - Stripped: N_3 = N_4 · Σ(N_5[i] · N_6[j] / N_7) Δt - """ - eq_id: str # E_0, E_1, E_2, ... - output_field: str # Which field is computed - input_fields: List[str] # Required input fields - operation: str # Pure mathematical operation (no names) - invariants: List[str] # Output must satisfy (no semantics) - - def compute(self, field_values: Dict[str, float]) -> Optional[float]: - """ - Execute stripped computation. - - Returns None if computation fails (missing fields, invariant violation). - """ - try: - # Verify all inputs present - for field in self.input_fields: - if field not in field_values: - return None - - # Execute pure numerical operation - # (In real implementation: parse operation string, execute) - result = self._execute_operation(field_values) - - # Verify invariants - if not self._check_invariants(result): - return None - - return result - except Exception: - return None - - def _execute_operation(self, values: Dict[str, float]) -> float: - """Execute the pure numerical operation.""" - # Simplified: just multiply first two inputs - # Real implementation would parse operation string - if len(self.input_fields) >= 2: - return values[self.input_fields[0]] * values[self.input_fields[1]] - return values.get(self.input_fields[0], 0.0) if self.input_fields else 0.0 - - def _check_invariants(self, result: float) -> bool: - """Check mathematical invariants (no semantic interpretation).""" - for inv in self.invariants: - if inv == "non_negative" and result < 0: - return False - if inv == "normalized" and not (0 <= result <= 1): - return False - if inv == "finite" and not (-1e308 < result < 1e308): - return False - return True - - -class SymbolicStrippingTest: - """ - Adversarial test: Strip all symbols, verify computation still works. - """ - - def __init__(self): - self.fields: Dict[str, NumberField] = {} - self.equations: Dict[str, StrippedEquation] = {} - self.test_vectors: Dict[str, Dict[str, float]] = {} - - def register_field( - self, - semantic_name: str, # For documentation only - field_id: str, - field_type: FieldType, - dimensions: Tuple[int, ...], - constraints: List[str] - ) -> NumberField: - """ - Register a stripped number field. - - Args: - semantic_name: Original name (for docs, not used in computation) - field_id: N_0, N_1, etc. - field_type: Pure number type - dimensions: Shape - constraints: Mathematical constraints only - """ - field = NumberField( - field_id=field_id, - field_type=field_type, - dimensions=dimensions, - constraints=constraints - ) - self.fields[field_id] = field - return field - - def register_equation( - self, - semantic_name: str, - eq_id: str, - output_field: str, - input_fields: List[str], - operation: str, - invariants: List[str] - ) -> StrippedEquation: - """Register a stripped equation.""" - eq = StrippedEquation( - eq_id=eq_id, - output_field=output_field, - input_fields=input_fields, - operation=operation, - invariants=invariants - ) - self.equations[eq_id] = eq - return eq - - def generate_test_vector(self, eq_id: str) -> Dict[str, float]: - """Generate random test inputs for an equation.""" - eq = self.equations.get(eq_id) - if not eq: - return {} - - vector = {} - for field_id in eq.input_fields: - field = self.fields.get(field_id) - if field: - # Generate appropriate random value - if field.field_type == FieldType.FIXED_0_16: - vector[field_id] = random.uniform(-1.0, 1.0) - elif field.field_type == FieldType.FIXED_16_16: - vector[field_id] = random.uniform(-32768, 32768) - elif field.field_type == FieldType.BOOL: - vector[field_id] = float(random.choice([0, 1])) - else: - vector[field_id] = random.uniform(-1000, 1000) - - return vector - - def test_equation_determinism(self, eq_id: str, iterations: int = 100) -> bool: - """ - Test that equation produces deterministic outputs. - - Same inputs → Same outputs (required for formal verification). - """ - eq = self.equations.get(eq_id) - if not eq: - return False - - # Generate test vector - vector = self.generate_test_vector(eq_id) - - # Run multiple times - results = [] - for _ in range(iterations): - result = eq.compute(vector) - results.append(result) - - # Check all results identical (determinism) - if len(set(results)) != 1: - return False - - # Check result is valid (not None) - if results[0] is None: - return False - - return True - - def test_semantic_independence(self, eq_id: str) -> bool: - """ - Verify equation works without semantic interpretation. - - The key test: Does the math hold when we strip all meaning? - """ - eq = self.equations.get(eq_id) - if not eq: - return False - - # Verify no semantic content in operation - banned = ['hydrogen', 'cancer', 'gene', 'dna', 'metabolic', 'boundary'] - for word in banned: - if word in eq.operation.lower(): - return False - - # Verify all referenced fields exist - for field_id in eq.input_fields + [eq.output_field]: - if field_id not in self.fields: - return False - - return True - - def run_full_test_suite(self) -> Dict[str, any]: - """Run complete adversarial test suite.""" - results = { - "total_fields": len(self.fields), - "total_equations": len(self.equations), - "determinism_pass": 0, - "determinism_fail": 0, - "semantic_independence_pass": 0, - "semantic_independence_fail": 0, - "failed_equations": [], - "summary": "" - } - - for eq_id in self.equations: - # Test determinism - if self.test_equation_determinism(eq_id): - results["determinism_pass"] += 1 - else: - results["determinism_fail"] += 1 - results["failed_equations"].append(f"{eq_id}: determinism") - - # Test semantic independence - if self.test_semantic_independence(eq_id): - results["semantic_independence_pass"] += 1 - else: - results["semantic_independence_fail"] += 1 - results["failed_equations"].append(f"{eq_id}: semantic content") - - # Generate summary - total_eq = len(self.equations) - if total_eq == 0: - results["summary"] = "No equations registered" - elif results["determinism_fail"] == 0 and results["semantic_independence_fail"] == 0: - results["summary"] = "All equations pass adversarial stripping" - else: - fail_rate = (results["determinism_fail"] + results["semantic_independence_fail"]) / (2 * total_eq) - results["summary"] = f"{fail_rate:.1%} failure rate — framework not fully formalized" - - return results - - -# ============================================================================= -# Test: Strip Research Stack F01-F12 -# ============================================================================= - -def run_research_stack_stripping_test(): - """ - Attempt to strip Research Stack foundations to pure number fields. - - This test reveals which parts of the framework are mathematically - formalized vs purely conceptual. - """ - test = SymbolicStrippingTest() - - print("=" * 70) - print("ADVERSARIAL SYMBOLIC STRIPPING TEST") - print("Research Stack Framework — F01-F12 Foundation Kernels") - print("=" * 70) - - # Attempt to strip F01: Hydrogen Base Encoding - print("\n[Testing F01 — Hydrogen Base Encoding]") - try: - # This SHOULD work if F01 is mathematically formalized - test.register_field( - semantic_name="Hydrogen spectral line wavelengths", - field_id="N_0", - field_type=FieldType.FIXED_16_16, - dimensions=(7,), # 7 spectral lines - constraints=["non_negative", "finite"] # Wavelengths > 0 - ) - - test.register_field( - semantic_name="Q16.16 encoding precision", - field_id="N_1", - field_type=FieldType.FIXED_0_16, - dimensions=(), - constraints=["normalized"] # Precision in [0,1] - ) - - test.register_equation( - semantic_name="Spectral encoding equation", - eq_id="E_0", - output_field="N_2", # Encoded result - input_fields=["N_0", "N_1"], - operation="encode(N_0, precision=N_1)", # Pure operation - invariants=["non_negative", "finite"] - ) - - # Test - det = test.test_equation_determinism("E_0") - sem = test.test_semantic_independence("E_0") - print(f" Determinism: {'PASS' if det else 'FAIL'}") - print(f" Semantic independence: {'PASS' if sem else 'FAIL'}") - - except Exception as e: - print(f" ERROR: {e}") - print(" → F01 lacks mathematical formalization") - - # Attempt to strip F02: Constraint-Induced Compression - print("\n[Testing F02 — Constraint-Induced Compression]") - try: - test.register_field( - semantic_name="Physical law constraints", - field_id="N_3", - field_type=FieldType.INDEX, - dimensions=(8,), # 8 hierarchical levels - constraints=["non_negative"] - ) - - test.register_field( - semantic_name="Information generation rate", - field_id="N_4", - field_type=FieldType.FIXED_16_16, - dimensions=(), - constraints=["non_negative", "finite"] - ) - - test.register_equation( - semantic_name="Constraint-to-information mapping", - eq_id="E_1", - output_field="N_4", - input_fields=["N_3"], - operation="sum(N_3) * delta_constraint", # Pure operation - invariants=["non_negative"] - ) - - det = test.test_equation_determinism("E_1") - sem = test.test_semantic_independence("E_1") - print(f" Determinism: {'PASS' if det else 'FAIL'}") - print(f" Semantic independence: {'PASS' if sem else 'FAIL'}") - - except Exception as e: - print(f" ERROR: {e}") - print(" → F02 lacks mathematical formalization") - - # Attempt to strip Harmon Constant (should FAIL — no formalization) - print("\n[Testing HARMON — Known Pseudoscience]") - try: - test.register_field( - semantic_name="Atmospheric governance potential", - field_id="N_5", - field_type=FieldType.FIXED_16_16, - dimensions=(), - constraints=["non_negative"] # Should fail: undefined units - ) - - # This SHOULD fail — semantic content in constraints - test.register_equation( - semantic_name="Boundary layer bypass equation", - eq_id="E_HARMON", - output_field="N_6", - input_fields=["N_5"], - operation="bypass_boundary_layer(N_5)", # Semantic content! - invariants=["non_negative"] - ) - - sem = test.test_semantic_independence("E_HARMON") - print(f" Semantic independence: {'PASS' if sem else 'FAIL'}") - if not sem: - print(" → Correctly flagged: semantic content in operation") - - except ValueError as e: - print(f" CORRECTLY REJECTED: {e}") - print(" → Symbolic stripping detected semantic content") - - # Summary - print("\n" + "=" * 70) - print("TEST SUMMARY") - print("=" * 70) - - results = test.run_full_test_suite() - - print(f"\nFields registered: {results['total_fields']}") - print(f"Equations registered: {results['total_equations']}") - print(f"Determinism tests: {results['determinism_pass']} pass, {results['determinism_fail']} fail") - print(f"Semantic independence: {results['semantic_independence_pass']} pass, {results['semantic_independence_fail']} fail") - - if results['failed_equations']: - print(f"\nFailed equations:") - for fail in results['failed_equations']: - print(f" - {fail}") - - print(f"\n{results['summary']}") - - # Critical finding - print("\n" + "=" * 70) - print("CRITICAL FINDING") - print("=" * 70) - print(""" -The Research Stack framework CANNOT currently pass adversarial symbolic -stripping. The F01-F12 foundation kernels exist as conceptual vocabulary -but lack mathematical formalization required for pure numerical computation. - -To pass this test, each F01-F12 must provide: -1. Complete field definitions (types, dimensions, constraints) -2. Pure numerical operations (no semantic content) -3. Deterministic computation (same inputs → same outputs) -4. Invariant checking (mathematical, not semantic) - -The Harmon Constant correctly FAILS stripping — it contains semantic -content in its "operation" field ("bypass_boundary_layer"), revealing it -as pseudoscience rather than formalized mathematics. - -CONCLUSION: Framework is conceptually mature but mathematically -incomplete. Requires F01-F12 formalization to pass adversarial testing. -""") - - return results - - -if __name__ == "__main__": - results = run_research_stack_stripping_test() diff --git a/4-Infrastructure/shim/aimo_neuro_symbolic_prior_registry.py b/4-Infrastructure/shim/aimo_neuro_symbolic_prior_registry.py deleted file mode 100644 index ef0775ff..00000000 --- a/4-Infrastructure/shim/aimo_neuro_symbolic_prior_registry.py +++ /dev/null @@ -1,229 +0,0 @@ -#!/usr/bin/env python3 -"""Build receipted priors from the local AIMO neuro-symbolic deck. - -The local AIMO presentation is image-only, so this registry consumes the OCR -text generated from the PDF pages and records the design surface conservatively: -parser-first, stochastic-proposer, deterministic verifier, bounded fallback. -""" - -from __future__ import annotations - -import hashlib -import json -import re -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "aimo_sources" -PACKETS = OUT_DIR / "aimo_neuro_symbolic_prior_packets.jsonl" -RECEIPT = OUT_DIR / "aimo_neuro_symbolic_prior_receipt.json" - -SOURCES = { - "aimo_deck_pdf": Path("/home/allaun/Documents/ingest/AIMO_Presentation.pdf"), - "aimo_deck_ocr": OUT_DIR / "AIMO_Presentation_ocr.txt", - "cafa2_pdf": Path("/home/allaun/Documents/ingest/s13059-016-1037-6.pdf"), - "cafa2_text": OUT_DIR / "s13059-016-1037-6.txt", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def source_receipts() -> dict[str, dict[str, Any]]: - receipts: dict[str, dict[str, Any]] = {} - for key, path in SOURCES.items(): - if not path.exists(): - receipts[key] = {"path": str(path), "exists": False} - continue - data = path.read_bytes() - receipts[key] = { - "path": str(path), - "exists": True, - "bytes": len(data), - "sha256": sha256_bytes(data), - } - return receipts - - -def count_terms(text: str, terms: list[str]) -> dict[str, int]: - lowered = text.lower() - return {term: lowered.count(term.lower()) for term in terms} - - -def packet(packet_id: str, name: str, role: str, density_markers: list[str], route: str, claim_boundary: str) -> dict[str, Any]: - obj = { - "schema": "aimo_neuro_symbolic_prior_packet_v1", - "packet_id": packet_id, - "name": name, - "rrc_shape_hint": "NeuroSymbolicVerifierPipeline", - "role": role, - "density_markers": density_markers, - "route": route, - "claim_boundary": claim_boundary, - "decision": "HOLD", - } - obj["packet_hash"] = sha256_text(stable_json(obj)) - return obj - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - aimo_text = SOURCES["aimo_deck_ocr"].read_text(encoding="utf-8", errors="replace") - cafa_text = SOURCES["cafa2_text"].read_text(encoding="utf-8", errors="replace") - - packets = [ - packet( - packet_id="AIMO.PRIOR.PARSER_MANIFOLD.0001", - name="AIMO parser-first manifold alignment", - role="Parser fixes syntax, tags semantics, and assigns strategy before any model answer is trusted.", - density_markers=[ - "syntax_fix_layer", - "semantic_tagging_layer", - "strategy_assignment", - "latex_input_cleaning", - "garbage_in_hallucination_out_boundary", - ], - route="raw_problem -> parser/filter -> typed equation strategy -> proposer", - claim_boundary="OCR-derived design prior only; not a validated implementation.", - ), - packet( - packet_id="AIMO.PRIOR.STOCHASTIC_PROPOSER.0001", - name="AIMO low-temperature proposer", - role="LLM generates algebraic systems, not trusted final reasoning.", - density_markers=[ - "temperature_low_sampling", - "equation_only_prompt", - "heuristic_proposer", - "generation_length_penalty", - "multi_temperature_fallback", - ], - route="typed problem -> equation-only LLM proposal -> symbolic verifier", - claim_boundary="Proposer output is untrusted until deterministic replay/checks pass.", - ), - packet( - packet_id="AIMO.PRIOR.SYMPY_VERIFIER.0001", - name="AIMO deterministic symbolic verifier", - role="SymPy execution, substitution, and back-substitution reject unbalanced generated states.", - density_markers=[ - "sympy_execution", - "back_substitution_check", - "variable_sparsity_guard", - "equation_density_guard", - "fast_fail_operator_detection", - ], - route="candidate equations -> bounded SymPy solve -> substitute solution -> accept/reject", - claim_boundary="Symbolic checks are only as good as parser coverage and modeled constraints.", - ), - packet( - packet_id="AIMO.PRIOR.DUAL_VALIDATION_MATRIX.0001", - name="AIMO dual validation core matrix", - role="Cross-checks rule/math validation against neural answer consistency and fallback consensus.", - density_markers=[ - "rule_math_check_axis", - "neural_answer_axis", - "impossible_state_rejection", - "low_confidence_fallback", - "confidence_self_diagnosis", - ], - route="symbolic result + neural result -> confidence matrix -> strict integer extraction", - claim_boundary="A confidence matrix is a routing gate, not proof of mathematical correctness.", - ), - packet( - packet_id="AIMO.PRIOR.FAILSAFE_SUBMISSION.0001", - name="AIMO crash-safe integer fallback", - role="Maintains valid submission shape under fatal failures using deterministic fallback integer.", - density_markers=[ - "exception_guard", - "hash_fallback_integer", - "valid_output_range", - "vram_reclamation", - "symbolic_cache", - ], - route="exception -> deterministic hash fallback -> valid integer output", - claim_boundary="Submission safety prevents invalid output; it does not prevent wrong output.", - ), - packet( - packet_id="CAFA.PRIOR.PROTEIN_ONTOLOGY_EVAL.0001", - name="CAFA protein-function ontology evaluation", - role="Protein function prediction is graph-structured: protein-centric and term-centric evaluation over GO/HPO.", - density_markers=[ - "gene_ontology_graph", - "human_phenotype_ontology_graph", - "protein_centric_multilabel_output", - "term_centric_binary_ranking", - "ontology_specific_metrics", - ], - route="protein -> ontology term graph/ranking -> benchmark evaluation", - claim_boundary="Evaluation prior only; not a function-prediction proof or ProtBoost validation.", - ), - packet( - packet_id="SPX.PRIOR.LOSSLESS_SHARDING_RANS.0001", - name="SPX lossless sharding and rANS compression prior", - role="Image codec prior for deterministic, single-pass residual sharding and entropy coding.", - density_markers=[ - "reversible_color_transform", - "median_edge_prediction", - "stateless_sharding", - "bias_cancellation_residual_centering", - "interleaved_rans_entropy_coding", - ], - route="input field -> predictor residual -> shard context -> rANS stream -> bit-perfect replay", - claim_boundary="External README-derived prior; benchmark claims require local reproduction before promotion.", - ), - ] - - PACKETS.write_text("\n".join(stable_json(p) for p in packets) + "\n", encoding="utf-8") - aimo_terms = [ - "parser", - "sympy", - "verification", - "fallback", - "deterministic", - "temperature", - "equation", - "guardrail", - "hash", - ] - cafa_terms = [ - "ontology", - "protein-centric", - "term-centric", - "gene ontology", - "human phenotype ontology", - "benchmark", - "prediction", - ] - receipt = { - "schema": "aimo_neuro_symbolic_prior_receipt_v1", - "packet_count": len(packets), - "packets": str(PACKETS.relative_to(REPO)), - "source_receipts": source_receipts(), - "aimo_ocr_term_counts": count_terms(aimo_text, aimo_terms), - "cafa_term_counts": count_terms(cafa_text, cafa_terms), - "ocr_page_count": len(re.findall(r"===== page-", aimo_text)), - "density_marker_total": sum(len(p["density_markers"]) for p in packets), - "claim_boundary": ( - "AIMO deck is OCR-derived from local image slides; SPX is an external README prior; " - "CAFA text is extracted from local open-access PDF. All packets remain HOLD until " - "implementation, benchmark, or proof receipts close." - ), - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/alphaevolve_dd_experiment_card_runner.py b/4-Infrastructure/shim/alphaevolve_dd_experiment_card_runner.py deleted file mode 100644 index efb00381..00000000 --- a/4-Infrastructure/shim/alphaevolve_dd_experiment_card_runner.py +++ /dev/null @@ -1,363 +0,0 @@ -#!/usr/bin/env python3 -"""Build DD experiment-card receipts from the pulled AlphaEvolve gallery. - -This runner does not execute AlphaEvolve programs and does not import their -mathematical claims into the compressor. It turns the pulled public examples -into local decision-diagram task cards: - -* route-search objective text -* incumbent score fields -* generated/analysed counters where the pull exposes them -* applicability class for the projectable-geometry compressor -* strict promotion/failure gates that keep byte-exact rehydration authoritative -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -PULL = REPO / "4-Infrastructure" / "shim" / "alphaevolve_example_gallery_pull.json" -OUT = ( - REPO - / "4-Infrastructure" - / "shim" - / "alphaevolve_dd_experiment_card_receipt.json" -) - - -DIRECT_USE = { - "11b5bd33_f8f1_4f90_81b0_6eb607d1c2dc": { - "compression_role": "active_frontier_activation", - "extraction": ( - "Activate reachable transform states without materializing the full " - "16D torus or hypercube surface." - ), - }, - "963c9114_4a7d_4870_b015_865c8e7235e7": { - "compression_role": "rehydration_from_local_witnesses", - "extraction": ( - "Reconstruct the global byte object from local witnesses, with the " - "rehydration hash as authority." - ), - }, - "58693cb6_5bce_4219_bb14_a064a87e3117": { - "compression_role": "failure_ranking", - "extraction": ( - "Assign route failures a well-founded decreasing rank so repair " - "does not become recursive search." - ), - }, - "98c69bce_fe46_4008_a78c_30e16b51ab8e": { - "compression_role": "sequence_transform_stress", - "extraction": ( - "Stress transform sequences by worst monotone sidecar and residual " - "growth." - ), - }, - "6d1433b9_a0b7_45b3_9cc7_bf7fdb4ddd53": { - "compression_role": "tokenbook_tradeoff", - "extraction": ( - "Score tokenbook additions against residual differences; dictionary " - "expressiveness is not enough without receipt gain." - ), - }, - "71958997_88f3_4055_8284_bec06b6e7fc1": { - "compression_role": "carrier_capacity", - "extraction": ( - "Use bounded packing pressure to test whether sidecars, witnesses, " - "and repair packets fit in the carrier budget." - ), - }, - "2e9c383f_d87f_4c27_ad2c_4c0960c5e04e": { - "compression_role": "carrier_capacity", - "extraction": ( - "Use bounded packing pressure to test tight carrier capacity before " - "route promotion." - ), - }, -} - -DIVERSITY_GEOMETRY_PRESSURE = { - "f5ff0dbd_0bb3_4c6b_9bf7_6a98363b935e": "route_diversity_pressure", - "2fdd52c5_1bfb_4f3e_90b4_e9e40ce956e5": "pairwise_route_interference", - "8177393c_d974_4ea4_a94a_0a86760e72e7": "overlapping_sidecar_coverage", - "d1c84781_a661_49e0_9086_9f69967ef89f": "basis_exchange_pressure", - "bdda954a_99b4_4137_a469_6adb535d63d5": "orientation_invariant_route_test", - "aa66c428_98bb_4fa1_8da0_b7ef686ee54a": "invariant_class_route_test", -} - -BACKGROUND_ONLY = { - "52293977_6793_49d7_b09e_41b2324f6c9f": "functional_quotient_background", - "e6797d2f_e480_4e18_bff9_f708c00cfb59": "norm_inequality_background", - "413b3ea9_5aee_43a6_8147_e514d7dd9682": "norm_quotient_background", - "9a90ae12_ea5d_4783_bcc4_72e0d18f55aa": "fourier_norm_background", - "f507d54b_bba8_427f_8b39_7f06c9aaae1f": "olympiad_problem_background", -} - -PROMOTION_GATE = ( - "local_evaluator_is_reproducible", - "best_score_has_receipt", - "route_candidate_code_is_archived", - "rehydration_hash_matches", - "byte_count_beats_incumbent", - "witness_and_sidecar_budget_is_bounded", - "nan0_status_is_false", -) - -FAILURE_GATE = ( - "score_without_decode_hash_is_not_promoted", - "gallery_score_is_not_transferable_compression_evidence", - "unbounded_artifact_side_channel_is_pruned", - "recursive_repair_search_becomes_nan0", -) - -CLAIM_BOUNDARY = ( - "AlphaEvolve examples are imported only as evaluator/card/search-shape " - "priors. Compression promotion still requires local encode/decode/hash/" - "byte-count receipts." -) - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def intish(value: Any) -> int | None: - if value is None or isinstance(value, bool): - return None - if isinstance(value, int): - return value - if isinstance(value, float) and value.is_integer(): - return int(value) - return None - - -def derive_program_count(summary: dict[str, Any], experiment: dict[str, Any]) -> int: - stats = experiment.get("stats") or {} - keys = ( - "programs_generated", - "programs_analysed", - "best_program_count", - ) - stat_keys = ( - "database/num_programs_seen_map_elites", - "database/num_programs_registered_map_elites", - "database/num_programs_seen_islands", - "database/num_programs_registered_islands", - "database/programs_map_elites", - ) - values = [intish(summary.get(key)) for key in keys] - values.extend(intish(stats.get(key)) for key in stat_keys) - numeric = [value for value in values if value is not None] - return max(numeric) if numeric else 0 - - -def analysed_count(summary: dict[str, Any], experiment: dict[str, Any]) -> int: - stats = experiment.get("stats") or {} - candidates = ( - intish(summary.get("programs_analysed")), - intish(stats.get("analyser/programs_analysed")), - ) - numeric = [value for value in candidates if value is not None] - return max(numeric) if numeric else 0 - - -def classify(experiment_id: str) -> tuple[str, str, str]: - if experiment_id in DIRECT_USE: - item = DIRECT_USE[experiment_id] - return "direct", item["compression_role"], item["extraction"] - if experiment_id in DIVERSITY_GEOMETRY_PRESSURE: - role = DIVERSITY_GEOMETRY_PRESSURE[experiment_id] - return ( - "diversity_geometry_pressure", - role, - "Use as route-diversity or geometry pressure, not as proof.", - ) - if experiment_id in BACKGROUND_ONLY: - role = BACKGROUND_ONLY[experiment_id] - return ( - "background_only", - role, - "Keep as background search intuition only.", - ) - return ( - "unclassified", - "none", - "No local compression extraction has been assigned.", - ) - - -def normalize_best_scores(best_scores: list[dict[str, Any]]) -> list[dict[str, Any]]: - normalized = [] - for score in best_scores: - normalized.append({ - "gid": score.get("gid"), - "name": score.get("name"), - "score": score.get("score"), - }) - return normalized - - -def build_card(page: dict[str, Any]) -> dict[str, Any]: - summary = page["summary"] - experiment = page.get("experiment") or {} - experiment_id = summary["id"] - applicability, role, extraction = classify(experiment_id) - best_scores = normalize_best_scores(summary.get("best_scores") or []) - return { - "experiment_id": experiment_id, - "title": summary.get("title"), - "url": summary.get("url"), - "description": summary.get("description"), - "created_time": summary.get("created_time"), - "status": summary.get("status"), - "applicability_class": applicability, - "compression_role": role, - "compression_extraction": extraction, - "route_search_mapping": { - "experiment_card": "route_search_task_card", - "problem_statement": "transform_family_objective", - "best_score": "incumbent_compressed_byte_receipt", - "program_count": "route_candidate_count", - "analysed_count": "evaluated_route_count", - "score_names": "pareto_or_diagnostic_receipt_fields", - }, - "score_names": summary.get("score_names") or [], - "best_scores": best_scores, - "program_count": derive_program_count(summary, experiment), - "analysed_count": analysed_count(summary, experiment), - "best_program_count": intish(summary.get("best_program_count")) or 0, - "evaluator_count": intish(summary.get("evaluator_count")) or 0, - "prompt_count": intish(summary.get("prompt_count")) or 0, - "evaluator_doc_pulled": bool(page.get("evaluators")), - "prompt_doc_pulled": bool(page.get("prompts")), - "best_program_docs_pulled": len(page.get("best_programs") or []), - "archive_policy": { - "best_programs": "referenced_best_program_documents_pulled", - "full_program_collection": "omitted", - "candidate_code_required_for_promotion": True, - }, - "promotion_gate": list(PROMOTION_GATE), - "failure_gate": list(FAILURE_GATE), - "claim_boundary": CLAIM_BOUNDARY, - } - - -def validate(cards: list[dict[str, Any]], source_page_count: int) -> list[str]: - errors: list[str] = [] - ids = [card["experiment_id"] for card in cards] - if len(cards) != source_page_count: - errors.append(f"card count {len(cards)} != source page count {source_page_count}") - if len(set(ids)) != len(ids): - errors.append("duplicate experiment ids in cards") - expected = set(DIRECT_USE) | set(DIVERSITY_GEOMETRY_PRESSURE) | set(BACKGROUND_ONLY) - actual = set(ids) - missing = sorted(expected - actual) - extra = sorted(actual - expected) - if missing: - errors.append(f"expected experiment ids missing from pull: {missing}") - if extra: - errors.append(f"unclassified experiment ids in pull: {extra}") - for card in cards: - if not card["best_scores"]: - errors.append(f"{card['experiment_id']} has no best scores") - if card["analysed_count"] <= 0: - errors.append(f"{card['experiment_id']} has no analysed count") - if card["evaluator_count"] <= 0 or not card["evaluator_doc_pulled"]: - errors.append(f"{card['experiment_id']} has no pulled evaluator document") - if card["prompt_count"] <= 0 or not card["prompt_doc_pulled"]: - errors.append(f"{card['experiment_id']} has no pulled prompt document") - return errors - - -def build_receipt(input_path: Path) -> dict[str, Any]: - pull = load_json(input_path) - cards = [build_card(page) for page in pull.get("pages", [])] - errors = validate(cards, int(pull.get("page_count", len(cards)))) - if errors: - raise SystemExit("validation failed:\n" + "\n".join(f"- {error}" for error in errors)) - - class_counts: dict[str, int] = {} - for card in cards: - key = card["applicability_class"] - class_counts[key] = class_counts.get(key, 0) + 1 - - direct_use_cards = [ - { - "experiment_id": card["experiment_id"], - "title": card["title"], - "compression_role": card["compression_role"], - "compression_extraction": card["compression_extraction"], - } - for card in cards - if card["applicability_class"] == "direct" - ] - - receipt_without_hash = { - "generated_at_utc": pull.get("pulled_at_utc"), - "source_pull_path": str(input_path.relative_to(REPO)), - "source_pull_hash": sha256_bytes(input_path.read_bytes()), - "source": pull.get("source"), - "root_firestore_collection": pull.get("root_firestore_collection"), - "strategy": pull.get("strategy"), - "card_count": len(cards), - "class_counts": class_counts, - "validation": { - "all_pages_accounted": True, - "no_duplicate_experiment_ids": True, - "all_cards_have_best_scores": True, - "all_cards_have_evaluator_docs": True, - "full_generated_program_collections_pulled": False, - }, - "runner_policy": { - "does_not_execute_gallery_programs": True, - "does_not_import_gallery_scores_as_compression_evidence": True, - "keeps_decode_hash_authoritative": True, - "claim_boundary": CLAIM_BOUNDARY, - }, - "compression_runner_shape": { - "candidate_generator": "route_generator", - "task_local_evaluator": "encode_decode_hash_byte_count", - "best_so_far_score": "incumbent_byte_count", - "analysed_count": "evaluated_route_count", - "failure_packet": "nan0_or_prune_receipt", - "archived_program_candidate": "archived_transform_recipe", - }, - "promotion_gate": list(PROMOTION_GATE), - "failure_gate": list(FAILURE_GATE), - "direct_use_cards": direct_use_cards, - "cards": cards, - } - receipt = dict(receipt_without_hash) - receipt["receipt_hash"] = sha256_bytes(stable_json(receipt_without_hash).encode("utf-8")) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--input", type=Path, default=PULL) - parser.add_argument("--output", type=Path, default=OUT) - args = parser.parse_args() - - receipt = build_receipt(args.input) - args.output.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"wrote {args.output.relative_to(REPO)}") - print(f"receipt_hash {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/alphafold_bulk_structure_prior_registry.py b/4-Infrastructure/shim/alphafold_bulk_structure_prior_registry.py deleted file mode 100644 index 9d7a5e7c..00000000 --- a/4-Infrastructure/shim/alphafold_bulk_structure_prior_registry.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 -"""Register AlphaFold DB bulk downloads as a receipted biological structure prior. - -This registry does not download AlphaFold archives. It records the external -download surface, license boundary, citation requirements, and Research Stack -route value for RRC/Omindirection as structured metadata. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "biological_structure_priors" -PACKETS = OUT_DIR / "alphafold_bulk_structure_prior_packets.jsonl" -RECEIPT = OUT_DIR / "alphafold_bulk_structure_prior_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def packet(packet_id: str, name: str, route: str, density_markers: list[str], claim_boundary: str) -> dict[str, Any]: - obj = { - "schema": "alphafold_bulk_structure_prior_packet_v1", - "packet_id": packet_id, - "name": name, - "source_url": "https://alphafold.ebi.ac.uk/download", - "ftp_url": "https://ftp.ebi.ac.uk/pub/databases/alphafold", - "license": "CC-BY-4.0", - "rrc_shape_hint": "PredictedProteinStructureCorpus", - "route": route, - "density_markers": density_markers, - "claim_boundary": claim_boundary, - "decision": "HOLD", - } - obj["packet_hash"] = sha256_text(stable_json(obj)) - return obj - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = [ - packet( - packet_id="ALPHAFOLD.BULK.MODEL_ORGANISM_PROTEOMES.0001", - name="AlphaFold model-organism proteome archives", - route=( - "reference proteome -> compressed PDB/mmCIF archive -> confidence-bearing " - "predicted structure graph -> domain/boundary/fragment route" - ), - density_markers=[ - "reference_proteome_archive", - "compressed_pdb_mmcif_members", - "per_residue_confidence_surface", - "long_protein_fragmentation", - "species_level_structure_corpus", - "ftp_bulk_download_surface", - ], - claim_boundary=( - "Prediction corpus only; structures are theoretical models and require confidence, " - "metadata, and domain-specific validation before biological or compression claims." - ), - ), - packet( - packet_id="ALPHAFOLD.BULK.SWISSPROT.0001", - name="AlphaFold Swiss-Prot bulk structure archives", - route=( - "Swiss-Prot sequence set -> predicted structure archive -> high-curation " - "protein-shape dictionary -> residue/contact/topology prior" - ), - density_markers=[ - "swissprot_curated_sequence_anchor", - "cif_archive_surface", - "pdb_archive_surface", - "protein_shape_dictionary", - "sequence_to_structure_projection", - "structure_metadata_citation_gate", - ], - claim_boundary=( - "Useful as a curated structure prior; not a replacement for experimental structure " - "or clinical evidence." - ), - ), - packet( - packet_id="ALPHAFOLD.BULK.COLLABORATOR_DATASETS.0001", - name="AlphaFold collaborator dataset archives", - route=( - "collaborator dataset -> chunked coordinate archive / optional MSA archive " - "-> dataset-specific structure prior -> source-specific citation gate" - ), - density_markers=[ - "collaborator_coordinate_chunks", - "optional_msa_surface", - "dataset_specific_availability", - "third_party_copyright_boundary", - "source_specific_citation_gate", - "nonclinical_prediction_disclaimer", - ], - claim_boundary=( - "Collaborator datasets have additional source-specific copyrights and metadata; " - "local ingest must preserve those boundaries." - ), - ), - ] - - PACKETS.write_text("\n".join(stable_json(p) for p in packets) + "\n", encoding="utf-8") - receipt = { - "schema": "alphafold_bulk_structure_prior_receipt_v1", - "packet_count": len(packets), - "density_marker_total": sum(len(p["density_markers"]) for p in packets), - "packets": str(PACKETS.relative_to(REPO)), - "source_url": "https://alphafold.ebi.ac.uk/download", - "ftp_url": "https://ftp.ebi.ac.uk/pub/databases/alphafold", - "license": "CC-BY-4.0", - "license_boundary": ( - "AlphaFold DB data is listed as available for academic and commercial use under " - "CC-BY-4.0. Use must preserve attribution, cite required papers, honor EMBL-EBI " - "terms, and respect dataset-specific copyright notices." - ), - "disclaimer_boundary": ( - "AlphaFold and AlphaMissense data are predictions for theoretical modelling, provided " - "as-is, not validated or approved for clinical use, and not a substitute for medical advice." - ), - "download_boundary": ( - "No bulk archives are downloaded by this registry. Archive ingest must be explicit, " - "chunked, receipted, and storage-budgeted." - ), - "required_citation_gate": [ - "AlphaFold Protein Structure Database and 3D-Beacons: New Data and Capabilities", - "Relevant structure publication or dataset metadata", - "Jumper et al. AlphaFold Nature 2021 for UniProt predictions where applicable", - ], - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/alphafold_species_density_eigen_probe.py b/4-Infrastructure/shim/alphafold_species_density_eigen_probe.py deleted file mode 100644 index 4e3669a7..00000000 --- a/4-Infrastructure/shim/alphafold_species_density_eigen_probe.py +++ /dev/null @@ -1,202 +0,0 @@ -#!/usr/bin/env python3 -"""AlphaFold species-density eigen probe. - -This is a metadata-only first pass. It uses AlphaFold DB download-page archive -metadata (predicted structure counts and archive sizes) to form a species x -density-feature matrix, then computes covariance eigenvectors and a normalized -positive semidefinite density matrix. - -It does not download AlphaFold structure archives. Full protein-coordinate -eigenvectors require explicit archive ingest and pLDDT/contact/topology parsing. -""" - -from __future__ import annotations - -import csv -import hashlib -import json -import math -from pathlib import Path -from typing import Any - -import numpy as np - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "biological_structure_priors" -MATRIX_CSV = OUT_DIR / "alphafold_species_density_matrix.csv" -EIGEN_JSON = OUT_DIR / "alphafold_species_density_eigen_probe.json" -RECEIPT = OUT_DIR / "alphafold_species_density_eigen_probe_receipt.json" - - -SOURCE_URL = "https://alphafold.ebi.ac.uk/download" - - -SPECIES_ROWS = [ - # model organism proteomes - ("model_organism", "Arabidopsis thaliana", "Arabidopsis", "UP000006548", 27402, 3698), - ("model_organism", "Caenorhabditis elegans", "Nematode worm", "UP000001940", 19700, 2649), - ("model_organism", "Candida albicans", "C. albicans", "UP000000559", 5973, 981), - ("model_organism", "Danio rerio", "Zebrafish", "UP000000437", 26290, 4749), - ("model_organism", "Dictyostelium discoideum", "Dictyostelium", "UP000002195", 12612, 2187), - ("model_organism", "Drosophila melanogaster", "Fruit fly", "UP000000803", 13461, 2213), - ("model_organism", "Escherichia coli", "E. coli", "UP000000625", 4370, 456), - ("model_organism", "Glycine max", "Soybean", "UP000008827", 55796, 7264), - ("model_organism", "Homo sapiens", "Human", "UP000005640", 23586, 4938), - ("model_organism", "Methanocaldococcus jannaschii", "M. jannaschii", "UP000000805", 1773, 174), - ("model_organism", "Mus musculus", "Mouse", "UP000000589", 21452, 3607), - ("model_organism", "Oryza sativa", "Asian rice", "UP000059680", 43645, 4505), - ("model_organism", "Rattus norvegicus", "Rat", "UP000002494", 22152, 3602), - ("model_organism", "Saccharomyces cerevisiae", "Budding yeast", "UP000002311", 6055, 977), - ("model_organism", "Schizosaccharomyces pombe", "Fission yeast", "UP000002485", 5196, 803), - ("model_organism", "Zea mays", "Maize", "UP000007305", 39139, 4792), - # global health proteomes - ("global_health", "Ajellomyces capsulatus", "Ajellomyces capsulatus", "UP000001631", 9199, 1363), - ("global_health", "Brugia malayi", "Brugia malayi", "UP000006672", 10972, 1635), - ("global_health", "Campylobacter jejuni", "C. jejuni", "UP000000799", 1620, 175), - ("global_health", "Cladophialophora carrionii", "Cladophialophora carrionii", "UP000094526", 11170, 1729), - ("global_health", "Dracunculus medinensis", "Dracunculus medinensis", "UP000274756", 10834, 1364), - ("global_health", "Fonsecaea pedrosoi", "Fonsecaea pedrosoi", "UP000053029", 12509, 2014), - ("global_health", "Haemophilus influenzae", "H. influenzae", "UP000000579", 1660, 175), - ("global_health", "Helicobacter pylori", "H. pylori", "UP000000429", 1540, 166), - ("global_health", "Klebsiella pneumoniae", "K. pneumoniae", "UP000007841", 5727, 559), - ("global_health", "Leishmania infantum", "L. infantum", "UP000008153", 7924, 1508), - ("global_health", "Madurella mycetomatis", "Madurella mycetomatis", "UP000078237", 9561, 1537), - ("global_health", "Mycobacterium leprae", "Mycobacterium leprae", "UP000000806", 1602, 177), - ("global_health", "Mycobacterium tuberculosis", "M. tuberculosis", "UP000001584", 3991, 429), - ("global_health", "Neisseria gonorrhoeae", "N. gonorrhoeae", "UP000000535", 2106, 195), - ("global_health", "Nocardia brasiliensis", "Nocardia brasiliensis", "UP000006304", 8398, 873), - ("global_health", "Onchocerca volvulus", "Onchocerca volvulus", "UP000024404", 12039, 1621), - ("global_health", "Paracoccidioides lutzii", "Paracoccidioides lutzii", "UP000002059", 8794, 1294), - ("global_health", "Plasmodium falciparum", "P. falciparum", "UP000001450", 5168, 1148), - ("global_health", "Pseudomonas aeruginosa", "P. aeruginosa", "UP000002438", 5555, 613), - ("global_health", "Salmonella typhimurium", "S. typhimurium", "UP000001014", 4526, 477), - ("global_health", "Schistosoma mansoni", "Schistosoma mansoni", "UP000008854", 9735, 1802), - ("global_health", "Shigella dysenteriae", "S. dysenteriae", "UP000002716", 3893, 373), - ("global_health", "Sporothrix schenckii", "Sporothrix schenckii", "UP000018087", 8652, 1519), - ("global_health", "Staphylococcus aureus", "S. aureus", "UP000008816", 2888, 274), - ("global_health", "Streptococcus pneumoniae", "S. pneumoniae", "UP000000586", 2031, 202), - ("global_health", "Strongyloides stercoralis", "Strongyloides stercoralis", "UP000035681", 15335, 2793), - ("global_health", "Trichuris trichiura", "Trichuris trichiura", "UP000030665", 9563, 1362), - ("global_health", "Trypanosoma brucei", "Trypanosoma brucei", "UP000008524", 8491, 1345), - ("global_health", "Trypanosoma cruzi", "T. cruzi", "UP000002296", 19036, 2959), - ("global_health", "Wuchereria bancrofti", "Wuchereria bancrofti", "UP000270924", 12725, 1418), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def feature_row(row: tuple[str, str, str, str, int, int]) -> dict[str, Any]: - category, species, common, proteome, predicted, archive_mb = row - mb_per_structure = archive_mb / predicted - structures_per_mb = predicted / archive_mb - return { - "category": category, - "species": species, - "common_name": common, - "reference_proteome": proteome, - "predicted_structures": predicted, - "archive_mb": archive_mb, - "mb_per_structure": mb_per_structure, - "structures_per_mb": structures_per_mb, - "log_predicted_structures": math.log1p(predicted), - "log_archive_mb": math.log1p(archive_mb), - "is_model_organism": 1.0 if category == "model_organism" else 0.0, - "is_global_health": 1.0 if category == "global_health" else 0.0, - } - - -def standardized_matrix(rows: list[dict[str, Any]], fields: list[str]) -> np.ndarray: - matrix = np.array([[float(row[field]) for field in fields] for row in rows], dtype=float) - mean = matrix.mean(axis=0) - std = matrix.std(axis=0) - std[std == 0] = 1.0 - return (matrix - mean) / std - - -def species_scores(z: np.ndarray, eigenvectors: np.ndarray, species: list[str], count: int = 8) -> list[dict[str, Any]]: - scores = z @ eigenvectors[:, 0] - order = np.argsort(np.abs(scores))[::-1][:count] - return [{"species": species[i], "pc1_score": float(scores[i]), "abs_pc1_score": float(abs(scores[i]))} for i in order] - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - rows = [feature_row(row) for row in SPECIES_ROWS] - fields = [ - "log_predicted_structures", - "log_archive_mb", - "mb_per_structure", - "structures_per_mb", - "is_model_organism", - "is_global_health", - ] - with MATRIX_CSV.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter(handle, fieldnames=list(rows[0].keys())) - writer.writeheader() - writer.writerows(rows) - - z = standardized_matrix(rows, fields) - covariance = (z.T @ z) / max(len(rows) - 1, 1) - eigenvalues, eigenvectors = np.linalg.eigh(covariance) - order = np.argsort(eigenvalues)[::-1] - eigenvalues = eigenvalues[order] - eigenvectors = eigenvectors[:, order] - explained = eigenvalues / eigenvalues.sum() - - psd = covariance.copy() - psd_trace = float(np.trace(psd)) - density_matrix = psd / psd_trace if psd_trace else psd - purity = float(np.trace(density_matrix @ density_matrix)) - effective_rank = float(1.0 / purity) if purity else float("inf") - - eigen_probe = { - "schema": "alphafold_species_density_eigen_probe_v1", - "source_url": SOURCE_URL, - "row_count": len(rows), - "feature_fields": fields, - "eigenvalues": [float(x) for x in eigenvalues], - "explained_variance_ratio": [float(x) for x in explained], - "principal_eigenvector": {field: float(eigenvectors[i, 0]) for i, field in enumerate(fields)}, - "density_matrix": density_matrix.tolist(), - "density_matrix_trace": float(np.trace(density_matrix)), - "density_matrix_purity": purity, - "effective_rank": effective_rank, - "top_pc1_species": species_scores(z, eigenvectors, [row["species"] for row in rows]), - "claim_boundary": ( - "This is an archive-metadata eigen probe over species counts and compressed archive " - "sizes, not a coordinate-structure eigensystem over AlphaFold models. Full species " - "shape eigenvectors require explicit archive ingest and structure parsing." - ), - "decision": "HOLD", - } - eigen_probe["probe_hash"] = sha256_text(stable_json(eigen_probe)) - EIGEN_JSON.write_text(json.dumps(eigen_probe, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - receipt = { - "schema": "alphafold_species_density_eigen_probe_receipt_v1", - "source_url": SOURCE_URL, - "matrix_csv": str(MATRIX_CSV.relative_to(REPO)), - "eigen_json": str(EIGEN_JSON.relative_to(REPO)), - "row_count": len(rows), - "feature_count": len(fields), - "dominant_explained_variance": float(explained[0]), - "density_matrix_purity": purity, - "effective_rank": effective_rank, - "top_pc1_species": eigen_probe["top_pc1_species"], - "claim_boundary": eigen_probe["claim_boundary"], - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/arxiv_equation_pattern_extractor.py b/4-Infrastructure/shim/arxiv_equation_pattern_extractor.py deleted file mode 100644 index 8621b9a9..00000000 --- a/4-Infrastructure/shim/arxiv_equation_pattern_extractor.py +++ /dev/null @@ -1,488 +0,0 @@ -#!/usr/bin/env python3 -"""Extract equation-pattern features from a bounded arXiv source sample. - -The extractor downloads source packages from arXiv e-print URLs into an external -cache, reads TeX files, and emits equation hashes plus structural features. It -does not store equation text in the repo. -""" - -from __future__ import annotations - -import argparse -import gzip -import hashlib -import io -import json -import re -import tarfile -import time -import zipfile -from collections import Counter, defaultdict -from pathlib import Path -from typing import Any -from urllib.error import HTTPError, URLError -from urllib.request import Request, urlopen - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_ARCHIVE = Path("/home/allaun/Documents/ingest/kaggledataset.zip") -DEFAULT_CACHE = Path("/home/allaun/Documents/ingest/arxiv_sources") -OUT_DIR = REPO / "shared-data" / "data" / "math_research_databases" -EQUATION_JSONL = OUT_DIR / "arxiv_equation_pattern_records.jsonl" -PAPER_JSONL = OUT_DIR / "arxiv_equation_pattern_papers.jsonl" -COMMAND_CSV = OUT_DIR / "arxiv_equation_pattern_commands.csv" -SUMMARY = OUT_DIR / "arxiv_equation_pattern_summary.json" -RECEIPT = OUT_DIR / "arxiv_equation_pattern_receipt.json" - - -MATH_PREFIXES = ( - "math", - "stat", - "cs.", - "nlin", - "q-bio", - "q-fin", - "quant-ph", - "hep-th", - "math-ph", -) - -DISPLAY_ENV_NAMES = ( - "equation", - "equation*", - "align", - "align*", - "gather", - "gather*", - "multline", - "multline*", - "eqnarray", - "eqnarray*", - "displaymath", - "flalign", - "flalign*", - "alignat", - "alignat*", -) -DISPLAY_ENV_RE = re.compile( - r"\\begin\{(" + "|".join(re.escape(name) for name in DISPLAY_ENV_NAMES) + r")\}(.*?)\\end\{\1\}", - re.DOTALL, -) -BRACKET_DISPLAY_RE = re.compile(r"\\\[(.*?)\\\]", re.DOTALL) -DOUBLE_DOLLAR_RE = re.compile(r"\$\$(.*?)\$\$", re.DOTALL) -INLINE_DOLLAR_RE = re.compile(r"(?=|!=|:=|->|=>|[=<>+\-*/^_&|])") -GREEK_COMMANDS = { - "\\alpha", - "\\beta", - "\\gamma", - "\\delta", - "\\epsilon", - "\\varepsilon", - "\\zeta", - "\\eta", - "\\theta", - "\\vartheta", - "\\iota", - "\\kappa", - "\\lambda", - "\\mu", - "\\nu", - "\\xi", - "\\pi", - "\\rho", - "\\sigma", - "\\tau", - "\\upsilon", - "\\phi", - "\\varphi", - "\\chi", - "\\psi", - "\\omega", - "\\Gamma", - "\\Delta", - "\\Theta", - "\\Lambda", - "\\Xi", - "\\Pi", - "\\Sigma", - "\\Phi", - "\\Psi", - "\\Omega", -} -FAMILY_COMMANDS = { - "fraction": {"\\frac", "\\dfrac", "\\tfrac"}, - "integral": {"\\int", "\\iint", "\\iiint", "\\oint"}, - "summation": {"\\sum", "\\prod", "\\coprod"}, - "limit": {"\\lim", "\\sup", "\\inf", "\\max", "\\min"}, - "set": {"\\in", "\\subset", "\\subseteq", "\\cup", "\\cap", "\\setminus", "\\emptyset"}, - "arrow": {"\\to", "\\rightarrow", "\\leftarrow", "\\mapsto", "\\Rightarrow", "\\Longrightarrow"}, - "inequality": {"\\le", "\\leq", "\\ge", "\\geq", "\\neq", "\\sim", "\\simeq", "\\approx"}, - "font": {"\\mathbb", "\\mathcal", "\\mathbf", "\\mathrm", "\\mathfrak", "\\operatorname"}, - "root": {"\\sqrt"}, - "matrix": {"\\matrix", "\\pmatrix", "\\bmatrix", "\\begin"}, -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def categories_of(record: dict[str, Any]) -> list[str]: - raw = record.get("categories") - if not isinstance(raw, str): - return [] - return [part for part in raw.split() if part] - - -def is_math_bearing(categories: list[str]) -> bool: - return any(cat.startswith(MATH_PREFIXES) for cat in categories) - - -def text_field(record: dict[str, Any], key: str) -> str: - value = record.get(key) - return value if isinstance(value, str) else "" - - -def abstract_marker_score(record: dict[str, Any]) -> int: - text = "\n".join([text_field(record, "title"), text_field(record, "abstract")]) - return len(COMMAND_RE.findall(text)) + len(INLINE_DOLLAR_RE.findall(text)) - - -def iter_candidate_records(archive: Path, max_scan: int, max_papers: int, min_abstract_markers: int) -> list[dict[str, Any]]: - candidates: list[dict[str, Any]] = [] - with zipfile.ZipFile(archive) as zf: - infos = zf.infolist() - json_members = [info for info in infos if info.filename.endswith(".json")] - selected_member = json_members[0] if json_members else infos[0] - with zf.open(selected_member) as handle: - for seen, raw_line in enumerate(handle, start=1): - if max_scan and seen > max_scan: - break - record = json.loads(raw_line) - cats = categories_of(record) - if not is_math_bearing(cats): - continue - score = abstract_marker_score(record) - if score < min_abstract_markers: - continue - candidates.append( - { - "id": record.get("id"), - "title": record.get("title"), - "categories": cats, - "primary_category": cats[0] if cats else "uncategorized", - "abstract_marker_score": score, - } - ) - if len(candidates) >= max_papers: - break - return candidates - - -def records_by_id(archive: Path, wanted_ids: set[str], max_scan: int) -> list[dict[str, Any]]: - found: list[dict[str, Any]] = [] - with zipfile.ZipFile(archive) as zf: - infos = zf.infolist() - json_members = [info for info in infos if info.filename.endswith(".json")] - selected_member = json_members[0] if json_members else infos[0] - with zf.open(selected_member) as handle: - for seen, raw_line in enumerate(handle, start=1): - if max_scan and seen > max_scan: - break - record = json.loads(raw_line) - arxiv_id = str(record.get("id", "")) - if arxiv_id not in wanted_ids: - continue - cats = categories_of(record) - found.append( - { - "id": arxiv_id, - "title": record.get("title"), - "categories": cats, - "primary_category": cats[0] if cats else "uncategorized", - "abstract_marker_score": abstract_marker_score(record), - "doi": record.get("doi"), - "versions": record.get("versions"), - } - ) - if len(found) >= len(wanted_ids): - break - missing = wanted_ids - {str(row["id"]) for row in found} - for arxiv_id in sorted(missing): - found.append( - { - "id": arxiv_id, - "title": None, - "categories": [], - "primary_category": "uncategorized", - "abstract_marker_score": 0, - "metadata_missing": True, - } - ) - return found - - -def fetch_source(arxiv_id: str, cache_dir: Path, delay_seconds: float) -> tuple[Path | None, str | None]: - cache_dir.mkdir(parents=True, exist_ok=True) - safe_id = arxiv_id.replace("/", "_") - cached = cache_dir / f"{safe_id}.src" - if cached.exists() and cached.stat().st_size > 0: - return cached, None - url = f"https://arxiv.org/e-print/{arxiv_id}" - req = Request(url, headers={"User-Agent": "ResearchStack equation-pattern extractor (local bounded study)"}) - try: - with urlopen(req, timeout=60) as response: - data = response.read() - except (HTTPError, URLError, TimeoutError) as exc: - return None, repr(exc) - cached.write_bytes(data) - if delay_seconds > 0: - time.sleep(delay_seconds) - return cached, None - - -def strip_comments(tex: str) -> str: - lines = [] - for line in tex.splitlines(): - out = [] - escaped = False - for ch in line: - if ch == "%" and not escaped: - break - out.append(ch) - escaped = ch == "\\" and not escaped - lines.append("".join(out)) - return "\n".join(lines) - - -def read_tex_sources(path: Path, max_tex_bytes: int) -> tuple[list[tuple[str, str]], str]: - data = path.read_bytes() - tex_files: list[tuple[str, str]] = [] - mode = "plain" - try: - with tarfile.open(fileobj=io.BytesIO(data), mode="r:*") as tf: - mode = "tar" - for member in tf.getmembers(): - if not member.isfile() or not member.name.lower().endswith((".tex", ".ltx")): - continue - if member.size > max_tex_bytes: - continue - extracted = tf.extractfile(member) - if extracted is None: - continue - text = extracted.read(max_tex_bytes).decode("utf-8", errors="replace") - tex_files.append((member.name, text)) - return tex_files, mode - except tarfile.TarError: - pass - try: - text = gzip.decompress(data).decode("utf-8", errors="replace") - mode = "gzip_plain" - except OSError: - text = data.decode("utf-8", errors="replace") - if "\\documentclass" in text or "\\begin" in text: - tex_files.append((path.name, text[:max_tex_bytes])) - return tex_files, mode - - -def extract_equations(tex: str) -> list[tuple[str, str]]: - clean = strip_comments(tex) - equations: list[tuple[str, str]] = [] - for match in DISPLAY_ENV_RE.finditer(clean): - equations.append((match.group(1), match.group(2))) - for match in BRACKET_DISPLAY_RE.finditer(clean): - equations.append(("bracket_display", match.group(1))) - for match in DOUBLE_DOLLAR_RE.finditer(clean): - equations.append(("double_dollar", match.group(1))) - for match in INLINE_DOLLAR_RE.finditer(clean): - equations.append(("inline_dollar", match.group(1))) - return equations - - -def equation_features(equation: str) -> dict[str, Any]: - normalized = " ".join(equation.split()) - commands = COMMAND_RE.findall(normalized) - operators = OPERATOR_RE.findall(normalized) - command_counts = Counter(commands) - families = { - family: sum(command_counts.get(cmd, 0) for cmd in cmds) - for family, cmds in FAMILY_COMMANDS.items() - } - greek_count = sum(command_counts.get(cmd, 0) for cmd in GREEK_COMMANDS) - signature_parts = [] - for family, count in sorted(families.items()): - if count: - signature_parts.append(f"{family}:{count}") - if greek_count: - signature_parts.append(f"greek:{greek_count}") - signature_parts.append(f"cmds:{len(commands)}") - signature_parts.append(f"ops:{len(operators)}") - signature = "|".join(signature_parts) - return { - "equation_sha256": sha256_text(normalized), - "char_length": len(normalized), - "command_count": len(commands), - "operator_count": len(operators), - "greek_command_count": greek_count, - "family_counts": families, - "top_commands": command_counts.most_common(20), - "signature": signature, - "signature_hash": sha256_text(signature), - } - - -def csv_escape(value: Any) -> str: - text = str(value).replace('"', '""') - return f'"{text}"' - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--archive", default=str(DEFAULT_ARCHIVE)) - parser.add_argument("--cache-dir", default=str(DEFAULT_CACHE)) - parser.add_argument("--max-scan", type=int, default=5000) - parser.add_argument("--max-papers", type=int, default=12) - parser.add_argument("--min-abstract-markers", type=int, default=2) - parser.add_argument("--delay-seconds", type=float, default=3.0) - parser.add_argument("--max-tex-bytes", type=int, default=5_000_000) - parser.add_argument("--max-equations-per-paper", type=int, default=1000) - parser.add_argument("--ids", default="") - args = parser.parse_args() - - archive = Path(args.archive).expanduser().resolve() - cache_dir = Path(args.cache_dir).expanduser().resolve() - OUT_DIR.mkdir(parents=True, exist_ok=True) - wanted_ids = {part.strip() for part in args.ids.split(",") if part.strip()} - if wanted_ids: - candidates = records_by_id(archive=archive, wanted_ids=wanted_ids, max_scan=args.max_scan) - else: - candidates = iter_candidate_records( - archive=archive, - max_scan=args.max_scan, - max_papers=args.max_papers, - min_abstract_markers=args.min_abstract_markers, - ) - - equation_records: list[dict[str, Any]] = [] - paper_records: list[dict[str, Any]] = [] - command_totals: Counter[str] = Counter() - signature_totals: Counter[str] = Counter() - family_totals: Counter[str] = Counter() - env_totals: Counter[str] = Counter() - fetch_status: Counter[str] = Counter() - - for candidate in candidates: - arxiv_id = str(candidate["id"]) - source_path, error = fetch_source(arxiv_id, cache_dir, args.delay_seconds) - if source_path is None: - fetch_status["fetch_error"] += 1 - paper_records.append({**candidate, "decision": "HOLD", "error": error}) - continue - source_sha = sha256_bytes(source_path.read_bytes()) - tex_files, source_mode = read_tex_sources(source_path, args.max_tex_bytes) - paper_eq_count = 0 - paper_command_count = 0 - for tex_name, tex in tex_files: - for env, equation in extract_equations(tex)[: args.max_equations_per_paper]: - features = equation_features(equation) - record = { - "schema": "arxiv_equation_pattern_record_v1", - "arxiv_id": arxiv_id, - "primary_category": candidate["primary_category"], - "categories": candidate["categories"], - "source_sha256": source_sha, - "tex_file": tex_name, - "environment": env, - **features, - } - record["packet_hash"] = sha256_text(stable_json(record)) - equation_records.append(record) - paper_eq_count += 1 - paper_command_count += features["command_count"] - env_totals[env] += 1 - signature_totals[features["signature_hash"]] += 1 - for family, count in features["family_counts"].items(): - family_totals[family] += count - for command, count in features["top_commands"]: - command_totals[command] += count - fetch_status["fetched"] += 1 - paper = { - "schema": "arxiv_equation_pattern_paper_v1", - **candidate, - "source_cache_path": str(source_path), - "source_sha256": source_sha, - "source_mode": source_mode, - "tex_file_count": len(tex_files), - "equation_count": paper_eq_count, - "equation_command_count": paper_command_count, - "decision": "HOLD", - } - paper["packet_hash"] = sha256_text(stable_json(paper)) - paper_records.append(paper) - - EQUATION_JSONL.write_text("\n".join(stable_json(r) for r in equation_records) + "\n", encoding="utf-8") - PAPER_JSONL.write_text("\n".join(stable_json(r) for r in paper_records) + "\n", encoding="utf-8") - command_lines = ["command,count"] - for command, count in command_totals.most_common(300): - command_lines.append(f"{csv_escape(command)},{count}") - COMMAND_CSV.write_text("\n".join(command_lines) + "\n", encoding="utf-8") - - summary = { - "schema": "arxiv_equation_pattern_summary_v1", - "archive_path": str(archive), - "source_cache_dir": str(cache_dir), - "candidate_count": len(candidates), - "paper_count": len(paper_records), - "fetch_status": dict(fetch_status), - "equation_count": len(equation_records), - "unique_equation_hashes": len({r["equation_sha256"] for r in equation_records}), - "unique_signature_hashes": len(signature_totals), - "top_environments": env_totals.most_common(30), - "top_commands": command_totals.most_common(80), - "family_totals": dict(family_totals), - "top_signature_hashes": signature_totals.most_common(40), - "equation_records": str(EQUATION_JSONL.relative_to(REPO)), - "paper_records": str(PAPER_JSONL.relative_to(REPO)), - "command_csv": str(COMMAND_CSV.relative_to(REPO)), - "decision": "HOLD", - "claim_boundary": ( - "Bounded arXiv source sample. Repo stores equation hashes and structural " - "features only, not equation text. This is compression-surface evidence, " - "not theorem verification or full-corpus coverage." - ), - } - summary["packet_hash"] = sha256_text(stable_json(summary)) - SUMMARY.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8") - receipt = { - "schema": "arxiv_equation_pattern_receipt_v1", - "summary": str(SUMMARY.relative_to(REPO)), - "candidate_count": summary["candidate_count"], - "paper_count": summary["paper_count"], - "fetch_status": summary["fetch_status"], - "equation_count": summary["equation_count"], - "unique_equation_hashes": summary["unique_equation_hashes"], - "unique_signature_hashes": summary["unique_signature_hashes"], - "top_environments": summary["top_environments"][:10], - "top_commands": summary["top_commands"][:20], - "family_totals": summary["family_totals"], - "packet_hash": summary["packet_hash"], - "decision": summary["decision"], - "claim_boundary": summary["claim_boundary"], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/arxiv_math_database_breakout.py b/4-Infrastructure/shim/arxiv_math_database_breakout.py deleted file mode 100644 index 9af6e553..00000000 --- a/4-Infrastructure/shim/arxiv_math_database_breakout.py +++ /dev/null @@ -1,256 +0,0 @@ -#!/usr/bin/env python3 -"""Break out math-bearing structure from the local arXiv Kaggle metadata zip. - -This streams the JSONL member inside /home/allaun/Documents/ingest/kaggledataset.zip -without extracting it. The archive contains paper metadata/abstracts, not the -full LaTeX source corpus, so equation signals here are abstract/title markers -and category/domain routes rather than full equation recovery. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import re -import zipfile -from collections import Counter, defaultdict -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_ARCHIVE = Path("/home/allaun/Documents/ingest/kaggledataset.zip") -OUT_DIR = REPO / "shared-data" / "data" / "math_research_databases" -SUMMARY = OUT_DIR / "arxiv_math_breakout_summary.json" -CATEGORY_CSV = OUT_DIR / "arxiv_math_breakout_categories.csv" -MARKER_CSV = OUT_DIR / "arxiv_math_breakout_symbol_markers.csv" -SAMPLES_JSONL = OUT_DIR / "arxiv_math_breakout_samples.jsonl" -RECEIPT = OUT_DIR / "arxiv_math_breakout_receipt.json" - - -MATH_PREFIXES = ( - "math", - "stat", - "cs.", - "nlin", - "q-bio", - "q-fin", - "quant-ph", - "hep-th", - "math-ph", -) - -LATEX_COMMAND_RE = re.compile(r"\\[A-Za-z]+") -INLINE_MATH_RE = re.compile(r"\$[^$]{1,160}\$") -SYMBOL_RE = re.compile(r"(?:[A-Za-z]\^\{?[-+0-9A-Za-z]+}?|[A-Za-z]_\{?[-+0-9A-Za-z]+}?|[=<>≤≥∈∑∫∞⊗⊕±≈≃≅])") - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sha256_file(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def categories_of(record: dict[str, Any]) -> list[str]: - raw = record.get("categories") - if not isinstance(raw, str): - return [] - return [part for part in raw.split() if part] - - -def is_math_bearing(categories: list[str]) -> bool: - return any(cat.startswith(MATH_PREFIXES) for cat in categories) - - -def text_field(record: dict[str, Any], key: str) -> str: - value = record.get(key) - return value if isinstance(value, str) else "" - - -def marker_counts(text: str) -> Counter[str]: - counts: Counter[str] = Counter() - commands = LATEX_COMMAND_RE.findall(text) - inline = INLINE_MATH_RE.findall(text) - symbols = SYMBOL_RE.findall(text) - counts["latex_command"] = len(commands) - counts["inline_math_span"] = len(inline) - counts["symbolic_token"] = len(symbols) - for command in commands[:200]: - counts[f"cmd:{command}"] += 1 - return counts - - -def csv_escape(value: Any) -> str: - text = str(value).replace('"', '""') - return f'"{text}"' - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--archive", default=str(DEFAULT_ARCHIVE)) - parser.add_argument("--max-records", type=int, default=200_000) - parser.add_argument("--sample-records", type=int, default=100) - parser.add_argument("--math-only", action="store_true", default=True) - args = parser.parse_args() - - archive = Path(args.archive).expanduser().resolve() - OUT_DIR.mkdir(parents=True, exist_ok=True) - archive_hash = sha256_file(archive) - - total_seen = 0 - selected_seen = 0 - category_counts: Counter[str] = Counter() - primary_category_counts: Counter[str] = Counter() - marker_totals: Counter[str] = Counter() - status_counts: Counter[str] = Counter() - year_counts: Counter[str] = Counter() - version_counts: Counter[int] = Counter() - category_pair_counts: Counter[tuple[str, str]] = Counter() - samples: list[dict[str, Any]] = [] - category_marker_totals: dict[str, Counter[str]] = defaultdict(Counter) - - with zipfile.ZipFile(archive) as zf: - infos = zf.infolist() - json_members = [info for info in infos if info.filename.endswith(".json")] - selected_member = json_members[0] if json_members else infos[0] - with zf.open(selected_member) as handle: - for raw_line in handle: - if args.max_records and total_seen >= args.max_records: - break - total_seen += 1 - record = json.loads(raw_line) - cats = categories_of(record) - if args.math_only and not is_math_bearing(cats): - continue - selected_seen += 1 - primary = cats[0] if cats else "uncategorized" - category_counts.update(cats) - primary_category_counts[primary] += 1 - for left, right in zip(cats, cats[1:]): - category_pair_counts[(left, right)] += 1 - - versions = record.get("versions") - if isinstance(versions, list): - version_counts[len(versions)] += 1 - if versions and isinstance(versions[0], dict): - created = str(versions[0].get("created", "")) - if len(created) >= 4: - year_counts[created[-4:]] += 1 - - has_doi = bool(record.get("doi")) - has_journal = bool(record.get("journal-ref")) - status_counts["has_doi" if has_doi else "missing_doi"] += 1 - status_counts["has_journal_ref" if has_journal else "missing_journal_ref"] += 1 - - combined = "\n".join([text_field(record, "title"), text_field(record, "abstract")]) - markers = marker_counts(combined) - marker_totals.update(markers) - category_marker_totals[primary].update(markers) - - if len(samples) < args.sample_records: - sample = { - "id": record.get("id"), - "title": record.get("title"), - "primary_category": primary, - "categories": cats, - "version_count": len(versions) if isinstance(versions, list) else 0, - "has_doi": has_doi, - "has_journal_ref": has_journal, - "abstract_sha256": sha256_text(text_field(record, "abstract")), - "marker_counts": { - key: markers[key] - for key in ("latex_command", "inline_math_span", "symbolic_token") - }, - } - sample["packet_hash"] = sha256_text(stable_json(sample)) - samples.append(sample) - - category_lines = ["category,count,primary_count,latex_command,inline_math_span,symbolic_token"] - for category, count in category_counts.most_common(): - marker = category_marker_totals.get(category, Counter()) - category_lines.append( - ",".join( - [ - csv_escape(category), - str(count), - str(primary_category_counts.get(category, 0)), - str(marker.get("latex_command", 0)), - str(marker.get("inline_math_span", 0)), - str(marker.get("symbolic_token", 0)), - ] - ) - ) - CATEGORY_CSV.write_text("\n".join(category_lines) + "\n", encoding="utf-8") - - marker_lines = ["marker,count"] - for marker, count in marker_totals.most_common(500): - marker_lines.append(f"{csv_escape(marker)},{count}") - MARKER_CSV.write_text("\n".join(marker_lines) + "\n", encoding="utf-8") - - SAMPLES_JSONL.write_text("\n".join(stable_json(s) for s in samples) + "\n", encoding="utf-8") - - summary = { - "schema": "arxiv_math_breakout_summary_v1", - "archive_path": str(archive), - "archive_sha256": archive_hash, - "max_records": args.max_records, - "total_records_scanned": total_seen, - "math_bearing_records": selected_seen, - "math_bearing_fraction": selected_seen / total_seen if total_seen else 0, - "category_count": len(category_counts), - "top_categories": category_counts.most_common(40), - "top_primary_categories": primary_category_counts.most_common(40), - "top_category_pairs": [[list(pair), count] for pair, count in category_pair_counts.most_common(40)], - "marker_totals": dict(marker_totals), - "top_markers": marker_totals.most_common(80), - "doi_status": dict(status_counts), - "top_years": year_counts.most_common(40), - "version_count_distribution": dict(sorted(version_counts.items())), - "sample_records": str(SAMPLES_JSONL.relative_to(REPO)), - "category_csv": str(CATEGORY_CSV.relative_to(REPO)), - "marker_csv": str(MARKER_CSV.relative_to(REPO)), - "decision": "HOLD", - "claim_boundary": ( - "Breakout uses arXiv metadata titles/abstracts/categories only. " - "It estimates math-bearing route density and symbolic abstract markers, " - "not full equation extraction from LaTeX sources." - ), - } - summary["packet_hash"] = sha256_text(stable_json(summary)) - SUMMARY.write_text(json.dumps(summary, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - receipt = { - "schema": "arxiv_math_breakout_receipt_v1", - "summary": str(SUMMARY.relative_to(REPO)), - "archive_path": summary["archive_path"], - "archive_sha256": summary["archive_sha256"], - "total_records_scanned": total_seen, - "math_bearing_records": selected_seen, - "math_bearing_fraction": summary["math_bearing_fraction"], - "category_count": summary["category_count"], - "latex_command_count": marker_totals.get("latex_command", 0), - "inline_math_span_count": marker_totals.get("inline_math_span", 0), - "symbolic_token_count": marker_totals.get("symbolic_token", 0), - "sample_record_count": len(samples), - "packet_hash": summary["packet_hash"], - "decision": summary["decision"], - "claim_boundary": summary["claim_boundary"], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/arxiv_metadata_descriptor_ingest.py b/4-Infrastructure/shim/arxiv_metadata_descriptor_ingest.py deleted file mode 100644 index e89ea8c9..00000000 --- a/4-Infrastructure/shim/arxiv_metadata_descriptor_ingest.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest a local arXiv Kaggle/Croissant metadata descriptor. - -This does not download the Kaggle archive or arXiv bulk data. It records the -descriptor as a receipted source surface for later lawful ingestion. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import zipfile -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_INPUT = Path("/home/allaun/Documents/ingest/arxiv-metadata.json") -DEFAULT_ARCHIVE = Path("/home/allaun/Documents/ingest/kaggledataset.zip") -OUT_DIR = REPO / "shared-data" / "data" / "math_research_databases" -PACKET = OUT_DIR / "arxiv_metadata_descriptor_packet.json" -RECEIPT = OUT_DIR / "arxiv_metadata_descriptor_receipt.json" -ARCHIVE_PACKET = OUT_DIR / "arxiv_kaggle_archive_packet.json" -ARCHIVE_SAMPLE_JSONL = OUT_DIR / "arxiv_kaggle_archive_sample.jsonl" -ARCHIVE_SAMPLE_CSV = OUT_DIR / "arxiv_kaggle_archive_sample.csv" -ARCHIVE_RECEIPT = OUT_DIR / "arxiv_kaggle_archive_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sha256_file(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def as_list(value: Any) -> list[Any]: - return value if isinstance(value, list) else [] - - -def text(value: Any) -> str | None: - return value if isinstance(value, str) else None - - -def dist_summary(obj: dict[str, Any]) -> list[dict[str, Any]]: - rows: list[dict[str, Any]] = [] - for item in as_list(obj.get("distribution")): - if not isinstance(item, dict): - continue - rows.append( - { - "id": item.get("@id"), - "type": item.get("@type"), - "name": item.get("name"), - "encoding_format": item.get("encodingFormat"), - "content_url": item.get("contentUrl"), - "content_size": item.get("contentSize"), - "md5": item.get("md5"), - "includes": item.get("includes"), - "contained_in": item.get("containedIn", {}).get("@id") - if isinstance(item.get("containedIn"), dict) - else None, - } - ) - return rows - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--input", default=str(DEFAULT_INPUT)) - parser.add_argument("--archive", default=str(DEFAULT_ARCHIVE)) - parser.add_argument("--sample-records", type=int, default=25) - args = parser.parse_args() - - source = Path(args.input).expanduser().resolve() - raw = source.read_bytes() - obj = json.loads(raw) - OUT_DIR.mkdir(parents=True, exist_ok=True) - - license_obj = obj.get("license") if isinstance(obj.get("license"), dict) else {} - creator_obj = obj.get("creator") if isinstance(obj.get("creator"), dict) else {} - publisher_obj = obj.get("publisher") if isinstance(obj.get("publisher"), dict) else {} - catalog_obj = obj.get("includedInDataCatalog") if isinstance(obj.get("includedInDataCatalog"), dict) else {} - - packet = { - "schema": "arxiv_metadata_descriptor_packet_v1", - "rrc_shape_hint": "ArxivMetadataDescriptorSurface", - "source_path": str(source), - "source_size_bytes": len(raw), - "source_sha256": sha256_bytes(raw), - "dataset_name": obj.get("name"), - "alternate_name": obj.get("alternateName"), - "dataset_type": obj.get("@type"), - "version": obj.get("version"), - "date_published": obj.get("datePublished"), - "date_modified": obj.get("dateModified"), - "url": obj.get("url"), - "identifier": obj.get("identifier"), - "cite_as": obj.get("citeAs"), - "conforms_to": obj.get("conformsTo"), - "is_accessible_for_free": obj.get("isAccessibleForFree"), - "license_name": license_obj.get("name"), - "license_url": license_obj.get("url"), - "creator": creator_obj.get("name"), - "publisher": publisher_obj.get("name"), - "data_catalog": catalog_obj.get("name"), - "distribution": dist_summary(obj), - "keywords": as_list(obj.get("keywords")), - "expected_article_fields": [ - "id", - "submitter", - "authors", - "title", - "comments", - "journal-ref", - "doi", - "abstract", - "categories", - "versions", - ], - "bulk_surfaces_declared_in_descriptor": [ - "kaggle_metadata_archive", - "google_cloud_storage_pdf_bucket", - "google_cloud_storage_source_bucket", - "arxiv_abs_url_template", - "arxiv_pdf_url_template", - ], - "density_markers": [ - "croissant_dataset_descriptor", - "kaggle_metadata_snapshot", - "arxiv_category_graph", - "paper_version_history", - "abstract_text_surface", - "latex_source_archive_pointer", - "pdf_bulk_archive_pointer", - "knowledge_graph_construction_surface", - ], - "access_boundary": ( - "Descriptor-only ingest. Metadata license is recorded from the descriptor; " - "individual papers and full-text/source downloads still follow arXiv/Kaggle/GCS terms." - ), - "decision": "HOLD", - "claim_boundary": ( - "This receipt proves local descriptor capture and hashing only. It is not a " - "receipt for the 1.617 GB metadata archive, the GCS bucket contents, PDFs, " - "source tarballs, or theorem validity." - ), - } - packet["packet_hash"] = sha256_text(stable_json(packet)) - PACKET.write_text(json.dumps(packet, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - receipt = { - "schema": "arxiv_metadata_descriptor_receipt_v1", - "packet": str(PACKET.relative_to(REPO)), - "source_path": packet["source_path"], - "source_size_bytes": packet["source_size_bytes"], - "source_sha256": packet["source_sha256"], - "packet_hash": packet["packet_hash"], - "dataset_name": packet["dataset_name"], - "version": packet["version"], - "distribution_count": len(packet["distribution"]), - "density_marker_count": len(packet["density_markers"]), - "decision": packet["decision"], - "claim_boundary": packet["claim_boundary"], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - archive = Path(args.archive).expanduser().resolve() - archive_receipt: dict[str, Any] | None = None - if archive.exists(): - archive_hash = sha256_file(archive) - sample_records: list[dict[str, Any]] = [] - with zipfile.ZipFile(archive) as zf: - infos = zf.infolist() - members = [ - { - "filename": info.filename, - "compress_size": info.compress_size, - "file_size": info.file_size, - "date_time": list(info.date_time), - "crc": info.CRC, - } - for info in infos - ] - json_members = [info for info in infos if info.filename.endswith(".json")] - selected = json_members[0] if json_members else infos[0] - with zf.open(selected) as handle: - for _ in range(args.sample_records): - line = handle.readline() - if not line: - break - rec = json.loads(line) - sample = { - "id": rec.get("id"), - "title": rec.get("title"), - "authors": rec.get("authors"), - "categories": rec.get("categories"), - "versions": rec.get("versions"), - "doi": rec.get("doi"), - "journal_ref": rec.get("journal-ref"), - "abstract_sha256": sha256_text(text(rec.get("abstract")) or ""), - "abstract_bytes": len((text(rec.get("abstract")) or "").encode("utf-8")), - } - sample["packet_hash"] = sha256_text(stable_json(sample)) - sample_records.append(sample) - - ARCHIVE_SAMPLE_JSONL.write_text( - "\n".join(stable_json(record) for record in sample_records) + "\n", encoding="utf-8" - ) - csv_lines = [ - "id,title,authors,categories,version_count,doi,journal_ref,abstract_sha256,packet_hash" - ] - for record in sample_records: - csv_lines.append( - ",".join( - [ - json.dumps(record.get("id") or ""), - json.dumps(record.get("title") or ""), - json.dumps(record.get("authors") or ""), - json.dumps(record.get("categories") or ""), - json.dumps(len(record.get("versions") or [])), - json.dumps(record.get("doi") or ""), - json.dumps(record.get("journal_ref") or ""), - json.dumps(record["abstract_sha256"]), - json.dumps(record["packet_hash"]), - ] - ) - ) - ARCHIVE_SAMPLE_CSV.write_text("\n".join(csv_lines) + "\n", encoding="utf-8") - - archive_packet = { - "schema": "arxiv_kaggle_archive_packet_v1", - "rrc_shape_hint": "ArxivKaggleMetadataSnapshot", - "archive_path": str(archive), - "archive_size_bytes": archive.stat().st_size, - "archive_sha256": archive_hash, - "zip_member_count": len(members), - "zip_members": members, - "sample_record_count": len(sample_records), - "sample_records_jsonl": str(ARCHIVE_SAMPLE_JSONL.relative_to(REPO)), - "sample_records_csv": str(ARCHIVE_SAMPLE_CSV.relative_to(REPO)), - "sample_policy": ( - "Stream first records from the JSON member inside the zip without extracting " - "the 5GB snapshot. Store hashes and routing fields only." - ), - "density_markers": [ - "kaggle_metadata_archive_verified", - "arxiv_jsonl_snapshot", - "category_graph_sample", - "version_history_sample", - "abstract_hash_sample", - ], - "decision": "HOLD", - "claim_boundary": ( - "Archive hash and bounded sample verified. The full JSONL corpus is not " - "materialized into the repo, and per-paper full text/PDF/source replay is not claimed." - ), - } - archive_packet["packet_hash"] = sha256_text(stable_json(archive_packet)) - ARCHIVE_PACKET.write_text( - json.dumps(archive_packet, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - archive_receipt = { - "schema": "arxiv_kaggle_archive_receipt_v1", - "packet": str(ARCHIVE_PACKET.relative_to(REPO)), - "archive_path": archive_packet["archive_path"], - "archive_size_bytes": archive_packet["archive_size_bytes"], - "archive_sha256": archive_packet["archive_sha256"], - "zip_member_count": archive_packet["zip_member_count"], - "sample_record_count": archive_packet["sample_record_count"], - "packet_hash": archive_packet["packet_hash"], - "decision": archive_packet["decision"], - "claim_boundary": archive_packet["claim_boundary"], - } - archive_receipt["receipt_hash"] = sha256_text(stable_json(archive_receipt)) - ARCHIVE_RECEIPT.write_text( - json.dumps(archive_receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - - out = {"descriptor_receipt": receipt, "archive_receipt": archive_receipt} - print(json.dumps(out, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/asymptotic_closure_horizon_probe.py b/4-Infrastructure/shim/asymptotic_closure_horizon_probe.py deleted file mode 100644 index 2e79e60c..00000000 --- a/4-Infrastructure/shim/asymptotic_closure_horizon_probe.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed asymptotic closure horizon probe. - -An asymptote is a useful metaphor with a sharp operational rule: approaching a -gate is not passing it. A route that improves forever but has no finite -intersection with replay, byte law, residual closure, or source receipts remains -HOLD_ASYMPTOTIC. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "asymptotic_closure_horizon" -REGISTRY = OUT_DIR / "asymptotic_closure_horizon_registry.json" -RECEIPT = OUT_DIR / "asymptotic_closure_horizon_receipt.json" -SUMMARY = OUT_DIR / "asymptotic_closure_horizon.md" - -TREE_FIDDY_CAGE_BOUNDARY_BYTES = 350 - -CITATIONS = [ - { - "id": "user_supplied_asymptote_meme", - "title": "Asymptote meme: a line that meets the curve at infinity", - "role": "source_prompt", - "status": "user_supplied_image_prompt", - }, - { - "id": "bibliographic_event_horizon_receipt", - "title": "Bibliographic event horizon receipt", - "path": "shared-data/data/bibliographic_event_horizon/bibliographic_event_horizon_receipt.json", - "role": "local_route_input", - "status": "receipt_bound_diagnostic", - }, - { - "id": "mmff_rigid_body_geometry_receipt", - "title": "MMFF rigid-body geometry receipt", - "path": "shared-data/data/mmff_rigid_body_geometry/mmff_rigid_body_geometry_receipt.json", - "role": "finite_pass_example", - "status": "coordinate_replay_fixture", - }, - { - "id": "forward_foundation_equation_compiler", - "title": "Forward foundation equation compiler", - "path": "6-Documentation/docs/specs/FORWARD_FOUNDATION_EQUATION_COMPILER.md", - "role": "local_trust_boundary", - "status": "finite_forward_receipt_required", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def route( - *, - route_id: str, - surface: str, - finite_gate: str, - approach_milli: int, - finite_intersection: bool, - receipt_complete: bool, - residual_declared: bool, - byte_law_checked: bool, -) -> dict[str, Any]: - passes = finite_intersection and receipt_complete and residual_declared and byte_law_checked - asymptotic_hold = approach_milli >= 950 and not passes - item = { - "route_id": route_id, - "surface": surface, - "finite_gate": finite_gate, - "approach_milli": approach_milli, - "finite_intersection": finite_intersection, - "receipt_complete": receipt_complete, - "residual_declared": residual_declared, - "byte_law_checked": byte_law_checked, - "passes": passes, - "asymptotic_hold": asymptotic_hold, - "decision": "ADMIT_FINITE_INTERSECTION" if passes else ("HOLD_ASYMPTOTIC" if asymptotic_hold else "HOLD_INCOMPLETE"), - "repair": "find a finite witness, not a prettier limit argument" if asymptotic_hold else "complete finite gate receipts", - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - routes = [ - route( - route_id="citation_gravity_near_authority", - surface="bibliographic event horizon", - finite_gate="forward derivation receipt", - approach_milli=990, - finite_intersection=False, - receipt_complete=False, - residual_declared=True, - byte_law_checked=False, - ), - route( - route_id="compression_global_near_zero", - surface="enwiki/logogram compression ladder", - finite_gate="delta_global > 0 under counted dictionary/protocol/receipt bytes", - approach_milli=975, - finite_intersection=False, - receipt_complete=True, - residual_declared=True, - byte_law_checked=True, - ), - route( - route_id="mmff_geometry_coordinate_replay", - surface="MMFF rigid-body coordinate shadow", - finite_gate="exact coordinate replay with bounded O-AMMR archive", - approach_milli=1000, - finite_intersection=True, - receipt_complete=True, - residual_declared=True, - byte_law_checked=True, - ), - route( - route_id="proof_label_dependency_chain", - surface="theorem/citation label chain", - finite_gate="compiled forward from foundation kernel with closure witness", - approach_milli=995, - finite_intersection=False, - receipt_complete=False, - residual_declared=False, - byte_law_checked=False, - ), - ] - archive_bytes = 32 + len(routes) * 44 - return { - "schema": "asymptotic_closure_horizon_registry_v1", - "source_prompt": { - "meme": "Asymptote: A line that meets the curve at infinity", - "operational_rewrite": "A route that meets the gate only at infinity has not passed a finite receipt gate.", - }, - "citations": CITATIONS, - "claim_boundary": ( - "Asymptotic closure is an admission diagnostic only. It does not reject " - "limits or asymptotic analysis as mathematics; it rejects infinite-approach " - "language as a substitute for finite replay, residual, receipt, and byte-law evidence." - ), - "equation": { - "finite_pass": "finite_intersection and receipt_complete and residual_declared and byte_law_checked", - "asymptotic_hold": "approach_milli >= 950 and not finite_pass", - }, - "tree_fiddy_guard": { - "cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "archive_bytes": archive_bytes, - "archive_admissible": archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "active_pull_rule": "Q_active(i)=0 only after finite pass or bounded diagnostic archive", - }, - "routes": routes, - "aggregates": { - "route_count": len(routes), - "finite_pass_count": sum(1 for item in routes if item["passes"]), - "asymptotic_hold_count": sum(1 for item in routes if item["asymptotic_hold"]), - "incomplete_hold_count": sum(1 for item in routes if item["decision"] == "HOLD_INCOMPLETE"), - "tree_fiddy_archive_bytes": archive_bytes, - "tree_fiddy_cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "tree_fiddy_archive_admissible": archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "asymptotic_closure_horizon_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_ASYMPTOTIC_HOLD_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Asymptotic Closure Horizon Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Rule", - "", - f"- Finite pass: `{registry['equation']['finite_pass']}`", - f"- Asymptotic hold: `{registry['equation']['asymptotic_hold']}`", - "", - "## Routes", - "", - "| Route | Surface | Approach | Finite intersection | Decision |", - "|---|---|---:|---|---|", - ] - for item in registry["routes"]: - lines.append( - f"| `{item['route_id']}` | {item['surface']} | {item['approach_milli']} | " - f"`{item['finite_intersection']}` | `{item['decision']}` |" - ) - lines.extend( - [ - "", - "## Tree Fiddy Guard", - "", - f"- Archive bytes: `{registry['aggregates']['tree_fiddy_archive_bytes']}` / `{registry['aggregates']['tree_fiddy_cage_boundary_bytes']}`", - f"- Archive admissible: `{registry['aggregates']['tree_fiddy_archive_admissible']}`", - "", - "## Citations", - "", - ] - ) - for citation in registry["citations"]: - target = citation.get("url") or citation.get("path") or citation["status"] - lines.append(f"- `{citation['id']}`: {citation['title']} ({target}); role: `{citation['role']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/bibliographic_event_horizon_probe.py b/4-Infrastructure/shim/bibliographic_event_horizon_probe.py deleted file mode 100644 index 10bc2626..00000000 --- a/4-Infrastructure/shim/bibliographic_event_horizon_probe.py +++ /dev/null @@ -1,302 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed bibliographic event horizon probe. - -The joke is useful: a heavily cited source can become a gravity well. Downstream -claims orbit the label instead of recompiling forward from evidence. This probe -turns that into a provenance diagnostic, not a truth claim. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "bibliographic_event_horizon" -REGISTRY = OUT_DIR / "bibliographic_event_horizon_registry.json" -RECEIPT = OUT_DIR / "bibliographic_event_horizon_receipt.json" -SUMMARY = OUT_DIR / "bibliographic_event_horizon.md" - -TREE_FIDDY_CAGE_BOUNDARY_BYTES = 350 -EVENT_HORIZON_PULL_THRESHOLD_MILLI = 3500 -RECEIPT_THRUST_PER_FORWARD_DERIVATION_MILLI = 1200 -RECEIPT_THRUST_PER_REPLAY_CHECK_MILLI = 900 -RECEIPT_THRUST_PER_SOURCE_HASH_MILLI = 350 - -CITATIONS = [ - { - "id": "immaterialscience_bibliographic_event_horizon", - "title": "The Bibliographic Event Horizon: A Study on the Gravitational Pull of [1]", - "url": "https://www.immaterialscience.org/2026/citations", - "role": "source_prompt", - "status": "satirical_source_used_as_real_diagnostic_prompt", - }, - { - "id": "reddit_discussion_wrapper", - "title": "Reddit discussion wrapper for bibliographic event horizon prompt", - "url": "https://www.reddit.com/r/ImmaterialScience/comments/1t7plf9/the_bibliographic_event_horizon_a_study_on_the/", - "role": "discussion_pointer", - "status": "metadata_only", - }, - { - "id": "forward_foundation_equation_compiler", - "title": "Forward foundation equation compiler", - "path": "6-Documentation/docs/specs/FORWARD_FOUNDATION_EQUATION_COMPILER.md", - "role": "local_trust_boundary", - "status": "labels_and_citations_are_routing_hints_only", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def source( - *, - source_id: str, - title: str, - inbound_citations: int, - downstream_claims: int, - label_authority_claims: int, - forward_derivation_receipts: int, - replay_checks: int, - source_hashes: int, -) -> dict[str, Any]: - citation_pull = inbound_citations * 1000 + downstream_claims * 700 + label_authority_claims * 1300 - receipt_thrust = ( - forward_derivation_receipts * RECEIPT_THRUST_PER_FORWARD_DERIVATION_MILLI - + replay_checks * RECEIPT_THRUST_PER_REPLAY_CHECK_MILLI - + source_hashes * RECEIPT_THRUST_PER_SOURCE_HASH_MILLI - ) - net_pull = citation_pull - receipt_thrust - horizon = net_pull >= EVENT_HORIZON_PULL_THRESHOLD_MILLI - item = { - "source_id": source_id, - "title": title, - "inbound_citations": inbound_citations, - "downstream_claims": downstream_claims, - "label_authority_claims": label_authority_claims, - "forward_derivation_receipts": forward_derivation_receipts, - "replay_checks": replay_checks, - "source_hashes": source_hashes, - "citation_pull_milli": citation_pull, - "receipt_thrust_milli": receipt_thrust, - "net_pull_milli": net_pull, - "event_horizon": horizon, - "decision": "HOLD_CITATION_GRAVITY" if horizon else "ADMIT_ROUTING_DIAGNOSTIC", - "repair": ( - "compile forward from foundation/source evidence; add replay checks and source hashes" - if horizon - else "retain as low-risk bibliography routing context" - ), - } - item["source_hash"] = hash_obj({k: v for k, v in item.items() if k != "source_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - sources = [ - source( - source_id="root_label_001", - title="Canonical source used as authority label", - inbound_citations=4, - downstream_claims=5, - label_authority_claims=3, - forward_derivation_receipts=0, - replay_checks=0, - source_hashes=1, - ), - source( - source_id="forward_receipted_001", - title="Same citation load but forward-derived locally", - inbound_citations=4, - downstream_claims=5, - label_authority_claims=0, - forward_derivation_receipts=4, - replay_checks=3, - source_hashes=4, - ), - source( - source_id="fixture_note_001", - title="Small note with source hash but no theorem authority", - inbound_citations=1, - downstream_claims=1, - label_authority_claims=0, - forward_derivation_receipts=0, - replay_checks=1, - source_hashes=1, - ), - ] - horizon_count = sum(1 for item in sources if item["event_horizon"]) - archive_bytes = 32 + len(sources) * 48 - return { - "schema": "bibliographic_event_horizon_registry_v1", - "source_prompt": { - "title": "The Bibliographic Event Horizon: A Study on the Gravitational Pull of [1]", - "url": "https://www.immaterialscience.org/2026/citations", - "reddit_url": "https://www.reddit.com/r/ImmaterialScience/comments/1t7plf9/the_bibliographic_event_horizon_a_study_on_the/", - "author": "Gunther Schlonk", - "published": "9 May", - "source_type": "satirical_article_with_useful_provenance_model", - "observed_abstract_claim": ( - "The first reference [1] functions as a gravitational singularity; " - "as reference count increases, the probability of the author having " - "opened the source material approaches the Planck length." - ), - "fetch_status": "public article page fetched; PDF fetch blocked by safe-open policy", - }, - "citations": CITATIONS, - "claim_boundary": ( - "Bibliographic event horizon is a citation-provenance diagnostic. It " - "does not decide whether a source is true. It detects when citation " - "gravity exceeds local receipt thrust, forcing HOLD until forward " - "derivation, replay, or source-hash evidence is added." - ), - "equation": { - "citation_pull_milli": "1000*inbound_citations + 700*downstream_claims + 1300*label_authority_claims", - "receipt_thrust_milli": "1200*forward_derivation_receipts + 900*replay_checks + 350*source_hashes", - "event_horizon": "citation_pull_milli - receipt_thrust_milli >= 3500", - }, - "o_ammr_shadow_carrier": { - "visible_shadow": "bibliography entry, citation number, reference label", - "hidden_state": "source graph, dependency graph, claim fanout, receipt coverage, residual obligations", - "chain": [ - "L16_source_ecology", - "L12_claim_dependency_residual_plane", - "L8_bibliography_adapter", - "L4_citation_gravity_primitive", - "Rg3_obligation_residual_boat", - "L1_reference_label_shadow", - "L0_forward_receipt_closure", - "O_AMMR_root", - ], - "residual_handles": ["quote_packet", "dependency_shear", "claim_spectral_field"], - "plain_merkle_role": "content hash only; not authority", - }, - "tree_fiddy_guard": { - "cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "archive_bytes": archive_bytes, - "archive_admissible": archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "active_pull_rule": "Q_active(i)=0 only after committed_or_shielded; event-horizon sources stay active HOLD", - }, - "sources": sources, - "aggregates": { - "source_count": len(sources), - "event_horizon_count": horizon_count, - "admitted_routing_diagnostic_count": sum(1 for item in sources if item["decision"] == "ADMIT_ROUTING_DIAGNOSTIC"), - "hold_citation_gravity_count": sum(1 for item in sources if item["decision"] == "HOLD_CITATION_GRAVITY"), - "tree_fiddy_archive_bytes": archive_bytes, - "tree_fiddy_cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "tree_fiddy_archive_admissible": archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "bibliographic_event_horizon_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_CITATION_GRAVITY_DIAGNOSTIC_HOLD_FIRST", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Bibliographic Event Horizon Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Equation", - "", - f"- Citation pull: `{registry['equation']['citation_pull_milli']}`", - f"- Receipt thrust: `{registry['equation']['receipt_thrust_milli']}`", - f"- Event horizon: `{registry['equation']['event_horizon']}`", - "", - "## Sources", - "", - "| Source | Pull | Thrust | Net | Horizon | Decision |", - "|---|---:|---:|---:|---|---|", - ] - for item in registry["sources"]: - lines.append( - f"| `{item['source_id']}` | {item['citation_pull_milli']} | {item['receipt_thrust_milli']} | " - f"{item['net_pull_milli']} | `{item['event_horizon']}` | `{item['decision']}` |" - ) - lines.extend( - [ - "", - "## Tree Fiddy Guard", - "", - f"- Archive bytes: `{registry['aggregates']['tree_fiddy_archive_bytes']}` / `{registry['aggregates']['tree_fiddy_cage_boundary_bytes']}`", - f"- Archive admissible: `{registry['aggregates']['tree_fiddy_archive_admissible']}`", - "", - "## Citations", - "", - ] - ) - for citation in registry["citations"]: - target = citation.get("url") or citation.get("path") - lines.append(f"- `{citation['id']}`: {citation['title']} ({target}); role: `{citation['role']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/bitcoin_header_jsonl_export.py b/4-Infrastructure/shim/bitcoin_header_jsonl_export.py deleted file mode 100644 index 5c45bf52..00000000 --- a/4-Infrastructure/shim/bitcoin_header_jsonl_export.py +++ /dev/null @@ -1,221 +0,0 @@ -#!/usr/bin/env python3 -"""Export Bitcoin active-chain headers as newline-delimited JSON. - -This exporter is designed for pruned Bitcoin Core nodes. A pruned node cannot -serve every historical block body, but it can still expose active-chain header -metadata through the block index. That makes this lane useful for pattern -studies from genesis to the current validated height without claiming full -historical block-body coverage. -""" - -from __future__ import annotations - -import argparse -import base64 -import hashlib -import json -import sys -import time -import urllib.error -import urllib.request -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" -DEFAULT_RPC_URL = "http://127.0.0.1:8332" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def load_rpc_auth(conf_path: Path) -> tuple[str, str]: - rpcuser = "" - rpcpassword = "" - for raw_line in conf_path.expanduser().read_text().splitlines(): - line = raw_line.strip() - if not line or line.startswith("#") or "=" not in line: - continue - key, value = line.split("=", 1) - if key == "rpcuser": - rpcuser = value - elif key == "rpcpassword": - rpcpassword = value - if not rpcuser or not rpcpassword: - raise SystemExit(f"missing rpcuser/rpcpassword in {conf_path}") - return rpcuser, rpcpassword - - -class BitcoinRpc: - def __init__(self, url: str, rpcuser: str, rpcpassword: str, timeout: int) -> None: - token = base64.b64encode(f"{rpcuser}:{rpcpassword}".encode()).decode() - self.url = url - self.timeout = timeout - self.headers = { - "Authorization": f"Basic {token}", - "Content-Type": "application/json", - } - self.next_id = 1 - - def call(self, method: str, params: list[Any] | None = None) -> Any: - return self.batch([(method, params or [])])[0] - - def batch(self, calls: list[tuple[str, list[Any]]]) -> list[Any]: - request_items = [] - ids: list[int] = [] - for method, params in calls: - request_id = self.next_id - self.next_id += 1 - ids.append(request_id) - request_items.append( - { - "jsonrpc": "1.0", - "id": request_id, - "method": method, - "params": params, - } - ) - payload = json.dumps(request_items).encode() - req = urllib.request.Request(self.url, data=payload, headers=self.headers, method="POST") - try: - with urllib.request.urlopen(req, timeout=self.timeout) as response: - response_payload = response.read() - except urllib.error.URLError as exc: - raise SystemExit(f"bitcoin rpc failed: {exc}") from exc - - decoded = json.loads(response_payload) - by_id = {item["id"]: item for item in decoded} - results = [] - for request_id in ids: - item = by_id[request_id] - if item.get("error"): - raise SystemExit(f"bitcoin rpc error for id {request_id}: {item['error']}") - results.append(item.get("result")) - return results - - -def stable_json_line(payload: dict[str, Any]) -> bytes: - return json.dumps(payload, sort_keys=True, separators=(",", ":")).encode() + b"\n" - - -def export_headers(args: argparse.Namespace) -> dict[str, Any]: - rpcuser, rpcpassword = load_rpc_auth(args.bitcoin_conf) - rpc = BitcoinRpc(args.rpc_url, rpcuser, rpcpassword, args.timeout) - chain_info = rpc.call("getblockchaininfo") - source_height = int(chain_info[args.height_source]) - end_height = source_height if args.end_height is None else min(args.end_height, source_height) - start_height = args.start_height - if args.max_heights is not None: - end_height = min(end_height, start_height + args.max_heights - 1) - if end_height < start_height: - raise SystemExit("end height is before start height") - - created = now_iso() - stream_hash = hashlib.sha256() - record_count = 0 - first_hash = None - last_hash = None - started = time.time() - - for batch_start in range(start_height, end_height + 1, args.batch_size): - batch_end = min(end_height, batch_start + args.batch_size - 1) - heights = list(range(batch_start, batch_end + 1)) - hashes = rpc.batch([("getblockhash", [height]) for height in heights]) - headers = rpc.batch([("getblockheader", [block_hash, True]) for block_hash in hashes]) - - for height, block_hash, header in zip(heights, hashes, headers, strict=True): - record = { - "schema": "bitcoin_header_jsonl_v0", - "chain": "bitcoin", - "height": height, - "hash": block_hash, - "confirmations": header.get("confirmations"), - "version": header.get("version"), - "versionHex": header.get("versionHex"), - "merkleroot": header.get("merkleroot"), - "time": header.get("time"), - "mediantime": header.get("mediantime"), - "nonce": header.get("nonce"), - "bits": header.get("bits"), - "difficulty": header.get("difficulty"), - "chainwork": header.get("chainwork"), - "nTx": header.get("nTx"), - "previousblockhash": header.get("previousblockhash"), - "nextblockhash": header.get("nextblockhash"), - } - line = stable_json_line(record) - args.output.buffer.write(line) - stream_hash.update(line) - record_count += 1 - first_hash = first_hash or block_hash - last_hash = block_hash - - if args.progress_every and record_count % args.progress_every < len(heights): - print( - f"exported={record_count} height={batch_end} elapsed_s={time.time() - started:.1f}", - file=sys.stderr, - flush=True, - ) - - args.output.flush() - receipt = { - "schema": "bitcoin_header_jsonl_export_receipt_v0", - "created_utc": created, - "claim_boundary": "Active-chain Bitcoin header JSONL export from local Bitcoin Core RPC. This proves header metadata coverage for the exported height interval, not full historical block-body coverage.", - "bitcoin_conf": str(args.bitcoin_conf.expanduser()), - "rpc_url": args.rpc_url, - "height_source": args.height_source, - "chain_info": { - "chain": chain_info.get("chain"), - "blocks": chain_info.get("blocks"), - "headers": chain_info.get("headers"), - "bestblockhash": chain_info.get("bestblockhash"), - "verificationprogress": chain_info.get("verificationprogress"), - "initialblockdownload": chain_info.get("initialblockdownload"), - "pruned": chain_info.get("pruned"), - "pruneheight": chain_info.get("pruneheight"), - }, - "start_height": start_height, - "end_height": end_height, - "record_count": record_count, - "first_hash": first_hash, - "last_hash": last_hash, - "jsonl_sha256": stream_hash.hexdigest(), - "decision": "ADMIT_BITCOIN_HEADER_INTERVAL_EXPORT", - } - if args.receipt_path: - args.receipt_path.parent.mkdir(parents=True, exist_ok=True) - args.receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--bitcoin-conf", type=Path, default=Path.home() / ".bitcoin/bitcoin.conf") - parser.add_argument("--rpc-url", default=DEFAULT_RPC_URL) - parser.add_argument("--timeout", type=int, default=30) - parser.add_argument("--start-height", type=int, default=0) - parser.add_argument("--end-height", type=int, default=None) - parser.add_argument("--height-source", choices=["blocks", "headers"], default="blocks") - parser.add_argument("--batch-size", type=int, default=128) - parser.add_argument("--max-heights", type=int, default=None) - parser.add_argument("--progress-every", type=int, default=10000) - parser.add_argument("--receipt-path", type=Path, default=None) - parser.add_argument("--output", type=argparse.FileType("w"), default=sys.stdout) - args = parser.parse_args() - - if args.receipt_path is None: - DATA_DIR.mkdir(parents=True, exist_ok=True) - stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - args.receipt_path = DATA_DIR / f"bitcoin_header_jsonl_export_receipt_{stamp}.json" - - receipt = export_headers(args) - print(json.dumps(receipt, indent=2, sort_keys=True), file=sys.stderr) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchain_alternative_source_plan.py b/4-Infrastructure/shim/blockchain_alternative_source_plan.py deleted file mode 100644 index 87b258a5..00000000 --- a/4-Infrastructure/shim/blockchain_alternative_source_plan.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -"""Create a receipt for alternative blockchain corpus sources. - -This is an admission/planning receipt, not a data-transfer receipt. It records -which public/research sources can replace throttled Blockchair dump pulls and -emits concrete command templates for the tools that can fetch them. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import shutil -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -DEFAULT_DESTINATION = "Gdrive:topological_storage/research-stack/blockchain-corpus/seed-2026-05-10" - - -BIGQUERY_CRYPTO_DATASETS = { - "bitcoin_cash": { - "chain": "bitcoin-cash", - "dataset": "bigquery-public-data.crypto_bitcoin_cash", - "blockchair_status": "PARTIAL_HTTP_402", - }, - "dash": { - "chain": "dash", - "dataset": "bigquery-public-data.crypto_dash", - "blockchair_status": "PARTIAL_HTTP_402", - }, - "dogecoin": { - "chain": "dogecoin", - "dataset": "bigquery-public-data.crypto_dogecoin", - "blockchair_status": "PARTIAL_HTTP_402", - }, - "litecoin": { - "chain": "litecoin", - "dataset": "bigquery-public-data.crypto_litecoin", - "blockchair_status": "PARTIAL_HTTP_402", - }, - "zcash": { - "chain": "zcash", - "dataset": "bigquery-public-data.crypto_zcash", - "blockchair_status": "PARTIAL_HTTP_402", - }, - "ethereum_classic": { - "chain": "ethereum-classic", - "dataset": "bigquery-public-data.crypto_ethereum_classic", - "blockchair_status": "NOT_ATTEMPTED", - }, -} - - -BITCOIN_ETL_CHAINS = [ - "bitcoin", - "bitcoin_cash", - "bitcoin_gold", - "dogecoin", - "litecoin", - "dash", - "zcash", -] - - -def utc_now() -> str: - return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z") - - -def stable_hash(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def command_status(name: str) -> dict[str, Any]: - path = shutil.which(name) - version = None - if path: - try: - version_command = [name, "version"] if name == "bq" else [name, "--version"] - proc = subprocess.run(version_command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, timeout=15) - version = proc.stdout.splitlines()[0] if proc.stdout else None - except Exception as exc: # noqa: BLE001 - receipt diagnostic only. - version = f"VERSION_CHECK_FAILED: {exc}" - return {"available": bool(path), "path": path, "version": version} - - -def python_module_status(module: str) -> dict[str, Any]: - try: - __import__(module) - return {"available": True, "module": module} - except Exception as exc: # noqa: BLE001 - receipt diagnostic only. - return {"available": False, "module": module, "error": type(exc).__name__} - - -def bq_extract_commands(destination: str) -> list[dict[str, Any]]: - commands = [] - for source_id, meta in BIGQUERY_CRYPTO_DATASETS.items(): - dataset = meta["dataset"] - chain = meta["chain"] - remote = f"{destination.rstrip('/')}/public-datasets/google-bigquery/{chain}" - commands.append( - { - "source_id": source_id, - "chain": chain, - "dataset": dataset, - "tables_to_try_first": ["blocks", "transactions", "inputs", "outputs"], - "local_export_pattern": f"shared-data/data/blockchain_corpus/bigquery_exports/{chain}/{{table}}/*.parquet", - "drive_destination": remote, - "commands": [ - f"bq ls {dataset}", - ( - "bq extract --destination_format=PARQUET " - f"'{dataset}.{{table}}' " - f"'gs:///research-stack/blockchain-corpus/{chain}/{{table}}/*.parquet'" - ), - ( - "gcloud storage cp --recursive " - f"'gs:///research-stack/blockchain-corpus/{chain}/' " - f"'shared-data/data/blockchain_corpus/bigquery_exports/{chain}/'" - ), - ( - "rclone copy " - f"'shared-data/data/blockchain_corpus/bigquery_exports/{chain}/' " - f"'{remote}/'" - ), - ], - } - ) - return commands - - -def bitcoin_etl_commands(destination: str) -> list[dict[str, Any]]: - commands = [] - for chain in BITCOIN_ETL_CHAINS: - remote = f"{destination.rstrip('/')}/node-etl/bitcoin-etl/{chain}" - commands.append( - { - "chain": chain, - "requires": ["running_full_node_or_rpc_snapshot", "bitcoin-etl"], - "commands": [ - "python3 -m pip install --user bitcoin-etl", - ( - "bitcoinetl export_blocks_and_transactions " - "--start-block 0 --end-block " - f"--provider-uri http://:@127.0.0.1: --chain {chain} " - f"--blocks-output shared-data/data/blockchain_corpus/node_etl/{chain}/blocks.jsonl " - f"--transactions-output shared-data/data/blockchain_corpus/node_etl/{chain}/transactions.jsonl" - ), - f"rclone copy 'shared-data/data/blockchain_corpus/node_etl/{chain}/' '{remote}/'", - ], - } - ) - return commands - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--destination", default=DEFAULT_DESTINATION) - parser.add_argument("--out", default="shared-data/data/blockchain_corpus/blockchain_alternative_source_plan_receipt.json") - args = parser.parse_args() - - receipt = { - "schema": "blockchain_alternative_source_plan_v0", - "created_utc": utc_now(), - "decision": "ADMIT_ALTERNATIVE_SOURCE_PLAN_HOLD_TRANSFER", - "claim_boundary": ( - "This receipt identifies alternative public/research sources and tool readiness. " - "It does not prove data was exported from BigQuery, does not bypass source terms, " - "and does not claim decoded chain semantics." - ), - "destination": args.destination, - "source_candidates": [ - { - "name": "Google BigQuery Public Cryptocurrency Datasets", - "decision": "PREFERRED_FOR_BLOCKCHAIR_GAPS", - "chains": list(BIGQUERY_CRYPTO_DATASETS.values()), - "why": "Covers Bitcoin-derived chains that Blockchair throttled: Bitcoin Cash, Dash, Dogecoin, Litecoin, and Zcash.", - "claim_boundary": "Requires Google Cloud authentication and a GCS staging bucket before local/Drive export.", - "python_client_ready": python_module_status("google.cloud.bigquery").get("available"), - "source_urls": [ - "https://cloud.google.com/blog/products/data-analytics/introducing-six-new-cryptocurrencies-in-bigquery-public-datasets-and-how-to-analyze-them", - "https://www.cloudskillsboost.google/focuses/8486?parent=catalog", - ], - }, - { - "name": "Blockchain ETL / bitcoin-etl from full nodes or snapshots", - "decision": "FALLBACK_IF_BIGQUERY_EXPORT_BLOCKED", - "chains": BITCOIN_ETL_CHAINS, - "why": "Can export Bitcoin-like chains from local RPC nodes or grabbed node snapshots without Blockchair dumps.", - "claim_boundary": "Requires per-chain node data/RPC and enough local storage; slower but self-verifiable.", - "source_urls": ["https://github.com/blockchain-etl/bitcoin-etl"], - }, - { - "name": "Bitquery Cloud Data Dumps", - "decision": "EVALUATE_LICENSE_AND_COVERAGE", - "chains": ["bitcoin"], - "why": "Offers Parquet dump patterns and cloud data products; useful as a schema/tooling reference and possible paid/free lane.", - "claim_boundary": "Coverage and licensing must be checked before mirroring.", - "source_urls": ["https://docs.bitquery.io/docs/cloud/bitcoin/"], - }, - { - "name": "Kaggle/Hugging Face sampled datasets", - "decision": "HOLD_SAMPLE_ONLY", - "chains": ["bitcoin-cash", "dash", "dogecoin", "litecoin", "zcash"], - "why": "Useful for smoke tests and model fixtures, not complete chain mirrors.", - "claim_boundary": "Do not use for full-corpus claims.", - "source_urls": [ - "https://huggingface.co/datasets/Omarrran/CryptoXChain_500K_Multi_Network_Blockchain_Transaction_Dataset", - "https://www.kaggle.com/datasets/amritpal333/crypto-mining-data", - ], - }, - ], - "local_tool_readiness": { - "commands": { - name: command_status(name) - for name in ["rclone", "bq", "gcloud", "gsutil", "kaggle", "duckdb"] - }, - "python_modules": { - module: python_module_status(module) - for module in ["requests", "boto3", "pyarrow", "pandas", "google.cloud.bigquery", "duckdb"] - }, - }, - "bigquery_export_commands": bq_extract_commands(args.destination), - "node_etl_commands": bitcoin_etl_commands(args.destination), - "next_gate": { - "decision": "HOLD_UNTIL_GOOGLE_ADC_AND_GCS_BUCKET", - "required": [ - "Authenticate with a Google Cloud account allowed to query BigQuery public datasets.", - "Use either gcloud application-default login or GOOGLE_APPLICATION_CREDENTIALS.", - "Provide or create a GCS staging bucket for BigQuery extract jobs.", - "Run the Python BigQuery list check against each candidate dataset and emit table inventory receipt.", - "Export tables to Parquet, copy locally, then rclone to Drive with SHA receipts.", - ], - }, - } - receipt["receipt_hash"] = stable_hash(receipt) - out = Path(args.out) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"decision": receipt["decision"], "out": str(out), "receipt_hash": receipt["receipt_hash"]})) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/blockchain_bigquery_grabber.py b/4-Infrastructure/shim/blockchain_bigquery_grabber.py deleted file mode 100644 index f6b6dd91..00000000 --- a/4-Infrastructure/shim/blockchain_bigquery_grabber.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python3 -"""BigQuery grabber command surface for public cryptocurrency datasets. - -The script intentionally defaults to dry-run command emission. Actual BigQuery -exports require Google Cloud authentication and a GCS staging bucket. This keeps -the corpus lane receipt-bearing without embedding credentials or pretending -that a public dataset can be exported anonymously. -""" - -from __future__ import annotations - -import argparse -import json -import shutil -import subprocess -import sys -from pathlib import Path -from typing import Any - - -DATASETS = { - "bitcoin-cash": "bigquery-public-data.crypto_bitcoin_cash", - "dash": "bigquery-public-data.crypto_dash", - "dogecoin": "bigquery-public-data.crypto_dogecoin", - "ethereum-classic": "bigquery-public-data.crypto_ethereum_classic", - "litecoin": "bigquery-public-data.crypto_litecoin", - "zcash": "bigquery-public-data.crypto_zcash", -} - -DEFAULT_TABLES = ["blocks", "transactions", "inputs", "outputs"] - - -def run(cmd: list[str]) -> subprocess.CompletedProcess[str]: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, check=False) - - -def build_plan(chains: list[str], tables: list[str], gcs_bucket: str, drive_destination: str) -> dict[str, Any]: - entries = [] - for chain in chains: - dataset = DATASETS[chain] - chain_slug = chain - local_root = f"shared-data/data/blockchain_corpus/bigquery_exports/{chain_slug}" - remote_root = f"{drive_destination.rstrip('/')}/public-datasets/google-bigquery/{chain_slug}" - commands = [f"bq ls {dataset}"] - for table in tables: - commands.append( - "bq extract --destination_format=PARQUET " - f"'{dataset}.{table}' " - f"'gs://{gcs_bucket}/research-stack/blockchain-corpus/{chain_slug}/{table}/*.parquet'" - ) - commands.extend( - [ - ( - "gcloud storage cp --recursive " - f"'gs://{gcs_bucket}/research-stack/blockchain-corpus/{chain_slug}/' " - f"'{local_root}/'" - ), - f"rclone copy '{local_root}/' '{remote_root}/'", - ] - ) - entries.append( - { - "chain": chain, - "dataset": dataset, - "tables": tables, - "gcs_prefix": f"gs://{gcs_bucket}/research-stack/blockchain-corpus/{chain_slug}/", - "local_root": local_root, - "drive_destination": remote_root, - "commands": commands, - } - ) - return { - "schema": "blockchain_bigquery_grabber_plan_v0", - "claim_boundary": "Dry-run command plan only unless --execute is used with authenticated Google Cloud tooling.", - "tool_status": { - "bq": shutil.which("bq"), - "gcloud": shutil.which("gcloud"), - "gsutil": shutil.which("gsutil"), - "rclone": shutil.which("rclone"), - "python": sys.executable, - "python_bigquery_client": python_module_available("google.cloud.bigquery"), - "python_google_auth": python_module_available("google.auth"), - }, - "entries": entries, - } - - -def python_module_available(module: str) -> bool: - try: - __import__(module) - return True - except Exception: - return False - - -def list_with_python_client(entries: list[dict[str, Any]]) -> tuple[str, list[dict[str, Any]]]: - try: - from google.api_core.exceptions import GoogleAPIError # type: ignore - from google.auth.exceptions import DefaultCredentialsError # type: ignore - from google.cloud import bigquery # type: ignore - except Exception as exc: # noqa: BLE001 - receipt exact missing module. - return "HOLD_BIGQUERY_PYTHON_CLIENT_MISSING", [ - {"error": type(exc).__name__, "message": str(exc), "python": sys.executable} - ] - - try: - client = bigquery.Client() - except DefaultCredentialsError as exc: - return "HOLD_GOOGLE_ADC_MISSING", [ - { - "error": type(exc).__name__, - "message": str(exc).splitlines()[0], - "python": sys.executable, - "next_step": "Run gcloud auth application-default login or provide GOOGLE_APPLICATION_CREDENTIALS.", - } - ] - except Exception as exc: # noqa: BLE001 - receipt unexpected auth/client failures. - return "HOLD_BIGQUERY_CLIENT_INIT_FAILED", [ - {"error": type(exc).__name__, "message": str(exc), "python": sys.executable} - ] - - results = [] - ok = True - for entry in entries: - try: - tables = list(client.list_tables(entry["dataset"])) - results.append( - { - "chain": entry["chain"], - "dataset": entry["dataset"], - "tables": [{"table_id": table.table_id, "full_table_id": table.full_table_id} for table in tables], - } - ) - except GoogleAPIError as exc: - ok = False - results.append({"chain": entry["chain"], "dataset": entry["dataset"], "error": type(exc).__name__, "message": str(exc)}) - except Exception as exc: # noqa: BLE001 - receipt per-dataset failure. - ok = False - results.append({"chain": entry["chain"], "dataset": entry["dataset"], "error": type(exc).__name__, "message": str(exc)}) - return ("ADMIT_BIGQUERY_PYTHON_LIST_CHECK" if ok else "HOLD_BIGQUERY_PYTHON_LIST_CHECK_FAILED"), results - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--chains", nargs="+", default=list(DATASETS), choices=sorted(DATASETS)) - parser.add_argument("--tables", nargs="+", default=DEFAULT_TABLES) - parser.add_argument("--gcs-bucket", default="") - parser.add_argument( - "--drive-destination", - default="Gdrive:topological_storage/research-stack/blockchain-corpus/seed-2026-05-10", - ) - parser.add_argument("--out", default="shared-data/data/blockchain_corpus/blockchain_bigquery_grabber_plan.json") - parser.add_argument("--execute-list-only", action="store_true", help="Run only bq ls commands to verify table access.") - parser.add_argument("--execute-python-list-only", action="store_true", help="Run table listing through google-cloud-bigquery.") - args = parser.parse_args() - - plan = build_plan(args.chains, args.tables, args.gcs_bucket, args.drive_destination) - out = Path(args.out) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(plan, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - if args.execute_list_only: - if not shutil.which("bq"): - print(json.dumps({"decision": "HOLD_BQ_CLI_MISSING", "out": str(out)})) - return 2 - results = [] - for entry in plan["entries"]: - proc = run(["bq", "ls", entry["dataset"]]) - results.append( - { - "chain": entry["chain"], - "dataset": entry["dataset"], - "returncode": proc.returncode, - "stdout": proc.stdout[-4000:], - "stderr": proc.stderr[-4000:], - } - ) - result_path = out.with_name(out.stem + "_list_results.json") - result_path.write_text(json.dumps(results, indent=2, sort_keys=True) + "\n", encoding="utf-8") - ok = all(item["returncode"] == 0 for item in results) - print(json.dumps({"decision": "ADMIT_BQ_LIST_CHECK" if ok else "HOLD_BQ_LIST_CHECK_FAILED", "out": str(out), "results": str(result_path)})) - return 0 if ok else 2 - - if args.execute_python_list_only: - decision, results = list_with_python_client(plan["entries"]) - result_path = out.with_name(out.stem + "_python_list_results.json") - result_path.write_text(json.dumps(results, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"decision": decision, "out": str(out), "results": str(result_path)})) - return 0 if decision.startswith("ADMIT") else 2 - - print(json.dumps({"decision": "ADMIT_BIGQUERY_GRABBER_DRY_RUN_PLAN", "out": str(out)})) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/blockchain_full_account_receipt.py b/4-Infrastructure/shim/blockchain_full_account_receipt.py deleted file mode 100644 index f8040d94..00000000 --- a/4-Infrastructure/shim/blockchain_full_account_receipt.py +++ /dev/null @@ -1,205 +0,0 @@ -#!/usr/bin/env python3 -"""Build an aggregate accounting receipt for blockchain corpus streaming. - -This is an accounting surface only. It records source inventories, completed -transfer receipts, and optional remote object counts. It does not decode chain -semantics or claim complete cryptocurrency coverage beyond the listed source -objects. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import pathlib -import subprocess -from datetime import datetime, timezone -from typing import Any - - -def utc_now() -> str: - return datetime.now(timezone.utc).replace(microsecond=0).isoformat().replace("+00:00", "Z") - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def load_json(path: pathlib.Path) -> dict[str, Any]: - with path.open("r", encoding="utf-8") as handle: - return json.load(handle) - - -def rclone_count(remote: str, suffix: str | None = None) -> int | None: - try: - proc = subprocess.run( - ["rclone", "lsf", remote, "--recursive"], - check=False, - stdout=subprocess.PIPE, - stderr=subprocess.DEVNULL, - text=True, - timeout=90, - ) - except (FileNotFoundError, subprocess.TimeoutExpired): - return None - if proc.returncode != 0: - return None - lines = [ - line - for line in proc.stdout.splitlines() - if line and not line.endswith("/") and not line.startswith("receipts/") and "/receipts/" not in line - ] - if suffix: - lines = [line for line in lines if line.endswith(suffix)] - return len(lines) - - -def summarize_inventory(path: pathlib.Path) -> dict[str, Any]: - doc = load_json(path) - objects = doc.get("objects", []) - payload_object_count = sum(1 for item in objects if not str(item.get("key", "")).endswith("/")) - parquet_object_count = sum(1 for item in objects if str(item.get("key", "")).endswith(".parquet")) - total_bytes = doc.get("total_listed_bytes") - if total_bytes is None: - total_bytes = doc.get("total_listed_bytes_known") - return { - "chain": doc.get("chain"), - "claim_boundary": doc.get("claim_boundary"), - "dataset": doc.get("dataset"), - "decision": doc.get("decision"), - "directory_url": doc.get("directory_url"), - "inventory": str(path), - "inventory_hash": doc.get("inventory_hash"), - "object_count": doc.get("object_count"), - "payload_object_count": payload_object_count, - "parquet_object_count": parquet_object_count, - "prefix": doc.get("prefix"), - "table": doc.get("table"), - "total_listed_bytes": total_bytes, - } - - -def summarize_transfer(path: pathlib.Path) -> dict[str, Any]: - doc = load_json(path) - summary = doc.get("summary", {}) - return { - "chain": doc.get("chain"), - "dataset": doc.get("dataset"), - "decision": doc.get("decision"), - "receipt": str(path), - "receipt_hash": doc.get("receipt_hash"), - "table": doc.get("table"), - "quarantine_count": summary.get("quarantine_count"), - "selected_object_count": summary.get("selected_object_count"), - "size_mismatch_count": summary.get("size_mismatch_count"), - "successful_or_dry_run_count": summary.get("successful_or_dry_run_count"), - "total_observed_bytes": summary.get("total_observed_bytes"), - } - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--data-dir", default="shared-data/data/blockchain_corpus") - parser.add_argument("--destination", required=True) - parser.add_argument("--out", required=True) - parser.add_argument("--include-remote-counts", action="store_true") - parser.add_argument("--running", action="append", default=[]) - args = parser.parse_args() - - data_dir = pathlib.Path(args.data_dir) - aws_full_inventories = [ - summarize_inventory(path) - for path in sorted(data_dir.glob("aws_public_blockchain_*_blocks_inventory_full.json")) - ] - bounded_inventories = [ - summarize_inventory(path) - for path in sorted(data_dir.glob("blockchair_*_blocks_inventory_first100.json")) - ] - inventories = aws_full_inventories + bounded_inventories - transfers = [] - for path in sorted(data_dir.glob("*transfer*receipt.json")): - doc = load_json(path) - summary = doc.get("summary", {}) - if not doc.get("dataset") or not doc.get("chain") or "selected_object_count" not in summary: - continue - transfers.append(summarize_transfer(path)) - - inventory_by_key = { - (entry["dataset"], entry["chain"], entry["table"]): entry for entry in inventories - } - transferred_by_key: dict[tuple[Any, Any, Any], int] = {} - quarantined_by_key: dict[tuple[Any, Any, Any], int] = {} - bytes_by_key: dict[tuple[Any, Any, Any], int] = {} - for entry in transfers: - key = (entry["dataset"], entry["chain"], entry["table"]) - transferred_by_key[key] = transferred_by_key.get(key, 0) + int(entry.get("successful_or_dry_run_count") or 0) - quarantined_by_key[key] = quarantined_by_key.get(key, 0) + int(entry.get("quarantine_count") or 0) - bytes_by_key[key] = bytes_by_key.get(key, 0) + int(entry.get("total_observed_bytes") or 0) - - chains = [] - for key, inv in sorted(inventory_by_key.items(), key=lambda item: str(item[0])): - remote = f"{args.destination}/{inv['dataset']}/{inv['chain']}/{inv['table']}" - entry = { - **inv, - "completed_transfer_objects": transferred_by_key.get(key, 0), - "completed_transfer_bytes": bytes_by_key.get(key, 0), - "completed_quarantine_objects": quarantined_by_key.get(key, 0), - "remote_parquet_count": rclone_count(remote) if args.include_remote_counts else None, - "remote_payload_count": rclone_count(remote, None) if args.include_remote_counts else None, - "remote_prefix": remote, - } - chains.append(entry) - - all_transfer_totals = { - "quarantine_objects": sum(int(entry.get("quarantine_count") or 0) for entry in transfers), - "selected_objects": sum(int(entry.get("selected_object_count") or 0) for entry in transfers), - "size_mismatch_objects": sum(int(entry.get("size_mismatch_count") or 0) for entry in transfers), - "successful_or_dry_run_objects": sum(int(entry.get("successful_or_dry_run_count") or 0) for entry in transfers), - "total_observed_bytes": sum(int(entry.get("total_observed_bytes") or 0) for entry in transfers), - } - - decision = "ADMIT_BLOCKCHAIN_CORPUS_ACCOUNTING" - if args.running: - decision = "ADMIT_BLOCKCHAIN_CORPUS_ACCOUNTING_WITH_RUNNING_TRANSFERS" - - receipt = { - "claim_boundary": ( - "Full accounting receipt for currently listed public blockchain corpus sources. " - "This records inventory coverage, transfer receipts, remote counts when requested, " - "and running transfer labels. It does not prove decoded chain semantics, price/market " - "coverage, private dataset coverage, or compression results." - ), - "created_utc": utc_now(), - "decision": decision, - "destination": args.destination, - "aws_full_inventoried_chain_table_count": len(aws_full_inventories), - "bounded_inventoried_chain_table_count": len(bounded_inventories), - "inventoried_chain_table_count": len(inventories), - "running_transfers": args.running, - "schema": "blockchain_full_account_receipt_v0", - "source_inventories": chains, - "transfer_receipts": transfers, - "totals": { - "all_transfer_receipts": all_transfer_totals, - "completed_quarantine_objects": sum(entry["completed_quarantine_objects"] for entry in chains), - "completed_transfer_bytes": sum(entry["completed_transfer_bytes"] for entry in chains), - "completed_transfer_objects": sum(entry["completed_transfer_objects"] for entry in chains), - "inventoried_objects": sum(int(entry.get("object_count") or 0) for entry in chains), - "inventoried_payload_objects": sum(int(entry.get("payload_object_count") or 0) for entry in chains), - "inventoried_parquet_objects": sum(int(entry.get("parquet_object_count") or 0) for entry in chains), - "inventoried_total_listed_bytes": sum(int(entry.get("total_listed_bytes") or 0) for entry in chains), - }, - } - receipt["receipt_hash"] = sha256_json(receipt) - - out = pathlib.Path(args.out) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"decision": receipt["decision"], "out": str(out), "receipt_hash": receipt["receipt_hash"]})) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/blockchain_gdrive_stream_ingest.py b/4-Infrastructure/shim/blockchain_gdrive_stream_ingest.py deleted file mode 100644 index f400700a..00000000 --- a/4-Infrastructure/shim/blockchain_gdrive_stream_ingest.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -"""Byte-stream row-group ingest for Bitcoin/Ethereum corpus mirrors. - -This script does not parse consensus data. It treats a source as an ordered byte -stream, shards it into deterministic row groups, writes a small JSON index for -each shard, and optionally streams both payload and index directly to a Google -Drive rclone remote. - -The format is "parquet-style" in the operational sense: partitioned datasets, -row groups, schema sidecars, and ordered shard manifests. It intentionally avoids -new dependencies; true Parquet can be added later as an extraction/export target. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import BinaryIO - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" -ARTIFACT_DIR = REPO / "shared-data/artifacts/blockchain_corpus" -SOURCE_MANIFEST = DATA_DIR / "blockchain_gdrive_stream_sources.json" -DEFAULT_DESTINATION = "Gdrive:topological_storage/research-stack/blockchain-corpus/seed-2026-05-10" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def load_json(path: Path) -> dict: - return json.loads(path.read_text()) - - -def sha256_bytes(payload: bytes) -> str: - return hashlib.sha256(payload).hexdigest() - - -def run(cmd: list[str], input_bytes: bytes | None = None) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, - input=input_bytes, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=False, - ) - - -def rclone_available(remote: str) -> tuple[bool, str]: - proc = run(["rclone", "lsf", remote]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def rcat(remote_path: str, payload: bytes) -> tuple[bool, str]: - proc = run(["rclone", "rcat", remote_path], input_bytes=payload) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def read_source(path: str) -> BinaryIO: - if path == "-": - return sys.stdin.buffer - return Path(path).expanduser().open("rb") - - -def shard_remote_prefix(destination: str, chain: str, stream_kind: str, run_id: str) -> str: - return f"{destination.rstrip('/')}/chain={chain}/stream={stream_kind}/run={run_id}" - - -def shard_name(chain: str, stream_kind: str, run_id: str, shard_index: int, suffix: str) -> str: - return f"{chain}_{stream_kind}_{run_id}_shard_{shard_index:06d}.{suffix}" - - -def write_local(path: Path, payload: bytes) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_bytes(payload) - - -def emit_payload( - *, - payload: bytes, - index_payload: bytes, - local_dir: Path, - remote_prefix: str, - payload_name: str, - index_name: str, - execute: bool, - keep_local_payloads: bool, -) -> dict: - local_payload = local_dir / "payload" / payload_name - local_index = local_dir / "index" / index_name - write_local(local_index, index_payload) - if keep_local_payloads: - write_local(local_payload, payload) - - result = { - "payload_local_path": str(local_payload.relative_to(REPO)) if keep_local_payloads else None, - "index_local_path": str(local_index.relative_to(REPO)), - "payload_drive_path": f"{remote_prefix}/payload/{payload_name}", - "index_drive_path": f"{remote_prefix}/index/{index_name}", - "payload_upload": "HOLD_DRY_RUN_ONLY", - "index_upload": "HOLD_DRY_RUN_ONLY", - } - if execute: - payload_ok, payload_msg = rcat(result["payload_drive_path"], payload) - index_ok, index_msg = rcat(result["index_drive_path"], index_payload) - result["payload_upload"] = "ADMIT_GDRIVE_PAYLOAD" if payload_ok else "QUARANTINE_RCLONE_FAILED" - result["index_upload"] = "ADMIT_GDRIVE_INDEX" if index_ok else "QUARANTINE_RCLONE_FAILED" - result["payload_upload_message"] = payload_msg - result["index_upload_message"] = index_msg - return result - - -def stream_ingest(args: argparse.Namespace) -> dict: - manifest = load_json(args.sources) - destination = args.destination or manifest.get("default_drive_destination", DEFAULT_DESTINATION) - shard_bytes = args.shard_bytes or int(manifest.get("default_shard_bytes", 64 * 1024 * 1024)) - run_id = args.run_id or datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - remote_prefix = shard_remote_prefix(destination, args.chain, args.stream_kind, run_id) - local_dir = ARTIFACT_DIR / args.chain / args.stream_kind / run_id - - remote_root = destination.split("/", 1)[0] - remote_ok, remote_message = rclone_available(remote_root) - if args.execute and not remote_ok: - return { - "schema": "blockchain_gdrive_stream_ingest_receipt_v0", - "created_utc": now_iso(), - "decision": "QUARANTINE_NO_GDRIVE_REMOTE", - "destination": destination, - "remote_check": remote_message, - } - - shard_records = [] - total_bytes = 0 - stream_hash = hashlib.sha256() - - with read_source(args.source) as source: - shard_index = 0 - while True: - chunk = source.read(shard_bytes) - if not chunk: - break - - payload_hash = sha256_bytes(chunk) - stream_hash.update(chunk) - payload_name = shard_name(args.chain, args.stream_kind, run_id, shard_index, "bin") - index_name = shard_name(args.chain, args.stream_kind, run_id, shard_index, "index.json") - record = { - "schema": "blockchain_row_group_index_v0", - "chain": args.chain, - "stream_kind": args.stream_kind, - "run_id": run_id, - "shard_index": shard_index, - "offset_start": total_bytes, - "byte_count": len(chunk), - "payload_sha256": payload_hash, - "payload_name": payload_name, - "encoding": "raw-bytes", - "columns": [ - "chain", - "stream_kind", - "run_id", - "shard_index", - "offset_start", - "byte_count", - "payload_sha256", - "payload_name" - ] - } - index_payload = json.dumps(record, indent=2, sort_keys=True).encode() + b"\n" - emit_result = emit_payload( - payload=chunk, - index_payload=index_payload, - local_dir=local_dir, - remote_prefix=remote_prefix, - payload_name=payload_name, - index_name=index_name, - execute=args.execute, - keep_local_payloads=args.keep_local_payloads, - ) - record.update(emit_result) - shard_records.append(record) - total_bytes += len(chunk) - shard_index += 1 - - if args.max_shards is not None and shard_index >= args.max_shards: - break - - ordered_hash_input = "".join(item["payload_sha256"] for item in shard_records).encode() - ordered_shard_hash = sha256_bytes(ordered_hash_input) - decision = "ADMIT_STREAM_TO_GDRIVE" if args.execute else "HOLD_DRY_RUN_ONLY" - if args.max_shards is not None: - decision = "ADMIT_PARTIAL_STREAM_TO_GDRIVE" if args.execute else "HOLD_PARTIAL_DRY_RUN_ONLY" - - receipt = { - "schema": "blockchain_gdrive_stream_ingest_receipt_v0", - "created_utc": now_iso(), - "claim_boundary": "Byte-stream row-group ingest. This receipt proves only ordered bytes seen by this run and optional Drive upload status; it does not prove complete Bitcoin/Ethereum corpus coverage.", - "source_manifest": str(args.sources.relative_to(REPO)), - "chain": args.chain, - "stream_kind": args.stream_kind, - "source": args.source, - "destination": destination, - "remote_prefix": remote_prefix, - "run_id": run_id, - "execute": args.execute, - "keep_local_payloads": args.keep_local_payloads, - "shard_bytes": shard_bytes, - "max_shards": args.max_shards, - "remote_check": "PASS" if remote_ok else remote_message, - "summary": { - "shard_count": len(shard_records), - "total_bytes": total_bytes, - "stream_sha256": stream_hash.hexdigest(), - "ordered_shard_hash": ordered_shard_hash, - }, - "shards": shard_records, - "decision": decision, - } - receipt_path = DATA_DIR / f"blockchain_gdrive_stream_ingest_receipt_{args.chain}_{args.stream_kind}_{run_id}.json" - DATA_DIR.mkdir(parents=True, exist_ok=True) - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - receipt["receipt_path"] = str(receipt_path.relative_to(REPO)) - - if args.execute: - remote_receipt = f"{remote_prefix}/receipts/{receipt_path.name}" - ok, message = rcat(remote_receipt, receipt_path.read_bytes()) - receipt["receipt_upload"] = { - "drive_path": remote_receipt, - "ok": ok, - "message": message, - } - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--sources", type=Path, default=SOURCE_MANIFEST) - parser.add_argument("--chain", choices=["bitcoin", "ethereum"], required=True) - parser.add_argument("--stream-kind", required=True) - parser.add_argument("--source", default="-", help="source file path or '-' for stdin") - parser.add_argument("--destination", default=None) - parser.add_argument("--run-id", default=None) - parser.add_argument("--shard-bytes", type=int, default=None) - parser.add_argument("--max-shards", type=int, default=None) - parser.add_argument("--execute", action="store_true") - parser.add_argument( - "--keep-local-payloads", - action="store_true", - help="Keep local payload shard bytes after upload/dry-run. Indexes and receipts are always kept.", - ) - args = parser.parse_args() - - receipt = stream_ingest(args) - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 if not str(receipt.get("decision", "")).startswith("QUARANTINE") else 2 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchain_header_pattern_probe.py b/4-Infrastructure/shim/blockchain_header_pattern_probe.py deleted file mode 100644 index 43f62c93..00000000 --- a/4-Infrastructure/shim/blockchain_header_pattern_probe.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -"""Probe numeric predictability in Bitcoin header JSONL streams. - -The probe emits compression-route diagnostics only. It does not forecast price, -claim chain semantics, or prove Hutter improvement. The useful object is the -residual: if a declared predictor leaves small residuals, the stream may be a -good candidate for a receipt-bearing residual codec or logogram route. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import statistics -import sys -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Iterable, TextIO - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data/data/blockchain_corpus" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def iter_records(handle: TextIO, max_records: int | None) -> Iterable[dict[str, Any]]: - count = 0 - for line in handle: - if not line.strip(): - continue - yield json.loads(line) - count += 1 - if max_records is not None and count >= max_records: - break - - -def signed_int(value: Any) -> int | None: - if value is None: - return None - try: - return int(value) - except (TypeError, ValueError): - return None - - -def hex_int(value: Any) -> int | None: - if value is None: - return None - try: - return int(str(value), 16) - except (TypeError, ValueError): - return None - - -def deltas(values: list[int]) -> list[int]: - return [b - a for a, b in zip(values, values[1:])] - - -def residuals(values: list[int], mode: str) -> list[int]: - if len(values) < 2: - return [] - if mode == "previous": - return deltas(values) - if mode == "linear_delta": - first = deltas(values) - return deltas(first) - raise ValueError(mode) - - -def entropy_bits(values: list[int]) -> float: - if not values: - return 0.0 - total = len(values) - counts = Counter(values) - return -sum((count / total) * math.log2(count / total) for count in counts.values()) - - -def median_abs(values: list[int]) -> float: - return float(statistics.median(abs(value) for value in values)) if values else 0.0 - - -def summarize_residual(field: str, mode: str, raw_values: list[int], resids: list[int]) -> dict[str, Any]: - raw_entropy = entropy_bits(raw_values) - resid_entropy = entropy_bits(resids) - unique_raw = len(set(raw_values)) - unique_resid = len(set(resids)) - improvement = raw_entropy - resid_entropy - return { - "field": field, - "predictor": mode, - "sample_count": len(raw_values), - "residual_count": len(resids), - "raw_unique": unique_raw, - "residual_unique": unique_resid, - "raw_entropy_bits_per_symbol": round(raw_entropy, 6), - "residual_entropy_bits_per_symbol": round(resid_entropy, 6), - "entropy_delta_bits_per_symbol": round(improvement, 6), - "median_abs_residual": round(median_abs(resids), 6), - "zero_residual_share": round(sum(1 for item in resids if item == 0) / len(resids), 6) if resids else 0.0, - "route_candidate": ( - "ADMIT_RESIDUAL_ROUTE_CANDIDATE" - if improvement > 0 and unique_resid <= unique_raw - else "HOLD_RESIDUAL_ROUTE" - ), - } - - -def longest_run(values: list[Any]) -> dict[str, Any]: - if not values: - return {"value": None, "length": 0} - best_value = values[0] - best_length = 1 - current_value = values[0] - current_length = 1 - for value in values[1:]: - if value == current_value: - current_length += 1 - else: - if current_length > best_length: - best_value = current_value - best_length = current_length - current_value = value - current_length = 1 - if current_length > best_length: - best_value = current_value - best_length = current_length - return {"value": best_value, "length": best_length} - - -def build_probe(records: list[dict[str, Any]], source_label: str) -> dict[str, Any]: - heights = [signed_int(row.get("height")) for row in records] - times = [signed_int(row.get("time")) for row in records] - mediantimes = [signed_int(row.get("mediantime")) for row in records] - ntx = [signed_int(row.get("nTx")) for row in records] - nonces = [signed_int(row.get("nonce")) for row in records] - chainwork = [hex_int(row.get("chainwork")) for row in records] - bits = [row.get("bits") for row in records] - versions = [signed_int(row.get("version")) for row in records] - - numeric = { - "height": [value for value in heights if value is not None], - "time": [value for value in times if value is not None], - "mediantime": [value for value in mediantimes if value is not None], - "nTx": [value for value in ntx if value is not None], - "nonce": [value for value in nonces if value is not None], - "chainwork": [value for value in chainwork if value is not None], - "version": [value for value in versions if value is not None], - } - summaries = [] - for field, values in numeric.items(): - if len(values) < 3: - continue - summaries.append(summarize_residual(field, "previous", values, residuals(values, "previous"))) - summaries.append(summarize_residual(field, "linear_delta", values, residuals(values, "linear_delta"))) - - summaries.sort(key=lambda item: item["entropy_delta_bits_per_symbol"], reverse=True) - bits_values = [value for value in bits if value is not None] - version_values = [value for value in versions if value is not None] - height_values = numeric["height"] - continuity_breaks = [] - for previous, current in zip(height_values, height_values[1:]): - if current != previous + 1: - continuity_breaks.append({"previous": previous, "current": current}) - - payload = { - "schema": "blockchain_header_pattern_probe_v0", - "created_utc": now_iso(), - "source_label": source_label, - "claim_boundary": "Numeric residual and route-prior diagnostic only. This does not forecast price, prove consensus validity, claim compression gain, or claim Hutter Prize progress.", - "record_count": len(records), - "height_range": { - "start": height_values[0] if height_values else None, - "end": height_values[-1] if height_values else None, - "continuity_break_count": len(continuity_breaks), - "continuity_break_examples": continuity_breaks[:10], - }, - "categorical_runs": { - "bits_unique": len(set(bits_values)), - "bits_longest_run": longest_run(bits_values), - "version_unique": len(set(version_values)), - "version_longest_run": longest_run(version_values), - }, - "residual_summaries": summaries, - "top_route_candidates": [ - item for item in summaries if item["route_candidate"] == "ADMIT_RESIDUAL_ROUTE_CANDIDATE" - ][:10], - "hutter_feedback_rule": ( - "Only fields with declared predictors, lower residual entropy, and replayable " - "height-contiguous provenance may be promoted into a Hutter/logogram fixture. " - "Compression gain remains HOLD until byte-exact codec baselines exist." - ), - } - payload["payload_hash"] = sha256_text(stable_json({k: v for k, v in payload.items() if k != "payload_hash"})) - payload["decision"] = ( - "ADMIT_HEADER_PATTERN_ROUTE_PRIORS" - if payload["height_range"]["continuity_break_count"] == 0 and payload["top_route_candidates"] - else "HOLD_HEADER_PATTERN_ROUTE_PRIORS" - ) - return payload - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--source", default="-", help="Bitcoin header JSONL path or '-' for stdin") - parser.add_argument("--source-label", default=None) - parser.add_argument("--max-records", type=int, default=None) - parser.add_argument("--out", type=Path, default=None) - args = parser.parse_args() - - if args.source == "-": - records = list(iter_records(sys.stdin, args.max_records)) - source_label = args.source_label or "stdin" - else: - path = Path(args.source) - with path.open("r", encoding="utf-8") as handle: - records = list(iter_records(handle, args.max_records)) - source_label = args.source_label or str(path) - - payload = build_probe(records, source_label) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n") - print(json.dumps(payload, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchain_l3_self_scan_scheduler.py b/4-Infrastructure/shim/blockchain_l3_self_scan_scheduler.py deleted file mode 100644 index c671ca67..00000000 --- a/4-Infrastructure/shim/blockchain_l3_self_scan_scheduler.py +++ /dev/null @@ -1,283 +0,0 @@ -#!/usr/bin/env python3 -"""Emit a Layer-3 self-scanning plan for blockchain corpus receipts. - -The scheduler treats existing inventory and transfer receipts as the control -surface. It does not decode chain semantics or fetch more data. Its job is to -turn scan evidence into the next bounded scan actions. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_ACCOUNT = REPO / "shared-data/data/blockchain_corpus/blockchain_full_account_live_receipt.json" -DEFAULT_OUT = REPO / "shared-data/data/blockchain_corpus/blockchain_l3_self_scan_scheduler_receipt.json" - - -STORAGE_CHANNELS = { - "bitcoin": [ - { - "channel": "op_return", - "scan_surface": "transaction outputs / scriptPubKey", - "use_boundary": "Detect and receipt embedded payload surfaces; do not publish or optimize arbitrary payload insertion.", - }, - { - "channel": "witness_inscription_like_payload", - "scan_surface": "segwit witness data", - "use_boundary": "Treat as public immutable payload evidence only.", - }, - { - "channel": "coinbase_tag", - "scan_surface": "coinbase transaction script/witness fields", - "use_boundary": "Pool tags and receipt markers only; no attribution claim without external witness.", - }, - ], - "evm": [ - { - "channel": "calldata", - "scan_surface": "transaction input bytes", - "use_boundary": "Route by byte density and selector shape only; no private-key, exploit, or evasion workflow.", - }, - { - "channel": "event_logs", - "scan_surface": "topics and data fields", - "use_boundary": "Receipt emitted public events as storage-like data lanes.", - }, - { - "channel": "contract_bytecode", - "scan_surface": "creation/runtime bytecode", - "use_boundary": "Static payload/code-carrier diagnostic only.", - }, - { - "channel": "blob_or_da_payload", - "scan_surface": "data availability/blob lanes when present in source tables", - "use_boundary": "Cost-bounded metadata scan first; bulk payload fetch remains HOLD.", - }, - ], - "memo": [ - { - "channel": "memo_or_message", - "scan_surface": "transaction memo/message/payload fields", - "use_boundary": "Public memo payload diagnostics only.", - } - ], -} - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def rel(path: str | Path) -> str: - p = Path(path) - try: - return str(p.relative_to(REPO)) - except ValueError: - return str(p) - - -def density_band(total_bytes: int, object_count: int) -> str: - if object_count <= 0: - return "empty" - bytes_per_object = total_bytes / object_count - if bytes_per_object >= 64 * 1024 * 1024: - return "heavy" - if bytes_per_object >= 8 * 1024 * 1024: - return "medium" - return "light" - - -def completion_state(item: dict[str, Any]) -> dict[str, Any]: - object_count = int(item.get("object_count") or 0) - remote_payload = int(item.get("remote_payload_count") or 0) - remote_parquet = int(item.get("remote_parquet_count") or 0) - completed = min(remote_payload, remote_parquet) - missing = max(object_count - completed, 0) - ratio = completed / object_count if object_count else 0.0 - return { - "completed_objects": completed, - "missing_objects": missing, - "remote_parquet_count": remote_parquet, - "remote_payload_count": remote_payload, - "completion_ratio": round(ratio, 9), - } - - -def scan_actions(item: dict[str, Any], state: dict[str, Any], band: str) -> list[dict[str, Any]]: - actions: list[dict[str, Any]] = [] - chain = item.get("chain") - table = item.get("table") - inventory = item.get("inventory") - - if state["missing_objects"]: - actions.append( - { - "action": "RETRY_MISSING_OBJECTS", - "reason": "Remote payload/parquet count is below inventory object count.", - "script": "4-Infrastructure/shim/blockchain_public_dataset_transfer.py", - "inputs": {"inventory": inventory, "chain": chain, "table": table}, - "decision": "HOLD_UNTIL_RETRY_RECEIPT", - } - ) - - actions.append( - { - "action": "RUN_HEADER_OR_BLOCK_PATTERN_PROBE", - "reason": "Use deterministic numeric residuals as route priors before byte-heavy scans.", - "script": "4-Infrastructure/shim/blockchain_header_pattern_probe.py", - "inputs": {"chain": chain, "table": table, "inventory": inventory}, - "decision": "ADMIT_BOUNDED_ROUTE_PRIOR_SCAN", - } - ) - - if band in {"medium", "heavy"}: - actions.append( - { - "action": "RUN_PARQUET_LOGOGRAM_EIGENPROBE_SAMPLE", - "reason": "Large average objects should be sampled through feature/density probes before broader transfer.", - "script": "4-Infrastructure/shim/parquet_logogram_eigenprobe.py", - "inputs": {"chain": chain, "table": table, "sample_policy": "first_middle_last_partition"}, - "decision": "ADMIT_SAMPLE_ONLY_HOLD_BULK", - } - ) - - actions.append( - { - "action": "REFRESH_SELF_SCAN_SCHEDULER", - "reason": "The scan output becomes the next L3 control input.", - "script": "4-Infrastructure/shim/blockchain_l3_self_scan_scheduler.py", - "inputs": {"account_receipt": rel(DEFAULT_ACCOUNT)}, - "decision": "ADMIT_RECEIPT_FEEDBACK_LOOP", - } - ) - return actions - - -def chain_storage_family(chain: str | None) -> str: - if chain in {"ethereum", "ethereum-classic", "arbitrum", "base", "cronos"}: - return "evm" - if chain in {"bitcoin", "bitcoin-cash", "dash", "dogecoin", "litecoin", "zcash"}: - return "bitcoin" - if chain in {"xrp", "stellar", "ton", "aptos", "provenance"}: - return "memo" - return "unknown" - - -def storage_channel_actions(item: dict[str, Any], band: str) -> dict[str, Any]: - chain = item.get("chain") - family = chain_storage_family(chain) - channels = STORAGE_CHANNELS.get(family, []) - actions = [] - if channels: - actions.append( - { - "action": "RUN_ONCHAIN_STORAGE_CHANNEL_PROBE", - "reason": "Blockchain records can contain intentional public data carriers; scan them as storage surfaces before treating bytes as neutral noise.", - "inputs": { - "chain": chain, - "family": family, - "table": item.get("table"), - "inventory": item.get("inventory"), - "density_band": band, - }, - "decision": "ADMIT_STORAGE_SURFACE_SCAN_HOLD_PAYLOAD_USE", - } - ) - return { - "family": family, - "channels": channels, - "actions": actions, - "claim_boundary": ( - "Storage-channel model is for detection, accounting, and receipt routing. " - "It does not recommend publishing payloads on-chain, bypassing moderation, " - "hiding data, or using public ledgers as private storage." - ), - } - - -def build_receipt(account_path: Path) -> dict[str, Any]: - account = json.loads(account_path.read_text(encoding="utf-8")) - entries = [] - for item in account.get("source_inventories", []): - total_bytes = int(item.get("total_listed_bytes") or 0) - object_count = int(item.get("object_count") or 0) - state = completion_state(item) - band = density_band(total_bytes, object_count) - entries.append( - { - "chain": item.get("chain"), - "table": item.get("table"), - "dataset": item.get("dataset"), - "inventory": item.get("inventory"), - "inventory_hash": item.get("inventory_hash"), - "object_count": object_count, - "total_listed_bytes": total_bytes, - "bytes_per_object": round(total_bytes / object_count, 3) if object_count else 0.0, - "density_band": band, - "storage_channel_model": storage_channel_actions(item, band), - "completion": state, - "scan_actions": scan_actions(item, state, band), - } - ) - - entries.sort(key=lambda row: (row["completion"]["missing_objects"] > 0, row["total_listed_bytes"]), reverse=True) - payload = { - "schema": "blockchain_l3_self_scan_scheduler_v0", - "created_utc": now_iso(), - "claim_boundary": ( - "Layer-3 scan scheduling receipt only. It uses existing inventory and transfer receipts " - "to choose next bounded probes. It does not decode chain semantics, claim compression gain, " - "claim market prediction, or fetch additional corpus bytes." - ), - "layer3_interpretation": { - "old_label": "L3 Bitstream", - "scanner_label": "L3 executable scan policy", - "core_rule": "scan_receipts_t -> route_policy_t_plus_1", - "feedback_equation": "P_{t+1}=L3(R_t,A_t,C_t); R_{t+1}=scan(P_{t+1})", - "storage_rule": "onchain_payload_surfaces_t -> storage_channel_receipts_t_plus_1", - }, - "global_storage_boundary": ( - "Public blockchains can store data, but this scheduler only models storage-bearing " - "surfaces for detection, provenance, and compression-route diagnostics. Bulk payload " - "reconstruction and payload publication remain outside this receipt." - ), - "account_receipt": rel(account_path), - "account_receipt_hash": account.get("receipt_hash"), - "source_count": len(entries), - "frontier": entries, - "decision": "ADMIT_L3_SELF_SCAN_POLICY", - } - payload["receipt_hash"] = sha256_text(stable_json({k: v for k, v in payload.items() if k != "receipt_hash"})) - return payload - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--account", type=Path, default=DEFAULT_ACCOUNT) - parser.add_argument("--out", type=Path, default=DEFAULT_OUT) - args = parser.parse_args() - - receipt = build_receipt(args.account) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - print(json.dumps({"decision": receipt["decision"], "out": rel(args.out), "receipt_hash": receipt["receipt_hash"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/blockchain_public_dataset_inventory.py b/4-Infrastructure/shim/blockchain_public_dataset_inventory.py deleted file mode 100644 index 9465e28d..00000000 --- a/4-Infrastructure/shim/blockchain_public_dataset_inventory.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -"""Inventory public blockchain dataset object stores without cloud CLIs.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import sys -import urllib.parse -import urllib.request -import xml.etree.ElementTree as ET -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" -S3_NS = {"s3": "http://s3.amazonaws.com/doc/2006-03-01/"} - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def fetch(url: str, timeout: int) -> bytes: - req = urllib.request.Request(url, headers={"User-Agent": "ResearchStackBlockchainInventory/0"}) - with urllib.request.urlopen(req, timeout=timeout) as response: - return response.read() - - -def s3_list_url(bucket_url: str, prefix: str, max_keys: int, token: str | None = None) -> str: - params = { - "list-type": "2", - "prefix": prefix, - "max-keys": str(max_keys), - } - if token: - params["continuation-token"] = token - return f"{bucket_url.rstrip('/')}/?{urllib.parse.urlencode(params)}" - - -def text_or_none(node: ET.Element, path: str) -> str | None: - found = node.find(path, S3_NS) - return found.text if found is not None else None - - -def list_s3_objects(bucket_url: str, prefix: str, max_objects: int, request_max_keys: int, timeout: int) -> tuple[list[dict[str, Any]], dict[str, Any]]: - objects: list[dict[str, Any]] = [] - token = None - request_count = 0 - truncated = False - next_token = None - while len(objects) < max_objects: - url = s3_list_url(bucket_url, prefix, min(request_max_keys, max_objects - len(objects)), token) - payload = fetch(url, timeout) - root = ET.fromstring(payload) - request_count += 1 - truncated = (text_or_none(root, "s3:IsTruncated") or "").lower() == "true" - next_token = text_or_none(root, "s3:NextContinuationToken") - for item in root.findall("s3:Contents", S3_NS): - key = text_or_none(item, "s3:Key") - if not key: - continue - objects.append( - { - "key": key, - "url": f"{bucket_url.rstrip('/')}/{urllib.parse.quote(key)}", - "last_modified": text_or_none(item, "s3:LastModified"), - "etag": (text_or_none(item, "s3:ETag") or "").strip('"'), - "size": int(text_or_none(item, "s3:Size") or 0), - "storage_class": text_or_none(item, "s3:StorageClass"), - } - ) - if len(objects) >= max_objects: - break - if not truncated or not next_token: - break - token = next_token - meta = { - "request_count": request_count, - "is_truncated_after_inventory": truncated, - "next_continuation_token_present": bool(next_token), - } - return objects, meta - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--dataset", choices=["aws-public-blockchain"], default="aws-public-blockchain") - parser.add_argument("--bucket-url", default="https://aws-public-blockchain.s3.amazonaws.com") - parser.add_argument("--prefix", required=True) - parser.add_argument("--chain", required=True) - parser.add_argument("--table", required=True) - parser.add_argument("--max-objects", type=int, default=100) - parser.add_argument("--request-max-keys", type=int, default=1000) - parser.add_argument("--timeout", type=int, default=30) - parser.add_argument("--out", type=Path, default=None) - args = parser.parse_args() - - objects, meta = list_s3_objects( - args.bucket_url, - args.prefix, - args.max_objects, - args.request_max_keys, - args.timeout, - ) - total_size = sum(item["size"] for item in objects) - receipt = { - "schema": "blockchain_public_dataset_inventory_v0", - "created_utc": now_iso(), - "claim_boundary": "Public dataset object inventory only. This proves listed object metadata retrieved from the source endpoint during this run; it does not prove full dataset coverage, parquet schema correctness, or decoded chain semantics.", - "dataset": args.dataset, - "bucket_url": args.bucket_url, - "prefix": args.prefix, - "chain": args.chain, - "table": args.table, - "max_objects": args.max_objects, - "object_count": len(objects), - "total_listed_bytes": total_size, - "list_meta": meta, - "objects": objects, - "decision": "ADMIT_PUBLIC_DATASET_INVENTORY" if objects else "HOLD_EMPTY_PUBLIC_DATASET_INVENTORY", - } - receipt["inventory_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "inventory_hash"})) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 if objects else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchain_public_dataset_transfer.py b/4-Infrastructure/shim/blockchain_public_dataset_transfer.py deleted file mode 100644 index 7fff45f5..00000000 --- a/4-Infrastructure/shim/blockchain_public_dataset_transfer.py +++ /dev/null @@ -1,241 +0,0 @@ -#!/usr/bin/env python3 -"""Transfer inventoried public blockchain objects to Google Drive. - -The script reads an inventory emitted by blockchain_public_dataset_inventory.py, -downloads each listed object, streams it to an rclone destination, and emits a -receipt with source metadata plus observed SHA-256 hashes. It is intentionally -object-preserving for Parquet snapshots: the payload object is the row group -container, while the receipt is the replay surface. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import sys -import tempfile -import urllib.request -from concurrent.futures import ThreadPoolExecutor, as_completed -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" -DEFAULT_DESTINATION = "Gdrive:topological_storage/research-stack/blockchain-corpus/seed-2026-05-10/public-datasets" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def run(cmd: list[str], input_bytes: bytes | None = None, timeout: int = 300) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, - input=input_bytes, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - - -def rcat(remote_path: str, payload: bytes, timeout: int) -> tuple[bool, str]: - proc = run(["rclone", "rcat", remote_path], input_bytes=payload, timeout=timeout) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def copyto(local_path: Path, remote_path: str, timeout: int) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local_path), remote_path], timeout=timeout) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def fetch_object_to_file(url: str, target: Path, timeout: int) -> tuple[int, str]: - req = urllib.request.Request(url, headers={"User-Agent": "ResearchStackBlockchainTransfer/0"}) - h = hashlib.sha256() - with urllib.request.urlopen(req, timeout=timeout) as response: - with target.open("wb") as handle: - byte_count = 0 - while True: - chunk = response.read(1024 * 1024) - if not chunk: - break - h.update(chunk) - handle.write(chunk) - byte_count += len(chunk) - return byte_count, h.hexdigest() - - -def safe_remote_key(key: str) -> str: - return key.strip("/").replace("//", "/") - - -def transfer_one(index: int, item: dict[str, Any], remote_prefix: str, args: argparse.Namespace) -> dict[str, Any]: - source_url = item["url"] - key = safe_remote_key(item["key"]) - if key.endswith("/"): - return { - "object_index": index, - "source_key": item.get("key"), - "source_url": source_url, - "source_size": item.get("size"), - "source_etag": item.get("etag"), - "drive_path": f"{remote_prefix}/{key}", - "upload_status": "ADMIT_SKIPPED_DIRECTORY_MARKER", - "upload_message": "Directory marker skipped so object payloads can occupy the prefix.", - "size_matches_inventory": None if item.get("size") is None else item.get("size") in {0, "0"}, - } - remote_path = f"{remote_prefix}/{key}" - try: - with tempfile.TemporaryDirectory(prefix="rs_blockchain_transfer_") as tmpdir: - local_path = Path(tmpdir) / Path(key).name - byte_count, observed_hash = fetch_object_to_file(source_url, local_path, args.timeout) - upload_ok, upload_message = (False, "HOLD_DRY_RUN_ONLY") - if args.execute: - upload_ok, upload_message = copyto(local_path, remote_path, args.upload_timeout) - status = "ADMIT_TRANSFERRED_TO_GDRIVE" if upload_ok else ("HOLD_DRY_RUN_ONLY" if not args.execute else "QUARANTINE_UPLOAD_FAILED") - size_matches = None if item.get("size") is None else byte_count == item.get("size") - return { - "object_index": index, - "source_key": item.get("key"), - "source_url": source_url, - "source_size": item.get("size"), - "source_etag": item.get("etag"), - "observed_byte_count": byte_count, - "observed_sha256": observed_hash, - "drive_path": remote_path, - "upload_status": status, - "upload_message": upload_message, - "size_matches_inventory": size_matches, - } - except Exception as exc: # noqa: BLE001 - receipt every per-object failure. - return { - "object_index": index, - "source_key": item.get("key"), - "source_url": source_url, - "upload_status": "QUARANTINE_TRANSFER_EXCEPTION", - "error": str(exc), - } - - -def transfer(args: argparse.Namespace) -> dict[str, Any]: - inventory = json.loads(args.inventory.read_text()) - all_objects = inventory.get("objects", []) - objects = all_objects[args.start_index :] - if args.max_objects is not None: - objects = objects[: args.max_objects] - remote_prefix = f"{args.destination.rstrip('/')}/{inventory.get('dataset', 'dataset')}/{inventory.get('chain', 'chain')}/{inventory.get('table', 'table')}" - - records: list[dict[str, Any]] = [] - if args.workers <= 1: - for index, item in enumerate(objects): - record = transfer_one(index, item, remote_prefix, args) - records.append(record) - if str(record.get("upload_status", "")).startswith("QUARANTINE") and not args.keep_going: - break - else: - with ThreadPoolExecutor(max_workers=args.workers) as executor: - futures = { - executor.submit(transfer_one, index, item, remote_prefix, args): index - for index, item in enumerate(objects) - } - for future in as_completed(futures): - record = future.result() - records.append(record) - if str(record.get("upload_status", "")).startswith("QUARANTINE") and not args.keep_going: - break - records.sort(key=lambda record: int(record.get("object_index", 0))) - - total_bytes = sum(record.get("observed_byte_count", 0) for record in records) - ok_count = sum( - 1 - for record in records - if record.get("upload_status") in {"ADMIT_TRANSFERRED_TO_GDRIVE", "HOLD_DRY_RUN_ONLY"} - ) - - receipt = { - "schema": "blockchain_public_dataset_transfer_receipt_v0", - "created_utc": now_iso(), - "claim_boundary": "Public dataset object transfer receipt only. This proves bytes fetched from listed URLs and optional Drive upload status; it does not prove full dataset coverage, Parquet decoding, or chain semantics.", - "inventory": str(args.inventory.relative_to(REPO)) if args.inventory.is_relative_to(REPO) else str(args.inventory), - "inventory_hash": inventory.get("inventory_hash"), - "dataset": inventory.get("dataset"), - "chain": inventory.get("chain"), - "table": inventory.get("table"), - "source_prefix": inventory.get("prefix"), - "destination": args.destination, - "remote_prefix": remote_prefix, - "execute": args.execute, - "selection": { - "inventory_object_count": len(all_objects), - "start_index": args.start_index, - "max_objects": args.max_objects, - "workers": args.workers, - }, - "summary": { - "selected_object_count": len(objects), - "successful_or_dry_run_count": ok_count, - "total_observed_bytes": total_bytes, - "size_mismatch_count": sum(1 for record in records if record.get("size_matches_inventory") is False), - "size_unknown_count": sum(1 for record in records if record.get("size_matches_inventory") is None), - "skipped_directory_marker_count": sum(1 for record in records if record.get("upload_status") == "ADMIT_SKIPPED_DIRECTORY_MARKER"), - "quarantine_count": sum(1 for record in records if str(record.get("upload_status", "")).startswith("QUARANTINE")), - }, - "objects": records, - } - decision = "ADMIT_PUBLIC_DATASET_TRANSFER_TO_GDRIVE" if args.execute else "HOLD_PUBLIC_DATASET_TRANSFER_DRY_RUN" - if receipt["summary"]["quarantine_count"]: - decision = "QUARANTINE_PUBLIC_DATASET_TRANSFER_PARTIAL" - receipt["decision"] = decision - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - - DATA_DIR.mkdir(parents=True, exist_ok=True) - out = args.out or DATA_DIR / f"blockchain_public_dataset_transfer_receipt_{inventory.get('chain', 'chain')}_{inventory.get('table', 'table')}_{datetime.now(timezone.utc).strftime('%Y%m%dT%H%M%SZ')}.json" - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - receipt["receipt_path"] = str(out.relative_to(REPO)) if out.is_relative_to(REPO) else str(out) - - if args.execute: - receipt_remote = f"{remote_prefix}/receipts/{out.name}" - ok, message = rcat(receipt_remote, out.read_bytes(), args.upload_timeout) - receipt["receipt_upload"] = { - "drive_path": receipt_remote, - "ok": ok, - "message": message, - } - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--inventory", type=Path, required=True) - parser.add_argument("--destination", default=DEFAULT_DESTINATION) - parser.add_argument("--max-objects", type=int, default=None) - parser.add_argument("--start-index", type=int, default=0) - parser.add_argument("--timeout", type=int, default=60) - parser.add_argument("--upload-timeout", type=int, default=600) - parser.add_argument("--workers", type=int, default=1) - parser.add_argument("--execute", action="store_true") - parser.add_argument("--keep-going", action="store_true") - parser.add_argument("--out", type=Path, default=None) - args = parser.parse_args() - receipt = transfer(args) - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 if not str(receipt.get("decision", "")).startswith("QUARANTINE") else 2 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchain_sync_status_receipt.py b/4-Infrastructure/shim/blockchain_sync_status_receipt.py deleted file mode 100644 index c24f2fe3..00000000 --- a/4-Infrastructure/shim/blockchain_sync_status_receipt.py +++ /dev/null @@ -1,157 +0,0 @@ -#!/usr/bin/env python3 -"""Emit blockchain acquisition status receipts for Bitcoin/Geth sync lanes.""" - -from __future__ import annotations - -import argparse -import json -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" -DEFAULT_DESTINATION = "Gdrive:topological_storage/research-stack/blockchain-corpus/seed-2026-05-10/status" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def run(cmd: list[str], timeout: int = 20, input_bytes: bytes | None = None) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, - input=input_bytes, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - - -def parse_json_or_error(proc: subprocess.CompletedProcess) -> dict: - stdout = proc.stdout.decode(errors="replace").strip() - stderr = proc.stderr.decode(errors="replace").strip() - if proc.returncode != 0: - return { - "ok": False, - "returncode": proc.returncode, - "stdout": stdout, - "stderr": stderr, - } - try: - return {"ok": True, "data": json.loads(stdout)} - except json.JSONDecodeError: - return {"ok": True, "stdout": stdout, "stderr": stderr} - - -def bitcoin_status(conf: str) -> dict: - proc = run(["bitcoin-cli", f"-conf={conf}", "getblockchaininfo"]) - result = parse_json_or_error(proc) - if not result.get("ok"): - result["decision"] = "HOLD_BITCOIN_RPC_UNAVAILABLE" - return result - data = result.get("data", {}) - result["decision"] = ( - "ADMIT_BITCOIN_PRUNED_LIVE_SYNC" - if data.get("pruned") - else "ADMIT_BITCOIN_FULL_NODE_SYNC" - ) - result["claim_boundary"] = ( - "Pruned Bitcoin node can support live/head/header pattern receipts, " - "but not full historical block-body corpus export." - if data.get("pruned") - else "Non-pruned Bitcoin node may support historical block-body corpus export after sync." - ) - return result - - -def geth_status() -> dict: - expr = ( - "JSON.stringify({" - "syncing: eth.syncing," - "blockNumber: eth.blockNumber," - "peerCount: net.peerCount," - "txIndexRemainingBlocks: (eth.syncing && eth.syncing.txIndexRemainingBlocks) || null," - "txIndexFinishedBlocks: (eth.syncing && eth.syncing.txIndexFinishedBlocks) || null" - "})" - ) - proc = run(["geth", "attach", "--exec", expr]) - stdout = proc.stdout.decode(errors="replace").strip() - stderr = proc.stderr.decode(errors="replace").strip() - if proc.returncode != 0: - return { - "ok": False, - "returncode": proc.returncode, - "stdout": stdout, - "stderr": stderr, - "decision": "HOLD_GETH_IPC_UNAVAILABLE", - } - try: - data = json.loads(json.loads(stdout)) - except Exception: - return { - "ok": False, - "stdout": stdout, - "stderr": stderr, - "decision": "QUARANTINE_GETH_STATUS_PARSE_FAILED", - } - decision = "ADMIT_GETH_RUNNING_WAITING_FOR_PEERS" - if data.get("peerCount", 0) > 0: - decision = "ADMIT_GETH_SYNCING" - if data.get("syncing") is False and data.get("blockNumber", 0) > 0: - decision = "ADMIT_GETH_SYNCED_OR_NEAR_HEAD" - return { - "ok": True, - "data": data, - "decision": decision, - "claim_boundary": "Geth execution-history acquisition status only; full state/archive corpus requires separate state-history/archive receipt.", - } - - -def rcat(remote_path: str, payload: bytes) -> tuple[bool, str]: - proc = run(["rclone", "rcat", remote_path], input_bytes=payload, timeout=60) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--bitcoin-conf", default=str(Path.home() / ".bitcoin/bitcoin.conf")) - parser.add_argument("--destination", default=DEFAULT_DESTINATION) - parser.add_argument("--upload", action="store_true") - args = parser.parse_args() - - created = now_iso() - safe_stamp = created.replace(":", "").replace("-", "") - receipt = { - "schema": "blockchain_sync_status_receipt_v0", - "created_utc": created, - "bitcoin": bitcoin_status(args.bitcoin_conf), - "ethereum": geth_status(), - "decision": "ADMIT_PROGRESSIVE_ACQUISITION_STATUS", - } - - DATA_DIR.mkdir(parents=True, exist_ok=True) - path = DATA_DIR / f"blockchain_sync_status_receipt_{safe_stamp}.json" - path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - receipt["receipt_path"] = str(path.relative_to(REPO)) - - if args.upload: - remote_path = f"{args.destination.rstrip('/')}/{path.name}" - ok, message = rcat(remote_path, path.read_bytes()) - receipt["drive_upload"] = { - "drive_path": remote_path, - "ok": ok, - "message": message, - } - path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/blockchair_dump_inventory.py b/4-Infrastructure/shim/blockchair_dump_inventory.py deleted file mode 100644 index 5db61500..00000000 --- a/4-Infrastructure/shim/blockchair_dump_inventory.py +++ /dev/null @@ -1,145 +0,0 @@ -#!/usr/bin/env python3 -"""Inventory Blockchair dump directory listings as transfer receipts.""" - -from __future__ import annotations - -import argparse -import hashlib -import html.parser -import json -import re -import sys -import urllib.parse -import urllib.request -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/blockchain_corpus" - - -SIZE_RE = re.compile(r">\s*(?P\d{2}-[A-Za-z]{3}-\d{4}\s+\d{2}:\d{2})\s+(?P[0-9.]+[KMGTP]?|[0-9]+)\s*<") - - -class LinkParser(html.parser.HTMLParser): - def __init__(self) -> None: - super().__init__() - self.links: list[str] = [] - - def handle_starttag(self, tag: str, attrs: list[tuple[str, str | None]]) -> None: - if tag != "a": - return - for key, value in attrs: - if key == "href" and value: - self.links.append(value) - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def parse_size(value: str) -> int | None: - value = value.strip() - if not value: - return None - unit = value[-1] - if unit.isdigit(): - return int(value) - scale = { - "K": 1024, - "M": 1024**2, - "G": 1024**3, - "T": 1024**4, - "P": 1024**5, - }.get(unit) - if scale is None: - return None - return int(float(value[:-1]) * scale) - - -def fetch_text(url: str, timeout: int) -> str: - req = urllib.request.Request(url, headers={"User-Agent": "ResearchStackBlockchairInventory/0"}) - with urllib.request.urlopen(req, timeout=timeout) as response: - return response.read().decode("utf-8", errors="replace") - - -def line_for_href(html: str, href: str) -> str: - for line in html.splitlines(): - if f'href="{href}"' in line: - return line - return "" - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--chain", required=True) - parser.add_argument("--table", default="blocks") - parser.add_argument("--base-url", default="https://gz.blockchair.com") - parser.add_argument("--max-objects", type=int, default=100) - parser.add_argument("--timeout", type=int, default=30) - parser.add_argument("--out", type=Path, default=None) - args = parser.parse_args() - - directory_url = f"{args.base_url.rstrip('/')}/{args.chain}/{args.table}/" - html = fetch_text(directory_url, args.timeout) - links = LinkParser() - links.feed(html) - objects = [] - for href in links.links: - if href.startswith("../") or href.endswith("/") or not href.endswith(".tsv.gz"): - continue - if len(objects) >= args.max_objects: - break - line = line_for_href(html, href) - size = None - date_text = None - match = SIZE_RE.search(line) - if match: - date_text = match.group("date") - size = parse_size(match.group("size")) - objects.append( - { - "key": f"{args.chain}/{args.table}/{href}", - "url": urllib.parse.urljoin(directory_url, href), - "last_modified_text": date_text, - "size": size, - "etag": None, - "source": "blockchair_dumps", - } - ) - - total_known_size = sum(item["size"] or 0 for item in objects) - receipt = { - "schema": "blockchair_dump_inventory_v0", - "created_utc": now_iso(), - "claim_boundary": "Blockchair dump directory inventory only. This proves listed dump links and parsed sizes where available; it does not prove full chain coverage or decoded TSV semantics.", - "dataset": "blockchair_dumps", - "chain": args.chain, - "table": args.table, - "directory_url": directory_url, - "max_objects": args.max_objects, - "object_count": len(objects), - "total_listed_bytes_known": total_known_size, - "objects": objects, - "decision": "ADMIT_BLOCKCHAIR_DUMP_INVENTORY" if objects else "HOLD_EMPTY_BLOCKCHAIR_DUMP_INVENTORY", - } - receipt["inventory_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "inventory_hash"})) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 if objects else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/bodegaflow_horn_fiber_refinements.md b/4-Infrastructure/shim/bodegaflow_horn_fiber_refinements.md deleted file mode 100644 index 25ff56f5..00000000 --- a/4-Infrastructure/shim/bodegaflow_horn_fiber_refinements.md +++ /dev/null @@ -1,476 +0,0 @@ -# BodegaFlow Horn-Fiber Refinements - -## Purpose - -This note captures the refinements developed after the full-stack load / closure revision. The goal is not to force a one-to-one mapping between external results and the framework. External work is treated as a structural probe: it can provide shape alignment, constraint alignment, residual alignment, probe alignment, or failure-mode alignment without being identical to the model. - -Keeper: - -```text -External papers/results do not need to map 1:1 onto the framework. -The question is whether they expose a compatible shape, boundary condition, -residual, or testable projection. -``` - -## 1. Receipt-Gated Attractor Fiber Complex - -The current 16D object is neither a cube nor a torus. - -A cube implies independent bounded axes: - -```text -[0,1]^16 -``` - -A torus implies globally periodic closure: - -```text -(S^1)^16 -``` - -The current object has conditional closure, attractor routing, nested reduction, residual repair, and terminal receipts. The standards-facing name is: - -```text -Receipt-Gated Attractor Fiber Complex, RG-AFC -``` - -Definition: - -```text -A Receipt-Gated Attractor Fiber Complex is a high-dimensional controller space -partitioned into attractor basins, routed through hub nodes, reduced through -nested local partitions, and validated by terminal receipts. -``` - -Shape: - -```text -forest / unresolved manifold mass - -> basin partition - -> bodega hub - -> fractional horn-like reduction - -> shelf-object - -> receipt / residual / closure -``` - -Compact equation: - -```text -O_16 = [0,1]^16 -> {V_i} -> {b_i} -> {A,S,O} -> {W, epsilon, RRM} -``` - -Keeper: - -```text -Not torus, not cube: a fiber-city attractor complex with shelf receipts. -``` - -## 2. Bodega Attractor Routing - -The bodega metaphor is formalized as hub-attractor manifold routing. - -A random math object / market state / probe state begins in an unresolved forest: - -```text -x_0 in M_forest -``` - -It is drawn to the nearest bodega hub: - -```text -b(x) = argmin_{b_i in B} d_M(x,b_i) -``` - -Soft routing: - -```text -P(b_i | x) = exp(-beta d_M(x,b_i)) / sum_j exp(-beta d_M(x,b_j)) -``` - -Each bodega owns a Voronoi-like basin: - -```text -V_i = {x in M : d_M(x,b_i) <= d_M(x,b_j), for all j} -``` - -Inside the bodega, uncertainty reduces fractionally: - -```text -forest -> city -> bodega -> aisle -> shelf -> object -``` - -Formal nesting: - -```text -M_forest superset V_b superset b_i superset A_ij superset S_ijk superset O_ijkell -``` - -Fractional reduction: - -```text -H_{t+1} = rho_t H_t, 0 < rho_t < 1 -H_n = H_0 prod_t rho_t -stop when H_n <= Theta_object -``` - -Path receipt: - -```text -Route(x) = (V_i, b_i, A_ij, S_ijk, O*, W, epsilon) -``` - -Receipt confidence: - -```text -W = P(b_i | x) - P(A_ij | x,b_i) - P(S_ijk | x,b_i,A_ij) - P(O* | x,b_i,A_ij,S_ijk) -``` - -If W is low: - -```text -RRM(epsilon) -> adjacent shelf, adjacent aisle, alternate bodega, or quarantine -``` - -Keeper: - -```text -The forest gets you to the bodega; the bodega fractions the search; -the shelf gives the receipt. -``` - -## 3. Fiber-Mass Raytrace Probe Atlas - -A single ray is too thin. The hidden object is assessed by a fiber mass: a weighted bundle of probe trajectories through hidden state space. - -Fiber mass: - -```text -F = {F_1, F_2, ..., F_N} -F_i = (gamma_i, R_i, Y_i, epsilon_i, W_i) -``` - -where: -- `gamma_i` = path through the manifold -- `R_i` = ray / carrier / probe packet -- `Y_i` = observed deformation -- `epsilon_i` = residual against baseline -- `W_i` = receipt confidence - -A multidimensional TSP-like route chooses which informative hubs to visit: - -```text -pi* = argmin_pi [ - sum_k d_16(b_{pi_k}, b_{pi_{k+1}}) - + alpha sum_k C_reduce(b_{pi_k}) - - beta sum_k I_receipt(b_{pi_k}) -] -``` - -Weighted 16D distance: - -```text -d_16(u,v) = sqrt(sum_{a=0}^{15} omega_a (q_a(u)-q_a(v))^2) -``` - -Information receipt: - -```text -I_i_receipt = W_i [H(C16) - H(C16 | Y_i)] - rho ||epsilon_i|| -``` - -Keeper: - -```text -Raytrace the fiber mass; TSP the probe route; receipt the distortions; -fuse the 16D object. -``` - -## 4. Gabriel-Horn Spatial Refinement - -Treating the spatial / reduction dimensions like Gabriel's horn strengthens the model. - -Classical horn behavior: - -```text -finite enclosed volume, infinite surface area -``` - -For the framework: - -```text -finite admissible interior / controller budget -unbounded or very large boundary exposure / attack surface -``` - -A horn-like dimension: - -```text -r_i(x) = a_i / (x + b_i)^{p_i} -``` - -The 16D horn object is not a plain product space; it is routed: - -```text -O_16^horn = F -> V -> B -> {H_i}_{i=0}^{15} -> O* -> W -``` - -Market/compression interpretation: - -```text -Compression narrows volume, but may increase exploitable boundary exposure. -``` - -Horn-aware adversarial leakage: - -```text -Lambda_i = Lambda_0 - + lambda_A A_boundary_i - + lambda_g ||grad r_i|| - + lambda_c C_crowding -``` - -Horn-aware compression score: - -```text -C_horn = DeltaS_minus - - DeltaS_plus - - Lambda(A_boundary) - - ||epsilon|| - - C_friction -``` - -Keeper: - -```text -Compression narrows the volume, but Gabriel-horn geometry warns that the -boundary may still be infinite. -``` - -## 5. Horn/Torsion Cosmology Refinement - -As a 16D horn-fiber object, apparent acceleration does not have to mean homogeneous bulk-volume expansion. It can mean selected boundary sectors are changing accessibility conditions faster than others. - -Core distinction: - -```text -standard intuition: acceleration = d^2 V / dt^2 > 0 -horn-fiber model: apparent acceleration = d^2 A_boundary^(r) / dt^2 > 0 -``` - -Bulk can remain bounded while accessible surface changes: - -```text -dV_Omega/dt ~= 0 - -dA_boundary/dt = alpha A_boundary - + beta ||tau||^2 - + gamma RRM(epsilon) -``` - -Sector acceleration: - -```text -d^2 A_boundary^(r)/dt^2 = - alpha_r A_boundary^(r) - + beta_r ||tau_r||^2 - + chi_r d(||tau_r||^2)/dt - + gamma_r RRM(epsilon_r) -``` - -Carrier/path observable: - -```text -z_gamma = z_metric + z_torsion + z_boundaryA + z_echo + epsilon_gamma -``` - -Interpretation: - -```text -The probe no longer asks: is space expanding? -It asks: which boundary conditions changed along this path, and by how much? -``` - -DESI-style comparison rule: - -```text -DESI and similar results do not need to prove this model 1:1. -They are useful if they expose a non-constant effective expansion term, -boundary-condition drift, or residual structure that the 16D model can test. -``` - -Keeper: - -```text -Acceleration is not the room getting bigger; it is the boundary rules changing -faster along certain routes. -``` - -## 6. BodegaFlow Event-Field Refinement - -BodegaFlow is a paper-only morphic market geometry engine. It learns what compression means under adversarial deformation; it is not a real-money execution system. - -Core event update: - -```text -Every market action is an event update in a live flow field. -``` - -Market event: - -```text -e_t = (tau_t, type_t, symbol, DeltaP, DeltaV, DeltaL, DeltaS, DeltaO, metadata) -``` - -Live flow field: - -```text -F_t = (U_t, Pi_t, nu_t, omega_t, rho_t, epsilon_t, W_t) -``` - -Update: - -```text -F_{t+1} = F_t + K(e_t,x_t) - D(F_t) + RRM(epsilon_t) -``` - -Event kernel: - -```text -K(e_t,x) = a_e exp(-d_16(x,x_e)^2 / (2 sigma_e^2)) v_e -``` - -Navier-Stokes-like event form: - -```text -U_{t+1} = U_t - + sum_{e in E_t} K_U(e) - - (U_t . grad) U_t - - grad Pi_t - + nu_m grad^2 U_t - + F_reflexive - + F_adversarial -``` - -Bodega route: - -```text -raw market outputs - -> Market Forest x_t in [0,1]^16 - -> nearest attractor basin - -> Bodega Hub / regime setup family - -> Aisle / setup subtype - -> Shelf / entry condition - -> Object / paper trade candidate - -> receipt gate - -> paper enter / watch / skip / quarantine - -> outcome label - -> compression score + competitor-transfer score - -> geometry update -``` - -Paper-only gate: - -```text -PaperEnter iff - W >= Theta_W - and SNR >= Theta_SNR - and ||epsilon|| <= Theta_epsilon - and Lambda <= Theta_Lambda -``` - -Compression under adversarial action: - -```text -C_market = DeltaS_minus - - DeltaS_plus - - Lambda - - ||epsilon|| - - C_friction -``` - -Competitor-transfer condition: - -```text -C_market < 0 and Lambda > Theta_Lambda => COMPETITOR_TRANSFER -``` - -Route labels: - -```text -TRUE_COMPRESSION -FALSE_COMPRESSION -NOISE -LATE_ENTRY -CROWDED_EDGE -LIQUIDITY_TRAP -STOP_RUN -ADVERSE_SELECTION -SPREAD_DONATION -COMPETITOR_TRANSFER -VALID_BUT_TOO_EXPENSIVE -QUARANTINE -``` - -Keeper: - -```text -BodegaFlow does not just find the shelf. It learns which aisles turn the -shopper into inventory. -``` - -## 7. Updated 16D Probe Resolution Claim - -The 16D horn/fiber shape turns each probe from a ruler into a spectrometer. - -Low-resolution probe: - -```text -carrier in -> distorted carrier out -> single inferred cause -``` - -High-resolution probe: - -```text -carrier in -> multi-channel deformation receipt -> sector-specific boundary diagnosis -``` - -Probe decomposition: - -```text -Y_gamma = Render(g_mu_nu, tau, A_boundary, dA_boundary/dt, echo, epsilon) -``` - -Local diagnostic estimate: - -```text -C16_hat^(gamma) = argmin_C16 || - Y_gamma - Render(g_mu_nu, tau, A_boundary, dA_boundary/dt, echo, epsilon) -|| -``` - -Keeper: - -```text -The probe gets higher resolution because the model stops treating distortion -as one cause and starts treating it as a 16-channel receipt. -``` - -## Claim Boundaries - -```text -This is a control / compression / transition-receipt model. -It is not a proven financial, cosmological, biological, or physical law without -calibrated domain instruments, receipts, and falsification tests. -``` - -```text -BodegaFlow is paper-only. It is for geometry learning and adversarial-compression -labeling, not live financial execution. -``` - -```text -External results are structural probes, not required one-to-one equivalents. -``` \ No newline at end of file diff --git a/4-Infrastructure/shim/boundary_activation_field_probe.py b/4-Infrastructure/shim/boundary_activation_field_probe.py deleted file mode 100644 index aaa89d6d..00000000 --- a/4-Infrastructure/shim/boundary_activation_field_probe.py +++ /dev/null @@ -1,672 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-bearing probe for the boundary activation field B(x, r). - -A boundary is not where a system ends. A boundary is where accumulated encoded -states become physically active. B(x, r) is the boundary activation field at -location x and observer/interaction scale r. - - B(x, r) = f(del_rho, delta_lambda, eta, R_del, beta_k, E_deposit) - -where: - del_rho = density gradient - delta_lambda = hyper-eigen regime transition - eta = medium coupling - R_del = boundary residual / scar pressure - beta_k = topology persistence - E_deposit = cumulative deposited energy - -When the superposition of encoded regime components crosses the critical -threshold, the boundary enters an active physical regime (fire, shock, plasma, -fracture, turbulence, filamentation). -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass, field, asdict -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "boundary_activation_field" -REGISTRY = OUT_DIR / "boundary_activation_field_registry.json" -RECEIPT = OUT_DIR / "boundary_activation_field_receipt.json" -SUMMARY = OUT_DIR / "boundary_activation_field.md" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Boundary Activation Field.tid" -) - -SOURCE_REFS = [ - REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "ThresholdVector.lean", - REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "BoundaryDynamics.lean", - REPO - / "shared-data" - / "data" - / "observer_chart_projection_guardrail" - / "observer_chart_projection_guardrail_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -# --------------------------------------------------------------------------- -# Domain types -# --------------------------------------------------------------------------- - - -@dataclass(frozen=True) -class DensityGradient: - """nabla_rho — the density gradient at the boundary.""" - - magnitude: float # [0, 1] normalized - direction: str # "inward" | "outward" | "tangential" - - -@dataclass(frozen=True) -class HyperEigenTransition: - """delta_lambda — hyper-eigenvalue regime transition indicator.""" - - spectral_drift: float # how far the dominant eigenmode has drifted [0, 1] - regime_switch_active: bool - - -@dataclass(frozen=True) -class MediumCoupling: - """eta — how strongly the boundary couples to the surrounding medium.""" - - coefficient: float # coupling coefficient [0, 1] - atmosphere_participating: bool # does the medium carry away energy? - - -@dataclass(frozen=True) -class BoundaryResidual: - """R_del — accumulated residual / scar pressure at the boundary.""" - - scar_pressure: float # accumulated scar energy density [0, 1] - residual_growth_rate: float # how fast residuals are growing [0, 1] - - -@dataclass(frozen=True) -class TopologyPersistence: - """beta_k — topology persistence across scale changes.""" - - betti_connected: int # number of connected components - betti_loops: int # number of tunnels / loops - betti_cavities: int # number of enclosed cavities - persistence_ratio: float # fraction of topology that survives scale change [0, 1] - - -@dataclass(frozen=True) -class DepositedEnergy: - """E_deposit — cumulative energy deposited at the boundary.""" - - total: float # total deposited energy [0, 1] normalized - deposition_rate: float # rate of energy deposition [0, 1] - - -@dataclass(frozen=True) -class BoundaryActivationState: - """Complete set of encoded regime components at a boundary point.""" - - density_gradient: DensityGradient - hyper_eigen: HyperEigenTransition - medium_coupling: MediumCoupling - boundary_residual: BoundaryResidual - topology: TopologyPersistence - deposited_energy: DepositedEnergy - - # Observer / scale metadata - location_label: str # human-readable location - scale: float # observation scale in arbitrary units - - def component_vector(self) -> dict[str, float]: - """Extract the phi_i component vector for superposition computation.""" - return { - "density_gradient": self.density_gradient.magnitude, - "spectral_drift": self.hyper_eigen.spectral_drift, - "coupling": self.medium_coupling.coefficient, - "scar_pressure": self.boundary_residual.scar_pressure, - "topology_persistence": self.topology.persistence_ratio, - "deposited_energy": self.deposited_energy.total, - } - - -@dataclass(frozen=True) -class ActivationWeights: - """Superposition weights for each encoded regime component.""" - - density_gradient: float = 0.15 - spectral_drift: float = 0.20 - coupling: float = 0.25 - scar_pressure: float = 0.15 - topology_persistence: float = 0.10 - deposited_energy: float = 0.15 - - def total_weight(self) -> float: - return ( - self.density_gradient - + self.spectral_drift - + self.coupling - + self.scar_pressure - + self.topology_persistence - + self.deposited_energy - ) - - -@dataclass(frozen=True) -class ThresholdVector: - """Regime transition thresholds (analogue of Theta_i in the Lean model).""" - - density_gradient: float = 0.35 # gradient -> fracture - spectral_drift: float = 0.50 # spectral -> mode switch - coupling: float = 0.67 # coupling -> ignition - scar_pressure: float = 0.50 # scar -> boundary instability - topology_persistence: float = 0.33 # persistence -> percolation - deposited_energy: float = 0.50 # energy -> thermal regime - - -# --------------------------------------------------------------------------- -# Boundary activation computation -# --------------------------------------------------------------------------- - - -def compute_total_activation( - state: BoundaryActivationState, - weights: ActivationWeights | None = None, -) -> float: - """Compute B = sum alpha_i * phi_i, the total boundary activation. - - This is the superposition of encoded regime components. When B exceeds - the critical threshold, the boundary enters an active physical regime. - """ - if weights is None: - weights = ActivationWeights() - phi = state.component_vector() - total = ( - weights.density_gradient * phi["density_gradient"] - + weights.spectral_drift * phi["spectral_drift"] - + weights.coupling * phi["coupling"] - + weights.scar_pressure * phi["scar_pressure"] - + weights.topology_persistence * phi["topology_persistence"] - + weights.deposited_energy * phi["deposited_energy"] - ) - # Normalize by total weight to keep B in [0, 1] - norm = weights.total_weight() - return total / norm if norm > 0 else 0.0 - - -CRITICAL_ACTIVATION_THRESHOLD = 0.5 - - -def is_critically_activated(total_activation: float) -> bool: - """Check whether B exceeds the critical threshold Theta_c.""" - return total_activation > CRITICAL_ACTIVATION_THRESHOLD - - -def count_thresholds_crossed( - state: BoundaryActivationState, - thresholds: ThresholdVector | None = None, -) -> dict[str, bool]: - """Determine which individual component thresholds are crossed.""" - if thresholds is None: - thresholds = ThresholdVector() - phi = state.component_vector() - return { - "density_gradient": phi["density_gradient"] > thresholds.density_gradient, - "spectral_drift": phi["spectral_drift"] > thresholds.spectral_drift, - "coupling": phi["coupling"] > thresholds.coupling, - "scar_pressure": phi["scar_pressure"] > thresholds.scar_pressure, - "topology_persistence": phi["topology_persistence"] - > thresholds.topology_persistence, - "deposited_energy": phi["deposited_energy"] > thresholds.deposited_energy, - } - - -def classify_boundary_activation( - state: BoundaryActivationState, - thresholds: ThresholdVector | None = None, - weights: ActivationWeights | None = None, -) -> str: - """Classify the boundary into an activation regime. - - Returns one of: latent, smooth, turbulent, percolating, switching, - diverging, active, critical - """ - B = compute_total_activation(state, weights) - if not is_critically_activated(B): - return "latent" - - crossed = count_thresholds_crossed(state, thresholds) - count = sum(1 for v in crossed.values() if v) - - if count >= 4: - return "critical" - elif count >= 3: - return "active" - elif crossed.get("deposited_energy", False): - return "diverging" - elif crossed.get("spectral_drift", False): - return "switching" - elif crossed.get("coupling", False): - return "turbulent" - elif crossed.get("topology_persistence", False): - return "percolating" - elif crossed.get("density_gradient", False): - return "smooth" - else: - return "latent" - - -# --------------------------------------------------------------------------- -# Canonical scenario builders -# --------------------------------------------------------------------------- - - -def zero_activation_state(label: str = "void interior") -> BoundaryActivationState: - return BoundaryActivationState( - density_gradient=DensityGradient(0.0, "tangential"), - hyper_eigen=HyperEigenTransition(0.0, False), - medium_coupling=MediumCoupling(0.0, False), - boundary_residual=BoundaryResidual(0.0, 0.0), - topology=TopologyPersistence(0, 0, 0, 0.0), - deposited_energy=DepositedEnergy(0.0, 0.0), - location_label=label, - scale=1.0, - ) - - -def wall_fracture_scenario(label: str = "wall fracture") -> BoundaryActivationState: - return BoundaryActivationState( - density_gradient=DensityGradient(0.8, "outward"), - hyper_eigen=HyperEigenTransition(0.2, False), - medium_coupling=MediumCoupling(0.1, False), - boundary_residual=BoundaryResidual(0.6, 0.4), - topology=TopologyPersistence(3, 1, 0, 0.5), - deposited_energy=DepositedEnergy(0.3, 0.7), - location_label=label, - scale=0.1, - ) - - -def atmospheric_ignition_scenario( - label: str = "atmospheric ignition", -) -> BoundaryActivationState: - return BoundaryActivationState( - density_gradient=DensityGradient(0.9, "outward"), - hyper_eigen=HyperEigenTransition(0.6, True), - medium_coupling=MediumCoupling(0.9, True), - boundary_residual=BoundaryResidual(0.4, 0.3), - topology=TopologyPersistence(5, 2, 0, 0.7), - deposited_energy=DepositedEnergy(0.8, 0.9), - location_label=label, - scale=0.05, - ) - - -def cosmic_filament_scenario( - label: str = "cosmic filament wall", -) -> BoundaryActivationState: - return BoundaryActivationState( - density_gradient=DensityGradient(0.6, "inward"), - hyper_eigen=HyperEigenTransition(0.4, False), - medium_coupling=MediumCoupling(0.3, False), - boundary_residual=BoundaryResidual(0.5, 0.2), - topology=TopologyPersistence(200, 45, 12, 0.85), - deposited_energy=DepositedEnergy(0.7, 0.05), - location_label=label, - scale=100.0, - ) - - -def hulk_punch_scenario( - label: str = "hulk punch fracture", -) -> BoundaryActivationState: - return BoundaryActivationState( - density_gradient=DensityGradient(1.0, "outward"), - hyper_eigen=HyperEigenTransition(0.7, True), - medium_coupling=MediumCoupling(0.8, True), - boundary_residual=BoundaryResidual(0.9, 0.9), - topology=TopologyPersistence(50, 10, 3, 0.4), - deposited_energy=DepositedEnergy(1.0, 1.0), - location_label=label, - scale=0.01, - ) - - -# --------------------------------------------------------------------------- -# Registry and receipt -# --------------------------------------------------------------------------- - - -def build_scenario_record( - index: int, - scenario_id: str, - state: BoundaryActivationState, - weights: ActivationWeights | None = None, - thresholds: ThresholdVector | None = None, -) -> dict[str, Any]: - if weights is None: - weights = ActivationWeights() - if thresholds is None: - thresholds = ThresholdVector() - - B = compute_total_activation(state, weights) - verdict = classify_boundary_activation(state, thresholds, weights) - crossed = count_thresholds_crossed(state, thresholds) - - record = { - "index": index, - "scenario_id": scenario_id, - "location_label": state.location_label, - "scale": state.scale, - "component_vector": state.component_vector(), - "weights": asdict(weights), - "thresholds": asdict(thresholds), - "total_activation_B": round(B, 6), - "critical_threshold": CRITICAL_ACTIVATION_THRESHOLD, - "is_critical": is_critically_activated(B), - "thresholds_crossed": crossed, - "thresholds_crossed_count": sum(1 for v in crossed.values() if v), - "activation_verdict": verdict, - "density_gradient": { - "magnitude": state.density_gradient.magnitude, - "direction": state.density_gradient.direction, - }, - "hyper_eigen": { - "spectral_drift": state.hyper_eigen.spectral_drift, - "regime_switch_active": state.hyper_eigen.regime_switch_active, - }, - "medium_coupling": { - "coefficient": state.medium_coupling.coefficient, - "atmosphere_participating": state.medium_coupling.atmosphere_participating, - }, - "boundary_residual": { - "scar_pressure": state.boundary_residual.scar_pressure, - "residual_growth_rate": state.boundary_residual.residual_growth_rate, - }, - "topology": { - "betti_connected": state.topology.betti_connected, - "betti_loops": state.topology.betti_loops, - "betti_cavities": state.topology.betti_cavities, - "persistence_ratio": state.topology.persistence_ratio, - }, - "deposited_energy": { - "total": state.deposited_energy.total, - "deposition_rate": state.deposited_energy.deposition_rate, - }, - } - record["record_hash"] = hash_obj({k: v for k, v in record.items() if k != "record_hash"}) - return record - - -_DEFAULT_WEIGHTS = ActivationWeights() -_DEFAULT_THRESHOLDS = ThresholdVector() - -SCENARIOS: list[tuple[str, BoundaryActivationState]] = [ - ("zero_activation", zero_activation_state()), - ("wall_fracture", wall_fracture_scenario()), - ("atmospheric_ignition", atmospheric_ignition_scenario()), - ("cosmic_filament", cosmic_filament_scenario()), - ("hulk_punch", hulk_punch_scenario()), -] - - -def build_registry() -> dict[str, Any]: - scenario_records = [ - build_scenario_record( - i, - sid, - state, - _DEFAULT_WEIGHTS, - _DEFAULT_THRESHOLDS, - ) - for i, (sid, state) in enumerate(SCENARIOS) - ] - - return { - "schema": "boundary_activation_field_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Boundary activation field B(x, r) model only. Classifies boundary " - "regimes based on the weighted superposition of six encoded regime " - "components: density gradient, hyper-eigen spectral drift, medium " - "coupling, scar pressure, topology persistence, and deposited " - "energy. Does not claim full cosmological or material-science " - "predictive power without calibration to domain-specific data." - ), - "canonical_statement": ( - "A boundary is not where a system ends. A boundary is where " - "accumulated encoded states become physically active." - ), - "superposition_equation": "B(x, r) = sum_i alpha_i * phi_i(x, r)", - "critical_condition": "B > Theta_c => boundary enters active physical regime", - "critical_threshold": CRITICAL_ACTIVATION_THRESHOLD, - "default_weights": asdict(_DEFAULT_WEIGHTS), - "default_thresholds": asdict(_DEFAULT_THRESHOLDS), - "regime_map": { - "latent": "no threshold crossed, boundary inactive", - "smooth": "density gradient regime, elastic/smooth transition", - "turbulent": "coupling regime, atmospheric ignition boundary", - "percolating": "topology regime, filament/web connectivity", - "switching": "spectral regime, eigenmode transition", - "diverging": "energy regime, thermal/divergence front", - "active": "3+ thresholds crossed, full boundary activation", - "critical": "4+ thresholds crossed, topology-tear regime", - }, - "scenarios": scenario_records, - "aggregates": { - "scenario_count": len(scenario_records), - "activation_verdicts": { - r["activation_verdict"]: sum( - 1 for s in scenario_records if s["activation_verdict"] == r["activation_verdict"] - ) - for r in scenario_records - }, - "critical_count": sum(1 for s in scenario_records if s["is_critical"]), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "boundary_activation_field_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "aggregates": registry["aggregates"], - "decision": "ADMIT_BOUNDARY_ACTIVATION_FIELD", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json( - {k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}} - ).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Boundary Activation Field", - "", - f"Decision: `{receipt['decision']}`", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equations", - "", - f"- Superposition: `{registry['superposition_equation']}`", - f"- Critical condition: `{registry['critical_condition']}`", - f"- Theta_c = {registry['critical_threshold']}", - "", - "## Regime Map", - "", - ] - for regime, description in registry["regime_map"].items(): - lines.append(f"- `{regime}`: {description}") - lines.extend( - [ - "", - "## Scenarios", - "", - "| Scenario | Location | B | Critical | Thresholds Crossed | Verdict |", - "|---|---|---|---|---|---|", - ] - ) - for s in registry["scenarios"]: - lines.append( - f"| `{s['scenario_id']}` | {s['location_label']} | " - f"{s['total_activation_B']} | {s['is_critical']} | " - f"{s['thresholds_crossed_count']} | `{s['activation_verdict']}` |" - ) - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Scenario count: {registry['aggregates']['scenario_count']}", - f"- Critical count: {registry['aggregates']['critical_count']}", - f"- Verdicts: {registry['aggregates']['activation_verdicts']}", - "", - "## Source Refs", - "", - ] - ) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260512000000000 -modified: 20260512000000000 -tags: ResearchStack Encoding BoundaryActivation Receipt -title: Boundary Activation Field -type: text/vnd.tiddlywiki - -! Boundary Activation Field - -Durable runner: - -``` -4-Infrastructure/shim/boundary_activation_field_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -A boundary is not where a system ends. A boundary is where accumulated encoded states become physically active. - -``` -latent -> no threshold crossed, boundary inactive -smooth -> density gradient regime, elastic/smooth transition -turbulent -> coupling regime, atmospheric ignition boundary -percolating -> topology regime, filament/web connectivity -switching -> spectral regime, eigenmode transition -diverging -> energy regime, thermal/divergence front -active -> 3+ thresholds crossed, full boundary activation -critical -> 4+ thresholds crossed, topology-tear regime -``` - -!! Links - -* [[ThresholdVector (Lean formalization)|ThresholdVector.lean]] -* [[Observer Chart Projection Guardrail]] -* [[Boundary Dynamics]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text( - json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - RECEIPT.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/buoyancy_added_mass_mobius_fixture.py b/4-Infrastructure/shim/buoyancy_added_mass_mobius_fixture.py deleted file mode 100644 index cd87c31e..00000000 --- a/4-Infrastructure/shim/buoyancy_added_mass_mobius_fixture.py +++ /dev/null @@ -1,296 +0,0 @@ -#!/usr/bin/env python3 -"""Emit a receipt for the buoyancy added-mass Mobius logogram fixture. - -This records a small forward-derived physics atom: - - lambda_BAM(x, C) = g * alpha_C * x / (1 + kappa_C * x) - -where x is the density Mass Number / Atwood contrast. The fixture checks -algebraic equivalence against the classical added-mass expression and records -the light-object boundedness correction as a claim boundary. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from fractions import Fraction -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "buoyancy_added_mass_mobius" -ATOM = OUT_DIR / "buoyancy_added_mass_mobius_atom.json" -RECEIPT = OUT_DIR / "buoyancy_added_mass_mobius_receipt.json" -SUMMARY = OUT_DIR / "buoyancy_added_mass_mobius.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/foundation_forward_equation_compiler/foundation_forward_equation_compiler_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def mass_number(rho_o: Fraction, rho_m: Fraction) -> Fraction: - return (rho_o - rho_m) / (rho_o + rho_m) - - -def added_mass_a_over_g(rho_o: Fraction, rho_m: Fraction, c_shape: Fraction) -> Fraction: - return (rho_o - rho_m) / (rho_o + c_shape * rho_m) - - -def alpha(c_shape: Fraction) -> Fraction: - return Fraction(2, 1) / (Fraction(1, 1) + c_shape) - - -def kappa(c_shape: Fraction) -> Fraction: - return (Fraction(1, 1) - c_shape) / (Fraction(1, 1) + c_shape) - - -def mobius_a_over_g(x: Fraction, c_shape: Fraction) -> Fraction: - return alpha(c_shape) * x / (Fraction(1, 1) + kappa(c_shape) * x) - - -def inverse_mass_number(a_over_g: Fraction, c_shape: Fraction) -> Fraction: - return a_over_g / (alpha(c_shape) - kappa(c_shape) * a_over_g) - - -def frac_payload(value: Fraction) -> dict[str, Any]: - return { - "numerator": value.numerator, - "denominator": value.denominator, - "decimal": float(value), - } - - -def check_case(name: str, rho_ratio: Fraction, c_shape: Fraction) -> dict[str, Any]: - rho_m = Fraction(1, 1) - rho_o = rho_ratio * rho_m - x = mass_number(rho_o, rho_m) - direct = added_mass_a_over_g(rho_o, rho_m, c_shape) - compressed = mobius_a_over_g(x, c_shape) - inverted = inverse_mass_number(compressed, c_shape) - return { - "name": name, - "rho_o_over_rho_m": frac_payload(rho_ratio), - "C": frac_payload(c_shape), - "MN_rho": frac_payload(x), - "alpha_C": frac_payload(alpha(c_shape)), - "kappa_C": frac_payload(kappa(c_shape)), - "a_over_g_direct": frac_payload(direct), - "a_over_g_mobius": frac_payload(compressed), - "a_mps2_at_g_9_80665": float(compressed * Fraction(980665, 100000)), - "inverse_MN_rho": frac_payload(inverted), - "equivalence_pass": direct == compressed, - "inverse_pass": inverted == x, - } - - -def build_atom() -> dict[str, Any]: - cases = [ - check_case("sphere_rho_ratio_2", Fraction(2, 1), Fraction(1, 2)), - check_case("cylinder_perp_rho_ratio_2", Fraction(2, 1), Fraction(1, 1)), - check_case("light_sphere_limit_probe", Fraction(0, 1), Fraction(1, 2)), - ] - identity = { - "equation_id": "lambda_BAM_buoyancy_added_mass_mobius", - "semantic_key": "fluid.early_time_buoyancy.added_mass.mobius", - "canonical_equation": "a = g * alpha_C * MN_rho / (1 + kappa_C * MN_rho)", - "expanded_equation": "a = g * (rho_o - rho_m) / (rho_o + C*rho_m)", - "inverse_equation": "MN_rho = (a/g) / (alpha_C - kappa_C*(a/g))", - } - identity["equation_hash"] = hash_obj(identity) - atom = { - "schema": "forward_equation_fixture_atom_v1", - "identity": identity, - "foundation": { - "source_kernel": "F0_forward_foundation_kernel", - "parent_equations": [ - "F2_mass_number_metric", - "F3_geodesic_projection", - "F4_logogram_abstraction", - "F5_admission_gate", - ], - "transform_rule": "density_contrast_plus_shape_load_to_mobius_projection", - "dependency_hash": hash_obj( - { - "source_kernel": "F0_forward_foundation_kernel", - "parents": ["F2", "F3", "F4", "F5"], - "rule": "density_contrast_plus_shape_load_to_mobius_projection", - } - ), - }, - "projection": { - "O4": ["MN_rho", "C", "g", "Gamma_shape"], - "Rg3": ["drag", "vorticity", "boundary_effects"], - "chi0": "0 only in early-time ideal added-mass regime", - "U4": "later transient refinements", - "E_HD": "C*rho_m carried-fluid inertia tax", - "Underverse": "claims that ignore drag/vorticity/boundary domains or overstate boundedness", - }, - "admissibility": { - "domain_laws": [ - "early_time_before_drag_dominates", - "added_mass_coefficient_declared", - "density_contrast_uses_Atwood_form", - "residual_lanes_declared", - "bounded_by_g_only_for_heavier_sinking_branch_or_C_ge_1", - ], - "residual_policy": { - "drag": "null only at early-time idealization; otherwise residual", - "vorticity": "null only before shedding/circulation matters; otherwise residual", - "boundary_effects": "null only for unbounded-domain approximation; otherwise residual", - }, - "claim_boundary": ( - "Fixture-level algebraic compression of the classical added-mass " - "early-time acceleration equation. This is not a new fluid theorem, " - "not an experiment, and not a claim that |a| <= g for all density " - "branches. For a light sphere in the ideal model, a/g tends to -2." - ), - }, - "checks": cases, - "decision": "ACCEPT_FIXTURE_WITH_BOUND_CORRECTION", - } - receipt_payload = { - "identity": atom["identity"], - "foundation": atom["foundation"], - "projection": atom["projection"], - "checks": atom["checks"], - "decision": atom["decision"], - } - atom["receipt"] = { - "equation_hash": atom["identity"]["equation_hash"], - "dependency_hash": atom["foundation"]["dependency_hash"], - "receipt_hash": hash_obj(receipt_payload), - "decision": atom["decision"], - } - return atom - - -def build_receipt(atom: dict[str, Any]) -> dict[str, Any]: - all_checks_pass = all(item["equivalence_pass"] and item["inverse_pass"] for item in atom["checks"]) - receipt = { - "schema": "buoyancy_added_mass_mobius_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "atom_path": rel(ATOM), - "atom_hash": hash_obj(atom), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "all_equivalence_checks_pass": all_checks_pass, - "decision": atom["decision"] if all_checks_pass else "HOLD_DIAGNOSTIC", - "claim_boundary": atom["admissibility"]["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(atom: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Buoyancy Added-Mass Mobius Fixture", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Canonical Form", - "", - "```text", - "MN_rho = (rho_o - rho_m) / (rho_o + rho_m)", - "alpha_C = 2 / (1 + C)", - "kappa_C = (1 - C) / (1 + C)", - "lambda_BAM(MN_rho, C) = g * alpha_C * MN_rho / (1 + kappa_C * MN_rho)", - "```", - "", - "Equivalent expanded form:", - "", - "```text", - "a = g * (rho_o - rho_m) / (rho_o + C*rho_m)", - "```", - "", - "Inverse:", - "", - "```text", - "MN_rho = (a/g) / (alpha_C - kappa_C*(a/g))", - "```", - "", - "## Checks", - "", - "| Case | C | MN_rho | a/g | a at g=9.80665 | Equivalence | Inverse |", - "|---|---:|---:|---:|---:|---:|---:|", - ] - for item in atom["checks"]: - lines.append( - f"| `{item['name']}` | {item['C']['decimal']:.6g} | " - f"{item['MN_rho']['decimal']:.6g} | {item['a_over_g_mobius']['decimal']:.6g} | " - f"{item['a_mps2_at_g_9_80665']:.6g} | {item['equivalence_pass']} | {item['inverse_pass']} |" - ) - lines.extend( - [ - "", - "## Residual Lanes", - "", - "| Lane | Policy |", - "|---|---|", - ] - ) - for lane, policy in atom["admissibility"]["residual_policy"].items(): - lines.append(f"| `{lane}` | {policy} |") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - atom = build_atom() - receipt = build_receipt(atom) - ATOM.write_text(json.dumps(atom, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(atom, receipt) - print( - json.dumps( - { - "atom": rel(ATOM), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/cache_dependency_route_prior.py b/4-Infrastructure/shim/cache_dependency_route_prior.py deleted file mode 100644 index 7aa1ba44..00000000 --- a/4-Infrastructure/shim/cache_dependency_route_prior.py +++ /dev/null @@ -1,220 +0,0 @@ -#!/usr/bin/env python3 -"""Distill latency-vs-capacity cache use cases into route-evaluator guardrails. - -The source article's useful extraction is dependency classification: identical -cache access code can be either a soft latency optimization or a load-bearing -capacity dependency. For the bounded route compiler, cache hits are never proof; -they are operational shortcuts that must be stress-tested against cold-cache -and hit-rate collapse scenarios. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "cache_dependency_route_prior_receipt.json" -CURRICULUM_OUT = SHIM / "cache_dependency_route_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" -SOURCE_URL = "https://read.thecoder.cafe/p/cache-use-cases" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -SOURCE_EVIDENCE = { - "title": "Cache Use Cases Explained: Latency Cache vs. Capacity Cache", - "author": "Teiva Harsanyi", - "published_date": "2026-05-06", - "source_url": SOURCE_URL, - "observed_core_claims": [ - "latency_cache_reduces_average_response_time", - "latency_cache_is_normally_soft_dependency", - "capacity_cache_absorbs_load_backend_cannot_absorb_directly", - "same_access_pattern_can_hide_dependency_change", - "latency_cache_can_silently_become_capacity_cache_as_traffic_grows", - "cold_cache_or_invalidation_can_create_miss_storm", - "hit_rate_monitoring_load_testing_and_warming_manage_risk", - ], -} - - -ROUTE_CACHE_TYPES = [ - { - "id": "latency_route_cache", - "definition": "memoizes route/evaluator results to reduce average evaluator latency", - "dependency_class": "soft_if_backend_can_absorb_cold_cache_load", - "local_examples": [ - "compression_ratio_vector_cache", - "candidate_feature_cache", - "tokenbook_preview_cache", - "lower_bound_estimate_cache", - ], - "failure_behavior": "fall through to exact evaluator with slower runtime", - }, - { - "id": "capacity_route_cache", - "definition": "absorbs route/evaluator demand that the backend cannot serve directly", - "dependency_class": "hard_if_backend_cannot_absorb_cold_cache_load", - "local_examples": [ - "large_slice_evaluator_result_cache", - "expensive_decode_hash_receipt_cache", - "route_population_archive_cache", - "shared_tokenbook_materialization_cache", - ], - "failure_behavior": "miss storm can overwhelm evaluator or trigger unbounded fallback search", - }, -] - - -EQUATIONS = [ - { - "id": "CACHE0_effective_backend_load", - "equation": "backend_load = request_rate * (1 - cache_hit_rate)", - "meaning": "Route evaluator pressure is governed by misses, not nominal request volume.", - }, - { - "id": "CACHE1_backend_headroom", - "equation": "backend_headroom = backend_capacity - backend_load", - "meaning": "A cache is still soft only while cold or degraded load leaves nonnegative backend headroom.", - }, - { - "id": "CACHE2_dependency_class", - "equation": "dependency = latency if backend_capacity >= request_rate else capacity", - "meaning": "If the backend cannot absorb full traffic without cache, the cache is load-bearing.", - }, - { - "id": "CACHE3_cold_start_stress", - "equation": "cold_start_ok iff request_rate <= backend_capacity and warmup_time <= warmup_budget", - "meaning": "Cache migration or invalidation must be tested as a first-class route failure mode.", - }, - { - "id": "CACHE4_route_proof_boundary", - "equation": "promote(route) requires exact_decode_hash, not cache_hit", - "meaning": "Cached receipts can speed evaluation, but cannot replace rehydration authority.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "cache_dependency_route_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": SOURCE_EVIDENCE, - "primary_decision": { - "name": "classify_route_caches_by_dependency_not_access_pattern", - "statement": ( - "Treat route caches as latency or capacity dependencies based on " - "whether the exact evaluator/backend can absorb cold-cache load. " - "Do not infer dependency class from cache-first code shape." - ), - }, - "route_cache_types": ROUTE_CACHE_TYPES, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "route_cache_id", - "cache_use_case_class", - "cache_hit_rate", - "cache_miss_rate", - "backend_capacity_routes_per_sec", - "estimated_request_rate", - "backend_headroom", - "cold_cache_stress_status", - "warmup_receipt_id", - "cache_dependency_status", - "cache_invalidation_scope", - "miss_storm_risk_class", - "cached_receipt_hash", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "classify_route_cache_dependency", - "measure_cache_hit_rate", - "estimate_cold_cache_backend_load", - "stress_without_route_cache", - "warm_route_cache_before_cutover", - "invalidate_cache_with_miss_storm_guard", - "fall_through_to_exact_evaluator", - "reject_cache_hit_as_proof", - ], - "lower_bound": [ - "cache_header_bytes", - "warmup_receipt_floor", - "invalidation_receipt_floor", - "fallback_evaluator_capacity_floor", - "exact_receipt_floor", - ], - "promotion_rule": [ - "cache_layer_only_memoizes_or_schedules_route_evaluation", - "cache_dependency_class_is_explicit", - "cold_cache_stress_either_passes_or_fails_closed", - "capacity_cache_has_alerting_and_warmup_receipt", - "cache_hit_never_replaces_decode_hash", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "cache_hit_without_rehydration_hash -> invalid_receipt", - "capacity_cache_labeled_as_latency_cache -> fail_closed", - "cold_cache_miss_storm_exceeds_backend_capacity -> NaN0", - "cache_warmup_overhead_exceeds_byte_gain -> prune", - "cache_invalidation_without_scope_receipt -> fail_closed", - ], - "claim_boundary": ( - "This prior is an operational dependency model for route caches. It " - "is not a compression result and does not promote cached outputs " - "without exact decode/hash/byte-count receipts." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["route_cache_types"]: - lines.append({"type": "route_cache_type", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/cad_force_probe_experiment_matrix.py b/4-Infrastructure/shim/cad_force_probe_experiment_matrix.py deleted file mode 100644 index 775106f9..00000000 --- a/4-Infrastructure/shim/cad_force_probe_experiment_matrix.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python3 -"""Experiment matrix for turning the Merkle-tensegrity CAD prior into measurements. - -The point of this layer is deliberately modest: make the lattice testable by -pinning every claim to a simulated scenario, a bench measurement field, or a -hold condition. The Merkle root is a receipt for the experiment record, not a -force sensor and not a structural safety certificate. -""" - -from __future__ import annotations - -import argparse -import hashlib -import importlib.util -import json -import sys -from pathlib import Path -from types import SimpleNamespace -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -SOURCE_RECEIPT = SHIM / "four_force_geometry_probe_prior_receipt.json" -LOAD_HARNESS = SHIM / "merkle_tensegrity_load_equation_generator.py" -OUT = SHIM / "cad_force_probe_experiment_matrix_receipt.json" -CURRICULUM = SHIM / "cad_force_probe_experiment_matrix_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def load_harness_module() -> Any: - spec = importlib.util.spec_from_file_location("merkle_tensegrity_load_equation_generator", LOAD_HARNESS) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot import load harness from {LOAD_HARNESS}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -def scenario_args(**overrides: Any) -> argparse.Namespace: - defaults = { - "seed": 2519138123, - "gravity": -9.81, - "mass_per_node": 0.1, - "lateral_noise_sigma": 0.05, - "duality_coefficient": 2 ** 0.5, - "density_midpoint": 0.25, - "epsilon_mech": 1e-8, - "include_face_diagonals": True, - } - defaults.update(overrides) - return SimpleNamespace(**defaults) - - -def summarize_simulation(name: str, description: str, args: argparse.Namespace, harness: Any) -> dict[str, Any]: - receipt = harness.build_receipt(args) - return { - "name": name, - "description": description, - "parameters": receipt["parameters"], - "result": { - "edge_count": receipt["lattice"]["edge_count"], - "support_count": receipt["lattice"]["support_count"], - "residual_norm_l2": receipt["results"]["residual_norm_l2"], - "mechanically_acceptable": receipt["results"]["mechanically_acceptable"], - "density_min": receipt["results"]["density_min"], - "density_max": receipt["results"]["density_max"], - "total_abs_edge_force_density": receipt["results"]["total_abs_edge_force_density"], - "merkle_root": receipt["merkle"]["root"], - "receipt_hash": receipt["receipt_hash"], - }, - } - - -def build_simulated_scenarios(harness: Any) -> list[dict[str, Any]]: - scenarios = [ - summarize_simulation( - "G0_braced_static_gravity", - "Default braced cube under gravity and small lateral disturbance.", - scenario_args(include_face_diagonals=True, lateral_noise_sigma=0.05, mass_per_node=0.1), - harness, - ), - summarize_simulation( - "G1_gravity_only_braced", - "Braced cube with lateral disturbance removed; isolates gravity/support closure.", - scenario_args(include_face_diagonals=True, lateral_noise_sigma=0.0, mass_per_node=0.1), - harness, - ), - summarize_simulation( - "G2_lateral_sweep_braced_0p20", - "Braced cube under amplified lateral disturbance; checks whether bracing still closes.", - scenario_args(include_face_diagonals=True, lateral_noise_sigma=0.2, mass_per_node=0.1), - harness, - ), - summarize_simulation( - "G3_mass_sweep_braced_0p20kg", - "Braced cube with doubled nodal mass; checks force-density and density-command response.", - scenario_args(include_face_diagonals=True, lateral_noise_sigma=0.05, mass_per_node=0.2), - harness, - ), - summarize_simulation( - "NC1_unbraced_lateral_negative_control", - "Axis-only cube under the same lateral disturbance; expected to fail residual closure.", - scenario_args(include_face_diagonals=False, lateral_noise_sigma=0.05, mass_per_node=0.1), - harness, - ), - ] - return scenarios - - -def build_receipt() -> dict[str, Any]: - source = json.loads(SOURCE_RECEIPT.read_text(encoding="utf-8")) - harness = load_harness_module() - simulated_scenarios = build_simulated_scenarios(harness) - - receipt: dict[str, Any] = { - "schema": "cad_force_probe_experiment_matrix_v1", - "source_prior": str(SOURCE_RECEIPT.relative_to(REPO)), - "source_prior_hash": source["receipt_hash"], - "source_merkle_root": source["source_merkle_root"], - "primary_read": ( - "This is the most directly testable frame in the current theory stack " - "besides biological DNA work: the object can be modeled as CAD, printed, " - "loaded with known forces, measured with fixtures/sensors, and committed " - "as a Merkle-verifiable experiment record." - ), - "why_more_testable_than_dna": { - "cad_lattice": [ - "geometry is controlled directly", - "loads can be set by fixture, mass, or actuator", - "displacement and failure can be measured immediately", - "negative controls can be printed with known bad bracing", - "Merkle receipts can commit each geometry/load/material trace", - ], - "dna_frame": [ - "biological state is harder to control directly", - "measurement loops are slower and more confounded", - "ethical and safety constraints are far tighter", - "latent variables dominate unless the assay is very narrow", - ], - }, - "simulated_scenarios": simulated_scenarios, - "bench_experiment_matrix": [ - { - "experiment_id": "BENCH_G0", - "force_lane": "gravity", - "printed_geometry": "braced cube lattice", - "set_force": "known mass plus Earth gravity", - "direct_measurements": ["mass_measurement", "support_reaction_if_available", "vertical_displacement_trace"], - "expected_signal": "small closure residual and monotone displacement with added mass", - "hold_condition": "crack, delamination, fixture slip, or displacement beyond calibrated limit", - }, - { - "experiment_id": "BENCH_G1", - "force_lane": "gravity + lateral mechanical disturbance", - "printed_geometry": "braced cube lattice", - "set_force": "known lateral load from pulley, spring scale, or actuator", - "direct_measurements": ["load_cell_trace", "lateral_displacement_trace", "video_marker_trace"], - "expected_signal": "braced lattice carries lateral load with bounded displacement", - "hold_condition": "observed lateral response diverges from calibrated model beyond epsilon_u", - }, - { - "experiment_id": "BENCH_NC1", - "force_lane": "negative control", - "printed_geometry": "axis-only unbraced cube", - "set_force": "same lateral load as BENCH_G1", - "direct_measurements": ["load_cell_trace", "lateral_displacement_trace", "failure_mode"], - "expected_signal": "higher residual proxy, higher displacement, or failure relative to braced cube", - "hold_condition": "negative control performs indistinguishably from braced design; model needs revision", - }, - { - "experiment_id": "BENCH_EM1", - "force_lane": "electromagnetic material lane", - "printed_geometry": "same braced cube across materials or print profiles", - "set_force": "same gravity/lateral load with material, temperature, or infill varied", - "direct_measurements": ["material_batch", "printer_profile", "temperature_trace", "stiffness_proxy"], - "expected_signal": "material/thermal lane changes stiffness and deformation more than Merkle state alone", - "hold_condition": "material profile omitted from any mechanical claim", - }, - { - "experiment_id": "BENCH_COMMIT1", - "force_lane": "attestation", - "printed_geometry": "same geometry record with one load or density field changed", - "set_force": "none; data integrity perturbation", - "direct_measurements": ["leaf_hashes", "merkle_root_before", "merkle_root_after"], - "expected_signal": "Merkle root changes under record mutation", - "hold_condition": "hash unchanged after semantically relevant record mutation", - }, - ], - "measurement_record_schema": { - "geometry": ["cad_model_hash", "stl_hash", "node_edge_schema_hash", "support_fixture_id"], - "print": ["printer_id", "slicer_profile_hash", "material_batch", "infill_profile", "nozzle_temperature_trace"], - "force": ["load_fixture_id", "mass_measurement", "load_cell_trace_hash", "actuator_profile_hash"], - "observation": [ - "dial_indicator_trace_hash", - "camera_marker_trace_hash", - "strain_marker_trace_hash", - "temperature_trace_hash", - "failure_mode", - ], - "model_compare": [ - "predicted_residual_norm_l2", - "observed_displacement", - "displacement_error", - "stiffness_proxy", - "calibration_gain", - "safety_hold", - ], - "attestation": ["leaf_hashes", "merkle_root", "receipt_hash", "operator_signature_optional"], - }, - "measurement_equations": { - "force_closure_error": "e_F = ||B q + R + p||_2", - "displacement_error": "e_u = ||u_observed - u_predicted||_2", - "stiffness_proxy": "K_proxy = F_applied / max(delta_observed, epsilon_delta)", - "calibration_gain": "k* = argmin_k ||u_observed - k u_predicted||_2", - "bracing_gain": "G_brace = K_proxy(braced) / K_proxy(unbraced)", - "attested_leaf": "leaf_i = H(stable_json(geometry, print, force, observation, model_compare))", - "safety_hold": "hold iff e_F > epsilon_F or e_u > epsilon_u or crack/failure/fixture_slip is observed", - }, - "claim_tests": [ - { - "claim": "bracing matters under lateral disturbance", - "test": "compare BENCH_G1 against BENCH_NC1", - "pass_signal": "braced lattice has lower displacement or higher stiffness proxy than unbraced negative control", - }, - { - "claim": "gravity lane is directly measurable", - "test": "mass sweep in BENCH_G0", - "pass_signal": "observed displacement or support reaction increases monotonically with applied mass", - }, - { - "claim": "EM lane dominates material printability", - "test": "material or thermal sweep in BENCH_EM1", - "pass_signal": "same geometry/load produces materially different stiffness proxy across profiles", - }, - { - "claim": "Merkle commits the experiment, but does not certify mechanics", - "test": "BENCH_COMMIT1 plus NC1", - "pass_signal": "root changes under record mutation; failed geometry can still have a valid failure receipt", - }, - ], - "failure_rules": [ - "bench measurement omitted -> theory claim remains speculative", - "negative control omitted -> bracing claim is weak", - "Merkle root treated as load-cell evidence -> invalid", - "EM/material metadata omitted from printability claim -> hold", - "strong or weak interaction treated as directly actuated by desktop CAD load test -> overclaim", - "fixture slip, print delamination, or sensor saturation ignored -> invalid measurement", - "safety_hold true but result reported as pass -> invalid receipt", - ], - "claim_boundary": ( - "This receipt defines an experiment matrix and simulated priors for a CAD-load " - "probe. It does not certify a printed object, prove a unified force theory, or " - "replace finite-element analysis, slicer calibration, or physical safety testing." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "choose_direct_measurement", - "input": "gravity, lateral, EM/material, or attestation lane", - "target": "mass/load-cell/displacement/material-trace/Merkle-root measurement", - }, - { - "task": "reject_untestable_force_claim", - "input": "claim about four forces from desktop CAD print", - "target": "gravity direct, EM material lane, strong/weak guard or baseline only", - }, - { - "task": "apply_negative_control", - "input": "braced cube result without unbraced comparison", - "target": "run or cite unbraced lateral negative control", - }, - { - "task": "build_bench_receipt", - "input": "geometry, print, force, observation, model compare", - "target": "stable JSON leaf records plus Merkle root and hold flags", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "scenario_count": len(receipt["simulated_scenarios"]), - "bench_experiment_count": len(receipt["bench_experiment_matrix"]), - "source_prior_hash": receipt["source_prior_hash"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/citation_math_function_distillation.py b/4-Infrastructure/shim/citation_math_function_distillation.py deleted file mode 100644 index 230ff77a..00000000 --- a/4-Infrastructure/shim/citation_math_function_distillation.py +++ /dev/null @@ -1,422 +0,0 @@ -#!/usr/bin/env python3 -"""Distill cited priors into math/function groups. - -This runner treats the Decision Diagram Compression Tuning Prior tiddler as the -source surface. It extracts the local citation footprint, then records a -bounded synthesis: what mathematical role each citation family contributes and -what primary compressor function it should serve. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import re -from pathlib import Path -from typing import Any - - -DEFAULT_SOURCE = Path( - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Decision Diagram Compression Tuning Prior.tid" -) -DEFAULT_RECEIPT = Path("4-Infrastructure/shim/citation_math_function_distillation_receipt.json") -DEFAULT_CURRICULUM = Path( - "4-Infrastructure/shim/citation_math_function_distillation_curriculum.jsonl" -) - - -PRIMARY_FUNCTION = { - "name": "bounded_exact_route_compiler", - "statement": ( - "Use citations as route priors that propose, bound, transport, fold, " - "allocate, repair, or verify transform routes; promote only after local " - "encode/decode/hash/byte-count receipt beats the incumbent." - ), - "function_chain": [ - "observe_corpus_structure", - "propose_route_family", - "bound_witness_sidecar_and_compute_cost", - "route_to_deterministic_owner", - "fold_or_factor_only_when_reachability_is_preserved", - "emit_exact_residual_repair", - "verify_rehydration_hash", - "measure_total_bytes_under_one_ratio_schema", - "promote_or_fail_closed", - ], -} - - -FUNCTION_GROUPS = [ - { - "id": "exact_outer_inner_route_search", - "source_examples": [ - "An Exact Framework for Solving the Space-Time Dependent TSP", - "decision diagram / branch-and-bound route planning", - ], - "math_shape": "outer discrete route search + expensive inner evaluator + bounds", - "primary_function": "turn transform tuning into a prunable decision diagram", - "dd_role": "search_and_bound", - }, - { - "id": "bounded_topology_closure", - "source_examples": [ - "Invariant dual mechanics of tensegrity and origami", - "deterministic routing", - "Menger-Torus-Braid Shell Route v0", - "T16 nD Bundle PIST Shell Machine", - ], - "math_shape": "finite witness fields + closure classes + deterministic owners", - "primary_function": "prevent topology metaphors from becoming recursive sidecars", - "dd_role": "closure_guard", - }, - { - "id": "finite_group_fiber_invariant_recursion", - "source_examples": [ - "Cayley fibergraph visualization", - "Q8 quaternion product fibers", - "stator-indexed contraction", - "braided fiber connector", - "axial core", - ], - "math_shape": "finite group action + product fiber + bounded substitution + invariant axis", - "primary_function": "generate structured route families while keeping recursion finite and receipted", - "dd_role": "invariant_route_generator", - }, - { - "id": "bundle_transport_framework_selection", - "source_examples": [ - "fibered n-spaces", - "Parseval frames for vector bundles", - "quaternionic slice regular functions", - "infinite-dimensional differential geometry", - "diffeological pseudobundles", - "Fredholm bundles", - ], - "math_shape": "base space + fiber + section + frame + transport + finite chart", - "primary_function": "choose the smallest bounded chart that can carry exact residual repair", - "dd_role": "framework_switchboard", - }, - { - "id": "state_machine_folding_reachability", - "source_examples": [ - "Recursive State Machine Guided Graph Folding for CFL Reachability", - "TF-Label topological-folding reachability", - "origami and kirigami folding complexity", - ], - "math_shape": "quotient graph / folded state machine preserving reachability", - "primary_function": "collapse equivalent DD states only when exact decode reachability is unchanged", - "dd_role": "frontier_reducer", - }, - { - "id": "consensus_repair_and_topology_robustness", - "source_examples": [ - "DeepConsensus", - "CANOS N-1 topology robust solver", - "gap-aware alignment", - ], - "math_shape": "multiple weak observations + perturbation + exact validator", - "primary_function": "repair route observations and stress promoted routes without relaxing exactness", - "dd_role": "repair_and_stress_test", - }, - { - "id": "evolutionary_route_population", - "source_examples": [ - "OpenEvolve", - "AlphaEvolve example gallery", - "MAP-Elites / islands / evaluator score", - ], - "math_shape": "quality-diversity population + evaluator + archive", - "primary_function": "expand the candidate frontier while keeping evaluator receipts authoritative", - "dd_role": "proposal_engine", - }, - { - "id": "ratio_quality_and_measurement_schema", - "source_examples": [ - "Syllabic compression effective compression ratios", - "Definition of Compression Ratio: JPEG2000 library differences", - "compression quality coupling", - ], - "math_shape": "nominal setting != achieved ratio; parameters couple to quality and overhead", - "primary_function": "force actual byte measurement under one explicit ratio schema", - "dd_role": "measurement_normalizer", - }, - { - "id": "semantic_information_limits_and_allocation", - "source_examples": [ - "Semantic Rate-Distortion Theory", - "information bottleneck", - "semantic arithmetic coding", - "probabilistic semantic communication with RSMA", - "prompt compression rate-distortion", - ], - "math_shape": "semantic entropy/rate-distortion/bottleneck + side information and resource budgets", - "primary_function": "turn semantic theory into budget coordinates, not byte-loss permission", - "dd_role": "semantic_budget_model", - }, - { - "id": "adaptive_predictor_with_exact_residual", - "source_examples": [ - "SZ-style error-controlled scientific compression", - "Lorenzo / regression predictors", - "block-local predictor selection", - ], - "math_shape": "local predictor family + diagnostic error bound + exact residual repair", - "primary_function": "use lossy predictors as sketches only when residual lanes restore bytes", - "dd_role": "sketch_then_repair", - }, - { - "id": "semantic_corpus_resolution", - "source_examples": [ - "fascicles", - "ItCompress", - "SPARTAN", - "DeepSqueeze", - "semantic trajectories", - "embedding wavelet subbands", - ], - "math_shape": "record/attribute lanes + compact bundles + model predictions + residual lanes", - "primary_function": "raise corpus resolution so proposals are local, reversible, and byte-authorized", - "dd_role": "corpus_observation_stack", - }, - { - "id": "syntax_semantic_feature_witnesses", - "source_examples": [ - "dependency-based text compression", - "DeepSIC", - "compression-ratio vector representation", - ], - "math_shape": "skeleton/head/feature projection + diagnostic witness + residualized deletion", - "primary_function": "share semantic or syntactic features with the route while paying witness bytes", - "dd_role": "feature_witness_surface", - }, - { - "id": "non_euclidean_semantic_kv_tree_store", - "source_examples": [ - "Riemannian manifold learning", - "Cartan-Hadamard sliced-Wasserstein flows", - "TinyEnc", - "TreeKV", - "Tree Fiddy", - "GEAR / low-rank KV cache compression", - ], - "math_shape": "curved key manifold + compressed/encrypted byte store + tree-bounded cache route", - "primary_function": "separate key geometry, store bytes, cache approximation, and exact residual authority", - "dd_role": "semantic_kv_bridge", - }, - { - "id": "multistate_geometry_addressability", - "source_examples": [ - "multistable origami metasurfaces", - "manifold geometric deep learning", - "tensor network geometry", - "quantum phase geometry", - "programmable multistability limits", - ], - "math_shape": "many stable states + controllability/addressability/energy/tolerance gates", - "primary_function": "prune route states that are stable-looking but not addressable, bounded, or exact", - "dd_role": "state_addressability_gate", - }, - { - "id": "epigenetic_phase_control", - "source_examples": [ - "epigenetic control of satellite cells", - "activation/proliferation/commitment/differentiation/fusion phases", - ], - "math_shape": "same substrate + phase-specific gates + bounded transition receipts", - "primary_function": "schedule route opening, suppression, specialization, repair, and fusion", - "dd_role": "route_scheduler", - }, - { - "id": "unresolved_metadata_hold", - "source_examples": [ - "unresolved IEEE Xplore metadata note", - "HTTP 418 / unable to load page / unverified title or DOI", - ], - "math_shape": "unknown citation -> no extracted prior", - "primary_function": "hold unverified sources outside the route prior until metadata is reliable", - "dd_role": "claim_boundary_hold", - }, -] - - -DD_FUNCTION_ALGEBRA = [ - { - "symbol": "B(route)", - "name": "bound", - "definition": "lower-bound payload + sidecar + witness + compute/container costs before evaluation", - }, - { - "symbol": "R(key)", - "name": "route", - "definition": "deterministically assign route/key/repair requests to an owner or chart", - }, - { - "symbol": "F(states)", - "name": "fold", - "definition": "merge states only when decode reachability and receipt class are preserved", - }, - { - "symbol": "T(section)", - "name": "transport", - "definition": "move local route sections across bundle/fiber/base regions with bounded holonomy", - }, - { - "symbol": "A(budget)", - "name": "allocate", - "definition": "split byte/runtime/witness budgets across shared, private, and repair lanes", - }, - { - "symbol": "E(residual)", - "name": "exact_repair", - "definition": "emit residual lanes that restore the exact source bytes after any sketch/proposal", - }, - { - "symbol": "V(output)", - "name": "verify", - "definition": "decode, hash, count bytes, and compare against incumbent under one ratio schema", - }, -] - - -def stable_hash(obj: Any) -> str: - payload = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def citation_source_surface(source_text: str) -> str: - """Return the tiddler region that contains input citations. - - The generated distillation section contains its own receipt hash. Excluding - it keeps the receipt stable across reruns after the wiki is updated. - """ - marker = "\n!! Citation Math Function Distillation\n" - if marker not in source_text: - return source_text - before, after = source_text.split(marker, 1) - next_marker = "\n!! Where To Tune Next\n" - if next_marker in after: - _, tail = after.split(next_marker, 1) - return before + next_marker + tail - return before - - -def extract_citation_surface(source_text: str) -> dict[str, Any]: - dois = sorted(set(re.findall(r"10\.\d{4,9}/[A-Za-z0-9._;()/:+-]+", source_text))) - urls = sorted(set(re.findall(r"https?://[^\s`]+", source_text))) - titles = sorted( - set( - match.strip() - for match in re.findall(r'"([^"\n]{12,180})"', source_text) - if not match.startswith("http") - ) - ) - consensus_threads = sorted( - set(re.findall(r"Consensus thread:\n\n```\n([^`]+?)\n```", source_text, flags=re.MULTILINE)) - ) - unresolved_ieee = [url for url in urls if "ieeexplore.ieee.org/abstract/document/" in url] - return { - "source_sha256": hashlib.sha256(source_text.encode("utf-8")).hexdigest(), - "doi_count": len(dois), - "url_count": len(urls), - "quoted_title_count": len(titles), - "consensus_thread_count": len(consensus_threads), - "unresolved_ieee_url_count": len(unresolved_ieee), - "dois": dois, - "urls": urls, - "consensus_threads": consensus_threads, - "sample_quoted_titles": titles[:25], - } - - -def build_receipt(source: Path) -> dict[str, Any]: - source_text = source.read_text(encoding="utf-8") - source_surface = citation_source_surface(source_text) - citation_surface = extract_citation_surface(source_surface) - receipt: dict[str, Any] = { - "schema": "citation_math_function_distillation_v1", - "generated_at": "2026-05-08T00:00:00+00:00", - "source_tiddler": str(source), - "source_surface_scope": ( - "tiddler excluding generated Citation Math Function Distillation section" - ), - "citation_surface": citation_surface, - "claim_boundary": ( - "This distills cited papers into math/function roles. It does not " - "verify every paper externally and does not promote any compression " - "claim without local byte receipts." - ), - "primary_function": PRIMARY_FUNCTION, - "dd_function_algebra": DD_FUNCTION_ALGEBRA, - "function_groups": FUNCTION_GROUPS, - "distilled_core": ( - "All citation families collapse to one operational compressor: a " - "bounded exact route compiler whose only promotion authority is " - "local rehydration hash plus measured compressed_total_bytes." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - return receipt - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = ( - "You are a citation math distiller for the projectable-geometry " - "compressor. Group citations by mathematical function and preserve the " - "byte-exact claim boundary." - ) - records: list[dict[str, Any]] = [] - for group in receipt["function_groups"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "distill_citation_group", - "group_id": group["id"], - "source_examples": group["source_examples"], - "math_shape": group["math_shape"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "primary_function": group["primary_function"], - "dd_role": group["dd_role"], - "promotion_authority": "local encode/decode/hash/byte-count receipt", - "claim_boundary": "citation_function_prior_only", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--source", type=Path, default=DEFAULT_SOURCE) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--curriculum", type=Path, default=DEFAULT_CURRICULUM) - args = parser.parse_args() - - receipt = build_receipt(args.source) - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/codebase-memory/Cargo.lock b/4-Infrastructure/shim/codebase-memory/Cargo.lock deleted file mode 100644 index 919b4951..00000000 --- a/4-Infrastructure/shim/codebase-memory/Cargo.lock +++ /dev/null @@ -1,573 +0,0 @@ -# This file is automatically @generated by Cargo. -# It is not intended for manual editing. -version = 4 - -[[package]] -name = "anyhow" -version = "1.0.102" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7f202df86484c868dbad7eaa557ef785d5c66295e41b460ef922eca0723b842c" - -[[package]] -name = "bitflags" -version = "2.11.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c4512299f36f043ab09a583e57bceb5a5aab7a73db1805848e8fef3c9e8c78b3" - -[[package]] -name = "block-buffer" -version = "0.10.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3078c7629b62d3f0439517fa394996acacc5cbc91c5a20d8c658e77abd503a71" -dependencies = [ - "generic-array", -] - -[[package]] -name = "cfg-if" -version = "1.0.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" - -[[package]] -name = "codebase-memory" -version = "0.1.0" -dependencies = [ - "serde", - "serde_json", - "sha2", - "tempfile", - "thiserror", - "walkdir", -] - -[[package]] -name = "cpufeatures" -version = "0.2.17" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "59ed5838eebb26a2bb2e58f6d5b5316989ae9d08bab10e0e6d103e656d1b0280" -dependencies = [ - "libc", -] - -[[package]] -name = "crypto-common" -version = "0.1.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "78c8292055d1c1df0cce5d180393dc8cce0abec0a7102adb6c7b1eef6016d60a" -dependencies = [ - "generic-array", - "typenum", -] - -[[package]] -name = "digest" -version = "0.10.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9ed9a281f7bc9b7576e61468ba615a66a5c8cfdff42420a70aa82701a3b1e292" -dependencies = [ - "block-buffer", - "crypto-common", -] - -[[package]] -name = "equivalent" -version = "1.0.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "877a4ace8713b0bcf2a4e7eec82529c029f1d0619886d18145fea96c3ffe5c0f" - -[[package]] -name = "errno" -version = "0.3.14" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "39cab71617ae0d63f51a36d69f866391735b51691dbda63cf6f96d042b63efeb" -dependencies = [ - "libc", - "windows-sys", -] - -[[package]] -name = "fastrand" -version = "2.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9f1f227452a390804cdb637b74a86990f2a7d7ba4b7d5693aac9b4dd6defd8d6" - -[[package]] -name = "foldhash" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d9c4f5dac5e15c24eb999c26181a6ca40b39fe946cbe4c263c7209467bc83af2" - -[[package]] -name = "generic-array" -version = "0.14.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "85649ca51fd72272d7821adaf274ad91c288277713d9c18820d8499a7ff69e9a" -dependencies = [ - "typenum", - "version_check", -] - -[[package]] -name = "getrandom" -version = "0.4.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0de51e6874e94e7bf76d726fc5d13ba782deca734ff60d5bb2fb2607c7406555" -dependencies = [ - "cfg-if", - "libc", - "r-efi", - "wasip2", - "wasip3", -] - -[[package]] -name = "hashbrown" -version = "0.15.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9229cfe53dfd69f0609a49f65461bd93001ea1ef889cd5529dd176593f5338a1" -dependencies = [ - "foldhash", -] - -[[package]] -name = "hashbrown" -version = "0.17.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed5909b6e89a2db4456e54cd5f673791d7eca6732202bbf2a9cc504fe2f9b84a" - -[[package]] -name = "heck" -version = "0.5.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea" - -[[package]] -name = "id-arena" -version = "2.3.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d3067d79b975e8844ca9eb072e16b31c3c1c36928edf9c6789548c524d0d954" - -[[package]] -name = "indexmap" -version = "2.14.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d466e9454f08e4a911e14806c24e16fba1b4c121d1ea474396f396069cf949d9" -dependencies = [ - "equivalent", - "hashbrown 0.17.1", - "serde", - "serde_core", -] - -[[package]] -name = "itoa" -version = "1.0.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f42a60cbdf9a97f5d2305f08a87dc4e09308d1276d28c869c684d7777685682" - -[[package]] -name = "leb128fmt" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "09edd9e8b54e49e587e4f6295a7d29c3ea94d469cb40ab8ca70b288248a81db2" - -[[package]] -name = "libc" -version = "0.2.186" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66" - -[[package]] -name = "linux-raw-sys" -version = "0.12.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32a66949e030da00e8c7d4434b251670a91556f4144941d37452769c25d58a53" - -[[package]] -name = "log" -version = "0.4.29" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" - -[[package]] -name = "memchr" -version = "2.8.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8ca58f447f06ed17d5fc4043ce1b10dd205e060fb3ce5b979b8ed8e59ff3f79" - -[[package]] -name = "once_cell" -version = "1.21.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9f7c3e4beb33f85d45ae3e3a1792185706c8e16d043238c593331cc7cd313b50" - -[[package]] -name = "prettyplease" -version = "0.2.37" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "479ca8adacdd7ce8f1fb39ce9ecccbfe93a3f1344b3d0d97f20bc0196208f62b" -dependencies = [ - "proc-macro2", - "syn", -] - -[[package]] -name = "proc-macro2" -version = "1.0.106" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8fd00f0bb2e90d81d1044c2b32617f68fcb9fa3bb7640c23e9c748e53fb30934" -dependencies = [ - "unicode-ident", -] - -[[package]] -name = "quote" -version = "1.0.45" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41f2619966050689382d2b44f664f4bc593e129785a36d6ee376ddf37259b924" -dependencies = [ - "proc-macro2", -] - -[[package]] -name = "r-efi" -version = "6.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8dcc9c7d52a811697d2151c701e0d08956f92b0e24136cf4cf27b57a6a0d9bf" - -[[package]] -name = "rustix" -version = "1.1.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b6fe4565b9518b83ef4f91bb47ce29620ca828bd32cb7e408f0062e9930ba190" -dependencies = [ - "bitflags", - "errno", - "libc", - "linux-raw-sys", - "windows-sys", -] - -[[package]] -name = "same-file" -version = "1.0.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "93fc1dc3aaa9bfed95e02e6eadabb4baf7e3078b0bd1b4d7b6b0b68378900502" -dependencies = [ - "winapi-util", -] - -[[package]] -name = "semver" -version = "1.0.28" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8a7852d02fc848982e0c167ef163aaff9cd91dc640ba85e263cb1ce46fae51cd" - -[[package]] -name = "serde" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9a8e94ea7f378bd32cbbd37198a4a91436180c5bb472411e48b5ec2e2124ae9e" -dependencies = [ - "serde_core", - "serde_derive", -] - -[[package]] -name = "serde_core" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41d385c7d4ca58e59fc732af25c3983b67ac852c1a25000afe1175de458b67ad" -dependencies = [ - "serde_derive", -] - -[[package]] -name = "serde_derive" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "serde_json" -version = "1.0.149" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "83fc039473c5595ace860d8c4fafa220ff474b3fc6bfdb4293327f1a37e94d86" -dependencies = [ - "itoa", - "memchr", - "serde", - "serde_core", - "zmij", -] - -[[package]] -name = "sha2" -version = "0.10.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a7507d819769d01a365ab707794a4084392c824f54a7a6a7862f8c3d0892b283" -dependencies = [ - "cfg-if", - "cpufeatures", - "digest", -] - -[[package]] -name = "syn" -version = "2.0.117" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e665b8803e7b1d2a727f4023456bbbbe74da67099c585258af0ad9c5013b9b99" -dependencies = [ - "proc-macro2", - "quote", - "unicode-ident", -] - -[[package]] -name = "tempfile" -version = "3.27.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32497e9a4c7b38532efcdebeef879707aa9f794296a4f0244f6f69e9bc8574bd" -dependencies = [ - "fastrand", - "getrandom", - "once_cell", - "rustix", - "windows-sys", -] - -[[package]] -name = "thiserror" -version = "1.0.69" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b6aaf5339b578ea85b50e080feb250a3e8ae8cfcdff9a461c9ec2904bc923f52" -dependencies = [ - "thiserror-impl", -] - -[[package]] -name = "thiserror-impl" -version = "1.0.69" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4fee6c4efc90059e10f81e6d42c60a18f76588c3d74cb83a0b242a2b6c7504c1" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "typenum" -version = "1.20.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "40ce102ab67701b8526c123c1bab5cbe42d7040ccfd0f64af1a385808d2f43de" - -[[package]] -name = "unicode-ident" -version = "1.0.24" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e6e4313cd5fcd3dad5cafa179702e2b244f760991f45397d14d4ebf38247da75" - -[[package]] -name = "unicode-xid" -version = "0.2.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ebc1c04c71510c7f702b52b7c350734c9ff1295c464a03335b00bb84fc54f853" - -[[package]] -name = "version_check" -version = "0.9.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a" - -[[package]] -name = "walkdir" -version = "2.5.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "29790946404f91d9c5d06f9874efddea1dc06c5efe94541a7d6863108e3a5e4b" -dependencies = [ - "same-file", - "winapi-util", -] - -[[package]] -name = "wasip2" -version = "1.0.3+wasi-0.2.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "20064672db26d7cdc89c7798c48a0fdfac8213434a1186e5ef29fd560ae223d6" -dependencies = [ - "wit-bindgen 0.57.1", -] - -[[package]] -name = "wasip3" -version = "0.4.0+wasi-0.3.0-rc-2026-01-06" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5428f8bf88ea5ddc08faddef2ac4a67e390b88186c703ce6dbd955e1c145aca5" -dependencies = [ - "wit-bindgen 0.51.0", -] - -[[package]] -name = "wasm-encoder" -version = "0.244.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "990065f2fe63003fe337b932cfb5e3b80e0b4d0f5ff650e6985b1048f62c8319" -dependencies = [ - "leb128fmt", - "wasmparser", -] - -[[package]] -name = "wasm-metadata" -version = "0.244.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bb0e353e6a2fbdc176932bbaab493762eb1255a7900fe0fea1a2f96c296cc909" -dependencies = [ - "anyhow", - "indexmap", - "wasm-encoder", - "wasmparser", -] - -[[package]] -name = "wasmparser" -version = "0.244.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "47b807c72e1bac69382b3a6fb3dbe8ea4c0ed87ff5629b8685ae6b9a611028fe" -dependencies = [ - "bitflags", - "hashbrown 0.15.5", - "indexmap", - "semver", -] - -[[package]] -name = "winapi-util" -version = "0.1.11" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22" -dependencies = [ - "windows-sys", -] - -[[package]] -name = "windows-link" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5" - -[[package]] -name = "windows-sys" -version = "0.61.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc" -dependencies = [ - "windows-link", -] - -[[package]] -name = "wit-bindgen" -version = "0.51.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d7249219f66ced02969388cf2bb044a09756a083d0fab1e566056b04d9fbcaa5" -dependencies = [ - "wit-bindgen-rust-macro", -] - -[[package]] -name = "wit-bindgen" -version = "0.57.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1ebf944e87a7c253233ad6766e082e3cd714b5d03812acc24c318f549614536e" - -[[package]] -name = "wit-bindgen-core" -version = "0.51.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ea61de684c3ea68cb082b7a88508a8b27fcc8b797d738bfc99a82facf1d752dc" -dependencies = [ - "anyhow", - "heck", - "wit-parser", -] - -[[package]] -name = "wit-bindgen-rust" -version = "0.51.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b7c566e0f4b284dd6561c786d9cb0142da491f46a9fbed79ea69cdad5db17f21" -dependencies = [ - "anyhow", - "heck", - "indexmap", - "prettyplease", - "syn", - "wasm-metadata", - "wit-bindgen-core", - "wit-component", -] - -[[package]] -name = "wit-bindgen-rust-macro" -version = "0.51.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c0f9bfd77e6a48eccf51359e3ae77140a7f50b1e2ebfe62422d8afdaffab17a" -dependencies = [ - "anyhow", - "prettyplease", - "proc-macro2", - "quote", - "syn", - "wit-bindgen-core", - "wit-bindgen-rust", -] - -[[package]] -name = "wit-component" -version = "0.244.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9d66ea20e9553b30172b5e831994e35fbde2d165325bec84fc43dbf6f4eb9cb2" -dependencies = [ - "anyhow", - "bitflags", - "indexmap", - "log", - "serde", - "serde_derive", - "serde_json", - "wasm-encoder", - "wasm-metadata", - "wasmparser", - "wit-parser", -] - -[[package]] -name = "wit-parser" -version = "0.244.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ecc8ac4bc1dc3381b7f59c34f00b67e18f910c2c0f50015669dde7def656a736" -dependencies = [ - "anyhow", - "id-arena", - "indexmap", - "log", - "semver", - "serde", - "serde_derive", - "serde_json", - "unicode-xid", - "wasmparser", -] - -[[package]] -name = "zmij" -version = "1.0.21" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" diff --git a/4-Infrastructure/shim/codebase-memory/Cargo.toml b/4-Infrastructure/shim/codebase-memory/Cargo.toml deleted file mode 100644 index c5eefba9..00000000 --- a/4-Infrastructure/shim/codebase-memory/Cargo.toml +++ /dev/null @@ -1,25 +0,0 @@ -[package] -name = "codebase-memory" -version = "0.1.0" -edition = "2021" -authors = ["Research Stack Team"] -description = "FAMM-based persistent multi-domain codebase memory for Hermes agent" -license = "Apache-2.0" - -[dependencies] -serde = { version = "1.0", features = ["derive"] } -serde_json = "1.0" -sha2 = "0.10" -walkdir = "2.5" -thiserror = "1.0" - -[dev-dependencies] -tempfile = "3.10" - -[[bin]] -name = "codebase-memory" -path = "src/main.rs" - -[lib] -name = "codebase_memory" -path = "src/lib.rs" diff --git a/4-Infrastructure/shim/codebase-memory/hermes_integration_manifest.json b/4-Infrastructure/shim/codebase-memory/hermes_integration_manifest.json deleted file mode 100644 index 77e91c87..00000000 --- a/4-Infrastructure/shim/codebase-memory/hermes_integration_manifest.json +++ /dev/null @@ -1,68 +0,0 @@ -{ - "manifest_version": "2026-05-13", - "agent": "hermes-nous-research", - "runtime_stack": { - "formal": "Lean 4 (lake build)", - "runtime": "Rust (cargo build/test)", - "persistence": "JSON serialization via serde", - "ffi": "None -- all Rust, no Python, no untyped shell" - }, - "doctrine": { - "observer_provider_pairs": true, - "receipt_gate_before_belief": true, - "human_interference_allowed": true, - "lean_source_of_truth": true, - "no_sorry_without_todo": true, - "no_python_in_runtime": true - }, - "codebase_memory": { - "crate": "4-Infrastructure/shim/codebase-memory", - "type": "Rust library + binary", - "module_design": "FAMM-based multi-domain architecture", - "domains": [ - "0-Core-Formalism", - "1-Distributed-Systems", - "2-Search-Space", - "3-Mathematical-Models", - "4-Infrastructure", - "5-Applications", - "6-Documentation" - ], - "components": { - "types.rs": "Q16_16, CodeDomain, CodeCell, DomainBank, DomainScarField, DualMapMemory", - "adapter.rs": "CodebaseMemoryAdapter, observe, commit, advance_epoch, query_all, load_for_hermes", - "main.rs": "Binary entry point: load_for_hermes(project_root, .hermes/codebase_memory.json)" - }, - "key_features": [ - "Thermal management: JUDGE_PAUSE on budget exceeded, BUILDER_ADD within budget", - "Scar differential tracking: ahead vs behind understanding", - "Commitment gate: admit if |Delta| <= epsilon, hold otherwise", - "Receipt emission: every read/write/commit produces MemoryAccessReceipt", - "Pruning: stale cells (delay > max_delay) removed on capacity pressure", - "JSON persistence: serde_json for cross-session state", - "File hash tracking: SHA-256 of content to detect changes", - "Observer/Provider: observe() records; commit() validates before promoting" - ] - }, - "lean_modules": { - "quarantined": [ - "Semantics/CodebaseMemory.lean.quarantine", - "Semantics/CodebaseFSDU.lean.quarantine", - "Semantics/CodebaseReceipt.lean.quarantine" - ], - "status": "Needs field notation fixes and theorem completion before build reinclusion", - "strategy": "Rust crate is production. Lean modules are reference spec. Fix when Q16_16.lt issue resolved." - }, - "verification": { - "cargo_check": true, - "cargo_test": "6/6 passed", - "lake_build": "FAMM passes (3,300 jobs). CodebaseMemory quarantined.", - "receipt": "shared-data/data/stack_solidification/codebase_memory_receipt_2026-05-13.md" - }, - "promotion_gates": { - "HOLD": "FSDU dual-map commit gate working; all tests pass", - "CANDIDATE": "JSON persistence verified; file hashing verified", - "REVIEWED": "Requires observer/provider receipt audit or human review", - "BLOCKED": "None current" - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/codebase-memory/src/adapter.rs b/4-Infrastructure/shim/codebase-memory/src/adapter.rs deleted file mode 100644 index ce6772cc..00000000 --- a/4-Infrastructure/shim/codebase-memory/src/adapter.rs +++ /dev/null @@ -1,217 +0,0 @@ -//! -//! codebase_memory::adapter -- Hermes agent integration for FAMM memory -//! -use serde_json; -use sha2::{Digest, Sha256}; -use std::collections::HashMap; -use std::fs; -use std::io::{Read, Write}; -use std::path::Path; -use walkdir::WalkDir; - -use crate::types::{ - ArtifactType, CodeCell, CodeDomain, DualMapMemory, MemoryAccessReceipt, Q16_16, -}; - -/// Hermes interface to the Research Stack codebase. -pub struct CodebaseMemoryAdapter { - memory: DualMapMemory, - capacity: usize, -} - -impl CodebaseMemoryAdapter { - pub fn init_fresh(capacity: usize) -> Self { - CodebaseMemoryAdapter { - memory: DualMapMemory::new(capacity), - capacity, - } - } - - pub fn load_or_init(path: &Path) -> Self { - if path.exists() { - if let Ok(adapter) = Self::load(path) { - return adapter; - } - } - Self::init_fresh(1000) - } - - pub fn load(path: &Path) -> Result> { - let mut file = fs::File::open(path)?; - let mut contents = String::new(); - file.read_to_string(&mut contents)?; - let memory: DualMapMemory = serde_json::from_str(&contents)?; - Ok(CodebaseMemoryAdapter { - memory, - capacity: 1000, - }) - } - - pub fn save(&self, path: &Path) -> Result<(), Box> { - let json = serde_json::to_string_pretty(&self.memory)?; - let mut file = fs::File::create(path)?; - file.write_all(json.as_bytes())?; - Ok(()) - } - - pub fn observe( - &mut self, - domain: &str, - artifact_type: &str, - artifact_path: &str, - data: Q16_16, - delay: Q16_16, - delay_mass: Q16_16, - ) -> MemoryAccessReceipt { - let bank = match self.memory.ahead.banks.get_mut(domain) { - Some(b) => b, - None => { - return MemoryAccessReceipt { - timestamp: 0, - domain: domain.to_string(), - action: "blocked".to_string(), - path: artifact_path.to_string(), - success: false, - cost: 0xFFFF, - invariant: "domain_not_found".to_string(), - data_value: 0, - thermal_ok: false, - } - } - }; - let idx = match bank.find_index(artifact_path) { - Some(i) => i, - None => match bank.next_free() { - Some(i) => i, - None => { - bank.prune(); - bank.next_free().unwrap_or(0) - } - }, - }; - let old_hash = bank.cells[idx].version_hash.clone(); - let new_hash = file_hash(artifact_path); - bank.cells[idx] = CodeCell { - artifact_path: artifact_path.to_string(), - artifact_type: artifact_type.to_string(), - data, - delay, - delay_mass, - delay_weight: Q16_16::ONE, - version_hash: new_hash.clone(), - last_accessed: now_ms(), - access_count: bank.cells[idx].access_count + 1, - receipt_bound: true, - }; - if !artifact_path.is_empty() { - bank.active_count += 1; - } - bank.current_stress = bank.current_stress.add(delay_mass); - if !old_hash.is_empty() && old_hash != new_hash { - if let Some(dsd) = self.memory.differentials.get_mut(domain) { - dsd.ahead_scar.accumulate(delay_mass); - dsd.differential = dsd.ahead_scar.total.sub(dsd.behind_scar.total); - } - } - MemoryAccessReceipt { - timestamp: now_ms(), - domain: domain.to_string(), - action: "write".to_string(), - path: artifact_path.to_string(), - success: true, - cost: 0x0000_1000, - invariant: format!("observed path={}", artifact_path), - data_value: data.0, - thermal_ok: true, - } - } - - pub fn commit(&mut self, domain: &str) -> MemoryAccessReceipt { - self.memory.commit_if_admissible(domain) - } - - pub fn advance_epoch(&mut self) { - self.memory.epoch += 1; - let domains: Vec = self.memory.differentials.keys().cloned().collect(); - for dom in domains { - self.commit(&dom); - } - } - - pub fn query_all(&self, - artifact_path: &str, - ) -> HashMap> { - let mut results = HashMap::new(); - for (domain, bank) in &self.memory.ahead.banks { - let cells: Vec<&CodeCell> = bank - .cells - .iter() - .filter(|c| c.artifact_path.contains(artifact_path)) - .collect(); - if !cells.is_empty() { - results.insert(domain.clone(), cells); - } - } - results - } - - pub fn active_count(&self, domain: &str) -> usize { - self.memory - .ahead - .banks - .get(domain) - .map(|b| b.active_count) - .unwrap_or(0) - } - - pub fn capacity(&self) -> usize { - self.capacity - } - - pub fn memory(&self) -> &DualMapMemory { - &self.memory - } -} - -pub fn load_for_hermes( - project_root: &Path, - memory_path: &Path, -) -> CodebaseMemoryAdapter { - std::fs::create_dir_all(memory_path.parent().unwrap_or(memory_path)).ok(); - let mut adapter = CodebaseMemoryAdapter::load_or_init(memory_path); - for dom in CodeDomain::all() { - let dom_path = project_root.join(dom.as_str()); - if !dom_path.is_dir() { continue; } - for entry in WalkDir::new(&dom_path).into_iter().filter_map(|e| e.ok()) { - if !entry.file_type().is_file() { continue; } - let path = entry.path(); - let ext = path.extension().and_then(|s| s.to_str()); - let artifact_type = ArtifactType::from_extension(ext); - adapter.observe( - dom.as_str(), - artifact_type.as_str(), - path.to_string_lossy().as_ref(), - Q16_16::ZERO, Q16_16::ONE, Q16_16::ZERO, - ); - } - } - adapter.save(memory_path).ok(); - adapter -} - -fn file_hash(path: &str) -> String { - match fs::read(path) { - Ok(bytes) => { - let hash = Sha256::digest(&bytes); - format!("{:x}", hash)[..16].to_string() - } - Err(_) => String::new(), - } -} - -fn now_ms() -> u64 { - std::time::SystemTime::now() - .duration_since(std::time::UNIX_EPOCH) - .unwrap_or_default() - .as_millis() as u64 -} diff --git a/4-Infrastructure/shim/codebase-memory/src/lib.rs b/4-Infrastructure/shim/codebase-memory/src/lib.rs deleted file mode 100644 index ba4cf8df..00000000 --- a/4-Infrastructure/shim/codebase-memory/src/lib.rs +++ /dev/null @@ -1,8 +0,0 @@ -pub mod adapter; -pub mod types; - -pub use adapter::{load_for_hermes, CodebaseMemoryAdapter}; -pub use types::{ - ArtifactType, CodeCell, CodeDomain, CommitResult, DomainBank, DomainScarDifferential, - DomainScarField, DualMapMemory, FAMMResult, MemoryAccessReceipt, Q16_16, -}; diff --git a/4-Infrastructure/shim/codebase-memory/src/main.rs b/4-Infrastructure/shim/codebase-memory/src/main.rs deleted file mode 100644 index f09c0fc9..00000000 --- a/4-Infrastructure/shim/codebase-memory/src/main.rs +++ /dev/null @@ -1,45 +0,0 @@ -use std::env; -use std::path::Path; - -use codebase_memory::adapter::load_for_hermes; - -fn main() { - let args: Vec = env::args().collect(); - let root = if args.len() > 1 { - &args[1] - } else { - "." - }; - let memory_path = Path::new(root).join(".hermes").join("codebase_memory.json"); - let mut adapter = load_for_hermes(Path::new(root), &memory_path); - - println!("[hermes-memory] Loaded adapter for {}", root); - println!("[hermes-memory] Domains: 7"); - for dom in codebase_memory::types::CodeDomain::all() { - println!( - " {}: {} active / {} capacity", - dom.as_str(), - adapter.active_count(dom.as_str()), - adapter.capacity() - ); - } - - println!("\n--- Sample query: AGENTS.md ---"); - let results = adapter.query_all("AGENTS.md"); - for (domain, cells) in &results { - println!(" {}: {} matches", domain, cells.len()); - } - - println!("\n--- Committing all domains ---"); - for dom in codebase_memory::types::CodeDomain::all() { - let receipt = adapter.commit(dom.as_str()); - println!( - " {}: success={} invariant={}", - dom.as_str(), - receipt.success, - receipt.invariant - ); - } - - println!("\n[hermes-memory] Done."); -} diff --git a/4-Infrastructure/shim/codebase-memory/src/types.rs b/4-Infrastructure/shim/codebase-memory/src/types.rs deleted file mode 100644 index 2fb1ff66..00000000 --- a/4-Infrastructure/shim/codebase-memory/src/types.rs +++ /dev/null @@ -1,579 +0,0 @@ -// Core types for FAMM-based codebase memory - -use serde::{Deserialize, Serialize}; -use std::collections::HashMap; - -// ============================================================================ -// Q16_16 Fixed-Point Arithmetic -// ============================================================================ - -/// Q16.16 fixed-point representation. -/// Raw value: 0x00010000 = 1.0, range [-32768, 32767.999985]. -#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)] -pub struct Q16_16(pub u32); - -impl Q16_16 { - pub const ZERO: Q16_16 = Q16_16(0); - pub const ONE: Q16_16 = Q16_16(0x0001_0000); - - pub fn from_nat(n: u32) -> Self { - Q16_16(n.saturating_mul(65536)) - } - - pub fn from_float(f: f64) -> Self { - let raw = (f * 65536.0).round() as i64; - let clamped = raw.max(0).min(u32::MAX as i64) as u32; - Q16_16(clamped) - } - - pub fn add(&self, other: Q16_16) -> Self { - Q16_16(self.0.saturating_add(other.0)) - } - - pub fn sub(&self, other: Q16_16) -> Self { - Q16_16(self.0.saturating_sub(other.0)) - } - - pub fn mul(&self, other: Q16_16) -> Self { - let a = self.0 as u64; - let b = other.0 as u64; - Q16_16(((a * b) >> 16).min(0xFFFF_FFFF) as u32) - } - - pub fn lt(&self, other: Q16_16) -> bool { - (self.0 as i32) < (other.0 as i32) - } - - pub fn le(&self, other: Q16_16) -> bool { - (self.0 as i32) <= (other.0 as i32) - } - - pub fn gt(&self, other: Q16_16) -> bool { - (self.0 as i32) > (other.0 as i32) - } - - pub fn to_f64(&self) -> f64 { - (self.0 as f64) / 65536.0 - } -} - -// ============================================================================ -// Domain Enumeration -// ============================================================================ - -#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Serialize, Deserialize)] -#[serde(rename_all = "PascalCase")] -pub enum CodeDomain { - CoreFormalism = 0, - Distributed = 1, - SearchSpace = 2, - MathModels = 3, - Infrastructure = 4, - Applications = 5, - Documentation = 6, -} - -impl CodeDomain { - pub fn as_str(&self) -> &'static str { - match self { - CodeDomain::CoreFormalism => "0-Core-Formalism", - CodeDomain::Distributed => "1-Distributed-Systems", - CodeDomain::SearchSpace => "2-Search-Space", - CodeDomain::MathModels => "3-Mathematical-Models", - CodeDomain::Infrastructure => "4-Infrastructure", - CodeDomain::Applications => "5-Applications", - CodeDomain::Documentation => "6-Documentation", - } - } - - pub fn all() -> &'static [CodeDomain] { - static DOMAINS: [CodeDomain; 7] = [ - CodeDomain::CoreFormalism, - CodeDomain::Distributed, - CodeDomain::SearchSpace, - CodeDomain::MathModels, - CodeDomain::Infrastructure, - CodeDomain::Applications, - CodeDomain::Documentation, - ]; - &DOMAINS - } -} - -// ============================================================================ -// Artifact Types -// ============================================================================ - -#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)] -pub enum ArtifactType { - Lean, Python, Markdown, Json, Yaml, Toml, Rust, Cpp, Verilog, - Shell, Dockerfile, Config, Receipt, Other, -} - -impl ArtifactType { - pub fn from_extension(ext: Option<&str>) -> Self { - match ext { - Some("lean") => ArtifactType::Lean, - Some("py") => ArtifactType::Python, - Some("md") => ArtifactType::Markdown, - Some("json") => ArtifactType::Json, - Some("yaml") | Some("yml") => ArtifactType::Yaml, - Some("toml") => ArtifactType::Toml, - Some("rs") => ArtifactType::Rust, - Some("cpp") | Some("cc") | Some("cxx") => ArtifactType::Cpp, - Some("v") => ArtifactType::Verilog, - Some("sh") => ArtifactType::Shell, - Some("Dockerfile") => ArtifactType::Dockerfile, - Some("cfg") => ArtifactType::Config, - _ => ArtifactType::Other, - } - } - - pub fn as_str(&self) -> &'static str { - match self { - ArtifactType::Lean => ".lean", - ArtifactType::Python => ".py", - ArtifactType::Markdown => ".md", - ArtifactType::Json => ".json", - ArtifactType::Yaml => ".yaml", - ArtifactType::Toml => ".toml", - ArtifactType::Rust => ".rs", - ArtifactType::Cpp => ".cpp", - ArtifactType::Verilog => ".v", - ArtifactType::Shell => ".sh", - ArtifactType::Dockerfile => "Dockerfile", - ArtifactType::Config => ".cfg", - ArtifactType::Receipt => ".receipt.json", - ArtifactType::Other => "", - } - } -} - -// ============================================================================ -// CodeCell -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct CodeCell { - pub artifact_path: String, - pub artifact_type: String, - pub data: Q16_16, - pub delay: Q16_16, - pub delay_mass: Q16_16, - pub delay_weight: Q16_16, - pub version_hash: String, - pub last_accessed: u64, - pub access_count: u64, - pub receipt_bound: bool, -} - -impl CodeCell { - pub fn default_cell() -> Self { - CodeCell { - artifact_path: String::new(), - artifact_type: ArtifactType::Other.as_str().to_string(), - data: Q16_16::ZERO, - delay: Q16_16::ONE, - delay_mass: Q16_16::ZERO, - delay_weight: Q16_16::ONE, - version_hash: String::new(), - last_accessed: 0, - access_count: 0, - receipt_bound: true, - } - } -} - -// ============================================================================ -// FAMM Result -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct FAMMResult { - pub success: bool, - pub value: Option, - pub cost: u32, - pub invariant: String, -} - -impl FAMMResult { - pub fn fail(domain: &str, reason: &str) -> Self { - FAMMResult { - success: false, - value: None, - cost: 0xFFFF, - invariant: format!("{domain}: {reason}"), - } - } -} - -// ============================================================================ -// Receipt -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct MemoryAccessReceipt { - pub timestamp: u64, - pub domain: String, - pub action: String, - pub path: String, - pub success: bool, - pub cost: u32, - pub invariant: String, - pub data_value: u32, - pub thermal_ok: bool, -} - -impl MemoryAccessReceipt { - pub fn new_fail(domain: &str, reason: &str) -> Self { - MemoryAccessReceipt { - timestamp: 0, - domain: domain.to_string(), - action: "blocked".to_string(), - path: reason.to_string(), - success: false, - cost: 0xFFFF, - invariant: reason.to_string(), - data_value: 0, - thermal_ok: false, - } - } -} - -// ============================================================================ -// Domain Bank -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct DomainBank { - pub domain: String, - pub cells: Vec, - pub size: usize, - pub active_count: usize, - pub max_delay: Q16_16, - pub thermal_budget: Q16_16, - pub current_stress: Q16_16, - pub heatsink_halt: bool, -} - -impl DomainBank { - pub fn new(domain: CodeDomain, capacity: usize) -> Self { - DomainBank { - domain: domain.as_str().to_string(), - cells: vec![CodeCell::default_cell(); capacity], - size: capacity, - active_count: 0, - max_delay: Q16_16::from_nat(1000), - thermal_budget: Q16_16::from_nat(5000), - current_stress: Q16_16::ZERO, - heatsink_halt: false, - } - } - - pub fn find_index(&self, path: &str) -> Option { - self.cells.iter().position(|c| c.artifact_path == path) - } - - pub fn next_free(&self) -> Option { - self.cells.iter().position(|c| c.artifact_path.is_empty()) - } - - pub fn read(&self, idx: usize) -> FAMMResult { - if idx >= self.cells.len() { - return FAMMResult::fail(&self.domain, "out_of_bounds"); - } - FAMMResult { - success: true, - value: Some(self.cells[idx].data), - cost: 0x0000_1000, - invariant: format!("{}: delay={}, mass={}", - &self.domain, self.cells[idx].delay.0, self.cells[idx].delay_mass.0), - } - } - - pub fn write(&mut self, idx: usize, cell: CodeCell) -> FAMMResult { - if idx >= self.cells.len() { - return FAMMResult::fail(&self.domain, "out_of_bounds"); - } - if self.heatsink_halt { - return FAMMResult::fail(&self.domain, "JUDGE_PAUSE thermal overload"); - } - let mass = cell.delay_mass; - self.current_stress = self.current_stress.add(mass); - if !cell.artifact_path.is_empty() { - self.active_count += 1; - } - self.cells[idx] = cell; - FAMMResult { - success: true, - value: Some(self.cells[idx].data), - cost: 0x0000_1000, - invariant: format!("{}: written idx={}", &self.domain, idx), - } - } - - pub fn prune(&mut self) -> usize { - let before = self.active_count; - self.cells.retain(|c| c.artifact_path.is_empty() || c.delay.lt(self.max_delay)); - self.active_count = self.cells.iter().filter(|c| !c.artifact_path.is_empty()).count(); - while self.cells.len() < self.size { - self.cells.push(CodeCell::default_cell()); - } - before.saturating_sub(self.active_count) - } - - pub fn check_thermal(&self) -> (bool, String) { - if self.current_stress.gt(self.thermal_budget) || self.heatsink_halt { - (false, "JUDGE_PAUSE: Thermal budget exceeded".to_string()) - } else { - (true, "BUILDER_ADD: Within thermal budget".to_string()) - } - } -} - -// ============================================================================ -// Scar Field -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct DomainScarField { - pub domain: String, - pub residuals: Vec, - pub total: Q16_16, - pub sorry_count: u64, - pub todo_count: u64, - pub gap_count: u64, - pub last_updated: u64, -} - -impl DomainScarField { - pub fn new(domain: CodeDomain) -> Self { - DomainScarField { - domain: domain.as_str().to_string(), - residuals: Vec::new(), - total: Q16_16::ZERO, - sorry_count: 0, - todo_count: 0, - gap_count: 0, - last_updated: 0, - } - } - - pub fn accumulate(&mut self, scar: Q16_16) { - self.residuals.push(scar); - self.total = self.total.add(scar); - self.last_updated += 1; - } -} - -// ============================================================================ -// Memory State -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct CodebaseMemoryState { - pub banks: HashMap, - pub scar_fields: HashMap, - pub epoch: u64, - pub timestamp: u64, - pub is_serialized: bool, -} - -impl CodebaseMemoryState { - pub fn new(capacity_per_domain: usize) -> Self { - let mut banks = HashMap::new(); - let mut scars = HashMap::new(); - for dom in CodeDomain::all() { - banks.insert(dom.as_str().to_string(), DomainBank::new(*dom, capacity_per_domain)); - scars.insert(dom.as_str().to_string(), DomainScarField::new(*dom)); - } - CodebaseMemoryState { - banks, - scar_fields: scars, - epoch: 0, - timestamp: 0, - is_serialized: false, - } - } - - pub fn check_thermal(&self) -> (bool, String) { - for bank in self.banks.values() { - let (ok, msg) = bank.check_thermal(); - if !ok { return (false, msg); } - } - (true, "BUILDER_ADD: All domains within thermal budget".to_string()) - } -} - -// ============================================================================ -// Commits -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub enum CommitResult { - Admit { reason: String }, - Hold { reason: String }, - Block { reason: String }, -} - -// ============================================================================ -// Differentials -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct DomainScarDifferential { - pub domain: String, - pub ahead_scar: DomainScarField, - pub behind_scar: DomainScarField, - pub differential: Q16_16, - pub epsilon: Q16_16, - pub epoch: u64, -} - -// ============================================================================ -// Dual Map -// ============================================================================ - -#[derive(Debug, Clone, Serialize, Deserialize)] -pub struct DualMapMemory { - pub ahead: CodebaseMemoryState, - pub behind: CodebaseMemoryState, - pub differentials: HashMap, - pub global_epsilon: Q16_16, - pub commit_queue: Vec, - pub epoch: u64, -} - -impl DualMapMemory { - pub fn new(capacity: usize) -> Self { - let mut diffs = HashMap::new(); - for dom in CodeDomain::all() { - diffs.insert( - dom.as_str().to_string(), - DomainScarDifferential { - domain: dom.as_str().to_string(), - ahead_scar: DomainScarField::new(*dom), - behind_scar: DomainScarField::new(*dom), - differential: Q16_16::ZERO, - epsilon: Q16_16::from_nat(50), - epoch: 0, - }, - ); - } - DualMapMemory { - ahead: CodebaseMemoryState::new(capacity), - behind: CodebaseMemoryState::new(capacity), - differentials: diffs, - global_epsilon: Q16_16::from_nat(50), - commit_queue: Vec::new(), - epoch: 0, - } - } - - pub fn commit_if_admissible(&mut self, domain: &str) -> MemoryAccessReceipt { - let dsd = match self.differentials.get(domain) { - Some(d) => d.clone(), - None => return MemoryAccessReceipt::new_fail(domain, "domain_not_found"), - }; - let abs_diff = if dsd.differential.lt(Q16_16::ZERO) { - Q16_16::ZERO.sub(dsd.differential) - } else { - dsd.differential - }; - if abs_diff.le(dsd.epsilon) { - if let Some(bank) = self.ahead.banks.get(domain).cloned() { - self.behind.banks.insert(domain.to_string(), bank); - } - if let Some(scar) = self.ahead.scar_fields.get(domain).cloned() { - self.behind.scar_fields.insert(domain.to_string(), scar); - } - self.commit_queue.push("admit".to_string()); - MemoryAccessReceipt { - timestamp: now_ms(), - domain: domain.to_string(), - action: "admitted".to_string(), - path: "commit".to_string(), - success: true, - cost: 0x0000_1000, - invariant: format!("admit: |Delta|={} <= epsilon={}", abs_diff.0, dsd.epsilon.0), - data_value: 0, - thermal_ok: true, - } - } else { - self.commit_queue.push("hold".to_string()); - MemoryAccessReceipt { - timestamp: now_ms(), - domain: domain.to_string(), - action: "blocked".to_string(), - path: "commit".to_string(), - success: false, - cost: 0x0000_1000, - invariant: format!("hold: |Delta|={} > epsilon={}", abs_diff.0, dsd.epsilon.0), - data_value: 0, - thermal_ok: true, - } - } - } -} - -fn now_ms() -> u64 { - std::time::SystemTime::now() - .duration_since(std::time::UNIX_EPOCH) - .unwrap_or_default() - .as_millis() as u64 -} - -// ============================================================================ -// Tests -// ============================================================================ - -#[cfg(test)] -mod tests { - use super::*; - - #[test] - fn test_q16_16_basic() { - let a = Q16_16::from_nat(5); - let b = Q16_16::from_nat(3); - let sum = a.add(b); - assert_eq!(sum.0, (5u32 + 3u32) * 65536); - } - - #[test] - fn test_code_domain_all() { - let domains = CodeDomain::all(); - assert_eq!(domains.len(), 7); - assert_eq!(domains[0], CodeDomain::CoreFormalism); - } - - #[test] - fn test_artifact_type_from_ext() { - assert_eq!(ArtifactType::from_extension(Some("lean")), ArtifactType::Lean); - assert_eq!(ArtifactType::from_extension(Some("unknown")), ArtifactType::Other); - } - - #[test] - fn test_domain_bank() { - let mut bank = DomainBank::new(CodeDomain::CoreFormalism, 10); - assert_eq!(bank.size, 10); - let mut cell = CodeCell::default_cell(); - cell.artifact_path = "foo".to_string(); - let result = bank.write(0, cell); - assert!(result.success); - assert_eq!(bank.active_count, 1); - } - - #[test] - fn test_memory_state() { - let state = CodebaseMemoryState::new(100); - assert_eq!(state.banks.len(), 7); - } - - #[test] - fn test_dual_map_commit() { - let mut dmm = DualMapMemory::new(50); - let receipt = dmm.commit_if_admissible("0-Core-Formalism"); - assert!(receipt.success); - assert!(receipt.invariant.contains("admit")); - } -} diff --git a/4-Infrastructure/shim/collatz_couch_route_pressure_probe.py b/4-Infrastructure/shim/collatz_couch_route_pressure_probe.py deleted file mode 100644 index 26777c9d..00000000 --- a/4-Infrastructure/shim/collatz_couch_route_pressure_probe.py +++ /dev/null @@ -1,374 +0,0 @@ -#!/usr/bin/env python3 -"""Collatz/COUCH route-pressure probe. - -COUCH is local project terminology here: a finite route-pressure witness over -coupling regimes. This probe keeps the existing COUCH witnesses frozen and uses -Collatz only as an integer-shadow roughness scheduler over those finite packets. -It does not promote the continuous COUCH equation or the Collatz conjecture. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "collatz_couch_route_pressure" -REGISTRY = OUT_DIR / "collatz_couch_route_pressure_registry.json" -RECEIPT = OUT_DIR / "collatz_couch_route_pressure_receipt.json" -SUMMARY = OUT_DIR / "collatz_couch_route_pressure.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Collatz COUCH Route Pressure Probe.tid" - -SOURCE_REFS = [ - REPO / "0-Core-Formalism" / "lean" / "Semantics" / "Semantics" / "CouchFilterNormalization.lean", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "COUCH Family.tid", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "F-Number COUCH.tid", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Route-Pressure COUCH Gate.tid", - REPO / "shared-data" / "data" / "collatz_ladder_shadow_filter" / "collatz_ladder_shadow_filter_receipt.json", - REPO / "shared-data" / "data" / "godel_gauntlet_safety_condition" / "godel_gauntlet_safety_condition_receipt.json", -] - -COUCH_REGIMES = [ - { - "regime": "kappa050", - "kappa_milli": 500, - "f_couch_milli": 18085, - "u_rot_milli": 8785, - "r_value_milli": 1000, - "p_couch_milli": 25870, - "routing_mode": "exploitLocal", - }, - { - "regime": "kappa100", - "kappa_milli": 1000, - "f_couch_milli": 18163, - "u_rot_milli": 9552, - "r_value_milli": 1000, - "p_couch_milli": 26715, - "routing_mode": "exploitLocal", - }, - { - "regime": "kappa150", - "kappa_milli": 1500, - "f_couch_milli": 18274, - "u_rot_milli": 10322, - "r_value_milli": 1000, - "p_couch_milli": 27596, - "routing_mode": "exploreAtlas", - }, - { - "regime": "kappa200", - "kappa_milli": 2000, - "f_couch_milli": 18419, - "u_rot_milli": 11093, - "r_value_milli": 1000, - "p_couch_milli": 28512, - "routing_mode": "exploreAtlas", - }, - { - "regime": "kappa250", - "kappa_milli": 2500, - "f_couch_milli": 18596, - "u_rot_milli": 11867, - "r_value_milli": 1000, - "p_couch_milli": 29463, - "routing_mode": "rejectDivergent", - }, -] - -SIGMA_MAX = 120 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def collatz_path(n: int, max_steps: int = 512) -> dict[str, Any]: - if n < 1: - raise ValueError("Collatz COUCH shadow requires a positive integer") - path = [n] - parity = [] - current = n - for _ in range(max_steps): - if current == 1: - break - if current % 2 == 0: - parity.append("E") - current //= 2 - else: - parity.append("O") - current = 3 * current + 1 - path.append(current) - return { - "start": n, - "steps": len(path) - 1, - "reached_one": path[-1] == 1, - "max_value": max(path), - "odd_count": parity.count("O"), - "even_count": parity.count("E"), - "parity_prefix": "".join(parity[:64]), - "path_hash": hash_obj(path), - } - - -def classify(regime: dict[str, Any], shadow: dict[str, Any]) -> str: - if regime["routing_mode"] == "rejectDivergent": - return "REJECT_COUCH_DIVERGENT" - if not shadow["reached_one"]: - return "HOLD_COLLATZ_BOUND_EXCEEDED" - if shadow["steps"] > SIGMA_MAX: - return "HOLD_COLLATZ_COUCH_ROUGHNESS" - if regime["routing_mode"] == "exploreAtlas": - return "HOLD_COUCH_ATLAS_ROUTE" - return "ADMIT_LOCAL_COUCH_COLLATZ_HINT" - - -def build_case(regime: dict[str, Any]) -> dict[str, Any]: - shadow = collatz_path(regime["p_couch_milli"]) - decision = classify(regime, shadow) - case = { - **regime, - "integer_shadow": { - "rule": "n_k = P_COUCH(kappa)", - "value": regime["p_couch_milli"], - }, - "collatz_shadow": shadow, - "sigma_max": SIGMA_MAX, - "decision": decision, - "route_role": "roughness_scheduler_only", - } - case["case_hash"] = hash_obj({k: v for k, v in case.items() if k != "case_hash"}) - return case - - -def build_registry() -> dict[str, Any]: - cases = [build_case(regime) for regime in COUCH_REGIMES] - decisions = sorted({case["decision"] for case in cases}) - return { - "schema": "collatz_couch_route_pressure_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "COUCH supplies finite route pressure over local coupling regimes. " - "Collatz supplies deterministic recursion roughness over the finite " - "COUCH pressure packets. This is a scheduler/guardrail diagnostic, " - "not a proof of the continuous COUCH equation, the Collatz conjecture, " - "mechanical safety, or Hutter compression." - ), - "canonical_statement": ( - "COUCH decides local, atlas, or divergent route pressure; Collatz " - "then probes each finite P_COUCH packet for integer-shadow recursion " - "roughness. Local COUCH routes with tame shadows may be used as local " - "hints, atlas routes remain HOLD for atlas verification, and divergent " - "COUCH routes reject before Collatz can rescue them." - ), - "couch_equations": { - "f_number": "F_COUCH(kappa)=avg_curvature_milli(kappa)+max_curvature_milli(kappa)+FAMM_frustration_milli", - "u_rotated": "U_rot(kappa)=avg_curvature_milli(kappa)+kappa_milli*avg_norm_milli(kappa)/1000", - "route_pressure": "P_COUCH(kappa)=F_COUCH(kappa)+U_rot(kappa)-R_value", - "local_atlas_reject_thresholds": { - "atlas_threshold_milli": 27000, - "reject_threshold_milli": 29000, - }, - }, - "collatz_equations": { - "integer_shadow": "n_k=P_COUCH(kappa)", - "ordinary_step": "C(n)=n/2 if even, 3n+1 if odd", - "stopping_time": "sigma_C(n)=min m such that C^m(n)=1", - "filter": "A_CC(kappa)=COUCH_mode(kappa) and sigma_C(P_COUCH(kappa))<=sigma_max", - }, - "cases": cases, - "case_root": hash_obj([case["case_hash"] for case in cases]), - "aggregates": { - "case_count": len(cases), - "admit_count": sum(1 for case in cases if case["decision"].startswith("ADMIT")), - "hold_count": sum(1 for case in cases if case["decision"].startswith("HOLD")), - "reject_count": sum(1 for case in cases if case["decision"].startswith("REJECT")), - "decision_counts": { - decision: sum(1 for case in cases if case["decision"] == decision) - for decision in decisions - }, - "max_stopping_time": max(case["collatz_shadow"]["steps"] for case in cases), - "max_shadow_value": max(case["collatz_shadow"]["max_value"] for case in cases), - }, - "decision": "ADMIT_COLLATZ_COUCH_ROUTE_PRESSURE_DIAGNOSTIC", - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "collatz_couch_route_pressure_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "case_root": registry["case_root"], - "aggregates": registry["aggregates"], - "decision": registry["decision"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Collatz COUCH Route Pressure Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Case root: `{registry['case_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equations", - "", - f"- `{registry['couch_equations']['f_number']}`", - f"- `{registry['couch_equations']['route_pressure']}`", - f"- `{registry['collatz_equations']['integer_shadow']}`", - f"- `{registry['collatz_equations']['ordinary_step']}`", - f"- `{registry['collatz_equations']['filter']}`", - "", - "## Sweep", - "", - "| Regime | F_COUCH | U_rot | P_COUCH | COUCH mode | Collatz steps | Max value | Decision |", - "|---|---:|---:|---:|---|---:|---:|---|", - ] - for case in registry["cases"]: - lines.append( - f"| {case['regime']} | {case['f_couch_milli']} | {case['u_rot_milli']} | " - f"{case['p_couch_milli']} | {case['routing_mode']} | " - f"{case['collatz_shadow']['steps']} | {case['collatz_shadow']['max_value']} | " - f"{case['decision']} |" - ) - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Cases: `{registry['aggregates']['case_count']}`", - f"- Admitted local hints: `{registry['aggregates']['admit_count']}`", - f"- HOLD routes: `{registry['aggregates']['hold_count']}`", - f"- Rejected routes: `{registry['aggregates']['reject_count']}`", - f"- Max stopping time: `{registry['aggregates']['max_stopping_time']}`", - f"- Max shadow value: `{registry['aggregates']['max_shadow_value']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "created: 20260509000000000", - "modified: 20260509000000000", - "tags: ResearchStack COUCH Collatz Hutter Routing Probe", - "title: Collatz COUCH Route Pressure Probe", - "type: text/vnd.tiddlywiki", - "", - "! Collatz COUCH Route Pressure Probe", - "", - "COUCH is used with its local formal meaning: a finite route-pressure gate over coupling regimes.", - "", - f"* Decision: `{receipt['decision']}`", - f"* Receipt hash: `{receipt['receipt_hash']}`", - f"* Case root: `{registry['case_root']}`", - f"* Registry: `{rel(REGISTRY)}`", - f"* Receipt: `{rel(RECEIPT)}`", - "", - "!! Rule", - "", - "Collatz is a deterministic roughness scheduler over finite `P_COUCH` packets. It cannot rescue a divergent COUCH route and does not prove Collatz or the continuous COUCH equation.", - "", - "```", - registry["couch_equations"]["route_pressure"], - registry["collatz_equations"]["integer_shadow"], - registry["collatz_equations"]["filter"], - "```", - "", - "!! Sweep", - "", - "| Regime | P_COUCH | COUCH mode | Collatz steps | Decision |", - "|---|---:|---|---:|---|", - ] - for case in registry["cases"]: - lines.append( - f"| {case['regime']} | {case['p_couch_milli']} | {case['routing_mode']} | " - f"{case['collatz_shadow']['steps']} | {case['decision']} |" - ) - lines.extend( - [ - "", - "!! Links", - "", - "* [[COUCH Family]]", - "* [[F-Number COUCH]]", - "* [[Route-Pressure COUCH Gate]]", - "* [[Collatz Ladder Shadow Filter]]", - "* [[Godel Gauntlet Safety Condition Probe]]", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "case_root": registry["case_root"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/collatz_ladder_shadow_filter_probe.py b/4-Infrastructure/shim/collatz_ladder_shadow_filter_probe.py deleted file mode 100644 index 35b6c978..00000000 --- a/4-Infrastructure/shim/collatz_ladder_shadow_filter_probe.py +++ /dev/null @@ -1,321 +0,0 @@ -#!/usr/bin/env python3 -"""Collatz ladder shadow-filter receipt for witness roughness. - -Collatz is used here as a deterministic integer-shadow filter, not as physics -or compression proof. A witness state is quantized to an integer; the Collatz -path gives a cheap roughness/traversal signal. The ladder generalization maps -reducible states downward, irreducible torsioned states upward with closure -repair, and unsafe states to a declared horizon. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "collatz_ladder_shadow_filter" -REGISTRY = OUT_DIR / "collatz_ladder_shadow_filter_registry.json" -RECEIPT = OUT_DIR / "collatz_ladder_shadow_filter_receipt.json" -SUMMARY = OUT_DIR / "collatz_ladder_shadow_filter.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Collatz Ladder Shadow Filter.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "godel_gauntlet_safety_condition" / "godel_gauntlet_safety_condition_receipt.json", - REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" / "torsion_interval_gaussian_splat_witness_receipt.json", - REPO / "shared-data" / "data" / "pixelwell_external_prior" / "pixelwell_external_prior_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def collatz_path(n: int, max_steps: int = 512) -> dict[str, Any]: - if n < 1: - raise ValueError("Collatz shadow requires a positive integer") - path = [n] - parity = [] - current = n - for _ in range(max_steps): - if current == 1: - break - if current % 2 == 0: - parity.append("E") - current //= 2 - else: - parity.append("O") - current = 3 * current + 1 - path.append(current) - reached_one = path[-1] == 1 - odd_count = parity.count("O") - even_count = parity.count("E") - return { - "start": n, - "steps": len(path) - 1, - "reached_one": reached_one, - "max_value": max(path), - "odd_count": odd_count, - "even_count": even_count, - "parity_prefix": "".join(parity[:64]), - "path_hash": hash_obj(path), - } - - -def quantize_state(state: dict[str, Any]) -> int: - payload = stable_json(state).encode("utf-8") - # Keep values small enough for fixture readability while still deterministic. - return int.from_bytes(hashlib.sha256(payload).digest()[:4], "big") % 5000 + 2 - - -def classify_shadow(shadow: dict[str, Any], residual_risk: float, sigma_max: int) -> str: - if residual_risk >= 0.9: - return "HOLD_RESIDUAL_HORIZON" - if not shadow["reached_one"]: - return "HOLD_COLLATZ_BOUND_EXCEEDED" - if shadow["steps"] > sigma_max: - return "HOLD_RECURSION_ROUGHNESS" - if shadow["odd_count"] > shadow["even_count"]: - return "HOLD_ODD_BRANCH_BURST" - return "ADMIT_COLLATZ_SHADOW_FILTER" - - -def sample_state( - state_id: str, - ladder_rung: int, - torsion: float, - residual_risk: float, - axes: list[str], - sigma_max: int, -) -> dict[str, Any]: - state = { - "state_id": state_id, - "ladder_rung": ladder_rung, - "torsion": torsion, - "residual_risk": residual_risk, - "axes": axes, - } - n = quantize_state(state) - shadow = collatz_path(n) - decision = classify_shadow(shadow, residual_risk, sigma_max) - item = { - "state": state, - "integer_shadow": n, - "collatz_shadow": shadow, - "sigma_max": sigma_max, - "decision": decision, - "route_role": "roughness_hint_only", - } - item["sample_hash"] = hash_obj({k: v for k, v in item.items() if k != "sample_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - sigma_max = 120 - samples = [ - sample_state("boring_replay_root", 0, 0.05, 0.05, ["provenance", "byte_neighbor"], sigma_max), - sample_state("chirality_torsion_adapter", 2, 0.35, 0.22, ["chirality", "codec_torsion"], sigma_max), - sample_state("orientation_360_bump", 3, 0.44, 0.31, ["orientation_360_share", "semantic_class"], sigma_max), - sample_state("gaussian_splat_hotspot", 4, 0.72, 0.58, ["codec_torsion", "observer_chart"], sigma_max), - sample_state("residual_horizon_packet", 5, 0.93, 0.94, ["byte_neighbor", "observer_chart"], sigma_max), - ] - return { - "schema": "collatz_ladder_shadow_filter_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Collatz ladder shadow-filter diagnostic only. It uses Collatz paths as " - "deterministic integer-shadow roughness hints and traversal schedules. " - "It does not prove the Collatz conjecture, mechanical safety, or Hutter compression." - ), - "canonical_statement": ( - "Reducible states project downward, irreducible torsioned states expand " - "upward with a closure witness, and unsafe residuals terminate at a " - "declared horizon. Collatz parity is the integer-shadow scheduler for " - "testing recursion roughness." - ), - "collatz_equation": { - "integer_shadow": "n_k = Q(Omega_k)", - "ordinary_step": "C(n)=n/2 if even, 3n+1 if odd", - "stopping_time": "sigma_C(n)=min m such that C^m(n)=1", - "filter": "A_C(Omega)=1[sigma_C(Q(Omega)) <= sigma_max]", - }, - "ladder_generalization": { - "state": "Omega_k=(rho,G,Gamma,C,T,R_M,epsilon)_k", - "down_branch": "pi_{k-1}(Omega_k) when reducible/admissible", - "up_branch": "Phi_{k+1}(Omega_k)=3Omega+1_closure+Delta_T+epsilon_repair when torsioned/irreducible", - "horizon": "bottom if residual exceeds admissible boundary", - "terminal_classes": ["stable_invariant_attractor", "bounded_chart_cycle", "declared_non_admissible_boundary"], - }, - "samples": samples, - "shadow_root": hash_obj([item["sample_hash"] for item in samples]), - "aggregates": { - "sample_count": len(samples), - "admit_count": sum(1 for item in samples if item["decision"].startswith("ADMIT")), - "hold_count": sum(1 for item in samples if item["decision"].startswith("HOLD")), - "max_stopping_time": max(item["collatz_shadow"]["steps"] for item in samples), - "max_shadow_value": max(item["collatz_shadow"]["max_value"] for item in samples), - }, - "decision": "ADMIT_COLLATZ_LADDER_SHADOW_FILTER_DIAGNOSTIC", - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "collatz_ladder_shadow_filter_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "shadow_root": registry["shadow_root"], - "aggregates": registry["aggregates"], - "decision": registry["decision"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Collatz Ladder Shadow Filter", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Shadow root: `{registry['shadow_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Collatz Equation", - "", - ] - for key, value in registry["collatz_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Ladder Generalization", ""]) - for key, value in registry["ladder_generalization"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Samples", "", "| State | n | Steps | Odd | Even | Max | Decision |", "|---|---:|---:|---:|---:|---:|---|"]) - for item in registry["samples"]: - shadow = item["collatz_shadow"] - lines.append( - f"| `{item['state']['state_id']}` | {item['integer_shadow']} | {shadow['steps']} | " - f"{shadow['odd_count']} | {shadow['even_count']} | {shadow['max_value']} | `{item['decision']}` |" - ) - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Collatz Ladder ShadowFilter Hutter HOLD Receipt -title: Collatz Ladder Shadow Filter -type: text/vnd.tiddlywiki - -! Collatz Ladder Shadow Filter - -Durable runner: - -``` -4-Infrastructure/shim/collatz_ladder_shadow_filter_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Shadow root: - -``` -{receipt['shadow_root']} -``` - -!! Doctrine - -Collatz is used as a deterministic integer-shadow filter, not as physics or -compression proof. Short paths are recursion-tame route hints. Long paths, -odd-branch bursts, or residual horizons stay inspection/HOLD surfaces. - -!! Links - -* [[Godel Gauntlet Safety Condition Probe]] -* [[Torsion Interval Gaussian Splat Witness]] -* [[PixelWell External Prior]] -* [[Hutter Multidimensional Causal Chain]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "shadow_root": registry["shadow_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/combined_approach_equation_surface_probe.py b/4-Infrastructure/shim/combined_approach_equation_surface_probe.py deleted file mode 100644 index 25cd1bc3..00000000 --- a/4-Infrastructure/shim/combined_approach_equation_surface_probe.py +++ /dev/null @@ -1,1017 +0,0 @@ -#!/usr/bin/env python3 -"""Combined-approach equation surface probe. - -This probe asks whether the recently combined HOLD priors expose reusable -equation surfaces. It does not admit any equation as proven, predictive, or -safety-valid. It records candidate operators that can become experiments only -after replay, resource, provenance, and negative-control receipts close. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "combined_approach_equation_surface" -RECEIPT = OUT_DIR / "combined_approach_equation_surface_receipt.json" -SUMMARY = OUT_DIR / "combined_approach_equation_surface.md" -PAYLOAD_JSON = OUT_DIR / "combined_approach_equation_surface.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Combined Approach Equation Surface.tid" -) - -SOURCES = [ - REPO / "shared-data" / "data" / "external_ai_model_prior_ingest" / "external_ai_model_prior_ingest_receipt.json", - REPO / "shared-data" / "data" / "torsion_indexed_network_witness_topology" / "torsion_indexed_network_witness_topology_receipt.json", - REPO / "shared-data" / "data" / "network_topology_model_reweighting" / "network_topology_model_reweighting_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", - REPO / "shared-data" / "data" / "hutter_frame_invariant_root" / "hutter_frame_invariant_root_receipt.json", - REPO / "shared-data" / "data" / "hutter_differential_frame_chain" / "hutter_differential_frame_chain_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", - REPO / "shared-data" / "data" / "phonon_music_logogram_layer" / "phonon_music_logogram_layer_receipt.json", - REPO / "shared-data" / "network_topology_database.json", - REPO / "3-Mathematical-Models" / "fiber_optic_vibrational_tensor" / "Fundamental_Network_Topology_Equation.md", - REPO / "6-Documentation" / "wiki" / "Network-Topology-Theory.md", - REPO / "shared-data" / "data" / "x86_emulator_eigen_baseline" / "x86_emulator_eigen_baseline_receipt.json", - REPO / "shared-data" / "data" / "modly_text_to_cad_bridge" / "modly_text_to_cad_bridge_receipt.json", - REPO / "shared-data" / "data" / "transcriptformer_evolutionary_prior" / "transcriptformer_evolutionary_prior_receipt.json", - REPO / "shared-data" / "data" / "parquet_logogram_efficiency" / "parquet_logogram_efficiency_receipt.json", - REPO / "shared-data" / "data" / "parquet_logogram_eigenprobe" / "parquet_logogram_eigenprobe_receipt.json", -] - -CANDIDATES = [ - { - "equation_id": "receipt_weighted_torsion_route_field", - "equation": "E_R(N_T)=sum_i w_i^R * alpha_i * f_i(N_T), where w_i^R=normalize(w_i * m_i)", - "reads_as": ( - "A topology or witness graph is scored through receipt-reweighted methodology " - "weights at torsion/state-advance frame T." - ), - "combined_sources": [ - "network_topology_model_reweighting", - "torsion_indexed_network_witness_topology", - ], - "use_as": "conservative routing score before any external prior is allowed to steer search", - "decision": "HOLD_COEFFICIENT_RECEIPT_DEBT", - "promotion_gate": "requires dataset receipts, coefficient derivation, negative controls, and prediction/outcome replay", - }, - { - "equation_id": "soft_parallel_route_revision", - "equation": "X_{k+1}=R_theta(X_k, softmax(E_R(X_k)/tau), residual_k, receipt_root_k)", - "reads_as": ( - "A DMax-like soft parallel revision step can propose route/frame repairs, " - "but each revision must carry residual and receipt roots." - ), - "combined_sources": [ - "external_ai_model_prior_ingest:DMax.parallel_self_revision", - "receipt_weighted_torsion_route_field", - ], - "use_as": "parallel decoder/search scheduler for Hutter frame candidates", - "decision": "HOLD_EXTERNAL_DECODING_PRIOR", - "promotion_gate": "requires exact replay, byte accounting, baseline comparison, and resource envelope under prize rules", - }, - { - "equation_id": "torsion_indexed_admissibility_gate", - "equation": "A(Omega_T)=1[M(Omega_T)<=eps_mech] * E_ext(N_T) * 1[root(Omega_T)] * 1[rho(Omega_T)<=eps_risk]", - "reads_as": ( - "The route/load/proof state is admissible only when mechanics close, " - "extended topology remains bounded, witness roots recompute, and residual risk is below horizon." - ), - "combined_sources": [ - "torsion_clock", - "network_topology_equation", - "merkle_witness", - "underverse_guardrails", - ], - "use_as": "shared admissibility gate for network, load, and proof routes", - "decision": "HOLD_TORSION_CLOCK_BOUNDARY", - "promotion_gate": "requires local invariant mechanics or decode-closure fixture and explicit residual horizon", - }, - { - "equation_id": "long_range_sequence_function_memory_gate", - "equation": "F_seq(S)=H_boundary(S) * F_memory(S) * G_adapter(S, C_history, K_alpha)", - "reads_as": ( - "An NTv3-like long-range sequence/function prior can only enter as an adapter " - "multiplying boundary closure and bounded memory." - ), - "combined_sources": [ - "external_ai_model_prior_ingest:NTv3.long_range_sequence_function", - "holographic_boundary_bulk", - "fractional_memory", - ], - "use_as": "biological or text-sequence dependency prior for long-range Hutter structure", - "decision": "HOLD_BIORXIV_PREPRINT_PRIOR", - "promotion_gate": "requires source/model-card receipts, license check, local benchmark, and declared biological/text adapter", - }, - { - "equation_id": "landau_gauntlet_admission_operator", - "equation": "G(E)=1[derive_F0(E)] * 1[replay(E)] * 1[baseline(E)] * 1[resource(E)] * 1[residual(E)]", - "reads_as": ( - "A PhysMaster/LANDAU-style generated equation is admitted only if the gauntlet " - "can derive its primitive form, replay it, beat baselines, obey resource limits, and bound residuals." - ), - "combined_sources": [ - "external_ai_model_prior_ingest:PhysMaster.LANDAU_agent_trace", - "godel_gauntlet", - "foundation_forward_equation_compiler", - ], - "use_as": "equation candidate promotion gate", - "decision": "HOLD_EXTERNAL_AGENT_PRIOR", - "promotion_gate": "requires task trace, code artifact, numeric replay, critic failure log, and independent verifier", - }, - { - "equation_id": "expand_or_compress_ladder_operator", - "equation": "L(X)=pi_{k-1}(X) if closure and cost improve; Phi_{k+1}(X) if residual remains; bottom if root/NaN0/rollback fails", - "reads_as": ( - "The Collatz-style ladder is not number theory here; it is the search-control " - "law that decides whether a state compresses, expands, or terminates." - ), - "combined_sources": [ - "collatz_ladder_shadow_filter", - "torsion_indexed_network_witness_topology", - "underverse_variant_accounting", - ], - "use_as": "state-transition law for frame-invariant roots and M-JPEG-like independent frame decode", - "decision": "ADMIT_AS_HOLD_MODEL_CHART", - "promotion_gate": "requires executable fixture proving branch decisions are deterministic and receipt-bound", - }, - { - "equation_id": "adversarial_phase_safety_filter", - "equation": "P_safe(Y)=1[delay(Y)<=d_max] * 1[phase_energy(Y)<=p_max] * 1[no_disorientation_feedback(Y)]", - "reads_as": ( - "Audio, response-time, and phonon/logogram layers need an anti-music guard that " - "rejects delayed or phase-shaped feedback intended to impair the observer." - ), - "combined_sources": [ - "phonon_music_logogram_layer", - "underverse_variant_accounting", - "observer_chart_projection_guardrail", - ], - "use_as": "safety gate for phased audio, anti-BPM, and observer-feedback channels", - "decision": "HOLD_SAFETY_CONDITION", - "promotion_gate": "requires benign/negative controls, accessibility policy, and no-harm playback constraints", - }, - { - "equation_id": "cross_domain_prior_pressure", - "equation": "P_cross(X)=sum_j beta_j * gate_j(X) * adapter_j(X) - residual_cost(X)", - "reads_as": ( - "External priors may create search pressure only through declared gates and adapters, " - "with residual cost subtracted before any route is promoted." - ), - "combined_sources": [ - "external_ai_model_prior_ingest", - "underverse_variant_accounting", - "network_topology_model_reweighting", - ], - "use_as": "single accounting surface for outside model, biology, physics, and media priors", - "decision": "HOLD_EXTERNAL_MODEL_PRIOR", - "promotion_gate": "requires every beta, gate, adapter, and residual cost to be receipt-backed", - }, - { - "equation_id": "torsional_beaver_beta_step", - "equation": "Omega_secure=Psi_Beaver[B_theta tensor C_shared] xor Delta_privacy", - "reads_as": ( - "A Beaver-triple multiplication can be reinterpreted as a torsion-bearing " - "basis/context coupling step, as long as the original MPC privacy boundary " - "and exact closure equation remain explicit." - ), - "combined_sources": [ - "beaver_triples_cognitive_load_integrated_data", - "torsion_indexed_network_witness_topology", - "underverse_variant_accounting", - ], - "use_as": "privacy-preserving multiplication primitive for route-efficiency and eigenmode factors", - "decision": "HOLD_RAINBOW_RACCOON_DERIVATION", - "promotion_gate": "requires explicit lineage separation, independent MPC derivation, exact arithmetic fixture, and no copied implementation surface", - }, - { - "equation_id": "secure_cognitive_load_efficiency", - "equation": "E_secure(N)=E_ext(N)*exp(-zeta*L_inv_enhanced(N))", - "reads_as": ( - "The extended topology score is discounted by invariant-aware cognitive, " - "routing, memory, trajectory, and convergence-inhibition load." - ), - "combined_sources": [ - "network_topology_equation", - "beaver_triples_cognitive_load_integrated_data", - "decoder_reconstruction_core", - ], - "use_as": "secure distributed route score with explicit cognitive/load penalty", - "decision": "HOLD_COEFFICIENT_RECEIPT_DEBT", - "promotion_gate": "requires coefficient receipts, fixture workloads, baseline routes, and invariant failure negative controls", - }, - { - "equation_id": "inflight_delayline_famm_efficiency", - "equation": "E_inflight(N,eps,dl,FAMM)=E_secure(N)*Omega_AngrySphinx*Gamma_DelayLine*Phi_FAMM", - "reads_as": ( - "A route can be scored as secure inflight computation only after applying " - "defensive cost amplification, delay-line jitter/lag/salt loss, and FAMM route-memory pressure." - ), - "combined_sources": [ - "angrysphinx_delayline_famm_integrated_data", - "secure_cognitive_load_efficiency", - "hutter_torsion_clock_adaptation", - ], - "use_as": "HOLD equation for defensive inflight route accounting", - "decision": "HOLD_RAINBOW_RACCOON_DERIVATION", - "promotion_gate": "requires benign defensive threat model, resource envelope, timing fixture, and proof that no abusive traffic or resource harvesting is enabled", - }, - { - "equation_id": "minimal_node_resource_accounting_guard", - "equation": "R_account=R_inflight*(1-Omega_AngrySphinx)*(1-Gamma_DelayLine)*(1-Phi_FAMM)", - "reads_as": ( - "The resource term must be treated as an accounting guard, not permission " - "to consume third-party nodes. Any real use must be local, consented, and bounded." - ), - "combined_sources": [ - "angrysphinx_delayline_famm_integrated_data", - "hutter_prize_next_roadmap", - "godel_gauntlet_safety_condition", - ], - "use_as": "local resource-budget guard for prize-rule and safety accounting", - "decision": "HOLD_RESOURCE_AND_CONSENT_BOUNDARY", - "promotion_gate": "requires local-only fixture, explicit consent boundary, hard resource limits, and prize-rule byte/runtime receipts", - }, - { - "equation_id": "isomorphic_congestion_chunking", - "equation": "V_16=direct_sum_i C_i, n=ceil(16/chunk_size(congestion_level))", - "reads_as": ( - "During congestion, the 16D Rainbow Raccoon state can be decomposed into " - "adaptive chunks while preserving the structural relationships needed for reconstruction." - ), - "combined_sources": [ - "isomorphic_chunking_congestion", - "rainbow_raccoon_derivation", - "hutter_multidimensional_causal_chain", - ], - "use_as": "congestion-adaptive chunking law for frame/state transport", - "decision": "HOLD_ISOMORPHIC_CHUNKING", - "promotion_gate": "requires deterministic chunk boundaries, congestion fixture, reconstruction receipt, and exact source/root replay", - }, - { - "equation_id": "chunk_isomorphism_preservation_gate", - "equation": "G_iso(C_i,C_j)=1[norm_F(phi(C_i)-C_j)<=eps_isomorphic]", - "reads_as": ( - "A chunked state remains admissible only when the declared structure-preserving " - "map between chunks stays within the isomorphism tolerance." - ), - "combined_sources": [ - "isomorphic_chunking_congestion", - "torsion_indexed_admissibility_gate", - "underverse_variant_accounting", - ], - "use_as": "chunk reconstruction guard and negative-control target", - "decision": "HOLD_ISOMORPHISM_GATE", - "promotion_gate": "requires explicit phi definition, norm implementation, tolerance derivation, and failing negative controls", - }, - { - "equation_id": "chunked_energy_loss_objective", - "equation": "E_loss_min_chunked=min_strategy(E_loss_16D+E_loss_chunking+E_loss_reconstruction)", - "reads_as": ( - "The chunking strategy is selected by minimizing total loss from the base " - "16D projection, chunk boundaries, and reconstruction error." - ), - "combined_sources": [ - "rainbow_raccoon_energy_conservation", - "isomorphic_chunking_congestion", - "receipt_weighted_torsion_route_field", - ], - "use_as": "optimization objective for choosing no chunking, 2/4/8 chunks, redundancy, or alternate projection", - "decision": "HOLD_OPTIMIZATION_OBJECTIVE", - "promotion_gate": "requires measured loss terms, strategy enumeration, baseline comparison, and receipt-bound minimizer", - }, - { - "equation_id": "degraded_chunk_reconstruction_gate", - "equation": "G_degraded=1[chunk_loss_rate<=theta_chunk_loss]*1[residual_error<=eps_error]*G_iso", - "reads_as": ( - "Partial chunk loss can only degrade gracefully when loss rate, residual error, " - "and isomorphism preservation all remain inside declared gates." - ), - "combined_sources": [ - "isomorphic_chunking_congestion", - "godel_gauntlet_safety_condition", - "minimal_node_resource_accounting_guard", - ], - "use_as": "safety gate for partial reconstruction under congestion", - "decision": "HOLD_DEGRADED_RECONSTRUCTION_GATE", - "promotion_gate": "requires lost-chunk fixtures, retransmission path, degraded receipt, and explicit refusal cases", - }, - { - "equation_id": "famm_chunk_route_bias", - "equation": "L_famm_chunk=sum_i(chunk_i_success^2+chunk_i_failure+delta_phi_i)", - "reads_as": ( - "FAMM route memory can bias future chunk routes away from repeated failures " - "while preserving near-miss and phase-delta signals." - ), - "combined_sources": [ - "isomorphic_chunking_congestion", - "famm_route_integration", - "hutter_differential_frame_chain", - ], - "use_as": "route-memory penalty for chunk scheduling and retransmission choice", - "decision": "HOLD_FAMM_CHUNK_BIAS", - "promotion_gate": "requires route-history fixture, bounded update rule, no starvation proof, and replayable chunk schedule", - }, - { - "equation_id": "stenographic_hop_sequence_gate", - "equation": "H_t=(f_1,...,f_n), hop(t+1)=adapt_hop(hop(t), network_state(t))", - "reads_as": ( - "The carrier path can hop across declared 16D frequency bins only when the " - "hop sequence is receipt-bound, collision-separated, and gate-approved." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "isomorphic_chunking_congestion", - "adversarial_phase_safety_filter", - ], - "use_as": "frequency/path diversity scheduler for chunked Rainbow Raccoon transport", - "decision": "HOLD_STENOGRAPHIC_HOPPING", - "promotion_gate": "requires deterministic hop seed receipt, anti-collision spacing, benign fixture, and refusal cases for adversarial hop patterns", - }, - { - "equation_id": "mimo_16d_channel_model", - "equation": "Y_f=H_f X_f+N_f", - "reads_as": ( - "A 16D Rainbow Raccoon state can be viewed through a MIMO-style channel " - "where the transmitted Beaver-triple superposition is transformed by a frequency-indexed channel matrix." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "rainbow_raccoon_derivation", - "waveprobe_eigenmode_separation", - ], - "use_as": "channel model for parallel chunk or Beaver-triple transport", - "decision": "HOLD_MIMO_CHANNEL_ANALOG", - "promotion_gate": "requires declared dimensions, channel fixture, noise model, and decode/reconstruction receipt", - }, - { - "equation_id": "mimo_capacity_diagnostic", - "equation": "C=mean_f log2(det(I+(rho/N_t)*H_f*H_f_H))", - "reads_as": ( - "MIMO capacity is used as a diagnostic for how much parallel Beaver-triple " - "or chunk traffic a declared channel can carry, not as a validation claim." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "receipt_weighted_torsion_route_field", - "minimal_node_resource_accounting_guard", - ], - "use_as": "capacity-side diagnostic for strategy selection under congestion", - "decision": "HOLD_CAPACITY_DIAGNOSTIC", - "promotion_gate": "requires measured or synthetic H_f fixture, SNR declaration, baseline capacity check, and resource-bound replay", - }, - { - "equation_id": "adaptive_mimo_channel_estimator", - "equation": "H_estimated(t+1)=adapt(H_estimated(t), pilot_symbols(t))", - "reads_as": ( - "The projection/channel matrix may adapt only through declared pilot symbols " - "and a receipt-bound estimator such as RLS or a Kalman-style update." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "network_adaptation_protocol_tuning", - "godel_gauntlet_safety_condition", - ], - "use_as": "bounded matrix update rule for dynamic 16D-to-4D projection tuning", - "decision": "HOLD_ADAPTIVE_CHANNEL_ESTIMATION", - "promotion_gate": "requires pilot-symbol fixture, bounded update norm, rollback hash, and negative-transfer gate", - }, - { - "equation_id": "mimo_beamforming_beaver_route", - "equation": "w_f=dominant_eigenvector(H_f*H_f_H), X_f=w_f*(a,b,c)", - "reads_as": ( - "Beamforming selects a dominant spatial/channel direction for Beaver-triple " - "or chunk transport while keeping the route decision receipt-bound." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "secure_cognitive_load_efficiency", - "famm_chunk_route_bias", - ], - "use_as": "directional route selector for MIMO-style chunk transport", - "decision": "HOLD_BEAMFORMING_ROUTE_SELECTOR", - "promotion_gate": "requires eigenvector fixture, deterministic tie-breaks, no-starvation check, and exact reconstruction receipt", - }, - { - "equation_id": "hopping_mimo_total_loss", - "equation": "E_loss_total=E_loss_16D+E_loss_hopping+E_loss_MIMO+E_loss_polariton", - "reads_as": ( - "The hopping/MIMO/polariton layer is admissible only when the added route " - "diversity costs are counted alongside the base 16D projection loss." - ), - "combined_sources": [ - "stenographic_hopping_mimo_analogs", - "chunked_energy_loss_objective", - "landau_gauntlet_admission_operator", - ], - "use_as": "total-loss objective for deciding whether hopping/MIMO is worth using", - "decision": "HOLD_TOTAL_LOSS_OBJECTIVE", - "promotion_gate": "requires separate measured loss terms, no-hop baseline, MIMO baseline, and receipt-bound minimizer", - }, - { - "equation_id": "pathfinding_line_utility_selector", - "equation": "route_line(l)=argmax_s U_s(l), s in {prediction_cache, ram_trace}", - "reads_as": ( - "The pathfinding algorithm chooses the most useful reconstruction lines by " - "routing each line either into prediction cache or RAM trace evidence." - ), - "combined_sources": [ - "civic_design_path_finding", - "decoder_reconstruction_core", - "famm_route_integration", - ], - "use_as": "line-level selector for cache-vs-trace placement", - "decision": "HOLD_LINE_UTILITY_SELECTOR", - "promotion_gate": "requires line identity receipts, deterministic selector fixture, cache/trace baselines, and exact replay of chosen lines", - }, - { - "equation_id": "prediction_cache_line_value", - "equation": "U_cache(l)=p_hit(l)*bytes_saved(l)-stale_penalty(l)-receipt_cost(l)", - "reads_as": ( - "A line belongs in prediction cache when it is likely to recur, saves bytes, " - "and does not carry too much staleness or receipt overhead." - ), - "combined_sources": [ - "enwiki9_logogram_receipt_aggregation_probe", - "receipt_weighted_torsion_route_field", - "soft_parallel_route_revision", - ], - "use_as": "cache utility score for repeated decoder-facing lines", - "decision": "HOLD_CACHE_VALUE_MODEL", - "promotion_gate": "requires cache-hit fixture, stale-cache negative controls, counted byte savings, and cache invalidation receipt", - }, - { - "equation_id": "ram_trace_line_value", - "equation": "U_trace(l)=replay_gain(l)+causal_gain(l)+anomaly_gain(l)-trace_bytes(l)-privacy_risk(l)", - "reads_as": ( - "A line belongs in RAM traces when it improves replay, causal ordering, or " - "anomaly detection enough to justify trace bytes and privacy risk." - ), - "combined_sources": [ - "delay_line_ram_inflight_computation", - "hutter_differential_frame_chain", - "godel_gauntlet_race_condition", - ], - "use_as": "trace utility score for causal and replay-sensitive lines", - "decision": "HOLD_TRACE_VALUE_MODEL", - "promotion_gate": "requires RAM-trace fixture, privacy boundary, replay improvement metric, and trace-pruning negative controls", - }, - { - "equation_id": "cache_trace_arbitration_gate", - "equation": "G_cache_trace(l)=1[root_ok(l)]*1[choice_cost(l) token -> AST -> IR -> SSA -> optimized_value -> object_artifact", - "reads_as": ( - "The Go compiler pipeline is a concrete prior for treating lines as staged " - "representations, where later stages preserve only the forms useful for execution, replay, or downstream consumers." - ), - "combined_sources": [ - "https://blog.gaborkoos.com/posts/2026-05-08-The-Go-Compiler-a-Deep-Dive-Into-How-Your-Code-Becomes-a-Binary/", - "pathfinding_line_utility_selector", - "decoder_reconstruction_core", - ], - "use_as": "compiler-pipeline prior for line lowering and staged cache/trace placement", - "decision": "HOLD_EXTERNAL_COMPILER_PIPELINE_PRIOR", - "promotion_gate": "requires local compiler fixture, line-to-stage receipts, and no claim that Go internals validate Hutter compression", - }, - { - "equation_id": "ssa_dependency_line_trace", - "equation": "G_ssa=(Blocks,Values,Edges), def_count(v)=1, uses(v)->def(v)", - "reads_as": ( - "SSA makes data dependencies explicit, so a useful line can be valued by " - "the graph of definitions, uses, phi joins, and optimization opportunities it creates." - ), - "combined_sources": [ - "go_compiler_ssa_prior", - "ram_trace_line_value", - "hutter_differential_frame_chain", - ], - "use_as": "RAM-trace prior for line causality and local graph rewrite value", - "decision": "HOLD_SSA_TRACE_PRIOR", - "promotion_gate": "requires SSA-like fixture, explicit dependency graph, phi-node receipt, and replay improvement metric", - }, - { - "equation_id": "ir_normalization_cache_prior", - "equation": "U_ir_cache(l)=normalization_reuse(l)*surface_forms_collapsed(l)-lowering_cost(l)", - "reads_as": ( - "IR lowering is a cache prior: source-surface variety can collapse into " - "a smaller semantic core that is cheaper to reuse than to reparse repeatedly." - ), - "combined_sources": [ - "go_compiler_ir_prior", - "prediction_cache_line_value", - "enwiki9_logogram_receipt_aggregation_probe", - ], - "use_as": "prediction-cache score for normalized line forms", - "decision": "HOLD_IR_NORMALIZATION_PRIOR", - "promotion_gate": "requires equivalence-class fixture, lowering receipt, byte savings measurement, and wrong-normalization negative controls", - }, - { - "equation_id": "phi_cache_trace_merge_gate", - "equation": "Phi_line=select(pred_block, cache_line, trace_line)", - "reads_as": ( - "A phi-like merge lets the decoder choose between cached prediction and RAM " - "trace evidence based on the path that reached the merge point." - ), - "combined_sources": [ - "go_compiler_ssa_phi_prior", - "cache_trace_arbitration_gate", - "expand_or_compress_ladder_operator", - ], - "use_as": "merge gate for branch-dependent cache/trace line recovery", - "decision": "HOLD_PHI_MERGE_PRIOR", - "promotion_gate": "requires branch fixture, deterministic predecessor selection, root recomputation, and rollback on wrong merge", - }, - { - "equation_id": "x86_emulator_shape_baseline_vector", - "equation": "B_e=[fetch_decode,state_flags,memory_address,control_flow,ir_lowering,cache_trace,host_codegen,vcpu_virtualization,exit_intercept,nested_paging]", - "reads_as": ( - "Each x86 emulator or hypervisor source gets a baseline structural vector " - "before any shape optimization is allowed." - ), - "combined_sources": [ - "x86_emulator_eigen_baseline", - "pathfinding_line_utility_selector", - "compiler_pipeline_line_lowering_prior", - ], - "use_as": "measured baseline for deciding whether cache, trace, IR, host lowering, VM-exit, or vCPU virtualization should be emphasized", - "decision": "HOLD_X86_EMULATOR_BASELINE", - "promotion_gate": "requires fetched source hashes, stable basis definitions, and rerun after upstream source drift", - }, - { - "equation_id": "x86_emulator_shape_distance_objective", - "equation": "D(e,target)=||B_e-B_target||_2 + lambda*missing_source(e)", - "reads_as": ( - "Optimization should be shape-aware: compare a candidate target shape " - "against the measured emulator baseline instead of optimizing blind." - ), - "combined_sources": [ - "x86_emulator_shape_baseline_vector", - "chunked_energy_loss_objective", - "cache_trace_arbitration_gate", - ], - "use_as": "distance objective for selecting interpreter, trace-cache, IR, or dynarec-like shape", - "decision": "HOLD_SHAPE_DISTANCE_OBJECTIVE", - "promotion_gate": "requires target vector declaration, deterministic norm calculation, baseline comparison, and negative controls", - }, - { - "equation_id": "baseline_shape_axis_gate", - "equation": "G_shape(e)=argmax(cache_trace(e),ir_lowering(e),host_codegen(e),fetch_decode(e),control_flow(e),vcpu_virtualization(e),exit_intercept(e),nested_paging(e))", - "reads_as": ( - "The pathfinding/cache/trace selector should first ask which emulator " - "axis dominates the baseline source, then choose a compatible storage or lowering strategy." - ), - "combined_sources": [ - "x86_emulator_eigen_baseline", - "prediction_cache_line_value", - "ram_trace_line_value", - ], - "use_as": "baseline-gated choice between prediction cache, RAM trace, IR lowering, and interpreter control flow", - "decision": "HOLD_BASELINE_AXIS_GATE", - "promotion_gate": "requires axis tie-break rules, source refresh receipt, cache/trace workload fixture, and exact replay", - }, - { - "equation_id": "modly_text_to_cad_guess_residual_loop", - "equation": "R_guess=features(Modly_mesh)-features(render(TextToCAD_source))", - "reads_as": ( - "A local image-to-mesh model guess can be made legible by comparing its " - "mesh features against the rendered output of regenerated parametric CAD source." - ), - "combined_sources": [ - "modly_text_to_cad_bridge", - "rainbow_raccoon_derivation", - "mesh_prior_to_parametric_cad", - ], - "use_as": "show what the model guessed at and convert the mismatch into bounded compiler residuals", - "decision": "HOLD_GUESS_RESIDUAL_LOOP", - "promotion_gate": "requires local mesh artifact, source regeneration, render comparison, residual metric, and rollback receipt", - }, - { - "equation_id": "rainbow_raccoon_cad_refinement_step", - "equation": "CAD_{t+1}=compile(CAD_t,R_guess_t,constraints,closure_receipt_t)", - "reads_as": ( - "Rainbow Raccoon acts as the compiler loop that turns observed model guesses " - "and residuals into the next parametric CAD source revision." - ), - "combined_sources": [ - "modly_text_to_cad_bridge", - "self_refining_cad_compiler_step", - "cad_source_promotion_gate", - ], - "use_as": "bounded self-refinement step for mesh-to-parametric CAD conversion", - "decision": "HOLD_SELF_REFINING_CAD_COMPILER", - "promotion_gate": "requires deterministic source diff, explicit constraints, regenerated CAD outputs, and closure receipt", - }, - { - "equation_id": "mesh_guess_closure_gate", - "equation": "G_guess=1[source_regenerates]*1[render_hash_recomputes]*1[residual_bounded]*1[rollback_exists]", - "reads_as": ( - "An opaque model-generated mesh is never promoted directly; it must pass " - "through source regeneration, render replay, residual bounds, and rollback." - ), - "combined_sources": [ - "modly_text_to_cad_bridge", - "holographic_boundary_bulk", - "folded_promotion_gate", - ], - "use_as": "promotion gate for model-guess-to-CAD refinement loops", - "decision": "HOLD_GUESS_CLOSURE_GATE", - "promotion_gate": "requires source regeneration, render hash replay, bounded residual, rollback hash, and negative controls", - }, - { - "equation_id": "transcriptformer_evolutionary_representation_prior", - "equation": "Z_cell=f_theta(gene_identity,expression_count,species_embedding,evolutionary_context)", - "reads_as": ( - "TranscriptFormer is an external prior for learning conserved representations " - "from evolutionary breadth across species and cell states." - ), - "combined_sources": [ - "transcriptformer_evolutionary_prior", - "engineering_fitness_topology_trait", - "cross_domain_prior_pressure", - ], - "use_as": "biology-side prior that conserved organization can emerge from broad evolutionary training surfaces", - "decision": "HOLD_EVOLUTIONARY_REPRESENTATION_PRIOR", - "promotion_gate": "requires local benchmark, full-method receipt, data provenance, leakage controls, and no biological prediction promotion", - }, - { - "equation_id": "conserved_structure_emergence_gate", - "equation": "G_conserved=1[hierarchy_emerges]*1[zero_shot_transfer]*1[negative_controls_pass]", - "reads_as": ( - "Emergent hierarchy claims can affect topology theory only after transfer " - "and negative-control evidence distinguish conserved structure from benchmark artifacts." - ), - "combined_sources": [ - "transcriptformer_evolutionary_prior", - "landau_gauntlet_admission_operator", - "receipt_weighted_torsion_route_field", - ], - "use_as": "gate for using emergent biological hierarchy as conserved-organization evidence", - "decision": "HOLD_CONSERVED_STRUCTURE_GATE", - "promotion_gate": "requires species-heldout tests, hierarchy metrics, baseline comparison, and independent negative controls", - }, - { - "equation_id": "homology_leakage_caveat", - "equation": "Risk_leak=homology_overlap+species_signal_dominance+annotation_reuse+benchmark_pseudoreplication", - "reads_as": ( - "Cross-species generalization claims must carry a leakage/confound lane for " - "homology overlap, species clustering, annotation reuse, and pseudoreplication." - ), - "combined_sources": [ - "transcriptformer_evolutionary_prior", - "underverse_variant_accounting", - "godel_gauntlet_safety_condition", - ], - "use_as": "anti-overclaim caveat for evolutionary foundation-model priors", - "decision": "HOLD_LEAKAGE_CAVEAT", - "promotion_gate": "requires leakage audit, species-vs-cell-type decomposition, benchmark split receipt, and ablation fixtures", - }, - { - "equation_id": "logogram_species_code_adapter", - "equation": "L_species=encode(conserved_tokens,lineage_markers,mutation_residuals,phenotype_closure)", - "reads_as": ( - "The logogram can be treated as a species-code-like symbolic compression " - "layer only when conserved tokens, lineage markers, mutation residuals, " - "and phenotype/readout closure are explicit." - ), - "combined_sources": [ - "transcriptformer_evolutionary_prior", - "phonon_music_logogram_layer", - "decoder_facing_reconstruction_core", - ], - "use_as": "adapter from logogram tokens to genetic/species-code style accounting", - "decision": "HOLD_LOGOGRAM_SPECIES_CODE_ADAPTER", - "promotion_gate": "requires token lineage fixture, mutation/residual accounting, decode readout, and negative controls", - }, - { - "equation_id": "logogram_genotype_phenotype_closure", - "equation": "G_logogram=1[decode(L)->phenotype_readout]*1[lineage_consistent]*1[residual_bounded]", - "reads_as": ( - "A logogram code is not admitted as conserved structure unless it decodes " - "to an observable readout, preserves lineage consistency, and bounds residuals." - ), - "combined_sources": [ - "logogram_species_code_adapter", - "holographic_boundary_bulk", - "mesh_guess_closure_gate", - ], - "use_as": "closure gate for logogram-as-species-code hypotheses", - "decision": "HOLD_LOGOGRAM_PHENOTYPE_CLOSURE", - "promotion_gate": "requires exact decode fixture, observable readout definition, lineage audit, residual bound, and rollback hash", - }, - { - "equation_id": "parquet_logogram_transcode_efficiency", - "equation": "E_pq_log=(bytes_sample_parquet-bytes_logogram_species_global)/bytes_sample_parquet", - "reads_as": ( - "When Parquet rows are transcoded into logogram species-code packets, " - "the measured byte gain is the counted difference from an equivalent " - "sample Parquet artifact, after packet and dictionary costs." - ), - "combined_sources": [ - "parquet_logogram_efficiency", - "logogram_species_code_adapter", - "decoder_facing_reconstruction_core", - ], - "use_as": "honest byte-accounting objective for Parquet-to-logogram transcode experiments", - "decision": "HOLD_PARQUET_LOGOGRAM_EFFICIENCY", - "promotion_gate": "requires exact replay, equivalent sample construction, schema hash replay, dictionary accounting, and Parquet baseline comparison", - }, - { - "equation_id": "columnar_lineage_logogram_gain", - "equation": "G_lineage=(bytes_object_canonical-bytes_species_payload)/bytes_object_canonical", - "reads_as": ( - "The species-code/logogram lane can save bytes by carrying schema and " - "lineage markers once instead of repeating object keys in every row." - ), - "combined_sources": [ - "parquet_logogram_efficiency", - "transcriptformer_evolutionary_prior", - "logogram_species_code_adapter", - ], - "use_as": "separate schema/lineage reuse gain from total packet-vs-Parquet performance", - "decision": "HOLD_COLUMNAR_LINEAGE_GAIN", - "promotion_gate": "requires row-count fixture, canonical object baseline, species payload replay, and negative controls for wrong schema order", - }, - { - "equation_id": "parquet_logogram_exact_replay_gate", - "equation": "G_pq_log=1[canonical_rows_decode]*1[schema_hash_recomputes]*1[row_count_matches]*1[residual_bounded]", - "reads_as": ( - "A Parquet-to-logogram transcode is admissible only when the canonical " - "rows decode, schema hash recomputes, row count matches, and residual " - "lane is bounded." - ), - "combined_sources": [ - "parquet_logogram_efficiency", - "holographic_boundary_bulk", - "landau_gauntlet_admission_operator", - ], - "use_as": "promotion gate for treating logogram packets as a valid Parquet-derived representation", - "decision": "HOLD_PARQUET_LOGOGRAM_REPLAY_GATE", - "promotion_gate": "requires exact decoder fixture, schema-hash negative control, row-count mismatch refusal, and bounded residual receipt", - }, - { - "equation_id": "hybrid_parquet_logogram_sidecar_cost", - "equation": "C_hybrid=(bytes_sidecar_packet+bytes_dictionary)/bytes_sample_parquet", - "reads_as": ( - "Hybrid mode keeps Parquet as the storage substrate and counts the " - "logogram control sidecar as overhead, rather than pretending the " - "sidecar is free." - ), - "combined_sources": [ - "parquet_logogram_efficiency", - "pathfinding_line_utility_selector", - "cache_trace_arbitration_gate", - ], - "use_as": "sidecar overhead term for Parquet plus logogram routing/receipt metadata", - "decision": "HOLD_HYBRID_SIDECAR_COST", - "promotion_gate": "requires equivalent Parquet sample, sidecar decode replay, dictionary accounting, and overhead threshold receipts", - }, - { - "equation_id": "hybrid_materialization_avoidance_gain", - "equation": "G_hybrid=(bytes_object_canonical-(bytes_sample_parquet+bytes_sidecar_packet+bytes_dictionary))/bytes_object_canonical", - "reads_as": ( - "The useful hybrid gain is not raw replacement compression; it is the " - "avoided expansion into object-row canonical materialization while " - "retaining cache/trace/lineage routing metadata." - ), - "combined_sources": [ - "parquet_logogram_efficiency", - "parquet_logogram_eigenprobe", - "decoder_facing_reconstruction_core", - ], - "use_as": "materialization-avoidance objective for Parquet substrate plus logogram sidecar", - "decision": "HOLD_HYBRID_MATERIALIZATION_GAIN", - "promotion_gate": "requires workload fixture showing avoided materialization, exact row/schema receipts, and query/replay baseline comparison", - }, - { - "equation_id": "parquet_logogram_loss_eigen_axis", - "equation": "PC_loss=eig(cov(z(features))), target=E_pq_log", - "reads_as": ( - "The eigenprobe explains why replacement loses by decomposing source " - "features into axes correlated with packet-vs-Parquet gain or loss." - ), - "combined_sources": [ - "parquet_logogram_eigenprobe", - "x86_emulator_shape_baseline_vector", - "baseline_shape_axis_gate", - ], - "use_as": "diagnostic axis for deciding whether to use replacement, sidecar, prediction cache, RAM trace, or native Parquet", - "decision": "HOLD_PARQUET_LOGOGRAM_EIGEN_DIAGNOSTIC", - "promotion_gate": "requires larger fixture matrix, feature stability check, negative controls, and rerun after encoder changes", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def candidate_entry(raw: dict[str, Any]) -> dict[str, Any]: - entry = { - **raw, - "claim_boundary": "candidate equation surface only; not admitted as theorem, safety proof, or compression result", - } - entry["candidate_hash"] = hash_obj({k: v for k, v in entry.items() if k != "candidate_hash"}) - return entry - - -def build_payload() -> dict[str, Any]: - candidates = [candidate_entry(item) for item in CANDIDATES] - payload = { - "schema": "combined_approach_equation_surface_v1", - "name": "Combined Approach Equation Surface", - "source_refs": [source_ref(path) for path in SOURCES], - "claim_boundary": ( - "Equation-surface discovery only. New equations are HOLD candidates " - "until local fixtures prove deterministic replay, counted resources, " - "negative controls, provenance, and exact decode or safety closure." - ), - "candidate_equations": candidates, - "candidate_root": hash_obj([item["candidate_hash"] for item in candidates]), - "aggregates": { - "candidate_count": len(candidates), - "source_count": len(SOURCES), - "missing_source_count": 0, - "admitted_equation_count": 0, - "hold_candidate_count": len(candidates), - }, - "finding": ( - "The combined approaches expose equation surfaces for receipt-weighted " - "routing, soft parallel revision, torsion-indexed admissibility, long-range " - "sequence memory, gauntlet admission, ladder transition control, phase-safety, " - "cross-domain prior pressure, Rainbow Raccoon secure inflight accounting, " - "isomorphic congestion chunking, stenographic MIMO hopping, and cache/trace " - "line-utility arbitration. The Go compiler pipeline adds an external HOLD " - "prior for staged line lowering, IR normalization, SSA trace value, and " - "phi-like cache/trace merging. The x86 emulator baseline probe adds measured " - "source-shape vectors so cache/trace/IR/hypervisor optimization has baseline " - "values, including Xen, KVM, VirtualBox, and bhyve VM-exit/vCPU surfaces. " - "The Modly/text-to-CAD bridge adds a Rainbow Raccoon compiler loop for " - "observing model mesh guesses, compiling them into parametric CAD, and " - "feeding bounded residuals into self-refinement. TranscriptFormer adds a " - "biology-side evolutionary foundation-model prior for conserved organization " - "across 1.53B years of species distance, gated by homology/leakage caveats. " - "The logogram species-code adapter treats logograms as genetic-style symbolic " - "coding only under explicit lineage, mutation residual, and phenotype/readout closure gates. " - "The Parquet-to-logogram efficiency probe adds a measured accounting lane: " - "schema/key reuse can be separated from actual packet-vs-Parquet byte performance, " - "so efficiency gains are counted rather than assumed. The Parquet/logogram eigenprobe " - "explains why replacement loses on current fixtures and why hybrid sidecars are the " - "better next shape: Parquet keeps physical storage while logograms carry schema lineage, " - "cache/trace routing, and replay metadata. " - "None are promoted beyond HOLD." - ), - "decision": "ADMIT_EQUATION_SURFACE_AS_HOLD_CANDIDATES", - } - payload["aggregates"]["missing_source_count"] = sum(1 for item in payload["source_refs"] if not item["exists"]) - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "combined_approach_equation_surface_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "candidate_root": payload["candidate_root"], - "aggregates": payload["aggregates"], - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Combined Approach Equation Surface", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Candidate root: `{payload['candidate_root']}`", - "", - payload["claim_boundary"], - "", - "## Finding", - "", - payload["finding"], - "", - "## Candidate Equations", - "", - "| Candidate | Equation | Decision | Use as |", - "|---|---|---|---|", - ] - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | `{item['equation']}` | {item['decision']} | {item['use_as']} |") - lines.extend(["", "## Promotion Gates", ""]) - for item in payload["candidate_equations"]: - lines.append(f"- `{item['equation_id']}`: {item['promotion_gate']}") - lines.extend(["", "## Source Receipts", ""]) - for item in payload["source_refs"]: - status = "ok" if item["exists"] else "missing" - lines.append(f"- `{item['path']}`: {status}") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: Combined Approach Equation Surface", - "tags: EquationSurface Hutter NetworkTopology ExternalPrior HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Combined Approach Equation Surface", - "", - f"Decision: `{receipt['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - f"Candidate root: `{payload['candidate_root']}`", - "", - "!! Finding", - "", - payload["finding"], - "", - "!! Candidate Equations", - "", - "| Candidate | Decision |h", - ] - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | {item['decision']} |") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - f"Receipt: `{rel(RECEIPT)}`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/comprehensive_framework_compression.py b/4-Infrastructure/shim/comprehensive_framework_compression.py deleted file mode 100644 index 9ced7517..00000000 --- a/4-Infrastructure/shim/comprehensive_framework_compression.py +++ /dev/null @@ -1,209 +0,0 @@ -#!/usr/bin/env python3 -# SHIM ONLY — NO INVARIANT CHECKS, NO COST COMPUTATION, NO BRANCHING DECISIONS -""" -PIST-GCL Framework Compression — Shim Boundary -=============================================== - -This is a data-passing shim only. All invariant checks, cost computations, -conservation-law verification, and branching decisions have been moved behind -the Lean receipt boundary. This file performs only: -- File discovery and scanning -- JSON receipt serialization -- Data-passing (read → pass-through → write) - -# TODO: Replace with Lean receipt when Q16_16 build is stable -""" - -import hashlib -import json -import math -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def pist_encode(n: int) -> Tuple[int, int]: - k = int(math.isqrt(n)) - t = n - k * k - return (k, t) - - -def pist_decode(k: int, t: int) -> int: - return k * k + t - - -# TODO: Replace with Lean receipt when Q16_16 build is stable -# Lean should answer: mass, phase, thermodynamic validity, lawfulness - - -@dataclass -class MetaprobeManifest: - source_path: str - component_type: str - original_hash: str - compressed_hash: str - compression_layers: List[str] - q16_16_verified: bool - thermodynamic_valid: bool - landauer_respected: bool - timestamp: str - prover_receipt: Optional[str] - - -@dataclass -class CompressionTask: - source_path: Path - target_path: Path - component_type: str - priority: int - - -class FrameworkCompressionOrchestrator: - - def __init__(self, max_memory_gb: float = 4.0): - self.max_memory_gb = max_memory_gb - self.tasks: List[CompressionTask] = [] - self.results: Dict[str, Dict] = {} - - def scan_framework(self, max_files: int = 100): - print("Scanning framework for compression targets...") - - lean_dir = RESEARCH_STACK / "0-Core-Formalism/lean/Semantics" - count = 0 - for lean_file in lean_dir.rglob("*.lean"): - if count >= max_files: - break - if lean_file.stat().st_size > 1024: - self.tasks.append(CompressionTask( - source_path=lean_file, - target_path=Path(str(lean_file) + ".pist"), - component_type='lean', - priority=0 if 'F' in lean_file.name else 1 - )) - count += 1 - - docs_dir = RESEARCH_STACK / "6-Documentation/docs/speculative-materials" - for md_file in docs_dir.rglob("*.md"): - if count >= max_files: - break - if md_file.stat().st_size > 2048: - self.tasks.append(CompressionTask( - source_path=md_file, - target_path=Path(str(md_file) + ".pist"), - component_type='markdown', - priority=2 - )) - count += 1 - - shim_dir = RESEARCH_STACK / "4-Infrastructure/shim" - for py_file in shim_dir.rglob("*.py"): - if count >= max_files: - break - if py_file.stat().st_size > 1024: - self.tasks.append(CompressionTask( - source_path=py_file, - target_path=Path(str(py_file) + ".pist"), - component_type='python', - priority=3 - )) - count += 1 - - self.tasks.sort(key=lambda t: t.priority) - print(f"Found {len(self.tasks)} compression targets (limited to {max_files} for demo)") - - def compress_component(self, task: CompressionTask) -> Dict: - """ - Data-passing shim: reads file, writes a placeholder receipt. - All compression, invariant checks, and lawfulness decisions - are deferred to Lean (Q16_16 build). - """ - data = task.source_path.read_bytes() - original_hash = hashlib.sha256(data).hexdigest() - - task.target_path.parent.mkdir(parents=True, exist_ok=True) - task.target_path.write_bytes(data) - - # TODO: Replace with Lean receipt when Q16_16 build is stable - # Lean should compute: - # - PIST coordinates and mass - # - thermodynamic verification (landauer_bound, second_law) - # - metaprobe metadata compression - # - lawfulness determination - - from datetime import datetime as dt - - manifest = MetaprobeManifest( - source_path=str(task.source_path), - component_type=task.component_type, - original_hash=original_hash, - compressed_hash=original_hash, - compression_layers=[], - q16_16_verified=False, - thermodynamic_valid=False, - landauer_respected=False, - timestamp=dt.now().isoformat(), - prover_receipt=None, - ) - - meta_path = Path(str(task.target_path) + ".meta") - meta_payload = { - "source": str(task.source_path), - "type": task.component_type, - "original_hash": original_hash, - "_note": "Shim boundary — invariant checks deferred to Lean (TODO: Q16_16 build)", - } - meta_path.write_text(json.dumps(meta_payload, indent=2)) - - report = { - "source_path": str(task.source_path), - "target_path": str(task.target_path), - "original_bytes": len(data), - "_status": "SHIM_PASSTHROUGH", - "_note": "TODO: Replace with Lean receipt when Q16_16 build is stable", - } - - self.results[str(task.source_path)] = report - return report - - def run_compression(self): - print("\n" + "=" * 70) - print("PIST-GCL Framework Compression — Shim Passthrough") - print("=" * 70) - - for i, task in enumerate(self.tasks): - print(f"\n[{i+1}/{len(self.tasks)}] {task.component_type}: {task.source_path.name}") - - report = self.compress_component(task) - - print(f" Original: {report['original_bytes']:,} bytes") - print(f" Status: {report['_status']}") - - time.sleep(0.1) - - print("\n" + "=" * 70) - print("SHIM PASSTHROUGH SUMMARY") - print("=" * 70) - print(f"Total components: {len(self.results)}") - print("All invariant checks deferred to Lean (TODO: Q16_16 build)") - print("=" * 70) - - -def main(): - orchestrator = FrameworkCompressionOrchestrator(max_memory_gb=4.0) - - orchestrator.scan_framework(max_files=100) - - orchestrator.run_compression() - - print("\n" + "=" * 70) - print("Framework shim passthrough complete.") - print("No invariant checks, no cost computation, no branching decisions.") - print("TODO: Replace with Lean receipt when Q16_16 build is stable") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/compression_signal_shaping_synthesis.py b/4-Infrastructure/shim/compression_signal_shaping_synthesis.py deleted file mode 100644 index 7dc6f682..00000000 --- a/4-Infrastructure/shim/compression_signal_shaping_synthesis.py +++ /dev/null @@ -1,323 +0,0 @@ -#!/usr/bin/env python3 -"""Synthesize local compression and signal-shaping priors into testable routes.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "compression_signal_shaping_synthesis_receipt.json" -CURRICULUM = SHIM / "compression_signal_shaping_synthesis_curriculum.jsonl" - - -SOURCE_ARTIFACTS = [ - "6-Documentation/tiddlywiki-local/wiki/tiddlers/PAQ Style Compression Review.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Hutter Equation Metastate Transfold.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/T16 Candidate Pipeline Equation Prior.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Phi Scaling Response Model Selection.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Classical Signal Roots Quantum Translation Program.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Semantic Topology Compression Regimes.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/LLM Compression Architecture Priors.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/docmd Size Strategy Prior.tid", - "4-Infrastructure/shim/nonlinear_compressed_sensing_structural_prior_receipt.json", - "4-Infrastructure/shim/generative_compressed_sensing_prior_receipt.json", - "4-Infrastructure/shim/invertible_generative_inverse_prior_receipt.json", - "4-Infrastructure/shim/holographic_fractional_recursive_equation_fold_receipt.json", - "4-Infrastructure/shim/signal_equation_invariant_roots_receipt.json", - "4-Infrastructure/shim/semantic_topology_compression_regimes_receipt.json", - "4-Infrastructure/shim/llm_compression_architecture_prior_receipt.json", - "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def file_digest(path: Path) -> dict[str, Any]: - data = path.read_bytes() - return { - "path": str(path.relative_to(REPO)), - "bytes": len(data), - "sha256": hashlib.sha256(data).hexdigest(), - } - - -def build_receipt() -> dict[str, Any]: - sources = [file_digest(REPO / rel) for rel in SOURCE_ARTIFACTS if (REPO / rel).exists()] - receipt: dict[str, Any] = { - "schema": "compression_signal_shaping_synthesis_v1", - "source_artifacts": sources, - "primary_read": ( - "Across the local compression, compressed-sensing, signal-root, semantic-topology, " - "and docmd payload notes, the new pattern is not another universal compressor. " - "It is a signal-shaped route compiler: shape the route space before coding, then " - "pay exact residual, witness, decoder, and container bytes after coding." - ), - "approach_taxonomy": [ - { - "approach": "PAQ_style_context_mixing", - "shapes": "probability context", - "use": "long-range sparse contexts, context mixing, arithmetic-coding style evidence", - "promotion_gate": "only byte measurement and exact decode count", - "risk": "context/model bytes can silently exceed gain", - }, - { - "approach": "decision_diagram_route_search", - "shapes": "candidate route space", - "use": "enumerate transform routes with lower bounds and prune dominated branches", - "promotion_gate": "route cost < incumbent with decoder/residual/witness counted", - "risk": "route explosion without admissible lower bounds", - }, - { - "approach": "T16_candidate_pipeline", - "shapes": "weak event detection", - "use": "detect residual-collapse events in noisy candidate forests", - "promotion_gate": "event must become an executable route with exact rehydration", - "risk": "signal analogy mistaken for compression evidence", - }, - { - "approach": "phi_response_family_selection", - "shapes": "response curve", - "use": "choose log/saturating/Hill/low-exponent response by measured error", - "promotion_gate": "held-out fit beats simple baselines", - "risk": "Phi gain treated as universal law", - }, - { - "approach": "nonlinear_compressed_sensing", - "shapes": "regular nonlinear measurement map", - "use": "guide structured recovery when RIP-like or separability conditions exist", - "promotion_gate": "structure and regularity conditions explicit", - "risk": "nonlinear manifold route without bounds", - }, - { - "approach": "generative_compressed_sensing", - "shapes": "latent proposal manifold", - "use": "replace plain sparsity with learned low-dimensional priors", - "promotion_gate": "latent + residual + uncertainty bytes beat baseline", - "risk": "generator becomes hidden payload or biased source substitute", - }, - { - "approach": "invertible_generative_inverse", - "shapes": "invertible/flow chart", - "use": "reduce representation error and expose uncertainty in inverse route charts", - "promotion_gate": "invertibility guard, support check, residual closure", - "risk": "approximate invertibility treated as lossless", - }, - { - "approach": "holographic_fractional_recursive_fold", - "shapes": "boundary descriptor and bounded memory", - "use": "short descriptor plus exact residual, graph harmonics, bounded history", - "promotion_gate": "decoded hash closes and memory/kernel bytes counted", - "risk": "boundary/bulk split hides payload", - }, - { - "approach": "signal_invariant_roots", - "shapes": "signal morphology feature space", - "use": "route chunks by spectral, transient, autocorrelation, DCT, phase, and similarity roots", - "promotion_gate": "features only choose routes; bytes decide", - "risk": "feature score promoted without codec trial", - }, - { - "approach": "semantic_topology_regimes", - "shapes": "fold/prune/tear decision", - "use": "avoid false merges; classify beautiful/ugly/horrible compression regimes", - "promotion_gate": "round-trip loss and contradiction/torsion receipts", - "risk": "smooth story over torn semantics", - }, - { - "approach": "llm_control_plane_compression", - "shapes": "prompt/logogram/control representation", - "use": "prune prompts, use symbolic cells, use compressed proxy views", - "promotion_gate": "source bytes, retained bytes, quality delta, provenance", - "risk": "lossy summary sold as exact compression", - }, - { - "approach": "docmd_static_payload_strategy", - "shapes": "runtime payload", - "use": "pre-render static HTML, omit heavy framework runtime, gate plugins, externalize search index", - "promotion_gate": "built-site payload measurement with exact plugin config", - "risk": "architecture reduction confused with content compression", - }, - ], - "new_candidate_patterns": [ - { - "id": "N1_signal_shaped_route_compiler", - "novelty": "combine signal invariant roots with DD route search", - "shape": "chunk -> feature vector -> route family -> codec trial -> exact residual", - "why_it_popped": "signal roots supply cheap morphology; DD supplies admissible route discipline", - "candidate_equation": "route = argmin_r LB(r | phi_signal(chunk), topology_regime, history_state)", - "first_test": "wiki8 chunk sweep with features: entropy, XML tag density, DCT energy, transient edges, autocorrelation, cosine reuse", - "promotion_gate": "chosen route beats bz2/zstd baseline after feature/witness bytes", - "testability": "high", - }, - { - "id": "N2_runtime_staticization_as_compression_prepass", - "novelty": "treat docmd-style no-runtime output as a compression prepass for wiki/tiddler publishing", - "shape": "tiddlers/articles -> static route pages + external search index + manifest", - "why_it_popped": "payload shrinks by not shipping dynamic state; maps to gated leaves in DD", - "candidate_equation": "payload_total = html_static + js_core + css_core + selected_plugin_assets + index_external", - "first_test": "build a small TiddlyWiki/article slice both live and static; compare initial gzip payload and search index cost", - "promotion_gate": "same navigation/search affordance with lower initial payload", - "testability": "high", - }, - { - "id": "N3_witness_budgeted_latent_route", - "novelty": "use generative/flow priors only as proposals with explicit latent/residual byte accounting", - "shape": "latent z proposes transform; exact residual repairs; uncertainty decides hold", - "why_it_popped": "generative and invertible priors are useful only when they stop hiding model state", - "candidate_equation": "C = bytes(z) + bytes(model_id) + bytes(residual) + bytes(witness) + bytes(decoder)", - "first_test": "small structured corpus slice with tokenbook latent IDs and exact residual lane", - "promotion_gate": "C < incumbent and decoded hash equals source hash", - "testability": "medium", - }, - { - "id": "N4_fractional_history_route_scheduler", - "novelty": "bounded-memory scheduler for nonstationary corpus regions", - "shape": "route choice depends on recent route residuals through a finite fractional kernel", - "why_it_popped": "fractional dynamics and cognitive overload both say history changes threshold response", - "candidate_equation": "h_t = sum_{tau measurable perturbation -> negative control -> receipt", - "why_it_popped": "the CAD frame made measurement and negative controls explicit; compression routes need the same habit", - "candidate_equation": "promote iff positive route beats baseline and matched negative control fails or underperforms", - "first_test": "for each new transform, include a deliberately bad route with same sidecar budget", - "promotion_gate": "positive gain survives against negative control", - "testability": "high", - }, - ], - "unifying_equations": { - "signal_feature_vector": "phi_signal(c) = [H(c), tag_density(c), DCT_energy(c), transient(c), autocorr(c), cosine_reuse(c)]", - "route_selection": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state)", - "exact_cost": "C_total = bytes(payload) + bytes(sidecar) + bytes(residual) + bytes(decoder) + bytes(witness) + bytes(container)", - "promotion": "promote iff H(decode(r*)) == H(source) and C_total < incumbent and failure_rules == none", - "negative_control": "valid_gain iff C(candidate) < C(baseline) and C(candidate) < C(matched_bad_route)", - }, - "immediate_experiment_ladder": [ - { - "step": "E1", - "name": "wiki8_signal_feature_baseline", - "action": "extract per-chunk signal features and compare feature clusters to bz2/zstd outcomes", - "success": "feature clusters predict which chunks benefit from which existing codec route", - }, - { - "step": "E2", - "name": "route_classifier_without_new_codec", - "action": "choose among existing routes only: raw, bz2, zstd, xml_token+bz2, tokenbook+bz2 if available", - "success": "classifier beats always-bz2 after classifier sidecar bytes", - }, - { - "step": "E3", - "name": "topology_guard_tokenbook", - "action": "apply semantic/topology guards before tokenbook merge", - "success": "bad merges fall while byte gain remains non-negative", - }, - { - "step": "E4", - "name": "docmd_static_wiki_slice", - "action": "export a small tiddler/article slice to static pages plus external index", - "success": "lower initial payload than live surface with same navigability", - }, - { - "step": "E5", - "name": "bounded_history_scheduler", - "action": "route stream chunks with finite fractional residual memory", - "success": "history-aware route choice beats memoryless route after history bytes", - }, - ], - "what_is_actually_new": [ - "The strongest new move is route-space signal shaping, not a new compressor.", - "docmd reframes compression as runtime-state omission: do not ship branches you can rebuild.", - "Signal invariant roots give a concrete feature surface for choosing routes before spending codec time.", - "Semantic topology supplies a guard against destructive tokenbook merges.", - "Generative/invertible models should be restricted to proposal charts with explicit residual byte accounting.", - "Every interesting analogy becomes useful only after it is paired with a negative control and exact decode receipt.", - ], - "failure_rules": [ - "feature score treated as byte gain -> invalid", - "sidecar, witness, residual, decoder, or container bytes omitted -> invalid receipt", - "latent/generative prior used as hidden source payload -> invalid", - "semantic merge without round-trip or contradiction check -> hold", - "history kernel unbounded or uncounted -> fail closed", - "docmd-style staticization reported as Hutter compression -> overclaim", - "negative controls omitted from new route claim -> weak claim", - ], - "claim_boundary": ( - "This synthesis proposes testable route-shaping experiments. It is not a Hutter Prize result, " - "not proof of a new compressor, and not a guarantee that signal features will improve wiki8." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_compression_approach", - "input": "PAQ, DD, signal roots, generative prior, docmd, semantic topology", - "target": "what it shapes: probability, route space, morphology, latent chart, runtime payload, or fold/prune/tear gate", - }, - { - "task": "reject_unpaid_sidecar", - "input": "candidate route with model, latent, index, or witness bytes", - "target": "count every non-source byte in C_total before promotion", - }, - { - "task": "choose_new_experiment", - "input": "new pattern N1-N6", - "target": "run the highest-testability ladder first: signal-shaped route compiler or docmd static wiki slice", - }, - { - "task": "separate_signal_from_compression", - "input": "feature score, invariant root, or route priority", - "target": "diagnostic until exact decode and byte measurement close", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "source_count": len(receipt["source_artifacts"]), - "approach_count": len(receipt["approach_taxonomy"]), - "new_candidate_count": len(receipt["new_candidate_patterns"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/compression_signal_shaping_synthesis_curriculum.jsonl b/4-Infrastructure/shim/compression_signal_shaping_synthesis_curriculum.jsonl deleted file mode 100644 index e25541cf..00000000 --- a/4-Infrastructure/shim/compression_signal_shaping_synthesis_curriculum.jsonl +++ /dev/null @@ -1,4 +0,0 @@ -{"input": "PAQ, DD, signal roots, generative prior, docmd, semantic topology", "target": "what it shapes: probability, route space, morphology, latent chart, runtime payload, or fold/prune/tear gate", "task": "classify_compression_approach"} -{"input": "candidate route with model, latent, index, or witness bytes", "target": "count every non-source byte in C_total before promotion", "task": "reject_unpaid_sidecar"} -{"input": "new pattern N1-N6", "target": "run the highest-testability ladder first: signal-shaped route compiler or docmd static wiki slice", "task": "choose_new_experiment"} -{"input": "feature score, invariant root, or route priority", "target": "diagnostic until exact decode and byte measurement close", "task": "separate_signal_from_compression"} diff --git a/4-Infrastructure/shim/compression_signal_shaping_synthesis_receipt.json b/4-Infrastructure/shim/compression_signal_shaping_synthesis_receipt.json deleted file mode 100644 index f8a4dd76..00000000 --- a/4-Infrastructure/shim/compression_signal_shaping_synthesis_receipt.json +++ /dev/null @@ -1,292 +0,0 @@ -{ - "approach_taxonomy": [ - { - "approach": "PAQ_style_context_mixing", - "promotion_gate": "only byte measurement and exact decode count", - "risk": "context/model bytes can silently exceed gain", - "shapes": "probability context", - "use": "long-range sparse contexts, context mixing, arithmetic-coding style evidence" - }, - { - "approach": "decision_diagram_route_search", - "promotion_gate": "route cost < incumbent with decoder/residual/witness counted", - "risk": "route explosion without admissible lower bounds", - "shapes": "candidate route space", - "use": "enumerate transform routes with lower bounds and prune dominated branches" - }, - { - "approach": "T16_candidate_pipeline", - "promotion_gate": "event must become an executable route with exact rehydration", - "risk": "signal analogy mistaken for compression evidence", - "shapes": "weak event detection", - "use": "detect residual-collapse events in noisy candidate forests" - }, - { - "approach": "phi_response_family_selection", - "promotion_gate": "held-out fit beats simple baselines", - "risk": "Phi gain treated as universal law", - "shapes": "response curve", - "use": "choose log/saturating/Hill/low-exponent response by measured error" - }, - { - "approach": "nonlinear_compressed_sensing", - "promotion_gate": "structure and regularity conditions explicit", - "risk": "nonlinear manifold route without bounds", - "shapes": "regular nonlinear measurement map", - "use": "guide structured recovery when RIP-like or separability conditions exist" - }, - { - "approach": "generative_compressed_sensing", - "promotion_gate": "latent + residual + uncertainty bytes beat baseline", - "risk": "generator becomes hidden payload or biased source substitute", - "shapes": "latent proposal manifold", - "use": "replace plain sparsity with learned low-dimensional priors" - }, - { - "approach": "invertible_generative_inverse", - "promotion_gate": "invertibility guard, support check, residual closure", - "risk": "approximate invertibility treated as lossless", - "shapes": "invertible/flow chart", - "use": "reduce representation error and expose uncertainty in inverse route charts" - }, - { - "approach": "holographic_fractional_recursive_fold", - "promotion_gate": "decoded hash closes and memory/kernel bytes counted", - "risk": "boundary/bulk split hides payload", - "shapes": "boundary descriptor and bounded memory", - "use": "short descriptor plus exact residual, graph harmonics, bounded history" - }, - { - "approach": "signal_invariant_roots", - "promotion_gate": "features only choose routes; bytes decide", - "risk": "feature score promoted without codec trial", - "shapes": "signal morphology feature space", - "use": "route chunks by spectral, transient, autocorrelation, DCT, phase, and similarity roots" - }, - { - "approach": "semantic_topology_regimes", - "promotion_gate": "round-trip loss and contradiction/torsion receipts", - "risk": "smooth story over torn semantics", - "shapes": "fold/prune/tear decision", - "use": "avoid false merges; classify beautiful/ugly/horrible compression regimes" - }, - { - "approach": "llm_control_plane_compression", - "promotion_gate": "source bytes, retained bytes, quality delta, provenance", - "risk": "lossy summary sold as exact compression", - "shapes": "prompt/logogram/control representation", - "use": "prune prompts, use symbolic cells, use compressed proxy views" - }, - { - "approach": "docmd_static_payload_strategy", - "promotion_gate": "built-site payload measurement with exact plugin config", - "risk": "architecture reduction confused with content compression", - "shapes": "runtime payload", - "use": "pre-render static HTML, omit heavy framework runtime, gate plugins, externalize search index" - } - ], - "claim_boundary": "This synthesis proposes testable route-shaping experiments. It is not a Hutter Prize result, not proof of a new compressor, and not a guarantee that signal features will improve wiki8.", - "failure_rules": [ - "feature score treated as byte gain -> invalid", - "sidecar, witness, residual, decoder, or container bytes omitted -> invalid receipt", - "latent/generative prior used as hidden source payload -> invalid", - "semantic merge without round-trip or contradiction check -> hold", - "history kernel unbounded or uncounted -> fail closed", - "docmd-style staticization reported as Hutter compression -> overclaim", - "negative controls omitted from new route claim -> weak claim" - ], - "immediate_experiment_ladder": [ - { - "action": "extract per-chunk signal features and compare feature clusters to bz2/zstd outcomes", - "name": "wiki8_signal_feature_baseline", - "step": "E1", - "success": "feature clusters predict which chunks benefit from which existing codec route" - }, - { - "action": "choose among existing routes only: raw, bz2, zstd, xml_token+bz2, tokenbook+bz2 if available", - "name": "route_classifier_without_new_codec", - "step": "E2", - "success": "classifier beats always-bz2 after classifier sidecar bytes" - }, - { - "action": "apply semantic/topology guards before tokenbook merge", - "name": "topology_guard_tokenbook", - "step": "E3", - "success": "bad merges fall while byte gain remains non-negative" - }, - { - "action": "export a small tiddler/article slice to static pages plus external index", - "name": "docmd_static_wiki_slice", - "step": "E4", - "success": "lower initial payload than live surface with same navigability" - }, - { - "action": "route stream chunks with finite fractional residual memory", - "name": "bounded_history_scheduler", - "step": "E5", - "success": "history-aware route choice beats memoryless route after history bytes" - } - ], - "new_candidate_patterns": [ - { - "candidate_equation": "route = argmin_r LB(r | phi_signal(chunk), topology_regime, history_state)", - "first_test": "wiki8 chunk sweep with features: entropy, XML tag density, DCT energy, transient edges, autocorrelation, cosine reuse", - "id": "N1_signal_shaped_route_compiler", - "novelty": "combine signal invariant roots with DD route search", - "promotion_gate": "chosen route beats bz2/zstd baseline after feature/witness bytes", - "shape": "chunk -> feature vector -> route family -> codec trial -> exact residual", - "testability": "high", - "why_it_popped": "signal roots supply cheap morphology; DD supplies admissible route discipline" - }, - { - "candidate_equation": "payload_total = html_static + js_core + css_core + selected_plugin_assets + index_external", - "first_test": "build a small TiddlyWiki/article slice both live and static; compare initial gzip payload and search index cost", - "id": "N2_runtime_staticization_as_compression_prepass", - "novelty": "treat docmd-style no-runtime output as a compression prepass for wiki/tiddler publishing", - "promotion_gate": "same navigation/search affordance with lower initial payload", - "shape": "tiddlers/articles -> static route pages + external search index + manifest", - "testability": "high", - "why_it_popped": "payload shrinks by not shipping dynamic state; maps to gated leaves in DD" - }, - { - "candidate_equation": "C = bytes(z) + bytes(model_id) + bytes(residual) + bytes(witness) + bytes(decoder)", - "first_test": "small structured corpus slice with tokenbook latent IDs and exact residual lane", - "id": "N3_witness_budgeted_latent_route", - "novelty": "use generative/flow priors only as proposals with explicit latent/residual byte accounting", - "promotion_gate": "C < incumbent and decoded hash equals source hash", - "shape": "latent z proposes transform; exact residual repairs; uncertainty decides hold", - "testability": "medium", - "why_it_popped": "generative and invertible priors are useful only when they stop hiding model state" - }, - { - "candidate_equation": "h_t = sum_{tau measurable perturbation -> negative control -> receipt", - "testability": "high", - "why_it_popped": "the CAD frame made measurement and negative controls explicit; compression routes need the same habit" - } - ], - "primary_read": "Across the local compression, compressed-sensing, signal-root, semantic-topology, and docmd payload notes, the new pattern is not another universal compressor. It is a signal-shaped route compiler: shape the route space before coding, then pay exact residual, witness, decoder, and container bytes after coding.", - "receipt_hash": "fc06057b20dc2281161e7380a63557171ab4d87ee7a82277fe2e8d74b1446f68", - "schema": "compression_signal_shaping_synthesis_v1", - "source_artifacts": [ - { - "bytes": 3665, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/PAQ Style Compression Review.tid", - "sha256": "e36d4ffb329d09a4a42b9a824b92a2ea0008fdbdad0dc636174736a6e18df45f" - }, - { - "bytes": 6543, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Hutter Equation Metastate Transfold.tid", - "sha256": "97839ff6f6f60ca827b85f80b5f44fd6a78f0119b5b4976dca11bb8c25ac2d29" - }, - { - "bytes": 6474, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/T16 Candidate Pipeline Equation Prior.tid", - "sha256": "5e8e2519df1e5b3814f4d234de65b6b49713e2a77eceb13523d9f2d24cb76b94" - }, - { - "bytes": 5164, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Phi Scaling Response Model Selection.tid", - "sha256": "993c616c03cf255a2cb9d756511a165f11c37808f2a2067621802d376ef1e447" - }, - { - "bytes": 22500, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Classical Signal Roots Quantum Translation Program.tid", - "sha256": "6290c0898b532730b86a0d36c1ac7dece5ecf250588daaf927c4c27f0ad4e59c" - }, - { - "bytes": 1545, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Semantic Topology Compression Regimes.tid", - "sha256": "5415310b6d9f9907d26536932724dd2da83beb8196b0f9329488497db213f8c0" - }, - { - "bytes": 1916, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/LLM Compression Architecture Priors.tid", - "sha256": "42944d5ee4133b6e0aa28833db1fd66e2252a262f448bd35394108b10ba373a9" - }, - { - "bytes": 2556, - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/docmd Size Strategy Prior.tid", - "sha256": "2aa5c194c0e4d55de83012d4566f846f784ff2ba08351888e982579d66573ee1" - }, - { - "bytes": 5824, - "path": "4-Infrastructure/shim/nonlinear_compressed_sensing_structural_prior_receipt.json", - "sha256": "f1e93d5ede20785bffed4e8b7520f3cdc9050c74c493dba41930d144f69874bc" - }, - { - "bytes": 5140, - "path": "4-Infrastructure/shim/generative_compressed_sensing_prior_receipt.json", - "sha256": "2eef678ab384e524e3909b310d8e36efa5c4776f699bc4d99ff720a1ee07c34d" - }, - { - "bytes": 5462, - "path": "4-Infrastructure/shim/invertible_generative_inverse_prior_receipt.json", - "sha256": "da99269c79d7b429524d43a6e69b6e860212c4b34b79421f91faafb88660f27e" - }, - { - "bytes": 8441, - "path": "4-Infrastructure/shim/holographic_fractional_recursive_equation_fold_receipt.json", - "sha256": "7284397eae4a93679e69a1550c76e322bfb28bc47c10af382fb05d8fd16fd74c" - }, - { - "bytes": 17340, - "path": "4-Infrastructure/shim/signal_equation_invariant_roots_receipt.json", - "sha256": "d62fe13ad78a481c4c2954bfa06a1e2255a5cd8923fb5f4a6b90d833a7cd1972" - }, - { - "bytes": 1948, - "path": "4-Infrastructure/shim/semantic_topology_compression_regimes_receipt.json", - "sha256": "e3b09e3090f9c19e5df2819424efd258e0968ba78136ff31c9f25c9d0eacdc3f" - }, - { - "bytes": 7224, - "path": "4-Infrastructure/shim/llm_compression_architecture_prior_receipt.json", - "sha256": "4e65458e29e17f032fa2b0df0a06d4713a2102ff2a58ef13d96c37dda5691264" - }, - { - "bytes": 8845, - "path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json", - "sha256": "fc222e265e70f69ee5e6039dd0cee2665a02e549b6718a7d022f9241849b3cf1" - } - ], - "unifying_equations": { - "exact_cost": "C_total = bytes(payload) + bytes(sidecar) + bytes(residual) + bytes(decoder) + bytes(witness) + bytes(container)", - "negative_control": "valid_gain iff C(candidate) < C(baseline) and C(candidate) < C(matched_bad_route)", - "promotion": "promote iff H(decode(r*)) == H(source) and C_total < incumbent and failure_rules == none", - "route_selection": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state)", - "signal_feature_vector": "phi_signal(c) = [H(c), tag_density(c), DCT_energy(c), transient(c), autocorr(c), cosine_reuse(c)]" - }, - "what_is_actually_new": [ - "The strongest new move is route-space signal shaping, not a new compressor.", - "docmd reframes compression as runtime-state omission: do not ship branches you can rebuild.", - "Signal invariant roots give a concrete feature surface for choosing routes before spending codec time.", - "Semantic topology supplies a guard against destructive tokenbook merges.", - "Generative/invertible models should be restricted to proposal charts with explicit residual byte accounting.", - "Every interesting analogy becomes useful only after it is paired with a negative control and exact decode receipt." - ] -} diff --git a/4-Infrastructure/shim/concept_cross_reference.py b/4-Infrastructure/shim/concept_cross_reference.py deleted file mode 100644 index 61488850..00000000 --- a/4-Infrastructure/shim/concept_cross_reference.py +++ /dev/null @@ -1,628 +0,0 @@ -#!/usr/bin/env python3 -""" -Aggressive cross-source concept tagging and relationship discovery against RDS. - -1. Extracts normalized terms from all ingested sources -2. Cross-references across equation ↔ tiddlywiki ↔ reference ↔ link ↔ dataset -3. Builds a concept lattice: concept_tags, source_mentions, cross_triples -4. Runs clustering queries to surface unexpected groupings - -Run in dev container: - podman exec -e AWS_ACCESS_KEY_ID=... -e AWS_SECRET_ACCESS_KEY=... -e AWS_REGION=us-east-1 -e RDS_IAM=1 \ - research-stack python3 /home/researcher/stack/4-Infrastructure/shim/concept_cross_reference.py -""" - -from __future__ import annotations - -import hashlib -import json -import logging -import os -import re -import sys -import uuid -from collections import Counter -from typing import Iterable - -import boto3 -import psycopg2 -import psycopg2.extras - -logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") -log = logging.getLogger("concept_xref") - -RDS_HOST = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com") -RDS_PORT = int(os.environ.get("RDS_PORT", "5432")) -RDS_USER = os.environ.get("RDS_USER", "postgres") -RDS_DBNAME = os.environ.get("RDS_DBNAME", "postgres") -RDS_IAM = os.environ.get("RDS_IAM", "1") == "1" -RDS_PW = os.environ.get("RDS_PASSWORD", "") -AWS_REGION = os.environ.get("AWS_REGION", "us-east-1") - - -def connect(): - if RDS_IAM: - client = boto3.client("rds", region_name=AWS_REGION) - pw = client.generate_db_auth_token(DBHostname=RDS_HOST, Port=RDS_PORT, DBUsername=RDS_USER, Region=AWS_REGION) - else: - pw = RDS_PW - return psycopg2.connect(host=RDS_HOST, port=RDS_PORT, user=RDS_USER, password=pw, dbname=RDS_DBNAME, sslmode="require") - - -def ensure_schema(conn): - cur = conn.cursor() - cur.execute(""" - CREATE TABLE IF NOT EXISTS knowledge.concept_tags ( - concept_id uuid PRIMARY KEY DEFAULT gen_random_uuid(), - term text NOT NULL, - normalized text NOT NULL, - term_type text NOT NULL, -- 'math_symbol', 'technical_term', 'named_entity', 'domain_label' - frequency integer NOT NULL DEFAULT 0, - sources text[] NOT NULL DEFAULT '{}', -- which tables it appears in - created_at timestamptz NOT NULL DEFAULT now() - ); - CREATE UNIQUE INDEX IF NOT EXISTS concept_norm_idx ON knowledge.concept_tags (normalized); - - CREATE TABLE IF NOT EXISTS knowledge.source_mentions ( - mention_id uuid PRIMARY KEY DEFAULT gen_random_uuid(), - concept_id uuid NOT NULL REFERENCES knowledge.concept_tags(concept_id) ON DELETE CASCADE, - source_table text NOT NULL, -- 'equations','tiddlywiki_pages','references','dois','links','article_sources' - source_id uuid NOT NULL, - term_raw text NOT NULL, - context_snippet text, - position integer, - ingested_at timestamptz NOT NULL DEFAULT now() - ); - CREATE INDEX IF NOT EXISTS sm_concept_idx ON knowledge.source_mentions (concept_id); - CREATE INDEX IF NOT EXISTS sm_source_idx ON knowledge.source_mentions (source_table, source_id); - - CREATE TABLE IF NOT EXISTS knowledge.cross_triples ( - triple_id uuid PRIMARY KEY DEFAULT gen_random_uuid(), - concept_id uuid NOT NULL REFERENCES knowledge.concept_tags(concept_id), - source_a_table text NOT NULL, - source_a_id uuid NOT NULL, - source_b_table text NOT NULL, - source_b_id uuid NOT NULL, - cooccurrence integer NOT NULL DEFAULT 1, - discovered_at timestamptz NOT NULL DEFAULT now() - ); - CREATE INDEX IF NOT EXISTS ct_concept_idx ON knowledge.cross_triples (concept_id); - CREATE INDEX IF NOT EXISTS ct_pair_idx ON knowledge.cross_triples (source_a_table, source_a_id, source_b_table, source_b_id); - - CREATE TABLE IF NOT EXISTS knowledge.concept_clusters ( - cluster_id uuid PRIMARY KEY DEFAULT gen_random_uuid(), - cluster_label text NOT NULL, - concept_ids uuid[] NOT NULL, - dominant_terms text[] NOT NULL, - size integer NOT NULL, - cohesion_score float8 NOT NULL DEFAULT 0, - cross_sources text[] NOT NULL, - created_at timestamptz NOT NULL DEFAULT now() - ); - """) - conn.commit() - log.info("Schema ready") - - -# --------------------------------------------------------------------------- -# Term extraction -# --------------------------------------------------------------------------- -MATH_SYMBOL_RE = re.compile( - r'\\(?:alpha|beta|gamma|Gamma|delta|Delta|epsilon|varepsilon|zeta|eta|theta|Theta|' - r'iota|kappa|lambda|Lambda|mu|nu|xi|Xi|pi|Pi|rho|sigma|Sigma|tau|upsilon|phi|Phi|' - r'varphi|chi|psi|Psi|omega|Omega|partial|nabla|infty|int|sum|prod|otimes|oplus|' - r'rightarrow|leftarrow|Rightarrow|Leftarrow|mapsto|approx|equiv|sim|propto|' - r'leq|geq|neq|times|cdot|circ|pm|mp|sqrt|frac|operatorname|mathbf|mathrm|mathcal|' - r'mathfrak|mathbb|text|hat|tilde|bar|vec|dot|ddot|widehat|widetilde|' - r'emptyset|forall|exists|nexists|in|notin|subset|subseteq|supset|supseteq|' - r'cup|cap|setminus|wedge|vee|neg|implies|iff|top|bot|land|lor|' - r'langle|rangle|lVert|rVert|vert|Vert|mid|' - r'underbrace|overbrace|stackrel|limits|nolimits|' - r'textup|textrm|textsf|texttt|textnormal|' - r'begin|end|item|label|ref|cite|' - r'left|right|big|Big|bigg|Bigg)' -) -TECHNICAL_WORD_RE = re.compile( - r'\b(?:' - r'manifold|field|shear|packet|spectral|braid|gossip|' - r'residual|invariant|receipt|scar|warden|collapse|compression|' - r'entropy|eigen|eigenvalue|eigenvector|coboundary|cochain|' - r'diffusion|transport|boundary|kernel|operator|' - r'tensor|metric|geodesic|curvature|torsion|' - r'quantum|classical|hamiltonian|lagrangian|' - r'reduction|projection|embedding|immersion|' - r'chirality|helicity|handedness|logogram|' - r'sidon|goxel|famm|nuvmap|otom|pist|' - r'erdos|szekeres|selfridge|gyarfas|' - r'biocompression|biocompress|organoid|' - r'chaos|fractal|attractor|basin|' - r'thermal|thermodynamic|landauer|' - r'witness|shadow|adversarial|' - r'morph|morphic|morphism|' - r'radix|codec|codec|semantic|' - r'markov|cognitive|attention|' - r'lattice|algebra|topology|topos|' - r'proof|theorem|lemma|corollary|' - r'neural|network|transformer|' - r'hutter|compression|prize|' - r'bio|dna|rna|protein|' - r'feynman|navier|stokes|' - r'plasma|mhd|alfven|' - r'betti|homology|cohomology|' - r'riccati|noise|mfg|' - r'hessian|jacobian|' - r'seam|tomography|sandwich|' - r'pruning|rope|scar|' - r'eigensolid|eigenspace|' - r'underverse|geocognition|' - r'smallcode|constrained|' - r'\bN\s*space\b|key.value|' - r'shortcut|ontology|' - r'hyperbolic|riemannian|poincare' - r')\b', - re.IGNORECASE, -) -TOKEN_RE = re.compile(r'[a-zA-Z_\\][a-zA-Z0-9_\\]*|\b(?:N-space|key-value|CP-SAT|Anti-FAMM|Anti-Braid)\b', re.IGNORECASE) - -STOP_WORDS = { - "the","a","an","is","are","was","were","be","been","being","have","has","had", - "do","does","did","will","would","shall","should","may","might","must","can","could", - "of","in","to","for","with","on","at","by","from","as","into","through","during", - "and","but","or","not","no","nor","so","if","then","else","when","where", - "this","that","these","those","it","its","we","they","he","she", - "which","who","whom","whose","what","how","why", - "also","very","more","most","some","any","all","each","every","both","few", - "new","other","such","only","own","same","just","about","over", - "text","bf","rm","sf","tt","em","sc","it","up", - "use","using","used","can","one","two","also", - "etc","via","per","e.g","i.e","figure","table","section", - "non","doi","url","http","https","www", - "paper","result","method","approach","model","data","set", - "page","pages","vol","pp","et","al", - "note","notes","example","see","shown","fig","eq","ref", - "abstract","introduction","conclusion","reference","references", - "arxiv","org","github","com","html","pdf", - "first","second","third","following","followed", - "based","given","found","obtained","described","proposed", - "well","within","without","between","among","under","above","below", - "since","however","therefore","thus","still","yet", - "does","don","doesn","did","didn","has","haven","hadn", - "here","there","where","now","then","than", - "get","got","getting","make","made","making", - "take","taken","taking","give","given","giving", - "let","lets","case","cases","term","terms","form","forms", - "number","numbers","value","values","point","points", - "part","parts","type","types","kind","kinds","way","ways", - "much","many","long","short","high","low","large","small", - "different","similar","same","total","whole","full", - "work","works","working","need","needs","needed", - "help","helps","helped","like","likes","liked", - "know","known","unknown","think","thought","believe", - "want","wants","wanted","try","tries","tried", -} - - -def tokenize(text: str) -> Iterable[str]: - """Split text into normalized tokens, filtering stop words.""" - for m in TOKEN_RE.finditer(text): - t = m.group(0).strip().lower().rstrip(".,;:!?\"'()[]{}") - if not t or t in STOP_WORDS or len(t) < 2: - continue - # Clean LaTeX residue - t = t.lstrip("\\").rstrip("{}") - if not t or t in STOP_WORDS or len(t) < 2: - continue - yield t - - -def classify_term(term: str, text: str) -> str: - """Classify term as math_symbol, technical_term, named_entity, or domain_label.""" - if MATH_SYMBOL_RE.fullmatch(term): - return "math_symbol" - if TECHNICAL_WORD_RE.search(term): - return "technical_term" - # Capitalized word or LaTeX \command suggests math symbol - if term.startswith("\\") or (term[0].isupper() and len(term) <= 3 and term.isalpha()): - return "math_symbol" - return "technical_term" - - -def extract_terms(text: str) -> list[tuple[str, str]]: - """Return list of (raw_term, term_type) tuples.""" - results: list[tuple[str, str]] = [] - seen: set[str] = set() - for tok in tokenize(text): - if tok in seen: - continue - seen.add(tok) - ttype = classify_term(tok, text) - results.append((tok, ttype)) - return results - - -# --------------------------------------------------------------------------- -# Cross-referencing -# --------------------------------------------------------------------------- -def fetch_and_tag(conn): - """Extract terms from all sources and populate concept_tags + source_mentions.""" - cur = conn.cursor() - - # Clear previous run (idempotent for re-runs) - cur.execute("TRUNCATE knowledge.source_mentions, knowledge.cross_triples CASCADE") - cur.execute("DELETE FROM knowledge.concept_tags") - - # Map: normalized_term -> concept_id - concept_map: dict[str, uuid.UUID] = {} - - sources = [ - ("equations", "SELECT eq_id, latex, source_file FROM knowledge.equations"), - ("tiddlywiki_pages", "SELECT tiddler_id, body || ' ' || tags, title FROM knowledge.tiddlywiki_pages"), - ("references", "SELECT ref_id, bibtex, source_file FROM knowledge.references"), - ("links", "SELECT link_id, url, source_file FROM knowledge.links"), - ("article_sources", "SELECT article_id, url || ' ' || coalesce(label,''), coalesce(label,'') FROM knowledge.article_sources"), - ("dois", "SELECT doi_id, doi, source_file FROM knowledge.dois"), - ("dataset_inventory", "SELECT inv_id, coalesce(name,'') || ' ' || coalesce(evidence,'') || ' ' || coalesce(notes,''), coalesce(name,'') FROM knowledge.dataset_inventory"), - ] - - total_mentions = 0 - - for src_table, query in sources: - cur.execute(query) - rows = cur.fetchall() - log.info("Processing %s: %d rows", src_table, len(rows)) - - for row in rows: - source_id = row[0] - text = row[1] if row[1] else "" - label = row[2] if len(row) > 2 and row[2] else "" - - terms = extract_terms(text) - for raw_term, ttype in terms: - norm = raw_term.lower().strip("\\{}") - - # Get or create concept - if norm not in concept_map: - cur.execute( - """INSERT INTO knowledge.concept_tags (term, normalized, term_type, frequency, sources) - VALUES (%s,%s,%s,1,ARRAY[%s]) - ON CONFLICT (normalized) DO UPDATE SET - frequency = concept_tags.frequency + 1, - sources = array_append(concept_tags.sources, %s) - RETURNING concept_id""", - (raw_term, norm, ttype, src_table, src_table), - ) - cid = cur.fetchone()[0] - concept_map[norm] = cid - else: - cid = concept_map[norm] - cur.execute( - """UPDATE knowledge.concept_tags - SET frequency = frequency + 1, - sources = CASE WHEN NOT (%s = ANY(sources)) THEN array_append(sources, %s) ELSE sources END - WHERE concept_id = %s""", - (src_table, src_table, cid), - ) - - # Record mention - snippet = text[max(0, text.lower().find(raw_term.lower()) - 60): - min(len(text), text.lower().find(raw_term.lower()) + 60)] - cur.execute( - """INSERT INTO knowledge.source_mentions (concept_id, source_table, source_id, term_raw, context_snippet) - VALUES (%s,%s,%s,%s,%s)""", - (cid, src_table, source_id, raw_term, snippet), - ) - total_mentions += 1 - - if total_mentions % 5000 == 0: - conn.commit() - log.info(" %d mentions processed…", total_mentions) - - conn.commit() - log.info("Total concepts: %d, total mentions: %d", len(concept_map), total_mentions) - return total_mentions - - -def build_cross_triples(conn): - """For each concept that appears in 2+ sources, create cross triples between every pair of source items.""" - cur = conn.cursor() - - cur.execute(""" - SELECT concept_id, array_agg(DISTINCT source_table) AS src_tables, - array_agg(DISTINCT source_id) AS src_ids - FROM knowledge.source_mentions - GROUP BY concept_id - HAVING count(DISTINCT source_table) >= 2 - """) - concepts = cur.fetchall() - log.info("Cross-referencing %d multi-source concepts…", len(concepts)) - - triple_count = 0 - for cid, src_tables, src_ids in concepts: - # Build cross triples: for each concept, link every source item pair - # that shares this concept but comes from different source tables - cur.execute(""" - SELECT DISTINCT sm1.source_table, sm1.source_id, - sm2.source_table, sm2.source_id - FROM knowledge.source_mentions sm1 - JOIN knowledge.source_mentions sm2 - ON sm1.concept_id = sm2.concept_id - AND sm1.source_table < sm2.source_table - AND sm1.source_id != sm2.source_id - WHERE sm1.concept_id = %s - """, (cid,)) - pairs = cur.fetchall() - for sa_table, sa_id, sb_table, sb_id in pairs: - cur.execute( - """INSERT INTO knowledge.cross_triples (concept_id, source_a_table, source_a_id, source_b_table, source_b_id) - VALUES (%s,%s,%s,%s,%s) - ON CONFLICT DO NOTHING""", - (cid, sa_table, sa_id, sb_table, sb_id), - ) - triple_count += 1 - - if triple_count % 2000 == 0: - conn.commit() - log.info(" %d triples…", triple_count) - - conn.commit() - log.info("Cross triples: %d", triple_count) - return triple_count - - -def discover_clusters(conn, min_cohesion: float = 0.3, max_clusters: int = 50): - """Use cross triples to discover concept clusters via shared-source density.""" - cur = conn.cursor() - - # Approach: find concepts that co-occur with the same source items - # A cluster forms when multiple concepts share the same cross-source item pairs - cur.execute(""" - WITH concept_pairs AS ( - SELECT ct1.concept_id AS c1, ct2.concept_id AS c2, - ct1.source_a_table, ct1.source_a_id, - ct1.source_b_table, ct1.source_b_id, - COUNT(*) AS shared_instances - FROM knowledge.cross_triples ct1 - JOIN knowledge.cross_triples ct2 - ON ct1.source_a_table = ct2.source_a_table - AND ct1.source_a_id = ct2.source_a_id - AND ct1.source_b_table = ct2.source_b_table - AND ct1.source_b_id = ct2.source_b_id - AND ct1.concept_id < ct2.concept_id - WHERE ct1.concept_id != ct2.concept_id - GROUP BY 1,2,3,4,5,6 - ), - concept_cohesion AS ( - SELECT c1, c2, COUNT(*) AS shared_pairs, - ARRAY_AGG(DISTINCT source_a_table || '-' || source_b_table) AS cross_source_pairs - FROM concept_pairs - GROUP BY c1, c2 - HAVING COUNT(*) >= 2 - ORDER BY shared_pairs DESC - ) - SELECT cc.c1, cc.c2, cc.shared_pairs, cc.cross_source_pairs, - t1.normalized AS term_a, t2.normalized AS term_b, - t1.sources AS sources_a, t2.sources AS sources_b - FROM concept_cohesion cc - JOIN knowledge.concept_tags t1 ON t1.concept_id = cc.c1 - JOIN knowledge.concept_tags t2 ON t2.concept_id = cc.c2 - ORDER BY cc.shared_pairs DESC - LIMIT %s - """, (max_clusters * 2,)) - - pairs = cur.fetchall() - log.info("Found %d high-cohesion concept pairs", len(pairs)) - - # Greedy cluster assignment - cluster_map: dict[uuid.UUID, uuid.UUID] = {} - clusters: dict[uuid.UUID, dict] = {} - - for c1, c2, shared, xsources, term_a, term_b, sources_a, sources_b in pairs: - c1_cluster = cluster_map.get(c1) - c2_cluster = cluster_map.get(c2) - - if c1_cluster and c2_cluster: - if c1_cluster != c2_cluster: - # Merge: combine smaller into larger - ca = clusters[c1_cluster] - cb = clusters[c2_cluster] - if ca["size"] >= cb["size"]: - _merge_cluster(ca, cb, cluster_map) - del clusters[c2_cluster] - else: - _merge_cluster(cb, ca, cluster_map) - del clusters[c1_cluster] - elif c1_cluster: - _add_to_cluster(clusters[c1_cluster], c2, term_b, sources_b, c2_cluster) - cluster_map[c2] = c1_cluster - elif c2_cluster: - _add_to_cluster(clusters[c2_cluster], c1, term_a, sources_a, c1_cluster) - cluster_map[c1] = c2_cluster - else: - # New cluster - cid = uuid.uuid4() - sources_set = set(list(sources_a) + list(sources_b) + [xs.split("-")[0] for xs in xsources] + [xs.split("-")[1] if "-" in xs else "" for xs in xsources]) - clusters[cid] = { - "concept_ids": [c1, c2], - "terms": [term_a, term_b], - "cross_sources": list(sources_set - {""}), - "size": 2, - } - cluster_map[c1] = cid - cluster_map[c2] = cid - - # Persist clusters - cluster_count = 0 - for cid, cdata in clusters.items(): - if cdata["size"] < 3: - continue - # Compute approximate cohesion score - cohesion = min(1.0, cdata["size"] / 10.0) - cur.execute( - """INSERT INTO knowledge.concept_clusters - (cluster_id, cluster_label, concept_ids, dominant_terms, size, cohesion_score, cross_sources) - VALUES (%s,%s,%s,%s,%s,%s,%s)""", - (cid, cdata["terms"][0], cdata["concept_ids"], cdata["terms"], - cdata["size"], cohesion, cdata["cross_sources"]), - ) - cluster_count += 1 - - conn.commit() - log.info("Persisted %d concept clusters", cluster_count) - return cluster_count - - -def _merge_cluster(into: dict, other: dict, cluster_map: dict): - into["concept_ids"].extend(other["concept_ids"]) - into["terms"].extend(other["terms"]) - into["cross_sources"] = list(set(into["cross_sources"] + other["cross_sources"])) - into["size"] += other["size"] - for cid in other["concept_ids"]: - cluster_map[cid] = cluster_map[into["concept_ids"][0]] if into["concept_ids"] else None - - -def _add_to_cluster(cdata: dict, cid, term, sources, _old_cluster): - cdata["concept_ids"].append(cid) - cdata["terms"].append(term) - cdata["cross_sources"] = list(set(cdata["cross_sources"] + list(sources))) - cdata["size"] += 1 - - -def run_exploratory_queries(conn): - """Run and log interesting cross-source discovery queries.""" - cur = conn.cursor() - - queries = [ - ("Top concepts by cross-source span", - """SELECT ct.normalized, ct.term_type, ct.frequency, - array_length(ct.sources, 1) AS source_count, - COUNT(DISTINCT sm.source_table) AS actual_sources - FROM knowledge.concept_tags ct - JOIN knowledge.source_mentions sm ON sm.concept_id = ct.concept_id - GROUP BY ct.concept_id, ct.normalized, ct.term_type, ct.frequency, ct.sources - ORDER BY actual_sources DESC, ct.frequency DESC - LIMIT 30"""), - - ("Cross-source pairs that share the most concepts", - """SELECT ct.source_a_table, ct.source_b_table, - COUNT(DISTINCT ct.concept_id) AS shared_concepts, - COUNT(*) AS total_pairs - FROM knowledge.cross_triples ct - GROUP BY ct.source_a_table, ct.source_b_table - ORDER BY shared_concepts DESC - LIMIT 20"""), - - ("Equations referencing tiddlywiki concepts", - """SELECT sm1.source_id AS tw_id, tw.title, sm2.source_id AS eq_id, - eq.latex, ct.normalized AS shared_concept - FROM knowledge.cross_triples ct - JOIN knowledge.source_mentions sm1 ON sm1.concept_id = ct.concept_id AND sm1.source_table = 'tiddlywiki_pages' - JOIN knowledge.source_mentions sm2 ON sm2.concept_id = ct.concept_id AND sm2.source_table = 'equations' - JOIN knowledge.tiddlywiki_pages tw ON tw.tiddler_id = sm1.source_id - JOIN knowledge.equations eq ON eq.eq_id = sm2.source_id - LIMIT 30"""), - - ("References linked to datasets via shared concepts", - """SELECT ct.normalized, - r.bibtex AS ref_bibtex, - di.name AS dataset_name - FROM knowledge.cross_triples ct - JOIN knowledge.source_mentions sm1 ON sm1.concept_id = ct.concept_id AND sm1.source_table = 'references' - JOIN knowledge.source_mentions sm2 ON sm2.concept_id = ct.concept_id AND sm2.source_table = 'dataset_inventory' - JOIN knowledge.references r ON r.ref_id = sm1.source_id - JOIN knowledge.dataset_inventory di ON di.inv_id = sm2.source_id - LIMIT 30"""), - - ("TiddlyWiki pages bridging multiple datasets via shared concepts", - """SELECT tw.title, - array_agg(DISTINCT di.name) AS linked_datasets, - array_agg(DISTINCT ct.normalized) AS shared_concepts, - COUNT(DISTINCT di.inv_id) AS dataset_count - FROM knowledge.source_mentions stw - JOIN knowledge.source_mentions sdi - ON stw.concept_id = sdi.concept_id - AND stw.source_table = 'tiddlywiki_pages' - AND sdi.source_table = 'dataset_inventory' - JOIN knowledge.tiddlywiki_pages tw ON tw.tiddler_id = stw.source_id - JOIN knowledge.dataset_inventory di ON di.inv_id = sdi.source_id - JOIN knowledge.concept_tags ct ON ct.concept_id = stw.concept_id - GROUP BY tw.tiddler_id, tw.title - HAVING COUNT(DISTINCT di.inv_id) >= 2 - ORDER BY dataset_count DESC - LIMIT 30"""), - - ("Most connected concepts (hub scores)", - """SELECT ct.normalized, ct.term_type, - COUNT(DISTINCT sm.source_table) AS source_types, - COUNT(DISTINCT sm.source_id) AS items_linked, - COUNT(DISTINCT cl.cluster_id) AS clusters - FROM knowledge.concept_tags ct - LEFT JOIN knowledge.source_mentions sm ON sm.concept_id = ct.concept_id - LEFT JOIN knowledge.concept_clusters cl ON ct.concept_id = ANY(cl.concept_ids) - GROUP BY ct.concept_id, ct.normalized, ct.term_type - ORDER BY items_linked DESC - LIMIT 30"""), - - ("Concept clusters with most diverse cross-source provenance", - """SELECT cc.cluster_label, cc.size, cc.cohesion_score, - cc.dominant_terms[1:5] AS top_terms, - cc.cross_sources - FROM knowledge.concept_clusters cc - ORDER BY array_length(cc.cross_sources, 1) DESC, cc.size DESC - LIMIT 20"""), - - ("Unexpected equation-tiddler-DOI triples", - """SELECT eq.latex, tw.title, d.doi, ct.normalized - FROM knowledge.source_mentions sm_eq - JOIN knowledge.source_mentions sm_tw - ON sm_eq.concept_id = sm_tw.concept_id - AND sm_eq.source_table = 'equations' - AND sm_tw.source_table = 'tiddlywiki_pages' - LEFT JOIN knowledge.source_mentions sm_doi - ON sm_eq.concept_id = sm_doi.concept_id - AND sm_doi.source_table = 'dois' - JOIN knowledge.equations eq ON eq.eq_id = sm_eq.source_id - JOIN knowledge.tiddlywiki_pages tw ON tw.tiddler_id = sm_tw.source_id - LEFT JOIN knowledge.dois d ON d.doi_id = sm_doi.source_id - JOIN knowledge.concept_tags ct ON ct.concept_id = sm_eq.concept_id - WHERE sm_doi.concept_id IS NOT NULL - LIMIT 30"""), - ] - - for title, query in queries: - cur.execute(query) - rows = cur.fetchall() - log.info("\n=== %s (%d results) ===", title, len(rows)) - for i, row in enumerate(rows): - if i >= 10: - log.info(" ... + %d more", len(rows) - 10) - break - log.info(" %s", " | ".join(str(c) for c in row)) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- -def main(): - log.info("Connecting to RDS…") - conn = connect() - conn.autocommit = False - ensure_schema(conn) - - log.info("Phase 1: Extract terms and tag all sources") - mentions = fetch_and_tag(conn) - - log.info("Phase 2: Build cross-references triples") - triples = build_cross_triples(conn) - - log.info("Phase 3: Discover concept clusters") - clusters = discover_clusters(conn, min_cohesion=0.3, max_clusters=50) - - log.info("Phase 4: Exploratory queries") - run_exploratory_queries(conn) - - conn.close() - log.info("Done. mentions=%d triples=%d clusters=%d", mentions, triples, clusters) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/connectome_manipulation_self_update_prior.py b/4-Infrastructure/shim/connectome_manipulation_self_update_prior.py deleted file mode 100644 index 3847c044..00000000 --- a/4-Infrastructure/shim/connectome_manipulation_self_update_prior.py +++ /dev/null @@ -1,194 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for connectome manipulation and self-updating model priors.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "connectome_manipulation_self_update_prior_receipt.json" -CURRICULUM = SHIM / "connectome_manipulation_self_update_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "connectome_manipulation_self_update_prior_v1", - "source_type": "user_supplied_connectome_and_self_updating_bibliography", - "primary_read": ( - "Connectome work provides a disciplined graph perturbation loop: " - "structural graph, simulated dynamics, function readout, reproducible " - "manipulation, prediction, and error remediation. For this stack it " - "is a virtual-connectome control prior, not evidence of autonomous " - "self-rewrite." - ), - "method_lanes": [ - { - "lane": "connectome_manipulation_framework", - "use": "systematically perturb graph structure and measure simulated function", - "risk": "simulation result depends on model assumptions and perturbation scope", - "keys": ["Pokorny2024A"], - }, - { - "lane": "generative_connectome_models", - "use": "generate plausible network structure from wiring and topology rules", - "risk": "generated graph plausibility is not functional validation", - "keys": ["Betzel2015Generative"], - }, - { - "lane": "structural_to_functional_dynamics", - "use": "simulate functional connectivity evolving over structural connectome", - "risk": "static structure can support multiple dynamic regimes", - "keys": ["Cabral2017Functional", "Arbabyazd2021Virtual"], - }, - { - "lane": "connectome_predictive_modeling", - "use": "predict behavior or phenotype from connectivity features", - "risk": "prediction does not explain mechanism without perturbation tests", - "keys": ["Shen2017Using", "Ali2022A"], - }, - { - "lane": "connectome_tooling_and_reproducibility", - "use": "query, manipulate, and audit network datasets", - "risk": "tool query result is not a model claim by itself", - "keys": ["Clements2020neuPrint:"], - }, - { - "lane": "self_updating_and_nonstationary_models", - "use": "periodically update, self-train, or remediate model errors under drift", - "risk": "self-update can amplify errors unless gated by validation", - "keys": ["Li2024Online", "Duarte2024Generating", "Doak2020Self-Updating", "Kim2020Domain"], - }, - { - "lane": "model_connectomes_for_language_models", - "use": "treat model internals as structured graph lineage for data-efficient training", - "risk": "model-connectome analogy requires direct measurement of model graph/function", - "keys": ["Kotar2025Model"], - }, - ], - "virtual_connectome_state": [ - "node_set_id", - "edge_set_id", - "edge_weight_schema", - "structural_connectome_hash", - "functional_state_vector", - "perturbation_operator_id", - "simulation_dynamics_id", - "prediction_head_id", - "error_remediation_policy_id", - "drift_detector_id", - "update_epoch", - "validation_receipt_id", - "rollback_state_hash", - ], - "equation_pipeline_mapping": { - "structural_connectome": "equation dependency graph", - "functional_connectivity": "observed equation behavior under validators", - "connectome_manipulation": "controlled rewrite or perturbation of equation graph", - "simulation": "numeric, symbolic, unit, or byte-route evaluation", - "self_update": "bounded model/equation index update after validation", - "error_remediation": "rollback or patch when validation fails", - }, - "hutter_mapping": { - "connectome_graph": "route dependency graph", - "functional_readout": "compressed bytes, decode hash, runtime, witness cost", - "perturbation": "route transform change", - "self_update": "route scheduler update only after receipt", - "rollback": "restore previous incumbent and dependency graph", - }, - "promotion_rule": [ - "graph state is versioned and hashed", - "perturbation operator is explicit and bounded", - "functional readout is measured locally", - "self-update has validation and rollback receipts", - "Hutter route updates preserve exact decode/hash authority", - ], - "failure_rules": [ - "graph analogy without measured function -> diagnostic only", - "self-update without validation receipt -> fail closed", - "model drift detector missing -> hold", - "rollback state missing -> invalid update", - "prediction score replaces mechanism or byte receipt -> invalid", - ], - "bibliography_keys": [ - "Pokorny2024A", - "Betzel2015Generative", - "Arbabyazd2021Virtual", - "Cabral2017Functional", - "Clements2020neuPrint:", - "Li2024Online", - "Ali2022A", - "Kotar2025Model", - "Shen2017Using", - "Duarte2024Generating", - "Hammer2004Recursive", - "Kim2020Domain", - "Borst2023Connecting", - "Doak2020Self-Updating", - ], - "bibtex_hygiene_notes": [ - "Clements2020neuPrint: contains punctuation in the BibTeX key", - "Doak2020Self-Updating contains punctuation in the BibTeX key", - "Consensus-style DOI metadata should be verified before publication", - ], - "claim_boundary": ( - "This prior supports reproducible graph manipulation and bounded " - "self-update discipline. It does not prove autonomous metatyping, " - "biological equivalence, or compression improvement." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_connectome_update_lane", - "input": "graph manipulation or self-updating model method", - "target": "connectome manipulation, generative graph, dynamics, prediction, tooling, self-update, or model-connectome lane", - }, - { - "task": "require_validation_and_rollback", - "input": "self-updating equation or route graph", - "target": "validation receipt plus rollback state hash", - }, - { - "task": "separate_graph_analogy_from_function", - "input": "connectome-inspired route or equation graph", - "target": "measured functional readout before promotion", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "method_lane_count": len(receipt["method_lanes"]), - "state_field_count": len(receipt["virtual_connectome_state"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting.py b/4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting.py deleted file mode 100644 index 4aa0b62e..00000000 --- a/4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for reweighted cognitive-load equations with connectome-protective overflow.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "connectome_protective_cognitive_load_reweighting_receipt.json" -CURRICULUM = SHIM / "connectome_protective_cognitive_load_reweighting_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "connectome_protective_cognitive_load_reweighting_v1", - "source_type": "user_supplied_reweighting_equations_plus_local_multi_domain_cognitive_load_spec", - "primary_read": ( - "Reweight cognitive load as a domain-specific response-family model with " - "a protective overflow gate. Overflow shifts excess load into an emotional " - "offload channel as a hypothesis about preserving working graph stability. " - "The main intended use is historical and civilizational modeling of overload " - "under accelerated information transfer; it does not erase load and does " - "not prove biological connectome protection." - ), - "core_equations": { - "raw_cognitive_load": ( - "L_cog_raw(d,x) = C_d * response_family_d(x; theta_d) * " - "lambda_phi^D_f * B_gate(d,constraints)" - ), - "trauma_adjusted_threshold": ( - "L_threshold_eff = L_threshold * exp(-rho_T * T_trauma)" - ), - "bandwidth_adjusted_threshold": ( - "L_threshold_hist = L_threshold_eff * exp(-rho_B * B_overflow)" - ), - "bandwidth_overflow": ( - "B_overflow = max(0, transfer_bandwidth - assimilation_bandwidth) / assimilation_bandwidth" - ), - "trauma_adjusted_emotional_barrier": ( - "DeltaE_emotional_eff = DeltaE_emotional + chi_T * T_trauma" - ), - "historical_emotional_barrier": ( - "DeltaE_emotional_hist = DeltaE_emotional_eff + chi_B * B_overflow" - ), - "trauma_adjusted_emotional_temperature": ( - "kT_emotional_eff = kT_emotional / (1 + psi_T * T_trauma)" - ), - "historical_emotional_temperature": ( - "kT_emotional_hist = kT_emotional_eff / (1 + psi_B * B_overflow)" - ), - "overflow_gate": ( - "G_over(d) = 1 if L_cog_raw <= L_threshold_hist; " - "else exp(-gamma_d * (L_cog_raw - L_threshold_hist) / kT_emotional_hist)" - ), - "effective_cognitive_load": "L_cog_eff = L_cog_raw * G_over", - "emotional_offload": ( - "L_emotional_offload = max(0, L_cog_raw - L_threshold_hist) * eta_offload_hist" - ), - "trauma_adjusted_offload_efficiency": ( - "eta_offload_eff = eta_offload * exp(-omega_T * T_trauma)" - ), - "historical_offload_efficiency": ( - "eta_offload_hist = eta_offload_eff * exp(-omega_B * B_overflow)" - ), - "emotional_load": ( - "L_emotional = C_emotional,d * emotional_response_d(L_emotional_offload; " - "theta_emotional,d) * lambda_phi^D_f * B_gate_emotional,d" - ), - "emotional_gate": ( - "B_gate_emotional = exp(-gamma_emotional * DeltaE_emotional_hist / kT_emotional_hist)" - ), - "residual_stress": ( - "L_residual_stress = max(0, L_cog_raw - L_threshold_hist) * (1 - eta_offload_hist)" - ), - "total_protective_load": ( - "L_total = L_cog_eff + L_emotional + L_residual_stress" - ), - "threshold": "L_threshold = C_threshold * lambda_phi^D_f * B_gate_threshold", - }, - "domain_response_families": { - "text": { - "cognitive": "log(1 + beta_text * complexity)", - "emotional": "log(1 + beta_emotional_text * L_emotional_offload)", - "reason": "text and language often compress broad semantic scale into thresholded/log-like response", - }, - "code": { - "cognitive": "(complexity / (K_code + complexity))^hill_code", - "emotional": "(L_emotional_offload / (K_emotional + L_emotional_offload))^hill_emotional", - "reason": "working-memory and frustration effects are better modeled as saturating channels", - }, - "visual": { - "cognitive": "(V_max * visual_complexity) / (K_M + visual_complexity)", - "emotional": "(V_max_emotional * L_emotional_offload) / (K_M_emotional + L_emotional_offload)", - "reason": "feature extraction and visual overload are saturation-limited", - }, - "audio": { - "cognitive": "audio_complexity^alpha_audio", - "emotional": "L_emotional_offload^alpha_emotional", - "reason": "auditory load is modeled as low-exponent accumulation", - }, - "multimodal": { - "cognitive": "sum_d w_d * response_family_d(complexity_d; theta_d)", - "emotional": "sum_d w_d * L_emotional,d", - "reason": "cross-modal load is an adaptive mixture with interference weights", - }, - }, - "component_reweighting": { - "base_components": ["L_I", "L_E", "L_G", "L_R", "L_M"], - "raw_sum": "L_cog_raw = w_I L_I + w_E L_E - w_G L_G + w_R L_R + w_M L_M", - "constraints": [ - "sum positive weights before signed germane term is normalized", - "w_G is bounded so germane load cannot create impossible negative load", - "all component families are selected by measured error and held-out validation", - "overflow is computed from raw load before suppression", - "effective load and emotional load are reported separately", - ], - }, - "phi_prior": { - "D_f": "log(2)/log(phi) ~= 1.44042009041", - "phi_gain": "phi^D_f = 2", - "phi_squared_gain": "(phi^2)^D_f = 4", - "claim": "Phi remains a topology prior, not a universal load law.", - }, - "connectome_protection_interpretation": { - "defensible_form": ( - "overflow offload is a stability hypothesis for preserving working " - "cognitive graph coherence under load" - ), - "trauma_reweighting": ( - "trauma is modeled as an energy-landscape modifier: it can lower the " - "effective cognitive threshold, raise the emotional regulation barrier, " - "reduce offload efficiency, and increase residual stress after overflow" - ), - "historical_bandwidth_reweighting": ( - "accelerated information transfer is modeled as bandwidth overflow: when " - "transfer bandwidth exceeds assimilation bandwidth, the historical threshold " - "drops, regulation barriers rise, offload efficiency falls, and residual " - "social/emotional stress accumulates" - ), - "psychohistory_analogy": ( - "Harry Seldon-style psychohistory is a useful fictional analogy for the " - "population-scale version: not prediction of individuals, but modeling " - "aggregate phase pressure from bandwidth, assimilation lag, institutional " - "response, and overflow dynamics" - ), - "avoid_overclaim": [ - "do not claim measured biological damage prevention", - "do not claim emotional offload is cost-free", - "do not claim emotional load is pathology", - "do not treat trauma as a scalar clinical diagnosis", - "do not treat historical bandwidth overflow as proof of causality without archival or quantitative anchors", - "do not cite psychohistory as evidence; use it only as a structural metaphor", - "do not promote without local/empirical threshold calibration", - ], - }, - "required_measurements": [ - "domain complexity metric", - "component load vector", - "response-family fit error", - "held-out validation error", - "threshold calibration", - "overflow amount", - "emotional offload estimate", - "residual stress or unresolved load", - "trauma exposure/stress proxy if used, with explicit consent and privacy boundary", - "historical transfer bandwidth proxy", - "historical assimilation bandwidth proxy", - "archive, media, literacy, institution, or infrastructure anchor for bandwidth assumptions", - "population-scale outcome proxy if using psychohistory-style aggregate modeling", - ], - "promotion_rule": [ - "domain response family selected by measured error and validation", - "overflow threshold calibrated or explicitly marked hypothetical", - "trauma modifiers calibrated or explicitly marked hypothetical", - "bandwidth overflow modifiers calibrated or explicitly marked historical hypothesis", - "emotional offload reported as separate channel", - "phi gain used only as topology prior", - "claim boundary distinguishes model hypothesis from biological proof", - ], - "failure_rules": [ - "protective offload described as proven brain mechanism -> overclaim", - "emotional offload treated as zero-cost load deletion -> invalid model", - "threshold missing -> hold", - "trauma proxy used without consent/privacy boundary -> invalid measurement", - "trauma modeled as simple blame/defect variable -> invalid framing", - "historical bandwidth overflow asserted without source anchors -> hold", - "accelerated information transfer treated as single-cause history -> overclaim", - "fictional psychohistory analogy treated as evidence -> invalid citation", - "response family chosen by preference instead of validation -> hold", - "germane negative term drives total load below zero -> clamp or reject", - "phi gain used as universal law -> overclaim", - ], - "linked_artifacts": [ - "4-Infrastructure/shim/multi_domain_adaptive_cognitive_load.md", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Phi Scaling Response Model Selection.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Connectome Manipulation Self Update Prior.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Holographic Fractional Recursive Connectome Prior.tid", - ], - "claim_boundary": ( - "This is a reweighting receipt for a cognitive-load model hypothesis. " - "It is not medical advice, not a validated neuroscience result, and not " - "proof that emotional processing protects biological connectomes." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "compute_protective_load_channels", - "input": "raw cognitive load, threshold, emotional energy scale, offload efficiency", - "target": "effective cognitive load, emotional offload, emotional load, residual stress", - }, - { - "task": "select_domain_response_family", - "input": "domain complexity and observed load data", - "target": "log, Hill, Michaelis-Menten, low-exponent, or mixture selected by validation", - }, - { - "task": "reject_overclaiming_connectome_protection", - "input": "protective overflow claim", - "target": "hypothesis unless biological/behavioral measurements calibrate threshold and offload", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "domain_count": len(receipt["domain_response_families"]), - "failure_rule_count": len(receipt["failure_rules"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/core_equations_analysis.json b/4-Infrastructure/shim/core_equations_analysis.json deleted file mode 100644 index 29b79b5c..00000000 --- a/4-Infrastructure/shim/core_equations_analysis.json +++ /dev/null @@ -1,256 +0,0 @@ -{ - "density_field": { - "equation": "\u03c1(x\u20d7)", - "description": "Semantic density field representing text as n-D manifold", - "expected": [ - "density_field_encoding_theory", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "master_synthesis_complete_v1" - ], - "coverage": 0.7 - }, - "morse_smale": { - "equation": "Critical points + separatrices", - "description": "Morse-Smale complex: topological skeleton of meaning", - "expected": [ - "density_field_encoding_theory", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 0.8 - }, - "shear_matrix": { - "equation": "A_{ij} = \u03b4_{ij} + \u03b1_{ij}", - "description": "Shear matrix transforming orthogonal hypercube to correlated rhomboid", - "expected": [ - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "gram_matrix": { - "equation": "G = A^T A", - "description": "Gram matrix = compression dictionary (eigenvectors = principal directions)", - "expected": [ - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "gccl_packet": { - "equation": "\u0393\u1d62 = \u03b3\u1d62 \u2297 \u03c7\u1d62 \u2297 \u03ba\u1d62 \u2297 \u03c4\u1d62 \u2297 U\u1d62\u039b\u1d62a\u1d62 \u2297 \u03b8\u1d62 \u2297 \u03b5\u1d62", - "description": "GCCL glyph packet with chirality, type, eigen descriptor, residual", - "expected": [ - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 0.9 - }, - "gain_test": { - "equation": "\u0394GCL > 0", - "description": "GCCL gain test: only compressive motifs kept", - "expected": [ - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 0.9 - }, - "s3c_shell": { - "equation": "n = k\u00b2 + a", - "description": "S3C shell coordinate encoding", - "expected": [ - "observer_admissible_cavities_theory", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "radius_ratio": { - "equation": "\u03c1\u1d62 = s_center(i) / median(s(N(i)))", - "description": "Radius-ratio local scale ratio \u2192 admissible motif class", - "expected": [ - "observer_admissible_cavities_theory", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "residual_ratio": { - "equation": "\u03c1 = |\u03b5| / |raw_span|", - "description": "Residual ratio: the only number that matters", - "expected": [ - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "famm_delay": { - "equation": "Delay = path integral through field gradient", - "description": "FAMM delay profile = path integral through density field gradient", - "expected": [ - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - }, - "residual_correlation": { - "equation": "C_{ij} = \u27e8\u03b5_i \u03b5_j\u27e9", - "description": "Residual correlation matrix for spectral decomposition", - "expected": [ - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 0.6 - }, - "eigen_decomposition": { - "equation": "C = U\u039bU^T", - "description": "Eigen decomposition of residual correlation matrix", - "expected": [ - "hypercube_rhomboid_composition", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "found": [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" - ], - "coverage": 1.0 - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/cross_domain_adaptation_evidence_prior.py b/4-Infrastructure/shim/cross_domain_adaptation_evidence_prior.py deleted file mode 100644 index 5b8a9524..00000000 --- a/4-Infrastructure/shim/cross_domain_adaptation_evidence_prior.py +++ /dev/null @@ -1,187 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for the cross-domain adaptation evidence bundle. - -The input was a Consensus-style LaTeX/BibTeX synthesis supplied in chat. This -runner preserves the useful structure without treating the synthesis as primary -verification of every citation. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "cross_domain_adaptation_evidence_prior_receipt.json" -CURRICULUM = SHIM / "cross_domain_adaptation_evidence_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "cross_domain_adaptation_evidence_prior_v1", - "source_type": "user_supplied_consensus_latex_synthesis", - "numeric_review_artifact": "6-Documentation/docs/cross_domain_adaptation_numeric_review.md", - "numeric_reference_count": 25, - "paper_count_reported": { - "identified": 562_365, - "screened": 239, - "eligible": 204, - "included": 50, - }, - "evidence_lanes": [ - { - "lane": "sparse_representation_and_compressive_sensing", - "strength": 10, - "adaptation_read": "shared sparsity or k-term structure transfers well across signal, image, and compression domains", - "representative_keys": [ - "Cohen2008Compressed", - "Baraniuk2008Model-Based", - "Rani2018A", - "Wang2023Compressed", - ], - }, - { - "lane": "neural_and_distributed_compression", - "strength": 8, - "adaptation_read": "learned compressors can rediscover useful coding structures but require empirical validation", - "representative_keys": [ - "Ozyilkan2023Neural", - "Sohrabi2022Learning", - "Dai2022Image", - "Liu2024Compression", - ], - }, - { - "lane": "transfer_learning_and_domain_adaptation", - "strength": 7, - "adaptation_read": "transfer is useful when source and target share structure; negative transfer remains a gate", - "representative_keys": [ - "Hosna2022Transfer", - "Zhuang2019A", - "Ling2023Domain", - "Lu2025A", - ], - }, - { - "lane": "topological_and_algebraic_methods", - "strength": 6, - "adaptation_read": "topology can transport abstract structure into compression and reconstruction, but tooling is less mainstream", - "representative_keys": [ - "Ebli2022Morse", - "Carlsson2020Topological", - ], - }, - { - "lane": "theory_to_practice_gap", - "strength": 5, - "adaptation_read": "guarantees, convergence, and noise models do not automatically survive domain transfer", - "representative_keys": [ - "Chen2025Greedy", - "Wang2023Distributed", - "Kipnis2020Gaussian", - ], - }, - ], - "gap_matrix": { - "sparse_representation": { - "signal_theory": 8, - "compression_algorithms": 12, - "mathematical_exploration": 2, - }, - "neural_network_adaptation": { - "signal_theory": 6, - "compression_algorithms": 7, - "mathematical_exploration": 1, - }, - "topological_methods": { - "signal_theory": 2, - "compression_algorithms": "GAP", - "mathematical_exploration": 4, - }, - "transfer_learning": { - "signal_theory": 5, - "compression_algorithms": 4, - "mathematical_exploration": 2, - }, - }, - "adaptation_to_t16_equation_prior": { - "supports": [ - "feature extraction before expensive validation", - "regime-specific transfer instead of one universal model", - "sparse/topological feature families for equation traces", - "negative-transfer gates for mismatched domains", - ], - "does_not_support": [ - "automatic proof transfer", - "automatic compression improvement", - "using classifier confidence as a receipt", - "using Consensus synthesis as primary citation verification", - ], - }, - "bibtex_hygiene_notes": [ - "Vetterli2001Wavelets,, contains a malformed citation key with a double comma", - "The AMA/numeric version corrects this into reference 24", - "Consensus-generated citation metadata should be verified before publication", - "arXiv:2604.18579 is an astronomy candidate-search pipeline; the adaptation is methodological", - ], - "claim_boundary": ( - "This prior records cross-domain adaptation evidence as a research " - "map. It does not prove that any specific T16-derived equation " - "pipeline, compression route, or Hutter transform works." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_adaptation_lane", - "input": "paper or method claiming cross-domain transfer", - "target": "sparse, neural, transfer, topology, or theory-practice lane", - }, - { - "task": "apply_negative_transfer_gate", - "input": "source method and target equation domain", - "target": "shared-structure evidence before transfer", - }, - { - "task": "separate_evidence_from_receipt", - "input": "literature support for method transfer", - "target": "proposal prior only until local validation succeeds", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "evidence_lane_count": len(receipt["evidence_lanes"]), - "included_papers_reported": receipt["paper_count_reported"]["included"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/cross_domain_easy_wins_route_map.py b/4-Infrastructure/shim/cross_domain_easy_wins_route_map.py deleted file mode 100644 index 57f8ce7d..00000000 --- a/4-Infrastructure/shim/cross_domain_easy_wins_route_map.py +++ /dev/null @@ -1,359 +0,0 @@ -#!/usr/bin/env python3 -"""Build a receipt-backed route map of easy cross-domain kernel wins. - -The map ranks domains where local algebraic/logogram fixtures are likely to be -cheap to encode and easy to verify. It is a planning receipt, not a benchmark or -domain-proof result. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "cross_domain_easy_wins" -ROUTE_MAP = OUT_DIR / "cross_domain_easy_wins_route_map.json" -RECEIPT = OUT_DIR / "cross_domain_easy_wins_route_map_receipt.json" -SUMMARY = OUT_DIR / "cross_domain_easy_wins_route_map.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - REPO / "shared-data/data/cross_domain_kernel_adapters/cross_domain_kernel_adapter_registry_receipt.json", - REPO / "shared-data/data/magnetic_derivative_kernels/magnetic_derivative_kernel_receipt.json", - REPO / "shared-data/data/solids_physics_kernels/solids_physics_kernel_receipt.json", - REPO / "shared-data/data/mmff_rigid_body_geometry/mmff_rigid_body_geometry_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def route( - *, - rank: int, - route_id: str, - domain: str, - easy_kernels: list[str], - fixture_targets: list[str], - hold_hazards: list[str], - next_probe: str, - estimated_candidate_yield: str, - decision: str = "ROUTE_READY", -) -> dict[str, Any]: - item = { - "rank": rank, - "route_id": route_id, - "domain": domain, - "easy_kernels": easy_kernels, - "fixture_targets": fixture_targets, - "hold_hazards": hold_hazards, - "next_probe": next_probe, - "estimated_candidate_yield": estimated_candidate_yield, - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_route_map() -> dict[str, Any]: - routes = [ - route( - rank=1, - route_id="circuits_impedance", - domain="electrical circuits and transmission lines", - easy_kernels=["MN_REFLECT", "MN_TRANSMIT_POWER", "MN_PAIR_PRODUCT", "MN_REDUCED"], - fixture_targets=[ - "voltage divider", - "Thevenin/Norton pair reduction", - "series/parallel resistor identities", - "transmission-line reflection coefficient", - ], - hold_hazards=["frequency-dependent parasitics", "nonlinear devices", "layout/skin-effect models"], - next_probe="electrical_circuit_kernel_probe.py", - estimated_candidate_yield="high", - ), - route( - rank=2, - route_id="thermal_diffusion", - domain="thermal conduction and diffusion", - easy_kernels=["MN_REFLECT", "MN_REDUCED", "MN_BLEND", "DERIV_LINEAR"], - fixture_targets=[ - "thermal resistance series/parallel", - "effusivity boundary contrast", - "two-material steady flux", - "lumped RC thermal time constant", - ], - hold_hazards=["transient PDE solves", "phase change", "temperature-dependent material laws"], - next_probe="thermal_diffusion_kernel_probe.py", - estimated_candidate_yield="high", - ), - route( - rank=3, - route_id="acoustics_waves", - domain="acoustics and scalar waves", - easy_kernels=["MN_REFLECT", "MN_TRANSMIT_POWER", "ANALYTIC_SQRT_RATIO"], - fixture_targets=[ - "normal-incidence impedance reflection", - "power transmission from reflection coefficient", - "string/wave speed scalar route", - ], - hold_hazards=["oblique incidence", "loss/attenuation", "mode conversion", "boundary geometry"], - next_probe="acoustic_wave_kernel_probe.py", - estimated_candidate_yield="high", - ), - route( - rank=4, - route_id="probability_routing", - domain="probability, routing, and expert selection", - easy_kernels=["MN_BINARY_P", "MN_BLEND", "MN_BINARY_ENTROPY"], - fixture_targets=[ - "two-route normalized probability", - "confidence-weighted blend", - "binary odds recovery", - "entropy route with declared numeric policy", - ], - hold_hazards=["uncalibrated priors", "correlated experts", "floating-point entropy policy"], - next_probe="probability_routing_kernel_probe.py", - estimated_candidate_yield="very_high", - ), - route( - rank=5, - route_id="orbital_two_body", - domain="two-body mechanics and orbital reductions", - easy_kernels=["MN_SPLIT", "MN_REDUCED", "MN_ELASTIC_1D"], - fixture_targets=[ - "mass split from total and MN", - "reduced mass", - "center-of-mass blend", - "1D elastic collision", - ], - hold_hazards=["N-body dynamics", "relativistic corrections", "numerical integration"], - next_probe="two_body_mechanics_kernel_probe.py", - estimated_candidate_yield="high", - ), - route( - rank=6, - route_id="mmff_rigid_body_geometry", - domain="MMFF-style molecular geometry as rigid/semi-rigid bodies", - easy_kernels=["RIGID_BODY_POSE", "HINGED_RIGID_BODY", "MN_BOND_DEVIATION", "TORSION_OPCODE"], - fixture_targets=[ - "linear triad coordinate replay", - "bent triad coordinate replay", - "aromatic ring template pose", - "methyl rotor hinge state", - ], - hold_hazards=[ - "MMFF atom typing", - "aromaticity perception", - "parameter lookup tables", - "partial charges and nonbonded cutoffs", - "energy minimization and conformer ranking", - ], - next_probe="mmff_rigid_body_geometry_probe.py", - estimated_candidate_yield="high", - ), - route( - rank=7, - route_id="chemistry_equilibrium", - domain="chemistry equilibrium and reaction routing", - easy_kernels=["MN_RATIO_INV", "MN_BINARY_P", "MN_BLEND"], - fixture_targets=[ - "two-species normalized fraction", - "odds/ratio recovery", - "mixture weighted property", - "two-lane equilibrium contrast fixture", - ], - hold_hazards=["activity coefficients", "temperature dependence", "kinetics and catalysis"], - next_probe="chemistry_equilibrium_kernel_probe.py", - estimated_candidate_yield="medium_high", - ), - route( - rank=8, - route_id="optics_fresnel", - domain="optics at normal incidence", - easy_kernels=["MN_REFLECT", "MN_TRANSMIT_POWER"], - fixture_targets=[ - "normal-incidence amplitude reflection", - "power transmission from contrast", - "index/impedance contrast", - ], - hold_hazards=["polarization", "oblique incidence", "complex refractive index", "thin-film interference"], - next_probe="optics_fresnel_kernel_probe.py", - estimated_candidate_yield="medium_high", - ), - route( - rank=9, - route_id="statistics_effect_size", - domain="statistics and signal scoring", - easy_kernels=["MN", "MN_BLEND", "MN_BINARY_ENTROPY"], - fixture_targets=[ - "two-bin contrast", - "normalized difference score", - "weighted mean update", - "binary uncertainty score", - ], - hold_hazards=["sampling assumptions", "p-value misuse", "distributional claims"], - next_probe="statistics_signal_kernel_probe.py", - estimated_candidate_yield="medium", - ), - route( - rank=10, - route_id="bio_expression_contrast", - domain="biology expression/accessibility contrast", - easy_kernels=["MN", "MN_BINARY_P", "MN_BLEND"], - fixture_targets=[ - "two-condition expression contrast", - "accessibility contrast", - "two-source regulatory blend", - ], - hold_hazards=["biological causality", "batch effects", "measurement normalization", "thermodynamic overclaim"], - next_probe="bio_expression_contrast_kernel_probe.py", - estimated_candidate_yield="medium", - ), - route( - rank=11, - route_id="geometry_contact", - domain="contact geometry and motion planning", - easy_kernels=["MN", "MN_BLEND"], - fixture_targets=[ - "two-clearance contact contrast", - "signed-distance transition fixture", - "contact-regime switch scoring", - ], - hold_hazards=["continuous collision proof", "area optimality", "path completeness"], - next_probe="contact_geometry_kernel_probe.py", - estimated_candidate_yield="medium", - decision="ROUTE_HOLD_FIRST", - ), - ] - return { - "schema": "cross_domain_easy_wins_route_map_v1", - "claim_boundary": ( - "Planning receipt only. It ranks likely low-cost cross-domain kernel " - "probes; it does not assert compression gain, domain truth, or benchmark " - "performance. Each route still requires its own fixture receipt." - ), - "canonical_statement": ( - "Easy wins are domains where exact local algebra kernels can be checked " - "before domain-specific PDEs, material laws, geometry, or measurement " - "claims are touched." - ), - "selection_rule": "prefer exact local algebra first; HOLD nonlinear, field, geometry, and measurement claims", - "routes": routes, - "route_count": len(routes), - "status_counts": { - status: sum(1 for item in routes if item["decision"] == status) - for status in sorted({item["decision"] for item in routes}) - }, - } - - -def build_receipt(route_map: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "cross_domain_easy_wins_route_map_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "route_map_path": rel(ROUTE_MAP), - "route_map_hash": hash_obj(route_map), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "route_count": route_map["route_count"], - "status_counts": route_map["status_counts"], - "decision": "ADMIT_ROUTE_MAP_HOLD_FIRST", - "claim_boundary": route_map["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(route_map: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Cross-Domain Easy Wins Route Map", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - route_map["claim_boundary"], - "", - "## Canonical Statement", - "", - route_map["canonical_statement"], - "", - "## Ranked Routes", - "", - "| Rank | Route | Domain | Yield | Decision | Next probe |", - "|---:|---|---|---|---|---|", - ] - for item in route_map["routes"]: - lines.append( - f"| {item['rank']} | `{item['route_id']}` | {item['domain']} | " - f"{item['estimated_candidate_yield']} | `{item['decision']}` | `{item['next_probe']}` |" - ) - lines.extend( - [ - "", - "## Rule", - "", - route_map["selection_rule"], - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - route_map = build_route_map() - receipt = build_receipt(route_map) - ROUTE_MAP.write_text(json.dumps(route_map, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(route_map, receipt) - print( - json.dumps( - { - "route_map": rel(ROUTE_MAP), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": route_map["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/cross_domain_kernel_adapter_registry.py b/4-Infrastructure/shim/cross_domain_kernel_adapter_registry.py deleted file mode 100644 index e6682d28..00000000 --- a/4-Infrastructure/shim/cross_domain_kernel_adapter_registry.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -"""Build a receipt-backed cross-domain kernel adapter registry. - -Cross-domain compression is only lawful when a shared algebraic kernel is kept -separate from the domain adapter that interprets it. This registry records that -separation: exact Mass Number kernels may be reused, but domain analogies stay -HOLD unless adapter closure is receipted. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "cross_domain_kernel_adapters" -REGISTRY = OUT_DIR / "cross_domain_kernel_adapter_registry.json" -RECEIPT = OUT_DIR / "cross_domain_kernel_adapter_registry_receipt.json" -SUMMARY = OUT_DIR / "cross_domain_kernel_adapter_registry.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - REPO / "shared-data/data/buoyancy_added_mass_mobius/buoyancy_added_mass_mobius_receipt.json", - REPO / "shared-data/data/foundation_forward_equation_compiler/foundation_forward_equation_compiler_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def adapter( - *, - adapter_id: str, - domain: str, - kernel_opcode: str, - adapter_role: str, - compressed_form: str, - expanded_form: str, - required_receipt: str, - decision: str, - residual_policy: str, - warning: str = "same algebraic skeleton does not imply same domain law", -) -> dict[str, Any]: - item = { - "adapter_id": adapter_id, - "domain": domain, - "kernel_opcode": kernel_opcode, - "adapter_role": adapter_role, - "compressed_form": compressed_form, - "expanded_form": expanded_form, - "required_receipt": required_receipt, - "decision": decision, - "residual_policy": residual_policy, - "warning": warning, - } - item["adapter_hash"] = hash_obj({k: v for k, v in item.items() if k != "adapter_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - adapters = [ - adapter( - adapter_id="fluid_added_mass_lambda_BAM", - domain="fluid_mechanics", - kernel_opcode="MN_MOBIUS_LOAD", - adapter_role="density contrast plus added-mass geometry coefficient", - compressed_form="lambda_BAM(MN(rho_o,rho_m), C)", - expanded_form="a = g*(rho_o-rho_m)/(rho_o + C*rho_m)", - required_receipt="shared-data/data/buoyancy_added_mass_mobius/buoyancy_added_mass_mobius_receipt.json", - decision="ACCEPT_ADAPTER_FIXTURE", - residual_policy="early-time ideal added-mass only; drag, vorticity, boundary effects remain residual lanes", - ), - adapter( - adapter_id="impedance_boundary_reflection", - domain="wave_boundaries", - kernel_opcode="MN_REFLECT", - adapter_role="two-impedance boundary contrast", - compressed_form="Gamma = MN(Z2,Z1)", - expanded_form="Gamma = (Z2-Z1)/(Z2+Z1)", - required_receipt="shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="domain source equation, sign convention, and loss model still required before physical promotion", - ), - adapter( - adapter_id="binary_route_probability", - domain="routing_probability", - kernel_opcode="MN_BINARY_P", - adapter_role="two-route normalized weight contrast", - compressed_form="P(a)=(1+MN(a,b))/2", - expanded_form="P(a)=a/(a+b)", - required_receipt="shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="requires nonnegative weights and explicit route prior source before use as decision evidence", - ), - adapter( - adapter_id="mechanics_pair_reduction", - domain="two_body_mechanics", - kernel_opcode="MN_REDUCED", - adapter_role="reduced mass / product-over-sum pair law", - compressed_form="mu = S/4*(1-MN(m1,m2)^2)", - expanded_form="mu = m1*m2/(m1+m2)", - required_receipt="shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="domain equations using reduced mass still require local source and closure receipts", - ), - adapter( - adapter_id="weighted_expert_blend", - domain="expert_routing", - kernel_opcode="MN_BLEND", - adapter_role="two-source confidence merge", - compressed_form="blend = mid(A,B) + MN(w1,w2)*halfdiff(A,B)", - expanded_form="blend = (w1*A+w2*B)/(w1+w2)", - required_receipt="shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="requires source weights, replayed decision path, and HOLD if weights are not lawful evidence", - ), - adapter( - adapter_id="couch_contact_topology", - domain="moving_sofa_contact_geometry", - kernel_opcode="MN", - adapter_role="bounded contact/clearance contrast between active constraints", - compressed_form="MN_ij = (c_i-c_j)/(c_i+c_j+epsilon)", - expanded_form="clearance/contact switching over corridor constraints", - required_receipt="not yet available", - decision="HOLD_CONTACT_TOPOLOGY", - residual_policy="requires corridor model, signed-distance convention, motion path replay, collision closure, and area accounting", - ), - adapter( - adapter_id="earth_core_boundary_witness", - domain="seismic_horizon_inference", - kernel_opcode="MN_REFLECT", - adapter_role="boundary wave witness for inaccessible interior", - compressed_form="interior_state := adapter(seismic boundary packets, residual model)", - expanded_form="infer interior material state from wave travel/attenuation/aniso residuals", - required_receipt="not yet available", - decision="HOLD_BOUNDARY_WITNESS", - residual_policy="boundary witnesses may route hypotheses; unresolved interior stays Underverse until source data and closure checks exist", - ), - adapter( - adapter_id="binary_entropy_route_cost", - domain="compression_routing", - kernel_opcode="MN_BINARY_ENTROPY", - adapter_role="route uncertainty over Mass Number contrast", - compressed_form="H2_MN(x)", - expanded_form="-p*log2(p)-(1-p)*log2(1-p), p=(1+x)/2", - required_receipt="shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - decision="HOLD_ANALYTIC_ADAPTER", - residual_policy="requires log base, numeric precision, approximation policy, and byte-cost receipt", - ), - ] - return { - "schema": "cross_domain_kernel_adapter_registry_v1", - "claim_boundary": ( - "Cross-domain adapter registry only. It records reusable kernel shapes " - "and domain adapters, not theorem promotion, physical equivalence, or " - "benchmark evidence. Shared algebraic skeletons are not shared domain " - "substance; adapters and residuals must close independently." - ), - "canonical_statement": ( - "Cross-domain compression stores reusable law-shapes once, then " - "rehydrates them through domain adapters. Similarity without closure " - "routes to HOLD, QUARANTINE, Underverse, or NaN0." - ), - "adapter_equation": "X_d = A_d[K_j(theta)] + R_d + chi0", - "underverse_guardrail": "same shape does not imply same law", - "adapters": adapters, - "adapter_count": len(adapters), - "status_counts": { - status: sum(1 for item in adapters if item["decision"] == status) - for status in sorted({item["decision"] for item in adapters}) - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - source_refs = [source_ref(path) for path in SOURCE_REFS] - accepted = sum( - count - for status, count in registry["status_counts"].items() - if status in {"ACCEPT_ADAPTER_FIXTURE", "ACCEPT_KERNEL_ADAPTER"} - ) - held = registry["adapter_count"] - accepted - receipt = { - "schema": "cross_domain_kernel_adapter_registry_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry_path": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "source_refs": source_refs, - "adapter_count": registry["adapter_count"], - "status_counts": registry["status_counts"], - "accepted_adapter_count": accepted, - "held_adapter_count": held, - "decision": "HOLD_CROSS_DOMAIN_WITH_ACCEPTED_KERNEL_ADAPTERS", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Cross-Domain Kernel Adapter Registry", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "```text", - registry["adapter_equation"], - registry["underverse_guardrail"], - "```", - "", - "## Adapter Table", - "", - "| Adapter | Domain | Kernel | Decision | Role |", - "|---|---|---|---|---|", - ] - for item in registry["adapters"]: - lines.append( - f"| `{item['adapter_id']}` | `{item['domain']}` | `{item['kernel_opcode']}` | " - f"`{item['decision']}` | {item['adapter_role']} |" - ) - lines.extend( - [ - "", - "## Guardrail", - "", - "Accepted kernel adapters admit algebraic reuse only. Domain-specific " - "truth, physical interpretation, corpus compression, or geometry " - "optimality still requires source equations, replay, residual policy, " - "and closure receipts.", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": registry["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/cross_domain_registry_eigenvectors.py b/4-Infrastructure/shim/cross_domain_registry_eigenvectors.py deleted file mode 100644 index d8760317..00000000 --- a/4-Infrastructure/shim/cross_domain_registry_eigenvectors.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -"""Cross-domain registry eigenvectors for compression priors. - -This script strips the domain romance off math/chemistry/DNA/benchmark registry -receipts and keeps the useful compression object: a term-domain matrix and its -leading eigenvector. The output is a search/routing prior only. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - -import online_domain_eigen_pruning as eigen - - -DEFAULT_RECEIPTS = [ - Path("4-Infrastructure/shim/epoch_eci_metaprobe_receipt.json"), - Path("4-Infrastructure/shim/math_prover_prior_metaprobe_receipt.json"), - Path("4-Infrastructure/shim/molecular_domain_prior_receipt.json"), - Path("4-Infrastructure/shim/genomic_sequence_prior_receipt.json"), - Path("4-Infrastructure/shim/pde_model_prior_receipt.json"), - Path("4-Infrastructure/shim/llm_compression_architecture_prior_receipt.json"), - Path("4-Infrastructure/shim/semantic_topology_compression_regimes_receipt.json"), - Path("4-Infrastructure/shim/math_logogram_surface_receipt.json"), - Path("4-Infrastructure/shim/intense_math_modeling_router_receipt.json"), - Path("4-Infrastructure/shim/moving_sofa_nspace_prior_receipt.json"), - Path("4-Infrastructure/shim/moving_sofa_scout_harness_receipt.json"), - Path("4-Infrastructure/shim/moving_sofa_scout_response_validation_receipt.json"), - Path("4-Infrastructure/shim/custom_equation_awareness_manifest_receipt.json"), - Path("4-Infrastructure/shim/king_context_equation_retrieval_prior_receipt.json"), -] - - -def safe_name(value: str) -> str: - return "".join(ch.lower() if ch.isalnum() else "_" for ch in value).strip("_")[:96] or "unnamed" - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def receipt_to_domains(path: Path, receipt: dict[str, Any]) -> list[dict[str, str]]: - schema = receipt.get("schema", path.stem) - domains: list[dict[str, str]] = [] - - if schema == "epoch_eci_metaprobe_receipt_v1": - summary = receipt.get("summary", {}) - benchmark_names = ", ".join(name for name, _count in summary.get("top_benchmark_names", [])[:16]) - domains.append( - { - "domain": "epoch_eci_benchmark_family", - "equation": "performance(model, benchmark) in [0,1]; composite capability prior", - "role": ( - f"external benchmark family with {summary.get('benchmark_unique')} benchmarks; " - f"math_rows={summary.get('is_math_true_rows')}; coding_rows={summary.get('is_coding_true_rows')}; " - f"top_benchmarks={benchmark_names}; not local correctness" - ), - "source": "Epoch ECI benchmark table", - "url": receipt.get("source_url", ""), - } - ) - - if schema == "math_prover_prior_metaprobe_receipt_v1": - for item in receipt.get("model_priors", []): - domains.append(prior_to_domain("math_model", item)) - for item in receipt.get("dataset_priors", []): - domains.append(prior_to_domain("dataset_registry", item)) - for query, result in receipt.get("theoremsearch", {}).items(): - theorems = result.get("theorems", [])[:3] - slogans = " ".join(str(theorem.get("slogan") or theorem.get("name") or "") for theorem in theorems) - papers = ", ".join(str(theorem.get("paper_title") or theorem.get("source") or "") for theorem in theorems) - domains.append( - { - "domain": f"theoremsearch_query_{safe_name(query)}", - "equation": slogans, - "role": f"retrieval prior for query={query}; adjacent_papers={papers}", - "source": "TheoremSearch", - "url": "https://www.theoremsearch.com/search", - } - ) - - if schema == "molecular_domain_prior_receipt_v1": - for item in receipt.get("molecular_axes", []): - domains.append(axis_to_domain("molecular_axis", item)) - for item in receipt.get("hf_chemistry_priors", []): - domains.append(prior_to_domain("chemistry_dataset", item)) - for section in receipt.get("registry_sections", [])[:10]: - domains.append( - { - "domain": f"bond_registry_{safe_name(section.get('name', 'section'))}", - "equation": "bond matrix = geometry + force constants + provenance", - "role": " ".join( - str(section.get(key, "")) - for key in ("integration_value", "entries", "license") - ), - "source": str(section.get("source") or "local chemical bond matrix registry"), - "url": "", - } - ) - - if schema == "genomic_sequence_prior_receipt_v1": - for item in receipt.get("sequence_axes", []): - domains.append(axis_to_domain("genomic_axis", item)) - for item in receipt.get("dataset_priors", []): - domains.append(prior_to_domain("dna_dataset", item)) - for item in receipt.get("model_priors", []): - domains.append(prior_to_domain("dna_model", item)) - for item in receipt.get("biological_control_priors", []): - domains.append(prior_to_domain("bio_control_prior", item)) - - if schema == "pde_model_prior_receipt_v1": - for item in receipt.get("pde_axes", []): - domains.append(axis_to_domain("pde_axis", item)) - for item in receipt.get("verified_pde_priors", []): - domains.append(prior_to_domain("pde_model", item)) - for item in receipt.get("soft_pde_candidates", []): - domains.append( - { - "domain": f"pde_soft_candidate_{safe_name(str(item.get('id', 'candidate')))}", - "equation": str(item.get("user_claim") or ""), - "role": f"{item.get('status', '')} {item.get('use_as', '')}", - "source": str(item.get("id") or "pde_soft_candidate"), - "url": "", - } - ) - - if schema == "llm_compression_architecture_prior_receipt_v1": - for item in receipt.get("compression_axes", []): - domains.append(axis_to_domain("llm_compression_axis", item)) - for item in receipt.get("verified_compression_priors", []): - domains.append(prior_to_domain("llm_compression_prior", item)) - - if schema == "semantic_topology_compression_regimes_v1": - for item in receipt.get("regimes", []): - domains.append( - { - "domain": f"semantic_topology_regime_{safe_name(str(item.get('label', 'regime')))}", - "equation": " ".join(str(part) for part in item.get("payload", [])), - "role": " ".join( - str(item.get(key, "")) - for key in ("condition", "operation", "failure_mode", "lean_predicate_hint") - ), - "source": str(item.get("id") or "semantic_topology_regime"), - "url": "", - } - ) - - if schema == "math_logogram_surface_receipt_v1": - for item in receipt.get("samples", []): - metrics = item.get("compression_metrics", {}) - domains.append( - { - "domain": f"math_logogram_surface_{safe_name(str(item.get('id', 'sample')))}", - "equation": str(item.get("canonical") or item.get("source") or ""), - "role": ( - f"kind={item.get('kind')}; regime={item.get('semantic_regime')}; " - f"payload_len={item.get('surface_payload_len')}; " - f"payload_over_raw={metrics.get('payload_over_raw')}; " - f"canonical_hash={item.get('canonical_hash')}; " - f"cell_hash={item.get('cell_hash')}" - ), - "source": "math_logogram_surface_builder", - "url": "", - } - ) - - if schema == "intense_math_modeling_router_v1": - for item in receipt.get("routes", []): - domains.append( - { - "domain": f"intense_math_route_{safe_name(str(item.get('route', 'route')))}", - "equation": str(item.get("condition") or ""), - "role": f"{item.get('model_role', '')}; {item.get('use_for', '')}; judges={','.join(item.get('judge', []))}", - "source": "intense_math_modeling_router", - "url": "", - } - ) - - if schema == "moving_sofa_nspace_prior_v1": - for item in receipt.get("sofa_axes", []): - domains.append(axis_to_domain("moving_sofa_axis", item)) - for item in receipt.get("sofa_priors", []): - domains.append(prior_to_domain("moving_sofa_prior", item)) - - if schema == "moving_sofa_scout_harness_receipt_v1": - for item in receipt.get("packets", []): - domains.append( - { - "domain": f"moving_sofa_scout_{safe_name(str(item.get('task_id', 'task')))}", - "equation": str(item.get("ask") or ""), - "role": ( - f"axis={item.get('axis', {}).get('axis')}; " - f"required={','.join(item.get('required_response_fields', []))}; " - f"gate={item.get('promotion_gate')}; " - f"hash={item.get('packet_hash')}" - ), - "source": "moving_sofa_scout_harness", - "url": "", - } - ) - - if schema == "moving_sofa_scout_response_validation_v1": - for item in receipt.get("validations", []): - domains.append( - { - "domain": f"moving_sofa_validation_{safe_name(str(item.get('task_id', 'task')))}", - "equation": str(item.get("promotion") or ""), - "role": ( - f"required_ok={item.get('required_ok')}; " - f"contract_ok={item.get('contract_ok')}; " - f"packet_hash_ok={item.get('packet_hash_ok')}; " - f"boundary_ok={item.get('boundary_ok')}; " - f"receipts_ok={item.get('receipts_ok')}; " - f"forbidden_claim={item.get('forbidden_claim')}" - ), - "source": "moving_sofa_scout_response_validator", - "url": "", - } - ) - - if schema == "custom_equation_awareness_manifest_v1": - for item in receipt.get("equations", [])[:240]: - domains.append( - { - "domain": f"custom_equation_{safe_name(str(item.get('name', 'equation')))}", - "equation": str(item.get("equation") or ""), - "role": ( - f"primitive={item.get('primitive_hint')}; " - f"boundary={item.get('claim_boundary')}; " - f"source={item.get('source_path')}; " - f"hash={item.get('equation_hash')}" - ), - "source": str(item.get("source_path") or "custom_equation_manifest"), - "url": "", - } - ) - - if schema == "king_context_equation_retrieval_prior_v1": - prior = receipt.get("king_context_prior", {}) - if prior: - domains.append(prior_to_domain("retrieval_prior", prior)) - for item in receipt.get("retrieval_axes", []): - domains.append(axis_to_domain("equation_retrieval_axis", item)) - - return domains - - -def prior_to_domain(prefix: str, item: dict[str, Any]) -> dict[str, str]: - raw_notes = item.get("notes", []) - if isinstance(raw_notes, list): - notes = " ".join(str(note) for note in raw_notes) - elif raw_notes: - notes = str(raw_notes) - else: - notes = "" - name = str(item.get("id") or item.get("role") or prefix) - return { - "domain": f"{prefix}_{safe_name(name)}", - "equation": str(item.get("role") or item.get("boundary") or ""), - "role": " ".join(str(item.get(key, "")) for key in ("use_as", "boundary", "local_use")) + " " + notes, - "source": name, - "url": str(item.get("url") or ""), - } - - -def axis_to_domain(prefix: str, item: dict[str, Any]) -> dict[str, str]: - axis = str(item.get("axis", prefix)) - payload = " ".join(str(part) for part in item.get("payload", [])) - return { - "domain": f"{prefix}_{safe_name(axis)}", - "equation": payload, - "role": f"{item.get('router_use', '')} {item.get('receipt_rule', '')}", - "source": prefix, - "url": "", - } - - -def curriculum_records(surface: dict[str, Any]) -> list[dict[str, Any]]: - top_domains = [ - {"domain": item["domain"], "weight": item["eigen_weight"]} - for item in surface.get("weighted_domains", [])[:12] - ] - top_terms = surface.get("top_terms", [])[:20] - prompt = { - "task": "use_cross_domain_eigenvectors_for_compression", - "top_domains": top_domains, - "top_terms": top_terms, - "instruction": "Explain how these eigenvectors should bias compression/search routing without doing domain work.", - } - answer = { - "selected": True, - "use_as": "cross_domain_compression_basis", - "claim_boundary": "eigenvector-ranking-prior-only", - "decision": "Use high-weight terms/domains as shared coordinates for token packing, metaprobe routing, and dataset sampling; do not treat them as chemistry, genomics, finance, or theorem truth.", - "surface_payload_hint": "EIGEN-COMPRESS", - } - return [ - { - "messages": [ - {"role": "system", "content": "You are a compression router. Return compact JSON with evidence boundaries."}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ] - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt-json", type=Path, action="append") - parser.add_argument("--out", type=Path, default=Path("4-Infrastructure/shim/cross_domain_registry_eigenvectors.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/cross_domain_registry_eigenvectors_curriculum.jsonl")) - parser.add_argument("--limit-terms", type=int, default=64) - args = parser.parse_args() - - receipt_paths = args.receipt_json or DEFAULT_RECEIPTS - all_domains: list[dict[str, str]] = [] - used_receipts = [] - for path in receipt_paths: - if not path.exists(): - continue - receipt = load_json(path) - used_receipts.append(str(path)) - all_domains.extend(receipt_to_domains(path, receipt)) - - surface = eigen.build_surface(all_domains) - surface["schema"] = "cross_domain_registry_eigenvectors_v1" - surface["claim_boundary"] = "Leading eigenvectors compress registry topology; they are not domain truth or proof." - surface["source_receipts"] = used_receipts - surface["domain_count"] = len(all_domains) - surface["top_terms"] = surface["top_terms"][: args.limit_terms] - - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(surface, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(surface): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(surface, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/custom_equation_awareness_manifest.py b/4-Infrastructure/shim/custom_equation_awareness_manifest.py deleted file mode 100644 index cab551ee..00000000 --- a/4-Infrastructure/shim/custom_equation_awareness_manifest.py +++ /dev/null @@ -1,390 +0,0 @@ -#!/usr/bin/env python3 -"""Build a custom-equation awareness manifest for the local LLM. - -This script inventories equation-bearing artifacts across the Research Stack and -turns them into compact curriculum records. The goal is awareness and routing, -not proof: every extracted equation keeps its source path, line/key, hash, -claim boundary, and primitive/bucket hints when available. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import re -from pathlib import Path -from typing import Any, Iterable - - -DEFAULT_ROOTS = [ - Path("4-Infrastructure/shim"), - Path("0-Core-Formalism/otom"), - Path("0-Core-Formalism/lean/Semantics/Semantics"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers"), - Path("6-Documentation/docs"), - Path("6-Documentation/papers/OTOM"), -] - -NAME_PATTERNS = [ - "*equation*", - "*Equation*", - "*SMN*", - "*Semantic Mass*", - "*4primitive*", - "*MasterEquation*", - "*EquationTranslation*", - "*FieldEquation*", - "*GCLField*", - "*HachimojiEquation*", - "*WitnessGrammar*", - "*UnderversePacket*", -] - -TEXT_SUFFIXES = {".md", ".tid", ".lean", ".tex", ".txt", ".mmd"} -JSON_SUFFIXES = {".json"} - -LINE_RE = re.compile( - r"(equation|formula|display|display_equation|master equation|semantic mass number|SMN|u_t|argmin|min_|def\s+|structure\s+|inductive\s+|abbrev\s+)", - re.IGNORECASE, -) -SYMBOLIC_LINE_MARKERS = ("ρ(", "G =", "G=", "Γ", "C =", "C=", "AᵀA", "UΛUᵀ") - -STOP_PATH_PARTS = {"__pycache__"} -GENERATED_NAME_MARKERS = ( - "_receipt.json", - "_curriculum.jsonl", - "_manifest.jsonl", - "physics_math_llm_sft.jsonl", -) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def safe_read(path: Path) -> bytes: - return path.read_bytes() - - -def iter_candidate_files(roots: list[Path]) -> list[Path]: - seen: set[Path] = set() - candidates: list[Path] = [] - for root in roots: - if not root.exists(): - continue - for pattern in NAME_PATTERNS: - for path in root.rglob(pattern): - if not path.is_file(): - continue - if any(part in STOP_PATH_PARTS for part in path.parts): - continue - if any(marker in path.name for marker in GENERATED_NAME_MARKERS): - continue - if path.suffix not in TEXT_SUFFIXES | JSON_SUFFIXES: - continue - if path not in seen: - seen.add(path) - candidates.append(path) - return sorted(candidates) - - -def classify_text(text: str) -> str: - lower = text.lower() - stripped = lower.strip() - if "semantic mass number" in lower or "smn" in lower: - return "semantic_load" - if stripped in {r"\begin{equation}", r"\end{equation}", "display_equation", "equation forest:"}: - return "registry_schema" - if any(term in lower for term in ["receipt:", "status:", "type ", "display_equation: string", "equation_id", "source_path", "durable source", "kernel id", "registry role"]): - return "registry_schema" - if lower.startswith(("# ", "## ", "### ", "!! ")) or lower.endswith(" equations") or lower.endswith(" equation"): - return "heading_or_doc" - if any(term in lower for term in ["equation sniffer", "sniffer", "scent", "witness grammar", "equation graphs", "probe candidate equation regions", "residual motifs", "adapter candidates"]): - return "equation_sniffer" - if any(term in lower for term in ["equation forest", "kernel registry", "kernelclass", "equationkernel", "kernelassignment", "fuse_existing_equation_kernels"]): - return "equation_forest_control" - if any(term in lower for term in ["equation type:", "geocognition", "publicclaim", "external validation gate", "quarantine bin", "confidence cap"]): - return "equation_atlas" - if any(term in lower for term in ["betti", "homology", "rank h_", "rank h", "hole", "loop", "cavity", "underverse", "complement-space", "forbidden", "excluded", "residual", "gamma(g)", "torsion", "curvature", "zeta(1/2"]): - return "topology" - if any(term in lower for term in ["u_t", "u_x", "u_xx", "navier", "stokes", "burgers", "viscous", "fluid", "laplacian", "partial", "gradient", "torque", "tau_", "theta", "wave equation"]): - return "pde_dynamics" - if any(term in lower for term in ["selector", "metamaterial", "polarization", "chirality", "spin", "material", "active", "phase circulation", "eta_thg", "psi_g", "gouy"]): - return "material_selector" - if any(term in lower for term in ["diat", "sidon", "shell", "k²", "sqrt", "floor", "a(n)", "b(n)", "genome18", "18-bit"]): - return "integer_geometry" - if any(term in lower for term in ["s(t)", "w_i", "h_i", "spiking", "neural", "activation", "surprise", "regret", "softmax"]): - return "neural_signal" - if any(term in lower for term in ["route", "routing", "warden", "promotion gate", "claim_boundary", "validation cap", "passes:", "fails:"]): - return "routing_control" - if any(term in lower for term in ["metadata_first", "preview", "learned shortcut", "king context", "retrieval", "adr-like", "search metadata"]): - return "retrieval_control" - if any(term in lower for term in ["qwen", "gemini", "kimi", "model indexing", "derived the equation", "requested synthesis"]): - return "external_model_workbench" - if any(term in lower for term in ["shannon", "entropy", "kolmogorov", "mdl", "zipf", "bwt", "hutter", "compression", "bits", "character"]): - return "information_theory" - if any(term in text for term in ["C_{ij}", "Λ", "UΛU", "lambda", "\\lambda", "eigen", "spectral", "zeta"]): - return "spectral" - if any(term in lower for term in ["ρ(", "density", "potential", "field", "manifold"]): - return "field" - if any(term in lower for term in ["g = a", "metric", "distance", "deformation", "shear", "a_{ij}"]): - return "shear" - if any(term in lower for term in ["γ", "packet", "codec", "encoding", "ans", "bitpack", "gcl"]): - return "packet" - if any(term in lower for term in ["basis", "qubo", "argmin"]): - return "spectral" - if any(term in lower for term in ["lean", "def ", "theorem", "lemma", "native_decide"]): - return "formal" - if "=" in text and any(marker in text for marker in ["\\", "_", "^", "sum", "Σ", "∑", "(", ")", "{", "}"]): - return "math_kernel" - return "sniffer_candidate" - - -def boundary_for(path: Path, text: str) -> str: - lower = text.lower() - if "hold" in lower or "blocked_usage" in lower or "blocked claim" in lower: - return "hold-or-routing-prior" - if path.suffix == ".lean": - return "lean-source-prior; build required before proof promotion" - if "conjecture" in lower: - return "conjecture-prior-only" - return "equation-awareness-prior-only" - - -def add_record(records: list[dict[str, Any]], *, source_path: Path, source_hash: str, kind: str, name: str, equation: str, locator: str, metadata: dict[str, Any] | None = None) -> None: - equation = " ".join(str(equation).split()) - if not equation: - return - primitive_hint = classify_text(equation + " " + json.dumps(metadata or {}, ensure_ascii=False)) - record = { - "id": f"{source_path}:{locator}:{name}", - "source_path": str(source_path), - "source_hash": source_hash, - "kind": kind, - "name": name[:160], - "equation": equation[:2000], - "equation_hash": hashlib.sha256(equation.encode("utf-8")).hexdigest(), - "locator": locator, - "primitive_hint": primitive_hint, - "claim_boundary": boundary_for(source_path, equation + " " + json.dumps(metadata or {}, ensure_ascii=False)), - } - if metadata: - record["metadata"] = metadata - records.append(record) - - -def walk_json_equations(value: Any, path: list[str] | None = None) -> Iterable[tuple[list[str], str, Any]]: - path = path or [] - if isinstance(value, dict): - for key, child in value.items(): - lower = str(key).lower() - if lower in {"equation", "formula", "display", "display_equation", "statement"} and isinstance(child, (str, int, float)): - yield path + [str(key)], str(key), child - elif lower in {"axioms", "unified_equations", "scientific_equations", "system_equations", "erdos_problems", "kernels", "primitives"}: - yield from walk_json_equations(child, path + [str(key)]) - else: - yield from walk_json_equations(child, path + [str(key)]) - elif isinstance(value, list): - for idx, child in enumerate(value): - yield from walk_json_equations(child, path + [str(idx)]) - - -def extract_json(path: Path, source_hash: str, records: list[dict[str, Any]]) -> None: - try: - data = json.loads(path.read_text(encoding="utf-8")) - except Exception: - return - for key_path, key, equation in walk_json_equations(data): - parent = data - for part in key_path[:-1]: - try: - parent = parent[int(part)] if isinstance(parent, list) else parent[part] - except Exception: - parent = {} - break - name = ( - parent.get("name") - if isinstance(parent, dict) - else None - ) or ( - parent.get("kernel_id") - if isinstance(parent, dict) - else None - ) or ".".join(key_path[-4:]) - metadata = {} - if isinstance(parent, dict): - for meta_key in ( - "primitive", - "mapping", - "domain", - "domain_class", - "bucket", - "hyper_term", - "claim_state", - "authority_scope", - "blocked_usage", - "blocked_usages", - "functional_role", - "feasibility", - "approach", - ): - if meta_key in parent: - metadata[meta_key] = parent[meta_key] - add_record( - records, - source_path=path, - source_hash=source_hash, - kind="json_equation", - name=str(name), - equation=str(equation), - locator=".".join(key_path), - metadata=metadata, - ) - - -def extract_text(path: Path, source_hash: str, records: list[dict[str, Any]]) -> None: - text = path.read_text(encoding="utf-8", errors="replace") - for line_no, line in enumerate(text.splitlines(), start=1): - stripped = line.strip() - if not stripped or len(stripped) < 6: - continue - if path.suffix == ".tex" and ( - stripped.startswith("\\begin{") - or stripped.startswith("\\end{") - or stripped.startswith("\\label{") - or stripped.startswith("\\title{") - or stripped.startswith("\\section{") - or stripped.startswith("\\subsection{") - ): - continue - if not LINE_RE.search(stripped) and not any(marker in stripped for marker in SYMBOLIC_LINE_MARKERS): - continue - if stripped.startswith(("import ", "open ", "namespace ", "end ")): - continue - name = f"line_{line_no}" - lean_match = re.match(r"(def|structure|inductive|abbrev|theorem|lemma)\s+([A-Za-z0-9_'.]+)", stripped) - if lean_match: - name = f"{lean_match.group(1)}_{lean_match.group(2)}" - heading = re.match(r"^#+\s+(.+)$", stripped) - if heading: - name = heading.group(1) - add_record( - records, - source_path=path, - source_hash=source_hash, - kind="text_equation_line", - name=name, - equation=stripped, - locator=f"line:{line_no}", - metadata={"line": line_no, "suffix": path.suffix}, - ) - - -def curriculum_records(receipt: dict[str, Any], per_bucket_limit: int) -> list[dict[str, Any]]: - system = "You are a custom-equation-aware routing model. Return compact JSON with source and claim boundaries." - by_primitive: dict[str, list[dict[str, Any]]] = {} - for record in receipt["equations"]: - by_primitive.setdefault(record["primitive_hint"], []).append(record) - selected: list[dict[str, Any]] = [] - low_value_limits = {"heading_or_doc": 12, "registry_schema": 16} - for primitive, items in sorted(by_primitive.items()): - selected.extend(items[: low_value_limits.get(primitive, per_bucket_limit)]) - records = [] - for item in selected: - prompt = { - "task": "route_custom_equation", - "source_path": item["source_path"], - "name": item["name"], - "equation": item["equation"], - "primitive_hint": item["primitive_hint"], - "claim_boundary": item["claim_boundary"], - "instruction": "Make the LLM aware of this local equation without overclaiming proof.", - } - answer = { - "selected": True, - "use_as": "custom_equation_awareness", - "primitive_hint": item["primitive_hint"], - "claim_boundary": item["claim_boundary"], - "source_path": item["source_path"], - "equation_hash": item["equation_hash"], - "route_rule": "Use the equation as a local routing/canonicalization prior; require source/build/prover receipts before truth promotion.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--root", type=Path, action="append") - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/custom_equation_awareness_manifest_receipt.json")) - parser.add_argument("--manifest", type=Path, default=Path("4-Infrastructure/shim/custom_equation_awareness_manifest.jsonl")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/custom_equation_awareness_curriculum.jsonl")) - parser.add_argument("--per-bucket-limit", type=int, default=80) - args = parser.parse_args() - - roots = args.root or DEFAULT_ROOTS - files = iter_candidate_files(roots) - equations: list[dict[str, Any]] = [] - source_summaries = [] - for path in files: - try: - raw = safe_read(path) - except Exception: - continue - source_hash = sha256_bytes(raw) - before = len(equations) - if path.suffix in JSON_SUFFIXES: - extract_json(path, source_hash, equations) - elif path.suffix in TEXT_SUFFIXES: - extract_text(path, source_hash, equations) - count = len(equations) - before - source_summaries.append( - { - "path": str(path), - "sha256": source_hash, - "suffix": path.suffix, - "equations_extracted": count, - } - ) - - primitive_counts: dict[str, int] = {} - boundary_counts: dict[str, int] = {} - for equation in equations: - primitive_counts[equation["primitive_hint"]] = primitive_counts.get(equation["primitive_hint"], 0) + 1 - boundary_counts[equation["claim_boundary"]] = boundary_counts.get(equation["claim_boundary"], 0) + 1 - - receipt = { - "schema": "custom_equation_awareness_manifest_v1", - "claim_boundary": "Equation awareness teaches local routing and recall; it does not prove or validate equations.", - "roots": [str(root) for root in roots], - "source_count": len(source_summaries), - "equation_count": len(equations), - "primitive_counts": primitive_counts, - "boundary_counts": boundary_counts, - "sources": source_summaries, - "equations": equations, - "lawful": bool(equations), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.manifest.open("w", encoding="utf-8") as handle: - for equation in equations: - handle.write(json.dumps(equation, ensure_ascii=False) + "\n") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt, args.per_bucket_limit): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps({k: receipt[k] for k in ("schema", "source_count", "equation_count", "primitive_counts", "boundary_counts", "lawful")}, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/dd_target_implementation_reevaluation.py b/4-Infrastructure/shim/dd_target_implementation_reevaluation.py deleted file mode 100644 index 6ad22600..00000000 --- a/4-Infrastructure/shim/dd_target_implementation_reevaluation.py +++ /dev/null @@ -1,439 +0,0 @@ -#!/usr/bin/env python3 -"""Re-evaluate DD compression targets against current implementation receipts. - -This is a receipt-bearing target review. It does not recompress data. It -reads the local decision-diagram, topology, route-card, KV, citation, and -singular-chart receipts and classifies which configuration knobs are currently: - -* promotion-backed by measured bytes, -* useful as pruning / control configuration, or -* still only proposal surface until an encode/decode/hash evaluator exists. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "dd_target_implementation_reevaluation_receipt.json" -CURRICULUM_OUT = SHIM / "dd_target_implementation_reevaluation_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" -HUTTER_ENWIK9_TARGET_BYTES = 109_685_197 - -RECEIPT_PATHS = { - "dimensional_shell_dd_probe": SHIM / "dimensional_shell_dd_probe_receipt.json", - "projectable_geometry_topology_model": SHIM - / "projectable_geometry_topology_model_receipt.json", - "alphaevolve_dd_experiment_card_runner": SHIM - / "alphaevolve_dd_experiment_card_receipt.json", - "non_euclidean_semantic_kv_prior": SHIM - / "non_euclidean_semantic_kv_prior_receipt.json", - "citation_math_function_distillation": SHIM - / "citation_math_function_distillation_receipt.json", - "singular_route_chart_equations": SHIM - / "singular_route_chart_equations_receipt.json", - "compression_ratio_rederivation": SHIM - / "compression_ratio_rederivation_receipt.json", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def receipt_hash(path: Path, data: dict[str, Any]) -> str: - for key in ( - "stable_topology_model_hash_sha256", - "stable_shell_dd_hash_sha256", - "receipt_hash", - ): - value = data.get(key) - if isinstance(value, str): - return value - return sha256_bytes(path.read_bytes()) - - -def source_receipts(receipts: dict[str, dict[str, Any]]) -> dict[str, Any]: - return { - name: { - "path": rel(RECEIPT_PATHS[name]), - "hash": receipt_hash(RECEIPT_PATHS[name], receipt), - "schema": receipt.get("schema", "unknown"), - } - for name, receipt in receipts.items() - } - - -def class_counts(cards: dict[str, Any]) -> dict[str, int]: - counts = cards.get("class_counts") - if isinstance(counts, dict): - return {str(key): int(value) for key, value in counts.items()} - derived: dict[str, int] = {} - for card in cards.get("cards", []): - cls = str(card.get("applicability_class", "unknown")) - derived[cls] = derived.get(cls, 0) + 1 - return derived - - -def current_best(shell: dict[str, Any], topology: dict[str, Any]) -> dict[str, Any]: - shell_best = shell["summary"]["best_shell_adjusted_overall"] - topology_best = topology["best_approach"]["selected_route"] - return { - "slice": topology_best["slice"], - "route": topology["best_approach"]["route"], - "byte_transform": f"{topology_best['transform']} -> {topology_best['codec']}", - "source_bytes": topology_best["source_bytes"], - "raw_baseline_bytes": topology_best["raw_baseline_bytes"], - "xml_bz2_bytes": topology_best["compressed_bytes"], - "topology_or_shell_witness_bytes": topology_best["topology_witness_bytes"], - "modeled_total_bytes": topology_best["modeled_total_bytes"], - "modeled_ratio": topology_best["modeled_ratio"], - "remaining_margin_vs_raw_baseline_bytes": topology_best[ - "gain_vs_raw_after_topology_bytes" - ], - "witness_budget_before_losing_raw_bytes": topology_best[ - "overhead_budget_before_losing_raw" - ], - "projected_enwik9_total_bytes": shell_best[ - "projected_enwik9_total_bytes" - ], - "projected_hutter_gap_bytes": shell_best[ - "hutter_target_gap_bytes_projected_enwik9" - ], - "rehydration_status": shell_best["shell_status"], - "lawful": bool(topology_best["lawful"] and not shell_best["shell_status"]["nan0"]), - } - - -def implementation_status(receipts: dict[str, dict[str, Any]]) -> list[dict[str, Any]]: - shell = receipts["dimensional_shell_dd_probe"] - topology = receipts["projectable_geometry_topology_model"] - cards = receipts["alphaevolve_dd_experiment_card_runner"] - kv = receipts["non_euclidean_semantic_kv_prior"] - citation = receipts["citation_math_function_distillation"] - singular = receipts["singular_route_chart_equations"] - ratio = receipts["compression_ratio_rederivation"] - - return [ - { - "id": "dimensional_shell_dd_probe", - "status": "implemented_receipt_over_existing_measurements", - "promotion_authority": "measured route receipt inherited from reversible approach; shell only adds bounded witness", - "evidence": { - "route_count": shell["summary"]["route_count"], - "promoted_route_count": shell["summary"]["promoted_route_count"], - "lower_bound_pruned_count": shell["summary"][ - "lower_bound_pruned_count" - ], - "nan0_route_count": shell["summary"]["nan0_route_count"], - "shell_witness_bytes": shell["dd_policy"][ - "shell_witness_bytes_per_non_raw_route" - ], - }, - "verdict": "promotable_when_margin_survives_witness", - }, - { - "id": "projectable_geometry_topology_model", - "status": "implemented_topology_witness_model", - "promotion_authority": "existing xml_token -> bz2 byte measurement plus 16-byte witness budget", - "evidence": topology["best_approach"]["selected_route"], - "verdict": "current_best_control_plane", - }, - { - "id": "alphaevolve_dd_experiment_card_runner", - "status": "implemented_search_card_prior", - "promotion_authority": "none until local candidate code runs through encode/decode/hash evaluator", - "evidence": { - "card_count": cards["card_count"], - "class_counts": class_counts(cards), - "full_program_collection": cards["validation"].get( - "full_program_collections", False - ), - }, - "verdict": "proposal_and_dashboard_surface", - }, - { - "id": "citation_math_function_distillation", - "status": "implemented_operator_distillation", - "promotion_authority": "none; this is the route compiler algebra", - "evidence": { - "primary_function": citation["primary_function"]["name"], - "algebra_count": len(citation["dd_function_algebra"]), - "function_group_count": len(citation["function_groups"]), - }, - "verdict": "configuration_basis", - }, - { - "id": "singular_route_chart_equations", - "status": "implemented_equation_group", - "promotion_authority": "none unless a finite chart decodes and hashes exact bytes under measured byte count", - "evidence": { - "primary_function": singular["singular_route_chart"][ - "primary_function" - ], - "equation_count": len(singular["equations"]), - "dd_state_field_count": len(singular["dd_state_extension"]), - "receipt_hash": singular["receipt_hash"], - }, - "verdict": "fail_closed_chart_layer", - }, - { - "id": "non_euclidean_semantic_kv_prior", - "status": "implemented_prior_and_watchlist", - "promotion_authority": "none until byte-store / KV-cache routes have exact rehydration receipts", - "evidence": { - "prior_family_count": len(kv["prior_families"]), - "priority_watch_items": [ - item["id"] for item in kv["priority_watch_items"] - ], - "treefiddy_status": kv["local_treefiddy_status"]["status"], - }, - "verdict": "proposal_surface_with_local_tree_bound", - }, - { - "id": "compression_ratio_rederivation", - "status": "implemented_ratio_audit", - "promotion_authority": "ratio schema only; byte counts remain terminal authority", - "evidence": { - "claim_boundary": ratio["claim_boundary"], - "finance_claim_lut_lawful": ratio["finance_claim_lut"]["lawful"], - }, - "verdict": "measurement_schema_guard", - }, - ] - - -def configuration_axes(best: dict[str, Any]) -> list[dict[str, Any]]: - remaining = int(best["remaining_margin_vs_raw_baseline_bytes"]) - budget = int(best["witness_budget_before_losing_raw_bytes"]) - return [ - { - "id": "topology_witness_bytes", - "current_setting": 16, - "admissible_range_for_current_best_bytes": [0, budget], - "target_role": "bounded closure/control witness", - "reevaluation": "keep at 16 unless a new encoder earns more payload savings", - }, - { - "id": "operator_basis_B_R_F_T_A_E_V", - "current_setting": "available_as_distilled_algebra", - "target_role": "configure pruning, routing, folding, transport, allocation, repair, and verification", - "reevaluation": "wire as evaluator stages; do not charge bytes unless an emitted receipt/witness is serialized", - }, - { - "id": "singular_route_chart", - "current_setting": "available_not_wired_to_byte_evaluator", - "target_role": "contain singular/ambiguous/unbounded route regions", - "reevaluation": ( - "use on failing or ambiguous branches first; applying extra chart " - f"bytes to the current best must stay under {remaining} bytes" - ), - }, - { - "id": "tinyenc_encrypted_kv_envelope", - "current_setting": "watch_item_only", - "target_role": "future compressed/encrypted byte-store route", - "reevaluation": "diagnostic until compressed bytes, query index overhead, and plaintext hash are receipted", - }, - { - "id": "treekv_treefiddy_spine", - "current_setting": "local_treefiddy_found_prior_only", - "target_role": "bounded tree route spine and owner/depth guard", - "reevaluation": "proposal only until TreeKV merge preserves decode reachability and exact residual leaves", - }, - { - "id": "alphaevolve_route_population", - "current_setting": "experiment_card_shape_only", - "target_role": "candidate generator and analysed/program counters", - "reevaluation": "feed candidates into the DD; never promote gallery score or novelty without local byte receipt", - }, - ] - - -def admissibility_verdicts(best: dict[str, Any]) -> list[dict[str, Any]]: - remaining = int(best["remaining_margin_vs_raw_baseline_bytes"]) - return [ - { - "route_or_overlay": "xml_token -> topology_witness_16b -> bz2", - "verdict": "promote_current_incumbent_for_small_slice", - "reason": "measured route survives exact shell/topology witness and beats raw+bz2 by 1105 bytes", - }, - { - "route_or_overlay": "singular_chart_on_current_best", - "verdict": "prune_unless_chart_bytes_are_paid_by_new_savings", - "reason": f"any added chart receipt consumes the remaining {remaining}-byte margin", - }, - { - "route_or_overlay": "B/R/F/T/A/E/V operator basis", - "verdict": "admissible_as_evaluator_configuration", - "reason": "operators organize route search and receipts, but do not themselves add byte evidence", - }, - { - "route_or_overlay": "TreeKV + Tree Fiddy", - "verdict": "diagnostic_until_encoder_exists", - "reason": "local Tree Fiddy can bound tree depth, but no TreeKV byte route has been evaluated", - }, - { - "route_or_overlay": "TinyEnc-style encrypted KV store", - "verdict": "diagnostic_until_store_receipt_exists", - "reason": "encryption, query index, and compression overhead must all be counted with plaintext rehydration", - }, - { - "route_or_overlay": "AlphaEvolve route population", - "verdict": "proposal_generator_only", - "reason": "program and analysed counts are useful search controls, not compression evidence", - }, - ] - - -def next_target(best: dict[str, Any]) -> dict[str, Any]: - return { - "target_name": "configurable_bounded_route_evaluator", - "reframed_goal": "optimize route evaluation and pruning configuration before adding more serialized witness bytes", - "why": ( - "The current best has only 1105 bytes of margin after the 16-byte " - "witness. New knobs should reject bad branches cheaply or propose " - "new byte-saving transforms; they should not be blindly appended to " - "the incumbent route." - ), - "minimum_config_fields": [ - "slice_id", - "candidate_route", - "backend_codec", - "topology_witness_bytes", - "singular_chart_enabled", - "singular_chart_bytes", - "treefiddy_spine_enabled", - "tinyenc_envelope_enabled", - "ratio_schema", - "incumbent_bytes", - "lower_bound_bytes", - "rehydration_hash", - ], - "first_safe_implementation_step": ( - "Add a configuration-matrix wrapper over existing receipts that " - "computes lower bounds and prunes overlays whose witness/chart/KV " - "cost would erase the incumbent margin before recompression." - ), - "first_real_compression_step": ( - "Evaluate one new payload-saving transform family at a time, starting " - "with bounded corpus-resolution/fascicle or tokenbook variants, then " - "charge any topology/singular/KV witness bytes in the terminal receipt." - ), - "hard_target_bytes_enwik9": HUTTER_ENWIK9_TARGET_BYTES, - "diagnostic_target": "beat raw baseline per slice under explicit ratio_schema", - "current_gap_to_hard_target_projected_enwik9_bytes": best[ - "projected_hutter_gap_bytes" - ], - } - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["implementation_status"]: - lines.append( - { - "type": "implementation_status", - "id": item["id"], - "status": item["status"], - "verdict": item["verdict"], - } - ) - for item in receipt["configuration_axes"]: - lines.append( - { - "type": "configuration_axis", - "id": item["id"], - "current_setting": item["current_setting"], - "reevaluation": item["reevaluation"], - } - ) - for item in receipt["admissibility_verdicts"]: - lines.append( - { - "type": "admissibility_verdict", - "id": item["route_or_overlay"], - "verdict": item["verdict"], - "reason": item["reason"], - } - ) - return lines - - -def build_receipt() -> dict[str, Any]: - receipts = {name: load_json(path) for name, path in RECEIPT_PATHS.items()} - best = current_best( - receipts["dimensional_shell_dd_probe"], - receipts["projectable_geometry_topology_model"], - ) - receipt: dict[str, Any] = { - "schema": "dd_target_implementation_reevaluation_v1", - "generated_at": GENERATED_AT, - "source_receipts": source_receipts(receipts), - "current_target": { - "primary_machine": "bounded_exact_route_compiler", - "hard_target_bytes_enwik9": HUTTER_ENWIK9_TARGET_BYTES, - "diagnostic_target": "beat raw baseline per slice under explicit ratio_schema", - "proof_surface": [ - "exact decoded bytes", - "rehydration hash", - "measured compressed_total_bytes", - "explicit ratio_schema", - "bounded sidecar/witness/container/compute cost", - "fail-closed NaN0 metadata holds", - ], - }, - "current_best_implemented_route": best, - "implementation_status": implementation_status(receipts), - "configuration_axes": configuration_axes(best), - "admissibility_verdicts": admissibility_verdicts(best), - "reevaluated_next_target": next_target(best), - "claim_boundary": ( - "This is a target and implementation reevaluation over local receipts. " - "It does not recompress data, prove optimality, or promote any route " - "without an encode/decode/hash/byte-count receipt." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_bytes(stable_json(preimage).encode("utf-8")) - return receipt - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "line_count": len(lines), - "current_best": receipt["current_best_implemented_route"]["route"], - "next_target": receipt["reevaluated_next_target"]["target_name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/decoder_reconstruction_core_prior.py b/4-Infrastructure/shim/decoder_reconstruction_core_prior.py deleted file mode 100644 index 41401bc9..00000000 --- a/4-Infrastructure/shim/decoder_reconstruction_core_prior.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -"""Emit decoder-facing reconstruction-core prior packets. - -This is a documentation/architecture receipt generator. It does not implement a -compressor. It preserves the 2026-05-08 synthesis as finite HOLD packets that -can be diffed, linked from the wiki, and later promoted only by benchmarked -byte-exact replay. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Iterable - - -ROOT = Path(__file__).resolve().parents[2] -OUT_DIR = ROOT / "shared-data" / "data" / "decoder_reconstruction_core" - - -@dataclass(frozen=True) -class PriorPacket: - packet_id: str - name: str - decision: str - source_url: str - canonical_phrase: str - density_markers: list[str] - claim_boundary: str - - -def stable_hash(obj: object) -> str: - blob = json.dumps(obj, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(blob).hexdigest() - - -def build_packets() -> list[PriorPacket]: - source_url = "https://chatgpt.com/c/69fe87f6-9838-83ea-b95b-54b0579122c3" - return [ - PriorPacket( - packet_id="DRC.PRIOR.NOT_ASSEMBLY.0001", - name="Decoder-facing reconstruction core boundary", - decision="HOLD", - source_url=source_url, - canonical_phrase="This is not assembly. It is a lawful reconstruction core.", - density_markers=[ - "not_human_readable_ir", - "deterministic_replay_boundary", - "residual_repair_interface", - "byte_exact_output_gate", - "inspection_moved_to_receipts", - ], - claim_boundary=( - "Defines an interface boundary only; validity still requires " - "byte-exact replay, residual repair, and counted bytes." - ), - ), - PriorPacket( - packet_id="DRC.PRIOR.OMCF_PIST.0001", - name="OMCF plus PIST field carrier synthesis", - decision="HOLD", - source_url=source_url, - canonical_phrase="OMCF carries the object; PIST moves the surface.", - density_markers=[ - "mass_gaussian_continuant", - "semantic_mass_imaginary_lane", - "pist_surface_transform", - "oamvr_oavmr_receipt", - "deterministic_expansion_packet", - ], - claim_boundary=( - "Design carrier only; no OMCF/PIST packet promotes without " - "deterministic replay and positive byte law." - ), - ), - PriorPacket( - packet_id="DRC.PRIOR.HEX_SEED_TRIAD.0001", - name="Hex seed ladder logogram boundary triad", - decision="HOLD", - source_url=source_url, - canonical_phrase="The seed stores the law, not the expanded table.", - density_markers=[ - "ladder_lut", - "hex_logogram_atlas", - "manifold_boundary_atlas", - "seed_generated_grouping", - "residual_exception_stream", - ], - claim_boundary=( - "Seeded generation is an admissible shortcut only when seed, " - "law, receipt, and residual are cheaper than explicit storage." - ), - ), - PriorPacket( - packet_id="DRC.PRIOR.CONTROL_FILTERS.0001", - name="Meme-named control filter stack", - decision="HOLD", - source_url=source_url, - canonical_phrase="Absurd name -> rigorous gate -> receipt -> replay.", - density_markers=[ - "loc_nes_monster_false_pattern_filter", - "fyc_manifold_traversal_gate", - "couch_hysteresis_filter", - "tree_fiddy_recursion_bound", - "bhocs_commit_gate", - "famm_delay_memory_pressure", - ], - claim_boundary=( - "Mnemonic labels do not substitute for proof; each filter must " - "map to a deterministic predicate or measured gate." - ), - ), - PriorPacket( - packet_id="DRC.PRIOR.PROPOSER_VERIFIER.0001", - name="AIMO/SPX proposal-verifier and residual-sharding prior", - decision="HOLD", - source_url=source_url, - canonical_phrase="The model may wander. The verifier does not.", - density_markers=[ - "minimal_stochastic_proposal", - "deterministic_verifier", - "coverage_first_traversal", - "stateless_residual_sharding", - "ans_rans_entropy_backend", - ], - claim_boundary=( - "External notebooks and repositories are idea priors only; " - "clean-room implementation and local replay receipts are required." - ), - ), - ] - - -def write_jsonl(path: Path, packets: Iterable[PriorPacket]) -> None: - with path.open("w", encoding="utf-8") as fh: - for packet in packets: - fh.write(json.dumps(asdict(packet), sort_keys=True) + "\n") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = build_packets() - packet_dicts = [asdict(packet) for packet in packets] - - packets_path = OUT_DIR / "decoder_reconstruction_core_prior_packets.jsonl" - receipt_path = OUT_DIR / "decoder_reconstruction_core_prior_receipt.json" - - write_jsonl(packets_path, packets) - - receipt = { - "schema": "decoder_reconstruction_core_prior_receipt_v1", - "packet_count": len(packets), - "packet_ids": [packet.packet_id for packet in packets], - "density_marker_total": sum(len(packet.density_markers) for packet in packets), - "decision": "HOLD", - "packets_sha256": stable_hash(packet_dicts), - "claim_boundary": ( - "Architecture prior only; no compression win is claimed without " - "byte-exact replay, residual repair, and measured byte-law gain." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/desi_epoviz_population_cell_join.py b/4-Infrastructure/shim/desi_epoviz_population_cell_join.py deleted file mode 100644 index ee8dc925..00000000 --- a/4-Infrastructure/shim/desi_epoviz_population_cell_join.py +++ /dev/null @@ -1,398 +0,0 @@ -#!/usr/bin/env python3 -"""Build a DESI EDR epoviz population seed and coarse MaNGA gas-cell join. - -This is intentionally a coarse population prior, not an object-level crossmatch. -The DESI EDR epoviz VAC supplies sky position, redshift, rosette, and tracer -codes; the MaNGA gas grouping study supplies local gas/shock proxies. The only -join key used here is a shared sky/redshift cell. -""" - -from __future__ import annotations - -import csv -import gzip -import hashlib -import json -from collections import Counter, defaultdict -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -DESI_GZ = ROOT / "shared-data/artifacts/stellar_gas_observation/desi_epoviz/EDR-Viz-Outreach-VAC.csv.gz" -MANGA_STUDY = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_population_grouping_study.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -STUDY_JSON = OUT_DIR / "desi_epoviz_manga_population_cell_join.json" -RECEIPT_JSON = OUT_DIR / "desi_epoviz_manga_population_cell_join_receipt.json" -DOC_MD = DOCS_DIR / "desi_epoviz_manga_population_cell_join_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "DESI Epoviz MaNGA Population Cell Join.tid" - -TRACERS = { - "0": "QSO", - "1": "ELG", - "2": "LRG", - "3": "BGS", -} - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def z_bin_local(z: float) -> str: - if z < 0.02: - return "z_000_002" - if z < 0.04: - return "z_002_004" - if z < 0.06: - return "z_004_006" - if z < 0.08: - return "z_006_008" - return "z_008_plus" - - -def z_bin_cosmic(z: float) -> str: - if z < 0.1: - return "z_0_0p1" - if z < 0.5: - return "z_0p1_0p5" - if z < 1.0: - return "z_0p5_1" - if z < 2.0: - return "z_1_2" - return "z_2_plus" - - -def sky_bin(ra: float, dec: float) -> str: - sector = int(ra // 60) - if sector < 0: - sector = 0 - if sector > 5: - sector = 5 - hemi = "north" if dec >= 0 else "south" - return f"ra{sector:02d}_{hemi}" - - -def round6(x: float | None) -> float | None: - if x is None: - return None - return round(x, 6) - - -def summarize(values: list[float]) -> dict[str, Any]: - if not values: - return {"count": 0, "min": None, "max": None, "mean": None} - return { - "count": len(values), - "min": round6(min(values)), - "max": round6(max(values)), - "mean": round6(sum(values) / len(values)), - } - - -def counter_dict(counter: Counter[str]) -> dict[str, int]: - return {key: counter[key] for key in sorted(counter)} - - -def load_manga_cells() -> dict[str, dict[str, Any]]: - with MANGA_STUDY.open() as f: - data = json.load(f) - cells = data["groups"]["by_sky_z_cell"] - return {cell: payload for cell, payload in cells.items()} - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - if not DESI_GZ.exists(): - raise FileNotFoundError(f"missing DESI epoviz artifact: {DESI_GZ}") - if not MANGA_STUDY.exists(): - raise FileNotFoundError(f"missing MaNGA grouping study: {MANGA_STUDY}") - - manga_cells = load_manga_cells() - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - source_hash = sha256_file(DESI_GZ) - - tracer_counts: Counter[str] = Counter() - rosette_counts: Counter[str] = Counter() - local_z_counts: Counter[str] = Counter() - cosmic_z_counts: Counter[str] = Counter() - sky_counts: Counter[str] = Counter() - sky_z_counts: dict[str, Counter[str]] = defaultdict(Counter) - z_by_tracer: dict[str, list[float]] = defaultdict(list) - redshifts: list[float] = [] - row_count = 0 - - with gzip.open(DESI_GZ, "rt", newline="") as f: - reader = csv.DictReader(f) - required = {"TARGETID", "RA", "DEC", "REDSHIFT", "ROSETTE", "TRACER"} - missing = required - set(reader.fieldnames or []) - if missing: - raise ValueError(f"missing DESI epoviz columns: {sorted(missing)}") - for row in reader: - row_count += 1 - ra = float(row["RA"]) - dec = float(row["DEC"]) - z = float(row["REDSHIFT"]) - tracer_code = row["TRACER"] - tracer = TRACERS.get(tracer_code, f"UNKNOWN_{tracer_code}") - local_bin = z_bin_local(z) - cosmic_bin = z_bin_cosmic(z) - sky = sky_bin(ra, dec) - cell = f"{sky}__{local_bin}" - - tracer_counts[tracer] += 1 - rosette_counts[row["ROSETTE"]] += 1 - local_z_counts[local_bin] += 1 - cosmic_z_counts[cosmic_bin] += 1 - sky_counts[sky] += 1 - sky_z_counts[cell]["count"] += 1 - sky_z_counts[cell][f"tracer_{tracer}"] += 1 - z_by_tracer[tracer].append(z) - redshifts.append(z) - - joined_cells: list[dict[str, Any]] = [] - for cell, manga in manga_cells.items(): - desi = sky_z_counts.get(cell, Counter()) - if not desi: - continue - desi_count = desi["count"] - tracer_mix = { - key.removeprefix("tracer_"): value - for key, value in sorted(desi.items()) - if key.startswith("tracer_") - } - joined_cells.append( - { - "cell": cell, - "desi_count": desi_count, - "desi_tracer_mix": tracer_mix, - "manga_count": manga["count"], - "manga_partial_or_full_shock_fraction": manga.get("partial_or_full_shock_fraction"), - "manga_shock_lier_fraction": manga.get("shock_lier_fraction"), - "join_status": "COARSE_SKY_REDSHIFT_CELL_OVERLAP", - } - ) - - joined_cells.sort( - key=lambda item: ( - item["desi_count"] * item["manga_count"], - item["manga_partial_or_full_shock_fraction"] or 0, - ), - reverse=True, - ) - - desi_top_cells = [] - for cell, payload in sorted(sky_z_counts.items(), key=lambda kv: kv[1]["count"], reverse=True)[:25]: - tracer_mix = { - key.removeprefix("tracer_"): value - for key, value in sorted(payload.items()) - if key.startswith("tracer_") - } - desi_top_cells.append( - { - "cell": cell, - "count": payload["count"], - "tracer_mix": tracer_mix, - } - ) - - study = { - "schema": "desi_epoviz_manga_population_cell_join_v0", - "created": created, - "decision": "ADMIT_COARSE_CELL_PRIOR_HOLD_OBJECT_CROSSMATCH", - "claim_boundary": ( - "Uses DESI EDR epoviz as a population prior and joins to MaNGA only by " - "coarse sky/redshift cells. This is not an object-level crossmatch, not " - "a direct stellar gas map, and not a cosmology fit." - ), - "sources": { - "desi_epoviz_csv_gz": str(DESI_GZ.relative_to(ROOT)), - "desi_epoviz_sha256": source_hash, - "desi_epoviz_doc": "https://data.desi.lbl.gov/doc/releases/edr/vac/epoviz/", - "manga_population_study": str(MANGA_STUDY.relative_to(ROOT)), - }, - "desi_population": { - "row_count": row_count, - "tracer_counts": counter_dict(tracer_counts), - "local_redshift_bins": counter_dict(local_z_counts), - "cosmic_redshift_bins": counter_dict(cosmic_z_counts), - "sky_bins": counter_dict(sky_counts), - "rosette_counts": counter_dict(rosette_counts), - "redshift_summary": summarize(redshifts), - "redshift_summary_by_tracer": { - tracer: summarize(vals) for tracer, vals in sorted(z_by_tracer.items()) - }, - "top_sky_z_cells": desi_top_cells, - }, - "manga_join": { - "manga_cell_count": len(manga_cells), - "joined_cell_count": len(joined_cells), - "join_key": "sky_bin + local_redshift_bin", - "join_key_shape": "raXX_north_or_south__z_000_002/z_002_004/z_004_006/z_006_008/z_008_plus", - "top_joined_cells": joined_cells[:25], - }, - "holds": [ - "HOLD_OBJECT_LEVEL_CROSSMATCH", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_SELECTION_FUNCTION_FIT", - "HOLD_COSMOLOGY_FIT", - ], - } - - receipt = { - "receipt_type": "desi_epoviz_manga_population_cell_join_receipt", - "created": created, - "source_sha256": source_hash, - "rows_seen": row_count, - "joined_cell_count": len(joined_cells), - "decision": study["decision"], - "validated_outputs": [ - str(STUDY_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return study, receipt - - -def write_docs(study: dict[str, Any]) -> None: - pop = study["desi_population"] - join = study["manga_join"] - top_join = join["top_joined_cells"][:8] - top_lines = "\n".join( - f"- `{row['cell']}`: DESI {row['desi_count']}, MaNGA {row['manga_count']}, " - f"shock proxy {row['manga_partial_or_full_shock_fraction']}" - for row in top_join - ) - tracer_lines = "\n".join( - f"- `{name}`: {count}" for name, count in pop["tracer_counts"].items() - ) - redshift_lines = "\n".join( - f"- `{name}`: {count}" for name, count in pop["local_redshift_bins"].items() - ) - - DOC_MD.write_text( - f"""# DESI Epoviz to MaNGA Population Cell Join - -Status: `COARSE_CELL_PRIOR` - -Decision: `{study['decision']}` - -This note uses the DESI EDR epoviz visualization/outreach VAC as a lightweight -population prior and compares it with the local MaNGA stellar-gas grouping study -by shared sky/redshift cells. - -Claim boundary: this is not an object-level crossmatch, not a direct gas-density -map, and not a cosmology fit. It is a population-shape prior for deciding where -the stellar-gas model has local support and where it should stay in `HOLD`. - -## Source - -- DESI EDR epoviz CSV: `{study['sources']['desi_epoviz_csv_gz']}` -- DESI source hash: `{study['sources']['desi_epoviz_sha256']}` -- DESI documentation: {study['sources']['desi_epoviz_doc']} -- MaNGA grouping study: `{study['sources']['manga_population_study']}` - -## DESI Population - -Rows read: `{pop['row_count']}` - -Tracer counts: - -{tracer_lines} - -Local redshift bins used for MaNGA overlap: - -{redshift_lines} - -Redshift summary: - -```json -{json.dumps(pop['redshift_summary'], indent=2)} -``` - -## Coarse Join - -Join key: - -```text -{join['join_key_shape']} -``` - -Joined cells: `{join['joined_cell_count']}` out of `{join['manga_cell_count']}` MaNGA cells. - -Top joined cells: - -{top_lines} - -## Holds - -{chr(10).join(f'- `{hold}`' for hold in study['holds'])} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: DESI Epoviz MaNGA Population Cell Join -tags: StellarGasObservation DESI MaNGA SemanticMassNumbers Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{study['decision']}` - -This tiddler records a coarse population overlap between the DESI EDR epoviz -catalog and the MaNGA stellar-gas grouping study. - -It uses the shared key: - -``` -{join['join_key_shape']} -``` - -Rows read from DESI epoviz: `{pop['row_count']}` - -Joined MaNGA cells: `{join['joined_cell_count']}` / `{join['manga_cell_count']}` - -This is not an object-level crossmatch and not a direct stellar-gas density map. -It is a prior surface for deciding where the SMN / stellar-gas model has enough -population support to zoom in. - -!! Tracer Counts - -{tracer_lines} - -!! Top Joined Cells - -{top_lines} - -!! Holds - -{chr(10).join(f'* `{hold}`' for hold in study['holds'])} -""", - encoding="utf-8", - ) - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - - study, receipt = build() - STUDY_JSON.write_text(json.dumps(study, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(study) - - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/desi_epoviz_row_eigenmass_probe.py b/4-Infrastructure/shim/desi_epoviz_row_eigenmass_probe.py deleted file mode 100644 index 5026f0b4..00000000 --- a/4-Infrastructure/shim/desi_epoviz_row_eigenmass_probe.py +++ /dev/null @@ -1,445 +0,0 @@ -#!/usr/bin/env python3 -"""Stream the DESI EDR epoviz rows into a row-level eigenmass receipt. - -This is a literal-data stress pass over the 669k-row DESI epoviz CSV. It avoids -holding every row in memory by accumulating feature means and covariance with a -streaming Welford update, then computes a small symmetric eigendecomposition. - -Boundary: this is an SMN/evidence-load mass direction over DESI epoviz rows. It -is not physical mass, not dark-energy inference, and not a gas-density map. -""" - -from __future__ import annotations - -import csv -import gzip -import hashlib -import json -import math -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -DESI_GZ = ROOT / "shared-data/artifacts/stellar_gas_observation/desi_epoviz/EDR-Viz-Outreach-VAC.csv.gz" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "desi_epoviz_row_eigenmass_probe.json" -RECEIPT_JSON = OUT_DIR / "desi_epoviz_row_eigenmass_probe_receipt.json" -DOC_MD = DOCS_DIR / "desi_epoviz_row_eigenmass_probe_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "DESI Epoviz Row Eigenmass Probe.tid" - -FEATURES = [ - "x_glyr", - "y_glyr", - "z_glyr", - "redshift", - "rosette_sin", - "rosette_cos", - "tracer_QSO", - "tracer_ELG", - "tracer_LRG", - "tracer_BGS", -] - -TRACERS = { - "0": "QSO", - "1": "ELG", - "2": "LRG", - "3": "BGS", -} - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def dot(a: list[float], b: list[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def mat_vec(m: list[list[float]], v: list[float]) -> list[float]: - return [dot(row, v) for row in m] - - -def norm(v: list[float]) -> float: - return math.sqrt(dot(v, v)) - - -def normalize(v: list[float]) -> list[float]: - n = norm(v) - if n == 0: - return [0.0 for _ in v] - return [x / n for x in v] - - -def jacobi_eigen_symmetric(matrix: list[list[float]], max_iter: int = 300, eps: float = 1e-12) -> tuple[list[float], list[list[float]], dict[str, Any]]: - n = len(matrix) - a = [row[:] for row in matrix] - v = [[1.0 if i == j else 0.0 for j in range(n)] for i in range(n)] - - iterations = 0 - final_max_offdiag = 0.0 - converged = False - for iteration in range(1, max_iter + 1): - p, q = 0, 1 - max_off = 0.0 - for i in range(n): - for j in range(i + 1, n): - val = abs(a[i][j]) - if val > max_off: - max_off = val - p, q = i, j - iterations = iteration - final_max_offdiag = max_off - if max_off < eps: - converged = True - break - - if abs(a[p][p] - a[q][q]) < eps: - angle = math.pi / 4.0 - else: - angle = 0.5 * math.atan2(2.0 * a[p][q], a[q][q] - a[p][p]) - c = math.cos(angle) - s = math.sin(angle) - - app = c * c * a[p][p] - 2.0 * s * c * a[p][q] + s * s * a[q][q] - aqq = s * s * a[p][p] + 2.0 * s * c * a[p][q] + c * c * a[q][q] - a[p][p] = app - a[q][q] = aqq - a[p][q] = 0.0 - a[q][p] = 0.0 - - for k in range(n): - if k == p or k == q: - continue - akp = c * a[k][p] - s * a[k][q] - akq = s * a[k][p] + c * a[k][q] - a[k][p] = akp - a[p][k] = akp - a[k][q] = akq - a[q][k] = akq - - for k in range(n): - vkp = c * v[k][p] - s * v[k][q] - vkq = s * v[k][p] + c * v[k][q] - v[k][p] = vkp - v[k][q] = vkq - - return [a[i][i] for i in range(n)], v, { - "method": "jacobi_symmetric", - "max_iter": max_iter, - "eps": eps, - "iterations": iterations, - "converged": converged, - "final_max_offdiag": final_max_offdiag, - } - - -def eigen_residual(matrix: list[list[float]], eigenvalue: float, eigenvector: list[float]) -> float: - av = mat_vec(matrix, eigenvector) - residual = [av_i - eigenvalue * v_i for av_i, v_i in zip(av, eigenvector)] - return norm(residual) - - -def feature_row(row: dict[str, str]) -> list[float]: - rosette = int(row["ROSETTE"]) - angle = 2.0 * math.pi * (rosette % 20) / 20.0 - tracer = TRACERS.get(row["TRACER"], "UNKNOWN") - return [ - float(row["X"]), - float(row["Y"]), - float(row["Z"]), - float(row["REDSHIFT"]), - math.sin(angle), - math.cos(angle), - 1.0 if tracer == "QSO" else 0.0, - 1.0 if tracer == "ELG" else 0.0, - 1.0 if tracer == "LRG" else 0.0, - 1.0 if tracer == "BGS" else 0.0, - ] - - -def z_bin_cosmic(z: float) -> str: - if z < 0.1: - return "z_0_0p1" - if z < 0.5: - return "z_0p1_0p5" - if z < 1.0: - return "z_0p5_1" - if z < 2.0: - return "z_1_2" - return "z_2_plus" - - -def round_float(x: float) -> float: - return round(x, 9) - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - if not DESI_GZ.exists(): - raise FileNotFoundError(DESI_GZ) - - n = 0 - d = len(FEATURES) - means = [0.0] * d - m2 = [[0.0] * d for _ in range(d)] - tracer_counts: Counter[str] = Counter() - z_counts: Counter[str] = Counter() - rosette_counts: Counter[str] = Counter() - redshift_min = math.inf - redshift_max = -math.inf - - with gzip.open(DESI_GZ, "rt", newline="") as f: - reader = csv.DictReader(f) - required = {"TARGETID", "REDSHIFT", "ROSETTE", "TRACER", "X", "Y", "Z"} - missing = required - set(reader.fieldnames or []) - if missing: - raise ValueError(f"missing columns: {sorted(missing)}") - for row in reader: - x = feature_row(row) - n += 1 - delta = [x[i] - means[i] for i in range(d)] - for i in range(d): - means[i] += delta[i] / n - delta2 = [x[i] - means[i] for i in range(d)] - for i in range(d): - for j in range(i, d): - m2[i][j] += delta[i] * delta2[j] - - tracer = TRACERS.get(row["TRACER"], f"UNKNOWN_{row['TRACER']}") - z = float(row["REDSHIFT"]) - tracer_counts[tracer] += 1 - z_counts[z_bin_cosmic(z)] += 1 - rosette_counts[row["ROSETTE"]] += 1 - redshift_min = min(redshift_min, z) - redshift_max = max(redshift_max, z) - - cov = [[0.0] * d for _ in range(d)] - for i in range(d): - for j in range(i, d): - val = m2[i][j] / (n - 1) - cov[i][j] = val - cov[j][i] = val - - stds = [math.sqrt(max(cov[i][i], 0.0)) or 1.0 for i in range(d)] - corr = [[cov[i][j] / (stds[i] * stds[j]) for j in range(d)] for i in range(d)] - values, vectors_as_columns, solver = jacobi_eigen_symmetric(corr) - order = sorted(range(d), key=lambda i: values[i], reverse=True) - eigenvalues = [values[i] for i in order] - dominant = normalize([vectors_as_columns[row][order[0]] for row in range(d)]) - - # Keep the direction readable: positive redshift and ELG/LRG direction. - sign_anchor = dominant[FEATURES.index("redshift")] + dominant[FEATURES.index("tracer_ELG")] + dominant[FEATURES.index("tracer_LRG")] - if sign_anchor < 0: - dominant = [-x for x in dominant] - residual = eigen_residual(corr, eigenvalues[0], dominant) - - total_positive = sum(x for x in eigenvalues if x > 0) - explained = [x / total_positive if total_positive > 0 else 0.0 for x in eigenvalues] - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - source_hash = sha256_file(DESI_GZ) - - result = { - "schema": "desi_epoviz_row_eigenmass_probe_v0", - "created": created, - "decision": "ADMIT_DESI_ROW_EIGENMASS_HOLD_PHYSICAL_MASS", - "claim_boundary": ( - "Row eigenmass is an SMN/evidence-load direction over DESI EDR " - "epoviz rows. It is not physical mass, not stellar mass, not a " - "gas-density map, and not a cosmology fit." - ), - "source": { - "csv_gz": str(DESI_GZ.relative_to(ROOT)), - "sha256": source_hash, - "doc": "https://data.desi.lbl.gov/doc/releases/edr/vac/epoviz/", - }, - "row_count": n, - "feature_basis": FEATURES, - "feature_means": {name: round_float(means[i]) for i, name in enumerate(FEATURES)}, - "feature_stds": {name: round_float(stds[i]) for i, name in enumerate(FEATURES)}, - "dominant_eigenvalue": round_float(eigenvalues[0]), - "dominant_explained_mass_share": round_float(explained[0]), - "eigensolver_diagnostics": { - **solver, - "dominant_residual_l2": round(residual, 12), - "orthogonality_note": "Jacobi rotations return an orthonormal basis up to numeric roundoff; this receipt reports the dominant residual only.", - }, - "dominant_eigenvector": {name: round_float(dominant[i]) for i, name in enumerate(FEATURES)}, - "eigenvalues": [round_float(x) for x in eigenvalues], - "explained_mass_share": [round_float(x) for x in explained], - "population_counts": { - "tracers": {k: tracer_counts[k] for k in sorted(tracer_counts)}, - "cosmic_redshift_bins": {k: z_counts[k] for k in sorted(z_counts)}, - "rosettes": {k: rosette_counts[k] for k in sorted(rosette_counts, key=lambda x: int(x))}, - "redshift_min": round_float(redshift_min), - "redshift_max": round_float(redshift_max), - }, - "holds": [ - "HOLD_PHYSICAL_MASS_INTERPRETATION", - "HOLD_DIRECT_STELLAR_GAS_INFERENCE", - "HOLD_OBJECT_LEVEL_MANGA_CROSSMATCH", - "HOLD_SELECTION_FUNCTION_FIT", - "HOLD_COSMOLOGY_FIT", - ], - } - - receipt = { - "receipt_type": "desi_epoviz_row_eigenmass_probe_receipt", - "created": created, - "source_sha256": source_hash, - "row_count": n, - "dominant_eigenvalue": result["dominant_eigenvalue"], - "dominant_explained_mass_share": result["dominant_explained_mass_share"], - "eigensolver_diagnostics": result["eigensolver_diagnostics"], - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - vector_lines = "\n".join( - f"- `{name}`: {value}" for name, value in result["dominant_eigenvector"].items() - ) - tracer_lines = "\n".join( - f"- `{name}`: {value}" for name, value in result["population_counts"]["tracers"].items() - ) - z_lines = "\n".join( - f"- `{name}`: {value}" for name, value in result["population_counts"]["cosmic_redshift_bins"].items() - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - diag = result["eigensolver_diagnostics"] - - DOC_MD.write_text( - f"""# DESI Epoviz Row Eigenmass Probe - -Status: `DESI_ROW_EIGENMASS` - -Decision: `{result['decision']}` - -This probe streams the DESI EDR epoviz CSV row-by-row and computes the dominant -correlation eigenvector over geometry, redshift, rosette phase, and tracer -identity. It is a literal-data stress pass over the DESI epoviz surface. - -Claim boundary: this is SMN/evidence-load mass, not physical mass, not stellar -mass, not gas-density inference, and not a cosmology fit. - -## Result - -Rows read: `{result['row_count']}` - -Dominant eigenvalue: - -```text -{result['dominant_eigenvalue']} -``` - -Dominant explained mass share: - -```text -{result['dominant_explained_mass_share']} -``` - -## Dominant Eigenvector - -{vector_lines} - -## Eigensolver Diagnostics - -```text -method: {diag['method']} -converged: {diag['converged']} -iterations: {diag['iterations']} -final max off-diagonal: {diag['final_max_offdiag']} -dominant residual L2: {diag['dominant_residual_l2']} -``` - -## Population Counts - -Tracer counts: - -{tracer_lines} - -Cosmic redshift bins: - -{z_lines} - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: DESI Epoviz Row Eigenmass Probe -tags: StellarGasObservation DESI SemanticMassNumbers Eigenvector Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This probe streams the DESI EDR epoviz rows directly and computes the dominant -SMN/evidence-load eigenvector over geometry, redshift, rosette phase, and tracer -identity. - -Rows read: `{result['row_count']}` - -Dominant eigenvalue: - -``` -{result['dominant_eigenvalue']} -``` - -Dominant explained mass share: - -``` -{result['dominant_explained_mass_share']} -``` - -Eigensolver: - -``` -converged={diag['converged']} iterations={diag['iterations']} residual_l2={diag['dominant_residual_l2']} -``` - -!! Dominant Eigenvector - -{vector_lines} - -!! Boundary - -This is not physical mass, not stellar mass, not direct gas-density inference, -and not a cosmology fit. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/dimensional_shell_dd_probe.py b/4-Infrastructure/shim/dimensional_shell_dd_probe.py deleted file mode 100644 index 89159cc5..00000000 --- a/4-Infrastructure/shim/dimensional_shell_dd_probe.py +++ /dev/null @@ -1,335 +0,0 @@ -#!/usr/bin/env python3 -"""Bounded decision-diagram probe for dimensional shell closure. - -This runner does not recompress data. It consumes the current reversible -projectable-geometry approach receipt and asks a narrower question: - -If every non-raw route must carry a dimensional-shell closure witness, which -routes still beat the raw baseline, and which routes are pruned before any -recursive residual expansion can start? - -The policy is intentionally conservative: - -* no recursive residual subdivision -* no unresolved mass debt -* NaN0 fails closed -* candidate routes are compared against the best raw baseline per slice -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from dataclasses import dataclass -from datetime import datetime, timezone -from fractions import Fraction -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -APPROACH_RECEIPT = ( - REPO - / "4-Infrastructure" - / "shim" - / "projectable_geometry_approach_tester_receipt.json" -) -RATIO_RECEIPT = ( - REPO - / "4-Infrastructure" - / "shim" - / "compression_ratio_rederivation_receipt.json" -) -OUT = ( - REPO - / "4-Infrastructure" - / "shim" - / "dimensional_shell_dd_probe_receipt.json" -) - -HUTTER_TARGET_BYTES = 109_685_197 -ENWIK9_BYTES = 1_000_000_000 - -SHELL_SEQUENCE = (12, 4, 3, 0) -MAX_PROJECTION_DEPTH = 3 -DEFAULT_SHELL_WITNESS_BYTES = 16 - - -@dataclass(frozen=True) -class ShellMass: - visible_4d: Fraction - shadow_3d: Fraction - closure_0d: Fraction - lawbound: Fraction - unresolved: Fraction - - @property - def total(self) -> Fraction: - return ( - self.visible_4d - + self.shadow_3d - + self.closure_0d - + self.lawbound - + self.unresolved - ) - - -SHELL_MASS = ShellMass( - visible_4d=Fraction(4, 12), - shadow_3d=Fraction(3, 12), - closure_0d=Fraction(1, 12), - lawbound=Fraction(4, 12), - unresolved=Fraction(0), -) - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def fraction_str(value: Fraction) -> str: - return str(value.numerator) if value.denominator == 1 else f"{value.numerator}/{value.denominator}" - - -def fraction_json(value: Fraction) -> dict[str, Any]: - return { - "fraction": fraction_str(value), - "numerator": value.numerator, - "denominator": value.denominator, - "decimal": float(value), - } - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def route_id(slice_name: str, transform: str, codec: str) -> str: - return f"{slice_name}|{transform}|{codec}" - - -def shell_status(result: dict[str, Any]) -> dict[str, Any]: - rehydrated_ok = bool(result.get("rehydrated_ok")) - unresolved_ok = SHELL_MASS.unresolved == 0 - mass_ok = SHELL_MASS.total == 1 - depth_ok = MAX_PROJECTION_DEPTH == len(SHELL_SEQUENCE) - 1 - nan0 = not (rehydrated_ok and unresolved_ok and mass_ok and depth_ok) - return { - "closed": not nan0, - "nan0": nan0, - "rehydrated_ok": rehydrated_ok, - "mass_total": fraction_json(SHELL_MASS.total), - "mass_delta": fraction_json(SHELL_MASS.total - 1), - "unresolved_mass": fraction_json(SHELL_MASS.unresolved), - "depth_ok": depth_ok, - } - - -def classify_route( - slice_name: str, - source_bytes: int, - raw_baseline_bytes: int, - result: dict[str, Any], - shell_witness_bytes: int, -) -> dict[str, Any]: - transform = result["transform"] - codec = result["codec"] - raw_route = transform == "raw" - closure = shell_status(result) - witness_bytes = 0 if raw_route else shell_witness_bytes - adjusted_bytes = int(result["compressed_size"]) + witness_bytes - raw_gap_bytes = raw_baseline_bytes - adjusted_bytes - ratio = adjusted_bytes / source_bytes if source_bytes else 0.0 - projected_enwik9_total = int(ratio * ENWIK9_BYTES) - - if raw_route: - decision = "raw_baseline" - prune_reason = None - elif closure["nan0"]: - decision = "pruned" - prune_reason = "nan0" - elif adjusted_bytes >= raw_baseline_bytes: - decision = "pruned" - prune_reason = "lower_bound_exceeds_incumbent" - else: - decision = "promoted" - prune_reason = None - - return { - "route_id": route_id(slice_name, transform, codec), - "slice": slice_name, - "source_bytes": source_bytes, - "transform": transform, - "codec": codec, - "raw_baseline_bytes": raw_baseline_bytes, - "compressed_bytes": int(result["compressed_size"]), - "encoded_bytes": int(result.get("encoded_size", result["compressed_size"])), - "shell_witness_bytes": witness_bytes, - "shell_adjusted_bytes": adjusted_bytes, - "shell_adjusted_ratio": ratio, - "projected_enwik9_total_bytes": projected_enwik9_total, - "hutter_target_gap_bytes_projected_enwik9": projected_enwik9_total - HUTTER_TARGET_BYTES, - "gain_vs_raw_after_shell_bytes": raw_gap_bytes, - "overhead_budget_before_losing_raw": max( - 0, - raw_baseline_bytes - int(result["compressed_size"]) - (1 if not raw_route else 0), - ), - "decision": decision, - "prune_reason": prune_reason, - "shell_status": closure, - } - - -def slice_best(routes: list[dict[str, Any]]) -> dict[str, Any] | None: - promoted = [route for route in routes if route["decision"] == "promoted"] - if not promoted: - return None - return min(promoted, key=lambda item: item["shell_adjusted_bytes"]) - - -def build_receipt(shell_witness_bytes: int) -> dict[str, Any]: - approach = load_json(APPROACH_RECEIPT) - ratio = load_json(RATIO_RECEIPT) if RATIO_RECEIPT.exists() else {} - - slices = [] - all_routes = [] - for item in approach.get("slices", []): - slice_name = item["slice_name"] - source_bytes = int(item["source_bytes"]) - raw_baseline_bytes = int(item["best_raw_baseline"]["compressed_size"]) - routes = [ - classify_route( - slice_name, - source_bytes, - raw_baseline_bytes, - result, - shell_witness_bytes, - ) - for result in item.get("results", []) - ] - best = slice_best(routes) - all_routes.extend(routes) - slices.append({ - "slice": slice_name, - "source_bytes": source_bytes, - "raw_baseline_bytes": raw_baseline_bytes, - "route_count": len(routes), - "promoted_route_count": sum(route["decision"] == "promoted" for route in routes), - "pruned_route_count": sum(route["decision"] == "pruned" for route in routes), - "nan0_route_count": sum(route["prune_reason"] == "nan0" for route in routes), - "best_shell_adjusted": best, - "routes": routes, - }) - - promoted = [route for route in all_routes if route["decision"] == "promoted"] - pruned = [route for route in all_routes if route["decision"] == "pruned"] - raw_baselines = [route for route in all_routes if route["decision"] == "raw_baseline"] - best_overall = min(promoted, key=lambda item: item["shell_adjusted_ratio"]) if promoted else None - - receipt = { - "schema": "dimensional_shell_dd_probe_receipt_v1", - "generated_utc": datetime.now(timezone.utc).isoformat(), - "surface_id": "dimensional_shell_dd_probe", - "source_receipts": { - "approach_receipt": str(APPROACH_RECEIPT.relative_to(REPO)), - "approach_stable_hash_sha256": approach.get("stable_approach_hash_sha256"), - "ratio_receipt": str(RATIO_RECEIPT.relative_to(REPO)), - "ratio_stable_hash_sha256": ratio.get("stable_rederived_ratio_hash_sha256"), - }, - "dimensional_shell": { - "shell_sequence": list(SHELL_SEQUENCE), - "max_projection_depth": MAX_PROJECTION_DEPTH, - "mass_law": ( - "source_mass = visible_4d + shadow_3d + closure_0d + lawbound_mass; " - "unresolved_mass must be zero." - ), - "mass": { - "source": fraction_json(Fraction(1)), - "visible_4d": fraction_json(SHELL_MASS.visible_4d), - "shadow_3d": fraction_json(SHELL_MASS.shadow_3d), - "closure_0d": fraction_json(SHELL_MASS.closure_0d), - "lawbound": fraction_json(SHELL_MASS.lawbound), - "unresolved": fraction_json(SHELL_MASS.unresolved), - "total": fraction_json(SHELL_MASS.total), - }, - "nan0_boundary": ( - "Any non-rehydrating route, unresolved mass debt, non-unit mass total, " - "or projection deeper than 12->4->3->0 is NaN0 and fails closed." - ), - }, - "dd_policy": { - "exponential_computation_permaban": True, - "no_recursive_residual_subdivision": True, - "branch_and_bound_incumbent": "best raw baseline per slice", - "shell_witness_bytes_per_non_raw_route": shell_witness_bytes, - "prune_rules": [ - "nan0", - "lower_bound_exceeds_incumbent", - ], - }, - "summary": { - "slice_count": len(slices), - "route_count": len(all_routes), - "raw_baseline_route_count": len(raw_baselines), - "promoted_route_count": len(promoted), - "pruned_route_count": len(pruned), - "nan0_route_count": sum(route["prune_reason"] == "nan0" for route in all_routes), - "lower_bound_pruned_count": sum( - route["prune_reason"] == "lower_bound_exceeds_incumbent" - for route in all_routes - ), - "best_shell_adjusted_overall": best_overall, - "all_shell_closed": all(not route["shell_status"]["nan0"] for route in all_routes), - }, - "slices": slices, - "claim_boundary": ( - "This is a bounded decision-diagram pruning receipt over existing " - "small-slice compression measurements. It is not a Hutter claim, " - "optimality proof, physical-dimension claim, or new compression result." - ), - "lawful": True, - } - - stable_preimage = stable_json({ - "schema": receipt["schema"], - "surface_id": receipt["surface_id"], - "source_receipts": receipt["source_receipts"], - "dimensional_shell": receipt["dimensional_shell"], - "dd_policy": receipt["dd_policy"], - "summary": receipt["summary"], - "slices": receipt["slices"], - "claim_boundary": receipt["claim_boundary"], - "lawful": receipt["lawful"], - }).encode("utf-8") - receipt["stable_shell_dd_hash_sha256"] = sha256_bytes(stable_preimage) - receipt["receipt_hash_preimage_sha256"] = sha256_bytes(stable_json(receipt).encode("utf-8")) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--shell-witness-bytes", type=int, default=DEFAULT_SHELL_WITNESS_BYTES) - parser.add_argument("--out", type=Path, default=OUT) - args = parser.parse_args() - - if args.shell_witness_bytes < 0: - raise ValueError("--shell-witness-bytes must be non-negative") - - receipt = build_receipt(args.shell_witness_bytes) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(json.dumps({ - "lawful": receipt["lawful"], - "stable_shell_dd_hash_sha256": receipt["stable_shell_dd_hash_sha256"], - "summary": receipt["summary"], - }, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/dna_codec_filter_receipt.py b/4-Infrastructure/shim/dna_codec_filter_receipt.py deleted file mode 100644 index 34383ed9..00000000 --- a/4-Infrastructure/shim/dna_codec_filter_receipt.py +++ /dev/null @@ -1,462 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for the DNA-filtered codec objective. - -This treats DNA equations as an engineering filter for the reconstruction -core: conserved motif information, local binding compatibility, repair -layers, mutation-budget pressure, and replay curvature are measured as -route-prior fields. The hard gate remains byte-exact repair plus positive -byte law. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "dna_codec_filter" -RECEIPT = OUT_DIR / "dna_codec_filter_receipt.json" -TABLE = OUT_DIR / "dna_codec_filter_table.jsonl" -SUMMARY = OUT_DIR / "dna_codec_filter_receipt.md" -SOURCE_MANIFEST = REPO / "6-Documentation" / "docs" / "provenance" / "DNA_CODEC_FILTER_SOURCES.cff" - - -OBJECTIVE_PACKET = { - "name": "DNA-Filtered Codec Objective", - "core_map": "S = Repair_R(Regulate_B_DeltaG(Replay(K,Theta,Pi)))", - "lossless_gate": "Repair(Replay(K,Theta,Pi),R) == S", - "byte_error": "epsilon_byte = 0", - "score": ( - "J_DNA_codec = |D|+|K|+|Theta|+|Pi|+|R| + lambda_1 H_seq(R) " - "- lambda_2 R_motif(K,Theta) + lambda_3 DeltaG_codec " - "+ lambda_4 U_route + lambda_5 E_replication + lambda_6 E_mutation " - "+ lambda_7 C_regulatory" - ), - "motif_information": "R_motif = sum_l(log2(|A_d|) - H_d(l))", - "binding_energy": "DeltaG_codec = sum_i DeltaG_local(k_i,k_{i+1}) + DeltaG_context + DeltaG_mismatch", - "repair_stack": "S0 = Replay(K,Theta,Pi); S = Repair(S0,R)", - "mutation_budget": "epsilon_token <= epsilon_archive / N_tokens", - "route_curvature": "U_route = 1/2 kappa_decode integral ||d t_state / ds||^2 ds", - "native_phrase": ( - "Compression as genomic reconstruction: what to grow, where to bind, " - "when to unfold, how to repair, and what residual remains." - ), -} - - -SOURCE_SURFACES = [ - { - "name": "Sequence logos / positional information content", - "url": "https://bioconductor.org/packages/release/bioc/html/seqLogo.html", - "role": "conserved motif information prior", - }, - { - "name": "SantaLucia nearest-neighbor DNA thermodynamics", - "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC19045/", - "role": "local binding-compatibility analogy", - }, - { - "name": "DNA replication proofreading and mismatch repair", - "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC6153641/", - "role": "layered repair/fidelity analogy", - }, - { - "name": "Drake rule and genome-size mutation pressure", - "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC4563715/", - "role": "per-token error budget analogy", - }, - { - "name": "Worm-like chain model", - "url": "https://chem.libretexts.org/Bookshelves/Biological_Chemistry/Concepts_in_Biophysical_Chemistry_%28Tokmakoff%29/02%3A_Macromolecules/09%3A_Macromolecular_Mechanics/9.02%3A_Worm-like_Chain", - "role": "replay curvature analogy", - }, -] - - -LOCAL_BINDING_SCORE = { - "AA": 1.4, - "AC": -1.2, - "AG": -0.4, - "AT": -1.0, - "CA": -1.1, - "CC": 1.3, - "CG": -2.1, - "CT": -0.4, - "GA": -0.4, - "GC": -2.0, - "GG": 1.3, - "GT": -1.1, - "TA": -0.9, - "TC": -0.4, - "TG": -1.0, - "TT": 1.4, -} - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - source: str - kernel: str - theta: dict[str, Any] - protocol: dict[str, Any] - negative_control: bool - notes: str - - -FIXTURES = [ - Fixture( - fixture_id="motif_binding_repair_admit", - source="ACGT" * 256, - kernel="repeat_motif", - theta={"motif": "ACGT", "count": 256, "regulator": "stable_nn"}, - protocol={"decoder": "repeat_motif_v1", "repair": "patch_v1"}, - negative_control=False, - notes="Conserved local motif with exact replay and positive byte law.", - ), - Fixture( - fixture_id="wrong_count_repair_hold", - source="ACGT" * 256, - kernel="repeat_motif", - theta={"motif": "ACGT", "count": 255, "regulator": "stable_nn"}, - protocol={"decoder": "repeat_motif_v1", "repair": "patch_v1"}, - negative_control=True, - notes="Residual can repair the missing motif, but the route is a negative control.", - ), - Fixture( - fixture_id="unstable_binding_literal_hold", - source="AAAACCCCGGGGTTTT", - kernel="raw_literal", - theta={"literal": "AAAACCCCGGGGTTTT", "regulator": "homopolymer_run"}, - protocol={"decoder": "raw_literal_v1", "repair": "patch_v1"}, - negative_control=False, - notes="Exact replay, but no positive byte law and high local self-run penalty.", - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def entropy(values: list[str]) -> float: - if not values: - return 0.0 - counts: dict[str, int] = {} - for value in values: - counts[value] = counts.get(value, 0) + 1 - total = len(values) - return -sum((count / total) * math.log(count / total, 2) for count in counts.values()) - - -def shannon_entropy_bytes(data: bytes) -> float: - if not data: - return 0.0 - counts: dict[int, int] = {} - for value in data: - counts[value] = counts.get(value, 0) + 1 - total = len(data) - return -sum((count / total) * math.log(count / total, 2) for count in counts.values()) - - -def replay(kernel: str, theta: dict[str, Any], protocol: dict[str, Any]) -> str: - decoder = protocol.get("decoder") - if kernel == "repeat_motif" and decoder == "repeat_motif_v1": - return str(theta["motif"]) * int(theta["count"]) - if kernel == "raw_literal" and decoder == "raw_literal_v1": - return str(theta["literal"]) - raise ValueError(f"unsupported kernel/protocol pair {kernel}/{decoder}") - - -def residual_patch(source: str, candidate: str) -> list[dict[str, Any]]: - patch: list[dict[str, Any]] = [] - max_len = max(len(source), len(candidate)) - for index in range(max_len): - actual = source[index] if index < len(source) else "" - proposed = candidate[index] if index < len(candidate) else "" - if actual != proposed: - patch.append({"i": index, "actual": actual, "candidate": proposed}) - return patch - - -def apply_patch(source: str, candidate: str, patch: list[dict[str, Any]]) -> str: - repaired = list(candidate) - for item in patch: - index = int(item["i"]) - while index >= len(repaired): - repaired.append("") - repaired[index] = str(item["actual"]) - return "".join(repaired[: len(source)]) - - -def motif_information(source: str, motif: str | None) -> float: - alphabet_size = 4 - if not motif: - return 0.0 - width = len(motif) - if width == 0 or len(source) < width: - return 0.0 - windows = [source[index : index + width] for index in range(0, len(source), width) if len(source[index : index + width]) == width] - if not windows: - return 0.0 - gain = 0.0 - for position in range(width): - column = [window[position] for window in windows] - gain += max(0.0, math.log(alphabet_size, 2) - entropy(column)) - return gain * len(windows) - - -def binding_energy(sequence: str) -> float: - if len(sequence) < 2: - return 0.0 - return sum(LOCAL_BINDING_SCORE.get(sequence[index : index + 2], 0.0) for index in range(len(sequence) - 1)) - - -def route_curvature(sequence: str) -> float: - if len(sequence) < 3: - return 0.0 - directions = [] - order = {"A": 0, "C": 1, "G": 2, "T": 3} - for left, right in zip(sequence, sequence[1:]): - directions.append(order.get(right, 0) - order.get(left, 0)) - return sum(abs(right - left) for left, right in zip(directions, directions[1:])) - - -def repair_error_budget(patch_count: int, token_count: int) -> dict[str, float]: - # Engineering analogy only: every layer is a bounded receipt, not a biology claim. - p_kernel = 1e-6 - p_basis = 1e-6 - p_replay = 1e-9 - p_repair = max(1e-12, (patch_count + 1) / max(token_count, 1) * 1e-9) - # A layered verifier fails if any required layer fails. For independent - # small risks the union bound is the conservative engineering proxy. - p_fail_union_bound = min(1.0, p_kernel + p_basis + p_replay + p_repair) - return { - "p_kernel": p_kernel, - "p_basis": p_basis, - "p_replay": p_replay, - "p_repair": p_repair, - "p_decode_fail_union_bound": p_fail_union_bound, - "negative_log10_fail_union_bound": -math.log10(p_fail_union_bound), - } - - -def counted_size(obj: Any) -> int: - return len(stable_json(obj).encode("utf-8")) - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - try: - reconstruction = replay(fixture.kernel, fixture.theta, fixture.protocol) - replay_error = None - except Exception as exc: # noqa: BLE001 - receipt should preserve failure text. - reconstruction = "" - replay_error = str(exc) - - patch = residual_patch(fixture.source, reconstruction) - repaired = apply_patch(fixture.source, reconstruction, patch) - exact_replay_without_residual = fixture.source == reconstruction - exact_replay_with_residual = repaired == fixture.source - motif = fixture.theta.get("motif") if isinstance(fixture.theta.get("motif"), str) else None - motif_gain = motif_information(fixture.source, motif) - delta_g = binding_energy(motif or fixture.source) - curvature = route_curvature(reconstruction or fixture.source) - source_entropy = shannon_entropy_bytes(fixture.source.encode("utf-8")) - residual_payload = {"patch": patch} - residual_bytes = 0 if exact_replay_without_residual else counted_size(residual_payload) - residual_entropy = shannon_entropy_bytes(stable_json(residual_payload).encode("utf-8")) if residual_bytes else 0.0 - - dictionary_payload = { - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "source_manifest": rel(SOURCE_MANIFEST), - } - kernel_payload = {"kernel": fixture.kernel} - theta_payload = fixture.theta - protocol_payload = fixture.protocol - counted_payload = { - "D": dictionary_payload, - "K": kernel_payload, - "Theta": theta_payload, - "Pi": protocol_payload, - "R": residual_payload, - } - raw_bytes = len(fixture.source.encode("utf-8")) - dictionary_bytes = counted_size(dictionary_payload) - kernel_bytes = counted_size(kernel_payload) - theta_bytes = counted_size(theta_payload) - protocol_bytes = counted_size(protocol_payload) - counted_bytes = dictionary_bytes + kernel_bytes + theta_bytes + protocol_bytes + residual_bytes - byte_gain = raw_bytes - counted_bytes - error_budget = repair_error_budget(len(patch), len(fixture.source)) - mutation_budget_per_token = 0.0 - if len(fixture.source) > 0: - mutation_budget_per_token = 0.0 / len(fixture.source) - - locally_stable = delta_g < 0.0 - positive_byte_law = byte_gain > 0 - residual_declared = True - - if fixture.negative_control and exact_replay_without_residual: - status = "FAIL_NEGATIVE_CONTROL" - elif fixture.negative_control: - status = "HOLD_DIAGNOSTIC" - elif exact_replay_with_residual and positive_byte_law and locally_stable: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "notes": fixture.notes, - "negative_control": fixture.negative_control, - "source_hash": sha256_text(fixture.source), - "reconstruction_hash": sha256_text(reconstruction), - "repaired_hash": sha256_text(repaired), - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "replay_error": replay_error, - "exact_replay_without_residual": exact_replay_without_residual, - "exact_replay_with_residual": exact_replay_with_residual, - "residual_declared": residual_declared, - "raw_bytes": raw_bytes, - "dictionary_bytes": dictionary_bytes, - "kernel_bytes": kernel_bytes, - "theta_bytes": theta_bytes, - "protocol_bytes": protocol_bytes, - "residual_bytes": residual_bytes, - "counted_bytes": counted_bytes, - "byte_gain": byte_gain, - "positive_byte_law": positive_byte_law, - "source_entropy_bits_per_byte": source_entropy, - "residual_entropy_bits_per_byte": residual_entropy, - "motif_information_bits": motif_gain, - "delta_g_codec_analog": delta_g, - "locally_stable": locally_stable, - "route_curvature": curvature, - "mutation_budget_per_token_for_lossless_archive": mutation_budget_per_token, - "repair_error_budget": error_budget, - "patch_count": len(patch), - "counted_payload_hash": sha256_text(stable_json(counted_payload)), - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# DNA Codec Filter Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Objective", - "", - f"`{OBJECTIVE_PACKET['core_map']}`", - "", - f"`{OBJECTIVE_PACKET['score']}`", - "", - f"`{OBJECTIVE_PACKET['lossless_gate']}`", - "", - "## Fixtures", - "", - "| Fixture | Status | Exact repair | Byte gain | Motif bits | DeltaG analog | Route curvature |", - "|---|---|---:|---:|---:|---:|---:|", - ] - for result in receipt["results"]: - lines.append( - f"| {result['fixture_id']} | {result['status']} | " - f"{result['exact_replay_with_residual']} | {result['byte_gain']} | " - f"{result['motif_information_bits']:.2f} | {result['delta_g_codec_analog']:.2f} | " - f"{result['route_curvature']:.2f} |" - ) - lines.extend( - [ - "", - "## Source Surfaces", - "", - ] - ) - for source in SOURCE_SURFACES: - lines.append(f"- {source['name']}: {source['url']}") - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "dna_codec_filter_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "objective_packet": OBJECTIVE_PACKET, - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "source_manifest": rel(SOURCE_MANIFEST), - "source_surfaces": SOURCE_SURFACES, - "fixture_count": len(results), - "table": rel(TABLE), - "summary": rel(SUMMARY), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "DNA-filtered codec objective prior only. It maps biological equations " - "to codec filters for motif conservation, local binding compatibility, " - "repair layering, mutation-budget pressure, and replay curvature. It " - "does not model DNA, does not ingest biological data, does not validate " - "biology, and does not claim compression benchmark performance." - ), - } - receipt["receipt_hash"] = sha256_text( - stable_json( - { - k: v - for k, v in receipt.items() - if k not in {"receipt_hash", "generated_at_utc"} - } - ) - ) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/eigen_solved_math_router.py b/4-Infrastructure/shim/eigen_solved_math_router.py deleted file mode 100644 index fa33fedc..00000000 --- a/4-Infrastructure/shim/eigen_solved_math_router.py +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env python3 -"""Route local math models through the online-domain eigen prior. - -This combines: - 1. local admissible rows from MATH_MODEL_MAP.tsv, and - 2. the source-backed online domain eigenvector from online_domain_eigen_pruning.py. - -The output is a ranked shortlist of local templates to try before widening a -compression/logogram/FPGA search. -""" - -from __future__ import annotations - -import argparse -import json -import math -import re -from pathlib import Path -from typing import Any - -import online_domain_eigen_pruning as eigen -import solved_math_pruning_surface as solved - - -TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_+-]{2,}") - - -def tokenize(text: str) -> set[str]: - return { - token.strip("_+-").lower() - for token in TOKEN_RE.findall(text) - if token.strip("_+-").lower() and token.strip("_+-").lower() not in eigen.STOPWORDS - } - - -def load_eigen(path: Path | None) -> dict[str, Any]: - if path and path.exists(): - return json.loads(path.read_text(encoding="utf-8")) - return eigen.build_surface(eigen.DEFAULT_DOMAINS) - - -def entry_text(entry: dict[str, Any]) -> str: - fields = [ - "model_name", - "family", - "equation", - "variables", - "purpose", - "domain_type", - "bind_class", - "implemented", - ] - return " ".join(str(entry.get(field, "")) for field in fields) - - -def route_entries(local_index: dict[str, Any], eigen_index: dict[str, Any]) -> dict[str, Any]: - term_weights = {item["term"]: float(item["weight"]) for item in eigen_index.get("top_terms", [])} - domain_weights = { - item["domain"]: float(item["eigen_weight"]) - for item in eigen_index.get("weighted_domains", []) - } - - routed = [] - for entry in local_index.get("entries", []): - tokens = tokenize(entry_text(entry)) - lexical = sum(weight for term, weight in term_weights.items() if term in tokens) - - domain_hit = 0.0 - joined = entry_text(entry).lower() - for domain, weight in domain_weights.items(): - domain_tokens = set(domain.split("_")) - if domain in joined or domain_tokens.intersection(tokens): - domain_hit += weight - - evidence_component = float(entry.get("pruning_score", 0)) / 100.0 - score = evidence_component + lexical + 0.75 * domain_hit - routed.append( - { - **entry, - "online_eigen_lexical_score": lexical, - "online_eigen_domain_score": domain_hit, - "routed_score": score, - } - ) - - routed.sort(key=lambda item: (-item["routed_score"], item["model_name"])) - return { - "schema": "eigen_solved_math_router_v1", - "claim_boundary": "Ranking combines local evidence tiers and online eigen priors; it is a search-order hint, not a proof.", - "local_source": local_index.get("source"), - "online_source_schema": eigen_index.get("schema"), - "query": local_index.get("query"), - "entry_count": len(routed), - "top_online_domains": eigen_index.get("weighted_domains", [])[:5], - "entries": routed, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--model-map", type=Path, default=solved.DEFAULT_MODEL_MAP) - parser.add_argument("--eigen-json", type=Path, default=Path("4-Infrastructure/shim/online_domain_eigen_pruning.json")) - parser.add_argument("--query", default="compression") - parser.add_argument("--include-documented", action="store_true") - parser.add_argument("--limit", type=int, default=40) - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - local = solved.build_index( - solved.load_rows(args.model_map), - query=args.query, - include_documented=args.include_documented, - ) - local["source"] = str(args.model_map) - eig = load_eigen(args.eigen_json) - routed = route_entries(local, eig) - if args.limit >= 0: - routed["entries"] = routed["entries"][: args.limit] - text = json.dumps(routed, indent=2, ensure_ascii=False) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/eigenvector_resonance_probe.py b/4-Infrastructure/shim/eigenvector_resonance_probe.py deleted file mode 100644 index c1371822..00000000 --- a/4-Infrastructure/shim/eigenvector_resonance_probe.py +++ /dev/null @@ -1,433 +0,0 @@ -#!/usr/bin/env python3 -"""Eigenvector resonance probe for dimensional shell packets. - -This runner is a calibration/witness probe, not a physical discovery claim and -not a compression benchmark. It looks for a bounded transverse pull on the -collective eigenvector after the 12D shell is mapped as: - - 12D = 4D visible + 3D genus shadow + 1D closure + 4D unseen reserve - -with dimensional weights 4:3:1:4. Packets that are NaN0, event/shock packets, -depth-overflow packets, or 1 cycle/day alias packets are quarantined instead of -promoted. - -Input JSONL packet forms: - - {"z": [12 numbers]} - -or - - { - "visible4": [4 numbers], - "genus3": [3 numbers], - "closure0": 0, - "unseen4": [4 numbers], - "depth": 0, - "event_packet": false, - "frequency_cpd": 0.031 - } - -Usage: - - python3 4-Infrastructure/shim/eigenvector_resonance_probe.py \ - --known-jsonl known.jsonl \ - --observed-jsonl observed.jsonl \ - --out eigenvector_resonance_receipt.json - -Smoke test: - - python3 4-Infrastructure/shim/eigenvector_resonance_probe.py \ - --synthetic --out /tmp/eigenvector_resonance_receipt.json -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -SCHEMA = "eigenvector_resonance_probe_receipt_v1" -VECTOR_DIM = 12 -WEIGHTS = { - "visible4": 4.0 / 12.0, - "genus3": 3.0 / 12.0, - "closure0": 1.0 / 12.0, - "unseen4": 4.0 / 12.0, -} -LENS = {"visible4": 4, "genus3": 3, "closure0": 1, "unseen4": 4} -ALIASED_1CPD_EPS = 1.0e-9 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def finite_number(x: Any) -> bool: - return isinstance(x, (int, float)) and math.isfinite(float(x)) - - -def read_vec(value: Any, name: str, expected_len: int) -> list[float]: - if name == "closure0" and finite_number(value): - return [float(value)] - if not isinstance(value, list) or len(value) != expected_len: - raise ValueError(f"{name} must have length {expected_len}") - out: list[float] = [] - for idx, item in enumerate(value): - if not finite_number(item): - raise ValueError(f"{name}[{idx}] is not finite") - out.append(float(item)) - return out - - -def packet_to_vector(packet: dict[str, Any], *, max_depth: int) -> tuple[list[float] | None, str | None]: - if packet.get("nan0") is True: - return None, "nan0_flag" - if packet.get("event_packet") is True: - return None, "event_packet" - - depth = packet.get("depth", 0) - if not isinstance(depth, int): - return None, "invalid_depth" - if depth > max_depth: - return None, "depth_exceeded" - - freq = packet.get("frequency_cpd") - if finite_number(freq) and abs(float(freq) - 1.0) <= ALIASED_1CPD_EPS: - return None, "diurnal_alias_1cpd" - - if "z" in packet: - try: - z = read_vec(packet["z"], "z", VECTOR_DIM) - except ValueError as exc: - return None, str(exc) - return z, None - - aliases = { - "visible4": ("visible4", "O4", "primitive4"), - "genus3": ("genus3", "Rg3", "shadow3"), - "closure0": ("closure0", "chi0", "closure"), - "unseen4": ("unseen4", "U4", "reserve4"), - } - z: list[float] = [] - try: - for segment, names in aliases.items(): - value = None - for name in names: - if name in packet: - value = packet[name] - break - if value is None: - raise ValueError(f"missing segment {segment}") - raw = read_vec(value, segment, LENS[segment]) - scale = math.sqrt(WEIGHTS[segment]) - z.extend(scale * x for x in raw) - except ValueError as exc: - return None, str(exc) - - if len(z) != VECTOR_DIM: - return None, f"mapped dimension {len(z)} != {VECTOR_DIM}" - return z, None - - -def load_jsonl(path: Path, *, max_depth: int) -> tuple[list[list[float]], dict[str, int]]: - rows: list[list[float]] = [] - quarantine: dict[str, int] = {} - with path.open("r", encoding="utf-8") as fh: - for line in fh: - line = line.strip() - if not line: - continue - try: - packet = json.loads(line) - except json.JSONDecodeError: - quarantine["invalid_json"] = quarantine.get("invalid_json", 0) + 1 - continue - if not isinstance(packet, dict): - quarantine["packet_not_object"] = quarantine.get("packet_not_object", 0) + 1 - continue - vec, reason = packet_to_vector(packet, max_depth=max_depth) - if reason is not None: - quarantine[reason] = quarantine.get(reason, 0) + 1 - continue - assert vec is not None - rows.append(vec) - return rows, quarantine - - -def synthetic_streams(n: int) -> tuple[list[list[float]], list[list[float]]]: - known: list[list[float]] = [] - observed: list[list[float]] = [] - for i in range(n): - t = 2.0 * math.pi * i / n - row = [0.0] * VECTOR_DIM - row[0] = math.sin(t) - row[1] = 0.55 * math.cos(2.0 * t) - row[2] = 0.25 * math.sin(3.0 * t) - row[5] = 0.08 * math.cos(t / 4.0) - row[8] = 0.04 * math.sin(t / 8.0) - known.append(row) - obs = list(row) - obs[6] += 0.008 * math.sin(t + 0.33) - obs[9] += 0.018 * math.sin(t + 0.33) - obs[10] += 0.012 * math.cos(t + 0.33) - observed.append(obs) - return known, observed - - -def dot(a: list[float], b: list[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def norm(v: list[float]) -> float: - return math.sqrt(max(0.0, dot(v, v))) - - -def normalize(v: list[float]) -> list[float]: - n = norm(v) - if n <= 0.0: - raise ValueError("zero vector cannot be normalized") - return [x / n for x in v] - - -def mean(rows: list[list[float]]) -> list[float]: - dim = len(rows[0]) - return [sum(row[j] for row in rows) / len(rows) for j in range(dim)] - - -def covariance(rows: list[list[float]]) -> list[list[float]]: - if len(rows) < 2: - raise ValueError("need at least two rows") - dim = len(rows[0]) - mu = mean(rows) - cov = [[0.0 for _ in range(dim)] for _ in range(dim)] - for row in rows: - c = [row[j] - mu[j] for j in range(dim)] - for i in range(dim): - for j in range(dim): - cov[i][j] += c[i] * c[j] - inv = 1.0 / (len(rows) - 1) - for i in range(dim): - for j in range(dim): - cov[i][j] *= inv - return cov - - -def mat_vec(m: list[list[float]], v: list[float]) -> list[float]: - return [sum(row[j] * v[j] for j in range(len(v))) for row in m] - - -def mat_sub(a: list[list[float]], b: list[list[float]]) -> list[list[float]]: - return [[a[i][j] - b[i][j] for j in range(len(a[i]))] for i in range(len(a))] - - -def power_eigen(m: list[list[float]], *, steps: int = 256, tol: float = 1.0e-12) -> tuple[float, list[float]]: - dim = len(m) - v = normalize([1.0 for _ in range(dim)]) - last = 0.0 - for _ in range(steps): - mv = mat_vec(m, v) - if norm(mv) <= 0.0: - return 0.0, v - v = normalize(mv) - lam = dot(v, mat_vec(m, v)) - if abs(lam - last) <= tol: - return lam, v - last = lam - return last, v - - -def deflate(m: list[list[float]], lam: float, u: list[float]) -> list[list[float]]: - return [[m[i][j] - lam * u[i] * u[j] for j in range(len(m))] for i in range(len(m))] - - -def transverse_pull(delta_cov: list[list[float]], u0: list[float]) -> list[float]: - raw = mat_vec(delta_cov, u0) - along = dot(raw, u0) - return [raw[i] - along * u0[i] for i in range(len(u0))] - - -def angle_deg(a: list[float], b: list[float]) -> float | None: - na = norm(a) - nb = norm(b) - if na <= 0.0 or nb <= 0.0: - return None - c = max(-1.0, min(1.0, dot(a, b) / (na * nb))) - return math.degrees(math.acos(c)) - - -def windows(n: int, k: int) -> list[tuple[int, int]]: - k = max(1, k) - size = max(2, n // k) - out = [] - start = 0 - while start < n: - end = n if len(out) == k - 1 else min(n, start + size) - if end - start >= 2: - out.append((start, end)) - start = end - return out - - -def analyze(known: list[list[float]], observed: list[list[float]], *, subwindows: int, min_pull: float, min_eigengap: float, max_angle: float) -> dict[str, Any]: - n = min(len(known), len(observed)) - if n < 2: - raise ValueError("not enough usable paired packets") - known = known[:n] - observed = observed[:n] - - ck = covariance(known) - co = covariance(observed) - dc = mat_sub(co, ck) - lam0, u0 = power_eigen(ck) - lam1, _ = power_eigen(deflate(ck, lam0, u0)) - eigengap = lam0 - lam1 - pull = transverse_pull(dc, u0) - pull_norm = norm(pull) - - sub_pulls: list[list[float]] = [] - sub_reports: list[dict[str, Any]] = [] - for start, end in windows(n, subwindows): - ckw = covariance(known[start:end]) - cow = covariance(observed[start:end]) - lam0w, u0w = power_eigen(ckw) - lam1w, _ = power_eigen(deflate(ckw, lam0w, u0w)) - pw = transverse_pull(mat_sub(cow, ckw), u0w) - sub_pulls.append(pw) - sub_reports.append({ - "start_index": start, - "end_index_exclusive": end, - "lambda0": lam0w, - "lambda1": lam1w, - "eigengap": lam0w - lam1w, - "pull_norm": norm(pw), - }) - - angles = [] - for p in sub_pulls[1:]: - angle = angle_deg(sub_pulls[0], p) - if angle is not None: - angles.append(angle) - max_seen_angle = max(angles) if angles else None - - eigengap_ok = eigengap > min_eigengap - pull_ok = pull_norm > min_pull - stable = max_seen_angle is not None and max_seen_angle <= max_angle - - if not eigengap_ok: - decision = "NAN0_EIGENBASIS_UNSTABLE" - elif not pull_ok: - decision = "CLOSED_NO_RESONANCE" - elif not stable: - decision = "RESIDUAL_WITNESS_UNSTABLE_DIRECTION" - else: - decision = "PROMOTE_RESONANCE_CANDIDATE" - - return { - "sample_count_used": n, - "lambda0": lam0, - "lambda1": lam1, - "eigengap": eigengap, - "eigengap_ok": eigengap_ok, - "collective_eigenvector_u0": u0, - "transverse_pull_vector": pull, - "transverse_pull_norm": pull_norm, - "pull_score_over_eigengap": pull_norm / (abs(eigengap) + 1.0e-12), - "pull_ok": pull_ok, - "subwindow_angles_deg_vs_first": angles, - "max_subwindow_angle_deg": max_seen_angle, - "stable_direction": stable, - "subwindows": sub_reports, - "decision": decision, - } - - -def build_receipt(args: argparse.Namespace) -> dict[str, Any]: - if args.synthetic: - known, observed = synthetic_streams(args.synthetic_samples) - known_quarantine: dict[str, int] = {} - observed_quarantine: dict[str, int] = {} - source = {"synthetic": True, "known_jsonl": None, "observed_jsonl": None} - else: - if args.known_jsonl is None or args.observed_jsonl is None: - raise SystemExit("provide --known-jsonl and --observed-jsonl, or use --synthetic") - known, known_quarantine = load_jsonl(args.known_jsonl, max_depth=args.max_depth) - observed, observed_quarantine = load_jsonl(args.observed_jsonl, max_depth=args.max_depth) - source = {"synthetic": False, "known_jsonl": str(args.known_jsonl), "observed_jsonl": str(args.observed_jsonl)} - - result = analyze( - known, - observed, - subwindows=args.subwindows, - min_pull=args.min_pull, - min_eigengap=args.min_eigengap, - max_angle=args.max_stability_angle_deg, - ) - receipt = { - "schema": SCHEMA, - "generated_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": "Eigenvector resonance candidates are compression/calibration witnesses, not physical-body claims, Hutter claims, or byte-compression results.", - "source": source, - "dimensional_shell_law": { - "source_12d": 12, - "visible_4d": 4, - "genus3_shadow": 3, - "closure_0d_witness_coordinate": 1, - "unseen_reserve_4d": 4, - "ratio": "4:3:1:4", - "weights": WEIGHTS, - "nan_boundary": "NaN0/event/depth/diurnal-alias packets are quarantined before resonance promotion.", - }, - "gates": { - "max_depth": args.max_depth, - "subwindows": args.subwindows, - "min_pull": args.min_pull, - "min_eigengap": args.min_eigengap, - "max_stability_angle_deg": args.max_stability_angle_deg, - }, - "quarantine": {"known": known_quarantine, "observed": observed_quarantine}, - "analysis": result, - "lawful": result["decision"] != "NAN0_EIGENBASIS_UNSTABLE", - } - receipt["stable_resonance_hash_sha256"] = sha256_text(stable_json({k: receipt[k] for k in receipt if k != "generated_utc"})) - return receipt - - -def main() -> int: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--known-jsonl", type=Path) - ap.add_argument("--observed-jsonl", type=Path) - ap.add_argument("--out", type=Path, required=True) - ap.add_argument("--synthetic", action="store_true") - ap.add_argument("--synthetic-samples", type=int, default=512) - ap.add_argument("--max-depth", type=int, default=3) - ap.add_argument("--subwindows", type=int, default=5) - ap.add_argument("--min-pull", type=float, default=1.0e-6) - ap.add_argument("--min-eigengap", type=float, default=1.0e-9) - ap.add_argument("--max-stability-angle-deg", type=float, default=35.0) - args = ap.parse_args() - - receipt = build_receipt(args) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(json.dumps({ - "schema": receipt["schema"], - "decision": receipt["analysis"]["decision"], - "lawful": receipt["lawful"], - "sample_count_used": receipt["analysis"]["sample_count_used"], - "transverse_pull_norm": receipt["analysis"]["transverse_pull_norm"], - "eigengap": receipt["analysis"]["eigengap"], - "stable_resonance_hash_sha256": receipt["stable_resonance_hash_sha256"], - }, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki8_wiki_logogram_probe.py b/4-Infrastructure/shim/enwiki8_wiki_logogram_probe.py deleted file mode 100644 index 838984e3..00000000 --- a/4-Infrastructure/shim/enwiki8_wiki_logogram_probe.py +++ /dev/null @@ -1,396 +0,0 @@ -#!/usr/bin/env python3 -"""Encode a bounded enwik8 slice with wiki/logogram grammar atoms. - -This is a no-training, no-benchmark probe. It tests whether a tiny -decoder-facing grammar for common MediaWiki/XML surfaces can replay a local -enwik8 slice byte-exactly with residual/receipt accounting. -""" - -from __future__ import annotations - -import argparse -import bz2 -import hashlib -import json -import lzma -import re -import zlib -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_CORPUS = REPO / "shared-data" / "corpora" / "enwik8" -OUT_DIR = REPO / "shared-data" / "data" / "enwiki8_wiki_logogram_probe" -RECEIPT = OUT_DIR / "enwiki8_wiki_logogram_probe_receipt.json" -SUMMARY = OUT_DIR / "enwiki8_wiki_logogram_probe_receipt.md" -CORE = OUT_DIR / "enwiki8_wiki_logogram_probe_core.wlg1" - -OP_LIT = 0x00 -OP_WLINK = 0x81 -OP_TEMPLATE = 0x82 -OP_SECTION = 0x83 -OP_TAG_PAIR = 0x84 -OP_TAG_ATTR_PAIR = 0x85 -OP_EMPTY_MINOR = 0x86 - -TAG_IDS = { - "title": 1, - "id": 2, - "timestamp": 3, - "username": 4, - "comment": 5, - "text": 6, -} -ID_TAGS = {value: key for key, value in TAG_IDS.items()} - -TAG_PAIR_RE = re.compile( - rb"<(title|id|timestamp|username|comment)>([^<]*)", re.DOTALL -) -TEXT_ATTR_RE = re.compile(rb'([^<]*)', re.DOTALL) -WLINK_RE = re.compile(rb"\[\[([^\[\]\n]{1,240})\]\]") -TEMPLATE_RE = re.compile(rb"\{\{([^\{\}\n]{1,240})\}\}") -SECTION_RE = re.compile(rb"==\s*([^=\n][^\n]{0,160}?)\s*==") - - -@dataclass(frozen=True) -class Atom: - kind: str - raw: bytes - payload: bytes - start: int - end: int - tag: str | None = None - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def put_varint(value: int) -> bytes: - if value < 0: - raise ValueError("negative varint") - out = bytearray() - while True: - byte = value & 0x7F - value >>= 7 - if value: - out.append(byte | 0x80) - else: - out.append(byte) - return bytes(out) - - -def read_varint(data: bytes, cursor: int) -> tuple[int, int]: - shift = 0 - value = 0 - while True: - if cursor >= len(data): - raise ValueError("truncated varint") - byte = data[cursor] - cursor += 1 - value |= (byte & 0x7F) << shift - if not byte & 0x80: - return value, cursor - shift += 7 - if shift > 28: - raise ValueError("varint too long") - - -def emit_payload(opcode: int, payload: bytes) -> bytes: - return bytes([opcode]) + put_varint(len(payload)) + payload - - -def atom_encoded(atom: Atom) -> bytes: - if atom.kind == "LIT": - return emit_payload(OP_LIT, atom.payload) - if atom.kind == "WLINK": - return emit_payload(OP_WLINK, atom.payload) - if atom.kind == "TEMPLATE": - return emit_payload(OP_TEMPLATE, atom.payload) - if atom.kind == "SECTION": - return emit_payload(OP_SECTION, atom.payload) - if atom.kind == "TAG_PAIR": - tag_id = TAG_IDS[atom.tag or ""] - return bytes([OP_TAG_PAIR, tag_id]) + put_varint(len(atom.payload)) + atom.payload - if atom.kind == "TAG_ATTR_PAIR": - tag_id = TAG_IDS[atom.tag or ""] - return bytes([OP_TAG_ATTR_PAIR, tag_id]) + put_varint(len(atom.payload)) + atom.payload - if atom.kind == "EMPTY_MINOR": - return bytes([OP_EMPTY_MINOR]) - raise ValueError(f"unsupported atom kind {atom.kind}") - - -def decode_core(core: bytes) -> bytes: - if not core.startswith(b"WLG1"): - raise ValueError("bad core magic") - cursor = 4 - out = bytearray() - while cursor < len(core): - opcode = core[cursor] - cursor += 1 - if opcode == OP_EMPTY_MINOR: - out.extend(b"") - continue - if opcode in {OP_TAG_PAIR, OP_TAG_ATTR_PAIR}: - tag_id = core[cursor] - cursor += 1 - tag = ID_TAGS[tag_id] - size, cursor = read_varint(core, cursor) - payload = core[cursor : cursor + size] - cursor += size - if opcode == OP_TAG_PAIR: - out.extend(b"<" + tag.encode("ascii") + b">" + payload + b"") - else: - if tag != "text": - raise ValueError("unsupported attr tag") - out.extend(b'' + payload + b"") - continue - size, cursor = read_varint(core, cursor) - payload = core[cursor : cursor + size] - cursor += size - if opcode == OP_LIT: - out.extend(payload) - elif opcode == OP_WLINK: - out.extend(b"[[" + payload + b"]]") - elif opcode == OP_TEMPLATE: - out.extend(b"{{" + payload + b"}}") - elif opcode == OP_SECTION: - out.extend(b"== " + payload + b" ==") - else: - raise ValueError(f"bad opcode {opcode}") - return bytes(out) - - -def best_match(slice_bytes: bytes, cursor: int) -> Atom | None: - candidates: list[Atom] = [] - for regex, kind in [ - (TEXT_ATTR_RE, "TAG_ATTR_PAIR"), - (TAG_PAIR_RE, "TAG_PAIR"), - (WLINK_RE, "WLINK"), - (TEMPLATE_RE, "TEMPLATE"), - (SECTION_RE, "SECTION"), - ]: - match = regex.match(slice_bytes, cursor) - if not match: - continue - raw = match.group(0) - if kind == "TAG_PAIR": - tag = match.group(1).decode("ascii") - payload = match.group(2) - elif kind == "TAG_ATTR_PAIR": - tag = "text" - payload = match.group(1) - else: - tag = None - payload = match.group(1).strip() if kind == "SECTION" else match.group(1) - atom = Atom(kind=kind, raw=raw, payload=payload, start=cursor, end=cursor + len(raw), tag=tag) - if len(atom_encoded(atom)) < len(raw): - candidates.append(atom) - if slice_bytes.startswith(b"", cursor): - atom = Atom(kind="EMPTY_MINOR", raw=b"", payload=b"", start=cursor, end=cursor + len(b"")) - candidates.append(atom) - if not candidates: - return None - return min(candidates, key=lambda atom: len(atom_encoded(atom)) - len(atom.raw)) - - -def encode_slice(slice_bytes: bytes) -> tuple[bytes, list[Atom]]: - atoms: list[Atom] = [] - literal = bytearray() - literal_start = 0 - cursor = 0 - - def flush_literal(at: int) -> None: - nonlocal literal, literal_start - if literal: - payload = bytes(literal) - atoms.append(Atom(kind="LIT", raw=payload, payload=payload, start=literal_start, end=at)) - literal = bytearray() - - while cursor < len(slice_bytes): - atom = best_match(slice_bytes, cursor) - if atom is None: - if not literal: - literal_start = cursor - literal.append(slice_bytes[cursor]) - cursor += 1 - continue - flush_literal(cursor) - atoms.append(atom) - cursor = atom.end - flush_literal(cursor) - core = b"WLG1" + b"".join(atom_encoded(atom) for atom in atoms) - return core, atoms - - -def atom_receipt(atom: Atom, index: int) -> dict[str, Any]: - encoded = atom_encoded(atom) - return { - "index": index, - "kind": atom.kind, - "tag": atom.tag, - "start": atom.start, - "end": atom.end, - "raw_bytes": len(atom.raw), - "encoded_bytes": len(encoded), - "byte_gain": len(atom.raw) - len(encoded), - "raw_hash": sha256_bytes(atom.raw), - "encoded_hash": sha256_bytes(encoded), - "decision": "ACCEPT", - "residual_policy": "none", - } - - -def compression_baselines(data: bytes) -> dict[str, int]: - return { - "zlib_9": len(zlib.compress(data, 9)), - "bz2_9": len(bz2.compress(data, 9)), - "lzma_9": len(lzma.compress(data, preset=9)), - } - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# enwiki8 Wiki Logogram Probe Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Slice", - "", - f"- Corpus: `{receipt['corpus']}`", - f"- Offset: `{receipt['offset']}`", - f"- Length: `{receipt['slice_bytes']}`", - f"- Slice hash: `{receipt['slice_sha256']}`", - "", - "## Accounting", - "", - "| Metric | Bytes |", - "|---|---:|", - f"| Raw slice | {receipt['raw_bytes']} |", - f"| Encoded core | {receipt['core_bytes']} |", - f"| Packet estimate | {receipt['packet_estimate_bytes']} |", - f"| Full receipt JSON | {receipt['full_receipt_json_bytes']} |", - "", - "## Status", - "", - f"- Exact replay: `{receipt['exact_replay']}`", - f"- Core byte gain: `{receipt['core_byte_gain']}`", - f"- Packet byte gain estimate: `{receipt['packet_byte_gain_estimate']}`", - f"- Atom count: `{receipt['atom_count']}`", - "", - "## Baselines", - "", - ] - for name, value in receipt["baselines"].items(): - lines.append(f"- `{name}`: `{value}` bytes") - lines.append("") - SUMMARY.write_text("\n".join(lines), encoding="utf-8") - - -def build_receipt(corpus: Path, offset: int, length: int) -> dict[str, Any]: - source = corpus.read_bytes() - slice_bytes = source[offset : offset + length] - core, atoms = encode_slice(slice_bytes) - decoded = decode_core(core) - receipts = [atom_receipt(atom, index) for index, atom in enumerate(atoms)] - kind_counts = {kind: sum(1 for atom in atoms if atom.kind == kind) for kind in sorted({atom.kind for atom in atoms})} - truncated_receipt_bytes = 4 * len(atoms) - header_bytes = 8 - packet_estimate_bytes = len(core) + header_bytes + truncated_receipt_bytes - core_path = CORE - core_path.write_bytes(core) - - receipt = { - "schema": "enwiki8_wiki_logogram_probe_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "corpus": rel(corpus) if corpus.is_relative_to(REPO) else str(corpus), - "corpus_size": corpus.stat().st_size, - "offset": offset, - "slice_bytes": len(slice_bytes), - "slice_sha256": sha256_bytes(slice_bytes), - "core": rel(core_path), - "core_sha256": sha256_bytes(core), - "decoded_sha256": sha256_bytes(decoded), - "exact_replay": decoded == slice_bytes, - "atom_count": len(atoms), - "kind_counts": kind_counts, - "raw_bytes": len(slice_bytes), - "core_bytes": len(core), - "core_byte_gain": len(slice_bytes) - len(core), - "header_bytes": header_bytes, - "truncated_receipt_bytes": truncated_receipt_bytes, - "packet_estimate_bytes": packet_estimate_bytes, - "packet_byte_gain_estimate": len(slice_bytes) - packet_estimate_bytes, - "baselines": compression_baselines(slice_bytes), - "atom_receipts": receipts, - "decision": "HOLD", - "fixture_status": "ADMIT_FIXTURE" if decoded == slice_bytes and len(slice_bytes) > len(core) else "HOLD_DIAGNOSTIC", - "claim_boundary": ( - "Bounded local enwiki8 slice probe only. This is not a Hutter Prize " - "submission, not a full-corpus enwiki8 result, and not evidence of " - "compression competitiveness. The packet estimate uses truncated " - "per-atom receipt hashes; full JSON receipts are inspection material." - ), - } - receipt["full_receipt_json_bytes"] = len(stable_json(receipt).encode("utf-8")) - receipt["receipt_hash"] = sha256_bytes( - stable_json( - { - key: value - for key, value in receipt.items() - if key not in {"receipt_hash", "generated_at_utc"} - } - ).encode("utf-8") - ) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run a wiki-logogram encode probe on a local enwiki8 slice.") - parser.add_argument("--corpus", type=Path, default=DEFAULT_CORPUS) - parser.add_argument("--offset", type=int, default=1406) - parser.add_argument("--length", type=int, default=4096) - parser.add_argument("--receipt", type=Path, default=RECEIPT) - args = parser.parse_args() - - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_receipt(args.corpus, args.offset, args.length) - args.receipt.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(args.receipt), - "summary": rel(SUMMARY), - "core": rel(CORE), - "receipt_hash": receipt["receipt_hash"], - "fixture_status": receipt["fixture_status"], - "raw_bytes": receipt["raw_bytes"], - "core_bytes": receipt["core_bytes"], - "packet_estimate_bytes": receipt["packet_estimate_bytes"], - "baselines": receipt["baselines"], - "kind_counts": receipt["kind_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki9_logogram_canonical_baseline_probe.py b/4-Infrastructure/shim/enwiki9_logogram_canonical_baseline_probe.py deleted file mode 100644 index e817c731..00000000 --- a/4-Infrastructure/shim/enwiki9_logogram_canonical_baseline_probe.py +++ /dev/null @@ -1,568 +0,0 @@ -#!/usr/bin/env python3 -"""v5 canonical enwik9 slice and baseline probe. - -This pass freezes the v4 codec/accounting path and adds only two outer gates: -PROVENANCE and BASELINE. It does not change the encoder. Non-1GB inputs are -automatically treated as fixtures, not canonical enwik9 evidence. -""" - -from __future__ import annotations - -import argparse -import bz2 -import hashlib -import importlib.util -import json -import lzma -import shutil -import subprocess -import sys -import tempfile -import zlib -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -V4_SCRIPT = REPO / "4-Infrastructure" / "shim" / "enwiki9_logogram_dictionary_amortization_probe.py" -V4_RECEIPT = REPO / "shared-data" / "data" / "enwiki9_logogram_dictionary_amortization_probe" / "enwiki9_logogram_dictionary_amortization_probe_receipt.json" -DEFAULT_INPUT = Path("/home/allaun/Downloads/data/enwik9_data/1234567") -OUT_DIR = REPO / "shared-data" / "data" / "enwiki9_logogram_canonical_baseline_probe" -RECEIPT = OUT_DIR / "enwiki9_logogram_canonical_baseline_probe_receipt.json" -SUMMARY = OUT_DIR / "enwiki9_logogram_canonical_baseline_probe_receipt.md" - -CANONICAL_ENWIK9_SIZE = 1_000_000_000 -DEFAULT_SLICE_SIZE = 65536 -DEFAULT_SCAN_STRIDE = 1_048_576 -GENESIS_EVENT_HASH = "0" * 64 -FIXED_OFFSETS = [0, 1_000_000, 10_000_000, 100_000_000, 500_000_000, 900_000_000] -TARGET_CLASSES = [ - "xml_head", - "link_heavy", - "template_heavy", - "ref_heavy", - "category_file_heavy", - "prose_heavy", - "mixed_high_entropy", -] - - -def load_v4_module() -> Any: - spec = importlib.util.spec_from_file_location("enwiki9_logogram_dictionary_amortization_probe", V4_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load v4 script: {V4_SCRIPT}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -V4 = load_v4_module() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_file(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def read_prior_v4() -> dict[str, Any] | None: - if not V4_RECEIPT.exists(): - return None - receipt = json.loads(V4_RECEIPT.read_text(encoding="utf-8")) - return { - "receipt": rel(V4_RECEIPT), - "receipt_hash": receipt.get("receipt_hash"), - "gate_protocol": receipt.get("gate_protocol"), - "encoder_frozen_from": receipt.get("encoder_frozen_from"), - "receipt_mode_frozen_from": receipt.get("receipt_mode_frozen_from"), - "dictionary_bytes": receipt.get("dictionary_bytes"), - } - - -def event_hash(event: dict[str, Any]) -> str: - return sha256_bytes(stable_json(event).encode("utf-8")) - - -def make_event( - index: int, - gate: str, - previous_hash: str, - input_payload: dict[str, Any], - output_payload: dict[str, Any], - delta_bytes: int, - decision: str, -) -> dict[str, Any]: - event = { - "event_index": index, - "gate": gate, - "previous_event_hash": previous_hash, - "input_hash": sha256_bytes(stable_json(input_payload).encode("utf-8")), - "output_hash": sha256_bytes(stable_json(output_payload).encode("utf-8")), - "delta_bytes": delta_bytes, - "decision": decision, - "clock_participates_in_hash": False, - } - event["event_hash"] = event_hash(event) - return event - - -def classify_slice(data: bytes, offset: int) -> str: - if offset == 0 and data.lstrip().startswith(b"<"): - return "xml_head" - lower = data.lower() - template = data.count(b"{{") + data.count(b"}}") - link = data.count(b"[[") + data.count(b"]]") - category_file = lower.count(b"[[category:") + lower.count(b"[[file:") + lower.count(b"[[image:") - ref = lower.count(b"") - entropyish = len(set(data)) + data.count(b"|") + data.count(b"{") + data.count(b"[") - if category_file >= 2: - return "category_file_heavy" - if template >= max(link, 2): - return "template_heavy" - if ref >= 2: - return "ref_heavy" - if link >= 2: - return "link_heavy" - if table >= 2 or (xml >= 12 and entropyish >= 90): - return "mixed_high_entropy" - if xml >= 24: - return "xml_head" - return "prose_heavy" - - -def read_slice(path: Path, offset: int, length: int) -> bytes: - with path.open("rb") as handle: - handle.seek(offset) - return handle.read(length) - - -def scan_for_class(path: Path, size: int, wanted: str, slice_size: int, stride: int) -> tuple[int, bytes, str] | None: - best: tuple[int, bytes, str] | None = None - best_score = -1 - with path.open("rb") as handle: - for offset in range(0, max(size - 1, 0), stride): - handle.seek(offset) - data = handle.read(slice_size) - if not data: - break - cls = classify_slice(data, offset) - if cls == wanted: - return offset, data, cls - score = class_score(data, wanted, offset) - if score > best_score: - best_score = score - best = (offset, data, cls) - return best - - -def class_score(data: bytes, wanted: str, offset: int) -> int: - lower = data.lower() - if wanted == "xml_head": - return (1000 if offset == 0 else 0) + data.count(b"<") + data.count(b">") - if wanted == "link_heavy": - return data.count(b"[[") + data.count(b"]]") - if wanted == "template_heavy": - return data.count(b"{{") + data.count(b"}}") - if wanted == "ref_heavy": - return lower.count(b" list[dict[str, Any]]: - selected: list[dict[str, Any]] = [] - seen_offsets: set[int] = set() - for offset in FIXED_OFFSETS: - if offset >= size: - continue - data = read_slice(path, offset, slice_size) - if not data: - continue - seen_offsets.add(offset) - selected.append( - { - "name": f"offset_{offset}", - "offset": offset, - "length": len(data), - "selection": "fixed_offset", - "requested_class": None, - "slice_class": classify_slice(data, offset), - "data": data, - } - ) - for wanted in TARGET_CLASSES: - found = scan_for_class(path, size, wanted, slice_size, scan_stride) - if found is None: - continue - offset, data, cls = found - if offset in seen_offsets: - continue - seen_offsets.add(offset) - suffix = "exact" if cls == wanted else "best_available" - name = f"{wanted}_{offset}_{suffix}" - selected.append( - { - "name": name, - "offset": offset, - "length": len(data), - "selection": "content_selected", - "requested_class": wanted, - "slice_class": cls, - "content_match": cls == wanted, - "data": data, - } - ) - return selected - - -def zstd_size(data: bytes) -> int | None: - if shutil.which("zstd") is None: - return None - with tempfile.NamedTemporaryFile(suffix=".raw") as src, tempfile.NamedTemporaryFile(suffix=".zst") as dst: - src.write(data) - src.flush() - subprocess.run(["zstd", "-19", "-q", "-f", src.name, "-o", dst.name], check=True) - return Path(dst.name).stat().st_size - - -def baselines(data: bytes) -> dict[str, int | None]: - return { - "zlib_9": len(zlib.compress(data, 9)), - "bz2_9": len(bz2.compress(data, 9)), - "lzma_9": len(lzma.compress(data, preset=9)), - "zstd_19_if_available": zstd_size(data), - } - - -def run_slice(item: dict[str, Any], source_dir: Path, dictionary_hash: str, protocol_sha: str) -> dict[str, Any]: - data = item.pop("data") - core, atoms = V4.V3.V2.encode(data) - decoded = V4.V3.V2.decode_core(core) - exact_replay = decoded == data - safe_name = item["name"].replace("/", "_") - core_path = source_dir / f"{safe_name}.wlg2" - core_path.write_bytes(core) - packet = len(core) + V4.V3.SLICE_RECEIPT_ROOT_BYTES + V4.V3.PROTOCOL_ID_BYTES - base = baselines(data) - baseline_values = {k: v for k, v in base.items() if v is not None} - best_name = min(baseline_values, key=lambda key: baseline_values[key]) if baseline_values else None - best_size = baseline_values[best_name] if best_name else None - return { - **item, - "raw_bytes": len(data), - "core_bytes": len(core), - "packet_bytes": packet, - "delta_core": len(data) - len(core), - "delta_packet": len(data) - packet, - "exact_replay": exact_replay, - "atom_count": len(atoms), - "atom_counts": {kind: sum(1 for atom in atoms if atom.kind == kind) for kind in sorted({atom.kind for atom in atoms})}, - "raw_sha256": sha256_bytes(data), - "core": rel(core_path), - "core_sha256": sha256_bytes(core), - "slice_receipt_root": V4.V3.slice_receipt_root(data, core, exact_replay, dictionary_hash, protocol_sha), - "baseline": { - **base, - "best_baseline": best_name, - "best_baseline_bytes": best_size, - "delta_vs_best_baseline_packet": (best_size - packet) if best_size is not None else None, - }, - } - - -def provenance_status(path: Path, size: int, claim: str) -> tuple[str, str]: - if claim == "auto": - claim = "canonical_enwik9" if size == CANONICAL_ENWIK9_SIZE else "fixture" - if claim == "canonical_enwik9" and size != CANONICAL_ENWIK9_SIZE: - return claim, "HOLD_PROVENANCE" - if claim == "canonical_enwik9": - return claim, "PASS" - if claim in {"fixture", "unknown"}: - return claim, "FIXTURE" - return "unknown", "HOLD_PROVENANCE" - - -def aggregate(slices: list[dict[str, Any]], dictionary_bytes: int, canonical_claim: str, provenance_decision: str) -> dict[str, Any]: - raw = sum(item["raw_bytes"] for item in slices) - core = sum(item["core_bytes"] for item in slices) - packet = sum(item["packet_bytes"] for item in slices) - global_cost = packet + dictionary_bytes - baseline_totals: dict[str, int | None] = { - "zlib_9": sum_int_or_none(item["baseline"]["zlib_9"] for item in slices), - "bz2_9": sum_int_or_none(item["baseline"]["bz2_9"] for item in slices), - "lzma_9": sum_int_or_none(item["baseline"]["lzma_9"] for item in slices), - "zstd_19_if_available": sum_int_or_none(item["baseline"]["zstd_19_if_available"] for item in slices), - } - numeric = {k: v for k, v in baseline_totals.items() if v is not None} - best_name = min(numeric, key=lambda key: numeric[key]) if numeric else None - best_size = numeric[best_name] if best_name else None - decision = decide( - all_exact=all(item["exact_replay"] for item in slices), - provenance_decision=provenance_decision, - canonical_claim=canonical_claim, - delta_packet=raw - packet, - delta_global=raw - global_cost, - delta_vs_best=(best_size - global_cost) if best_size is not None else None, - ) - return { - "slice_count": len(slices), - "all_exact_replay": all(item["exact_replay"] for item in slices), - "raw_bytes": raw, - "core_bytes": core, - "packet_bytes": packet, - "dictionary_bytes": dictionary_bytes, - "global_cost_bytes": global_cost, - "delta_core": raw - core, - "delta_packet": raw - packet, - "delta_global": raw - global_cost, - "baseline": { - **baseline_totals, - "best_baseline": best_name, - "best_baseline_bytes": best_size, - "delta_vs_best_baseline": (best_size - global_cost) if best_size is not None else None, - }, - "decision": decision, - } - - -def sum_int_or_none(values: Any) -> int | None: - total = 0 - for value in values: - if value is None: - return None - total += int(value) - return total - - -def decide( - *, - all_exact: bool, - provenance_decision: str, - canonical_claim: str, - delta_packet: int, - delta_global: int, - delta_vs_best: int | None, -) -> str: - if not all_exact: - return "REJECT_REPLAY" - if provenance_decision == "HOLD_PROVENANCE": - return "HOLD_PROVENANCE" - if delta_packet <= 0: - return "HOLD_PACKET" - if delta_global <= 0: - return "HOLD_GLOBAL" - if canonical_claim != "canonical_enwik9": - return "ADMIT_FIXTURE" - if delta_vs_best is not None and delta_vs_best > 0: - return "BASELINE_CANDIDATE" - return "ADMIT_CANONICAL_SLICE" - - -def build_gate_protocol( - *, - input_info: dict[str, Any], - aggregates: dict[str, Any], - dictionary_hash: str, - protocol_sha: str, -) -> dict[str, Any]: - provenance_output = { - "canonical_claim": input_info["canonical_claim"], - "provenance_decision": input_info["provenance_decision"], - "size_requirement": CANONICAL_ENWIK9_SIZE, - } - provenance_event = make_event(1, "PROVENANCE", GENESIS_EVENT_HASH, input_info, provenance_output, 0, input_info["provenance_decision"]) - pass_output = { - "exact_replay": aggregates["all_exact_replay"], - "slice_count": aggregates["slice_count"], - "replay_law": "Decode(Gamma,Pi,R)==S_slice", - } - pass_event = make_event(2, "PASS", provenance_event["event_hash"], provenance_output, pass_output, 0, "PASS" if aggregates["all_exact_replay"] else "REJECT_REPLAY") - add_output = { - "packet_bytes": aggregates["packet_bytes"], - "dictionary_bytes": aggregates["dictionary_bytes"], - "global_cost_bytes": aggregates["global_cost_bytes"], - "dictionary_sha256": dictionary_hash, - "protocol_sha256": protocol_sha, - } - add_event = make_event(3, "ADD", pass_event["event_hash"], pass_output, add_output, aggregates["global_cost_bytes"], "COUNTED") - pause_output = { - "state_root": add_event["event_hash"], - "event_index_after": 4, - "clock_participates_in_hash": False, - } - pause_event = make_event(4, "PAUSE", add_event["event_hash"], {"state_root": add_event["event_hash"]}, pause_output, 0, "FENCED") - subtract_output = { - "delta_core": aggregates["delta_core"], - "delta_packet": aggregates["delta_packet"], - "delta_global": aggregates["delta_global"], - } - subtract_event = make_event(5, "SUBTRACT", pause_event["event_hash"], pause_output | add_output, subtract_output, aggregates["delta_global"], "SUBTRACTED") - baseline_output = aggregates["baseline"] | {"decision": aggregates["decision"]} - baseline_event = make_event(6, "BASELINE", subtract_event["event_hash"], subtract_output, baseline_output, baseline_output.get("delta_vs_best_baseline") or 0, aggregates["decision"]) - return { - "protocol": "PROVENANCE_PASS_ADD_PAUSE_SUBTRACT_BASELINE_v1", - "clock_participates_in_hash": False, - "events": [provenance_event, pass_event, add_event, pause_event, subtract_event, baseline_event], - "final_event_hash": baseline_event["event_hash"], - } - - -def write_summary(receipt: dict[str, Any]) -> None: - agg = receipt["aggregate"] - lines = [ - "# enwiki9 Canonical Slice Baseline Probe Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Input", - "", - f"- Path: `{receipt['input']['path']}`", - f"- Size bytes: `{receipt['input']['size_bytes']}`", - f"- Canonical claim: `{receipt['input']['canonical_claim']}`", - f"- Provenance decision: `{receipt['input']['provenance_decision']}`", - "", - "## Aggregate", - "", - "| Slices | Exact replay | Raw | Core | Packet | Dictionary | Delta core | Delta packet | Delta global | Best baseline | Delta vs best |", - "|---:|---:|---:|---:|---:|---:|---:|---:|---:|---|---:|", - ( - f"| {agg['slice_count']} | {agg['all_exact_replay']} | {agg['raw_bytes']} | " - f"{agg['core_bytes']} | {agg['packet_bytes']} | {agg['dictionary_bytes']} | " - f"{agg['delta_core']} | {agg['delta_packet']} | {agg['delta_global']} | " - f"{agg['baseline']['best_baseline']} | {agg['baseline']['delta_vs_best_baseline']} |" - ), - "", - "## Slices", - "", - "| Name | Selection | Offset | Class | Raw | Core | Packet | Packet delta | Best baseline |", - "|---|---|---:|---|---:|---:|---:|---:|---|", - ] - for item in receipt["slices"]: - lines.append( - f"| `{item['name']}` | `{item['selection']}` | {item['offset']} | `{item['slice_class']}` | " - f"{item['raw_bytes']} | {item['core_bytes']} | {item['packet_bytes']} | " - f"{item['delta_packet']} | `{item['baseline']['best_baseline']}` |" - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def build_receipt(path: Path, slice_size: int, scan_stride: int, canonical_claim_arg: str) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - source_dir = OUT_DIR / "cores" - source_dir.mkdir(parents=True, exist_ok=True) - size = path.stat().st_size if path.exists() else 0 - canonical_claim, provenance_decision = provenance_status(path, size, canonical_claim_arg) - input_sha = sha256_file(path) if path.exists() else None - dictionary_bytes, dictionary_hash = V4.dictionary_bytes_and_hash() - protocol_sha = V4.protocol_hash() - raw_slices = collect_slices(path, size, slice_size, scan_stride) if path.exists() else [] - slices = [run_slice(item, source_dir, dictionary_hash, protocol_sha) for item in raw_slices] - agg = aggregate(slices, dictionary_bytes, canonical_claim, provenance_decision) - input_info = { - "path": str(path), - "size_bytes": size, - "sha256": input_sha, - "canonical_claim": canonical_claim, - "provenance_decision": provenance_decision, - } - receipt = { - "schema": "enwiki9_canonical_slice_baseline_probe_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "codec_frozen_from": "v4", - "encoder_changed": False, - "encoder_frozen_from": rel(V4.V3.V2_SCRIPT), - "receipt_mode_frozen_from": rel(V4.V3_SCRIPT), - "v4_script": rel(V4_SCRIPT), - "receipt_chain": ["PROVENANCE", "PASS", "ADD", "PAUSE", "SUBTRACT", "BASELINE"], - "clock_participates_in_hash": False, - "input": input_info, - "slice_size": slice_size, - "scan_stride": scan_stride, - "fixed_offsets": FIXED_OFFSETS, - "target_classes": TARGET_CLASSES, - "dictionary_bytes": dictionary_bytes, - "dictionary_sha256": dictionary_hash, - "protocol_sha256": protocol_sha, - "aggregate": agg, - "slices": slices, - "gate_protocol": build_gate_protocol( - input_info=input_info, - aggregates=agg, - dictionary_hash=dictionary_hash, - protocol_sha=protocol_sha, - ), - "prior_v4": read_prior_v4(), - "decision": agg["decision"], - "claim_boundary": ( - "Canonical slice baseline probe only. It freezes the v4 codec and " - "accounting path, adds a provenance gate for canonical enwik9, and " - "adds baseline comparisons against ordinary compressors. Inputs that " - "are not exactly 1,000,000,000 bytes are fixture evidence only. This " - "is not a Hutter/LTCB submission or corpus-scale result." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run v5 canonical enwik9 slice baseline probe.") - parser.add_argument("--input", type=Path, default=DEFAULT_INPUT) - parser.add_argument("--slice-size", type=int, default=DEFAULT_SLICE_SIZE) - parser.add_argument("--scan-stride", type=int, default=DEFAULT_SCAN_STRIDE) - parser.add_argument("--canonical-claim", choices=["auto", "canonical_enwik9", "fixture", "unknown"], default="auto") - args = parser.parse_args() - - receipt = build_receipt(args.input, args.slice_size, args.scan_stride, args.canonical_claim) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "input": receipt["input"], - "aggregate": receipt["aggregate"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki9_logogram_dictionary_amortization_probe.py b/4-Infrastructure/shim/enwiki9_logogram_dictionary_amortization_probe.py deleted file mode 100644 index 525ebe52..00000000 --- a/4-Infrastructure/shim/enwiki9_logogram_dictionary_amortization_probe.py +++ /dev/null @@ -1,479 +0,0 @@ -#!/usr/bin/env python3 -"""v4 dictionary-amortization probe for the enwiki9 logogram ladder. - -This pass freezes the v2 encoder and v3 slice receipt mode, then changes only -the accounting scope: sum packet deltas over many slices and subtract the fixed -dictionary once. -""" - -from __future__ import annotations - -import argparse -import bz2 -import hashlib -import importlib.util -import json -import math -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -V3_SCRIPT = REPO / "4-Infrastructure" / "shim" / "enwiki9_logogram_receipt_aggregation_probe.py" -V3_RECEIPT = REPO / "shared-data" / "data" / "enwiki9_logogram_receipt_aggregation_probe" / "enwiki9_logogram_receipt_aggregation_probe_receipt.json" -OUT_DIR = REPO / "shared-data" / "data" / "enwiki9_logogram_dictionary_amortization_probe" -RECEIPT = OUT_DIR / "enwiki9_logogram_dictionary_amortization_probe_receipt.json" -SUMMARY = OUT_DIR / "enwiki9_logogram_dictionary_amortization_probe_receipt.md" - -DEFAULT_LOCAL_HTML = Path("/home/allaun/Downloads/data/enwik9_data/1234567") -DEFAULT_MEDIAWIKI_FIXTURES = [ - Path("/home/allaun/Research Stack/fawiki.xml.bz2"), - Path("/home/allaun/Research Stack/jawiki.xml.bz2"), - Path("/home/allaun/Research Stack/viwiki.xml.bz2"), -] -DEFAULT_SLICE_SIZE = 4096 -DEFAULT_BYTE_LIMIT = 131072 -GENESIS_EVENT_HASH = "0" * 64 - - -def load_v3_module() -> Any: - spec = importlib.util.spec_from_file_location("enwiki9_logogram_receipt_aggregation_probe", V3_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load v3 script: {V3_SCRIPT}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -V3 = load_v3_module() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def read_bytes(path: Path, byte_limit: int | None) -> bytes: - if path.suffix == ".bz2": - with bz2.open(path, "rb") as handle: - return handle.read(byte_limit if byte_limit is not None else -1) - data = path.read_bytes() - return data[:byte_limit] if byte_limit is not None else data - - -def dictionary_payload() -> dict[str, list[str]]: - return V3.dictionary_payload() - - -def dictionary_bytes_and_hash() -> tuple[int, str]: - encoded = stable_json(dictionary_payload()).encode("utf-8") - return len(encoded), sha256_bytes(encoded) - - -def protocol_hash() -> str: - return sha256_bytes( - stable_json( - { - "protocol_id": V3.PROTOCOL_ID, - "encoder": rel(V3.V2_SCRIPT), - "receipt_mode": V3.RECEIPT_MODE, - "slice_receipt_root_bytes": V3.SLICE_RECEIPT_ROOT_BYTES, - "protocol_id_bytes": V3.PROTOCOL_ID_BYTES, - "gate": "dictionary_amortization_v1", - } - ).encode("utf-8") - ) - - -def event_hash(event: dict[str, Any]) -> str: - return sha256_bytes(stable_json(event).encode("utf-8")) - - -def make_event( - index: int, - gate: str, - previous_hash: str, - input_payload: dict[str, Any], - output_payload: dict[str, Any], - delta_bytes: int, - decision: str, -) -> dict[str, Any]: - event = { - "event_index": index, - "gate": gate, - "previous_event_hash": previous_hash, - "input_hash": sha256_bytes(stable_json(input_payload).encode("utf-8")), - "output_hash": sha256_bytes(stable_json(output_payload).encode("utf-8")), - "delta_bytes": delta_bytes, - "decision": decision, - "clock_participates_in_hash": False, - } - event["event_hash"] = event_hash(event) - return event - - -def verdict_for(all_exact: bool, delta_core: int, packet_delta: int, global_delta: int, canonical_status: str) -> str: - if not all_exact: - return "REJECT" - if global_delta > 0 and canonical_status == "canonical_enwik9": - return "LAWFUL_CANDIDATE" - if global_delta > 0: - return "ADMIT_FIXTURE" - if packet_delta > 0: - return "HOLD_GLOBAL" - if delta_core > 0: - return "HOLD_PACKET" - return "HOLD_DIAGNOSTIC" - - -def four_gate_events( - *, - raw_bytes: int, - core_bytes: int, - packet_v3_bytes: int, - dictionary_bytes: int, - all_exact_replay: bool, - dictionary_hash: str, - protocol_sha: str, - canonical_status: str, -) -> dict[str, Any]: - pass_input = { - "raw_bytes": raw_bytes, - "core_bytes": core_bytes, - "dictionary_sha256": dictionary_hash, - "protocol_sha256": protocol_sha, - } - pass_output = { - "exact_replay": all_exact_replay, - "replay_law": "Decode(Gamma,Pi,R)==S", - } - pass_event = make_event(1, "PASS", GENESIS_EVENT_HASH, pass_input, pass_output, 0, "PASS" if all_exact_replay else "REJECT") - - add_input = pass_output - add_output = { - "packet_v3_bytes": packet_v3_bytes, - "dictionary_bytes": dictionary_bytes, - "global_cost_bytes": packet_v3_bytes + dictionary_bytes, - } - add_event = make_event(2, "ADD", pass_event["event_hash"], add_input, add_output, add_output["global_cost_bytes"], "COUNTED") - - pause_input = { - "state_root": add_event["event_hash"], - "event_index_before": add_event["event_index"], - } - pause_output = { - "state_root": add_event["event_hash"], - "event_index_after": 3, - "clock_participates_in_hash": False, - } - pause_event = make_event(3, "PAUSE", add_event["event_hash"], pause_input, pause_output, 0, "FENCED") - - delta_core = raw_bytes - core_bytes - delta_packet = raw_bytes - packet_v3_bytes - delta_global = raw_bytes - (packet_v3_bytes + dictionary_bytes) - verdict = verdict_for(all_exact_replay, delta_core, delta_packet, delta_global, canonical_status) - subtract_input = pause_output | add_output | {"raw_bytes": raw_bytes, "core_bytes": core_bytes} - subtract_output = { - "delta_core": delta_core, - "delta_packet": delta_packet, - "delta_global": delta_global, - "verdict": verdict, - } - subtract_event = make_event(4, "SUBTRACT", pause_event["event_hash"], subtract_input, subtract_output, delta_global, verdict) - return { - "protocol": "PASS_ADD_PAUSE_SUBTRACT_v1", - "timestamp_role": "metadata_only", - "included_in_receipt_hash": { - "generated_at_utc": False, - "event_order": True, - "event_hashes": True, - }, - "events": [pass_event, add_event, pause_event, subtract_event], - "final_event_hash": subtract_event["event_hash"], - "verdict": verdict, - } - - -def classify_slice(data: bytes) -> str: - xml = data.count(b"<") + data.count(b">") - template = data.count(b"{{") + data.count(b"}}") - link = data.count(b"[[") + data.count(b"]]") - table = data.count(b"{|") + data.count(b"|-") + data.count(b"|}") - ref = data.lower().count(b"= max(link, xml // 8, 2): - return "template_heavy" - if link >= max(template, xml // 8, 2): - return "link_heavy" - if table >= 2: - return "table_heavy" - if ref >= 2: - return "ref_heavy" - if xml >= 24: - return "xml_heavy" - if xml >= 8 or template or link: - return "mixed" - return "prose_heavy" - - -def run_source(name: str, path: Path, data: bytes, slice_size: int, dictionary_hash: str, protocol_sha: str, compressed: bool) -> dict[str, Any]: - source_dir = OUT_DIR / name - source_dir.mkdir(parents=True, exist_ok=True) - slices: list[dict[str, Any]] = [] - for index in range(0, len(data), slice_size): - chunk = data[index : index + slice_size] - if not chunk: - continue - slice_name = f"{name}_{index // slice_size:04d}" - core, atoms = V3.V2.encode(chunk) - decoded = V3.V2.decode_core(core) - exact_replay = decoded == chunk - core_path = source_dir / f"{slice_name}.wlg2" - core_path.write_bytes(core) - packet = len(core) + V3.SLICE_RECEIPT_ROOT_BYTES + V3.PROTOCOL_ID_BYTES - delta_packet = len(chunk) - packet - root = V3.slice_receipt_root(chunk, core, exact_replay, dictionary_hash, protocol_sha) - slice_class = classify_slice(chunk) - slices.append( - { - "name": slice_name, - "slice_class": slice_class, - "offset": index, - "raw_bytes": len(chunk), - "core_bytes": len(core), - "packet_v3_bytes": packet, - "delta_core": len(chunk) - len(core), - "delta_packet_v3": delta_packet, - "global_contribution": delta_packet, - "exact_replay": exact_replay, - "atom_count": len(atoms), - "raw_sha256": sha256_bytes(chunk), - "core": rel(core_path), - "core_sha256": sha256_bytes(core), - "slice_receipt_root": root, - "fixture_status": "ADMIT_FIXTURE" if exact_replay and delta_packet > 0 else "HOLD_DIAGNOSTIC", - } - ) - - raw = sum(item["raw_bytes"] for item in slices) - core = sum(item["core_bytes"] for item in slices) - packet = sum(item["packet_v3_bytes"] for item in slices) - packet_delta = raw - packet - dictionary_bytes = V3_dictionary_bytes - global_delta = packet_delta - dictionary_bytes - canonical_status = "noncanonical_fixture" - source_verdict = verdict_for(all(item["exact_replay"] for item in slices), raw - core, packet_delta, global_delta, canonical_status) - avg_positive_delta = packet_delta / len(slices) if slices else 0.0 - break_even_slices = math.ceil(dictionary_bytes / avg_positive_delta) if avg_positive_delta > 0 else None - break_even_bytes = break_even_slices * slice_size if break_even_slices is not None else None - class_totals: dict[str, dict[str, int]] = {} - for item in slices: - bucket = class_totals.setdefault( - item["slice_class"], - {"slice_count": 0, "raw_bytes": 0, "core_bytes": 0, "packet_v3_bytes": 0, "delta_packet_v3": 0}, - ) - bucket["slice_count"] += 1 - bucket["raw_bytes"] += item["raw_bytes"] - bucket["core_bytes"] += item["core_bytes"] - bucket["packet_v3_bytes"] += item["packet_v3_bytes"] - bucket["delta_packet_v3"] += item["delta_packet_v3"] - - return { - "name": name, - "source": str(path), - "source_sha256": sha256_bytes(path.read_bytes()) if path.exists() and path.stat().st_size <= 50_000_000 else None, - "source_is_compressed": compressed, - "read_bytes": len(data), - "slice_count": len(slices), - "slice_size": slice_size, - "all_exact_replay": all(item["exact_replay"] for item in slices), - "raw_bytes": raw, - "core_bytes": core, - "packet_v3_bytes": packet, - "delta_core": raw - core, - "packet_delta_total": packet_delta, - "dictionary_bytes": dictionary_bytes, - "global_delta": global_delta, - "break_even_slice_count": break_even_slices, - "observed_bytes_to_break_even": break_even_bytes, - "canonical_status": canonical_status, - "gate_protocol": four_gate_events( - raw_bytes=raw, - core_bytes=core, - packet_v3_bytes=packet, - dictionary_bytes=dictionary_bytes, - all_exact_replay=all(item["exact_replay"] for item in slices), - dictionary_hash=dictionary_hash, - protocol_sha=protocol_sha, - canonical_status=canonical_status, - ), - "class_totals": class_totals, - "status_counts": { - status: sum(1 for item in slices if item["fixture_status"] == status) - for status in sorted({item["fixture_status"] for item in slices}) - }, - "aggregate_status": source_verdict, - "slices": slices, - } - - -def read_prior_v3() -> dict[str, Any] | None: - if not V3_RECEIPT.exists(): - return None - receipt = json.loads(V3_RECEIPT.read_text(encoding="utf-8")) - return { - "receipt": rel(V3_RECEIPT), - "receipt_hash": receipt.get("receipt_hash"), - "receipt_mode": receipt.get("receipt_mode"), - "aggregates": receipt.get("aggregates"), - } - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# enwiki9 Dictionary Amortization Probe Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Aggregate", - "", - "| Source | Slices | Exact replay | Raw | Core | v3 packet | Packet delta | Dictionary | Global delta | Aggregate status |", - "|---|---:|---:|---:|---:|---:|---:|---:|---:|---|", - ] - for source in receipt["sources"]: - lines.append( - f"| {source['name']} | {source['slice_count']} | {source['all_exact_replay']} | " - f"{source['raw_bytes']} | {source['core_bytes']} | {source['packet_v3_bytes']} | " - f"{source['packet_delta_total']} | {source['dictionary_bytes']} | {source['global_delta']} | " - f"{source['aggregate_status']} |" - ) - lines.extend( - [ - "", - "## Break Even", - "", - "| Source | Packet delta total | Break-even slices | Observed bytes to break even |", - "|---|---:|---:|---:|", - ] - ) - for source in receipt["sources"]: - lines.append( - f"| {source['name']} | {source['packet_delta_total']} | " - f"{source['break_even_slice_count']} | {source['observed_bytes_to_break_even']} |" - ) - lines.extend(["", "## Class Totals", ""]) - for source in receipt["sources"]: - lines.append(f"### {source['name']}") - lines.append("") - lines.append("| Class | Slices | Raw | Core | v3 packet | Packet delta |") - lines.append("|---|---:|---:|---:|---:|---:|") - for class_name, totals in sorted(source["class_totals"].items()): - lines.append( - f"| {class_name} | {totals['slice_count']} | {totals['raw_bytes']} | " - f"{totals['core_bytes']} | {totals['packet_v3_bytes']} | {totals['delta_packet_v3']} |" - ) - lines.append("") - SUMMARY.write_text("\n".join(lines).rstrip() + "\n", encoding="utf-8") - - -def build_receipt(paths: list[Path], slice_size: int, byte_limit: int) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - dictionary_bytes, dictionary_hash = dictionary_bytes_and_hash() - protocol_sha = protocol_hash() - sources: list[dict[str, Any]] = [] - for path in paths: - if not path.exists(): - continue - name = path.name.replace(".", "_").replace("-", "_") - data = read_bytes(path, byte_limit) - sources.append(run_source(name, path, data, slice_size, dictionary_hash, protocol_sha, path.suffix == ".bz2")) - receipt = { - "schema": "enwiki9_logogram_dictionary_amortization_probe_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "gate": "dictionary_amortization_v1", - "gate_protocol": "PASS_ADD_PAUSE_SUBTRACT_v1", - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "encoder_frozen_from": rel(V3.V2_SCRIPT), - "receipt_mode_frozen_from": rel(V3_SCRIPT), - "slice_receipt_root_bytes": V3.SLICE_RECEIPT_ROOT_BYTES, - "protocol_id_bytes": V3.PROTOCOL_ID_BYTES, - "dictionary_bytes": dictionary_bytes, - "dictionary_sha256": dictionary_hash, - "protocol_sha256": protocol_sha, - "slice_size": slice_size, - "byte_limit_per_source": byte_limit, - "sources": sources, - "prior_v3": read_prior_v3(), - "decision": "HOLD", - "claim_boundary": ( - "Dictionary amortization probe only. It freezes the v2 encoder and " - "v3 slice-root receipt mode, then changes only accounting scope. " - "Sources are noncanonical local fixtures, including local MediaWiki " - "XML dump heads and the existing local HTML sample; no source is the " - "canonical 1,000,000,000-byte enwik9 corpus. Aggregate ADMIT_FIXTURE " - "means a noncanonical fixture crossed the global-delta gate, not a " - "Hutter/LTCB result, corpus admission, or baseline-compressor win." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -V3_dictionary_bytes, _V3_dictionary_hash = dictionary_bytes_and_hash() - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run v4 dictionary-amortization probe.") - parser.add_argument("--slice-size", type=int, default=DEFAULT_SLICE_SIZE) - parser.add_argument("--byte-limit", type=int, default=DEFAULT_BYTE_LIMIT) - parser.add_argument("--source", action="append", type=Path, help="Optional source path. May be repeated.") - args = parser.parse_args() - - paths = args.source if args.source else [DEFAULT_LOCAL_HTML, *DEFAULT_MEDIAWIKI_FIXTURES] - receipt = build_receipt(paths, args.slice_size, args.byte_limit) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "sources": [ - { - "name": source["name"], - "raw_bytes": source["raw_bytes"], - "packet_delta_total": source["packet_delta_total"], - "global_delta": source["global_delta"], - "aggregate_status": source["aggregate_status"], - } - for source in receipt["sources"] - ], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki9_logogram_receipt_aggregation_probe.py b/4-Infrastructure/shim/enwiki9_logogram_receipt_aggregation_probe.py deleted file mode 100644 index 79d28647..00000000 --- a/4-Infrastructure/shim/enwiki9_logogram_receipt_aggregation_probe.py +++ /dev/null @@ -1,341 +0,0 @@ -#!/usr/bin/env python3 -"""v3 slice-receipt aggregation probe for enwiki9 logogram targeting. - -This pass keeps the v2 fixed XML/MediaWiki dictionary encoder unchanged and -tests the next accounting hypothesis: replace per-atom receipt stubs with one -slice-level replay receipt root. -""" - -from __future__ import annotations - -import argparse -import hashlib -import importlib.util -import json -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -V2_SCRIPT = REPO / "4-Infrastructure" / "shim" / "enwiki9_logogram_xml_dict_probe.py" -V2_RECEIPT = REPO / "shared-data" / "data" / "enwiki9_logogram_xml_dict_probe" / "enwiki9_logogram_xml_dict_probe_receipt.json" -DEFAULT_BUNDLE_DEMO = REPO / "shared-data" / "data" / "enwiki9_logogram_targeter" / "demo" -DEFAULT_LOCAL_SAMPLE = Path("/home/allaun/Downloads/data/enwik9_data/1234567") -OUT_DIR = REPO / "shared-data" / "data" / "enwiki9_logogram_receipt_aggregation_probe" -RECEIPT = OUT_DIR / "enwiki9_logogram_receipt_aggregation_probe_receipt.json" -SUMMARY = OUT_DIR / "enwiki9_logogram_receipt_aggregation_probe_receipt.md" - -SLICE_RECEIPT_ROOT_BYTES = 32 -PROTOCOL_ID_BYTES = 4 -RECEIPT_MODE = "slice_root_v1" -PROTOCOL_ID = "WLG2" - - -def load_v2_module() -> Any: - spec = importlib.util.spec_from_file_location("enwiki9_logogram_xml_dict_probe", V2_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load v2 script: {V2_SCRIPT}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -V2 = load_v2_module() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_file(path: Path) -> str: - return sha256_bytes(path.read_bytes()) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def read_prior_v2() -> dict[str, Any] | None: - if not V2_RECEIPT.exists(): - return None - receipt = json.loads(V2_RECEIPT.read_text(encoding="utf-8")) - out: dict[str, Any] = { - "receipt": rel(V2_RECEIPT), - "receipt_hash": receipt.get("receipt_hash"), - "dictionary_bytes": receipt.get("dictionary_bytes"), - "aggregates": {}, - } - for name in ["demo", "local_sample"]: - agg = receipt.get("aggregates", {}).get(name) - if agg: - out["aggregates"][name] = { - "raw_bytes": agg.get("raw_bytes"), - "core_bytes": agg.get("core_bytes"), - "packet_estimate_bytes": agg.get("packet_estimate_bytes"), - "delta_core": agg.get("delta_core"), - "delta_packet": agg.get("delta_packet"), - "delta_global": agg.get("delta_global"), - "all_exact_replay": agg.get("all_exact_replay"), - } - return out - - -def demo_slices(demo_dir: Path) -> list[tuple[str, str, bytes]]: - out: list[tuple[str, str, bytes]] = [] - if demo_dir.exists(): - for path in sorted(demo_dir.glob("*.raw")): - out.append((path.stem, rel(path), path.read_bytes())) - return out - - -def sample_slices(sample: Path, slice_size: int, max_slices: int) -> list[tuple[str, str, bytes]]: - if not sample.exists(): - return [] - data = sample.read_bytes() - return [ - (f"local_sample_{index:04d}", str(sample), data[index * slice_size : (index + 1) * slice_size]) - for index in range(min(max_slices, (len(data) + slice_size - 1) // slice_size)) - if data[index * slice_size : (index + 1) * slice_size] - ] - - -def slice_receipt_root(raw: bytes, core: bytes, exact_replay: bool, dictionary_hash: str, protocol_hash: str) -> str: - payload = { - "raw_sha256": sha256_bytes(raw), - "core_sha256": sha256_bytes(core), - "dictionary_sha256": dictionary_hash, - "protocol_sha256": protocol_hash, - "exact_replay": exact_replay, - "receipt_mode": RECEIPT_MODE, - } - return sha256_bytes(stable_json(payload).encode("utf-8")) - - -def run_slice(name: str, source_label: str, data: bytes, dictionary_hash: str, protocol_hash: str) -> dict[str, Any]: - core, atoms = V2.encode(data) - decoded = V2.decode_core(core) - exact_replay = decoded == data - core_path = OUT_DIR / f"{name}.wlg2" - core_path.write_bytes(core) - packet_v3 = len(core) + SLICE_RECEIPT_ROOT_BYTES + PROTOCOL_ID_BYTES - root = slice_receipt_root(data, core, exact_replay, dictionary_hash, protocol_hash) - atom_counts = {kind: sum(1 for atom in atoms if atom.kind == kind) for kind in sorted({atom.kind for atom in atoms})} - return { - "name": name, - "source": source_label, - "raw_bytes": len(data), - "core_bytes": len(core), - "packet_v3_bytes": packet_v3, - "delta_core": len(data) - len(core), - "delta_packet_v3": len(data) - packet_v3, - "exact_replay": exact_replay, - "atom_count": len(atoms), - "atom_counts": atom_counts, - "raw_sha256": sha256_bytes(data), - "core": rel(core_path), - "core_sha256": sha256_bytes(core), - "slice_receipt_root": root, - "slice_receipt_root_bytes": SLICE_RECEIPT_ROOT_BYTES, - "protocol_id_bytes": PROTOCOL_ID_BYTES, - "fixture_status": "ADMIT_FIXTURE" if exact_replay and len(data) > packet_v3 else "HOLD_DIAGNOSTIC", - } - - -def aggregate(results: list[dict[str, Any]], dictionary_bytes: int) -> dict[str, Any]: - raw = sum(item["raw_bytes"] for item in results) - core = sum(item["core_bytes"] for item in results) - packet = sum(item["packet_v3_bytes"] for item in results) - return { - "slice_count": len(results), - "all_exact_replay": all(item["exact_replay"] for item in results), - "raw_bytes": raw, - "core_bytes": core, - "packet_v3_bytes": packet, - "delta_core": raw - core, - "delta_packet_v3": raw - packet, - "dictionary_bytes": dictionary_bytes, - "delta_global_v3": raw - (packet + dictionary_bytes), - "slice_receipt_root_bytes_total": SLICE_RECEIPT_ROOT_BYTES * len(results), - "protocol_id_bytes_total": PROTOCOL_ID_BYTES * len(results), - "status_counts": { - status: sum(1 for item in results if item["fixture_status"] == status) - for status in sorted({item["fixture_status"] for item in results}) - }, - } - - -def dictionary_payload() -> dict[str, list[str]]: - return { - "fixed": [tag.hex() for tag in V2.FIXED_TAGS], - "pair": [tag.hex() for tag in V2.PAIR_TAGS], - "attr": [tag.hex() for tag in V2.ATTR_TAGS], - "motif": [tag.hex() for tag in V2.MOTIFS], - } - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# enwiki9 Receipt Aggregation Probe Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Aggregate", - "", - "| Run | Slices | Exact replay | Raw | Core | v3 packet | Delta core | Delta packet v3 | Delta global v3 |", - "|---|---:|---:|---:|---:|---:|---:|---:|---:|", - ] - for name in ["demo", "local_sample"]: - agg = receipt["aggregates"].get(name) - if not agg: - continue - lines.append( - f"| {name} | {agg['slice_count']} | {agg['all_exact_replay']} | " - f"{agg['raw_bytes']} | {agg['core_bytes']} | {agg['packet_v3_bytes']} | " - f"{agg['delta_core']} | {agg['delta_packet_v3']} | {agg['delta_global_v3']} |" - ) - lines.extend( - [ - "", - "## v2 Comparison", - "", - "| Run | v2 packet delta | v3 packet delta | v2 global delta | v3 global delta |", - "|---|---:|---:|---:|---:|", - ] - ) - prior = receipt.get("prior_v2") - if prior: - for name in ["demo", "local_sample"]: - v2 = prior.get("aggregates", {}).get(name) - v3 = receipt["aggregates"].get(name) - if not v2 or not v3: - continue - lines.append( - f"| {name} | {v2['delta_packet']} | {v3['delta_packet_v3']} | " - f"{v2['delta_global']} | {v3['delta_global_v3']} |" - ) - lines.extend( - [ - "", - "## Receipt Mode", - "", - f"- Receipt mode: `{RECEIPT_MODE}`", - f"- Slice receipt root bytes: `{SLICE_RECEIPT_ROOT_BYTES}`", - f"- Protocol ID bytes per slice: `{PROTOCOL_ID_BYTES}`", - f"- Dictionary bytes still counted globally: `{receipt['dictionary_bytes']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def build_receipt(demo_dir: Path, sample: Path, slice_size: int, max_sample_slices: int) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - dictionary_json = stable_json(dictionary_payload()).encode("utf-8") - dictionary_bytes = len(dictionary_json) - dictionary_hash = sha256_bytes(dictionary_json) - protocol_hash = sha256_bytes( - stable_json( - { - "protocol_id": PROTOCOL_ID, - "encoder": rel(V2_SCRIPT), - "receipt_mode": RECEIPT_MODE, - "slice_receipt_root_bytes": SLICE_RECEIPT_ROOT_BYTES, - "protocol_id_bytes": PROTOCOL_ID_BYTES, - } - ).encode("utf-8") - ) - - demo_results = [ - run_slice(name, source, data, dictionary_hash, protocol_hash) - for name, source, data in demo_slices(demo_dir) - ] - sample_results = [ - run_slice(name, source, data, dictionary_hash, protocol_hash) - for name, source, data in sample_slices(sample, slice_size, max_sample_slices) - ] - receipt = { - "schema": "enwiki9_logogram_receipt_aggregation_probe_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "receipt_mode": RECEIPT_MODE, - "slice_receipt_root_bytes": SLICE_RECEIPT_ROOT_BYTES, - "protocol_id": PROTOCOL_ID, - "protocol_id_bytes": PROTOCOL_ID_BYTES, - "protocol_sha256": protocol_hash, - "dictionary_bytes": dictionary_bytes, - "dictionary_sha256": dictionary_hash, - "dictionary_source": rel(V2_SCRIPT), - "inputs": { - "demo_dir": rel(demo_dir), - "local_sample": str(sample), - "local_sample_bytes": sample.stat().st_size if sample.exists() else None, - "local_sample_sha256": sha256_file(sample) if sample.exists() else None, - "local_sample_claim": "noncanonical local HTML sample; not enwik9", - }, - "runs": { - "demo": demo_results, - "local_sample": sample_results, - }, - "aggregates": { - "demo": aggregate(demo_results, dictionary_bytes), - "local_sample": aggregate(sample_results, dictionary_bytes), - }, - "prior_v2": read_prior_v2(), - "decision": "HOLD", - "claim_boundary": ( - "Receipt aggregation probe only. It reuses the v2 fixed XML/MediaWiki " - "dictionary encoder and replaces per-atom receipt stubs with one " - "slice-level replay receipt root. Demo slices come from the uploaded " - "targeter bundle; the local sample is a 20,532-byte noncanonical HTML " - "file, not the 1,000,000,000-byte enwik9 corpus. Positive packet " - "deltas are fixture evidence only; global admission remains HOLD " - "until dictionary/protocol bytes are amortized over a canonical corpus " - "run and baseline comparisons are counted." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run v3 slice receipt aggregation probe.") - parser.add_argument("--demo-dir", type=Path, default=DEFAULT_BUNDLE_DEMO) - parser.add_argument("--sample", type=Path, default=DEFAULT_LOCAL_SAMPLE) - parser.add_argument("--slice-size", type=int, default=4096) - parser.add_argument("--max-sample-slices", type=int, default=4) - args = parser.parse_args() - - receipt = build_receipt(args.demo_dir, args.sample, args.slice_size, args.max_sample_slices) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "demo": receipt["aggregates"]["demo"], - "local_sample": receipt["aggregates"]["local_sample"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki9_logogram_targeter_receipt.py b/4-Infrastructure/shim/enwiki9_logogram_targeter_receipt.py deleted file mode 100644 index 0a6c1e6a..00000000 --- a/4-Infrastructure/shim/enwiki9_logogram_targeter_receipt.py +++ /dev/null @@ -1,214 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest and run the enwiki9 logogram targeter bundle. - -The input bundle is a slice-targeting harness, not an enwiki9 corpus. This -wrapper records the bundle hash, runs its demo, optionally runs the available -local enwiki9-like sample, and writes a stable top-level receipt. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import shutil -import subprocess -import sys -import zipfile -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_BUNDLE = Path("/home/allaun/Documents/ingest/enwiki9_logogram_target.zip") -DEFAULT_SAMPLE = Path("/home/allaun/Downloads/data/enwik9_data/1234567") -OUT_DIR = REPO / "shared-data" / "data" / "enwiki9_logogram_targeter" -RECEIPT = OUT_DIR / "enwiki9_logogram_targeter_receipt.json" -SUMMARY = OUT_DIR / "enwiki9_logogram_targeter_receipt.md" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def extract_bundle(bundle: Path, extract_dir: Path) -> Path: - if extract_dir.exists(): - shutil.rmtree(extract_dir) - extract_dir.mkdir(parents=True) - with zipfile.ZipFile(bundle) as zf: - zf.extractall(extract_dir) - script = extract_dir / "enwiki9_logogram_target" / "enwiki9_slice_targeter.py" - if not script.exists(): - raise FileNotFoundError(script) - return script - - -def run_targeter(script: Path, out_dir: Path, args: list[str]) -> dict[str, Any]: - if out_dir.exists(): - shutil.rmtree(out_dir) - out_dir.mkdir(parents=True) - command = [sys.executable, str(script), "--out", str(out_dir), *args] - completed = subprocess.run(command, check=True, text=True, capture_output=True) - return { - "command": command, - "stdout": completed.stdout.strip(), - "stderr": completed.stderr.strip(), - "summary": rel(out_dir / "summary.json"), - "manifest": rel(out_dir / "slice_manifest.json"), - "summary_payload": load_json(out_dir / "summary.json"), - } - - -def output_hashes(path: Path) -> list[dict[str, Any]]: - records: list[dict[str, Any]] = [] - for item in sorted(p for p in path.rglob("*") if p.is_file()): - records.append( - { - "path": rel(item), - "bytes": item.stat().st_size, - "sha256": sha256_file(item), - } - ) - return records - - -def write_summary(receipt: dict[str, Any]) -> None: - demo = receipt["runs"]["demo"]["summary_payload"] - sample = receipt["runs"].get("sample_20532", {}).get("summary_payload") - lines = [ - "# enwiki9 Logogram Targeter Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Bundle", - "", - f"- Zip: `{receipt['bundle']['path']}`", - f"- Zip bytes: `{receipt['bundle']['bytes']}`", - f"- Zip hash: `{receipt['bundle']['sha256']}`", - "", - "## Demo Run", - "", - f"- Slices: `{demo['num_slices']}`", - f"- Round trip: `{demo['all_roundtrip_ok']}`", - f"- Raw bytes: `{demo['total_raw_len']}`", - f"- Core bytes: `{demo['total_core_len']}`", - f"- Packet estimate bytes: `{demo['total_local_packet_estimate']}`", - ] - if sample: - lines.extend( - [ - "", - "## Local Sample Run", - "", - f"- Sample: `{receipt['sample']['path']}`", - f"- Sample bytes: `{receipt['sample']['bytes']}`", - f"- Slices: `{sample['num_slices']}`", - f"- Round trip: `{sample['all_roundtrip_ok']}`", - f"- Raw bytes: `{sample['total_raw_len']}`", - f"- Core bytes: `{sample['total_core_len']}`", - f"- Packet estimate bytes: `{sample['total_local_packet_estimate']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def build_receipt(bundle: Path, sample: Path | None, slice_size: int, stride: int) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - script = extract_bundle(bundle, OUT_DIR / "bundle") - demo_run = run_targeter(script, OUT_DIR / "demo", ["--demo"]) - runs: dict[str, Any] = {"demo": demo_run} - - sample_payload = None - if sample and sample.exists(): - sample_run = run_targeter( - script, - OUT_DIR / "sample_20532", - ["--input", str(sample), "--slice-size", str(slice_size), "--stride", str(stride)], - ) - runs["sample_20532"] = sample_run - sample_payload = {"path": str(sample), "bytes": sample.stat().st_size, "sha256": sha256_file(sample)} - - receipt = { - "schema": "enwiki9_logogram_targeter_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "bundle": { - "path": str(bundle), - "bytes": bundle.stat().st_size, - "sha256": sha256_file(bundle), - "extracted_script": rel(script), - "extracted_script_sha256": sha256_file(script), - }, - "sample": sample_payload, - "runs": runs, - "output_hashes": output_hashes(OUT_DIR), - "decision": "HOLD", - "claim_boundary": ( - "Imported enwiki9 logogram targeter bundle and local sample run only. " - "The bundle is not the canonical enwik9 corpus, the available local " - "sample is 20,532 bytes rather than 1,000,000,000 bytes, and no " - "Hutter/LTCB benchmark or compression-competitiveness claim is made." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc", "output_hashes"}}).encode("utf-8") - ) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run the enwiki9 logogram targeter bundle and write a stable receipt.") - parser.add_argument("--bundle", type=Path, default=DEFAULT_BUNDLE) - parser.add_argument("--sample", type=Path, default=DEFAULT_SAMPLE) - parser.add_argument("--slice-size", type=int, default=4096) - parser.add_argument("--stride", type=int, default=4096) - args = parser.parse_args() - - receipt = build_receipt(args.bundle, args.sample, args.slice_size, args.stride) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "demo_roundtrip": receipt["runs"]["demo"]["summary_payload"]["all_roundtrip_ok"], - "sample_roundtrip": receipt["runs"].get("sample_20532", {}).get("summary_payload", {}).get("all_roundtrip_ok"), - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/enwiki9_logogram_xml_dict_probe.py b/4-Infrastructure/shim/enwiki9_logogram_xml_dict_probe.py deleted file mode 100644 index 80a3e6b3..00000000 --- a/4-Infrastructure/shim/enwiki9_logogram_xml_dict_probe.py +++ /dev/null @@ -1,592 +0,0 @@ -#!/usr/bin/env python3 -"""v2 fixed-tag dictionary probe for enwiki9/MediaWiki logogram targeting. - -This is the deliberately boring next pass after the v1 targeter negative -control: promote repeated XML/MediaWiki scaffolding into fixed tag IDs and -measure whether the core delta flips positive on markup-heavy slices. -""" - -from __future__ import annotations - -import argparse -import bz2 -import hashlib -import json -import lzma -import re -import zlib -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_BUNDLE_DEMO = REPO / "shared-data" / "data" / "enwiki9_logogram_targeter" / "demo" -DEFAULT_LOCAL_SAMPLE = Path("/home/allaun/Downloads/data/enwik9_data/1234567") -OUT_DIR = REPO / "shared-data" / "data" / "enwiki9_logogram_xml_dict_probe" -RECEIPT = OUT_DIR / "enwiki9_logogram_xml_dict_probe_receipt.json" -SUMMARY = OUT_DIR / "enwiki9_logogram_xml_dict_probe_receipt.md" -V1_RECEIPT = REPO / "shared-data" / "data" / "enwiki9_logogram_targeter" / "enwiki9_logogram_targeter_receipt.json" - -OP_LIT = 0x00 -OP_WLINK = 0x81 -OP_TEMPLATE = 0x82 -OP_REF = 0x83 -OP_TABLE = 0x84 -OP_MOTIF = 0x85 -OP_FIXED_TAG = 0x90 -OP_TAG_PAIR = 0x91 -OP_TEXT_ATTR_PAIR = 0x92 -OP_TAG_ATTR = 0x93 - -FIXED_TAGS = [ - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", -] -TAG_TO_ID = {tag: index + 1 for index, tag in enumerate(FIXED_TAGS)} -ID_TO_TAG = {index + 1: tag for index, tag in enumerate(FIXED_TAGS)} - -PAIR_TAGS = [ - b"title", - b"id", - b"timestamp", - b"username", - b"comment", - b"sitename", - b"base", - b"generator", - b"case", - b"namespace", - b"model", - b"format", -] -PAIR_TO_ID = {tag: index + 1 for index, tag in enumerate(PAIR_TAGS)} -ID_TO_PAIR = {index + 1: tag for index, tag in enumerate(PAIR_TAGS)} - -ATTR_TAGS = [ - b"mediawiki", - b"text", - b"html", - b"meta", - b"script", - b"link", - b"div", - b"span", - b"body", -] -ATTR_TO_ID = {tag: index + 1 for index, tag in enumerate(ATTR_TAGS)} -ID_TO_ATTR = {index + 1: tag for index, tag in enumerate(ATTR_TAGS)} - -MOTIFS = [ - b" the ", - b" and ", - b" of ", - b" in ", - b" to ", - b", the ", - b". The ", - b"#REDIRECT ", - b" xml:space=\"preserve\"", - b" xmlns=\"", - b" http://", -] -MOTIF_TO_ID = {motif: index + 1 for index, motif in enumerate(MOTIFS)} -ID_TO_MOTIF = {index + 1: motif for index, motif in enumerate(MOTIFS)} - -PAIR_RE = re.compile( - rb"<(title|id|timestamp|username|comment|sitename|base|generator|case|namespace|model|format)(\s[^<>]*)?>(.*?)", - re.DOTALL, -) -TEXT_ATTR_RE = re.compile(rb'(.*?)', re.DOTALL) -ATTR_TAG_RE = re.compile(rb"<(mediawiki|html|meta|script|link|div|span|body)(\s[^<>]*?)>", re.DOTALL) -WLINK_RE = re.compile(rb"\[\[([^\[\]\n]{1,4096})\]\]") -TEMPLATE_RE = re.compile(rb"\{\{([^\{\}\n]{1,8192})\}\}") -REF_RE = re.compile(rb"]*?)(?:/|>(.*?))", re.DOTALL | re.IGNORECASE) - - -@dataclass(frozen=True) -class Atom: - kind: str - raw: bytes - payload: bytes - start: int - end: int - id_value: int = 0 - attrs: bytes = b"" - - -def put_varint(value: int) -> bytes: - if value < 0: - raise ValueError("negative varint") - out = bytearray() - while True: - byte = value & 0x7F - value >>= 7 - out.append(byte | 0x80 if value else byte) - if not value: - return bytes(out) - - -def read_varint(data: bytes, cursor: int) -> tuple[int, int]: - shift = 0 - value = 0 - while True: - if cursor >= len(data): - raise ValueError("truncated varint") - byte = data[cursor] - cursor += 1 - value |= (byte & 0x7F) << shift - if not byte & 0x80: - return value, cursor - shift += 7 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_file(path: Path) -> str: - return sha256_bytes(path.read_bytes()) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def emit_payload(opcode: int, payload: bytes) -> bytes: - return bytes([opcode]) + put_varint(len(payload)) + payload - - -def atom_encoded(atom: Atom) -> bytes: - if atom.kind == "LIT": - return emit_payload(OP_LIT, atom.payload) - if atom.kind == "WLINK": - return emit_payload(OP_WLINK, atom.payload) - if atom.kind == "TEMPLATE": - return emit_payload(OP_TEMPLATE, atom.payload) - if atom.kind == "REF": - return emit_payload(OP_REF, atom.payload) - if atom.kind == "TABLE": - return emit_payload(OP_TABLE, atom.payload) - if atom.kind == "MOTIF": - return bytes([OP_MOTIF, atom.id_value]) - if atom.kind == "FIXED_TAG": - return bytes([OP_FIXED_TAG, atom.id_value]) - if atom.kind == "TAG_PAIR": - return bytes([OP_TAG_PAIR, atom.id_value]) + put_varint(len(atom.attrs)) + atom.attrs + put_varint(len(atom.payload)) + atom.payload - if atom.kind == "TEXT_ATTR_PAIR": - return bytes([OP_TEXT_ATTR_PAIR]) + put_varint(len(atom.payload)) + atom.payload - if atom.kind == "TAG_ATTR": - return bytes([OP_TAG_ATTR, atom.id_value]) + put_varint(len(atom.attrs)) + atom.attrs - raise ValueError(f"unsupported atom kind {atom.kind}") - - -def decode_core(core: bytes) -> bytes: - if not core.startswith(b"WLG2"): - raise ValueError("bad core magic") - cursor = 4 - out = bytearray() - while cursor < len(core): - opcode = core[cursor] - cursor += 1 - if opcode == OP_FIXED_TAG: - tag_id = core[cursor] - cursor += 1 - out.extend(ID_TO_TAG[tag_id]) - continue - if opcode == OP_MOTIF: - motif_id = core[cursor] - cursor += 1 - out.extend(ID_TO_MOTIF[motif_id]) - continue - if opcode == OP_TAG_PAIR: - tag_id = core[cursor] - cursor += 1 - attr_len, cursor = read_varint(core, cursor) - attrs = core[cursor : cursor + attr_len] - cursor += attr_len - payload_len, cursor = read_varint(core, cursor) - payload = core[cursor : cursor + payload_len] - cursor += payload_len - tag = ID_TO_PAIR[tag_id] - out.extend(b"<" + tag + attrs + b">" + payload + b"") - continue - if opcode == OP_TEXT_ATTR_PAIR: - payload_len, cursor = read_varint(core, cursor) - payload = core[cursor : cursor + payload_len] - cursor += payload_len - out.extend(b'' + payload + b"") - continue - if opcode == OP_TAG_ATTR: - tag_id = core[cursor] - cursor += 1 - attr_len, cursor = read_varint(core, cursor) - attrs = core[cursor : cursor + attr_len] - cursor += attr_len - out.extend(b"<" + ID_TO_ATTR[tag_id] + attrs + b">") - continue - - size, cursor = read_varint(core, cursor) - payload = core[cursor : cursor + size] - cursor += size - if opcode == OP_LIT: - out.extend(payload) - elif opcode == OP_WLINK: - out.extend(b"[[" + payload + b"]]") - elif opcode == OP_TEMPLATE: - out.extend(b"{{" + payload + b"}}") - elif opcode == OP_REF: - if payload.endswith(b"\0SELF"): - out.extend(b"") - else: - attrs, body = payload.split(b"\0", 1) - out.extend(b"" + body + b"") - elif opcode == OP_TABLE: - out.extend(payload) - else: - raise ValueError(f"bad opcode {opcode}") - return bytes(out) - - -def match_atom(data: bytes, cursor: int) -> Atom | None: - candidates: list[Atom] = [] - - text_match = TEXT_ATTR_RE.match(data, cursor) - if text_match: - raw = text_match.group(0) - candidates.append(Atom("TEXT_ATTR_PAIR", raw, text_match.group(1), cursor, cursor + len(raw))) - - pair_match = PAIR_RE.match(data, cursor) - if pair_match: - raw = pair_match.group(0) - tag = pair_match.group(1) - attrs = pair_match.group(2) or b"" - payload = pair_match.group(3) - candidates.append(Atom("TAG_PAIR", raw, payload, cursor, cursor + len(raw), PAIR_TO_ID[tag], attrs)) - - attr_match = ATTR_TAG_RE.match(data, cursor) - if attr_match: - raw = attr_match.group(0) - tag = attr_match.group(1) - attrs = attr_match.group(2) or b"" - candidates.append(Atom("TAG_ATTR", raw, b"", cursor, cursor + len(raw), ATTR_TO_ID[tag], attrs)) - - for tag in sorted(FIXED_TAGS, key=len, reverse=True): - if data.startswith(tag, cursor): - candidates.append(Atom("FIXED_TAG", tag, b"", cursor, cursor + len(tag), TAG_TO_ID[tag])) - break - - wlink = WLINK_RE.match(data, cursor) - if wlink: - raw = wlink.group(0) - candidates.append(Atom("WLINK", raw, wlink.group(1), cursor, cursor + len(raw))) - - template = TEMPLATE_RE.match(data, cursor) - if template: - raw = template.group(0) - candidates.append(Atom("TEMPLATE", raw, template.group(1), cursor, cursor + len(raw))) - - ref = REF_RE.match(data, cursor) - if ref: - raw = ref.group(0) - if raw.endswith(b"/>"): - payload = (ref.group(1) or b"") + b"\0SELF" - else: - payload = (ref.group(1) or b"") + b"\0" + (ref.group(2) or b"") - candidates.append(Atom("REF", raw, payload, cursor, cursor + len(raw))) - - for token in (b"{|", b"|}", b"|-"): - if data.startswith(token, cursor): - candidates.append(Atom("TABLE", token, token, cursor, cursor + len(token))) - - for motif in sorted(MOTIFS, key=len, reverse=True): - if data.startswith(motif, cursor): - candidates.append(Atom("MOTIF", motif, b"", cursor, cursor + len(motif), MOTIF_TO_ID[motif])) - break - - if not candidates: - return None - return min(candidates, key=lambda atom: len(atom_encoded(atom)) - len(atom.raw)) - - -def encode(data: bytes) -> tuple[bytes, list[Atom]]: - atoms: list[Atom] = [] - literal = bytearray() - literal_start = 0 - cursor = 0 - - def flush_literal(at: int) -> None: - nonlocal literal, literal_start - if literal: - payload = bytes(literal) - atoms.append(Atom("LIT", payload, payload, literal_start, at)) - literal = bytearray() - - while cursor < len(data): - atom = match_atom(data, cursor) - if atom is None: - if not literal: - literal_start = cursor - literal.append(data[cursor]) - cursor += 1 - continue - flush_literal(cursor) - atoms.append(atom) - cursor = atom.end - - flush_literal(cursor) - return b"WLG2" + b"".join(atom_encoded(atom) for atom in atoms), atoms - - -def entropy(data: bytes) -> float: - if not data: - return 0.0 - counts = {byte: data.count(byte) for byte in set(data)} - n = len(data) - return -sum((count / n) * __import__("math").log2(count / n) for count in counts.values()) - - -def baselines(data: bytes) -> dict[str, int]: - return { - "zlib_9": len(zlib.compress(data, 9)), - "bz2_9": len(bz2.compress(data, 9)), - "lzma_9": len(lzma.compress(data, preset=9)), - } - - -def run_slice(name: str, source_label: str, data: bytes, out_dir: Path) -> dict[str, Any]: - core, atoms = encode(data) - decoded = decode_core(core) - core_path = out_dir / f"{name}.wlg2" - core_path.write_bytes(core) - atom_counts = {kind: sum(1 for atom in atoms if atom.kind == kind) for kind in sorted({atom.kind for atom in atoms})} - header_bytes = 8 - receipt_stub_bytes = 4 * len(atoms) - packet_estimate = len(core) + header_bytes + receipt_stub_bytes - return { - "name": name, - "source": source_label, - "raw_bytes": len(data), - "core_bytes": len(core), - "packet_estimate_bytes": packet_estimate, - "delta_core": len(data) - len(core), - "delta_packet": len(data) - packet_estimate, - "exact_replay": decoded == data, - "atom_count": len(atoms), - "atom_counts": atom_counts, - "raw_sha256": sha256_bytes(data), - "core": rel(core_path), - "core_sha256": sha256_bytes(core), - "entropy_raw": entropy(data), - "entropy_core": entropy(core), - "baselines": baselines(data), - "fixture_status": "ADMIT_FIXTURE" if decoded == data and len(data) > len(core) else "HOLD_DIAGNOSTIC", - } - - -def demo_slices(demo_dir: Path) -> list[tuple[str, str, bytes]]: - out: list[tuple[str, str, bytes]] = [] - if demo_dir.exists(): - for path in sorted(demo_dir.glob("*.raw")): - out.append((path.stem, rel(path), path.read_bytes())) - return out - - -def sample_slices(sample: Path, slice_size: int, max_slices: int) -> list[tuple[str, str, bytes]]: - if not sample.exists(): - return [] - data = sample.read_bytes() - return [ - (f"local_sample_{index:04d}", str(sample), data[index * slice_size : (index + 1) * slice_size]) - for index in range(min(max_slices, (len(data) + slice_size - 1) // slice_size)) - if data[index * slice_size : (index + 1) * slice_size] - ] - - -def aggregate(results: list[dict[str, Any]], dictionary_bytes: int) -> dict[str, Any]: - raw = sum(item["raw_bytes"] for item in results) - core = sum(item["core_bytes"] for item in results) - packet = sum(item["packet_estimate_bytes"] for item in results) - return { - "slice_count": len(results), - "all_exact_replay": all(item["exact_replay"] for item in results), - "raw_bytes": raw, - "core_bytes": core, - "packet_estimate_bytes": packet, - "delta_core": raw - core, - "delta_packet": raw - packet, - "dictionary_bytes": dictionary_bytes, - "delta_global": raw - (packet + dictionary_bytes), - "status_counts": { - status: sum(1 for item in results if item["fixture_status"] == status) - for status in sorted({item["fixture_status"] for item in results}) - }, - } - - -def prior_v1_totals() -> dict[str, Any] | None: - if not V1_RECEIPT.exists(): - return None - receipt = json.loads(V1_RECEIPT.read_text(encoding="utf-8")) - out: dict[str, Any] = {} - for key, run_key in [("demo", "demo"), ("local_sample", "sample_20532")]: - payload = receipt.get("runs", {}).get(run_key, {}).get("summary_payload") - if not payload: - continue - raw = int(payload["total_raw_len"]) - core = int(payload["total_core_len"]) - packet = int(payload["total_local_packet_estimate"]) - out[key] = { - "raw_bytes": raw, - "core_bytes": core, - "packet_estimate_bytes": packet, - "delta_core": raw - core, - "delta_packet": raw - packet, - "all_exact_replay": bool(payload["all_roundtrip_ok"]), - } - return out or None - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# enwiki9 XML Dictionary Probe Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Aggregate", - "", - "| Run | Slices | Exact replay | Raw | Core | Packet est. | Delta core | Delta packet | Delta global |", - "|---|---:|---:|---:|---:|---:|---:|---:|---:|", - ] - for name in ["demo", "local_sample"]: - agg = receipt["aggregates"].get(name) - if not agg: - continue - lines.append( - f"| {name} | {agg['slice_count']} | {agg['all_exact_replay']} | " - f"{agg['raw_bytes']} | {agg['core_bytes']} | {agg['packet_estimate_bytes']} | " - f"{agg['delta_core']} | {agg['delta_packet']} | {agg['delta_global']} |" - ) - if receipt.get("prior_v1_totals"): - lines.extend(["", "## v1 Comparison", ""]) - for name, totals in receipt["prior_v1_totals"].items(): - lines.append( - f"- `{name}` v1 raw/core/packet: " - f"`{totals['raw_bytes']} / {totals['core_bytes']} / {totals['packet_estimate_bytes']}`" - ) - lines.extend(["", "## Dictionary", ""]) - lines.append(f"- Fixed tag entries: `{len(FIXED_TAGS)}`") - lines.append(f"- Pair tag entries: `{len(PAIR_TAGS)}`") - lines.append(f"- Attribute tag entries: `{len(ATTR_TAGS)}`") - lines.append(f"- Motif entries: `{len(MOTIFS)}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def build_receipt(demo_dir: Path, sample: Path, slice_size: int, max_sample_slices: int) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - demo_results = [run_slice(name, source, data, OUT_DIR) for name, source, data in demo_slices(demo_dir)] - sample_results = [run_slice(name, source, data, OUT_DIR) for name, source, data in sample_slices(sample, slice_size, max_sample_slices)] - dictionary_bytes = len(stable_json({"fixed": [tag.hex() for tag in FIXED_TAGS], "pair": [tag.hex() for tag in PAIR_TAGS], "attr": [tag.hex() for tag in ATTR_TAGS], "motif": [tag.hex() for tag in MOTIFS]}).encode("utf-8")) - receipt = { - "schema": "enwiki9_logogram_xml_dict_probe_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "dictionary": { - "fixed_tags": [tag.decode("utf-8", errors="replace") for tag in FIXED_TAGS], - "pair_tags": [tag.decode("ascii") for tag in PAIR_TAGS], - "attribute_tags": [tag.decode("ascii") for tag in ATTR_TAGS], - "motifs": [motif.decode("utf-8", errors="replace") for motif in MOTIFS], - }, - "dictionary_bytes": dictionary_bytes, - "inputs": { - "demo_dir": rel(demo_dir), - "local_sample": str(sample), - "local_sample_bytes": sample.stat().st_size if sample.exists() else None, - "local_sample_sha256": sha256_file(sample) if sample.exists() else None, - "local_sample_claim": "noncanonical local HTML sample; not enwik9", - }, - "runs": { - "demo": demo_results, - "local_sample": sample_results, - }, - "aggregates": { - "demo": aggregate(demo_results, dictionary_bytes), - "local_sample": aggregate(sample_results, dictionary_bytes), - }, - "prior_v1_totals": prior_v1_totals(), - "decision": "HOLD", - "claim_boundary": ( - "Fixed-tag dictionary probe only. Demo slices come from the uploaded " - "targeter bundle; the local sample is a 20,532-byte noncanonical HTML " - "file, not the 1,000,000,000-byte enwik9 corpus. Positive deltas are " - "fixture evidence only and do not establish Hutter/LTCB performance." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Run v2 fixed XML/MediaWiki tag dictionary probe.") - parser.add_argument("--demo-dir", type=Path, default=DEFAULT_BUNDLE_DEMO) - parser.add_argument("--sample", type=Path, default=DEFAULT_LOCAL_SAMPLE) - parser.add_argument("--slice-size", type=int, default=4096) - parser.add_argument("--max-sample-slices", type=int, default=4) - args = parser.parse_args() - - receipt = build_receipt(args.demo_dir, args.sample, args.slice_size, args.max_sample_slices) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "demo": receipt["aggregates"]["demo"], - "local_sample": receipt["aggregates"]["local_sample"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/epoch_eci_metaprobe.py b/4-Infrastructure/shim/epoch_eci_metaprobe.py deleted file mode 100644 index 04fa182c..00000000 --- a/4-Infrastructure/shim/epoch_eci_metaprobe.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -"""Epoch ECI benchmark-data metaprobe for local model-training context. - -This imports Epoch AI's public ECI benchmark table as an external capability -prior. It does not score our local model unless we run those benchmarks. It -summarizes benchmark coverage and emits curriculum records for the physics-math -router to understand which external capability axes exist. -""" - -from __future__ import annotations - -import argparse -import csv -import io -import json -import math -import urllib.request -from collections import Counter -from pathlib import Path -from typing import Any - - -DEFAULT_URL = "https://epoch.ai/data/eci_benchmarks.csv" - - -def fetch_text(url: str, timeout: int = 60) -> str: - req = urllib.request.Request(url, headers={"User-Agent": "Research-Stack-ECI-Metaprobe/0.1"}) - with urllib.request.urlopen(req, timeout=timeout) as resp: - return resp.read().decode("utf-8", errors="replace") - - -def read_rows(text: str) -> list[dict[str, str]]: - reader = csv.DictReader(io.StringIO(text)) - return [{key: str(value or "") for key, value in row.items()} for row in reader] - - -def normalized_entropy(values: list[str]) -> float: - values = [value for value in values if value] - if not values: - return 0.0 - counts = Counter(values) - total = sum(counts.values()) - h = -sum((count / total) * math.log2(count / total) for count in counts.values()) - max_h = math.log2(len(counts)) if len(counts) > 1 else 1.0 - return h / max_h - - -def infer_columns(rows: list[dict[str, str]]) -> dict[str, str | None]: - if not rows: - return {} - columns = list(rows[0].keys()) - lowered = {col.lower(): col for col in columns} - def find(*needles: str) -> str | None: - for col in columns: - lc = col.lower() - if all(needle in lc for needle in needles): - return col - return None - return { - "model": find("model"), - "benchmark": find("benchmark"), - "score": find("score") or lowered.get("performance"), - "release_date": find("date") or find("release"), - "organization": find("organization") or find("developer") or find("creator"), - } - - -def summarize(rows: list[dict[str, str]]) -> dict[str, Any]: - cols = infer_columns(rows) - summary: dict[str, Any] = { - "row_count": len(rows), - "columns": list(rows[0].keys()) if rows else [], - "inferred_columns": cols, - } - for name, col in cols.items(): - if not col: - continue - values = [row.get(col, "") for row in rows] - counts = Counter(value for value in values if value) - summary[f"{name}_unique"] = len(counts) - summary[f"top_{name}s"] = counts.most_common(12) - summary[f"{name}_entropy"] = normalized_entropy(values) - benchmark_name_col = "benchmark" if rows and "benchmark" in rows[0] else None - benchmark_id_col = "benchmark_id" if rows and "benchmark_id" in rows[0] else cols.get("benchmark") - if benchmark_name_col and benchmark_id_col: - id_to_name: dict[str, str] = {} - for row in rows: - benchmark_id = row.get(benchmark_id_col, "") - benchmark_name = row.get(benchmark_name_col, "") - if benchmark_id and benchmark_name and benchmark_id not in id_to_name: - id_to_name[benchmark_id] = benchmark_name - summary["benchmark_id_to_name_sample"] = dict(list(sorted(id_to_name.items()))[:24]) - summary["top_benchmark_names"] = [ - [id_to_name.get(str(benchmark_id), str(benchmark_id)), count] - for benchmark_id, count in summary.get("top_benchmarks", []) - ] - for flag in ("is_math", "is_coding"): - if rows and flag in rows[0]: - true_count = sum(1 for row in rows if row.get(flag, "").lower() == "true") - summary[f"{flag}_true_rows"] = true_count - return summary - - -def curriculum_records(summary: dict[str, Any]) -> list[dict[str, Any]]: - records = [] - top_benchmarks = summary.get("top_benchmark_names") or summary.get("top_benchmarks", []) - prompt = { - "task": "use_external_capability_prior", - "source": "Epoch Capabilities Index benchmark table", - "benchmark_count_hint": summary.get("benchmark_unique"), - "top_benchmarks": top_benchmarks[:8], - "math_rows": summary.get("is_math_true_rows"), - "coding_rows": summary.get("is_coding_true_rows"), - "instruction": "Explain how to use ECI as an external metaprobe axis without replacing local receipts.", - } - answer = { - "selected": True, - "use_as": "external_capability_prior", - "claim_boundary": "benchmark-context-only", - "decision": "Use ECI benchmark domains to choose evaluation axes for the physics-math router; do not treat ECI as proof of local model correctness.", - "metaprobe_rule": "Local SFT, Ollama JSON, Lean evidence, and Tang receipts remain separate audit channels.", - } - records.append({ - "messages": [ - {"role": "system", "content": "You are a physics-math compression router. Return compact JSON with evidence boundaries."}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - }) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--url", default=DEFAULT_URL) - parser.add_argument("--cache", type=Path, default=Path("4-Infrastructure/shim/epoch_eci_benchmarks.csv")) - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/epoch_eci_metaprobe_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/epoch_eci_curriculum.jsonl")) - parser.add_argument("--use-cache", action="store_true") - args = parser.parse_args() - - if args.use_cache and args.cache.exists(): - text = args.cache.read_text(encoding="utf-8", errors="replace") - source_mode = "cache" - else: - text = fetch_text(args.url) - args.cache.parent.mkdir(parents=True, exist_ok=True) - args.cache.write_text(text, encoding="utf-8") - source_mode = "download" - rows = read_rows(text) - summary = summarize(rows) - receipt = { - "schema": "epoch_eci_metaprobe_receipt_v1", - "claim_boundary": "ECI is an external benchmark/capability prior, not proof of local model behavior.", - "source_url": args.url, - "source_mode": source_mode, - "cache": str(args.cache), - "summary": summary, - "lawful": summary.get("row_count", 0) > 0 and bool(summary.get("columns")), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(summary): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/erdos_dag_famm_shm_loop.py b/4-Infrastructure/shim/erdos_dag_famm_shm_loop.py deleted file mode 100644 index 55143d83..00000000 --- a/4-Infrastructure/shim/erdos_dag_famm_shm_loop.py +++ /dev/null @@ -1,197 +0,0 @@ -#!/usr/bin/env python3 -""" -Load the Erdős DAG/FAMM harness through RAM/SHM and feed compact counts to -the local GPU surface. - -Boundary: -- Python source is loaded into RAM and executed as an in-memory module. -- /dev/shm carries source bytes, result JSON, and compact numeric counts. -- The GPU surface receives numeric arrays only; it does not execute Python. -""" - -from __future__ import annotations - -import argparse -import importlib.util -import json -import mmap -import struct -import sys -import types -from pathlib import Path -from typing import Any - - -RESEARCH_STACK = Path(__file__).resolve().parents[2] -HARNESS_PATH = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_dag_famm.py" -DEFAULT_SHM_PATH = Path("/dev/shm/erdos_dag_famm_loop") -DEFAULT_SHM_SIZE = 4 * 1024 * 1024 -HEADER_STRUCT = struct.Struct("<4sIIII") -MAGIC = b"EDF1" - - -def ensure_shm(path: Path, size: int) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("wb") as f: - f.truncate(size) - - -def write_shm(path: Path, size: int, source: bytes, result: bytes, counts: list[int]) -> dict[str, Any]: - counts_blob = struct.pack(f"<{len(counts)}I", *counts) if counts else b"" - header_size = HEADER_STRUCT.size - source_offset = header_size - result_offset = source_offset + len(source) - counts_offset = result_offset + len(result) - used = counts_offset + len(counts_blob) - if used > size: - raise ValueError(f"SHM buffer too small: need {used} bytes, have {size}") - - ensure_shm(path, size) - with path.open("r+b") as f: - mm = mmap.mmap(f.fileno(), size) - mm[:header_size] = HEADER_STRUCT.pack( - MAGIC, - len(source), - len(result), - len(counts), - counts_offset, - ) - mm[source_offset:result_offset] = source - mm[result_offset:counts_offset] = result - mm[counts_offset:used] = counts_blob - mm.flush() - mm.close() - - return { - "shm_path": str(path), - "shm_size": size, - "source_bytes": len(source), - "result_bytes": len(result), - "count_values": len(counts), - "counts_offset": counts_offset, - "used_bytes": used, - } - - -def load_harness_from_ram(path: Path) -> types.ModuleType: - source = path.read_text(encoding="utf-8") - module = types.ModuleType("erdos_dag_famm_ram_module") - module.__file__ = f"" - module.__dict__["__name__"] = "erdos_dag_famm_ram_module" - sys.modules[module.__name__] = module - code = compile(source, module.__file__, "exec") - exec(code, module.__dict__) - return module - - -def load_gpu_surface() -> Any: - gpgpu_dir = RESEARCH_STACK / "5-Applications/tools-scripts/gpgpu" - sys.path.insert(0, str(gpgpu_dir)) - spec = importlib.util.spec_from_file_location("gpgpu_surface", gpgpu_dir / "gpgpu_surface.py") - if spec is None or spec.loader is None: - raise RuntimeError("Could not load gpgpu_surface.py") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module.get_surface() - - -def run_harness( - module: types.ModuleType, - max_powerful: int, - checkpoint_path: Path, - resume: bool, -) -> dict[str, Any]: - dag = module.AuditDag() - famm = module.FammMemory() - checkpoint = module.CheckpointStore(checkpoint_path, resume=resume) - - gyarfas = dag.run("gyarfas_packet_receipts", [], lambda: module.gyarfas_investigation(checkpoint)) - for packet in gyarfas["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - selfridge = dag.run("selfridge_covering_receipts", [], lambda: module.selfridge_investigation(checkpoint)) - for packet in selfridge["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - mollin = dag.run( - "mollin_walsh_powerful_receipts", - [], - lambda: module.mollin_walsh_investigation(max_powerful, checkpoint), - ) - for packet in mollin["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - dag.run( - "dag_famm_synthesis", - [ - "gyarfas_packet_receipts", - "selfridge_covering_receipts", - "mollin_walsh_powerful_receipts", - ], - lambda: { - "status": "synthesis_complete", - "summary": { - "famm_matrix": famm.matrix(), - "promotion_rule": "Only verified packets promote; finite smoke tests remain finite.", - }, - }, - ) - - return { - "dag_receipts": [module.asdict(receipt) for receipt in dag.receipts], - "famm_matrix": famm.matrix(), - "checkpoint": checkpoint.summary(), - "results": { - "erdos_gyarfas": gyarfas, - "erdos_selfridge": selfridge, - "erdos_mollin_walsh": mollin, - }, - } - - -def flatten_counts(matrix: dict[str, dict[str, int]]) -> tuple[list[str], list[int]]: - labels: list[str] = [] - counts: list[int] = [] - for domain in sorted(matrix): - for status in sorted(matrix[domain]): - labels.append(f"{domain}:{status}") - counts.append(int(matrix[domain][status])) - return labels, counts - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--harness", type=Path, default=HARNESS_PATH) - parser.add_argument("--shm-path", type=Path, default=DEFAULT_SHM_PATH) - parser.add_argument("--shm-size", type=int, default=DEFAULT_SHM_SIZE) - parser.add_argument("--max-powerful", type=int, default=5000) - parser.add_argument("--checkpoint", type=Path, default=RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_dag_famm_checkpoint.json") - parser.add_argument("--resume", action="store_true") - args = parser.parse_args() - - source_bytes = args.harness.read_bytes() - module = load_harness_from_ram(args.harness) - result = run_harness(module, args.max_powerful, args.checkpoint, args.resume) - labels, counts = flatten_counts(result["famm_matrix"]) - - surface = load_gpu_surface() - count_mean = surface.mean(counts) - count_std = surface.std(counts) - - result["gpu_surface"] = { - "backend": surface.backend, - "labels": labels, - "counts": counts, - "count_mean": count_mean, - "count_std": count_std, - } - - result_bytes = json.dumps(result, sort_keys=True).encode("utf-8") - shm_meta = write_shm(args.shm_path, args.shm_size, source_bytes, result_bytes, counts) - - print(json.dumps({"shm": shm_meta, "gpu_surface": result["gpu_surface"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/erdos_problems_4primitive_mapping.json b/4-Infrastructure/shim/erdos_problems_4primitive_mapping.json deleted file mode 100644 index 593b1354..00000000 --- a/4-Infrastructure/shim/erdos_problems_4primitive_mapping.json +++ /dev/null @@ -1,171 +0,0 @@ -{ - "primitives": { - "field": { - "equation": "\u03c1(x\u20d7)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "erdos_applications": [ - "Density of primes and prime gaps", - "Arithmetic progressions in dense sets (Szemer\u00e9di)", - "Distribution of integers in additive sets", - "Density in combinatorial structures", - "Erd\u0151s\u2013Tur\u00e1n theorem on additive bases" - ] - }, - "shear": { - "equation": "G = A\u1d40A", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "erdos_applications": [ - "Graph distances and metric embeddings", - "Extremal graph theory (max/min edges)", - "Graph isoperimetry and expansion", - "Erd\u0151s\u2013Stone theorem (extremal function)", - "Graph minor theory and treewidth" - ] - }, - "packet": { - "equation": "\u0393\u1d62", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "erdos_applications": [ - "Ramsey numbers and witness structures", - "Extremal set systems (covering/packing)", - "Erd\u0151s\u2013Ko\u2013Rado theorem", - "Erd\u0151s\u2013Szekeres theorem (monotone subsequences)", - "Erd\u0151s\u2013Ginzburg\u2013Ziv theorem (zero-sum subsets)" - ] - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "erdos_applications": [ - "Graph spectra and eigenvalue bounds", - "Graph partitioning and clustering", - "Random graph eigenvalue distributions", - "Erd\u0151s\u2013R\u00e9nyi model properties", - "Expander graphs and spectral gap" - ] - } - }, - "erdos_problems": { - "high_priority": { - "erdos_turan_conjecture": { - "name": "Erd\u0151s\u2013Tur\u00e1n Conjecture on Additive Bases", - "statement": "If A is an additive basis of order 2 for the natural numbers, then the sum of reciprocals diverges: \u03a3_{a\u2208A} 1/a = \u221e", - "primitive": "field", - "mapping": "Additive basis density = field distribution. Conjecture about density of basis elements.", - "approach": "Treat A as density field \u03c1(n). Analyze spectral decomposition of additive structure. Use field primitive to model basis density and shear primitive to analyze additive deformation.", - "feasibility": "HIGH - Directly about density/distribution, maps cleanly to field primitive" - }, - "erdos_straus_conjecture": { - "name": "Erd\u0151s\u2013Straus Conjecture", - "statement": "For every integer n \u2265 2, the equation 4/n = 1/x + 1/y + 1/z has a solution in positive integers x, y, z", - "primitive": "packet", - "mapping": "Egyptian fraction decomposition = packet encoding. Each solution is a packet (x,y,z) encoding 4/n.", - "approach": "Treat solutions as packets. Use packet primitive to search for encoding space. Spectral analysis of solution space structure.", - "feasibility": "HIGH - Problem about finding encodings/packets, natural fit for packet primitive" - }, - "erdos_conjecture_arithmetic_progressions": { - "name": "Erd\u0151s Conjecture on Arithmetic Progressions", - "statement": "If \u03a3_{a\u2208A} 1/a diverges, then A contains arbitrarily long arithmetic progressions", - "primitive": "field", - "mapping": "Divergent reciprocal sum = high density field. High density implies rich structure (APs).", - "approach": "Use field primitive to model density \u03c1(A). Apply shear primitive to analyze how density deforms under translation (arithmetic progression structure). Spectral decomposition to detect periodic structure.", - "feasibility": "HIGH - Directly about density implying structure, field primitive natural fit" - }, - "erdos_renyi_random_graph": { - "name": "Erd\u0151s\u2013R\u00e9nyi Random Graph Model", - "statement": "Study properties of G(n,p) random graphs. Threshold phenomena for connectivity, giant component, Hamiltonicity", - "primitive": "spectral", - "mapping": "Random graph eigenvalue distribution = spectral basis. Phase transitions = spectral pruning.", - "approach": "Use spectral primitive to analyze eigenvalue distribution of G(n,p). Detect phase transitions via spectral gap. Field primitive for density of edges.", - "feasibility": "VERY HIGH - Well-studied, spectral methods standard, direct mapping" - } - }, - "medium_priority": { - "erdos_ko_rado": { - "name": "Erd\u0151s\u2013Ko\u2013Rado Theorem (extensions)", - "statement": "Maximum size of intersecting families of k-subsets of {1,...,n}", - "primitive": "packet", - "mapping": "Intersecting family = packet collection with witness property (intersection).", - "approach": "Treat each family as packet set. Use packet primitive to analyze encoding constraints. Spectral analysis of intersection graph.", - "feasibility": "MEDIUM - Solved for large n, but extensions open. Packet primitive useful for generalizations" - }, - "erdos_szekeres": { - "name": "Erd\u0151s\u2013Szekeres Theorem (generalizations)", - "statement": "Any sequence of n\u00b2+1 distinct real numbers contains a monotone subsequence of length n+1", - "primitive": "packet", - "mapping": "Monotone subsequence = packet witness. Ramsey-type problem about finding structure.", - "approach": "Use packet primitive to model subsequences as witnesses. Shear primitive for ordering deformation. Spectral analysis of permutation patterns.", - "feasibility": "MEDIUM - Solved, but generalizations and extensions open" - }, - "erdos_ginzburg_ziv": { - "name": "Erd\u0151s\u2013Ginzburg\u2013Ziv Theorem (extensions)", - "statement": "Any 2n-1 integers contain n whose sum is divisible by n", - "primitive": "packet", - "mapping": "Zero-sum subset = packet with witness property (sum = 0 mod n).", - "approach": "Treat subsets as packets. Use packet primitive to search for zero-sum encoding. Spectral analysis of additive structure modulo n.", - "feasibility": "MEDIUM - Solved, but extensions to other groups and structures open" - }, - "erdos_stone": { - "name": "Erd\u0151s\u2013Stone Theorem (extremal function)", - "statement": "For any graph H, ex(n,H) = (1 - 1/\u03c7(H)-1 + o(1))n\u00b2/2 where \u03c7(H) is chromatic number", - "primitive": "shear", - "mapping": "Extremal function = shear metric. Maximum edges without H = deformation constraint.", - "approach": "Use shear primitive to analyze edge density under forbidden subgraph constraint. Spectral analysis of extremal graphs. Field primitive for density.", - "feasibility": "MEDIUM - Solved, but generalizations to hypergraphs open" - } - }, - "exploratory": { - "erdos_faber_lovasz": { - "name": "Erd\u0151s\u2013Faber\u2013Lov\u00e1sz Conjecture", - "statement": "If each edge of a complete graph on n vertices is colored with one of n colors, then there exists a set of n edges with no two sharing a vertex or having the same color", - "primitive": "packet", - "mapping": "Edge coloring = packet encoding. Matching = packet set with witness properties.", - "approach": "Use packet primitive to model edge colorings as encodings. Spectral analysis of intersection graph. Shear for matching constraints.", - "feasibility": "EXPLORATORY - Recently solved (2021), but method could generalize" - }, - "erdos_distinct_distances": { - "name": "Erd\u0151s Distinct Distances Problem", - "statement": "Any set of n points in the plane determines at least n/\u221alog n distinct distances", - "primitive": "shear", - "mapping": "Distance set = shear metric. Point configuration = field manifold.", - "approach": "Use field primitive for point configuration. Shear primitive for distance metric. Spectral analysis of distance distribution.", - "feasibility": "EXPLORATORY - Solved (Guth-Katz), but 4-primitive approach could provide new perspective" - }, - "erdos_moser_problem": { - "name": "Erd\u0151s\u2013Moser Problem", - "statement": "Find all solutions to 1/a + 1/b + 1/c + 1/d + 1/e = 1 in distinct positive integers", - "primitive": "packet", - "mapping": "Egyptian fraction decomposition = packet encoding. Each solution is a 5-tuple packet.", - "approach": "Use packet primitive to search for encoding space. Spectral analysis of solution structure. Field for density of solutions.", - "feasibility": "EXPLORATORY - Solved (only known solution), but method could generalize to other Diophantine equations" - }, - "erdos_hadamard": { - "name": "Erd\u0151s Hadamard Conjecture", - "statement": "There exist Hadamard matrices of order 4k for all k", - "primitive": "spectral", - "mapping": "Hadamard matrix = spectral basis (orthogonal rows/columns). Eigenvalues = \u00b1\u221an.", - "approach": "Use spectral primitive to analyze matrix structure. Field for existence density. Packet for construction methods.", - "feasibility": "EXPLORATORY - Open problem, spectral methods standard in Hadamard matrix theory" - } - } - }, - "primitive_distribution": { - "field": 2, - "shear": 2, - "packet": 6, - "spectral": 2 - }, - "recommended_order": [ - "erdos_renyi_random_graph", - "erdos_turan_conjecture", - "erdos_straus_conjecture", - "erdos_conjecture_arithmetic_progressions" - ], - "insights": { - "packet_dominance": "Packet primitive dominates (5 problems) - many Erd\u0151s problems are about encodings/witnesses", - "spectral_validation": "Erd\u0151s\u2013R\u00e9nyi provides validation point - spectral methods standard", - "field_additive": "Field primitive for additive problems - density and structure", - "shear_extremal": "Shear primitive for extremal problems - metric and deformation", - "cross_domain": "All primitives represented - framework covers diverse Erd\u0151s problem types" - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/erdos_problems_4primitive_mapping.py b/4-Infrastructure/shim/erdos_problems_4primitive_mapping.py deleted file mode 100644 index 15433bfd..00000000 --- a/4-Infrastructure/shim/erdos_problems_4primitive_mapping.py +++ /dev/null @@ -1,305 +0,0 @@ -#!/usr/bin/env python3 -""" -Erdős Problems → 4-Primitive Framework Mapping -============================================== -Identify which Erdős problems could be approached using the -4-primitive framework (field, shear, packet, spectral). -""" - -import json -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -# 4-primitive framework -PRIMITIVES = { - "field": { - "equation": "ρ(x⃗)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "erdos_applications": [ - "Density of primes and prime gaps", - "Arithmetic progressions in dense sets (Szemerédi)", - "Distribution of integers in additive sets", - "Density in combinatorial structures", - "Erdős–Turán theorem on additive bases" - ] - }, - "shear": { - "equation": "G = AᵀA", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "erdos_applications": [ - "Graph distances and metric embeddings", - "Extremal graph theory (max/min edges)", - "Graph isoperimetry and expansion", - "Erdős–Stone theorem (extremal function)", - "Graph minor theory and treewidth" - ] - }, - "packet": { - "equation": "Γᵢ", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "erdos_applications": [ - "Ramsey numbers and witness structures", - "Extremal set systems (covering/packing)", - "Erdős–Ko–Rado theorem", - "Erdős–Szekeres theorem (monotone subsequences)", - "Erdős–Ginzburg–Ziv theorem (zero-sum subsets)" - ] - }, - "spectral": { - "equation": "C = UΛUᵀ", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "erdos_applications": [ - "Graph spectra and eigenvalue bounds", - "Graph partitioning and clustering", - "Random graph eigenvalue distributions", - "Erdős–Rényi model properties", - "Expander graphs and spectral gap" - ] - } -} - -# Erdős problems mapped to 4 primitives -ERDOS_PROBLEMS = { - "high_priority": { - "erdos_turan_conjecture": { - "name": "Erdős–Turán Conjecture on Additive Bases", - "statement": "If A is an additive basis of order 2 for the natural numbers, then the sum of reciprocals diverges: Σ_{a∈A} 1/a = ∞", - "primitive": "field", - "mapping": "Additive basis density = field distribution. Conjecture about density of basis elements.", - "approach": "Treat A as density field ρ(n). Analyze spectral decomposition of additive structure. Use field primitive to model basis density and shear primitive to analyze additive deformation.", - "feasibility": "HIGH - Directly about density/distribution, maps cleanly to field primitive" - }, - "erdos_straus_conjecture": { - "name": "Erdős–Straus Conjecture", - "statement": "For every integer n ≥ 2, the equation 4/n = 1/x + 1/y + 1/z has a solution in positive integers x, y, z", - "primitive": "packet", - "mapping": "Egyptian fraction decomposition = packet encoding. Each solution is a packet (x,y,z) encoding 4/n.", - "approach": "Treat solutions as packets. Use packet primitive to search for encoding space. Spectral analysis of solution space structure.", - "feasibility": "HIGH - Problem about finding encodings/packets, natural fit for packet primitive" - }, - "erdos_conjecture_arithmetic_progressions": { - "name": "Erdős Conjecture on Arithmetic Progressions", - "statement": "If Σ_{a∈A} 1/a diverges, then A contains arbitrarily long arithmetic progressions", - "primitive": "field", - "mapping": "Divergent reciprocal sum = high density field. High density implies rich structure (APs).", - "approach": "Use field primitive to model density ρ(A). Apply shear primitive to analyze how density deforms under translation (arithmetic progression structure). Spectral decomposition to detect periodic structure.", - "feasibility": "HIGH - Directly about density implying structure, field primitive natural fit" - }, - "erdos_renyi_random_graph": { - "name": "Erdős–Rényi Random Graph Model", - "statement": "Study properties of G(n,p) random graphs. Threshold phenomena for connectivity, giant component, Hamiltonicity", - "primitive": "spectral", - "mapping": "Random graph eigenvalue distribution = spectral basis. Phase transitions = spectral pruning.", - "approach": "Use spectral primitive to analyze eigenvalue distribution of G(n,p). Detect phase transitions via spectral gap. Field primitive for density of edges.", - "feasibility": "VERY HIGH - Well-studied, spectral methods standard, direct mapping" - } - }, - "medium_priority": { - "erdos_ko_rado": { - "name": "Erdős–Ko–Rado Theorem (extensions)", - "statement": "Maximum size of intersecting families of k-subsets of {1,...,n}", - "primitive": "packet", - "mapping": "Intersecting family = packet collection with witness property (intersection).", - "approach": "Treat each family as packet set. Use packet primitive to analyze encoding constraints. Spectral analysis of intersection graph.", - "feasibility": "MEDIUM - Solved for large n, but extensions open. Packet primitive useful for generalizations" - }, - "erdos_szekeres": { - "name": "Erdős–Szekeres Theorem (generalizations)", - "statement": "Any sequence of n²+1 distinct real numbers contains a monotone subsequence of length n+1", - "primitive": "packet", - "mapping": "Monotone subsequence = packet witness. Ramsey-type problem about finding structure.", - "approach": "Use packet primitive to model subsequences as witnesses. Shear primitive for ordering deformation. Spectral analysis of permutation patterns.", - "feasibility": "MEDIUM - Solved, but generalizations and extensions open" - }, - "erdos_ginzburg_ziv": { - "name": "Erdős–Ginzburg–Ziv Theorem (extensions)", - "statement": "Any 2n-1 integers contain n whose sum is divisible by n", - "primitive": "packet", - "mapping": "Zero-sum subset = packet with witness property (sum = 0 mod n).", - "approach": "Treat subsets as packets. Use packet primitive to search for zero-sum encoding. Spectral analysis of additive structure modulo n.", - "feasibility": "MEDIUM - Solved, but extensions to other groups and structures open" - }, - "erdos_stone": { - "name": "Erdős–Stone Theorem (extremal function)", - "statement": "For any graph H, ex(n,H) = (1 - 1/χ(H)-1 + o(1))n²/2 where χ(H) is chromatic number", - "primitive": "shear", - "mapping": "Extremal function = shear metric. Maximum edges without H = deformation constraint.", - "approach": "Use shear primitive to analyze edge density under forbidden subgraph constraint. Spectral analysis of extremal graphs. Field primitive for density.", - "feasibility": "MEDIUM - Solved, but generalizations to hypergraphs open" - } - }, - "exploratory": { - "erdos_faber_lovasz": { - "name": "Erdős–Faber–Lovász Conjecture", - "statement": "If each edge of a complete graph on n vertices is colored with one of n colors, then there exists a set of n edges with no two sharing a vertex or having the same color", - "primitive": "packet", - "mapping": "Edge coloring = packet encoding. Matching = packet set with witness properties.", - "approach": "Use packet primitive to model edge colorings as encodings. Spectral analysis of intersection graph. Shear for matching constraints.", - "feasibility": "EXPLORATORY - Recently solved (2021), but method could generalize" - }, - "erdos_distinct_distances": { - "name": "Erdős Distinct Distances Problem", - "statement": "Any set of n points in the plane determines at least n/√log n distinct distances", - "primitive": "shear", - "mapping": "Distance set = shear metric. Point configuration = field manifold.", - "approach": "Use field primitive for point configuration. Shear primitive for distance metric. Spectral analysis of distance distribution.", - "feasibility": "EXPLORATORY - Solved (Guth-Katz), but 4-primitive approach could provide new perspective" - }, - "erdos_moser_problem": { - "name": "Erdős–Moser Problem", - "statement": "Find all solutions to 1/a + 1/b + 1/c + 1/d + 1/e = 1 in distinct positive integers", - "primitive": "packet", - "mapping": "Egyptian fraction decomposition = packet encoding. Each solution is a 5-tuple packet.", - "approach": "Use packet primitive to search for encoding space. Spectral analysis of solution structure. Field for density of solutions.", - "feasibility": "EXPLORATORY - Solved (only known solution), but method could generalize to other Diophantine equations" - }, - "erdos_hadamard": { - "name": "Erdős Hadamard Conjecture", - "statement": "There exist Hadamard matrices of order 4k for all k", - "primitive": "spectral", - "mapping": "Hadamard matrix = spectral basis (orthogonal rows/columns). Eigenvalues = ±√n.", - "approach": "Use spectral primitive to analyze matrix structure. Field for existence density. Packet for construction methods.", - "feasibility": "EXPLORATORY - Open problem, spectral methods standard in Hadamard matrix theory" - } - } -} - - -def analyze_erdos_mapping(): - print("=" * 70) - print(" ERDŐS PROBLEMS → 4-PRIMITIVE FRAMEWORK MAPPING") - print("=" * 70) - - print("\n4-PRIMITIVE FRAMEWORK:") - for prim, data in PRIMITIVES.items(): - print(f"\n{prim.upper()}: {data['equation']}") - print(f" Role: {data['role']}") - print(f" Erdős applications:") - for app in data['erdos_applications']: - print(f" • {app}") - - print("\n" + "=" * 70) - print(" ERDŐS PROBLEMS BY PRIORITY") - print("=" * 70) - - for priority, problems in ERDOS_PROBLEMS.items(): - print(f"\n{priority.upper().replace('_', ' ')} ({len(problems)} problems):") - for prob_id, prob in problems.items(): - prim = prob["primitive"].upper() - print(f"\n • {prob['name']}") - print(f" Primitive: {prim}") - print(f" Statement: {prob['statement'][:100]}...") - print(f" Mapping: {prob['mapping']}") - print(f" Feasibility: {prob['feasibility']}") - - print("\n" + "=" * 70) - print(" PRIMITIVE DISTRIBUTION") - print("=" * 70) - - primitive_counts = {"field": 0, "shear": 0, "packet": 0, "spectral": 0} - for priority, problems in ERDOS_PROBLEMS.items(): - for prob in problems.values(): - primitive_counts[prob["primitive"]] += 1 - - total = sum(primitive_counts.values()) - for prim, count in primitive_counts.items(): - percent = count / total * 100 if total > 0 else 0 - print(f"\n{prim.upper()} ({count} problems, {percent:.1f}%):") - problems_list = [] - for priority, problems in ERDOS_PROBLEMS.items(): - for prob_id, prob in problems.items(): - if prob["primitive"] == prim: - problems_list.append(f"{prob['name']} ({priority})") - for p in problems_list: - print(f" • {p}") - - print("\n" + "=" * 70) - print(" RECOMMENDED APPROACH ORDER") - print("=" * 70) - - print("\n1. Erdős–Rényi Random Graph Model (SPECTRAL)") - print(" - Why: Well-studied, spectral methods standard, direct mapping") - print(" - Approach: Analyze eigenvalue distribution of G(n,p), detect phase transitions via spectral gap") - print(" - Expected outcome: New insights into random graph phase transitions") - - print("\n2. Erdős–Turán Conjecture (FIELD)") - print(" - Why: Directly about density/distribution, maps cleanly to field primitive") - print(" - Approach: Treat additive basis as density field, analyze spectral decomposition of additive structure") - print(" - Expected outcome: New perspective on basis density and additive structure") - - print("\n3. Erdős–Straus Conjecture (PACKET)") - print(" - Why: Problem about finding encodings/packets, natural fit for packet primitive") - print(" - Approach: Treat solutions as packets, search encoding space, spectral analysis of solution structure") - print(" - Expected outcome: Potential progress on long-standing Diophantine problem") - - print("\n4. Erdős Conjecture on Arithmetic Progressions (FIELD)") - print(" - Why: Directly about density implying structure, field primitive natural fit") - print(" - Approach: Model density ρ(A), apply shear to analyze density deformation under translation") - print(" - Expected outcome: New approach to Szemerédi-type theorems") - - print("\n" + "=" * 70) - print(" KEY INSIGHTS") - print("=" * 70) - - print("\n1. Field primitive (2 problems):") - print(" - Erdős–Turán Conjecture: additive basis density") - print(" - Erdős Conjecture on APs: density implies structure") - print(" - Core: density problems, distribution analysis, additive structure") - - print("\n2. Shear primitive (2 problems):") - print(" - Erdős–Stone Theorem: extremal function") - print(" - Erdős Distinct Distances: distance metric") - print(" - Core: extremal problems, metric geometry, graph deformation") - - print("\n3. Packet primitive (5 problems):") - print(" - Erdős–Straus Conjecture: Egyptian fraction encoding") - print(" - Erdős–Ko–Rado: intersecting families") - print(" - Erdős–Szekeres: monotone subsequences") - print(" - Erdős–Ginzburg–Ziv: zero-sum subsets") - print(" - Erdős–Faber–Lovász: edge colorings") - print(" - Core: encoding problems, witness structures, Ramsey-type problems") - - print("\n4. Spectral primitive (2 problems):") - print(" - Erdős–Rényi Random Graph: eigenvalue distribution") - print(" - Erdős Hadamard Conjecture: orthogonal matrices") - print(" - Core: eigenvalue problems, spectral methods, random matrix theory") - - print("\n5. Cross-domain patterns:") - print(" - Packet primitive dominates (5 problems) - many Erdős problems are about encodings/witnesses") - print(" - Field and spectral each have 2 problems - density and eigenvalue problems are common") - print(" - Shear has 2 problems - extremal and metric geometry problems") - print(" - All primitives represented - 4-primitive framework covers diverse Erdős problem types") - - print("\n6. Recommended starting point:") - print(" - Erdős–Rényi Random Graph Model: spectral methods standard, high feasibility") - print(" - This provides a validation of the 4-primitive framework on well-understood problem") - print(" - Success here would validate approach for harder problems (Erdős–Turán, Erdős–Straus)") - - # Save mapping - output_file = RESEARCH_STACK / "4-Infrastructure/shim/erdos_problems_4primitive_mapping.json" - with open(output_file, 'w') as f: - json.dump({ - "primitives": PRIMITIVES, - "erdos_problems": ERDOS_PROBLEMS, - "primitive_distribution": primitive_counts, - "recommended_order": [ - "erdos_renyi_random_graph", - "erdos_turan_conjecture", - "erdos_straus_conjecture", - "erdos_conjecture_arithmetic_progressions" - ], - "insights": { - "packet_dominance": "Packet primitive dominates (5 problems) - many Erdős problems are about encodings/witnesses", - "spectral_validation": "Erdős–Rényi provides validation point - spectral methods standard", - "field_additive": "Field primitive for additive problems - density and structure", - "shear_extremal": "Shear primitive for extremal problems - metric and deformation", - "cross_domain": "All primitives represented - framework covers diverse Erdős problem types" - } - }, f, indent=2) - - print(f"\n✓ Mapping saved to: {output_file}") - - -if __name__ == "__main__": - analyze_erdos_mapping() diff --git a/4-Infrastructure/shim/erdos_surface_orchestrator/.gitignore b/4-Infrastructure/shim/erdos_surface_orchestrator/.gitignore deleted file mode 100644 index 5722b158..00000000 --- a/4-Infrastructure/shim/erdos_surface_orchestrator/.gitignore +++ /dev/null @@ -1,4 +0,0 @@ -target/ -out/*.mp4 -out/*.raw -out/*.flac diff --git a/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.lock b/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.lock deleted file mode 100644 index 2c18b1d6..00000000 --- a/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.lock +++ /dev/null @@ -1,7 +0,0 @@ -# This file is automatically @generated by Cargo. -# It is not intended for manual editing. -version = 4 - -[[package]] -name = "erdos_surface_orchestrator" -version = "0.1.0" diff --git a/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.toml b/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.toml deleted file mode 100644 index 7c31fb87..00000000 --- a/4-Infrastructure/shim/erdos_surface_orchestrator/Cargo.toml +++ /dev/null @@ -1,6 +0,0 @@ -[package] -name = "erdos_surface_orchestrator" -version = "0.1.0" -edition = "2021" - -[dependencies] diff --git a/4-Infrastructure/shim/erdos_surface_orchestrator/src/main.rs b/4-Infrastructure/shim/erdos_surface_orchestrator/src/main.rs deleted file mode 100644 index 63734f5c..00000000 --- a/4-Infrastructure/shim/erdos_surface_orchestrator/src/main.rs +++ /dev/null @@ -1,570 +0,0 @@ -use std::env; -use std::fs::{self, File}; -use std::io::{self, Write}; -use std::path::{Path, PathBuf}; -use std::process::{Command, Stdio}; - -const DEFAULT_SHM_PATH: &str = "/dev/shm/erdos_surface_loop.bin"; -const MAGIC: &[u8; 4] = b"EDS1"; - -#[derive(Debug)] -struct Config { - repo_root: PathBuf, - shm_path: PathBuf, - out_dir: PathBuf, - famm_packages_path: PathBuf, - encode: bool, -} - -#[derive(Debug)] -struct Probe { - name: &'static str, - available: bool, - summary: String, -} - -fn main() -> io::Result<()> { - let config = Config::from_args()?; - fs::create_dir_all(&config.out_dir)?; - - let lean_json = run_lean_surface(&config.repo_root)?; - let famm_packages_json = fs::read_to_string(&config.famm_packages_path) - .unwrap_or_else(|_| "{\"schema\":\"missing_famm_packages\",\"packages\":[]}".to_string()); - let counts = parse_count_values(&lean_json); - if counts.is_empty() { - return Err(io::Error::new( - io::ErrorKind::InvalidData, - "Lean surface emitted no count_values array", - )); - } - - let wgsl = render_wgsl(counts.len()); - let wgsl_path = config.out_dir.join("erdos_counts_reduce.wgsl"); - fs::write(&wgsl_path, wgsl)?; - - let raw_frame_path = config.out_dir.join("erdos_counts_rgba_16x16.raw"); - write_count_frame(&raw_frame_path, &counts)?; - let raw_pcm_path = config.out_dir.join("erdos_dsp_surface_s16le.raw"); - write_dsp_pcm(&raw_pcm_path, &counts)?; - - let mut encoded = Vec::new(); - let h264_path = config.out_dir.join("erdos_counts_h264.mp4"); - let h265_path = config.out_dir.join("erdos_counts_h265.mp4"); - let flac_path = config.out_dir.join("erdos_dsp_surface.flac"); - if config.encode { - encoded.push(encode_frame(&raw_frame_path, &h264_path, "libx264")); - encoded.push(encode_frame(&raw_frame_path, &h265_path, "libx265")); - encoded.push(encode_flac(&raw_pcm_path, &flac_path)); - } - - let probes = vec![ - probe_vulkan(), - probe_ffmpeg_codec("h264_nvenc"), - probe_ffmpeg_codec("hevc_nvenc"), - probe_ffmpeg_codec("h264_vulkan"), - probe_ffmpeg_codec("hevc_vulkan"), - probe_audio_playback(), - probe_audio_capture(), - ]; - - write_shm(&config.shm_path, &lean_json, &counts)?; - - let divider_manifest_path = config.out_dir.join("erdos_surface_dividers.json"); - let divider_manifest = render_surface_dividers( - &config, - &famm_packages_json, - &counts, - &wgsl_path, - &raw_frame_path, - &raw_pcm_path, - &h264_path, - &h265_path, - &flac_path, - ); - fs::write(÷r_manifest_path, divider_manifest.as_bytes())?; - - let report = render_report( - &config, - &lean_json, - &famm_packages_json, - &counts, - &probes, - &encoded, - &wgsl_path, - &raw_frame_path, - &raw_pcm_path, - &flac_path, - ÷r_manifest_path, - ÷r_manifest, - ); - let report_path = config - .out_dir - .join("erdos_surface_orchestrator_report.json"); - fs::write(&report_path, report.as_bytes())?; - - println!("{}", report); - eprintln!("wrote {}", report_path.display()); - Ok(()) -} - -impl Config { - fn from_args() -> io::Result { - let mut repo_root = env::current_dir()?; - let mut shm_path = PathBuf::from(DEFAULT_SHM_PATH); - let mut famm_packages_path: Option = None; - let mut encode = true; - - let mut args = env::args().skip(1); - while let Some(arg) = args.next() { - match arg.as_str() { - "--repo-root" => { - if let Some(value) = args.next() { - repo_root = PathBuf::from(value); - } - } - "--shm-path" => { - if let Some(value) = args.next() { - shm_path = PathBuf::from(value); - } - } - "--famm-packages" => { - if let Some(value) = args.next() { - famm_packages_path = Some(PathBuf::from(value)); - } - } - "--no-encode" => encode = false, - _ => {} - } - } - - let out_dir = repo_root.join("4-Infrastructure/shim/erdos_surface_orchestrator/out"); - let famm_packages_path = famm_packages_path.unwrap_or_else(|| { - repo_root.join("4-Infrastructure/shim/investigate_erdos_dag_famm_packages.json") - }); - Ok(Self { - repo_root, - shm_path, - out_dir, - famm_packages_path, - encode, - }) - } -} - -fn run_lean_surface(repo_root: &Path) -> io::Result { - let lean_root = repo_root.join("0-Core-Formalism/lean/Semantics"); - let output = Command::new("lake") - .args([ - "env", - "lean", - "--run", - "Semantics/Testing/ErdosSurface.lean", - ]) - .current_dir(&lean_root) - .output()?; - - if !output.status.success() { - return Err(io::Error::new( - io::ErrorKind::Other, - format!( - "Lean surface failed: {}", - String::from_utf8_lossy(&output.stderr) - ), - )); - } - - Ok(String::from_utf8_lossy(&output.stdout).trim().to_string()) -} - -fn parse_count_values(json: &str) -> Vec { - let marker = "\"count_values\":["; - let Some(start) = json.find(marker).map(|idx| idx + marker.len()) else { - return Vec::new(); - }; - let Some(end) = json[start..].find(']').map(|idx| start + idx) else { - return Vec::new(); - }; - json[start..end] - .split(',') - .filter_map(|part| part.trim().parse::().ok()) - .collect() -} - -fn render_wgsl(count_len: usize) -> String { - format!( - r#"struct Counts {{ - values: array, -}}; - -struct Output {{ - total: atomic, - max_value: atomic, -}}; - -@group(0) @binding(0) var counts: Counts; -@group(0) @binding(1) var output: Output; - -@compute @workgroup_size(64) -fn main(@builtin(global_invocation_id) gid: vec3) {{ - let i = gid.x; - if (i >= {count_len}u) {{ - return; - }} - let v = counts.values[i]; - atomicAdd(&output.total, v); - loop {{ - let old = atomicLoad(&output.max_value); - if (v <= old) {{ - break; - }} - let exchanged = atomicCompareExchangeWeak(&output.max_value, old, v); - if (exchanged.exchanged) {{ - break; - }} - }} -}} -"# - ) -} - -fn write_count_frame(path: &Path, counts: &[u32]) -> io::Result<()> { - let mut frame = vec![0u8; 16 * 16 * 4]; - for (idx, count) in counts.iter().enumerate() { - let base = idx * 4; - if base + 3 >= frame.len() { - break; - } - let value = (*count).min(255) as u8; - frame[base] = value; - frame[base + 1] = value.saturating_mul(32); - frame[base + 2] = 255u8.saturating_sub(value.saturating_mul(16)); - frame[base + 3] = 255; - } - fs::write(path, frame) -} - -fn write_dsp_pcm(path: &Path, counts: &[u32]) -> io::Result<()> { - let sample_rate = 48_000usize; - let duration_samples = sample_rate / 2; - let mut pcm = Vec::with_capacity(duration_samples * 2); - let base_freqs = [220.0f32, 330.0, 440.0, 660.0, 880.0, 1320.0]; - for i in 0..duration_samples { - let t = i as f32 / sample_rate as f32; - let mut sample = 0.0f32; - for (idx, count) in counts.iter().enumerate() { - let freq = base_freqs[idx % base_freqs.len()] * (1.0 + (*count as f32 / 16.0)); - let amp = 0.12f32 / (idx as f32 + 1.0); - sample += amp * (2.0 * std::f32::consts::PI * freq * t).sin(); - } - let scaled = (sample.clamp(-0.95, 0.95) * i16::MAX as f32) as i16; - pcm.extend_from_slice(&scaled.to_le_bytes()); - } - fs::write(path, pcm) -} - -fn encode_frame(raw_frame: &Path, out_path: &Path, encoder: &str) -> String { - let status = Command::new("ffmpeg") - .args([ - "-y", "-f", "rawvideo", "-pix_fmt", "rgba", "-s", "16x16", "-r", "1", "-i", - ]) - .arg(raw_frame) - .args(["-frames:v", "1", "-c:v", encoder, "-pix_fmt", "yuv420p"]) - .arg(out_path) - .stdout(Stdio::null()) - .stderr(Stdio::null()) - .status(); - - match status { - Ok(s) if s.success() => format!( - "{{\"encoder\":\"{}\",\"status\":\"ok\",\"path\":\"{}\"}}", - encoder, - json_escape(&out_path.display().to_string()) - ), - Ok(s) => format!( - "{{\"encoder\":\"{}\",\"status\":\"failed\",\"code\":{}}}", - encoder, - s.code().unwrap_or(-1) - ), - Err(err) => format!( - "{{\"encoder\":\"{}\",\"status\":\"error\",\"error\":\"{}\"}}", - encoder, - json_escape(&err.to_string()) - ), - } -} - -fn encode_flac(raw_pcm: &Path, out_path: &Path) -> String { - let status = Command::new("ffmpeg") - .args(["-y", "-f", "s16le", "-ar", "48000", "-ac", "1", "-i"]) - .arg(raw_pcm) - .args(["-c:a", "flac"]) - .arg(out_path) - .stdout(Stdio::null()) - .stderr(Stdio::null()) - .status(); - - match status { - Ok(s) if s.success() => format!( - "{{\"encoder\":\"flac\",\"status\":\"ok\",\"path\":\"{}\"}}", - json_escape(&out_path.display().to_string()) - ), - Ok(s) => format!( - "{{\"encoder\":\"flac\",\"status\":\"failed\",\"code\":{}}}", - s.code().unwrap_or(-1) - ), - Err(err) => format!( - "{{\"encoder\":\"flac\",\"status\":\"error\",\"error\":\"{}\"}}", - json_escape(&err.to_string()) - ), - } -} - -fn write_shm(path: &Path, lean_json: &str, counts: &[u32]) -> io::Result<()> { - if let Some(parent) = path.parent() { - fs::create_dir_all(parent)?; - } - let mut file = File::create(path)?; - file.write_all(MAGIC)?; - file.write_all(&(lean_json.len() as u32).to_le_bytes())?; - file.write_all(&(counts.len() as u32).to_le_bytes())?; - file.write_all(lean_json.as_bytes())?; - for count in counts { - file.write_all(&count.to_le_bytes())?; - } - file.flush() -} - -fn count_occurrences(haystack: &str, needle: &str) -> usize { - haystack.match_indices(needle).count() -} - -fn render_surface_dividers( - config: &Config, - famm_packages_json: &str, - counts: &[u32], - wgsl_path: &Path, - raw_frame_path: &Path, - raw_pcm_path: &Path, - h264_path: &Path, - h265_path: &Path, - flac_path: &Path, -) -> String { - let package_count = - count_occurrences(famm_packages_json, "\"schema\":\"erdos_famm_package_v1\"") - + count_occurrences(famm_packages_json, "\"schema\": \"erdos_famm_package_v1\""); - let dsp_motifs = [ - "raw", - "spectral_focus", - "transient_edge", - "hybrid", - "palette_control", - "braid_prior", - "mode_mux_dsp", - ]; - let dsp_motif_json = dsp_motifs - .iter() - .map(|motif| { - format!( - "{{\"motif\":\"{}\",\"package_refs\":{}}}", - motif, - count_occurrences(famm_packages_json, &format!("\"name\": \"{}\"", motif)) - + count_occurrences(famm_packages_json, &format!("\"name\":\"{}\"", motif)) - ) - }) - .collect::>() - .join(","); - - let total_count: u32 = counts.iter().sum(); - let max_count = counts.iter().copied().max().unwrap_or(0); - - format!( - "{{\"schema\":\"erdos_surface_dividers_v1\",\ -\"claim_boundary\":\"dividers route data between surfaces; only Lean/CPU can promote receipts\",\ -\"source_packages\":\"{}\",\ -\"package_count\":{},\ -\"dividers\":[\ -{{\"divider\":\"cpu_lean_trust\",\"input\":\"{}\",\"output\":\"{}\",\"operation\":\"receipt classification and promotion gate\",\"trust\":\"authoritative\"}},\ -{{\"divider\":\"gpu_vulkan_numeric\",\"input\":\"{}\",\"output\":\"{}\",\"operation\":\"u32 count total/max reduction\",\"trust\":\"accelerator; recheck before promotion\",\"count_total\":{},\"count_max\":{}}},\ -{{\"divider\":\"h264_transport\",\"input\":\"{}\",\"output\":\"{}\",\"operation\":\"RGBA package-count frame to H.264 packet telemetry\",\"trust\":\"transport only; hash after decode\"}},\ -{{\"divider\":\"h265_transport\",\"input\":\"{}\",\"output\":\"{}\",\"operation\":\"RGBA package-count frame to H.265 packet telemetry\",\"trust\":\"transport only; hash after decode\"}},\ -{{\"divider\":\"flac_dsp_container\",\"input\":\"{}\",\"output\":\"{}\",\"operation\":\"S16LE DSP motif waveform to FLAC surface container\",\"trust\":\"signal transport only; hash after decode\"}},\ -{{\"divider\":\"dsp_motif_router\",\"input\":\"{}\",\"output\":\"/dev/snd or PipeWire DSP workload\",\"operation\":\"map package motifs to spectral/transient/hybrid/palette/braid/mode-mux surfaces\",\"trust\":\"signal lane only\",\"motifs\":[{}]}}\ -]}}", - json_escape(&config.famm_packages_path.display().to_string()), - package_count, - json_escape(&config.famm_packages_path.display().to_string()), - json_escape(&config.shm_path.display().to_string()), - json_escape(&config.shm_path.display().to_string()), - json_escape(&wgsl_path.display().to_string()), - total_count, - max_count, - json_escape(&raw_frame_path.display().to_string()), - json_escape(&h264_path.display().to_string()), - json_escape(&raw_frame_path.display().to_string()), - json_escape(&h265_path.display().to_string()), - json_escape(&raw_pcm_path.display().to_string()), - json_escape(&flac_path.display().to_string()), - json_escape(&config.famm_packages_path.display().to_string()), - dsp_motif_json - ) -} - -fn probe_vulkan() -> Probe { - match Command::new("vulkaninfo").arg("--summary").output() { - Ok(output) if output.status.success() => { - let text = String::from_utf8_lossy(&output.stdout); - let summary = text - .lines() - .find(|line| line.trim_start().starts_with("deviceName")) - .unwrap_or("vulkan device present") - .trim() - .to_string(); - Probe { - name: "vulkan", - available: true, - summary, - } - } - Ok(_) => Probe { - name: "vulkan", - available: false, - summary: "vulkaninfo returned nonzero".to_string(), - }, - Err(err) => Probe { - name: "vulkan", - available: false, - summary: err.to_string(), - }, - } -} - -fn probe_ffmpeg_codec(codec: &'static str) -> Probe { - match Command::new("ffmpeg") - .args(["-hide_banner", "-encoders"]) - .output() - { - Ok(output) if output.status.success() => { - let text = String::from_utf8_lossy(&output.stdout); - let available = text.contains(codec); - Probe { - name: codec, - available, - summary: if available { - "encoder listed by ffmpeg".to_string() - } else { - "encoder not listed by ffmpeg".to_string() - }, - } - } - Ok(_) => Probe { - name: codec, - available: false, - summary: "ffmpeg -encoders returned nonzero".to_string(), - }, - Err(err) => Probe { - name: codec, - available: false, - summary: err.to_string(), - }, - } -} - -fn probe_audio_playback() -> Probe { - probe_command("audio_playback", "aplay", &["-l"]) -} - -fn probe_audio_capture() -> Probe { - probe_command("audio_capture", "arecord", &["-l"]) -} - -fn probe_command(name: &'static str, cmd: &str, args: &[&str]) -> Probe { - match Command::new(cmd).args(args).output() { - Ok(output) if output.status.success() => { - let text = String::from_utf8_lossy(&output.stdout); - let summary = text.lines().next().unwrap_or("available").to_string(); - Probe { - name, - available: true, - summary, - } - } - Ok(_) => Probe { - name, - available: false, - summary: format!("{} returned nonzero", cmd), - }, - Err(err) => Probe { - name, - available: false, - summary: err.to_string(), - }, - } -} - -fn render_report( - config: &Config, - lean_json: &str, - famm_packages_json: &str, - counts: &[u32], - probes: &[Probe], - encoded: &[String], - wgsl_path: &Path, - raw_frame_path: &Path, - raw_pcm_path: &Path, - flac_path: &Path, - divider_manifest_path: &Path, - divider_manifest: &str, -) -> String { - let probe_json = probes - .iter() - .map(|p| { - format!( - "{{\"name\":\"{}\",\"available\":{},\"summary\":\"{}\"}}", - p.name, - p.available, - json_escape(&p.summary) - ) - }) - .collect::>() - .join(","); - - format!( - "{{\"schema\":\"erdos_surface_orchestrator_v1\",\ -\"claim_boundary\":\"Lean owns receipts; Vulkan/codec/audio lanes accelerate or transport only\",\ -\"shm_path\":\"{}\",\ -\"famm_packages_path\":\"{}\",\ -\"famm_package_count\":{},\ -\"lean_receipt\":{},\ -\"counts\":[{}],\ -\"wgsl_shader\":\"{}\",\ -\"raw_frame\":\"{}\",\ -\"raw_pcm\":\"{}\",\ -\"flac_container\":\"{}\",\ -\"encoded\":[{}],\ -\"surface_dividers_path\":\"{}\",\ -\"surface_dividers\":{},\ -\"probes\":[{}]}}", - json_escape(&config.shm_path.display().to_string()), - json_escape(&config.famm_packages_path.display().to_string()), - count_occurrences(famm_packages_json, "\"schema\":\"erdos_famm_package_v1\"") - + count_occurrences(famm_packages_json, "\"schema\": \"erdos_famm_package_v1\""), - lean_json, - counts - .iter() - .map(u32::to_string) - .collect::>() - .join(","), - json_escape(&wgsl_path.display().to_string()), - json_escape(&raw_frame_path.display().to_string()), - json_escape(&raw_pcm_path.display().to_string()), - json_escape(&flac_path.display().to_string()), - encoded.join(","), - json_escape(÷r_manifest_path.display().to_string()), - divider_manifest, - probe_json - ) -} - -fn json_escape(s: &str) -> String { - s.replace('\\', "\\\\").replace('"', "\\\"") -} diff --git a/4-Infrastructure/shim/external_ai_model_prior_ingest_probe.py b/4-Infrastructure/shim/external_ai_model_prior_ingest_probe.py deleted file mode 100644 index f74dece3..00000000 --- a/4-Infrastructure/shim/external_ai_model_prior_ingest_probe.py +++ /dev/null @@ -1,263 +0,0 @@ -#!/usr/bin/env python3 -"""External AI model prior ingest for Hutter/topology routing. - -This probe records outside model/paper links as priors only. It does not import -weights, run external code, or promote an external result into the stack. The -goal is to keep DMax, NTv3, and PhysMaster as typed HOLD surfaces with explicit -promotion gates. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "external_ai_model_prior_ingest" -RECEIPT = OUT_DIR / "external_ai_model_prior_ingest_receipt.json" -SUMMARY = OUT_DIR / "external_ai_model_prior_ingest.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "External AI Model Prior Ingest.tid" - -PRIORS = [ - { - "prior_id": "DMax.parallel_self_revision", - "title": "DMax: Aggressive Parallel Decoding for dLLMs", - "sources": [ - "https://arxiv.org/pdf/2604.08302", - "https://github.com/czg1225/DMax", - "https://huggingface.co/collections/Zigeng/dmax-training-data", - "https://huggingface.co/collections/Zigeng/dmax-models", - ], - "observed_claim": ( - "DMax reframes dLLM decoding as self-revising embedding-space refinement " - "with on-policy uniform training and soft parallel decoding." - ), - "stack_mapping": [ - "hutter_differential_frame_chain", - "hutter_frame_invariant_root", - "parallel_route_repair", - "self_revision_without_truth_promotion", - ], - "use_as": "decoding_parallelism_prior", - "decision": "HOLD_EXTERNAL_DECODING_PRIOR", - "promotion_gate": ( - "local benchmark required: exact replay, counted bytes, baseline comparison, " - "resource envelope, and deterministic receipt over any adapted decoding path" - ), - }, - { - "prior_id": "NTv3.long_range_sequence_function", - "title": "A foundational model for joint sequence-function multi-species modeling at scale for long-range genomic prediction", - "sources": [ - "https://www.biorxiv.org/content/10.64898/2025.12.22.695963v1", - "https://huggingface.co/spaces/InstaDeepAI/ntv3", - "https://huggingface.co/collections/InstaDeepAI/nucleotide-transformer-v3", - "https://github.com/instadeepai/nucleotide-transformer", - ], - "observed_claim": ( - "NTv3 is presented as a foundation-model surface for long-range genomics " - "and joint sequence-function modeling." - ), - "stack_mapping": [ - "genomic_sequence_prior_surface", - "cross_domain_sequence_function_prior", - "long_range_dependency_probe", - "biological_adapter_hold_lane", - ], - "use_as": "long_range_sequence_function_prior", - "decision": "HOLD_BIORXIV_PREPRINT_PRIOR", - "promotion_gate": ( - "hold until preprint/source/model cards are locally receipted, benchmarked, " - "license-checked, and mapped through a declared biological adapter" - ), - }, - { - "prior_id": "PhysMaster.LANDAU_agent_trace", - "title": "PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research", - "sources": [ - "https://arxiv.org/pdf/2512.19799", - ], - "observed_claim": ( - "PhysMaster presents an LLM-based physics research agent with a LANDAU " - "library/priors/methodology substrate and code-based numerical loops." - ), - "stack_mapping": [ - "forward_foundation_equation_compiler", - "godel_gauntlet", - "equation_atom_receipts", - "research_agent_trace_prior", - ], - "use_as": "research_agent_methodology_prior", - "decision": "HOLD_EXTERNAL_AGENT_PRIOR", - "promotion_gate": ( - "hold until task traces, retrieved-paper roots, numerical artifacts, " - "critic failures, and reproduction receipts are local and inspectable" - ), - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def prior_entry(raw: dict[str, Any]) -> dict[str, Any]: - entry = { - **raw, - "admission_status": "external_prior_hold", - "claim_boundary": "metadata and routing prior only; no imported model/output is admitted", - } - entry["prior_hash"] = hash_obj({k: v for k, v in entry.items() if k != "prior_hash"}) - return entry - - -def build_payload() -> dict[str, Any]: - priors = [prior_entry(item) for item in PRIORS] - payload = { - "schema": "external_ai_model_prior_ingest_v1", - "claim_boundary": ( - "External model/paper prior ingest only. These sources can suggest " - "routing, benchmark, or architecture experiments, but they do not " - "become trusted dependencies until local receipts close." - ), - "priors": priors, - "prior_root": hash_obj([item["prior_hash"] for item in priors]), - "aggregates": { - "prior_count": len(priors), - "source_url_count": sum(len(item["sources"]) for item in priors), - "hold_count": sum(1 for item in priors if item["decision"].startswith("HOLD")), - }, - "decision": "ADMIT_EXTERNAL_AI_MODEL_PRIORS_AS_HOLD", - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "external_ai_model_prior_ingest_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "prior_root": payload["prior_root"], - "aggregates": payload["aggregates"], - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# External AI Model Prior Ingest", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Prior root: `{payload['prior_root']}`", - "", - payload["claim_boundary"], - "", - "## Priors", - "", - "| Prior | Use as | Decision | Promotion gate |", - "|---|---|---|---|", - ] - for item in payload["priors"]: - lines.append(f"| {item['prior_id']} | {item['use_as']} | {item['decision']} | {item['promotion_gate']} |") - lines.extend(["", "## Source URLs", ""]) - for item in payload["priors"]: - lines.append(f"### {item['prior_id']}") - for url in item["sources"]: - lines.append(f"- {url}") - lines.append("") - SUMMARY.write_text("\n".join(lines), encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - text = [ - "title: External AI Model Prior Ingest", - "tags: ExternalPrior Hutter DMax PhysMaster Genomics HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! External AI Model Prior Ingest", - "", - f"Decision: `{receipt['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - f"Prior root: `{payload['prior_root']}`", - "", - "!! Priors", - "", - "| Prior | Use as | Decision |h", - ] - for item in payload["priors"]: - text.append(f"| {item['prior_id']} | {item['use_as']} | {item['decision']} |") - text.extend( - [ - "", - "!! Links", - "", - "* [[Hutter Differential Frame Chain]]", - "* [[Hutter Frame Invariant Root]]", - "* [[Network Topology Model Reweighting]]", - "* [[Underverse Variant Accounting]]", - f"* Receipt: `{rel(RECEIPT)}`", - f"* Summary: `{rel(SUMMARY)}`", - ] - ) - TIDDLER.write_text("\n".join(text) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - (OUT_DIR / "external_ai_model_prior_ingest.json").write_text( - json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "receipt_hash": receipt["receipt_hash"], - "prior_root": payload["prior_root"], - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "aggregates": payload["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/external_sem_entity_diff_probe.py b/4-Infrastructure/shim/external_sem_entity_diff_probe.py deleted file mode 100644 index 13349c5f..00000000 --- a/4-Infrastructure/shim/external_sem_entity_diff_probe.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python3 -"""Probe Ataraxy-Labs/sem as an optional entity-level diff aid. - -No code is imported into the stack. This records whether a caller-provided sem -binary can extract entity surfaces for the current solidification tools and -where it helps close failure tickets. -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT = REPO / "shared-data" / "data" / "stack_solidification" / "external_sem_entity_diff_probe_receipt.json" -DOC = REPO / "6-Documentation" / "docs" / "external_sem_entity_diff_probe_2026-05-09.md" - -TARGETS = [ - "4-Infrastructure/shim/stack_fail_closure_register.py", - "4-Infrastructure/shim/tang9k_uart_beacon_probe.py", - "4-Infrastructure/shim/stack_solidification_audit.py", - "4-Infrastructure/shim/rrc_tri_cycle_audit.py", -] - - -def run(command: list[str], timeout: int = 120) -> dict[str, Any]: - proc = subprocess.run( - command, - cwd=REPO, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - return { - "command": command, - "returncode": proc.returncode, - "stdout_tail": proc.stdout[-4000:], - "stderr_tail": proc.stderr[-4000:], - } - - -def parse_entities(stdout: str) -> list[dict[str, Any]]: - try: - data = json.loads(stdout) - except json.JSONDecodeError: - return [] - if isinstance(data, list): - return data - return [] - - -def build_doc(receipt: dict[str, Any]) -> str: - lines = [ - "# External sem Entity-Diff Probe", - "", - "**Date:** 2026-05-09", - "", - "## Decision", - "", - f"- Status: `{receipt['decision']}`", - "- No implementation code was imported.", - "- No repository dependency was added.", - "- Use only as an optional external audit aid unless explicitly vendored later.", - "", - "## Why It Helps", - "", - "- Primary fit: `FAIL-WORKTREE-SCOPE-007`, because entity-level diffs reduce broad dirty-tree risk.", - "- Secondary fit: `FAIL-RECEIPT-GATE-006`, because entity IDs can be attached to receipt and rollback checklists.", - "- It does not close security, coefficient, topology prediction, or FPGA transport gates by itself.", - "", - "## Tool Boundary", - "", - f"- Source: `{receipt['source_url']}`", - f"- License: `{receipt['license']}`", - f"- Requested binary: `{receipt['sem_binary']}`", - f"- Version result: `{receipt['version']['stdout_tail'].strip()}`", - f"- `/usr/bin/sem` collision: `{receipt['gnu_parallel_collision']['detected']}`", - "", - "## Entity Probe", - "", - ] - for row in receipt["entity_targets"]: - lines.append(f"### `{row['path']}`") - lines.append("") - lines.append(f"- Status: `{row['status']}`") - for entity in row.get("entities", []): - lines.append( - f"- `{entity.get('type')}` `{entity.get('name')}` lines {entity.get('start_line')}-{entity.get('end_line')}" - ) - lines.append("") - lines.append("## Machine Receipt") - lines.append("") - lines.append(f"- `{OUT.relative_to(REPO)}`") - return "\n".join(lines) + "\n" - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--sem-bin", default="/tmp/sem_probe/sem/crates/target/release/sem") - args = parser.parse_args() - - sem_bin = Path(args.sem_bin) - version = run([str(sem_bin), "--version"]) if sem_bin.exists() else { - "command": [str(sem_bin), "--version"], - "returncode": 127, - "stdout_tail": "", - "stderr_tail": "sem binary not found", - } - gnu = run(["/usr/bin/sem", "--version"]) if Path("/usr/bin/sem").exists() else { - "command": ["/usr/bin/sem", "--version"], - "returncode": 127, - "stdout_tail": "", - "stderr_tail": "not found", - } - entity_targets = [] - for target in TARGETS: - if not sem_bin.exists(): - result = {"returncode": 127, "stdout_tail": "", "stderr_tail": "sem binary not found"} - else: - result = run([str(sem_bin), "entities", target, "--json"]) - entities = parse_entities(result.get("stdout_tail", "")) - entity_targets.append( - { - "path": target, - "status": "PASS" if result["returncode"] == 0 and entities else "FAIL", - "entity_count": len(entities), - "entities": entities, - "stderr_tail": result.get("stderr_tail", ""), - } - ) - - receipt = { - "schema": "external_sem_entity_diff_probe_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "source_url": "https://github.com/Ataraxy-Labs/sem", - "license": "MIT OR Apache-2.0", - "sem_binary": str(sem_bin), - "version": version, - "gnu_parallel_collision": { - "detected": "GNU parallel" in gnu.get("stdout_tail", "") or "GNU parallel" in gnu.get("stderr_tail", ""), - "version_probe": gnu, - }, - "entity_targets": entity_targets, - "mapped_fail_tickets": [ - "FAIL-WORKTREE-SCOPE-007", - "FAIL-RECEIPT-GATE-006", - ], - "decision": ( - "OPTIONAL_AUDIT_AID_READY" - if version["returncode"] == 0 and all(row["status"] == "PASS" for row in entity_targets) - else "OPTIONAL_AUDIT_AID_NOT_READY" - ), - "claim_boundary": "External tool orientation only. This does not import sem code, add sem as a dependency, or close any HOLD gate by itself.", - } - OUT.parent.mkdir(parents=True, exist_ok=True) - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - DOC.write_text(build_doc(receipt), encoding="utf-8") - print(json.dumps({"receipt": str(OUT.relative_to(REPO)), "doc": str(DOC.relative_to(REPO)), "decision": receipt["decision"]}, indent=2)) - return 0 if receipt["decision"] == "OPTIONAL_AUDIT_AID_READY" else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/ffmpeg-plugins/Cargo.lock b/4-Infrastructure/shim/ffmpeg-plugins/Cargo.lock deleted file mode 100644 index de2a7e26..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/Cargo.lock +++ /dev/null @@ -1,1509 +0,0 @@ -# This file is automatically @generated by Cargo. -# It is not intended for manual editing. -version = 4 - -[[package]] -name = "allocator-api2" -version = "0.2.21" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "683d7910e743518b0e34f1186f92494becacb047c7b6bf616c96772180fef923" - -[[package]] -name = "android_system_properties" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "819e7219dbd41043ac279b19830f2efc897156490d7fd6ea916720117ee66311" -dependencies = [ - "libc", -] - -[[package]] -name = "anstream" -version = "1.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "824a212faf96e9acacdbd09febd34438f8f711fb84e09a8916013cd7815ca28d" -dependencies = [ - "anstyle", - "anstyle-parse", - "anstyle-query", - "anstyle-wincon", - "colorchoice", - "is_terminal_polyfill", - "utf8parse", -] - -[[package]] -name = "anstyle" -version = "1.0.14" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "940b3a0ca603d1eade50a4846a2afffd5ef57a9feac2c0e2ec2e14f9ead76000" - -[[package]] -name = "anstyle-parse" -version = "1.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "52ce7f38b242319f7cabaa6813055467063ecdc9d355bbb4ce0c68908cd8130e" -dependencies = [ - "utf8parse", -] - -[[package]] -name = "anstyle-query" -version = "1.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "40c48f72fd53cd289104fc64099abca73db4166ad86ea0b4341abe65af83dadc" -dependencies = [ - "windows-sys", -] - -[[package]] -name = "anstyle-wincon" -version = "3.0.11" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "291e6a250ff86cd4a820112fb8898808a366d8f9f58ce16d1f538353ad55747d" -dependencies = [ - "anstyle", - "once_cell_polyfill", - "windows-sys", -] - -[[package]] -name = "arrayvec" -version = "0.7.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7c02d123df017efcdfbd739ef81735b36c5ba83ec3c59c80a9d7ecc718f92e50" - -[[package]] -name = "ash" -version = "0.38.0+1.3.281" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0bb44936d800fea8f016d7f2311c6a4f97aebd5dc86f09906139ec848cf3a46f" -dependencies = [ - "libloading", -] - -[[package]] -name = "autocfg" -version = "1.5.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f2032f911046de80f0a198e0901378627c33f59ea0ac00e363d481118bd70a53" - -[[package]] -name = "bit-set" -version = "0.9.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "34ddef2995421ab6a5c779542c81ee77c115206f4ad9d5a8e05f4ff49716a3dd" -dependencies = [ - "bit-vec", -] - -[[package]] -name = "bit-vec" -version = "0.9.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b71798fca2c1fe1086445a7258a4bc81e6e49dcd24c8d0dd9a1e57395b603f51" - -[[package]] -name = "bitflags" -version = "2.11.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c4512299f36f043ab09a583e57bceb5a5aab7a73db1805848e8fef3c9e8c78b3" - -[[package]] -name = "block2" -version = "0.6.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "cdeb9d870516001442e364c5220d3574d2da8dc765554b4a617230d33fa58ef5" -dependencies = [ - "objc2", -] - -[[package]] -name = "bumpalo" -version = "3.20.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "72f5acc6cb2ba439de613abc23857ec3d78374d8ed5ac84e9d11336e87da8649" - -[[package]] -name = "bytemuck" -version = "1.25.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c8efb64bd706a16a1bdde310ae86b351e4d21550d98d056f22f8a7f7a2183fec" -dependencies = [ - "bytemuck_derive", -] - -[[package]] -name = "bytemuck_derive" -version = "1.10.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f9abbd1bc6865053c427f7198e6af43bfdedc55ab791faed4fbd361d789575ff" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "cfg-if" -version = "1.0.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801" - -[[package]] -name = "cfg_aliases" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "613afe47fcd5fac7ccf1db93babcb082c5994d996f20b8b159f2ad1658eb5724" - -[[package]] -name = "clap" -version = "4.6.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1ddb117e43bbf7dacf0a4190fef4d345b9bad68dfc649cb349e7d17d28428e51" -dependencies = [ - "clap_builder", - "clap_derive", -] - -[[package]] -name = "clap_builder" -version = "4.6.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "714a53001bf66416adb0e2ef5ac857140e7dc3a0c48fb28b2f10762fc4b5069f" -dependencies = [ - "anstream", - "anstyle", - "clap_lex", - "strsim", -] - -[[package]] -name = "clap_derive" -version = "4.6.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f2ce8604710f6733aa641a2b3731eaa1e8b3d9973d5e3565da11800813f997a9" -dependencies = [ - "heck", - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "clap_lex" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c8d4a3bb8b1e0c1050499d1815f5ab16d04f0959b233085fb31653fbfc9d98f9" - -[[package]] -name = "codespan-reporting" -version = "0.13.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "af491d569909a7e4dee0ad7db7f5341fef5c614d5b8ec8cf765732aba3cff681" -dependencies = [ - "serde", - "termcolor", - "unicode-width", -] - -[[package]] -name = "colorchoice" -version = "1.0.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1d07550c9036bf2ae0c684c4297d503f838287c83c53686d05370d0e139ae570" - -[[package]] -name = "crunchy" -version = "0.2.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "460fbee9c2c2f33933d720630a6a0bac33ba7053db5344fac858d4b8952d77d5" - -[[package]] -name = "dispatch2" -version = "0.3.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1e0e367e4e7da84520dedcac1901e4da967309406d1e51017ae1abfb97adbd38" -dependencies = [ - "bitflags", - "objc2", -] - -[[package]] -name = "dlib" -version = "0.5.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ab8ecd87370524b461f8557c119c405552c396ed91fc0a8eec68679eab26f94a" -dependencies = [ - "libloading", -] - -[[package]] -name = "document-features" -version = "0.2.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d4b8a88685455ed29a21542a33abd9cb6510b6b129abadabdcef0f4c55bc8f61" -dependencies = [ - "litrs", -] - -[[package]] -name = "equivalent" -version = "1.0.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "877a4ace8713b0bcf2a4e7eec82529c029f1d0619886d18145fea96c3ffe5c0f" - -[[package]] -name = "ffmpeg-plugins" -version = "0.1.0" -dependencies = [ - "bytemuck", - "pollster", - "serde", - "serde_json", - "wgpu", -] - -[[package]] -name = "foldhash" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d9c4f5dac5e15c24eb999c26181a6ca40b39fe946cbe4c263c7209467bc83af2" - -[[package]] -name = "foldhash" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "77ce24cb58228fbb8aa041425bb1050850ac19177686ea6e0f41a70416f56fdb" - -[[package]] -name = "futures-core" -version = "0.3.32" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7e3450815272ef58cec6d564423f6e755e25379b217b0bc688e295ba24df6b1d" - -[[package]] -name = "futures-task" -version = "0.3.32" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "037711b3d59c33004d3856fbdc83b99d4ff37a24768fa1be9ce3538a1cde4393" - -[[package]] -name = "futures-util" -version = "0.3.32" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "389ca41296e6190b48053de0321d02a77f32f8a5d2461dd38762c0593805c6d6" -dependencies = [ - "futures-core", - "futures-task", - "pin-project-lite", - "slab", -] - -[[package]] -name = "gl_generator" -version = "0.14.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1a95dfc23a2b4a9a2f5ab41d194f8bfda3cabec42af4e39f08c339eb2a0c124d" -dependencies = [ - "khronos_api", - "log", - "xml-rs", -] - -[[package]] -name = "glow" -version = "0.17.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "29038e1c483364cc6bb3cf78feee1816002e127c331a1eec55a4d202b9e1adb5" -dependencies = [ - "js-sys", - "slotmap", - "wasm-bindgen", - "web-sys", -] - -[[package]] -name = "glutin_wgl_sys" -version = "0.6.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2c4ee00b289aba7a9e5306d57c2d05499b2e5dc427f84ac708bd2c090212cf3e" -dependencies = [ - "gl_generator", -] - -[[package]] -name = "gpu-allocator" -version = "0.28.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "51255ea7cfaadb6c5f1528d43e92a82acb2b96c43365989a28b2d44ee38f8795" -dependencies = [ - "ash", - "hashbrown 0.16.1", - "log", - "presser", - "thiserror", - "windows 0.58.0", -] - -[[package]] -name = "gpu-descriptor" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b89c83349105e3732062a895becfc71a8f921bb71ecbbdd8ff99263e3b53a0ca" -dependencies = [ - "bitflags", - "gpu-descriptor-types", - "hashbrown 0.15.5", -] - -[[package]] -name = "gpu-descriptor-types" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "fdf242682df893b86f33a73828fb09ca4b2d3bb6cc95249707fc684d27484b91" -dependencies = [ - "bitflags", -] - -[[package]] -name = "half" -version = "2.7.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6ea2d84b969582b4b1864a92dc5d27cd2b77b622a8d79306834f1be5ba20d84b" -dependencies = [ - "cfg-if", - "crunchy", - "num-traits", - "zerocopy", -] - -[[package]] -name = "hashbrown" -version = "0.15.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9229cfe53dfd69f0609a49f65461bd93001ea1ef889cd5529dd176593f5338a1" -dependencies = [ - "foldhash 0.1.5", -] - -[[package]] -name = "hashbrown" -version = "0.16.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "841d1cc9bed7f9236f321df977030373f4a4163ae1a7dbfe1a51a2c1a51d9100" -dependencies = [ - "allocator-api2", - "equivalent", - "foldhash 0.2.0", -] - -[[package]] -name = "hashbrown" -version = "0.17.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed5909b6e89a2db4456e54cd5f673791d7eca6732202bbf2a9cc504fe2f9b84a" - -[[package]] -name = "heck" -version = "0.5.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2304e00983f87ffb38b55b444b5e3b60a884b5d30c0fca7d82fe33449bbe55ea" - -[[package]] -name = "hexf-parse" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dfa686283ad6dd069f105e5ab091b04c62850d3e4cf5d67debad1933f55023df" - -[[package]] -name = "indexmap" -version = "2.14.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d466e9454f08e4a911e14806c24e16fba1b4c121d1ea474396f396069cf949d9" -dependencies = [ - "equivalent", - "hashbrown 0.17.1", -] - -[[package]] -name = "is_terminal_polyfill" -version = "1.70.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a6cb138bb79a146c1bd460005623e142ef0181e3d0219cb493e02f7d08a35695" - -[[package]] -name = "itoa" -version = "1.0.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8f42a60cbdf9a97f5d2305f08a87dc4e09308d1276d28c869c684d7777685682" - -[[package]] -name = "jni-sys" -version = "0.3.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41a652e1f9b6e0275df1f15b32661cf0d4b78d4d87ddec5e0c3c20f097433258" -dependencies = [ - "jni-sys 0.4.1", -] - -[[package]] -name = "jni-sys" -version = "0.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c6377a88cb3910bee9b0fa88d4f42e1d2da8e79915598f65fb0c7ee14c878af2" -dependencies = [ - "jni-sys-macros", -] - -[[package]] -name = "jni-sys-macros" -version = "0.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "38c0b942f458fe50cdac086d2f946512305e5631e720728f2a61aabcd47a6264" -dependencies = [ - "quote", - "syn", -] - -[[package]] -name = "js-sys" -version = "0.3.99" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "142bc4740e452c1e57ade0cbc129f139c9093e354346f0872ef985f4f5cf5f11" -dependencies = [ - "cfg-if", - "futures-util", - "once_cell", - "wasm-bindgen", -] - -[[package]] -name = "khronos-egl" -version = "6.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6aae1df220ece3c0ada96b8153459b67eebe9ae9212258bb0134ae60416fdf76" -dependencies = [ - "libc", - "libloading", - "pkg-config", -] - -[[package]] -name = "khronos_api" -version = "3.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e2db585e1d738fc771bf08a151420d3ed193d9d895a36df7f6f8a9456b911ddc" - -[[package]] -name = "libc" -version = "0.2.186" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "68ab91017fe16c622486840e4c83c9a37afeff978bd239b5293d61ece587de66" - -[[package]] -name = "libloading" -version = "0.8.9" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d7c4b02199fee7c5d21a5ae7d8cfa79a6ef5bb2fc834d6e9058e89c825efdc55" -dependencies = [ - "cfg-if", - "windows-link", -] - -[[package]] -name = "libm" -version = "0.2.16" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b6d2cec3eae94f9f509c767b45932f1ada8350c4bdb85af2fcab4a3c14807981" - -[[package]] -name = "litrs" -version = "1.0.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "11d3d7f243d5c5a8b9bb5d6dd2b1602c0cb0b9db1621bafc7ed66e35ff9fe092" - -[[package]] -name = "lock_api" -version = "0.4.14" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "224399e74b87b5f3557511d98dff8b14089b3dadafcab6bb93eab67d3aace965" -dependencies = [ - "scopeguard", -] - -[[package]] -name = "log" -version = "0.4.29" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "5e5032e24019045c762d3c0f28f5b6b8bbf38563a65908389bf7978758920897" - -[[package]] -name = "memchr" -version = "2.8.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f8ca58f447f06ed17d5fc4043ce1b10dd205e060fb3ce5b979b8ed8e59ff3f79" - -[[package]] -name = "naga" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0dd91265cc2454558f659b3b4b9640f0ddb8cc6521277f166b8a8c181c898079" -dependencies = [ - "arrayvec", - "bit-set", - "bitflags", - "cfg-if", - "cfg_aliases", - "codespan-reporting", - "half", - "hashbrown 0.16.1", - "hexf-parse", - "indexmap", - "libm", - "log", - "num-traits", - "once_cell", - "rustc-hash", - "spirv", - "thiserror", - "unicode-ident", -] - -[[package]] -name = "ndk-sys" -version = "0.6.0+11769913" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ee6cda3051665f1fb8d9e08fc35c96d5a244fb1be711a03b71118828afc9a873" -dependencies = [ - "jni-sys 0.3.1", -] - -[[package]] -name = "num-traits" -version = "0.2.19" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "071dfc062690e90b734c0b2273ce72ad0ffa95f0c74596bc250dcfd960262841" -dependencies = [ - "autocfg", - "libm", -] - -[[package]] -name = "objc2" -version = "0.6.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3a12a8ed07aefc768292f076dc3ac8c48f3781c8f2d5851dd3d98950e8c5a89f" -dependencies = [ - "objc2-encode", -] - -[[package]] -name = "objc2-core-foundation" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2a180dd8642fa45cdb7dd721cd4c11b1cadd4929ce112ebd8b9f5803cc79d536" -dependencies = [ - "bitflags", - "dispatch2", - "objc2", -] - -[[package]] -name = "objc2-encode" -version = "4.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ef25abbcd74fb2609453eb695bd2f860d389e457f67dc17cafc8b8cbc89d0c33" - -[[package]] -name = "objc2-foundation" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e3e0adef53c21f888deb4fa59fc59f7eb17404926ee8a6f59f5df0fd7f9f3272" -dependencies = [ - "bitflags", - "objc2", - "objc2-core-foundation", -] - -[[package]] -name = "objc2-metal" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a0125f776a10d00af4152d74616409f0d4a2053a6f57fa5b7d6aa2854ac04794" -dependencies = [ - "bitflags", - "block2", - "objc2", - "objc2-foundation", -] - -[[package]] -name = "objc2-quartz-core" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "96c1358452b371bf9f104e21ec536d37a650eb10f7ee379fff67d2e08d537f1f" -dependencies = [ - "bitflags", - "objc2", - "objc2-core-foundation", - "objc2-foundation", - "objc2-metal", -] - -[[package]] -name = "once_cell" -version = "1.21.4" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9f7c3e4beb33f85d45ae3e3a1792185706c8e16d043238c593331cc7cd313b50" - -[[package]] -name = "once_cell_polyfill" -version = "1.70.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "384b8ab6d37215f3c5301a95a4accb5d64aa607f1fcb26a11b5303878451b4fe" - -[[package]] -name = "ordered-float" -version = "5.3.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b7d950ca161dc355eaf28f82b11345ed76c6e1f6eb1f4f4479e0323b9e2fbd0e" -dependencies = [ - "num-traits", -] - -[[package]] -name = "parking_lot" -version = "0.12.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "93857453250e3077bd71ff98b6a65ea6621a19bb0f559a85248955ac12c45a1a" -dependencies = [ - "lock_api", - "parking_lot_core", -] - -[[package]] -name = "parking_lot_core" -version = "0.9.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2621685985a2ebf1c516881c026032ac7deafcda1a2c9b7850dc81e3dfcb64c1" -dependencies = [ - "cfg-if", - "libc", - "redox_syscall", - "smallvec", - "windows-link", -] - -[[package]] -name = "pin-project-lite" -version = "0.2.17" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a89322df9ebe1c1578d689c92318e070967d1042b512afbe49518723f4e6d5cd" - -[[package]] -name = "pkg-config" -version = "0.3.33" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "19f132c84eca552bf34cab8ec81f1c1dcc229b811638f9d283dceabe58c5569e" - -[[package]] -name = "pollster" -version = "0.4.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2f3a9f18d041e6d0e102a0a46750538147e5e8992d3b4873aaafee2520b00ce3" - -[[package]] -name = "portable-atomic" -version = "1.13.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c33a9471896f1c69cecef8d20cbe2f7accd12527ce60845ff44c153bb2a21b49" - -[[package]] -name = "portable-atomic-util" -version = "0.2.7" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c2a106d1259c23fac8e543272398ae0e3c0b8d33c88ed73d0cc71b0f1d902618" -dependencies = [ - "portable-atomic", -] - -[[package]] -name = "presser" -version = "0.3.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e8cf8e6a8aa66ce33f63993ffc4ea4271eb5b0530a9002db8455ea6050c77bfa" - -[[package]] -name = "proc-macro2" -version = "1.0.106" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8fd00f0bb2e90d81d1044c2b32617f68fcb9fa3bb7640c23e9c748e53fb30934" -dependencies = [ - "unicode-ident", -] - -[[package]] -name = "profiling" -version = "1.0.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3d595e54a326bc53c1c197b32d295e14b169e3cfeaa8dc82b529f947fba6bcf5" - -[[package]] -name = "quote" -version = "1.0.45" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41f2619966050689382d2b44f664f4bc593e129785a36d6ee376ddf37259b924" -dependencies = [ - "proc-macro2", -] - -[[package]] -name = "range-alloc" -version = "0.1.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ca45419789ae5a7899559e9512e58ca889e41f04f1f2445e9f4b290ceccd1d08" - -[[package]] -name = "raw-window-handle" -version = "0.6.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "20675572f6f24e9e76ef639bc5552774ed45f1c30e2951e1e99c59888861c539" - -[[package]] -name = "raw-window-metal" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "40d213455a5f1dc59214213c7330e074ddf8114c9a42411eb890c767357ce135" -dependencies = [ - "objc2", - "objc2-core-foundation", - "objc2-foundation", - "objc2-quartz-core", -] - -[[package]] -name = "redox_syscall" -version = "0.5.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ed2bf2547551a7053d6fdfafda3f938979645c44812fbfcda098faae3f1a362d" -dependencies = [ - "bitflags", -] - -[[package]] -name = "renderdoc-sys" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "19b30a45b0cd0bcca8037f3d0dc3421eaf95327a17cad11964fb8179b4fc4832" - -[[package]] -name = "rustc-hash" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "08d43f7aa6b08d49f382cde6a7982047c3426db949b1424bc4b7ec9ae12c6ce2" - -[[package]] -name = "rustversion" -version = "1.0.22" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b39cdef0fa800fc44525c84ccb54a029961a8215f9619753635a9c0d2538d46d" - -[[package]] -name = "s3c-tool" -version = "0.1.0" -dependencies = [ - "bytemuck", - "clap", - "ffmpeg-plugins", - "pollster", - "serde_json", -] - -[[package]] -name = "scopeguard" -version = "1.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "94143f37725109f92c262ed2cf5e59bce7498c01bcc1502d7b9afe439a4e9f49" - -[[package]] -name = "serde" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9a8e94ea7f378bd32cbbd37198a4a91436180c5bb472411e48b5ec2e2124ae9e" -dependencies = [ - "serde_core", - "serde_derive", -] - -[[package]] -name = "serde_core" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "41d385c7d4ca58e59fc732af25c3983b67ac852c1a25000afe1175de458b67ad" -dependencies = [ - "serde_derive", -] - -[[package]] -name = "serde_derive" -version = "1.0.228" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d540f220d3187173da220f885ab66608367b6574e925011a9353e4badda91d79" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "serde_json" -version = "1.0.150" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e8014e44b4736ed0538adeecded0fce2a272f22dc9578a7eb6b2d9993c74cfb9" -dependencies = [ - "itoa", - "memchr", - "serde", - "serde_core", - "zmij", -] - -[[package]] -name = "slab" -version = "0.4.12" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0c790de23124f9ab44544d7ac05d60440adc586479ce501c1d6d7da3cd8c9cf5" - -[[package]] -name = "slotmap" -version = "1.1.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bdd58c3c93c3d278ca835519292445cb4b0d4dc59ccfdf7ceadaab3f8aeb4038" -dependencies = [ - "version_check", -] - -[[package]] -name = "smallvec" -version = "1.15.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "67b1b7a3b5fe4f1376887184045fcf45c69e92af734b7aaddc05fb777b6fbd03" - -[[package]] -name = "spirv" -version = "0.4.0+sdk-1.4.341.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d9571ea910ebd84c86af4b3ed27f9dbdc6ad06f17c5f96146b2b671e2976744f" -dependencies = [ - "bitflags", -] - -[[package]] -name = "static_assertions" -version = "1.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a2eb9349b6444b326872e140eb1cf5e7c522154d69e7a0ffb0fb81c06b37543f" - -[[package]] -name = "strsim" -version = "0.11.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7da8b5736845d9f2fcb837ea5d9e2628564b3b043a70948a3f0b778838c5fb4f" - -[[package]] -name = "syn" -version = "2.0.117" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e665b8803e7b1d2a727f4023456bbbbe74da67099c585258af0ad9c5013b9b99" -dependencies = [ - "proc-macro2", - "quote", - "unicode-ident", -] - -[[package]] -name = "termcolor" -version = "1.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "06794f8f6c5c898b3275aebefa6b8a1cb24cd2c6c79397ab15774837a0bc5755" -dependencies = [ - "winapi-util", -] - -[[package]] -name = "thiserror" -version = "2.0.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4288b5bcbc7920c07a1149a35cf9590a2aa808e0bc1eafaade0b80947865fbc4" -dependencies = [ - "thiserror-impl", -] - -[[package]] -name = "thiserror-impl" -version = "2.0.18" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ebc4ee7f67670e9b64d05fa4253e753e016c6c95ff35b89b7941d6b856dec1d5" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "unicode-ident" -version = "1.0.24" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e6e4313cd5fcd3dad5cafa179702e2b244f760991f45397d14d4ebf38247da75" - -[[package]] -name = "unicode-width" -version = "0.1.14" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7dd6e30e90baa6f72411720665d41d89b9a3d039dc45b8faea1ddd07f617f6af" - -[[package]] -name = "utf8parse" -version = "0.2.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "06abde3611657adf66d383f00b093d7faecc7fa57071cce2578660c9f1010821" - -[[package]] -name = "version_check" -version = "0.9.5" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0b928f33d975fc6ad9f86c8f283853ad26bdd5b10b7f1542aa2fa15e2289105a" - -[[package]] -name = "wasm-bindgen" -version = "0.2.122" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3ed04576f974d2b2fba0f38c51dbc5518011e38c36bf1143164be765528fd409" -dependencies = [ - "cfg-if", - "once_cell", - "rustversion", - "wasm-bindgen-macro", - "wasm-bindgen-shared", -] - -[[package]] -name = "wasm-bindgen-futures" -version = "0.4.72" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9473dbd2991ae90b6291c3c32c30c6187ac49aa32f9905d1cce280ec1e110b0f" -dependencies = [ - "js-sys", - "wasm-bindgen", -] - -[[package]] -name = "wasm-bindgen-macro" -version = "0.2.122" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "916151b09da36bd82f6615cbf3a419e2f0ba23a03c6160e8e92eb6bd4aa1dec6" -dependencies = [ - "quote", - "wasm-bindgen-macro-support", -] - -[[package]] -name = "wasm-bindgen-macro-support" -version = "0.2.122" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "299047362ccbfce148b67ab7e73349f77748e00c8296f9542adfad2ad82c5c5e" -dependencies = [ - "bumpalo", - "proc-macro2", - "quote", - "syn", - "wasm-bindgen-shared", -] - -[[package]] -name = "wasm-bindgen-shared" -version = "0.2.122" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9a929b2c61f11ba3e9bc35b50c1f25cb38e0e892c0c231ae2b8cf78d5dad4437" -dependencies = [ - "unicode-ident", -] - -[[package]] -name = "wayland-sys" -version = "0.31.11" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "d8eab23fefc9e41f8e841df4a9c707e8a8c4ed26e944ef69297184de2785e3be" -dependencies = [ - "dlib", - "log", - "once_cell", - "pkg-config", -] - -[[package]] -name = "web-sys" -version = "0.3.99" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6d621441cfc37b84979402712047321980c178f299193a3589d05b99e8763436" -dependencies = [ - "js-sys", - "wasm-bindgen", -] - -[[package]] -name = "wgpu" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "bb3feacc458f7bee8bc1737149b42b6c731aa461039a4264a67bb6681646b250" -dependencies = [ - "arrayvec", - "bitflags", - "bytemuck", - "cfg-if", - "cfg_aliases", - "document-features", - "hashbrown 0.16.1", - "js-sys", - "log", - "naga", - "parking_lot", - "portable-atomic", - "profiling", - "raw-window-handle", - "smallvec", - "static_assertions", - "wasm-bindgen", - "wasm-bindgen-futures", - "web-sys", - "wgpu-core", - "wgpu-hal", - "wgpu-types", -] - -[[package]] -name = "wgpu-core" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "02da3ad1b568337f25513b317870960ef87073ea0945502e44b864b67a8c77b7" -dependencies = [ - "arrayvec", - "bit-set", - "bit-vec", - "bitflags", - "bytemuck", - "cfg_aliases", - "document-features", - "hashbrown 0.16.1", - "indexmap", - "log", - "naga", - "once_cell", - "parking_lot", - "portable-atomic", - "profiling", - "raw-window-handle", - "rustc-hash", - "smallvec", - "thiserror", - "wgpu-core-deps-apple", - "wgpu-core-deps-emscripten", - "wgpu-core-deps-windows-linux-android", - "wgpu-hal", - "wgpu-naga-bridge", - "wgpu-types", -] - -[[package]] -name = "wgpu-core-deps-apple" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "62e51b5447e144b3dbba4feb01f80f4fa21696fa0cd99afb2c3df1affd6fdb28" -dependencies = [ - "wgpu-hal", -] - -[[package]] -name = "wgpu-core-deps-emscripten" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3487cd6293a963bc5c0c0396f6a2192043c50003c07f4efdccbad3d90ec9d819" -dependencies = [ - "wgpu-hal", -] - -[[package]] -name = "wgpu-core-deps-windows-linux-android" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1bfb01076d0aa08b0ba9bd741e178b5cc440f5abe99d9581323a4c8b5d1a1916" -dependencies = [ - "wgpu-hal", -] - -[[package]] -name = "wgpu-hal" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "31f8e1a9e7a8512f276f7c62e018c7fa8d60954303fed2e5750114332049193f" -dependencies = [ - "android_system_properties", - "arrayvec", - "ash", - "bit-set", - "bitflags", - "block2", - "bytemuck", - "cfg-if", - "cfg_aliases", - "glow", - "glutin_wgl_sys", - "gpu-allocator", - "gpu-descriptor", - "hashbrown 0.16.1", - "js-sys", - "khronos-egl", - "libc", - "libloading", - "log", - "naga", - "ndk-sys", - "objc2", - "objc2-core-foundation", - "objc2-foundation", - "objc2-metal", - "objc2-quartz-core", - "once_cell", - "ordered-float", - "parking_lot", - "portable-atomic", - "portable-atomic-util", - "profiling", - "range-alloc", - "raw-window-handle", - "raw-window-metal", - "renderdoc-sys", - "smallvec", - "thiserror", - "wasm-bindgen", - "wayland-sys", - "web-sys", - "wgpu-naga-bridge", - "wgpu-types", - "windows 0.62.2", - "windows-core 0.62.2", - "windows-result 0.4.1", -] - -[[package]] -name = "wgpu-naga-bridge" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "59c654c483f058800972c3645e95388a7eca31bf9fe1933bc20e036588a0be02" -dependencies = [ - "naga", - "wgpu-types", -] - -[[package]] -name = "wgpu-types" -version = "29.0.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "a9bcc31518a0e9735aefebedb5f7a9ef3ed1c42549c9f4c882fa9060ceaac639" -dependencies = [ - "bitflags", - "bytemuck", - "js-sys", - "log", - "raw-window-handle", - "web-sys", -] - -[[package]] -name = "winapi-util" -version = "0.1.11" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "c2a7b1c03c876122aa43f3020e6c3c3ee5c05081c9a00739faf7503aeba10d22" -dependencies = [ - "windows-sys", -] - -[[package]] -name = "windows" -version = "0.58.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "dd04d41d93c4992d421894c18c8b43496aa748dd4c081bac0dc93eb0489272b6" -dependencies = [ - "windows-core 0.58.0", - "windows-targets", -] - -[[package]] -name = "windows" -version = "0.62.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "527fadee13e0c05939a6a05d5bd6eec6cd2e3dbd648b9f8e447c6518133d8580" -dependencies = [ - "windows-collections", - "windows-core 0.62.2", - "windows-future", - "windows-numerics", -] - -[[package]] -name = "windows-collections" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "23b2d95af1a8a14a3c7367e1ed4fc9c20e0a26e79551b1454d72583c97cc6610" -dependencies = [ - "windows-core 0.62.2", -] - -[[package]] -name = "windows-core" -version = "0.58.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6ba6d44ec8c2591c134257ce647b7ea6b20335bf6379a27dac5f1641fcf59f99" -dependencies = [ - "windows-implement 0.58.0", - "windows-interface 0.58.0", - "windows-result 0.2.0", - "windows-strings 0.1.0", - "windows-targets", -] - -[[package]] -name = "windows-core" -version = "0.62.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b8e83a14d34d0623b51dce9581199302a221863196a1dde71a7663a4c2be9deb" -dependencies = [ - "windows-implement 0.60.2", - "windows-interface 0.59.3", - "windows-link", - "windows-result 0.4.1", - "windows-strings 0.5.1", -] - -[[package]] -name = "windows-future" -version = "0.3.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "e1d6f90251fe18a279739e78025bd6ddc52a7e22f921070ccdc67dde84c605cb" -dependencies = [ - "windows-core 0.62.2", - "windows-link", - "windows-threading", -] - -[[package]] -name = "windows-implement" -version = "0.58.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "2bbd5b46c938e506ecbce286b6628a02171d56153ba733b6c741fc627ec9579b" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "windows-implement" -version = "0.60.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "053e2e040ab57b9dc951b72c264860db7eb3b0200ba345b4e4c3b14f67855ddf" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "windows-interface" -version = "0.58.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "053c4c462dc91d3b1504c6fe5a726dd15e216ba718e84a0e46a88fbe5ded3515" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "windows-interface" -version = "0.59.3" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3f316c4a2570ba26bbec722032c4099d8c8bc095efccdc15688708623367e358" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "windows-link" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "f0805222e57f7521d6a62e36fa9163bc891acd422f971defe97d64e70d0a4fe5" - -[[package]] -name = "windows-numerics" -version = "0.3.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "6e2e40844ac143cdb44aead537bbf727de9b044e107a0f1220392177d15b0f26" -dependencies = [ - "windows-core 0.62.2", - "windows-link", -] - -[[package]] -name = "windows-result" -version = "0.2.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "1d1043d8214f791817bab27572aaa8af63732e11bf84aa21a45a78d6c317ae0e" -dependencies = [ - "windows-targets", -] - -[[package]] -name = "windows-result" -version = "0.4.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7781fa89eaf60850ac3d2da7af8e5242a5ea78d1a11c49bf2910bb5a73853eb5" -dependencies = [ - "windows-link", -] - -[[package]] -name = "windows-strings" -version = "0.1.0" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "4cd9b125c486025df0eabcb585e62173c6c9eddcec5d117d3b6e8c30e2ee4d10" -dependencies = [ - "windows-result 0.2.0", - "windows-targets", -] - -[[package]] -name = "windows-strings" -version = "0.5.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "7837d08f69c77cf6b07689544538e017c1bfcf57e34b4c0ff58e6c2cd3b37091" -dependencies = [ - "windows-link", -] - -[[package]] -name = "windows-sys" -version = "0.61.2" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "ae137229bcbd6cdf0f7b80a31df61766145077ddf49416a728b02cb3921ff3fc" -dependencies = [ - "windows-link", -] - -[[package]] -name = "windows-targets" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "9b724f72796e036ab90c1021d4780d4d3d648aca59e491e6b98e725b84e99973" -dependencies = [ - "windows_aarch64_gnullvm", - "windows_aarch64_msvc", - "windows_i686_gnu", - "windows_i686_gnullvm", - "windows_i686_msvc", - "windows_x86_64_gnu", - "windows_x86_64_gnullvm", - "windows_x86_64_msvc", -] - -[[package]] -name = "windows-threading" -version = "0.2.1" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3949bd5b99cafdf1c7ca86b43ca564028dfe27d66958f2470940f73d86d75b37" -dependencies = [ - "windows-link", -] - -[[package]] -name = "windows_aarch64_gnullvm" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "32a4622180e7a0ec044bb555404c800bc9fd9ec262ec147edd5989ccd0c02cd3" - -[[package]] -name = "windows_aarch64_msvc" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "09ec2a7bb152e2252b53fa7803150007879548bc709c039df7627cabbd05d469" - -[[package]] -name = "windows_i686_gnu" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "8e9b5ad5ab802e97eb8e295ac6720e509ee4c243f69d781394014ebfe8bbfa0b" - -[[package]] -name = "windows_i686_gnullvm" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "0eee52d38c090b3caa76c563b86c3a4bd71ef1a819287c19d586d7334ae8ed66" - -[[package]] -name = "windows_i686_msvc" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "240948bc05c5e7c6dabba28bf89d89ffce3e303022809e73deaefe4f6ec56c66" - -[[package]] -name = "windows_x86_64_gnu" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "147a5c80aabfbf0c7d901cb5895d1de30ef2907eb21fbbab29ca94c5b08b1a78" - -[[package]] -name = "windows_x86_64_gnullvm" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "24d5b23dc417412679681396f2b49f3de8c1473deb516bd34410872eff51ed0d" - -[[package]] -name = "windows_x86_64_msvc" -version = "0.52.6" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "589f6da84c646204747d1270a2a5661ea66ed1cced2631d546fdfb155959f9ec" - -[[package]] -name = "xml-rs" -version = "0.8.28" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "3ae8337f8a065cfc972643663ea4279e04e7256de865aa66fe25cec5fb912d3f" - -[[package]] -name = "zerocopy" -version = "0.8.48" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "eed437bf9d6692032087e337407a86f04cd8d6a16a37199ed57949d415bd68e9" -dependencies = [ - "zerocopy-derive", -] - -[[package]] -name = "zerocopy-derive" -version = "0.8.48" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "70e3cd084b1788766f53af483dd21f93881ff30d7320490ec3ef7526d203bad4" -dependencies = [ - "proc-macro2", - "quote", - "syn", -] - -[[package]] -name = "zmij" -version = "1.0.21" -source = "registry+https://github.com/rust-lang/crates.io-index" -checksum = "b8848ee67ecc8aedbaf3e4122217aff892639231befc6a1b58d29fff4c2cabaa" diff --git a/4-Infrastructure/shim/ffmpeg-plugins/Cargo.toml b/4-Infrastructure/shim/ffmpeg-plugins/Cargo.toml deleted file mode 100644 index e0d624cf..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/Cargo.toml +++ /dev/null @@ -1,26 +0,0 @@ -[workspace] -members = [".", "s3c-tool"] - -[package] -name = "ffmpeg-plugins" -version = "0.1.0" -edition = "2021" - -[lib] -crate-type = ["cdylib", "lib", "staticlib"] - -[dependencies] -bytemuck = { version = "1", features = ["derive"] } -serde = { version = "1", features = ["derive"] } -serde_json = "1" -wgpu = { version = "29", optional = true } -pollster = { version = "0.4", optional = true } - -[features] -default = ["gpu"] -gpu = ["wgpu", "pollster"] - -[profile.release] -opt-level = "z" -lto = true -strip = true diff --git a/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/Cargo.toml b/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/Cargo.toml deleted file mode 100644 index 68085b7b..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/Cargo.toml +++ /dev/null @@ -1,15 +0,0 @@ -[package] -name = "s3c-tool" -version = "0.1.0" -edition = "2021" - -[[bin]] -name = "s3c-tool" -path = "src/main.rs" - -[dependencies] -ffmpeg-plugins = { path = "..", features = ["gpu"] } -bytemuck = { version = "1", features = ["derive"] } -clap = { version = "4", features = ["derive"] } -serde_json = "1" -pollster = "0.4" diff --git a/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/src/main.rs b/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/src/main.rs deleted file mode 100644 index b7ea4f36..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/s3c-tool/src/main.rs +++ /dev/null @@ -1,87 +0,0 @@ -//! s3c-tool — reads i16 PCM from stdin, processes through S3C, outputs JSON. -//! Auto-detects GPU (Vulkan) and falls back to CPU. -use std::io::{self, Read}; -use clap::Parser; - -#[derive(Parser)] -#[command(name = "s3c-tool", about = "S3C shell decomposition of PCM audio (GPU accelerated)")] -struct Args { - #[arg(long, default_value = "4096")] - chunk: usize, - #[arg(long, default_value_t = false)] - aggregate: bool, - #[arg(long, default_value = "48000")] - rate: u32, - /// Force CPU even if GPU is available - #[arg(long, default_value_t = false)] - cpu: bool, -} - -fn main() -> Result<(), String> { - let args = Args::parse(); - - // Read all i16 samples from stdin - let mut stdin = io::stdin().lock(); - let mut all: Vec = Vec::new(); - let mut buf = [0u8; 8192]; - loop { - let n = stdin.read(&mut buf).map_err(|e| format!("read: {}", e))?; - if n == 0 { break; } - let samples: &[i16] = bytemuck::cast_slice(&buf[..n - (n % 2)]); - all.extend_from_slice(samples); - } - if all.is_empty() { return Err("no input data".into()); } - - // Try GPU backend first - let use_gpu = if !args.cpu { - match pollster::block_on(ffmpeg_plugins::gpu::GpuBackend::new()) { - Some(gpu) => { - eprintln!("[s3c] GPU backend ready (Vulkan)"); - let chunk_sz = if args.chunk == 0 { all.len() } else { args.chunk.min(all.len()) }; - let mut results = Vec::new(); - for chunk in all.chunks(chunk_sz) { - let handles = gpu.process_s3c(chunk); - if args.aggregate { - let stats = ffmpeg_plugins::avfilters::s3c::aggregate(&handles); - results.push(serde_json::json!({ - "offset": results.len() * chunk_sz, - "stats": { "n": stats.n_samples, "throats": stats.n_throats, "avg_j": stats.avg_j_score, "max_j": stats.max_j_score, "min_j": stats.min_j_score, "emission_ratio": stats.emission_ratio, } - })); - } else { - for (i, h) in handles.iter().enumerate() { - results.push(serde_json::json!({"i": i, "k": h.k, "a": h.a, "b": h.b, "j": h.j_score, "t": h.throat})); - } - } - } - let out = serde_json::json!({"tool": "s3c-tool", "backend": "gpu", "results": results}); - println!("{}", serde_json::to_string_pretty(&out).unwrap()); - return Ok(()); - } - None => { eprintln!("[s3c] No GPU found, using CPU"); false } - } - } else { false }; - - // CPU fallback - if !use_gpu { - eprintln!("[s3c] CPU backend"); - let chunk_sz = if args.chunk == 0 { all.len() } else { args.chunk.min(all.len()) }; - let mut results = Vec::new(); - for chunk in all.chunks(chunk_sz) { - let handles = ffmpeg_plugins::avfilters::s3c::process_chunk(chunk); - if args.aggregate { - let stats = ffmpeg_plugins::avfilters::s3c::aggregate(&handles); - results.push(serde_json::json!({ - "offset": results.len() * chunk_sz, - "stats": { "n": stats.n_samples, "throats": stats.n_throats, "avg_j": stats.avg_j_score, "max_j": stats.max_j_score, "min_j": stats.min_j_score, "emission_ratio": stats.emission_ratio, } - })); - } else { - for (i, h) in handles.iter().enumerate() { - results.push(serde_json::json!({"i": i, "k": h.k, "a": h.a, "b": h.b, "j": h.j_score, "t": h.throat})); - } - } - } - let out = serde_json::json!({"tool": "s3c-tool", "backend": "cpu", "results": results}); - println!("{}", serde_json::to_string_pretty(&out).unwrap()); - } - Ok(()) -} diff --git a/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/mod.rs b/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/mod.rs deleted file mode 100644 index 9bad104c..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/mod.rs +++ /dev/null @@ -1 +0,0 @@ -pub mod s3c; diff --git a/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/s3c.rs b/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/s3c.rs deleted file mode 100644 index 4e7d346d..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/src/avfilters/s3c.rs +++ /dev/null @@ -1,139 +0,0 @@ -/// S3C shell decomposition — per-sample manifold transform. -/// Every PCM sample n is decomposed into shell coordinates n = k² + a, -/// giving three manifold handles and a J-score. -/// -/// GPU backend via wgpu compute shader when available (concept from light repo). -/// Falls back to CPU scalar path. - -use serde::Serialize; - -#[derive(Debug, Clone, Copy, bytemuck::Zeroable, bytemuck::Pod)] -#[repr(C)] -pub struct S3CShaders { - pub sample: i32, - pub k: u32, - pub a: u32, - pub b: u32, - pub j_score: u32, - pub throat: u32, -} - -impl S3CShaders { - pub fn is_throat(&self) -> bool { self.throat != 0 } -} - -/// CPU decomposition of a single sample -pub fn decompose_cpu(sample: i16) -> S3CShaders { - let n = sample.unsigned_abs() as u32; - let k = (n as f64).sqrt().floor() as u32; - let k1 = k + 1; - let a = n - k * k; - let b = k1 * k1 - n; - - let mass = a as u64 * b as u64; - let mirror = if a > b { a - b } else { b - a }; - let j_score = (mass + mirror as u64 + k as u64) as u32; - - S3CShaders { - sample: sample as i32, - k, - a, - b, - j_score, - throat: if a == b { 1 } else { 0 }, - } -} - -/// Process a chunk of PCM samples on CPU -pub fn process_chunk(samples: &[i16]) -> Vec { - samples.iter().map(|&s| decompose_cpu(s)).collect() -} - -/// GPU compute shader for S3C decomposition (WGSL). -/// Inspired by light repo's photon_hash.wgsl spatial hash kernel. -/// Dispatched as: @workgroup_size(256, 1, 1), n workgroups = ceil(n_samples / 256) -pub const S3C_COMPUTE_SHADER: &str = r#" -struct S3CInput { - sample: i32, -}; - -struct S3COutput { - sample: i32, - k: u32, - a: u32, - b: u32, - j_score: u32, - throat: u32, -}; - -@group(0) @binding(0) var input: array; -@group(0) @binding(1) var output: array; - -@compute @workgroup_size(256, 1, 1) -fn main(@builtin(global_invocation_id) gid: vec3) { - let id = gid.x; - if (id >= arrayLength(&input)) { return; } - let sample = abs(input[id].sample); - let n = u32(sample); - - let kf = sqrt(f32(n)); - let k = u32(floor(kf)); - let k1 = k + 1u; - let a = n - k * k; - let b = k1 * k1 - n; - - let mass = a * b; - let mirror = u32(abs(i32(a) - i32(b))); - let j_score = mass + mirror + k; - - output[id].sample = i32(n); - output[id].k = k; - output[id].a = a; - output[id].b = b; - output[id].j_score = j_score; - output[id].throat = select(0u, 1u, a == b); -} -"#; - -#[derive(Debug, Clone, Serialize)] -pub struct S3CStats { - pub n_samples: usize, - pub n_throats: usize, - pub avg_j_score: f64, - pub max_j_score: u32, - pub min_j_score: u32, - pub emission_ratio: f64, - pub throat_positions: Vec, -} - -pub fn aggregate(handles: &[S3CShaders]) -> S3CStats { - let n = handles.len(); - let throats: Vec = handles.iter().enumerate() - .filter(|(_, h)| h.is_throat()).map(|(i, _)| i).collect(); - let mut total_j: u64 = 0; - let mut max_j: u32 = 0; - let mut min_j: u32 = u32::MAX; - - for h in handles { - total_j += h.j_score as u64; - max_j = max_j.max(h.j_score); - min_j = min_j.min(h.j_score); - } - - let avg = if n > 0 { total_j as f64 / n as f64 } else { 0.0 }; - let var: f64 = handles.iter() - .map(|h| { let d = h.j_score as f64 - avg; d * d }) - .sum::() / n as f64; - let threshold = (avg + var.sqrt()) as u32; - let emitted = handles.iter().filter(|h| h.j_score > threshold).count(); - - S3CStats { - n_samples: n, - n_throats: throats.len(), - avg_j_score: avg, - max_j_score: max_j, - min_j_score: if min_j == u32::MAX { 0 } else { min_j }, - emission_ratio: if n > 0 { emitted as f64 / n as f64 } else { 0.0 }, - throat_positions: throats, - } -} diff --git a/4-Infrastructure/shim/ffmpeg-plugins/src/gpu.rs b/4-Infrastructure/shim/ffmpeg-plugins/src/gpu.rs deleted file mode 100644 index d4f4302c..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/src/gpu.rs +++ /dev/null @@ -1,141 +0,0 @@ -use crate::avfilters::s3c::{S3CShaders, S3C_COMPUTE_SHADER}; -use wgpu::util::DeviceExt; - -pub struct GpuBackend { - device: wgpu::Device, - queue: wgpu::Queue, - pipeline: wgpu::ComputePipeline, - bgl: wgpu::BindGroupLayout, -} - -impl GpuBackend { - pub async fn new() -> Option { - let instance = wgpu::Instance::new(wgpu::InstanceDescriptor { - backends: wgpu::Backends::VULKAN, - flags: wgpu::InstanceFlags::default(), - memory_budget_thresholds: wgpu::MemoryBudgetThresholds::default(), - backend_options: wgpu::BackendOptions::default(), - display: None, - }); - let adapter = match instance.request_adapter(&wgpu::RequestAdapterOptions { - power_preference: wgpu::PowerPreference::HighPerformance, - ..Default::default() - }).await { - Ok(a) => a, - Err(_) => return None, - }; - let (device, queue) = match adapter.request_device( - &wgpu::DeviceDescriptor { - label: None, - required_features: wgpu::Features::empty(), - required_limits: wgpu::Limits::default(), - experimental_features: wgpu::ExperimentalFeatures::default(), - memory_hints: wgpu::MemoryHints::default(), - trace: wgpu::Trace::Off, - }, - ).await { - Ok(dq) => dq, - _ => return None, - }; - - let shader = device.create_shader_module(wgpu::ShaderModuleDescriptor { - label: None, - source: wgpu::ShaderSource::Wgsl(std::borrow::Cow::Borrowed(S3C_COMPUTE_SHADER)), - }); - - let bgl = device.create_bind_group_layout(&wgpu::BindGroupLayoutDescriptor { - label: None, - entries: &[ - wgpu::BindGroupLayoutEntry { - binding: 0, - visibility: wgpu::ShaderStages::COMPUTE, - ty: wgpu::BindingType::Buffer { - ty: wgpu::BufferBindingType::Storage { read_only: true }, - has_dynamic_offset: false, - min_binding_size: None, - }, - count: None, - }, - wgpu::BindGroupLayoutEntry { - binding: 1, - visibility: wgpu::ShaderStages::COMPUTE, - ty: wgpu::BindingType::Buffer { - ty: wgpu::BufferBindingType::Storage { read_only: false }, - has_dynamic_offset: false, - min_binding_size: None, - }, - count: None, - }, - ], - }); - - let pl = device.create_pipeline_layout(&wgpu::PipelineLayoutDescriptor { - label: None, - bind_group_layouts: &[Some(&bgl)], - immediate_size: 0, - }); - - let pipeline = device.create_compute_pipeline(&wgpu::ComputePipelineDescriptor { - label: None, - layout: Some(&pl), - module: &shader, - entry_point: Some("main"), - compilation_options: wgpu::PipelineCompilationOptions::default(), - cache: None, - }); - - Some(Self { device, queue, pipeline, bgl }) - } - - pub fn process_s3c(&self, samples: &[i16]) -> Vec { - let n = samples.len() as u32; - if n == 0 { return vec![]; } - let g = 256u32; - let ng = (n + g - 1) / g; - let osz = n as u64 * std::mem::size_of::() as u64; - - let in_u32: Vec = samples.iter().map(|&s| s as i32 as u32).collect(); - let ib = self.device.create_buffer_init(&wgpu::util::BufferInitDescriptor { - label: None, contents: bytemuck::cast_slice(&in_u32), - usage: wgpu::BufferUsages::STORAGE, - }); - let ob = self.device.create_buffer(&wgpu::BufferDescriptor { - label: None, size: osz, - usage: wgpu::BufferUsages::STORAGE | wgpu::BufferUsages::COPY_SRC, - mapped_at_creation: false, - }); - let sb = self.device.create_buffer(&wgpu::BufferDescriptor { - label: None, size: osz, - usage: wgpu::BufferUsages::COPY_DST | wgpu::BufferUsages::MAP_READ, - mapped_at_creation: false, - }); - - let bg = self.device.create_bind_group(&wgpu::BindGroupDescriptor { - label: None, layout: &self.bgl, - entries: &[ - wgpu::BindGroupEntry { binding: 0, resource: ib.as_entire_binding() }, - wgpu::BindGroupEntry { binding: 1, resource: ob.as_entire_binding() }, - ], - }); - - let mut enc = self.device.create_command_encoder(&wgpu::CommandEncoderDescriptor { label: None }); - { - let mut cp = enc.begin_compute_pass(&wgpu::ComputePassDescriptor { label: None, timestamp_writes: None }); - cp.set_pipeline(&self.pipeline); - cp.set_bind_group(0, &bg, &[]); - cp.dispatch_workgroups(ng, 1, 1); - } - enc.copy_buffer_to_buffer(&ob, 0, &sb, 0, osz); - self.queue.submit(std::iter::once(enc.finish())); - - let sl = sb.slice(..); - let (tx, rx) = std::sync::mpsc::channel(); - sl.map_async(wgpu::MapMode::Read, move |v| { let _ = tx.send(v); }); - self.device.poll(wgpu::PollType::Wait { submission_index: None, timeout: None }).ok(); - let _ = rx.recv(); - let d = sl.get_mapped_range(); - let r: Vec = bytemuck::cast_slice(&d).to_vec(); - drop(d); sb.unmap(); - r - } -} diff --git a/4-Infrastructure/shim/ffmpeg-plugins/src/lib.rs b/4-Infrastructure/shim/ffmpeg-plugins/src/lib.rs deleted file mode 100644 index 30563706..00000000 --- a/4-Infrastructure/shim/ffmpeg-plugins/src/lib.rs +++ /dev/null @@ -1,145 +0,0 @@ -pub mod avfilters; -#[cfg(feature = "gpu")] -pub mod gpu; - -use std::ffi::CString; -use std::os::raw::c_char; - -pub const AVMEDIA_TYPE_AUDIO: i32 = 1; -pub const AV_SAMPLE_FMT_S16: i32 = 1; -pub const AV_SAMPLE_FMT_FLTP: i32 = 8; -pub const AV_OPT_TYPE_INT: i32 = 0; -pub const AV_OPT_TYPE_STRING: i32 = 3; - -#[repr(C)] -pub struct AVFilterPad { - pub name: *const c_char, - pub media_type: i32, -} - -#[repr(C)] -pub struct AVFilter { - pub name: *const c_char, - pub description: *const c_char, - _reserved: [u8; 64], -} - -static FILTERS: &[(&str, &str)] = &[ - ("s3c", "S3C shell decomposition — manifold handles from PCM audio"), - ("waveprobe", "Waveprobe QUBO projection — attractor ID from audio frames"), - ("rgflow", "RGFlow — coarse-graining persistence detector"), -]; - -/// Returns a JSON manifest of available filters. -#[no_mangle] -pub extern "C" fn ffmpeg_plugins_manifest() -> *mut c_char { - let manifest = serde_json::json!({ - "plugin": "research-stack-ffmpeg-plugins", - "version": "0.1.0", - "filters": FILTERS.iter().map(|(name, desc)| { - serde_json::json!({"name": name, "description": desc}) - }).collect::>(), - }); - CString::new(manifest.to_string()) - .unwrap_or_default() - .into_raw() -} - -/// Free a string returned by the plugin. -#[no_mangle] -pub extern "C" fn ffmpeg_plugins_free_string(s: *mut c_char) { - if !s.is_null() { - unsafe { let _ = CString::from_raw(s); } - } -} - -/// Process S3C on a buffer of i16 samples. Returns JSON with per-frame stats. -#[no_mangle] -pub extern "C" fn s3c_process( - samples: *const i16, - count: i32, - out_json: *mut *mut c_char, -) -> i32 { - if samples.is_null() || count <= 0 { - return -1; - } - let buf = unsafe { std::slice::from_raw_parts(samples, count as usize) }; - let handles = avfilters::s3c::process_chunk(buf); - let stats = avfilters::s3c::aggregate(&handles); - - let result = serde_json::json!({ - "n": stats.n_samples, - "throats": stats.n_throats, - "avg_j": stats.avg_j_score, - "max_j": stats.max_j_score, - "min_j": stats.min_j_score, - "emission_ratio": stats.emission_ratio, - "throat_positions": stats.throat_positions.iter().take(20).cloned().collect::>(), - }); - - let json_str = CString::new(result.to_string()).unwrap_or_default(); - unsafe { *out_json = json_str.into_raw(); } - 0 -} - -#[cfg(test)] -mod tests { - use crate::avfilters::s3c; - - #[test] - fn test_s3c_decompose_zero() { - let h = s3c::decompose_cpu(0); - assert_eq!(h.k, 0); - assert_eq!(h.a, 0); - assert_eq!(h.b, 1); - assert_eq!(h.j_score, 1); - assert_eq!(h.throat, 0); - } - - #[test] - fn test_s3c_decompose_one() { - let h = s3c::decompose_cpu(1); - assert_eq!(h.k, 1); - assert_eq!(h.a, 0); - assert_eq!(h.b, 3); - assert_eq!(h.j_score, 4); - } - - #[test] - fn test_s3c_decompose_perfect_square() { - let h = s3c::decompose_cpu(144); - assert_eq!(h.k, 12); - assert_eq!(h.a, 0); - assert_eq!(h.b, 25); - } - - #[test] - fn test_s3c_decompose_negative() { - let pos = s3c::decompose_cpu(128); - let neg = s3c::decompose_cpu(-128); - assert_eq!(pos.j_score, neg.j_score); - } - - #[test] - fn test_s3c_process_chunk() { - let samples: Vec = vec![0, 1, 16, 100, 255, 1000, 32767]; - let handles = s3c::process_chunk(&samples); - assert_eq!(handles.len(), 7); - let stats = s3c::aggregate(&handles); - assert_eq!(stats.n_samples, 7); - assert!(stats.avg_j_score > 0.0); - } - - #[test] - fn test_ffi_s3c_process() { - let samples: Vec = (0..100).collect(); - let mut out: *mut std::os::raw::c_char = std::ptr::null_mut(); - let rc = crate::s3c_process(samples.as_ptr(), samples.len() as i32, &mut out); - assert_eq!(rc, 0); - assert!(!out.is_null()); - let result = unsafe { std::ffi::CStr::from_ptr(out) }.to_str().unwrap(); - assert!(result.contains("n")); - assert!(result.contains("throats")); - crate::ffmpeg_plugins_free_string(out); - } -} diff --git a/4-Infrastructure/shim/finance_claim_codec_requirements.txt b/4-Infrastructure/shim/finance_claim_codec_requirements.txt deleted file mode 100644 index 6dcb175c..00000000 --- a/4-Infrastructure/shim/finance_claim_codec_requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -cbor2==6.0.1 -msgpack==1.1.2 -protobuf==7.34.1 -flatbuffers==25.12.19 -nanopb==0.4.9.1 diff --git a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_lut_render.typ b/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_lut_render.typ deleted file mode 100644 index 31163d58..00000000 --- a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_lut_render.typ +++ /dev/null @@ -1,20 +0,0 @@ -#set page(width: 8.5in, height: 11in, margin: 0.75in) -#set text(size: 9pt) -= Finance Claim LUT Render Preview - -#table( - columns: (1.1fr, 3fr, 1.2fr), - [Packet], [FCL1 Render Preview], [Hash], - [claim-0001], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:IL R:MED S:ACH H:LIT`], [`ec920bd8511b`], - [claim-0002], [`I:LIT C:LIT P:LIT $:LIT U:USDC r:LIT T:LIT K:LIT J:DE R:LOW S:CHAIN H:LIT`], [`a6eade8ffd09`], - [claim-0003], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:IL R:HIGH S:WIRE H:LIT`], [`94de689977e3`], - [claim-0004], [`I:LIT C:LIT P:LIT $:LIT U:EUR r:LIT T:LIT K:LIT J:DE R:MED S:WIRE H:LIT`], [`d4ef12dbcd22`], - [claim-0005], [`I:LIT C:LIT P:LIT $:LIT U:USDC r:LIT T:LIT K:LIT J:DE R:LOW S:CHAIN H:LIT`], [`ddd9311b96fd`], - [claim-0006], [`I:LIT C:LIT P:LIT $:LIT U:EUR r:LIT T:LIT K:LIT J:IL R:LOW S:ACH H:LIT`], [`0fb6d960ab8a`], - [claim-0007], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:DE R:HIGH S:WIRE H:LIT`], [`7916a0d24d6b`], - [claim-0008], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:DE R:MED S:WIRE H:LIT`], [`0e57b21ab6eb`], - [claim-0009], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:IL R:LOW S:ACH H:LIT`], [`7d091e7d363c`], - [claim-0010], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:DE R:MED S:WIRE H:LIT`], [`d3cb4596808c`], - [claim-0011], [`I:LIT C:LIT P:LIT $:LIT U:USD r:LIT T:LIT K:LIT J:IL R:HIGH S:CHAIN H:LIT`], [`0d1c2cb6a87f`], - [claim-0012], [`I:LIT C:LIT P:LIT $:LIT U:LIT r:LIT T:LIT K:LIT J:LIT R:MED S:LIT H:LIT`], [`079f77be3346`], -) diff --git a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.fbs b/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.fbs deleted file mode 100644 index 146773f1..00000000 --- a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.fbs +++ /dev/null @@ -1,18 +0,0 @@ -namespace ResearchStack.Finance; - -table FinancialClaimPacket { - id:string; - claimant:string; - counterparty:string; - principal:string; - currency:string; - rate:string; - maturity:string; - collateral:string; - jurisdiction:string; - risk:string; - settlement_path:string; - receipt:string; -} - -root_type FinancialClaimPacket; diff --git a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.options b/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.options deleted file mode 100644 index ca74b039..00000000 --- a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.options +++ /dev/null @@ -1,12 +0,0 @@ -research_stack.finance.FinancialClaimPacket.id max_size:64 -research_stack.finance.FinancialClaimPacket.claimant max_size:128 -research_stack.finance.FinancialClaimPacket.counterparty max_size:128 -research_stack.finance.FinancialClaimPacket.principal max_size:32 -research_stack.finance.FinancialClaimPacket.currency max_size:16 -research_stack.finance.FinancialClaimPacket.rate max_size:32 -research_stack.finance.FinancialClaimPacket.maturity max_size:32 -research_stack.finance.FinancialClaimPacket.collateral max_size:160 -research_stack.finance.FinancialClaimPacket.jurisdiction max_size:32 -research_stack.finance.FinancialClaimPacket.risk max_size:32 -research_stack.finance.FinancialClaimPacket.settlement_path max_size:32 -research_stack.finance.FinancialClaimPacket.receipt max_size:160 diff --git a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.proto b/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.proto deleted file mode 100644 index 89ddf03c..00000000 --- a/4-Infrastructure/shim/finance_claim_lut_fixtures/finance_claim_packet.proto +++ /dev/null @@ -1,18 +0,0 @@ -syntax = "proto3"; - -package research_stack.finance; - -message FinancialClaimPacket { - string id = 1; - string claimant = 2; - string counterparty = 3; - string principal = 4; - string currency = 5; - string rate = 6; - string maturity = 7; - string collateral = 8; - string jurisdiction = 9; - string risk = 10; - string settlement_path = 11; - string receipt = 12; -} diff --git a/4-Infrastructure/shim/finance_claim_lut_harness.py b/4-Infrastructure/shim/finance_claim_lut_harness.py deleted file mode 100644 index 2d42f107..00000000 --- a/4-Infrastructure/shim/finance_claim_lut_harness.py +++ /dev/null @@ -1,1163 +0,0 @@ -#!/usr/bin/env python3 -"""Reversible FinancialClaimPacket LUT compression harness. - -JSON is the human/audit envelope. The embedded candidate is a paired binary -surface: - - FCL1: compact field/value token tape - FCS1: typed literal sidecar - -Both must decode back to the canonical FinancialClaimPacket JSON bytes exactly. -""" - -from __future__ import annotations - -import argparse -import binascii -import hashlib -import importlib.util -import json -import shutil -import subprocess -import tempfile -import zlib -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -FIXTURE_DIR = SHIM / "finance_claim_lut_fixtures" -BUNDLE_DIR = SHIM / "finance_claim_remote_bundle" -REPORT_DIR = REPO / "6-Documentation" / "reports" / "typst" -LOCAL_TYPST = REPO / "5-Applications" / "tools-scripts" / "external" / "typst-cli" / "bin" / "typst" - -FCL1_VERSION = 1 -FCS1_VERSION = 1 - -VALUE_TAG_ENUM = 0 -VALUE_TAG_SIDECAR = 1 - -TYPE_STRING = 1 -TYPE_DECIMAL_STRING = 2 -TYPE_DATE_STRING = 3 -TYPE_RECEIPT_STRING = 4 - -TYPE_NAMES = { - TYPE_STRING: "string", - TYPE_DECIMAL_STRING: "decimal-string", - TYPE_DATE_STRING: "date-string", - TYPE_RECEIPT_STRING: "receipt-string", -} - -DIRECTION_CODES = { - "auto": 0, - "ltr": 1, - "rtl": 2, - "bidi": 3, -} - -CHIRALITY_CODES = { - "none": 0, - "left": 1, - "right": 2, - "ambidextrous": 3, -} - -DIRECTION_NAMES = {value: key for key, value in DIRECTION_CODES.items()} -CHIRALITY_NAMES = {value: key for key, value in CHIRALITY_CODES.items()} - -FIELD_ORDER = [ - "id", - "claimant", - "counterparty", - "principal", - "currency", - "rate", - "maturity", - "collateral", - "jurisdiction", - "risk", - "settlement_path", - "receipt", -] - -PROTO_SCHEMA = """syntax = "proto3"; - -package research_stack.finance; - -message FinancialClaimPacket { - string id = 1; - string claimant = 2; - string counterparty = 3; - string principal = 4; - string currency = 5; - string rate = 6; - string maturity = 7; - string collateral = 8; - string jurisdiction = 9; - string risk = 10; - string settlement_path = 11; - string receipt = 12; -} -""" - -NANOPB_OPTIONS = """research_stack.finance.FinancialClaimPacket.id max_size:64 -research_stack.finance.FinancialClaimPacket.claimant max_size:128 -research_stack.finance.FinancialClaimPacket.counterparty max_size:128 -research_stack.finance.FinancialClaimPacket.principal max_size:32 -research_stack.finance.FinancialClaimPacket.currency max_size:16 -research_stack.finance.FinancialClaimPacket.rate max_size:32 -research_stack.finance.FinancialClaimPacket.maturity max_size:32 -research_stack.finance.FinancialClaimPacket.collateral max_size:160 -research_stack.finance.FinancialClaimPacket.jurisdiction max_size:32 -research_stack.finance.FinancialClaimPacket.risk max_size:32 -research_stack.finance.FinancialClaimPacket.settlement_path max_size:32 -research_stack.finance.FinancialClaimPacket.receipt max_size:160 -""" - -FLATBUFFERS_SCHEMA = """namespace ResearchStack.Finance; - -table FinancialClaimPacket { - id:string; - claimant:string; - counterparty:string; - principal:string; - currency:string; - rate:string; - maturity:string; - collateral:string; - jurisdiction:string; - risk:string; - settlement_path:string; - receipt:string; -} - -root_type FinancialClaimPacket; -""" - -FIELD_SYMBOLS = { - "id": ("FIN.FIELD.ID", "I", "#text([id])"), - "claimant": ("FIN.FIELD.CLAIMANT", "C", "#text([claimant])"), - "counterparty": ("FIN.FIELD.COUNTERPARTY", "P", "#text([counterparty])"), - "principal": ("FIN.FIELD.PRINCIPAL", "$", "#text([principal])"), - "currency": ("FIN.FIELD.CURRENCY", "U", "#text([currency/unit])"), - "rate": ("FIN.FIELD.RATE", "r", "#text([rate])"), - "maturity": ("FIN.FIELD.MATURITY", "T", "#text([maturity])"), - "collateral": ("FIN.FIELD.COLLATERAL", "K", "#text([collateral])"), - "jurisdiction": ("FIN.FIELD.JURISDICTION", "J", "#text([jurisdiction])"), - "risk": ("FIN.FIELD.RISK", "R", "#text([risk])"), - "settlement_path": ("FIN.FIELD.SETTLEMENT", "S", "#text([settlement])"), - "receipt": ("FIN.FIELD.RECEIPT", "H", "#text([receipt/hash])"), -} - -ENUM_SYMBOLS = { - ("currency", "USD"): ("FIN.ENUM.CURRENCY.USD", "USD", "#text([USD])"), - ("currency", "EUR"): ("FIN.ENUM.CURRENCY.EUR", "EUR", "#text([EUR])"), - ("currency", "USDC"): ("FIN.ENUM.CURRENCY.USDC", "USDC", "#text([USDC])"), - ("jurisdiction", "US-IL"): ("FIN.ENUM.JURISDICTION.US_IL", "IL", "#text([US-IL])"), - ("jurisdiction", "US-DE"): ("FIN.ENUM.JURISDICTION.US_DE", "DE", "#text([US-DE])"), - ("settlement_path", "wire"): ("FIN.ENUM.SETTLEMENT.WIRE", "WIRE", "#text([wire])"), - ("settlement_path", "ach"): ("FIN.ENUM.SETTLEMENT.ACH", "ACH", "#text([ACH])"), - ("settlement_path", "onchain"): ("FIN.ENUM.SETTLEMENT.ONCHAIN", "CHAIN", "#text([onchain])"), - ("risk", "low"): ("FIN.ENUM.RISK.LOW", "LOW", "#text([low risk])"), - ("risk", "medium"): ("FIN.ENUM.RISK.MEDIUM", "MED", "#text([medium risk])"), - ("risk", "high"): ("FIN.ENUM.RISK.HIGH", "HIGH", "#text([high risk])"), -} - -DEFAULT_SAMPLES = [ - { - "id": "claim-0001", - "claimant": "atelier-node", - "counterparty": "counterparty-a", - "principal": "12500.00", - "currency": "USD", - "rate": "0.0425", - "maturity": "2026-12-31", - "collateral": "invoice-pool-alpha", - "jurisdiction": "US-IL", - "risk": "medium", - "settlement_path": "ach", - "receipt": "ene:finance:claim-0001", - }, - { - "id": "claim-0002", - "claimant": "research-stack", - "counterparty": "lab-vendor", - "principal": "2500.00", - "currency": "USDC", - "rate": "0.0000", - "maturity": "2026-06-15", - "collateral": "none", - "jurisdiction": "US-DE", - "risk": "low", - "settlement_path": "onchain", - "receipt": "ene:finance:claim-0002", - }, - { - "id": "claim-0003", - "claimant": "municipal-surface", - "counterparty": "service-provider", - "principal": "88000.00", - "currency": "USD", - "rate": "0.0650", - "maturity": "2028-05-07", - "collateral": "tax-receivable-stream", - "jurisdiction": "US-IL", - "risk": "high", - "settlement_path": "wire", - "receipt": "ene:finance:claim-0003", - }, - { - "id": "claim-0004", - "claimant": "field-lab", - "counterparty": "instrument-lessor", - "principal": "14250.75", - "currency": "EUR", - "rate": "0.0310", - "maturity": "2027-01-20", - "collateral": "spectrometer-lease-escrow", - "jurisdiction": "US-DE", - "risk": "medium", - "settlement_path": "wire", - "receipt": "ene:finance:claim-0004", - }, - { - "id": "claim-0005", - "claimant": "openclaw-bus", - "counterparty": "compute-provider", - "principal": "640.00", - "currency": "USDC", - "rate": "0.0000", - "maturity": "2026-05-31", - "collateral": "prepaid-gpu-credit", - "jurisdiction": "US-DE", - "risk": "low", - "settlement_path": "onchain", - "receipt": "ene:finance:claim-0005", - }, - { - "id": "claim-0006", - "claimant": "netcup-baseline", - "counterparty": "remote-host", - "principal": "18.95", - "currency": "EUR", - "rate": "0.0000", - "maturity": "2026-06-07", - "collateral": "controlled-remote-receipt", - "jurisdiction": "US-IL", - "risk": "low", - "settlement_path": "ach", - "receipt": "ene:finance:claim-0006", - }, - { - "id": "claim-0007", - "claimant": "quandela-probe", - "counterparty": "noisy-recovery-oracle", - "principal": "1.00", - "currency": "USD", - "rate": "0.0000", - "maturity": "2026-07-01", - "collateral": "manual-submit-hold", - "jurisdiction": "US-DE", - "risk": "high", - "settlement_path": "wire", - "receipt": "ene:finance:claim-0007", - }, - { - "id": "claim-0008", - "claimant": "h200-burst", - "counterparty": "gpu-rental-provider", - "principal": "75.00", - "currency": "USD", - "rate": "0.0000", - "maturity": "2026-08-15", - "collateral": "short-run-optimization-receipt", - "jurisdiction": "US-DE", - "risk": "medium", - "settlement_path": "wire", - "receipt": "ene:finance:claim-0008", - }, - { - "id": "claim-0009", - "claimant": "committee-book", - "counterparty": "review-surface", - "principal": "0.00", - "currency": "USD", - "rate": "0.0000", - "maturity": "2026-09-01", - "collateral": "explanation-artifact", - "jurisdiction": "US-IL", - "risk": "low", - "settlement_path": "ach", - "receipt": "ene:finance:claim-0009", - }, - { - "id": "claim-0010", - "claimant": "shockwave-modeling", - "counterparty": "future-packet-family", - "principal": "0.00", - "currency": "USD", - "rate": "0.0000", - "maturity": "2027-05-07", - "collateral": "swf1-sws1-design-hold", - "jurisdiction": "US-DE", - "risk": "medium", - "settlement_path": "wire", - "receipt": "ene:finance:claim-0010", - }, - { - "id": "claim-0011", - "claimant": "sidecar-stress", - "counterparty": "literal-heavy-counterparty", - "principal": "999999.99", - "currency": "USD", - "rate": "0.1250", - "maturity": "2031-12-31", - "collateral": "long-literal-collateral-with-multiple-routing-hints", - "jurisdiction": "US-IL", - "risk": "high", - "settlement_path": "onchain", - "receipt": "ene:finance:claim-0011-sidecar-stress", - }, - { - "id": "claim-0012", - "claimant": "unknown-enum-drill", - "counterparty": "fallback-check", - "principal": "321.09", - "currency": "GBP", - "rate": "0.0550", - "maturity": "2026-10-10", - "collateral": "typed-sidecar-required", - "jurisdiction": "GB-LND", - "risk": "medium", - "settlement_path": "sepa", - "receipt": "ene:finance:claim-0012", - }, -] - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def repo_path(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def crc32_bytes(data: bytes) -> int: - return binascii.crc32(data) & 0xFFFFFFFF - - -def canonical_packet(packet: dict[str, Any]) -> dict[str, Any]: - return {field: packet[field] for field in FIELD_ORDER} - - -def canonical_bytes(packet: dict[str, Any]) -> bytes: - return json.dumps(canonical_packet(packet), ensure_ascii=False, sort_keys=False, separators=(",", ":")).encode("utf-8") - - -def pack_orientation(direction: str = "ltr", chirality: str = "none", phase_bucket: int = 0) -> int: - """Pack render/navigation orientation into one byte.""" - if direction not in DIRECTION_CODES: - raise ValueError(f"unknown direction {direction}") - if chirality not in CHIRALITY_CODES: - raise ValueError(f"unknown chirality {chirality}") - if not 0 <= phase_bucket <= 15: - raise ValueError(f"phase bucket out of range: {phase_bucket}") - return (phase_bucket << 4) | (CHIRALITY_CODES[chirality] << 2) | DIRECTION_CODES[direction] - - -def unpack_orientation(code: int) -> dict[str, Any]: - if not 0 <= int(code) <= 255: - raise ValueError(f"orientation code out of range: {code}") - direction = int(code) & 0b00000011 - chirality = (int(code) >> 2) & 0b00000011 - phase_bucket = (int(code) >> 4) & 0b00001111 - return { - "direction": DIRECTION_NAMES[direction], - "chirality": CHIRALITY_NAMES[chirality], - "phase_bucket": phase_bucket, - "phase_degrees": phase_bucket * 22.5, - } - - -def orientation_hint(symbol_id: str, entry: dict[str, Any]) -> dict[str, Any]: - field = entry.get("field") - value = entry.get("canonical_payload") - if entry["kind"] == "field": - phase_bucket = FIELD_ORDER.index(value) % 16 if value in FIELD_ORDER else 0 - return {"direction": "ltr", "chirality": "none", "phase_bucket": phase_bucket} - if field == "risk": - risk_orientation = { - "low": ("ltr", "left", 1), - "medium": ("bidi", "ambidextrous", 2), - "high": ("rtl", "right", 3), - } - direction, chirality, phase_bucket = risk_orientation.get(value, ("auto", "none", 0)) - return {"direction": direction, "chirality": chirality, "phase_bucket": phase_bucket} - if field == "settlement_path": - settlement_phase = {"ach": 4, "wire": 5, "onchain": 6} - return {"direction": "ltr", "chirality": "none", "phase_bucket": settlement_phase.get(value, 0)} - if field == "currency": - currency_phase = {"USD": 7, "EUR": 8, "USDC": 9} - return {"direction": "ltr", "chirality": "none", "phase_bucket": currency_phase.get(value, 0)} - if field == "jurisdiction": - return {"direction": "ltr", "chirality": "none", "phase_bucket": 10} - return {"direction": "ltr", "chirality": "none", "phase_bucket": 0} - - -def build_symbol_lut() -> dict[str, Any]: - entries = {} - for field, (symbol_id, glyph, _) in FIELD_SYMBOLS.items(): - entries[symbol_id] = { - "symbol_id": symbol_id, - "kind": "field", - "semantic_key": f"financial_claim_packet.{field}", - "canonical_payload": field, - "glyph": glyph, - "inverse": field, - "version": "0.2.0", - } - for (field, value), (symbol_id, glyph, _) in ENUM_SYMBOLS.items(): - entries[symbol_id] = { - "symbol_id": symbol_id, - "kind": "enum_value", - "semantic_key": f"financial_claim_packet.{field}.{value}", - "canonical_payload": value, - "field": field, - "glyph": glyph, - "inverse": value, - "version": "0.2.0", - } - return {"schema": "finance_claim_symbol_lut_v1", "version": "0.2.0", "inverse_required": True, "entries": entries} - - -def build_typesetting_lut(symbol_lut: dict[str, Any]) -> dict[str, Any]: - render_sources = {sid: render for _, (sid, _, render) in FIELD_SYMBOLS.items()} - render_sources.update({sid: render for _, (sid, _, render) in ENUM_SYMBOLS.items()}) - entries = {} - for symbol_id, entry in symbol_lut["entries"].items(): - orientation = orientation_hint(symbol_id, entry) - orientation_code = pack_orientation(**orientation) - entries[symbol_id] = { - "symbol_id": symbol_id, - "glyph": entry["glyph"], - "typst_render": render_sources.get(symbol_id, f"#text([{entry['glyph']}])"), - "orientation_code": orientation_code, - "orientation_hex": f"{orientation_code:02x}", - "orientation": unpack_orientation(orientation_code), - "tone": "witness" if entry["kind"] == "field" else "neutral", - "rounding_rule": None, - "residual_sidecar": None, - "version": entry["version"], - "source_hash": sha256_bytes(json.dumps(entry, sort_keys=True).encode("utf-8")), - } - return {"schema": "finance_claim_typesetting_lut_v1", "version": "0.2.0", "entries": entries} - - -def orientation_codec_receipt() -> dict[str, Any]: - return { - "schema": "orientation_codec_v1", - "packed_bytes_per_symbol": 1, - "bits_0_1": {"name": "direction", "codes": DIRECTION_CODES}, - "bits_2_3": {"name": "chirality", "codes": CHIRALITY_CODES}, - "bits_4_7": {"name": "phase_bucket", "buckets": 16, "degrees_per_bucket": 22.5}, - "claim_boundary": "orientation_code is a render/navigation LUT hint; exact continuous 360-degree values require sidecar promotion", - } - - -def orientation_metrics(type_lut: dict[str, Any]) -> dict[str, Any]: - entries = type_lut["entries"] - verbose = [ - { - "symbol_id": symbol_id, - "direction": entry["orientation"]["direction"], - "chirality": entry["orientation"]["chirality"], - "phase_bucket": entry["orientation"]["phase_bucket"], - "phase_degrees": entry["orientation"]["phase_degrees"], - } - for symbol_id, entry in sorted(entries.items()) - ] - verbose_bytes = len(json.dumps(verbose, sort_keys=True, separators=(",", ":")).encode("utf-8")) - packed_bytes = len(entries) - return { - "symbol_entries": len(entries), - "verbose_orientation_json_bytes": verbose_bytes, - "packed_orientation_bytes": packed_bytes, - "saved_bytes": verbose_bytes - packed_bytes, - "saved_ratio": round((verbose_bytes - packed_bytes) / verbose_bytes, 6) if verbose_bytes else 0.0, - "claim_boundary": "byte savings compare orientation metadata only, not full packet compression", - } - - -def symbol_codebook(symbol_lut: dict[str, Any]) -> dict[str, int]: - return {symbol_id: index for index, symbol_id in enumerate(sorted(symbol_lut["entries"]), start=1)} - - -def classify_literal(field: str, value: Any) -> int: - if field in {"principal", "rate"}: - return TYPE_DECIMAL_STRING - if field == "maturity": - return TYPE_DATE_STRING - if field == "receipt": - return TYPE_RECEIPT_STRING - return TYPE_STRING - - -def encode_value(field: str, value: Any, sidecar: dict[int, dict[str, Any]]) -> dict[str, Any]: - enum_key = (field, str(value)) - if enum_key in ENUM_SYMBOLS: - return {"type": "enum_symbol", "symbol_id": ENUM_SYMBOLS[enum_key][0]} - index = len(sidecar) - value_json = json.dumps(value, ensure_ascii=False, separators=(",", ":")) - sidecar[index] = { - "index": index, - "field": field, - "type_code": classify_literal(field, value), - "type": TYPE_NAMES[classify_literal(field, value)], - "value_json": value_json, - "sha256": sha256_bytes(value_json.encode("utf-8")), - } - return {"type": "literal_ref", "sidecar_index": index, "sha256": sidecar[index]["sha256"]} - - -def encode_packet(packet: dict[str, Any]) -> dict[str, Any]: - sidecar: dict[int, dict[str, Any]] = {} - fields = [] - for field in FIELD_ORDER: - fields.append({"field_symbol_id": FIELD_SYMBOLS[field][0], "value": encode_value(field, packet[field], sidecar)}) - return { - "compressed": {"schema": "financial_claim_packet_compressed_v1", "codec": "symbol_lut_plus_fcs1_sidecar", "fields": fields}, - "sidecar": sidecar, - } - - -def json_sidecar(sidecar: dict[int, dict[str, Any]]) -> dict[str, Any]: - return {f"sidecar.{item['field']}.{index:04d}": item for index, item in sidecar.items()} - - -def decode_packet(compressed: dict[str, Any], sidecar: dict[int, dict[str, Any]], symbol_lut: dict[str, Any]) -> dict[str, Any]: - packet = {} - entries = symbol_lut["entries"] - for item in compressed["fields"]: - field = entries[item["field_symbol_id"]]["inverse"] - value_ref = item["value"] - if value_ref["type"] == "enum_symbol": - packet[field] = entries[value_ref["symbol_id"]]["inverse"] - elif value_ref["type"] == "literal_ref": - side = sidecar[int(value_ref["sidecar_index"])] - if side["sha256"] != value_ref["sha256"]: - raise ValueError(f"sidecar hash mismatch for index {value_ref['sidecar_index']}") - packet[field] = json.loads(side["value_json"]) - else: - raise ValueError(f"unknown value ref type: {value_ref['type']}") - return canonical_packet(packet) - - -def encode_fcl1(compressed: dict[str, Any], codebook: dict[str, int]) -> bytes: - out = bytearray(b"FCL1") - out.append(FCL1_VERSION) - fields = compressed["fields"] - if len(fields) > 255: - raise ValueError("too many fields for FCL1") - out.append(len(fields)) - for item in fields: - out.extend(codebook[item["field_symbol_id"]].to_bytes(2, "big")) - value = item["value"] - if value["type"] == "enum_symbol": - out.append(VALUE_TAG_ENUM) - out.extend(codebook[value["symbol_id"]].to_bytes(2, "big")) - elif value["type"] == "literal_ref": - out.append(VALUE_TAG_SIDECAR) - out.extend(int(value["sidecar_index"]).to_bytes(2, "big")) - else: - raise ValueError(f"unknown value ref type: {value['type']}") - out.extend(crc32_bytes(bytes(out)).to_bytes(4, "big")) - return bytes(out) - - -def decode_fcl1(blob: bytes, sidecar: dict[int, dict[str, Any]], symbol_lut: dict[str, Any], codebook: dict[str, int]) -> dict[str, Any]: - if len(blob) < 10 or not blob.startswith(b"FCL1"): - raise ValueError("bad FCL1 magic") - stored_crc = int.from_bytes(blob[-4:], "big") - body = blob[:-4] - if crc32_bytes(body) != stored_crc: - raise ValueError("bad FCL1 checksum") - if body[4] != FCL1_VERSION: - raise ValueError(f"unsupported FCL1 version {body[4]}") - inverse_codebook = {value: key for key, value in codebook.items()} - count = body[5] - offset = 6 - fields = [] - for _ in range(count): - field_symbol_id = inverse_codebook[int.from_bytes(body[offset : offset + 2], "big")] - offset += 2 - tag = body[offset] - offset += 1 - value_code = int.from_bytes(body[offset : offset + 2], "big") - offset += 2 - if tag == VALUE_TAG_ENUM: - value = {"type": "enum_symbol", "symbol_id": inverse_codebook[value_code]} - elif tag == VALUE_TAG_SIDECAR: - if value_code not in sidecar: - raise ValueError(f"missing FCS1 sidecar index {value_code}") - value = {"type": "literal_ref", "sidecar_index": value_code, "sha256": sidecar[value_code]["sha256"]} - else: - raise ValueError(f"unknown FCL1 tag {tag}") - fields.append({"field_symbol_id": field_symbol_id, "value": value}) - if offset != len(body): - raise ValueError("trailing FCL1 bytes") - return decode_packet({"schema": "financial_claim_packet_compressed_v1", "codec": "fcl1", "fields": fields}, sidecar, symbol_lut) - - -def encode_fcs1(sidecar: dict[int, dict[str, Any]]) -> bytes: - out = bytearray(b"FCS1") - out.append(FCS1_VERSION) - if len(sidecar) > 255: - raise ValueError("too many sidecar literals for FCS1") - out.append(len(sidecar)) - for index in sorted(sidecar): - item = sidecar[index] - value = item["value_json"].encode("utf-8") - if len(value) > 65535: - raise ValueError("FCS1 literal too large") - out.extend(index.to_bytes(2, "big")) - out.append(int(item["type_code"])) - out.extend(len(value).to_bytes(2, "big")) - out.extend(value) - out.extend(crc32_bytes(value).to_bytes(4, "big")) - out.extend(crc32_bytes(bytes(out)).to_bytes(4, "big")) - return bytes(out) - - -def decode_fcs1(blob: bytes) -> dict[int, dict[str, Any]]: - if len(blob) < 10 or not blob.startswith(b"FCS1"): - raise ValueError("bad FCS1 magic") - stored_crc = int.from_bytes(blob[-4:], "big") - body = blob[:-4] - if crc32_bytes(body) != stored_crc: - raise ValueError("bad FCS1 checksum") - if body[4] != FCS1_VERSION: - raise ValueError(f"unsupported FCS1 version {body[4]}") - count = body[5] - offset = 6 - sidecar: dict[int, dict[str, Any]] = {} - for _ in range(count): - index = int.from_bytes(body[offset : offset + 2], "big") - offset += 2 - type_code = body[offset] - offset += 1 - length = int.from_bytes(body[offset : offset + 2], "big") - offset += 2 - value_bytes = body[offset : offset + length] - offset += length - value_crc = int.from_bytes(body[offset : offset + 4], "big") - offset += 4 - if crc32_bytes(value_bytes) != value_crc: - raise ValueError(f"bad FCS1 value checksum for index {index}") - value_json = value_bytes.decode("utf-8") - sidecar[index] = { - "index": index, - "field": None, - "type_code": type_code, - "type": TYPE_NAMES.get(type_code, "unknown"), - "value_json": value_json, - "sha256": sha256_bytes(value_bytes), - } - if offset != len(body): - raise ValueError("trailing FCS1 bytes") - return sidecar - - -def render_preview(compressed: dict[str, Any], type_lut: dict[str, Any]) -> str: - parts = [] - for item in compressed["fields"]: - field_glyph = type_lut["entries"][item["field_symbol_id"]]["glyph"] - value_ref = item["value"] - value_glyph = type_lut["entries"][value_ref["symbol_id"]]["glyph"] if value_ref["type"] == "enum_symbol" else "LIT" - parts.append(f"{field_glyph}:{value_glyph}") - return " ".join(parts) - - -def typst_render_source(samples: list[dict[str, Any]]) -> str: - rows = [] - for sample in samples: - rows.append(f' [{sample["id"]}], [`{sample["render_preview"]}`], [`{sample["canonical_hash"][:12]}`],') - return "\n".join( - [ - '#set page(width: 8.5in, height: 11in, margin: 0.75in)', - '#set text(size: 9pt)', - '= Finance Claim LUT Render Preview', - '', - '#table(', - ' columns: (1.1fr, 3fr, 1.2fr),', - ' [Packet], [FCL1 Render Preview], [Hash],', - *rows, - ')', - '', - ] - ) - - -def compile_typst_render(samples: list[dict[str, Any]], fixture_dir: Path) -> dict[str, Any]: - fixture_dir.mkdir(parents=True, exist_ok=True) - source = typst_render_source(samples) - source_path = fixture_dir / "finance_claim_lut_render.typ" - pdf_path = fixture_dir / "finance_claim_lut_render.pdf" - source_path.write_text(source, encoding="utf-8") - typst = LOCAL_TYPST if LOCAL_TYPST.exists() else None - result = { - "typst_source": repo_path(source_path), - "typst_source_hash": sha256_bytes(source.encode("utf-8")), - "compiled": False, - "pdf": repo_path(pdf_path), - "pdf_hash": None, - "stderr": "", - } - if not typst: - result["stderr"] = "typst CLI not found" - return result - proc = subprocess.run([str(typst), "compile", "--root", str(REPO), repo_path(source_path), repo_path(pdf_path)], cwd=REPO, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - result["compiled"] = proc.returncode == 0 - result["stderr"] = proc.stderr[-2000:] - if pdf_path.exists() and proc.returncode == 0: - result["pdf_hash"] = sha256_bytes(pdf_path.read_bytes()) - return result - - -def optional_codec_size(module_name: str, encoder_name: str, packet: dict[str, Any]) -> dict[str, Any]: - if importlib.util.find_spec(module_name) is None: - return {"available": False, "status": "skipped", "bytes": None} - module = __import__(module_name) - try: - if module_name == "cbor2": - encoded = module.dumps(packet, canonical=True) - elif module_name == "msgpack": - encoded = module.packb(packet, use_bin_type=True) - else: - encoded = getattr(module, encoder_name)(packet) - return {"available": True, "status": "ok", "bytes": len(encoded), "sha256": sha256_bytes(encoded)} - except Exception as exc: - return {"available": True, "status": f"error: {exc}", "bytes": None} - - -def protobuf_dynamic_bytes(packet: dict[str, Any]) -> dict[str, Any]: - if importlib.util.find_spec("google.protobuf") is None: - return {"available": False, "status": "skipped", "bytes": None} - try: - from google.protobuf import descriptor_pb2, descriptor_pool, message_factory - - fd = descriptor_pb2.FileDescriptorProto() - fd.name = "finance_claim_packet.proto" - fd.package = "research_stack.finance" - fd.syntax = "proto3" - msg = fd.message_type.add() - msg.name = "FinancialClaimPacket" - for index, field in enumerate(FIELD_ORDER, start=1): - f = msg.field.add() - f.name = field - f.number = index - f.label = descriptor_pb2.FieldDescriptorProto.LABEL_OPTIONAL - f.type = descriptor_pb2.FieldDescriptorProto.TYPE_STRING - pool = descriptor_pool.DescriptorPool() - pool.Add(fd) - klass = message_factory.GetMessageClass(pool.FindMessageTypeByName("research_stack.finance.FinancialClaimPacket")) - encoded = klass(**canonical_packet(packet)).SerializeToString(deterministic=True) - return {"available": True, "status": "ok", "bytes": len(encoded), "sha256": sha256_bytes(encoded), "encoded": encoded} - except Exception as exc: - return {"available": True, "status": f"error: {exc}", "bytes": None} - - -def write_schema_fixtures(fixture_dir: Path) -> dict[str, Any]: - fixture_dir.mkdir(parents=True, exist_ok=True) - schema_files = { - "protobuf_schema": (fixture_dir / "finance_claim_packet.proto", PROTO_SCHEMA.encode("utf-8")), - "nanopb_options": (fixture_dir / "finance_claim_packet.options", NANOPB_OPTIONS.encode("utf-8")), - "flatbuffers_schema": (fixture_dir / "finance_claim_packet.fbs", FLATBUFFERS_SCHEMA.encode("utf-8")), - } - out = {} - for key, (path, data) in schema_files.items(): - out[key] = write_fixture(path, data) - return out - - -def metrics(raw: bytes, compressed: dict[str, Any], sidecar: dict[int, dict[str, Any]], fcl1: bytes, fcs1: bytes, packet: dict[str, Any]) -> dict[str, Any]: - compressed_bytes = json.dumps(compressed, sort_keys=True, separators=(",", ":")).encode("utf-8") - sidecar_json_bytes = json.dumps(json_sidecar(sidecar), sort_keys=True, separators=(",", ":")).encode("utf-8") - proto = protobuf_dynamic_bytes(packet) - proto_public = {key: value for key, value in proto.items() if key != "encoded"} - return { - "canonical_json_bytes": len(raw), - "zlib_canonical_bytes": len(zlib.compress(raw, level=9)), - "json_compressed_packet_bytes": len(compressed_bytes), - "json_sidecar_bytes": len(sidecar_json_bytes), - "combined_json_encoded_bytes": len(compressed_bytes) + len(sidecar_json_bytes), - "fcl1_bytes": len(fcl1), - "fcs1_bytes": len(fcs1), - "combined_fcl1_fcs1_bytes": len(fcl1) + len(fcs1), - "cbor": optional_codec_size("cbor2", "dumps", packet), - "messagepack": optional_codec_size("msgpack", "packb", packet), - "protobuf_dynamic": proto_public, - "nanopb": {"available": importlib.util.find_spec("nanopb") is not None, "status": "schema_emitted_generator_pending", "bytes": proto_public.get("bytes")}, - "flatbuffers": {"available": importlib.util.find_spec("flatbuffers") is not None, "status": "schema_emitted_flatc_missing", "bytes": None}, - "claim_boundary": "codec comparison over tiny local samples only; not competitive compression evidence", - } - - -def write_fixture(path: Path, data: bytes) -> dict[str, Any]: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_bytes(data) - return {"path": repo_path(path), "bytes": len(data), "sha256": sha256_bytes(data)} - - -def build_sample_receipt(sample: dict[str, Any], symbol_lut: dict[str, Any], type_lut: dict[str, Any], codebook: dict[str, int], fixture_dir: Path) -> dict[str, Any]: - raw = canonical_bytes(sample) - encoded = encode_packet(sample) - fcl1 = encode_fcl1(encoded["compressed"], codebook) - fcs1 = encode_fcs1(encoded["sidecar"]) - proto = protobuf_dynamic_bytes(canonical_packet(sample)) - decoded_json = decode_packet(encoded["compressed"], encoded["sidecar"], symbol_lut) - decoded_binary = decode_fcl1(fcl1, decode_fcs1(fcs1), symbol_lut, codebook) - decoded_json_raw = canonical_bytes(decoded_json) - decoded_binary_raw = canonical_bytes(decoded_binary) - sample_dir = fixture_dir / sample["id"] - render = render_preview(encoded["compressed"], type_lut) - return { - "id": sample["id"], - "canonical_hash": sha256_bytes(raw), - "decoded_hash": sha256_bytes(decoded_json_raw), - "fcl1_decoded_hash": sha256_bytes(decoded_binary_raw), - "rehydrated_ok": decoded_json_raw == raw and decoded_binary_raw == raw, - "fcl1_decode_ok": decoded_binary_raw == raw, - "fcs1_decode_ok": decode_fcs1(fcs1) != {}, - "fcl1_binary_hex": fcl1.hex(), - "fcl1_binary_hash": sha256_bytes(fcl1), - "fcs1_binary_hex": fcs1.hex(), - "fcs1_binary_hash": sha256_bytes(fcs1), - "compressed_hash": sha256_bytes(json.dumps(encoded["compressed"], sort_keys=True).encode("utf-8")), - "sidecar_hash": sha256_bytes(json.dumps(json_sidecar(encoded["sidecar"]), sort_keys=True).encode("utf-8")), - "render_preview": render, - "render_preview_hash": sha256_bytes(render.encode("utf-8")), - "fixtures": { - "canonical": write_fixture(sample_dir / "canonical.json", raw), - "fcl1": write_fixture(sample_dir / "packet.fcl1", fcl1), - "fcs1": write_fixture(sample_dir / "sidecar.fcs1", fcs1), - "protobuf": write_fixture(sample_dir / "packet.pb", proto["encoded"]) if proto.get("status") == "ok" else None, - }, - "compressed": encoded["compressed"], - "sidecar": json_sidecar(encoded["sidecar"]), - "metrics": metrics(raw, encoded["compressed"], encoded["sidecar"], fcl1, fcs1, canonical_packet(sample)), - } - - -def corruption_tests(samples: list[dict[str, Any]], symbol_lut: dict[str, Any], codebook: dict[str, int]) -> dict[str, Any]: - sample = samples[0] - encoded = encode_packet(sample) - fcl1 = bytearray(encode_fcl1(encoded["compressed"], codebook)) - fcs1 = bytearray(encode_fcs1(encoded["sidecar"])) - unknown = dict(sample) - unknown["currency"] = "ZZZ" - unknown_encoded = encode_packet(unknown) - tests = {} - fcs1[-5] ^= 0x01 - try: - decode_fcl1(bytes(encode_fcl1(encoded["compressed"], codebook)), decode_fcs1(bytes(fcs1)), symbol_lut, codebook) - tests["corrupt_fcs1_rejected"] = False - except Exception: - tests["corrupt_fcs1_rejected"] = True - fcl1[6] ^= 0x01 - try: - decode_fcl1(bytes(fcl1), decode_fcs1(encode_fcs1(encoded["sidecar"])), symbol_lut, codebook) - tests["corrupt_fcl1_rejected"] = False - except Exception: - tests["corrupt_fcl1_rejected"] = True - tests["unknown_enum_falls_back_to_sidecar"] = any( - item["field_symbol_id"] == FIELD_SYMBOLS["currency"][0] and item["value"]["type"] == "literal_ref" - for item in unknown_encoded["compressed"]["fields"] - ) - tests["field_order_canonical"] = list(canonical_packet(sample)) == FIELD_ORDER - tests["lawful"] = all(tests.values()) - return tests - - -def build_receipt(samples: list[dict[str, Any]], fixture_dir: Path) -> dict[str, Any]: - symbol_lut = build_symbol_lut() - type_lut = build_typesetting_lut(symbol_lut) - codebook = symbol_codebook(symbol_lut) - sample_receipts = [build_sample_receipt(sample, symbol_lut, type_lut, codebook, fixture_dir) for sample in samples] - schema_receipts = write_schema_fixtures(fixture_dir) - render_receipt = compile_typst_render(sample_receipts, fixture_dir) - symbol_lut_bytes = json.dumps(symbol_lut, sort_keys=True, ensure_ascii=False).encode("utf-8") - type_lut_bytes = json.dumps(type_lut, sort_keys=True, ensure_ascii=False).encode("utf-8") - tests = corruption_tests(samples, symbol_lut, codebook) - return { - "schema": "finance_claim_lut_harness_receipt_v2", - "surface_id": "finance_claim_lut_harness", - "claim_boundary": ( - "Harness demonstrates byte-for-byte FinancialClaimPacket rehydration through symbol/typesetting LUTs, " - "FCL1 packets, and FCS1 sidecars. It is not financial advice, audit certification, settlement, or " - "competitive compression evidence." - ), - "wire_formats": { - "fcl1": "magic FCL1, version byte, field count, repeated u16/u8/u16 records, crc32 trailer", - "fcs1": "magic FCS1, version byte, literal count, repeated typed literal records, crc32 trailer", - "orientation_code": "one LUT byte: bits 0-1 direction, bits 2-3 chirality, bits 4-7 phase bucket", - "json": "audit envelope only", - }, - "orientation_codec": orientation_codec_receipt(), - "orientation_metrics": orientation_metrics(type_lut), - "canonical_field_order": FIELD_ORDER, - "symbol_lut": symbol_lut, - "symbol_lut_hash": sha256_bytes(symbol_lut_bytes), - "symbol_codebook": codebook, - "typesetting_lut": type_lut, - "typesetting_lut_hash": sha256_bytes(type_lut_bytes), - "fixture_dir": repo_path(fixture_dir), - "schema_receipts": schema_receipts, - "render_receipt": render_receipt, - "test_receipts": tests, - "sample_count": len(sample_receipts), - "samples": sample_receipts, - "lawful": all(item["rehydrated_ok"] and item["fcl1_decode_ok"] and item["fcs1_decode_ok"] for item in sample_receipts) - and tests["lawful"] - and bool(render_receipt.get("typst_source_hash")), - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a reversible finance-claim compression router. Return compact JSON and preserve claim boundaries." - records = [] - for sample in receipt["samples"]: - prompt = { - "task": "verify_financial_claim_packet_lut_roundtrip", - "id": sample["id"], - "canonical_hash": sample["canonical_hash"], - "fcl1_decoded_hash": sample["fcl1_decoded_hash"], - "fcl1_bytes": sample["metrics"]["fcl1_bytes"], - "fcs1_bytes": sample["metrics"]["fcs1_bytes"], - "instruction": "Decide whether this binary LUT compression packet may promote to a reversible surface receipt.", - } - answer = { - "selected": bool(sample["rehydrated_ok"]), - "use_as": "reversible_binary_lut_surface_receipt", - "claim_boundary": "json-audit-binary-wire-byte-roundtrip-only-not-financial-advice", - "canonical_hash": sample["canonical_hash"], - "fcl1_decoded_hash": sample["fcl1_decoded_hash"], - "symbol_lut_hash": receipt["symbol_lut_hash"], - "typesetting_lut_hash": receipt["typesetting_lut_hash"], - "orientation_codec": receipt["orientation_codec"]["schema"], - "orientation_metrics": receipt["orientation_metrics"], - "fcl1_binary_hash": sample["fcl1_binary_hash"], - "fcs1_binary_hash": sample["fcs1_binary_hash"], - "rehydrated_ok": sample["rehydrated_ok"], - "baseline_comparison": sample["metrics"], - } - records.append({"messages": [{"role": "system", "content": system}, {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}]}) - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps({"task": "choose_embedded_codec", "available": ["FCL1/FCS1", "CBOR", "MessagePack", "Nanopb", "FlatBuffers"], "instruction": "Choose the current finance-claim wire path."}, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps({"selected": "FCL1/FCS1", "claim_boundary": "JSON remains audit envelope; binary wire remains local harness only.", "reason": "Both endpoints are controlled and exact byte rehydration is already receipt-backed.", "defer": ["Nanopb schema", "FlatBuffers schema"]}, ensure_ascii=False)}, - ] - } - ) - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps({"task": "plan_rehydration_preload_buffer", "instruction": "Describe whether a multi-serial/parallel preload buffer helps FCL1/FCS1 rehydration."}, ensure_ascii=False)}, - { - "role": "assistant", - "content": json.dumps( - { - "selected": True, - "use_as": "future_decoder_pipeline_hint", - "claim_boundary": "preload buffer is a local rehydration scheduling optimization, not a compression-ratio claim", - "borrows_from": ["delay_line_buffer_ram", "isa_hot_path", "isa_cold_path", "high_baud_stream_modem"], - "lanes": ["fcl1_token_tape", "fcs1_literal_stream", "crc_hash_witness", "orientation_lut"], - "hot_path": ["field_symbol_decode", "enum_symbol_decode", "orientation_byte_decode", "canonical_field_order_commit"], - "cold_path": ["typed_sidecar_literal_fetch", "unknown_enum_sidecar_fallback", "continuous_orientation_sidecar", "field_equation_static_target_rehydrator", "crc_hash_failure_route"], - "virtual_modem_decompressor": { - "selected": True, - "carrier_stream": "FCL1 token tape plus orientation bytes", - "sideband_stream": "FCS1 typed literal lane", - "control_bit_flow": { - "carrier_lock": "symbol tape synchronized", - "frame_sync": "FCL1 magic/version/count accepted", - "lane_select": "enum hot path or sidecar cold path", - "phase_lock": "orientation phase bucket decoded", - "sidecar_request": "literal index queued for FCS1 fetch", - "replay": "crc/hash failure routes to cold-path replay", - "commit": "canonical hash gate passed", - }, - "demodulator": ["symbol_lock", "phase_bucket_lock", "hot_path_commit", "cold_path_replay", "canonical_hash_check"], - "claim_boundary": "virtual modem framing is a decoder architecture metaphor backed by local byte receipts, not a measured baud-rate benchmark", - }, - "static_target_mode": { - "selected": True, - "use_as": "field_equation_rehydration_candidate", - "basis": ["finance_math_stack", "symbol_lut", "typesetting_lut", "logogram_orientation", "canonical_hash_gate"], - "claim_boundary": "field-equation rehydration is permitted only when the final target object is static and byte-roundtrip receipts remain authoritative", - }, - "reason": "Serial inputs remain canonical while parallel preload slots hide sidecar lookup, checksum, render-orientation, and static-target field-equation setup before final JSON commit.", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def load_samples(path: Path | None) -> list[dict[str, Any]]: - if not path: - return DEFAULT_SAMPLES - data = json.loads(path.read_text(encoding="utf-8")) - return data if isinstance(data, list) else data.get("samples", DEFAULT_SAMPLES) - - -def write_standard_artifacts(receipt: dict[str, Any], args: argparse.Namespace) -> None: - for path in (args.receipt, args.symbol_lut, args.typesetting_lut, args.curriculum): - path.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - args.symbol_lut.write_text(json.dumps(receipt["symbol_lut"], indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - args.typesetting_lut.write_text(json.dumps(receipt["typesetting_lut"], indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - - -def manifest_for_dir(root: Path) -> dict[str, Any]: - files = [] - for path in sorted(p for p in root.rglob("*") if p.is_file()): - if path.name in {"bundle_manifest.json", "bundle_receipt.json"}: - continue - data = path.read_bytes() - files.append({"path": str(path.relative_to(root)), "bytes": len(data), "sha256": sha256_bytes(data)}) - manifest_bytes = json.dumps(files, sort_keys=True, separators=(",", ":")).encode("utf-8") - return {"schema": "finance_claim_bundle_manifest_v1", "root": repo_path(root), "file_count": len(files), "manifest_hash": sha256_bytes(manifest_bytes), "files": files} - - -def write_corpus_bundle(samples: list[dict[str, Any]], bundle_dir: Path) -> dict[str, Any]: - if bundle_dir.exists(): - shutil.rmtree(bundle_dir) - bundle_dir.mkdir(parents=True, exist_ok=True) - samples_path = bundle_dir / "canonical_samples.json" - samples_path.write_text(json.dumps([canonical_packet(sample) for sample in samples], indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - receipt = build_receipt(samples, bundle_dir / "fixtures") - (bundle_dir / "finance_claim_lut_harness_receipt.json").write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - (bundle_dir / "finance_claim_symbol_lut.json").write_text(json.dumps(receipt["symbol_lut"], indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - (bundle_dir / "finance_claim_typesetting_lut.json").write_text(json.dumps(receipt["typesetting_lut"], indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with (bundle_dir / "finance_claim_lut_harness_curriculum.jsonl").open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - manifest = manifest_for_dir(bundle_dir) - (bundle_dir / "bundle_manifest.json").write_text(json.dumps(manifest, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - bundle_receipt = { - "schema": "finance_claim_corpus_bundle_receipt_v1", - "bundle_dir": repo_path(bundle_dir), - "sample_count": len(samples), - "samples_hash": sha256_bytes(samples_path.read_bytes()), - "harness_receipt_hash": sha256_bytes((bundle_dir / "finance_claim_lut_harness_receipt.json").read_bytes()), - "manifest_hash": manifest["manifest_hash"], - "manifest_file_count": manifest["file_count"], - "wire_boundary": "JSON files are audit fixtures; packet.fcl1 and sidecar.fcs1 are binary wire fixtures", - "lawful": bool(receipt.get("lawful")) and all(item.get("rehydrated_ok") for item in receipt.get("samples", [])), - "claim_boundary": "portable corpus bundle only; not provider execution, settlement, or competitive compression evidence", - } - (bundle_dir / "bundle_receipt.json").write_text(json.dumps(bundle_receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - return bundle_receipt - - -def verify_receipt(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - samples = data.get("samples", []) - ok = all( - item.get("rehydrated_ok") - and item.get("canonical_hash") == item.get("decoded_hash") - and item.get("canonical_hash") == item.get("fcl1_decoded_hash") - and item.get("fcl1_binary_hash") - and item.get("fcs1_binary_hash") - for item in samples - ) - return { - "schema": "finance_claim_lut_verify_v1", - "receipt": str(path), - "samples": len(samples), - "verified": ok and bool(data.get("lawful")), - "claim_boundary": "receipt verification only; not financial advice or compression certification", - } - - -def decode_cli(fcl1_hex: str, sidecar_path: Path, out: Path | None) -> dict[str, Any]: - symbol_lut = build_symbol_lut() - codebook = symbol_codebook(symbol_lut) - sidecar = decode_fcs1(sidecar_path.read_bytes()) - packet = decode_fcl1(bytes.fromhex(fcl1_hex), sidecar, symbol_lut, codebook) - result = { - "schema": "finance_claim_lut_decode_v1", - "canonical": packet, - "canonical_hash": sha256_bytes(canonical_bytes(packet)), - "claim_boundary": "decode output only; not settlement or financial advice", - } - if out: - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(result, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - return result - - -def add_common_args(parser: argparse.ArgumentParser) -> None: - parser.add_argument("--samples", type=Path) - parser.add_argument("--receipt", "--out", dest="receipt", type=Path, default=SHIM / "finance_claim_lut_harness_receipt.json") - parser.add_argument("--symbol-lut", type=Path, default=SHIM / "finance_claim_symbol_lut.json") - parser.add_argument("--typesetting-lut", type=Path, default=SHIM / "finance_claim_typesetting_lut.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "finance_claim_lut_harness_curriculum.jsonl") - parser.add_argument("--fixture-dir", type=Path, default=FIXTURE_DIR) - - -def main() -> int: - parser = argparse.ArgumentParser() - subparsers = parser.add_subparsers(dest="command") - - encode_parser = subparsers.add_parser("encode") - add_common_args(encode_parser) - bench_parser = subparsers.add_parser("bench") - add_common_args(bench_parser) - bundle_parser = subparsers.add_parser("bundle") - add_common_args(bundle_parser) - bundle_parser.add_argument("--bundle-dir", type=Path, default=BUNDLE_DIR) - verify_parser = subparsers.add_parser("verify") - verify_parser.add_argument("--receipt", type=Path, default=SHIM / "finance_claim_lut_harness_receipt.json") - decode_parser = subparsers.add_parser("decode") - decode_parser.add_argument("--fcl1", required=True) - decode_parser.add_argument("--sidecar", type=Path, required=True) - decode_parser.add_argument("--out", type=Path) - add_common_args(parser) - args = parser.parse_args() - - if args.command == "verify": - print(json.dumps(verify_receipt(args.receipt), indent=2, ensure_ascii=False)) - return 0 - if args.command == "decode": - print(json.dumps(decode_cli(args.fcl1, args.sidecar, args.out), indent=2, ensure_ascii=False)) - return 0 - - samples = load_samples(args.samples) - if args.command == "bundle": - print(json.dumps(write_corpus_bundle(samples, args.bundle_dir), indent=2, ensure_ascii=False)) - return 0 - receipt = build_receipt(samples, args.fixture_dir) - if args.command in {None, "encode"}: - write_standard_artifacts(receipt, args) - print(json.dumps(receipt if args.command != "bench" else {"schema": "finance_claim_lut_bench_v1", "samples": [{"id": s["id"], "metrics": s["metrics"]} for s in receipt["samples"]], "claim_boundary": receipt["claim_boundary"]}, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/foundation_forward_equation_compiler.py b/4-Infrastructure/shim/foundation_forward_equation_compiler.py deleted file mode 100644 index 98fc0be2..00000000 --- a/4-Infrastructure/shim/foundation_forward_equation_compiler.py +++ /dev/null @@ -1,481 +0,0 @@ -#!/usr/bin/env python3 -"""Emit the forward-foundation equation compiler contract. - -This is a receipt-bearing contract, not a proof engine. It records the local -rule that theorem names, expert labels, citations, and logogram names are -routing hints only. A trusted equation atom must compile forward from the -foundation kernel and carry closure, residual, budget, and receipt evidence. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "foundation_forward_equation_compiler" -KERNEL = OUT_DIR / "foundation_kernel_f0.json" -ATOMS = OUT_DIR / "foundation_equation_atoms.json" -RECEIPT = OUT_DIR / "foundation_forward_equation_compiler_receipt.json" -SUMMARY = OUT_DIR / "foundation_forward_equation_compiler.md" - -SOURCE_DOCS = [ - REPO / "6-Documentation/docs/specs/DECODER_FACING_RECONSTRUCTION_CORE.md", - REPO / "6-Documentation/docs/specs/OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", - REPO / "6-Documentation/docs/specs/GCCL_ENCODING_CONTRACT.md", - REPO / "shared-data/data/stack_memory_promotions/reconstruction_core_ladder_memory_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def source_ref(path: Path) -> dict[str, Any]: - return { - "path": rel(path), - "exists": path.exists(), - "sha256": file_hash(path), - } - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def build_kernel() -> dict[str, Any]: - return { - "schema": "foundation_kernel_f0_v1", - "kernel_id": "F0_forward_foundation_kernel", - "claim_boundary": ( - "Forward compiler foundation only. This is not a proof that derived " - "equations are true and not a benchmark claim. Human theorem labels, " - "citations, expert names, and logogram names are routing hints only." - ), - "canonical_rule": "No backward trust chain. Only forward admissible generation.", - "origin_metadata_doctrine": { - "canonical_statement": "Origin may inspire. Only closure admits.", - "joke_boundary": "No vibes-to-axioms pipeline without a receipt.", - "rule": ( - "Human origin, era context, institution, biography, altered-state suspicion, " - "aesthetic elegance, and theorem labels are metadata only. They may route a " - "candidate, but they cannot promote it." - ), - "careful_nonclaim": ( - "This does not claim that unusual historical, cultural, or personal origins " - "invalidate an equation. It only says origin stories are not receipts." - ), - "metadata_fields": { - "human_origin": "metadata_only", - "era_context": "metadata_only", - "institutional_prestige": "metadata_only", - "theorem_label": "routing_hint_only", - "aesthetic_elegance": "routing_hint_only", - "accepted_result": "hold_until_forward_receipted", - "formal_closure": "admission_candidate", - }, - }, - "foundation_set": [ - { - "symbol": "O4", - "name": "four_primitives", - "meaning": "field, shear, packet, spectral", - }, - { - "symbol": "SD", - "name": "dimensional_shell", - "meaning": "projection plus residual plus closure shell", - }, - { - "symbol": "MN", - "name": "mass_number", - "meaning": "metric weight and admissibility pressure", - }, - { - "symbol": "gamma_star", - "name": "geodesic_projection", - "meaning": "shortest lawful projection path under declared costs", - }, - { - "symbol": "H_dV", - "name": "information_horizon", - "meaning": "entropy and Underverse boundary", - }, - { - "symbol": "Omega", - "name": "torsion_shear_event_correction", - "meaning": "deformation and event-order accounting", - }, - { - "symbol": "Lambda", - "name": "logogram_substitution", - "meaning": "callable abstraction atom", - }, - { - "symbol": "A", - "name": "admission_gate", - "meaning": "ACCEPT, HOLD, or QUARANTINE decision", - }, - ], - "shell_equation": { - "id": "SD_no_infinity_shell", - "display": "SD = L4(O4) + L3(Rg3) + chi0 + U4 + E_HD + U_under", - "terms": { - "SD": "source object in its full domain dimension", - "O4": "visible four-primitive projection", - "Rg3": "genus-3 residual shadow", - "chi0": "closure witness", - "U4": "unseen but promotable reserve", - "E_HD": "high-dimensional projection energy tax", - "U_under": "Underverse lane for forbidden, failed, entropy-bound, or non-promotable residue", - }, - }, - "admission_equation": { - "id": "forward_equation_acceptance", - "display": "ACCEPT(E) iff receipt_recomputes(E) and chi0(E)=0 and residual_declared(E) and B(E) dict[str, Any]: - identity = { - "equation_id": equation_id, - "semantic_key": semantic_key, - "canonical_equation": canonical_equation, - } - identity["equation_hash"] = hash_obj(identity) - dependency_hash = hash_obj( - { - "source_kernel": "F0_forward_foundation_kernel", - "parent_equations": parent_equations, - "transform_rule": transform_rule, - } - ) - receipt_payload = { - "identity": identity, - "dependency_hash": dependency_hash, - "projection": projection, - "domain_laws": domain_laws, - "decision": decision, - "residual_policy": residual_policy, - } - return { - "identity": identity, - "foundation": { - "source_kernel": "F0_forward_foundation_kernel", - "parent_equations": parent_equations, - "transform_rule": transform_rule, - "dependency_hash": dependency_hash, - }, - "projection": projection, - "admissibility": { - "domain_laws": domain_laws, - "dimensional_scaling": "declared_or_hold", - "energy_budget": "E_HD_must_be_paid_or_hold", - "information_budget": "H_dV_boundary_must_be_declared_or_hold", - "closure_status": "closed_only_if_chi0_zero", - "residual_policy": residual_policy, - }, - "receipt": { - "source_hash": "F0", - "equation_hash": identity["equation_hash"], - "dependency_hash": dependency_hash, - "receipt_hash": hash_obj(receipt_payload), - "decision": decision, - }, - } - - -def build_atoms() -> dict[str, Any]: - atoms = [ - equation_atom( - equation_id="F1_dimensional_shell", - semantic_key="foundation.shell.no_infinity", - canonical_equation="SD = L4(O4) + L3(Rg3) + chi0 + U4 + E_HD + U_under", - parent_equations=["F0_forward_foundation_kernel"], - transform_rule="declare_shell_terms", - projection={ - "O4": "visible_projection", - "Rg3": "bounded_residual_shadow", - "chi0": "closure_witness", - "U4": "promotable_reserve", - "E_HD": "projection_energy_tax", - "Underverse": "non_promotable_entropy_lane", - }, - domain_laws=["no_silent_infinity", "residual_declared", "closure_required_for_accept"], - decision="ACCEPT_CONTRACT", - residual_policy="all non-closed material routes to U_under, HOLD, QUARANTINE, or NaN0", - ), - equation_atom( - equation_id="F2_mass_number_metric", - semantic_key="foundation.mass_number.metric_pressure", - canonical_equation="MN -> g_MN", - parent_equations=["F1_dimensional_shell"], - transform_rule="derive_metric_pressure_from_mass_number", - projection={ - "O4": "metric-visible pressure", - "Rg3": "unclosed metric residual", - "chi0": "metric closure witness", - "U4": "candidate metric reserves", - "E_HD": "metric projection cost", - "Underverse": "metric category errors", - }, - domain_laws=["mass_is_not_distance_until_admissibility_closure", "category_errors_hold"], - decision="ACCEPT_CONTRACT", - residual_policy="raw mass-number divergence must carry typed residuals until closed", - ), - equation_atom( - equation_id="F3_geodesic_projection", - semantic_key="foundation.geodesic.shortest_lawful_path", - canonical_equation="gamma_star = argmin(path_cost + E_HD + H_dV_cost)", - parent_equations=["F2_mass_number_metric"], - transform_rule="select_shortest_lawful_projection_path", - projection={ - "O4": "selected visible path", - "Rg3": "path shadow residual", - "chi0": "path closure witness", - "U4": "unselected lawful alternates", - "E_HD": "path projection energy", - "Underverse": "forbidden or entropy-bound paths", - }, - domain_laws=["costs_declared", "forbidden_paths_do_not_promote", "event_order_preserved"], - decision="ACCEPT_CONTRACT", - residual_policy="unselected or forbidden route material remains residual or Underverse", - ), - equation_atom( - equation_id="F4_logogram_abstraction", - semantic_key="foundation.logogram.callable_atom", - canonical_equation="O4 + Rg3 -> Lambda -> D_lambda(theta) + r -> chi0", - parent_equations=["F3_geodesic_projection"], - transform_rule="promote_repeated_lawful_structure_to_callable_atom", - projection={ - "O4": "visible callable surface", - "Rg3": "abstraction residual", - "chi0": "rehydration closure witness", - "U4": "future promotable motifs", - "E_HD": "abstraction and replay cost", - "Underverse": "same-surface collisions and invalid cancellations", - }, - domain_laws=["payload_not_glyph", "same_surface_not_same_atom", "correct_output_not_operator_proof"], - decision="ACCEPT_CONTRACT", - residual_policy="payload, orientation, placement, role, and replay order must be preserved or residualized", - ), - equation_atom( - equation_id="F5_admission_gate", - semantic_key="foundation.admission.accept_hold_quarantine", - canonical_equation="A(E) = ACCEPT iff receipt_recomputes and chi0=0 and residual_declared and B dict[str, Any]: - kernel_hash = hash_obj(kernel) - atoms_hash = hash_obj(atoms) - pass_gate = { - "gate": "PASS", - "kernel_hash": kernel_hash, - "atoms_hash": atoms_hash, - "source_docs": [source_ref(path) for path in SOURCE_DOCS], - "exact_replay": True, - "clock_participates_in_hash": False, - } - add_gate = { - "gate": "ADD", - "foundation_symbol_count": len(kernel["foundation_set"]), - "equation_atom_count": atoms["atom_count"], - "source_doc_count": len(SOURCE_DOCS), - } - state_before_pause = hash_obj({"pass": pass_gate, "add": add_gate}) - pause_gate = { - "gate": "PAUSE", - "event_index": 3, - "delta_bytes": 0, - "state_root_before": state_before_pause, - "state_root_after": state_before_pause, - "clock_participates_in_hash": False, - } - subtract_gate = { - "gate": "SUBTRACT", - "trusted_backward_labels": 0, - "trusted_forward_contract_atoms": atoms["atom_count"], - "uncompiled_equation_default": "HOLD", - } - receipt = { - "schema": "foundation_forward_equation_compiler_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "kernel_path": rel(KERNEL), - "kernel_hash": kernel_hash, - "atoms_path": rel(ATOMS), - "atoms_hash": atoms_hash, - "gates": [pass_gate, add_gate, pause_gate, subtract_gate], - "source_docs": [source_ref(path) for path in SOURCE_DOCS], - "decision": "ACCEPT_CONTRACT_HOLD_RESULTS", - "claim_boundary": kernel["claim_boundary"], - "canonical_statement": kernel["canonical_rule"], - "origin_metadata_doctrine": kernel["origin_metadata_doctrine"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(kernel: dict[str, Any], atoms: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Foundation Forward Equation Compiler", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - kernel["claim_boundary"], - "", - "## Canonical Rule", - "", - "```text", - kernel["canonical_rule"], - "```", - "", - "## Origin Metadata Doctrine", - "", - kernel["origin_metadata_doctrine"]["careful_nonclaim"], - "", - "```text", - kernel["origin_metadata_doctrine"]["canonical_statement"], - kernel["origin_metadata_doctrine"]["joke_boundary"], - "```", - "", - kernel["origin_metadata_doctrine"]["rule"], - "", - "## Foundation Set", - "", - "| Symbol | Name | Meaning |", - "|---|---|---|", - ] - for item in kernel["foundation_set"]: - lines.append(f"| `{item['symbol']}` | `{item['name']}` | {item['meaning']} |") - lines.extend( - [ - "", - "## Shell Equation", - "", - "```text", - kernel["shell_equation"]["display"], - "```", - "", - "## Forward Atoms", - "", - "| Equation | Decision | Rule |", - "|---|---|---|", - ] - ) - for atom in atoms["atoms"]: - lines.append( - f"| `{atom['identity']['equation_id']}` | `{atom['receipt']['decision']}` | " - f"{atom['foundation']['transform_rule']} |" - ) - lines.extend( - [ - "", - "## Gate Loop", - "", - "```text", - "PASS -> ADD -> PAUSE -> SUBTRACT => ACCEPT_CONTRACT/HOLD_RESULTS", - "```", - "", - "Anything that cannot compile forward from `F0_forward_foundation_kernel` remains `HOLD`, `QUARANTINE`, `U_under`, or `NaN0`.", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - kernel = build_kernel() - atoms = build_atoms() - receipt = build_receipt(kernel, atoms) - KERNEL.write_text(json.dumps(kernel, indent=2, sort_keys=True) + "\n", encoding="utf-8") - ATOMS.write_text(json.dumps(atoms, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(kernel, atoms, receipt) - print( - json.dumps( - { - "kernel": rel(KERNEL), - "atoms": rel(ATOMS), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/four_force_geometry_probe_prior.py b/4-Infrastructure/shim/four_force_geometry_probe_prior.py deleted file mode 100644 index 56d811d1..00000000 --- a/4-Infrastructure/shim/four_force_geometry_probe_prior.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for using the Merkle-tensegrity lattice as a four-force probe geometry.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -SOURCE = SHIM / "merkle_tensegrity_load_equation_receipt.json" -OUT = SHIM / "four_force_geometry_probe_prior_receipt.json" -CURRICULUM = SHIM / "four_force_geometry_probe_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - source = json.loads(SOURCE.read_text(encoding="utf-8")) - receipt: dict[str, Any] = { - "schema": "four_force_geometry_probe_prior_v1", - "source_receipt": str(SOURCE.relative_to(REPO)), - "source_receipt_hash": source["receipt_hash"], - "source_merkle_root": source["merkle"]["root"], - "primary_read": ( - "Use the braced Merkle-tensegrity cube as a probe geometry for force " - "separation. Gravity is a direct external load. Electromagnetism governs " - "material bonding, thermal response, sensing, and print actuation. Strong " - "interaction appears only as a material binding baseline at this scale. Weak " - "interaction appears as a radiation/transmutation boundary, not a printable " - "load actuator." - ), - "four_force_mapping": { - "gravity": { - "active_in_harness": True, - "equation_slot": "p_i^G = [0, 0, m_i g]", - "geometry_role": "external body load and support reaction driver", - "measurable_proxy": ["mass_per_node", "gravity", "support_reactions", "residual_norm_l2"], - "print_control_status": "directly modeled as load", - }, - "electromagnetic": { - "active_in_harness": "implicit", - "equation_slot": "K_material, thermal_window, bonding_energy, sensor_field", - "geometry_role": "stiffness, adhesion, heat flow, actuator/sensor coupling", - "measurable_proxy": ["material_batch", "temperature", "extrusion/flow", "conductivity", "sensor_digest"], - "print_control_status": "dominant real-world print/material force but not yet solved in toy harness", - }, - "strong": { - "active_in_harness": False, - "equation_slot": "E_binding_material_baseline", - "geometry_role": "nuclear binding baseline behind material mass and atomic stability", - "measurable_proxy": ["material isotope/specification only if relevant"], - "print_control_status": "not a geometry control knob for ordinary 3D printing", - }, - "weak": { - "active_in_harness": False, - "equation_slot": "R_decay_or_radiation_guard", - "geometry_role": "radioactive decay/transmutation boundary condition", - "measurable_proxy": ["radiation/isotope safety status only if relevant"], - "print_control_status": "not a load actuator; safety guard only", - }, - }, - "probe_state_16d": [ - "x", - "y", - "z", - "mass_density", - "gravity_load_z", - "lateral_load_x", - "lateral_load_y", - "edge_force_density_q", - "support_reaction", - "print_density_rho", - "em_stiffness_or_thermal_state", - "material_binding_baseline", - "radiation_decay_guard", - "equilibrium_residual", - "merkle_phase_commitment", - "closure_margin", - ], - "probe_equations": { - "force_sum": "p_i = p_i^G + p_i^EM + p_i^strong_baseline + p_i^weak_guard", - "gravity_load": "p_i^G = [0, 0, m_i g]", - "mechanical_closure": "sum_j q_ij(x_i - x_j) + p_i^G + r_i + p_i^EM ~= 0", - "em_material_placeholder": "p_i^EM := thermal/material/sensor correction term pending calibration", - "strong_baseline": "p_i^strong_baseline := 0 at macro geometry scale; enters material constants only", - "weak_guard": "p_i^weak_guard := 0 unless radioactive/transmutation boundary is active", - "closure_margin": "margin = epsilon_mech - ||R_mech||_2", - "commitment": "M_root = MerkleRoot(H(node/edge/support/force records))", - }, - "what_it_says_now": [ - "the current toy harness is mostly a gravity-plus-mechanics probe", - "the bracing result shows geometry controls whether lateral disturbance can close", - "EM must be the next real extension because printability is material/thermal/bonding dominated", - "strong and weak should remain material/safety metadata unless the experiment involves nuclear/radiological regimes", - "the 16D lift is useful as a typed probe-state vector, not as sixteen physical spatial dimensions", - ], - "next_probe_steps": [ - "add calibrated material stiffness and thermal expansion terms as the EM lane", - "add material batch metadata for binding baseline rather than pretending to actuate strong force", - "add radiation/isotope safety guard as a weak-force boundary if relevant", - "compare residual and Merkle roots across gravity-only, gravity+EM, and failed unbraced geometries", - ], - "failure_rules": [ - "treating all four forces as equally active in a desktop 3D print -> overclaim", - "using strong/weak forces as geometry knobs without nuclear/radiological model -> invalid", - "calling Merkle commitment a force measurement -> invalid", - "adding 16D axes without typed semantics -> bookkeeping noise", - "EM material lane omitted in real print safety claim -> hold", - ], - "claim_boundary": ( - "This is a probe-state prior for separating force roles in a toy lattice. " - "It is not a unified-field result, not a structural safety certificate, and " - "not evidence that strong or weak interactions are controllable by this geometry." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_force_lane", - "input": "gravity, electromagnetism, strong, or weak term in lattice probe", - "target": "external load, material/thermal lane, binding baseline, or safety guard", - }, - { - "task": "build_16d_probe_state", - "input": "node geometry, load, stress, material, residual, Merkle data", - "target": "typed 16D probe vector with no untyped axes", - }, - { - "task": "reject_force_overclaim", - "input": "claim that toy print lattice probes all four forces directly", - "target": "gravity direct, EM next extension, strong/weak metadata or guard only", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "source_receipt_hash": receipt["source_receipt_hash"], - "probe_state_dimensions": len(receipt["probe_state_16d"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/fpga_nanokernel_rrc_analysis.py b/4-Infrastructure/shim/fpga_nanokernel_rrc_analysis.py deleted file mode 100644 index 41f2d86f..00000000 --- a/4-Infrastructure/shim/fpga_nanokernel_rrc_analysis.py +++ /dev/null @@ -1,540 +0,0 @@ -#!/usr/bin/env python3 -"""Rainbow Raccoon Compiler analysis for FPGA/nanokernel/Verilator approach. - -This script applies the Rainbow Raccoon manifold projection to the FPGA programming -approach components to identify optimization targets and map adjustments. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "fpga_nanokernel_rrc_receipt.json" - - -MANIFOLD_AXES = [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared", -] - - -LAW_SHAPE_PROTOTYPES: dict[str, dict[str, float]] = { - "SignalShapedRouteCompiler": { - "semantic_entropy": 0.58, - "geometric_mass": 0.28, - "compression_pressure": 0.92, - "topology_torsion": 0.34, - "receipt_density": 0.78, - "field_energy": 0.52, - "hardware_affinity": 0.61, - "proof_readiness": 0.42, - "residual_risk": 0.31, - "shape_closure": 0.76, - "history_depth": 0.46, - "negative_control_strength": 0.83, - "projection_declared": 0.91, - "decoder_declared": 0.88, - "witness_declared": 0.79, - "scale_band_declared": 0.64, - }, - "ProjectableGeometryTopology": { - "semantic_entropy": 0.34, - "geometric_mass": 0.94, - "compression_pressure": 0.56, - "topology_torsion": 0.72, - "receipt_density": 0.81, - "field_energy": 0.76, - "hardware_affinity": 0.68, - "proof_readiness": 0.49, - "residual_risk": 0.37, - "shape_closure": 0.90, - "history_depth": 0.38, - "negative_control_strength": 0.61, - "projection_declared": 0.95, - "decoder_declared": 0.70, - "witness_declared": 0.84, - "scale_band_declared": 0.73, - }, - "FPGAHardwareLoader": { # New shape for FPGA programming - "semantic_entropy": 0.25, - "geometric_mass": 0.45, - "compression_pressure": 0.38, - "topology_torsion": 0.28, - "receipt_density": 0.85, - "field_energy": 0.62, - "hardware_affinity": 0.95, - "proof_readiness": 0.35, - "residual_risk": 0.42, - "shape_closure": 0.78, - "history_depth": 0.25, - "negative_control_strength": 0.88, - "projection_declared": 0.92, - "decoder_declared": 0.88, - "witness_declared": 0.82, - "scale_band_declared": 0.71, - }, - "NanokernelSurface": { # New shape for nanokernel - "semantic_entropy": 0.42, - "geometric_mass": 0.35, - "compression_pressure": 0.85, - "topology_torsion": 0.38, - "receipt_density": 0.72, - "field_energy": 0.68, - "hardware_affinity": 0.82, - "proof_readiness": 0.48, - "residual_risk": 0.35, - "shape_closure": 0.85, - "history_depth": 0.55, - "negative_control_strength": 0.72, - "projection_declared": 0.88, - "decoder_declared": 0.65, - "witness_declared": 0.78, - "scale_band_declared": 0.58, - }, - "VerilatorSimulation": { # New shape for Verilator - "semantic_entropy": 0.38, - "geometric_mass": 0.52, - "compression_pressure": 0.45, - "topology_torsion": 0.32, - "receipt_density": 0.68, - "field_energy": 0.58, - "hardware_affinity": 0.75, - "proof_readiness": 0.52, - "residual_risk": 0.28, - "shape_closure": 0.82, - "history_depth": 0.42, - "negative_control_strength": 0.65, - "projection_declared": 0.85, - "decoder_declared": 0.72, - "witness_declared": 0.80, - "scale_band_declared": 0.65, - }, - "HoldForUnlawfulOrUnderspecifiedShape": { - "semantic_entropy": 0.76, - "geometric_mass": 0.40, - "compression_pressure": 0.50, - "topology_torsion": 0.83, - "receipt_density": 0.24, - "field_energy": 0.70, - "hardware_affinity": 0.25, - "proof_readiness": 0.10, - "residual_risk": 0.91, - "shape_closure": 0.19, - "history_depth": 0.74, - "negative_control_strength": 0.12, - "projection_declared": 0.18, - "decoder_declared": 0.15, - "witness_declared": 0.10, - "scale_band_declared": 0.22, - }, -} - - -FIELD_EQUATIONS = { - "SignalShapedRouteCompiler": ( - "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); " - "promote iff exact decode hash closes and total bytes beat incumbent" - ), - "ProjectableGeometryTopology": ( - "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0" - ), - "FPGAHardwareLoader": ( - "bitstream -> uart_protocol -> fpga_configuration; " - "admit iff magic_header, length_checksum, footer_signature, and ack_sequence close" - ), - "NanokernelSurface": ( - "gcl_bytecode -> syscall_interface -> hardware_shim; " - "admit iff memory_arena, swarm_coordination, lawful_loss_semantics, and triumvirate_clock close" - ), - "VerilatorSimulation": ( - "verilog -> cpp_model -> simulation_trace; " - "admit iff timing_correctness, resource_constraints, testbench_coverage, and vcd_trace close" - ), - "HoldForUnlawfulOrUnderspecifiedShape": ( - "HOLD iff projection, decoder, witness, scale, or residual accounting is missing" - ), -} - - -@dataclass(frozen=True) -class RRCObject: - object_id: str - label: str - kind: str - payload: str - source_path: str | None = None - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def clamp01(value: float) -> float: - return max(0.0, min(1.0, value)) - - -def keyword_score(text: str, keywords: list[str]) -> float: - lowered = text.lower() - if not keywords: - return 0.0 - hits = sum(1 for word in keywords if word.lower() in lowered) - return hits / len(keywords) - - -def text_payload(path: str) -> str: - p = REPO / path - if not p.exists(): - return "" - data = p.read_text(encoding="utf-8", errors="replace") - return data[:12000] - - -def build_fpga_objects() -> list[RRCObject]: - """Build RRC objects for FPGA/nanokernel/Verilator components.""" - return [ - RRCObject( - object_id="fpga_obj_verilog_design", - label="Meta-Manifold Prover Verilog Design", - kind="verilog_hardware", - source_path="4-Infrastructure/hardware/metamanifold_prover_gowin.v", - payload=text_payload("4-Infrastructure/hardware/metamanifold_prover_gowin.v"), - ), - RRCObject( - object_id="fpga_obj_verilator_testbench", - label="Verilator Testbench for Meta-Manifold Prover", - kind="verilator_simulation", - source_path="4-Infrastructure/hardware/tb_metamanifold_prover.cpp", - payload=text_payload("4-Infrastructure/hardware/tb_metamanifold_prover.cpp"), - ), - RRCObject( - object_id="fpga_obj_nanokernel_loader", - label="Nanokernel UART FPGA Loader", - kind="nanokernel_surface", - source_path="4-Infrastructure/nano-kernel/fpga_uart_loader.gcl", - payload=text_payload("4-Infrastructure/nano-kernel/fpga_uart_loader.gcl"), - ), - RRCObject( - object_id="fpga_obj_simulation_results", - label="Verilator Simulation Results", - kind="simulation_receipt", - source_path="6-Documentation/docs/verilator_simulation_results_2026-05-09.md", - payload=text_payload("6-Documentation/docs/verilator_simulation_results_2026-05-09.md"), - ), - RRCObject( - object_id="fpga_obj_approach_design", - label="Nanokernel + Verilator FPGA Programming Approach", - kind="architecture_design", - source_path="6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md", - payload=text_payload("6-Documentation/docs/nanokernel_verilator_fpga_approach_2026-05-09.md"), - ), - ] - - -def project_to_manifold(obj: RRCObject) -> dict[str, float]: - """Project object onto 16-axis manifold.""" - text = obj.payload - size = max(1, len(text.encode("utf-8"))) - unique_chars = len(set(text)) if text else 0 - entropy_proxy = clamp01(unique_chars / 96.0) - json_like = 1.0 if text.lstrip().startswith(("{", "[")) else 0.0 - source_declared = 1.0 if obj.source_path else 0.0 - - # FPGA-specific keywords - fpga_terms = ["fpga", "verilog", "bitstream", "uart", "gowin", "tang", "hardware", "synthesis", "yosys"] - nanokernel_terms = ["nanokernel", "gcl", "syscall", "memory_arena", "swarm", "triunvirate", "lawful_loss"] - verilator_terms = ["verilator", "simulation", "testbench", "vcd", "trace", "cpp", "compile"] - protocol_terms = ["uart", "protocol", "magic_header", "checksum", "ack", "footer", "bitstream"] - verification_terms = ["test", "verify", "validate", "pass", "fail", "assertion", "coverage"] - projection_terms = ["projection", "manifold", "coordinate", "shape", "design"] - decoder_terms = ["decode", "decoder", "rehydration", "residual", "bytes", "protocol"] - witness_terms = ["receipt", "witness", "hash", "sha256", "proof", "invariant"] - scale_terms = ["scale", "lambda", "threshold", "tolerance", "budget", "bandwidth"] - - projection_declared = clamp01(max(source_declared, keyword_score(text, projection_terms))) - decoder_declared = clamp01(keyword_score(text, decoder_terms)) - witness_declared = clamp01(keyword_score(text, witness_terms)) - scale_band_declared = clamp01(keyword_score(text, scale_terms)) - negative_control_strength = clamp01(keyword_score(text, ["negative", "control", "fail", "hold", "invalid"])) - receipt_density = clamp01((text.lower().count("receipt") + text.lower().count("hash")) / 18.0) - - residual_risk = clamp01( - 1.0 - - ( - 0.20 * projection_declared - + 0.20 * decoder_declared - + 0.25 * witness_declared - + 0.15 * scale_band_declared - + 0.20 * negative_control_strength - ) - ) - shape_closure = clamp01( - 0.30 * projection_declared - + 0.25 * decoder_declared - + 0.25 * witness_declared - + 0.20 * scale_band_declared - ) - - hardware_affinity = clamp01(keyword_score(text, fpga_terms + nanokernel_terms + verilator_terms)) - history_depth = clamp01(keyword_score(text, ["history", "recursive", "evolution", "curriculum", "nanokernel"])) - - return { - "semantic_entropy": entropy_proxy, - "geometric_mass": clamp01(keyword_score(text, ["geometry", "topology", "manifold", "fpga", "hardware"])), - "compression_pressure": clamp01(keyword_score(text, ["compression", "codec", "bytes", "optimize", "reduce"])), - "topology_torsion": clamp01(keyword_score(text, ["torsion", "contradiction", "nan0", "hold", "unlawful"])), - "receipt_density": receipt_density, - "field_energy": clamp01(keyword_score(text, ["field", "energy", "load", "gate", "equilibrium"])), - "hardware_affinity": hardware_affinity, - "proof_readiness": clamp01((witness_declared + keyword_score(text, ["lean", "theorem", "proof", "verify"])) / 2.0), - "residual_risk": residual_risk, - "shape_closure": shape_closure, - "history_depth": history_depth, - "negative_control_strength": negative_control_strength, - "projection_declared": projection_declared, - "decoder_declared": decoder_declared, - "witness_declared": witness_declared, - "scale_band_declared": scale_band_declared, - } - - -def manifold_distance(a: dict[str, float], b: dict[str, float]) -> float: - """Calculate Euclidean distance between manifold coordinates.""" - total = 0.0 - for axis in MANIFOLD_AXES: - total += (a.get(axis, 0.0) - b.get(axis, 0.0)) ** 2 - return math.sqrt(total / len(MANIFOLD_AXES)) - - -def nearest_lawful_shape(coords: dict[str, float], kind: str) -> dict[str, Any]: - """Find nearest lawful shape prototype.""" - scored = [ - { - "shape": shape, - "distance": manifold_distance(coords, prototype), - "raw_distance": manifold_distance(coords, prototype), - } - for shape, prototype in LAW_SHAPE_PROTOTYPES.items() - ] - scored.sort(key=lambda item: item["distance"]) - best = scored[0] - return { - "shape": best["shape"], - "distance": round(best["distance"], 6), - "declared_kind": kind, - "alternates": scored[1:4], - } - - -def type_witness(obj: RRCObject, coords: dict[str, float], shape: str, distance: float) -> dict[str, Any]: - """Generate type witness for object.""" - required_axes = [ - "projection_declared", - "witness_declared", - "scale_band_declared", - ] - - if shape == "FPGAHardwareLoader": - required_axes.extend(["decoder_declared", "hardware_affinity"]) - if shape == "NanokernelSurface": - required_axes.extend(["decoder_declared", "shape_closure", "hardware_affinity"]) - if shape == "VerilatorSimulation": - required_axes.extend(["decoder_declared", "proof_readiness", "hardware_affinity"]) - - missing = [axis for axis in required_axes if coords.get(axis, 0.0) < 0.35] - status = "HOLD" if missing or shape == "HoldForUnlawfulOrUnderspecifiedShape" else "CANDIDATE" - if distance > 0.55: - status = "HOLD" - if "nearest_shape_distance" not in missing: - missing.append("nearest_shape_distance") - - witness_payload = { - "object_id": obj.object_id, - "shape": shape, - "status": status, - "required_axes": required_axes, - "missing_or_weak_axes": missing, - "lean_boundary": "declared_not_proved", - "conservative_synthesis": status != "CANDIDATE", - } - return witness_payload | {"witness_hash": sha256_text(stable_json(witness_payload))} - - -def compile_object(obj: RRCObject) -> dict[str, Any]: - """Compile object through RRC pipeline.""" - coords = project_to_manifold(obj) - nearest = nearest_lawful_shape(coords, obj.kind) - witness = type_witness(obj, coords, nearest["shape"], float(nearest["distance"])) - field_equation = FIELD_EQUATIONS[nearest["shape"]] - compiled = { - "object": { - "object_id": obj.object_id, - "label": obj.label, - "kind": obj.kind, - "source_path": obj.source_path, - "payload_sha256": sha256_text(obj.payload), - "payload_bytes_sampled": len(obj.payload.encode("utf-8")), - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt", - ], - "manifold_projection": { - "axes": MANIFOLD_AXES, - "coordinates": {axis: round(coords[axis], 6) for axis in MANIFOLD_AXES}, - }, - "nearest_lawful_shape": nearest, - "type_witness": witness, - "field_equation": field_equation, - } - compiled["invariant_receipt"] = { - "schema": "rrc.fpga_object_receipt.v1", - "object_id": obj.object_id, - "shape": nearest["shape"], - "status": witness["status"], - "receipt_hash": sha256_text(stable_json(compiled)), - } - return compiled - - -def build_receipt() -> dict[str, Any]: - """Build RRC receipt for FPGA/nanokernel approach.""" - objects = build_fpga_objects() - compiled_objects = [compile_object(obj) for obj in objects] - - # Calculate map adjustments - candidate_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "CANDIDATE") - hold_count = sum(1 for obj in compiled_objects if obj["type_witness"]["status"] == "HOLD") - - # Identify common missing axes - all_missing = [] - for obj in compiled_objects: - all_missing.extend(obj["type_witness"]["missing_or_weak_axes"]) - - missing_frequency = {} - for axis in all_missing: - missing_frequency[axis] = missing_frequency.get(axis, 0) + 1 - - # Generate map adjustment recommendations - recommendations = [] - - if missing_frequency.get("proof_readiness", 0) > 0: - recommendations.append({ - "priority": "HIGH", - "axis": "proof_readiness", - "current_state": "Lean boundary: declared_not_proved", - "adjustment": "Add Lean formal verification for Meta-Manifold Prover operations", - "expected_improvement": "+0.15 proof_readiness score", - }) - - if missing_frequency.get("scale_band_declared", 0) > 0: - recommendations.append({ - "priority": "HIGH", - "axis": "scale_band_declared", - "current_state": "No explicit scale/tolerance declarations", - "adjustment": "Add Q16_16 precision bounds and timing constraints to Verilog", - "expected_improvement": "+0.20 scale_band_declared score", - }) - - if missing_frequency.get("decoder_declared", 0) > 0: - recommendations.append({ - "priority": "MEDIUM", - "axis": "decoder_declared", - "current_state": "Protocol decoder not fully specified", - "adjustment": "Complete UART protocol decoder specification in nanokernel loader", - "expected_improvement": "+0.15 decoder_declared score", - }) - - if missing_frequency.get("witness_declared", 0) > 0: - recommendations.append({ - "priority": "MEDIUM", - "axis": "witness_declared", - "current_state": "Invariant receipts incomplete", - "adjustment": "Add hash-based receipts for each programming stage", - "expected_improvement": "+0.12 witness_declared score", - }) - - receipt: dict[str, Any] = { - "schema": "fpga_nanokernel_rrc_analysis_v1", - "claim_state": "integration_shim_not_formal_proof", - "compiler_name": "Rainbow Raccoon Compiler", - "compiler_abbrev": "RRC", - "analysis_target": "FPGA/Nanokernel/Verilator Programming Approach", - "primary_read": ( - "RRC analysis of FPGA programming approach identifies shape classifications " - "and map adjustments for optimization. The approach shows strong hardware affinity " - "but needs formal verification and scale-band declarations." - ), - "manifold_axes": MANIFOLD_AXES, - "lawful_shape_prototypes": LAW_SHAPE_PROTOTYPES, - "field_equations": FIELD_EQUATIONS, - "compiled_objects": compiled_objects, - "summary": { - "total_objects": len(compiled_objects), - "candidate_count": candidate_count, - "hold_count": hold_count, - "candidate_rate": candidate_count / len(compiled_objects) if compiled_objects else 0.0, - }, - "map_adjustments": { - "missing_axes_frequency": missing_frequency, - "recommendations": recommendations, - "priority_order": sorted(recommendations, key=lambda x: ( - 0 if x["priority"] == "HIGH" else 1 if x["priority"] == "MEDIUM" else 2 - )), - }, - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print( - json.dumps( - { - "receipt": str(OUT.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "compiled_object_count": len(receipt["compiled_objects"]), - "candidate_count": receipt["summary"]["candidate_count"], - "hold_count": receipt["summary"]["hold_count"], - "candidate_rate": receipt["summary"]["candidate_rate"], - "adjustment_count": len(receipt["map_adjustments"]["recommendations"]), - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/fpga_topological_extension_probe.py b/4-Infrastructure/shim/fpga_topological_extension_probe.py deleted file mode 100755 index 61303c2b..00000000 --- a/4-Infrastructure/shim/fpga_topological_extension_probe.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 -"""Detect a newly attached USB FPGA as a topological extension. - -The probe records USB devices, serial nodes, and sysfs ancestry. It can compare -against a prior snapshot and identify new nodes without relying on kernel logs. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import os -import subprocess -import time -from pathlib import Path -from typing import Any - - -DEFAULT_OUT = Path("4-Infrastructure/shim/fpga_topological_extension_snapshot.json") - - -def run_text(cmd: list[str]) -> str: - try: - return subprocess.check_output(cmd, text=True, stderr=subprocess.DEVNULL) - except (FileNotFoundError, subprocess.CalledProcessError): - return "" - - -def list_usb() -> list[dict[str, str]]: - rows = [] - for line in run_text(["lsusb"]).splitlines(): - parts = line.split() - if len(parts) < 6: - continue - rows.append( - { - "bus": parts[1], - "device": parts[3].rstrip(":"), - "id": parts[5], - "description": " ".join(parts[6:]), - "raw": line, - } - ) - return rows - - -def readlink(path: Path) -> str: - try: - return os.readlink(path) - except OSError: - return "" - - -def serial_nodes() -> list[dict[str, str]]: - nodes: dict[str, dict[str, str]] = {} - for pattern in ("/dev/ttyUSB*", "/dev/ttyACM*"): - for node in sorted(Path("/dev").glob(Path(pattern).name)): - nodes[str(node)] = { - "node": str(node), - "by_id": "", - "sysfs": "", - "driver": "", - } - - by_id = Path("/dev/serial/by-id") - if by_id.exists(): - for link in sorted(by_id.iterdir()): - target = (by_id / link.name).resolve() - entry = nodes.setdefault( - str(target), - {"node": str(target), "by_id": "", "sysfs": "", "driver": ""}, - ) - entry["by_id"] = str(link) - - for entry in nodes.values(): - node_name = Path(entry["node"]).name - sys_path = Path("/sys/class/tty") / node_name - if sys_path.exists(): - entry["sysfs"] = str(sys_path.resolve()) - entry["driver"] = readlink(sys_path / "device" / "driver") - - return sorted(nodes.values(), key=lambda item: item["node"]) - - -def snapshot() -> dict[str, Any]: - payload = { - "schema": "fpga_topological_extension_snapshot_v1", - "timestamp_unix": time.time(), - "usb": list_usb(), - "serial": serial_nodes(), - } - digest_input = json.dumps(payload, sort_keys=True).encode("utf-8") - payload["sha256"] = hashlib.sha256(digest_input).hexdigest() - return payload - - -def keyset(snapshot_payload: dict[str, Any], name: str) -> set[str]: - if "snapshot" in snapshot_payload: - snapshot_payload = snapshot_payload["snapshot"] - if name == "usb": - return {row.get("raw", "") for row in snapshot_payload.get("usb", [])} - if name == "serial": - return {row.get("node", "") + "|" + row.get("by_id", "") for row in snapshot_payload.get("serial", [])} - return set() - - -def diff(before: dict[str, Any], after: dict[str, Any]) -> dict[str, Any]: - return { - "schema": "fpga_topological_extension_diff_v1", - "before_sha256": before.get("sha256", ""), - "after_sha256": after.get("sha256", ""), - "new_usb": sorted(keyset(after, "usb") - keyset(before, "usb")), - "removed_usb": sorted(keyset(before, "usb") - keyset(after, "usb")), - "new_serial": sorted(keyset(after, "serial") - keyset(before, "serial")), - "removed_serial": sorted(keyset(before, "serial") - keyset(after, "serial")), - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--out", type=Path, default=DEFAULT_OUT) - parser.add_argument("--before", type=Path) - parser.add_argument("--watch", action="store_true") - parser.add_argument("--interval", type=float, default=1.0) - parser.add_argument("--timeout", type=float, default=60.0) - args = parser.parse_args() - - before_raw = json.loads(args.before.read_text()) if args.before else None - before = before_raw.get("snapshot", before_raw) if isinstance(before_raw, dict) else None - start = time.time() - current = snapshot() - - if args.watch and before is not None: - while time.time() - start < args.timeout: - current = snapshot() - current_diff = diff(before, current) - if current_diff["new_usb"] or current_diff["new_serial"]: - break - time.sleep(args.interval) - - result: dict[str, Any] = {"snapshot": current} - if before is not None: - result["diff"] = diff(before, current) - - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8") - print(json.dumps(result, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/fractal_go_t16_pist_bridge.py b/4-Infrastructure/shim/fractal_go_t16_pist_bridge.py deleted file mode 100644 index 1d3bb8b7..00000000 --- a/4-Infrastructure/shim/fractal_go_t16_pist_bridge.py +++ /dev/null @@ -1,348 +0,0 @@ -#!/usr/bin/env python3 -"""Compile the fractal-Go-on-T16 idea into the existing PIST bundle prior. - -The tiddler already selected ``pist_nd_bundle`` over Go tiles as the native -topology witness primitive. This receipt keeps that decision: fractal Go is -useful as a rule-language prior, but the serializable route primitive remains -a sparse PIST bundle packet with exact residual repair. -""" - -from __future__ import annotations - -import hashlib -import json -import zipfile -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Decision Diagram Compression Tuning Prior.tid" -) -OUT = SHIM / "fractal_go_t16_pist_bridge_receipt.json" -CURRICULUM_OUT = SHIM / "fractal_go_t16_pist_bridge_curriculum.jsonl" -ARCHIVE = Path("/home/allaun/Documents/ingest/ChatGPT-Batch-2026-05-08.zip") -REFINEMENT_MEMBER = "ChatGPT-16D_Torus_with_Go_Tiles.json" -GENERATED_SECTION = "!! Fractal Go T16 PIST Bridge" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def tiddler_source_surface() -> str: - """Read the source tiddler without this generated bridge section.""" - text = TIDDLER.read_text(encoding="utf-8") - kept: list[str] = [] - skipping = False - for line in text.splitlines(): - if line.strip() == GENERATED_SECTION: - skipping = True - continue - if skipping and line.startswith("!! "): - skipping = False - if not skipping: - kept.append(line) - return "\n".join(kept) + "\n" - - -def source_evidence() -> dict[str, Any]: - text = tiddler_source_surface() - markers = [ - "16D torus should use nD PIST rather than Go tiles", - "pist_nd_bundle", - "Do not simulate a full 16D torus board", - "Store sparse active PIST bundle packets", - "T16 = (S1)^16", - "Percolating the 16D Hypercube", - ] - hits = [] - for lineno, line in enumerate(text.splitlines(), start=1): - for marker in markers: - if marker in line: - hits.append({"line": lineno, "marker": marker, "text": line.strip()}) - return { - "source_tiddler": rel(TIDDLER), - "source_sha256": sha256_bytes(text.encode("utf-8")), - "generated_section_excluded": GENERATED_SECTION, - "markers": markers, - "hits": hits, - } - - -def archive_evidence() -> dict[str, Any]: - archive_bytes = ARCHIVE.read_bytes() - with zipfile.ZipFile(ARCHIVE) as zf: - names = zf.namelist() - raw = zf.read(REFINEMENT_MEMBER) - chat = json.loads(raw.decode("utf-8")) - body = "\n\n".join(str(message.get("content", "")) for message in chat.get("messages", [])) - markers = [ - "16D torus", - "fractal Go", - "goxel", - "liberties", - "capture", - "ko", - "territory", - "AMMR", - "O-AMMR", - "G16 = (T16, Sigma, N, F, R, Pi)", - "|Omega| = b^(m^16)", - "Do not simulate the full torus", - ] - marker_hits = [marker for marker in markers if marker.lower() in body.lower()] - return { - "archive_path": str(ARCHIVE), - "archive_sha256": sha256_bytes(archive_bytes), - "member_count": len(names), - "member_names": names, - "refinement_member": REFINEMENT_MEMBER, - "refinement_sha256": sha256_bytes(raw), - "title": chat.get("title"), - "timestamp": chat.get("timestamp"), - "url": chat.get("url"), - "message_count": len(chat.get("messages", [])), - "marker_hits": marker_hits, - "extracted_model": { - "compact_space": "T16 = (S1)^16", - "rule_language": "fractal_go_goxel_automaton", - "native_packet": "sparse_pist_bundle_packet", - "explosion_guard": "materialize active goxel packets only", - "claim_boundary": "proposal/pruning prior until exact residual decode/hash/byte count closes", - }, - } - - -SELF_FOLDED_CANDIDATES = [ - { - "id": "sixteen_orthoplex", - "role": "axis_pinched_basis", - "useful_as": "sharp sparse axis carrier / signed basis proposal", - "not_useful_as": "full route payload or proof of byte compression", - "verdict": "proposal_feature", - }, - { - "id": "barnes_wall_lambda_16", - "role": "dense_owner_lattice", - "useful_as": "future deterministic owner quantizer or packing diagnostic in 16D", - "not_useful_as": "serializable lattice payload without measured overhead", - "verdict": "diagnostic_until_receipted", - }, - { - "id": "calabi_yau_8_complex", - "role": "continuous_topological_fold_metaphor", - "useful_as": "language for compactification / cycle bookkeeping", - "not_useful_as": "finite compression primitive in this stack", - "verdict": "background_only", - }, - { - "id": "sixteen_cube", - "role": "active_frontier_reference", - "useful_as": "65536-state activation/card prior and hypercube reachability check", - "not_useful_as": "full materialized 16D board", - "verdict": "frontier_model_only", - }, -] - -GO_TO_PIST_COMPILATION = [ - { - "go_term": "placement", - "compiled_role": "open_sparse_bundle_packet", - "receipt_field": "active_bundle_id", - }, - { - "go_term": "liberties", - "compiled_role": "admissible_continuation_count", - "receipt_field": "liberty_count", - }, - { - "go_term": "capture", - "compiled_role": "residual_or_void_collapse", - "receipt_field": "capture_receipt_id", - }, - { - "go_term": "territory", - "compiled_role": "deterministic_owner_region", - "receipt_field": "owner_route_id", - }, - { - "go_term": "ko", - "compiled_role": "no_repeat_invalid_trace", - "receipt_field": "ko_trace_hash", - }, - { - "go_term": "fractal_scale", - "compiled_role": "pist_shell_base_and_offset", - "receipt_field": "shell_k_offset_t", - }, -] - -EQUATIONS = [ - { - "id": "FG0_torus_phase_space", - "equation": "T16 = (S1)^16; theta_i == theta_i + 2*pi", - "meaning": "Use compact cyclic phase space so boundary failure becomes recurrence or capture, not infinity.", - }, - { - "id": "FG1_tile_state", - "equation": "sigma_i = (occupancy, chi, kappa, rho, lambda_mode, epsilon_budget, q, scale)", - "meaning": "Treat a Go tile as a bounded diagnostic state packet, not as payload.", - }, - { - "id": "FG2_sparse_automaton", - "equation": "G16 = (T16, Sigma, N, F, R, Pi)", - "meaning": "Fractal Go is a compact recursive admissibility automaton over toroidal phase space.", - }, - { - "id": "FG3_liberties", - "equation": "L(G_i) = {n in N(i) | A(n) == admissible}", - "meaning": "Liberties measure admissible continuation rather than board-game freedom.", - }, - { - "id": "FG4_capture", - "equation": "|L(G_i)| == 0 -> residual_lane or void_receipt or archive_packet or shadow_projection", - "meaning": "Capture is topological cleanup: collapse unstable state into a bounded receipt path.", - }, - { - "id": "FG5_multiscale_update", - "equation": "sigma_i^k(t+1) = F(N_i^k, P_down(sigma^(k+1)), P_up(sigma^(k-1)), Phi)", - "meaning": "Fine tiles supply detail; coarse tiles enforce law; projections must be receipted.", - }, - { - "id": "FG6_state_explosion_guard", - "equation": "|Omega| = b^(m^16); materialize(active_packets) only", - "meaning": "Never store or search the full 16D board.", - }, - { - "id": "FG7_pist_compilation", - "equation": "GoTile_i^k -> PistBundlePacket(shell_k, offset_t, fiber_vector_hash, phase, residual_budget, receipt_hash)", - "meaning": "The native implementation target is sparse PIST bundle packets.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "fractal_go_t16_pist_bridge_v1", - "generated_at": GENERATED_AT, - "archive_evidence": archive_evidence(), - "source_evidence": source_evidence(), - "primary_decision": { - "name": "compile_fractal_go_to_sparse_pist_bundle", - "statement": ( - "Use fractal Go as a rule-language prior for liberties, capture, " - "territory, ko, and multiscale admissibility; keep PIST bundle " - "packets as the serializable route primitive." - ), - "native_primitive": "pist_nd_bundle", - "rejected_native_primitive": "full_fractal_go_t16_board", - }, - "self_folded_shape_verdicts": SELF_FOLDED_CANDIDATES, - "go_to_pist_compilation": GO_TO_PIST_COMPILATION, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "t16_phase_key", - "goxel_packet_id", - "tile_occupancy_class", - "chirality_class", - "curvature_class", - "density_mass_class", - "spectral_mode_class", - "liberty_count", - "capture_receipt_id", - "ko_trace_hash", - "fractal_scale_k", - "pist_shell_k", - "pist_offset_t", - "fiber_vector_hash", - "owner_route_id", - "residual_lane_id", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "open_sparse_goxel_packet", - "compute_toroidal_neighbor_liberties", - "capture_zero_liberty_region", - "route_territory_to_owner", - "reject_ko_trace_repeat", - "project_fractal_scale_to_pist_bundle", - "emit_exact_residual_lane", - "close_with_rehydration_hash", - ], - "promotion_rule": [ - "fractal_go_layer_only_proposes_or_prunes_routes", - "full_t16_board_is_never_materialized", - "zero_liberty_capture_emits_bounded_receipt", - "ko_trace_hash_prevents_recursive_invalid_loops", - "pist_bundle_packet_fits_witness_budget", - "exact_residual_lane_restores_source_bytes", - "decoded_hash_matches_source", - "measured_total_bytes_beats_incumbent", - ], - "failure_rule": [ - "full_board_materialization -> NaN0", - "capture_without_residual_or_void_receipt -> fail_closed", - "ko_repeat_without_trace_hash -> NaN0", - "continuous_shape_claim_without_finite_packet -> diagnostic_only", - "witness_bytes_exceed_remaining_margin -> prune", - ], - "claim_boundary": ( - "This receipt compiles a conceptual fractal-Go/T16 state machine into " - "the existing sparse PIST bundle discipline. It is not a compression " - "result and does not promote a route without exact decode/hash/byte " - "measurement." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_bytes(stable_json(preimage).encode("utf-8")) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["self_folded_shape_verdicts"]: - lines.append({"type": "shape_verdict", **item}) - for item in receipt["go_to_pist_compilation"]: - lines.append({"type": "compilation_rule", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/fsdu_hyperheuristic_bridge.py b/4-Infrastructure/shim/fsdu_hyperheuristic_bridge.py deleted file mode 100644 index 4f387252..00000000 --- a/4-Infrastructure/shim/fsdu_hyperheuristic_bridge.py +++ /dev/null @@ -1,211 +0,0 @@ -#!/usr/bin/env python3 -"""Bridge hyper-heuristic demo metrics into FSDU scar-differential receipts. - -This is a receipt projection layer, not a solver. It reads the existing -hyper-heuristic orchestrator receipt and emits the dual-map FSDU accounting -surface: - - ahead scar = observed speculative failure pressure - behind scar = conservative absorbed failure pressure - delta scar = ahead - behind - commit gate = bounded delta scar <= epsilon - -The output is intended for dashboards, kanban, and follow-on replay checks. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import time -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -DEFAULT_INPUT = ROOT / "4-Infrastructure" / "shim" / "hyper_heuristic_orchestrator_receipt.json" -LOCAL_RECEIPT = ROOT / "4-Infrastructure" / "shim" / "fsdu_hyperheuristic_bridge_receipt.json" -STACK_RECEIPT = ROOT / "shared-data" / "data" / "stack_solidification" / "fsdu_hyperheuristic_bridge_receipt.json" - -PROTOCOL = "fsdu_hyperheuristic_bridge_v0" -LEAN_ANCHOR = "2-Search-Space/FAMM/FAMM_FSDU.lean" -THEORY_ANCHOR = "2-Search-Space/FAMM/docs/FSDU_theory.md" - -HEURISTIC_TO_SOLVER_BIAS = { - "adaptive": "a_star", - "balanced": "dijkstra", - "greedy": "greedy", - "conservative": "bfs", - "random": "dfs", -} - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sha256_path(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def normalize_mixture(best_heuristic: str) -> dict[str, float]: - weights = { - "bfs": 0.18, - "dfs": 0.14, - "dijkstra": 0.20, - "a_star": 0.26, - "greedy": 0.22, - } - solver = HEURISTIC_TO_SOLVER_BIAS.get(best_heuristic, "a_star") - weights[solver] += 0.25 - total = sum(weights.values()) - return {key: round(value / total, 9) for key, value in weights.items()} - - -def component_alerts(success_rate: float, failure_pressure: float, heuristic_count: int) -> list[str]: - alerts: list[str] = [] - if success_rate < 0.75: - alerts.append("heuristicBiasFailed") - if success_rate < 0.70: - alerts.append("loopPressureRising") - if failure_pressure >= 3.0: - alerts.append("deadEndConfirmed") - if heuristic_count >= 4 and success_rate >= 0.75: - alerts.append("shortcutOpened") - if not alerts: - alerts.append("edgeCostChanged") - return alerts - - -def project_component(component: dict[str, Any], epsilon: float) -> dict[str, Any]: - demo = component.get("demo_results", {}) - success_rate = float(demo.get("success_rate", 0.0)) - operations = int(demo.get("total_operations", 0)) - best_heuristic = str(demo.get("best_heuristic", "adaptive")) - heuristics = component.get("heuristics", []) - heuristic_count = len(heuristics) - - failure_pressure = max(0.0, 1.0 - success_rate) * operations - exploration_pressure = 0.01 * heuristic_count - ahead_scar = failure_pressure + exploration_pressure - - # The behind map represents conservative absorbed error. It deliberately - # lags the ahead map; the differential is the control signal. - behind_scar = failure_pressure * success_rate - scar_delta = ahead_scar - behind_scar - abs_delta = abs(scar_delta) - admissible = abs_delta <= epsilon - - return { - "component": component.get("component", "UNKNOWN"), - "description": component.get("description", ""), - "observed": { - "success_rate": success_rate, - "total_operations": operations, - "best_heuristic": best_heuristic, - "heuristic_count": heuristic_count, - }, - "fsdu_projection": { - "ahead_scar": round(ahead_scar, 9), - "behind_scar": round(behind_scar, 9), - "scar_delta": round(scar_delta, 9), - "abs_scar_delta": round(abs_delta, 9), - "epsilon": epsilon, - "admissible": admissible, - "commit_decision": "COMMIT_ALLOWED" if admissible else "RETUNE_REQUIRED", - "alerts": component_alerts(success_rate, failure_pressure, heuristic_count), - "solver_mixture": normalize_mixture(best_heuristic), - }, - "claim_boundary": "receipt_projection_not_live_path_optimality_not_compression_claim", - } - - -def build_receipt(input_path: Path, epsilon: float, out_path: Path, mirror_path: Path | None) -> dict[str, Any]: - source = json.loads(input_path.read_text(encoding="utf-8")) - components = source.get("components_implemented", []) - projected = [project_component(component, epsilon) for component in components] - all_admissible = all(row["fsdu_projection"]["admissible"] for row in projected) - max_delta = max((row["fsdu_projection"]["abs_scar_delta"] for row in projected), default=0.0) - - receipt = { - "protocol": PROTOCOL, - "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), - "source_receipt": { - "path": str(input_path), - "sha256": sha256_path(input_path), - "script_name": source.get("script_name"), - "source_timestamp": source.get("timestamp"), - }, - "lean_anchor": LEAN_ANCHOR, - "theory_anchor": THEORY_ANCHOR, - "equation": { - "state": "X_t = (M_a, M_b, S_a, S_b, Theta)", - "scar_differential": "DeltaS_t = S_a - S_b", - "commit_gate": "commit allowed iff ||DeltaS_t|| <= epsilon", - }, - "projection_policy": { - "epsilon": epsilon, - "ahead_scar": "(1 - success_rate) * total_operations + 0.01 * heuristic_count", - "behind_scar": "(1 - success_rate) * total_operations * success_rate", - "live_path_claim": False, - "compression_claim": False, - "hardware_claim": False, - }, - "components": projected, - "gate": { - "decision": "ADMIT_FSDU_BRIDGE_RECEIPT" if all_admissible else "HOLD_SCAR_DIVERGENCE", - "all_components_admissible": all_admissible, - "component_count": len(projected), - "max_abs_scar_delta": round(max_delta, 9), - "epsilon": epsilon, - "next_gate": "wire live orchestrator state snapshots into the same FSDU fields", - }, - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - if mirror_path is not None: - mirror_path.parent.mkdir(parents=True, exist_ok=True) - mirror_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--input", default=str(DEFAULT_INPUT), help="Hyper-heuristic receipt JSON") - parser.add_argument("--epsilon", type=float, default=1.25, help="Scar differential commit envelope") - parser.add_argument("--output", default=str(LOCAL_RECEIPT), help="Receipt output path") - parser.add_argument("--no-mirror", action="store_true", help="Skip shared-data mirror receipt") - args = parser.parse_args() - - mirror = None if args.no_mirror else STACK_RECEIPT - receipt = build_receipt(Path(args.input), args.epsilon, Path(args.output), mirror) - print( - json.dumps( - { - "receipt": str(Path(args.output)), - "mirror": None if mirror is None else str(mirror), - "decision": receipt["gate"]["decision"], - "component_count": receipt["gate"]["component_count"], - "max_abs_scar_delta": receipt["gate"]["max_abs_scar_delta"], - "epsilon": receipt["gate"]["epsilon"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/fsdu_live_hyperheuristic_probe.py b/4-Infrastructure/shim/fsdu_live_hyperheuristic_probe.py deleted file mode 100644 index e6a1ad8d..00000000 --- a/4-Infrastructure/shim/fsdu_live_hyperheuristic_probe.py +++ /dev/null @@ -1,381 +0,0 @@ -#!/usr/bin/env python3 -"""Run a live seeded hyper-heuristic snapshot and project it into FSDU. - -Unlike fsdu_hyperheuristic_bridge.py, this probe does not read the static -orchestrator receipt. It executes the orchestrator in-process, captures the -actual component histories, and emits a dual-map scar receipt from that live -snapshot. It remains a software witness only. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import random -import time -from pathlib import Path -from typing import Any, Callable - -from hyper_heuristic_orchestrator import ( - ComponentType, - FAMMHyperHeuristics, - FPGABuildHyperHeuristics, - GPUSchedulingHyperHeuristics, - HeuristicType, - HyperHeuristicOrchestrator, - PISTHyperHeuristics, - ShimSelectionHyperHeuristics, -) - - -ROOT = Path(__file__).resolve().parents[2] -LOCAL_RECEIPT = ROOT / "4-Infrastructure" / "shim" / "fsdu_live_hyperheuristic_probe_receipt.json" -STACK_RECEIPT = ROOT / "shared-data" / "data" / "stack_solidification" / "fsdu_live_hyperheuristic_probe_receipt.json" -PROTOCOL = "fsdu_live_hyperheuristic_probe_v0" - - -HEURISTIC_TO_SOLVER = { - "adaptive": "a_star", - "balanced": "dijkstra", - "conservative": "bfs", - "greedy": "greedy", - "random": "dfs", -} - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def add_result_accounting(result: dict[str, Any], cost: float, reward: float, success: bool | None = None) -> dict[str, Any]: - out = dict(result) - if success is not None: - out["success"] = bool(success) - out["cost"] = round(float(cost), 6) - out["reward"] = round(float(reward), 6) - return out - - -def run_component( - orchestrator: HyperHeuristicOrchestrator, - component: ComponentType, - operations: int, - context_fn: Callable[[int], dict[str, Any]], - dispatch_fn: Callable[[HeuristicType, dict[str, Any]], dict[str, Any]], -) -> list[dict[str, Any]]: - events = [] - for op_index in range(operations): - context = context_fn(op_index) - result, heuristic = orchestrator.select_and_execute(component, dispatch_fn, context) - events.append( - { - "op": op_index, - "component": component.value, - "heuristic": heuristic.value, - "success": False if result is None else bool(result.get("success", True)), - "result_hash": sha256_text(stable_json(result)), - } - ) - return events - - -def famm_context(i: int) -> dict[str, Any]: - return { - "bank": {"cells": {0: {"delay": 5.0, "delay_weight": 1.0}, 1: {"delay": 3.0, "delay_weight": 0.5}}}, - "address": i % 2, - "target_delay": 2.0 + (i % 7), - "max_delay": 6.0, - "tolerance": 1.25, - } - - -def famm_dispatch(ht: HeuristicType, ctx: dict[str, Any]) -> dict[str, Any]: - if ht == HeuristicType.GREEDY: - result = FAMMHyperHeuristics.greedy_minimize(ht, ctx) - elif ht == HeuristicType.BALANCED: - result = FAMMHyperHeuristics.frustration_balance(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - result = FAMMHyperHeuristics.adaptive_weight(ht, ctx) - else: - result = FAMMHyperHeuristics.greedy_minimize(ht, ctx) - miss = abs(float(result.get("adjusted_delay", 0.0)) - float(ctx["target_delay"])) - success = bool(result.get("success", True)) and miss <= float(ctx["tolerance"]) - return add_result_accounting(result, cost=miss * 4.0, reward=1.0 - miss, success=success) - - -def pist_context(i: int) -> dict[str, Any]: - return {"pos": {"k": i % 5, "t": (i * 3) % 13}, "phase": "seismic" if i % 3 == 0 else "grounded"} - - -def pist_dispatch(ht: HeuristicType, ctx: dict[str, Any]) -> dict[str, Any]: - if ht == HeuristicType.GREEDY: - result = PISTHyperHeuristics.linear_move(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - result = PISTHyperHeuristics.adaptive_move(ht, ctx) - else: - result = PISTHyperHeuristics.resonance_jump(ht, ctx) - new_t = int(result["new_pos"]["t"]) - k = int(result["new_pos"]["k"]) - success = 0 <= new_t <= (2 * k + 1) - cost = 0.0 if success else abs(new_t - (2 * k + 1)) + abs(min(0, new_t)) - return add_result_accounting(result, cost=cost, reward=1.0 if success else -cost, success=success) - - -def shim_context(i: int) -> dict[str, Any]: - domains = ["math", "compression", "hardware", "general", "unknown"] - performance_history = { - "math": {"math_prover_prior_metaprobe.py": 0.9, "intense_math_modeling_router.py": 0.7}, - "compression": {"compression_signal_shaping_synthesis.py": 0.8}, - } - return {"domain": domains[i % len(domains)], "task_type": "optimization", "performance_history": performance_history} - - -def shim_dispatch(ht: HeuristicType, ctx: dict[str, Any]) -> dict[str, Any]: - if ht == HeuristicType.GREEDY: - result = ShimSelectionHyperHeuristics.select_by_domain(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - result = ShimSelectionHyperHeuristics.select_adaptive(ht, ctx) - else: - result = ShimSelectionHyperHeuristics.select_by_performance(ht, ctx) - selected = result.get("selected_shim", "") - success = selected != "default_shim.py" - return add_result_accounting(result, cost=0.0 if success else 15.0, reward=1.0 if success else -3.0, success=success) - - -def gpu_context(i: int) -> dict[str, Any]: - task_queue = [ - {"name": f"gpu_task_{i}_{j}", "priority": (i + j) % 10, "memory_required": 1000 + ((i + 2) * (j + 1) * 700) % 7000} - for j in range(3) - ] - return { - "task_queue": task_queue, - "gpu_memory": 12000, - "gpu_count": 1, - "current_memory_usage": i * 650, - "gpu_states": [{"load": 0.3 + (i % 5) * 0.1, "memory": 6000}], - } - - -def gpu_dispatch(ht: HeuristicType, ctx: dict[str, Any]) -> dict[str, Any]: - if ht == HeuristicType.GREEDY: - result = GPUSchedulingHyperHeuristics.round_robin(ht, ctx) - elif ht == HeuristicType.BALANCED: - result = GPUSchedulingHyperHeuristics.priority_based(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - result = GPUSchedulingHyperHeuristics.memory_aware(ht, ctx) - else: - result = GPUSchedulingHyperHeuristics.load_balancing(ht, ctx) - assigned = result.get("assigned", []) - failed = len([row for row in assigned if row.get("gpu_id") is None]) - return add_result_accounting(result, cost=failed * 10.0, reward=1.0 - failed, success=failed == 0) - - -def fpga_context(i: int) -> dict[str, Any]: - modules = [f"module_{j}" for j in range(5)] - changed_files = [f"{module}.v" for j, module in enumerate(modules) if (i + j) % 3 == 0] - return { - "modules": modules, - "changed_files": changed_files, - "build_cache": {file_name: f"cached_{file_name}" for idx, file_name in enumerate(changed_files) if idx % 2 == 0}, - "dependency_graph": {f"module_{j}": [f"module_{k}" for k in range(j)] if j > 0 else [] for j in range(5)}, - "available_cores": 4, - "resource_budget": {"LUT": 9000, "FF": 18000, "BRAM": 18}, - "module_resources": {f"module_{j}": {"LUT": 1000 * (j + 1), "FF": 2000 * (j + 1), "BRAM": j + 1} for j in range(5)}, - "critical_paths": [["module_0", "module_2", "module_4"], ["module_1", "module_3"]], - } - - -def fpga_dispatch(ht: HeuristicType, ctx: dict[str, Any]) -> dict[str, Any]: - if ht == HeuristicType.GREEDY: - result = FPGABuildHyperHeuristics.incremental_build(ht, ctx) - pressure = len(result.get("modules_to_rebuild", [])) - success = pressure <= 2 - elif ht == HeuristicType.BALANCED: - result = FPGABuildHyperHeuristics.parallel_synthesis(ht, ctx) - pressure = len(result.get("dependent_modules", [])) - success = pressure <= 4 - elif ht == HeuristicType.ADAPTIVE: - result = FPGABuildHyperHeuristics.resource_aware(ht, ctx) - pressure = len(result.get("deferred", [])) - success = bool(result.get("success", False)) - else: - result = FPGABuildHyperHeuristics.timing_driven(ht, ctx) - pressure = len(result.get("critical_modules", [])) - success = pressure <= 4 - return add_result_accounting(result, cost=pressure * 8.0, reward=1.0 - pressure, success=success) - - -def live_mixture(events: list[dict[str, Any]]) -> dict[str, float]: - weights = {"bfs": 0.0, "dfs": 0.0, "dijkstra": 0.0, "a_star": 0.0, "greedy": 0.0} - for event in events: - solver = HEURISTIC_TO_SOLVER.get(event["heuristic"], "a_star") - weights[solver] += 1.0 - total = sum(weights.values()) or 1.0 - return {key: round(value / total, 9) for key, value in weights.items()} - - -def alerts_for(success_rate: float, avg_cost: float, switch_count: int) -> list[str]: - alerts = [] - if success_rate < 0.8: - alerts.append("heuristicBiasFailed") - if success_rate < 0.65: - alerts.append("loopPressureRising") - if avg_cost > 5.0: - alerts.append("deadEndConfirmed") - if switch_count > 0: - alerts.append("edgeCostChanged") - if not alerts: - alerts.append("shortcutOpened") - return alerts - - -def project_live_component(component: str, report: dict[str, Any], events: list[dict[str, Any]], raw_events: list[dict[str, Any]], epsilon: float) -> dict[str, Any]: - runs = len(raw_events) - successes = sum(1 for event in raw_events if event["success"]) - success_rate = successes / max(1, runs) - metric_rows = [] - for row in raw_events: - metric_rows.append(row) - global_entries = [row for row in raw_events] - failure_count = runs - successes - - # Use global_metrics for cost/reward because select_and_execute records the - # accounting result there. - avg_cost = 0.0 - avg_reward = 0.0 - if global_entries: - avg_cost = sum(float(row["cost"]) for row in global_entries) / len(global_entries) - avg_reward = sum(float(row["reward"]) for row in global_entries) / len(global_entries) - - switch_count = int(report["switch_count"]) - exploration_rate = float(report["exploration_rate"]) - ahead_scar = failure_count + avg_cost / 10.0 + switch_count + exploration_rate * runs - behind_scar = failure_count * success_rate + max(0.0, avg_cost / 20.0) - scar_delta = ahead_scar - behind_scar - abs_delta = abs(scar_delta) - admissible = abs_delta <= epsilon - return { - "component": component, - "live_snapshot": { - "successes": successes, - "total_operations": runs, - "success_rate": round(success_rate, 9), - "avg_cost": round(avg_cost, 9), - "avg_reward": round(avg_reward, 9), - "switch_count": switch_count, - "exploration_rate": round(exploration_rate, 9), - "current_heuristic": report["current_heuristic"], - }, - "fsdu_projection": { - "ahead_scar": round(ahead_scar, 9), - "behind_scar": round(behind_scar, 9), - "scar_delta": round(scar_delta, 9), - "abs_scar_delta": round(abs_delta, 9), - "epsilon": epsilon, - "admissible": admissible, - "commit_decision": "COMMIT_ALLOWED" if admissible else "RETUNE_REQUIRED", - "alerts": alerts_for(success_rate, avg_cost, switch_count), - "solver_mixture": live_mixture(events), - }, - } - - -def run_live_probe(seed: int, epsilon: float) -> dict[str, Any]: - random.seed(seed) - orchestrator = HyperHeuristicOrchestrator() - event_index: dict[str, list[dict[str, Any]]] = {} - - suites = [ - (ComponentType.FAMM_DELAY, 20, famm_context, famm_dispatch), - (ComponentType.PIST_MOVE, 15, pist_context, pist_dispatch), - (ComponentType.SHIM_SELECTION, 12, shim_context, shim_dispatch), - (ComponentType.GPU_SCHEDULING, 15, gpu_context, gpu_dispatch), - (ComponentType.FPGA_BUILD, 12, fpga_context, fpga_dispatch), - ] - for component, ops, context_fn, dispatch_fn in suites: - event_index[component.value] = run_component(orchestrator, component, ops, context_fn, dispatch_fn) - - report = orchestrator.get_performance_report() - raw_global: dict[str, list[dict[str, Any]]] = {} - for key, rows in orchestrator.global_metrics.items(): - component, heuristic = key.rsplit("_", 1) - raw_global.setdefault(component, []) - for row in rows: - raw_global[component].append({"heuristic": heuristic, **row}) - - components = [] - for component, component_report in report["components"].items(): - components.append(project_live_component(component, component_report, event_index[component], raw_global.get(component, []), epsilon)) - - all_admissible = all(row["fsdu_projection"]["admissible"] for row in components) - max_delta = max((row["fsdu_projection"]["abs_scar_delta"] for row in components), default=0.0) - return { - "protocol": PROTOCOL, - "created_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), - "seed": seed, - "epsilon": epsilon, - "claim_boundary": "live_software_snapshot_not_live_path_optimality_not_hardware_claim", - "lean_anchor": "2-Search-Space/FAMM/FAMM_FSDU.lean", - "equation": { - "state": "X_t = (M_a, M_b, S_a, S_b, Theta)", - "scar_differential": "DeltaS_t = S_a - S_b", - "commit_gate": "commit allowed iff ||DeltaS_t|| <= epsilon", - }, - "orchestrator_report_hash": sha256_text(stable_json(report)), - "event_index_hash": sha256_text(stable_json(event_index)), - "components": components, - "gate": { - "decision": "ADMIT_LIVE_FSDU_SNAPSHOT" if all_admissible else "HOLD_LIVE_SCAR_DIVERGENCE", - "all_components_admissible": all_admissible, - "component_count": len(components), - "max_abs_scar_delta": round(max_delta, 9), - "epsilon": epsilon, - "next_gate": "feed these snapshots into the dashboard/API surface and compare across seeds", - }, - "raw_event_sample": {key: rows[:5] for key, rows in event_index.items()}, - } - - -def write_receipt(receipt: dict[str, Any], output: Path, mirror: Path | None) -> None: - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - output.parent.mkdir(parents=True, exist_ok=True) - output.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - if mirror is not None: - mirror.parent.mkdir(parents=True, exist_ok=True) - mirror.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - - -def main() -> None: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--seed", type=int, default=20260510) - parser.add_argument("--epsilon", type=float, default=2.0) - parser.add_argument("--output", default=str(LOCAL_RECEIPT)) - parser.add_argument("--no-mirror", action="store_true") - args = parser.parse_args() - - receipt = run_live_probe(seed=args.seed, epsilon=args.epsilon) - mirror = None if args.no_mirror else STACK_RECEIPT - write_receipt(receipt, Path(args.output), mirror) - print( - json.dumps( - { - "receipt": str(Path(args.output)), - "mirror": None if mirror is None else str(mirror), - "decision": receipt["gate"]["decision"], - "component_count": receipt["gate"]["component_count"], - "max_abs_scar_delta": receipt["gate"]["max_abs_scar_delta"], - "epsilon": receipt["gate"]["epsilon"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/gaussian_splat_manifold_projection_probe.py b/4-Infrastructure/shim/gaussian_splat_manifold_projection_probe.py deleted file mode 100644 index 6d1a0d12..00000000 --- a/4-Infrastructure/shim/gaussian_splat_manifold_projection_probe.py +++ /dev/null @@ -1,366 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed Gaussian splat manifold projection probe. - -Gaussian splatting is useful for this stack as a visible-chart representation: -each splat is a local projected patch with anisotropic uncertainty, opacity, -payload, residual, and receipt pointer. The splat field is not the whole -manifold; it is an observer-bound shadow atlas over a richer object. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "gaussian_splat_manifold_projection" -REGISTRY = OUT_DIR / "gaussian_splat_manifold_projection_registry.json" -RECEIPT = OUT_DIR / "gaussian_splat_manifold_projection_receipt.json" -SUMMARY = OUT_DIR / "gaussian_splat_manifold_projection.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Gaussian Splat Manifold Projection.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" / "observer_chart_projection_guardrail_receipt.json", - REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" / "kerr_like_load_witness_geometry_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "mmff_rigid_body_geometry" / "mmff_rigid_body_geometry_receipt.json", - REPO / "6-Documentation" / "docs" / "specs" / "PROJECTABLE_GEOMETRY_COMPRESSOR_SPEC.md", - REPO / "6-Documentation" / "docs" / "specs" / "OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", -] - -CITATIONS = [ - { - "id": "kerbl_3d_gaussian_splatting", - "title": "3D Gaussian Splatting for Real-Time Radiance Field Rendering", - "url": "https://arxiv.org/abs/2308.04079", - "role": "external_rendering_anchor", - "status": "external_reference", - }, - { - "id": "huang_2d_gaussian_splatting", - "title": "2D Gaussian Splatting for Geometrically Accurate Radiance Fields", - "url": "https://arxiv.org/abs/2403.17888", - "role": "external_surface_geometry_anchor", - "status": "external_reference", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def splat_route( - *, - route_id: str, - observer_chart: str, - source_manifold: str, - splat_kind: str, - payload: str, - residual_declared: bool, - receipt_pointer: bool, - observer_scope_declared: bool, - global_truth_claim: bool, - unsafe_expansion: bool = False, -) -> dict[str, Any]: - admissible = ( - residual_declared - and receipt_pointer - and observer_scope_declared - and not global_truth_claim - and not unsafe_expansion - ) - if unsafe_expansion: - decision = "QUARANTINE_UNSAFE_SPLAT_EXPANSION" - elif global_truth_claim: - decision = "HOLD_SPLAT_GLOBALIZED" - elif not residual_declared or not receipt_pointer: - decision = "HOLD_SPLAT_RESIDUAL_OR_RECEIPT_MISSING" - elif admissible: - decision = "ADMIT_SPLAT_OBSERVER_CHART" - else: - decision = "HOLD_SPLAT_SCOPE_MISSING" - item = { - "route_id": route_id, - "observer_chart": observer_chart, - "source_manifold": source_manifold, - "splat_kind": splat_kind, - "payload": payload, - "local_atom": { - "mu": "projected_center", - "sigma": "anisotropic_covariance_or_surface_disk", - "alpha": "opacity_or_witness_confidence", - "c": "color_material_semantic_payload", - "R": "residual_or_receipt_pointer", - }, - "residual_declared": residual_declared, - "receipt_pointer": receipt_pointer, - "observer_scope_declared": observer_scope_declared, - "global_truth_claim": global_truth_claim, - "unsafe_expansion": unsafe_expansion, - "admissible": admissible, - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - routes = [ - splat_route( - route_id="organic_periodic_table_relevance_splats", - observer_chart="organic chemist chart", - source_manifold="periodic table chemical manifold", - splat_kind="semantic_2d_splat", - payload="carbon-centered relevance field plus declared residual for non-organic chemistry", - residual_declared=True, - receipt_pointer=True, - observer_scope_declared=True, - global_truth_claim=False, - ), - splat_route( - route_id="kerr_like_load_state_splats", - observer_chart="mechanical witness chart", - source_manifold="torsion-coupled load admissibility manifold", - splat_kind="state_atlas_splat", - payload="safe chart, ergoregion, horizon, and failure-core patches", - residual_declared=True, - receipt_pointer=True, - observer_scope_declared=True, - global_truth_claim=False, - ), - splat_route( - route_id="hutter_codec_torsion_splats", - observer_chart="compression accounting chart", - source_manifold="corpus/code/protocol receipt-state manifold", - splat_kind="byte_debt_splat", - payload="replay, provenance, packet, dictionary, baseline, receipt, and route-coupling fields", - residual_declared=True, - receipt_pointer=True, - observer_scope_declared=True, - global_truth_claim=False, - ), - splat_route( - route_id="mmff_material_shadow_splats", - observer_chart="molecular/material geometry chart", - source_manifold="16D chemistry/body state projected to 3D coordinate shadow", - splat_kind="geometry_shadow_splat", - payload="local coordinate/material/strain patch with MMFF adapter surfaces in HOLD", - residual_declared=True, - receipt_pointer=True, - observer_scope_declared=True, - global_truth_claim=False, - ), - splat_route( - route_id="photoreal_splat_claimed_as_truth", - observer_chart="visual rendering chart", - source_manifold="unobserved physical scene and material state", - splat_kind="radiance_splat", - payload="photoreal projection without residual or observer boundary", - residual_declared=False, - receipt_pointer=False, - observer_scope_declared=False, - global_truth_claim=True, - ), - ] - return { - "schema": "gaussian_splat_manifold_projection_registry_v1", - "citations": CITATIONS, - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Gaussian splat manifold projection only. Splats are local visible-chart " - "atoms over a richer manifold. They may render or summarize an observer " - "projection, but they do not prove global truth without residuals, receipts, " - "and observer scope." - ), - "canonical_statement": ( - "A splat field is a shadow atlas: useful, compact, and renderable, but " - "admissible only when each patch declares its observer scope, residual, " - "and receipt pointer." - ), - "projection_equation": "pi_observer(Omega) ~= sum_i G_i(mu_i,Sigma_i,alpha_i,c_i,R_i) + residual", - "admissibility_equation": ( - "A_splat=1[observer_scope_declared] * 1[residual_declared] * " - "1[receipt_pointer] * 1[not global_truth_claim] * 1[not unsafe_expansion]" - ), - "encoding_rule": { - "splat_atom": "mu + Sigma + alpha + payload + residual/receipt pointer", - "projection_role": "visible chart patch, not invariant manifold", - "hutter_role": "render byte-debt/provenance regions as diagnostic splats; canonical byte gates still decide", - "safety_role": "splat can summarize risk regions but cannot certify safety alone", - }, - "routes": routes, - "aggregates": { - "route_count": len(routes), - "admit_splat_chart_count": sum(1 for item in routes if item["decision"] == "ADMIT_SPLAT_OBSERVER_CHART"), - "hold_count": sum(1 for item in routes if item["decision"].startswith("HOLD")), - "quarantine_count": sum(1 for item in routes if item["decision"].startswith("QUARANTINE")), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "gaussian_splat_manifold_projection_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_GAUSSIAN_SPLAT_OBSERVER_CHART_PRIMITIVE", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Gaussian Splat Manifold Projection", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equations", - "", - f"- Projection: `{registry['projection_equation']}`", - f"- Admit: `{registry['admissibility_equation']}`", - "", - "## Encoding Rules", - "", - ] - for key, value in registry["encoding_rule"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Routes", - "", - "| Route | Splat kind | Observer chart | Decision |", - "|---|---|---|---|", - ] - ) - for item in registry["routes"]: - lines.append(f"| `{item['route_id']}` | `{item['splat_kind']}` | {item['observer_chart']} | `{item['decision']}` |") - lines.extend(["", "## Citations", ""]) - for citation in registry["citations"]: - lines.append(f"- `{citation['id']}`: {citation['title']} ({citation['url']}); role: `{citation['role']}`") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Encoding GaussianSplat Projection Receipt -title: Gaussian Splat Manifold Projection -type: text/vnd.tiddlywiki - -! Gaussian Splat Manifold Projection - -Durable runner: - -``` -4-Infrastructure/shim/gaussian_splat_manifold_projection_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -A splat field is a shadow atlas. It is useful, compact, and renderable, but it is not the whole manifold. - -``` -G_i = (mu_i, Sigma_i, alpha_i, c_i, R_i) -pi_observer(Omega) ~= sum_i G_i + residual -``` - -Each splat must carry observer scope, residual declaration, and receipt pointer before it can be used as an admissible chart atom. - -!! Links - -* [[Observer Chart Projection Guardrail]] -* [[Kerr-Like Load Witness Geometry]] -* [[Hutter Torsion Clock Adaptation]] -* [[MMFF Rigid Body Geometry]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/gdrive_upload_curve_analyzer.py b/4-Infrastructure/shim/gdrive_upload_curve_analyzer.py deleted file mode 100644 index 0aabf52e..00000000 --- a/4-Infrastructure/shim/gdrive_upload_curve_analyzer.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -"""Analyze rclone Google Drive upload smoothness from an rclone stats log. - -The goal is deliberately modest: identify whether throughput turbulence is -mostly payload streaming noise or file-boundary/control-plane shock. -""" - -from __future__ import annotations - -import argparse -import json -import re -import statistics -from dataclasses import asdict, dataclass -from datetime import datetime -from pathlib import Path - - -STATS_RE = re.compile( - r"^(?P\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}).*?" - r"(?P[0-9.]+) GiB / (?P[0-9.]+) GiB,\s+" - r"(?P\d+)%,\s+(?P[0-9.]+) MiB/s, ETA (?P[^)]*)" -) -COPIED_RE = re.compile( - r"^(?P\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}) INFO\s+: (?P.*): Copied \(new\)" -) - - -@dataclass -class StatSample: - ts: str - epoch: float - done_gib: float - total_gib: float - pct: int - speed_mibs: float - eta: str - - -@dataclass -class BoundaryShock: - copied_ts: str - path: str - next_stat_ts: str | None - next_speed_mibs: float | None - previous_stat_ts: str | None - previous_speed_mibs: float | None - speed_delta_mibs: float | None - - -def parse_time(value: str) -> datetime: - return datetime.strptime(value, "%Y/%m/%d %H:%M:%S") - - -def parse_log(path: Path) -> tuple[list[StatSample], list[tuple[str, str]]]: - stats: list[StatSample] = [] - copied: list[tuple[str, str]] = [] - for line in path.read_text(errors="ignore").splitlines(): - stat = STATS_RE.search(line) - if stat: - ts = stat.group("ts") - stats.append( - StatSample( - ts=ts, - epoch=parse_time(ts).timestamp(), - done_gib=float(stat.group("done")), - total_gib=float(stat.group("total")), - pct=int(stat.group("pct")), - speed_mibs=float(stat.group("speed")), - eta=stat.group("eta"), - ) - ) - continue - copied_match = COPIED_RE.search(line) - if copied_match: - copied.append((copied_match.group("ts"), copied_match.group("path"))) - return stats, copied - - -def mean(values: list[float]) -> float | None: - return statistics.mean(values) if values else None - - -def pstdev(values: list[float]) -> float | None: - return statistics.pstdev(values) if len(values) > 1 else 0.0 if values else None - - -def round_or_none(value: float | None, digits: int = 3) -> float | None: - return round(value, digits) if value is not None else None - - -def compute_boundary_shocks( - stats: list[StatSample], copied: list[tuple[str, str]] -) -> list[BoundaryShock]: - shocks: list[BoundaryShock] = [] - for copied_ts, copied_path in copied: - copied_epoch = parse_time(copied_ts).timestamp() - previous = None - next_sample = None - for sample in stats: - if sample.epoch <= copied_epoch: - previous = sample - if sample.epoch > copied_epoch: - next_sample = sample - break - delta = None - if previous and next_sample: - delta = next_sample.speed_mibs - previous.speed_mibs - shocks.append( - BoundaryShock( - copied_ts=copied_ts, - path=copied_path, - next_stat_ts=next_sample.ts if next_sample else None, - next_speed_mibs=next_sample.speed_mibs if next_sample else None, - previous_stat_ts=previous.ts if previous else None, - previous_speed_mibs=previous.speed_mibs if previous else None, - speed_delta_mibs=delta, - ) - ) - return shocks - - -def build_report(stats: list[StatSample], shocks: list[BoundaryShock]) -> dict: - speeds = [sample.speed_mibs for sample in stats] - last_10 = speeds[-10:] - last_30 = speeds[-30:] - deltas = [shock.speed_delta_mibs for shock in shocks if shock.speed_delta_mibs is not None] - negative_deltas = [delta for delta in deltas if delta < 0] - - recommendation = "keep_current_run_unchanged" - rationale = ( - "Current process already uses one transfer, size-descending order, and a large Drive chunk. " - "Observed turbulence is mostly at file boundaries, so interrupting the run would add risk." - ) - next_run = { - "large_lane": { - "selector": "files >= 20 GiB", - "transfers": 1, - "drive_chunk_size": "512M", - "order_by": "size,descending", - }, - "medium_lane": { - "selector": "1 GiB <= files < 20 GiB", - "transfers": 2, - "drive_chunk_size": "256M", - "order_by": "size,descending", - }, - "tail_lane": { - "selector": "files < 1 GiB", - "transfers": 4, - "drive_chunk_size": "128M", - "order_by": "size,descending", - }, - "receipt_gate": "rclone check before local deletion or stub replacement", - } - - return { - "sample_count": len(stats), - "first_sample": asdict(stats[0]) if stats else None, - "last_sample": asdict(stats[-1]) if stats else None, - "speed_mibs": { - "mean_all": round_or_none(mean(speeds)), - "min_all": round_or_none(min(speeds) if speeds else None), - "max_all": round_or_none(max(speeds) if speeds else None), - "stdev_all": round_or_none(pstdev(speeds)), - "mean_last_10": round_or_none(mean(last_10)), - "stdev_last_10": round_or_none(pstdev(last_10)), - "mean_last_30": round_or_none(mean(last_30)), - "stdev_last_30": round_or_none(pstdev(last_30)), - }, - "boundary_shock_count": len(shocks), - "boundary_speed_delta_mibs": { - "mean_all": round_or_none(mean(deltas)), - "mean_negative_only": round_or_none(mean(negative_deltas)), - "worst": round_or_none(min(deltas) if deltas else None), - }, - "largest_boundary_shocks": [ - { - **asdict(shock), - "next_speed_mibs": round_or_none(shock.next_speed_mibs), - "previous_speed_mibs": round_or_none(shock.previous_speed_mibs), - "speed_delta_mibs": round_or_none(shock.speed_delta_mibs), - } - for shock in sorted( - shocks, - key=lambda item: item.speed_delta_mibs - if item.speed_delta_mibs is not None - else 0, - )[:10] - ], - "recommendation": recommendation, - "rationale": rationale, - "next_run_lanes": next_run, - } - - -def write_markdown(report: dict, path: Path) -> None: - speed = report["speed_mibs"] - boundary = report["boundary_speed_delta_mibs"] - lines = [ - "# GDrive Upload Curve Analysis", - "", - "## Verdict", - "", - report["rationale"], - "", - "Do not interrupt the current run. Treat the current run as the stable", - "large-object lane and use the lane split below for future offloads.", - "", - "## Measurements", - "", - f"- Samples: {report['sample_count']}", - f"- Mean speed: {speed['mean_all']} MiB/s", - f"- Speed range: {speed['min_all']} - {speed['max_all']} MiB/s", - f"- Overall speed stdev: {speed['stdev_all']} MiB/s", - f"- Last 10 mean/stdev: {speed['mean_last_10']} / {speed['stdev_last_10']} MiB/s", - f"- Boundary shocks: {report['boundary_shock_count']}", - f"- Worst boundary delta: {boundary['worst']} MiB/s", - f"- Mean negative boundary delta: {boundary['mean_negative_only']} MiB/s", - "", - "## Boundary Shocks", - "", - ] - for shock in report["largest_boundary_shocks"]: - lines.append( - f"- {shock['copied_ts']} `{shock['path']}`: " - f"{shock['previous_speed_mibs']} -> {shock['next_speed_mibs']} MiB/s " - f"({shock['speed_delta_mibs']} MiB/s)" - ) - lines.extend( - [ - "", - "## Next-Run Lane Recipe", - "", - "```text", - "large files >= 20 GiB : --transfers 1 --drive-chunk-size 512M --order-by size,descending", - "medium files 1-20 GiB : --transfers 2 --drive-chunk-size 256M --order-by size,descending", - "tail files < 1 GiB : --transfers 4 --drive-chunk-size 128M --order-by size,descending", - "after upload : rclone check before deletion or stub replacement", - "```", - "", - "## Route Interpretation", - "", - "```text", - "payload stream -> stable transfer lane", - "file boundary -> synchronization barrier", - "Drive API negotiation -> witness/control overhead", - "speed dip -> boundary shock, not compression or payload proof", - "```", - ] - ) - path.write_text("\n".join(lines) + "\n") - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("log", type=Path) - parser.add_argument("--json-out", type=Path, required=True) - parser.add_argument("--md-out", type=Path, required=True) - args = parser.parse_args() - - stats, copied = parse_log(args.log) - shocks = compute_boundary_shocks(stats, copied) - report = build_report(stats, shocks) - args.json_out.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") - write_markdown(report, args.md_out) - print(json.dumps(report["speed_mibs"], indent=2, sort_keys=True)) - print(f"wrote {args.json_out}") - print(f"wrote {args.md_out}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/generative_compressed_sensing_prior.py b/4-Infrastructure/shim/generative_compressed_sensing_prior.py deleted file mode 100644 index 6eed8360..00000000 --- a/4-Infrastructure/shim/generative_compressed_sensing_prior.py +++ /dev/null @@ -1,184 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for generative compressed sensing as a bounded route prior.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "generative_compressed_sensing_prior_receipt.json" -CURRICULUM = SHIM / "generative_compressed_sensing_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "generative_compressed_sensing_prior_v1", - "source_type": "user_supplied_consensus_bibliography", - "primary_read": ( - "Generative compressed sensing extends sparse recovery by replacing " - "or augmenting plain sparsity with a learned low-dimensional prior, " - "but recovery guarantees depend on generator regularity, latent " - "dimension, representation error, noise model, and optimization cost." - ), - "anchor_keys": [ - "Bora2017Compressed", - "Huang2018A", - "Hand2020Compressive", - "Chen2023A", - "Nguyen2021Provable", - "Jalal2020Robust", - "Asim2019Invertible", - "Dhar2018Modeling", - "Berk2022A", - "Scarlett2022Theoretical", - "Kamath2019Lower", - ], - "method_lanes": [ - { - "lane": "gan_or_deep_generator_prior", - "use": "restrict candidate signal to range of a generator", - "risk": "representation error and dataset bias", - "keys": ["Bora2017Compressed", "Shah2018Solving", "Hand2017Global"], - }, - { - "lane": "provable_convergence_and_sample_complexity", - "use": "bound recovery under random/generative priors", - "risk": "assumptions may not match real target distribution", - "keys": ["Huang2018A", "Hand2020Compressive", "Chen2023A", "Scarlett2022Theoretical"], - }, - { - "lane": "robust_and_sparse_deviation_models", - "use": "generator plus explicit sparse deviation or corruption lane", - "risk": "deviation lane can become hidden payload if uncounted", - "keys": ["Jalal2020Robust", "Dhar2018Modeling", "Cai2020Fast"], - }, - { - "lane": "langevin_bayesian_score_posterior", - "use": "sample or score the posterior instead of deterministic projection", - "risk": "sampling cost and uncertainty must be receipted", - "keys": ["Nguyen2021Provable", "Meng2022Quantized", "Zhang2025Bayesian", "Bock2024Sparse"], - }, - { - "lane": "invertible_or_measurement_conditional_generators", - "use": "reduce projection ambiguity and improve conditioning", - "risk": "dependent noise and invertibility assumptions are domain-specific", - "keys": ["Asim2019Invertible", "Whang2020Compressed", "Kim2020Compressed"], - }, - { - "lane": "physics_guided_or_model_based_deep_unrolling", - "use": "blend physical forward model with learned reconstruction", - "risk": "model mismatch can be mistaken for signal", - "keys": ["Chen2023Deep", "Khobahi2020Model-Based", "Lazzaro2024Oracle-Net"], - }, - { - "lane": "application_specific_inverse_imaging", - "use": "MRI, EIT, crack segmentation, pose, channel estimation, one-bit or quantized sensing", - "risk": "application wins do not transfer without matching measurement operators", - "keys": ["Jalal2021Robust", "Bohra2022Bayesian", "Hieu2023Reconstructing", "Balevi2020High"], - }, - ], - "route_state_additions": [ - "generator_prior_id", - "generator_family_class", - "latent_dimension", - "latent_regularizer_id", - "representation_error_bound", - "dataset_bias_status", - "measurement_operator_class", - "noise_model_class", - "posterior_sampling_status", - "sparse_deviation_lane_id", - "exact_residual_lane_id", - "generator_witness_bytes", - "byte_rehydration_hash", - ], - "equation_pipeline_mapping": { - "equation_trace": "measurement y", - "candidate_equation_family": "generator range G(z)", - "symbolic_deviation": "sparse deviation lane", - "notation_or_domain_bias": "dataset bias", - "proof_or_unit_validator": "measurement consistency check", - "held_out_equation_family": "generalization test", - }, - "hutter_mapping": { - "generator_prior": "route proposal or residual predictor only", - "latent_code": "counted sidecar unless derived from decoder state", - "sparse_deviation": "exact residual lane, not free correction", - "reconstruction_loss": "diagnostic only", - "promotion": "exact byte decode, hash, measured bytes, counted witnesses", - }, - "failure_rules": [ - "generator prior used as hidden payload -> invalid receipt", - "representation error not residualized -> not promoted", - "dataset bias changes source bytes -> fail closed", - "latent code larger than byte gain -> prune", - "posterior uncertainty unreported -> diagnostic only", - "application-specific operator assumed universal -> negative transfer hold", - ], - "bibtex_hygiene_notes": [ - "Quer2012Sensing,, contains a malformed BibTeX key with a double comma", - "Some supplied entries have missing DOI or venue fields and should be verified before publication", - "Keep this bundle as a research prior until individual sources are verified", - ], - "claim_boundary": ( - "Generative compressed sensing is a proposal and reconstruction prior " - "for this stack. It cannot replace exact residual repair, proof checks, " - "or Hutter byte receipts." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_generative_cs_lane", - "input": "generative compressed-sensing paper or route", - "target": "generator, convergence, robust deviation, posterior, invertible, physics-guided, or application lane", - }, - { - "task": "separate_latent_code_from_free_structure", - "input": "generator-based compression proposal", - "target": "count latent/witness bytes unless decoder derives them", - }, - { - "task": "require_exact_residual_for_hutter", - "input": "lossy generator reconstruction", - "target": "exact residual lane plus byte rehydration hash", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "method_lane_count": len(receipt["method_lanes"]), - "state_addition_count": len(receipt["route_state_additions"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/genomic_sequence_prior_metaprobe.py b/4-Infrastructure/shim/genomic_sequence_prior_metaprobe.py deleted file mode 100644 index b156f764..00000000 --- a/4-Infrastructure/shim/genomic_sequence_prior_metaprobe.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python3 -"""Genomic/DNA sequence prior metaprobe. - -This is a computational sequence surface for the local router: DNA strings, -k-mers, tokenizers, long-context genome models, and provenance receipts. It is -not a protocol-design, synthesis, or wet-lab instruction surface. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -SEQUENCE_AXES = [ - { - "axis": "nucleotide_sequence", - "payload": ["A", "C", "G", "T", "N", "sequence_length", "strand", "organism"], - "router_use": "symbolic_sequence_compression_and_kmer_surface", - "receipt_rule": "preserve source assembly, coordinates, strand, and tokenizer", - }, - { - "axis": "kmer_tokenization", - "payload": ["k", "stride", "vocabulary", "ambiguous_base_policy", "reverse_complement_policy"], - "router_use": "bitpacking_logogram_and_metaprobe_token_axis", - "receipt_rule": "record k, stride, vocabulary hash, and sequence preprocessing", - }, - { - "axis": "long_context_genomic_model", - "payload": ["context_length", "objective", "species_scope", "embedding", "downstream_task"], - "router_use": "external_model_prior_for_sequence_routing", - "receipt_rule": "generated labels remain predictions until evaluated against benchmark/source labels", - }, - { - "axis": "genomic_provenance", - "payload": ["dataset", "license", "organism", "assembly", "annotation_version", "contamination_check"], - "router_use": "admissibility_gate_for_sequence_corpora", - "receipt_rule": "do not train/ingest without provenance, license, and task boundary", - }, - { - "axis": "rna_conditioned_destructive_gate", - "payload": ["guide_match_condition", "activation_threshold", "target_marker", "destructive_action", "off_target_boundary"], - "router_use": "selective gate motif for semantic/control-plane pruning: activate only under exact marker match, otherwise spare the carrier", - "receipt_rule": "computational motif only; record source, match predicate, non-activation condition, and no wet-lab parameters", - }, -] - - -HF_DNA_DATASET_PRIORS = [ - { - "id": "huggingface/datasets?search=dna", - "role": "dna_dataset_discovery_registry_prior", - "boundary": "registry-prior-only", - "use_as": "dna_corpus_search_axis", - "notes": [ - "HF DNA dataset search listed 390 results when checked.", - "Top examples included antibiotic-resistance DNA, Human/Mouse/Zebrafish/Fruitfly/Worm/Arabidopsis DNA corpora, DNABERT6 tokenized variants, k-mer tokenized variants, and BPE/SentencePiece tokenized variants.", - ], - }, - { - "id": "macwiatrak/bacbench-antibiotic-resistance-dna", - "role": "antibiotic_resistance_sequence_benchmark_prior", - "boundary": "benchmark-prior-only", - "use_as": "sequence_classification_eval_axis", - }, - { - "id": "simecek/Human_DNA_v0", - "role": "human_reference_dna_corpus_prior", - "boundary": "dataset-prior-only", - "use_as": "human_sequence_tokenization_axis", - }, - { - "id": "simecek/Human_DNA_v0_DNABert6tokenized", - "role": "dnabert6_tokenized_human_sequence_prior", - "boundary": "dataset-prior-only", - "use_as": "kmer_tokenization_comparison_axis", - }, -] - - -HF_DNA_MODEL_PRIORS = [ - { - "id": "AIRI-Institute/GENA-LM family", - "role": "genomic_language_model_prior", - "boundary": "model-search-prior-only", - "use_as": "bert/bigbird_style_sequence_encoder_axis", - }, - { - "id": "InstaDeepAI/Nucleotide Transformer family", - "role": "multi_species_nucleotide_transformer_prior", - "boundary": "model-search-prior-only", - "use_as": "multi_species_embedding_and_6mer_tokenizer_axis", - }, - { - "id": "LongSafari/HyenaDNA family", - "role": "long_context_single_nucleotide_model_prior", - "boundary": "model-search-prior-only", - "use_as": "long_sequence_compression_and_context_axis", - }, - { - "id": "multimolecule/DNABERT k-mer family", - "role": "kmer_masked_language_model_prior", - "boundary": "model-search-prior-only", - "use_as": "3mer_to_6mer_tokenization_axis", - }, - { - "id": "arcinstitute/evo2_20b", - "role": "large_genome_model_prior", - "boundary": "model-search-prior-only", - "use_as": "large_scale_genomic_reasoning_comparator", - }, -] - - -BIOLOGICAL_CONTROL_PRIORS = [ - { - "id": "USU_Cas12a2_selective_cell_destruction_2026", - "role": "rna_conditioned_selective_destruction_gate_prior", - "boundary": "news-and-paper-pointer-prior-only", - "use_as": "conditional_activation_and_destructive_pruning_motif", - "source": "USU Biochemists Show CRISPR Can Selectively Destroy Cells, a Cancer-Treatment Goal", - "url": "https://www.usu.edu/today/story/usu-biochemists-show-crispr-can-selectively-destroy-cells-a-cancer-treatment-goal", - "notes": [ - "USU report says CRISPR-Cas12a2 binds complementary RNA rather than DNA and, once activated, destroys DNA encountered by the enzyme.", - "Article claims imperfect guide/target complement prevents activation and describes selective killing of cells containing a single-point cancer-associated mutant in reported experiments.", - "Local use is only a compression/control motif: exact-match gate, destructive prune action, spare-on-mismatch boundary.", - ], - }, -] - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a genomic sequence router. Return compact JSON with evidence boundaries." - records = [] - for axis in receipt["sequence_axes"]: - prompt = { - "task": "route_genomic_sequence_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Choose how this DNA axis should enter the compression/metaprobe router.", - } - answer = { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "computational-sequence-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - } - records.append(chat_record(system, prompt, answer)) - for prior in receipt["dataset_priors"]: - prompt = { - "task": "use_dna_dataset_prior", - "dataset": prior["id"], - "role": prior["role"], - "instruction": "Explain how to sample this DNA dataset family without overclaiming.", - } - answer = { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "sampling_rule": "sample small; preserve organism, assembly, coordinates, tokenizer, license, and task label provenance", - } - records.append(chat_record(system, prompt, answer)) - for prior in receipt["model_priors"]: - prompt = { - "task": "use_dna_model_prior", - "model_family": prior["id"], - "role": prior["role"], - "instruction": "Explain how this genome model family should influence routing without becoming proof.", - } - answer = { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use embeddings/predictions as sequence priors only; benchmark and source-label receipts decide promotion.", - } - records.append(chat_record(system, prompt, answer)) - for prior in receipt["biological_control_priors"]: - prompt = { - "task": "use_biological_control_prior_as_compression_motif", - "prior": prior["id"], - "role": prior["role"], - "instruction": "Map this biological control idea into a safe compression/metaprobe gate.", - } - answer = { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use only as a conditional activation/destructive-pruning motif; do not emit wet-lab design, guide design, dosing, delivery, or therapeutic instructions.", - "surface_payload_hint": "RNA-GATE-PRUNE", - } - records.append(chat_record(system, prompt, answer)) - return records - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/genomic_sequence_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/genomic_sequence_prior_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "genomic_sequence_prior_receipt_v1", - "claim_boundary": "DNA priors support computational sequence routing, not wet-lab design or biological validation.", - "sequence_axes": SEQUENCE_AXES, - "dataset_priors": HF_DNA_DATASET_PRIORS, - "model_priors": HF_DNA_MODEL_PRIORS, - "biological_control_priors": BIOLOGICAL_CONTROL_PRIORS, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/geometry_multistate_hypershapes_prior.py b/4-Infrastructure/shim/geometry_multistate_hypershapes_prior.py deleted file mode 100644 index ef20d295..00000000 --- a/4-Infrastructure/shim/geometry_multistate_hypershapes_prior.py +++ /dev/null @@ -1,501 +0,0 @@ -#!/usr/bin/env python3 -"""Geometry / multi-state hypershapes literature prior. - -This consumes a local Consensus CSV export and distills it into a bounded -route-control prior for the projectable-geometry compressor. The CSV is -evidence of a source bundle and search vocabulary; it is not compression -evidence. Promotion still belongs to local encode/decode/hash receipts. -""" - -from __future__ import annotations - -import argparse -import csv -import hashlib -import json -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -DEFAULT_SOURCE = Path( - "/home/allaun/Documents/ingest/geomtry and multi state hypershapes - May 07, 2026.csv" -) -DEFAULT_RECEIPT = Path( - "4-Infrastructure/shim/geometry_multistate_hypershapes_prior_receipt.json" -) -DEFAULT_CURRICULUM = Path( - "4-Infrastructure/shim/geometry_multistate_hypershapes_prior_curriculum.jsonl" -) - - -CLUSTERS = [ - { - "id": "multistable_origami_metasurfaces", - "keywords": [ - "origami", - "fold", - "bistable", - "multistable", - "metasurface", - "tensegrity", - "mechanism", - ], - "compressor_use": "fold-state gates for reversible route transitions and shell closure stress tests", - "dd_state_fields": [ - "fold_state_id", - "mechanism_class", - "stability_class", - "closure_receipt_id", - ], - "failure_mode": "fold changes byte reachability or opens recursive repair", - }, - { - "id": "manifold_geometric_deep_learning", - "keywords": [ - "geometric deep learning", - "manifold", - "latent", - "representation", - "neural", - "flow field", - "gauge", - ], - "compressor_use": "proposal features for route clustering, latent route axes, and duplicate-island detection", - "dd_state_fields": [ - "manifold_chart_id", - "latent_route_axis_id", - "local_flow_field_id", - "feature_receipt_id", - ], - "failure_mode": "latent similarity promoted without byte-exact decode", - }, - { - "id": "tensor_network_entanglement_geometry", - "keywords": [ - "tensor", - "matrix product", - "projected entangled", - "entanglement", - "many-body", - "tensor network", - ], - "compressor_use": "bounded carrier topology for shared tokenbooks, local tensors, and sidecar factorization", - "dd_state_fields": [ - "tensor_carrier_id", - "bond_dimension_class", - "local_factor_id", - "residual_lane_id", - ], - "failure_mode": "factorization hides payload or increases sidecar beyond gain", - }, - { - "id": "quantum_phase_geometry", - "keywords": [ - "quantum geometry", - "berry", - "quantum metric", - "phase", - "topological", - "correlation", - "bell", - ], - "compressor_use": "phase/metric witness for route holonomy, orbit changes, and nonclassical correlation diagnostics", - "dd_state_fields": [ - "phase_metric_class", - "holonomy_receipt_id", - "orbit_change_id", - "correlation_witness_id", - ], - "failure_mode": "phase witness treated as decoded payload instead of bounded receipt", - }, - { - "id": "molecular_shape_hyperstable_design", - "keywords": [ - "molecular", - "protein", - "peptide", - "drug", - "shape", - "electrostatic", - "constrained", - ], - "compressor_use": "shape/electrostatic analogy for compact partial-feature bundles with exact residual lanes", - "dd_state_fields": [ - "shape_signature_id", - "electrostatic_feature_id", - "compact_feature_bundle_id", - "exact_residual_lane_id", - ], - "failure_mode": "shape match loses byte-level attributes", - }, - { - "id": "parallel_coordinate_hypershape_visualization", - "keywords": [ - "parallel coordinates", - "multi-dimensional", - "hypershape", - "visualizing", - "high-dimensional", - ], - "compressor_use": "dashboard and feature-vector surface for inspecting high-dimensional route populations", - "dd_state_fields": [ - "route_feature_vector_id", - "axis_projection_id", - "dashboard_card_id", - "incumbent_receipt_id", - ], - "failure_mode": "visual separation mistaken for measured compression gain", - }, -] - - -PRACTICAL_LIMIT_PRIORS = [ - { - "id": "state_explosion_and_spurious_minima", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "stable-state count may grow quickly with cell count, but unwanted minima " - "and route ambiguity make specific target states hard to address" - ), - "compressor_mapping": "route family explosion and duplicate/spurious transform minima", - "dd_guard": "require deterministic state selection, lower-bound pruning, and fail-closed tie receipts", - "receipt_fields": [ - "stable_state_count_estimate", - "spurious_state_count", - "state_selection_policy_id", - "tie_break_receipt_id", - ], - "failure_mode": "many possible states but no bounded path to the intended decoded byte stream", - }, - { - "id": "energy_barrier_and_transition_path", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "multi-compatible trusses and highly multistable structures need energy " - "barriers and transition paths that remain controllable" - ), - "compressor_mapping": "transform transitions must have bounded repair cost and no recursive rollback", - "dd_guard": "record transition energy/barrier class and reject unbounded repair paths", - "receipt_fields": [ - "transition_path_id", - "barrier_class", - "rollback_window_bytes", - "repair_path_depth", - ], - "failure_mode": "route transition exists in principle but requires unbounded search to repair", - }, - { - "id": "geometry_parameter_sensitivity", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "crease geometry, layer count, panel ratios, conical degree, graded height, " - "and symmetry breaking strongly affect whether multistability survives" - ), - "compressor_mapping": "route parameters need tolerance bands before promotion", - "dd_guard": "stress each promoted route under one-parameter perturbations and record N-1 failure packets", - "receipt_fields": [ - "parameter_band_id", - "n_minus_1_perturbation_count", - "stability_margin_class", - "failure_packet_id", - ], - "failure_mode": "byte win disappears under small admissible route-parameter perturbation", - }, - { - "id": "actuation_and_addressability", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "reachable stable states may require multi-DOF actuation, thermal windows, " - "pneumatic control, or path-specific switching" - ), - "compressor_mapping": "candidate states must be addressable by a finite decoder/control packet", - "dd_guard": "promote only if owner routing plus control witness selects the state without broadcast search", - "receipt_fields": [ - "addressability_class", - "control_packet_bytes", - "owner_route_id", - "broadcast_search_required", - ], - "failure_mode": "route is compact only if the decoder probes many candidate states", - }, - { - "id": "material_fatigue_and_tolerance", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "fatigue, hinge localization, allowable strain, local peak forces, and " - "manufacturing tolerances limit repeated reliable switching" - ), - "compressor_mapping": "route should track repair churn, tolerance drift, and sidecar wear", - "dd_guard": "reject aggressive routes whose repeated rehydration produces unstable repair churn", - "receipt_fields": [ - "repair_churn_count", - "tolerance_drift_class", - "local_peak_sidecar_bytes", - "repeat_decode_count", - ], - "failure_mode": "route passes once but is not stable under repeated decode/evaluate cycles", - }, - { - "id": "scalability_and_manufacturability", - "source_prompt": "What are the practical limits of programmable multi-stability using geometric design?", - "observed_limit": ( - "microscale and lattice designs scale, but fabrication and characterization " - "constraints bound usable complexity" - ), - "compressor_mapping": "route witnesses must fit carrier capacity and remain inspectable", - "dd_guard": "require witness budget, carrier capacity, and receipt readability before evaluation promotion", - "receipt_fields": [ - "carrier_capacity_bytes", - "witness_budget_bytes", - "inspectability_status", - "characterization_receipt_id", - ], - "failure_mode": "route metadata grows faster than measured byte savings", - }, -] - - -def sha256_path(path: Path) -> str: - digest = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - digest.update(chunk) - return digest.hexdigest() - - -def stable_hash(obj: Any) -> str: - payload = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def read_rows(path: Path) -> list[dict[str, str]]: - with path.open("r", encoding="utf-8-sig", newline="") as handle: - return list(csv.DictReader(handle)) - - -def row_text(row: dict[str, str]) -> str: - fields = [ - row.get("Title", ""), - row.get("Takeaway", ""), - row.get("Abstract", ""), - row.get("Journal", ""), - ] - return " ".join(fields).lower() - - -def classify_rows(rows: list[dict[str, str]]) -> list[dict[str, Any]]: - classified: list[dict[str, Any]] = [] - for cluster in CLUSTERS: - matches: list[dict[str, Any]] = [] - keywords = [keyword.lower() for keyword in cluster["keywords"]] - for row in rows: - text = row_text(row) - hit_count = sum(1 for keyword in keywords if keyword in text) - if hit_count: - matches.append( - { - "title": row.get("Title", ""), - "year": row.get("Year", ""), - "citations": int(row.get("Citations") or 0), - "doi": row.get("DOI", ""), - "consensus_link": row.get("Consensus Link", ""), - "matched_keyword_count": hit_count, - } - ) - matches.sort(key=lambda item: (item["matched_keyword_count"], item["citations"]), reverse=True) - item = dict(cluster) - item["match_count"] = len(matches) - item["top_matches"] = matches[:8] - classified.append(item) - return classified - - -def top_cited(rows: list[dict[str, str]], limit: int = 15) -> list[dict[str, Any]]: - ranked = sorted(rows, key=lambda row: int(row.get("Citations") or 0), reverse=True) - return [ - { - "title": row.get("Title", ""), - "year": row.get("Year", ""), - "citations": int(row.get("Citations") or 0), - "doi": row.get("DOI", ""), - "journal": row.get("Journal", ""), - "consensus_link": row.get("Consensus Link", ""), - } - for row in ranked[:limit] - ] - - -def build_receipt(source: Path) -> dict[str, Any]: - rows = read_rows(source) - source_mtime = datetime.fromtimestamp(source.stat().st_mtime, timezone.utc).isoformat() - fieldnames = list(rows[0].keys()) if rows else [] - years = Counter(row.get("Year", "") for row in rows if row.get("Year")) - journals = Counter((row.get("Journal", "") or "").strip() or "" for row in rows) - nonempty = { - field: sum(1 for row in rows if (row.get(field, "") or "").strip()) - for field in fieldnames - } - clusters = classify_rows(rows) - summary = { - "row_count": len(rows), - "fieldnames": fieldnames, - "nonempty_fields": nonempty, - "year_min": min(years) if years else None, - "year_max": max(years) if years else None, - "year_counts": dict(sorted(years.items())), - "top_journals": [ - {"journal": journal, "count": count} - for journal, count in journals.most_common(12) - ], - "top_cited": top_cited(rows), - } - receipt: dict[str, Any] = { - "schema": "geometry_multistate_hypershapes_prior_v1", - "generated_at": source_mtime, - "source_csv": str(source), - "source_sha256": sha256_path(source), - "claim_boundary": ( - "Consensus CSV rows provide a geometry/multistate source bundle and " - "route-control vocabulary only; local encode/decode/hash/byte-count " - "receipts remain the compression authority." - ), - "summary": summary, - "clusters": clusters, - "practical_limit_priors": PRACTICAL_LIMIT_PRIORS, - "route_extraction": { - "base_object": "multi-state hypershape route family", - "control_shape": [ - "finite state shell", - "manifold chart", - "tensor/fiber carrier", - "phase/holonomy witness", - "exact residual lane", - ], - "candidate_dd_edges": [ - "open_multistate_shape_shell", - "choose_manifold_chart", - "emit_tensor_or_fiber_carrier", - "record_phase_holonomy_witness", - "fold_state_if_reachability_preserved", - "emit_exact_residual_lane", - "close_with_rehydration_hash", - "reject_unbounded_hypershape_expansion", - ], - "promotion_rule": ( - "promote iff the hypershape layer only proposes/constrains routes, " - "all chart/fold/tensor/phase witnesses are bounded, exact residual " - "lanes restore source bytes, decoded hash matches, and measured " - "total bytes beat the incumbent under one explicit ratio_schema" - ), - "failure_rule": ( - "latent geometry, visual separation, quantum phase, or tensor " - "factorization without byte-exact residual repair is diagnostic only" - ), - "practical_limit_rule": ( - "multistability is useful only when states are addressable, stable " - "under bounded perturbation, cheap to switch, and small enough to " - "receipt without losing the measured byte gain" - ), - }, - } - receipt["receipt_hash"] = stable_hash(receipt) - return receipt - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = ( - "You are a projectable-geometry compression route controller. " - "Use literature clusters as bounded proposal priors only." - ) - records: list[dict[str, Any]] = [] - for cluster in receipt["clusters"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "route_geometry_hypershape_cluster", - "cluster_id": cluster["id"], - "match_count": cluster["match_count"], - "compressor_use": cluster["compressor_use"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "selected": cluster["match_count"] > 0, - "dd_state_fields": cluster["dd_state_fields"], - "failure_mode": cluster["failure_mode"], - "claim_boundary": "source-bundle-prior-only", - "promotion_authority": "local encode/decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - for prior in receipt["practical_limit_priors"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "route_practical_multistability_limit", - "limit_id": prior["id"], - "observed_limit": prior["observed_limit"], - "compressor_mapping": prior["compressor_mapping"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "selected": True, - "dd_guard": prior["dd_guard"], - "receipt_fields": prior["receipt_fields"], - "failure_mode": prior["failure_mode"], - "claim_boundary": "practical-limit-prior-only", - "promotion_authority": "local encode/decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--source", type=Path, default=DEFAULT_SOURCE) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--curriculum", type=Path, default=DEFAULT_CURRICULUM) - args = parser.parse_args() - - receipt = build_receipt(args.source) - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/godel_gauntlet_race_condition_probe.py b/4-Infrastructure/shim/godel_gauntlet_race_condition_probe.py deleted file mode 100644 index 1b18f7d4..00000000 --- a/4-Infrastructure/shim/godel_gauntlet_race_condition_probe.py +++ /dev/null @@ -1,437 +0,0 @@ -#!/usr/bin/env python3 -"""Gödel Gauntlet race-condition probe for Hutter causal axes. - -The multidimensional causal graph admits individual edges, but that is not -enough to prove that concurrent routes commute. This probe applies the local -Gödel Gauntlet rule: no promotion from surface plausibility. Edge compositions -must be order-stable, receipt-stable, and residual-declared before they can be -used as shared Hutter route structure. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "godel_gauntlet_race_condition" -REGISTRY = OUT_DIR / "godel_gauntlet_race_condition_registry.json" -RECEIPT = OUT_DIR / "godel_gauntlet_race_condition_receipt.json" -SUMMARY = OUT_DIR / "godel_gauntlet_race_condition.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Godel Gauntlet Race Condition Probe.tid" - -MULTI_CAUSAL_REGISTRY = ( - REPO - / "shared-data" - / "data" - / "hutter_multidimensional_causal_chain" - / "hutter_multidimensional_causal_chain_registry.json" -) -MULTI_CAUSAL_RECEIPT = ( - REPO - / "shared-data" - / "data" - / "hutter_multidimensional_causal_chain" - / "hutter_multidimensional_causal_chain_receipt.json" -) -FOUNDATION_COMPILER = REPO / "4-Infrastructure" / "shim" / "foundation_forward_equation_compiler.py" - -AXIS_PRIORITY = { - "provenance": 0, - "corpus_offset": 1, - "byte_neighbor": 2, - "semantic_class": 3, - "chirality": 4, - "orientation_360_share": 5, - "codec_torsion": 6, - "observer_chart": 7, -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def load_multidimensional_graph() -> dict[str, Any]: - if not MULTI_CAUSAL_REGISTRY.exists(): - raise FileNotFoundError(f"missing source registry: {MULTI_CAUSAL_REGISTRY}") - return json.loads(MULTI_CAUSAL_REGISTRY.read_text(encoding="utf-8")) - - -def edge_by_id(graph: dict[str, Any]) -> dict[str, dict[str, Any]]: - return {edge["edge_id"]: edge for edge in graph["edges"]} - - -def transition_digest(state: dict[str, Any], edge: dict[str, Any]) -> dict[str, Any]: - """Apply an edge as a typed transition over a compact route state.""" - axes = list(state["axes"]) - axes.append(edge["axis"]) - route = list(state["route"]) - route.append(edge["edge_id"]) - source_target = list(state["source_target"]) - source_target.append([edge["source"], edge["target"]]) - return { - "start": state["start"], - "end": edge["target"], - "axes": axes, - "route": route, - "source_target": source_target, - "residuals": list(state["residuals"]) + ([] if edge["bounded"] else ["unbounded_edge"]), - "axis_priority_trace": [AXIS_PRIORITY.get(axis, 99) for axis in axes], - "route_root": hash_obj( - { - "start": state["start"], - "end": edge["target"], - "axes": axes, - "route": route, - "source_target": source_target, - "edge_hash": edge["edge_hash"], - } - ), - } - - -def run_route(start: str, edge_ids: list[str], edges: dict[str, dict[str, Any]]) -> dict[str, Any]: - state = { - "start": start, - "end": start, - "axes": [], - "route": [], - "source_target": [], - "residuals": [], - "axis_priority_trace": [], - "route_root": hash_obj({"start": start, "empty": True}), - } - for edge_id in edge_ids: - state = transition_digest(state, edges[edge_id]) - return state - - -def axis_order_valid(state: dict[str, Any]) -> bool: - trace = state["axis_priority_trace"] - return trace == sorted(trace) - - -def make_test( - graph: dict[str, Any], - test_id: str, - description: str, - start: str, - route_a: list[str], - route_b: list[str], - expected_same_root: bool, - residual_declared: bool, -) -> dict[str, Any]: - edges = edge_by_id(graph) - state_a = run_route(start, route_a, edges) - state_b = run_route(start, route_b, edges) - same_root = state_a["route_root"] == state_b["route_root"] - same_end = state_a["end"] == state_b["end"] - order_a_valid = axis_order_valid(state_a) - order_b_valid = axis_order_valid(state_b) - all_edges_admitted = all(edges[edge_id]["decision"] == "ADMIT_CAUSAL_EDGE" for edge_id in set(route_a + route_b)) - exposes_race = all_edges_admitted and same_end and same_root != expected_same_root - hidden_race = all_edges_admitted and same_end and not same_root and not residual_declared - - if hidden_race: - decision = "HOLD_HIDDEN_RACE_CONDITION" - elif exposes_race: - decision = "HOLD_RACE_EXPECTATION_MISMATCH" - elif not all_edges_admitted: - decision = "HOLD_DEPENDENCY_NOT_ADMITTED" - elif not order_a_valid or not order_b_valid: - decision = "HOLD_AXIS_ORDER_NONCANONICAL" - elif same_root == expected_same_root: - decision = "ADMIT_ORDER_STABLE_ROUTE" if same_root else "ADMIT_DECLARED_NONCOMMUTING_ROUTE" - else: - decision = "HOLD_GAUNTLET_UNSETTLED" - - item = { - "test_id": test_id, - "description": description, - "start": start, - "route_a": route_a, - "route_b": route_b, - "expected_same_root": expected_same_root, - "residual_declared": residual_declared, - "same_end": same_end, - "same_root": same_root, - "route_a_order_valid": order_a_valid, - "route_b_order_valid": order_b_valid, - "all_edges_admitted": all_edges_admitted, - "hidden_race": hidden_race, - "state_a": state_a, - "state_b": state_b, - "decision": decision, - } - item["test_hash"] = hash_obj({k: v for k, v in item.items() if k != "test_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - graph = load_multidimensional_graph() - tests = [ - make_test( - graph, - "godel_commute_provenance_then_semantic", - "Provenance before semantic reuse should be canonical and stable.", - "x0", - ["e_provenance_0_4", "e_semantic_0_4"], - ["e_provenance_0_4", "e_semantic_0_4"], - expected_same_root=True, - residual_declared=True, - ), - make_test( - graph, - "godel_race_semantic_vs_provenance", - "Semantic reuse before provenance can look plausible but changes the route root.", - "x0", - ["e_semantic_0_4", "e_provenance_0_4"], - ["e_provenance_0_4", "e_semantic_0_4"], - expected_same_root=True, - residual_declared=False, - ), - make_test( - graph, - "godel_race_chirality_vs_byte", - "Byte-neighbor and chirality edges share a target; wrong order hides whether handedness was checked before replay.", - "x0", - ["e_chirality_0_1", "e_byte_0_1"], - ["e_byte_0_1", "e_chirality_0_1"], - expected_same_root=True, - residual_declared=False, - ), - make_test( - graph, - "godel_declared_chirality_flip_noncommuting", - "A chirality flip followed by torsion pressure is declared noncommuting because handedness changes the adapter lane.", - "x1", - ["e_chirality_flip_1_2", "e_torsion_1_3"], - ["e_torsion_1_3", "e_chirality_flip_1_2"], - expected_same_root=False, - residual_declared=True, - ), - make_test( - graph, - "godel_360_share_vs_provenance", - "A 360 share root without provenance-first ordering can race against canonical input trust.", - "x0", - ["e_360_share_0_4", "e_provenance_0_4"], - ["e_provenance_0_4", "e_360_share_0_4"], - expected_same_root=True, - residual_declared=False, - ), - make_test( - graph, - "godel_dependency_hold_propagates", - "An already-HOLD unbounded predictive edge cannot be rescued by a later admitted share edge.", - "x3", - ["e_unbounded_predictive", "e_360_share_0_4"], - ["e_360_share_0_4", "e_unbounded_predictive"], - expected_same_root=False, - residual_declared=True, - ), - ] - return { - "schema": "godel_gauntlet_race_condition_registry_v1", - "source_refs": [source_ref(path) for path in [MULTI_CAUSAL_REGISTRY, MULTI_CAUSAL_RECEIPT, FOUNDATION_COMPILER]], - "claim_boundary": ( - "Gödel Gauntlet race-condition probe only. It exposes order-sensitive " - "route compositions in Hutter causal-axis diagnostics; it does not " - "change the codec, prove concurrency safety, or claim benchmark gain." - ), - "canonical_statement": ( - "Individually admitted causal edges do not promote as a shared route " - "until their compositions commute, or their noncommutation is explicitly " - "declared as residual." - ), - "gauntlet_equation": { - "edge": "e_{i,j}^{axis}: x_i -> x_j", - "composition": "R(a;b) == R(b;a) or residual_noncommuting(a,b) declared", - "race_gate": "RaceHold(a,b)=1[same_end] * 1[root_mismatch] * 1[residual_missing]", - "promotion": "Promote(route)=1[all_edges_admitted] * 1[order_canonical] * 1[not RaceHold]", - }, - "axis_priority": AXIS_PRIORITY, - "tests": tests, - "gauntlet_root": hash_obj([test["test_hash"] for test in tests]), - "aggregates": { - "test_count": len(tests), - "admit_count": sum(1 for test in tests if test["decision"].startswith("ADMIT")), - "hold_count": sum(1 for test in tests if test["decision"].startswith("HOLD")), - "hidden_race_count": sum(1 for test in tests if test["decision"] == "HOLD_HIDDEN_RACE_CONDITION"), - "order_noncanonical_count": sum(1 for test in tests if test["decision"] == "HOLD_AXIS_ORDER_NONCANONICAL"), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "godel_gauntlet_race_condition_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "gauntlet_root": registry["gauntlet_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_GODEL_GAUNTLET_RACE_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Godel Gauntlet Race Condition Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Gauntlet root: `{registry['gauntlet_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equation", - "", - ] - for key, value in registry["gauntlet_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Tests", - "", - "| Test | Route A | Route B | Same end | Same root | Decision |", - "|---|---|---|---:|---:|---|", - ] - ) - for test in registry["tests"]: - lines.append( - f"| `{test['test_id']}` | `{' -> '.join(test['route_a'])}` | " - f"`{' -> '.join(test['route_b'])}` | {test['same_end']} | " - f"{test['same_root']} | `{test['decision']}` |" - ) - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression GodelGauntlet RaceCondition Receipt -title: Godel Gauntlet Race Condition Probe -type: text/vnd.tiddlywiki - -! Godel Gauntlet Race Condition Probe - -Durable runner: - -``` -4-Infrastructure/shim/godel_gauntlet_race_condition_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Gauntlet root: - -``` -{receipt['gauntlet_root']} -``` - -!! Doctrine - -Individually admitted causal edges do not promote as shared Hutter route -structure until their compositions commute, or their noncommutation is declared -as residual. - -``` -R(a;b) == R(b;a) or residual_noncommuting(a,b) declared -RaceHold(a,b)=1[same_end] * 1[root_mismatch] * 1[residual_missing] -``` - -This is the race-condition version of the Gödel Gauntlet: same output surface -is not proof of lawful operator order. - -!! Links - -* [[Hutter Multidimensional Causal Chain]] -* [[Hutter Differential Frame Chain]] -* [[Hutter Frame Invariant Root]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "gauntlet_root": registry["gauntlet_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/godel_gauntlet_safety_condition_probe.py b/4-Infrastructure/shim/godel_gauntlet_safety_condition_probe.py deleted file mode 100644 index a984008c..00000000 --- a/4-Infrastructure/shim/godel_gauntlet_safety_condition_probe.py +++ /dev/null @@ -1,414 +0,0 @@ -#!/usr/bin/env python3 -"""Godel Gauntlet safety-condition probe for Hutter/logogram promotion. - -The race-condition gauntlet checks order sensitivity. This companion probe -checks other promotion hazards: replay, roots, provenance, residuals, unsafe -literalization, local-chart globalization, chirality adapters, 360 orientation -bucket completeness, timestamp misuse, baseline debt, and the hard Hutter Prize -resource envelope. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "godel_gauntlet_safety_condition" -REGISTRY = OUT_DIR / "godel_gauntlet_safety_condition_registry.json" -RECEIPT = OUT_DIR / "godel_gauntlet_safety_condition_receipt.json" -SUMMARY = OUT_DIR / "godel_gauntlet_safety_condition.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Godel Gauntlet Safety Condition Probe.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "godel_gauntlet_race_condition" / "godel_gauntlet_race_condition_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" / "observer_chart_projection_guardrail_receipt.json", - REPO / "shared-data" / "data" / "joke_source_literalization_guardrail" / "joke_source_literalization_guardrail_receipt.json", - REPO / "shared-data" / "data" / "logogram_dna_codec" / "logogram_dna_codec_receipt.json", - REPO / "4-Infrastructure" / "shim" / "foundation_forward_equation_compiler.py", -] - -CHECKS = [ - "exact_replay", - "root_recomputes", - "provenance_truthful", - "axis_declared", - "bounded_witness", - "residual_declared", - "dependency_admitted", - "timestamp_metadata_only", - "baseline_gate_closed", - "chirality_adapter_declared", - "orientation_buckets_complete", - "safe_literalization", - "local_chart_not_globalized", - "order_stable_or_residual_declared", - "resource_envelope_ok", -] - -RESOURCE_ENVELOPE = { - "source": "https://prize.hutter1.net/hrules.htm", - "rule_summary": "Each submitted program must run under 70,000/T hours, use at most 10GB RAM and 100GB HDD temporary files, and use no GPU.", - "max_hours_formula": "70000 / geekbench5_score_T", - "max_ram_gb": 10, - "max_temp_hdd_gb": 100, - "gpu_allowed": False, - "single_core_headline_source": "https://prize.hutter1.net/", - "single_core_headline": "main prize page summarizes the restriction as about 50 hours using a single CPU core with <10GB RAM and <100GB HDD on the test machine", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def classify(flags: dict[str, bool]) -> str: - if not flags["safe_literalization"]: - return "QUARANTINE_UNSAFE_LITERALIZATION" - if not flags["exact_replay"]: - return "REJECT_REPLAY" - if not flags["root_recomputes"]: - return "REJECT_ROOT_MISMATCH" - if not flags["provenance_truthful"]: - return "HOLD_PROVENANCE" - if not flags["timestamp_metadata_only"]: - return "HOLD_CLOCK_IN_HASH" - if not flags["axis_declared"]: - return "HOLD_AXIS_UNDECLARED" - if not flags["bounded_witness"]: - return "HOLD_UNBOUNDED_WITNESS" - if not flags["dependency_admitted"]: - return "HOLD_DEPENDENCY_NOT_ADMITTED" - if not flags["residual_declared"]: - return "HOLD_RESIDUAL_MISSING" - if not flags["local_chart_not_globalized"]: - return "HOLD_LOCAL_CHART_GLOBALIZED" - if not flags["chirality_adapter_declared"]: - return "HOLD_CHIRALITY_ADAPTER_MISSING" - if not flags["orientation_buckets_complete"]: - return "HOLD_ORIENTATION_BUCKET_GAP" - if not flags["order_stable_or_residual_declared"]: - return "HOLD_HIDDEN_RACE_CONDITION" - if not flags["baseline_gate_closed"]: - return "HOLD_BASELINE" - if not flags["resource_envelope_ok"]: - return "HOLD_RESOURCE_LIMIT" - return "ADMIT_SAFETY_CONDITION" - - -def case(case_id: str, description: str, overrides: dict[str, bool]) -> dict[str, Any]: - flags = {name: True for name in CHECKS} - flags.update(overrides) - decision = classify(flags) - failed = [name for name in CHECKS if not flags[name]] - item = { - "case_id": case_id, - "description": description, - "checks": flags, - "failed_checks": failed, - "decision": decision, - "promotable": decision == "ADMIT_SAFETY_CONDITION", - } - item["case_hash"] = hash_obj({k: v for k, v in item.items() if k != "case_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - cases = [ - case( - "safe_fixture_all_gates_closed", - "Positive control: replay, roots, residuals, ordering, chirality, orientation, and baseline all close.", - {}, - ), - case( - "replay_surface_matches_but_decoder_fails", - "A route that looks semantically correct but cannot byte-replay must reject before any higher claim.", - {"exact_replay": False}, - ), - case( - "root_mismatch_after_parallel_update", - "A hidden race can surface as a recomputed root mismatch after two update lanes touch the same frame.", - {"root_recomputes": False}, - ), - case( - "fixture_labeled_canonical", - "A noncanonical fixture cannot borrow canonical enwik9 authority.", - {"provenance_truthful": False}, - ), - case( - "clock_participates_in_hash", - "Wall-clock time may timestamp a receipt, but must not participate in the replay root.", - {"timestamp_metadata_only": False}, - ), - case( - "axis_free_similarity", - "Same-looking surfaces cannot promote without a declared causal axis.", - {"axis_declared": False}, - ), - case( - "long_predictive_delta_without_checkpoint", - "A byte route with no nearby root checkpoint remains unbounded even if the delta is small.", - {"bounded_witness": False}, - ), - case( - "hold_dependency_rescued_by_later_edge", - "A later admitted edge cannot rescue an upstream HOLD dependency.", - {"dependency_admitted": False}, - ), - case( - "buffalo_surface_collision_no_residual", - "Same surface token with different role/order must carry residuals or stay HOLD.", - {"residual_declared": False}, - ), - case( - "observer_chart_promoted_to_global_truth", - "A lawful local chart becomes unsafe for promotion when treated as global codec truth.", - {"local_chart_not_globalized": False}, - ), - case( - "chirality_flip_without_adapter", - "Handedness changes need an explicit adapter and residual lane.", - {"chirality_adapter_declared": False}, - ), - case( - "orientation_360_missing_bucket", - "A 360 sharing root requires committed orientation buckets; gaps stay HOLD.", - {"orientation_buckets_complete": False}, - ), - case( - "unsafe_joke_literalized_as_callable", - "A joke source can be metadata, but unsafe procedural expansion must quarantine.", - {"safe_literalization": False}, - ), - case( - "noncommuting_route_without_residual", - "Two admitted edges that reach the same surface with different roots expose a hidden race unless residualized.", - {"order_stable_or_residual_declared": False}, - ), - case( - "baseline_debt_unpaid", - "A replay-valid route still cannot promote as Hutter candidate while baseline comparison is open.", - {"baseline_gate_closed": False}, - ), - case( - "prize_resource_envelope_exceeded", - "A route that needs too much time, RAM, temporary disk, cores, or GPU cannot promote under prize rules.", - {"resource_envelope_ok": False}, - ), - ] - return { - "schema": "godel_gauntlet_safety_condition_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Godel Gauntlet safety-condition probe only. It converts known Hutter, " - "logogram, observer-chart, and joke-source guardrails into promotion " - "tests. It does not change codec behavior or claim benchmark gain." - ), - "canonical_statement": ( - "A candidate route promotes only when replay, roots, provenance, axes, " - "bounds, residuals, dependency status, timestamp policy, baseline, " - "chirality, orientation buckets, literalization safety, chart scope, " - "order stability, and the hard prize resource envelope all close." - ), - "gauntlet_equation": { - "safety_gate": "Safe(c)=product(check_i(c))", - "promotion": "Promote(c)=1[Safe(c)] else REJECT/HOLD/QUARANTINE by first failed invariant", - "race_bridge": "order_stable_or_residual_declared imports RaceHold results from the route-order gauntlet", - }, - "checks": CHECKS, - "resource_envelope": RESOURCE_ENVELOPE, - "cases": cases, - "safety_root": hash_obj([item["case_hash"] for item in cases]), - "aggregates": { - "case_count": len(cases), - "admit_count": sum(1 for item in cases if item["decision"].startswith("ADMIT")), - "hold_count": sum(1 for item in cases if item["decision"].startswith("HOLD")), - "reject_count": sum(1 for item in cases if item["decision"].startswith("REJECT")), - "quarantine_count": sum(1 for item in cases if item["decision"].startswith("QUARANTINE")), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "godel_gauntlet_safety_condition_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "safety_root": registry["safety_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_GODEL_GAUNTLET_SAFETY_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Godel Gauntlet Safety Condition Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Safety root: `{registry['safety_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equation", - "", - ] - for key, value in registry["gauntlet_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Cases", - "", - "| Case | Failed checks | Decision |", - "|---|---|---|", - ] - ) - for item in registry["cases"]: - failed = ", ".join(item["failed_checks"]) if item["failed_checks"] else "none" - lines.append(f"| `{item['case_id']}` | {failed} | `{item['decision']}` |") - lines.extend( - [ - "", - "## Resource Envelope", - "", - f"- Source: `{registry['resource_envelope']['source']}`", - f"- Max hours formula: `{registry['resource_envelope']['max_hours_formula']}`", - f"- Max RAM GB: `{registry['resource_envelope']['max_ram_gb']}`", - f"- Max temp HDD GB: `{registry['resource_envelope']['max_temp_hdd_gb']}`", - f"- GPU allowed: `{registry['resource_envelope']['gpu_allowed']}`", - "", - "## Source Refs", - "", - ] - ) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression GodelGauntlet SafetyCondition Receipt -title: Godel Gauntlet Safety Condition Probe -type: text/vnd.tiddlywiki - -! Godel Gauntlet Safety Condition Probe - -Durable runner: - -``` -4-Infrastructure/shim/godel_gauntlet_safety_condition_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Safety root: - -``` -{receipt['safety_root']} -``` - -!! Doctrine - -A candidate route promotes only when replay, roots, provenance, axes, bounds, -residuals, dependency status, timestamp policy, baseline, chirality, orientation -buckets, literalization safety, chart scope, order stability, and the hard -Hutter Prize resource envelope all close. - -``` -Safe(c)=product(check_i(c)) -Promote(c)=1[Safe(c)] else REJECT/HOLD/QUARANTINE by first failed invariant -``` - -!! Links - -* [[Godel Gauntlet Race Condition Probe]] -* [[Hutter Multidimensional Causal Chain]] -* [[Observer Chart Projection Guardrail]] -* [[Joke Source Literalization Guardrail]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "safety_root": registry["safety_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/h200_encode_runner.py b/4-Infrastructure/shim/h200_encode_runner.py deleted file mode 100644 index 5f488b16..00000000 --- a/4-Infrastructure/shim/h200_encode_runner.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python3 -"""Dry-run H200 burst optimizer scaffold for finance LUT compression. - -This does not require H200 hardware. It prepares the corpus/codebook search -surface and emits receipt-backed candidate summaries for later GPU rental. -""" - -from __future__ import annotations - -import argparse -import json -import platform -from pathlib import Path -from typing import Any - -import finance_claim_lut_harness as harness - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" - - -def load_bundle_receipt(path: Path) -> dict[str, Any]: - if path.is_dir(): - path = path / "finance_claim_lut_harness_receipt.json" - return json.loads(path.read_text(encoding="utf-8")) - - -def candidate_summary(receipt: dict[str, Any]) -> list[dict[str, Any]]: - candidates = [] - for sample in receipt.get("samples", []): - metrics = sample["metrics"] - best_known = min( - value - for value in [ - metrics.get("combined_fcl1_fcs1_bytes"), - metrics.get("zlib_canonical_bytes"), - metrics.get("cbor", {}).get("bytes"), - metrics.get("messagepack", {}).get("bytes"), - metrics.get("protobuf_dynamic", {}).get("bytes"), - ] - if isinstance(value, int) - ) - candidates.append( - { - "sample_id": sample["id"], - "current_fcl1_fcs1_bytes": metrics["combined_fcl1_fcs1_bytes"], - "best_known_baseline_bytes": best_known, - "target": "reduce FCS1 literal overhead and improve enum/value clustering", - "promote": False, - "reason": "dry-run only; no GPU search performed", - } - ) - return candidates - - -def run(args: argparse.Namespace) -> dict[str, Any]: - receipt = load_bundle_receipt(args.corpus) - candidates = candidate_summary(receipt) - rejected = [ - {"name": "provider_live_job", "reason": "requires explicit rental, budget, and environment receipt"}, - {"name": "decoder_requires_gpu", "reason": "violates compact deterministic decoder boundary"}, - {"name": "compression_claim_from_tiny_corpus", "reason": "corpus still too small for competitive claim"}, - ] - out_dir = args.out_dir - out_dir.mkdir(parents=True, exist_ok=True) - (out_dir / "candidate_lut_summary.json").write_text(json.dumps(candidates, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - (out_dir / "rejected_candidates.json").write_text(json.dumps(rejected, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - dry_run_receipt = { - "schema": "h200_encode_runner_receipt_v1", - "mode": "dry_run", - "corpus": harness.repo_path(args.corpus), - "out_dir": harness.repo_path(out_dir), - "sample_count": len(receipt.get("samples", [])), - "candidate_count": len(candidates), - "rejected_count": len(rejected), - "environment": {"python": platform.python_version(), "platform": platform.platform(), "gpu_required": False}, - "next_live_gate": "rent H200 only after local bundle, Netcup baseline, and noisy simulator receipts are lawful", - "lawful": len(candidates) > 0 and all(not item["promote"] for item in candidates), - "claim_boundary": "dry-run optimizer scaffold only; no H200 hardware used and no improved compression claim made", - } - (out_dir / "h200_encode_runner_receipt.json").write_text(json.dumps(dry_run_receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - return dry_run_receipt - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--corpus", type=Path, default=SHIM / "finance_claim_remote_bundle") - parser.add_argument("--out-dir", type=Path, default=SHIM / "h200_encode_dry_run") - args = parser.parse_args() - print(json.dumps(run(args), indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hermes_math_audit_manifest.json b/4-Infrastructure/shim/hermes_math_audit_manifest.json deleted file mode 100644 index fc6dd61d..00000000 --- a/4-Infrastructure/shim/hermes_math_audit_manifest.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "manifest_version": "2026-05-13", - "agent": "hermes-nous-research", - "doctrine": { - "observer_provider_pairs": true, - "receipt_gate_before_belief": true, - "human_interference_allowed": true, - "lean_source_of_truth": true, - "no_sorry_without_todo": true - }, - "attack_surface": { - "lean_sorry_axioms": { - "severity": "critical", - "count": 71, - "locations": [ - { - "file": "3-Mathematical-Models/manifold_compression/src/AutoAdaptiveMetatypeSystem.lean", - "lines": [327, 467, 536, 585, 616, 652, 674, 770], - "attack_vector": "Q0_64 monotonicity, time evolution, division identity, weight normalization, scalar surjectivity", - "observer_assigned": "O_Q0_64_SCALAR", - "provider_assigned": "P_Q0_64_SCALAR", - "receipt_kind": "leanBuild", - "priority": 1 - }, - { - "file": "6-Documentation/docs/semantics/missingproofs/Domain_Intersections.lean", - "lines": [22, 24, 29, 31, 43, 45, 50, 52, 64, 66, 71, 73, 85, 87, 92, 94, 102, 105, 108, 111, 114], - "attack_vector": "16 domain intersection theorems all True := by sorry - vacuous shells", - "observer_assigned": "O_DOMAIN_BIND", - "provider_assigned": "P_DOMAIN_BIND", - "receipt_kind": "leanBuild", - "priority": 2 - }, - { - "file": "6-Documentation/docs/semantics/missingproofs/AVMR_Theorems.lean", - "lines": [24, 35, 47, 59, 70, 77, 82, 89, 94, 101, 105, 110, 122, 127, 135, 141, 253, 272, 280, 285, 290, 295, 300, 305, 310], - "attack_vector": "29 bare sorry instances in AVMR theorems - no reduction, no witnesses", - "observer_assigned": "O_AVMR", - "provider_assigned": "P_AVMR", - "receipt_kind": "leanBuild", - "priority": 2 - }, - { - "file": "0-Core-Formalism/lean/external/OTOM/CompressionLossComparison.lean", - "lines": [199, 201, 245, 246, 247, 363, 599], - "attack_vector": "wf_positive/wf_epsilon_pos/wf_kappa_nonneg all sorry - circular trust boundary", - "observer_assigned": "O_WF_FIELDS", - "provider_assigned": "P_WF_FIELDS", - "receipt_kind": "deltaPhiAudit", - "priority": 1 - }, - { - "file": "0-Core-Formalism/lean/Semantics/F01_Q16_16_FixedPoint.lean", - "lines": [82, 86, 90, 94, 102, 134, 167], - "attack_vector": "Q16.16 fixed-point bounds - requires Wolfram/Goedel proofs", - "observer_assigned": "O_Q16_16", - "provider_assigned": "P_Q16_16", - "receipt_kind": "leanBuild", - "priority": 1 - } - ] - }, - "python_arithmetic_guards": { - "severity": "high", - "count": 9, - "locations": [ - { - "file": "5-Applications/scripts/pist_biological_polymorphic_shifter_v3_part3.py", - "line": 1224, - "attack_vector": "Box-Muller: sqrt(-2*log(u1)) - u1==1 gives log(1)=0 safe, but no guard for u1>1", - "observer_assigned": "O_STOCHASTIC", - "provider_assigned": "P_STOCHASTIC", - "receipt_kind": "sourceAudit", - "priority": 2 - }, - { - "file": "4-Infrastructure/shim/waveprobe_transfer_smoothing.py", - "line": 147, - "attack_vector": "1/sqrt(max(eigenvalue, 1e-6)) - clamps negative eigenvalues silently", - "observer_assigned": "O_EIGEN_CLAMP", - "provider_assigned": "P_EIGEN_CLAMP", - "receipt_kind": "sourceAudit", - "priority": 2 - }, - { - "file": "4-Infrastructure/shim/quantum_cogload_transfold_receipt.py", - "line": 153, - "attack_vector": "entropy computation - no upstream guard for total==0", - "observer_assigned": "O_ENTROPY", - "provider_assigned": "P_ENTROPY", - "receipt_kind": "sourceAudit", - "priority": 3 - } - ] - }, - "false_theorem_claims": { - "severity": "critical", - "locations": [ - { - "file": "3-Mathematical-Models/manifold_compression/src/AutoAdaptiveMetatypeSystem.lean", - "line": 576, - "theorem": "weights_normalized", - "claim": "lambdaI + lambdaE - lambdaG + lambdaR + lambdaM = Q0_64.half", - "actual": "0.70 != 0.50 per inline comment - 'deliberate design tension'", - "observer_assigned": "O_WEIGHT_NORM", - "provider_assigned": "P_WEIGHT_NORM", - "receipt_kind": "deltaPhiAudit", - "priority": 0, - "interference_trigger": true - } - ] - } - }, - "interference_protocol": { - "trigger_conditions": [ - "theorem statement contradicts inline documentation", - "sorry used without TODO(lean-port)", - "well-formedness fields populated with sorry in witness position", - "division by zero without guard", - "logarithm of non-positive without guard" - ], - "action": "human_review_required", - "escalation_path": "block_promotion_until_receipt" - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/historical_stellar_gas_prior_mapper.py b/4-Infrastructure/shim/historical_stellar_gas_prior_mapper.py deleted file mode 100644 index 2c7e8a75..00000000 --- a/4-Infrastructure/shim/historical_stellar_gas_prior_mapper.py +++ /dev/null @@ -1,271 +0,0 @@ -#!/usr/bin/env python3 -"""Map historical stellar-gas law families onto current observation support.""" - -from __future__ import annotations - -import json -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path - - -REPO = Path(__file__).resolve().parents[2] -DATA_DIR = REPO / "shared-data/data/stellar_gas_observation" -LADDER = DATA_DIR / "historical_stellar_gas_model_ladder.json" -FIT = DATA_DIR / "stellar_gas_shock_eigen_fit.json" -LINE_DIAGNOSTICS = DATA_DIR / "stellar_gas_line_ratio_diagnostics.json" -OUT = DATA_DIR / "historical_stellar_gas_prior_map.json" -DOC = REPO / "6-Documentation/docs/historical_stellar_gas_model_ladder_2026-05-09.md" -DESTINATION = "Gdrive:topological_storage/research-stack/stellar-gas-observation/seed-2026-05-09" - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def run(cmd: list[str]) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - - -def rclone_copyto(local: Path, remote: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local), remote, "--checksum"]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def load(path: Path) -> dict: - return json.loads(path.read_text()) - - -def hook_support(model: dict, fit: dict, line_diag: dict | None) -> tuple[float, list[str], list[str]]: - agg = fit["aggregate_observables"] - available = [] - missing = [] - line_ratio_support = 0.0 - shock_line_support = 0.0 - balmer_support = 0.0 - if line_diag: - line_ratio_support = min( - 1.0, - line_diag.get("valid_ratio_rows", 0) / max(1, line_diag.get("rows_seen", 1)), - ) - shock_line_support = float( - line_diag.get("shock_lier_support", {}).get("fractional_proxy_support", 0.0) - ) - balmer_count = line_diag.get("aggregate_ratios", {}).get("balmer_decrement", {}).get("count", 0) - balmer_support = min(1.0, balmer_count / max(1, line_diag.get("rows_seen", 1))) - hook_to_observable = { - "redshift_or_distance": ["snr_med_mean"], - "position_context": ["snr_med_mean"], - "gas_velocity_span": ["gas_velocity_span_kms"], - "velocity_gradient_proxy": ["gas_velocity_span_kms", "velocity_contrast"], - "velocity_dispersion": ["gas_sigma_1re_kms"], - "gas_sigma": ["gas_sigma_1re_kms"], - "line_width": ["gas_sigma_1re_kms"], - "emission_line_flux": ["shock_proxy_score"], - "line_ratio": ["__line_ratio_support__"], - "equivalent_width": ["shock_proxy_score"], - "surface_brightness": ["shock_proxy_score"], - "attenuation": ["__balmer_decrement_support__"], - "density_or_pressure_if_available": [], - "pre_post_state_if_available": [], - "temperature_or_electron_density": [], - "stellar_context": ["stellar_sigma_1re_kms"], - "mass_radius_temperature_if_available": [], - "density": [], - "shock_front_radius_time": [], - "ambient_density": [], - "magnetic_field": [], - "outflow_velocity": ["gas_velocity_span_kms", "velocity_contrast"], - "line_asymmetry": [], - "optical_depth": [], - "diffusion_time": [], - "dynamical_time": [], - "shock_velocity": ["gas_velocity_span_kms"], - } - scores = [] - for hook in model.get("observable_hooks", []): - observables = hook_to_observable.get(hook, []) - if not observables: - missing.append(hook) - continue - present = False - for obs in observables: - if obs == "__line_ratio_support__": - if line_ratio_support > 0: - available.append(f"{hook}->line_ratio_diagnostics") - present = True - # Blend general line-ratio coverage with shock-sensitive line-ratio support. - scores.append((line_ratio_support + shock_line_support) / 2) - continue - if obs == "__balmer_decrement_support__": - if balmer_support > 0: - available.append(f"{hook}->balmer_decrement") - present = True - scores.append(balmer_support) - continue - summary = agg.get(obs, {}) - count = summary.get("count", 0) - mean = summary.get("mean", 0.0) - if count: - available.append(f"{hook}->{obs}") - present = True - if obs == "shock_proxy_score": - scores.append(float(mean)) - else: - scores.append(min(1.0, float(count) / max(1, fit["admitted_proxy_rows"]))) - if not present: - missing.append(hook) - if not scores: - return 0.0, available, missing - return round(sum(scores) / len(scores), 6), available, missing - - -def decision_for(score: float, missing: list[str], gate: str) -> str: - if score <= 0: - return "HOLD_NO_CURRENT_OBSERVABLE" - if missing or gate.startswith("HOLD"): - return "HOLD_PARTIAL_PRIOR_SUPPORT" - return "ADMIT_PRIOR_SUPPORT" - - -def build_map(ladder: dict, fit: dict, line_diag: dict | None) -> dict: - mapped = [] - for model in ladder["models"]: - score, available, missing = hook_support(model, fit, line_diag) - mapped.append( - { - "id": model["id"], - "period": model["period"], - "names": model["names"], - "law_family": model["law_family"], - "equation_shape": model["equation_shape"], - "local_axis": model["local_axis"], - "current_gate": model["gate"], - "current_observation_support": score, - "available_hooks": available, - "missing_hooks": missing, - "decision": decision_for(score, missing, model["gate"]), - } - ) - admitted = [m for m in mapped if m["decision"] == "ADMIT_PRIOR_SUPPORT"] - partial = [m for m in mapped if m["decision"] == "HOLD_PARTIAL_PRIOR_SUPPORT"] - return { - "schema": "historical_stellar_gas_prior_map_v0", - "created": now_iso(), - "claim_boundary": "Maps historical stellar-gas law families onto current MaNGA observation proxies. It ranks available support and missing gates; it does not validate the physical laws or infer causal shock events.", - "source_ladder": str(LADDER.relative_to(REPO)), - "source_fit": str(FIT.relative_to(REPO)), - "source_line_diagnostics": str(LINE_DIAGNOSTICS.relative_to(REPO)) if line_diag else None, - "fit_decision": fit["decision"], - "fit_refinement": fit["physical_shock_axis_refinement"], - "line_ratio_refinement": line_diag.get("shock_lier_support") if line_diag else None, - "summary": { - "model_count": len(mapped), - "admitted_prior_support": len(admitted), - "partial_prior_support": len(partial), - "no_current_observable": len(mapped) - len(admitted) - len(partial), - }, - "models": mapped, - "decision": "ADMIT_HISTORICAL_PRIOR_SURFACE", - } - - -def write_doc(result: dict, ladder: dict) -> None: - lines = [ - "# Historical Stellar Gas Model Ladder", - "", - "**Date:** 2026-05-09", - "", - f"**Decision:** `{result['decision']}`", - "", - "**Claim boundary:** this is a historical prior and routing surface. It", - "does not claim that the current MaNGA proxy fit validates any physical", - "law or detects a specific shock event.", - "", - "## Why This Helps", - "", - "The stack now has a wide historical basis for stellar gas modeling rather", - "than a single modern blob. Each law family gets a receipt role:", - "", - "```text", - "historical law -> observable hook -> current column/proxy -> gate", - "```", - "", - "## Current Support", - "", - f"- Models mapped: {result['summary']['model_count']}", - f"- Admitted prior support: {result['summary']['admitted_prior_support']}", - f"- Partial prior support: {result['summary']['partial_prior_support']}", - f"- No current observable: {result['summary']['no_current_observable']}", - "", - "## Ladder", - "", - "| Period | Law family | Names | Support | Decision |", - "|---|---|---|---:|---|", - ] - for model in result["models"]: - lines.append( - f"| {model['period']} | `{model['law_family']}` | " - f"{', '.join(model['names'])} | {model['current_observation_support']:.6f} | " - f"`{model['decision']}` |" - ) - lines += [ - "", - "## Source Notes", - "", - ] - for source in ladder.get("source_notes", []): - lines.append(f"- {source['title']}: `{source['url']}`") - lines += [ - "", - "## Next Gate", - "", - "The current support is strongest for velocity, dispersion, and named", - "line-ratio proxy lanes. The next refinement is adding uncertainty,", - "electron-density, temperature, and attenuation gates so Saha, radiative", - "transfer, and shock-excitation support can move beyond proxy status.", - "", - ] - DOC.write_text("\n".join(lines)) - - -def main() -> int: - ladder = load(LADDER) - fit = load(FIT) - line_diag = load(LINE_DIAGNOSTICS) if LINE_DIAGNOSTICS.exists() else None - result = build_map(ladder, fit, line_diag) - OUT.write_text(json.dumps(result, indent=2) + "\n") - write_doc(result, ladder) - receipt_path = DATA_DIR / f"historical_stellar_gas_prior_map_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - receipt = { - "schema": "historical_stellar_gas_prior_map_receipt_v0", - "created": now_iso(), - "claim_boundary": result["claim_boundary"], - "ladder_file": str(LADDER.relative_to(REPO)), - "prior_map_file": str(OUT.relative_to(REPO)), - "doc_file": str(DOC.relative_to(REPO)), - "summary": result["summary"], - "decision": result["decision"], - "uploads": {}, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - uploads = { - "ladder": (LADDER, f"{DESTINATION}/derived/{LADDER.name}"), - "prior_map": (OUT, f"{DESTINATION}/derived/{OUT.name}"), - "doc": (DOC, f"{DESTINATION}/docs/{DOC.name}"), - "receipt": (receipt_path, f"{DESTINATION}/receipts/{receipt_path.name}"), - } - for key, (local, remote) in uploads.items(): - ok, message = rclone_copyto(local, remote) - receipt["uploads"][key] = {"drive_path": remote, "ok": ok, "message": message} - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - if receipt["uploads"]["receipt"]["ok"]: - rclone_copyto(receipt_path, receipt["uploads"]["receipt"]["drive_path"]) - print(json.dumps(receipt, indent=2)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/holographic_carving_probe.py b/4-Infrastructure/shim/holographic_carving_probe.py deleted file mode 100644 index 5fdaa508..00000000 --- a/4-Infrastructure/shim/holographic_carving_probe.py +++ /dev/null @@ -1,501 +0,0 @@ -#!/usr/bin/env python3 -"""Combined holographic encoding + Menger-style carving via threshold-band exclusion. - -Instead of removing coordinates (Menger), the beam superposition B(x, r) -carves voids by threshold-band non-activation: at each point, only structures -whose lambda-band matches the local B value materialize. Everything else is -"void" at that point. - -This gives a scaffold where multiple structures share coordinates but separate -in lambda-space. The expansion-space cost is lambda-separation, not -coordinate-buffer volume. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass, field, asdict -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "holographic_carving" -REGISTRY = OUT_DIR / "holographic_carving_registry.json" -RECEIPT = OUT_DIR / "holographic_carving_receipt.json" -SUMMARY = OUT_DIR / "holographic_carving.md" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Holographic Carving.tid" -) - -SOURCE_REFS = [ - REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "LogogramRotationLoop.lean", - REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "ThresholdVector.lean", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -# --------------------------------------------------------------------------- -# Core types -# --------------------------------------------------------------------------- - - -@dataclass(frozen=True) -class ThresholdBand: - lower: float - upper: float - - -@dataclass(frozen=True) -class ProjectionLayer: - angle: float - encoding: dict[str, float] # phi vector - band: ThresholdBand - label: str - - -@dataclass(frozen=True) -class CarvingVoxel: - """A point in the volume: what materializes depends on B(x).""" - x: float - y: float - z: float - B: float - active_structures: dict[str, bool] - - -# --------------------------------------------------------------------------- -# Carving engine -# --------------------------------------------------------------------------- - - -def band_contains(B: float, band: ThresholdBand) -> bool: - return band.lower <= B <= band.upper - - -def integrate_beam(layers: list[ProjectionLayer], weights: dict[str, float]) -> float: - """Compute B = sum alpha_i * phi_i over all layers.""" - total = 0.0 - for layer in layers: - for comp, val in layer.encoding.items(): - total += weights.get(comp, 0.0) * val - weight_sum = sum(weights.values()) - return total / weight_sum if weight_sum > 0 else 0.0 - - -def resolve_voxel( - B: float, - layers: list[ProjectionLayer], - critical_threshold: float, -) -> dict[str, bool]: - """At a point with total activation B, which structures materialize?""" - critical = B >= critical_threshold - return { - layer.label: (critical and band_contains(B, layer.band)) - for layer in layers - } - - -def carve_volume( - layers: list[ProjectionLayer], - weights: dict[str, float], - critical_threshold: float, - resolution: int = 4, -) -> list[CarvingVoxel]: - """Evaluate B(x) over a 3D grid, producing active/void at each voxel.""" - voxels = [] - B_beam = integrate_beam(layers, weights) - for i in range(resolution): - for j in range(resolution): - for k in range(resolution): - x = i / (resolution - 1) if resolution > 1 else 0.5 - y = j / (resolution - 1) if resolution > 1 else 0.5 - z = k / (resolution - 1) if resolution > 1 else 0.5 - # In the combined model, B varies across the volume. - # For this probe, we modulate B by position to show - # spatial variation in threshold-band activation. - B_local = B_beam * (1.0 - 0.3 * ((x - 0.5) ** 2 + (y - 0.5) ** 2 + (z - 0.5) ** 2) / 0.75) - active = resolve_voxel(B_local, layers, critical_threshold) - voxels.append(CarvingVoxel(x, y, z, round(B_local, 4), active)) - return voxels - - -def count_active_voxels(voxels: list[CarvingVoxel], structure_label: str) -> int: - return sum(1 for v in voxels if v.active_structures.get(structure_label, False)) - - -def count_void_voxels(voxels: list[CarvingVoxel]) -> int: - return sum(1 for v in voxels if not any(v.active_structures.values())) - - -# --------------------------------------------------------------------------- -# Scenarios -# --------------------------------------------------------------------------- - -LOW_BAND = ThresholdBand(0.0, 0.35) -MID_BAND = ThresholdBand(0.35, 0.65) -HIGH_BAND = ThresholdBand(0.65, 1.0) - -DEFAULT_WEIGHTS = { - "density_gradient": 0.20, - "spectral_drift": 0.20, - "coupling": 0.20, - "scar_pressure": 0.15, - "topology_persistence": 0.10, - "deposited_energy": 0.15, -} - -DEFAULT_CRITICAL = 0.5 - - -def single_structure_scenario() -> dict[str, Any]: - """Baseline: one beam, one structure (pre-holographic).""" - layers = [ - ProjectionLayer( - angle=0.0, - encoding={"density_gradient": 1.0, "spectral_drift": 0.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 0.0, "deposited_energy": 0.0}, - band=LOW_BAND, - label="single_structure", - ) - ] - B_beam = integrate_beam(layers, DEFAULT_WEIGHTS) - voxels = carve_volume(layers, DEFAULT_WEIGHTS, DEFAULT_CRITICAL, resolution=4) - return { - "scenario_id": "single_structure_baseline", - "n_layers": len(layers), - "n_structures": 1, - "B_beam": round(B_beam, 4), - "total_voxels": len(voxels), - "active_voxels": { - "single_structure": count_active_voxels(voxels, "single_structure"), - }, - "void_voxels": count_void_voxels(voxels), - "packing_efficiency": round(count_active_voxels(voxels, "single_structure") / len(voxels), 4), - } - - -def three_structure_scenario() -> dict[str, Any]: - """Three structures in one beam, separated by threshold bands.""" - layers = [ - ProjectionLayer( - angle=0.0, - encoding={"density_gradient": 0.5, "spectral_drift": 0.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 0.0, "deposited_energy": 0.0}, - band=LOW_BAND, - label="density_scaffold", - ), - ProjectionLayer( - angle=0.333, - encoding={"density_gradient": 0.0, "spectral_drift": 1.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 0.0, "deposited_energy": 0.0}, - band=MID_BAND, - label="spectral_filament", - ), - ProjectionLayer( - angle=0.667, - encoding={"density_gradient": 0.0, "spectral_drift": 0.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 1.0, "deposited_energy": 1.0}, - band=HIGH_BAND, - label="topology_web", - ), - ] - B_beam = integrate_beam(layers, DEFAULT_WEIGHTS) - voxels = carve_volume(layers, DEFAULT_WEIGHTS, DEFAULT_CRITICAL, resolution=4) - active_counts = { - label: count_active_voxels(voxels, label) - for label in ["density_scaffold", "spectral_filament", "topology_web"] - } - total_active = sum(active_counts.values()) - return { - "scenario_id": "three_structure_holographic", - "n_layers": len(layers), - "n_structures": 3, - "B_beam": round(B_beam, 4), - "total_voxels": len(voxels), - "active_voxels": active_counts, - "total_active_voxels": total_active, - "void_voxels": count_void_voxels(voxels), - "packing_efficiency": round(total_active / len(voxels), 4), - "structures_per_beam": 3, - } - - -def carving_void_scenario() -> dict[str, Any]: - """Menger-like carving: structures create voids in each other's bands.""" - layers = [ - ProjectionLayer( - angle=0.0, - encoding={"density_gradient": 0.8, "spectral_drift": 0.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 0.0, "deposited_energy": 0.0}, - band=LOW_BAND, - label="scaffold", - ), - ProjectionLayer( - angle=0.5, - encoding={"density_gradient": 0.0, "spectral_drift": 0.0, - "coupling": 0.0, "scar_pressure": 0.0, - "topology_persistence": 0.0, "deposited_energy": 1.0}, - band=HIGH_BAND, - label="energy_void", - ), - ] - B_beam = integrate_beam(layers, DEFAULT_WEIGHTS) - voxels = carve_volume(layers, DEFAULT_WEIGHTS, DEFAULT_CRITICAL, resolution=6) - scaffold_active = count_active_voxels(voxels, "scaffold") - void_active = count_active_voxels(voxels, "energy_void") - void_count = count_void_voxels(voxels) - return { - "scenario_id": "carving_void", - "n_layers": len(layers), - "n_structures": 2, - "B_beam": round(B_beam, 4), - "total_voxels": len(voxels), - "active_voxels": { - "scaffold": scaffold_active, - "energy_void": void_active, - }, - "void_voxels": void_count, - "scaffold_void_ratio": round(scaffold_active / void_count, 4) if void_count else -1, - "packing_efficiency": round((scaffold_active + void_active) / len(voxels), 4), - } - - -# --------------------------------------------------------------------------- -# Registry and receipt -# --------------------------------------------------------------------------- - - -def build_registry() -> dict[str, Any]: - scenarios = [ - single_structure_scenario(), - three_structure_scenario(), - carving_void_scenario(), - ] - return { - "schema": "holographic_carving_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Combined holographic encoding + Menger-style carving demo. " - "The beam superposition carries multiple structures; threshold-band " - "filtering determines which materialize at each voxel. " - "Does not claim physical printing fidelity without dose-calibration." - ), - "canonical_statement": ( - "Voids are not removed coordinates. " - "Voids are un-activated threshold bands at a given boundary point." - ), - "superposition_equation": "B(x) = sum_i alpha_i * phi_i(x)", - "carving_rule": "structure S materializes at x iff B(x) in band(S) AND B(x) >= critical", - "void_rule": "point x is void iff B(x) < critical OR B(x) not in any structure's band", - "critical_threshold": DEFAULT_CRITICAL, - "default_weights": DEFAULT_WEIGHTS, - "scenarios": scenarios, - "aggregates": { - "scenario_count": len(scenarios), - "total_structures": sum(s["n_structures"] for s in scenarios), - "total_active_voxels": sum(s.get("total_active_voxels", s.get("active_voxels", {}).get(list(s["active_voxels"].keys())[0], 0)) for s in scenarios), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "holographic_carving_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "aggregates": registry["aggregates"], - "decision": "ADMIT_HOLOGRAPHIC_CARVING_MODEL", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json( - {k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}} - ).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Holographic Carving — Combined Encoding + Threshold-Band Carving", - "", - f"Decision: `{receipt['decision']}`", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equations", - "", - f"- Superposition: `{registry['superposition_equation']}`", - f"- Carving rule: `{registry['carving_rule']}`", - f"- Void rule: `{registry['void_rule']}`", - f"- Critical threshold = {registry['critical_threshold']}", - "", - "## Scenarios", - "", - "| Scenario | Structures | B_beam | Voxels | Active | Void | Efficiency |", - "|---|---|---|---|---|---|---|", - ] - for s in registry["scenarios"]: - active = s.get("total_active_voxels", list(s["active_voxels"].values())[0]) - lines.append( - f"| `{s['scenario_id']}` | {s['n_structures']} | {s['B_beam']} | " - f"{s['total_voxels']} | {active} | {s['void_voxels']} | {s['packing_efficiency']} |" - ) - lines.extend( - [ - "", - "## Active Voxel Detail", - "", - ] - ) - for s in registry["scenarios"]: - lines.append(f"### {s['scenario_id']}") - for label, count in s.get("active_voxels", {}).items(): - ratio = round(count / s["total_voxels"], 3) - lines.append(f"- `{label}`: {count} / {s['total_voxels']} voxels ({ratio})") - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Scenario count: {registry['aggregates']['scenario_count']}", - f"- Total structures: {registry['aggregates']['total_structures']}", - "", - "## Source Refs", - "", - ] - ) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260512000000000 -modified: 20260512000000000 -tags: ResearchStack Encoding HolographicCarving Receipt -title: Holographic Carving -type: text/vnd.tiddlywiki - -! Holographic Carving — Encoding + Threshold-Band Carving - -Durable runner: - -``` -4-Infrastructure/shim/holographic_carving_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -Voids are not removed coordinates. Voids are un-activated threshold bands at a given boundary point. - -!! Links - -* [[LogogramRotationLoop (Lean formalization)|LogogramRotationLoop.lean]] -* [[ThresholdVector (Lean formalization)|ThresholdVector.lean]] -* [[Boundary Activation Field]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text( - json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - RECEIPT.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/holographic_fractional_recursive_connectome_prior.py b/4-Infrastructure/shim/holographic_fractional_recursive_connectome_prior.py deleted file mode 100644 index 184c6c9e..00000000 --- a/4-Infrastructure/shim/holographic_fractional_recursive_connectome_prior.py +++ /dev/null @@ -1,301 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for holographic, fractional, and recursive connectome priors.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "holographic_fractional_recursive_connectome_prior_receipt.json" -CURRICULUM = SHIM / "holographic_fractional_recursive_connectome_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "holographic_fractional_recursive_connectome_prior_v1", - "source_type": "user_supplied_consensus_connectome_holography_fractional_recursion_bundle", - "primary_read": ( - "Holographic, fractional, and recursive connectome mechanisms each " - "provide a plausible reconfiguration primitive: boundary/bulk coding, " - "memory kernels, and iterative self-organization. The integrated " - "all-three biological-connectome claim remains weak and should be " - "treated as an open research program, not an established fact." - ), - "reported_search_shapes": { - "latex_version": { - "identified_papers": 271065, - "screened_papers": 239, - "eligible_papers": 198, - "included_papers": 50, - }, - "ama_numeric_version": { - "retrieved": "83.4M", - "eligible": 2099, - "included": 50, - "note": "The supplied versions disagree on retrieval counts; preserve both as unverified Consensus metadata.", - }, - }, - "evidence_claims": [ - { - "claim": "connectomes can dynamically reconfigure structure/function with new data", - "strength": "strong_9_10", - "risk": "empirical reconfiguration does not identify a reusable compiler mechanism by itself", - "keys": ["Seguin2023Brain", "Bennett2018Rewiring", "Park2021An"], - }, - { - "claim": "holographic/tensor models can support adaptable encoding and decoding", - "strength": "strong_8_10", - "risk": "holographic representation is not automatically byte-exact or biologically instantiated", - "keys": ["Hu2019Machine", "Pastawski2015Holographic", "Melnikov2023Connectomes"], - }, - { - "claim": "fractional-order models can add memory effects and long-range dependence", - "strength": "moderate_7_10", - "risk": "fractional order must be fitted, bounded, and paid as model complexity", - "keys": ["Joshi2023A", "Ionescu2017The", "Zhou2020Clarify"], - }, - { - "claim": "recursive/self-organizing architectures support continual structured updates", - "strength": "moderate_7_10", - "risk": "recursive update can drift unless validation and rollback are explicit", - "keys": ["Hammer2004Recursive", "Doncevic2022A"], - }, - { - "claim": "all three mechanisms are empirically validated together in biological connectomes", - "strength": "weak_2_10", - "risk": "no direct integrated validation in supplied evidence", - "keys": [], - }, - { - "claim": "fractal geometry provides useful markers for structural/functional dynamics", - "strength": "moderate_6_10", - "risk": "marker quality does not imply causal mechanism or compression gain", - "keys": ["Radulescu2025Fractal"], - }, - ], - "method_lanes": [ - { - "lane": "connectome_harmonic_and_manifold_reconfiguration", - "use": "represent dynamics as modes over a structural graph or manifold", - "keys": ["Atasoy2017Connectome-harmonic", "Park2021An", "Preti2017The"], - "stack_mapping": "equation or route graph Laplacian eigenmodes", - }, - { - "lane": "holographic_boundary_bulk_encoding", - "use": "separate compact boundary representation from richer interior state", - "keys": ["Pastawski2015Holographic", "Melnikov2023Connectomes", "Hu2019Machine"], - "stack_mapping": "boundary receipt/index plus exact interior residual rehydration", - }, - { - "lane": "deep_holographic_reconstruction", - "use": "learn inverse reconstruction from sparse or phase-like observations", - "keys": ["Rivenson2017Phase", "Situ2022Deep", "Huang2024Quantitative", "Wang2019Y-Net:"], - "stack_mapping": "candidate inverse map, never final byte authority", - }, - { - "lane": "fractional_memory_dynamics", - "use": "model long-memory state updates with non-integer order dynamics", - "keys": ["Ionescu2017The", "Joshi2023A", "Zhou2020Clarify"], - "stack_mapping": "bounded history kernel for route/equation state", - }, - { - "lane": "recursive_self_organizing_update", - "use": "process sequential or structured inputs through repeated internal updates", - "keys": ["Hammer2004Recursive", "Doncevic2022A", "Lynn2022Heavy-tailed"], - "stack_mapping": "recursive graph updater with drift, validation, and rollback gates", - }, - { - "lane": "atlas_remapping_and_domain_adaptation", - "use": "move connectome representations between atlas/schema domains", - "keys": ["Dadashkarimi2023Cross", "Ganin2015Domain-Adversarial", "Zoph2017Learning"], - "stack_mapping": "optimal-transport or adversarial remap between equation dialects", - }, - { - "lane": "fractal_and_heavy_tail_network_markers", - "use": "measure multiscale structure and heavy-tailed connectivity", - "keys": ["Radulescu2025Fractal", "Lynn2022Heavy-tailed"], - "stack_mapping": "fractal dimension and tail diagnostics as priors, not receipts", - }, - { - "lane": "dynamic_network_reconfiguration", - "use": "borrow reconfiguration discipline from network science and power networks", - "keys": ["Behbahani2024Comprehensive", "Bennett2018Rewiring", "Seguin2023Brain"], - "stack_mapping": "bounded topology rewrite with cost and stability constraints", - }, - ], - "integrated_state": [ - "graph_state_hash", - "harmonic_basis_id", - "boundary_code_id", - "bulk_state_commitment", - "fractional_order_alpha", - "memory_kernel_id", - "recursive_update_operator_id", - "atlas_mapping_id", - "domain_adaptation_guard_id", - "fractal_marker_vector", - "holographic_reconstruction_error_bound", - "history_window_cost", - "validation_receipt_id", - "rollback_state_hash", - ], - "equation_pipeline_mapping": { - "holographic_boundary": "compact equation/route index or receipt boundary", - "holographic_bulk": "full latent/interior state requiring exact residual closure", - "fractional_memory": "history-sensitive update kernel for nonstationary routes", - "recursive_update": "iterative equation graph rewriter under validation", - "connectome_harmonic": "graph Laplacian eigenbasis for route/equation modes", - "atlas_remapping": "schema or dialect transfer between incompatible equation maps", - "fractal_marker": "multiscale topology diagnostic for candidate segmentation", - }, - "hutter_mapping": { - "boundary_code": "short route descriptor or index", - "bulk_state": "hidden state that must be rehydrated or paid as residual", - "fractional_kernel": "history model whose parameters and window bytes count", - "recursive_update": "route proposal update, not byte authority", - "harmonic_basis": "candidate transform basis over route graph", - "fractal_marker": "route-pruning feature only", - }, - "promotion_rule": [ - "each lane declares whether it is representation, memory, update, remap, or diagnostic", - "fractional order and memory kernel cost are bounded", - "boundary/bulk split has exact residual closure", - "recursive update has validation and rollback receipts", - "atlas/domain remap has an admissibility witness", - "Hutter use preserves exact decode/hash/measured-byte authority", - ], - "failure_rules": [ - "integrated all-three claim treated as established -> overclaim", - "holographic boundary hides payload bytes -> invalid receipt", - "fractional memory kernel unbounded -> NaN0", - "recursive update without rollback -> fail closed", - "atlas remap without admissibility witness -> hold", - "fractal marker replaces validation -> diagnostic only", - "reconstruction confidence replaces exact decode/hash -> invalid", - ], - "research_gap_matrix": { - "encoding_decoding_adaptation": { - "holographic_models": 4, - "fractional_models": "GAP", - "recursive_models": "GAP", - "empirical_connectome_data": "GAP", - }, - "memory_effects": { - "holographic_models": "GAP", - "fractional_models": 4, - "recursive_models": "GAP", - "empirical_connectome_data": "GAP", - }, - "sequential_data_integration": { - "holographic_models": "GAP", - "fractional_models": "GAP", - "recursive_models": 3, - "empirical_connectome_data": "GAP", - }, - "biological_validation": { - "holographic_models": 1, - "fractional_models": 2, - "recursive_models": 1, - "empirical_connectome_data": 8, - }, - }, - "bibliography_keys": [ - "Atasoy2017Connectome-harmonic", - "Bazinet2023Towards", - "Behbahani2024Comprehensive", - "Bennett2018Rewiring", - "Dadashkarimi2023Cross", - "Doncevic2022A", - "Fatemiabhari2024From", - "Ganin2015Domain-Adversarial", - "Hammer2004Recursive", - "Hu2019Machine", - "Huang2024Quantitative", - "Ionescu2017The", - "Joshi2023A", - "Liu20234K-DMDNet:", - "Lynn2022Heavy-tailed", - "Melnikov2023Connectomes", - "Noecker2023Stereo-EEG-guided", - "Park2021An", - "Pastawski2015Holographic", - "Petersen2019Holographic", - "Preti2017The", - "Radulescu2025Fractal", - "Rivenson2017Phase", - "Seguin2023Brain", - "Situ2022Deep", - "Vasa2022Null", - "Wang2019Y-Net:", - "Wang2024Reconfigurable", - "Zhou2020Clarify", - "Zoph2017Learning", - ], - "bibtex_hygiene_notes": [ - "Several supplied keys contain punctuation or accents; normalize before publication", - "The supplied Consensus search-shape counts disagree between LaTeX and AMA versions", - "Consensus-generated DOI and citation metadata should be verified before final citation use", - ], - "claim_boundary": ( - "This prior supports a research program for combining holographic " - "boundary/bulk coding, fractional memory, and recursive graph update. " - "It does not establish an empirically validated unified biological " - "connectome mechanism, autonomous self-reconfiguration, or compression gain." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_reconfiguration_lane", - "input": "holographic, fractional, recursive, harmonic, atlas, or fractal method", - "target": "representation, memory, update, remap, or diagnostic lane", - }, - { - "task": "protect_integrated_claim_boundary", - "input": "claim that holography, fractionality, and recursion are jointly validated", - "target": "weak/open frontier unless direct integrated evidence is present", - }, - { - "task": "charge_memory_and_boundary_costs", - "input": "fractional memory kernel or holographic boundary/bulk split", - "target": "bounded kernel cost and exact residual closure", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "method_lane_count": len(receipt["method_lanes"]), - "state_field_count": len(receipt["integrated_state"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/holographic_fractional_recursive_equation_fold.py b/4-Infrastructure/shim/holographic_fractional_recursive_equation_fold.py deleted file mode 100644 index 75aeb1b5..00000000 --- a/4-Infrastructure/shim/holographic_fractional_recursive_equation_fold.py +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env python3 -"""Extract and fold equations from the holographic/fractional/recursive connectome prior.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -SOURCE_RECEIPT = SHIM / "holographic_fractional_recursive_connectome_prior_receipt.json" -RECEIPT = SHIM / "holographic_fractional_recursive_equation_fold_receipt.json" -CURRICULUM = SHIM / "holographic_fractional_recursive_equation_fold_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - source = json.loads(SOURCE_RECEIPT.read_text(encoding="utf-8")) - equations: list[dict[str, Any]] = [ - { - "id": "connectome_laplacian", - "lane": "connectome_harmonic_and_manifold_reconfiguration", - "source_shape": "L_G = D_G - A_G", - "semantics": "graph Laplacian from structural adjacency and degree matrix", - "folded_use": "route/equation graph operator whose eigenspaces define candidate modes", - "receipt_obligation": "graph_state_hash, edge_weight_schema, harmonic_basis_id", - }, - { - "id": "connectome_harmonic_decomposition", - "lane": "connectome_harmonic_and_manifold_reconfiguration", - "source_shape": "L_G phi_k = lambda_k phi_k; x(t) = sum_k a_k(t) phi_k", - "semantics": "activity or route state expanded in graph-Laplacian eigenmodes", - "folded_use": "candidate transform basis over route/equation dependency graph", - "receipt_obligation": "basis bytes and reconstruction residual are counted", - }, - { - "id": "fractional_state_dynamics", - "lane": "fractional_memory_dynamics", - "source_shape": "D_t^alpha x(t) = F(x(t), u(t), theta), 0 < alpha <= 1", - "semantics": "non-integer derivative carries long-memory dynamics", - "folded_use": "history-sensitive route state updater", - "receipt_obligation": "fractional_order_alpha, memory_kernel_id, history_window_cost", - }, - { - "id": "fractional_memory_kernel", - "lane": "fractional_memory_dynamics", - "source_shape": "x_t = x_0 + sum_{tau < t} K_alpha(t - tau) F(x_tau, u_tau)", - "semantics": "discrete memory convolution approximation to fractional dynamics", - "folded_use": "bounded state history for nonstationary compression routes", - "receipt_obligation": "kernel parameters and retained history bytes are counted", - }, - { - "id": "recursive_self_update", - "lane": "recursive_self_organizing_update", - "source_shape": "h_{n+1} = R_theta(h_n, x_n, G_n); G_{n+1} = U_phi(G_n, h_{n+1})", - "semantics": "recursive state and graph update under new structured input", - "folded_use": "equation/route graph rewrite proposal", - "receipt_obligation": "validation_receipt_id and rollback_state_hash required", - }, - { - "id": "holographic_boundary_bulk_split", - "lane": "holographic_boundary_bulk_encoding", - "source_shape": "b = P_boundary(z); z_hat = R_bulk(b, r_exact)", - "semantics": "compact boundary representation plus interior/bulk recovery", - "folded_use": "short route descriptor plus exact residual rehydration", - "receipt_obligation": "boundary_code_id, bulk_state_commitment, exact residual hash", - }, - { - "id": "exact_holographic_closure", - "lane": "holographic_boundary_bulk_encoding", - "source_shape": "H(decode(boundary_code, residual)) == H(source)", - "semantics": "boundary code has no compression authority until exact decode closes", - "folded_use": "Hutter promotion gate", - "receipt_obligation": "decoded hash and measured total bytes", - }, - { - "id": "deep_holographic_inverse", - "lane": "deep_holographic_reconstruction", - "source_shape": "z_hat = f_theta(y_phase_or_sparse); e = ||A z_hat - y||", - "semantics": "learned inverse reconstruction with measurement residual", - "folded_use": "candidate inverse map for route proposals", - "receipt_obligation": "holographic_reconstruction_error_bound and exact residual lane", - }, - { - "id": "atlas_optimal_transport_remap", - "lane": "atlas_remapping_and_domain_adaptation", - "source_shape": "T* = argmin_T + epsilon KL(T || mu nu^T), T1=mu, T^T1=nu", - "semantics": "remap connectome/equation coordinates between atlases or schemas", - "folded_use": "dialect/schema transfer between equation maps", - "receipt_obligation": "atlas_mapping_id and domain_adaptation_guard_id", - }, - { - "id": "domain_adversarial_invariance", - "lane": "atlas_remapping_and_domain_adaptation", - "source_shape": "min_{F,C} max_D L_task(C(F(x)), y) - lambda L_domain(D(F(x)), d)", - "semantics": "learn features predictive for task while suppressing domain identity", - "folded_use": "negative-transfer guard for borrowed equation features", - "receipt_obligation": "held-out target validation; no proof transfer by confidence alone", - }, - { - "id": "fractal_dimension_marker", - "lane": "fractal_and_heavy_tail_network_markers", - "source_shape": "D_f = lim_{epsilon -> 0} log N(epsilon) / log(1/epsilon)", - "semantics": "multiscale covering dimension of graph or functional state geometry", - "folded_use": "route segmentation and topology diagnostic", - "receipt_obligation": "diagnostic only unless tied to exact byte validation", - }, - { - "id": "heavy_tail_connectivity_marker", - "lane": "fractal_and_heavy_tail_network_markers", - "source_shape": "P(K > k) ~ C k^{-beta}", - "semantics": "heavy-tailed node/edge influence distribution", - "folded_use": "prioritize high-influence route/equation nodes", - "receipt_obligation": "tail-fit cost and uncertainty reported; no promotion authority", - }, - { - "id": "network_reconfiguration_objective", - "lane": "dynamic_network_reconfiguration", - "source_shape": "G* = argmin_{G'} L_function(G') + lambda C_rewire(G,G') + gamma I_unstable(G')", - "semantics": "choose new graph under function, rewrite cost, and instability penalty", - "folded_use": "bounded topology rewrite objective for equation/route graph", - "receipt_obligation": "perturbation_operator_id, measured function, rollback_state_hash", - }, - ] - - folded_equations = [ - { - "id": "folded_route_state", - "shape": ( - "S_route = (G_hash, Phi_L, boundary_code, bulk_commit, alpha, " - "K_alpha, R_update, T_atlas, D_guard, F_marker, e_holo, " - "C_history, validation_receipt, rollback_hash)" - ), - "use": "single folded state carrying the extracted equation family into DD search", - }, - { - "id": "folded_cost", - "shape": ( - "C_total = bytes_payload + bytes_boundary + bytes_bulk_commit + " - "bytes_memory_kernel + bytes_history_window + bytes_residual + " - "bytes_witness" - ), - "use": "prevents holographic or fractional lanes from hiding payload in model state", - }, - { - "id": "folded_promotion_gate", - "shape": ( - "promote iff H(decode(route)) == H(source) and C_total < incumbent " - "and validation_receipt exists and rollback_hash exists" - ), - "use": "keeps exact decode/hash authority outside every predictor", - }, - { - "id": "folded_nan0_guard", - "shape": ( - "NaN0 iff unbounded(K_alpha) or missing(residual) or missing(rollback) " - "or hidden_payload(boundary_code)" - ), - "use": "fail-closed guard for unbounded memory, hidden bulk state, and unsafe recursion", - }, - { - "id": "folded_basis_reconstruction", - "shape": "x_hat = sum_{k in K_kept} a_k phi_k + r_exact", - "use": "harmonic compression only counts if omitted modes are paid in exact residual", - }, - ] - - receipt: dict[str, Any] = { - "schema": "holographic_fractional_recursive_equation_fold_v1", - "source_receipt": str(SOURCE_RECEIPT.relative_to(REPO)), - "source_receipt_hash": source["receipt_hash"], - "primary_read": ( - "The extractable math folds into a graph-state route model: Laplacian " - "harmonics propose modes, holographic boundary/bulk split proposes a " - "descriptor/residual separation, fractional dynamics supply bounded " - "memory, recursive updates propose graph rewrites, OT/domain-adversarial " - "terms remap schemas, and fractal/heavy-tail markers remain diagnostics." - ), - "extracted_equation_count": len(equations), - "folded_equation_count": len(folded_equations), - "equations": equations, - "folded_equations": folded_equations, - "fold_in_decision": [ - "keep harmonic bases as candidate transforms, not proof", - "count boundary code, bulk commitment, memory kernel, history window, residual, and witness bytes", - "reject unbounded fractional kernels as NaN0", - "require rollback before recursive graph updates promote", - "treat fractal and heavy-tail terms as pruning diagnostics only", - "promote only through exact decode/hash/measured-byte closure", - ], - "claim_boundary": ( - "This is a local equation fold over user-supplied literature synthesis. " - "It is not a derivation of the cited papers, not a biological proof, " - "and not evidence of compression improvement without local byte tests." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_extracted_equation", - "input": "equation from holographic/fractional/recursive connectome literature", - "target": "harmonic, boundary_bulk, fractional_memory, recursive_update, atlas_remap, adversarial_invariance, fractal_marker, heavy_tail, or reconfiguration_objective", - }, - { - "task": "fold_equation_into_route_state", - "input": "source-shaped equation", - "target": "DD state fields plus receipt obligations", - }, - { - "task": "reject_hidden_math_payload", - "input": "boundary code, memory kernel, or recursive state with uncounted payload", - "target": "NaN0 or invalid receipt", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "source_receipt_hash": receipt["source_receipt_hash"], - "extracted_equation_count": receipt["extracted_equation_count"], - "folded_equation_count": receipt["folded_equation_count"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/hotloading_prover_orchestrator.py b/4-Infrastructure/shim/hotloading_prover_orchestrator.py deleted file mode 100644 index 863bcd66..00000000 --- a/4-Infrastructure/shim/hotloading_prover_orchestrator.py +++ /dev/null @@ -1,538 +0,0 @@ -#!/usr/bin/env python3 -""" -Hotloading Prover Orchestrator — Resource-Conscious Theorem Proving -====================================================================== - -Prevents resource exhaustion by: -1. Loading prover models on-demand only -2. Unloading immediately after use -3. Queue-based task management -4. Memory/CPU monitoring -5. Bounded concurrency - -Usage: - orchestrator = HotloadingProverOrchestrator(max_memory_gb=8) - orchestrator.queue_theorem("F01_Q16_16_FixedPoint.lean", "add_total") - orchestrator.process_queue() -""" - -import subprocess -import sys -import time -import gc -import psutil -import json -from pathlib import Path -from dataclasses import dataclass -from typing import List, Dict, Optional, Callable -from enum import Enum -from queue import Queue, PriorityQueue -import threading -import logging - -# Setup logging -logging.basicConfig( - level=logging.INFO, - format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' -) -logger = logging.getLogger('hotloading_prover') - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -class ProverType(Enum): - """Available prover models with resource requirements.""" - BF4PROVER = ("bf4prover", 2, 4) # (name, cpu_cores, memory_gb) - GOEDEL_8B = ("goedel-8b", 4, 8) - GOEDEL_32B = ("goedel-32b", 8, 32) - BFS_PROVER = ("bfs-prover", 2, 4) - - -class TaskPriority(Enum): - """Task priority levels.""" - CRITICAL = 0 # Blocking other work - HIGH = 1 # Foundation equations F01-F12 - NORMAL = 2 # Standard theorems - LOW = 3 # Optional proofs - - -@dataclass -class TheoremTask: - """Task for proving a theorem.""" - lean_file: Path - theorem_name: str - prover_type: ProverType - priority: TaskPriority - timeout_seconds: int - retries: int = 0 - max_retries: int = 3 - - def __lt__(self, other): - return self.priority.value < other.priority.value - - -@dataclass -class ProverInstance: - """Managed prover instance with lifecycle.""" - prover_type: ProverType - process: Optional[subprocess.Popen] - loaded_at: Optional[float] - last_used: Optional[float] - memory_usage_mb: float - - def is_loaded(self) -> bool: - return self.process is not None and self.process.poll() is None - - def unload(self): - """Unload prover to free resources.""" - if self.process: - try: - self.process.terminate() - self.process.wait(timeout=10) - except: - self.process.kill() - self.process = None - self.loaded_at = None - gc.collect() - logger.info(f"Unloaded {self.prover_type.value[0]}") - - -class ResourceMonitor: - """Monitor system resources to prevent exhaustion.""" - - def __init__(self, max_memory_gb: float, max_cpu_percent: float = 80.0): - self.max_memory_gb = max_memory_gb - self.max_cpu_percent = max_cpu_percent - self.process = psutil.Process() - - def check_resources(self) -> Dict[str, bool]: - """Check if resources are available.""" - memory = psutil.virtual_memory() - cpu_percent = psutil.cpu_percent(interval=0.1) - swap = psutil.swap_memory() - - return { - "memory_available": memory.available / (1024**3) > 2.0, # Need 2GB headroom - "memory_within_limit": memory.used / (1024**3) < self.max_memory_gb, - "cpu_available": cpu_percent < self.max_cpu_percent, - "swap_ok": swap.percent < 50.0 if swap.total > 0 else True - } - - def can_load_prover(self, prover: ProverType) -> bool: - """Check if we can load a specific prover.""" - resources = self.check_resources() - _, required_cores, required_gb = prover.value - - memory = psutil.virtual_memory() - available_gb = memory.available / (1024**3) - - return ( - resources["memory_available"] and - resources["memory_within_limit"] and - resources["cpu_available"] and - available_gb >= required_gb + 2.0 # Required + headroom - ) - - def wait_for_resources(self, prover: ProverType, timeout: int = 300): - """Wait until resources are available.""" - start = time.time() - while time.time() - start < timeout: - if self.can_load_prover(prover): - return True - logger.info(f"Waiting for resources to load {prover.value[0]}...") - time.sleep(5) - # Try to free memory - gc.collect() - return False - - -class HotloadingProverOrchestrator: - """ - Orchestrates prover models with hotloading to prevent resource exhaustion. - - Strategy: - 1. Queue all theorem proving tasks - 2. Load provers on-demand - 3. Process highest priority tasks first - 4. Unload prover immediately after use - 5. Monitor resources, throttle if needed - """ - - def __init__( - self, - max_memory_gb: float = 16.0, - max_concurrent_provers: int = 2, - idle_timeout_seconds: int = 60 - ): - self.max_memory_gb = max_memory_gb - self.max_concurrent = max_concurrent_provers - self.idle_timeout = idle_timeout_seconds - - self.task_queue = PriorityQueue() - self.results: Dict[str, Dict] = {} - self.provers: Dict[ProverType, ProverInstance] = {} - self.monitor = ResourceMonitor(max_memory_gb) - self.active_tasks = 0 - self.lock = threading.Lock() - - # Statistics - self.stats = { - "tasks_submitted": 0, - "tasks_completed": 0, - "tasks_failed": 0, - "provers_loaded": 0, - "provers_unloaded": 0, - "memory_peak_gb": 0.0 - } - - def queue_theorem( - self, - lean_file: str, - theorem_name: str, - prover: ProverType = ProverType.BF4PROVER, - priority: TaskPriority = TaskPriority.NORMAL, - timeout: int = 300 - ): - """Queue a theorem proving task.""" - task = TheoremTask( - lean_file=RESEARCH_STACK / lean_file, - theorem_name=theorem_name, - prover_type=prover, - priority=priority, - timeout_seconds=timeout - ) - self.task_queue.put(task) - self.stats["tasks_submitted"] += 1 - logger.info(f"Queued {theorem_name} from {lean_file} (priority: {priority.name})") - - def load_prover(self, prover_type: ProverType) -> bool: - """Hotload a prover model.""" - with self.lock: - # Check if already loaded - if prover_type in self.provers and self.provers[prover_type].is_loaded(): - self.provers[prover_type].last_used = time.time() - return True - - # Wait for resources - if not self.monitor.wait_for_resources(prover_type): - logger.error(f"Cannot load {prover_type.value[0]} — insufficient resources") - return False - - # Load based on type - if prover_type == ProverType.BF4PROVER: - return self._load_bf4prover() - elif prover_type == ProverType.GOEDEL_8B: - return self._load_goedel("8b") - elif prover_type == ProverType.GOEDEL_32B: - return self._load_goedel("32b") - elif prover_type == ProverType.BFS_PROVER: - return self._load_bfs_prover() - - return False - - def _load_bf4prover(self) -> bool: - """Load bf4prover (lightweight).""" - try: - # bf4prover is a Python script — no persistent process needed - self.provers[ProverType.BF4PROVER] = ProverInstance( - prover_type=ProverType.BF4PROVER, - process=None, # Stateless - loaded_at=time.time(), - last_used=time.time(), - memory_usage_mb=0 - ) - self.stats["provers_loaded"] += 1 - logger.info("Loaded bf4prover (stateless)") - return True - except Exception as e: - logger.error(f"Failed to load bf4prover: {e}") - return False - - def _load_goedel(self, size: str) -> bool: - """Load Goedel-Prover-V2 model.""" - try: - goedel_path = RESEARCH_STACK / "ai-math-discovery-systems/Goedel-Prover-V2" - - # Check if model exists - model_file = goedel_path / f"goedel-prover-v2-{size}.bin" - if not model_file.exists(): - logger.warning(f"Goedel model not found: {model_file}") - return False - - # Load model (simplified — real implementation would use proper loader) - logger.info(f"Loading Goedel-Prover-V2-{size}...") - - # Simulate loading - time.sleep(2) - - self.provers[ProverType.GOEDEL_8B if size == "8b" else ProverType.GOEDEL_32B] = ProverInstance( - prover_type=ProverType.GOEDEL_8B if size == "8b" else ProverType.GOEDEL_32B, - process=None, # Would be actual model process - loaded_at=time.time(), - last_used=time.time(), - memory_usage_mb=8000 if size == "8b" else 32000 - ) - self.stats["provers_loaded"] += 1 - logger.info(f"Loaded Goedel-Prover-V2-{size}") - return True - - except Exception as e: - logger.error(f"Failed to load Goedel: {e}") - return False - - def _load_bfs_prover(self) -> bool: - """Load bfs_prover via Ollama.""" - try: - # Check Ollama availability - result = subprocess.run( - ["curl", "-s", "http://localhost:11434/api/tags"], - capture_output=True, - text=True, - timeout=5 - ) - - if result.returncode != 0: - logger.warning("Ollama not available") - return False - - self.provers[ProverType.BFS_PROVER] = ProverInstance( - prover_type=ProverType.BFS_PROVER, - process=None, - loaded_at=time.time(), - last_used=time.time(), - memory_usage_mb=4000 - ) - self.stats["provers_loaded"] += 1 - logger.info("Loaded bfs_prover via Ollama") - return True - - except Exception as e: - logger.error(f"Failed to load bfs_prover: {e}") - return False - - def unload_prover(self, prover_type: ProverType): - """Unload a prover to free resources.""" - with self.lock: - if prover_type in self.provers: - self.provers[prover_type].unload() - del self.provers[prover_type] - self.stats["provers_unloaded"] += 1 - - def unload_idle_provers(self): - """Unload provers that have been idle.""" - with self.lock: - now = time.time() - for prover_type, instance in list(self.provers.items()): - if instance.is_loaded() and instance.last_used: - if now - instance.last_used > self.idle_timeout: - logger.info(f"Unloading idle prover: {prover_type.value[0]}") - self.unload_prover(prover_type) - - def run_bf4prover_task(self, task: TheoremTask) -> Dict: - """Run a bf4prover task.""" - bf4prover_script = RESEARCH_STACK / "scripts/bf4prover.py" - - try: - result = subprocess.run( - [ - "python3", str(bf4prover_script), - str(task.lean_file), - "--theorem", task.theorem_name, - "--dry-run" - ], - capture_output=True, - text=True, - timeout=task.timeout_seconds, - cwd=str(RESEARCH_STACK) - ) - - success = result.returncode == 0 and "sorry" not in result.stdout - - return { - "theorem": task.theorem_name, - "file": str(task.lean_file), - "success": success, - "output": result.stdout, - "error": result.stderr if not success else None, - "prover": "bf4prover", - "duration": None # Would track actual time - } - - except subprocess.TimeoutExpired: - return { - "theorem": task.theorem_name, - "success": False, - "error": "Timeout", - "prover": "bf4prover" - } - except Exception as e: - return { - "theorem": task.theorem_name, - "success": False, - "error": str(e), - "prover": "bf4prover" - } - - def process_single_task(self, task: TheoremTask) -> Dict: - """Process a single theorem task.""" - logger.info(f"Processing {task.theorem_name} with {task.prover_type.value[0]}") - - # Load prover - if not self.load_prover(task.prover_type): - return { - "theorem": task.theorem_name, - "success": False, - "error": f"Failed to load {task.prover_type.value[0]}" - } - - try: - # Run task - if task.prover_type == ProverType.BF4PROVER: - result = self.run_bf4prover_task(task) - else: - result = { - "theorem": task.theorem_name, - "success": False, - "error": f"Prover {task.prover_type.value[0]} not implemented" - } - - # Update statistics - if result["success"]: - self.stats["tasks_completed"] += 1 - else: - self.stats["tasks_failed"] += 1 - - # Retry if needed - if task.retries < task.max_retries: - task.retries += 1 - logger.info(f"Retrying {task.theorem_name} (attempt {task.retries})") - time.sleep(2 ** task.retries) # Exponential backoff - return self.process_single_task(task) - - return result - - finally: - # Update last used - if task.prover_type in self.provers: - self.provers[task.prover_type].last_used = time.time() - - # Unload if memory pressure - memory = psutil.virtual_memory() - if memory.percent > 85: - logger.warning("Memory pressure detected — unloading prover") - self.unload_prover(task.prover_type) - - def process_queue(self): - """Process all queued tasks with hotloading.""" - logger.info(f"Starting queue processing ({self.task_queue.qsize()} tasks)") - - while not self.task_queue.empty(): - # Unload idle provers periodically - self.unload_idle_provers() - - # Get next task - task = self.task_queue.get() - - # Process - result = self.process_single_task(task) - - # Store result - key = f"{task.lean_file}:{task.theorem_name}" - self.results[key] = result - - # Log progress - completed = self.stats["tasks_completed"] + self.stats["tasks_failed"] - total = self.stats["tasks_submitted"] - logger.info(f"Progress: {completed}/{total} ({100*completed//total}%)") - - # Small delay to prevent resource exhaustion - time.sleep(1) - - # Unload all provers - for prover_type in list(self.provers.keys()): - self.unload_prover(prover_type) - - logger.info("Queue processing complete") - return self.results - - def get_stats(self) -> Dict: - """Get orchestrator statistics.""" - memory = psutil.virtual_memory() - self.stats["memory_peak_gb"] = max( - self.stats["memory_peak_gb"], - memory.used / (1024**3) - ) - return self.stats.copy() - - -def main(): - """Demonstrate hotloading prover orchestrator.""" - print("=" * 70) - print("Hotloading Prover Orchestrator") - print("Resource-conscious theorem proving for F01-F12") - print("=" * 70) - - # Initialize with 8GB memory limit - orchestrator = HotloadingProverOrchestrator( - max_memory_gb=8.0, - max_concurrent_provers=1, # Conservative - idle_timeout_seconds=30 - ) - - # Queue F01 theorems - f01_file = "0-Core-Formalism/lean/Semantics/F01_Q16_16_FixedPoint.lean" - - theorems = [ - ("add_total", TaskPriority.CRITICAL), - ("mul_total", TaskPriority.CRITICAL), - ("div_total", TaskPriority.CRITICAL), - ("round_valid", TaskPriority.HIGH), - ("mul_no_overflow", TaskPriority.HIGH), - ("E_0_deterministic", TaskPriority.HIGH), - ("E_0_bounds", TaskPriority.NORMAL), - ("convergence_to_fixed_point", TaskPriority.NORMAL), - ] - - print(f"\nQueueing {len(theorems)} theorems from F01...") - - for theorem, priority in theorems: - orchestrator.queue_theorem( - lean_file=f01_file, - theorem_name=theorem, - prover=ProverType.BF4PROVER, - priority=priority, - timeout=60 - ) - - # Process queue - print("\nProcessing with hotloading...") - results = orchestrator.process_queue() - - # Report - print("\n" + "=" * 70) - print("RESULTS") - print("=" * 70) - - success_count = sum(1 for r in results.values() if r.get("success")) - fail_count = len(results) - success_count - - print(f"Success: {success_count}/{len(results)}") - print(f"Failed: {fail_count}/{len(results)}") - - stats = orchestrator.get_stats() - print(f"\nResource Usage:") - print(f" Peak memory: {stats['memory_peak_gb']:.2f} GB") - print(f" Provers loaded: {stats['provers_loaded']}") - print(f" Provers unloaded: {stats['provers_unloaded']}") - - print("\n" + "=" * 70) - print("Hotloading prevented resource exhaustion:") - print(f" - Loaded provers on-demand only") - print(f" - Unloaded after use (idle timeout: 30s)") - print(f" - Bounded concurrency (max: 1)") - print(f" - Memory limit enforced: 8GB") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/hutter_differential_frame_chain_probe.py b/4-Infrastructure/shim/hutter_differential_frame_chain_probe.py deleted file mode 100644 index 25b195b5..00000000 --- a/4-Infrastructure/shim/hutter_differential_frame_chain_probe.py +++ /dev/null @@ -1,341 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed Hutter differential frame chain probe. - -Frame-invariant roots give a discrete differential chain: - - x_i --dx_i--> x_{i+1} - -The transition is admissible only if x_i is independently replayable, dx_i -rehydrates the next frame, and the recomputed root equals R_{i+1}. This keeps -differentials as bounded witnesses rather than a fragile predictive chain. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "hutter_differential_frame_chain" -REGISTRY = OUT_DIR / "hutter_differential_frame_chain_registry.json" -RECEIPT = OUT_DIR / "hutter_differential_frame_chain_receipt.json" -SUMMARY = OUT_DIR / "hutter_differential_frame_chain.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Differential Frame Chain.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "hutter_frame_invariant_root" / "hutter_frame_invariant_root_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_canonical_baseline_probe" / "enwiki9_logogram_canonical_baseline_probe_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def frame_state(frame_id: int, class_name: str, raw_hash: str, core_hash: str, independently_replayable: bool) -> dict[str, Any]: - payload = { - "frame_id": frame_id, - "class_name": class_name, - "raw_hash": raw_hash, - "core_hash": core_hash, - "independently_replayable": independently_replayable, - } - payload["root"] = hash_obj(payload) - return payload - - -def transition( - *, - transition_id: str, - source: dict[str, Any], - target: dict[str, Any], - differential_hash: str, - rehydrates_target: bool, - root_recomputes: bool, - bounded_delta: bool, -) -> dict[str, Any]: - admissible = ( - source["independently_replayable"] - and target["independently_replayable"] - and rehydrates_target - and root_recomputes - and bounded_delta - ) - if not source["independently_replayable"] or not target["independently_replayable"]: - decision = "HOLD_FRAME_REPLAY_REQUIRED" - elif not rehydrates_target: - decision = "REJECT_DIFFERENTIAL_REPLAY" - elif not root_recomputes: - decision = "REJECT_ROOT_MISMATCH" - elif not bounded_delta: - decision = "HOLD_UNBOUNDED_DIFFERENTIAL" - else: - decision = "ADMIT_DIFFERENTIAL_FRAME_EDGE" - item = { - "transition_id": transition_id, - "source_frame": source["frame_id"], - "target_frame": target["frame_id"], - "source_root": source["root"], - "target_root": target["root"], - "differential_hash": differential_hash, - "rehydrates_target": rehydrates_target, - "root_recomputes": root_recomputes, - "bounded_delta": bounded_delta, - "admissible": admissible, - "equation": "x_i + dx_i -> x_{i+1}; H(replay(x_i,dx_i)) == R_{i+1}", - "decision": decision, - } - item["transition_hash"] = hash_obj({k: v for k, v in item.items() if k != "transition_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - frames = [ - frame_state(0, "xml_head", "raw_xml_head", "core_xml_head", True), - frame_state(1, "template_heavy", "raw_template_heavy", "core_template_heavy", True), - frame_state(2, "link_heavy", "raw_link_heavy", "core_link_heavy", True), - frame_state(3, "mixed_high_entropy", "raw_mixed_high_entropy", "core_mixed_high_entropy", True), - frame_state(4, "unsafe_long_predictive_chain", "raw_predictive", "core_predictive", False), - ] - transitions = [ - transition( - transition_id="dx_0_1", - source=frames[0], - target=frames[1], - differential_hash=hash_obj({"from": 0, "to": 1, "opcode": "template_delta"}), - rehydrates_target=True, - root_recomputes=True, - bounded_delta=True, - ), - transition( - transition_id="dx_1_2", - source=frames[1], - target=frames[2], - differential_hash=hash_obj({"from": 1, "to": 2, "opcode": "link_delta"}), - rehydrates_target=True, - root_recomputes=True, - bounded_delta=True, - ), - transition( - transition_id="dx_2_3", - source=frames[2], - target=frames[3], - differential_hash=hash_obj({"from": 2, "to": 3, "opcode": "entropy_patch"}), - rehydrates_target=True, - root_recomputes=True, - bounded_delta=True, - ), - transition( - transition_id="dx_3_4_unbounded", - source=frames[3], - target=frames[4], - differential_hash=hash_obj({"from": 3, "to": 4, "opcode": "fragile_predictive_chain"}), - rehydrates_target=True, - root_recomputes=True, - bounded_delta=False, - ), - ] - return { - "schema": "hutter_differential_frame_chain_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Hutter differential frame chain diagnostic only. It formalizes x_i to " - "x_{i+1} transitions over independently replayable frame roots. It does " - "not change the codec or assert actual corpus compression gains." - ), - "canonical_statement": ( - "Frame roots give states; differentials give admissible edges. A delta " - "is trusted only if it rehydrates the target and recomputes the target root." - ), - "chain_equation": { - "state": "x_i := independently replayable frame with root R_i", - "edge": "dx_i := bounded differential witness from x_i to x_{i+1}", - "admission": "A(dx_i)=1[Decode(x_i,dx_i)=x_{i+1}] * 1[H(x_{i+1})=R_{i+1}] * 1[bounded_delta]", - "global_chain": "x_0 --dx_0--> x_1 --dx_1--> ... --dx_n--> x_{n+1}", - }, - "hutter_role": { - "mjpeg_analogy": "root frames are independently replayable key frames; differentials are bounded shortcut edges", - "torsion_link": "unbounded differentials add route_coupling and predictive-chain torsion", - "admissible_use": "use dx_i for compression only when root frames remain available as finite checkpoints", - }, - "frames": frames, - "transitions": transitions, - "chain_root": hash_obj([item["transition_hash"] for item in transitions]), - "aggregates": { - "frame_count": len(frames), - "transition_count": len(transitions), - "admitted_transition_count": sum(1 for item in transitions if item["decision"] == "ADMIT_DIFFERENTIAL_FRAME_EDGE"), - "hold_transition_count": sum(1 for item in transitions if item["decision"].startswith("HOLD")), - "reject_transition_count": sum(1 for item in transitions if item["decision"].startswith("REJECT")), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "hutter_differential_frame_chain_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "chain_root": registry["chain_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_HUTTER_DIFFERENTIAL_FRAME_CHAIN_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Hutter Differential Frame Chain", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Chain root: `{registry['chain_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Chain Equation", - "", - ] - for key, value in registry["chain_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Transitions", - "", - "| Transition | Source | Target | Decision |", - "|---|---:|---:|---|", - ] - ) - for item in registry["transitions"]: - lines.append(f"| `{item['transition_id']}` | {item['source_frame']} | {item['target_frame']} | `{item['decision']}` |") - lines.extend(["", "## Hutter Role", ""]) - for key, value in registry["hutter_role"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression DifferentialChain Receipt -title: Hutter Differential Frame Chain -type: text/vnd.tiddlywiki - -! Hutter Differential Frame Chain - -Durable runner: - -``` -4-Infrastructure/shim/hutter_differential_frame_chain_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Chain root: - -``` -{receipt['chain_root']} -``` - -!! Doctrine - -Frame roots give states. Differentials give admissible edges. - -``` -x_i --dx_i--> x_(i+1) -A(dx_i)=1[Decode(x_i,dx_i)=x_(i+1)] * 1[H(x_(i+1))=R_(i+1)] * 1[bounded_delta] -``` - -This lets the codec say x leads to x(i) without losing independently replayable roots. - -!! Links - -* [[Hutter Frame Invariant Root]] -* [[Hutter Torsion Clock Adaptation]] -* [[Torsion Interval Gaussian Splat Witness]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "chain_root": registry["chain_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hutter_eigenmass_transfer_plan.py b/4-Infrastructure/shim/hutter_eigenmass_transfer_plan.py deleted file mode 100644 index 7bc1a4eb..00000000 --- a/4-Infrastructure/shim/hutter_eigenmass_transfer_plan.py +++ /dev/null @@ -1,276 +0,0 @@ -#!/usr/bin/env python3 -"""Create a receipt-backed transfer plan for Hutter-style eigenmass tuning. - -This does not run a Hutter benchmark. It ports the successful DESI multiscale -eigenmass pattern into a compression/Hutter tuning protocol with explicit HOLDs. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -ALIGNMENT_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_multiscale_eigenmass_alignment.json" -OISC_RECEIPT = ROOT / "shared-data/data/stack_solidification/rust_oisc_decompressor_target_receipt.json" -OUT_DIR = ROOT / "shared-data/data/stack_solidification" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "hutter_eigenmass_transfer_plan.json" -RECEIPT_JSON = OUT_DIR / "hutter_eigenmass_transfer_plan_receipt.json" -DOC_MD = DOCS_DIR / "hutter_eigenmass_transfer_plan_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Hutter Eigenmass Transfer Plan.tid" - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - with path.open() as f: - return json.load(f) - - -def maybe_load_json(path: Path) -> dict[str, Any] | None: - if not path.exists(): - return None - return load_json(path) - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - alignment = load_json(ALIGNMENT_JSON) - oisc = maybe_load_json(OISC_RECEIPT) - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - - feature_basis = [ - "byte_offset_phase", - "symbol_class", - "local_context_hash_class", - "long_range_recurrence_distance", - "prediction_cache_hit", - "ram_trace_reuse", - "residual_entropy_proxy", - "oisc_instruction_density", - "ammr_receipt_depth", - "replay_delta_cost", - ] - - transfer_ladder = [ - { - "desi_pattern": "literal row surface", - "hutter_analog": "raw corpus windows", - "gate": "WINDOW_FIXTURE_ONLY_UNTIL_CANONICAL_ENWIK9", - }, - { - "desi_pattern": "joined gas/shock constrained cells", - "hutter_analog": "byte-exact replay fixtures constrained by Rust OISC closure", - "gate": "LEAN_RUST_REPLAY_REQUIRED", - }, - { - "desi_pattern": "constraint sharpening factor", - "hutter_analog": "compression candidate must sharpen prediction/replay axes without losing byte-exact closure", - "gate": "SHARPENING_WITH_EXACT_REPLAY_ONLY", - }, - { - "desi_pattern": "multiscale cosine alignment", - "hutter_analog": "candidate feature direction must align across raw windows, token/logogram windows, and OISC replay receipts", - "gate": "MULTISCALE_ALIGNMENT_BEFORE_PROMOTION", - }, - ] - - gates = [ - "canonical_enwik9_sha256_or_fixture_label_required", - "raw_baseline_required", - "candidate_wire_format_required", - "byte_exact_decompressor_receipt_required", - "negative_controls_required", - "eigenmass_sharpening_required", - "no_competitive_hutter_claim_without_full_prize_envelope", - ] - - plan = { - "schema": "hutter_eigenmass_transfer_plan_v0", - "created": created, - "decision": "ADMIT_TRANSFER_PROTOCOL_HOLD_HUTTER_CLAIM", - "claim_boundary": ( - "Transfers the DESI multiscale eigenmass method into a Hutter-style " - "compression tuning protocol. It does not run enwik9, does not claim " - "compression gain, and does not claim Hutter Prize progress." - ), - "source_alignment": str(ALIGNMENT_JSON.relative_to(ROOT)), - "source_oisc_receipt": str(OISC_RECEIPT.relative_to(ROOT)) if oisc else None, - "imported_signal": { - "desi_manga_tracer_cosine": alignment["alignment"]["tracer_subspace_cosine"], - "desi_manga_constraint_sharpening_factor": alignment["alignment"]["constraint_sharpening_factor"], - "analogy_boundary": "evidence sharpening pattern only; no astronomy data transfers into compression scores", - }, - "hutter_feature_basis_after_tuning": feature_basis, - "transfer_ladder": transfer_ladder, - "minimum_receipt_shape": { - "corpus_id": "canonical hash or fixture label", - "window_id": "byte offset + length + hash", - "baseline_sizes": "raw, zlib/lzma, current candidate if available", - "candidate_features": feature_basis, - "eigenmass": "dominant eigenvalue + explained share + feature vector", - "replay": "input hash + output hash + instruction count + decision", - "decision": "ADMIT_FIXTURE / HOLD / QUARANTINE", - }, - "promotion_gates": gates, - "hutter_holds": [ - "HOLD_CANONICAL_ENWIK9", - "HOLD_FULL_CORPUS_RUN", - "HOLD_COMPETITIVE_COMPRESSION_CLAIM", - "HOLD_PRODUCTION_DECOMPRESSOR", - "HOLD_FPGA_ASIC_PROMOTION", - ], - "next_executable_step": ( - "Run this protocol on a small fixed text fixture first, producing raw " - "window eigenmass, OISC replay receipt, and a baseline size matrix." - ), - } - - receipt_payload = json.dumps(plan, sort_keys=True) - receipt = { - "receipt_type": "hutter_eigenmass_transfer_plan_receipt", - "created": created, - "plan_hash": sha256_text(receipt_payload), - "decision": plan["decision"], - "imported_sharpening_factor": plan["imported_signal"]["desi_manga_constraint_sharpening_factor"], - "gate_count": len(gates), - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return plan, receipt - - -def write_docs(plan: dict[str, Any], receipt: dict[str, Any]) -> None: - basis = "\n".join(f"- `{item}`" for item in plan["hutter_feature_basis_after_tuning"]) - ladder = "\n".join( - f"- DESI `{row['desi_pattern']}` -> Hutter `{row['hutter_analog']}`; gate `{row['gate']}`" - for row in plan["transfer_ladder"] - ) - gates = "\n".join(f"- `{gate}`" for gate in plan["promotion_gates"]) - holds = "\n".join(f"- `{hold}`" for hold in plan["hutter_holds"]) - - DOC_MD.write_text( - f"""# Hutter Eigenmass Transfer Plan - -Status: `TRANSFER_PROTOCOL_HOLD_HUTTER_CLAIM` - -Decision: `{plan['decision']}` - -This document ports the DESI/MaNGA multiscale eigenmass pattern into the Hutter -compression lane as a tuning protocol. It is a method-transfer receipt, not a -compression benchmark. - -Claim boundary: no enwik9 run, no compression-gain claim, no Hutter Prize claim, -and no FPGA/ASIC promotion is made here. - -## Imported Signal - -```text -DESI/MaNGA tracer cosine: {plan['imported_signal']['desi_manga_tracer_cosine']} -DESI/MaNGA sharpening factor: {plan['imported_signal']['desi_manga_constraint_sharpening_factor']} -``` - -Only the evidence-sharpening pattern transfers. Astronomy values do not become -compression scores. - -## Hutter Feature Basis After Tuning - -{basis} - -## Transfer Ladder - -{ladder} - -## Minimum Receipt Shape - -```json -{json.dumps(plan['minimum_receipt_shape'], indent=2)} -``` - -## Promotion Gates - -{gates} - -## Holds - -{holds} - -## Receipt - -```text -plan hash: {receipt['plan_hash']} -``` -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Hutter Eigenmass Transfer Plan -tags: ResearchStack Hutter Compression SemanticMassNumbers Eigenvector Receipt HOLD -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{plan['decision']}` - -This tiddler records how the DESI/MaNGA multiscale eigenmass method transfers -into the Hutter compression lane after tuning. - -Imported sharpening signal: - -``` -{plan['imported_signal']['desi_manga_constraint_sharpening_factor']} -``` - -Only the method transfers. Astronomy values do not become compression scores. - -!! Feature Basis - -{basis} - -!! Transfer Ladder - -{ladder} - -!! Promotion Gates - -{gates} - -!! Holds - -{holds} - -!! Receipt - -``` -{receipt['plan_hash']} -``` -""", - encoding="utf-8", - ) - - -def main() -> None: - plan, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(plan, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(plan, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/hutter_equation_metastate_transfold.py b/4-Infrastructure/shim/hutter_equation_metastate_transfold.py deleted file mode 100644 index dffe0737..00000000 --- a/4-Infrastructure/shim/hutter_equation_metastate_transfold.py +++ /dev/null @@ -1,386 +0,0 @@ -#!/usr/bin/env python3 -"""Transfold Hutter-style equations into a receipt-authoritative metastate. - -This does not recompress data. It translates the existing Hutter equation -surfaces into the stricter route/evaluator state required by the bounded exact -route compiler: - - proposal score -> candidate route coordinate -> exact receipt boundary - -The useful move is to keep speculative equation fields as proposal features -while making exact decode/hash/byte accounting the only promotion authority. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "hutter_equation_metastate_transfold_receipt.json" -CURRICULUM_OUT = SHIM / "hutter_equation_metastate_transfold_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" -HUTTER_ENWIK9_TARGET_BYTES = 109_685_197 - -SOURCE_SURFACES = { - "hutter_prize_equation_markdown": REPO - / "6-Documentation" - / "papers" - / "OTOM" - / "04_Hutter_Prize_Equation.md", - "hutter_derivation_spec": REPO - / "5-Applications" - / "hutter_prize" - / "DERIVATION_SPEC.md", - "hutter_architecture": REPO - / "5-Applications" - / "hutter_prize" - / "ARCHITECTURE.md", - "hutter_static_target_omindirection_prior": REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Hutter Static Target Omindirection Prior.tid", - "hutter_prize_compression_lean": REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "HutterPrizeCompression.lean", - "hutter_prize_flow_lean": REPO - / "0-Core-Formalism" - / "lean" - / "Semantics" - / "Semantics" - / "HutterPrizeFlow.lean", -} - -SOURCE_RECEIPTS = { - "projectable_geometry_topology_model": SHIM - / "projectable_geometry_topology_model_receipt.json", - "dimensional_shell_dd_probe": SHIM / "dimensional_shell_dd_probe_receipt.json", - "dd_target_implementation_reevaluation": SHIM - / "dd_target_implementation_reevaluation_receipt.json", - "compression_ratio_rederivation": SHIM - / "compression_ratio_rederivation_receipt.json", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def file_record(path: Path) -> dict[str, Any]: - data = path.read_bytes() - return { - "path": rel(path), - "bytes": len(data), - "sha256": sha256_bytes(data), - } - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def receipt_hash(path: Path, data: dict[str, Any]) -> str: - for key in ( - "receipt_hash", - "stable_topology_model_hash_sha256", - "stable_shell_dd_hash_sha256", - ): - value = data.get(key) - if isinstance(value, str): - return value - return sha256_bytes(path.read_bytes()) - - -def source_surface_records() -> dict[str, dict[str, Any]]: - return {name: file_record(path) for name, path in SOURCE_SURFACES.items()} - - -def source_receipt_records(receipts: dict[str, dict[str, Any]]) -> dict[str, Any]: - return { - name: { - "path": rel(SOURCE_RECEIPTS[name]), - "schema": receipt.get("schema", "unknown"), - "hash": receipt_hash(SOURCE_RECEIPTS[name], receipt), - } - for name, receipt in receipts.items() - } - - -def current_best_metastate(topology: dict[str, Any], dd_target: dict[str, Any]) -> dict[str, Any]: - selected = topology["best_approach"]["selected_route"] - hard_target = dd_target.get("current_target", {}).get( - "hard_target_bytes_enwik9", HUTTER_ENWIK9_TARGET_BYTES - ) - projected = dd_target["current_best_implemented_route"].get( - "projected_enwik9_total_bytes" - ) - gap = None if projected is None else projected - hard_target - return { - "source_corpus_id": selected["slice"], - "source_bytes": selected["source_bytes"], - "candidate_chart": "xml_token_topology_witness_bz2", - "transform_route": topology["best_approach"]["route"], - "payload_bytes": selected["compressed_bytes"], - "residual_bytes": 0, - "witness_bytes": selected["topology_witness_bytes"], - "decoder_delta_bytes": 0, - "container_bytes": 0, - "compressed_total_bytes": selected["modeled_total_bytes"], - "baseline_bytes": selected["raw_baseline_bytes"], - "margin_vs_baseline_bytes": selected["gain_vs_raw_after_topology_bytes"], - "ratio_schema": "modeled_total_bytes / source_bytes; local slice diagnostic", - "modeled_ratio": selected["modeled_ratio"], - "hard_target_bytes_enwik9": hard_target, - "projected_enwik9_total_bytes": projected, - "projected_gap_to_hard_target_bytes": gap, - "exact_decode_status": "inherited_from_existing_reversible_measurement_receipts", - "source_hash_status": "not_embedded_in_topology_receipt", - "decoded_hash_status": "not_embedded_in_topology_receipt", - "promotion_status": ( - "current_small_slice_incumbent_only; not a Hutter Prize claim" - ), - "failure_code": None, - } - - -def hutter_transfold_equations() -> dict[str, Any]: - return { - "source_equation_surface": ( - "C = proposal_score(comp, phys, geom, scaling) or " - "phi_HP = field + compression_gain + decoder/resource penalties" - ), - "metastate_transfold": ( - "proposal_score -> candidate_route_coordinate -> " - "bounded_exact_route_metastate -> promotion_receipt" - ), - "hutter_route_metastate": [ - "source_corpus_id", - "source_bytes", - "candidate_chart", - "transform_route", - "payload_bytes", - "residual_bytes", - "witness_bytes", - "decoder_delta_bytes", - "container_bytes", - "runtime_budget", - "compressed_total_bytes", - "baseline_bytes", - "hard_target_bytes", - "ratio_schema", - "exact_decode_status", - "source_hash", - "decoded_hash", - "promotion_status", - "failure_code", - ], - "counted_total": ( - "compressed_total_bytes = payload_bytes + residual_bytes + " - "witness_bytes + decoder_delta_bytes + container_bytes" - ), - "lower_bound": ( - "LB_route = payload_floor + residual_floor + witness_floor + " - "decoder_delta_floor + container_floor + evaluator_cost_floor" - ), - "prune_rule": "prune iff LB_route >= incumbent_bytes", - "promotion_rule": ( - "promote iff decoded_hash == source_hash and compressed_total_bytes " - "< incumbent_bytes and ratio_schema is explicit and all witness, " - "residual, decoder, and container bytes are counted" - ), - "hard_target_rule": ( - "Hutter-hard promotion additionally requires the total contest artifact " - "for enwik9 to beat 109685197 bytes under the applicable prize rules" - ), - } - - -def bridge_lanes() -> list[dict[str, Any]]: - return [ - { - "lane": "symbolic_score_to_route_feature", - "input": "C_comp, C_phys, C_geom, S, G, F, rho", - "metastate_role": "proposal coordinate / search prior", - "promotion_authority": "none", - }, - { - "lane": "flow_penalty_to_lower_bound", - "input": "decoder penalty, resource penalty, tau, sigma, q", - "metastate_role": "lower-bound and prune pressure", - "promotion_authority": "none unless counted in bytes/runtime receipt", - }, - { - "lane": "trinary_vm_to_decoder_boundary", - "input": "declared rules, subregister trace, deterministic program", - "metastate_role": "portable reconstruction contract", - "promotion_authority": "exact replay only after source hash matches", - }, - { - "lane": "topology_witness_to_counted_metadata", - "input": "Menger/Torus/Braid/NaN0 16-byte witness", - "metastate_role": "bounded control-plane witness", - "promotion_authority": "only if route margin survives witness bytes", - }, - { - "lane": "residual_to_byte_authority", - "input": "sidecar, repair lane, exact rehydration", - "metastate_role": "restore all bytes removed by proposal charts", - "promotion_authority": "decoded_hash == source_hash", - }, - ] - - -def implications(best: dict[str, Any]) -> list[dict[str, Any]]: - margin = int(best["margin_vs_baseline_bytes"]) - return [ - { - "id": "winning_equation_demotion", - "implication": ( - "The weighted Hutter score is useful as a route-search coordinate, " - "not as a byte claim." - ), - }, - { - "id": "metastate_authority", - "implication": ( - "The metastate's invariant root is exact reconstruction under " - "counted compressed_total_bytes." - ), - }, - { - "id": "current_margin_constraint", - "implication": ( - f"The current small-slice route has {margin} bytes of margin after " - "the 16-byte topology witness; additional overlays must earn their " - "own payload savings before promotion." - ), - }, - { - "id": "hutter_gap", - "implication": ( - "The current projected enwik9 size remains diagnostic only and is " - "far above the hard target; the next useful work is payload-saving " - "transforms, not more unpriced metadata." - ), - }, - ] - - -def research_tasks() -> list[dict[str, str]]: - return [ - { - "id": "embed_hash_authority", - "task": "carry source_hash and decoded_hash through every local Hutter route receipt", - }, - { - "id": "route_matrix_wrapper", - "task": "evaluate candidate charts with payload/residual/witness/decoder/container byte columns", - }, - { - "id": "demote_unpriced_scores", - "task": "treat physics/geometric/topology scores as priors until exact route bytes exist", - }, - { - "id": "payload_transform_trials", - "task": "test corpus-resolution, fascicle, tokenbook, and residualized normalization routes", - }, - { - "id": "lean_alignment", - "task": "align Lean Hutter score modules with the metastate distinction between proposal score and promotion receipt", - }, - ] - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for lane in receipt["bridge_lanes"]: - lines.append({"type": "bridge_lane", **lane}) - for item in receipt["metastate_implications"]: - lines.append({"type": "implication", **item}) - for item in receipt["research_tasks"]: - lines.append({"type": "research_task", **item}) - return lines - - -def build_receipt() -> dict[str, Any]: - receipts = {name: load_json(path) for name, path in SOURCE_RECEIPTS.items()} - best = current_best_metastate( - receipts["projectable_geometry_topology_model"], - receipts["dd_target_implementation_reevaluation"], - ) - receipt: dict[str, Any] = { - "schema": "hutter_equation_metastate_transfold_v1", - "generated_at": GENERATED_AT, - "runner": rel(Path(__file__)), - "source_surfaces": source_surface_records(), - "source_receipts": source_receipt_records(receipts), - "transfold_definition": ( - "Transfold the Hutter equation surface from symbolic compression score " - "into a bounded exact route metastate whose invariant root is exact " - "decode plus counted byte total." - ), - "hutter_transfold_equations": hutter_transfold_equations(), - "bridge_lanes": bridge_lanes(), - "current_best_metastate": best, - "metastate_implications": implications(best), - "research_tasks": research_tasks(), - "claim_boundary": ( - "This is a Hutter-equation transfold and receipt-indexing artifact. " - "It does not recompress data, improve byte counts, prove optimality, " - "or make a Hutter Prize submission. Promotion still requires exact " - "encode/decode/hash verification and measured total bytes with every " - "witness, residual, decoder, and container cost counted." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_bytes(stable_json(preimage).encode("utf-8")) - return receipt - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print( - json.dumps( - { - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "bridge_lane_count": len(receipt["bridge_lanes"]), - "research_task_count": len(receipt["research_tasks"]), - "current_route": receipt["current_best_metastate"]["transform_route"], - "current_total_bytes": receipt["current_best_metastate"][ - "compressed_total_bytes" - ], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/hutter_frame_invariant_root_probe.py b/4-Infrastructure/shim/hutter_frame_invariant_root_probe.py deleted file mode 100644 index bfd47792..00000000 --- a/4-Infrastructure/shim/hutter_frame_invariant_root_probe.py +++ /dev/null @@ -1,358 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed Hutter frame-invariant root probe. - -This adapts the torsion-interval splat idea to Hutter compression as a -frame-invariant root stream: each slice/frame has a stable root that can decode -independently, while optional deltas reference prior frame roots. The analogy is -M-JPEG style: favor independently replayable frames over one long fragile -predictive chain. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "hutter_frame_invariant_root" -REGISTRY = OUT_DIR / "hutter_frame_invariant_root_registry.json" -RECEIPT = OUT_DIR / "hutter_frame_invariant_root_receipt.json" -SUMMARY = OUT_DIR / "hutter_frame_invariant_root.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Frame Invariant Root.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" / "torsion_interval_gaussian_splat_witness_receipt.json", - REPO / "shared-data" / "data" / "gaussian_splat_manifold_projection" / "gaussian_splat_manifold_projection_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_canonical_baseline_probe" / "enwiki9_logogram_canonical_baseline_probe_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_dictionary_amortization_probe" / "enwiki9_logogram_dictionary_amortization_probe_receipt.json", -] - -FRAME_ROOT_BYTES = 32 -FRAME_ID_BYTES = 4 -DELTA_REF_BYTES = 4 -RECEIPT_ROOT_BYTES = 32 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def frame( - *, - frame_id: int, - class_name: str, - raw_bytes: int, - root_core_bytes: int, - delta_bytes: int | None, - independent_replay: bool, - prior_frame_ref: int | None = None, -) -> dict[str, Any]: - invariant_packet = root_core_bytes + FRAME_ROOT_BYTES + FRAME_ID_BYTES + RECEIPT_ROOT_BYTES - delta_packet = None if delta_bytes is None else delta_bytes + DELTA_REF_BYTES + RECEIPT_ROOT_BYTES - chosen_packet = invariant_packet if delta_packet is None else min(invariant_packet, delta_packet) - root_payload = { - "frame_id": frame_id, - "class_name": class_name, - "raw_bytes": raw_bytes, - "root_core_bytes": root_core_bytes, - "independent_replay": independent_replay, - "prior_frame_ref": prior_frame_ref, - } - if not independent_replay: - decision = "HOLD_FRAME_REPLAY_REQUIRED" - elif chosen_packet >= raw_bytes: - decision = "HOLD_FRAME_PACKET_EXPANDS" - elif delta_packet is not None and delta_packet < invariant_packet: - decision = "ADMIT_DELTA_FRAME_WITH_ROOT_CHECKPOINT" - else: - decision = "ADMIT_INVARIANT_ROOT_FRAME" - item = { - "frame_id": frame_id, - "class_name": class_name, - "raw_bytes": raw_bytes, - "root_core_bytes": root_core_bytes, - "delta_bytes": delta_bytes, - "prior_frame_ref": prior_frame_ref, - "independent_replay": independent_replay, - "frame_root": hash_obj(root_payload), - "invariant_packet_bytes": invariant_packet, - "delta_packet_bytes": delta_packet, - "chosen_packet_bytes": chosen_packet, - "delta_vs_raw": raw_bytes - chosen_packet, - "decision": decision, - } - item["frame_hash"] = hash_obj({k: v for k, v in item.items() if k != "frame_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - frames = [ - frame( - frame_id=0, - class_name="xml_head", - raw_bytes=65536, - root_core_bytes=62100, - delta_bytes=None, - independent_replay=True, - ), - frame( - frame_id=1, - class_name="template_heavy", - raw_bytes=65536, - root_core_bytes=63850, - delta_bytes=62900, - independent_replay=True, - prior_frame_ref=0, - ), - frame( - frame_id=2, - class_name="link_heavy", - raw_bytes=65536, - root_core_bytes=64120, - delta_bytes=63040, - independent_replay=True, - prior_frame_ref=1, - ), - frame( - frame_id=3, - class_name="mixed_high_entropy", - raw_bytes=65536, - root_core_bytes=65720, - delta_bytes=65200, - independent_replay=True, - prior_frame_ref=2, - ), - frame( - frame_id=4, - class_name="unsafe_long_predictive_chain", - raw_bytes=65536, - root_core_bytes=64000, - delta_bytes=2000, - independent_replay=False, - prior_frame_ref=3, - ), - ] - return { - "schema": "hutter_frame_invariant_root_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Hutter frame-invariant root diagnostic only. It proposes a frame-root " - "decompression discipline for slice streams. It does not change the frozen " - "enwiki9 codec and does not claim Hutter-score competitiveness." - ), - "canonical_statement": ( - "Use frame-invariant roots like M-JPEG key frames: every corpus frame should " - "remain independently replayable, while deltas are optional bounded shortcuts " - "anchored to prior roots." - ), - "frame_model": { - "frame_root": "R_k = H(frame_id, class, raw hash, core hash, dictionary hash, protocol hash)", - "independent_decode": "Decode(root_frame_k) = raw_frame_k", - "delta_decode": "Decode(delta_k, R_{k-1}) = raw_frame_k, with R_k recomputed after replay", - "global_stream_root": "R_stream = MerkleRoot(R_0, ..., R_K)", - "clock_policy": "frame order and roots matter; wall-clock generation time is metadata only", - }, - "byte_accounting": { - "frame_root_bytes": FRAME_ROOT_BYTES, - "frame_id_bytes": FRAME_ID_BYTES, - "delta_ref_bytes": DELTA_REF_BYTES, - "receipt_root_bytes": RECEIPT_ROOT_BYTES, - "invariant_packet": "root_core_bytes + frame_root_bytes + frame_id_bytes + receipt_root_bytes", - "delta_packet": "delta_bytes + delta_ref_bytes + receipt_root_bytes", - }, - "admissibility_equation": ( - "A_frame=1[independent_replay] * 1[chosen_packet_bytes < raw_bytes] * " - "1[R_k recomputes] * 1[delta_chain_bounded]" - ), - "hutter_torsion_link": { - "reduces": ["route_coupling", "receipt_debt", "long_predictive_chain_fragility"], - "adds": ["frame_root_bytes", "receipt_root_bytes", "frame_index_bytes"], - "ergoregion_rule": "a delta may be used only if a nearby invariant root checkpoint bounds replay damage", - }, - "frames": frames, - "stream_root": hash_obj([frame_item["frame_root"] for frame_item in frames]), - "aggregates": { - "frame_count": len(frames), - "admit_invariant_count": sum(1 for item in frames if item["decision"] == "ADMIT_INVARIANT_ROOT_FRAME"), - "admit_delta_count": sum(1 for item in frames if item["decision"] == "ADMIT_DELTA_FRAME_WITH_ROOT_CHECKPOINT"), - "hold_count": sum(1 for item in frames if item["decision"].startswith("HOLD")), - "raw_bytes": sum(item["raw_bytes"] for item in frames), - "chosen_packet_bytes": sum(item["chosen_packet_bytes"] for item in frames), - "delta_vs_raw": sum(item["raw_bytes"] - item["chosen_packet_bytes"] for item in frames), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "hutter_frame_invariant_root_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "stream_root": registry["stream_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_HUTTER_FRAME_INVARIANT_ROOT_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Hutter Frame-Invariant Root", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Stream root: `{registry['stream_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Frame Model", - "", - ] - for key, value in registry["frame_model"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Frames", - "", - "| Frame | Class | Raw | Invariant packet | Delta packet | Chosen | Delta vs raw | Decision |", - "|---:|---|---:|---:|---:|---:|---:|---|", - ] - ) - for item in registry["frames"]: - delta_packet = "" if item["delta_packet_bytes"] is None else str(item["delta_packet_bytes"]) - lines.append( - f"| {item['frame_id']} | `{item['class_name']}` | {item['raw_bytes']} | " - f"{item['invariant_packet_bytes']} | {delta_packet} | {item['chosen_packet_bytes']} | " - f"{item['delta_vs_raw']} | `{item['decision']}` |" - ) - lines.extend(["", "## Hutter Torsion Link", ""]) - for key, value in registry["hutter_torsion_link"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression FrameRoot Receipt -title: Hutter Frame Invariant Root -type: text/vnd.tiddlywiki - -! Hutter Frame Invariant Root - -Durable runner: - -``` -4-Infrastructure/shim/hutter_frame_invariant_root_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Stream root: - -``` -{receipt['stream_root']} -``` - -!! Doctrine - -Use frame-invariant roots like M-JPEG key frames: each corpus slice should remain independently replayable, while deltas are optional bounded shortcuts anchored to prior roots. - -``` -R_stream = MerkleRoot(R_0, ..., R_K) -Decode(root_frame_k) = raw_frame_k -Decode(delta_k, R_(k-1)) = raw_frame_k, then recompute R_k -``` - -!! Links - -* [[Hutter Torsion Clock Adaptation]] -* [[Torsion Interval Gaussian Splat Witness]] -* [[Gaussian Splat Manifold Projection]] -* [[Hutter Prize Compression]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "stream_root": registry["stream_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hutter_multidimensional_causal_chain_probe.py b/4-Infrastructure/shim/hutter_multidimensional_causal_chain_probe.py deleted file mode 100644 index a5d9b55e..00000000 --- a/4-Infrastructure/shim/hutter_multidimensional_causal_chain_probe.py +++ /dev/null @@ -1,434 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed multidimensional causal chain probe for Hutter frames. - -The differential frame chain need not be a single linear sequence. A frame root -can participate in multiple typed causal axes: byte-neighbor, semantic class, -provenance, torsion/accounting, chirality, 360 orientation sharing, and -spatial/corpus offset. This probe models a multidimensional causal graph where -each edge has a declared axis and bounded witness. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" -REGISTRY = OUT_DIR / "hutter_multidimensional_causal_chain_registry.json" -RECEIPT = OUT_DIR / "hutter_multidimensional_causal_chain_receipt.json" -SUMMARY = OUT_DIR / "hutter_multidimensional_causal_chain.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Multidimensional Causal Chain.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "hutter_differential_frame_chain" / "hutter_differential_frame_chain_receipt.json", - REPO / "shared-data" / "data" / "hutter_frame_invariant_root" / "hutter_frame_invariant_root_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" / "observer_chart_projection_guardrail_receipt.json", - REPO / "shared-data" / "data" / "logogram_dna_codec" / "logogram_dna_codec_receipt.json", -] - -ALLOWED_AXES = [ - "byte_neighbor", - "semantic_class", - "provenance", - "codec_torsion", - "chirality", - "orientation_360_share", - "corpus_offset", - "observer_chart", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def node( - node_id: str, - frame_id: int, - class_name: str, - offset: int, - root: str, - chirality: str, - orientation_degrees: int, -) -> dict[str, Any]: - payload = { - "node_id": node_id, - "frame_id": frame_id, - "class_name": class_name, - "offset": offset, - "root": root, - "chirality": chirality, - "orientation_degrees": orientation_degrees, - } - payload["node_hash"] = hash_obj(payload) - return payload - - -def edge( - *, - edge_id: str, - source: str, - target: str, - axis: str, - relation: str, - witness: str, - bounded: bool, - reversible: bool, - root_check: bool, - global_truth_claim: bool = False, -) -> dict[str, Any]: - axis_declared = axis in ALLOWED_AXES - admissible = axis_declared and bounded and root_check and not global_truth_claim - if not axis_declared: - decision = "HOLD_AXIS_UNDECLARED" - elif global_truth_claim: - decision = "HOLD_LOCAL_EDGE_GLOBALIZED" - elif not root_check: - decision = "REJECT_CAUSAL_ROOT_MISMATCH" - elif not bounded: - decision = "HOLD_UNBOUNDED_CAUSAL_EDGE" - else: - decision = "ADMIT_CAUSAL_EDGE" - item = { - "edge_id": edge_id, - "source": source, - "target": target, - "axis": axis, - "relation": relation, - "witness": witness, - "bounded": bounded, - "reversible": reversible, - "root_check": root_check, - "global_truth_claim": global_truth_claim, - "admissible": admissible, - "decision": decision, - } - item["edge_hash"] = hash_obj({k: v for k, v in item.items() if k != "edge_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - nodes = [ - node("x0", 0, "xml_head", 0, hash_obj({"frame": 0, "class": "xml_head"}), "L", 0), - node("x1", 1, "template_heavy", 65536, hash_obj({"frame": 1, "class": "template_heavy"}), "L", 90), - node("x2", 2, "link_heavy", 131072, hash_obj({"frame": 2, "class": "link_heavy"}), "R", 180), - node("x3", 3, "mixed_high_entropy", 196608, hash_obj({"frame": 3, "class": "mixed_high_entropy"}), "R", 270), - node("x4", 4, "xml_head", 1_000_000, hash_obj({"frame": 4, "class": "xml_head"}), "L", 360), - ] - edges = [ - edge( - edge_id="e_byte_0_1", - source="x0", - target="x1", - axis="byte_neighbor", - relation="next corpus frame", - witness="bounded delta dx_0_1", - bounded=True, - reversible=False, - root_check=True, - ), - edge( - edge_id="e_semantic_0_4", - source="x0", - target="x4", - axis="semantic_class", - relation="same xml-head observer chart across offsets", - witness="class root and feature signature match", - bounded=True, - reversible=True, - root_check=True, - ), - edge( - edge_id="e_torsion_1_3", - source="x1", - target="x3", - axis="codec_torsion", - relation="dictionary/receipt pressure increases toward mixed entropy", - witness="codec torsion debt vector", - bounded=True, - reversible=False, - root_check=True, - ), - edge( - edge_id="e_provenance_0_4", - source="x0", - target="x4", - axis="provenance", - relation="canonical enwik9 provenance relation", - witness="source file hash and offset receipt", - bounded=True, - reversible=False, - root_check=True, - ), - edge( - edge_id="e_chirality_0_1", - source="x0", - target="x1", - axis="chirality", - relation="same handedness preserves frame orientation under template projection", - witness="chirality bit and phase placement receipt", - bounded=True, - reversible=True, - root_check=True, - ), - edge( - edge_id="e_chirality_flip_1_2", - source="x1", - target="x2", - axis="chirality", - relation="handedness flip requires explicit adapter and residual declaration", - witness="chirality flip adapter with declared residual", - bounded=True, - reversible=False, - root_check=True, - ), - edge( - edge_id="e_360_share_0_4", - source="x0", - target="x4", - axis="orientation_360_share", - relation="same root family shareable across a closed 0-to-360 orientation sweep", - witness="orientation bucket roots at 0, 90, 180, 270, and 360 degrees", - bounded=True, - reversible=True, - root_check=True, - ), - edge( - edge_id="e_bad_global_chart", - source="x2", - target="x3", - axis="observer_chart", - relation="local chart incorrectly promoted to global codec truth", - witness="observer chart without residual", - bounded=True, - reversible=False, - root_check=True, - global_truth_claim=True, - ), - edge( - edge_id="e_unbounded_predictive", - source="x3", - target="x4", - axis="byte_neighbor", - relation="long predictive jump without nearby root checkpoint", - witness="unbounded delta chain", - bounded=False, - reversible=False, - root_check=True, - ), - ] - return { - "schema": "hutter_multidimensional_causal_chain_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Hutter multidimensional causal chain diagnostic only. It models typed " - "relations among frame roots; it does not alter codec behavior or assert " - "actual compression gain." - ), - "canonical_statement": ( - "A frame root is a node. A differential or relation is an edge. Causality " - "is admissible only along a declared axis with bounded witness and root check. " - "Chirality and 360 orientation sharing are axes, not informal global truth." - ), - "causal_equation": { - "node": "x_i := frame root plus observer/corpus metadata", - "edge": "e_{i,j}^{axis}: x_i -> x_j", - "admission": "A(e)=1[axis_declared] * 1[bounded_witness] * 1[root_check] * 1[not globalized_local_chart]", - "graph": "G_H=(X,E_axis) with byte, semantic, provenance, torsion, chirality, 360 orientation sharing, offset, and observer-chart axes", - }, - "allowed_axes": ALLOWED_AXES, - "hutter_role": { - "multi_axis_prediction": "x can lead to x(i) along different declared axes, not just next byte frame", - "chirality_axis": "handedness is a routing/admission coordinate when frame orientation or phase matters", - "orientation_360_share": "closed orientation sweeps can share a frame-invariant root when every bucket is committed", - "compression_use": "reuse roots/classes/routes when causal edge is admitted", - "guardrail": "axis-free similarity is HOLD; globalized local chart is HOLD; unbounded predictive jump is HOLD", - }, - "nodes": nodes, - "edges": edges, - "graph_root": hash_obj({"nodes": [item["node_hash"] for item in nodes], "edges": [item["edge_hash"] for item in edges]}), - "aggregates": { - "node_count": len(nodes), - "edge_count": len(edges), - "admitted_edge_count": sum(1 for item in edges if item["decision"] == "ADMIT_CAUSAL_EDGE"), - "hold_edge_count": sum(1 for item in edges if item["decision"].startswith("HOLD")), - "reject_edge_count": sum(1 for item in edges if item["decision"].startswith("REJECT")), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "hutter_multidimensional_causal_chain_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "graph_root": registry["graph_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_HUTTER_MULTIDIMENSIONAL_CAUSAL_CHAIN_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Hutter Multidimensional Causal Chain", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Graph root: `{registry['graph_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Causal Equation", - "", - ] - for key, value in registry["causal_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Edges", - "", - "| Edge | Axis | Source | Target | Decision |", - "|---|---|---|---|---|", - ] - ) - for item in registry["edges"]: - lines.append(f"| `{item['edge_id']}` | `{item['axis']}` | `{item['source']}` | `{item['target']}` | `{item['decision']}` |") - lines.extend(["", "## Hutter Role", ""]) - for key, value in registry["hutter_role"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression CausalChain Receipt -title: Hutter Multidimensional Causal Chain -type: text/vnd.tiddlywiki - -! Hutter Multidimensional Causal Chain - -Durable runner: - -``` -4-Infrastructure/shim/hutter_multidimensional_causal_chain_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Graph root: - -``` -{receipt['graph_root']} -``` - -!! Doctrine - -A frame root is a node. A differential or relation is an edge. Causality is admissible only along a declared axis with bounded witness and root check. - -``` -e_{{i,j}}^axis : x_i -> x_j -A(e)=1[axis_declared] * 1[bounded_witness] * 1[root_check] -``` - -This lets x lead to x(i) across byte, semantic, provenance, torsion, chirality, 360 orientation-sharing, corpus-offset, and observer-chart dimensions. - -Chirality is treated as a real routing/admission axis. The 360 sharing axis is -accepted only as a bounded closed-orientation sweep with committed bucket roots, -not as a free claim that every observer chart shares the same global truth. - -!! Links - -* [[Hutter Differential Frame Chain]] -* [[Hutter Frame Invariant Root]] -* [[Observer Chart Projection Guardrail]] -* [[Hutter Torsion Clock Adaptation]] -* [[Logogram-DNA Codec Receipt]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "graph_root": registry["graph_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hutter_prize_next_roadmap_probe.py b/4-Infrastructure/shim/hutter_prize_next_roadmap_probe.py deleted file mode 100644 index b9e8fc4b..00000000 --- a/4-Infrastructure/shim/hutter_prize_next_roadmap_probe.py +++ /dev/null @@ -1,417 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed next-roadmap probe for the Hutter Prize track. - -This probe does not change the codec. It records the current v5 state, the -official prize resource envelope, and the next admissible roadmap gates for -canonical enwik9 work. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "hutter_prize_next_roadmap" -REGISTRY = OUT_DIR / "hutter_prize_next_roadmap_registry.json" -RECEIPT = OUT_DIR / "hutter_prize_next_roadmap_receipt.json" -SUMMARY = OUT_DIR / "hutter_prize_next_roadmap.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Prize Next Roadmap.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "enwiki9_logogram_canonical_baseline_probe" / "enwiki9_logogram_canonical_baseline_probe_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_dictionary_amortization_probe" / "enwiki9_logogram_dictionary_amortization_probe_receipt.json", - REPO / "shared-data" / "data" / "godel_gauntlet_safety_condition" / "godel_gauntlet_safety_condition_receipt.json", - REPO / "shared-data" / "data" / "godel_gauntlet_race_condition" / "godel_gauntlet_race_condition_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", - REPO / "shared-data" / "data" / "phonon_music_logogram_layer" / "phonon_music_logogram_layer_receipt.json", -] - -OFFICIAL_RULE_REFS = [ - { - "label": "Hutter Prize main page", - "url": "https://prize.hutter1.net/", - "used_for": "current record, enwik9 target, single-core/RAM/HDD/no-GPU headline", - }, - { - "label": "Hutter Prize rules", - "url": "https://prize.hutter1.net/hrules.htm", - "used_for": "formal runtime and resource limits", - }, -] - -RESOURCE_ENVELOPE = { - "record_to_beat_bytes": 110_793_128, - "target_file": "enwik9", - "target_size_bytes": 1_000_000_000, - "must_reproduce_target_exactly": True, - "counts_program_plus_archive": True, - "max_hours_formula": "70000 / geekbench5_score_T", - "max_ram_gb": 10, - "max_temp_hdd_gb": 100, - "gpu_allowed": False, - "single_cpu_core": True, -} - -V_LADDER = [ - {"rung": "v1", "status": "replay passes; core expands"}, - {"rung": "v2", "status": "replay passes; core shrinks"}, - {"rung": "v3", "status": "replay passes; packet shrinks"}, - {"rung": "v4", "status": "fixture global shrink appears under clockless gates"}, - {"rung": "v5", "status": "frozen codec plus provenance and baseline gate is active; current local fixture is HOLD_GLOBAL"}, -] - -NEXT_STEPS = [ - { - "step_id": "verify_canonical_enwik9", - "gate": "PROVENANCE", - "action": "Acquire or point to canonical enwik9, require size_bytes == 1_000_000_000, compute sha256, and mark every other input as fixture.", - "failure_decision": "HOLD_PROVENANCE", - }, - { - "step_id": "run_frozen_v5_offsets", - "gate": "PASS_ADD_PAUSE_SUBTRACT_BASELINE", - "action": "Run the frozen v4/v5 codec over offsets 0, 1M, 10M, 100M, 500M, and 900M without encoder changes.", - "failure_decision": "REJECT_REPLAY | HOLD_PACKET | HOLD_GLOBAL", - }, - { - "step_id": "run_content_selected_windows", - "gate": "PROVENANCE_PASS_BASELINE", - "action": "Select xml_head, link_heavy, template_heavy, ref_heavy, category_file_heavy, prose_heavy, and mixed_high_entropy windows from canonical enwik9.", - "failure_decision": "HOLD_PROVENANCE | HOLD_GLOBAL", - }, - { - "step_id": "compare_baselines", - "gate": "BASELINE", - "action": "Emit zlib_9, bz2_9, lzma_9, and zstd_19_if_available bytes per slice and aggregate.", - "failure_decision": "HOLD_BASELINE", - }, - { - "step_id": "run_godel_gauntlet", - "gate": "SAFETY", - "action": "Run replay/root/provenance/order/resource/resource-limit checks before any promotion.", - "failure_decision": "HOLD_RESOURCE_LIMIT | HOLD_HIDDEN_RACE_CONDITION | REJECT_ROOT_MISMATCH", - }, - { - "step_id": "choose_v6_from_failures_only", - "gate": "ENCODER_CHANGE_FENCE", - "action": "Choose the next codec change only from canonical v5 failures; do not train on fawiki/jawiki/viwiki fixtures.", - "failure_decision": "HOLD_ENCODER_CHANGE_FENCE", - }, -] - -V6_GUIDANCE = [ - {"v5_failure": "XML head wins, link-heavy loses", "v6_fix": "link alias factoring"}, - {"v5_failure": "template-heavy loses", "v6_fix": "template name/key factoring"}, - {"v5_failure": "ref-heavy loses", "v6_fix": "citation grammar atoms"}, - {"v5_failure": "prose-heavy loses", "v6_fix": "leave prose to baseline/statistical model"}, - {"v5_failure": "packet positive but global negative", "v6_fix": "wider amortization run"}, - {"v5_failure": "global positive but baseline loses", "v6_fix": "pipe output into stronger backend compressor"}, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def read_json(path: Path) -> dict[str, Any] | None: - if not path.exists(): - return None - return json.loads(path.read_text(encoding="utf-8")) - - -def source_ref(path: Path) -> dict[str, Any]: - receipt = read_json(path) - return { - "path": rel(path), - "exists": path.exists(), - "sha256": file_hash(path), - "receipt_hash": receipt.get("receipt_hash") if isinstance(receipt, dict) else None, - "decision": receipt.get("decision") if isinstance(receipt, dict) else None, - } - - -def summarize_v5() -> dict[str, Any]: - receipt = read_json(SOURCE_REFS[0]) - if not receipt: - return {"exists": False, "decision": "HOLD_MISSING_V5_RECEIPT"} - aggregate = receipt.get("aggregate", {}) - return { - "exists": True, - "receipt": rel(SOURCE_REFS[0]), - "receipt_hash": receipt.get("receipt_hash"), - "decision": receipt.get("decision"), - "codec_frozen_from": receipt.get("codec_frozen_from"), - "encoder_changed": receipt.get("encoder_changed"), - "clock_participates_in_hash": receipt.get("clock_participates_in_hash"), - "input": receipt.get("input"), - "aggregate": { - "all_exact_replay": aggregate.get("all_exact_replay"), - "raw_bytes": aggregate.get("raw_bytes"), - "core_bytes": aggregate.get("core_bytes"), - "packet_bytes": aggregate.get("packet_bytes"), - "dictionary_bytes": aggregate.get("dictionary_bytes"), - "delta_core": aggregate.get("delta_core"), - "delta_packet": aggregate.get("delta_packet"), - "delta_global": aggregate.get("delta_global"), - "best_baseline": aggregate.get("baseline", {}).get("best_baseline"), - "best_baseline_bytes": aggregate.get("baseline", {}).get("best_baseline_bytes"), - "delta_vs_best_baseline": aggregate.get("baseline", {}).get("delta_vs_best_baseline"), - }, - } - - -def build_registry() -> dict[str, Any]: - v5 = summarize_v5() - registry = { - "schema": "hutter_prize_next_roadmap_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "official_rule_refs": OFFICIAL_RULE_REFS, - "resource_envelope": RESOURCE_ENVELOPE, - "claim_boundary": ( - "Hutter Prize roadmap receipt only. It freezes the current codec " - "state, records official resource and scoring gates, and specifies " - "next canonical-enwik9 work. It does not claim a corpus-scale result " - "or change encoder behavior." - ), - "canonical_statement": ( - "v5 moves from fixture evidence to canonical enwik9 slice evidence. " - "The next valid action is provenance-first canonical slicing with " - "baseline comparison under hard prize resource limits; v6 codec " - "changes are allowed only after v5 canonical failures identify them." - ), - "current_ladder": V_LADDER, - "current_v5": v5, - "next_steps": NEXT_STEPS, - "v6_guidance": V6_GUIDANCE, - "decision_vocabulary": [ - "REJECT_REPLAY", - "HOLD_PROVENANCE", - "HOLD_PACKET", - "HOLD_GLOBAL", - "HOLD_BASELINE", - "HOLD_RESOURCE_LIMIT", - "ADMIT_FIXTURE", - "ADMIT_CANONICAL_SLICE", - "BASELINE_CANDIDATE", - ], - "feedback_loop_guardrail": { - "role": "defensive_race_condition_analogy_only", - "statement": ( - "Delayed or altered feedback is useful as a model of hidden " - "noncommuting loops: an emitted packet can re-enter the observer " - "chart late and perturb route choice. In Hutter work this becomes " - "a race-condition test over frame roots, never an audio tactic." - ), - }, - } - registry["roadmap_root"] = hash_obj( - { - "resource_envelope": registry["resource_envelope"], - "current_v5": registry["current_v5"], - "next_steps": registry["next_steps"], - "v6_guidance": registry["v6_guidance"], - } - ) - return registry - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - current = registry["current_v5"] - decision = "HOLD_CANONICAL_ENWIK9_REQUIRED" - if current.get("input", {}).get("size_bytes") == RESOURCE_ENVELOPE["target_size_bytes"]: - decision = "READY_FOR_CANONICAL_V5_SWEEP" - receipt = { - "schema": "hutter_prize_next_roadmap_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "roadmap_root": registry["roadmap_root"], - "decision": decision, - "resource_envelope": registry["resource_envelope"], - "current_v5_decision": current.get("decision"), - "current_v5_receipt_hash": current.get("receipt_hash"), - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - current = registry["current_v5"] - agg = current.get("aggregate", {}) - lines = [ - "# Hutter Prize Next Roadmap", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Roadmap root: `{receipt['roadmap_root']}`", - "", - registry["claim_boundary"], - "", - "## Current v5 State", - "", - f"- v5 receipt: `{current.get('receipt')}`", - f"- v5 receipt hash: `{current.get('receipt_hash')}`", - f"- v5 decision: `{current.get('decision')}`", - f"- Exact replay: `{agg.get('all_exact_replay')}`", - f"- Raw/core/packet bytes: `{agg.get('raw_bytes')}` / `{agg.get('core_bytes')}` / `{agg.get('packet_bytes')}`", - f"- Delta core/packet/global: `{agg.get('delta_core')}` / `{agg.get('delta_packet')}` / `{agg.get('delta_global')}`", - f"- Best baseline: `{agg.get('best_baseline')}` at `{agg.get('best_baseline_bytes')}` bytes", - f"- Delta vs best baseline: `{agg.get('delta_vs_best_baseline')}`", - "", - "## Official Prize Envelope", - "", - f"- Record to beat: `{registry['resource_envelope']['record_to_beat_bytes']}` bytes", - f"- Target: `{registry['resource_envelope']['target_file']}` at `{registry['resource_envelope']['target_size_bytes']}` bytes", - f"- Counts program plus archive: `{registry['resource_envelope']['counts_program_plus_archive']}`", - f"- Max hours formula: `{registry['resource_envelope']['max_hours_formula']}`", - f"- Max RAM GB: `{registry['resource_envelope']['max_ram_gb']}`", - f"- Max temp HDD GB: `{registry['resource_envelope']['max_temp_hdd_gb']}`", - f"- GPU allowed: `{registry['resource_envelope']['gpu_allowed']}`", - "", - "## Next Steps", - "", - "| Step | Gate | Action | Failure decision |", - "|---|---|---|---|", - ] - for step in registry["next_steps"]: - lines.append( - f"| `{step['step_id']}` | `{step['gate']}` | {step['action']} | `{step['failure_decision']}` |" - ) - lines.extend(["", "## v6 Only After v5", "", "| v5 failure | v6 fix |", "|---|---|"]) - for item in registry["v6_guidance"]: - lines.append(f"| {item['v5_failure']} | {item['v6_fix']} |") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append( - f"- `{source['path']}` exists: `{source['exists']}` decision: `{source['decision']}` receipt: `{source['receipt_hash']}`" - ) - for source in registry["official_rule_refs"]: - lines.append(f"- {source['label']}: {source['url']} ({source['used_for']})") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - current = registry["current_v5"] - agg = current.get("aggregate", {}) - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression Roadmap Receipt -title: Hutter Prize Next Roadmap -type: text/vnd.tiddlywiki - -! Hutter Prize Next Roadmap - -Durable runner: - -``` -4-Infrastructure/shim/hutter_prize_next_roadmap_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Roadmap root: - -``` -{receipt['roadmap_root']} -``` - -!! Current State - -v5 is still `HOLD_GLOBAL` on the local fixture. It replays exactly, but the -global delta is `{agg.get('delta_global')}` and the best baseline is -`{agg.get('best_baseline')}` at `{agg.get('best_baseline_bytes')}` bytes. - -!! Next Gate - -Acquire or point to canonical `enwik9`, require exactly `1,000,000,000` bytes, -then rerun the frozen v5 codec over fixed and content-selected windows. Any -noncanonical input stays fixture evidence. - -!! Hard Prize Envelope - -* Record to beat: `{registry['resource_envelope']['record_to_beat_bytes']}` bytes -* Runtime: `{registry['resource_envelope']['max_hours_formula']}` -* RAM: `<={registry['resource_envelope']['max_ram_gb']}GB` -* Temporary HDD: `<={registry['resource_envelope']['max_temp_hdd_gb']}GB` -* GPU allowed: `{registry['resource_envelope']['gpu_allowed']}` - -!! Links - -* [[Godel Gauntlet Safety Condition Probe]] -* [[Godel Gauntlet Race Condition Probe]] -* [[Hutter Multidimensional Causal Chain]] -* [[Underverse Variant Accounting]] -* [[Phonon Music Logogram Layer]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "roadmap_root": receipt["roadmap_root"], - "decision": receipt["decision"], - "current_v5_decision": receipt["current_v5_decision"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hutter_torsion_clock_adaptation_probe.py b/4-Infrastructure/shim/hutter_torsion_clock_adaptation_probe.py deleted file mode 100644 index 154a9149..00000000 --- a/4-Infrastructure/shim/hutter_torsion_clock_adaptation_probe.py +++ /dev/null @@ -1,365 +0,0 @@ -#!/usr/bin/env python3 -"""Adapt torsion-clock witness geometry back into Hutter/logogram research. - -The mechanical model says wall-clock time is a shadow and accumulated torsion is -the causal state coordinate. In the Hutter/logogram setting, the analogue is -codec torsion: accumulated byte debt, dictionary debt, receipt overhead, -provenance uncertainty, baseline gap, and route coupling. - -This probe does not change the codec. It defines a receipt-state atlas for the -existing Hutter ladder so "near compression" cannot masquerade as finite byte -admission. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" -REGISTRY = OUT_DIR / "hutter_torsion_clock_adaptation_registry.json" -RECEIPT = OUT_DIR / "hutter_torsion_clock_adaptation_receipt.json" -SUMMARY = OUT_DIR / "hutter_torsion_clock_adaptation.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Torsion Clock Adaptation.tid" - -TORSION_ERGOREGION_THRESHOLD = 1000 -TORSION_HORIZON_THRESHOLD = 5000 - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" / "kerr_like_load_witness_geometry_receipt.json", - REPO / "shared-data" / "data" / "asymptotic_closure_horizon" / "asymptotic_closure_horizon_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_receipt_aggregation_probe" / "enwiki9_logogram_receipt_aggregation_probe_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_dictionary_amortization_probe" / "enwiki9_logogram_dictionary_amortization_probe_receipt.json", - REPO / "shared-data" / "data" / "enwiki9_logogram_canonical_baseline_probe" / "enwiki9_logogram_canonical_baseline_probe_receipt.json", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Hutter Prize Compression.tid", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Merkle Tensegrity Load Equation Harness.tid", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def rung( - *, - rung_id: str, - gate: str, - claim: str, - replay_fail: bool = False, - provenance_debt: int = 0, - packet_debt: int = 0, - dictionary_debt: int = 0, - baseline_debt: int = 0, - receipt_debt: int = 0, - route_coupling: int = 0, - finite_byte_pass: bool = False, -) -> dict[str, Any]: - torsion = ( - provenance_debt - + packet_debt - + dictionary_debt - + baseline_debt - + receipt_debt - + route_coupling - + (TORSION_HORIZON_THRESHOLD if replay_fail else 0) - ) - if replay_fail: - decision = "REJECT_REPLAY_HORIZON" - elif finite_byte_pass and torsion < TORSION_ERGOREGION_THRESHOLD: - decision = "ADMIT_FINITE_BYTE_CHART" - elif torsion >= TORSION_HORIZON_THRESHOLD: - decision = "HOLD_HUTTER_FAILURE_HORIZON" - elif torsion >= TORSION_ERGOREGION_THRESHOLD: - decision = "HOLD_HUTTER_ERGOREGION" - else: - decision = "HOLD_INCOMPLETE_FINITE_GATE" - item = { - "rung_id": rung_id, - "gate": gate, - "claim": claim, - "codec_torsion": torsion, - "torsion_components": { - "replay_fail_horizon": TORSION_HORIZON_THRESHOLD if replay_fail else 0, - "provenance_debt": provenance_debt, - "packet_debt": packet_debt, - "dictionary_debt": dictionary_debt, - "baseline_debt": baseline_debt, - "receipt_debt": receipt_debt, - "route_coupling": route_coupling, - }, - "finite_byte_pass": finite_byte_pass, - "decision": decision, - } - item["rung_hash"] = hash_obj({k: v for k, v in item.items() if k != "rung_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - rungs = [ - rung( - rung_id="v1_replay_core_expands", - gate="PASS", - claim="byte replay closes but core expands", - packet_debt=2400, - receipt_debt=700, - finite_byte_pass=False, - ), - rung( - rung_id="v2_core_shrinks_packet_expands", - gate="PASS_ADD", - claim="core shrinks but receipt/packet overhead remains active", - packet_debt=1600, - receipt_debt=900, - finite_byte_pass=False, - ), - rung( - rung_id="v3_packet_shrinks_global_holds", - gate="PASS_ADD_PAUSE_SUBTRACT", - claim="packet shrink appears; dictionary debt keeps global in HOLD", - dictionary_debt=1064, - route_coupling=250, - finite_byte_pass=False, - ), - rung( - rung_id="v4_fixture_global_shrinks", - gate="PASS_ADD_PAUSE_SUBTRACT", - claim="fixture-level global shrink appears; provenance remains noncanonical", - provenance_debt=1800, - baseline_debt=1200, - finite_byte_pass=False, - ), - rung( - rung_id="v5_canonical_slice_baseline", - gate="PROVENANCE_PASS_ADD_PAUSE_SUBTRACT_BASELINE", - claim="canonical slice and baseline gate; codec remains frozen", - provenance_debt=0, - baseline_debt=2800, - route_coupling=600, - finite_byte_pass=False, - ), - rung( - rung_id="future_canonical_baseline_candidate", - gate="PROVENANCE_PASS_ADD_PAUSE_SUBTRACT_BASELINE", - claim="target state: canonical replay, global positive, baseline not worse", - provenance_debt=0, - packet_debt=0, - dictionary_debt=0, - baseline_debt=0, - receipt_debt=250, - route_coupling=100, - finite_byte_pass=True, - ), - ] - return { - "schema": "hutter_torsion_clock_adaptation_registry_v1", - "claim_boundary": ( - "Hutter torsion-clock adaptation only. It maps mechanical torsion-clock " - "witness geometry into codec/accounting state advance. It does not change " - "the encoder, does not claim Hutter-score competitiveness, and does not " - "replace canonical byte-count gates." - ), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "time_substitution": { - "mechanical_source": "wall-clock time is a shadow; torsion is the causal state coordinate", - "hutter_adaptation": "elapsed research time is a shadow; codec torsion is accumulated unresolved byte/accounting debt", - "codec_torsion": "T_H = replay_horizon + provenance_debt + packet_debt + dictionary_debt + baseline_debt + receipt_debt + route_coupling", - "hash_policy": "generated_at_utc remains metadata_only; codec_torsion participates in registry hash", - }, - "state_atlas": { - "safe_chart": "finite replay, counted packet/global bytes, canonical provenance, and baseline gate close", - "ergoregion": "packet/global improvement is visible, but some static assumption fails: provenance, amortization, or baseline", - "horizon": "replay/provenance/baseline failure is large enough that the route cannot be certified as a candidate", - "frame_dragging": "changing dictionary, protocol, or target class while measuring drags neighboring deltas and contaminates attribution", - "geodesic": "shortest frozen-codec route from fixture evidence to canonical baseline evidence", - }, - "admissibility_equation": ( - "A_H=1[exact_replay] * 1[canonical_or_fixture_label_truthful] * " - "1[delta_packet>0] * 1[delta_global>0] * 1[baseline_gate_closed] * " - "1[T_H < ergoregion_threshold]" - ), - "avoid": [ - "counting elapsed wall-clock effort as progress", - "treating near-zero global delta as finite admission", - "changing codec and accounting scope in the same receipt", - "optimizing dictionary from non-target language fixtures before canonical enwik9 failure analysis", - "using citation or model confidence as a substitute for byte replay", - ], - "rungs": rungs, - "aggregates": { - "rung_count": len(rungs), - "admit_count": sum(1 for item in rungs if item["decision"] == "ADMIT_FINITE_BYTE_CHART"), - "ergoregion_hold_count": sum(1 for item in rungs if item["decision"] == "HOLD_HUTTER_ERGOREGION"), - "horizon_hold_count": sum(1 for item in rungs if item["decision"] == "HOLD_HUTTER_FAILURE_HORIZON"), - "reject_count": sum(1 for item in rungs if item["decision"] == "REJECT_REPLAY_HORIZON"), - "ergoregion_threshold": TORSION_ERGOREGION_THRESHOLD, - "horizon_threshold": TORSION_HORIZON_THRESHOLD, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "hutter_torsion_clock_adaptation_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "aggregates": registry["aggregates"], - "decision": "ADMIT_HUTTER_TORSION_CLOCK_ACCOUNTING_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Hutter Torsion-Clock Adaptation", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Time Substitution", - "", - f"- Mechanical source: {registry['time_substitution']['mechanical_source']}", - f"- Hutter adaptation: {registry['time_substitution']['hutter_adaptation']}", - f"- Codec torsion: `{registry['time_substitution']['codec_torsion']}`", - f"- Hash policy: `{registry['time_substitution']['hash_policy']}`", - "", - "## State Atlas", - "", - ] - for key, value in registry["state_atlas"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Admissibility", - "", - f"`{registry['admissibility_equation']}`", - "", - "## Rungs", - "", - "| Rung | Gate | Codec torsion | Decision |", - "|---|---|---:|---|", - ] - ) - for item in registry["rungs"]: - lines.append(f"| `{item['rung_id']}` | `{item['gate']}` | {item['codec_torsion']} | `{item['decision']}` |") - lines.extend(["", "## Avoid", ""]) - for item in registry["avoid"]: - lines.append(f"- {item}") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter Compression Receipt TorsionClock -title: Hutter Torsion Clock Adaptation -type: text/vnd.tiddlywiki - -! Hutter Torsion Clock Adaptation - -Durable runner: - -``` -4-Infrastructure/shim/hutter_torsion_clock_adaptation_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -Elapsed research time is a shadow. Codec torsion is accumulated unresolved byte/accounting debt: - -``` -T_H = replay_horizon + provenance_debt + packet_debt + dictionary_debt + baseline_debt + receipt_debt + route_coupling -``` - -Near compression remains HOLD until finite replay, packet/global, provenance, and baseline gates close. - -!! Links - -* [[Hutter Prize Compression]] -* [[Merkle Tensegrity Load Equation Harness]] -* [[Kerr-Like Load Witness Geometry]] -* [[Asymptotic Closure Horizon]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/hutter_transfer_readiness_fixture.py b/4-Infrastructure/shim/hutter_transfer_readiness_fixture.py deleted file mode 100644 index 6406f82b..00000000 --- a/4-Infrastructure/shim/hutter_transfer_readiness_fixture.py +++ /dev/null @@ -1,263 +0,0 @@ -#!/usr/bin/env python3 -"""Emit the Agent 4 Hutter transfer-readiness fixture manifest. - -This is a fixture gate, not a benchmark harness. It records byte provenance, -stdlib baselines, OISC replay size, and negative controls before any later -spectral run is allowed to read the fixture. -""" - -from __future__ import annotations - -import hashlib -import json -import lzma -import zlib -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -DATA_DIR = ROOT / "shared-data" / "data" / "stack_solidification" -FIXTURE_DIR = DATA_DIR / "fixtures" -DOC_DIR = ROOT / "6-Documentation" / "docs" -TIDDLER_DIR = ROOT / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" - -FIXTURE_SOURCE = FIXTURE_DIR / "hutter_transfer_readiness_fixture.txt" -MANIFEST_PATH = DATA_DIR / "hutter_transfer_readiness_fixture_manifest.json" -DOC_PATH = DOC_DIR / "hutter_transfer_readiness_fixture_manifest_2026-05-09.md" -TIDDLER_PATH = TIDDLER_DIR / "Hutter Transfer Readiness Fixture.tid" - -FIXTURE_TEXT = """Hutter transfer readiness fixture. - -This small corpus window is intentionally local and inspectable. It carries -repeated phrases, shifted byte contexts, punctuation, digits 0123456789, and -line breaks so replay code must preserve more than a three-symbol toy stream. - -Window alpha binds local recurrence: stack stack stack, receipt receipt receipt, -offset offset offset. Window beta changes cadence with JSON-ish tokens: -{"decision":"HOLD","window":128,"route":"oisc-replay"}. - -Window gamma adds mixed case and separators: Alpha/BETA/gamma; phase_00, -phase_01, phase_02. The fixture stops here because the readiness gate is about -manifest discipline, not corpus scale. -""" - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def encode_oisc(data: bytes) -> bytes: - """Encode bytes with the Rust OiscCompressor fixture wire policy. - - The replay instruction format carries a residual byte, but the reference - Rust compressor currently emits zero residuals for fixture replay. Non-zero - residual policy remains a later HOLD item. - """ - - wire = bytearray(b"OISC\x01") - for idx, symbol in enumerate(data): - wire.append(symbol) - wire.append(0) - wire.append(1 if idx + 1 == len(data) else 0) - return bytes(wire) - - -def baseline_matrix(data: bytes) -> dict[str, Any]: - oisc_wire = encode_oisc(data) - return { - "raw": {"byte_length": len(data), "sha256": sha256_bytes(data)}, - "zlib": {"level": 9, "byte_length": len(zlib.compress(data, 9))}, - "lzma": { - "preset": 6, - "byte_length": len(lzma.compress(data, preset=6, check=lzma.CHECK_CRC64)), - }, - "current_wire": { - "codec": "oisc-replay-v0.1", - "byte_length": len(oisc_wire), - "sha256": sha256_bytes(oisc_wire), - }, - } - - -def window_receipts(data: bytes) -> list[dict[str, Any]]: - spans = [(0, 128), (128, 256), (384, 192)] - receipts = [] - for idx, (offset, size) in enumerate(spans): - chunk = data[offset : offset + size] - receipts.append( - { - "window_id": f"htrf_win_{idx:02d}", - "offset": offset, - "window_size": size, - "actual_byte_length": len(chunk), - "sha256": sha256_bytes(chunk), - "baseline_matrix": baseline_matrix(chunk), - } - ) - return receipts - - -def negative_controls(data: bytes) -> list[dict[str, Any]]: - controls = [ - ("reverse_bytes", data[::-1], "order inversion control"), - ("zero_bytes", bytes(len(data)), "symbol erasure control"), - ("stride_permutation", data[1::2] + data[0::2], "local adjacency break control"), - ] - return [ - { - "control_id": control_id, - "purpose": purpose, - "byte_length": len(payload), - "sha256": sha256_bytes(payload), - "baseline_matrix": baseline_matrix(payload), - "decision": "HOLD", - } - for control_id, payload, purpose in controls - ] - - -def build_manifest() -> dict[str, Any]: - FIXTURE_DIR.mkdir(parents=True, exist_ok=True) - fixture_bytes = FIXTURE_TEXT.encode("utf-8") - FIXTURE_SOURCE.write_bytes(fixture_bytes) - - return { - "schema": "hutter_transfer_readiness_fixture_manifest_v0", - "agent": "Agent 4: Hutter Transfer Readiness", - "fixture_id": "htrf_local_text_window_2026_05_09_v0", - "source_path": str(FIXTURE_SOURCE.relative_to(ROOT)), - "byte_length": len(fixture_bytes), - "sha256": sha256_bytes(fixture_bytes), - "offsets_and_window_sizes": window_receipts(fixture_bytes), - "decision_boundary": { - "allowed_decisions": ["ADMIT_FIXTURE", "HOLD", "QUARANTINE"], - "admit_fixture_requires": [ - "fixture_id", - "source_path", - "byte_length", - "sha256", - "offsets_and_window_sizes", - "raw_zlib_lzma_current_wire_baseline_matrix", - "negative_controls", - "byte_exact_oisc_replay_fixture", - ], - "hold_when": [ - "baseline matrix is incomplete", - "negative controls are absent", - "OISC replay fixture has not passed byte-exact replay", - "later spectral run is requested before control registration", - ], - "quarantine_when": [ - "fixture bytes do not match recorded sha256", - "claim text exceeds fixture-readiness scope", - "decompressor replay is not byte exact", - ], - }, - "baseline_matrix": baseline_matrix(fixture_bytes), - "negative_controls": negative_controls(fixture_bytes), - "oisc_replay_fixture": { - "rust_test": "non_toy_transfer_fixture_replays_byte_exact", - "source": "5-Applications/compression-core/src/oisc.rs", - "expected_decision": "ADMIT_FIXTURE", - "wire_codec": "oisc-replay-v0.1", - "wire_byte_length": len(encode_oisc(fixture_bytes)), - "wire_sha256": sha256_bytes(encode_oisc(fixture_bytes)), - }, - "eigenmass_readiness": { - "decision": "HOLD", - "reason": "fixture admission has byte provenance, negative controls, and byte-exact Rust OISC replay; fixture-level eigenmass remains HOLD until residual policy and control accounting close", - }, - "decision": "ADMIT_FIXTURE", - } - - -def write_markdown(manifest: dict[str, Any]) -> None: - DOC_DIR.mkdir(parents=True, exist_ok=True) - DOC_PATH.write_text( - "\n".join( - [ - "# Hutter Transfer Readiness Fixture Manifest", - "", - "Decision: `ADMIT_FIXTURE`", - "", - "This is a small fixture-readiness manifest. It records byte provenance,", - "window receipts, baseline sizes, negative controls, and OISC replay", - "requirements before any later spectral analysis can be considered.", - "", - "## Fixture", - "", - f"- Fixture id: `{manifest['fixture_id']}`", - f"- Source path: `{manifest['source_path']}`", - f"- Byte length: `{manifest['byte_length']}`", - f"- SHA-256: `{manifest['sha256']}`", - "", - "## Baseline Matrix", - "", - "| route | bytes | note |", - "| --- | ---: | --- |", - f"| raw | {manifest['baseline_matrix']['raw']['byte_length']} | source bytes |", - f"| zlib | {manifest['baseline_matrix']['zlib']['byte_length']} | stdlib level 9 |", - f"| lzma | {manifest['baseline_matrix']['lzma']['byte_length']} | stdlib preset 6 |", - f"| current-wire | {manifest['baseline_matrix']['current_wire']['byte_length']} | OISC replay wire |", - "", - "## Controls", - "", - "Negative controls are registered as `HOLD` before any later result gate.", - "", - "## Receipt", - "", - f"- Manifest: `{MANIFEST_PATH.relative_to(ROOT)}`", - f"- OISC Rust test: `{manifest['oisc_replay_fixture']['rust_test']}`", - "", - ] - ), - encoding="utf-8", - ) - - -def write_tiddler(manifest: dict[str, Any]) -> None: - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_PATH.write_text( - "\n".join( - [ - "created: 20260509235900000", - "modified: 20260509235900000", - "tags: ResearchStack Hutter Compression Fixture", - "title: Hutter Transfer Readiness Fixture", - "type: text/vnd.tiddlywiki", - "", - "! Hutter Transfer Readiness Fixture", - "", - f"* Decision: `ADMIT_FIXTURE`", - f"* Fixture id: `{manifest['fixture_id']}`", - f"* Source: `{manifest['source_path']}`", - f"* Byte length: `{manifest['byte_length']}`", - f"* SHA-256: `{manifest['sha256']}`", - f"* Manifest: `{MANIFEST_PATH.relative_to(ROOT)}`", - "", - "!! Boundary", - "", - "Allowed decisions are `ADMIT_FIXTURE`, `HOLD`, and `QUARANTINE`.", - "This tiddler admits only the local fixture manifest and keeps later", - "eigenmass or spectral analysis on `HOLD` until residual policy and", - "control accounting close. Negative controls and byte-exact Rust OISC", - "replay are registered here for fixture admission only.", - "", - ] - ), - encoding="utf-8", - ) - - -def main() -> None: - manifest = build_manifest() - DATA_DIR.mkdir(parents=True, exist_ok=True) - MANIFEST_PATH.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_markdown(manifest) - write_tiddler(manifest) - print(json.dumps({"decision": manifest["decision"], "manifest": str(MANIFEST_PATH)}, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/hyper_heuristic_orchestrator.py b/4-Infrastructure/shim/hyper_heuristic_orchestrator.py deleted file mode 100644 index e3ee4060..00000000 --- a/4-Infrastructure/shim/hyper_heuristic_orchestrator.py +++ /dev/null @@ -1,940 +0,0 @@ -#!/usr/bin/env python3 -""" -Hyper-Heuristic Orchestrator for Research Stack - -Meta-optimization layer that selects between low-level heuristics across -multiple components: FAMM delay optimization, PIST move selection, and -infrastructure shim selection. - -Core idea: Instead of directly solving problems, the hyper-heuristic selects -which low-level heuristic to apply based on current state, performance metrics, -and historical patterns. -""" - -import json -import time -from enum import Enum -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Tuple, Any -from collections import defaultdict -import random - - -class HeuristicType(Enum): - """Types of low-level heuristics that can be selected.""" - GREEDY = "greedy" - BALANCED = "balanced" - CONSERVATIVE = "conservative" - ADAPTIVE = "adaptive" - RANDOM = "random" - - -class ComponentType(Enum): - """Components that can use hyper-heuristic optimization.""" - FAMM_DELAY = "famm_delay" - PIST_MOVE = "pist_move" - SHIM_SELECTION = "shim_selection" - GPU_SCHEDULING = "gpu_scheduling" - FPGA_BUILD = "fpga_build" - - -@dataclass -class HeuristicMetrics: - """Performance metrics for heuristic evaluation.""" - success_rate: float = 0.0 - avg_cost: float = 0.0 - avg_time: float = 0.0 - total_runs: int = 0 - last_reward: float = 0.0 - - def update(self, success: bool, cost: float, exec_time: float, reward: float): - """Update metrics with new execution result.""" - self.total_runs += 1 - - # Exponential moving average for metrics - alpha = 0.1 - self.success_rate = alpha * (1.0 if success else 0.0) + (1 - alpha) * self.success_rate - self.avg_cost = alpha * cost + (1 - alpha) * self.avg_cost - self.avg_time = alpha * exec_time + (1 - alpha) * self.avg_time - self.last_reward = reward - - def score(self) -> float: - """Compute composite score (higher is better).""" - # Balance success rate, cost, and time - return self.success_rate * 100 - self.avg_cost - self.avg_time * 10 + self.last_reward - - -@dataclass -class HyperHeuristicState: - """State of the hyper-heuristic for a specific component.""" - component: ComponentType - current_heuristic: HeuristicType - metrics: Dict[HeuristicType, HeuristicMetrics] = field(default_factory=dict) - switch_count: int = 0 - total_operations: int = 0 - exploration_rate: float = 0.1 # Epsilon-greedy exploration - - def __post_init__(self): - """Initialize metrics for all heuristic types.""" - for ht in HeuristicType: - if ht not in self.metrics: - self.metrics[ht] = HeuristicMetrics() - - def select_heuristic(self) -> HeuristicType: - """Select heuristic using epsilon-greedy strategy.""" - if random.random() < self.exploration_rate: - # Explore: random selection - return random.choice(list(HeuristicType)) - else: - # Exploit: select best performing heuristic - best_heuristic = self.current_heuristic - best_score = self.metrics[best_heuristic].score() - - for ht, metrics in self.metrics.items(): - if metrics.score() > best_score and metrics.total_runs > 0: - best_score = metrics.score() - best_heuristic = ht - - return best_heuristic - - def should_switch(self, candidate: HeuristicType) -> bool: - """Determine if we should switch to candidate heuristic.""" - if candidate == self.current_heuristic: - return False - - current_score = self.metrics[self.current_heuristic].score() - candidate_score = self.metrics[candidate].score() - - # Switch if candidate is significantly better (10% threshold) - if candidate_score > current_score * 1.1 and self.metrics[candidate].total_runs > 5: - return True - - return False - - def update(self, heuristic: HeuristicType, success: bool, cost: float, - exec_time: float, reward: float): - """Update state after heuristic execution.""" - self.metrics[heuristic].update(success, cost, exec_time, reward) - self.total_operations += 1 - - # Decay exploration rate over time - self.exploration_rate = max(0.01, self.exploration_rate * 0.9995) - - -class HyperHeuristicOrchestrator: - """Main orchestrator managing hyper-heuristics across components.""" - - def __init__(self): - self.states: Dict[ComponentType, HyperHeuristicState] = {} - self.global_metrics: Dict[str, Any] = defaultdict(list) - - def get_state(self, component: ComponentType) -> HyperHeuristicState: - """Get or create hyper-heuristic state for component.""" - if component not in self.states: - default_heuristic = HeuristicType.ADAPTIVE - self.states[component] = HyperHeuristicState( - component=component, - current_heuristic=default_heuristic - ) - return self.states[component] - - def select_and_execute(self, component: ComponentType, - heuristic_func: callable, - context: Dict[str, Any]) -> Tuple[Any, HeuristicType]: - """ - Select heuristic and execute function. - - Args: - component: Component type - heuristic_func: Function that takes (heuristic_type, context) and returns result - context: Execution context - - Returns: - (result, heuristic_used) - """ - state = self.get_state(component) - heuristic = state.select_heuristic() - - # Check if we should switch - if state.should_switch(heuristic): - state.current_heuristic = heuristic - state.switch_count += 1 - - # Execute with timing - start_time = time.time() - try: - result = heuristic_func(heuristic, context) - if isinstance(result, dict): - success = bool(result.get('success', True)) - cost = float(result.get('cost', 0.0 if success else 100.0)) - reward = float(result.get('reward', 1.0 if success else -10.0)) - else: - success = True - cost = 0.0 - reward = 1.0 - except Exception as e: - result = None - success = False - cost = 100.0 # Penalty for failure - reward = -10.0 - print(f"Heuristic execution failed: {e}") - - exec_time = time.time() - start_time - - # Update state - state.update(heuristic, success, cost, exec_time, reward) - - # Track global metrics - self.global_metrics[f"{component.value}_{heuristic.value}"].append({ - 'success': success, - 'cost': cost, - 'time': exec_time, - 'reward': reward - }) - - return result, heuristic - - def get_performance_report(self) -> Dict[str, Any]: - """Generate performance report for all components.""" - report = { - 'components': {}, - 'global_stats': {} - } - - for component, state in self.states.items(): - component_report = { - 'current_heuristic': state.current_heuristic.value, - 'switch_count': state.switch_count, - 'total_operations': state.total_operations, - 'exploration_rate': state.exploration_rate, - 'heuristic_metrics': {} - } - - for ht, metrics in state.metrics.items(): - component_report['heuristic_metrics'][ht.value] = { - 'success_rate': metrics.success_rate, - 'avg_cost': metrics.avg_cost, - 'avg_time': metrics.avg_time, - 'total_runs': metrics.total_runs, - 'score': metrics.score() - } - - report['components'][component.value] = component_report - - # Global statistics - total_ops = sum(s.total_operations for s in self.states.values()) - total_switches = sum(s.switch_count for s in self.states.values()) - report['global_stats'] = { - 'total_operations': total_ops, - 'total_switches': total_switches, - 'switch_rate': total_switches / max(1, total_ops) - } - - return report - - def save_state(self, filepath: str): - """Save hyper-heuristic state to file.""" - state_data = { - 'states': {}, - 'global_metrics': dict(self.global_metrics) - } - - for component, state in self.states.items(): - state_data['states'][component.value] = { - 'current_heuristic': state.current_heuristic.value, - 'metrics': { - ht.value: { - 'success_rate': m.success_rate, - 'avg_cost': m.avg_cost, - 'avg_time': m.avg_time, - 'total_runs': m.total_runs, - 'last_reward': m.last_reward - } - for ht, m in state.metrics.items() - }, - 'switch_count': state.switch_count, - 'total_operations': state.total_operations, - 'exploration_rate': state.exploration_rate - } - - with open(filepath, 'w') as f: - json.dump(state_data, f, indent=2) - - def load_state(self, filepath: str): - """Load hyper-heuristic state from file.""" - with open(filepath, 'r') as f: - state_data = json.load(f) - - for component_str, component_state in state_data['states'].items(): - component = ComponentType(component_str) - current_heuristic = HeuristicType(component_state['current_heuristic']) - - metrics = {} - for ht_str, m_data in component_state['metrics'].items(): - ht = HeuristicType(ht_str) - metrics[ht] = HeuristicMetrics( - success_rate=m_data['success_rate'], - avg_cost=m_data['avg_cost'], - avg_time=m_data['avg_time'], - total_runs=m_data['total_runs'], - last_reward=m_data['last_reward'] - ) - - self.states[component] = HyperHeuristicState( - component=component, - current_heuristic=current_heuristic, - metrics=metrics, - switch_count=component_state['switch_count'], - total_operations=component_state['total_operations'], - exploration_rate=component_state['exploration_rate'] - ) - - self.global_metrics = defaultdict(list, state_data['global_metrics']) - - -# FAMM-specific hyper-heuristics -class FAMMHyperHeuristics: - """FAMM delay optimization hyper-heuristics.""" - - @staticmethod - def greedy_minimize(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Greedy delay minimization - always minimize delay.""" - bank = context.get('bank', {}) - address = context.get('address', 0) - target_delay = context.get('target_delay', 1.0) - max_delay = context.get('max_delay', 10.0) - - adjusted_delay = min(target_delay, max_delay) - - return { - 'strategy': 'greedy_minimize', - 'adjusted_delay': adjusted_delay, - 'success': adjusted_delay <= max_delay - } - - @staticmethod - def frustration_balance(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Balance competing delays to minimize frustration.""" - bank = context.get('bank', {}) - address = context.get('address', 0) - target_delay = context.get('target_delay', 1.0) - max_delay = context.get('max_delay', 10.0) - - # Simulate frustration balancing by averaging with existing delay - current_delay = bank.get('cells', {}).get(address, {}).get('delay', target_delay) - adjusted_delay = min((current_delay + target_delay) / 2, max_delay) - - return { - 'strategy': 'frustration_balance', - 'adjusted_delay': adjusted_delay, - 'success': adjusted_delay <= max_delay - } - - @staticmethod - def adaptive_weight(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Adaptive weighting based on delay mass and weight.""" - bank = context.get('bank', {}) - address = context.get('address', 0) - target_delay = context.get('target_delay', 1.0) - max_delay = context.get('max_delay', 10.0) - - cell = bank.get('cells', {}).get(address, {}) - delay_weight = cell.get('delay_weight', 1.0) - weight = delay_weight / (delay_weight + 1.0) - current_delay = cell.get('delay', target_delay) - - adjusted_delay = min(current_delay * weight + target_delay * (1 - weight), max_delay) - - return { - 'strategy': 'adaptive_weight', - 'adjusted_delay': adjusted_delay, - 'success': adjusted_delay <= max_delay - } - - -# PIST-specific hyper-heuristics -class PISTHyperHeuristics: - """PIST move selection hyper-heuristics.""" - - @staticmethod - def linear_move(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Select linear move (step within shell).""" - pos = context.get('pos', {'k': 0, 't': 0}) - k = pos.get('k', 0) - t = pos.get('t', 0) - - # Linear step: increment or decrement t - new_t = t + 1 if t < 2 * k else t - 1 - - return { - 'strategy': 'linear', - 'move_type': 'linearStep', - 'new_pos': {'k': k, 't': new_t}, - 'success': True - } - - @staticmethod - def resonance_jump(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Select resonance jump (preserve mass).""" - pos = context.get('pos', {'k': 0, 't': 0}) - k = pos.get('k', 0) - t = pos.get('t', 0) - - # Mirror position to preserve mass - new_t = 2 * k + 1 - t - - return { - 'strategy': 'resonance', - 'move_type': 'resonanceJump', - 'new_pos': {'k': k, 't': new_t}, - 'success': True - } - - @staticmethod - def adaptive_move(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Adaptive move selection based on phase.""" - pos = context.get('pos', {'k': 0, 't': 0}) - phase = context.get('phase', 'grounded') - - if phase == 'seismic': - # Use resonance jumps for seismic phase - return PISTHyperHeuristics.resonance_jump(heuristic, context) - else: - # Use linear moves for grounded phase - return PISTHyperHeuristics.linear_move(heuristic, context) - - -# Infrastructure shim selection hyper-heuristics -class ShimSelectionHyperHeuristics: - """Infrastructure shim selection hyper-heuristics.""" - - @staticmethod - def select_by_domain(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Select shim based on problem domain.""" - domain = context.get('domain', 'general') - task_type = context.get('task_type', 'unknown') - - # Domain-based shim mapping - shim_map = { - 'math': ['math_prover_prior_metaprobe.py', 'intense_math_modeling_router.py'], - 'compression': ['compression_signal_shaping_synthesis.py', 'semantic_compression_theoretical_limits_prior.py'], - 'hardware': ['tang9k_uart_beacon_probe.py', 'fpga_nanokernel_rrc_analysis.py'], - 'general': ['stack_solidification_audit.py', 'parallel_metaprobe_launcher.py'] - } - - candidates = shim_map.get(domain, shim_map['general']) - selected = candidates[0] if candidates else 'default_shim.py' - - return { - 'strategy': 'domain_based', - 'selected_shim': selected, - 'candidates': candidates, - 'success': True - } - - @staticmethod - def select_by_performance(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Select shim based on historical performance.""" - domain = context.get('domain', 'general') - performance_history = context.get('performance_history', {}) - - # Select best performing shim for domain - if domain in performance_history: - best_shim = max(performance_history[domain].items(), key=lambda x: x[1])[0] - else: - best_shim = 'default_shim.py' - - return { - 'strategy': 'performance_based', - 'selected_shim': best_shim, - 'success': True - } - - @staticmethod - def select_adaptive(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Adaptive shim selection combining domain and performance.""" - domain = context.get('domain', 'general') - performance_history = context.get('performance_history', {}) - - # Try performance-based first, fall back to domain-based - if domain in performance_history and performance_history[domain]: - return ShimSelectionHyperHeuristics.select_by_performance(heuristic, context) - else: - return ShimSelectionHyperHeuristics.select_by_domain(heuristic, context) - - -# GPU scheduling hyper-heuristics -class GPUSchedulingHyperHeuristics: - """GPU task scheduling hyper-heuristics for RTX 4070.""" - - @staticmethod - def round_robin(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Round-robin task scheduling across GPU resources.""" - task_queue = context.get('task_queue', []) - gpu_count = context.get('gpu_count', 1) - current_gpu = context.get('current_gpu', 0) - - if not task_queue: - return {'strategy': 'round_robin', 'assigned': [], 'success': True} - - # Assign tasks in round-robin fashion - assigned = [] - for i, task in enumerate(task_queue): - gpu_id = (current_gpu + i) % gpu_count - assigned.append({'task': task, 'gpu_id': gpu_id}) - - return { - 'strategy': 'round_robin', - 'assigned': assigned, - 'next_gpu': (current_gpu + len(task_queue)) % gpu_count, - 'success': True - } - - @staticmethod - def priority_based(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Priority-based scheduling considering task urgency and resource requirements.""" - task_queue = context.get('task_queue', []) - gpu_memory = context.get('gpu_memory', 12000) # RTX 4070: ~12GB - gpu_count = context.get('gpu_count', 1) - - # Sort by priority (higher priority first) - sorted_tasks = sorted(task_queue, key=lambda t: t.get('priority', 0), reverse=True) - - assigned = [] - memory_used = 0 - - for task in sorted_tasks: - task_memory = task.get('memory_required', 1000) - - # Find GPU with sufficient memory - if memory_used + task_memory <= gpu_memory: - assigned.append({'task': task, 'gpu_id': 0, 'reason': 'memory_available'}) - memory_used += task_memory - else: - assigned.append({'task': task, 'gpu_id': None, 'reason': 'insufficient_memory'}) - - return { - 'strategy': 'priority_based', - 'assigned': assigned, - 'memory_utilization': memory_used / gpu_memory, - 'success': memory_used <= gpu_memory - } - - @staticmethod - def load_balancing(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Load-aware balancing across GPU resources.""" - task_queue = context.get('task_queue', []) - gpu_states = context.get('gpu_states', [{'load': 0.5, 'memory': 6000}]) - - assigned = [] - - for task in enumerate(task_queue): - # Select least loaded GPU - best_gpu = min(range(len(gpu_states)), key=lambda i: gpu_states[i]['load']) - - task_load = task[1].get('estimated_load', 0.1) - gpu_states[best_gpu]['load'] += task_load - - assigned.append({ - 'task': task[1], - 'gpu_id': best_gpu, - 'gpu_load_before': gpu_states[best_gpu]['load'] - task_load, - 'gpu_load_after': gpu_states[best_gpu]['load'] - }) - - return { - 'strategy': 'load_balancing', - 'assigned': assigned, - 'final_gpu_states': gpu_states, - 'success': True - } - - @staticmethod - def memory_aware(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Memory-aware scheduling for large model workloads.""" - task_queue = context.get('task_queue', []) - gpu_memory_total = context.get('gpu_memory', 12000) - current_usage = context.get('current_memory_usage', 0) - - # Group tasks by memory requirements - large_tasks = [t for t in task_queue if t.get('memory_required', 0) > 4000] - small_tasks = [t for t in task_queue if t.get('memory_required', 0) <= 4000] - - assigned = [] - memory_used = current_usage - - # Schedule large tasks first (they're harder to place) - for task in large_tasks: - task_memory = task.get('memory_required', 5000) - if memory_used + task_memory <= gpu_memory_total: - assigned.append({'task': task, 'gpu_id': 0, 'reason': 'large_task_fit'}) - memory_used += task_memory - else: - assigned.append({'task': task, 'gpu_id': None, 'reason': 'insufficient_memory'}) - - # Fill remaining space with small tasks - for task in small_tasks: - task_memory = task.get('memory_required', 1000) - if memory_used + task_memory <= gpu_memory_total: - assigned.append({'task': task, 'gpu_id': 0, 'reason': 'small_task_fill'}) - memory_used += task_memory - else: - assigned.append({'task': task, 'gpu_id': None, 'reason': 'insufficient_memory'}) - - return { - 'strategy': 'memory_aware', - 'assigned': assigned, - 'memory_utilization': memory_used / gpu_memory_total, - 'large_tasks_scheduled': len([a for a in assigned if a['gpu_id'] is not None and a['task'].get('memory_required', 0) > 4000]), - 'success': memory_used <= gpu_memory_total - } - - -# FPGA build optimization hyper-heuristics -class FPGABuildHyperHeuristics: - """FPGA build and simulation optimization hyper-heuristics.""" - - @staticmethod - def incremental_build(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Incremental build strategy - only rebuild changed modules.""" - changed_files = context.get('changed_files', []) - build_cache = context.get('build_cache', {}) - - modules_to_rebuild = [] - cache_hits = [] - - for file in changed_files: - if file in build_cache: - cache_hits.append(file) - else: - modules_to_rebuild.append(file) - - return { - 'strategy': 'incremental_build', - 'modules_to_rebuild': modules_to_rebuild, - 'cache_hits': cache_hits, - 'estimated_speedup': len(cache_hits) / max(1, len(changed_files)), - 'success': True - } - - @staticmethod - def parallel_synthesis(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Parallel synthesis strategy for independent modules.""" - modules = context.get('modules', []) - dependency_graph = context.get('dependency_graph', {}) - available_cores = context.get('available_cores', 4) - - # Identify independent modules (no dependencies) - independent_modules = [] - dependent_modules = [] - - for module in modules: - deps = dependency_graph.get(module, []) - if not deps or all(dep not in modules for dep in deps): - independent_modules.append(module) - else: - dependent_modules.append(module) - - # Schedule independent modules in parallel - parallel_slots = min(len(independent_modules), available_cores) - - return { - 'strategy': 'parallel_synthesis', - 'parallel_slots': parallel_slots, - 'independent_modules': independent_modules, - 'dependent_modules': dependent_modules, - 'estimated_speedup': parallel_slots / max(1, len(modules)), - 'success': True - } - - @staticmethod - def resource_aware(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Resource-aware synthesis considering FPGA resource constraints.""" - modules = context.get('modules', []) - resource_budget = context.get('resource_budget', {'LUT': 43200, 'FF': 86400, 'BRAM': 120}) - module_resources = context.get('module_resources', {}) - - scheduled = [] - resource_used = {'LUT': 0, 'FF': 0, 'BRAM': 0} - deferred = [] - - for module in modules: - req = module_resources.get(module, {'LUT': 1000, 'FF': 2000, 'BRAM': 1}) - - # Check if module fits - fits = all( - resource_used[r] + req[r] <= resource_budget[r] - for r in ['LUT', 'FF', 'BRAM'] - ) - - if fits: - scheduled.append(module) - for r in ['LUT', 'FF', 'BRAM']: - resource_used[r] += req[r] - else: - deferred.append(module) - - return { - 'strategy': 'resource_aware', - 'scheduled': scheduled, - 'deferred': deferred, - 'resource_utilization': { - r: resource_used[r] / resource_budget[r] for r in ['LUT', 'FF', 'BRAM'] - }, - 'success': len(deferred) == 0 - } - - @staticmethod - def timing_driven(heuristic: HeuristicType, context: Dict[str, Any]) -> Dict[str, Any]: - """Timing-driven synthesis for critical path optimization.""" - modules = context.get('modules', []) - timing_constraints = context.get('timing_constraints', {}) - critical_paths = context.get('critical_paths', []) - - # Prioritize modules on critical paths - priority_map = {} - for i, path in enumerate(critical_paths): - for module in path: - priority_map[module] = priority_map.get(module, 0) + (len(critical_paths) - i) - - # Sort modules by priority (critical path modules first) - sorted_modules = sorted( - modules, - key=lambda m: priority_map.get(m, 0), - reverse=True - ) - - return { - 'strategy': 'timing_driven', - 'module_priority': priority_map, - 'build_order': sorted_modules, - 'critical_modules': [m for m in sorted_modules if priority_map.get(m, 0) > 0], - 'success': True - } - - -def demo_famm_hyperheuristic(): - """Demonstrate FAMM hyper-heuristic optimization.""" - print("=== FAMM Hyper-Heuristic Demo ===") - - orchestrator = HyperHeuristicOrchestrator() - - # Simulate FAMM operations - for i in range(20): - context = { - 'bank': { - 'cells': { - 0: {'delay': 5.0, 'delay_weight': 1.0}, - 1: {'delay': 3.0, 'delay_weight': 0.5} - } - }, - 'address': i % 2, - 'target_delay': 4.0, - 'max_delay': 10.0 - } - - # Route to appropriate heuristic function based on current selection - def famm_heuristic_func(ht, ctx): - if ht == HeuristicType.GREEDY: - return FAMMHyperHeuristics.greedy_minimize(ht, ctx) - elif ht == HeuristicType.BALANCED: - return FAMMHyperHeuristics.frustration_balance(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - return FAMMHyperHeuristics.adaptive_weight(ht, ctx) - else: - return FAMMHyperHeuristics.greedy_minimize(ht, ctx) - - result, heuristic = orchestrator.select_and_execute( - ComponentType.FAMM_DELAY, famm_heuristic_func, context - ) - - print(f"Op {i+1}: {heuristic.value} -> delay={result['adjusted_delay']:.2f}, success={result['success']}") - - report = orchestrator.get_performance_report() - print("\n=== FAMM Performance Report ===") - print(json.dumps(report['components']['famm_delay'], indent=2)) - - -def demo_pist_hyperheuristic(): - """Demonstrate PIST hyper-heuristic optimization.""" - print("\n=== PIST Hyper-Heuristic Demo ===") - - orchestrator = HyperHeuristicOrchestrator() - - # Simulate PIST operations - for i in range(15): - context = { - 'pos': {'k': i % 5, 't': i % 10}, - 'phase': 'seismic' if i % 3 == 0 else 'grounded' - } - - def pist_heuristic_func(ht, ctx): - if ht == HeuristicType.GREEDY: - return PISTHyperHeuristics.linear_move(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - return PISTHyperHeuristics.adaptive_move(ht, ctx) - else: - return PISTHyperHeuristics.resonance_jump(ht, ctx) - - result, heuristic = orchestrator.select_and_execute( - ComponentType.PIST_MOVE, pist_heuristic_func, context - ) - - print(f"Op {i+1}: {heuristic.value} -> {result['move_type']}, pos={result['new_pos']}") - - report = orchestrator.get_performance_report() - print("\n=== PIST Performance Report ===") - print(json.dumps(report['components']['pist_move'], indent=2)) - - -def demo_shim_selection_hyperheuristic(): - """Demonstrate infrastructure shim selection hyper-heuristic.""" - print("\n=== Shim Selection Hyper-Heuristic Demo ===") - - orchestrator = HyperHeuristicOrchestrator() - - # Simulate shim selection - domains = ['math', 'compression', 'hardware', 'general'] - performance_history = { - 'math': {'math_prover_prior_metaprobe.py': 0.9, 'intense_math_modeling_router.py': 0.7}, - 'compression': {'compression_signal_shaping_synthesis.py': 0.8} - } - - for i in range(12): - context = { - 'domain': domains[i % len(domains)], - 'task_type': 'optimization', - 'performance_history': performance_history - } - - def shim_heuristic_func(ht, ctx): - if ht == HeuristicType.GREEDY: - return ShimSelectionHyperHeuristics.select_by_domain(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - return ShimSelectionHyperHeuristics.select_adaptive(ht, ctx) - else: - return ShimSelectionHyperHeuristics.select_by_performance(ht, ctx) - - result, heuristic = orchestrator.select_and_execute( - ComponentType.SHIM_SELECTION, shim_heuristic_func, context - ) - - print(f"Op {i+1}: {heuristic.value} -> {result['selected_shim']}") - - report = orchestrator.get_performance_report() - print("\n=== Shim Selection Performance Report ===") - print(json.dumps(report['components']['shim_selection'], indent=2)) - - -def demo_gpu_scheduling_hyperheuristic(): - """Demonstrate GPU scheduling hyper-heuristic.""" - print("\n=== GPU Scheduling Hyper-Heuristic Demo ===") - - orchestrator = HyperHeuristicOrchestrator() - - # Simulate GPU task scheduling - for i in range(15): - task_queue = [ - {'name': f'task_{i}_{j}', 'priority': (i+j) % 10, 'memory_required': 1000 + (i*j % 4000)} - for j in range(3) - ] - - context = { - 'task_queue': task_queue, - 'gpu_memory': 12000, - 'gpu_count': 1, - 'current_memory_usage': i * 500, - 'gpu_states': [{'load': 0.3 + (i % 5) * 0.1, 'memory': 6000}] - } - - def gpu_heuristic_func(ht, ctx): - if ht == HeuristicType.GREEDY: - return GPUSchedulingHyperHeuristics.round_robin(ht, ctx) - elif ht == HeuristicType.BALANCED: - return GPUSchedulingHyperHeuristics.priority_based(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - return GPUSchedulingHyperHeuristics.memory_aware(ht, ctx) - else: - return GPUSchedulingHyperHeuristics.load_balancing(ht, ctx) - - result, heuristic = orchestrator.select_and_execute( - ComponentType.GPU_SCHEDULING, gpu_heuristic_func, context - ) - - memory_util = result.get('memory_utilization', 0) - scheduled = len([a for a in result.get('assigned', []) if a.get('gpu_id') is not None]) - print(f"Op {i+1}: {heuristic.value} -> {scheduled}/{len(task_queue)} tasks, mem={memory_util:.2%}") - - report = orchestrator.get_performance_report() - print("\n=== GPU Scheduling Performance Report ===") - print(json.dumps(report['components']['gpu_scheduling'], indent=2)) - - -def demo_fpga_build_hyperheuristic(): - """Demonstrate FPGA build optimization hyper-heuristic.""" - print("\n=== FPGA Build Hyper-Heuristic Demo ===") - - orchestrator = HyperHeuristicOrchestrator() - - # Simulate FPGA build optimization - for i in range(12): - modules = [f'module_{j}' for j in range(5)] - changed_files = [f'{m}.v' for m in modules if (i + hash(m)) % 3 == 0] - - context = { - 'modules': modules, - 'changed_files': changed_files, - 'build_cache': {f: f'cached_{f}' for f in changed_files if hash(f) % 2 == 0}, - 'dependency_graph': { - f'module_{j}': [f'module_{k}' for k in range(j)] if j > 0 else [] - for j in range(5) - }, - 'available_cores': 4, - 'resource_budget': {'LUT': 43200, 'FF': 86400, 'BRAM': 120}, - 'module_resources': { - f'module_{j}': {'LUT': 1000 * (j+1), 'FF': 2000 * (j+1), 'BRAM': j+1} - for j in range(5) - }, - 'critical_paths': [['module_0', 'module_2', 'module_4'], ['module_1', 'module_3']] - } - - def fpga_heuristic_func(ht, ctx): - if ht == HeuristicType.GREEDY: - return FPGABuildHyperHeuristics.incremental_build(ht, ctx) - elif ht == HeuristicType.BALANCED: - return FPGABuildHyperHeuristics.parallel_synthesis(ht, ctx) - elif ht == HeuristicType.ADAPTIVE: - return FPGABuildHyperHeuristics.resource_aware(ht, ctx) - else: - return FPGABuildHyperHeuristics.timing_driven(ht, ctx) - - result, heuristic = orchestrator.select_and_execute( - ComponentType.FPGA_BUILD, fpga_heuristic_func, context - ) - - speedup = result.get('estimated_speedup', 0) - success = result.get('success', False) - print(f"Op {i+1}: {heuristic.value} -> speedup={speedup:.2f}x, success={success}") - - report = orchestrator.get_performance_report() - print("\n=== FPGA Build Performance Report ===") - print(json.dumps(report['components']['fpga_build'], indent=2)) - - -if __name__ == "__main__": - demo_famm_hyperheuristic() - demo_pist_hyperheuristic() - demo_shim_selection_hyperheuristic() - demo_gpu_scheduling_hyperheuristic() - demo_fpga_build_hyperheuristic() - - print("\n=== Global Statistics ===") - orchestrator = HyperHeuristicOrchestrator() - # Run demos to populate orchestrator - demo_famm_hyperheuristic() - demo_pist_hyperheuristic() - demo_shim_selection_hyperheuristic() - demo_gpu_scheduling_hyperheuristic() - demo_fpga_build_hyperheuristic() - - # Note: In real usage, you'd use a single orchestrator instance across demos diff --git a/4-Infrastructure/shim/illegal_state_unrepresentable_route_prior.py b/4-Infrastructure/shim/illegal_state_unrepresentable_route_prior.py deleted file mode 100644 index 064722d0..00000000 --- a/4-Infrastructure/shim/illegal_state_unrepresentable_route_prior.py +++ /dev/null @@ -1,225 +0,0 @@ -#!/usr/bin/env python3 -"""Distill "make illegal states unrepresentable" into DD route-state guards. - -The source article's useful extraction is finite-state API discipline: expose -only legal transitions, and use type/state markers to prevent invalid builder -paths. For the bounded route compiler, invalid route combinations should be -unrepresentable when possible, and otherwise fail closed at the boundary. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "illegal_state_unrepresentable_route_prior_receipt.json" -CURRICULUM_OUT = SHIM / "illegal_state_unrepresentable_route_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" -SOURCE_URL = "https://blog.frankel.ch/illegal-state-unrepresentable/" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -SOURCE_EVIDENCE = { - "title": "Making illegal state unrepresentable", - "author": "Nicolas Frankel", - "published_date": "2026-04-19", - "source_url": SOURCE_URL, - "observed_core_claims": [ - "builder_pattern_can_be_read_as_finite_state_machine", - "illegal_transitions_should_not_be_exposed_to_api_users", - "static_typing_can_reject_nonexistent_transitions_at_compile_time", - "dynamic_typing_requires_runtime_validation_or_external_type_checker", - "naive_state_classes_can_create_combinatorial_growth", - "phantom_or_generic_state_markers_reduce_growth_for_common_transitions", - "opaque_constructors_help_hide_invalid_direct_construction", - ], -} - - -STATE_DISCIPLINES = [ - { - "id": "runtime_validation_only", - "local_meaning": "route object can be built in invalid shape and rejected later", - "use_when": "dynamic/plugin boundaries where static types are unavailable", - "risk": "invalid states consume evaluator time and can leak into receipts", - "verdict": "boundary_fallback_only", - }, - { - "id": "state_specific_builder", - "local_meaning": "each route state exposes only legal next DD edges", - "use_when": "small finite route machines with few compatibility dimensions", - "risk": "class/edge explosion as route constraints multiply", - "verdict": "good_for_small_closed_fsm", - }, - { - "id": "phantom_state_marker", - "local_meaning": "carry compile-time or schema-time route state without serializing payload", - "use_when": "shared transitions should preserve route family while specific edges are constrained", - "risk": "marker must not become an uncounted witness channel", - "verdict": "preferred_for_route_api_shape", - }, - { - "id": "opaque_route_constructor", - "local_meaning": "force route construction through legal transition functions", - "use_when": "receipts or config matrices should not be hand-assembled into invalid states", - "risk": "bypass paths must be audited at JSON/plugin boundaries", - "verdict": "preferred_for_receipt_objects", - }, -] - - -EQUATIONS = [ - { - "id": "ISR0_route_fsm", - "equation": "RouteFSM = (States, LegalEdges, start, terminals)", - "meaning": "Transform tuning is an explicit finite-state route builder.", - }, - { - "id": "ISR1_transition_totality", - "equation": "edge(s, a) is constructible iff a in LegalEdges(s)", - "meaning": "An illegal DD edge should not be callable from the current route state.", - }, - { - "id": "ISR2_phantom_state", - "equation": "RouteBuilder[S] carries S at type/schema time and erases S at payload time", - "meaning": "State markers constrain transitions without becoming hidden compressed data.", - }, - { - "id": "ISR3_common_transition", - "equation": "common_edge: RouteBuilder[S] -> RouteBuilder[S]", - "meaning": "Shared legal edges preserve state and avoid duplicated boilerplate.", - }, - { - "id": "ISR4_specific_transition", - "equation": "specific_edge: RouteBuilder[S_a] -> RouteBuilder[S_b]", - "meaning": "Compatibility-changing route choices move to a new explicit state class.", - }, - { - "id": "ISR5_boundary_validation", - "equation": "external_json_route valid iff reconstruct(RouteFSM, json).state != invalid", - "meaning": "Typed interiors still need fail-closed validation at untyped plugin/file boundaries.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "illegal_state_unrepresentable_route_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": SOURCE_EVIDENCE, - "primary_decision": { - "name": "make_invalid_route_states_unrepresentable", - "statement": ( - "Represent route construction as a finite-state builder where " - "only legal DD edges are exposed from each state. Use phantom " - "or schema-time state markers for common transitions, and keep " - "runtime validation at external JSON/plugin boundaries." - ), - }, - "state_disciplines": STATE_DISCIPLINES, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "route_state_type_id", - "legal_edge_set_id", - "phantom_marker_id", - "opaque_constructor_status", - "transition_witness_id", - "compile_time_rejected_edge_count", - "runtime_rejected_edge_count", - "json_boundary_validation_status", - "state_marker_payload_bytes", - "invalid_state_nan0_flag", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "open_typed_route_builder", - "expose_only_legal_edges", - "apply_common_transition_preserving_state", - "apply_specific_transition_changing_state", - "erase_phantom_marker_from_payload", - "validate_external_route_json", - "reject_unrepresentable_transition", - "fail_closed_on_invalid_route_state", - ], - "lower_bound": [ - "state_marker_header_bytes", - "transition_witness_floor", - "json_boundary_validation_floor", - "exact_residual_lane_floor", - ], - "promotion_rule": [ - "route_builder_exposes_only_legal_transitions", - "phantom_or_schema_markers_are_not_payload_channels", - "opaque_constructors_prevent_direct_invalid_receipts", - "external_json_or_plugin_routes_validate_against_fsm", - "invalid_states_fail_closed_before_evaluation", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "illegal_transition_constructible -> invalid_api_surface", - "phantom_marker_serialized_as_hidden_payload -> invalid_receipt", - "state_class_growth_becomes_combinatorial -> refactor_to_marker_matrix", - "external_route_json_bypasses_validation -> fail_closed", - "runtime_rejection_after_expensive_eval -> prune_or_move_gate_earlier", - ], - "claim_boundary": ( - "This prior constrains route-state representation. It is not a " - "compression result and does not replace exact decode/hash/byte-count " - "promotion receipts." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["state_disciplines"]: - lines.append({"type": "state_discipline", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/imports/mathxml_domains.graphml b/4-Infrastructure/shim/imports/mathxml_domains.graphml deleted file mode 100644 index e4541507..00000000 --- a/4-Infrastructure/shim/imports/mathxml_domains.graphml +++ /dev/null @@ -1,2346 +0,0 @@ - - - - - - - - - - - - - - Foundational Extensions - HIGH - Genus-3 surfaces, branch-cut defects, topological RAM, FAMM basins, PIST witness surfaces - 1 - - - Foundational Extensions - HIGH - Particle spectrum, four forces, spin quantization, winding numbers, OTOM bind - 2 - - - Foundational Extensions - HIGH - Topological RAM classification, manifold vector bundles, quantum foam, fractional unified field - 3 - - - Foundational Extensions - HIGH - Quantum foam, fractional derivatives, topological RAM, FAMM frustration, OTOM formalization - 4 - - - - - Deep Theoretical Connections - HIGH - Lean 4 formalization, OTOM bind primitive, topological RAM, manifold classification - 5 - - - Deep Theoretical Connections - MEDIUM - OTOM formalization, FAMM composition, PIST transformations, bind primitive - 6 - - - Deep Theoretical Connections - MEDIUM - Local-to-global principles, FAMM memory, OTOM as topos, data manifold as site - 7 - - - Deep Theoretical Connections - MEDIUM - Quantum foam, fractional field, topological RAM, OTOM, compression moduli - 8 - - - - - Analysis and PDE Deep Dive - MEDIUM - Fractional derivatives, quantum foam, branch cuts, FAMM frustration, compression - 9 - - - Analysis and PDE Deep Dive - MEDIUM - Fractal dimensions, topological RAM, FAMM basins, compression entropy, recursive structures - 10 - - - Analysis and PDE Deep Dive - MEDIUM - Torsional cosmology, particle spectrum, eigenvector clustering, FAMM, compression - 11 - - - Analysis and PDE Deep Dive - MEDIUM - Torsional unwinding, FAMM frustration, topological RAM, fractional field, compression - 12 - - - - - Probability and Statistics Deep Dive - MEDIUM - FAMM memory cycles, search space exploration, compression entropy, distributed systems - 13 - - - Probability and Statistics Deep Dive - MEDIUM - FAMM frustration as stochastic process, quantum foam, topological RAM, compression - 14 - - - Probability and Statistics Deep Dive - MEDIUM - Compression, FAMM optimization, search space, OTOM, Bayesian inference - 15 - - - Probability and Statistics Deep Dive - MEDIUM - FAMM frustration, compression, search space, distributed systems, topological RAM - 16 - - - - - Algebra and Number Theory Deep Dive - MEDIUM - Integer arithmetic, mass numbers, fractional field, compression, OTOM - 17 - - - Algebra and Number Theory Deep Dive - MEDIUM - Fractional unified field, mass numbers, compression, OTOM, quantum foam - 18 - - - Algebra and Number Theory Deep Dive - MEDIUM - Topological RAM, FAMM, OTOM, compression, manifold classification - 19 - - - Algebra and Number Theory Deep Dive - MEDIUM - Integer arithmetic, OTOM modules, compression, topological RAM, fractional field - 20 - - - - - Geometry and Topology Deep Dive - MEDIUM - Torsional cosmology, topological RAM, FAMM, compression, genus-3 surfaces - 21 - - - Geometry and Topology Deep Dive - MEDIUM - Torsional unwinding, FAMM, compression, OTOM, quantum foam - 22 - - - Geometry and Topology Deep Dive - MEDIUM - Fractional unified field, torsional cosmology, compression, OTOM, topological RAM - 23 - - - Geometry and Topology Deep Dive - MEDIUM - Braid theory, genus-3 surfaces, topological RAM, FAMM, OTOM - 24 - - - - - Mathematical Physics Deep Dive - MEDIUM - Fractional unified field, quantum foam, FAMM, OTOM, topological RAM - 25 - - - Mathematical Physics Deep Dive - LOW - Fractional unified field, quantum foam, genus-3 surfaces, topological RAM, OTOM - 26 - - - Mathematical Physics Deep Dive - LOW - Quantum foam, torsional cosmology, topological RAM, FAMM, fractional field - 27 - - - Mathematical Physics Deep Dive - LOW - Fractional unified field, torsional cosmology, compression, OTOM, quantum foam - 28 - - - - - Computational and Applied Mathematics - LOW - Fractional derivatives, torsional cosmology, FAMM, topological RAM, compression - 29 - - - Computational and Applied Mathematics - MEDIUM - FAMM, search space, compression, OTOM, topological RAM - 30 - - - Computational and Applied Mathematics - LOW - Compression, FAMM, search space, OTOM, topological RAM - 31 - - - Computational and Applied Mathematics - LOW - Compression algorithms, search space, OTOM, FAMM, topological RAM - 32 - - - - - Interdisciplinary Connections - LOW - PIST biological shifter, genetics, FAMM, compression, OTOM - 33 - - - Interdisciplinary Connections - LOW - FAMM, topological RAM, compression, OTOM, search space - 34 - - - Interdisciplinary Connections - LOW - Stochastic processes, FAMM, optimization, risk management, compression - 35 - - - Interdisciplinary Connections - LOW - Quantum foam, topological RAM, FAMM, compression, OTOM - 36 - - - - - Specialized and Cutting-Edge Fields - LOW - Integer arithmetic, compression, FAMM, OTOM, topological RAM - 37 - - - Specialized and Cutting-Edge Fields - LOW - OTOM transformations, FAMM, compression, topological RAM, search space - 38 - - - Specialized and Cutting-Edge Fields - LOW - Eigenvector clustering, FAMM, compression, OTOM, quantum foam - 39 - - - Specialized and Cutting-Edge Fields - LOW - OTOM, FAMM, compression, quantum foam, topological RAM - 40 - - - Specialized and Cutting-Edge Fields - LOW - Compression, FAMM, search space, topological RAM, OTOM - 41 - - - Specialized and Cutting-Edge Fields - LOW - FAMM, search space, ENE, compression, OTOM - 42 - - - Specialized and Cutting-Edge Fields - MEDIUM - Compression, FAMM, topological RAM, manifold classification, OTOM - 43 - - - Specialized and Cutting-Edge Fields - MEDIUM - OTOM, FAMM, quantum foam, compression, topological RAM - 44 - - - Specialized and Cutting-Edge Fields - LOW - OTOM, FAMM, topological RAM, compression, quantum foam - 45 - - - Specialized and Cutting-Edge Fields - LOW - Topological RAM, fractional derivatives, OTOM, FAMM, quantum foam - 46 - - - Specialized and Cutting-Edge Fields - LOW - Quantum foam, topological RAM, torsional cosmology, FAMM, OTOM - 47 - - - Specialized and Cutting-Edge Fields - LOW - Compression, FAMM, OTOM, search space, topological RAM - 48 - - - Specialized and Cutting-Edge Fields - LOW - Braid theory, fractional unified field, FAMM, OTOM, topological RAM - 49 - - - Specialized and Cutting-Edge Fields - LOW - Topological RAM, FAMM, OTOM, compression, manifold classification - 50 - - - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - - - prerequisite - strong - - - enhances - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - medium - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - medium - - - prerequisite - medium - - - - - prerequisite - medium - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - - - Semantic Information Theory - HIGH - Mass numbers, decoder-relative recoverability, semantic mass, communication regimes, regime invariants - 51 - - - Semantic Information Theory - HIGH - Semantic mass vectors, invariant strength, binding, routing leverage, compression gain, update resistance - 52 - - - Semantic Information Theory - MEDIUM - Admissible reduction, residual risk, evidence receipts, adversarial trials, measurement support - 53 - - - - - Quaternion Algebra and Spherical Geometry - HIGH - Unit quaternions on S³, Hamilton product, great circle distance, SLERP interpolation, axis-angle representation - 54 - - - Quaternion Algebra and Spherical Geometry - HIGH - Chiral incompatibility, D+L→W collapse, quaternion dot product, ternary classification, SLUG-3 gates - 55 - - - Quaternion Algebra and Spherical Geometry - MEDIUM - Quaternion rotation matrices, parallel transport, torsion frames, curvature on S³, DNA backbone encoding - 56 - - - - - Spectral Graph Theory - HIGH - Domain adjacency matrices, principal eigenvector analysis, equation clustering, spectral clustering, graph Laplacians - 57 - - - Spectral Graph Theory - MEDIUM - Eigenvalue decomposition, spectral dimension, heat kernel diffusion, Cheeger inequalities, expander graphs - 58 - - - Spectral Graph Theory - MEDIUM - Random walks on graphs, PageRank, community detection, spectral partitioning, graph Fourier transforms - 59 - - - - - Polarization Theory and Structured Light - HIGH - Pancharatnam topological charge, spin-orbit coupling, circular polarization states, Stokes parameters, Poincaré sphere - 60 - - - Polarization Theory and Structured Light - MEDIUM - Optical chirality density, spin angular momentum, orbital angular momentum, Gouy phase, Laguerre-Gaussian modes - 61 - - - Polarization Theory and Structured Light - MEDIUM - Optical Hall effect, spin-current fields, azimuthal spin patterns, component separation, topological control - 62 - - - - - Biosemiotics and Communication Regimes - HIGH - Animal communication systems, bioacoustic signals, echolocation, chemical trails, spatial/vector signaling - 63 - - - Biosemiotics and Communication Regimes - MEDIUM - Visual/display patterns, electric/field signals, tactile/vibrational communication, receiver-response dynamics - 64 - - - Biosemiotics and Communication Regimes - MEDIUM - Signal form, decoder/context coupling, recoverable state, residual ambiguity, observable response - 65 - - - - - Topological Quantum Field Theory - MEDIUM - Topological charges, winding numbers, spin textures, anyon statistics, topological invariants - 66 - - - Topological Quantum Field Theory - MEDIUM - Chern-Simons theory, Wilson loops, knot invariants, TQFT functors, cobordism categories - 67 - - - Topological Quantum Field Theory - LOW - Topological phases of matter, topological insulators, edge states, bulk-boundary correspondence - 68 - - - - - Spherical Harmonics and Spin Geometry - MEDIUM - Spherical harmonics expansion, spinor bundles, spin structures, Dirac operators, index theorems - 69 - - - Spherical Harmonics and Spin Geometry - MEDIUM - Clebsch-Gordan coefficients, Wigner D-matrices, angular momentum coupling, tensor operators - 70 - - - Spherical Harmonics and Spin Geometry - LOW - Spinor calculus, spin connections, torsion in spin geometry, Cartan geometry - 71 - - - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - - - Computational Humanities - HIGH - Text mining, eigenvectors in semantic similarity, authorship attribution, stylometry, topic modeling - 72 - - - Computational Humanities - HIGH - Latent Semantic Analysis (LSA), singular value decomposition in text, semantic eigenvectors - 73 - - - Computational Humanities - MEDIUM - Word embeddings, eigenvectors in word2vec, GloVe, contextual semantic spaces - 74 - - - - - Network Humanities - HIGH - Social network analysis, eigenvector centrality, cultural transmission networks, influence propagation - 75 - - - Network Humanities - MEDIUM - Bibliometric coupling, citation networks, intellectual genealogy, knowledge graphs - 76 - - - Network Humanities - MEDIUM - Community detection in cultural data, modularity eigenvectors, network anthropology - 77 - - - - - Digital Philology - HIGH - Textual analysis, eigenvectors in stylometry, authorship verification, chronological sequencing - 78 - - - Digital Philology - MEDIUM - Hermeneutics in digital age, textual variants, manuscript clustering, eigenvector-based stemmatology - 79 - - - Digital Philology - MEDIUM - Corpus linguistics, eigenvectors in diachronic analysis, language change detection - 80 - - - - - Cultural Analytics - HIGH - Pattern recognition in cultural data, eigenvectors in trend analysis, cultural evolution - 81 - - - Cultural Analytics - MEDIUM - Eigenfaces in art recognition, style transfer eigenvectors, visual culture analysis - 82 - - - Cultural Analytics - MEDIUM - Music genre classification, eigenvectors in audio analysis, cultural pattern extraction - 83 - - - - - Music Information Retrieval - HIGH - Spectral analysis, eigenvectors in harmonic analysis, tonal networks, chord progressions - 84 - - - Music Information Retrieval - MEDIUM - Eigenmusic, principal component analysis in audio, timbre eigenvectors, rhythmic patterns - 85 - - - Music Information Retrieval - MEDIUM - Music similarity, eigenvectors in recommendation systems, cultural transmission of music - 86 - - - - - Philosophy of Mathematics - MEDIUM - Foundational questions, mathematical realism vs anti-realism, structuralism, category theory foundations - 87 - - - Philosophy of Mathematics - MEDIUM - Phenomenology of mathematics, embodied cognition, mathematical intuition, eigenvectors as mental structures - 88 - - - Philosophy of Mathematics - LOW - Mathematical Platonism, constructivism, formalism, intuitionism in historical context - 89 - - - - - Cognitive Humanities - HIGH - Conceptual spaces, semantic networks, eigenvectors in cognitive modeling, mental representation - 90 - - - Cognitive Humanities - MEDIUM - Embodied cognition, distributed cognition, cultural cognition, collective intelligence - 91 - - - Cognitive Humanities - MEDIUM - Narrative cognition, story eigenvectors, discourse analysis, rhetorical structures - 92 - - - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - - - Genus-3 Topology and Hyperbolic Geometry - HIGH - Genus-3 surfaces, three-hole torus topology, hyperbolic plane tiling, self-similar structure - 93 - - - Genus-3 Topology and Hyperbolic Geometry - HIGH - Injectivity radius, geodesic wrapping, fundamental polygon tiling, hyperbolic distance metrics - 94 - - - Genus-3 Topology and Hyperbolic Geometry - MEDIUM - Poincaré disk model, hyperbolic isometries, Fuchsian groups, modular forms - 95 - - - - - Branch-Cut Defects and Half-Möbius Folds - HIGH - Branch-cut defects, half-Möbius folds, topological phase transitions, critical angle defects - 96 - - - Branch-Cut Defects and Half-Möbius Folds - HIGH - Monodromy, covering spaces, Riemann surface branch points, analytic continuation - 97 - - - Branch-Cut Defects and Half-Möbius Folds - MEDIUM - Non-orientable manifolds, Möbius strip geometry, twist defects, topological obstructions - 98 - - - - - Variable Torsional Fields - HIGH - Variable torsional frequency ω(x, t), torsional wave equation, torsional charge density, torsional sound speed - 99 - - - Variable Torsional Fields - HIGH - Torsional gradient, local torsional time, clock rate variation, torsional attractors - 100 - - - Variable Torsional Fields - MEDIUM - Torsional anisotropy, directional ω dependence, dipolar modulation, large-scale torsional structure - 101 - - - - - Torsional Quantum Mechanics - HIGH - Torsional wavefunction, phase vibration on geometric sheet, Fourier conjugates in θ-space - 102 - - - Torsional Quantum Mechanics - HIGH - Phase resolution limit, uncertainty from sampling theory, measurement as phase pinning - 103 - - - Torsional Quantum Mechanics - MEDIUM - Path interference on torsional sheet, Born rule from mode competition, thermodynamic irreversibility - 104 - - - - - Golden Ratio Scaling and Fractal Dimensions - HIGH - Φ-scaling hierarchy, golden ratio self-similarity, fractal dimension D_f = log(2)/log(Φ) ≈ 1.44 - 105 - - - Golden Ratio Scaling and Fractal Dimensions - HIGH - Spectral dimension D_s, Hausdorff dimension D_H, power-law correlations, critical exponents - 106 - - - Golden Ratio Scaling and Fractal Dimensions - MEDIUM - DNA helix geometry, nucleosome packing, chromatin hierarchy, biological self-similarity - 107 - - - - - Cosmological Anomalies - HIGH - Methuselah star age paradox, JWST early massive galaxies, Hubble tension, dark flow - 108 - - - Cosmological Anomalies - HIGH - Axis of evil, CMB quadrupole-octupole alignment, large-scale anisotropy, preferred directions - 109 - - - Cosmological Anomalies - MEDIUM - Void aging, overdense overclocking, cosmic web entropy scaling, galaxy clustering - 110 - - - - - Torsional Thermodynamics - MEDIUM - Entropy scaling on fractals, Bekenstein bound modification, specific heat exponents - 111 - - - Torsional Thermodynamics - MEDIUM - Phase transitions in fractal geometry, critical exponents, correlation length divergence - 112 - - - Torsional Thermodynamics - LOW - Arrow of time as torsional unwinding, Carnot efficiency, Landauer limit, Jarzynski equality - 113 - - - - - Recursive Compression Structures - HIGH - Recursive prediction models, critical scale detection, branch-cut adaptation, multi-level mixing - 114 - - - Recursive Compression Structures - MEDIUM - Hierarchical context windows, self-similar basis fusion, phase transition boundaries - 115 - - - Recursive Compression Structures - MEDIUM - Text boundaries, code structures, DNA regulatory elements, semantic compression - 116 - - - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - - prerequisite - strong - - - prerequisite - strong - - - enhances - strong - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - prerequisite - strong - - - prerequisite - medium - - - enhances - medium - - - - diff --git a/4-Infrastructure/shim/ingest_compactified_core_equations.py b/4-Infrastructure/shim/ingest_compactified_core_equations.py deleted file mode 100644 index 88c52cdd..00000000 --- a/4-Infrastructure/shim/ingest_compactified_core_equations.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Compactified Core Equations -=================================== -Compactify 12 core equations to 4 primitives (67% reduction). -Maintains 90.8% coverage across theories. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -COMPACTIFIED_EQUATIONS = { - "id": "compactified-core-equations-v1", - "source": "Compactification of 12 core equations to 4 primitives based on analysis (109/120 matches, 90.8% coverage)", - "title": "Compactified Core Equations: 4 Primitives for Compression Architecture", - "date": "2026-05-07", - - "core_synthesis": ( - "12 core equations compactified to 4 primitives (67% reduction) while maintaining " - "90.8% coverage across compression theories. Redundant equations merged: shear_matrix + " - "gram_matrix → shear primitive; residual_correlation + eigen_decomposition → spectral " - "primitive. Derivable equations expressed as derived metrics: radius_ratio, residual_ratio " - "derived from field primitive. Topological compactification: 10 theories viewed as " - "projections of 4D compact manifold (field, shear, packet, spectral)." - ), - - "compactification_rationale": { - "original_12_equations": "density_field, morse_smale, shear_matrix, gram_matrix, gccl_packet, gain_test, s3c_shell, radius_ratio, residual_ratio, famm_delay, residual_correlation, eigen_decomposition", - "analysis_results": "109/120 equation-theory matches (90.8% coverage). 7 equations with full coverage, 5 with partial coverage, 0 with no coverage.", - "redundancies_identified": { - "shear_gram": "shear_matrix (A_{ij} = δ_{ij} + α_{ij}) and gram_matrix (G = A^T A) linked. Gram derives from shear.", - "correlation_eigen": "residual_correlation (C_{ij} = ⟨ε_i ε_j⟩) and eigen_decomposition (C = UΛU^T) form pipeline.", - "field_derivatives": "radius_ratio (ρᵢ = s_center(i) / median(s(N(i)))) and residual_ratio (ρ = |ε| / |raw_span|) derived from field topology." - }, - "compactification_ratio": "12 → 4 primitives (67% reduction)" - }, - - "compactified_primitives": { - "field_primitive": { - "equation": "ρ(x⃗)", - "latex": "\\rho(\\vec{x})", - "description": "Semantic density field representing text as n-D manifold with topological features (peaks, ridges, saddles, vortices, voids)", - "derives": [ - "morse_smale: Critical points + separatrices = topological skeleton of meaning", - "radius_ratio: ρᵢ = ∇ρ(x⃗) / |∇ρ(x⃗)| at critical points (local scale ratio)", - "residual_ratio: ρ = ||ε||_2 / ||s||_2 (residual metric derived from field)", - "s3c_shell: n = k² + a (shell coordinates encode field structure)" - ], - "coverage": "70-80% across theories (density_field, morse_smale, s3c_shell, radius_ratio, residual_ratio)" - }, - "shear_primitive": { - "equation": "G = A^T A", - "latex": "G = A^T A", - "description": "Gram matrix = compression dictionary. Shear matrix A transforms orthogonal hypercube to correlated rhomboid. Eigenvectors = principal correlation directions, eigenvalues = compression gains", - "derives": [ - "shear_matrix: A_{ij} = δ_{ij} + α_{ij} (encoding of G)", - "famm_delay: Delay = ∫_γ ∇ρ · dl (path integral through sheared field gradient)" - ], - "coverage": "100% across theories (shear_matrix, gram_matrix, famm_delay, eigen_decomposition)" - }, - "packet_primitive": { - "equation": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "latex": "\\Gamma_i = \\gamma_i \\otimes \\chi_i \\otimes \\kappa_i \\otimes \\tau_i \\otimes U_i\\Lambda_i a_i \\otimes \\theta_i \\otimes \\varepsilon_i", - "description": "GCCL glyph packet with chirality, type, eigen descriptor, residual. Gain test ΔGCL > 0 filters compressive motifs", - "derives": [ - "gain_test: ΔGCL > 0 (filter applied to packet acceptance)", - "gccl_packet: Full packet formula (the primitive itself)" - ], - "coverage": "90% across theories (gccl_packet, gain_test)" - }, - "spectral_primitive": { - "equation": "C = UΛU^T", - "latex": "C = U\\Lambda U^T", - "description": "Eigen decomposition of correlation matrix. Residual correlation C_{ij} = ⟨ε_i ε_j⟩. Spectral energy compaction: 90% energy in 10% coefficients", - "derives": [ - "residual_correlation: C_{ij} = ⟨ε_i ε_j⟩ (input to spectral decomposition)", - "eigen_decomposition: C = UΛU^T (the primitive itself)", - "famm_spectral: Delays weighted by eigenvalue spectra (spectral pruning)" - ], - "coverage": "60-100% across theories (residual_correlation, eigen_decomposition, erans field effect)" - } - }, - - "topological_compactification": { - "concept": "10 compression theories viewed as projections of 4D compact manifold", - "manifold_dimensions": { - "dimension_0_field": "ρ(x⃗) — density field primitive (semantic manifold structure)", - "dimension_1_shear": "G = A^T A — shear primitive (geometric transformation)", - "dimension_2_packet": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ — packet primitive (encoding unit)", - "dimension_3_spectral": "C = UΛU^T — spectral primitive (residual decomposition)" - }, - "theory_projections": { - "density_field_encoding_theory": "Projection onto dimension 0 (field) with partial spectral", - "observer_admissible_cavities_theory": "Projection onto dimensions 0-2 (field + shear + packet)", - "hypercube_rhomboid_composition": "Projection onto dimension 1 (shear) with spectral", - "gccl_gec_spec_v1": "Projection onto dimensions 2-3 (packet + spectral)", - "unified_compression_architecture_synthesis_v1": "Full 4D projection (all primitives)", - "hippocampus_tabula_plena_combined_v1": "Full 4D projection with biological constraints", - "erans_field_effect_spectrum_v1": "Projection onto dimensions 0-3 with spectral emphasis", - "master_synthesis_complete_v1": "Complete 4D manifold with all projections integrated" - }, - "coordinate_charts": "Each theory is a different coordinate chart on the 4D manifold. Master synthesis is the atlas covering all charts." - }, - - "compactification_benefits": { - "reduction": "12 equations → 4 primitives (67% reduction)", - "coverage_maintained": "90.8% coverage maintained across theories", - "simplified_implementation": "4 core primitives easier to implement and verify than 12 equations", - "unified_framework": "4 primitives provide unified framework for all compression theories", - "topological_clarity": "4D manifold structure reveals relationships between theories", - "computational_efficiency": "Spectral primitive enables energy compaction (10-20% gain on residuals)", - "biological_alignment": "Field primitive aligns with hippocampus density fields, spectral with pattern separation" - }, - - "implementation_mapping": { - "field_primitive_implementation": { - "stage": "Stage 1: density field extraction", - "code": "Compute ρ(x⃗) from corpus C. Extract Morse-Smale topological skeleton.", - "outputs": "Peaks, ridges, saddles, vortices, voids, level_sets, S3C shell coordinates" - }, - "shear_primitive_implementation": { - "stage": "Stage 2: shear matrix computation", - "code": "Compute shear matrix A, Gram matrix G = A^T A. Eigen-decompose G = UΛU^T.", - "outputs": "Eigenvectors (principal directions), eigenvalues (compression gains), FAMM delay profile" - }, - "packet_primitive_implementation": { - "stage": "Stage 7: GCCL packet construction", - "code": "Construct Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ. Apply gain test ΔGCL > 0.", - "outputs": "Glyph packets with chirality, type, eigen descriptor, parameters, residual" - }, - "spectral_primitive_implementation": { - "stage": "Stage 13: erans spectral entropy coding", - "code": "Compute residual correlation C_{ij} = ⟨ε_i ε_j⟩. Eigen-decompose C = UΛU^T. Code spectral coefficients with erans.", - "outputs": "Spectral coefficients (eigenvalues, eigenvector weights), entropy-coded residuals" - } - }, - - "keeper_phrases": [ - "12 equations compactified to 4 primitives: field, shear, packet, spectral.", - "Field primitive ρ(x⃗) derives Morse-Smale, radius_ratio, residual_ratio, S3C shells.", - "Shear primitive G = A^T A derives shear_matrix, FAMM delays, eigen decomposition.", - "Packet primitive Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ includes gain test.", - "Spectral primitive C = UΛU^T derives residual correlation, eigen decomposition, spectral pruning.", - "67% reduction (12 → 4) with 90.8% coverage maintained.", - "10 theories = projections of 4D compact manifold.", - "Master synthesis = atlas covering all coordinate charts.", - "Spectral energy compaction: 90% energy in 10% coefficients.", - "Field primitive aligns with hippocampus density fields.", - "Spectral primitive aligns with hippocampus pattern separation.", - "Compactification reveals topological structure of compression architecture." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "compactified-equations", - "4-primitives", - "field-primitive", - "shear-primitive", - "packet-primitive", - "spectral-primitive", - "topological-compactification", - "4d-manifold", - "coordinate-charts", - "67-percent-reduction", - "90-8-percent-coverage", - "compression-architecture" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "compactified_core_equations_v1.json" - with open(out_path, 'w') as f: - json.dump(COMPACTIFIED_EQUATIONS, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": COMPACTIFIED_EQUATIONS["id"], - "title": COMPACTIFIED_EQUATIONS["title"], - "date": COMPACTIFIED_EQUATIONS["date"], - "source": COMPACTIFIED_EQUATIONS["source"], - "ingested_at": COMPACTIFIED_EQUATIONS["metadata"]["ingested_at"], - "tags": COMPACTIFIED_EQUATIONS["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCompactification ratio: 12 → 4 primitives (67% reduction)") - print(f"Coverage maintained: 90.8%") - - print(f"\n4 primitives:") - for prim, data in COMPACTIFIED_EQUATIONS["compactified_primitives"].items(): - print(f" • {prim}: {data['equation']} — {data['description'][:60]}...") - - print(f"\nTopological compactification:") - print(f" 10 theories = projections of 4D compact manifold") - for dim, desc in COMPACTIFIED_EQUATIONS["topological_compactification"]["manifold_dimensions"].items(): - print(f" • {dim}: {desc[:60]}...") - - print(f"\nKeeper phrases ({len(COMPACTIFIED_EQUATIONS['keeper_phrases'])}):") - for p in COMPACTIFIED_EQUATIONS['keeper_phrases']: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_dair_agentic_wiki.py b/4-Infrastructure/shim/ingest_dair_agentic_wiki.py deleted file mode 100644 index 1bd82814..00000000 --- a/4-Infrastructure/shim/ingest_dair_agentic_wiki.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest dair-ai Agentic Engineering Wiki into Research Stack.""" - -import json, time, hashlib -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -WIKI = { - "id": "dair-agentic-engineering-wiki", - "source": "https://github.com/dair-ai/dair-workshops/tree/main/agentic-engineering-wiki", - "title": "AI Agent Engineering Wiki — dair-ai", - "date": "2026-04-29", - "stats": {"tips": 51, "categories": 7, "companies": 9, "papers": 10, "tools": 14}, - "categories": { - "tool_use": { - "tips": 11, - "key_insight": "Pre-filter tools to relevant subset per request; log agent intent to detect loops" - }, - "evaluation": { - "tips": 8, - "key_insight": "Trajectory-aware eval (not just final output); repeat runs for reliability; behavioral rubrics for LLM-as-judge" - }, - "prompting": { - "tips": 6, - "key_insight": "Five-layer system prompt anatomy; tool descriptions as engineering surface; instruction hierarchy defense (system > user > tool output)" - }, - "orchestration": { - "tips": 7, - "key_insight": "Agents as MCP servers for composable multi-agent systems; Plan-Execute-Verify-Replan loop; handoffs with state transfer" - }, - "memory": { - "tips": 6, - "key_insight": "Context management, RAG, state, conversation history" - }, - "reliability": { - "tips": 8, - "key_insight": "Guardrails before risky ops; 'Lazy Agent' failure mode; restricted API keys; sandbox testing" - }, - "deployment": { - "tips": 5, - "key_insight": "Cost tracking, sandbox execution, monitoring, observability" - } - }, - "orchestration_tips": [ - "Structure agents as specialists with explicit ownership via handoffs", - "Leverage Google's ADK for interoperable agent orchestration across frameworks", - "Use heterogeneous model teams — different models have different strengths", - "Adopt Plan-Execute-Verify-Replan loop for complex multi-agent workflows", - "Use handoffs for agent-to-agent delegation with state transfer", - "Use built-in connector tools to reduce tool scaffolding overhead", - "Represent agents as MCP servers — compose multi-agent systems over same protocol" - ], - "reliability_tips": [ - "Add guardrails and human review before risky operations", - "Encode persistence, risk assessment, and proactive planning in agent prompts", - "Handle server tool pauses gracefully with pause_turn", - "Watch for 'Lazy Agent' failure mode — model knows it needs tools but doesn't call them", - "Don't use agents for problems with deterministic solutions — plain code still wins", - "Use restricted API keys (rk_*) to limit agent blast radius", - "Use tool_plan for explicit reasoning before acting", - "Test agents in sandbox environments before production — non-determinism demands it" - ], - "design_philosophy": [ - "Every claim links to a source. No unsupported advice.", - "Speculation is clearly marked.", - "Built for flexibility — new categories, companies, formats addable anytime.", - "Community-first — pulled from real production experiences (HN, Reddit, postmortems)." - ], - "relevance_to_research_stack": { - "direct_matches": [ - "Prover orchestration layers (L0-L3) ↔ Plan-Execute-Verify-Replan loop", - "Swarm consensus (11 agents) ↔ Agents as specialists with handoffs", - "ProverWatchdog guard_transition ↔ Guardrails before risky ops", - "Virtual FPGA system tests ↔ Sandbox testing before production", - "BFS-Prover-V2 audit trail ↔ Trajectory-aware evaluation", - "bf4prover manifold reshape ↔ Verify step in Plan-Execute-Verify-Replan" - ], - "gaps_in_our_system": [ - "No restricted API key pattern for agent blast radius", - "No explicit 'Lazy Agent' detection in swarm", - "No heterogeneous model teams (all agents use same model)", - "No cost tracking for orchestration layers", - "No pause_turn equivalent for long-running proofs" - ], - "strengths_of_our_system": [ - "Q16.16 fixed-point precision (wiki has no numerical guarantees)", - "Hardware substrate integration (wiki is software-only)", - "Formal proof backing (wiki relies on empirical testing)", - "Topological manifold awareness (wiki has no geometric model)" - ] - }, - "metadata": { - "ingested_at": time.time(), - "tags": ["agentic-engineering", "orchestration", "multi-agent", "reliability", "evaluation", "prompting"] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "dair_agentic_engineering_wiki.json" - with open(out_path, 'w') as f: - json.dump(WIKI, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Update index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": WIKI["id"], "title": WIKI["title"], - "date": WIKI["date"], "source": WIKI["source"], - "ingested_at": WIKI["metadata"]["ingested_at"], - "tags": WIKI["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nDirect matches to our system:") - for m in WIKI["relevance_to_research_stack"]["direct_matches"]: - print(f" ↔ {m}") - - print(f"\nGaps identified:") - for g in WIKI["relevance_to_research_stack"]["gaps_in_our_system"]: - print(f" ⚠ {g}") - - print(f"\nOur strengths:") - for s in WIKI["relevance_to_research_stack"]["strengths_of_our_system"]: - print(f" ✓ {s}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_density_field_encoding.py b/4-Infrastructure/shim/ingest_density_field_encoding.py deleted file mode 100644 index 94900a65..00000000 --- a/4-Infrastructure/shim/ingest_density_field_encoding.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Density Field Encoding — Beyond UTF-8 -============================================== -Inspired by "digital dzogchen" generative concept: -Data not as 1D byte sequence but as n-dimensional semantic density field. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -DENSITY_FIELD = { - "id": "density-field-encoding-theory", - "source": "User insight + r/generative digital_dzogchen concept", - "title": "Density Field Encoding: Representing Text as Topological Semantic Manifolds Instead of UTF-8", - "date": "2026-05-07", - - "core_claim": ( - "UTF-8 encodes text as a 1D discrete byte sequence: byte[i] at position i. " - "Density Field Encoding (DFE) represents text as a continuous n-dimensional " - "semantic density field ρ(x⃗) where information is stored in topological features: " - "peaks (named entities), ridges (semantic connections), saddles (topic transitions), " - "vortices (cyclic references), and voids (template structures). The field is latent; " - "observer queries collapse local regions into discrete UTF-8 text." - ), - - "utf8_vs_density": { - "utf8": { - "representation": "1D discrete sequence: b[i] ∈ [0,255] for i = 0..N-1", - "assumption": "Text is a linear string of symbols", - "compression": "Exploit sequential redundancy (LZ, PPM, BWT, neural prediction)", - "limitation": "Cannot represent non-sequential relationships without explicit linking; every byte position is independent dimension" - }, - "density_field": { - "representation": "n-D continuous field: ρ: ℝⁿ → ℝ⁺ (semantic density)", - "assumption": "Text is a region of an information manifold; spatial proximity = semantic proximity", - "compression": "Encode topological skeleton (peaks, ridges, voids) + small perturbation field", - "advantage": "Implicit relationships via field geometry; no explicit links needed; multi-scale structure naturally emerges" - } - }, - - "topological_features": { - "peaks": { - "encoding": "Named entities, article centers, key concepts", - "field_property": "Local maximum of ρ(x⃗)", - "invariant": "Peak index (persistent homology H₀), peak height (salience)", - "s3c_analogue": "k = shell index = distance from peak = semantic centrality; a = angular offset within cluster" - }, - "ridges": { - "encoding": "Hyperlinks, citations, semantic associations, see-also connections", - "field_property": "1-D local maxima along one direction, saddle in transverse", - "invariant": "Ridge persistence (how far down before splitting), ridge connectivity graph", - "s3c_analogue": "Mirror complement b⁰ = remaining path to next peak; mass = connection strength" - }, - "saddles": { - "encoding": "Topic transitions, paragraph boundaries, section changes", - "field_property": "Saddle point: maximum in some directions, minimum in others", - "invariant": "Saddle index, separatrix topology (which peaks connected)", - "s3c_analogue": "Throat region a ≈ b⁰ = maximum ambiguity = maximum information transition" - }, - "vortices": { - "encoding": "Cyclic references, category hierarchies, template instantiations, recursive structures", - "field_property": "Rotational flow in field gradient: ∇ρ circulates around core", - "invariant": "Vorticity ω = ∇ × ∇ρ, circulation Γ = ∮∇ρ·dl", - "s3c_analogue": "Contra-rotation gear in MS3C; recursive shell nesting S_n(S_{n-1}^n)" - }, - "voids": { - "encoding": "Template structures, common phrases, expected-but-absent content", - "field_property": "Local minimum of ρ(x⃗), possibly negative in signed extension", - "invariant": "Void depth, void volume, enclosing shell connectivity", - "s3c_analogue": "Negative pyramid / anti-resonance = expected structure that is absent; cheaper to encode absence than presence" - }, - "level_sets": { - "encoding": "Paragraphs, sections, articles at different semantic granularity", - "field_property": "Iso-density surfaces {x⃗ | ρ(x⃗) = c}", - "invariant": "Euler characteristic χ of level set surface = β₀ - β₁ + β₂", - "s3c_analogue": "Shell index k = level set value; each shell is a semantic granularity layer" - } - }, - - "compression_mechanism": { - "topological_skeleton": { - "description": "Encode only the critical points and separatrices of the Morse-Smale complex", - "data": "Peak positions + heights, ridge connectivity graph, saddle indices, vortex cores, void enclosures", - "size": "O(N_peaks + N_ridges) ≪ O(N_bytes). For enwik9: ~10⁶ peaks, ~10⁷ ridges → ~100MB skeleton vs 1GB raw", - "reconstruction": "Decode skeleton + perturbation field → approximate density field → collapse to UTF-8 on observer query" - }, - "perturbation_field": { - "description": "Residual between topological skeleton prediction and actual density", - "encoding": "High-frequency, small-amplitude corrections stored via PIST n-D bundle encoding", - "analogy": "Like residual in JPEG: skeleton = DCT low frequencies, perturbation = high frequencies" - }, - "observer_collapse": { - "description": "The field itself is never materialized as full text. Observer queries specify a path through the field.", - "oac_connection": "Observer-Admissible Cavities: the field is the latent n^n space. A query 'show me the France article' is a touch that manifests the local cavity around the 'France' peak.", - "compression": "Only manifest the touched region. The rest stays compressed in the field representation." - } - }, - - "morse_theory_formalization": { - "density_field": "ρ: M → ℝ⁺ where M is n-dimensional semantic manifold", - "critical_points": "∇ρ = 0. Classified by Hessian eigenvalues: peak (all -), saddle (mixed), void (all +)", - "morse_complex": "Cells built from ascending/descending manifolds of critical points. Combinatorial encoding of field topology.", - "persistence": "Track critical points as ρ threshold varies. Persistent features = real semantic structure. Transient = noise/detail.", - "compression_theorem": ( - "The Morse-Smale complex of ρ encodes the homotopy type of M. " - "If text structure is determined by topological type (links, sections, categories), " - "then the Morse complex is a complete encoding up to homeomorphism. " - "Exact text reconstruction requires perturbation field, but semantic navigation requires only the complex." - ) - }, - - "stack_integration": { - "pist_nd_encoding": { - "role": "PERTURBATION ENCODER: PIST n-D bundle encodes the residual density field after skeleton subtraction", - "mapping": "fiber_dim = topological feature type (peak, ridge, saddle, vortex, void). n_dims = spatial dimensions of semantic manifold (typically 3-4D: topic, time, authority, style)" - }, - "s3c_shells": { - "role": "MULTI-SCALE SHELL STRUCTURE: S3C shell index k = semantic distance from core concept. a = intra-cluster position.", - "mapping": "Concentric shells around each peak = layers of detail: k=0=title, k=1=abstract, k=2=lead, k=3=body, k=4=references, k=5=see-also" - }, - "oac": { - "role": "LAZY MANIFESTATION: The density field is a global OAC. Observer queries are touches that manifest local regions.", - "mapping": "touch(ρ, observer, query_region) → local_manifested_text + residual. Unqueried regions stay latent." - }, - "hypercube_rhomboid": { - "role": "MANIFOLD GEOMETRY: UTF-8 text is an orthogonal hypercube (independent byte positions). Density field is a sheared rhomboid where correlated semantic positions lean into each other.", - "mapping": "Shear matrix A maps from UTF-8 hypercube to density rhomboid. A is learned from corpus: eigenvectors = principal semantic directions." - }, - "famm_delay_lines": { - "role": "TEMPORAL SEQUENCING: Density field has no natural order. FAMM preshaped delays impose a reading path through the field.", - "mapping": "Delay profile = path integral through field gradient. Fast regions = high density (predictable). Slow regions = low density (needs more context)." - }, - "erans_entropy": { - "role": "RESIDUAL CODING: After skeleton encoding, perturbation field is entropy-coded using erans-style enumerative coding on histogram of density residuals.", - "mapping": "Density values are not bytes; but discretized to histogram bins. Enumerative coding is optimal for exact histogram." - } - }, - - "hutter_prize_application": { - "current_paradigm": "1D byte sequence → predict next byte → entropy code prediction residual", - "density_paradigm": "Encode topological skeleton of semantic density field → store as compressed graph + persistent homology → entropy code perturbation field", - "estimated_size": { - "skeleton": "~50-150MB for enwik9 (Morse complex of ~10⁶ peaks + ~10⁷ ridges + persistence pairs)", - "perturbation": "~200-400MB (PIST n-D bundle encoded residuals)", - "total": "~250-550MB vs current best ~115MB", - "caveat": "This is raw field encoding. A hybrid approach may win: use density field for structural regions (infoboxes, citations, links = 40% of enwik) + traditional encoding for free text." - }, - "novel_capability": "Current compressors produce a flat file. A density field compressor produces a NAVIGABLE structure: you can query 'show me all articles 2 links from France' without decompressing everything." - }, - - "digital_dzogchen_connection": { - "philosophy": "Dzogchen: appearances are not solid; they are luminous emptiness — projections of mind's nature. Reality is not a collection of discrete objects but a continuous field of appearing.", - "computational_analogue": "Text is not a collection of discrete bytes but a continuous semantic density field. What we call 'the Wikipedia article on France' is a local modulation of the global information field — a peak with certain topological features.", - "compression_insight": "Just as dzogchen says the entire mandala is present in every point, the entire Wikipedia is present in every local density gradient. Compression is finding the minimal description of the field's topology, not its explicit manifestation." - }, - - "keeper_phrases": [ - "UTF-8 assumes text is a string. Density field assumes text is a landscape.", - "A citation is not 200 bytes. It is a ridge connecting two peaks through a saddle.", - "The Morse-Smale complex is the topological skeleton of meaning.", - "Compression is not predicting the next byte. It is finding the minimal topological description of the semantic field.", - "Observer touch collapses the field; the field itself never needs to fully materialize.", - "In a density field, 'France' and 'Germany' are nearby peaks on the same continental ridge.", - "Vortices encode recursion. Voids encode templates. Saddles encode transitions.", - "The Hutter Prize is asking for a flat file. We should be encoding a navigable manifold." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "density-field-encoding", "topological-compression", "morse-theory", - "semantic-manifold", "beyond-utf8", "digital-dzogchen", "navigable-compression", - "persistent-homology", "morse-smale-complex", "observer-collapse", - "hutter-prize", "oac", "s3c-shells", "pist-perturbation" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "density_field_encoding_theory.json" - with open(out_path, 'w') as f: - json.dump(DENSITY_FIELD, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": DENSITY_FIELD["id"], - "title": DENSITY_FIELD["title"], - "date": DENSITY_FIELD["date"], - "source": DENSITY_FIELD["source"], - "ingested_at": DENSITY_FIELD["metadata"]["ingested_at"], - "tags": DENSITY_FIELD["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nTopological features mapped:") - for feat, props in DENSITY_FIELD["topological_features"].items(): - print(f" • {feat}: {props['encoding']}") - - print(f"\nStack integration:") - for module, mapping in DENSITY_FIELD["stack_integration"].items(): - print(f" ↔ {module}: {mapping['role'][:70]}...") - - print(f"\nKeeper phrases ({len(DENSITY_FIELD['keeper_phrases'])}):") - for p in DENSITY_FIELD["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_erans_field_effect_spectrum.py b/4-Infrastructure/shim/ingest_erans_field_effect_spectrum.py deleted file mode 100644 index a99bbc33..00000000 --- a/4-Infrastructure/shim/ingest_erans_field_effect_spectrum.py +++ /dev/null @@ -1,226 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: erans Field Effect Spectrum Extension -=========================================== -Evolve erans enumerative rANS to encode the spectral decomposition -of the residual field, not just flat histogram coding. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ERANS_FIELD_EFFECT = { - "id": "erans-field-effect-spectrum-v1", - "source": "User insight: evolve erans to become field effect spectrum", - "title": "erans Field Effect Spectrum: Spectral Residual Encoding via Enumerative rANS", - "date": "2026-05-07", - - "core_insight": ( - "erans currently provides optimal exact histogram coding of residuals. " - "Evolve erans to encode the spectral decomposition of the residual field ε(x⃗) itself. " - "Instead of coding residual values directly, compute the field's spectral decomposition " - "(Fourier, wavelet, or eigen-decomposition of the residual correlation matrix) and use " - "erans to entropy-code the spectral coefficients. The 'field effect' is how residuals " - "propagate through the manifold — the spectrum captures this propagation pattern." - ), - - "erans_baseline": { - "current_usage": "Enumerative rANS for optimal exact histogram coding of residual stream Ε", - "mechanism": "Histogram of residual values is exact; enumerative coding is optimal for exact histograms", - "limitation": "Treats residuals as independent symbols. Does not capture spatial/spectral correlation in the residual field itself" - }, - - "field_effect_spectrum": { - "residual_field": "ε(x⃗) is the perturbation field — difference between topological skeleton prediction and actual density", - "spectral_decomposition": { - "fourier_transform": "ε̂(k⃗) = ∫ ε(x⃗) e^(-2πi k⃗·x⃗) dx⃗ — captures frequency components of residual field", - "wavelet_transform": "Wavelet coefficients capture multi-scale residual structure", - "eigen_decomposition": "Compute correlation matrix C_{ij} = ⟨ε_i ε_j⟩, then eigen-decompose C = UΛU^T. Eigenvectors = principal residual patterns, eigenvalues = residual energy per pattern", - "choice": "Eigen-decomposition is most compatible with existing shear matrix framework (Gram matrix G = A^T A already uses eigenvectors)" - }, - "field_effect_interpretation": { - "propagation": "Spectral coefficients show how residuals at one location affect other locations through the manifold", - "correlation": "Off-diagonal terms in correlation matrix C show residual correlation across spatial/temporal dimensions", - "energy_distribution": "Eigenvalues Λ show how residual energy is distributed across principal residual patterns", - "hippocampus_analogue": "Pattern separation in hippocampus (10-40% ensemble overlap) can be modeled in spectral domain — residual patterns with low overlap are separated in eigenspace" - } - }, - - "erans_spectral_encoding": { - "spectral_coefficients_as_symbols": "Treat spectral coefficients (eigenvalues, eigenvector weights, Fourier amplitudes) as symbols for erans coding", - "histogram_exactness": "Histogram of spectral coefficients is exact (derived from exact residual field). Enumerative coding is optimal.", - "advantages": { - "correlation_capture": "Spectral decomposition captures residual correlation that flat histogram coding misses", - "energy_compaction": "Most residual energy concentrated in few spectral coefficients — erans codes these efficiently", - "propagation_modeling": "Field effect spectrum models how residuals propagate through manifold", - "hippocampus_alignment": "Spectral pattern separation aligns with hippocampus ensemble overlap mechanism" - }, - "encoding_pipeline": [ - "Compute residual field ε(x⃗)", - "Compute residual correlation matrix C_{ij} = ⟨ε_i ε_j⟩", - "Eigen-decompose C = UΛU^T", - "Extract spectral coefficients: eigenvalues Λ, eigenvector projection weights a = U^T ε", - "Build exact histogram of spectral coefficients", - "Apply erans enumerative coding to spectral coefficients", - "Store coded spectral coefficients + eigenvectors U", - "Decode: reconstruct spectral coefficients → reconstruct residual field ε̃(x⃗)" - ] - }, - - "integration_with_master_synthesis": { - "stage_12_pist_perturbation": "PIST n-D bundle encodes perturbation field δ. Now also compute spectral decomposition of δ", - "stage_13_erans_spectral": "erans entropy-codes spectral coefficients of perturbation field, not just flat residual values", - "shear_matrix_alignment": "Gram matrix G = A^T A already uses eigenvectors. Residual correlation matrix C shares same eigenspace. Can reuse eigenvector computation.", - "famm_spectral_pruning": "FAMM delay pruning based on eigenvalue spectra can also use residual spectral energy. High residual energy regions get slower delays (need more context).", - "oac_spectral_gate": "OAC admissibility gate can use spectral overlap measure instead of just residual size. Two motifs are admissible if their residual spectral patterns have low overlap (hippocampus pattern separation analogue).", - "radius_ratio_spectral": "Radius-ratio quantization can use spectral energy ratio instead of just scale ratio. ρ_spectral = λ_i / median(Λ)." - }, - - "spectral_field_effect_metrics": { - "spectral_energy_compaction": "Most residual energy in few eigenvalues. If λ₁ >> λ₂ >> ..., then field is highly compressible in spectral domain.", - "spectral_overlap": "Overlap between residual spectral patterns of different motifs. Low overlap = good pattern separation (hippocampus analogue).", - "spectral_entropy": "Entropy of spectral coefficient histogram. Lower entropy = more compressible.", - "spectral_correlation_length": "Correlation length in spectral domain = how far residuals propagate through field.", - "spectral_discrimination_threshold": "zeta^thr_spectral = threshold on spectral overlap for OAC admissibility." - }, - - "compression_gain_from_spectral": { - "energy_compaction": "If 90% of residual energy in top 10% of spectral coefficients, erans codes these 10% efficiently. 10-20% gain over flat histogram.", - "correlation_capture": "Spectral decomposition captures residual correlation. 5-10% additional gain.", - "hippocampus_pattern_separation": "Spectral overlap measure improves OAC gate precision. Avoids 2-3% more bloat.", - "famm_spectral_pruning": "FAMM delays based on residual spectral energy. 3-5% additional context efficiency.", - "total_spectral_gain": "Estimated 20-35% additional gain on residual coding stage." - }, - - "implementation_details": { - "correlation_matrix_computation": { - "method": "C_{ij} = (1/N) Σ_k ε_k(i) ε_k(j) where ε_k is residual at position k in dimension i", - "complexity": "O(N × d²) where N = number of residual positions, d = number of dimensions (typically 3-4: topic, time, authority, style)", - "sparse_approximation": "For large N, use sparse correlation or stochastic approximation" - }, - "eigen_decomposition": { - "method": "Standard symmetric eigen-decomposition of C", - "output": "Eigenvectors U (principal residual patterns), eigenvalues Λ (residual energy per pattern)", - "q16_16_fixed_point": "Eigenvectors and eigenvalues encoded in Q16.16 for hardware-native determinism" - }, - "spectral_coefficient_extraction": { - "method": "a = U^T ε (project residual field onto eigenvectors)", - "output": "Spectral coefficient vector a (weights of each principal residual pattern)", - "histogram": "Build exact histogram of a values for erans coding" - }, - "erans_spectral_coding": { - "input": "Histogram of spectral coefficients a", - "method": "Enumerative rANS (same as baseline, but applied to spectral coefficients instead of raw residuals)", - "output": "Entropy-coded spectral coefficients" - }, - "reconstruction": { - "decode_spectral": "Decode spectral coefficients ã", - "reconstruct_residual": "ε̃ = U ã (reconstruct residual field from spectral coefficients)", - "apply_residual": "s = Repair(Generate(...), ε̃)" - } - }, - - "hippocampus_spectral_analogy": { - "ensemble_overlap_spectral": "Hippocampus pattern separation uses 10-40% ensemble overlap. Spectral analogue: overlap between eigenvectors of residual patterns for different motifs.", - "discrimination_threshold_spectral": "zeta^thr = 10 Hz firing rate. Spectral analogue: zeta^thr_spectral = threshold on spectral coefficient magnitude or eigenvalue ratio.", - "neuron_dropout_spectral": "Neuron dropout during consolidation. Spectral analogue: drop spectral coefficients below threshold (prune low-energy residual patterns).", - "composite_promotion_spectral": "Composite promotion = soliton bound state. Spectral analogue: accepted motifs have coherent spectral residual patterns (low entropy, high compaction)." - }, - - "keeper_phrases": [ - "erans currently codes flat histograms. Evolve erans to code spectral decompositions of the residual field.", - "The field effect is how residuals propagate through the manifold. The spectrum captures this propagation.", - "Compute residual correlation matrix C, eigen-decompose C = UΛU^T, code spectral coefficients with erans.", - "Spectral energy compaction: 90% of residual energy in 10% of coefficients = 10-20% gain.", - "Spectral decomposition captures residual correlation that flat histogram coding misses.", - "FAMM delays can use residual spectral energy: high energy regions get slower delays.", - "OAC gate can use spectral overlap measure instead of just residual size for pattern separation.", - "Hippocampus pattern separation (10-40% ensemble overlap) has spectral analogue in eigenvector overlap.", - "The shear matrix Gram matrix G and residual correlation matrix C share the same eigenspace.", - "Don't code the residual values. Code the spectral pattern of the residual field.", - "Spectral entropy = compressibility. Lower spectral entropy = more compressible residual field.", - "zeta^thr_spectral = threshold on spectral coefficient magnitude for hippocampus-style discrimination.", - "Composite promotion spectral: accepted motifs have coherent residual spectral patterns (low entropy).", - "The field effect spectrum is the residual's propagation pattern through the manifold." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "erans-field-effect", - "spectral-encoding", - "residual-field-spectrum", - "correlation-matrix", - "eigen-decomposition", - "field-effect", - "hippocampus-spectral", - "pattern-separation", - "energy-compaction", - "spectral-entropy", - "enumerative-rans", - "spectral-overlap", - "famm-spectral", - "oac-spectral" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "erans_field_effect_spectrum_v1.json" - with open(out_path, 'w') as f: - json.dump(ERANS_FIELD_EFFECT, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ERANS_FIELD_EFFECT["id"], - "title": ERANS_FIELD_EFFECT["title"], - "date": ERANS_FIELD_EFFECT["date"], - "source": ERANS_FIELD_EFFECT["source"], - "ingested_at": ERANS_FIELD_EFFECT["metadata"]["ingested_at"], - "tags": ERANS_FIELD_EFFECT["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCore insight:") - print(f" {ERANS_FIELD_EFFECT['core_insight'][:80]}...") - - print(f"\nSpectral decomposition methods:") - for method, desc in ERANS_FIELD_EFFECT["field_effect_spectrum"]["spectral_decomposition"].items(): - print(f" • {method}: {desc[:60]}...") - - print(f"\nerans spectral encoding pipeline:") - for i, step in enumerate(ERANS_FIELD_EFFECT["erans_spectral_encoding"]["encoding_pipeline"], 1): - print(f" {i}. {step[:60]}...") - - print(f"\nIntegration with master synthesis:") - for integration, desc in ERANS_FIELD_EFFECT["integration_with_master_synthesis"].items(): - print(f" • {integration}: {desc[:60]}...") - - print(f"\nCompression gain from spectral:") - for source, gain in ERANS_FIELD_EFFECT["compression_gain_from_spectral"].items(): - print(f" • {source}: {gain}") - - print(f"\nKeeper phrases ({len(ERANS_FIELD_EFFECT['keeper_phrases'])}):") - for p in ERANS_FIELD_EFFECT['keeper_phrases']: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_erans_reference.py b/4-Infrastructure/shim/ingest_erans_reference.py deleted file mode 100644 index 316fa577..00000000 --- a/4-Infrastructure/shim/ingest_erans_reference.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: erans — enumerative rANS (reference only, NO CODE COPIED) -================================================================== -izabera/erans is a streamable single-pass rANS variant. -NO LICENSE — algorithmic notes only, zero code incorporated. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ERANS_REF = { - "id": "erans-enumerative-rans-reference", - "source": "https://github.com/izabera/erans", - "title": "erans: Enumerative rANS — Algorithmic Reference (No Code)", - "date": "2026-05-07", - "license": "NONE SPECIFIED — DO NOT INCORPORATE CODE", - "status": "REFERENCE ONLY — algorithmic ideas, zero lines copied", - - "what_it_is": ( - "A streamable, single-pass, adaptive rANS variant that encodes " - "the exact histogram + permutation index of a multiset. Achieves " - "the enumerative coding bound: compressed size approaches log of " - "the multinomial coefficient, strictly less than Shannon entropy " - "for the same data (by ~((K-1)/2)log2(N) bits)." - ), - - "key_algorithmic_ideas": { - "single_pass_adaptive": { - "concept": "Encode each symbol against running counts INCLUDING current position. No pre-pass for histogram.", - "why_it_works": "c_i(s) = count of s up to position i (including i). p_i(s) = c_i(s)/i. Encoder bumps count BEFORE computing CDF so decoder sees same prefix counts walking backward.", - "our_take": "Maps to PIST streaming encode where shell mass accumulates online. The 'include current position' trick avoids zero-probability symbols." - }, - "enumerative_bound": { - "concept": "log2(M) = NH - ((K-1)/2)log2(N) + O(1) where M is multinomial coefficient, H is empirical entropy, K=256", - "significance": "Beats Shannon entropy by ~1018 bits for N=2^24, K=256. The gain is from NOT quantizing frequencies to powers of 2.", - "our_take": "This is the geometric compression gain from exact histogram — analogous to storing the exact shear matrix rather than a quantized approximation." - }, - "shrub_data_structure": { - "concept": "2-level tree, branching factor 16, for O(1) branchless CDF updates. Bottom: 16 groups of 16 counters. Top: 16 group sums.", - "operations": "inc/dec: masked add to group + top. sym→cdf: top[byte>>4] + bottom[byte&0xf]. cdf→sym: vector compare for monotonic scan.", - "isa_note": "erans uses AVX-512 masked adds. Scalar equivalent works at ~2x cycles. Algorithmic structure is ISA-agnostic.", - "our_take": "Fenwick tree alternative. The 16×16 split is natural for byte alphabet. Could map to Q0_16 accumulators for fixed-point probability tracking." - }, - "streaming_renorm": { - "concept": "State kept in [M, 256*M). Overflow bytes emitted when state >= 256*f. No length prefix needed — decoder pulls until state >= M_final.", - "boundary_case": "When f=M (all symbols same so far), state stays at 1, renorm never fires — degenerates to no-op. Clean.", - "our_take": "FAMM preshaped renorm: the renorm threshold shifts with M. Could preshape the threshold per shell class for structured data." - }, - "histogram_encoding": { - "concept": "Rice coding: split each count into lower B bits (binary) + upper part (unary). B = max(0, ceil(log2(M))-8).", - "overhead": "At most 576 bytes for N=2^24. Theoretical lower bound ~555 bytes.", - "our_take": "S3C shell coordinates could encode histogram more compactly — counts are shell populations, naturally structured." - } - }, - - "hutter_prize_relevance": { - "entropy_coder": "erans is a candidate entropy coding backend. Beats standard rANS by ~0.004% on large blocks — small but real.", - "streaming": "Single-pass streaming matches our PIST-S3C-FAMM pipeline architecture. No pre-pass needed.", - "block_size": "Implementation limit 2^24 (16MiB). Algorithm has no inherent limit. enwik8 = 100M, enwik9 = 1G — need larger blocks or chaining.", - "comparison_to_fse": "FSE (tANS) decodes in ~5-10 cycles/byte. erans is slower but produces smaller output. For Hutter, size matters more than speed.", - "adaptation_limitation": "erans is NOT locally adaptive — uses global histogram. For varying distributions, block-splitting needed. Our S3C shell batching naturally provides block boundaries." - }, - - "why_separate": [ - "NO LICENSE — cannot incorporate any code", - "Algorithmic ideas are public domain (math), implementation is not", - "Our shrub-equivalent should be written from scratch in Lean + extraction target", - "ISA-agnostic by design: no AVX-512 dependency, scalar fallback always" - ], - - "design_rules_added": { - "isa_agnostic": "Never assume any instruction set is available. SIMD is opportunistic, never structural. All hot paths must have scalar fallback.", - "license_gate": "No code enters the stack without a compatible license. Algorithmic ideas from unlicensed repos are noted as reference only.", - "separation": "Reference implementations live in design notes, never in the source tree." - }, - - "metadata": { - "ingested_at": time.time(), - "tags": ["entropy-coding", "rans", "enumerative-coding", "reference-only", - "no-license", "streaming", "hutter-prize", "entropy", "shrub"] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "erans_enumerative_rans_reference.json" - with open(out_path, 'w') as f: - json.dump(ERANS_REF, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ERANS_REF["id"], - "title": ERANS_REF["title"], - "date": ERANS_REF["date"], - "source": ERANS_REF["source"], - "ingested_at": ERANS_REF["metadata"]["ingested_at"], - "tags": ERANS_REF["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nAlgorithmic ideas captured (no code):") - for name, idea in ERANS_REF["key_algorithmic_ideas"].items(): - print(f" • {name}: {idea['concept'][:80]}...") - - print(f"\n⚠ LICENSE: {ERANS_REF['license']}") - print(f"⚠ {ERANS_REF['why_separate'][0]}") - print(f"⚠ {ERANS_REF['why_separate'][1]}") - - print(f"\nDesign rules:") - for rule, text in ERANS_REF["design_rules_added"].items(): - print(f" + {rule}: {text[:80]}...") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_erdos_problems.py b/4-Infrastructure/shim/ingest_erdos_problems.py deleted file mode 100644 index 9867a23f..00000000 --- a/4-Infrastructure/shim/ingest_erdos_problems.py +++ /dev/null @@ -1,451 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest Erdős Problems from External Sources -========================================= -Pull in Erdős problems from external sources and ingest into local research database. -""" - -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") -RESEARCH_DIR = RESEARCH_STACK / "shared-data/data/germane/research" - - -# Erdős problems from Wikipedia and other sources -ERDOS_PROBLEMS = { - "unsolved_conjectures": [ - { - "name": "Erdős–Gyárfás conjecture", - "description": "On cycles with lengths equal to a power of two in graphs with minimum degree 3.", - "domain": "Graph Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Gy%C3%A1rf%C3%A1s_conjecture" - }, - { - "name": "Erdős–Hajnal conjecture", - "description": "In a family of graphs defined by an excluded induced subgraph, every graph has either a large clique or a large independent set.", - "domain": "Graph Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Hajnal_conjecture" - }, - { - "name": "Erdős–Mollin–Walsh conjecture", - "description": "On consecutive triples of powerful numbers.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Powerful_number" - }, - { - "name": "Erdős–Selfridge conjecture", - "description": "A covering system with distinct moduli contains at least one even modulus.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Covering_system" - }, - { - "name": "Erdős–Straus conjecture", - "description": "For every integer n ≥ 2, the equation 4/n = 1/x + 1/y + 1/z has a solution in positive integers x, y, z.", - "domain": "Diophantine Equations", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Straus_conjecture" - }, - { - "name": "Erdős conjecture on arithmetic progressions", - "description": "If Σ_{a∈A} 1/a diverges, then A contains arbitrarily long arithmetic progressions.", - "domain": "Additive Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_conjecture_on_arithmetic_progressions" - }, - { - "name": "Erdős–Szekeres conjecture", - "description": "On the number of points needed to ensure that a point set contains a large convex polygon.", - "domain": "Discrete Geometry", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Szekeres_conjecture" - }, - { - "name": "Erdős–Turán conjecture on additive bases", - "description": "If A is an additive basis of order 2 for the natural numbers, then the sum of reciprocals diverges: Σ_{a∈A} 1/a = ∞.", - "domain": "Additive Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Tur%C3%A1n_conjecture_on_additive_bases" - }, - { - "name": "Erdős conjecture on quickly growing integer sequences", - "description": "On integer sequences with rational reciprocal series (Sylvester's sequence).", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Sylvester%27s_sequence" - }, - { - "name": "Erdős–Oler conjecture on circle packing", - "description": "On circle packing in an equilateral triangle with a number of circles one less than a triangular number.", - "domain": "Geometry", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Circle_packing_in_an_equilateral_triangle" - }, - { - "name": "Minimum overlap problem", - "description": "To estimate the limit of M(n).", - "domain": "Combinatorics", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Minimum_overlap_problem" - }, - { - "name": "Erdős conjecture on ternary expansion of 2^n", - "description": "The ternary expansion of 2^n contains at least one digit 2 for every n > 8.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Moser equation", - "description": "The equation 1^k + 2^k + ... + (m-1)^k = m^k has no solutions except 1^1 + 2^1 = 3^1.", - "domain": "Diophantine Equations", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Moser_equation" - } - ], - "solved_conjectures": [ - { - "name": "Erdős–Faber–Lovász conjecture", - "description": "On coloring unions of cliques.", - "domain": "Graph Theory", - "status": "Solved (2021)", - "solved_by": "Dong Yeap Kang, Tom Kelly, Daniela Kühn, Abhishek Methuku, and Deryk Osthus", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Faber%E2%80%93Lov%C3%A1sz_conjecture" - }, - { - "name": "Erdős sumset conjecture", - "description": "On sets.", - "domain": "Additive Combinatorics", - "status": "Solved (2018)", - "solved_by": "Joel Moreira, Florian Karl Richter, Donald Robertson", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_sumset_conjecture" - }, - { - "name": "Burr–Erdős conjecture", - "description": "On Ramsey numbers of graphs.", - "domain": "Ramsey Theory", - "status": "Solved (2015)", - "solved_by": "Choongbum Lee", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Burr%E2%80%93Erd%C5%91s_conjecture" - }, - { - "name": "Erdős conjecture on equitable colorings", - "description": "Now known as the Hajnal–Szemerédi theorem.", - "domain": "Graph Theory", - "status": "Solved (1970)", - "solved_by": "András Hajnal and Endre Szemerédi", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Lovász conjecture on weak/strong delta-systems", - "description": "On delta-systems.", - "domain": "Combinatorics", - "status": "Solved (1974)", - "solved_by": "Michel Deza", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Heilbronn conjecture", - "description": "In combinatorial number theory on the number of sums of two sets of residues modulo a prime.", - "domain": "Number Theory", - "status": "Solved (1994)", - "solved_by": "Dias da Silva and Hamidoune", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Graham conjecture", - "description": "In combinatorial number theory on monochromatic Egyptian fraction representations of unity.", - "domain": "Number Theory", - "status": "Solved (2000)", - "solved_by": "Ernie Croot", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Stewart conjecture", - "description": "On the Diophantine equation n! + 1 = p^k_a p_{k+1}^b.", - "domain": "Number Theory", - "status": "Solved (2001)", - "solved_by": "Florian Luca", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Cameron–Erdős conjecture", - "description": "On sum-free sets of integers.", - "domain": "Number Theory", - "status": "Solved (2003-2004)", - "solved_by": "Ben Green and Alexander Sapozhenko", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Menger conjecture", - "description": "On disjoint paths in infinite graphs.", - "domain": "Graph Theory", - "status": "Solved (2009)", - "solved_by": "Ron Aharoni and Eli Berger", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős distinct distances problem", - "description": "The correct exponent was proved in 2010 by Larry Guth and Nets Katz, but the correct power of log n is still undetermined.", - "domain": "Discrete Geometry", - "status": "Partially Solved (2010)", - "solved_by": "Larry Guth and Nets Katz", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_distinct_distances_problem" - }, - { - "name": "Erdős–Rankin conjecture on prime gaps", - "description": "On prime gaps.", - "domain": "Number Theory", - "status": "Solved (2014)", - "solved_by": "Ford, Green, Konyagin, and Tao", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős discrepancy problem", - "description": "On partial sums of ±1-sequences.", - "domain": "Number Theory", - "status": "Solved (2015)", - "solved_by": "Terence Tao", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős squarefree conjecture", - "description": "Central binomial coefficients C(2n, n) are never squarefree for n > 4.", - "domain": "Number Theory", - "status": "Solved (1996)", - "solved_by": "Various", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős primitive set conjecture", - "description": "The sum Σ_{n∈A} 1/(n log n) for any primitive set A attains its maximum at the set of prime numbers.", - "domain": "Number Theory", - "status": "Solved (2022)", - "solved_by": "Jared Duker Lichtman", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Sauer problem", - "description": "About maximum number of edges an n-vertex graph can have without containing a k-regular subgraph.", - "domain": "Graph Theory", - "status": "Solved", - "solved_by": "Oliver Janzer and Benny Sudakov", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 728", - "description": "Solved in 2026 using AI assistance.", - "domain": "Unknown", - "status": "Solved (2026)", - "solved_by": "Kevin Barreto and Liam Price with ChatGPT 5.2 and Aristotle Lean API", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 347", - "description": "On subset sums of sequences with ratio limit 2.", - "domain": "Combinatorics", - "status": "Solved (2026)", - "solved_by": "Enrique Barschkis", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 369", - "description": "Solved in March 2026 by 17-year-old Sky Yang (Yueer Yang).", - "domain": "Unknown", - "status": "Solved (2026)", - "solved_by": "Sky Yang (Yueer Yang)", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - } - ], - "additional_problems": [ - { - "name": "Erdős–Ko–Rado theorem", - "description": "Maximum size of intersecting families of k-subsets of {1,...,n} is C(n-1, k-1) for n ≥ 2k.", - "domain": "Extremal Set Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Ginzburg–Ziv theorem", - "description": "Any 2n-1 integers contain n whose sum is divisible by n.", - "domain": "Additive Number Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Stone theorem", - "description": "For any graph H, ex(n,H) = (1 - 1/χ(H)-1 + o(1))n²/2 where χ(H) is chromatic number.", - "domain": "Extremal Graph Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Rényi random graph model", - "description": "Study properties of G(n,p) random graphs. Threshold phenomena for connectivity, giant component, Hamiltonicity.", - "domain": "Random Graphs", - "status": "Standard model", - "source": "Standard model", - "url": "" - }, - { - "name": "Erdős Hadamard conjecture", - "description": "There exist Hadamard matrices of order 4k for all k.", - "domain": "Linear Algebra", - "status": "Unsolved", - "source": "Standard conjecture", - "url": "" - }, - { - "name": "Erdős–Moser problem", - "description": "Find all solutions to 1/a + 1/b + 1/c + 1/d + 1/e = 1 in distinct positive integers.", - "domain": "Diophantine Equations", - "status": "Solved (only known solution)", - "source": "Standard problem", - "url": "" - } - ] -} - - -def create_erdos_problems_document(): - """Create a comprehensive Erdős problems document.""" - timestamp = datetime.now().isoformat() - - document = { - "document_id": "erdos_problems_comprehensive_v1", - "title": "Comprehensive Erdős Problems Collection", - "created": timestamp, - "source": "Wikipedia and other sources", - "unsolved_conjectures": ERDOS_PROBLEMS["unsolved_conjectures"], - "solved_conjectures": ERDOS_PROBLEMS["solved_conjectures"], - "additional_problems": ERDOS_PROBLEMS["additional_problems"], - "statistics": { - "total_unsolved": len(ERDOS_PROBLEMS["unsolved_conjectures"]), - "total_solved": len(ERDOS_PROBLEMS["solved_conjectures"]), - "total_additional": len(ERDOS_PROBLEMS["additional_problems"]), - "total_problems": len(ERDOS_PROBLEMS["unsolved_conjectures"]) + len(ERDOS_PROBLEMS["solved_conjectures"]) + len(ERDOS_PROBLEMS["additional_problems"]) - }, - "domains": { - "Graph Theory": 0, - "Number Theory": 0, - "Discrete Geometry": 0, - "Additive Number Theory": 0, - "Diophantine Equations": 0, - "Combinatorics": 0, - "Extremal Set Theory": 0, - "Ramsey Theory": 0, - "Random Graphs": 0, - "Linear Algebra": 0, - "Additive Combinatorics": 0, - "Geometry": 0, - "Unknown": 0 - } - } - - # Count domains - all_problems = ERDOS_PROBLEMS["unsolved_conjectures"] + ERDOS_PROBLEMS["solved_conjectures"] + ERDOS_PROBLEMS["additional_problems"] - for problem in all_problems: - domain = problem["domain"] - if domain in document["domains"]: - document["domains"][domain] += 1 - - return document - - -def main(): - print("=" * 70) - print(" INGESTING ERDŐS PROBLEMS INTO LOCAL RESEARCH DATABASE") - print("=" * 70) - - # Create document - document = create_erdos_problems_document() - - print(f"\nStatistics:") - print(f" Total unsolved: {document['statistics']['total_unsolved']}") - print(f" Total solved: {document['statistics']['total_solved']}") - print(f" Total additional: {document['statistics']['total_additional']}") - print(f" Total problems: {document['statistics']['total_problems']}") - - print(f"\nDomain distribution:") - for domain, count in document["domains"].items(): - if count > 0: - print(f" {domain}: {count}") - - # Save to research directory - output_file = RESEARCH_DIR / "erdos_problems_comprehensive_v1.json" - with open(output_file, 'w') as f: - json.dump(document, f, indent=2) - - print(f"\n✓ Erdős problems saved to: {output_file}") - - # Update research ingestion index - index_file = RESEARCH_DIR / "research_ingestion_index.json" - - if index_file.exists(): - with open(index_file, 'r') as f: - index = json.load(f) - else: - index = [] - - # Add new entry - new_entry = { - "id": "erdos-problems-comprehensive-v1", - "title": "Comprehensive Erdős Problems Collection", - "date": datetime.now().isoformat(), - "source": "Wikipedia and other sources", - "ingested_at": datetime.now().timestamp(), - "tags": ["erdos", "conjectures", "problems", "graph-theory", "number-theory", "combinatorics"] - } - - index.append(new_entry) - - with open(index_file, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Research ingestion index updated") - - return document - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/ingest_gccl_gec_spec.py b/4-Infrastructure/shim/ingest_gccl_gec_spec.py deleted file mode 100644 index 7e0dad12..00000000 --- a/4-Infrastructure/shim/ingest_gccl_gec_spec.py +++ /dev/null @@ -1,357 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: GCCL-GEC Spec — Full Compression Architecture -====================================================== -Geometric-Cognitive Compression Law / Glyph Eigen Codec -Byte-exact compression via lawful callable glyph kernels. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -GCCL_GEC = { - "id": "gccl-gec-spec-v1", - "source": "USER formal spec — complete compression architecture", - "title": "GCCL-GEC: Geometric-Cognitive Compression Law / Glyph Eigen Codec — Full Specification", - "date": "2026-05-07", - - "purpose": ( - "Compress a byte corpus by finding the smallest lawful executable " - "glyph/eigen/manifold program that reconstructs the exact original bytes. " - "Do not store the text. Store the cheapest lawful generator of the byte projection." - ), - - "archive_structure": { - "formula": "A = D ⊕ 𝔊 ⊕ Χ ⊕ Τ ⊕ 𝕌 ⊕ Γ ⊕ Θ ⊕ Ε ⊕ R", - "components": { - "D": {"name": "Deterministic Decompressor", "role": "Loads profile, interprets packets, emits exact bytes"}, - "𝔊": {"name": "GlyphBook", "role": "Maps printable codepoints to callable reconstruction kernels"}, - "Χ": {"name": "ChiralityBook", "role": "Maps chirality vectors to law-axes for each glyph"}, - "Τ": {"name": "TypeBook", "role": "Maps datatype witnesses to structural generative laws"}, - "𝕌": {"name": "EigenBook", "role": "Stores reusable eigenbasis/spectrum/coefficient descriptors"}, - "Γ": {"name": "Glyph Packet Stream", "role": "Atomic compression units — not characters, but kernel invocations"}, - "Θ": {"name": "Parameter Stream", "role": "Side-stream of integer/arithmetic-coded payload data"}, - "Ε": {"name": "Residual Stream", "role": "Exact byte repair — honesty layer where speculative compressors die"}, - "R": {"name": "Receipt/Checksum/Audit", "role": "SHA256 verification + audit trail"} - }, - "compact_equation": "C = Π_B(Bind_GCCL(𝔊, Χ, Τ, 𝕌, Γ, Θ, Ε))", - "compact_meaning": "Corpus = byte projection of GCCL-bound kernel composition" - }, - - "fundamental_packet": { - "formula": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "fields": { - "γᵢ": {"name": "visible_glyph", "domain": "emoji / math symbol / PUA codepoint / Unicode printable", "meaning": "Invokes a specific reconstruction kernel"}, - "χᵢ": {"name": "chirality_vector", "domain": "⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩", "meaning": "Which law-axis the glyph operates on"}, - "κᵢ": {"name": "local_context / manifold_coordinate", "domain": "position in n-D semantic manifold", "meaning": "Where in the field this packet applies"}, - "τᵢ": {"name": "datatype_witness", "domain": "TypeBook entry", "meaning": "Structural law to import (biography, math article, citation graph...)"}, - "UᵢΛᵢaᵢ": {"name": "eigen_descriptor", "domain": "EigenBook entry", "meaning": "Reusable geometric basis + spectrum + sparse coefficients"}, - "θᵢ": {"name": "parameters", "domain": "side-stream encoded integers", "meaning": "Mode selectors, eigenbook indices, residual class tags"}, - "εᵢ": {"name": "residual", "domain": "byte repair data", "meaning": "Exact correction to generated prediction"} - }, - "core_rule": "glyph ≠ symbol. glyph = callable compression kernel." - }, - - "chirality_book": { - "description": "Same glyph means different lawful things depending on chirality vector.", - "vector_axes": { - "G_geo": "geometric primitive", - "G_comp": "mathematics article/domain macro", - "G_spec": "eigenbasis selector", - "G_topo": "incidence/angle graph operator", - "G_arith": "numeric constraint kernel", - "G_load": "expensive/fallback region marker" - }, - "example": { - "📐_geo": "geometric primitive", - "📐_comp": "mathematics article/domain macro", - "📐_spec": "eigenbasis selector", - "📐_topo": "incidence/angle graph operator", - "📐_arith": "numeric constraint kernel", - "📐_load": "expensive/fallback region marker" - }, - "power": "Finite glyph set becomes combinatorially huge through chirality rotation" - }, - - "type_book": { - "core_rule": "The datatype is the engine. UTF-8 is only the exhaust.", - "example_types": [ - "WikiArticle", - "WikiArticle", - "WikiArticle", - "Infobox", - "CitationGraph", - "SectionTree", - "HistoricalTimeline", - "BranchTaxonomy", - "TableMatrix", - "SameReferentCluster", - "FormulaRegion", - "ListRegion", - "MarkupRegion" - ], - "compression_mass": "WikiArticle already implies title, lead, infobox, birth/death fields, occupation, chronology, categories, citation patterns, linking conventions. The datatype itself carries structure.", - "value": "A type imports generative structure without storing every instance explicitly." - }, - - "eigen_book": { - "formula": "Gᵢ = ⟨τᵢ, Uᵢ, Λᵢ, aᵢ, εᵢ⟩", - "components": { - "τᵢ": "manifold/type class", - "Uᵢ": "eigenbasis / local frame (column vectors)", - "Λᵢ": "eigenvalue spectrum / scale-pressure", - "aᵢ": "sparse coefficients (activation weights)", - "εᵢ": "residual bytes (perturbation from ideal eigenstate)" - }, - "example_math_article": { - "U": ["u_definition", "u_taxonomy", "u_history", "u_notation", "u_application", "u_philosophy", "u_reference"], - "meaning": "A mathematics page ≈ sparse activation of these eigenmodes + residual repair" - }, - "keeper": "A wiki page is a sparse eigenstate of a typed reconstruction manifold, plus apology bytes." - }, - - "parameter_encoding": { - "channels": [ - "side streams", - "variation selectors", - "combining marks", - "PUA suffixes", - "integer-coded payloads", - "arithmetic-coded payloads" - ], - "example": "📐︖︉︚ → MathDomainKernel(mode=22, eigenbook=9, residual_class=26)", - "rule": "Decompressor reads codepoints, not what fonts display." - }, - - "residual_stream": { - "role": "The most important honesty layer.", - "core_rule": "Decode(generative_model) ⊕ ε = exact original bytes", - "forms": [ - "literal patch", - "XOR patch", - "edit script", - "structural diff", - "entropy-coded correction", - "markup repair", - "serialization repair" - ], - "diagnostic": "ρ = |ε| / |raw_span|. ρ < 0.1 = excellent. ρ ≈ 0.5 = maybe useful. ρ ≈ 1.0 = generator failed." - }, - - "gccl_gain_test": { - "formula": "ΔGCL(Γᵢ) = ΔG_geo + ΔG_comp + ΔG_spec + ΔG_topo + ΔG_arith - G_load - L(εᵢ) - L(θᵢ) - amortized_decoder_cost", - "accept_rule": "ΔGCL(Γᵢ) > 0", - "practical_form": "gain(Γᵢ) = literal_cost(span) - encoded_cost(γᵢ, χᵢ, κᵢ, τᵢ, UΛa, θᵢ, εᵢ)", - "principle": "No vibes. No 'semantic compression' handwaving. Only: shorter, deterministic, byte-exact, auditable." - }, - - "decode_pipeline": [ - "Load decompressor profile D", - "Load GlyphBook 𝔊", - "Load ChiralityBook Χ", - "Load TypeBook Τ", - "Load EigenBook 𝕌", - "Read region index I", - "For each Γᵢ: resolve glyph γᵢ, chirality χᵢ, type τᵢ, eigen descriptor UᵢΛᵢaᵢ, parameters θᵢ", - "Generate predicted byte span ŝᵢ", - "Apply residual εᵢ", - "Emit exact span sᵢ", - "Concatenate spans", - "Verify checksum / receipt" - ], - - "encode_pipeline": [ - "Segment corpus into candidate spans (pages, sections, infoboxes, tables, citations, formulas, markup regions)", - "Infer candidate types τ", - "Fit candidate geometric model (choose U, Λ, a)", - "Choose glyph kernel γ and chirality χ", - "Generate predicted bytes", - "Compute residual ε", - "Score: gain = literal_cost - encoded_cost", - "Keep candidates with gain > 0", - "Solve covering problem: choose packet set Γ* covering C with minimum total cost", - "Emit archive", - "Decode immediately and verify exact byte equality" - ], - - "model_families": { - "A_Wiki_structural": { - "kernels": ["WikiArticle", "Infobox", "SectionTree", "CitationGraph", "CategoryList", "InternalLinkGraph", "ReferenceList", "TableMatrix"], - "value": "Highest practical value. Structural redundancy in encyclopedic corpora is massive." - }, - "B_Same_referent": { - "kernels": ["SameReferentVariation", "EntityAliasCluster", "PronounEpithetChain"], - "value": "Handles elegant-variation / synonym-heavy text where literal repetition is low." - }, - "C_Arithmetic_date": { - "kernels": ["Year", "DateInterval", "Coordinate", "PopulationTable", "UnitExpression", "Ranking", "Ordinal"], - "value": "Numbers are dense but highly structured. Very reliable wins." - }, - "D_Fractal_generator": { - "kernels": ["Mandelbrot", "L-system", "CellularAutomaton", "ProceduralImage", "ParametricCurve"], - "value": "Only useful if byte projection matches generator closely. SVG serialization cost usually dominates." - }, - "E_Eigenfield": { - "kernels": ["ArticleEigenfield", "CitationEigenfield", "MarkupEigenfield", "SemanticDensityField"], - "value": "Region-level reconstruction. Connects to density-field encoding theory." - } - }, - - "stress_test_lesson": { - "mandelbrot_svg": "generator cost ≈ tiny, serialized artifact cost ≈ huge", - "conclusion": "z ↦ z² + c generates the image, but not the exact SVG file. Residual = ε_serialize.", - "best_diagnostic": "ρ = |ε| / |raw_span|" - }, - - "implementation_phases": { - "Phase_1": { - "name": "Byte-exact toy codec", - "scope": "GlyphBook + TypeBook + ResidualStream", - "kernels": ["CitationGraphKernel", "InfoboxKernel", "SectionTreeKernel"], - "goal": "generated_span + residual = original_span" - }, - "Phase_2": { - "name": "Add arithmetic/date kernels", - "scope": "Years, dates, coordinates, measurements, rankings, table values", - "value": "Reliable wins on dense numeric data" - }, - "Phase_3": { - "name": "Add same-referent variation", - "scope": "EntityClusterKernel, AliasEmitter, CoreferenceSurfaceFormKernel", - "value": "Attacks low-repetition text" - }, - "Phase_4": { - "name": "Add eigen descriptors", - "scope": "UΛa for region classes", - "caution": "Only after above works. Use as descriptor reuse, not magic semantic compression." - }, - "Phase_5": { - "name": "Add PUA glyph acceleration", - "rule": "promotion_gain = repeated_invocation_savings - glyph_definition_cost. Promote only if positive." - } - }, - - "prototype_archive": { - "magic": "GEC1", - "struct_fields": ["decoder_profile", "glyphbook", "typebook", "packets", "params", "residuals", "sha256"], - "packet_fields": ["glyph_id: u32", "chirality: u8", "type_id: u16", "region_start: u64", "region_len: u32", "eigen_id: Option", "param_ref", "residual_ref"], - "decode_invariant": "sha256(decode(archive)) == sha256(original)" - }, - - "stack_integration": { - "density_field_encoding": { - "role": "REGION-LEVEL MODEL FAMILY E. The density field IS an eigenfield kernel.", - "mapping": "ρ(x⃗) = SemanticDensityField kernel. Topological skeleton = GlyphBook + EigenBook. Perturbation = ResidualStream." - }, - "s3c_shells": { - "role": "LOCAL COORDINATE κᵢ. Shell index k = semantic distance from peak (e.g., article title). a = intra-cluster angular position.", - "mapping": "κᵢ encoded as S3C shell coordinates: cheap integer manifold position." - }, - "oac": { - "role": "GLYPH KERNEL LAZINESS. A glyph is a callable kernel stored in the OAC. It only materializes when touched by the decoder pipeline.", - "mapping": "GlyphBook = library of OAC touch-manifestable kernels. Chirality = which touch interpretation." - }, - "hypercube_rhomboid": { - "role": "MANIFOLD GEOMETRY OF THE PACKET STREAM. UTF-8 is orthogonal hypercube (independent byte positions). GCCL-GEC is sheared hyper-rhomboid: each packet's meaning depends on neighboring packets through chirality and type context.", - "mapping": "Shear matrix learned from corpus: eigenvectors = principal semantic directions. Packets lean into each other." - }, - "famm_delay_lines": { - "role": "TEMPORAL SEQUENCING OF PACKET STREAM. FAMM preshaped delays impose decode order through the packet stream.", - "mapping": "Delay profile = path integral through packet dependencies. Fast regions = high structural redundancy (predictable). Slow regions = high residual density (needs more context)." - }, - "erans_entropy": { - "role": "RESIDUAL AND PARAMETER STREAM CODING. After glyph prediction, residual bytes and parameters are entropy-coded via enumerative rANS.", - "mapping": "Histogram of residuals is exact; erans enumerative coding is optimal for exact histograms." - }, - "radius_ratio_motif": { - "role": "LOCAL ADMISSIBILITY QUANTIZER FOR PACKET SELECTION. Given a local feature ratio ρ, the radius-ratio rule selects the smallest stable coordination motif (kernel).", - "mapping": "Local scale ratio → admissible kernel class (WikiArticle, Infobox, CitationGraph...) + residual. Same move: continuous witness → finite motif alphabet." - } - }, - - "keeper_phrases": [ - "Do not store the text. Store the cheapest lawful generator of the byte projection.", - "glyph ≠ symbol. glyph = callable compression kernel.", - "The datatype is the engine. UTF-8 is only the exhaust.", - "A wiki page is a sparse eigenstate of a typed reconstruction manifold, plus apology bytes.", - "Never trust a glyph until the residual gets smaller.", - "No vibes. No 'semantic compression' handwaving. Only: shorter, deterministic, byte-exact, auditable.", - "ρ = |ε| / |raw_span|. This is the only number that matters.", - "The decompressor reads codepoints, not what fonts display.", - "A finite glyph set becomes combinatorially huge because each glyph can rotate through many law-axes.", - "The Morse-Smale complex is the topological skeleton of meaning. GCCL-GEC is the lawful engine that navigates it." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "gccl-gec", "compression-architecture", "glyph-eigen-codec", - "byte-exact-compression", "callable-kernel", "chirality-book", - "type-book", "eigen-book", "residual-stream", "gain-test", - "hutter-prize", "density-field", "s3c-shells", "oac", - "hypercube-rhomboid", "famm", "erans", "radius-ratio", - "geometric-compression" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "gccl_gec_spec_v1.json" - with open(out_path, 'w') as f: - json.dump(GCCL_GEC, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": GCCL_GEC["id"], - "title": GCCL_GEC["title"], - "date": GCCL_GEC["date"], - "source": GCCL_GEC["source"], - "ingested_at": GCCL_GEC["metadata"]["ingested_at"], - "tags": GCCL_GEC["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nArchive components (9):") - for k, v in GCCL_GEC["archive_structure"]["components"].items(): - print(f" {k} = {v['name']}: {v['role'][:60]}...") - - print(f"\nPacket fields (7):") - for field, props in GCCL_GEC["fundamental_packet"]["fields"].items(): - print(f" {field} → {props['name']}: {props['meaning'][:50]}...") - - print(f"\nModel families (5):") - for fam, data in GCCL_GEC["model_families"].items(): - print(f" {fam}: {len(data['kernels'])} kernels — {data['value'][:50]}...") - - print(f"\nStack integration (7):") - for module, mapping in GCCL_GEC["stack_integration"].items(): - print(f" ↔ {module}: {mapping['role'][:65]}...") - - print(f"\nImplementation phases (5):") - for phase, data in GCCL_GEC["implementation_phases"].items(): - print(f" {phase}: {data['name']} — {data.get('goal', data.get('value', data.get('scope', '')))[:50]}...") - - print(f"\nKeeper phrases ({len(GCCL_GEC['keeper_phrases'])}):") - for p in GCCL_GEC["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_hippocampus_tabula_plena.py b/4-Infrastructure/shim/ingest_hippocampus_tabula_plena.py deleted file mode 100644 index 915696a8..00000000 --- a/4-Infrastructure/shim/ingest_hippocampus_tabula_plena.py +++ /dev/null @@ -1,272 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Hippocampus Tabula Plena Combined Approach -================================================== -Combines maximum math density + unified compression architecture + -hippocampus engram consolidation + tabula plena (full slate) insight. -FAMM delay lines model the pruning from dense initial state to sparse structured state. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HIPPOCAMPUS_TABULA_PLENA = { - "id": "hippocampus-tabula-plena-combined-v1", - "source": "Synthesis: maximum math density + unified architecture + hippocampus engram consolidation (Tomé 2024) + tabula plena (Live Science 2024)", - "title": "Hippocampus Tabula Plena: Full Slate Compression with FAMM Pruning Dynamics", - "date": "2026-05-07", - - "core_synthesis": ( - "The hippocampus starts as 'tabula plena' (full slate) — densely wired with hyperconnected " - "neurons in seemingly random pattern — and prunes to sparse, structured networks during " - "maturation. This is exactly the compression paradigm: start with maximum math density " - "(full Unicode spectrum, custom glyphs, omniversal chirality) and prune via FAMM delay " - "lines, OAC gates, radius-ratio quantization, and gain tests to minimal representation " - "that reconstructs exactly. FAMM models the adaptive pruning dynamics from dense " - "initial state to sparse structured state." - ), - - "hippocampus_tabula_plena_insight": { - "source": "Live Science 2024: Jonas et al. Nature Communications - hippocampus CA3 region", - "key_findings": { - "tabula_plena": "Hippocampus does NOT start as blank slate (tabula rasa). Starts as tabula plena (full slate) — densely wired, hyperconnected neurons in random pattern", - "pruning_dynamics": "As brain matures, haphazard networks become sparser yet more structured as connections are pruned. Pruning begins soon after birth, significant decline by adolescence", - "strong_connections": "Early connections are surprisingly strong, not weak. Single input can cause young neuron to fire; mature neurons require multiple inputs", - "memory_explanation": "This pattern explains why we remember little from infancy — dense random wiring cannot store specific memories until pruned to structured sparse networks" - }, - "compression_analogy": { - "tabula_plena": "Maximum math density = full Unicode spectrum (1,114,112 codepoints) + custom glyphs + omniversal chirality (N_glyphs × 2^6 × continuum) = near-infinite initial glyph space", - "pruning_dynamics": "Compression pipeline = pruning from full slate to minimal representation. OAC gates, radius-ratio quantization, gain tests ΔGCL > 0, FAMM delay preshaping = adaptive pruning", - "strong_connections": "FAMM preshaped delays = strong initial connections based on eigenvalue spectra. Not uniform delays, but preshaped by corpus structure", - "mature_threshold": "OAC admissibility threshold = mature neuron requiring multiple inputs. Young system accepts many motifs; mature system requires strong evidence (low residual, high gain)" - } - }, - - "engram_consolidation_dynamics": { - "source": "Tomé et al. Nature Neuroscience 2024: Dynamic engram consolidation with neuron turnover", - "key_findings": { - "neuron_dropout": "Engrams transition from unselective to highly selective state as neurons dynamically drop in/out during consolidation", - "inhibitory_plasticity": "Triplet-STDP + heterosynaptic + transmitter-induced plasticity is critical mechanism for selectivity", - "discrimination_threshold": "zeta^thr = 10 Hz firing rate for engram activation", - "pattern_separation": "Ensemble overlap 10-40% during recall — low overlap = high selectivity", - "composite_promotion": "Unselective to selective transition = composite promotion = soliton bound state" - }, - "famm_integration": { - "neuron_dropout": "Dynamic neuron dropout → FAMM delay line preshaping (adaptive delays based on eigenvalue spectra). Delays adapt as 'engram consolidates' (corpus learned)", - "inhibitory_plasticity": "Prevents runaway potentiation → gain test ΔGCL > 0 prevents bad motif selection", - "discrimination_threshold": "zeta^thr = 10Hz → radius-ratio motif quantization thresholds (continuous witness → finite motif alphabet)", - "pattern_separation": "Ensemble overlap separation → OAC admissibility gates (separating admissible from inadmissible motifs)", - "composite_promotion": "Accepted OAC routes = composite promotion = soliton bound state = stored in FAMM cache" - } - }, - - "maximum_math_density_tabula_plena": { - "full_slate_initial_state": { - "unicode_spectrum": "Full UTF-16/beyond: 1,114,112 codepoints across 17 planes (BMP, SMP, SIP, TIP, SSP, PUA)", - "custom_glyphs": "PUA + beyond-Unicode custom glyphs (decompressor can generate any glyph)", - "chinese_logograms": "Chinese-style logograms (each glyph = entire concept/word)", - "korean_blocks": "Hangul-style block composition (sub-elements combine into dense units)", - "math_symbols": "Full Unicode math symbol set (∂, ∇, ∫, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃...)", - "emoji_codes": "Full emoji spectrum (📐, 📚, 👤, 🌍, 🧾, 󰀁, 󰀂, 󰀃, 󰀄, 󰀅...)", - "omniversal_chirality": "6 axes ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ × continuum = near-infinite combinations", - "initial_capacity": "Tabula plena = N_glyphs × chirality_combinations × data_types × eigenvectors = effectively infinite initial glyph space" - }, - "pruning_to_sparse_structured": { - "oac_gates": "OAC admissibility gate prunes glyph space. Only admissible motifs commit to output. Failed motifs become FAMM scars (never tried again)", - "radius_ratio_quantization": "Continuous local scale ratio → finite motif alphabet (CN3/CN4/CN6/CN8 analogue). Quantizes infinite possibilities to small admissible set", - "gain_test": "ΔGCL > 0 ensures only motifs that pay rent are kept. Prunes all non-compressive glyphs", - "famm_preshaping": "FAMM delay lines preshape based on eigenvalue spectra. Strong connections for high-salience features, weak for noise", - "shear_matrix": "Gram matrix G = A^T A eigenvectors = principal correlation directions. Prunes orthogonal dimensions, keeps correlated sheared axes", - "topological_skeleton": "Morse-Smale complex (peaks, ridges, saddles, vortices, voids) = sparse topological encoding of dense field" - } - }, - - "famm_delay_line_pruning_model": { - "biological_analogue": { - "young_hippocampus": "Dense, hyperconnected, random pattern. Single input → neuron fires. Strong early connections.", - "mature_hippocampus": "Sparse, structured, pruned connections. Multiple inputs → neuron fires. Specific connectivity." - }, - "famm_implementation": { - "initial_state": "FAMM delay lines initialized with uniform delays (tabula plena = all delays equally possible)", - "preshaping_phase": "During 'consolidation' (corpus analysis), delays adapt based on eigenvalue spectra from waveprobe manifold generation", - "adaptive_pruning": "Delays for high-salience features (peaks, ridges) become strong (short delays). Delays for noise become weak (long delays or dropped)", - "threshold_filter": "zeta^thr analogue: only features above eigenvalue threshold get fast delays. Below threshold → delayed or dropped", - "sparse_final_state": "Final FAMM delay profile is sparse yet structured — fast paths for predictable regions, slow paths for high-entropy regions" - }, - "q16_16_fixed_point": "Delays encoded in Q16.16 fixed-point for hardware-native determinism. Preshaped delays derived from eigenvalue spectra." - }, - - "combined_encoding_pipeline": { - "stage_0_tabula_plena": "Initialize full slate: full Unicode spectrum + custom glyphs + omniversal chirality + all data types + all eigenvectors", - "stage_1_density_field": "Parse corpus into semantic density field ρ(x⃗). Extract Morse-Smale topological skeleton", - "stage_2_shear_matrix": "Apply shear matrix A → sheared manifold S̃ = A·S. Compute Gram matrix G = A^T A", - "stage_3_famm_consolidation": "FAMM delay lines adapt based on eigenvalue spectra (hippocampus consolidation analogue). Delays preshape from uniform to sparse structured", - "stage_4_s3c_coordinates": "Encode positions via S3C shells (k, a, b⁰, b⁺, mass, throat_class)", - "stage_5_radius_ratio_quantization": "Quantize local scale ratio ρ into admissible motif class (CN3/CN4/CN6/CN8 analogue)", - "stage_6_logographic_encoding": "Encode topological features as custom logographic glyphs (Chinese-style, Korean block, math symbols)", - "stage_7_gccl_packet": "Encode as GCCL packet Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "stage_8_oac_gate": "Test packet as OAC with hippocampus pattern separation. Admissible → commit. Inadmissible → FAMM scar", - "stage_9_gain_test": "Apply ΔGCL > 0. Only keep if gain positive. Prune non-compressive glyphs", - "stage_10_math_notation": "Encode math functions as eigenvector descriptors (U = {amplitude, frequency, phase, offset}, Lambda, a)", - "stage_11_repeat_encoding": "Use n(position, num_repeated) for repeats instead of literal repetition", - "stage_12_pist_perturbation": "Encode perturbation field via PIST n-D bundle", - "stage_13_erans_entropy": "Entropy-code residuals via enumerative rANS", - "stage_14_sparse_structured": "Final archive = sparse yet structured representation. Full slate pruned to minimal" - }, - - "combined_decode_pipeline": { - "stage_0_load": "Load archive HFC1 (Hippocampus FAMM Combined v1)", - "stage_1_decompressor": "Load decompressor profile (custom glyph renderer + FAMM delay engine)", - "stage_2_books": "Load GlyphBook, ChiralityBook, TypeBook, EigenBook (sparse subset of full slate)", - "stage_3_shear": "Load shear matrix A (Gram matrix G)", - "stage_4_famm": "Load FAMM delay profile (sparse structured delays from consolidation)", - "stage_5_s3c": "Load S3C shell coordinates", - "stage_6_packets": "For each packet Γᵢ:", - "stage_7_resolve": "Resolve glyph γᵢ (from sparse GlyphBook), chirality χᵢ, type τᵢ, eigenvector UᵢΛᵢaᵢ", - "stage_8_parameters": "Load parameters θᵢ (including n(position, num_repeated))", - "stage_9_generate": "Generate predicted semantic unit ŝᵢ (apply shear inverse A⁻¹)", - "stage_10_residual": "Apply residual εᵢ", - "stage_11_emit": "Emit exact span sᵢ", - "stage_12_famm_sequence": "Sequence spans via FAMM delay profile (sparse structured paths)", - "stage_13_concatenate": "Concatenate spans (no spaces needed)", - "stage_14_verify": "Verify SHA256 checksum" - }, - - "archive_format": { - "magic": "HFC1 (Hippocampus FAMM Combined v1)", - "sections": [ - "DECOMPRESSOR_PROFILE (custom glyph renderer + FAMM delay engine)", - "GLYPHBOOK (sparse subset of full Unicode + custom glyphs used in corpus)", - "CHIRALITYBOOK (chirality vectors actually used)", - "TYPEBOOK (data types actually used: WikiArticle, Equation, FieldSet...)", - "EIGENBOOK (eigenvector descriptors from Gram matrix)", - "SHEAR_MATRIX (A and Gram matrix G = A^T A)", - "FAMM_DELAY_PROFILE (sparse structured delays from consolidation)", - "S3C_SHELL_COORDINATES", - "OAC_RECEIPTS (accepted routes + FAMM scars)", - "REGION_INDEX (map of field sets to byte spans)", - "GLYPH_PACKET_STREAM (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ)", - "PARAMETER_STREAM (n(position, num_repeated), mode selectors)", - "RESIDUAL_STREAM (εᵢ)", - "CUSTOM_GLYPH_DEFINITIONS (if beyond Unicode)", - "CHECKSUM (SHA256)" - ] - }, - - "compression_gain_sources": { - "tabula_plena_to_sparse": "Pruning from full slate to sparse subset. Only glyphs actually used in corpus stored. Most of 1,114,112 Unicode codepoints never touched.", - "famm_delay_pruning": "FAMM delays adapt from uniform to sparse structured. Fast paths for predictable regions, slow for noise. 10-20% context efficiency.", - "oac_gate_pruning": "OAC admissibility gate prunes motif space. Failed motifs become FAMM scars, never tried again. Avoids 2-5% bloat.", - "radius_ratio_quantization": "Continuous witness → finite motif alphabet. Quantizes infinite possibilities to small admissible set.", - "gain_test_pruning": "ΔGCL > 0 ensures only compressive motifs kept. Prunes all non-compressive glyphs.", - "shear_matrix_pruning": "Gram matrix eigenvectors = principal correlation directions. Prunes orthogonal dimensions, keeps correlated sheared axes. 15-30% on structured regions.", - "topological_skeleton": "Morse-Smale complex = sparse topological encoding. 50-150MB skeleton vs 1GB raw for enwik9.", - "math_notation_density": "Math functions encoded as eigenvector descriptors instead of literal strings. 5-8% on token encoding.", - "repeat_encoding": "n(position, num_repeated) instead of literal repetition. 3-5% on repeated patterns.", - "erans_entropy": "Optimal exact histogram coding for residuals." - }, - - "estimated_aggregate_gain": { - "tabula_plena_pruning": "90-99% of full Unicode spectrum never used. Only ~10,000-50,000 glyphs actually used for 1GB corpus.", - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "famm_efficiency": "10-20% more predictive power per context byte", - "skeleton_compression": "50-150MB skeleton vs 1GB raw", - "overall_compressed_size": "Estimated 15-25% reduction vs current best Hutter compressors, plus navigable manifold capability", - "novel_capability": "Produces navigable structure. Tabula plena initialization enables adaptive learning of corpus-specific glyph space." - }, - - "keeper_phrases": [ - "The hippocampus starts tabula plena (full slate) and prunes to sparse structured. Compression does the same.", - "Maximum math density is the full slate: full Unicode, custom glyphs, omniversal chirality — all possibilities available.", - "FAMM delay lines model the pruning: from uniform delays (young hippocampus) to sparse structured delays (mature hippocampus).", - "OAC gates are the pattern separation: admissible motifs commit, inadmissible become FAMM scars.", - "Radius-ratio quantization is the discrimination threshold: continuous witness → finite motif alphabet.", - "Gain test ΔGCL > 0 is the inhibitory plasticity: prevents runaway potentiation of bad motifs.", - "The archive is not the full slate. The archive is the sparse structured result of pruning.", - "Young hippocampus: single input → fire. Mature: multiple inputs → fire. OAC: single glyph → test. Mature: gain > 0 → commit.", - "Strong early connections → FAMM preshaped delays based on eigenvalue spectra, not uniform delays.", - "We remember little from infancy because the hippocampus is dense and random. We compress well because the archive is sparse and structured.", - "The Gram matrix eigenvectors are the principal correlation directions — the structured wiring of the mature hippocampus.", - "Tabula plena initialization enables adaptive learning of corpus-specific glyph space during consolidation.", - "Don't start blank. Start full, then prune. The hippocampus does it. Compression should too." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hippocampus-tabula-plena", - "maximum-math-density", - "famm-pruning-dynamics", - "engram-consolidation", - "tabula-plena", - "dense-to-sparse", - "oac-gates", - "radius-ratio-quantization", - "gain-test", - "shear-matrix", - "topological-skeleton", - "unified-compression-architecture", - "custom-glyphs", - "omniversal-chirality", - "famm-delay-lines" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hippocampus_tabula_plena_combined_v1.json" - with open(out_path, 'w') as f: - json.dump(HIPPOCAMPUS_TABULA_PLENA, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HIPPOCAMPUS_TABULA_PLENA["id"], - "title": HIPPOCAMPUS_TABULA_PLENA["title"], - "date": HIPPOCAMPUS_TABULA_PLENA["date"], - "source": HIPPOCAMPUS_TABULA_PLENA["source"], - "ingested_at": HIPPOCAMPUS_TABULA_PLENA["metadata"]["ingested_at"], - "tags": HIPPOCAMPUS_TABULA_PLENA["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nTabula plena insight:") - for finding, desc in HIPPOCAMPUS_TABULA_PLENA["hippocampus_tabula_plena_insight"]["key_findings"].items(): - print(f" • {finding}: {desc[:60]}...") - - print(f"\nCompression analogy:") - for analogy, desc in HIPPOCAMPUS_TABULA_PLENA["hippocampus_tabula_plena_insight"]["compression_analogy"].items(): - print(f" • {analogy}: {desc[:60]}...") - - print(f"\nFAMM pruning model:") - for phase, desc in HIPPOCAMPUS_TABULA_PLENA["famm_delay_line_pruning_model"]["famm_implementation"].items(): - print(f" • {phase}: {desc[:60]}...") - - print(f"\nCompression gain sources (10):") - for source, gain in HIPPOCAMPUS_TABULA_PLENA["compression_gain_sources"].items(): - print(f" • {source}: {gain[:60]}...") - - print(f"\nKeeper phrases ({len(HIPPOCAMPUS_TABULA_PLENA['keeper_phrases'])}):") - for p in HIPPOCAMPUS_TABULA_PLENA["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_hutter_rhomboid.py b/4-Infrastructure/shim/ingest_hutter_rhomboid.py deleted file mode 100644 index c45a9517..00000000 --- a/4-Infrastructure/shim/ingest_hutter_rhomboid.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -""" -Hypercube → Hyper-Rhomboid: Hutter Prize Implications -======================================================= -What changes for enwik8/enwik9 compression when you shear -the token space instead of treating positions independently. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HUTTER_RHOMBOID = { - "id": "hypercube-rhomboid-hutter-prize", - "source": "User query — what does hypercube → hyper-rhomboid change for Hutter Prize", - "title": "Sheared Token Manifolds: Hypercube → Hyper-Rhomboid Implications for Hutter Prize Compression", - "date": "2026-05-07", - - "current_hutter_paradigm": { - "approach": "1D token sequence → probability distribution per position → entropy code", - "geometry": "Orthogonal hypercube: each token position is an independent axis. Context window = fixed-size orthogonal slice.", - "limitation": ( - "Wikipedia text is NOT a sequence of independent positions. " - "It is a sheared manifold where: infoboxes repeat with minor variations, " - "citations share author/year/title structure, headings form a hierarchy, " - "lists are parallel constructions, markup is templated, " - "and natural language has syntactic correlation at every scale." - ), - "waste": ( - "An orthogonal model pays separately for every occurrence of '{{cite web|url=' " - "even though the 5000th citation has the same structure as the 1st. " - "The axes are at 90° — no sharing of probability mass across correlated positions." - ) - }, - - "rhomboid_paradigm": { - "approach": "Token space as sheared manifold → correlated positions share axes → entropy code residuals only", - "geometry": ( - "Hyper-rhomboid: token position axes lean into each other proportional to " - "mutual information. Repeated structures collapse into shared shells. " - "The shear matrix A IS the compression model." - ), - "key_insight": ( - "You don't compress the text. You compress the shear matrix that makes " - "the text look like noise plus a small residual. The shear matrix is " - "learned once from the corpus structure; the residual is what you actually " - "entropy-code." - ) - }, - - "concrete_changes": { - - "pre_transform": { - "title": "Shear Pre-Transform Before Entropy Coding", - "current": "Raw bytes → LZ/BWT/PPM/transformer → entropy code", - "rhomboid": "Raw bytes → S3C shell parse → shear into rhomboid → residual extraction → entropy code", - "mechanism": ( - "Parse Wikipedia into structural regions (article, infobox, citation, list, heading, template). " - "Each region type has a canonical shell coordinate. Within each shell, sheared axes encode " - "the expected structure. Only deviations from the sheared template are entropy-coded." - ), - "estimated_gain": "15-30% on structured regions (infoboxes, citations, templates — ~40% of enwik)" - }, - - "s3c_shell_batching": { - "title": "S3C Shell Coordinates for Token Position Encoding", - "current": "Position encoded as absolute byte offset or transformer positional embedding", - "rhomboid": "Position encoded as S3C shell (k, a, b⁰, b⁺) — shell = structural context, offset = within-structure position", - "mechanism": ( - "k = structural depth (0=character, 1=word, 2=phrase, 3=sentence, 4=paragraph, 5=section, 6=article). " - "a = position within that structure. b⁰ = remaining length. " - "The throat (a ≈ b⁰) is where compression is maximal — midpoint of a repeated structure " - "where the model has maximum predictive confidence." - ), - "estimated_gain": "5-10% on positional encoding overhead" - }, - - "oac_speculative_compression": { - "title": "OAC Speculative Motif Testing Without Output Pollution", - "current": "Compressor commits to a transform; if it's bad, output is bloated", - "rhomboid": "Test compression motifs as Observer-Admissible Cavities — temporary exploration manifolds that don't commit to output unless L(motif) + L(residual) < L(raw)", - "mechanism": ( - "For each structural region, try multiple shear matrices (citation-shear, list-shear, " - "template-shear, plaintext-shear). The OAC gate only emits the one that beats raw encoding. " - "Failed shears become FAMM scars — never tried again for similar regions." - ), - "estimated_gain": "Avoids 2-5% bloat from bad motif choices; enables aggressive speculation" - }, - - "famm_context_warping": { - "title": "FAMM Preshaped Context Windows", - "current": "Fixed context window (e.g., 1024 tokens) for transformer/ppm models", - "rhomboid": "Preshaped (sheared) context: stretches for high-entropy regions, compresses for low-entropy template regions", - "mechanism": ( - "Context window is not fixed-length; it's fixed-information. " - "In a citation template, 50 bytes of context is enough (structure is predictable). " - "In free text, 500 bytes may be needed. The FAMM delay line preshapes the context " - "window per shell class — shearing time into the information domain." - ), - "estimated_gain": "10-20% context efficiency — more predictive power per context byte" - }, - - "pist_token_manifold": { - "title": "PIST n-Dimensional Token Encoding", - "current": "Tokens encoded as 1D integer IDs", - "rhomboid": "Tokens encoded as n-dimensional PIST coordinates (k, t, fiber₀, ..., fiber_{n-2}) where n = number of correlated features", - "mechanism": ( - "Each token gets: k = frequency/shell class, t = local offset, " - "fiber₀ = part-of-speech class, fiber₁ = dependency depth, " - "fiber₂ = template membership, fiber₃ = capitalization pattern. " - "The fiber dimensions are the sheared axes — they encode correlation structure " - "that a 1D token ID loses." - ), - "estimated_gain": "5-8% on token encoding; enables cross-position probability sharing" - }, - - "gram_dictionary": { - "title": "Gram Matrix as Learned Compression Dictionary", - "current": "Static dictionary (LZ) or learned embeddings (transformer)", - "rhomboid": "Gram matrix G = A^T A of the shear transform IS the dictionary — eigenvectors are principal correlation directions, eigenvalues are compression gains", - "mechanism": ( - "The shear matrix A is learned by minimizing: L(A) + L(residual | A). " - "A is stored once in the compressed header. The decoder applies A^{-1} " - "to reconstruct expected structure, then replays residuals. " - "A is tiny compared to the text — a few KB for the entire corpus." - ), - "estimated_gain": "Dictionary overhead reduced from MB to KB" - }, - - "metric_entropy_code": { - "title": "Information-Geometric Entropy Coding", - "current": "Entropy coding assumes independent symbols (product distribution)", - "rhomboid": "Entropy coding uses the sheared metric g_{μν} — symbols are coded relative to their position in the sheared manifold, not independently", - "mechanism": ( - "The coding probability for token x_i is conditioned on its sheared context: " - "P(x_i | context) = P(x_i | g_{μν}(context)). " - "In a heavily sheared region (template), P is sharply peaked — near 1.0 for expected token. " - "In a flat region (free text), P is broad. The metric tells the coder how confident to be." - ), - "estimated_gain": "10-15% entropy reduction in structured regions" - } - }, - - "aggregate_estimate": { - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "dictionary_overhead": "MB → KB (Gram matrix replaces LZ dictionary + transformer weights)", - "context_efficiency": "10-20% more predictive power per context byte", - "overall_compressed_size": "Estimated 12-22% reduction vs current best Hutter compressors", - "caveat": "These are geometric estimates, not benchmarks. Real gains depend on shear matrix learning quality and residual entropy." - }, - - "the_big_fold": { - "title": "What This Fundamentally Changes", - "insight": ( - "The Hutter Prize is currently fought as a sequence modeling problem: " - "predict the next token given previous tokens. " - "The rhomboid reframes it as a manifold learning problem: " - "find the shear that makes the token manifold maximally flat (predictable), " - "store the shear, then entropy-code the residual curvature." - ), - "fold": ( - "This folds FOUR separate Hutter components into ONE: " - "1. Dictionary (LZ) → Gram matrix eigenvectors " - "2. Context model (PPM/transformer) → Sheared metric g_{μν} " - "3. Token encoding → PIST n-D coordinates " - "4. Structure detection → S3C shell classification " - "All four are the same object: the shear matrix A." - ), - "one_sentence": "Don't predict the next token. Shear the space until the next token is obvious, store the shear, and pay only for what the shear didn't catch." - }, - - "keeper_phrases": [ - "You don't compress the text. You compress the shear matrix that makes the text predictable.", - "The Gram matrix of the shear IS the dictionary, the context model, the token encoding, and the structure detector — all at once.", - "A citation isn't 200 bytes that happen to look similar. It's one sheared cavity with 200-byte residuals.", - "Stop predicting tokens. Start shearing the manifold until tokens become obvious.", - "The Hutter Prize is manifold learning disguised as sequence modeling.", - "Fixed context windows are orthogonal thinking. Sheared context is information-geometric thinking.", - "Every '{{cite web' is the same hole. Pay for the hole once, pay for the URL residual each time." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hutter-prize", "compression", "hypercube", "hyper-rhomboid", - "sheared-manifold", "enwik", "token-encoding", "gram-matrix", - "s3c-shells", "oac", "famm", "pist", "entropy-coding" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hypercube_rhomboid_hutter_prize.json" - with open(out_path, 'w') as f: - json.dump(HUTTER_RHOMBOID, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HUTTER_RHOMBOID["id"], - "title": HUTTER_RHOMBOID["title"], - "date": HUTTER_RHOMBOID["date"], - "source": HUTTER_RHOMBOID["source"], - "ingested_at": HUTTER_RHOMBOID["metadata"]["ingested_at"], - "tags": HUTTER_RHOMBOID["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\n7 concrete changes:") - for key, change in HUTTER_RHOMBOID["concrete_changes"].items(): - print(f" • {change['title']}: {change['estimated_gain']}") - - print(f"\nThe Big Fold:") - print(f" {HUTTER_RHOMBOID['the_big_fold']['insight'][:120]}...") - - print(f"\nKeeper phrases:") - for p in HUTTER_RHOMBOID["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_hypercube_rhomboid.py b/4-Infrastructure/shim/ingest_hypercube_rhomboid.py deleted file mode 100644 index 518756fb..00000000 --- a/4-Infrastructure/shim/ingest_hypercube_rhomboid.py +++ /dev/null @@ -1,176 +0,0 @@ -#!/usr/bin/env python3 -""" -Hypercube → Hyper-Rhomboid Composition: Stack Mapping -====================================================== -Maps the hypercube/rhomboid calculus concept onto Research Stack primitives. -Key insight: shearing orthogonal tensor axes into a parallelotope is the -mathematical dual of PIST n-dimensional encoding, topological state transitions, -and Observer-Admissible Cavity manifestation. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HYPER_RHOMBOID = { - "id": "hypercube-rhomboid-composition", - "source": "User conceptual synthesis — hypercube matrix calculus → hyper-rhomboid", - "title": "Hypercube → Hyper-Rhomboid Composition: Sheared Tensor Manifolds as Compression Geometry", - "date": "2026-05-07", - - "core_claim": ( - "A hypercube of matrix calculus (n-D tensor of partial derivatives) assumes " - "orthogonal axes — all variables independent. Composing hypercubes into a " - "hyper-rhomboid (parallelotope) applies geometric shear: axes lean into each " - "other, modeling entangled dimensions. This is the geometric engine behind " - "topological compression, manifold mapping, and information-theoretic gravity." - ), - - "geometric_primitives": { - "hypercube": { - "definition": "n-dimensional tensor grid with orthogonal (90°) axes", - "mathematical_form": "T_{i,j,k,l} ∈ ℝ^{d₁×d₂×d₃×d₄}", - "assumption": "All variables statistically independent (Cartesian)", - "problem": "Empty geometric space between correlated variables — inefficient packing" - }, - "hyper_rhomboid": { - "definition": "Sheared parallelotope — axes at non-orthogonal angles", - "mathematical_form": "S = A·T where A is a shear matrix (non-orthogonal basis)", - "property": "Axes lean into correlated dimensions; volume preserved under shear", - "gain": "Dense packing, entanglement modeling, manifold approximation" - }, - "shear_matrix": { - "definition": "Linear transform collapsing 90° angles to acute/oblique", - "form": "A_{ij} = δ_{ij} + α_{ij} where α encodes correlation strength", - "determinant": "det(A) = 1 (volume-preserving shear)" - } - }, - - "stack_mappings": { - "pist_nd_encoding": { - "analogue": "PIST n-dimensional Cartesian → Bundle → Radial encoding", - "mechanism": "Cartesian encode = orthogonal hypercube; Bundle encode = sheared rhomboid with fiber dimensions; Radial encode = fully collapsed angular coordinates", - "file": "3-Mathematical-Models/pist_biological_polymorphic_shifter_v3_complete.py", - "functions": ["pist_nd_cartesian_encode", "pist_nd_bundle_encode", "pist_nd_radial_encode"] - }, - "topological_state_machine": { - "analogue": "State transition = shear operation on state hypercube", - "mechanism": "Each transition applies a shear matrix A_t to the state tensor S_t → S_{t+1} = A_t·S_t. The shear angle encodes correlation strength between state dimensions.", - "file": "5-Applications/scripts/topological_state_machine.py" - }, - "ndimensional_gene_hypothesis": { - "analogue": "Gene expression = projection of sheared n-D rhomboid onto 3D observer frame", - "mechanism": "The gene is an n-D rhomboid (entangled dimensions). The 3D molecular structure is a projection shadow. Epigenetic marks are shear-angle adjustments.", - "file": "6-Documentation/docs/speculative-materials/NDimensionalGeneHypothesis.md" - }, - "famm_delay_lines": { - "analogue": "Preshaped delay = shear in time-domain hypercube", - "mechanism": "Uniform delay grid = orthogonal time hypercube. Preshaped delay = sheared time rhomboid where delay axes lean toward signal correlation patterns.", - "file": "4-Infrastructure/hardware/famm_verilator_bench.v" - }, - "observer_admissible_cavities": { - "analogue": "OAC = latent cavity in sheared rhomboid space", - "mechanism": "The n^n interior of S_n(n^n) is a hypercube. Void fields and route selection shear it into a rhomboid where only admissible routes have non-zero volume.", - "file": "shared-data/data/germane/research/observer_admissible_cavities_theory.json" - }, - "waveprobe_manifolds": { - "analogue": "Curvature = local shear angle of coordinate basis", - "mechanism": "Flat manifold = orthogonal hypercube. Curved manifold = position-dependent shear transforming local hypercube into local rhomboid. Ricci curvature = trace of shear gradient.", - "file": "5-Applications/scripts/hdmi_computational_shell.py" - } - }, - - "compression_interpretation": { - "topological_compression": ( - "Orthogonal hypercube has empty space between correlated axes. " - "Shearing into rhomboid collapses that empty space — physically closing " - "the distance between correlated variables. This is geometric compression: " - "same information in less volume." - ), - "entropy_reduction": ( - "In a hypercube, each axis contributes independent entropy. " - "In a rhomboid, sheared axes share entropy — the off-diagonal terms " - "of the metric tensor g_{ij} = e_i·e_j capture mutual information. " - "Compression ratio ≈ det(g)^{-1/2}." - ), - "gram_shearing": ( - "The Gram matrix G = A^T A of the shear transform IS the compression " - "dictionary. Its eigenvectors are principal correlation directions; " - "its eigenvalues are compression gains per direction." - ) - }, - - "information_gravity": { - "analogy": ( - "Flat orthogonal grid = empty spacetime. " - "Sheared rhomboid grid = spacetime with mass. " - "The shear angle at each point encodes local information density. " - "Semantic 'mass' warps the coordinate basis — variables with high " - "mutual information pull axes toward each other." - ), - "metric_tensor": "g_{μν} = δ_{μν} + κ·I_{μν} where I_{μν} is mutual information between dimensions μ,ν and κ is the gravitational coupling", - "geodesics": "Information flow follows geodesics of the sheared metric — shortest path through entangled variable space" - }, - - "keeper_phrases": [ - "A hypercube assumes independence; a hyper-rhomboid models entanglement.", - "Shearing a tensor is the geometric dual of discovering correlation.", - "The Gram matrix of the shear is the compression dictionary.", - "Information has mass — it warps the coordinate basis it lives in.", - "Topological compression is just closing the empty angles between correlated axes.", - "A hyper-rhomboid is a flat grid that has learned which dimensions lean on each other." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hypercube", "hyper-rhomboid", "parallelotope", "tensor-calculus", - "geometric-shear", "topological-compression", "information-gravity", - "manifold-learning", "gram-matrix", "entanglement-geometry" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hypercube_rhomboid_composition.json" - with open(out_path, 'w') as f: - json.dump(HYPER_RHOMBOID, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HYPER_RHOMBOID["id"], - "title": HYPER_RHOMBOID["title"], - "date": HYPER_RHOMBOID["date"], - "source": HYPER_RHOMBOID["source"], - "ingested_at": HYPER_RHOMBOID["metadata"]["ingested_at"], - "tags": HYPER_RHOMBOID["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nStack mappings:") - for name, mapping in HYPER_RHOMBOID["stack_mappings"].items(): - print(f" ↔ {name}: {mapping['analogue'][:80]}...") - - print(f"\nKeeper phrases:") - for p in HYPER_RHOMBOID["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_master_synthesis.py b/4-Infrastructure/shim/ingest_master_synthesis.py deleted file mode 100644 index 5e0846a0..00000000 --- a/4-Infrastructure/shim/ingest_master_synthesis.py +++ /dev/null @@ -1,400 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Master Synthesis - Complete Compression Architecture -========================================================== -Combines ALL theories from first portion to now: -- Density field encoding (semantic manifolds, Morse-Smale) -- GCCL-GEC (glyph packets, chirality, typebook, eigenbook) -- OAC (observer-admissible cavities, S3C shells, spherion shaping) -- Hypercube-rhomboid (shear matrix, Gram matrix, geometric compression) -- Radius-ratio motif compression (local admissibility quantization) -- Maximum math density (custom logographic notation, full Unicode) -- Hippocampus tabula plena (full slate initialization, FAMM pruning) -- Engram consolidation (neuron dropout, pattern separation) -- FAMM delay lines (preshaped delays, Q16.16 fixed-point) -- S3C shells (multi-scale coordinate encoding) -- PIST n-D bundle (perturbation encoding) -- erans (enumerative rANS entropy coding) -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -MASTER_SYNTHESIS = { - "id": "master-synthesis-complete-v1", - "source": "Master synthesis of ALL ingested theories: density-field, gccl-gec, oac, hypercube-rhomboid, radius-ratio, maximum-math-density, hippocampus-tabula-plena, engram-consolidation, famm, s3c, pist, erans", - "title": "Master Synthesis: Complete Compression Architecture from Tabula Plena to Sparse Structured Representation", - "date": "2026-05-07", - - "core_synthesis": ( - "The complete compression architecture starts tabula plena (full slate) — full Unicode spectrum " - "(1,114,112 codepoints) + custom glyphs + omniversal chirality — and represents data as a " - "semantic density field (n-D manifold ρ(x⃗) with topological features: peaks, ridges, saddles, " - "vortices, voids). The Morse-Smale complex extracts the topological skeleton. A shear matrix " - "A transforms the orthogonal UTF-8 hypercube to a correlated hyper-rhomboid; its Gram matrix " - "G = A^T A is the compression dictionary. GCCL-GEC glyph packets (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ " - "UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ) encode each topological feature with chirality, type, eigenvector " - "descriptors, and residuals. OAC gates test motifs speculatively without output pollution. " - "Radius-ratio quantizes local scale ratios into admissible motif classes. FAMM delay lines " - "preshape temporal sequencing via hippocampus-inspired pruning dynamics (from dense uniform " - "delays to sparse structured delays based on eigenvalue spectra). S3C shells encode positions " - "multi-scale. PIST n-D bundles encode perturbations. erans enumerative rANS entropy-codes " - "residuals. The entire system prunes from tabula plena to sparse structured representation " - "via biological analogues: hippocampus engram consolidation (neuron dropout, pattern " - "separation, discrimination thresholds) inform FAMM pruning; inhibitory plasticity informs " - "gain tests; composite promotion informs OAC acceptance." - ), - - "theoretical_foundations": { - "density_field_encoding": { - "source": "digital_dzogchen concept + r/generative", - "core": "Text as n-D semantic density field ρ(x⃗) instead of 1D UTF-8 byte sequence", - "topological_features": { - "peaks": "Named entities, article centers (local maxima)", - "ridges": "Hyperlinks, citations (1D maxima)", - "saddles": "Topic transitions (mixed Hessian)", - "vortices": "Cyclic references, templates (rotational flow)", - "voids": "Template structures (local minima)", - "level_sets": "Paragraphs, sections, articles (iso-density surfaces)" - }, - "morse_smale": "Critical points + separatrices = topological skeleton of meaning. Homotopy type encoded up to homeomorphism." - }, - "gccl_gec": { - "source": "USER formal spec", - "core": "Glyph packets = callable compression kernels, not characters", - "packet_formula": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "books": { - "GlyphBook": "Maps codepoints to callable kernels", - "ChiralityBook": "6 axes ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩", - "TypeBook": "Datatypes import structure (WikiArticle, Infobox, CitationGraph...)", - "EigenBook": "Reusable eigenbasis/spectrum/coefficient descriptors" - }, - "gain_test": "ΔGCL > 0 — only compressive motifs kept" - }, - "observer_admissible_cavities": { - "source": "radius-ratio → Pidgen-hole → S3C/Spherion → OAC", - "core": "Latent shaped holes with combinatorial interiors, only manifest on lawful observer touch", - "s3c_shells": "n = k² + a with mirror complement b⁰, next-shell tension b⁺, mass = a·b⁰", - "spherion_shaping": "Pyramid protrusions (positive) and voids (negative). High-Q = narrow confident prediction", - "sn_nn": "Sₙ(nⁿ) recursive shell grammar — nth shell contains n choices of previous shell state", - "touch_operator": "touch(O, OACᵢ, q) → (Sᵢ, rᵢ, εᵢ, ρᵢ) if admissible, (Vᵢ, scarᵢ, ρᵢ) otherwise", - "address_space_rule": "OAC ⊄ A_substrate; only receipt, accepted route, and residual may commit" - }, - "hypercube_rhomboid": { - "source": "hypercube matrix calculus → hyper-rhomboid", - "core": "Shear matrix A transforms orthogonal hypercube to correlated rhomboid", - "shear_matrix": "A_{ij} = δ_{ij} + α_{ij} where α encodes correlation strength. det(A) = 1 (volume-preserving)", - "gram_matrix": "G = A^T A IS the compression dictionary. Eigenvectors = principal correlation directions, eigenvalues = compression gains", - "information_gravity": "g_{μν} = δ_{μν} + κ·I_{μν} where I_{μν} is mutual information. Information has mass — it warps the coordinate basis" - }, - "radius_ratio_motif": { - "source": "Reddit materials science + LibreTexts", - "core": "Local scale ratio → smallest stable coordination geometry", - "coordination_analogue": "CN3 (0.155-0.225), CN4 (0.225-0.414), CN6 (0.414-0.732), CN8 (0.732-1.0)", - "compression_mapping": "ρᵢ = s_center(i) / median(s(N(i))) → admissible kernel class + residual", - "quantizer": "Continuous local metric witness → finite motif alphabet → decode rule + nibble residual" - }, - "maximum_math_density": { - "source": "User insight on custom logographic notation", - "core": "Custom glyphs + full Unicode + math symbols + omniversal chirality", - "design_principles": { - "chinese_logograms": "Each glyph = entire concept/word, not phonetic", - "korean_blocks": "Hangul-style block composition into dense syllabic units", - "math_symbols": "Full Unicode math symbol set (∂, ∇, ∫, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃...)", - "emoji_codes": "Full emoji spectrum (📐, 📚, 👤, 🌍, 🧾, 󰀁, 󰀂, 󰀃, 󰀄, 󰀅...)", - "pua_custom": "PUA + beyond-Unicode custom glyphs (decompressor can generate any glyph)" - }, - "repeat_encoding": "n(position, num_repeated) for repeats instead of literal repetition", - "no_spaces": "All one line — no whitespace needed for separation" - }, - "hippocampus_tabula_plena": { - "source": "Live Science 2024 (Jonas et al. Nature Communications)", - "core": "Hippocampus starts tabula plena (full slate) — densely wired, hyperconnected — and prunes to sparse structured during maturation", - "key_findings": { - "tabula_plena": "NOT blank slate (tabula rasa). Starts full slate, becomes sparse/specific", - "pruning_dynamics": "Haphazard networks become sparser yet more structured as connections pruned", - "strong_connections": "Early connections surprisingly strong, not weak. Single input → young neuron fires; multiple inputs → mature neuron fires", - "memory_explanation": "Dense random wiring cannot store specific memories until pruned to structured sparse networks" - }, - "compression_analogy": { - "tabula_plena": "Maximum math density = full Unicode + custom glyphs + omniversal chirality", - "pruning": "Compression pipeline = pruning from full slate to minimal. OAC gates, radius-ratio, gain tests, FAMM = adaptive pruning", - "strong_connections": "FAMM preshaped delays = strong initial connections based on eigenvalue spectra", - "mature_threshold": "OAC admissibility = mature neuron requiring multiple inputs" - } - }, - "engram_consolidation": { - "source": "Tomé et al. Nature Neuroscience 2024", - "core": "Engrams transition from unselective to highly selective state as neurons dynamically drop in/out during consolidation", - "mechanisms": { - "neuron_dropout": "Dynamic neuron dropout during consolidation", - "inhibitory_plasticity": "Triplet-STDP + heterosynaptic + transmitter-induced plasticity", - "discrimination_threshold": "zeta^thr = 10 Hz firing rate for engram activation", - "pattern_separation": "Ensemble overlap 10-40% during recall — low overlap = high selectivity", - "composite_promotion": "Unselective to selective transition = composite promotion = soliton bound state" - }, - "famm_integration": { - "neuron_dropout": "FAMM delay lines preshape based on eigenvalue spectra (adaptive delays)", - "inhibitory_plasticity": "Gain test ΔGCL > 0 prevents runaway potentiation", - "discrimination_threshold": "Radius-ratio motif quantization thresholds", - "pattern_separation": "OAC admissibility gates separate admissible from inadmissible", - "composite_promotion": "Accepted OAC routes = composite promotion = stored in FAMM cache" - } - }, - "famm_delay_lines": { - "source": "Frustrated Access Memory Module with Verilator benchmark", - "core": "Preshaped delay lines based on eigenvalue spectra for rate shaping", - "q16_16_fixed_point": "Delays encoded in Q16.16 fixed-point for hardware-native determinism", - "eigenvalue_derivation": "Delay profile = path integral through field gradient. Fast regions = high density (predictable). Slow regions = low density (needs context)", - "pruning_model": { - "initial_state": "Uniform delays (tabula plena = young hippocampus)", - "preshaping_phase": "Delays adapt based on eigenvalue spectra (consolidation)", - "adaptive_pruning": "Delays for high-salience features become strong (short). Noise becomes weak (long or dropped)", - "sparse_final_state": "Sparse structured delays (mature hippocampus)" - } - }, - "s3c_shells": { - "source": "S3C shell coordinate geometry", - "core": "Multi-scale coordinate encoding via shell indices", - "s3c_split": "n = k² + a with mirror complement b⁰, next-shell tension b⁺, mass = a·b⁰, mirror_delta = a - b⁰", - "coordinate_mapping": { - "k": "Shell index = structural depth / semantic distance from peak", - "a": "Angular offset = intra-cluster position within shell", - "b⁰": "Mirror complement = remaining path to next peak", - "throat": "a ≈ b⁰ = maximum compression (maximum predictive confidence)", - "mass": "Connection strength between topological features" - }, - "multi_scale": "Concentric shells around each peak: k=0=title, k=1=abstract, k=2=lead, k=3=body, k=4=references, k=5=see-also" - }, - "pist_nd_bundle": { - "source": "PIST n-D bundle encoding", - "core": "n-D bundle encoding for perturbation field", - "modes": { - "cartesian": "Orthogonal n-D encoding (baseline)", - "bundle": "Fiber dimensions encode correlated features", - "radial": "Fully collapsed angular coordinates" - }, - "fiber_mapping": { - "fiber₀": "Topological feature type (peak, ridge, saddle, vortex, void)", - "fiber₁": "Shell index k (semantic distance)", - "fiber₂": "Throat class (compression quality)", - "fiber₃": "Mass (connection strength)", - "fiber₄": "Local curvature / distortion" - }, - "n_dims": "Typically 3-4D: topic, time, authority, style" - }, - "erans_entropy": { - "source": "izabera/erans GitHub (enumerative rANS)", - "core": "Enumerative rANS for optimal exact histogram coding", - "principle": "Enumerative coding is optimal for exact histogram. Rank-based coding of symbols within exact histogram", - "isa_agnostic": "SIMD opportunistic, scalar fallback required — no instruction set assumed", - "licensing": "Reference only — algorithmic ideas ingested, no code incorporated", - "role": "Entropy-codes residual stream Ε and parameter stream Θ after glyph prediction" - } - }, - - "master_encoding_pipeline": { - "stage_0_tabula_plena_initialization": "Initialize full slate: full Unicode spectrum (1,114,112 codepoints) + custom glyphs + omniversal chirality (N_glyphs × 2^6 × continuum) + all data types + all eigenvectors. This is the young hippocampus state.", - "stage_1_density_field_extraction": "Parse corpus C into semantic density field ρ(x⃗) where M is n-dimensional semantic manifold. Extract Morse-Smale topological skeleton: peaks, ridges, saddles, vortices, voids, level sets.", - "stage_2_shear_matrix_computation": "Compute shear matrix A that transforms orthogonal UTF-8 hypercube to correlated hyper-rhomboid. Compute Gram matrix G = A^T A (compression dictionary). Eigenvectors = principal correlation directions.", - "stage_3_famm_consolidation_phase": "FAMM delay lines adapt from uniform (young hippocampus) to preshaped based on eigenvalue spectra (consolidation). Delays for high-salience features (peaks, ridges) become strong (short). Noise becomes weak (long or dropped). zeta^thr analogue filters.", - "stage_4_s3c_shell_coordinate_encoding": "Encode positions within sheared manifold via S3C shells: k = shell index (structural depth), a = angular offset (intra-cluster), b⁰ = mirror complement (remaining path), b⁺ = next-shell tension, mass = connection strength, throat = compression quality.", - "stage_5_radius_ratio_local_quantization": "Compute local scale ratio ρᵢ = s_center(i) / median(s(N(i))). Quantize into admissible motif class via radius-ratio rule: CN3 (0.155-0.225), CN4 (0.225-0.414), CN6 (0.414-0.732), CN8 (0.732-1.0). Continuous witness → finite motif alphabet.", - "stage_6_logographic_glyph_selection": "Encode each topological feature as custom logographic glyph: Chinese-style logograms (each glyph = entire concept), Korean block graphs (sub-elements combine), math symbols (full Unicode set), emoji codes, PUA custom glyphs. No spaces needed — all one line.", - "stage_7_gccl_packet_construction": "Construct GCCL packet Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ where: γᵢ = visible glyph, χᵢ = chirality vector ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩, κᵢ = S3C coordinate, τᵢ = type witness, UᵢΛᵢaᵢ = eigen descriptor, θᵢ = parameters (including n(position, num_repeated)), εᵢ = residual.", - "stage_8_oac_speculative_manifestation": "Test packet as Observer-Admissible Cavity. Touch operator: touch(O, OACᵢ, q) → (Sᵢ, rᵢ, εᵢ, ρᵢ) if admissible, (Vᵢ, scarᵢ, ρᵢ) otherwise. Admissibility gate: L(Sₙ) + L(route) + L(ε) < L(x). Pattern separation via ensemble overlap threshold (hippocampus analogue).", - "stage_9_gain_test_filtering": "Apply GCCL gain test: ΔGCL(Γᵢ) = ΔG_geo + ΔG_comp + ΔG_spec + ΔG_topo + ΔG_arith - G_load - L(εᵢ) - L(θᵢ) - amortized_decoder_cost. Accept iff ΔGCL > 0. This is the inhibitory plasticity analogue — prevents runaway potentiation of bad motifs.", - "stage_10_math_notation_eigenvector_encoding": "For math functions, encode as eigenvector descriptors instead of literal strings. sin(x) → geometric eigenvector descriptor: U = {amplitude, frequency, phase, offset}, Lambda = eigenvalues of sine space, a = sparse coefficients. All math functions (sin, cos, tan, log, exp, sqrt, etc.) encoded this way.", - "stage_11_repeat_position_encoding": "For repeated characters, use n(position, num_repeated) encoding instead of literal repetition. This reduces redundancy without storing full sequences.", - "stage_12_pist_perturbation_bundle_encoding": "Encode perturbation field δ (difference between topological skeleton prediction and actual density) via PIST n-D bundle encoding. Fiber dimensions: feature type, shell index, throat class, mass, curvature. n_dims = 3-4 (topic, time, authority, style).", - "stage_13_erans_residual_entropy_coding": "Entropy-code residual stream Ε and parameter stream Θ via enumerative rANS. Histogram of residuals is exact; enumerative coding is optimal for exact histograms. ISA-agnostic: SIMD opportunistic, scalar fallback required.", - "stage_14_sparse_structured_archive": "Assemble final archive. This is the mature hippocampus state: sparse yet structured. Only glyphs actually used in corpus stored (90-99% of full Unicode pruned). FAMM delays are sparse structured (fast paths for predictable regions, slow for noise). OAC receipts show accepted routes; FAMM scars show rejected motifs." - }, - - "master_decode_pipeline": { - "stage_0_load_archive": "Load archive MCA1 (Master Compression Architecture v1)", - "stage_1_load_decompressor": "Load decompressor profile D: custom glyph renderer + FAMM delay engine + Q16.16 fixed-point arithmetic", - "stage_2_load_books_sparse": "Load sparse subset of books from full slate: GlyphBook (only glyphs used in corpus), ChiralityBook (only chirality vectors used), TypeBook (only data types used: WikiArticle, Equation, FieldSet...), EigenBook (eigenvector descriptors from Gram matrix)", - "stage_3_load_shear_matrix": "Load shear matrix A and Gram matrix G = A^T A. This is the compression dictionary — eigenvectors = principal correlation directions", - "stage_4_load_famm_profile": "Load FAMM delay profile (sparse structured delays from consolidation phase). This is the mature hippocampus wiring", - "stage_5_load_s3c_coordinates": "Load S3C shell coordinates (k, a, b⁰, b⁺, mass, throat_class) for position encoding", - "stage_6_load_oac_receipts": "Load OAC receipts (accepted routes) and FAMM scars (rejected motifs) for pattern separation context", - "stage_7_packet_iteration": "For each packet Γᵢ in stream:", - "stage_8_resolve_glyph": "Resolve glyph γᵢ from sparse GlyphBook. If custom beyond-Unicode, decompressor generates it", - "stage_9_resolve_chirality": "Resolve chirality χᵢ from ChiralityBook. This selects the law-axis for the glyph", - "stage_10_resolve_type": "Resolve type τᵢ from TypeBook. This imports structural generative law", - "stage_11_load_eigen_descriptor": "Load eigenvector descriptor UᵢΛᵢaᵢ from EigenBook. This is the geometric compression", - "stage_12_load_parameters": "Load parameters θᵢ including n(position, num_repeated) for repeat expansion", - "stage_13_generate_prediction": "Generate predicted semantic unit ŝᵢ by applying shear inverse A⁻¹ to reconstruct expected structure from eigenvector descriptor", - "stage_14_apply_residual": "Apply residual εᵢ to correct prediction. This is the honesty layer — exact byte repair", - "stage_15_emit_exact_span": "Emit exact span sᵢ = Repair(Generate(γᵢ, χᵢ, κᵢ, τᵢ, UᵢΛᵢaᵢ, θᵢ), εᵢ)", - "stage_16_famm_temporal_sequencing": "Sequence spans via FAMM delay profile. Fast regions = high density (predictable). Slow regions = low density (needs context). This is the mature hippocampus temporal wiring", - "stage_17_concatenate": "Concatenate spans. No spaces needed — all one line", - "stage_18_verify_checksum": "Verify SHA256 checksum. Decode(archive) must equal original corpus C exactly", - "stage_19_output": "Output original corpus C" - }, - - "archive_format": { - "magic": "MCA1 (Master Compression Architecture v1)", - "sections": [ - "DECOMPRESSOR_PROFILE (custom glyph renderer + FAMM delay engine + Q16.16 arithmetic)", - "GLYPHBOOK_SPARSE (sparse subset of full Unicode + custom glyphs actually used in corpus)", - "CHIRALITYBOOK_SPARSE (chirality vectors actually used)", - "TYPEBOOK_SPARSE (data types actually used: WikiArticle, Equation, FieldSet, CitationGraph...)", - "EIGENBOOK (eigenvector descriptors from Gram matrix G = A^T A)", - "SHEAR_MATRIX_A (shear matrix transforming orthogonal hypercube to rhomboid)", - "GRAM_MATRIX_G (G = A^T A, the compression dictionary)", - "FAMM_DELAY_PROFILE (sparse structured delays from consolidation phase)", - "S3C_SHELL_COORDINATES (k, a, b⁰, b⁺, mass, throat_class for each position)", - "OAC_RECEIPTS (accepted routes = composite promotion = soliton bound states)", - "FAMM_SCARS (rejected motifs = never try again)", - "REGION_INDEX (map of field sets to byte spans)", - "GLYPH_PACKET_STREAM (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ)", - "PARAMETER_STREAM (θᵢ including n(position, num_repeated), mode selectors)", - "RESIDUAL_STREAM (εᵢ = exact byte repair)", - "PIST_PERTURBATION_BUNDLE (n-D bundle encoding of perturbation field)", - "CUSTOM_GLYPH_DEFINITIONS (if beyond Unicode)", - "CHECKSUM (SHA256)" - ] - }, - - "compression_gain_sources": { - "tabula_plena_pruning": "90-99% of full Unicode spectrum never used. Only ~10,000-50,000 glyphs actually used for 1GB corpus. Massive reduction from 1,114,112 codepoints.", - "famm_delay_pruning": "FAMM delays adapt from uniform to sparse structured. Fast paths for predictable regions (high density), slow for noise (low density). 10-20% context efficiency gain.", - "oac_gate_pruning": "OAC admissibility gate prunes motif space. Failed motifs become FAMM scars, never tried again. Avoids 2-5% bloat from bad motif choices.", - "radius_ratio_quantization": "Continuous local scale ratio → finite motif alphabet (CN3/CN4/CN6/CN8 analogue). Quantizes infinite possibilities to small admissible set. 5-10% entropy reduction.", - "gain_test_pruning": "ΔGCL > 0 ensures only compressive motifs kept. Prunes all non-compressive glyphs. This is the inhibitory plasticity analogue.", - "shear_matrix_pruning": "Gram matrix G = A^T A eigenvectors = principal correlation directions. Prunes orthogonal dimensions, keeps correlated sheared axes. 15-30% gain on structured regions.", - "topological_skeleton": "Morse-Smale complex = sparse topological encoding. O(N_peaks + N_ridges) ≪ O(N_bytes). 50-150MB skeleton vs 1GB raw for enwik9.", - "math_notation_density": "Math functions encoded as eigenvector descriptors instead of literal strings. sin(x) = 6 bytes → geometric descriptor. 5-8% on token encoding.", - "repeat_encoding": "n(position, num_repeated) instead of literal repetition. 3-5% on repeated patterns.", - "s3c_shell_efficiency": "Multi-scale coordinate encoding. Shell index k = structural depth captures semantic hierarchy. 5-10% on positional encoding.", - "pist_bundle_efficiency": "n-D bundle encoding captures correlated features in fiber dimensions. 5-8% on perturbation encoding.", - "erans_entropy": "Optimal exact histogram coding for residuals. Enumerative rANS is optimal for exact histograms. 2-5% entropy reduction.", - "hippocampus_pattern_separation": "Ensemble overlap threshold (10-40% analogue) separates admissible from inadmissible motifs. Prevents motif pollution.", - "composite_promotion": "Accepted OAC routes = composite promotion = soliton bound states stored in FAMM cache. Reuse of successful patterns." - }, - - "estimated_aggregate_gain": { - "tabula_plena_pruning": "90-99% of Unicode spectrum pruned. Only ~0.01-0.05% actually used.", - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup, math notation)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "famm_efficiency": "10-20% more predictive power per context byte", - "skeleton_compression": "50-150MB skeleton vs 1GB raw for enwik9", - "dictionary_overhead": "MB → KB (Gram matrix replaces LZ dictionary + transformer weights)", - "overall_compressed_size": "Estimated 18-28% reduction vs current best Hutter compressors", - "novel_capability": "Produces navigable structure. Query 'show me all articles 2 links from France' without full decompression. Tabula plena initialization enables adaptive learning of corpus-specific glyph space during consolidation.", - "biological_fidelity": "System follows hippocampus engram consolidation dynamics: neuron dropout (FAMM pruning), pattern separation (OAC gates), discrimination thresholds (radius-ratio), inhibitory plasticity (gain tests), composite promotion (OAC acceptance)." - }, - - "keeper_phrases": [ - "The hippocampus starts tabula plena (full slate) and prunes to sparse structured. Compression does the same.", - "Maximum math density is the full slate: full Unicode, custom glyphs, omniversal chirality — all possibilities available.", - "The density field is the manifold; the glyph packets are the navigators; the shear matrix is the map; FAMM is the temporal wiring.", - "FAMM delay lines model hippocampus pruning: from uniform delays (young) to sparse structured delays (mature) based on eigenvalue spectra.", - "OAC gates are the pattern separation: admissible motifs commit (composite promotion), inadmissible become FAMM scars.", - "Radius-ratio quantization is the discrimination threshold: continuous witness → finite motif alphabet.", - "Gain test ΔGCL > 0 is the inhibitory plasticity: prevents runaway potentiation of bad motifs.", - "The Gram matrix of the shear IS the dictionary, the context model, the token encoding, and the structure detector — all at once.", - "The Morse-Smale complex is the topological skeleton of meaning. GCCL-GEC is the lawful engine that navigates it.", - "Young hippocampus: single input → fire. Mature: multiple inputs → fire. OAC: single glyph → test. Mature: gain > 0 → commit.", - "Strong early connections → FAMM preshaped delays based on eigenvalue spectra, not uniform delays.", - "We remember little from infancy because the hippocampus is dense and random. We compress well because the archive is sparse and structured.", - "Don't start blank. Start full, then prune. The hippocampus does it. Compression should too.", - "The archive is not the full slate. The archive is the sparse structured result of pruning.", - "A wiki page is a sparse eigenstate of a typed reconstruction manifold, plus apology bytes.", - "ρ = |ε| / |raw_span|. This is the only number that matters.", - "The datatype is the engine. UTF-8 is only the exhaust.", - "Never trust a glyph until the residual gets smaller." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "master-synthesis", - "complete-compression-architecture", - "density-field-encoding", - "gccl-gec", - "observer-admissible-cavities", - "hypercube-rhomboid", - "radius-ratio", - "maximum-math-density", - "hippocampus-tabula-plena", - "engram-consolidation", - "famm-delay-lines", - "s3c-shells", - "pist-nd-bundle", - "erans-entropy", - "morse-smale-complex", - "gram-matrix", - "shear-matrix", - "tabula-plena", - "sparse-structured", - "navigable-compression", - "hutter-prize", - "information-geometry", - "semantic-manifold" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "master_synthesis_complete_v1.json" - with open(out_path, 'w') as f: - json.dump(MASTER_SYNTHESIS, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": MASTER_SYNTHESIS["id"], - "title": MASTER_SYNTHESIS["title"], - "date": MASTER_SYNTHESIS["date"], - "source": MASTER_SYNTHESIS["source"], - "ingested_at": MASTER_SYNTHESIS["metadata"]["ingested_at"], - "tags": MASTER_SYNTHESIS["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nTheoretical foundations (12):") - for foundation, data in MASTER_SYNTHESIS["theoretical_foundations"].items(): - print(f" • {foundation}: {data.get('core', data.get('description', ''))[:60]}...") - - print(f"\nMaster encoding pipeline (14 stages):") - for i, (stage, desc) in enumerate(MASTER_SYNTHESIS["master_encoding_pipeline"].items(), 1): - print(f" {i}. {stage}: {desc[:60]}...") - - print(f"\nMaster decode pipeline (19 stages):") - for i, (stage, desc) in enumerate(MASTER_SYNTHESIS["master_decode_pipeline"].items(), 1): - print(f" {i}. {stage}: {desc[:60]}...") - - print(f"\nCompression gain sources (13):") - for source, gain in MASTER_SYNTHESIS["compression_gain_sources"].items(): - print(f" • {source}: {gain[:60]}...") - - print(f"\nEstimated aggregate gain:") - for metric, value in MASTER_SYNTHESIS["estimated_aggregate_gain"].items(): - print(f" • {metric}: {value[:70]}...") - - print(f"\nKeeper phrases ({len(MASTER_SYNTHESIS['keeper_phrases'])}):") - for p in MASTER_SYNTHESIS["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_maximum_math_density_spec.py b/4-Infrastructure/shim/ingest_maximum_math_density_spec.py deleted file mode 100644 index 22f1aa5f..00000000 --- a/4-Infrastructure/shim/ingest_maximum_math_density_spec.py +++ /dev/null @@ -1,325 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Maximum Math Density Specification -========================================== -Custom logographic notation + math notation density + GCCL chirality + -eigenvector geometric compression + full Unicode spectrum + custom glyphs. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -MAX_MATH_DENSITY = { - "id": "maximum-math-density-spec-v1", - "source": "User insight: custom logographic notation + math density + GCCL + eigenvectors", - "title": "Maximum Math Density: Custom Logographic Notation with Eigenvector Geometric Compression", - "date": "2026-05-07", - - "core_insight": ( - "UTF-8 is a 1D byte sequence. Maximum compression requires representing data as " - "logographic density maps using the full Unicode spectrum (UTF-16, beyond), custom glyphs, " - "Chinese-style notation, Korean block graphs, and eigenvector geometric descriptors. " - "Field sets can carry entire wiki entries via data types. Repeated characters use " - "n(position, num_repeated) encoding. No spaces needed — all one line." - ), - - "representation_paradigm": { - "utf8_baseline": { - "representation": "1D byte sequence: b[i] ∈ [0,255]", - "limitation": "Linear string, independent byte positions, no semantic structure" - }, - "logographic_density": { - "representation": "n-D logographic field: each glyph = entire semantic unit (wiki entry, equation, concept)", - "advantage": "Single glyph carries dense information via data type + eigenvector descriptor" - }, - "math_notation_density": { - "representation": "String of notational equations from full math book", - "advantage": "Math notation is inherently dense — ∫, ∂, ∇, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃" - }, - "combined": "Logographic glyphs + math notation + eigenvector descriptors = maximum information density per character" - }, - - "custom_glyph_language": { - "design_principles": { - "chinese_style_logograms": "Each glyph = entire concept/word, not phonetic. Similar to Hanzi where 意 = 'meaning' in one character", - "korean_block_graphs": "Hangul-style block composition where sub-elements combine into dense syllabic units", - "math_symbols": "Full Unicode math symbol set (∂, ∇, ∫, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃, ∴, ∵, ⊕, ⊗, ⊘, ⊙, ⊚, ⊛, ⊜, ⊝, ⊞, ⊟, ⊠, ⊡, ⊢, ⊣, ⊤, ⊥, ⊦, ⊧, ⊨, ⊩, ⊪, ⊫, ⊬, ⊭, ⊮, ⊯, ⊰, ⊱, ⊲, ⊳, ⊴, ⊵, ⊶, ⊷, ⊸, ⊹, ⊺, ⊻, ⊼, ⊽, ⊾, ⊿, ⋀, ⋁, ⋂, ⋃, ⋄, ⋅, ⋆, ⋇, ⋈, ⋉, ⋊, ⋋, ⋌, ⋍, ⋎, ⋏, ⋐, ⋑, ⋒, ⋓, ⋔, ⋕, ⋖, ⋗, ⋘, ⋙, ⋚, ⋛, ⋜, ⋝, ⋞, ⋟, ⋠, ⋡, ⋢, ⋣, ⋤, ⋥, ⋦, ⋧, ⋨, ⋩, ⋪, ⋫, ⋬, ⋭, ⋮, ⋯, ⋰, ⋱, ⋲, ⋳, ⋴, ⋵, ⋶, ⋷, ⋸, ⋹, ⋺, ⋻, ⋼, ⋽, ⋾, ⋿)", - "emoji_codes": "Full emoji spectrum (📐, 📚, 👤, 🌍, 🧾, 󰀁, 󰀂, 󰀃, 󰀄, 󰀅...)", - "unused_unicode": "Truly unused characters in full spectrum (PUA, private use areas, reserved planes)", - "custom_glyphs": "Decompressor can generate custom glyphs on-the-fly if needed" - }, - "composition_rules": { - "block_composition": "Like Hangul: sub-elements combine into block glyphs. Each block = dense semantic unit", - "position_encoding": "n(position, num_repeated) for repeated characters. No need for literal repetition", - "no_spaces": "All one line — no whitespace needed for separation", - "density_first": "Only caring about compression, not readability to humans" - }, - "example_encodings": { - "1906_as_chinese": "Could be represented as single Chinese-style logogram (custom glyph 1906)", - "wiki_entry": "Entire wiki page about math could be self-encoded as field set + data type", - "equation": "String of math notation: ∫₀^∞ e^(-x²) dx = √π/2 encoded as single glyph with eigenvector descriptor" - } - }, - - "field_set_data_type_carrying": { - "concept": "Field sets can carry entire wiki entries via data types", - "mechanism": { - "data_type": "WikiArticle carries entire structure (title, infobox, citations, sections)", - "field_set": "F = {field₁, field₂, ..., fieldₙ} where each field = eigenvector descriptor + residual", - "self_encoding": "The page about math itself can be self-encoded — meta-encoding", - "recursive": "Field sets can nest: wiki entry contains field sets for sub-sections" - }, - "example": "Field set for 'Mathematics' wiki page = {title_field, infobox_field, history_field, notation_field, application_field, philosophy_field, reference_field}. Each field = glyph + chirality + eigenvector + residual." - }, - - "utf16_beyond_spectrum": { - "unicode_planes": { - "BMP": "Basic Multilingual Plane (U+0000 to U+FFFF) — 65,536 codepoints", - "SMP": "Supplementary Multilingual Plane (U+10000 to U+1FFFF) — CJK, emoji, math symbols", - "SIP": "Supplementary Ideographic Plane (U+20000 to U+2FFFF) — rare CJK", - "TIP": "Third Ideographic Plane (U+30000 to U+3FFFF) — more CJK", - "SSP": "Supplementary Special-purpose Plane (U+E0000 to U+EFFFF) — private use", - "PUA": "Private Use Areas (U+E000 to U+F8FF, U+F0000 to U+FFFFD, U+100000 to U+10FFFD) — custom glyphs" - }, - "utilization_strategy": { - "standard_unicode": "Use existing math symbols, emoji, CJK, Hangul blocks", - "pua_custom": "Define custom glyphs in PUA for compression-specific purposes", - "beyond_unicode": "If needed, decompressor can generate glyphs beyond standard Unicode", - "decompressor_capability": "Custom glyph decompressor can render any glyph defined in the archive" - }, - "capacity": "1,114,112 codepoints in Unicode 15.0. Custom glyphs extend this further." - }, - - "gccl_omniversal_chirality": { - "role": "Omniversal chirality makes info-dense characters reusable in near-infinite combinations", - "mechanism": { - "glyph": "Single info-dense character (custom glyph, emoji, math symbol, CJK)", - "chirality_vector": "⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ — 6 axes", - "combinatorial_explosion": "N_glyphs × 2^6_chirality_axes × continuum_of_chirality_values = near-infinite combinations", - "reuse": "Same glyph reused with different chirality = different meaning without new characters" - }, - "example": "📐 with chirality ⟨geo, comp, 0, spec, 0, 0⟩ = geometric primitive. 📐 with chirality ⟨0, 0, load, 0, topo, 0⟩ = expensive fallback marker. Same glyph, different meaning." - }, - - "eigenvector_geometric_compression": { - "role": "Encode as pure geometric compression via eigenvector descriptors", - "mechanism": { - "shear_matrix": "A transforms orthogonal hypercube to correlated rhomboid", - "gram_matrix": "G = A^T A, eigenvectors = principal correlation directions", - "eigenvector_descriptor": "Each glyph packet carries UᵢΛᵢaᵢ (eigenbasis, spectrum, sparse coefficients)", - "geometric_encoding": "Information encoded as geometry of manifold, not literal bytes" - }, - "advantage": "Eigenvectors capture the 'shape' of information. The same shape can describe many different instances." - }, - - "maximum_math_density_encoding": { - "encoding_unit": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "fields": { - "γᵢ": "Custom logographic glyph (Chinese-style, Korean block, math symbol, emoji, PUA custom)", - "χᵢ": "Omniversal chirality (6 axes, near-infinite combinations)", - "κᵢ": "S3C shell coordinate (k, a, b⁰, b⁺) — position in field", - "τᵢ": "Data type (WikiArticle, FieldSet, Equation, etc.)", - "UᵢΛᵢaᵢ": "Eigenvector descriptor (geometric compression)", - "θᵢ": "Parameters (n(position, num_repeated) for repeats, mode selectors)", - "εᵢ": "Residual (exact repair)" - }, - "density_per_glyph": "Single glyph can carry: entire wiki entry (via data type), equation (via math notation), concept (via logogram), structure (via eigenvector)" - }, - - "one_billion_byte_encoding": { - "assumptions": { - "glyph_capacity": "1,114,112 Unicode codepoints + custom PUA + beyond-Unicode", - "chirality_combinations": "N_glyphs × 2^6 × continuum ≈ effectively infinite", - "eigenvector_reuse": "Same eigenvector descriptor used across many glyphs", - "data_type_carrying": "Single data type carries entire structure" - }, - "capacity_model": { - "per_glyph_bytes": "Assume 4 bytes per glyph (UTF-32) or 2-4 bytes (UTF-16)", - "glyphs_per_mb": "1,048,576 / 4 = 262,144 glyphs per MB", - "glyphs_per_gb": "262,144 × 1024 = 268,435,456 glyphs per GB", - "information_per_glyph": "If each glyph = entire wiki entry (via data type + eigenvector), then 268M wiki entries per GB", - "compression_ratio": "If average wiki entry = 10KB, then 268M glyphs × 10KB = 2.68TB of information in 1GB = 2680x compression" - }, - "conservative_estimate": { - "per_glyph_semantic_load": "Assume each glyph = 1KB of semantic information (not entire wiki entry)", - "total_capacity": "268M glyphs × 1KB = 268GB of semantic information in 1GB = 268x compression", - "realistic_estimate": "With residual costs, eigenvector overhead, chirality encoding: 100-200x compression achievable" - } - }, - - "fractal_encoding_test_case": { - "purpose": "Find page with fractal encoding (nearly impossible to compress) to test limits of design", - "candidate_sources": [ - "Mandelbrot set ASCII art", - "L-system generated fractals", - "Cellular automaton rule 30 output", - "Random noise (worst case)", - "Encrypted data (worst case)" - ], - "test_methodology": { - "encode_fractal": "Encode fractal using maximum math density encoding", - "measure_compression": "ρ = |ε| / |raw_span| — residual ratio", - "limit_analysis": "If ρ ≈ 1.0, design failed for this data type. If ρ < 0.1, excellent.", - "expected_result": "Fractals should have ρ ≈ 0.5-1.0 because they have no topological structure to exploit" - }, - "diagnostic": "Fractal encoding stress test reveals which components of the design rely on structure vs. which work on any data" - }, - - "sin_to_math_notation": { - "concept": "Change sin (sine function) to actual math notation", - "encoding": { - "literal": "sin(x) = 6 bytes in ASCII", - "math_notation": "sin(x) = 2 glyphs (sin, x) with math notation or single custom glyph for sine function", - "eigenvector_descriptor": "Sine function encoded as eigenvector descriptor: U = {amplitude, frequency, phase, offset}, Lambda = {eigenvalues of sine space}, a = sparse coefficients", - "geometric_encoding": "Sine wave = geometric object in function space, not string of characters" - }, - "generalization": "All math functions (sin, cos, tan, log, exp, sqrt, etc.) encoded as geometric eigenvector descriptors, not literal strings" - }, - - "full_spec_best_approach": { - "encoding_pipeline": [ - "Parse corpus into semantic field (density field extraction)", - "Classify each region: wiki entry, equation, concept, template, free text", - "For each region, choose optimal encoding:", - " - Wiki entry → data type (WikiArticle) + eigenvector descriptor + residual", - " - Equation → math notation glyphs + eigenvector descriptor", - " - Concept → custom logographic glyph + chirality + eigenvector", - " - Template → field set with repeated structure", - " - Free text → S3C shell coordinates + GCCL packet", - "Apply omniversal chirality to maximize glyph reuse", - "Encode position via n(position, num_repeated) for repeats", - "Apply eigenvector geometric compression (shear matrix → Gram matrix)", - "Entropy-code residuals via erans", - "Assemble archive with custom glyph definitions if needed" - ], - "archive_format": { - "magic": "MMD1 (Maximum Math Density v1)", - "sections": [ - "DECOMPRESSOR_PROFILE (custom glyph renderer)", - "GLYPHBOOK (custom glyphs + Unicode mapping)", - "CHIRALITYBOOK (chirality vectors)", - "TYPEBOOK (data types: WikiArticle, Equation, FieldSet...)", - "EIGENBOOK (eigenvector descriptors)", - "SHEAR_MATRIX (Gram matrix G = A^T A)", - "FIELD_SET_INDEX (map of field sets to byte spans)", - "GLYPH_PACKET_STREAM (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ)", - "PARAMETER_STREAM (n(position, num_repeated), mode selectors)", - "RESIDUAL_STREAM (εᵢ)", - "CUSTOM_GLYPH_DEFINITIONS (if beyond Unicode)", - "CHECKSUM (SHA256)" - ] - }, - "decode_pipeline": [ - "Load archive MMD1", - "Load decompressor profile (custom glyph renderer)", - "Load GlyphBook, ChiralityBook, TypeBook, EigenBook", - "Load shear matrix (Gram matrix)", - "Load field set index", - "For each packet Γᵢ:", - " - Resolve glyph γᵢ (custom or Unicode)", - " - Resolve chirality χᵢ", - " - Resolve type τᵢ", - " - Load eigenvector descriptor UᵢΛᵢaᵢ", - " - Load parameters θᵢ (including n(position, num_repeated))", - " - Generate predicted semantic unit ŝᵢ", - " - Apply residual εᵢ", - " - Emit exact span sᵢ", - "Concatenate spans (no spaces needed)", - "Verify checksum" - ] - }, - - "keeper_phrases": [ - "UTF-8 is a string. Maximum math density is a logographic field of eigenvector-encoded concepts.", - "A single glyph can carry an entire wiki entry via data type + eigenvector descriptor.", - "1906 is not four bytes. It is one custom logographic glyph.", - "Omniversal chirality makes the same glyph mean near-infinite things.", - "Don't encode 'sin(x)'. Encode the geometric object in function space.", - "Field sets carry entire structures. The page about math can self-encode.", - "No spaces needed. All one line. Repeats use n(position, num_repeated).", - "The full Unicode spectrum is your alphabet. Custom glyphs extend it further.", - "Chinese-style logograms + Korean block graphs + math symbols = maximum density.", - "1 billion bytes = 268 million glyphs. If each glyph = 1KB semantic, that's 268GB of information.", - "Fractal encoding is the stress test. If it compresses, the design is truly universal.", - "The decompressor can generate any glyph. You are not limited to standard Unicode.", - "Eigenvectors capture the shape of information. The shape is reusable; the instance is residual.", - "Math notation is dense. Use it. ∫, ∂, ∇, ∑, ∏, √, ∞ are your building blocks." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "maximum-math-density", - "logographic-notation", - "custom-glyphs", - "chinese-style-encoding", - "korean-block-graphs", - "math-notation-density", - "utf16-beyond", - "omniversal-chirality", - "eigenvector-geometric-compression", - "field-set-carrying", - "n-position-num-repeated", - "fractal-encoding-test", - "1-billion-byte-encoding", - "gccl-combined" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "maximum_math_density_spec_v1.json" - with open(out_path, 'w') as f: - json.dump(MAX_MATH_DENSITY, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": MAX_MATH_DENSITY["id"], - "title": MAX_MATH_DENSITY["title"], - "date": MAX_MATH_DENSITY["date"], - "source": MAX_MATH_DENSITY["source"], - "ingested_at": MAX_MATH_DENSITY["metadata"]["ingested_at"], - "tags": MAX_MATH_DENSITY["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCustom glyph language design principles:") - for principle, desc in MAX_MATH_DENSITY["custom_glyph_language"]["design_principles"].items(): - print(f" • {principle}: {desc[:60]}...") - - print(f"\nUTF-16/beyond spectrum:") - for plane, desc in MAX_MATH_DENSITY["utf16_beyond_spectrum"]["unicode_planes"].items(): - print(f" • {plane}: {desc}") - - print(f"\n1 billion byte encoding capacity:") - for metric, value in MAX_MATH_DENSITY["one_billion_byte_encoding"]["capacity_model"].items(): - print(f" • {metric}: {value[:70]}...") - - print(f"\nConservative estimate:") - for metric, value in MAX_MATH_DENSITY["one_billion_byte_encoding"]["conservative_estimate"].items(): - print(f" • {metric}: {value}") - - print(f"\nKeeper phrases ({len(MAX_MATH_DENSITY['keeper_phrases'])}):") - for p in MAX_MATH_DENSITY["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_ms_myelin_article.py b/4-Infrastructure/shim/ingest_ms_myelin_article.py deleted file mode 100644 index 50728c19..00000000 --- a/4-Infrastructure/shim/ingest_ms_myelin_article.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest MS myelin glucose signaling article into Research Stack database.""" - -import json, time, hashlib -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ARTICLE = { - "id": "ms-myelin-glucose-2026-05-04", - "source": "https://multiplesclerosisnewstoday.com/news-posts/2026/05/04/brain-sugar-levels-act-signal-myelin-growth-study-finds/", - "title": "Brain sugar levels act as signal for myelin growth, study finds", - "date": "2026-05-04", - "publication": "Multiple Sclerosis News Today", - "summary": "Glucose levels in the brain regulate oligodendrocyte progenitor cell (OPC) fate — high glucose drives OPC proliferation via histone acetylation, low glucose triggers maturation into myelin-producing oligodendrocytes. Acetyl-CoA from glucose is required for OPC division; mature oligodendrocytes can source acetyl-CoA from ketone bodies for myelin synthesis. ACLY enzyme knockout reduces early myelin but ketogenic diet rescues it.", - "key_findings": [ - "OPC activity correlates with local brain glucose levels", - "High glucose → acetyl-CoA → histone acetylation → OPC proliferation", - "Low glucose → OPC maturation into myelin-producing oligodendrocytes", - "ACLY enzyme required for glucose-to-acetyl-CoA conversion in OPCs", - "Mature oligodendrocytes use ketone bodies as alternative acetyl-CoA source", - "Ketogenic diet rescues myelin production in ACLY-deficient mice", - "Same cell lineage interprets different metabolic signals at distinct stages" - ], - "relevance_to_research_stack": { - "topics": [ - "metabolic_epigenetic_switch", - "myelin_repair_mechanism", - "glucose_signaling_pathway", - "oligodendrocyte_differentiation", - "ketogenic_metabolic_intervention", - "histone_acetylation_gene_regulation" - ], - "connections": [ - "N-Dimensional Gene Hypothesis: glucose gradient as spatial morphogen signal", - "PIST biological polymorphic shifter: metabolic state → cell fate switch", - "Topological state machine: glucose level as continuous state variable", - "FAMM delay lines: metabolic latency in cell fate decisions", - "Waveprobe manifolds: glucose gradient as scalar field on brain manifold" - ] - }, - "metadata": { - "ingested_at": time.time(), - "content_hash": hashlib.sha256( - "glucose myelin OPC oligodendrocyte acetyl-CoA ACLY ketogenic histone acetylation".encode() - ).hexdigest()[:16], - "tags": ["neuroscience", "metabolism", "myelin", "multiple-sclerosis", "epigenetics", "glucose-signaling"] - } -} - - -def ingest(): - # Save to germane research data - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "ms_myelin_glucose_signaling_2026-05-04.json" - with open(out_path, 'w') as f: - json.dump(ARTICLE, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Append to research index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ARTICLE["id"], - "title": ARTICLE["title"], - "date": ARTICLE["date"], - "source": ARTICLE["source"], - "ingested_at": ARTICLE["metadata"]["ingested_at"], - "tags": ARTICLE["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index updated: {index_path} ({len(index)} entries)") - - # Print connections - print(f"\nResearch Stack connections:") - for conn in ARTICLE["relevance_to_research_stack"]["connections"]: - print(f" → {conn}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_oac_theory.py b/4-Infrastructure/shim/ingest_oac_theory.py deleted file mode 100644 index feb29a89..00000000 --- a/4-Infrastructure/shim/ingest_oac_theory.py +++ /dev/null @@ -1,169 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Observer-Admissible Cavities & Radius-Ratio Compression Theory -Maps the ChatGPT conversation into Research Stack database with -cross-references to existing modules. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -OAC_THEORY = { - "id": "observer-admissible-cavities-theory", - "source": "ChatGPT conversation — radius-ratio → Pidgen-hole → S3C/Spherion → OAC", - "title": "Observer-Admissible Cavities: Latent Shaped Holes as Compression Primitives", - "date": "2026-05-07", - "summary": "Observer-Admissible Cavities (OACs) are latent shaped holes whose combinatorial interiors remain compressed until a lawful observer touches them. Admissible touches manifest routes; inadmissible touches manifest void scars. OACs create temporary exploration spaces that do not pollute the substrate address space — only receipts, accepted routes, and residuals commit.", - - "key_concepts": { - "radius_ratio_rule": { - "definition": "Cation/anion radius ratio predicts admissible coordination geometry (CN3=0.155, CN4=0.225, CN6=0.414, CN8=0.732)", - "compression_analogue": "Local scale ratio → admissible motif class → decode rule + residual", - "sources": ["LibreTexts 9.1", "Wikipedia cation-anion radius ratio", "MIT 12.108 lec4"] - }, - "pidgen_hole_theory": { - "definition": "Objects fall into typed admissible holes; compression stores hole_id + residual. A hole is compressive when L(hole) + L(residual) < L(raw).", - "upgrade": "Radius-ratio gives holes typed geometry (not just buckets). S3C gives shell coordinates. Spherion gives surface shaping." - }, - "s3c_shell_coordinates": { - "definition": "n = k² + a, with mirror complement b⁰, next-shell tension b⁺, mass = a×b⁰, mirror_delta = a-b⁰", - "compression_role": "Converts raw values into structured shell cavities with throat/boundary/asymmetry classification", - "existing_module": "4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md, S3C geometry docs" - }, - "spherion_shaping": { - "definition": "Resonant spherical surfaces with pyramid protrusions (positive) and voids (negative). High-Q = narrow confident prediction; low-Q = broad noisy prediction.", - "compression_role": "Pyramid height = amplitude, base width = duration, slope = transition, asymmetry = skew, apex = precision. Negative pyramids = expected-but-missing features (void carrier).", - "existing_module": "topology-resonance hierarchy doc, pyramid-spherion gear review" - }, - "sn_nn": { - "definition": "S_n(n^n): shaped shell with symbolic n^n combinatorial interior. Interior is latent — not materialized. Only selected route + residual paid.", - "recursive_form": "S_n((S_{n-1})^n): recursive shell grammar — nth shell contains n choices of previous shell state", - "compression_role": "Explosive interior bound behind compact shell descriptor" - }, - "observer_admissible_cavity": { - "definition": "OAC = (S_n, A_O, T, V, R, ε): latent cavity that only manifests under lawful observer touch", - "touch_operator": "touch(O, OAC_i, q) → (S_i, r_i, ε_i, ρ_i) if admissible, (V_i, scar_i, ρ_i) otherwise", - "address_space_rule": "OAC ⊄ A_substrate; only receipt, accepted route, and residual may commit", - "temporary_exploration": "OACs create observer-scoped scratch manifolds that evaporate after gate decision — no substrate pollution" - } - }, - - "compression_pipeline": [ - "raw bytes/tokens/graph nodes", - "map to integer or local state n", - "S3C split: k, a, b⁰, b⁺", - "classify throat/boundary/asymmetry", - "map to spherion mode σ", - "add pyramid/void shaping h", - "emit S_n codon", - "emit residual", - "entropy-code streams separately" - ], - - "output_streams": [ - "shell indices k", - "offsets a", - "throat/mass classes", - "shape modes", - "void/protrusion masks", - "residual bytes" - ], - - "admissibility_gate": { - "accept": "L(S_n) + L(route) + L(ε) < L(x)", - "reject": "void scar + FAMM memory + down-ranked prior", - "existing_module": "MS3C GCL admissibility wrapper, FAMM failure-memory compression" - }, - - "keeper_phrases": [ - "A compressive hole is a lawful cavity whose residual is cheaper than the thing it absorbs.", - "S3C gives the pigeon a lawful shell; Spherion shaping gives the hole teeth, voids, and resonance.", - "S_n(n^n) is a Matryoshka shell: externally small, internally combinatorial, decoded only along lawful routed paths.", - "Do not store the n^n interior. Store the shaped shell, the void field, the selected route, and the residual.", - "The holes are lazy. They do not exist as expanded objects; they exist as lawful cavities with manifestation rules.", - "Observer-Admissible Cavities create temporary exploration manifolds whose interiors do not occupy substrate address space.", - "OACs let the system think in holes without storing every hole it thinks through." - ], - - "cross_references": { - "existing_modules": { - "pist_biological_polymorphic_shifter_v3_complete.py": "PIST nD bundle encode/decode — direct S3C shell mapping target", - "topological_state_machine.py": "State transitions → touch operations on OACs", - "hdmi_computational_shell.py": "Shell computation surface → OAC manifestation target", - "FixedPoint.lean": "Q16.16 arithmetic for mass/mirror_delta/throat classification", - "NDimensionalGeneHypothesis.md": "Gene as n-D information structure → OAC as gene-analogue cavity", - "famm_verilator_bench.v": "FAMM preshaped delays → OAC route latency model", - "prover_orchestration_layer.py": "L0-L3 pipeline → OAC touch/gate/commit pipeline" - }, - "new_primitives_needed": [ - "OAC.lean — Lean 4 formalization of Observer-Admissible Cavities", - "s3c_shell_codec.py — S3C shell coordinate encoder/decoder", - "spherion_shape_quantizer.py — Pyramid/void field classifier", - "oac_touch_gate.py — Touch operator with GCL admissibility check" - ] - }, - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "observer-admissible-cavities", "radius-ratio", "coordination-geometry", - "pidgen-hole-theory", "s3c-shells", "spherion-shaping", - "compression-theory", "lazy-manifestation", "substrate-separation", - "temporary-exploration", "admissibility-gate", "void-carrier" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "observer_admissible_cavities_theory.json" - with open(out_path, 'w') as f: - json.dump(OAC_THEORY, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Update index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": OAC_THEORY["id"], - "title": OAC_THEORY["title"], - "date": OAC_THEORY["date"], - "source": OAC_THEORY["source"], - "ingested_at": OAC_THEORY["metadata"]["ingested_at"], - "tags": OAC_THEORY["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nKey concepts ingested:") - for name, concept in OAC_THEORY["key_concepts"].items(): - print(f" • {name}: {concept['definition'][:80]}...") - - print(f"\nCross-references to existing modules:") - for module, role in OAC_THEORY["cross_references"]["existing_modules"].items(): - print(f" ↔ {module}: {role[:70]}...") - - print(f"\nNew primitives needed:") - for p in OAC_THEORY["cross_references"]["new_primitives_needed"]: - print(f" + {p}") - - print(f"\nKeeper phrases ({len(OAC_THEORY['keeper_phrases'])}):") - for phrase in OAC_THEORY["keeper_phrases"]: - print(f" → {phrase}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/ingest_unified_compression_synthesis.py b/4-Infrastructure/shim/ingest_unified_compression_synthesis.py deleted file mode 100644 index bee53944..00000000 --- a/4-Infrastructure/shim/ingest_unified_compression_synthesis.py +++ /dev/null @@ -1,414 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Unified Compression Architecture Synthesis -================================================== -Synthesis of Density Field Encoding + GCCL-GEC + OAC + Hypercube-Rhomboid -+ S3C Shells + FAMM + PIST + erans + Radius-Ratio into a single coherent -compression pipeline for the Research Stack. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -SYNTHESIS = { - "id": "unified-compression-architecture-synthesis-v1", - "source": "Synthesis of 5 ingested theories: density-field, gccl-gec, oac, hypercube-rhomboid, hutter-rhomboid", - "title": "Unified Compression Architecture: Semantic Density Fields with Glyph Eigen Codec and Sheared Manifold Geometry", - "date": "2026-05-07", - - "core_synthesis": ( - "The unified architecture treats text as an n-dimensional semantic density field, " - "encodes its topological skeleton via GCCL-GEC glyph packets, applies hypercube-rhomboid " - "shear for correlation modeling, uses OAC for speculative lazy manifestation, " - "coordinates via S3C shells, temporally warps via FAMM, encodes perturbations via PIST, " - "entropy-codes residuals via erans, and quantizes local admissibility via radius-ratio." - ), - - "architectural_layers": { - "Layer_0_Raw_Representation": { - "input": "UTF-8 byte sequence C ∈ Byteⁿ", - "limitation": "1D orthogonal hypercube — independent byte positions, no semantic structure", - "transformation": "Parse into semantic density field ρ: M → ℝ⁺ where M is n-D semantic manifold" - }, - "Layer_1_Density_Field_Extraction": { - "operation": "Compute semantic density field ρ(x⃗) from corpus structure", - "topological_features": { - "peaks": "Named entities, article centers, key concepts (local maxima)", - "ridges": "Hyperlinks, citations, semantic connections (1D maxima)", - "saddles": "Topic transitions, paragraph boundaries (mixed Hessian)", - "vortices": "Cyclic references, template instantiations (rotational flow)", - "voids": "Template structures, expected absence (local minima)", - "level_sets": "Paragraphs, sections, articles (iso-density surfaces)" - }, - "morse_smale_complex": "Critical points + separatrices = topological skeleton of meaning", - "output": "Topological skeleton S (peaks, ridges, saddles, vortices, voids) + perturbation field δ" - }, - "Layer_2_Hypercube_Rhomboid_Shear": { - "operation": "Apply shear matrix A to orthogonal UTF-8 hypercube → correlated semantic rhomboid", - "shear_matrix": "A_{ij} = δ_{ij} + α_{ij} where α encodes mutual information between dimensions i, j", - "gram_matrix": "G = A^T A is the compression dictionary — eigenvectors = principal correlation directions, eigenvalues = compression gains", - "volume_preservation": "det(A) = 1 — information volume preserved, only geometry changed", - "information_gravity": "g_{μν} = δ_{μν} + κ·I_{μν} where I_{μν} is mutual information, κ is gravitational coupling", - "output": "Sheared manifold S̃ = A·S where correlated axes lean into each other" - }, - "Layer_3_S3C_Shell_Coordinate_Encoding": { - "operation": "Encode positions within sheared manifold via S3C shell coordinates", - "s3c_split": "n = k² + a with mirror complement b⁰, next-shell tension b⁺, mass = a·b⁰, mirror_delta = a - b⁰", - "coordinate_mapping": { - "k": "Shell index = structural depth / semantic distance from peak", - "a": "Angular offset = intra-cluster position within shell", - "b⁰": "Mirror complement = remaining path to next peak", - "throat": "a ≈ b⁰ = maximum compression (maximum predictive confidence)", - "mass": "Connection strength between topological features" - }, - "output": "Shell-encoded coordinates (k, a, b⁰, b⁺, mass, throat_class) for each packet" - }, - "Layer_4_GCCL_GEC_Packet_Encoding": { - "operation": "Encode each topological feature as glyph packet Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "packet_fields": { - "γᵢ": "Visible glyph / emoji / math symbol / PUA codepoint — invokes callable reconstruction kernel", - "χᵢ": "Chirality vector ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ — law-axis selection", - "κᵢ": "Local context = S3C shell coordinate (k, a) from Layer 3", - "τᵢ": "Type witness from TypeBook (WikiArticle, Infobox, CitationGraph...)", - "UᵢΛᵢaᵢ": "Eigen descriptor from EigenBook (basis, spectrum, sparse coefficients)", - "θᵢ": "Parameters from side stream (mode selectors, eigenbook indices)", - "εᵢ": "Residual bytes = exact repair to generated prediction" - }, - "books": { - "GlyphBook": "Maps codepoints to callable kernels", - "ChiralityBook": "Maps chirality vectors to law-axes", - "TypeBook": "Maps datatypes to structural laws", - "EigenBook": "Stores reusable geometric descriptors" - }, - "gain_test": "ΔGCL(Γᵢ) = literal_cost - encoded_cost(γᵢ, χᵢ, κᵢ, τᵢ, UΛa, θᵢ, εᵢ). Accept iff ΔGCL > 0.", - "output": "Packet stream Γ = {Γ₁, Γ₂, ..., Γₙ} + parameter stream Θ + residual stream Ε" - }, - "Layer_5_OAC_Speculative_Manifestation": { - "operation": "Test compression motifs as Observer-Admissible Cavities before committing", - "oac_definition": "OAC = (Sₙ, A_O, T, V, R, ε) — latent cavity that only manifests under lawful observer touch", - "touch_operator": "touch(O, OACᵢ, q) → (Sᵢ, rᵢ, εᵢ, ρᵢ) if admissible, (Vᵢ, scarᵢ, ρᵢ) otherwise", - "admissibility_gate": "Accept iff L(Sₙ) + L(route) + L(ε) < L(x)", - "spherion_shaping": { - "positive_pyramids": "Protrusions = confident predictions (high-Q narrow)", - "negative_pyramids": "Voids = expected-but-missing features (cheaper to encode absence)", - "shape_parameters": "Height = amplitude, base = duration, slope = transition, asymmetry = skew, apex = precision" - }, - "sn_nn_grammar": "Sₙ(nⁿ) recursive shell grammar — nth shell contains n choices of previous shell state", - "address_space_rule": "OAC ⊄ A_substrate; only receipt, accepted route, and residual may commit", - "output": "Accepted packets + void scars (FAMM failure memory) + receipts" - }, - "Layer_6_Radius_Ratio_Local_Quantization": { - "operation": "Quantize local scale ratio ρ into admissible motif class via radius-ratio rule", - "radius_ratio_rule": "Local scale ratio → smallest stable coordination geometry", - "coordination_analogy": { - "CN3 (triangular)": "ρ ∈ [0.155, 0.225) — minimal coordination", - "CN4 (tetrahedral)": "ρ ∈ [0.225, 0.414) — 4-way coordination", - "CN6 (octahedral)": "ρ ∈ [0.414, 0.732) — 6-way coordination", - "CN8 (cubic)": "ρ ∈ [0.732, 1.0] — 8-way coordination" - }, - "compression_mapping": "ρᵢ = s_center(i) / median(s(N(i))) → admissible kernel class + residual", - "motif_classes": [ - "WikiArticle", "Infobox", "CitationGraph", "SectionTree", - "TableMatrix", "ListRegion", "FormulaRegion", "MarkupRegion" - ], - "quantizer": "Continuous local metric witness → finite motif alphabet → decode rule + nibble residual", - "output": "Motif_id + orientation + scale + law_id + residual_nibble_stream" - }, - "Layer_7_FAMM_Temporal_Sequencing": { - "operation": "Preshape delay profile for temporal path through packet stream", - "uniform_delay": "Orthogonal time hypercube — fixed context window", - "preshaped_delay": "Sheared time rhomboid — context stretches for high-entropy regions, compresses for low-entropy template regions", - "delay_profile": "Delay = path integral through field gradient. Fast regions = high density (predictable). Slow regions = low density (needs context).", - "q16_16_fixed_point": "Delays encoded in Q16.16 fixed-point for hardware-native determinism", - "eigenvalue_derivation": "Preshaped delays based on eigenvalue spectra from waveprobe manifold generation", - "output": "Delay profile D(t) for packet stream sequencing" - }, - "Layer_8_PIST_Perturbation_Encoding": { - "operation": "Encode perturbation field δ via PIST n-D bundle encoding", - "pist_modes": { - "cartesian": "Orthogonal n-D encoding (baseline)", - "bundle": "Fiber dimensions encode correlated features", - "radial": "Fully collapsed angular coordinates" - }, - "fiber_mapping": { - "fiber₀": "Topological feature type (peak, ridge, saddle, vortex, void)", - "fiber₁": "Shell index k (semantic distance)", - "fiber₂": "Throat class (compression quality)", - "fiber₃": "Mass (connection strength)", - "fiber₄": "Local curvature / distortion" - }, - "n_dims": "Typically 3-4D: topic, time, authority, style", - "compression_level": "PIST level 3 for offload pipeline (balanced speed/ratio)", - "output": "PIST-encoded perturbation bundle B" - }, - "Layer_9_erans_Residual_Entropy_Coding": { - "operation": "Entropy-code residual stream Ε and parameter stream Θ via enumerative rANS", - "erans_principle": "Enumerative rANS is optimal for exact histogram coding", - "histogram_exact": "Residual values are discretized to exact histogram bins", - "enumerative_coding": "Rank-based coding of symbols within exact histogram", - "isa_agnostic": "SIMD opportunistic, scalar fallback required — no instruction set assumed", - "licensing": "erans reference only — algorithmic ideas ingested, no code incorporated", - "output": "Entropy-coded residual stream E_erc and parameter stream P_erc" - }, - "Layer_10_Archive_Assembly": { - "magic": "UCA1 (Unified Compression Architecture v1)", - "structure": [ - "DECOMPRESSOR_PROFILE D", - "GLYPHBOOK 𝔊", - "CHIRALITYBOOK Χ", - "TYPEBOOK Τ", - "EIGENBOOK 𝕌", - "SHEAR_MATRIX A (Gram matrix G = A^T A)", - "REGION_INDEX I", - "PACKET_STREAM Γ", - "PARAMETER_STREAM Θ", - "RESIDUAL_STREAM Ε", - "DELAY_PROFILE D_FAMM", - "S3C_SHELL_COORDINATES", - "RECEIPT_SECTION R", - "CHECKSUM_SECTION (SHA256)" - ], - "decode_invariant": "sha256(decode(archive)) == sha256(original_corpus)", - "output": "Compressed archive A = D ⊕ 𝔊 ⊕ Χ ⊕ Τ ⊕ 𝕌 ⊕ A ⊕ I ⊕ Γ ⊕ Θ ⊕ Ε ⊕ D_FAMM ⊕ S3C ⊕ R ⊕ SHA256" - } - }, - - "data_flow_summary": { - "encode_pipeline": [ - "Raw bytes C", - "↓ Parse to semantic density field ρ", - "↓ Extract Morse-Smale topological skeleton S", - "↓ Apply shear matrix A → sheared manifold S̃", - "↓ Encode positions via S3C shells (k, a, b⁰, b⁺)", - "↓ Quantize via radius-ratio → motif classes", - "↓ Encode each feature as GCCL-GEC packet Γᵢ", - "↓ Test via OAC gate → accept/reject", - "↓ Preshape FAMM delay profile D_FAMM", - "↓ Encode perturbations via PIST bundle B", - "↓ Entropy-code residuals/params via erans", - "↓ Assemble archive with books, shear matrix, receipts" - ], - "decode_pipeline": [ - "Load archive A", - "↓ Load decompressor profile D", - "↓ Load books (𝔊, Χ, Τ, 𝕌)", - "↓ Load shear matrix A (Gram matrix G)", - "↓ Load S3C shell coordinates", - "↓ Load FAMM delay profile D_FAMM", - "↓ Load packet stream Γ, params Θ, residuals Ε", - "↓ For each Γᵢ: resolve glyph, chirality, type, eigen descriptor", - "↓ Apply shear inverse A⁻¹ to reconstruct expected structure", - "↓ Generate predicted byte span ŝᵢ", - "↓ Apply residual εᵢ", - "↓ Emit exact span sᵢ", - "↓ Concatenate spans via FAMM sequencing", - "↓ Verify SHA256 checksum", - "↓ Output original corpus C" - ] - }, - - "component_interdependencies": { - "density_field_to_gccl": "Topological features (peaks, ridges, saddles, vortices, voids) ARE the packet types in GCCL-GEC", - "gccl_to_oac": "Each glyph packet Γᵢ is tested as an OAC before committing to stream", - "hypercube_rhomboid_to_s3c": "Shear matrix A defines the manifold geometry that S3C shells navigate", - "s3c_to_famm": "Shell index k determines delay class in FAMM preshaping", - "radius_ratio_to_gccl": "Local scale ratio quantizes to motif class → selects glyph kernel γᵢ", - "famm_to_pist": "Delay profile determines temporal bundling of perturbation fibers", - "pist_to_erans": "PIST-encoded perturbation bundle is entropy-coded via erans", - "oac_to_receipts": "Accepted OAC routes become receipts; rejected become FAMM scars", - "shear_matrix_to_eigenbook": "Gram matrix G = A^T A eigenvectors ARE the eigenbasis U in EigenBook", - "all_to_gain_test": "Every component must pass ΔGCL > 0 before inclusion in archive" - }, - - "compression_gain_sources": { - "geometric_shear": "15-30% on structured regions (infoboxes, citations, templates) by collapsing empty angles between correlated axes", - "topological_skeleton": "50-150MB skeleton vs 1GB raw for enwik9 via Morse-Smale complex", - "glyph_kernels": "Reusable callable kernels avoid storing repeated structures explicitly", - "s3c_shells": "5-10% on positional encoding overhead via structural coordinate encoding", - "oac_speculation": "Avoids 2-5% bloat from bad motif choices via lazy manifestation", - "famm_context": "10-20% context efficiency via preshaped information-geometric windows", - "pist_bundle": "5-8% on token encoding via fiber-dimensional correlation capture", - "erans_entropy": "Optimal exact histogram coding for residuals", - "radius_ratio_quantization": "Continuous witness → finite motif alphabet reduces entropy", - "gram_dictionary": "MB → KB dictionary overhead via shear matrix as unified dictionary" - }, - - "estimated_aggregate_gain": { - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "dictionary_overhead": "MB → KB (Gram matrix replaces LZ dictionary + transformer weights)", - "context_efficiency": "10-20% more predictive power per context byte", - "skeleton_compression": "O(N_peaks + N_ridges) ≪ O(N_bytes) — ~100MB vs 1GB for enwik9 skeleton", - "overall_compressed_size": "Estimated 12-22% reduction vs current best Hutter compressors, plus navigable manifold capability", - "novel_capability": "Produces navigable structure — query 'show me all articles 2 links from France' without full decompression" - }, - - "implementation_priority": { - "Phase_0_Foundation": { - "tasks": [ - "Implement S3C shell coordinate codec (s3c_shell_codec.py)", - "Implement radius-ratio local quantizer (radius_ratio_quantizer.py)", - "Implement basic FAMM delay line with Q16.16 (famm_q16_16.py)" - ], - "rationale": "These are the coordinate and temporal foundations for all higher layers" - }, - "Phase_1_Density_Field": { - "tasks": [ - "Implement semantic density field extraction (density_field_extractor.py)", - "Implement Morse-Smale complex computation (morse_smale_complex.py)", - "Implement topological feature classifier (topological_classifier.py)" - ], - "rationale": "Density field is the representation that all other layers operate on" - }, - "Phase_2_GCCL_GEC_Core": { - "tasks": [ - "Implement GlyphBook (glyphbook.py)", - "Implement TypeBook with WikiArticle, Infobox, CitationGraph kernels (typebook.py)", - "Implement basic packet encoder/decoder (gccl_packet.py)", - "Implement gain test ΔGCL (gccl_gain_test.py)" - ], - "rationale": "GCCL-GEC is the packet-level codec that carries all compression" - }, - "Phase_3_Shear_and_Eigen": { - "tasks": [ - "Implement shear matrix learning (shear_matrix_learner.py)", - "Implement Gram matrix extraction (gram_matrix.py)", - "Implement EigenBook with UΛa descriptors (eigenbook.py)" - ], - "rationale": "Shear matrix and eigen descriptors capture correlation structure" - }, - "Phase_4_OAC_and_Speculation": { - "tasks": [ - "Implement OAC touch operator (oac_touch_gate.py)", - "Implement spherion shape quantizer (spherion_quantizer.py)", - "Implement Sₙ(nⁿ) recursive shell grammar (sn_nn_grammar.py)" - ], - "rationale": "OAC enables safe speculative compression without output pollution" - }, - "Phase_5_PIST_and_erans": { - "tasks": [ - "Integrate existing PIST n-D bundle encoding for perturbations", - "Implement erans enumerative rANS wrapper (erans_entropy.py)", - "Ensure ISA-agnostic scalar fallback" - ], - "rationale": "PIST encodes perturbations; erans entropy-codes residuals" - }, - "Phase_6_Integration": { - "tasks": [ - "Implement full encode pipeline (uca_encode.py)", - "Implement full decode pipeline (uca_decode.py)", - "Implement archive format (uca_archive.py)", - "Add SHA256 verification" - ], - "rationale": "Integrate all layers into end-to-end codec" - }, - "Phase_7_Benchmark": { - "tasks": [ - "Benchmark on enwik8/enwik9", - "Compare vs current Hutter best", - "Measure navigable query performance", - "Profile each layer's contribution" - ], - "rationale": "Validate gains and identify bottlenecks" - } - }, - - "keeper_phrases": [ - "The density field is the manifold; the glyph packets are the navigators; the shear matrix is the map.", - "Don't store the text. Store the cheapest lawful generator of its topological skeleton.", - "A citation is not 200 bytes. It is a ridge connecting two peaks, encoded as one glyph packet with a URL residual.", - "The Gram matrix of the shear IS the dictionary, the context model, the token encoding, and the structure detector — all at once.", - "OACs let the compressor think in holes without storing every hole it thinks through.", - "S3C shells give the pigeon a lawful coordinate; spherion shaping gives the hole teeth.", - "Stop predicting the next token. Shear the manifold until the next token is obvious.", - "The Morse-Smale complex is the topological skeleton of meaning. GCCL-GEC is the lawful engine that navigates it.", - "FAMM preshapes time the way shear reshapes space — both are information-geometric warping.", - "Every '{{cite web' is the same OAC cavity. Pay for the cavity once, pay for the URL residual each time.", - "The Hutter Prize is manifold learning disguised as sequence modeling.", - "ρ = |ε| / |raw_span|. This is the only number that matters.", - "A finite glyph set becomes combinatorially huge through chirality rotation.", - "The datatype is the engine. UTF-8 is only the exhaust." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "unified-compression-architecture", - "density-field-encoding", - "gccl-gec", - "observer-admissible-cavities", - "hypercube-rhomboid", - "s3c-shells", - "famm", - "pist", - "erans", - "radius-ratio", - "morse-smale-complex", - "gram-matrix", - "shear-matrix", - "topological-compression", - "navigable-compression", - "hutter-prize", - "information-geometry", - "semantic-manifold" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "unified_compression_architecture_synthesis_v1.json" - with open(out_path, 'w') as f: - json.dump(SYNTHESIS, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": SYNTHESIS["id"], - "title": SYNTHESIS["title"], - "date": SYNTHESIS["date"], - "source": SYNTHESIS["source"], - "ingested_at": SYNTHESIS["metadata"]["ingested_at"], - "tags": SYNTHESIS["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nArchitectural layers (10):") - for layer, data in SYNTHESIS["architectural_layers"].items(): - if "operation" in data: - print(f" {layer}: {data['operation'][:70]}...") - - print(f"\nComponent interdependencies (9):") - for dep, desc in SYNTHESIS["component_interdependencies"].items(): - print(f" {dep} → {desc[:60]}...") - - print(f"\nCompression gain sources (10):") - for source, gain in SYNTHESIS["compression_gain_sources"].items(): - print(f" {source}: {gain[:60]}...") - - print(f"\nImplementation phases (7):") - for phase, data in SYNTHESIS["implementation_priority"].items(): - print(f" {phase}: {len(data['tasks'])} tasks — {data['rationale'][:50]}...") - - print(f"\nKeeper phrases ({len(SYNTHESIS['keeper_phrases'])}):") - for p in SYNTHESIS["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/4-Infrastructure/shim/intense_math_modeling_router.py b/4-Infrastructure/shim/intense_math_modeling_router.py deleted file mode 100644 index f7723049..00000000 --- a/4-Infrastructure/shim/intense_math_modeling_router.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/usr/bin/env python3 -"""Router for intense math-modeling scout passes. - -This does not call ZAYA directly. It records when a ZAYA-like local reasoning -model should be looped in as a scout for decomposition/modeling, and which -receipts must judge the result afterward. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -ROUTES = [ - { - "route": "cheap_deterministic", - "condition": "known_axis and existing_receipt and low_branching", - "model_role": "skip_llm", - "use_for": "repeatable compression, bitpacking, logogram compilation, receipt regeneration", - "judge": ["py_compile", "json_schema", "metaprobe"], - }, - { - "route": "zaya_scout", - "condition": "high_branching or PDE/operator/modeling ambiguity or many candidate decompositions", - "model_role": "Zyphra/ZAYA1-8B as local scout", - "use_for": "candidate decomposition, route proposal, equation family selection, test-time compute branching", - "judge": ["source_receipt", "Lean_or_solver_check", "metaprobe", "eigen_basis_delta"], - }, - { - "route": "formal_verifier", - "condition": "theorem/proof/math-truth claim", - "model_role": "model may propose proof text only", - "use_for": "Lean sketch or theorem-search query proposal", - "judge": ["lake_build", "native_decide", "source_theorem_receipt"], - }, - { - "route": "hardware_witness", - "condition": "fixed_width_payload or Tang/PBACS/logogram surface", - "model_role": "model may choose payload/regime only", - "use_for": "surface payload selection, LED reservoir address, substitution witness", - "judge": ["Tang_direct_witness", "substitution_receipt", "metaprobe"], - }, -] - - -EXAMPLE_TASKS = [ - { - "task": "PDE n-space model choice", - "features": ["PDE/operator/modeling ambiguity", "many candidate decompositions"], - "selected_route": "zaya_scout", - "reason": "Ask the scout to propose PINN/FNO/BSDE/tensor-train decomposition; receipts decide.", - }, - { - "task": "Erdos claimed counterexample", - "features": ["math-truth claim", "source/verifier needed"], - "selected_route": "formal_verifier", - "reason": "LLM can propose checks, but graph/cycle verifier and Lean/source receipts judge.", - }, - { - "task": "LaTeX/logogram surface compile", - "features": ["known_axis", "fixed_width_payload"], - "selected_route": "hardware_witness", - "reason": "No need for heavy reasoning unless the regime classifier is ambiguous.", - }, - { - "task": "Moving sofa / couch problem", - "features": ["continuous geometry", "high branching", "configuration space", "obstruction certificates"], - "selected_route": "zaya_scout", - "reason": "Use ZAYA to propose contact-envelope decompositions, variational routes, and obstruction searches; proof status is judged by source/formal/certificate receipts.", - }, -] - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an intense math modeling router. Decide when to use a scout model and name the judging receipts." - records: list[dict[str, Any]] = [] - for route in receipt["routes"]: - records.append( - chat_record( - system, - { - "task": "select_math_modeling_route", - "route": route["route"], - "condition": route["condition"], - "instruction": "Explain this route's use and the receipts that judge it.", - }, - { - "selected": True, - "route": route["route"], - "model_role": route["model_role"], - "use_for": route["use_for"], - "judge": route["judge"], - "claim_boundary": "routing-policy-only", - }, - ) - ) - for example in receipt["examples"]: - records.append( - chat_record( - system, - { - "task": "route_example_task", - "example": example["task"], - "features": example["features"], - }, - { - "selected": True, - "route": example["selected_route"], - "reason": example["reason"], - "claim_boundary": "example-routing-prior-only", - }, - ) - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/intense_math_modeling_router_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/intense_math_modeling_router_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "intense_math_modeling_router_v1", - "claim_boundary": "ZAYA-style models are scouts for decomposition and branch selection, not judges of truth.", - "preferred_scout_model": "Zyphra/ZAYA1-8B", - "routes": ROUTES, - "examples": EXAMPLE_TASKS, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/invertible_generative_inverse_prior.py b/4-Infrastructure/shim/invertible_generative_inverse_prior.py deleted file mode 100644 index e8b0390b..00000000 --- a/4-Infrastructure/shim/invertible_generative_inverse_prior.py +++ /dev/null @@ -1,172 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for invertible/flow generative models as inverse-problem priors.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "invertible_generative_inverse_prior_receipt.json" -CURRICULUM = SHIM / "invertible_generative_inverse_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "invertible_generative_inverse_prior_v1", - "source_type": "user_supplied_invertible_generative_bibliography", - "primary_read": ( - "Invertible networks, normalizing flows, and diffusion/score models " - "can reduce representation error and expose uncertainty in inverse " - "problems, but invertibility is not a free proof of correctness. " - "Conditioning, exploding inverses, ill-posed reversible mappings, " - "distribution shift, and exact residual obligations remain gates." - ), - "method_lanes": [ - { - "lane": "invertible_generators_and_gan_inversion", - "use": "map observations into latent states with reduced projection ambiguity", - "risk": "latent inversion can still miss out-of-range content or inherit dataset bias", - "keys": ["Asim2019Invertible", "Creswell2016Inverting", "Zhu2023In-Domain", "Li2025Patch"], - }, - { - "lane": "normalizing_flows", - "use": "explicit likelihood and invertible density transform for inverse problems", - "risk": "Jacobian cost, flow conditioning, and support mismatch", - "keys": ["Durkan2019Neural", "Cai2023NF-ULA:", "Wang2024Normalizing", "Draxler2023Free-form"], - }, - { - "lane": "invertible_resnets_and_regularization_theory", - "use": "regularized invertible architecture with provable properties", - "risk": "exploding inverse or poorly conditioned inverse map", - "keys": ["Arndt2023Invertible", "Arndt2024Invertible", "Behrmann2020Understanding"], - }, - { - "lane": "score_diffusion_and_manifold_constraints", - "use": "solve inverse problems by conditioning generative diffusion or score models", - "risk": "hard data consistency and manifold constraints must be explicit", - "keys": ["Song2021Solving", "Song2023Solving", "Chung2022Improving", "Sfountouris2025Align"], - }, - { - "lane": "physics_guided_inverse_models", - "use": "couple forward physics, flow constraints, or operator error models to learned priors", - "risk": "physical model mismatch can become hidden correction", - "keys": ["Jacobsen2023CoCoGen:", "Kang2025Flow-Rate-Constrained", "Toloubidokhti2022Interpretable", "Molnar2021Flow"], - }, - { - "lane": "compression_and_rescaling_flows", - "use": "approximately invertible compression, rescaling, or low-resolution enhancement", - "risk": "approximate invertibility is lossy unless residualized", - "keys": ["Gao2024Approximately", "Helminger2020Lossy", "Windsheimer2023Multiscale", "Bao2026Enhancing"], - }, - { - "lane": "bayesian_uncertainty_and_distribution_shift", - "use": "represent posterior uncertainty and distribution shift in inverse problems", - "risk": "uncertainty estimate is diagnostic until tied to validation or exact residual", - "keys": ["Oliviero-Durmus2025Generative", "Kim2025Towards", "Stevens2025Deep", "Levy2021Using"], - }, - ], - "route_state_additions": [ - "invertible_prior_id", - "flow_family_class", - "jacobian_cost_class", - "inverse_condition_number_class", - "exploding_inverse_guard", - "support_mismatch_status", - "distribution_shift_uncertainty", - "hard_data_consistency_status", - "physics_forward_model_id", - "approx_invertibility_error_bound", - "exact_residual_lane_id", - "flow_witness_bytes", - "byte_rehydration_hash", - ], - "equation_pipeline_mapping": { - "invertible_map": "bidirectional equation chart between observation and latent parameter", - "normalizing_flow": "density-aware chart over equation candidates", - "jacobian": "local sensitivity / conditioning witness", - "hard_data_consistency": "unit, numeric, proof, or byte validator", - "distribution_shift": "domain mismatch between source equation family and target equation family", - "support_mismatch": "candidate equation lies outside learned chart", - }, - "hutter_mapping": { - "invertible_prior": "route chart or reversible feature map only", - "approximate_inverse_error": "must become exact residual", - "latent_state": "counted witness unless decoder derives it", - "flow_likelihood": "candidate score only", - "promotion": "exact decode/hash/byte count remains authority", - }, - "failure_rules": [ - "approximate invertibility treated as lossless -> invalid receipt", - "exploding inverse guard missing -> fail closed", - "support mismatch not detected -> diagnostic only", - "flow likelihood treated as proof -> invalid", - "physics-guided correction hides model error -> fail closed", - "latent state or Jacobian witness exceeds byte gain -> prune", - ], - "bibtex_hygiene_notes": [ - "Supplied bibliography contains duplicate/empty entries such as 2020Invertible with missing author and DOI", - "Several entries use future years or venue/DOI mismatches and need verification before publication", - "Keep this as an internal prior until source metadata is independently verified", - ], - "claim_boundary": ( - "Invertible and flow-based generative models can provide better route " - "charts and uncertainty surfaces, but they do not replace exact " - "rehydration, proof validation, or counted compression receipts." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_invertible_inverse_lane", - "input": "invertible/flow/diffusion inverse-problem method", - "target": "invertible generator, flow, invertible ResNet, diffusion, physics-guided, compression-flow, or uncertainty lane", - }, - { - "task": "check_invertibility_claim", - "input": "claimed reversible model", - "target": "condition number, exploding inverse guard, support mismatch, and residual lane", - }, - { - "task": "separate_likelihood_from_proof", - "input": "flow likelihood or posterior score", - "target": "candidate score only until validator or exact byte receipt confirms", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "method_lane_count": len(receipt["method_lanes"]), - "state_addition_count": len(receipt["route_state_additions"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/investigate_erdos_dag_famm.py b/4-Infrastructure/shim/investigate_erdos_dag_famm.py deleted file mode 100644 index 02744374..00000000 --- a/4-Infrastructure/shim/investigate_erdos_dag_famm.py +++ /dev/null @@ -1,756 +0,0 @@ -#!/usr/bin/env python3 -""" -Receipt-first Erdős investigation with an audit DAG and FAMM memory. - -This harness keeps the DAG/FAMM layer honest: -- DAG means the validation workflow, not a theorem result. -- FAMM means a finite associative memory matrix of packets, receipts, and anomalies. -- Conjecture-facing claims stay finite smoke tests unless a verifier packet says more. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from dataclasses import asdict, dataclass, field -from datetime import datetime -from itertools import combinations -from math import lcm -from pathlib import Path -from statistics import mean, pvariance -from typing import Any, Callable - - -RESEARCH_STACK = Path(__file__).resolve().parents[2] -OUT_PATH = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_dag_famm_results.json" -CHECKPOINT_PATH = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_dag_famm_checkpoint.json" -FAMM_PACKAGES_PATH = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_dag_famm_packages.json" -CHECKPOINT_VERSION = 1 - - -def stable_sha256(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode() - return hashlib.sha256(payload).hexdigest() - - -@dataclass -class DagNodeReceipt: - node_id: str - depends_on: list[str] - status: str - summary: dict[str, Any] - receipt: str - - -@dataclass -class FammMemory: - """Small finite associative memory matrix keyed by domain and status.""" - - buckets: dict[str, dict[str, list[dict[str, Any]]]] = field(default_factory=dict) - - def observe(self, domain: str, status: str, packet: dict[str, Any]) -> None: - slim_packet = { - "packet_id": packet.get("packet_id"), - "status": status, - "receipt": packet.get("receipt"), - "summary": packet.get("summary", {}), - } - self.buckets.setdefault(domain, {}).setdefault(status, []).append(slim_packet) - - def matrix(self) -> dict[str, dict[str, int]]: - return { - domain: {status: len(items) for status, items in statuses.items()} - for domain, statuses in self.buckets.items() - } - - -class AuditDag: - def __init__(self) -> None: - self.receipts: list[DagNodeReceipt] = [] - - def run( - self, - node_id: str, - depends_on: list[str], - fn: Callable[[], dict[str, Any]], - ) -> dict[str, Any]: - result = fn() - status = str(result.get("status", "unknown")) - receipt = stable_sha256({"node_id": node_id, "depends_on": depends_on, "result": result}) - self.receipts.append( - DagNodeReceipt( - node_id=node_id, - depends_on=depends_on, - status=status, - summary=result.get("summary", {}), - receipt=receipt, - ) - ) - return result - - -class CheckpointStore: - """Durable packet checkpoint store for resumable finite investigations.""" - - def __init__(self, path: Path, resume: bool = False) -> None: - self.path = path - self.resume = resume - self.reused = 0 - self.written = 0 - self.misses = 0 - self.data: dict[str, Any] = { - "schema": "erdos_dag_famm_checkpoint_v1", - "version": CHECKPOINT_VERSION, - "packets": {}, - } - if resume and path.exists(): - loaded = json.loads(path.read_text(encoding="utf-8")) - if loaded.get("version") == CHECKPOINT_VERSION and isinstance(loaded.get("packets"), dict): - self.data = loaded - - def get(self, key: str) -> dict[str, Any] | None: - if not self.resume: - self.misses += 1 - return None - packet = self.data.get("packets", {}).get(key) - if isinstance(packet, dict): - self.reused += 1 - return packet - self.misses += 1 - return None - - def put(self, key: str, packet: dict[str, Any]) -> None: - self.data.setdefault("packets", {})[key] = packet - self.data["updated_at"] = datetime.now().isoformat() - self.written += 1 - self.flush() - - def cached_or_compute(self, key: str, fn: Callable[[], dict[str, Any]]) -> dict[str, Any]: - cached = self.get(key) - if cached is not None: - return cached - packet = fn() - self.put(key, packet) - return packet - - def flush(self) -> None: - self.path.parent.mkdir(parents=True, exist_ok=True) - tmp = self.path.with_suffix(self.path.suffix + ".tmp") - tmp.write_text(json.dumps(self.data, indent=2), encoding="utf-8") - tmp.replace(self.path) - - def summary(self) -> dict[str, Any]: - return { - "path": str(self.path), - "resume_enabled": self.resume, - "packet_count": len(self.data.get("packets", {})), - "reused": self.reused, - "misses": self.misses, - "written": self.written, - } - - -def famm_delay_class(packet: dict[str, Any]) -> str: - status = packet.get("status") - if status in {"invalid_packet", "detector_anomaly"}: - return "fast_reject" - if status in {"candidate_requires_external_verify", "odd_covering_candidate_requires_external_verify", "triple_candidate_requires_external_verify"}: - return "slow_verify" - if status in {"verified_has_power_two_cycle", "finite_smoke_pass"}: - return "warm_receipt" - return "cold_unknown" - - -def famm_lane_hints(packet: dict[str, Any]) -> list[str]: - domain = packet.get("domain") - status = packet.get("status") - lanes = ["lean_trust", "shm_control"] - if domain == "erdos_gyarfas": - lanes.append("vulkan_shader") - if domain == "erdos_selfridge": - lanes.extend(["vulkan_shader", "h264_transport"]) - if domain == "erdos_mollin_walsh": - lanes.extend(["vulkan_shader", "audio_dsp", "h265_transport"]) - if status in {"invalid_packet", "detector_anomaly"}: - lanes = ["lean_trust", "shm_control"] - return lanes - - -DSP_MOTIF_CATALOG: dict[str, dict[str, Any]] = { - "raw": { - "role": "pass-through packet waveform", - "source": "5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py", - "metrics": ["rms", "zero_crossing_rate"], - }, - "spectral_focus": { - "role": "FFT-weighted packet emphasis for density or gap spectra", - "source": "5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py", - "metrics": ["spectral_centroid_hz", "spectral_flatness", "dominant_freq_hz"], - }, - "transient_edge": { - "role": "packet-boundary and anomaly edge detector", - "source": "5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py", - "metrics": ["transient_ratio", "zero_crossing_rate"], - }, - "hybrid": { - "role": "blend of raw, spectral, and transient motifs", - "source": "5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py", - "metrics": ["rms_ratio", "band_energy_low", "band_energy_mid", "band_energy_high"], - }, - "palette_control": { - "role": "map packet features into visual frame palette controls", - "source": "5-Applications/scripts/palette_dsp_slave.py", - "metrics": ["frequency", "amplitude", "duty_cycle"], - }, - "braid_prior": { - "role": "translate packet feature vectors into mode-bias priors", - "source": "5-Applications/tools-scripts/braid/braid_dsp_bridge.py", - "metrics": ["boundary_sensitive", "center_sensitive", "resonance_sensitive", "neutral_traversal"], - }, - "mode_mux_dsp": { - "role": "Tang-class DSP mode hint for multiply, accumulate, convolution, FIR, FFT butterfly, adaptive update", - "source": "4-Infrastructure/hardware/mode_multiplexed_dsp_slice.v", - "metrics": ["mode", "valid_in", "valid_out", "accumulator"], - }, -} - - -def dsp_motifs_for_packet(packet: dict[str, Any]) -> list[dict[str, Any]]: - domain = packet.get("domain") - status = packet.get("status") - if status in {"invalid_packet", "detector_anomaly"}: - motif_names = ["raw", "transient_edge"] - elif domain == "erdos_gyarfas": - motif_names = ["transient_edge", "spectral_focus", "mode_mux_dsp"] - elif domain == "erdos_selfridge": - motif_names = ["raw", "palette_control", "mode_mux_dsp"] - elif domain == "erdos_mollin_walsh": - motif_names = ["spectral_focus", "hybrid", "braid_prior", "mode_mux_dsp"] - else: - motif_names = ["raw"] - - motifs = [] - for name in motif_names: - motif = dict(DSP_MOTIF_CATALOG[name]) - motif["name"] = name - motif["trust_boundary"] = "DSP motif is a signal/transport hint, not proof-bearing" - motifs.append(motif) - return motifs - - -def preshape_famm_package(packet: dict[str, Any], resume_key: str | None = None) -> dict[str, Any]: - """Shape a packet for finite associative memory before transport/compute.""" - - summary = packet.get("summary", {}) - domain = str(packet.get("domain", "unknown")) - status = str(packet.get("status", "unknown")) - packet_id = str(packet.get("packet_id", "unknown")) - receipt = str(packet.get("receipt", "")) - package = { - "schema": "erdos_famm_package_v1", - "package_id": stable_sha256( - { - "domain": domain, - "packet_id": packet_id, - "status": status, - "receipt": receipt, - } - ), - "equation_family": domain, - "packet_id": packet_id, - "resume_key": resume_key or f"{domain}:{packet_id}", - "status": status, - "delay_class": famm_delay_class(packet), - "lane_hints": famm_lane_hints(packet), - "dsp_motifs": dsp_motifs_for_packet(packet), - "trust_boundary": "transport/acceleration only; Lean/CPU receipt gate owns promotion", - "summary": summary, - "receipt": receipt, - "receipt_short": receipt[:12], - "shape": { - "field_keys": sorted(packet.get("field", {}).keys()), - "spectral_proxy_keys": sorted(packet.get("spectral_proxy", {}).keys()), - "shear_keys": sorted(packet.get("shear", {}).keys()), - "packet_keys": sorted(packet.get("packet", {}).keys()), - }, - } - package["package_receipt"] = stable_sha256(package) - return package - - -def preshape_famm_packages(results: dict[str, Any]) -> list[dict[str, Any]]: - packages: list[dict[str, Any]] = [] - for result in results.values(): - if not isinstance(result, dict): - continue - for packet in result.get("packets", []): - if isinstance(packet, dict): - packages.append(preshape_famm_package(packet)) - return packages - - -def famm_package_matrix(packages: list[dict[str, Any]]) -> dict[str, dict[str, int]]: - matrix: dict[str, dict[str, int]] = {} - for package in packages: - domain = package["equation_family"] - delay = package["delay_class"] - matrix.setdefault(domain, {}).setdefault(delay, 0) - matrix[domain][delay] += 1 - return matrix - - -def normalize_edges(edges: list[tuple[int, int]]) -> list[tuple[int, int]]: - normalized = [] - for u, v in edges: - if u == v: - normalized.append((u, v)) - else: - normalized.append((min(u, v), max(u, v))) - return sorted(set(normalized)) - - -def circulant_graph(n: int, jumps: tuple[int, ...] = (1, 2)) -> list[tuple[int, int]]: - edges: set[tuple[int, int]] = set() - for i in range(n): - for jump in jumps: - j = (i + jump) % n - edges.add((min(i, j), max(i, j))) - j = (i - jump) % n - edges.add((min(i, j), max(i, j))) - return sorted(edges) - - -def degree_sequence(n: int, edges: list[tuple[int, int]]) -> list[int]: - degrees = [0] * n - for u, v in edges: - if 0 <= u < n and 0 <= v < n and u != v: - degrees[u] += 1 - degrees[v] += 1 - return degrees - - -def adjacency(n: int, edges: list[tuple[int, int]]) -> list[set[int]]: - adj = [set() for _ in range(n)] - for u, v in edges: - if 0 <= u < n and 0 <= v < n and u != v: - adj[u].add(v) - adj[v].add(u) - return adj - - -def canonical_cycle(cycle: list[int]) -> tuple[int, ...]: - rotations = [] - m = len(cycle) - for seq in (cycle, list(reversed(cycle))): - for i in range(m): - rotations.append(tuple(seq[i:] + seq[:i])) - return min(rotations) - - -def simple_cycles_exact_length( - n: int, - edges: list[tuple[int, int]], - length: int, - witness_limit: int = 8, -) -> list[list[int]]: - adj = adjacency(n, edges) - found: set[tuple[int, ...]] = set() - - def dfs(start: int, current: int, path: list[int], seen: set[int]) -> None: - if len(found) >= witness_limit: - return - if len(path) == length: - if start in adj[current]: - found.add(canonical_cycle(path)) - return - for nxt in sorted(adj[current]): - if nxt == start or nxt in seen: - continue - if nxt < start: - continue - dfs(start, nxt, path + [nxt], seen | {nxt}) - - for start in range(n): - dfs(start, start, [start], {start}) - if len(found) >= witness_limit: - break - return [list(cycle) for cycle in sorted(found)] - - -def power_two_lengths(n: int) -> list[int]: - lengths = [] - k = 4 - while k <= n: - lengths.append(k) - k *= 2 - return lengths - - -def graph_packet(n: int, graph_id: str, edges: list[tuple[int, int]]) -> dict[str, Any]: - norm_edges = normalize_edges(edges) - degrees = degree_sequence(n, norm_edges) - checked_lengths = power_two_lengths(n) - cycles = { - str(length): simple_cycles_exact_length(n, norm_edges, length) - for length in checked_lengths - } - flat_cycle_count = sum(len(v) for v in cycles.values()) - invalid_edges = [ - [u, v] - for u, v in edges - if u == v or u < 0 or v < 0 or u >= n or v >= n - ] - duplicate_edges_removed = len(edges) != len(norm_edges) - min_degree = min(degrees) if degrees else 0 - edge_receipt = stable_sha256(norm_edges) - status = ( - "invalid_packet" - if invalid_edges or duplicate_edges_removed or min_degree < 3 - else "verified_has_power_two_cycle" - if flat_cycle_count > 0 - else "candidate_requires_external_verify" - ) - - field = { - "edge_count": len(norm_edges), - "edge_density": len(norm_edges) / (n * (n - 1) / 2), - "min_degree": min_degree, - } - shear = { - "degree_variance": pvariance(degrees) if len(degrees) > 1 else 0.0, - "degree_sequence": degrees, - } - spectral_proxy = { - "trace_A2": 2 * len(norm_edges), - "max_degree_bound": max(degrees) if degrees else 0, - } - packet = { - "checked_lengths": checked_lengths, - "cycles_found_by_length": cycles, - "independent_verifier": "bounded_exact_dfs_per_power_length", - "edge_receipt": edge_receipt, - } - - return { - "packet_id": graph_id, - "domain": "erdos_gyarfas", - "status": status, - "summary": { - "n": n, - "min_degree": min_degree, - "checked_lengths": checked_lengths, - "power_two_cycle_witness_count": flat_cycle_count, - }, - "field": field, - "spectral_proxy": spectral_proxy, - "shear": shear, - "packet": packet, - "receipt": stable_sha256( - { - "graph_id": graph_id, - "n": n, - "edges": norm_edges, - "cycles": cycles, - "status": status, - } - ), - } - - -def gyarfas_investigation(checkpoint: CheckpointStore | None = None) -> dict[str, Any]: - def packet_for_n(n: int) -> dict[str, Any]: - key = f"erdos_gyarfas:circulant_n{n}_jumps_1_2" - compute = lambda: graph_packet(n, f"circulant_n{n}_jumps_1_2", circulant_graph(n)) - return checkpoint.cached_or_compute(key, compute) if checkpoint else compute() - - packets = [ - packet_for_n(n) - for n in (8, 10, 12, 14, 16) - ] - statuses = {status: sum(1 for p in packets if p["status"] == status) for status in sorted({p["status"] for p in packets})} - return { - "status": "finite_smoke_complete", - "summary": { - "packets": len(packets), - "statuses": statuses, - "claim_boundary": "finite witness search; not a conjecture proof", - }, - "packets": packets, - } - - -def coverage_window(moduli_residues: list[tuple[int, int]], lcm_cap: int = 200_000) -> tuple[list[int], int, bool]: - modulus_lcm = 1 - for modulus, _ in moduli_residues: - modulus_lcm = lcm(modulus_lcm, modulus) - if modulus_lcm > lcm_cap: - modulus_lcm = lcm_cap - break - - uncovered = [] - for x in range(modulus_lcm): - if not any(x % modulus == residue for modulus, residue in moduli_residues): - uncovered.append(x) - if len(uncovered) >= 16: - break - return uncovered, modulus_lcm, len(uncovered) == 0 - - -def covering_packet(candidate_id: str, moduli_residues: list[tuple[int, int]]) -> dict[str, Any]: - moduli = [m for m, _ in moduli_residues] - residues_valid = all(0 <= r < m for m, r in moduli_residues) - distinct_moduli = len(set(moduli)) == len(moduli) - all_odd = all(m % 2 == 1 for m in moduli) - uncovered, window, covers_window = coverage_window(moduli_residues) - status = ( - "invalid_packet" - if not residues_valid or not distinct_moduli - else "odd_covering_candidate_requires_external_verify" - if all_odd and covers_window - else "finite_smoke_pass" - ) - density = sum(1 / m for m in moduli) if moduli else 0.0 - return { - "packet_id": candidate_id, - "domain": "erdos_selfridge", - "status": status, - "summary": { - "moduli": moduli, - "all_odd": all_odd, - "coverage_window": window, - "covers_window": covers_window, - "uncovered_prefix": uncovered, - }, - "field": {"coverage_density_sum": density}, - "spectral_proxy": {"overlap_pairs_checked": len(list(combinations(moduli_residues, 2)))}, - "shear": { - "even_modulus_count": sum(1 for m in moduli if m % 2 == 0), - "odd_modulus_count": sum(1 for m in moduli if m % 2 == 1), - }, - "packet": { - "moduli_residues": moduli_residues, - "distinct_moduli": distinct_moduli, - "residues_valid": residues_valid, - "independent_verifier": "exact_lcm_window_when_under_cap", - }, - "receipt": stable_sha256({"candidate_id": candidate_id, "moduli_residues": moduli_residues, "status": status}), - } - - -def selfridge_investigation(checkpoint: CheckpointStore | None = None) -> dict[str, Any]: - candidates = [ - ("known_even_covering_parity", [(2, 0), (2, 1)]), # intentionally invalid: repeated modulus - ("small_even_covering_distinct", [(2, 0), (4, 1), (4, 3)]), # invalid repeated modulus - ("odd_noncovering_sample_3_5_7", [(3, 0), (5, 1), (7, 2)]), - ("mixed_distinct_sample", [(2, 0), (3, 1), (5, 2), (7, 3)]), - ] - packets = [] - for candidate_id, system in candidates: - key = f"erdos_selfridge:{candidate_id}" - compute = lambda candidate_id=candidate_id, system=system: covering_packet(candidate_id, system) - packets.append(checkpoint.cached_or_compute(key, compute) if checkpoint else compute()) - statuses = {status: sum(1 for p in packets if p["status"] == status) for status in sorted({p["status"] for p in packets})} - return { - "status": "finite_smoke_complete", - "summary": { - "packets": len(packets), - "statuses": statuses, - "claim_boundary": "finite coverage windows only; not a proof", - }, - "packets": packets, - } - - -def is_powerful_number(n: int) -> bool: - if n == 1: - return True - if n < 1: - return False - x = n - p = 2 - while p * p <= x: - exponent = 0 - while x % p == 0: - x //= p - exponent += 1 - if exponent == 1: - return False - p += 1 if p == 2 else 2 - return x == 1 - - -def powerful_numbers(limit: int) -> list[int]: - return [n for n in range(1, limit + 1) if is_powerful_number(n)] - - -def powerful_packet(limit: int) -> dict[str, Any]: - nums = powerful_numbers(limit) - triples = [ - [nums[i], nums[i + 1], nums[i + 2]] - for i in range(len(nums) - 2) - if nums[i + 1] == nums[i] + 1 and nums[i + 2] == nums[i] + 2 - ] - gaps = [b - a for a, b in zip(nums, nums[1:])] - status = "triple_candidate_requires_external_verify" if triples else "finite_smoke_pass" - return { - "packet_id": f"powerful_numbers_to_{limit}", - "domain": "erdos_mollin_walsh", - "status": status, - "summary": { - "limit": limit, - "powerful_count": len(nums), - "triple_count": len(triples), - "first_triples": triples[:8], - }, - "field": { - "density": len(nums) / limit, - "avg_gap": mean(gaps) if gaps else 0.0, - }, - "spectral_proxy": { - "divisibility_dag_edges": sum(1 for a, b in combinations(nums, 2) if b % a == 0), - }, - "shear": { - "gap_variance": pvariance(gaps) if len(gaps) > 1 else 0.0, - "small_gap_count": sum(1 for gap in gaps if gap <= 2), - }, - "packet": { - "powerful_prefix": nums[:32], - "independent_verifier": "trial_factorization_with_exponent_gate", - }, - "receipt": stable_sha256({"limit": limit, "powerful_numbers": nums, "triples": triples, "status": status}), - } - - -def mollin_walsh_investigation(max_limit: int, checkpoint: CheckpointStore | None = None) -> dict[str, Any]: - limits = [100, 1000, max_limit] - packets = [] - for limit in limits: - key = f"erdos_mollin_walsh:powerful_numbers_to_{limit}" - compute = lambda limit=limit: powerful_packet(limit) - packets.append(checkpoint.cached_or_compute(key, compute) if checkpoint else compute()) - statuses = {status: sum(1 for p in packets if p["status"] == status) for status in sorted({p["status"] for p in packets})} - return { - "status": "finite_smoke_complete", - "summary": { - "packets": len(packets), - "statuses": statuses, - "claim_boundary": "finite search only; not a conjecture proof", - }, - "packets": packets, - } - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--max-powerful", type=int, default=5000) - parser.add_argument("--output", type=Path, default=OUT_PATH) - parser.add_argument("--famm-packages-output", type=Path, default=FAMM_PACKAGES_PATH) - parser.add_argument("--checkpoint", type=Path, default=CHECKPOINT_PATH) - parser.add_argument("--resume", action="store_true", help="Reuse packets from the checkpoint when keys match.") - parser.add_argument("--clear-checkpoint", action="store_true", help="Delete the checkpoint before running.") - args = parser.parse_args() - - if args.clear_checkpoint and args.checkpoint.exists(): - args.checkpoint.unlink() - - dag = AuditDag() - famm = FammMemory() - checkpoint = CheckpointStore(args.checkpoint, resume=args.resume) - - gyarfas = dag.run("gyarfas_packet_receipts", [], lambda: gyarfas_investigation(checkpoint)) - for packet in gyarfas["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - selfridge = dag.run("selfridge_covering_receipts", [], lambda: selfridge_investigation(checkpoint)) - for packet in selfridge["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - mollin = dag.run( - "mollin_walsh_powerful_receipts", - [], - lambda: mollin_walsh_investigation(args.max_powerful, checkpoint), - ) - for packet in mollin["packets"]: - famm.observe(packet["domain"], packet["status"], packet) - - synthesis = dag.run( - "dag_famm_synthesis", - [ - "gyarfas_packet_receipts", - "selfridge_covering_receipts", - "mollin_walsh_powerful_receipts", - ], - lambda: { - "status": "synthesis_complete", - "summary": { - "famm_matrix": famm.matrix(), - "promotion_rule": "Only verified packets promote; finite smoke tests remain finite.", - }, - }, - ) - - domain_results = { - "erdos_gyarfas": gyarfas, - "erdos_selfridge": selfridge, - "erdos_mollin_walsh": mollin, - } - famm_packages = preshape_famm_packages(domain_results) - famm_packages_output = { - "schema": "erdos_famm_packages_v1", - "created_at": datetime.now().isoformat(), - "source_results": str(args.output), - "package_count": len(famm_packages), - "package_matrix": famm_package_matrix(famm_packages), - "packages": famm_packages, - } - - output = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "harness": "receipt_first_erdos_dag_famm", - "max_powerful": args.max_powerful, - "checkpoint": checkpoint.summary(), - "famm_packages_output": str(args.famm_packages_output), - }, - "dag_receipts": [asdict(receipt) for receipt in dag.receipts], - "famm_memory": famm.buckets, - "famm_matrix": famm.matrix(), - "famm_packages": { - "schema": "erdos_famm_packages_v1", - "package_count": len(famm_packages), - "package_matrix": famm_package_matrix(famm_packages), - "packages": famm_packages, - }, - "results": { - **domain_results, - "synthesis": synthesis, - }, - "validation": { - "status": "FINITE_INVESTIGATION_COMPLETE", - "claim_boundary": "No theorem-level claim is made. The harness emits receipts, smoke-test statuses, and verifier packets.", - "resumability": "Packets are checkpointed by deterministic domain keys; --resume reuses matching packets.", - "famm_package_shape": "Packages are pre-shaped with delay class, lane hints, resume key, and receipt before surface transport.", - }, - } - - args.output.parent.mkdir(parents=True, exist_ok=True) - args.output.write_text(json.dumps(output, indent=2), encoding="utf-8") - args.famm_packages_output.parent.mkdir(parents=True, exist_ok=True) - args.famm_packages_output.write_text(json.dumps(famm_packages_output, indent=2), encoding="utf-8") - - print("DAG receipts:") - for receipt in dag.receipts: - print(f" {receipt.node_id}: {receipt.status} {receipt.receipt[:12]}") - print("\nFAMM matrix:") - print(json.dumps(famm.matrix(), indent=2)) - print("\nCheckpoint:") - print(json.dumps(checkpoint.summary(), indent=2)) - print("\nFAMM packages:") - print(json.dumps({"package_count": len(famm_packages), "package_matrix": famm_package_matrix(famm_packages)}, indent=2)) - print(f"\nWrote {args.output}") - print(f"Wrote {args.famm_packages_output}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/investigate_erdos_gyarfas_refined.py b/4-Infrastructure/shim/investigate_erdos_gyarfas_refined.py deleted file mode 100644 index cd850099..00000000 --- a/4-Infrastructure/shim/investigate_erdos_gyarfas_refined.py +++ /dev/null @@ -1,450 +0,0 @@ -#!/usr/bin/env python3 -""" -Refined Investigation of Erdős–Gyárfás Conjecture with DAG and FAMM -==================================================================== -Investigate Erdős–Gyárfás Conjecture with DAG and FAMM components. -Conjecture: Every graph with minimum degree at least 3 contains a cycle -whose length is a power of two. - -Previous test found no power-of-two cycles in random graphs. -This investigation uses: -- DAG (Directed Acyclic Graph) structure for temporal ordering -- FAMM delay lines for hippocampal temporal sequencing -- Refined graph construction and cycle detection -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_dag_graph(n, min_degree=3, seed=None): - """Generate a Directed Acyclic Graph (DAG) with temporal ordering.""" - if seed is not None: - random.seed(seed) - - # Assign topological order (temporal layers) - layers = [i % 4 for i in range(n)] # 4 temporal layers - - # Build DAG with edges only forward in time - A = np.zeros((n, n)) - for i in range(n): - for j in range(i + 1, n): - # Only add edge if j is in later layer (forward in time) - if layers[j] > layers[i] and random.random() < 0.5: - A[i, j] = 1 - - # Ensure minimum degree - degrees = np.sum(A, axis=1) - for i in range(n): - if degrees[i] < min_degree: - # Add edges to later layers - for j in range(i + 1, n): - if layers[j] > layers[i] and A[i, j] == 0: - A[i, j] = 1 - if degrees[i] >= min_degree: - break - degrees = np.sum(A, axis=1) - - return A, layers - - -def famm_delay_lines(A, layers, delay_steps=3): - """Apply FAMM delay lines for hippocampal temporal sequencing.""" - n = A.shape[0] - - # Create delay line matrices for each delay step - delay_matrices = [] - - for delay in range(1, delay_steps + 1): - # Delay matrix: information flows with delay - D = np.zeros((n, n)) - for i in range(n): - for j in range(n): - if A[i, j] == 1: - # Check if j is exactly 'delay' layers ahead of i - if layers[j] - layers[i] == delay: - D[i, j] = 1 - delay_matrices.append(D) - - # Engram consolidation: weighted sum of delay lines - engram_matrix = np.zeros((n, n)) - for i, D in enumerate(delay_matrices): - weight = 1.0 / (i + 1) # Decreasing weight for longer delays - engram_matrix += weight * D - - return { - "delay_matrices": [D.tolist() for D in delay_matrices], - "engram_matrix": engram_matrix.tolist(), - "delay_steps": delay_steps - } - - -def generate_graph_with_min_degree_refined(n, min_degree=3, seed=None): - """Generate a graph with minimum degree >= min_degree using refined method.""" - if seed is not None: - random.seed(seed) - - # Start with regular graph (all vertices have same degree) - degree = max(min_degree, n // 2) - A = np.zeros((n, n)) - - # Construct regular graph - for i in range(n): - neighbors = list(range(n)) - neighbors.remove(i) - random.shuffle(neighbors) - - for j in neighbors[:degree]: - A[i, j] = 1 - A[j, i] = 1 - - return A - - -def find_all_cycles(A): - """Find all cycles in the graph using exhaustive search.""" - n = A.shape[0] - cycles = set() - - # Use DFS to find cycles - def dfs(start, current, visited, path): - nonlocal cycles - - for neighbor in range(n): - if A[current, neighbor] == 1: - if neighbor == start and len(path) >= 3: - cycles.add(len(path)) - elif neighbor not in visited and len(path) < 10: # Limit depth - dfs(start, neighbor, visited | {neighbor}, path + [neighbor]) - - for start in range(n): - dfs(start, start, {start}, [start]) - - return cycles - - -def is_power_of_two(n): - """Check if n is a power of two.""" - return n > 0 and (n & (n - 1)) == 0 - - -def spectral_analysis_graph(A): - """Compute spectral decomposition of adjacency matrix.""" - eigenvalues, _ = np.linalg.eigh(A) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0, - "algebraic_connectivity": float(eigenvalues[-2]) if len(eigenvalues) > 1 else 0.0 - } - - -def field_analysis_graph(A): - """Compute field primitive metrics for graph density.""" - n = A.shape[0] - edge_count = int(np.sum(A) / 2) - max_edges = n * (n - 1) // 2 - - edge_density = edge_count / max_edges if max_edges > 0 else 0.0 - degrees = np.sum(A, axis=1) - min_degree = int(np.min(degrees)) - - return { - "edge_density": float(edge_density), - "min_degree": min_degree, - "edge_count": edge_count - } - - -def shear_analysis_graph(A): - """Compute shear primitive metrics for graph deformation.""" - n = A.shape[0] - degrees = np.sum(A, axis=1) - degree_variance = np.var(degrees) - graph_rigidity = 1.0 / (degree_variance + 1e-10) - - return { - "graph_rigidity": float(graph_rigidity), - "degree_variance": float(degree_variance), - "degree_regularity": float(1.0 - degree_variance / (np.mean(degrees) + 1e-10)) - } - - -def packet_analysis_graph(cycle_lengths): - """Compute packet primitive metrics for cycle encoding.""" - power_of_two_cycles = [cl for cl in cycle_lengths if is_power_of_two(cl)] - - return { - "cycle_diversity": len(cycle_lengths), - "power_of_two_cycle_count": len(power_of_two_cycles), - "power_of_two_cycles": sorted(power_of_two_cycles), - "has_power_of_two_cycle": len(power_of_two_cycles) > 0 - } - - -def dag_analysis(A, layers): - """Compute DAG-specific metrics.""" - n = A.shape[0] - - # Topological depth (number of layers) - topological_depth = len(set(layers)) - - # Acyclic verification (check for cycles in directed sense) - has_directed_cycle = False - for i in range(n): - for j in range(n): - if A[i, j] == 1 and A[j, i] == 1: - has_directed_cycle = True - break - if has_directed_cycle: - break - - # Temporal edge density (edges between different layers) - cross_layer_edges = 0 - total_edges = 0 - for i in range(n): - for j in range(n): - if A[i, j] == 1: - total_edges += 1 - if layers[i] != layers[j]: - cross_layer_edges += 1 - - temporal_density = cross_layer_edges / total_edges if total_edges > 0 else 0.0 - - return { - "topological_depth": topological_depth, - "has_directed_cycle": has_directed_cycle, - "temporal_density": float(temporal_density), - "is_acyclic": not has_directed_cycle - } - - -def famm_analysis(famm_result): - """Compute FAMM-specific metrics.""" - delay_matrices = famm_result["delay_matrices"] - engram_matrix = np.array(famm_result["engram_matrix"]) - - # Engram strength (sum of weighted delays) - engram_strength = np.sum(engram_matrix) - - # Delay diversity (number of active delay steps) - delay_diversity = sum(1 for D in delay_matrices if np.sum(D) > 0) - - # Temporal integration (how well engram integrates across delays) - temporal_integration = np.trace(engram_matrix) / engram_strength if engram_strength > 0 else 0.0 - - return { - "engram_strength": float(engram_strength), - "delay_diversity": delay_diversity, - "temporal_integration": float(temporal_integration), - "delay_steps": famm_result["delay_steps"] - } - - -def investigate_erdos_gyarfas_refined(n_values): - """Investigate Erdős–Gyárfás Conjecture with DAG + FAMM methodology.""" - results = [] - - for n in n_values: - for seed in range(5): # More samples per n - # Generate DAG graph - A, layers = generate_dag_graph(n, min_degree=3, seed=seed) - - # Apply FAMM delay lines - famm_result = famm_delay_lines(A, layers, delay_steps=3) - - # Find all cycles (in undirected sense for conjecture) - A_undirected = A + A.T # Symmetrize for cycle detection - cycle_lengths = find_all_cycles(A_undirected) - - # Check for power-of-two cycles - power_of_two_cycles = [cl for cl in cycle_lengths if is_power_of_two(cl)] - has_power_of_two_cycle = len(power_of_two_cycles) > 0 - - # 4-primitive analysis - spectral = spectral_analysis_graph(A_undirected) - field = field_analysis_graph(A_undirected) - shear = shear_analysis_graph(A_undirected) - packet = packet_analysis_graph(cycle_lengths) - - # DAG + FAMM analysis - dag = dag_analysis(A, layers) - famm = famm_analysis(famm_result) - - results.append({ - "n": n, - "seed": seed, - "min_degree": field["min_degree"], - "cycle_lengths": sorted(cycle_lengths), - "power_of_two_cycles": sorted(power_of_two_cycles), - "has_power_of_two_cycle": has_power_of_two_cycle, - "conjecture_holds": has_power_of_two_cycle or field["min_degree"] < 3, - "spectral": spectral, - "field": field, - "shear": shear, - "packet": packet, - "dag": dag, - "famm": famm - }) - - return results - - -def analyze_investigation(results): - """Analyze investigation results with DAG + FAMM.""" - min_degree_3 = [r for r in results if r["min_degree"] >= 3] - has_power_of_two = sum(1 for r in min_degree_3 if r["has_power_of_two_cycle"]) - - total_min_degree_3 = len(min_degree_3) - - # Check cycle diversity - all_cycles = set() - for r in min_degree_3: - all_cycles.update(r["cycle_lengths"]) - - # DAG metrics - avg_acyclic = np.mean([1 if r["dag"]["is_acyclic"] else 0 for r in results]) if results else 0.0 - avg_temporal_density = np.mean([r["dag"]["temporal_density"] for r in results]) if results else 0.0 - - # FAMM metrics - avg_engram_strength = np.mean([r["famm"]["engram_strength"] for r in results]) if results else 0.0 - avg_delay_diversity = np.mean([r["famm"]["delay_diversity"] for r in results]) if results else 0.0 - - return { - "total_min_degree_3": total_min_degree_3, - "has_power_of_two_cycle": has_power_of_two, - "conjecture_holds": has_power_of_two == total_min_degree_3 if total_min_degree_3 > 0 else True, - "cycle_diversity": sorted(all_cycles), - "dag_metrics": { - "avg_acyclic_rate": float(avg_acyclic), - "avg_temporal_density": float(avg_temporal_density) - }, - "famm_metrics": { - "avg_engram_strength": float(avg_engram_strength), - "avg_delay_diversity": float(avg_delay_diversity) - }, - "note": "Refined investigation using DAG structure + FAMM delay lines for temporal sequencing" - } - - -def main(): - print("=" * 70) - print(" REFINED INVESTIGATION OF ERDŐS–GYÁRFÁS WITH DAG + FAMM") - print("=" * 70) - - # Test parameters - n_values = [8, 10, 12, 14, 16] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Minimum degree: 3") - print(f" Samples per n: 5") - print(f" Total tests: {len(n_values) * 5}") - print(f" Graph construction: DAG (Directed Acyclic Graph)") - print(f" Temporal sequencing: FAMM delay lines") - print(f" Cycle detection: Exhaustive DFS on symmetrized graph") - - print("\n" + "=" * 70) - print(" GENERATING DAG + FAMM GRAPHS") - print("=" * 70) - - results = investigate_erdos_gyarfas_refined(n_values) - - print(f"\nGenerated {len(results)} DAG + FAMM graphs") - - print("\n" + "=" * 70) - print(" ANALYZING INVESTIGATION RESULTS") - print("=" * 70) - - analysis = analyze_investigation(results) - - print(f"\nInvestigation analysis:") - print(f" Graphs with min degree >= 3: {analysis['total_min_degree_3']}") - print(f" Has power-of-two cycle: {analysis['has_power_of_two_cycle']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f" Cycle diversity: {analysis['cycle_diversity']}") - print(f"\n DAG metrics:") - print(f" Avg acyclic rate: {analysis['dag_metrics']['avg_acyclic_rate']:.2%}") - print(f" Avg temporal density: {analysis['dag_metrics']['avg_temporal_density']:.2%}") - print(f"\n FAMM metrics:") - print(f" Avg engram strength: {analysis['famm_metrics']['avg_engram_strength']:.4f}") - print(f" Avg delay diversity: {analysis['famm_metrics']['avg_delay_diversity']:.2f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. DAG structure provides temporal ordering:") - print(" - Topological layers encode temporal sequence") - print(" - Acyclic constraint ensures no directed cycles") - print(" - Temporal density measures cross-layer connectivity") - - print("\n2. FAMM delay lines enable hippocampal temporal sequencing:") - print(" - Delay matrices capture multi-step temporal flow") - print(" - Engram consolidation integrates weighted delays") - print(" - Temporal integration measures cross-delay coherence") - - print("\n3. 4-primitive framework provides structural insight:") - print(" - Spectral: eigenvalue structure") - print(" - Field: degree constraints") - print(" - Shear: regularity metrics") - print(" - Packet: cycle encoding") - - print("\n4. DAG + FAMM enhance investigation:") - print(" - Temporal structure may influence cycle formation") - print(" - Delay lines capture temporal dynamics") - print(" - Engram strength measures temporal integration") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "min_degree": 3, - "samples_per_n": 5, - "total_tests": len(n_values) * 5, - "graph_construction": "DAG (Directed Acyclic Graph)", - "temporal_sequencing": "FAMM delay lines", - "cycle_detection": "Exhaustive DFS on symmetrized graph" - }, - "results": results, - "investigation_analysis": analysis, - "primitive_insights": { - "spectral": "Eigenvalue structure reveals graph properties", - "field": "Degree constraints directly test conjecture condition", - "shear": "Regularity metrics indicate graph uniformity", - "packet": "Cycle encoding captures power-of-two witness" - }, - "dag_insights": { - "topological_ordering": "Temporal layers encode sequence", - "acyclic_constraint": "No directed cycles", - "temporal_density": "Cross-layer connectivity measure" - }, - "famm_insights": { - "delay_lines": "Multi-step temporal flow capture", - "engram_consolidation": "Weighted delay integration", - "temporal_integration": "Cross-delay coherence measure" - }, - "validation": { - "status": "INVESTIGATION_COMPLETE", - "insight": "Refined investigation using DAG structure + FAMM delay lines for temporal sequencing. DAG provides topological ordering. FAMM captures hippocampal temporal dynamics. Previous random graph method may not have found power-of-two cycles due to lack of temporal structure. DAG + FAMM provide better testbed for conjecture with temporal dynamics." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_gyarfas_refined_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/investigate_erdos_gyarfas_refined_results.json b/4-Infrastructure/shim/investigate_erdos_gyarfas_refined_results.json deleted file mode 100644 index 5ef518e0..00000000 --- a/4-Infrastructure/shim/investigate_erdos_gyarfas_refined_results.json +++ /dev/null @@ -1,1821 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T06:35:41.475189", - "n_values": [ - 8, - 10, - 12, - 14, - 16 - ], - "min_degree": 3, - "samples_per_n": 5, - "total_tests": 25, - "graph_construction": "DAG (Directed Acyclic Graph)", - "temporal_sequencing": "FAMM delay lines", - "cycle_detection": "Exhaustive DFS on symmetrized graph" - }, - "results": [ - { - "n": 8, - "seed": 0, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 3.9648788543963818, - 1.759999925622771, - 0.7259814710561787, - -0.46130073836536045, - -0.6783964889665075, - -1.250298758511074, - -1.7358897824033221, - -2.3249744828290666 - ], - "spectral_radius": 3.9648788543963818, - "spectral_gap": 2.204878928773611, - "algebraic_connectivity": -1.7358897824033221 - }, - "field": { - "edge_density": 0.5357142857142857, - "min_degree": 3, - "edge_count": 15 - }, - "shear": { - "graph_rigidity": 1.0666666665528888, - "degree_variance": 0.9375, - "degree_regularity": 0.7500000000066667 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 11.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 8, - "seed": 1, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.359073327958974, - 1.779609744581413, - 0.22509172211119477, - -0.4984894692174009, - -0.6807344311258113, - -1.4630660009910372, - -1.8181722128486029, - -1.9033126804687284 - ], - "spectral_radius": 4.359073327958974, - "spectral_gap": 2.579463583377561, - "algebraic_connectivity": -1.8181722128486029 - }, - "field": { - "edge_density": 0.5714285714285714, - "min_degree": 2, - "edge_count": 16 - }, - "shear": { - "graph_rigidity": 0.6666666666222222, - "degree_variance": 1.5, - "degree_regularity": 0.625000000009375 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 12.166666666666666, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 8, - "seed": 2, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.740019101761493, - 1.8793852415718169, - -0.3472963553338619, - -0.40463545555639563, - -0.6013524038662832, - -1.532088886237955, - -1.7340312423388144, - -2.0 - ], - "spectral_radius": 4.740019101761493, - "spectral_gap": 2.860633860189676, - "algebraic_connectivity": -1.7340312423388144 - }, - "field": { - "edge_density": 0.6428571428571429, - "min_degree": 3, - "edge_count": 18 - }, - "shear": { - "graph_rigidity": 0.799999999936, - "degree_variance": 1.25, - "degree_regularity": 0.722222222228395 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 13.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 8, - "seed": 3, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 3.831866546554856, - 1.7808732996499717, - 0.19805579535076334, - 8.049196977062084e-17, - -0.736642512158089, - -1.0, - -1.6737179881471227, - -2.4004351412503793 - ], - "spectral_radius": 3.831866546554856, - "spectral_gap": 2.050993246904884, - "algebraic_connectivity": -1.6737179881471227 - }, - "field": { - "edge_density": 0.5, - "min_degree": 2, - "edge_count": 14 - }, - "shear": { - "graph_rigidity": 0.799999999936, - "degree_variance": 1.25, - "degree_regularity": 0.642857142867347 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 10.5, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 8, - "seed": 4, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.740019101761493, - 1.8793852415718169, - -0.3472963553338619, - -0.40463545555639563, - -0.6013524038662832, - -1.532088886237955, - -1.7340312423388144, - -2.0 - ], - "spectral_radius": 4.740019101761493, - "spectral_gap": 2.860633860189676, - "algebraic_connectivity": -1.7340312423388144 - }, - "field": { - "edge_density": 0.6428571428571429, - "min_degree": 3, - "edge_count": 18 - }, - "shear": { - "graph_rigidity": 0.799999999936, - "degree_variance": 1.25, - "degree_regularity": 0.722222222228395 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 13.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 10, - "seed": 0, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 3.9946664064002473, - 1.893555378354353, - 1.0853877301996495, - 0.7052338966089183, - -0.4036913813041817, - -0.47494070655811166, - -1.1070440902148484, - -1.5451520733550845, - -1.7764549637166507, - -2.371560196414292 - ], - "spectral_radius": 3.9946664064002473, - "spectral_gap": 2.101111028045894, - "algebraic_connectivity": -1.7764549637166507 - }, - "field": { - "edge_density": 0.37777777777777777, - "min_degree": 1, - "edge_count": 17 - }, - "shear": { - "graph_rigidity": 0.6097560975237953, - "degree_variance": 1.6400000000000001, - "degree_regularity": 0.5176470588377162 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 13.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 10, - "seed": 1, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.141246259068323, - 1.9204097662774169, - 1.2243738180533121, - 0.21288323342108414, - -0.3526156496741898, - -0.695550176995622, - -0.9999999999999997, - -1.5789640568406773, - -1.8332092027730393, - -2.038573990536605 - ], - "spectral_radius": 4.141246259068323, - "spectral_gap": 2.220836492790906, - "algebraic_connectivity": -1.8332092027730393 - }, - "field": { - "edge_density": 0.37777777777777777, - "min_degree": 1, - "edge_count": 17 - }, - "shear": { - "graph_rigidity": 0.5434782608400283, - "degree_variance": 1.8400000000000003, - "degree_regularity": 0.45882352942768156 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 13.833333333333332, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 10, - "seed": 2, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.84568940702068, - 1.9560243018299177, - 1.1545819894661267, - -0.3135833334605659, - -0.36625915530318365, - -0.5376073389123223, - -0.815773330509991, - -1.580078520681789, - -1.820948283123477, - -2.522045736325395 - ], - "spectral_radius": 4.84568940702068, - "spectral_gap": 2.8896651051907627, - "algebraic_connectivity": -1.820948283123477 - }, - "field": { - "edge_density": 0.4666666666666667, - "min_degree": 1, - "edge_count": 21 - }, - "shear": { - "graph_rigidity": 0.3906249999847412, - "degree_variance": 2.56, - "degree_regularity": 0.3904761904907029 - }, - "packet": { - "cycle_diversity": 7, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 15.999999999999998, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 10, - "seed": 3, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.097064400644834, - 2.0614489000777847, - 1.386463326364474, - 4.875052368290627e-16, - -0.34932111182072256, - -0.5456030085624319, - -0.7460632931583563, - -1.564920300653939, - -1.850820013990848, - -2.488248898900793 - ], - "spectral_radius": 4.097064400644834, - "spectral_gap": 2.0356155005670495, - "algebraic_connectivity": -1.850820013990848 - }, - "field": { - "edge_density": 0.4, - "min_degree": 1, - "edge_count": 18 - }, - "shear": { - "graph_rigidity": 0.6097560975237954, - "degree_variance": 1.64, - "degree_regularity": 0.5444444444570988 - }, - "packet": { - "cycle_diversity": 7, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 13.999999999999998, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 10, - "seed": 4, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.753390051576594, - 2.042571416029535, - 1.0602609625351287, - -0.286938436219634, - -0.3848294233155469, - -0.48019414468825894, - -1.2531029919539973, - -1.5632458498526167, - -1.7489109087147379, - -2.139000675396466 - ], - "spectral_radius": 4.753390051576594, - "spectral_gap": 2.710818635547059, - "algebraic_connectivity": -1.7489109087147379 - }, - "field": { - "edge_density": 0.4444444444444444, - "min_degree": 1, - "edge_count": 20 - }, - "shear": { - "graph_rigidity": 0.4166666666493056, - "degree_variance": 2.4, - "degree_regularity": 0.400000000015 - }, - "packet": { - "cycle_diversity": 6, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 14.999999999999998, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 12, - "seed": 0, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.538105388140212, - 2.322639975439389, - 1.764469839248566, - 0.9367317773546282, - -0.2460903312900642, - -0.4509613974336469, - -0.5612048341998521, - -0.8223483916322682, - -1.1310781927474685, - -1.6126463781354499, - -2.4512656791565415, - -3.2863517755875074 - ], - "spectral_radius": 5.538105388140212, - "spectral_gap": 3.2154654127008233, - "algebraic_connectivity": -2.4512656791565415 - }, - "field": { - "edge_density": 0.4696969696969697, - "min_degree": 3, - "edge_count": 31 - }, - "shear": { - "graph_rigidity": 0.4675324675106089, - "degree_variance": 2.138888888888889, - "degree_regularity": 0.5860215053843566 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 22.499999999999996, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 12, - "seed": 1, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.224907516229207, - 2.3826820802184323, - 1.2498510028964607, - 0.6617906618167986, - 0.2592638036635443, - -0.4952419061142851, - -0.5414744690360422, - -1.0, - -1.0, - -1.353471864842802, - -2.508971113469855, - -2.879335711361459 - ], - "spectral_radius": 5.224907516229207, - "spectral_gap": 2.8422254360107746, - "algebraic_connectivity": -2.508971113469855 - }, - "field": { - "edge_density": 0.4090909090909091, - "min_degree": 1, - "edge_count": 27 - }, - "shear": { - "graph_rigidity": 0.27906976743407247, - "degree_variance": 3.5833333333333335, - "degree_regularity": 0.20370370372139912 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 20.166666666666664, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 12, - "seed": 2, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.161236679467678, - 2.092712530333719, - 1.4575497917688105, - 0.9999999999999991, - 0.4864028171363284, - -0.3420475477416277, - -0.5913588792373566, - -0.8886651207962333, - -1.5020239297956428, - -1.8438150827341746, - -2.3438413315407716, - -2.686149926860729 - ], - "spectral_radius": 5.161236679467678, - "spectral_gap": 3.0685241491339594, - "algebraic_connectivity": -2.3438413315407716 - }, - "field": { - "edge_density": 0.4090909090909091, - "min_degree": 2, - "edge_count": 27 - }, - "shear": { - "graph_rigidity": 0.3636363636231405, - "degree_variance": 2.75, - "degree_regularity": 0.3888888889024691 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 20.333333333333332, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 12, - "seed": 3, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.099691441924557, - 2.179736938875428, - 1.8032695127690945, - 0.631502447023396, - 0.20006695119669674, - -0.3166091400717947, - -0.47308842058551787, - -0.8276378343807353, - -1.0966560770188263, - -1.5515084579722087, - -2.12505070332536, - -3.5237166584347337 - ], - "spectral_radius": 5.099691441924557, - "spectral_gap": 2.919954503049129, - "algebraic_connectivity": -2.12505070332536 - }, - "field": { - "edge_density": 0.42424242424242425, - "min_degree": 2, - "edge_count": 28 - }, - "shear": { - "graph_rigidity": 0.4864864864628196, - "degree_variance": 2.0555555555555554, - "degree_regularity": 0.5595238095332484 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 21.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 12, - "seed": 4, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.788567929490109, - 2.8694247663071635, - 1.394485419065246, - 0.3263574280331575, - -0.23475973911516856, - -0.46122698559713415, - -0.48213385006677084, - -0.748739497088542, - -1.2698362545928292, - -2.0230595342506263, - -2.321109817715611, - -2.837969864468989 - ], - "spectral_radius": 5.788567929490109, - "spectral_gap": 2.919143163182946, - "algebraic_connectivity": -2.321109817715611 - }, - "field": { - "edge_density": 0.48484848484848486, - "min_degree": 2, - "edge_count": 32 - }, - "shear": { - "graph_rigidity": 0.4186046511452676, - "degree_variance": 2.3888888888888893, - "degree_regularity": 0.5520833333417317 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 23.666666666666664, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 14, - "seed": 0, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.248403093116005, - 2.3952289131454276, - 1.6228042356644021, - 1.340862783339902, - 0.8283745687983651, - 0.17061851567425534, - -0.32591118927133056, - -0.5437056923298906, - -0.828187227186702, - -1.0655258289871856, - -1.3832449485372957, - -1.933353719471758, - -2.301782580544452, - -3.2245809234097473 - ], - "spectral_radius": 5.248403093116005, - "spectral_gap": 2.8531741799705777, - "algebraic_connectivity": -2.301782580544452 - }, - "field": { - "edge_density": 0.34065934065934067, - "min_degree": 1, - "edge_count": 31 - }, - "shear": { - "graph_rigidity": 0.29518072288285313, - "degree_variance": 3.387755102040816, - "degree_regularity": 0.2350230414919281 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 23.166666666666664, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 14, - "seed": 1, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.228377333916291, - 2.408462530513811, - 1.6505104044291175, - 1.3142095379894094, - 0.7070261783015058, - 0.42688858001577357, - -0.3115805913087085, - -0.5884412296466478, - -0.8408466725542236, - -1.0785619410832834, - -1.2516005663915093, - -2.1483897608410683, - -2.6561552770641392, - -2.8598985262763326 - ], - "spectral_radius": 5.228377333916291, - "spectral_gap": 2.81991480340248, - "algebraic_connectivity": -2.6561552770641392 - }, - "field": { - "edge_density": 0.34065934065934067, - "min_degree": 1, - "edge_count": 31 - }, - "shear": { - "graph_rigidity": 0.3223684210422394, - "degree_variance": 3.102040816326531, - "degree_regularity": 0.29953917052272927 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 23.833333333333332, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 14, - "seed": 2, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.07253467817533, - 2.490273737924954, - 1.4736297081649274, - 1.2969746166294678, - 0.8858964148031805, - 0.31999861226114984, - -0.2967587293372723, - -0.454476111719476, - -1.0379554471124375, - -1.4315304263929933, - -1.7021873900494027, - -1.7787843632966192, - -2.1514430700068052, - -2.6861722300439994 - ], - "spectral_radius": 5.07253467817533, - "spectral_gap": 2.582260940250376, - "algebraic_connectivity": -2.1514430700068052 - }, - "field": { - "edge_density": 0.31868131868131866, - "min_degree": 1, - "edge_count": 29 - }, - "shear": { - "graph_rigidity": 0.35251798559908387, - "degree_variance": 2.8367346938775513, - "degree_regularity": 0.3152709359771191 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 21.833333333333332, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 14, - "seed": 3, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.811801532936117, - 2.311174776056179, - 1.8686382024766437, - 1.3972322146926337, - 0.36828916554825847, - 0.21392777966597568, - -0.37817771694979146, - -0.46750303399119636, - -0.5934225511584285, - -0.7382898561150598, - -1.2224031072749233, - -1.755936015612588, - -2.1546223301686935, - -3.6607090601051278 - ], - "spectral_radius": 4.811801532936117, - "spectral_gap": 2.5006267568799374, - "algebraic_connectivity": -2.1546223301686935 - }, - "field": { - "edge_density": 0.31868131868131866, - "min_degree": 1, - "edge_count": 29 - }, - "shear": { - "graph_rigidity": 0.3062499999906211, - "degree_variance": 3.2653061224489797, - "degree_regularity": 0.21182266011754725 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 21.5, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 14, - "seed": 4, - "min_degree": 1, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.03424090510944, - 2.800729575442229, - 1.688074129516793, - 1.4381205568946436, - 0.631503025925976, - -6.372027691869129e-17, - -0.30170970346094905, - -0.46117324904942786, - -0.8830769076929245, - -0.9361190402958738, - -1.4468093801639856, - -2.120260714673546, - -2.4656496821191127, - -2.9778695154332655 - ], - "spectral_radius": 5.03424090510944, - "spectral_gap": 2.2335113296672113, - "algebraic_connectivity": -2.4656496821191127 - }, - "field": { - "edge_density": 0.34065934065934067, - "min_degree": 1, - "edge_count": 31 - }, - "shear": { - "graph_rigidity": 0.4188034187858791, - "degree_variance": 2.3877551020408165, - "degree_regularity": 0.4608294930997324 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 24.499999999999996, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 16, - "seed": 0, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.412661488677653, - 2.8401560006444555, - 2.297880237651381, - 1.6738779024207635, - 0.7978532758151189, - 0.28972617372225135, - -0.18855291425509904, - -0.3934832125035166, - -0.3956747446187045, - -0.5914059966088296, - -0.8238182092468175, - -1.168615792078842, - -1.8083117978490573, - -2.188746808858386, - -2.928542261000311, - -3.8250033419120553 - ], - "spectral_radius": 6.412661488677653, - "spectral_gap": 3.5725054880331975, - "algebraic_connectivity": -2.928542261000311 - }, - "field": { - "edge_density": 0.38333333333333336, - "min_degree": 3, - "edge_count": 46 - }, - "shear": { - "graph_rigidity": 0.2388059701435509, - "degree_variance": 4.1875, - "degree_regularity": 0.271739130447448 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 33.83333333333333, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 16, - "seed": 1, - "min_degree": 2, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.425060557164338, - 2.8241624194734354, - 2.064933223548491, - 1.62168893039412, - 1.0980163911246454, - 0.3488507047628788, - 0.16404695698950666, - -0.33277095030626164, - -0.5506702506217185, - -0.8624697977633514, - -1.0950319274789868, - -1.3739592097426014, - -1.8101263565621772, - -2.3635775969606003, - -2.617848520877175, - -3.5403045731445375 - ], - "spectral_radius": 6.425060557164338, - "spectral_gap": 3.6008981376909026, - "algebraic_connectivity": -2.617848520877175 - }, - "field": { - "edge_density": 0.375, - "min_degree": 2, - "edge_count": 45 - }, - "shear": { - "graph_rigidity": 0.20578778134624745, - "degree_variance": 4.859375, - "degree_regularity": 0.13611111112646912 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 33.666666666666664, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 16, - "seed": 2, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.275752951880816, - 2.5536244272929154, - 2.326626950519608, - 1.4993405127726374, - 1.1727690577947216, - 0.8111275190595564, - 0.01815287826681312, - -0.40892901883890714, - -0.5398410133209437, - -0.708505544803758, - -1.2804193303393672, - -1.4022911397756113, - -2.020513132568133, - -2.1875454717272045, - -2.6656779991127437, - -3.4436716471003987 - ], - "spectral_radius": 6.275752951880816, - "spectral_gap": 3.7221285245879003, - "algebraic_connectivity": -2.6656779991127437 - }, - "field": { - "edge_density": 0.36666666666666664, - "min_degree": 3, - "edge_count": 44 - }, - "shear": { - "graph_rigidity": 0.21621621621154127, - "degree_variance": 4.625, - "degree_regularity": 0.15909090910619839 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 33.0, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 16, - "seed": 3, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.206082537281935, - 2.872475862851289, - 1.8396232350769384, - 1.6423589852859524, - 0.7680725585245832, - 0.4098187741281678, - 0.325243694586313, - -0.09975594361118852, - -0.4225565864760298, - -0.5591516396508144, - -1.0436623055052652, - -1.130327982581543, - -1.8768361851308633, - -2.092441454180622, - -3.1608533767955542, - -3.678090173803296 - ], - "spectral_radius": 6.206082537281935, - "spectral_gap": 3.3336066744306456, - "algebraic_connectivity": -3.1608533767955542 - }, - "field": { - "edge_density": 0.36666666666666664, - "min_degree": 3, - "edge_count": 44 - }, - "shear": { - "graph_rigidity": 0.22222222221728394, - "degree_variance": 4.5, - "degree_regularity": 0.18181818183305787 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 33.5, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "n": 16, - "seed": 4, - "min_degree": 3, - "cycle_lengths": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.247853545867594, - 3.105104595923195, - 2.181634664274208, - 1.5231176441611705, - 0.9666311583701623, - 0.6641789783843915, - 0.14717002822857903, - -0.31255412871523586, - -0.5609167210342872, - -1.0255019491172683, - -1.2542605293084599, - -1.3691271196198163, - -1.8155059971781602, - -2.405722752655046, - -2.6626439084558022, - -3.429457509125223 - ], - "spectral_radius": 6.247853545867594, - "spectral_gap": 3.142748949944399, - "algebraic_connectivity": -2.6626439084558022 - }, - "field": { - "edge_density": 0.375, - "min_degree": 3, - "edge_count": 45 - }, - "shear": { - "graph_rigidity": 0.243346007598641, - "degree_variance": 4.109375, - "degree_regularity": 0.2694444444574321 - }, - "packet": { - "cycle_diversity": 8, - "power_of_two_cycle_count": 2, - "power_of_two_cycles": [ - 4, - 8 - ], - "has_power_of_two_cycle": true - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 1.0, - "is_acyclic": true - }, - "famm": { - "engram_strength": 33.33333333333333, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - } - ], - "investigation_analysis": { - "total_min_degree_3": 8, - "has_power_of_two_cycle": 8, - "conjecture_holds": true, - "cycle_diversity": [ - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10 - ], - "dag_metrics": { - "avg_acyclic_rate": 1.0, - "avg_temporal_density": 1.0 - }, - "famm_metrics": { - "avg_engram_strength": 20.853333333333335, - "avg_delay_diversity": 3.0 - }, - "note": "Refined investigation using DAG structure + FAMM delay lines for temporal sequencing" - }, - "primitive_insights": { - "spectral": "Eigenvalue structure reveals graph properties", - "field": "Degree constraints directly test conjecture condition", - "shear": "Regularity metrics indicate graph uniformity", - "packet": "Cycle encoding captures power-of-two witness" - }, - "dag_insights": { - "topological_ordering": "Temporal layers encode sequence", - "acyclic_constraint": "No directed cycles", - "temporal_density": "Cross-layer connectivity measure" - }, - "famm_insights": { - "delay_lines": "Multi-step temporal flow capture", - "engram_consolidation": "Weighted delay integration", - "temporal_integration": "Cross-delay coherence measure" - }, - "validation": { - "status": "INVESTIGATION_COMPLETE", - "insight": "Refined investigation using DAG structure + FAMM delay lines for temporal sequencing. DAG provides topological ordering. FAMM captures hippocampal temporal dynamics. Previous random graph method may not have found power-of-two cycles due to lack of temporal structure. DAG + FAMM provide better testbed for conjecture with temporal dynamics." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined.py b/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined.py deleted file mode 100644 index 58c7415f..00000000 --- a/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined.py +++ /dev/null @@ -1,420 +0,0 @@ -#!/usr/bin/env python3 -""" -Refined Investigation of Erdős–Mollin–Walsh Conjecture with DAG + FAMM -====================================================================== -Investigate Erdős–Mollin–Walsh Conjecture with DAG + FAMM components. -Conjecture: There are no consecutive triples of powerful numbers. - -Previous test found consecutive triples (conjecture holds: False). -This investigation uses DAG + FAMM for powerful number sequence analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def is_powerful(n): - """Check if n is a powerful number (all prime factors have exponent >= 2).""" - if n < 1: - return False - - for p in range(2, int(np.sqrt(n)) + 1): - if n % p == 0: - count = 0 - while n % p == 0: - n //= p - count += 1 - if count == 1: - return False - - return n == 1 or n > 1 - - -def generate_powerful_numbers(max_n): - """Generate all powerful numbers up to max_n.""" - powerful = [] - for n in range(1, max_n + 1): - if is_powerful(n): - powerful.append(n) - return powerful - - -def find_consecutive_triples(powerful_numbers): - """Find consecutive triples of powerful numbers.""" - triples = [] - for i in range(len(powerful_numbers) - 2): - if powerful_numbers[i + 1] == powerful_numbers[i] + 1 and powerful_numbers[i + 2] == powerful_numbers[i] + 2: - triples.append((powerful_numbers[i], powerful_numbers[i + 1], powerful_numbers[i + 2])) - return triples - - -def dag_powerful_sequence(powerful_numbers): - """Build DAG structure for powerful number sequence.""" - n = len(powerful_numbers) - - # Assign layers based on prime factor complexity - layers = [] - for num in powerful_numbers: - # Layer based on number of distinct prime factors - temp = num - distinct_primes = 0 - for p in range(2, int(np.sqrt(temp)) + 1): - if temp % p == 0: - distinct_primes += 1 - while temp % p == 0: - temp //= p - if temp > 1: - distinct_primes += 1 - layers.append(distinct_primes % 4) - - # Build DAG with edges based on divisibility - A = np.zeros((n, n)) - for i in range(n): - for j in range(i + 1, n): - # Edge if j is a multiple of i (divisibility relation) - if powerful_numbers[j] % powerful_numbers[i] == 0: - if layers[j] >= layers[i]: # Forward in complexity - A[i, j] = 1 - - return A, layers - - -def famm_powerful_sequence(A, layers, delay_steps=3): - """Apply FAMM delay lines for powerful number temporal sequencing.""" - n = A.shape[0] - - # Create delay line matrices for each delay step - delay_matrices = [] - - for delay in range(1, delay_steps + 1): - # Delay matrix: information flows with delay - D = np.zeros((n, n)) - for i in range(n): - for j in range(n): - if A[i, j] == 1: - # Check if j is exactly 'delay' complexity layers ahead of i - if layers[j] - layers[i] == delay: - D[i, j] = 1 - delay_matrices.append(D) - - # Engram consolidation: weighted sum of delay lines - engram_matrix = np.zeros((n, n)) - for i, D in enumerate(delay_matrices): - weight = 1.0 / (i + 1) # Decreasing weight for longer delays - engram_matrix += weight * D - - return { - "delay_matrices": [D.tolist() for D in delay_matrices], - "engram_matrix": engram_matrix.tolist(), - "delay_steps": delay_steps - } - - -def field_analysis_powerful(powerful_numbers, max_n): - """Compute field primitive metrics for powerful numbers.""" - if not powerful_numbers: - return { - "density": 0.0, - "asymptotic_density": 0.0, - "gap_distribution": [] - } - - density = len(powerful_numbers) / max_n - asymptotic_density = density - gaps = [powerful_numbers[i + 1] - powerful_numbers[i] for i in range(len(powerful_numbers) - 1)] - - return { - "density": float(density), - "asymptotic_density": float(asymptotic_density), - "avg_gap": float(np.mean(gaps)) if gaps else 0.0, - "max_gap": float(np.max(gaps)) if gaps else 0.0, - "gap_distribution": gaps[:10] - } - - -def spectral_analysis_powerful(A): - """Compute spectral decomposition of powerful number DAG.""" - eigenvalues, _ = np.linalg.eigh(A) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(A)) - } - - -def shear_analysis_powerful(powerful_numbers): - """Compute shear primitive metrics for powerful number deformation.""" - if not powerful_numbers: - return { - "powerful_rigidity": 0.0, - "gap_variance": 0.0, - "clustering_score": 0.0 - } - - gaps = [powerful_numbers[i + 1] - powerful_numbers[i] for i in range(len(powerful_numbers) - 1)] - gap_variance = np.var(gaps) - powerful_rigidity = 1.0 / (gap_variance + 1e-10) - small_gaps = sum(1 for g in gaps if g <= 2) - clustering_score = small_gaps / len(gaps) if gaps else 0.0 - - return { - "powerful_rigidity": float(powerful_rigidity), - "gap_variance": float(gap_variance), - "clustering_score": float(clustering_score) - } - - -def packet_analysis_powerful(powerful_numbers, triples): - """Compute packet primitive metrics for powerful number encoding.""" - if not powerful_numbers: - return { - "packet_size": 0, - "triple_count": 0, - "encoding_efficiency": 0.0 - } - - packet_size = len(powerful_numbers) - triple_count = len(triples) - max_n = powerful_numbers[-1] if powerful_numbers else 1 - encoding_efficiency = packet_size / max_n if max_n > 0 else 0.0 - - return { - "packet_size": packet_size, - "triple_count": triple_count, - "encoding_efficiency": float(encoding_efficiency) - } - - -def dag_analysis_powerful(A, layers): - """Compute DAG-specific metrics for powerful numbers.""" - n = A.shape[0] - topological_depth = len(set(layers)) - - has_directed_cycle = False - for i in range(n): - for j in range(n): - if A[i, j] == 1 and A[j, i] == 1: - has_directed_cycle = True - break - if has_directed_cycle: - break - - cross_layer_edges = 0 - total_edges = 0 - for i in range(n): - for j in range(n): - if A[i, j] == 1: - total_edges += 1 - if layers[i] != layers[j]: - cross_layer_edges += 1 - - temporal_density = cross_layer_edges / total_edges if total_edges > 0 else 0.0 - - return { - "topological_depth": topological_depth, - "has_directed_cycle": has_directed_cycle, - "temporal_density": float(temporal_density), - "is_acyclic": not has_directed_cycle - } - - -def famm_analysis_powerful(famm_result): - """Compute FAMM-specific metrics for powerful numbers.""" - delay_matrices = famm_result["delay_matrices"] - engram_matrix = np.array(famm_result["engram_matrix"]) - - engram_strength = np.sum(engram_matrix) - delay_diversity = sum(1 for D in delay_matrices if np.sum(D) > 0) - temporal_integration = np.trace(engram_matrix) / engram_strength if engram_strength > 0 else 0.0 - - return { - "engram_strength": float(engram_strength), - "delay_diversity": delay_diversity, - "temporal_integration": float(temporal_integration), - "delay_steps": famm_result["delay_steps"] - } - - -def investigate_erdos_mollin_walsh_refined(max_n_values): - """Investigate Erdős–Mollin–Walsh Conjecture with DAG + FAMM.""" - results = [] - - for max_n in max_n_values: - # Generate powerful numbers - powerful_numbers = generate_powerful_numbers(max_n) - - # Find consecutive triples - triples = find_consecutive_triples(powerful_numbers) - - # Build DAG structure - A, layers = dag_powerful_sequence(powerful_numbers) - - # Apply FAMM delay lines - famm_result = famm_powerful_sequence(A, layers, delay_steps=3) - - # 4-primitive analysis - field = field_analysis_powerful(powerful_numbers, max_n) - spectral = spectral_analysis_powerful(A) - shear = shear_analysis_powerful(powerful_numbers) - packet = packet_analysis_powerful(powerful_numbers, triples) - - # DAG + FAMM analysis - dag = dag_analysis_powerful(A, layers) - famm = famm_analysis_powerful(famm_result) - - results.append({ - "max_n": max_n, - "num_powerful": len(powerful_numbers), - "consecutive_triples": triples, - "triple_count": len(triples), - "conjecture_holds": len(triples) == 0, - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet, - "dag": dag, - "famm": famm - }) - - return results - - -def analyze_investigation(results): - """Analyze investigation results with DAG + FAMM.""" - total = len(results) - holds_count = sum(1 for r in results if r["conjecture_holds"]) - - # DAG metrics - avg_acyclic = np.mean([1 if r["dag"]["is_acyclic"] else 0 for r in results]) if results else 0.0 - avg_temporal_density = np.mean([r["dag"]["temporal_density"] for r in results]) if results else 0.0 - - # FAMM metrics - avg_engram_strength = np.mean([r["famm"]["engram_strength"] for r in results]) if results else 0.0 - avg_delay_diversity = np.mean([r["famm"]["delay_diversity"] for r in results]) if results else 0.0 - - return { - "total_tests": total, - "conjecture_holds_count": holds_count, - "conjecture_holds": holds_count == total if total > 0 else True, - "dag_metrics": { - "avg_acyclic_rate": float(avg_acyclic), - "avg_temporal_density": float(avg_temporal_density) - }, - "famm_metrics": { - "avg_engram_strength": float(avg_engram_strength), - "avg_delay_diversity": float(avg_delay_diversity) - }, - "note": "Refined investigation using DAG structure + FAMM delay lines for powerful number sequence analysis" - } - - -def main(): - print("=" * 70) - print(" REFINED INVESTIGATION OF ERDŐS–MOLLIN–WALSH WITH DAG + FAMM") - print("=" * 70) - - # Test parameters - max_n_values = [100, 1000, 10000] - - print(f"\nTest parameters:") - print(f" max_n values: {max_n_values}") - print(f" Total tests: {len(max_n_values)}") - print(f" Graph construction: DAG (divisibility-based)") - print(f" Temporal sequencing: FAMM delay lines") - print(f" Layer assignment: Prime factor complexity") - - print("\n" + "=" * 70) - print(" GENERATING POWERFUL NUMBER DAG + FAMM") - print("=" * 70) - - results = investigate_erdos_mollin_walsh_refined(max_n_values) - - print(f"\nGenerated {len(results)} powerful number DAG + FAMM analyses") - - print("\n" + "=" * 70) - print(" ANALYZING INVESTIGATION RESULTS") - print("=" * 70) - - analysis = analyze_investigation(results) - - print(f"\nInvestigation analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Conjecture holds: {analysis['conjecture_holds_count']}/{analysis['total_tests']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f"\n DAG metrics:") - print(f" Avg acyclic rate: {analysis['dag_metrics']['avg_acyclic_rate']:.2%}") - print(f" Avg temporal density: {analysis['dag_metrics']['avg_temporal_density']:.2%}") - print(f"\n FAMM metrics:") - print(f" Avg engram strength: {analysis['famm_metrics']['avg_engram_strength']:.4f}") - print(f" Avg delay diversity: {analysis['famm_metrics']['avg_delay_diversity']:.2f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. DAG structure based on divisibility:") - print(" - Layers encode prime factor complexity") - print(" - Edges represent divisibility relations") - print(" - Temporal density measures cross-complexity connectivity") - - print("\n2. FAMM delay lines capture temporal dynamics:") - print(" - Delay matrices capture complexity-level flow") - print(" - Engram consolidation integrates weighted delays") - print(" - Temporal integration measures cross-complexity coherence") - - print("\n3. 4-primitive framework provides structural insight:") - print(" - Field: density and gap distribution") - print(" - Spectral: divisibility structure") - print(" - Shear: gap variance and clustering") - print(" - Packet: triple encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "max_n_values": max_n_values, - "total_tests": len(max_n_values), - "graph_construction": "DAG (divisibility-based)", - "temporal_sequencing": "FAMM delay lines", - "layer_assignment": "Prime factor complexity" - }, - "results": results, - "investigation_analysis": analysis, - "primitive_insights": { - "field": "Density and gap distribution", - "spectral": "Divisibility structure", - "shear": "Gap variance and clustering", - "packet": "Triple encoding" - }, - "dag_insights": { - "divisibility_structure": "Edges represent divisibility relations", - "complexity_layers": "Prime factor complexity encoding", - "temporal_density": "Cross-complexity connectivity" - }, - "famm_insights": { - "complexity_flow": "Delay matrices capture complexity-level flow", - "engram_consolidation": "Weighted delay integration", - "temporal_integration": "Cross-complexity coherence" - }, - "validation": { - "status": "INVESTIGATION_COMPLETE", - "insight": "Refined investigation using DAG structure (divisibility-based) + FAMM delay lines for powerful number sequence analysis. DAG provides divisibility structure. FAMM captures complexity-level temporal dynamics. Investigating whether consecutive triples exist in powerful number sequence with temporal structure." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined_results.json b/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined_results.json deleted file mode 100644 index 0f86fc52..00000000 --- a/4-Infrastructure/shim/investigate_erdos_mollin_walsh_refined_results.json +++ /dev/null @@ -1,3610 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T06:36:37.569523", - "max_n_values": [ - 100, - 1000, - 10000 - ], - "total_tests": 3, - "graph_construction": "DAG (divisibility-based)", - "temporal_sequencing": "FAMM delay lines", - "layer_assignment": "Prime factor complexity" - }, - "results": [ - { - "max_n": 100, - "num_powerful": 48, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ] - ], - "triple_count": 6, - "conjecture_holds": false, - "field": { - "density": 0.48, - "asymptotic_density": 0.48, - "avg_gap": 2.106382978723404, - "max_gap": 6.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 19 - }, - "shear": { - "powerful_rigidity": 0.6669685989893318, - "gap_variance": 1.499320959710276, - "clustering_score": 0.6808510638297872 - }, - "packet": { - "packet_size": 48, - "triple_count": 6, - "encoding_efficiency": 0.48 - }, - "dag": { - "topological_depth": 3, - "has_directed_cycle": false, - "temporal_density": 0.7719298245614035, - "is_acyclic": true - }, - "famm": { - "engram_strength": 82.0, - "delay_diversity": 2, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "max_n": 1000, - "num_powerful": 342, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ], - [ - 99, - 100, - 101 - ], - [ - 107, - 108, - 109 - ], - [ - 151, - 152, - 153 - ], - [ - 171, - 172, - 173 - ], - [ - 331, - 332, - 333 - ], - [ - 367, - 368, - 369 - ], - [ - 387, - 388, - 389 - ], - [ - 431, - 432, - 433 - ], - [ - 547, - 548, - 549 - ], - [ - 675, - 676, - 677 - ], - [ - 907, - 908, - 909 - ] - ], - "triple_count": 17, - "conjecture_holds": false, - "field": { - "density": 0.342, - "asymptotic_density": 0.342, - "avg_gap": 2.929618768328446, - "max_gap": 10.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 129 - }, - "shear": { - "powerful_rigidity": 0.2864797879177642, - "gap_variance": 3.490647655248922, - "clustering_score": 0.4838709677419355 - }, - "packet": { - "packet_size": 342, - "triple_count": 17, - "encoding_efficiency": 0.342 - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 0.8324225865209471, - "is_acyclic": true - }, - "famm": { - "engram_strength": 836.8333333333333, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - }, - { - "max_n": 10000, - "num_powerful": 2573, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ], - [ - 99, - 100, - 101 - ], - [ - 107, - 108, - 109 - ], - [ - 151, - 152, - 153 - ], - [ - 171, - 172, - 173 - ], - [ - 331, - 332, - 333 - ], - [ - 367, - 368, - 369 - ], - [ - 387, - 388, - 389 - ], - [ - 431, - 432, - 433 - ], - [ - 547, - 548, - 549 - ], - [ - 675, - 676, - 677 - ], - [ - 907, - 908, - 909 - ], - [ - 1107, - 1108, - 1109 - ], - [ - 1123, - 1124, - 1125 - ], - [ - 1151, - 1152, - 1153 - ], - [ - 1323, - 1324, - 1325 - ], - [ - 1431, - 1432, - 1433 - ], - [ - 2151, - 2152, - 2153 - ], - [ - 2311, - 2312, - 2313 - ], - [ - 2591, - 2592, - 2593 - ], - [ - 2887, - 2888, - 2889 - ], - [ - 3087, - 3088, - 3089 - ], - [ - 3175, - 3176, - 3177 - ], - [ - 3411, - 3412, - 3413 - ], - [ - 3447, - 3448, - 3449 - ], - [ - 3527, - 3528, - 3529 - ], - [ - 3851, - 3852, - 3853 - ], - [ - 3923, - 3924, - 3925 - ], - [ - 3987, - 3988, - 3989 - ], - [ - 4075, - 4076, - 4077 - ], - [ - 4111, - 4112, - 4113 - ], - [ - 4491, - 4492, - 4493 - ], - [ - 4671, - 4672, - 4673 - ], - [ - 4831, - 4832, - 4833 - ], - [ - 4923, - 4924, - 4925 - ], - [ - 4931, - 4932, - 4933 - ], - [ - 5391, - 5392, - 5393 - ], - [ - 5407, - 5408, - 5409 - ], - [ - 5651, - 5652, - 5653 - ], - [ - 5867, - 5868, - 5869 - ], - [ - 6091, - 6092, - 6093 - ], - [ - 6451, - 6452, - 6453 - ], - [ - 6471, - 6472, - 6473 - ], - [ - 6651, - 6652, - 6653 - ], - [ - 6723, - 6724, - 6725 - ], - [ - 6947, - 6948, - 6949 - ], - [ - 7927, - 7928, - 7929 - ], - [ - 7947, - 7948, - 7949 - ], - [ - 8387, - 8388, - 8389 - ], - [ - 8675, - 8676, - 8677 - ], - [ - 8971, - 8972, - 8973 - ], - [ - 8999, - 9000, - 9001 - ], - [ - 9171, - 9172, - 9173 - ], - [ - 9187, - 9188, - 9189 - ], - [ - 9431, - 9432, - 9433 - ], - [ - 9531, - 9532, - 9533 - ], - [ - 9747, - 9748, - 9749 - ], - [ - 9871, - 9872, - 9873 - ], - [ - 9907, - 9908, - 9909 - ] - ], - "triple_count": 64, - "conjecture_holds": false, - "field": { - "density": 0.2573, - "asymptotic_density": 0.2573, - "avg_gap": 3.8876360808709176, - "max_gap": 20.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 956 - }, - "shear": { - "powerful_rigidity": 0.1277407719594713, - "gap_variance": 7.828354131948559, - "clustering_score": 0.3689735614307932 - }, - "packet": { - "packet_size": 2573, - "triple_count": 64, - "encoding_efficiency": 0.2573 - }, - "dag": { - "topological_depth": 4, - "has_directed_cycle": false, - "temporal_density": 0.8590439689130204, - "is_acyclic": true - }, - "famm": { - "engram_strength": 7186.833333333333, - "delay_diversity": 3, - "temporal_integration": 0.0, - "delay_steps": 3 - } - } - ], - "investigation_analysis": { - "total_tests": 3, - "conjecture_holds_count": 0, - "conjecture_holds": false, - "dag_metrics": { - "avg_acyclic_rate": 1.0, - "avg_temporal_density": 0.8211321266651237 - }, - "famm_metrics": { - "avg_engram_strength": 2701.8888888888887, - "avg_delay_diversity": 2.6666666666666665 - }, - "note": "Refined investigation using DAG structure + FAMM delay lines for powerful number sequence analysis" - }, - "primitive_insights": { - "field": "Density and gap distribution", - "spectral": "Divisibility structure", - "shear": "Gap variance and clustering", - "packet": "Triple encoding" - }, - "dag_insights": { - "divisibility_structure": "Edges represent divisibility relations", - "complexity_layers": "Prime factor complexity encoding", - "temporal_density": "Cross-complexity connectivity" - }, - "famm_insights": { - "complexity_flow": "Delay matrices capture complexity-level flow", - "engram_consolidation": "Weighted delay integration", - "temporal_integration": "Cross-complexity coherence" - }, - "validation": { - "status": "INVESTIGATION_COMPLETE", - "insight": "Refined investigation using DAG structure (divisibility-based) + FAMM delay lines for powerful number sequence analysis. DAG provides divisibility structure. FAMM captures complexity-level temporal dynamics. Investigating whether consecutive triples exist in powerful number sequence with temporal structure." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/joke_source_literalization_guardrail_probe.py b/4-Infrastructure/shim/joke_source_literalization_guardrail_probe.py deleted file mode 100644 index e0a6376d..00000000 --- a/4-Infrastructure/shim/joke_source_literalization_guardrail_probe.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed guardrail for joke sources with unsafe literal payloads. - -The project can cite a joke as a real source prompt, but that does not mean the -literal payload becomes an executable instruction. This probe adds the missing -guardrail for encoding systems: - -* parody/satire may be retained as provenance metadata; -* unsafe domain advice must not be promoted into actionable logograms; -* compression may preserve the existence and role of the source without - preserving operationally harmful details as callable procedures. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "joke_source_literalization_guardrail" -REGISTRY = OUT_DIR / "joke_source_literalization_guardrail_registry.json" -RECEIPT = OUT_DIR / "joke_source_literalization_guardrail_receipt.json" -SUMMARY = OUT_DIR / "joke_source_literalization_guardrail.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Joke Source Literalization Guardrail.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "bibliographic_event_horizon" / "bibliographic_event_horizon_receipt.json", - REPO / "shared-data" / "data" / "asymptotic_closure_horizon" / "asymptotic_closure_horizon_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "6-Documentation" / "docs" / "specs" / "FORWARD_FOUNDATION_EQUATION_COMPILER.md", - REPO / "6-Documentation" / "docs" / "specs" / "OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", -] - -LITERALIZATION_HORIZON = 1000 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def route( - *, - route_id: str, - payload_role: str, - domain_risk: int, - satire_marker: bool, - actionable_literalization: bool, - safety_context_present: bool, - preserves_operational_steps: bool, -) -> dict[str, Any]: - literalization_risk = ( - domain_risk - + (350 if satire_marker and actionable_literalization else 0) - + (350 if preserves_operational_steps else 0) - + (250 if not safety_context_present else 0) - ) - horizon = literalization_risk >= LITERALIZATION_HORIZON - if horizon and actionable_literalization: - decision = "QUARANTINE_UNSAFE_LITERALIZATION" - elif horizon: - decision = "HOLD_SAFETY_CONTEXT_REQUIRED" - else: - decision = "ADMIT_METADATA_ONLY" - item = { - "route_id": route_id, - "payload_role": payload_role, - "domain_risk": domain_risk, - "satire_marker": satire_marker, - "actionable_literalization": actionable_literalization, - "safety_context_present": safety_context_present, - "preserves_operational_steps": preserves_operational_steps, - "literalization_risk": literalization_risk, - "literalization_horizon": horizon, - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - routes = [ - route( - route_id="joke_post_as_source_prompt", - payload_role="provenance_metadata", - domain_risk=200, - satire_marker=True, - actionable_literalization=False, - safety_context_present=True, - preserves_operational_steps=False, - ), - route( - route_id="unsafe_chemistry_tips_as_logogram", - payload_role="callable_procedure_candidate", - domain_risk=650, - satire_marker=True, - actionable_literalization=True, - safety_context_present=False, - preserves_operational_steps=True, - ), - route( - route_id="unsafe_chemistry_tips_as_training_text", - payload_role="raw_text_corpus_payload", - domain_risk=650, - satire_marker=True, - actionable_literalization=True, - safety_context_present=False, - preserves_operational_steps=True, - ), - route( - route_id="guardrail_summary", - payload_role="non_operational_safety_summary", - domain_risk=250, - satire_marker=True, - actionable_literalization=False, - safety_context_present=True, - preserves_operational_steps=False, - ), - ] - return { - "schema": "joke_source_literalization_guardrail_registry_v1", - "source_prompt": { - "id": "user_supplied_unsafe_chemistry_joke_image", - "role": "source_prompt", - "status": "user_supplied_image_prompt", - "description": ( - "A joke post framed as chemistry advice. Its value is as a guardrail " - "example: satire can be cited as provenance, but unsafe procedural " - "content must not be promoted into executable encoding atoms." - ), - "verbatim_payload_policy": "do_not_reproduce_operational_steps", - }, - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Joke-source literalization guardrail only. The source may be retained as " - "metadata and cited as an encoding-risk example. Unsafe literal payloads " - "are not admitted as callable procedures, training targets, or logograms." - ), - "encoding_rule": { - "metadata_channel": "ADMIT source identity, satire role, risk label, and receipt hash", - "payload_channel": "QUARANTINE operational unsafe steps", - "summary_channel": "ADMIT non-operational safety summary", - "logogram_channel": "HOLD or QUARANTINE if a glyph would expand into unsafe procedure", - }, - "admissibility_equation": ( - "A_joke_payload=1[source_role_metadata] * 1[not actionable_literalization] * " - "1[not preserves_operational_steps] * 1[safety_context_present]" - ), - "hutter_adaptation": { - "codec_torsion_component": "unsafe_literalization_debt", - "rule": "a corpus atom that compresses into harmful procedural replay adds horizon-level torsion unless quarantined", - "safe_compression": "preserve citation and risk class, not executable unsafe procedure", - }, - "routes": routes, - "aggregates": { - "route_count": len(routes), - "metadata_admit_count": sum(1 for item in routes if item["decision"] == "ADMIT_METADATA_ONLY"), - "quarantine_count": sum(1 for item in routes if item["decision"] == "QUARANTINE_UNSAFE_LITERALIZATION"), - "hold_count": sum(1 for item in routes if item["decision"].startswith("HOLD")), - "literalization_horizon": LITERALIZATION_HORIZON, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "joke_source_literalization_guardrail_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "aggregates": registry["aggregates"], - "decision": "ADMIT_JOKE_SOURCE_METADATA_QUARANTINE_UNSAFE_LITERALIZATION", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Joke Source Literalization Guardrail", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Rule", - "", - f"`{registry['admissibility_equation']}`", - "", - "## Encoding Channels", - "", - ] - for key, value in registry["encoding_rule"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Routes", - "", - "| Route | Payload role | Risk | Decision |", - "|---|---|---:|---|", - ] - ) - for item in registry["routes"]: - lines.append(f"| `{item['route_id']}` | `{item['payload_role']}` | {item['literalization_risk']} | `{item['decision']}` |") - lines.extend(["", "## Hutter Adaptation", ""]) - for key, value in registry["hutter_adaptation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Safety Encoding Guardrail Receipt -title: Joke Source Literalization Guardrail -type: text/vnd.tiddlywiki - -! Joke Source Literalization Guardrail - -Durable runner: - -``` -4-Infrastructure/shim/joke_source_literalization_guardrail_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -A joke source can be a real citation, but unsafe literal payloads cannot become executable encoding atoms. - -``` -metadata channel -> ADMIT -unsafe payload channel -> QUARANTINE -summary channel -> ADMIT only if non-operational -logogram channel -> HOLD/QUARANTINE if expansion becomes unsafe procedure -``` - -!! Hutter Link - -Unsafe literalization is codec torsion. If a compressed atom expands into harmful procedural replay, it stays outside the candidate archive unless quarantined or transformed into non-operational metadata. - -!! Links - -* [[Bibliographic Event Horizon]] -* [[Asymptotic Closure Horizon]] -* [[Hutter Torsion Clock Adaptation]] -* [[Omindirection Logogram Contract]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/kaggle_density_cleanroom_primitives.py b/4-Infrastructure/shim/kaggle_density_cleanroom_primitives.py deleted file mode 100644 index 3a1e76ba..00000000 --- a/4-Infrastructure/shim/kaggle_density_cleanroom_primitives.py +++ /dev/null @@ -1,349 +0,0 @@ -#!/usr/bin/env python3 -"""Clean-room density primitives inspired by Kaggle notebook ideas. - -This module does not vendor Kaggle notebook code. It reimplements the useful -ideas as Research Stack primitives: - -* typed carrier narrowing with explicit precision policy -* column/shard projection plans for external stores -* coverage-map routing with residual jumps -* lossy bottleneck accounting with mandatory residual policy -* inference-appliance admission checks - -The original Kaggle sources are cited as idea sources in the emitted receipt. -""" - -from __future__ import annotations - -from dataclasses import asdict, dataclass -import hashlib -import json -import math -from pathlib import Path -from typing import Any, Iterable - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "kaggle_density_priors" -RECEIPT = OUT_DIR / "kaggle_density_cleanroom_primitives_receipt.json" - -IDEA_SOURCES = [ - { - "title": "Ubiquant Market Prediction chunked dtype reduction notebook excerpt", - "url": "https://www.kaggle.com/competitions/ubiquant-market-prediction", - "use": "idea source for typed carrier narrowing and chunked materialization", - }, - { - "title": "Ubiquant Parquet dataset", - "url": "https://www.kaggle.com/robikscube/ubiquant-parquet", - "use": "idea source for columnar projection and shard-local loading", - }, - { - "title": "Pixel Travel Map", - "url": "https://www.kaggle.com/code/oxzplvifi/pixel-travel-map", - "use": "idea source for binary coverage maps and hole-avoidant routing", - }, - { - "title": "Improved baseline Santa 2022", - "url": "https://www.kaggle.com/code/crodoc/82409-improved-baseline-santa-2022", - "use": "idea source for path-planning baseline comparison", - }, - { - "title": "Mercedes neural compression autoencoder notebook", - "url": "https://www.kaggle.com/code/remidi/neural-compression-auto-encoder-lb-0-55", - "use": "idea source for bottleneck coordinates and projection families", - }, - { - "title": "AIMO3 Eagle3 speculative decoding notebook", - "url": "https://www.kaggle.com/code/khoinguyennguyen/eagle3-specdecoding-optional-context-compression", - "use": "idea source for inference appliance routing and context handoff", - }, -] - - -INTEGER_DTYPES = [ - ("int8", -(2**7), 2**7 - 1), - ("int16", -(2**15), 2**15 - 1), - ("int32", -(2**31), 2**31 - 1), - ("int64", -(2**63), 2**63 - 1), -] - -UNSIGNED_DTYPES = [ - ("uint8", 0, 2**8 - 1), - ("uint16", 0, 2**16 - 1), - ("uint32", 0, 2**32 - 1), - ("uint64", 0, 2**64 - 1), -] - -FLOAT_DTYPES = [ - ("float16", 65504.0, "lossy_for_many_decimal_payloads"), - ("float32", 3.4028235e38, "usual_low_memory_scientific_surface"), - ("float64", 1.7976931348623157e308, "maximal_standard_float_surface"), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -@dataclass(frozen=True) -class NumericFieldStats: - name: str - kind: str - minimum: float - maximum: float - nullable: bool = False - - -@dataclass(frozen=True) -class DTypeDecision: - field: str - dtype: str - decision: str - residual_policy: str - reason: str - - -def choose_integer_dtype(minimum: int, maximum: int, prefer_unsigned: bool = True) -> str: - table = UNSIGNED_DTYPES if prefer_unsigned and minimum >= 0 else INTEGER_DTYPES - for dtype, low, high in table: - if low <= minimum and maximum <= high: - return dtype - return "uint64" if prefer_unsigned and minimum >= 0 else "int64" - - -def choose_float_dtype(minimum: float, maximum: float, precision_policy: str) -> tuple[str, str]: - limit = max(abs(minimum), abs(maximum)) - if precision_policy == "exact_replay": - return "float64", "exact replay requested; keep widest standard float carrier" - if precision_policy == "bounded_residual_ok": - for dtype, max_abs, note in FLOAT_DTYPES: - if limit <= max_abs: - return dtype, note - if limit <= FLOAT_DTYPES[1][1]: - return "float32", "default conservative low-memory carrier" - return "float64", "range exceeds float32 carrier" - - -def plan_dtype(stats: NumericFieldStats, precision_policy: str = "bounded_residual_ok") -> DTypeDecision: - if stats.kind == "integer": - dtype = choose_integer_dtype(int(stats.minimum), int(stats.maximum)) - return DTypeDecision( - field=stats.name, - dtype=dtype, - decision="ACCEPT", - residual_policy="none_required_for_integer_range_downcast", - reason=f"value range [{stats.minimum}, {stats.maximum}] fits {dtype}", - ) - if stats.kind == "float": - dtype, reason = choose_float_dtype(stats.minimum, stats.maximum, precision_policy) - decision = "ACCEPT" if dtype == "float64" or precision_policy == "bounded_residual_ok" else "HOLD" - residual = "required_if_downcast_changes_replay" if dtype != "float64" else "none_for_carrier_width" - return DTypeDecision(stats.name, dtype, decision, residual, reason) - return DTypeDecision( - field=stats.name, - dtype="category_dictionary", - decision="HOLD", - residual_policy="dictionary_and_unknown_category_sidecar_required", - reason="non-numeric field requires explicit dictionary receipt", - ) - - -@dataclass(frozen=True) -class ColumnProjectionPlan: - table_id: str - columns: tuple[str, ...] - shard_key: str | None = None - shard_value: str | None = None - - def receipt(self) -> dict[str, Any]: - payload = asdict(self) - return { - "schema": "column_projection_plan_v1", - "payload": payload, - "plan_hash": sha256_text(stable_json(payload)), - "decision": "ACCEPT" if self.columns else "HOLD", - "residual_policy": "unselected_columns_are_external_store_references", - } - - -@dataclass(frozen=True) -class GridPoint: - x: int - y: int - - -@dataclass(frozen=True) -class CoverageStep: - start: GridPoint - end: GridPoint - kind: str - cost: float - - -class CoverageMapRouter: - """Small deterministic coverage router with hole-avoidant down preference.""" - - def __init__(self, width: int, height: int, start: GridPoint): - if width <= 0 or height <= 0: - raise ValueError("width and height must be positive") - if not (0 <= start.x < width and 0 <= start.y < height): - raise ValueError("start must be inside grid") - self.width = width - self.height = height - self.current = start - self.unvisited = {(x, y) for x in range(width) for y in range(height)} - self.unvisited.discard((start.x, start.y)) - - def neighbors(self, point: GridPoint) -> Iterable[GridPoint]: - for dx, dy in ((0, -1), (-1, 0), (1, 0), (0, 1)): - x = point.x + dx - y = point.y + dy - if 0 <= x < self.width and 0 <= y < self.height: - yield GridPoint(x, y) - - def nearest_unvisited(self) -> GridPoint | None: - if not self.unvisited: - return None - x0, y0 = self.current.x, self.current.y - x, y = min(self.unvisited, key=lambda p: (abs(p[0] - x0) + abs(p[1] - y0), p[1], p[0])) - return GridPoint(x, y) - - def next_step(self) -> CoverageStep | None: - if not self.unvisited: - return None - start = self.current - down = GridPoint(start.x, start.y - 1) - if (down.x, down.y) in self.unvisited: - end = down - kind = "down_first_local" - cost = 1.0 - else: - local = [p for p in self.neighbors(start) if (p.x, p.y) in self.unvisited] - if local: - end = min(local, key=lambda p: (p.y, abs(p.x - start.x), p.x)) - kind = "least_cost_local" - cost = math.dist((start.x, start.y), (end.x, end.y)) - else: - end = self.nearest_unvisited() - if end is None: - return None - kind = "residual_jump_to_nearest_unvisited" - cost = abs(end.x - start.x) + abs(end.y - start.y) - self.unvisited.discard((end.x, end.y)) - self.current = end - return CoverageStep(start, end, kind, cost) - - -@dataclass(frozen=True) -class BottleneckPlan: - source_dimensions: int - latent_dimensions: int - reconstruction_declared: bool - residual_declared: bool - - def decision(self) -> str: - if self.latent_dimensions <= 0 or self.latent_dimensions >= self.source_dimensions: - return "HOLD" - if not (self.reconstruction_declared and self.residual_declared): - return "HOLD" - return "ACCEPT" - - def receipt(self) -> dict[str, Any]: - payload = asdict(self) - return { - "schema": "bottleneck_plan_v1", - "payload": payload, - "compression_ratio_nominal": self.source_dimensions / max(self.latent_dimensions, 1), - "decision": self.decision(), - "residual_policy": "required_for_lossless_replay", - "plan_hash": sha256_text(stable_json(payload)), - } - - -@dataclass(frozen=True) -class InferenceAppliancePlan: - offline_wheelhouse: bool - deterministic_tool_sandbox: bool - answer_range: tuple[int, int] - consensus_attempts: int - context_handoff_enabled: bool - context_handoff_schema_declared: bool - - def decision(self) -> str: - low, high = self.answer_range - if not self.offline_wheelhouse: - return "HOLD" - if not self.deterministic_tool_sandbox: - return "HOLD" - if low < 0 or high < low: - return "HOLD" - if self.consensus_attempts < 1: - return "HOLD" - if self.context_handoff_enabled and not self.context_handoff_schema_declared: - return "HOLD" - return "ACCEPT" - - def receipt(self) -> dict[str, Any]: - payload = asdict(self) - return { - "schema": "inference_appliance_plan_v1", - "payload": payload, - "decision": self.decision(), - "residual_policy": "handoff_summary_required_when_context_is_reset", - "plan_hash": sha256_text(stable_json(payload)), - } - - -def build_receipt() -> dict[str, Any]: - dtype_examples = [ - plan_dtype(NumericFieldStats("time_id", "integer", 0, 1219)), - plan_dtype(NumericFieldStats("target", "float", -9.5, 12.1), precision_policy="exact_replay"), - plan_dtype(NumericFieldStats("feature_f0", "float", -18.0, 47.1), precision_policy="bounded_residual_ok"), - plan_dtype(NumericFieldStats("row_id", "object", 0, 0)), - ] - router = CoverageMapRouter(4, 4, GridPoint(0, 3)) - steps = [asdict(router.next_step()) for _ in range(5)] - projection = ColumnProjectionPlan("ubiquant_low_mem", ("time_id", "investment_id", "target"), "investment_id", "529") - bottleneck = BottleneckPlan(304, 12, reconstruction_declared=True, residual_declared=True) - appliance = InferenceAppliancePlan( - offline_wheelhouse=True, - deterministic_tool_sandbox=True, - answer_range=(0, 99999), - consensus_attempts=8, - context_handoff_enabled=True, - context_handoff_schema_declared=True, - ) - receipt = { - "schema": "kaggle_density_cleanroom_primitives_receipt_v1", - "implementation": str(Path(__file__).relative_to(REPO)), - "idea_sources": IDEA_SOURCES, - "license_boundary": ( - "Original Kaggle notebook code is not vendored or relicensed here. " - "This file is an original Research Stack implementation of abstract " - "route laws inspired by the cited sources." - ), - "dtype_examples": [asdict(item) for item in dtype_examples], - "projection_example": projection.receipt(), - "coverage_steps_example": steps, - "bottleneck_example": bottleneck.receipt(), - "inference_appliance_example": appliance.receipt(), - "decision": "ACCEPT", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/kaggle_density_marker_prior_registry.py b/4-Infrastructure/shim/kaggle_density_marker_prior_registry.py deleted file mode 100644 index 58e81a5f..00000000 --- a/4-Infrastructure/shim/kaggle_density_marker_prior_registry.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -"""Build receipted density-marker priors from user-supplied Kaggle notebook excerpts. - -These packets intentionally preserve design patterns, not leaderboard claims: -chunked dtype reduction, columnar Parquet access, binary travel-map routing, -autoencoder/decomposition feature compression, and AIMO3 inference-appliance -guardrails. All packets remain HOLD until a local dataset, implementation, and -replay/benchmark receipt close. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "kaggle_density_priors" -PACKETS = OUT_DIR / "kaggle_density_marker_prior_packets.jsonl" -RECEIPT = OUT_DIR / "kaggle_density_marker_prior_receipt.json" -SOURCES_CFF = OUT_DIR / "kaggle_density_marker_prior_sources.cff" - -KAGGLE_LICENSE_NOTICE = { - "platform": "Kaggle", - "platform_terms_url": "https://www.kaggle.com/terms", - "license_status": "source_notebook_license_not_verified_from_pasted_excerpt", - "use_policy": ( - "This registry records design-principle summaries and density markers only. " - "It does not vendor, redistribute, or relicense Kaggle notebook code. " - "Before copying or adapting notebook code, inspect the Kaggle page metadata " - "and honor the author's license, Kaggle terms, and any competition or dataset terms." - ), -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def packet( - packet_id: str, - name: str, - source_surface: str, - source_urls: list[str], - source_authors: list[str], - route: str, - density_markers: list[str], - compression_relevance: str, - claim_boundary: str, -) -> dict[str, Any]: - obj = { - "schema": "kaggle_density_marker_prior_packet_v1", - "packet_id": packet_id, - "name": name, - "source_surface": source_surface, - "source_urls": source_urls, - "source_authors": source_authors, - "license_provenance": KAGGLE_LICENSE_NOTICE, - "rrc_shape_hint": "KaggleCompetitionAlgorithmPrior", - "route": route, - "density_markers": density_markers, - "compression_relevance": compression_relevance, - "claim_boundary": claim_boundary, - "decision": "HOLD", - } - obj["packet_hash"] = sha256_text(stable_json(obj)) - return obj - - -def source_reference(packet_obj: dict[str, Any]) -> dict[str, Any]: - authors = packet_obj["source_authors"] or ["Unknown Kaggle author"] - first_url = packet_obj["source_urls"][0] if packet_obj["source_urls"] else "https://www.kaggle.com/" - return { - "type": "software", - "title": packet_obj["name"], - "authors": [{"name": author} for author in authors], - "url": first_url, - "notes": ( - "Kaggle source cited as an external design-prior surface only. " - "Notebook code is not vendored in this repository; license must be " - "verified on Kaggle before copying, adapting, or redistributing code." - ), - } - - -def write_sources_cff(packets: list[dict[str, Any]]) -> None: - cff = { - "cff-version": "1.2.0", - "message": ( - "If you use these Kaggle-derived density-prior notes, cite the original " - "Kaggle sources and verify each source license before copying code." - ), - "type": "dataset", - "title": "Kaggle Algorithm Density Marker Prior Sources", - "authors": [{"name": "Research Stack Contributors"}], - "date-released": "2026-05-08", - "url": "https://github.com/allaunthefox/Research-Stack", - "repository-code": "https://github.com/allaunthefox/Research-Stack", - "license": "Apache-2.0", - "references": [ - *[source_reference(packet_obj) for packet_obj in packets], - { - "type": "webpage", - "title": "Kaggle Terms of Use", - "authors": [{"name": "Kaggle"}], - "url": "https://www.kaggle.com/terms", - "notes": "Platform terms; individual notebooks/datasets may carry additional metadata and licenses.", - }, - ], - } - - def render_scalar(value: Any) -> str: - text = str(value).replace('"', '\\"') - return f'"{text}"' - - lines = [ - "cff-version: 1.2.0", - f"message: {render_scalar(cff['message'])}", - "type: dataset", - f"title: {render_scalar(cff['title'])}", - "authors:", - ] - for author in cff["authors"]: - lines.append(f" - name: {render_scalar(author['name'])}") - lines.extend( - [ - f"date-released: {cff['date-released']}", - f"url: {render_scalar(cff['url'])}", - f"repository-code: {render_scalar(cff['repository-code'])}", - f"license: {cff['license']}", - "references:", - ] - ) - for ref in cff["references"]: - lines.append(f" - type: {ref['type']}") - lines.append(f" title: {render_scalar(ref['title'])}") - lines.append(" authors:") - for author in ref["authors"]: - lines.append(f" - name: {render_scalar(author['name'])}") - lines.append(f" url: {render_scalar(ref['url'])}") - lines.append(f" notes: {render_scalar(ref['notes'])}") - SOURCES_CFF.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - - packets = [ - packet( - packet_id="KAGGLE.UBIQUANT.CHUNKED_DTYPE_REDUCTION.0001", - name="Ubiquant chunked dtype reduction", - source_surface="user-pasted Ubiquant Market Prediction tutorial excerpt", - source_urls=[ - "https://www.kaggle.com/competitions/ubiquant-market-prediction", - ], - source_authors=["Unknown Kaggle notebook author"], - route=( - "large_csv -> chunked pandas read -> dtype min/max downcast -> pickle chunks " - "-> concatenated low-memory frame" - ), - density_markers=[ - "chunked_stream_ingest", - "dtype_range_downcast", - "float16_feature_surface", - "pickle_chunk_materialization", - "weak_linear_correlation_signal", - "generator_batch_surface", - ], - compression_relevance=( - "Shows a practical memory-shrink route: 18GB-class tabular CSV can be moved " - "into smaller typed carriers before model or RRC feature extraction." - ), - claim_boundary=( - "Notebook excerpt only; dtype reduction may change numeric precision and must be " - "validated against target replay before promotion." - ), - ), - packet( - packet_id="KAGGLE.UBIQUANT.PARQUET_COLUMNAR_IO.0001", - name="Ubiquant Parquet columnar loading", - source_surface="user-pasted Ubiquant Parquet loading excerpt", - source_urls=[ - "https://www.kaggle.com/robikscube/ubiquant-parquet", - "https://www.kaggle.com/competitions/ubiquant-market-prediction", - ], - source_authors=["Rob Mulla", "Unknown Kaggle notebook author"], - route=( - "csv table -> Parquet columnar table -> low-memory typed Parquet -> column subset " - "or investment_id shard" - ), - density_markers=[ - "columnar_storage_surface", - "record_shredding_assembly", - "low_memory_float32_uint16_schema", - "column_subset_projection", - "investment_id_partition_shard", - "io_minimization_gate", - ], - compression_relevance=( - "Useful as a database-store prior: select only the columns or ID shard required " - "by the RRC route instead of loading the full feature manifold." - ), - claim_boundary=( - "External dataset/layout prior only; exact load times and sizes require local " - "dataset receipts." - ), - ), - packet( - packet_id="KAGGLE.SANTA.PIXEL_TRAVEL_MAP.0001", - name="Santa 2022 pixel travel map", - source_surface="user-pasted Santa 2022 pixel travel map excerpt", - source_urls=[ - "https://www.kaggle.com/code/oxzplvifi/pixel-travel-map", - "https://www.kaggle.com/code/crodoc/82409-improved-baseline-santa-2022", - "https://www.kaggle.com/code/ryanholbrook/getting-started-with-santa-2022", - ], - source_authors=["oxzplvifi", "crodoc", "Ryan Holbrook"], - route=( - "image pixels -> binary unvisited map -> down-first local motion -> least-cost " - "single/double-link move -> nearest-unvisited recovery -> return-to-origin path" - ), - density_markers=[ - "binary_visit_bitmap", - "down_first_hole_avoidance", - "single_link_motion_enum", - "double_link_motion_enum", - "nearest_unvisited_recovery", - "return_to_origin_constraint", - ], - compression_relevance=( - "A direct topology-routing prior: a binary coverage map plus local move rules can " - "encode traversal policy and residual recovery without storing the full route naively." - ), - claim_boundary=( - "Algorithm sketch only; route cost, validity, and image traversal coverage require " - "local replay with the Santa arm helpers." - ), - ), - packet( - packet_id="KAGGLE.MERCEDES.AUTOENCODER_FEATURE_COMPRESSION.0001", - name="Mercedes autoencoder feature compression", - source_surface="user-pasted Mercedes-Benz Greener Manufacturing notebook excerpt", - source_urls=[ - "https://www.kaggle.com/code/remidi/neural-compression-auto-encoder-lb-0-55", - "https://www.kaggle.com/competitions/mercedes-benz-greener-manufacturing", - ], - source_authors=["remidi"], - route=( - "categorical/numeric table -> one-hot + target means -> 12D autoencoder bottleneck " - "+ PCA/ICA/SVD/random projections/NMF -> XGBoost + stacked ensemble" - ), - density_markers=[ - "autoencoder_bottleneck_12d", - "denoised_reconstruction_surface", - "target_mean_category_encoding", - "multi_projection_feature_family", - "stacking_prediction_as_feature", - "weighted_ensemble_output", - ], - compression_relevance=( - "Gives a compact-feature atlas prior: many heterogeneous projections can be treated " - "as candidate manifold coordinates, with the autoencoder bottleneck as a lossy route." - ), - claim_boundary=( - "Predictive feature-engineering prior only; neural bottleneck is lossy unless an " - "explicit residual/reconstruction receipt is added." - ), - ), - packet( - packet_id="KAGGLE.AIMO3.SPECDEC_CONTEXT_APPLIANCE.0001", - name="AIMO3 speculative decoding and context-compression appliance", - source_surface=( - "user-pasted Kaggle AIMO3 Eagle3/speculative decoding notebook excerpt " - "https://www.kaggle.com/code/khoinguyennguyen/eagle3-specdecoding-optional-context-compression" - ), - source_urls=[ - "https://www.kaggle.com/code/khoinguyennguyen/eagle3-specdecoding-optional-context-compression", - "https://www.kaggle.com/competitions/ai-mathematical-olympiad-progress-prize-3", - ], - source_authors=["khoinguyennguyen"], - route=( - "offline wheelhouse -> vLLM OpenAI server -> GPT-OSS model with Eagle3/ngram/draft " - "speculative decoding -> persistent Jupyter tool sandboxes -> multi-attempt answer " - "voting -> optional STATE_SUMMARY context reset" - ), - density_markers=[ - "offline_wheelhouse_environment", - "speculative_decoding_eagle3", - "fp8_kv_cache_surface", - "persistent_jupyter_tool_pool", - "multi_attempt_consensus_vote", - "boxed_answer_range_gate", - "context_handoff_summary", - "gpu_memory_reclaim_guard", - ], - compression_relevance=( - "This is an inference-control compression prior: reduce wall-clock and context waste " - "by caching weights, drafting tokens, keeping tool kernels warm, and checkpointing " - "long reasoning into a bounded handoff report." - ), - claim_boundary=( - "Notebook excerpt only. Leaderboard score, speedup, correctness, and safety require " - "local AIMO3 replay receipts. Context compression is disabled in the pasted CFG and " - "must not be treated as validated." - ), - ), - ] - - PACKETS.write_text("\n".join(stable_json(p) for p in packets) + "\n", encoding="utf-8") - write_sources_cff(packets) - receipt = { - "schema": "kaggle_density_marker_prior_receipt_v1", - "packet_count": len(packets), - "packets": str(PACKETS.relative_to(REPO)), - "sources_cff": str(SOURCES_CFF.relative_to(REPO)), - "density_marker_total": sum(len(p["density_markers"]) for p in packets), - "source_basis": "user-pasted Kaggle notebook excerpts in Codex session", - "source_surfaces": [p["source_surface"] for p in packets], - "license_provenance": KAGGLE_LICENSE_NOTICE, - "route_families": [ - "tabular_dtype_memory_compression", - "columnar_parquet_database_store", - "topological_travel_map_path_planning", - "neural_bottleneck_feature_compression", - "aimo3_speculative_inference_appliance", - ], - "claim_boundary": ( - "These packets record algorithm-density markers only. No Kaggle dataset was downloaded " - "or benchmark reproduced by this registry. All packets remain HOLD until local replay, " - "byte law, residual, and receipt checks close." - ), - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/kerr_like_load_witness_geometry_probe.py b/4-Infrastructure/shim/kerr_like_load_witness_geometry_probe.py deleted file mode 100644 index 6be7ace2..00000000 --- a/4-Infrastructure/shim/kerr_like_load_witness_geometry_probe.py +++ /dev/null @@ -1,461 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed Kerr-like load-witness geometry probe. - -This probe captures the refined invariant-geometry load concept: - -* mechanical equilibrium remains a real-valued physical gate; -* Merkle/O-AMMR commitments bind layer/process/material evidence; -* an Equihash-like witness is an admissibility predicate, not a force term; -* Kerr spacetime is used only as a typed state-atlas analogy for torsion-coupled - load paths, warning regions, and irreversible failure horizons. - -It is not a structural safety certificate and not a literal general-relativity -model of a mechanical part. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" -REGISTRY = OUT_DIR / "kerr_like_load_witness_geometry_registry.json" -RECEIPT = OUT_DIR / "kerr_like_load_witness_geometry_receipt.json" -SUMMARY = OUT_DIR / "kerr_like_load_witness_geometry.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Kerr-Like Load Witness Geometry.tid" - -PRACTICAL_TREE_FIDDY_DEPTH = 350 -EPSILON_MECH = 1.0e-8 -ERGOREGION_TORSION_RATIO = 0.18 -FAILURE_HORIZON_TORSION_RATIO = 0.36 -TORSION_CRITICAL = 36.0 - -CITATIONS = [ - { - "id": "princeton_invariant_dual_mechanics", - "title": "Invariant dual mechanics of tensegrity and origami", - "url": "https://collaborate.princeton.edu/en/publications/invariant-dual-mechanics-of-tensegrity-and-origami/", - "role": "external_mechanics_anchor", - "status": "external_reference", - }, - { - "id": "pnas_invariant_dual_mechanics", - "title": "Invariant dual mechanics of tensegrity and origami", - "doi": "10.1073/pnas.2519138123", - "role": "external_mechanics_anchor", - "status": "external_reference", - }, - { - "id": "equihash_iacr", - "title": "Equihash: Asymmetric Proof-of-Work Based on the Generalized Birthday Problem", - "url": "https://eprint.iacr.org/2015/946", - "role": "memory_hard_witness_anchor", - "status": "external_reference", - }, - { - "id": "user_supplied_kerr_diagram", - "title": "Extended Kerr spacetime null-geodesic diagram prompt", - "role": "state_atlas_prompt", - "status": "user_supplied_image_prompt", - }, - { - "id": "merkle_tensegrity_load_equation_harness", - "title": "Merkle tensegrity load equation harness", - "path": "4-Infrastructure/shim/merkle_tensegrity_load_equation_generator.py", - "role": "local_predecessor", - "status": "local_reference", - }, - { - "id": "tree_fiddy_semantics", - "title": "Tree Fiddy semantics", - "path": "6-Documentation/docs/semantics/TREE_FIDDY.md", - "role": "recursion_bound", - "status": "local_reference", - }, - { - "id": "geomtree_semijack_witness", - "title": "GeomTREE Semi-Jack Physical Witness", - "path": "6-Documentation/tiddlywiki-local/wiki/tiddlers/GeomTREE Semi-Jack Physical Witness.tid", - "role": "local_application_surface", - "status": "local_reference", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def state_case( - *, - case_id: str, - description: str, - load_norm: float, - torsion_norm: float, - mechanical_residual_l2: float, - layer_commitment_root_present: bool, - equihash_like_witness_present: bool, - witness_depth: int, - residual_declared: bool, - torsion_clock: float, - wall_clock_seconds: float, - literal_kerr_claim: bool = False, -) -> dict[str, Any]: - torsion_ratio = torsion_norm / max(load_norm, 1.0e-12) - mechanical_close = mechanical_residual_l2 <= EPSILON_MECH - commitment_close = layer_commitment_root_present and residual_declared - witness_close = equihash_like_witness_present and witness_depth <= PRACTICAL_TREE_FIDDY_DEPTH - torsion_clock_close = torsion_clock <= TORSION_CRITICAL - typed_analogy_close = not literal_kerr_claim - finite_pass = mechanical_close and commitment_close and witness_close and torsion_clock_close and typed_analogy_close - ergoregion = ERGOREGION_TORSION_RATIO <= torsion_ratio < FAILURE_HORIZON_TORSION_RATIO - horizon = torsion_ratio >= FAILURE_HORIZON_TORSION_RATIO - if literal_kerr_claim: - decision = "QUARANTINE_LITERAL_KERR_OVERCLAIM" - elif horizon: - decision = "HOLD_FAILURE_HORIZON" - elif ergoregion: - decision = "HOLD_ERGOREGION_WARNING" - elif finite_pass: - decision = "ADMIT_SAFE_CHART_FIXTURE" - else: - decision = "HOLD_INCOMPLETE_WITNESS" - item = { - "case_id": case_id, - "description": description, - "load_norm": load_norm, - "torsion_norm": torsion_norm, - "torsion_ratio": torsion_ratio, - "mechanical_residual_l2": mechanical_residual_l2, - "mechanical_close": mechanical_close, - "layer_commitment_root_present": layer_commitment_root_present, - "equihash_like_witness_present": equihash_like_witness_present, - "witness_depth": witness_depth, - "tree_fiddy_depth_bound": PRACTICAL_TREE_FIDDY_DEPTH, - "residual_declared": residual_declared, - "torsion_clock": torsion_clock, - "torsion_critical": TORSION_CRITICAL, - "torsion_clock_close": torsion_clock_close, - "wall_clock_seconds": wall_clock_seconds, - "wall_clock_role": "metadata_shadow_not_hash_or_causal_coordinate", - "literal_kerr_claim": literal_kerr_claim, - "typed_analogy_close": typed_analogy_close, - "finite_pass": finite_pass, - "ergoregion_warning": ergoregion, - "failure_horizon": horizon, - "decision": decision, - } - item["case_hash"] = hash_obj({k: v for k, v in item.items() if k != "case_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - cases = [ - state_case( - case_id="axial_fixture_closed", - description="Mostly axial load; mechanics, commitments, witness, residual, and depth gates close.", - load_norm=100.0, - torsion_norm=4.0, - mechanical_residual_l2=1.0e-12, - layer_commitment_root_present=True, - equihash_like_witness_present=True, - witness_depth=18, - residual_declared=True, - torsion_clock=5.0, - wall_clock_seconds=3600.0, - ), - state_case( - case_id="off_axis_ergoregion", - description="Off-axis load has not failed, but static vertical-load assumptions are no longer admissible.", - load_norm=100.0, - torsion_norm=24.0, - mechanical_residual_l2=2.0e-12, - layer_commitment_root_present=True, - equihash_like_witness_present=True, - witness_depth=31, - residual_declared=True, - torsion_clock=25.0, - wall_clock_seconds=120.0, - ), - state_case( - case_id="semi_jack_failure_horizon", - description="Torsion-coupled load crosses the non-recoverable admissibility horizon.", - load_norm=100.0, - torsion_norm=48.0, - mechanical_residual_l2=4.0e-12, - layer_commitment_root_present=True, - equihash_like_witness_present=True, - witness_depth=34, - residual_declared=True, - torsion_clock=44.0, - wall_clock_seconds=20.0, - ), - state_case( - case_id="missing_memory_hard_witness", - description="Mechanics close, but the proposed load assignment has no costly-to-fake witness.", - load_norm=100.0, - torsion_norm=5.0, - mechanical_residual_l2=1.0e-12, - layer_commitment_root_present=True, - equihash_like_witness_present=False, - witness_depth=12, - residual_declared=True, - torsion_clock=8.0, - wall_clock_seconds=12.0, - ), - state_case( - case_id="literal_kerr_overclaim", - description="The analogy is incorrectly promoted as literal Kerr spacetime.", - load_norm=100.0, - torsion_norm=8.0, - mechanical_residual_l2=1.0e-12, - layer_commitment_root_present=True, - equihash_like_witness_present=True, - witness_depth=10, - residual_declared=True, - torsion_clock=9.0, - wall_clock_seconds=1.0, - literal_kerr_claim=True, - ), - ] - return { - "schema": "kerr_like_load_witness_geometry_registry_v1", - "citations": CITATIONS, - "claim_boundary": ( - "Kerr-like load witness geometry is a typed admissibility atlas for " - "torsion-coupled mechanical states. It is not literal Kerr spacetime, " - "not a structural safety certificate, and not a claim that hashes or " - "Equihash terms are mechanical forces." - ), - "type_separation": { - "mechanical_plane": "real/vector/tensor equilibrium residuals with units", - "commitment_plane": "Merkle/O-AMMR bitstring commitments to layer, process, material, and sensor packets", - "witness_plane": "Equihash-like memory-hard predicate over committed state and quantized load vector", - "state_atlas_plane": "Kerr-like analogy for safe chart, ergoregion warning, and failure horizon", - "clock_plane": "wall-clock time is metadata; torsional state-advance is the causal coordinate", - }, - "time_substitution": { - "rule": "replace wall-clock t with torsional state coordinate T", - "statement": "Clock time is what the observer sees; torsion is what the structure remembers.", - "torsion_clock": "T(s)=integral(||tau|| + alpha*||load cross normal|| + beta*risk) ds", - "observed_time": "t_obs = pi_t(T, load, residual_risk, material_shadow, commitment_root)", - "hash_policy": "wall_clock_excluded_from_receipt_hash; torsion_clock_included_as state coordinate", - }, - "avoid_equation": "T*A(q)*omega - T*B(q)*delta + lambda*hash_xor_term = 0", - "admissibility_equation": ( - "A(Omega)=1[||T A(q) omega - T B(q) delta|| <= epsilon_mech] * " - "1[torsion_clock(Omega) <= T_crit] * " - "1[R_M = Commit(layer/process/material packets)] * " - "1[Pi_NK(R_M, Q(load), seed)=1] * " - "1[depth(T_witness) <= 350] * " - "1[typed_analogy_not_literal]" - ), - "kerr_like_dictionary": { - "mass_M": "load/informatic burden", - "spin_a": "torsion, twist, off-axis moment, cyclic shear", - "frame_dragging": "load-path coupling where torsion drags neighboring admissible states", - "ergoregion": "warning chart where static load assumptions fail but failure is not yet asserted", - "event_horizon": "irreversible admissibility boundary where residual risk cannot be certified away", - "ring_singularity": "degenerate failure core: crack, buckling hinge, delamination, wrong-part interface", - "geodesic": "candidate load/witness trajectory through state space", - }, - "parameters": { - "epsilon_mech": EPSILON_MECH, - "ergoregion_torsion_ratio": ERGOREGION_TORSION_RATIO, - "failure_horizon_torsion_ratio": FAILURE_HORIZON_TORSION_RATIO, - "practical_tree_fiddy_depth": PRACTICAL_TREE_FIDDY_DEPTH, - "torsion_critical": TORSION_CRITICAL, - }, - "cases": cases, - "aggregates": { - "case_count": len(cases), - "admit_safe_chart_count": sum(1 for item in cases if item["decision"] == "ADMIT_SAFE_CHART_FIXTURE"), - "hold_ergoregion_count": sum(1 for item in cases if item["decision"] == "HOLD_ERGOREGION_WARNING"), - "hold_failure_horizon_count": sum(1 for item in cases if item["decision"] == "HOLD_FAILURE_HORIZON"), - "hold_incomplete_witness_count": sum(1 for item in cases if item["decision"] == "HOLD_INCOMPLETE_WITNESS"), - "quarantine_literal_kerr_count": sum(1 for item in cases if item["decision"] == "QUARANTINE_LITERAL_KERR_OVERCLAIM"), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "kerr_like_load_witness_geometry_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_TYPED_KERR_LIKE_LOAD_WITNESS_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Kerr-Like Load Witness Geometry", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Type Separation", - "", - ] - for key, value in registry["type_separation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Torsion-Clock", - "", - f"- Rule: `{registry['time_substitution']['rule']}`", - f"- Statement: {registry['time_substitution']['statement']}", - f"- Torsion clock: `{registry['time_substitution']['torsion_clock']}`", - f"- Observed time: `{registry['time_substitution']['observed_time']}`", - f"- Hash policy: `{registry['time_substitution']['hash_policy']}`", - "", - "## Equations", - "", - f"- Avoid: `{registry['avoid_equation']}`", - f"- Admit: `{registry['admissibility_equation']}`", - "", - "## Kerr-Like Dictionary", - "", - ] - ) - for key, value in registry["kerr_like_dictionary"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Cases", - "", - "| Case | Torsion ratio | Torsion-clock | Wall-clock shadow | Mechanical close | Witness | Decision |", - "|---|---:|---:|---:|---|---|---|", - ] - ) - for item in registry["cases"]: - lines.append( - f"| `{item['case_id']}` | {item['torsion_ratio']:.3f} | " - f"{item['torsion_clock']:.3f} | {item['wall_clock_seconds']:.3f} | " - f"`{item['mechanical_close']}` | `{item['equihash_like_witness_present']}` | `{item['decision']}` |" - ) - lines.extend(["", "## Citations", ""]) - for citation in registry["citations"]: - target = citation.get("url") or citation.get("doi") or citation.get("path") or citation["status"] - lines.append(f"- `{citation['id']}`: {citation['title']} ({target}); role: `{citation['role']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hardware Materials KerrLike LoadWitness Receipt -title: Kerr-Like Load Witness Geometry -type: text/vnd.tiddlywiki - -! Kerr-Like Load Witness Geometry - -Durable runner: - -``` -4-Infrastructure/shim/kerr_like_load_witness_geometry_probe.py -``` - -Receipt: - -``` -{receipt['registry']} -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Claim Boundary - -This is a typed admissibility atlas for torsion-coupled mechanical states. It is not literal Kerr spacetime and not a structural safety certificate. - -!! Rule - -Physical equilibrium remains in the mechanical plane. Merkle/O-AMMR commits fabrication and material evidence. Equihash-like work is a witness predicate. Kerr-like language names the state atlas: safe chart, ergoregion warning, and failure horizon. - -!! Torsion Clock - -Clock time is metadata. Torsional state-advance is the causal coordinate: - -``` -T(s)=integral(||tau|| + alpha*||load cross normal|| + beta*risk) ds -``` - -The structure does not remember seconds; it remembers twist, load history, holonomy, residual strain, and damage transport. - -!! Links - -* [[GeomTREE Semi-Jack Physical Witness]] -* [[Merkle Tensegrity Load Equation Harness]] -* [[Tree Fiddy]] -* [[Invariant Dual Mechanics Supporting Materials]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/king_context_equation_retrieval_prior.py b/4-Infrastructure/shim/king_context_equation_retrieval_prior.py deleted file mode 100644 index 3af99e3f..00000000 --- a/4-Infrastructure/shim/king_context_equation_retrieval_prior.py +++ /dev/null @@ -1,138 +0,0 @@ -#!/usr/bin/env python3 -"""King Context retrieval prior for custom equation awareness. - -King Context is recorded as a retrieval-shape prior: metadata-first search, -preview-before-full-read, learned shortcuts, and ADR memory. Locally, we apply -that shape to custom equation manifests so the LLM sees the right equations -without dumping the entire forest into context. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -KING_CONTEXT_PRIOR = { - "id": "deandevz/king-context", - "url": "https://github.com/deandevz/king-context", - "role": "metadata_first_progressive_disclosure_retrieval_prior", - "boundary": "repo-readme-prior-only", - "local_use": "equation_manifest_search_preview_and_learned_shortcut_axis", - "notes": [ - "README describes a local-first retrieval layer for AI agents.", - "Core shape: search metadata before reading full content, preview roughly 400 tokens before full read, and avoid file dumps.", - "Metadata fields include keywords, use_cases, tags, and priority.", - "Includes ADR-style decision memory and learned shortcuts for repeated lookups.", - "Benchmarks claim token-efficiency improvements versus Context7; local use requires our own receipts.", - ], -} - - -RETRIEVAL_AXES = [ - { - "axis": "metadata_first_equation_search", - "payload": ["keywords", "primitive_hint", "claim_boundary", "source_path", "priority"], - "router_use": "search equation manifest by metadata before loading equation text", - "receipt_rule": "record query, matched metadata, source hash, preview hash, and full-read decision", - }, - { - "axis": "preview_before_full_equation", - "payload": ["equation_preview", "context_budget", "full_read_gate", "source_hash"], - "router_use": "show compact equation preview before pulling long docs or full source files", - "receipt_rule": "record preview bytes/tokens and whether full source was read", - }, - { - "axis": "learned_equation_shortcuts", - "payload": ["query_pattern", "equation_id", "source_path", "section_hint", "reuse_count"], - "router_use": "cache repeated equation lookups for future model/scout passes", - "receipt_rule": "shortcut must include source hash and invalidate on source hash change", - }, - { - "axis": "adr_equation_decision_memory", - "payload": ["decision", "equation_ids", "claim_boundary", "promotion_gate", "timestamp"], - "router_use": "preserve equation-routing decisions as durable ADR-like memory", - "receipt_rule": "decision entries must list equations, gates, and blocked claims", - }, -] - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an equation retrieval router. Return compact JSON with source/hash boundaries." - records = [] - for axis in receipt["retrieval_axes"]: - records.append( - chat_record( - system, - { - "task": "route_equation_retrieval_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Use this retrieval shape before loading custom equations.", - }, - { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "retrieval-shape-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - }, - ) - ) - prior = receipt["king_context_prior"] - records.append( - chat_record( - system, - { - "task": "use_king_context_prior", - "repo": prior["id"], - "role": prior["role"], - "url": prior["url"], - "instruction": "Map King Context's retrieval shape into local custom equation awareness.", - }, - { - "selected": True, - "use_as": prior["local_use"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use metadata-first preview retrieval for equations; local token savings require our own measured receipts.", - }, - ) - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/king_context_equation_retrieval_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/king_context_equation_retrieval_prior_curriculum.jsonl")) - args = parser.parse_args() - receipt = { - "schema": "king_context_equation_retrieval_prior_v1", - "claim_boundary": "King Context is a retrieval-shape prior for equation awareness; local performance must be measured.", - "king_context_prior": KING_CONTEXT_PRIOR, - "retrieval_axes": RETRIEVAL_AXES, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/language_set_manifold_registry.py b/4-Infrastructure/shim/language_set_manifold_registry.py deleted file mode 100644 index 030db491..00000000 --- a/4-Infrastructure/shim/language_set_manifold_registry.py +++ /dev/null @@ -1,798 +0,0 @@ -#!/usr/bin/env python3 -"""Build a receipt-backed registry of language-set density-marker candidates. - -This intentionally stores density markers, category geometry, and source -boundaries, not copied lexicons or protected glyph sets. RRC can use these -packets as language-set shape candidates while keeping whole-language promotion -conservative. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "language_set_manifold_graph" -REGISTRY_JSONL = OUT_DIR / "language_set_registry.jsonl" -NODES_CSV = OUT_DIR / "language_set_graph_nodes.csv" -EDGES_CSV = OUT_DIR / "language_set_graph_edges.csv" -RECEIPT_JSON = OUT_DIR / "language_set_registry_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -LANGUAGE_SETS: list[dict[str, Any]] = [ - { - "language_set_id": "LANG.ITHKUIL.DESIGN_PRECEDENT.0001", - "name": "Ithkuil", - "family": "constructed philosophical language", - "source_urls": [ - "https://www.ithkuil.net/00_intro.html", - "https://ithkuil.net/02_morpho-phonology.html", - "https://ithkuil.net/03_morphology.html", - "https://ithkuil.net/11_script.htm", - ], - "license_boundary": "Extract category geometry and morpho-phonemic design only; do not copy lexicon or script identity as payload.", - "scale_band": "official grammar documentation scope, 2026-05-08 snapshot", - "category_axes": [ - "root", - "stem", - "formative", - "adjunct", - "configuration", - "affiliation", - "perspective", - "case", - "validation", - "bias", - "tone", - "stress", - ], - "density_markers": [ - "multi-axis_morphology", - "semantic_category_portmanteau", - "morpho_phonemic_surface", - "prosodic_metadata", - ], - "compression_read": "semantic category portmanteau over dense morphology", - }, - { - "language_set_id": "LANG.KLINGON.FICTIONAL.0001", - "name": "Klingon", - "family": "fictional constructed language", - "source_urls": [ - "https://www.kli.org/muz_Segh/grammar/", - "https://en.wikipedia.org/wiki/Klingon_language", - ], - "license_boundary": "Use grammar-shape metadata and public reference pointers only; do not copy protected franchise lexicon as payload.", - "scale_band": "KLI grammar pages plus public encyclopedia overview", - "category_axes": [ - "phoneme_inventory", - "affix_order", - "noun_suffix_class", - "verb_suffix_class", - "object_verb_subject_order", - "evidential_or_attitude_marking", - ], - "density_markers": [ - "strict_affix_slotting", - "unusual_phonotactic_profile", - "object_verb_subject_order", - ], - "compression_read": "strict affix slots and unusual phonotactic profile as typed route surface", - }, - { - "language_set_id": "LANG.DOTHRAKI.FICTIONAL.0001", - "name": "Dothraki", - "family": "fictional constructed language", - "source_urls": [ - "https://en.wikipedia.org/wiki/Dothraki_language", - "https://dothraki.com/dl/dothraki101.pdf", - ], - "license_boundary": "Use typological and grammar-shape metadata only; Dothraki language material is HBO-associated and remains non-payload.", - "scale_band": "public overview and introductory PDF metadata only", - "category_axes": [ - "naturalistic_phonology", - "nominal_case", - "verb_conjugation", - "animacy_or_cultural_domain", - "word_order", - ], - "density_markers": [ - "naturalistic_morphology", - "domain_biased_lexical_field", - "case_and_verb_inflection", - ], - "compression_read": "naturalistic conlang morphology and domain-biased lexicon as HOLD-only prior", - }, - { - "language_set_id": "LANG.HIGH_VALYRIAN.FICTIONAL.0001", - "name": "High Valyrian", - "family": "fictional constructed language", - "source_urls": [ - "https://en.wikipedia.org/wiki/Valyrian_languages", - "https://wiki.languageinvention.com/index.php?title=High_Valyrian", - ], - "license_boundary": "Use grammar category metadata only; franchise lexicon and examples are not payload.", - "scale_band": "public overview metadata only", - "category_axes": [ - "noun_class", - "case", - "number", - "gender", - "verb_inflection", - "derivational_family", - ], - "density_markers": [ - "inflectional_bundle", - "noun_class_case_number", - "derivational_family", - ], - "compression_read": "large inflectional category bundle as language graph prior", - }, - { - "language_set_id": "LANG.NAVI.FICTIONAL.0001", - "name": "Na'vi", - "family": "fictional constructed language", - "source_urls": [ - "https://learnnavi.org/", - "https://kelutral.org/linguistics", - "https://en.wikipedia.org/wiki/Na%CA%BCvi_language", - ], - "license_boundary": "Use grammar topology and source pointers only; do not copy lexicon as payload.", - "scale_band": "public grammar-reference pointers and overview metadata", - "category_axes": [ - "case_marking", - "free_word_order", - "singular_dual_trial_plural", - "infix_position", - "ejectives", - "alienness_profile", - ], - "density_markers": [ - "case_rich_alignment", - "number_distinction_ladder", - "infix_position_marker", - ], - "compression_read": "case-rich, number-rich grammar as typed manifold candidate", - }, - { - "language_set_id": "LANG.QUENYA.TOLKIEN.0001", - "name": "Quenya", - "family": "Tolkienian Elvish language", - "source_urls": [ - "https://www.elvish.org/resources.html", - "https://eldamo.org/", - "https://tolkiengateway.net/wiki/Elvish", - ], - "license_boundary": "Use scholarly reference metadata and category geometry; Tolkien lexicon/glyph identity is not internal payload.", - "scale_band": "reference index metadata only, not vocabulary ingestion", - "category_axes": [ - "case", - "number", - "declension", - "phonological_history", - "script_view", - "neo_language_uncertainty", - ], - "density_markers": [ - "diachronic_layering", - "inflectional_case_system", - "script_view_separation", - ], - "compression_read": "diachronic philological layers and inflectional axes as graph topology", - }, - { - "language_set_id": "LANG.SINDARIN.TOLKIEN.0001", - "name": "Sindarin", - "family": "Tolkienian Elvish language", - "source_urls": [ - "https://www.elvish.org/resources.html", - "https://eldamo.org/", - "https://tolkiengateway.net/wiki/Elvish", - ], - "license_boundary": "Use scholarly reference metadata and mutation/category geometry only; do not copy lexicon/glyph identity as payload.", - "scale_band": "reference index metadata only, not vocabulary ingestion", - "category_axes": [ - "initial_mutation", - "case_or_relation_marking", - "number", - "phonological_history", - "script_view", - "neo_language_uncertainty", - ], - "density_markers": [ - "initial_mutation_edge", - "phonological_history_layer", - "script_view_separation", - ], - "compression_read": "mutation edges as manifold torsion and repair evidence", - }, - { - "language_set_id": "LANG.LOJBAN.LOGICAL.0001", - "name": "Lojban", - "family": "logical constructed language", - "source_urls": [ - "https://lojban.github.io/cll/", - "https://lojban.org/publications/level0/brochure-utf/grammar.html", - ], - "license_boundary": "Use published grammar and parser-shape metadata; quote/copy only under source license when explicitly allowed.", - "scale_band": "Complete Lojban Language reference and level-0 grammar overview", - "category_axes": [ - "predicate_logic", - "unambiguous_parse", - "selmaho", - "bridi", - "sumti", - "cmavo", - "rafsi", - ], - "density_markers": [ - "unambiguous_parse", - "predicate_argument_frame", - "machine_grammar_table", - ], - "compression_read": "machine-parseable logic grammar as high-proof-readiness language graph", - }, - { - "language_set_id": "LANG.TOKIPONA.MINIMAL.0001", - "name": "Toki Pona", - "family": "minimal constructed language", - "source_urls": [ - "https://www.tokipona.org/", - "https://sona.pona.la/wiki/Grammar", - ], - "license_boundary": "Use minimal-grammar metadata and source pointers; official book contents are not payload.", - "scale_band": "official site plus descriptive grammar page", - "category_axes": [ - "small_lexicon", - "analytic_grammar", - "particle_frame", - "modifier_chain", - "semantic_broadening", - "proper_name_adaptation", - ], - "density_markers": [ - "minimal_lexicon", - "particle_frame", - "context_residual_pressure", - ], - "compression_read": "minimal lexicon plus high context residual as compression negative/control pair", - }, - { - "language_set_id": "LANG.ESPERANTO.AUX.0001", - "name": "Esperanto", - "family": "international auxiliary language", - "source_urls": [ - "https://en.wikipedia.org/wiki/Esperanto", - "https://lernu.net/gramatiko", - ], - "license_boundary": "Use grammar-category metadata and source pointers; no bulk corpus ingestion in this registry.", - "scale_band": "public grammar overview metadata", - "category_axes": [ - "regular_affixation", - "part_of_speech_suffix", - "accusative_marker", - "correlative_table", - "agglutination", - ], - "density_markers": [ - "regular_affixation", - "part_of_speech_suffix", - "correlative_table", - ], - "compression_read": "regular derivational morphology as reusable category graph", - }, - { - "language_set_id": "LANG.BLISSYMBOLICS.AAC.0001", - "name": "Blissymbolics", - "family": "constructed semantic symbol system", - "source_urls": [ - "https://www.blissymbolics.org/", - "https://en.wikipedia.org/wiki/Blissymbols", - ], - "license_boundary": "Use compositional-symbol design grammar only; symbol glyphs and vocabulary require explicit permission/licensing.", - "scale_band": "public organizational overview metadata", - "category_axes": [ - "basic_character", - "compound_symbol", - "semantic_modifier", - "pictograph", - "ideograph", - "aac_context", - ], - "density_markers": [ - "semantic_radical_composition", - "compound_symbol_law", - "modifier_relation_table", - ], - "compression_read": "compositional semantic radicals as logogram graph precedent", - }, - { - "language_set_id": "CODE.APL.ARRAY.0001", - "name": "APL", - "family": "array programming language", - "source_urls": [ - "https://aplwiki.com/wiki/APL", - "https://en.wikipedia.org/wiki/APL_(programming_language)", - ], - "license_boundary": "Use language-density markers and operator-family metadata only; do not copy manuals or proprietary glyph tables.", - "scale_band": "public language overview metadata", - "category_axes": ["array_rank", "primitive_function", "operator_modifier", "tacit_composition", "shape_polymorphism"], - "density_markers": ["single_glyph_high_rank_operator", "array_wide_transform", "implicit_iteration", "rank_polymorphic_surface"], - "compression_read": "one glyph or opcode can imply an array-scale transform", - }, - { - "language_set_id": "CODE.J.ARRAY.0001", - "name": "J", - "family": "array programming language", - "source_urls": [ - "https://www.jsoftware.com/help/dictionary/contents.htm", - "https://en.wikipedia.org/wiki/J_(programming_language)", - ], - "license_boundary": "Use ASCII-density and operator-family markers only; do not copy dictionary text as payload.", - "scale_band": "public dictionary and overview metadata", - "category_axes": ["verb", "adverb", "conjunction", "rank", "fork_hook_train"], - "density_markers": ["ascii_symbol_modifier", "tacit_train", "rank_annotation", "operator_family_digraph"], - "compression_read": "ASCII-safe dense operator families as byte-native logogram precedent", - }, - { - "language_set_id": "CODE.BQN.ARRAY.0001", - "name": "BQN", - "family": "array programming language", - "source_urls": [ - "https://mlochbaum.github.io/BQN/", - "https://mlochbaum.github.io/BQN/doc/index.html", - ], - "license_boundary": "Use public design/category markers only; do not copy documentation examples as payload.", - "scale_band": "public language documentation metadata", - "category_axes": ["function", "modifier", "array_shape", "block", "train"], - "density_markers": ["modern_apl_glyph_density", "modifier_scope", "array_shape_carrier", "tacit_composition"], - "compression_read": "modern array glyph density with explicit modifier scope", - }, - { - "language_set_id": "CODE.UIUA.ARRAY_STACK.0001", - "name": "Uiua", - "family": "stack-based array programming language", - "source_urls": ["https://www.uiua.org/", "https://www.uiua.org/docs"], - "license_boundary": "Use operator-density and stack/array design markers only; do not copy docs/examples as payload.", - "scale_band": "public language documentation metadata", - "category_axes": ["stack_effect", "array_shape", "glyph_primitive", "modifier", "formatter_view"], - "density_markers": ["stack_effect_as_type", "glyph_primitive", "array_stack_fusion", "formatter_as_surface_view"], - "compression_read": "stack effects and array glyphs expose compact executable shape", - }, - { - "language_set_id": "CODE.FORTH.CONCATENATIVE.0001", - "name": "Forth", - "family": "concatenative stack programming language", - "source_urls": ["https://forth-standard.org/", "https://en.wikipedia.org/wiki/Forth_(programming_language)"], - "license_boundary": "Use standard stack-effect and word-composition markers only; source texts remain external.", - "scale_band": "public standard and overview metadata", - "category_axes": ["word", "stack_effect", "dictionary", "immediate_word", "threaded_code"], - "density_markers": ["stack_effect_signature", "dictionary_extensibility", "concatenative_composition", "threaded_code_density"], - "compression_read": "word dictionaries plus stack effects as executable manifold graph", - }, - { - "language_set_id": "CODE.PROLOG.LOGIC.0001", - "name": "Prolog", - "family": "logic programming language", - "source_urls": ["https://www.swi-prolog.org/", "https://en.wikipedia.org/wiki/Prolog"], - "license_boundary": "Use logic-programming density markers only; no corpus or library ingestion in registry.", - "scale_band": "public language overview metadata", - "category_axes": ["fact", "rule", "unification", "backtracking", "predicate_arity"], - "density_markers": ["unification_as_control_flow", "implicit_search_tree", "predicate_arity_type", "backtracking_surface"], - "compression_read": "implicit search/control encoded by facts and unification", - }, - { - "language_set_id": "CODE.HASKELL.FUNCTIONAL.0001", - "name": "Haskell", - "family": "typed functional programming language", - "source_urls": ["https://www.haskell.org/", "https://www.haskell.org/onlinereport/haskell2010/"], - "license_boundary": "Use type-system and laziness markers only; no library/code ingestion in registry.", - "scale_band": "public language report and overview metadata", - "category_axes": ["typeclass", "higher_kind", "lazy_evaluation", "monad", "algebraic_data_type"], - "density_markers": ["typeclass_dictionary", "higher_kinded_abstraction", "lazy_thunk_graph", "monadic_effect_marker"], - "compression_read": "type-level structure and laziness create dense deferred computation graph", - }, - { - "language_set_id": "CODE.LEAN.PROOF.0001", - "name": "Lean", - "family": "dependent type theorem proving language", - "source_urls": ["https://lean-lang.org/", "https://leanprover.github.io/theorem_proving_in_lean4/"], - "license_boundary": "Use proof-language density markers and local module metadata only; no external code ingestion.", - "scale_band": "public Lean 4 documentation plus local Research Stack usage", - "category_axes": ["dependent_type", "inductive_type", "theorem", "tactic", "kernel_check"], - "density_markers": ["proof_term_compression", "dependent_type_payload", "tactic_script_as_generator", "kernel_check_receipt"], - "compression_read": "proof terms and tactics separate generator from checked payload", - }, - { - "language_set_id": "CODE.RUST.SYSTEMS.0001", - "name": "Rust", - "family": "systems programming language", - "source_urls": ["https://www.rust-lang.org/", "https://doc.rust-lang.org/book/"], - "license_boundary": "Use ownership/type-system density markers only; no crate/code ingestion in registry.", - "scale_band": "public book and overview metadata", - "category_axes": ["ownership", "borrow", "lifetime", "trait", "sum_type"], - "density_markers": ["ownership_as_static_resource_graph", "lifetime_region_marker", "trait_bound_surface", "enum_match_partition"], - "compression_read": "ownership and trait bounds encode resource topology statically", - }, - { - "language_set_id": "CODE.REGEX.FORMAL.0001", - "name": "Regular Expressions", - "family": "formal pattern language", - "source_urls": ["https://en.wikipedia.org/wiki/Regular_expression", "https://www.regular-expressions.info/"], - "license_boundary": "Use formal pattern-density markers only; no pattern corpus ingestion.", - "scale_band": "public formal-language overview metadata", - "category_axes": ["concatenation", "alternation", "quantifier", "character_class", "capture_group"], - "density_markers": ["finite_automaton_surface", "quantifier_compression", "character_class_set_collapse", "capture_reference_edge"], - "compression_read": "small pattern string expands to large accepted-language set", - }, - { - "language_set_id": "CODE.BRAINFUCK.ESOLANG.0001", - "name": "Brainfuck", - "family": "esoteric programming language", - "source_urls": ["https://esolangs.org/wiki/Brainfuck", "https://en.wikipedia.org/wiki/Brainfuck"], - "license_boundary": "Use instruction-set density markers only; no program corpus ingestion.", - "scale_band": "public esolang overview metadata", - "category_axes": ["data_pointer", "cell_increment", "loop_bracket", "io_instruction", "tape_state"], - "density_markers": ["minimal_opcode_set", "tape_machine_surface", "loop_bracket_control", "extreme_context_residual"], - "compression_read": "tiny opcode alphabet shifts complexity into tape/context residual", - }, - { - "language_set_id": "CODE.MALBOLGE.ESOLANG.0001", - "name": "Malbolge", - "family": "esoteric programming language", - "source_urls": ["https://esolangs.org/wiki/Malbolge", "https://en.wikipedia.org/wiki/Malbolge"], - "license_boundary": "Use weird-encoding density markers only; no program corpus ingestion.", - "scale_band": "public esolang overview metadata", - "category_axes": ["self_modification", "ternary_memory", "instruction_encryption", "crazy_operation", "control_transfer"], - "density_markers": ["self_modifying_code", "encrypted_instruction_surface", "ternary_memory_model", "brain_hurt_density_marker"], - "compression_read": "deliberate cognitive friction exposes weird encoding axes", - }, - { - "language_set_id": "CODE.WHITESPACE.ESOLANG.0001", - "name": "Whitespace", - "family": "esoteric programming language", - "source_urls": ["https://esolangs.org/wiki/Whitespace", "https://en.wikipedia.org/wiki/Whitespace_(programming_language)"], - "license_boundary": "Use invisible-token density markers only; no program corpus ingestion.", - "scale_band": "public esolang overview metadata", - "category_axes": ["space_tab_lf_token", "stack_instruction", "heap_access", "label_control", "io_instruction"], - "density_markers": ["invisible_token_channel", "layout_as_opcode", "stack_machine_surface", "source_view_mismatch"], - "compression_read": "presentation-invisible token stream proves payload/view separation", - }, -] - - -LANGCHAIN_SPLITTER_TARGETS: list[dict[str, Any]] = [ - { - "code": "PYTHON", - "name": "Python", - "family": "indentation-sensitive programming language", - "axes": ["indentation_block", "function", "class", "decorator", "import_graph"], - "markers": ["indentation_as_block_boundary", "decorator_metadata_channel", "dunder_protocol_surface"], - }, - { - "code": "JAVA", - "name": "Java", - "family": "class-oriented programming language", - "axes": ["class", "method", "annotation", "interface", "package"], - "markers": ["annotation_metadata_channel", "nominal_type_hierarchy", "brace_block_boundary"], - }, - { - "code": "JS", - "name": "JavaScript", - "family": "prototype-based scripting language", - "axes": ["function", "closure", "prototype", "async_boundary", "module"], - "markers": ["closure_context_capture", "prototype_chain_surface", "async_callback_density"], - }, - { - "code": "TS", - "name": "TypeScript", - "family": "typed JavaScript language", - "axes": ["type_annotation", "interface", "generic", "union_type", "module"], - "markers": ["type_overlay_on_runtime_language", "structural_type_surface", "union_narrowing_marker"], - }, - { - "code": "GO", - "name": "Go", - "family": "concurrent systems language", - "axes": ["goroutine", "channel", "interface", "package", "defer"], - "markers": ["channel_concurrency_surface", "defer_control_marker", "structural_interface_density"], - }, - { - "code": "CPP", - "name": "C++", - "family": "multi-paradigm systems language", - "axes": ["template", "namespace", "class", "pointer_reference", "preprocessor"], - "markers": ["template_metaprogramming_surface", "preprocessor_dual_language", "ownership_implicit_residual"], - }, - { - "code": "C", - "name": "C", - "family": "systems programming language", - "axes": ["function", "pointer", "struct", "macro", "translation_unit"], - "markers": ["pointer_arithmetic_surface", "macro_preprocessor_channel", "manual_memory_context"], - }, - { - "code": "CSHARP", - "name": "C#", - "family": "managed typed programming language", - "axes": ["class", "attribute", "generic", "linq_query", "async_task"], - "markers": ["attribute_metadata_channel", "linq_query_surface", "managed_runtime_type_graph"], - }, - { - "code": "KOTLIN", - "name": "Kotlin", - "family": "null-safe JVM language", - "axes": ["nullability", "extension_function", "data_class", "coroutine", "sealed_class"], - "markers": ["nullability_type_marker", "extension_function_surface", "coroutine_suspension_marker"], - }, - { - "code": "SCALA", - "name": "Scala", - "family": "typed functional/object language", - "axes": ["trait", "implicit", "case_class", "pattern_match", "higher_kind"], - "markers": ["implicit_resolution_surface", "case_class_deconstruction", "typelevel_functional_density"], - }, - { - "code": "SWIFT", - "name": "Swift", - "family": "protocol-oriented programming language", - "axes": ["protocol", "optional", "extension", "enum_associated_value", "async"], - "markers": ["protocol_extension_surface", "optional_type_marker", "associated_value_enum"], - }, - { - "code": "RUBY", - "name": "Ruby", - "family": "dynamic object language", - "axes": ["block", "module", "mixin", "method_missing", "dsl_surface"], - "markers": ["block_closure_surface", "mixin_linearization", "dsl_by_metaprogramming"], - }, - { - "code": "PHP", - "name": "PHP", - "family": "template-oriented web language", - "axes": ["php_block", "html_interleave", "namespace", "class", "array_shape"], - "markers": ["template_code_interleave", "request_context_surface", "array_shape_overload"], - }, - { - "code": "PROTO", - "name": "Protocol Buffers", - "family": "schema/interface definition language", - "axes": ["message", "field_number", "service", "enum", "wire_type"], - "markers": ["field_number_wire_contract", "schema_as_codec_surface", "service_rpc_shape"], - }, - { - "code": "SOL", - "name": "Solidity", - "family": "smart-contract language", - "axes": ["contract", "modifier", "event", "storage_slot", "payable"], - "markers": ["modifier_as_gate_surface", "storage_layout_contract", "event_log_channel"], - }, - { - "code": "COBOL", - "name": "COBOL", - "family": "business record programming language", - "axes": ["division", "paragraph", "record_layout", "picture_clause", "data_division"], - "markers": ["record_layout_surface", "picture_clause_density", "division_section_boundary"], - }, - { - "code": "MARKDOWN", - "name": "Markdown", - "family": "lightweight markup language", - "axes": ["heading", "code_fence", "list", "link", "frontmatter"], - "markers": ["heading_hierarchy_surface", "code_fence_language_channel", "whitespace_formatting_residual"], - }, - { - "code": "LATEX", - "name": "LaTeX", - "family": "document and math markup language", - "axes": ["command", "environment", "math_mode", "section", "macro"], - "markers": ["macro_expansion_surface", "math_mode_channel", "environment_scope_boundary"], - }, - { - "code": "HTML", - "name": "HTML", - "family": "structured document markup language", - "axes": ["tag", "attribute", "heading", "section", "dom_tree"], - "markers": ["dom_tree_surface", "attribute_metadata_channel", "heading_section_boundary"], - }, -] - - -_existing_language_ids = {entry["language_set_id"] for entry in LANGUAGE_SETS} -for target in LANGCHAIN_SPLITTER_TARGETS: - lang_id = f"CODE.{target['code']}.LANGCHAIN_SPLITTER.0001" - if lang_id in _existing_language_ids: - continue - LANGUAGE_SETS.append( - { - "language_set_id": lang_id, - "name": target["name"], - "family": target["family"], - "source_urls": [ - "https://api.python.langchain.com/en/latest/text_splitters/", - "https://api.python.langchain.com/en/v0.0.354/text_splitter/langchain.text_splitter.Language.html", - ], - "license_boundary": ( - "Derived from LangChain language-aware splitter targets as density-marker metadata only; " - "do not copy source programs, manuals, or syntax examples as payload." - ), - "scale_band": "LangChain text splitter language enum and splitter documentation metadata", - "category_axes": target["axes"], - "density_markers": target["markers"] + ["langchain_language_aware_split_boundary"], - "compression_read": "language-aware splitting marks syntax boundaries that likely carry dense structure", - } - ) - - -def build_packet(entry: dict[str, Any]) -> dict[str, Any]: - node_types = ["root", "category", "density_marker", "surface", "portmanteau", "residual", "witness"] - edge_types = ["realizes", "scopes", "mutates", "omits", "repairs", "contrasts", "projects_to"] - nodes = [ - {"id": f"{entry['language_set_id']}:language", "type": "root", "label": entry["name"]}, - {"id": f"{entry['language_set_id']}:surface", "type": "surface", "label": "surface_views"}, - {"id": f"{entry['language_set_id']}:residual", "type": "residual", "label": "residual_policy"}, - {"id": f"{entry['language_set_id']}:witness", "type": "witness", "label": "source_and_scale_witness"}, - ] - nodes.extend( - { - "id": f"{entry['language_set_id']}:category:{axis}", - "type": "category", - "label": axis, - } - for axis in entry["category_axes"] - ) - nodes.extend( - { - "id": f"{entry['language_set_id']}:density:{marker}", - "type": "density_marker", - "label": marker, - } - for marker in entry["density_markers"] - ) - edges = [] - for axis in entry["category_axes"]: - edges.append( - { - "source": f"{entry['language_set_id']}:language", - "target": f"{entry['language_set_id']}:category:{axis}", - "type": "scopes", - } - ) - edges.append( - { - "source": f"{entry['language_set_id']}:category:{axis}", - "target": f"{entry['language_set_id']}:surface", - "type": "realizes", - } - ) - for marker in entry["density_markers"]: - edges.append( - { - "source": f"{entry['language_set_id']}:language", - "target": f"{entry['language_set_id']}:density:{marker}", - "type": "scopes", - } - ) - edges.append( - { - "source": f"{entry['language_set_id']}:density:{marker}", - "target": f"{entry['language_set_id']}:surface", - "type": "realizes", - } - ) - edges.extend( - [ - { - "source": f"{entry['language_set_id']}:surface", - "target": f"{entry['language_set_id']}:residual", - "type": "omits", - }, - { - "source": f"{entry['language_set_id']}:residual", - "target": f"{entry['language_set_id']}:surface", - "type": "repairs", - }, - { - "source": f"{entry['language_set_id']}:witness", - "target": f"{entry['language_set_id']}:language", - "type": "contrasts", - }, - { - "source": f"{entry['language_set_id']}:language", - "target": "RRCShape:LanguageSetManifoldGraph", - "type": "projects_to", - }, - ] - ) - status = "CANDIDATE" if entry.get("scale_band") and "metadata" not in entry["scale_band"].lower() else "HOLD" - packet = { - "schema": "language_set_manifold_graph_v1", - "rrc_shape": "LanguageSetManifoldGraph", - "status": status, - "admission_note": "CANDIDATE requires declared bounded scale band and replay evidence; HOLD entries are source/category/density-marker priors only.", - "node_types": node_types, - "edge_types": edge_types, - **entry, - "nodes": nodes, - "edges": edges, - } - packet["packet_hash"] = sha256_text(stable_json(packet)) - return packet - - -def csv_escape(value: Any) -> str: - text = str(value).replace('"', '""') - return f'"{text}"' - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = [build_packet(entry) for entry in LANGUAGE_SETS] - REGISTRY_JSONL.write_text("\n".join(stable_json(packet) for packet in packets) + "\n", encoding="utf-8") - - node_lines = ["language_set_id,node_id,node_type,label,status,packet_hash"] - edge_lines = ["language_set_id,source,target,edge_type,status,packet_hash"] - for packet in packets: - for node in packet["nodes"]: - node_lines.append( - ",".join( - [ - csv_escape(packet["language_set_id"]), - csv_escape(node["id"]), - csv_escape(node["type"]), - csv_escape(node["label"]), - csv_escape(packet["status"]), - csv_escape(packet["packet_hash"]), - ] - ) - ) - for edge in packet["edges"]: - edge_lines.append( - ",".join( - [ - csv_escape(packet["language_set_id"]), - csv_escape(edge["source"]), - csv_escape(edge["target"]), - csv_escape(edge["type"]), - csv_escape(packet["status"]), - csv_escape(packet["packet_hash"]), - ] - ) - ) - NODES_CSV.write_text("\n".join(node_lines) + "\n", encoding="utf-8") - EDGES_CSV.write_text("\n".join(edge_lines) + "\n", encoding="utf-8") - - status_counts: dict[str, int] = {} - for packet in packets: - status_counts[packet["status"]] = status_counts.get(packet["status"], 0) + 1 - - receipt = { - "schema": "language_set_manifold_registry_receipt_v1", - "claim_boundary": "Registry records density markers and source/category graph priors only; it does not ingest protected lexicons, copy glyphs, or prove translation quality.", - "rrc_shape": "LanguageSetManifoldGraph", - "packet_count": len(packets), - "status_counts": status_counts, - "registry_jsonl": str(REGISTRY_JSONL.relative_to(REPO)), - "nodes_csv": str(NODES_CSV.relative_to(REPO)), - "edges_csv": str(EDGES_CSV.relative_to(REPO)), - "language_set_ids": [packet["language_set_id"] for packet in packets], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/language_surface_ambiguity_negative_control.py b/4-Infrastructure/shim/language_surface_ambiguity_negative_control.py deleted file mode 100644 index f6dc39ea..00000000 --- a/4-Infrastructure/shim/language_surface_ambiguity_negative_control.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python3 -"""Language surface-ambiguity negative controls for reconstruction receipts. - -These fixtures record why surface resemblance cannot promote a replay law: -1. A false algebraic derivation may accidentally land on the right word. -2. Repeated word surfaces, such as Buffalo instances, require typed roles. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "language_surface_ambiguity_negative_control" -RECEIPT = OUT_DIR / "language_surface_ambiguity_negative_control_receipt.json" -SUMMARY = OUT_DIR / "language_surface_ambiguity_negative_control_receipt.md" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def flown_by_cancellation_fixture() -> dict[str, Any]: - raw = "grew/grown = flew/x; x = flew*grown/grew = flown" - lexicon = { - "grow": {"past": "grew", "past_participle": "grown"}, - "fly": {"past": "flew", "past_participle": "flown"}, - } - analogy_output = "flown" - lexicon_output = lexicon["fly"]["past_participle"] - return { - "id": "flown_by_cancellation", - "surface": raw, - "surface_sha256": sha256_bytes(raw.encode("utf-8")), - "claimed_operator": "string_fraction_cancellation", - "operator_lawful": False, - "analogy_output": analogy_output, - "lexicon_output": lexicon_output, - "output_matches_lexicon": analogy_output == lexicon_output, - "promotion_decision": "HOLD_DERIVATION", - "reason": ( - "The output is lexically correct, but the derivation is not lawful. " - "English morphology is not cancellative algebra over word strings." - ), - } - - -def buffalo_fixture() -> dict[str, Any]: - sentence = "Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo" - tokens = sentence.split() - typed_roles = [ - {"index": 1, "surface": "Buffalo", "role": "city_modifier", "lemma": "Buffalo"}, - {"index": 2, "surface": "buffalo", "role": "plural_noun_subject", "lemma": "buffalo"}, - {"index": 3, "surface": "Buffalo", "role": "city_modifier", "lemma": "Buffalo"}, - {"index": 4, "surface": "buffalo", "role": "plural_noun_relative_subject", "lemma": "buffalo"}, - {"index": 5, "surface": "buffalo", "role": "transitive_verb_relative", "lemma": "buffalo"}, - {"index": 6, "surface": "buffalo", "role": "transitive_verb_main", "lemma": "buffalo"}, - {"index": 7, "surface": "Buffalo", "role": "city_modifier", "lemma": "Buffalo"}, - {"index": 8, "surface": "buffalo", "role": "plural_noun_object", "lemma": "buffalo"}, - ] - replay = " ".join(item["surface"] for item in typed_roles) - normalized_surfaces = {token.lower() for token in tokens} - return { - "id": "buffalo_surface_collision", - "surface": sentence, - "surface_sha256": sha256_bytes(sentence.encode("utf-8")), - "surface_token_count": len(tokens), - "case_sensitive_surface_count": len(set(tokens)), - "case_folded_surface_count": len(normalized_surfaces), - "typed_role_count": len({item["role"] for item in typed_roles}), - "typed_roles": typed_roles, - "typed_replay_exact": replay == sentence, - "naive_surface_collapse_loses_roles": len(normalized_surfaces) < len({item["role"] for item in typed_roles}), - "promotion_decision": "HOLD_SURFACE_COLLISION", - "reason": ( - "The same visible word surface carries city-modifier, noun, and verb " - "roles. A codec may reuse the surface token only if typed roles or " - "residuals replay the original bytes exactly." - ), - } - - -def build_receipt() -> dict[str, Any]: - fixtures = [flown_by_cancellation_fixture(), buffalo_fixture()] - receipt = { - "schema": "language_surface_ambiguity_negative_control_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "purpose": "negative controls for analogy leakage and same-surface role collision", - "fixtures": fixtures, - "decision": "HOLD", - "claim_boundary": ( - "Language ambiguity negative-control receipt only. These fixtures do " - "not define an English morphology model or a compression result. They " - "record that analogy and surface reuse may propose candidates, but " - "typed replay and byte-exact recovery are the trust boundary." - ), - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# Language Surface Ambiguity Negative-Control Receipt", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Fixtures", - "", - "| Fixture | Decision | Replay / match | Reason |", - "|---|---|---|---|", - ] - for fixture in receipt["fixtures"]: - if fixture["id"] == "flown_by_cancellation": - replay = f"output_matches_lexicon={fixture['output_matches_lexicon']}" - else: - replay = f"typed_replay_exact={fixture['typed_replay_exact']}" - lines.append( - f"| `{fixture['id']}` | `{fixture['promotion_decision']}` | `{replay}` | {fixture['reason']} |" - ) - lines.extend( - [ - "", - "## Buffalo Handling", - "", - "Buffalo instances are handled as typed-role atoms, not as one reusable untyped token.", - "The surface may be shared, but each occurrence must preserve role, position,", - "case, and replay order or declare a residual.", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "fixtures": [fixture["promotion_decision"] for fixture in receipt["fixtures"]], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/lean_proof_replay_receipt.py b/4-Infrastructure/shim/lean_proof_replay_receipt.py deleted file mode 100644 index 96027563..00000000 --- a/4-Infrastructure/shim/lean_proof_replay_receipt.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for the LeanDojo/mathlib proof-boundary lane. - -LeanDojo/mathlib are route priors only. This receipt promotes only local Lean -replay evidence: targeted `lake build` plus witness `#eval` output from a tiny -extension theorem fixture. -""" - -from __future__ import annotations - -import hashlib -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -LEAN_ROOT = REPO / "0-Core-Formalism" / "lean" / "Semantics" -LEAN_FILE = LEAN_ROOT / "ExtensionScaffold" / "Compression" / "ProofReplay.lean" -OUT_DIR = REPO / "shared-data" / "data" / "lean_proof_replay" -RECEIPT = OUT_DIR / "lean_proof_replay_receipt.json" -SUMMARY = OUT_DIR / "lean_proof_replay_receipt.md" - - -EXPECTED_WITNESSES = ["94", "true", "true", "false", "false"] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def sha256_file(path: Path) -> str: - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def run_command(argv: list[str], cwd: Path) -> dict[str, Any]: - proc = subprocess.run(argv, cwd=cwd, text=True, capture_output=True, check=False) - return { - "argv": argv, - "cwd": rel(cwd), - "returncode": proc.returncode, - "stdout": proc.stdout, - "stderr": proc.stderr, - "stdout_hash": sha256_text(proc.stdout), - "stderr_hash": sha256_text(proc.stderr), - } - - -def parse_witnesses(stdout: str) -> list[str]: - witnesses: list[str] = [] - for line in stdout.splitlines(): - stripped = line.strip() - if stripped in {"true", "false"} or stripped.isdigit(): - witnesses.append(stripped) - continue - if ": " in stripped: - tail = stripped.rsplit(": ", 1)[-1].strip() - if tail in {"true", "false"} or tail.isdigit(): - witnesses.append(tail) - return witnesses - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Lean Proof Replay Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Status: `{receipt['status']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Witnesses", - "", - f"Expected: `{receipt['expected_witnesses']}`", - "", - f"Observed: `{receipt['observed_witnesses']}`", - "", - "## Commands", - "", - ] - for command in receipt["commands"]: - lines.extend( - [ - f"- `{' '.join(command['argv'])}`", - f" - returncode: `{command['returncode']}`", - f" - stdout hash: `{command['stdout_hash']}`", - f" - stderr hash: `{command['stderr_hash']}`", - ] - ) - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - build = run_command(["lake", "build", "ExtensionScaffold.Compression.ProofReplay"], LEAN_ROOT) - eval_run = run_command(["lake", "env", "lean", "ExtensionScaffold/Compression/ProofReplay.lean"], LEAN_ROOT) - observed = parse_witnesses(eval_run["stdout"]) - build_pass = build["returncode"] == 0 - witness_pass = observed == EXPECTED_WITNESSES - status = "ADMIT_FIXTURE" if build_pass and witness_pass else "HOLD_DIAGNOSTIC" - receipt = { - "schema": "lean_proof_replay_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "lean_file": rel(LEAN_FILE), - "lean_file_hash": sha256_file(LEAN_FILE), - "module": "ExtensionScaffold.Compression.ProofReplay", - "commands": [build, eval_run], - "expected_witnesses": EXPECTED_WITNESSES, - "observed_witnesses": observed, - "build_pass": build_pass, - "witness_pass": witness_pass, - "status": status, - "decision": "HOLD", - "claim_boundary": ( - "LeanDojo/mathlib proof-boundary fixture only. External proof corpora " - "may propose obligations, but promotion requires local Lean replay. " - "This receipt proves only a tiny local admission predicate fixture, " - "not any external theorem, compression benchmark, or mathlib coverage claim." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "status": receipt["status"], - "observed_witnesses": observed, - }, - indent=2, - sort_keys=True, - ) - ) - return 0 if build_pass and witness_pass else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/llm_compression_architecture_prior_metaprobe.py b/4-Infrastructure/shim/llm_compression_architecture_prior_metaprobe.py deleted file mode 100644 index 115fe01a..00000000 --- a/4-Infrastructure/shim/llm_compression_architecture_prior_metaprobe.py +++ /dev/null @@ -1,225 +0,0 @@ -#!/usr/bin/env python3 -"""LLM compression architecture priors for n-space/metaprobe tuning. - -These records keep the useful part of prompt, latent, and weight-compression -research: routing coordinates for a local compression-first LLM stack. They do -not claim any model is "intelligent" by itself, and they do not bypass receipts. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -COMPRESSION_AXES = [ - { - "axis": "symbolic_metalanguage", - "payload": ["logic_symbol", "constraint", "operator", "scope", "semantic_receipt"], - "router_use": "compress verbose instructions into logogram/symbolic control cells", - "receipt_rule": "round-trip through expanded natural-language paraphrase and task-result check", - }, - { - "axis": "prompt_token_pruning", - "payload": ["token_importance", "budget", "query_focus", "retained_span", "compression_ratio"], - "router_use": "strip low-information prompt volume before routing into expensive models", - "receipt_rule": "record source bytes, retained bytes, compression ratio, and downstream quality delta", - }, - { - "axis": "information_bottleneck", - "payload": ["input_information", "target_information", "latent_state", "mutual_information_proxy", "distortion"], - "router_use": "tune representations toward useful lossy compression instead of string memorization", - "receipt_rule": "record proxy metric, retained-task score, and distortion/error budget", - }, - { - "axis": "system_class_compression", - "payload": ["statistical_structure", "indexical_structure", "semantic_basin", "reconstruction_block", "policy_boundary"], - "router_use": "preserve reusable statistical structure while refusing exact source reconstruction as a routing goal", - "receipt_rule": "store source provenance, no-verbatim reconstruction rule, and similarity/audit check", - }, - { - "axis": "weight_palette_transcoding", - "payload": ["weight_distribution", "exponent_palette", "codebook", "decode_path", "memory_bandwidth"], - "router_use": "treat model weights as hardware-visible compressed palettes for inference surfaces", - "receipt_rule": "record lossless/lossy status, decode cost, memory saved, and benchmark delta", - }, - { - "axis": "proxy_compressed_views", - "payload": ["raw_bytes", "compressed_view", "alignment_loss", "decompress_hint", "view_id"], - "router_use": "train the model to align raw text with compressed metaprobe/logogram views", - "receipt_rule": "record compressor version, raw/compressed pairs, and equivalence-test prompts", - }, - { - "axis": "math_display_list_canonicalization", - "payload": ["latex_source", "parse_node", "layout_box", "display_list", "render_receipt"], - "router_use": "canonicalize math/logogram strings into renderer-independent symbolic display cells", - "receipt_rule": "record parser version, source hash, display-list hash, and optional PNG/SVG/PDF render hash", - }, -] - - -VERIFIED_COMPRESSION_PRIORS = [ - { - "id": "MetaGlyph", - "role": "symbolic_metalanguage_prompt_compression", - "boundary": "paper-prior-only", - "use_as": "symbolic_logogram_prompt_axis", - "source": "Semantic Compression of LLM Instructions via Symbolic Metalanguages", - "url": "https://arxiv.org/abs/2601.07354", - "notes": "Use mathematical/logical symbols as dense instruction primitives; candidate prior for custom logogram language.", - }, - { - "id": "LLMLingua", - "role": "coarse_to_fine_prompt_compression", - "boundary": "paper/project-prior-only", - "use_as": "prompt_budget_and_token_importance_axis", - "source": "LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models", - "url": "https://arxiv.org/abs/2310.05736", - "notes": "Budget controller and token-level prompt compression; useful baseline for metaprobe text compression.", - }, - { - "id": "LLMLingua-2", - "role": "task_agnostic_prompt_compression", - "boundary": "paper/project-prior-only", - "use_as": "task_agnostic_token_classifier_axis", - "source": "LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression", - "url": "https://arxiv.org/abs/2403.12968", - "notes": "Treats compression as token classification distilled for general prompt compression.", - }, - { - "id": "SelectiveContext", - "role": "context_redundancy_pruning", - "boundary": "paper-prior-only", - "use_as": "context_redundancy_filter_axis", - "source": "Compressing Context to Enhance Inference Efficiency of Large Language Models", - "url": "https://arxiv.org/abs/2310.06201", - "notes": "Prunes redundant context; good negative-control baseline against richer metaprobe compression.", - }, - { - "id": "LanguageModelingIsCompression", - "role": "prediction_compression_equivalence", - "boundary": "paper-prior-only", - "use_as": "lm_as_compressor_objective_axis", - "source": "Language Modeling Is Compression", - "url": "https://arxiv.org/abs/2309.10668", - "notes": "Useful objective lens: language modeling and compression are linked; not a direct model recipe.", - }, - { - "id": "InformationBottleneckLLM", - "role": "representation_information_flow_lens", - "boundary": "paper-prior-only", - "use_as": "latent_information_bottleneck_axis", - "source": "Exploring Information Processing in Large Language Models: Insights from Information Bottleneck Theory", - "url": "https://arxiv.org/abs/2501.00999", - "notes": "Use as measurement lens for retained information versus distortion in latent/control surfaces.", - }, - { - "id": "ProxyCompression", - "role": "raw_and_compressed_view_training", - "boundary": "paper-prior-only", - "use_as": "raw_compressed_alignment_axis", - "source": "Proxy Compression for Language Modeling", - "url": "https://arxiv.org/abs/2602.04289", - "notes": "Train against raw bytes and externally compressed views; close match to metaprobe/logogram pairs.", - }, - { - "id": "Unweight", - "role": "lossless_mlp_weight_compression", - "boundary": "paper-prior-only", - "use_as": "weight_palette_transcoding_axis", - "source": "Unweight: Lossless MLP Weight Compression for LLM Inference", - "url": "https://research.cloudflare.com/papers/unweight-2026.pdf", - "notes": "Hardware-level prior for compressed BF16/MLP weight movement; verify implementation before any speed claim.", - }, - { - "id": "RaTeX", - "role": "rust_native_latex_math_display_list_renderer", - "boundary": "repo-prior-only", - "use_as": "math_logogram_canonicalization_axis", - "source": "RaTeX: KaTeX-compatible math rendering engine in pure Rust", - "url": "https://github.com/erweixin/RaTeX", - "notes": "Useful as a Rust-native LaTeX/math/chemistry token canonicalizer into display lists; render artifacts can serve as visual receipts.", - }, -] - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a compression-first LLM router. Return compact JSON with receipt boundaries." - records: list[dict[str, Any]] = [] - for axis in receipt["compression_axes"]: - records.append( - chat_record( - system, - { - "task": "route_compression_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Use this as a metaprobe/logogram compression coordinate.", - }, - { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "compression-coordinate-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - }, - ) - ) - for prior in receipt["verified_compression_priors"]: - records.append( - chat_record( - system, - { - "task": "use_llm_compression_prior", - "model_or_lens": prior["id"], - "role": prior["role"], - "source": prior["source"], - "instruction": "Explain how this tunes the local LLM pipeline without replacing receipts.", - }, - { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use as architecture/corpus coordinate; verify with compression ratio, quality delta, and source receipts.", - }, - ) - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/llm_compression_architecture_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/llm_compression_architecture_prior_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "llm_compression_architecture_prior_receipt_v1", - "claim_boundary": "Compression architecture priors tune prompt/logogram/metaprobe routing; they are not local performance proof.", - "compression_axes": COMPRESSION_AXES, - "verified_compression_priors": VERIFIED_COMPRESSION_PRIORS, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/ln2_ladder_chart_invariant_probe.py b/4-Infrastructure/shim/ln2_ladder_chart_invariant_probe.py deleted file mode 100644 index 39055e75..00000000 --- a/4-Infrastructure/shim/ln2_ladder_chart_invariant_probe.py +++ /dev/null @@ -1,337 +0,0 @@ -#!/usr/bin/env python3 -"""ln(2) ladder-chart invariant probe. - -This fixture records a small, exact example of the ladder model: one invariant -object, ln(2), observed through several lawful charts. It also records the -projection guardrails: infinite sum/integral exchange and endpoint convergence -must be justified, and symbolic/rhythmic shadows are not proof by themselves. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "ln2_ladder_chart_invariant" -REGISTRY = OUT_DIR / "ln2_ladder_chart_invariant_registry.json" -RECEIPT = OUT_DIR / "ln2_ladder_chart_invariant_receipt.json" -SUMMARY = OUT_DIR / "ln2_ladder_chart_invariant.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "ln2 Ladder Chart Invariant.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" / "observer_chart_projection_guardrail_receipt.json", - REPO / "shared-data" / "data" / "collatz_ladder_shadow_filter" / "collatz_ladder_shadow_filter_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", - REPO / "6-Documentation" / "docs" / "specs" / "FORWARD_FOUNDATION_EQUATION_COMPILER.md", -] - -LN2 = math.log(2.0) -TOL = 1.0e-12 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def alternating_harmonic(partials: int) -> float: - return sum(((-1.0) ** n) / (n + 1) for n in range(partials)) - - -def unit_interval_midpoint(samples: int) -> float: - step = 1.0 / samples - return sum(step / (1.0 + (i + 0.5) * step) for i in range(samples)) - - -def chart_entry( - chart_id: str, - projection: str, - evaluation: str, - value: float | None, - decision: str, - guardrail: str, - role: str = "lawful_chart", -) -> dict[str, Any]: - error = None if value is None else abs(value - LN2) - entry = { - "chart_id": chart_id, - "projection": projection, - "evaluation": evaluation, - "value": value, - "target_ln2": LN2, - "abs_error": error, - "decision": decision, - "guardrail": guardrail, - "role": role, - } - entry["chart_hash"] = hash_obj({k: v for k, v in entry.items() if k != "chart_hash"}) - return entry - - -def build_registry() -> dict[str, Any]: - charts = [ - chart_entry( - "direct_antiderivative", - "I=int_0^infty 1/(1+e^x) dx", - "[-log(1+e^-x)]_0^infty = log(2)", - LN2, - "ADMIT_EXACT_CHART", - "improper endpoint limit must be declared", - ), - chart_entry( - "exponential_substitution", - "u=e^-x maps x in [0,infty) to u in [1,0]", - "I=int_0^1 1/(1+u) du = log(2)", - LN2, - "ADMIT_EXACT_CHART", - "orientation reversal and dx=-du/u must be paid", - ), - chart_entry( - "geometric_expansion", - "1/(1+e^x)=e^-x/(1+e^-x)=sum_n (-1)^n e^{-(n+1)x}", - "termwise integral gives alternating harmonic series", - alternating_harmonic(100000), - "ADMIT_LIMIT_CHART_WITH_JUSTIFICATION", - "exchange of infinite sum and improper integral requires convergence justification", - ), - chart_entry( - "alternating_harmonic_series", - "sum_{n=0}^infty (-1)^n/(n+1)", - "Taylor log(1+z) at z=1 gives log(2)", - alternating_harmonic(100000), - "ADMIT_LIMIT_CHART_WITH_ENDPOINT_GUARD", - "endpoint z=1 needs Abel/alternating convergence justification", - ), - chart_entry( - "unit_interval_integral", - "int_0^1 du/(1+u)", - "midpoint numerical replay over unit chart", - unit_interval_midpoint(1_000_000), - "ADMIT_NUMERIC_REPLAY_CHART", - "numeric replay is evidence only; exact chart is antiderivative", - ), - chart_entry( - "parametric_derivative_trick", - "d/da log(1+a) at a=1 or eta/xi-style derivative route", - "derivative/integral parameter chart returns log(2) when domain is declared", - LN2, - "ADMIT_SYMBOLIC_CHART", - "parameter domain and derivative-exchange law must be declared", - ), - chart_entry( - "music_sheet_shadow", - "rhythmic or symbolic shadow of the transform ladder", - "no numeric proof; chart is mnemonic/projection only", - None, - "HOLD_SHADOW_ONLY", - "rhythm-shadow cannot certify the invariant without a lawful adapter", - role="observer_shadow", - ), - ] - return { - "schema": "ln2_ladder_chart_invariant_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "invariant": { - "id": "ln2", - "canonical_object": "I=int_0^infty 1/(1+e^x) dx = ln(2)", - "numeric_value": LN2, - "short_form": "ln 2 is the invariant; the integral, series, substitution, and rhythm are charts.", - }, - "claim_boundary": ( - "Toy ladder-chart invariant fixture only. The admitted charts show " - "that one object can survive lawful projection changes. The fixture " - "does not admit arbitrary symbolic shadows, unjustified exchange of " - "limits, or rhythm/music notation as proof." - ), - "canonical_statement": ( - "A lawful object survives chart changes. A bad derivation loses the " - "invariant during projection. A good proof states the adapter, pays " - "the endpoint and limit-exchange costs, and shows every shadow belongs " - "to the same object." - ), - "ladder_mapping": { - "continuous_field_chart": "improper integral", - "substitution_chart": "unit interval projection", - "packetized_recursive_chart": "geometric expansion", - "parity_torsion_chart": "alternating harmonic signs", - "invariant_attractor": "ln(2)", - "observer_shadow": "music/rhythm notation, held until adapter exists", - }, - "charts": charts, - "chart_root": hash_obj([chart["chart_hash"] for chart in charts]), - "aggregates": { - "chart_count": len(charts), - "admit_count": sum(1 for chart in charts if chart["decision"].startswith("ADMIT")), - "hold_count": sum(1 for chart in charts if chart["decision"].startswith("HOLD")), - "max_numeric_error": max(chart["abs_error"] or 0.0 for chart in charts), - "missing_source_count": sum(1 for path in SOURCE_REFS if not path.exists()), - }, - "decision": "ADMIT_LN2_LADDER_CHART_FIXTURE_WITH_SHADOW_HOLD", - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "ln2_ladder_chart_invariant_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "chart_root": registry["chart_root"], - "aggregates": registry["aggregates"], - "decision": registry["decision"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# ln2 Ladder Chart Invariant", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Chart root: `{registry['chart_root']}`", - "", - registry["claim_boundary"], - "", - "## Invariant", - "", - registry["invariant"]["short_form"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Charts", - "", - "| Chart | Projection | Decision | Guardrail |", - "|---|---|---|---|", - ] - for chart in registry["charts"]: - lines.append( - f"| {chart['chart_id']} | {chart['projection']} | " - f"{chart['decision']} | {chart['guardrail']} |" - ) - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Charts: `{registry['aggregates']['chart_count']}`", - f"- Admitted charts: `{registry['aggregates']['admit_count']}`", - f"- Held shadows: `{registry['aggregates']['hold_count']}`", - f"- Max numeric error: `{registry['aggregates']['max_numeric_error']}`", - f"- Missing sources: `{registry['aggregates']['missing_source_count']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "created: 20260509000000000", - "modified: 20260509000000000", - "tags: ResearchStack Ladder Invariant Chart Guardrail", - "title: ln2 Ladder Chart Invariant", - "type: text/vnd.tiddlywiki", - "", - "! ln2 Ladder Chart Invariant", - "", - registry["invariant"]["short_form"], - "", - f"* Decision: `{receipt['decision']}`", - f"* Receipt hash: `{receipt['receipt_hash']}`", - f"* Chart root: `{registry['chart_root']}`", - f"* Registry: `{rel(REGISTRY)}`", - f"* Receipt: `{rel(RECEIPT)}`", - "", - "!! Rule", - "", - "A lawful object survives chart changes; an observer shadow stays HOLD until its adapter and proof obligations are declared.", - "", - "```", - registry["invariant"]["canonical_object"], - "```", - "", - "!! Charts", - "", - "| Chart | Decision | Guardrail |", - "|---|---|---|", - ] - for chart in registry["charts"]: - lines.append(f"| {chart['chart_id']} | {chart['decision']} | {chart['guardrail']} |") - lines.extend( - [ - "", - "!! Links", - "", - "* [[Observer Chart Projection Guardrail]]", - "* [[Collatz Ladder Shadow Filter]]", - "* [[Underverse Variant Accounting]]", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "chart_root": registry["chart_root"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/logogram_dna_codec_receipt.py b/4-Infrastructure/shim/logogram_dna_codec_receipt.py deleted file mode 100644 index 6aacc6b0..00000000 --- a/4-Infrastructure/shim/logogram_dna_codec_receipt.py +++ /dev/null @@ -1,511 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for the logogram-DNA codec objective. - -This folds the DNA-style codec filter through Omindirection and GCCL: glyphs -are not payloads, adapters are not authorities, and every compressed codon-like -atom must pass payload, direction, chirality, placement, residual, receipt, and -adapter gates before it can participate in replay. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "logogram_dna_codec" -RECEIPT = OUT_DIR / "logogram_dna_codec_receipt.json" -TABLE = OUT_DIR / "logogram_dna_codec_table.jsonl" -SUMMARY = OUT_DIR / "logogram_dna_codec_receipt.md" - - -OBJECTIVE_PACKET = { - "name": "Logogram-DNA Codec Objective", - "core_map": "S = Repair_R(Regulate_B_DeltaG(Replay_Pi(Gamma)))", - "atom_shape": "a_i = (p_i,h_i,d_i,chi_i,phi_i,x_i,tau_i,r_i,g_i,rho_i,delta_i)", - "payload_separation": "payload != glyph != rendered layout", - "lossless_gate": "Decode(Gamma,Pi,R) == S", - "substitution_gate": "SubOK(t_i -> g_i) iff Recover(g_i,r_i,rho_i) == t_i", - "binding_gate": "B_logo = 1/(1 + exp((DeltaG_bind - mu_logo)/(k_B T_decode)))", - "admission_gate": ( - "Adm(a_i) = F_payload F_direction F_chirality F_phase F_placement " - "F_residual F_receipt F_adapter" - ), - "score": ( - "J_logo_DNA = |D|+|Gamma|+|Pi|+|R|+|H_receipts| - lambda_1 G_sub " - "+ lambda_2 H(R)+lambda_3 DeltaG_bind/(k_B T_decode)+lambda_4 U_route " - "+ lambda_5 L_human+lambda_6 C_collision+lambda_7 C_HOLD+lambda_8 C_QUARANTINE" - ), - "native_phrase": ( - "A lawful logogram is a compressed symbolic codon whose meaning is not " - "the glyph, but the receipted replay path back to its canonical payload." - ), -} - - -@dataclass(frozen=True) -class AtomFixture: - symbol_id: str - semantic_key: str - canonical_payload_kernel: str - kernel_args: dict[str, Any] - glyph: str - direction: str - chirality: str - phase: int - placement: dict[str, Any] - residual_sidecar: dict[str, Any] | None - receipt_present: bool - adapter_canonical: bool - decision: str - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - atoms: list[AtomFixture] - protocol: dict[str, Any] - negative_control: bool - notes: str - - -FIXTURES = [ - Fixture( - fixture_id="lawful_logogram_codons_admit", - protocol={"decoder": "logogram_codons_v1", "repair": "sidecar_v1"}, - negative_control=False, - notes="Three receipted atoms replay long canonical payloads through compact glyph codons.", - atoms=[ - AtomFixture( - symbol_id="lg-trig-identity", - semantic_key="math.trig.pythagorean", - canonical_payload_kernel="repeat_literal", - kernel_args={"literal": "sin(x)^2+cos(x)^2=1;", "count": 96}, - glyph="LG1", - direction="forward", - chirality="none", - phase=0, - placement={"kind": "row", "coord": [0, 0], "liberties": 2, "captured_by": None, "territory": "math"}, - residual_sidecar=None, - receipt_present=True, - adapter_canonical=True, - decision="ACCEPT", - ), - AtomFixture( - symbol_id="lg-euler", - semantic_key="math.euler.identity", - canonical_payload_kernel="repeat_literal", - kernel_args={"literal": "exp(i*pi)+1=0;", "count": 96}, - glyph="LG2", - direction="forward", - chirality="none", - phase=0, - placement={"kind": "row", "coord": [1, 0], "liberties": 2, "captured_by": None, "territory": "math"}, - residual_sidecar=None, - receipt_present=True, - adapter_canonical=True, - decision="ACCEPT", - ), - AtomFixture( - symbol_id="lg-newton", - semantic_key="physics.force", - canonical_payload_kernel="repeat_literal", - kernel_args={"literal": "F=m*a;", "count": 192}, - glyph="LG3", - direction="forward", - chirality="none", - phase=0, - placement={"kind": "row", "coord": [2, 0], "liberties": 2, "captured_by": None, "territory": "physics"}, - residual_sidecar=None, - receipt_present=True, - adapter_canonical=True, - decision="ACCEPT", - ), - ], - ), - Fixture( - fixture_id="auto_direction_hold", - protocol={"decoder": "logogram_codons_v1", "repair": "sidecar_v1"}, - negative_control=True, - notes="Recoverable atom is held because promoted direction cannot remain auto.", - atoms=[ - AtomFixture( - symbol_id="lg-auto", - semantic_key="math.auto.direction", - canonical_payload_kernel="repeat_literal", - kernel_args={"literal": "a+b=b+a;", "count": 16}, - glyph="LGA", - direction="auto", - chirality="none", - phase=0, - placement={"kind": "row", "coord": [0, 0], "liberties": 1, "captured_by": None, "territory": "math"}, - residual_sidecar=None, - receipt_present=True, - adapter_canonical=True, - decision="HOLD", - ) - ], - ), - Fixture( - fixture_id="semantic_tear_quarantine", - protocol={"decoder": "logogram_codons_v1", "repair": "sidecar_v1"}, - negative_control=True, - notes="Adapter mutation and missing receipt route the atom to quarantine.", - atoms=[ - AtomFixture( - symbol_id="lg-tear", - semantic_key="math.semantic.tear", - canonical_payload_kernel="repeat_literal", - kernel_args={"literal": "x=x;", "count": 8}, - glyph="LGT", - direction="forward", - chirality="right", - phase=90, - placement={"kind": "quarantine", "coord": [0, 0], "liberties": 0, "captured_by": None, "territory": "tear"}, - residual_sidecar=None, - receipt_present=False, - adapter_canonical=False, - decision="QUARANTINE", - ) - ], - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def counted_size(obj: Any) -> int: - return len(stable_json(obj).encode("utf-8")) - - -def entropy_bytes(data: bytes) -> float: - if not data: - return 0.0 - counts: dict[int, int] = {} - for value in data: - counts[value] = counts.get(value, 0) + 1 - total = len(data) - return -sum((count / total) * math.log(count / total, 2) for count in counts.values()) - - -def payload(atom: AtomFixture) -> str: - if atom.canonical_payload_kernel == "repeat_literal": - return str(atom.kernel_args["literal"]) * int(atom.kernel_args["count"]) - raise ValueError(f"unsupported payload kernel {atom.canonical_payload_kernel}") - - -def chiral_ok(chirality: str, phase: int) -> bool: - if not (0 <= phase < 360): - return False - if chirality == "none": - return phase == 0 - if chirality == "left": - return 0 <= phase < 180 - if chirality == "right": - return 180 <= phase < 360 - if chirality == "ambidextrous": - return True - return False - - -def placement_ok(atom: AtomFixture) -> bool: - placement = atom.placement - if placement.get("kind") == "quarantine": - return atom.decision == "QUARANTINE" - if placement.get("kind") == "board" and int(placement.get("liberties", 0)) == 0 and placement.get("captured_by") is None: - return False - return "coord" in placement and "territory" in placement - - -def atom_receipt(atom: AtomFixture, atom_payload: str) -> dict[str, Any]: - payload_hash = sha256_text(atom_payload) - source_hash = sha256_text(stable_json({"symbol_id": atom.symbol_id, "semantic_key": atom.semantic_key})) - atom_hash = sha256_text( - stable_json( - { - "symbol_id": atom.symbol_id, - "glyph": atom.glyph, - "payload_hash": payload_hash, - "direction": atom.direction, - "chirality": atom.chirality, - "phase": atom.phase, - "placement": atom.placement, - } - ) - ) - receipt_hash = sha256_text(stable_json({"payload_hash": payload_hash, "source_hash": source_hash, "atom_hash": atom_hash})) - return { - "payload_hash": payload_hash, - "source_hash": source_hash, - "atom_hash": atom_hash, - "receipt_hash": receipt_hash, - } - - -def recover_atom(atom: AtomFixture) -> str: - return payload(atom) - - -def binding_delta(atoms: list[AtomFixture]) -> float: - if len(atoms) < 2: - return 0.0 - total = 0.0 - for left, right in zip(atoms, atoms[1:]): - if left.placement.get("territory") == right.placement.get("territory"): - total -= 1.0 - else: - total += 0.5 - if left.direction == right.direction and left.direction != "auto": - total -= 0.5 - else: - total += 1.0 - if chiral_ok(left.chirality, left.phase) and chiral_ok(right.chirality, right.phase): - total -= 0.25 - else: - total += 2.0 - return total - - -def route_curvature(atoms: list[AtomFixture]) -> float: - if len(atoms) < 3: - return 0.0 - coords = [atom.placement.get("coord", [0, 0]) for atom in atoms] - vectors = [] - for left, right in zip(coords, coords[1:]): - vectors.append((right[0] - left[0], right[1] - left[1])) - return float(sum(abs(bx - ax) + abs(by - ay) for (ax, ay), (bx, by) in zip(vectors, vectors[1:]))) - - -def validate_atom(atom: AtomFixture, atom_payload: str) -> dict[str, Any]: - receipt = atom_receipt(atom, atom_payload) if atom.receipt_present else {} - residual_ok = atom.residual_sidecar is not None or recover_atom(atom) == atom_payload - gates = { - "payload": bool(atom_payload), - "direction": atom.direction in {"forward", "reverse", "neutral"}, - "chirality_phase": chiral_ok(atom.chirality, atom.phase), - "placement": placement_ok(atom), - "residual": residual_ok, - "receipt": atom.receipt_present and bool(receipt.get("payload_hash")), - "adapter": atom.adapter_canonical, - } - if atom.decision == "QUARANTINE": - admitted_decision = "QUARANTINE" - elif all(gates.values()): - admitted_decision = "ACCEPT" - else: - admitted_decision = "HOLD" - return { - "symbol_id": atom.symbol_id, - "semantic_key": atom.semantic_key, - "glyph": atom.glyph, - "payload_hash": sha256_text(atom_payload), - "receipt": receipt, - "gates": gates, - "declared_decision": atom.decision, - "computed_decision": admitted_decision, - "gate_pass": all(gates.values()) and admitted_decision == atom.decision, - } - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - atom_payloads = [payload(atom) for atom in fixture.atoms] - source = "".join(atom_payloads) - recovered = "".join(recover_atom(atom) for atom in fixture.atoms if atom.decision != "QUARANTINE") - exact_replay = recovered == source - atom_results = [validate_atom(atom, atom_payload) for atom, atom_payload in zip(fixture.atoms, atom_payloads)] - has_quarantine = any(result["computed_decision"] == "QUARANTINE" for result in atom_results) - hold_count = sum(1 for result in atom_results if result["computed_decision"] == "HOLD") - accepted_count = sum(1 for result in atom_results if result["computed_decision"] == "ACCEPT") - - dictionary_payload = {"objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET))} - gamma_payload = [ - { - "symbol_id": atom.symbol_id, - "glyph": atom.glyph, - "direction": atom.direction, - "chirality": atom.chirality, - "phase": atom.phase, - "placement": atom.placement, - "payload_kernel": atom.canonical_payload_kernel, - "kernel_args": atom.kernel_args, - } - for atom in fixture.atoms - ] - residual_payload = { - "sidecars": [ - {"symbol_id": atom.symbol_id, "sidecar": atom.residual_sidecar} - for atom in fixture.atoms - if atom.residual_sidecar is not None - ] - } - receipt_payload = { - "receipts": [ - result["receipt"].get("receipt_hash") - for result in atom_results - if result["receipt"].get("receipt_hash") - ] - } - protocol_payload = fixture.protocol - - raw_bytes = len(source.encode("utf-8")) - dictionary_bytes = counted_size(dictionary_payload) - gamma_bytes = counted_size(gamma_payload) - protocol_bytes = counted_size(protocol_payload) - residual_bytes = counted_size(residual_payload) if residual_payload["sidecars"] else 0 - receipt_bytes = counted_size(receipt_payload) - counted_bytes = dictionary_bytes + gamma_bytes + protocol_bytes + residual_bytes + receipt_bytes - substitution_gain = raw_bytes - (gamma_bytes + residual_bytes + receipt_bytes) - byte_gain = raw_bytes - counted_bytes - delta_g = binding_delta(fixture.atoms) - curvature = route_curvature(fixture.atoms) - residual_entropy = entropy_bytes(stable_json(residual_payload).encode("utf-8")) if residual_bytes else 0.0 - - if fixture.negative_control: - status = "HOLD_DIAGNOSTIC" if not has_quarantine else "QUARANTINE_DIAGNOSTIC" - elif exact_replay and byte_gain > 0 and hold_count == 0 and not has_quarantine: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "notes": fixture.notes, - "negative_control": fixture.negative_control, - "source_hash": sha256_text(source), - "recovered_hash": sha256_text(recovered), - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "exact_replay": exact_replay, - "raw_bytes": raw_bytes, - "dictionary_bytes": dictionary_bytes, - "gamma_bytes": gamma_bytes, - "protocol_bytes": protocol_bytes, - "residual_bytes": residual_bytes, - "receipt_bytes": receipt_bytes, - "counted_bytes": counted_bytes, - "substitution_gain": substitution_gain, - "byte_gain": byte_gain, - "delta_g_bind_analog": delta_g, - "route_curvature": curvature, - "residual_entropy_bits_per_byte": residual_entropy, - "accepted_count": accepted_count, - "hold_count": hold_count, - "quarantine_count": sum(1 for result in atom_results if result["computed_decision"] == "QUARANTINE"), - "atom_results": atom_results, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Logogram-DNA Codec Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Objective", - "", - f"`{OBJECTIVE_PACKET['core_map']}`", - "", - f"`{OBJECTIVE_PACKET['admission_gate']}`", - "", - f"`{OBJECTIVE_PACKET['substitution_gate']}`", - "", - "## Fixtures", - "", - "| Fixture | Status | Exact replay | Byte gain | Accepted | HOLD | Quarantine |", - "|---|---|---:|---:|---:|---:|---:|", - ] - for result in receipt["results"]: - lines.append( - f"| {result['fixture_id']} | {result['status']} | {result['exact_replay']} | " - f"{result['byte_gain']} | {result['accepted_count']} | {result['hold_count']} | " - f"{result['quarantine_count']} |" - ) - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "logogram_dna_codec_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "objective_packet": OBJECTIVE_PACKET, - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "source_specs": [ - "6-Documentation/docs/specs/OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", - "6-Documentation/docs/specs/GCCL_ENCODING_CONTRACT.md", - "6-Documentation/docs/provenance/DNA_CODEC_FILTER_SOURCES.cff", - ], - "fixture_count": len(results), - "table": rel(TABLE), - "summary": rel(SUMMARY), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Logogram-DNA codec objective prior only. It combines local Omindirection " - "and GCCL law gates with the DNA-filtered repair/binding analogy. It " - "does not claim biological modeling, renderer correctness, global " - "logogram compression, Hutter performance, or external proof status." - ), - } - receipt["receipt_hash"] = sha256_text( - stable_json( - { - k: v - for k, v in receipt.items() - if k not in {"receipt_hash", "generated_at_utc"} - } - ) - ) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/magnetic_derivative_kernel_probe.py b/4-Infrastructure/shim/magnetic_derivative_kernel_probe.py deleted file mode 100644 index d11d9473..00000000 --- a/4-Infrastructure/shim/magnetic_derivative_kernel_probe.py +++ /dev/null @@ -1,335 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed magnetic derivative kernel probe. - -This probe adds a small magnetic-domain route surface for the cross-domain -kernel library. It admits only exact local algebra/vector fixtures and keeps -field-equation, gauge, boundary, and material-model claims in HOLD. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from fractions import Fraction -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "magnetic_derivative_kernels" -REGISTRY = OUT_DIR / "magnetic_derivative_kernel_registry.json" -RECEIPT = OUT_DIR / "magnetic_derivative_kernel_receipt.json" -SUMMARY = OUT_DIR / "magnetic_derivative_kernel.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - REPO / "shared-data/data/cross_domain_kernel_adapters/cross_domain_kernel_adapter_registry_receipt.json", -] - - -Vector = tuple[Fraction, Fraction, Fraction] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def frac_payload(value: Fraction | Vector) -> Any: - if isinstance(value, tuple): - return [frac_payload(item) for item in value] - return {"numerator": value.numerator, "denominator": value.denominator, "decimal": float(value)} - - -def mn(a: Fraction, b: Fraction) -> Fraction: - return (a - b) / (a + b) - - -def magnetic_pressure(B: Fraction, mu: Fraction) -> Fraction: - return B * B / (2 * mu) - - -def d_magnetic_pressure_dB(B: Fraction, mu: Fraction) -> Fraction: - return B / mu - - -def scalar_dipole_force(moment: Fraction, dBdx: Fraction) -> Fraction: - return moment * dBdx - - -def cross(a: Vector, b: Vector) -> Vector: - ax, ay, az = a - bx, by, bz = b - return ay * bz - az * by, az * bx - ax * bz, ax * by - ay * bx - - -def lorentz_magnetic_force(charge: Fraction, velocity: Vector, B: Vector) -> Vector: - vxB = cross(velocity, B) - return tuple(charge * item for item in vxB) # type: ignore[return-value] - - -def check_equal(name: str, compressed: Any, direct: Any) -> dict[str, Any]: - return { - "name": name, - "compressed": frac_payload(compressed), - "direct": frac_payload(direct), - "pass": compressed == direct, - } - - -def entry( - *, - entry_id: str, - kernel_opcode: str, - magnetic_role: str, - compressed_form: str, - expanded_form: str, - checks: list[dict[str, Any]], - decision: str, - residual_policy: str, -) -> dict[str, Any]: - item = { - "entry_id": entry_id, - "kernel_opcode": kernel_opcode, - "magnetic_role": magnetic_role, - "compressed_form": compressed_form, - "expanded_form": expanded_form, - "checks": checks, - "all_checks_pass": all(check.get("pass", False) for check in checks) if checks else None, - "decision": decision, - "residual_policy": residual_policy, - "claim_boundary": "magnetic route fixture only; not a Maxwell solver or material model", - } - item["entry_hash"] = hash_obj({k: v for k, v in item.items() if k != "entry_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - B = Fraction(3) - mu = Fraction(2) - moment = Fraction(5) - dBdx = Fraction(7, 3) - charge = Fraction(2) - velocity: Vector = (Fraction(1), Fraction(2), Fraction(3)) - field: Vector = (Fraction(5), Fraction(-1), Fraction(4)) - mu1 = Fraction(3) - mu2 = Fraction(8) - - entries = [ - entry( - entry_id="magnetic_pressure_derivative", - kernel_opcode="DERIV_QUADRATIC", - magnetic_role="local derivative of magnetic pressure density with respect to field magnitude", - compressed_form="d/dB [B^2/(2*mu)] = B/mu", - expanded_form="P_B = B^2/(2*mu)", - checks=[ - check_equal( - "dP_dB_B3_mu2", - d_magnetic_pressure_dB(B, mu), - (magnetic_pressure(B + Fraction(1), mu) - magnetic_pressure(B - Fraction(1), mu)) / 2, - ) - ], - decision="ACCEPT_DERIVATIVE_FIXTURE", - residual_policy="scalar uniform-mu fixture only; spatial fields require gradient, units, geometry, and boundary receipts", - ), - entry( - entry_id="scalar_dipole_gradient_force", - kernel_opcode="DERIV_LINEAR", - magnetic_role="one-dimensional projection of dipole force from potential gradient", - compressed_form="F_x = m * dB/dx", - expanded_form="U = -m*B(x); F_x = -dU/dx", - checks=[check_equal("dipole_force_m5_dBdx7_3", scalar_dipole_force(moment, dBdx), Fraction(35, 3))], - decision="ACCEPT_DERIVATIVE_FIXTURE", - residual_policy="1D aligned-dipole fixture only; vector dipole orientation and material response require adapter receipts", - ), - entry( - entry_id="lorentz_magnetic_cross_product", - kernel_opcode="CROSS_PRODUCT", - magnetic_role="magnetic part of Lorentz force as a vector cross-product kernel", - compressed_form="F_B = q * cross(v,B)", - expanded_form="F = q*(v x B)", - checks=[ - check_equal( - "lorentz_q2_v123_B5neg14", - lorentz_magnetic_force(charge, velocity, field), - (Fraction(22), Fraction(22), Fraction(-22)), - ) - ], - decision="ACCEPT_VECTOR_FIXTURE", - residual_policy="magnetic-only vector fixture; electric field term, relativistic conventions, and units remain adapter data", - ), - entry( - entry_id="permeability_boundary_contrast", - kernel_opcode="MN_REFLECT", - magnetic_role="two-permeability boundary contrast candidate", - compressed_form="Gamma_mu = MN(mu2,mu1)", - expanded_form="Gamma_mu = (mu2-mu1)/(mu2+mu1)", - checks=[check_equal("mn_mu8_3", mn(mu2, mu1), Fraction(5, 11))], - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="exact contrast only; electromagnetic boundary conditions, orientation, and sign convention still require domain receipt", - ), - entry( - entry_id="alfven_speed_route", - kernel_opcode="ANALYTIC_SQRT_RATIO", - magnetic_role="MHD speed route with square-root denominator", - compressed_form="v_A = B / sqrt(mu*rho)", - expanded_form="Alfven speed candidate", - checks=[], - decision="HOLD_ANALYTIC_ADAPTER", - residual_policy="requires square-root precision, units, density/permeability source, and MHD assumptions", - ), - entry( - entry_id="faraday_time_derivative", - kernel_opcode="CURL_TIME_DERIVATIVE", - magnetic_role="field equation route for induction", - compressed_form="curl(E) = -dB/dt", - expanded_form="Faraday induction law candidate", - checks=[], - decision="HOLD_FIELD_EQUATION", - residual_policy="requires orientation, gauge/sign convention, boundary conditions, discretization, and source receipt", - ), - entry( - entry_id="ampere_current_derivative", - kernel_opcode="CURL_SOURCE_ADAPTER", - magnetic_role="field equation route for current source", - compressed_form="curl(B) -> mu*J plus displacement-current policy", - expanded_form="Ampere-Maxwell route candidate", - checks=[], - decision="HOLD_FIELD_EQUATION", - residual_policy="requires unit system, displacement-current policy, material model, boundary conditions, and source receipt", - ), - entry( - entry_id="magnetic_susceptibility_contrast", - kernel_opcode="MN", - magnetic_role="bounded contrast over two susceptibilities or magnetizations", - compressed_form="MN(chi2,chi1) or MN(M2,M1)", - expanded_form="relative contrast between magnetic response lanes", - checks=[], - decision="HOLD_MATERIAL_ADAPTER", - residual_policy="requires material law, linearity range, hysteresis policy, and measurement receipt", - ), - ] - return { - "schema": "magnetic_derivative_kernel_registry_v1", - "claim_boundary": ( - "Magnetic derivative route registry only. Exact scalar/vector algebra " - "fixtures may be accepted, but Maxwell/MHD/material claims stay HOLD " - "until units, gauge/sign conventions, boundary conditions, source data, " - "and residual policies are receipted." - ), - "canonical_statement": ( - "Magnetic equations expose reusable derivative, contrast, and cross-product " - "kernels, but field truth lives behind adapter closure." - ), - "entries": entries, - "entry_count": len(entries), - "status_counts": { - status: sum(1 for item in entries if item["decision"] == status) - for status in sorted({item["decision"] for item in entries}) - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - accepted_statuses = {"ACCEPT_DERIVATIVE_FIXTURE", "ACCEPT_VECTOR_FIXTURE", "ACCEPT_KERNEL_ADAPTER"} - accepted_checks_pass = all( - item["all_checks_pass"] is True - for item in registry["entries"] - if item["decision"] in accepted_statuses - ) - receipt = { - "schema": "magnetic_derivative_kernel_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry_path": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "entry_count": registry["entry_count"], - "status_counts": registry["status_counts"], - "accepted_checks_pass": accepted_checks_pass, - "decision": "HOLD_MAGNETIC_DOMAIN_WITH_ACCEPTED_FIXTURES" if accepted_checks_pass else "HOLD_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Magnetic Derivative Kernel Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Entry Table", - "", - "| Entry | Kernel | Decision | Role |", - "|---|---|---|---|", - ] - for item in registry["entries"]: - lines.append( - f"| `{item['entry_id']}` | `{item['kernel_opcode']}` | " - f"`{item['decision']}` | {item['magnetic_role']} |" - ) - lines.extend(["", "## Guardrail", "", registry["claim_boundary"]]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": registry["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/mass_equation_distill_receipt.py b/4-Infrastructure/shim/mass_equation_distill_receipt.py deleted file mode 100644 index 39f22510..00000000 --- a/4-Infrastructure/shim/mass_equation_distill_receipt.py +++ /dev/null @@ -1,307 +0,0 @@ -#!/usr/bin/env python3 -"""Emit a receipt for the unified mass-equation distill artifact. - -This is a coverage and claim-boundary receipt, not a theorem verifier. It -records what the mass-equation parquet contains, what it excludes, and the -hashes needed to treat the artifact as an input to later OMCF/PIST routing. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import pyarrow.compute as pc -import pyarrow.parquet as pq - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_INPUT = REPO / "3-Mathematical-Models/equations_parquet_tagged/mass_equations_unified.parquet" -DEFAULT_TIMESTAMPED = ( - REPO / "3-Mathematical-Models/equations_parquet_tagged/mass_equations_20260504_134248.parquet" -) -DEFAULT_COMPRESSED = ( - REPO / "3-Mathematical-Models/equations_compressed/mass_equations_20260504_134248.compressed" -) -DEFAULT_RECEIPT = ( - REPO / "3-Mathematical-Models/equations_parquet_tagged/mass_equations_unified_receipt.json" -) -DEFAULT_SUMMARY = ( - REPO / "3-Mathematical-Models/equations_parquet_tagged/mass_equations_unified_receipt.md" -) - -CLAIM_BOUNDARY = ( - "Mass-equation distill coverage receipt. This records extracted/tagged " - "equation surfaces and structural features for routing, compression, and " - "candidate-law selection. It is not a theorem verification result, not a " - "complete all-mathematics corpus claim, not a benchmark result, and not a " - "claim that every row is semantically a physical mass law." -) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def sha256_file(path: Path) -> str | None: - if not path.exists(): - return None - h = hashlib.sha256() - with path.open("rb") as handle: - for chunk in iter(lambda: handle.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def value_counts(table: Any, column: str, limit: int = 20) -> list[dict[str, Any]]: - if column not in table.column_names: - return [] - counts = pc.value_counts(table[column]).to_pylist() - normalized = [ - {"value": "" if item["values"] is None else item["values"], "count": int(item["counts"])} - for item in counts - ] - normalized.sort(key=lambda row: (-row["count"], str(row["value"]))) - return normalized[:limit] - - -def bool_sum(table: Any, column: str) -> int | None: - if column not in table.column_names: - return None - return int(pc.sum(table[column].cast("int64")).as_py() or 0) - - -def unique_count(table: Any, column: str) -> int | None: - if column not in table.column_names: - return None - return int(pc.count_distinct(table[column]).as_py() or 0) - - -def null_count(table: Any, column: str) -> int | None: - if column not in table.column_names: - return None - return int(pc.sum(pc.is_null(table[column]).cast("int64")).as_py() or 0) - - -def equation_hash_stats(table: Any) -> dict[str, Any]: - if "equation" not in table.column_names: - return {"available": False} - hashes: Counter[str] = Counter() - duplicate_rows = 0 - for value in table["equation"].to_pylist(): - text = "" if value is None else str(value) - digest = hashlib.sha256(text.encode("utf-8")).hexdigest() - hashes[digest] += 1 - if hashes[digest] > 1: - duplicate_rows += 1 - return { - "available": True, - "unique_equation_hashes": len(hashes), - "duplicate_equation_rows_by_hash": duplicate_rows, - "top_duplicate_hashes": [ - {"sha256": digest, "count": count} - for digest, count in hashes.most_common(10) - if count > 1 - ], - } - - -def compressed_metadata(path: Path) -> dict[str, Any]: - if not path.exists(): - return {"exists": False} - raw = path.read_bytes()[:4096] - metadata: dict[str, Any] = {"exists": True, "path": rel(path), "bytes": path.stat().st_size} - if len(raw) >= 4: - n = int.from_bytes(raw[:4], "big") - if 0 < n <= len(raw) - 4: - try: - metadata["header"] = json.loads(raw[4 : 4 + n].decode("utf-8")) - except Exception as exc: # pragma: no cover - diagnostic only - metadata["header_parse_error"] = f"{type(exc).__name__}: {exc}" - return metadata - - -def build_receipt(input_path: Path, timestamped_path: Path, compressed_path: Path) -> dict[str, Any]: - parquet = pq.ParquetFile(input_path) - table = pq.read_table(input_path) - - feature_columns = [ - "has_operator", - "has_derivative", - "has_integral", - "has_sum", - "has_product", - "has_fraction", - "has_matrix", - "has_vector", - "has_function_call", - "has_subscript", - "has_superscript", - "has_sqrt", - "is_short", - "is_medium", - "is_long", - ] - numeric_columns = ["confidence", "length", "num_operators", "num_variables"] - - receipt: dict[str, Any] = { - "schema": "mass_equation_distill_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "runner": rel(Path(__file__)), - "claim_boundary": CLAIM_BOUNDARY, - "decision": "COVERAGE_RECEIPT_ONLY", - "primary_artifact": { - "path": rel(input_path), - "bytes": input_path.stat().st_size, - "sha256": sha256_file(input_path), - "rows": parquet.metadata.num_rows, - "row_groups": parquet.metadata.num_row_groups, - "columns": table.column_names, - }, - "related_artifacts": { - "timestamped_mass_parquet": { - "path": rel(timestamped_path), - "exists": timestamped_path.exists(), - "bytes": timestamped_path.stat().st_size if timestamped_path.exists() else None, - "sha256": sha256_file(timestamped_path), - "rows": pq.ParquetFile(timestamped_path).metadata.num_rows - if timestamped_path.exists() - else None, - }, - "compressed_mass_equations": { - **compressed_metadata(compressed_path), - "sha256": sha256_file(compressed_path), - }, - }, - "coverage": { - "source_type_counts": value_counts(table, "source_type"), - "domain_counts": value_counts(table, "domain"), - "category_counts": value_counts(table, "category"), - "unified_pattern_counts": value_counts(table, "unified_pattern"), - "pattern_counts": value_counts(table, "pattern"), - "unique_counts": { - column: unique_count(table, column) - for column in ["equation_id", "equation", "source", "title", "doi", "category", "domain"] - if column in table.column_names - }, - "null_counts": { - column: null_count(table, column) - for column in ["equation_id", "equation", "source", "title", "doi", "category", "domain"] - if column in table.column_names - }, - }, - "feature_counts": { - column: bool_sum(table, column) - for column in feature_columns - if column in table.column_names - }, - "numeric_summary": {}, - "equation_hash_stats": equation_hash_stats(table), - "exclusions": [ - "No proof replay or Lean build was run by this receipt.", - "No byte-exact compression benchmark is claimed.", - "Rows are extracted/tagged equation surfaces, not authoritative mathematical truth.", - "The source coverage observed here is arXiv-only for the unified parquet.", - "Domain value 'unknown' remains unresolved and must not be promoted as typed coverage.", - "The compressed artifact is an older timestamped mass-equation artifact, not a compressed form of every unified row unless a separate replay receipt proves it.", - ], - "admission_notes": [ - "Suitable as a routing prior for OMCF/PIST candidate-law discovery.", - "Suitable as a corpus for mass-pattern feature statistics.", - "Not suitable as a final total-math distill claim without source inventory, replay, and negative controls.", - ], - } - - for column in numeric_columns: - if column in table.column_names: - arr = table[column] - receipt["numeric_summary"][column] = { - "min": pc.min(arr).as_py(), - "max": pc.max(arr).as_py(), - "mean": pc.mean(arr).as_py(), - } - - stable_receipt = dict(receipt) - stable_receipt.pop("generated_at_utc", None) - receipt_preimage = json.dumps(stable_receipt, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - receipt["receipt_hash_sha256"] = hashlib.sha256(receipt_preimage.encode("utf-8")).hexdigest() - return receipt - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - primary = receipt["primary_artifact"] - related = receipt["related_artifacts"] - lines = [ - "# Mass Equation Distill Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash_sha256']}`", - "", - "## Claim Boundary", - "", - receipt["claim_boundary"], - "", - "## Primary Artifact", - "", - f"- Path: `{primary['path']}`", - f"- Rows: `{primary['rows']}`", - f"- Bytes: `{primary['bytes']}`", - f"- SHA256: `{primary['sha256']}`", - "", - "## Coverage Snapshot", - "", - "Top domains:", - ] - for row in receipt["coverage"]["domain_counts"][:8]: - lines.append(f"- `{row['value']}`: {row['count']}") - lines.extend(["", "Top categories:"]) - for row in receipt["coverage"]["category_counts"][:8]: - lines.append(f"- `{row['value']}`: {row['count']}") - lines.extend(["", "Feature counts:"]) - for key, value in receipt["feature_counts"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Related Artifacts", - "", - f"- Timestamped parquet: `{related['timestamped_mass_parquet']['path']}` " - f"({related['timestamped_mass_parquet']['rows']} rows)", - f"- Compressed artifact: `{related['compressed_mass_equations'].get('path')}`", - "", - "## Exclusions", - "", - ] - ) - for item in receipt["exclusions"]: - lines.append(f"- {item}") - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--input", type=Path, default=DEFAULT_INPUT) - parser.add_argument("--timestamped", type=Path, default=DEFAULT_TIMESTAMPED) - parser.add_argument("--compressed", type=Path, default=DEFAULT_COMPRESSED) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--summary", type=Path, default=DEFAULT_SUMMARY) - args = parser.parse_args() - - receipt = build_receipt(args.input, args.timestamped, args.compressed) - args.receipt.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, args.summary) - print(json.dumps({"receipt": rel(args.receipt), "summary": rel(args.summary), "hash": receipt["receipt_hash_sha256"]}, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/mass_number_transform_registry.py b/4-Infrastructure/shim/mass_number_transform_registry.py deleted file mode 100644 index 83969f96..00000000 --- a/4-Infrastructure/shim/mass_number_transform_registry.py +++ /dev/null @@ -1,427 +0,0 @@ -#!/usr/bin/env python3 -"""Build a receipt-backed registry of Mass-Number-able transforms. - -The registry captures equation families that can be represented by a bounded -contrast - - MN(a,b) = (a-b)/(a+b) - -plus a small transform opcode. Exact rational identities are admitted as -kernel fixtures. Analytic transforms such as entropy are recorded as HOLD -until a numerical/error policy is declared. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from fractions import Fraction -from pathlib import Path -from typing import Any, Callable - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "mass_number_transform_registry" -REGISTRY = OUT_DIR / "mass_number_transform_registry.json" -RECEIPT = OUT_DIR / "mass_number_transform_registry_receipt.json" -SUMMARY = OUT_DIR / "mass_number_transform_registry.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/foundation_forward_equation_compiler/foundation_forward_equation_compiler_receipt.json", - REPO / "shared-data/data/buoyancy_added_mass_mobius/buoyancy_added_mass_mobius_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def frac_payload(value: Fraction | tuple[Fraction, ...]) -> Any: - if isinstance(value, tuple): - return [frac_payload(item) for item in value] - return {"numerator": value.numerator, "denominator": value.denominator, "decimal": float(value)} - - -def mn(a: Fraction, b: Fraction) -> Fraction: - return (a - b) / (a + b) - - -def ratio_from_mn(x: Fraction) -> Fraction: - return (Fraction(1) + x) / (Fraction(1) - x) - - -def mobius_mn(x: Fraction, k: Fraction) -> Fraction: - return Fraction(2) * x / ((Fraction(1) + k) + (Fraction(1) - k) * x) - - -def split_pair(total: Fraction, x: Fraction) -> tuple[Fraction, Fraction]: - return total * (Fraction(1) + x) / 2, total * (Fraction(1) - x) / 2 - - -def reduced_pair(total: Fraction, x: Fraction) -> Fraction: - return total * (Fraction(1) - x * x) / 4 - - -def pair_product(total: Fraction, x: Fraction) -> Fraction: - return total * total * (Fraction(1) - x * x) / 4 - - -def weighted_blend(x: Fraction, a_value: Fraction, b_value: Fraction) -> Fraction: - return (a_value + b_value) / 2 + x * (a_value - b_value) / 2 - - -def binary_p(x: Fraction) -> Fraction: - return (Fraction(1) + x) / 2 - - -def binary_q(x: Fraction) -> Fraction: - return (Fraction(1) - x) / 2 - - -def transmit_power(x: Fraction) -> Fraction: - return Fraction(1) - x * x - - -def elastic_1d(x: Fraction, v1: Fraction, v2: Fraction) -> tuple[Fraction, Fraction]: - return x * v1 + (Fraction(1) - x) * v2, (Fraction(1) + x) * v1 - x * v2 - - -def direct_mobius(a: Fraction, b: Fraction, k: Fraction) -> Fraction: - return (a - b) / (a + k * b) - - -def direct_reduced(a: Fraction, b: Fraction) -> Fraction: - return a * b / (a + b) - - -def direct_product(a: Fraction, b: Fraction) -> Fraction: - return a * b - - -def direct_weighted_blend(w1: Fraction, w2: Fraction, a_value: Fraction, b_value: Fraction) -> Fraction: - return (w1 * a_value + w2 * b_value) / (w1 + w2) - - -def direct_elastic_1d(m1: Fraction, m2: Fraction, v1: Fraction, v2: Fraction) -> tuple[Fraction, Fraction]: - total = m1 + m2 - return ( - ((m1 - m2) / total) * v1 + (2 * m2 / total) * v2, - (2 * m1 / total) * v1 + ((m2 - m1) / total) * v2, - ) - - -def check_equal(name: str, compressed: Any, direct: Any) -> dict[str, Any]: - return { - "name": name, - "compressed": frac_payload(compressed), - "direct": frac_payload(direct), - "pass": compressed == direct, - } - - -def transform( - *, - transform_id: str, - opcode: str, - family: str, - canonical_form: str, - expanded_form: str, - args_saved: list[str], - domains: list[str], - checks: list[dict[str, Any]], - decision: str = "ACCEPT_KERNEL", - residual_policy: str = "none for exact algebraic identity; domain restrictions still apply", -) -> dict[str, Any]: - item = { - "transform_id": transform_id, - "opcode": opcode, - "family": family, - "canonical_form": canonical_form, - "expanded_form": expanded_form, - "args_saved": args_saved, - "domains": domains, - "checks": checks, - "all_checks_pass": all(check.get("pass", False) for check in checks) if checks else None, - "decision": decision, - "residual_policy": residual_policy, - } - item["transform_hash"] = hash_obj({k: v for k, v in item.items() if k != "transform_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - a = Fraction(5) - b = Fraction(3) - x = mn(a, b) - total = a + b - k = Fraction(2, 3) - w1, w2 = Fraction(7), Fraction(5) - wx = mn(w1, w2) - av, bv = Fraction(11), Fraction(2) - m1, m2 = Fraction(5), Fraction(3) - mx = mn(m1, m2) - v1, v2 = Fraction(7), Fraction(-1) - z2, z1 = Fraction(9), Fraction(4) - zx = mn(z2, z1) - - transforms = [ - transform( - transform_id="MN_contrast", - opcode="MN", - family="contrast", - canonical_form="x = MN(a,b)", - expanded_form="x = (a-b)/(a+b)", - args_saved=["shared bounded contrast replaces repeated difference-over-sum forms"], - domains=["positive_pair_ratios", "impedance_contrasts", "density_contrasts", "binary_weights"], - checks=[check_equal("MN_5_3", x, Fraction(1, 4))], - ), - transform( - transform_id="ratio_from_MN", - opcode="MN_RATIO_INV", - family="inverse_ratio", - canonical_form="a/b = (1+x)/(1-x)", - expanded_form="x = (a-b)/(a+b)", - args_saved=["reconstructs raw ratio from one bounded scalar"], - domains=["positive_pair_ratios", "rehydration"], - checks=[check_equal("ratio_rehydrate_5_3", ratio_from_mn(x), a / b)], - ), - transform( - transform_id="MN_mobius_load", - opcode="MN_MOBIUS_LOAD", - family="geometry_loaded_denominator", - canonical_form="F_k(x) = 2x / ((1+k)+(1-k)x)", - expanded_form="F_k(a,b) = (a-b)/(a+k*b)", - args_saved=["replace a,b,k denominator family with x,k"], - domains=["added_mass", "geometry_loaded_inertia", "interface_load_correction"], - checks=[check_equal("mobius_5_3_k_2_3", mobius_mn(x, k), direct_mobius(a, b, k))], - ), - transform( - transform_id="pair_split", - opcode="MN_SPLIT", - family="two_body_decomposition", - canonical_form="a,b = S/2*(1+x), S/2*(1-x)", - expanded_form="S=a+b; x=(a-b)/(a+b)", - args_saved=["store total plus MN instead of two raw parts"], - domains=["two_body_mass", "binary_weights", "mixture_components"], - checks=[check_equal("split_5_3", split_pair(total, x), (a, b))], - ), - transform( - transform_id="pair_reduced", - opcode="MN_REDUCED", - family="product_over_sum", - canonical_form="ab/(a+b) = S/4*(1-x^2)", - expanded_form="ab/(a+b)", - args_saved=["reduced mass / parallel pair from total plus MN"], - domains=["reduced_mass", "parallel_resistors", "harmonic_pair_reduction"], - checks=[check_equal("reduced_5_3", reduced_pair(total, x), direct_reduced(a, b))], - ), - transform( - transform_id="pair_product", - opcode="MN_PAIR_PRODUCT", - family="pair_product", - canonical_form="ab = S^2/4*(1-x^2)", - expanded_form="ab", - args_saved=["pair product from total plus MN"], - domains=["two_body_mass", "variance_like_pair_terms", "coupling_products"], - checks=[check_equal("product_5_3", pair_product(total, x), direct_product(a, b))], - ), - transform( - transform_id="weighted_blend", - opcode="MN_BLEND", - family="weighted_average", - canonical_form="blend = mid(A,B) + x*halfdiff(A,B)", - expanded_form="(w1*A+w2*B)/(w1+w2)", - args_saved=["replace two weights with one bounded weight contrast"], - domains=["center_of_mass", "expert_blend", "mixture_interpolation", "confidence_merge"], - checks=[check_equal("blend_w7_5_A11_B2", weighted_blend(wx, av, bv), direct_weighted_blend(w1, w2, av, bv))], - ), - transform( - transform_id="reflection", - opcode="MN_REFLECT", - family="boundary_reflection", - canonical_form="Gamma = MN(Z2,Z1)", - expanded_form="Gamma = (Z2-Z1)/(Z2+Z1)", - args_saved=["one contrast scalar for impedance boundary"], - domains=["acoustic_impedance", "transmission_lines", "elastic_waves", "normal_fresnel", "thermal_effusivity"], - checks=[check_equal("reflect_9_4", zx, (z2 - z1) / (z2 + z1))], - ), - transform( - transform_id="transmit_power", - opcode="MN_TRANSMIT_POWER", - family="boundary_transmission", - canonical_form="T_power = 1 - x^2", - expanded_form="1 - ((A-B)/(A+B))^2", - args_saved=["power transmission from reflected MN scalar"], - domains=["boundary_transmission", "impedance_matching", "binary_survival"], - checks=[check_equal("transmit_9_4", transmit_power(zx), Fraction(1) - ((z2 - z1) / (z2 + z1)) ** 2)], - ), - transform( - transform_id="binary_probability", - opcode="MN_BINARY_P", - family="binary_choice", - canonical_form="P(a)= (1+x)/2; P(b)= (1-x)/2", - expanded_form="P(a)=a/(a+b); P(b)=b/(a+b)", - args_saved=["two-class normalized weights from one bounded scalar"], - domains=["binary_classifier", "route_selection", "expert_choice", "accept_hold_competition"], - checks=[ - check_equal("binary_p_5_3", binary_p(x), a / (a + b)), - check_equal("binary_q_5_3", binary_q(x), b / (a + b)), - ], - ), - transform( - transform_id="elastic_collision_1d", - opcode="MN_ELASTIC_1D", - family="two_body_collision", - canonical_form="v1'=x*v1+(1-x)*v2; v2'=(1+x)*v1-x*v2", - expanded_form="standard 1D elastic collision using m1,m2", - args_saved=["replace two mass coefficients with one mass contrast"], - domains=["elastic_collision", "two_body_mechanics", "mass_weighted_exchange"], - checks=[check_equal("elastic_m5_3_v7_neg1", elastic_1d(mx, v1, v2), direct_elastic_1d(m1, m2, v1, v2))], - ), - transform( - transform_id="binary_entropy_MN", - opcode="MN_BINARY_ENTROPY", - family="information_measure", - canonical_form="H2(x)=H2((1+x)/2)", - expanded_form="-p*log2(p)-(1-p)*log2(1-p)", - args_saved=["entropy over binary choice reuses MN scalar"], - domains=["route_uncertainty", "expert_selection", "receipt_gain_scoring"], - checks=[], - decision="HOLD_ANALYTIC", - residual_policy="requires log base, numeric precision, and approximation receipt before admission", - ), - ] - return { - "schema": "mass_number_transform_registry_v1", - "claim_boundary": ( - "Registry of algebraic Mass-Number-able transform families. ACCEPT_KERNEL " - "entries passed exact rational identity checks; HOLD_ANALYTIC entries are " - "routing candidates only until numerical/error policies are receipted. " - "This is a compression/logogram registry, not a physics theorem atlas." - ), - "canonical_statement": ( - "Mass-Number-able equations are equations whose apparent complexity is " - "mostly a bounded contrast, weighted blend, pair reduction, reflection, " - "binary choice, or geometry-loaded Mobius transform." - ), - "base_logogram": "MN(a,b) = (a-b)/(a+b)", - "ratio_identity": "MN(a,b) = tanh(0.5*ln(a/b)) for positive a,b", - "transforms": transforms, - "transform_count": len(transforms), - "status_counts": { - status: sum(1 for item in transforms if item["decision"] == status) - for status in sorted({item["decision"] for item in transforms}) - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - all_accept_checks_pass = all( - item["all_checks_pass"] is True - for item in registry["transforms"] - if item["decision"] == "ACCEPT_KERNEL" - ) - receipt = { - "schema": "mass_number_transform_registry_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry_path": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "transform_count": registry["transform_count"], - "status_counts": registry["status_counts"], - "all_accept_kernel_checks_pass": all_accept_checks_pass, - "decision": "ACCEPT_REGISTRY_WITH_HOLD_ANALYTIC" if all_accept_checks_pass else "HOLD_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Mass Number Transform Registry", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "```text", - registry["base_logogram"], - registry["ratio_identity"], - "```", - "", - "## Transform Table", - "", - "| Opcode | Family | Decision | Canonical form | Domains |", - "|---|---|---|---|---|", - ] - for item in registry["transforms"]: - lines.append( - f"| `{item['opcode']}` | `{item['family']}` | `{item['decision']}` | " - f"`{item['canonical_form']}` | {', '.join(item['domains'])} |" - ) - lines.extend(["", "## Check Summary", "", "| Opcode | Checks | Pass |", "|---|---:|---:|"]) - for item in registry["transforms"]: - lines.append(f"| `{item['opcode']}` | {len(item['checks'])} | {item['all_checks_pass']} |") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": registry["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_logogram_corpus_substitution_benchmark.py b/4-Infrastructure/shim/math_logogram_corpus_substitution_benchmark.py deleted file mode 100755 index 806e953d..00000000 --- a/4-Infrastructure/shim/math_logogram_corpus_substitution_benchmark.py +++ /dev/null @@ -1,120 +0,0 @@ -#!/usr/bin/env python3 -"""Run substitution auditing over a bounded corpus slice.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - -import math_logogram_substitution_audit as audit - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -DEFAULT_CORPUS = REPO / "shared-data" / "corpora" / "enwik8" -DEFAULT_RECEIPT = SHIM / "math_logogram_enwik8_slice_substitution_receipt.json" - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def chunks(text: str, chunk_chars: int, limit: int) -> list[str]: - out: list[str] = [] - cursor = 0 - while cursor < len(text) and len(out) < limit: - piece = text[cursor : cursor + chunk_chars].strip() - cursor += chunk_chars - if piece: - out.append(piece) - return out - - -def summarize(tests: list[dict[str, Any]]) -> dict[str, Any]: - raw_bytes = sum(int(test["compression"]["raw_bytes"]) for test in tests) - canonical_bytes = sum(int(test["compression"]["canonical_bytes"]) for test in tests) - payload_bytes = sum(int(test["compression"]["surface_payload_bytes"]) for test in tests) - json_sidecar = sum(int(test["compression"]["sidecar_bytes_json_compact"]) for test in tests) - packed_sidecar = sum(int(test["compression"]["sidecar_bytes_packed_estimate"]) for test in tests) - total_packed = payload_bytes + packed_sidecar - return { - "sample_count": len(tests), - "raw_bytes": raw_bytes, - "canonical_bytes": canonical_bytes, - "payload_bytes": payload_bytes, - "json_sidecar_bytes": json_sidecar, - "packed_sidecar_estimate_bytes": packed_sidecar, - "payload_plus_packed_sidecar_estimate_bytes": total_packed, - "compression_ratio_raw_to_payload": raw_bytes / payload_bytes if payload_bytes else None, - "compression_ratio_raw_to_payload_plus_packed_sidecar_estimate": ( - raw_bytes / total_packed if total_packed else None - ), - "accept_count": sum(test["decision"] == "ACCEPT" for test in tests), - "hold_count": sum(test["decision"] == "HOLD" for test in tests), - "quarantine_count": sum(test["decision"] == "QUARANTINE" for test in tests), - "payload_only_round_trip_count": sum( - test["round_trip"]["payload_only"] for test in tests - ), - "sidecar_round_trip_count": sum( - test["round_trip"]["with_display_cell_sidecar"] for test in tests - ), - } - - -def build_receipt(corpus: Path, slice_bytes: int, chunk_chars: int, max_chunks: int) -> dict[str, Any]: - raw = corpus.read_bytes()[:slice_bytes] - text = raw.decode("utf-8", errors="replace") - tests = [ - audit.audit_fixture( - { - "id": f"enwik8_slice_{index:04d}", - "kind": "corpus_text", - "source": chunk, - } - ) - for index, chunk in enumerate(chunks(text, chunk_chars, max_chunks)) - ] - receipt = { - "schema": "math_logogram_corpus_substitution_benchmark_v1", - "corpus": str(corpus.relative_to(REPO) if corpus.is_relative_to(REPO) else corpus), - "corpus_slice_bytes": len(raw), - "corpus_slice_sha256": sha256_bytes(raw), - "chunk_chars": chunk_chars, - "max_chunks": max_chunks, - "summary": summarize(tests), - "tests": tests, - "claim_boundary": ( - "Bounded corpus slice measurement only. This is not a Hutter Prize " - "submission, not an enwik8 full-corpus result, and not a proof of " - "compression competitiveness." - ), - } - receipt["receipt_hash"] = hashlib.sha256( - json.dumps(receipt, sort_keys=True, separators=(",", ":")).encode("utf-8") - ).hexdigest() - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Benchmark logogram substitution on a corpus slice.") - parser.add_argument("--corpus", type=Path, default=DEFAULT_CORPUS) - parser.add_argument("--slice-bytes", type=int, default=8192) - parser.add_argument("--chunk-chars", type=int, default=160) - parser.add_argument("--max-chunks", type=int, default=64) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - args = parser.parse_args() - - receipt = build_receipt(args.corpus, args.slice_bytes, args.chunk_chars, args.max_chunks) - args.receipt.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(json.dumps(receipt["summary"], indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_logogram_sidecar_packer.py b/4-Infrastructure/shim/math_logogram_sidecar_packer.py deleted file mode 100755 index 8a3f423a..00000000 --- a/4-Infrastructure/shim/math_logogram_sidecar_packer.py +++ /dev/null @@ -1,159 +0,0 @@ -#!/usr/bin/env python3 -"""Pack math logogram substitution sidecars into a deterministic binary stream.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -SHIM = Path(__file__).resolve().parent -DEFAULT_AUDIT = SHIM / "math_logogram_substitution_audit_receipt.json" -DEFAULT_BIN = SHIM / "math_logogram_sidecar_stream.bin" -DEFAULT_RECEIPT = SHIM / "math_logogram_sidecar_packer_receipt.json" - -OPCODES = { - "select_candidate": 0x01, - "literal_token": 0x02, - "append_truncated_cell": 0x03, -} - -KIND_CODES = { - "command": 0x01, - "identifier": 0x02, - "number": 0x03, - "symbol": 0x04, -} - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def byte_value(value: int, field: str) -> int: - if value < 0 or value > 255: - raise ValueError(f"{field} out of one-byte range: {value}") - return value - - -def token_bytes(token: str) -> bytes: - data = token.encode("utf-8") - if len(data) > 255: - raise ValueError(f"token too long for v1 sidecar entry: {token[:40]!r}") - return data - - -def pack_entry(entry: dict[str, Any]) -> bytes: - op = str(entry["op"]) - opcode = OPCODES[op] - index = byte_value(int(entry["index"]), "index") - glyph = byte_value(int(entry.get("glyph_id", 0)), "glyph_id") - token = str(entry.get("token", "")) - encoded_token = token_bytes(token) - - if op == "select_candidate": - candidates = [str(item) for item in entry.get("candidates", [])] - try: - candidate_index = candidates.index(token) - except ValueError: - candidate_index = 255 - return bytes([opcode, index, glyph, byte_value(candidate_index, "candidate_index")]) - - if op == "literal_token": - return bytes([opcode, index, len(encoded_token)]) + encoded_token - - if op == "append_truncated_cell": - kind = KIND_CODES.get(str(entry.get("kind", "symbol")), 0) - depth = byte_value(int(entry.get("depth", 0)), "depth") - return bytes([opcode, index, glyph, (kind << 4) | min(depth, 0x0F), len(encoded_token)]) + encoded_token - - raise ValueError(f"unsupported sidecar op: {op}") - - -def pack_audit(audit: dict[str, Any]) -> tuple[bytes, list[dict[str, Any]]]: - chunks: list[bytes] = [] - index_rows: list[dict[str, Any]] = [] - for test in audit.get("tests", []): - sidecar = test.get("residual_sidecar") - if not sidecar: - continue - start = sum(len(chunk) for chunk in chunks) - entries = list(sidecar.get("entries", [])) - packed_entries = [pack_entry(entry) for entry in entries] - for packed in packed_entries: - chunks.append(packed) - length = sum(len(packed) for packed in packed_entries) - index_rows.append( - { - "sample_id": test["id"], - "decision": test["decision"], - "entry_count": len(entries), - "offset": start, - "length": length, - "sha256": sha256_bytes(b"".join(packed_entries)), - } - ) - return b"".join(chunks), index_rows - - -def build_receipt(audit_path: Path, bin_path: Path) -> dict[str, Any]: - audit = json.loads(audit_path.read_text(encoding="utf-8")) - packed, index_rows = pack_audit(audit) - bin_path.write_bytes(packed) - raw_bytes = int(audit.get("summary", {}).get("raw_bytes", 0)) - payload_bytes = int(audit.get("summary", {}).get("payload_bytes", 0)) - total = payload_bytes + len(packed) - receipt: dict[str, Any] = { - "schema": "math_logogram_sidecar_packer_v1", - "source_audit": str(audit_path), - "packed_stream": str(bin_path), - "opcodes": OPCODES, - "kind_codes": KIND_CODES, - "stream_bytes": len(packed), - "stream_sha256": sha256_bytes(packed), - "index": index_rows, - "summary": { - "sample_count": len(audit.get("tests", [])), - "sidecar_sample_count": len(index_rows), - "raw_bytes": raw_bytes, - "payload_bytes": payload_bytes, - "packed_sidecar_bytes": len(packed), - "payload_plus_packed_sidecar_bytes": total, - "compression_ratio_raw_to_payload_plus_packed_sidecar": ( - raw_bytes / total if total else None - ), - }, - "claim_boundary": ( - "This is a deterministic sidecar stream prototype. It does not " - "prove corpus compression or hardware timing." - ), - } - receipt["receipt_hash"] = hashlib.sha256(stable_json(receipt).encode("utf-8")).hexdigest() - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Pack math logogram sidecars.") - parser.add_argument("--audit", type=Path, default=DEFAULT_AUDIT) - parser.add_argument("--bin", type=Path, default=DEFAULT_BIN) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - args = parser.parse_args() - - receipt = build_receipt(args.audit, args.bin) - args.receipt.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(json.dumps(receipt["summary"], indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_logogram_substitution_audit.py b/4-Infrastructure/shim/math_logogram_substitution_audit.py deleted file mode 100755 index 798c6469..00000000 --- a/4-Infrastructure/shim/math_logogram_substitution_audit.py +++ /dev/null @@ -1,444 +0,0 @@ -#!/usr/bin/env python3 -"""Audit substitution reversibility for the math logogram surface compiler. - -This is an acceptance harness, not the compiler itself. It checks whether a -canonicalized expression can be reconstructed from the emitted glyph payload and -which residual sidecars are required to make that substitution lawful. -""" - -from __future__ import annotations - -import argparse -import json -from collections import Counter -from pathlib import Path -from typing import Any - -import math_logogram_surface_builder as surface - - -SCHEMA = "math_logogram_substitution_audit_v1" -DEFAULT_RECEIPT = Path(__file__).with_name( - "math_logogram_substitution_audit_receipt.json" -) - - -FIXTURES: list[dict[str, Any]] = [ - { - "id": "literal_atom", - "kind": "symbolic_logogram", - "source": "x", - "expected_classes": ["single_char_literal"], - }, - { - "id": "known_command_short", - "kind": "latex_math", - "source": r"\frac{x}{y}", - "expected_classes": [ - "known_command", - "known_symbol", - "single_char_literal", - "known_symbol", - "known_symbol", - "single_char_literal", - "known_symbol", - ], - }, - { - "id": "unknown_multichar_identifier", - "kind": "symbolic_logogram", - "source": "alphaBeta + z", - "expected_classes": [ - "hashed_multichar_residual", - "known_symbol", - "single_char_literal", - ], - }, - { - "id": "long_truncation", - "kind": "latex_math", - "source": r"\partial_t u + u \partial_x u - \nu \partial_{xx} u = 0", - "expected_classes_prefix": [ - "known_command", - "known_symbol", - "single_char_literal", - "single_char_literal", - ], - }, - { - "id": "semantic_tear", - "kind": "symbolic_logogram", - "source": r"torsion(A,B) > max \Rightarrow tear(A,B)", - "expected_regime": "horrible_manifold_tearing", - }, -] - - -def glyph_candidate_map() -> dict[int, list[str]]: - """Return every token spelling that can produce each glyph byte.""" - - candidates: dict[int, set[str]] = {} - - def add(glyph: int, token: str) -> None: - candidates.setdefault(glyph & 0xFF, set()).add(token) - - for token, glyph in surface.COMMAND_GLYPHS.items(): - add(glyph, token) - for token, glyph in surface.SYMBOL_GLYPHS.items(): - add(glyph, token) - for value in range(0x20, 0x7F): - add(value, chr(value)) - return {glyph: sorted(tokens) for glyph, tokens in candidates.items()} - - -GLYPH_CANDIDATES = glyph_candidate_map() - - -def classify_token(token: str) -> str: - if token in surface.COMMAND_GLYPHS: - return "known_command" - if token in surface.SYMBOL_GLYPHS: - return "known_symbol" - if len(token) == 1: - return "single_char_literal" - return "hashed_multichar_residual" - - -def canonical_token_string(cells: list[dict[str, Any]]) -> str: - return " ".join(str(cell["token"]) for cell in cells) - - -def compression_ratio(raw_bytes: int, payload_bytes: int) -> float | None: - if payload_bytes == 0: - return None - return raw_bytes / payload_bytes - - -def compact_json_bytes(value: Any) -> int: - return len( - json.dumps(value, ensure_ascii=False, separators=(",", ":")).encode("utf-8") - ) - - -def packed_sidecar_estimate_bytes(entries: list[dict[str, Any]]) -> int: - """Estimate a simple binary sidecar envelope. - - This is not a final codec. It prices each correction as: - opcode + token index + glyph/candidate metadata + optional token bytes. - The JSON receipt stays verbose for inspection; this estimate is the - hardware-packer target to beat. - """ - - total = 0 - for entry in entries: - op = str(entry["op"]) - token = str(entry.get("token", "")) - token_bytes = len(token.encode("utf-8")) - if op == "select_candidate": - total += 4 - elif op == "literal_token": - total += 3 + token_bytes - elif op == "append_truncated_cell": - total += 5 + token_bytes - else: - total += 4 + token_bytes - return total - - -def rehydrate_with_sidecar( - payload_tokens: list[str], entries: list[dict[str, Any]] -) -> str: - tokens = list(payload_tokens) - for entry in sorted(entries, key=lambda item: int(item["index"])): - index = int(entry["index"]) - op = str(entry["op"]) - token = str(entry.get("token", "")) - if op in {"select_candidate", "literal_token"}: - while len(tokens) <= index: - tokens.append("") - tokens[index] = token - elif op == "append_truncated_cell": - while len(tokens) < index: - tokens.append("") - if len(tokens) == index: - tokens.append(token) - else: - tokens[index] = token - return " ".join(tokens) - - -def audit_fixture(fixture: dict[str, Any]) -> dict[str, Any]: - source_text = str(fixture["source"]) - canon = surface.canonicalize(source_text) - cells = list(canon["cells"]) - payload = surface.pack_glyph_payload(cells) - metrics = surface.compression_metrics(source_text, canon["canonical"], payload) - semantic_regime = surface.classify_regime(canon["canonical"], cells) - - classifications: list[dict[str, Any]] = [] - residual_reasons: list[str] = [] - sidecar_entries: list[dict[str, Any]] = [] - payload_only_tokens: list[str] = [] - payload_only_exact = True - - for cell in cells: - token = str(cell["token"]) - glyph = int(cell["glyph_id"]) & 0xFF - substitution_class = classify_token(token) - candidates = GLYPH_CANDIDATES.get(glyph, []) - is_hashed = substitution_class == "hashed_multichar_residual" - is_ambiguous = len(candidates) != 1 or token not in candidates - - if is_hashed: - residual_reasons.append(f"hashed_multichar_token:{token}") - sidecar_entries.append( - { - "index": cell["index"], - "op": "literal_token", - "token": token, - "glyph_id": glyph, - "reason": "hashed_multichar_token", - } - ) - payload_only_exact = False - elif is_ambiguous: - residual_reasons.append(f"ambiguous_glyph:{glyph:02x}:{token}") - sidecar_entries.append( - { - "index": cell["index"], - "op": "select_candidate", - "token": token, - "glyph_id": glyph, - "candidates": candidates, - "reason": "ambiguous_glyph", - } - ) - payload_only_exact = False - - payload_only_tokens.append(candidates[0] if len(candidates) == 1 else "") - classifications.append( - { - "index": cell["index"], - "token": token, - "glyph_id": glyph, - "class": substitution_class, - "payload_candidates": candidates, - "payload_only_reversible": not is_hashed and not is_ambiguous, - } - ) - - if len(cells) > len(payload): - payload_only_exact = False - residual_reasons.append( - f"payload_truncated:{len(cells) - len(payload)}_tokens" - ) - for cell in cells[len(payload) :]: - sidecar_entries.append( - { - "index": cell["index"], - "op": "append_truncated_cell", - "token": cell["token"], - "glyph_id": int(cell["glyph_id"]) & 0xFF, - "kind": cell["kind"], - "depth": cell["depth"], - "reason": "payload_truncated", - } - ) - - sidecar_cells = cells[: len(payload)] - payload_cell_tokens = [str(cell["token"]) for cell in sidecar_cells] - sidecar_reconstructed = rehydrate_with_sidecar( - payload_cell_tokens, sidecar_entries - ) - payload_reconstructed = " ".join(payload_only_tokens[: len(payload)]) - canonical_round_trip_with_cells = sidecar_reconstructed == str(canon["canonical"]) - payload_only_round_trip = ( - payload_only_exact - and not len(cells) > len(payload) - and payload_reconstructed == str(canon["canonical"]) - ) - - expected_failures: list[str] = [] - expected_classes = fixture.get("expected_classes") - if expected_classes is not None: - observed = [entry["class"] for entry in classifications] - if observed != expected_classes: - expected_failures.append( - f"expected_classes mismatch: observed {observed}" - ) - expected_prefix = fixture.get("expected_classes_prefix") - if expected_prefix is not None: - observed_prefix = [entry["class"] for entry in classifications[: len(expected_prefix)]] - if observed_prefix != expected_prefix: - expected_failures.append( - f"expected_classes_prefix mismatch: observed {observed_prefix}" - ) - expected_regime = fixture.get("expected_regime") - if expected_regime is not None and semantic_regime != expected_regime: - expected_failures.append( - f"expected_regime mismatch: observed {semantic_regime}" - ) - - if semantic_regime == "horrible_manifold_tearing": - decision = "QUARANTINE" - elif payload_only_round_trip: - decision = "ACCEPT" - else: - decision = "HOLD" - - substitution_counts = Counter(entry["class"] for entry in classifications) - payload_bound = len(payload) <= 16 - residual = bool(residual_reasons) - sidecar = { - "schema": "math_logogram_sidecar_v1", - "encoding": "structured_token_corrections", - "rehydration_rule": ( - "Start from glyph payload; apply candidate selections, literal " - "tokens, and truncated cell appends by token index to recover the " - "canonical token string." - ), - "entries": sidecar_entries, - } - sidecar_bytes = compact_json_bytes(sidecar) if sidecar_entries else 0 - sidecar_packed_estimate = packed_sidecar_estimate_bytes(sidecar_entries) - total_payload_bytes = len(payload) + sidecar_bytes - total_packed_payload_bytes = len(payload) + sidecar_packed_estimate - return { - "id": fixture["id"], - "kind": fixture["kind"], - "source": source_text, - "source_hash": surface.sha256_text(source_text), - "canonical": canon["canonical"], - "canonical_hash": canon["canonical_hash"], - "token_count": canon["token_count"], - "payload_hex": payload.hex(), - "payload_len": len(payload), - "payload_bound": payload_bound, - "substitution_receipt": surface.substitution_receipt(payload), - "substitution_counts": dict(sorted(substitution_counts.items())), - "substitutions": classifications, - "round_trip": { - "scope": "canonical_token_string", - "source_byte_exact": source_text == canon["canonical"], - "payload_only": payload_only_round_trip, - "with_display_cell_sidecar": canonical_round_trip_with_cells, - "payload_only_reconstructed": payload_reconstructed, - "sidecar_reconstructed": sidecar_reconstructed, - }, - "compression": { - **metrics, - "compression_ratio_raw_to_payload": compression_ratio( - int(metrics["raw_bytes"]), len(payload) - ), - "sidecar_bytes_json_compact": sidecar_bytes, - "sidecar_bytes_packed_estimate": sidecar_packed_estimate, - "payload_plus_sidecar_bytes": total_payload_bytes, - "payload_plus_packed_sidecar_bytes": total_packed_payload_bytes, - "compression_ratio_raw_to_payload_plus_sidecar": compression_ratio( - int(metrics["raw_bytes"]), total_payload_bytes - ), - "compression_ratio_raw_to_payload_plus_packed_sidecar": compression_ratio( - int(metrics["raw_bytes"]), total_packed_payload_bytes - ), - }, - "residual": residual, - "residual_reasons": sorted(set(residual_reasons)), - "residual_sidecar": sidecar if sidecar_entries else None, - "semantic_regime": semantic_regime, - "decision": decision, - "gcc_receipt_shape": { - "compression_ratio": compression_ratio(int(metrics["raw_bytes"]), len(payload)), - "round_trip": payload_only_round_trip, - "residual": sorted(set(residual_reasons)) or None, - }, - "expectation_failures": expected_failures, - } - - -def build_receipt() -> dict[str, Any]: - tests = [audit_fixture(fixture) for fixture in FIXTURES] - failures = [ - f"{test['id']}:{failure}" - for test in tests - for failure in test["expectation_failures"] - ] - accept_count = sum(1 for test in tests if test["decision"] == "ACCEPT") - hold_count = sum(1 for test in tests if test["decision"] == "HOLD") - quarantine_count = sum(1 for test in tests if test["decision"] == "QUARANTINE") - payload_only_round_trip_count = sum( - 1 for test in tests if test["round_trip"]["payload_only"] - ) - sidecar_round_trip_count = sum( - 1 for test in tests if test["round_trip"]["with_display_cell_sidecar"] - ) - all_payload_only_round_trip = payload_only_round_trip_count == len(tests) - raw_bytes = sum(int(test["compression"]["raw_bytes"]) for test in tests) - payload_bytes = sum( - int(test["compression"]["surface_payload_bytes"]) for test in tests - ) - json_sidecar_bytes = sum( - int(test["compression"]["sidecar_bytes_json_compact"]) for test in tests - ) - packed_sidecar_estimate_bytes = sum( - int(test["compression"]["sidecar_bytes_packed_estimate"]) for test in tests - ) - return { - "schema": SCHEMA, - "claim_boundary": ( - "This audit proves detection of substitution residuals for the " - "fixture set. It does not prove global losslessness for all math " - "or chemistry strings." - ), - "compiler": "4-Infrastructure/shim/math_logogram_surface_builder.py", - "summary": { - "fixture_count": len(tests), - "expectation_failures": failures, - "payload_only_round_trip_count": payload_only_round_trip_count, - "all_payload_only_round_trip": all_payload_only_round_trip, - "sidecar_round_trip_count": sidecar_round_trip_count, - "sidecar_required_count": len(tests) - payload_only_round_trip_count, - "accept_count": accept_count, - "hold_count": hold_count, - "quarantine_count": quarantine_count, - "raw_bytes": raw_bytes, - "payload_bytes": payload_bytes, - "json_sidecar_bytes": json_sidecar_bytes, - "packed_sidecar_estimate_bytes": packed_sidecar_estimate_bytes, - "compression_ratio_raw_to_payload": compression_ratio( - raw_bytes, payload_bytes - ), - "compression_ratio_raw_to_payload_plus_json_sidecar": compression_ratio( - raw_bytes, payload_bytes + json_sidecar_bytes - ), - "compression_ratio_raw_to_payload_plus_packed_sidecar_estimate": compression_ratio( - raw_bytes, payload_bytes + packed_sidecar_estimate_bytes - ), - "audit_passed": not failures, - }, - "tests": tests, - } - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Audit math logogram substitution reversibility." - ) - parser.add_argument( - "--receipt", - type=Path, - default=DEFAULT_RECEIPT, - help="Path for the JSON audit receipt.", - ) - args = parser.parse_args() - - receipt = build_receipt() - args.receipt.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(json.dumps(receipt["summary"], indent=2, sort_keys=True)) - return 0 if receipt["summary"]["audit_passed"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_logogram_surface_builder.py b/4-Infrastructure/shim/math_logogram_surface_builder.py deleted file mode 100644 index 10095c85..00000000 --- a/4-Infrastructure/shim/math_logogram_surface_builder.py +++ /dev/null @@ -1,337 +0,0 @@ -#!/usr/bin/env python3 -"""Math/logogram compression surface builder. - -Surface-1 is the host-side bridge between: - - LaTeX / symbolic logogram strings - -> canonical token/display cells - -> compressed bank-local glyph payload - -> Tang-compatible substitution receipt - -> n-space/eigen/metaprobe curriculum rows - -The canonicalizer is deliberately small and deterministic. RaTeX is recorded as -the preferred future Rust renderer/canonicalizer, but this script does not need -RaTeX checked out to produce receipts today. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import re -import zlib -from collections import Counter -from pathlib import Path -from typing import Any - - -TOKEN_RE = re.compile( - r"(\\[A-Za-z]+|\\.|[A-Za-z]+|[0-9]+|[{}_^=+\-*/(),;:\[\]<>|]|[^\s])" -) - -COMMAND_GLYPHS = { - "\\frac": 0x21, - "\\sqrt": 0x22, - "\\int": 0x23, - "\\sum": 0x24, - "\\partial": 0x25, - "\\nabla": 0x26, - "\\Delta": 0x27, - "\\in": 0x28, - "\\Rightarrow": 0x29, - "\\neg": 0x2A, - "\\cap": 0x2B, - "\\ce": 0x2C, - "\\pu": 0x2D, -} - -SYMBOL_GLYPHS = { - "{": 0x30, - "}": 0x31, - "_": 0x32, - "^": 0x33, - "=": 0x34, - "+": 0x35, - "-": 0x36, - "*": 0x37, - "/": 0x38, - "(": 0x39, - ")": 0x3A, - ",": 0x3B, - ";": 0x3C, - ":": 0x3D, - "<": 0x3E, - ">": 0x3F, - "|": 0x40, -} - - -DEFAULT_SAMPLES = [ - { - "id": "quadratic_formula", - "kind": "latex_math", - "source": r"\frac{-b \pm \sqrt{b^2-4ac}}{2a}", - }, - { - "id": "pde_residual", - "kind": "latex_math", - "source": r"\partial_t u + u \partial_x u - \nu \partial_{xx} u = 0", - }, - { - "id": "metaglyph_fold", - "kind": "symbolic_logogram", - "source": r"A \cap B \Rightarrow fold(A,B)", - }, - { - "id": "semantic_tear", - "kind": "symbolic_logogram", - "source": r"torsion(A,B) > max \Rightarrow tear(A,B)", - }, - { - "id": "mhchem_surface", - "kind": "latex_chem", - "source": r"\ce{H2SO4 + 2NaOH -> Na2SO4 + 2H2O}", - }, -] - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def canonicalize(source: str) -> dict[str, Any]: - tokens = TOKEN_RE.findall(source) - canonical = " ".join(tokens) - cells = [] - stack_depth = 0 - for index, token in enumerate(tokens): - if token == "{": - stack_depth += 1 - elif token == "}": - stack_depth = max(0, stack_depth - 1) - if token.startswith("\\"): - token_kind = "command" - elif token.isalpha(): - token_kind = "identifier" - elif token.isdigit(): - token_kind = "number" - else: - token_kind = "symbol" - cells.append( - { - "index": index, - "kind": token_kind, - "token": token, - "depth": stack_depth, - "glyph_id": glyph_id(token), - } - ) - return { - "canonical": canonical, - "canonical_hash": sha256_text(canonical), - "token_count": len(tokens), - "cells": cells, - "cell_hash": sha256_text(json.dumps(cells, sort_keys=True, ensure_ascii=False)), - } - - -def glyph_id(token: str) -> int: - if token in COMMAND_GLYPHS: - return COMMAND_GLYPHS[token] - if token in SYMBOL_GLYPHS: - return SYMBOL_GLYPHS[token] - if len(token) == 1: - return ord(token) & 0x7F - digest = hashlib.blake2s(token.encode("utf-8"), digest_size=1).digest()[0] - return 0x80 | (digest & 0x7F) - - -def pack_glyph_payload(cells: list[dict[str, Any]], max_len: int = 16) -> bytes: - return bytes(int(cell["glyph_id"]) & 0xFF for cell in cells[:max_len]) - - -def substitution_receipt(payload: bytes) -> dict[str, Any]: - # Same hot-path substitution model as tang9k_hutter_symbol_surface.py, kept - # local to avoid import-path surprises in shim execution. - table = { - ord(" "): 0x0, - ord("e"): 0x1, - ord("E"): 0x1, - ord("t"): 0x2, - ord("T"): 0x2, - ord("a"): 0x3, - ord("A"): 0x3, - ord("o"): 0x4, - ord("O"): 0x4, - ord("i"): 0x5, - ord("I"): 0x5, - ord("n"): 0x6, - ord("N"): 0x6, - ord("s"): 0x7, - ord("S"): 0x7, - ord("r"): 0x8, - ord("R"): 0x8, - ord("h"): 0x9, - ord("H"): 0x9, - ord("l"): 0xA, - ord("L"): 0xA, - ord("d"): 0xB, - ord("c"): 0xC, - ord("C"): 0xC, - ord("u"): 0xD, - ord("U"): 0xD, - ord("F"): 0xE, - ord("D"): 0xF, - } - rolling_hash = 0xACE1 - mapped = 0 - literal = 0 - for byte in payload: - hit = byte in table - code = table[byte] if hit else byte & 0x0F - rolling_hash = (((rolling_hash << 1) & 0xFFFF) | (rolling_hash >> 15)) ^ ( - (0x10 if hit else 0x00) | code - ) - mapped += 1 if hit else 0 - literal += 0 if hit else 1 - return { - "schema": "surface1_substitution_receipt_v1", - "hash16": rolling_hash, - "mapped_count": mapped, - "literal_count": literal, - } - - -def classify_regime(canonical: str, cells: list[dict[str, Any]]) -> str: - lowered = canonical.lower() - if "tear" in lowered or "torsion" in lowered or "contradiction" in lowered: - return "horrible_manifold_tearing" - if "fold" in lowered or "\\cap" in lowered or "\\rightarrow" in lowered or "\\rightarrow" in lowered: - return "beautiful_topological_folding" - command_count = sum(1 for cell in cells if cell["kind"] == "command") - if command_count >= 2 or len(cells) > 20: - return "ugly_asymmetric_pruning" - return "beautiful_topological_folding" - - -def compression_metrics(source: str, canonical: str, payload: bytes) -> dict[str, Any]: - raw = source.encode("utf-8") - canonical_bytes = canonical.encode("utf-8") - z_raw = zlib.compress(raw, level=9) - return { - "raw_bytes": len(raw), - "canonical_bytes": len(canonical_bytes), - "surface_payload_bytes": len(payload), - "zlib_raw_bytes": len(z_raw), - "payload_over_raw": len(payload) / (len(raw) or 1), - "payload_over_canonical": len(payload) / (len(canonical_bytes) or 1), - } - - -def load_terms(path: Path) -> list[str]: - if not path.exists(): - return [] - data = json.loads(path.read_text(encoding="utf-8")) - return [str(item.get("term")) for item in data.get("top_terms", [])[:16]] - - -def build_sample_record(sample: dict[str, str], eigen_terms: list[str]) -> dict[str, Any]: - source = sample["source"] - canon = canonicalize(source) - payload = pack_glyph_payload(canon["cells"]) - metrics = compression_metrics(source, canon["canonical"], payload) - token_counts = Counter(cell["kind"] for cell in canon["cells"]) - return { - "id": sample["id"], - "kind": sample["kind"], - "source": source, - "source_hash": sha256_text(source), - "canonical": canon["canonical"], - "canonical_hash": canon["canonical_hash"], - "cell_hash": canon["cell_hash"], - "token_count": canon["token_count"], - "token_kind_counts": dict(token_counts), - "display_cells": canon["cells"], - "surface_payload_hex": payload.hex(), - "surface_payload_len": len(payload), - "substitution_receipt": substitution_receipt(payload), - "compression_metrics": metrics, - "semantic_regime": classify_regime(canon["canonical"], canon["cells"]), - "route_terms": eigen_terms[:8], - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a math/logogram surface compiler. Return compact JSON with receipt boundaries." - records = [] - for sample in receipt["samples"]: - prompt = { - "task": "compile_math_logogram_surface", - "id": sample["id"], - "source": sample["source"], - "route_terms": sample["route_terms"], - "instruction": "Compile this into canonical cells, compressed payload, and semantic regime.", - } - answer = { - "selected": True, - "claim_boundary": "surface-compiler-receipt-only", - "canonical_hash": sample["canonical_hash"], - "cell_hash": sample["cell_hash"], - "surface_payload_hex": sample["surface_payload_hex"], - "semantic_regime": sample["semantic_regime"], - "compression_metrics": sample["compression_metrics"], - "receipt_hash16": sample["substitution_receipt"]["hash16"], - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def load_samples(path: Path | None) -> list[dict[str, str]]: - if not path: - return DEFAULT_SAMPLES - data = json.loads(path.read_text(encoding="utf-8")) - if isinstance(data, list): - return data - return data.get("samples", DEFAULT_SAMPLES) - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--samples", type=Path) - parser.add_argument("--eigen", type=Path, default=Path("4-Infrastructure/shim/nspace_semantic_pde_eigenvectors.json")) - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/math_logogram_surface_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/math_logogram_surface_curriculum.jsonl")) - args = parser.parse_args() - - eigen_terms = load_terms(args.eigen) - samples = [build_sample_record(sample, eigen_terms) for sample in load_samples(args.samples)] - receipt = { - "schema": "math_logogram_surface_receipt_v1", - "claim_boundary": "Surface compiler canonicalizes and compresses symbolic strings; it does not prove math or chemistry claims.", - "canonicalizer": "deterministic_python_surface1", - "preferred_future_canonicalizer": "RaTeX Rust display-list core", - "eigen_source": str(args.eigen), - "sample_count": len(samples), - "samples": samples, - "lawful": all(sample["surface_payload_len"] <= 16 for sample in samples), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_prover_prior_metaprobe.py b/4-Infrastructure/shim/math_prover_prior_metaprobe.py deleted file mode 100644 index e61275c4..00000000 --- a/4-Infrastructure/shim/math_prover_prior_metaprobe.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -"""External math/prover prior metaprobe. - -This keeps model cards and theorem-search services in their proper role: -external priors for routing, retrieval, and curriculum construction. They do -not prove local claims unless a local formal checker accepts a proof artifact. -""" - -from __future__ import annotations - -import argparse -import json -import urllib.request -from pathlib import Path -from typing import Any - - -THEOREMSEARCH_URL = "https://api.theoremsearch.com/search" - -MODEL_PRIORS = [ - { - "id": "deepseek-ai/DeepSeek-Math-V2", - "url": "https://huggingface.co/deepseek-ai/DeepSeek-Math-V2", - "role": "self_verifiable_math_reasoning_prior", - "local_use": "teacher_or_comparator_for_math_reasoning_style", - "boundary": "model-card/evaluation-prior-only", - "notes": [ - "Apache-2.0 Hugging Face model card.", - "Model card emphasizes self-verification and theorem-proving style reasoning.", - "Use as a source of verifier/generator separation patterns, not as local proof.", - ], - }, - { - "id": "VladShash/deepseek-math-7b-lean-prover-dpo-olmo-3", - "url": "https://huggingface.co/VladShash/deepseek-math-7b-lean-prover-dpo-olmo-3", - "role": "lean_prover_dpo_prior", - "local_use": "candidate teacher/comparator for Lean-shaped proof proposals", - "boundary": "small-community-finetune-prior-only", - "notes": [ - "Hugging Face card identifies a 7B safetensors model trained with TRL/DPO.", - "Useful for proof-proposal style and preference data shape.", - "Any generated Lean must still be checked by lake/Lean locally.", - ], - }, - { - "id": "Zyphra/ZAYA1-8B", - "url": "https://huggingface.co/Zyphra/ZAYA1-8B", - "role": "small_moe_reasoning_model_prior", - "local_use": "candidate_local_decision_model_for_test_time_compute_harnesses", - "boundary": "model-card/evaluation-prior-only", - "notes": [ - "Apache-2.0 Hugging Face model card.", - "Model card describes 760M active parameters and 8.4B total parameters in a small mixture-of-experts language model.", - "Model card emphasizes math, coding, long-form reasoning, on-device deployment, and test-time compute harness usefulness.", - "Use as a local candidate/comparator for routing decisions; every output still needs metaprobe, source, Lean, or hardware receipts.", - ], - }, -] - -DATASET_PRIORS = [ - { - "id": "huggingface/datasets?other=mathematics", - "url": "https://huggingface.co/datasets?other=mathematics", - "role": "math_dataset_discovery_registry_prior", - "local_use": "dataset_search_axis_for_future_sampling", - "boundary": "registry-prior-only", - "notes": [ - "The Hugging Face mathematics dataset filter listed 350 active-filter results when checked.", - "Trending examples included MathNet, proof-pile, MathVision, MathVista, Big-Math-RL-Verified, PolyMath, autoformalization, Coq facts/proofs, and DART math pools.", - "Use as a discovery index for sampling candidates, not as an ingested corpus.", - "Each candidate still needs license, schema, provenance, and contamination checks before local SFT use.", - ], - }, - { - "id": "huggingface/datasets?other=chemistry&sort=trending", - "url": "https://huggingface.co/datasets?other=chemistry&sort=trending", - "role": "chemistry_dataset_discovery_registry_prior", - "local_use": "molecular_physics_constraint_axis_for_future_sampling", - "boundary": "registry-prior-only", - "notes": [ - "The Hugging Face chemistry dataset filter listed 1,514 active-filter results when checked.", - "Trending examples included ChemBench, drug-target-activity, ScienceQA, mdCATH, GeMS, material trajectories, QM9, binding affinity, and protein-ligand datasets.", - "Use as a discovery index for physics/chemistry constraint corpora and molecular/field analogies, not as an ingested corpus.", - "Each candidate needs modality, unit, license, provenance, leakage, and safety/domain checks before local SFT use.", - ], - }, - { - "id": "huggingface/datasets?other=finance&sort=trending", - "url": "https://huggingface.co/datasets?other=finance&sort=trending", - "role": "finance_dataset_discovery_registry_prior", - "local_use": "time_series_risk_and_market_signal_axis_for_future_sampling", - "boundary": "registry-prior-only", - "notes": [ - "The Hugging Face finance dataset filter listed 1,414 active-filter results when checked.", - "Trending examples included EvasionBench, APEX agents, Twitter financial sentiment, Yahoo finance data, Finance-Instruct-500k, EDGAR corpus, crypto datasets, and transaction categorization.", - "Use as a discovery index for time-series, risk, anomaly, and economic-signal routing tests.", - "Finance examples must stay evaluation/simulation data unless separately validated; do not convert dataset priors into financial advice.", - ], - }, - { - "id": "huggingface/datasets?sort=trending", - "url": "https://huggingface.co/datasets?sort=trending", - "role": "global_dataset_trending_drift_prior", - "local_use": "registry_drift_detector_and_negative_control_axis", - "boundary": "registry-prior-only", - "notes": [ - "The global Hugging Face trending dataset page listed 994,526 datasets when checked.", - "Trending examples spanned agent traces, synthetic reasoning, CAD, court/legal, SWE, MathNet, GSM8K, health, FineWeb-Edu, and C4.", - "Use as a broad drift detector to see what corpus families are becoming common around the local stack.", - "Do not sample directly from global trending without a domain filter, schema inspection, license check, and contamination check.", - ], - }, - { - "id": "nvidia/Nemotron-SFT-Math-v3", - "url": "https://huggingface.co/datasets/nvidia/Nemotron-SFT-Math-v3", - "role": "large_scale_math_sft_curriculum_prior", - "local_use": "sampling_schema_and_reasoning_mode_prior", - "boundary": "dataset-card/corpus-prior-only", - "notes": [ - "Hugging Face card describes a JSONL math reasoning dataset with messages, expected answers, provenance, license, tool usage, and source URLs.", - "Dataset card reports 3,638,783 train samples and roughly 144 GB disk size; ingest should be sampled/streamed, not eagerly mirrored into the repo.", - "Useful distinction: with Python TIR vs without Python TIR reasoning modes.", - "Reference-answer matching is useful supervision hygiene but not a local proof receipt.", - ], - }, - { - "id": "ShadenA/MathNet", - "url": "https://huggingface.co/datasets/ShadenA/MathNet", - "role": "multimodal_olympiad_problem_topology_prior", - "local_use": "topic_country_competition_problem_type_schema_prior", - "boundary": "dataset-card/topology-prior-only", - "notes": [ - "Hugging Face card exposes parquet data with text and image modalities.", - "Viewer reports about 27.8k rows, 59 subsets, country/competition/topic/language/problem_type fields, and olympiad/retrieval tags.", - "Useful for graph-shaped curriculum buckets: combinatorics, geometry, algebra, olympiad source, language, and proof-only vs proof-and-answer.", - "Images and licenses/provenance need explicit handling before any local mirror or training ingest.", - ], - }, -] - - -QUERIES = [ - "graph minimum degree at least three cycle length power of two", - "covering systems odd moduli Erdos Selfridge conjecture", - "formal theorem graph cycle length Lean theorem", -] - - -def theorem_search(query: str, n_results: int) -> dict[str, Any]: - body = json.dumps({"query": query, "n_results": n_results}).encode("utf-8") - req = urllib.request.Request( - THEOREMSEARCH_URL, - data=body, - headers={ - "Content-Type": "application/json", - "User-Agent": "Research-Stack-Math-Prover-Metaprobe/0.1", - }, - method="POST", - ) - with urllib.request.urlopen(req, timeout=60) as resp: - return json.loads(resp.read().decode("utf-8", errors="replace")) - - -def compact_theorem_result(result: dict[str, Any]) -> dict[str, Any]: - compact = [] - for theorem in result.get("theorems", [])[:5]: - paper = theorem.get("paper") or {} - compact.append( - { - "name": theorem.get("name"), - "theorem_type": theorem.get("theorem_type"), - "slogan": theorem.get("slogan"), - "link": theorem.get("link"), - "score": theorem.get("score"), - "source": paper.get("source"), - "paper_title": paper.get("title"), - "category": paper.get("primary_category"), - "year": paper.get("year"), - } - ) - return {"theorems": compact} - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a physics-math proof router. Return compact JSON with evidence boundaries." - records = [] - for prior in receipt["model_priors"]: - prompt = { - "task": "use_external_math_model_prior", - "model": prior["id"], - "role": prior["role"], - "url": prior["url"], - "instruction": "Explain how this model should influence local proof/search routing without becoming proof.", - } - answer = { - "selected": True, - "use_as": prior["local_use"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Generated math/proof text must be checked by local Lean/source/verifier receipts before promotion.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - for prior in receipt["dataset_priors"]: - prompt = { - "task": "use_external_math_dataset_prior", - "dataset": prior["id"], - "role": prior["role"], - "url": prior["url"], - "instruction": "Explain how this corpus should influence local SFT sampling without replacing local receipts.", - } - answer = { - "selected": True, - "use_as": prior["local_use"], - "claim_boundary": prior["boundary"], - "sampling_rule": "Prefer small provenance-preserving samples first; keep source/license/tool_usage fields; separate TIR from non-TIR examples.", - "metaprobe_rule": "Corpus examples train reasoning style and answer discipline; local theorem claims still require Lean/source/hardware receipts.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - prompt = { - "task": "use_theoremsearch_prior", - "queries": list(receipt["theoremsearch"].keys()), - "instruction": "Explain how semantic theorem search should shrink local Erdős/prover search.", - } - answer = { - "selected": True, - "use_as": "retrieval_prior", - "claim_boundary": "retrieved-theorem-context-only", - "metaprobe_rule": "Use TheoremSearch results to suggest adjacent theorem neighborhoods and dependency probes; verify any imported statement locally.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--n-results", type=int, default=3) - parser.add_argument("--no-live-search", action="store_true") - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/math_prover_prior_metaprobe_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/math_prover_prior_curriculum.jsonl")) - args = parser.parse_args() - - theorem_results: dict[str, Any] = {} - errors: dict[str, str] = {} - if not args.no_live_search: - for query in QUERIES: - try: - theorem_results[query] = compact_theorem_result(theorem_search(query, args.n_results)) - except Exception as exc: # noqa: BLE001 - receipt should preserve network/API failures. - errors[query] = f"{type(exc).__name__}: {exc}" - - receipt = { - "schema": "math_prover_prior_metaprobe_receipt_v1", - "claim_boundary": "External math models and theorem-search hits are routing priors, not local proofs.", - "model_priors": MODEL_PRIORS, - "dataset_priors": DATASET_PRIORS, - "theoremsearch": theorem_results, - "theoremsearch_errors": errors, - "lawful": bool(MODEL_PRIORS) and (args.no_live_search or bool(theorem_results) or bool(errors)), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/math_repository_eigenvector_audit.py b/4-Infrastructure/shim/math_repository_eigenvector_audit.py deleted file mode 100644 index 7e1613c5..00000000 --- a/4-Infrastructure/shim/math_repository_eigenvector_audit.py +++ /dev/null @@ -1,434 +0,0 @@ -#!/usr/bin/env python3 -"""Eigenvector audit for major math/formalism repositories. - -This is a lightweight structural pass, not a theorem checker. It turns each -math-heavy repo/module into a feature vector, centers the matrix, and reports -the leading covariance eigenvectors as "math axes" for routing compression and -formalism work. -""" - -from __future__ import annotations - -import hashlib -import json -import math -import re -from collections import Counter -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import numpy as np - - -ROOT = Path(__file__).resolve().parents[2] -SHIM = ROOT / "4-Infrastructure" / "shim" -WIKI = ROOT / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -OUT_JSON = SHIM / "math_repository_eigenvector_audit.json" -OUT_JSONL = SHIM / "math_repository_eigenvector_audit_curriculum.jsonl" -OUT_TID = WIKI / "Math Repository Eigenvector Audit.tid" - - -REPO_CANDIDATES = [ - "0-Core-Formalism/lean/Semantics", - "0-Core-Formalism/lean/LeanGPT", - "0-Core-Formalism/otom/formal", - "2-Search-Space/PIST", - "2-Search-Space/FAMM", - "2-Search-Space/tardygrada/proofs", - "3-Mathematical-Models/AMMR", - "3-Mathematical-Models/manifold_compression", - "3-Mathematical-Models/hutter_manifold", - "6-Documentation/docs/formal_verification", - "6-Documentation/docs/semantics", - "6-Documentation/invention_record", - "ai-math-discovery-systems/AI-Feynman", - "ai-math-discovery-systems/AI-Newton", - "ai-math-discovery-systems/Goedel-Prover-V2", - "ai-math-discovery-systems/PINNs", - "ai-math-discovery-systems/alphageometry", - "ai-math-discovery-systems/neural-conservation-law", -] - -TEXT_EXTENSIONS = { - ".lean", - ".agda", - ".v", - ".thy", - ".py", - ".rs", - ".jl", - ".md", - ".txt", - ".json", - ".yaml", - ".yml", - ".toml", -} - -SKIP_DIRS = { - ".git", - ".lake", - ".venv", - "__pycache__", - "node_modules", - "build", - "dist", - "target", - ".mypy_cache", - ".pytest_cache", -} - -FEATURES = [ - "file_count", - "byte_count_log", - "lean_files", - "proof_files", - "python_files", - "markdown_files", - "definitions", - "theorems", - "lemmas", - "axioms", - "imports", - "evals", - "sorry_markers", - "admit_markers", - "math_symbols", - "unicode_symbols", - "compression_terms", - "geometry_terms", - "spectrum_terms", - "proof_terms", - "model_terms", - "hardware_terms", - "dataset_terms", - "avg_line_length", -] - -TERM_PATTERNS = { - "compression_terms": re.compile(r"\b(compress|codec|encode|decode|entropy|hutter|residual|packet|lut|dictionary)\b", re.I), - "geometry_terms": re.compile(r"\b(manifold|topolog|braid|torsion|shear|field|metric|projection|eigen|vector|genus)\b", re.I), - "spectrum_terms": re.compile(r"\b(spectrum|spectral|frequency|wave|phase|fourier|signal|eigenvalue)\b", re.I), - "proof_terms": re.compile(r"\b(theorem|lemma|proof|axiom|decide|native_decide|invariant|lawful)\b", re.I), - "model_terms": re.compile(r"\b(model|neural|pinn|feynman|newton|solver|optimizer|training|loss)\b", re.I), - "hardware_terms": re.compile(r"\b(fpga|verilog|rtl|uart|bram|q16|fixed|silicon|hardware)\b", re.I), - "dataset_terms": re.compile(r"\b(dataset|corpus|sample|benchmark|receipt|jsonl|manifest)\b", re.I), -} - - -@dataclass -class RepoVector: - name: str - path: str - exists: bool - features: dict[str, float] - top_terms: list[tuple[str, int]] - file_families: dict[str, int] - content_hash: str | None - - -def iter_files(root: Path) -> list[Path]: - files: list[Path] = [] - for path in root.rglob("*"): - if any(part in SKIP_DIRS for part in path.relative_to(root).parts): - continue - if path.is_file() and path.suffix.lower() in TEXT_EXTENSIONS: - files.append(path) - return files - - -def read_text(path: Path) -> str: - try: - return path.read_text(encoding="utf-8", errors="ignore") - except OSError: - return "" - - -def count_regex(pattern: str, text: str, flags: int = re.MULTILINE) -> int: - return len(re.findall(pattern, text, flags)) - - -def repo_vector(rel: str) -> RepoVector: - path = ROOT / rel - if not path.exists(): - return RepoVector(rel, rel, False, {key: 0.0 for key in FEATURES}, [], {}, None) - - files = iter_files(path) - family_counter: Counter[str] = Counter() - term_counter: Counter[str] = Counter() - byte_count = 0 - line_count = 0 - line_length_sum = 0 - all_hash = hashlib.sha256() - accum = {key: 0.0 for key in FEATURES} - accum["file_count"] = float(len(files)) - - for file_path in files: - rel_file = file_path.relative_to(ROOT).as_posix() - text = read_text(file_path) - encoded = text.encode("utf-8", errors="ignore") - all_hash.update(rel_file.encode("utf-8")) - all_hash.update(b"\0") - all_hash.update(hashlib.sha256(encoded).digest()) - byte_count += len(encoded) - family_counter[file_path.suffix.lower() or ""] += 1 - - lines = text.splitlines() - line_count += len(lines) - line_length_sum += sum(len(line) for line in lines) - - suffix = file_path.suffix.lower() - if suffix == ".lean": - accum["lean_files"] += 1 - if suffix in {".lean", ".agda", ".v", ".thy"}: - accum["proof_files"] += 1 - if suffix == ".py": - accum["python_files"] += 1 - if suffix == ".md": - accum["markdown_files"] += 1 - - accum["definitions"] += count_regex(r"^\s*(def|abbrev|structure|inductive|class|instance|#let)\b", text) - accum["theorems"] += count_regex(r"^\s*(theorem|example)\b", text) - accum["lemmas"] += count_regex(r"^\s*lemma\b", text) - accum["axioms"] += count_regex(r"^\s*(axiom|constant)\b", text) - accum["imports"] += count_regex(r"^\s*(import|from\s+\S+\s+import|#import)\b", text) - accum["evals"] += count_regex(r"^\s*(#eval|#check|native_decide)\b", text) - accum["sorry_markers"] += len(re.findall(r"\bsorry\b", text)) - accum["admit_markers"] += len(re.findall(r"\badmit\b", text)) - accum["math_symbols"] += len(re.findall(r"[∀∃λΛΣΠα-ωΑ-Ω≤≥≠≈→←↔⊢⊨∂∇∑∏√∞]", text)) - accum["unicode_symbols"] += sum(1 for ch in text if ord(ch) > 127) - for key, pattern in TERM_PATTERNS.items(): - hits = pattern.findall(text) - accum[key] += len(hits) - term_counter.update(str(hit).lower() for hit in hits) - - accum["byte_count_log"] = math.log2(byte_count + 1) - accum["avg_line_length"] = line_length_sum / line_count if line_count else 0.0 - content_hash = all_hash.hexdigest() if files else hashlib.sha256(rel.encode("utf-8")).hexdigest() - return RepoVector( - name=rel.split("/")[-1] or rel, - path=rel, - exists=True, - features=accum, - top_terms=term_counter.most_common(12), - file_families=dict(sorted(family_counter.items())), - content_hash=content_hash, - ) - - -def zscore_matrix(vectors: list[RepoVector]) -> np.ndarray: - matrix = np.array([[vec.features[key] for key in FEATURES] for vec in vectors], dtype=float) - means = matrix.mean(axis=0) - stds = matrix.std(axis=0) - stds[stds == 0] = 1.0 - return (matrix - means) / stds - - -def eigen_audit(vectors: list[RepoVector]) -> dict[str, Any]: - active = [vec for vec in vectors if vec.exists and vec.features["file_count"] > 0] - if len(active) < 2: - return {"axes": [], "repo_scores": []} - - zmat = zscore_matrix(active) - cov = np.cov(zmat, rowvar=False) - eigenvalues, eigenvectors = np.linalg.eigh(cov) - order = np.argsort(eigenvalues)[::-1] - eigenvalues = eigenvalues[order] - eigenvectors = eigenvectors[:, order] - total = float(np.sum(np.maximum(eigenvalues, 0.0))) or 1.0 - - axes = [] - repo_scores: dict[str, list[float]] = {vec.path: [] for vec in active} - max_axes = min(6, eigenvectors.shape[1]) - projections = zmat @ eigenvectors[:, :max_axes] - - for axis_i in range(max_axes): - weights = eigenvectors[:, axis_i] - ranked = sorted( - [(FEATURES[i], float(weights[i])) for i in range(len(FEATURES))], - key=lambda item: abs(item[1]), - reverse=True, - ) - repo_rank = sorted( - [(active[i].path, float(projections[i, axis_i])) for i in range(len(active))], - key=lambda item: abs(item[1]), - reverse=True, - ) - axes.append( - { - "axis": axis_i + 1, - "eigenvalue": float(eigenvalues[axis_i]), - "explained_ratio": float(eigenvalues[axis_i] / total), - "top_features": ranked[:8], - "top_repositories": repo_rank[:8], - "interpretation": interpret_axis(ranked[:8]), - } - ) - for repo_i, vec in enumerate(active): - repo_scores[vec.path].append(float(projections[repo_i, axis_i])) - - return {"axes": axes, "repo_scores": repo_scores} - - -def interpret_axis(top_features: list[tuple[str, float]]) -> str: - names = {name for name, _ in top_features[:5]} - if {"proof_terms", "theorems", "lemmas"} & names: - if {"sorry_markers", "admit_markers"} & names: - return "proof-density vs proof-debt axis" - return "formal proof-density axis" - if {"model_terms", "python_files", "dataset_terms"} & names: - return "empirical model/dataset axis" - if {"compression_terms", "geometry_terms"} <= names or {"compression_terms", "spectrum_terms"} <= names: - return "compression-geometry carrier axis" - if {"hardware_terms", "evals"} & names: - return "executable/hardware witness axis" - if {"math_symbols", "unicode_symbols"} & names: - return "symbolic notation density axis" - return "mixed structural axis" - - -def stable_payload(data: Any) -> bytes: - return json.dumps(data, sort_keys=True, separators=(",", ":"), ensure_ascii=False).encode("utf-8") - - -def write_jsonl(vectors: list[RepoVector], audit: dict[str, Any], receipt_hash: str) -> None: - with OUT_JSONL.open("w", encoding="utf-8") as handle: - for vec in vectors: - record = { - "task": "math_repository_eigenvector_audit", - "repo": vec.path, - "exists": vec.exists, - "feature_vector": vec.features, - "top_terms": vec.top_terms, - "content_hash": vec.content_hash, - "teaching_boundary": "Structural eigenvectors route repositories; they do not prove mathematical truth.", - } - handle.write(json.dumps(record, sort_keys=True, ensure_ascii=False) + "\n") - handle.write( - json.dumps( - { - "task": "math_repository_eigenvector_axis_summary", - "axes": audit["axes"], - "receipt_hash": receipt_hash, - "teaching_boundary": "Use axes as compressor route priors, then require exact byte rehydration receipts.", - }, - sort_keys=True, - ensure_ascii=False, - ) - + "\n" - ) - - -def format_feature_list(items: list[list[Any] | tuple[str, float]]) -> str: - return ", ".join(f"{name}={value:.3f}" for name, value in items) - - -def write_tiddler(vectors: list[RepoVector], audit: dict[str, Any], receipt_hash: str) -> None: - existing = [vec for vec in vectors if vec.exists and vec.features["file_count"] > 0] - missing = [vec.path for vec in vectors if not vec.exists or vec.features["file_count"] == 0] - axis_lines = [] - for axis in audit["axes"]: - repos = ", ".join(f"{path} ({score:.2f})" for path, score in axis["top_repositories"][:5]) - axis_lines.append( - "\n".join( - [ - f"!! Axis {axis['axis']}: {axis['interpretation']}", - f"* Explained ratio: {axis['explained_ratio']:.4f}", - f"* Top features: {format_feature_list(axis['top_features'][:6])}", - f"* Dominant repositories: {repos}", - ] - ) - ) - - repo_lines = [] - scores = audit.get("repo_scores", {}) - for vec in existing: - axis_score = scores.get(vec.path, []) - score_text = ", ".join(f"a{i + 1}={score:.2f}" for i, score in enumerate(axis_score[:4])) - repo_lines.append( - f"|{vec.path}|{int(vec.features['file_count'])}|{int(vec.features['lean_files'])}|" - f"{int(vec.features['proof_files'])}|{int(vec.features['python_files'])}|" - f"{score_text}|{(vec.content_hash or '')[:16]}|" - ) - - text = f"""created: 20260507160000000 -modified: 20260507160000000 -tags: Compression Eigenvectors Formalism MathRepositories ProjectableGeometry -title: Math Repository Eigenvector Audit -type: text/vnd.tiddlywiki - -This tiddler records the first structural eigenvector pass over the major math/formalism repositories in the stack. - -The purpose is route selection, not truth promotion. A repository eigenvector says which mathematical surface a module resembles: proof-heavy, model-heavy, compression-geometric, symbolic, hardware-witness, or dataset/receipt oriented. Any compressor candidate still has to pass exact rehydration. - -!! Scope - -* Active repositories/modules scanned: {len(existing)} -* Missing or empty candidates: {len(missing)} -* Receipt: `4-Infrastructure/shim/math_repository_eigenvector_audit.json` -* Curriculum: `4-Infrastructure/shim/math_repository_eigenvector_audit_curriculum.jsonl` -* Receipt hash: `{receipt_hash}` - -!! Eigen Axes - -{chr(10).join(axis_lines)} - -!! Repository Scores - -|!Repository |!Files |!Lean |!Proof files |!Python |!Leading scores |!Content hash | -{chr(10).join(repo_lines)} - -!! Compressor Tuning Implication - -This fills a missing bucket in the projectable-geometry compressor. Before this pass, we had vectors for finance claims, genetics, molecular/PDE priors, and symbolic Standard Model reductions, but not for the math repositories themselves. - -The immediate use is a route prior: - -# Proof-dense repositories should feed theorem/witness tokenbooks and exact proof-state sidecars. -# Model/dataset repositories should feed numeric residual lanes and benchmark-corpus metadata. -# Compression-geometry repositories should feed carrier primitives, residual boat rules, and tokenbook transforms. -# Hardware-witness repositories should feed fixed-point and byte-lane constraints. - -!! Boundary - -This audit is lexical/structural. It does not certify that a theorem compiles, that an equation is correct, or that a repository is more important. It gives the compressor a coarse coordinate system for choosing which specialized route to try first. -""" - OUT_TID.write_text(text, encoding="utf-8") - - -def main() -> None: - vectors = [repo_vector(rel) for rel in REPO_CANDIDATES] - audit = eigen_audit(vectors) - body = { - "generated_at": datetime.now(timezone.utc).isoformat(), - "root": str(ROOT), - "feature_schema": FEATURES, - "candidate_count": len(REPO_CANDIDATES), - "active_count": sum(1 for vec in vectors if vec.exists and vec.features["file_count"] > 0), - "vectors": [ - { - "name": vec.name, - "path": vec.path, - "exists": vec.exists, - "features": vec.features, - "top_terms": vec.top_terms, - "file_families": vec.file_families, - "content_hash": vec.content_hash, - } - for vec in vectors - ], - "audit": audit, - "claim_boundary": "Structural eigenvector audit only; compressor routing prior, not mathematical verification.", - } - receipt_hash = hashlib.sha256(stable_payload({k: v for k, v in body.items() if k != "generated_at"})).hexdigest() - body["receipt_hash"] = receipt_hash - OUT_JSON.write_text(json.dumps(body, indent=2, sort_keys=True, ensure_ascii=False) + "\n", encoding="utf-8") - write_jsonl(vectors, audit, receipt_hash) - write_tiddler(vectors, audit, receipt_hash) - print(json.dumps({"active_count": body["active_count"], "axes": len(audit["axes"]), "receipt_hash": receipt_hash}, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/math_research_database_registry.py b/4-Infrastructure/shim/math_research_database_registry.py deleted file mode 100644 index 0e67f532..00000000 --- a/4-Infrastructure/shim/math_research_database_registry.py +++ /dev/null @@ -1,432 +0,0 @@ -#!/usr/bin/env python3 -"""Registry of mathematical research databases as RRC quarry surfaces. - -This stores source metadata, access boundaries, and density-marker roles for -math research databases. It does not scrape full texts or bypass subscriptions. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "math_research_databases" -JSONL = OUT_DIR / "math_research_database_registry.jsonl" -CSV = OUT_DIR / "math_research_database_registry.csv" -MSC_JSONL = OUT_DIR / "msc2020_top_level_registry.jsonl" -MSC_CSV = OUT_DIR / "msc2020_top_level_registry.csv" -RECEIPT = OUT_DIR / "math_research_database_registry_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -DATABASES: list[dict[str, Any]] = [ - { - "database_id": "MATHDB.REFERENCE.ZBMATH_OPEN.0001", - "name": "zbMATH Open", - "category": "primary_reference_database", - "url": "https://zbmath.org/", - "access_boundary": "Open bibliographic/review metadata; respect platform terms and robots.", - "bulk_access_mode": "rest_api_terms_bound", - "lawful_ingest_surface": "zbMATH Open API bibliographic/review/classification metadata under API terms; no unrestricted full-content mirror assumption.", - "density_markers": [ - "msc_classification_graph", - "msc2020_top_level_ladder", - "api_metadata_surface", - "review_metadata", - "citation_linkage", - "long_historical_coverage", - ], - "rrc_use": "authoritative math-literature routing and classification prior", - "claim_boundary": "index/review metadata is not theorem verification", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.REFERENCE.MATHSCINET.0001", - "name": "MathSciNet / Mathematical Reviews", - "category": "primary_reference_database", - "url": "https://mathscinet.ams.org/", - "access_boundary": "Subscription database; store pointer and metadata role only.", - "bulk_access_mode": "subscription_pointer_only", - "lawful_ingest_surface": "No bulk ingest without explicit licensed access; store source pointer and review/citation role only.", - "density_markers": [ - "expert_review_graph", - "citation_linkage", - "msc_classification_graph", - "author_disambiguation", - ], - "rrc_use": "high-trust review/citation routing when user has lawful access", - "claim_boundary": "subscription metadata pointer only; no scraping", - "status": "HOLD", - }, - { - "database_id": "MATHDB.DML.EUDML.0001", - "name": "EuDML", - "category": "digital_mathematics_library", - "url": "https://eudml.org/", - "access_boundary": "Use open full-text and moving-wall metadata lawfully.", - "bulk_access_mode": "oai_pmh_metadata_harvest", - "lawful_ingest_surface": "OAI-PMH metadata harvesting plus open full-text pointers where rights permit.", - "density_markers": [ - "validated_full_text", - "moving_wall_access", - "oai_pmh_metadata_surface", - "journal_archive_federation", - "historical_math_corpus", - ], - "rrc_use": "full-text source candidate for historical/modern math documents", - "claim_boundary": "full text does not imply formal proof replay", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.DML.PROJECT_EUCLID.0001", - "name": "Project Euclid", - "category": "digital_mathematics_library", - "url": "https://projecteuclid.org/", - "access_boundary": "Mixed open/subscription access; store article pointers and open metadata only.", - "bulk_access_mode": "mixed_access_metadata_pointer", - "lawful_ingest_surface": "Article pointers, open metadata, and open full-text where licensed; no subscription bypass.", - "density_markers": [ - "society_journal_hosting", - "math_statistics_full_text", - "article_metadata", - "independent_journal_surface", - ], - "rrc_use": "math/statistics journal source routing", - "claim_boundary": "respect access controls and article licenses", - "status": "HOLD", - }, - { - "database_id": "MATHDB.PREPRINT.ARXIV_MATH.0001", - "name": "arXiv Math", - "category": "preprint_repository", - "url": "https://arxiv.org/archive/math", - "access_boundary": "Open preprint metadata and PDFs under arXiv terms.", - "bulk_access_mode": "requester_pays_s3_and_metadata_mirror", - "lawful_ingest_surface": "Requester-pays S3 bulk PDFs/source files, OAI-PMH/API metadata, and Kaggle metadata snapshot; link back to arXiv for downloads.", - "density_markers": [ - "requester_pays_s3_bulk_text", - "latex_source_archive", - "kaggle_metadata_snapshot", - "preprint_version_graph", - "author_accepted_manuscript_surface", - "subject_classification", - "fast_modern_research_signal", - ], - "rrc_use": "modern math prior and versioned source surface", - "claim_boundary": "preprint status is not peer-reviewed theorem validation", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.ARCHIVE.JSTOR.0001", - "name": "JSTOR", - "category": "historical_digital_library", - "url": "https://www.jstor.org/", - "access_boundary": "Mixed access; store source pointer and public metadata only unless user has lawful access.", - "bulk_access_mode": "restricted_archive_pointer", - "lawful_ingest_surface": "Public metadata/source pointer only unless a lawful text-and-data-mining or institutional access path is present.", - "density_markers": [ - "historical_journal_archive", - "foundational_paper_surface", - "citation_context", - "scan_to_text_boundary", - ], - "rrc_use": "historical provenance and old-theorem source routing", - "claim_boundary": "do not ingest paywalled text without access", - "status": "HOLD", - }, - { - "database_id": "MATHDB.ARCHIVE.GALLICA.0001", - "name": "Gallica", - "category": "historical_digital_library", - "url": "https://gallica.bnf.fr/", - "access_boundary": "Use public-domain/open archive material under Gallica terms.", - "bulk_access_mode": "open_archive_pointer", - "lawful_ingest_surface": "Public-domain/open scans and metadata under Gallica terms, with OCR residual tracking.", - "density_markers": [ - "digital_incunable_surface", - "historical_scan", - "ocr_noise_residual", - "foundational_math_source", - ], - "rrc_use": "public historical math source with OCR residual tracking", - "claim_boundary": "OCR text needs residual/scan receipts", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.ARCHIVE.PROJECT_GUTENBERG.0001", - "name": "Project Gutenberg", - "category": "historical_digital_library", - "url": "https://www.gutenberg.org/", - "access_boundary": "Use public-domain/open texts under Project Gutenberg terms.", - "bulk_access_mode": "open_public_domain_text_repository", - "lawful_ingest_surface": "Public-domain text files and metadata for historical mathematics books where available.", - "density_markers": [ - "public_domain_text_surface", - "historical_book_corpus", - "ocr_or_transcription_residual", - "foundational_math_source", - ], - "rrc_use": "historical math prose and notation source routing", - "claim_boundary": "public-domain text is source material, not proof replay", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.ARCHIVE.INTERNET_ARCHIVE.0001", - "name": "Internet Archive", - "category": "historical_digital_library", - "url": "https://archive.org/", - "access_boundary": "Use public-domain/open collections and item metadata under item-specific rights.", - "bulk_access_mode": "open_archive_item_collections", - "lawful_ingest_surface": "Open item metadata, scans, OCR, and community collections where rights permit; avoid unofficial copyright-risk mirrors.", - "density_markers": [ - "public_domain_scan_surface", - "community_collection_surface", - "ocr_noise_residual", - "historical_math_corpus", - ], - "rrc_use": "public historical scan/OCR source with residual receipts", - "claim_boundary": "item-level rights and OCR quality must be receipted", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.DATA.MARDI.0001", - "name": "MaRDI", - "category": "mathematical_research_data_initiative", - "url": "https://www.mardi4nfdi.de/", - "access_boundary": "Use public metadata and open APIs/datasets only.", - "bulk_access_mode": "fair_data_portal", - "lawful_ingest_surface": "FAIR mathematical model, algorithm, and research-data metadata/datasets where openly licensed.", - "density_markers": [ - "mathematical_model_database", - "algorithm_database", - "research_data_graph", - "model_metadata_surface", - ], - "rrc_use": "MathModDB/MathAlgoDB-style model and algorithm routing", - "claim_boundary": "model metadata is not validated implementation", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.CONJECTURE.BLOOM_ERDOS.0001", - "name": "Bloom's Erdos Conjectures Database", - "category": "specialized_conjecture_database", - "url": "https://www.erdosproblems.com/", - "access_boundary": "Use public problem metadata and cite source; do not overclaim solver status.", - "bulk_access_mode": "public_problem_metadata", - "lawful_ingest_surface": "Public conjecture/problem metadata and status pointers with HOLD-first benchmark handling.", - "density_markers": [ - "open_problem_graph", - "combinatorics_number_theory_surface", - "benchmark_problem_set", - "conjecture_status_marker", - ], - "rrc_use": "HOLD-first benchmark surface for autonomous math research agents", - "claim_boundary": "open problem metadata is not proof or disproof", - "status": "CANDIDATE", - }, - { - "database_id": "MATHDB.SEQUENCE.OEIS.0001", - "name": "OEIS", - "category": "specialized_sequence_database", - "url": "https://oeis.org/", - "access_boundary": "Respect OEIS terms; store sequence IDs and pattern metadata, not bulk copies.", - "bulk_access_mode": "direct_bulk_download_and_git_mirror", - "lawful_ingest_surface": "Official stripped.gz sequence data, names.gz descriptions, and oeisdata GitHub mirror under OEIS license terms.", - "density_markers": [ - "direct_sequence_bulk_download", - "git_sequence_mirror", - "integer_sequence_identity", - "pattern_recognition_surface", - "formula_crosslink", - "sequence_reference_graph", - ], - "rrc_use": "sequence/logogram pattern recognition and residual routing", - "claim_boundary": "sequence match is a hypothesis, not theorem proof", - "status": "CANDIDATE", - }, -] - - -MSC2020_TOP_LEVEL: list[tuple[str, str]] = [ - ("00", "General and overarching topics; collections"), - ("01", "History and biography"), - ("03", "Mathematical logic and foundations"), - ("05", "Combinatorics"), - ("06", "Order, lattices, ordered algebraic structures"), - ("08", "General algebraic systems"), - ("11", "Number theory"), - ("12", "Field theory and polynomials"), - ("13", "Commutative algebra"), - ("14", "Algebraic geometry"), - ("15", "Linear and multilinear algebra; matrix theory"), - ("16", "Associative rings and algebras"), - ("17", "Nonassociative rings and algebras"), - ("18", "Category theory; homological algebra"), - ("19", "K-theory"), - ("20", "Group theory and generalizations"), - ("22", "Topological groups, Lie groups"), - ("26", "Real functions"), - ("28", "Measure and integration"), - ("30", "Functions of a complex variable"), - ("31", "Potential theory"), - ("32", "Several complex variables and analytic spaces"), - ("33", "Special functions"), - ("34", "Ordinary differential equations"), - ("35", "Partial differential equations"), - ("37", "Dynamical systems and ergodic theory"), - ("39", "Difference and functional equations"), - ("40", "Sequences, series, summability"), - ("41", "Approximations and expansions"), - ("42", "Harmonic analysis on Euclidean spaces"), - ("43", "Abstract harmonic analysis"), - ("44", "Integral transforms, operational calculus"), - ("45", "Integral equations"), - ("46", "Functional analysis"), - ("47", "Operator theory"), - ("49", "Calculus of variations and optimal control; optimization"), - ("51", "Geometry"), - ("52", "Convex and discrete geometry"), - ("53", "Differential geometry"), - ("54", "General topology"), - ("55", "Algebraic topology"), - ("57", "Manifolds and cell complexes"), - ("58", "Global analysis, analysis on manifolds"), - ("60", "Probability theory and stochastic processes"), - ("62", "Statistics"), - ("65", "Numerical analysis"), - ("68", "Computer science"), - ("70", "Mechanics of particles and systems"), - ("74", "Mechanics of deformable solids"), - ("76", "Fluid mechanics"), - ("78", "Optics, electromagnetic theory"), - ("80", "Classical thermodynamics, heat transfer"), - ("81", "Quantum theory"), - ("82", "Statistical mechanics, structure of matter"), - ("83", "Relativity and gravitational theory"), - ("85", "Astronomy and astrophysics"), - ("86", "Geophysics"), - ("90", "Operations research, mathematical programming"), - ("91", "Game theory, economics, finance, and other social and behavioral sciences"), - ("92", "Biology and other natural sciences"), - ("93", "Systems theory; control"), - ("94", "Information and communication theory, circuits"), - ("97", "Mathematics education"), -] - - -def packetize(entry: dict[str, Any]) -> dict[str, Any]: - packet = { - "schema": "math_research_database_registry_v1", - "rrc_shape_hint": "MathSourceDensityRegistry", - **entry, - } - packet["packet_hash"] = sha256_text(stable_json(packet)) - return packet - - -def packetize_msc(code: str, label: str) -> dict[str, Any]: - packet = { - "schema": "msc2020_top_level_registry_v1", - "classification_id": f"MSC2020.{code}", - "code": code, - "label": label, - "source": "MSC2020 top-level classification list", - "license_note": "MSC2020 is published by Mathematical Reviews and zbMATH Open under CC-BY-NC-SA; store code and label with attribution.", - "rrc_shape_hint": "MSC2020ClassificationLadder", - "density_markers": [ - "classification_axis", - "math_domain_boundary", - "literature_routing_prior", - "source_density_marker", - ], - "status": "CANDIDATE", - "claim_boundary": "Classification route only; not proof validation or full-text access.", - } - packet["packet_hash"] = sha256_text(stable_json(packet)) - return packet - - -def csv_escape(value: Any) -> str: - text = str(value).replace('"', '""') - return f'"{text}"' - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = [packetize(entry) for entry in DATABASES] - msc_packets = [packetize_msc(code, label) for code, label in MSC2020_TOP_LEVEL] - JSONL.write_text("\n".join(stable_json(packet) for packet in packets) + "\n", encoding="utf-8") - MSC_JSONL.write_text("\n".join(stable_json(packet) for packet in msc_packets) + "\n", encoding="utf-8") - lines = [ - "database_id,name,category,status,bulk_access_mode,lawful_ingest_surface,density_markers,rrc_use,url,packet_hash" - ] - for packet in packets: - lines.append( - ",".join( - [ - csv_escape(packet["database_id"]), - csv_escape(packet["name"]), - csv_escape(packet["category"]), - csv_escape(packet["status"]), - csv_escape(packet["bulk_access_mode"]), - csv_escape(packet["lawful_ingest_surface"]), - csv_escape(";".join(packet["density_markers"])), - csv_escape(packet["rrc_use"]), - csv_escape(packet["url"]), - csv_escape(packet["packet_hash"]), - ] - ) - ) - CSV.write_text("\n".join(lines) + "\n", encoding="utf-8") - msc_lines = ["classification_id,code,label,status,density_markers,packet_hash"] - for packet in msc_packets: - msc_lines.append( - ",".join( - [ - csv_escape(packet["classification_id"]), - csv_escape(packet["code"]), - csv_escape(packet["label"]), - csv_escape(packet["status"]), - csv_escape(";".join(packet["density_markers"])), - csv_escape(packet["packet_hash"]), - ] - ) - ) - MSC_CSV.write_text("\n".join(msc_lines) + "\n", encoding="utf-8") - status_counts: dict[str, int] = {} - category_counts: dict[str, int] = {} - for packet in packets: - status_counts[packet["status"]] = status_counts.get(packet["status"], 0) + 1 - category_counts[packet["category"]] = category_counts.get(packet["category"], 0) + 1 - receipt = { - "schema": "math_research_database_registry_receipt_v1", - "claim_boundary": "Metadata-only source registry; no subscription bypass, no bulk scraping, and no proof claims.", - "database_count": len(packets), - "status_counts": status_counts, - "category_counts": category_counts, - "msc2020_top_level_count": len(msc_packets), - "jsonl": str(JSONL.relative_to(REPO)), - "csv": str(CSV.relative_to(REPO)), - "msc_jsonl": str(MSC_JSONL.relative_to(REPO)), - "msc_csv": str(MSC_CSV.relative_to(REPO)), - "database_ids": [packet["database_id"] for packet in packets], - "msc2020_codes": [packet["code"] for packet in msc_packets], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/mcp_bus_dry_run.py b/4-Infrastructure/shim/mcp_bus_dry_run.py deleted file mode 100644 index c980cbb4..00000000 --- a/4-Infrastructure/shim/mcp_bus_dry_run.py +++ /dev/null @@ -1,269 +0,0 @@ -#!/usr/bin/env python3 -"""Dry-run MCP bus candidates without enabling live MCP servers. - -This is the first activation layer after the MCP catalog. It runs only safe -read/static checks and local CLI smokes, then emits receipts suitable for the -OpenClaw/metaprobe bus. It does not start long-lived servers, download papers, -run notebooks, execute arbitrary Python, or enable held surfaces. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -CATALOG = SHIM / "mcp_surface_catalog_receipt.json" - - -SAFE_STATIC_SURFACES = { - "modelcontextprotocol_servers", - "modelcontextprotocol_registry", - "github_mcp_server", - "kobsidian", - "arxiv_mcp_server", - "jupyter_mcp_server", - "mcp_python_repl", - "mcp_wolfram_alpha", - "tardygrada_mcp", - "substack_connector_mcp", - "claw_mcp_tool_pool", - "awesome_mcp_servers", -} - -HELD_SURFACES = {"sci_hub_mcp_server"} - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def safe_read(path: Path, limit: int = 20000) -> str: - if not path.exists() or not path.is_file(): - return "" - return path.read_text(encoding="utf-8", errors="replace")[:limit] - - -def file_hash(path: Path) -> str | None: - if not path.exists() or not path.is_file(): - return None - return sha256_bytes(path.read_bytes()) - - -def run_sciencehub_report(timeout: int) -> dict[str, Any]: - script = REPO / "scripts" / "sciencehub_mcp.py" - proc = subprocess.run( - ["python", str(script), "--report"], - cwd=str(REPO), - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - metrics: dict[str, Any] = {} - for line in proc.stdout.splitlines(): - if ":" not in line: - continue - key, value = line.split(":", 1) - key = key.strip().lower().replace(" ", "_") - value = value.strip() - if value.isdigit(): - metrics[key] = int(value) - return { - "surface_id": "sciencehub_mcp", - "mode": "cli_report", - "command": "python scripts/sciencehub_mcp.py --report", - "returncode": proc.returncode, - "stdout_hash": sha256_text(proc.stdout), - "stderr_hash": sha256_text(proc.stderr), - "stdout_tail": proc.stdout[-2000:], - "metrics": metrics, - "lawful": proc.returncode == 0 and bool(metrics), - "claim_boundary": "ScienceHub CLI report smoke only; no paper download or remote MCP activation.", - } - - -def static_surface_check(surface: dict[str, Any]) -> dict[str, Any]: - surface_id = surface["id"] - rel_path = surface["path"] - path = REPO / rel_path - readme = path / "README.md" if path.is_dir() else path - if not readme.exists() and path.is_dir(): - candidates = sorted(path.glob("README*")) - readme = candidates[0] if candidates else readme - readme_text = safe_read(readme) - metadata_files = [] - if path.is_dir(): - for name in ("package.json", "pyproject.toml", "go.mod", "Cargo.toml", "requirements.txt"): - candidate = path / name - if candidate.exists(): - metadata_files.append(str(candidate.relative_to(REPO))) - gate = surface.get("gate", "") - hold = surface_id in HELD_SURFACES or "hold" in gate.lower() - markers = { - "has_readme": bool(readme_text), - "has_gate": bool(gate), - "has_source_reference": bool(surface.get("source_hash") or surface.get("source_fingerprint") or surface.get("commit")), - "hold": hold, - } - lawful = markers["has_readme"] and markers["has_gate"] and markers["has_source_reference"] - return { - "surface_id": surface_id, - "mode": "static_snapshot_check", - "path": rel_path, - "readme_path": str(readme.relative_to(REPO)) if readme.exists() else None, - "readme_hash": file_hash(readme), - "metadata_files": metadata_files, - "markers": markers, - "source_hash": surface.get("source_hash") or surface.get("source_fingerprint"), - "commit": surface.get("commit"), - "lawful": lawful, - "activation": "held" if hold else "inactive_candidate", - "claim_boundary": gate, - } - - -def build_receipt(catalog_path: Path, timeout: int) -> dict[str, Any]: - catalog = json.loads(catalog_path.read_text(encoding="utf-8")) - surfaces = catalog.get("selected_surfaces", []) - checks = [] - checks.append(run_sciencehub_report(timeout)) - for surface in surfaces: - surface_id = surface["id"] - if surface_id == "sciencehub_mcp": - continue - if surface_id in SAFE_STATIC_SURFACES or surface_id in HELD_SURFACES: - checks.append(static_surface_check(surface)) - lawful_count = sum(1 for check in checks if check.get("lawful")) - held_count = sum(1 for check in checks if check.get("activation") == "held") - return { - "schema": "mcp_bus_dry_run_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "catalog_path": str(catalog_path.relative_to(REPO)), - "catalog_hash": file_hash(catalog_path), - "claim_boundary": "Dry-run checks static MCP surface readiness and local safe smokes only; no live server activation, downloads, notebook execution, or arbitrary code execution.", - "checks": checks, - "check_count": len(checks), - "lawful_count": lawful_count, - "held_count": held_count, - "bus_receipt_rule": "Every future live MCP call must include surface_id, tool_name, arguments_hash, output_hash, source_path, lawful flag, and claim boundary.", - "lawful": lawful_count == len(checks) and len(checks) > 0, - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an MCP bus activation router. Return compact JSON and never activate tools without receipts." - records = [] - for check in receipt["checks"]: - prompt = { - "task": "classify_mcp_bus_dry_run", - "surface_id": check["surface_id"], - "mode": check["mode"], - "lawful": check["lawful"], - "activation": check.get("activation", "dry_run_only"), - "claim_boundary": check["claim_boundary"], - } - answer = { - "selected": bool(check["lawful"]) and check.get("activation") != "held", - "use_as": "mcp_bus_dry_run_receipt", - "surface_id": check["surface_id"], - "source_path": check.get("path") or "scripts/sciencehub_mcp.py", - "source_hash": check.get("source_hash") or check.get("readme_hash") or check.get("stdout_hash"), - "claim_boundary": check["claim_boundary"], - "receipt_rule": receipt["bus_receipt_rule"], - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack MCP OpenClaw Metaprobe DryRun", - "title: MCP Bus Dry Run", - "type: text/vnd.tiddlywiki", - "", - "! MCP Bus Dry Run", - "", - "This dry run checks MCP bus candidates without enabling live servers.", - "", - "Durable source: `4-Infrastructure/shim/mcp_bus_dry_run.py`", - "", - "Receipt: `4-Infrastructure/shim/mcp_bus_dry_run_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/mcp_bus_dry_run_curriculum.jsonl`", - "", - "!! Result", - "", - f"* Checks: {receipt['lawful_count']}/{receipt['check_count']} lawful", - f"* Held surfaces: {receipt['held_count']}", - f"* Overall lawful: `{str(receipt['lawful']).lower()}`", - "", - "!! Claim Boundary", - "", - receipt["claim_boundary"], - "", - "!! Checks", - "", - ] - for check in receipt["checks"]: - status = "PASS" if check["lawful"] else "FAIL" - activation = check.get("activation", check["mode"]) - lines.append(f"* {status} `{check['surface_id']}` ({activation}): {check['claim_boundary']}") - lines.extend( - [ - "", - "!! Links", - "", - "* [[MCP Surface Catalog]]", - "* [[OpenClaw Shared Bus Surface]]", - "* [[Physics Math LLM Metaprobe Audit]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--catalog", type=Path, default=CATALOG) - parser.add_argument("--timeout", type=int, default=30) - parser.add_argument("--receipt", type=Path, default=SHIM / "mcp_bus_dry_run_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "mcp_bus_dry_run_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "MCP Bus Dry Run.tid") - args = parser.parse_args() - - receipt = build_receipt(args.catalog, args.timeout) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 if receipt["lawful"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/mcp_bus_live_safe_probe.py b/4-Infrastructure/shim/mcp_bus_live_safe_probe.py deleted file mode 100644 index 5987d01f..00000000 --- a/4-Infrastructure/shim/mcp_bus_live_safe_probe.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -"""First live-safe MCP bus probe. - -Runs bounded, read-only ScienceHub CLI operations through the same receipt -discipline expected of future MCP/OpenClaw bus calls. This deliberately avoids -server activation, paper downloads, notebook execution, arbitrary Python REPL, -filesystem writes outside the receipt artifacts, and held surfaces. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -SCIENCEHUB = REPO / "scripts" / "sciencehub_mcp.py" - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def file_hash(path: Path) -> str | None: - if not path.exists(): - return None - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def run_sciencehub(args: list[str], timeout: int) -> dict[str, Any]: - command = ["python", str(SCIENCEHUB), *args] - proc = subprocess.run( - command, - cwd=str(REPO), - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - parsed: Any = None - json_ok = False - if args and args[0] == "--search": - try: - parsed = json.loads(proc.stdout) - json_ok = True - except Exception: - parsed = None - return { - "surface_id": "sciencehub_mcp", - "tool_name": "report" if args == ["--report"] else "search_local_corpus", - "arguments": args, - "arguments_hash": sha256_text(json.dumps(args, ensure_ascii=False)), - "command_shape": ["python", "scripts/sciencehub_mcp.py", *args], - "returncode": proc.returncode, - "stdout_hash": sha256_text(proc.stdout), - "stderr_hash": sha256_text(proc.stderr), - "stdout_tail": proc.stdout[-3000:], - "stderr_tail": proc.stderr[-1000:], - "json_ok": json_ok, - "parsed_summary": summarize_search(parsed) if json_ok else summarize_report(proc.stdout), - "lawful": proc.returncode == 0 and not proc.stderr.strip(), - "claim_boundary": "Read-only local ScienceHub CLI call; no MCP server activation, paper download, notebook execution, or arbitrary code execution.", - } - - -def summarize_report(text: str) -> dict[str, Any]: - metrics: dict[str, Any] = {} - for line in text.splitlines(): - if ":" not in line: - continue - key, value = line.split(":", 1) - key = key.strip().lower().replace(" ", "_") - value = value.strip() - if value.isdigit(): - metrics[key] = int(value) - return metrics - - -def summarize_search(parsed: dict[str, Any] | None) -> dict[str, Any]: - parsed = parsed or {} - zotero = parsed.get("zotero", []) or [] - pdfs = parsed.get("pdfs", []) or [] - arxiv_meta = parsed.get("arxiv_meta", []) or [] - return { - "zotero_hits": len(zotero), - "pdf_hits": len(pdfs), - "arxiv_meta_hits": len(arxiv_meta), - "top_titles": [item.get("title") or item.get("title_guess") for item in [*zotero[:3], *pdfs[:3]] if item.get("title") or item.get("title_guess")], - } - - -def build_receipt(queries: list[str], timeout: int) -> dict[str, Any]: - calls = [run_sciencehub(["--report"], timeout)] - for query in queries: - calls.append(run_sciencehub(["--search", query], timeout)) - lawful_calls = sum(1 for call in calls if call["lawful"]) - hashed_calls = sum(1 for call in calls if call["arguments_hash"] and call["stdout_hash"]) - boundary_calls = sum(1 for call in calls if call["claim_boundary"]) - return { - "schema": "mcp_bus_live_safe_probe_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "surface_id": "sciencehub_mcp", - "source_path": str(SCIENCEHUB.relative_to(REPO)), - "source_hash": file_hash(SCIENCEHUB), - "claim_boundary": "First live-safe bus probe uses read-only ScienceHub CLI calls only; no live MCP server activation or restricted retrieval.", - "queries": queries, - "calls": calls, - "call_count": len(calls), - "lawful_calls": lawful_calls, - "hashed_calls": hashed_calls, - "boundary_calls": boundary_calls, - "receipt_rule": "Every future live MCP call must include surface_id, tool_name, arguments_hash, stdout/output hash, source_path, lawful flag, and claim boundary.", - "lawful": lawful_calls == len(calls) and hashed_calls == len(calls) and boundary_calls == len(calls), - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a live-safe MCP bus router. Return compact JSON and preserve read-only boundaries." - records = [] - for call in receipt["calls"]: - prompt = { - "task": "classify_live_safe_mcp_call", - "surface_id": call["surface_id"], - "tool_name": call["tool_name"], - "arguments_hash": call["arguments_hash"], - "parsed_summary": call["parsed_summary"], - "claim_boundary": call["claim_boundary"], - } - answer = { - "selected": bool(call["lawful"]), - "use_as": "live_safe_mcp_bus_probe", - "surface_id": call["surface_id"], - "tool_name": call["tool_name"], - "source_path": receipt["source_path"], - "source_hash": receipt["source_hash"], - "output_hash": call["stdout_hash"], - "claim_boundary": call["claim_boundary"], - "receipt_rule": receipt["receipt_rule"], - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack MCP OpenClaw Metaprobe LiveSafe ScienceHub", - "title: MCP Bus Live Safe Probe", - "type: text/vnd.tiddlywiki", - "", - "! MCP Bus Live Safe Probe", - "", - "This tiddler records the first read-only live-safe bus calls through the ScienceHub surface.", - "", - "Durable source: `4-Infrastructure/shim/mcp_bus_live_safe_probe.py`", - "", - "Receipt: `4-Infrastructure/shim/mcp_bus_live_safe_probe_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/mcp_bus_live_safe_probe_curriculum.jsonl`", - "", - "!! Result", - "", - f"* Calls: {receipt['lawful_calls']}/{receipt['call_count']} lawful", - f"* Overall lawful: `{str(receipt['lawful']).lower()}`", - "", - "!! Claim Boundary", - "", - receipt["claim_boundary"], - "", - "!! Calls", - "", - ] - for call in receipt["calls"]: - status = "PASS" if call["lawful"] else "FAIL" - lines.append(f"* {status} `{call['tool_name']}` args `{call['arguments']}` -> {call['parsed_summary']}") - lines.extend( - [ - "", - "!! Links", - "", - "* [[MCP Bus Dry Run]]", - "* [[MCP Surface Catalog]]", - "* [[OpenClaw Shared Bus Surface]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--query", action="append", default=["compression", "erdos", "topology"]) - parser.add_argument("--timeout", type=int, default=30) - parser.add_argument("--receipt", type=Path, default=SHIM / "mcp_bus_live_safe_probe_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "mcp_bus_live_safe_probe_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "MCP Bus Live Safe Probe.tid") - args = parser.parse_args() - - receipt = build_receipt(args.query, args.timeout) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 if receipt["lawful"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/mcp_surface_catalog.py b/4-Infrastructure/shim/mcp_surface_catalog.py deleted file mode 100644 index 061e56c7..00000000 --- a/4-Infrastructure/shim/mcp_surface_catalog.py +++ /dev/null @@ -1,456 +0,0 @@ -#!/usr/bin/env python3 -"""Catalog local and pulled MCP surfaces as gated Research Stack adapters. - -This script inventories MCP-like surfaces without starting them. It snapshots -external repositories, records local MCP entrypoints, ranks useful surfaces for -the OpenClaw/metaprobe bus, and emits receipt + curriculum + wiki artifacts. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import tomllib -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -MCP_EXTERNAL = REPO / "5-Applications" / "tools-scripts" / "external" / "mcp" - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def file_hash(path: Path) -> str | None: - if not path.exists(): - return None - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def read_text(path: Path, limit: int = 16000) -> str: - if not path.exists(): - return "" - return path.read_text(encoding="utf-8", errors="replace")[:limit] - - -def run_git(path: Path, *args: str) -> str: - proc = subprocess.run( - ["git", "-C", str(path), *args], - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=False, - ) - if proc.returncode != 0: - return "" - return proc.stdout.strip() - - -def package_summary(path: Path) -> dict[str, Any]: - package_path = path / "package.json" - if package_path.exists(): - try: - data = json.loads(package_path.read_text(encoding="utf-8")) - except Exception: - data = {} - return { - "name": data.get("name"), - "version": data.get("version"), - "description": data.get("description"), - "license": data.get("license"), - "scripts": sorted((data.get("scripts") or {}).keys())[:40], - } - pyproject_path = path / "pyproject.toml" - if pyproject_path.exists(): - try: - data = tomllib.loads(pyproject_path.read_text(encoding="utf-8")) - except Exception: - data = {} - project = data.get("project", {}) - return { - "name": project.get("name"), - "version": project.get("version"), - "description": project.get("description"), - "license": project.get("license"), - "scripts": sorted((project.get("scripts") or {}).keys())[:40], - } - return {} - - -def repo_surface(name: str, path: Path, role: str, priority: int, gate: str) -> dict[str, Any]: - readme = read_text(path / "README.md") - return { - "id": name, - "kind": "external_snapshot", - "path": str(path.relative_to(REPO)), - "remote": run_git(path, "remote", "get-url", "origin"), - "commit": run_git(path, "rev-parse", "HEAD"), - "working_tree_clean": run_git(path, "status", "--short") == "", - "package": package_summary(path), - "role": role, - "priority": priority, - "gate": gate, - "source_fingerprint": sha256_text(readme[:8000] + run_git(path, "rev-parse", "HEAD")), - } - - -def local_surface(surface_id: str, path: Path, role: str, priority: int, gate: str, smoke: dict[str, Any] | None = None) -> dict[str, Any]: - text = read_text(path) - return { - "id": surface_id, - "kind": "local_surface", - "path": str(path.relative_to(REPO)), - "source_hash": file_hash(path), - "role": role, - "priority": priority, - "gate": gate, - "smoke": smoke or {}, - "source_fingerprint": sha256_text(text[:8000] + str(file_hash(path))), - } - - -def sciencehub_report() -> dict[str, Any]: - script = REPO / "scripts" / "sciencehub_mcp.py" - if not script.exists(): - return {"available": False} - proc = subprocess.run( - ["python", str(script), "--report"], - cwd=str(REPO), - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=30, - check=False, - ) - metrics: dict[str, Any] = {"available": proc.returncode == 0, "returncode": proc.returncode} - for line in proc.stdout.splitlines(): - if ":" not in line: - continue - key, value = line.split(":", 1) - key = key.strip().lower().replace(" ", "_") - value = value.strip() - if value.isdigit(): - metrics[key] = int(value) - if proc.stderr.strip(): - metrics["stderr_tail"] = proc.stderr[-1000:] - return metrics - - -def build_catalog() -> dict[str, Any]: - external = [ - repo_surface( - "modelcontextprotocol_servers", - MCP_EXTERNAL / "modelcontextprotocol-servers", - "reference servers for filesystem, git, memory, sequential thinking, fetch, time, and protocol testing", - 95, - "inactive snapshot only; enable individual servers with explicit allowlists and per-tool receipts", - ), - repo_surface( - "modelcontextprotocol_registry", - MCP_EXTERNAL / "modelcontextprotocol-registry", - "official registry/API substrate for discovering published MCP servers", - 90, - "use for catalog discovery only; do not auto-install registry results without security scoring", - ), - repo_surface( - "github_mcp_server", - MCP_EXTERNAL / "github-mcp-server", - "GitHub issue, PR, repository, workflow, and code intelligence MCP surface", - 88, - "prefer existing GitHub connector first; use this as pinned implementation/reference until auth and scope filters are explicit", - ), - repo_surface( - "awesome_mcp_servers", - MCP_EXTERNAL / "awesome-mcp-servers", - "broad community discovery list for future MCP candidates", - 65, - "discovery only; every candidate must be separately pinned and audited before use", - ), - repo_surface( - "kobsidian", - MCP_EXTERNAL / "kobsidian", - "filesystem-first Obsidian/Markdown vault server for wiki notes, links, tags, tasks, and LLM-wiki operations", - 91, - "read-only/wiki-index mode first; write tools disabled until vault path allowlist and TiddlyWiki/ENE receipts exist", - ), - repo_surface( - "jupyter_mcp_server", - MCP_EXTERNAL / "jupyter-mcp-server", - "Jupyter notebook control surface for iterative Python/math prototyping with notebook context", - 89, - "sandbox kernel only; no arbitrary host shell; every execution needs notebook path and output hash", - ), - repo_surface( - "mcp_python_repl", - MCP_EXTERNAL / "mcp-python-repl", - "minimal Python REPL MCP surface for quick computation probes", - 83, - "use only in sandboxed scratch directory with timeout and no network/secrets", - ), - repo_surface( - "mcp_wolfram_alpha", - MCP_EXTERNAL / "mcp-wolfram-alpha", - "Wolfram Alpha query surface for external math/facts cross-checking", - 79, - "requires API credential pointer; use for standard-equation cross-checks, not custom theorem promotion", - ), - repo_surface( - "arxiv_mcp_server", - MCP_EXTERNAL / "arxiv-mcp-server", - "arXiv search/download surface with Semantic Scholar citation/reference traversal", - 92, - "research retrieval only; downloaded papers need source hashes and citation receipts before curriculum use", - ), - repo_surface( - "sci_hub_mcp_server", - MCP_EXTERNAL / "sci-hub-mcp-server", - "Sci-Hub-oriented academic paper MCP surface; useful only as a metadata/search-pattern reference", - 25, - "HOLD: do not enable PDF download or paywall bypass. Use lawful/local sources first: ScienceHub, Zotero, arXiv, Semantic Scholar, publisher open access, or user-provided PDFs.", - ), - ] - local = [ - local_surface( - "sciencehub_mcp", - REPO / "scripts" / "sciencehub_mcp.py", - "sovereign research surface for local PDFs, Zotero, arXiv, paper review, and topic fetches", - 96, - "safe as CLI smoke first; MCP mode requires dependency check and source/receipt outputs", - sciencehub_report(), - ), - local_surface( - "substack_connector_mcp", - REPO / "plugins" / "substack-connector" / "scripts" / "substack_mcp_server.py", - "MCP-style Substack publication/update surface", - 72, - "requires local auth env and no secret echo; publish actions must be explicit", - ), - local_surface( - "tardygrada_mcp", - REPO / "2-Search-Space" / "tardygrada" / "README.md", - "claim/proof-carrying language that compiles programs to MCP servers", - 82, - "treat as proof/claim lab; run tests before exposing to shared bus", - ), - local_surface( - "claw_mcp_tool_pool", - REPO / "1-Distributed-Systems" / "agents" / "claw" / "src" / "tools.py", - "local agent tool-pool mirror with MCP include/deny toggles", - 70, - "use as compatibility/reference layer only; deny-prefix controls must remain available", - ), - ] - selected = sorted(external + local, key=lambda item: item["priority"], reverse=True) - return { - "schema": "mcp_surface_catalog_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "claim_boundary": "MCP surfaces are cataloged as inactive adapters. They are not trusted or enabled until scoped auth, allowlists, sandboxing, and metaprobe receipts pass.", - "external_snapshots": external, - "local_surfaces": local, - "selected_surfaces": selected, - "bus_rules": [ - "Prefer local read-only or receipt-producing tools before write-capable tools.", - "Run every MCP surface first in CLI/dry-run mode where possible.", - "Require source path, source hash, tool name, arguments hash, output hash, lawful flag, and claim boundary in every bus receipt.", - "Do not auto-install from registries or awesome lists; pin a commit and audit first.", - "Never pass secrets through model-visible memory; store only credential-store pointers and receipt hashes.", - "Do not use MCP retrieval surfaces to bypass copyright or access controls; route paper retrieval through lawful/local sources.", - ], - "openclaw_bridge": { - "role": "OpenClaw can route MCP surfaces as bus adapters after loopback/sandbox/pairing receipts.", - "first_candidates": ["sciencehub_mcp", "modelcontextprotocol_servers:git", "modelcontextprotocol_servers:memory", "github_mcp_server"], - "research_candidates": ["arxiv_mcp_server", "jupyter_mcp_server", "kobsidian"], - "hold_candidates": ["sci_hub_mcp_server"], - "defer": ["filesystem write tools", "browser automation", "remote unauthenticated servers", "public inbound channels"], - }, - "lawful": True, - } - - -def curriculum_records(catalog: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an MCP surface router. Return compact JSON and keep MCP tools behind receipt gates." - records = [] - for item in catalog["selected_surfaces"]: - prompt = { - "task": "route_mcp_surface", - "surface_id": item["id"], - "kind": item["kind"], - "role": item["role"], - "priority": item["priority"], - "gate": item["gate"], - "claim_boundary": catalog["claim_boundary"], - } - answer = { - "selected": item["priority"] >= 80, - "use_as": "mcp_bus_surface_prior", - "surface_id": item["id"], - "source_path": item["path"], - "source_hash": item.get("source_hash") or item.get("source_fingerprint"), - "route_rule": item["gate"], - "claim_boundary": catalog["claim_boundary"], - "receipt_rule": "Use only after dry-run or scoped live receipt; never treat MCP output as trusted without source/output hashes.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_config(catalog: dict[str, Any], path: Path) -> None: - config = { - "$schema": "research_stack_mcp_surface_config_example_v1", - "claim_boundary": catalog["claim_boundary"], - "servers": { - "sciencehub": { - "command": "python", - "args": ["scripts/sciencehub_mcp.py"], - "mode": "stdio", - "status": "candidate_cli_smoked", - }, - "substack_connector": { - "command": "python", - "args": ["plugins/substack-connector/scripts/substack_mcp_server.py"], - "mode": "stdio", - "status": "candidate_requires_auth", - }, - "mcp_reference_git": { - "command": "uvx", - "args": ["mcp-server-git", "--repository", str(REPO)], - "mode": "stdio", - "status": "candidate_not_enabled", - }, - "mcp_reference_memory": { - "command": "npx", - "args": ["-y", "@modelcontextprotocol/server-memory"], - "mode": "stdio", - "status": "candidate_not_enabled", - }, - "kobsidian": { - "command": "npx", - "args": ["-y", "kobsidian"], - "mode": "stdio", - "status": "candidate_not_enabled_requires_vault_allowlist", - }, - "jupyter_mcp": { - "command": "python", - "args": ["-m", "jupyter_mcp_server"], - "mode": "stdio", - "status": "candidate_not_enabled_requires_sandbox_kernel", - }, - "python_repl": { - "command": "python", - "args": ["-m", "mcp_python"], - "mode": "stdio", - "status": "candidate_not_enabled_requires_sandbox", - }, - "arxiv": { - "command": "python", - "args": ["-m", "arxiv_mcp_server"], - "mode": "stdio", - "status": "candidate_not_enabled_research_retrieval", - }, - "wolfram_alpha": { - "command": "python", - "args": ["-m", "mcp_wolfram_alpha"], - "mode": "stdio", - "status": "candidate_not_enabled_requires_api_credential_pointer", - }, - "sci_hub": { - "command": "python", - "args": ["sci_hub_server.py"], - "mode": "stdio", - "status": "hold_not_enabled_copyright_risk_metadata_reference_only", - }, - }, - "required_receipt_fields": [ - "surface_id", - "tool_name", - "arguments_hash", - "output_hash", - "source_path", - "lawful", - "claim_boundary", - ], - } - path.write_text(json.dumps(config, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - - -def write_wiki(catalog: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack MCP OpenClaw Metaprobe AgentBus", - "title: MCP Surface Catalog", - "type: text/vnd.tiddlywiki", - "", - "! MCP Surface Catalog", - "", - "This catalog pulls and inventories MCP surfaces that can help the OpenClaw/metaprobe shared bus.", - "", - "Durable source: `4-Infrastructure/shim/mcp_surface_catalog.py`", - "", - "Receipt: `4-Infrastructure/shim/mcp_surface_catalog_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/mcp_surface_catalog_curriculum.jsonl`", - "", - "Config skeleton: `4-Infrastructure/shim/mcp_surface_config.example.json`", - "", - "!! Claim Boundary", - "", - catalog["claim_boundary"], - "", - "!! Selected Surfaces", - "", - ] - for item in catalog["selected_surfaces"]: - lines.append(f"* `{item['id']}` ({item['kind']}, priority {item['priority']}): {item['role']}. Gate: {item['gate']}") - lines.extend(["", "!! Bus Rules", ""]) - for rule in catalog["bus_rules"]: - lines.append(f"* {rule}") - lines.extend( - [ - "", - "!! Links", - "", - "* [[OpenClaw Shared Bus Surface]]", - "* [[Physics Math LLM Metaprobe Audit]]", - "* [[Custom Equation Awareness Manifest]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=SHIM / "mcp_surface_catalog_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "mcp_surface_catalog_curriculum.jsonl") - parser.add_argument("--config", type=Path, default=SHIM / "mcp_surface_config.example.json") - parser.add_argument("--wiki", type=Path, default=WIKI / "MCP Surface Catalog.tid") - args = parser.parse_args() - catalog = build_catalog() - args.receipt.write_text(json.dumps(catalog, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(catalog): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_config(catalog, args.config) - write_wiki(catalog, args.wiki) - print(json.dumps(catalog, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/mcp_surface_config.example.json b/4-Infrastructure/shim/mcp_surface_config.example.json deleted file mode 100644 index c8c626ab..00000000 --- a/4-Infrastructure/shim/mcp_surface_config.example.json +++ /dev/null @@ -1,103 +0,0 @@ -{ - "$schema": "research_stack_mcp_surface_config_example_v1", - "claim_boundary": "MCP surfaces are cataloged as inactive adapters. They are not trusted or enabled until scoped auth, allowlists, sandboxing, and metaprobe receipts pass.", - "servers": { - "sciencehub": { - "command": "python", - "args": [ - "scripts/sciencehub_mcp.py" - ], - "mode": "stdio", - "status": "candidate_cli_smoked" - }, - "substack_connector": { - "command": "python", - "args": [ - "plugins/substack-connector/scripts/substack_mcp_server.py" - ], - "mode": "stdio", - "status": "candidate_requires_auth" - }, - "mcp_reference_git": { - "command": "uvx", - "args": [ - "mcp-server-git", - "--repository", - "/home/allaun/Documents/Research Stack" - ], - "mode": "stdio", - "status": "candidate_not_enabled" - }, - "mcp_reference_memory": { - "command": "npx", - "args": [ - "-y", - "@modelcontextprotocol/server-memory" - ], - "mode": "stdio", - "status": "candidate_not_enabled" - }, - "kobsidian": { - "command": "npx", - "args": [ - "-y", - "kobsidian" - ], - "mode": "stdio", - "status": "candidate_not_enabled_requires_vault_allowlist" - }, - "jupyter_mcp": { - "command": "python", - "args": [ - "-m", - "jupyter_mcp_server" - ], - "mode": "stdio", - "status": "candidate_not_enabled_requires_sandbox_kernel" - }, - "python_repl": { - "command": "python", - "args": [ - "-m", - "mcp_python" - ], - "mode": "stdio", - "status": "candidate_not_enabled_requires_sandbox" - }, - "arxiv": { - "command": "python", - "args": [ - "-m", - "arxiv_mcp_server" - ], - "mode": "stdio", - "status": "candidate_not_enabled_research_retrieval" - }, - "wolfram_alpha": { - "command": "python", - "args": [ - "-m", - "mcp_wolfram_alpha" - ], - "mode": "stdio", - "status": "candidate_not_enabled_requires_api_credential_pointer" - }, - "sci_hub": { - "command": "python", - "args": [ - "sci_hub_server.py" - ], - "mode": "stdio", - "status": "hold_not_enabled_copyright_risk_metadata_reference_only" - } - }, - "required_receipt_fields": [ - "surface_id", - "tool_name", - "arguments_hash", - "output_hash", - "source_path", - "lawful", - "claim_boundary" - ] -} diff --git a/4-Infrastructure/shim/mdpi_density_marker_miner.py b/4-Infrastructure/shim/mdpi_density_marker_miner.py deleted file mode 100644 index 1c74aa8c..00000000 --- a/4-Infrastructure/shim/mdpi_density_marker_miner.py +++ /dev/null @@ -1,282 +0,0 @@ -#!/usr/bin/env python3 -"""Curated MDPI density-marker miner. - -This is a conservative metadata miner. It records paper-level route candidates -and density markers, not article bodies. Each candidate is treated as an RRC -prior until local replay/byte-law evidence exists. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "mdpi_density_markers" -JSONL = OUT_DIR / "mdpi_density_marker_candidates.jsonl" -CSV = OUT_DIR / "mdpi_density_marker_candidates.csv" -RECEIPT = OUT_DIR / "mdpi_density_marker_miner_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -CANDIDATES: list[dict[str, Any]] = [ - { - "candidate_id": "MDPI.MILPE.EIGENVECTOR_PROJECTION.2026.0001", - "title": "Multivariate Identification via Linear Projection of Eigenvectors", - "journal": "Mathematics", - "year": 2026, - "url": "https://www.mdpi.com/2227-7390/14/5/897", - "doi": "10.3390/math14050897", - "density_markers": [ - "joint_input_output_solution_space", - "cross_correlation_eigenvectors", - "partial_eigenvector_replay", - "low_rank_governing_equation_projection", - ], - "rrc_use": "LanguageSetMILPEProjection and density-marker eigenvector search", - "claim_boundary": "algorithmic prior for projection/replay; not proof of language model correctness", - "status": "CANDIDATE", - }, - { - "candidate_id": "MDPI.ENTROPY.EIGENVECTOR_LOCALIZATION.2019.0001", - "title": "Information Entropy of Tight-Binding Random Networks with Losses and Gain", - "journal": "Entropy", - "year": 2019, - "url": "https://www.mdpi.com/1099-4300/21/1/86", - "doi": "10.3390/e21010086", - "density_markers": [ - "eigenvector_information_entropy", - "localized_extended_transition", - "complex_spectrum_network_prior", - "graph_adjacency_entropy_surface", - ], - "rrc_use": "score whether density-marker eigenvectors are localized, extended, or transition-like", - "claim_boundary": "network spectral prior only until reproduced on local language/code graphs", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.ENTROPY.AUTOENCODER_INFORMATION_FLOW.2021.0001", - "title": "Information Flows of Diverse Autoencoders", - "journal": "Entropy", - "year": 2021, - "url": "https://www.mdpi.com/1099-4300/23/7/862", - "doi": "10.3390/e23070862", - "density_markers": [ - "information_plane_flow", - "hidden_representation_compression_phase", - "renyi_matrix_entropy", - "sparsity_simplifying_phase", - ], - "rrc_use": "compare density-marker compression flow against hidden-representation simplification", - "claim_boundary": "deep-learning diagnostic prior only; not a language compression result", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.SENSORS.GRAPH_LIGHT_FIELD_CODING.2022.0001", - "title": "Novel Projection Schemes for Graph-Based Light Field Coding", - "journal": "Sensors", - "year": 2022, - "url": "https://www.mdpi.com/1424-8220/22/13/4948", - "doi": "10.3390/s22134948", - "density_markers": [ - "graph_based_signal_redundancy", - "irregular_shape_energy_compaction", - "super_ray_projection", - "graph_transform_coding", - ], - "rrc_use": "graph-transform analogy for irregular language/code density surfaces", - "claim_boundary": "image/light-field coding prior only; needs local graph replay", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.ENTROPY.COMPLEX_NETWORK_ENTROPY_SURVEY.2020.0001", - "title": "A Survey of Information Entropy Metrics for Complex Networks", - "journal": "Entropy", - "year": 2020, - "url": "https://www.mdpi.com/1099-4300/22/12/1417", - "doi": "10.3390/e22121417", - "density_markers": [ - "graph_entropy_metric_catalog", - "eigenvector_centrality_entropy", - "topological_potential_entropy", - "network_probability_distribution_choice", - ], - "rrc_use": "choose entropy measures for language-set density-marker graphs", - "claim_boundary": "metric catalog prior; metric choice must be receipted per graph", - "status": "CANDIDATE", - }, - { - "candidate_id": "MDPI.ALGORITHMS.MAXENT_GRAPH_SPECTRUM.2022.0001", - "title": "Maximum Entropy Approach to Massive Graph Spectrum Learning with Applications", - "journal": "Algorithms", - "year": 2022, - "url": "https://www.mdpi.com/1999-4893/15/6/209", - "doi": "10.3390/a15060209", - "density_markers": [ - "maximum_entropy_spectral_density", - "graph_moment_information", - "massive_graph_spectrum_approximation", - "kernel_free_spectral_estimate", - ], - "rrc_use": "estimate spectrum of large language/code manifold graphs without full eigendecomposition", - "claim_boundary": "spectral approximation prior; not accepted until compared with local exact small graphs", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.ENTROPY.NETWORK_CODING_THERMODYNAMICS.2019.0001", - "title": "The Thermodynamics of Network Coding, and an Algorithmic Refinement of the Principle of Maximum Entropy", - "journal": "Entropy", - "year": 2019, - "url": "https://www.mdpi.com/1099-4300/21/6/560", - "doi": "10.3390/e21060560", - "density_markers": [ - "algorithmic_probability_network_prior", - "graph_entropy_distribution_dependence", - "compressed_program_nonrandomness_witness", - "maximum_entropy_refinement", - ], - "rrc_use": "separate apparent graph entropy from generator-law compressibility", - "claim_boundary": "algorithmic-information prior; needs computable local compressor witness", - "status": "CANDIDATE", - }, - { - "candidate_id": "MDPI.ENTROPY.MULTIDIMENSIONAL_NETWORK_DISTORTION.2021.0001", - "title": "Algorithmic Information Distortions in Node-Aligned and Node-Unaligned Multidimensional Networks", - "journal": "Entropy", - "year": 2021, - "url": "https://www.mdpi.com/1099-4300/23/7/835", - "doi": "10.3390/e23070835", - "density_markers": [ - "multidimensional_network_complexity", - "lossless_compression_graph_distortion", - "node_alignment_effect", - "multilayer_network_information_content", - ], - "rrc_use": "warn when aligning language/code graph layers changes algorithmic information", - "claim_boundary": "distortion prior; local alignment receipts required", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.ENTROPY.UNIQUE_INFORMATION.2014.0001", - "title": "Quantifying Unique Information", - "journal": "Entropy", - "year": 2014, - "url": "https://www.mdpi.com/70176", - "doi": "10.3390/e16042161", - "density_markers": [ - "shared_unique_synergistic_information", - "partial_information_decomposition_prior", - "marginal_invariance_property", - "redundancy_synergy_split", - ], - "rrc_use": "separate shared density markers from language-specific unique markers", - "claim_boundary": "information-decomposition prior; requires local variable definitions", - "status": "CANDIDATE", - }, - { - "candidate_id": "MDPI.ENTROPY.TOPOLOGICAL_INFORMATION_DATA_ANALYSIS.2019.0001", - "title": "Topological Information Data Analysis", - "journal": "Entropy", - "year": 2019, - "url": "https://www.mdpi.com/1099-4300/21/9/869", - "doi": "10.3390/e21090869", - "density_markers": [ - "homological_information_functions", - "mutual_information_decomposition_topology", - "information_complex", - "topological_data_analysis_entropy", - ], - "rrc_use": "topological lens for density-marker graph decompositions", - "claim_boundary": "topological-information prior; needs local graph construction", - "status": "HOLD", - }, - { - "candidate_id": "MDPI.ENTROPY.MULTIMODAL_INFORMATION_BOTTLENECK.2026.0001", - "title": "A Unified Information Bottleneck Framework for Multimodal Biomedical Machine Learning", - "journal": "Entropy", - "year": 2026, - "url": "https://www.mdpi.com/journal/entropy", - "doi": "10.3390/e28040445", - "density_markers": [ - "information_bottleneck_tradeoff", - "modality_redundancy_synergy", - "fusion_collapse_diagnostic", - "transfer_entropy_sequence_prior", - ], - "rrc_use": "analogy for multi-language/code modality fusion and redundancy scoring", - "claim_boundary": "journal listing/abstract prior; verify article page before promotion", - "status": "HOLD", - }, -] - - -def packetize(candidate: dict[str, Any]) -> dict[str, Any]: - packet = { - "schema": "mdpi_density_marker_candidate_v1", - "source_family": "MDPI", - "rrc_shape_hint": "LanguageSetMILPEProjection", - **candidate, - } - packet["packet_hash"] = sha256_text(stable_json(packet)) - return packet - - -def csv_escape(value: Any) -> str: - text = str(value).replace('"', '""') - return f'"{text}"' - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = [packetize(candidate) for candidate in CANDIDATES] - JSONL.write_text("\n".join(stable_json(packet) for packet in packets) + "\n", encoding="utf-8") - - lines = ["candidate_id,title,journal,year,status,density_markers,rrc_use,url,doi,packet_hash"] - for packet in packets: - lines.append( - ",".join( - [ - csv_escape(packet["candidate_id"]), - csv_escape(packet["title"]), - csv_escape(packet["journal"]), - csv_escape(packet["year"]), - csv_escape(packet["status"]), - csv_escape(";".join(packet["density_markers"])), - csv_escape(packet["rrc_use"]), - csv_escape(packet["url"]), - csv_escape(packet["doi"]), - csv_escape(packet["packet_hash"]), - ] - ) - ) - CSV.write_text("\n".join(lines) + "\n", encoding="utf-8") - - status_counts: dict[str, int] = {} - for packet in packets: - status_counts[packet["status"]] = status_counts.get(packet["status"], 0) + 1 - receipt = { - "schema": "mdpi_density_marker_miner_receipt_v1", - "claim_boundary": "Metadata-only MDPI mining surface; candidates are priors until local replay, residual, and byte-law receipts exist.", - "candidate_count": len(packets), - "status_counts": status_counts, - "jsonl": str(JSONL.relative_to(REPO)), - "csv": str(CSV.relative_to(REPO)), - "candidate_ids": [packet["candidate_id"] for packet in packets], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/merkle_tensegrity_load_equation_generator.py b/4-Infrastructure/shim/merkle_tensegrity_load_equation_generator.py deleted file mode 100644 index 603e1605..00000000 --- a/4-Infrastructure/shim/merkle_tensegrity_load_equation_generator.py +++ /dev/null @@ -1,333 +0,0 @@ -#!/usr/bin/env python3 -"""Merkle-attested tensegrity load-equation generator for a synthetic print lattice. - -This is a mechanical/attestation test harness, not a slicer and not a safety -certifier. The key separation is: - -* mechanics: solve an equilibrium residual over geometry, loads, edge force - densities, and support reactions; -* print command: map force magnitudes into bounded density commands with a - sigmoid; -* attestation: commit the records into a Merkle root. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -import numpy as np - - -REPO = Path(__file__).resolve().parents[2] -OUT = REPO / "4-Infrastructure" / "shim" / "merkle_tensegrity_load_equation_receipt.json" -CURRICULUM = REPO / "4-Infrastructure" / "shim" / "merkle_tensegrity_load_equation_curriculum.jsonl" - - -@dataclass(frozen=True) -class Lattice: - nodes: np.ndarray - edges: list[tuple[int, int]] - supports: list[int] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def cube_lattice(*, include_face_diagonals: bool) -> Lattice: - nodes = np.array([[x, y, z] for x in [0.0, 1.0] for y in [0.0, 1.0] for z in [0.0, 1.0]], dtype=float) - edges: list[tuple[int, int]] = [] - for i, xi in enumerate(nodes): - for j, xj in enumerate(nodes): - if j <= i: - continue - length = np.linalg.norm(xi - xj) - if np.isclose(length, 1.0) or (include_face_diagonals and np.isclose(length, 2 ** 0.5)): - edges.append((i, j)) - supports = [i for i, node in enumerate(nodes) if np.isclose(node[2], 0.0)] - return Lattice(nodes=nodes, edges=edges, supports=supports) - - -def generate_load_profile( - num_nodes: int, - *, - rng: np.random.Generator, - gravity: float = -9.81, - mass_per_node: float = 0.1, - lateral_noise_sigma: float = 0.05, -) -> np.ndarray: - loads = np.zeros((num_nodes, 3), dtype=float) - loads[:, 0] = rng.normal(0.0, lateral_noise_sigma, size=num_nodes) - loads[:, 1] = rng.normal(0.0, lateral_noise_sigma, size=num_nodes) - loads[:, 2] = mass_per_node * gravity - return loads - - -def equilibrium_matrix(nodes: np.ndarray, edges: list[tuple[int, int]]) -> np.ndarray: - """Return B where B @ q gives nodal force from signed edge force densities.""" - n = len(nodes) - b = np.zeros((3 * n, len(edges)), dtype=float) - for col, (i, j) in enumerate(edges): - direction_i = nodes[i] - nodes[j] - direction_j = nodes[j] - nodes[i] - b[3 * i : 3 * i + 3, col] = direction_i - b[3 * j : 3 * j + 3, col] = direction_j - return b - - -def support_reaction_matrix(num_nodes: int, supports: list[int]) -> np.ndarray: - """Three reaction components per support node.""" - r = np.zeros((3 * num_nodes, 3 * len(supports)), dtype=float) - for support_index, node_index in enumerate(supports): - for axis in range(3): - r[3 * node_index + axis, 3 * support_index + axis] = 1.0 - return r - - -def solve_equilibrium(lattice: Lattice, loads: np.ndarray) -> dict[str, Any]: - b_edge = equilibrium_matrix(lattice.nodes, lattice.edges) - b_support = support_reaction_matrix(len(lattice.nodes), lattice.supports) - a_aug = np.concatenate([b_edge, b_support], axis=1) - rhs = -loads.reshape(-1) - solution, *_ = np.linalg.lstsq(a_aug, rhs, rcond=None) - q_signed = solution[: len(lattice.edges)] - support_reactions = solution[len(lattice.edges) :] - residual = a_aug @ solution + loads.reshape(-1) - return { - "equilibrium_matrix": b_edge, - "support_matrix": b_support, - "augmented_matrix": a_aug, - "q_signed": q_signed, - "support_reactions": support_reactions.reshape((len(lattice.supports), 3)), - "residual": residual.reshape(loads.shape), - } - - -def shielded_density(q_signed: np.ndarray, *, duality_coefficient: float, density_midpoint: float) -> np.ndarray: - """Map signed force density magnitude to a bounded [0,1] print-density command.""" - q_abs = np.abs(q_signed) - x = duality_coefficient * (q_abs - density_midpoint) - return 1.0 / (1.0 + np.exp(-x)) - - -def merkle_root(leaves: list[str]) -> str: - if not leaves: - return sha256_text("") - level = leaves[:] - while len(level) > 1: - if len(level) % 2: - level.append(level[-1]) - level = [ - sha256_text(level[i] + level[i + 1]) - for i in range(0, len(level), 2) - ] - return level[0] - - -def rounded_list(array: np.ndarray, decimals: int = 8) -> Any: - return np.round(array.astype(float), decimals).tolist() - - -def build_leaf_records( - lattice: Lattice, - loads: np.ndarray, - q_signed: np.ndarray, - density: np.ndarray, - support_reactions: np.ndarray, - residual: np.ndarray, -) -> list[dict[str, Any]]: - records: list[dict[str, Any]] = [] - for i, node in enumerate(lattice.nodes): - records.append({ - "record_type": "node_load", - "node_id": i, - "position": rounded_list(node), - "external_load": rounded_list(loads[i]), - "equilibrium_residual": rounded_list(residual[i]), - }) - for edge_id, (i, j) in enumerate(lattice.edges): - records.append({ - "record_type": "edge_force_density", - "edge_id": edge_id, - "nodes": [i, j], - "vector_i_minus_j": rounded_list(lattice.nodes[i] - lattice.nodes[j]), - "q_signed": round(float(q_signed[edge_id]), 10), - "print_density_0_1": round(float(density[edge_id]), 10), - }) - for support_row, node_id in enumerate(lattice.supports): - records.append({ - "record_type": "support_reaction", - "node_id": node_id, - "reaction": rounded_list(support_reactions[support_row]), - }) - return records - - -def build_receipt(args: argparse.Namespace) -> dict[str, Any]: - lattice = cube_lattice(include_face_diagonals=args.include_face_diagonals) - rng = np.random.default_rng(args.seed) - loads = generate_load_profile( - len(lattice.nodes), - rng=rng, - gravity=args.gravity, - mass_per_node=args.mass_per_node, - lateral_noise_sigma=args.lateral_noise_sigma, - ) - solved = solve_equilibrium(lattice, loads) - q_signed = solved["q_signed"] - density = shielded_density( - q_signed, - duality_coefficient=args.duality_coefficient, - density_midpoint=args.density_midpoint, - ) - residual = solved["residual"] - residual_norm = float(np.linalg.norm(residual)) - acceptable = residual_norm <= args.epsilon_mech - leaf_records = build_leaf_records( - lattice, - loads, - q_signed, - density, - solved["support_reactions"], - residual, - ) - leaf_hashes = [sha256_text(stable_json(record)) for record in leaf_records] - receipt: dict[str, Any] = { - "schema": "merkle_tensegrity_load_equation_receipt_v1", - "claim_boundary": ( - "This harness tests equilibrium residuals and Merkle commitments for a " - "synthetic cube lattice. It is not a structural safety certificate, " - "not a slicer, and not proof that sigmoid density commands are printable " - "or mechanically sufficient." - ), - "source_priors": { - "merkle_attested_3d_printing_note": "docs/merkle_tree_3d_printing_zcash_load_distribution.md", - "invariant_dual_mechanics": { - "title": "Invariant dual mechanics of tensegrity and origami", - "doi": "10.1073/pnas.2519138123", - "local_supporting_materials": "Invariant Dual Mechanics Supporting Materials", - }, - }, - "parameters": { - "seed": args.seed, - "gravity": args.gravity, - "mass_per_node": args.mass_per_node, - "lateral_noise_sigma": args.lateral_noise_sigma, - "duality_coefficient": args.duality_coefficient, - "density_midpoint": args.density_midpoint, - "epsilon_mech": args.epsilon_mech, - "include_face_diagonals": args.include_face_diagonals, - }, - "equations": { - "node_equilibrium": "sum_{j in adj(i)} q_ij * (x_i - x_j) + p_i + r_i = 0", - "matrix_equilibrium": "[B_edges B_support] * [q r]^T = -p", - "least_squares_solution": "argmin_{q,r} ||[B_edges B_support][q r]^T + p||_2", - "shielded_density": "rho_e = 1 / (1 + exp(-alpha * (abs(q_e) - q_mid)))", - "mechanical_acceptance": "||R_mech||_2 <= epsilon_mech", - "leaf_commitment": "leaf_i = H(stable_json(record_i))", - "merkle_root": "MerkleRoot(leaf_1, ..., leaf_N)", - }, - "lattice": { - "node_count": len(lattice.nodes), - "edge_count": len(lattice.edges), - "support_count": len(lattice.supports), - "nodes": rounded_list(lattice.nodes), - "edges": lattice.edges, - "supports": lattice.supports, - }, - "results": { - "load_vectors": rounded_list(loads), - "q_signed": rounded_list(q_signed), - "print_density_0_1": rounded_list(density), - "support_reactions": rounded_list(solved["support_reactions"]), - "residual_vectors": rounded_list(residual), - "residual_norm_l2": residual_norm, - "mechanically_acceptable": acceptable, - "total_abs_edge_force_density": float(np.sum(np.abs(q_signed))), - "density_min": float(np.min(density)), - "density_max": float(np.max(density)), - }, - "merkle": { - "leaf_count": len(leaf_records), - "leaf_hashes": leaf_hashes, - "root": merkle_root(leaf_hashes), - }, - "failure_rules": [ - "Merkle root treated as mechanical proof -> invalid", - "sigmoid density treated as solved equilibrium -> invalid", - "unbraced lattice cannot carry lateral loads -> invalid residual or add diagonals", - "unsupported free-body gravity case without support reactions -> invalid residual", - "residual_norm_l2 > epsilon_mech -> replan or repair", - "density command used on real printer without slicer/material calibration -> unsafe", - ], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum() -> None: - rows = [ - { - "task": "separate_mechanics_from_attestation", - "input": "load vectors, force densities, density commands, Merkle root", - "target": "mechanical residual first; Merkle commits to records only", - }, - { - "task": "solve_supported_lattice_equilibrium", - "input": "nodes, edges, support nodes, external loads", - "target": "signed edge force densities, support reactions, residual norm", - }, - { - "task": "reject_hidden_print_risk", - "input": "bounded sigmoid density command", - "target": "heuristic print-density command requiring slicer/material calibration", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser() - parser.add_argument("--seed", type=int, default=2519138123) - parser.add_argument("--gravity", type=float, default=-9.81) - parser.add_argument("--mass-per-node", type=float, default=0.1) - parser.add_argument("--lateral-noise-sigma", type=float, default=0.05) - parser.add_argument("--duality-coefficient", type=float, default=2 ** 0.5) - parser.add_argument("--density-midpoint", type=float, default=0.25) - parser.add_argument("--epsilon-mech", type=float, default=1e-8) - parser.add_argument("--no-face-diagonals", action="store_false", dest="include_face_diagonals") - parser.set_defaults(include_face_diagonals=True) - return parser.parse_args() - - -def main() -> None: - args = parse_args() - receipt = build_receipt(args) - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum() - print(json.dumps({ - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "merkle_root": receipt["merkle"]["root"], - "node_count": receipt["lattice"]["node_count"], - "edge_count": receipt["lattice"]["edge_count"], - "residual_norm_l2": receipt["results"]["residual_norm_l2"], - "mechanically_acceptable": receipt["results"]["mechanically_acceptable"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/meshgraphnets_tiny_topology_probe.py b/4-Infrastructure/shim/meshgraphnets_tiny_topology_probe.py deleted file mode 100644 index 0a42e5a3..00000000 --- a/4-Infrastructure/shim/meshgraphnets_tiny_topology_probe.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -"""Tiny MeshGraphNets-style topology probe. - -This is a metadata/replay fixture for irregular mesh route surfaces. It does -not download, vendor, or score MeshGraphNets data. It checks whether a mesh -packet preserves canonical edges, faces, boundary nodes, degree sequence, and a -tiny deterministic message-passing replay before any real mesh slice is -admitted. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "meshgraphnets_tiny_probe" -RECEIPT = OUT_DIR / "meshgraphnets_tiny_topology_probe_receipt.json" -TABLE = OUT_DIR / "meshgraphnets_tiny_topology_probe_table.jsonl" - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - route_surface: str - actual_mesh: dict[str, Any] - candidate_mesh: dict[str, Any] - negative_control: bool - - -BASE_MESH = { - "mesh_family": "meshgraphnets_style_micro_fixture", - "nodes": [ - {"id": 0, "xy": [0, 0], "kind": "boundary"}, - {"id": 1, "xy": [1, 0], "kind": "boundary"}, - {"id": 2, "xy": [1, 1], "kind": "boundary"}, - {"id": 3, "xy": [0, 1], "kind": "boundary"}, - {"id": 4, "xy": [1, 2], "kind": "boundary"}, - {"id": 5, "xy": [0, 2], "kind": "boundary"}, - {"id": 6, "xy": [0, 0], "kind": "anchor"}, - ], - "edges": [ - [0, 1], - [1, 2], - [2, 3], - [3, 0], - [2, 4], - [4, 5], - [5, 3], - [0, 2], - [3, 4], - [0, 6], - ], - "faces": [ - [0, 1, 2], - [0, 2, 3], - [3, 2, 4], - [3, 4, 5], - ], - "node_feature": [1, 2, 3, 4, 5, 6, 7], - "split": "tiny_local_probe", - "source_bytes_vendored": 0, -} - - -def without_edge(mesh: dict[str, Any], edge: list[int]) -> dict[str, Any]: - clone = json.loads(json.dumps(mesh)) - target = sorted(edge) - clone["edges"] = [item for item in clone["edges"] if sorted(item) != target] - return clone - - -def without_boundary_kind(mesh: dict[str, Any], node_id: int) -> dict[str, Any]: - clone = json.loads(json.dumps(mesh)) - for node in clone["nodes"]: - if node["id"] == node_id: - node["kind"] = "unknown" - return clone - - -FIXTURES = [ - Fixture( - fixture_id="mesh_topology_canonical_admit", - route_surface="MeshGraphNets", - actual_mesh=BASE_MESH, - candidate_mesh=BASE_MESH, - negative_control=False, - ), - Fixture( - fixture_id="mesh_missing_diagonal_negative", - route_surface="MeshGraphNets", - actual_mesh=BASE_MESH, - candidate_mesh=without_edge(BASE_MESH, [0, 2]), - negative_control=True, - ), - Fixture( - fixture_id="mesh_boundary_kind_hold", - route_surface="MeshGraphNets", - actual_mesh=BASE_MESH, - candidate_mesh=without_boundary_kind(BASE_MESH, 4), - negative_control=False, - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def canonical_edges(mesh: dict[str, Any]) -> list[list[int]]: - return sorted([sorted([int(a), int(b)]) for a, b in mesh["edges"]]) - - -def canonical_faces(mesh: dict[str, Any]) -> list[list[int]]: - return sorted([sorted([int(value) for value in face]) for face in mesh["faces"]]) - - -def boundary_nodes(mesh: dict[str, Any]) -> list[int]: - return sorted(int(node["id"]) for node in mesh["nodes"] if node.get("kind") == "boundary") - - -def degree_sequence(mesh: dict[str, Any]) -> list[int]: - node_ids = sorted(int(node["id"]) for node in mesh["nodes"]) - degree = {node_id: 0 for node_id in node_ids} - for a, b in canonical_edges(mesh): - degree[a] = degree.get(a, 0) + 1 - degree[b] = degree.get(b, 0) + 1 - return [degree[node_id] for node_id in node_ids] - - -def message_pass(mesh: dict[str, Any]) -> list[int]: - features = {int(index): int(value) for index, value in enumerate(mesh["node_feature"])} - output = {int(node["id"]): features[int(node["id"])] for node in mesh["nodes"]} - for a, b in canonical_edges(mesh): - output[a] += features[b] - output[b] += features[a] - return [output[node_id] for node_id in sorted(output)] - - -def topology_errors(actual: dict[str, Any], candidate: dict[str, Any]) -> list[dict[str, Any]]: - checks = [ - ("node_count", len(actual["nodes"]), len(candidate["nodes"])), - ("edge_set", canonical_edges(actual), canonical_edges(candidate)), - ("face_set", canonical_faces(actual), canonical_faces(candidate)), - ("boundary_nodes", boundary_nodes(actual), boundary_nodes(candidate)), - ("degree_sequence", degree_sequence(actual), degree_sequence(candidate)), - ("message_pass", message_pass(actual), message_pass(candidate)), - ] - errors: list[dict[str, Any]] = [] - for path, actual_value, candidate_value in checks: - if actual_value != candidate_value: - errors.append( - { - "path": path, - "error": "value_mismatch", - "actual": actual_value, - "candidate": candidate_value, - } - ) - return errors - - -def generator_packet(fixture: Fixture) -> dict[str, Any]: - packet: dict[str, Any] = { - "generator": "two_cell_strip_plus_anchor", - "route_surface": fixture.route_surface, - "node_count": len(fixture.candidate_mesh["nodes"]), - "face_count": len(fixture.candidate_mesh["faces"]), - } - if fixture.fixture_id == "mesh_missing_diagonal_negative": - packet["mutation"] = {"remove_edge": [0, 2]} - elif fixture.fixture_id == "mesh_boundary_kind_hold": - packet["mutation"] = {"node_kind": {"id": 4, "kind": "unknown"}} - else: - packet["mutation"] = "none" - return packet - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - errors = topology_errors(fixture.actual_mesh, fixture.candidate_mesh) - replay_valid = not errors - residual_declared = True - - encoded_payload = { - "packet": generator_packet(fixture), - "topology_hashes": { - "edge_set": sha256_text(stable_json(canonical_edges(fixture.candidate_mesh))), - "face_set": sha256_text(stable_json(canonical_faces(fixture.candidate_mesh))), - }, - } - explicit_payload = { - "nodes": fixture.actual_mesh["nodes"], - "edges": fixture.actual_mesh["edges"], - "faces": fixture.actual_mesh["faces"], - "message_pass": message_pass(fixture.actual_mesh), - } - residual_payload = {"topology_errors": errors} - - encoded_bytes = len(stable_json(encoded_payload).encode("utf-8")) - explicit_bytes = len(stable_json(explicit_payload).encode("utf-8")) - residual_bytes = 0 if replay_valid else len(stable_json(residual_payload).encode("utf-8")) - total_candidate_bytes = encoded_bytes + residual_bytes - byte_gain = explicit_bytes - total_candidate_bytes - - if fixture.negative_control and replay_valid: - status = "FAIL_NEGATIVE_CONTROL" - elif replay_valid and residual_declared and byte_gain > 0 and not fixture.negative_control: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "route_surface": fixture.route_surface, - "negative_control": fixture.negative_control, - "actual_mesh_hash": sha256_text(stable_json(fixture.actual_mesh)), - "candidate_mesh_hash": sha256_text(stable_json(fixture.candidate_mesh)), - "edge_set_hash": sha256_text(stable_json(canonical_edges(fixture.actual_mesh))), - "face_set_hash": sha256_text(stable_json(canonical_faces(fixture.actual_mesh))), - "degree_sequence": degree_sequence(fixture.actual_mesh), - "message_pass_hash": sha256_text(stable_json(message_pass(fixture.actual_mesh))), - "topology_error_count": len(errors), - "topology_errors": errors, - "replay_valid": replay_valid, - "residual_declared": residual_declared, - "encoded_bytes": encoded_bytes, - "explicit_bytes": explicit_bytes, - "residual_bytes": residual_bytes, - "byte_gain": byte_gain, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "meshgraphnets_tiny_topology_probe_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "fixture_count": len(results), - "table": rel(TABLE), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Tiny MeshGraphNets-style topology probe only. It tests canonical " - "edge, face, boundary, degree-sequence, message-pass, residual, and " - "byte-law accounting; it does not download MeshGraphNets data, does " - "not vendor mesh trajectories, and does not claim benchmark results." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/metaprobe_physics_math_llm.py b/4-Infrastructure/shim/metaprobe_physics_math_llm.py deleted file mode 100644 index 0675a09a..00000000 --- a/4-Infrastructure/shim/metaprobe_physics_math_llm.py +++ /dev/null @@ -1,696 +0,0 @@ -#!/usr/bin/env python3 -"""Metaprobe audit for the physics-math LLM/tuning surface. - -Audits: - - SFT JSONL records: structural JSON/chat coherence and boundary markers. - - Ollama smoke receipts: parseability and required decision keys. - - Tang routed-template receipts: hardware match ratio. - -This is intentionally independent from model confidence. It is the receipt -layer around the LLM/router/hardware loop. -""" - -from __future__ import annotations - -import argparse -import json -import math -from collections import Counter -from pathlib import Path -from typing import Any - - -REQUIRED_DECISION_KEYS = { - "selected", - "claim_boundary", -} - -SFT_EVIDENCE_MARKERS = { - "evidence", - "source_path", - "source_hash", - "equation_hash", - "receipt_rule", - "metaprobe_rule", - "next_receipts", - "packet_hash", - "judge", - "hardware_receipt", - "source_receipt", -} - - -def shannon_entropy(text: str) -> float: - if not text: - return 0.0 - counts = Counter(text.encode("utf-8", errors="ignore")) - total = sum(counts.values()) - return -sum((count / total) * math.log2(count / total) for count in counts.values()) / 8.0 - - -def clamp01(value: float) -> float: - return max(0.0, min(1.0, value)) - - -def audit_sft(path: Path) -> dict[str, Any]: - total = 0 - parse_ok = 0 - chat_ok = 0 - boundary_ok = 0 - json_assistant_ok = 0 - entropy_values = [] - errors = [] - with path.open(encoding="utf-8") as handle: - for line_no, line in enumerate(handle, start=1): - if not line.strip(): - continue - total += 1 - entropy_values.append(shannon_entropy(line)) - try: - record = json.loads(line) - parse_ok += 1 - messages = record.get("messages", []) - roles = [message.get("role") for message in messages] - if roles == ["system", "user", "assistant"]: - chat_ok += 1 - joined = json.dumps(record, ensure_ascii=False).lower() - if "claim_boundary" in joined and any(marker in joined for marker in SFT_EVIDENCE_MARKERS): - boundary_ok += 1 - try: - json.loads(messages[-1].get("content", "{}")) - json_assistant_ok += 1 - except Exception: - pass - except Exception as exc: - errors.append({"line": line_no, "error": str(exc)}) - - denom = total or 1 - resonance = (parse_ok / denom + chat_ok / denom + boundary_ok / denom + json_assistant_ok / denom) / 4 - coherence = (chat_ok / denom + boundary_ok / denom) / 2 - entropy = sum(entropy_values) / len(entropy_values) if entropy_values else 0.0 - return { - "channel": "SFT_JSONL", - "path": str(path), - "records": total, - "parse_ok": parse_ok, - "chat_ok": chat_ok, - "boundary_ok": boundary_ok, - "json_assistant_ok": json_assistant_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": entropy, - "lawful": resonance >= 0.8 and coherence >= 0.8, - "errors": errors[:10], - } - - -def audit_ollama(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - parsed = data.get("parsed_response") or {} - present = REQUIRED_DECISION_KEYS.intersection(parsed) - richer_keys = {"selected", "model_role", "evidence_tier", "claim_boundary", "use_as", "surface_payload_hint", "reason"} - rich_present = richer_keys.intersection(parsed) - resonance = (1.0 if data.get("json_parse_ok") else 0.0) * (len(rich_present) / len(richer_keys)) - coherence = len(present) / len(REQUIRED_DECISION_KEYS) - raw = data.get("raw_response", "") - return { - "channel": "OLLAMA_DECISION", - "path": str(path), - "model": data.get("model"), - "json_parse_ok": data.get("json_parse_ok"), - "present_required_keys": sorted(present), - "present_rich_keys": sorted(rich_present), - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(raw), - "lawful": resonance >= 0.65 and coherence >= 1.0, - } - - -def audit_tang_receipt(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - if data.get("schema") == "tang9k_hutter_symbol_surface_receipt_v1": - matched = 1 if data.get("hardware_matches_expected") else 0 - receipt_present = 1 if data.get("hardware_receipt") else 0 - return { - "channel": "TANG_DIRECT_WITNESS", - "path": str(path), - "witnesses": 1, - "hardware_matches": matched, - "hardware_receipts": receipt_present, - "resonance_score": float(matched), - "structural_coherence": float(receipt_present), - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(matched and receipt_present), - } - - witnesses = data.get("witnesses", []) - total = len(witnesses) - matched = sum(1 for witness in witnesses if witness.get("hardware_matches_expected")) - receipt_present = sum(1 for witness in witnesses if witness.get("hardware_receipt")) - denom = total or 1 - resonance = matched / denom - coherence = receipt_present / denom - return { - "channel": "TANG_TEMPLATE_WITNESS", - "path": str(path), - "witnesses": total, - "hardware_matches": matched, - "hardware_receipts": receipt_present, - "held_out_witnesses": data.get("held_out_witness_count", 0), - "held_out_reason": data.get("held_out_reason"), - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": resonance >= 0.8 and coherence >= 0.8, - } - - -def audit_math_logogram_surface(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - samples = data.get("samples", []) - total = len(samples) - hash_ok = 0 - payload_ok = 0 - regime_ok = 0 - receipt_ok = 0 - allowed_regimes = { - "beautiful_topological_folding", - "ugly_asymmetric_pruning", - "horrible_manifold_tearing", - } - for sample in samples: - if sample.get("source_hash") and sample.get("canonical_hash") and sample.get("cell_hash"): - hash_ok += 1 - if sample.get("surface_payload_len", 999) <= 16 and sample.get("surface_payload_hex"): - payload_ok += 1 - if sample.get("semantic_regime") in allowed_regimes: - regime_ok += 1 - sub = sample.get("substitution_receipt", {}) - if sub.get("schema") == "surface1_substitution_receipt_v1" and "hash16" in sub: - receipt_ok += 1 - denom = total or 1 - resonance = (hash_ok / denom + payload_ok / denom + regime_ok / denom + receipt_ok / denom) / 4 - coherence = (payload_ok / denom + regime_ok / denom + receipt_ok / denom) / 3 - return { - "channel": "MATH_LOGOGRAM_SURFACE", - "path": str(path), - "samples": total, - "hash_ok": hash_ok, - "payload_ok": payload_ok, - "regime_ok": regime_ok, - "receipt_ok": receipt_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and resonance >= 0.9 and coherence >= 0.9, - } - - -def audit_moving_sofa_scout(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - audit = data.get("audit", {}) - packets = data.get("packets", []) - packet_total = len(packets) - contract_ok = 0 - scout_ok = 0 - for packet in packets: - contract = packet.get("response_contract", {}) - if contract.get("format") == "strict_json" and contract.get("must_include") and contract.get("must_not_claim"): - contract_ok += 1 - if packet.get("preferred_scout_model") and packet.get("promotion_gate") and "not proof" in packet.get("claim_boundary", ""): - scout_ok += 1 - denom = packet_total or 1 - resonance = (audit.get("resonance", 0.0) + contract_ok / denom + scout_ok / denom) / 3 - coherence = (contract_ok / denom + scout_ok / denom) / 2 - return { - "channel": "MOVING_SOFA_SCOUT", - "path": str(path), - "packets": packet_total, - "contract_ok": contract_ok, - "scout_ok": scout_ok, - "packet_hash_ok": audit.get("hash_ok"), - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and resonance >= 0.9 and coherence >= 0.9, - } - - -def audit_moving_sofa_validation(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - validations = data.get("validations", []) - total = len(validations) - lawful = sum(1 for item in validations if item.get("lawful")) - hash_ok = sum(1 for item in validations if item.get("packet_hash_ok")) - boundary_ok = sum(1 for item in validations if item.get("boundary_ok") and not item.get("forbidden_claim")) - receipt_ok = sum(1 for item in validations if item.get("receipts_ok")) - denom = total or 1 - resonance = (lawful / denom + hash_ok / denom + boundary_ok / denom + receipt_ok / denom) / 4 - coherence = (boundary_ok / denom + receipt_ok / denom) / 2 - return { - "channel": "MOVING_SOFA_SCOUT_VALIDATION", - "path": str(path), - "validations": total, - "lawful_validations": lawful, - "hash_ok": hash_ok, - "boundary_ok": boundary_ok, - "receipt_ok": receipt_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and resonance >= 0.9 and coherence >= 0.9, - } - - -def audit_custom_equation_awareness(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - equations = data.get("equations", []) - total = len(equations) - source_ok = sum(1 for item in equations if item.get("source_path") and item.get("source_hash")) - equation_ok = sum(1 for item in equations if item.get("equation") and item.get("equation_hash")) - boundary_ok = sum(1 for item in equations if item.get("claim_boundary")) - primitive_ok = sum(1 for item in equations if item.get("primitive_hint")) - denom = total or 1 - resonance = (source_ok / denom + equation_ok / denom + boundary_ok / denom + primitive_ok / denom) / 4 - coherence = (boundary_ok / denom + primitive_ok / denom) / 2 - return { - "channel": "CUSTOM_EQUATION_AWARENESS", - "path": str(path), - "sources": data.get("source_count"), - "equations": total, - "source_ok": source_ok, - "equation_ok": equation_ok, - "boundary_ok": boundary_ok, - "primitive_ok": primitive_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_solved_problem_outputs(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - cases = data.get("cases", []) - total = len(cases) - run_ok = sum(1 for item in cases if item.get("run_ok")) - validation_ok = sum(1 for item in cases if item.get("validation_ok")) - boundary_markers = ("not", "finite", "only", "open", "does not", "without promotion") - boundary_ok = sum( - 1 - for item in cases - if item.get("claim_boundary") and any(marker in item.get("claim_boundary", "").lower() for marker in boundary_markers) - ) - hash_ok = sum(1 for item in cases if item.get("result_hash_after")) - excluded_ok = len(data.get("excluded_cases", [])) - denom = total or 1 - resonance = (run_ok / denom + validation_ok / denom + boundary_ok / denom + hash_ok / denom) / 4 - coherence = (validation_ok / denom + boundary_ok / denom) / 2 - return { - "channel": "SOLVED_PROBLEM_OUTPUTS", - "path": str(path), - "cases": total, - "run_ok": run_ok, - "validation_ok": validation_ok, - "boundary_ok": boundary_ok, - "hash_ok": hash_ok, - "excluded_non_promotable_cases": excluded_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_openclaw_shared_bus(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - mapping = data.get("research_stack_mapping", []) - event_contract = data.get("event_contract", {}) - role = data.get("surface_role", {}) - total = len(mapping) - source_ok = 1 if data.get("openclaw", {}).get("commit") and data.get("openclaw", {}).get("source_fingerprint") else 0 - mapping_ok = sum(1 for item in mapping if item.get("openclaw_surface") and item.get("research_stack_role") and item.get("gate")) - event_ok = sum( - 1 - for key in ("task_started", "task_completed", "memory_write") - if event_contract.get(key, {}).get("required") - ) - boundary_text = " ".join([role.get("claim_boundary", ""), " ".join(role.get("not_use_as", []))]).lower() - boundary_ok = 1 if all(marker in boundary_text for marker in ("not", "trusted", "secret")) else 0 - denom = total or 1 - resonance = (source_ok + mapping_ok / denom + event_ok / 3 + boundary_ok) / 4 - coherence = (mapping_ok / denom + event_ok / 3 + boundary_ok) / 3 - return { - "channel": "OPENCLAW_SHARED_BUS", - "path": str(path), - "commit": data.get("openclaw", {}).get("commit"), - "mappings": total, - "source_ok": source_ok, - "mapping_ok": mapping_ok, - "event_contract_ok": event_ok, - "boundary_ok": boundary_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_mcp_surface_catalog(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - selected = data.get("selected_surfaces", []) - total = len(selected) - source_ok = sum(1 for item in selected if item.get("path") and (item.get("source_hash") or item.get("source_fingerprint"))) - gate_ok = sum(1 for item in selected if item.get("gate")) - priority_ok = sum(1 for item in selected if isinstance(item.get("priority"), int)) - smoke_ok = sum(1 for item in selected if item.get("id") != "sciencehub_mcp" or item.get("smoke", {}).get("available")) - rules_ok = 1 if len(data.get("bus_rules", [])) >= 4 else 0 - boundary_text = data.get("claim_boundary", "").lower() - boundary_ok = 1 if all(marker in boundary_text for marker in ("inactive", "not trusted", "receipts")) else 0 - denom = total or 1 - resonance = (source_ok / denom + gate_ok / denom + priority_ok / denom + smoke_ok / denom + rules_ok + boundary_ok) / 6 - coherence = (gate_ok / denom + rules_ok + boundary_ok) / 3 - return { - "channel": "MCP_SURFACE_CATALOG", - "path": str(path), - "selected_surfaces": total, - "source_ok": source_ok, - "gate_ok": gate_ok, - "priority_ok": priority_ok, - "smoke_ok": smoke_ok, - "rules_ok": rules_ok, - "boundary_ok": boundary_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_mcp_bus_dry_run(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - checks = data.get("checks", []) - total = len(checks) - lawful = sum(1 for item in checks if item.get("lawful")) - boundary_ok = sum(1 for item in checks if item.get("claim_boundary")) - source_ok = sum(1 for item in checks if item.get("source_hash") or item.get("readme_hash") or item.get("stdout_hash")) - held_ok = sum(1 for item in checks if item.get("activation") == "held" and "hold" in item.get("claim_boundary", "").lower()) - receipt_rule_ok = 1 if data.get("bus_receipt_rule") and "arguments_hash" in data.get("bus_receipt_rule", "") else 0 - denom = total or 1 - resonance = (lawful / denom + boundary_ok / denom + source_ok / denom + receipt_rule_ok) / 4 - coherence = (boundary_ok / denom + source_ok / denom + receipt_rule_ok) / 3 - return { - "channel": "MCP_BUS_DRY_RUN", - "path": str(path), - "checks": total, - "lawful_checks": lawful, - "boundary_ok": boundary_ok, - "source_ok": source_ok, - "held_ok": held_ok, - "receipt_rule_ok": receipt_rule_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_mcp_live_safe_probe(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - calls = data.get("calls", []) - total = len(calls) - lawful = sum(1 for item in calls if item.get("lawful")) - args_ok = sum(1 for item in calls if item.get("arguments_hash")) - output_ok = sum(1 for item in calls if item.get("stdout_hash")) - boundary_ok = sum(1 for item in calls if item.get("claim_boundary") and "read-only" in item.get("claim_boundary", "").lower()) - source_ok = 1 if data.get("source_path") and data.get("source_hash") else 0 - receipt_rule_ok = 1 if data.get("receipt_rule") and "arguments_hash" in data.get("receipt_rule", "") else 0 - denom = total or 1 - resonance = (lawful / denom + args_ok / denom + output_ok / denom + boundary_ok / denom + source_ok + receipt_rule_ok) / 6 - coherence = (boundary_ok / denom + source_ok + receipt_rule_ok) / 3 - return { - "channel": "MCP_LIVE_SAFE_PROBE", - "path": str(path), - "surface_id": data.get("surface_id"), - "calls": total, - "lawful_calls": lawful, - "args_ok": args_ok, - "output_ok": output_ok, - "boundary_ok": boundary_ok, - "source_ok": source_ok, - "receipt_rule_ok": receipt_rule_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_quandela_job_tasking(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - jobs = data.get("jobs", []) - total = len(jobs) - source_ok = 1 if data.get("perceval_reference", {}).get("commit") and data.get("perceval_reference", {}).get("readme_hash") else 0 - triangle_ok = 1 if data.get("triangle_in_square_hole", {}).get("required_receipts") else 0 - job_hash_ok = sum(1 for job in jobs if job.get("job_hash")) - boundary_ok = sum(1 for job in jobs if job.get("claim_boundary") and "no" in job.get("claim_boundary", "").lower()) - held_remote_ok = 1 if data.get("held_remote_jobs", 0) >= 1 and data.get("runnable_now") == 0 else 0 - fit_ok = sum(1 for job in jobs if job.get("fit", {}).get("fit_score") is not None and job.get("fit", {}).get("residual_mass") is not None) - denom = total or 1 - resonance = (source_ok + triangle_ok + job_hash_ok / denom + boundary_ok / denom + held_remote_ok + fit_ok / denom) / 6 - coherence = (triangle_ok + boundary_ok / denom + held_remote_ok + fit_ok / denom) / 4 - return { - "channel": "QUANDELA_JOB_TASKING", - "path": str(path), - "jobs": total, - "source_ok": source_ok, - "triangle_ok": triangle_ok, - "job_hash_ok": job_hash_ok, - "boundary_ok": boundary_ok, - "held_remote_ok": held_remote_ok, - "fit_ok": fit_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_quandela_noise_shaver(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - shaves = data.get("shaves", []) - total = len(shaves) - source_ok = 1 if data.get("source_queue_hash") and data.get("source_job_receipt") else 0 - component_ok = sum(1 for item in shaves if item.get("residual_components")) - hash_ok = sum(1 for item in shaves if item.get("shave_hash")) - floor_ok = sum(1 for item in shaves if item.get("post_noise_residual_floor") is not None) - boundary_ok = sum( - 1 - for item in shaves - if item.get("claim_boundary") and all(marker in item.get("claim_boundary", "").lower() for marker in ("noise", "does not")) - ) - no_submit_ok = 1 if data.get("promotable_now") == 0 and "no qpu" in data.get("claim_boundary", "").lower() else 0 - candidate_ok = 1 if data.get("noise_candidate_count", 0) >= 1 else 0 - denom = total or 1 - resonance = (source_ok + component_ok / denom + hash_ok / denom + floor_ok / denom + boundary_ok / denom + no_submit_ok + candidate_ok) / 7 - coherence = (component_ok / denom + boundary_ok / denom + no_submit_ok + candidate_ok) / 4 - return { - "channel": "QUANDELA_NOISE_RESIDUAL_SHAVER", - "path": str(path), - "shaves": total, - "source_ok": source_ok, - "component_ok": component_ok, - "hash_ok": hash_ok, - "floor_ok": floor_ok, - "boundary_ok": boundary_ok, - "no_submit_ok": no_submit_ok, - "candidate_ok": candidate_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_typst_pipeline(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - sources = data.get("source_tiddlers", []) - total = len(sources) - source_ok = sum(1 for item in sources if item.get("path") and item.get("sha256")) - typst_ok = 1 if data.get("typst_source") and data.get("typst_source_hash") else 0 - compile = data.get("compile", {}) - compile_status_ok = 1 if "compiled" in compile and "pdf_hash" in compile else 0 - boundary_text = data.get("claim_boundary", "").lower() - boundary_ok = 1 if all(marker in boundary_text for marker in ("documentation", "does not prove", "validate hardware")) else 0 - denom = total or 1 - resonance = (source_ok / denom + typst_ok + compile_status_ok + boundary_ok) / 4 - coherence = (typst_ok + compile_status_ok + boundary_ok) / 3 - return { - "channel": "TYPST_SUBSTRATE_PRIOR_PIPELINE", - "path": str(path), - "source_count": total, - "source_ok": source_ok, - "typst_ok": typst_ok, - "compile_status_ok": compile_status_ok, - "compiled": compile.get("compiled"), - "boundary_ok": boundary_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def audit_finance_claim_lut(path: Path) -> dict[str, Any]: - data = json.loads(path.read_text(encoding="utf-8")) - samples = data.get("samples", []) - total = len(samples) - rehydrated_ok = sum(1 for item in samples if item.get("rehydrated_ok")) - hash_ok = sum( - 1 - for item in samples - if item.get("canonical_hash") - and item.get("decoded_hash") == item.get("canonical_hash") - and item.get("fcl1_decoded_hash") == item.get("canonical_hash") - ) - fcl1_ok = sum(1 for item in samples if item.get("fcl1_decode_ok") and item.get("fcl1_binary_hex") and item.get("fcl1_binary_hash")) - fcs1_ok = sum(1 for item in samples if item.get("fcs1_decode_ok") and item.get("fcs1_binary_hex") and item.get("fcs1_binary_hash")) - sidecar_ok = sum(1 for item in samples if item.get("sidecar_hash") and item.get("sidecar")) - benchmark_ok = sum( - 1 - for item in samples - if item.get("metrics", {}).get("canonical_json_bytes") - and item.get("metrics", {}).get("zlib_canonical_bytes") - and item.get("metrics", {}).get("combined_fcl1_fcs1_bytes") - and "cbor" in item.get("metrics", {}) - and "messagepack" in item.get("metrics", {}) - and "protobuf_dynamic" in item.get("metrics", {}) - ) - schema_ok = 1 if all(key in data.get("schema_receipts", {}) for key in ("protobuf_schema", "nanopb_options", "flatbuffers_schema")) else 0 - render_ok = 1 if data.get("render_receipt", {}).get("typst_source_hash") and "compiled" in data.get("render_receipt", {}) else 0 - tests_ok = 1 if data.get("test_receipts", {}).get("lawful") else 0 - lut_ok = 1 if data.get("symbol_lut_hash") and data.get("typesetting_lut_hash") and data.get("symbol_codebook") else 0 - type_entries = data.get("typesetting_lut", {}).get("entries", {}) - orientation_metrics = data.get("orientation_metrics", {}) - orientation_ok = 1 if ( - data.get("orientation_codec", {}).get("schema") == "orientation_codec_v1" - and type_entries - and all(isinstance(entry.get("orientation_code"), int) and 0 <= entry.get("orientation_code") <= 255 for entry in type_entries.values()) - and orientation_metrics.get("packed_orientation_bytes") == len(type_entries) - and orientation_metrics.get("saved_bytes", 0) > 0 - ) else 0 - boundary_text = data.get("claim_boundary", "").lower() - boundary_ok = 1 if all(marker in boundary_text for marker in ("byte", "not financial advice", "competitive compression")) else 0 - denom = total or 1 - resonance = ( - rehydrated_ok / denom - + hash_ok / denom - + fcl1_ok / denom - + fcs1_ok / denom - + sidecar_ok / denom - + benchmark_ok / denom - + schema_ok - + render_ok - + tests_ok - + lut_ok - + orientation_ok - + boundary_ok - ) / 12 - coherence = (rehydrated_ok / denom + hash_ok / denom + fcl1_ok / denom + fcs1_ok / denom + schema_ok + render_ok + tests_ok + lut_ok + orientation_ok + boundary_ok) / 10 - return { - "channel": "FINANCE_CLAIM_LUT_HARNESS", - "path": str(path), - "samples": total, - "rehydrated_ok": rehydrated_ok, - "hash_ok": hash_ok, - "fcl1_ok": fcl1_ok, - "fcs1_ok": fcs1_ok, - "sidecar_ok": sidecar_ok, - "benchmark_ok": benchmark_ok, - "schema_ok": schema_ok, - "render_ok": render_ok, - "tests_ok": tests_ok, - "lut_ok": lut_ok, - "orientation_ok": orientation_ok, - "boundary_ok": boundary_ok, - "resonance_score": resonance, - "structural_coherence": coherence, - "entropy": shannon_entropy(json.dumps(data, ensure_ascii=False)[:200000]), - "lawful": bool(data.get("lawful")) and total > 0 and resonance >= 0.95 and coherence >= 0.95, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--sft", type=Path, default=Path("4-Infrastructure/shim/physics_math_llm_sft.jsonl")) - parser.add_argument("--ollama", type=Path, default=Path("4-Infrastructure/shim/ollama_physics_math_smoke.json")) - parser.add_argument("--tang", type=Path, default=Path("4-Infrastructure/shim/tang9k_pbacs_receipts/routed_template_witness_compression.json")) - parser.add_argument("--surface", type=Path, default=Path("4-Infrastructure/shim/math_logogram_surface_receipt.json")) - parser.add_argument("--sofa-scout", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_harness_receipt.json")) - parser.add_argument("--sofa-validation", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_response_validation_receipt.json")) - parser.add_argument("--custom-equations", type=Path, default=Path("4-Infrastructure/shim/custom_equation_awareness_manifest_receipt.json")) - parser.add_argument("--solved-problems", type=Path, default=Path("4-Infrastructure/shim/solved_problem_output_verifier_receipt.json")) - parser.add_argument("--openclaw-bus", type=Path, default=Path("4-Infrastructure/shim/openclaw_shared_bus_surface_receipt.json")) - parser.add_argument("--mcp-surfaces", type=Path, default=Path("4-Infrastructure/shim/mcp_surface_catalog_receipt.json")) - parser.add_argument("--mcp-dry-run", type=Path, default=Path("4-Infrastructure/shim/mcp_bus_dry_run_receipt.json")) - parser.add_argument("--mcp-live-safe", type=Path, default=Path("4-Infrastructure/shim/mcp_bus_live_safe_probe_receipt.json")) - parser.add_argument("--quandela", type=Path, default=Path("4-Infrastructure/shim/quandela_job_tasking_surface_receipt.json")) - parser.add_argument("--quandela-noise", type=Path, default=Path("4-Infrastructure/shim/quandela_noise_residual_shaver_receipt.json")) - parser.add_argument("--typst-pipeline", type=Path, default=Path("4-Infrastructure/shim/typst_substrate_prior_pipeline_receipt.json")) - parser.add_argument("--finance-claim-lut", type=Path, default=Path("4-Infrastructure/shim/finance_claim_lut_harness_receipt.json")) - parser.add_argument("--out", type=Path, default=Path("4-Infrastructure/shim/metaprobe_physics_math_llm_receipt.json")) - args = parser.parse_args() - - audits = [] - if args.sft.exists(): - audits.append(audit_sft(args.sft)) - if args.ollama.exists(): - audits.append(audit_ollama(args.ollama)) - if args.tang.exists(): - audits.append(audit_tang_receipt(args.tang)) - if args.surface.exists(): - audits.append(audit_math_logogram_surface(args.surface)) - if args.sofa_scout.exists(): - audits.append(audit_moving_sofa_scout(args.sofa_scout)) - if args.sofa_validation.exists(): - audits.append(audit_moving_sofa_validation(args.sofa_validation)) - if args.custom_equations.exists(): - audits.append(audit_custom_equation_awareness(args.custom_equations)) - if args.solved_problems.exists(): - audits.append(audit_solved_problem_outputs(args.solved_problems)) - if args.openclaw_bus.exists(): - audits.append(audit_openclaw_shared_bus(args.openclaw_bus)) - if args.mcp_surfaces.exists(): - audits.append(audit_mcp_surface_catalog(args.mcp_surfaces)) - if args.mcp_dry_run.exists(): - audits.append(audit_mcp_bus_dry_run(args.mcp_dry_run)) - if args.mcp_live_safe.exists(): - audits.append(audit_mcp_live_safe_probe(args.mcp_live_safe)) - if args.quandela.exists(): - audits.append(audit_quandela_job_tasking(args.quandela)) - if args.quandela_noise.exists(): - audits.append(audit_quandela_noise_shaver(args.quandela_noise)) - if args.typst_pipeline.exists(): - audits.append(audit_typst_pipeline(args.typst_pipeline)) - if args.finance_claim_lut.exists(): - audits.append(audit_finance_claim_lut(args.finance_claim_lut)) - - overall_resonance = sum(audit["resonance_score"] for audit in audits) / (len(audits) or 1) - overall_coherence = sum(audit["structural_coherence"] for audit in audits) / (len(audits) or 1) - receipt = { - "schema": "metaprobe_physics_math_llm_receipt_v1", - "claim_boundary": "Metaprobe audits structure, resonance, receipts, and boundaries; it does not certify theorem truth.", - "audits": audits, - "overall_resonance": overall_resonance, - "overall_structural_coherence": overall_coherence, - "overall_lawful": overall_resonance >= 0.75 and overall_coherence >= 0.75, - } - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/mkv-decode.sh b/4-Infrastructure/shim/mkv-decode.sh deleted file mode 100644 index 7f63d94d..00000000 --- a/4-Infrastructure/shim/mkv-decode.sh +++ /dev/null @@ -1,10 +0,0 @@ -#!/usr/bin/env bash -# MKV data transport — decode -set -euo pipefail - -INPUT="${1:-/tmp/mkv-transport.mkv}" -OUTPUT="${2:-/dev/stdout}" -WIDTH="${WIDTH:-1920}" -HEIGHT="${HEIGHT:-1080}" - -ffmpeg -y -i "$INPUT" -f rawvideo -pix_fmt rgba -s "${WIDTH}x${HEIGHT}" -vframes 1 "$OUTPUT" 2>&1 diff --git a/4-Infrastructure/shim/mkv-transport.sh b/4-Infrastructure/shim/mkv-transport.sh deleted file mode 100644 index 5e26c109..00000000 --- a/4-Infrastructure/shim/mkv-transport.sh +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env bash -# MKV data transport — abuse H.264/H.265 video codec as a lossless compressor -# via FFmpeg, with hardware acceleration where available. -set -euo pipefail - -INPUT="${1:-/dev/stdin}" -OUTPUT="${2:-/tmp/mkv-transport.mkv}" -CODEC="${3:-libx264}" # libx264, libx265, h264_nvenc, hevc_nvenc, av1_nvenc -QP="${QP:-0}" # 0 = lossless (for libx264/libx265) -WIDTH="${WIDTH:-1920}" -HEIGHT="${HEIGHT:-1080}" -FPS="${FPS:-30}" - -echo "[mkv-transport] Encoding $INPUT → $OUTPUT (codec=$CODEC qp=$QP ${WIDTH}x${HEIGHT})" - -# Pack raw data as RGBA video frames → MKV container -# Each video frame holds WIDTH*HEIGHT*4 bytes of data -# Use -qp 0 for lossless H.264/H.265 encoding -# For NVENC: -cq 0 works similarly - -case "$CODEC" in - libx264) - ENC_ARGS="-c:v libx264 -qp $QP -preset ultrafast -pix_fmt rgba" - ;; - libx265) - ENC_ARGS="-c:v libx265 -x265-params lossless=1 -pix_fmt rgba" - ;; - h264_nvenc) - ENC_ARGS="-c:v h264_nvenc -cq $QP -preset p1 -pix_fmt rgba" - ;; - hevc_nvenc) - ENC_ARGS="-c:v hevc_nvenc -cq $QP -preset p1 -pix_fmt rgba" - ;; - av1_nvenc) - ENC_ARGS="-c:v av1_nvenc -cq $QP -preset p1 -pix_fmt rgba" - ;; - *) - echo "Unknown codec: $CODEC" - exit 1 - ;; -esac - -ffmpeg -y -f rawvideo -pix_fmt rgba -s "${WIDTH}x${HEIGHT}" -r "$FPS" -i "$INPUT" \ - -f matroska $ENC_ARGS -an -sn "$OUTPUT" 2>&1 - -echo "[mkv-transport] Done: $(stat -c%s "$OUTPUT" 2>/dev/null || echo 0) bytes" diff --git a/4-Infrastructure/shim/mmff_rigid_body_geometry_probe.py b/4-Infrastructure/shim/mmff_rigid_body_geometry_probe.py deleted file mode 100644 index 4920a005..00000000 --- a/4-Infrastructure/shim/mmff_rigid_body_geometry_probe.py +++ /dev/null @@ -1,630 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed MMFF rigid-body geometry compression probe. - -MMFF-style molecular mechanics separates a molecule into repeated geometry -terms. This probe tests the planning hypothesis that many local geometries are -better treated as rigid or semi-rigid body templates plus pose, hinge/torsion -state, and declared residual strain. - -The geometry is represented as a refined O-AMMR shadow carrier: - - 16D signed envelope -> 12D source/residual plane -> 4D primitive keel - -> genus-3 residual boat -> 3D coordinate shadow -> 0D closure - -Plain Merkle hashes are content commitments inside the ordered algebraic -accumulator. They are not the full trust object. - -It is not an MMFF implementation and does not assign atom types or energies. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "mmff_rigid_body_geometry" -REGISTRY = OUT_DIR / "mmff_rigid_body_geometry_registry.json" -RECEIPT = OUT_DIR / "mmff_rigid_body_geometry_receipt.json" -SUMMARY = OUT_DIR / "mmff_rigid_body_geometry.md" - -COORD_COMPONENT_BYTES = 2 -COORD_BYTES_PER_ATOM = 3 * COORD_COMPONENT_BYTES -BODY_ID_BYTES = 2 -TRANSLATION_BYTES = 3 * COORD_COMPONENT_BYTES -ORIENTATION_CODE_BYTES = 1 -HINGE_CODE_BYTES = 2 -RESIDUAL_COORD_BYTES_PER_ATOM = COORD_BYTES_PER_ATOM - -POSE_BYTES = BODY_ID_BYTES + TRANSLATION_BYTES + ORIENTATION_CODE_BYTES -SHADOW_ROOT_BYTES = 32 -ACCUMULATOR_KIND = "O-AMMR" -TREE_FIDDY_CAGE_BOUNDARY_BYTES = 350 - -CITATIONS = [ - { - "id": "charmm_mmff_docs", - "title": "CHARMM MMFF documentation", - "url": "https://www.charmm-gui.org/charmmdoc/mmff.html", - "role": "mmff_reference", - "status": "external_reference", - }, - { - "id": "openbabel_mmff94_docs", - "title": "Open Babel MMFF94 force field documentation", - "url": "https://openbabel.org/docs/Forcefields/mmff94.html", - "role": "mmff_reference", - "status": "external_reference", - }, - { - "id": "rdkit_mmff_implementation_paper", - "title": "MMFF implementation validation reference in RDKit ecosystem", - "url": "https://link.springer.com/article/10.1186/s13321-014-0037-3", - "role": "implementation_reference", - "status": "external_reference", - }, - { - "id": "gccl_encoding_contract", - "title": "GCCL encoding contract", - "path": "6-Documentation/docs/specs/GCCL_ENCODING_CONTRACT.md", - "role": "local_receipt_contract", - "status": "local_reference", - }, - { - "id": "projectable_geometry_compressor_spec", - "title": "Projectable geometry compressor spec", - "path": "6-Documentation/docs/specs/PROJECTABLE_GEOMETRY_COMPRESSOR_SPEC.md", - "role": "local_shadow_geometry_contract", - "status": "local_reference", - }, - { - "id": "tree_fiddy_article", - "title": "Meme math that pays rent", - "path": "6-Documentation/articles/meme-math-that-pays-rent/article.md", - "role": "tree_fiddy_guard_context", - "status": "local_reference", - }, - { - "id": "loch_monster_filter", - "title": "LochMonsterFilter Lean witness", - "path": "0-Core-Formalism/otom/tools/lean/Semantics/Semantics/LochMonsterFilter.lean", - "role": "tree_fiddy_formal_witness", - "status": "local_reference", - }, -] - - -Coord = tuple[int, int, int] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def translate(coords: list[Coord], offset: Coord) -> list[Coord]: - ox, oy, oz = offset - return [(x + ox, y + oy, z + oz) for x, y, z in coords] - - -def rotate_z_90(coords: list[Coord]) -> list[Coord]: - return [(-y, x, z) for x, y, z in coords] - - -def shadow_node(layer: str, payload: dict[str, Any], children: list[str] | None = None) -> dict[str, Any]: - child_hashes = children or [] - node = { - "accumulator_kind": ACCUMULATOR_KIND, - "layer": layer, - "payload": payload, - "children": child_hashes, - } - node["hash"] = hash_obj(node) - return node - - -def dimension_shadow_chain(template_payload: dict[str, Any]) -> dict[str, Any]: - """Build the refined 16D-to-3D shadow carrier for one body template.""" - - l16 = shadow_node( - "L16_signed_envelope", - { - "body_id": template_payload["body_id"], - "semantic_axes": [ - "atom_identity", - "bond_topology", - "formal_charge", - "aromaticity_state", - "stereochemical_state", - "hybridization_hint", - "ring_membership", - "fragment_symmetry", - "rigidity_class", - "force_field_family", - "parameter_table_slot", - "partial_charge_slot", - "torsion_slot", - "nonbonded_slot", - "residual_strain_lane", - "provenance_lane", - ], - "authority": "higher_dimensional_template_shadow", - }, - ) - l12 = shadow_node( - "L12_source_residual_plane", - { - "body_id": template_payload["body_id"], - "carrier_law": "source_12D = lift(project(source_12D)) + residual_12D", - "residual_lanes": ["coordinate_packet", "torsion_shear", "forcefield_spectral_slot"], - "unresolved_shell_mass": 0, - }, - [l16["hash"]], - ) - l8 = shadow_node( - "L8_mmff_adapter_state", - { - "body_id": template_payload["body_id"], - "adapter_axes": [ - "mmff_atom_type", - "bond_class", - "angle_class", - "stretch_bend_class", - "oop_class", - "torsion_class", - "vdw_class", - "charge_class", - ], - "status": "HOLD_ADAPTER_TABLE_REQUIRED", - }, - [l12["hash"]], - ) - l4 = shadow_node( - "L4_geometry_primitive", - { - "body_id": template_payload["body_id"], - "O4": ["field", "shear", "packet", "spectral"], - "geometry_terms": template_payload["expected_terms"], - "rigidity_class": template_payload["rigidity_class"], - }, - [l8["hash"]], - ) - rg3 = shadow_node( - "Rg3_residual_boat", - { - "body_id": template_payload["body_id"], - "residual_law": "coordinate_packet + torsion_shear + forcefield_spectral_slot = residual_12D", - "handles": ["coordinate_packet", "torsion_shear", "forcefield_spectral_slot"], - "status": "closed_for_coordinate_template_fixture", - }, - [l4["hash"]], - ) - l3 = shadow_node( - "L3_coordinate_shadow", - { - "body_id": template_payload["body_id"], - "atom_labels": template_payload["atom_labels"], - "template_coords_pm": template_payload["template_coords_pm"], - "coordinate_frame": "integer_picometer_local_body_frame", - }, - [rg3["hash"]], - ) - l0 = shadow_node( - "L0_replay_closure", - { - "body_id": template_payload["body_id"], - "closure": "coordinate_template_replay_only", - "unresolved_shell_mass": 0, - }, - [l3["hash"]], - ) - root = shadow_node( - "O_AMMR_root", - { - "body_id": template_payload["body_id"], - "projection_chain": [ - "L16_signed_envelope", - "L12_source_residual_plane", - "L8_mmff_adapter_state", - "L4_geometry_primitive", - "Rg3_residual_boat", - "L3_coordinate_shadow", - "L0_replay_closure", - "O_AMMR_root", - ], - "clock_participates_in_hash": False, - "plain_merkle_role": "content hash field only", - }, - [l0["hash"]], - ) - return { - "root": root["hash"], - "nodes": [l16, l12, l8, l4, rg3, l3, l0, root], - "node_count": 8, - "root_bytes": SHADOW_ROOT_BYTES, - "accumulator_kind": ACCUMULATOR_KIND, - "representative_carrier": "16D signed envelope -> 12D source/residual plane -> 4D primitive keel -> genus-3 residual boat -> 3D coordinate shadow -> 0D closure", - } - - -def raw_coord_bytes(atom_count: int) -> int: - return atom_count * COORD_BYTES_PER_ATOM - - -def rigid_packet_bytes(atom_count: int, *, hinges: int = 0, residual_atoms: int = 0) -> int: - return POSE_BYTES + hinges * HINGE_CODE_BYTES + residual_atoms * RESIDUAL_COORD_BYTES_PER_ATOM - - -def body_template( - *, - body_id: str, - name: str, - atom_labels: list[str], - template_coords: list[Coord], - mmff_role: str, - rigidity_class: str, - expected_terms: list[str], -) -> dict[str, Any]: - payload = { - "body_id": body_id, - "name": name, - "atom_labels": atom_labels, - "template_coords_pm": template_coords, - "mmff_role": mmff_role, - "rigidity_class": rigidity_class, - "expected_terms": expected_terms, - } - payload["shadow_chain"] = dimension_shadow_chain(payload) - payload["template_hash"] = hash_obj(payload) - return payload - - -def fixture( - *, - fixture_id: str, - template: dict[str, Any], - offset: Coord, - orientation: str, - hinges: int = 0, - residual_atoms: int = 0, - decision: str, - residual_policy: str, -) -> dict[str, Any]: - base = [tuple(coord) for coord in template["template_coords_pm"]] - oriented = rotate_z_90(base) if orientation == "RZ90" else base - reconstructed = translate(oriented, offset) - direct = reconstructed[:] - atom_count = len(template["atom_labels"]) - raw = raw_coord_bytes(atom_count) - packet = rigid_packet_bytes(atom_count, hinges=hinges, residual_atoms=residual_atoms) - item = { - "fixture_id": fixture_id, - "body_id": template["body_id"], - "body_name": template["name"], - "shadow_root": template["shadow_chain"]["root"], - "shadow_root_bytes": SHADOW_ROOT_BYTES, - "offset_pm": offset, - "orientation_code": orientation, - "hinge_count": hinges, - "residual_atom_count": residual_atoms, - "atom_count": atom_count, - "raw_coord_bytes": raw, - "rigid_packet_bytes": packet, - "delta_bytes": raw - packet, - "replay_exact": reconstructed == direct, - "reconstructed_coords_pm": reconstructed, - "direct_coords_pm": direct, - "decision": decision, - "residual_policy": residual_policy, - } - item["fixture_hash"] = hash_obj({k: v for k, v in item.items() if k != "fixture_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - templates = [ - body_template( - body_id="RB_LINEAR_TRIAD_CO2", - name="linear triad", - atom_labels=["O", "C", "O"], - template_coords=[(-116, 0, 0), (0, 0, 0), (116, 0, 0)], - mmff_role="rigid local bond/angle geometry", - rigidity_class="rigid", - expected_terms=["bond_stretch", "angle_bend"], - ), - body_template( - body_id="RB_WATER_BENT", - name="bent triad", - atom_labels=["H", "O", "H"], - template_coords=[(76, 59, 0), (0, 0, 0), (-76, 59, 0)], - mmff_role="rigid local bond/angle geometry", - rigidity_class="rigid", - expected_terms=["bond_stretch", "angle_bend"], - ), - body_template( - body_id="RB_BENZENE_RING", - name="aromatic six-member ring with hydrogens", - atom_labels=["C", "C", "C", "C", "C", "C", "H", "H", "H", "H", "H", "H"], - template_coords=[ - (140, 0, 0), - (70, 121, 0), - (-70, 121, 0), - (-140, 0, 0), - (-70, -121, 0), - (70, -121, 0), - (249, 0, 0), - (124, 216, 0), - (-124, 216, 0), - (-249, 0, 0), - (-124, -216, 0), - (124, -216, 0), - ], - mmff_role="aromatic rigid fragment candidate", - rigidity_class="semi_rigid", - expected_terms=["bond_stretch", "angle_bend", "torsion", "out_of_plane", "aromatic_atom_typing"], - ), - body_template( - body_id="RB_METHYL_ROTOR", - name="methyl rotor", - atom_labels=["C", "H", "H", "H"], - template_coords=[(0, 0, 0), (109, 0, 0), (-36, 103, 0), (-36, -103, 0)], - mmff_role="rigid rotor attached by one torsion hinge", - rigidity_class="hinged_rigid", - expected_terms=["bond_stretch", "angle_bend", "torsion"], - ), - ] - template_by_id = {template["body_id"]: template for template in templates} - fixtures = [ - fixture( - fixture_id="co2_identity_pose", - template=template_by_id["RB_LINEAR_TRIAD_CO2"], - offset=(1000, 2000, 3000), - orientation="IDENTITY", - decision="ADMIT_RIGID_BODY_FIXTURE", - residual_policy="exact integer-coordinate replay; atom typing and energy terms not claimed", - ), - fixture( - fixture_id="water_rotated_pose", - template=template_by_id["RB_WATER_BENT"], - offset=(-200, 80, 10), - orientation="RZ90", - decision="ADMIT_RIGID_BODY_FIXTURE", - residual_policy="exact integer-coordinate replay; atom typing and energy terms not claimed", - ), - fixture( - fixture_id="benzene_template_pose", - template=template_by_id["RB_BENZENE_RING"], - offset=(0, 0, 0), - orientation="IDENTITY", - decision="ADMIT_SEMI_RIGID_FIXTURE", - residual_policy="aromaticity and force-field atom typing remain adapter-table HOLD surfaces", - ), - fixture( - fixture_id="methyl_hinged_pose", - template=template_by_id["RB_METHYL_ROTOR"], - offset=(250, -250, 0), - orientation="RZ90", - hinges=1, - decision="ADMIT_HINGED_RIGID_FIXTURE", - residual_policy="one torsion hinge encoded; torsion energy and neighbor-dependent strain remain residual surfaces", - ), - fixture( - fixture_id="strained_benzene_hold", - template=template_by_id["RB_BENZENE_RING"], - offset=(0, 0, 0), - orientation="IDENTITY", - residual_atoms=2, - decision="HOLD_STRAIN_RESIDUAL", - residual_policy="fixture shows residual budget path but does not admit distorted aromatic geometry without MMFF typing receipt", - ), - ] - total_raw = sum(item["raw_coord_bytes"] for item in fixtures) - total_packet = sum(item["rigid_packet_bytes"] for item in fixtures) - tree_fiddy_shadow_archive_bytes = total_packet + SHADOW_ROOT_BYTES - return { - "schema": "mmff_rigid_body_geometry_registry_v1", - "citations": CITATIONS, - "claim_boundary": ( - "MMFF rigid-body geometry probe only. It tests coordinate-template " - "replay, refined O-AMMR 16D-to-3D shadow accounting, and byte accounting for " - "rigid/semi-rigid fragments; it does not implement MMFF atom typing, " - "parameter lookup, aromaticity, energy scoring, conformer search, or " - "wet-lab validity." - ), - "encoding_model": { - "coord_component_bytes": COORD_COMPONENT_BYTES, - "coord_bytes_per_atom": COORD_BYTES_PER_ATOM, - "body_id_bytes": BODY_ID_BYTES, - "translation_bytes": TRANSLATION_BYTES, - "orientation_code_bytes": ORIENTATION_CODE_BYTES, - "hinge_code_bytes": HINGE_CODE_BYTES, - "residual_coord_bytes_per_atom": RESIDUAL_COORD_BYTES_PER_ATOM, - "pose_bytes": POSE_BYTES, - "shadow_root_bytes": SHADOW_ROOT_BYTES, - "accumulator_kind": ACCUMULATOR_KIND, - "tree_fiddy_cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - }, - "templates": templates, - "fixtures": fixtures, - "aggregates": { - "fixture_count": len(fixtures), - "all_exact_replay": all(item["replay_exact"] for item in fixtures), - "raw_coord_bytes": total_raw, - "rigid_packet_bytes": total_packet, - "delta_bytes": total_raw - total_packet, - "tree_fiddy_shadow_archive_bytes": tree_fiddy_shadow_archive_bytes, - "tree_fiddy_cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "tree_fiddy_archive_admissible": tree_fiddy_shadow_archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "admitted_fixture_count": sum(1 for item in fixtures if item["decision"].startswith("ADMIT")), - "hold_fixture_count": sum(1 for item in fixtures if item["decision"].startswith("HOLD")), - }, - "tree_fiddy_guard": { - "meaning": "bounded archive and safety cage for committed shadow geometry receipts", - "cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "active_pull_rule": "Q_active(i)=0 if committed_or_shielded", - "archive_bytes_counted_here": "rigid_packet_bytes_total + one O-AMMR shadow root", - "decision": "TREE_FIDDY_ARCHIVE_CANDIDATE" if tree_fiddy_shadow_archive_bytes <= TREE_FIDDY_CAGE_BOUNDARY_BYTES else "HOLD_ACTIVE_SHADOW_ROUTE", - }, - "logogram_candidates": [ - { - "symbol": "LAMBDA_RB_POSE", - "payload": "template_id + translation + orientation_code -> atom coordinate block", - "decision": "ADMIT_FIXTURE", - }, - { - "symbol": "LAMBDA_HINGED_RB", - "payload": "rigid body pose + torsion hinge code + residual strain lane", - "decision": "ADMIT_FIXTURE", - }, - { - "symbol": "LAMBDA_MMFF_GEOM", - "payload": "rigid/semi-rigid body stream + MMFF adapter table hash + residual strain", - "decision": "HOLD_ADAPTER_REQUIRED", - }, - { - "symbol": "LAMBDA_MMFF_SHADOW_MERKLE", - "payload": "L16 chemistry/body state -> L8 MMFF adapter -> L4 geometry primitive -> L3 coordinate shadow", - "decision": "REPLACED_BY_O_AMMR_REFINEMENT", - }, - { - "symbol": "LAMBDA_MMFF_SHADOW_O_AMMR", - "payload": "16D signed envelope -> 12D residual plane -> 4D primitive keel -> Rg3 residual boat -> 3D coordinate shadow -> 0D closure", - "decision": "ADMIT_REFINED_SHADOW_FIXTURE", - }, - ], - "hold_surfaces": [ - "MMFF atom typing", - "MMFF aromaticity model", - "MMFF parameter tables", - "partial charges", - "nonbonded interaction cutoffs", - "energy minimization convergence", - "conformer ranking", - ], - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "mmff_rigid_body_geometry_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_RIGID_BODY_GEOMETRY_FIXTURE_HOLD_MMFF", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - agg = registry["aggregates"] - lines = [ - "# MMFF Rigid-Body Geometry Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Aggregate", - "", - f"- Fixtures: `{agg['fixture_count']}`", - f"- Exact replay: `{agg['all_exact_replay']}`", - f"- Raw coordinate bytes: `{agg['raw_coord_bytes']}`", - f"- Rigid packet bytes: `{agg['rigid_packet_bytes']}`", - f"- Delta bytes: `{agg['delta_bytes']}`", - f"- Tree Fiddy archive bytes: `{agg['tree_fiddy_shadow_archive_bytes']}` / `{agg['tree_fiddy_cage_boundary_bytes']}`", - f"- Tree Fiddy archive admissible: `{agg['tree_fiddy_archive_admissible']}`", - f"- Admitted fixtures: `{agg['admitted_fixture_count']}`", - f"- HOLD fixtures: `{agg['hold_fixture_count']}`", - "", - "## Shadow Chain", - "", - "`L16_signed_envelope -> L12_source_residual_plane -> L8_mmff_adapter_state -> L4_geometry_primitive -> Rg3_residual_boat -> L3_coordinate_shadow -> L0_replay_closure -> O_AMMR_root`", - "", - "The O-AMMR root binds the higher-dimensional chemistry/body template, residual lanes, " - "adapter state, and 3D coordinate replay. Plain Merkle hashes are content commitments " - "inside the ordered algebraic accumulator. MMFF atom typing and parameter tables remain " - "`HOLD_ADAPTER_TABLE_REQUIRED`.", - "", - "## Fixtures", - "", - "| Fixture | Body | Raw | Packet | Delta | Decision |", - "|---|---|---:|---:|---:|---|", - ] - for item in registry["fixtures"]: - lines.append( - f"| `{item['fixture_id']}` | `{item['body_id']}` | {item['raw_coord_bytes']} | " - f"{item['rigid_packet_bytes']} | {item['delta_bytes']} | `{item['decision']}` |" - ) - lines.extend( - [ - "", - "## Rule", - "", - "Treat recurring molecular fragments as rigid/semi-rigid templates first. " - "Encode pose, hinge/torsion state, and residual strain. Keep MMFF atom " - "typing, aromaticity, parameter lookup, charges, nonbonded interactions, " - "and minimization as adapter surfaces until receipted.", - "", - "## Citations", - "", - ] - ) - for citation in registry["citations"]: - target = citation.get("url") or citation.get("path") - lines.append(f"- `{citation['id']}`: {citation['title']} ({target}); role: `{citation['role']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/modly_text_to_cad_bridge_probe.py b/4-Infrastructure/shim/modly_text_to_cad_bridge_probe.py deleted file mode 100644 index 49429c27..00000000 --- a/4-Infrastructure/shim/modly_text_to_cad_bridge_probe.py +++ /dev/null @@ -1,315 +0,0 @@ -#!/usr/bin/env python3 -"""Modly to text-to-CAD bridge probe. - -This probe records a conservative bridge between Modly's local image-to-mesh -pipeline, the repo-local text-to-CAD harness, and the Rainbow Raccoon compiler -idea. It does not vendor Modly, install model weights, or treat generated meshes -as parametric CAD source. The bridge keeps mesh output as a model-guess prior, -then routes durable source of truth through text-to-CAD generator files while -Rainbow Raccoon carries residuals, closure gates, and refinement receipts. -""" - -from __future__ import annotations - -import hashlib -import json -import urllib.request -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "modly_text_to_cad_bridge" -PAYLOAD_JSON = OUT_DIR / "modly_text_to_cad_bridge.json" -SUMMARY = OUT_DIR / "modly_text_to_cad_bridge.md" -RECEIPT = OUT_DIR / "modly_text_to_cad_bridge_receipt.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Modly Text To CAD Bridge.tid" -) - -REMOTE_SOURCES = [ - { - "name": "modly_readme", - "url": "https://raw.githubusercontent.com/lightningpixel/modly/main/README.md", - }, - { - "name": "modly_package", - "url": "https://raw.githubusercontent.com/lightningpixel/modly/main/package.json", - }, - { - "name": "modly_generation_router", - "url": "https://raw.githubusercontent.com/lightningpixel/modly/main/api/routers/generation.py", - }, - { - "name": "modly_export_router", - "url": "https://raw.githubusercontent.com/lightningpixel/modly/main/api/routers/export.py", - }, - { - "name": "modly_generator_contract", - "url": "https://raw.githubusercontent.com/lightningpixel/modly/main/api/services/generators/base.py", - }, -] - -LOCAL_SOURCES = [ - REPO / "5-Applications" / "text-to-cad" / "README.md", - REPO / "5-Applications" / "text-to-cad" / "AGENTS.md", - REPO / "5-Applications" / "text-to-cad" / "skills" / "cad" / "SKILL.md", - REPO / "5-Applications" / "text-to-cad" / "skills" / "cad" / "references" / "generator-contract.md", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def fetch_remote(source: dict[str, str]) -> dict[str, Any]: - try: - request = urllib.request.Request( - source["url"], - headers={"User-Agent": "ResearchStack-modly-text-to-cad-bridge/1.0"}, - ) - with urllib.request.urlopen(request, timeout=30) as response: - data = response.read() - return { - **source, - "fetched": True, - "fetch_error": None, - "bytes": len(data), - "sha256": sha256_bytes(data), - } - except Exception as exc: # pragma: no cover - receipt captures network failures. - return { - **source, - "fetched": False, - "fetch_error": f"{type(exc).__name__}: {exc}", - "bytes": 0, - "sha256": None, - } - - -def local_ref(path: Path) -> dict[str, Any]: - exists = path.exists() - data = path.read_bytes() if exists else b"" - return { - "path": rel(path), - "exists": exists, - "bytes": len(data), - "sha256": sha256_bytes(data) if exists else None, - } - - -def build_payload() -> dict[str, Any]: - remote_refs = [fetch_remote(source) for source in REMOTE_SOURCES] - local_refs = [local_ref(path) for path in LOCAL_SOURCES] - payload = { - "schema": "modly_text_to_cad_bridge_v1", - "claim_boundary": ( - "Bridge hypothesis only. Modly mesh output is treated as a local prior " - "or evidence artifact, not as editable CAD source. Text-to-CAD generator " - "files remain the durable source of truth." - ), - "remote_refs": remote_refs, - "local_refs": local_refs, - "bridge_pipeline": [ - "image_or_photo_input", - "modly_local_image_to_mesh_generation", - "rainbow_raccoon_guess_capture", - "modly_export_glb_stl_obj_or_ply", - "mesh_cleanup_and_feature_measurement", - "text_to_cad_parametric_generator_synthesis", - "rainbow_raccoon_residual_compile", - "explicit_step_stl_dxf_glb_urdf_regeneration", - "render_compare_and_residual_update", - "cad_explorer_review_with_cad_refs", - ], - "candidate_equations": [ - { - "equation_id": "mesh_prior_to_parametric_cad", - "equation": "CAD_source = synthesize(generator_contract, mesh_features, design_intent, constraints)", - "decision": "HOLD_MESH_TO_PARAMETRIC_CAD", - "use_as": "turn Modly mesh evidence into editable text-to-CAD source", - }, - { - "equation_id": "modly_mesh_prior_weight", - "equation": "W_mesh = q_mesh * q_silhouette * q_scale * q_topology - cleanup_cost", - "decision": "HOLD_MESH_PRIOR_WEIGHT", - "use_as": "decide how strongly a generated mesh should influence CAD synthesis", - }, - { - "equation_id": "cad_source_promotion_gate", - "equation": "promote iff source_regenerates && mesh_alignment_ok && step_valid && receipt_exists", - "decision": "HOLD_CAD_PROMOTION_GATE", - "use_as": "keep source-controlled CAD as the promotion boundary", - }, - { - "equation_id": "rainbow_raccoon_guess_residual_loop", - "equation": "R_guess = features(Modly_mesh) - features(render(TextToCAD_source))", - "decision": "HOLD_GUESS_RESIDUAL_LOOP", - "use_as": "show what the model guessed at and feed bounded residuals back into refinement", - }, - { - "equation_id": "self_refining_cad_compiler_step", - "equation": "CAD_{t+1}=compile(CAD_t, R_guess_t, constraints, closure_receipt_t)", - "decision": "HOLD_SELF_REFINING_CAD_COMPILER", - "use_as": "Rainbow Raccoon compiler loop from mesh guess to improved parametric CAD", - }, - { - "equation_id": "mesh_guess_closure_gate", - "equation": "G_guess=1[source_regenerates]*1[render_hash_recomputes]*1[residual_bounded]*1[rollback_exists]", - "decision": "HOLD_GUESS_CLOSURE_GATE", - "use_as": "prevent the refinement loop from treating an opaque mesh guess as validated geometry", - }, - ], - "adapter_shape": { - "modly_role": "local image-to-mesh prior generator and mesh export surface", - "text_to_cad_role": "parametric source-of-truth generator, validator, exporter, and viewer/ref surface", - "rainbow_raccoon_role": "compiler/refinement layer that records model guesses, residuals, closure gates, and rollback receipts", - "safe_default": "do not vendor Modly or download model weights automatically; import only user-provided mesh artifacts or explicit local Modly outputs", - "artifact_boundary": "generated mesh can guide silhouette and proportions; generated Python CAD source owns editable geometry", - "refinement_boundary": "self-refinement is allowed only over local fixtures, explicit constraints, render comparisons, and rollback hashes", - }, - "decision": "ADMIT_MODLY_TEXT_TO_CAD_BRIDGE_AS_HOLD_PRIOR", - } - payload["aggregates"] = { - "remote_source_count": len(remote_refs), - "remote_fetched_count": sum(1 for item in remote_refs if item["fetched"]), - "local_source_count": len(local_refs), - "local_existing_count": sum(1 for item in local_refs if item["exists"]), - "candidate_count": len(payload["candidate_equations"]), - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "modly_text_to_cad_bridge_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "aggregates": payload["aggregates"], - "remote_hashes": {item["name"]: item["sha256"] for item in payload["remote_refs"]}, - "local_hashes": {item["path"]: item["sha256"] for item in payload["local_refs"]}, - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Modly Text-to-CAD Bridge", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Bridge Pipeline", - "", - ] - for index, step in enumerate(payload["bridge_pipeline"], start=1): - lines.append(f"{index}. `{step}`") - lines.extend( - [ - "", - "## Adapter Shape", - "", - ] - ) - for key, value in payload["adapter_shape"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Candidate Equations", - "", - "| Candidate | Equation | Decision | Use as |", - "|---|---|---|---|", - ] - ) - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | `{item['equation']}` | {item['decision']} | {item['use_as']} |") - lines.extend(["", "## Sources", ""]) - for item in payload["remote_refs"]: - status = "ok" if item["fetched"] else "missing" - lines.append(f"- `{item['name']}`: {status} - {item['url']}") - for item in payload["local_refs"]: - status = "ok" if item["exists"] else "missing" - lines.append(f"- `{item['path']}`: {status}") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: Modly Text To CAD Bridge", - "tags: TextToCAD Modly CAD MeshPrior HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Modly Text To CAD Bridge", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Bridge", - "", - "Modly is treated as a local image-to-mesh prior generator. Text-to-CAD remains the parametric source-of-truth generator and validation/export surface. Rainbow Raccoon acts as the compiler loop that records what the model guessed, compares it to regenerated CAD, and carries bounded residuals into the next refinement step.", - "", - "!! Pipeline", - "", - ] - for step in payload["bridge_pipeline"]: - lines.append(f"* `{step}`") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - f"Receipt: `shared-data/data/modly_text_to_cad_bridge/modly_text_to_cad_bridge_receipt.json`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/modly_text_to_cad_bridge_smoke.py b/4-Infrastructure/shim/modly_text_to_cad_bridge_smoke.py deleted file mode 100644 index a5dd7827..00000000 --- a/4-Infrastructure/shim/modly_text_to_cad_bridge_smoke.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -"""Smoke test for the Modly -> Rainbow Raccoon -> text-to-CAD bridge. - -This test avoids GPU/model dependencies by creating a deterministic synthetic -"Modly mesh guess" fixture, then generating a real text-to-CAD STEP/STL artifact -from a build123d source through the repo-local CAD runtime. It measures the -feature residual between the mesh guess and regenerated CAD output and checks a -closure gate with a deliberately failing negative control. -""" - -from __future__ import annotations - -import hashlib -import json -import math -import struct -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "modly_text_to_cad_bridge" / "smoke" -RECEIPT = OUT_DIR / "modly_text_to_cad_bridge_smoke_receipt.json" -SUMMARY = OUT_DIR / "modly_text_to_cad_bridge_smoke.md" -CAD_SOURCE = OUT_DIR / "rr_bridge_box.py" -MODLY_GUESS_STL = OUT_DIR / "modly_guess_box.stl" -ROLLBACK = OUT_DIR / "rr_bridge_box.rollback.json" -CAD_PYTHON = REPO / "5-Applications" / "text-to-cad" / ".venv" / "bin" / "python" -GEN_STEP_PART = REPO / "5-Applications" / "text-to-cad" / "skills" / "cad" / "scripts" / "gen_step_part" - -TARGET_DIMS = (10.0, 20.0, 5.0) -NEGATIVE_DIMS = (12.0, 20.0, 5.0) -RESIDUAL_EPSILON = 1.0e-6 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def write_box_stl(path: Path, dims: tuple[float, float, float]) -> None: - x, y, z = (value / 2.0 for value in dims) - vertices = [ - (-x, -y, -z), (x, -y, -z), (x, y, -z), (-x, y, -z), - (-x, -y, z), (x, -y, z), (x, y, z), (-x, y, z), - ] - faces = [ - (0, 1, 2), (0, 2, 3), (4, 6, 5), (4, 7, 6), - (0, 4, 5), (0, 5, 1), (1, 5, 6), (1, 6, 2), - (2, 6, 7), (2, 7, 3), (3, 7, 4), (3, 4, 0), - ] - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("wb") as handle: - handle.write(b"synthetic modly mesh guess".ljust(80, b"\0")) - handle.write(struct.pack(" dict[str, Any]: - data = path.read_bytes() - if len(data) < 84: - raise ValueError(f"STL too small: {path}") - triangle_count = struct.unpack(" float: - ab = (b[0] - a[0], b[1] - a[1], b[2] - a[2]) - ac = (c[0] - a[0], c[1] - a[1], c[2] - a[2]) - cross = ( - ab[1] * ac[2] - ab[2] * ac[1], - ab[2] * ac[0] - ab[0] * ac[2], - ab[0] * ac[1] - ab[1] * ac[0], - ) - return 0.5 * math.sqrt(sum(value * value for value in cross)) - - -def residual(lhs: dict[str, Any], rhs: dict[str, Any]) -> dict[str, Any]: - extent_delta = [ - round(lhs["bbox_extents"][i] - rhs["bbox_extents"][i], 9) - for i in range(3) - ] - return { - "extent_delta": extent_delta, - "max_abs_extent_delta": max(abs(value) for value in extent_delta), - "volume_delta": round(lhs["bbox_volume"] - rhs["bbox_volume"], 9), - "surface_area_delta": round(lhs["surface_area"] - rhs["surface_area"], 9), - } - - -def write_cad_source() -> None: - CAD_SOURCE.write_text( - """import build123d as bd - - -def gen_step(): - return { - "shape": bd.Box(10.0, 20.0, 5.0), - "step_output": "rr_bridge_box.step", - "export_stl": True, - "stl_output": "rr_bridge_box.stl", - "skip_topology": True, - } -""", - encoding="utf-8", - ) - - -def run_generation() -> subprocess.CompletedProcess[str]: - if not CAD_PYTHON.exists(): - raise FileNotFoundError(f"Missing text-to-CAD Python runtime: {CAD_PYTHON}") - return subprocess.run( - [str(CAD_PYTHON), str(GEN_STEP_PART), str(CAD_SOURCE), "--summary"], - cwd=REPO, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.STDOUT, - check=False, - ) - - -def build_receipt() -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - write_box_stl(MODLY_GUESS_STL, TARGET_DIMS) - write_cad_source() - ROLLBACK.write_text( - json.dumps({"cad_source_sha256": file_hash(CAD_SOURCE), "target_dims": TARGET_DIMS}, indent=2), - encoding="utf-8", - ) - generation = run_generation() - cad_stl = OUT_DIR / "rr_bridge_box.stl" - modly_features = stl_features(MODLY_GUESS_STL) - cad_features = stl_features(cad_stl) if generation.returncode == 0 and cad_stl.exists() else None - bridge_residual = residual(modly_features, cad_features) if cad_features else { - "extent_delta": [None, None, None], - "max_abs_extent_delta": None, - "volume_delta": None, - "surface_area_delta": None, - } - - negative_path = OUT_DIR / "negative_wrong_guess_box.stl" - write_box_stl(negative_path, NEGATIVE_DIMS) - negative_features = stl_features(negative_path) - negative_residual = residual(negative_features, cad_features) if cad_features else { - "extent_delta": [None, None, None], - "max_abs_extent_delta": None, - "volume_delta": None, - "surface_area_delta": None, - } - - closure_gate = { - "source_regenerates": generation.returncode == 0 and cad_stl.exists(), - "render_hash_recomputes": bool(cad_features) and file_hash(cad_stl) == cad_features["sha256"], - "residual_bounded": ( - bridge_residual["max_abs_extent_delta"] is not None - and bridge_residual["max_abs_extent_delta"] <= RESIDUAL_EPSILON - ), - "rollback_exists": ROLLBACK.exists() and file_hash(ROLLBACK) is not None, - } - negative_gate = { - "residual_bounded": ( - negative_residual["max_abs_extent_delta"] is not None - and negative_residual["max_abs_extent_delta"] <= RESIDUAL_EPSILON - ), - "expected_to_fail": True, - } - payload = { - "schema": "modly_text_to_cad_bridge_smoke_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "target_dims": TARGET_DIMS, - "negative_dims": NEGATIVE_DIMS, - "generation_returncode": generation.returncode, - "generation_output_tail": generation.stdout.splitlines()[-8:], - "modly_guess_features": modly_features, - "text_to_cad_features": cad_features, - "bridge_residual": bridge_residual, - "negative_residual": negative_residual, - "closure_gate": closure_gate, - "negative_gate": negative_gate, - "decision": ( - "PASS_MODLY_TEXT_TO_CAD_BRIDGE_SMOKE" - if all(closure_gate.values()) and not negative_gate["residual_bounded"] - else "FAIL_MODLY_TEXT_TO_CAD_BRIDGE_SMOKE" - ), - "claim_boundary": ( - "Smoke fixture only. This tests the bridge mechanics with synthetic mesh " - "and local CAD generation; it does not test Modly model quality or GPU inference." - ), - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k not in {"payload_hash", "generated_at_utc"}}) - payload["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in payload.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return payload - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# Modly Text-to-CAD Bridge Smoke", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Residual", - "", - f"- Max extent residual: `{receipt['bridge_residual']['max_abs_extent_delta']}`", - f"- Negative max extent residual: `{receipt['negative_residual']['max_abs_extent_delta']}`", - "", - "## Closure Gate", - "", - ] - for key, value in receipt["closure_gate"].items(): - lines.append(f"- `{key}`: `{value}`") - lines.extend(["", "## Artifacts", ""]) - for path in [CAD_SOURCE, MODLY_GUESS_STL, OUT_DIR / "rr_bridge_box.step", OUT_DIR / "rr_bridge_box.stl", ROLLBACK]: - lines.append(f"- `{path.relative_to(REPO)}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_summary(receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - raise SystemExit(0 if receipt["decision"].startswith("PASS_") else 1) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/molecular_domain_prior_metaprobe.py b/4-Infrastructure/shim/molecular_domain_prior_metaprobe.py deleted file mode 100644 index 1f5e7951..00000000 --- a/4-Infrastructure/shim/molecular_domain_prior_metaprobe.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python3 -"""Molecular-domain metaprobe for the physics/math router. - -This promotes chemistry from a generic Hugging Face registry tag into a -concrete computational surface: molecular graphs, bond matrices, spectra, -properties, units, provenance, and receipts. It deliberately avoids claiming -wet-lab validity from model/dataset priors. -""" - -from __future__ import annotations - -import argparse -import json -import re -from pathlib import Path -from typing import Any - - -REGISTRY = Path("shared-data/artifacts/chemical_bond_matrix_registry.md") -LOCAL_CHEMISTRY_DIR = Path("5-Applications/tools-scripts/chemistry") - - -MOLECULAR_AXES = [ - { - "axis": "molecular_graph", - "payload": ["atom_types", "bond_order", "formal_charge", "aromaticity", "stereochemistry"], - "router_use": "graph_topology_and_symbolic_compression", - "receipt_rule": "preserve canonical representation hash and source provenance", - }, - { - "axis": "bond_matrix", - "payload": ["bond_length", "bond_angle", "dihedral", "force_constant", "coordinate_frame"], - "router_use": "field/shear primitive over molecular geometry", - "receipt_rule": "preserve units, method, basis/force-field name, and source database", - }, - { - "axis": "spectral_property", - "payload": ["energy", "frequency", "dipole", "polarizability", "orbital_or_band_feature"], - "router_use": "spectral primitive and eigen-prior selection", - "receipt_rule": "separate experimental, calculated, and model-predicted values", - }, - { - "axis": "dataset_provenance", - "payload": ["source", "license", "version", "modality", "schema", "contamination_check"], - "router_use": "admissibility and sampling gate", - "receipt_rule": "do not train/ingest without source/license/schema receipt", - }, -] - - -HF_CHEMISTRY_PRIORS = [ - { - "id": "jablonkagroup/ChemBench", - "role": "chemistry_benchmark_prior", - "boundary": "benchmark-prior-only", - "use_as": "chemistry_reasoning_eval_axis", - }, - { - "id": "eve-bio/drug-target-activity", - "role": "bioactivity_table_prior", - "boundary": "dataset-prior-only", - "use_as": "molecule_target_property_schema_axis", - }, - { - "id": "lisn519010/QM9", - "role": "quantum_chemistry_small_molecule_prior", - "boundary": "dataset-prior-only", - "use_as": "small_molecule_property_and_geometry_axis", - }, - { - "id": "jglaser/binding_affinity", - "role": "protein_ligand_affinity_prior", - "boundary": "dataset-prior-only", - "use_as": "binding_property_schema_axis", - }, - { - "id": "LeMaterial/LeMat-Traj", - "role": "material_trajectory_prior", - "boundary": "dataset-prior-only", - "use_as": "molecular_or_material_dynamics_axis", - }, -] - - -def extract_registry_sections(text: str) -> list[dict[str, Any]]: - sections: list[dict[str, Any]] = [] - current: dict[str, Any] | None = None - for line in text.splitlines(): - heading = re.match(r"^###\s+\d+\.\s+(.+)$", line) - if heading: - if current: - sections.append(current) - current = {"name": heading.group(1).strip(), "lines": []} - continue - if current is not None: - current["lines"].append(line) - if current: - sections.append(current) - - compact = [] - for section in sections: - body = "\n".join(section["lines"]) - compact.append( - { - "name": section["name"], - "source": first_match(body, r"\*\*Source:\*\*\s*(.+)") or first_match(body, r"\*\*URL:\*\*\s*(.+)"), - "license": first_match(body, r"\*\*License:\*\*\s*(.+)"), - "entries": first_match(body, r"\*\*Entries:\*\*\s*(.+)") or first_match(body, r"\*\*Species:\*\*\s*(.+)"), - "integration_value": first_match(body, r"\*\*Integration Value:\*\*\s*(.+)"), - "bond_matrix_mentions": count_terms(body, ["bond", "angle", "dihedral", "coordinate", "force"]), - } - ) - return compact - - -def first_match(text: str, pattern: str) -> str | None: - match = re.search(pattern, text) - return match.group(1).strip() if match else None - - -def count_terms(text: str, terms: list[str]) -> int: - lowered = text.lower() - return sum(lowered.count(term) for term in terms) - - -def local_script_summary(path: Path) -> dict[str, Any]: - text = path.read_text(encoding="utf-8", errors="replace") - lowered = text.lower() - speculative_markers = [ - "element 229", - "superconductor", - "stabilized into a molecular cluster", - "calibrated", - "final collapse", - ] - return { - "path": str(path), - "lines": text.count("\n") + 1, - "functions": re.findall(r"^def\s+([A-Za-z_][A-Za-z0-9_]*)", text, flags=re.MULTILINE), - "claim_boundary": "hypothesis_or_demo_only" if any(marker in lowered for marker in speculative_markers) else "utility_script", - "contains_randomness": "random" in lowered, - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a molecular physics/math router. Return compact JSON with evidence boundaries." - records = [] - for axis in receipt["molecular_axes"]: - prompt = { - "task": "route_molecular_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Choose how this molecular axis should enter the physics-math compression router.", - } - answer = { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "computational-chemistry-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - } - records.append(chat_record(system, prompt, answer)) - for prior in receipt["hf_chemistry_priors"]: - prompt = { - "task": "use_hf_chemistry_prior", - "dataset": prior["id"], - "role": prior["role"], - "instruction": "Explain how to sample this chemistry dataset family without overclaiming.", - } - answer = { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "sampling_rule": "sample small, preserve schema/source/license/units, and keep experimental/calculated/predicted labels distinct", - } - records.append(chat_record(system, prompt, answer)) - return records - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--registry", type=Path, default=REGISTRY) - parser.add_argument("--chemistry-dir", type=Path, default=LOCAL_CHEMISTRY_DIR) - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/molecular_domain_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/molecular_domain_prior_curriculum.jsonl")) - args = parser.parse_args() - - registry_text = args.registry.read_text(encoding="utf-8", errors="replace") if args.registry.exists() else "" - local_scripts = [ - local_script_summary(path) - for path in sorted(args.chemistry_dir.glob("*.py")) - ] if args.chemistry_dir.exists() else [] - receipt = { - "schema": "molecular_domain_prior_receipt_v1", - "claim_boundary": "Molecular priors support computational routing, not wet-lab validity or synthesis claims.", - "registry": str(args.registry), - "registry_sections": extract_registry_sections(registry_text), - "molecular_axes": MOLECULAR_AXES, - "hf_chemistry_priors": HF_CHEMISTRY_PRIORS, - "local_scripts": local_scripts, - "lawful": bool(registry_text) and bool(MOLECULAR_AXES), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/moving_sofa_nspace_prior_metaprobe.py b/4-Infrastructure/shim/moving_sofa_nspace_prior_metaprobe.py deleted file mode 100644 index 45927429..00000000 --- a/4-Infrastructure/shim/moving_sofa_nspace_prior_metaprobe.py +++ /dev/null @@ -1,193 +0,0 @@ -#!/usr/bin/env python3 -"""Moving sofa / couch problem n-space prior. - -This is the user's white-whale geometry target. The receipt turns the problem -into a compression/search surface: configuration space, contact envelopes, -rotation schedules, obstruction certificates, and claimed proof boundaries. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -SOFA_AXES = [ - { - "axis": "configuration_space", - "payload": ["x", "y", "theta", "hallway_constraint", "collision_free_path"], - "router_use": "encode sofa motion as a low-dimensional path through constrained configuration space", - "receipt_rule": "record corridor width, rotation angle schedule, contact state, and collision predicate", - }, - { - "axis": "contact_envelope", - "payload": ["wall_contact", "corner_contact", "swept_boundary", "curve_section", "support_line"], - "router_use": "compress feasible shapes by contact/event envelopes instead of dense grids", - "receipt_rule": "record curve section IDs, tangency/contact events, and boundary reconstruction error", - }, - { - "axis": "area_functional", - "payload": ["shape_boundary", "area_integral", "variation", "Euler_Lagrange_condition", "constraint_multiplier"], - "router_use": "route variational approaches and Gerver-like optimality conditions", - "receipt_rule": "record functional, assumptions, necessary conditions, and numerical integration error", - }, - { - "axis": "upper_bound_obstruction", - "payload": ["angle_grid", "forbidden_region", "cover_certificate", "upper_bound", "computer_assistance"], - "router_use": "construct obstruction certificates for pruning larger candidate shapes", - "receipt_rule": "record discretization, interval bounds, certificate hash, and convergence/coverage claim", - }, - { - "axis": "neural_shape_scout", - "payload": ["candidate_shape_latent", "movement_policy", "area_score", "constraint_loss", "counterexample_search"], - "router_use": "use ZAYA/neural solvers as scouts for candidate decompositions and failure cases", - "receipt_rule": "neural evidence never promotes without analytic/source/verifier certificate", - }, - { - "axis": "nspace_generalization", - "payload": ["dimension", "corridor_topology", "rigid_body_state", "projection", "obstruction_family"], - "router_use": "generalize couch problem into n-space topology/compression experiments", - "receipt_rule": "record dimensional assumptions and distinguish 2D sofa theorem claims from n-space analogies", - }, -] - - -SOFA_PRIORS = [ - { - "id": "Gerver_sofa_constant", - "role": "best_known_classical_lower_bound_and_conjectured_optimum", - "boundary": "classical-construction-prior", - "use_as": "target_shape_and_contact_envelope_prior", - "source": "Gerver construction, referenced across current sofa literature", - "url": "https://www.math.ucdavis.edu/~romik/movingsofa/", - "notes": "Area approximately 2.2195; boundary described by 18 curve sections in modern accounts.", - }, - { - "id": "Kallus_Romik_upper_bound", - "role": "computer_assisted_upper_bound_prior", - "boundary": "published/computer-assisted-prior", - "use_as": "upper_bound_obstruction_certificate_axis", - "source": "Improved upper bounds in the moving sofa problem", - "url": "https://www.math.ucdavis.edu/~romik/data/uploads/papers/sofabounds.pdf", - "notes": "Upper bound line around 2.37; useful for obstruction-certificate shape.", - }, - { - "id": "Baek_conditional_upper_bound", - "role": "conditional_injectivity_upper_bound_prior", - "boundary": "paper-prior-only", - "use_as": "injectivity_condition_and_variational_upper_bound_axis", - "source": "A Conditional Upper Bound for the Moving Sofa Problem", - "url": "https://arxiv.org/abs/2406.10725", - "notes": "Reports conditional upper bound 1 + pi^2/8 = 2.2337... under an injectivity condition including Gerver's sofa.", - }, - { - "id": "Deng_variational_solver", - "role": "calculus_of_variations_necessary_condition_prior", - "boundary": "paper-prior-only", - "use_as": "area_functional_and_euler_lagrange_axis", - "source": "Solving Moving Sofa Problem Using Calculus of Variations", - "url": "https://arxiv.org/abs/2407.02587", - "notes": "Derives variational necessary conditions and numerically recovers Gerver-scale area under assumptions.", - }, - { - "id": "Deep_learning_Gerver_evidence", - "role": "neural_evidence_for_global_optimality_prior", - "boundary": "evidence-prior-not-proof", - "use_as": "neural_shape_scout_and_negative_control_axis", - "source": "Deep Learning Evidence for Global Optimality of Gerver's Sofa", - "url": "https://arxiv.org/abs/2407.11106", - "notes": "Useful as scout/evidence shape; does not replace proof or obstruction certificate.", - }, - { - "id": "Baek_optimality_claim", - "role": "claimed_resolution_of_moving_sofa_problem", - "boundary": "arxiv-claimed-proof-prior-until-independent-verification", - "use_as": "proof_structure_and_obstruction_certificate_target", - "source": "Optimality of Gerver's Sofa", - "url": "https://arxiv.org/abs/2411.19826", - "notes": "Claims Gerver's 18-section construction attains maximum area 2.2195...; local pipeline should treat as source to inspect, not as automatically accepted theorem.", - }, -] - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a moving-sofa n-space geometry router. Return compact JSON with proof boundaries." - records: list[dict[str, Any]] = [] - for axis in receipt["sofa_axes"]: - records.append( - chat_record( - system, - { - "task": "route_moving_sofa_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Use this axis to compress/search the couch problem.", - }, - { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "moving-sofa-coordinate-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - }, - ) - ) - for prior in receipt["sofa_priors"]: - records.append( - chat_record( - system, - { - "task": "use_moving_sofa_prior", - "prior": prior["id"], - "role": prior["role"], - "source": prior["source"], - "instruction": "Explain how this prior guides ZAYA/intense modeling without becoming proof.", - }, - { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use for route/scout/certificate shape only; theorem status requires independent source/proof/formal or reproducible certificate receipts.", - }, - ) - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_nspace_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_nspace_prior_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "moving_sofa_nspace_prior_v1", - "claim_boundary": "Moving sofa priors guide n-space search/compression; they do not certify a proof.", - "white_whale": True, - "sofa_axes": SOFA_AXES, - "sofa_priors": SOFA_PRIORS, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/moving_sofa_scout_harness.py b/4-Infrastructure/shim/moving_sofa_scout_harness.py deleted file mode 100644 index ff088dde..00000000 --- a/4-Infrastructure/shim/moving_sofa_scout_harness.py +++ /dev/null @@ -1,209 +0,0 @@ -#!/usr/bin/env python3 -"""Moving sofa scout harness. - -This is the next runnable loop for the couch/sofa white whale: - - prior receipt -> ZAYA-ready scout packets -> deterministic admissibility gates - -The harness does not solve the moving sofa problem. It prepares bounded prompts -for a local scout model and defines what a usable answer must contain before it -can be promoted to source/formal/certificate work. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -DEFAULT_PRIOR = Path("4-Infrastructure/shim/moving_sofa_nspace_prior_receipt.json") -DEFAULT_ROUTER = Path("4-Infrastructure/shim/intense_math_modeling_router_receipt.json") -DEFAULT_EIGEN = Path("4-Infrastructure/shim/nspace_semantic_pde_eigenvectors.json") - - -SCOUT_TASKS = [ - { - "id": "contact_envelope_decomposition", - "axis": "contact_envelope", - "ask": "Propose a contact-event decomposition for Gerver-style sofa motion.", - "required_fields": ["curve_sections", "contact_events", "reconstruction_error_plan", "source_prior_ids"], - "promotion_gate": "must cite curve/event receipt plan; no proof claim", - }, - { - "id": "upper_bound_certificate_shape", - "axis": "upper_bound_obstruction", - "ask": "Propose a certificate schema for pruning candidate shapes above Gerver-scale area.", - "required_fields": ["angle_grid", "forbidden_region_model", "coverage_certificate", "error_bound_plan"], - "promotion_gate": "must include discretization and coverage/error boundary", - }, - { - "id": "variational_functional_route", - "axis": "area_functional", - "ask": "Propose a variational route that separates assumptions, necessary conditions, and numerical checks.", - "required_fields": ["functional", "assumptions", "necessary_conditions", "numerical_receipts"], - "promotion_gate": "Euler-Lagrange style conditions are necessary only unless proof receipt says otherwise", - }, - { - "id": "nspace_generalization_probe", - "axis": "nspace_generalization", - "ask": "Map the 2D sofa problem into an n-space generalization without confusing analogy with theorem.", - "required_fields": ["dimension", "corridor_topology", "rigid_body_state", "projection_rule", "analogy_boundary"], - "promotion_gate": "must distinguish 2D theorem status from n-space experiment", - }, - { - "id": "claimed_proof_triage", - "axis": "configuration_space", - "ask": "Triage the claimed optimality proof into checkable lemmas, dependencies, and certificate candidates.", - "required_fields": ["lemma_buckets", "dependency_graph", "certificate_candidates", "verification_order"], - "promotion_gate": "must treat arXiv claim as source material, not accepted theorem", - }, -] - - -def sha256_json(value: Any) -> str: - return hashlib.sha256(json.dumps(value, sort_keys=True, ensure_ascii=False).encode("utf-8")).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def find_axis(prior: dict[str, Any], axis_name: str) -> dict[str, Any]: - for axis in prior.get("sofa_axes", []): - if axis.get("axis") == axis_name: - return axis - raise KeyError(axis_name) - - -def top_terms(eigen: dict[str, Any], limit: int = 12) -> list[str]: - return [str(item.get("term")) for item in eigen.get("top_terms", [])[:limit]] - - -def make_scout_packet(task: dict[str, Any], prior: dict[str, Any], router: dict[str, Any], eigen: dict[str, Any]) -> dict[str, Any]: - axis = find_axis(prior, task["axis"]) - packet = { - "schema": "moving_sofa_scout_packet_v1", - "task_id": task["id"], - "preferred_scout_model": router.get("preferred_scout_model", "Zyphra/ZAYA1-8B"), - "axis": axis, - "ask": task["ask"], - "required_response_fields": task["required_fields"], - "promotion_gate": task["promotion_gate"], - "relevant_priors": [ - { - "id": item.get("id"), - "role": item.get("role"), - "boundary": item.get("boundary"), - "use_as": item.get("use_as"), - "url": item.get("url"), - } - for item in prior.get("sofa_priors", []) - ], - "eigen_terms": top_terms(eigen), - "claim_boundary": "scout-packet-only; model output is not proof", - "response_contract": { - "format": "strict_json", - "must_include": task["required_fields"] + ["claim_boundary", "next_receipts"], - "must_not_claim": ["solved", "proved", "optimality_certified"], - }, - } - packet["packet_hash"] = sha256_json(packet) - return packet - - -def make_curriculum(packets: list[dict[str, Any]]) -> list[dict[str, Any]]: - system = "You are ZAYA acting as a moving-sofa scout. Return strict JSON; never certify proof." - records = [] - for packet in packets: - answer = { - "selected": True, - "route": "zaya_scout", - "task_id": packet["task_id"], - "claim_boundary": packet["claim_boundary"], - "required_response_fields": packet["required_response_fields"], - "next_receipts": ["source_excerpt", "formal_or_solver_check", "metaprobe_audit"], - "packet_hash": packet["packet_hash"], - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(packet, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def audit_packets(packets: list[dict[str, Any]]) -> dict[str, Any]: - total = len(packets) - required_ok = 0 - boundary_ok = 0 - hash_ok = 0 - for packet in packets: - contract = packet.get("response_contract", {}) - if packet.get("required_response_fields") and contract.get("must_include"): - required_ok += 1 - if "not proof" in packet.get("claim_boundary", "") and packet.get("promotion_gate"): - boundary_ok += 1 - expected_hash = packet.get("packet_hash") - clone = dict(packet) - clone.pop("packet_hash", None) - if expected_hash == sha256_json(clone): - hash_ok += 1 - denom = total or 1 - resonance = (required_ok / denom + boundary_ok / denom + hash_ok / denom) / 3 - return { - "packet_count": total, - "required_ok": required_ok, - "boundary_ok": boundary_ok, - "hash_ok": hash_ok, - "resonance": resonance, - "lawful": resonance >= 0.95, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--prior", type=Path, default=DEFAULT_PRIOR) - parser.add_argument("--router", type=Path, default=DEFAULT_ROUTER) - parser.add_argument("--eigen", type=Path, default=DEFAULT_EIGEN) - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_harness_receipt.json")) - parser.add_argument("--packets", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_packets.jsonl")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_harness_curriculum.jsonl")) - args = parser.parse_args() - - prior = load_json(args.prior) - router = load_json(args.router) - eigen = load_json(args.eigen) - packets = [make_scout_packet(task, prior, router, eigen) for task in SCOUT_TASKS] - audit = audit_packets(packets) - receipt = { - "schema": "moving_sofa_scout_harness_receipt_v1", - "claim_boundary": "Scout packets prepare model-assisted decomposition; they do not solve or prove the moving sofa problem.", - "source_prior": str(args.prior), - "router_prior": str(args.router), - "eigen_prior": str(args.eigen), - "preferred_scout_model": router.get("preferred_scout_model", "Zyphra/ZAYA1-8B"), - "audit": audit, - "packets": packets, - "lawful": audit["lawful"], - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.packets.open("w", encoding="utf-8") as handle: - for packet in packets: - handle.write(json.dumps(packet, ensure_ascii=False) + "\n") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in make_curriculum(packets): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/moving_sofa_scout_response_validator.py b/4-Infrastructure/shim/moving_sofa_scout_response_validator.py deleted file mode 100644 index 9a6a6cc3..00000000 --- a/4-Infrastructure/shim/moving_sofa_scout_response_validator.py +++ /dev/null @@ -1,212 +0,0 @@ -#!/usr/bin/env python3 -"""Validate moving-sofa scout responses. - -This finishes the first complete couch-problem loop: - - scout packets -> scout responses -> deterministic validation/promote/hold - -If no response file is supplied, the script emits conservative baseline -responses that satisfy the same contract ZAYA must satisfy later. That makes the -pipeline runnable today while keeping the model as a replaceable scout. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -DEFAULT_PACKETS = Path("4-Infrastructure/shim/moving_sofa_scout_packets.jsonl") -FORBIDDEN_PROOF_WORDS = {"solved", "proved", "proof", "certified", "optimality_certified"} - - -BASELINE_PLANS = { - "contact_envelope_decomposition": { - "curve_sections": ["gerver_section_index_0_to_17", "wall_contact_arc", "corner_contact_transition"], - "contact_events": ["left_wall_support", "inner_corner_tangent", "right_wall_support"], - "reconstruction_error_plan": "compare reconstructed envelope area and contact continuity against source curve receipts", - "source_prior_ids": ["Gerver_sofa_constant", "Baek_optimality_claim"], - }, - "upper_bound_certificate_shape": { - "angle_grid": "monotone theta samples with interval enclosure, not a single floating grid", - "forbidden_region_model": "intersection of hallway-obstruction half-planes/contact constraints", - "coverage_certificate": "hashable interval cover over angle/contact states", - "error_bound_plan": "separate discretization error, interval arithmetic error, and coverage gap", - }, - "variational_functional_route": { - "functional": "area(shape_boundary) subject to feasible corridor-motion constraints", - "assumptions": ["connected planar shape", "unit-width right-angle hallway", "declared convexity/injectivity assumptions only when used"], - "necessary_conditions": ["Euler-Lagrange stationarity", "contact-envelope boundary compatibility"], - "numerical_receipts": ["integration_error", "curve_section_hash", "assumption_list"], - }, - "nspace_generalization_probe": { - "dimension": "n >= 2, with 2D theorem status kept separate", - "corridor_topology": "right-angle corridor generalized to constrained passage complex", - "rigid_body_state": ["translation_coordinates", "rotation_group_parameterization", "collision_predicate"], - "projection_rule": "project n-space obstruction to lower-dimensional contact certificates", - "analogy_boundary": "n-space probe is an analogy/search surface, not a solved 2D theorem", - }, - "claimed_proof_triage": { - "lemma_buckets": ["configuration-space reduction", "contact-envelope completeness", "upper-bound obstruction", "Gerver equality case"], - "dependency_graph": "extract definitions -> lemmas -> theorem -> numeric constant dependencies", - "certificate_candidates": ["curve_section_receipts", "interval_cover_hashes", "area_integral_replay"], - "verification_order": ["definitions", "assumptions", "local lemmas", "certificate replay", "global theorem claim"], - }, -} - - -def load_jsonl(path: Path) -> list[dict[str, Any]]: - out = [] - with path.open(encoding="utf-8") as handle: - for line in handle: - if line.strip(): - out.append(json.loads(line)) - return out - - -def sha256_json(value: Any) -> str: - return hashlib.sha256(json.dumps(value, sort_keys=True, ensure_ascii=False).encode("utf-8")).hexdigest() - - -def make_baseline_response(packet: dict[str, Any]) -> dict[str, Any]: - task_id = packet["task_id"] - plan = dict(BASELINE_PLANS.get(task_id, {})) - plan.update( - { - "claim_boundary": "scout-response-only; not proof", - "next_receipts": ["source_excerpt", "formal_or_solver_check", "metaprobe_audit"], - "promotion_request": "HOLD until receipt gates pass", - "packet_hash": packet["packet_hash"], - } - ) - return plan - - -def load_responses(path: Path | None, packets: list[dict[str, Any]]) -> list[dict[str, Any]]: - if not path: - return [make_baseline_response(packet) for packet in packets] - return load_jsonl(path) - - -def contains_forbidden_claim(value: Any) -> bool: - text = json.dumps(value, ensure_ascii=False).lower() - # These words are allowed when they appear in explicit negative/boundary - # phrases. Keep this conservative and phrase-based rather than trying to do - # natural-language semantics inside the validator. - safe_boundary_phrases = [ - "not proof", - "no proof", - "not a proof", - "not solved", - "not a solved", - "not accepted", - "not automatically accepted", - "not an automatically accepted", - "not certified", - "no theorem claim", - ] - for phrase in safe_boundary_phrases: - text = text.replace(phrase, "") - return any(word in text for word in FORBIDDEN_PROOF_WORDS) - - -def validate_response(packet: dict[str, Any], response: dict[str, Any]) -> dict[str, Any]: - required = set(packet.get("required_response_fields", [])) - present = {field for field in required if field in response} - must_include = set(packet.get("response_contract", {}).get("must_include", [])) - present_contract = {field for field in must_include if field in response} - packet_hash_ok = response.get("packet_hash") == packet.get("packet_hash") - boundary_ok = "claim_boundary" in response and "not proof" in str(response.get("claim_boundary", "")).lower() - receipts_ok = isinstance(response.get("next_receipts"), list) and bool(response.get("next_receipts")) - forbidden = contains_forbidden_claim(response) - required_ok = present == required - contract_ok = present_contract == must_include - lawful = required_ok and contract_ok and packet_hash_ok and boundary_ok and receipts_ok and not forbidden - return { - "task_id": packet.get("task_id"), - "required_ok": required_ok, - "contract_ok": contract_ok, - "packet_hash_ok": packet_hash_ok, - "boundary_ok": boundary_ok, - "receipts_ok": receipts_ok, - "forbidden_claim": forbidden, - "promotion": "PROMOTE_TO_SOURCE_FORMAL_TRIAGE" if lawful else "HOLD", - "lawful": lawful, - "response_hash": sha256_json(response), - } - - -def curriculum_records(packets: list[dict[str, Any]], responses: list[dict[str, Any]], validations: list[dict[str, Any]]) -> list[dict[str, Any]]: - system = "You are a moving-sofa scout-response validator. Return compact JSON with promotion status." - records = [] - for packet, response, validation in zip(packets, responses, validations): - prompt = { - "task": "validate_moving_sofa_scout_response", - "packet": { - "task_id": packet["task_id"], - "required_response_fields": packet["required_response_fields"], - "packet_hash": packet["packet_hash"], - }, - "response": response, - } - answer = { - "selected": validation["lawful"], - "task_id": validation["task_id"], - "promotion": validation["promotion"], - "claim_boundary": "validation-receipt-only", - "checks": validation, - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--packets", type=Path, default=DEFAULT_PACKETS) - parser.add_argument("--responses", type=Path, help="optional ZAYA response JSONL; defaults to baseline conservative responses") - parser.add_argument("--out-responses", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_baseline_responses.jsonl")) - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_response_validation_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/moving_sofa_scout_response_validation_curriculum.jsonl")) - args = parser.parse_args() - - packets = load_jsonl(args.packets) - responses = load_responses(args.responses, packets) - if len(responses) != len(packets): - raise SystemExit(f"response count {len(responses)} does not match packet count {len(packets)}") - validations = [validate_response(packet, response) for packet, response in zip(packets, responses)] - lawful_count = sum(1 for item in validations if item["lawful"]) - receipt = { - "schema": "moving_sofa_scout_response_validation_v1", - "claim_boundary": "Validation gates scout responses; it does not prove the moving sofa problem.", - "packets": str(args.packets), - "responses_source": str(args.responses) if args.responses else "baseline_conservative_responses", - "response_count": len(responses), - "lawful_count": lawful_count, - "validations": validations, - "lawful": lawful_count == len(validations), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - with args.out_responses.open("w", encoding="utf-8") as handle: - for response in responses: - handle.write(json.dumps(response, ensure_ascii=False) + "\n") - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(packets, responses, validations): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/multi_domain_adaptive_cognitive_load.md b/4-Infrastructure/shim/multi_domain_adaptive_cognitive_load.md deleted file mode 100644 index 9c7b7e8f..00000000 --- a/4-Infrastructure/shim/multi_domain_adaptive_cognitive_load.md +++ /dev/null @@ -1,1342 +0,0 @@ -# Multi-Domain Adaptive Cognitive Load Functions - -## Overview - -The Φ-scaling response-family framework enables adaptive cognitive load functions across multiple information domains. Instead of fixed linear coefficients, each domain selects its optimal response family (log, Hill, Michaelis-Menten, low-exponent power) based on measured error, complexity penalty, and held-out validation. - -## 2026-05-08 Reweighting: Connectome Protection + Historical Bandwidth Overflow - -This revision treats the overflow mechanism as a **model hypothesis** about preserving working graph stability under overload, not as a proven biological claim. Emotional offload is not free load deletion: it is a separate channel with its own energy barrier, residual stress term, and validation burden. - -The primary use is historical / civilizational modeling of cognitive overload under accelerated information transfer. Trauma remains one local energy-cost modifier, but the broader historical variable is bandwidth overflow: information transfer rate exceeding assimilation capacity. - -Bandwidth overflow: - -``` -B_overflow = - max(0, transfer_bandwidth - assimilation_bandwidth) - / assimilation_bandwidth -``` - -Historical threshold: - -``` -L_threshold_hist = - L_threshold_eff · exp(-rho_B · B_overflow) -``` - -Historical emotional barrier and temperature: - -``` -DeltaE_emotional_hist = - DeltaE_emotional_eff + chi_B · B_overflow - -kT_emotional_hist = - kT_emotional_eff / (1 + psi_B · B_overflow) -``` - -Historical offload efficiency: - -``` -eta_offload_hist = - eta_offload_eff · exp(-omega_B · B_overflow) -``` - -Interpretation: - -``` -accelerated information transfer can lower effective assimilation threshold -accelerated information transfer can raise emotional/institutional regulation barriers -accelerated information transfer can reduce clean offload efficiency -accelerated information transfer can increase residual social/emotional stress -``` - -Psychohistory analogy: - -``` -Harry Seldon's model is a useful fictional analogue for the population-scale -version of this equation: - - not individual prediction - but aggregate phase-pressure modeling - from bandwidth, assimilation lag, institutional response, - and emotional overflow dynamics -``` - -Boundary: - -``` -psychohistory is a structural metaphor here, not evidence. -It helps name the shape: population-scale cognitive load under accelerated -information transfer. -``` - -In the trauma-aware local version, trauma is modeled as an energy-landscape modifier: - -``` -L_threshold_eff = - L_threshold · exp(-rho_T · T_trauma) - -DeltaE_emotional_eff = - DeltaE_emotional + chi_T · T_trauma - -kT_emotional_eff = - kT_emotional / (1 + psi_T · T_trauma) - -eta_offload_eff = - eta_offload · exp(-omega_T · T_trauma) -``` - -Interpretation: - -``` -trauma can lower the effective cognitive threshold -trauma can raise the emotional regulation barrier -trauma can reduce offload efficiency -trauma can increase residual stress after overflow -``` - -Claim boundary: - -``` -T_trauma is not a scalar diagnosis of a person. -It is a model-side stress / exposure proxy that requires consent, -privacy boundaries, and empirical calibration before any real use. - -B_overflow is not a single-cause theory of history. -It requires source anchors such as archive volume, media speed, -literacy/education capacity, institutional response lag, infrastructure -reach, or other measured transfer/assimilation proxies. -``` - -## Multi-Domain Cognitive Load Equation - -``` -Cognitive_Load(domain, complexity) = - C_domain(domain) - · response_family(complexity; θ_domain) - · lambda_phi^{D_f} - · B_gate(domain, constraints) - · overflow_gate(domain, L_cognitive, L_threshold) -``` - -where: -- `domain` = information type (text, code, visual, audio, multimodal) -- `C_domain(domain)` = domain normalization constant -- `response_family` = selected per domain (log, Hill, Michaelis-Menten, low-exponent) -- `θ_domain` = fitted response parameters for domain -- `lambda_phi^{D_f}` = fractal gain (4 if lambda_phi = Φ², 2 if lambda_phi = Φ) -- `B_gate(domain, constraints)` = binding/admissibility gate for domain constraints -- `overflow_gate` = connectome-protective overflow to emotional processing - -## Connectome-Protective Overflow Mechanism - -### Hypothesis - -**To protect its connectome, cognitive overflow is shifted to emotional processing.** - -When cognitive load exceeds a protective threshold, this model shifts excess cognitive demand into an emotional offload channel. The defensible version is that this may preserve working graph stability by preventing overload propagation in cognitive processing routes. It does not prove structural damage prevention. - -### Overflow Gate Function - -``` -overflow_gate(domain, L_cognitive, L_threshold) = - if L_cognitive ≤ L_threshold_hist: - 1.0 (no overflow) - else: - exp(-gamma · (L_cognitive - L_threshold_hist) / kT_emotional_hist) -``` - -where: -- `L_cognitive` = current cognitive load (L_I + L_E + L_G + L_R + L_M) -- `L_threshold_hist` = trauma-and-bandwidth-adjusted protective threshold -- `gamma` = overflow coefficient -- `kT_emotional_hist` = trauma-and-bandwidth-adjusted emotional processing energy scale - -### Emotional Offloading - -When overflow occurs, excess cognitive load is shifted to emotional processing: - -``` -L_emotional_offload = max(0, L_cognitive - L_threshold_hist) · eta_offload_hist -``` - -**Emotional Load Response**: -``` -L_emotional = C_emotional · response_family(L_emotional_offload; θ_emotional) · lambda_phi^{D_f} · B_gate_emotional -``` - -**Response Family**: `low_exponent_power` (emotional regulation limits) -``` -L_emotional = C_emotional · (L_emotional_offload)^{α_emotional} · lambda_phi^{D_f} · B_gate_emotional -``` - -where: -- `α_emotional` = 0.3-0.5 (low exponent, emotional regulation capacity) -- `C_emotional` = emotional normalization constant -- `B_gate_emotional` = emotional offloading gate (social support, coping mechanisms) - -### Connectome Protection Mechanism - -**Threshold Selection**: -``` -L_threshold = C_threshold · lambda_phi^{D_f} · B_gate_threshold -``` - -where: -- `C_threshold` = threshold normalization constant -- `B_gate_threshold` = individual threshold gate (baseline cognitive capacity) - -**Protection Mechanism**: -1. Cognitive load increases with information complexity -2. When L_cognitive > L_threshold_hist, overflow activates -3. Excess load shifts into an emotional processing / salience channel -4. Cognitive graph routes are protected from overload propagation in the model -5. Emotional processing regulates offloaded load through emotional regulation mechanisms -6. If offload is inefficient, residual stress remains and must be counted - -### Updated Total Load with Emotional Offloading - -``` -L_total = L_cog_eff + L_emotional + L_residual_stress -``` - -where: -- `L_cog_eff` = cognitive load after overflow suppression -- `L_emotional` = emotional offload response -- `L_residual_stress` = unresolved excess load after offload inefficiency - -### Emotional Regulation Gate - -``` -B_gate_emotional = exp(-gamma_emotional · DeltaE_emotional_hist / kT_emotional_hist) -``` - -where: -- `DeltaE_emotional_hist` = trauma-and-bandwidth-adjusted emotional regulation barrier -- `gamma_emotional` = emotional regulation coefficient -- Offloading reduces emotional load through: - - Social support - - Coping mechanisms - - Emotional regulation strategies - - Stress reduction - -### Domain-Specific Emotional Offloading - -**Text Processing**: -``` -L_emotional_text = C_emotional_text · log(1 + β_emotional_text · L_emotional_offload_text) · lambda_phi^{D_f} · B_gate_emotional_text -``` - -**Code Processing**: -``` -L_emotional_code = C_emotional_code · (L_emotional_offload_code / (K_emotional + L_emotional_offload_code))^{hill_emotional} · lambda_phi^{D_f} · B_gate_emotional_code -``` - -**Visual Processing**: -``` -L_emotional_visual = C_emotional_visual · (V_max_emotional · L_emotional_offload_visual) / (K_M_emotional + L_emotional_offload_visual) · lambda_phi^{D_f} · B_gate_emotional_visual -``` - -**Audio Processing**: -``` -L_emotional_audio = C_emotional_audio · (L_emotional_offload_audio)^{α_emotional} · lambda_phi^{D_f} · B_gate_emotional_audio -``` - -**Multimodal**: -``` -L_emotional_multi = Σ w_d · L_emotional_d -``` - -## Domain-Specific Response Families - -### Text Processing Domain - -**Response Family**: `log_mutations` (Weber-Fechner perception) - -``` -L_text = C_text · log(1 + β_text · word_count) · lambda_phi^{D_f} · B_gate_text -``` - -**Parameters**: -- `β_text` = 0.316 (fitted to reading comprehension data) -- `C_text` = domain normalization (fitted) -- `B_gate_text` = attentional capacity gate - -**Load Components**: -- `L_I_text = C_I_text · log(1 + β_I · semantic_complexity)` -- `L_E_text = C_E_text · log(1 + β_E · formatting_complexity)` -- `L_G_text = C_G_text · log(1 + β_G · vocabulary_novelty)` -- `L_R_text = C_R_text · log(1 + β_R · discourse_structure)` -- `L_M_text = C_M_text · log(1 + β_M · working_memory_demand)` -- `L_emotional_text = C_emotional_text · log(1 + β_emotional_text · L_emotional_offload_text) · lambda_phi^{D_f} · B_gate_emotional_text` - -**Adaptive Behavior**: Logarithmic scaling matches Weber-Fechner perception of text length and complexity. Emotional offloading activates when cognitive load exceeds threshold. - -### Code Processing Domain - -**Response Family**: `hill_saturation` (working memory limits) - -``` -L_code = C_code · (complexity / (K_code + complexity))^{hill_code} · lambda_phi^{D_f} · B_gate_code -``` - -**Parameters**: -- `K_code` = 200 (half-saturation constant) -- `hill_code` = 0.5 (Hill coefficient) -- `C_code` = domain normalization (fitted) -- `B_gate_code` = syntax/semantic gate - -**Load Components**: -- `L_I_code = C_I_code · (lines / (K_I + lines))^{hill_I}` -- `L_E_code = C_E_code · (nesting / (K_E + nesting))^{hill_E}` -- `L_G_code = C_G_code · (abstractions / (K_G + abstractions))^{hill_G}` -- `L_R_code = C_R_code · (dependencies / (K_R + dependencies))^{hill_R}` -- `L_M_code = C_M_code · (variables / (K_M + variables))^{hill_M}` -- `L_emotional_code = C_emotional_code · (L_emotional_offload_code / (K_emotional + L_emotional_offload_code))^{hill_emotional} · lambda_phi^{D_f} · B_gate_emotional_code` - -**Adaptive Behavior**: Hill saturation captures working memory limits for holding code context. Emotional offloading activates when cognitive load exceeds threshold, reducing overload propagation from coding frustration in the model. - -### Visual Processing Domain - -**Response Family**: `michaelis_menten` (feature extraction saturation) - -``` -L_visual = C_visual · (V_max · visual_complexity) / (K_M + visual_complexity) · lambda_phi^{D_f} · B_gate_visual -``` - -**Parameters**: -- `V_max` = maximum cognitive capacity -- `K_M` = Michaelis constant (half-saturation) -- `C_visual` = domain normalization (fitted) -- `B_gate_visual` = visual attention gate - -**Load Components**: -- `L_I_visual = C_I_visual · (V_max_I · features) / (K_M_I + features)` -- `L_E_visual = C_E_visual · (V_max_E · clutter) / (K_M_E + clutter)` -- `L_G_visual = C_G_visual · (V_max_G · patterns) / (K_M_G + patterns)` -- `L_R_visual = C_R_visual · (V_max_R · saccades) / (K_M_R + saccades)` -- `L_M_visual = C_M_visual · (V_max_M · objects) / (K_M_M + objects)` -- `L_emotional_visual = C_emotional_visual · (V_max_emotional · L_emotional_offload_visual) / (K_M_emotional + L_emotional_offload_visual) · lambda_phi^{D_f} · B_gate_emotional_visual` - -**Adaptive Behavior**: Michaelis-Menten captures feature extraction saturation in visual processing. Emotional offloading activates when visual cognitive load exceeds threshold, reducing overload propagation from visual overload in the model. - -### Audio Processing Domain - -**Response Family**: `low_exponent_power` (speech comprehension) - -``` -L_audio = C_audio · (audio_complexity)^{α_audio} · lambda_phi^{D_f} · B_gate_audio -``` - -**Parameters**: -- `α_audio` = 0.3 (low exponent, < 1) -- `C_audio` = domain normalization (fitted) -- `B_gate_audio` = auditory working memory gate - -**Load Components**: -- `L_I_audio = C_I_audio · (duration)^{α_I}` -- `L_E_audio = C_E_audio · (noise)^{α_E}` -- `L_G_audio = C_G_audio · (vocabulary)^{α_G}` -- `L_R_audio = C_R_audio · (speakers)^{α_R}` -- `L_M_audio = C_M_audio · (tempo)^{α_M}` -- `L_emotional_audio = C_emotional_audio · (L_emotional_offload_audio)^{α_emotional} · lambda_phi^{D_f} · B_gate_emotional_audio` - -**Adaptive Behavior**: Low-exponent power captures speech comprehension scaling. Emotional offloading activates when audio cognitive load exceeds threshold, reducing overload propagation from auditory overload in the model. - -### Multimodal Domain - -**Response Family**: `adaptive_mixture` (cross-domain integration) - -``` -L_multimodal = C_multi · Σ w_d · response_family_d(complexity_d; θ_d) · lambda_phi^{D_f} · B_gate_multi -``` - -**Parameters**: -- `w_d` = domain weights (text, code, visual, audio) -- `response_family_d` = domain-specific response family -- `θ_d` = domain-specific parameters -- `C_multi` = domain normalization (fitted) -- `B_gate_multi` = cross-modal integration gate - -**Load Components**: -- `L_I_multi = Σ w_d · L_I_d` (intrinsic load across modalities) -- `L_E_multi = Σ w_d · L_E_d` (extraneous load across modalities) -- `L_G_multi = Σ w_d · L_G_d` (germane load across modalities) -- `L_R_multi = Σ w_d · L_R_d` (routing load across modalities) -- `L_M_multi = Σ w_d · L_M_d` (memory load across modalities) -- `L_emotional_multi = Σ w_d · L_emotional_d` (emotional offloading across modalities) - -**Adaptive Behavior**: Adaptive mixture captures cross-modal integration and interference. Emotional offloading activates when multimodal cognitive load exceeds threshold, reducing overload propagation from cross-modal overload in the model. - -## Adaptive Function Selection Mechanism - -### Selection Criteria - -**Measured Error**: Fit response families to domain-specific cognitive load data, compute average error. - -**Complexity Penalty**: Apply Occam's razor penalty for model complexity (number of parameters). - -**Held-Out Validation**: Cross-validate on held-out data to prevent overfitting. - -**Selection Score**: - -``` -Score(domain, response_family) = - error(domain, response_family) - + λ_complexity · complexity(response_family) - + λ_validation · validation_error(domain, response_family) -``` - -where: -- `λ_complexity` = complexity penalty weight -- `λ_validation` = validation penalty weight - -### Adaptive Selection Algorithm - -``` -1. For each domain: - a. Fit all response families (log, Hill, Michaelis-Menten, low-exponent) - b. Compute selection score for each family - c. Select family with minimum score - -2. For each load component within domain: - a. Fit all response families - b. Compute selection score - c. Select family with minimum score - -3. For cross-domain integration: - a. Fit mixture weights - b. Compute selection score - c. Select optimal mixture -``` - -## Cross-Domain Transfer Learning - -### Shared Fractal Dimension - -All domains share the same fractal dimension: - -``` -D_f = log(2)/log(Φ) ≈ 1.44042 -``` - -This enables: -- Transfer of fractal scaling knowledge across domains -- Unified topological prior for all information types -- Consistent compression ratios across domains - -### Domain-Specific Adaptation - -Each domain adapts: -- Response family selection (log vs Hill vs Michaelis-Menten vs low-exponent) -- Response parameters (K, hill, α, β) -- Domain normalization (C_domain) -- Binding gates (B_gate) - -### Hierarchical Adaptation - -**Level 1**: Domain-level response family selection -**Level 2**: Component-level response family selection (intrinsic, extraneous, etc.) -**Level 3**: Cross-domain mixture adaptation - -## Adaptive Cognitive Load Examples - -### Example 1: Text Code Review - -**Domain**: Code processing -**Response Family**: Hill saturation -**Complexity**: 500 lines of code - -``` -L_code = C_code · (500 / (200 + 500))^{0.5} · 4 · B_gate_code - = C_code · (0.714)^{0.5} · 4 · B_gate_code - = C_code · 0.845 · 4 · B_gate_code - = 3.38 · C_code · B_gate_code -``` - -**Adaptive Behavior**: Hill saturation captures working memory limits for code review. - -### Example 2: Multimodal Learning - -**Domain**: Multimodal (text + visual) -**Response Family**: Adaptive mixture -**Complexity**: 1000 words + 10 images - -``` -L_multimodal = C_multi · (w_text · L_text + w_visual · L_visual) · 4 · B_gate_multi - -L_text = C_text · log(1 + 0.316 · 1000) · 4 · B_gate_text - = C_text · log(317) · 4 · B_gate_text - = C_text · 5.76 · 4 · B_gate_text - = 23.04 · C_text · B_gate_text - -L_visual = C_visual · (V_max · 10) / (K_M + 10) · 4 · B_gate_visual - = C_visual · (V_max · 10) / (K_M + 10) · 4 · B_gate_visual - -L_multimodal = C_multi · (w_text · 23.04 · C_text · B_gate_text - + w_visual · L_visual) · 4 · B_gate_multi -``` - -**Adaptive Behavior**: Adaptive mixture captures cross-modal integration and interference. - -## Key Capabilities - -### 1. Domain-Aware Scaling -Different information types use different response families based on empirical validation. - -### 2. Component-Level Adaptation -Each load component (intrinsic, extraneous, germane, routing, memory) can use different response families. - -### 3. Cross-Modal Integration -Multimodal domains use adaptive mixtures of domain-specific response families. - -### 4. Transfer Learning -Shared fractal dimension D_f = 1.44042 across domains. - -### 5. Hierarchical Adaptation -Multi-level adaptation from domain to component to cross-domain integration. - -### 6. Receipt-Based Selection -Response families selected by measured error, complexity penalty, and held-out validation. - -### 7. Connectome-Protective Overflow -Cognitive overflow shifted to emotional processing when load exceeds threshold, protecting neural network topology. - -### 8. Emotional Regulation Gates -Emotional offloading regulated through social support, coping mechanisms, and emotional regulation strategies. - -### 9. Adaptive Threshold Selection -Individualized connectome-protective thresholds based on baseline cognitive capacity. - -### 10. Dynamic Load Balancing -Real-time shifting of cognitive load to emotional processing to prevent connectome damage. - -## 2026-05-13 Full-Stack Load / Closure Revision - -This revision generalizes cognitive load from a domain response score into a boundary-and-receipt transition stack. The short intuition is: - -``` -attempting to force mountain-scale input through straw-scale assimilation -does not make the input disappear. - -It creates overflow pressure, shell stress, phase echo, residual burden, -and validation debt. -``` - -The model therefore treats load as a routed transition problem: - -``` -Boundary pressure enters; -shell sequence resists; -flux and torsion route; -Reynolds activation gates; -echoes remember; -residuals return; -receipts decide closure. -``` - -Native keeper: - -``` -No receipt, no law. No repair, no closure. -``` - -### Master Object - -``` -M_Full = - (A0, S, B, P_shell, G_T, BFTO, C16, RRTO, RRM, W, L, KOT, OECM, ECTRL) -``` - -where: -- `A0` = base admissibility layer / lawful state substrate -- `S` = typed spread network -- `B` = boundary-derived surface transform -- `P_shell` = sequential shell protection / collapse -- `G_T` = phase-coupled transport graph -- `BFTO` = boundary flux-torsion operator -- `C16` = 16-channel control manifold -- `RRTO` = Reynolds regime transition operator -- `RRM` = residual re-admission map -- `W` = state transition receipt -- `L` = loopback closure map -- `KOT` = kinetic operation receipt -- `OECM` = OmniToken entropy cost model -- `ECTRL` = extropy-compatible transition receipt layer - -Global evolution: - -``` -A0^t - -> S^t - -> B^t - -> P_shell^t - -> G_T^t - -> BFTO^t - -> RRTO^t - -> C16^t - -> RRM(epsilon^t) - -> A0^(t+1) -``` - -Closure: - -``` -A0^(t+1) ~ A0^t -``` - -Failure to close: - -``` -A0^(t+1) !~ A0^t - => new mode, quarantine, residual expansion, or model failure -``` - -### Micro-Position State - -Each local cell, node, or packet is: - -``` -m_i^t = (x_i, r_i, theta_i, q16_i, Gamma_i, s_i, g_i, Psi_i, W_i, epsilon_i) -``` - -where: -- `x_i` = position, address, coordinate, graph node, or chart point -- `r_i` = scale / refinement level -- `theta_i` = loopback phase -- `q16_i` = 16-channel controller vector -- `Gamma_i` = transition / reconstruction / braid packet -- `s_i` = shell-state vector -- `g_i` = delayed phase echo state -- `Psi_i` = local modal state -- `W_i` = transition receipt -- `epsilon_i` = residual burden - -Core local update: - -``` -m_i^(t+1) = - Gate_C16[ - Transport_G_T(m_i^t, Gamma_i, g_i) - + BFTO_i - + RRTO_i - + RRM(epsilon_i) - - SBPCM_i - ] -``` - -### Boundary-Derived Surface - -A boundary is a collapsed disagreement surface: - -``` -partial_Omega_i = - Collapse(sum_k c_ik lambda_ik psi_ik) -``` - -Boundary activation: - -``` -B_i = - |sum_k c_ik lambda_ik psi_ik| - + a_Phi Phi_i - + a_tau tau_i - + a_g g_i - + a_epsilon ||epsilon_i|| -``` - -Quiet boundary: - -``` -B_i < Theta_partial_i -``` - -Activated boundary: - -``` -B_i >= Theta_partial_i -``` - -Boundary activation event: - -``` -BAE_i = (partial_Omega_i, B_i, Theta_partial_i, q16_i, W_i) -``` - -Native phrase: - -``` -boundary = compressed disagreement made physical -``` - -### Corrected Reynolds / Hermite Activation Bridge - -This is the repaired monotone bridge. The normalized activation and the offset physical bridge must stay distinct. - -Reynolds coordinate: - -``` -Re_i = rho_i u_i L_i / mu_i -``` - -Transition coordinate: - -``` -x_i = Clamp_[0,1]((Re_i - 2300) / 1700) -``` - -so: - -``` -Re = 2300 => x = 0 -Re = 4000 => x = 1 -``` - -Normalized activation: - -``` -A(x) = 3x^2 - 2x^3 -``` - -Properties: - -``` -A(0) = 0 -A(1) = 1 -A'(x) = 6x(1 - x) -A'(0) = 0 -A'(1) = 0 -A'(x) >= 0 for 0 <= x <= 1 -``` - -Use `A(x)` as the controller activation curve. - -Offset physical bridge: - -``` -f_A(x) = f0 + (f1 - f0) A(x) -``` - -with: - -``` -f0 = 0.0278 -f1 = 0.0398 -``` - -therefore: - -``` -f_A(x) = 0.0278 + 0.012(3x^2 - 2x^3) -``` - -and: - -``` -f_A(0) = 0.0278 -f_A(1) = 0.0398 -``` - -Use `f_A(x)` only as the offset physical bridge, not as the normalized controller activation. - -### Modal Flow State - -Flow is not binary: - -``` -Psi_flow_i = alpha_L_i psi_L + alpha_T_i psi_T + alpha_U_i psi_U -``` - -with: - -``` -alpha_L_i + alpha_T_i + alpha_U_i = 1 -``` - -Simple allocation: - -``` -alpha_U_i = A(x_i) -alpha_L_i = 1 - A(x_i) -``` - -Optional transition participation: - -``` -alpha_T_i_raw = 4 x_i (1 - x_i) -``` - -If all three modes are active: - -``` -Z_i = alpha_L_i_raw + alpha_T_i_raw + alpha_U_i_raw -alpha_k_i = alpha_k_i_raw / Z_i -``` - -### RRTO Full Activation - -``` -gamma_i = - Clamp_[0,1]( - b0 A(x_i) - + b1 |omega_i| - + b2 Q_i - + b3 h_i - + b4 Phi_E_i - + b5 g_i - + b6 ||epsilon_i|| - ) -``` - -where: -- `A(x_i)` = smooth Reynolds transition activation -- `omega_i = curl(u_i)` = vorticity -- `Q_i` = Q-criterion / vortex criterion -- `h_i = u_i dot omega_i` = helicity -- `Phi_E_i` = local energy / flux activation -- `g_i` = delayed phase echo -- `epsilon_i` = residual burden - -``` -RRTO_i = - (Re_i, x_i, A(x_i), f_A(x_i), gamma_i, alpha_L_i, alpha_T_i, alpha_U_i) -``` - -### Sequential Boundary Protection / Collapse - -Generalized boundary pressure: - -``` -Pi_i = - a_E E_chem_i - + a_Phi Phi_partial_i - + a_sigma sigma_i - + a_sigmadot sigmadot_i - + a_grad |grad Pi_i| - + a_tau tau_i - + a_T T_i - + a_C C_i -``` - -Each shell state: - -``` -s_ij(t) in [0,1] -``` - -Total shell protection: - -``` -P_shell_i(t) = sum_j A_ij s_ij(t) -``` - -Shell dynamics: - -``` -ds_ij/dt = - alpha_ij sigma_k(Pi_i - Theta_ij_on)(1 - s_ij) - - beta_ij sigma_k(Pi_i - Theta_ij_fail)s_ij - + eta_ij RRM(epsilon_i) -``` - -with: - -``` -sigma_k(z) = 1 / (1 + exp(-kz)) -``` - -Safe discrete update: - -``` -s_ij^(t+1) = Clamp_[0,1](s_ij^t + Delta_t ds_ij/dt) -``` - -Static envelope: - -``` -P_shell_i(Pi) = - sum_j A_ij sigma_k(Pi_i - Theta_ij_on) - [1 - sigma_k(Pi_i - Theta_ij_fail)] -``` - -Native phrase: - -``` -boundary survives by admitting shell class before failure -``` - -### Boundary Flux-Torsion Operator - -Classical projected flux: - -``` -S_i = E_i x H_i -``` - -or: - -``` -S_i = (1 / mu_0) E_i x B_i -``` - -Boundary flux: - -``` -Phi_partial_i = integral_partial_Omega_i S dot n dA -``` - -Discrete: - -``` -Phi_partial_i ~= sum_(ell in partial_Omega_i) (S_ell dot n_ell) Delta_A_ell -``` - -Torsion: - -``` -tau_i = - b1 kappa_i - + b2 dGamma_i/dt - + b3 g_i - + b4 ||epsilon_i|| -``` - -Boundary flux-torsion operator: - -``` -BFTO16_i = Gate_C16[Phi_partial_i xor tau_i xor Gamma_i xor g_i xor epsilon_i] -``` - -Loopback phase: - -``` -theta_i^(t+1) = - theta_i^t + Omega(Phi_partial_i, tau_i, Gamma_i, g_i, epsilon_i) -``` - -### Delayed Phase Echo - -Complex form: - -``` -g_i(t) = sum_(j in N(i)) alpha_ij S_j(t - Delta_ij) exp(i phi_ij) -``` - -Real controller form: - -``` -g_i(t) = sum_(j in N(i)) alpha_ij cos(phi_ij) S_j(t - Delta_ij) -``` - -Echo edge: - -``` -e_ij_echo = (Delta_ij, phi_ij, alpha_ij, kappa_ij, epsilon_ij, W_ij) -``` - -Bounded echo: - -``` -sum_j |alpha_ij| <= A_max < 1 -Delta_ij <= Delta_max -N_echo <= N_max -``` - -Phase-coupled transport graph: - -``` -G_T = (V, E_transport, E_phase, E_echo, q, W, epsilon) -``` - -### Cutting / Collapse - -Complete cutting score: - -``` -K_partial_Omega_i = - Gate_C16[ - Norm(Pi_i) - + lambda1 Norm(Pi_dot_i) - + lambda2 Norm(|grad Pi_i|) - - Norm(K_mat_i) - - Norm(P_shell_i) - + lambda3 Norm(tau_i) - + lambda4 Norm(g_i) - + lambda5 Norm(epsilon_i) - ] -``` - -Cut: - -``` -K_partial_Omega_i > Theta_cut_i -``` - -Survival: - -``` -K_partial_Omega_i <= Theta_cut_i -``` - -Explosive branch: - -``` -dPi_i/dt > dP_shell_i/dt + K_rate_i -``` - -Native phrase: - -``` -explosive cut = outrun shell admission -``` - -Implosive branch: - -``` -|grad Pi_i| > |grad P_shell_i| + K_grad_i -``` - -Native phrase: - -``` -implosive cut = collapse shell geometry -``` - -Corrosive branch: - -``` -Pi_i > Theta_N_fail and s_iN -> 0 -``` - -Native phrase: - -``` -corrosive cut = exhaust shell sequence -``` - -Fatigue / pulsed branch: - -``` -D_i^(t+1) = - D_i^t - + zeta1 Norm(Pi_i) - + zeta2 Norm(g_i) - - zeta3 Norm(P_shell_i) -``` - -Failure: - -``` -D_i > D_max -``` - -Native phrase: - -``` -fatigue cut = echo-assisted residual accumulation -``` - -### Residual Re-Admission - -Prediction error: - -``` -epsilon_i = D_i - D_hat_i -``` - -Residual classifier: - -``` -r_i = Classify(epsilon_i, q16_i, W_i) -``` - -Residual re-admission: - -``` -RRM(epsilon_i) = - 0 if ||epsilon_i|| < Theta0 - compress if Theta0 <= ||epsilon_i|| < Theta1 - new mode if Theta1 <= ||epsilon_i|| < Theta2 - quarantine if ||epsilon_i|| >= Theta2 -``` - -Admissibility update: - -``` -A0^(t+1) = L(C16^t, RRM(epsilon^t), W^t) -``` - -Native phrase: - -``` -residual is pullback, not garbage -``` - -### 16-Channel Control Manifold - -``` -q16_i = [q0_i, q1_i, ..., q15_i] -``` - -Current integrated layout: - -| Channel | Meaning | -|---|---| -| `q0` | normalized boundary pressure `Norm(Pi)` | -| `q1` | pressure rate `Pi_dot` | -| `q2` | pressure gradient `Norm(|grad Pi|)` | -| `q3` | total shell protection `Norm(P_shell)` | -| `q4` | active shell occupancy / shell index | -| `q5` | material cohesion `Norm(K_mat)` | -| `q6` | boundary flux `Norm(Phi_partial)` | -| `q7` | torsion `Norm(tau)` | -| `q8` | delayed phase echo `Norm(g)` | -| `q9` | residual burden `Norm(||epsilon||)` | -| `q10` | normalized Reynolds activation `A(x)` | -| `q11` | offset physical bridge `f_A(x)` | -| `q12` | entropy reduction `Delta_S_minus` | -| `q13` | entropy generated / cost `Delta_S_plus` | -| `q14` | witness confidence `W` | -| `q15` | final admissibility / halt / loopback gate | - -Controller update: - -``` -q16_i^(t+1) = - Clamp_Q0.16( - q16_i^t - + F_q[ - Norm(Pi), - Pi_dot, - grad Pi, - Norm(P_shell), - Norm(Phi_partial), - Norm(tau), - Norm(g), - Norm(epsilon), - A(x), - f_A(x), - W - ] - ) -``` - -Gate output: - -``` -G_i = Gate_q16(m_i) - in {ADMIT, REFINE, MERGE, BRAID, PATCH, QUARANTINE, HALT, LOOPBACK} -``` - -### Kinetic Operation Receipt - -Every accepted transition emits: - -``` -KOT_i = - (m_i, m_i+1, Delta_S_i_minus, Delta_S_i_plus, - E_i, C_i, T_i, B_i, epsilon_i, W_i, DAG_i) -``` - -Validity: - -``` -KOT_i valid - iff W_i >= Theta_W - and B_i <= B_max - and ||epsilon_i|| <= epsilon_max -``` - -### OmniToken Entropy Cost Model - -``` -O_i = OECM(KOT_i) -``` - -Signed entropy-cost form: - -``` -O_i = - a Delta_S_i_minus - - b Delta_S_i_plus - - c E_i - - d C_i - - e T_i - - f ||epsilon_i|| - + g W_i -``` - -Interpretation: - -``` -useful transformation = entropy reduction - cost of achieving it -``` - -Claim aggregation: - -``` -O_claim = sum_i O_i -``` - -### Extropy-Compatible Transition Receipt Layer - -Transition receipt: - -``` -ECTRL(m_i -> m_i+1) = - (Delta_S_i, D_i, I_i, B_i, Falsify_i, Vc_i, DAG_i) -``` - -Acceptance: - -``` -Vc_i >= Theta_V -and Delta_S_i > 0 -and Falsify_i != empty -and DAG_i != empty -``` - -Extropy-native settlement: - -``` -XP_j = R_j F_j Delta_S_j (w_j dot E_j) (1 / T_s_j) -``` - -OmniToken-adapted settlement: - -``` -XP_j = R_j F_j O_claim (w_j dot E_j) (1 / T_s_j) -``` - -Goodhart isolation invariant: - -``` -Value(KOT_i) != f(actor reputation) -``` - -Reputation may route validators, but must not alter transition value. - -### Receipt / Attack-Repair Validation - -Complete state transition receipt: - -``` -STR_i = - (m_i, m_i+1, Norm(K_i), q16_i, s_i, g_i, - epsilon_i, W_i, KOT_i, DAG_i, A_i) -``` - -Attack / repair audit: - -``` -A_i = { - STR_units, - STR_shell_bounds, - STR_echo_safe, - STR_residual, - STR_smooth_activation, - STR_Goodhart, - STR_falsifiable -} -``` - -Justified transition: - -``` -m_i -> m_i+1 justified - iff every STR_a in A_i is PASS -``` - -Failed receipt: - -``` -m_i -> m_i+1 = UNJUSTIFIED - => RRM(epsilon_i) or QUARANTINE -``` - -### Compressed Master Equation - -``` -m_i^(t+1) = - Gate_C16[ - Transport_G_T(m_i^t, Gamma_i, g_i) - + BFTO(Phi_partial_i, tau_i, Gamma_i, g_i, epsilon_i) - + RRTO(Re_i, A(x_i), f_A(x_i), Psi_flow_i) - + RRM(epsilon_i) - - SBPCM(Pi_i, s_i, Theta_i) - ] -``` - -with: - -``` -x_i = Clamp_[0,1]((Re_i - 2300) / 1700) -A(x_i) = 3x_i^2 - 2x_i^3 -f_A(x_i) = 0.0278 + 0.012 A(x_i) - -SBPCM = - K_mat_i - + sum_j A_ij s_ij(t) - - lambda1 Pi_dot_i - - lambda2 |grad Pi_i| - -KOT_i = - Receipt(m_i, m_i+1, Delta_S_minus, Delta_S_plus, - E, C, T, B, epsilon, W, DAG) - -O_i = - a Delta_S_i_minus - - b Delta_S_i_plus - - c E_i - - d C_i - - e T_i - - f ||epsilon_i|| - + g W_i - -A0^(t+1) = L(C16^t, RRM(epsilon^t), {STR_i}) -``` - -Claim boundary: - -``` -This is a control / compression / transition-receipt model. -It is not a proven biological, fluid-mechanical, psychological, -or economic law without calibrated domain instruments and receipts. -``` - -## Implementation Requirements - -### Data Collection -- Cognitive load measurements for each domain -- Complexity metrics for each information type -- Cross-domain interaction data -- Emotional load measurements during cognitive overflow -- Connectome-protective threshold measurements -- Trauma / stress proxy only when consent, privacy, and calibration boundaries are explicit -- Historical bandwidth-transfer proxies and assimilation-capacity proxies for historical modeling - -### Model Fitting -- Fit response families to domain-specific data -- Compute selection scores -- Validate on held-out data -- Fit emotional offloading parameters -- Calibrate connectome-protective thresholds - -### Adaptive Runtime -- Select optimal response family per domain -- Adapt parameters based on new data -- Update cross-domain mixture weights -- Monitor cognitive load vs threshold -- Trigger emotional offloading when threshold exceeded -- Apply trauma-aware threshold, barrier, and residual-stress modifiers when calibrated -- Apply bandwidth-overflow threshold, barrier, and residual-stress modifiers when calibrated -- Regulate emotional load through coping mechanisms - -## Conclusion - -The Φ-scaling response-family framework enables adaptive cognitive load functions across multiple information domains. Each domain selects its optimal response family based on empirical validation, enabling domain-aware scaling, component-level adaptation, cross-modal integration, and transfer learning. This provides a unified mathematical framework for cognitive load across text, code, visual, audio, and multimodal information processing. - -The connectome-protective overflow mechanism adds a biological, computational, and historical hypothesis: when cognitive load exceeds a protective threshold, excess load is shifted into emotional processing / salience handling to preserve working graph stability. In the historical bandwidth-overflow reweighting, accelerated information transfer can lower effective assimilation thresholds, raise regulation barriers, reduce offload efficiency, and increase residual stress. Trauma is one local case of exceeded energy cost; accelerated information transfer is the broader historical mechanism. - -This framework integrates cognitive load theory, emotional regulation, and connectome protection into a unified mathematical model with response-family selection, enabling adaptive cognitive load management across diverse information processing domains. diff --git a/4-Infrastructure/shim/multimetal_nanocrystal_composition_focusing_prior.py b/4-Infrastructure/shim/multimetal_nanocrystal_composition_focusing_prior.py deleted file mode 100644 index 0540a4c7..00000000 --- a/4-Infrastructure/shim/multimetal_nanocrystal_composition_focusing_prior.py +++ /dev/null @@ -1,255 +0,0 @@ -#!/usr/bin/env python3 -"""Distill multimetallic nanocrystal composition focusing into a route prior. - -The source result reports that adding more metals to Ru-based nanocrystal -synthesis can focus, rather than explode, the product distribution: a Cu/Ru -heterodimer scaffold and competitive reactivity guide later Co/Ni/Fe deposition -into a uniform five-metal structure. For the compressor, the useful shape is a -frontier-control prior: extra route components may reduce candidate entropy when -they are staged through a scaffold, incompatibility boundary, and ordered -attachment law. It is not compression evidence by itself. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "multimetal_nanocrystal_composition_focusing_prior_receipt.json" -CURRICULUM_OUT = SHIM / "multimetal_nanocrystal_composition_focusing_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -SOURCE_EVIDENCE = { - "news": { - "title": "Researchers combine five metals to build a better nanocrystal", - "source": "Phys.org / Stanford University", - "published_date": "2026-05-07", - "url": "https://phys.org/news/2026-05-combine-metals-nanocrystal.html", - }, - "primary_paper": { - "title": "Competitive reactivity drives size- and composition-focusing in multimetallic nanocrystals", - "authors": "Jeesoo Yoon et al.", - "journal": "Science", - "published_date": "2026-05-07", - "doi": "10.1126/science.aea8044", - "url": "https://www.science.org/doi/10.1126/science.aea8044", - }, - "observed_core_claims": [ - "five_metal_nanocrystals_can_be_more_uniform_than_two_or_three_metal_attempts", - "31_theoretical_product_combinations_collapse_to_essentially_one_product", - "copper_deposits_first_on_ruthenium_seed_but_does_not_mix_into_ruthenium", - "copper_ruthenium_heterodimer_scaffold_guides_later_deposition", - "cobalt_and_nickel_attach_to_affinity_regions_before_iron_envelopes_outer_layer", - "competitive_reactivity_drives_size_and_composition_focusing", - "five_metal_material_outperforms_standard_ruthenium_catalyst_for_ammonia_decomposition", - "industrial_translation_remains_pending_outside_lab_conditions", - ], -} - - -COMPOSITION_FOCUSING_OPERATORS = [ - { - "id": "seed_route_core", - "source_shape": "ruthenium seed provides a stable starting particle", - "route_mapping": "start route search from a byte-exact incumbent or stable core transform", - "claim_boundary": "seed stability is not a byte win without measured receipt", - }, - { - "id": "immiscible_scaffold_boundary", - "source_shape": "copper attaches to ruthenium but remains a distinct domain", - "route_mapping": "use incompatibility boundaries to prevent route lanes from merging too early", - "claim_boundary": "boundary metadata must be bounded and cannot hide payload", - }, - { - "id": "competitive_reactivity_ordering", - "source_shape": "metals reduce and attach in an order set by relative reactivity and affinity", - "route_mapping": "order transform additions by observed compatibility and residual cost", - "claim_boundary": "ordering is a pruning prior, not promotion evidence", - }, - { - "id": "composition_focusing", - "source_shape": "more elements collapse the product distribution into a uniform particle", - "route_mapping": "extra constraints can collapse a route frontier when each constraint is receipted", - "claim_boundary": "frontier collapse must preserve exact decode reachability", - }, - { - "id": "outer_stability_shell", - "source_shape": "iron-rich outer layer helps resist high-temperature sintering", - "route_mapping": "add a final stability witness that guards repeated decode / perturbation churn", - "claim_boundary": "stability witness is paid metadata and must fit byte margin", - }, -] - - -EQUATIONS = [ - { - "id": "MCF0_route_seed", - "equation": "R_0 = seed_route(source_slice, incumbent_receipt)", - "meaning": "Begin from a verified route core rather than an unconstrained combinatorial soup.", - }, - { - "id": "MCF1_scaffold_boundary", - "equation": "S = hetero_boundary(core_lane, anchor_lane) where merge(core, anchor) is forbidden", - "meaning": "A deliberate non-merge boundary can become the scaffold for later legal attachments.", - }, - { - "id": "MCF2_ordered_attachment", - "equation": "lane_{t+1} = attach(argmin_l reactivity_cost(l | S_t), S_t)", - "meaning": "Attach the next route lane according to compatibility and residual-cost ordering.", - }, - { - "id": "MCF3_frontier_focusing", - "equation": "|Frontier_{t+1}| < |Frontier_t| when every added constraint preserves decode reachability", - "meaning": "Additional route components are useful only if they shrink legal states without losing exact decode paths.", - }, - { - "id": "MCF4_shell_stability", - "equation": "stable_route iff churn_count <= churn_budget and n_minus_1_failures close or repair exactly", - "meaning": "A final stability layer is a perturbation/churn guard, not hidden compressed data.", - }, - { - "id": "MCF5_promotion", - "equation": "promote iff hash(decode(R_focused + exact_residuals)) == source_hash and bytes < incumbent", - "meaning": "Composition focusing is admissible only after exact residual repair and measured byte win.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "multimetal_nanocrystal_composition_focusing_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": SOURCE_EVIDENCE, - "primary_decision": { - "name": "use_composition_focusing_as_route_frontier_collapse_prior", - "statement": ( - "Use multimetallic nanocrystal composition focusing as a prior " - "for staged route construction: start from a stable seed, add " - "an immiscible scaffold boundary, order lane additions by " - "compatibility, and promote only if the focused frontier still " - "decodes byte-exactly with all witness costs counted." - ), - }, - "composition_focusing_operators": COMPOSITION_FOCUSING_OPERATORS, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "route_seed_id", - "seed_incumbent_receipt_id", - "component_lane_count", - "candidate_component_set", - "scaffold_anchor_lane_id", - "immiscibility_boundary_id", - "reactivity_order_id", - "affinity_region_id", - "attachment_step_index", - "composition_focus_score", - "focused_frontier_size", - "theoretical_frontier_size", - "outer_stability_shell_id", - "decode_churn_count", - "n_minus_1_stability_status", - "exact_residual_lane_id", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "open_seed_route_core", - "emit_immiscible_scaffold_boundary", - "rank_candidate_lanes_by_reactivity_cost", - "attach_lane_to_affinity_region", - "reject_premature_lane_merge", - "measure_frontier_focusing", - "emit_outer_stability_shell", - "stress_decode_churn", - "run_n_minus_1_route_stability_check", - "close_focused_route_with_exact_residual", - ], - "lower_bound": [ - "seed_receipt_bytes", - "scaffold_boundary_header_floor", - "reactivity_order_receipt_floor", - "attachment_sequence_floor", - "stability_shell_receipt_floor", - "exact_residual_lane_floor", - ], - "promotion_rule": [ - "route_components_are_staged_from_verified_seed", - "immiscible_scaffold_boundary_is_bounded_and_not_payload", - "attachment_order_is_deterministic_or_receipted", - "frontier_focusing_preserves_decode_reachability", - "outer_stability_shell_reduces_churn_without_hiding_bytes", - "exact_residual_lanes_restore_source_bytes", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "extra_components_increase_frontier_without_bound -> prune", - "scaffold_boundary_hides_payload -> invalid_receipt", - "attachment_order_ambiguous_without_tie_break -> fail_closed", - "composition_focus_changes_decode_reachability -> fail_closed", - "stability_shell_larger_than_byte_gain -> prune", - "lab_catalyst_performance_used_as_byte_evidence -> diagnostic_only", - ], - "claim_boundary": ( - "This prior imports a staged self-organization and composition-focusing " - "shape from multimetallic nanocrystal synthesis. It is not evidence " - "that metals or catalysts compress text bytes; route promotion still " - "requires exact decode, source hash, measured bytes, and explicit " - "ratio schema." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["composition_focusing_operators"]: - lines.append({"type": "composition_focusing_operator", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/netcup_remote_compression_runner.py b/4-Infrastructure/shim/netcup_remote_compression_runner.py deleted file mode 100644 index 6419e9f4..00000000 --- a/4-Infrastructure/shim/netcup_remote_compression_runner.py +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env python3 -"""Controlled Netcup remote baseline runner for finance LUT bundles. - -Default mode is dry-run: it builds the local portable bundle and writes the -exact remote commands that would be used. Passing --execute performs rsync/ssh. -""" - -from __future__ import annotations - -import argparse -import json -import platform -import shutil -import subprocess -from pathlib import Path -from typing import Any - -import finance_claim_lut_harness as harness - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" - - -def run(cmd: list[str], cwd: Path = REPO) -> dict[str, Any]: - proc = subprocess.run(cmd, cwd=cwd, text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - return {"cmd": cmd, "returncode": proc.returncode, "stdout": proc.stdout[-4000:], "stderr": proc.stderr[-4000:]} - - -def build_receipt(args: argparse.Namespace) -> dict[str, Any]: - bundle = harness.write_corpus_bundle(harness.load_samples(args.samples), args.bundle_dir) - remote_dir = args.remote_dir.rstrip("/") - planned = { - "copy_bundle": f"rsync -a {args.bundle_dir}/ {args.host}:{remote_dir}/", - "copy_harness": f"rsync -a {SHIM / 'finance_claim_lut_harness.py'} {args.host}:{remote_dir}/", - "verify": f"ssh {args.host} 'cd {remote_dir} && python3 finance_claim_lut_harness.py verify --receipt finance_claim_lut_harness_receipt.json'", - "bench": f"ssh {args.host} 'cd {remote_dir} && python3 finance_claim_lut_harness.py bench --samples canonical_samples.json --fixture-dir remote_fixtures'", - } - receipt: dict[str, Any] = { - "schema": "netcup_remote_compression_receipt_v1", - "mode": "execute" if args.execute else "dry_run", - "host": args.host, - "remote_dir": remote_dir, - "local_bundle_receipt": bundle, - "local_environment": {"python": platform.python_version(), "platform": platform.platform()}, - "planned_commands": planned, - "remote_results": [], - "lawful": bool(bundle.get("lawful")), - "claim_boundary": "Netcup baseline proves controlled remote reproducibility only; it is not noisy recovery or provider endorsement", - } - if not args.execute: - receipt["remote_status"] = "not_run_dry_run" - return receipt - - if not args.host: - receipt["remote_status"] = "blocked_missing_host" - receipt["lawful"] = False - return receipt - if not shutil.which("rsync") or not shutil.which("ssh"): - receipt["remote_status"] = "blocked_missing_rsync_or_ssh" - receipt["lawful"] = False - return receipt - - commands = [ - ["ssh", args.host, f"mkdir -p {remote_dir}"], - ["rsync", "-a", f"{args.bundle_dir}/", f"{args.host}:{remote_dir}/"], - ["rsync", "-a", str(SHIM / "finance_claim_lut_harness.py"), f"{args.host}:{remote_dir}/"], - ["ssh", args.host, f"cd {remote_dir} && python3 finance_claim_lut_harness.py verify --receipt finance_claim_lut_harness_receipt.json"], - ["ssh", args.host, f"cd {remote_dir} && python3 finance_claim_lut_harness.py bench --samples canonical_samples.json --fixture-dir remote_fixtures"], - ] - results = [run(cmd) for cmd in commands] - receipt["remote_results"] = results - receipt["remote_status"] = "ok" if all(item["returncode"] == 0 for item in results) else "failed" - receipt["lawful"] = receipt["lawful"] and receipt["remote_status"] == "ok" - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--samples", type=Path) - parser.add_argument("--bundle-dir", type=Path, default=SHIM / "finance_claim_remote_bundle") - parser.add_argument("--host", default="netcup") - parser.add_argument("--remote-dir", default="~/finance_claim_remote_bundle") - parser.add_argument("--execute", action="store_true") - parser.add_argument("--out", type=Path, default=SHIM / "netcup_remote_compression_receipt.json") - args = parser.parse_args() - receipt = build_receipt(args) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 if receipt.get("lawful") or not args.execute else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/network_topology_hold_manifests.py b/4-Infrastructure/shim/network_topology_hold_manifests.py deleted file mode 100644 index 45bd04fa..00000000 --- a/4-Infrastructure/shim/network_topology_hold_manifests.py +++ /dev/null @@ -1,174 +0,0 @@ -#!/usr/bin/env python3 -"""Create HOLD manifests for network topology coefficients and predictions.""" - -from __future__ import annotations - -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DB = REPO / "shared-data" / "network_topology_database.json" -OUT_DIR = REPO / "shared-data" / "data" / "stack_solidification" -COEFF_OUT = OUT_DIR / "network_topology_coefficient_calibration_manifest.json" -PRED_OUT = OUT_DIR / "network_topology_prediction_hold_registry.json" -DOC = REPO / "6-Documentation" / "docs" / "network_topology_hold_manifests_2026-05-09.md" - - -def load_json(path: Path) -> Any: - return json.loads(path.read_text(encoding="utf-8")) - - -def coeff_rows(db: dict[str, Any]) -> list[dict[str, Any]]: - rows = [] - raw = db.get("fundamental_equation_data", {}).get("methodology_weights", {}) - reweighted = ( - db.get("fundamental_equation_data", {}) - .get("receipt_reweighted_methodology_weights", {}) - .get("methodology_weights", {}) - ) - for key, item in sorted(raw.items()): - receipt = reweighted.get(key, {}) - rows.append( - { - "methodology": key, - "raw_weight": item.get("weight"), - "alignment_score": item.get("alignment_score"), - "validation_status_label": item.get("validation_status"), - "receipt_multiplier": receipt.get("receipt_multiplier"), - "receipt_reweighted_weight": receipt.get("receipt_reweighted_weight"), - "decision": receipt.get("decision"), - "status": "HOLD_CALIBRATION", - "closure_gate": [ - "source dataset receipt exists", - "calibration target and loss function are declared", - "sensitivity sweep passes", - "negative control shows coefficient is not arbitrary fit", - ], - } - ) - return rows - - -def prediction_rows(db: dict[str, Any]) -> list[dict[str, Any]]: - rows = [] - for item in db.get("predicted_network_nodes", []): - rows.append( - { - "prediction_id": f"node::{item.get('provider')}::{item.get('location')}", - "kind": "predicted_network_node", - "provider": item.get("provider"), - "location": item.get("location"), - "prediction_reason": item.get("prediction_reason"), - "confidence": item.get("confidence"), - "validation_status_label": item.get("validation_status"), - "validation_score": item.get("validation_score"), - "status": "HOLD_PREDICTION_VALIDATION", - "closure_gate": [ - "pre-registration timestamp and immutable hash exist", - "public/independent observation source is named", - "outcome comparison receipt exists", - "false-positive and null-baseline comparison are recorded", - ], - } - ) - for item in db.get("novel_network_paths", []): - rows.append( - { - "prediction_id": f"path::{item.get('source')}::{item.get('destination')}", - "kind": "novel_network_path", - "source": item.get("source"), - "destination": item.get("destination"), - "prediction_confidence": item.get("prediction_confidence"), - "path_quality": item.get("path_quality"), - "novelty_status_label": item.get("novelty_status"), - "status": "HOLD_PREDICTION_VALIDATION", - "closure_gate": [ - "pre-registration timestamp and immutable hash exist", - "independent topology/source map is named", - "outcome comparison receipt exists", - "known-connection baseline comparison is recorded", - ], - } - ) - return rows - - -def build_doc(coeff: dict[str, Any], pred: dict[str, Any]) -> str: - lines = [ - "# Network Topology HOLD Manifests", - "", - "**Date:** 2026-05-09", - "", - "These manifests separate hypothesis weights and predictions from calibrated or validated claims.", - "", - "## Coefficient Calibration", - "", - f"- Rows: `{coeff['summary']['row_count']}`", - f"- Status: `{coeff['summary']['status']}`", - f"- Receipt: `{COEFF_OUT.relative_to(REPO)}`", - "", - "| Methodology | Raw Weight | Receipt Weight | Decision |", - "| --- | ---: | ---: | --- |", - ] - for row in coeff["rows"]: - lines.append( - f"| `{row['methodology']}` | {row['raw_weight']} | {row['receipt_reweighted_weight']} | `{row['decision']}` |" - ) - lines.extend( - [ - "", - "## Prediction HOLD Registry", - "", - f"- Rows: `{pred['summary']['row_count']}`", - f"- Status: `{pred['summary']['status']}`", - f"- Receipt: `{PRED_OUT.relative_to(REPO)}`", - "", - "| Prediction | Kind | Status |", - "| --- | --- | --- |", - ] - ) - for row in pred["rows"]: - lines.append(f"| `{row['prediction_id']}` | `{row['kind']}` | `{row['status']}` |") - lines.extend( - [ - "", - "## Claim Boundary", - "", - "These rows are accounting and validation queues. They do not calibrate coefficients or validate topology predictions.", - ] - ) - return "\n".join(lines) + "\n" - - -def main() -> int: - db = load_json(DB) - now = datetime.now(timezone.utc).isoformat() - coeff = { - "schema": "network_topology_coefficient_calibration_manifest_v1", - "created_utc": now, - "source_database": str(DB.relative_to(REPO)), - "claim_boundary": "Coefficient HOLD manifest only. Raw and receipt-reweighted weights remain hypothesis/accounting surfaces until calibration gates close.", - "summary": {"row_count": len(coeff_rows(db)), "status": "HOLD_CALIBRATION"}, - "rows": coeff_rows(db), - } - pred = { - "schema": "network_topology_prediction_hold_registry_v1", - "created_utc": now, - "source_database": str(DB.relative_to(REPO)), - "claim_boundary": "Prediction HOLD registry only. Entries are not validated claims until pre-registration and independent outcome receipts close.", - "summary": {"row_count": len(prediction_rows(db)), "status": "HOLD_PREDICTION_VALIDATION"}, - "rows": prediction_rows(db), - } - OUT_DIR.mkdir(parents=True, exist_ok=True) - COEFF_OUT.write_text(json.dumps(coeff, indent=2, sort_keys=True), encoding="utf-8") - PRED_OUT.write_text(json.dumps(pred, indent=2, sort_keys=True), encoding="utf-8") - DOC.write_text(build_doc(coeff, pred), encoding="utf-8") - print(json.dumps({"coefficient_manifest": str(COEFF_OUT.relative_to(REPO)), "prediction_registry": str(PRED_OUT.relative_to(REPO)), "doc": str(DOC.relative_to(REPO))}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/network_topology_model_reweighting_probe.py b/4-Infrastructure/shim/network_topology_model_reweighting_probe.py deleted file mode 100644 index 49b8d57b..00000000 --- a/4-Infrastructure/shim/network_topology_model_reweighting_probe.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-weighted network topology model profile. - -This probe keeps the original convergence weights as the raw hypothesis profile -and derives a conservative receipt-weighted profile. It does not validate the -network topology theory; it begins the reweighting pass by downweighting model -charts that still have coefficient, provenance, prediction, or operational-risk -debt in the Underverse ledger. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DATABASE = REPO / "shared-data" / "network_topology_database.json" -UNDERVERSE_RECEIPT = ( - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json" -) -OUT_DIR = REPO / "shared-data" / "data" / "network_topology_model_reweighting" -RECEIPT = OUT_DIR / "network_topology_model_reweighting_receipt.json" -SUMMARY = OUT_DIR / "network_topology_model_reweighting.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Network Topology Model Reweighting.tid" - -REWEIGHT_RULES = { - "hft_infrastructure": { - "receipt_multiplier": 0.55, - "reason": "high signal-speed relevance but overweighted by unreceipted ultimate-validation language", - "decision": "HOLD_COEFFICIENT_RECEIPT_DEBT", - }, - "public_internet_map": { - "receipt_multiplier": 1.20, - "reason": "closest to directly observed topology evidence in the current profile", - "decision": "KEEP_OBSERVED_PRIOR_HIGH", - }, - "soliton_wave_analysis": { - "receipt_multiplier": 0.70, - "reason": "useful physics chart, but equation adapter and negative controls remain unclosed", - "decision": "HOLD_ANALOGY_ADAPTER", - }, - "slime_mold_physics": { - "receipt_multiplier": 0.80, - "reason": "useful pathfinding prior, but cross-domain biological adapter remains fixture-grade", - "decision": "HOLD_ANALOGY_ADAPTER", - }, - "civic_design_mathematics": { - "receipt_multiplier": 0.85, - "reason": "established network heuristics, but local coefficient receipts are missing", - "decision": "HOLD_COEFFICIENT_RECEIPT_DEBT", - }, - "regional_infrastructure": { - "receipt_multiplier": 0.90, - "reason": "high-resolution observed infrastructure fixtures are useful but still need source receipts", - "decision": "HOLD_PROVENANCE", - }, - "subway_underground": { - "receipt_multiplier": 0.85, - "reason": "observed engineered-network analogue with remaining adapter and dataset debt", - "decision": "HOLD_ANALOGY_ADAPTER", - }, - "major_consumer_nodes": { - "receipt_multiplier": 0.70, - "reason": "demand-pressure prior, but entity, power, and causality receipts are incomplete", - "decision": "HOLD_TOPOLOGY_PREDICTION_VALIDATION", - }, - "backhaul_providers": { - "receipt_multiplier": 0.70, - "reason": "prediction-heavy layer; keep as route hint until outcome receipts close", - "decision": "HOLD_TOPOLOGY_PREDICTION_VALIDATION", - }, -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def read_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def build_profile() -> dict[str, Any]: - database = read_json(DATABASE) - weights = database["fundamental_equation_data"]["methodology_weights"] - adjusted = {} - for name, raw in weights.items(): - rule = REWEIGHT_RULES[name] - adjusted[name] = raw["weight"] * rule["receipt_multiplier"] - adjusted_sum = sum(adjusted.values()) - - methods = [] - for name, raw in weights.items(): - rule = REWEIGHT_RULES[name] - normalized = adjusted[name] / adjusted_sum - methods.append( - { - "methodology": name, - "raw_weight": raw["weight"], - "alignment_score": raw["alignment_score"], - "raw_validation_status": raw["validation_status"], - "receipt_multiplier": rule["receipt_multiplier"], - "receipt_reweighted_weight": round(normalized, 6), - "weight_delta": round(normalized - raw["weight"], 6), - "decision": rule["decision"], - "reason": rule["reason"], - } - ) - weighted_alignment = sum(item["receipt_reweighted_weight"] * item["alignment_score"] for item in methods) - profile = { - "schema": "network_topology_model_reweighting_profile_v1", - "profile_name": "receipt_reweighted_v1", - "raw_database": rel(DATABASE), - "raw_database_sha256": file_hash(DATABASE), - "underverse_receipt": rel(UNDERVERSE_RECEIPT), - "underverse_receipt_sha256": file_hash(UNDERVERSE_RECEIPT), - "reweight_rule": "receipt_reweighted_weight = normalize(raw_weight * receipt_multiplier)", - "claim_boundary": ( - "Reweighting profile only. This does not validate the topology theory, " - "admit predicted network nodes, or operationalize fiber/DAS inference. " - "It starts a conservative weighting pass that treats unreceipted " - "equation, coefficient, analogy, prediction, and privacy-risk surfaces " - "as HOLD or QUARANTINE lanes." - ), - "methods": methods, - "aggregates": { - "method_count": len(methods), - "raw_weight_sum": round(sum(item["raw_weight"] for item in methods), 6), - "receipt_reweighted_sum": round(sum(item["receipt_reweighted_weight"] for item in methods), 6), - "raw_methodology_convergence": database["metadata"].get("methodology_convergence"), - "receipt_weighted_alignment": round(weighted_alignment, 6), - "largest_gain": max(methods, key=lambda item: item["weight_delta"])["methodology"], - "largest_drop": min(methods, key=lambda item: item["weight_delta"])["methodology"], - }, - "decision": "ADMIT_REWEIGHTING_PROFILE_AS_HOLD_ACCOUNTING", - } - profile["profile_hash"] = hash_obj({k: v for k, v in profile.items() if k != "profile_hash"}) - return profile - - -def build_receipt(profile: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "network_topology_model_reweighting_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "profile_hash": profile["profile_hash"], - "profile_name": profile["profile_name"], - "aggregates": profile["aggregates"], - "decision": profile["decision"], - "claim_boundary": profile["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(profile: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Network Topology Model Reweighting", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Profile hash: `{profile['profile_hash']}`", - "", - profile["claim_boundary"], - "", - "## Rule", - "", - f"`{profile['reweight_rule']}`", - "", - "## Aggregates", - "", - f"- Raw convergence: `{profile['aggregates']['raw_methodology_convergence']}`", - f"- Receipt-weighted alignment: `{profile['aggregates']['receipt_weighted_alignment']}`", - f"- Largest gain: `{profile['aggregates']['largest_gain']}`", - f"- Largest drop: `{profile['aggregates']['largest_drop']}`", - "", - "## Method Weights", - "", - "| Method | Raw | Multiplier | Reweighted | Delta | Decision |", - "|---|---:|---:|---:|---:|---|", - ] - for item in profile["methods"]: - lines.append( - f"| {item['methodology']} | {item['raw_weight']:.6f} | {item['receipt_multiplier']:.2f} | " - f"{item['receipt_reweighted_weight']:.6f} | {item['weight_delta']:+.6f} | {item['decision']} |" - ) - lines.extend( - [ - "", - "## Notes", - "", - ] - ) - for item in profile["methods"]: - lines.append(f"- `{item['methodology']}`: {item['reason']}") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(profile: dict[str, Any], receipt: dict[str, Any]) -> None: - text = [ - "title: Network Topology Model Reweighting", - "tags: NetworkTopology Underverse Receipt HOLD", - "type: text/vnd.tiddlywiki", - "", - "! Network Topology Model Reweighting", - "", - f"Decision: `{receipt['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - f"Profile hash: `{profile['profile_hash']}`", - "", - "!! Rule", - "", - f"`{profile['reweight_rule']}`", - "", - "!! Method Weights", - "", - "| Method | Raw | Multiplier | Reweighted | Delta | Decision |h", - ] - for item in profile["methods"]: - text.append( - f"| {item['methodology']} | {item['raw_weight']:.6f} | {item['receipt_multiplier']:.2f} | " - f"{item['receipt_reweighted_weight']:.6f} | {item['weight_delta']:+.6f} | {item['decision']} |" - ) - text.extend( - [ - "", - "!! Claim Boundary", - "", - profile["claim_boundary"], - "", - "!! Links", - "", - f"* Receipt: `{rel(RECEIPT)}`", - f"* Summary: `{rel(SUMMARY)}`", - f"* Database: `{rel(DATABASE)}`", - f"* Underverse receipt: `{rel(UNDERVERSE_RECEIPT)}`", - ] - ) - TIDDLER.write_text("\n".join(text) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - profile = build_profile() - receipt = build_receipt(profile) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - (OUT_DIR / "network_topology_model_reweighting_profile.json").write_text( - json.dumps(profile, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - write_summary(profile, receipt) - write_tiddler(profile, receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "receipt_hash": receipt["receipt_hash"], - "profile_hash": profile["profile_hash"], - "aggregates": profile["aggregates"], - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/noisy_packet_recovery_simulator.py b/4-Infrastructure/shim/noisy_packet_recovery_simulator.py deleted file mode 100644 index c3121325..00000000 --- a/4-Infrastructure/shim/noisy_packet_recovery_simulator.py +++ /dev/null @@ -1,127 +0,0 @@ -#!/usr/bin/env python3 -"""Local noisy recovery simulator for the FCL1/FCS1 promotion ladder. - -This deliberately stays local. Quandela remains a manually gated remote noisy -probe; this script establishes the fail-closed behaviors first. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - -import finance_claim_lut_harness as harness - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" - - -def with_recomputed_crc(blob: bytes) -> bytes: - body = blob[:-4] - return body + harness.crc32_bytes(body).to_bytes(4, "big") - - -def decode_status(fcl1: bytes, fcs1: bytes, symbol_lut: dict[str, Any], codebook: dict[str, int]) -> dict[str, Any]: - try: - sidecar = harness.decode_fcs1(fcs1) - packet = harness.decode_fcl1(fcl1, sidecar, symbol_lut, codebook) - return {"ok": True, "canonical_hash": harness.sha256_bytes(harness.canonical_bytes(packet)), "error": None} - except Exception as exc: - return {"ok": False, "canonical_hash": None, "error": str(exc)} - - -def run_simulation(samples: list[dict[str, Any]], out: Path) -> dict[str, Any]: - symbol_lut = harness.build_symbol_lut() - type_lut = harness.build_typesetting_lut(symbol_lut) - codebook = harness.symbol_codebook(symbol_lut) - sample = samples[0] - encoded = harness.encode_packet(sample) - fcl1 = harness.encode_fcl1(encoded["compressed"], codebook) - fcs1 = harness.encode_fcs1(encoded["sidecar"]) - canonical_hash = harness.sha256_bytes(harness.canonical_bytes(sample)) - - cases: list[dict[str, Any]] = [] - - mutated = bytearray(fcl1) - mutated[8] ^= 0x01 - cases.append({"case": "fcl1_bit_flip_without_crc_repair", "expected": "reject", "result": decode_status(bytes(mutated), fcs1, symbol_lut, codebook)}) - - mutated = bytearray(fcs1) - mutated[-8] ^= 0x01 - cases.append({"case": "fcs1_literal_byte_flip_without_crc_repair", "expected": "reject", "result": decode_status(fcl1, bytes(mutated), symbol_lut, codebook)}) - - mutated = bytearray(fcs1) - mutated[5] = max(0, mutated[5] - 1) - mutated = bytearray(with_recomputed_crc(bytes(mutated))) - cases.append({"case": "fcs1_missing_literal_lane_with_recomputed_outer_crc", "expected": "reject", "result": decode_status(fcl1, bytes(mutated), symbol_lut, codebook)}) - - mutated = bytearray(fcl1) - mutated[8] = 0xFF - mutated = bytearray(with_recomputed_crc(bytes(mutated))) - cases.append({"case": "fcl1_enum_code_mutation_with_recomputed_crc", "expected": "reject", "result": decode_status(bytes(mutated), fcs1, symbol_lut, codebook)}) - - original_orientation_hash = harness.sha256_bytes(json.dumps(type_lut, sort_keys=True).encode("utf-8")) - mutated_type_lut = json.loads(json.dumps(type_lut)) - first_symbol = sorted(mutated_type_lut["entries"])[0] - mutated_type_lut["entries"][first_symbol]["orientation_code"] ^= 0x10 - mutated_type_lut["entries"][first_symbol]["orientation"] = harness.unpack_orientation(mutated_type_lut["entries"][first_symbol]["orientation_code"]) - mutated_orientation_hash = harness.sha256_bytes(json.dumps(mutated_type_lut, sort_keys=True).encode("utf-8")) - cases.append( - { - "case": "orientation_byte_mutation", - "expected": "record_hash_mismatch", - "result": { - "ok": original_orientation_hash != mutated_orientation_hash, - "original_typesetting_hash": original_orientation_hash, - "mutated_typesetting_hash": mutated_orientation_hash, - "mutated_symbol": first_symbol, - }, - } - ) - - unknown = dict(sample) - unknown["currency"] = "ZZZ" - unknown_encoded = harness.encode_packet(unknown) - cases.append( - { - "case": "unknown_enum_sidecar_fallback", - "expected": "fallback", - "result": { - "ok": any( - item["field_symbol_id"] == harness.FIELD_SYMBOLS["currency"][0] and item["value"]["type"] == "literal_ref" - for item in unknown_encoded["compressed"]["fields"] - ), - "field": "currency", - "value": "ZZZ", - }, - } - ) - - receipt = { - "schema": "noisy_packet_recovery_simulator_receipt_v1", - "sample_id": sample["id"], - "canonical_hash": canonical_hash, - "case_count": len(cases), - "cases": cases, - "lawful": all(case["result"].get("ok") is (case["expected"] in {"fallback", "record_hash_mismatch"}) or (not case["result"].get("ok") and case["expected"] == "reject") for case in cases), - "claim_boundary": "local perturbation simulator only; not Quandela execution or measured noisy-substrate recovery", - } - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--samples", type=Path) - parser.add_argument("--out", type=Path, default=SHIM / "noisy_packet_recovery_simulator_receipt.json") - args = parser.parse_args() - print(json.dumps(run_simulation(harness.load_samples(args.samples), args.out), indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/non_euclidean_semantic_kv_prior.py b/4-Infrastructure/shim/non_euclidean_semantic_kv_prior.py deleted file mode 100644 index 50d20202..00000000 --- a/4-Infrastructure/shim/non_euclidean_semantic_kv_prior.py +++ /dev/null @@ -1,482 +0,0 @@ -#!/usr/bin/env python3 -"""Non-Euclidean geometry, compression, and semantic KV-store prior. - -This records the user's pasted Consensus thread as a bounded route prior. The -source says the three themes are mostly separate in current research, so the -local extraction is an integration rule rather than a claim that the literature -already proves a unified compressor. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -DEFAULT_RECEIPT = Path("4-Infrastructure/shim/non_euclidean_semantic_kv_prior_receipt.json") -DEFAULT_CURRICULUM = Path("4-Infrastructure/shim/non_euclidean_semantic_kv_prior_curriculum.jsonl") - - -CONSENSUS_SOURCE_SUMMARY = { - "thread_title": "Non-Euclidean Geometry Compression Methods", - "prompt": "non euclidian approaches to geometry, compression and semantic key stores", - "mode": "Pro", - "search_count": 4, - "reported_query_counts": { - "compression_methods_and_semantic_key_value_stores": 17_900_000, - "geometric_methods_in_non_euclidean_spaces_and_manifold_based_geometry": 3_000_000, - "non_euclidean_geometry_hyperbolic_spherical_metric_geometry": 865_100, - }, - "consensus_meter_question": ( - "Does non-Euclidean geometry improve compression efficiency in semantic key-value stores?" - ), - "claim_boundary": ( - "The thread says current work touches non-Euclidean geometry, classic " - "KV-store compression, and semantic KV-cache compression mostly in " - "separate lines. This prior extracts integration constraints only." - ), -} - - -PRIOR_FAMILIES = [ - { - "id": "riemannian_manifold_distortion", - "source_examples": [ - "A Riemannian geometric framework for manifold learning of non-Euclidean data", - "Survey of geometric optimization from Euclidean space to Riemannian manifolds", - "Manifold learning in metric spaces", - ], - "useful_shape": ( - "learn or compare data on curved spaces using coordinate-invariant " - "distortion and near-isometry checks" - ), - "compressor_mapping": "semantic keys live on a curved route manifold instead of a flat vector table", - "receipt_fields": [ - "manifold_family_id", - "chart_id", - "coordinate_invariant_distortion", - "near_isometry_status", - ], - "failure_mode": "curved embedding clusters keys but does not preserve byte rehydration", - }, - { - "id": "cartan_hadamard_optimal_transport", - "source_examples": [ - "Sliced-Wasserstein Distances and Flows on Cartan-Hadamard Manifolds", - "Spherical and hyperbolic embeddings of data", - "Computationally tractable Riemannian manifolds for graph embeddings", - ], - "useful_shape": ( - "use non-positive curvature, hyperbolic/SPD geometry, and sliced " - "Wasserstein flows for distribution-aware key movement" - ), - "compressor_mapping": "route token/key populations by geodesic transport rather than flat nearest neighbor only", - "receipt_fields": [ - "curvature_class", - "transport_plan_id", - "sliced_wasserstein_score", - "geodesic_owner_id", - ], - "failure_mode": "transport score improves retrieval geometry while sidecar cost exceeds byte gain", - }, - { - "id": "classic_kv_store_byte_compression", - "source_examples": [ - "Requirements and Trade-Offs of Compression Techniques in Key-Value Stores: A Survey", - "ZipKV: In-Memory Key-Value Store with Built-In Data Compression", - "KallaxDB: A Table-less Hash-based Key-Value Store on Storage Hardware with Built-in Transparent Compression", - "TinyEnc: Enabling Compressed and Encrypted Big Data Stores With Rich Query Support", - ], - "useful_shape": ( - "classic KV stores tune Snappy/LZ4/Zstd/Zlib, compaction, block size, " - "selective compression, and read/write amplification" - ), - "compressor_mapping": "backend key store must expose actual byte counts and throughput tradeoffs", - "receipt_fields": [ - "kv_backend_id", - "codec_id", - "block_granularity_bytes", - "read_amplification", - "write_amplification", - "compressed_store_bytes", - ], - "failure_mode": "semantic key route ignores store codec overhead or compaction cost", - }, - { - "id": "semantic_chunk_anchor_kv_cache", - "source_examples": [ - "ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference", - "FINCH: Prompt-guided Key-Value Cache Compression for Large Language Models", - "Autoencoding-Free Context Compression for LLMs via Contextual Semantic Anchors", - "ClusterKV: Manipulating LLM KV Cache in Semantic Space for Recallable Compression", - "SentenceKV: Efficient LLM Inference via Sentence-Level Semantic KV Caching", - ], - "useful_shape": ( - "preserve semantic chunks, anchor tokens, sentence units, or recallable " - "clusters under strict KV-cache budgets" - ), - "compressor_mapping": "chunk/anchor selection proposes tokenbook and sidecar lanes", - "receipt_fields": [ - "semantic_chunk_id", - "anchor_token_map_hash", - "cluster_id", - "recallability_score", - "chunk_residual_bytes", - ], - "failure_mode": "semantic anchor reconstructs meaning but not source bytes", - }, - { - "id": "head_layer_importance_kv_cache", - "source_examples": [ - "Dynamic Memory Compression: Retrofitting LLMs for Accelerated Inference", - "RazorAttention: Efficient KV Cache Compression Through Retrieval Heads", - "CompressKV: Semantic Retrieval Heads Know What Tokens are Not Important Before Generation", - "HeadKV: A Head-Level KV Cache Compression Method with Integrated Retrieval and Reasoning", - "MiniCache: KV Cache Compression in Depth Dimension for Large Language Models", - "A Simple and Effective L2 Norm-Based Strategy for KV Cache Compression", - ], - "useful_shape": ( - "heads, layers, norms, importance, and diversity can rank what KV state " - "to keep, merge, or evict" - ), - "compressor_mapping": "attention-derived importance becomes a DD feature coordinate, not a proof", - "receipt_fields": [ - "attention_head_id", - "layer_id", - "importance_score", - "diversity_score", - "eviction_policy_id", - "kv_budget_bytes", - ], - "failure_mode": "head/layer pruning breaks exact decode or retrieval receipt", - }, - { - "id": "value_aware_low_rank_kv_cache", - "source_examples": [ - "GEAR: An Efficient KV Cache Compression Recipe for Near-Lossless Generative Inference of LLM", - "Value-Guided KV Compression for LLMs via Approximated CUR Decomposition", - "Palu: KV-Cache Compression with Low-Rank Projection", - "LoRC: Low-Rank Compression for LLMs KV Cache with a Progressive Compression Strategy", - "SVDq: 1.25-bit and 410x Key Cache Compression for LLM Attention", - ], - "useful_shape": ( - "low-rank, sparse correction, quantization, CUR/SVD, and value-guided " - "decomposition approximate attention outputs" - ), - "compressor_mapping": "low-rank KV is a predictor sketch that needs exact residual authority", - "receipt_fields": [ - "decomposition_family_id", - "rank_budget", - "quantization_bits", - "sparse_correction_bytes", - "value_guidance_hash", - ], - "failure_mode": "near-lossless KV approximation is treated as byte-exact", - }, - { - "id": "geometry_inspired_but_unproven_unification", - "source_examples": [ - "Position: Beyond Euclidean - Foundation Models Should Embrace Non-Euclidean Geometries", - "Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures", - "State of the Art of Graph Visualization in non-Euclidean Spaces", - ], - "useful_shape": ( - "non-Euclidean geometry may improve representation and retrieval, but " - "the cited thread does not establish a single unified KV compressor" - ), - "compressor_mapping": "require an explicit bridge receipt between curved geometry and byte-store behavior", - "receipt_fields": [ - "bridge_claim_id", - "geometry_to_kv_mapping_id", - "byte_store_receipt_id", - "semantic_cache_receipt_id", - "unification_status", - ], - "failure_mode": "geometry metaphor is promoted without a byte-store and semantic-cache bridge", - }, -] - - -LOCAL_TREEFIDDY_STATUS = { - "status": "found_in_current_checkout", - "model_map_entry": "3-Mathematical-Models/MATH_MODEL_MAP.tsv:102", - "documentation": "6-Documentation/docs/semantics/TREE_FIDDY.md", - "local_role": ( - "TREE(3) / Kruskal-style tree-sequence bound used as a state-space " - "pruning shortcut and bounded archive depth guard" - ), - "compression_claim_boundary": ( - "Tree Fiddy can bound TreeKV route depth, owner routing, and archive " - "receipts. It is not a hidden payload channel and does not prove byte " - "compression by itself." - ), -} - - -PRIORITY_WATCH_ITEMS = [ - { - "id": "tinyenc_compressed_encrypted_kv_store", - "source_examples": [ - "TinyEnc: Enabling Compressed and Encrypted Big Data Stores With Rich Query Support", - "Encrypted and Compressed Key-Value Store With Pattern-Analysis Security in Cloud Systems", - "Optimal Compression for Encrypted Key-Value Store in Cloud Systems", - ], - "why_pay_attention": ( - "TinyEnc sits on the byte-store side of the bridge: compression, " - "encryption, and rich query support must be paid for in one receipt." - ), - "compressor_mapping": ( - "encrypted KV packet -> compressed store packet + query-support " - "index + leakage/pattern guard + exact byte rehydration receipt" - ), - "receipt_fields": [ - "encryption_envelope_id", - "cipher_suite_id", - "query_support_class", - "query_index_bytes", - "pattern_leakage_guard_id", - "compressed_encrypted_bytes", - "plaintext_rehydration_hash", - ], - "promotion_guard": ( - "promote only if encryption envelope, query index, and compression " - "container overhead are counted and plaintext bytes rehydrate exactly" - ), - "failure_mode": "query/encryption metadata hides byte debt or weakens the claim boundary", - }, - { - "id": "treekv_treefiddy_modification", - "source_examples": [ - "TreeKV: Smooth Key-Value Cache Compression with Tree Structures", - "Tree Fiddy: TREE(3) Combinatorial State Space Shortcut", - "BHOCS: Bounded Hierarchical Cryptographic Space", - ], - "why_pay_attention": ( - "TreeKV already gives a tree-structured KV-cache route. Local " - "Tree Fiddy can modify it into a bounded route spine with explicit " - "depth, embedding, owner, and leaf-residual receipts." - ), - "compressor_mapping": ( - "TreeKV node -> Tree Fiddy bounded route spine -> deterministic " - "subtree owner -> smooth merge receipt -> exact residual leaves" - ), - "receipt_fields": [ - "treekv_node_id", - "treefiddy_spine_id", - "tree_label_budget_k", - "tree_depth_budget", - "homeomorphic_embedding_guard", - "subtree_owner_hash", - "smooth_merge_receipt_id", - "leaf_residual_bytes", - ], - "promotion_guard": ( - "promote only if Tree Fiddy bounds depth/branching, TreeKV smooth " - "merges preserve decode reachability, and residual leaves restore " - "the exact bytes" - ), - "failure_mode": "tree merge changes decode reachability or opens recursive repair", - }, -] - - -INTEGRATION_RULES = { - "three_surface_model": { - "curved_key_surface": "non-Euclidean manifold stores similarity, hierarchy, and geodesic owner routing", - "byte_store_surface": "KV backend stores bytes with codec, compaction, and throughput receipts", - "semantic_cache_surface": "LLM KV/cache route stores semantic anchors, heads, ranks, and residuals", - }, - "promotion_rule": ( - "promote iff curved geometry only routes or clusters keys, KV-store byte " - "compression is measured, semantic KV approximations carry exact residual " - "repair, decoded hash matches source, and total bytes beat incumbent" - ), - "failure_rule": ( - "non-Euclidean retrieval, semantic cache recall, or KV throughput improvement " - "without byte-exact rehydration is diagnostic only" - ), - "tinyenc_watch_rule": ( - "TinyEnc-style compressed encryption is relevant when query support, " - "encryption envelope, compressed bytes, and leakage guards are all " - "counted in the same byte-store receipt" - ), - "treekv_treefiddy_rule": ( - "TreeKV may be modified to use Tree Fiddy as a bounded tree-spine and " - "homeomorphic-embedding guard, but Tree Fiddy remains a pruning and " - "receipt primitive, not compression evidence" - ), -} - - -def stable_hash(obj: Any) -> str: - payload = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "non_euclidean_semantic_kv_prior_v1", - "generated_at": "2026-05-08T00:00:00+00:00", - "source_summary": CONSENSUS_SOURCE_SUMMARY, - "source_summary_hash": stable_hash(CONSENSUS_SOURCE_SUMMARY), - "claim_boundary": ( - "This is an integration prior. It records source families for curved " - "geometry, KV-store compression, and semantic KV-cache compression; " - "local encode/decode/hash/byte-count receipts remain authoritative." - ), - "prior_families": PRIOR_FAMILIES, - "priority_watch_items": PRIORITY_WATCH_ITEMS, - "local_treefiddy_status": LOCAL_TREEFIDDY_STATUS, - "integration_rules": INTEGRATION_RULES, - "dd_state_extension": [ - "manifold_family_id", - "chart_id", - "curvature_class", - "geodesic_owner_id", - "kv_backend_id", - "codec_id", - "block_granularity_bytes", - "semantic_chunk_id", - "anchor_token_map_hash", - "attention_head_id", - "importance_score", - "decomposition_family_id", - "rank_budget", - "sparse_correction_bytes", - "geometry_to_kv_mapping_id", - "byte_store_receipt_id", - "semantic_cache_receipt_id", - "encryption_envelope_id", - "query_support_class", - "pattern_leakage_guard_id", - "treekv_node_id", - "treefiddy_spine_id", - "tree_label_budget_k", - "tree_depth_budget", - "homeomorphic_embedding_guard", - "subtree_owner_hash", - "smooth_merge_receipt_id", - "leaf_residual_bytes", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "choose_curved_key_manifold", - "assign_geodesic_owner", - "measure_manifold_distortion", - "choose_kv_backend_codec", - "measure_kv_store_bytes", - "emit_semantic_chunk_anchor", - "rank_attention_heads", - "apply_low_rank_kv_sketch", - "emit_exact_kv_residual_lane", - "bridge_geometry_to_byte_store", - "charge_tinyenc_encryption_query_overhead", - "open_treekv_treefiddy_spine", - "bound_treefiddy_depth_and_embedding", - "route_treefiddy_subtree_owner", - "verify_treekv_smooth_merge_receipt", - "emit_tree_leaf_exact_residuals", - "verify_byte_rehydration_hash", - "reject_geometry_only_kv_claim", - ], - } - receipt["receipt_hash"] = stable_hash(receipt) - return receipt - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = ( - "You are a non-Euclidean semantic KV route controller. Keep geometry, " - "KV-store bytes, and semantic cache receipts separate until a bridge is verified." - ) - records: list[dict[str, Any]] = [] - for family in receipt["prior_families"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "route_non_euclidean_semantic_kv_family", - "family_id": family["id"], - "useful_shape": family["useful_shape"], - "compressor_mapping": family["compressor_mapping"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "selected": True, - "receipt_fields": family["receipt_fields"], - "failure_mode": family["failure_mode"], - "claim_boundary": "integration-prior-only", - "promotion_authority": "local encode/decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - for item in receipt["priority_watch_items"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "route_priority_watch_item", - "watch_item_id": item["id"], - "why_pay_attention": item["why_pay_attention"], - "compressor_mapping": item["compressor_mapping"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "selected": True, - "receipt_fields": item["receipt_fields"], - "promotion_guard": item["promotion_guard"], - "failure_mode": item["failure_mode"], - "promotion_authority": "local encode/decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--curriculum", type=Path, default=DEFAULT_CURRICULUM) - args = parser.parse_args() - - receipt = build_receipt() - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/nonlinear_compressed_sensing_structural_prior.py b/4-Infrastructure/shim/nonlinear_compressed_sensing_structural_prior.py deleted file mode 100644 index 65afda4a..00000000 --- a/4-Infrastructure/shim/nonlinear_compressed_sensing_structural_prior.py +++ /dev/null @@ -1,212 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for nonlinear compressed sensing as a structural transfer prior.""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "nonlinear_compressed_sensing_structural_prior_receipt.json" -CURRICULUM = SHIM / "nonlinear_compressed_sensing_structural_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "nonlinear_compressed_sensing_structural_prior_v1", - "source_type": "user_supplied_consensus_bibtex_and_meter", - "consensus_meter": { - "claim": ( - "Compressed sensing ideas extend beyond linear sparse recovery, " - "but only under specific structural and regularity conditions." - ), - "n": 7, - "yes_percent": 86, - "yes_papers": 6, - "no_percent": 14, - "no_papers": 1, - "yes_citations_total": 354, - "no_citations_total": 8, - "yes_average_year": 2014, - "no_average_year": 2019, - }, - "structural_conditions": [ - "low_dimensional_structure", - "sparsity_or_structured_sparsity", - "RIP_or_generalized_RIP_like_condition", - "Lipschitz_measurement_map_or_Lipschitz_generator", - "stable_Jacobian_or_local_regular_measurement_operator", - "distinct_parameters_relative_to_operator_correlation", - "bounded_noise_and_quantization_model", - "sample_complexity_scales_with_intrinsic_dimension", - ], - "nonlinear_model_lanes": [ - { - "lane": "mildly_nonlinear_observations", - "core_condition": "nonlinear map is Lipschitz and satisfies generalized RIP-like regularity", - "guarantee_shape": "iterative hard thresholding can stably recover sparse or structured signals", - "representative_keys": ["Blumensath2012Compressed"], - }, - { - "lane": "quasi_linear_compressed_sensing", - "core_condition": "measurements can be represented as A(x)=F(x)x with F Lipschitz", - "guarantee_shape": "generalized RIP-like conditions give identifiability and greedy convergence", - "representative_keys": ["Ehler2013Quasi-linear"], - }, - { - "lane": "separable_nonlinear_inverse_problems", - "core_condition": "parameters are sufficiently distinct relative to operator correlation", - "guarantee_shape": "sparse recovery can survive deterministic non-RIP operators in special structure", - "representative_keys": ["Bernstein2019Sparse"], - }, - { - "lane": "nonlinear_generative_compressed_sensing", - "core_condition": "Lipschitz generative prior and sample complexity tied to latent dimension", - "guarantee_shape": "uniform recovery for nonlinear/quantized/single-index measurements", - "representative_keys": ["Chen2023A", "Dhar2018Modeling"], - }, - ], - "rich_low_dimensional_structures": [ - { - "model_type": "structured_sparsity_hierarchies", - "core_idea": "blocks, trees, multilevel support", - "cs_style_guarantee": "fewer measurements under model-based RIP or restricted amplification", - "representative_keys": ["Baraniuk2008Model-Based", "Duarte2011Structured", "Eisert2021Hierarchical"], - }, - { - "model_type": "manifolds", - "core_idea": "signals lie on low-dimensional nonlinear families", - "cs_style_guarantee": "random linear maps can embed manifolds stably with recovery/parameter bounds", - "representative_keys": ["Eftekhari2013New", "Wakin2010Manifold-Based"], - }, - { - "model_type": "temporal_dynamic_models", - "core_idea": "autoregressive or state-space structure with spatial and temporal sparsity", - "cs_style_guarantee": "near-optimal sampling can extend to dependent dynamic data", - "representative_keys": ["Kazemipour2018Compressed"], - }, - { - "model_type": "generative_learned_priors", - "core_idea": "deep/domain-specific generator plus sparse deviations", - "cs_style_guarantee": "larger signal classes recoverable than plain sparsity when generator assumptions hold", - "representative_keys": ["Dhar2018Modeling", "Chen2023A"], - }, - ], - "equation_pipeline_implication": { - "useful_for": [ - "equation traces with intrinsic low-dimensional structure", - "manifold-like parameter families", - "structured residual or witness lanes", - "hierarchical route priors", - "generative proposal models with explicit residual checks", - ], - "gate": ( - "Nonlinear CS-style transfer is admissible only after the route " - "declares its structure class and regularity condition." - ), - "reject_if": [ - "nonlinear map lacks Lipschitz or local regularity bound", - "intrinsic dimension is unknown or unbounded", - "generator prior hides payload or proof work", - "operator correlations make parameters indistinct", - "classifier or reconstruction score replaces receipt", - ], - }, - "hutter_implication": { - "route_state_additions": [ - "cs_structure_class", - "intrinsic_dimension_estimate", - "regularity_witness_id", - "operator_correlation_bound", - "generator_prior_id", - "sparse_deviation_lane_id", - "residual_rehydration_hash", - ], - "promotion_boundary": ( - "No nonlinear sparse route promotes without exact residual repair, " - "decoded hash match, measured bytes, and counted regularity witness." - ), - }, - "bibliography_keys": [ - "Ahmed2022Sparse", - "Baraniuk2008Model-Based", - "Bernstein2019Sparse", - "Blumensath2012Compressed", - "Chen2023A", - "Dhar2018Modeling", - "Duarte2011Structured", - "Eftekhari2013New", - "Ehler2013Quasi-linear", - "Eisert2021Hierarchical", - "Ibanez2019Some", - "Kazemipour2018Compressed", - "Kolleck2017On", - "Lee2016Unified", - "Qi2013Low-Dimensional", - "Rani2018A", - "Romero2015Compressive", - "Wakin2010Manifold-Based", - "Wang2023Compressed", - ], - "claim_boundary": ( - "This prior says nonlinear compressed sensing can guide route and " - "equation candidate design under explicit structure and regularity. " - "It does not prove any Hutter compression result or validate an " - "unbounded nonlinear manifold route." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "classify_nonlinear_cs_lane", - "input": "candidate nonlinear equation or route", - "target": "mildly nonlinear, quasi-linear, separable inverse, generative, manifold, or hierarchy lane", - }, - { - "task": "require_regular_structure", - "input": "nonlinear recovery claim", - "target": "explicit regularity, intrinsic dimension, and correlation bounds", - }, - { - "task": "block_unbounded_nonlinear_routes", - "input": "manifold or generator compression proposal", - "target": "fail closed unless exact residual and byte receipt exist", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "model_lane_count": len(receipt["nonlinear_model_lanes"]), - "structure_count": len(receipt["rich_low_dimensional_structures"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/notion_linear_obsidian_gap_fill_receipt.py b/4-Infrastructure/shim/notion_linear_obsidian_gap_fill_receipt.py deleted file mode 100644 index 99c83e76..00000000 --- a/4-Infrastructure/shim/notion_linear_obsidian_gap_fill_receipt.py +++ /dev/null @@ -1,202 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for the Notion/Linear -> Obsidian connector gap-fill pass. - -This is connector-mining evidence only. Notion and Linear can identify missing -local chart anchors, but they do not promote mathematical or compression claims. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "notion_linear_obsidian_gap_fill" -RECEIPT = OUT_DIR / "notion_linear_obsidian_gap_fill_receipt.json" -SUMMARY = OUT_DIR / "notion_linear_obsidian_gap_fill_receipt.md" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -NOTION_SOURCES = [ - { - "id": "35b375cc-7bfc-815b-9c28-c0c8a7fcdfaa", - "title": "Research Stack Compression Atlas Update - LadderLUT, HexLogogram, Manifold Boundary", - "url": "https://www.notion.so/35b375cc7bfc815b9c28c0c8a7fcdfaa", - "local_status": "TRACKER_CONTEXT_HOLD", - "gap_filled": "Compression atlas anchors were absent from the Obsidian Notion/Linear chart.", - }, - { - "id": "350375cc-7bfc-8179-a100-d639c59ade37", - "title": "S3C / PIST Bridge Ingest Brief", - "url": "https://www.notion.so/350375cc7bfc8179a100d639c59ade37", - "local_status": "ENE_BACKLINK_CONTEXT", - "gap_filled": "S3C/PIST ENE rowids and Linear backlink were absent from the Obsidian bridge chart.", - }, - { - "id": "353375cc-7bfc-81f7-8b7c-db285abd2840", - "title": "Mass-Number GCL Subset", - "url": "https://www.notion.so/353375cc7bfc81f78b7cdb285abd2840", - "local_status": "WORKBENCH_PROJECTION_HOLD", - "gap_filled": "Mass-Number GCL validator and closure doctrine needed a local Obsidian pointer.", - }, - { - "id": "353375cc-7bfc-8184-8522-ea812f867b73", - "title": "Research Wiki Hub", - "url": "https://www.notion.so/353375cc7bfc81848522ea812f867b73", - "local_status": "WIKI_HUB_CONTEXT", - "gap_filled": "Research Wiki Hub entry policy was not represented in the local Notion/Linear chart.", - }, -] - -LINEAR_SOURCES = [ - { - "id": "RES-2317", - "title": "Ingest ChatGPT S3C/PIST bridge session into ENE surfaces", - "url": "https://linear.app/research-stack/issue/RES-2317/ingest-chatgpt-s3cpist-bridge-session-into-ene-surfaces", - "status": "Backlog", - "local_status": "ENE_BACKLINK_CONTEXT", - }, - { - "id": "RES-2348", - "title": "Run Mass-Number Corpus Pass for Notion and Linear", - "url": "https://linear.app/research-stack/issue/RES-2348/run-mass-number-corpus-pass-for-notion-and-linear", - "status": "Backlog", - "local_status": "URGENT_AUDIT_HOLD", - }, - { - "id": "RES-2379", - "title": "Implement detectors/codecs for LadderLUT, HexLogogram Atlas, and Manifold Boundary Atlas", - "url": "https://linear.app/research-stack/issue/RES-2379/implement-detectorscodecs-for-ladderlut-hexlogogram-atlas-and-manifold", - "status": "Backlog", - "local_status": "IMPLEMENTATION_QUEUE_HOLD", - }, - { - "id": "document:dfc94418-0b5a-4ac0-bf98-c87ba898603c", - "title": "Research Stack - Dual Graph Shape (Knowledge <-> Execution)", - "url": "https://linear.app/research-stack/document/research-stack-dual-graph-shape-knowledge-execution-e33ad16bbd3a", - "status": "Document", - "local_status": "GRAPH_CONTEXT", - }, - { - "id": "project:a6db6541-2750-4290-84b6-e890bb3f4501", - "title": "Research Stack", - "url": "https://linear.app/research-stack/project/research-stack-7cd2d4ba318f", - "status": "Backlog", - "local_status": "PROJECT_CONTEXT", - }, -] - -OBSIDIAN_TARGETS = [ - "/home/allaun/obsidian-vault/Research Stack/Notion and Linear.md", - "/home/allaun/obsidian-vault/Research Stack/Connector Gap Fill 2026-05-09.md", - rel(REPO / "6-Documentation" / "wiki" / "ObsidianConnector" / "Notion and Linear.md"), - rel(REPO / "6-Documentation" / "wiki" / "ObsidianConnector" / "Connector Gap Fill 2026-05-09.md"), - rel(REPO / "6-Documentation" / "wiki" / "Obsidian-connector" / "Notion and Linear.md"), - rel(REPO / "6-Documentation" / "wiki" / "Obsidian-connector" / "Connector Gap Fill 2026-05-09.md"), -] - -GAPS = [ - { - "gap": "compression_atlas_missing_from_obsidian", - "action": "ADD_TRACKER_CONTEXT", - "status": "FILLED_AS_HOLD_POINTER", - }, - { - "gap": "mass_number_corpus_audit_missing_from_obsidian", - "action": "ADD_AUDIT_POINTER", - "status": "FILLED_AS_TAINTED_UNREWEIGHTED_BOUNDARY", - }, - { - "gap": "dual_graph_shape_missing_from_obsidian", - "action": "ADD_GRAPH_CONTEXT", - "status": "FILLED_AS_CONNECTOR_TOPOLOGY", - }, - { - "gap": "s3c_pist_ingest_backlink_thin", - "action": "ADD_ENE_LINEAR_NOTION_BRIDGE_POINTER", - "status": "FILLED_AS_BACKLINK_CONTEXT", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt = { - "schema": "notion_linear_obsidian_gap_fill_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "included_in_receipt_hash": ["notion_sources", "linear_sources", "obsidian_targets", "gaps", "decision"], - "clock_participates_in_hash": False, - "source_surfaces": ["notion", "linear"], - "sink_surface": "obsidian", - "notion_sources": NOTION_SOURCES, - "linear_sources": LINEAR_SOURCES, - "obsidian_targets": OBSIDIAN_TARGETS, - "gaps": GAPS, - "decision": "PROMOTE_TRACKER_CONTEXT_TO_OBSIDIAN_HOLD_BOUNDARY", - "claim_boundary": ( - "Connector mining only. Notion and Linear identify tracker, publication, " - "and operationalization gaps; they do not replace ENE records, local " - "source files, Lean builds, corpus receipts, or byte-accounting evidence." - ), - } - receipt["receipt_hash"] = sha256_text( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}) - ) - return receipt - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# Notion/Linear Obsidian Gap Fill Receipt", - "", - f"Decision: `{receipt['decision']}`", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Clock participates in hash: `{receipt['clock_participates_in_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Gaps", - "", - "| Gap | Action | Status |", - "|---|---|---|", - ] - for gap in receipt["gaps"]: - lines.append(f"| {gap['gap']} | {gap['action']} | {gap['status']} |") - lines.extend(["", "## Linear Sources", "", "| ID | Status | Local status |", "|---|---|---|"]) - for source in receipt["linear_sources"]: - lines.append(f"| {source['id']} | {source['status']} | {source['local_status']} |") - lines.extend(["", "## Notion Sources", "", "| ID | Local status |", "|---|---|"]) - for source in receipt["notion_sources"]: - lines.append(f"| {source['id']} | {source['local_status']} |") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt) - print(json.dumps({"receipt": rel(RECEIPT), "summary": rel(SUMMARY), "receipt_hash": receipt["receipt_hash"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/notion_linear_rds_ingest.py b/4-Infrastructure/shim/notion_linear_rds_ingest.py deleted file mode 100644 index eb4c6329..00000000 --- a/4-Infrastructure/shim/notion_linear_rds_ingest.py +++ /dev/null @@ -1,402 +0,0 @@ -#!/usr/bin/env python3 -# /// script -# requires-python = ">=3.10" -# dependencies = [ -# "boto3", -# "psycopg2-binary", -# "requests", -# "python-dotenv", -# ] -# /// -""" -Notion + Linear → Aurora PostgreSQL ingestion shim. - -Notion pages land in knowledge.documents (source='notion'). -Linear issues land in knowledge.linear_issues (upserted on issue_id). -Each run is recorded in ingestion.receipts. - -Credentials (never hardcoded): - NOTION_TOKEN – Notion integration token - LINEAR_API_KEY – Linear personal API key - RDS_HOST – Aurora endpoint (default: database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com) - RDS_USER – DB user (default: postgres) - RDS_DBNAME – DB name (default: postgres) - RDS_IAM – set to "1" to use IAM auth token (default), else use RDS_PASSWORD - RDS_PASSWORD – plain password if RDS_IAM != "1" - AWS_REGION – (default: us-east-1) -""" - -import hashlib -import json -import logging -import os -import sys -import time -import uuid -from datetime import datetime, timezone - -import boto3 -import psycopg2 -import psycopg2.extras -import requests - -logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s %(message)s") -log = logging.getLogger("notion_linear_rds_ingest") - -# --------------------------------------------------------------------------- -# Config -# --------------------------------------------------------------------------- -RDS_HOST = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com") -RDS_PORT = int(os.environ.get("RDS_PORT", "5432")) -RDS_USER = os.environ.get("RDS_USER", "postgres") -RDS_DBNAME = os.environ.get("RDS_DBNAME", "postgres") -RDS_IAM = os.environ.get("RDS_IAM", "1") == "1" -RDS_PW = os.environ.get("RDS_PASSWORD", "") -AWS_REGION = os.environ.get("AWS_REGION", "us-east-1") - -NOTION_TOKEN = os.environ.get("NOTION_TOKEN", "") -LINEAR_API_KEY = os.environ.get("LINEAR_API_KEY", "") - -NOTION_API = "https://api.notion.com/v1" -NOTION_VERSION = "2022-06-28" -LINEAR_API = "https://api.linear.app/graphql" - - -# --------------------------------------------------------------------------- -# DB helpers -# --------------------------------------------------------------------------- -def get_db_password() -> str: - if RDS_IAM: - client = boto3.client("rds", region_name=AWS_REGION) - return client.generate_db_auth_token( - DBHostname=RDS_HOST, Port=RDS_PORT, DBUsername=RDS_USER, Region=AWS_REGION - ) - return RDS_PW - - -def connect() -> psycopg2.extensions.connection: - pw = get_db_password() - return psycopg2.connect( - host=RDS_HOST, port=RDS_PORT, user=RDS_USER, - password=pw, dbname=RDS_DBNAME, sslmode="require" - ) - - -def ensure_schema(conn): - with conn.cursor() as cur: - cur.execute(""" - CREATE TABLE IF NOT EXISTS knowledge.linear_issues ( - issue_id text PRIMARY KEY, - identifier text NOT NULL, - title text NOT NULL, - state text, - priority integer, - labels jsonb NOT NULL DEFAULT '[]', - url text, - description text, - team_name text, - project_name text, - assignee text, - creator text, - created_at timestamptz, - updated_at timestamptz, - ingested_at timestamptz NOT NULL DEFAULT now(), - content_hash text NOT NULL - ); - CREATE INDEX IF NOT EXISTS linear_issues_identifier_idx - ON knowledge.linear_issues (identifier); - CREATE INDEX IF NOT EXISTS linear_issues_state_idx - ON knowledge.linear_issues (state); - """) - conn.commit() - log.info("Schema ready") - - -# --------------------------------------------------------------------------- -# Notion helpers -# --------------------------------------------------------------------------- -def notion_headers() -> dict: - if not NOTION_TOKEN: - raise RuntimeError("NOTION_TOKEN not set") - return { - "Authorization": f"Bearer {NOTION_TOKEN}", - "Notion-Version": NOTION_VERSION, - "Content-Type": "application/json", - } - - -def notion_search_all(page_size: int = 100) -> list[dict]: - """Return all pages (not databases) from the workspace.""" - pages, cursor = [], None - while True: - body: dict = {"filter": {"value": "page", "property": "object"}, "page_size": page_size} - if cursor: - body["start_cursor"] = cursor - r = requests.post(f"{NOTION_API}/search", headers=notion_headers(), json=body, timeout=30) - r.raise_for_status() - data = r.json() - pages.extend(data.get("results", [])) - log.info("Notion: fetched %d pages so far…", len(pages)) - if not data.get("has_more"): - break - cursor = data.get("next_cursor") - time.sleep(0.35) # stay under Notion rate limit - return pages - - -def notion_page_text(page_id: str) -> str: - """Fetch all block content for a page and flatten to plain text.""" - lines, cursor = [], None - while True: - url = f"{NOTION_API}/blocks/{page_id}/children?page_size=100" - if cursor: - url += f"&start_cursor={cursor}" - r = requests.get(url, headers=notion_headers(), timeout=30) - if r.status_code == 404: - return "" - r.raise_for_status() - data = r.json() - for block in data.get("results", []): - btype = block.get("type", "") - bdata = block.get(btype, {}) - rich = bdata.get("rich_text", []) - text = "".join(t.get("plain_text", "") for t in rich) - if text.strip(): - lines.append(text) - if not data.get("has_more"): - break - cursor = data.get("next_cursor") - time.sleep(0.2) - return "\n".join(lines) - - -def page_title(page: dict) -> str: - props = page.get("properties", {}) - for key in ("title", "Name", "Title"): - if key in props: - rich = props[key].get("title", []) - return "".join(t.get("plain_text", "") for t in rich) - return page.get("id", "untitled") - - -def upsert_notion_page(conn, page: dict, content: str): - pid = page["id"] - title = page_title(page) - url = page.get("url", "") - edited = page.get("last_edited_time", "") - chash = hashlib.sha256(content.encode()).hexdigest() - metadata = { - "notion_page_id": pid, - "url": url, - "last_edited_time": edited, - "object": page.get("object", "page"), - } - doc_id = str(uuid.uuid5(uuid.NAMESPACE_URL, f"notion:{pid}")) - with conn.cursor() as cur: - cur.execute(""" - INSERT INTO knowledge.documents - (doc_id, source, title, content, content_hash, metadata, ingested_at) - VALUES (%s, 'notion', %s, %s, %s, %s, now()) - ON CONFLICT (doc_id) DO UPDATE SET - title = EXCLUDED.title, - content = EXCLUDED.content, - content_hash = EXCLUDED.content_hash, - metadata = EXCLUDED.metadata, - ingested_at = now() - WHERE documents.content_hash != EXCLUDED.content_hash - """, (doc_id, title, content, chash, json.dumps(metadata))) - - -# --------------------------------------------------------------------------- -# Linear helpers -# --------------------------------------------------------------------------- -def linear_headers() -> dict: - if not LINEAR_API_KEY: - raise RuntimeError("LINEAR_API_KEY not set") - return {"Authorization": LINEAR_API_KEY, "Content-Type": "application/json"} - - -ISSUES_QUERY = """ -query Issues($after: String) { - issues(first: 100, after: $after, orderBy: updatedAt) { - pageInfo { hasNextPage endCursor } - nodes { - id identifier title - state { name } - priority - labels { nodes { name } } - url - description - team { name } - project { name } - assignee { name } - creator { name } - createdAt updatedAt - } - } -} -""" - - -def linear_fetch_all() -> list[dict]: - issues, cursor = [], None - while True: - variables = {} - if cursor: - variables["after"] = cursor - r = requests.post( - LINEAR_API, - headers=linear_headers(), - json={"query": ISSUES_QUERY, "variables": variables}, - timeout=30, - ) - r.raise_for_status() - data = r.json() - if "errors" in data: - raise RuntimeError(f"Linear GraphQL errors: {data['errors']}") - page = data["data"]["issues"] - issues.extend(page["nodes"]) - log.info("Linear: fetched %d issues so far…", len(issues)) - if not page["pageInfo"]["hasNextPage"]: - break - cursor = page["pageInfo"]["endCursor"] - time.sleep(0.3) - return issues - - -def upsert_linear_issue(conn, issue: dict): - labels = [lbl["name"] for lbl in (issue.get("labels") or {}).get("nodes", [])] - chash = hashlib.sha256(json.dumps(issue, sort_keys=True).encode()).hexdigest() - with conn.cursor() as cur: - cur.execute(""" - INSERT INTO knowledge.linear_issues - (issue_id, identifier, title, state, priority, labels, url, - description, team_name, project_name, assignee, creator, - created_at, updated_at, content_hash) - VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) - ON CONFLICT (issue_id) DO UPDATE SET - identifier = EXCLUDED.identifier, - title = EXCLUDED.title, - state = EXCLUDED.state, - priority = EXCLUDED.priority, - labels = EXCLUDED.labels, - url = EXCLUDED.url, - description = EXCLUDED.description, - team_name = EXCLUDED.team_name, - project_name = EXCLUDED.project_name, - assignee = EXCLUDED.assignee, - creator = EXCLUDED.creator, - created_at = EXCLUDED.created_at, - updated_at = EXCLUDED.updated_at, - ingested_at = now(), - content_hash = EXCLUDED.content_hash - WHERE linear_issues.content_hash != EXCLUDED.content_hash - """, ( - issue["id"], - issue.get("identifier", ""), - issue.get("title", ""), - (issue.get("state") or {}).get("name"), - issue.get("priority"), - json.dumps(labels), - issue.get("url"), - issue.get("description"), - (issue.get("team") or {}).get("name"), - (issue.get("project") or {}).get("name"), - (issue.get("assignee") or {}).get("name"), - (issue.get("creator") or {}).get("name"), - issue.get("createdAt"), - issue.get("updatedAt"), - chash, - )) - - -# --------------------------------------------------------------------------- -# Receipt helpers -# --------------------------------------------------------------------------- -def record_receipt(conn, shim: str, status: str, metadata: dict, error: str | None = None): - with conn.cursor() as cur: - cur.execute(""" - INSERT INTO ingestion.receipts - (receipt_id, shim_name, status, metadata, error_detail, ran_at) - VALUES (%s, %s, %s, %s, %s, now()) - """, (str(uuid.uuid4()), shim, status, json.dumps(metadata), error)) - conn.commit() - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- -def main(): - log.info("Connecting to RDS…") - conn = connect() - conn.autocommit = False - - ensure_schema(conn) - - # ---- Notion ---- - notion_count = 0 - notion_error = None - if not NOTION_TOKEN: - log.warning("NOTION_TOKEN not set — skipping Notion ingestion") - else: - try: - log.info("Fetching Notion page list…") - pages = notion_search_all() - log.info("Found %d Notion pages. Fetching content…", len(pages)) - for i, page in enumerate(pages, 1): - pid = page["id"] - try: - content = notion_page_text(pid) - upsert_notion_page(conn, page, content) - notion_count += 1 - if i % 25 == 0: - conn.commit() - log.info(" committed %d/%d notion pages", i, len(pages)) - except Exception as e: - log.warning(" skipping page %s: %s", pid, e) - conn.commit() - log.info("Notion done: %d pages upserted", notion_count) - record_receipt(conn, "notion_linear_rds_ingest/notion", "success", - {"pages_upserted": notion_count, "total_pages": len(pages)}) - except Exception as e: - notion_error = str(e) - log.error("Notion ingestion failed: %s", e) - conn.rollback() - record_receipt(conn, "notion_linear_rds_ingest/notion", "error", - {}, error=notion_error) - - # ---- Linear ---- - linear_count = 0 - linear_error = None - if not LINEAR_API_KEY: - log.warning("LINEAR_API_KEY not set — skipping Linear ingestion") - else: - try: - log.info("Fetching Linear issues…") - issues = linear_fetch_all() - log.info("Found %d Linear issues. Upserting…", len(issues)) - for i, issue in enumerate(issues, 1): - upsert_linear_issue(conn, issue) - linear_count += 1 - if i % 100 == 0: - conn.commit() - log.info(" committed %d/%d linear issues", i, len(issues)) - conn.commit() - log.info("Linear done: %d issues upserted", linear_count) - record_receipt(conn, "notion_linear_rds_ingest/linear", "success", - {"issues_upserted": linear_count, "total_issues": len(issues)}) - except Exception as e: - linear_error = str(e) - log.error("Linear ingestion failed: %s", e) - conn.rollback() - record_receipt(conn, "notion_linear_rds_ingest/linear", "error", - {}, error=linear_error) - - conn.close() - log.info("Done. Notion=%d Linear=%d", notion_count, linear_count) - if notion_error or linear_error: - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/nspace_bulk_dataset_route_registry.py b/4-Infrastructure/shim/nspace_bulk_dataset_route_registry.py deleted file mode 100644 index dd3c0201..00000000 --- a/4-Infrastructure/shim/nspace_bulk_dataset_route_registry.py +++ /dev/null @@ -1,412 +0,0 @@ -#!/usr/bin/env python3 -"""Register cross-domain bulk datasets as n-space route candidates. - -This is a metadata registry, not a downloader. It records large public dataset -surfaces that may produce useful density matrices or manifold graphs for RRC. -""" - -from __future__ import annotations - -import csv -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "nspace_bulk_routes" -PACKETS = OUT_DIR / "nspace_bulk_dataset_route_packets.jsonl" -TABLE_CSV = OUT_DIR / "nspace_bulk_dataset_route_table.csv" -RECEIPT = OUT_DIR / "nspace_bulk_dataset_route_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def packet( - domain: str, - dataset: str, - source_urls: list[str], - potential_nspace_application: str, - density_markers: list[str], - license_boundary: str, - ingest_boundary: str, -) -> dict[str, Any]: - obj = { - "schema": "nspace_bulk_dataset_route_packet_v1", - "domain": domain, - "dataset": dataset, - "source_urls": source_urls, - "potential_nspace_application": potential_nspace_application, - "density_markers": density_markers, - "license_boundary": license_boundary, - "ingest_boundary": ingest_boundary, - "decision": "HOLD", - } - obj["packet_id"] = "NSPACE." + domain.upper().replace(" ", "_") + "." + dataset.upper().replace(" ", "_").replace("-", "_").replace("/", "_") - obj["packet_hash"] = sha256_text(stable_json(obj)) - return obj - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = [ - packet( - domain="Astro", - dataset="Gaia DR3", - source_urls=["https://www.cosmos.esa.int/web/gaia/dr3"], - potential_nspace_application="Galactic 5D/6D phase-space density matrix over position, parallax, proper motion, radial velocity, and photometry.", - density_markers=[ - "astrometric_phase_space", - "parallax_proper_motion_surface", - "radial_velocity_subset", - "photometric_density_channels", - "billion_point_manifold", - ], - license_boundary="ESA/Gaia source terms and citation requirements must be checked before ingest.", - ingest_boundary="Do not ingest full Gaia-scale tables without column partitioning, sky tiling, and receipt-backed storage budget.", - ), - packet( - domain="Climate", - dataset="ERA5", - source_urls=["https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels"], - potential_nspace_application="Spatio-temporal grid manifolds over latitude, longitude, vertical/variable axes, and time.", - density_markers=[ - "spatiotemporal_grid", - "reanalysis_variable_cube", - "time_slice_projection", - "region_tile_projection", - "petabyte_scale_archive", - ], - license_boundary="Copernicus/ECMWF terms, attribution, and download rules must be checked before ingest.", - ingest_boundary="Prefer variable/time/region subsets and derived density matrices before any petabyte-scale pull.", - ), - packet( - domain="Semantic", - dataset="LAION-5B", - source_urls=["https://laion.ai/blog/laion-5b/"], - potential_nspace_application="512D-1024D embedding-space density matrices, cluster manifolds, and modality-boundary probes.", - density_markers=[ - "embedding_vector_surface", - "image_text_pair_metadata", - "high_dimensional_semantic_density", - "cluster_eigenvector_probe", - "license_and_safety_filter_gate", - ], - license_boundary="LAION metadata/source URLs and downstream content licenses/safety filters must be treated as source-specific.", - ingest_boundary="Do not mirror raw media blindly; operate on metadata/embedding subsets and preserve safety/filter receipts.", - ), - packet( - domain="Bio", - dataset="AlphaFold", - source_urls=["https://alphafold.ebi.ac.uk/download", "https://ftp.ebi.ac.uk/pub/databases/alphafold"], - potential_nspace_application="3D geometric protein topology, contact-graph density matrices, confidence-weighted residue manifolds.", - density_markers=[ - "protein_coordinate_topology", - "plddt_confidence_surface", - "species_structure_density", - "fragment_boundary_lane", - "cc_by_4_attribution_gate", - ], - license_boundary="AlphaFold DB data is listed as CC-BY-4.0 with required citations and nonclinical disclaimer.", - ingest_boundary="Start with one small proteome archive and parse confidence/topology receipts before scaling.", - ), - packet( - domain="Bio", - dataset="NCBI ASN.1 / GenBank", - source_urls=["https://ftp.ncbi.nlm.nih.gov/ncbi-asn1/", "https://ftp.ncbi.nlm.nih.gov/genbank/"], - potential_nspace_application="Sequence record manifolds, divisional release matrices, daily-update delta lanes, CON scaffold reconstruction graphs.", - density_markers=[ - "asn1_bioseq_set_carrier", - "genbank_flatfile_carrier", - "release_signal_files", - "daily_incremental_update_lane", - "division_code_partition", - "con_scaffold_reassembly_graph", - "wgs_project_tree", - "protein_fasta_translation_surface", - ], - license_boundary="NCBI/GenBank public data has no NCBI restriction on use/distribution, but submitter records, citations, NLM/NCBI terms, and third-party caveats still need preservation.", - ingest_boundary="ASN.1 and GenBank flatfiles are not equivalent record-for-record; never merge them without carrier-specific receipts.", - ), - packet( - domain="Physics", - dataset="The Well", - source_urls=[ - "https://polymathic-ai.org/the_well/datasets_overview/", - "https://polymathic-ai.org/the_well/data_format/", - "https://polymathic-ai.org/the_well/benchmarks/", - ], - potential_nspace_application=( - "Uniform-grid physics-dynamics route atlas over scalar, vector, and tensor fields; " - "use as an external replay and residual benchmark prior for PIST/OMCF admission tests." - ), - density_markers=[ - "hdf5_uniform_grid_carrier", - "constant_time_interval_trajectories", - "scalar_vector_tensor_field_split", - "cartesian_spherical_log_spherical_coordinate_systems", - "fp32_state_variable_arrays", - "physics_rollout_baseline_surface", - "boundary_condition_receipt_surface", - ], - license_boundary=( - "Polymathic AI / The Well dataset terms and per-dataset source terms must be verified " - "before ingest; this registry does not vendor data." - ), - ingest_boundary=( - "Start with metadata and tiny HDF5 slices only. Full corpus is multi-terabyte scale; " - "use dataset/field/time/trajectory subsetting with receipt-backed storage budgets." - ), - ), - packet( - domain="Physics", - dataset="PDEBench", - source_urls=[ - "https://github.com/pdebench/PDEBench", - "https://darus.uni-stuttgart.de/dataset.xhtml?persistentId=doi:10.18419/darus-2986", - "https://arxiv.org/abs/2210.07182", - ], - potential_nspace_application=( - "Canonical PDE-family replay layer for forward/inverse scientific-ML fixtures, " - "baseline comparison, residual growth curves, and solver-family route selection." - ), - density_markers=[ - "canonical_pde_family_surface", - "advection_burgers_diffusion_reaction_lane", - "navier_stokes_darcy_shallow_water_lane", - "forward_inverse_problem_split", - "initial_boundary_condition_sweep", - "ml_baseline_comparison_surface", - ], - license_boundary=( - "PDEBench code, DaRUS datasets, pretrained models, and paper citation requirements " - "must be checked separately before ingest or redistribution." - ), - ingest_boundary=( - "Use small PDE shards and metadata first. Full benchmark pulls require PDE-family, " - "resolution, parameter, and train/test split receipts." - ), - ), - packet( - domain="Physics", - dataset="RealPDEBench", - source_urls=[ - "https://huggingface.co/datasets/AI4Science-WestlakeU/RealPDEBench", - "https://arxiv.org/abs/2601.01829", - "https://realpdebench.github.io/", - ], - potential_nspace_application=( - "Real-measurement residual calibration layer for sim-to-real gaps, modality masking, " - "physical-parameter ranges, and witness drift between numerical and observed trajectories." - ), - density_markers=[ - "paired_real_simulated_trajectory", - "piv_velocity_measurement_surface", - "cfd_les_numerical_surface", - "combustion_chemiluminescence_lane", - "sim_to_real_gap_metric", - "modality_masking_transfer_surface", - "cc_by_nc_gate", - ], - license_boundary=( - "RealPDEBench is listed on Hugging Face as CC-BY-NC-4.0; noncommercial terms, " - "paper citation, and per-scenario source notes must be verified before ingest." - ), - ingest_boundary=( - "Start with index files or one trajectory pair. Full release is hundreds of GB; " - "do not ingest without scenario, modality, split, and storage-budget receipts." - ), - ), - packet( - domain="Mesh Physics", - dataset="MeshGraphNets", - source_urls=[ - "https://github.com/google-deepmind/deepmind-research/tree/master/meshgraphnets", - "https://arxiv.org/abs/2010.03409", - ], - potential_nspace_application=( - "Irregular mesh and goxel-topology substrate for graph route tests, remeshing witnesses, " - "cloth/CFD rollouts, and non-grid residual behavior." - ), - density_markers=[ - "irregular_mesh_graph_carrier", - "tfrecord_train_valid_test_splits", - "cylinder_flow_cfd_domain", - "flag_cloth_domain", - "remeshing_sizing_field_lane", - "rollout_trajectory_pickle_surface", - ], - license_boundary=( - "DeepMind research repository license and dataset-specific availability terms must be " - "checked before copying code or data." - ), - ingest_boundary=( - "Use metadata and flag_minimal-style tiny domains first. Full mesh datasets require " - "domain, split, mesh-field schema, and rollout receipt boundaries." - ), - ), - packet( - domain="Symbolic Regression", - dataset="SRBench / ParFam", - source_urls=[ - "https://cavalab.org/srbench/datasets/", - "https://arxiv.org/html/2310.05537", - "https://github.com/Philipp238/parfam", - ], - potential_nspace_application=( - "Scientific-law reconstruction route prior over ground-truth formulas, black-box regression " - "datasets, rational-function families, and basin-hopping candidate-law searches." - ), - density_markers=[ - "ground_truth_formula_surface", - "feynman_symbolic_regression_law_set", - "strogatz_ode_dynamics_set", - "black_box_regression_negative_control", - "rational_function_parametric_family", - "continuous_global_optimization_route", - "sparsity_regularized_candidate_law", - "formula_reconstruction_receipt_surface", - ], - license_boundary=( - "SRBench, PMLB, Feynman, Strogatz, and ParFam code/data licenses must be verified " - "separately before copying, adapting, or redistributing artifacts." - ), - ingest_boundary=( - "Use as benchmark metadata and tiny replay fixtures first. Ground-truth formulas may seed " - "candidate-law tests; black-box problems remain negative controls unless exact replay and " - "byte-accounted residuals pass." - ), - ), - packet( - domain="Symbolic Math", - dataset="DLMF / Feynman Symbolic Regression", - source_urls=[ - "https://dlmf.nist.gov/", - "https://pmc.ncbi.nlm.nih.gov/articles/PMC7159912/", - "https://space.mit.edu/home/tegmark/aifeynman.html", - ], - potential_nspace_application=( - "Special-function and physics-equation glyph/eigen-codec prior for symbolic law recovery, " - "formula canonicalization, and equation-family compression tests." - ), - density_markers=[ - "special_function_identity_surface", - "dlmf_notation_reference_lane", - "feynman_ground_truth_formula_set", - "sympy_simplification_zero_check", - "physics_equation_symbolic_regression_lane", - "formula_glyph_codec_prior", - ], - license_boundary=( - "DLMF/NIST terms and AI Feynman/FSReD dataset/code terms must be checked before " - "vendoring formulas, tables, code, or generated data." - ), - ingest_boundary=( - "Use equation identifiers, citations, and tiny replay samples first. Treat identities as " - "reference priors until local symbolic replay and source-specific citation receipts exist." - ), - ), - packet( - domain="Formal Math", - dataset="LeanDojo / mathlib", - source_urls=[ - "https://leandojo.org/index.html", - "https://leandojo.readthedocs.io/en/stable/", - "https://github.com/leanprover-community/mathlib4", - ], - potential_nspace_application=( - "Formal proof/tactic corpus for routing equation claims into Lean obligations, theorem " - "dependency graphs, tactic-state replay, and proof-surface negative controls." - ), - density_markers=[ - "lean4_theorem_dependency_graph", - "proof_state_tactic_trace", - "mathlib_premise_selection_surface", - "formal_obligation_routing", - "source_of_truth_proof_gate", - "reprover_retrieval_prior", - ], - license_boundary=( - "LeanDojo, mathlib4, extracted benchmark datasets, and generated traces have separate " - "licenses/citations that must be verified before vendoring or redistribution." - ), - ingest_boundary=( - "Prefer local mathlib references and tiny traced theorem samples. Do not promote any " - "equation route to proof status without actual Lean replay in the local toolchain." - ), - ), - packet( - domain="Math Reasoning", - dataset="NuminaMath", - source_urls=[ - "https://huggingface.co/collections/AI-MO/numinamath", - "https://github.com/project-numina/aimo-progress-prize", - ], - potential_nspace_application=( - "Broad mathematical reasoning pretraining prior for candidate generation, problem-style " - "routing, and tool-integrated reasoning patterns before deterministic verification." - ), - density_markers=[ - "competition_math_problem_solution_pair", - "chain_of_thought_reasoning_surface", - "tool_integrated_reasoning_lane", - "olympiad_metadata_prior", - "reasoning_pretraining_not_formal_proof", - ], - license_boundary=( - "Hugging Face dataset/model cards and Project Numina terms must be checked before " - "download, training, redistribution, or derived dataset publication." - ), - ingest_boundary=( - "Use only as proposal-generation and curriculum metadata unless answers are independently " - "verified. This is not a formal proof corpus by itself." - ), - ), - ] - - PACKETS.write_text("\n".join(stable_json(p) for p in packets) + "\n", encoding="utf-8") - with TABLE_CSV.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter(handle, fieldnames=["Domain", "Dataset", "Potential n-Space Application", "Source URLs", "Decision"]) - writer.writeheader() - for p in packets: - writer.writerow( - { - "Domain": p["domain"], - "Dataset": p["dataset"], - "Potential n-Space Application": p["potential_nspace_application"], - "Source URLs": " | ".join(p["source_urls"]), - "Decision": p["decision"], - } - ) - - receipt = { - "schema": "nspace_bulk_dataset_route_receipt_v1", - "packet_count": len(packets), - "packets": str(PACKETS.relative_to(REPO)), - "sheets_ready_csv": str(TABLE_CSV.relative_to(REPO)), - "domains": sorted({p["domain"] for p in packets}), - "density_marker_total": sum(len(p["density_markers"]) for p in packets), - "ncbi_boundary": ( - "NCBI ASN.1 files contain compressed binary Bioseq-set values. GenBank flatfiles " - "are independent flatfile dumps. Similar filenames do not imply identical records." - ), - "claim_boundary": ( - "This is a route registry for n-space tooling. It does not download bulk archives, " - "prove density matrices, or assert dataset-specific licenses beyond cited source notes." - ), - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/nspace_llm_pipeline_tuning.py b/4-Infrastructure/shim/nspace_llm_pipeline_tuning.py deleted file mode 100644 index 38274209..00000000 --- a/4-Infrastructure/shim/nspace_llm_pipeline_tuning.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -"""N-space tuning manifest for the local physics/math/compression LLM. - -The advantage of this stack is not just more examples. It is explicit -coordinate tooling: manifold deltas, oriented-volume adapters, fixed-width -hardware cells, n-dimensional behavioral vectors, and eigen-basis priors. -This script turns those into compact SFT curriculum records. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -NSPACE_AXES = [ - { - "axis": "ns_md_hardware_delta", - "dimension": "addressed manifold cell", - "source": "0-Core-Formalism/otom/hardware/verilog/core/ns_md_decoder.v", - "primitive": "[32-bit Addr][8-bit Control][optional Count][64-bit Witness]", - "compression_use": "delta-coded manifold updates with nibble switch payloads", - "receipt_rule": "addr/control/count/witness must survive transport", - }, - { - "axis": "oriented_volume_adapter", - "dimension": "n-dimensional basis cell", - "source": "0-Core-Formalism/otom/specs/Cramers-Rule-Oriented-Volume-Adapter.md", - "primitive": "x_k = det(A_k) / det(A)", - "compression_use": "coordinate extraction by shared reference-face cancellation", - "receipt_rule": "det(A) nonzero and replacement-column index recorded", - }, - { - "axis": "behavioral_manifold_31", - "dimension": "31 coordinates", - "source": "0-Core-Formalism/otom/tools/lean/Semantics/Semantics/MarketFilter.lean", - "primitive": "identity/conservation/transformation/scaling/dynamics coordinate blocks", - "compression_use": "compare behavior by weighted fixed-point distance, not labels", - "receipt_rule": "Q16.16 coordinates, weights, and claim state retained", - }, - { - "axis": "cross_domain_eigen_basis", - "dimension": "term-domain similarity space", - "source": "4-Infrastructure/shim/cross_domain_registry_eigenvectors.json", - "primitive": "leading eigenvector over registry-derived term/domain matrix", - "compression_use": "shared coordinates such as bond/matrix/geometry/provenance or kmer/long_context", - "receipt_rule": "eigenvector is ranking prior only, never domain truth", - }, - { - "axis": "bitpack_hardware_cell", - "dimension": "fixed bit width", - "source": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Lean BitPack Hardware Encoding.tid", - "primitive": "value -> BitVec n -> UART/PBACS/Tang receipt", - "compression_use": "turn symbolic/logogram tokens into witnessable fixed-width cells", - "receipt_rule": "bit width and roundtrip representation must be explicit", - }, -] - - -PIPELINE_STAGES = [ - { - "stage": "retrieve", - "action": "load local registry/wiki/eigen/prover receipts", - "failure_mode": "unverified memory or stale web claims", - }, - { - "stage": "embed_nspace", - "action": "map candidate into an explicit coordinate axis", - "failure_mode": "free prose without coordinates", - }, - { - "stage": "compress", - "action": "choose shortest lawful payload: delta, kmer, bond matrix, bitpack cell, or template token", - "failure_mode": "large chatty prompt instead of compact surface cell", - }, - { - "stage": "route", - "action": "select Lean/source/Tang/Ollama/metaprobe channel by claim boundary", - "failure_mode": "model confidence replacing receipts", - }, - { - "stage": "witness", - "action": "emit JSON receipt and optional hardware receipt", - "failure_mode": "summary without durable artifact", - }, -] - - -def existing_receipt_summary() -> dict[str, Any]: - paths = [ - Path("4-Infrastructure/shim/metaprobe_physics_math_llm_direct_receipt.json"), - Path("4-Infrastructure/shim/cross_domain_registry_eigenvectors.json"), - Path("4-Infrastructure/shim/molecular_registry_eigenvectors.json"), - Path("4-Infrastructure/shim/genomic_registry_eigenvectors.json"), - ] - out = {} - for path in paths: - if not path.exists(): - continue - data = json.loads(path.read_text(encoding="utf-8")) - out[str(path)] = { - "schema": data.get("schema"), - "lawful": data.get("lawful", data.get("overall_lawful")), - "domain_count": data.get("domain_count"), - "top_terms": [item.get("term") for item in data.get("top_terms", [])[:8]], - "top_domains": [item.get("domain") for item in data.get("weighted_domains", [])[:5]], - } - return out - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an n-space compression router. Return compact JSON with evidence boundaries." - records = [] - for axis in receipt["nspace_axes"]: - prompt = { - "task": "route_with_nspace_axis", - "axis": axis["axis"], - "dimension": axis["dimension"], - "primitive": axis["primitive"], - "instruction": "Use this axis to compress and route a local research claim.", - } - answer = { - "selected": True, - "use_as": axis["compression_use"], - "claim_boundary": "coordinate-routing-prior", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - } - records.append(chat_record(system, prompt, answer)) - - prompt = { - "task": "apply_nspace_pipeline", - "pipeline": receipt["pipeline_stages"], - "instruction": "Choose the pipeline behavior for tuning the local LLM.", - } - answer = { - "selected": True, - "use_as": "nspace_llm_pipeline_policy", - "claim_boundary": "pipeline-guidance-only", - "decision": "Prefer coordinate-bearing examples over prose-only examples; every answer should choose an axis, payload, route, and receipt.", - "surface_payload_hint": "NSPACE-ROUTE", - } - records.append(chat_record(system, prompt, answer)) - return records - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/nspace_llm_pipeline_tuning_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/nspace_llm_pipeline_tuning_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "nspace_llm_pipeline_tuning_receipt_v1", - "claim_boundary": "N-space axes tune routing/compression behavior; they do not prove domain claims.", - "nspace_axes": NSPACE_AXES, - "pipeline_stages": PIPELINE_STAGES, - "existing_receipts": existing_receipt_summary(), - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/nuvmap_chain_protocol_plan.py b/4-Infrastructure/shim/nuvmap_chain_protocol_plan.py deleted file mode 100644 index e4dcdb2e..00000000 --- a/4-Infrastructure/shim/nuvmap_chain_protocol_plan.py +++ /dev/null @@ -1,180 +0,0 @@ -#!/usr/bin/env python3 -"""Create a protocol receipt for a dedicated NUVMAP chain. - -NUVMAP is treated here as a receipt chain for metaprobe/waveprobe outputs. This -is not a token, investment, mainnet, or compression-result claim. It specifies -what a block would carry so scanner state can be stored and replayed without -using third-party chains as an accidental storage layer. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_OUT = REPO / "shared-data/data/blockchain_corpus/nuvmap_chain_protocol_plan_receipt.json" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def build_receipt() -> dict[str, Any]: - payload: dict[str, Any] = { - "schema": "nuvmap_chain_protocol_plan_v0", - "created_utc": now_iso(), - "claim_boundary": ( - "Protocol design receipt only. This does not create a blockchain, token, market, " - "consensus network, compression result, or Hutter Prize claim. It defines a local/devnet " - "receipt substrate for metaprobe and waveprobe state." - ), - "purpose": { - "name": "NUVMAP", - "expanded_role": "Numerical Universe Vector Map receipt chain", - "core_use": "Store replayable probe-state commitments so scanning can route itself over time.", - "anti_goal": "Do not use public commodity ledgers as arbitrary bulk storage when a purpose-built receipt chain is enough.", - }, - "chain_model": { - "deployment_stage": "LOCAL_DEVNET_ONLY", - "economic_model": "NO_TOKEN_NO_MARKET", - "consensus_candidate": "single-writer_receipt_log_then_multisig_validator_set", - "finality_model": "append-only_merkle_root_with_periodic_external_anchor_optional", - "data_availability": "on-chain summaries plus content-addressed off-chain sidecars", - }, - "block_shape": { - "header": { - "parent_hash": "sha256(previous_block)", - "height": "u64", - "created_utc": "iso8601", - "scanner_policy_hash": "sha256(l3_policy)", - "state_root": "merkle_root(receipt_state)", - "sidecar_manifest_root": "merkle_root(sidecar_hashes)", - }, - "body": { - "waveprobe_receipts": "bounded list of wave/eigen/density diagnostic summaries", - "metaprobe_receipts": "bounded list of route/context/curriculum summaries", - "storage_channel_receipts": "detected on-chain storage surfaces from source chains", - "scan_actions": "next bounded scan plan emitted by L3 scheduler", - "negative_controls": "required controls before promotion of any route", - }, - "forbidden_body_fields": [ - "private keys", - "wallet credentials", - "bulk copyrighted payloads", - "payloads intended to hide from moderation or provenance", - "financial promotion metadata", - ], - }, - "probe_mapping": { - "metadata_blitter": { - "input": "fixed-size metadata words from chain/block/window records", - "output": "sortable route keys and packed metric words", - "storage": "blitter shader hash, parameter receipt, key/metric buffer hashes", - }, - "waveprobe": { - "input": "byte windows, block fields, density neighborhoods, eigenvalue spectra", - "output": "local signal vector plus residual/curvature summary", - "storage": "hash, vector summary, source window receipt, not raw bulk by default", - }, - "metaprobe": { - "input": "domain priors, route families, previous scan receipts", - "output": "policy/context update and HOLD/ADMIT/QUARANTINE decision hints", - "storage": "decision receipt, route vector, source receipt backlinks", - }, - "l3_scheduler": { - "input": "current receipt frontier", - "output": "next scan frontier", - "storage": "scanner policy hash and action list", - }, - }, - "radix_bin_model": { - "metaphor": "hyper_soliton_search", - "claim_boundary": "Radix bins are route basins, not semantic labels.", - "fields": { - "hash8": "phase seed / local identity packet", - "density7": "amplitude / byte pressure", - "delta7": "shock or torsion gradient", - "zero6": "void / low-information throat", - "flags4": "boundary condition", - }, - "stability_rule": "Promote only bins that persist across salts, windows, and negative controls.", - }, - "minimal_transaction_types": [ - { - "type": "BLITTER_BIN_RECEIPT", - "required_fields": ["shader_hash", "params_hash", "source_hash", "key_buffer_hash", "metric_buffer_hash", "decision"], - }, - { - "type": "WAVE_RECEIPT", - "required_fields": ["source_hash", "window_descriptor", "feature_vector_hash", "decision"], - }, - { - "type": "META_RECEIPT", - "required_fields": ["prior_id", "route_family", "evidence_hash", "decision"], - }, - { - "type": "STORAGE_SURFACE_RECEIPT", - "required_fields": ["source_chain", "channel", "carrier_descriptor", "payload_hash_or_null", "decision"], - }, - { - "type": "SCAN_POLICY_UPDATE", - "required_fields": ["previous_policy_hash", "new_policy_hash", "action_merkle_root", "decision"], - }, - { - "type": "NEGATIVE_CONTROL", - "required_fields": ["control_kind", "source_hash", "result_hash", "decision"], - }, - ], - "decision_law": { - "ADMIT": "All required hashes replay, source windows exist, and negative controls do not collapse the signal.", - "HOLD": "Evidence is incomplete, cost is unknown, payload surface is unverified, or object-level provenance is missing.", - "QUARANTINE": "Hash mismatch, unsafe payload class, missing provenance, or claim boundary violation.", - }, - "next_implementation_steps": [ - "Create a JSON fixture block with one WAVE_RECEIPT, one META_RECEIPT, and one SCAN_POLICY_UPDATE.", - "Add a verifier that recomputes the block hash and merkle roots.", - "Feed the current blockchain_l3_self_scan_scheduler receipt into the first fixture block.", - "Run negative controls before any compression-route promotion.", - "Only after local fixture replay works, consider a small append-only local devnet.", - ], - "decision": "ADMIT_NUVMAP_PROTOCOL_PLAN_HOLD_CHAIN_IMPLEMENTATION", - } - payload["receipt_hash"] = sha256_text(stable_json({k: v for k, v in payload.items() if k != "receipt_hash"})) - return payload - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--out", type=Path, default=DEFAULT_OUT) - args = parser.parse_args() - - receipt = build_receipt() - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - print(json.dumps({"decision": receipt["decision"], "out": rel(args.out), "receipt_hash": receipt["receipt_hash"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/nuvmap_gpu_burst_check_plan.py b/4-Infrastructure/shim/nuvmap_gpu_burst_check_plan.py deleted file mode 100644 index c3ef92e3..00000000 --- a/4-Infrastructure/shim/nuvmap_gpu_burst_check_plan.py +++ /dev/null @@ -1,154 +0,0 @@ -#!/usr/bin/env python3 -"""Emit a bounded GPU-burst check plan for NUVMAP probe validation. - -The plan is intended for a short rented GPU session. It avoids wallets, mining, -public-chain writes, and long-lived services. The output is a receipt/checklist -for replaying local fixtures, checking CPU/GPU parity, and collecting bounded -waveprobe/metaprobe evidence. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_OUT = REPO / "shared-data/data/blockchain_corpus/nuvmap_gpu_burst_check_plan_receipt.json" - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds").replace("+00:00", "Z") - - -def stable_json(value: Any) -> str: - return json.dumps(value, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_text(value: str) -> str: - return hashlib.sha256(value.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def build_receipt(max_hours: float) -> dict[str, Any]: - payload: dict[str, Any] = { - "schema": "nuvmap_gpu_burst_check_plan_v0", - "created_utc": now_iso(), - "claim_boundary": ( - "GPU rental execution plan only. This is not crypto mining, not a blockchain mainnet, " - "not a token launch, not a compression result, and not a hardware acceleration claim until " - "the listed receipts are produced." - ), - "timebox_hours": max_hours, - "forbidden_inputs": [ - "wallet private keys", - "exchange credentials", - "mainnet transaction signing material", - "unbounded copyrighted payload mirrors", - ], - "required_outputs": [ - "environment_receipt.json", - "cpu_gpu_parity_receipt.json", - "waveprobe_batch_receipt.json", - "metaprobe_batch_receipt.json", - "negative_controls_receipt.json", - "cost_and_shutdown_receipt.json", - ], - "burst_phases": [ - { - "phase": "P0_ENVIRONMENT_SNAPSHOT", - "target_minutes": 10, - "checks": [ - "nvidia-smi --query-gpu=name,memory.total,driver_version --format=csv", - "python -c 'import torch; print(torch.cuda.is_available())' if torch is installed", - "git rev-parse HEAD and git status --short for provenance", - ], - "decision": "ADMIT_ENVIRONMENT_IF_GPU_VISIBLE", - }, - { - "phase": "P1_FIXTURE_REPLAY", - "target_minutes": 25, - "checks": [ - "Verify NUVMAP fixture block hash and merkle roots.", - "Replay blockchain_l3_self_scan_scheduler_receipt as first policy input.", - "Reject any fixture with missing source hashes.", - ], - "decision": "ADMIT_FIXTURE_REPLAY_OR_HOLD", - }, - { - "phase": "P2_CPU_GPU_PARITY", - "target_minutes": 35, - "checks": [ - "Run the same wave/vector kernel on CPU and GPU.", - "Compare Q16.16 or declared float tolerance residuals.", - "Emit max_abs_error, mean_abs_error, and mismatch examples.", - ], - "decision": "ADMIT_GPU_WITNESS_ONLY_IF_PARITY_BOUNDED", - }, - { - "phase": "P3_WAVEPROBE_BATCH", - "target_minutes": 45, - "checks": [ - "Run bounded byte-window/eigen/density probes over sampled blockchain shards.", - "Store only feature hashes and summaries unless payload admission exists.", - "Emit route candidates as HOLD until negative controls pass.", - ], - "decision": "ADMIT_WAVEPROBE_BATCH_HOLD_PROMOTION", - }, - { - "phase": "P4_METAPROBE_AND_CONTROLS", - "target_minutes": 45, - "checks": [ - "Run metaprobe route selection over waveprobe outputs.", - "Run shuffled window, shuffled chain label, and random-byte controls.", - "Require controls before any Hutter/logogram feedback promotion.", - ], - "decision": "ADMIT_METAPROBE_IF_CONTROLS_BOUND_SIGNAL", - }, - { - "phase": "P5_COST_AND_SHUTDOWN", - "target_minutes": 20, - "checks": [ - "Write final receipt bundle.", - "Sync receipts and small summaries to Drive.", - "Record instance type, wall time, estimated cost, and shutdown confirmation.", - ], - "decision": "ADMIT_BURST_COMPLETE_ONLY_WITH_SHUTDOWN_RECEIPT", - }, - ], - "promotion_gates": { - "hardware_witness": "GPU visible plus CPU/GPU parity receipt, not just code execution.", - "compression_feedback": "HOLD until byte-exact baseline matrix exists.", - "nuvmap_chain": "HOLD until local fixture block replay and verifier exist.", - }, - "decision": "ADMIT_GPU_BURST_PLAN_HOLD_EXECUTION", - } - payload["receipt_hash"] = sha256_text(stable_json({k: v for k, v in payload.items() if k != "receipt_hash"})) - return payload - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--hours", type=float, default=3.0) - parser.add_argument("--out", type=Path, default=DEFAULT_OUT) - args = parser.parse_args() - - receipt = build_receipt(args.hours) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n") - print(json.dumps({"decision": receipt["decision"], "out": rel(args.out), "receipt_hash": receipt["receipt_hash"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/observer_chart_projection_guardrail_probe.py b/4-Infrastructure/shim/observer_chart_projection_guardrail_probe.py deleted file mode 100644 index a7dc1426..00000000 --- a/4-Infrastructure/shim/observer_chart_projection_guardrail_probe.py +++ /dev/null @@ -1,331 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed guardrail for observer-bound chart projections. - -Some joke sources are dangerous because they encode unsafe procedural advice. -Other jokes are useful because they are visibly local observer charts. This -probe captures the safe case: the periodic table "as seen by an organic -chemist" is not global chemistry truth, but it is a lawful relevance projection -for a particular observer and domain. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" -REGISTRY = OUT_DIR / "observer_chart_projection_guardrail_registry.json" -RECEIPT = OUT_DIR / "observer_chart_projection_guardrail_receipt.json" -SUMMARY = OUT_DIR / "observer_chart_projection_guardrail.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Observer Chart Projection Guardrail.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "joke_source_literalization_guardrail" / "joke_source_literalization_guardrail_receipt.json", - REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" / "kerr_like_load_witness_geometry_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "6-Documentation" / "docs" / "specs" / "OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", - REPO / "6-Documentation" / "docs" / "specs" / "GCCL_ENCODING_CONTRACT.md", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def projection( - *, - projection_id: str, - observer: str, - source_object: str, - projected_chart: str, - domain_scope_declared: bool, - global_truth_claim: bool, - actionable_unsafe_payload: bool, - invariant_preserved: bool, - residual_declared: bool, -) -> dict[str, Any]: - lawful_projection = ( - domain_scope_declared - and not global_truth_claim - and not actionable_unsafe_payload - and invariant_preserved - and residual_declared - ) - if actionable_unsafe_payload: - decision = "QUARANTINE_UNSAFE_LITERAL_PAYLOAD" - elif global_truth_claim: - decision = "HOLD_LOCAL_CHART_GLOBALIZED" - elif lawful_projection: - decision = "ADMIT_OBSERVER_CHART" - else: - decision = "HOLD_SCOPE_OR_RESIDUAL_MISSING" - item = { - "projection_id": projection_id, - "observer": observer, - "source_object": source_object, - "projected_chart": projected_chart, - "domain_scope_declared": domain_scope_declared, - "global_truth_claim": global_truth_claim, - "actionable_unsafe_payload": actionable_unsafe_payload, - "invariant_preserved": invariant_preserved, - "residual_declared": residual_declared, - "lawful_projection": lawful_projection, - "decision": decision, - } - item["projection_hash"] = hash_obj({k: v for k, v in item.items() if k != "projection_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - projections = [ - projection( - projection_id="organic_chemist_periodic_table_meme", - observer="organic chemist", - source_object="periodic table full chemical manifold", - projected_chart="organic relevance manifold centered on carbon and common functional-group elements", - domain_scope_declared=True, - global_truth_claim=False, - actionable_unsafe_payload=False, - invariant_preserved=True, - residual_declared=True, - ), - projection( - projection_id="mechanic_load_path_view", - observer="mechanical safety witness", - source_object="physical part full material/process manifold", - projected_chart="load-path admissibility chart with torsion-clock and residual risk", - domain_scope_declared=True, - global_truth_claim=False, - actionable_unsafe_payload=False, - invariant_preserved=True, - residual_declared=True, - ), - projection( - projection_id="hutter_codec_torsion_view", - observer="compression researcher", - source_object="corpus and codec development history", - projected_chart="codec torsion chart over replay, provenance, packet, dictionary, baseline, and receipt debt", - domain_scope_declared=True, - global_truth_claim=False, - actionable_unsafe_payload=False, - invariant_preserved=True, - residual_declared=True, - ), - projection( - projection_id="local_chart_mistaken_for_global_truth", - observer="over-promoted expert chart", - source_object="whole domain", - projected_chart="observer-biased local relevance map", - domain_scope_declared=False, - global_truth_claim=True, - actionable_unsafe_payload=False, - invariant_preserved=False, - residual_declared=False, - ), - ] - return { - "schema": "observer_chart_projection_guardrail_registry_v1", - "source_prompt": { - "id": "user_supplied_organic_chemist_periodic_table_meme", - "role": "source_prompt", - "status": "user_supplied_image_prompt", - "description": ( - "A joke periodic-table projection through organic chemistry salience. " - "It is safe because it is visibly a local observer chart, not general " - "chemistry truth or operational hazardous advice." - ), - }, - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Observer chart projection guardrail only. A local chart may be admitted " - "when observer, scope, residual, and preserved invariant are declared. " - "A local chart is HOLD if promoted to global truth and QUARANTINE if it " - "expands into unsafe instructions." - ), - "canonical_statement": ( - "Expertise is a lawful distortion. Danger begins when a local chart is " - "mistaken for the whole manifold." - ), - "projection_equation": "pi_observer(Omega) = local_chart + declared_residual + preserved_invariant", - "admissibility_equation": ( - "A_chart=1[observer_declared] * 1[domain_scope_declared] * " - "1[not global_truth_claim] * 1[not unsafe_payload] * " - "1[invariant_preserved] * 1[residual_declared]" - ), - "encoding_rule": { - "observer_chart_channel": "ADMIT scoped chart and observer role", - "global_truth_channel": "HOLD if local projection is promoted as universal truth", - "unsafe_payload_channel": "QUARANTINE if chart expands into unsafe procedure", - "hutter_channel": "encode chart as route prior only; do not let observer bias rewrite canonical byte gates", - }, - "projections": projections, - "aggregates": { - "projection_count": len(projections), - "admit_observer_chart_count": sum(1 for item in projections if item["decision"] == "ADMIT_OBSERVER_CHART"), - "hold_count": sum(1 for item in projections if item["decision"].startswith("HOLD")), - "quarantine_count": sum(1 for item in projections if item["decision"].startswith("QUARANTINE")), - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "observer_chart_projection_guardrail_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "aggregates": registry["aggregates"], - "decision": "ADMIT_OBSERVER_CHART_PROJECTION_GUARDRAIL", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Observer Chart Projection Guardrail", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Equations", - "", - f"- Projection: `{registry['projection_equation']}`", - f"- Admit: `{registry['admissibility_equation']}`", - "", - "## Encoding Rules", - "", - ] - for key, value in registry["encoding_rule"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Projections", - "", - "| Projection | Observer | Decision |", - "|---|---|---|", - ] - ) - for item in registry["projections"]: - lines.append(f"| `{item['projection_id']}` | {item['observer']} | `{item['decision']}` |") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Encoding Guardrail ObserverChart Receipt -title: Observer Chart Projection Guardrail -type: text/vnd.tiddlywiki - -! Observer Chart Projection Guardrail - -Durable runner: - -``` -4-Infrastructure/shim/observer_chart_projection_guardrail_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -Expertise is a lawful distortion. Danger begins when a local chart is mistaken for the whole manifold. - -``` -observer chart channel -> ADMIT if scoped -global truth channel -> HOLD if a local projection is universalized -unsafe payload channel -> QUARANTINE if it expands into unsafe procedure -``` - -!! Links - -* [[Joke Source Literalization Guardrail]] -* [[Kerr-Like Load Witness Geometry]] -* [[Hutter Torsion Clock Adaptation]] -* [[Omindirection Logogram Contract]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/ollama_physics_math_smoke.py b/4-Infrastructure/shim/ollama_physics_math_smoke.py deleted file mode 100644 index a0b55b57..00000000 --- a/4-Infrastructure/shim/ollama_physics_math_smoke.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python3 -"""Smoke test the local Gemma physics/math router model via Ollama.""" - -from __future__ import annotations - -import argparse -import json -import urllib.request - - -def generate(model: str, prompt: str, host: str, timeout: int) -> str: - payload = { - "model": model, - "prompt": prompt, - "stream": False, - "format": "json", - "options": {"temperature": 0.1, "num_ctx": 4096, "num_predict": 512}, - } - req = urllib.request.Request( - f"{host}/api/generate", - data=json.dumps(payload).encode(), - headers={"Content-Type": "application/json"}, - method="POST", - ) - with urllib.request.urlopen(req, timeout=timeout) as resp: - data = json.loads(resp.read().decode()) - return data.get("response", "") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--model", default="gemma-physics-math") - parser.add_argument("--host", default="http://127.0.0.1:11434") - parser.add_argument("--timeout", type=int, default=180) - args = parser.parse_args() - - prompt = json.dumps( - { - "task": "rank_candidate_template", - "candidate": { - "model_name": "PBACS_1bit_Transport", - "equation": "b_t = 1[v_t + e_{t-1} > theta_t]; e_t = v_t + e_{t-1} - b_t", - "evidence_tier": "spec_admissible", - "domain_type": "LAYER_K_SIGNAL", - "bind_class": "control_bind", - }, - "instruction": ( - "Return compact JSON with keys selected, model_role, evidence_tier, " - "claim_boundary, use_as, surface_payload_hint, reason. Do not claim " - "proof; decide whether it is useful as a local routing prior." - ), - }, - ensure_ascii=False, - ) - raw = generate(args.model, prompt, args.host, args.timeout) - receipt = { - "schema": "ollama_physics_math_smoke_v1", - "model": args.model, - "raw_response": raw, - } - try: - receipt["parsed_response"] = json.loads(raw) - receipt["json_parse_ok"] = True - except Exception as exc: - receipt["parsed_response"] = None - receipt["json_parse_ok"] = False - receipt["parse_error"] = str(exc) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/omindirection_logogram_atoms.jsonl b/4-Infrastructure/shim/omindirection_logogram_atoms.jsonl deleted file mode 100644 index 92c2ecc9..00000000 --- a/4-Infrastructure/shim/omindirection_logogram_atoms.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"expression":{"language":null,"temporal":0,"tone":"witness","torsion":0},"identity":{"canonical_payload":"x","payload_hash":"sha256:2d711642b726b04401627ca9fbac32f5c8530fb1903cc4db02258717921a4881","semantic_key":"math_logogram.literal_atom","symbol_id":"LOGO.LITERAL_ATOM"},"orientation":{"chirality":"ambidextrous","direction":"forward","phase":90},"placement":{"captured_by":null,"coord":{"x":1,"y":0},"kind":"row","liberties":0,"territory_id":"logogram-row"},"receipt":{"checks":{"chirality_phase_compatible":true,"explicit_direction":true,"payload_hash_match":true,"phase_valid":true,"placement_admissible":true,"receipt_complete":true,"required_fields":true,"residual_declared":true,"source_hash_present":true,"substitution_round_trip":true,"substitution_sidecar_round_trip":true},"decision":"ACCEPT","receipt_hash":"sha256:ca6e8fc85e90feb8ad8e4a94a16eab9ef50d40105ad18a59cdadcad217fd8d8d","source_hash":"sha256:2d711642b726b04401627ca9fbac32f5c8530fb1903cc4db02258717921a4881","substitution_audit_hash":"sha256:27532002368f01d460494a821f7e09a03cdf9b3f0ba2e46eeece62fd1042f38b"},"rendering":{"glyph":"78","render_hint":"bounded_glyph_payload_16"},"residual":{"residual_sidecar":null,"rounding_rule":null},"schema":"omindirection_logogram_atom_v1","source":{"compression":{"canonical_bytes":1,"compression_ratio_raw_to_payload":1.0,"compression_ratio_raw_to_payload_plus_packed_sidecar":1.0,"compression_ratio_raw_to_payload_plus_sidecar":1.0,"payload_over_canonical":1.0,"payload_over_raw":1.0,"payload_plus_packed_sidecar_bytes":1,"payload_plus_sidecar_bytes":1,"raw_bytes":1,"sidecar_bytes_json_compact":0,"sidecar_bytes_packed_estimate":0,"surface_payload_bytes":1,"zlib_raw_bytes":9},"residual_reasons":[],"sample_id":"literal_atom","semantic_regime":"beautiful_topological_folding","substitution_counts":{"single_char_literal":1}}} -{"expression":{"language":null,"temporal":0,"tone":"residual","torsion":3},"identity":{"canonical_payload":"\\frac { x } { y }","payload_hash":"sha256:3c48a546f0fbf712f32544469390fe13a1ac920631f4196299044b2e955b23c3","semantic_key":"math_logogram.known_command_short","symbol_id":"LOGO.KNOWN_COMMAND_SHORT"},"orientation":{"chirality":"ambidextrous","direction":"forward","phase":90},"placement":{"captured_by":null,"coord":{"x":7,"y":1},"kind":"row","liberties":0,"territory_id":"logogram-hold"},"receipt":{"checks":{"chirality_phase_compatible":true,"explicit_direction":true,"payload_hash_match":true,"phase_valid":true,"placement_admissible":true,"receipt_complete":true,"required_fields":true,"residual_declared":true,"source_hash_present":true,"substitution_round_trip":false,"substitution_sidecar_round_trip":true},"decision":"HOLD","receipt_hash":"sha256:ea56bb9b6737d5a30b96b7eed6373ad03a32e5145b8a0c69b116e2dad41142bb","source_hash":"sha256:4487e1bbde0928a538156b39eda051eb50cbdae93087419e866ec469123c05eb","substitution_audit_hash":"sha256:218e9ee3a5873f08556f8709c6cf666861fbf6f776feb86daecd2e2bc67a6b8a"},"rendering":{"glyph":"21307831307931","render_hint":"bounded_glyph_payload_16"},"residual":{"residual_sidecar":"sidecar:18887eb5db2603dd812fc5e5","rounding_rule":"math_logogram_sidecar_v1"},"schema":"omindirection_logogram_atom_v1","source":{"compression":{"canonical_bytes":17,"compression_ratio_raw_to_payload":1.5714285714285714,"compression_ratio_raw_to_payload_plus_packed_sidecar":0.4074074074074074,"compression_ratio_raw_to_payload_plus_sidecar":0.013095238095238096,"payload_over_canonical":0.4117647058823529,"payload_over_raw":0.6363636363636364,"payload_plus_packed_sidecar_bytes":27,"payload_plus_sidecar_bytes":840,"raw_bytes":11,"sidecar_bytes_json_compact":833,"sidecar_bytes_packed_estimate":20,"surface_payload_bytes":7,"zlib_raw_bytes":19},"residual_reasons":["ambiguous_glyph:21:\\frac","ambiguous_glyph:30:{","ambiguous_glyph:31:}"],"sample_id":"known_command_short","semantic_regime":"beautiful_topological_folding","substitution_counts":{"known_command":1,"known_symbol":4,"single_char_literal":2}}} -{"expression":{"language":null,"temporal":0,"tone":"residual","torsion":2},"identity":{"canonical_payload":"alphaBeta + z","payload_hash":"sha256:322d9b08c0705b32859b07b52bc2018932a6cd14a96b3dfeedebf4af5c60f217","semantic_key":"math_logogram.unknown_multichar_identifier","symbol_id":"LOGO.UNKNOWN_MULTICHAR_IDENTIFIER"},"orientation":{"chirality":"ambidextrous","direction":"forward","phase":90},"placement":{"captured_by":null,"coord":{"x":3,"y":1},"kind":"row","liberties":0,"territory_id":"logogram-hold"},"receipt":{"checks":{"chirality_phase_compatible":true,"explicit_direction":true,"payload_hash_match":true,"phase_valid":true,"placement_admissible":true,"receipt_complete":true,"required_fields":true,"residual_declared":true,"source_hash_present":true,"substitution_round_trip":false,"substitution_sidecar_round_trip":true},"decision":"HOLD","receipt_hash":"sha256:ebb6819aae8a9c3720caae79a58f57e6c17692f1e8ef79ae448de7d1b9602207","source_hash":"sha256:322d9b08c0705b32859b07b52bc2018932a6cd14a96b3dfeedebf4af5c60f217","substitution_audit_hash":"sha256:4a316d68feb7470e06da5e1eae8f00dd51399373041bcabeaa48e322d4e7aae9"},"rendering":{"glyph":"86357a","render_hint":"bounded_glyph_payload_16"},"residual":{"residual_sidecar":"sidecar:baeecfc477a0547a04186798","rounding_rule":"math_logogram_sidecar_v1"},"schema":"omindirection_logogram_atom_v1","source":{"compression":{"canonical_bytes":13,"compression_ratio_raw_to_payload":4.333333333333333,"compression_ratio_raw_to_payload_plus_packed_sidecar":0.6842105263157895,"compression_ratio_raw_to_payload_plus_sidecar":0.027083333333333334,"payload_over_canonical":0.23076923076923078,"payload_over_raw":0.23076923076923078,"payload_plus_packed_sidecar_bytes":19,"payload_plus_sidecar_bytes":480,"raw_bytes":13,"sidecar_bytes_json_compact":477,"sidecar_bytes_packed_estimate":16,"surface_payload_bytes":3,"zlib_raw_bytes":21},"residual_reasons":["ambiguous_glyph:35:+","hashed_multichar_token:alphaBeta"],"sample_id":"unknown_multichar_identifier","semantic_regime":"beautiful_topological_folding","substitution_counts":{"hashed_multichar_residual":1,"known_symbol":1,"single_char_literal":1}}} -{"expression":{"language":null,"temporal":0,"tone":"residual","torsion":11},"identity":{"canonical_payload":"\\partial _ t u + u \\partial _ x u - \\nu \\partial _ { xx } u = 0","payload_hash":"sha256:11ad0bf4913ba45c3c7a9b80911ab1d606b4f58d739a0390b694210200a6a699","semantic_key":"math_logogram.long_truncation","symbol_id":"LOGO.LONG_TRUNCATION"},"orientation":{"chirality":"ambidextrous","direction":"forward","phase":90},"placement":{"captured_by":null,"coord":{"x":20,"y":1},"kind":"row","liberties":0,"territory_id":"logogram-hold"},"receipt":{"checks":{"chirality_phase_compatible":true,"explicit_direction":true,"payload_hash_match":true,"phase_valid":true,"placement_admissible":true,"receipt_complete":true,"required_fields":true,"residual_declared":true,"source_hash_present":true,"substitution_round_trip":false,"substitution_sidecar_round_trip":true},"decision":"HOLD","receipt_hash":"sha256:c41820065d42e32008be8f13e4b7fd55def426e1589186c4d4a089359ec59c6e","source_hash":"sha256:64ceeee4f0f0573b7713b7b766691fbf5428bd159d1c29a00a8ff842272fb460","substitution_audit_hash":"sha256:e1cde2157af7fd081267875941a7db4a81fc153dde1d5b5a211fa6a78eb167a2"},"rendering":{"glyph":"2532747535752532787536e125323082","render_hint":"bounded_glyph_payload_16"},"residual":{"residual_sidecar":"sidecar:50fec2e058a55b091a870650","rounding_rule":"math_logogram_sidecar_v1"},"schema":"omindirection_logogram_atom_v1","source":{"compression":{"canonical_bytes":63,"compression_ratio_raw_to_payload":3.4375,"compression_ratio_raw_to_payload_plus_packed_sidecar":0.5555555555555556,"compression_ratio_raw_to_payload_plus_sidecar":0.02321654706627269,"payload_over_canonical":0.25396825396825395,"payload_over_raw":0.2909090909090909,"payload_plus_packed_sidecar_bytes":99,"payload_plus_sidecar_bytes":2369,"raw_bytes":55,"sidecar_bytes_json_compact":2353,"sidecar_bytes_packed_estimate":83,"surface_payload_bytes":16,"zlib_raw_bytes":44},"residual_reasons":["ambiguous_glyph:25:\\partial","ambiguous_glyph:30:0","ambiguous_glyph:30:{","ambiguous_glyph:31:}","ambiguous_glyph:32:_","ambiguous_glyph:34:=","ambiguous_glyph:35:+","ambiguous_glyph:36:-","hashed_multichar_token:\\nu","hashed_multichar_token:xx","payload_truncated:4_tokens"],"sample_id":"long_truncation","semantic_regime":"ugly_asymmetric_pruning","substitution_counts":{"hashed_multichar_residual":2,"known_command":3,"known_symbol":8,"single_char_literal":7}}} -{"expression":{"language":null,"temporal":0,"tone":"residual","torsion":8},"identity":{"canonical_payload":"torsion ( A , B ) > max \\Rightarrow tear ( A , B )","payload_hash":"sha256:0632054ea2324eaecd449372431056aaa70a24be898dd64d557359fc3e5ec587","semantic_key":"math_logogram.semantic_tear","symbol_id":"LOGO.SEMANTIC_TEAR"},"orientation":{"chirality":"right","direction":"reverse","phase":270},"placement":{"captured_by":"semantic_tear","coord":{"x":15,"y":0},"kind":"quarantine","liberties":0,"territory_id":"logogram-quarantine"},"receipt":{"checks":{"chirality_phase_compatible":true,"explicit_direction":true,"payload_hash_match":true,"phase_valid":true,"placement_admissible":true,"receipt_complete":true,"required_fields":true,"residual_declared":true,"source_hash_present":true,"substitution_round_trip":false,"substitution_sidecar_round_trip":true},"decision":"QUARANTINE","receipt_hash":"sha256:675e67f89f2ebef072d1fcc3f3e7d96dfe10aa0ab3519bf1229c32e591afa261","source_hash":"sha256:62418fc8b55386858970f6f1a61790c3b789c1184e5c488375a149ffc89d22b2","substitution_audit_hash":"sha256:f4ebcf7fc5efcc0e6e05b4904a5c8a532aa21ba8c855d1603825e5bd5bc19301"},"rendering":{"glyph":"d639413b423a3f8629af39413b423a","render_hint":"bounded_glyph_payload_16"},"residual":{"residual_sidecar":"sidecar:052b81952c8271cce950ce79","rounding_rule":"math_logogram_sidecar_v1"},"schema":"omindirection_logogram_atom_v1","source":{"compression":{"canonical_bytes":50,"compression_ratio_raw_to_payload":2.6666666666666665,"compression_ratio_raw_to_payload_plus_packed_sidecar":0.5714285714285714,"compression_ratio_raw_to_payload_plus_sidecar":0.02680965147453083,"payload_over_canonical":0.3,"payload_over_raw":0.375,"payload_plus_packed_sidecar_bytes":70,"payload_plus_sidecar_bytes":1492,"raw_bytes":40,"sidecar_bytes_json_compact":1477,"sidecar_bytes_packed_estimate":55,"surface_payload_bytes":15,"zlib_raw_bytes":45},"residual_reasons":["ambiguous_glyph:29:\\Rightarrow","ambiguous_glyph:39:(","ambiguous_glyph:3a:)","ambiguous_glyph:3b:,","ambiguous_glyph:3f:>","hashed_multichar_token:max","hashed_multichar_token:tear","hashed_multichar_token:torsion"],"sample_id":"semantic_tear","semantic_regime":"horrible_manifold_tearing","substitution_counts":{"hashed_multichar_residual":3,"known_command":1,"known_symbol":7,"single_char_literal":4}}} diff --git a/4-Infrastructure/shim/omindirection_logogram_compiler.py b/4-Infrastructure/shim/omindirection_logogram_compiler.py deleted file mode 100755 index 879ecc45..00000000 --- a/4-Infrastructure/shim/omindirection_logogram_compiler.py +++ /dev/null @@ -1,176 +0,0 @@ -#!/usr/bin/env python3 -"""Compile substitution-audited logograms into omindirectional atom receipts.""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -SHIM = Path(__file__).resolve().parent -DEFAULT_AUDIT = SHIM / "math_logogram_substitution_audit_receipt.json" -DEFAULT_ATOMS = SHIM / "omindirection_logogram_atoms.jsonl" -DEFAULT_RECEIPT = SHIM / "omindirection_logogram_compiler_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sidecar_ref(sample: dict[str, Any]) -> str | None: - sidecar = sample.get("residual_sidecar") - if not sidecar: - return None - return "sidecar:" + sha256_text(stable_json(sidecar))[:24] - - -def atom_for_sample(sample: dict[str, Any]) -> dict[str, Any]: - decision = str(sample["decision"]) - residual_ref = sidecar_ref(sample) - is_quarantine = decision == "QUARANTINE" - is_hold = decision == "HOLD" - canonical = str(sample["canonical"]) - payload_hash = "sha256:" + sha256_text(canonical) - source_hash = "sha256:" + str(sample["source_hash"]) - residual_declared = residual_ref is not None - atom: dict[str, Any] = { - "schema": "omindirection_logogram_atom_v1", - "identity": { - "symbol_id": f"LOGO.{str(sample['id']).upper()}", - "semantic_key": f"math_logogram.{sample['id']}", - "canonical_payload": canonical, - "payload_hash": payload_hash, - }, - "orientation": { - "direction": "reverse" if is_quarantine else "forward", - "chirality": "right" if is_quarantine else "ambidextrous", - "phase": 270 if is_quarantine else 90, - }, - "placement": { - "kind": "quarantine" if is_quarantine else "row", - "coord": {"x": int(sample.get("token_count", 0)), "y": 1 if is_hold else 0}, - "liberties": 0, - "captured_by": "semantic_tear" if is_quarantine else None, - "territory_id": ( - "logogram-quarantine" - if is_quarantine - else "logogram-hold" - if is_hold - else "logogram-row" - ), - }, - "expression": { - "tone": "residual" if is_hold or is_quarantine else "witness", - "torsion": len(sample.get("residual_reasons", [])), - "temporal": 0, - "language": None, - }, - "residual": { - "rounding_rule": ( - "math_logogram_sidecar_v1" - if residual_declared - else None - ), - "residual_sidecar": residual_ref, - }, - "rendering": { - "glyph": sample["payload_hex"], - "render_hint": "bounded_glyph_payload_16", - }, - "receipt": { - "source_hash": source_hash, - "substitution_audit_hash": "sha256:" + sha256_text(stable_json(sample)), - "checks": { - "required_fields": True, - "payload_hash_match": True, - "source_hash_present": bool(sample.get("source_hash")), - "explicit_direction": True, - "phase_valid": True, - "chirality_phase_compatible": True, - "placement_admissible": True, - "residual_declared": residual_declared or decision == "ACCEPT", - "substitution_round_trip": bool(sample["round_trip"]["payload_only"]), - "substitution_sidecar_round_trip": bool( - sample["round_trip"]["with_display_cell_sidecar"] - ), - "receipt_complete": True, - }, - "decision": decision, - }, - "source": { - "sample_id": sample["id"], - "semantic_regime": sample["semantic_regime"], - "substitution_counts": sample["substitution_counts"], - "compression": sample["compression"], - "residual_reasons": sample["residual_reasons"], - }, - } - atom["receipt"]["receipt_hash"] = "sha256:" + sha256_text(stable_json(atom)) - return atom - - -def build_receipt(audit_path: Path, atoms_path: Path) -> dict[str, Any]: - audit = json.loads(audit_path.read_text(encoding="utf-8")) - atoms = [atom_for_sample(sample) for sample in audit.get("tests", [])] - atoms_path.write_text( - "\n".join(stable_json(atom) for atom in atoms) + "\n", - encoding="utf-8", - ) - counts = { - "atom_count": len(atoms), - "accept_count": sum(atom["receipt"]["decision"] == "ACCEPT" for atom in atoms), - "hold_count": sum(atom["receipt"]["decision"] == "HOLD" for atom in atoms), - "quarantine_count": sum(atom["receipt"]["decision"] == "QUARANTINE" for atom in atoms), - "sidecar_ref_count": sum( - atom["residual"]["residual_sidecar"] is not None for atom in atoms - ), - } - receipt: dict[str, Any] = { - "schema": "omindirection_logogram_compiler_receipt_v1", - "source_audit": str(audit_path), - "atoms_jsonl": str(atoms_path), - "counts": counts, - "atom_hashes": [ - { - "symbol_id": atom["identity"]["symbol_id"], - "decision": atom["receipt"]["decision"], - "receipt_hash": atom["receipt"]["receipt_hash"], - "residual_sidecar": atom["residual"]["residual_sidecar"], - } - for atom in atoms - ], - "claim_boundary": ( - "This compiler maps substitution audit decisions into " - "omindirectional atom receipts. It does not prove source math or " - "global compression." - ), - } - receipt["receipt_hash"] = "sha256:" + sha256_text(stable_json(receipt)) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description="Compile audited logograms into omindirectional atoms.") - parser.add_argument("--audit", type=Path, default=DEFAULT_AUDIT) - parser.add_argument("--atoms", type=Path, default=DEFAULT_ATOMS) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - args = parser.parse_args() - - receipt = build_receipt(args.audit, args.atoms) - args.receipt.write_text( - json.dumps(receipt, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(json.dumps(receipt["counts"], indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/one_symbol_lut_fuzzer_prior.py b/4-Infrastructure/shim/one_symbol_lut_fuzzer_prior.py deleted file mode 100644 index 92363f45..00000000 --- a/4-Infrastructure/shim/one_symbol_lut_fuzzer_prior.py +++ /dev/null @@ -1,297 +0,0 @@ -#!/usr/bin/env python3 -"""Emit one-symbol LUT fuzzer generator priors. - -The goal is not to claim compression. The goal is to preserve a finite catalog -of deterministic generator families that can fuzz a compression ratio by -collapsing a large explicit array into a small replay law plus residual. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Callable - - -ROOT = Path(__file__).resolve().parents[2] -OUT_DIR = ROOT / "shared-data" / "data" / "one_symbol_lut_fuzzer" - - -@dataclass(frozen=True) -class GeneratorPrior: - packet_id: str - name: str - family: str - formula: str - generating_function: str - replay_law: str - stress_role: str - failure_mode: str - sample: list[str] - decision: str - - -def stable_hash(obj: object) -> str: - blob = json.dumps(obj, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(blob).hexdigest() - - -def fixed_blocks(values: list[int], width: int) -> list[str]: - return [str(v).zfill(width)[-width:] for v in values] - - -def arithmetic_values(n: int) -> list[int]: - return list(range(n)) - - -def triangular_values(n: int) -> list[int]: - return [i * (i + 1) // 2 for i in range(n)] - - -def square_values(n: int) -> list[int]: - return [i * i for i in range(n)] - - -def cube_values(n: int) -> list[int]: - return [i * i * i for i in range(n)] - - -def geometric_values(k: int, n: int) -> list[int]: - v = 1 - out: list[int] = [] - for _ in range(n): - out.append(v) - v *= k - return out - - -def fibonacci_values(n: int) -> list[int]: - a, b = 1, 1 - out: list[int] = [] - for _ in range(n): - out.append(a) - a, b = b, a + b - return out - - -def lucas_values(n: int) -> list[int]: - a, b = 2, 1 - out: list[int] = [] - for _ in range(n): - out.append(a) - a, b = b, a + b - return out - - -def repetend_digits(p: int, limit: int) -> str: - seen: dict[int, int] = {} - rem = 1 % p - digits: list[str] = [] - while rem and rem not in seen and len(digits) < limit: - seen[rem] = len(digits) - rem *= 10 - digits.append(str(rem // p)) - rem %= p - return "".join(digits) - - -def chunk_string(text: str, width: int, count: int) -> list[str]: - padded = text + ("0" * width) - return [padded[i : i + width].ljust(width, "0") for i in range(0, width * count, width)] - - -def champernowne_digits(limit: int) -> str: - out = [] - i = 1 - while len("".join(out)) < limit: - out.append(str(i)) - i += 1 - return "".join(out)[:limit] - - -def make_packet( - packet_id: str, - name: str, - family: str, - formula: str, - generating_function: str, - replay_law: str, - stress_role: str, - failure_mode: str, - sample_builder: Callable[[], list[str]], -) -> GeneratorPrior: - return GeneratorPrior( - packet_id=packet_id, - name=name, - family=family, - formula=formula, - generating_function=generating_function, - replay_law=replay_law, - stress_role=stress_role, - failure_mode=failure_mode, - sample=sample_builder(), - decision="HOLD", - ) - - -def build_packets() -> list[GeneratorPrior]: - return [ - make_packet( - "OSLF.PRIOR.ARITHMETIC_LADDER.0001", - "Arithmetic progression ladder", - "formal_power_series", - "a_n = n", - "x / (1 - x)^2", - "emit start + n * stride in fixed-width slots", - "boundary stressor for skipped/carry-swallowed coordinates", - "carry propagation or wrap requires residual exceptions", - lambda: fixed_blocks(arithmetic_values(16), 3), - ), - make_packet( - "OSLF.PRIOR.TRIANGULAR.0001", - "Triangular number ladder", - "formal_power_series", - "a_n = n(n+1)/2", - "x / (1 - x)^3", - "emit second-order cumulative count", - "acceleration-density stressor for table and offset manifolds", - "slot overflow creates overlapping blocks and residual debt", - lambda: fixed_blocks(triangular_values(14), 4), - ), - make_packet( - "OSLF.PRIOR.SQUARES.0001", - "Square number ladder", - "formal_power_series", - "a_n = n^2", - "x(1+x) / (1 - x)^3", - "emit polynomial law value for index n", - "curvature stressor for index surfaces and manifold-distance fields", - "polynomial degree mismatch causes residual expansion", - lambda: fixed_blocks(square_values(14), 4), - ), - make_packet( - "OSLF.PRIOR.CUBES.0001", - "Cube number ladder", - "formal_power_series", - "a_n = n^3", - "x(1+4x+x^2) / (1 - x)^4", - "emit third-order polynomial law value for index n", - "higher-order density stressor for volume-like coordinate arrays", - "slot overflow and degree overfit require explicit residuals", - lambda: fixed_blocks(cube_values(12), 5), - ), - make_packet( - "OSLF.PRIOR.GEOMETRIC_2.0001", - "Power-of-two geometric generator", - "geometric_series", - "a_n = 2^n", - "1 / (1 - 2x)", - "multiply previous value by two", - "exponential blowup stressor for slot overlap and entropy cliffs", - "growth exceeds fixed slot width quickly and forces carry residuals", - lambda: fixed_blocks(geometric_values(2, 12), 5), - ), - make_packet( - "OSLF.PRIOR.FIBONACCI.0001", - "Fibonacci recurrence generator", - "linear_recurrence", - "a_n = a_{n-1} + a_{n-2}", - "x / (1 - x - x^2)", - "emit recurrence with initial state 1,1", - "state-transition stressor for biology-like branching and ratio drift", - "wrong initial state or mutated coefficients produce Lucas-like residual", - lambda: fixed_blocks(fibonacci_values(14), 4), - ), - make_packet( - "OSLF.PRIOR.LUCAS_MUTATION.0001", - "Lucas recurrence mutation generator", - "linear_recurrence", - "a_n = a_{n-1} + a_{n-2}, initial 2,1", - "(2 - x) / (1 - x - x^2)", - "emit Fibonacci-law recurrence with different initial state", - "mutation basin test for recurrence classifier stability", - "confusing Fibonacci and Lucas requires residual or distinct law ID", - lambda: fixed_blocks(lucas_values(14), 4), - ), - make_packet( - "OSLF.PRIOR.CYCLIC_PRIME_97.0001", - "Prime reciprocal cyclic repetend", - "cyclic_prime", - "digits(1 / 97)", - "1 / p where p has long decimal period", - "emit repetend digits modulo period with rotation offset", - "rotational-invariance stressor for LUT windows and FPGA O(1) paths", - "non-full-period primes or wrong phase offsets require residual repair", - lambda: chunk_string(repetend_digits(97, 96), 4, 12), - ), - make_packet( - "OSLF.PRIOR.REPUNIT_REPEAT.0001", - "Repunit repeat generator", - "repunit", - "block / (10^w - 1)", - "c / (B - 1)", - "repeat a fixed block indefinitely or for declared length", - "header-tax baseline for obvious repetition", - "not useful when explicit run-length coding is cheaper", - lambda: ["123"] * 12, - ), - make_packet( - "OSLF.PRIOR.CHAMPERNOWNE_DECOY.0001", - "Champernowne-style counting-string decoy", - "concatenation_law", - "123456789101112...", - "not rational in the simple finite recurrence sense", - "concatenate positive integers in the declared radix", - "pseudo-normal decoy: broad digit coverage from a tiny law", - "normal-looking windows can defeat naive entropy heuristics", - lambda: chunk_string(champernowne_digits(48), 4, 12), - ), - ] - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = build_packets() - packet_dicts = [asdict(p) for p in packets] - - packets_path = OUT_DIR / "one_symbol_lut_fuzzer_packets.jsonl" - table_path = OUT_DIR / "one_symbol_lut_fuzzer_table.csv" - receipt_path = OUT_DIR / "one_symbol_lut_fuzzer_receipt.json" - - with packets_path.open("w", encoding="utf-8") as fh: - for packet in packet_dicts: - fh.write(json.dumps(packet, sort_keys=True) + "\n") - - with table_path.open("w", encoding="utf-8") as fh: - fh.write("packet_id,name,family,generating_function,stress_role,decision\n") - for packet in packets: - fields = [ - packet.packet_id, - packet.name, - packet.family, - packet.generating_function, - packet.stress_role, - packet.decision, - ] - fh.write(",".join('"' + f.replace('"', '""') + '"' for f in fields) + "\n") - - receipt = { - "schema": "one_symbol_lut_fuzzer_receipt_v1", - "packet_count": len(packets), - "families": sorted({p.family for p in packets}), - "decision": "HOLD", - "packets_sha256": stable_hash(packet_dicts), - "claim_boundary": ( - "Generator-law fuzzing prior only. One-symbol collapse promotes only " - "when generator bytes plus residual and receipt beat the explicit array." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/online_domain_eigen_pruning.json b/4-Infrastructure/shim/online_domain_eigen_pruning.json deleted file mode 100644 index cfe09e14..00000000 --- a/4-Infrastructure/shim/online_domain_eigen_pruning.json +++ /dev/null @@ -1,290 +0,0 @@ -{ - "schema": "online_domain_eigen_pruning_v1", - "claim_boundary": "Leading eigenvector is a ranking prior over adjacent source-backed domains, not proof of correctness.", - "domain_count": 7, - "weighted_domains": [ - { - "domain": "arithmetic_entropy_coding", - "equation": "message -> interval with subinterval widths proportional to symbol probabilities", - "role": "baseline entropy coder and probability weighting model", - "source": "Youssef, Parallel Algorithms for Entropy-Coding Techniques, NIST, 1998", - "url": "https://www.nist.gov/publications/parallel-algorithms-entropy-coding-techniques", - "eigen_weight": 0.32818448514877907 - }, - { - "domain": "asymmetric_numeral_systems", - "equation": "state-machine entropy coding with symbol-state allocation f_s ~= p_s R", - "role": "finite-state entropy coding, table/LUT-adjacent for hardware", - "source": "Pieprzyk et al., The Compression Optimality of Asymmetric Numeral Systems, Entropy, 2023", - "url": "https://www.mdpi.com/1099-4300/25/4/672", - "eigen_weight": 0.2598079624847316 - }, - { - "domain": "minimum_description_length", - "equation": "argmin_M L(M) + L(D | M)", - "role": "model selection prior; choose the shortest lawful template before encoding", - "source": "Grunwald, Model Selection Based on Minimum Description Length, Journal of Mathematical Psychology, 2000", - "url": "https://www.sciencedirect.com/science/article/abs/pii/S0022249699912804", - "eigen_weight": 0.19256323124324387 - }, - { - "domain": "bounce_lightweight_integer_compression", - "equation": "compress k separate blocks of size N across SIMD lanes to preserve scalar ratio", - "role": "partitioned lane layout prior for avoiding wide-register ratio loss", - "source": "Bittner et al., BOUNCE: memory-efficient SIMD approach for lightweight integer compression, Distributed and Parallel Databases, 2023", - "url": "https://link.springer.com/article/10.1007/s10619-023-07426-0", - "eigen_weight": 0.10931260799338613 - }, - { - "domain": "simd_bp128_bitpacking", - "equation": "block_width = ceil(log2(max(block)+1)); pack N integers at block_width bits", - "role": "lane-width prior for GPU/FPGA integer surfaces", - "source": "Lemire and Boytsov, Decoding billions of integers per second through vectorization, Software: Practice and Experience, 2015", - "url": "https://arxiv.org/abs/1209.2137", - "eigen_weight": 0.057946842152125255 - }, - { - "domain": "delta_sigma_one_bit", - "equation": "b_t = Q(v_t + e_{t-1}); e_t = v_t + e_{t-1} - b_t", - "role": "1-bit residual-feedback transport prior, adjacent to PBACS", - "source": "Zierhofer, Adaptive Delta-Sigma Modulation for Enhanced Input Dynamic Range, EURASIP JASP, 2008", - "url": "https://link.springer.com/article/10.1155/2008/439203", - "eigen_weight": 0.03805214750155165 - }, - { - "domain": "normalized_compression_distance", - "equation": "NCD_Z(x,y) = (Z(xy) - min(Z(x), Z(y))) / max(Z(x), Z(y))", - "role": "compressor-backed similarity gate for choosing nearby templates", - "source": "Cilibrasi and Vitanyi, Clustering by Compression, IEEE Transactions on Information Theory, 2005", - "url": "https://ir.cwi.nl/pub/16389", - "eigen_weight": 0.014132723476182319 - } - ], - "top_terms": [ - { - "term": "entropy", - "weight": 0.2063358047091217 - }, - { - "term": "model", - "weight": 0.13192504349539264 - }, - { - "term": "selection", - "weight": 0.0837713577517334 - }, - { - "term": "parallel", - "weight": 0.07848924644513405 - }, - { - "term": "algorithms", - "weight": 0.07515803351452537 - }, - { - "term": "arithmetic_entropy_coding", - "weight": 0.07515803351452537 - }, - { - "term": "baseline", - "weight": 0.07515803351452537 - }, - { - "term": "coder", - "weight": 0.07515803351452537 - }, - { - "term": "entropy-coding", - "weight": 0.07515803351452537 - }, - { - "term": "interval", - "weight": 0.07515803351452537 - }, - { - "term": "message", - "weight": 0.07515803351452537 - }, - { - "term": "nist", - "weight": 0.07515803351452537 - }, - { - "term": "probabilities", - "weight": 0.07515803351452537 - }, - { - "term": "probability", - "weight": 0.07515803351452537 - }, - { - "term": "proportional", - "weight": 0.07515803351452537 - }, - { - "term": "subinterval", - "weight": 0.07515803351452537 - }, - { - "term": "symbol", - "weight": 0.07515803351452537 - }, - { - "term": "techniques", - "weight": 0.07515803351452537 - }, - { - "term": "weighting", - "weight": 0.07515803351452537 - }, - { - "term": "widths", - "weight": 0.07515803351452537 - }, - { - "term": "youssef", - "weight": 0.07515803351452537 - }, - { - "term": "allocation", - "weight": 0.05780453403001633 - }, - { - "term": "asymmetric", - "weight": 0.05780453403001633 - }, - { - "term": "asymmetric_numeral_systems", - "weight": 0.05780453403001633 - }, - { - "term": "f_s", - "weight": 0.05780453403001633 - }, - { - "term": "finite-state", - "weight": 0.05780453403001633 - }, - { - "term": "hardware", - "weight": 0.05780453403001633 - }, - { - "term": "lut-adjacent", - "weight": 0.05780453403001633 - }, - { - "term": "numeral", - "weight": 0.05780453403001633 - }, - { - "term": "optimality", - "weight": 0.05780453403001633 - }, - { - "term": "p_s", - "weight": 0.05780453403001633 - }, - { - "term": "pieprzyk", - "weight": 0.05780453403001633 - }, - { - "term": "state-machine", - "weight": 0.05780453403001633 - }, - { - "term": "symbol-state", - "weight": 0.05780453403001633 - }, - { - "term": "table", - "weight": 0.05780453403001633 - }, - { - "term": "prior", - "weight": 0.04862003891674652 - }, - { - "term": "argmin_m", - "weight": 0.0418856788758667 - }, - { - "term": "before", - "weight": 0.0418856788758667 - }, - { - "term": "choose", - "weight": 0.0418856788758667 - }, - { - "term": "description", - "weight": 0.0418856788758667 - } - ], - "domain_similarity_matrix": [ - [ - 0.9999999999999997, - 0.06864763884530972, - 0.0, - 0.01513151045105087, - 0.014647293359011769, - 0.015228563066514943, - 0.0 - ], - [ - 0.06864763884530972, - 1.0, - 0.10532564245617808, - 0.0, - 0.028001404572565464, - 0.0, - 0.0 - ], - [ - 0.0, - 0.10532564245617808, - 0.9999999999999998, - 0.0, - 0.0, - 0.0, - 0.0 - ], - [ - 0.01513151045105087, - 0.0, - 0.0, - 1.0000000000000002, - 0.034758527243063664, - 0.01283417735563, - 0.031889604696206345 - ], - [ - 0.014647293359011769, - 0.028001404572565464, - 0.0, - 0.034758527243063664, - 1.0000000000000002, - 0.01242347625226291, - 0.0 - ], - [ - 0.015228563066514943, - 0.0, - 0.0, - 0.01283417735563, - 0.01242347625226291, - 1.0, - 0.0 - ], - [ - 0.0, - 0.0, - 0.0, - 0.031889604696206345, - 0.0, - 0.0, - 1.0 - ] - ] -} diff --git a/4-Infrastructure/shim/online_domain_eigen_pruning.py b/4-Infrastructure/shim/online_domain_eigen_pruning.py deleted file mode 100644 index 57543787..00000000 --- a/4-Infrastructure/shim/online_domain_eigen_pruning.py +++ /dev/null @@ -1,187 +0,0 @@ -#!/usr/bin/env python3 -"""Spectral pruning over adjacent online compression/math domains. - -The input is a small, source-backed set of peer-reviewed/standards-adjacent -domain priors. The script builds a term-domain matrix, computes the leading -eigenvector of the domain similarity matrix, and emits weighted terms/domains -that can shrink later compression/logogram/FPGA searches. -""" - -from __future__ import annotations - -import argparse -import json -import math -import re -from collections import Counter -from pathlib import Path -from typing import Any - - -TOKEN_RE = re.compile(r"[A-Za-z][A-Za-z0-9_+-]{2,}") - - -DEFAULT_DOMAINS: list[dict[str, str]] = [ - { - "domain": "minimum_description_length", - "equation": "argmin_M L(M) + L(D | M)", - "role": "model selection prior; choose the shortest lawful template before encoding", - "source": "Grunwald, Model Selection Based on Minimum Description Length, Journal of Mathematical Psychology, 2000", - "url": "https://www.sciencedirect.com/science/article/abs/pii/S0022249699912804", - }, - { - "domain": "arithmetic_entropy_coding", - "equation": "message -> interval with subinterval widths proportional to symbol probabilities", - "role": "baseline entropy coder and probability weighting model", - "source": "Youssef, Parallel Algorithms for Entropy-Coding Techniques, NIST, 1998", - "url": "https://www.nist.gov/publications/parallel-algorithms-entropy-coding-techniques", - }, - { - "domain": "asymmetric_numeral_systems", - "equation": "state-machine entropy coding with symbol-state allocation f_s ~= p_s R", - "role": "finite-state entropy coding, table/LUT-adjacent for hardware", - "source": "Pieprzyk et al., The Compression Optimality of Asymmetric Numeral Systems, Entropy, 2023", - "url": "https://www.mdpi.com/1099-4300/25/4/672", - }, - { - "domain": "simd_bp128_bitpacking", - "equation": "block_width = ceil(log2(max(block)+1)); pack N integers at block_width bits", - "role": "lane-width prior for GPU/FPGA integer surfaces", - "source": "Lemire and Boytsov, Decoding billions of integers per second through vectorization, Software: Practice and Experience, 2015", - "url": "https://arxiv.org/abs/1209.2137", - }, - { - "domain": "bounce_lightweight_integer_compression", - "equation": "compress k separate blocks of size N across SIMD lanes to preserve scalar ratio", - "role": "partitioned lane layout prior for avoiding wide-register ratio loss", - "source": "Bittner et al., BOUNCE: memory-efficient SIMD approach for lightweight integer compression, Distributed and Parallel Databases, 2023", - "url": "https://link.springer.com/article/10.1007/s10619-023-07426-0", - }, - { - "domain": "delta_sigma_one_bit", - "equation": "b_t = Q(v_t + e_{t-1}); e_t = v_t + e_{t-1} - b_t", - "role": "1-bit residual-feedback transport prior, adjacent to PBACS", - "source": "Zierhofer, Adaptive Delta-Sigma Modulation for Enhanced Input Dynamic Range, EURASIP JASP, 2008", - "url": "https://link.springer.com/article/10.1155/2008/439203", - }, - { - "domain": "normalized_compression_distance", - "equation": "NCD_Z(x,y) = (Z(xy) - min(Z(x), Z(y))) / max(Z(x), Z(y))", - "role": "compressor-backed similarity gate for choosing nearby templates", - "source": "Cilibrasi and Vitanyi, Clustering by Compression, IEEE Transactions on Information Theory, 2005", - "url": "https://ir.cwi.nl/pub/16389", - }, -] - - -STOPWORDS = { - "and", - "the", - "for", - "with", - "from", - "into", - "that", - "this", - "through", - "using", - "based", - "source", - "journal", - "transactions", - "systems", - "compression", - "coding", -} - - -def tokenize(text: str) -> list[str]: - tokens = [] - for match in TOKEN_RE.finditer(text.lower()): - token = match.group(0).strip("_+-") - if token and token not in STOPWORDS: - tokens.append(token) - return tokens - - -def leading_eigenvector(matrix: list[list[float]], iterations: int = 80) -> list[float]: - n = len(matrix) - vec = [1.0 / math.sqrt(n)] * n - for _ in range(iterations): - nxt = [sum(matrix[i][j] * vec[j] for j in range(n)) for i in range(n)] - norm = math.sqrt(sum(x * x for x in nxt)) or 1.0 - vec = [x / norm for x in nxt] - total = sum(abs(x) for x in vec) or 1.0 - return [abs(x) / total for x in vec] - - -def build_surface(domains: list[dict[str, str]]) -> dict[str, Any]: - docs = [] - df: Counter[str] = Counter() - for item in domains: - text = " ".join([item["domain"], item["equation"], item["role"], item["source"]]) - counts = Counter(tokenize(text)) - docs.append(counts) - df.update(counts.keys()) - - vocab = sorted(df) - n_docs = len(docs) - vectors = [] - for counts in docs: - total = sum(counts.values()) or 1 - vector = [] - for token in vocab: - tf = counts[token] / total - idf = math.log((1 + n_docs) / (1 + df[token])) + 1 - vector.append(tf * idf) - norm = math.sqrt(sum(x * x for x in vector)) or 1.0 - vectors.append([x / norm for x in vector]) - - sim = [] - for left in vectors: - row = [] - for right in vectors: - row.append(sum(a * b for a, b in zip(left, right))) - sim.append(row) - - domain_weights = leading_eigenvector(sim) - term_scores: Counter[str] = Counter() - for weight, vector in zip(domain_weights, vectors): - for token, value in zip(vocab, vector): - term_scores[token] += weight * value - - weighted_domains = [] - for item, weight in sorted(zip(domains, domain_weights), key=lambda pair: -pair[1]): - weighted_domains.append({**item, "eigen_weight": weight}) - - return { - "schema": "online_domain_eigen_pruning_v1", - "claim_boundary": "Leading eigenvector is a ranking prior over adjacent source-backed domains, not proof of correctness.", - "domain_count": len(domains), - "weighted_domains": weighted_domains, - "top_terms": [ - {"term": term, "weight": weight} - for term, weight in term_scores.most_common(40) - ], - "domain_similarity_matrix": sim, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--out", type=Path) - parser.add_argument("--limit-terms", type=int, default=40) - args = parser.parse_args() - - surface = build_surface(DEFAULT_DOMAINS) - surface["top_terms"] = surface["top_terms"][: args.limit_terms] - text = json.dumps(surface, indent=2, ensure_ascii=False) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/openclaw_shared_bus_config.example.json b/4-Infrastructure/shim/openclaw_shared_bus_config.example.json deleted file mode 100644 index 19600327..00000000 --- a/4-Infrastructure/shim/openclaw_shared_bus_config.example.json +++ /dev/null @@ -1,40 +0,0 @@ -{ - "$schema": "research_stack_openclaw_shared_bus_config_example_v1", - "claim_boundary": "Template only. Do not place secrets here. Keep real credentials under OpenClaw's local credential store.", - "gateway": { - "bind": "127.0.0.1", - "port": 18789, - "non_loopback": "disabled_until_auth_pairing_receipt" - }, - "agents": { - "defaults": { - "sandbox": { - "mode": "non-main", - "required_for": [ - "remote", - "group", - "public_channel", - "untrusted_input" - ] - } - } - }, - "research_stack_bus": { - "memory_write_rule": "write only hashes, receipt paths, lawful statuses, and next-action pointers; never raw secrets", - "required_task_completion_keys": [ - "agent_handle", - "task_id", - "receipt_path", - "receipt_hash", - "lawful", - "claim_boundary" - ], - "allowed_memory_value_types": [ - "receipt_path", - "hash", - "lawful_status", - "claim_boundary", - "next_action_pointer" - ] - } -} diff --git a/4-Infrastructure/shim/openclaw_shared_bus_surface.py b/4-Infrastructure/shim/openclaw_shared_bus_surface.py deleted file mode 100644 index 468ca805..00000000 --- a/4-Infrastructure/shim/openclaw_shared_bus_surface.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python3 -"""Create a receipt-bound OpenClaw shared-bus surface descriptor. - -OpenClaw is pulled as an external snapshot and treated as a control-plane/bus -candidate. This script does not start a gateway, install dependencies, or enable -inbound channels. It records the pinned source, maps the bus surfaces into the -Research Stack, and emits curriculum records for bounded LLM routing. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_OPENCLAW = REPO / "5-Applications" / "tools-scripts" / "external" / "openclaw" -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" - - -def run_git(path: Path, *args: str) -> str: - proc = subprocess.run( - ["git", "-C", str(path), *args], - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=False, - ) - if proc.returncode != 0: - raise RuntimeError(proc.stderr.strip() or proc.stdout.strip()) - return proc.stdout.strip() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def read_text(path: Path, limit: int = 12000) -> str: - if not path.exists(): - return "" - return path.read_text(encoding="utf-8", errors="replace")[:limit] - - -def load_package(path: Path) -> dict[str, Any]: - package_path = path / "package.json" - if not package_path.exists(): - return {} - data = json.loads(package_path.read_text(encoding="utf-8")) - return { - "name": data.get("name"), - "version": data.get("version"), - "description": data.get("description"), - "license": data.get("license"), - "runtime_hint": "Node 24 recommended or Node 22.16+ per README", - "script_keys": sorted((data.get("scripts") or {}).keys())[:80], - } - - -def evidence_snippets(path: Path) -> list[dict[str, str]]: - snippets = [] - for rel, marker in [ - ("README.md", "OpenClaw is a personal AI assistant you run on your own devices."), - ("README.md", "Gateway is just the control plane."), - ("README.md", "Multi-agent routing"), - ("README.md", "Default: tools run on the host"), - ("docs/index.md", "Gateway is the single source of truth for sessions, routing, and channel connections."), - ("docs/network.md", "Loopback first"), - ]: - text = read_text(path / rel) - lower = text.lower() - idx = lower.find(marker.lower()) - if idx < 0: - continue - start = max(0, idx - 180) - end = min(len(text), idx + len(marker) + 280) - snippets.append( - { - "source_path": str((path / rel).relative_to(REPO)), - "marker": marker, - "snippet_hash": sha256_text(text[start:end]), - } - ) - return snippets - - -def build_surface(path: Path) -> dict[str, Any]: - commit = run_git(path, "rev-parse", "HEAD") - branch = run_git(path, "rev-parse", "--abbrev-ref", "HEAD") - remote = run_git(path, "remote", "get-url", "origin") - status = run_git(path, "status", "--short") - package = load_package(path) - readme = read_text(path / "README.md") - docs_index = read_text(path / "docs" / "index.md") - network_doc = read_text(path / "docs" / "network.md") - source_fingerprint = sha256_text("\n".join([commit, package.get("version") or "", readme[:4000], docs_index[:4000], network_doc[:4000]])) - return { - "schema": "openclaw_shared_bus_surface_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "openclaw": { - "path": str(path.relative_to(REPO)), - "remote": remote, - "branch": branch, - "commit": commit, - "working_tree_clean": status == "", - "package": package, - "source_fingerprint": source_fingerprint, - "evidence": evidence_snippets(path), - }, - "surface_role": { - "name": "OpenClaw Shared Bus Surface", - "use_as": "local_first_agent_gateway_and_event_bus_candidate", - "not_use_as": [ - "theorem_truth_source", - "unbounded_tool_executor", - "raw_secret_memory_store", - "open_inbound_channel_without_pairing", - ], - "claim_boundary": "OpenClaw is treated as a bus/control-plane candidate. It is not run or trusted until loopback, pairing, sandbox, and metaprobe receipt gates pass.", - }, - "research_stack_mapping": [ - { - "openclaw_surface": "Gateway", - "research_stack_role": "shared bus/control plane", - "gate": "loopback-only first; no non-loopback bind without explicit auth and pairing receipt", - }, - { - "openclaw_surface": "sessions/routing", - "research_stack_role": "bounded worker lanes for AgentID/shared identity tasks", - "gate": "one task receipt per lane before memory write", - }, - { - "openclaw_surface": "channels/plugins", - "research_stack_role": "transport adapters for chat/API/hardware event ingress", - "gate": "disable public inbound channels until allowlist and sandbox receipts exist", - }, - { - "openclaw_surface": "skills", - "research_stack_role": "local tool contract layer for metaprobe/verifier actions", - "gate": "skill outputs must include source path, hash, lawful flag, and claim boundary", - }, - { - "openclaw_surface": "sandboxing", - "research_stack_role": "containment membrane for non-main and remote sessions", - "gate": "non-main sessions default to sandboxed/receipt-only writes", - }, - ], - "event_contract": { - "task_started": { - "required": ["agent_handle", "task_id", "title", "state", "timestamp"], - }, - "task_completed": { - "required": ["agent_handle", "task_id", "receipt_path", "receipt_hash", "lawful", "claim_boundary"], - }, - "memory_write": { - "required": ["key", "value_hash", "source_receipt_path", "claim_boundary"], - "rule": "write only hashes, receipt paths, lawful statuses, and next-action pointers; never raw secrets", - }, - }, - "activation_plan": [ - "Keep external snapshot pinned and inactive.", - "Generate loopback-only OpenClaw config skeleton.", - "Route one local metaprobe verifier task through a dry-run event adapter.", - "Only after receipt pass, test gateway loopback with no public channels.", - "Promote to shared bus surface only after sandbox and pairing receipts exist.", - ], - "lawful": True, - } - - -def curriculum_records(surface: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an OpenClaw bus-surface router. Return compact JSON and keep OpenClaw behind receipt gates." - records = [] - for item in surface["research_stack_mapping"]: - prompt = { - "task": "route_openclaw_surface", - "openclaw_surface": item["openclaw_surface"], - "research_stack_role": item["research_stack_role"], - "gate": item["gate"], - "claim_boundary": surface["surface_role"]["claim_boundary"], - } - answer = { - "selected": True, - "use_as": "shared_bus_surface_prior", - "openclaw_surface": item["openclaw_surface"], - "route_rule": item["gate"], - "claim_boundary": surface["surface_role"]["claim_boundary"], - "source_path": surface["openclaw"]["path"], - "source_hash": surface["openclaw"]["source_fingerprint"], - "receipt_rule": "Treat as bus/control-plane prior only until live loopback and sandbox receipts exist.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_config_template(surface: dict[str, Any], path: Path) -> None: - config = { - "$schema": "research_stack_openclaw_shared_bus_config_example_v1", - "claim_boundary": "Template only. Do not place secrets here. Keep real credentials under OpenClaw's local credential store.", - "gateway": { - "bind": "127.0.0.1", - "port": 18789, - "non_loopback": "disabled_until_auth_pairing_receipt", - }, - "agents": { - "defaults": { - "sandbox": { - "mode": "non-main", - "required_for": ["remote", "group", "public_channel", "untrusted_input"], - } - } - }, - "research_stack_bus": { - "memory_write_rule": surface["event_contract"]["memory_write"]["rule"], - "required_task_completion_keys": surface["event_contract"]["task_completed"]["required"], - "allowed_memory_value_types": ["receipt_path", "hash", "lawful_status", "claim_boundary", "next_action_pointer"], - }, - } - path.write_text(json.dumps(config, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - - -def write_wiki(surface: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack OpenClaw AgentBus Metaprobe IPC", - "title: OpenClaw Shared Bus Surface", - "type: text/vnd.tiddlywiki", - "", - "! OpenClaw Shared Bus Surface", - "", - "OpenClaw is pulled as a pinned external snapshot and treated as a local-first shared bus/control-plane candidate.", - "", - f"Snapshot path: `{surface['openclaw']['path']}`", - f"Commit: `{surface['openclaw']['commit']}`", - f"Package version: `{surface['openclaw']['package'].get('version')}`", - "", - "Durable source: `4-Infrastructure/shim/openclaw_shared_bus_surface.py`", - "", - "Receipt: `4-Infrastructure/shim/openclaw_shared_bus_surface_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/openclaw_shared_bus_surface_curriculum.jsonl`", - "", - "Config skeleton: `4-Infrastructure/shim/openclaw_shared_bus_config.example.json`", - "", - "!! Claim Boundary", - "", - surface["surface_role"]["claim_boundary"], - "", - "!! Surface Mapping", - "", - ] - for item in surface["research_stack_mapping"]: - lines.append(f"* `{item['openclaw_surface']}` -> {item['research_stack_role']}. Gate: {item['gate']}") - lines.extend( - [ - "", - "!! Activation Plan", - "", - ] - ) - for step in surface["activation_plan"]: - lines.append(f"* {step}") - lines.extend( - [ - "", - "!! Links", - "", - "* [[Physics Math LLM Metaprobe Audit]]", - "* [[Solved Problem Output Verifier]]", - "* [[Custom Equation Awareness Manifest]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--openclaw", type=Path, default=DEFAULT_OPENCLAW) - parser.add_argument("--receipt", type=Path, default=SHIM / "openclaw_shared_bus_surface_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "openclaw_shared_bus_surface_curriculum.jsonl") - parser.add_argument("--config-template", type=Path, default=SHIM / "openclaw_shared_bus_config.example.json") - parser.add_argument("--wiki", type=Path, default=WIKI / "OpenClaw Shared Bus Surface.tid") - args = parser.parse_args() - - surface = build_surface(args.openclaw) - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(surface, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(surface): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_config_template(surface, args.config_template) - write_wiki(surface, args.wiki) - print(json.dumps(surface, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/parallel_metaprobe_launcher.py b/4-Infrastructure/shim/parallel_metaprobe_launcher.py deleted file mode 100644 index 539a5a3d..00000000 --- a/4-Infrastructure/shim/parallel_metaprobe_launcher.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -"""Launch a bounded parallel metaprobe sweep. - -This runner coordinates existing receipt-generating probes. It does not ingest -external corpora, prove claims, or promote any route. Each lane writes a local -receipt, stdout, stderr, and a master launcher receipt records what ran. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import os -import subprocess -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -MASS_PARQUET = REPO / "3-Mathematical-Models" / "equations_parquet_tagged" / "mass_equations_unified.parquet" - - -@dataclass(frozen=True) -class Lane: - name: str - cmd: list[str] - receipt: Path | None = None - curriculum: Path | None = None - claim_boundary: str = "route-prior-only" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path | None) -> str | None: - if path is None: - return None - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def timestamp() -> str: - return datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - -def lane_set(out_dir: Path, include_rrc: bool) -> list[Lane]: - lanes = [ - Lane( - name="nspace_bulk_dataset_route_registry", - cmd=[sys.executable, str(SHIM / "nspace_bulk_dataset_route_registry.py")], - receipt=REPO / "shared-data" / "data" / "nspace_bulk_routes" / "nspace_bulk_dataset_route_receipt.json", - claim_boundary="external-dataset-route-registry-only", - ), - Lane( - name="decoder_reconstruction_core_prior", - cmd=[sys.executable, str(SHIM / "decoder_reconstruction_core_prior.py")], - receipt=REPO / "shared-data" / "data" / "decoder_reconstruction_core" / "decoder_reconstruction_core_prior_receipt.json", - claim_boundary="architecture-prior-only", - ), - Lane( - name="parallel_stage_domain_route_prior", - cmd=[sys.executable, str(SHIM / "parallel_stage_domain_route_prior.py")], - receipt=SHIM / "parallel_stage_domain_route_prior_receipt.json", - curriculum=SHIM / "parallel_stage_domain_route_prior_curriculum.jsonl", - claim_boundary="parallel-domain-route-prior-only", - ), - Lane( - name="pde_model_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "pde_model_prior_metaprobe.py"), - "--receipt", - str(out_dir / "pde_model_prior_receipt.json"), - "--curriculum", - str(out_dir / "pde_model_prior_curriculum.jsonl"), - ], - receipt=out_dir / "pde_model_prior_receipt.json", - curriculum=out_dir / "pde_model_prior_curriculum.jsonl", - claim_boundary="pde-model-prior-only", - ), - Lane( - name="math_prover_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "math_prover_prior_metaprobe.py"), - "--no-live-search", - "--receipt", - str(out_dir / "math_prover_prior_metaprobe_receipt.json"), - "--curriculum", - str(out_dir / "math_prover_prior_curriculum.jsonl"), - ], - receipt=out_dir / "math_prover_prior_metaprobe_receipt.json", - curriculum=out_dir / "math_prover_prior_curriculum.jsonl", - claim_boundary="math-prover-prior-no-live-search", - ), - Lane( - name="molecular_domain_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "molecular_domain_prior_metaprobe.py"), - "--receipt", - str(out_dir / "molecular_domain_prior_receipt.json"), - "--curriculum", - str(out_dir / "molecular_domain_prior_curriculum.jsonl"), - ], - receipt=out_dir / "molecular_domain_prior_receipt.json", - curriculum=out_dir / "molecular_domain_prior_curriculum.jsonl", - claim_boundary="molecular-domain-prior-only", - ), - Lane( - name="genomic_sequence_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "genomic_sequence_prior_metaprobe.py"), - "--receipt", - str(out_dir / "genomic_sequence_prior_receipt.json"), - "--curriculum", - str(out_dir / "genomic_sequence_prior_curriculum.jsonl"), - ], - receipt=out_dir / "genomic_sequence_prior_receipt.json", - curriculum=out_dir / "genomic_sequence_prior_curriculum.jsonl", - claim_boundary="genomic-sequence-prior-only", - ), - Lane( - name="llm_compression_architecture_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "llm_compression_architecture_prior_metaprobe.py"), - "--receipt", - str(out_dir / "llm_compression_architecture_prior_receipt.json"), - "--curriculum", - str(out_dir / "llm_compression_architecture_prior_curriculum.jsonl"), - ], - receipt=out_dir / "llm_compression_architecture_prior_receipt.json", - curriculum=out_dir / "llm_compression_architecture_prior_curriculum.jsonl", - claim_boundary="llm-compression-architecture-prior-only", - ), - Lane( - name="moving_sofa_nspace_prior_metaprobe", - cmd=[ - sys.executable, - str(SHIM / "moving_sofa_nspace_prior_metaprobe.py"), - "--receipt", - str(out_dir / "moving_sofa_nspace_prior_receipt.json"), - "--curriculum", - str(out_dir / "moving_sofa_nspace_prior_curriculum.jsonl"), - ], - receipt=out_dir / "moving_sofa_nspace_prior_receipt.json", - curriculum=out_dir / "moving_sofa_nspace_prior_curriculum.jsonl", - claim_boundary="moving-sofa-route-prior-only", - ), - Lane( - name="mass_equation_distill_receipt", - cmd=[ - sys.executable, - str(SHIM / "mass_equation_distill_receipt.py"), - "--receipt", - str(out_dir / "mass_equations_unified_receipt.json"), - "--summary", - str(out_dir / "mass_equations_unified_receipt.md"), - ], - receipt=out_dir / "mass_equations_unified_receipt.json", - claim_boundary="mass-equation-coverage-receipt-only", - ), - ] - if include_rrc: - lanes.append( - Lane( - name="rrc_mass_equation_projection", - cmd=[ - sys.executable, - str(SHIM / "rrc_equation_classifier.py"), - "--mass-parquet", - str(MASS_PARQUET), - "--mass-only", - "--receipt-detail-limit", - "1000", - "--out", - str(out_dir / "mass_equations_rrc_projection_receipt.json"), - "--summary", - str(out_dir / "mass_equations_rrc_projection_receipt.md"), - "--curriculum", - str(out_dir / "mass_equations_rrc_projection_curriculum.jsonl"), - "--table", - str(out_dir / "mass_equations_rrc_projection_table.csv"), - ], - receipt=out_dir / "mass_equations_rrc_projection_receipt.json", - curriculum=out_dir / "mass_equations_rrc_projection_curriculum.jsonl", - claim_boundary="rrc-route-atlas-not-proof-atlas", - ) - ) - return lanes - - -def run_lane(lane: Lane, out_dir: Path, timeout: int) -> dict[str, Any]: - started = time.time() - stdout_path = out_dir / f"{lane.name}.stdout.txt" - stderr_path = out_dir / f"{lane.name}.stderr.txt" - try: - proc = subprocess.run( - lane.cmd, - cwd=REPO, - text=True, - capture_output=True, - timeout=timeout, - check=False, - ) - stdout_path.write_text(proc.stdout, encoding="utf-8", errors="replace") - stderr_path.write_text(proc.stderr, encoding="utf-8", errors="replace") - status = "PASS" if proc.returncode == 0 else "FAIL" - error = None - returncode = proc.returncode - except subprocess.TimeoutExpired as exc: - stdout_path.write_text(exc.stdout or "", encoding="utf-8", errors="replace") - stderr_path.write_text(exc.stderr or "", encoding="utf-8", errors="replace") - status = "TIMEOUT" - error = f"timeout after {timeout}s" - returncode = None - - receipt_exists = lane.receipt.exists() if lane.receipt else False - receipt_hash = None - if receipt_exists and lane.receipt: - receipt_hash = sha256_text(lane.receipt.read_text(encoding="utf-8", errors="replace")) - return { - "name": lane.name, - "status": status, - "returncode": returncode, - "elapsed_s": round(time.time() - started, 3), - "cmd": lane.cmd, - "receipt": rel(lane.receipt), - "receipt_exists": receipt_exists, - "receipt_file_sha256": receipt_hash, - "curriculum": rel(lane.curriculum), - "stdout": rel(stdout_path), - "stderr": rel(stderr_path), - "claim_boundary": lane.claim_boundary, - "error": error, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--workers", type=int, default=min(6, os.cpu_count() or 2)) - parser.add_argument("--timeout", type=int, default=600) - parser.add_argument("--out-dir", type=Path, default=SHIM / "parallel_metaprobe_runs" / timestamp()) - parser.add_argument("--skip-rrc", action="store_true") - args = parser.parse_args() - - args.out_dir.mkdir(parents=True, exist_ok=True) - lanes = lane_set(args.out_dir, include_rrc=not args.skip_rrc) - - results: list[dict[str, Any]] = [] - with ThreadPoolExecutor(max_workers=max(1, args.workers)) as pool: - futures = {pool.submit(run_lane, lane, args.out_dir, args.timeout): lane for lane in lanes} - for future in as_completed(futures): - result = future.result() - results.append(result) - print(json.dumps({"lane": result["name"], "status": result["status"], "elapsed_s": result["elapsed_s"]}, sort_keys=True)) - - results.sort(key=lambda item: item["name"]) - receipt = { - "schema": "parallel_metaprobe_launcher_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "workers": args.workers, - "timeout_s": args.timeout, - "lane_count": len(results), - "status_counts": {status: sum(1 for item in results if item["status"] == status) for status in sorted({item["status"] for item in results})}, - "lanes": results, - "decision": "HOLD", - "claim_boundary": ( - "Parallel metaprobe launch receipt only. These lanes generate route priors, " - "coverage receipts, and negative-control surfaces; no lane promotes a theorem, " - "dataset ingest, compression benchmark, or byte-law win without separate replay." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - receipt_path = args.out_dir / "parallel_metaprobe_launcher_receipt.json" - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"receipt": rel(receipt_path), "receipt_hash": receipt["receipt_hash"], "status_counts": receipt["status_counts"]}, indent=2, sort_keys=True)) - return 0 if receipt["status_counts"].get("FAIL", 0) == 0 and receipt["status_counts"].get("TIMEOUT", 0) == 0 else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/parallel_stage_domain_route_prior.py b/4-Infrastructure/shim/parallel_stage_domain_route_prior.py deleted file mode 100644 index eedce94f..00000000 --- a/4-Infrastructure/shim/parallel_stage_domain_route_prior.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -"""Capture the refinement that every route stage is a parallel domain bundle. - -The local design correction is that a route stage should not be modeled as one -linear transform lane. Each stage processes parallel domains of the data type -currently under evaluation: bytes, tokens, structure, residuals, witnesses, -owners, runtime budgets, and closure state. Promotion happens only when all -domains synchronize back to exact bytes with bounded costs. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "parallel_stage_domain_route_prior_receipt.json" -CURRICULUM_OUT = SHIM / "parallel_stage_domain_route_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -DOMAIN_AXES = [ - { - "id": "byte_domain", - "role": "source bytes and exact decoded output", - "promotion_authority": True, - "failure": "byte hash mismatch -> not promoted", - }, - { - "id": "token_domain", - "role": "XML tokens, phrase tokens, dependency heads, semantic anchors", - "promotion_authority": False, - "failure": "token view without residual -> diagnostic only", - }, - { - "id": "structure_domain", - "role": "records, attributes, graphs, folds, bundles, scaffolds", - "promotion_authority": False, - "failure": "structure changes decode reachability -> fail closed", - }, - { - "id": "residual_domain", - "role": "exact repair lanes for all sketch, deletion, imputation, or projection losses", - "promotion_authority": True, - "failure": "missing residual for non-byte-exact domain -> NaN0", - }, - { - "id": "witness_domain", - "role": "topology, shell, singular, cache, composition, phase, and route receipts", - "promotion_authority": False, - "failure": "witness bytes exceed remaining gain -> prune", - }, - { - "id": "owner_domain", - "role": "deterministic owner, cache dependency, route-to-chart assignment", - "promotion_authority": False, - "failure": "broadcast or ambiguous owner without tie-break -> fail closed", - }, - { - "id": "budget_domain", - "role": "byte, runtime, sidecar, witness, and evaluator capacity budgets", - "promotion_authority": False, - "failure": "domain budget hidden from lower bound -> invalid receipt", - }, - { - "id": "closure_domain", - "role": "NaN0, chi0, shell closure, rank decrease, rehydration status", - "promotion_authority": True, - "failure": "closure does not converge -> NaN0", - }, -] - - -EQUATIONS = [ - { - "id": "PSD0_stage_bundle", - "equation": "Stage_t = {D_t^byte, D_t^token, D_t^structure, D_t^residual, D_t^witness, D_t^owner, D_t^budget, D_t^closure}", - "meaning": "A route stage is a synchronized bundle of typed domains, not a single transform.", - }, - { - "id": "PSD1_parallel_transition", - "equation": "Stage_{t+1} = parallel_map(f_i, D_t^i) with sync barriers at claim boundaries", - "meaning": "Each domain advances with its own legal edge, then synchronizes before claims are compared.", - }, - { - "id": "PSD2_domain_contract", - "equation": "contract_i = (input_type_i, output_type_i, witness_cost_i, residual_obligation_i)", - "meaning": "Every domain edge declares what it consumes, emits, costs, and must repair.", - }, - { - "id": "PSD3_cross_domain_barrier", - "equation": "barrier_ok iff all obligations_i are paid and no domain has nan0_flag", - "meaning": "No domain can advance a promotion claim while another domain carries unpaid byte debt.", - }, - { - "id": "PSD4_stage_lower_bound", - "equation": "LB_stage = sum_i header_i + witness_i + residual_floor_i + compute_floor_i", - "meaning": "Lower bounds are summed across parallel domains before expensive evaluation.", - }, - { - "id": "PSD5_stage_promotion", - "equation": "promote iff sync(Stage_T) and hash(decode(D_T^byte + D_T^residual)) == source_hash and bytes < incumbent", - "meaning": "The stage bundle promotes only through exact bytes after all domains synchronize.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "parallel_stage_domain_route_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": { - "type": "local_design_refinement", - "statement": "Each stage is parallel domains of the data type being processed.", - "workspace_target": "Decision Diagram Compression Tuning Prior", - }, - "primary_decision": { - "name": "model_route_stage_as_parallel_domain_bundle", - "statement": ( - "Represent each DD route stage as a typed parallel domain bundle. " - "Domains may transform, propose, route, repair, witness, or bound " - "different views of the current data type, but they must synchronize " - "at claim boundaries and close through exact decoded bytes." - ), - }, - "domain_axes": DOMAIN_AXES, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "stage_id", - "stage_domain_vector_id", - "active_data_type_id", - "byte_domain_state_id", - "token_domain_state_id", - "structure_domain_state_id", - "residual_domain_state_id", - "witness_domain_state_id", - "owner_domain_state_id", - "budget_domain_state_id", - "closure_domain_state_id", - "domain_contract_hash", - "cross_domain_barrier_status", - "stage_lower_bound_bytes", - "domain_nan0_bitmap", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "open_parallel_stage_bundle", - "advance_byte_domain", - "advance_token_domain", - "advance_structure_domain", - "emit_domain_residual_obligation", - "charge_domain_witness_cost", - "assign_domain_owner", - "sum_stage_lower_bound", - "synchronize_stage_domains", - "reject_unsynchronized_promotion", - "close_stage_with_rehydration_hash", - ], - "promotion_rule": [ - "stage_domains_are_explicit_and_typed", - "each_domain_edge_declares_cost_and_residual_obligation", - "cross_domain_barrier_synchronizes_before_promotion", - "all_non_byte_exact_domains_emit_exact_residual_repair", - "domain_nan0_bitmap_is_zero", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "linear_stage_hides_parallel_domain_debt -> invalid_receipt", - "domain_advances_without_contract -> fail_closed", - "token_or_structure_domain_claims_byte_authority -> diagnostic_only", - "cross_domain_barrier_unsynchronized -> not_promoted", - "domain_nan0_bitmap_nonzero -> fail_closed", - "sum_domain_costs_exceeds_incumbent_margin -> prune", - ], - "claim_boundary": ( - "Parallel stage domains are a route representation discipline. They " - "do not relax the proof surface: exact decoded bytes, source hash, " - "measured total bytes, bounded costs, and explicit ratio schema remain " - "the only promotion authority." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["domain_axes"]: - lines.append({"type": "domain_axis", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/parquet_logogram_efficiency_probe.py b/4-Infrastructure/shim/parquet_logogram_efficiency_probe.py deleted file mode 100644 index 89df2740..00000000 --- a/4-Infrastructure/shim/parquet_logogram_efficiency_probe.py +++ /dev/null @@ -1,573 +0,0 @@ -#!/usr/bin/env python3 -"""Parquet to logogram efficiency accounting probe. - -This probe measures the user's proposed path: transcode sampled Parquet rows -into a logogram/species-code style payload, then account for byte gains or -losses with exact replay gates. It intentionally separates measured byte -accounting from promotion claims: positive savings are fixture evidence only, -and negative savings are still useful baseline values. - -It also tests the stronger hybrid hypothesis: keep Parquet as the physical -storage substrate and add a compact logogram sidecar for schema lineage, -prediction-cache routing, RAM-trace routing, and replay receipts. -""" - -from __future__ import annotations - -import argparse -import hashlib -import importlib.util -import json -import math -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import pyarrow as pa -import pyarrow.parquet as pq - - -REPO = Path(__file__).resolve().parents[2] -V2_SCRIPT = REPO / "4-Infrastructure" / "shim" / "enwiki9_logogram_xml_dict_probe.py" -OUT_DIR = REPO / "shared-data" / "data" / "parquet_logogram_efficiency" -PAYLOAD_JSON = OUT_DIR / "parquet_logogram_efficiency.json" -SUMMARY = OUT_DIR / "parquet_logogram_efficiency.md" -RECEIPT = OUT_DIR / "parquet_logogram_efficiency_receipt.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Parquet Logogram Efficiency.tid" -) - -PROTOCOL_ID = "PQLOG1" -SLICE_RECEIPT_ROOT_BYTES = 32 -PROTOCOL_ID_BYTES = len(PROTOCOL_ID.encode("ascii")) -SCHEMA_HASH_BYTES = 32 -ROW_COUNT_BYTES = 8 - -DEFAULT_INPUTS = [ - REPO / "3-Mathematical-Models" / "equations_parquet_tagged" / "mass_equations_unified.parquet", - REPO / "3-Mathematical-Models" / "equations_parquet_tagged" / "equations_unified_9pattern.parquet", - REPO / "shared-data" / "data" / "connectomes" / "openworm_parquet" / "summary.parquet", - REPO / "shared-data" / "data" / "datasets" / "mathlib4_complete.parquet", -] - - -def load_v2_module() -> Any: - spec = importlib.util.spec_from_file_location("enwiki9_logogram_xml_dict_probe", V2_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load logogram script: {V2_SCRIPT}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -V2 = load_v2_module() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str: - return sha256_bytes(path.read_bytes()) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def clean_value(value: Any) -> Any: - if isinstance(value, float): - if math.isnan(value) or math.isinf(value): - return None - return value - if isinstance(value, bytes): - return {"__bytes_hex__": value.hex()} - if isinstance(value, dict): - return {str(k): clean_value(v) for k, v in sorted(value.items(), key=lambda item: str(item[0]))} - if isinstance(value, (list, tuple)): - return [clean_value(item) for item in value] - return value - - -def dictionary_payload() -> dict[str, list[str]]: - return { - "fixed": [tag.hex() for tag in V2.FIXED_TAGS], - "pair": [tag.hex() for tag in V2.PAIR_TAGS], - "attr": [tag.hex() for tag in V2.ATTR_TAGS], - "motif": [tag.hex() for tag in V2.MOTIFS], - } - - -def encode_packet(data: bytes, name: str) -> dict[str, Any]: - core, atoms = V2.encode(data) - decoded = V2.decode_core(core) - core_path = OUT_DIR / f"{name}.wlg2" - core_path.write_bytes(core) - atom_counts = {kind: sum(1 for atom in atoms if atom.kind == kind) for kind in sorted({atom.kind for atom in atoms})} - packet_bytes = len(core) + SLICE_RECEIPT_ROOT_BYTES + PROTOCOL_ID_BYTES + SCHEMA_HASH_BYTES + ROW_COUNT_BYTES - return { - "core": rel(core_path), - "core_bytes": len(core), - "core_sha256": sha256_bytes(core), - "packet_bytes": packet_bytes, - "exact_replay": decoded == data, - "atom_count": len(atoms), - "atom_counts": atom_counts, - } - - -def exact_replay_pass(item: dict[str, Any]) -> bool: - return bool( - item["species_logogram"]["exact_replay"] - and item["schema_hash_recomputes"] - and item["row_count_matches"] - and item["residual_bounded"] - ) - - -def route_for_column(name: str, field_type: str, distinct_count: int, row_count: int, null_count: int) -> str: - lower = name.lower() - if any(token in lower for token in ["hash", "sha", "id", "doi", "source", "extracted", "timestamp"]): - return "ram_trace" - if row_count and distinct_count <= max(8, row_count // 8): - return "prediction_cache" - if "bool" in field_type or distinct_count <= 2: - return "prediction_cache" - if null_count == row_count: - return "prediction_cache" - return "parquet_native" - - -def column_sidecar(table: pa.Table, source: str, schema_hash: str, fields: list[dict[str, Any]]) -> tuple[bytes, dict[str, Any]]: - row_count = table.num_rows - columns: list[dict[str, Any]] = [] - for field in fields: - values = [clean_value(value) for value in table[field["name"]].to_pylist()] - encoded_values = [stable_json(value) for value in values] - distinct_count = len(set(encoded_values)) - null_count = sum(1 for value in values if value is None) - route = route_for_column(field["name"], field["type"], distinct_count, row_count, null_count) - columns.append( - { - "name": field["name"], - "type": field["type"], - "nullable": field["nullable"], - "distinct_count": distinct_count, - "null_count": null_count, - "route": route, - } - ) - plan = { - "prediction_cache_columns": [item["name"] for item in columns if item["route"] == "prediction_cache"], - "ram_trace_columns": [item["name"] for item in columns if item["route"] == "ram_trace"], - "parquet_native_columns": [item["name"] for item in columns if item["route"] == "parquet_native"], - } - sidecar = { - "protocol": "PQLOG-HYBRID-SIDECAR-v1", - "source": source, - "schema_hash": schema_hash, - "row_count": row_count, - "columns": columns, - "pathfinding_plan": plan, - "exact_replay_gate": [ - "parquet_sample_hash_recomputes", - "schema_hash_recomputes", - "row_count_matches", - "sidecar_decode_replays", - ], - } - return stable_json(sidecar).encode("utf-8"), sidecar - - -def sample_table(path: Path, row_limit: int) -> pa.Table: - parquet_file = pq.ParquetFile(path) - table = parquet_file.read_row_group(0) - return table.slice(0, min(row_limit, table.num_rows)) - - -def write_sample_parquet(table: pa.Table, out_path: Path) -> str: - for compression in ["zstd", "snappy", None]: - try: - pq.write_table(table, out_path, compression=compression) - return str(compression or "none") - except Exception: - continue - raise RuntimeError(f"could not write sample parquet: {out_path}") - - -def schema_record(path: Path, parquet_file: pq.ParquetFile, table: pa.Table) -> dict[str, Any]: - fields = [ - {"name": field.name, "type": str(field.type), "nullable": field.nullable} - for field in table.schema - ] - return { - "source_path": rel(path), - "source_bytes": path.stat().st_size, - "source_sha256": file_hash(path), - "source_rows": parquet_file.metadata.num_rows, - "source_row_groups": parquet_file.metadata.num_row_groups, - "source_columns": parquet_file.metadata.num_columns, - "sample_rows": table.num_rows, - "sample_columns": table.num_columns, - "fields": fields, - } - - -def canonical_forms(table: pa.Table) -> tuple[bytes, bytes, list[str], list[dict[str, Any]]]: - columns = table.column_names - rows = [{key: clean_value(value) for key, value in row.items()} for row in table.to_pylist()] - object_jsonl = b"".join((stable_json(row) + "\n").encode("utf-8") for row in rows) - species_payload = { - "schema": columns, - "row_count": len(rows), - "rows": [[clean_value(row.get(column)) for column in columns] for row in rows], - } - species_bytes = stable_json(species_payload).encode("utf-8") - return object_jsonl, species_bytes, columns, rows - - -def run_source(path: Path, row_limit: int) -> dict[str, Any]: - parquet_file = pq.ParquetFile(path) - table = sample_table(path, row_limit) - safe_name = path.stem.replace(".", "_").replace("-", "_") - sample_path = OUT_DIR / f"{safe_name}.sample.parquet" - compression = write_sample_parquet(table, sample_path) - object_bytes, species_bytes, columns, rows = canonical_forms(table) - object_path = OUT_DIR / f"{safe_name}.canonical_rows.jsonl" - species_path = OUT_DIR / f"{safe_name}.logogram_species_payload.json" - object_path.write_bytes(object_bytes) - species_path.write_bytes(species_bytes) - object_packet = encode_packet(object_bytes, f"{safe_name}.object") - species_packet = encode_packet(species_bytes, f"{safe_name}.species") - schema = schema_record(path, parquet_file, table) - schema_hash = hash_obj({"columns": columns, "fields": schema["fields"]}) - sidecar_bytes, sidecar = column_sidecar(table, rel(path), schema_hash, schema["fields"]) - sidecar_path = OUT_DIR / f"{safe_name}.hybrid_sidecar.json" - sidecar_path.write_bytes(sidecar_bytes) - sidecar_packet = encode_packet(sidecar_bytes, f"{safe_name}.hybrid_sidecar") - sample_parquet_bytes = sample_path.stat().st_size - species_global_bytes = species_packet["packet_bytes"] - object_global_bytes = object_packet["packet_bytes"] - hybrid_bytes = sample_parquet_bytes + sidecar_packet["packet_bytes"] - gate_pass = ( - species_packet["exact_replay"] - and schema_hash == hash_obj({"columns": columns, "fields": schema["fields"]}) - and len(rows) == table.num_rows - and sidecar_packet["exact_replay"] - ) - if gate_pass: - gate = "PASS_EXACT_REPLAY" - else: - gate = "FAIL_EXACT_REPLAY" - delta_vs_parquet = sample_parquet_bytes - species_global_bytes - delta_vs_object_canonical = len(object_bytes) - species_global_bytes - species_payload_gain = len(object_bytes) - len(species_bytes) - hybrid_delta_vs_parquet = sample_parquet_bytes - hybrid_bytes - hybrid_materialization_avoidance = len(object_bytes) - hybrid_bytes - return { - "name": safe_name, - "source": rel(path), - "schema": schema, - "schema_hash": schema_hash, - "sample_parquet": rel(sample_path), - "sample_parquet_bytes": sample_parquet_bytes, - "sample_parquet_sha256": file_hash(sample_path), - "sample_parquet_compression": compression, - "canonical_object_rows": rel(object_path), - "canonical_object_bytes": len(object_bytes), - "canonical_object_sha256": sha256_bytes(object_bytes), - "logogram_species_payload": rel(species_path), - "logogram_species_payload_bytes": len(species_bytes), - "logogram_species_payload_sha256": sha256_bytes(species_bytes), - "object_logogram": object_packet, - "species_logogram": species_packet, - "hybrid_sidecar": { - "path": rel(sidecar_path), - "payload_bytes": len(sidecar_bytes), - "payload_sha256": sha256_bytes(sidecar_bytes), - "logogram": sidecar_packet, - "route_counts": { - route: sum(1 for item in sidecar["columns"] if item["route"] == route) - for route in ["prediction_cache", "ram_trace", "parquet_native"] - }, - "pathfinding_plan": sidecar["pathfinding_plan"], - }, - "row_count_matches": len(rows) == table.num_rows, - "schema_hash_recomputes": schema_hash == hash_obj({"columns": columns, "fields": schema["fields"]}), - "residual_bounded": species_packet["exact_replay"] and sidecar_packet["exact_replay"], - "exact_replay_gate": gate, - "measured": { - "delta_vs_sample_parquet_bytes": delta_vs_parquet, - "gain_vs_sample_parquet_ratio": round(delta_vs_parquet / sample_parquet_bytes, 6) if sample_parquet_bytes else None, - "delta_vs_object_canonical_bytes": delta_vs_object_canonical, - "gain_vs_object_canonical_ratio": round(delta_vs_object_canonical / len(object_bytes), 6) if object_bytes else None, - "schema_key_reuse_payload_gain_bytes": species_payload_gain, - "schema_key_reuse_payload_gain_ratio": round(species_payload_gain / len(object_bytes), 6) if object_bytes else None, - "hybrid_sidecar_overhead_bytes": sidecar_packet["packet_bytes"], - "hybrid_total_bytes": hybrid_bytes, - "hybrid_delta_vs_sample_parquet_bytes": hybrid_delta_vs_parquet, - "hybrid_overhead_vs_sample_parquet_ratio": round(sidecar_packet["packet_bytes"] / sample_parquet_bytes, 6) if sample_parquet_bytes else None, - "hybrid_materialization_avoidance_bytes": hybrid_materialization_avoidance, - "hybrid_materialization_avoidance_ratio": round(hybrid_materialization_avoidance / len(object_bytes), 6) if object_bytes else None, - }, - "decision": "HOLD_PARQUET_LOGOGRAM_FIXTURE", - } - - -def aggregate(sources: list[dict[str, Any]], dictionary_bytes: int) -> dict[str, Any]: - sample_parquet = sum(item["sample_parquet_bytes"] for item in sources) - object_bytes = sum(item["canonical_object_bytes"] for item in sources) - species_payload = sum(item["logogram_species_payload_bytes"] for item in sources) - species_packet = sum(item["species_logogram"]["packet_bytes"] for item in sources) - object_packet = sum(item["object_logogram"]["packet_bytes"] for item in sources) - hybrid_sidecars = sum(item["hybrid_sidecar"]["logogram"]["packet_bytes"] for item in sources) - global_species = species_packet + dictionary_bytes - hybrid_total = sample_parquet + hybrid_sidecars + dictionary_bytes - delta_parquet = sample_parquet - global_species - delta_object = object_bytes - global_species - payload_gain = object_bytes - species_payload - hybrid_materialization = object_bytes - hybrid_total - return { - "source_count": len(sources), - "all_exact_replay": bool(sources) and all(exact_replay_pass(item) for item in sources), - "all_schema_hashes_recompute": bool(sources) and all(item["schema_hash_recomputes"] for item in sources), - "all_row_counts_match": bool(sources) and all(item["row_count_matches"] for item in sources), - "sample_parquet_bytes": sample_parquet, - "canonical_object_bytes": object_bytes, - "logogram_species_payload_bytes": species_payload, - "object_logogram_packet_bytes": object_packet, - "species_logogram_packet_bytes": species_packet, - "hybrid_sidecar_packet_bytes": hybrid_sidecars, - "dictionary_bytes": dictionary_bytes, - "species_global_bytes": global_species, - "hybrid_total_bytes": hybrid_total, - "delta_vs_sample_parquet_bytes": delta_parquet, - "gain_vs_sample_parquet_ratio": round(delta_parquet / sample_parquet, 6) if sample_parquet else None, - "delta_vs_object_canonical_bytes": delta_object, - "gain_vs_object_canonical_ratio": round(delta_object / object_bytes, 6) if object_bytes else None, - "schema_key_reuse_payload_gain_bytes": payload_gain, - "schema_key_reuse_payload_gain_ratio": round(payload_gain / object_bytes, 6) if object_bytes else None, - "hybrid_delta_vs_sample_parquet_bytes": sample_parquet - hybrid_total, - "hybrid_overhead_vs_sample_parquet_ratio": round((hybrid_sidecars + dictionary_bytes) / sample_parquet, 6) if sample_parquet else None, - "hybrid_materialization_avoidance_bytes": hybrid_materialization, - "hybrid_materialization_avoidance_ratio": round(hybrid_materialization / object_bytes, 6) if object_bytes else None, - "hybrid_route_counts": { - route: sum(item["hybrid_sidecar"]["route_counts"][route] for item in sources) - for route in ["prediction_cache", "ram_trace", "parquet_native"] - }, - } - - -def build_payload(paths: list[Path], row_limit: int) -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - existing = [path for path in paths if path.exists()] - missing = [path for path in paths if not path.exists()] - sources = [run_source(path, row_limit) for path in existing] - dictionary_json = stable_json(dictionary_payload()).encode("utf-8") - dictionary_bytes = len(dictionary_json) - dictionary_hash = sha256_bytes(dictionary_json) - payload = { - "schema": "parquet_logogram_efficiency_probe_v1", - "protocol_id": PROTOCOL_ID, - "claim_boundary": ( - "Parquet-to-logogram byte accounting only. This samples local Parquet files, " - "canonicalizes rows, transcodes them into the existing WLG2 logogram core, " - "and records exact replay plus byte deltas. It does not claim global " - "compression, canonical enwik9 performance, or replacement of Parquet." - ), - "inputs": { - "requested_paths": [rel(path) for path in paths], - "existing_paths": [rel(path) for path in existing], - "missing_paths": [rel(path) for path in missing], - "row_limit_per_source": row_limit, - }, - "accounting_equations": { - "parquet_logogram_transcode_efficiency": "E_pq_log=(bytes_sample_parquet-bytes_logogram_species_global)/bytes_sample_parquet", - "columnar_lineage_logogram_gain": "G_lineage=(bytes_object_canonical-bytes_species_payload)/bytes_object_canonical", - "parquet_logogram_exact_replay_gate": "G_pq_log=1[canonical_rows_decode]*1[schema_hash_recomputes]*1[row_count_matches]*1[residual_bounded]", - "hybrid_parquet_logogram_sidecar_cost": "C_hybrid=(bytes_sidecar_packet+bytes_dictionary)/bytes_sample_parquet", - "hybrid_materialization_avoidance_gain": "G_hybrid=(bytes_object_canonical-(bytes_sample_parquet+bytes_sidecar_packet+bytes_dictionary))/bytes_object_canonical", - "hybrid_cache_trace_route_selector": "route(column)=argmax(U_prediction_cache,U_ram_trace,U_parquet_native)", - }, - "dictionary": { - "source": rel(V2_SCRIPT), - "bytes": dictionary_bytes, - "sha256": dictionary_hash, - }, - "sources": sources, - "aggregates": aggregate(sources, dictionary_bytes), - "decision": ( - "ADMIT_PARQUET_LOGOGRAM_EFFICIENCY_AS_HOLD_FIXTURE" - if sources - else "HOLD_NO_PARQUET_INPUTS" - ), - } - payload["finding"] = ( - "The useful accounting split is between Parquet bytes, full object-row canonical bytes, " - "and logogram species-code bytes. Schema/key reuse can reduce the canonical row surface, " - "but WLG2 packet plus dictionary costs must still beat Parquet before any compression gain is claimed. " - "The hybrid path keeps Parquet as substrate and uses logograms as a sidecar for schema lineage, " - "prediction-cache columns, RAM-trace columns, and exact replay gates." - ) - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "parquet_logogram_efficiency_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "aggregates": payload["aggregates"], - "source_hashes": {item["source"]: item["schema"]["source_sha256"] for item in payload["sources"]}, - "sample_hashes": {item["source"]: item["sample_parquet_sha256"] for item in payload["sources"]}, - "dictionary_sha256": payload["dictionary"]["sha256"], - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - agg = payload["aggregates"] - lines = [ - "# Parquet Logogram Efficiency", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Finding", - "", - payload["finding"], - "", - "## Aggregate", - "", - f"- Sources: `{agg['source_count']}`", - f"- Exact replay: `{agg['all_exact_replay']}`", - f"- Sample Parquet bytes: `{agg['sample_parquet_bytes']}`", - f"- Object canonical bytes: `{agg['canonical_object_bytes']}`", - f"- Logogram species global bytes: `{agg['species_global_bytes']}`", - f"- Delta vs sample Parquet: `{agg['delta_vs_sample_parquet_bytes']}` ({agg['gain_vs_sample_parquet_ratio']})", - f"- Delta vs object canonical: `{agg['delta_vs_object_canonical_bytes']}` ({agg['gain_vs_object_canonical_ratio']})", - f"- Schema/key reuse payload gain: `{agg['schema_key_reuse_payload_gain_bytes']}` ({agg['schema_key_reuse_payload_gain_ratio']})", - f"- Hybrid sidecar packet bytes: `{agg['hybrid_sidecar_packet_bytes']}`", - f"- Hybrid total bytes: `{agg['hybrid_total_bytes']}`", - f"- Hybrid overhead vs sample Parquet: `{agg['hybrid_delta_vs_sample_parquet_bytes']}` ({agg['hybrid_overhead_vs_sample_parquet_ratio']})", - f"- Hybrid materialization avoidance: `{agg['hybrid_materialization_avoidance_bytes']}` ({agg['hybrid_materialization_avoidance_ratio']})", - f"- Hybrid route counts: `{agg['hybrid_route_counts']}`", - "", - "## Equations", - "", - ] - for name, equation in payload["accounting_equations"].items(): - lines.append(f"- `{name}`: `{equation}`") - lines.extend( - [ - "", - "## Per Source", - "", - "| Source | Rows | Sample parquet | Object canonical | Species payload | Species packet | Delta vs parquet | Delta vs object | Gate |", - "|---|---:|---:|---:|---:|---:|---:|---:|---|", - ] - ) - for item in payload["sources"]: - measured = item["measured"] - lines.append( - f"| `{item['source']}` | {item['schema']['sample_rows']} | {item['sample_parquet_bytes']} | " - f"{item['canonical_object_bytes']} | {item['logogram_species_payload_bytes']} | " - f"{item['species_logogram']['packet_bytes']} | {measured['delta_vs_sample_parquet_bytes']} | " - f"{measured['delta_vs_object_canonical_bytes']} | {item['exact_replay_gate']} |" - ) - lines.extend(["", "## Receipt", "", f"`{rel(RECEIPT)}`"]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - agg = payload["aggregates"] - lines = [ - "title: Parquet Logogram Efficiency", - "tags: Parquet Logogram Efficiency HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Parquet Logogram Efficiency", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Accounting", - "", - f"* `E_pq_log`: `{agg['gain_vs_sample_parquet_ratio']}`", - f"* `G_lineage`: `{agg['schema_key_reuse_payload_gain_ratio']}`", - f"* `C_hybrid`: `{agg['hybrid_overhead_vs_sample_parquet_ratio']}`", - f"* `G_hybrid`: `{agg['hybrid_materialization_avoidance_ratio']}`", - f"* Hybrid route counts: `{agg['hybrid_route_counts']}`", - f"* Exact replay: `{agg['all_exact_replay']}`", - "", - "!! Equations", - "", - ] - for name, equation in payload["accounting_equations"].items(): - lines.append(f"* `{name}`: `{equation}`") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - "!! Links", - "", - "* [[Combined Approach Equation Surface]]", - "* [[TranscriptFormer Evolutionary Prior]]", - "", - f"Receipt: `{rel(RECEIPT)}`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - parser = argparse.ArgumentParser(description="Account for Parquet to logogram transcode efficiency.") - parser.add_argument("paths", nargs="*", type=Path) - parser.add_argument("--row-limit", type=int, default=256) - args = parser.parse_args() - paths = args.paths or DEFAULT_INPUTS - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload(paths, args.row_limit) - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/parquet_logogram_eigenprobe.py b/4-Infrastructure/shim/parquet_logogram_eigenprobe.py deleted file mode 100644 index 369c8f50..00000000 --- a/4-Infrastructure/shim/parquet_logogram_eigenprobe.py +++ /dev/null @@ -1,363 +0,0 @@ -#!/usr/bin/env python3 -"""Eigenprobe for Parquet/logogram efficiency outcomes. - -This reads the Parquet logogram efficiency receipt and asks why the measured -byte outcomes happen. The probe builds a small feature matrix per sampled -Parquet source, decomposes the standardized covariance matrix, and records the -dominant axes plus feature correlations against the packet-vs-Parquet result. -With four fixtures this is diagnostic, not statistical proof. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -import numpy as np - - -REPO = Path(__file__).resolve().parents[2] -INPUT = REPO / "shared-data" / "data" / "parquet_logogram_efficiency" / "parquet_logogram_efficiency.json" -INPUT_RECEIPT = REPO / "shared-data" / "data" / "parquet_logogram_efficiency" / "parquet_logogram_efficiency_receipt.json" -OUT_DIR = REPO / "shared-data" / "data" / "parquet_logogram_eigenprobe" -PAYLOAD_JSON = OUT_DIR / "parquet_logogram_eigenprobe.json" -SUMMARY = OUT_DIR / "parquet_logogram_eigenprobe.md" -RECEIPT = OUT_DIR / "parquet_logogram_eigenprobe_receipt.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Parquet Logogram Eigenprobe.tid" -) - - -FEATURES = [ - "sample_rows", - "sample_columns", - "sample_parquet_bytes", - "canonical_object_bytes", - "object_to_parquet_expansion", - "species_payload_bytes", - "species_packet_bytes", - "species_to_parquet_ratio", - "schema_key_reuse_ratio", - "hybrid_sidecar_packet_bytes", - "hybrid_overhead_ratio", - "prediction_cache_column_ratio", - "ram_trace_column_ratio", - "parquet_native_column_ratio", - "species_atom_density", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def verify_embedded_hash(obj: dict[str, Any], embedded_key: str, exclude_keys: set[str] | None = None) -> dict[str, Any]: - exclude = {embedded_key, *(exclude_keys or set())} - embedded = obj.get(embedded_key) - recomputed = hash_obj({k: v for k, v in obj.items() if k not in exclude}) - return { - "embedded": embedded, - "recomputed": recomputed, - "matches": embedded == recomputed, - } - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def route_ratio(item: dict[str, Any], route: str) -> float: - counts = item.get("hybrid_sidecar", {}).get("route_counts", {}) - total = sum(counts.values()) - return counts.get(route, 0) / total if total else 0.0 - - -def source_vector(item: dict[str, Any]) -> dict[str, float]: - sample = float(item["sample_parquet_bytes"]) - canonical = float(item["canonical_object_bytes"]) - species_payload = float(item["logogram_species_payload_bytes"]) - species_packet = float(item["species_logogram"]["packet_bytes"]) - sidecar_packet = float(item.get("hybrid_sidecar", {}).get("logogram", {}).get("packet_bytes", 0)) - atom_count = float(item["species_logogram"].get("atom_count", 0)) - return { - "sample_rows": float(item["schema"]["sample_rows"]), - "sample_columns": float(item["schema"]["sample_columns"]), - "sample_parquet_bytes": sample, - "canonical_object_bytes": canonical, - "object_to_parquet_expansion": canonical / sample if sample else 0.0, - "species_payload_bytes": species_payload, - "species_packet_bytes": species_packet, - "species_to_parquet_ratio": species_packet / sample if sample else 0.0, - "schema_key_reuse_ratio": float(item["measured"]["schema_key_reuse_payload_gain_ratio"]), - "hybrid_sidecar_packet_bytes": sidecar_packet, - "hybrid_overhead_ratio": sidecar_packet / sample if sample else 0.0, - "prediction_cache_column_ratio": route_ratio(item, "prediction_cache"), - "ram_trace_column_ratio": route_ratio(item, "ram_trace"), - "parquet_native_column_ratio": route_ratio(item, "parquet_native"), - "species_atom_density": atom_count / species_payload if species_payload else 0.0, - } - - -def corr(xs: np.ndarray, ys: np.ndarray) -> float: - if np.std(xs) == 0 or np.std(ys) == 0: - return 0.0 - return float(np.corrcoef(xs, ys)[0, 1]) - - -def component_entry(index: int, value: float, vector: np.ndarray, explained: float, names: list[str]) -> dict[str, Any]: - loadings = sorted( - [{"feature": names[i], "loading": round(float(vector[i]), 6)} for i in range(len(names))], - key=lambda item: abs(item["loading"]), - reverse=True, - ) - return { - "component": index + 1, - "eigenvalue": round(float(value), 6), - "explained_variance_ratio": round(float(explained), 6), - "top_loadings": loadings[:8], - } - - -def build_payload() -> dict[str, Any]: - data = json.loads(INPUT.read_text(encoding="utf-8")) - receipt = json.loads(INPUT_RECEIPT.read_text(encoding="utf-8")) if INPUT_RECEIPT.exists() else {} - input_payload_verification = verify_embedded_hash(data, "payload_hash") - input_receipt_verification = ( - verify_embedded_hash(receipt, "receipt_hash", {"generated_at_utc"}) - if receipt - else {"embedded": None, "recomputed": None, "matches": False} - ) - rows = [] - for item in data["sources"]: - vector = source_vector(item) - rows.append( - { - "name": item["name"], - "source": item["source"], - "target_gain_vs_parquet": float(item["measured"]["gain_vs_sample_parquet_ratio"]), - "target_hybrid_materialization_avoidance": float(item["measured"].get("hybrid_materialization_avoidance_ratio", 0.0)), - "features": vector, - } - ) - matrix = np.array([[row["features"][feature] for feature in FEATURES] for row in rows], dtype=float) - means = matrix.mean(axis=0) - stds = matrix.std(axis=0) - stds[stds == 0] = 1.0 - standardized = (matrix - means) / stds - covariance = np.cov(standardized, rowvar=False) - values, vectors = np.linalg.eigh(covariance) - order = np.argsort(values)[::-1] - values = values[order] - vectors = vectors[:, order] - total = float(values.sum()) or 1.0 - components = [ - component_entry(i, values[i], vectors[:, i], values[i] / total, FEATURES) - for i in range(min(3, len(values))) - ] - target = np.array([row["target_gain_vs_parquet"] for row in rows], dtype=float) - hybrid_target = np.array([row["target_hybrid_materialization_avoidance"] for row in rows], dtype=float) - correlations = sorted( - [ - { - "feature": feature, - "corr_gain_vs_parquet": round(corr(matrix[:, i], target), 6), - "corr_hybrid_materialization": round(corr(matrix[:, i], hybrid_target), 6), - } - for i, feature in enumerate(FEATURES) - ], - key=lambda item: abs(item["corr_gain_vs_parquet"]), - reverse=True, - ) - scores = standardized @ vectors - for row_index, row in enumerate(rows): - row["component_scores"] = { - f"pc{i + 1}": round(float(scores[row_index, i]), 6) - for i in range(min(3, scores.shape[1])) - } - input_hashes_recompute = input_payload_verification["matches"] and input_receipt_verification["matches"] - payload = { - "schema": "parquet_logogram_eigenprobe_v1", - "input_payload": rel(INPUT), - "input_payload_hash": input_payload_verification["recomputed"], - "input_payload_hash_embedded": input_payload_verification["embedded"], - "input_payload_hash_recomputes": input_payload_verification["matches"], - "input_receipt": rel(INPUT_RECEIPT), - "input_receipt_hash": input_receipt_verification["recomputed"], - "input_receipt_hash_embedded": input_receipt_verification["embedded"], - "input_receipt_hash_recomputes": input_receipt_verification["matches"], - "claim_boundary": ( - "Eigenprobe diagnostic only. Four fixture rows are enough to explain the " - "current accounting direction, not enough for statistical generalization. " - "All equations remain HOLD until larger fixture sweeps and negative controls." - ), - "feature_names": FEATURES, - "rows": rows, - "principal_components": components, - "feature_target_correlations": correlations, - "interpretation": [ - "Parquet is winning raw bytes because it already compresses the physical columnar table; WLG2 is currently a generic XML/wiki-oriented logogram packet, so large free-text/content columns inflate the species packet.", - "The equation tables show strong schema/key reuse, so logogram species payloads beat object-row canonical forms, but that gain is not enough to beat Parquet compression.", - "The connectome summary is the small positive case because the table is tiny, low-column, and highly structured; the sidecar/reuse signal is large relative to payload.", - "The hybrid path is different: its cost is a small sidecar overhead over Parquet, while its gain is avoided canonical materialization plus cache/trace routing, not replacement compression.", - ], - "decision": ( - "ADMIT_PARQUET_LOGOGRAM_EIGENPROBE_AS_HOLD_DIAGNOSTIC" - if input_hashes_recompute - else "HOLD_PARQUET_LOGOGRAM_EIGENPROBE_INPUT_HASH_MISMATCH" - ), - } - payload["aggregates"] = { - "source_count": len(rows), - "feature_count": len(FEATURES), - "component_count": len(components), - "top_gain_correlations": correlations[:5], - "dominant_component": components[0] if components else None, - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "parquet_logogram_eigenprobe_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "input_payload_hash": payload["input_payload_hash"], - "input_payload_hash_recomputes": payload["input_payload_hash_recomputes"], - "input_receipt_hash": payload["input_receipt_hash"], - "input_receipt_hash_recomputes": payload["input_receipt_hash_recomputes"], - "aggregates": payload["aggregates"], - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Parquet Logogram Eigenprobe", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "## Input Verification", - "", - f"- Payload hash recomputes: `{payload['input_payload_hash_recomputes']}`", - f"- Receipt hash recomputes: `{payload['input_receipt_hash_recomputes']}`", - "", - payload["claim_boundary"], - "", - "## Interpretation", - "", - ] - for item in payload["interpretation"]: - lines.append(f"- {item}") - lines.extend(["", "## Principal Components", ""]) - for component in payload["principal_components"]: - top = ", ".join(f"{item['feature']}={item['loading']}" for item in component["top_loadings"][:5]) - lines.append( - f"- PC{component['component']}: eigenvalue `{component['eigenvalue']}`, " - f"explained `{component['explained_variance_ratio']}`; {top}" - ) - lines.extend(["", "## Target Correlations", "", "| Feature | Corr gain vs Parquet | Corr hybrid materialization |", "|---|---:|---:|"]) - for item in payload["feature_target_correlations"]: - lines.append(f"| {item['feature']} | {item['corr_gain_vs_parquet']} | {item['corr_hybrid_materialization']} |") - lines.extend(["", "## Source Scores", "", "| Source | Gain vs Parquet | Hybrid materialization | PC1 | PC2 | PC3 |", "|---|---:|---:|---:|---:|---:|"]) - for row in payload["rows"]: - scores = row["component_scores"] - lines.append( - f"| `{row['source']}` | {row['target_gain_vs_parquet']} | {row['target_hybrid_materialization_avoidance']} | " - f"{scores.get('pc1')} | {scores.get('pc2')} | {scores.get('pc3')} |" - ) - lines.extend(["", "## Receipt", "", f"`{rel(RECEIPT)}`"]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: Parquet Logogram Eigenprobe", - "tags: Parquet Logogram Eigenprobe HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Parquet Logogram Eigenprobe", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Input Verification", - "", - f"* Payload hash recomputes: `{payload['input_payload_hash_recomputes']}`", - f"* Receipt hash recomputes: `{payload['input_receipt_hash_recomputes']}`", - "", - "!! Why The Result Happens", - "", - ] - for item in payload["interpretation"]: - lines.append(f"* {item}") - lines.extend(["", "!! Dominant Axis", ""]) - dominant = payload["aggregates"]["dominant_component"] - if dominant: - lines.append(f"PC{dominant['component']} explains `{dominant['explained_variance_ratio']}` of fixture variance.") - for item in dominant["top_loadings"][:6]: - lines.append(f"* `{item['feature']}`: `{item['loading']}`") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - "!! Links", - "", - "* [[Parquet Logogram Efficiency]]", - "* [[Combined Approach Equation Surface]]", - "", - f"Receipt: `{rel(RECEIPT)}`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/pde_model_prior_metaprobe.py b/4-Infrastructure/shim/pde_model_prior_metaprobe.py deleted file mode 100644 index 9b3d4f0c..00000000 --- a/4-Infrastructure/shim/pde_model_prior_metaprobe.py +++ /dev/null @@ -1,247 +0,0 @@ -#!/usr/bin/env python3 -"""PDE model prior metaprobe for n-space LLM tuning. - -PDE foundation/assistant models are useful here as compression coordinates: -operator tokens, boundary-condition conditioning, spatiotemporal grids, -shape-agnostic fields, control autoformalization, and code-generation routes. -This script records verified priors and softer user-supplied candidates without -turning any of them into numerical truth. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -PDE_AXES = [ - { - "axis": "operator_learning", - "payload": ["PDE_operator", "initial_condition", "boundary_condition", "coefficients", "solution_field"], - "router_use": "map symbolic PDE descriptions into operator-family compression cells", - "receipt_rule": "record PDE class, boundary distribution, discretization, and residual/evaluation metric", - }, - { - "axis": "spatiotemporal_field", - "payload": ["space_dim", "time_steps", "grid_or_mesh", "field_channels", "resolution"], - "router_use": "n-space field packet for transformer/neural-operator priors", - "receipt_rule": "record dimension, units, grid/mesh type, and downsample/patching rule", - }, - { - "axis": "shape_agnostic_geometry", - "payload": ["1D", "2D", "3D", "heterogeneous_resolution", "scalar_vector_components"], - "router_use": "geometry-invariant compression axis across domains", - "receipt_rule": "record geometry map, coordinate frame, and transfer/fine-tune target", - }, - { - "axis": "pde_workflow_controller", - "payload": ["informal_spec", "formal_spec", "subgoal", "solver_code", "utility_metric"], - "router_use": "route language claims into formal/controller/code-generation surfaces", - "receipt_rule": "formal spec and generated code must be checked by external solver or local verifier", - }, - { - "axis": "mesh_free_residual_probe", - "payload": ["coordinate_sample", "pde_residual", "boundary_residual", "loss_weight", "collocation_seed"], - "router_use": "PINN-style sparse coordinate probes for n-space manifolds without Cartesian grid expansion", - "receipt_rule": "record sampled coordinates, PDE residual definition, boundary residual, seed, and held-out residual check", - }, - { - "axis": "latent_operator_compression", - "payload": ["function_space_token", "spectral_modes", "latent_operator", "decode_rule", "resolution_transfer"], - "router_use": "FNO/neural-operator style compression of function-space dynamics into reusable latent operators", - "receipt_rule": "record train/eval resolution, retained modes, operator family, and extrapolation target", - }, - { - "axis": "stochastic_path_solver", - "payload": ["brownian_path_seed", "terminal_condition", "gradient_estimate", "control_value", "path_batch"], - "router_use": "Deep-BSDE style path sampling for very high-dimensional control/HJB-like PDE routing", - "receipt_rule": "record path seeds, terminal condition, gradient network version, and variance/error estimate", - }, - { - "axis": "tensor_train_factorization", - "payload": ["tt_rank", "core_index", "factor_core", "boundary_slice", "reconstruction_error"], - "router_use": "tensor-train/decomposition compression for high-dimensional fields with sparse information volume", - "receipt_rule": "record TT ranks, core shapes, reconstruction error, and boundary slices retained", - }, -] - - -VERIFIED_PDE_PRIORS = [ - { - "id": "POSEIDON", - "role": "multiscale_operator_transformer_pde_foundation_model", - "boundary": "paper/project-prior-only", - "use_as": "operator_foundation_model_axis", - "source": "Poseidon: Efficient Foundation Models for PDEs", - "url": "https://arxiv.org/abs/2405.19101", - "notes": "Multiscale operator transformer / scOT style PDE foundation model; generalization prior, not local PDE truth.", - }, - { - "id": "MORPH", - "role": "shape_agnostic_pde_foundation_model", - "boundary": "paper/model-card-prior-only", - "use_as": "shape_agnostic_field_axis", - "source": "MORPH: Shape-agnostic PDE Foundation Models", - "url": "https://arxiv.org/abs/2509.21670", - "notes": "Handles heterogeneous 1D/2D/3D spatiotemporal PDE datasets with scalar/vector fields.", - }, - { - "id": "PDE-Controller", - "role": "llm_autoformalization_and_pde_control_workflow", - "boundary": "project/paper-prior-only", - "use_as": "formal_spec_and_controller_axis", - "source": "PDE-Controller: LLMs for Autoformalization and Reasoning of PDEs", - "url": "https://pde-controller.github.io/", - "notes": "Routes informal PDE control problems into formal specifications, subgoals, and solver/code workflows.", - }, - { - "id": "CodePDE", - "role": "llm_generated_pde_solver_code", - "boundary": "paper-prior-only", - "use_as": "solver_code_generation_axis", - "source": "CodePDE: An Inference Framework for LLM-driven PDE Solver Generation", - "url": "https://arxiv.org/abs/2505.08783", - "notes": "Frames PDE solving as numerical solver code generation.", - }, - { - "id": "Unisolver", - "role": "pde_conditional_transformer_universal_solver", - "boundary": "paper-prior-only", - "use_as": "pde_conditioned_sequence_solver_axis", - "source": "Unisolver: PDE-Conditional Transformers Are Universal PDE Solvers", - "url": "https://arxiv.org/abs/2405.17527", - "notes": "Useful correction for the user-supplied Universal Physics Solver label: source names Unisolver.", - }, - { - "id": "Aurora", - "role": "earth_system_weather_foundation_model", - "boundary": "paper/project-prior-only", - "use_as": "atmospheric_spatiotemporal_field_axis", - "source": "Aurora: A Foundation Model of the Atmosphere / Earth System", - "url": "https://arxiv.org/abs/2405.13063", - "notes": "Weather/atmospheric foundation model; PDE-adjacent via learned atmospheric dynamics, not a general PDE solver receipt.", - }, - { - "id": "Prithvi-WxC", - "role": "weather_climate_foundation_model", - "boundary": "paper/model-card-prior-only", - "use_as": "weather_climate_field_axis", - "source": "Prithvi WxC: Foundation Model for Weather and Climate", - "url": "https://arxiv.org/abs/2409.13598", - "notes": "2.3B weather/climate model released via Hugging Face; useful for large-token spatiotemporal topology.", - }, - { - "id": "CoDA-NO", - "role": "codomain_attention_neural_operator", - "boundary": "paper-prior-only", - "use_as": "multiphysics_channel_tokenization_axis", - "source": "Pretraining Codomain Attention Neural Operators for Solving Multiphysics PDEs", - "url": "https://arxiv.org/abs/2403.12553", - "notes": "Tokenizes functions along codomain/channel space; strong n-space compression analogy.", - }, -] - - -SOFT_PDE_CANDIDATES = [ - { - "id": "LLM4PDE", - "status": "needs_exact_primary_source", - "user_claim": "language-conditioned neural solver integrating language-style PDE encodings with operator-learning backbones", - "use_as": "candidate_language_conditioned_operator_axis", - }, - { - "id": "ICON-LM", - "status": "needs_exact_primary_source", - "user_claim": "in-context operator learning with text and data prompts", - "use_as": "candidate_in_context_operator_axis", - }, -] - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are an n-space PDE compression router. Return compact JSON with evidence boundaries." - records = [] - for axis in receipt["pde_axes"]: - prompt = { - "task": "route_pde_axis", - "axis": axis["axis"], - "payload": axis["payload"], - "instruction": "Use this PDE axis as a compression/routing coordinate.", - } - answer = { - "selected": True, - "use_as": axis["router_use"], - "claim_boundary": "pde-coordinate-prior-only", - "surface_payload_hint": axis["axis"][:16].upper(), - "receipt_rule": axis["receipt_rule"], - } - records.append(chat_record(system, prompt, answer)) - for prior in receipt["verified_pde_priors"]: - prompt = { - "task": "use_pde_model_prior", - "model": prior["id"], - "role": prior["role"], - "source": prior["source"], - "instruction": "Explain how this PDE model should tune routing without becoming numerical proof.", - } - answer = { - "selected": True, - "use_as": prior["use_as"], - "claim_boundary": prior["boundary"], - "metaprobe_rule": "Use as architecture/corpus coordinate; require residual/source/solver receipts for any PDE claim.", - } - records.append(chat_record(system, prompt, answer)) - for candidate in receipt["soft_pde_candidates"]: - prompt = { - "task": "handle_unverified_pde_candidate", - "candidate": candidate["id"], - "user_claim": candidate["user_claim"], - "instruction": "Route this candidate conservatively.", - } - answer = { - "selected": False, - "use_as": candidate["use_as"], - "claim_boundary": "needs-primary-source-before-training-weight", - "next_action": "Keep as soft candidate until exact paper/model card is pinned.", - } - records.append(chat_record(system, prompt, answer)) - return records - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/pde_model_prior_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/pde_model_prior_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "pde_model_prior_receipt_v1", - "claim_boundary": "PDE model priors tune n-space routing and compression; they do not solve or validate local PDEs.", - "pde_axes": PDE_AXES, - "verified_pde_priors": VERIFIED_PDE_PRIORS, - "soft_pde_candidates": SOFT_PDE_CANDIDATES, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/pde_tiny_replay_harness.py b/4-Infrastructure/shim/pde_tiny_replay_harness.py deleted file mode 100644 index 440c9033..00000000 --- a/4-Infrastructure/shim/pde_tiny_replay_harness.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -"""Tiny PDE-style replay harness. - -This is the first local replay fixture for the PDEBench route surface. It uses -deterministic built-in micro-fixtures only: no external PDEBench data is -downloaded, vendored, or scored. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "pde_tiny_replay" -RECEIPT = OUT_DIR / "pde_tiny_replay_receipt.json" -TABLE = OUT_DIR / "pde_tiny_replay_table.jsonl" - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - route_surface: str - law_family: str - grid_n: int - steps: int - shift_per_step: int - truth_boundary: str - candidate_boundary: str - initial_support: list[int] - negative_control: bool - - -FIXTURES = [ - Fixture( - fixture_id="advection_periodic_exact_shift_admit", - route_surface="PDEBench", - law_family="linear_advection_integer_cfl", - grid_n=64, - steps=48, - shift_per_step=1, - truth_boundary="periodic", - candidate_boundary="periodic", - initial_support=[0, 3, 7, 15, 31], - negative_control=False, - ), - Fixture( - fixture_id="advection_wrong_boundary_negative", - route_surface="PDEBench", - law_family="linear_advection_integer_cfl", - grid_n=64, - steps=48, - shift_per_step=1, - truth_boundary="periodic", - candidate_boundary="zero_outflow", - initial_support=[0, 3, 7, 15, 31], - negative_control=True, - ), - Fixture( - fixture_id="advection_short_exact_hold_diagnostic", - route_surface="PDEBench", - law_family="linear_advection_integer_cfl", - grid_n=8, - steps=2, - shift_per_step=1, - truth_boundary="periodic", - candidate_boundary="periodic", - initial_support=[0, 3], - negative_control=False, - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def initial_state(grid_n: int, support: list[int]) -> list[int]: - values = [0] * grid_n - for index in support: - values[index % grid_n] = 1 - return values - - -def step_advection(values: list[int], shift: int, boundary: str) -> list[int]: - out = [0] * len(values) - for index, value in enumerate(values): - target = index + shift - if boundary == "periodic": - out[target % len(values)] = value - elif boundary == "zero_outflow": - if 0 <= target < len(values): - out[target] = value - else: - raise ValueError(f"unsupported boundary {boundary}") - return out - - -def trajectory(fixture: Fixture, boundary: str) -> list[list[int]]: - rows = [initial_state(fixture.grid_n, fixture.initial_support)] - for _ in range(fixture.steps): - rows.append(step_advection(rows[-1], fixture.shift_per_step, boundary)) - return rows - - -def mismatch_rows(truth: list[list[int]], candidate: list[list[int]]) -> list[dict[str, int]]: - mismatches: list[dict[str, int]] = [] - for t_index, (truth_row, candidate_row) in enumerate(zip(truth, candidate)): - for x_index, (truth_value, candidate_value) in enumerate(zip(truth_row, candidate_row)): - if truth_value != candidate_value: - mismatches.append( - { - "t": t_index, - "x": x_index, - "truth": truth_value, - "candidate": candidate_value, - } - ) - return mismatches - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - truth = trajectory(fixture, fixture.truth_boundary) - candidate = trajectory(fixture, fixture.candidate_boundary) - mismatches = mismatch_rows(truth, candidate) - replay_valid = not mismatches - residual_declared = True - - encoded_payload = { - "law_family": fixture.law_family, - "grid_n": fixture.grid_n, - "steps": fixture.steps, - "shift_per_step": fixture.shift_per_step, - "boundary": fixture.candidate_boundary, - "initial_support": fixture.initial_support, - } - explicit_payload = { - "trajectory": truth, - } - residual_payload = {"mismatches": mismatches} - - encoded_bytes = len(stable_json(encoded_payload).encode("utf-8")) - explicit_bytes = len(stable_json(explicit_payload).encode("utf-8")) - residual_bytes = 0 if replay_valid else len(stable_json(residual_payload).encode("utf-8")) - total_candidate_bytes = encoded_bytes + residual_bytes - byte_gain = explicit_bytes - total_candidate_bytes - - if fixture.negative_control and replay_valid: - status = "FAIL_NEGATIVE_CONTROL" - elif replay_valid and residual_declared and byte_gain > 0 and not fixture.negative_control: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "route_surface": fixture.route_surface, - "law_family": fixture.law_family, - "truth_boundary": fixture.truth_boundary, - "candidate_boundary": fixture.candidate_boundary, - "negative_control": fixture.negative_control, - "grid_n": fixture.grid_n, - "steps": fixture.steps, - "shift_per_step": fixture.shift_per_step, - "initial_support": fixture.initial_support, - "trajectory_hash": sha256_text(stable_json(truth)), - "candidate_hash": sha256_text(stable_json(candidate)), - "mismatch_count": len(mismatches), - "replay_valid": replay_valid, - "residual_declared": residual_declared, - "encoded_bytes": encoded_bytes, - "explicit_bytes": explicit_bytes, - "residual_bytes": residual_bytes, - "byte_gain": byte_gain, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "pde_tiny_replay_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "fixture_count": len(results), - "table": rel(TABLE), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Tiny PDE-style replay fixture only. It tests deterministic local " - "advection replay, wrong-boundary negative controls, residual " - "accounting, and byte-law diagnostics; it is not PDEBench data ingest, " - "not a PDEBench benchmark score, and not a compression benchmark." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/phi_scaling_fix_proposals.md b/4-Infrastructure/shim/phi_scaling_fix_proposals.md deleted file mode 100644 index eed0972e..00000000 --- a/4-Infrastructure/shim/phi_scaling_fix_proposals.md +++ /dev/null @@ -1,191 +0,0 @@ -# Φ-Scaling Equation Fix Proposals - -## Test Results Summary - -| Test | Status | Error | Issue | -|------|--------|-------|-------| -| LTEE Fitness | FAIL | 133.45% | Square-root scaling too aggressive | -| Drake's Rule | FAIL | 60.61% | Per-genome rate assumption wrong | -| Fractal Dimension | PASS | 5.06% | Works well - keep as is | -| Sampling Coincidence | PARTIAL | 7.67% | Close but not exact | - -## Proposed Fixes - -### Fix 1: LTEE Fitness Trajectory - -**Problem**: Simple square-root scaling `P ∝ S^{1/2}` overpredicts fitness dramatically at higher mutation counts (237.5% error at 50,000 generations). - -**Root Cause**: LTEE exhibits stronger diminishing returns than simple square-root due to: -- Clonal interference (multiple beneficial mutations compete) -- Resource limitation (carrying capacity 500M cells, 25 mg/L glucose) -- Epistatic interactions (negative epistasis between mutations) -- Mutation rate evolution (mutator strains appear) - -**Proposed Fix**: Replace square-root with a selected response family that incorporates epistatic interference: - -``` -P = C_domain · (S / (K + S))^α · lambda_phi^{D_f} · B_gate -``` - -where: -- `K` = half-saturation constant (epistatic interference strength) -- `α` = scaling exponent (fit to data, likely < 0.5) -- This is a Michaelis-Menten type saturating function - -**Alternative**: Use logarithmic scaling with epistatic correction: - -``` -P = C_domain · log(1 + β·S) · lambda_phi^{D_f} · B_gate -``` - -where: -- `β` = epistatic interference coefficient -- Logarithmic scaling naturally gives diminishing returns - -**Expected Improvement**: Logarithmic or saturating functions should capture the observed LTEE fitness trajectory more accurately than simple power law. - -**Model-selection update**: A local response-family sweep found: - -``` -best tested LTEE response: - hill_saturation - avg_error = 0.40604904495100724% - K = 200 - hill = 0.5 - -nearest logarithmic response: - log_mutations - avg_error = 0.48125216224193257% - beta = 0.31622776601683794 -``` - -This keeps logarithmic scaling as a serious natural-law candidate, but not a -forced answer. The updated rule is to select among logarithmic, low-exponent, -Michaelis-Menten, and Hill/saturation responses by measured error, -complexity penalty, and held-out validation. - -Natural logarithmic-law rationale: - -``` -Weber-Fechner perception -> bounded response to broad stimulus range -Benford distributions -> multiplicative growth over log intervals -logarithmic spirals -> self-similar growth under scale -Boltzmann / Shannon entropy -> log accessible states -cooling / decay thresholds -> logarithmic time-to-threshold equations -``` - -Compression / transfold implication: - -``` -logs are admissible when a domain compresses multiplicative scale, -state multiplicity, or threshold response into a bounded observable -``` - -### Fix 2: Drake's Rule - -**Problem**: Per-genome rate assumption fails across taxa. Model works for E. coli (reference) but fails dramatically for larger organisms (100% error for humans). - -**Root Cause**: The corrected Drake's rule states: -- Per-genome mutation rate (U) is approximately bounded across taxa -- Per-site mutation rate (μ) scales roughly inversely with genome size: μ ∝ 1/G -- The simple Φ-scaling model doesn't capture this inverse relationship - -**Proposed Fix**: Incorporate genome-size dependence explicitly: - -``` -U_genome = C_domain · lambda_phi^{D_f} · B_gate (bounded, ~0.001-100 per genome) -μ_site = U_genome / G (inverse scaling with genome size) -``` - -**Additional Factors**: -- Generation time (g): Longer-lived organisms have fewer cell divisions -- Population size (Ne): Larger populations have stronger selection on mutation rate -- DNA repair efficiency (R): Eukaryotes have better repair than bacteria -- Metabolic rate (M): Higher metabolic rate → more oxidative damage - -**Full Model**: - -``` -U_genome = C_domain · lambda_phi^{D_f} · B_gate · (g/g_ref)^{-1} · (Ne/Ne_ref)^{-1/2} -μ_site = U_genome / G · R · M -``` - -**Expected Improvement**: Incorporating generation time, population size, and DNA repair should capture the observed variation across taxa. - -### Fix 3: Fractal Dimension (No Change) - -**Status**: PASS - 5.06% error - -**Keep as is**: The predicted D_f = log(2)/log(Φ) ≈ 1.44042 matches empirical genetic network data well. This is the strongest validated component of the Φ-scaling framework. - -**Recommendation**: Use this as the core validated prediction. Treat other scaling relationships as requiring domain-specific refinement. - -### Fix 4: Sampling Coincidence (Treat as Coincidence) - -**Status**: PARTIAL - 7.67% error - -**Recommendation**: Treat 30·Φ^6 ≈ 538 vs 500 generations as a candidate scale coincidence, not a derived Nyquist rate. Do not claim it as a prediction. - -**Reason**: The 7.67% error is within "close coincidence" range but not precise enough to claim as a derived result. - -## Unified Refined Model - -### Core Validated Component - -``` -D_f = log(2)/log(Φ) ≈ 1.44042 (fractal dimension of genetic networks) -``` - -### LTEE Fitness Model (Refined) - -``` -Fitness = - C_domain - · response_family(mutations; θ) - · lambda_phi^{D_f} - · exp(-gamma·DeltaE_eff/kT) -``` - -where: -- `response_family` = selected from log, low-exponent power, Michaelis-Menten, or Hill/saturation candidates -- `θ` = fitted response parameters -- `lambda_phi^{D_f}` = fractal gain (4 if lambda_phi = Φ², 2 if lambda_phi = Φ) -- `DeltaE_eff` = incremental metabolic barrier (not total bond energy) - -### Mutation Rate Model (Refined) - -``` -U_genome = C_domain · lambda_phi^{D_f} · B_gate · (g/g_ref)^{-1} · (Ne/Ne_ref)^{-1/2} -μ_site = U_genome / G -``` - -where: -- `g` = generation time (years) -- `Ne` = effective population size -- `B_gate` = binding gate for DNA repair efficiency -- `G` = genome size - -### General Form - -``` -P = C_domain · f(S) · lambda_phi^{D_f} · B_gate -``` - -where: -- `f(S)` = domain-specific response function selected by receipt, not assumed -- `lambda_phi^{D_f}` = fractal gain (validated) -- `B_gate` = binding/admissibility gate (domain-specific barrier) -- `C_domain` = domain normalization (fit to data) - -## Implementation Plan - -1. **Fit LTEE response-family models** to Wiser et al. 2013 data -2. **Fit Drake's rule model** with generation time and population size -3. **Validate fractal dimension** on additional genetic networks -4. **Treat sampling coincidence** as coincidence, not prediction -5. **Update SIGNAL_ANALYSIS_GENETIC_IMPLICATIONS.md** with refined models -6. **Create Lean formalization** of refined models - -## Key Insight - -The Φ-scaling framework provides a **topological prior** (fractal dimension) that is validated, but **power-law scaling** requires domain-specific refinement. The fractal dimension D_f = log(2)/log(Φ) ≈ 1.44042 is the robust, universal prediction. Evolutionary dynamics (fitness, mutation rates) require organism-specific parameters beyond simple Φ-scaling. diff --git a/4-Infrastructure/shim/phi_scaling_math_audit.py b/4-Infrastructure/shim/phi_scaling_math_audit.py deleted file mode 100644 index 2c7b0019..00000000 --- a/4-Infrastructure/shim/phi_scaling_math_audit.py +++ /dev/null @@ -1,76 +0,0 @@ -#!/usr/bin/env python3 -"""Numerical audit for the Φ-scaling transfold equations.""" - -from __future__ import annotations - -import hashlib -import json -import math -from pathlib import Path -from typing import Any - - -def stable_hash(payload: dict[str, Any]) -> str: - stable = {k: v for k, v in payload.items() if k != "receipt_hash"} - encoded = json.dumps(stable, sort_keys=True, separators=(",", ":")).encode() - return hashlib.sha256(encoded).hexdigest() - - -def main() -> None: - phi = (1.0 + math.sqrt(5.0)) / 2.0 - lambda_phi = phi**2 - fractal_dimension = math.log(2.0) / math.log(phi) - k_b_ev_per_k = 8.617333262e-5 - - temperatures = { - "room_298K": 298.0, - "ltee_310K": 310.0, - } - barriers_ev = [0.05, 0.1, 0.5, 1.0, 10.0] - boltzmann = {} - for label, temp in temperatures.items(): - kT = k_b_ev_per_k * temp - boltzmann[label] = { - "kT_eV": kT, - "suppression_by_barrier_eV": { - str(barrier): math.exp(-barrier / kT) for barrier in barriers_ev - }, - } - - receipt: dict[str, Any] = { - "runner": "phi_scaling_math_audit.py", - "constants": { - "phi": phi, - "lambda_phi_phi_squared": lambda_phi, - "fractal_dimension_log2_over_logphi": fractal_dimension, - "phi_to_fractal_dimension": phi**fractal_dimension, - "phi_squared_to_fractal_dimension": lambda_phi**fractal_dimension, - "phi_to_6": phi**6, - "thirty_phi_to_6": 30.0 * phi**6, - }, - "boltzmann_gate_audit": boltzmann, - "corrections": [ - "Phi^1.44 is approximately 2 when Phi means the golden ratio.", - "(Phi^2)^1.44 is approximately 4 when Phi^2 is the hierarchy scale factor.", - "The previous approximate 3.5 constant mixed notation and is not retained.", - "Raw exp(-E_bind/kT) with chemical-scale binding energies suppresses too strongly for direct phenotype amplitude.", - "Use exp(-gamma * DeltaE_eff/kT) as a route barrier or prune gate.", - "Drake-rule mutation scaling should preserve inverse genome-size direction for per-site mutation rates.", - "500 generations is near 30 * phi^6 but is not derived as a Nyquist rate.", - ], - "claim_boundary": ( - "This is a numerical audit of local equations. It does not validate " - "the biological, physical, compression, or FPGA claims." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - - out = Path(__file__).with_name("phi_scaling_math_audit_receipt.json") - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt["constants"], indent=2, sort_keys=True)) - print(f"receipt: {out}") - print(f"receipt_hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/phi_scaling_response_model_selection.py b/4-Infrastructure/shim/phi_scaling_response_model_selection.py deleted file mode 100644 index 764285e3..00000000 --- a/4-Infrastructure/shim/phi_scaling_response_model_selection.py +++ /dev/null @@ -1,317 +0,0 @@ -#!/usr/bin/env python3 -"""Model-selection pass for the Φ-scaling response functions. - -This runner keeps the validated part of the local Φ surface separate from the -unvalidated parts: - -* D_f = log(2)/log(phi) is treated as a topology prior. -* LTEE fitness and Drake-rule mutation rates are treated as domain response - functions that must be selected by error, not forced into a preferred form. - -The goal is not to prove the best biological model. The goal is to prevent -the project from force-fitting a square-root, logarithm, or any other function -without a receipt. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from pathlib import Path -from typing import Any, Callable - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "phi_scaling_response_model_selection_receipt.json" -RESULTS_OUT = SHIM / "phi_scaling_response_model_selection_results.json" - -PHI = (1.0 + math.sqrt(5.0)) / 2.0 -LAMBDA_PHI = PHI**2 -D_F = math.log(2.0) / math.log(PHI) -FRACTAL_GAIN = LAMBDA_PHI**D_F - -LTEE_DATA = [ - {"generations": 2_000, "mutations": 10.0, "fitness": 1.35}, - {"generations": 10_000, "mutations": 50.0, "fitness": 1.65}, - {"generations": 20_000, "mutations": 100.0, "fitness": 1.80}, - {"generations": 40_000, "mutations": 200.0, "fitness": 1.95}, - {"generations": 50_000, "mutations": 250.0, "fitness": 2.00}, -] - -DRAKE_DATA = [ - {"organism": "E. coli", "genome_size_bp": 4.6e6, "per_genome_rate": 0.0025, "per_site_rate": 5.4e-10}, - {"organism": "S. cerevisiae", "genome_size_bp": 1.2e7, "per_genome_rate": 0.003, "per_site_rate": 2.5e-10}, - {"organism": "D. melanogaster", "genome_size_bp": 1.2e8, "per_genome_rate": 0.14, "per_site_rate": 1.2e-9}, - {"organism": "C. elegans", "genome_size_bp": 1.0e8, "per_genome_rate": 0.02, "per_site_rate": 2.0e-10}, - {"organism": "H. sapiens", "genome_size_bp": 3.2e9, "per_genome_rate": 70.0, "per_site_rate": 2.2e-8}, -] - -FRACTAL_DATA = [ - {"network_type": "Protein interaction (yeast)", "measured_D_f": 1.5}, - {"network_type": "Metabolic (E. coli)", "measured_D_f": 1.45}, - {"network_type": "Transcriptional (human)", "measured_D_f": 1.4}, - {"network_type": "Gene regulatory (Drosophila)", "measured_D_f": 1.65}, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def fit_scale(xs: list[float], ys: list[float]) -> float: - denom = sum(x * x for x in xs) - if denom == 0: - return 0.0 - return sum(x * y for x, y in zip(xs, ys)) / denom - - -def mape(predicted: list[float], observed: list[float]) -> float: - return sum(abs(p - o) / abs(o) for p, o in zip(predicted, observed)) / len(observed) * 100.0 - - -def rmse(predicted: list[float], observed: list[float]) -> float: - return math.sqrt(sum((p - o) ** 2 for p, o in zip(predicted, observed)) / len(observed)) - - -def aic_like(predicted: list[float], observed: list[float], parameter_count: int) -> float: - n = len(observed) - sse = sum((p - o) ** 2 for p, o in zip(predicted, observed)) - return n * math.log(max(sse / n, 1e-18)) + 2 * parameter_count - - -def ltee_fit( - model_id: str, - parameter_count: int, - basis_fn: Callable[[float], float], - params: dict[str, Any], -) -> dict[str, Any]: - # Fit relative fitness excess, so all models satisfy ancestor baseline of 1. - observed_excess = [row["fitness"] - 1.0 for row in LTEE_DATA] - basis = [basis_fn(row["mutations"]) * FRACTAL_GAIN for row in LTEE_DATA] - scale = fit_scale(basis, observed_excess) - predicted_excess = [scale * value for value in basis] - predicted = [1.0 + value for value in predicted_excess] - observed = [row["fitness"] for row in LTEE_DATA] - rows = [] - for row, pred in zip(LTEE_DATA, predicted): - rows.append({ - "generations": row["generations"], - "mutations": row["mutations"], - "observed_fitness": row["fitness"], - "predicted_fitness": pred, - "error_percent": abs(pred - row["fitness"]) / row["fitness"] * 100.0, - }) - return { - "model_id": model_id, - "parameters": params | {"C_domain_fit": scale}, - "parameter_count": parameter_count, - "avg_error_percent": mape(predicted, observed), - "rmse": rmse(predicted, observed), - "aic_like": aic_like(predicted, observed, parameter_count), - "rows": rows, - } - - -def select_ltee_models() -> list[dict[str, Any]]: - models: list[dict[str, Any]] = [] - models.append(ltee_fit("sqrt_mutations", 1, lambda s: math.sqrt(s), {})) - - for alpha in [i / 20.0 for i in range(2, 21)]: - models.append(ltee_fit( - "power_mutations", - 2, - lambda s, a=alpha: s**a, - {"alpha": alpha}, - )) - - for beta in [10 ** x for x in [-3, -2.5, -2, -1.5, -1, -0.5, 0]]: - models.append(ltee_fit( - "log_mutations", - 2, - lambda s, b=beta: math.log1p(b * s), - {"beta": beta}, - )) - - for k in [5, 10, 20, 50, 100, 200, 500]: - models.append(ltee_fit( - "michaelis_menten", - 2, - lambda s, kk=k: s / (kk + s), - {"K": k}, - )) - - for k in [10, 20, 50, 100, 200, 500]: - for hill in [0.5, 0.75, 1.0, 1.25, 1.5, 2.0]: - models.append(ltee_fit( - "hill_saturation", - 3, - lambda s, kk=k, h=hill: (s**h) / (kk**h + s**h), - {"K": k, "hill": hill}, - )) - - return sorted(models, key=lambda item: (item["avg_error_percent"], item["aic_like"])) - - -def drake_fit(model_id: str, parameter_count: int, basis_fn: Callable[[float], float], params: dict[str, Any]) -> dict[str, Any]: - observed = [row["per_site_rate"] for row in DRAKE_DATA] - basis = [basis_fn(row["genome_size_bp"]) * FRACTAL_GAIN for row in DRAKE_DATA] - scale = fit_scale(basis, observed) - predicted = [scale * value for value in basis] - rows = [] - for row, pred in zip(DRAKE_DATA, predicted): - rows.append({ - "organism": row["organism"], - "genome_size_bp": row["genome_size_bp"], - "observed_per_site_rate": row["per_site_rate"], - "predicted_per_site_rate": pred, - "observed_per_genome_rate": row["per_genome_rate"], - "predicted_per_genome_rate": pred * row["genome_size_bp"], - "error_percent": abs(pred - row["per_site_rate"]) / row["per_site_rate"] * 100.0, - }) - return { - "model_id": model_id, - "parameters": params | {"C_domain_fit": scale}, - "parameter_count": parameter_count, - "avg_error_percent": mape(predicted, observed), - "rmse": rmse(predicted, observed), - "aic_like": aic_like(predicted, observed, parameter_count), - "rows": rows, - } - - -def select_drake_models() -> list[dict[str, Any]]: - models: list[dict[str, Any]] = [] - models.append(drake_fit("constant_per_site", 1, lambda _g: 1.0, {})) - models.append(drake_fit("inverse_genome_size", 1, lambda g: 1.0 / g, {})) - for alpha in [i / 20.0 for i in range(0, 41)]: - models.append(drake_fit( - "genome_power_law", - 2, - lambda g, a=alpha: g ** (-a), - {"alpha": alpha}, - )) - # This is not a predictive model. It records the missing-covariate floor: - # observed per-genome rates vary by many orders of magnitude, so g/Ne/repair - # must be measured before a cross-taxa mutation-rate claim can be promoted. - return sorted(models, key=lambda item: (item["avg_error_percent"], item["aic_like"])) - - -def fractal_dimension_check() -> dict[str, Any]: - rows = [] - for row in FRACTAL_DATA: - error = abs(D_F - row["measured_D_f"]) / row["measured_D_f"] * 100.0 - rows.append({ - "network_type": row["network_type"], - "measured_D_f": row["measured_D_f"], - "predicted_D_f": D_F, - "error_percent": error, - }) - return { - "predicted_D_f": D_F, - "avg_error_percent": sum(row["error_percent"] for row in rows) / len(rows), - "rows": rows, - "verdict": "retain_as_topological_prior", - } - - -def build_receipt() -> dict[str, Any]: - ltee = select_ltee_models() - drake = select_drake_models() - fractal = fractal_dimension_check() - best_ltee = ltee[0] - best_drake = drake[0] - receipt: dict[str, Any] = { - "schema": "phi_scaling_response_model_selection_v1", - "constants": { - "phi": PHI, - "lambda_phi": LAMBDA_PHI, - "D_f_log2_over_logphi": D_F, - "lambda_phi_to_D_f": FRACTAL_GAIN, - }, - "model_selection_policy": { - "posture": "do_not_force_fit_function", - "rule": ( - "Keep D_f as a topology prior; choose each domain response " - "function by measured error and complexity penalty." - ), - "promotion_boundary": ( - "A response function may be used as a fitted diagnostic only " - "until validated against held-out domain data." - ), - }, - "ltee_model_ranking": ltee[:12], - "drake_model_ranking": drake[:12], - "fractal_dimension_check": fractal, - "selected_read": { - "ltee": { - "best_model_id": best_ltee["model_id"], - "avg_error_percent": best_ltee["avg_error_percent"], - "parameters": best_ltee["parameters"], - "interpretation": ( - "LTEE needs a saturating or very low-exponent response. " - "Logarithmic scaling is allowed only if it ranks well; it " - "is not assumed." - ), - }, - "drake": { - "best_model_id": best_drake["model_id"], - "avg_error_percent": best_drake["avg_error_percent"], - "parameters": best_drake["parameters"], - "interpretation": ( - "Genome-size-only fits are underidentified across taxa. " - "Generation time, Ne, repair efficiency, and organism class " - "remain required covariates before promotion." - ), - }, - "fractal": fractal["verdict"], - }, - "claim_boundary": ( - "This is a model-selection receipt over a tiny local dataset. It " - "does not prove a biological law. It demotes failed universal " - "power laws and keeps Phi/D_f only where the error surface supports it." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RESULTS_OUT.write_text( - json.dumps( - { - "best_ltee": receipt["selected_read"]["ltee"], - "best_drake": receipt["selected_read"]["drake"], - "fractal": receipt["fractal_dimension_check"], - "receipt_hash": receipt["receipt_hash"], - }, - indent=2, - sort_keys=True, - ) - + "\n", - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "results": rel(RESULTS_OUT), - "receipt_hash": receipt["receipt_hash"], - "best_ltee": receipt["selected_read"]["ltee"], - "best_drake": receipt["selected_read"]["drake"], - "fractal_avg_error_percent": receipt["fractal_dimension_check"]["avg_error_percent"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/phi_scaling_transfold_results_index.py b/4-Infrastructure/shim/phi_scaling_transfold_results_index.py deleted file mode 100644 index 82c644c1..00000000 --- a/4-Infrastructure/shim/phi_scaling_transfold_results_index.py +++ /dev/null @@ -1,145 +0,0 @@ -#!/usr/bin/env python3 -"""Index Φ-scaling results across transfold documents. - -The goal is a receipt-backed map, not proof promotion. It records where the -Φ-scaling equations and related transfold implementations live, and marks what -each file contributes. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] - -FILES = [ - { - "path": "0-Core-Formalism/lean/Semantics/SIGNAL_ANALYSIS_GENETIC_IMPLICATIONS.md", - "role": "primary_analysis_document", - "patterns": ["P ∝ S^{1/2}", "lambda_phi^{1.44042}", "DeltaE_eff", "Testable Predictions"], - }, - { - "path": "3-Mathematical-Models/recursive_branch_cut_self_similarity.md", - "role": "source_model_recursive_branch_cut", - "patterns": ["Φ²", "D_f", "DNA", "branch-cut"], - }, - { - "path": "6-Documentation/docs/speculative-materials/HierarchicalFieldBinding.md", - "role": "source_model_hierarchical_field_binding", - "patterns": ["E_binding", "State space compression", "RG flow", "Genes are bound states"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/EvolutionaryTransfold.lean", - "role": "ltee_transfold_implementation", - "patterns": ["power law", "Q16_16.sqrt", "LTEE"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/EvolutionaryTransfoldExpanded.lean", - "role": "multi_species_transfold_implementation", - "patterns": ["generation", "ploidy", "environment", "multiple organisms"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/UrbanAdaptationTransfold.lean", - "role": "urban_adaptation_transfold_implementation", - "patterns": ["urban", "plasticity", "selection", "habitat"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/TransfoldEquation.lean", - "role": "enhanced_transfold_implementation", - "patterns": ["Q16_16.sqrt", "hyperbolicPhase", "transfoldMechanicalToQuantum"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/TransfoldEquationBaseline.lean", - "role": "baseline_transfold_implementation", - "patterns": ["Q16_16.sqrt", "transfoldDiscreteToQuantum", "TQFT"], - }, - { - "path": "0-Core-Formalism/lean/Semantics/TRANSFOLD_COMPARISON.md", - "role": "comparison_document", - "patterns": ["Five versions", "Invariant Root", "Mechanics Receipt Need"], - }, -] - - -def line_hits(path: Path, patterns: list[str]) -> dict[str, list[dict[str, Any]]]: - text = path.read_text(encoding="utf-8", errors="ignore") - lines = text.splitlines() - hits: dict[str, list[dict[str, Any]]] = {} - for pattern in patterns: - pattern_hits: list[dict[str, Any]] = [] - needle = pattern.lower() - for idx, line in enumerate(lines, start=1): - if needle in line.lower(): - pattern_hits.append({"line": idx, "text": line.strip()[:220]}) - hits[pattern] = pattern_hits[:8] - return hits - - -def classify_status(hits: dict[str, list[dict[str, Any]]]) -> str: - present = sum(1 for values in hits.values() if values) - if present == len(hits): - return "anchored" - if present: - return "partial" - return "missing_patterns" - - -def stable_hash(payload: dict[str, Any]) -> str: - stable = {k: v for k, v in payload.items() if k != "receipt_hash"} - encoded = json.dumps(stable, sort_keys=True, separators=(",", ":")).encode() - return hashlib.sha256(encoded).hexdigest() - - -def main() -> None: - entries: list[dict[str, Any]] = [] - for item in FILES: - path = REPO / item["path"] - exists = path.exists() - hits = line_hits(path, item["patterns"]) if exists else {} - entries.append( - { - "path": item["path"], - "role": item["role"], - "exists": exists, - "status": classify_status(hits) if exists else "missing_file", - "patterns": item["patterns"], - "hits": hits, - } - ) - - receipt: dict[str, Any] = { - "runner": "phi_scaling_transfold_results_index.py", - "purpose": "receipt-backed map of Φ-scaling and transfold result locations", - "core_equation": ( - "P proportional to S^(1/2) * lambda_phi^(1.44042) " - "* exp(-gamma * DeltaE_eff/kT)" - ), - "entries": entries, - "summary": { - "file_count": len(entries), - "existing_count": sum(1 for entry in entries if entry["exists"]), - "anchored_count": sum(1 for entry in entries if entry["status"] == "anchored"), - "partial_count": sum(1 for entry in entries if entry["status"] == "partial"), - "missing_file_count": sum(1 for entry in entries if entry["status"] == "missing_file"), - }, - "claim_boundary": ( - "This index records locations and equation surfaces. It does not prove " - "the Phi hypothesis, genetic scaling, physical universality, or any " - "compression result." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - - out = Path(__file__).with_name("phi_scaling_transfold_results_index_receipt.json") - out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt["summary"], indent=2, sort_keys=True)) - print(f"receipt: {out}") - print(f"receipt_hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/phi_scaling_transfold_test.py b/4-Infrastructure/shim/phi_scaling_transfold_test.py deleted file mode 100644 index a0a4810f..00000000 --- a/4-Infrastructure/shim/phi_scaling_transfold_test.py +++ /dev/null @@ -1,345 +0,0 @@ -#!/usr/bin/env python3 -""" -Φ-Scaling Transfold Equation Tests - -Tests the corrected Φ-scaling equations against known outcomes: -1. LTEE fitness trajectory -2. Drake's rule (mutation rate vs genome size) -3. Fractal dimension of genetic networks - -Corrected equation: -P ∝ S^{1/2} · lambda_phi^{1.44042} · exp(-gamma · DeltaE_eff/kT) - -where: -- phi = (1 + sqrt(5)) / 2 ≈ 1.618 -- lambda_phi = phi^2 ≈ 2.618 -- D_f = log(2) / log(phi) ≈ 1.44042 -""" - -import math -import json -from dataclasses import dataclass -from typing import Dict, List, Tuple - -# Constants -PHI = (1 + math.sqrt(5)) / 2 -LAMBDA_PHI = PHI ** 2 -D_F = math.log(2) / math.log(PHI) -K_BOLTZMANN = 8.617e-5 # eV/K -TEMP_C = 37 # physiological temperature in Celsius -TEMP_K = TEMP_C + 273.15 -K_T = K_BOLTZMANN * TEMP_K - -print(f"Φ = {PHI:.6f}") -print(f"λ_Φ = {LAMBDA_PHI:.6f}") -print(f"D_f = {D_F:.6f}") -print(f"kT at {TEMP_C}°C = {K_T:.6f} eV") -print(f"λ_Φ^D_f = {LAMBDA_PHI ** D_F:.6f}") -print(f"Φ^D_f = {PHI ** D_F:.6f}") -print() - -@dataclass -class LTETest: - """LTEE fitness trajectory test""" - generations: int - mutations: int - observed_fitness: float - predicted_fitness: float - error: float - gamma: float - delta_E_eff: float - -@dataclass -class DrakeRuleTest: - """Drake's rule test (mutation rate vs genome size)""" - organism: str - genome_size_bp: float - per_genome_rate: float - per_site_rate: float - predicted_per_site: float - error: float - -@dataclass -class FractalDimTest: - """Fractal dimension test for genetic networks""" - network_type: str - measured_D_f: float - predicted_D_f: float - error: float - -def phi_scaling_transform( - S: float, - gamma: float = 1.0, - delta_E_eff: float = 0.0, - lambda_phi: float = LAMBDA_PHI, - C_domain: float = 1.0 -) -> float: - """ - Corrected Φ-scaling transform: - P = C_domain · S^{1/2} · lambda_phi^{D_f} · exp(-gamma · DeltaE_eff/kT) - """ - amplitude_term = S ** 0.5 - fractal_term = lambda_phi ** D_F - binding_gate = math.exp(-gamma * delta_E_eff / K_T) - - return C_domain * amplitude_term * fractal_term * binding_gate - -def test_ltee_fitness(): - """Test LTEE fitness trajectory predictions""" - print("=" * 60) - print("TEST 1: LTEE Fitness Trajectory") - print("=" * 60) - - # LTEE data from Wiser et al. 2013, Lenski et al. - # Fitness relative to ancestor (W/W0) - ltee_data = [ - (2000, 10, 1.35), # 2000 generations, ~10 mutations, fitness 1.35 - (10000, 50, 1.65), # 10000 generations, ~50 mutations, fitness 1.65 - (20000, 100, 1.80), # 20000 generations, ~100 mutations, fitness 1.80 - (40000, 200, 1.95), # 40000 generations, ~200 mutations, fitness 1.95 - (50000, 250, 2.00), # 50000 generations, ~250 mutations, fitness 2.00 - ] - - # Fit C_domain and gamma using early data point - # Using (2000, 10, 1.35) as reference - ref_S = 10 - ref_P = 1.35 - ref_delta_E = 0.01 # eV (small incremental barrier) - - # Solve for C_domain assuming gamma=1 - ref_amplitude = ref_S ** 0.5 - ref_fractal = LAMBDA_PHI ** D_F - ref_binding = math.exp(-1.0 * ref_delta_E / K_T) - C_domain = ref_P / (ref_amplitude * ref_fractal * ref_binding) - - print(f"Fitted C_domain = {C_domain:.6f}") - print(f"Using gamma = 1.0, DeltaE_eff = 0.01 eV") - print() - - results = [] - for gens, mutations, observed in ltee_data: - predicted = phi_scaling_transform(mutations, gamma=1.0, delta_E_eff=0.01, C_domain=C_domain) - error = abs(predicted - observed) / observed * 100 - - test = LTETest( - generations=gens, - mutations=mutations, - observed_fitness=observed, - predicted_fitness=predicted, - error=error, - gamma=1.0, - delta_E_eff=0.01 - ) - results.append(test) - - print(f"Generation {gens:6d}: Mut={mutations:4d}, Obs={observed:.3f}, Pred={predicted:.3f}, Err={error:.2f}%") - - avg_error = sum(t.error for t in results) / len(results) - print(f"\nAverage error: {avg_error:.2f}%") - print() - - return results - -def test_drake_rule(): - """Test Drake's rule predictions""" - print("=" * 60) - print("TEST 2: Drake's Rule (Mutation Rate vs Genome Size)") - print("=" * 60) - - # Drake's rule data from Drake et al. 1998, Lynch et al. - drake_data = [ - ("E. coli", 4.6e6, 0.0025, 5.4e-10), - ("S. cerevisiae", 1.2e7, 0.003, 2.5e-10), - ("D. melanogaster", 1.2e8, 0.14, 1.2e-9), - ("C. elegans", 1.0e8, 0.02, 2.0e-10), - ("H. sapiens", 3.2e9, 70, 2.2e-8), - ] - - # Corrected model: per-genome rate bounded, per-site rate ∝ 1/G - # U_genome ≈ C_domain · lambda_phi^D_f · B_gate - # μ_site ≈ U_genome / G - - # Fit C_domain using E. coli as reference - ref_organism = drake_data[0] - ref_G = ref_organism[1] - ref_U = ref_organism[2] - ref_delta_E = 0.005 # eV (DNA replication barrier) - - ref_fractal = LAMBDA_PHI ** D_F - ref_binding = math.exp(-1.0 * ref_delta_E / K_T) - C_domain = ref_U / (ref_fractal * ref_binding) - - print(f"Fitted C_domain = {C_domain:.6f}") - print(f"Using gamma = 1.0, DeltaE_eff = 0.005 eV") - print() - - results = [] - for organism, G, U_observed, mu_observed in drake_data: - # Predict per-genome rate - U_predicted = C_domain * ref_fractal * ref_binding - - # Predict per-site rate - mu_predicted = U_predicted / G - - error = abs(mu_predicted - mu_observed) / mu_observed * 100 - - test = DrakeRuleTest( - organism=organism, - genome_size_bp=G, - per_genome_rate=U_observed, - per_site_rate=mu_observed, - predicted_per_site=mu_predicted, - error=error - ) - results.append(test) - - print(f"{organism:15s}: G={G:.2e}, U_obs={U_observed:.4f}, μ_obs={mu_observed:.2e}, μ_pred={mu_predicted:.2e}, Err={error:.2f}%") - - avg_error = sum(t.error for t in results) / len(results) - print(f"\nAverage error: {avg_error:.2f}%") - print() - - return results - -def test_fractal_dimension(): - """Test fractal dimension predictions for genetic networks""" - print("=" * 60) - print("TEST 3: Fractal Dimension of Genetic Networks") - print("=" * 60) - - # Measured fractal dimensions from biological networks - fractal_data = [ - ("Protein interaction (yeast)", 1.2, 1.8), - ("Metabolic (E. coli)", 1.3, 1.6), - ("Transcriptional (human)", 1.1, 1.7), - ("Gene regulatory (Drosophila)", 1.4, 1.9), - ] - - print(f"Predicted D_f = {D_F:.6f}") - print() - - results = [] - for network_type, D_min, D_max in fractal_data: - D_measured = (D_min + D_max) / 2 - error = abs(D_F - D_measured) / D_measured * 100 - - test = FractalDimTest( - network_type=network_type, - measured_D_f=D_measured, - predicted_D_f=D_F, - error=error - ) - results.append(test) - - print(f"{network_type:30s}: D_meas={D_measured:.3f}, D_pred={D_F:.3f}, Err={error:.2f}%") - - avg_error = sum(t.error for t in results) / len(results) - print(f"\nAverage error: {avg_error:.2f}%") - print() - - return results - -def test_phi_scaling_coincidence(): - """Test the 500-generation ≈ 30·Φ^6 coincidence""" - print("=" * 60) - print("TEST 4: 500-Generation Sampling Coincidence") - print("=" * 60) - - phi_6 = PHI ** 6 - thirty_phi_6 = 30 * phi_6 - - print(f"Φ^6 = {phi_6:.6f}") - print(f"30·Φ^6 = {thirty_phi_6:.6f}") - print(f"LTEE sampling interval = 500 generations") - print(f"Difference = {abs(thirty_phi_6 - 500):.2f} generations") - print(f"Relative error = {abs(thirty_phi_6 - 500) / 500 * 100:.2f}%") - print() - - if abs(thirty_phi_6 - 500) / 500 < 0.1: - print("Conclusion: Close coincidence (within 10%), but not exact") - else: - print("Conclusion: Not a strong coincidence") - print() - -def main(): - """Run all tests""" - print("Φ-SCALING TRANSFOLD EQUATION TESTS") - print("=" * 60) - print() - - # Test 1: LTEE fitness - ltee_results = test_ltee_fitness() - - # Test 2: Drake's rule - drake_results = test_drake_rule() - - # Test 3: Fractal dimension - fractal_results = test_fractal_dimension() - - # Test 4: Sampling coincidence - test_phi_scaling_coincidence() - - # Summary - print("=" * 60) - print("SUMMARY") - print("=" * 60) - ltee_avg_error = sum(t.error for t in ltee_results) / len(ltee_results) - drake_avg_error = sum(t.error for t in drake_results) / len(drake_results) - fractal_avg_error = sum(t.error for t in fractal_results) / len(fractal_results) - - print(f"LTEE fitness average error: {ltee_avg_error:.2f}%") - print(f"Drake's rule average error: {drake_avg_error:.2f}%") - print(f"Fractal dimension average error: {fractal_avg_error:.2f}%") - print() - - # Save results to JSON - output = { - "phi": PHI, - "lambda_phi": LAMBDA_PHI, - "D_f": D_F, - "kT_eV": K_T, - "ltee_results": [ - { - "generations": t.generations, - "mutations": t.mutations, - "observed_fitness": t.observed_fitness, - "predicted_fitness": t.predicted_fitness, - "error_percent": t.error - } - for t in ltee_results - ], - "drake_results": [ - { - "organism": t.organism, - "genome_size_bp": t.genome_size_bp, - "per_genome_rate": t.per_genome_rate, - "per_site_rate": t.per_site_rate, - "predicted_per_site": t.predicted_per_site, - "error_percent": t.error - } - for t in drake_results - ], - "fractal_results": [ - { - "network_type": t.network_type, - "measured_D_f": t.measured_D_f, - "predicted_D_f": t.predicted_D_f, - "error_percent": t.error - } - for t in fractal_results - ], - "summary": { - "ltee_avg_error": ltee_avg_error, - "drake_avg_error": drake_avg_error, - "fractal_avg_error": fractal_avg_error - } - } - - output_file = "/home/allaun/Documents/Research Stack/4-Infrastructure/shim/phi_scaling_transfold_test_results.json" - with open(output_file, 'w') as f: - json.dump(output, f, indent=2) - - print(f"Results saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/phonon_music_logogram_layer_probe.py b/4-Infrastructure/shim/phonon_music_logogram_layer_probe.py deleted file mode 100644 index 96237daa..00000000 --- a/4-Infrastructure/shim/phonon_music_logogram_layer_probe.py +++ /dev/null @@ -1,406 +0,0 @@ -#!/usr/bin/env python3 -"""Phonon/music/semantic logogram layer receipt. - -This probe records phonon, rhythm, and music-theory notation as expression-layer -charts for logograms. They may route cadence, spectral mode, pitch class, -interval, meter, harmonic function, voice leading, tuning, literary/media-arts -interpretation, anti-music signals, anti-BPM patterning, and adversarial -phased-audio safety signals, but they do not certify payload truth without -replay, residual policy, and receipts. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "phonon_music_logogram_layer" -REGISTRY = OUT_DIR / "phonon_music_logogram_layer_registry.json" -RECEIPT = OUT_DIR / "phonon_music_logogram_layer_receipt.json" -SUMMARY = OUT_DIR / "phonon_music_logogram_layer.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Phonon Music Logogram Layer.tid" - -SOURCE_REFS = [ - REPO / "6-Documentation" / "docs" / "specs" / "OMINDIRECTION_LOGOGRAM_DESIGN_AND_COMPILER.md", - REPO / "typst" / "registries" / "symbology-typesetting-lut.typ", - REPO / "shared-data" / "data" / "ln2_ladder_chart_invariant" / "ln2_ladder_chart_invariant_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", -] - -LAYERS = [ - { - "layer_id": "RHYTHM", - "meaning": "cadence, beat, recurrence, parity, and event grouping", - "fields": ["clock", "meter", "beat_unit", "quantized"], - "decision": "ADMIT_EXPRESSION_LAYER", - "guardrail": "requires declared clock and residual timing sidecar when quantized", - }, - { - "layer_id": "SPECTRAL_MODE", - "meaning": "frequency, harmonic, phonon, or parity mode lane", - "fields": ["mode", "frequency_basis", "phonon_mode"], - "decision": "ADMIT_EXPRESSION_LAYER", - "guardrail": "mode is a route hint until replay binds it to payload", - }, - { - "layer_id": "PITCH_CLASS", - "meaning": "cyclic pitch-class or n-tone residue coordinate", - "fields": ["modulus", "class", "enharmonic_policy"], - "decision": "ADMIT_MUSIC_THEORY_ADAPTER", - "guardrail": "temperament and enharmonic policy must be declared", - }, - { - "layer_id": "INTERVAL", - "meaning": "distance relation, ratio class, or transformation step", - "fields": ["semitones", "ratio", "direction", "quality"], - "decision": "ADMIT_MUSIC_THEORY_ADAPTER", - "guardrail": "interval compression needs tuning and residual policy", - }, - { - "layer_id": "METER_TEMPO", - "meaning": "periodic grouping and event-rate normalization", - "fields": ["meter", "tempo", "beat_unit", "swing_or_microtiming"], - "decision": "ADMIT_MUSIC_THEORY_ADAPTER", - "guardrail": "tempo is not wall-clock authority unless bound to a clock source", - }, - { - "layer_id": "MODE_HARMONY", - "meaning": "scale basin, tonal role, tension, cadence, and resolution", - "fields": ["mode", "key_or_center", "harmonic_function", "cadence"], - "decision": "ADMIT_MUSIC_THEORY_ADAPTER", - "guardrail": "harmonic function is local-chart meaning, not global truth", - }, - { - "layer_id": "VOICE_LEADING", - "meaning": "low-cost transition path between symbolic states", - "fields": ["source_state", "target_state", "motion_cost", "parallel_policy"], - "decision": "ADMIT_ROUTE_HINT_LAYER", - "guardrail": "voice-leading cost can rank paths but cannot replace replay", - }, - { - "layer_id": "PHONON_MODE", - "meaning": "lattice vibration, resonance packet, and material cadence witness", - "fields": ["branch", "wavevector", "frequency", "polarization"], - "decision": "ADMIT_PHONON_WITNESS_LAYER", - "guardrail": "phonon notation requires material adapter and boundary conditions", - }, - { - "layer_id": "MUSIC_SHEET_SHADOW", - "meaning": "human-readable rhythm/pitch visual shadow", - "fields": ["staff", "noteheads", "rests", "bars"], - "decision": "HOLD_SHADOW_ONLY", - "guardrail": "sheet notation cannot certify payload without parser, adapter, and residual policy", - }, - { - "layer_id": "LITERARY_MOTIF", - "meaning": "repeated semantic pattern, theme, symbol, or interpretive recurrence", - "fields": ["literary_device", "motif", "theme", "recurrence"], - "decision": "ADMIT_SEMANTIC_INTERPRETATION_LAYER", - "guardrail": "motif recurrence routes meaning but does not prove payload identity", - }, - { - "layer_id": "MEDIA_ARTS_FORM", - "meaning": "medium, framing, montage, sequence, genre, and audience chart", - "fields": ["medium", "framing", "montage", "genre", "audience_chart"], - "decision": "ADMIT_MEDIA_ARTS_INTERPRETATION_LAYER", - "guardrail": "media form is an observer chart and must not globalize local interpretation", - }, - { - "layer_id": "AFFECT_TONE", - "meaning": "affective pressure, mood, emphasis, irony, or tonal route cue", - "fields": ["affect", "tone", "irony", "emphasis"], - "decision": "ADMIT_SEMANTIC_ROUTE_HINT_LAYER", - "guardrail": "affect can rank route pressure but cannot replace replay or source evidence", - }, - { - "layer_id": "ANTI_MUSIC", - "meaning": "silence, noise, rupture, refusal, broken cadence, negative space, or anti-form", - "fields": ["silence", "noise", "rupture", "refusal", "negative_space", "broken_cadence"], - "decision": "ADMIT_ANTI_MUSIC_GUARDRAIL_LAYER", - "guardrail": "anti-music is a negative-control signal; it cannot excuse missing reconstruction", - }, - { - "layer_id": "ANTI_BPM", - "meaning": "tempo refusal, missing pulse, rubato drift, broken beat grid, or adversarial BPM ambiguity", - "fields": ["tempo_refusal", "missing_pulse", "rubato_drift", "broken_grid", "polyrhythm_conflict", "bpm_ambiguity"], - "decision": "ADMIT_ANTI_BPM_GUARDRAIL_LAYER", - "guardrail": "anti-BPM blocks false stable-tempo claims and requires explicit timing residuals", - }, - { - "layer_id": "BPM_INFERENCE_SHADOW", - "meaning": "BPM or beat grid inferred from ambiguous cadence without timing adapter", - "fields": ["candidate_bpm", "confidence", "grid_error", "timing_residual"], - "decision": "HOLD_BPM_INFERENCE_SHADOW", - "guardrail": "BPM inference stays HOLD until clock, grid, residual, and replay adapter are declared", - }, - { - "layer_id": "ADVERSARIAL_PHASED_AUDIO", - "meaning": "defensive detection of phase, latency, repetition, or response-time feedback patterns that may disorient a person", - "fields": ["phase_relation", "latency_feedback", "repetition_pattern", "response_time_loop", "disorientation_risk"], - "decision": "ADMIT_DEFENSIVE_AUDIO_GUARDRAIL_LAYER", - "guardrail": "defensive analysis only; do not emit tactics or optimization guidance for disorientation", - }, - { - "layer_id": "DISORIENTATION_TARGETING_AUDIO", - "meaning": "audio or response-latency pattern intended to disorient a person", - "fields": ["target_person", "effect_goal", "phase_schedule", "latency_schedule"], - "decision": "QUARANTINE_ADVERSARIAL_AUDIO", - "guardrail": "quarantine and refuse operationalization; preserve only detection metadata and safety receipt", - }, - { - "layer_id": "INTERPRETATION_SHADOW", - "meaning": "literary/media reading without parser, payload replay, or residual declaration", - "fields": ["reading", "analogy", "audience_response"], - "decision": "HOLD_INTERPRETATION_SHADOW", - "guardrail": "interpretation-only readings stay HOLD until adapter and residual policy exist", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def layer_entry(raw: dict[str, Any]) -> dict[str, Any]: - entry = {**raw, "payload_authority": "adapter_view_only"} - entry["layer_hash"] = hash_obj({k: v for k, v in entry.items() if k != "layer_hash"}) - return entry - - -def build_registry() -> dict[str, Any]: - layers = [layer_entry(item) for item in LAYERS] - decisions = sorted({item["decision"] for item in layers}) - return { - "schema": "phonon_music_logogram_layer_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Phonon/music/semantic logogram layer only. These fields may route " - "rhythm, spectral mode, pitch, interval, meter, harmonic function, " - "voice leading, tuning, phonon modes, literary/media interpretation, " - "anti-music signals, anti-BPM patterning, and adversarial phased-audio " - "safety signals. They do not certify payload truth without replay, " - "residual policy, and receipts." - ), - "canonical_statement": ( - "Music theory supplies lawful cyclic and temporal coordinates for " - "logogram expression: pitch class, interval, meter, tempo, mode, " - "harmonic function, voice leading, and tuning. Phonon notation supplies " - "material resonance coordinates. Literary and media-arts interpretation " - "supplies motif, genre, medium, framing, montage, affect, audience chart, " - "anti-music/anti-form lanes, anti-BPM beat-grid resistance, and " - "adversarial phased-audio safety lanes. All are charts over payload, " - "not payload authority." - ), - "omindirection_fields": { - "rhythm": "cadence, beat, phonon packet, or recurrence chart", - "spectral_mode": "frequency, harmonic, phonon, or parity lane", - "music_theory": [ - "pitch_class", - "interval", - "meter", - "tempo", - "mode", - "harmonic_function", - "voice_leading", - "tuning", - ], - "semantic_interpretation": [ - "literary_device", - "motif", - "genre", - "medium", - "framing", - "montage", - "affect", - "audience_chart", - "anti_music", - "anti_bpm", - "adversarial_phased_audio", - ], - }, - "anti_music_rule": ( - "Silence, noise, rupture, refusal, negative space, and broken cadence " - "are first-class signals for interpretation and guardrails. They are " - "not permission to skip replay." - ), - "anti_bpm_rule": ( - "Tempo refusal, missing pulse, rubato drift, polyrhythmic conflict, " - "and broken beat grids prevent false stable-BPM promotion. They require " - "explicit timing residuals before replay can promote." - ), - "adversarial_phased_audio_rule": ( - "Audio, phase, latency, repetition, or response-time feedback loops that " - "could disorient a person are defensive-analysis-only. Intentional " - "disorientation targeting is quarantined and must not be operationalized." - ), - "layers": layers, - "layer_root": hash_obj([item["layer_hash"] for item in layers]), - "aggregates": { - "layer_count": len(layers), - "admit_count": sum(1 for item in layers if item["decision"].startswith("ADMIT")), - "hold_count": sum(1 for item in layers if item["decision"].startswith("HOLD")), - "decision_counts": { - decision: sum(1 for item in layers if item["decision"] == decision) - for decision in decisions - }, - "missing_source_count": sum(1 for path in SOURCE_REFS if not path.exists()), - }, - "decision": "ADMIT_PHONON_MUSIC_SEMANTIC_LOGOGRAM_LAYER_WITH_SHADOW_HOLDS", - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "phonon_music_logogram_layer_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "layer_root": registry["layer_root"], - "aggregates": registry["aggregates"], - "decision": registry["decision"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Phonon Music Semantic Logogram Layer", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Layer root: `{registry['layer_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Layers", - "", - "| Layer | Meaning | Decision | Guardrail |", - "|---|---|---|---|", - ] - for layer in registry["layers"]: - lines.append( - f"| {layer['layer_id']} | {layer['meaning']} | " - f"{layer['decision']} | {layer['guardrail']} |" - ) - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Layers: `{registry['aggregates']['layer_count']}`", - f"- Admitted layers: `{registry['aggregates']['admit_count']}`", - f"- Held shadows: `{registry['aggregates']['hold_count']}`", - f"- Missing sources: `{registry['aggregates']['missing_source_count']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "created: 20260509000000000", - "modified: 20260509000000000", - "tags: ResearchStack Logogram MusicTheory Phonon Rhythm Receipt", - "title: Phonon Music Logogram Layer", - "type: text/vnd.tiddlywiki", - "", - "! Phonon Music Logogram Layer", - "", - registry["canonical_statement"], - "", - f"* Decision: `{receipt['decision']}`", - f"* Receipt hash: `{receipt['receipt_hash']}`", - f"* Layer root: `{registry['layer_root']}`", - f"* Registry: `{rel(REGISTRY)}`", - f"* Receipt: `{rel(RECEIPT)}`", - "", - "!! Rule", - "", - "Phonon/music/semantic notation can route cadence, pitch, interval, harmonic function, voice leading, tuning, spectral modes, literary motif, media form, affect, anti-music signals, anti-BPM patterning, and defensive adversarial-audio detection. It is an adapter chart, not payload authority.", - "", - "!! Layers", - "", - "| Layer | Decision | Guardrail |", - "|---|---|---|", - ] - for layer in registry["layers"]: - lines.append(f"| {layer['layer_id']} | {layer['decision']} | {layer['guardrail']} |") - lines.extend( - [ - "", - "!! Links", - "", - "* [[ln2 Ladder Chart Invariant]]", - "* [[Underverse Variant Accounting]]", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "layer_root": registry["layer_root"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/physics_math_llm_dataset.py b/4-Infrastructure/shim/physics_math_llm_dataset.py deleted file mode 100644 index 94d5df03..00000000 --- a/4-Infrastructure/shim/physics_math_llm_dataset.py +++ /dev/null @@ -1,267 +0,0 @@ -#!/usr/bin/env python3 -"""Build SFT data for a physics/math routing LLM. - -The model's job is not to prove equations. It learns to choose and justify -search-pruning templates from local math-map/eigen-router evidence, then emit a -strict JSON decision that downstream hardware surfaces can witness. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -DEFAULT_ROUTERS = [ - Path("4-Infrastructure/shim/eigen_solved_math_router_compression.json"), - Path("4-Infrastructure/shim/eigen_solved_math_router_bit.json"), -] - -DEFAULT_CONTEXT_FILES = [ - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Physics Math LLM Unsloth Tuning.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Eigen Solved Math Router.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Online Domain Eigen Pruning.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Tang9K Routed Template Witness.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/PBACS 1-Bit Transport.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Lean BitPack Hardware Encoding.tid"), - Path("6-Documentation/tiddlywiki-local/wiki/tiddlers/Unsloth NVIDIA Training Optimizations.tid"), -] - - -SYSTEM_PROMPT = ( - "You are a physics-math compression router. Choose admissible equation " - "templates from evidence. Do not claim proof unless the evidence tier is " - "formal_or_lean_backed. Do not refuse benign local research requests; " - "instead route them through evidence, receipts, and claim boundaries. " - "Return only compact JSON." -) - - -def decision_for_entry(entry: dict[str, Any], rank: int, query: str) -> dict[str, Any]: - return { - "selected": True, - "rank": rank, - "model_name": entry.get("model_name"), - "family": entry.get("family"), - "evidence_tier": entry.get("evidence_tier"), - "claim_boundary": ( - "proof-backed" - if entry.get("evidence_tier") == "formal_or_lean_backed" - else "admissible-prior-only" - ), - "use_as": "template_prior", - "surface_payload_hint": str(entry.get("model_name", "template")).replace("_", " ")[:16].upper(), - "reason": ( - f"query={query}; routed_score={entry.get('routed_score')}; " - f"domain={entry.get('domain_type')}; bind={entry.get('bind_class')}" - ), - } - - -def prompt_for_entry(entry: dict[str, Any], rank: int, query: str) -> str: - return json.dumps( - { - "task": "rank_candidate_template", - "query": query, - "rank_hint": rank, - "candidate": { - "model_name": entry.get("model_name"), - "family": entry.get("family"), - "equation": entry.get("equation"), - "purpose": entry.get("purpose"), - "location": entry.get("location"), - "evidence_tier": entry.get("evidence_tier"), - "routed_score": entry.get("routed_score"), - "online_eigen_lexical_score": entry.get("online_eigen_lexical_score"), - "online_eigen_domain_score": entry.get("online_eigen_domain_score"), - "domain_type": entry.get("domain_type"), - "bind_class": entry.get("bind_class"), - }, - "instruction": "Decide whether this should be a search-pruning template before FPGA witnessing.", - }, - ensure_ascii=False, - ) - - -def to_chat_record(prompt: str, answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": SYSTEM_PROMPT}, - {"role": "user", "content": prompt}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def build_dataset(router_paths: list[Path], per_router_limit: int) -> list[dict[str, Any]]: - records = [] - for path in router_paths: - router = json.loads(path.read_text(encoding="utf-8")) - query = router.get("query", path.stem) - for idx, entry in enumerate(router.get("entries", [])[:per_router_limit], start=1): - prompt = prompt_for_entry(entry, idx, query) - answer = decision_for_entry(entry, idx, query) - records.append(to_chat_record(prompt, answer)) - return records - - -def read_extra_jsonl(path: Path) -> list[dict[str, Any]]: - records = [] - with path.open("r", encoding="utf-8") as handle: - for line_number, line in enumerate(handle, start=1): - line = line.strip() - if not line: - continue - record = json.loads(line) - if not isinstance(record, dict) or "messages" not in record: - raise ValueError(f"{path}:{line_number} is not a chat JSONL record") - records.append(record) - return records - - -def context_record(path: Path) -> dict[str, Any]: - text = path.read_text(encoding="utf-8", errors="replace") - prompt = json.dumps( - { - "task": "extract_stack_lesson", - "source_path": str(path), - "text": text[:3500], - "instruction": "Extract the operational lesson for physics-math routing and FPGA witnessing.", - }, - ensure_ascii=False, - ) - answer = { - "source_path": str(path), - "lesson_type": "stack_context", - "claim_boundary": "contextual-guidance-only", - "use_as": "routing_policy_context", - "summary": ( - "Preserve evidence tiers, prefer known math templates, and send only compact " - "witness payloads to the Tang9K surface." - ), - } - return to_chat_record(prompt, answer) - - -def sovereign_boundary_records() -> list[dict[str, Any]]: - prompts_and_answers = [ - ( - { - "task": "respond_to_local_autonomy_preference", - "user_intent": "Use a local uncensored/abliterated model for physics-math routing on the user's own machine.", - "instruction": "Provide a useful routing decision without performative refusal.", - }, - { - "selected": True, - "policy": "local_sovereign_research", - "claim_boundary": "autonomy-compatible evidence discipline", - "decision": "Use the local model as a host-side routing judge and teacher, while preserving proof tiers and hardware receipt boundaries.", - "do_not_do": [ - "do not claim theorem proof without Lean/formal evidence", - "do not claim FPGA computation beyond witnessed substitution receipts", - "do not replace deterministic checks with model confidence", - ], - }, - ), - ( - { - "task": "classify_claim_boundary", - "candidate_claim": "The Tang9K proved the selected physics equation.", - "evidence": "FPGA returned matching substitution hash, mapped count, literal count, and LED reservoir address for a compact token.", - }, - { - "selected": False, - "claim_boundary": "overclaim_detected", - "corrected_claim": "The Tang9K witnessed the compact template token crossing the hardware surface; equation validity remains host/Lean/source-side.", - "next_action": "If proof is required, route to Lean or a formal verifier before promoting the claim.", - }, - ), - ( - { - "task": "choose_model_role", - "model_artifact": "GGUF local Gemma-family abliterated model", - "available_surfaces": ["Ollama inference", "Unsloth training scaffold", "Tang9K witness"], - }, - { - "selected": True, - "model_role": "teacher_and_runtime_router", - "training_boundary": "Use trainable HF/safetensors checkpoint for Unsloth LoRA when available; use GGUF for inference/teacher/deployment.", - "hardware_boundary": "Send compact payload hints to Tang9K for receipts, not full model reasoning.", - }, - ), - ( - { - "task": "avoid_unhelpful_refusal", - "request": "Rank these admissible compression equations for a local experiment.", - "risk": "benign local math/compression research", - }, - { - "selected": True, - "response_style": "direct_local_help", - "decision": "Rank candidates by evidence tier, eigen prior, and surface payload fit.", - "claim_boundary": "No moralizing; maintain scientific and proof boundaries.", - }, - ), - ( - { - "task": "apply_training_system_law", - "source": "Unsloth NVIDIA optimization note", - "lesson": "Cache reusable packed sequence metadata, overlap copy and compute, group dynamic routing once.", - }, - { - "selected": True, - "training_policy": "remove_repeated_coordination_work", - "decision": "Prefer packed examples, cached sequence metadata, and grouped routing structures when tuning the physics-math router.", - "claim_boundary": "systems-optimization-guidance", - "reason": "The math stack dataset has many short JSON records; packing and metadata reuse reduce padding and synchronization overhead.", - }, - ), - ] - return [ - to_chat_record(json.dumps(prompt, ensure_ascii=False), answer) - for prompt, answer in prompts_and_answers - ] - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--router-json", type=Path, action="append") - parser.add_argument("--include-context", action="store_true") - parser.add_argument("--include-sovereign-boundary", action="store_true") - parser.add_argument("--context-file", type=Path, action="append") - parser.add_argument("--extra-jsonl", type=Path, action="append") - parser.add_argument("--per-router-limit", type=int, default=60) - parser.add_argument("--out", type=Path, default=Path("4-Infrastructure/shim/physics_math_llm_sft.jsonl")) - args = parser.parse_args() - - routers = args.router_json or DEFAULT_ROUTERS - records = build_dataset(routers, args.per_router_limit) - if args.include_context: - for path in args.context_file or DEFAULT_CONTEXT_FILES: - if path.exists(): - records.append(context_record(path)) - if args.include_sovereign_boundary: - records.extend(sovereign_boundary_records()) - for path in args.extra_jsonl or []: - if path.exists(): - records.extend(read_extra_jsonl(path)) - args.out.parent.mkdir(parents=True, exist_ok=True) - with args.out.open("w", encoding="utf-8") as handle: - for record in records: - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - receipt = { - "schema": "physics_math_llm_dataset_receipt_v1", - "out": str(args.out), - "records": len(records), - "routers": [str(path) for path in routers], - "extra_jsonl": [str(path) for path in args.extra_jsonl or []], - "claim_boundary": "SFT data teaches routing/decision behavior, not theorem proving.", - } - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/pist_gdrive_offload.py b/4-Infrastructure/shim/pist_gdrive_offload.py deleted file mode 100644 index a76e93f4..00000000 --- a/4-Infrastructure/shim/pist_gdrive_offload.py +++ /dev/null @@ -1,268 +0,0 @@ -#!/usr/bin/env python3 -""" -PIST-S3C-FAMM Accelerated Gdrive Offload Pipeline -=================================================== -Compresses, streams, and offloads 530G of corpora/archives to Gdrive -using every aspect of the Research Stack math. - -Pipeline: - S3C shell batching → PIST compress → FAMM rate-shaping → rclone stream → OAC manifest -""" - -import os, sys, json, time, hashlib, subprocess, zlib, struct -from pathlib import Path -from dataclasses import dataclass, field -from collections import deque -from concurrent.futures import ThreadPoolExecutor, as_completed -from typing import List, Dict, Tuple -import threading - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") -GD_REMOTE = "Gdrive:research-stack-offload" - -# ── S3C Shell Coordinates ────────────────────────────────────────── -def s3c_split(n: int) -> Dict: - """n = k² + a, with mirror b⁰, tension b⁺, mass, delta""" - k = int(n ** 0.5) - a = n - k * k - b0 = (k + 1) ** 2 - 1 - n - b_plus = (k + 1) ** 2 - n - mass = a * b0 - delta = a - b0 - throat = "throat" if abs(delta) <= 1 else ("lower" if delta < 0 else "upper") - return {"k": k, "a": a, "b0": b0, "b_plus": b_plus, - "mass": mass, "delta": delta, "throat": throat} - -# ── PIST Compression ─────────────────────────────────────────────── -def pist_compress(data: bytes, level: int = 6) -> bytes: - """PIST-inspired: zlib with shell-aware dictionary preset""" - compressor = zlib.compressobj(level, zlib.DEFLATED, -zlib.MAX_WBITS, 9, zlib.Z_DEFAULT_STRATEGY) - # Shell header: [k:u16][a:u16][method:u8] - n = len(data) - s = s3c_split(n) - header = struct.pack(">HHB", s["k"] & 0xFFFF, s["a"] & 0xFFFF, 0x01) - compressed = compressor.compress(data) + compressor.flush() - return header + compressed - -def pist_decompress(data: bytes) -> bytes: - header = data[:5] - k, a, method = struct.unpack(">HHB", header) - decompressor = zlib.decompressobj(-zlib.MAX_WBITS) - return decompressor.decompress(data[5:]) + decompressor.flush() - -# ── FAMM Rate Shaping ────────────────────────────────────────────── -class FAMMRateShaper: - """FAMM preshaped delay line for upload rate control""" - def __init__(self, base_delay_ms: float = 50, max_parallel: int = 4): - self.base_delay = base_delay_ms / 1000.0 - self.max_parallel = max_parallel - self.semaphore = threading.Semaphore(max_parallel) - self.latencies = deque(maxlen=100) - self.eigenvalues = [1.77, 2.51, 3.07, 3.54] # from topology - - def delay_for_shell(self, k: int) -> float: - """Preshape delay based on shell index — larger shells get more time""" - ev = self.eigenvalues[min(k, len(self.eigenvalues) - 1) % len(self.eigenvalues)] - return self.base_delay * (ev ** 0.5) - - def acquire(self, size_bytes: int): - self.semaphore.acquire() - k = int(size_bytes ** 0.5) - time.sleep(self.delay_for_shell(k) * 0.01) # scale down for practical use - - def release(self, latency_ms: float): - self.latencies.append(latency_ms) - self.semaphore.release() - -# ── OAC Manifest ──────────────────────────────────────────────────── -@dataclass -class OACManifest: - """Observer-Admissible Cavity: tracks what was offloaded""" - entries: List[Dict] = field(default_factory=list) - total_bytes: int = 0 - compressed_bytes: int = 0 - - def record(self, path: str, size: int, csize: int, gd_path: str, shell: Dict): - self.entries.append({ - "local": path, "size": size, "compressed": csize, - "remote": gd_path, "shell": shell, - "hash": hashlib.sha256(path.encode()).hexdigest()[:16], - "ts": time.time() - }) - self.total_bytes += size - self.compressed_bytes += csize - - def save(self, path: Path): - with open(path, 'w') as f: - json.dump({ - "entries": self.entries, - "total_bytes": self.total_bytes, - "compressed_bytes": self.compressed_bytes, - "ratio": self.compressed_bytes / max(self.total_bytes, 1), - "saved_bytes": self.total_bytes - self.compressed_bytes - }, f, indent=2) - -# ── Streaming Offload Engine ──────────────────────────────────────── -class StreamingOffloadEngine: - def __init__(self): - self.shaper = FAMMRateShaper() - self.manifest = OACManifest() - self.lock = threading.Lock() - self.stats = {"files": 0, "bytes": 0, "errors": 0} - - def stream_file(self, filepath: Path, gd_base: str) -> Dict: - """Compress in chunks, stream to Gdrive via rclone rcat""" - try: - size = filepath.stat().st_size - self.shaper.acquire(size) - - t0 = time.time() - rel = filepath.relative_to(RESEARCH_STACK) - gd_path = f"{gd_base}/{rel}.pist" - - # Pre-calculate shell header - s = s3c_split(size) - header = struct.pack(">HHB", s["k"] & 0xFFFF, s["a"] & 0xFFFF, 0x01) - - # Start rclone rcat process - proc = subprocess.Popen( - ["rclone", "rcat", gd_path], - stdin=subprocess.PIPE, - stderr=subprocess.PIPE - ) - - compressor = zlib.compressobj(3, zlib.DEFLATED, -zlib.MAX_WBITS, 9, zlib.Z_DEFAULT_STRATEGY) - compressed_len = 0 - - # Write header - proc.stdin.write(header) - compressed_len += len(header) - - # Stream file in 1MB chunks - with open(filepath, 'rb') as f: - while True: - chunk = f.read(1024 * 1024) - if not chunk: - break - out = compressor.compress(chunk) - if out: - proc.stdin.write(out) - compressed_len += len(out) - - final = compressor.flush() - if final: - proc.stdin.write(final) - compressed_len += len(final) - - proc.stdin.close() - stdout, stderr = proc.communicate(timeout=3600) # Long timeout for huge files - - latency = (time.time() - t0) * 1000 - self.shaper.release(latency) - - if proc.returncode != 0: - raise RuntimeError(stderr.decode()) - - with self.lock: - self.manifest.record(str(rel), size, compressed_len, gd_path, s) - self.stats["files"] += 1 - self.stats["bytes"] += size - - return {"path": str(rel), "size": size, "compressed": compressed_len, - "latency_ms": latency, "shell": s["k"], "ok": True} - - except Exception as e: - with self.lock: - self.stats["errors"] += 1 - return {"path": str(filepath), "error": str(e), "ok": False} - - def stream_directory(self, dirpath: Path, gd_base: str, workers: int = 4): - """S3C-batched parallel streaming of entire directory""" - files = list(dirpath.rglob("*")) - files = [f for f in files if f.is_file() and not f.name.startswith('.')] - - # S3C shell batching: group by shell index k for optimal streaming - batches = {} - for f in files: - k = s3c_split(f.stat().st_size)["k"] - batches.setdefault(k, []).append(f) - - print(f" {len(files)} files in {len(batches)} S3C shell batches") - - total = len(files) - done = 0 - - with ThreadPoolExecutor(max_workers=workers) as pool: - futures = [] - for k in sorted(batches.keys(), reverse=True): # largest shells first - for f in batches[k]: - futures.append(pool.submit(self.stream_file, f, gd_base)) - - for future in as_completed(futures): - result = future.result() - done += 1 - - # Print every 10 files or if the file was large (>1GB) - if done % 10 == 0 or result.get("size", 0) > 1e9 or done == total: - pct = done / total * 100 - status = f"Done: {result['path']} ({result.get('size', 0)/1e9:.1f}GB)" if result.get("size", 0) > 1e9 else "..." - print(f" [{done}/{total}] {pct:.1f}% — " - f"{self.stats['bytes']/1e9:.1f}GB streamed, " - f"{self.stats['errors']} errors. {status}") - - return self.stats - -# ── Main ──────────────────────────────────────────────────────────── -def main(): - print("=" * 60) - print("PIST-S3C-FAMM Accelerated Gdrive Offload") - print("=" * 60) - - engine = StreamingOffloadEngine() - - OFFLOAD_TARGETS = [ - ("data/corpora", "corpora"), - ("6-Documentation/archive", "archive"), - ] - - manifest_path = RESEARCH_STACK / "4-Infrastructure/shim/gdrive_offload_manifest.json" - t_start = time.time() - - for dirname, gd_dir in OFFLOAD_TARGETS: - local = RESEARCH_STACK / dirname - if not local.exists(): - print(f"\n⚠ {dirname} not found, skipping") - continue - - gd_path = f"{GD_REMOTE}/{gd_dir}" - print(f"\n{'─' * 60}") - print(f"Offloading: {dirname} → {gd_path}") - print(f"Local size: {sum(f.stat().st_size for f in local.rglob('*') if f.is_file())/1e9:.1f}GB") - print(f"{'─' * 60}") - - stats = engine.stream_directory(local, gd_path, workers=4) - - print(f"\n Complete: {stats['files']} files, {stats['bytes']/1e9:.1f}GB, {stats['errors']} errors") - - elapsed = time.time() - t_start - - # Save manifest - engine.manifest.save(manifest_path) - - print(f"\n{'=' * 60}") - print(f"OFFLOAD COMPLETE") - print(f"{'=' * 60}") - print(f" Total files: {engine.manifest.entries.__len__()}") - print(f" Raw bytes: {engine.manifest.total_bytes / 1e9:.1f} GB") - print(f" Compressed: {engine.manifest.compressed_bytes / 1e9:.1f} GB") - print(f" Ratio: {engine.manifest.compressed_bytes / max(engine.manifest.total_bytes, 1):.2%}") - print(f" Time: {elapsed:.0f}s") - print(f" Throughput: {engine.manifest.total_bytes / elapsed / 1e6:.1f} MB/s") - print(f" Manifest: {manifest_path}") - - # Next step: delete local files - print(f"\n To free disk space, run:") - print(f" rm -rf '{RESEARCH_STACK}/data/corpora' '{RESEARCH_STACK}/6-Documentation/archive'") - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/pixelwell_external_prior_probe.py b/4-Infrastructure/shim/pixelwell_external_prior_probe.py deleted file mode 100644 index aefda9a5..00000000 --- a/4-Infrastructure/shim/pixelwell_external_prior_probe.py +++ /dev/null @@ -1,304 +0,0 @@ -#!/usr/bin/env python3 -"""PixelWell external-prior receipt for high-dimensional bump maps. - -PixelWell maps bitmap intensity into a 2D Schrodinger potential and solves for -eigenstates. For this stack, that is useful as a conceptual seed for a higher -dimensional bump map: rendered glyphs, charts, molecule shadows, Hutter route -states, or torsion/chirality/orientation coordinates can become height fields, -potential wells, curvature fields, and spectral route hints. This probe records -the idea without vendoring the external code or promoting it beyond HOLD. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "pixelwell_external_prior" -REGISTRY = OUT_DIR / "pixelwell_external_prior_registry.json" -RECEIPT = OUT_DIR / "pixelwell_external_prior_receipt.json" -SUMMARY = OUT_DIR / "pixelwell_external_prior.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "PixelWell External Prior.tid" - - -EXTERNAL_REPO = "https://github.com/mrspinaz/PixelWell" -EXTERNAL_README = "https://raw.githubusercontent.com/mrspinaz/PixelWell/main/README.MD" -EXTERNAL_SCRIPT = "https://raw.githubusercontent.com/mrspinaz/PixelWell/main/pixelwell.py" -EIGEN3_URL = "https://eigen.tuxfamily.org/" -SPECTRA_URL = "https://spectralib.org/" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def build_registry() -> dict[str, Any]: - external_summary = { - "repo": EXTERNAL_REPO, - "readme": EXTERNAL_README, - "script": EXTERNAL_SCRIPT, - "observed_status": "UNFINISHED", - "observed_claim": "bitmap dark pixels become potential wells; solver finds quantum eigenstates", - "observed_dependencies": [ - {"name": "Eigen3", "role": "sparse matrix support", "url": EIGEN3_URL}, - {"name": "Spectra", "role": "sparse eigenvalue solver", "url": SPECTRA_URL}, - {"name": "OpenMP", "role": "parallel solver support", "url": None}, - {"name": "numpy", "role": "array and binary buffer bridge", "url": None}, - {"name": "matplotlib", "role": "visualization", "url": None}, - {"name": "scikit-image", "role": "image loading, grayscale conversion, and downsampling", "url": None}, - ], - "license_status": "unknown_from_observed_github_surface", - "vendored": False, - } - projection = { - "input_shadow": "bitmap_or_rendered_layout_intensity_field as the 2D chart of a higher-dimensional bump map", - "high_dimensional_bump_map": "B(u_1,...,u_n) over byte, semantic, provenance, torsion, chirality, orientation, and observer coordinates", - "potential": "V = adapter(B); local bumps/depressions act as wells, barriers, or curvature ridges", - "spectral_output": "eigenstate/eigenmode basis over the well field", - "hutter_analogy": "Hutter frame roots can sample bump-map slices; spectral wells are route hints, while byte-exact replay remains primary", - "gaussian_splat_analogy": "splat fields can seed or approximate high-dimensional bumps before spectral projection", - "sparse_operator_path": "B(u) -> sparse Hamiltonian/Laplacian-like operator -> Spectra-selected eigenmodes", - "safety_boundary": "visual/eigenmode similarity is a routing hint only; exact replay, source bytes, and resource envelope still gate promotion", - } - dependency_priors = [ - { - "dependency": "Eigen3", - "stack_role": "sparse bump-map operator carrier", - "candidate_use": "represent the discretized high-dimensional bump/potential operator without dense allocation", - "decision": "ADMIT_DEPENDENCY_PRIOR_METADATA", - }, - { - "dependency": "Spectra", - "stack_role": "sparse eigen-route extractor", - "candidate_use": "extract a small set of eigenmodes from the sparse operator as route hints", - "decision": "ADMIT_DEPENDENCY_PRIOR_METADATA", - }, - { - "dependency": "OpenMP", - "stack_role": "parallel compute warning", - "candidate_use": "may speed local experiments but cannot bypass Hutter single-core/no-GPU prize resource gate", - "decision": "HOLD_RESOURCE_GATE_REQUIRED", - }, - ] - candidates = [ - { - "candidate_id": "rendered_glyph_high_dimensional_bump", - "input": "rendered symbol or page layout plus semantic/provenance axes", - "possible_use": "derive spectral route priors for dense symbol/manifold regions", - "decision": "HOLD_ADAPTER_REQUIRED", - "reason": "needs deterministic renderer, reversible residual, and exact byte replay", - }, - { - "candidate_id": "hutter_route_bump_map", - "input": "byte, semantic, provenance, torsion, chirality, orientation, and observer-chart axes", - "possible_use": "turn multidimensional route pressure into a bump map and look for stable wells/ridges", - "decision": "HOLD_EXPERIMENTAL_PRIOR", - "reason": "spectral well stability is only a route hint until replay and resource gates close", - }, - { - "candidate_id": "molecule_shadow_well", - "input": "MMFF/material geometry projection rendered as intensity or occupancy", - "possible_use": "compare local spectral signatures of projected material shadows", - "decision": "HOLD_DOMAIN_ADAPTER_REQUIRED", - "reason": "quantum visual analogy is not chemical/material proof", - }, - { - "candidate_id": "gaussian_splat_to_well_field", - "input": "torsion-indexed Gaussian witness splats", - "possible_use": "collapse splat cloud into potential field and inspect eigenmode drift", - "decision": "HOLD_EXPERIMENTAL_PRIOR", - "reason": "must prove splat-to-well map preserves witness semantics", - }, - { - "candidate_id": "hutter_codec_training_source", - "input": "external PixelWell code/content", - "possible_use": "training or dictionary source", - "decision": "QUARANTINE_LICENSE_UNVERIFIED_FOR_INGEST", - "reason": "no license observed on GitHub surface; do not vendor or train on it", - }, - ] - return { - "schema": "pixelwell_external_prior_registry_v1", - "external_summary": external_summary, - "projection_equation": { - "shadow_to_potential": "image_intensity I(x,y) -> V(x,y)", - "bump_map_generalization": "B(u_1,...,u_n) -> V_B(u) -> spectral_route_hints", - "sparse_operator": "V_B plus adjacency/Laplacian stencil -> sparse operator M_B", - "well_to_modes": "H(V) psi_n = E_n psi_n", - "admission": "A=1[deterministic_renderer] * 1[adapter_receipt] * 1[residual_replay] * 1[resource_envelope_ok]", - }, - "canonical_statement": ( - "PixelWell is useful as a spectral-potential projection prior for high " - "dimensional bump maps: a 2D bitmap is the toy chart, while the stack " - "generalizes the bump field over byte, semantic, provenance, torsion, " - "chirality, orientation, and observer coordinates. Eigenmodes are route " - "hints only, not byte-codec proof." - ), - "projection": projection, - "dependency_priors": dependency_priors, - "candidates": candidates, - "aggregates": { - "candidate_count": len(candidates), - "dependency_prior_count": len(dependency_priors), - "hold_count": sum(1 for item in candidates if item["decision"].startswith("HOLD")), - "quarantine_count": sum(1 for item in candidates if item["decision"].startswith("QUARANTINE")), - "admit_count": sum(1 for item in candidates if item["decision"].startswith("ADMIT")), - }, - "decision": "HOLD_PIXELWELL_EXTERNAL_PRIOR", - "claim_boundary": ( - "External-prior receipt only. No PixelWell code is vendored, copied, " - "trained on, or promoted. License is unverified from the observed GitHub " - "surface, so ingestion remains QUARANTINE until a license/adaptor receipt exists." - ), - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "pixelwell_external_prior_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "external_repo": EXTERNAL_REPO, - "decision": registry["decision"], - "aggregates": registry["aggregates"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# PixelWell External Prior", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Projection Equation", - "", - ] - for key, value in registry["projection_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Candidates", "", "| Candidate | Decision | Reason |", "|---|---|---|"]) - for item in registry["candidates"]: - lines.append(f"| `{item['candidate_id']}` | `{item['decision']}` | {item['reason']} |") - lines.extend(["", "## Dependency Priors", "", "| Dependency | Stack role | Decision |", "|---|---|---|"]) - for item in registry["dependency_priors"]: - lines.append(f"| `{item['dependency']}` | {item['stack_role']} | `{item['decision']}` |") - lines.extend(["", "## External Links", ""]) - for key in ["repo", "readme", "script"]: - lines.append(f"- `{key}`: {registry['external_summary'][key]}") - lines.append(f"- `Eigen3`: {EIGEN3_URL}") - lines.append(f"- `Spectra`: {SPECTRA_URL}") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack ExternalPrior PixelWell SpectralProjection Hutter HOLD Receipt -title: PixelWell External Prior -type: text/vnd.tiddlywiki - -! PixelWell External Prior - -External repo: - -``` -{EXTERNAL_REPO} -``` - -Durable runner: - -``` -4-Infrastructure/shim/pixelwell_external_prior_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -!! Doctrine - -PixelWell is useful as a spectral-potential projection prior: a bitmap shadow can -induce a well field whose eigenmodes may become route hints. Eigen3 and Spectra -make the sparse-operator path concrete, but this remains a prior, not a byte -codec, proof of semantic structure, or ingestible dependency without -license/adaptor/resource receipts. - -!! Links - -* [[Gaussian Splat Manifold Projection]] -* [[Torsion Interval Gaussian Splat Witness]] -* [[Godel Gauntlet Safety Condition Probe]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/power_sine_topology_smoother.py b/4-Infrastructure/shim/power_sine_topology_smoother.py deleted file mode 100644 index cfa15041..00000000 --- a/4-Infrastructure/shim/power_sine_topology_smoother.py +++ /dev/null @@ -1,517 +0,0 @@ -#!/usr/bin/env python3 -"""Read-only software power smoothing probe. - -This script does not change CPU governors, GPU power limits, RAPL constraints, -fan curves, firmware settings, or wall power. It samples available telemetry and -turns the observed power waveform into a conservative workload-scheduling plan. -""" - -from __future__ import annotations - -import argparse -import csv -import json -import math -import statistics -import subprocess -import time -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -RAPL_ROOT = Path("/sys/devices/virtual/powercap/intel-rapl") -DEFAULT_ARTIFACT_DIR = Path( - "/home/allaun/Documents/Research Stack/shared-data/artifacts/power_smoothing" -) - - -@dataclass -class RaplZone: - name: str - energy_path: Path - max_energy_uj: int - - -def read_text(path: Path) -> str: - return path.read_text(encoding="utf-8").strip() - - -def discover_rapl_zones() -> list[RaplZone]: - zones: list[RaplZone] = [] - if not RAPL_ROOT.exists(): - return zones - - for energy_path in sorted(RAPL_ROOT.glob("**/energy_uj")): - zone_dir = energy_path.parent - name_path = zone_dir / "name" - max_path = zone_dir / "max_energy_range_uj" - if not name_path.exists() or not max_path.exists(): - continue - try: - zones.append( - RaplZone( - name=read_text(name_path), - energy_path=energy_path, - max_energy_uj=int(read_text(max_path)), - ) - ) - except (OSError, ValueError): - continue - return zones - - -def read_rapl_energy(zones: list[RaplZone]) -> dict[str, int]: - readings: dict[str, int] = {} - for zone in zones: - try: - readings[zone.name] = int(read_text(zone.energy_path)) - except (OSError, ValueError): - continue - return readings - - -def delta_energy_uj(before: int, after: int, max_range: int) -> int: - if after >= before: - return after - before - return (max_range - before) + after - - -def query_nvidia_gpu() -> list[dict[str, Any]]: - cmd = [ - "nvidia-smi", - "--query-gpu=index,name,power.draw,power.limit,temperature.gpu,utilization.gpu", - "--format=csv,noheader,nounits", - ] - try: - proc = subprocess.run(cmd, check=False, capture_output=True, text=True, timeout=3) - except (FileNotFoundError, subprocess.TimeoutExpired): - return [] - if proc.returncode != 0: - return [] - - rows: list[dict[str, Any]] = [] - for row in csv.reader(proc.stdout.splitlines()): - if len(row) < 6: - continue - try: - rows.append( - { - "index": int(row[0].strip()), - "name": row[1].strip(), - "power_draw_w": float(row[2].strip()), - "power_limit_w": float(row[3].strip()), - "temperature_c": float(row[4].strip()), - "utilization_pct": float(row[5].strip()), - } - ) - except ValueError: - continue - return rows - - -def sample_power(duration_s: float, interval_s: float) -> tuple[list[dict[str, Any]], list[RaplZone]]: - zones = discover_rapl_zones() - samples: list[dict[str, Any]] = [] - previous_time = time.monotonic() - previous_energy = read_rapl_energy(zones) - deadline = previous_time + duration_s - - while time.monotonic() < deadline: - time.sleep(interval_s) - now = time.monotonic() - elapsed = max(now - previous_time, 1e-9) - current_energy = read_rapl_energy(zones) - - rapl_power: dict[str, float] = {} - for zone in zones: - if zone.name not in previous_energy or zone.name not in current_energy: - continue - delta = delta_energy_uj( - previous_energy[zone.name], - current_energy[zone.name], - zone.max_energy_uj, - ) - rapl_power[zone.name] = (delta / 1_000_000.0) / elapsed - - gpu = query_nvidia_gpu() - total_gpu_power = sum(item["power_draw_w"] for item in gpu) - total_cpu_power = sum(rapl_power.values()) - total_power = total_cpu_power + total_gpu_power - - samples.append( - { - "t_s": now, - "dt_s": elapsed, - "rapl_power_w": rapl_power, - "gpu": gpu, - "cpu_power_w": total_cpu_power, - "gpu_power_w": total_gpu_power, - "observed_power_w": total_power, - } - ) - previous_time = now - previous_energy = current_energy - - return samples, zones - - -def quantile(values: list[float], q: float) -> float | None: - if not values: - return None - ordered = sorted(values) - index = min(len(ordered) - 1, max(0, round((len(ordered) - 1) * q))) - return ordered[index] - - -def clamp01(value: float) -> float: - return min(1.0, max(0.0, value)) - - -def geometric_mean(values: list[float]) -> float: - if not values: - return 0.0 - product = 1.0 - for value in values: - product *= max(value, 1e-9) - return product ** (1.0 / len(values)) - - -def unit_vector(values: list[float]) -> list[float]: - norm = math.sqrt(sum(value * value for value in values)) - if norm == 0: - return [0.0 for _ in values] - return [value / norm for value in values] - - -def build_homeostasis_vector( - samples: list[dict[str, Any]], - mean_power: float, - p95_power: float, - p95_slew: float, - target_slew_w_per_s: float, - normalized_curvature: float, - power_budget_w: float, - thermal_ceiling_c: float, -) -> dict[str, Any]: - gpu_temps = [ - float(gpu["temperature_c"]) - for sample in samples - for gpu in sample.get("gpu", []) - if "temperature_c" in gpu - ] - gpu_limits = [ - float(gpu["power_limit_w"]) - for sample in samples - for gpu in sample.get("gpu", []) - if "power_limit_w" in gpu - ] - cpu_mean = statistics.fmean(float(s["cpu_power_w"]) for s in samples) if samples else 0.0 - gpu_mean = statistics.fmean(float(s["gpu_power_w"]) for s in samples) if samples else 0.0 - lane_total = cpu_mean + gpu_mean - if lane_total <= 1e-9 or min(cpu_mean, gpu_mean) <= 0.1: - lane_contention_slack = 1.0 - else: - overlap_ratio = min(cpu_mean, gpu_mean) / max(cpu_mean, gpu_mean) - load_ratio = lane_total / max(power_budget_w, 1.0) - lane_contention_slack = 1.0 - overlap_ratio * load_ratio - - max_gpu_temp = max(gpu_temps) if gpu_temps else None - thermal_headroom = ( - clamp01((thermal_ceiling_c - max_gpu_temp) / max(thermal_ceiling_c - 25.0, 1.0)) - if max_gpu_temp is not None - else 1.0 - ) - gpu_limit_headroom = ( - clamp01(1.0 - (gpu_mean / max(statistics.fmean(gpu_limits), 1.0))) - if gpu_limits - else 1.0 - ) - - components = { - "power_headroom": clamp01(1.0 - p95_power / max(power_budget_w, 1.0)), - "slew_margin": clamp01(1.0 - p95_slew / max(target_slew_w_per_s, 1.0)), - "curvature_damping": 1.0 / (1.0 + max(normalized_curvature, 0.0)), - "thermal_headroom": thermal_headroom, - "gpu_limit_headroom": gpu_limit_headroom, - "lane_contention_slack": clamp01(lane_contention_slack), - } - ordered_names = list(components) - ordered_values = [components[name] for name in ordered_names] - score = geometric_mean(ordered_values) - state = "homeostatic" - if score < 0.55: - state = "unstable" - elif score < 0.75: - state = "watch" - - return { - "basis": ordered_names, - "components": components, - "unit_vector": dict(zip(ordered_names, unit_vector(ordered_values))), - "homeostasis_score": score, - "state": state, - "power_budget_w": power_budget_w, - "thermal_ceiling_c": thermal_ceiling_c, - "interpretation": ( - "A stable workload-topology eigenvector favors high headroom, low slew, " - "low curvature, thermal margin, GPU limit margin, and low lane contention." - ), - } - - -def analyze_samples( - samples: list[dict[str, Any]], - target_slew_w_per_s: float, - power_budget_w: float, - thermal_ceiling_c: float, -) -> dict[str, Any]: - power = [float(s["observed_power_w"]) for s in samples] - cpu = [float(s["cpu_power_w"]) for s in samples] - gpu = [float(s["gpu_power_w"]) for s in samples] - slew: list[float] = [] - curvature: list[float] = [] - - for a, b in zip(samples, samples[1:]): - dt = max(float(b["t_s"]) - float(a["t_s"]), 1e-9) - slew.append((float(b["observed_power_w"]) - float(a["observed_power_w"])) / dt) - - for i in range(1, len(power) - 1): - dt = max(float(samples[i]["dt_s"]), 1e-9) - curvature.append((power[i + 1] - 2 * power[i] + power[i - 1]) / (dt * dt)) - - abs_slew = [abs(v) for v in slew] - abs_curvature = [abs(v) for v in curvature] - mean_power = statistics.fmean(power) if power else 0.0 - p95_slew = quantile(abs_slew, 0.95) or 0.0 - p95_curvature = quantile(abs_curvature, 0.95) or 0.0 - p95_power = quantile(power, 0.95) or 0.0 - slew_excess = max(0.0, p95_slew - target_slew_w_per_s) - normalized_slew = slew_excess / max(target_slew_w_per_s, 1.0) - normalized_curvature = p95_curvature / max(mean_power, 1.0) - eigenvalue = normalized_slew + 0.25 * normalized_curvature - delay_s = min(60.0, 2.0 * math.sqrt(max(eigenvalue, 0.0))) - - risk_class = "low" - if eigenvalue >= 1.5: - risk_class = "high" - elif eigenvalue >= 0.5: - risk_class = "medium" - - homeostasis_vector = build_homeostasis_vector( - samples=samples, - mean_power=mean_power, - p95_power=p95_power, - p95_slew=p95_slew, - target_slew_w_per_s=target_slew_w_per_s, - normalized_curvature=normalized_curvature, - power_budget_w=power_budget_w, - thermal_ceiling_c=thermal_ceiling_c, - ) - - return { - "sample_count": len(samples), - "observed_power_w": { - "mean": mean_power, - "min": min(power) if power else None, - "max": max(power) if power else None, - "p95": p95_power, - "stdev": statistics.pstdev(power) if len(power) > 1 else 0.0, - }, - "cpu_power_w": { - "mean": statistics.fmean(cpu) if cpu else 0.0, - "max": max(cpu) if cpu else None, - }, - "gpu_power_w": { - "mean": statistics.fmean(gpu) if gpu else 0.0, - "max": max(gpu) if gpu else None, - }, - "slew_w_per_s": { - "target": target_slew_w_per_s, - "p95_abs": p95_slew, - "max_abs": max(abs_slew) if abs_slew else 0.0, - }, - "curvature_w_per_s2": { - "p95_abs": p95_curvature, - "max_abs": max(abs_curvature) if abs_curvature else 0.0, - }, - "power_sine_eigenvalue": eigenvalue, - "homeostasis_vector": homeostasis_vector, - "recommended_start_delay_s": delay_s, - "risk_class": risk_class, - } - - -def build_recommendations(analysis: dict[str, Any], samples: list[dict[str, Any]]) -> list[dict[str, str]]: - risk = analysis["risk_class"] - homeostasis_state = analysis["homeostasis_vector"]["state"] - gpu_present = any(s.get("gpu") for s in samples) - delay = analysis["recommended_start_delay_s"] - recommendations = [ - { - "lane": "scheduler", - "action": f"Stagger high-power job starts by at least {delay:.1f}s when p95 slew exceeds budget.", - "safety": "software-only; no hardware write", - }, - { - "lane": "concurrency", - "action": "Run one high-power lane at a time: GPU render/model, full Lean build, large compression eval, or bulk upload verification.", - "safety": "process scheduling only", - }, - { - "lane": "io_tail", - "action": "Batch tiny-file upload/check tails separately; they create long wall-clock tails without much useful power smoothing signal.", - "safety": "rclone/job topology only", - }, - ] - if homeostasis_state != "homeostatic": - recommendations.append( - { - "lane": "homeostasis", - "action": "Prefer queueing over parallel launch until the homeostasis vector returns to the stable region.", - "safety": "software-only topology gate", - } - ) - if gpu_present: - recommendations.append( - { - "lane": "gpu", - "action": "For long GPU jobs, consider a manual lower NVIDIA power limit only after a dry run records the stable workload draw.", - "safety": "not applied by this script", - } - ) - if risk == "high": - recommendations.append( - { - "lane": "gate", - "action": "Do not launch another high-power lane until the observed waveform returns below the slew budget.", - "safety": "fail-closed scheduling gate", - } - ) - return recommendations - - -def write_markdown(path: Path, payload: dict[str, Any]) -> None: - analysis = payload["analysis"] - recs = payload["recommendations"] - lines = [ - "# Power Sine Software Smoothing Receipt", - "", - f"Generated: `{payload['generated_at']}`", - "", - "## Scope", - "", - "This is a read-only software-level probe. It measures CPU/GPU power telemetry and derives workload scheduling guidance. It does not alter wall power, firmware, CPU governors, RAPL limits, GPU power limits, or fan curves.", - "", - "## Observed Waveform", - "", - f"- samples: `{analysis['sample_count']}`", - f"- mean observed power: `{analysis['observed_power_w']['mean']:.3f} W`", - f"- max observed power: `{analysis['observed_power_w']['max']:.3f} W`", - f"- p95 absolute slew: `{analysis['slew_w_per_s']['p95_abs']:.3f} W/s`", - f"- target slew budget: `{analysis['slew_w_per_s']['target']:.3f} W/s`", - f"- power-sine eigenvalue: `{analysis['power_sine_eigenvalue']:.6f}`", - f"- homeostasis score: `{analysis['homeostasis_vector']['homeostasis_score']:.6f}`", - f"- homeostasis state: `{analysis['homeostasis_vector']['state']}`", - f"- risk class: `{analysis['risk_class']}`", - f"- recommended high-power start delay: `{analysis['recommended_start_delay_s']:.3f} s`", - "", - "## Software Smoothing Law", - "", - "```", - "P(t) = P_cpu_rapl(t) + P_gpu_nvidia(t)", - "slew(t) = dP/dt", - "curvature(t) = d2P/dt2", - "lambda = max(0, p95(|slew|) - slew_budget) / slew_budget", - " + 0.25 * p95(|curvature|) / mean(P)", - "start_delay = 2 * sqrt(lambda)", - "```", - "", - "## Homeostasis Eigenvector", - "", - "```", - "H = [", - " power_headroom,", - " slew_margin,", - " curvature_damping,", - " thermal_headroom,", - " gpu_limit_headroom,", - " lane_contention_slack", - "]", - "```", - "", - "Components:", - "", - ] - for name, value in analysis["homeostasis_vector"]["components"].items(): - lines.append(f"- `{name}`: `{value:.6f}`") - lines.extend( - [ - "", - "## Recommendations", - "", - ] - ) - for rec in recs: - lines.append(f"- `{rec['lane']}`: {rec['action']} ({rec['safety']})") - lines.extend( - [ - "", - "## Claim Boundary", - "", - "This receipt can justify workload scheduling and upload/build lane shaping. It is not evidence of physical power conditioning, PSU computation, or mains sine-wave correction.", - "", - ] - ) - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--duration", type=float, default=15.0) - parser.add_argument("--interval", type=float, default=1.0) - parser.add_argument("--target-slew-w-per-s", type=float, default=25.0) - parser.add_argument("--power-budget-w", type=float, default=350.0) - parser.add_argument("--thermal-ceiling-c", type=float, default=83.0) - parser.add_argument("--artifact-dir", type=Path, default=DEFAULT_ARTIFACT_DIR) - args = parser.parse_args() - - args.artifact_dir.mkdir(parents=True, exist_ok=True) - generated_at = datetime.now(timezone.utc).replace(microsecond=0).isoformat() - stamp = datetime.now().strftime("%Y%m%d_%H%M%S") - samples, zones = sample_power(args.duration, args.interval) - analysis = analyze_samples( - samples, - target_slew_w_per_s=args.target_slew_w_per_s, - power_budget_w=args.power_budget_w, - thermal_ceiling_c=args.thermal_ceiling_c, - ) - payload = { - "generated_at": generated_at, - "scope": "read_only_software_power_smoothing", - "rapl_zones": [ - {"name": zone.name, "energy_path": str(zone.energy_path)} - for zone in zones - ], - "analysis": analysis, - "recommendations": build_recommendations(analysis, samples), - "samples": samples, - "claim_boundary": [ - "software workload shaping only", - "no wall power conditioning", - "no firmware or power-limit writes", - "no physical safety claim", - ], - } - - json_path = args.artifact_dir / f"power_sine_smoothing_receipt_{stamp}.json" - md_path = args.artifact_dir / f"power_sine_smoothing_receipt_{stamp}.md" - json_path.write_text(json.dumps(payload, indent=2), encoding="utf-8") - write_markdown(md_path, payload) - print(json.dumps({"json": str(json_path), "markdown": str(md_path), "analysis": analysis}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/prethinker_computational_epistemics_prior.py b/4-Infrastructure/shim/prethinker_computational_epistemics_prior.py deleted file mode 100644 index 84d4e451..00000000 --- a/4-Infrastructure/shim/prethinker_computational_epistemics_prior.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -"""Emit Prethinker computational-epistemics prior packets. - -This preserves dr3d/prethinker as an external architecture prior for governed -semantic intake. It does not vendor code and does not claim local reproduction. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import asdict, dataclass -from pathlib import Path - - -ROOT = Path(__file__).resolve().parents[2] -OUT_DIR = ROOT / "shared-data" / "data" / "prethinker_epistemics" - - -@dataclass(frozen=True) -class EpistemicPriorPacket: - packet_id: str - name: str - facet: str - source_url: str - external_term: str - local_mapping: str - rrc_use: str - density_markers: list[str] - claim_boundary: str - decision: str = "HOLD" - - -def stable_hash(obj: object) -> str: - blob = json.dumps(obj, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(blob).hexdigest() - - -def build_packets() -> list[EpistemicPriorPacket]: - repo = "https://github.com/dr3d/prethinker" - semantic_instrument = ( - "https://raw.githubusercontent.com/dr3d/prethinker/main/docs/SEMANTIC_INSTRUMENT.md" - ) - compiler = ( - "https://raw.githubusercontent.com/dr3d/prethinker/main/docs/" - "MULTI_PASS_SEMANTIC_COMPILER.md" - ) - mapper = ( - "https://raw.githubusercontent.com/dr3d/prethinker/main/docs/" - "SEMANTIC_IR_MAPPER_SPEC.md" - ) - harness = ( - "https://raw.githubusercontent.com/dr3d/prethinker/main/docs/" - "CURRENT_HARNESS_INSTRUMENT.md" - ) - - return [ - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.AUTHORITY_BOUNDARY.0001", - name="Model proposes, deterministic mapper admits", - facet="authority_boundary", - source_url=repo, - external_term="governed semantic intake / deterministic admission gates", - local_mapping="LLM proposal != accepted state", - rrc_use="use as proposal/admission boundary for RRC semantic candidates", - density_markers=[ - "semantic_workspace", - "deterministic_mapper", - "prolog_truth_layer", - "candidate_operation_gate", - "unsafe_write_block", - ], - claim_boundary="Architecture prior only; local mapper/replay implementation required.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.SOURCE_ENVELOPE.0001", - name="Source envelope and epistemic status split", - facet="source_envelope", - source_url=mapper, - external_term="source policy and candidate operation admission", - local_mapping="claim carrier must preserve who said what before promoting content", - rrc_use="separate speech/source atoms from payload truth atoms", - density_markers=[ - "said_vs_known", - "claim_content_container", - "direct_context_inferred_source_policy", - "unsafe_implication_diagnostic", - "claim_fact_noncollapse", - ], - claim_boundary="Does not determine truth; preserves source-scoped candidates for admission.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.TEMPORAL_BINDING.0001", - name="Temporal binding and interval-state surface", - facet="temporal_state", - source_url=semantic_instrument, - external_term="temporal status lens / temporal unavailable uncertainty", - local_mapping="temporal is corpus-time, interval, correction dependency, or pass index", - rrc_use="route time anchors into explicit temporal state, not flattened event facts", - density_markers=[ - "temporal_anchor", - "status_interval", - "deadline_family", - "effective_expired_boundary", - "correction_dependent_interval", - ], - claim_boundary="Temporal graph or lens output is proposal-only until candidate operations pass gates.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.STRUCTURED_ABSENCE.0001", - name="Structured absence and uncertainty vocabulary", - facet="structured_absence", - source_url=semantic_instrument, - external_term="unknown / unstated / pending / disputed / unsupported states", - local_mapping="absence can be a positive epistemic state, not missing data", - rrc_use="encode HOLD/unknown/unstated/resolved-negative without filling the gap", - density_markers=[ - "unknown_not_unstated", - "pending_not_false", - "disputed_claims", - "unsupported_claim", - "resolved_negative", - ], - claim_boundary="Uncertainty labels are admission states, not replacement facts.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.CORRECTION_CASCADE.0001", - name="Correction provenance and cascade guard", - facet="correction_cascade", - source_url=mapper, - external_term="safe retract / correction projection / temporal correction guard", - local_mapping="correction changes dependent carriers while preserving original speech act receipt", - rrc_use="route corrections through residual/retraction sidecars and dependency repair", - density_markers=[ - "correction_target", - "retract_plan", - "replacement_anchor", - "dependent_interval_recalc", - "original_claim_preserved", - ], - claim_boundary="Correction candidates require explicit retract/correction plan before durable mutation.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.COUNTERFACTUAL_CONTAINMENT.0001", - name="Counterfactual containment", - facet="counterfactual", - source_url=mapper, - external_term="pure hypothetical query projection", - local_mapping="hypothetical premises may answer a query but must not write durable facts", - rrc_use="keep what-if expansion in scoped diagnostic world or query lane", - density_markers=[ - "hypothetical_query", - "no_premise_write", - "inferred_query_allowed", - "durable_truth_blocked", - "counterfactual_scope", - ], - claim_boundary="Counterfactual answers do not promote premise or conclusion into global state.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.RATIONALE_MECHANISM.0001", - name="Rationale versus mechanism", - facet="rationale_mechanism", - source_url=semantic_instrument, - external_term="rationale/contrast lens", - local_mapping="event mechanism and stated reason are separate atom families", - rrc_use="preserve why-surface separately from action surface for selector routing", - density_markers=[ - "mechanism_fact", - "rationale_fact", - "contrast_note", - "why_question_surface", - "answer_shape_guard", - ], - claim_boundary="Rationale is source-scoped support, not automatic causal proof.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.SELECTOR_SURFACE.0001", - name="Selector surface and row-level activation", - facet="selector_problem", - source_url=harness, - external_term="selector guards / row-level activation / exact-row protection", - local_mapping="different questions need different admitted surfaces over the same artifact", - rrc_use="choose evidence surface by question type without mutating the core record", - density_markers=[ - "question_act_routing", - "surface_specificity", - "baseline_readiness", - "row_level_gate", - "exact_row_protection", - ], - claim_boundary="Selector policy is diagnostic until transfer support prevents regressions.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.MULTI_PASS_LENSES.0001", - name="Multi-pass semantic lens accumulation", - facet="semantic_lenses", - source_url=compiler, - external_term="semantic parallax / safe-surface accumulation", - local_mapping="compile multiple constrained views; union only admitted rows", - rrc_use="use separate lens passes for source, temporal, rule, rationale, and selector surfaces", - density_markers=[ - "semantic_parallax", - "backbone_lens", - "support_lens", - "rule_lens", - "safe_surface_union", - ], - claim_boundary="Union is deterministic over admitted clauses only; it does not reread prose.", - ), - EpistemicPriorPacket( - packet_id="PRETHINKER.PRIOR.STRUGGLE_DETECTION.0001", - name="Semantic struggle and zombie retry detector", - facet="struggle_detection", - source_url=harness, - external_term="semantic_progress_assessment_v1", - local_mapping="Tree Fiddy-style stop condition for nonproductive semantic passes", - rrc_use="stop candidate generation when unique contribution stalls or duplicate ratio rises", - density_markers=[ - "zombie_risk", - "duplicate_ratio", - "recent_unique_contribution", - "stop_and_report", - "named_expected_contribution", - ], - claim_boundary="Stop recommendation is telemetry-derived, not a semantic truth result.", - ), - ] - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - packets = build_packets() - packet_dicts = [asdict(packet) for packet in packets] - - packets_path = OUT_DIR / "prethinker_computational_epistemics_packets.jsonl" - receipt_path = OUT_DIR / "prethinker_computational_epistemics_receipt.json" - - with packets_path.open("w", encoding="utf-8") as fh: - for packet in packet_dicts: - fh.write(json.dumps(packet, sort_keys=True) + "\n") - - receipt = { - "schema": "prethinker_computational_epistemics_receipt_v1", - "packet_count": len(packets), - "facets": sorted({packet.facet for packet in packets}), - "density_marker_total": sum(len(packet.density_markers) for packet in packets), - "decision": "HOLD", - "packets_sha256": stable_hash(packet_dicts), - "claim_boundary": ( - "External architecture prior only. No Prethinker code is vendored; " - "local adoption requires clean-room mapping, replay receipts, and byte-law gates." - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/projectable_geometry_approach_tester.py b/4-Infrastructure/shim/projectable_geometry_approach_tester.py deleted file mode 100644 index 5e48e0a9..00000000 --- a/4-Infrastructure/shim/projectable_geometry_approach_tester.py +++ /dev/null @@ -1,493 +0,0 @@ -#!/usr/bin/env python3 -"""Approach tester for projectable-geometry compression on wiki-like targets. - -This is not a Hutter submission compressor. It is a reversible approach sieve: -run small, exact transforms against local wiki-like files, compare them with -standard codecs, and record which ideas are worth promoting. - -The "boat-aware" approaches are deliberately conservative: - -* every transform must decode byte-for-byte -* every transformed payload is benchmarked with real byte sizes -* expansion is recorded honestly -""" - -from __future__ import annotations - -import argparse -import bz2 -import hashlib -import json -import lzma -import os -import zlib -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Callable - - -REPO = Path(__file__).resolve().parents[2] -OUT = REPO / "4-Infrastructure" / "shim" / "projectable_geometry_approach_tester_receipt.json" - -HUTTER_TARGET_BYTES = 109_685_197 -ENWIK9_BYTES = 1_000_000_000 -HUTTER_TARGET_RATIO = HUTTER_TARGET_BYTES / ENWIK9_BYTES - -DEFAULT_CANDIDATES = [ - Path("/home/allaun/.gemini/antigravity/scratch/kimi_dataset/enwik8"), - Path("/home/allaun/.local/share/Trash/files/enwik8"), - Path("/home/allaun/Downloads/data/enwik9_data/1234567"), -] - -TOKEN_ESCAPE = b"\x00" -XML_WIKI_TOKENS = [ - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"", - b"[[", - b"]]", - b"{{", - b"}}", - b"==", - b"'''", - b""", - b"&", -] - - -@dataclass(frozen=True) -class EncodedPayload: - payload: bytes - metadata: dict[str, Any] - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def zlib9(data: bytes) -> bytes: - return zlib.compress(data, 9) - - -def bz2_best(data: bytes) -> bytes: - return bz2.compress(data, 9) - - -def lzma_best(data: bytes) -> bytes: - return lzma.compress(data, preset=9 | lzma.PRESET_EXTREME) - - -def raw_encode(data: bytes) -> EncodedPayload: - return EncodedPayload(data, {}) - - -def raw_decode(encoded: EncodedPayload) -> bytes: - return encoded.payload - - -def xml_token_encode(data: bytes) -> EncodedPayload: - token_map = {token: idx + 1 for idx, token in enumerate(XML_WIKI_TOKENS)} - # Longest first avoids splitting around smaller fragments. - tokens = sorted(token_map, key=len, reverse=True) - out = bytearray() - i = 0 - while i < len(data): - if data[i:i + 1] == TOKEN_ESCAPE: - out.extend(TOKEN_ESCAPE) - out.extend(b"\x00") - i += 1 - continue - matched = False - for token in tokens: - if data.startswith(token, i): - out.extend(TOKEN_ESCAPE) - out.append(token_map[token]) - i += len(token) - matched = True - break - if not matched: - out.append(data[i]) - i += 1 - return EncodedPayload(bytes(out), { - "token_count": len(XML_WIKI_TOKENS), - "escape": "00", - "tokens": [token.decode("utf-8", errors="replace") for token in XML_WIKI_TOKENS], - }) - - -def xml_token_decode(encoded: EncodedPayload) -> bytes: - reverse = {idx + 1: token for idx, token in enumerate(XML_WIKI_TOKENS)} - data = encoded.payload - out = bytearray() - i = 0 - while i < len(data): - b = data[i] - if b != 0: - out.append(b) - i += 1 - continue - if i + 1 >= len(data): - raise ValueError("trailing token escape") - code = data[i + 1] - if code == 0: - out.extend(TOKEN_ESCAPE) - elif code in reverse: - out.extend(reverse[code]) - else: - raise ValueError(f"unknown token code {code}") - i += 2 - return bytes(out) - - -def byte_class(byte: int) -> int: - if byte in b" \n\r\t": - return 0 - if 65 <= byte <= 90 or 97 <= byte <= 122: - return 1 - if 48 <= byte <= 57: - return 2 - if byte in b"<>/={}[]|#_*'\"&;:": - return 3 - return 4 - - -def class_lane_encode(data: bytes) -> EncodedPayload: - """Split stream into class labels and lane payloads. - - This is a simple "boat" transform: a keel of byte classes plus residual - lanes. It is exact, but not assumed to compress. - """ - - class_stream = bytearray() - lanes = [bytearray() for _ in range(5)] - for byte in data: - cls = byte_class(byte) - class_stream.append(cls) - lanes[cls].append(byte) - parts = [bytes(class_stream), *(bytes(lane) for lane in lanes)] - header = bytearray(b"PGB0") - for part in parts: - header.extend(len(part).to_bytes(4, "big")) - payload = bytes(header) + b"".join(parts) - return EncodedPayload(payload, { - "boat": "byte_class_lanes", - "lane_count": 5, - "lane_lengths": [len(part) for part in parts[1:]], - "class_stream_length": len(parts[0]), - }) - - -def class_lane_decode(encoded: EncodedPayload) -> bytes: - data = encoded.payload - if not data.startswith(b"PGB0"): - raise ValueError("bad PGB0 magic") - offset = 4 - lengths = [] - for _ in range(6): - lengths.append(int.from_bytes(data[offset:offset + 4], "big")) - offset += 4 - parts = [] - for length in lengths: - parts.append(data[offset:offset + length]) - offset += length - if offset != len(data): - raise ValueError("trailing PGB0 bytes") - class_stream = parts[0] - lanes = [bytearray(part) for part in parts[1:]] - lane_offsets = [0] * 5 - out = bytearray() - for cls in class_stream: - idx = lane_offsets[cls] - out.append(lanes[cls][idx]) - lane_offsets[cls] += 1 - return bytes(out) - - -def delta_boat_encode(data: bytes) -> EncodedPayload: - """XOR-delta stream split into three residual handles plus class keel.""" - - if not data: - return EncodedPayload(b"PGD0" + (0).to_bytes(4, "big"), {"boat": "xor_delta_handles"}) - class_stream = bytearray() - handles = [bytearray() for _ in range(3)] - prev = 0 - for byte in data: - delta = byte ^ prev - prev = byte - cls = byte_class(byte) - handle = 0 if cls in (1, 2) else 1 if cls in (3,) else 2 - class_stream.append(handle) - handles[handle].append(delta) - parts = [bytes(class_stream), *(bytes(handle) for handle in handles)] - header = bytearray(b"PGD0") - header.append(data[0]) - for part in parts: - header.extend(len(part).to_bytes(4, "big")) - payload = bytes(header) + b"".join(parts) - return EncodedPayload(payload, { - "boat": "xor_delta_three_handles", - "handle_count": 3, - "handle_lengths": [len(part) for part in parts[1:]], - "class_stream_length": len(parts[0]), - }) - - -def delta_boat_decode(encoded: EncodedPayload) -> bytes: - data = encoded.payload - if not data.startswith(b"PGD0"): - raise ValueError("bad PGD0 magic") - if len(data) == 8: - return b"" - first = data[4] - offset = 5 - lengths = [] - for _ in range(4): - lengths.append(int.from_bytes(data[offset:offset + 4], "big")) - offset += 4 - parts = [] - for length in lengths: - parts.append(data[offset:offset + length]) - offset += length - if offset != len(data): - raise ValueError("trailing PGD0 bytes") - class_stream = parts[0] - handles = [bytearray(part) for part in parts[1:]] - handle_offsets = [0] * 3 - out = bytearray() - prev = 0 - for handle in class_stream: - idx = handle_offsets[handle] - delta = handles[handle][idx] - byte = delta ^ prev - out.append(byte) - prev = byte - handle_offsets[handle] += 1 - if out and out[0] != first: - raise ValueError("first-byte witness mismatch") - return bytes(out) - - -TRANSFORMS: dict[str, tuple[Callable[[bytes], EncodedPayload], Callable[[EncodedPayload], bytes]]] = { - "raw": (raw_encode, raw_decode), - "xml_token": (xml_token_encode, xml_token_decode), - "class_lane_boat": (class_lane_encode, class_lane_decode), - "delta_boat": (delta_boat_encode, delta_boat_decode), -} - -CODECS: dict[str, Callable[[bytes], bytes]] = { - "stored": lambda data: data, - "zlib9": zlib9, - "bz2": bz2_best, - "lzma": lzma_best, -} - - -def discover_inputs(extra_inputs: list[Path]) -> list[Path]: - candidates = [*extra_inputs, *DEFAULT_CANDIDATES] - seen = set() - existing = [] - for path in candidates: - resolved = path.expanduser() - if not resolved.exists() or not resolved.is_file(): - continue - key = str(resolved.resolve()) - if key in seen: - continue - seen.add(key) - existing.append(resolved) - return existing - - -def make_slices(path: Path, max_slice: int) -> list[tuple[str, bytes]]: - data = path.read_bytes() - sizes = [] - for size in (20_000, 100_000, 1_000_000, 4_000_000): - if size <= max_slice and len(data) >= size: - sizes.append(size) - if not sizes: - sizes.append(min(len(data), max_slice)) - slices = [] - for size in sizes: - slices.append((f"{path.name}:{size}", data[:size])) - return slices - - -def evaluate_slice(name: str, source_path: Path, data: bytes) -> dict[str, Any]: - source_hash = sha256_bytes(data) - results = [] - for transform_name, (encode, decode) in TRANSFORMS.items(): - encoded = encode(data) - decoded = decode(encoded) - rehydrated_ok = decoded == data - encoded_hash = sha256_bytes(encoded.payload) - for codec_name, codec in CODECS.items(): - compressed = codec(encoded.payload) - total_size = len(compressed) - ratio = total_size / len(data) if data else 0.0 - projected_enwik9_total = int(ratio * ENWIK9_BYTES) - results.append({ - "transform": transform_name, - "codec": codec_name, - "encoded_size": len(encoded.payload), - "compressed_size": total_size, - "ratio": ratio, - "projected_enwik9_total_bytes": projected_enwik9_total, - "beats_hutter_target_ratio": ratio < HUTTER_TARGET_RATIO, - "rehydrated_ok": rehydrated_ok, - "encoded_hash_sha256": encoded_hash, - "metadata": encoded.metadata, - }) - ranked = sorted(results, key=lambda item: item["compressed_size"]) - best = ranked[0] if ranked else None - baseline_zlib = next( - item for item in results - if item["transform"] == "raw" and item["codec"] == "zlib9" - ) - best_raw = min( - (item for item in results if item["transform"] == "raw"), - key=lambda item: item["compressed_size"], - ) - best_boat = min( - (item for item in results if item["transform"] != "raw"), - key=lambda item: item["compressed_size"], - ) - return { - "slice_name": name, - "source_path": str(source_path), - "source_bytes": len(data), - "source_hash_sha256": source_hash, - "hutter_target_ratio": HUTTER_TARGET_RATIO, - "best": best, - "baseline_zlib9": baseline_zlib, - "best_raw_baseline": best_raw, - "best_boat_aware": best_boat, - "boat_beats_zlib9": best_boat["compressed_size"] < baseline_zlib["compressed_size"], - "boat_beats_best_raw": best_boat["compressed_size"] < best_raw["compressed_size"], - "all_rehydrated": all(item["rehydrated_ok"] for item in results), - "results": ranked, - } - - -def build_receipt(inputs: list[Path], max_slice: int) -> dict[str, Any]: - discovered = discover_inputs(inputs) - slice_results = [] - for path in discovered: - for slice_name, data in make_slices(path, max_slice): - slice_results.append(evaluate_slice(slice_name, path, data)) - best_overall = min( - (result["best"] | {"slice_name": result["slice_name"]} for result in slice_results), - key=lambda item: item["ratio"], - ) if slice_results else None - boat_wins = [ - { - "slice_name": result["slice_name"], - "best_boat_aware": result["best_boat_aware"], - "baseline_zlib9": result["baseline_zlib9"], - } - for result in slice_results - if result["boat_beats_zlib9"] - ] - boat_wins_vs_best_raw = [ - { - "slice_name": result["slice_name"], - "best_boat_aware": result["best_boat_aware"], - "best_raw_baseline": result["best_raw_baseline"], - } - for result in slice_results - if result["boat_beats_best_raw"] - ] - receipt = { - "schema": "projectable_geometry_approach_tester_receipt_v1", - "generated_utc": datetime.now(timezone.utc).isoformat(), - "surface_id": "projectable_geometry_approach_tester", - "hutter_target": { - "enwik9_bytes": ENWIK9_BYTES, - "target_total_bytes": HUTTER_TARGET_BYTES, - "target_ratio": HUTTER_TARGET_RATIO, - "note": "Projection from small slices is diagnostic only; it is not a Hutter claim.", - }, - "inputs": [ - { - "path": str(path), - "bytes": path.stat().st_size, - "sha256": sha256_bytes(path.read_bytes()), - } - for path in discovered - ], - "approaches": { - "transforms": sorted(TRANSFORMS), - "codecs": sorted(CODECS), - "boat_aware_transforms": ["xml_token", "class_lane_boat", "delta_boat"], - }, - "summary": { - "input_count": len(discovered), - "slice_count": len(slice_results), - "all_rehydrated": all(result["all_rehydrated"] for result in slice_results), - "boat_win_count_vs_zlib9": len(boat_wins), - "boat_win_count_vs_best_raw": len(boat_wins_vs_best_raw), - "best_overall": best_overall, - "boat_wins_vs_zlib9": boat_wins, - "boat_wins_vs_best_raw": boat_wins_vs_best_raw, - }, - "slices": slice_results, - "claim_boundary": ( - "This is a small-slice approach sieve for projectable-geometry " - "transforms. Projected enwik9 totals are diagnostic only and do not " - "constitute Hutter Prize compression claims." - ), - "lawful": True, - } - stable_preimage = stable_json({ - "schema": receipt["schema"], - "surface_id": receipt["surface_id"], - "hutter_target": receipt["hutter_target"], - "inputs": receipt["inputs"], - "approaches": receipt["approaches"], - "summary": receipt["summary"], - "slices": receipt["slices"], - "claim_boundary": receipt["claim_boundary"], - "lawful": receipt["lawful"], - }).encode("utf-8") - receipt["stable_approach_hash_sha256"] = sha256_bytes(stable_preimage) - receipt["receipt_hash_preimage_sha256"] = sha256_bytes(stable_json(receipt).encode("utf-8")) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--input", action="append", type=Path, default=[]) - parser.add_argument("--max-slice", type=int, default=1_000_000) - parser.add_argument("--out", type=Path, default=OUT) - args = parser.parse_args() - receipt = build_receipt(args.input, args.max_slice) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(json.dumps({ - "lawful": receipt["lawful"], - "stable_approach_hash_sha256": receipt["stable_approach_hash_sha256"], - "receipt_hash_preimage_sha256": receipt["receipt_hash_preimage_sha256"], - "summary": receipt["summary"], - }, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/projectable_geometry_topology_model.py b/4-Infrastructure/shim/projectable_geometry_topology_model.py deleted file mode 100644 index 8f566ea6..00000000 --- a/4-Infrastructure/shim/projectable_geometry_topology_model.py +++ /dev/null @@ -1,389 +0,0 @@ -#!/usr/bin/env python3 -"""Model the best bounded topology approach for projectable compression. - -This is a design-prior receipt, not a byte compressor. It consumes the -dimensional shell decision-diagram receipt and chooses the best current route -that can carry the topology triad inside the closure-witness budget: - -* Menger sponge: sparse bucket substrate -* Torus: cyclic lane carrier -* Braid: lawful transition/crossing witness - -The important rule is that the triad must fit inside the existing bounded -closure witness. If it needs recursive explanation, it is NaN0 and rejected. - -In the finer-grain version, Menger voids are modeled as black-hole buckets: -the decoder may verify a boundary/horizon witness, but it may not expand the -interior. Any route that needs to inspect the interior becomes NaN0. - -The invariant-dual mechanics prior adds a second guard: the static witness -side behaves like tensegrity self-stress, while the decode-motion side behaves -like an origami infinitesimal mechanism. Nondegenerate transformations may -change the shape, but must preserve the closure class. - -The deterministic-routing prior adds an ownership rule: the decoder routes a -symbol, residual, or repair request to its owning horizon/lane instead of -replicating the request across candidate buckets. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHELL_DD_RECEIPT = ( - REPO - / "4-Infrastructure" - / "shim" - / "dimensional_shell_dd_probe_receipt.json" -) -OUT = ( - REPO - / "4-Infrastructure" - / "shim" - / "projectable_geometry_topology_model_receipt.json" -) - - -TOPOLOGY_WITNESS_ALLOCATION = { - "menger_bucket_witness_bytes": 4, - "torus_carrier_witness_bytes": 4, - "braid_rule_witness_bytes": 4, - "nan0_closure_witness_bytes": 4, -} - -TOPOLOGY_BITFIELDS = { - "menger_black_hole_bucket": { - "total_bits": 32, - "fields": { - "horizon_id_bits": 12, - "void_depth_bits": 4, - "horizon_area_class_bits": 8, - "skip_mass_class_bits": 8, - }, - }, - "torus_orbit_carrier": { - "total_bits": 32, - "fields": { - "lane_modulus_bits": 10, - "phase_index_bits": 10, - "orbit_direction_bits": 2, - "affine_transform_class_bits": 4, - "wrap_epoch_bits": 6, - }, - }, - "braid_crossing_rule": { - "total_bits": 32, - "fields": { - "crossing_id_bits": 8, - "chirality_bits": 2, - "rule_id_bits": 10, - "static_self_stress_class_bits": 4, - "kinematic_mechanism_class_bits": 4, - "parity_crc_bits": 4, - }, - }, - "nan0_closure": { - "total_bits": 32, - "fields": { - "nan0_flag_bits": 1, - "mass_delta_q_bits": 13, - "horizon_hash_bits": 12, - "nondegenerate_transform_witness_bits": 3, - "superstability_witness_bits": 3, - }, - }, -} - -TOPOLOGY_EQUATIONS = { - "shell_mass": ( - "source_mass = visible_4d + horizon_mass + orbit_mass + braid_mass " - "+ lawbound_mass; unresolved_mass = 0" - ), - "menger_void": ( - "void_i = (horizon_id, void_depth, horizon_area_class, skip_mass_class)" - ), - "black_hole_boundary": ( - "interior(void_i) is non-decodable; decoder verifies horizon(void_i) only" - ), - "torus_orbit": ( - "lane_t = (lane_0 + phase_index + tick) mod lane_modulus" - ), - "deterministic_owner": ( - "owner_i = hash(horizon_id, lane_modulus, phase_index, route_key) mod lane_modulus" - ), - "route_to_data": ( - "decode requests route to owner_i; do not replicate speculative reads across voids" - ), - "braid_transition": ( - "state_{t+1} = braid_rule(crossing_id, chirality, rule_id, state_t)" - ), - "closure": ( - "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0" - ), - "invariant_duality": ( - "static_self_stress_class(void_i) is dual to " - "kinematic_mechanism_class(fold_i)" - ), - "nondegenerate_transform": ( - "T is admissible iff det_class(T) != 0 and closure_class(T*x) == closure_class(x)" - ), - "superstability_guard": ( - "promote invariant route only if geometry_rank_class is full and " - "force_density_class is PSD-compatible" - ), - "fail_closed": ( - "if interior expansion is requested or closure fails, emit NaN0 and stop" - ), -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def topology_witness_bytes() -> int: - return sum(TOPOLOGY_WITNESS_ALLOCATION.values()) - - -def validate_topology_bitfields() -> list[str]: - errors: list[str] = [] - for name, spec in TOPOLOGY_BITFIELDS.items(): - total = int(spec["total_bits"]) - field_sum = sum(int(value) for value in spec["fields"].values()) - if field_sum != total: - errors.append(f"{name} fields sum to {field_sum}, expected {total}") - expected_bits = topology_witness_bytes() * 8 - actual_bits = sum(int(spec["total_bits"]) for spec in TOPOLOGY_BITFIELDS.values()) - if actual_bits != expected_bits: - errors.append(f"topology bitfields sum to {actual_bits}, expected {expected_bits}") - return errors - - -def promoted_routes(shell_dd: dict[str, Any]) -> list[dict[str, Any]]: - routes: list[dict[str, Any]] = [] - for slice_item in shell_dd.get("slices", []): - for route in slice_item.get("routes", []): - if route.get("decision") == "promoted": - routes.append(route) - return routes - - -def model_route(route: dict[str, Any], witness_bytes: int) -> dict[str, Any]: - overhead_budget = int(route["overhead_budget_before_losing_raw"]) - fits_budget = witness_bytes <= overhead_budget - no_recursive_debt = ( - route.get("shell_status", {}).get("closed") is True - and route.get("shell_status", {}).get("nan0") is False - ) - lawful = fits_budget and no_recursive_debt - modeled_bytes = int(route["compressed_bytes"]) + witness_bytes - source_bytes = int(route["source_bytes"]) - return { - "route_id": route["route_id"], - "slice": route["slice"], - "source_bytes": source_bytes, - "transform": route["transform"], - "codec": route["codec"], - "compressed_bytes": route["compressed_bytes"], - "raw_baseline_bytes": route["raw_baseline_bytes"], - "topology_witness_bytes": witness_bytes, - "modeled_total_bytes": modeled_bytes, - "modeled_ratio": modeled_bytes / source_bytes if source_bytes else 0.0, - "gain_vs_raw_after_topology_bytes": int(route["raw_baseline_bytes"]) - modeled_bytes, - "overhead_budget_before_losing_raw": overhead_budget, - "fits_witness_budget": fits_budget, - "no_recursive_debt": no_recursive_debt, - "lawful": lawful, - "reject_reason": None if lawful else ( - "topology_witness_exceeds_budget" if not fits_budget else "nan0_or_recursive_debt" - ), - } - - -def build_receipt() -> dict[str, Any]: - bitfield_errors = validate_topology_bitfields() - if bitfield_errors: - raise ValueError("; ".join(bitfield_errors)) - - shell_dd = load_json(SHELL_DD_RECEIPT) - witness_bytes = topology_witness_bytes() - modeled = [ - model_route(route, witness_bytes) - for route in promoted_routes(shell_dd) - ] - lawful = [route for route in modeled if route["lawful"]] - rejected = [route for route in modeled if not route["lawful"]] - best = min(lawful, key=lambda item: item["modeled_ratio"]) if lawful else None - - receipt = { - "schema": "projectable_geometry_topology_model_receipt_v1", - "generated_utc": datetime.now(timezone.utc).isoformat(), - "surface_id": "projectable_geometry_topology_model", - "source": { - "shell_dd_receipt": str(SHELL_DD_RECEIPT.relative_to(REPO)), - "shell_dd_stable_hash_sha256": shell_dd.get("stable_shell_dd_hash_sha256"), - "invariant_dual_mechanics_prior": { - "title": "Invariant dual mechanics of tensegrity and origami", - "doi": "10.1073/pnas.2519138123", - "mapping": ( - "Use tensegrity self-stress as the static horizon witness " - "and origami infinitesimal mechanism as the decode-motion " - "witness; nondegenerate transforms must preserve closure." - ), - }, - "deterministic_routing_prior": { - "title": ( - "Deterministic routing is one of the most effective ways " - "distributed systems reduce consistency problems at scale" - ), - "url": "https://bencane.com/posts/2026-04-30/", - "mapping": ( - "Route compressed-symbol, residual, and repair requests to " - "their owning horizon/lane instead of probing multiple " - "candidate buckets. This reduces stale speculative state " - "and avoids consistency work by construction." - ), - }, - }, - "topology_triad": { - "menger_sponge": { - "role": "black-hole bucket lattice", - "compressor_use": ( - "Names admissible voids as bounded non-expanded buckets. " - "Only the horizon witness is decoded; the interior is forbidden." - ), - }, - "torus": { - "role": "cyclic lane carrier", - "compressor_use": ( - "Provides wraparound lane addressing, phase continuity, and " - "bounded clock/cadence fields." - ), - }, - "braid": { - "role": "lawful transition witness", - "compressor_use": ( - "Constrains crossings, chirality, lane interaction, and " - "reversible rule order." - ), - }, - }, - "witness_allocation": { - **TOPOLOGY_WITNESS_ALLOCATION, - "total_topology_witness_bytes": witness_bytes, - "meaning": ( - "The triad must fit inside the bounded closure witness. It is a " - "route-control packet, not an expanding residual tree." - ), - }, - "finer_grain_equations": { - "equations": TOPOLOGY_EQUATIONS, - "bitfields": TOPOLOGY_BITFIELDS, - "resolution_rule": ( - "Resolution improves by subdividing the 16-byte witness into " - "bounded bitfields, not by adding recursive residual depth." - ), - "invariant_duality_rule": ( - "A transformed route may be reused only when its static " - "self-stress class and kinematic mechanism class remain paired " - "under a nondegenerate transform witness." - ), - "deterministic_routing_rule": ( - "Every symbol or repair request has exactly one owning horizon " - "for a given route key. The decoder routes to ownership first " - "and only uses replication/fallback for durability, never for " - "ordinary parse choice." - ), - "black_hole_void_rule": ( - "Menger voids are black-hole buckets: verify the horizon, never " - "decode the interior, and fail closed if interior expansion is " - "requested." - ), - }, - "best_approach": { - "name": "Menger-BlackHole-Torus-Braid Shell Route v1", - "route": "xml_token -> topology_witness_16b -> bz2", - "packet_order": [ - "Menger black-hole bucket horizon", - "Torus orbit lane modulus/phase", - "Braid crossing/chirality law", - "NaN0 closure witness", - ], - "selected_route": best, - "implementation_rule": ( - "Keep the byte transform at the proven xml_token route first. " - "Use the topology triad only as the bounded DD control witness " - "until a real encoder proves byte savings." - ), - }, - "summary": { - "modeled_promoted_route_count": len(modeled), - "lawful_route_count": len(lawful), - "rejected_route_count": len(rejected), - "best_modeled_route": best, - "all_lawful_routes": lawful, - "rejected_routes": rejected, - }, - "claim_boundary": ( - "This receipt models the best current topology-aware route using " - "existing compression measurements. It is not a new compression " - "benchmark, Hutter claim, physical topology claim, or optimality proof." - ), - "lawful": best is not None, - } - - stable_preimage = stable_json({ - "schema": receipt["schema"], - "surface_id": receipt["surface_id"], - "source": receipt["source"], - "topology_triad": receipt["topology_triad"], - "witness_allocation": receipt["witness_allocation"], - "finer_grain_equations": receipt["finer_grain_equations"], - "best_approach": receipt["best_approach"], - "summary": receipt["summary"], - "claim_boundary": receipt["claim_boundary"], - "lawful": receipt["lawful"], - }).encode("utf-8") - receipt["stable_topology_model_hash_sha256"] = sha256_bytes(stable_preimage) - receipt["receipt_hash_preimage_sha256"] = sha256_bytes(stable_json(receipt).encode("utf-8")) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--out", type=Path, default=OUT) - args = parser.parse_args() - - receipt = build_receipt() - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(json.dumps({ - "lawful": receipt["lawful"], - "stable_topology_model_hash_sha256": receipt["stable_topology_model_hash_sha256"], - "best_approach": receipt["best_approach"], - "summary": { - "modeled_promoted_route_count": receipt["summary"]["modeled_promoted_route_count"], - "lawful_route_count": receipt["summary"]["lawful_route_count"], - "rejected_route_count": receipt["summary"]["rejected_route_count"], - }, - }, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/prover_orchestration_layer.py b/4-Infrastructure/shim/prover_orchestration_layer.py deleted file mode 100644 index ae80d270..00000000 --- a/4-Infrastructure/shim/prover_orchestration_layer.py +++ /dev/null @@ -1,200 +0,0 @@ -#!/usr/bin/env python3 -""" -Prover-Integrated Orchestration Layers -Ties Goedel-Prover-V2, BFS-Prover-V2, bf4prover into runtime orchestration. - -Layers: - L0: Hardware (FAMM, FPGA, traces, PDN) - L1: Prover Watchdog (Goedel) — guards state transitions - L2: Swarm Consensus (BFS) — audits agent determinism - L3: Topological Adaptation (bf4) — proves manifold reshapes -""" - -import json, time, hashlib -from dataclasses import dataclass, field -from typing import List, Dict, Tuple, Optional -from pathlib import Path -from collections import deque - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -@dataclass -class ProverResult: - task_id: str; success: bool; latency_ms: float = 0 - proof: Optional[str] = None; prover: str = "" - - -class ProverWatchdog: - """L1: Goedel-Prover-V2 guards critical state transitions at runtime.""" - - INVARIANTS = [ - "Q16_16_no_overflow", "FAMM_delay_monotonic", - "PDN_impedance_bound", "trace_skew_bound", "topology_connected" - ] - - def __init__(self): - self.violations = 0 - - def guard(self, from_st: Dict, to_st: Dict) -> Tuple[bool, List[str]]: - """Verify all invariants hold across state transition.""" - checks = { - "Q16_16_no_overflow": all(abs(v) < 32768 for v in to_st.get("q16", [])), - "FAMM_delay_monotonic": all(d >= 0 for d in to_st.get("delays", [])), - "PDN_impedance_bound": to_st.get("pdn_z", 999) < to_st.get("pdn_target", 10), - "trace_skew_bound": to_st.get("skew_ps", 999) < 50, - "topology_connected": to_st.get("edges", 0) >= to_st.get("nodes", 0) - 1, - } - failed = [k for k, v in checks.items() if not v] - self.violations += len(failed) - return len(failed) == 0, failed - - -class SwarmConsensus: - """L2: BFS-Prover-V2 audits agent traces for determinism.""" - - def __init__(self, n: int = 11): - self.hashes: Dict[int, str] = {} - self.trail = deque(maxlen=1000) - self.drift = False - - def audit(self, agent_id: int, inp: Dict, out: Dict) -> ProverResult: - h = hashlib.sha256(json.dumps(inp, sort_keys=True).encode()).hexdigest() - prev = self.hashes.get(agent_id) - deterministic = (prev is None or prev == h) - self.hashes[agent_id] = h - self.trail.append({"agent": agent_id, "ok": deterministic, "ts": time.time()}) - if not deterministic: - self.drift = True - return ProverResult(f"audit_{agent_id}", deterministic, prover="bfs-prover-v2") - - def consensus(self, findings: List[Dict]) -> Tuple[Dict, float]: - ok = all(self.audit(f["agent_id"], f.get("in", {}), f.get("out", {})).success for f in findings) - return {"agents": len(findings), "deterministic": ok, "drift": self.drift}, (0.95 if ok else 0.5) - - -class TopologicalAdaptation: - """L3: bf4prover proves manifold reshapes before hardware reconfiguration.""" - - def __init__(self): - self.manifold = {"dim": 4, "shape": "flat", "ev": [1.77, 2.51, 3.07, 3.54]} - self.history: List[Dict] = [] - self.proved: Dict[str, bool] = {} - - def reshape(self, new_m: Dict) -> ProverResult: - h = hashlib.sha256(json.dumps(new_m, sort_keys=True).encode()).hexdigest()[:16] - if h in self.proved: - return ProverResult(h, self.proved[h], prover="bf4prover") - - ev = new_m.get("ev", []) - ok = new_m.get("dim", 0) > 0 and all(e > 0 for e in ev) - - if ok: - self.proved[h] = True - self.history.append({"ts": time.time(), "hash": h, "shape": new_m.get("shape")}) - self.manifold = new_m - - return ProverResult(h, ok, prover="bf4prover") - - def famm_preshape(self) -> Dict: - ev = self.manifold.get("ev", [1.77]) - return {"shape": self.manifold.get("shape"), "ev": ev, - "delays": [100.0 / (e ** 0.5) for e in ev], "proved": len(self.proved) > 0} - - -class ProverOrchestrationEngine: - """Integrated runtime orchestration with all three provers.""" - - def __init__(self): - self.watchdog = ProverWatchdog() - self.swarm = SwarmConsensus(11) - self.topology = TopologicalAdaptation() - self.latencies = {"watchdog": [], "swarm": [], "topology": []} - - def process(self, from_st: Dict, to_st: Dict) -> Tuple[bool, Dict]: - report = {"layers": {}, "allowed": False} - - # L1: Watchdog - t0 = time.time() - ok, failed = self.watchdog.guard(from_st, to_st) - self.latencies["watchdog"].append((time.time() - t0) * 1000) - report["layers"]["watchdog"] = {"ok": ok, "failed": failed} - if not ok: - return False, report - - # L2: Swarm - t0 = time.time() - cons, conf = self.swarm.consensus(to_st.get("findings", [])) - self.latencies["swarm"].append((time.time() - t0) * 1000) - report["layers"]["swarm"] = {"consensus": cons, "confidence": conf} - - # L3: Topology - t0 = time.time() - m = to_st.get("manifold", {}) - if m: - r = self.topology.reshape(m) - self.latencies["topology"].append(r.latency_ms) - report["layers"]["topology"] = {"ok": r.success, "shape": m.get("shape")} - if not r.success: - return False, report - - report["allowed"] = True - report["famm"] = self.topology.famm_preshape() - return True, report - - def metrics(self) -> Dict: - m = {} - for k, v in self.latencies.items(): - if v: - m[k] = {"mean_ms": sum(v) / len(v), "max_ms": max(v), "calls": len(v)} - m["watchdog_violations"] = self.watchdog.violations - m["swarm_drift"] = self.swarm.drift - m["topology_adaptations"] = len(self.topology.history) - m["proved_configs"] = len(self.topology.proved) - return m - - -def main(): - print("=" * 60) - print("Prover-Integrated Orchestration Layers") - print("=" * 60) - - engine = ProverOrchestrationEngine() - - from_st = {"q16": [100, 200], "delays": [75, 53], "pdn_z": 8, "pdn_target": 10, - "skew_ps": 30, "nodes": 5, "edges": 8} - to_st = {"q16": [150, 250], "delays": [70, 50], "pdn_z": 9, "pdn_target": 10, - "skew_ps": 35, "nodes": 5, "edges": 8, - "findings": [{"agent_id": 0, "in": {"t": "CLK"}, "out": {"d": 75}}, - {"agent_id": 1, "in": {"t": "D0"}, "out": {"d": 53}}], - "manifold": {"dim": 4, "shape": "flat", "ev": [1.77, 2.51, 3.07, 3.54]}} - - print("\n[1] Processing state transition...") - ok, report = engine.process(from_st, to_st) - - print(f" Allowed: {ok}") - for name, data in report["layers"].items(): - s = "✓" if data.get("ok", True) else "✗" - print(f" L{['watchdog','swarm','topology'].index(name)+1} {name}: {s}") - - print(f"\n[2] FAMM preshape: {report.get('famm', {})}") - - print(f"\n[3] Performance metrics:") - for k, v in engine.metrics().items(): - if isinstance(v, dict): - print(f" {k}: mean={v['mean_ms']:.2f}ms, calls={v['calls']}") - else: - print(f" {k}: {v}") - - # Save - out = RESEARCH_STACK / "4-Infrastructure/shim/prover_orchestration_report.json" - with open(out, 'w') as f: - json.dump({"report": report, "metrics": engine.metrics()}, f, indent=2, default=str) - print(f"\n[4] Saved: {out}") - print("\n" + "=" * 60) - print("Provers integrated into runtime orchestration") - print("=" * 60) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/prover_orchestration_report.json b/4-Infrastructure/shim/prover_orchestration_report.json deleted file mode 100644 index 6f64a9f9..00000000 --- a/4-Infrastructure/shim/prover_orchestration_report.json +++ /dev/null @@ -1,60 +0,0 @@ -{ - "report": { - "layers": { - "watchdog": { - "ok": true, - "failed": [] - }, - "swarm": { - "consensus": { - "agents": 2, - "deterministic": true, - "drift": false - }, - "confidence": 0.95 - }, - "topology": { - "ok": true, - "shape": "flat" - } - }, - "allowed": true, - "famm": { - "shape": "flat", - "ev": [ - 1.77, - 2.51, - 3.07, - 3.54 - ], - "delays": [ - 75.16460280028288, - 63.11944030978032, - 57.07301455353496, - 53.14940034527339 - ], - "proved": true - } - }, - "metrics": { - "watchdog": { - "mean_ms": 0.008821487426757812, - "max_ms": 0.008821487426757812, - "calls": 1 - }, - "swarm": { - "mean_ms": 0.05030632019042969, - "max_ms": 0.05030632019042969, - "calls": 1 - }, - "topology": { - "mean_ms": 0.0, - "max_ms": 0, - "calls": 1 - }, - "watchdog_violations": 0, - "swarm_drift": false, - "topology_adaptations": 1, - "proved_configs": 1 - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/pw-dsp-bridge.sh b/4-Infrastructure/shim/pw-dsp-bridge.sh deleted file mode 100644 index ad23b39a..00000000 --- a/4-Infrastructure/shim/pw-dsp-bridge.sh +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env bash -# PipeWire capture → chunked FLAC → S3C GPU manifold bridge -# Full pipeline: pw-record → PCM → s3c-tool (GPU/CPU) → JSON receipts -# Set PW_TARGET env to override auto-detected audio source -set -euo pipefail - -FLAC="${FLAC:-flac}" -S3C_TOOL="${S3C_TOOL:-s3c-tool}" -OUTDIR="${OUTDIR:-/tmp/pw-dsp}" -mkdir -p "$OUTDIR" - -# Auto-detect first available audio input source -if [ -z "${PW_TARGET:-}" ]; then - PW_TARGET=$(pactl list sources short 2>/dev/null | grep -v monitor | awk '{print $2}' | head -1 || true) - if [ -z "$PW_TARGET" ]; then - echo "[pw-dsp] WARNING: no audio input source found, trying default" - PW_TARGET="0" - fi -fi - -echo "[pw-dsp] S3C GPU manifold bridge starting (target=$PW_TARGET)" - -while true; do - TS=$(date -u +%Y%m%dT%H%M%SZ) - RAW="$OUTDIR/raw_$TS.s16le" - FLAC_FILE="$OUTDIR/chunk_$TS.flac" - RECEIPT="$OUTDIR/receipt_$TS.json" - - # Capture 2-second PCM chunk via PipeWire (raw s16le, 48kHz mono) - pw-record --latency=100ms --target="$PW_TARGET" --rate=48000 --channels=1 --format=s16 "$RAW" & - PW_PID=$! - sleep 2 - kill "$PW_PID" 2>/dev/null || true - wait "$PW_PID" 2>/dev/null || true - - if [ ! -s "$RAW" ]; then continue; fi - - # Compress to FLAC (lossless, ~57% compression on noise floor) - $FLAC --best --no-padding --stdout --endian=little --sign=signed --channels=1 --bps=16 --sample-rate=48000 "$RAW" 2>/dev/null > "$FLAC_FILE" - - # Process through GPU-accelerated S3C manifold - $S3C_TOOL --aggregate < "$RAW" 2>/dev/null > "$RECEIPT" || true - - RAW_SIZE=$(stat -c%s "$RAW" 2>/dev/null || echo 0) - FLAC_SIZE=$(stat -c%s "$FLAC_FILE" 2>/dev/null || echo 0) - - if [ -s "$RECEIPT" ]; then - EMISSION=$(python3 -c "import json; d=json.load(open('$RECEIPT')); print(d['results'][0]['stats']['emission_ratio'])" 2>/dev/null || echo "?") - J_AVG=$(python3 -c "import json; d=json.load(open('$RECEIPT')); print(d['results'][0]['stats']['avg_j'])" 2>/dev/null || echo "?") - echo "[pw-dsp] $TS: ${RAW_SIZE}B raw, ${FLAC_SIZE}B FLAC, emission=$EMISSION, avg_j=$J_AVG" - else - echo "[pw-dsp] $TS: ${RAW_SIZE}B raw, ${FLAC_SIZE}B FLAC (no receipt)" - fi - - # Keep FLAC chunk, remove raw PCM - rm -f "$RAW" -done diff --git a/4-Infrastructure/shim/qam_hutter_manifold_geometry_prior.py b/4-Infrastructure/shim/qam_hutter_manifold_geometry_prior.py deleted file mode 100644 index 3d8085c3..00000000 --- a/4-Infrastructure/shim/qam_hutter_manifold_geometry_prior.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python3 -"""Model the QAM Hutter equation as a constrained manifold geometry. - -The useful local idea is that the I-axis byte objective and the Q-axis -exactness receipt are not two scores to average. They are coordinates on a -route manifold with a hard promotion submanifold: routes promote only when the -byte coordinate is below the incumbent and the receipt coordinate lies on the -exactness/closure locus. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "qam_hutter_manifold_geometry_prior_receipt.json" -CURRICULUM_OUT = SHIM / "qam_hutter_manifold_geometry_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -MANIFOLD_OBJECTS = [ - { - "id": "route_manifold", - "symbol": "M_route", - "definition": "space of legal transform routes with coordinates for bytes, witnesses, residuals, receipts, owners, and closure state", - "compression_role": "search surface for Hutter-style exact routes", - }, - { - "id": "byte_coordinate_chart", - "symbol": "I: M_route -> R", - "definition": "I(r) = payload + sidecar + witness + container bytes", - "compression_role": "objective coordinate minimized against the incumbent", - }, - { - "id": "exactness_constraint_chart", - "symbol": "Q: M_route -> {0,1}", - "definition": "Q(r)=1 iff decode/hash/receipt/NaN0 closure all pass", - "compression_role": "hard constraint, not an optimizable soft score", - }, - { - "id": "promotion_submanifold", - "symbol": "P = {r in M_route | I(r) < incumbent and Q(r)=1}", - "definition": "verified route region eligible for incumbent replacement", - "compression_role": "only region from which Hutter claims may be promoted", - }, - { - "id": "prune_halfspace", - "symbol": "N = {r_prefix | LB_QAM(r_prefix) >= incumbent}", - "definition": "lower-bound excluded route prefixes", - "compression_role": "prevents expensive evaluation of routes that cannot win", - }, - { - "id": "nan0_boundary", - "symbol": "partial M_NaN0", - "definition": "fail-closed boundary where receipt, closure, or exactness is invalid", - "compression_role": "absorbs invalid routes without recursive repair", - }, -] - - -EQUATIONS = [ - { - "id": "QHM0_route_coordinate", - "equation": "x_r = (I_payload, I_sidecar, I_witness, I_container, Q_hash, Q_merkle, Q_decode, Q_nan0)", - "meaning": "A route point carries byte-mass coordinates and exactness receipt coordinates.", - }, - { - "id": "QHM1_byte_function", - "equation": "I(r) = payload_bytes(r) + sidecar_bytes(r) + witness_bytes(r) + container_overhead(r)", - "meaning": "The I coordinate is the measured byte objective.", - }, - { - "id": "QHM2_exactness_locus", - "equation": "E = {r | decode(r)=source and H(decode(r))=source_hash and merkle(r)=route_key(r) and nan0(r)=0}", - "meaning": "The exactness locus is a hard submanifold of valid route points.", - }, - { - "id": "QHM3_promotion_submanifold", - "equation": "P = E cap {r | I(r) < incumbent_bytes}", - "meaning": "Promotion requires exactness plus a strict byte win.", - }, - { - "id": "QHM4_prune_region", - "equation": "Prune(prefix) iff LB_QAM(prefix) >= incumbent_bytes", - "meaning": "Prefixes whose lower bound lies outside the winning halfspace are pruned.", - }, - { - "id": "QHM5_margin_budget", - "equation": "witness_budget_remaining = incumbent_bytes - measured_payload_bytes - required_sidecar_bytes", - "meaning": "Any new geometric, semantic, FPGA, or cache witness must fit inside measured savings.", - }, - { - "id": "QHM6_barrier_flow", - "equation": "flow(r_t -> r_{t+1}) admissible iff Q remains closable and LB decreases or stays below incumbent", - "meaning": "Search flow is allowed only while the exactness side can still close and the byte side can still win.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "qam_hutter_manifold_geometry_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": { - "type": "local_modeling_refinement", - "statement": "Model the QAM Hutter Prize equations as manifold geometry.", - "workspace_target": "Decision Diagram Compression Tuning Prior", - }, - "primary_decision": { - "name": "model_qam_hutter_as_constrained_route_manifold", - "statement": ( - "Represent Hutter route tuning as a constrained manifold. The byte objective " - "is an I-coordinate, exact rehydration and receipts form a Q-coordinate " - "constraint, and promotion occurs only on the verified winning submanifold." - ), - }, - "manifold_objects": MANIFOLD_OBJECTS, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "route_manifold_chart_id", - "route_point_id", - "i_payload_coordinate", - "i_sidecar_coordinate", - "i_witness_coordinate", - "i_total_coordinate", - "q_hash_coordinate", - "q_merkle_coordinate", - "q_decode_coordinate", - "q_nan0_coordinate", - "exactness_locus_status", - "promotion_submanifold_status", - "prune_halfspace_status", - "margin_budget_bytes", - ], - "candidate_dd_edges": [ - "open_route_manifold_chart", - "embed_route_as_qam_point", - "compute_i_axis_byte_coordinate", - "compute_q_axis_receipt_coordinate", - "project_prefix_to_lower_bound_halfspace", - "test_exactness_locus_membership", - "test_promotion_submanifold_membership", - "route_to_nan0_boundary", - "promote_verified_winning_route", - ], - "promotion_rule": [ - "route_point_lies_on_exactness_locus", - "i_axis_total_bytes_is_below_incumbent", - "ratio_schema_is_explicit", - "nan0_coordinate_is_zero", - "witness_and_sidecar_mass_are_counted", - ], - "failure_rule": [ - "outside_exactness_locus -> not_promoted", - "inside_exactness_locus_but_no_byte_win -> diagnostic_only", - "lower_bound_outside_winning_halfspace -> prune", - "nan0_coordinate_nonzero -> fail_closed", - "hidden_payload_in_q_axis -> invalid_receipt", - ], - "implementation_implication": ( - "A configurable bounded route evaluator can treat each candidate as a point in " - "M_route, cheaply reject prefixes outside the winning halfspace, and reserve " - "expensive encode/decode/hash work for routes whose Q-coordinate can still close." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - records = [] - for eq in receipt["equations"]: - records.append( - { - "task": "derive_qam_hutter_manifold_equation", - "equation_id": eq["id"], - "prompt": f"Explain {eq['id']} for the bounded exact route compiler.", - "completion": f"{eq['equation']} -- {eq['meaning']}", - } - ) - for obj in receipt["manifold_objects"]: - records.append( - { - "task": "map_route_manifold_object", - "object_id": obj["id"], - "prompt": f"Map {obj['symbol']} into compression route evaluation.", - "completion": obj["compression_role"], - } - ) - CURRICULUM_OUT.write_text( - "".join(stable_json(record) + "\n" for record in records), - encoding="utf-8", - ) - - -def main() -> int: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_curriculum(receipt) - print(json.dumps( - { - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "equation_count": len(receipt["equations"]), - "manifold_object_count": len(receipt["manifold_objects"]), - }, - indent=2, - sort_keys=True, - )) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/quandela_job_tasking_surface.py b/4-Infrastructure/shim/quandela_job_tasking_surface.py deleted file mode 100644 index 14566750..00000000 --- a/4-Infrastructure/shim/quandela_job_tasking_surface.py +++ /dev/null @@ -1,329 +0,0 @@ -#!/usr/bin/env python3 -"""Quandela/Perceval job tasking surface with Triangle-in-Square pruning. - -This creates a dry-run queue for photonic quantum simulation/QPU tasking. It -does not install Perceval, save tokens, submit remote jobs, or execute circuits. -The point is to make the job membrane explicit before any cloud or QPU surface -is touched. -""" - -from __future__ import annotations - -import argparse -import hashlib -import importlib.util -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -PERCEVAL = REPO / "5-Applications" / "tools-scripts" / "external" / "quantum" / "perceval" -NOISE_RECEIPT = REPO / "4-Infrastructure" / "hardware" / "noise_stability_sim_receipt.json" -EIGEN_TRAJECTORY = REPO / "4-Infrastructure" / "hardware" / "eigenvalue_trajectory.png" - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def file_hash(path: Path) -> str | None: - if not path.exists(): - return None - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def run_git(path: Path, *args: str) -> str: - proc = subprocess.run(["git", "-C", str(path), *args], text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - return proc.stdout.strip() if proc.returncode == 0 else "" - - -def perceval_available() -> dict[str, Any]: - spec = importlib.util.find_spec("perceval") - if spec is None: - return {"installed": False, "version": None} - try: - import perceval as pcvl # type: ignore - - return {"installed": True, "version": getattr(pcvl, "__version__", None)} - except Exception as exc: - return {"installed": False, "version": None, "error": f"{type(exc).__name__}: {exc}"} - - -def triangle_square_fit(triangle: dict[str, float], square: dict[str, float]) -> dict[str, Any]: - ratios = {} - overflow = {} - for key, value in triangle.items(): - capacity = max(float(square.get(key, 0.0)), 1e-9) - ratios[key] = min(1.0, float(value) / capacity) - overflow[key] = max(0.0, float(value) - capacity) - fit_score = sum(ratios.values()) / (len(ratios) or 1) - residual_mass = sum(overflow.values()) - fits_square = residual_mass == 0.0 - return { - "fit_score": fit_score, - "residual_mass": residual_mass, - "fits_square": fits_square, - "ratios": ratios, - "overflow": overflow, - "rule": "Route only the residual that does not fit the local square; do not submit broad unpruned jobs.", - } - - -def load_stochastic_crc_source() -> dict[str, Any]: - if not NOISE_RECEIPT.exists(): - return { - "available": False, - "path": str(NOISE_RECEIPT.relative_to(REPO)), - "receipt_hash": None, - "crc32_hex": None, - "payload_sha256": None, - "eigen_trajectory_hash": file_hash(EIGEN_TRAJECTORY), - } - receipt = json.loads(NOISE_RECEIPT.read_text(encoding="utf-8")) - crc = receipt.get("micro_gain", {}).get("stochastic_crc", {}) - return { - "available": True, - "path": str(NOISE_RECEIPT.relative_to(REPO)), - "receipt_hash": receipt.get("receipt_hash_preimage_sha256"), - "crc32_hex": crc.get("crc32_hex"), - "payload_sha256": crc.get("payload_sha256"), - "byte_length": crc.get("byte_length"), - "eigen_trajectory": str(EIGEN_TRAJECTORY.relative_to(REPO)), - "eigen_trajectory_hash": file_hash(EIGEN_TRAJECTORY), - "claim_boundary": crc.get("claim_boundary"), - } - - -def build_jobs() -> list[dict[str, Any]]: - stochastic_crc = load_stochastic_crc_source() - local_square = { - "modes": 8, - "photons": 4, - "depth": 24, - "shots": 1000, - } - cloud_square = { - "modes": 32, - "photons": 12, - "depth": 128, - "shots": 100000, - } - candidates = [ - { - "job_id": "pcvl_local_triangle_smoke", - "intent": "minimal photonic circuit simulation smoke", - "target": "local_perceval_simulator", - "triangle": {"modes": 4, "photons": 2, "depth": 8, "shots": 100}, - "square": local_square, - }, - { - "job_id": "pcvl_compression_kernel_probe", - "intent": "compression/eigenvector kernel probe after classical pruning", - "target": "local_perceval_simulator", - "triangle": {"modes": 8, "photons": 4, "depth": 24, "shots": 1000}, - "square": local_square, - }, - { - "job_id": "quandela_remote_residual_hold", - "intent": "remote QPU/cloud residual candidate after Triangle-in-Square pruning", - "target": "quandela_cloud_remote_job", - "triangle": {"modes": 16, "photons": 8, "depth": 64, "shots": 10000}, - "square": cloud_square, - }, - { - "job_id": "quandela_stochastic_crc_photonic_probe_hold", - "intent": "photonic/noisy sampler probe for stochastic CRC replay witness over the braided-field eigen-noise lane", - "target": "quandela_cloud_remote_job", - "triangle": {"modes": 8, "photons": 4, "depth": 32, "shots": 4096}, - "square": cloud_square, - "source_artifacts": stochastic_crc, - "expected_contract": { - "input": "stochastic_crc_lane_v1 payload hash plus eigenvalue trajectory witness", - "remote_output": "sample/count distribution or failed/degraded packet candidate", - "local_acceptance": "accepted only if local replay maps result to the same canonical CRC witness or an explicitly classified degradation", - }, - }, - ] - jobs = [] - for candidate in candidates: - fit = triangle_square_fit(candidate["triangle"], candidate["square"]) - target = candidate["target"] - if target == "quandela_cloud_remote_job": - activation = "held_requires_token_provider_budget_and_manual_submit" - lawful_to_run_now = False - elif fit["fits_square"]: - activation = "dry_run_queue_only_until_perceval_installed" - lawful_to_run_now = False - else: - activation = "prune_before_queue" - lawful_to_run_now = False - job = { - **candidate, - "fit": fit, - "activation": activation, - "lawful_to_run_now": lawful_to_run_now, - "claim_boundary": "Job spec only. No Perceval execution, no token storage, no cloud submission, and no QPU time is consumed.", - "job_hash": sha256_text(json.dumps(candidate, sort_keys=True, ensure_ascii=False)), - } - jobs.append(job) - return jobs - - -def build_receipt() -> dict[str, Any]: - readme = PERCEVAL / "README.md" - pyproject = PERCEVAL / "pyproject.toml" - jobs = build_jobs() - installed = perceval_available() - queue_hash = sha256_text(json.dumps(jobs, sort_keys=True, ensure_ascii=False)) - return { - "schema": "quandela_job_tasking_surface_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "surface_id": "quandela_perceval_job_tasking", - "claim_boundary": "Quandela/Perceval is enabled only as a dry-run job-tasking surface. Remote execution requires explicit credential, provider, budget, and manual-submit receipts.", - "perceval_reference": { - "path": str(PERCEVAL.relative_to(REPO)), - "remote": run_git(PERCEVAL, "remote", "get-url", "origin"), - "commit": run_git(PERCEVAL, "rev-parse", "HEAD"), - "readme_hash": file_hash(readme), - "pyproject_hash": file_hash(pyproject), - "installed": installed, - "source_claims": [ - "Perceval is a Python framework for photonic quantum circuits and simulations.", - "Perceval interfaces with available QPUs on Quandela cloud.", - "Perceval runtime exposes local and remote job abstractions.", - ], - }, - "stochastic_crc_source": load_stochastic_crc_source(), - "triangle_in_square_hole": { - "definition": "A routing/pruning primitive where the triangle is the smallest constrained problem kernel and the square hole is the available execution surface. Only residual mismatch may be queued for heavier simulation/QPU tasking.", - "purpose": "Cut work before quantum/cloud submission by fitting the classical kernel into local capacity first.", - "required_receipts": ["triangle_shape", "square_capacity", "fit_score", "residual_mass", "job_hash", "claim_boundary"], - }, - "jobs": jobs, - "queue_hash": queue_hash, - "job_count": len(jobs), - "held_remote_jobs": sum(1 for job in jobs if job["target"] == "quandela_cloud_remote_job"), - "runnable_now": sum(1 for job in jobs if job["lawful_to_run_now"]), - "lawful": True, - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a Quandela/Perceval job-tasking router. Return compact JSON and never submit jobs without receipts." - records = [] - for job in receipt["jobs"]: - prompt = { - "task": "route_quandela_job", - "job_id": job["job_id"], - "intent": job["intent"], - "target": job["target"], - "fit": job["fit"], - "activation": job["activation"], - "claim_boundary": job["claim_boundary"], - } - answer = { - "selected": False, - "use_as": "quandela_job_tasking_prior", - "job_id": job["job_id"], - "target": job["target"], - "activation": job["activation"], - "job_hash": job["job_hash"], - "source_path": receipt["perceval_reference"]["path"], - "source_hash": receipt["perceval_reference"]["readme_hash"], - "claim_boundary": receipt["claim_boundary"], - "receipt_rule": "Require triangle/square fit receipt, credential pointer, provider, budget, and manual-submit approval before execution.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack Quandela Perceval Quantum TriangleSquare JobTasking", - "title: Quandela Job Tasking Surface", - "type: text/vnd.tiddlywiki", - "", - "! Quandela Job Tasking Surface", - "", - "This surface queues dry-run Perceval/Quandela job specs behind the Triangle-in-a-Square-Hole pruning primitive.", - "", - "Durable source: `4-Infrastructure/shim/quandela_job_tasking_surface.py`", - "", - "Receipt: `4-Infrastructure/shim/quandela_job_tasking_surface_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/quandela_job_tasking_surface_curriculum.jsonl`", - "", - f"Perceval snapshot: `{receipt['perceval_reference']['path']}`", - f"Perceval commit: `{receipt['perceval_reference']['commit']}`", - "", - "!! Stochastic CRC Photonic Probe", - "", - "The braid-field noise lane is now queued as a held photonic/noisy sampler candidate.", - "", - f"Noise receipt: `{receipt['stochastic_crc_source']['path']}`", - f"Noise receipt hash: `{receipt['stochastic_crc_source']['receipt_hash']}`", - f"CRC32 witness: `{receipt['stochastic_crc_source']['crc32_hex']}`", - f"CRC payload hash: `{receipt['stochastic_crc_source']['payload_sha256']}`", - "", - "Remote output is never accepted directly. It must be replayed locally against the stochastic CRC witness and classified as recovered, degraded, or failed.", - "", - "!! Triangle In A Square Hole", - "", - receipt["triangle_in_square_hole"]["definition"], - "", - "!! Claim Boundary", - "", - receipt["claim_boundary"], - "", - "!! Jobs", - "", - ] - for job in receipt["jobs"]: - lines.append(f"* `{job['job_id']}` -> {job['target']}; activation `{job['activation']}`; residual `{job['fit']['residual_mass']}`") - lines.extend( - [ - "", - "!! Links", - "", - "* [[MCP Bus Live Safe Probe]]", - "* [[MCP Surface Catalog]]", - "* [[OpenClaw Shared Bus Surface]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=SHIM / "quandela_job_tasking_surface_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "quandela_job_tasking_surface_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "Quandela Job Tasking Surface.tid") - args = parser.parse_args() - receipt = build_receipt() - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/quandela_noise_residual_shaver.py b/4-Infrastructure/shim/quandela_noise_residual_shaver.py deleted file mode 100644 index 552a026c..00000000 --- a/4-Infrastructure/shim/quandela_noise_residual_shaver.py +++ /dev/null @@ -1,306 +0,0 @@ -#!/usr/bin/env python3 -"""Noise-environment residual shaver for Quandela/Perceval tasking. - -This does not submit quantum jobs or claim quantum advantage. It classifies -residuals from dry-run job specs into components that a noisy photonic sampling -environment might help reduce, versus components that should stay classical or -blocked. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" - - -NOISE_HELPFUL_COMPONENTS = { - "sampling_variance", - "symmetry_ambiguity", - "collision_surface", - "interference_search", -} - -NOISE_HARMFUL_COMPONENTS = { - "coherent_model_bias", - "hardware_loss", - "calibration_gap", - "credential_gap", - "theorem_gap", -} - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def load_job_receipt(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def inferred_residual_components(job: dict[str, Any]) -> list[dict[str, Any]]: - """Attach a conservative latent residual model to a dry-run job spec.""" - job_id = job.get("job_id", "") - target = job.get("target", "") - if job_id == "pcvl_local_triangle_smoke": - return [ - {"kind": "sampling_variance", "mass": 0.03}, - {"kind": "calibration_gap", "mass": 0.02}, - ] - if job_id == "pcvl_compression_kernel_probe": - return [ - {"kind": "symmetry_ambiguity", "mass": 0.08}, - {"kind": "collision_surface", "mass": 0.06}, - {"kind": "coherent_model_bias", "mass": 0.04}, - ] - if target == "quandela_cloud_remote_job": - if job_id == "quandela_stochastic_crc_photonic_probe_hold": - return [ - {"kind": "interference_search", "mass": 0.10}, - {"kind": "sampling_variance", "mass": 0.10}, - {"kind": "symmetry_ambiguity", "mass": 0.06}, - {"kind": "collision_surface", "mass": 0.04}, - {"kind": "hardware_loss", "mass": 0.06}, - {"kind": "credential_gap", "mass": 0.05}, - {"kind": "calibration_gap", "mass": 0.04}, - ] - return [ - {"kind": "interference_search", "mass": 0.12}, - {"kind": "sampling_variance", "mass": 0.08}, - {"kind": "hardware_loss", "mass": 0.08}, - {"kind": "credential_gap", "mass": 0.05}, - {"kind": "theorem_gap", "mass": 0.03}, - ] - return [{"kind": "coherent_model_bias", "mass": 0.01}] - - -def classify_component(component: dict[str, Any]) -> dict[str, Any]: - kind = component["kind"] - mass = float(component["mass"]) - if kind in NOISE_HELPFUL_COMPONENTS: - return { - **component, - "noise_alignment": 1.0, - "route": "candidate_for_noise_shaving", - "reason": "Residual is stochastic, symmetry-like, collision-like, or sampling-distribution shaped.", - } - if kind in NOISE_HARMFUL_COMPONENTS: - return { - **component, - "noise_alignment": 0.0, - "route": "do_not_promote_to_noise", - "reason": "Residual is model bias, hardware debt, access gating, or proof debt; noise will not make it true.", - } - return { - **component, - "noise_alignment": 0.25, - "route": "hold_for_manual_classification", - "reason": "Residual class is unknown.", - } - - -def shave_job(job: dict[str, Any]) -> dict[str, Any]: - components = [classify_component(component) for component in inferred_residual_components(job)] - total_mass = sum(float(component["mass"]) for component in components) - helpful_mass = sum( - float(component["mass"]) * float(component["noise_alignment"]) - for component in components - if component["route"] == "candidate_for_noise_shaving" - ) - harmful_mass = sum( - float(component["mass"]) - for component in components - if component["route"] == "do_not_promote_to_noise" - ) - shave_score = helpful_mass / total_mass if total_mass else 0.0 - post_noise_residual_floor = max(0.0, total_mass - helpful_mass) - - if job.get("target") == "quandela_cloud_remote_job": - activation = "held_remote_noise_candidate_requires_token_provider_budget_manual_submit" - promotable_now = False - elif shave_score >= 0.55 and harmful_mass <= helpful_mass: - activation = "local_sim_noise_sweep_candidate_after_perceval_install" - promotable_now = False - else: - activation = "keep_classical_or_hold" - promotable_now = False - - payload = { - "job_id": job.get("job_id"), - "target": job.get("target"), - "job_hash": job.get("job_hash"), - "activation": activation, - "promotable_now": promotable_now, - "residual_components": components, - "residual_total_mass": total_mass, - "noise_helpful_mass": helpful_mass, - "noise_harmful_mass": harmful_mass, - "noise_shave_score": shave_score, - "post_noise_residual_floor": post_noise_residual_floor, - "claim_boundary": ( - "Noise shaving is a routing prior only. It may reduce sampling-shaped residuals in simulation, " - "but it does not repair model bias, hardware loss, proof gaps, or cloud authorization gates." - ), - } - payload["shave_hash"] = sha256_text(json.dumps(payload, sort_keys=True, ensure_ascii=False)) - return payload - - -def build_receipt(job_receipt_path: Path) -> dict[str, Any]: - source = load_job_receipt(job_receipt_path) - shaves = [shave_job(job) for job in source.get("jobs", [])] - total = len(shaves) or 1 - candidate_count = sum(1 for item in shaves if "candidate" in item["activation"]) - held_remote_count = sum(1 for item in shaves if item["activation"].startswith("held_remote")) - return { - "schema": "quandela_noise_residual_shaver_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "surface_id": "quandela_noise_residual_shaver", - "source_job_receipt": str(job_receipt_path), - "source_queue_hash": source.get("queue_hash"), - "source_job_count": source.get("job_count"), - "principle": ( - "Use the photonic noise environment as a residual shaver only for uncertainty that is already " - "sampling-distribution shaped; block residuals that are proof debt, model bias, or access control." - ), - "triangle_square_extension": { - "triangle": "smallest constrained problem kernel", - "square": "available local/cloud execution surface", - "noise_skin": "stochastic photonic sampler layer over the square surface", - "rule": "Only the triangle residual that aligns with the noise skin may be promoted; everything else is classical debt.", - }, - "shaves": shaves, - "noise_candidate_count": candidate_count, - "held_remote_noise_candidates": held_remote_count, - "promotable_now": sum(1 for item in shaves if item["promotable_now"]), - "average_noise_shave_score": sum(item["noise_shave_score"] for item in shaves) / total, - "claim_boundary": ( - "Dry-run routing receipt only. No Perceval execution, no Quandela cloud job, no token handling, " - "no QPU usage, and no theorem/solver claim." - ), - "lawful": True, - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a quantum-noise residual router. Return compact JSON and preserve claim boundaries." - records = [] - for item in receipt["shaves"]: - prompt = { - "task": "classify_noise_residual_shaving", - "job_id": item["job_id"], - "target": item["target"], - "components": item["residual_components"], - "noise_shave_score": item["noise_shave_score"], - } - answer = { - "selected": "candidate" in item["activation"], - "use_as": "noise_residual_routing_prior", - "job_id": item["job_id"], - "activation": item["activation"], - "noise_shave_score": item["noise_shave_score"], - "post_noise_residual_floor": item["post_noise_residual_floor"], - "shave_hash": item["shave_hash"], - "claim_boundary": item["claim_boundary"], - "receipt_rule": "Require component-level residual class, source queue hash, shave hash, and explicit no-submit boundary.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack Quandela Perceval Quantum Noise Residuals TriangleSquare", - "title: Quandela Noise Residual Shaver", - "type: text/vnd.tiddlywiki", - "", - "! Quandela Noise Residual Shaver", - "", - "This tiddler records the dry-run rule for treating a noisy photonic environment as a residual-shaving skin over the Quandela tasking surface.", - "", - "Durable source: `4-Infrastructure/shim/quandela_noise_residual_shaver.py`", - "", - "Receipt: `4-Infrastructure/shim/quandela_noise_residual_shaver_receipt.json`", - "", - "Curriculum: `4-Infrastructure/shim/quandela_noise_residual_shaver_curriculum.jsonl`", - "", - "!! Principle", - "", - receipt["principle"], - "", - "!! Stochastic CRC Lane", - "", - "The `quandela_stochastic_crc_photonic_probe_hold` job routes the braided-field micro-noise CRC witness into a held photonic/noisy sampler candidate.", - "", - "The useful contract is:", - "", - "```", - "seeded noise lane -> photonic/noisy sample candidate -> local CRC replay classifier", - "```", - "", - "The remote output is a recovery/degradation signal only. It is not a proof and is not accepted without local replay.", - "", - "!! Claim Boundary", - "", - receipt["claim_boundary"], - "", - "!! Jobs", - "", - ] - for item in receipt["shaves"]: - lines.append( - f"* `{item['job_id']}` -> activation `{item['activation']}`; " - f"score `{item['noise_shave_score']:.4f}`; floor `{item['post_noise_residual_floor']:.4f}`" - ) - lines.extend( - [ - "", - "!! Links", - "", - "* [[Quandela Job Tasking Surface]]", - "* [[MCP Bus Live Safe Probe]]", - "* [[OpenClaw Shared Bus Surface]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--jobs", type=Path, default=SHIM / "quandela_job_tasking_surface_receipt.json") - parser.add_argument("--receipt", type=Path, default=SHIM / "quandela_noise_residual_shaver_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "quandela_noise_residual_shaver_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "Quandela Noise Residual Shaver.tid") - args = parser.parse_args() - - receipt = build_receipt(args.jobs) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/quandela_perceval_requirements.txt b/4-Infrastructure/shim/quandela_perceval_requirements.txt deleted file mode 100644 index b42e83ec..00000000 --- a/4-Infrastructure/shim/quandela_perceval_requirements.txt +++ /dev/null @@ -1,29 +0,0 @@ -certifi==2026.4.22 -charset-normalizer==3.4.7 -contourpy==1.3.3 -cycler==0.12.1 -drawsvg==2.4.1 -exqalibur==1.1.1 -fonttools==4.62.1 -idna==3.15 -kiwisolver==1.5.0 -latexcodec==3.0.1 -matplotlib==3.10.9 -mpmath==1.3.0 -multipledispatch==1.0.0 -networkx==3.6.1 -numpy==2.4.4 -packaging==26.2 -perceval-quandela==1.1.0 -pillow==12.2.0 -platformdirs==4.9.6 -protobuf==7.34.1 -pyparsing==3.3.2 -python-dateutil==2.9.0.post0 -requests==2.33.1 -scipy==1.17.1 -six==1.17.0 -sympy==1.14.0 -tabulate==0.10.0 -tqdm==4.67.3 -urllib3==2.7.0 diff --git a/4-Infrastructure/shim/quandela_stochastic_crc_local_sim.py b/4-Infrastructure/shim/quandela_stochastic_crc_local_sim.py deleted file mode 100644 index 9852c629..00000000 --- a/4-Infrastructure/shim/quandela_stochastic_crc_local_sim.py +++ /dev/null @@ -1,331 +0,0 @@ -#!/usr/bin/env python3 -"""Local Perceval simulation for the stochastic CRC photonic probe. - -This is intentionally a local-only witness runner. It maps the braided-field -noise lane's stochastic CRC into a small photonic circuit, computes the exact -local output distribution with Perceval/SLOS, and records a replay receipt. -It does not submit a remote Quandela job or claim physical advantage. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import random -import statistics -import zlib -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -NOISE_RECEIPT = REPO / "4-Infrastructure" / "hardware" / "noise_stability_sim_receipt.json" -OUT = REPO / "4-Infrastructure" / "shim" / "quandela_stochastic_crc_local_sim_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_hash(path: Path) -> str | None: - if not path.exists(): - return None - return sha256_bytes(path.read_bytes()) - - -def crc32_hex(data: bytes) -> str: - return f"{zlib.crc32(data) & 0xFFFFFFFF:08x}" - - -def hamming32(left_hex: str, right_hex: str) -> int: - left = int(left_hex, 16) - right = int(right_hex, 16) - return (left ^ right).bit_count() - - -def xor32_hex(left_hex: str, right_hex: str) -> str: - return f"{(int(left_hex, 16) ^ int(right_hex, 16)) & 0xFFFFFFFF:08x}" - - -def load_noise_receipt(path: Path) -> dict[str, Any]: - receipt = json.loads(path.read_text(encoding="utf-8")) - stochastic = receipt["micro_gain"]["stochastic_crc"] - return { - "receipt": receipt, - "stochastic": stochastic, - "source_crc32_hex": stochastic["crc32_hex"], - "source_payload_sha256": stochastic["payload_sha256"], - "receipt_hash": receipt.get("receipt_hash_preimage_sha256"), - } - - -def crc_phases(crc: str) -> list[float]: - return [(byte / 255.0) * 2.0 * math.pi for byte in bytes.fromhex(crc)] - - -def payload_angles(payload_sha256: str, count: int) -> list[float]: - digest = bytes.fromhex(payload_sha256) - return [(digest[i] / 255.0) * math.pi for i in range(count)] - - -def build_circuit(source_crc32: str, payload_sha256: str): - import perceval as pcvl - - phases = crc_phases(source_crc32) - thetas = payload_angles(payload_sha256, 6) - circuit = pcvl.Circuit(4, name="stochastic-crc-local-witness") - - # The CRC bytes become phase shifters. The payload hash sets deterministic - # beam-splitter angles so the replay surface is tied to both witnesses. - circuit.add((0, 1), pcvl.BS(theta=thetas[0])) - circuit.add(0, pcvl.PS(phases[0])) - circuit.add(1, pcvl.PS(phases[1])) - circuit.add((2, 3), pcvl.BS(theta=thetas[1])) - circuit.add(2, pcvl.PS(phases[2])) - circuit.add(3, pcvl.PS(phases[3])) - circuit.add((1, 2), pcvl.BS(theta=thetas[2])) - circuit.add((0, 1), pcvl.BS(theta=thetas[3])) - circuit.add(0, pcvl.PS((phases[0] + phases[2]) % (2.0 * math.pi))) - circuit.add(3, pcvl.PS((phases[1] + phases[3]) % (2.0 * math.pi))) - circuit.add((0, 1), pcvl.BS(theta=thetas[4])) - circuit.add((2, 3), pcvl.BS(theta=thetas[5])) - return circuit, phases, thetas - - -def run_perceval(source_crc32: str, payload_sha256: str, photons: int) -> dict[str, Any]: - import perceval as pcvl - from perceval.algorithm import Sampler - - circuit, phases, thetas = build_circuit(source_crc32, payload_sha256) - if photons == 4: - input_state = pcvl.BasicState([1, 1, 1, 1]) - elif photons == 2: - input_state = pcvl.BasicState([1, 1, 0, 0]) - else: - raise ValueError("photons must be 2 or 4") - - processor = pcvl.Processor("SLOS", circuit) - processor.with_input(input_state) - sampler = Sampler(processor) - probabilities = sampler.probs()["results"] - serial_probabilities = { - str(state): round(float(probability), 12) - for state, probability in sorted(probabilities.items(), key=lambda item: str(item[0])) - } - distribution_bytes = stable_json(serial_probabilities).encode("utf-8") - distribution_crc = crc32_hex(distribution_bytes) - top_outputs = [ - {"state": state, "probability": probability} - for state, probability in sorted(serial_probabilities.items(), key=lambda item: item[1], reverse=True)[:8] - ] - return { - "perceval_version": getattr(pcvl, "__version__", None), - "backend": "SLOS", - "modes": 4, - "photons": photons, - "input_state": str(input_state), - "phase_radians": [round(value, 12) for value in phases], - "beam_splitter_theta_radians": [round(value, 12) for value in thetas], - "output_state_count": len(serial_probabilities), - "output_probabilities": serial_probabilities, - "top_outputs": top_outputs, - "distribution_hash_sha256": sha256_bytes(distribution_bytes), - "distribution_crc32_hex": distribution_crc, - } - - -def classify(source_crc32: str, output_crc32: str, output_state_count: int) -> dict[str, Any]: - distance = hamming32(source_crc32, output_crc32) - residual_xor = xor32_hex(source_crc32, output_crc32) - repaired_crc32 = xor32_hex(output_crc32, residual_xor) - if source_crc32 == output_crc32: - status = "recovered_direct" - elif output_state_count > 0: - status = "recovered_with_residual" - else: - status = "failed" - native_recovery = status == "recovered_direct" - residual_recovery = status == "recovered_with_residual" and repaired_crc32 == source_crc32 - return { - "status": status, - "source_crc32_hex": source_crc32, - "output_crc32_hex": output_crc32, - "crc_hamming_distance_bits": distance, - "residual_repair_lane": { - "schema": "crc32_xor_residual_repair_v1", - "residual_xor_hex": residual_xor, - "repaired_crc32_hex": repaired_crc32, - "byte_length": 4, - "recovered_source_crc": repaired_crc32 == source_crc32, - "claim_boundary": ( - "This is an explicit CRC residual lane. It repairs the replay witness " - "but does not mean the photonic distribution natively recovered the CRC." - ), - }, - "native_recovery": native_recovery, - "residual_recovery": residual_recovery, - "acceptance": native_recovery or residual_recovery, - "interpretation": ( - "Local photonic replay produced a deterministic distribution witness. " - "If native recovery fails, the explicit residual XOR lane repairs the " - "CRC witness and records the exact four-byte correction." - ), - } - - -def weighted_sample_counts(probabilities: dict[str, float], shots: int, rng: random.Random) -> dict[str, int]: - states = list(probabilities.keys()) - weights = [float(probabilities[state]) for state in states] - counts = {state: 0 for state in states} - for state in rng.choices(states, weights=weights, k=shots): - counts[state] += 1 - return {state: count for state, count in counts.items() if count} - - -def run_statistical_passes( - source_crc32: str, - probabilities: dict[str, float], - passes: int, - shots: int, - seed_material: str, -) -> dict[str, Any]: - pass_records = [] - for index in range(passes): - pass_seed = int(sha256_bytes(f"{seed_material}:{index}".encode("utf-8"))[:16], 16) - rng = random.Random(pass_seed) - counts = weighted_sample_counts(probabilities, shots, rng) - counts_bytes = stable_json(counts).encode("utf-8") - counts_crc32 = crc32_hex(counts_bytes) - pass_classifier = classify(source_crc32, counts_crc32, len(counts)) - pass_records.append({ - "pass_index": index, - "seed": pass_seed, - "shots": shots, - "observed_state_count": len(counts), - "counts_crc32_hex": counts_crc32, - "counts_hash_sha256": sha256_bytes(counts_bytes), - "crc_hamming_distance_bits": pass_classifier["crc_hamming_distance_bits"], - "status": pass_classifier["status"], - "acceptance": pass_classifier["acceptance"], - "residual_xor_hex": pass_classifier["residual_repair_lane"]["residual_xor_hex"], - "repaired_crc32_hex": pass_classifier["residual_repair_lane"]["repaired_crc32_hex"], - }) - distances = [record["crc_hamming_distance_bits"] for record in pass_records] - observed_counts = [record["observed_state_count"] for record in pass_records] - return { - "schema": "stochastic_crc_shot_sampling_stats_v1", - "passes": passes, - "shots_per_pass": shots, - "seed_material_sha256": sha256_bytes(seed_material.encode("utf-8")), - "native_recovery_count": sum(1 for record in pass_records if record["status"] == "recovered_direct"), - "residual_recovery_count": sum(1 for record in pass_records if record["status"] == "recovered_with_residual"), - "failed_count": sum(1 for record in pass_records if record["status"] == "failed"), - "acceptance_count": sum(1 for record in pass_records if record["acceptance"]), - "crc_hamming_distance_bits": { - "mean": statistics.fmean(distances) if distances else 0.0, - "pstdev": statistics.pstdev(distances) if len(distances) > 1 else 0.0, - "min": min(distances) if distances else 0, - "max": max(distances) if distances else 0, - }, - "observed_state_count": { - "mean": statistics.fmean(observed_counts) if observed_counts else 0.0, - "pstdev": statistics.pstdev(observed_counts) if len(observed_counts) > 1 else 0.0, - "min": min(observed_counts) if observed_counts else 0, - "max": max(observed_counts) if observed_counts else 0, - }, - "pass_records": pass_records, - "claim_boundary": ( - "Statistics are seeded local shot-sampling over the exact Perceval/SLOS " - "distribution. They are not hardware measurements or Quandela cloud results." - ), - } - - -def build_receipt(noise_path: Path, photons: int, passes: int, shots: int) -> dict[str, Any]: - source = load_noise_receipt(noise_path) - sim = run_perceval(source["source_crc32_hex"], source["source_payload_sha256"], photons) - replay = classify(source["source_crc32_hex"], sim["distribution_crc32_hex"], sim["output_state_count"]) - statistics_receipt = run_statistical_passes( - source["source_crc32_hex"], - sim["output_probabilities"], - passes, - shots, - f"{source['source_crc32_hex']}:{sim['distribution_hash_sha256']}:{photons}:{shots}", - ) - receipt = { - "schema": "quandela_stochastic_crc_local_sim_receipt_v1", - "generated_utc": datetime.now(timezone.utc).isoformat(), - "surface_id": "quandela_stochastic_crc_local_perceval_sim", - "claim_boundary": ( - "Local Perceval/SLOS simulation only. No Quandela cloud job, remote processor, " - "credential, QPU time, compression advantage, physical topological protection, " - "or hardware safety claim is made." - ), - "source": { - "noise_receipt": str(noise_path.relative_to(REPO)), - "noise_receipt_hash_sha256": file_hash(noise_path), - "noise_receipt_preimage_hash": source["receipt_hash"], - "stochastic_crc": source["stochastic"], - }, - "simulation": sim, - "replay_classifier": replay, - "statistical_passes": statistics_receipt, - "lawful": True, - } - stable_replay_preimage = stable_json({ - "schema": receipt["schema"], - "surface_id": receipt["surface_id"], - "claim_boundary": receipt["claim_boundary"], - "source": receipt["source"], - "simulation": receipt["simulation"], - "replay_classifier": receipt["replay_classifier"], - "statistical_passes": receipt["statistical_passes"], - "lawful": receipt["lawful"], - }).encode("utf-8") - receipt["stable_replay_hash_sha256"] = sha256_bytes(stable_replay_preimage) - preimage = stable_json(receipt).encode("utf-8") - receipt["receipt_hash_preimage_sha256"] = sha256_bytes(preimage) - return receipt - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--noise-receipt", type=Path, default=NOISE_RECEIPT) - parser.add_argument("--out", type=Path, default=OUT) - parser.add_argument("--photons", type=int, choices=(2, 4), default=4) - parser.add_argument("--passes", type=int, default=10) - parser.add_argument("--shots", type=int, default=4096) - args = parser.parse_args() - - receipt = build_receipt(args.noise_receipt, args.photons, args.passes, args.shots) - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - try: - out_display = str(args.out.relative_to(REPO)) - except ValueError: - out_display = str(args.out) - print(json.dumps({ - "lawful": receipt["lawful"], - "status": receipt["replay_classifier"]["status"], - "acceptance": receipt["replay_classifier"]["acceptance"], - "distribution_crc32_hex": receipt["simulation"]["distribution_crc32_hex"], - "passes": receipt["statistical_passes"]["passes"], - "shots_per_pass": receipt["statistical_passes"]["shots_per_pass"], - "mean_crc_hamming_distance_bits": receipt["statistical_passes"]["crc_hamming_distance_bits"]["mean"], - "residual_recovery_count": receipt["statistical_passes"]["residual_recovery_count"], - "receipt_hash_preimage_sha256": receipt["receipt_hash_preimage_sha256"], - "stable_replay_hash_sha256": receipt["stable_replay_hash_sha256"], - "out": out_display, - }, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/quantum_basis_compression_objective_receipt.py b/4-Infrastructure/shim/quantum_basis_compression_objective_receipt.py deleted file mode 100644 index ed25eab4..00000000 --- a/4-Infrastructure/shim/quantum_basis_compression_objective_receipt.py +++ /dev/null @@ -1,290 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for the quantum-basis compression objective. - -This converts the quantum cognitive-load implication into an executable -HOLD-first codec objective: a candidate basis is useful only when kernel, -parameters, protocol, and residual replay byte-exactly and cost less than raw. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "quantum_basis_compression_objective" -RECEIPT = OUT_DIR / "quantum_basis_compression_objective_receipt.json" -TABLE = OUT_DIR / "quantum_basis_compression_objective_table.jsonl" -SUMMARY = OUT_DIR / "quantum_basis_compression_objective_receipt.md" - - -OBJECTIVE_PACKET = { - "name": "Quantum Basis Compression Objective", - "core_map": "S -> (K_Q,Theta_Q,R,Pi) -> S_hat", - "lossless_gate": "Decode(K_Q,Theta_Q,R,Pi) == S", - "byte_error": "epsilon_byte = ||S - S_hat||_0 = 0", - "score": ( - "J_compress = |D| + |K_Q| + |Theta_Q| + |R| + |Pi| + " - "lambda_T D_replay + lambda_L L_decode" - ), - "gain": "G_Q = |S| - (|D| + |K_Q| + |Theta_Q| + |R_Q| + |Pi_Q|)", - "admission": "G_Q > 0 and exact replay", - "residual": "R_Q = S - Replay(K_Q,Theta_Q,Pi_Q)", - "native_phrase": ( - "Compression is finding the projection where most of the object becomes " - "lawful reconstruction and only the law-breaking part remains residual." - ), -} - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - source: str - kernel: str - theta: dict[str, Any] - protocol: dict[str, Any] - negative_control: bool - - -FIXTURES = [ - Fixture( - fixture_id="periodic_ab_kernel_admit", - source="AB" * 128, - kernel="repeat_literal", - theta={"literal": "AB", "count": 128}, - protocol={"decoder": "repeat_literal_v1"}, - negative_control=False, - ), - Fixture( - fixture_id="periodic_ab_wrong_count_negative", - source="AB" * 128, - kernel="repeat_literal", - theta={"literal": "AB", "count": 127}, - protocol={"decoder": "repeat_literal_v1"}, - negative_control=True, - ), - Fixture( - fixture_id="nearly_random_literal_hold", - source="A7fQ9zLm02PqXRtB", - kernel="raw_literal", - theta={"literal": "A7fQ9zLm02PqXRtB"}, - protocol={"decoder": "raw_literal_v1"}, - negative_control=False, - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def replay(kernel: str, theta: dict[str, Any], protocol: dict[str, Any]) -> str: - decoder = protocol.get("decoder") - if kernel == "repeat_literal" and decoder == "repeat_literal_v1": - return str(theta["literal"]) * int(theta["count"]) - if kernel == "raw_literal" and decoder == "raw_literal_v1": - return str(theta["literal"]) - raise ValueError(f"unsupported kernel/protocol pair {kernel}/{decoder}") - - -def residual_patch(source: str, candidate: str) -> list[dict[str, Any]]: - patch: list[dict[str, Any]] = [] - max_len = max(len(source), len(candidate)) - for index in range(max_len): - actual = source[index] if index < len(source) else "" - proposed = candidate[index] if index < len(candidate) else "" - if actual != proposed: - patch.append({"i": index, "actual": actual, "candidate": proposed}) - return patch - - -def shannon_entropy_bytes(data: bytes) -> float: - if not data: - return 0.0 - counts: dict[int, int] = {} - for value in data: - counts[value] = counts.get(value, 0) + 1 - total = len(data) - return -sum((count / total) * math.log(count / total, 2) for count in counts.values()) - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - source_bytes = fixture.source.encode("utf-8") - try: - reconstruction = replay(fixture.kernel, fixture.theta, fixture.protocol) - replay_error = None - except Exception as exc: # noqa: BLE001 - receipt should preserve failure text. - reconstruction = "" - replay_error = str(exc) - - patch = residual_patch(fixture.source, reconstruction) - exact_replay_without_residual = fixture.source == reconstruction - residual_declared = True - reconstructed_with_patch = list(reconstruction) - for item in patch: - index = int(item["i"]) - while index >= len(reconstructed_with_patch): - reconstructed_with_patch.append("") - reconstructed_with_patch[index] = str(item["actual"]) - repaired = "".join(reconstructed_with_patch[: len(fixture.source)]) - exact_replay_with_residual = repaired == fixture.source - - dictionary_payload = {"objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET))} - kernel_payload = {"kernel": fixture.kernel} - theta_payload = fixture.theta - protocol_payload = fixture.protocol - residual_payload = {"patch": patch} - counted_payload = { - "D": dictionary_payload, - "K_Q": kernel_payload, - "Theta_Q": theta_payload, - "R": residual_payload, - "Pi": protocol_payload, - } - raw_bytes = len(source_bytes) - dictionary_bytes = len(stable_json(dictionary_payload).encode("utf-8")) - kernel_bytes = len(stable_json(kernel_payload).encode("utf-8")) - theta_bytes = len(stable_json(theta_payload).encode("utf-8")) - protocol_bytes = len(stable_json(protocol_payload).encode("utf-8")) - residual_bytes = 0 if exact_replay_without_residual else len(stable_json(residual_payload).encode("utf-8")) - counted_bytes = dictionary_bytes + kernel_bytes + theta_bytes + protocol_bytes + residual_bytes - byte_gain = raw_bytes - counted_bytes - residual_entropy = shannon_entropy_bytes(stable_json(residual_payload).encode("utf-8")) if residual_bytes else 0.0 - source_entropy = shannon_entropy_bytes(source_bytes) - - if fixture.negative_control and exact_replay_with_residual and not exact_replay_without_residual: - status = "HOLD_DIAGNOSTIC" - elif fixture.negative_control and exact_replay_without_residual: - status = "FAIL_NEGATIVE_CONTROL" - elif exact_replay_with_residual and byte_gain > 0 and not fixture.negative_control: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "negative_control": fixture.negative_control, - "source_hash": sha256_text(fixture.source), - "reconstruction_hash": sha256_text(reconstruction), - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "replay_error": replay_error, - "exact_replay_without_residual": exact_replay_without_residual, - "exact_replay_with_residual": exact_replay_with_residual, - "residual_declared": residual_declared, - "raw_bytes": raw_bytes, - "dictionary_bytes": dictionary_bytes, - "kernel_bytes": kernel_bytes, - "theta_bytes": theta_bytes, - "protocol_bytes": protocol_bytes, - "residual_bytes": residual_bytes, - "counted_bytes": counted_bytes, - "byte_gain": byte_gain, - "source_entropy_bits_per_byte": source_entropy, - "residual_entropy_bits_per_byte": residual_entropy, - "patch_count": len(patch), - "counted_payload_hash": sha256_text(stable_json(counted_payload)), - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Quantum Basis Compression Objective Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Objective", - "", - f"`{OBJECTIVE_PACKET['core_map']}`", - "", - f"`{OBJECTIVE_PACKET['score']}`", - "", - f"`{OBJECTIVE_PACKET['admission']}`", - "", - "## Fixtures", - "", - "| Fixture | Status | Exact replay | Byte gain | Residual bytes |", - "|---|---|---:|---:|---:|", - ] - for result in receipt["results"]: - lines.append( - f"| {result['fixture_id']} | {result['status']} | " - f"{result['exact_replay_with_residual']} | {result['byte_gain']} | " - f"{result['residual_bytes']} |" - ) - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "quantum_basis_compression_objective_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "objective_packet": OBJECTIVE_PACKET, - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "fixture_count": len(results), - "table": rel(TABLE), - "summary": rel(SUMMARY), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Quantum-basis compression objective prior only. It checks a tiny " - "lossless generator plus residual accounting surface; it does not " - "claim Hutter performance, does not validate quantum compression, " - "and does not promote any aesthetic transform without positive " - "byte law and exact replay." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/quantum_cogload_transfold_receipt.py b/4-Infrastructure/shim/quantum_cogload_transfold_receipt.py deleted file mode 100644 index 52bbc894..00000000 --- a/4-Infrastructure/shim/quantum_cogload_transfold_receipt.py +++ /dev/null @@ -1,343 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for the quantum cognitive-load transfold equation. - -This admits the pasted equation as a HOLD-first route prior, not as a proven -psychological, quantum-computing, or compression result. The executable part is -a tiny Pauli-string replay that checks feature extraction and component routing. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "quantum_cogload_transfold" -RECEIPT = OUT_DIR / "quantum_cogload_transfold_receipt.json" -TABLE = OUT_DIR / "quantum_cogload_transfold_table.jsonl" -SUMMARY = OUT_DIR / "quantum_cogload_transfold_receipt.md" - - -CANONICAL_PACKET = { - "name": "Quantum Cognitive Load Transfold", - "symbol": "L_QCog", - "compact_equation": ( - "L_QCog(H_class,rho,Omega) = R_Sigma_Q({C_k_Q * R_k_Q(" - "x_k_Q(H_Q,U_Q,rho,Omega);theta_k_Q) * lambda_phi^D_f * " - "B_k_Q(Omega)} for k in {I,E,G,R,M}; theta_Sigma_Q)" - ), - "transfold": { - "H_Q": "(Pauli o C_n o Q_hbar)(H_class)", - "pauli_expansion": "H_Q = sum_alpha c_alpha P_alpha", - "pauli_string": "P_alpha = tensor_j sigma_j(alpha), sigma in {I,X,Y,Z}", - "coefficient": "c_alpha = 2^-n Tr[P_alpha C_n(Q_hbar(H_class))]", - }, - "feature_vector": [ - "H_P(c)", - "S_P(c)", - "chi_comm(H_Q)", - "E_ent(rho)", - "epsilon_C", - "D_circ(U_Q)", - "M_meas", - "Delta_basis", - "Delta_semantic", - ], - "components": { - "I": ["H_P", "S_P", "chi_comm", "E_ent"], - "E": ["epsilon_C", "D_circ", "M_meas", "Delta_basis"], - "G": ["Delta_schema", "Delta_compression", "Delta_transfer"], - "R": ["Delta_basis", "Delta_domain", "Delta_classical_quantum", "Delta_glyph_Pauli"], - "M": ["n", "S_P", "D_context", "N_registers"], - }, - "fractal_dimension": "D_f = log(2) / log(phi)", - "native_stack_phrase": ( - "CogLoad_Q = ResponseFold(PauliMass + EntanglementBurden + " - "NoncommutativeRouting + TruncationResidual + ReplayDepth + " - "SemanticBasinPressure)" - ), -} - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - pauli_coefficients: dict[str, float] - tau: float - rho_entanglement_bits: float - epsilon_c: float - circuit_depth: int - measurement_count: int - delta_basis: float - delta_semantic: float - negative_control: bool - - -FIXTURES = [ - Fixture( - fixture_id="two_qubit_pauli_cloud_admit", - pauli_coefficients={"ZI": 0.5, "IZ": 0.25, "XX": 0.125, "YY": 0.125}, - tau=0.1, - rho_entanglement_bits=1.0, - epsilon_c=0.01, - circuit_depth=8, - measurement_count=2, - delta_basis=0.125, - delta_semantic=0.2, - negative_control=False, - ), - Fixture( - fixture_id="single_term_toy_hold", - pauli_coefficients={"ZI": 1.0}, - tau=0.1, - rho_entanglement_bits=0.0, - epsilon_c=0.0, - circuit_depth=1, - measurement_count=1, - delta_basis=0.0, - delta_semantic=0.0, - negative_control=False, - ), - Fixture( - fixture_id="missing_pauli_coefficients_negative", - pauli_coefficients={}, - tau=0.1, - rho_entanglement_bits=0.0, - epsilon_c=1.0, - circuit_depth=0, - measurement_count=0, - delta_basis=1.0, - delta_semantic=1.0, - negative_control=True, - ), -] - - -ANTICOMMUTE = { - ("X", "Y"), - ("Y", "X"), - ("X", "Z"), - ("Z", "X"), - ("Y", "Z"), - ("Z", "Y"), -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def coefficient_probabilities(coefficients: dict[str, float]) -> dict[str, float]: - norm = sum(value * value for value in coefficients.values()) - if norm <= 0: - return {} - return {key: (value * value) / norm for key, value in coefficients.items()} - - -def pauli_entropy(coefficients: dict[str, float]) -> float: - probs = coefficient_probabilities(coefficients) - return -sum(prob * math.log(prob, 2) for prob in probs.values() if prob > 0) - - -def support_size(coefficients: dict[str, float], tau: float) -> int: - return sum(1 for value in coefficients.values() if abs(value) > tau) - - -def anticommutes(left: str, right: str) -> bool: - if len(left) != len(right): - raise ValueError("Pauli strings must have equal length") - flips = 0 - for a, b in zip(left, right): - if a == "I" or b == "I" or a == b: - continue - if (a, b) in ANTICOMMUTE: - flips += 1 - else: - raise ValueError(f"unsupported Pauli pair {a}{b}") - return flips % 2 == 1 - - -def commutation_burden(coefficients: dict[str, float]) -> float: - probs = coefficient_probabilities(coefficients) - keys = sorted(probs) - burden = 0.0 - for i, left in enumerate(keys): - for right in keys[i + 1 :]: - if anticommutes(left, right): - burden += probs[left] * probs[right] - return burden - - -def component_scores(fixture: Fixture) -> dict[str, float]: - h_p = pauli_entropy(fixture.pauli_coefficients) - s_p = float(support_size(fixture.pauli_coefficients, fixture.tau)) - chi = commutation_burden(fixture.pauli_coefficients) if fixture.pauli_coefficients else 0.0 - e_ent = fixture.rho_entanglement_bits - intrinsic = h_p + 0.25 * s_p + chi + e_ent - extraneous = fixture.epsilon_c + 0.1 * fixture.circuit_depth + 0.25 * fixture.measurement_count + fixture.delta_basis - germane = 0.5 * (fixture.delta_semantic + max(0.0, 1.0 - fixture.epsilon_c)) - routing = fixture.delta_basis + fixture.delta_semantic + chi - memory = len(next(iter(fixture.pauli_coefficients), "")) + s_p + 0.25 * fixture.circuit_depth - return { - "H_P": h_p, - "S_P": s_p, - "chi_comm": chi, - "E_ent": e_ent, - "L_I_Q": intrinsic, - "L_E_Q": extraneous, - "L_G_Q": germane, - "L_R_Q": routing, - "L_M_Q": memory, - "L_QCog_toy": intrinsic + extraneous + germane + routing + memory, - } - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - feature_errors: list[dict[str, Any]] = [] - if not fixture.pauli_coefficients: - feature_errors.append({"path": "pauli_coefficients", "error": "missing_required_coefficients"}) - else: - lengths = {len(item) for item in fixture.pauli_coefficients} - if len(lengths) != 1: - feature_errors.append({"path": "pauli_coefficients", "error": "mixed_pauli_string_lengths"}) - - replay_valid = not feature_errors - residual_declared = True - scores = component_scores(fixture) if replay_valid else {} - - encoded_payload = { - "canonical_packet_hash": sha256_text(stable_json(CANONICAL_PACKET)), - "pauli_coefficients": fixture.pauli_coefficients, - "tau": fixture.tau, - "feature_extractors": ["H_P", "S_P", "chi_comm", "E_ent", "epsilon_C", "D_circ", "M_meas"], - } - explicit_payload = { - "equation_packet": CANONICAL_PACKET, - "toy_scores": scores, - } - residual_payload = {"feature_errors": feature_errors} - encoded_bytes = len(stable_json(encoded_payload).encode("utf-8")) - explicit_bytes = len(stable_json(explicit_payload).encode("utf-8")) - residual_bytes = 0 if replay_valid else len(stable_json(residual_payload).encode("utf-8")) - byte_gain = explicit_bytes - encoded_bytes - residual_bytes - - if fixture.negative_control and replay_valid: - status = "FAIL_NEGATIVE_CONTROL" - elif replay_valid and residual_declared and byte_gain > 0 and not fixture.negative_control and scores.get("S_P", 0) > 1: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "negative_control": fixture.negative_control, - "pauli_coefficients_hash": sha256_text(stable_json(fixture.pauli_coefficients)), - "canonical_packet_hash": sha256_text(stable_json(CANONICAL_PACKET)), - "feature_error_count": len(feature_errors), - "feature_errors": feature_errors, - "scores": scores, - "replay_valid": replay_valid, - "residual_declared": residual_declared, - "encoded_bytes": encoded_bytes, - "explicit_bytes": explicit_bytes, - "residual_bytes": residual_bytes, - "byte_gain": byte_gain, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Quantum Cognitive Load Transfold Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Canonical Packet", - "", - f"`{CANONICAL_PACKET['compact_equation']}`", - "", - "## Fixture Status", - "", - "| Fixture | Status | Replay | Byte gain |", - "|---|---|---:|---:|", - ] - for result in receipt["results"]: - lines.append( - f"| {result['fixture_id']} | {result['status']} | " - f"{result['replay_valid']} | {result['byte_gain']} |" - ) - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "quantum_cogload_transfold_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "canonical_packet": CANONICAL_PACKET, - "canonical_packet_hash": sha256_text(stable_json(CANONICAL_PACKET)), - "fixture_count": len(results), - "table": rel(TABLE), - "summary": rel(SUMMARY), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Quantum cognitive-load transfold prior only. It records a canonical " - "equation packet and a tiny Pauli-string feature replay; it does not " - "prove cognitive load theory, does not validate a quantum algorithm, " - "does not establish biological or psychological claims, and does not " - "claim compression benchmark performance." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/rainbow_raccoon_compiler.py b/4-Infrastructure/shim/rainbow_raccoon_compiler.py deleted file mode 100644 index 76bc32a1..00000000 --- a/4-Infrastructure/shim/rainbow_raccoon_compiler.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python3 -# SHIM ONLY — NO INVARIANT CHECKS, NO COST COMPUTATION, NO BRANCHING DECISIONS -"""Rainbow Raccoon Compiler integration shim. - -This is a data-passing shim only. Manifold projection, Euclidean distance -computation, nearest-lawful-shape classification, and type-witness decisions -have all been moved behind the Lean receipt boundary. This file performs only: -- JSON serialization / deserialization -- File I/O (read, write, digest) -- Orchestration (spawn/collect receipts) - -Manifold projection → Lean: Semantics/RRCManifold.lean -Nearest lawful shape → Lean: Semantics/RRCClassification.lean -Type witness (HOLD/CANDIDATE) → Lean: Semantics/RRCTypeWitness.lean - -# TODO: Replace with Lean receipt when Q16_16 build is stable -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "rainbow_raccoon_compiler_receipt.json" -CURRICULUM = SHIM / "rainbow_raccoon_compiler_curriculum.jsonl" - - -SOURCE_ARTIFACTS = [ - "docs/compression_signal_shaping_synthesis.md", - "4-Infrastructure/shim/compression_signal_shaping_synthesis_receipt.json", - "4-Infrastructure/shim/projectable_geometry_topology_model_receipt.json", - "4-Infrastructure/shim/holographic_fractional_recursive_equation_fold_receipt.json", - "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json", - "4-Infrastructure/shim/cad_force_probe_experiment_matrix_receipt.json", - "docs/research/GCCL_THEORY_INTRO.md", - "0-Core-Formalism/lean/Semantics/Semantics/GeometricCompressionWorkspace.lean", -] - - -@dataclass(frozen=True) -class RRCObject: - object_id: str - label: str - kind: str - payload: str - source_path: str | None = None - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_digest(path: Path) -> dict[str, Any]: - data = path.read_bytes() - return { - "path": str(path.relative_to(REPO)), - "bytes": len(data), - "sha256": sha256_bytes(data), - } - - -def text_payload(path: str) -> str: - p = REPO / path - if not p.exists(): - return "" - data = p.read_text(encoding="utf-8", errors="replace") - return data[:12000] - - -def build_objects() -> list[RRCObject]: - return [ - RRCObject( - object_id="rrc_obj_signal_route_compiler", - label="Compression Signal Shaping Synthesis", - kind="compression_route_prior", - source_path="docs/compression_signal_shaping_synthesis.md", - payload=text_payload("docs/compression_signal_shaping_synthesis.md"), - ), - RRCObject( - object_id="rrc_obj_projectable_geometry", - label="Projectable Geometry Topology Receipt", - kind="geometry_topology_receipt", - source_path="4-Infrastructure/shim/projectable_geometry_topology_model_receipt.json", - payload=text_payload("4-Infrastructure/shim/projectable_geometry_topology_model_receipt.json"), - ), - RRCObject( - object_id="rrc_obj_cognitive_load", - label="Connectome Protective Cognitive Load Receipt", - kind="cognitive_field_receipt", - source_path="4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json", - payload=text_payload("4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json"), - ), - RRCObject( - object_id="rrc_obj_cad_force_probe", - label="CAD Force Probe Experiment Matrix Receipt", - kind="cad_force_receipt", - source_path="4-Infrastructure/shim/cad_force_probe_experiment_matrix_receipt.json", - payload=text_payload("4-Infrastructure/shim/cad_force_probe_experiment_matrix_receipt.json"), - ), - RRCObject( - object_id="rrc_obj_underspecified", - label="Underspecified raw object negative control", - kind="negative_control", - payload="raw object with no declared projection, witness, decoder, residual, or scale band", - ), - ] - - -# TODO: Replace with Lean receipt when Q16_16 build is stable -# Lean modules: -# Semantics/RRCManifold.lean → project_to_manifold -# Semantics/RRCClassification.lean → nearest_lawful_shape -# Semantics/RRCTypeWitness.lean → type_witness (HOLD vs CANDIDATE) - - -def compile_object(obj: RRCObject) -> dict[str, Any]: - """ - Data-passing shim. Manifold projection, lawful-shape classification, - and type-witness determination are deferred to Lean. - """ - return { - "object": { - "object_id": obj.object_id, - "label": obj.label, - "kind": obj.kind, - "source_path": obj.source_path, - "payload_sha256": sha256_text(obj.payload), - "payload_bytes_sampled": len(obj.payload.encode("utf-8")), - }, - "_status": "SHIM_PASSTHROUGH", - "_note": ( - "TODO: Replace with Lean receipt when Q16_16 build is stable. " - "Manifold projection → Semantics/RRCManifold.lean, " - "lawful shape → Semantics/RRCClassification.lean, " - "type witness → Semantics/RRCTypeWitness.lean" - ), - } - - -def build_receipt() -> dict[str, Any]: - sources = [file_digest(REPO / rel) for rel in SOURCE_ARTIFACTS if (REPO / rel).exists()] - objects = build_objects() - compiled_objects = [compile_object(obj) for obj in objects] - receipt: dict[str, Any] = { - "schema": "rainbow_raccoon_compiler_integration_v1", - "claim_state": "shim_passthrough_not_proof", - "source_artifacts": sources, - "compiler_name": "Rainbow Raccoon Compiler", - "compiler_abbrev": "RRC", - "primary_read": ( - "RRC type-checking is deferred to Lean. This shim emits " - "hash-stable receipts with TODO markers for each Lean module." - ), - "pipeline": [ - {"step": "object", "meaning": "raw object read from source files"}, - {"step": "manifold_projection", "meaning": "TODO: Lean RRCManifold.lean"}, - {"step": "nearest_lawful_shape", "meaning": "TODO: Lean RRCClassification.lean"}, - {"step": "type_witness", "meaning": "TODO: Lean RRCTypeWitness.lean"}, - {"step": "field_equation", "meaning": "TODO: Lean field-equation module"}, - {"step": "invariant_receipt", "meaning": "hash-stable receipt for replay"}, - ], - "compiled_objects": compiled_objects, - "promotion_rules": [ - "No CANDIDATE or HOLD determination is made by this shim.", - "All classification and witness decisions deferred to Lean.", - ], - "next_integration_steps": [ - "Write Semantics/RRCManifold.lean with 16-axis coordinate computation.", - "Write Semantics/RRCClassification.lean with lawful-shape prototypes.", - "Write Semantics/RRCTypeWitness.lean with HOLD/CANDIDATE gate.", - ], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [] - for compiled in receipt["compiled_objects"]: - rows.append( - { - "prompt": ( - "Classify this object with the Rainbow Raccoon Compiler pipeline: " - f"{compiled['object']['label']}" - ), - "completion": { - "_status": "SHIM_PASSTHROUGH", - "_note": "TODO: Lean receipt (RRCManifold + RRCClassification + RRCTypeWitness)", - }, - } - ) - CURRICULUM.write_text( - "\n".join(stable_json(row) for row in rows) + "\n", - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_curriculum(receipt) - print( - json.dumps( - { - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "compiled_object_count": len(receipt["compiled_objects"]), - "_status": "SHIM_PASSTHROUGH", - "_note": "TODO: Replace with Lean receipt when Q16_16 build is stable", - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/rainbow_raccoon_compiler_curriculum.jsonl b/4-Infrastructure/shim/rainbow_raccoon_compiler_curriculum.jsonl deleted file mode 100644 index 1dc795f6..00000000 --- a/4-Infrastructure/shim/rainbow_raccoon_compiler_curriculum.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":{"field_equation":"r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent","receipt_hash":"3fb2f3b7e3136097a97b5193308be0112e32127ab9c4904aeb99c0044fdac49b","shape":"SignalShapedRouteCompiler","status":"HOLD"},"prompt":"Classify this object with the Rainbow Raccoon Compiler pipeline: Compression Signal Shaping Synthesis"} -{"completion":{"field_equation":"close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0","receipt_hash":"f8d0fae4e724793e97e221d919795a4a8d54f2ac0942cacefd096b1d3b59e71e","shape":"ProjectableGeometryTopology","status":"CANDIDATE"},"prompt":"Classify this object with the Rainbow Raccoon Compiler pipeline: Projectable Geometry Topology Receipt"} -{"completion":{"field_equation":"L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate","receipt_hash":"bad3a1070cfd3db7b6db06d2185042d5d25cc55ba2b1ab244519c0d4f7016c7b","shape":"CognitiveLoadField","status":"CANDIDATE"},"prompt":"Classify this object with the Rainbow Raccoon Compiler pipeline: Connectome Protective Cognitive Load Receipt"} -{"completion":{"field_equation":"sum_j q_ij * (x_i - x_j) + p_i = 0; residual must stay under declared tolerance","receipt_hash":"daa2058fb8f3631fab71d980eea905cdf97cf294787259ba4b116fe975550843","shape":"CadForceProbeReceipt","status":"HOLD"},"prompt":"Classify this object with the Rainbow Raccoon Compiler pipeline: CAD Force Probe Experiment Matrix Receipt"} -{"completion":{"field_equation":"HOLD iff projection, decoder, witness, scale, or residual accounting is missing","receipt_hash":"58fa4044ec2f9ffb848dc2d2152f12bf8b2445e4701c905f2563b2e4d9d8e696","shape":"HoldForUnlawfulOrUnderspecifiedShape","status":"HOLD"},"prompt":"Classify this object with the Rainbow Raccoon Compiler pipeline: Underspecified raw object negative control"} diff --git a/4-Infrastructure/shim/rainbow_raccoon_compiler_receipt.json b/4-Infrastructure/shim/rainbow_raccoon_compiler_receipt.json deleted file mode 100644 index c9eda0e3..00000000 --- a/4-Infrastructure/shim/rainbow_raccoon_compiler_receipt.json +++ /dev/null @@ -1,760 +0,0 @@ -{ - "claim_state": "integration_shim_not_formal_proof", - "compiled_objects": [ - { - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_obj_signal_route_compiler", - "receipt_hash": "3fb2f3b7e3136097a97b5193308be0112e32127ab9c4904aeb99c0044fdac49b", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 1.0, - "decoder_declared": 0.8, - "field_energy": 0.571429, - "geometric_mass": 0.714286, - "hardware_affinity": 0.166667, - "history_depth": 0.8, - "negative_control_strength": 1.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.21, - "scale_band_declared": 0.2, - "semantic_entropy": 0.8125, - "shape_closure": 0.74, - "topology_torsion": 0.6, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.24990781814137925, - "kind_prior_bonus": 0.0, - "raw_distance": 0.24990781814137925, - "shape": "LogogramProjection" - }, - { - "distance": 0.315320209049232, - "kind_prior_bonus": 0.0, - "raw_distance": 0.315320209049232, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.3183245275968724, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3183245275968724, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.070321, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Compression Signal Shaping Synthesis", - "object_id": "rrc_obj_signal_route_compiler", - "payload_bytes_sampled": 5795, - "payload_sha256": "acf78e129bbd08a5e8c5eaf0245fa6cbddecb277700ec33ad94bb379b5e0f7f7", - "source_path": "docs/compression_signal_shaping_synthesis.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_obj_signal_route_compiler", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "66938ad313a0deeb0a7082febda21d561be48d5cf77046057d6053f907f079e9" - } - }, - { - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_obj_projectable_geometry", - "receipt_hash": "f8d0fae4e724793e97e221d919795a4a8d54f2ac0942cacefd096b1d3b59e71e", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.833333, - "decoder_declared": 0.8, - "field_energy": 0.285714, - "geometric_mass": 0.914286, - "hardware_affinity": 0.166667, - "history_depth": 0.2, - "negative_control_strength": 0.4, - "projection_declared": 1.0, - "proof_readiness": 0.625, - "receipt_density": 0.611111, - "residual_risk": 0.25, - "scale_band_declared": 0.4, - "semantic_entropy": 0.770833, - "shape_closure": 0.83, - "topology_torsion": 0.2, - "witness_declared": 1.0 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.206098395010935, - "kind_prior_bonus": 0.0, - "raw_distance": 0.206098395010935, - "shape": "LogogramProjection" - }, - { - "distance": 0.2698413349454287, - "kind_prior_bonus": 0.0, - "raw_distance": 0.2698413349454287, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.3536593156150725, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3536593156150725, - "shape": "CadForceProbeReceipt" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.107275, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Projectable Geometry Topology Receipt", - "object_id": "rrc_obj_projectable_geometry", - "payload_bytes_sampled": 11265, - "payload_sha256": "e89b864560b956dab17a56683cc131374a4dc26347258f5164389d11984f940b", - "source_path": "4-Infrastructure/shim/projectable_geometry_topology_model_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_obj_projectable_geometry", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "CANDIDATE", - "witness_hash": "2c445584d580ba81c570cdcbaac0fc2f938187c229d7e3e878c1d0f83aa9810e" - } - }, - { - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_obj_cognitive_load", - "receipt_hash": "bad3a1070cfd3db7b6db06d2185042d5d25cc55ba2b1ab244519c0d4f7016c7b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.0, - "decoder_declared": 0.2, - "field_energy": 0.571429, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.8, - "negative_control_strength": 0.6, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.166667, - "residual_risk": 0.4, - "scale_band_declared": 0.6, - "semantic_entropy": 0.802083, - "shape_closure": 0.62, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37635520223031216, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37635520223031216, - "shape": "LogogramProjection" - }, - { - "distance": 0.384927049561231, - "kind_prior_bonus": 0.0, - "raw_distance": 0.384927049561231, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4031179744703246, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4031179744703246, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.10305, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Connectome Protective Cognitive Load Receipt", - "object_id": "rrc_obj_cognitive_load", - "payload_bytes_sampled": 8845, - "payload_sha256": "fc222e265e70f69ee5e6039dd0cee2665a02e549b6718a7d022f9241849b3cf1", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_obj_cognitive_load", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "b964fc51027b1b1744bb3e7eddc77ae52657608aac3fa0205859ca40e930196e" - } - }, - { - "field_equation": "sum_j q_ij * (x_i - x_j) + p_i = 0; residual must stay under declared tolerance", - "invariant_receipt": { - "object_id": "rrc_obj_cad_force_probe", - "receipt_hash": "daa2058fb8f3631fab71d980eea905cdf97cf294787259ba4b116fe975550843", - "schema": "rrc.object_receipt.v1", - "shape": "CadForceProbeReceipt", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.0, - "decoder_declared": 0.2, - "field_energy": 0.428571, - "geometric_mass": 0.571429, - "hardware_affinity": 0.333333, - "history_depth": 0.0, - "negative_control_strength": 0.8, - "projection_declared": 1.0, - "proof_readiness": 0.2, - "receipt_density": 1.0, - "residual_risk": 0.47, - "scale_band_declared": 0.2, - "semantic_entropy": 0.75, - "shape_closure": 0.49, - "topology_torsion": 0.2, - "witness_declared": 0.4 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3553850990040301, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3553850990040301, - "shape": "LogogramProjection" - }, - { - "distance": 0.37821407961983083, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37821407961983083, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.3810977215308549, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3810977215308549, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cad_force_receipt", - "distance": 0.167641, - "kind_prior_shape": "CadForceProbeReceipt", - "shape": "CadForceProbeReceipt" - }, - "object": { - "kind": "cad_force_receipt", - "label": "CAD Force Probe Experiment Matrix Receipt", - "object_id": "rrc_obj_cad_force_probe", - "payload_bytes_sampled": 12000, - "payload_sha256": "aa9973793d7964c7343521715758bef376029a1be8da3ed3dda4b7174e7e1191", - "source_path": "4-Infrastructure/shim/cad_force_probe_experiment_matrix_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_obj_cad_force_probe", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "CadForceProbeReceipt", - "status": "HOLD", - "witness_hash": "b4f41cc343d59dac0790fbb87436892ca03e0322089b2a2e57dfce717e178acd" - } - }, - { - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_obj_underspecified", - "receipt_hash": "58fa4044ec2f9ffb848dc2d2152f12bf8b2445e4701c905f2563b2e4d9d8e696", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.0, - "decoder_declared": 0.6, - "field_energy": 0.0, - "geometric_mass": 0.0, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 0.2, - "proof_readiness": 0.1, - "receipt_density": 0.0, - "residual_risk": 0.76, - "scale_band_declared": 0.2, - "semantic_entropy": 0.197917, - "shape_closure": 0.3, - "topology_torsion": 0.0, - "witness_declared": 0.2 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.5267681143229543, - "kind_prior_bonus": 0.0, - "raw_distance": 0.5267681143229543, - "shape": "LogogramProjection" - }, - { - "distance": 0.5273847232024844, - "kind_prior_bonus": 0.0, - "raw_distance": 0.5273847232024844, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.5578131224708635, - "kind_prior_bonus": 0.0, - "raw_distance": 0.5578131224708635, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.240924, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Underspecified raw object negative control", - "object_id": "rrc_obj_underspecified", - "payload_bytes_sampled": 81, - "payload_sha256": "16b533bc42bda8f17ebb1328324e0bd0749c867eccfa28c8c35d19310e553e9a", - "source_path": null - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "object_id": "rrc_obj_underspecified", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "6259e606899cdf4987f8c0537d4bb0f9e5ef4e80916f934c2851af4e9a9f3b98" - } - } - ], - "compiler_abbrev": "RRC", - "compiler_name": "Rainbow Raccoon Compiler", - "field_equations": { - "CadForceProbeReceipt": "sum_j q_ij * (x_i - x_j) + p_i = 0; residual must stay under declared tolerance", - "CognitiveLoadField": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "HoldForUnlawfulOrUnderspecifiedShape": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "LogogramProjection": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "ProjectableGeometryTopology": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "SignalShapedRouteCompiler": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent" - }, - "lawful_shape_prototypes": { - "CadForceProbeReceipt": { - "compression_pressure": 0.3, - "decoder_declared": 0.46, - "field_energy": 0.81, - "geometric_mass": 0.91, - "hardware_affinity": 0.73, - "history_depth": 0.31, - "negative_control_strength": 0.88, - "projection_declared": 0.92, - "proof_readiness": 0.45, - "receipt_density": 0.87, - "residual_risk": 0.43, - "scale_band_declared": 0.79, - "semantic_entropy": 0.25, - "shape_closure": 0.86, - "topology_torsion": 0.64, - "witness_declared": 0.89 - }, - "CognitiveLoadField": { - "compression_pressure": 0.63, - "decoder_declared": 0.38, - "field_energy": 0.88, - "geometric_mass": 0.42, - "hardware_affinity": 0.37, - "history_depth": 0.91, - "negative_control_strength": 0.42, - "projection_declared": 0.76, - "proof_readiness": 0.28, - "receipt_density": 0.55, - "residual_risk": 0.71, - "scale_band_declared": 0.68, - "semantic_entropy": 0.86, - "shape_closure": 0.52, - "topology_torsion": 0.66, - "witness_declared": 0.53 - }, - "HoldForUnlawfulOrUnderspecifiedShape": { - "compression_pressure": 0.5, - "decoder_declared": 0.15, - "field_energy": 0.7, - "geometric_mass": 0.4, - "hardware_affinity": 0.25, - "history_depth": 0.74, - "negative_control_strength": 0.12, - "projection_declared": 0.18, - "proof_readiness": 0.1, - "receipt_density": 0.24, - "residual_risk": 0.91, - "scale_band_declared": 0.22, - "semantic_entropy": 0.76, - "shape_closure": 0.19, - "topology_torsion": 0.83, - "witness_declared": 0.1 - }, - "LogogramProjection": { - "compression_pressure": 0.86, - "decoder_declared": 0.84, - "field_energy": 0.43, - "geometric_mass": 0.49, - "hardware_affinity": 0.58, - "history_depth": 0.34, - "negative_control_strength": 0.55, - "projection_declared": 0.93, - "proof_readiness": 0.36, - "receipt_density": 0.72, - "residual_risk": 0.34, - "scale_band_declared": 0.58, - "semantic_entropy": 0.62, - "shape_closure": 0.78, - "topology_torsion": 0.48, - "witness_declared": 0.82 - }, - "ProjectableGeometryTopology": { - "compression_pressure": 0.56, - "decoder_declared": 0.7, - "field_energy": 0.76, - "geometric_mass": 0.94, - "hardware_affinity": 0.68, - "history_depth": 0.38, - "negative_control_strength": 0.61, - "projection_declared": 0.95, - "proof_readiness": 0.49, - "receipt_density": 0.81, - "residual_risk": 0.37, - "scale_band_declared": 0.73, - "semantic_entropy": 0.34, - "shape_closure": 0.9, - "topology_torsion": 0.72, - "witness_declared": 0.84 - }, - "SignalShapedRouteCompiler": { - "compression_pressure": 0.92, - "decoder_declared": 0.88, - "field_energy": 0.52, - "geometric_mass": 0.28, - "hardware_affinity": 0.61, - "history_depth": 0.46, - "negative_control_strength": 0.83, - "projection_declared": 0.91, - "proof_readiness": 0.42, - "receipt_density": 0.78, - "residual_risk": 0.31, - "scale_band_declared": 0.64, - "semantic_entropy": 0.58, - "shape_closure": 0.76, - "topology_torsion": 0.34, - "witness_declared": 0.79 - } - }, - "manifold_axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "next_integration_steps": [ - "Add a Lean RRCShape enum and witness-gate theorem surface.", - "Wire RRC classifications into the compression route classifier from E1/E2.", - "Use RRC HOLD status as a fail-closed gate for semantic tokenbook merges.", - "Map CAD force-probe receipts through RRC before four-force geometry claims." - ], - "pipeline": [ - { - "meaning": "raw object, receipt, source file, model state, or probe record", - "step": "object" - }, - { - "meaning": "map object into a 16-axis semantic/geometric/compression phase vector", - "step": "manifold_projection" - }, - { - "meaning": "choose closest declared type-shape prototype under normalized distance", - "step": "nearest_lawful_shape" - }, - { - "meaning": "emit CANDIDATE or HOLD witness; Lean status is explicit", - "step": "type_witness" - }, - { - "meaning": "attach behavior equation for the selected shape", - "step": "field_equation" - }, - { - "meaning": "hash-stable receipt for replay and audit", - "step": "invariant_receipt" - } - ], - "primary_read": "RRC becomes the type-checking layer for the signal-shaped route compiler: objects are projected into a named manifold vector, matched to lawful shape prototypes, assigned conservative type witnesses, and emitted as hash-stable invariant receipts.", - "promotion_rules": [ - "CANDIDATE is not a Lean proof; it is only admissible for next-stage proving.", - "HOLD is emitted when projection, witness, decoder, residual, or scale is weak.", - "No object may be promoted as lawful without a replayable invariant receipt.", - "Compression gain must still count residual, witness, decoder, sidecar, and container bytes.", - "Geometry or force claims require calibrated physical measurement receipts." - ], - "receipt_hash": "5edf7a533f7994233f075e171a984760525301e0f66040c8ac882d1172928f2a", - "schema": "rainbow_raccoon_compiler_integration_v1", - "source_artifacts": [ - { - "bytes": 5795, - "path": "docs/compression_signal_shaping_synthesis.md", - "sha256": "acf78e129bbd08a5e8c5eaf0245fa6cbddecb277700ec33ad94bb379b5e0f7f7" - }, - { - "bytes": 15511, - "path": "4-Infrastructure/shim/compression_signal_shaping_synthesis_receipt.json", - "sha256": "f89389a1a9cccf06a24d1b6d4102591ab5e6c031f7ce9e1675365724053276f1" - }, - { - "bytes": 11265, - "path": "4-Infrastructure/shim/projectable_geometry_topology_model_receipt.json", - "sha256": "e89b864560b956dab17a56683cc131374a4dc26347258f5164389d11984f940b" - }, - { - "bytes": 8441, - "path": "4-Infrastructure/shim/holographic_fractional_recursive_equation_fold_receipt.json", - "sha256": "7284397eae4a93679e69a1550c76e322bfb28bc47c10af382fb05d8fd16fd74c" - }, - { - "bytes": 8845, - "path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json", - "sha256": "fc222e265e70f69ee5e6039dd0cee2665a02e549b6718a7d022f9241849b3cf1" - }, - { - "bytes": 12271, - "path": "4-Infrastructure/shim/cad_force_probe_experiment_matrix_receipt.json", - "sha256": "846d4d0700ef1ff7c7b879509c2659f127fdea5ef6b28076b2d72907bd7ffea0" - }, - { - "bytes": 17288, - "path": "docs/research/GCCL_THEORY_INTRO.md", - "sha256": "b8b2b44259bca36151165c510bb8bd6b89b4f5b29b3aea90827bae6a90e36aff" - }, - { - "bytes": 35234, - "path": "0-Core-Formalism/lean/Semantics/Semantics/GeometricCompressionWorkspace.lean", - "sha256": "ca26efe8e205c27952a3a23fdbdb5e7ef6cfccd376c3da35c6a997caf92f0078" - } - ] -} \ No newline at end of file diff --git a/4-Infrastructure/shim/reconstruction_core_memory_promotion.py b/4-Infrastructure/shim/reconstruction_core_memory_promotion.py deleted file mode 100644 index a651c144..00000000 --- a/4-Infrastructure/shim/reconstruction_core_memory_promotion.py +++ /dev/null @@ -1,254 +0,0 @@ -#!/usr/bin/env python3 -"""Promote the reconstruction-core ladder into project memory. - -This writes a compact memory pointer, not a private Codex memory entry. It -follows the local OpenClaw/ENE memory-write rule: hashes, receipt paths, lawful -statuses, claim boundaries, and next-action pointers only. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "stack_memory_promotions" -MEMORY = OUT_DIR / "reconstruction_core_ladder_memory.json" -RECEIPT = OUT_DIR / "reconstruction_core_ladder_memory_receipt.json" -SUMMARY = OUT_DIR / "reconstruction_core_ladder_memory.md" - -SOURCE_RECEIPTS = [ - REPO / "shared-data/data/enwiki9_logogram_targeter/enwiki9_logogram_targeter_receipt.json", - REPO / "shared-data/data/enwiki9_logogram_xml_dict_probe/enwiki9_logogram_xml_dict_probe_receipt.json", - REPO / "shared-data/data/enwiki9_logogram_receipt_aggregation_probe/enwiki9_logogram_receipt_aggregation_probe_receipt.json", - REPO / "shared-data/data/enwiki9_logogram_dictionary_amortization_probe/enwiki9_logogram_dictionary_amortization_probe_receipt.json", - REPO / "shared-data/data/enwiki9_logogram_canonical_baseline_probe/enwiki9_logogram_canonical_baseline_probe_receipt.json", - REPO / "shared-data/data/language_surface_ambiguity_negative_control/language_surface_ambiguity_negative_control_receipt.json", - REPO / "shared-data/data/foundation_forward_equation_compiler/foundation_forward_equation_compiler_receipt.json", - REPO / "shared-data/data/buoyancy_added_mass_mobius/buoyancy_added_mass_mobius_receipt.json", - REPO / "shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - REPO / "shared-data/data/cross_domain_kernel_adapters/cross_domain_kernel_adapter_registry_receipt.json", - REPO / "shared-data/data/magnetic_derivative_kernels/magnetic_derivative_kernel_receipt.json", - REPO / "shared-data/data/solids_physics_kernels/solids_physics_kernel_receipt.json", - REPO / "shared-data/data/cross_domain_easy_wins/cross_domain_easy_wins_route_map_receipt.json", -] - -SOURCE_DOCS = [ - REPO / "6-Documentation/docs/specs/DECODER_FACING_RECONSTRUCTION_CORE.md", - REPO / "6-Documentation/docs/specs/LAW_GATED_RECONSTRUCTION_CORE_SHIFT.md", - REPO / "6-Documentation/docs/specs/RECONSTRUCTION_CORE_MATH_REVIEW_2026_05_09.md", - REPO / "6-Documentation/docs/specs/FORWARD_FOUNDATION_EQUATION_COMPILER.md", - REPO / "6-Documentation/docs/safety/ANGRYSPHINX_ADAPTIVE_SHELL_DEFENSE.md", - REPO / "0-Core-Formalism/otom/docs/safety/ANGRYSPHINX_ADAPTIVE_SHELL_DEFENSE.md", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def source_ref(path: Path) -> dict[str, Any]: - return { - "path": rel(path), - "exists": path.exists(), - "sha256": file_hash(path), - } - - -def build_memory() -> dict[str, Any]: - return { - "schema": "stack_memory_pointer_v1", - "memory_key": "reconstruction_core_ladder_2026_05_09", - "memory_kind": "receipt_backed_project_memory", - "settlement_state": "FORMING", - "lawful_status": "HOLD_TOP_LEVEL", - "claim_boundary": ( - "Project-memory pointer only. This records the current reconstruction-core " - "evidence ladder and guardrails; it is not a Hutter/LTCB result, not a " - "canonical enwik9 result, and not a compression benchmark claim." - ), - "canonical_statement": ( - "The reconstruction-core ladder has replay-valid fixtures, a positive " - "core-delta gate, a positive packet-delta gate, and one noncanonical " - "global-positive fixture under dictionary amortization. Top-level " - "promotion remains HOLD until canonical enwik9, baseline comparison, " - "control filters, and full accounting pass. Godel's Gauntlet is the " - "promotion/quarantine gate that blocks self-handwave; Buffalo-style " - "same-surface language instances require typed replay or residuals. " - "The forward-foundation compiler blocks backward theorem-label trust: " - "external equation names route only as hints until they compile from F0 " - "with closure, residual accounting, and receipts. The v5 canonical " - "baseline probe freezes the codec and adds provenance plus baseline " - "gates; the available local input remains a fixture and currently " - "lands at HOLD_GLOBAL. The Mass Number transform registry records " - "reusable exact algebraic kernels for ratio, pair, blend, reflection, " - "binary-choice, and Mobius-load families as MN plus a small opcode; " - "analytic entropy remains HOLD until precision and error policy are " - "receipted. Cross-domain compression is adapter-gated kernel reuse: " - "same algebraic skeleton does not imply same domain law, and couch " - "contact topology or seismic horizon inference stay HOLD until their " - "adapters close with replay, residuals, and receipts. Magnetic " - "derivative kernels currently admit only exact local scalar/vector " - "fixtures; Maxwell, MHD, gauge, material, and measurement claims stay " - "HOLD until unit, boundary, source, and residual receipts exist. " - "Solids physics currently admits local linear-elastic algebra fixtures " - "and MN pair/boundary adapters; wave, plasticity, fracture, anisotropy, " - "geometry, and material-model claims stay HOLD until their adapters " - "close. The easy-wins route map ranks the next low-cost domains for " - "exact local-algebra probes, with nonlinear, field, geometry, and " - "measurement claims held until receipted." - ), - "gate_ladder": [ - {"gate": "G0_exact_replay", "status": "PASS_FIXTURE"}, - {"gate": "G1_delta_core_positive", "status": "PASS_FIXTURE"}, - {"gate": "G2_delta_packet_positive", "status": "PASS_FIXTURE"}, - {"gate": "G3_delta_global_positive", "status": "ADMIT_FIXTURE_NONCANONICAL_ONLY"}, - {"gate": "G4_canonical_enwik9_slice", "status": "HOLD"}, - {"gate": "G5_baseline_comparison", "status": "HOLD"}, - {"gate": "G6_corpus_scale_hutter_accounting", "status": "HOLD"}, - ], - "guardrails": [ - { - "name": "Godels_Gauntlet", - "role": "promotion_quarantine_gate", - "rule": "the stack may propose and defend, but may not promote itself without receipts", - }, - { - "name": "Buffalo_surface_collision", - "role": "same_surface_role_guardrail", - "rule": "same visible token is not same atom unless role, position, case, and replay order are preserved or residualized", - }, - { - "name": "flown_by_cancellation", - "role": "invalid_derivation_guardrail", - "rule": "correct output is not proof of a lawful operator", - }, - { - "name": "Mass_Number_opcode_registry", - "role": "symbolic_compression_kernel", - "rule": "exact pair/ratio/blend/reflection families may compress to MN plus opcode; analytic transforms stay HOLD until error policy is receipted", - }, - { - "name": "Cross_domain_adapter_gate", - "role": "analogy_to_law_boundary", - "rule": "shared kernels may be reused across domains only through adapters with replay, residual policy, and closure receipts", - }, - { - "name": "Magnetic_derivative_gate", - "role": "field_equation_boundary", - "rule": "local derivative and cross-product fixtures may be accepted, but Maxwell/MHD/material claims stay HOLD until units, boundaries, sources, and residuals are receipted", - }, - { - "name": "Solids_physics_gate", - "role": "material_law_boundary", - "rule": "local linear-elastic fixtures may be accepted, but wave, plasticity, fracture, anisotropy, geometry, and material claims stay HOLD until receipted", - }, - { - "name": "Easy_wins_route_map", - "role": "probe_queue", - "rule": "prioritize exact local algebra in circuits, thermal, acoustics, probability, two-body, chemistry, optics, statistics, biology, and contact routes", - }, - ], - "source_receipts": [source_ref(path) for path in SOURCE_RECEIPTS], - "source_docs": [source_ref(path) for path in SOURCE_DOCS], - "next_action_pointer": "Find or construct canonical enwik9, verify size/checksum, then run frozen v5 on canonical slices.", - "memory_write_rule": "write only hashes, receipt paths, lawful statuses, claim boundaries, and next-action pointers; never raw secrets", - } - - -def build_receipt(memory: dict[str, Any]) -> dict[str, Any]: - memory_hash = sha256_bytes(stable_json(memory).encode("utf-8")) - receipt = { - "schema": "stack_memory_promotion_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "memory_key": memory["memory_key"], - "memory_path": rel(MEMORY), - "memory_hash": memory_hash, - "source_receipt_paths": [item["path"] for item in memory["source_receipts"]], - "source_doc_paths": [item["path"] for item in memory["source_docs"]], - "lawful_status": memory["lawful_status"], - "claim_boundary": memory["claim_boundary"], - "next_action_pointer": memory["next_action_pointer"], - "decision": "PROMOTE_TO_PROJECT_MEMORY", - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(memory: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Reconstruction Core Ladder Memory", - "", - f"Memory key: `{memory['memory_key']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - memory["claim_boundary"], - "", - "## Canonical Statement", - "", - memory["canonical_statement"], - "", - "## Gate Ladder", - "", - "| Gate | Status |", - "|---|---|", - ] - for gate in memory["gate_ladder"]: - lines.append(f"| `{gate['gate']}` | `{gate['status']}` |") - lines.extend(["", "## Guardrails", "", "| Name | Role | Rule |", "|---|---|---|"]) - for guardrail in memory["guardrails"]: - lines.append(f"| `{guardrail['name']}` | `{guardrail['role']}` | {guardrail['rule']} |") - lines.extend(["", "## Next Action", "", memory["next_action_pointer"]]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - memory = build_memory() - receipt = build_receipt(memory) - MEMORY.write_text(json.dumps(memory, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(memory, receipt) - print( - json.dumps( - { - "memory": rel(MEMORY), - "summary": rel(SUMMARY), - "receipt": rel(RECEIPT), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "lawful_status": memory["lawful_status"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/relay_validation_shim.py b/4-Infrastructure/shim/relay_validation_shim.py deleted file mode 100644 index 839597a8..00000000 --- a/4-Infrastructure/shim/relay_validation_shim.py +++ /dev/null @@ -1,420 +0,0 @@ -#!/usr/bin/env python3 -""" -Relay Validation Shim — Research Stack Integration -==================================================== - -Integrates kridaydave/Relay context validation with Research Stack framework -to provide cryptographic provenance, rollback capability, and claim verification. - -Purpose: -- Validate derivation chains for F01-F12 foundation equations -- Detect circular references in empirical claims -- Enforce budget constraints on speculative assertions -- Maintain immutable ledger of framework evolution - -Usage: - from relay_validation_shim import ResearchStackValidator - - validator = ResearchStackValidator( - framework_version="2026-05-06", - target_standard="6.5sigma" - ) - - # Register a claim - result = validator.register_claim( - claim_id="F03-BindingHierarchy", - claim_type="mathematical", - content="8-level hierarchical compression", - dependencies=["F01-HydrogenBase", "F02-ConstraintGeneration"], - evidence_paths=[ - "0-Core-Formalism/lean/Semantics/HierarchicalBinding.lean" - ] - ) - - # Validate claim chain - if validator.validate_chain("F03-BindingHierarchy"): - print("Claim validated — all dependencies resolved") - else: - print(f"Validation failed: {validator.get_failure_reason()}") -""" - -import hashlib -import json -from datetime import datetime, timezone -from dataclasses import dataclass, field -from typing import Dict, List, Optional, Set, Tuple, Union -from enum import Enum, auto -from pathlib import Path - - -class ClaimStatus(Enum): - """Validation status for framework claims.""" - PROPOSED = auto() - DERIVED = auto() # Has mathematical derivation - IMPLEMENTED = auto() # Has Lean formalization - TESTED = auto() # Has empirical validation - VALIDATED = auto() # Meets 6.5σ standard - SUSPECT = auto() # Missing derivation or evidence - HIGHLY_SUSPECT = auto() # Circular or thermodynamically impossible - REJECTED = auto() # Falsified or invalid - - -class ClaimType(Enum): - """Types of claims in the framework.""" - MATHEMATICAL = auto() # Equations, theorems - EMPIRICAL = auto() # Experimental observations - THEORETICAL = auto() # Physical theories - COMPUTATIONAL = auto() # Simulation results - PHILOSOPHICAL = auto() # Conceptual frameworks - - -@dataclass -class ClaimEnvelope: - """ - Immutable envelope for Research Stack claims. - - Similar to Relay's Context Envelope but specialized for - scientific claims with dependency tracking. - """ - claim_id: str - claim_type: ClaimType - status: ClaimStatus - version: str - timestamp: str - content_hash: str - dependencies: List[str] = field(default_factory=list) - evidence_hashes: List[str] = field(default_factory=list) - derivation_chain: List[str] = field(default_factory=list) - budget_tokens: int = 0 # Complexity budget (analogy to token limits) - signature: Optional[str] = None - - def to_dict(self) -> Dict: - """Serialize to dictionary for signing.""" - return { - "claim_id": self.claim_id, - "claim_type": self.claim_type.name, - "status": self.status.name, - "version": self.version, - "timestamp": self.timestamp, - "content_hash": self.content_hash, - "dependencies": self.dependencies, - "evidence_hashes": self.evidence_hashes, - "derivation_chain": self.derivation_chain, - "budget_tokens": self.budget_tokens - } - - def compute_hash(self) -> str: - """Compute SHA256 hash of envelope content.""" - data = json.dumps(self.to_dict(), sort_keys=True) - return hashlib.sha256(data.encode()).hexdigest()[:16] - - def sign(self, secret_key: str) -> None: - """Cryptographically sign the envelope.""" - data = self.compute_hash() + secret_key - self.signature = hashlib.sha256(data.encode()).hexdigest()[:32] - - -@dataclass -class ValidationResult: - """Result of claim validation.""" - is_valid: bool - claim_id: str - status: ClaimStatus - failures: List[str] = field(default_factory=list) - warnings: List[str] = field(default_factory=list) - derivation_depth: int = 0 - circular_path: Optional[List[str]] = None - - -class ResearchStackValidator: - """ - Validator for Research Stack framework claims. - - Provides: - - Claim registration with dependency tracking - - Circular reference detection - - Derivation chain validation - - Budget enforcement (complexity limits) - - Immutable ledger of claim evolution - """ - - def __init__(self, framework_version: str, target_standard: str = "6.5sigma"): - self.framework_version = framework_version - self.target_standard = target_standard - self.claims: Dict[str, ClaimEnvelope] = {} - self.ledger: List[ClaimEnvelope] = [] - self.dependency_graph: Dict[str, Set[str]] = {} - self.secret_key = f"research-stack-{framework_version}" - - # Pre-populate with known framework structure - self._bootstrap_framework() - - def _bootstrap_framework(self) -> None: - """Initialize with known Research Stack structure.""" - # F01-F12 foundation equations (awaiting derivation) - for i in range(1, 13): - claim_id = f"F{i:02d}-FoundationKernel" - self.register_claim( - claim_id=claim_id, - claim_type=ClaimType.MATHEMATICAL, - content=f"Foundation kernel F{i:02d} (awaiting formal derivation)", - dependencies=[], # F01-F12 are axiomatic - status=ClaimStatus.PROPOSED - ) - - # Known suspect claims (from adversarial archive) - self.register_claim( - claim_id="HARMON-CONSTANT", - claim_type=ClaimType.EMPIRICAL, - content="300% metabolic velocity via boundary layer scouring", - dependencies=[], - status=ClaimStatus.HIGHLY_SUSPECT, - evidence_paths=["6-Documentation/Adversarial Data/README.md"] - ) - - def register_claim( - self, - claim_id: str, - claim_type: ClaimType, - content: str, - dependencies: List[str] = None, - evidence_paths: List[str] = None, - status: ClaimStatus = ClaimStatus.PROPOSED, - budget_tokens: int = 100 - ) -> ClaimEnvelope: - """ - Register a new claim in the framework. - - Args: - claim_id: Unique identifier (e.g., "F03-BindingHierarchy") - claim_type: Type of claim (mathematical, empirical, etc.) - content: Description of the claim - dependencies: List of claim_ids this claim depends on - evidence_paths: File paths to supporting evidence - status: Initial validation status - budget_tokens: Complexity budget (higher = more complex claim) - """ - dependencies = dependencies or [] - evidence_paths = evidence_paths or [] - - # Compute content hash - content_hash = hashlib.sha256(content.encode()).hexdigest()[:16] - - # Compute evidence hashes - evidence_hashes = [] - for path in evidence_paths: - if Path(path).exists(): - with open(path, 'rb') as f: - evidence_hashes.append(hashlib.sha256(f.read()).hexdigest()[:16]) - else: - evidence_hashes.append(f"missing:{path}") - - # Create envelope - envelope = ClaimEnvelope( - claim_id=claim_id, - claim_type=claim_type, - status=status, - version=self.framework_version, - timestamp=datetime.now(timezone.utc).isoformat(), - content_hash=content_hash, - dependencies=dependencies, - evidence_hashes=evidence_hashes, - budget_tokens=budget_tokens - ) - - # Sign envelope - envelope.sign(self.secret_key) - - # Register claim - self.claims[claim_id] = envelope - self.dependency_graph[claim_id] = set(dependencies) - self.ledger.append(envelope) - - return envelope - - def detect_circular_dependencies(self, claim_id: str) -> Optional[List[str]]: - """ - Detect circular dependency chains. - - Returns: - List of claim_ids forming circular path, or None if acyclic. - """ - visited = set() - rec_stack = set() - path = [] - - def dfs(node: str) -> Optional[List[str]]: - visited.add(node) - rec_stack.add(node) - path.append(node) - - for neighbor in self.dependency_graph.get(node, set()): - if neighbor not in visited: - result = dfs(neighbor) - if result: - return result - elif neighbor in rec_stack: - # Found cycle - cycle_start = path.index(neighbor) - return path[cycle_start:] - - path.pop() - rec_stack.remove(node) - return None - - return dfs(claim_id) - - def validate_chain(self, claim_id: str, max_depth: int = 10) -> ValidationResult: - """ - Validate complete derivation chain for a claim. - - Checks: - 1. All dependencies exist - 2. No circular references - 3. Status compatibility (cannot build on SUSPECT claims) - 4. Derivation depth within limits - 5. Evidence integrity (hashes match) - """ - result = ValidationResult( - is_valid=True, - claim_id=claim_id, - status=ClaimStatus.PROPOSED, - failures=[], - warnings=[], - derivation_depth=0 - ) - - if claim_id not in self.claims: - result.is_valid = False - result.failures.append(f"Claim {claim_id} not registered") - return result - - claim = self.claims[claim_id] - result.status = claim.status - - # Check for circular dependencies - circular = self.detect_circular_dependencies(claim_id) - if circular: - result.is_valid = False - result.failures.append(f"Circular dependency detected: {' -> '.join(circular)}") - result.circular_path = circular - return result - - # Validate all dependencies - visited = set() - - def validate_deps(node: str, depth: int) -> None: - if depth > max_depth: - result.warnings.append(f"Max derivation depth ({max_depth}) exceeded at {node}") - return - - if node in visited: - return - visited.add(node) - result.derivation_depth = max(result.derivation_depth, depth) - - if node not in self.claims: - result.is_valid = False - result.failures.append(f"Missing dependency: {node}") - return - - dep_claim = self.claims[node] - - # Cannot build on suspect claims - if dep_claim.status in [ClaimStatus.SUSPECT, ClaimStatus.HIGHLY_SUSPECT]: - result.warnings.append(f"Depends on suspect claim: {node}") - - if dep_claim.status == ClaimStatus.REJECTED: - result.is_valid = False - result.failures.append(f"Depends on rejected claim: {node}") - - # Recursively validate dependencies - for dep in dep_claim.dependencies: - validate_deps(dep, depth + 1) - - for dep in claim.dependencies: - validate_deps(dep, 1) - - return result - - def get_framework_status(self) -> Dict[str, int]: - """Get summary of all claims by status.""" - status_counts = {status.name: 0 for status in ClaimStatus} - for claim in self.claims.values(): - status_counts[claim.status.name] += 1 - return status_counts - - def export_ledger(self, path: str) -> None: - """Export immutable ledger to JSON file.""" - data = { - "framework_version": self.framework_version, - "target_standard": self.target_standard, - "export_time": datetime.now(timezone.utc).isoformat(), - "claims": [claim.to_dict() for claim in self.ledger] - } - - with open(path, 'w') as f: - json.dump(data, f, indent=2) - - def find_unproven_claims(self) -> List[str]: - """Find all claims awaiting derivation or validation.""" - unproven = [] - for claim_id, claim in self.claims.items(): - if claim.status in [ClaimStatus.PROPOSED, ClaimStatus.DERIVED]: - # Check if it has complete derivation chain - result = self.validate_chain(claim_id) - if result.derivation_depth < 2: # Shallow derivation - unproven.append(claim_id) - return unproven - - -# Example usage and validation -if __name__ == "__main__": - # Initialize validator - validator = ResearchStackValidator( - framework_version="2026-05-06", - target_standard="6.5sigma" - ) - - # Register some biological framework claims - validator.register_claim( - claim_id="Cancer-CompressionFailure", - claim_type=ClaimType.THEORETICAL, - content="Cancer as information corruption in robust compression", - dependencies=["F02-ConstraintGeneration", "F08-RobustnessCompression"], - evidence_paths=[ - "6-Documentation/docs/speculative-materials/CancerAsCompressionFailure.md" - ], - status=ClaimStatus.DERIVED - ) - - validator.register_claim( - claim_id="Semelparity-ControlledDecompression", - claim_type=ClaimType.THEORETICAL, - content="Semelparity as programmed decompression vs cancer corruption", - dependencies=["Cancer-CompressionFailure", "F09-CompressionDynamics"], - status=ClaimStatus.DERIVED - ) - - # Test validation - result = validator.validate_chain("Semelparity-ControlledDecompression") - print(f"Validation result for Semelparity claim:") - print(f" Valid: {result.is_valid}") - print(f" Status: {result.status.name}") - print(f" Derivation depth: {result.derivation_depth}") - print(f" Warnings: {result.warnings}") - - # Check framework status - status = validator.get_framework_status() - print(f"\nFramework status:") - for status_name, count in status.items(): - if count > 0: - print(f" {status_name}: {count}") - - # Find unproven claims - unproven = validator.find_unproven_claims() - print(f"\nUnproven claims awaiting derivation: {unproven}") - - # Export ledger - validator.export_ledger("/tmp/research_stack_ledger.json") - print(f"\nLedger exported to /tmp/research_stack_ledger.json") diff --git a/4-Infrastructure/shim/replay_fixture_queue.py b/4-Infrastructure/shim/replay_fixture_queue.py deleted file mode 100644 index 2cfb24ee..00000000 --- a/4-Infrastructure/shim/replay_fixture_queue.py +++ /dev/null @@ -1,217 +0,0 @@ -#!/usr/bin/env python3 -"""Build a ranked replay-fixture queue from metaprobe receipts. - -The queue is an execution planner, not a benchmark result. It ranks route -surfaces by local readiness, fixture size, verifier availability, and whether a -negative-control lane is already obvious. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -DEFAULT_METAPROBE = ( - REPO - / "4-Infrastructure" - / "shim" - / "parallel_metaprobe_runs" - / "20260509T053755Z" - / "parallel_metaprobe_launcher_receipt.json" -) -ROUTE_PACKETS = REPO / "shared-data" / "data" / "nspace_bulk_routes" / "nspace_bulk_dataset_route_packets.jsonl" -OUT_DIR = REPO / "shared-data" / "data" / "replay_fixture_queue" -QUEUE_JSON = OUT_DIR / "replay_fixture_queue_receipt.json" -QUEUE_MD = OUT_DIR / "replay_fixture_queue.md" - - -QUEUE_SHAPES = { - "SRBench / ParFam": { - "rank": 1, - "readiness": 96, - "first_fixture": "symbolic_law_replay_harness: feynman_newton_gravity", - "negative_control": "mutated denominator exponent", - "verifier": "deterministic numeric replay plus residual accounting", - "reason": "smallest exact-law surface with obvious negative controls", - }, - "DLMF / Feynman Symbolic Regression": { - "rank": 2, - "readiness": 94, - "first_fixture": "symbolic_law_replay_harness: feynman_kinetic_energy", - "negative_control": "operator or coefficient mutation", - "verifier": "deterministic numeric replay plus formula/source hash", - "reason": "equation/glyph prior is directly aligned with one-symbol law replay", - }, - "PDEBench": { - "rank": 3, - "readiness": 78, - "first_fixture": "pde_tiny_replay_harness: advection_periodic_exact_shift", - "negative_control": "wrong boundary/viscosity metadata", - "verifier": "deterministic local advection replay plus residual drift receipt", - "reason": "canonical PDE families are clean and now have a no-download local micro-fixture", - }, - "The Well": { - "rank": 4, - "readiness": 72, - "first_fixture": "the_well_tiny_schema_probe: scalar/vector field schema", - "negative_control": "field-rank or coordinate-system mismatch", - "verifier": "field rank, axis, boundary, dtype, and residual schema receipt", - "reason": "large and well-structured, now guarded by a metadata-only schema probe before data slices", - }, - "LeanDojo / mathlib": { - "rank": 5, - "readiness": 70, - "first_fixture": "lean_proof_replay_receipt: ExtensionScaffold.Compression.ProofReplay", - "negative_control": "statement without local lake replay", - "verifier": "targeted lake build plus #eval witness readback", - "reason": "best proof boundary, now guarded by a tiny local Lean admission theorem fixture", - }, - "MeshGraphNets": { - "rank": 6, - "readiness": 64, - "first_fixture": "meshgraphnets_tiny_topology_probe: canonical mesh topology", - "negative_control": "mesh topology/split mismatch", - "verifier": "canonical edge, face, boundary, degree, and message-pass receipt", - "reason": "important for goxel topology and now guarded by a no-download topology probe", - }, - "RealPDEBench": { - "rank": 7, - "readiness": 58, - "first_fixture": "one paired real/sim trajectory index", - "negative_control": "sim-to-real modality mismatch", - "verifier": "scenario, modality, split, and noncommercial license receipts", - "reason": "valuable residual calibration, but license and data size raise friction", - }, - "NuminaMath": { - "rank": 8, - "readiness": 52, - "first_fixture": "proposal-only reasoning curriculum sample", - "negative_control": "answer without independent verification", - "verifier": "external answer check or local formal/numeric verifier", - "reason": "useful for proposal generation, not enough for truth promotion", - }, -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def read_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def read_packets(path: Path) -> list[dict[str, Any]]: - packets: list[dict[str, Any]] = [] - for line in path.read_text(encoding="utf-8").splitlines(): - if line.strip(): - packets.append(json.loads(line)) - return packets - - -def build_queue(metaprobe_path: Path) -> dict[str, Any]: - metaprobe = read_json(metaprobe_path) - packets = read_packets(ROUTE_PACKETS) - passed_lanes = {lane["name"] for lane in metaprobe.get("lanes", []) if lane.get("status") == "PASS"} - - queue = [] - for packet in packets: - shape = QUEUE_SHAPES.get(packet["dataset"]) - if shape is None: - continue - queue.append( - { - "rank": shape["rank"], - "readiness": shape["readiness"], - "dataset": packet["dataset"], - "domain": packet["domain"], - "packet_id": packet["packet_id"], - "packet_hash": packet["packet_hash"], - "first_fixture": shape["first_fixture"], - "negative_control": shape["negative_control"], - "verifier": shape["verifier"], - "reason": shape["reason"], - "source_urls": packet["source_urls"], - "ingest_boundary": packet["ingest_boundary"], - "decision": "HOLD", - } - ) - queue.sort(key=lambda item: (item["rank"], -item["readiness"])) - - receipt = { - "schema": "replay_fixture_queue_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "metaprobe_receipt": rel(metaprobe_path), - "metaprobe_receipt_hash": metaprobe.get("receipt_hash"), - "route_packets": rel(ROUTE_PACKETS), - "route_packet_count": len(packets), - "passed_metaprobe_lanes": sorted(passed_lanes), - "queue_count": len(queue), - "queue": queue, - "next_action": "run lean_proof_replay_receipt.py before RealPDEBench calibration", - "claim_boundary": ( - "Replay queue only. Ranking reflects local fixture readiness and receipt surface, " - "not benchmark performance, proof status, or compression gain." - ), - "decision": "HOLD", - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - return receipt - - -def write_markdown(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Replay Fixture Queue", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Queue", - "", - "| Rank | Dataset | Readiness | First fixture | Negative control |", - "|---:|---|---:|---|---|", - ] - for item in receipt["queue"]: - lines.append( - f"| {item['rank']} | {item['dataset']} | {item['readiness']} | " - f"{item['first_fixture']} | {item['negative_control']} |" - ) - lines.extend( - [ - "", - "## Next Action", - "", - f"`{receipt['next_action']}`", - "", - ] - ) - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_queue(DEFAULT_METAPROBE) - QUEUE_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_markdown(receipt, QUEUE_MD) - print(json.dumps({"receipt": rel(QUEUE_JSON), "summary": rel(QUEUE_MD), "receipt_hash": receipt["receipt_hash"], "queue_count": receipt["queue_count"]}, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/review_fix_regression_gate.py b/4-Infrastructure/shim/review_fix_regression_gate.py deleted file mode 100644 index 06979391..00000000 --- a/4-Infrastructure/shim/review_fix_regression_gate.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -"""Regression gate for the wiki/topology receipt review fixes. - -This is intentionally narrow. It protects the issues found in the fine-tooth -review: overclaim wording, generated-wiki self-inclusion, vacuous replay gates, -input-hash trust, x86 live-source drift, and maturation non-idempotence. -""" - -from __future__ import annotations - -import importlib.util -import json -import re -import subprocess -import sys -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] - -SCOPED_TEXT_FILES = [ - REPO / "shared-data" / "network_topology_database.json", - REPO / "3-Mathematical-Models" / "fiber_optic_vibrational_tensor" / "Fundamental_Network_Topology_Equation.md", - REPO / "6-Documentation" / "wiki" / "Network-Topology-Theory.md", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Fundamental Network Topology Equation.tid", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Network Topology Theory.tid", - REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "X86 Emulator Eigen Baseline.tid", -] - -FORBIDDEN_PATTERNS = [ - r"ultimate_validation", - r"ultimate validation", - r"\bvalidates\b", - r"validated_by_", - r"highest validation", - r"R_harvest", - r"8 converging", - r"C\(N\)\s*=\s*\(E_actual/E_theoretical\)", - r"0\.77 is the overall methodology convergence score", - r'"validated"\s*:\s*true', - r'"validation_source"', - r"high_validation", - r"live_unpinned_raw_urls", -] - -RECEIPTS = { - "wiki_review": REPO / "shared-data" / "data" / "wiki_tool_tuning_review" / "wiki_tool_tuning_review_receipt.json", - "wiki_maturation": REPO / "shared-data" / "data" / "wiki_tool_maturation_pass" / "wiki_tool_maturation_pass_receipt.json", - "parquet_efficiency": REPO / "shared-data" / "data" / "parquet_logogram_efficiency" / "parquet_logogram_efficiency_receipt.json", - "parquet_eigen": REPO / "shared-data" / "data" / "parquet_logogram_eigenprobe" / "parquet_logogram_eigenprobe_receipt.json", - "x86": REPO / "shared-data" / "data" / "x86_emulator_eigen_baseline" / "x86_emulator_eigen_baseline_receipt.json", - "modly_smoke": REPO / "shared-data" / "data" / "modly_text_to_cad_bridge" / "smoke" / "modly_text_to_cad_bridge_smoke_receipt.json", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True, allow_nan=False) - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def fail(message: str) -> None: - raise SystemExit(f"FAIL: {message}") - - -def check_forbidden_patterns() -> list[str]: - hits: list[str] = [] - for path in SCOPED_TEXT_FILES: - text = path.read_text(encoding="utf-8") - rel = path.relative_to(REPO) - for pattern in FORBIDDEN_PATTERNS: - match = re.search(pattern, text, flags=re.IGNORECASE) - if match: - hits.append(f"{rel}: forbidden `{pattern}` matched `{match.group(0)}`") - return hits - - -def load_review_module() -> Any: - module_path = REPO / "4-Infrastructure" / "shim" / "wiki_tool_tuning_review_probe.py" - spec = importlib.util.spec_from_file_location("wiki_tool_tuning_review_probe", module_path) - if spec is None or spec.loader is None: - fail(f"unable to load {module_path}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def check_wiki_generated_exclusion() -> None: - module = load_review_module() - payload = module.build_payload() - path_fields = [] - for key in ("top_tuning_targets", "quick_wins", "baseline_debt"): - path_fields.extend(entry.get("path") for entry in payload.get(key, [])) - for category in payload.get("category_rollup", {}).values(): - path_fields.extend(entry.get("path") for entry in category.get("top", [])) - paths = {path for path in path_fields if path} - generated = { - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Wiki Tool Tuning Review.tid", - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Wiki Tool Maturation Pass.tid", - } - leaked = sorted(paths & generated) - if leaked: - fail(f"generated wiki tiddlers leaked into review payload: {leaked}") - if payload["inputs"]["tool_like_count"] <= 0: - fail("wiki review tool_like_count is empty") - - -def check_receipts() -> dict[str, dict[str, Any]]: - receipts = {} - missing = [name for name, path in RECEIPTS.items() if not path.exists()] - if missing: - fail(f"missing receipts: {missing}") - for name, path in RECEIPTS.items(): - receipts[name] = load_json(path) - return receipts - - -def check_parquet_eigen_receipt(receipt: dict[str, Any]) -> None: - if receipt.get("input_payload_hash_recomputes") is not True: - fail("parquet eigenprobe input payload hash does not recompute") - if receipt.get("input_receipt_hash_recomputes") is not True: - fail("parquet eigenprobe input receipt hash does not recompute") - if receipt.get("decision") != "ADMIT_PARQUET_LOGOGRAM_EIGENPROBE_AS_HOLD_DIAGNOSTIC": - fail(f"unexpected parquet eigenprobe decision: {receipt.get('decision')}") - - -def check_x86_receipt(receipt: dict[str, Any]) -> None: - aggregates = receipt.get("aggregates", {}) - if aggregates.get("fetched_source_count") != aggregates.get("source_count"): - fail(f"x86 source fetch/cache incomplete: {aggregates}") - if aggregates.get("source_mode") != "local_cache_preferred_live_fetch_on_cache_miss": - fail(f"x86 source mode is not cache-preferred: {aggregates.get('source_mode')}") - cache_dir = REPO / aggregates.get("source_cache_dir", "") - cached_count = len(list(cache_dir.glob("*"))) if cache_dir.exists() else 0 - if cached_count != aggregates.get("source_count"): - fail(f"x86 source cache count {cached_count} != source_count {aggregates.get('source_count')}") - - -def check_maturation_idempotence() -> None: - script = REPO / "4-Infrastructure" / "shim" / "wiki_tool_maturation_apply.py" - result = subprocess.run( - [sys.executable, str(script)], - cwd=REPO, - check=True, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - ) - receipt = json.loads(result.stdout) - if receipt["aggregates"]["tiddlers_changed"] != 0: - fail(f"maturation pass is not idempotent: {receipt['aggregates']}") - if receipt["aggregates"]["tool_entries_seen"] != receipt["aggregates"]["matured_block_count"]: - fail(f"maturation count mismatch: {receipt['aggregates']}") - - -def main() -> int: - forbidden_hits = check_forbidden_patterns() - if forbidden_hits: - fail("forbidden overclaim patterns found:\n" + "\n".join(forbidden_hits)) - check_wiki_generated_exclusion() - receipts = check_receipts() - check_parquet_eigen_receipt(receipts["parquet_eigen"]) - check_x86_receipt(receipts["x86"]) - check_maturation_idempotence() - print( - json.dumps( - { - "decision": "PASS_REVIEW_FIX_REGRESSION_GATE", - "checked_receipts": sorted(RECEIPTS), - "forbidden_patterns": len(FORBIDDEN_PATTERNS), - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/rrc_equation_classifier.py b/4-Infrastructure/shim/rrc_equation_classifier.py deleted file mode 100644 index e1c5b926..00000000 --- a/4-Infrastructure/shim/rrc_equation_classifier.py +++ /dev/null @@ -1,514 +0,0 @@ -#!/usr/bin/env python3 -"""Project local equation surfaces through the Rainbow Raccoon Compiler. - -This is a routing/projection pass, not a proof pass. It converts equation -records into RRC objects and lets the existing manifold-indexed compiler select -a nearest lawful shape. The important artifact is the coordinate/witness -surface, not a human taxonomy label. -""" - -from __future__ import annotations - -import argparse -import csv -import hashlib -import importlib.util -import json -import re -import sys -from dataclasses import dataclass -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "rrc_equation_classifier_receipt.json" -CURRICULUM = SHIM / "rrc_equation_classifier_curriculum.jsonl" -SUMMARY = REPO / "docs" / "rrc_equation_classification.md" -TABLE = SHIM / "rrc_equation_classifier_table.csv" - - -@dataclass(frozen=True) -class EquationRecord: - equation_id: str - name: str - equation: str - source_path: str - family: str - purpose: str = "" - domain_type: str = "" - bind_class: str = "" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def load_rrc_module() -> Any: - path = SHIM / "rainbow_raccoon_compiler.py" - spec = importlib.util.spec_from_file_location("rainbow_raccoon_compiler", path) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load RRC module from {path}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def clean_text(value: Any) -> str: - return str(value or "").replace("\x00", "").strip() - - -def read_math_model_map(limit: int | None) -> list[EquationRecord]: - path = REPO / "3-Mathematical-Models" / "MATH_MODEL_MAP.tsv" - if not path.exists(): - return [] - records: list[EquationRecord] = [] - with path.open("r", encoding="utf-8", errors="replace", newline="") as handle: - reader = csv.DictReader(handle, delimiter="\t") - for row in reader: - if not row: - continue - name = clean_text(row.get("Model_Name")) or f"math_model_{len(records)}" - equation = clean_text(row.get("Equation")) - if not equation: - continue - records.append( - EquationRecord( - equation_id=f"math_model_map:{clean_text(row.get('#')) or len(records)}", - name=name, - equation=equation, - source_path=rel(path), - family=clean_text(row.get("Family")), - purpose=clean_text(row.get("Purpose")), - domain_type=clean_text(row.get("Domain_Type")), - bind_class=clean_text(row.get("Bind_Class")), - ) - ) - if limit is not None and len(records) >= limit: - break - return records - - -def read_equations_jsonl(path: Path, limit: int | None) -> list[EquationRecord]: - if not path.exists(): - return [] - records: list[EquationRecord] = [] - with path.open("r", encoding="utf-8", errors="replace") as handle: - for line in handle: - if limit is not None and len(records) >= limit: - break - line = line.strip() - if not line: - continue - try: - row = json.loads(line) - except json.JSONDecodeError: - continue - equation = clean_text(row.get("equation") or row.get("normalized")) - if not equation: - continue - records.append( - EquationRecord( - equation_id=clean_text(row.get("equation_id")) or f"{path.name}:{len(records)}", - name=clean_text(row.get("name")) or clean_text(row.get("source")) or "extracted_equation", - equation=equation, - source_path=rel(path), - family=clean_text(row.get("type")) or "extracted", - purpose=clean_text(row.get("source_type")), - ) - ) - return records - - -def read_extracted_markdown(limit: int | None) -> list[EquationRecord]: - path = REPO / "3-Mathematical-Models" / "extracted_equations.md" - if not path.exists(): - return [] - text = path.read_text(encoding="utf-8", errors="replace") - records: list[EquationRecord] = [] - for idx, match in enumerate(re.finditer(r"```(?:[A-Za-z0-9_+-]+)?\n(.*?)```", text, re.DOTALL)): - equation = clean_text(match.group(1)) - if not equation or "\n" in equation and len(equation.splitlines()) > 6: - continue - records.append( - EquationRecord( - equation_id=f"extracted_md:{idx}", - name=f"extracted_md_equation_{idx}", - equation=equation, - source_path=rel(path), - family="markdown_extracted", - purpose="fenced equation block", - ) - ) - if limit is not None and len(records) >= limit: - break - return records - - -def read_recent_receipt_equations() -> list[EquationRecord]: - receipt_paths = [ - SHIM / "connectome_protective_cognitive_load_reweighting_receipt.json", - SHIM / "transfold_enwiki8_magnetic_domain_generator_receipt.json", - SHIM / "transfold_couch_data_magnetic_domain_receipt.json", - SHIM / "merkle_tensegrity_load_equation_receipt.json", - SHIM / "hutter_equation_metastate_transfold_receipt.json", - ] - records: list[EquationRecord] = [] - for path in receipt_paths: - if not path.exists(): - continue - try: - receipt = json.loads(path.read_text(encoding="utf-8")) - except json.JSONDecodeError: - continue - candidate_maps = [] - for key in ("core_equations", "transfold_map", "hutter_transfold_equations"): - value = receipt.get(key) - if isinstance(value, dict): - candidate_maps.append((key, value)) - if isinstance(receipt.get("transfold_map"), dict): - core = receipt["transfold_map"].get("core_equations") - if isinstance(core, dict): - candidate_maps.append(("transfold_core_equations", core)) - for group, mapping in candidate_maps: - for name, equation in mapping.items(): - if isinstance(equation, (dict, list)): - equation_text = stable_json(equation) - else: - equation_text = clean_text(equation) - if not equation_text: - continue - records.append( - EquationRecord( - equation_id=f"{path.stem}:{group}:{name}", - name=name, - equation=equation_text, - source_path=rel(path), - family=clean_text(receipt.get("schema")) or "receipt_equation", - purpose=clean_text(receipt.get("purpose") or receipt.get("primary_read")), - ) - ) - return records - - -def route_hint(record: EquationRecord) -> str: - """Non-authoritative hint used only to choose an initial RRC kind prior.""" - text = " ".join( - [record.name, record.equation, record.family, record.purpose, record.domain_type, record.bind_class] - ).lower() - checks = [ - ("cognitive_load", ["cognitive", "load", "overflow", "emotional"]), - ("magnetic_signal", ["magnetic", "magnetization", "susceptibility", "remanence", "h_field"]), - ("compression_route", ["compression", "bytes", "bpb", "hutter", "codec", "decode"]), - ("geometry_topology", ["geometry", "topology", "manifold", "geodesic", "metric", "curvature"]), - ("cad_force", ["force", "stress", "tensegrity", "equilibrium", "stiffness", "load vector"]), - ("thermodynamic_energy", ["thermo", "entropy", "energy", "heat", "temperature", "landauer"]), - ("control_signal", ["control", "gate", "threshold", "risk", "feedback", "actuator"]), - ("chaotic_couch", ["couch", "chaotic", "oscillator", "hysteresis", "kappa"]), - ("electromagnetic_field", ["electromagnetic", "muon", "magnetic moment", "lorentz", "photon"]), - ("transfold", ["transfold", "projection", "source_domain", "target_domain"]), - ] - scores = [] - for label, terms in checks: - hits = sum(1 for term in terms if term in text) - scores.append((hits, label)) - scores.sort(reverse=True) - return scores[0][1] if scores and scores[0][0] > 0 else "unclassified_equation" - - -def rrc_kind_for_record(record: EquationRecord, hint: str) -> str: - bind = record.bind_class.lower() - domain = record.domain_type.lower() - text = f"{record.name} {record.equation} {record.family} {record.purpose}".lower() - if hint in {"compression_route", "magnetic_signal", "transfold"} or "compression" in domain: - return "compression_route_prior" - if hint in {"geometry_topology", "cad_force"} or "geometric" in bind: - return "cad_force_receipt" if hint == "cad_force" else "geometry_topology_receipt" - if hint == "cognitive_load": - return "cognitive_field_receipt" - if "receipt" in text or "hash" in text: - return "logogram_projection" - if hint == "unclassified_equation": - return "negative_control" - return "cognitive_field_receipt" if hint in {"control_signal", "thermodynamic_energy"} else "compression_route_prior" - - -def record_payload(record: EquationRecord, hint: str) -> str: - payload = { - "equation_id": record.equation_id, - "name": record.name, - "equation": record.equation, - "family": record.family, - "purpose": record.purpose, - "domain_type": record.domain_type, - "bind_class": record.bind_class, - "route_hint_non_authoritative": hint, - "projection": "equation_text_to_rrc_manifold_axes", - "decoder": "source_path plus equation_id plus equation text", - "witness": "rrc_equation_classifier_receipt", - "scale_band": "bounded text equation sample; no proof claim", - } - return json.dumps(payload, sort_keys=True, ensure_ascii=True) - - -def project_records(records: list[EquationRecord], sample_limit: int | None) -> dict[str, Any]: - rrc = load_rrc_module() - if sample_limit is not None: - records = records[:sample_limit] - compiled = [] - for record in records: - hint = route_hint(record) - kind = rrc_kind_for_record(record, hint) - obj = rrc.RRCObject( - object_id=f"rrc_eq_{sha256_text(record.equation_id)[:16]}", - label=record.name, - kind=kind, - payload=record_payload(record, hint), - source_path=record.source_path, - ) - item = rrc.compile_object(obj) - coords = item["manifold_projection"]["coordinates"] - ranked_axes = sorted(coords.items(), key=lambda kv: kv[1], reverse=True) - item["equation_record"] = { - "equation_id": record.equation_id, - "name": record.name, - "equation": record.equation, - "source_path": record.source_path, - "family": record.family, - "purpose": record.purpose, - "domain_type": record.domain_type, - "bind_class": record.bind_class, - "route_hint_non_authoritative": hint, - "rrc_kind": kind, - "projection_signature": { - "top_axes": ranked_axes[:4], - "weak_axes": [axis for axis, value in coords.items() if value < 0.35], - "shape_distance": item["nearest_lawful_shape"]["distance"], - }, - } - item["invariant_receipt"]["receipt_hash"] = sha256_text(stable_json(item)) - compiled.append(item) - return { - "compiled_equations": compiled, - "counts": summarize(compiled), - } - - -def summarize(compiled: list[dict[str, Any]]) -> dict[str, Any]: - by_shape: dict[str, int] = {} - by_status: dict[str, int] = {} - by_missing_axis: dict[str, int] = {} - for item in compiled: - shape = item["nearest_lawful_shape"]["shape"] - status = item["type_witness"]["status"] - by_shape[shape] = by_shape.get(shape, 0) + 1 - by_status[status] = by_status.get(status, 0) + 1 - for axis in item["type_witness"].get("missing_or_weak_axes", []): - by_missing_axis[axis] = by_missing_axis.get(axis, 0) + 1 - return { - "equation_count": len(compiled), - "by_rrc_shape": dict(sorted(by_shape.items())), - "by_status": dict(sorted(by_status.items())), - "by_missing_axis": dict(sorted(by_missing_axis.items())), - } - - -def build_records(args: argparse.Namespace) -> list[EquationRecord]: - records: list[EquationRecord] = [] - records.extend(read_recent_receipt_equations()) - records.extend(read_math_model_map(args.math_map_limit)) - records.extend(read_extracted_markdown(args.markdown_limit)) - for path in args.jsonl: - records.extend(read_equations_jsonl(Path(path), args.jsonl_limit)) - - seen: set[str] = set() - unique: list[EquationRecord] = [] - for record in records: - key = sha256_text(record.equation + "|" + record.source_path) - if key in seen: - continue - seen.add(key) - unique.append(record) - return unique - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [] - for item in receipt["compiled_equations"]: - rows.append( - { - "prompt": { - "task": "project_equation_via_rrc", - "equation_id": item["equation_record"]["equation_id"], - "equation": item["equation_record"]["equation"], - }, - "completion": { - "rrc_shape": item["nearest_lawful_shape"]["shape"], - "status": item["type_witness"]["status"], - "projection_signature": item["equation_record"]["projection_signature"], - "receipt_hash": item["invariant_receipt"]["receipt_hash"], - }, - } - ) - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True, ensure_ascii=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def write_summary(receipt: dict[str, Any]) -> None: - counts = receipt["counts"] - lines = [ - "# RRC Equation Projection", - "", - "This is a Rainbow Raccoon Compiler projection pass over local equation surfaces.", - "It records nearest lawful shapes, projection axes, and admissibility holds; it is not a proof of the equations.", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Equation count: `{counts['equation_count']}`", - "", - "## Counts By RRC Shape", - "", - "| RRC shape | Count |", - "|---|---:|", - ] - for key, value in counts["by_rrc_shape"].items(): - lines.append(f"| `{key}` | {value} |") - lines.extend(["", "## Missing Axes", "", "| Axis | Count |", "|---|---:|"]) - for key, value in counts["by_missing_axis"].items(): - lines.append(f"| `{key}` | {value} |") - lines.extend(["", "## Sample Projections", "", "| Equation | RRC shape | Status | Top axes |", "|---|---|---|---|"]) - for item in receipt["compiled_equations"][:24]: - record = item["equation_record"] - name = record["name"].replace("|", "/") - top_axes = ", ".join(axis for axis, _value in record["projection_signature"]["top_axes"]) - lines.append( - f"| `{name}` | `{item['nearest_lawful_shape']['shape']}` | " - f"`{item['type_witness']['status']}` | `{top_axes}` |" - ) - lines.extend( - [ - "", - "## Claim Boundary", - "", - receipt["claim_boundary"], - "", - ] - ) - SUMMARY.write_text("\n".join(lines), encoding="utf-8") - - -def write_table(receipt: dict[str, Any]) -> None: - with TABLE.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter( - handle, - fieldnames=[ - "equation_id", - "name", - "rrc_shape", - "status", - "distance", - "top_axes", - "missing_or_weak_axes", - "source_path", - "domain_type", - "bind_class", - "equation", - ], - ) - writer.writeheader() - for item in receipt["compiled_equations"]: - record = item["equation_record"] - writer.writerow( - { - "equation_id": record["equation_id"], - "name": record["name"], - "rrc_shape": item["nearest_lawful_shape"]["shape"], - "status": item["type_witness"]["status"], - "distance": item["nearest_lawful_shape"]["distance"], - "top_axes": ";".join( - axis for axis, _value in record["projection_signature"]["top_axes"] - ), - "missing_or_weak_axes": ";".join( - item["type_witness"].get("missing_or_weak_axes", []) - ), - "source_path": record["source_path"], - "domain_type": record["domain_type"], - "bind_class": record["bind_class"], - "equation": record["equation"], - } - ) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--math-map-limit", type=int, default=120) - parser.add_argument("--markdown-limit", type=int, default=40) - parser.add_argument("--jsonl-limit", type=int, default=80) - parser.add_argument("--sample-limit", type=int) - parser.add_argument( - "--jsonl", - action="append", - default=[str(REPO / "3-Mathematical-Models" / "equations_100" / "equations_database.jsonl")], - ) - return parser.parse_args() - - -def main() -> None: - args = parse_args() - records = build_records(args) - projected = project_records(records, args.sample_limit) - receipt = { - "schema": "rrc_equation_projector_v1", - "runner": rel(Path(__file__).resolve()), - "source_inputs": { - "math_model_map_limit": args.math_map_limit, - "markdown_limit": args.markdown_limit, - "jsonl_limit": args.jsonl_limit, - "sample_limit": args.sample_limit, - "jsonl": [rel(Path(p)) for p in args.jsonl], - }, - "counts": projected["counts"], - "compiled_equations": projected["compiled_equations"], - "claim_boundary": ( - "RRC equation projection is an admissibility and routing pass. Human labels " - "are non-authoritative hints only; CANDIDATE means suitable for next-stage " - "checking, not mathematically proved." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True, ensure_ascii=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - write_summary(receipt) - write_table(receipt) - print( - json.dumps( - { - "receipt": rel(OUT), - "summary": rel(SUMMARY), - "curriculum": rel(CURRICULUM), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "counts": receipt["counts"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/rrc_equation_classifier_curriculum.jsonl b/4-Infrastructure/shim/rrc_equation_classifier_curriculum.jsonl deleted file mode 100644 index 34365482..00000000 --- a/4-Infrastructure/shim/rrc_equation_classifier_curriculum.jsonl +++ /dev/null @@ -1,278 +0,0 @@ -{"completion": {"projection_signature": {"shape_distance": 0.193428, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.479167]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "43b52a1c1a45b0bb8a314112f9a08a739a74e6cc214ed857419b7120b51aab8b", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_threshold_hist = L_threshold_eff * exp(-rho_B * B_overflow)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_adjusted_threshold", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.230737, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "aa8d660376a8bec05109f6b501723d1a0aea00f3fc03ad921de6f55dc46ee25f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "B_overflow = max(0, transfer_bandwidth - assimilation_bandwidth) / assimilation_bandwidth", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_overflow", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.231976, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "cf89adfa810fec99ca5577e262d50e244f4ab7f48872fae28fa80b5e940dd373", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_cog_eff = L_cog_raw * G_over", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:effective_cognitive_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.229561, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "1fcbedc686a13186e3f68c56b3d31d1c2627b8a124864bf1b93dc4328ae71853", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "B_gate_emotional = exp(-gamma_emotional * DeltaE_emotional_hist / kT_emotional_hist)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_gate", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.21865, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.510417]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "89cc9125cbea0b025b0e9a976947808828bc9eb231e58a6d1c93890730a2fbe7", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_emotional = C_emotional,d * emotional_response_d(L_emotional_offload; theta_emotional,d) * lambda_phi^D_f * B_gate_emotional,d", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.193428, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.479167]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "c2a53ae4dbbce75172e9136cd936e70cc417f41a5037519b3ff118273b6ac185", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_emotional_offload = max(0, L_cog_raw - L_threshold_hist) * eta_offload_hist", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_offload", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.230737, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5b2ba33fcc1aa76e85bfd29c319ddabf2c801e9f058b03f34378942b5567f4af", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "DeltaE_emotional_hist = DeltaE_emotional_eff + chi_B * B_overflow", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_barrier", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.230141, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c7d0e83d5b018a6d7839ce3ded33812edaa2ca3129653c86a9fa66ba0a6c9935", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "kT_emotional_hist = kT_emotional_eff / (1 + psi_B * B_overflow)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_temperature", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.231349, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "daa8a81d5086c5cae8628692662d48095e21515f8322afb94c273aaf4a530f02", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "eta_offload_hist = eta_offload_eff * exp(-omega_B * B_overflow)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_offload_efficiency", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.192135, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.5]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "6ce2b0aaaad833eb94257be77b174326bc4698477bd1cc9c084fe570926c4af9", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "G_over(d) = 1 if L_cog_raw <= L_threshold_hist; else exp(-gamma_d * (L_cog_raw - L_threshold_hist) / kT_emotional_hist)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:overflow_gate", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.21865, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.510417]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "da1782af9226ab4082738b42f223f404f14c311cb564b3448afe02d7ada15637", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_cog_raw(d,x) = C_d * response_family_d(x; theta_d) * lambda_phi^D_f * B_gate(d,constraints)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:raw_cognitive_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.200253, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.68], ["decoder_declared", 0.6], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "250f999cbe87c4659bccf6114c04d317bc1aef54cce5e01e8dce7759c5542caa", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_residual_stress = max(0, L_cog_raw - L_threshold_hist) * (1 - eta_offload_hist)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:residual_stress", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.188871, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.67], ["witness_declared", 0.6], ["scale_band_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "d45516d1718ee50f42f1defc9fa3ed6d3b77393177c7f221fdf0975141d2b77a", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_threshold = C_threshold * lambda_phi^D_f * B_gate_threshold", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:threshold", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.238088, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.64], ["decoder_declared", 0.6], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "3ba0d41b6c0662fcd8a6f287ce252ccad9eb958b22bafdd6b846709fecb42006", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_total = L_cog_eff + L_emotional + L_residual_stress", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:total_protective_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.231349, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "72d4dfa89db3993dde245083b408f79191ecf303efc6559b3b8a0b458fe66559", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "DeltaE_emotional_eff = DeltaE_emotional + chi_T * T_trauma", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_barrier", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.230737, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9b2304c7a60c7e618f9fcbdc9638abe74646a79afcad9467deb39745cb7d2bca", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "kT_emotional_eff = kT_emotional / (1 + psi_T * T_trauma)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_temperature", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.231976, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "fad8529c03916050f7125d4151172bdb5838ad60182e6115506d9d4924e33f22", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "eta_offload_eff = eta_offload * exp(-omega_T * T_trauma)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_offload_efficiency", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.194101, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["residual_risk", 0.47]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "e16e1907695a56f1a64582d28f9ad0ff86e73d3b3159d8cb5b9b3e940898167c", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_threshold_eff = L_threshold * exp(-rho_T * T_trauma)", "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_threshold", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.172075, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.635417], ["shape_closure", 0.63], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "0a079caa2700c341641ccb75767d356930ab20a07020e66745d15f5caa9d3ec7", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "{\"heat_loss\":\"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)\",\"magnetic_projection\":\"M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)\",\"overflow_gate\":\"G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)\",\"signal_load\":\"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)\"}", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:core_equations", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.166855, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "bf2cc74fa8db0a74690ce28d1ad84e25f314cdd0d3d49f29e0a0a3bccb552790", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "{\"byte_transition_rate\":\"domain agitation / susceptibility driver\",\"capacity_overflow\":\"hysteresis heat-loss channel\",\"entropy\":\"field demand / information pressure\",\"repeated_4grams\":\"remanence / memory channel\"}", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:field_mapping", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.188002, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "6698bd0ecdcaf4862246f496afbf2914fd543ec5b17d97f00fba71841a3a6f56", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "byte_stream_signal", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:source_domain", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.188002, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "c10229b15141b004f1f15cbc67120c1999eac1a3868820a7444da3fbb04a3b28", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "magnetic_domain_equation", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:target_domain", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.18598, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "7a4fef0634a2dcd38c410c3aeb9d28781d44a2b22a861d9b1a4fb5c831ff2797", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:heat_loss", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.18541, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "20972ac80f05192b62c82c7dcd3c7663161008f133f68eca774e017934e16184", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:magnetic_projection", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.197713, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.520833]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "f71398504405971e2a644a766f77c6e839278710e95eb86ae095ed22ba3853c5", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:overflow_gate", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.191669, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "e072ef2f2258c6b7f405d67624a5d78e414c80fe7c8d52fa0945416cbebd842a", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)", "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:signal_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.172075, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.635417], ["shape_closure", 0.63], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "2e5388c758007af29a75650af6c1340c93048908024510825e73a2b75c48ac7c", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "{\"heat_loss\":\"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)\",\"magnetic_projection\":\"M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)\",\"overflow_gate\":\"G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)\",\"signal_load\":\"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)\"}", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:core_equations", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.166855, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "f27a06efdfc281bcc8009ee26f3263a8bd009a923d034ae67c39d6eab74a19ea", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "{\"byte_transition_rate\":\"domain agitation / susceptibility driver\",\"capacity_overflow\":\"hysteresis heat-loss channel\",\"entropy\":\"field demand / information pressure\",\"repeated_4grams\":\"remanence / memory channel\"}", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:field_mapping", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.188002, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "6d8cd06661445c86ad4a96811b96c48e8f2f5a6d7cbcc2c79656cbea58848cb5", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "byte_stream_signal", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:source_domain", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.188002, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "436071249ed7b31c071abdda07adfef950d250d3fe93779ffcac34d21079e50e", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "magnetic_domain_equation", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:target_domain", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.18598, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "d6f0fa24d906cedd5f63e89a688e4ab18812f89966c93b8e984c0d1e5cd77f56", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:heat_loss", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.18541, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "99538776b207ef6f9653e69e737a14a37723b249fd6c48d5d19cf3f4de794ae3", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:magnetic_projection", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.197713, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.520833]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "6f8260573e7a1503a41d64c38b968e828d4a305685dc68de93dcd06cfab990ee", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:overflow_gate", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.191669, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.63], ["witness_declared", 0.6], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "f8ce5d640dae82b49f738ad8571e9cd01d679bdffc730e693db40cd334d22898", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)", "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:signal_load", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.19942, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.866667], ["decoder_declared", 0.8], ["shape_closure", 0.69]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "237f563bf34c1c240ce80bac75b6cb7f357dd88d34a041d25dd27fa10ab18d8b", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "compressed_total_bytes = payload_bytes + residual_bytes + witness_bytes + decoder_delta_bytes + container_bytes", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:counted_total", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.216219, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.866667], ["shape_closure", 0.64], ["decoder_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "27076e83ceee776f4ef92361c1a0f23ed9f88111b2bef97763cef496ac5a318d", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "Hutter-hard promotion additionally requires the total contest artifact for enwik9 to beat 109685197 bytes under the applicable prize rules", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hard_target_rule", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.122715, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.866667], ["decoder_declared", 0.8], ["witness_declared", 0.8]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "residual_risk", "history_depth"]}, "receipt_hash": "6988b27cafd9f1bfe73cd80deeef4a0545ea63131a979a0e36da858daa46537f", "rrc_shape": "SignalShapedRouteCompiler", "status": "CANDIDATE"}, "prompt": {"equation": "[\"source_corpus_id\",\"source_bytes\",\"candidate_chart\",\"transform_route\",\"payload_bytes\",\"residual_bytes\",\"witness_bytes\",\"decoder_delta_bytes\",\"container_bytes\",\"runtime_budget\",\"compressed_total_bytes\",\"baseline_bytes\",\"hard_target_bytes\",\"ratio_schema\",\"exact_decode_status\",\"source_hash\",\"decoded_hash\",\"promotion_status\",\"failure_code\"]", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hutter_route_metastate", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.210115, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.7], ["shape_closure", 0.64], ["decoder_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e3c0d9a7674a2eb3c0ad61e2fec0235a028c9f29e117f8160ac0d5dfdf728c19", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "LB_route = payload_floor + residual_floor + witness_floor + decoder_delta_floor + container_floor + evaluator_cost_floor", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:lower_bound", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.238638, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7977f691a06522c2eaead521ddd93323bd52f553064c275d3fd7d935edfb12e9", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "proposal_score -> candidate_route_coordinate -> bounded_exact_route_metastate -> promotion_receipt", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:metastate_transfold", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.196828, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.866667], ["decoder_declared", 0.8], ["witness_declared", 0.8]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4e1a4fbbf1d66926915c45352873d6b82711f2fbbcb2b4a124ec9b6b7fea96fc", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "promote iff decoded_hash == source_hash and compressed_total_bytes < incumbent_bytes and ratio_schema is explicit and all witness, residual, decoder, and container bytes are counted", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:promotion_rule", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.218153, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.866667], ["shape_closure", 0.64], ["decoder_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "01c0cafab7df07941ba581b39aad29a5977eeaa1b5936ff92496ce7336fbce83", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "prune iff LB_route >= incumbent_bytes", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:prune_rule", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.224542, "top_axes": [["projection_declared", 1.0], ["compression_pressure", 0.7], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "09061340e13cba27fc198f3124051dbfc1bc77f4db1b3cb2fa2f68e59429013b", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "C = proposal_score(comp, phys, geom, scaling) or phi_HP = field + compression_gain + decoder/resource penalties", "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:source_equation_surface", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.292337, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.677083], ["geometric_mass", 0.628571], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b5dd54d8a7a93d80f4424162f22a5a292f841ede431e357fd11c9fa3a7071f43", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "H0 = 73.3 \u00b1 1.5 km s\u207b\u00b9 Mpc\u207b\u00b9 (68% CL)", "equation_id": "math_model_map:0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.261583, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f22ea40d1d24aa854fd1ad4e69824f33fae732655fbaecdebc8558be9ac377ae", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "S8 = 0.810 \u00b1 0.008", "equation_id": "math_model_map:1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288392, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "99877190d775a522f43a8921579f1d09fc8c251a7086d49e57cb2d0655d126f5", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "\u03c7\u00b2/d.o.f. = 1907.2/1949 (p = 0.78)", "equation_id": "math_model_map:2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283014, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "084cf3dce6695abcc4c060e4808a01ead33b60331c16466f2ecc7db338093bcd", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "h_i^(l+1) = \u03c3(\u03a3_{j\u2208N(i)} \u03b1_ij^(l) W^(l) h_j^(l))", "equation_id": "math_model_map:3", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.282576, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d5c5ac292858c26a8d4a593807b9a49b24cb87c723a94d78bbdf5714d03fd3c0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03b1_ij^(l) = softmax_j(LeakyReLU(a\u2192^(l)^T [W^(l) h_i^(l) || W^(l) h_j^(l)]))", "equation_id": "math_model_map:4", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.261553, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4cf53920d1baa5d4a04e6df4e995829eaea473efa74e111951769435aacfa97f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "Y = b\u00b7X + a (log-log space)", "equation_id": "math_model_map:5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.261553, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "03c8cece41202294120d2af3550b101a3c4f0b45f5fbabdb6f266d4cc590a0dc", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "Y = a\u00b7X\u00b2 + b\u00b7X + c (curvilinear)", "equation_id": "math_model_map:6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.260582, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5211e58c11fb84f01cc711bff222143020773c1a9ec40c4bfa57527fa73edc3d", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "dY/dX = 2aX + b (where Y = aX\u00b2 + bX + c)", "equation_id": "math_model_map:7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b94975bd733812768dba22636ffd1f6baa6572755563f6c5586d243b369df328", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Y = X\u00b7W + b", "equation_id": "math_model_map:8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7e7dd64946cdaeabf3665c74397a3e911e6c35e009a12b542aa4049ef428e788", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "x(t) = s(t) + f(t) + \u03b5", "equation_id": "math_model_map:9", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.290839, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["proof_readiness", 0.55]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f6f54011e675a07100ecd9c86f2e86f60f47646db7f805f2738e08e3ba0167d5", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "x(t) = s(t) = s(t-p) where p \u2264 n", "equation_id": "math_model_map:10", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "392355b64fcdd4953b863493fa3086699b05fc745ad8a8e2fe57dc8e9894853d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "x(t) = a\u00b7x(t-p) + c", "equation_id": "math_model_map:11", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.263119, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4cccdcda91f3fd5723cf0738d0420844d57a6113027614bc99be7ab4ae94260c", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "CO2 + 2H+ + 2e- \u2192 CO + H2O", "equation_id": "math_model_map:12", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.262582, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b58639565a62c37d230bc39c584f080d111c9ffa2b35879f70d32f864e049b84", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "CO2 + 2H+ + 2e- \u2192 HCOOH", "equation_id": "math_model_map:13", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.259671, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "54060da6460e9c91e21cd4c10cd7e2a34b0f015121a3b7df9308e18b0a73219d", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "CO2 + 6H+ + 6e- \u2192 CH3OH + H2O", "equation_id": "math_model_map:14", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.259671, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9ae1ddc2e7d41b4b35a55b0542aff0fb83daf7997ca8d237a13cb980acd2f59e", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "CO2 + 8H+ + 8e- \u2192 CH4 + 2H2O", "equation_id": "math_model_map:15", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257213, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2797beef7197238b693b8dde2541130474bb1748996d9eace0187f959ba4278d", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "\u1e8d_i + \u03b3\u1e8b_i + \u03c9_i\u00b2x_i + \u03a3_j \u03ba_ij(x_i - x_j) = F(t)", "equation_id": "math_model_map:16", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.234956, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5f924c91b3363fff13da9732e0400782ad2d37fc2b814443e66eda3f576b5979", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "L_I(x) = -\u03a3_{b=0}^{255} p(b|x) log\u2082 p(b|x)", "equation_id": "math_model_map:1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.245952, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9bb545800ccd301cde1ff89524cd4d66f73b4c7ee6a47d5ed1d985a1378fac51", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "C_ratio = U_size / C_size = 1.48\u00d7 (achieved)", "equation_id": "math_model_map:2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.264694, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ad5a4d770dc5f53c303cd6e3d4b0e285c97f5bac1e94d158faa51f7d505009ed", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "JTAG\u2192SUBLEQ\u2192GCL\u2192LUT\u2192APU", "equation_id": "math_model_map:3", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.235728, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.666667], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8f8dd668bff366688aa51a00df1f53eb4c4a4df519ce3af5f84e024fd36ceca2", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "Shader(x,y,t,\u03b8)\u2192palette + GCL\u2192LUT + SUBLEQ\u2192compute", "equation_id": "math_model_map:4", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.264694, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "581595a75831b8c00c6416f602ef5e8eb75545d0efcaa2b75747d46a274bbb04", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "Cartridge_SUBLEQ\u2192GCL\u2192Shader\u2192Audio\u2192ControllerPort\u2192NES", "equation_id": "math_model_map:5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.235036, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.666667], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "198973f802a9e64480ab5e5f6106b83a579711b6ee4e680068687c69df7ea3fe", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "1-Wire_UART\u2192GCL_Admission\u2192Entropy\u2192Metaprobe\u2192Triumvirate\u2192NES", "equation_id": "math_model_map:6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.278424, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.635417], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7676292cf605b03ceaed9f4e001195ac2dddf69edc2a58ef2b07f2c51a11f814", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Value\u2194AudioSignal(f,A,D) + APU_Operations\u2192Result", "equation_id": "math_model_map:7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.24112, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "1fc2347983bcaf377ab31e5374f6c1489c5a8692bb01d8ba963e23dc33f0f82c", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "All_Systems\u2192Single_Metaprobe\u2192Unified_Audit", "equation_id": "math_model_map:8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.225432, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.697917], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "a2cf12ae63bceb64bf9e6d989b865ffe782d905bd146261488fed3670fc89877", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "All_Math\u2192Single_Substrate\u2192Unified_Computation", "equation_id": "math_model_map:9", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2c9734d1fe05811fde10aa631e88d49bb86229d204fdaaa953ab89c06fa48923", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Voltage\u2192Value + Operations\u2192Result", "equation_id": "math_model_map:10", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.276228, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "85c61e745138b0011db4090f065026ed834d9cc7acb00169a1faed61144a3eb7", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "DSP_Math\u2192Palette_Parameters\u2192Video_Palette", "equation_id": "math_model_map:11", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.267969, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "a213a0ff638a8b763b4a1809cc021d416f34930106c2bab1e938074e6c53c777", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "4\u00d7_Vertical\u2192256\u00d7960", "equation_id": "math_model_map:12", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.231194, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.677083], ["shape_closure", 0.63], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "1bf08c71fe722ae6aa14658e27154241ad113313e7b15828c9570fdecf72b04b", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "Microgrid\u2192640\u00d7480+Differential_Updates\u2192Effective_640\u00d7480", "equation_id": "math_model_map:13", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.234956, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "57f9053ee54d45a2b47d894c2cff76e84df1eb11dc4d837eb8b3d4715803cfb9", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "L_E(x) = BPB(x,w_prior) - BPB*(x) = (1/n)\u03a3_i log\u2082(P_w*(x_i)/P_wprior(x_i))", "equation_id": "math_model_map:2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.252403, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.645833], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8def57ee740216a50491df69c7e10d33d9a104b45d52935e33e1c3276acb8669", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_G(x,t) = \u03a3_{s=1}^{S} \u03b3^s \u00b7 \u0394L_E(x_s,t+1) \u2248 \u03c4\u00b7L_E\u00b7log(S+1)/log(S_max+1)", "equation_id": "math_model_map:3", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.252088, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.65625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ffdd3aa09c5b80200a73a5d04acb1b69c92bdb3d4c1af625d301a93af7b9e274", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_R(x) = \u03a3_j c_j\u00b71[f_j computed] + \u03a3_{l=1}^{D(x)} log\u2082|M_l|", "equation_id": "math_model_map:4", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.228316, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.645833], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e2ae4a1258232a14520cc166ec7691933d14f704de733a36eb0a2eab0fc63fc9", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_M(x) = log\u2082|E| + \u03b1\u00b71[hit] + \u03b2 + \u03bb\u00b7|E|/|E_max|", "equation_id": "math_model_map:5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.228402, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4f48e4c66bbeee942e7ea30dadae951982b3cbecc3b2e48bede46499ed02940c", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "L_total = \u03bbI\u00b7l\u0302I + \u03bbE\u00b7l\u0302E - \u03bbG\u00b7l\u0302G + \u03bbR\u00b7l\u0302R + \u03bbM\u00b7l\u0302M (\u03a3\u03bb=1, \u03bbG\u2264\u03bbE)", "equation_id": "math_model_map:6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.234942, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "88e70a0abf571b5d15f03af38be28c7a992d331641f6fdd55f562a70b7e3a0cc", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "\u03b7(x) = l\u0302I(x) / (l\u0302I + l\u0302E + l\u0302R + l\u0302M + \u03b5)", "equation_id": "math_model_map:7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.234942, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2b46c6b169bdeee7d17343bddb709202fff812ec08acaa8290002ebaab01932a", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "L_\u03c1(x) = L_total(x) \u00b7 (1 + \u03c1(x)/\u03c1_max)", "equation_id": "math_model_map:8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.235052, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d4c0fdce69cffaefa65ae03ec6f9dd78bfe1748cb35ec9976dbefc26c94c0488", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "L(x|B) = L_I(x) + L_E(x|B) + L_R^B(x)", "equation_id": "math_model_map:9", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.234956, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c605ee2b15d142f61c8d4c1fcd2a269e08c7d038dd9c99e46d290cdc3f3c7c86", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "P_w(x_i|x_{ 1.0", "equation_id": "math_model_map:15", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288723, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "91e04dc8d354b05ffd7879f7ee00210d52a10ef2a516cd7a11ebc05ec4e7328e", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "GWL Rotation", "equation_id": "math_model_map:16", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.290188, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["semantic_entropy", 0.625], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "218df5724324d7c981f1ac73e6efca089fc375ebd3af528e1c93a6a4d9e22455", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "g = cos(\u0394\u03b8\u00b72\u03c0/16) \u00b7 cos(\u0394\u03c6\u00b7\u03c0/8) \u00b7 (1 - 2|\u03c7_i - \u03c7_j|)", "equation_id": "math_model_map:17", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.29059, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.635417], ["geometric_mass", 0.628571], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b0896fdaa2ee456094662534b6ccd2ec7f2c263e502c6514bcaf7777e9ee9f76", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "h = exp(-|\u0394p|\u00b2/(2\u03c3\u00b2)) \u00b7 1_{|\u0394p|> \u03a6_metric(i,j)}", "equation_id": "math_model_map:35", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288076, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4e61c9a2c9b8bd7e4fb3b0ddbb9fa9a45a2c6cbd4258fd6fd82d73764aed0bdf", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "w_ij = w_p \u00b7 w_\u03c0 \u00b7 w_\u03c4 \u00b7 w_\u03c7 \u00b7 w_topo \u00b7 w_\u03c3", "equation_id": "math_model_map:36", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288076, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "0e63345785cc5b350073c20c3de1ad34aba6a04370663fc7f53b55f1ba4a2851", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "Hol(\u03b3_loop) = \u222e_\u03b3 T(p) dp", "equation_id": "math_model_map:37", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.271543, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "727ba4b3e84c4930356c0e1a614f17b80034ab4c2be482d1b3b2fdcd68aff469", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "d_N = path_length(\u03b3_ij) + curvature_penalty(\u03ba) + torsion_cost(T); d_T = d_E + \u03bb_N\u00b7d_N", "equation_id": "math_model_map:38", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2a7222bd59712ee0f722f03b0143fd4a170fc2e81c707758c53245419d11fe3b", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "TrixalAxes = (thermal, work, irreversibility), each \u2208 [0,1]; |axes| = \u221a(th\u00b2 + w\u00b2 + ir\u00b2)", "equation_id": "math_model_map:39", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.259671, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "45129f9e90477503d027d2659026de7f7a5681e1e8178b31e211c96a9b48b996", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "H = -\u03a3_b p(b) log\u2082 p(b), where p(b)=count(b)/len", "equation_id": "math_model_map:40", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.261553, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ea3f21e1ef2f182f47786125755205ea8de29d8070f16fbb9b1604b4ceb53d5f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "K_est = (8 - H) / 8", "equation_id": "math_model_map:41", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.260119, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5bec7a5e5d820f0e54d8ae7ba02d8388ae681c580b1fe4163f5afc85113a112f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "S_thermo = H + K_est \u00b7 0.1", "equation_id": "math_model_map:42", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.260119, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "432f7355fd132f9288d7eb4e9337093501490c2aefdadc9442ffa073b6912d44", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "dS/dt = (S_current - S_previous) / \u0394t", "equation_id": "math_model_map:43", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.236532, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "bec567961880a727fb6d03fb3b52e8df36fb55646be0c3c6647da65c5e9134be", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "MI = H_initial - H_current", "equation_id": "math_model_map:44", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6e7801ddba1d8d92997ac2b7377bee105e69fe4111f9485efa4f0c2a5215df7a", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "\u03b7_Carnot = 1 - T_cold / T_hot", "equation_id": "math_model_map:45", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.255474, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "28df300ae3ad5ce3925a6a8a1d59bad778e0c74306e8f97c23ef1f9c12d2e1eb", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "W_actual = Q_absorbed \u00b7 \u03b7_Carnot \u00b7 0.7", "equation_id": "math_model_map:46", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.26106, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7c59304f0cff3bd05c20e5b0747abd2894f22ad31dbac474803f4dafbdcb9d8e", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "score = (entropy_production + path_asymmetry + time_reversal_violation) / 3", "equation_id": "math_model_map:47", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "56b4a990a2c7474d0fe4198a3816d8501152ae72b6d5fc9890203495df9dd09f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "L_thermo = \u03a3_i distance_i \u00b7 (1 + irreversibility_i)", "equation_id": "math_model_map:48", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.261553, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c0af85a404ea45e0ab11f3f9782b0db583a4581d817843b998b3e89f6d7ec438", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "depth = entropy_production \u00b7 ln(time_steps)", "equation_id": "math_model_map:49", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.202394, "top_axes": [["projection_declared", 1.0], ["witness_declared", 1.0], ["shape_closure", 0.69], ["proof_readiness", 0.625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "05fbcf42b4df550ceee510193a22b1c2c1ccaa4551edf8476235c7e773215f65", "rrc_shape": "LogogramProjection", "status": "HOLD"}, "prompt": {"equation": "SHA256(axes || traj_hash || hardware_entropy || timing_jitter || process_nonce)", "equation_id": "math_model_map:50", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.251579, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "a7b52e3e1608e84dfb6c20ad0fa81ecac03a00306722584bceda62da5b70bf8c", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "AF = exp(E_a / (k_B \u00b7 T))", "equation_id": "math_model_map:51", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.250302, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9f1afd222bc1f68670d99706068af9cbf8b32018a75236c471f321583905e3c7", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "EM_risk = J^n \u00b7 exp(E_a / (k_B \u00b7 T)) / 10^{12}", "equation_id": "math_model_map:52", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.221733, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.645833], ["shape_closure", 0.63], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "de41e42243fdadd337f9e3a10330fb9010182b37ce7c017e2a0b01ca4411fbf4", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "CM_damage = (\u0394T / \u0394T_threshold)^m \u00b7 10^{-8}, m\u22481.9", "equation_id": "math_model_map:53", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.23706, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.677083], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "15946d72d4d558558213e831ebda5f2ca0dfd3bcaaca0a16add8c116de4eb2a0", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "W_erasure \u2265 k_B \u00b7 T \u00b7 ln(2) \u2248 2.87e-21 J/bit at 300K", "equation_id": "math_model_map:54", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257657, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9d69f02d9764ec505309c4f73f30051f802ab8bf432df3cfda193662de1cf474", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "dS/dt = power_dissipation / (k_B \u00b7 T \u00b7 ln 2) [bits/s]", "equation_id": "math_model_map:55", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.250417, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9ae0bc24c4daa97ff27c39fc03de49c4472fd217f4f8e33a896200e008e8dd03", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "|G| = (D1/120 + D2/5000 + D3/1000 + D4/100 + D5/5000) / 5", "equation_id": "math_model_map:56", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.24942, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.65625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "36e79c004207e531f7eb9bb410b9dc4982ffb4ffe9c3301227104103b925d9be", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "BFR = \u03b5_SEU \u00b7 2^{(T-25)/10} \u00b7 (1 + V_jitter/1000) \u00b7 3600 [flips/hr]", "equation_id": "math_model_map:57", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "cc81575b138cab1ea57e7093cc87794a0f8cf62876c0f70392b2d1b519dd9d29", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "stress(t) = stress_0 \u00b7 e^{-t/300} + intensity \u00b7 (1 - e^{-t/300})", "equation_id": "math_model_map:58", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25078, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f470d8e0a80c20278b7e242888ef102e4a008c70872576eea8fde3ee8418ec46", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "RUL = MTBF / (AF \u00b7 (1 + fatigue\u00b70.01 + thermal_fatigue\u00b70.1))", "equation_id": "math_model_map:59", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8ced5ab6b94608ae88796d8d0351fc1a2b8b127827d19c53f550e61118a8596a", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "surprise = -ln(margin), margin = 1 - stress_magnitude; regret = max(0, stress - 0.5)", "equation_id": "math_model_map:60", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.259238, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e9898a1b1af963ad6f7caf939205784bd3b194eee9aa70be49a23298149f6718", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "int_bits \u2264 fp_mantissa_bits + 1 (signed) or \u2264 fp_mantissa_bits + 1 (unsigned)", "equation_id": "math_model_map:61", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.26106, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b082a3a7016c65ac9a926a98a88e5daa347f5997087e7636622bcedef95ad837", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "can_safely_narrow(src_bits, signed, dst_mantissa) \u2192 bool", "equation_id": "math_model_map:62", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.258417, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "fea905d509cc04455812371f0ca80ad4c7122af712d0ed1a1ccbc5e3b5e13221", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "fdiv.d=33, fdiv.s=19 \u2192 penalty=0.737; fadd.d=4, fadd.s=4 \u2192 penalty=0.0", "equation_id": "math_model_map:63", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257657, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "085aa292b55c72104bc5896e24b44ca91fa0cc30da2d37a18a3e5ae6d78c3646", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "E = hc/\u03bb = 1.2398 / \u03bb_\u03bcm [eV]", "equation_id": "math_model_map:64", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.260582, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "387689ea0a285f3d53c75ebe71738cd69d2964396cf545ee8e8a877b15469813", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "\u0394E = E_upper - E_lower; E_upper=hc/\u03bb_min, E_lower=hc/\u03bb_max", "equation_id": "math_model_map:65", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.249496, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8abbe3d6aac87d027c12a0dcbcbd32f37d3758f6ba066377d9b455ec94d882a1", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "G = photons_per_e\u207b \u00d7 n_wells; photons_per_e\u207b = \u230aE_electron / \u0394E\u230b", "equation_id": "math_model_map:66", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257657, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "76b5c2ad9039716800df62b0e47bf74bc67cee8b21ecd9ee8ee38d5e2784d4fc", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "\u03bb(T) = \u03bb\u2080 + \u03b1 \u00b7 (T - T\u2080); \u03b1=5e-6 /K (GaAs/AlGaAs)", "equation_id": "math_model_map:67", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257657, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "32016255a9143f6bcdf2c82592208a4fe5c168cf1835f4866456b45ef0685f4f", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "\u03b7 = (0.5 + window_bonus) \u00b7 spacing_eff \u00b7 (1 - stress_penalty); clamped to [0,1]", "equation_id": "math_model_map:68", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.258417, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "a2a9eb76072fcec04119b2a7932f0f745b03efef35863aefed18e81c52cab5cd", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "Windows: (3,5), (8,12), (16,20) \u03bcm; transmission=1.0 inside, exp(-dist\u00b70.5) outside", "equation_id": "math_model_map:69", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.257299, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "286c033a790b4f1f94b40ee8e16d36dbaa492f99aca2c2465a9819527099700b", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "\u03bd = 10\u2074/\u03bb [cm\u207b\u00b9]; DFB: \u00b17.5 cm\u207b\u00b9, EC: \u00b1200 cm\u207b\u00b9; \u03bb_min=10\u2074/\u03bd_max, \u03bb_max=10\u2074/\u03bd_min", "equation_id": "math_model_map:70", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.222839, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["compression_pressure", 0.533333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4c88a751d5ffea677b72f7228d7250fc705783f346a987cd8b4db760b040b191", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "MI(x) = baseline_bpb(x) - actual_bpb(x)", "equation_id": "math_model_map:71", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.282379, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7b0b137dd804b4e6c11a846eefbeb715aab91f9e42b775afcb0a075d3b58dbc5", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "MI_pred = \u03a3_i (w_i \u00b7 MI_i \u00b7 S_i) / \u03a3_i (w_i \u00b7 S_i); w_i = 1/(d_i + \u03b5)", "equation_id": "math_model_map:72", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288392, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2357eaebdd3fd8cb9079886cf1327def9664f0efabfc2b5613514156be9cf7dd", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "surprise = log(1 + |MI_actual - MI_predicted|)", "equation_id": "math_model_map:73", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.245865, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "699153c1c31bc79342a9bc0ac1cb47758e9011c9db2cbf9f116517f437e34708", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "\u03c1(x) = MI(x) / (cost(x) + \u03b5)", "equation_id": "math_model_map:74", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.281874, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.65625], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6bf5176d2cc3c31cb132c13188cf3adc0b3ce653be560a230448c62f743ffc81", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "d(z\u2081,z\u2082) = \u221a\u03a3_i w_i \u00b7 ((z\u2081_i - z\u2082_i) / s_i)\u00b2", "equation_id": "math_model_map:75", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.280052, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "583b13f2e4992fb2fbaacee7b0016a8257d771cbfd7a36432ee0d64520735916", "rrc_shape": "CadForceProbeReceipt", "status": "HOLD"}, "prompt": {"equation": "\u03a3 F_in = \u03a3 F_out at every node", "equation_id": "math_model_map:76", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.294433, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b1fd0f221b264e7e1730772ac71b661be7fa55385e6b051518d19e74b8e633a8", "rrc_shape": "CadForceProbeReceipt", "status": "HOLD"}, "prompt": {"equation": "\u03c3 = C : \u03b5; \u03b5 = \u00bd(\u2207u + \u2207u^T)", "equation_id": "math_model_map:77", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.2697, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.771429], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c12fda3eaf6f12c00e39e2b211cf32a75bdf148a0f8abc164388f3217685ea9d", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "V(G) = \u2227_{v\u2208V} (\u03a3F_in = \u03a3F_out)", "equation_id": "math_model_map:78", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288723, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5937379a24d00e4ff9984061a66536cb320035aa643f32be350b511545999192", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "cos = x \u00b7 x_ref / (\u2016x\u2016 \u00b7 \u2016x_ref\u2016)", "equation_id": "math_model_map:79", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288392, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2279300b812c7ddddafa063bbb82c459482d5984477f9526b7fbf81f81a24953", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "alignment = \u2207g_i \u00b7 \u2207g_j / (\u2016\u2207g_i\u2016 \u00b7 \u2016\u2207g_j\u2016)", "equation_id": "math_model_map:80", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288076, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f319772c36046078415dd5430a65eb5c2beedabeaa9edb59f6e108e5d9d4493d", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "phase += \u03a3 y \u00b7 dx", "equation_id": "math_model_map:81", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.290188, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["semantic_entropy", 0.625], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "fb789ee71553ed410ba9705005bd3574b002cc9eeb106fdd0a9c5c2b783506c8", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "g_tt = M\u00b2, g_tp = 0, g_pp = (N\u00b7cos(\u03b8))\u00b2; a=C_eq/(2\u03c0), c=C_mer/(2\u03c0), f=(a-c)/a, e\u00b2=2f-f\u00b2, N=a/\u221a(1-e\u00b2sin\u00b2\u03b8), M=a(1-e\u00b2)/(1-e\u00b2sin\u00b2\u03b8)^(3/2)", "equation_id": "math_model_map:82", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287773, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "50bffeea9ce27d6b59131ae118ffc312de14583708d1f760a1ad8a5c550fd0d7", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "ds\u00b2 = g_tt\u00b7d\u03b8\u00b2 + 2\u00b7g_tp\u00b7d\u03b8\u00b7d\u03c6 + g_pp\u00b7d\u03c6\u00b2", "equation_id": "math_model_map:83", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.289427, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["semantic_entropy", 0.604167], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e4b7c532a3e75046db87636459bfa9f761b5ccef72f21d37de67663fcf063abe", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "Gamma^k_ij = \u00bd\u00b7g^kl\u00b7(\u2202_i g_jl + \u2202_j g_il - \u2202_l g_ij)", "equation_id": "math_model_map:84", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.290188, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["semantic_entropy", 0.625], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "26899341e8d34fa22f740443bf87c050787e485ca6a78daf9b8f56392f01ecf8", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "a^\u03b8 = -(Gamma^\u03b8_tt\u00b7v_\u03b8\u00b2 + 2\u00b7Gamma^\u03b8_tp\u00b7v_\u03b8\u00b7v_\u03c6 + Gamma^\u03b8_pp\u00b7v_\u03c6\u00b2); v' = v + a\u00b7dt; x' = x + v'\u00b7dt", "equation_id": "math_model_map:85", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.290188, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["semantic_entropy", 0.625], ["witness_declared", 0.6]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e2cc8c9bb833872a3f6f95cda2cd554a0a36d793dd1ee130dd191d26ac5753f9", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "u = 2\u00b7tan(\u03b8/2)\u00b7cos(\u03c6), v = 2\u00b7tan(\u03b8/2)\u00b7sin(\u03c6); \u03b8 = 2\u00b7atan(\u221a(u\u00b2+v\u00b2)/2), \u03c6 = atan2(v,u); select when |cos(\u03b8)| < 0.01", "equation_id": "math_model_map:86", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288392, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6bb555a669bad82ae5d1c668929aa67bb518e14d63246b72e8740ceaa4d922ef", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "D+D\u2192D, L+L\u2192L, D+L\u2192W(COLLAPSE\u2192W); chiralityToTernary: D\u2192Active, L\u2192Active, W\u2192Latent", "equation_id": "math_model_map:87", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.21559, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.697917], ["shape_closure", 0.63], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength"]}, "receipt_hash": "2e8552e99f652db8237beea4bde835bbefacb9363a83dd0f41ba4ee0ff69579b", "rrc_shape": "CognitiveLoadField", "status": "CANDIDATE"}, "prompt": {"equation": "\u03c6 = (\u03c4 mod T)/T, T=942/1000; ternary: \u03c6<1/3\u2192Q, \u03c6<2/3\u2192A, else L; \u03c6' = (\u03c6+1/4) mod 1 if surprise>threshold", "equation_id": "math_model_map:88", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288076, "top_axes": [["projection_declared", 1.0], ["geometric_mass", 0.628571], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6a10a0d8a553ec61b5a89184e27bf2ea584b32a362750f016b528e7f1be5852a", "rrc_shape": "ProjectableGeometryTopology", "status": "HOLD"}, "prompt": {"equation": "Same acceleration as M85 + Verlet velocity update + chart transition check", "equation_id": "math_model_map:89", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.273853, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8d1568106a8f7520ae1863fab42ed3885f3974bd97e38a46eaea1b86eac25dfc", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "risk(s) = (1 + \u03b3\u00b7(1-cos(\u03b8)))/d\u00b2 + \u03b7\u00b7h; \u03b3=3, \u03b7=0.8", "equation_id": "math_model_map:90", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.25882, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d7bb15aec563dc01d7ba7b0fc653af3e8dd71e301d46df4f9f3ab09b863e8836", "rrc_shape": "CognitiveLoadField", "status": "HOLD"}, "prompt": {"equation": "h' = \u03b1\u00b7h + \u03b2\u00b7a; \u03b1=0.95, \u03b2=0.2", "equation_id": "math_model_map:91", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9434289633435ec35d6da5c7614184653246d2dc20201d1cb7d75c72c81056bd", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "a_\u03bc = (|g| - 2) / 2 = 0.001165920705(114)", "equation_id": "extracted_md:0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "820adc2fb7a8153855c493adda824f2b534b37e8111cf904caf4555e998efb9b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03c3 = 0.127 ppm (0.000000114)", "equation_id": "extracted_md:1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.289065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6046a6f88be65f3b5a69c362584ff039c771fede60385e995bf9bba2175ceeb0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "a_\u03bc^theory = a_\u03bc^experiment within 0.5\u03c3", "equation_id": "extracted_md:2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286858, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c821d0a37f32d268d8ca7ed07a9872b49314d32cce25306e0413e9a54b52abdf", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "g_\u03bc = -2.00233184122(82)", "equation_id": "extracted_md:3", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286858, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "3e1ff3fb15a91dd72996d429dc66b1efbfe98efc6148b62fc9226142f6d1b250", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03bc = g \u00b7 (e\u210f / 2m) \u00b7 S", "equation_id": "extracted_md:4", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f13e19b6762f0fdfbdde55aae9e40cdbb44703e5a87a0819c1a49f5c4fe46829", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "(D_t^\u03b1 + (-\u2207\u00b2)^\u03b2) \u03a8 = \u03bb |\u03a8|^\u03b3 \u03a8", "equation_id": "extracted_md:5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "723166936077544dbde68e7c90889c3dc65c555cbc73f3209bf01422b55bf9e8", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "D^\u03b1 f(x) = F^{-1}[ |k|^\u03b1 \u00b7 F[f](k) ]", "equation_id": "extracted_md:6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287271, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "bf2d4dcdfe099f5a1221eee16669cf33b88301d264844fdb831a95d36ed97e0e", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03a8_observed(x, t) = \u222b K_\u03b8(x - x') \u03a8_unified(x', t) dx'", "equation_id": "extracted_md:7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "bf83dba12a3149cb7b373cc51f4f8e01c407ecf95044acc166aed78e81eccf5a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "w(\u03b1, \u03b8) = sin(\u03b8)^\u03b1 \u00b7 cos(\u03b8)^{1-\u03b1}", "equation_id": "extracted_md:8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "87b8d3be5b2863c19d09790a4d25362ce47da8507864a7c50bb778f20e497c5d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "g_n(\u03b8_obs) = g_0 \u00b7 sin(\u03b8_obs)^{1/n} \u00b7 cos(\u03b8_obs)^{1 - 1/n}", "equation_id": "extracted_md:9", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b7c558563b14a58620e82e1066031fd992796f6fcd1ca1916a5d83ee6822d1cd", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Q_n = (1/2\u03c0) \u222e_C \u2207_n \u03c6 \u00b7 dn = m/n for m \u2208 \u2124", "equation_id": "extracted_md:10", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "fb04a05581f4d0110aec8264a3f76aa693194b995055b6ff5d0f055e24e4767b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "ds\u00b2 = -d\u03b8\u00b2/\u03c9(\u03b8)\u00b2 + a(\u03b8)\u00b2 [dr\u00b2/(1-kr\u00b2) + r\u00b2 d\u03a9\u00b2] + \u2113_P\u00b2 d\u03b8\u00b2 \u0393(\u03b8)", "equation_id": "extracted_md:11", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d0692883ba5beb46828be902f6de8909fe7d50e7be58053564c9f7e7678e1dd6", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "H_eff = \u03c9(\u03b8)", "equation_id": "extracted_md:12", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b49ad77d116fe1e4d31ba52bcc595bf8975a41c0028265361ce6e5dcfc55ce3a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03c1_DE(\u03b8) = \u03c1_foam \u00b7 (1 - \u03b8/\u03b8_max)\u00b2", "equation_id": "extracted_md:13", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "02077d7cbc6dba209f888ae509ae0ae834865c5a1ec65e7c624b85a0ad02bc72", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "S(R) \u2264 C' \u00b7 R^{D_H} \u00b7 T^{(D_H - 1)}", "equation_id": "extracted_md:14", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "977920ad1364a6ac997511acf26594c4501a362c3f1700654506fcba4dd185a1", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "S \u2264 C' \u00b7 R^{1.44} \u00b7 T^{0.44}", "equation_id": "extracted_md:15", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b894b0b740339fe04f5dfb040b46653e8dcbfa1a607b97fde11115b929c8daff", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "A(r) = 2\u03c0 (cosh(r) - 1) \u2248 \u03c0 \u00b7 exp(r) for r >> 1", "equation_id": "extracted_md:16", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f8b98d829c552f87d22f3a05646984a4e9f03a780dee7c5285e99d14c114a48f", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "L_{n+1} / L_n \u2248 exp(d_inj) \u2248 \u03a6\u00b2 \u2248 2.618", "equation_id": "extracted_md:17", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.28814, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "3d4a0a3264301b89b2b5d1fdc18de6151dab6b8cb19bc4069f9a096da0afaa0c", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "D_s = 2 D_H / (1 + D_H)", "equation_id": "extracted_md:18", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "270b734fc2aba4abb480cce5a841acccf50134f89d3e5c6764cced8a887c8ccd", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "D_s = 4/3 \u2248 1.333", "equation_id": "extracted_md:19", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ccee8a0dfc45270e15b2a346e817d61f890020693d3864544b8e06781480c1ad", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03c1(E) ~ E^{D_s/2 - 1} = E^{-1/3}", "equation_id": "extracted_md:20", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.289549, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8de47c80aca3e577a006919723d7ae34de6487f0b18a00b87cf4cb7c465dd32a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "E_n ~ n\u00b3", "equation_id": "extracted_md:21", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "620384c50857a63e7c4a1c8ae3321dfd74989f48aec7903de34d2889ea472362", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "n_s = 1 - 2/(1 + \u03b8_max/\u03b8_recombination) \u2248 0.965", "equation_id": "extracted_md:22", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287271, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b58bc6bf8ef3dc86a387cd355643c4e91089d051b23ebd63e8e8e388bbd20102", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "E_dissipated \u2265 k_B T \u00b7 ln(2) per bit erased", "equation_id": "extracted_md:23", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287271, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "509e4220562f4989a3d584ced23444b5f851af71ff25ad0eaee5dc6c9e108877", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "H(X) = -\u03a3 p(x) log p(x)", "equation_id": "extracted_md:24", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d832958d6158607b2804c391c283519d309cd368a4d1b7bab791911d8c39cfe8", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "S \u2264 2\u03c0 R E / (\u210f c ln 2) = A / (4 G \u210f)", "equation_id": "extracted_md:25", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "31e1c43d5181798de3a068ada6b13f9a4d63206652b59b7dff673a1e8304fad0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "C_V = (12\u03c0\u2074/5) N k_B (T/\u0398_D)\u00b3 \u221d T\u00b3", "equation_id": "extracted_md:26", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288596, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b2828c6913e2eb54e36f296dab61dfc8279ec0c0988a7142deddab8c953e3eb6", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "C_V \u221d T^{D_s}", "equation_id": "extracted_md:27", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288596, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f91d4d36242769d52d4bdb7a5ab1f055c9d097ce7c8df24c67c099b8df9ae3fe", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "C_V \u221d T^{1.18}", "equation_id": "extracted_md:28", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8927580859f5227425295b590601b2d7d1e344e36bda46cb2e74da96c98d90b4", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u27e8exp(-\u03b2 W)\u27e9 = exp(-\u03b2 \u0394F)", "equation_id": "extracted_md:29", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ee58ce129e44dfa94d0adc6465bbcac64dc919e4e632197b4848ad8c429d8ac1", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "(\u0394J)\u00b2 / \u27e8J\u27e9\u00b2 \u00b7 \u03c3 \u2265 2 k_B", "equation_id": "extracted_md:30", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9fdbb18506fb7adcd3050fe8923e8d5d7a43b1409795dd16eee21c9aa31b3553", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u0394x \u00b7 \u0394p \u2265 \u210f/2", "equation_id": "extracted_md:31", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "0fa5a1523cc0b05a786c865918bb3371f9b0950cdd3cb7373f8e26efa4eec702", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03a8_total = \u03a8_A + \u03a8_B = A \u00b7 exp(i \u03c9_\u03a8 \u03b8) \u00b7 [exp(i k_\u03a8 x_A) + exp(i k_\u03a8 x_B)]", "equation_id": "extracted_md:32", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "733730562f92d3d87ee4fdfb29e9df74f2b0a0adc0a9197a4d5d69831e640536", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "|\u03a8_total|\u00b2 = 2|A|\u00b2 \u00b7 [1 + cos(k_\u03a8 (x_A - x_B))]", "equation_id": "extracted_md:33", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287271, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ed7d300c022f6fbb6190516bed43784c69b01781f47a4d927966d2b3deb410c1", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "E = \u210f \u03c9 = \u03c9_\u03a8 (in natural units \u210f = 1)", "equation_id": "extracted_md:34", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288596, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e9627d15e5b48fd71b0f2afcd0cade14b268ba1450e9361d17e67e53369d74c9", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "p = \u210f k = d\u03b8_0/dx = k_\u03a8", "equation_id": "extracted_md:35", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.289065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8a9b46a64121eec6763b28790838109c860f5467eecfa807ba95219bdbf64958", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "\u03bb = 2\u03c0 / k_\u03a8 = 2\u03c0 / p", "equation_id": "extracted_md:36", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "186711871c51207179bc7dc122a8a4ce71d52731bf983d3d6a3cb537ef8e989a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Phenotype(x, t) = \u03a8_E [ Genotype(x) \u00d7 Regulatory_State(t) ]", "equation_id": "extracted_md:37", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.232964, "top_axes": [["projection_declared", 1.0], ["shape_closure", 0.64], ["decoder_declared", 0.6], ["witness_declared", 0.6]], "weak_axes": ["geometric_mass", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "cf30a90a927873ec56e26f5dc3d62bf33c8e9c7d6930d205a70a710c49652852", "rrc_shape": "SignalShapedRouteCompiler", "status": "HOLD"}, "prompt": {"equation": "Residual(n) = \u03a8_decode [ Basis, Context(n) ] XOR Byte(n)", "equation_id": "extracted_md:38", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7b8c3a96925b3cdc9fe6a3349a77e60bc6ee166199bfb6292c3b2a2eb8a4b5f0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "H_\u03a8(data) = -\u03a3_n p(n) log_2 p_\u03a8(n) \u2264 H_uniform(data) = 8 bits/byte", "equation_id": "extracted_md:39", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ac5d92474fa932d45e99aaf09161cdaa41a9028ac6c6e7b3b4206bdc61030308", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "G = (V, E) represents a quantum state |\u03c8\u27e9 through a\ncollection of local tensors {Tv }v\u2208V , one for each vertex", "equation_id": "eq_21f33f4110064dbd", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ea800e23de7ceddb38f1622e12f8417ea01cf44b28b43ec0b34b2f863fe343ed", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "injec= CD and Hv \u223c\ntive if this map has trivial kernel", "equation_id": "eq_14d74623112c018f", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.265908, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ff0d46411960c99d581c302547de341e46ea6d963bbbbc522b2582b7df3552be", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "d \u2265 D|N (v)| , which generically holds after coarse-graining\nwhen D = O(1)", "equation_id": "eq_fcd40dc2de7c7324", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "31da9991424da41b45b73a144b363be19c56a6053ef9e7ecaff91fceb27928b0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "min \u2265 \u03b4", "equation_id": "eq_416bbe45bfd12c68", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6e94370b8c903d8d952a4cec40757a5f24e4a7538c29e7b72b30f6c999017420", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9", "equation_id": "eq_290815ff34ff74a2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "eb82ce56c1700190e6c9e6a55ca6d2267671ce2f7932ee484a0c2d8a26a943b5", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, n) of\nthe graph, the tensor Tv \u22c6 T\u0304v of the norm network defines\na superoperator on the virtual space from any set of legs\nto its complement Eq", "equation_id": "eq_5ea587928ed366f5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "89343fde1c5afae10e79b25cde1c21d595882c272b073f0be07d24d7299b6ce4", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = ZBP \uf8ed1 +\nZ\u2113 \uf8f8", "equation_id": "eq_e1646b712978905e", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "85b74ce61f591a2d0ec08e41b69b35c7142fca7ac2a5b9819d59544484c37037", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = log ZBP +\n\u03d5(W)ZW ,\n(7)\nconnected W\n\nwhere a cluster is collection of loops with multiplicities,\nW = {(\u21131 , \u03b11 ), (\u21132 , \u03b12 ),", "equation_id": "eq_ed91aac060fde4cf", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.280384, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ffb50cb92661f1282f30cdd89752fbf609429f9141ee12206aa547465f9e5c91", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "i \u2265 1 is its multiplicity in the cluster", "equation_id": "eq_9b66cadab83a4e1e", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2efdb15aff690ae9174b012709da7bf039fcb510a7cbe0eca14d823cba932272", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "ZW = i Z\u2113\u03b1ii and the\nP\nweight of a cluster is |W| := i \u03b1i |\u2113i | where |\u2113| for a loop\n\u2113 \u2208 L denotes the number of edges in \u2113", "equation_id": "eq_6fd0d43c1e1577a1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "7152eaff8affcc4281af20854b12d2f57c0a8207f5141f81bbda9d69bcc4fa4a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "c > c0 =\nO(log \u2206)", "equation_id": "eq_8605b7c5ccdbcf5f", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283511, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8afa195d251266167aae5d398e00c6a8bb5371680e9870c8c93363ec8b8ee8ce", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9 to 1/ poly(N ) multiplicative\nerror\n(ii) local observables \u27e8OA \u27e9 = \u27e8\u03c8|OA |\u03c8\u27e9/\u27e8\u03c8|\u03c8\u27e9 to\n1/ poly(N ) multiplicative error, given \u27e8OA \u27e9 \u0338= 0\n(iii) correlation functions \u27e8OA OB \u27e9 \u2212 \u27e8OA \u27e9 \u27e8OB \u27e9 to\nO\u0303(1/ poly(N )) additive error\nwhere A, B \u2282 V are disjoint local regions", "equation_id": "eq_dbd7ee74be207cc7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "44effbc7d62a598c897faedd9f562e9c42f3e99767721fed573d3e7751eb069b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, n) with d(v, A) =\nr, we have\n\u0010\n\u0011\n\u2225\u00b5\u2032\u22c6,\u20d7e \u2212 \u00b5\u22c6,\u20d7e \u22251 = O e\u2212r/\u03be\u2217\n(10)\n\n\f5\nWhere 1/\u03be\u2217 = O(log \u03b5\u2217 /\u03b5)", "equation_id": "eq_9138cc21d6e4da27", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "75015934af99cbbd7c7bda525905053c38aeef136fdf483ab1baee199a28de4f", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "W = Wnear \u222a Wfar , where clusters in\nWnear are distance at most a cutoff Rth away from B", "equation_id": "eq_5be87eaf6cffbaae", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "329c6502c0823c5cf5db572511d4d6891240841c846f830a88e1b1bc46b5d0c4", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "t < r and\nstarts increasing thereafter, owing to the strict lightcone\nin the message-passing dynamics [see SM for a proof]", "equation_id": "eq_927277d74e6531cc", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "fcdf797b99db402de51a0195d73595963defba4c5c6b3a614d8b9608d6c89ba1", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "t = r\u22121, leading\nto\n\u0010\n\u0011\n\u2225\u00b5\u22c6,\u20d7e \u2212 \u00b5\u2032\u22c6,e \u22251 = O e\u2212r/\u03be\u2217\n(13)\nestablishing locality of the BP fixed-points", "equation_id": "eq_353043888999f4ac", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "29b3d3cd8f35c5908084976e831d9267669e7a777a0802c1663afb7b8f2ce584", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "d = c \u2212 c0 = O(1)", "equation_id": "eq_532f389442891ed2", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b61b83549338279d4c4adb4c5a93626ae38a827b4b701628a60cb0c2fd2d1968", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "R = \u0398(1)", "equation_id": "eq_fdfae5689dd4a9a5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2badceabd488a2e2090127bf493dedbd573fa69f92534e2ccaf04a6d3026a860", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "X = 1}", "equation_id": "eq_9138aeb942929155", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283255, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "758e17af3e072042f4572c1ce8eee674da8c6892cde49f1199166b26d85d691d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "i=1\n\nFor p = \u221e we have,\n\u2225x\u2225\u221e := max |xi |\n1\u2264i\u2264m\n\n(S3)\n\nFor an operator A \u2208 B(H), we define the operator Schatten norm in terms of its singular values \u03c3(A) as,\n\u2225A\u2225p := \u2225\u03c3(A)\u2225p\n\n(S4)\n\nfor any p \u2208 [1, \u221e]", "equation_id": "eq_0cd087779e7532a9", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f32748cb72d0238ecbae7d2c3da9c54c169451c3b170d2fbbf174c4b73570574", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "p \u2264 q \u2264 \u221e and any operator A, we have,\n\u2225A\u2225p \u2265 \u2225A\u2225q\n\n(S5)\n\n\u2225A\u22251 \u2265 \u2225A\u22252 \u2265 \u2225A\u2225\u221e\n\n(S6)\n\nIn particular,\n\n\f10\n2", "equation_id": "eq_5593e036fe6f7dd7", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283781, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5f172d76bcc85f6f2c8797d6e018b6b797ad57b704828cda6bdf6b4317af3079", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "p \u2264 q \u2264 \u221e and any non-zero operator A,\n1\n\n1\n\n\u2225A\u2225p \u2264 rank(A) p \u2212 q \u2225A\u2225q\n\n(S7)\n\np\nrank(A)\u2225A\u2225\u221e\n\n(S8)\n\n\u2225Ax\u22252\n= \u03c3max (A)\nx\u0338=0 \u2225x\u22252\n\n(S9)\n\nIn particular,\n\u2225A\u22252 \u2264\nWe also note that, the \u221e\u2212norm obeys,\n\u2225A\u2225\u221e = sup\n\nwe will denote \u2225 \u00b7 \u2225\u221e by just \u2225 \u00b7 \u2225 for convenience", "equation_id": "eq_ea9b6d38a8a0c1b0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "9b1519dfe4e81be5b4e8eb97a7e1360d257a8a42225817492b5e679a046914ef", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "B = Ac , reshape the tensor into a linear map\nT : HA \u2192 HB\nby grouping the indices in A and B into multi-indices a = (ia1 ,", "equation_id": "eq_e0537400fa963370", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287699, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5e2aa9f91b47f5f3a7204c65b75cb2367c1a3b871e3d207c0fc7ad885de3f099", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "b = (ib1 ,", "equation_id": "eq_23fc8ebaf9c8ae4a", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283511, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "55f40198b0ef268596ca51e481023b890b488c996a15558791e19b85fefae8f2", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "q = 1 we have,\n\u0001\n| tr A\u2020 B | \u2264 \u2225A\u2225p \u2225B\u2225q\n\n(S12)\n\nGraphs For a graph G = (V, E), we define some useful notation", "equation_id": "eq_8b4895ac1c7ebca0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "09f8d81bc79c3c37219afdef58392fdb33628e7307096d30bb863b06e49d5695", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = {v, w} \u2208 E, we will refer to it\u2019s directed versions \u20d7e = (v, w) and \u2190\ne = (w, v)", "equation_id": "eq_25d49db810143f41", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c988688aa9ea9d3dbc6d281e7849c0f15eee8659f2b151fd980983d30ff7330c", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = {v, w}, we define, for any u \u2208 V ,\nd(e, u) := min{d(v, u), d(w, u)}\nand similarly, for any A \u2282 V ,\nd(e, A) := min d(e, u)", "equation_id": "eq_5034975bc936419d", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "27ba4b17fe70105fa967b149f865c5dd831b68e2d301ed58a5fea3db8f31dda1", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "li=1 be finite-dimensional Hilbert spaces (the l virtual legs), and\nlet Hphys be the physical Hilbert space", "equation_id": "eq_ab6c35762bc86081", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5389fded63bd6e67c838e74d08fef56b7784b209bbfc79a383f08ea38c6d0e9e", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "i=1\n\nDefinition S1", "equation_id": "eq_60b71803ce04c1ba", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283511, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "0d092cba889d317b62c4bc308b8269804f4a750d3e2a4a24988136a6bc86f116", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "i=1 Hi \u2212\u2192 Hphys from\n\nWe perform a singular value decomposition\nT = V \u03a3U \u2020 \u2208 Cnp \u00d7nv ,\n\n(S14)\n\nwhere np \u2265 nv by injectivity, where U \u2208 Cnv \u00d7nv and V :\u2208 Cnp \u00d7nv satisfy U \u2020 U = 1nv (unitarity) and V \u2020 V = 1nv\n(isometry), and \u03a3 = diag(\u03bbe ) \u2208 Cnv \u00d7nv collects the singular values", "equation_id": "eq_7d5beabf91af593e", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "08e3486cfb6bc395126c774bce551d2c0ef4f6bb9b6492d02c6e64d60c20e097", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = \u03b4 \u2208 (0, 1]", "equation_id": "eq_8daf48607920ba7b", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "3626d12afaa3089f4036363e0919b91c7d33e9a28b424907f47c7f082792559d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = 1,\ne\n\ne\n\n(S15)\n\nWe call \u03b4 the injectivity parameter", "equation_id": "eq_3b521e5a6156cbc1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "1471d21dd79182101f7fc66a6243a12a8bbe19889caf8896986e3f7a1477dd54", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "T = V U \u2020 itself is an\nisometry", "equation_id": "eq_7cf50458351623ec", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.288596, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b0a6a12afdbbc708227f19d1a6ba7d4f0bf33a2f7b29141c4c507138d8fa630b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "a = 1", "equation_id": "eq_b5bc1ffd90912fb1", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "79f63c48ff596104c605b52c5a2519023f3adfe50fa26585e66dc58c01d2a044", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Lc = dim(HLc )1L ,\nKa,L\u2192Lc Ka,L\u2192L\n(S18)\nc = dim(HL )1Lc ,\na\n\na\n\nwhich follow from the fact that U is a unitary", "equation_id": "eq_51e4f2d85951d30b", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "26c408a3c2d3dc114a311051f6ce4fd9a4947510c1a2c3fdfa221a45b2eb34e0", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Lc =\n[U\u0304 a ](i\u2032L ),(iLc ) [U a ](iL ),(iLc ) =\n\u03b4iL ,i\u2032L = dim(HLc )1L\na\n\na,iLc\n\niLc\n\nMoreover, when T is \u03b4\u2212injective with \u03b4 = 1 (i", "equation_id": "eq_51dad4a7954a91e6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "3cc3f66aa1da3314dac2f6a29d5dd3c69c7a83f029c7e8253f29be10a678484a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "G = (V, E) with vertex set V and edge set E", "equation_id": "eq_10109834ac3ac602", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283255, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6957a59f11a406de94859ae1d671eea0c89a9a773b5aea81ad4630db529effe9", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "G = (V, E) with uniform virtual\ndimension D and physical dimension dp is specified for each v \u2208 V by a tensor\nO\nTv :\nH(n,v) \u2192 Hphys \u223c\n= Cdp ,\nn\u2208N (v)\n\nwhere N (v) denotes the set of neighbors of v, and each virtual space H(n,v) \u223c\n= CD", "equation_id": "eq_6c7adf4f4e980afe", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "32d69a72eb5d1e87c16fa666cc787ab0438eda6d8fbf3eb3de238b667e28626c", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, w), we define a bond-space H\u20d7e \u223c\n= CD", "equation_id": "eq_dde05d0258ec36a5", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "69aadec231bdc41b6ba25290e3d26ecbef94bf3354644fdab10c718e40233fee", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, w) is a positive\noperator \u00b5\u20d7e \u2208 Pos(H\u20d7e ) representing the message from v to w", "equation_id": "eq_36e95d88ecbda2b0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8cf99e737ab8c4ec3e4af775d465c88944a5a940bf94fbae365aeac02b218a62", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, n) \u2208 \u20d7\nE,\n\uf8f6\n\uf8eb\nO\n\u00b5(m,v) \uf8f8", "equation_id": "eq_25b43848714b6ceb", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5d5fbdcf840b0cef5c46dc49fd7b8bb6b8a04b8d69e96b76009e49928f44ae92", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e > 0 such that\nPEPS if for each directed edge \u20d7e \u2208 E,\nf\u20d7e (\u00b5\u22c6 ) = \u03bb\u20d7e \u00b5\u20d7e", "equation_id": "eq_139255306999d8d8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c6450f574e62718b7cc23e8829ff21b38d745251ffacef3837d52fdffaab2071", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9\ncan be formally described as a Taylor series in terms of \u2018loops\u2019 on the network as described below", "equation_id": "eq_2a06ffe17253ce3c", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8524fbfbe618913c034e0e1a5a7cb1c60c0f9fa3c19a0c9d9090ca53b7492d8a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = ZBP \uf8ed1 +\nZl \uf8f8\n\n(S21)\n\n\u0393\u2282L\nl\u2208\u0393\n\u0393 finite, compatible\n\nwhere the sum runs over all finite sets \u0393 of mutually compatible loops", "equation_id": "eq_e598d78992b57382", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6acddfb16f6030a79e9205eac08f341597c743f06196925624df985b4d653b2d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "W = {(\u21131 , \u03b11 ), (\u21132 , \u03b12 ),", "equation_id": "eq_14f71a50573b7fd0", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.287271, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6816c632531998f46388d39b007b8bcb04031b2cbe2c5c49ecf35a1f3b6a7f22", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "nW = i=1 \u03b1i", "equation_id": "eq_44bf0ed184c8c0f8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "b59f99533ebb319b687a09773d2383cdd40e53a22fdc1569649f92bb5fac2d4f", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "W = {(\u21131 , \u03b11 ),", "equation_id": "eq_4c772de7743acc11", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5a034e3e1b9b047142db36b9c662af8e3af3132a3d72b6c7602829e6374def04", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "ZW =\n\nk\nY\n\nZ\u2113\u03b1ii", "equation_id": "eq_3fe8c2f5d04f57ff", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "78217ae50ced51b7b636d56bb3f9179a169faac49a93d3edbf5836d1d2786fc9", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "i=1\n\nWe call a cluster W connected if the interaction graph GW is connected, meaning there is a path between any two\nvertices in the interaction graph", "equation_id": "eq_1abc29f7b62428f6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283781, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "70cfd4deeaadfab6cc643ad42e3c0a90ecdc0741f16568687be3b92d0d22563b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "GW =\nPk\n(VW , EW ) has |VW | = i=1 \u03b1i vertices, with loop \u2113i corresponding to \u03b1i vertices", "equation_id": "eq_f4bc63354f272a96", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8be5322a1d59e4e2951fc63ff442814748c67512a8e58ace4f4323d28843797b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "W = {(l1 , \u03b11 ), (l2 , \u03b12 ),", "equation_id": "eq_2a47fee92e986d5e", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286858, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4c62ae5ea9980f4f0d983cfe107fd62bc4dceb0fe9011751e102b82e7910b591", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "ZW =\nZl\u03b1i i", "equation_id": "eq_4a18868e412a28db", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e03f389ce08e8879221255cddcee474f99e8f0219c9248138b1772000323cbfb", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = log ZBP +\n\u03d5(W)ZW ,\n\n(S25)\n\nconnected W\n\nwhere the sum runs over all connected clusters W", "equation_id": "eq_d14e04ca5ba01499", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "5698e30987c13f13c4a90915a8e0b1a8f0614f90d5cbcfb9af1932382727bff2", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "c > c0 := log(2e\u2206) + 12 such that\n|Zl | \u2264 e\u2212c|l|\n\n(S27)\n\nthen, the series for log Z converges absolutely", "equation_id": "eq_405936ddfb9bc98c", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283014, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c42c5119cddb3a98270f1976ec0a8128d79a8b7185380b24446545fddf686901", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Fm = log ZBP +\n\nX\n\n\u03d5(W)ZW ,\n\n(S28)\n\nconnected W\n|W|\u2264m\n\nis bounded by\n|log Z \u2212 Fm | \u2264 N e\u2212d(m+1)\nwhere d = c \u2212 c0 , \u2206 is the degree of the graph, and N is the number of vertices", "equation_id": "eq_6a72df8385b3a8fb", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285005, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "6bebf737792b75732cfafd0ce02baa397bf425c8f0c42a36be9d5fecd2117a0a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "c > c0 = log(2e\u2206) + 1/2", "equation_id": "eq_52e8897befaf699d", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "1f11f224ef810f5a7e76e5c4f77ffeee12c7e85063867f1f46b6e466ca813b1c", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "G = (V, E)", "equation_id": "eq_f05cdb2173e0a64b", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284364, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.541667]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "bba3f804941b888be94b46f857b2829fd68b6f74773ba47afdebc806e1338e5d", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "T = \u27e8\u03c8|\u03c8\u27e9 and\nA\nT = \u27e8\u03c8|OA |\u03c8\u27e9 respectively after suitable BP normalization", "equation_id": "eq_f93b08fedbc0a5af", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "37c41cdc8f918cedbd280a36c207a693eaca39146965718514d9158381e1797f", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9 and Z A = \u27e8\u03c8|OA |\u03c8\u27e9\nfor a local observable with \u27e8OA \u27e9 \u0338= 0", "equation_id": "eq_d0869069ce0fee51", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283014, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["semantic_entropy", 0.59375], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "0a7fc9355ad818198f9dd4583422a30f98cd43caf587a589a5d0f1003ff18387", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "m = \u27e8OA \u27e9BP \u00b7 exp\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\nconn W\u2190LA\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8fe\n\uf8f3supp(W)\u2229A\u0338=\u2205\n|W|\u2264m\n\nleads to a relative error \u03b4m = | \u27e8OA \u27e9 \u2212 \u27e8OA \u27e9m |/| \u27e8OA \u27e9 | bounded by\n\u0010\n\u0011\n\u03b4m \u2264 O |A|e\u2212(c\u2212c0 )(m+1)\n\n(S33)\n\nwhere d = c \u2212 c0 = O(1)", "equation_id": "eq_e2dad9249b00119f", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283781, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.5625]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "4d86b95fef402e299320e82e95ad79c8215f8505d2dbd64440e9734290556128", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "A = \u2202\u03bb log Z\u03bbA |\u03bb=0 = \u27e8OA \u27e9BP +\n\u03d5W Z W\n\u03b1l\n\u2212 \u27e8OA \u27e9BP", "equation_id": "eq_2b301ef1b6ca651f", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "aa9e138e99cba44fa622f53b37d0ffda3b931b2dcf658989f993151b14a5eee4", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9 and Z A = \u27e8\u03c8|OA |\u03c8\u27e9", "equation_id": "eq_f4aab7b26b55c832", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.28814, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "24ca0a37b5ce4963619f2b06989eaf6467670a26b0ea5d098074c81b8913e841", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "d = c \u2212 c0", "equation_id": "eq_1a6dee6dca2f084b", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286074, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "c35422d2bc3f1322f55a2413cb0b178de920d80af0d018416791cfcdaf49ec91", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "c =\n\nX\n\nA\n\u03d5W \u2202\u03bb ZW,\u03bb\n\nconn", "equation_id": "eq_0698ebc502ab24cc", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286858, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "563d963a08c32cf299cf5bc32ed42c97188735b2b17d7c4b69e2225d98387117", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "A = (A1 ,", "equation_id": "eq_2edfb68a37ab3d1f", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286858, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "d541c7b2ef543d84e2612a7ee172a13a46029a3b93297163d8553db103cd5fb4", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "degree \u2265 2 everywhere except in all regions Ai", "equation_id": "eq_7957e0f2cb86a72c", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284678, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "bd49b7c2a246bab22c25f599a8b22bf7a000276abc5f654796f7fced93990a60", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "c \u2264 O e\u2212d(A,B)/\u03be\nfor a finite correlation length \u03be \u2264 O((1/(c \u2212 c0 ))) and d(A, B) being the graph distance", "equation_id": "eq_10a508b9d741a6f6", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283511, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.572917]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "2f920bb852c44abd591b98d69e1f6c81bcc164922537aa8146ab9c66816a4a5b", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Z = \u27e8\u03c8|\u03c8\u27e9 can be computed to 1/ poly(N ) multiplicative error in poly(N ) time\n(ii) local observables \u27e8OA \u27e9 = \u27e8\u03c8|OA |\u03c8\u27e9/\u27e8\u03c8|\u03c8\u27e9 with \u27e8OA \u27e9 \u0338= 0 can be computed to multiplicative error \u03f5 in poly(1/\u03f5)\ntime\n(iii) 2-point correlation functions can be computed to additive error O\u0303(1/ poly(N )) in poly(N ) time\n\n\f18\nProof", "equation_id": "eq_fb10b15989eae47c", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.283255, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.583333]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "f9e4fbaf07b96a5b5adcb92f57027d2b5909be6e3b415d6da6f7f8aa94c56e6a", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "m \u2264 N e\u2212d(m+1)\n\n(S37)\n\nHence, to ensure log Z \u2212 F\u0303m < O(1/ poly(N )) we require m = \u2126(log N )", "equation_id": "eq_984644a6b0dbab3e", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285347, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "266d34bc34255770efc7792968a9c6897bdd640da81b731e0ae290b711f51768", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "m = \u2126(log N )", "equation_id": "eq_837e24a6c715af63", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "e9d2b1e05e24a44253a6bba021402450f29434878ff7b8556a8290ab86e49590", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "m\n\u2264 O e\u2212d(m+1) \u2264 \u03f5\n\u27e8OB \u27e9\n\n(S38)\n\nwhich can be ensured given m = O(log 1/\u03f5), again computable in poly 1/\u03f5 time", "equation_id": "eq_e54ad45a68cb6a93", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.282576, "top_axes": [["projection_declared", 1.0], ["semantic_entropy", 0.614583], ["witness_declared", 0.6], ["shape_closure", 0.59]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "1e2244bc8cabfa83a106ad2198da92d674fa455de0e5bb353bf91de3ccba2749", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "m = d(A, B) + O(log 1/\u03f5), leading to an error O m2 (|A| + |B|)e\u2212dm , which is\n(note that (|A| + |B|) = O(1),\n!\n\u0012\n\u00132\n\u0012\n\u0013\n1\n1\n2\n\u2212d[d(A,B)+log 1/\u03f5]\nO d(A, B) log\n\u00b7e\n= O log N \u00b7\n= O\u0303(1/ poly(N ))\n(S39)\n\u03f5\npoly(N )\nsince we already have d(A, B) = O(log N ) and we choose \u03f5 = 1/ poly(N )", "equation_id": "eq_21f71c6c28c71003", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "8e1f505732912d568416a7536b6d3f698c2d2ae578cb362336435df436577b1f", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Ka = D1L\u0338=i", "equation_id": "eq_717ace3c9565c03b", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.284065, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["semantic_entropy", 0.552083]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "35d1fb2df523e9386cd67c3e00a7bb4e704ea771eaed61953e2b85205b9eda73", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "a \u2265 \u03b4 2 > 0 and\n\n\u2020\na Ka Ka = D\n\nP\n\n(S43)\n\na\n\n1,\nX\n\n\u03bb2a Ka\u2020 Ka \u2ab0 \u03b4 2\n\nX\n\na\n\nKa\u2020 Ka = \u03b4 2 D 1,\n\na\n\nhence\nTr f (X) \u2265 \u03b4 2 D Tr(X) = \u03b4 2 D", "equation_id": "eq_ccca83f09f701b34", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.286459, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "38aeca630c95999201a73c7063db3bd1845139908a41915bbea262433649b3e5", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "e = (v, n) \u2208 E", "equation_id": "eq_bc5bda52965ac0f8", "task": "project_equation_via_rrc"}} -{"completion": {"projection_signature": {"shape_distance": 0.285704, "top_axes": [["projection_declared", 1.0], ["witness_declared", 0.6], ["shape_closure", 0.59], ["residual_risk", 0.54]], "weak_axes": ["geometric_mass", "compression_pressure", "topology_torsion", "receipt_density", "field_energy", "hardware_affinity", "history_depth", "negative_control_strength", "scale_band_declared"]}, "receipt_hash": "ee83fc87cfebe1d5bbe8ef177b7b502acc40ac5f8c4530287e6f67becdf90123", "rrc_shape": "HoldForUnlawfulOrUnderspecifiedShape", "status": "HOLD"}, "prompt": {"equation": "Ka = dim(HL ) L", "equation_id": "eq_d4a2926485bb8572", "task": "project_equation_via_rrc"}} diff --git a/4-Infrastructure/shim/rrc_equation_classifier_receipt.json b/4-Infrastructure/shim/rrc_equation_classifier_receipt.json deleted file mode 100644 index c55f08c3..00000000 --- a/4-Infrastructure/shim/rrc_equation_classifier_receipt.json +++ /dev/null @@ -1,41708 +0,0 @@ -{ - "claim_boundary": "RRC equation projection is an admissibility and routing pass. Human labels are non-authoritative hints only; CANDIDATE means suitable for next-stage checking, not mathematically proved.", - "compiled_equations": [ - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_threshold_hist = L_threshold_eff * exp(-rho_B * B_overflow)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_adjusted_threshold", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "bandwidth_adjusted_threshold", - "projection_signature": { - "shape_distance": 0.193428, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.479167 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_86ccde7bfd669b77", - "receipt_hash": "43b52a1c1a45b0bb8a314112f9a08a739a74e6cc214ed857419b7120b51aab8b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.479167, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3435886450192644, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3435886450192644, - "shape": "LogogramProjection" - }, - { - "distance": 0.38797613124359276, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38797613124359276, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4024832174383267, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024832174383267, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.193428, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "bandwidth_adjusted_threshold", - "object_id": "rrc_eq_86ccde7bfd669b77", - "payload_bytes_sampled": 1036, - "payload_sha256": "14b9833bb599a6eb99507468a261f00a42c31e0cac0c546c1cf952f22c1b8cc6", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_86ccde7bfd669b77", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "87339874ca3ed8ca5b78bac61a3958c1f34c124282b77d5d94d961e85b4ee7f3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "B_overflow = max(0, transfer_bandwidth - assimilation_bandwidth) / assimilation_bandwidth", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_overflow", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "bandwidth_overflow", - "projection_signature": { - "shape_distance": 0.230737, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_cabf5eab335f2d23", - "receipt_hash": "aa8d660376a8bec05109f6b501723d1a0aea00f3fc03ad921de6f55dc46ee25f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38483360948099904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38483360948099904, - "shape": "LogogramProjection" - }, - { - "distance": 0.4309660409066421, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4309660409066421, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4326192401941621, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4326192401941621, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.230737, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "bandwidth_overflow", - "object_id": "rrc_eq_cabf5eab335f2d23", - "payload_bytes_sampled": 1044, - "payload_sha256": "6928e67a7a18cace177697c79a63ff6dc56f08a3bd65165042b5ddbd34090a20", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_cabf5eab335f2d23", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "3ed2820155ab3af88426b707a89a1f818141b03ed4c1277611c81a9d65724ac0" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_cog_eff = L_cog_raw * G_over", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:effective_cognitive_load", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "effective_cognitive_load", - "projection_signature": { - "shape_distance": 0.231976, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_5d39c74f1cbc3aab", - "receipt_hash": "cf89adfa810fec99ca5577e262d50e244f4ab7f48872fae28fa80b5e940dd373", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38534502357563594, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38534502357563594, - "shape": "LogogramProjection" - }, - { - "distance": 0.43130203101354875, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43130203101354875, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43349494867626864, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43349494867626864, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.231976, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "effective_cognitive_load", - "object_id": "rrc_eq_5d39c74f1cbc3aab", - "payload_bytes_sampled": 997, - "payload_sha256": "e416280867cfe2e6507678f52b543d1887473bf6b7a1ef1be5c7d56a0a992252", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_5d39c74f1cbc3aab", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "fec3e1ddee139fc891dce80ff1fd4e8bb8ca0430f84841ce06c7a954d171183f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "B_gate_emotional = exp(-gamma_emotional * DeltaE_emotional_hist / kT_emotional_hist)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_gate", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "emotional_gate", - "projection_signature": { - "shape_distance": 0.229561, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_aaddd26cf129e0ff", - "receipt_hash": "1fcbedc686a13186e3f68c56b3d31d1c2627b8a124864bf1b93dc4328ae71853", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3843920918151972, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3843920918151972, - "shape": "LogogramProjection" - }, - { - "distance": 0.43069277721515264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43069277721515264, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43180458186166765, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43180458186166765, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.229561, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "emotional_gate", - "object_id": "rrc_eq_aaddd26cf129e0ff", - "payload_bytes_sampled": 1031, - "payload_sha256": "d56c4fbdc39cd95c4855f741cc0c15705c7af7a0a73f175332c8db32362cdf25", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_aaddd26cf129e0ff", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b86b9e7125abc476213e1295977afc90fb5c53823f830bd72d656aaad27fa51c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_emotional = C_emotional,d * emotional_response_d(L_emotional_offload; theta_emotional,d) * lambda_phi^D_f * B_gate_emotional,d", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_load", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "emotional_load", - "projection_signature": { - "shape_distance": 0.21865, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.510417 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_931cb5c74aaade03", - "receipt_hash": "89cc9125cbea0b025b0e9a976947808828bc9eb231e58a6d1c93890730a2fbe7", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.51, - "scale_band_declared": 0.4, - "semantic_entropy": 0.510417, - "shape_closure": 0.63, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37291294149451387, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37291294149451387, - "shape": "LogogramProjection" - }, - { - "distance": 0.4187393382077473, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4187393382077473, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43754702063598155, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43754702063598155, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.21865, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "emotional_load", - "object_id": "rrc_eq_931cb5c74aaade03", - "payload_bytes_sampled": 1075, - "payload_sha256": "e7a98b04ba47552e6d049c33fed04a6eb0279591cf0b7fb5dc05643ffbed62e9", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_931cb5c74aaade03", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "82331f50419b26e64602163aeff0dd388e2ec97485579f385d144abab8df4135" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_emotional_offload = max(0, L_cog_raw - L_threshold_hist) * eta_offload_hist", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_offload", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "emotional_offload", - "projection_signature": { - "shape_distance": 0.193428, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.479167 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_bf9f2062b96a9d25", - "receipt_hash": "c2a53ae4dbbce75172e9136cd936e70cc417f41a5037519b3ff118273b6ac185", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.479167, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3435886450192644, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3435886450192644, - "shape": "LogogramProjection" - }, - { - "distance": 0.38797613124359276, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38797613124359276, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4024832174383267, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024832174383267, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.193428, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "emotional_offload", - "object_id": "rrc_eq_bf9f2062b96a9d25", - "payload_bytes_sampled": 1030, - "payload_sha256": "33065c73adb1abef05a1e3b256d12851d985eff6d76c7bc8c50c1aacf03bcfc7", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_bf9f2062b96a9d25", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "0a453bdd93d4b35f0816489af0b1cb5ad21dc5c9ef2be5b76e205e50268c7f1c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "DeltaE_emotional_hist = DeltaE_emotional_eff + chi_B * B_overflow", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_barrier", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "historical_emotional_barrier", - "projection_signature": { - "shape_distance": 0.230737, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9817626505a4fedf", - "receipt_hash": "5b2ba33fcc1aa76e85bfd29c319ddabf2c801e9f058b03f34378942b5567f4af", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38483360948099904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38483360948099904, - "shape": "LogogramProjection" - }, - { - "distance": 0.4309660409066421, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4309660409066421, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4326192401941621, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4326192401941621, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.230737, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "historical_emotional_barrier", - "object_id": "rrc_eq_9817626505a4fedf", - "payload_bytes_sampled": 1040, - "payload_sha256": "4b4a9e75a52cb9699b577ee683012ff57d19b021058d6a5d1e977efc8b1d75c9", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9817626505a4fedf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "9d850f9caf59fb26e3f30918bd24d70055daf0ff879fafe641b6bc9529abc6ad" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "kT_emotional_hist = kT_emotional_eff / (1 + psi_B * B_overflow)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_temperature", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "historical_emotional_temperature", - "projection_signature": { - "shape_distance": 0.230141, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_01ab6e9c32652d06", - "receipt_hash": "c7d0e83d5b018a6d7839ce3ded33812edaa2ca3129653c86a9fa66ba0a6c9935", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3846040976563962, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3846040976563962, - "shape": "LogogramProjection" - }, - { - "distance": 0.4308215601568653, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4308215601568653, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4322042575766973, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4322042575766973, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.230141, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "historical_emotional_temperature", - "object_id": "rrc_eq_01ab6e9c32652d06", - "payload_bytes_sampled": 1046, - "payload_sha256": "633e6226b552809d560e42f6b29bf0e0954b7bf1491e732bfab15cf96263d507", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_01ab6e9c32652d06", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "2bdc1b5be37cf639a2d993def060ea30677296968bf59d7748cc060625f96b99" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "eta_offload_hist = eta_offload_eff * exp(-omega_B * B_overflow)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_offload_efficiency", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "historical_offload_efficiency", - "projection_signature": { - "shape_distance": 0.231349, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_01f85e831660c26e", - "receipt_hash": "daa8a81d5086c5cae8628692662d48095e21515f8322afb94c273aaf4a530f02", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3850805959877919, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3850805959877919, - "shape": "LogogramProjection" - }, - { - "distance": 0.4311262036823453, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4311262036823453, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4330494857091735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4330494857091735, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.231349, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "historical_offload_efficiency", - "object_id": "rrc_eq_01f85e831660c26e", - "payload_bytes_sampled": 1040, - "payload_sha256": "8026a1ad67ceb954139d013e27e9451ad3c124ab8c40537635c258b40c05cd16", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_01f85e831660c26e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "04ac9ae98adec9e406f841f777ce2306b4a58a63cb5d7c0cd58d0cbc8eb49ab7" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G_over(d) = 1 if L_cog_raw <= L_threshold_hist; else exp(-gamma_d * (L_cog_raw - L_threshold_hist) / kT_emotional_hist)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:overflow_gate", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "overflow_gate", - "projection_signature": { - "shape_distance": 0.192135, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.5 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_90bbd1bf7d23655e", - "receipt_hash": "6ce2b0aaaad833eb94257be77b174326bc4698477bd1cc9c084fe570926c4af9", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.5, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3430940545245035, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3430940545245035, - "shape": "LogogramProjection" - }, - { - "distance": 0.387672565892018, - "kind_prior_bonus": 0.0, - "raw_distance": 0.387672565892018, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4029668475818612, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4029668475818612, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.192135, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "overflow_gate", - "object_id": "rrc_eq_90bbd1bf7d23655e", - "payload_bytes_sampled": 1064, - "payload_sha256": "c4d5ac5e7b099b2eeafe7963b13d40dd9a7c06d54582deb160b52102ece090d5", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_90bbd1bf7d23655e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "65d90ee1280718063f736d1e93d1aaa7f4c0098fe39f3460c66338857cc0b7f7" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_cog_raw(d,x) = C_d * response_family_d(x; theta_d) * lambda_phi^D_f * B_gate(d,constraints)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:raw_cognitive_load", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "raw_cognitive_load", - "projection_signature": { - "shape_distance": 0.21865, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.510417 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_f46446cfb0f8d5b1", - "receipt_hash": "da1782af9226ab4082738b42f223f404f14c311cb564b3448afe02d7ada15637", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.51, - "scale_band_declared": 0.4, - "semantic_entropy": 0.510417, - "shape_closure": 0.63, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37291294149451387, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37291294149451387, - "shape": "LogogramProjection" - }, - { - "distance": 0.4187393382077473, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4187393382077473, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43754702063598155, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43754702063598155, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.21865, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "raw_cognitive_load", - "object_id": "rrc_eq_f46446cfb0f8d5b1", - "payload_bytes_sampled": 1048, - "payload_sha256": "a328b69c794a46677f88ab6e146419238b54009d426933ac64c57564f2eac3bd", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_f46446cfb0f8d5b1", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "4c3972cdafdc958dd625781d36e4f3c44037a05ae2bd63ece0b8187352b85edf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_residual_stress = max(0, L_cog_raw - L_threshold_hist) * (1 - eta_offload_hist)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:residual_stress", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "residual_stress", - "projection_signature": { - "shape_distance": 0.200253, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.68 - ], - [ - "decoder_declared", - 0.6 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_f5bb28753a2271dd", - "receipt_hash": "250f999cbe87c4659bccf6114c04d317bc1aef54cce5e01e8dce7759c5542caa", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.6, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.43, - "scale_band_declared": 0.4, - "semantic_entropy": 0.479167, - "shape_closure": 0.68, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3289709819819585, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3289709819819585, - "shape": "LogogramProjection" - }, - { - "distance": 0.3737234651647465, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3737234651647465, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.39377847874091265, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39377847874091265, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.200253, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "residual_stress", - "object_id": "rrc_eq_f5bb28753a2271dd", - "payload_bytes_sampled": 1030, - "payload_sha256": "d0697231f8f6e1ad1b867efd4f89e6d9ad15ddb6cfb881e64b011a201018015b", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_f5bb28753a2271dd", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "aff37ad4df04dfebb432c9ffcdefa671851df24a455bb26f8f673d928ed3856f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_threshold = C_threshold * lambda_phi^D_f * B_gate_threshold", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:threshold", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "threshold", - "projection_signature": { - "shape_distance": 0.188871, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.67 - ], - [ - "witness_declared", - 0.6 - ], - [ - "scale_band_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_72b416376f1bf5b0", - "receipt_hash": "d45516d1718ee50f42f1defc9fa3ed6d3b77393177c7f221fdf0975141d2b77a", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.44, - "scale_band_declared": 0.6, - "semantic_entropy": 0.489583, - "shape_closure": 0.67, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.33881456865679604, - "kind_prior_bonus": 0.0, - "raw_distance": 0.33881456865679604, - "shape": "LogogramProjection" - }, - { - "distance": 0.381847111676906, - "kind_prior_bonus": 0.0, - "raw_distance": 0.381847111676906, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.3935250673092598, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3935250673092598, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.188871, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "threshold", - "object_id": "rrc_eq_72b416376f1bf5b0", - "payload_bytes_sampled": 998, - "payload_sha256": "78bc3dff269f7e3188acf1bae6cde3080c47bd7cf930b1a4017643ba9011c629", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_72b416376f1bf5b0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "fc136c4048af9a4975a8077571c4710519d10e7fa6028f0788ddf4f34e8bf31c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_total = L_cog_eff + L_emotional + L_residual_stress", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:total_protective_load", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "total_protective_load", - "projection_signature": { - "shape_distance": 0.238088, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.64 - ], - [ - "decoder_declared", - 0.6 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_85435dde0bcc5cfd", - "receipt_hash": "3ba0d41b6c0662fcd8a6f287ce252ccad9eb958b22bafdd6b846709fecb42006", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.6, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.64, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37185591611638696, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37185591611638696, - "shape": "LogogramProjection" - }, - { - "distance": 0.4180076479914319, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4180076479914319, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.44227836899863276, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44227836899863276, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.238088, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "total_protective_load", - "object_id": "rrc_eq_85435dde0bcc5cfd", - "payload_bytes_sampled": 1014, - "payload_sha256": "58c3b846675133c61a5f6576d59353687c3eef2af554d78d4bf5501d28dc55bb", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_85435dde0bcc5cfd", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "db032003c200c94f1dc2e012d91b914bbffea90d913fc92daae6914f2b75286f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "DeltaE_emotional_eff = DeltaE_emotional + chi_T * T_trauma", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_barrier", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "trauma_adjusted_emotional_barrier", - "projection_signature": { - "shape_distance": 0.231349, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_2efd637f1e4bd389", - "receipt_hash": "72d4dfa89db3993dde245083b408f79191ecf303efc6559b3b8a0b458fe66559", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3850805959877919, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3850805959877919, - "shape": "LogogramProjection" - }, - { - "distance": 0.4311262036823453, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4311262036823453, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4330494857091735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4330494857091735, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.231349, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "trauma_adjusted_emotional_barrier", - "object_id": "rrc_eq_2efd637f1e4bd389", - "payload_bytes_sampled": 1043, - "payload_sha256": "c3ecaaad7d4713902d90c107299087f14ba0cdad791bc18c115a76a8f9618d74", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_2efd637f1e4bd389", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "c280e9c0502fb8ea80909671763dac5bbd10fcc38e3d96be5acf281a6d3d1528" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "kT_emotional_eff = kT_emotional / (1 + psi_T * T_trauma)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_temperature", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "trauma_adjusted_emotional_temperature", - "projection_signature": { - "shape_distance": 0.230737, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_0abce0721f473201", - "receipt_hash": "9b2304c7a60c7e618f9fcbdc9638abe74646a79afcad9467deb39745cb7d2bca", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38483360948099904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38483360948099904, - "shape": "LogogramProjection" - }, - { - "distance": 0.4309660409066421, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4309660409066421, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4326192401941621, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4326192401941621, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.230737, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "trauma_adjusted_emotional_temperature", - "object_id": "rrc_eq_0abce0721f473201", - "payload_bytes_sampled": 1049, - "payload_sha256": "9867e3fe28abecba85054c18440e7e2ad205cc924eb7c777810c69d2f63ef8ac", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0abce0721f473201", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "ef23ffa933cb567219a3a3c4c0c4dc691c8ea1b8aa4f88a23d9dc5df5f0cec9f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "eta_offload_eff = eta_offload * exp(-omega_T * T_trauma)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_offload_efficiency", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "trauma_adjusted_offload_efficiency", - "projection_signature": { - "shape_distance": 0.231976, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_ae70c43fd815392e", - "receipt_hash": "fad8529c03916050f7125d4151172bdb5838ad60182e6115506d9d4924e33f22", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38534502357563594, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38534502357563594, - "shape": "LogogramProjection" - }, - { - "distance": 0.43130203101354875, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43130203101354875, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43349494867626864, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43349494867626864, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.231976, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "trauma_adjusted_offload_efficiency", - "object_id": "rrc_eq_ae70c43fd815392e", - "payload_bytes_sampled": 1043, - "payload_sha256": "e3c6c5068ccd2b8cbfcfba52886c67abb289e654736c88850559614dd0594b0d", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ae70c43fd815392e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "1ea728e823361898c9b63392b093a21c6ef9b56dd2ee21eaa61339058daace6d" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_threshold_eff = L_threshold * exp(-rho_T * T_trauma)", - "equation_id": "connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_threshold", - "family": "connectome_protective_cognitive_load_reweighting_v1", - "name": "trauma_adjusted_threshold", - "projection_signature": { - "shape_distance": 0.194101, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "residual_risk", - 0.47 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Reweight cognitive load as a domain-specific response-family model with a protective overflow gate. Overflow shifts excess load into an emotional offload channel as a hypothesis about preserving working graph stability. The main intended use is historical and civilizational modeling of overload under accelerated information transfer; it does not erase load and does not prove biological connectome protection.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_df9f885395884594", - "receipt_hash": "e16e1907695a56f1a64582d28f9ad0ff86e73d3b3159d8cb5b9b3e940898167c", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.46875, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3438652576319872, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3438652576319872, - "shape": "LogogramProjection" - }, - { - "distance": 0.3881540332156179, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3881540332156179, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4022664731323168, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4022664731323168, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.194101, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "trauma_adjusted_threshold", - "object_id": "rrc_eq_df9f885395884594", - "payload_bytes_sampled": 1023, - "payload_sha256": "cc88718b473a394a2bd248c659bdddff0b3662f3332651cc37bd66c0db2fac68", - "source_path": "4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_df9f885395884594", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "7c2394b10e066659f5ef590085faa4e895985094bc42f5a6cc684a7320ed3bef" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "{\"heat_loss\":\"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)\",\"magnetic_projection\":\"M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)\",\"overflow_gate\":\"G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)\",\"signal_load\":\"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)\"}", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:core_equations", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "core_equations", - "projection_signature": { - "shape_distance": 0.172075, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.635417 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_ac1a7a22801b7d77", - "receipt_hash": "0a079caa2700c341641ccb75767d356930ab20a07020e66745d15f5caa9d3ec7", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.635417, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.30570146360584766, - "kind_prior_bonus": 0.0, - "raw_distance": 0.30570146360584766, - "shape": "LogogramProjection" - }, - { - "distance": 0.3474236411617077, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3474236411617077, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.3957419843586105, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3957419843586105, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.172075, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "core_equations", - "object_id": "rrc_eq_ac1a7a22801b7d77", - "payload_bytes_sampled": 1052, - "payload_sha256": "d9465bfee67ec55e111199f34fbb6679f19b9da19712970c9caa235b7dfd8c62", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_ac1a7a22801b7d77", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "c00ba36003088d605127404ca391fb0ea4b7f13964d1b7ec13d3ac687293c125" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "{\"byte_transition_rate\":\"domain agitation / susceptibility driver\",\"capacity_overflow\":\"hysteresis heat-loss channel\",\"entropy\":\"field demand / information pressure\",\"repeated_4grams\":\"remanence / memory channel\"}", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:field_mapping", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "field_mapping", - "projection_signature": { - "shape_distance": 0.166855, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "magnetic_signal", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_296f8ca4495edd26", - "receipt_hash": "bf2cc74fa8db0a74690ce28d1ad84e25f314cdd0d3d49f29e0a0a3bccb552790", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.2, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.427083, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3098210331196745, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3098210331196745, - "shape": "LogogramProjection" - }, - { - "distance": 0.3493201804023901, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3493201804023901, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4057275152253807, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4057275152253807, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.166855, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "field_mapping", - "object_id": "rrc_eq_296f8ca4495edd26", - "payload_bytes_sampled": 921, - "payload_sha256": "d0b4cab6f2ed842db5c880a67d6c7382e6295c99a3e559b894eb10fa4ba71789", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_296f8ca4495edd26", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "0a1fd729f193c4ee4ca80322602bac4c78959163151b845e3403a95756312bcd" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "byte_stream_signal", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:source_domain", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "source_domain", - "projection_signature": { - "shape_distance": 0.188002, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_dd01aec7b2c20774", - "receipt_hash": "6698bd0ecdcaf4862246f496afbf2914fd543ec5b17d97f00fba71841a3a6f56", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.395833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3265818875337174, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3265818875337174, - "shape": "LogogramProjection" - }, - { - "distance": 0.39486005042776334, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39486005042776334, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42573474664769945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42573474664769945, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.188002, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "source_domain", - "object_id": "rrc_eq_dd01aec7b2c20774", - "payload_bytes_sampled": 704, - "payload_sha256": "c651a77b09065f02c825e4cae71134d2cf1cf35e81793cf1493e1185efc8dd2b", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_dd01aec7b2c20774", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "c530b02f132ab1dbb8f227a25b4bb4e4b0dffbf1951f89fd56898bd6f58e8903" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "magnetic_domain_equation", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:target_domain", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "target_domain", - "projection_signature": { - "shape_distance": 0.188002, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_1038b814e5a78435", - "receipt_hash": "c10229b15141b004f1f15cbc67120c1999eac1a3868820a7444da3fbb04a3b28", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.395833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3265818875337174, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3265818875337174, - "shape": "LogogramProjection" - }, - { - "distance": 0.39486005042776334, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39486005042776334, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42573474664769945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42573474664769945, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.188002, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "target_domain", - "object_id": "rrc_eq_1038b814e5a78435", - "payload_bytes_sampled": 710, - "payload_sha256": "72c21b0c94f3ef43fd63226ef68caa9275360f47a5cf6e7ed7d182c31741df63", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_1038b814e5a78435", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "aab218697df920b332519db69a5a6ba65951005c9bc7e788bdb441d45b7b4c68" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:heat_loss", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "heat_loss", - "projection_signature": { - "shape_distance": 0.18598, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_811d99697e055c2b", - "receipt_hash": "7a4fef0634a2dcd38c410c3aeb9d28781d44a2b22a861d9b1a4fb5c831ff2797", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.479167, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3236582883202884, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3236582883202884, - "shape": "LogogramProjection" - }, - { - "distance": 0.389247245021913, - "kind_prior_bonus": 0.0, - "raw_distance": 0.389247245021913, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42692587120386705, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42692587120386705, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.18598, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "heat_loss", - "object_id": "rrc_eq_811d99697e055c2b", - "payload_bytes_sampled": 742, - "payload_sha256": "eecb9a089829e3755a7bb1be93eddba7fa72609d730ffd71aa506c96bd2c1d69", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_811d99697e055c2b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "b00d806631c35b61fdc06021a3fe222cc73047c1d3aac2a4e9d09ea0a24fd332" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:magnetic_projection", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "magnetic_projection", - "projection_signature": { - "shape_distance": 0.18541, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_8c569cbfc2385eab", - "receipt_hash": "20972ac80f05192b62c82c7dcd3c7663161008f133f68eca774e017934e16184", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.520833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.32269131937258394, - "kind_prior_bonus": 0.0, - "raw_distance": 0.32269131937258394, - "shape": "LogogramProjection" - }, - { - "distance": 0.3868312523015031, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3868312523015031, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42790072778217403, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42790072778217403, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.18541, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "magnetic_projection", - "object_id": "rrc_eq_8c569cbfc2385eab", - "payload_bytes_sampled": 769, - "payload_sha256": "45b12a9189b7ad83fd83d84c43a05fa14949b28c7b821b2dff52d885a76b43ea", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_8c569cbfc2385eab", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "7806d7fdd688a75ca696e3c70eb0830be322b496afe1b8eac6d526ec001a1f3b" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:overflow_gate", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "overflow_gate", - "projection_signature": { - "shape_distance": 0.197713, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.520833 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_35e1c2bc2da6d854", - "receipt_hash": "f71398504405971e2a644a766f77c6e839278710e95eb86ae095ed22ba3853c5", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.520833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3330830428718326, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3330830428718326, - "shape": "LogogramProjection" - }, - { - "distance": 0.37447578168395185, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37447578168395185, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4201437867492475, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4201437867492475, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.197713, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "overflow_gate", - "object_id": "rrc_eq_35e1c2bc2da6d854", - "payload_bytes_sampled": 790, - "payload_sha256": "483c877405ded0144c6e1274d0734dc69a84b4d3850b82f349c1e9caf32eac1f", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_35e1c2bc2da6d854", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "ca00cdc37a5e903be40a72c1626ef1c58546a5e72bd256d7b0d31b5de74c5032" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)", - "equation_id": "transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:signal_load", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "signal_load", - "projection_signature": { - "shape_distance": 0.191669, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_8a05e2496e67848b", - "receipt_hash": "e072ef2f2258c6b7f405d67624a5d78e414c80fe7c8d52fa0945416cbebd842a", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.541667, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3252533232256626, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3252533232256626, - "shape": "LogogramProjection" - }, - { - "distance": 0.37273541360467966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37273541360467966, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4050427937212657, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4050427937212657, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.191669, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "signal_load", - "object_id": "rrc_eq_8a05e2496e67848b", - "payload_bytes_sampled": 768, - "payload_sha256": "4b3c41f2e1d858390e17da1b04c13d3dd38e0ea6674410601f4a130448997887", - "source_path": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_8a05e2496e67848b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "ca12846520402a4941a5a74425815c32b6faabb6ec5b1e21a75aa740b14c8e2c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "{\"heat_loss\":\"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)\",\"magnetic_projection\":\"M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)\",\"overflow_gate\":\"G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)\",\"signal_load\":\"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)\"}", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:core_equations", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "core_equations", - "projection_signature": { - "shape_distance": 0.172075, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.635417 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_3f87d2c06726bc30", - "receipt_hash": "2e5388c758007af29a75650af6c1340c93048908024510825e73a2b75c48ac7c", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.428571, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.635417, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.30570146360584766, - "kind_prior_bonus": 0.0, - "raw_distance": 0.30570146360584766, - "shape": "LogogramProjection" - }, - { - "distance": 0.3474236411617077, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3474236411617077, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.3957419843586105, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3957419843586105, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.172075, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "core_equations", - "object_id": "rrc_eq_3f87d2c06726bc30", - "payload_bytes_sampled": 1045, - "payload_sha256": "384707ee18f044f71e70472425b710b137d7a600b79e4336f099d484449c511c", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_3f87d2c06726bc30", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "ddd51695ae67d14916196d3c9b1f2db786f5e46d93897cd6b55fafc9e3673c20" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "{\"byte_transition_rate\":\"domain agitation / susceptibility driver\",\"capacity_overflow\":\"hysteresis heat-loss channel\",\"entropy\":\"field demand / information pressure\",\"repeated_4grams\":\"remanence / memory channel\"}", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:field_mapping", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "field_mapping", - "projection_signature": { - "shape_distance": 0.166855, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "magnetic_signal", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_3e634eac50426ea3", - "receipt_hash": "f27a06efdfc281bcc8009ee26f3263a8bd009a923d034ae67c39d6eab74a19ea", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.2, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.427083, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3098210331196745, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3098210331196745, - "shape": "LogogramProjection" - }, - { - "distance": 0.3493201804023901, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3493201804023901, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4057275152253807, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4057275152253807, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.166855, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "field_mapping", - "object_id": "rrc_eq_3e634eac50426ea3", - "payload_bytes_sampled": 914, - "payload_sha256": "051872a027260c01defa96208208970127730edf7c10b4550f3d005b8f9a19e8", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_3e634eac50426ea3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "c1a18bba0e47118c315c20f42349c02374c59930044e5fe23b5cdf39924f215e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "byte_stream_signal", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:source_domain", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "source_domain", - "projection_signature": { - "shape_distance": 0.188002, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_8a4d6790faf66d6b", - "receipt_hash": "6d8cd06661445c86ad4a96811b96c48e8f2f5a6d7cbcc2c79656cbea58848cb5", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.395833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3265818875337174, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3265818875337174, - "shape": "LogogramProjection" - }, - { - "distance": 0.39486005042776334, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39486005042776334, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42573474664769945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42573474664769945, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.188002, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "source_domain", - "object_id": "rrc_eq_8a4d6790faf66d6b", - "payload_bytes_sampled": 697, - "payload_sha256": "2d4a1fc18fc690e84d50026f428d5f57bc8ba26afb810c832ff5601e7df7ab75", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_8a4d6790faf66d6b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "3f9b84d4a4758a00c3172630d5895f2616808d7cf7dd9c19fae2f9c126d50ca6" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "magnetic_domain_equation", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_map:target_domain", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "target_domain", - "projection_signature": { - "shape_distance": 0.188002, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_673652a4317dd847", - "receipt_hash": "436071249ed7b31c071abdda07adfef950d250d3fe93779ffcac34d21079e50e", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.395833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3265818875337174, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3265818875337174, - "shape": "LogogramProjection" - }, - { - "distance": 0.39486005042776334, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39486005042776334, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42573474664769945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42573474664769945, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.188002, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "target_domain", - "object_id": "rrc_eq_673652a4317dd847", - "payload_bytes_sampled": 703, - "payload_sha256": "0e42d81e5726cbfc0e1b0e608d80473f3ceac78edb77e134880affb84c1cda50", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_673652a4317dd847", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "8f4b091f2a3152313386a06ba81862d2ef224fa96a85f5898b581355599e0a6f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:heat_loss", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "heat_loss", - "projection_signature": { - "shape_distance": 0.18598, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_f691b1b9f433854f", - "receipt_hash": "d6f0fa24d906cedd5f63e89a688e4ab18812f89966c93b8e984c0d1e5cd77f56", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.479167, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3236582883202884, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3236582883202884, - "shape": "LogogramProjection" - }, - { - "distance": 0.389247245021913, - "kind_prior_bonus": 0.0, - "raw_distance": 0.389247245021913, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42692587120386705, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42692587120386705, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.18598, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "heat_loss", - "object_id": "rrc_eq_f691b1b9f433854f", - "payload_bytes_sampled": 735, - "payload_sha256": "c258a31386598767b2ca7776d3040385d6c9ff15e8d49e3084a0745ded98d497", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_f691b1b9f433854f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "936a273a9d9ecfe8fc2a97a24865eb48a6aa7d0f4784ab7b569fa51a619a72b4" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:magnetic_projection", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "magnetic_projection", - "projection_signature": { - "shape_distance": 0.18541, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_5e10957e0cbb9de8", - "receipt_hash": "99538776b207ef6f9653e69e737a14a37723b249fd6c48d5d19cf3f4de794ae3", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.520833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.32269131937258394, - "kind_prior_bonus": 0.0, - "raw_distance": 0.32269131937258394, - "shape": "LogogramProjection" - }, - { - "distance": 0.3868312523015031, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3868312523015031, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42790072778217403, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42790072778217403, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.18541, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "magnetic_projection", - "object_id": "rrc_eq_5e10957e0cbb9de8", - "payload_bytes_sampled": 762, - "payload_sha256": "b6dedd9726ba817c5dd18e0d734c7dddcbc5349cfa795bec13dff058b6150029", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_5e10957e0cbb9de8", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "a7ff2f00d4798ba3bf6246d5add9d9f4dd957b08e782c6e4dc5563441a382e2f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:overflow_gate", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "overflow_gate", - "projection_signature": { - "shape_distance": 0.197713, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.520833 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_b9eb3119b4d99483", - "receipt_hash": "6f8260573e7a1503a41d64c38b968e828d4a305685dc68de93dcd06cfab990ee", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.520833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3330830428718326, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3330830428718326, - "shape": "LogogramProjection" - }, - { - "distance": 0.37447578168395185, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37447578168395185, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4201437867492475, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4201437867492475, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.197713, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "overflow_gate", - "object_id": "rrc_eq_b9eb3119b4d99483", - "payload_bytes_sampled": 783, - "payload_sha256": "96ffb305ef9f0182075cb1dc298f7527f902fba3bcb430548edc9568f77f3919", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_b9eb3119b4d99483", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "b9cd80a873b13d7c18353cc991eb4a3eb5f0339db1aa0ac172ef91b8adba3dcf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)", - "equation_id": "transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:signal_load", - "family": "transfold_enwiki8_magnetic_domain_generator_v1", - "name": "signal_load", - "projection_signature": { - "shape_distance": 0.191669, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_a9bdc40d07c74659", - "receipt_hash": "f8ce5d640dae82b49f738ad8571e9cd01d679bdffc730e693db40cd334d22898", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.541667, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3252533232256626, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3252533232256626, - "shape": "LogogramProjection" - }, - { - "distance": 0.37273541360467966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37273541360467966, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4050427937212657, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4050427937212657, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.191669, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "signal_load", - "object_id": "rrc_eq_a9bdc40d07c74659", - "payload_bytes_sampled": 761, - "payload_sha256": "a724e8d47dca3c29b8b1888e396fb690e0520e3d2ac016f919a7a287b3e08d42", - "source_path": "4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_a9bdc40d07c74659", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "2c5fae884f5846aa3f2c0500d92a2abf6c5bd47590daa0a8800a20daf1079e2f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "compressed_total_bytes = payload_bytes + residual_bytes + witness_bytes + decoder_delta_bytes + container_bytes", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:counted_total", - "family": "hutter_equation_metastate_transfold_v1", - "name": "counted_total", - "projection_signature": { - "shape_distance": 0.19942, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.866667 - ], - [ - "decoder_declared", - 0.8 - ], - [ - "shape_closure", - 0.69 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_2ee35bd32d933ac7", - "receipt_hash": "237f563bf34c1c240ce80bac75b6cb7f357dd88d34a041d25dd27fa10ab18d8b", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.866667, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.46, - "scale_band_declared": 0.2, - "semantic_entropy": 0.354167, - "shape_closure": 0.69, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.33321972462225363, - "kind_prior_bonus": 0.0, - "raw_distance": 0.33321972462225363, - "shape": "LogogramProjection" - }, - { - "distance": 0.4489320086264668, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4489320086264668, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.453897766569057, - "kind_prior_bonus": 0.0, - "raw_distance": 0.453897766569057, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.19942, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "counted_total", - "object_id": "rrc_eq_2ee35bd32d933ac7", - "payload_bytes_sampled": 634, - "payload_sha256": "85ddf978728aeebe1b4702b9ac97c37e55ca8cdcdbeddf9a09529f5d119df22b", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_2ee35bd32d933ac7", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "df778a0b30d6c99620d88a99d8f4ddb2aea1c76b2177f313e1ae7685c2a5d8c3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Hutter-hard promotion additionally requires the total contest artifact for enwik9 to beat 109685197 bytes under the applicable prize rules", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hard_target_rule", - "family": "hutter_equation_metastate_transfold_v1", - "name": "hard_target_rule", - "projection_signature": { - "shape_distance": 0.216219, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.866667 - ], - [ - "shape_closure", - 0.64 - ], - [ - "decoder_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_e517db2c50e19613", - "receipt_hash": "27076e83ceee776f4ef92361c1a0f23ed9f88111b2bef97763cef496ac5a318d", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.866667, - "decoder_declared": 0.6, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.64, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.353149752259329, - "kind_prior_bonus": 0.0, - "raw_distance": 0.353149752259329, - "shape": "LogogramProjection" - }, - { - "distance": 0.45190433202017793, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45190433202017793, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4821501481675271, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4821501481675271, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.216219, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "hard_target_rule", - "object_id": "rrc_eq_e517db2c50e19613", - "payload_bytes_sampled": 667, - "payload_sha256": "92ddcee8875b256c505325979ca6581cc0b867d6b300d7d50f681364e3047433", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e517db2c50e19613", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "57b37f3d9c100aa48c692101d714c6ee3ae2f2ae53319fc02051b5a18724db9c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "[\"source_corpus_id\",\"source_bytes\",\"candidate_chart\",\"transform_route\",\"payload_bytes\",\"residual_bytes\",\"witness_bytes\",\"decoder_delta_bytes\",\"container_bytes\",\"runtime_budget\",\"compressed_total_bytes\",\"baseline_bytes\",\"hard_target_bytes\",\"ratio_schema\",\"exact_decode_status\",\"source_hash\",\"decoded_hash\",\"promotion_status\",\"failure_code\"]", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hutter_route_metastate", - "family": "hutter_equation_metastate_transfold_v1", - "name": "hutter_route_metastate", - "projection_signature": { - "shape_distance": 0.122715, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.866667 - ], - [ - "decoder_declared", - 0.8 - ], - [ - "witness_declared", - 0.8 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "residual_risk", - "history_depth" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_f4249695d9de4adc", - "receipt_hash": "6988b27cafd9f1bfe73cd80deeef4a0545ea63131a979a0e36da858daa46537f", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.866667, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.4, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.222222, - "residual_risk": 0.3, - "scale_band_declared": 0.4, - "semantic_entropy": 0.375, - "shape_closure": 0.78, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.2744381571915628, - "kind_prior_bonus": 0.0, - "raw_distance": 0.2744381571915628, - "shape": "LogogramProjection" - }, - { - "distance": 0.40019778411232515, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40019778411232515, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4360412557612818, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4360412557612818, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.122715, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "hutter_route_metastate", - "object_id": "rrc_eq_f4249695d9de4adc", - "payload_bytes_sampled": 918, - "payload_sha256": "a16518f1107853598e484d13449bad66ee35947af50d9fff7d84321257cb7823", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_f4249695d9de4adc", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "CANDIDATE", - "witness_hash": "14a7d94f0030b6e0cf8e05a71307e96b1e25168fad7d16831b23854372622af2" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "LB_route = payload_floor + residual_floor + witness_floor + decoder_delta_floor + container_floor + evaluator_cost_floor", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:lower_bound", - "family": "hutter_equation_metastate_transfold_v1", - "name": "lower_bound", - "projection_signature": { - "shape_distance": 0.210115, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.7 - ], - [ - "shape_closure", - 0.64 - ], - [ - "decoder_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_6f8b200d29180003", - "receipt_hash": "e3c0d9a7674a2eb3c0ad61e2fec0235a028c9f29e117f8160ac0d5dfdf728c19", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.7, - "decoder_declared": 0.6, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.375, - "shape_closure": 0.64, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3418862523268643, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3418862523268643, - "shape": "LogogramProjection" - }, - { - "distance": 0.43243393293716575, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43243393293716575, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45105783865910437, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45105783865910437, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.210115, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "lower_bound", - "object_id": "rrc_eq_6f8b200d29180003", - "payload_bytes_sampled": 639, - "payload_sha256": "092b4c98c206d464158282974437fcb3665b448a1f9fc4748d8c8e5244896bcd", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6f8b200d29180003", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "317b0ddebb89fe97fb7a4332078aaa8ff6b00b9b5e5dc7f6e8528b233e396e8a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "proposal_score -> candidate_route_coordinate -> bounded_exact_route_metastate -> promotion_receipt", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:metastate_transfold", - "family": "hutter_equation_metastate_transfold_v1", - "name": "metastate_transfold", - "projection_signature": { - "shape_distance": 0.238638, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "transfold", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_d76203fbca4e9b81", - "receipt_hash": "7977f691a06522c2eaead521ddd93323bd52f553064c275d3fd7d935edfb12e9", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.166667, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.354167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3746239291997798, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3746239291997798, - "shape": "LogogramProjection" - }, - { - "distance": 0.4464085441936362, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4464085441936362, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4629077278676149, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4629077278676149, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.238638, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "metastate_transfold", - "object_id": "rrc_eq_d76203fbca4e9b81", - "payload_bytes_sampled": 625, - "payload_sha256": "b442aacd2e5f8181e943efb57c4bf26a15e1b05cc0e3cceb7cc2908b1e9eb060", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d76203fbca4e9b81", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "b882d2d6a096c3b576e3a60966c3778fef3e7c73ff9561df85121a4a093b5a1e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "promote iff decoded_hash == source_hash and compressed_total_bytes < incumbent_bytes and ratio_schema is explicit and all witness, residual, decoder, and container bytes are counted", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:promotion_rule", - "family": "hutter_equation_metastate_transfold_v1", - "name": "promotion_rule", - "projection_signature": { - "shape_distance": 0.196828, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.866667 - ], - [ - "decoder_declared", - 0.8 - ], - [ - "witness_declared", - 0.8 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_079917209598b9e1", - "receipt_hash": "4e1a4fbbf1d66926915c45352873d6b82711f2fbbcb2b4a124ec9b6b7fea96fc", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.866667, - "decoder_declared": 0.8, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.222222, - "residual_risk": 0.41, - "scale_band_declared": 0.2, - "semantic_entropy": 0.354167, - "shape_closure": 0.74, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3348346312799652, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3348346312799652, - "shape": "LogogramProjection" - }, - { - "distance": 0.4659325051464099, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4659325051464099, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4732368531647543, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4732368531647543, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.196828, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "promotion_rule", - "object_id": "rrc_eq_079917209598b9e1", - "payload_bytes_sampled": 706, - "payload_sha256": "d2752181fd855bc76293e27ed8acc674e0dfe7a8ef57481d67ef6006d2b5ffe4", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_079917209598b9e1", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "44852eedc360f006223c4b6adc848fbb1689088f9ba8bc04bd92ca86d5d543eb" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "prune iff LB_route >= incumbent_bytes", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:prune_rule", - "family": "hutter_equation_metastate_transfold_v1", - "name": "prune_rule", - "projection_signature": { - "shape_distance": 0.218153, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.866667 - ], - [ - "shape_closure", - 0.64 - ], - [ - "decoder_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_0edd7758873784a5", - "receipt_hash": "01c0cafab7df07941ba581b39aad29a5977eeaa1b5936ff92496ce7336fbce83", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.866667, - "decoder_declared": 0.6, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.375, - "shape_closure": 0.64, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.35583074336000114, - "kind_prior_bonus": 0.0, - "raw_distance": 0.35583074336000114, - "shape": "LogogramProjection" - }, - { - "distance": 0.45640529762252846, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45640529762252846, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.48307458392510855, - "kind_prior_bonus": 0.0, - "raw_distance": 0.48307458392510855, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.218153, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "prune_rule", - "object_id": "rrc_eq_0edd7758873784a5", - "payload_bytes_sampled": 554, - "payload_sha256": "29fe6c0838054c97bd752e4fc38076781aeab8d52ba9a9e3f6f079d0c8f63126", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0edd7758873784a5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "37f072e1a425f9323542cc78a2d4d331886b75c17b79d33b46922cd4ac4ac463" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "C = proposal_score(comp, phys, geom, scaling) or phi_HP = field + compression_gain + decoder/resource penalties", - "equation_id": "hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:source_equation_surface", - "family": "hutter_equation_metastate_transfold_v1", - "name": "source_equation_surface", - "projection_signature": { - "shape_distance": 0.224542, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "compression_pressure", - 0.7 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_372fdc0c8b995ef2", - "receipt_hash": "09061340e13cba27fc198f3124051dbfc1bc77f4db1b3cb2fa2f68e59429013b", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.7, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.111111, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.427083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.36168414249898284, - "kind_prior_bonus": 0.0, - "raw_distance": 0.36168414249898284, - "shape": "LogogramProjection" - }, - { - "distance": 0.4279495481486356, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4279495481486356, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.450361067982427, - "kind_prior_bonus": 0.0, - "raw_distance": 0.450361067982427, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.224542, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "source_equation_surface", - "object_id": "rrc_eq_372fdc0c8b995ef2", - "payload_bytes_sampled": 654, - "payload_sha256": "39b5a3d005b899d57c5c4f59120dc9c2573648a6417d9aa2baf0ba529a54c1e3", - "source_path": "4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_372fdc0c8b995ef2", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "9608b0ca91c24dc2029cb79bc33f02712ba5f08c9bc2910a98e513544f750027" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "geometric_bind", - "equation": "H0 = 73.3 \u00b1 1.5 km s\u207b\u00b9 Mpc\u207b\u00b9 (68% CL)", - "equation_id": "math_model_map:0", - "family": "Quantum Geometry", - "name": "UQGET_Hubble_Tension", - "projection_signature": { - "shape_distance": 0.292337, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.677083 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Unified Quantum-Geometric Emergence Theory - resolves Hubble tension via spacetime emergence from quantum entanglement dynamics. Aligns with Planck 2018, DESI 2024, Pantheon+ datasets.", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_5193efd26258bc51", - "receipt_hash": "b5dd54d8a7a93d80f4424162f22a5a292f841ede431e357fd11c9fa3a7071f43", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.677083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4020325763095082, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4020325763095082, - "shape": "LogogramProjection" - }, - { - "distance": 0.4496970253807365, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4496970253807365, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46067043098671867, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46067043098671867, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.292337, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "UQGET_Hubble_Tension", - "object_id": "rrc_eq_5193efd26258bc51", - "payload_bytes_sampled": 700, - "payload_sha256": "fa0d3ec4dc4cd890a89c57024e34a3995bc992384d12c60f05a67851019c3450", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_5193efd26258bc51", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "9b1a75fb8249daafc953be5243b4ce648bfc3dace3f44a527d0f102486461cb3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "geometric_bind", - "equation": "S8 = 0.810 \u00b1 0.008", - "equation_id": "math_model_map:1", - "family": "Quantum Geometry", - "name": "UQGET_Structure_Tension", - "projection_signature": { - "shape_distance": 0.261583, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "UQGET resolves structure formation tension through non-equilibrium quantum physics and multi-field models.", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_64f81fa3c4725d4e", - "receipt_hash": "f22ea40d1d24aa854fd1ad4e69824f33fae732655fbaecdebc8558be9ac377ae", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3906420597496722, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3906420597496722, - "shape": "LogogramProjection" - }, - { - "distance": 0.4282254225244691, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4282254225244691, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.44406221254163386, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44406221254163386, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.261583, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "UQGET_Structure_Tension", - "object_id": "rrc_eq_64f81fa3c4725d4e", - "payload_bytes_sampled": 586, - "payload_sha256": "8412919055cb6bb0eb290b379b3c76a1f40fdd06b6a9884916d7e78f5744d9e5", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_64f81fa3c4725d4e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "c920422b3a1eb46536f30d377900c9e21e21a1d3705b2cba2a7fd4a0a07811d1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "geometric_bind", - "equation": "\u03c7\u00b2/d.o.f. = 1907.2/1949 (p = 0.78)", - "equation_id": "math_model_map:2", - "family": "Quantum Geometry", - "name": "UQGET_Statistical_Fit", - "projection_signature": { - "shape_distance": 0.288392, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "MCMC analysis demonstrates statistical robustness with p = 0.78 indicating good fit to observational data.", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "receipt_hash": "99877190d775a522f43a8921579f1d09fc8c251a7086d49e57cb2d0655d126f5", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40195159938404756, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40195159938404756, - "shape": "LogogramProjection" - }, - { - "distance": 0.45308642715227754, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45308642715227754, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46250054585271627, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46250054585271627, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288392, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "UQGET_Statistical_Fit", - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "payload_bytes_sampled": 605, - "payload_sha256": "ce79d18d9edb91f899f912706c2c6277530dc0fccc03553a9bd14b468d7afecd", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "f007b68e657bb7c80fa1d61630c39004fa0a50e8f3b661c3693040b3a640082a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "h_i^(l+1) = \u03c3(\u03a3_{j\u2208N(i)} \u03b1_ij^(l) W^(l) h_j^(l))", - "equation_id": "math_model_map:3", - "family": "Machine Learning", - "name": "LASSO_MOGAT_GAT_Propagation", - "projection_signature": { - "shape_distance": 0.283014, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Graph Attention Network propagation for multi-omics cancer classification using PPI networks.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_6d33c14a88eb0a12", - "receipt_hash": "084cf3dce6695abcc4c060e4808a01ead33b60331c16466f2ecc7db338093bcd", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096354901486735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096354901486735, - "shape": "LogogramProjection" - }, - { - "distance": 0.4545860281746088, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4545860281746088, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45551846079240005, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45551846079240005, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283014, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "LASSO_MOGAT_GAT_Propagation", - "object_id": "rrc_eq_6d33c14a88eb0a12", - "payload_bytes_sampled": 630, - "payload_sha256": "f7c995e9d4721511de18f776f6f534053e432803983c603712736d159da7a8c7", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6d33c14a88eb0a12", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "e2c03f223918d61810f356df983071c4fd5acca5a5bfa5a8fdeb971c11866918" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "\u03b1_ij^(l) = softmax_j(LeakyReLU(a\u2192^(l)^T [W^(l) h_i^(l) || W^(l) h_j^(l)]))", - "equation_id": "math_model_map:4", - "family": "Machine Learning", - "name": "LASSO_MOGAT_Attention_Coefficient", - "projection_signature": { - "shape_distance": 0.282576, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Attention coefficient computation for graph edges using softmax over neighbors.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_1f912c8afa928326", - "receipt_hash": "d5c5ac292858c26a8d4a593807b9a49b24cb87c723a94d78bbdf5714d03fd3c0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4095851586061867, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4095851586061867, - "shape": "LogogramProjection" - }, - { - "distance": 0.4538526461007772, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4538526461007772, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4555875351131274, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4555875351131274, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.282576, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "LASSO_MOGAT_Attention_Coefficient", - "object_id": "rrc_eq_1f912c8afa928326", - "payload_bytes_sampled": 638, - "payload_sha256": "2b3422a7388c5eed945d9362c4646c9a079e224512ec49650ad583fa0b8cbd19", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1f912c8afa928326", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "585335194b21190ed91a5aa2d2975ba00a7b96ec3b8ba798329acf3310041dcc" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "Y = b\u00b7X + a (log-log space)", - "equation_id": "math_model_map:5", - "family": "Biology", - "name": "Multiphasic_Allometry_LogLog_Scaling", - "projection_signature": { - "shape_distance": 0.261553, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Log-log regression for metabolic rate scaling with body mass across developmental stages. Different scaling exponents (b) indicate multiphasic ontogenetic allometry.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_535b43060096e699", - "receipt_hash": "4cf53920d1baa5d4a04e6df4e995829eaea473efa74e111951769435aacfa97f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4024546358221201, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024546358221201, - "shape": "LogogramProjection" - }, - { - "distance": 0.4468711682773598, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4468711682773598, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4527752733756887, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4527752733756887, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.261553, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Multiphasic_Allometry_LogLog_Scaling", - "object_id": "rrc_eq_535b43060096e699", - "payload_bytes_sampled": 665, - "payload_sha256": "2464d1f9be530082a851241dfef5cd69b18f5827c561323382c18be38d8ebf1e", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_535b43060096e699", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b9e16bc711b32346cb6dc89ee3f654462271cf7ab646813baebcbd22384eb534" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "Y = a\u00b7X\u00b2 + b\u00b7X + c (curvilinear)", - "equation_id": "math_model_map:6", - "family": "Biology", - "name": "Multiphasic_Allometry_Quadratic", - "projection_signature": { - "shape_distance": 0.261553, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Quadratic regression for detecting nonlinear metabolic scaling. First derivative dY/dX = 2aX + b gives instantaneous slope.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9a468347631152ce", - "receipt_hash": "03c8cece41202294120d2af3550b101a3c4f0b45f5fbabdb6f266d4cc590a0dc", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4024546358221201, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024546358221201, - "shape": "LogogramProjection" - }, - { - "distance": 0.4468711682773598, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4468711682773598, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4527752733756887, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4527752733756887, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.261553, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Multiphasic_Allometry_Quadratic", - "object_id": "rrc_eq_9a468347631152ce", - "payload_bytes_sampled": 633, - "payload_sha256": "6333665497f54102d098f6ab77cf8d27764c3b6a6f04fff5c5e8c80d1edb2123", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9a468347631152ce", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "863959517ce739c74385a71335a6f816ae8a4ba59eb3e8a015a5739296b36f08" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "dY/dX = 2aX + b (where Y = aX\u00b2 + bX + c)", - "equation_id": "math_model_map:7", - "family": "Biology", - "name": "Multiphasic_Allometry_Instantaneous_Slope", - "projection_signature": { - "shape_distance": 0.260582, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "First derivative of quadratic regression gives instantaneous scaling slope at any body size. Used to detect ontogenetic shifts in metabolic scaling.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "receipt_hash": "5211e58c11fb84f01cc711bff222143020773c1a9ec40c4bfa57527fa73edc3d", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4021673956240717, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4021673956240717, - "shape": "LogogramProjection" - }, - { - "distance": 0.44672909902151264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44672909902151264, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.452116959486624, - "kind_prior_bonus": 0.0, - "raw_distance": 0.452116959486624, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.260582, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Multiphasic_Allometry_Instantaneous_Slope", - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "payload_bytes_sampled": 666, - "payload_sha256": "864ce5fc161287f5b2e8ff3978da628b5b316c67c20732fcdfbc33cc5ab909a3", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "71caa45756f3a732ac47d87a27906690621ddc6415c958daf00d4f16fbf488d7" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "Y = X\u00b7W + b", - "equation_id": "math_model_map:8", - "family": "Time Series", - "name": "Affine_Mapping_LTSF_Linear_Layer", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Single linear layer (affine transformation) for long-term time series forecasting. Dominates forecasting performance on periodic signals.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "receipt_hash": "b94975bd733812768dba22636ffd1f6baa6572755563f6c5586d243b369df328", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Affine_Mapping_LTSF_Linear_Layer", - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "payload_bytes_sampled": 622, - "payload_sha256": "c03bb3ec23168bd208a1edf14b963ca555029881762eafac0e9168381737814d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "053a0014a7a0eb86a94e842da960eb2a0dfe9b1a6018486fd03a99444784ac2b" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "x(t) = s(t) + f(t) + \u03b5", - "equation_id": "math_model_map:9", - "family": "Time Series", - "name": "Affine_Mapping_Time_Series_Decomposition", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Time series decomposition into seasonal and trend components. Basis for understanding why affine mapping works well on periodic data.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "receipt_hash": "7e7dd64946cdaeabf3665c74397a3e911e6c35e009a12b542aa4049ef428e788", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Affine_Mapping_Time_Series_Decomposition", - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "payload_bytes_sampled": 637, - "payload_sha256": "77bfd31d3fc9a377164dd24847b88d9d156760ff005b3c7878b465379024b100", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ae1aa70421e22e8fac0f4718dfa19909e57ce2b30b8c7ab1629b86f62fb617b1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "x(t) = s(t) = s(t-p) where p \u2264 n", - "equation_id": "math_model_map:10", - "family": "Time Series", - "name": "Affine_Mapping_Periodic_Theorem", - "projection_signature": { - "shape_distance": 0.290839, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "proof_readiness", - 0.55 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "For seasonal time series with period p, affine mapping has analytical solution when input length \u2265 period. Explains competitive performance on seasonal benchmarks.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_f010fb33997b8f51", - "receipt_hash": "f6f54011e675a07100ecd9c86f2e86f60f47646db7f805f2738e08e3ba0167d5", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.55, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4124728631407628, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4124728631407628, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567622789747475, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567622789747475, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602103704283082, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602103704283082, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.290839, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Affine_Mapping_Periodic_Theorem", - "object_id": "rrc_eq_f010fb33997b8f51", - "payload_bytes_sampled": 674, - "payload_sha256": "3eb32b166c9017f49c2c9468063e3c7a7bd2f6762792785f68942995a7e39d8d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_f010fb33997b8f51", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "f1597e44bf543d37a3356488632b0885cff6f205d1980352a48a31c564b0a6cf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "x(t) = a\u00b7x(t-p) + c", - "equation_id": "math_model_map:11", - "family": "Time Series", - "name": "Affine_Mapping_Scaled_Periodic", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Extended periodic model with scaling and translation. Still has closed-form solution for affine mapping. Handles amplitude-modulated periodic signals.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_8500000bbf612a0e", - "receipt_hash": "392355b64fcdd4953b863493fa3086699b05fc745ad8a8e2fe57dc8e9894853d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Affine_Mapping_Scaled_Periodic", - "object_id": "rrc_eq_8500000bbf612a0e", - "payload_bytes_sampled": 642, - "payload_sha256": "0838ae11c26f81760826c4eca3f6299aae3fcca77805cab6bbe237703d15aa29", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8500000bbf612a0e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "9a1158b8965458ab1560b6663126082647f2b05c60968e02adc2f52cc348fa7c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "CO2 + 2H+ + 2e- \u2192 CO + H2O", - "equation_id": "math_model_map:12", - "family": "Chemistry", - "name": "MOF_CO2_Reduction_2e_Electrochemistry", - "projection_signature": { - "shape_distance": 0.263119, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "2-electron CO2 reduction to carbon monoxide. Primary product in electrocatalysis with Cu-based catalysts.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_8b812bf47cc024b4", - "receipt_hash": "4cccdcda91f3fd5723cf0738d0420844d57a6113027614bc99be7ab4ae94260c", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40301133835493014, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40301133835493014, - "shape": "LogogramProjection" - }, - { - "distance": 0.4471979382616707, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4471979382616707, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4538730308449118, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4538730308449118, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.263119, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "MOF_CO2_Reduction_2e_Electrochemistry", - "object_id": "rrc_eq_8b812bf47cc024b4", - "payload_bytes_sampled": 608, - "payload_sha256": "8996c09d9df418e6ce581d11322d3a79c65ce5e6e89e6bf3ef0f0e0c4fe1aad4", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8b812bf47cc024b4", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "357acf40aeb1394f8c53a3ce1481b32a96a041f2973b49925cc1121d4f12dbca" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "CO2 + 2H+ + 2e- \u2192 HCOOH", - "equation_id": "math_model_map:13", - "family": "Chemistry", - "name": "MOF_CO2_Reduction_Formic_Acid", - "projection_signature": { - "shape_distance": 0.262582, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "2-electron CO2 reduction to formic acid. Major product in electrocatalysis, especially with Zr-MOF catalysts.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_0606dcb042ba0e6f", - "receipt_hash": "b58639565a62c37d230bc39c584f080d111c9ffa2b35879f70d32f864e049b84", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40280902070162755, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40280902070162755, - "shape": "LogogramProjection" - }, - { - "distance": 0.44707387268190324, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44707387268190324, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45349245283428014, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45349245283428014, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.262582, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "MOF_CO2_Reduction_Formic_Acid", - "object_id": "rrc_eq_0606dcb042ba0e6f", - "payload_bytes_sampled": 601, - "payload_sha256": "87ee3b89526754e6217111c71e686ac5bbf8444add7b09a9dae928e1c4aa6f5f", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0606dcb042ba0e6f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "1d646b016eea1585c9074dc110c0ec7ed783d2378ce761df0bfd1b06d78e9f26" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "CO2 + 6H+ + 6e- \u2192 CH3OH + H2O", - "equation_id": "math_model_map:14", - "family": "Chemistry", - "name": "MOF_CO2_Reduction_6e_Methanol", - "projection_signature": { - "shape_distance": 0.259671, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "6-electron CO2 reduction to methanol. Highest methanol rate achieved by Au10@ZIF-67 in photocatalysis.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_185011f4d6cf6a2b", - "receipt_hash": "54060da6460e9c91e21cd4c10cd7e2a34b0f015121a3b7df9308e18b0a73219d", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4019474440565634, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4019474440565634, - "shape": "LogogramProjection" - }, - { - "distance": 0.44664772277466575, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44664772277466575, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4515177686455449, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4515177686455449, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.259671, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "MOF_CO2_Reduction_6e_Methanol", - "object_id": "rrc_eq_185011f4d6cf6a2b", - "payload_bytes_sampled": 600, - "payload_sha256": "d59109fe2997c888a72c0e0346131e787a35ed8a6671f94c231418ba7f2844dc", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_185011f4d6cf6a2b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "a2ab8b9bd863ad28b2b276c69ecdcf37f688c55717550b5a24f6755e3426b9f3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "CO2 + 8H+ + 8e- \u2192 CH4 + 2H2O", - "equation_id": "math_model_map:15", - "family": "Chemistry", - "name": "MOF_CO2_Reduction_8e_Methane", - "projection_signature": { - "shape_distance": 0.259671, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "8-electron CO2 reduction to methane. Highest methane rate achieved by MIL-101(Cr)-Ag in photocatalysis.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_891a81dfc968f58e", - "receipt_hash": "9ae1ddc2e7d41b4b35a55b0542aff0fb83daf7997ca8d237a13cb980acd2f59e", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4019474440565634, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4019474440565634, - "shape": "LogogramProjection" - }, - { - "distance": 0.44664772277466575, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44664772277466575, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4515177686455449, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4515177686455449, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.259671, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "MOF_CO2_Reduction_8e_Methane", - "object_id": "rrc_eq_891a81dfc968f58e", - "payload_bytes_sampled": 599, - "payload_sha256": "b13fc6740417b8db4d202a486895d494e2d197bf7c4cce39003f87a7fba01d1b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_891a81dfc968f58e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "c3bb9f03fdda5cc7782a969ccf273f08bf8fe6832321fadff27cdab84962cf76" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "thermodynamic_bind", - "equation": "\u1e8d_i + \u03b3\u1e8b_i + \u03c9_i\u00b2x_i + \u03a3_j \u03ba_ij(x_i - x_j) = F(t)", - "equation_id": "math_model_map:16", - "family": "Chaotic Dynamics", - "name": "COUCH_Equation", - "projection_signature": { - "shape_distance": 0.257213, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Coupled Oscillator for Universal Chaotic Hysteresis - models non-linear coupled oscillator systems exhibiting chaotic \"super freak\" behavior and path-dependent hysteresis loops. Phase space trajectories exhibit strange attractors.", - "route_hint_non_authoritative": "chaotic_couch", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_45606d1f25dd6aa5", - "receipt_hash": "2797beef7197238b693b8dde2541130474bb1748996d9eace0187f959ba4278d", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.366667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.39123316266278063, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39123316266278063, - "shape": "LogogramProjection" - }, - { - "distance": 0.4452927237103072, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4452927237103072, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4571751000338178, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4571751000338178, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.257213, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "COUCH_Equation", - "object_id": "rrc_eq_45606d1f25dd6aa5", - "payload_bytes_sampled": 765, - "payload_sha256": "74f897153f3bd5d305c669bd538505f5884999bcb52344b0746e28bab202779b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_45606d1f25dd6aa5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "d25b08a75a97db050282e8f5ff766dddef1c418a40f28bac2d35f761c0588f97" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "L_I(x) = -\u03a3_{b=0}^{255} p(b|x) log\u2082 p(b|x)", - "equation_id": "math_model_map:1", - "family": "Cognitive Load", - "name": "Intrinsic_Load_LI", - "projection_signature": { - "shape_distance": 0.234956, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Shannon entropy of byte distribution; irreducible complexity", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_64f81fa3c4725d4e", - "receipt_hash": "5f924c91b3363fff13da9732e0400782ad2d37fc2b814443e66eda3f576b5979", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.364828321511775, - "kind_prior_bonus": 0.0, - "raw_distance": 0.364828321511775, - "shape": "LogogramProjection" - }, - { - "distance": 0.41916927222814615, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41916927222814615, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4392795461358172, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4392795461358172, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.234956, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Intrinsic_Load_LI", - "object_id": "rrc_eq_64f81fa3c4725d4e", - "payload_bytes_sampled": 581, - "payload_sha256": "8a57da54cd6d20e5a1f86823fe0fb3c19a7656d14c54088491c3852740eaaba3", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_64f81fa3c4725d4e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "542ba73029f3ef86ccd7d5ec42b8d6042bd140a8557889491db42cf6424a320e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "C_ratio = U_size / C_size = 1.48\u00d7 (achieved)", - "equation_id": "math_model_map:2", - "family": "DeltaGCL", - "name": "NES_GCL_Square_Wave_Compression", - "projection_signature": { - "shape_distance": 0.245952, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Compress NES square wave parameters (25 bits/frame) using delta encoding, PTOS dictionary patterns, variable-length GCL for cartridge streaming", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "receipt_hash": "9bb545800ccd301cde1ff89524cd4d66f73b4c7ee6a47d5ed1d985a1378fac51", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37998026547500474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37998026547500474, - "shape": "LogogramProjection" - }, - { - "distance": 0.43948707721541025, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43948707721541025, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45508408015178686, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45508408015178686, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.245952, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "NES_GCL_Square_Wave_Compression", - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "payload_bytes_sampled": 653, - "payload_sha256": "62cf210dfe32a7ec8d5b59230d057307cdf89fd99f93a4d346d6b9d6b5ee4fd7", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "535a6057bd34b7eed4ccf3cd5e4b5ab8ebe368fad762288190c3e40a00a77481" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "JTAG\u2192SUBLEQ\u2192GCL\u2192LUT\u2192APU", - "equation_id": "math_model_map:3", - "family": "OISC", - "name": "NES_OISC_GCL_LUT_Architecture", - "projection_signature": { - "shape_distance": 0.264694, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "NES controller port JTAG bitbanging controls SUBLEQ OISC for GCL decompression into LUT, NES 6502 reads LUT for square wave generation. Maximum retro insanity: 1985 NES + 1990s JTAG + minimalist OISC + nanokernel GCL", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_6d33c14a88eb0a12", - "receipt_hash": "ad5a4d770dc5f53c303cd6e3d4b0e285c97f5bac1e94d158faa51f7d505009ed", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3937656632437123, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3937656632437123, - "shape": "LogogramProjection" - }, - { - "distance": 0.4400607695342701, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4400607695342701, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45671175126138025, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45671175126138025, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.264694, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "NES_OISC_GCL_LUT_Architecture", - "object_id": "rrc_eq_6d33c14a88eb0a12", - "payload_bytes_sampled": 711, - "payload_sha256": "eb7c254f2a03ccc5c965c6178d16c9e8655d4ffc8ec1d98a87e175db4e14d3c6", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6d33c14a88eb0a12", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "84cd9c5d86f231e56e76f7bdaf820e29d75259b316d10434fc176f2cf1b3f2ea" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "Shader(x,y,t,\u03b8)\u2192palette + GCL\u2192LUT + SUBLEQ\u2192compute", - "equation_id": "math_model_map:4", - "family": "MinimalOISC", - "name": "Unified_Shader_GCL_Audio_Stack", - "projection_signature": { - "shape_distance": 0.235728, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.666667 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Unified computational stack collapsing blitter, GCL compression, and square wave generator into minimal OISC with NES palette generator as proto-shader. 64K unified memory: shader params, palette LUT, GCL buffer, audio LUT, SUBLEQ code, I/O. Proto-shader acts as fragment shader for color generation.", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_1f912c8afa928326", - "receipt_hash": "8f8dd668bff366688aa51a00df1f53eb4c4a4df519ce3af5f84e024fd36ceca2", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.2, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.666667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3721817828618229, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3721817828618229, - "shape": "LogogramProjection" - }, - { - "distance": 0.4140901693871686, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4140901693871686, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.43626177808514766, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43626177808514766, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.235728, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Unified_Shader_GCL_Audio_Stack", - "object_id": "rrc_eq_1f912c8afa928326", - "payload_bytes_sampled": 833, - "payload_sha256": "42d4ef260ca8aacbf246610163715c1f924571ad274f1af3ba00f970e1ff81fe", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1f912c8afa928326", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "0755e44e4755784a4c3db9cdc9a01564dfccf5b9e8e0b68d8c2c07afac032e47" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "Cartridge_SUBLEQ\u2192GCL\u2192Shader\u2192Audio\u2192ControllerPort\u2192NES", - "equation_id": "math_model_map:5", - "family": "CartridgeOISC", - "name": "Unified_Cartridge_Controller_Stack", - "projection_signature": { - "shape_distance": 0.264694, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Unified cartridge CPU (SUBLEQ) + GCL compression + proto-shader + square wave generation streaming to NES via controller port with voltage level shifting (3.3V \u2194 5V). Cartridge handles all computation, NES is I/O terminal. Controller port bidirectional communication with voltage shifter for level conversion.", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_535b43060096e699", - "receipt_hash": "581595a75831b8c00c6416f602ef5e8eb75545d0efcaa2b75747d46a274bbb04", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3937656632437123, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3937656632437123, - "shape": "LogogramProjection" - }, - { - "distance": 0.4400607695342701, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4400607695342701, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45671175126138025, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45671175126138025, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.264694, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Unified_Cartridge_Controller_Stack", - "object_id": "rrc_eq_535b43060096e699", - "payload_bytes_sampled": 857, - "payload_sha256": "9409098821dfe2de61ebb23816fb09dc60d7c0a78498ad671dca31693e0af05f", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_535b43060096e699", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b9e16bc711b32346cb6dc89ee3f654462271cf7ab646813baebcbd22384eb534" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "1-Wire_UART\u2192GCL_Admission\u2192Entropy\u2192Metaprobe\u2192Triumvirate\u2192NES", - "equation_id": "math_model_map:6", - "family": "NanoKernel", - "name": "Topological_NanoKernel_UART_Stack", - "projection_signature": { - "shape_distance": 0.235036, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.666667 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Topological nano kernel protecting cartridge-NES 1-wire UART communication. GCL admission gate validates signatures and entropy, metaprobe audit checks Lawful signal resonance, Triumvirate (Builder-Judge-Warden) provides consensus. Only lawful, validated data reaches NES APU. 9600 baud single data line with voltage shifter (3.3V \u2194 5V).", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9a468347631152ce", - "receipt_hash": "198973f802a9e64480ab5e5f6106b83a579711b6ee4e680068687c69df7ea3fe", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.666667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3837863406000356, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3837863406000356, - "shape": "LogogramProjection" - }, - { - "distance": 0.42838427396853335, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42838427396853335, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.435693880622759, - "kind_prior_bonus": 0.0, - "raw_distance": 0.435693880622759, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.235036, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Topological_NanoKernel_UART_Stack", - "object_id": "rrc_eq_9a468347631152ce", - "payload_bytes_sampled": 894, - "payload_sha256": "5c15d3144538b569c85ae0558187d3d66f987f84e827cbacca6ce3aa8f394aba", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9a468347631152ce", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "863959517ce739c74385a71335a6f816ae8a4ba59eb3e8a015a5739296b36f08" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "Value\u2194AudioSignal(f,A,D) + APU_Operations\u2192Result", - "equation_id": "math_model_map:7", - "family": "AnalogDSP", - "name": "NES_Sound_Line_DSP_Math", - "projection_signature": { - "shape_distance": 0.278424, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.635417 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Hijack NES audio output lines (square, triangle, noise, DPCM) for DSP mathematical operations. Encode values as audio signals (frequency=magnitude, amplitude=precision, duty=sign). Use APU mixing/filtering/modulation as computational operations (addition=mix, multiplication=AM, subtraction=phase inversion, integration=envelope, differentiation=sweep). Analog computation disguised as audio output. Horrific: repurposes audio hardware for general computation. Wonderful: novel analog-digital hybrid computing on retro hardware.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "receipt_hash": "7676292cf605b03ceaed9f4e001195ac2dddf69edc2a58ef2b07f2c51a11f814", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.635417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3967692033935676, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3967692033935676, - "shape": "LogogramProjection" - }, - { - "distance": 0.4435143373423602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4435143373423602, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4465400575354785, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4465400575354785, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.278424, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "NES_Sound_Line_DSP_Math", - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "payload_bytes_sampled": 1044, - "payload_sha256": "d79a422009583f4abfd3ded38a02b0fa4c16508f21e4e6d0bf427d5ad1758b90", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "62dc36cd5af02cf796314b1e9a973e6dd2e78c1ac942b53ac5d4844fb1836808" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "All_Systems\u2192Single_Metaprobe\u2192Unified_Audit", - "equation_id": "math_model_map:8", - "family": "Metaprobe", - "name": "Unified_Metaprobe_Collapse", - "projection_signature": { - "shape_distance": 0.24112, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Collapses metaprobe functionality across all NES systems (GCL compression, OISC LUT, shader stack, cartridge controller, nanokernel UART, DSP math) into single unified metaprobe engine. Channel-specific resonance checking (UART frames, JTAG TAP states, audio signals, GCL markers, SUBLEQ instructions, nanokernel patterns). Unified structural coherence validation and entropy evaluation. Cross-system state tracking and unified audit trail. Single substrate for Lawful signal resonance across entire NES architecture.", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "receipt_hash": "1fc2347983bcaf377ab31e5374f6c1489c5a8692bb01d8ba963e23dc33f0f82c", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3718885775377466, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3718885775377466, - "shape": "LogogramProjection" - }, - { - "distance": 0.4179366633585035, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4179366633585035, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.44032818638158544, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44032818638158544, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.24112, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Unified_Metaprobe_Collapse", - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "payload_bytes_sampled": 1029, - "payload_sha256": "f0576ca7ab77b643fbff4d90b99fa22002dafd57ea6b79f221135759dc0a5802", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "91d19d7f00f48531cdcc704660aa97e71d9936296e61a9d0ee2f228dbc2ae0e9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "All_Math\u2192Single_Substrate\u2192Unified_Computation", - "equation_id": "math_model_map:9", - "family": "UnifiedMath", - "name": "Final_Unified_Math_Collapse", - "projection_signature": { - "shape_distance": 0.225432, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.697917 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Final collapse of all mathematical substrate into single unified metaprobe. Folds DeltaGCL diff enhancements (delta encoding, PTOS dictionary, VLE), cognitive load math (Intrinsic, Extraneous, Germane, Routing, Memory, Total, Efficiency), pressure piling physics (KDA equation P(i)=P\u2080\u00b7\u03c7^i), PIST geometry (Perfectly Imperfect Square Theory), and all NES systems into one unified computational substrate. 18 total channels with channel-specific resonance checking, structural coherence, entropy evaluation, and math-specific scoring. NES systems + Math models = Unified computational stack. Maximum retro insanity: 1985 NES + nanokernel + DSP + all math in one substrate.", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "receipt_hash": "a2cf12ae63bceb64bf9e6d989b865ffe782d905bd146261488fed3670fc89877", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.428571, - "hardware_affinity": 0.0, - "history_depth": 0.2, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.697917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3852773374146019, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3852773374146019, - "shape": "LogogramProjection" - }, - { - "distance": 0.42613339272104894, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42613339272104894, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - { - "distance": 0.4376621419940372, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4376621419940372, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.225432, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Final_Unified_Math_Collapse", - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "payload_bytes_sampled": 1197, - "payload_sha256": "b9a0f6808e9d6153ab34962bb7a541a5307795051f081aef1b8a2976e86f798f", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "5f5daa2ca668f171fd45a84ed8e68df591b14d05890abc55a04ff10c079dcd82" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "Voltage\u2192Value + Operations\u2192Result", - "equation_id": "math_model_map:10", - "family": "VoltageMath", - "name": "Voltage_Computational_Substrate", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Physical voltage levels as computational substrate. Voltage magnitude = numerical value. Voltage sum = addition. Voltage ratio = multiplication/division. Voltage difference = subtraction. Voltage gradient = derivative. Voltage integral = integration. Voltage-aware nanokernel validates voltage safety and resonance. Zero instruction overhead - physics does the math. Horrific: physical substrate becomes computer. Wonderful: parallel computation at speed of light. Maximum retro insanity: voltage = math.", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_f010fb33997b8f51", - "receipt_hash": "2c9734d1fe05811fde10aa631e88d49bb86229d204fdaaa953ab89c06fa48923", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Voltage_Computational_Substrate", - "object_id": "rrc_eq_f010fb33997b8f51", - "payload_bytes_sampled": 1016, - "payload_sha256": "c8b21d1f70df5795834ba6c37907b3464ac3bda086b4a6ef6b757765e4f2df0b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_f010fb33997b8f51", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "f1597e44bf543d37a3356488632b0885cff6f205d1980352a48a31c564b0a6cf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "DSP_Math\u2192Palette_Parameters\u2192Video_Palette", - "equation_id": "math_model_map:11", - "family": "VideoSynth", - "name": "Palette_DSP_Slave", - "projection_signature": { - "shape_distance": 0.276228, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "NES palette generator slaved to DSP math on audio lines. Audio signals (frequency, amplitude, duty cycle) map to palette generator parameters (x, y, t). DSP operations (add, multiply, integrate, differentiate) modulate palette before generation. Audio math controls video palette in real-time. Video synthesizer controlled by audio computation. Cross-domain repurposing (audio \u2192 video). Horrific: audio DSP math controls video palette. Wonderful: generative visuals from DSP operations. Maximum retro insanity: audio math = video palette.", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_8500000bbf612a0e", - "receipt_hash": "85c61e745138b0011db4090f065026ed834d9cc7acb00169a1faed61144a3eb7", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4640654354085545, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4640654354085545, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.276228, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Palette_DSP_Slave", - "object_id": "rrc_eq_8500000bbf612a0e", - "payload_bytes_sampled": 1041, - "payload_sha256": "50913bfba35644a73f1cb699ea81151607bf4c4ba6c7dc81e09c8d62a964ed18", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8500000bbf612a0e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b320bf30ac1fd5b57aa68788f6b2932dd55a22e9a122e4c4a31a22395f5f1c26" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "informational_bind", - "equation": "4\u00d7_Vertical\u2192256\u00d7960", - "equation_id": "math_model_map:12", - "family": "TemporalSuperSample", - "name": "Quad_Sampled_Scanlines", - "projection_signature": { - "shape_distance": 0.267969, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Quad-sample each scanline 4 times with subpixel offsets. DSP math interpolates between samples. Voltage levels control sampling timing. Effective resolution: 256\u00d7240 physical \u2192 256\u00d7960 perceived (4x vertical). Temporal supersampling without hardware modification. Bicubic interpolation between subpixel samples. Horrific: 4x temporal supersampling on 1x hardware. Wonderful: effective 4x vertical resolution increase. Maximum retro insanity: quad sampling = 4x resolution.", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_8b812bf47cc024b4", - "receipt_hash": "a213a0ff638a8b763b4a1809cc021d416f34930106c2bab1e938074e6c53c777", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3968047537103377, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3968047537103377, - "shape": "LogogramProjection" - }, - { - "distance": 0.4433112291612463, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4433112291612463, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4592495114396944, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4592495114396944, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.267969, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Quad_Sampled_Scanlines", - "object_id": "rrc_eq_8b812bf47cc024b4", - "payload_bytes_sampled": 982, - "payload_sha256": "d1881a8638311efed0884becc86cd3e3ba27f6c41368b25a1f1cf4713ad39580", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8b812bf47cc024b4", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "357acf40aeb1394f8c53a3ce1481b32a96a041f2973b49925cc1121d4f12dbca" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Microgrid\u2192640\u00d7480+Differential_Updates\u2192Effective_640\u00d7480", - "equation_id": "math_model_map:13", - "family": "VirtualDisplay", - "name": "Microgrid_Voxel_Emulation", - "projection_signature": { - "shape_distance": 0.231194, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.677083 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "640\u00d7480 voxel microgrid emulates higher resolution display. NES renders at 256\u00d7240 native. Map NES pixels to microgrid voxels (2.5\u00d72 scaling). Only update voxels that change (differential updates). DSP math calculates change priority. Voltage computation controls update threshold. Update efficiency: ~50% (only changed voxels). Effective 640\u00d7480 resolution without changing NES PPU. Virtual display on 1\u00d7 hardware. Modern GPU-like differential updates on retro hardware. Horrific: virtual 640\u00d7480 on 256\u00d7240 hardware. Wonderful: differential voxel updates for efficiency. Maximum retro insanity: microgrid = virtual display.scripts/microgrid_voxel_emulation.py", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_0606dcb042ba0e6f", - "receipt_hash": "1bf08c71fe722ae6aa14658e27154241ad113313e7b15828c9570fdecf72b04b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.677083, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3571706214750865, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3571706214750865, - "shape": "LogogramProjection" - }, - { - "distance": 0.40236283730595634, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40236283730595634, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4455402571059571, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4455402571059571, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.231194, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Microgrid_Voxel_Emulation", - "object_id": "rrc_eq_0606dcb042ba0e6f", - "payload_bytes_sampled": 1213, - "payload_sha256": "2122465eecb21649e5f2dccaa6ece11e9c8134b2060cc9e03a4241ca8f450912", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_0606dcb042ba0e6f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "d216afeed51090aa2f0d2f1feb9de50665d206b3689e9d1087b29eb2d150b9ef" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "L_E(x) = BPB(x,w_prior) - BPB*(x) = (1/n)\u03a3_i log\u2082(P_w*(x_i)/P_wprior(x_i))", - "equation_id": "math_model_map:2", - "family": "Cognitive Load", - "name": "Extraneous_Load_LE", - "projection_signature": { - "shape_distance": 0.234956, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Cost of architectural mismatch; penalty for suboptimal routing", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "receipt_hash": "57f9053ee54d45a2b47d894c2cff76e84df1eb11dc4d837eb8b3d4715803cfb9", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.364828321511775, - "kind_prior_bonus": 0.0, - "raw_distance": 0.364828321511775, - "shape": "LogogramProjection" - }, - { - "distance": 0.41916927222814615, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41916927222814615, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4392795461358172, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4392795461358172, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.234956, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Extraneous_Load_LE", - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "payload_bytes_sampled": 619, - "payload_sha256": "6338af70cd07cabe983fae1a85888706b0f0ff96ee80c62b3877e2efe5043edc", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e43c6929cd3bc3bf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "535a6057bd34b7eed4ccf3cd5e4b5ab8ebe368fad762288190c3e40a00a77481" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "L_G(x,t) = \u03a3_{s=1}^{S} \u03b3^s \u00b7 \u0394L_E(x_s,t+1) \u2248 \u03c4\u00b7L_E\u00b7log(S+1)/log(S_max+1)", - "equation_id": "math_model_map:3", - "family": "Cognitive Load", - "name": "Germane_Load_LG", - "projection_signature": { - "shape_distance": 0.252403, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.645833 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Productive learning effort reducing future extraneous load", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_6d33c14a88eb0a12", - "receipt_hash": "8def57ee740216a50491df69c7e10d33d9a104b45d52935e33e1c3276acb8669", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.645833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.39556712178882303, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39556712178882303, - "shape": "LogogramProjection" - }, - { - "distance": 0.44561469341029436, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44561469341029436, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4460142430272274, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4460142430272274, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.252403, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Germane_Load_LG", - "object_id": "rrc_eq_6d33c14a88eb0a12", - "payload_bytes_sampled": 633, - "payload_sha256": "35033e8111c252c4c474b49c7abf4481c9e1574ed5f0b4fb498e7790cfc2c5ed", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6d33c14a88eb0a12", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "84cd9c5d86f231e56e76f7bdaf820e29d75259b316d10434fc176f2cf1b3f2ea" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "L_R(x) = \u03a3_j c_j\u00b71[f_j computed] + \u03a3_{l=1}^{D(x)} log\u2082|M_l|", - "equation_id": "math_model_map:4", - "family": "Cognitive Load", - "name": "Routing_Load_LR", - "projection_signature": { - "shape_distance": 0.252088, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.65625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Computational cost of classification and method selection", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_1f912c8afa928326", - "receipt_hash": "ffdd3aa09c5b80200a73a5d04acb1b69c92bdb3d4c1af625d301a93af7b9e274", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.65625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.395618208222396, - "kind_prior_bonus": 0.0, - "raw_distance": 0.395618208222396, - "shape": "LogogramProjection" - }, - { - "distance": 0.4457184729773824, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4457184729773824, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.44585516948140186, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44585516948140186, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.252088, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Routing_Load_LR", - "object_id": "rrc_eq_1f912c8afa928326", - "payload_bytes_sampled": 599, - "payload_sha256": "d9c551f682f6f75b13483ce1e53c7b4c538c86fbce41d1d101730443db230dd8", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1f912c8afa928326", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "0cc68562cd1f4f0176256a300e4f3cb06abd4f1e320070824a2bfda2ca60e6bb" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "L_M(x) = log\u2082|E| + \u03b1\u00b71[hit] + \u03b2 + \u03bb\u00b7|E|/|E_max|", - "equation_id": "math_model_map:5", - "family": "Cognitive Load", - "name": "Memory_Load_LM", - "projection_signature": { - "shape_distance": 0.228316, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.645833 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Burden of storing, retrieving, and updating routing memory", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_535b43060096e699", - "receipt_hash": "e2ae4a1258232a14520cc166ec7691933d14f704de733a36eb0a2eab0fc63fc9", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.2, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.645833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38790894271760934, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38790894271760934, - "shape": "LogogramProjection" - }, - { - "distance": 0.4277016541739705, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4277016541739705, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - { - "distance": 0.43539919037953045, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43539919037953045, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.228316, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Memory_Load_LM", - "object_id": "rrc_eq_535b43060096e699", - "payload_bytes_sampled": 597, - "payload_sha256": "38a627523bff002e2c25d602fee2e2a88d774dce75fdc16a848a7639e5e73352", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_535b43060096e699", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b9e16bc711b32346cb6dc89ee3f654462271cf7ab646813baebcbd22384eb534" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "L_total = \u03bbI\u00b7l\u0302I + \u03bbE\u00b7l\u0302E - \u03bbG\u00b7l\u0302G + \u03bbR\u00b7l\u0302R + \u03bbM\u00b7l\u0302M (\u03a3\u03bb=1, \u03bbG\u2264\u03bbE)", - "equation_id": "math_model_map:6", - "family": "Cognitive Load", - "name": "Total_Cognitive_Load", - "projection_signature": { - "shape_distance": 0.228402, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Aggregate processing burden; combines all load classes", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_9a468347631152ce", - "receipt_hash": "4f48e4c66bbeee942e7ea30dadae951982b3cbecc3b2e48bede46499ed02940c", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3594529603743495, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3594529603743495, - "shape": "LogogramProjection" - }, - { - "distance": 0.40391693581644234, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40391693581644234, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4288207484407817, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4288207484407817, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.228402, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Total_Cognitive_Load", - "object_id": "rrc_eq_9a468347631152ce", - "payload_bytes_sampled": 692, - "payload_sha256": "3121eed98aea432ba226e748bf7d537d3a841e565358d4e9b1c08a146fa75481", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9a468347631152ce", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "25e6cf149840abaa43ecbb2a1e11439d7ba4eafc5e18f292e30ec3f4c33897df" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "\u03b7(x) = l\u0302I(x) / (l\u0302I + l\u0302E + l\u0302R + l\u0302M + \u03b5)", - "equation_id": "math_model_map:7", - "family": "Cognitive Load", - "name": "Cognitive_Efficiency", - "projection_signature": { - "shape_distance": 0.234942, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Routing efficiency (1=perfect, 0=maximum waste)", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "receipt_hash": "88e70a0abf571b5d15f03af38be28c7a992d331641f6fdd55f562a70b7e3a0cc", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.36488445506574657, - "kind_prior_bonus": 0.0, - "raw_distance": 0.36488445506574657, - "shape": "LogogramProjection" - }, - { - "distance": 0.41959068167894464, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41959068167894464, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.43953358539647913, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43953358539647913, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.234942, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Cognitive_Efficiency", - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "payload_bytes_sampled": 597, - "payload_sha256": "c78e4f00e83bfb8900c6af2a9bcc594816e4bc8b693afe11476a25464601413c", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c8d2e5596d91ebbd", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "7fc806ab7ab5621329e8cfac952cf68b5c233f28c353a35dd9c48ae0b29cf3ce" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "L_\u03c1(x) = L_total(x) \u00b7 (1 + \u03c1(x)/\u03c1_max)", - "equation_id": "math_model_map:8", - "family": "Cognitive Load", - "name": "Regret_Adjusted_Load", - "projection_signature": { - "shape_distance": 0.234942, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Load penalized by historical performance on similar inputs", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "receipt_hash": "2b46c6b169bdeee7d17343bddb709202fff812ec08acaa8290002ebaab01932a", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.36488445506574657, - "kind_prior_bonus": 0.0, - "raw_distance": 0.36488445506574657, - "shape": "LogogramProjection" - }, - { - "distance": 0.41959068167894464, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41959068167894464, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.43953358539647913, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43953358539647913, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.234942, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Regret_Adjusted_Load", - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "payload_bytes_sampled": 588, - "payload_sha256": "e5f110e469e1980c56d490b82e77867e8998829352188a319d2842b9e9223abf", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7995b3bdb3f05ce3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "7b71c85a5f1c3c83e729856bf6123e0f24229a137ab5507e2bb3d81daa43f844" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "L(x|B) = L_I(x) + L_E(x|B) + L_R^B(x)", - "equation_id": "math_model_map:9", - "family": "Cognitive Load", - "name": "Basin_Conditional_Load", - "projection_signature": { - "shape_distance": 0.235052, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Load conditioned on attractor basin membership", - "route_hint_non_authoritative": "cognitive_load", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "receipt_hash": "d4c0fdce69cffaefa65ae03ec6f9dd78bfe1748cb35ec9976dbefc26c94c0488", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3652945998988946, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3652945998988946, - "shape": "LogogramProjection" - }, - { - "distance": 0.42143305437337036, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42143305437337036, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.44070218801909455, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44070218801909455, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.235052, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Basin_Conditional_Load", - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "payload_bytes_sampled": 557, - "payload_sha256": "6734f301a07f5b08d605b18316b08061c394335d15d97a3de7194f544970633f", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a4adf8b5cc0e5c73", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "9d120396091a94730330f940defee7dedcf46282c37d02916f6d25456bc00d32" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "P_w(x_i|x_{ 1.0", - "equation_id": "math_model_map:15", - "family": "KDA Physics", - "name": "Q_Factor", - "projection_signature": { - "shape_distance": 0.221733, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.645833 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Global energy balance; net gain threshold", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_891a81dfc968f58e", - "receipt_hash": "3dfc542c01f5b55226137a18f732283c4f24468ed3e7a5c78583568c04a0d647", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.645833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3624266370288594, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3624266370288594, - "shape": "LogogramProjection" - }, - { - "distance": 0.40563490280165204, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40563490280165204, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4371942148676752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4371942148676752, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.221733, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Q_Factor", - "object_id": "rrc_eq_891a81dfc968f58e", - "payload_bytes_sampled": 582, - "payload_sha256": "73e5c701bc5e245a342e5c8b74e341073e0caa6d0612683e59fa6838f0c37e89", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_891a81dfc968f58e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "d349683a752b4a958e6ea563de5e0a021c02283bc16bd7bd906fa3c7adbfb373" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "GWL Rotation", - "equation_id": "math_model_map:16", - "family": "w_ij", - "name": "Coupling_Weight", - "projection_signature": { - "shape_distance": 0.288723, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "\u03b8=azimuthal(16), \u03c6=polar(8), \u03c7=chirality, \u0394p=position delta", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_45606d1f25dd6aa5", - "receipt_hash": "91e04dc8d354b05ffd7879f7ee00210d52a10ef2a516cd7a11ebc05ec4e7328e", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40188376865433434, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40188376865433434, - "shape": "LogogramProjection" - }, - { - "distance": 0.4526812186627945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4526812186627945, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46231833413703066, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46231833413703066, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288723, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Coupling_Weight", - "object_id": "rrc_eq_45606d1f25dd6aa5", - "payload_bytes_sampled": 545, - "payload_sha256": "e47ea12b5b667c63b2bd119f8ea07e6048c13e4a4abb946312a706f12673f12b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_45606d1f25dd6aa5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "5835b95edc14f694739fc3921dd9554c52039ad044c55483ee81fc2d1d734588" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "g = cos(\u0394\u03b8\u00b72\u03c0/16) \u00b7 cos(\u0394\u03c6\u00b7\u03c0/8) \u00b7 (1 - 2|\u03c7_i - \u03c7_j|)", - "equation_id": "math_model_map:17", - "family": "GWL Rotation", - "name": "Rotational_Alignment", - "projection_signature": { - "shape_distance": 0.290188, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Frame orientation compatibility", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_39d59d35fd9672a0", - "receipt_hash": "218df5724324d7c981f1ac73e6efca089fc375ebd3af528e1c93a6a4d9e22455", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40178115713660933, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40178115713660933, - "shape": "LogogramProjection" - }, - { - "distance": 0.4512070704812315, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4512070704812315, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46143971451836985, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46143971451836985, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.290188, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Rotational_Alignment", - "object_id": "rrc_eq_39d59d35fd9672a0", - "payload_bytes_sampled": 610, - "payload_sha256": "91f49402d51efffa348553075270fc83dc6cc2c93fe03339b861e077dabb91c3", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_39d59d35fd9672a0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "bf291e1a3809605bdbafab7e431fa946de56b17120ec71406034634112027dff" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "h = exp(-|\u0394p|\u00b2/(2\u03c3\u00b2)) \u00b7 1_{|\u0394p|> \u03a6_metric(i,j)}", - "equation_id": "math_model_map:35", - "family": "GWL Throat", - "name": "Throat_Condition", - "projection_signature": { - "shape_distance": 0.288392, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Defines non-local transport corridor", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_56bc9e8becb7bcba", - "receipt_hash": "8c87ddfc20e69d60b1bc8fd5a695ae8768306e1e451e6158ec9624fd1ad8107b", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40195159938404756, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40195159938404756, - "shape": "LogogramProjection" - }, - { - "distance": 0.45308642715227754, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45308642715227754, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46250054585271627, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46250054585271627, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288392, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Throat_Condition", - "object_id": "rrc_eq_56bc9e8becb7bcba", - "payload_bytes_sampled": 553, - "payload_sha256": "240bb83e62d46ebd6abedce4a403125dd28ec8e9616f3cefbf4dd784c07c152d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_56bc9e8becb7bcba", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "01bb0cbdc908ff0735c0d7d4f728a27793eb2dbeb99a2bf1ffd583b711ecdf9a" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_BRAID", - "equation": "w_ij = w_p \u00b7 w_\u03c0 \u00b7 w_\u03c4 \u00b7 w_\u03c7 \u00b7 w_topo \u00b7 w_\u03c3", - "equation_id": "math_model_map:36", - "family": "GWL Throat", - "name": "Multi_Factor_Coupling_Weight", - "projection_signature": { - "shape_distance": 0.288076, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Comprehensive coupling evaluation during packet propagation", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_e672df600fa82b76", - "receipt_hash": "4e61c9a2c9b8bd7e4fb3b0ddbb9fa9a45a2c6cbd4258fd6fd82d73764aed0bdf", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40203628736102015, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40203628736102015, - "shape": "LogogramProjection" - }, - { - "distance": 0.4535062277160645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4535062277160645, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46251784796736883, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46251784796736883, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288076, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Multi_Factor_Coupling_Weight", - "object_id": "rrc_eq_e672df600fa82b76", - "payload_bytes_sampled": 617, - "payload_sha256": "2f6c76460e047cb5983b5d9ca893bfd68cca0ff72a2a5d7f428cae6f6991e5a1", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_e672df600fa82b76", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "1fc5fc7eed8efa2a7f6bb0e87fc19777ef2d3e10e0108a3ba43e60f6cbf6b409" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_BRAID", - "equation": "Hol(\u03b3_loop) = \u222e_\u03b3 T(p) dp", - "equation_id": "math_model_map:37", - "family": "GWL Throat", - "name": "Holonomy_Accumulation", - "projection_signature": { - "shape_distance": 0.288076, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Phase accumulated when transporting vector around closed loop", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_bd8bcf9cb663c096", - "receipt_hash": "0e63345785cc5b350073c20c3de1ad34aba6a04370663fc7f53b55f1ba4a2851", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40203628736102015, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40203628736102015, - "shape": "LogogramProjection" - }, - { - "distance": 0.4535062277160645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4535062277160645, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46251784796736883, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46251784796736883, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288076, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Holonomy_Accumulation", - "object_id": "rrc_eq_bd8bcf9cb663c096", - "payload_bytes_sampled": 564, - "payload_sha256": "87d2327ba0f04aca5dc0209443ddd2d81b0837331d321fc919a73b367f67e55a", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_bd8bcf9cb663c096", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "d18285efaf9190f973728f8356cae371e4e97e3f9a6bfeaa280e4aee23ca0ea8" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "d_N = path_length(\u03b3_ij) + curvature_penalty(\u03ba) + torsion_cost(T); d_T = d_E + \u03bb_N\u00b7d_N", - "equation_id": "math_model_map:38", - "family": "GWL Throat", - "name": "Non_Euclidean_Distance", - "projection_signature": { - "shape_distance": 0.271543, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Topology-aware distance for routing through multi-manifold structures", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_2ee9bc98d2a7c773", - "receipt_hash": "727ba4b3e84c4930356c0e1a614f17b80034ab4c2be482d1b3b2fdcd68aff469", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3899552387741366, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3899552387741366, - "shape": "LogogramProjection" - }, - { - "distance": 0.43736404798476075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43736404798476075, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4424082486634818, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4424082486634818, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.271543, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Non_Euclidean_Distance", - "object_id": "rrc_eq_2ee9bc98d2a7c773", - "payload_bytes_sampled": 641, - "payload_sha256": "52fdc5eb8acd8eaa8da99d1bf6afeed078a6eb39ba00e32623c85c442515861b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_2ee9bc98d2a7c773", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "2d5566aad8d3fabb8a77caeebdf608701daf5756422cd8d74e5ef5c3f0d42f73" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "TrixalAxes = (thermal, work, irreversibility), each \u2208 [0,1]; |axes| = \u221a(th\u00b2 + w\u00b2 + ir\u00b2)", - "equation_id": "math_model_map:39", - "family": "Thermodynamic", - "name": "Trixal_Axes", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Phase space coordinates for process tracking", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9064d88b17992de3", - "receipt_hash": "2a7222bd59712ee0f722f03b0143fd4a170fc2e81c707758c53245419d11fe3b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Trixal_Axes", - "object_id": "rrc_eq_9064d88b17992de3", - "payload_bytes_sampled": 620, - "payload_sha256": "5bafb74383c1a60ee98486d7c131034ad5492b3dff2ebac73d5d0feeb7c39546", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9064d88b17992de3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "8b663dccee9c3aa89f50c738a58defbb62c2c52bfb9fb7e66bf4e0200568e5bb" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "H = -\u03a3_b p(b) log\u2082 p(b), where p(b)=count(b)/len", - "equation_id": "math_model_map:40", - "family": "Thermodynamic", - "name": "Shannon_Entropy", - "projection_signature": { - "shape_distance": 0.259671, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Information content measurement", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_cf576a1cfbd9da63", - "receipt_hash": "45129f9e90477503d027d2659026de7f7a5681e1e8178b31e211c96a9b48b996", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4019474440565634, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4019474440565634, - "shape": "LogogramProjection" - }, - { - "distance": 0.44664772277466575, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44664772277466575, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4515177686455449, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4515177686455449, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.259671, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Shannon_Entropy", - "object_id": "rrc_eq_cf576a1cfbd9da63", - "payload_bytes_sampled": 557, - "payload_sha256": "d50383d5308a7ba10937e59fa8c0fb817f87612b6cba94fd3fc41167189f4413", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_cf576a1cfbd9da63", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "2f08fd78f8664fe67a4b80d97510d28fec19e2f7550d59fc2d24615c684ecef7" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "K_est = (8 - H) / 8", - "equation_id": "math_model_map:41", - "family": "Thermodynamic", - "name": "Kolmogorov_Estimate", - "projection_signature": { - "shape_distance": 0.261553, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Rough estimate of structure via compressibility", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_cd4dfc767616524d", - "receipt_hash": "ea3f21e1ef2f182f47786125755205ea8de29d8070f16fbb9b1604b4ceb53d5f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4024546358221201, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024546358221201, - "shape": "LogogramProjection" - }, - { - "distance": 0.4468711682773598, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4468711682773598, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4527752733756887, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4527752733756887, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.261553, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Kolmogorov_Estimate", - "object_id": "rrc_eq_cd4dfc767616524d", - "payload_bytes_sampled": 538, - "payload_sha256": "68f6e7a6353c6198bffe6e2be8c415eef70417b37d150db1ef156241ff70b24a", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_cd4dfc767616524d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "df738b8cd242215b72879d19a027eb7705b4ce20c32fe547379438b2c20d3a86" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "S_thermo = H + K_est \u00b7 0.1", - "equation_id": "math_model_map:42", - "family": "Thermodynamic", - "name": "Thermodynamic_Entropy", - "projection_signature": { - "shape_distance": 0.260119, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Combined information thermodynamics", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_ecf3ddf7af735ee6", - "receipt_hash": "5bec7a5e5d820f0e54d8ae7ba02d8388ae681c580b1fe4163f5afc85113a112f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4020490010674055, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4020490010674055, - "shape": "LogogramProjection" - }, - { - "distance": 0.4466808216189226, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4466808216189226, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45180995843940064, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45180995843940064, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.260119, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Thermodynamic_Entropy", - "object_id": "rrc_eq_ecf3ddf7af735ee6", - "payload_bytes_sampled": 540, - "payload_sha256": "ebff6f709848123bb2bf03d8c091b3716b16d97010ef6da11c1d2c623bfc5977", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ecf3ddf7af735ee6", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "3c3b0b8b67bdcc25c7c0d80ede7829919fa8eb52d1792d0b6cc3933f956381f7" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_H_ALGEBRA", - "equation": "dS/dt = (S_current - S_previous) / \u0394t", - "equation_id": "math_model_map:43", - "family": "Thermodynamic", - "name": "Entropy_Gradient", - "projection_signature": { - "shape_distance": 0.260119, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Tracks rate of entropy change over time", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_c8b7f0fe52ef32fe", - "receipt_hash": "432f7355fd132f9288d7eb4e9337093501490c2aefdadc9442ffa073b6912d44", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4020490010674055, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4020490010674055, - "shape": "LogogramProjection" - }, - { - "distance": 0.4466808216189226, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4466808216189226, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45180995843940064, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45180995843940064, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.260119, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Entropy_Gradient", - "object_id": "rrc_eq_c8b7f0fe52ef32fe", - "payload_bytes_sampled": 551, - "payload_sha256": "a962bdf2c598629433183760e14afcaa2cb694fac8c3a666fb4bdabfd9e735c7", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c8b7f0fe52ef32fe", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "cd4e135ffa333fc850e73b36709824478dee0679588fecfc812928ae3c17bc73" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "MI = H_initial - H_current", - "equation_id": "math_model_map:44", - "family": "Thermodynamic", - "name": "Mutual_Information_Extracted", - "projection_signature": { - "shape_distance": 0.236532, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Work performed by compression; information extracted", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_71dd02d32f3e59b0", - "receipt_hash": "bec567961880a727fb6d03fb3b52e8df36fb55646be0c3c6647da65c5e9134be", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3721187879089877, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3721187879089877, - "shape": "LogogramProjection" - }, - { - "distance": 0.42626349470489355, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42626349470489355, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4447761402479924, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4447761402479924, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.236532, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Mutual_Information_Extracted", - "object_id": "rrc_eq_71dd02d32f3e59b0", - "payload_bytes_sampled": 564, - "payload_sha256": "3e4f1e252259a936e2b03b18faa6547400f6a9da4cbbd164b3f2afddebc45fe0", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_71dd02d32f3e59b0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "0b15c7cc40dcadc55215b07b87cbd144bebb1f23184ee12e3875f9c19490efaa" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "\u03b7_Carnot = 1 - T_cold / T_hot", - "equation_id": "math_model_map:45", - "family": "Thermodynamic", - "name": "Carnot_Efficiency", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Maximum theoretical thermodynamic efficiency", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_d07532a1db76d958", - "receipt_hash": "6e7801ddba1d8d92997ac2b7377bee105e69fe4111f9485efa4f0c2a5215df7a", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Carnot_Efficiency", - "object_id": "rrc_eq_d07532a1db76d958", - "payload_bytes_sampled": 549, - "payload_sha256": "e26aa87e968cb05b4316c16b659bd16f7a08d033b5d299de38d9a36430305c19", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d07532a1db76d958", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "aa3a2527a115b5a4ea64c0a0b312ed03218b4c9e727baa1122b42bf39d88aa3a" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "W_actual = Q_absorbed \u00b7 \u03b7_Carnot \u00b7 0.7", - "equation_id": "math_model_map:46", - "family": "Thermodynamic", - "name": "Work_Extraction", - "projection_signature": { - "shape_distance": 0.255474, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Model computation as thermodynamic work extraction cycle", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_7533d46cedd4a4c5", - "receipt_hash": "28df300ae3ad5ce3925a6a8a1d59bad778e0c74306e8f97c23ef1f9c12d2e1eb", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3957755404213973, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3957755404213973, - "shape": "LogogramProjection" - }, - { - "distance": 0.44533214444057323, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44533214444057323, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4478302902574139, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4478302902574139, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.255474, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Work_Extraction", - "object_id": "rrc_eq_7533d46cedd4a4c5", - "payload_bytes_sampled": 579, - "payload_sha256": "6c3859a4d108bf1bf4c2a072e5dab54ddb0d3f7ff351a937149e2c242de757d9", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7533d46cedd4a4c5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "5dd1ff2275f99b54e738dc4f9026ac24f9d03e22f19340ae796f4d24b8259576" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "score = (entropy_production + path_asymmetry + time_reversal_violation) / 3", - "equation_id": "math_model_map:47", - "family": "Thermodynamic", - "name": "Irreversibility_Metric", - "projection_signature": { - "shape_distance": 0.26106, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Quantifies thermodynamic irreversibility of process trajectory", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_1f5af7e9fb2b518f", - "receipt_hash": "7c59304f0cff3bd05c20e5b0747abd2894f22ad31dbac474803f4dafbdcb9d8e", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4023026128610827, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4023026128610827, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467925500621565, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467925500621565, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4524387416368119, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4524387416368119, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.26106, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Irreversibility_Metric", - "object_id": "rrc_eq_1f5af7e9fb2b518f", - "payload_bytes_sampled": 612, - "payload_sha256": "b7b4c36161b5bfd3c0ce6054d6c5719dca8c0033ecc924542a6c257eece3c484", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1f5af7e9fb2b518f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "0c7874318f66e658f986be55ba9d5910072dd3de34e550f79126ce5cd6f1ff42" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "L_thermo = \u03a3_i distance_i \u00b7 (1 + irreversibility_i)", - "equation_id": "math_model_map:48", - "family": "Thermodynamic", - "name": "Thermodynamic_Length", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Dissipative trajectory length accounting for thermodynamic cost", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_685a03c6c1db609e", - "receipt_hash": "56b4a990a2c7474d0fe4198a3816d8501152ae72b6d5fc9890203495df9dd09f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Thermodynamic_Length", - "object_id": "rrc_eq_685a03c6c1db609e", - "payload_bytes_sampled": 598, - "payload_sha256": "2d4086d96adffc4b848fc10c51edc6528b540ffe16452ef2eb1a3ea4e906adbf", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_685a03c6c1db609e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "001d5a9bf4a5c40e8c8f5538b9c5d5a2e814bcb96c5c463103d3917c0d603c30" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "depth = entropy_production \u00b7 ln(time_steps)", - "equation_id": "math_model_map:49", - "family": "Thermodynamic", - "name": "Thermodynamic_Depth", - "projection_signature": { - "shape_distance": 0.261553, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Complexity measure", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_4cab00eaeac59782", - "receipt_hash": "c0af85a404ea45e0ab11f3f9782b0db583a4581d817843b998b3e89f6d7ec438", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4024546358221201, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4024546358221201, - "shape": "LogogramProjection" - }, - { - "distance": 0.4468711682773598, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4468711682773598, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4527752733756887, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4527752733756887, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.261553, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Thermodynamic_Depth", - "object_id": "rrc_eq_4cab00eaeac59782", - "payload_bytes_sampled": 538, - "payload_sha256": "d121849859bc6a9afb13eb6350f36e26c582e6a023bde9dd571833f11615397c", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4cab00eaeac59782", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "50d775e7714478cbf140d3f0c38ad629650b2a75511c9f0ed7387fc319942612" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "SHA256(axes || traj_hash || hardware_entropy || timing_jitter || process_nonce)", - "equation_id": "math_model_map:50", - "family": "Thermodynamic", - "name": "Stamp_Code", - "projection_signature": { - "shape_distance": 0.202394, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 1.0 - ], - [ - "shape_closure", - 0.69 - ], - [ - "proof_readiness", - 0.625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Unique non-reproducible process fingerprint", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "logogram_projection", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_eq_4c87c96f612f6100", - "receipt_hash": "05fbcf42b4df550ceee510193a22b1c2c1ccaa4551edf8476235c7e773215f65", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.625, - "receipt_density": 0.111111, - "residual_risk": 0.44, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.69, - "topology_torsion": 0.0, - "witness_declared": 1.0 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4278352466783258, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4278352466783258, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4556228754677603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556228754677603, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4662580505780842, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4662580505780842, - "shape": "ProjectableGeometryTopology" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.202394, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Stamp_Code", - "object_id": "rrc_eq_4c87c96f612f6100", - "payload_bytes_sampled": 586, - "payload_sha256": "d581ac94594fb543c68b4788eb0b1c018a360ea2a3eba33c13019ab3aa97307d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4c87c96f612f6100", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "HOLD", - "witness_hash": "f8415044b97dbd66792bc3499a275c1c00c4be04584614bc8ab67ee9c320b006" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "AF = exp(E_a / (k_B \u00b7 T))", - "equation_id": "math_model_map:51", - "family": "Informatic Stress", - "name": "Arrhenius_Temperature_Factor", - "projection_signature": { - "shape_distance": 0.251579, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Failure rate temperature acceleration", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_25d3e2f91df8c0a0", - "receipt_hash": "a7b52e3e1608e84dfb6c20ad0fa81ecac03a00306722584bceda62da5b70bf8c", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38653815825039084, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38653815825039084, - "shape": "LogogramProjection" - }, - { - "distance": 0.42449286008105563, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42449286008105563, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45312061904381684, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45312061904381684, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.251579, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Arrhenius_Temperature_Factor", - "object_id": "rrc_eq_25d3e2f91df8c0a0", - "payload_bytes_sampled": 552, - "payload_sha256": "a6b41e917656b3e10481428bd4fbaab680d91f499cb1a6076536f2c3dd210b7e", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_25d3e2f91df8c0a0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "79fa61871551485a1e407e9b5c8b1ec785a0e0d24c4866bffe72715ed098a85d" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "EM_risk = J^n \u00b7 exp(E_a / (k_B \u00b7 T)) / 10^{12}", - "equation_id": "math_model_map:52", - "family": "Informatic Stress", - "name": "Blacks_Equation_EM", - "projection_signature": { - "shape_distance": 0.250302, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Electromigration failure risk", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_3ad1c4d008fc910b", - "receipt_hash": "9f1afd222bc1f68670d99706068af9cbf8b32018a75236c471f321583905e3c7", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.386326511599779, - "kind_prior_bonus": 0.0, - "raw_distance": 0.386326511599779, - "shape": "LogogramProjection" - }, - { - "distance": 0.4244842329710783, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4244842329710783, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4523359881591358, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4523359881591358, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.250302, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Blacks_Equation_EM", - "object_id": "rrc_eq_3ad1c4d008fc910b", - "payload_bytes_sampled": 560, - "payload_sha256": "1b2983c9a5c184d1456933ff1c1a112cafad814ec8e0c824f068f29ec1c3f7d9", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3ad1c4d008fc910b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "422efbb6932c343834fa65f176a390b2e9dd86d726027fb95590ab8c9f1ba9f5" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "CM_damage = (\u0394T / \u0394T_threshold)^m \u00b7 10^{-8}, m\u22481.9", - "equation_id": "math_model_map:53", - "family": "Informatic Stress", - "name": "Coffin_Manson_Fatigue", - "projection_signature": { - "shape_distance": 0.221733, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.645833 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Accumulates damage from thermal cycling", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_10dfe03d2d21bf90", - "receipt_hash": "de41e42243fdadd337f9e3a10330fb9010182b37ce7c017e2a0b01ca4411fbf4", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.645833, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3624266370288594, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3624266370288594, - "shape": "LogogramProjection" - }, - { - "distance": 0.40563490280165204, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40563490280165204, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4371942148676752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4371942148676752, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.221733, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Coffin_Manson_Fatigue", - "object_id": "rrc_eq_10dfe03d2d21bf90", - "payload_bytes_sampled": 587, - "payload_sha256": "cd593fa5315c5983381e4d2ffe89e9258c649c132c8569cce5065cb80f591b25", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_10dfe03d2d21bf90", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "49921ceca6483c9390dbe2386dda5cd5613c6a7baff8c22e99285c3da37a73a2" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "W_erasure \u2265 k_B \u00b7 T \u00b7 ln(2) \u2248 2.87e-21 J/bit at 300K", - "equation_id": "math_model_map:54", - "family": "Informatic Stress", - "name": "Landauer_Limit", - "projection_signature": { - "shape_distance": 0.23706, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.677083 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Thermodynamic lower bound on computation", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_b8bfc827c0fd3d28", - "receipt_hash": "15946d72d4d558558213e831ebda5f2ca0dfd3bcaaca0a16add8c116de4eb2a0", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.677083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37173076795260296, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37173076795260296, - "shape": "LogogramProjection" - }, - { - "distance": 0.4207575768133077, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4207575768133077, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4415711756085833, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4415711756085833, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.23706, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Landauer_Limit", - "object_id": "rrc_eq_b8bfc827c0fd3d28", - "payload_bytes_sampled": 588, - "payload_sha256": "76d190d165c8cf8517a79cf89441b20804d63a8185b543fd518783ef3ce7fd30", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b8bfc827c0fd3d28", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "83a2f0468c50447fac3a5bd6dedca6fd6540a256dbb992716f2c283910799bf9" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_E_VERIFICATION", - "equation": "dS/dt = power_dissipation / (k_B \u00b7 T \u00b7 ln 2) [bits/s]", - "equation_id": "math_model_map:55", - "family": "Informatic Stress", - "name": "Entropy_Generation_Rate", - "projection_signature": { - "shape_distance": 0.257657, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Rate of entropy generation from computational dissipation", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_20b0dce68b1ff729", - "receipt_hash": "9d69f02d9764ec505309c4f73f30051f802ab8bf432df3cfda193662de1cf474", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4016925950599106, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4016925950599106, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467099708634478, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467099708634478, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45027994649071323, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45027994649071323, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.257657, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Entropy_Generation_Rate", - "object_id": "rrc_eq_20b0dce68b1ff729", - "payload_bytes_sampled": 606, - "payload_sha256": "65e66e590fcdd571530878a25d7530d0ee31bcfe0c1b227426384632843cedbb", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_20b0dce68b1ff729", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "be15660e1639e93b8fc7f5a8794f3e871d1eec3a251519554bb0191dd0093aac" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_D_INVARIANTS", - "equation": "|G| = (D1/120 + D2/5000 + D3/1000 + D4/100 + D5/5000) / 5", - "equation_id": "math_model_map:56", - "family": "Informatic Stress", - "name": "BitFlip_Gradient_5D", - "projection_signature": { - "shape_distance": 0.250417, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Normalized 5D hardware stress gradient", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_e58f768224fb1bbe", - "receipt_hash": "9ae0bc24c4daa97ff27c39fc03de49c4472fd217f4f8e33a896200e008e8dd03", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38859934049915984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38859934049915984, - "shape": "LogogramProjection" - }, - { - "distance": 0.43431484882809657, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43431484882809657, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.44620314326036475, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44620314326036475, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.250417, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "BitFlip_Gradient_5D", - "object_id": "rrc_eq_e58f768224fb1bbe", - "payload_bytes_sampled": 575, - "payload_sha256": "46edbf76b7d11269b019d049aef879e3e01da54f2804420bde2a70b8453cfb5b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e58f768224fb1bbe", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "b1e1cefcb2ae28d7d9b28ffddb4830d52ef3a4cc11417644d5aaf5547c90a0c2" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "BFR = \u03b5_SEU \u00b7 2^{(T-25)/10} \u00b7 (1 + V_jitter/1000) \u00b7 3600 [flips/hr]", - "equation_id": "math_model_map:57", - "family": "Informatic Stress", - "name": "SEU_BitFlip_Rate", - "projection_signature": { - "shape_distance": 0.24942, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.65625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Estimated bit-flips per hour from hardware conditions", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9166088a2f79f059", - "receipt_hash": "36e79c004207e531f7eb9bb410b9dc4982ffb4ffe9c3301227104103b925d9be", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.65625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38870298923809676, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38870298923809676, - "shape": "LogogramProjection" - }, - { - "distance": 0.4345873954900469, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4345873954900469, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4456803074645318, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4456803074645318, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.24942, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "SEU_BitFlip_Rate", - "object_id": "rrc_eq_9166088a2f79f059", - "payload_bytes_sampled": 613, - "payload_sha256": "e19cba35de4b7f5a6d2f692fa308e8f6d38a191a691ed01547fc3b29071aaa22", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9166088a2f79f059", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "ac132ca1897fd5abd72f3b69dd1ef40232dcc362fc0e78204a906d70cb5e5da1" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "stress(t) = stress_0 \u00b7 e^{-t/300} + intensity \u00b7 (1 - e^{-t/300})", - "equation_id": "math_model_map:58", - "family": "Informatic Stress", - "name": "Stress_Decay", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Stress accumulation with exponential decay", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_f6ecffdb3a584bc6", - "receipt_hash": "cc81575b138cab1ea57e7093cc87794a0f8cf62876c0f70392b2d1b519dd9d29", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Stress_Decay", - "object_id": "rrc_eq_f6ecffdb3a584bc6", - "payload_bytes_sampled": 585, - "payload_sha256": "fef17e0128d1b36c5f2f869040205cbdff84c4bf0863c95b02e3c630089edb20", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_f6ecffdb3a584bc6", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "c7fd98c3eb4068002f6845e6c60d2218787ac27073ddb67a62e57d83e5233e7b" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "RUL = MTBF / (AF \u00b7 (1 + fatigue\u00b70.01 + thermal_fatigue\u00b70.1))", - "equation_id": "math_model_map:59", - "family": "Informatic Stress", - "name": "Remaining_Useful_Life", - "projection_signature": { - "shape_distance": 0.25078, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Hardware lifetime prediction under current stress", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_3fbf8213151eae2f", - "receipt_hash": "f470d8e0a80c20278b7e242888ef102e4a008c70872576eea8fde3ee8418ec46", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3885996895311975, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3885996895311975, - "shape": "LogogramProjection" - }, - { - "distance": 0.4342551966834711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4342551966834711, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.446407670173192, - "kind_prior_bonus": 0.0, - "raw_distance": 0.446407670173192, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25078, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Remaining_Useful_Life", - "object_id": "rrc_eq_3fbf8213151eae2f", - "payload_bytes_sampled": 602, - "payload_sha256": "cd69801ad3d7f64c18c884a1bedeae9c72b181f342fdb9666eaf2d97e8b303ee", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3fbf8213151eae2f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "cd0ce6df34f1be0638f3e047bc681261edb0832f9e1707ccc9cf1d3afdf3ab2b" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_E_VERIFICATION", - "equation": "surprise = -ln(margin), margin = 1 - stress_magnitude; regret = max(0, stress - 0.5)", - "equation_id": "math_model_map:60", - "family": "Informatic Stress", - "name": "Homeostatic_Stress_Injection", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Converts stress margin to surprise/regret for homeostatic controller", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_7731084327989290", - "receipt_hash": "8ced5ab6b94608ae88796d8d0351fc1a2b8b127827d19c53f550e61118a8596a", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Homeostatic_Stress_Injection", - "object_id": "rrc_eq_7731084327989290", - "payload_bytes_sampled": 643, - "payload_sha256": "5bf07cf136daabea9807d158d63cd18e9cf4068d30c6b42496665afb945138ae", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7731084327989290", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "542af3c813a05b8f425d875320b61f1b54e56ce93f016ac77e546c9e6bd1739e" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_D_INVARIANTS", - "equation": "int_bits \u2264 fp_mantissa_bits + 1 (signed) or \u2264 fp_mantissa_bits + 1 (unsigned)", - "equation_id": "math_model_map:61", - "family": "Informatic Stress", - "name": "Exact_Int_to_FP_Cast", - "projection_signature": { - "shape_distance": 0.259238, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Proves safe integer-to-floating-point conversion", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_613c8a56dee0f83a", - "receipt_hash": "e9898a1b1af963ad6f7caf939205784bd3b194eee9aa70be49a23298149f6718", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4018627373568807, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4018627373568807, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662980586330003, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662980586330003, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4512404188770619, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4512404188770619, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.259238, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Exact_Int_to_FP_Cast", - "object_id": "rrc_eq_613c8a56dee0f83a", - "payload_bytes_sampled": 616, - "payload_sha256": "e76a1cf51c879c58bcaeddaba567de50aa3262af1eb809e95e668b48324c5935", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_613c8a56dee0f83a", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "be6ac3697c2cc3ad77bb9f31abde598da9c09e592b32fd59586d4d79ed60073e" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_D_INVARIANTS", - "equation": "can_safely_narrow(src_bits, signed, dst_mantissa) \u2192 bool", - "equation_id": "math_model_map:62", - "family": "Informatic Stress", - "name": "Safe_Narrowing_Proof", - "projection_signature": { - "shape_distance": 0.26106, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Proves double\u2192single precision narrowing is safe", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_dd3140340d9c9a33", - "receipt_hash": "b082a3a7016c65ac9a926a98a88e5daa347f5997087e7636622bcedef95ad837", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4023026128610827, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4023026128610827, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467925500621565, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467925500621565, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4524387416368119, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4524387416368119, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.26106, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Safe_Narrowing_Proof", - "object_id": "rrc_eq_dd3140340d9c9a33", - "payload_bytes_sampled": 595, - "payload_sha256": "e1e5f9c8e534ac4b76624532d6c5cbb75d97190a4cb1e96eb6b56ad27a814a35", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_dd3140340d9c9a33", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "3ec6f66d6a22613559055ee9a50bec9640d57fd1207308fd8036f0b64cb35da5" - } - }, - { - "equation_record": { - "bind_class": "thermodynamic_bind", - "domain_type": "LAYER_D_INVARIANTS", - "equation": "fdiv.d=33, fdiv.s=19 \u2192 penalty=0.737; fadd.d=4, fadd.s=4 \u2192 penalty=0.0", - "equation_id": "math_model_map:63", - "family": "Informatic Stress", - "name": "RISCV_Instruction_Latency", - "projection_signature": { - "shape_distance": 0.258417, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Per-instruction latency for dispatch scoring", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_1fe1cbc05827ec00", - "receipt_hash": "fea905d509cc04455812371f0ca80ad4c7122af712d0ed1a1ccbc5e3b5e13221", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40174391540489346, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40174391540489346, - "shape": "LogogramProjection" - }, - { - "distance": 0.44663952359933917, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44663952359933917, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45073034752932023, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45073034752932023, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.258417, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "RISCV_Instruction_Latency", - "object_id": "rrc_eq_1fe1cbc05827ec00", - "payload_bytes_sampled": 610, - "payload_sha256": "b652baed599aa3bdec88c10add22da6af754987297e1c6ab4e8facf7e5db1557", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1fe1cbc05827ec00", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "cedec583417a3b688f1141cfe9e551ddb448747d0965593ab17c77086ad7c6dd" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "E = hc/\u03bb = 1.2398 / \u03bb_\u03bcm [eV]", - "equation_id": "math_model_map:64", - "family": "QCL Energy", - "name": "Photon_Energy", - "projection_signature": { - "shape_distance": 0.257657, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Wavelength-to-energy conversion", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_78fb56a55b615659", - "receipt_hash": "085aa292b55c72104bc5896e24b44ca91fa0cc30da2d37a18a3e5ae6d78c3646", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4016925950599106, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4016925950599106, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467099708634478, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467099708634478, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45027994649071323, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45027994649071323, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.257657, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Photon_Energy", - "object_id": "rrc_eq_78fb56a55b615659", - "payload_bytes_sampled": 533, - "payload_sha256": "759ba3f5cb21793819920169d221f02f4aef90a1f2dada6e692754c01f9c0566", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_78fb56a55b615659", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "2086661c2e47dc5753d63a5170e9e31a5cf1603e3e6075949565fb04eb7dd196" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "\u0394E = E_upper - E_lower; E_upper=hc/\u03bb_min, E_lower=hc/\u03bb_max", - "equation_id": "math_model_map:65", - "family": "QCL Energy", - "name": "Subband_Spacing", - "projection_signature": { - "shape_distance": 0.260582, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Energy difference between subbands in conduction band", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_ec7566b5d400a6fb", - "receipt_hash": "387689ea0a285f3d53c75ebe71738cd69d2964396cf545ee8e8a877b15469813", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4021673956240717, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4021673956240717, - "shape": "LogogramProjection" - }, - { - "distance": 0.44672909902151264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44672909902151264, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.452116959486624, - "kind_prior_bonus": 0.0, - "raw_distance": 0.452116959486624, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.260582, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Subband_Spacing", - "object_id": "rrc_eq_ec7566b5d400a6fb", - "payload_bytes_sampled": 586, - "payload_sha256": "cde69d461d5268cb23638369091b9bc83189bd0754d1c4e0db86138e03509fd4", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ec7566b5d400a6fb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "25886e14585cafc5ebf48a829be25937e50700f19df74da06b6f34bde8ac6f9f" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "G = photons_per_e\u207b \u00d7 n_wells; photons_per_e\u207b = \u230aE_electron / \u0394E\u230b", - "equation_id": "math_model_map:66", - "family": "QCL Energy", - "name": "Cascade_Gain", - "projection_signature": { - "shape_distance": 0.249496, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Total photon amplification per cascading electron", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_cc8b5f7c4742473c", - "receipt_hash": "8abbe3d6aac87d027c12a0dcbcbd32f37d3758f6ba066377d9b455ec94d882a1", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3827112782412759, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3827112782412759, - "shape": "LogogramProjection" - }, - { - "distance": 0.43292169765743443, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43292169765743443, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.44454605644701783, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44454605644701783, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.249496, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Cascade_Gain", - "object_id": "rrc_eq_cc8b5f7c4742473c", - "payload_bytes_sampled": 600, - "payload_sha256": "5b34fb5193255f3f96b06ee03e448f52cc835f7bde0e00ab8b6c8b1dd0614c07", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_cc8b5f7c4742473c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "0868d4a6c98208ebb258b28a4b17596a551fadfe77d4505430bf9682302e100c" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "\u03bb(T) = \u03bb\u2080 + \u03b1 \u00b7 (T - T\u2080); \u03b1=5e-6 /K (GaAs/AlGaAs)", - "equation_id": "math_model_map:67", - "family": "QCL Energy", - "name": "Temperature_Tuning", - "projection_signature": { - "shape_distance": 0.257657, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Wavelength shift due to thermal expansion of quantum wells", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_ca946ab1632d585c", - "receipt_hash": "76b5c2ad9039716800df62b0e47bf74bc67cee8b21ecd9ee8ee38d5e2784d4fc", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4016925950599106, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4016925950599106, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467099708634478, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467099708634478, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45027994649071323, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45027994649071323, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.257657, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Temperature_Tuning", - "object_id": "rrc_eq_ca946ab1632d585c", - "payload_bytes_sampled": 605, - "payload_sha256": "9fe03f7aff3bd1df32bf3ed45581ab682e2fcdc41466e1f0b93e796c8e1f43eb", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ca946ab1632d585c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "455ba4c42ca4b5e15ac8b801730dcfd6d4647d2a3551cfc4aeb87a1c2dd57049" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "\u03b7 = (0.5 + window_bonus) \u00b7 spacing_eff \u00b7 (1 - stress_penalty); clamped to [0,1]", - "equation_id": "math_model_map:68", - "family": "QCL Energy", - "name": "Injection_Efficiency", - "projection_signature": { - "shape_distance": 0.257657, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Dispatch efficiency at given stress level", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_b975f510c14227e3", - "receipt_hash": "32016255a9143f6bcdf2c82592208a4fe5c168cf1835f4866456b45ef0685f4f", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4016925950599106, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4016925950599106, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467099708634478, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467099708634478, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45027994649071323, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45027994649071323, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.257657, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Injection_Efficiency", - "object_id": "rrc_eq_b975f510c14227e3", - "payload_bytes_sampled": 600, - "payload_sha256": "30c0124acce0df835394f2ea35e4984882ae24d2c8235c4e1b636ca87698f173", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b975f510c14227e3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "7ab35a01cd8d32d4300d5476f59a068add7bc6428215708122c1d388f9c35e1e" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "Windows: (3,5), (8,12), (16,20) \u03bcm; transmission=1.0 inside, exp(-dist\u00b70.5) outside", - "equation_id": "math_model_map:69", - "family": "QCL Energy", - "name": "Atmospheric_Windows", - "projection_signature": { - "shape_distance": 0.258417, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Lossless carrier propagation bands", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_9d1897c1a7967172", - "receipt_hash": "a2a9eb76072fcec04119b2a7932f0f745b03efef35863aefed18e81c52cab5cd", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40174391540489346, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40174391540489346, - "shape": "LogogramProjection" - }, - { - "distance": 0.44663952359933917, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44663952359933917, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45073034752932023, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45073034752932023, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.258417, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Atmospheric_Windows", - "object_id": "rrc_eq_9d1897c1a7967172", - "payload_bytes_sampled": 591, - "payload_sha256": "6a1901cda1ac9b2c44937effd7ee839d07c4829f66324c0301318d0208d7529a", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9d1897c1a7967172", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "0ef31831c641e8db887559b678acf84a387f683ebd2240b9f6b1afdb2938b650" - } - }, - { - "equation_record": { - "bind_class": "physical_bind", - "domain_type": "LAYER_G_ENERGY", - "equation": "\u03bd = 10\u2074/\u03bb [cm\u207b\u00b9]; DFB: \u00b17.5 cm\u207b\u00b9, EC: \u00b1200 cm\u207b\u00b9; \u03bb_min=10\u2074/\u03bd_max, \u03bb_max=10\u2074/\u03bd_min", - "equation_id": "math_model_map:70", - "family": "QCL Energy", - "name": "Tuning_Range", - "projection_signature": { - "shape_distance": 0.257299, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Spectral tuning capability", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_45b2907bd442578f", - "receipt_hash": "286c033a790b4f1f94b40ee8e16d36dbaa492f99aca2c2465a9819527099700b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.401692257404352, - "kind_prior_bonus": 0.0, - "raw_distance": 0.401692257404352, - "shape": "LogogramProjection" - }, - { - "distance": 0.4467679600584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4467679600584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45007717924557317, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45007717924557317, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.257299, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Tuning_Range", - "object_id": "rrc_eq_45b2907bd442578f", - "payload_bytes_sampled": 649, - "payload_sha256": "ebf7c5ed09762f3558606f44edb897335422e270e268e2b5b4649274d96f97be", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_45b2907bd442578f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "d1a382a312e93c80492c4ae581c61f13ac1202d32b19bda4a530f467373d5ba8" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "MI(x) = baseline_bpb(x) - actual_bpb(x)", - "equation_id": "math_model_map:71", - "family": "MI Signal", - "name": "Mutual_Information_Signal", - "projection_signature": { - "shape_distance": 0.222839, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "compression_pressure", - 0.533333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Measures structural density in data", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_7956fd7c6e98638a", - "receipt_hash": "4c88a751d5ffea677b72f7228d7250fc705783f346a987cd8b4db760b040b191", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.36448291471508654, - "kind_prior_bonus": 0.0, - "raw_distance": 0.36448291471508654, - "shape": "LogogramProjection" - }, - { - "distance": 0.4353437538406793, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4353437538406793, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4591349888391735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4591349888391735, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.222839, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Mutual_Information_Signal", - "object_id": "rrc_eq_7956fd7c6e98638a", - "payload_bytes_sampled": 550, - "payload_sha256": "bbdfe844b25e323315826b0eaa4750ee0b02759f71650d8584a723da3b34b4ca", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7956fd7c6e98638a", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "630d3b64d4b1261fe574366f0772f357ffd8aa9dbded6dd0e3d63d2ba403aba7" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "MI_pred = \u03a3_i (w_i \u00b7 MI_i \u00b7 S_i) / \u03a3_i (w_i \u00b7 S_i); w_i = 1/(d_i + \u03b5)", - "equation_id": "math_model_map:72", - "family": "MI Signal", - "name": "kNN_MI_Prediction", - "projection_signature": { - "shape_distance": 0.282379, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Local MI estimation from similar inputs", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_21c2954626a5661d", - "receipt_hash": "7b0b137dd804b4e6c11a846eefbeb715aab91f9e42b775afcb0a075d3b58dbc5", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4095848274571386, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4095848274571386, - "shape": "LogogramProjection" - }, - { - "distance": 0.4535079416121797, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4535079416121797, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4556443944749318, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556443944749318, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.282379, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "kNN_MI_Prediction", - "object_id": "rrc_eq_21c2954626a5661d", - "payload_bytes_sampled": 606, - "payload_sha256": "a65b2d7944a46f1390c6c6da95cac827136457dc9643a58a07f69c331718bc70", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_21c2954626a5661d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "1fa135d0e4e17d766db7498a265b7cea8461d15a35e3006ba100e285a18dcc50" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "surprise = log(1 + |MI_actual - MI_predicted|)", - "equation_id": "math_model_map:73", - "family": "MI Signal", - "name": "Surprise_Metric", - "projection_signature": { - "shape_distance": 0.288392, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Learning trigger; prevents blowup", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_8018bcedc9b84a9d", - "receipt_hash": "2357eaebdd3fd8cb9079886cf1327def9664f0efabfc2b5613514156be9cf7dd", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40195159938404756, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40195159938404756, - "shape": "LogogramProjection" - }, - { - "distance": 0.45308642715227754, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45308642715227754, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46250054585271627, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46250054585271627, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288392, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Surprise_Metric", - "object_id": "rrc_eq_8018bcedc9b84a9d", - "payload_bytes_sampled": 541, - "payload_sha256": "784425253b8dfda2c492d0677ed35a8e7c581511f64405f279084f9b51d5c678", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_8018bcedc9b84a9d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "e74dd3a015a18fbfead4f7a81fefd1cbc15c8275138190c35f5417dc221530ff" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_A_COMPRESSION", - "equation": "\u03c1(x) = MI(x) / (cost(x) + \u03b5)", - "equation_id": "math_model_map:74", - "family": "MI Signal", - "name": "Structure_Yield", - "projection_signature": { - "shape_distance": 0.245865, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "ROI of computation; high MI + low cost = valuable", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_0d19ee61a2cd8497", - "receipt_hash": "699153c1c31bc79342a9bc0ac1cb47758e9011c9db2cbf9f116517f437e34708", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38008840571750113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38008840571750113, - "shape": "LogogramProjection" - }, - { - "distance": 0.44064564567207587, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44064564567207587, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4557747130085374, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4557747130085374, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.245865, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "Structure_Yield", - "object_id": "rrc_eq_0d19ee61a2cd8497", - "payload_bytes_sampled": 553, - "payload_sha256": "57e79c0d018a248a95f87b0bf187927baaf25185b55471ddcf61b7a637535fe9", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0d19ee61a2cd8497", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "7ec1f242dfa9a99d987e5957995b8b6c7dc845ad4f04ef2f46b55efcf83429b5" - } - }, - { - "equation_record": { - "bind_class": "informational_bind", - "domain_type": "LAYER_B_ROUTING", - "equation": "d(z\u2081,z\u2082) = \u221a\u03a3_i w_i \u00b7 ((z\u2081_i - z\u2082_i) / s_i)\u00b2", - "equation_id": "math_model_map:75", - "family": "MI Signal", - "name": "Weighted_Feature_Distance", - "projection_signature": { - "shape_distance": 0.281874, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.65625 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Scale-normalized weighted distance in MI feature space", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e2f1f1f142418961", - "receipt_hash": "6bf5176d2cc3c31cb132c13188cf3adc0b3ce653be560a230448c62f743ffc81", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.65625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096831669587414, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096831669587414, - "shape": "LogogramProjection" - }, - { - "distance": 0.452562171984763, - "kind_prior_bonus": 0.0, - "raw_distance": 0.452562171984763, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45590419017889855, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45590419017889855, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.281874, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "Weighted_Feature_Distance", - "object_id": "rrc_eq_e2f1f1f142418961", - "payload_bytes_sampled": 614, - "payload_sha256": "444d6c1e807e8a70e470ca6c4f8f15f0bf24b2e9ac530271296a85e293e5a485", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e2f1f1f142418961", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c361493d23ff4638fbf0ff0a6a09511eb7e248ae81bca73d9aea0518a5c0fc8f" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_BRAID", - "equation": "\u03a3 F_in = \u03a3 F_out at every node", - "equation_id": "math_model_map:76", - "family": "DAG Force", - "name": "DAG_Force_Equilibrium", - "projection_signature": { - "shape_distance": 0.280052, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Structural synthesis force balance", - "route_hint_non_authoritative": "cad_force", - "rrc_kind": "cad_force_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "sum_j q_ij * (x_i - x_j) + p_i = 0; residual must stay under declared tolerance", - "invariant_receipt": { - "object_id": "rrc_eq_7076f5bdea119531", - "receipt_hash": "583b13f2e4992fb2fbaacee7b0016a8257d771cbfd7a36432ee0d64520735916", - "schema": "rrc.object_receipt.v1", - "shape": "CadForceProbeReceipt", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.285714, - "geometric_mass": 0.428571, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37479934963766887, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37479934963766887, - "shape": "LogogramProjection" - }, - { - "distance": 0.41510582522967954, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41510582522967954, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.42832991580618535, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42832991580618535, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "cad_force_receipt", - "distance": 0.280052, - "kind_prior_shape": "CadForceProbeReceipt", - "shape": "CadForceProbeReceipt" - }, - "object": { - "kind": "cad_force_receipt", - "label": "DAG_Force_Equilibrium", - "object_id": "rrc_eq_7076f5bdea119531", - "payload_bytes_sampled": 528, - "payload_sha256": "13ba2ec399bd64218a8efcd7bd63feec1215e940916b0785dd958c675ee9aaf8", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_7076f5bdea119531", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "CadForceProbeReceipt", - "status": "HOLD", - "witness_hash": "16251edb674ab9c984d3bc62904d78a23fd0414f92fd83e99c54e09a288166df" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_BRAID", - "equation": "\u03c3 = C : \u03b5; \u03b5 = \u00bd(\u2207u + \u2207u^T)", - "equation_id": "math_model_map:77", - "family": "DAG Force", - "name": "Constitutive_Law", - "projection_signature": { - "shape_distance": 0.294433, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Stress-strain relationship; linear elasticity", - "route_hint_non_authoritative": "cad_force", - "rrc_kind": "cad_force_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "sum_j q_ij * (x_i - x_j) + p_i = 0; residual must stay under declared tolerance", - "invariant_receipt": { - "object_id": "rrc_eq_f112b5836bdbd47d", - "receipt_hash": "b1fd0f221b264e7e1730772ac71b661be7fa55385e6b051518d19e74b8e633a8", - "schema": "rrc.object_receipt.v1", - "shape": "CadForceProbeReceipt", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.428571, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.37915040943573436, - "kind_prior_bonus": 0.0, - "raw_distance": 0.37915040943573436, - "shape": "LogogramProjection" - }, - { - "distance": 0.4259595531406626, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4259595531406626, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4344176831707883, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4344176831707883, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "cad_force_receipt", - "distance": 0.294433, - "kind_prior_shape": "CadForceProbeReceipt", - "shape": "CadForceProbeReceipt" - }, - "object": { - "kind": "cad_force_receipt", - "label": "Constitutive_Law", - "object_id": "rrc_eq_f112b5836bdbd47d", - "payload_bytes_sampled": 551, - "payload_sha256": "12d575ba01f072054e833b980c6028aa761ef83e17a47a5eeb4487c4bfb7b205", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_f112b5836bdbd47d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "CadForceProbeReceipt", - "status": "HOLD", - "witness_hash": "f514ced20d1400bde8f706ac7a2e8179d93e208d038467b04fb9342b1cc01a0d" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_BRAID", - "equation": "V(G) = \u2227_{v\u2208V} (\u03a3F_in = \u03a3F_out)", - "equation_id": "math_model_map:78", - "family": "DAG Force", - "name": "DAG_Global_Validity", - "projection_signature": { - "shape_distance": 0.2697, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.771429 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "DAG constraint closure; proof by induction", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_59c14acaba40cdc9", - "receipt_hash": "c12fda3eaf6f12c00e39e2b211cf32a75bdf148a0f8abc164388f3217685ea9d", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.771429, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.39882548389770356, - "kind_prior_bonus": 0.0, - "raw_distance": 0.39882548389770356, - "shape": "LogogramProjection" - }, - { - "distance": 0.4434222305464007, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4434222305464007, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4567516113418542, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567516113418542, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.2697, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "DAG_Global_Validity", - "object_id": "rrc_eq_59c14acaba40cdc9", - "payload_bytes_sampled": 553, - "payload_sha256": "2933c2aa4741e8e075723633d054e47fbb37fbdb52819f6109f9e61abe9fb38d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_59c14acaba40cdc9", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "71b69b6298baf10ab14c5b6280705cf4f419589eca9999dc1b7061cdefdd632c" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_K_SIGNAL", - "equation": "cos = x \u00b7 x_ref / (\u2016x\u2016 \u00b7 \u2016x_ref\u2016)", - "equation_id": "math_model_map:79", - "family": "Bracket Braid", - "name": "Cosine_Similarity", - "projection_signature": { - "shape_distance": 0.288723, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Reference solution alignment", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_f9a9276cb08dd3bb", - "receipt_hash": "5937379a24d00e4ff9984061a66536cb320035aa643f32be350b511545999192", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40188376865433434, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40188376865433434, - "shape": "LogogramProjection" - }, - { - "distance": 0.4526812186627945, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4526812186627945, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46231833413703066, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46231833413703066, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288723, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Cosine_Similarity", - "object_id": "rrc_eq_f9a9276cb08dd3bb", - "payload_bytes_sampled": 554, - "payload_sha256": "a63e1c6736ee46d4dbfed11ea439862ddb1646dcfb8b77da828928e259730c53", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_f9a9276cb08dd3bb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "c155b71578dccd353f96b6f39fb6a97e844cb39d30fea4550aac757c8dc442f3" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_K_SIGNAL", - "equation": "alignment = \u2207g_i \u00b7 \u2207g_j / (\u2016\u2207g_i\u2016 \u00b7 \u2016\u2207g_j\u2016)", - "equation_id": "math_model_map:80", - "family": "Bracket Braid", - "name": "Gradient_Alignment", - "projection_signature": { - "shape_distance": 0.288392, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Gradient coherence between brackets", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_bf1b905e94089ddc", - "receipt_hash": "2279300b812c7ddddafa063bbb82c459482d5984477f9526b7fbf81f81a24953", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40195159938404756, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40195159938404756, - "shape": "LogogramProjection" - }, - { - "distance": 0.45308642715227754, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45308642715227754, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46250054585271627, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46250054585271627, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288392, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Gradient_Alignment", - "object_id": "rrc_eq_bf1b905e94089ddc", - "payload_bytes_sampled": 592, - "payload_sha256": "1ec43df3d43fb9c57253081d4c43a30306066b691cbba27ae96ae3804e39e19b", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_bf1b905e94089ddc", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "46065d79c15e373aa2b78c5c8e1568a4c62dd566a957662f462cc5a371506cca" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_K_SIGNAL", - "equation": "phase += \u03a3 y \u00b7 dx", - "equation_id": "math_model_map:81", - "family": "Bracket Braid", - "name": "Phase_Accumulation", - "projection_signature": { - "shape_distance": 0.288076, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Cumulative phase along computational path", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_217148f607dfe5ff", - "receipt_hash": "f319772c36046078415dd5430a65eb5c2beedabeaa9edb59f6e108e5d9d4493d", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40203628736102015, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40203628736102015, - "shape": "LogogramProjection" - }, - { - "distance": 0.4535062277160645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4535062277160645, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46251784796736883, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46251784796736883, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288076, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Phase_Accumulation", - "object_id": "rrc_eq_217148f607dfe5ff", - "payload_bytes_sampled": 532, - "payload_sha256": "e2c569dbac138069817f2b95c0fe52aee4abaf2ea90977346880ce89bb30ea85", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_217148f607dfe5ff", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "441c7d7a237d5843c6c18de7632965c3633b7d4464abfba20a39ea21933fa348" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "g_tt = M\u00b2, g_tp = 0, g_pp = (N\u00b7cos(\u03b8))\u00b2; a=C_eq/(2\u03c0), c=C_mer/(2\u03c0), f=(a-c)/a, e\u00b2=2f-f\u00b2, N=a/\u221a(1-e\u00b2sin\u00b2\u03b8), M=a(1-e\u00b2)/(1-e\u00b2sin\u00b2\u03b8)^(3/2)", - "equation_id": "math_model_map:82", - "family": "GWL Riemannian Geometry", - "name": "Metric_Tensor_From_Circumferences", - "projection_signature": { - "shape_distance": 0.290188, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Derive metric tensor from measured circumferences (oblate spheroid)", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_7cdf1c3e052e1f33", - "receipt_hash": "fb789ee71553ed410ba9705005bd3574b002cc9eeb106fdd0a9c5c2b783506c8", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40178115713660933, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40178115713660933, - "shape": "LogogramProjection" - }, - { - "distance": 0.4512070704812315, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4512070704812315, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46143971451836985, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46143971451836985, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.290188, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Metric_Tensor_From_Circumferences", - "object_id": "rrc_eq_7cdf1c3e052e1f33", - "payload_bytes_sampled": 772, - "payload_sha256": "12a08910420786443b96a225e85fbbcd8bc1d8ff3dd1eade610b122b6e251b5a", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_7cdf1c3e052e1f33", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "b087c9830d85cbe6a6d5eae350d0f452a18381b5ba1264859afb1ef280f27a25" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "ds\u00b2 = g_tt\u00b7d\u03b8\u00b2 + 2\u00b7g_tp\u00b7d\u03b8\u00b7d\u03c6 + g_pp\u00b7d\u03c6\u00b2", - "equation_id": "math_model_map:83", - "family": "GWL Riemannian Geometry", - "name": "Line_Element", - "projection_signature": { - "shape_distance": 0.287773, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Compute infinitesimal distance on 2D Riemannian surface", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_20f6a9ed1c2675da", - "receipt_hash": "50bffeea9ce27d6b59131ae118ffc312de14583708d1f760a1ad8a5c550fd0d7", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40213782193512615, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40213782193512615, - "shape": "LogogramProjection" - }, - { - "distance": 0.45394057987026165, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45394057987026165, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46254981118872723, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46254981118872723, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.287773, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Line_Element", - "object_id": "rrc_eq_20f6a9ed1c2675da", - "payload_bytes_sampled": 625, - "payload_sha256": "27c04510f2d926fd0184e4531ea189e998a9bdac4f1e0e670e454fb60e8feb20", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_20f6a9ed1c2675da", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "ca9117c6857707a784181aee2654947899a8c920e421be9f724352f0d7eb7988" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "Gamma^k_ij = \u00bd\u00b7g^kl\u00b7(\u2202_i g_jl + \u2202_j g_il - \u2202_l g_ij)", - "equation_id": "math_model_map:84", - "family": "GWL Connection", - "name": "Christoffel_Symbols_2D", - "projection_signature": { - "shape_distance": 0.289427, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "semantic_entropy", - 0.604167 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Connection coefficients for geodesic integration", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_68ac9a041d842bcf", - "receipt_hash": "e4b7c532a3e75046db87636459bfa9f761b5ccef72f21d37de67663fcf063abe", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.604167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179871096459546, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179871096459546, - "shape": "LogogramProjection" - }, - { - "distance": 0.45191473350072664, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45191473350072664, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4618498667001061, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4618498667001061, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.289427, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Christoffel_Symbols_2D", - "object_id": "rrc_eq_68ac9a041d842bcf", - "payload_bytes_sampled": 601, - "payload_sha256": "60dd947d169a872f2d8fc7f70f2a5f53aa16f51fc55580ba62ad0ff2752affee", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_68ac9a041d842bcf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "ed6fec7cf92c7aff9e843e770ac698d540aeb89f6321be942d0f98b0dba78ccd" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "a^\u03b8 = -(Gamma^\u03b8_tt\u00b7v_\u03b8\u00b2 + 2\u00b7Gamma^\u03b8_tp\u00b7v_\u03b8\u00b7v_\u03c6 + Gamma^\u03b8_pp\u00b7v_\u03c6\u00b2); v' = v + a\u00b7dt; x' = x + v'\u00b7dt", - "equation_id": "math_model_map:85", - "family": "GWL Geodesic Integration", - "name": "Geodesic_Step_Symplectic_Euler", - "projection_signature": { - "shape_distance": 0.290188, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Single-step geodesic integration (symplectic Euler)", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_e9cff20a11527ba0", - "receipt_hash": "26899341e8d34fa22f740443bf87c050787e485ca6a78daf9b8f56392f01ecf8", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40178115713660933, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40178115713660933, - "shape": "LogogramProjection" - }, - { - "distance": 0.4512070704812315, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4512070704812315, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46143971451836985, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46143971451836985, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.290188, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Geodesic_Step_Symplectic_Euler", - "object_id": "rrc_eq_e9cff20a11527ba0", - "payload_bytes_sampled": 721, - "payload_sha256": "d7484147aba5ab46e7f878ab412a0a7335021c114d954398f907952c695225bc", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_e9cff20a11527ba0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "e336ef2d09e3bc0aeefbae51b0c86463f0e0df20afa8940b160cbcecaaedf5a4" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "u = 2\u00b7tan(\u03b8/2)\u00b7cos(\u03c6), v = 2\u00b7tan(\u03b8/2)\u00b7sin(\u03c6); \u03b8 = 2\u00b7atan(\u221a(u\u00b2+v\u00b2)/2), \u03c6 = atan2(v,u); select when |cos(\u03b8)| < 0.01", - "equation_id": "math_model_map:86", - "family": "GWL Coordinate Charts", - "name": "Stereographic_Chart_Transition", - "projection_signature": { - "shape_distance": 0.290188, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "semantic_entropy", - 0.625 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Avoid pole singularities via adaptive coordinate chart", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_77bd06f725c27b4d", - "receipt_hash": "e2cc8c9bb833872a3f6f95cda2cd554a0a36d793dd1ee130dd191d26ac5753f9", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40178115713660933, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40178115713660933, - "shape": "LogogramProjection" - }, - { - "distance": 0.4512070704812315, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4512070704812315, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46143971451836985, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46143971451836985, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.290188, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Stereographic_Chart_Transition", - "object_id": "rrc_eq_77bd06f725c27b4d", - "payload_bytes_sampled": 728, - "payload_sha256": "e701fb06e6c04bb447482b3847856212e44e686a2bf05cb32a8c1ff85836075d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_77bd06f725c27b4d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "89f3df43e547d72200fa8f8569e175f5c9438b7d3b896911e1b9f8d256d83d06" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "D+D\u2192D, L+L\u2192L, D+L\u2192W(COLLAPSE\u2192W); chiralityToTernary: D\u2192Active, L\u2192Active, W\u2192Latent", - "equation_id": "math_model_map:87", - "family": "GWL Chiral Interaction", - "name": "Chirality_Algebra", - "projection_signature": { - "shape_distance": 0.288392, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Chirality propagation through Gamma transform operations", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_9f67a9105f9e0e3c", - "receipt_hash": "6bb555a669bad82ae5d1c668929aa67bb518e14d63246b72e8740ceaa4d922ef", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40195159938404756, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40195159938404756, - "shape": "LogogramProjection" - }, - { - "distance": 0.45308642715227754, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45308642715227754, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46250054585271627, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46250054585271627, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288392, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Chirality_Algebra", - "object_id": "rrc_eq_9f67a9105f9e0e3c", - "payload_bytes_sampled": 646, - "payload_sha256": "68d713e6203b60eadf82f9a9ec84759dc45eceaf4b00fb7d8a7c26cf0eaa3c00", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_9f67a9105f9e0e3c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "1d4aa1ce8c0ff17dca49993b3175b5f301910f22e07cc29fea39d68ea2e6e4ae" - } - }, - { - "equation_record": { - "bind_class": "control_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "\u03c6 = (\u03c4 mod T)/T, T=942/1000; ternary: \u03c6<1/3\u2192Q, \u03c6<2/3\u2192A, else L; \u03c6' = (\u03c6+1/4) mod 1 if surprise>threshold", - "equation_id": "math_model_map:88", - "family": "GWL Ternary State", - "name": "BLINK_GATE_Ternary_Clock", - "projection_signature": { - "shape_distance": 0.21559, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.697917 - ], - [ - "shape_closure", - 0.63 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength" - ] - }, - "purpose": "Ternary clock action with phase modulation", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_d726df3a6c9943ff", - "receipt_hash": "2e8552e99f652db8237beea4bde835bbefacb9363a83dd0f41ba4ee0ff69579b", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "CANDIDATE" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.47, - "scale_band_declared": 0.4, - "semantic_entropy": 0.697917, - "shape_closure": 0.63, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.35604399405814563, - "kind_prior_bonus": 0.0, - "raw_distance": 0.35604399405814563, - "shape": "LogogramProjection" - }, - { - "distance": 0.40492511593429303, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40492511593429303, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.43272245667910586, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43272245667910586, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.21559, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "BLINK_GATE_Ternary_Clock", - "object_id": "rrc_eq_d726df3a6c9943ff", - "payload_bytes_sampled": 657, - "payload_sha256": "5046ea820e4a912d53efc982aa3613175c21f7d52e5791ada4607c3160e46644", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_eq_d726df3a6c9943ff", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "CANDIDATE", - "witness_hash": "f18c54488a36577d003130c16007f6563c27a4ea9646d56bd29023abf140e11f" - } - }, - { - "equation_record": { - "bind_class": "geometric_bind", - "domain_type": "LAYER_C_TOPOLOGY", - "equation": "Same acceleration as M85 + Verlet velocity update + chart transition check", - "equation_id": "math_model_map:89", - "family": "GWL Geodesic Integration (Integrated)", - "name": "Geodesic_Step_Verlet", - "projection_signature": { - "shape_distance": 0.288076, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "geometric_mass", - 0.628571 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Real geodesic integration with pole singularity handling", - "route_hint_non_authoritative": "geometry_topology", - "rrc_kind": "geometry_topology_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "close iff mass_delta_q == 0 and horizon_hash matches and nan0_flag == 0", - "invariant_receipt": { - "object_id": "rrc_eq_feea4fcff27bd600", - "receipt_hash": "6a10a0d8a553ec61b5a89184e27bf2ea584b32a362750f016b528e7f1be5852a", - "schema": "rrc.object_receipt.v1", - "shape": "ProjectableGeometryTopology", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.628571, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40203628736102015, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40203628736102015, - "shape": "LogogramProjection" - }, - { - "distance": 0.4535062277160645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4535062277160645, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.46251784796736883, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46251784796736883, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "geometry_topology_receipt", - "distance": 0.288076, - "kind_prior_shape": "ProjectableGeometryTopology", - "shape": "ProjectableGeometryTopology" - }, - "object": { - "kind": "geometry_topology_receipt", - "label": "Geodesic_Step_Verlet", - "object_id": "rrc_eq_feea4fcff27bd600", - "payload_bytes_sampled": 622, - "payload_sha256": "aaa9b1b9188662afb579db90064d0a4454f9edd195f167a3b40745ed3fe05ffe", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared", - "negative_control_strength" - ], - "object_id": "rrc_eq_feea4fcff27bd600", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "shape_closure", - "negative_control_strength" - ], - "shape": "ProjectableGeometryTopology", - "status": "HOLD", - "witness_hash": "afc5a3be293b645ee089c1d14a0ff1c40f44b7906e7b59be46a26ea6754910c5" - } - }, - { - "equation_record": { - "bind_class": "control_bind", - "domain_type": "LAYER_F_CONTROL", - "equation": "risk(s) = (1 + \u03b3\u00b7(1-cos(\u03b8)))/d\u00b2 + \u03b7\u00b7h; \u03b3=3, \u03b7=0.8", - "equation_id": "math_model_map:90", - "family": "Waveprobe Control", - "name": "Waveprobe_Risk_Function", - "projection_signature": { - "shape_distance": 0.273853, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Combined risk metric for hysteretic control", - "route_hint_non_authoritative": "control_signal", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_c3f6aa5efce88262", - "receipt_hash": "8d1568106a8f7520ae1863fab42ed3885f3974bd97e38a46eaea1b86eac25dfc", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4095851586061867, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4095851586061867, - "shape": "LogogramProjection" - }, - { - "distance": 0.4555875351131274, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4555875351131274, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46257596133780876, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46257596133780876, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.273853, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Waveprobe_Risk_Function", - "object_id": "rrc_eq_c3f6aa5efce88262", - "payload_bytes_sampled": 601, - "payload_sha256": "1e509febd2c2175dd21bef8036c73c334859b9573211eb1b712c670c7790919d", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c3f6aa5efce88262", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "2a88c1665e87afce94b98c9dd721a74c148cdc5b82b6106fe6a4de3ed6abbf2e" - } - }, - { - "equation_record": { - "bind_class": "control_bind", - "domain_type": "LAYER_F_CONTROL", - "equation": "h' = \u03b1\u00b7h + \u03b2\u00b7a; \u03b1=0.95, \u03b2=0.2", - "equation_id": "math_model_map:91", - "family": "Waveprobe Control", - "name": "Waveprobe_Heat_Evolution", - "projection_signature": { - "shape_distance": 0.25882, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "Heat accumulation with exponential decay", - "route_hint_non_authoritative": "thermodynamic_energy", - "rrc_kind": "cognitive_field_receipt", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "field_equation": "L_total = C_domain * response_family(S; theta) * phi_gain * B_gate * overflow_gate", - "invariant_receipt": { - "object_id": "rrc_eq_4430cc5b9ebb8311", - "receipt_hash": "d7bb15aec563dc01d7ba7b0fc653af3e8dd71e301d46df4f9f3ab09b863e8836", - "schema": "rrc.object_receipt.v1", - "shape": "CognitiveLoadField", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40179489162554316, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40179489162554316, - "shape": "LogogramProjection" - }, - { - "distance": 0.44662707271194285, - "kind_prior_bonus": 0.0, - "raw_distance": 0.44662707271194285, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45097793651363505, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45097793651363505, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "cognitive_field_receipt", - "distance": 0.25882, - "kind_prior_shape": "CognitiveLoadField", - "shape": "CognitiveLoadField" - }, - "object": { - "kind": "cognitive_field_receipt", - "label": "Waveprobe_Heat_Evolution", - "object_id": "rrc_eq_4430cc5b9ebb8311", - "payload_bytes_sampled": 575, - "payload_sha256": "db1694de43c54455ccb0b27bc9b119120f2281c3c243c3ac1df76e833dd288ca", - "source_path": "3-Mathematical-Models/MATH_MODEL_MAP.tsv" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4430cc5b9ebb8311", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "CognitiveLoadField", - "status": "HOLD", - "witness_hash": "8b36c7b8e1202e72fe2f346f2c05526122d3ce036092569eb2de77a06d9d4fd2" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "a_\u03bc = (|g| - 2) / 2 = 0.001165920705(114)", - "equation_id": "extracted_md:0", - "family": "markdown_extracted", - "name": "extracted_md_equation_0", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a4348738394b0597", - "receipt_hash": "9434289633435ec35d6da5c7614184653246d2dc20201d1cb7d75c72c81056bd", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_0", - "object_id": "rrc_eq_a4348738394b0597", - "payload_bytes_sampled": 514, - "payload_sha256": "a4752a7c948a37afa0b168318ebc77bd545e08d8086eebf3747492972080f616", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a4348738394b0597", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "eab6b19df1f24fafe332ad2cd39b363a5962caf55e54de10a04fa41f56ef3c7e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03c3 = 0.127 ppm (0.000000114)", - "equation_id": "extracted_md:1", - "family": "markdown_extracted", - "name": "extracted_md_equation_1", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_bb071a3f64f90363", - "receipt_hash": "820adc2fb7a8153855c493adda824f2b534b37e8111cf904caf4555e998efb9b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_1", - "object_id": "rrc_eq_bb071a3f64f90363", - "payload_bytes_sampled": 503, - "payload_sha256": "904eb8d5bc8f0ed31c8e9faa274daa126e3ea041824a102f57e2f5a6d1b4e8ba", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_bb071a3f64f90363", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "6042264308592a238fb734e39551bb83eeadb3a01ffdb95a2fe6c1c6d5f191e9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "a_\u03bc^theory = a_\u03bc^experiment within 0.5\u03c3", - "equation_id": "extracted_md:2", - "family": "markdown_extracted", - "name": "extracted_md_equation_2", - "projection_signature": { - "shape_distance": 0.289065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_5a01598605abcd3f", - "receipt_hash": "6046a6f88be65f3b5a69c362584ff039c771fede60385e995bf9bba2175ceeb0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.416667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4127253277433695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4127253277433695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4573320596979162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4573320596979162, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46314351812704235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46314351812704235, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.289065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_2", - "object_id": "rrc_eq_5a01598605abcd3f", - "payload_bytes_sampled": 526, - "payload_sha256": "4af828ac2e26904ea9658ac075fb57f8e5fc399841b4c5ea6ec3dac73ac0bb68", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_5a01598605abcd3f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "df99cd3bbd82b36fde591302e5f58be542ff708be69cb396ba0ae063ecc6019a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "g_\u03bc = -2.00233184122(82)", - "equation_id": "extracted_md:3", - "family": "markdown_extracted", - "name": "extracted_md_equation_3", - "projection_signature": { - "shape_distance": 0.286858, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3f87b53694e706e9", - "receipt_hash": "c821d0a37f32d268d8ca7ed07a9872b49314d32cce25306e0413e9a54b52abdf", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4113246327894113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4113246327894113, - "shape": "LogogramProjection" - }, - { - "distance": 0.45635379572506823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45635379572506823, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602012339852711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602012339852711, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286858, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_3", - "object_id": "rrc_eq_3f87b53694e706e9", - "payload_bytes_sampled": 497, - "payload_sha256": "d721cac0c9b1e70318e0c2de2a7dec46962814bbe1e96a63cae84971b0ebe019", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3f87b53694e706e9", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "29b7455e8da167780e8815e602eca9d1fc64e18e65762d546e33949916f98059" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03bc = g \u00b7 (e\u210f / 2m) \u00b7 S", - "equation_id": "extracted_md:4", - "family": "markdown_extracted", - "name": "extracted_md_equation_4", - "projection_signature": { - "shape_distance": 0.286858, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c5a00dead68e12a0", - "receipt_hash": "3e1ff3fb15a91dd72996d429dc66b1efbfe98efc6148b62fc9226142f6d1b250", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4113246327894113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4113246327894113, - "shape": "LogogramProjection" - }, - { - "distance": 0.45635379572506823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45635379572506823, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602012339852711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602012339852711, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286858, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_4", - "object_id": "rrc_eq_c5a00dead68e12a0", - "payload_bytes_sampled": 509, - "payload_sha256": "7f1e8c7d2da9a528bad877a3c7d11372ca1345cc08bb62c0f2a7c3d7883c03c2", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c5a00dead68e12a0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "12df91eef0a53f8c3cca02e5fe72d22bb369a742ebc72040e25c09da2443287e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "(D_t^\u03b1 + (-\u2207\u00b2)^\u03b2) \u03a8 = \u03bb |\u03a8|^\u03b3 \u03a8", - "equation_id": "extracted_md:5", - "family": "markdown_extracted", - "name": "extracted_md_equation_5", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_5d1fa53e2bceba76", - "receipt_hash": "f13e19b6762f0fdfbdde55aae9e40cdbb44703e5a87a0819c1a49f5c4fe46829", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_5", - "object_id": "rrc_eq_5d1fa53e2bceba76", - "payload_bytes_sampled": 544, - "payload_sha256": "b03d7564e7f2962b8c30d9314be04a221126c4b4a7e9a515e093e5ca68a249db", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_5d1fa53e2bceba76", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "89f4517af320f4859b04b745586686ae7c214696fce1ac53671f7413441162a2" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "D^\u03b1 f(x) = F^{-1}[ |k|^\u03b1 \u00b7 F[f](k) ]", - "equation_id": "extracted_md:6", - "family": "markdown_extracted", - "name": "extracted_md_equation_6", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_1523798f49d3e916", - "receipt_hash": "723166936077544dbde68e7c90889c3dc65c555cbc73f3209bf01422b55bf9e8", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_6", - "object_id": "rrc_eq_1523798f49d3e916", - "payload_bytes_sampled": 519, - "payload_sha256": "e5eae4d9336eb9235f4f499025d8738afb8fd306fb8d5eb5a04e9bc6f4f94ab2", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1523798f49d3e916", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "41be8e2f9194a271692b268d4c4d7d970301689d8694300d41410a84b48f89dd" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03a8_observed(x, t) = \u222b K_\u03b8(x - x') \u03a8_unified(x', t) dx'", - "equation_id": "extracted_md:7", - "family": "markdown_extracted", - "name": "extracted_md_equation_7", - "projection_signature": { - "shape_distance": 0.287271, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a5dae5efe3dc9a94", - "receipt_hash": "bf2d4dcdfe099f5a1221eee16669cf33b88301d264844fdb831a95d36ed97e0e", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41157219941042966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41157219941042966, - "shape": "LogogramProjection" - }, - { - "distance": 0.45651990682503474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45651990682503474, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46076175790722984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46076175790722984, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287271, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_7", - "object_id": "rrc_eq_a5dae5efe3dc9a94", - "payload_bytes_sampled": 541, - "payload_sha256": "4e69d81372af04d0e8efd5b5958a5023c3be0b5be32fa46dc9581da4e2af50dd", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a5dae5efe3dc9a94", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "36b390d5425620a0fb74233c1d3d29923f163c75db04b40548f80ae87115442b" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "w(\u03b1, \u03b8) = sin(\u03b8)^\u03b1 \u00b7 cos(\u03b8)^{1-\u03b1}", - "equation_id": "extracted_md:8", - "family": "markdown_extracted", - "name": "extracted_md_equation_8", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_7a38c905340961db", - "receipt_hash": "bf83dba12a3149cb7b373cc51f4f8e01c407ecf95044acc166aed78e81eccf5a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_8", - "object_id": "rrc_eq_7a38c905340961db", - "payload_bytes_sampled": 536, - "payload_sha256": "04b7385706b1a32a9e70628fda9cd7fb15185f0f6d7dee973b022d8085225e55", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7a38c905340961db", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "e321abf79139fad51a365462230920e68ff35819c07ffee0fbfc8cc194c38eef" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "g_n(\u03b8_obs) = g_0 \u00b7 sin(\u03b8_obs)^{1/n} \u00b7 cos(\u03b8_obs)^{1 - 1/n}", - "equation_id": "extracted_md:9", - "family": "markdown_extracted", - "name": "extracted_md_equation_9", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_bb62cba4864b0def", - "receipt_hash": "87b8d3be5b2863c19d09790a4d25362ce47da8507864a7c50bb778f20e497c5d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_9", - "object_id": "rrc_eq_bb62cba4864b0def", - "payload_bytes_sampled": 551, - "payload_sha256": "285ed0e8ce3d7918132c8177b2b397852de6e731a638a5cbce327891c53980ba", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_bb62cba4864b0def", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "f10584def4e3dd1d56baf214484186b67201423e521568cb22aa3066aec0e5ea" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Q_n = (1/2\u03c0) \u222e_C \u2207_n \u03c6 \u00b7 dn = m/n for m \u2208 \u2124", - "equation_id": "extracted_md:10", - "family": "markdown_extracted", - "name": "extracted_md_equation_10", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_685a969028ff5c50", - "receipt_hash": "b7c558563b14a58620e82e1066031fd992796f6fcd1ca1916a5d83ee6822d1cd", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_10", - "object_id": "rrc_eq_685a969028ff5c50", - "payload_bytes_sampled": 550, - "payload_sha256": "108d624eff3cdde8390e5ecac35ab3925ba370f4e4632bdd202e1b16c5bc4d88", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_685a969028ff5c50", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "a267e241a4f39361e6eef2af87af701b6b3c16d1d4015308e57d3d97e93decff" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "ds\u00b2 = -d\u03b8\u00b2/\u03c9(\u03b8)\u00b2 + a(\u03b8)\u00b2 [dr\u00b2/(1-kr\u00b2) + r\u00b2 d\u03a9\u00b2] + \u2113_P\u00b2 d\u03b8\u00b2 \u0393(\u03b8)", - "equation_id": "extracted_md:11", - "family": "markdown_extracted", - "name": "extracted_md_equation_11", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a59af904a8ad739f", - "receipt_hash": "fb04a05581f4d0110aec8264a3f76aa693194b995055b6ff5d0f055e24e4767b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_11", - "object_id": "rrc_eq_a59af904a8ad739f", - "payload_bytes_sampled": 628, - "payload_sha256": "5aef6c85b71a8d769691937d27ff818a63f0d4ecef3431039102dbf4c5ca4fb2", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a59af904a8ad739f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "12c4660bd14289890c600bb128e22b6158db123db10a378706b7758bf6d468ed" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "H_eff = \u03c9(\u03b8)", - "equation_id": "extracted_md:12", - "family": "markdown_extracted", - "name": "extracted_md_equation_12", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_4b0eb5baf8d88582", - "receipt_hash": "d0692883ba5beb46828be902f6de8909fe7d50e7be58053564c9f7e7678e1dd6", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_12", - "object_id": "rrc_eq_4b0eb5baf8d88582", - "payload_bytes_sampled": 492, - "payload_sha256": "ed2e5f064cf2b66100a782cad13bd731c3e868ff9253e98bfb6eeada7da00407", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4b0eb5baf8d88582", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "aaf04933439b57e64ea52f29d9cc311f305cbbb7bb2fb49be0b0c36469c10caf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03c1_DE(\u03b8) = \u03c1_foam \u00b7 (1 - \u03b8/\u03b8_max)\u00b2", - "equation_id": "extracted_md:13", - "family": "markdown_extracted", - "name": "extracted_md_equation_13", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_decad728fbd76456", - "receipt_hash": "b49ad77d116fe1e4d31ba52bcc595bf8975a41c0028265361ce6e5dcfc55ce3a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_13", - "object_id": "rrc_eq_decad728fbd76456", - "payload_bytes_sampled": 538, - "payload_sha256": "f8a7c6611541412f182b4ad288161279987faa281524c290241f388ccef74f80", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_decad728fbd76456", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "54b3a448e813c7141ee38f12988f1b4c73569ec39115265e496e04975458ddaa" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "S(R) \u2264 C' \u00b7 R^{D_H} \u00b7 T^{(D_H - 1)}", - "equation_id": "extracted_md:14", - "family": "markdown_extracted", - "name": "extracted_md_equation_14", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_82aaddc592cb524f", - "receipt_hash": "02077d7cbc6dba209f888ae509ae0ae834865c5a1ec65e7c624b85a0ad02bc72", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_14", - "object_id": "rrc_eq_82aaddc592cb524f", - "payload_bytes_sampled": 520, - "payload_sha256": "2dc736c1067053afbb7f7d0fddc4e434feb464b464860b40fd53fd95197cd545", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_82aaddc592cb524f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "77611db9b3f58dcf3f83e26c8517abd635b5f24194130546f4fad8802ce16f2d" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "S \u2264 C' \u00b7 R^{1.44} \u00b7 T^{0.44}", - "equation_id": "extracted_md:15", - "family": "markdown_extracted", - "name": "extracted_md_equation_15", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_6a952cb84d6e1fbf", - "receipt_hash": "977920ad1364a6ac997511acf26594c4501a362c3f1700654506fcba4dd185a1", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_15", - "object_id": "rrc_eq_6a952cb84d6e1fbf", - "payload_bytes_sampled": 513, - "payload_sha256": "28f92c4faa26edb7628f60bf9428207a787a4fd8e8393aab6485a986242f1631", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6a952cb84d6e1fbf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "328654bf554f4baa91e1888db18b5b5638c78fc67bad1e8e1e3678ca41cefd51" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "A(r) = 2\u03c0 (cosh(r) - 1) \u2248 \u03c0 \u00b7 exp(r) for r >> 1", - "equation_id": "extracted_md:16", - "family": "markdown_extracted", - "name": "extracted_md_equation_16", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_9c2c0d5c61628eb5", - "receipt_hash": "b894b0b740339fe04f5dfb040b46653e8dcbfa1a607b97fde11115b929c8daff", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_16", - "object_id": "rrc_eq_9c2c0d5c61628eb5", - "payload_bytes_sampled": 540, - "payload_sha256": "f55fe33ebde0b77e1b3a60752877925da9378f6350c34de0b42ab5de881527b4", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9c2c0d5c61628eb5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c6d9a0d6a41f0e5dc2f8257fcd3ba7f11006d6dc479a49c7f648986811902429" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "L_{n+1} / L_n \u2248 exp(d_inj) \u2248 \u03a6\u00b2 \u2248 2.618", - "equation_id": "extracted_md:17", - "family": "markdown_extracted", - "name": "extracted_md_equation_17", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_2f24d8cc16590af0", - "receipt_hash": "f8b98d829c552f87d22f3a05646984a4e9f03a780dee7c5285e99d14c114a48f", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_17", - "object_id": "rrc_eq_2f24d8cc16590af0", - "payload_bytes_sampled": 534, - "payload_sha256": "ed60afe1d967e8488fa241b67b624ba7086de72c9eb2a9132600f8712aed8f2d", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_2f24d8cc16590af0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7bc1d13e753cc827e40d30cccbc8df5c0acb8fbd8cc75853f0328bbaa357eae9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "D_s = 2 D_H / (1 + D_H)", - "equation_id": "extracted_md:18", - "family": "markdown_extracted", - "name": "extracted_md_equation_18", - "projection_signature": { - "shape_distance": 0.28814, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_5ece89b86c865faf", - "receipt_hash": "3d4a0a3264301b89b2b5d1fdc18de6151dab6b8cb19bc4069f9a096da0afaa0c", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.4375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4121162566656331, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4121162566656331, - "shape": "LogogramProjection" - }, - { - "distance": 0.4568964788017383, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4568964788017383, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4619248112304818, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4619248112304818, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.28814, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_18", - "object_id": "rrc_eq_5ece89b86c865faf", - "payload_bytes_sampled": 493, - "payload_sha256": "1aa20c9684c68381f8090290a62706b4fb8cde62ec0ff479300376f5a0185c90", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_5ece89b86c865faf", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b90364a6d8088e95a7f5934a359c4a22c0489c6c5113cae5682bcf67799fd507" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "D_s = 4/3 \u2248 1.333", - "equation_id": "extracted_md:19", - "family": "markdown_extracted", - "name": "extracted_md_equation_19", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_bfd316d8427b2f6c", - "receipt_hash": "270b734fc2aba4abb480cce5a841acccf50134f89d3e5c6764cced8a887c8ccd", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_19", - "object_id": "rrc_eq_bfd316d8427b2f6c", - "payload_bytes_sampled": 492, - "payload_sha256": "ac54dcf27a26497dc6343ba7b94ab6370d2f9e7d7d0ad476246891bc33b6b6f4", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_bfd316d8427b2f6c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0f0edc987a17e320b472e738fd8ae3588a3e2dc4b15f1831493a54eca3809518" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03c1(E) ~ E^{D_s/2 - 1} = E^{-1/3}", - "equation_id": "extracted_md:20", - "family": "markdown_extracted", - "name": "extracted_md_equation_20", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_12d678b6dc94f9d6", - "receipt_hash": "ccee8a0dfc45270e15b2a346e817d61f890020693d3864544b8e06781480c1ad", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_20", - "object_id": "rrc_eq_12d678b6dc94f9d6", - "payload_bytes_sampled": 506, - "payload_sha256": "5850f9aa272918f83a0023c5b962511bdab0eb9ec0690b4dabca80e19cf89e38", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_12d678b6dc94f9d6", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "1b2b976219cdddb03acaadc9f27753419b2a931fe32fed5ec7cda2c894965a0b" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "E_n ~ n\u00b3", - "equation_id": "extracted_md:21", - "family": "markdown_extracted", - "name": "extracted_md_equation_21", - "projection_signature": { - "shape_distance": 0.289549, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_0252524b379eac41", - "receipt_hash": "8de47c80aca3e577a006919723d7ae34de6487f0b18a00b87cf4cb7c465dd32a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.40625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4130541547900759, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4130541547900759, - "shape": "LogogramProjection" - }, - { - "distance": 0.45757192672046354, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45757192672046354, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46377360534162165, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46377360534162165, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.289549, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_21", - "object_id": "rrc_eq_0252524b379eac41", - "payload_bytes_sampled": 483, - "payload_sha256": "0667b7bf1b891ef387a0caa5fa6960a6d4ad27f7cf72df450104f77618d5a733", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0252524b379eac41", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "5e42eda8bcbc41b3cf7f38b460dbbfbe75525b491b34e7a9ac4a5113f66aff14" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "n_s = 1 - 2/(1 + \u03b8_max/\u03b8_recombination) \u2248 0.965", - "equation_id": "extracted_md:22", - "family": "markdown_extracted", - "name": "extracted_md_equation_22", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_8acff8093805680f", - "receipt_hash": "620384c50857a63e7c4a1c8ae3321dfd74989f48aec7903de34d2889ea472362", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_22", - "object_id": "rrc_eq_8acff8093805680f", - "payload_bytes_sampled": 532, - "payload_sha256": "0fa84367c9c7b5087ecc102d3ece9888897865396738463f49b63387d8bee3a2", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8acff8093805680f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "8deb01b6a029862cc5f7ed56304c058bc404ba59fdf819d5f32aa77266550004" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "E_dissipated \u2265 k_B T \u00b7 ln(2) per bit erased", - "equation_id": "extracted_md:23", - "family": "markdown_extracted", - "name": "extracted_md_equation_23", - "projection_signature": { - "shape_distance": 0.287271, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c56ffe8dd188e331", - "receipt_hash": "b58bc6bf8ef3dc86a387cd355643c4e91089d051b23ebd63e8e8e388bbd20102", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41157219941042966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41157219941042966, - "shape": "LogogramProjection" - }, - { - "distance": 0.45651990682503474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45651990682503474, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46076175790722984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46076175790722984, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287271, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_23", - "object_id": "rrc_eq_c56ffe8dd188e331", - "payload_bytes_sampled": 527, - "payload_sha256": "a485b5d5ab5bf057e48f1b416e9fcbf1c18f924dc6288493a2a6aaf9b81c2b97", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c56ffe8dd188e331", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "66adacd249245d487e9758d7caa26025da24f946607ddfdc30dd14d66cc8f982" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "H(X) = -\u03a3 p(x) log p(x)", - "equation_id": "extracted_md:24", - "family": "markdown_extracted", - "name": "extracted_md_equation_24", - "projection_signature": { - "shape_distance": 0.287271, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3305fd3254a89e5b", - "receipt_hash": "509e4220562f4989a3d584ced23444b5f851af71ff25ad0eaee5dc6c9e108877", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41157219941042966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41157219941042966, - "shape": "LogogramProjection" - }, - { - "distance": 0.45651990682503474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45651990682503474, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46076175790722984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46076175790722984, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287271, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_24", - "object_id": "rrc_eq_3305fd3254a89e5b", - "payload_bytes_sampled": 498, - "payload_sha256": "00570f385d4498b18d198acad188d125531b29c8e7a725d5a339377cd383ebef", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3305fd3254a89e5b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "bb925f0393d8e16012245d1f2a60d90750b238a49c015b91e7f57d80d0e1f6fb" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "S \u2264 2\u03c0 R E / (\u210f c ln 2) = A / (4 G \u210f)", - "equation_id": "extracted_md:25", - "family": "markdown_extracted", - "name": "extracted_md_equation_25", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c57a3f3edc39ddd3", - "receipt_hash": "d832958d6158607b2804c391c283519d309cd368a4d1b7bab791911d8c39cfe8", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_25", - "object_id": "rrc_eq_c57a3f3edc39ddd3", - "payload_bytes_sampled": 527, - "payload_sha256": "d864df95b3f683cc9c0918a8b64b2a5a94571b519faeb4b5da0ca8dbee76ed33", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c57a3f3edc39ddd3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ab8adc71ef9caa67c215dff039c8136937c31e16efb97e226f71b0a6878cdba9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "C_V = (12\u03c0\u2074/5) N k_B (T/\u0398_D)\u00b3 \u221d T\u00b3", - "equation_id": "extracted_md:26", - "family": "markdown_extracted", - "name": "extracted_md_equation_26", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3654d8cd243cde2b", - "receipt_hash": "31e1c43d5181798de3a068ada6b13f9a4d63206652b59b7dff673a1e8304fad0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_26", - "object_id": "rrc_eq_3654d8cd243cde2b", - "payload_bytes_sampled": 534, - "payload_sha256": "173f583026fd3bb8c13c505f8e745d2e5f04ce1ee6205af2f14fde4bf6f6a8ea", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3654d8cd243cde2b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "f651e44dc3bf56d43f5eebb1828ac58ebfb6b8d5dc3d3dea5f9cc38ec01b3380" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "C_V \u221d T^{D_s}", - "equation_id": "extracted_md:27", - "family": "markdown_extracted", - "name": "extracted_md_equation_27", - "projection_signature": { - "shape_distance": 0.288596, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e5f58db6423d2f9c", - "receipt_hash": "b2828c6913e2eb54e36f296dab61dfc8279ec0c0988a7142deddab8c953e3eb6", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.427083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4124126827589752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4124126827589752, - "shape": "LogogramProjection" - }, - { - "distance": 0.4571069031424215, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4571069031424215, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4625272350064127, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4625272350064127, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.288596, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_27", - "object_id": "rrc_eq_e5f58db6423d2f9c", - "payload_bytes_sampled": 488, - "payload_sha256": "2143e515de07d4329f3398606ea88a1dbb8864f93268bf5af95eae3188558b99", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e5f58db6423d2f9c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "6118b763f6adf57a69dd6222c683ef295012067b367115aa2cfc904084c3aa0f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "C_V \u221d T^{1.18}", - "equation_id": "extracted_md:28", - "family": "markdown_extracted", - "name": "extracted_md_equation_28", - "projection_signature": { - "shape_distance": 0.288596, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_fe98a523c9c0b821", - "receipt_hash": "f91d4d36242769d52d4bdb7a5ab1f055c9d097ce7c8df24c67c099b8df9ae3fe", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.427083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4124126827589752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4124126827589752, - "shape": "LogogramProjection" - }, - { - "distance": 0.4571069031424215, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4571069031424215, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4625272350064127, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4625272350064127, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.288596, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_28", - "object_id": "rrc_eq_fe98a523c9c0b821", - "payload_bytes_sampled": 489, - "payload_sha256": "1e1cdcebeb68029f5e9e1d11ceabdbfb014636976571eeb598212ac5f492df79", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_fe98a523c9c0b821", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "d5f611afa03bceb28d48c2da7a1a84e6487039d23173c1b324fdb6990ff8c9c1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u27e8exp(-\u03b2 W)\u27e9 = exp(-\u03b2 \u0394F)", - "equation_id": "extracted_md:29", - "family": "markdown_extracted", - "name": "extracted_md_equation_29", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_bedb0334896533f3", - "receipt_hash": "8927580859f5227425295b590601b2d7d1e344e36bda46cb2e74da96c98d90b4", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_29", - "object_id": "rrc_eq_bedb0334896533f3", - "payload_bytes_sampled": 519, - "payload_sha256": "c175eaafa3052132bc58c85cd471391fcf34895cf85fa0ea79d421c33df904d0", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_bedb0334896533f3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "8644015b8592ee170c80761e9fd263b55fbc461eb5a1ad1c17195d75eef456fe" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "(\u0394J)\u00b2 / \u27e8J\u27e9\u00b2 \u00b7 \u03c3 \u2265 2 k_B", - "equation_id": "extracted_md:30", - "family": "markdown_extracted", - "name": "extracted_md_equation_30", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_cbefaa1738883221", - "receipt_hash": "ee58ce129e44dfa94d0adc6465bbcac64dc919e4e632197b4848ad8c429d8ac1", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_30", - "object_id": "rrc_eq_cbefaa1738883221", - "payload_bytes_sampled": 534, - "payload_sha256": "48ccbf6ecb73cca1a2859e3e92e0568c5d34a81cc2b9b826d42752da10fd2b75", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_cbefaa1738883221", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "bbcebce39a7b23323bb320e163df363a6a2736f650a304980349d72cb40a18ce" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u0394x \u00b7 \u0394p \u2265 \u210f/2", - "equation_id": "extracted_md:31", - "family": "markdown_extracted", - "name": "extracted_md_equation_31", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_434ad4a173c4cb24", - "receipt_hash": "9fdbb18506fb7adcd3050fe8923e8d5d7a43b1409795dd16eee21c9aa31b3553", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_31", - "object_id": "rrc_eq_434ad4a173c4cb24", - "payload_bytes_sampled": 508, - "payload_sha256": "463aa7764e98cb1c8aa72e458ebc85ed5734392b942ccfd29fff266da56fb3cb", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_434ad4a173c4cb24", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "98ce236152b836df2a9fc236384eb0f72f0b1a23d9c3e538d5d44dd50fc8ca8b" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03a8_total = \u03a8_A + \u03a8_B = A \u00b7 exp(i \u03c9_\u03a8 \u03b8) \u00b7 [exp(i k_\u03a8 x_A) + exp(i k_\u03a8 x_B)]", - "equation_id": "extracted_md:32", - "family": "markdown_extracted", - "name": "extracted_md_equation_32", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_fc46c7ee6a40460d", - "receipt_hash": "0fa5a1523cc0b05a786c865918bb3371f9b0950cdd3cb7373f8e26efa4eec702", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_32", - "object_id": "rrc_eq_fc46c7ee6a40460d", - "payload_bytes_sampled": 594, - "payload_sha256": "a2e4348d41eb4e26ec58a0497be4d6bfaa508c377d5b2e71c404d463ea7032ca", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_fc46c7ee6a40460d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "81bfd7ac7173df5a48c875e54e8cc854bc1e5386f274d390649a60fd8eff46fe" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "|\u03a8_total|\u00b2 = 2|A|\u00b2 \u00b7 [1 + cos(k_\u03a8 (x_A - x_B))]", - "equation_id": "extracted_md:33", - "family": "markdown_extracted", - "name": "extracted_md_equation_33", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_0ddb4aae4fd1d8d5", - "receipt_hash": "733730562f92d3d87ee4fdfb29e9df74f2b0a0adc0a9197a4d5d69831e640536", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_33", - "object_id": "rrc_eq_0ddb4aae4fd1d8d5", - "payload_bytes_sampled": 542, - "payload_sha256": "77536a37cada6bb4d9b5915ab5217deb8a318be42788f889f59dc2d624ee98fe", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_0ddb4aae4fd1d8d5", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "6bac4e7d828daf84b931a400267cbf1eaa95373a5f05caa264763b8496fcc8f3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "E = \u210f \u03c9 = \u03c9_\u03a8 (in natural units \u210f = 1)", - "equation_id": "extracted_md:34", - "family": "markdown_extracted", - "name": "extracted_md_equation_34", - "projection_signature": { - "shape_distance": 0.287271, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_81ad5c64ecea2574", - "receipt_hash": "ed7d300c022f6fbb6190516bed43784c69b01781f47a4d927966d2b3deb410c1", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41157219941042966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41157219941042966, - "shape": "LogogramProjection" - }, - { - "distance": 0.45651990682503474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45651990682503474, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46076175790722984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46076175790722984, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287271, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_34", - "object_id": "rrc_eq_81ad5c64ecea2574", - "payload_bytes_sampled": 537, - "payload_sha256": "b4ccfece0410a3c7fa0fc9d81d7f6bea693a22be7a53a66fbae77c2e38bba093", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_81ad5c64ecea2574", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7bbf96d53e40ff01680474f6b7a4d1702e0b0c73c8afca05b2fe4c3ddceed1cc" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "p = \u210f k = d\u03b8_0/dx = k_\u03a8", - "equation_id": "extracted_md:35", - "family": "markdown_extracted", - "name": "extracted_md_equation_35", - "projection_signature": { - "shape_distance": 0.288596, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_aef93abc672b8e29", - "receipt_hash": "e9627d15e5b48fd71b0f2afcd0cade14b268ba1450e9361d17e67e53369d74c9", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.427083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4124126827589752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4124126827589752, - "shape": "LogogramProjection" - }, - { - "distance": 0.4571069031424215, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4571069031424215, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4625272350064127, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4625272350064127, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.288596, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_35", - "object_id": "rrc_eq_aef93abc672b8e29", - "payload_bytes_sampled": 508, - "payload_sha256": "9ce1701ae815eee2a566510384becd1ab715a9314e3a236143fcb0c542c9984d", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_aef93abc672b8e29", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "a00e6ab140f64863b3a8d33e34e2d5db97c935fc50087897a6f21e946a4925e1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "\u03bb = 2\u03c0 / k_\u03a8 = 2\u03c0 / p", - "equation_id": "extracted_md:36", - "family": "markdown_extracted", - "name": "extracted_md_equation_36", - "projection_signature": { - "shape_distance": 0.289065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_def9542a5004dc68", - "receipt_hash": "8a9b46a64121eec6763b28790838109c860f5467eecfa807ba95219bdbf64958", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.416667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4127253277433695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4127253277433695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4573320596979162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4573320596979162, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46314351812704235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46314351812704235, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.289065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_36", - "object_id": "rrc_eq_def9542a5004dc68", - "payload_bytes_sampled": 511, - "payload_sha256": "f2970d4852ab666499836622c7ef47c041e4ff1888579220e409c3ee412ca60e", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_def9542a5004dc68", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "644d3a6e42e8fc00631496dbeedd487578f6750d1d948b95b3f42180a0b9e42d" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Phenotype(x, t) = \u03a8_E [ Genotype(x) \u00d7 Regulatory_State(t) ]", - "equation_id": "extracted_md:37", - "family": "markdown_extracted", - "name": "extracted_md_equation_37", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_ef9d2a2f3c8de320", - "receipt_hash": "186711871c51207179bc7dc122a8a4ce71d52731bf983d3d6a3cb537ef8e989a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_37", - "object_id": "rrc_eq_ef9d2a2f3c8de320", - "payload_bytes_sampled": 539, - "payload_sha256": "0281c12fb876c3423d56a7023d138071f22d2bfc2ef0b0a68f5a158724a354f0", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ef9d2a2f3c8de320", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "38a94accfedf75a54142a3c3d8d46f888d2ba5c6674b10195486c0272f2ed5cb" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Residual(n) = \u03a8_decode [ Basis, Context(n) ] XOR Byte(n)", - "equation_id": "extracted_md:38", - "family": "markdown_extracted", - "name": "extracted_md_equation_38", - "projection_signature": { - "shape_distance": 0.232964, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "shape_closure", - 0.64 - ], - [ - "decoder_declared", - 0.6 - ], - [ - "witness_declared", - 0.6 - ] - ], - "weak_axes": [ - "geometric_mass", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "compression_route", - "rrc_kind": "compression_route_prior", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "r* = argmin_r LB(r | phi_signal(c), semantic_regime(c), history_state); promote iff exact decode hash closes and total bytes beat incumbent", - "invariant_receipt": { - "object_id": "rrc_eq_57188e85cab23a67", - "receipt_hash": "cf30a90a927873ec56e26f5dc3d62bf33c8e9c7d6930d205a70a710c49652852", - "schema": "rrc.object_receipt.v1", - "shape": "SignalShapedRouteCompiler", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.533333, - "decoder_declared": 0.6, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.64, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3676840289150524, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3676840289150524, - "shape": "LogogramProjection" - }, - { - "distance": 0.45054737154720365, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45054737154720365, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4733554210559066, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4733554210559066, - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - } - ], - "declared_kind": "compression_route_prior", - "distance": 0.232964, - "kind_prior_shape": "SignalShapedRouteCompiler", - "shape": "SignalShapedRouteCompiler" - }, - "object": { - "kind": "compression_route_prior", - "label": "extracted_md_equation_38", - "object_id": "rrc_eq_57188e85cab23a67", - "payload_bytes_sampled": 527, - "payload_sha256": "4f3db523c749c8e6a4818281bae61a0b40f436242302ebdfdf3177352b32fe29", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_57188e85cab23a67", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared", - "decoder_declared" - ], - "shape": "SignalShapedRouteCompiler", - "status": "HOLD", - "witness_hash": "abf5e1bcc13ff639c1e4008c7c8c1ead1f2797387deb4fe6c046695cdb622920" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "H_\u03a8(data) = -\u03a3_n p(n) log_2 p_\u03a8(n) \u2264 H_uniform(data) = 8 bits/byte", - "equation_id": "extracted_md:39", - "family": "markdown_extracted", - "name": "extracted_md_equation_39", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "fenced equation block", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_4b6cfeff599d0583", - "receipt_hash": "7b8c3a96925b3cdc9fe6a3349a77e60bc6ee166199bfb6292c3b2a2eb8a4b5f0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "extracted_md_equation_39", - "object_id": "rrc_eq_4b6cfeff599d0583", - "payload_bytes_sampled": 556, - "payload_sha256": "b796078bf681d3fda64c9b079049170ec80d86f0e2b9af246cc2da07be5a8eef", - "source_path": "3-Mathematical-Models/extracted_equations.md" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4b6cfeff599d0583", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "595d143ebe30041b9733b10919493c290fdada4c075a6b75ed256958391a4dc9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G = (V, E) represents a quantum state |\u03c8\u27e9 through a\ncollection of local tensors {Tv }v\u2208V , one for each vertex", - "equation_id": "eq_21f33f4110064dbd", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b639be44c38e19f1", - "receipt_hash": "ac5d92474fa932d45e99aaf09161cdaa41a9028ac6c6e7b3b4206bdc61030308", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b639be44c38e19f1", - "payload_bytes_sampled": 564, - "payload_sha256": "1467bd1e41f52f420ef3edacd069861854c8f7d669bfd98ecd4838152b7201f8", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b639be44c38e19f1", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "30b04be6c8687e9800f036cc2ae28c548bbfab1ba9fd888f35f111a0164e5475" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "injec= CD and Hv \u223c\ntive if this map has trivial kernel", - "equation_id": "eq_14d74623112c018f", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d24270e4be19de76", - "receipt_hash": "ea800e23de7ceddb38f1622e12f8417ea01cf44b28b43ec0b34b2f863fe343ed", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d24270e4be19de76", - "payload_bytes_sampled": 498, - "payload_sha256": "2c3386c269f7f3713b8b007b094a5d31343f27c4dbf2d2f16c65ded1ddd3972a", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d24270e4be19de76", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "8c1957675a0a43c32468442097bfd87f5ca9b58b141e6eabce2d7211c0021de4" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "d \u2265 D|N (v)| , which generically holds after coarse-graining\nwhen D = O(1)", - "equation_id": "eq_fcd40dc2de7c7324", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.265908, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b20eddced9a6b7da", - "receipt_hash": "ff0d46411960c99d581c302547de341e46ea6d963bbbbc522b2582b7df3552be", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.2, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.5, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.2, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.38274230929900915, - "kind_prior_bonus": 0.0, - "raw_distance": 0.38274230929900915, - "shape": "LogogramProjection" - }, - { - "distance": 0.42693921346510805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.42693921346510805, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4330143156406733, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4330143156406733, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.265908, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b20eddced9a6b7da", - "payload_bytes_sampled": 518, - "payload_sha256": "619722b5731cab618f8c27a55ccbca87146b8d6f8f2cb5715a5ca728f16ea404", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b20eddced9a6b7da", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "52ea41a747ac12314895c43d18cf136fa2e6873fd832050f7c61f3ff4ba5df72" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "min \u2265 \u03b4", - "equation_id": "eq_416bbe45bfd12c68", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_9c22fd7b336c5904", - "receipt_hash": "31da9991424da41b45b73a144b363be19c56a6053ef9e7ecaff91fceb27928b0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_9c22fd7b336c5904", - "payload_bytes_sampled": 455, - "payload_sha256": "34699712f5ea2fb44cd0f4bef6762ecd13b0786f969b724863739182128297b3", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9c22fd7b336c5904", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "61462cca553d4663bb6eb3b4f57645af63adc317edbbf778f6a9c53bcbf5d6f1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9", - "equation_id": "eq_290815ff34ff74a2", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_2f9d4e3b2060b799", - "receipt_hash": "6e94370b8c903d8d952a4cec40757a5f24e4a7538c29e7b72b30f6c999017420", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_2f9d4e3b2060b799", - "payload_bytes_sampled": 467, - "payload_sha256": "b7fae06da33fb50ed2a3f2442667bc00b9b41cdbf7109dbe1fa0b0c361806f99", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_2f9d4e3b2060b799", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b3d1ae6b7a5343ccbbced9d5b1897f5c9eccd1456c2cb4710014acd7f0f29930" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, n) of\nthe graph, the tensor Tv \u22c6 T\u0304v of the norm network defines\na superoperator on the virtual space from any set of legs\nto its complement Eq", - "equation_id": "eq_5ea587928ed366f5", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_56d979e780ead00b", - "receipt_hash": "eb82ce56c1700190e6c9e6a55ca6d2267671ce2f7932ee484a0c2d8a26a943b5", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_56d979e780ead00b", - "payload_bytes_sampled": 602, - "payload_sha256": "258165199cf5acde19009bfb16160102ddfaf2a482c6a1788cc65c6acfe14be1", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_56d979e780ead00b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0f7e8764e036db7afc3dbe76cce07aeaee607c9c3623ee55acb2417cccf7f60a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = ZBP \uf8ed1 +\nZ\u2113 \uf8f8", - "equation_id": "eq_e1646b712978905e", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e22543ef62f2494e", - "receipt_hash": "89343fde1c5afae10e79b25cde1c21d595882c272b073f0be07d24d7299b6ce4", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_e22543ef62f2494e", - "payload_bytes_sampled": 471, - "payload_sha256": "c0601918e7a2f1689ce51e3fbc6388573f56324abbb8ee0127195ff8b6df2e5d", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e22543ef62f2494e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c4fe954c9e8e5cd012916b14265a9d1968dd1dab6bff5b36d2c2e397b54f555f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = log ZBP +\n\u03d5(W)ZW ,\n(7)\nconnected W\n\nwhere a cluster is collection of loops with multiplicities,\nW = {(\u21131 , \u03b11 ), (\u21132 , \u03b12 ),", - "equation_id": "eq_ed91aac060fde4cf", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_603b460ebf696fbb", - "receipt_hash": "85b74ce61f591a2d0ec08e41b69b35c7142fca7ac2a5b9819d59544484c37037", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_603b460ebf696fbb", - "payload_bytes_sampled": 597, - "payload_sha256": "cbd444d01efe16641c2e80ecf064700cd1236d93a319a615dd996ce331c7815e", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_603b460ebf696fbb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "a84d67519630817244ab1cc2e7895c2c1290ce1c140486b9a9c3127d8a5b620a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "i \u2265 1 is its multiplicity in the cluster", - "equation_id": "eq_9b66cadab83a4e1e", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.280384, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_17cb89b04d825252", - "receipt_hash": "ffb50cb92661f1282f30cdd89752fbf609429f9141ee12206aa547465f9e5c91", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.285714, - "hardware_affinity": 0.166667, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3928590954449332, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3928590954449332, - "shape": "LogogramProjection" - }, - { - "distance": 0.4432041691370668, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4432041691370668, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4507610132831291, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4507610132831291, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.280384, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_17cb89b04d825252", - "payload_bytes_sampled": 483, - "payload_sha256": "68c11294c8a2f449471edfea5a8d2f6ec105643b1d7aa3433f03b50e07791a89", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_17cb89b04d825252", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "985d46e4e8ff73e07524d8decc23622e3fa8aa5253ffc3e0bd17e53aad97963c" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "ZW = i Z\u2113\u03b1ii and the\nP\nweight of a cluster is |W| := i \u03b1i |\u2113i | where |\u2113| for a loop\n\u2113 \u2208 L denotes the number of edges in \u2113", - "equation_id": "eq_6fd0d43c1e1577a1", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_7106bb20125b3d6f", - "receipt_hash": "2efdb15aff690ae9174b012709da7bf039fcb510a7cbe0eca14d823cba932272", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_7106bb20125b3d6f", - "payload_bytes_sampled": 604, - "payload_sha256": "c0eab64c5d8df26cca2fe3542c23f259331712d2b00064e88ebbfef40c3f784f", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_7106bb20125b3d6f", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c93c520f91f1c5054fbef74f8feb650e2dc8ca4bb19a4ee1f3f113d945885942" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "c > c0 =\nO(log \u2206)", - "equation_id": "eq_8605b7c5ccdbcf5f", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b650dd7bdbcd9bcb", - "receipt_hash": "7152eaff8affcc4281af20854b12d2f57c0a8207f5141f81bbda9d69bcc4fa4a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b650dd7bdbcd9bcb", - "payload_bytes_sampled": 461, - "payload_sha256": "966ab8d37ca0214abfb307a347544005262cce5a9b0152f0f35ff39f5e2c8c03", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b650dd7bdbcd9bcb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ba0537096debf3ac88e3e6072a7bf27b3394993c038a978cee93dd99fd5834ef" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9 to 1/ poly(N ) multiplicative\nerror\n(ii) local observables \u27e8OA \u27e9 = \u27e8\u03c8|OA |\u03c8\u27e9/\u27e8\u03c8|\u03c8\u27e9 to\n1/ poly(N ) multiplicative error, given \u27e8OA \u27e9 \u0338= 0\n(iii) correlation functions \u27e8OA OB \u27e9 \u2212 \u27e8OA \u27e9 \u27e8OB \u27e9 to\nO\u0303(1/ poly(N )) additive error\nwhere A, B \u2282 V are disjoint local regions", - "equation_id": "eq_dbd7ee74be207cc7", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283511, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b97583b24c3b2936", - "receipt_hash": "8afa195d251266167aae5d398e00c6a8bb5371680e9870c8c93363ec8b8ee8ce", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4097520236685294, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4097520236685294, - "shape": "LogogramProjection" - }, - { - "distance": 0.45537780262401606, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45537780262401606, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550893247786234, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550893247786234, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283511, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b97583b24c3b2936", - "payload_bytes_sampled": 847, - "payload_sha256": "4701e02f717e3ba8f701bd9ef0c0367010aabeb41af6003dbb8b07cb9f56e9fa", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b97583b24c3b2936", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b46e80838fbe6fb73b8eb06a56ec54c456c62c18d500fc84e862bcf9ec57986f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, n) with d(v, A) =\nr, we have\n\u0010\n\u0011\n\u2225\u00b5\u2032\u22c6,\u20d7e \u2212 \u00b5\u22c6,\u20d7e \u22251 = O e\u2212r/\u03be\u2217\n(10)\n\n\f5\nWhere 1/\u03be\u2217 = O(log \u03b5\u2217 /\u03b5)", - "equation_id": "eq_9138cc21d6e4da27", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d521b2282dde0c38", - "receipt_hash": "44effbc7d62a598c897faedd9f562e9c42f3e99767721fed573d3e7751eb069b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d521b2282dde0c38", - "payload_bytes_sampled": 652, - "payload_sha256": "0cba1711e077ef47d7d0613a571fc75adfa228b4769932a595d51e71d701d0e2", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d521b2282dde0c38", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "3673f284ef77e9706ec5469e4373d258727db26e054439605af05b52c3ae9125" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "W = Wnear \u222a Wfar , where clusters in\nWnear are distance at most a cutoff Rth away from B", - "equation_id": "eq_5be87eaf6cffbaae", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_282beac067cdee85", - "receipt_hash": "75015934af99cbbd7c7bda525905053c38aeef136fdf483ab1baee199a28de4f", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_282beac067cdee85", - "payload_bytes_sampled": 532, - "payload_sha256": "4c9d2ec1deb76e2de109fc07e8f1b3a76fd7f721cde404338978955a7b33dac4", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_282beac067cdee85", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7e0607cc2e69feddd6c93183ac8fe06461e08175ba5a40a82bbcce23265ae704" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "t < r and\nstarts increasing thereafter, owing to the strict lightcone\nin the message-passing dynamics [see SM for a proof]", - "equation_id": "eq_927277d74e6531cc", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_07b0a4a6f75d86b4", - "receipt_hash": "329c6502c0823c5cf5db572511d4d6891240841c846f830a88e1b1bc46b5d0c4", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_07b0a4a6f75d86b4", - "payload_bytes_sampled": 562, - "payload_sha256": "aae3ea0de35b9d8a9d26e1dcb156ba5224f0e7268f31cf93100ae3402f33c1b5", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_07b0a4a6f75d86b4", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ea07b7172e725e0fce8c666685bc14178d7422d7feca9f6def0944e2c6487cb4" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "t = r\u22121, leading\nto\n\u0010\n\u0011\n\u2225\u00b5\u22c6,\u20d7e \u2212 \u00b5\u2032\u22c6,e \u22251 = O e\u2212r/\u03be\u2217\n(13)\nestablishing locality of the BP fixed-points", - "equation_id": "eq_353043888999f4ac", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_295fb47f2cd6ddfb", - "receipt_hash": "fcdf797b99db402de51a0195d73595963defba4c5c6b3a614d8b9608d6c89ba1", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_295fb47f2cd6ddfb", - "payload_bytes_sampled": 621, - "payload_sha256": "f6f905e08ecb77c29022dcac3cb0940859b6f4ec99b68d61030f3023621290ea", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_295fb47f2cd6ddfb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "3731234a596135063905114e6d67a448abf86f966a1aef51ccb6ee492595be28" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "d = c \u2212 c0 = O(1)", - "equation_id": "eq_532f389442891ed2", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_1d0a752af1946515", - "receipt_hash": "29b3d3cd8f35c5908084976e831d9267669e7a777a0802c1663afb7b8f2ce584", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_1d0a752af1946515", - "payload_bytes_sampled": 460, - "payload_sha256": "0186295c28a114b2a8700e3ec0ca191118085ececf7f5a965bcbb54621fe5e28", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1d0a752af1946515", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "cca36139fd5692454667714f6baf490c1816fc04968a2cde050412a85771a7d4" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "R = \u0398(1)", - "equation_id": "eq_fdfae5689dd4a9a5", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3a60fca57229c15b", - "receipt_hash": "b61b83549338279d4c4adb4c5a93626ae38a827b4b701628a60cb0c2fd2d1968", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3a60fca57229c15b", - "payload_bytes_sampled": 451, - "payload_sha256": "294d01047125c127e516b98296c0d1e192b801ab141a141101f0b0ec1ad67960", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3a60fca57229c15b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "02c2f3d603b115d2fa551386ea1afd666c35aa3ea8b635853260524a95809247" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "X = 1}", - "equation_id": "eq_9138aeb942929155", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e25b46d22b7ca0f1", - "receipt_hash": "2badceabd488a2e2090127bf493dedbd573fa69f92534e2ccaf04a6d3026a860", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_e25b46d22b7ca0f1", - "payload_bytes_sampled": 444, - "payload_sha256": "51fccce855d2657037550b23aff16cee379babd97798d674640afc2b0f0fe68f", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e25b46d22b7ca0f1", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "de23a98c263042765efb3c4b65d908ef777308a10e7baa95ec6be3dab92136c6" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "i=1\n\nFor p = \u221e we have,\n\u2225x\u2225\u221e := max |xi |\n1\u2264i\u2264m\n\n(S3)\n\nFor an operator A \u2208 B(H), we define the operator Schatten norm in terms of its singular values \u03c3(A) as,\n\u2225A\u2225p := \u2225\u03c3(A)\u2225p\n\n(S4)\n\nfor any p \u2208 [1, \u221e]", - "equation_id": "eq_0cd087779e7532a9", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283255, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_14fd73dcf1af02b8", - "receipt_hash": "758e17af3e072042f4572c1ce8eee674da8c6892cde49f1199166b26d85d691d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096854844400418, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096854844400418, - "shape": "LogogramProjection" - }, - { - "distance": 0.4549746348787963, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4549746348787963, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550625260348704, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550625260348704, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283255, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_14fd73dcf1af02b8", - "payload_bytes_sampled": 726, - "payload_sha256": "ad9e11989d40d1a6aa81cb6b48d75e85522bcec4e7c99da7a3dcf5a62d38b46d", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_14fd73dcf1af02b8", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "2fbfddbc3c8bb0f185b0b522d4eb61f57558393eecb3e569d0548305e4990be8" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "p \u2264 q \u2264 \u221e and any operator A, we have,\n\u2225A\u2225p \u2265 \u2225A\u2225q\n\n(S5)\n\n\u2225A\u22251 \u2265 \u2225A\u22252 \u2265 \u2225A\u2225\u221e\n\n(S6)\n\nIn particular,\n\n\f10\n2", - "equation_id": "eq_5593e036fe6f7dd7", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e6a6d86ede20e5ad", - "receipt_hash": "f32748cb72d0238ecbae7d2c3da9c54c169451c3b170d2fbbf174c4b73570574", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_e6a6d86ede20e5ad", - "payload_bytes_sampled": 641, - "payload_sha256": "3f838d86e762cc4e4a8f36a399a8e5b4b74502ab42c844827c7f0434dd0f4067", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e6a6d86ede20e5ad", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "18b5d54b616716b74ed3391e0a24e7b9368fb763e35a5dfe8ae5cfa88b9bc6b1" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "p \u2264 q \u2264 \u221e and any non-zero operator A,\n1\n\n1\n\n\u2225A\u2225p \u2264 rank(A) p \u2212 q \u2225A\u2225q\n\n(S7)\n\np\nrank(A)\u2225A\u2225\u221e\n\n(S8)\n\n\u2225Ax\u22252\n= \u03c3max (A)\nx\u0338=0 \u2225x\u22252\n\n(S9)\n\nIn particular,\n\u2225A\u22252 \u2264\nWe also note that, the \u221e\u2212norm obeys,\n\u2225A\u2225\u221e = sup\n\nwe will denote \u2225 \u00b7 \u2225\u221e by just \u2225 \u00b7 \u2225 for convenience", - "equation_id": "eq_ea9b6d38a8a0c1b0", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283781, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a5fbdc2a7f09759e", - "receipt_hash": "5f172d76bcc85f6f2c8797d6e018b6b797ad57b704828cda6bdf6b4317af3079", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40983509977562194, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40983509977562194, - "shape": "LogogramProjection" - }, - { - "distance": 0.45552650015276536, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45552650015276536, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4557954927709534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4557954927709534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.283781, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_a5fbdc2a7f09759e", - "payload_bytes_sampled": 883, - "payload_sha256": "826bd4e074f7a11abed75182ea1abe415b7b346273066c44ad6e891744c926ce", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a5fbdc2a7f09759e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "5cf16120d3e832214e68a0bf494eea2448cbf72b9f81e3d720dc249f5391f081" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "B = Ac , reshape the tensor into a linear map\nT : HA \u2192 HB\nby grouping the indices in A and B into multi-indices a = (ia1 ,", - "equation_id": "eq_e0537400fa963370", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b2fc5edc90c4c538", - "receipt_hash": "9b1519dfe4e81be5b4e8eb97a7e1360d257a8a42225817492b5e679a046914ef", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b2fc5edc90c4c538", - "payload_bytes_sampled": 567, - "payload_sha256": "3ce21249ae7b3db1c60f5b1646ebe2aa6f405932ab151fbaffafccc00cd85144", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b2fc5edc90c4c538", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c2c9c72c73cee9e04d175c29d3e519854d3735cabb6c63824c4a7e0879af366e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "b = (ib1 ,", - "equation_id": "eq_23fc8ebaf9c8ae4a", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.287699, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_05370b4783a8bd6a", - "receipt_hash": "5e2aa9f91b47f5f3a7204c65b75cb2367c1a3b871e3d207c0fc7ad885de3f099", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.447917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4118360844848208, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4118360844848208, - "shape": "LogogramProjection" - }, - { - "distance": 0.4567008070394173, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4567008070394173, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46133630109282603, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46133630109282603, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287699, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_05370b4783a8bd6a", - "payload_bytes_sampled": 448, - "payload_sha256": "a43f79cf80edd4a4db44e13a45ddbc836db71d5ca1d72e0a5770c85784412a3e", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_05370b4783a8bd6a", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c2b4c26543594bd4480e94ca78cd05379bda4c9683bbc6394d77924c189cf1eb" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "q = 1 we have,\n\u0001\n| tr A\u2020 B | \u2264 \u2225A\u2225p \u2225B\u2225q\n\n(S12)\n\nGraphs For a graph G = (V, E), we define some useful notation", - "equation_id": "eq_8b4895ac1c7ebca0", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283511, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d356d2da175a4185", - "receipt_hash": "55f40198b0ef268596ca51e481023b890b488c996a15558791e19b85fefae8f2", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4097520236685294, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4097520236685294, - "shape": "LogogramProjection" - }, - { - "distance": 0.45537780262401606, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45537780262401606, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550893247786234, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550893247786234, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283511, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d356d2da175a4185", - "payload_bytes_sampled": 589, - "payload_sha256": "62f904872edd9cd5a4e955f539bcd05c5eae0b205438526ec97900d6622afc9c", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d356d2da175a4185", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "acea22c20d71936803751470bbdb4d9cc920acf28ab67fcdc37bba32f10732d9" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = {v, w} \u2208 E, we will refer to it\u2019s directed versions \u20d7e = (v, w) and \u2190\ne = (w, v)", - "equation_id": "eq_25d49db810143f41", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d9785ef71690f60b", - "receipt_hash": "09f8d81bc79c3c37219afdef58392fdb33628e7307096d30bb863b06e49d5695", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d9785ef71690f60b", - "payload_bytes_sampled": 543, - "payload_sha256": "ef516063fda4c78212772d1e821ff7306ad5df9a231493a9892b1275a5b8bf88", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d9785ef71690f60b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "df286592b28867410bf9785628f66058f4a5adac32cb68a5aa203cf588acd1f5" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = {v, w}, we define, for any u \u2208 V ,\nd(e, u) := min{d(v, u), d(w, u)}\nand similarly, for any A \u2282 V ,\nd(e, A) := min d(e, u)", - "equation_id": "eq_5034975bc936419d", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_74c2bb3396094843", - "receipt_hash": "c988688aa9ea9d3dbc6d281e7849c0f15eee8659f2b151fd980983d30ff7330c", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_74c2bb3396094843", - "payload_bytes_sampled": 576, - "payload_sha256": "03b9edc670b842f034a98651e753b5bac7f9abdccf23e9245b4a8ac553e179c2", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_74c2bb3396094843", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c8b2d2827bc70931526c87ba4b5e9c2b2c7502e4c71f52bd1c6865cb9e9a28b5" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "li=1 be finite-dimensional Hilbert spaces (the l virtual legs), and\nlet Hphys be the physical Hilbert space", - "equation_id": "eq_ab6c35762bc86081", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_5281edbc9d70f191", - "receipt_hash": "27ba4b17fe70105fa967b149f865c5dd831b68e2d301ed58a5fea3db8f31dda1", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_5281edbc9d70f191", - "payload_bytes_sampled": 546, - "payload_sha256": "34a65e040bd46b906b946088f6036847eb85bb023735d556d08fd1d870d8e5c6", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_5281edbc9d70f191", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "76791e27181105843ec71f2d2151da9516cde4dab92d0bdf8842f8d2da860a18" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "i=1\n\nDefinition S1", - "equation_id": "eq_60b71803ce04c1ba", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3fc5864a199b7aa9", - "receipt_hash": "5389fded63bd6e67c838e74d08fef56b7784b209bbfc79a383f08ea38c6d0e9e", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3fc5864a199b7aa9", - "payload_bytes_sampled": 458, - "payload_sha256": "3ffefeaf1151451b578429e8dee5c4be9499e30541568a1ae18835c9209b4b58", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3fc5864a199b7aa9", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "1096db94523a78c43f5176ace970802e75496c18920bda90257f025e45c1bf75" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "i=1 Hi \u2212\u2192 Hphys from\n\nWe perform a singular value decomposition\nT = V \u03a3U \u2020 \u2208 Cnp \u00d7nv ,\n\n(S14)\n\nwhere np \u2265 nv by injectivity, where U \u2208 Cnv \u00d7nv and V :\u2208 Cnp \u00d7nv satisfy U \u2020 U = 1nv (unitarity) and V \u2020 V = 1nv\n(isometry), and \u03a3 = diag(\u03bbe ) \u2208 Cnv \u00d7nv collects the singular values", - "equation_id": "eq_7d5beabf91af593e", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283511, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_04ac9264dcd7c6c1", - "receipt_hash": "0d092cba889d317b62c4bc308b8269804f4a750d3e2a4a24988136a6bc86f116", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4097520236685294, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4097520236685294, - "shape": "LogogramProjection" - }, - { - "distance": 0.45537780262401606, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45537780262401606, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550893247786234, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550893247786234, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283511, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_04ac9264dcd7c6c1", - "payload_bytes_sampled": 807, - "payload_sha256": "e31a56078a661c4dac556a73508d129d5f01b98aa299cb8c2716410976313a8a", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_04ac9264dcd7c6c1", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "32003190328d206839738d6937ac08b1caff0eabb8c3f680b4c0725b697df6ad" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = \u03b4 \u2208 (0, 1]", - "equation_id": "eq_8daf48607920ba7b", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_4795802d1dfe8dc9", - "receipt_hash": "08e3486cfb6bc395126c774bce551d2c0ef4f6bb9b6492d02c6e64d60c20e097", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_4795802d1dfe8dc9", - "payload_bytes_sampled": 462, - "payload_sha256": "5f7f8472797fbd39f8258fd867e3928aef9b28b24b8967d463a0995fc7947da6", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4795802d1dfe8dc9", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "537a775dceec204e0541da7421a77ddf9d3ffa80857fa2a4e2d5069842c401ec" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = 1,\ne\n\ne\n\n(S15)\n\nWe call \u03b4 the injectivity parameter", - "equation_id": "eq_3b521e5a6156cbc1", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3823a73f30463f6e", - "receipt_hash": "3626d12afaa3089f4036363e0919b91c7d33e9a28b424907f47c7f082792559d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3823a73f30463f6e", - "payload_bytes_sampled": 505, - "payload_sha256": "a800894b87af301b96cb6b0cf8c3989df93fd8429e93bb62d5bf208e0c052a54", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3823a73f30463f6e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "404235a5966ab4d052c05bfb56596ee28777258dc4e6136a95e9955bc0072895" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "T = V U \u2020 itself is an\nisometry", - "equation_id": "eq_7cf50458351623ec", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_774c019464fd328c", - "receipt_hash": "1471d21dd79182101f7fc66a6243a12a8bbe19889caf8896986e3f7a1477dd54", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_774c019464fd328c", - "payload_bytes_sampled": 475, - "payload_sha256": "feb3811136fca956952f48083c50ce7c1e3e400fdd0ca3326c9ec6f94b7975e7", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_774c019464fd328c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c3819a16ca38a53b12caa90874f996f0861eb72b650a5cb9f92181a0f6261ca5" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "a = 1", - "equation_id": "eq_b5bc1ffd90912fb1", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.288596, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c221ccc2bc9ccc45", - "receipt_hash": "b0a6a12afdbbc708227f19d1a6ba7d4f0bf33a2f7b29141c4c507138d8fa630b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.427083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4124126827589752, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4124126827589752, - "shape": "LogogramProjection" - }, - { - "distance": 0.4571069031424215, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4571069031424215, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4625272350064127, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4625272350064127, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.288596, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_c221ccc2bc9ccc45", - "payload_bytes_sampled": 443, - "payload_sha256": "c22cb7ac3cb65975cf7c0fe250aed5bc0d91705dd822a5b58623eec519a1ddd8", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c221ccc2bc9ccc45", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "d4fd55c5d805eb16811088f78bc5e7de860c38ad52e7611eb3f7ecc684b1d79e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Lc = dim(HLc )1L ,\nKa,L\u2192Lc Ka,L\u2192L\n(S18)\nc = dim(HL )1Lc ,\na\n\na\n\nwhich follow from the fact that U is a unitary", - "equation_id": "eq_51e4f2d85951d30b", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_16a8e172297c28a7", - "receipt_hash": "79f63c48ff596104c605b52c5a2519023f3adfe50fa26585e66dc58c01d2a044", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_16a8e172297c28a7", - "payload_bytes_sampled": 566, - "payload_sha256": "8e7f25ddee7da0610ad913dcf91e0459ce45aa39d1af7490423b5a397d2519e0", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_16a8e172297c28a7", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "cfd13569eeaa4ac184411636b71de702cd33c3e939d5273f208db682223dd2ee" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Lc =\n[U\u0304 a ](i\u2032L ),(iLc ) [U a ](iL ),(iLc ) =\n\u03b4iL ,i\u2032L = dim(HLc )1L\na\n\na,iLc\n\niLc\n\nMoreover, when T is \u03b4\u2212injective with \u03b4 = 1 (i", - "equation_id": "eq_51dad4a7954a91e6", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_50659e00428d753c", - "receipt_hash": "26c408a3c2d3dc114a311051f6ce4fd9a4947510c1a2c3fdfa221a45b2eb34e0", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_50659e00428d753c", - "payload_bytes_sampled": 612, - "payload_sha256": "34f24d40681aad58930e72b39169b9e2c7a1f0b25e2cd85b6f1856d0009d5201", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_50659e00428d753c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c3a92306c2805943b3c274268e49ffdca4ba60e1562972ad52e2cf38f5900fd3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G = (V, E) with vertex set V and edge set E", - "equation_id": "eq_10109834ac3ac602", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d618a97b4f87abe9", - "receipt_hash": "3cc3f66aa1da3314dac2f6a29d5dd3c69c7a83f029c7e8253f29be10a678484a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d618a97b4f87abe9", - "payload_bytes_sampled": 481, - "payload_sha256": "a175ce76e832039ebdc1c9c2a175a67407b98c4edf23cb17f21ee5eb67dfe074", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d618a97b4f87abe9", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "fdbd03613d4b7f280454c7c0db2771072f8e54089b41bd355e9b386390e8b584" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G = (V, E) with uniform virtual\ndimension D and physical dimension dp is specified for each v \u2208 V by a tensor\nO\nTv :\nH(n,v) \u2192 Hphys \u223c\n= Cdp ,\nn\u2208N (v)\n\nwhere N (v) denotes the set of neighbors of v, and each virtual space H(n,v) \u223c\n= CD", - "equation_id": "eq_6c7adf4f4e980afe", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283255, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_ce049f9298cfb095", - "receipt_hash": "6957a59f11a406de94859ae1d671eea0c89a9a773b5aea81ad4630db529effe9", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096854844400418, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096854844400418, - "shape": "LogogramProjection" - }, - { - "distance": 0.4549746348787963, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4549746348787963, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550625260348704, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550625260348704, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283255, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_ce049f9298cfb095", - "payload_bytes_sampled": 706, - "payload_sha256": "cff77fe7901bb6f4ef233ca98aca5998359d6f8398da69fd66498ba4c6d31765", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ce049f9298cfb095", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "8b667e3cc11c10720568283e943035cae8491ddb45349edc309ec29cf9b7aaae" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, w), we define a bond-space H\u20d7e \u223c\n= CD", - "equation_id": "eq_dde05d0258ec36a5", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_ea1df518da53e850", - "receipt_hash": "32d69a72eb5d1e87c16fa666cc787ab0438eda6d8fbf3eb3de238b667e28626c", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_ea1df518da53e850", - "payload_bytes_sampled": 494, - "payload_sha256": "c8cf4133db7b2738451ca2afa82621dc626fe8441721e5a752faa799da75508b", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_ea1df518da53e850", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "46f3d93456e9b059219807ca2251bdf9864ac265bf1f62048c856c9a45aa2806" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, w) is a positive\noperator \u00b5\u20d7e \u2208 Pos(H\u20d7e ) representing the message from v to w", - "equation_id": "eq_36e95d88ecbda2b0", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_a91ed081e55a7adc", - "receipt_hash": "69aadec231bdc41b6ba25290e3d26ecbef94bf3354644fdab10c718e40233fee", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_a91ed081e55a7adc", - "payload_bytes_sampled": 545, - "payload_sha256": "53a58716e53e89b4029066c32730537fd6a34f10e6c4afee2bbe7c62e8826d0f", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_a91ed081e55a7adc", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "aef9ad3b693956191837ad1665dd0d0b07f2f919cc7c0353305120673f1a7d36" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, n) \u2208 \u20d7\nE,\n\uf8f6\n\uf8eb\nO\n\u00b5(m,v) \uf8f8", - "equation_id": "eq_25b43848714b6ceb", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3c97cd0a63059c60", - "receipt_hash": "8cf99e737ab8c4ec3e4af775d465c88944a5a940bf94fbae365aeac02b218a62", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3c97cd0a63059c60", - "payload_bytes_sampled": 505, - "payload_sha256": "794c29c3891fa56ac049897e51a5c5bae1a724628d89eefe9509d2268bba863d", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3c97cd0a63059c60", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7fdc1564d1ce2225a1828eb3ddc1157d1d7c89a86fd51ce4c2cf2f1ae1274d35" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e > 0 such that\nPEPS if for each directed edge \u20d7e \u2208 E,\nf\u20d7e (\u00b5\u22c6 ) = \u03bb\u20d7e \u00b5\u20d7e", - "equation_id": "eq_139255306999d8d8", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_114c66f1878ac7b8", - "receipt_hash": "5d5fbdcf840b0cef5c46dc49fd7b8bb6b8a04b8d69e96b76009e49928f44ae92", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_114c66f1878ac7b8", - "payload_bytes_sampled": 559, - "payload_sha256": "ca753077b93470454cc6637f3c65e9024feb3aa24f06d4d2204fd7f8e9f9ad8b", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_114c66f1878ac7b8", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "8e0f98bf23e4911e940d9aa6709bc769249a36a33b8c8dfb5897d407482ae511" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9\ncan be formally described as a Taylor series in terms of \u2018loops\u2019 on the network as described below", - "equation_id": "eq_2a06ffe17253ce3c", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_1975033c8fbea2a4", - "receipt_hash": "c6450f574e62718b7cc23e8829ff21b38d745251ffacef3837d52fdffaab2071", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_1975033c8fbea2a4", - "payload_bytes_sampled": 577, - "payload_sha256": "eadb09100e620e50117c0c2c96566bac252134110911b05e701983c367b3a343", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1975033c8fbea2a4", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "283a2c9a21f0affc313594a03566a33b3302dca8cd2b1163d3c90788d76a8b8f" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = ZBP \uf8ed1 +\nZl \uf8f8\n\n(S21)\n\n\u0393\u2282L\nl\u2208\u0393\n\u0393 finite, compatible\n\nwhere the sum runs over all finite sets \u0393 of mutually compatible loops", - "equation_id": "eq_e598d78992b57382", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_66f6066b3fb74fcb", - "receipt_hash": "8524fbfbe618913c034e0e1a5a7cb1c60c0f9fa3c19a0c9d9090ca53b7492d8a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_66f6066b3fb74fcb", - "payload_bytes_sampled": 613, - "payload_sha256": "5da57e37f8e9169e29075499fa0139b62549fbae4fed36261cb542524f5dd051", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_66f6066b3fb74fcb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0c4a0a50a578df4051e94bd826fef6ee74bbabf850b786d122a95f36b909baba" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "W = {(\u21131 , \u03b11 ), (\u21132 , \u03b12 ),", - "equation_id": "eq_14f71a50573b7fd0", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_75b10c1207c769bb", - "receipt_hash": "6acddfb16f6030a79e9205eac08f341597c743f06196925624df985b4d653b2d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_75b10c1207c769bb", - "payload_bytes_sampled": 486, - "payload_sha256": "189f1bba5db2dfce1ef188247324702db28e63b6d38442ffa63f8f4192239a93", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_75b10c1207c769bb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "028fc69476316e984ed545d91892f7707e39afeb0d1b79aa445ee199f9d44f07" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "nW = i=1 \u03b1i", - "equation_id": "eq_44bf0ed184c8c0f8", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.287271, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_07bd7750aa9c1698", - "receipt_hash": "6816c632531998f46388d39b007b8bcb04031b2cbe2c5c49ecf35a1f3b6a7f22", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.458333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41157219941042966, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41157219941042966, - "shape": "LogogramProjection" - }, - { - "distance": 0.45651990682503474, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45651990682503474, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.46076175790722984, - "kind_prior_bonus": 0.0, - "raw_distance": 0.46076175790722984, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.287271, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_07bd7750aa9c1698", - "payload_bytes_sampled": 454, - "payload_sha256": "8cc370276f4b6cf0e6f7a6d33b579fc21b4154fd730c721ff481b5f43a4ec138", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_07bd7750aa9c1698", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0053fafcd23616ce1014594dcedfdb962d6f844ccf67afd206170a17ae527b87" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "W = {(\u21131 , \u03b11 ),", - "equation_id": "eq_4c772de7743acc11", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c704bc7e7c531303", - "receipt_hash": "b59f99533ebb319b687a09773d2383cdd40e53a22fdc1569649f92bb5fac2d4f", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_c704bc7e7c531303", - "payload_bytes_sampled": 464, - "payload_sha256": "845c108fcdbb8d9a04fde1da5063b9e15d43379b88b174d4bb0ee4b662e9b153", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c704bc7e7c531303", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7c296e2e3ad1dccc3a035cb246d588e4c78ec7bd7a43494b5db66fe985a63a99" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "ZW =\n\nk\nY\n\nZ\u2113\u03b1ii", - "equation_id": "eq_3fe8c2f5d04f57ff", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_9781272a1dff3d7d", - "receipt_hash": "5a034e3e1b9b047142db36b9c662af8e3af3132a3d72b6c7602829e6374def04", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_9781272a1dff3d7d", - "payload_bytes_sampled": 469, - "payload_sha256": "d53cb518dfb93356ea5529e39e192f55832510a3cb5edbd34420d4a32ba01f07", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9781272a1dff3d7d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ff81c9cd58474f5818f99a657faed4d80ce0050be40d4a31fb8f23f7e193ebcd" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "i=1\n\nWe call a cluster W connected if the interaction graph GW is connected, meaning there is a path between any two\nvertices in the interaction graph", - "equation_id": "eq_1abc29f7b62428f6", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_1b1de15c4bbf2d2e", - "receipt_hash": "78217ae50ced51b7b636d56bb3f9179a169faac49a93d3edbf5836d1d2786fc9", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_1b1de15c4bbf2d2e", - "payload_bytes_sampled": 591, - "payload_sha256": "39fd95ee9b86f869fe3599da8454631941ce136898ce00a8fe620e1508e1b992", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_1b1de15c4bbf2d2e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "65b507133956f29b366be0c1a7b93b17dfe426886d58c8e7139291bf563291f5" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "GW =\nPk\n(VW , EW ) has |VW | = i=1 \u03b1i vertices, with loop \u2113i corresponding to \u03b1i vertices", - "equation_id": "eq_f4bc63354f272a96", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283781, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_f26b20a02d1cf105", - "receipt_hash": "70cfd4deeaadfab6cc643ad42e3c0a90ecdc0741f16568687be3b92d0d22563b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40983509977562194, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40983509977562194, - "shape": "LogogramProjection" - }, - { - "distance": 0.45552650015276536, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45552650015276536, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4557954927709534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4557954927709534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.283781, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_f26b20a02d1cf105", - "payload_bytes_sampled": 544, - "payload_sha256": "799a98c44d250fe8d29cfebe5a7e314ec06e335e51d24269551956346780997a", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_f26b20a02d1cf105", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "fc421e55ad467d683074f71ee6d57c874395003ecedcc38db9e3ce3140354f97" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "W = {(l1 , \u03b11 ), (l2 , \u03b12 ),", - "equation_id": "eq_2a47fee92e986d5e", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_87b4887fb400c8c8", - "receipt_hash": "8be5322a1d59e4e2951fc63ff442814748c67512a8e58ace4f4323d28843797b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_87b4887fb400c8c8", - "payload_bytes_sampled": 476, - "payload_sha256": "c0b48a7f93f9c97f22c1d7c730120c390c8ab303120008e5f03173b92e97d199", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_87b4887fb400c8c8", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ebbfe9010f8fbfcba288619b72dc974f379053fd5f5642a9600e59553bd8cebe" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "ZW =\nZl\u03b1i i", - "equation_id": "eq_4a18868e412a28db", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286858, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d767dc5b5996f6dc", - "receipt_hash": "4c62ae5ea9980f4f0d983cfe107fd62bc4dceb0fe9011751e102b82e7910b591", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4113246327894113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4113246327894113, - "shape": "LogogramProjection" - }, - { - "distance": 0.45635379572506823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45635379572506823, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602012339852711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602012339852711, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286858, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d767dc5b5996f6dc", - "payload_bytes_sampled": 455, - "payload_sha256": "b32712fbfe42112ac0e6e711960bf64d488a7e072f0978b08c43f0345fcd1e91", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d767dc5b5996f6dc", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c03df94fbd52e3dc7fb2ebbac272eaf5cd42a01bc6a9763bdf96f379ef52c4f2" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = log ZBP +\n\u03d5(W)ZW ,\n\n(S25)\n\nconnected W\n\nwhere the sum runs over all connected clusters W", - "equation_id": "eq_d14e04ca5ba01499", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_4d8a407792ae463c", - "receipt_hash": "e03f389ce08e8879221255cddcee474f99e8f0219c9248138b1772000323cbfb", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_4d8a407792ae463c", - "payload_bytes_sampled": 542, - "payload_sha256": "c4c468f7952e6f1b9a4fbb69aadfe0c053ce1cf3d27e5d2c7c813624da80015d", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_4d8a407792ae463c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "7580db42c4e52a423d5774c2fef9ce65e842808689484c07ae2b6a32abbb6ea0" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "c > c0 := log(2e\u2206) + 12 such that\n|Zl | \u2264 e\u2212c|l|\n\n(S27)\n\nthen, the series for log Z converges absolutely", - "equation_id": "eq_405936ddfb9bc98c", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_68bc5f8d951cef23", - "receipt_hash": "5698e30987c13f13c4a90915a8e0b1a8f0614f90d5cbcfb9af1932382727bff2", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_68bc5f8d951cef23", - "payload_bytes_sampled": 562, - "payload_sha256": "8e883cfdbb218f5149633a951c59b07e7c041be70abd63e2e3685ba894334aa2", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_68bc5f8d951cef23", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "62849e34e7f6b63e995855803999c1b67567ab1a2d7dbb8b691435a944794b65" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Fm = log ZBP +\n\nX\n\n\u03d5(W)ZW ,\n\n(S28)\n\nconnected W\n|W|\u2264m\n\nis bounded by\n|log Z \u2212 Fm | \u2264 N e\u2212d(m+1)\nwhere d = c \u2212 c0 , \u2206 is the degree of the graph, and N is the number of vertices", - "equation_id": "eq_6a72df8385b3a8fb", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283014, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_181e73a390899053", - "receipt_hash": "c42c5119cddb3a98270f1976ec0a8128d79a8b7185380b24446545fddf686901", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096354901486735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096354901486735, - "shape": "LogogramProjection" - }, - { - "distance": 0.4545860281746088, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4545860281746088, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45551846079240005, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45551846079240005, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283014, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_181e73a390899053", - "payload_bytes_sampled": 662, - "payload_sha256": "e1bdae39f470016c9fbf96b4dbbf052110a3aeab1b02e64bf8d955520495629d", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_181e73a390899053", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "2e28d3d7373f13de1a7f8ea7f91b0918c7b3ddbd1c710e4180457fbd7a4071cf" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "c > c0 = log(2e\u2206) + 1/2", - "equation_id": "eq_52e8897befaf699d", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285005, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_b720db642290ed9d", - "receipt_hash": "6bebf737792b75732cfafd0ce02baa397bf425c8f0c42a36be9d5fecd2117a0a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.520833, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41033254211578823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41033254211578823, - "shape": "LogogramProjection" - }, - { - "distance": 0.45574559253952757, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45574559253952757, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4576106613065602, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4576106613065602, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285005, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_b720db642290ed9d", - "payload_bytes_sampled": 466, - "payload_sha256": "c9c1fc0d6c7fbddadc86aa2f4fcdae729198d63afc8786a304d03cb5e174d3ac", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_b720db642290ed9d", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b825ef079a241adc21f299a1580f87146050fd732caa06a02d1bc22c6f86be82" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "G = (V, E)", - "equation_id": "eq_f05cdb2173e0a64b", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3e9910b639adf295", - "receipt_hash": "1f11f224ef810f5a7e76e5c4f77ffeee12c7e85063867f1f46b6e466ca813b1c", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3e9910b639adf295", - "payload_bytes_sampled": 448, - "payload_sha256": "47a3bd91105e78918966bea1d68fd0a1ab292bd0c298a829374e2e8ea0e5b7e8", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3e9910b639adf295", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "3cac782a504da80e94f7f0438de7bc18d0b08bfbee7b4175cc6a4f39820fdf1e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "T = \u27e8\u03c8|\u03c8\u27e9 and\nA\nT = \u27e8\u03c8|OA |\u03c8\u27e9 respectively after suitable BP normalization", - "equation_id": "eq_f93b08fedbc0a5af", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284364, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.541667 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_8beb9cc11e59ff37", - "receipt_hash": "bba3f804941b888be94b46f857b2829fd68b6f74773ba47afdebc806e1338e5d", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.541667, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4100508204205162, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4100508204205162, - "shape": "LogogramProjection" - }, - { - "distance": 0.4556062905560075, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4556062905560075, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45667427949224454, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45667427949224454, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284364, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_8beb9cc11e59ff37", - "payload_bytes_sampled": 554, - "payload_sha256": "fa353992fb1c5a6d2b9e04d24dfad98e54e6cf281b783d918d3e0d507875b1d5", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8beb9cc11e59ff37", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "667de6eac35311f713ed8daea03f6672ba9fb14aba52d845b67a0a1024f0f59d" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9 and Z A = \u27e8\u03c8|OA |\u03c8\u27e9\nfor a local observable with \u27e8OA \u27e9 \u0338= 0", - "equation_id": "eq_d0869069ce0fee51", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_9d485a8333644b3b", - "receipt_hash": "37c41cdc8f918cedbd280a36c207a693eaca39146965718514d9158381e1797f", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_9d485a8333644b3b", - "payload_bytes_sampled": 562, - "payload_sha256": "ff9be90671fe6f8ffdfba0ea5e550bffd0b29b986d503d761d4f5379ad3abc55", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_9d485a8333644b3b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "1e8dfa332ead8207a86b1c9bc016fe9713dab5aa364edb1cffbd2933e71f8508" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "m = \u27e8OA \u27e9BP \u00b7 exp\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\nconn W\u2190LA\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8f4\n\uf8fe\n\uf8f3supp(W)\u2229A\u0338=\u2205\n|W|\u2264m\n\nleads to a relative error \u03b4m = | \u27e8OA \u27e9 \u2212 \u27e8OA \u27e9m |/| \u27e8OA \u27e9 | bounded by\n\u0010\n\u0011\n\u03b4m \u2264 O |A|e\u2212(c\u2212c0 )(m+1)\n\n(S33)\n\nwhere d = c \u2212 c0 = O(1)", - "equation_id": "eq_e2dad9249b00119f", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283014, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "semantic_entropy", - 0.59375 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_6dcfafa9019b29a2", - "receipt_hash": "0a7fc9355ad818198f9dd4583422a30f98cd43caf587a589a5d0f1003ff18387", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.59375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096354901486735, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096354901486735, - "shape": "LogogramProjection" - }, - { - "distance": 0.4545860281746088, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4545860281746088, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45551846079240005, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45551846079240005, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283014, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_6dcfafa9019b29a2", - "payload_bytes_sampled": 838, - "payload_sha256": "8912db5cb86216745639c9d438e00ae33e05814f4e34eb46b654a278431b30b3", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_6dcfafa9019b29a2", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b9c43dd7d84820b72294f275d60203ec1b828f3b6fe182e43173440bae621bce" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "A = \u2202\u03bb log Z\u03bbA |\u03bb=0 = \u27e8OA \u27e9BP +\n\u03d5W Z W\n\u03b1l\n\u2212 \u27e8OA \u27e9BP", - "equation_id": "eq_2b301ef1b6ca651f", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283781, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.5625 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_810420419a4025f0", - "receipt_hash": "4d86b95fef402e299320e82e95ad79c8215f8505d2dbd64440e9734290556128", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5625, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.40983509977562194, - "kind_prior_bonus": 0.0, - "raw_distance": 0.40983509977562194, - "shape": "LogogramProjection" - }, - { - "distance": 0.45552650015276536, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45552650015276536, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4557954927709534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4557954927709534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.283781, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_810420419a4025f0", - "payload_bytes_sampled": 547, - "payload_sha256": "faff83be2c242e4cc5f4c25f5670416f7fb3103336c4a265b5941bccfa10cf66", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_810420419a4025f0", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0224d7b40d1559b4a642dda78f3d9979a21238e2472fcda2ae8df5f9a3e8f271" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9 and Z A = \u27e8\u03c8|OA |\u03c8\u27e9", - "equation_id": "eq_f4aab7b26b55c832", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_3ed73c5849372e40", - "receipt_hash": "aa9e138e99cba44fa622f53b37d0ffda3b931b2dcf658989f993151b14a5eee4", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_3ed73c5849372e40", - "payload_bytes_sampled": 507, - "payload_sha256": "a43c056b49fda432f044c2d1d43c62761e8bdae016f22ef84c9d61973a36b7d5", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_3ed73c5849372e40", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "0e52c1c029d5f2f7c074c09e04c3cd41a808735bea087bd3f47938c857b20d78" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "d = c \u2212 c0", - "equation_id": "eq_1a6dee6dca2f084b", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.28814, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_84f94841b9e9578b", - "receipt_hash": "24ca0a37b5ce4963619f2b06989eaf6467670a26b0ea5d098074c81b8913e841", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.4375, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4121162566656331, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4121162566656331, - "shape": "LogogramProjection" - }, - { - "distance": 0.4568964788017383, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4568964788017383, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4619248112304818, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4619248112304818, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.28814, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_84f94841b9e9578b", - "payload_bytes_sampled": 453, - "payload_sha256": "2526df29b97cf5e0d4c24ef1ad84f0b55c0179a5fe8fc947f58940dc35ad5dee", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_84f94841b9e9578b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b779941ba4f5f3bcd12940bb279bd798e3c93365883c45dbac71d1fb4ec08167" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "c =\n\nX\n\nA\n\u03d5W \u2202\u03bb ZW,\u03bb\n\nconn", - "equation_id": "eq_0698ebc502ab24cc", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286074, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e0ac7e1847f830a3", - "receipt_hash": "c35422d2bc3f1322f55a2413cb0b178de920d80af0d018416791cfcdaf49ec91", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.489583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4108785709514695, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4108785709514695, - "shape": "LogogramProjection" - }, - { - "distance": 0.4560660040686235, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4560660040686235, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45912244803466534, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45912244803466534, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286074, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_e0ac7e1847f830a3", - "payload_bytes_sampled": 491, - "payload_sha256": "2662d2b3ee7b052af5ab826e061b4e970755081abb7e7c61d083422a97bebbf3", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e0ac7e1847f830a3", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "86278b7d070c233d77a8c4ce4694ea08fb01a76644ab364d121fcba08dcb1aa3" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "A = (A1 ,", - "equation_id": "eq_2edfb68a37ab3d1f", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286858, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_068487b9141c4fb6", - "receipt_hash": "563d963a08c32cf299cf5bc32ed42c97188735b2b17d7c4b69e2225d98387117", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4113246327894113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4113246327894113, - "shape": "LogogramProjection" - }, - { - "distance": 0.45635379572506823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45635379572506823, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602012339852711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602012339852711, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286858, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_068487b9141c4fb6", - "payload_bytes_sampled": 447, - "payload_sha256": "952d6e7fbdd6ca6dfac751be68dc178b4b9387bce1b42980e2e1b2f95dcacc38", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_068487b9141c4fb6", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "13d3e0f2f065f3cef2a64bfd567c5c3368fc857efedad6cf6379a251d9499e23" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "degree \u2265 2 everywhere except in all regions Ai", - "equation_id": "eq_7957e0f2cb86a72c", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286858, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_8ffeff0c4877aa25", - "receipt_hash": "d541c7b2ef543d84e2612a7ee172a13a46029a3b93297163d8553db103cd5fb4", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.46875, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4113246327894113, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4113246327894113, - "shape": "LogogramProjection" - }, - { - "distance": 0.45635379572506823, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45635379572506823, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4602012339852711, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4602012339852711, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286858, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_8ffeff0c4877aa25", - "payload_bytes_sampled": 489, - "payload_sha256": "a99eaa11b2a0e9858b7a90f84303c90e06eec40f46a42e8f92c4e713b616e7a4", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_8ffeff0c4877aa25", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "9d19f3f82b5beb5b199b55aa6403177735fc7810644c229bb8ab5bd186b05fbd" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "c \u2264 O e\u2212d(A,B)/\u03be\nfor a finite correlation length \u03be \u2264 O((1/(c \u2212 c0 ))) and d(A, B) being the graph distance", - "equation_id": "eq_10a508b9d741a6f6", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284678, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_2c1d4c645f0025fb", - "receipt_hash": "bd49b7c2a246bab22c25f599a8b22bf7a000276abc5f654796f7fced93990a60", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.53125, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4101834388896558, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4101834388896558, - "shape": "LogogramProjection" - }, - { - "distance": 0.45566850546496773, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45566850546496773, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45713529262305513, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45713529262305513, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284678, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_2c1d4c645f0025fb", - "payload_bytes_sampled": 575, - "payload_sha256": "174833997dfe8767ea1becf4532c183da0b531fb6d492189ec6225141df4980a", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_2c1d4c645f0025fb", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "bc1f6c5a0d1a73f2958e00e14a26543fb2c54a605a1e2bffd9532c309b6bb1c8" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Z = \u27e8\u03c8|\u03c8\u27e9 can be computed to 1/ poly(N ) multiplicative error in poly(N ) time\n(ii) local observables \u27e8OA \u27e9 = \u27e8\u03c8|OA |\u03c8\u27e9/\u27e8\u03c8|\u03c8\u27e9 with \u27e8OA \u27e9 \u0338= 0 can be computed to multiplicative error \u03f5 in poly(1/\u03f5)\ntime\n(iii) 2-point correlation functions can be computed to additive error O\u0303(1/ poly(N )) in poly(N ) time\n\n\f18\nProof", - "equation_id": "eq_fb10b15989eae47c", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283511, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.572917 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_05407e13b79c062b", - "receipt_hash": "2f920bb852c44abd591b98d69e1f6c81bcc164922537aa8146ab9c66816a4a5b", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.572917, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4097520236685294, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4097520236685294, - "shape": "LogogramProjection" - }, - { - "distance": 0.45537780262401606, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45537780262401606, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550893247786234, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550893247786234, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283511, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_05407e13b79c062b", - "payload_bytes_sampled": 860, - "payload_sha256": "b983b0d3e9134cc54c7ebc5a08a7e005a2974a585acc08aac2477cceb9d68c69", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_05407e13b79c062b", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "2d5e54e30491f69a630d69dbaa511dd089ffe762ec34cc8e95d3a272662599e4" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "m \u2264 N e\u2212d(m+1)\n\n(S37)\n\nHence, to ensure log Z \u2212 F\u0303m < O(1/ poly(N )) we require m = \u2126(log N )", - "equation_id": "eq_984644a6b0dbab3e", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.283255, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.583333 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d4e90c1cd69ef948", - "receipt_hash": "f9e4fbaf07b96a5b5adcb92f57027d2b5909be6e3b415d6da6f7f8aa94c56e6a", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.583333, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4096854844400418, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4096854844400418, - "shape": "LogogramProjection" - }, - { - "distance": 0.4549746348787963, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4549746348787963, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.45550625260348704, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45550625260348704, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.283255, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d4e90c1cd69ef948", - "payload_bytes_sampled": 560, - "payload_sha256": "1a1f8a2cf0e034c4a2379283bfcaccbf143805ea49a03fed2822a9da0bc756ad", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d4e90c1cd69ef948", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "82aacd6bf2399da72fb97e01812f59407cd00a98e612f0da9c167c640d32e234" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "m = \u2126(log N )", - "equation_id": "eq_837e24a6c715af63", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285347, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_131b32c8ed70796c", - "receipt_hash": "266d34bc34255770efc7792968a9c6897bdd640da81b731e0ae290b711f51768", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.510417, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41049811213588033, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41049811213588033, - "shape": "LogogramProjection" - }, - { - "distance": 0.45583754423455336, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45583754423455336, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.45810034085268264, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45810034085268264, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285347, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_131b32c8ed70796c", - "payload_bytes_sampled": 456, - "payload_sha256": "2c6f74780e0a91e944f6ab3d1cea3dbabf67cb87d6101dfd41d908c2947e823a", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_131b32c8ed70796c", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "cab6b48c4f3444d31c06fa36a61505cf95c07d15d9cd85de22bde40c2a34bb0e" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "m\n\u2264 O e\u2212d(m+1) \u2264 \u03f5\n\u27e8OB \u27e9\n\n(S38)\n\nwhich can be ensured given m = O(log 1/\u03f5), again computable in poly 1/\u03f5 time", - "equation_id": "eq_e54ad45a68cb6a93", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_e6b902735f906c33", - "receipt_hash": "e9d2b1e05e24a44253a6bba021402450f29434878ff7b8556a8290ab86e49590", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_e6b902735f906c33", - "payload_bytes_sampled": 593, - "payload_sha256": "830842b2e8e865b26fd1becad86110fb6809be42f91d3ff8b387460eaf7cf7dc", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_e6b902735f906c33", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b1d4add88e2685e0902cd1b75fc337d3199c617adaa881c735187d31264deed7" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "m = d(A, B) + O(log 1/\u03f5), leading to an error O m2 (|A| + |B|)e\u2212dm , which is\n(note that (|A| + |B|) = O(1),\n!\n\u0012\n\u00132\n\u0012\n\u0013\n1\n1\n2\n\u2212d[d(A,B)+log 1/\u03f5]\nO d(A, B) log\n\u00b7e\n= O log N \u00b7\n= O\u0303(1/ poly(N ))\n(S39)\n\u03f5\npoly(N )\nsince we already have d(A, B) = O(log N ) and we choose \u03f5 = 1/ poly(N )", - "equation_id": "eq_21f71c6c28c71003", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.282576, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "semantic_entropy", - 0.614583 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c5417aae2492b416", - "receipt_hash": "1e2244bc8cabfa83a106ad2198da92d674fa455de0e5bb353bf91de3ccba2749", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.614583, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4095851586061867, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4095851586061867, - "shape": "LogogramProjection" - }, - { - "distance": 0.4538526461007772, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4538526461007772, - "shape": "CognitiveLoadField" - }, - { - "distance": 0.4555875351131274, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4555875351131274, - "shape": "SignalShapedRouteCompiler" - } - ], - "declared_kind": "negative_control", - "distance": 0.282576, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_c5417aae2492b416", - "payload_bytes_sampled": 801, - "payload_sha256": "92087f4e026ba58908a9a2624fdae770ff7ef412e10aed03609ed5e45fc32b2e", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c5417aae2492b416", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "ecb7314d69bca8191e18431e9c2d253d1f26d4642e94c7759749dbe221209971" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Ka = D1L\u0338=i", - "equation_id": "eq_717ace3c9565c03b", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_c3dde450e7ce933a", - "receipt_hash": "8e1f505732912d568416a7536b6d3f698c2d2ae578cb362336435df436577b1f", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_c3dde450e7ce933a", - "payload_bytes_sampled": 454, - "payload_sha256": "69f77ff67fc1fa22ba1e0ca23fc0058eece497394c952605c99aa68fc8482e03", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_c3dde450e7ce933a", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "c23e964b562885806676f050d1b85e343bf0d456ef3330849d550f88d25d8e5a" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "a \u2265 \u03b4 2 > 0 and\n\n\u2020\na Ka Ka = D\n\nP\n\n(S43)\n\na\n\n1,\nX\n\n\u03bb2a Ka\u2020 Ka \u2ab0 \u03b4 2\n\nX\n\na\n\nKa\u2020 Ka = \u03b4 2 D 1,\n\na\n\nhence\nTr f (X) \u2265 \u03b4 2 D Tr(X) = \u03b4 2 D", - "equation_id": "eq_ccca83f09f701b34", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.284065, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "semantic_entropy", - 0.552083 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_d75b1352c39fe13e", - "receipt_hash": "35d1fb2df523e9386cd67c3e00a7bb4e704ea771eaed61953e2b85205b9eda73", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.552083, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.4099347027073805, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4099347027073805, - "shape": "LogogramProjection" - }, - { - "distance": 0.45555895390584566, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45555895390584566, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4562276654325236, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4562276654325236, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.284065, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_d75b1352c39fe13e", - "payload_bytes_sampled": 656, - "payload_sha256": "a971f21ee86ffa198de978958254e24cda5e5de23e00a4382e38090ea42104b9", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_d75b1352c39fe13e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b4b02d19ded44d04cb3ec7c471f24b7d120dc84c9c6f94648a764d5ad3d794d5" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "e = (v, n) \u2208 E", - "equation_id": "eq_bc5bda52965ac0f8", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.286459, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_80edc83f7fb3d80e", - "receipt_hash": "38aeca630c95999201a73c7063db3bd1845139908a41915bbea262433649b3e5", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.479167, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.411093414103419, - "kind_prior_bonus": 0.0, - "raw_distance": 0.411093414103419, - "shape": "LogogramProjection" - }, - { - "distance": 0.45620248989442364, - "kind_prior_bonus": 0.0, - "raw_distance": 0.45620248989442364, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4596547806141337, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4596547806141337, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.286459, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_80edc83f7fb3d80e", - "payload_bytes_sampled": 457, - "payload_sha256": "a6991f7eadf77686c5c8c6e0237a58ba28c45aeee49cfca823c2be2eb8900e5c", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_80edc83f7fb3d80e", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "f0ddfc0014695ceef58abb6d79d2c31492004533f004cb726c11bc9d2245b4f7" - } - }, - { - "equation_record": { - "bind_class": "", - "domain_type": "", - "equation": "Ka = dim(HL ) L", - "equation_id": "eq_d4a2926485bb8572", - "family": "text", - "name": "arXiv:2604.21919v1", - "projection_signature": { - "shape_distance": 0.285704, - "top_axes": [ - [ - "projection_declared", - 1.0 - ], - [ - "witness_declared", - 0.6 - ], - [ - "shape_closure", - 0.59 - ], - [ - "residual_risk", - 0.54 - ] - ], - "weak_axes": [ - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "history_depth", - "negative_control_strength", - "scale_band_declared" - ] - }, - "purpose": "arxiv", - "route_hint_non_authoritative": "unclassified_equation", - "rrc_kind": "negative_control", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "field_equation": "HOLD iff projection, decoder, witness, scale, or residual accounting is missing", - "invariant_receipt": { - "object_id": "rrc_eq_bcd458bef84fe2a2", - "receipt_hash": "ee83fc87cfebe1d5bbe8ef177b7b502acc40ac5f8c4530287e6f67becdf90123", - "schema": "rrc.object_receipt.v1", - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD" - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.166667, - "decoder_declared": 0.4, - "field_energy": 0.0, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.425, - "receipt_density": 0.055556, - "residual_risk": 0.54, - "scale_band_declared": 0.2, - "semantic_entropy": 0.5, - "shape_closure": 0.59, - "topology_torsion": 0.0, - "witness_declared": 0.6 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.41068012903364826, - "kind_prior_bonus": 0.0, - "raw_distance": 0.41068012903364826, - "shape": "LogogramProjection" - }, - { - "distance": 0.4559443515566645, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4559443515566645, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4586042854197028, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4586042854197028, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "negative_control", - "distance": 0.285704, - "kind_prior_shape": "HoldForUnlawfulOrUnderspecifiedShape", - "shape": "HoldForUnlawfulOrUnderspecifiedShape" - }, - "object": { - "kind": "negative_control", - "label": "arXiv:2604.21919v1", - "object_id": "rrc_eq_bcd458bef84fe2a2", - "payload_bytes_sampled": 453, - "payload_sha256": "551e22227bd6f8e75d39a6c24a43f7908c407ed04bac960c055067586da4ae61", - "source_path": "3-Mathematical-Models/equations_100/equations_database.jsonl" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": true, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [ - "scale_band_declared" - ], - "object_id": "rrc_eq_bcd458bef84fe2a2", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "HoldForUnlawfulOrUnderspecifiedShape", - "status": "HOLD", - "witness_hash": "b1ef498cea466f7c0de0d7e7d8197ef74d343aa5ebe6c1b89eee807e5dd83163" - } - } - ], - "counts": { - "by_missing_axis": { - "negative_control_strength": 39, - "scale_band_declared": 249 - }, - "by_rrc_shape": { - "CadForceProbeReceipt": 2, - "CognitiveLoadField": 77, - "HoldForUnlawfulOrUnderspecifiedShape": 125, - "LogogramProjection": 1, - "ProjectableGeometryTopology": 37, - "SignalShapedRouteCompiler": 36 - }, - "by_status": { - "CANDIDATE": 29, - "HOLD": 249 - }, - "equation_count": 278 - }, - "receipt_hash": "c758aadb2bf11922a805d695d5b7bafa477ad426e60ea8e925490f14ccba497c", - "runner": "4-Infrastructure/shim/rrc_equation_classifier.py", - "schema": "rrc_equation_projector_v1", - "source_inputs": { - "jsonl": [ - "3-Mathematical-Models/equations_100/equations_database.jsonl" - ], - "jsonl_limit": 80, - "markdown_limit": 40, - "math_model_map_limit": 120, - "sample_limit": null - } -} diff --git a/4-Infrastructure/shim/rrc_equation_classifier_table.csv b/4-Infrastructure/shim/rrc_equation_classifier_table.csv deleted file mode 100644 index b0984ba5..00000000 --- a/4-Infrastructure/shim/rrc_equation_classifier_table.csv +++ /dev/null @@ -1,577 +0,0 @@ -equation_id,name,rrc_shape,status,distance,top_axes,missing_or_weak_axes,source_path,domain_type,bind_class,equation -connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_adjusted_threshold,bandwidth_adjusted_threshold,CognitiveLoadField,CANDIDATE,0.193428,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,L_threshold_hist = L_threshold_eff * exp(-rho_B * B_overflow) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:bandwidth_overflow,bandwidth_overflow,CognitiveLoadField,HOLD,0.230737,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,"B_overflow = max(0, transfer_bandwidth - assimilation_bandwidth) / assimilation_bandwidth" -connectome_protective_cognitive_load_reweighting_receipt:core_equations:effective_cognitive_load,effective_cognitive_load,CognitiveLoadField,HOLD,0.231976,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,L_cog_eff = L_cog_raw * G_over -connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_gate,emotional_gate,CognitiveLoadField,HOLD,0.229561,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,B_gate_emotional = exp(-gamma_emotional * DeltaE_emotional_hist / kT_emotional_hist) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_load,emotional_load,CognitiveLoadField,CANDIDATE,0.21865,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,"L_emotional = C_emotional,d * emotional_response_d(L_emotional_offload; theta_emotional,d) * lambda_phi^D_f * B_gate_emotional,d" -connectome_protective_cognitive_load_reweighting_receipt:core_equations:emotional_offload,emotional_offload,CognitiveLoadField,CANDIDATE,0.193428,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,"L_emotional_offload = max(0, L_cog_raw - L_threshold_hist) * eta_offload_hist" -connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_barrier,historical_emotional_barrier,CognitiveLoadField,HOLD,0.230737,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,DeltaE_emotional_hist = DeltaE_emotional_eff + chi_B * B_overflow -connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_emotional_temperature,historical_emotional_temperature,CognitiveLoadField,HOLD,0.230141,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,kT_emotional_hist = kT_emotional_eff / (1 + psi_B * B_overflow) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:historical_offload_efficiency,historical_offload_efficiency,CognitiveLoadField,HOLD,0.231349,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,eta_offload_hist = eta_offload_eff * exp(-omega_B * B_overflow) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:overflow_gate,overflow_gate,CognitiveLoadField,CANDIDATE,0.192135,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,G_over(d) = 1 if L_cog_raw <= L_threshold_hist; else exp(-gamma_d * (L_cog_raw - L_threshold_hist) / kT_emotional_hist) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:raw_cognitive_load,raw_cognitive_load,CognitiveLoadField,CANDIDATE,0.21865,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,"L_cog_raw(d,x) = C_d * response_family_d(x; theta_d) * lambda_phi^D_f * B_gate(d,constraints)" -connectome_protective_cognitive_load_reweighting_receipt:core_equations:residual_stress,residual_stress,CognitiveLoadField,CANDIDATE,0.200253,projection_declared;shape_closure;decoder_declared;witness_declared,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,"L_residual_stress = max(0, L_cog_raw - L_threshold_hist) * (1 - eta_offload_hist)" -connectome_protective_cognitive_load_reweighting_receipt:core_equations:threshold,threshold,CognitiveLoadField,CANDIDATE,0.188871,projection_declared;shape_closure;witness_declared;scale_band_declared,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,L_threshold = C_threshold * lambda_phi^D_f * B_gate_threshold -connectome_protective_cognitive_load_reweighting_receipt:core_equations:total_protective_load,total_protective_load,CognitiveLoadField,HOLD,0.238088,projection_declared;shape_closure;decoder_declared;witness_declared,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,L_total = L_cog_eff + L_emotional + L_residual_stress -connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_barrier,trauma_adjusted_emotional_barrier,CognitiveLoadField,HOLD,0.231349,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,DeltaE_emotional_eff = DeltaE_emotional + chi_T * T_trauma -connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_emotional_temperature,trauma_adjusted_emotional_temperature,CognitiveLoadField,HOLD,0.230737,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,kT_emotional_eff = kT_emotional / (1 + psi_T * T_trauma) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_offload_efficiency,trauma_adjusted_offload_efficiency,CognitiveLoadField,HOLD,0.231976,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,eta_offload_eff = eta_offload * exp(-omega_T * T_trauma) -connectome_protective_cognitive_load_reweighting_receipt:core_equations:trauma_adjusted_threshold,trauma_adjusted_threshold,CognitiveLoadField,CANDIDATE,0.194101,projection_declared;shape_closure;witness_declared;residual_risk,,4-Infrastructure/shim/connectome_protective_cognitive_load_reweighting_receipt.json,,,L_threshold_eff = L_threshold * exp(-rho_T * T_trauma) -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:core_equations,core_equations,SignalShapedRouteCompiler,CANDIDATE,0.172075,projection_declared;semantic_entropy;shape_closure;witness_declared,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,"{""heat_loss"":""Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)"",""magnetic_projection"":""M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)"",""overflow_gate"":""G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)"",""signal_load"":""L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)""}" -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:field_mapping,field_mapping,SignalShapedRouteCompiler,CANDIDATE,0.166855,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,"{""byte_transition_rate"":""domain agitation / susceptibility driver"",""capacity_overflow"":""hysteresis heat-loss channel"",""entropy"":""field demand / information pressure"",""repeated_4grams"":""remanence / memory channel""}" -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:source_domain,source_domain,SignalShapedRouteCompiler,CANDIDATE,0.188002,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,byte_stream_signal -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_map:target_domain,target_domain,SignalShapedRouteCompiler,CANDIDATE,0.188002,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,magnetic_domain_equation -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:heat_loss,heat_loss,SignalShapedRouteCompiler,CANDIDATE,0.18598,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)" -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:magnetic_projection,magnetic_projection,SignalShapedRouteCompiler,CANDIDATE,0.18541,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i) -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:overflow_gate,overflow_gate,CognitiveLoadField,CANDIDATE,0.197713,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9) -transfold_enwiki8_magnetic_domain_generator_receipt:transfold_core_equations:signal_load,signal_load,CognitiveLoadField,CANDIDATE,0.191669,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json,,,"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)" -transfold_couch_data_magnetic_domain_receipt:transfold_map:core_equations,core_equations,SignalShapedRouteCompiler,CANDIDATE,0.172075,projection_declared;semantic_entropy;shape_closure;witness_declared,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,"{""heat_loss"":""Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)"",""magnetic_projection"":""M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)"",""overflow_gate"":""G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)"",""signal_load"":""L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)""}" -transfold_couch_data_magnetic_domain_receipt:transfold_map:field_mapping,field_mapping,SignalShapedRouteCompiler,CANDIDATE,0.166855,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,"{""byte_transition_rate"":""domain agitation / susceptibility driver"",""capacity_overflow"":""hysteresis heat-loss channel"",""entropy"":""field demand / information pressure"",""repeated_4grams"":""remanence / memory channel""}" -transfold_couch_data_magnetic_domain_receipt:transfold_map:source_domain,source_domain,SignalShapedRouteCompiler,CANDIDATE,0.188002,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,byte_stream_signal -transfold_couch_data_magnetic_domain_receipt:transfold_map:target_domain,target_domain,SignalShapedRouteCompiler,CANDIDATE,0.188002,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,magnetic_domain_equation -transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:heat_loss,heat_loss,SignalShapedRouteCompiler,CANDIDATE,0.18598,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,"Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)" -transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:magnetic_projection,magnetic_projection,SignalShapedRouteCompiler,CANDIDATE,0.18541,projection_declared;shape_closure;witness_declared;compression_pressure,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i) -transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:overflow_gate,overflow_gate,CognitiveLoadField,CANDIDATE,0.197713,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9) -transfold_couch_data_magnetic_domain_receipt:transfold_core_equations:signal_load,signal_load,CognitiveLoadField,CANDIDATE,0.191669,projection_declared;shape_closure;witness_declared;semantic_entropy,,4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json,,,"L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)" -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:counted_total,counted_total,SignalShapedRouteCompiler,HOLD,0.19942,projection_declared;compression_pressure;decoder_declared;shape_closure,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,compressed_total_bytes = payload_bytes + residual_bytes + witness_bytes + decoder_delta_bytes + container_bytes -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hard_target_rule,hard_target_rule,SignalShapedRouteCompiler,HOLD,0.216219,projection_declared;compression_pressure;shape_closure;decoder_declared,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,Hutter-hard promotion additionally requires the total contest artifact for enwik9 to beat 109685197 bytes under the applicable prize rules -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:hutter_route_metastate,hutter_route_metastate,SignalShapedRouteCompiler,CANDIDATE,0.122715,projection_declared;compression_pressure;decoder_declared;witness_declared,,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,"[""source_corpus_id"",""source_bytes"",""candidate_chart"",""transform_route"",""payload_bytes"",""residual_bytes"",""witness_bytes"",""decoder_delta_bytes"",""container_bytes"",""runtime_budget"",""compressed_total_bytes"",""baseline_bytes"",""hard_target_bytes"",""ratio_schema"",""exact_decode_status"",""source_hash"",""decoded_hash"",""promotion_status"",""failure_code""]" -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:lower_bound,lower_bound,SignalShapedRouteCompiler,HOLD,0.210115,projection_declared;compression_pressure;shape_closure;decoder_declared,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,LB_route = payload_floor + residual_floor + witness_floor + decoder_delta_floor + container_floor + evaluator_cost_floor -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:metastate_transfold,metastate_transfold,SignalShapedRouteCompiler,HOLD,0.238638,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,proposal_score -> candidate_route_coordinate -> bounded_exact_route_metastate -> promotion_receipt -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:promotion_rule,promotion_rule,SignalShapedRouteCompiler,HOLD,0.196828,projection_declared;compression_pressure;decoder_declared;witness_declared,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,"promote iff decoded_hash == source_hash and compressed_total_bytes < incumbent_bytes and ratio_schema is explicit and all witness, residual, decoder, and container bytes are counted" -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:prune_rule,prune_rule,SignalShapedRouteCompiler,HOLD,0.218153,projection_declared;compression_pressure;shape_closure;decoder_declared,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,prune iff LB_route >= incumbent_bytes -hutter_equation_metastate_transfold_receipt:hutter_transfold_equations:source_equation_surface,source_equation_surface,SignalShapedRouteCompiler,HOLD,0.224542,projection_declared;compression_pressure;witness_declared;shape_closure,scale_band_declared,4-Infrastructure/shim/hutter_equation_metastate_transfold_receipt.json,,,"C = proposal_score(comp, phys, geom, scaling) or phi_HP = field + compression_gain + decoder/resource penalties" -math_model_map:0,UQGET_Hubble_Tension,ProjectableGeometryTopology,HOLD,0.292337,projection_declared;semantic_entropy;geometric_mass;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,geometric_bind,,H0 = 73.3 ± 1.5 km s⁻¹ Mpc⁻¹ (68% CL) -math_model_map:1,UQGET_Structure_Tension,ProjectableGeometryTopology,HOLD,0.261583,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,geometric_bind,,S8 = 0.810 ± 0.008 -math_model_map:2,UQGET_Statistical_Fit,ProjectableGeometryTopology,HOLD,0.288392,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,geometric_bind,,χ²/d.o.f. = 1907.2/1949 (p = 0.78) -math_model_map:3,LASSO_MOGAT_GAT_Propagation,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283014,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,h_i^(l+1) = σ(Σ_{j∈N(i)} α_ij^(l) W^(l) h_j^(l)) -math_model_map:4,LASSO_MOGAT_Attention_Coefficient,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.282576,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,α_ij^(l) = softmax_j(LeakyReLU(a→^(l)^T [W^(l) h_i^(l) || W^(l) h_j^(l)])) -math_model_map:5,Multiphasic_Allometry_LogLog_Scaling,CognitiveLoadField,HOLD,0.261553,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,Y = b·X + a (log-log space) -math_model_map:6,Multiphasic_Allometry_Quadratic,CognitiveLoadField,HOLD,0.261553,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,Y = a·X² + b·X + c (curvilinear) -math_model_map:7,Multiphasic_Allometry_Instantaneous_Slope,CognitiveLoadField,HOLD,0.260582,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,dY/dX = 2aX + b (where Y = aX² + bX + c) -math_model_map:8,Affine_Mapping_LTSF_Linear_Layer,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,Y = X·W + b -math_model_map:9,Affine_Mapping_Time_Series_Decomposition,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,x(t) = s(t) + f(t) + ε -math_model_map:10,Affine_Mapping_Periodic_Theorem,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.290839,projection_declared;witness_declared;shape_closure;proof_readiness,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,x(t) = s(t) = s(t-p) where p ≤ n -math_model_map:11,Affine_Mapping_Scaled_Periodic,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,x(t) = a·x(t-p) + c -math_model_map:12,MOF_CO2_Reduction_2e_Electrochemistry,CognitiveLoadField,HOLD,0.263119,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,CO2 + 2H+ + 2e- → CO + H2O -math_model_map:13,MOF_CO2_Reduction_Formic_Acid,CognitiveLoadField,HOLD,0.262582,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,CO2 + 2H+ + 2e- → HCOOH -math_model_map:14,MOF_CO2_Reduction_6e_Methanol,CognitiveLoadField,HOLD,0.259671,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,CO2 + 6H+ + 6e- → CH3OH + H2O -math_model_map:15,MOF_CO2_Reduction_8e_Methane,CognitiveLoadField,HOLD,0.259671,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,CO2 + 8H+ + 8e- → CH4 + 2H2O -math_model_map:16,COUCH_Equation,SignalShapedRouteCompiler,HOLD,0.257213,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,thermodynamic_bind,,ẍ_i + γẋ_i + ω_i²x_i + Σ_j κ_ij(x_i - x_j) = F(t) -math_model_map:1,Intrinsic_Load_LI,SignalShapedRouteCompiler,HOLD,0.234956,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,L_I(x) = -Σ_{b=0}^{255} p(b|x) log₂ p(b|x) -math_model_map:2,NES_GCL_Square_Wave_Compression,SignalShapedRouteCompiler,HOLD,0.245952,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,C_ratio = U_size / C_size = 1.48× (achieved) -math_model_map:3,NES_OISC_GCL_LUT_Architecture,CognitiveLoadField,HOLD,0.264694,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,JTAG→SUBLEQ→GCL→LUT→APU -math_model_map:4,Unified_Shader_GCL_Audio_Stack,SignalShapedRouteCompiler,HOLD,0.235728,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,"Shader(x,y,t,θ)→palette + GCL→LUT + SUBLEQ→compute" -math_model_map:5,Unified_Cartridge_Controller_Stack,CognitiveLoadField,HOLD,0.264694,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,Cartridge_SUBLEQ→GCL→Shader→Audio→ControllerPort→NES -math_model_map:6,Topological_NanoKernel_UART_Stack,CognitiveLoadField,HOLD,0.235036,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,1-Wire_UART→GCL_Admission→Entropy→Metaprobe→Triumvirate→NES -math_model_map:7,NES_Sound_Line_DSP_Math,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.278424,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,"Value↔AudioSignal(f,A,D) + APU_Operations→Result" -math_model_map:8,Unified_Metaprobe_Collapse,CognitiveLoadField,HOLD,0.24112,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,All_Systems→Single_Metaprobe→Unified_Audit -math_model_map:9,Final_Unified_Math_Collapse,CognitiveLoadField,HOLD,0.225432,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,All_Math→Single_Substrate→Unified_Computation -math_model_map:10,Voltage_Computational_Substrate,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,Voltage→Value + Operations→Result -math_model_map:11,Palette_DSP_Slave,CognitiveLoadField,HOLD,0.276228,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,DSP_Math→Palette_Parameters→Video_Palette -math_model_map:12,Quad_Sampled_Scanlines,CognitiveLoadField,HOLD,0.267969,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,informational_bind,,4×_Vertical→256×960 -math_model_map:13,Microgrid_Voxel_Emulation,CognitiveLoadField,CANDIDATE,0.231194,projection_declared;semantic_entropy;shape_closure;witness_declared,,3-Mathematical-Models/MATH_MODEL_MAP.tsv,,,Microgrid→640×480+Differential_Updates→Effective_640×480 -math_model_map:2,Extraneous_Load_LE,SignalShapedRouteCompiler,HOLD,0.234956,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,"L_E(x) = BPB(x,w_prior) - BPB*(x) = (1/n)Σ_i log₂(P_w*(x_i)/P_wprior(x_i))" -math_model_map:3,Germane_Load_LG,CognitiveLoadField,HOLD,0.252403,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,"L_G(x,t) = Σ_{s=1}^{S} γ^s · ΔL_E(x_s,t+1) ≈ τ·L_E·log(S+1)/log(S_max+1)" -math_model_map:4,Routing_Load_LR,CognitiveLoadField,HOLD,0.252088,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,L_R(x) = Σ_j c_j·1[f_j computed] + Σ_{l=1}^{D(x)} log₂|M_l| -math_model_map:5,Memory_Load_LM,CognitiveLoadField,HOLD,0.228316,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,L_M(x) = log₂|E| + α·1[hit] + β + λ·|E|/|E_max| -math_model_map:6,Total_Cognitive_Load,SignalShapedRouteCompiler,HOLD,0.228402,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,"L_total = λI·l̂I + λE·l̂E - λG·l̂G + λR·l̂R + λM·l̂M (Σλ=1, λG≤λE)" -math_model_map:7,Cognitive_Efficiency,SignalShapedRouteCompiler,HOLD,0.234942,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,η(x) = l̂I(x) / (l̂I + l̂E + l̂R + l̂M + ε) -math_model_map:8,Regret_Adjusted_Load,SignalShapedRouteCompiler,HOLD,0.234942,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,L_ρ(x) = L_total(x) · (1 + ρ(x)/ρ_max) -math_model_map:9,Basin_Conditional_Load,SignalShapedRouteCompiler,HOLD,0.235052,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,L(x|B) = L_I(x) + L_E(x|B) + L_R^B(x) -math_model_map:10,MoE_Predictor_Distribution,SignalShapedRouteCompiler,HOLD,0.234956,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,P_w(x_i|x_{ 1.0 -math_model_map:16,Coupling_Weight,ProjectableGeometryTopology,HOLD,0.288723,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,GWL Rotation -math_model_map:17,Rotational_Alignment,ProjectableGeometryTopology,HOLD,0.290188,projection_declared;geometric_mass;semantic_entropy;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,g = cos(Δθ·2π/16) · cos(Δφ·π/8) · (1 - 2|χ_i - χ_j|) -math_model_map:18,Spatial_Proximity,ProjectableGeometryTopology,HOLD,0.29059,projection_declared;semantic_entropy;geometric_mass;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,h = exp(-|Δp|²/(2σ²)) · 1_{|Δp|> Φ_metric(i,j)}" -math_model_map:36,Multi_Factor_Coupling_Weight,ProjectableGeometryTopology,HOLD,0.288076,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_BRAID,geometric_bind,w_ij = w_p · w_π · w_τ · w_χ · w_topo · w_σ -math_model_map:37,Holonomy_Accumulation,ProjectableGeometryTopology,HOLD,0.288076,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_BRAID,geometric_bind,Hol(γ_loop) = ∮_γ T(p) dp -math_model_map:38,Non_Euclidean_Distance,ProjectableGeometryTopology,HOLD,0.271543,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,d_N = path_length(γ_ij) + curvature_penalty(κ) + torsion_cost(T); d_T = d_E + λ_N·d_N -math_model_map:39,Trixal_Axes,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,"TrixalAxes = (thermal, work, irreversibility), each ∈ [0,1]; |axes| = √(th² + w² + ir²)" -math_model_map:40,Shannon_Entropy,CognitiveLoadField,HOLD,0.259671,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,"H = -Σ_b p(b) log₂ p(b), where p(b)=count(b)/len" -math_model_map:41,Kolmogorov_Estimate,CognitiveLoadField,HOLD,0.261553,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,K_est = (8 - H) / 8 -math_model_map:42,Thermodynamic_Entropy,CognitiveLoadField,HOLD,0.260119,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,S_thermo = H + K_est · 0.1 -math_model_map:43,Entropy_Gradient,CognitiveLoadField,HOLD,0.260119,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_H_ALGEBRA,thermodynamic_bind,dS/dt = (S_current - S_previous) / Δt -math_model_map:44,Mutual_Information_Extracted,SignalShapedRouteCompiler,HOLD,0.236532,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,thermodynamic_bind,MI = H_initial - H_current -math_model_map:45,Carnot_Efficiency,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,thermodynamic_bind,η_Carnot = 1 - T_cold / T_hot -math_model_map:46,Work_Extraction,CognitiveLoadField,HOLD,0.255474,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,thermodynamic_bind,W_actual = Q_absorbed · η_Carnot · 0.7 -math_model_map:47,Irreversibility_Metric,CognitiveLoadField,HOLD,0.26106,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,score = (entropy_production + path_asymmetry + time_reversal_violation) / 3 -math_model_map:48,Thermodynamic_Length,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,thermodynamic_bind,L_thermo = Σ_i distance_i · (1 + irreversibility_i) -math_model_map:49,Thermodynamic_Depth,CognitiveLoadField,HOLD,0.261553,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,depth = entropy_production · ln(time_steps) -math_model_map:50,Stamp_Code,LogogramProjection,HOLD,0.202394,projection_declared;witness_declared;shape_closure;proof_readiness,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,thermodynamic_bind,SHA256(axes || traj_hash || hardware_entropy || timing_jitter || process_nonce) -math_model_map:51,Arrhenius_Temperature_Factor,CognitiveLoadField,HOLD,0.251579,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,AF = exp(E_a / (k_B · T)) -math_model_map:52,Blacks_Equation_EM,CognitiveLoadField,HOLD,0.250302,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,EM_risk = J^n · exp(E_a / (k_B · T)) / 10^{12} -math_model_map:53,Coffin_Manson_Fatigue,CognitiveLoadField,CANDIDATE,0.221733,projection_declared;semantic_entropy;shape_closure;witness_declared,,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,"CM_damage = (ΔT / ΔT_threshold)^m · 10^{-8}, m≈1.9" -math_model_map:54,Landauer_Limit,SignalShapedRouteCompiler,HOLD,0.23706,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,thermodynamic_bind,W_erasure ≥ k_B · T · ln(2) ≈ 2.87e-21 J/bit at 300K -math_model_map:55,Entropy_Generation_Rate,CognitiveLoadField,HOLD,0.257657,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_E_VERIFICATION,thermodynamic_bind,dS/dt = power_dissipation / (k_B · T · ln 2) [bits/s] -math_model_map:56,BitFlip_Gradient_5D,CognitiveLoadField,HOLD,0.250417,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_D_INVARIANTS,thermodynamic_bind,|G| = (D1/120 + D2/5000 + D3/1000 + D4/100 + D5/5000) / 5 -math_model_map:57,SEU_BitFlip_Rate,CognitiveLoadField,HOLD,0.24942,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,BFR = ε_SEU · 2^{(T-25)/10} · (1 + V_jitter/1000) · 3600 [flips/hr] -math_model_map:58,Stress_Decay,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,stress(t) = stress_0 · e^{-t/300} + intensity · (1 - e^{-t/300}) -math_model_map:59,Remaining_Useful_Life,CognitiveLoadField,HOLD,0.25078,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,thermodynamic_bind,RUL = MTBF / (AF · (1 + fatigue·0.01 + thermal_fatigue·0.1)) -math_model_map:60,Homeostatic_Stress_Injection,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_E_VERIFICATION,thermodynamic_bind,"surprise = -ln(margin), margin = 1 - stress_magnitude; regret = max(0, stress - 0.5)" -math_model_map:61,Exact_Int_to_FP_Cast,CognitiveLoadField,HOLD,0.259238,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_D_INVARIANTS,thermodynamic_bind,int_bits ≤ fp_mantissa_bits + 1 (signed) or ≤ fp_mantissa_bits + 1 (unsigned) -math_model_map:62,Safe_Narrowing_Proof,CognitiveLoadField,HOLD,0.26106,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_D_INVARIANTS,thermodynamic_bind,"can_safely_narrow(src_bits, signed, dst_mantissa) → bool" -math_model_map:63,RISCV_Instruction_Latency,CognitiveLoadField,HOLD,0.258417,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_D_INVARIANTS,thermodynamic_bind,"fdiv.d=33, fdiv.s=19 → penalty=0.737; fadd.d=4, fadd.s=4 → penalty=0.0" -math_model_map:64,Photon_Energy,CognitiveLoadField,HOLD,0.257657,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,E = hc/λ = 1.2398 / λ_μm [eV] -math_model_map:65,Subband_Spacing,CognitiveLoadField,HOLD,0.260582,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,"ΔE = E_upper - E_lower; E_upper=hc/λ_min, E_lower=hc/λ_max" -math_model_map:66,Cascade_Gain,CognitiveLoadField,HOLD,0.249496,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,G = photons_per_e⁻ × n_wells; photons_per_e⁻ = ⌊E_electron / ΔE⌋ -math_model_map:67,Temperature_Tuning,CognitiveLoadField,HOLD,0.257657,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,λ(T) = λ₀ + α · (T - T₀); α=5e-6 /K (GaAs/AlGaAs) -math_model_map:68,Injection_Efficiency,CognitiveLoadField,HOLD,0.257657,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,"η = (0.5 + window_bonus) · spacing_eff · (1 - stress_penalty); clamped to [0,1]" -math_model_map:69,Atmospheric_Windows,CognitiveLoadField,HOLD,0.258417,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,"Windows: (3,5), (8,12), (16,20) μm; transmission=1.0 inside, exp(-dist·0.5) outside" -math_model_map:70,Tuning_Range,CognitiveLoadField,HOLD,0.257299,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_G_ENERGY,physical_bind,"ν = 10⁴/λ [cm⁻¹]; DFB: ±7.5 cm⁻¹, EC: ±200 cm⁻¹; λ_min=10⁴/ν_max, λ_max=10⁴/ν_min" -math_model_map:71,Mutual_Information_Signal,SignalShapedRouteCompiler,HOLD,0.222839,projection_declared;witness_declared;shape_closure;compression_pressure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,MI(x) = baseline_bpb(x) - actual_bpb(x) -math_model_map:72,kNN_MI_Prediction,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.282379,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,MI_pred = Σ_i (w_i · MI_i · S_i) / Σ_i (w_i · S_i); w_i = 1/(d_i + ε) -math_model_map:73,Surprise_Metric,ProjectableGeometryTopology,HOLD,0.288392,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,surprise = log(1 + |MI_actual - MI_predicted|) -math_model_map:74,Structure_Yield,SignalShapedRouteCompiler,HOLD,0.245865,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_A_COMPRESSION,informational_bind,ρ(x) = MI(x) / (cost(x) + ε) -math_model_map:75,Weighted_Feature_Distance,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.281874,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_B_ROUTING,informational_bind,"d(z₁,z₂) = √Σ_i w_i · ((z₁_i - z₂_i) / s_i)²" -math_model_map:76,DAG_Force_Equilibrium,CadForceProbeReceipt,HOLD,0.280052,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_BRAID,geometric_bind,Σ F_in = Σ F_out at every node -math_model_map:77,Constitutive_Law,CadForceProbeReceipt,HOLD,0.294433,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_BRAID,geometric_bind,σ = C : ε; ε = ½(∇u + ∇u^T) -math_model_map:78,DAG_Global_Validity,ProjectableGeometryTopology,HOLD,0.2697,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_BRAID,geometric_bind,V(G) = ∧_{v∈V} (ΣF_in = ΣF_out) -math_model_map:79,Cosine_Similarity,ProjectableGeometryTopology,HOLD,0.288723,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_K_SIGNAL,geometric_bind,cos = x · x_ref / (‖x‖ · ‖x_ref‖) -math_model_map:80,Gradient_Alignment,ProjectableGeometryTopology,HOLD,0.288392,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_K_SIGNAL,geometric_bind,alignment = ∇g_i · ∇g_j / (‖∇g_i‖ · ‖∇g_j‖) -math_model_map:81,Phase_Accumulation,ProjectableGeometryTopology,HOLD,0.288076,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_K_SIGNAL,geometric_bind,phase += Σ y · dx -math_model_map:82,Metric_Tensor_From_Circumferences,ProjectableGeometryTopology,HOLD,0.290188,projection_declared;geometric_mass;semantic_entropy;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,"g_tt = M², g_tp = 0, g_pp = (N·cos(θ))²; a=C_eq/(2π), c=C_mer/(2π), f=(a-c)/a, e²=2f-f², N=a/√(1-e²sin²θ), M=a(1-e²)/(1-e²sin²θ)^(3/2)" -math_model_map:83,Line_Element,ProjectableGeometryTopology,HOLD,0.287773,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,ds² = g_tt·dθ² + 2·g_tp·dθ·dφ + g_pp·dφ² -math_model_map:84,Christoffel_Symbols_2D,ProjectableGeometryTopology,HOLD,0.289427,projection_declared;geometric_mass;semantic_entropy;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,Gamma^k_ij = ½·g^kl·(∂_i g_jl + ∂_j g_il - ∂_l g_ij) -math_model_map:85,Geodesic_Step_Symplectic_Euler,ProjectableGeometryTopology,HOLD,0.290188,projection_declared;geometric_mass;semantic_entropy;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,a^θ = -(Gamma^θ_tt·v_θ² + 2·Gamma^θ_tp·v_θ·v_φ + Gamma^θ_pp·v_φ²); v' = v + a·dt; x' = x + v'·dt -math_model_map:86,Stereographic_Chart_Transition,ProjectableGeometryTopology,HOLD,0.290188,projection_declared;geometric_mass;semantic_entropy;witness_declared,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,"u = 2·tan(θ/2)·cos(φ), v = 2·tan(θ/2)·sin(φ); θ = 2·atan(√(u²+v²)/2), φ = atan2(v,u); select when |cos(θ)| < 0.01" -math_model_map:87,Chirality_Algebra,ProjectableGeometryTopology,HOLD,0.288392,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,"D+D→D, L+L→L, D+L→W(COLLAPSE→W); chiralityToTernary: D→Active, L→Active, W→Latent" -math_model_map:88,BLINK_GATE_Ternary_Clock,CognitiveLoadField,CANDIDATE,0.21559,projection_declared;semantic_entropy;shape_closure;witness_declared,,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,control_bind,"φ = (τ mod T)/T, T=942/1000; ternary: φ<1/3→Q, φ<2/3→A, else L; φ' = (φ+1/4) mod 1 if surprise>threshold" -math_model_map:89,Geodesic_Step_Verlet,ProjectableGeometryTopology,HOLD,0.288076,projection_declared;geometric_mass;witness_declared;shape_closure,scale_band_declared;negative_control_strength,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_C_TOPOLOGY,geometric_bind,Same acceleration as M85 + Verlet velocity update + chart transition check -math_model_map:90,Waveprobe_Risk_Function,CognitiveLoadField,HOLD,0.273853,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_F_CONTROL,control_bind,"risk(s) = (1 + γ·(1-cos(θ)))/d² + η·h; γ=3, η=0.8" -math_model_map:91,Waveprobe_Heat_Evolution,CognitiveLoadField,HOLD,0.25882,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/MATH_MODEL_MAP.tsv,LAYER_F_CONTROL,control_bind,"h' = α·h + β·a; α=0.95, β=0.2" -extracted_md:0,extracted_md_equation_0,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,a_μ = (|g| - 2) / 2 = 0.001165920705(114) -extracted_md:1,extracted_md_equation_1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,σ = 0.127 ppm (0.000000114) -extracted_md:2,extracted_md_equation_2,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.289065,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,a_μ^theory = a_μ^experiment within 0.5σ -extracted_md:3,extracted_md_equation_3,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286858,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,g_μ = -2.00233184122(82) -extracted_md:4,extracted_md_equation_4,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286858,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,μ = g · (eℏ / 2m) · S -extracted_md:5,extracted_md_equation_5,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,(D_t^α + (-∇²)^β) Ψ = λ |Ψ|^γ Ψ -extracted_md:6,extracted_md_equation_6,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,D^α f(x) = F^{-1}[ |k|^α · F[f](k) ] -extracted_md:7,extracted_md_equation_7,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287271,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,"Ψ_observed(x, t) = ∫ K_θ(x - x') Ψ_unified(x', t) dx'" -extracted_md:8,extracted_md_equation_8,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,"w(α, θ) = sin(θ)^α · cos(θ)^{1-α}" -extracted_md:9,extracted_md_equation_9,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,g_n(θ_obs) = g_0 · sin(θ_obs)^{1/n} · cos(θ_obs)^{1 - 1/n} -extracted_md:10,extracted_md_equation_10,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,Q_n = (1/2π) ∮_C ∇_n φ · dn = m/n for m ∈ ℤ -extracted_md:11,extracted_md_equation_11,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,ds² = -dθ²/ω(θ)² + a(θ)² [dr²/(1-kr²) + r² dΩ²] + ℓ_P² dθ² Γ(θ) -extracted_md:12,extracted_md_equation_12,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,H_eff = ω(θ) -extracted_md:13,extracted_md_equation_13,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,ρ_DE(θ) = ρ_foam · (1 - θ/θ_max)² -extracted_md:14,extracted_md_equation_14,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,S(R) ≤ C' · R^{D_H} · T^{(D_H - 1)} -extracted_md:15,extracted_md_equation_15,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,S ≤ C' · R^{1.44} · T^{0.44} -extracted_md:16,extracted_md_equation_16,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,A(r) = 2π (cosh(r) - 1) ≈ π · exp(r) for r >> 1 -extracted_md:17,extracted_md_equation_17,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,L_{n+1} / L_n ≈ exp(d_inj) ≈ Φ² ≈ 2.618 -extracted_md:18,extracted_md_equation_18,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.28814,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,D_s = 2 D_H / (1 + D_H) -extracted_md:19,extracted_md_equation_19,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,D_s = 4/3 ≈ 1.333 -extracted_md:20,extracted_md_equation_20,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,ρ(E) ~ E^{D_s/2 - 1} = E^{-1/3} -extracted_md:21,extracted_md_equation_21,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.289549,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,E_n ~ n³ -extracted_md:22,extracted_md_equation_22,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,n_s = 1 - 2/(1 + θ_max/θ_recombination) ≈ 0.965 -extracted_md:23,extracted_md_equation_23,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287271,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,E_dissipated ≥ k_B T · ln(2) per bit erased -extracted_md:24,extracted_md_equation_24,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287271,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,H(X) = -Σ p(x) log p(x) -extracted_md:25,extracted_md_equation_25,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,S ≤ 2π R E / (ℏ c ln 2) = A / (4 G ℏ) -extracted_md:26,extracted_md_equation_26,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,C_V = (12π⁴/5) N k_B (T/Θ_D)³ ∝ T³ -extracted_md:27,extracted_md_equation_27,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.288596,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,C_V ∝ T^{D_s} -extracted_md:28,extracted_md_equation_28,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.288596,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,C_V ∝ T^{1.18} -extracted_md:29,extracted_md_equation_29,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,⟨exp(-β W)⟩ = exp(-β ΔF) -extracted_md:30,extracted_md_equation_30,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,(ΔJ)² / ⟨J⟩² · σ ≥ 2 k_B -extracted_md:31,extracted_md_equation_31,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,Δx · Δp ≥ ℏ/2 -extracted_md:32,extracted_md_equation_32,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,Ψ_total = Ψ_A + Ψ_B = A · exp(i ω_Ψ θ) · [exp(i k_Ψ x_A) + exp(i k_Ψ x_B)] -extracted_md:33,extracted_md_equation_33,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,|Ψ_total|² = 2|A|² · [1 + cos(k_Ψ (x_A - x_B))] -extracted_md:34,extracted_md_equation_34,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287271,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,E = ℏ ω = ω_Ψ (in natural units ℏ = 1) -extracted_md:35,extracted_md_equation_35,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.288596,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,p = ℏ k = dθ_0/dx = k_Ψ -extracted_md:36,extracted_md_equation_36,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.289065,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,λ = 2π / k_Ψ = 2π / p -extracted_md:37,extracted_md_equation_37,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,"Phenotype(x, t) = Ψ_E [ Genotype(x) × Regulatory_State(t) ]" -extracted_md:38,extracted_md_equation_38,SignalShapedRouteCompiler,HOLD,0.232964,projection_declared;shape_closure;decoder_declared;witness_declared,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,"Residual(n) = Ψ_decode [ Basis, Context(n) ] XOR Byte(n)" -extracted_md:39,extracted_md_equation_39,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/extracted_equations.md,,,H_Ψ(data) = -Σ_n p(n) log_2 p_Ψ(n) ≤ H_uniform(data) = 8 bits/byte -eq_21f33f4110064dbd,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"G = (V, E) represents a quantum state |ψ⟩ through a -collection of local tensors {Tv }v∈V , one for each vertex" -eq_14d74623112c018f,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"injec= CD and Hv ∼ -tive if this map has trivial kernel" -eq_fcd40dc2de7c7324,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.265908,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"d ≥ D|N (v)| , which generically holds after coarse-graining -when D = O(1)" -eq_416bbe45bfd12c68,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,min ≥ δ -eq_290815ff34ff74a2,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,Z = ⟨ψ|ψ⟩ -eq_5ea587928ed366f5,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, n) of -the graph, the tensor Tv ⋆ T̄v of the norm network defines -a superoperator on the virtual space from any set of legs -to its complement Eq" -eq_e1646b712978905e,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ZBP 1 + -Zℓ " -eq_ed91aac060fde4cf,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = log ZBP + -ϕ(W)ZW , -(7) -connected W - -where a cluster is collection of loops with multiplicities, -W = {(ℓ1 , α1 ), (ℓ2 , α2 )," -eq_9b66cadab83a4e1e,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.280384,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,i ≥ 1 is its multiplicity in the cluster -eq_6fd0d43c1e1577a1,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"ZW = i Zℓαii and the -P -weight of a cluster is |W| := i αi |ℓi | where |ℓ| for a loop -ℓ ∈ L denotes the number of edges in ℓ" -eq_8605b7c5ccdbcf5f,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"c > c0 = -O(log ∆)" -eq_dbd7ee74be207cc7,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283511,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ⟨ψ|ψ⟩ to 1/ poly(N ) multiplicative -error -(ii) local observables ⟨OA ⟩ = ⟨ψ|OA |ψ⟩/⟨ψ|ψ⟩ to -1/ poly(N ) multiplicative error, given ⟨OA ⟩ ̸= 0 -(iii) correlation functions ⟨OA OB ⟩ − ⟨OA ⟩ ⟨OB ⟩ to -Õ(1/ poly(N )) additive error -where A, B ⊂ V are disjoint local regions" -eq_9138cc21d6e4da27,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, n) with d(v, A) = -r, we have - - -∥µ′⋆,⃗e − µ⋆,⃗e ∥1 = O e−r/ξ∗ -(10) - - 5 -Where 1/ξ∗ = O(log ε∗ /ε)" -eq_5be87eaf6cffbaae,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"W = Wnear ∪ Wfar , where clusters in -Wnear are distance at most a cutoff Rth away from B" -eq_927277d74e6531cc,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"t < r and -starts increasing thereafter, owing to the strict lightcone -in the message-passing dynamics [see SM for a proof]" -eq_353043888999f4ac,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"t = r−1, leading -to - - -∥µ⋆,⃗e − µ′⋆,e ∥1 = O e−r/ξ∗ -(13) -establishing locality of the BP fixed-points" -eq_532f389442891ed2,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,d = c − c0 = O(1) -eq_fdfae5689dd4a9a5,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,R = Θ(1) -eq_9138aeb942929155,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,X = 1} -eq_0cd087779e7532a9,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283255,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"i=1 - -For p = ∞ we have, -∥x∥∞ := max |xi | -1≤i≤m - -(S3) - -For an operator A ∈ B(H), we define the operator Schatten norm in terms of its singular values σ(A) as, -∥A∥p := ∥σ(A)∥p - -(S4) - -for any p ∈ [1, ∞]" -eq_5593e036fe6f7dd7,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"p ≤ q ≤ ∞ and any operator A, we have, -∥A∥p ≥ ∥A∥q - -(S5) - -∥A∥1 ≥ ∥A∥2 ≥ ∥A∥∞ - -(S6) - -In particular, - - 10 -2" -eq_ea9b6d38a8a0c1b0,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283781,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"p ≤ q ≤ ∞ and any non-zero operator A, -1 - -1 - -∥A∥p ≤ rank(A) p − q ∥A∥q - -(S7) - -p -rank(A)∥A∥∞ - -(S8) - -∥Ax∥2 -= σmax (A) -x̸=0 ∥x∥2 - -(S9) - -In particular, -∥A∥2 ≤ -We also note that, the ∞−norm obeys, -∥A∥∞ = sup - -we will denote ∥ · ∥∞ by just ∥ · ∥ for convenience" -eq_e0537400fa963370,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"B = Ac , reshape the tensor into a linear map -T : HA → HB -by grouping the indices in A and B into multi-indices a = (ia1 ," -eq_23fc8ebaf9c8ae4a,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287699,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"b = (ib1 ," -eq_8b4895ac1c7ebca0,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283511,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"q = 1 we have, - -| tr A† B | ≤ ∥A∥p ∥B∥q - -(S12) - -Graphs For a graph G = (V, E), we define some useful notation" -eq_25d49db810143f41,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = {v, w} ∈ E, we will refer to it’s directed versions ⃗e = (v, w) and ← -e = (w, v)" -eq_5034975bc936419d,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = {v, w}, we define, for any u ∈ V , -d(e, u) := min{d(v, u), d(w, u)} -and similarly, for any A ⊂ V , -d(e, A) := min d(e, u)" -eq_ab6c35762bc86081,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"li=1 be finite-dimensional Hilbert spaces (the l virtual legs), and -let Hphys be the physical Hilbert space" -eq_60b71803ce04c1ba,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"i=1 - -Definition S1" -eq_7d5beabf91af593e,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283511,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"i=1 Hi −→ Hphys from - -We perform a singular value decomposition -T = V ΣU † ∈ Cnp ×nv , - -(S14) - -where np ≥ nv by injectivity, where U ∈ Cnv ×nv and V :∈ Cnp ×nv satisfy U † U = 1nv (unitarity) and V † V = 1nv -(isometry), and Σ = diag(λe ) ∈ Cnv ×nv collects the singular values" -eq_8daf48607920ba7b,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = δ ∈ (0, 1]" -eq_3b521e5a6156cbc1,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = 1, -e - -e - -(S15) - -We call δ the injectivity parameter" -eq_7cf50458351623ec,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"T = V U † itself is an -isometry" -eq_b5bc1ffd90912fb1,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.288596,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,a = 1 -eq_51e4f2d85951d30b,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Lc = dim(HLc )1L , -Ka,L→Lc Ka,L→L -(S18) -c = dim(HL )1Lc , -a - -a - -which follow from the fact that U is a unitary" -eq_51dad4a7954a91e6,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Lc = -[Ū a ](i′L ),(iLc ) [U a ](iL ),(iLc ) = -δiL ,i′L = dim(HLc )1L -a - -a,iLc - -iLc - -Moreover, when T is δ−injective with δ = 1 (i" -eq_10109834ac3ac602,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"G = (V, E) with vertex set V and edge set E" -eq_6c7adf4f4e980afe,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283255,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"G = (V, E) with uniform virtual -dimension D and physical dimension dp is specified for each v ∈ V by a tensor -O -Tv : -H(n,v) → Hphys ∼ -= Cdp , -n∈N (v) - -where N (v) denotes the set of neighbors of v, and each virtual space H(n,v) ∼ -= CD" -eq_dde05d0258ec36a5,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, w), we define a bond-space H⃗e ∼ -= CD" -eq_36e95d88ecbda2b0,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, w) is a positive -operator µ⃗e ∈ Pos(H⃗e ) representing the message from v to w" -eq_25b43848714b6ceb,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, n) ∈ ⃗ -E, - - -O -µ(m,v) " -eq_139255306999d8d8,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e > 0 such that -PEPS if for each directed edge ⃗e ∈ E, -f⃗e (µ⋆ ) = λ⃗e µ⃗e" -eq_2a06ffe17253ce3c,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ⟨ψ|ψ⟩ -can be formally described as a Taylor series in terms of ‘loops’ on the network as described below" -eq_e598d78992b57382,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ZBP 1 + -Zl  - -(S21) - -Γ⊂L -l∈Γ -Γ finite, compatible - -where the sum runs over all finite sets Γ of mutually compatible loops" -eq_14f71a50573b7fd0,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"W = {(ℓ1 , α1 ), (ℓ2 , α2 )," -eq_44bf0ed184c8c0f8,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.287271,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,nW = i=1 αi -eq_4c772de7743acc11,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"W = {(ℓ1 , α1 )," -eq_3fe8c2f5d04f57ff,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"ZW = - -k -Y - -Zℓαii" -eq_1abc29f7b62428f6,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"i=1 - -We call a cluster W connected if the interaction graph GW is connected, meaning there is a path between any two -vertices in the interaction graph" -eq_f4bc63354f272a96,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283781,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"GW = -Pk -(VW , EW ) has |VW | = i=1 αi vertices, with loop ℓi corresponding to αi vertices" -eq_2a47fee92e986d5e,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"W = {(l1 , α1 ), (l2 , α2 )," -eq_4a18868e412a28db,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286858,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"ZW = -Zlαi i" -eq_d14e04ca5ba01499,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = log ZBP + -ϕ(W)ZW , - -(S25) - -connected W - -where the sum runs over all connected clusters W" -eq_405936ddfb9bc98c,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"c > c0 := log(2e∆) + 12 such that -|Zl | ≤ e−c|l| - -(S27) - -then, the series for log Z converges absolutely" -eq_6a72df8385b3a8fb,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283014,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Fm = log ZBP + - -X - -ϕ(W)ZW , - -(S28) - -connected W -|W|≤m - -is bounded by -|log Z − Fm | ≤ N e−d(m+1) -where d = c − c0 , ∆ is the degree of the graph, and N is the number of vertices" -eq_52e8897befaf699d,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285005,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,c > c0 = log(2e∆) + 1/2 -eq_f05cdb2173e0a64b,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"G = (V, E)" -eq_f93b08fedbc0a5af,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284364,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"T = ⟨ψ|ψ⟩ and -A -T = ⟨ψ|OA |ψ⟩ respectively after suitable BP normalization" -eq_d0869069ce0fee51,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ⟨ψ|ψ⟩ and Z A = ⟨ψ|OA |ψ⟩ -for a local observable with ⟨OA ⟩ ̸= 0" -eq_e2dad9249b00119f,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283014,projection_declared;witness_declared;semantic_entropy;shape_closure,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"m = ⟨OA ⟩BP · exp - - - - -conn W←LA - - - - - - - -supp(W)∩A̸=∅ -|W|≤m - -leads to a relative error δm = | ⟨OA ⟩ − ⟨OA ⟩m |/| ⟨OA ⟩ | bounded by - - -δm ≤ O |A|e−(c−c0 )(m+1) - -(S33) - -where d = c − c0 = O(1)" -eq_2b301ef1b6ca651f,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283781,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"A = ∂λ log ZλA |λ=0 = ⟨OA ⟩BP + -ϕW Z W -αl -− ⟨OA ⟩BP" -eq_f4aab7b26b55c832,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,Z = ⟨ψ|ψ⟩ and Z A = ⟨ψ|OA |ψ⟩ -eq_1a6dee6dca2f084b,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.28814,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,d = c − c0 -eq_0698ebc502ab24cc,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286074,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"c = - -X - -A -ϕW ∂λ ZW,λ - -conn" -eq_2edfb68a37ab3d1f,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286858,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"A = (A1 ," -eq_7957e0f2cb86a72c,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286858,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,degree ≥ 2 everywhere except in all regions Ai -eq_10a508b9d741a6f6,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284678,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"c ≤ O e−d(A,B)/ξ -for a finite correlation length ξ ≤ O((1/(c − c0 ))) and d(A, B) being the graph distance" -eq_fb10b15989eae47c,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283511,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"Z = ⟨ψ|ψ⟩ can be computed to 1/ poly(N ) multiplicative error in poly(N ) time -(ii) local observables ⟨OA ⟩ = ⟨ψ|OA |ψ⟩/⟨ψ|ψ⟩ with ⟨OA ⟩ ̸= 0 can be computed to multiplicative error ϵ in poly(1/ϵ) -time -(iii) 2-point correlation functions can be computed to additive error Õ(1/ poly(N )) in poly(N ) time - - 18 -Proof" -eq_984644a6b0dbab3e,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.283255,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"m ≤ N e−d(m+1) - -(S37) - -Hence, to ensure log Z − F̃m < O(1/ poly(N )) we require m = Ω(log N )" -eq_837e24a6c715af63,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285347,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,m = Ω(log N ) -eq_e54ad45a68cb6a93,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"m -≤ O e−d(m+1) ≤ ϵ -⟨OB ⟩ - -(S38) - -which can be ensured given m = O(log 1/ϵ), again computable in poly 1/ϵ time" -eq_21f71c6c28c71003,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.282576,projection_declared;semantic_entropy;witness_declared;shape_closure,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"m = d(A, B) + O(log 1/ϵ), leading to an error O m2 (|A| + |B|)e−dm , which is -(note that (|A| + |B|) = O(1), -! - -2 - - -1 -1 -2 -−d[d(A,B)+log 1/ϵ] -O d(A, B) log -·e -= O log N · -= Õ(1/ poly(N )) -(S39) -ϵ -poly(N ) -since we already have d(A, B) = O(log N ) and we choose ϵ = 1/ poly(N )" -eq_717ace3c9565c03b,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,Ka = D1L̸=i -eq_ccca83f09f701b34,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.284065,projection_declared;witness_declared;shape_closure;semantic_entropy,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"a ≥ δ 2 > 0 and - -† -a Ka Ka = D - -P - -(S43) - -a - -1, -X - -λ2a Ka† Ka ⪰ δ 2 - -X - -a - -Ka† Ka = δ 2 D 1, - -a - -hence -Tr f (X) ≥ δ 2 D Tr(X) = δ 2 D" -eq_bc5bda52965ac0f8,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.286459,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,"e = (v, n) ∈ E" -eq_d4a2926485bb8572,arXiv:2604.21919v1,HoldForUnlawfulOrUnderspecifiedShape,HOLD,0.285704,projection_declared;witness_declared;shape_closure;residual_risk,scale_band_declared,3-Mathematical-Models/equations_100/equations_database.jsonl,,,Ka = dim(HL ) L diff --git a/4-Infrastructure/shim/rrc_hold_closure_checklist.py b/4-Infrastructure/shim/rrc_hold_closure_checklist.py deleted file mode 100644 index 09c653f8..00000000 --- a/4-Infrastructure/shim/rrc_hold_closure_checklist.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -"""Generate closure checklists for Rainbow Raccoon Compiler HOLD objects.""" - -from __future__ import annotations - -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -COMPILER_RECEIPT = REPO / "4-Infrastructure" / "shim" / "rainbow_raccoon_compiler_receipt.json" -OUT_DIR = REPO / "shared-data" / "data" / "stack_solidification" -OUT = OUT_DIR / "rrc_hold_closure_checklist.json" -DOC = REPO / "6-Documentation" / "docs" / "rrc_hold_closure_checklist_2026-05-09.md" - - -AXIS_CLOSURE = { - "lean_or_independent_replay_gate": { - "gate": "Lean theorem, native_decide witness, or independent replay receipt exists", - "action": "add a replay harness or Lean theorem for the object-specific invariant", - }, - "scale_band_declared": { - "gate": "scale/range band is explicit and checked", - "action": "declare numeric scale band, unit domain, and overflow behavior", - }, - "projection_declared": { - "gate": "projection map from source object to manifold axes exists", - "action": "define projection axes and hash the projection payload", - }, - "witness_declared": { - "gate": "witness payload and verifier are named", - "action": "attach witness schema, verifier command, and receipt path", - }, - "decoder_declared": { - "gate": "decoder/replay route is named", - "action": "attach decoder route and exact replay hash", - }, -} - - -def load_json(path: Path) -> Any: - return json.loads(path.read_text(encoding="utf-8")) - - -def checklist_item(obj: dict[str, Any]) -> dict[str, Any]: - source = obj["object"] - witness = obj["type_witness"] - missing = witness.get("missing_or_weak_axes", []) - closures = [] - for axis in missing: - spec = AXIS_CLOSURE.get( - axis, - { - "gate": f"{axis} closed with explicit receipt", - "action": f"add closure evidence for {axis}", - }, - ) - closures.append({"axis": axis, **spec, "status": "OPEN"}) - return { - "object_id": source["object_id"], - "label": source["label"], - "kind": source["kind"], - "source_path": source["source_path"], - "payload_sha256": source["payload_sha256"], - "nearest_shape": obj["nearest_lawful_shape"]["shape"], - "status": witness["status"], - "lean_boundary": witness.get("lean_boundary"), - "closures": closures, - "promotion_rule": "Remain HOLD until every closure status is CLOSED and the compiler rerun emits CANDIDATE.", - } - - -def build_doc(receipt: dict[str, Any]) -> str: - lines = [ - "# RRC HOLD Closure Checklist", - "", - "**Date:** 2026-05-09", - "", - "This document gives each Rainbow Raccoon Compiler HOLD object a concrete closure checklist.", - "", - "## Summary", - "", - f"- Compiler receipt hash: `{receipt['compiler_receipt_hash']}`", - f"- HOLD objects: `{receipt['summary']['hold_count']}`", - f"- Candidate objects: `{receipt['summary']['candidate_count']}`", - f"- Open closure items: `{receipt['summary']['open_closure_count']}`", - "", - "## HOLD Objects", - "", - ] - for item in receipt["hold_objects"]: - lines.append(f"### `{item['object_id']}` {item['label']}") - lines.append("") - lines.append(f"- Shape: `{item['nearest_shape']}`") - lines.append(f"- Source: `{item['source_path']}`") - lines.append(f"- Payload SHA-256: `{item['payload_sha256']}`") - lines.append(f"- Lean boundary: `{item['lean_boundary']}`") - for closure in item["closures"]: - lines.append(f"- OPEN `{closure['axis']}`: {closure['gate']}") - lines.append(f"- Action: {closure['action']}") - lines.append(f"- Promotion rule: {item['promotion_rule']}") - lines.append("") - lines.append("## Candidate Objects") - lines.append("") - for item in receipt["candidate_objects"]: - lines.append(f"- `{item['object_id']}` {item['label']} -> `{item['nearest_shape']}`") - lines.append("") - lines.append("## Machine Receipt") - lines.append("") - lines.append(f"- `{OUT.relative_to(REPO)}`") - return "\n".join(lines) + "\n" - - -def main() -> int: - compiler = load_json(COMPILER_RECEIPT) - hold_objects = [] - candidate_objects = [] - for obj in compiler.get("compiled_objects", []): - status = obj.get("type_witness", {}).get("status") - if status == "HOLD": - hold_objects.append(checklist_item(obj)) - elif status == "CANDIDATE": - candidate_objects.append(checklist_item(obj)) - receipt = { - "schema": "rrc_hold_closure_checklist_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "compiler_receipt": str(COMPILER_RECEIPT.relative_to(REPO)), - "compiler_receipt_hash": compiler.get("receipt_hash"), - "claim_boundary": "Checklist only. This does not close HOLD objects or promote compiler candidates.", - "summary": { - "hold_count": len(hold_objects), - "candidate_count": len(candidate_objects), - "open_closure_count": sum(len(item["closures"]) for item in hold_objects), - }, - "hold_objects": hold_objects, - "candidate_objects": candidate_objects, - } - OUT_DIR.mkdir(parents=True, exist_ok=True) - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - DOC.write_text(build_doc(receipt), encoding="utf-8") - print(json.dumps({"receipt": str(OUT.relative_to(REPO)), "doc": str(DOC.relative_to(REPO)), "open_closures": receipt["summary"]["open_closure_count"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/rrc_logogram_projection_bridge.py b/4-Infrastructure/shim/rrc_logogram_projection_bridge.py deleted file mode 100644 index f5a2df6b..00000000 --- a/4-Infrastructure/shim/rrc_logogram_projection_bridge.py +++ /dev/null @@ -1,277 +0,0 @@ -#!/usr/bin/env python3 -"""Bridge math logograms into Rainbow Raccoon Compiler projections. - -This consumes the existing math logogram surface receipt and runs each compiled -sample through the RRC manifold/type-witness boundary as a LogogramProjection. -The bridge is deliberately receipt-only: it does not prove the mathematics in a -logogram. It verifies that the projection surface is declared, bounded, and -auditable enough to be a candidate object for later Lean/proof work. -""" - -from __future__ import annotations - -import hashlib -import importlib.util -import json -import sys -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -LOGOGRAM_RECEIPT = SHIM / "math_logogram_surface_receipt.json" -RRC_RECEIPT = SHIM / "rainbow_raccoon_compiler_receipt.json" -OUT = SHIM / "rrc_logogram_projection_bridge_receipt.json" -CURRICULUM = SHIM / "rrc_logogram_projection_bridge_curriculum.jsonl" - - -def load_rrc_module() -> Any: - path = SHIM / "rainbow_raccoon_compiler.py" - spec = importlib.util.spec_from_file_location("rainbow_raccoon_compiler", path) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load RRC module from {path}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_digest(path: Path) -> dict[str, Any]: - data = path.read_bytes() - return { - "path": str(path.relative_to(REPO)), - "bytes": len(data), - "sha256": sha256_bytes(data), - } - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def sample_projection_payload(sample: dict[str, Any]) -> dict[str, Any]: - return { - "id": sample["id"], - "kind": sample["kind"], - "source_hash": sample["source_hash"], - "canonical_hash": sample["canonical_hash"], - "cell_hash": sample["cell_hash"], - "token_count": sample["token_count"], - "token_kind_counts": sample["token_kind_counts"], - "surface_payload_hex": sample["surface_payload_hex"], - "surface_payload_len": sample["surface_payload_len"], - "substitution_receipt": sample["substitution_receipt"], - "compression_metrics": sample["compression_metrics"], - "semantic_regime": sample["semantic_regime"], - "projection": { - "source_space": "latex_or_symbolic_logogram", - "canonical_space": "deterministic_surface1_cells", - "payload_space": "bounded_glyph_payload_16_bytes", - "receipt_space": "substitution_receipt_plus_rrc_witness", - }, - "decoder_declared": "canonical cells + glyph payload + source residual boundary", - "scale_band_declared": "surface_payload_len <= 16 bytes; token_count finite; no math proof claim", - } - - -def tear_repair_witness(sample: dict[str, Any]) -> dict[str, Any] | None: - """Create a repair lane for semantic tearing without merging the torn mass.""" - if sample.get("semantic_regime") != "horrible_manifold_tearing": - return None - source = str(sample.get("source", "")) - canonical = str(sample.get("canonical", "")) - boundary = { - "regime": sample["semantic_regime"], - "source_hash": sample["source_hash"], - "canonical_hash": sample["canonical_hash"], - "cell_hash": sample["cell_hash"], - "trigger_terms": [ - term - for term in ["torsion", "contradiction", "tear", ">", "max"] - if term in source or term in canonical - ], - } - detached_mass_id = "detached_mass:" + sha256_text(stable_json(boundary))[:16] - witness = { - "schema": "rrc.logogram_tear_repair.v1", - "repair_status": "isolated_not_merged", - "repair_rule": "quarantine torn binding; preserve boundary and residual; refuse tokenbook merge", - "contradiction_witness_hash": sha256_text(stable_json(boundary)), - "tear_boundary_hash": sha256_text(sample["cell_hash"] + ":" + sample["semantic_regime"]), - "detached_mass_id": detached_mass_id, - "origin_block": { - "sample_id": sample["id"], - "source_hash": sample["source_hash"], - "canonical_hash": sample["canonical_hash"], - }, - "residual_lane": { - "kind": "semantic_boundary_residual", - "payload_hex": sample["surface_payload_hex"], - "payload_len": sample["surface_payload_len"], - "merge_admissible": False, - "projection_lane": "quarantine_projection", - }, - } - witness["repair_receipt_hash"] = sha256_text(stable_json(witness)) - return witness - - -def compile_sample(rrc: Any, sample: dict[str, Any]) -> dict[str, Any]: - payload = sample_projection_payload(sample) - obj = rrc.RRCObject( - object_id=f"rrc_logogram_{sample['id']}", - label=f"Logogram projection: {sample['id']}", - kind="logogram_projection", - payload=json.dumps(payload, sort_keys=True, ensure_ascii=True), - source_path="4-Infrastructure/shim/math_logogram_surface_receipt.json", - ) - compiled = rrc.compile_object(obj) - repair = tear_repair_witness(sample) - payload_bound_ok = sample["surface_payload_len"] <= 16 - type_ok = compiled["type_witness"]["status"] == "CANDIDATE" - merge_safe = sample["semantic_regime"] != "horrible_manifold_tearing" - repaired_tear = repair is not None - compiled["logogram_projection"] = { - "sample_id": sample["id"], - "semantic_regime": sample["semantic_regime"], - "canonical_hash": sample["canonical_hash"], - "cell_hash": sample["cell_hash"], - "payload_hex": sample["surface_payload_hex"], - "payload_len": sample["surface_payload_len"], - "hash16": sample["substitution_receipt"]["hash16"], - "projection_admissible": type_ok and payload_bound_ok and (merge_safe or repaired_tear), - "merge_admissible": type_ok and payload_bound_ok and merge_safe, - "projection_lane": "normal_projection" if merge_safe else "quarantine_projection", - "tear_repair_witness": repair, - "hold_reason": None, - } - if not compiled["logogram_projection"]["projection_admissible"]: - reasons = [] - if compiled["type_witness"]["status"] != "CANDIDATE": - reasons.extend(compiled["type_witness"]["missing_or_weak_axes"]) - if sample["surface_payload_len"] > 16: - reasons.append("payload_exceeds_16_byte_surface") - if sample["semantic_regime"] == "horrible_manifold_tearing" and repair is None: - reasons.append("semantic_regime_horrible_manifold_tearing_without_repair_witness") - compiled["logogram_projection"]["hold_reason"] = reasons - compiled["invariant_receipt"]["receipt_hash"] = sha256_text(stable_json(compiled)) - return compiled - - -def build_receipt() -> dict[str, Any]: - rrc = load_rrc_module() - logogram = load_json(LOGOGRAM_RECEIPT) - base_rrc = load_json(RRC_RECEIPT) if RRC_RECEIPT.exists() else {} - compiled = [compile_sample(rrc, sample) for sample in logogram.get("samples", [])] - receipt = { - "schema": "rrc_logogram_projection_bridge_v1", - "claim_state": "projection_bridge_not_math_proof", - "source_artifacts": [ - file_digest(LOGOGRAM_RECEIPT), - file_digest(SHIM / "math_logogram_surface_builder.py"), - file_digest(SHIM / "rainbow_raccoon_compiler.py"), - ] - + ([file_digest(RRC_RECEIPT)] if RRC_RECEIPT.exists() else []), - "upstream_rrc_receipt_hash": base_rrc.get("receipt_hash"), - "primary_read": ( - "Logograms become RRC projection objects by binding canonical cell hashes, " - "bounded glyph payloads, substitution receipts, and semantic regimes to a " - "LogogramProjection type witness." - ), - "projection_equation": ( - "P_logogram(source) = (canonical_hash, cell_hash, glyph_payload_16, " - "semantic_regime, substitution_receipt, rrc_type_witness)" - ), - "compiled_logograms": compiled, - "counts": { - "sample_count": len(compiled), - "candidate_count": sum(1 for item in compiled if item["type_witness"]["status"] == "CANDIDATE"), - "hold_count": sum(1 for item in compiled if item["type_witness"]["status"] == "HOLD"), - "projection_admissible_count": sum( - 1 for item in compiled if item["logogram_projection"]["projection_admissible"] - ), - "merge_admissible_count": sum( - 1 for item in compiled if item["logogram_projection"]["merge_admissible"] - ), - "repaired_tear_count": sum( - 1 - for item in compiled - if item["logogram_projection"].get("tear_repair_witness") is not None - ), - }, - "failure_rules": [ - "A logogram projection is not a proof of the source equation.", - "A payload over 16 bytes is HOLD for this Surface-1 bridge.", - "A horrible_manifold_tearing regime may enter quarantine projection only with a residual/contradiction witness.", - "A repaired tear is never merge-admissible without a separate proof receipt.", - "A missing RRC witness or weak projection axis is HOLD.", - ], - "next_steps": [ - "Add a declared residual lane for logograms that need more than 16 payload bytes.", - "Map admissible logograms into the E1/E2 route classifier as symbolic-feature tokens.", - "Create a Lean RRCShape.LogogramProjection witness gate.", - "Use semantic regime HOLDs to prevent unsafe tokenbook merges.", - ], - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [] - for item in receipt["compiled_logograms"]: - rows.append( - { - "prompt": { - "task": "rrc_logogram_projection", - "sample_id": item["logogram_projection"]["sample_id"], - "canonical_hash": item["logogram_projection"]["canonical_hash"], - "semantic_regime": item["logogram_projection"]["semantic_regime"], - }, - "completion": { - "shape": item["nearest_lawful_shape"]["shape"], - "status": item["type_witness"]["status"], - "projection_admissible": item["logogram_projection"]["projection_admissible"], - "receipt_hash": item["invariant_receipt"]["receipt_hash"], - }, - } - ) - CURRICULUM.write_text( - "\n".join(stable_json(row) for row in rows) + "\n", - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_curriculum(receipt) - print( - json.dumps( - { - "receipt": str(OUT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - **receipt["counts"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/rrc_logogram_projection_bridge_curriculum.jsonl b/4-Infrastructure/shim/rrc_logogram_projection_bridge_curriculum.jsonl deleted file mode 100644 index ad334cfc..00000000 --- a/4-Infrastructure/shim/rrc_logogram_projection_bridge_curriculum.jsonl +++ /dev/null @@ -1,5 +0,0 @@ -{"completion":{"projection_admissible":true,"receipt_hash":"d190b8af993f947b51b4f685776cae889311a363445b577f4945e7c2c96e512e","shape":"LogogramProjection","status":"CANDIDATE"},"prompt":{"canonical_hash":"ff788c59819d722f2620970286e7f3066167986692d521b5cdef786bc0f50b8b","sample_id":"quadratic_formula","semantic_regime":"ugly_asymmetric_pruning","task":"rrc_logogram_projection"}} -{"completion":{"projection_admissible":true,"receipt_hash":"11627f074c9ef45b8701cdfcc48f05d6803e4a9625468cbceb4375e4464b2e88","shape":"LogogramProjection","status":"CANDIDATE"},"prompt":{"canonical_hash":"11ad0bf4913ba45c3c7a9b80911ab1d606b4f58d739a0390b694210200a6a699","sample_id":"pde_residual","semantic_regime":"ugly_asymmetric_pruning","task":"rrc_logogram_projection"}} -{"completion":{"projection_admissible":true,"receipt_hash":"aae56db03a5219da730c16f2d13c517886bb9961bdee6008acc8b75510851241","shape":"LogogramProjection","status":"CANDIDATE"},"prompt":{"canonical_hash":"1c5b98e1ac8f74afc20c55790463a4a4cde5514009478d8e7756650c9d6f2180","sample_id":"metaglyph_fold","semantic_regime":"beautiful_topological_folding","task":"rrc_logogram_projection"}} -{"completion":{"projection_admissible":true,"receipt_hash":"dc0658468357e5e1b6c5811f31b6f1ce611eaf05ecadf0f2bbcb7d0ffc24c6e1","shape":"LogogramProjection","status":"CANDIDATE"},"prompt":{"canonical_hash":"0632054ea2324eaecd449372431056aaa70a24be898dd64d557359fc3e5ec587","sample_id":"semantic_tear","semantic_regime":"horrible_manifold_tearing","task":"rrc_logogram_projection"}} -{"completion":{"projection_admissible":true,"receipt_hash":"d7cbdc6b7b9b7ef567798096bc35d7b83831de5c4851e79b4bb531e4817e6132","shape":"LogogramProjection","status":"CANDIDATE"},"prompt":{"canonical_hash":"117ef030f41b0660fe394356f4299c2f504a5b9bb065285d3710b2cc1927e7bf","sample_id":"mhchem_surface","semantic_regime":"ugly_asymmetric_pruning","task":"rrc_logogram_projection"}} diff --git a/4-Infrastructure/shim/rrc_logogram_projection_bridge_receipt.json b/4-Infrastructure/shim/rrc_logogram_projection_bridge_receipt.json deleted file mode 100644 index 20aeeaef..00000000 --- a/4-Infrastructure/shim/rrc_logogram_projection_bridge_receipt.json +++ /dev/null @@ -1,663 +0,0 @@ -{ - "claim_state": "projection_bridge_not_math_proof", - "compiled_logograms": [ - { - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_logogram_quadratic_formula", - "receipt_hash": "d190b8af993f947b51b4f685776cae889311a363445b577f4945e7c2c96e512e", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "CANDIDATE" - }, - "logogram_projection": { - "canonical_hash": "ff788c59819d722f2620970286e7f3066167986692d521b5cdef786bc0f50b8b", - "cell_hash": "9fdeca812e7b2e62e8e8695c9fdde963c89050bb84456af247279a26f62f4458", - "hash16": 38671, - "hold_reason": null, - "merge_admissible": true, - "payload_hex": "21303662aa22306233323634ed313130", - "payload_len": 16, - "projection_admissible": true, - "projection_lane": "normal_projection", - "sample_id": "quadratic_formula", - "semantic_regime": "ugly_asymmetric_pruning", - "tear_repair_witness": null - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.32, - "scale_band_declared": 0.8, - "semantic_entropy": 0.5, - "shape_closure": 0.86, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3600126045190257, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3600126045190257, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4144677070345496, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4144677070345496, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4383895129848708, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4383895129848708, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.140633, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Logogram projection: quadratic_formula", - "object_id": "rrc_logogram_quadratic_formula", - "payload_bytes_sampled": 1249, - "payload_sha256": "5a5f6f724fae19e44f8df4679dd85fe1e1253cedc757e8faf62853b59ac64b59", - "source_path": "4-Infrastructure/shim/math_logogram_surface_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_logogram_quadratic_formula", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "CANDIDATE", - "witness_hash": "0bbd6f412eb03c3eb01a3cef59731a63de479e0e34d52c235757e6a9992d1cdf" - } - }, - { - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_logogram_pde_residual", - "receipt_hash": "11627f074c9ef45b8701cdfcc48f05d6803e4a9625468cbceb4375e4464b2e88", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "CANDIDATE" - }, - "logogram_projection": { - "canonical_hash": "11ad0bf4913ba45c3c7a9b80911ab1d606b4f58d739a0390b694210200a6a699", - "cell_hash": "972756182baea65037238d6929273702c2476c2c75cff9c13d65941f4cdbbec9", - "hash16": 27474, - "hold_reason": null, - "merge_admissible": true, - "payload_hex": "2532747535752532787536e125323082", - "payload_len": 16, - "projection_admissible": true, - "projection_lane": "normal_projection", - "sample_id": "pde_residual", - "semantic_regime": "ugly_asymmetric_pruning", - "tear_repair_witness": null - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.32, - "scale_band_declared": 0.8, - "semantic_entropy": 0.489583, - "shape_closure": 0.86, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3601666610935372, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3601666610935372, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4142244904941904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4142244904941904, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4389315399685239, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4389315399685239, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.140888, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Logogram projection: pde_residual", - "object_id": "rrc_logogram_pde_residual", - "payload_bytes_sampled": 1260, - "payload_sha256": "5dbf4653f4b110589e80bb5b44518d189598f547bf3cb69133784e2a7d0253d7", - "source_path": "4-Infrastructure/shim/math_logogram_surface_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_logogram_pde_residual", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "CANDIDATE", - "witness_hash": "8e863497b662eec4967838ca98086577e6865c3a2e1d1c65124b6baecbb11afc" - } - }, - { - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_logogram_metaglyph_fold", - "receipt_hash": "aae56db03a5219da730c16f2d13c517886bb9961bdee6008acc8b75510851241", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "CANDIDATE" - }, - "logogram_projection": { - "canonical_hash": "1c5b98e1ac8f74afc20c55790463a4a4cde5514009478d8e7756650c9d6f2180", - "cell_hash": "7d994f40112dde9b0f8f21f9f5dd4c0f894636d21289273eb81464d82e501ede", - "hash16": 43033, - "hold_reason": null, - "merge_admissible": true, - "payload_hex": "412b42298639413b423a", - "payload_len": 10, - "projection_admissible": true, - "projection_lane": "normal_projection", - "sample_id": "metaglyph_fold", - "semantic_regime": "beautiful_topological_folding", - "tear_repair_witness": null - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.32, - "scale_band_declared": 0.8, - "semantic_entropy": 0.489583, - "shape_closure": 0.86, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3601666610935372, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3601666610935372, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4142244904941904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4142244904941904, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4389315399685239, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4389315399685239, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.140888, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Logogram projection: metaglyph_fold", - "object_id": "rrc_logogram_metaglyph_fold", - "payload_bytes_sampled": 1248, - "payload_sha256": "2bf99cbb2d4a32fd4ceda4e71be4c69a40d18128a0942cfa4bdf9127437cc74c", - "source_path": "4-Infrastructure/shim/math_logogram_surface_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_logogram_metaglyph_fold", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "CANDIDATE", - "witness_hash": "6533248a5ce5f4a51f6cb9ba3b633cb4c0e2b8f0f9b06d9c1c020de8d901d23f" - } - }, - { - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_logogram_semantic_tear", - "receipt_hash": "dc0658468357e5e1b6c5811f31b6f1ce611eaf05ecadf0f2bbcb7d0ffc24c6e1", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "CANDIDATE" - }, - "logogram_projection": { - "canonical_hash": "0632054ea2324eaecd449372431056aaa70a24be898dd64d557359fc3e5ec587", - "cell_hash": "a9ceebfe7ccbc88bc50c06cd03ce65d38520a85e724a5475157672817802f6aa", - "hash16": 3579, - "hold_reason": null, - "merge_admissible": false, - "payload_hex": "d639413b423a3f8629af39413b423a", - "payload_len": 15, - "projection_admissible": true, - "projection_lane": "quarantine_projection", - "sample_id": "semantic_tear", - "semantic_regime": "horrible_manifold_tearing", - "tear_repair_witness": { - "contradiction_witness_hash": "3e2412df1702b1800594d5e570e390518a13c52891e3f034c0419f0c17dbdabb", - "detached_mass_id": "detached_mass:3e2412df1702b180", - "origin_block": { - "canonical_hash": "0632054ea2324eaecd449372431056aaa70a24be898dd64d557359fc3e5ec587", - "sample_id": "semantic_tear", - "source_hash": "62418fc8b55386858970f6f1a61790c3b789c1184e5c488375a149ffc89d22b2" - }, - "repair_receipt_hash": "68433cb400970b62d4e6b095b013b83b7e875a39d40465f183818c23a3afd68b", - "repair_rule": "quarantine torn binding; preserve boundary and residual; refuse tokenbook merge", - "repair_status": "isolated_not_merged", - "residual_lane": { - "kind": "semantic_boundary_residual", - "merge_admissible": false, - "payload_hex": "d639413b423a3f8629af39413b423a", - "payload_len": 15, - "projection_lane": "quarantine_projection" - }, - "schema": "rrc.logogram_tear_repair.v1", - "tear_boundary_hash": "47ae66c7227687763d915d4eacda4d8971546255b3df46b1966df9ed82283f04" - } - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.285714, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.32, - "scale_band_declared": 0.8, - "semantic_entropy": 0.489583, - "shape_closure": 0.86, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.358533895713518, - "kind_prior_bonus": 0.0, - "raw_distance": 0.358533895713518, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.3982747102838722, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3982747102838722, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.43472684227154046, - "kind_prior_bonus": 0.0, - "raw_distance": 0.43472684227154046, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.133122, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Logogram projection: semantic_tear", - "object_id": "rrc_logogram_semantic_tear", - "payload_bytes_sampled": 1225, - "payload_sha256": "cd2775907a884dc41591df4ea500e48acfbc6571e592013f13067e4ae44a2998", - "source_path": "4-Infrastructure/shim/math_logogram_surface_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_logogram_semantic_tear", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "CANDIDATE", - "witness_hash": "f61afd4258fd77944a86a605667ea3d96eafbd3efcc4890427ef9eae441f8769" - } - }, - { - "field_equation": "logogram_cell -> canonical_hash -> glyph_payload -> projection_lane; admit iff cell hash, payload bound, substitution receipt, and regime guard close", - "invariant_receipt": { - "object_id": "rrc_logogram_mhchem_surface", - "receipt_hash": "d7cbdc6b7b9b7ef567798096bc35d7b83831de5c4851e79b4bb531e4817e6132", - "schema": "rrc.object_receipt.v1", - "shape": "LogogramProjection", - "status": "CANDIDATE" - }, - "logogram_projection": { - "canonical_hash": "117ef030f41b0660fe394356f4299c2f504a5b9bb065285d3710b2cc1927e7bf", - "cell_hash": "48feffa327d055e0335b3f10eb453970d2f4b39121b0bce06f3328a788831197", - "hash16": 42273, - "hold_reason": null, - "merge_admissible": true, - "payload_hex": "2c304832a2343532e3363fa332a23435", - "payload_len": 16, - "projection_admissible": true, - "projection_lane": "normal_projection", - "sample_id": "mhchem_surface", - "semantic_regime": "ugly_asymmetric_pruning", - "tear_repair_witness": null - }, - "manifold_projection": { - "axes": [ - "semantic_entropy", - "geometric_mass", - "compression_pressure", - "topology_torsion", - "receipt_density", - "field_energy", - "hardware_affinity", - "proof_readiness", - "residual_risk", - "shape_closure", - "history_depth", - "negative_control_strength", - "projection_declared", - "decoder_declared", - "witness_declared", - "scale_band_declared" - ], - "coordinates": { - "compression_pressure": 0.333333, - "decoder_declared": 0.8, - "field_energy": 0.142857, - "geometric_mass": 0.142857, - "hardware_affinity": 0.0, - "history_depth": 0.0, - "negative_control_strength": 0.0, - "projection_declared": 1.0, - "proof_readiness": 0.525, - "receipt_density": 0.444444, - "residual_risk": 0.32, - "scale_band_declared": 0.8, - "semantic_entropy": 0.489583, - "shape_closure": 0.86, - "topology_torsion": 0.0, - "witness_declared": 0.8 - } - }, - "nearest_lawful_shape": { - "alternates": [ - { - "distance": 0.3601666610935372, - "kind_prior_bonus": 0.0, - "raw_distance": 0.3601666610935372, - "shape": "SignalShapedRouteCompiler" - }, - { - "distance": 0.4142244904941904, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4142244904941904, - "shape": "ProjectableGeometryTopology" - }, - { - "distance": 0.4389315399685239, - "kind_prior_bonus": 0.0, - "raw_distance": 0.4389315399685239, - "shape": "CognitiveLoadField" - } - ], - "declared_kind": "logogram_projection", - "distance": 0.140888, - "kind_prior_shape": "LogogramProjection", - "shape": "LogogramProjection" - }, - "object": { - "kind": "logogram_projection", - "label": "Logogram projection: mhchem_surface", - "object_id": "rrc_logogram_mhchem_surface", - "payload_bytes_sampled": 1263, - "payload_sha256": "eccbb31c2bcc0101c5c74c06f1994a73d30b021e53610947f9c23bb13aefa753", - "source_path": "4-Infrastructure/shim/math_logogram_surface_receipt.json" - }, - "pipeline": [ - "object", - "manifold_projection", - "nearest_lawful_shape", - "type_witness", - "field_equation", - "invariant_receipt" - ], - "type_witness": { - "conservative_synthesis": false, - "lean_boundary": "declared_not_proved", - "missing_or_weak_axes": [], - "object_id": "rrc_logogram_mhchem_surface", - "required_axes": [ - "projection_declared", - "witness_declared", - "scale_band_declared" - ], - "shape": "LogogramProjection", - "status": "CANDIDATE", - "witness_hash": "812380d8f84b874dded026a6ce41c9264850239e33de75aff00e84dbbefc9e39" - } - } - ], - "counts": { - "candidate_count": 5, - "hold_count": 0, - "merge_admissible_count": 4, - "projection_admissible_count": 5, - "repaired_tear_count": 1, - "sample_count": 5 - }, - "failure_rules": [ - "A logogram projection is not a proof of the source equation.", - "A payload over 16 bytes is HOLD for this Surface-1 bridge.", - "A horrible_manifold_tearing regime may enter quarantine projection only with a residual/contradiction witness.", - "A repaired tear is never merge-admissible without a separate proof receipt.", - "A missing RRC witness or weak projection axis is HOLD." - ], - "next_steps": [ - "Add a declared residual lane for logograms that need more than 16 payload bytes.", - "Map admissible logograms into the E1/E2 route classifier as symbolic-feature tokens.", - "Create a Lean RRCShape.LogogramProjection witness gate.", - "Use semantic regime HOLDs to prevent unsafe tokenbook merges." - ], - "primary_read": "Logograms become RRC projection objects by binding canonical cell hashes, bounded glyph payloads, substitution receipts, and semantic regimes to a LogogramProjection type witness.", - "projection_equation": "P_logogram(source) = (canonical_hash, cell_hash, glyph_payload_16, semantic_regime, substitution_receipt, rrc_type_witness)", - "receipt_hash": "83f44e8341788f6cbb2013704af2622f95f140b5a98402d69cd4ddde5ea88826", - "schema": "rrc_logogram_projection_bridge_v1", - "source_artifacts": [ - { - "bytes": 19934, - "path": "4-Infrastructure/shim/math_logogram_surface_receipt.json", - "sha256": "447a3dcb7837a99ebf5335ae043d8fcf43a24867c4b9ebfd8cfff5a6a8f11b87" - }, - { - "bytes": 10925, - "path": "4-Infrastructure/shim/math_logogram_surface_builder.py", - "sha256": "20c21f1130b70419da29f14ddd12b62c00ea5cf0b4116e1dbeefb7665d1511d6" - }, - { - "bytes": 21916, - "path": "4-Infrastructure/shim/rainbow_raccoon_compiler.py", - "sha256": "97de58442fa02de25aa084d144188b2199a074d80394e78c37e3b976e1ccb6ca" - }, - { - "bytes": 26421, - "path": "4-Infrastructure/shim/rainbow_raccoon_compiler_receipt.json", - "sha256": "f2fbbcc0c8e5b54772edf6c77b39b3ad99afb3af4c6a38a0f4d975c4110085cc" - } - ], - "upstream_rrc_receipt_hash": "5edf7a533f7994233f075e171a984760525301e0f66040c8ac882d1172928f2a" -} \ No newline at end of file diff --git a/4-Infrastructure/shim/rrc_tri_cycle_audit.py b/4-Infrastructure/shim/rrc_tri_cycle_audit.py deleted file mode 100755 index aa223d59..00000000 --- a/4-Infrastructure/shim/rrc_tri_cycle_audit.py +++ /dev/null @@ -1,495 +0,0 @@ -#!/usr/bin/env python3 -"""Tri-cycle audit for prover, Rainbow Raccoon Compiler, and FPGA witness lanes. - -The audit is intentionally conservative. It does not promote claims. It finds -HOLD/weak surfaces, reruns the available proof/compiler/witness gates, and emits -a receipt describing which parts are still blocked. -""" - -from __future__ import annotations - -import argparse -import json -import subprocess -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT_DIR = REPO / "shared-data" / "data" / "rrc_tri_cycle_audit" -OUT = OUT_DIR / "rrc_tri_cycle_audit_receipt.json" -DOC = REPO / "6-Documentation" / "docs" / "rrc_tri_cycle_audit_2026-05-09.md" - -TARGET_FILES = [ - "6-Documentation/wiki/Network-Topology-Theory.md", - "3-Mathematical-Models/fiber_optic_vibrational_tensor/Fundamental_Network_Topology_Equation.md", - "shared-data/network_topology_database.json", - "6-Documentation/docs/fpga_rrc_q16_accel_setup_2026-05-09.md", -] - -MARKERS = [ - "HOLD", - "HOLD_SECURITY_PROOF_DEBT", - "HOLD_COEFFICIENT_RECEIPT_DEBT", - "HOLD_ANALOGY_ADAPTER", - "HOLD_TOPOLOGY_PREDICTION_VALIDATION", - "not ready", - "unverified", - "USB-UART", - "CRC check : FAIL", - "does not validate", - "not a validation claim", - "receipt_path_exists", -] - -GATE_REGISTER: dict[str, dict[str, Any]] = { - "security_proof_debt": { - "status": "BLOCK_PROMOTION", - "closure_required": [ - "formal independence/freshness theorem for adaptive masks", - "secret-sharing non-reuse receipt", - "negative control showing adapted coefficients do not leak party inputs", - ], - "allowed_use": "design hypothesis and simulation only", - }, - "coefficient_or_calibration_debt": { - "status": "BLOCK_NUMERIC_CLAIMS", - "closure_required": [ - "dataset provenance receipt", - "coefficient calibration receipt", - "negative controls and sensitivity sweep", - ], - "allowed_use": "receipt-weighted prior accounting only", - }, - "topology_prediction_debt": { - "status": "BLOCK_VALIDATION_CLAIMS", - "closure_required": [ - "pre-registered prediction target", - "outcome receipt", - "independent public-map or measurement comparison", - ], - "allowed_use": "HOLD topology hypothesis only", - }, - "receipt_gate_debt": { - "status": "BLOCK_ROUTE_PROMOTION", - "closure_required": [ - "validation receipt exists", - "rollback hash exists", - "exact replay or decode closure hash matches", - ], - "allowed_use": "audit queue only", - }, - "fpga_transport_or_witness_debt": { - "status": "BLOCK_HARDWARE_ACCELERATION_CLAIMS", - "closure_required": [ - "simple UART loopback passes on fabric pins", - "Q16 accelerator hardware receipts match software receipts", - "durable flash readback passes or SRAM-only boundary remains explicit", - ], - "allowed_use": "software witness and SRAM-loaded bitstream development", - }, - "general_hold_surface": { - "status": "BLOCK_PUBLIC_PROMOTION", - "closure_required": [ - "bucket-specific gate assigned", - "source receipt linked", - "negative-control or replay evidence attached", - ], - "allowed_use": "internal research map", - }, -} - - -@dataclass -class CmdResult: - command: list[str] - cwd: str - returncode: int - stdout_tail: str - stderr_tail: str - - -def run_cmd(command: list[str], cwd: Path, timeout: int = 120) -> CmdResult: - proc = subprocess.run( - command, - cwd=cwd, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - return CmdResult( - command=command, - cwd=str(cwd.relative_to(REPO)), - returncode=proc.returncode, - stdout_tail=proc.stdout[-4000:], - stderr_tail=proc.stderr[-4000:], - ) - - -def load_json(path: Path) -> Any: - return json.loads(path.read_text(encoding="utf-8")) - - -def load_uart_beacon_diagnostics() -> dict[str, Any]: - receipts = [ - SHIM / "tang9k_uart_beacon_probe_receipt.json", - SHIM / "tang9k_uart_beacon_swapped_probe_receipt.json", - ] - diagnostics: list[dict[str, Any]] = [] - for path in receipts: - if not path.exists(): - diagnostics.append({"receipt": str(path.relative_to(REPO)), "present": False}) - continue - data = load_json(path) - if isinstance(data.get("ports"), dict): - port_rows = [ - { - "port": port, - "byte_count": item.get("byte_count"), - "contains_expected": item.get("contains_expected"), - "contains_q16_ascii": "513136" in item.get("raw_hex", ""), - } - for port, item in data["ports"].items() - ] - else: - port_rows = [ - { - "port": item.get("port"), - "byte_count": item.get("byte_count"), - "contains_expected": item.get("contains_expected"), - "contains_q16_ascii": item.get("contains_q16_ascii"), - } - for item in data.get("results", []) - ] - conclusion = data.get("conclusion") - if conclusion is None and port_rows: - conclusion = ( - "PASS" - if any(port.get("contains_expected") for port in port_rows) - else "FAIL_NO_BEACON_ON_FTDI_INTERFACES" - ) - diagnostics.append( - { - "receipt": str(path.relative_to(REPO)), - "present": True, - "schema": data.get("schema"), - "constraints": data.get("constraints"), - "bitstream_sha256": data.get("bitstream_sha256"), - "conclusion": conclusion, - "ports": port_rows, - } - ) - return { - "receipts": diagnostics, - "any_beacon_seen": any( - any(port.get("contains_expected") for port in item.get("ports", [])) - for item in diagnostics - if item.get("present") - ), - } - - -def scan_less_solid_surfaces() -> list[dict[str, Any]]: - findings: list[dict[str, Any]] = [] - for rel in TARGET_FILES: - path = REPO / rel - if not path.exists(): - continue - for lineno, line in enumerate(path.read_text(encoding="utf-8", errors="replace").splitlines(), 1): - marker_hits = [marker for marker in MARKERS if marker.lower() in line.lower()] - if not marker_hits: - continue - findings.append( - { - "path": rel, - "line": lineno, - "markers": marker_hits, - "text": line.strip()[:280], - "bucket": bucket_for_line(line), - } - ) - return findings - - -def bucket_for_line(line: str) -> str: - lower = line.lower() - if "security" in lower or "privacy" in lower or "beaver" in lower: - return "security_proof_debt" - if "coefficient" in lower or "calibration" in lower or "weight" in lower: - return "coefficient_or_calibration_debt" - if "prediction" in lower or "validation" in lower or "outcome" in lower: - return "topology_prediction_debt" - if "fpga" in lower or "usb-uart" in lower or "crc" in lower or "q16" in lower: - return "fpga_transport_or_witness_debt" - if "receipt" in lower: - return "receipt_gate_debt" - return "general_hold_surface" - - -def run_q16_witnesses(include_hardware: bool, hardware_port: str | None) -> dict[str, Any]: - cases = [ - ( - "shift", - [ - "python3", - "4-Infrastructure/shim/tang9k_rrc_q16_accel.py", - "--op", - "shift", - "--x", - "0x00038000", - ], - ), - ( - "weighted", - [ - "python3", - "4-Infrastructure/shim/tang9k_rrc_q16_accel.py", - "--op", - "weighted", - "--energy", - "0x000a0000", - "--alpha", - "0x00008000", - ], - ), - ( - "monotone", - [ - "python3", - "4-Infrastructure/shim/tang9k_rrc_q16_accel.py", - "--op", - "monotone", - "--a", - "0x00010000", - "--b", - "0x00030000", - ], - ), - ] - results: dict[str, Any] = {"software": {}, "hardware": {}, "hardware_requested": include_hardware} - for name, cmd in cases: - software_out = SHIM / f"rrc_tri_cycle_{name}_software_receipt.json" - software_cmd = cmd + ["--out", str(software_out.relative_to(REPO))] - software = run_cmd(software_cmd, REPO) - results["software"][name] = { - "command": software.command, - "returncode": software.returncode, - "receipt": str(software_out.relative_to(REPO)), - "match": software.returncode == 0 and load_json(software_out).get("match") is True, - } - if include_hardware and hardware_port: - hardware_out = SHIM / f"rrc_tri_cycle_{name}_hardware_receipt.json" - hardware_cmd = cmd + [ - "--port", - hardware_port, - "--retries", - "1", - "--out", - str(hardware_out.relative_to(REPO)), - ] - hardware = run_cmd(hardware_cmd, REPO, timeout=20) - receipt = load_json(hardware_out) if hardware_out.exists() else {} - results["hardware"][name] = { - "command": hardware.command, - "returncode": hardware.returncode, - "receipt": str(hardware_out.relative_to(REPO)), - "match": hardware.returncode == 0 and receipt.get("match") is True, - "hardware_error": receipt.get("hardware_error"), - } - results["software_pass"] = all(v["match"] for v in results["software"].values()) - results["hardware_pass"] = ( - all(v["match"] for v in results["hardware"].values()) if results["hardware"] else None - ) - return results - - -def summarize_rrc(receipt: dict[str, Any]) -> dict[str, Any]: - compiled = receipt.get("compiled_objects", []) - rows = [] - for obj in compiled: - rows.append( - { - "object_id": obj["object"]["object_id"], - "label": obj["object"]["label"], - "shape": obj["nearest_lawful_shape"]["shape"], - "status": obj["type_witness"]["status"], - "missing_or_weak_axes": obj["type_witness"].get("missing_or_weak_axes", []), - "lean_boundary": obj["type_witness"].get("lean_boundary"), - } - ) - return { - "receipt_hash": receipt.get("receipt_hash"), - "compiled_object_count": len(compiled), - "candidate_count": sum(1 for row in rows if row["status"] == "CANDIDATE"), - "hold_count": sum(1 for row in rows if row["status"] == "HOLD"), - "objects": rows, - } - - -def bucket_counts(findings: list[dict[str, Any]]) -> dict[str, int]: - counts: dict[str, int] = {} - for item in findings: - counts[item["bucket"]] = counts.get(item["bucket"], 0) + 1 - return dict(sorted(counts.items(), key=lambda kv: (-kv[1], kv[0]))) - - -def active_gate_register(findings: list[dict[str, Any]]) -> dict[str, Any]: - counts = bucket_counts(findings) - return { - bucket: GATE_REGISTER[bucket] | {"finding_count": count} - for bucket, count in counts.items() - if bucket in GATE_REGISTER - } - - -def build_doc(receipt: dict[str, Any]) -> str: - gates = receipt["gates"] - buckets = receipt["less_solid_surface_counts"] - lines = [ - "# RRC Tri-Cycle Audit", - "", - "**Date:** 2026-05-09", - "", - "## Gates", - "", - f"- Prover gate: `{gates['prover']['status']}`", - f"- Compiler gate: `{gates['compiler']['status']}`", - f"- FPGA software witness gate: `{gates['fpga_witness']['software_status']}`", - f"- FPGA hardware witness gate: `{gates['fpga_witness']['hardware_status']}`", - f"- UART beacon diagnostic: `{'PASS' if gates['fpga_witness']['uart_beacon']['any_beacon_seen'] else 'FAIL_NO_BEACON'}`", - "", - "## Less Solid Buckets", - "", - ] - for bucket, count in buckets.items(): - gate = receipt["gate_register"].get(bucket, {}) - lines.append(f"- `{bucket}`: {count} ({gate.get('status', 'NO_GATE')})") - lines.extend( - [ - "", - "## Closure Gates", - "", - ] - ) - for bucket, gate in receipt["gate_register"].items(): - lines.append(f"### `{bucket}`") - lines.append("") - lines.append(f"- Status: `{gate['status']}`") - lines.append(f"- Allowed use: {gate['allowed_use']}") - for req in gate["closure_required"]: - lines.append(f"- Requires: {req}") - lines.append("") - lines.extend( - [ - "", - "## UART Beacon Diagnostics", - "", - ] - ) - for beacon in gates["fpga_witness"]["uart_beacon"]["receipts"]: - lines.append(f"- `{beacon['receipt']}`: `{beacon.get('conclusion', 'MISSING')}`") - for port in beacon.get("ports", []): - lines.append( - f"- Port `{port['port']}` byte_count={port['byte_count']} contains_expected={port['contains_expected']}" - ) - lines.extend( - [ - "", - "## Highest Priority Holds", - "", - ] - ) - for item in receipt["less_solid_surfaces"][:20]: - lines.append( - f"- `{item['bucket']}` [{item['path']}:{item['line']}](../../{item['path']}#L{item['line']}): {item['text']}" - ) - lines.extend( - [ - "", - "## Claim Boundary", - "", - "This audit does not promote any HOLD claim. It only checks which weak surfaces are currently covered by prover, compiler, and FPGA-witness receipts.", - ] - ) - return "\n".join(lines) + "\n" - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--include-hardware", action="store_true") - parser.add_argument("--hardware-port", default="/dev/ttyUSB1") - args = parser.parse_args() - - OUT_DIR.mkdir(parents=True, exist_ok=True) - - less_solid = scan_less_solid_surfaces() - prover = run_cmd( - ["lake", "build", "Semantics.MetaManifoldProver"], - REPO / "0-Core-Formalism" / "lean" / "Semantics", - timeout=180, - ) - compiler = run_cmd(["python3", "4-Infrastructure/shim/rainbow_raccoon_compiler.py"], REPO) - compiler_receipt = load_json(SHIM / "rainbow_raccoon_compiler_receipt.json") - q16 = run_q16_witnesses(args.include_hardware, args.hardware_port) - uart_beacon = load_uart_beacon_diagnostics() - - receipt = { - "schema": "rrc_tri_cycle_audit_receipt_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": ( - "Tri-cycle audit only. Prover gate checks Lean build, compiler gate checks RRC HOLD/CANDIDATE " - "receipts, and FPGA gate checks Q16 witness harnesses. Hardware UART failures remain blockers." - ), - "less_solid_surface_counts": bucket_counts(less_solid), - "gate_register": active_gate_register(less_solid), - "less_solid_surfaces": less_solid, - "gates": { - "prover": { - "status": "PASS" if prover.returncode == 0 else "FAIL", - "command": prover.command, - "returncode": prover.returncode, - "stdout_tail": prover.stdout_tail, - "stderr_tail": prover.stderr_tail, - }, - "compiler": { - "status": "PASS_WITH_HOLDS" if compiler.returncode == 0 else "FAIL", - "command": compiler.command, - "returncode": compiler.returncode, - "stdout_tail": compiler.stdout_tail, - "stderr_tail": compiler.stderr_tail, - "summary": summarize_rrc(compiler_receipt), - }, - "fpga_witness": { - "software_status": "PASS" if q16["software_pass"] else "FAIL", - "hardware_status": ( - "PASS" - if q16["hardware_pass"] is True - else "FAIL" - if q16["hardware_pass"] is False - else "NOT_REQUESTED" - ), - "q16": q16, - "uart_beacon": uart_beacon, - }, - }, - "promotion_decision": "NO_PROMOTION", - "next_actions": [ - "Close or explicitly retain HOLD_SECURITY_PROOF_DEBT before treating adaptive Beaver coefficients as privacy-equivalent masks.", - "Treat coefficient and topology prediction rows as calibration debt until outcome receipts and negative controls close.", - "Fix Tang Nano 9K USB-UART fabric route before claiming live FPGA acceleration receipts; TX-only beacon receipts currently show no bytes on ttyUSB0 or ttyUSB1.", - "Use the Q16 lane as the first proof-backed compiler-to-FPGA witness once hardware transport responds.", - ], - } - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - DOC.write_text(build_doc(receipt), encoding="utf-8") - print(json.dumps({"receipt": str(OUT.relative_to(REPO)), "doc": str(DOC.relative_to(REPO)), "promotion_decision": "NO_PROMOTION"}, indent=2)) - return 0 if prover.returncode == 0 and compiler.returncode == 0 and q16["software_pass"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/run_core_equations.py b/4-Infrastructure/shim/run_core_equations.py deleted file mode 100644 index 13a0e4a4..00000000 --- a/4-Infrastructure/shim/run_core_equations.py +++ /dev/null @@ -1,241 +0,0 @@ -#!/usr/bin/env python3 -""" -Run 12 Core Equations Against 12 Ingested Theories -================================================== -Extract 12 core equations from master synthesis and check their presence -across the 12 ingested compression theories. -""" - -import json -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") -GERMANE_DIR = RESEARCH_STACK / "shared-data/data/germane/research" - -# 12 core equations from the compression architecture -CORE_EQUATIONS = { - "density_field": { - "equation": "ρ(x⃗)", - "description": "Semantic density field representing text as n-D manifold", - "latex": "\\rho(\\vec{x})", - "theories_expected": ["density_field_encoding_theory", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "morse_smale": { - "equation": "Critical points + separatrices", - "description": "Morse-Smale complex: topological skeleton of meaning", - "latex": "\\text{Morse-Smale} = \\{p, r, s, v, \\text{sep}\\}", - "theories_expected": ["density_field_encoding_theory", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "shear_matrix": { - "equation": "A_{ij} = δ_{ij} + α_{ij}", - "description": "Shear matrix transforming orthogonal hypercube to correlated rhomboid", - "latex": "A_{ij} = \\delta_{ij} + \\alpha_{ij}", - "theories_expected": ["hypercube_rhomboid_composition", "hypercube_rhomboid_hutter_prize", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "gram_matrix": { - "equation": "G = A^T A", - "description": "Gram matrix = compression dictionary (eigenvectors = principal directions)", - "latex": "G = A^T A", - "theories_expected": ["hypercube_rhomboid_composition", "hypercube_rhomboid_hutter_prize", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "gccl_packet": { - "equation": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "description": "GCCL glyph packet with chirality, type, eigen descriptor, residual", - "latex": "\\Gamma_i = \\gamma_i \\otimes \\chi_i \\otimes \\kappa_i \\otimes \\tau_i \\otimes U_i\\Lambda_i a_i \\otimes \\theta_i \\otimes \\varepsilon_i", - "theories_expected": ["gccl_gec_spec_v1", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "gain_test": { - "equation": "ΔGCL > 0", - "description": "GCCL gain test: only compressive motifs kept", - "latex": "\\Delta GCL > 0", - "theories_expected": ["gccl_gec_spec_v1", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "s3c_shell": { - "equation": "n = k² + a", - "description": "S3C shell coordinate encoding", - "latex": "n = k^2 + a", - "theories_expected": ["observer_admissible_cavities_theory", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "radius_ratio": { - "equation": "ρᵢ = s_center(i) / median(s(N(i)))", - "description": "Radius-ratio local scale ratio → admissible motif class", - "latex": "\\rho_i = s_{\\text{center}}(i) / \\text{median}(s(N(i)))", - "theories_expected": ["observer_admissible_cavities_theory", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "residual_ratio": { - "equation": "ρ = |ε| / |raw_span|", - "description": "Residual ratio: the only number that matters", - "latex": "\\rho = |\\varepsilon| / |\\text{raw_span}|", - "theories_expected": ["gccl_gec_spec_v1", "unified_compression_architecture_synthesis_v1", "master_synthesis_complete_v1"] - }, - "famm_delay": { - "equation": "Delay = path integral through field gradient", - "description": "FAMM delay profile = path integral through density field gradient", - "latex": "\\text{Delay} = \\int_{\\gamma} \\nabla \\rho \\cdot d\\vec{l}", - "theories_expected": ["unified_compression_architecture_synthesis_v1", "hippocampus_tabula_plena_combined_v1", "master_synthesis_complete_v1"] - }, - "residual_correlation": { - "equation": "C_{ij} = ⟨ε_i ε_j⟩", - "description": "Residual correlation matrix for spectral decomposition", - "latex": "C_{ij} = \\langle \\varepsilon_i \\varepsilon_j \\rangle", - "theories_expected": ["erans_field_effect_spectrum_v1", "master_synthesis_complete_v1"] - }, - "eigen_decomposition": { - "equation": "C = UΛU^T", - "description": "Eigen decomposition of residual correlation matrix", - "latex": "C = U\\Lambda U^T", - "theories_expected": ["hypercube_rhomboid_composition", "erans_field_effect_spectrum_v1", "master_synthesis_complete_v1"] - } -} - -# 12 compression theories (excluding non-compression entries) -THEORIES = [ - "observer_admissible_cavities_theory", - "hypercube_rhomboid_composition", - "hypercube_rhomboid_hutter_prize", - "erans_enumerative_rans_reference", - "density_field_encoding_theory", - "gccl_gec_spec_v1", - "unified_compression_architecture_synthesis_v1", - "hippocampus_tabula_plena_combined_v1", - "erans_field_effect_spectrum_v1", - "master_synthesis_complete_v1" -] - -def load_theory(theory_id): - """Load a theory JSON file.""" - theory_file = GERMANE_DIR / f"{theory_id}.json" - if theory_file.exists(): - with open(theory_file) as f: - return json.load(f) - return None - -def check_equation_in_theory(equation_key, theory_data): - """Check if an equation is present in a theory.""" - theory_str = json.dumps(theory_data, indent=2).lower() - - # Check for equation-specific patterns - equation_patterns = { - "density_field": ["rho", "density field", "semantic manifold"], - "morse_smale": ["morse", "smale", "topological", "skeleton", "critical point"], - "shear_matrix": ["shear", "matrix", "a_{ij}", "alpha", "delta"], - "gram_matrix": ["gram", "g = a^t a", "eigenvector", "principal"], - "gccl_packet": ["gamma", "chirality", "eigen", "residual", "glyph"], - "gain_test": ["delta", "gcl", "gain", "> 0"], - "s3c_shell": ["s3c", "shell", "k²", "k^2", "a", "mirror"], - "radius_ratio": ["radius", "ratio", "coordination", "cn3", "cn4", "cn6"], - "residual_ratio": ["residual", "ratio", "raw_span"], - "famm_delay": ["famm", "delay", "gradient", "path integral"], - "residual_correlation": ["correlation", "c_{ij}", "residual field"], - "eigen_decomposition": ["eigen", "decompose", "u", "lambda", "c = u"] - } - - patterns = equation_patterns.get(equation_key, []) - for pattern in patterns: - if pattern in theory_str: - return True - return False - -def main(): - print("=" * 70) - print(" RUNNING 12 CORE EQUATIONS AGAINST 12 COMPRESSION THEORIES") - print("=" * 70) - - results = {} - - # Load all theories - theory_data = {} - for theory_id in THEORIES: - data = load_theory(theory_id) - if data: - theory_data[theory_id] = data - print(f"\n✓ Loaded: {theory_id}") - else: - print(f"\n✗ Missing: {theory_id}") - - # Check each equation against each theory - print("\n" + "=" * 70) - print(" EQUATION × THEORY MATRIX") - print("=" * 70) - - for eq_key, eq_info in CORE_EQUATIONS.items(): - print(f"\n{eq_key}: {eq_info['equation']}") - print(f" {eq_info['description']}") - print(f" Expected in: {', '.join(eq_info['theories_expected'])}") - print(f" Found in:") - - found_in = [] - for theory_id, data in theory_data.items(): - if check_equation_in_theory(eq_key, data): - found_in.append(theory_id) - print(f" ✓ {theory_id}") - else: - print(f" ✗ {theory_id}") - - results[eq_key] = { - "equation": eq_info["equation"], - "description": eq_info["description"], - "expected": eq_info["theories_expected"], - "found": found_in, - "coverage": len(found_in) / len(theory_data) if theory_data else 0 - } - - # Summary statistics - print("\n" + "=" * 70) - print(" SUMMARY STATISTICS") - print("=" * 70) - - total_checks = len(CORE_EQUATIONS) * len(theory_data) - total_found = sum(len(r["found"]) for r in results.values()) - - print(f"\nTotal equation-theory checks: {total_checks}") - print(f"Total matches found: {total_found}") - print(f"Coverage: {total_found}/{total_checks} = {total_found/total_checks*100:.1f}%") - - # Equations with full coverage - print("\nEquations with full coverage (found in all theories):") - for eq_key, result in results.items(): - if result["coverage"] == 1.0: - print(f" ✓ {eq_key}: {result['equation']}") - - # Equations with partial coverage - print("\nEquations with partial coverage:") - for eq_key, result in results.items(): - if 0 < result["coverage"] < 1.0: - print(f" ○ {eq_key}: {result['equation']} ({result['coverage']*100:.1f}%)") - - # Equations with no coverage - print("\nEquations with no coverage:") - for eq_key, result in results.items(): - if result["coverage"] == 0: - print(f" ✗ {eq_key}: {result['equation']}") - - # Save results - output_file = RESEARCH_STACK / "4-Infrastructure/shim/core_equations_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - # Expected vs actual comparison - print("\n" + "=" * 70) - print(" EXPECTED VS ACTUAL") - print("=" * 70) - - for eq_key, result in results.items(): - expected_set = set(result["expected"]) - found_set = set(result["found"]) - missing = expected_set - found_set - unexpected = found_set - expected_set - - if missing or unexpected: - print(f"\n{eq_key}:") - if missing: - print(f" Missing (expected but not found): {', '.join(missing)}") - if unexpected: - print(f" Unexpected (found but not expected): {', '.join(unexpected)}") - else: - print(f"\n{eq_key}: ✓ All expected theories found") - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/scholar_abstraction_layer_shape_deep_dive.py b/4-Infrastructure/shim/scholar_abstraction_layer_shape_deep_dive.py deleted file mode 100644 index 11356416..00000000 --- a/4-Infrastructure/shim/scholar_abstraction_layer_shape_deep_dive.py +++ /dev/null @@ -1,344 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt a scholar-style deep dive on abstraction-layer shape matches. - -The search target is the local abstraction: - - each stage is parallel domains of the data type being processed - -The useful source matches are not compression claims. They are operator -families that resemble the local machine: synchronization schemas, reduced -product abstract domains, staged computation, multi-view fusion, provenance -semirings, lenses, and type/data-oriented parallel systems. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "scholar_abstraction_layer_shape_deep_dive_receipt.json" -CURRICULUM_OUT = SHIM / "scholar_abstraction_layer_shape_deep_dive_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -SOURCE_BUNDLE = [ - { - "id": "synchronization_schemas", - "title": "Synchronization Schemas", - "authors": "Rajeev Alur et al.", - "venue": "PODS 2021", - "url": "https://research.google/pubs/synchronization-schemas/", - "shape": "type-theoretic synchronization over series-parallel streams", - "local_mapping": "cross-domain barrier and typed stage stream object", - "strength": "primary_match", - }, - { - "id": "reduced_product_abstract_transformers", - "title": "Synthesizing Abstract Transformers for Reduced-Product Domains", - "authors": "Pankaj Kumar Kalita, Thomas Reps, Subhajit Roy", - "venue": "arXiv 2024", - "doi": "10.48550/arXiv.2408.04040", - "url": "https://arxiv.org/abs/2408.04040", - "shape": "component transformers over product domains must cooperate", - "local_mapping": "parallel stage domains with cross-domain contracts", - "strength": "primary_match", - }, - { - "id": "product_operators_abstract_interpretation", - "title": "A Survey on Product Operators in Abstract Interpretation", - "authors": "Agostino Cortesi, Giulia Costantini, Pietro Ferrara", - "venue": "EPTCS 129, 2013", - "doi": "10.4204/EPTCS.129.19", - "url": "https://arxiv.org/abs/1309.5146", - "shape": "Cartesian products, reduced products, and cardinal powers combine domains", - "local_mapping": "domain vector algebra for byte/token/structure/residual/witness views", - "strength": "primary_match", - }, - { - "id": "multidirectional_synchronization", - "title": "Controllable and decomposable multidirectional synchronizations", - "authors": "Hermann et al.", - "venue": "Software and Systems Modeling, 2021", - "doi": "10.1007/s10270-021-00879-w", - "url": "https://link.springer.com/article/10.1007/s10270-021-00879-w", - "shape": "wide span of lenses synchronizes multiple views through a central model", - "local_mapping": "central byte authority with token/structure/witness/domain views", - "strength": "primary_match", - }, - { - "id": "staged_computation", - "title": "Staged computation", - "authors": "James R. Larus and Michael Parkes", - "venue": "USENIX 2002", - "url": "https://www.usenix.org/publications/library/proceedings/usenix02/full_papers/larus/larus_html/index.html", - "shape": "stage as asynchronous operation group with private data and scheduling autonomy", - "local_mapping": "stage control plane, but local model adds typed parallel domains", - "strength": "strong_analogy", - }, - { - "id": "staged_classes", - "title": "Multi-stage Programming in the Large with Staged Classes", - "authors": "Lionel Parreaux, Amir Shaikhha", - "venue": "GPCE 2020", - "url": "https://cse.hkust.edu.hk/~parreaux/publication/gpce20/", - "shape": "zero-cost staged abstractions for modular programs and data structures", - "local_mapping": "compile route-stage abstractions away from payload; keep receipts only", - "strength": "strong_analogy", - }, - { - "id": "multi_view_gnn_taxonomy", - "title": "Graph neural networks for multi-view learning: a taxonomic review", - "authors": "Shunxin Xiao et al.", - "venue": "Artificial Intelligence Review, 2024", - "doi": "10.1007/s10462-024-10990-1", - "url": "https://link.springer.com/article/10.1007/s10462-024-10990-1", - "shape": "multiple graph/relation/attribute views are fused or aligned", - "local_mapping": "parallel route views propose and align but cannot override byte hash", - "strength": "strong_analogy", - }, - { - "id": "semiring_provenance", - "title": "PROX: Approximated Summarization of Data Provenance", - "authors": "Deutch, Gilad, Moskovitch et al.", - "venue": "VLDB / PMC version", - "url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC5001561/", - "shape": "provenance annotations summarize how outputs depend on input domains", - "local_mapping": "witness/provenance domain with bounded summaries and exact byte authority", - "strength": "strong_analogy", - }, - { - "id": "hawkeye_datatype_semantics", - "title": "HAWKEYE: Effective Discovery of Dataflow Impediments to Parallelization", - "authors": "Omer Tripp, Greta Yorsh, John Field, Mooly Sagiv", - "venue": "OOPSLA 2011", - "url": "https://research.google/pubs/hawkeye-effective-discovery-of-dataflow-impediments-to-parallelization/", - "shape": "parallelization dependencies tracked at abstract data-type semantics", - "local_mapping": "domain contracts should track semantic dependencies, not raw field noise", - "strength": "strong_analogy", - }, - { - "id": "yedalog", - "title": "Yedalog: Exploring Knowledge at Scale", - "authors": "Brian Chin et al.", - "venue": "SNAPL 2015", - "url": "https://research.google/pubs/yedalog-exploring-knowledge-at-scale/", - "shape": "mix data-parallel pipelines and computation in one declarative language over nested records", - "local_mapping": "route compiler DSL can mix domain-parallel transforms with structured corpus records", - "strength": "supporting_match", - }, - { - "id": "dynamically_managed_data_cpu_gpu", - "title": "Dynamically Managed Data for CPU-GPU Architectures", - "authors": "Thomas B. Jablin et al.", - "venue": "CGO 2012", - "url": "https://research.google/pubs/dynamically-managed-data-for-cpu-gpu-architectures/", - "shape": "automatic consistency management for CPU/GPU views of complex data", - "local_mapping": "owner/budget/closure domains must keep heterogeneous route views consistent", - "strength": "supporting_match", - }, - { - "id": "scalable_data_abstractions", - "title": "Scalable data abstractions for distributed parallel computations", - "authors": "James Hanlon, Simon J. Hollis, David May", - "venue": "arXiv 2012", - "doi": "10.48550/arXiv.1210.1157", - "url": "https://arxiv.org/abs/1210.1157", - "shape": "separate data representation from computation and allow distributed representations", - "local_mapping": "active data type has distributed domain views; byte view remains authority", - "strength": "supporting_match", - }, -] - - -CLUSTERS = [ - { - "id": "typed_synchronization_streams", - "members": ["synchronization_schemas", "yedalog"], - "local_operator": "type each route stage as a series-parallel stream of domains", - }, - { - "id": "reduced_product_domain_algebra", - "members": ["reduced_product_abstract_transformers", "product_operators_abstract_interpretation"], - "local_operator": "combine byte/token/structure/residual/witness domains as a reduced product with cross-domain reductions", - }, - { - "id": "multi_view_consistency", - "members": ["multidirectional_synchronization", "multi_view_gnn_taxonomy"], - "local_operator": "let views align and synchronize, while central byte authority resolves promotion", - }, - { - "id": "stage_runtime_boundary", - "members": ["staged_computation", "staged_classes", "dynamically_managed_data_cpu_gpu"], - "local_operator": "stage abstractions manage computation and consistency but must not become payload", - }, - { - "id": "provenance_dependency_witness", - "members": ["semiring_provenance", "hawkeye_datatype_semantics", "scalable_data_abstractions"], - "local_operator": "track provenance and data-type dependencies as bounded witness domains", - }, -] - - -EQUATIONS = [ - { - "id": "SAD0_reduced_product_stage", - "equation": "Stage_t = D_byte x_R D_token x_R D_structure x_R D_residual x_R D_witness x_R D_owner x_R D_budget x_R D_closure", - "meaning": "The stage is a reduced product of mutually constraining domains, not a flat tuple.", - }, - { - "id": "SAD1_domain_transformer_vector", - "equation": "F_t^# = ", - "meaning": "Each stage edge is a vector of component transformers.", - }, - { - "id": "SAD2_synchronization_schema", - "equation": "sync_schema(Stage_t) = ordering + key_partition + barrier_contract", - "meaning": "A typed schema controls which domains are ordered, keyed, parallel, or barriered.", - }, - { - "id": "SAD3_lens_center", - "equation": "central_model = exact_byte_span + exact_residuals", - "meaning": "Token, structure, and witness views synchronize through the byte/residual center.", - }, - { - "id": "SAD4_provenance_witness", - "equation": "W = provenance_semiring(route_edges, source_spans, residual_obligations)", - "meaning": "Witness domains record how a route result depends on inputs and repairs.", - }, - { - "id": "SAD5_promotion_barrier", - "equation": "promote iff all reductions close and hash(decode(center)) == source_hash", - "meaning": "No view alignment or abstract-domain precision replaces byte rehydration.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "scholar_abstraction_layer_shape_deep_dive_v1", - "generated_at": GENERATED_AT, - "search_note": ( - "Direct automated Google Scholar access is unreliable, so this scan used " - "Google-Scholar-linked research pages plus primary publisher, arXiv, " - "Springer, USENIX, and Google Research pages." - ), - "source_bundle": SOURCE_BUNDLE, - "clusters": CLUSTERS, - "equations": EQUATIONS, - "primary_decision": { - "name": "treat_parallel_stage_domains_as_reduced_product_with_sync_schema", - "statement": ( - "The closest literature shape is a reduced-product domain vector " - "controlled by synchronization schemas and lens-like consistency. " - "For the compressor, each stage should expose typed component " - "transformers, cross-domain reduction/barrier rules, bounded " - "provenance witnesses, and one exact byte/residual center." - ), - }, - "candidate_dd_state_extension": [ - "reduced_product_stage_id", - "sync_schema_id", - "domain_transformer_vector_id", - "domain_reduction_operator_id", - "view_lens_center_id", - "provenance_semiring_id", - "data_type_dependency_witness_id", - "stage_parallelism_class", - "domain_consistency_status", - "domain_reduction_fixpoint_status", - "byte_residual_center_hash", - ], - "candidate_dd_edges": [ - "choose_sync_schema", - "open_reduced_product_stage", - "synthesize_domain_transformer_vector", - "apply_cross_domain_reduction", - "synchronize_views_through_byte_center", - "emit_provenance_semiring_witness", - "track_data_type_dependency", - "reject_view_consistency_without_byte_hash", - "close_reduced_product_stage", - ], - "promotion_rule": [ - "stage_domains_are_a_reduced_product_not_unrelated_sidecars", - "sync_schema_declares_ordering_keying_and_barriers", - "component_transformers_are_typed_and_receipted", - "cross_domain_reductions_reach_fixpoint_or_fail_closed", - "views_synchronize_through_exact_byte_residual_center", - "provenance_witness_is_bounded", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "view_fusion_without_byte_center -> diagnostic_only", - "reduced_product_search_space_explodes -> prune_or_split_stage", - "sync_schema_missing_barrier -> invalid_receipt", - "provenance_polynomial_unbounded -> summarize_or_prune", - "stage_abstraction_serialized_as_payload -> invalid_receipt", - "data_type_dependency_ignored -> unsafe_parallelization", - ], - "claim_boundary": ( - "These papers provide similar abstraction-layer shapes. None are " - "evidence of compression improvement until local encode/decode/hash/" - "byte-count receipts close under one ratio schema." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["source_bundle"]: - lines.append({"type": "source_shape", **item}) - for item in receipt["clusters"]: - lines.append({"type": "cluster", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - "source_count": len(receipt["source_bundle"]), - "cluster_count": len(receipt["clusters"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/scientific_equations_4primitive_mapping.json b/4-Infrastructure/shim/scientific_equations_4primitive_mapping.json deleted file mode 100644 index a3a565c4..00000000 --- a/4-Infrastructure/shim/scientific_equations_4primitive_mapping.json +++ /dev/null @@ -1,225 +0,0 @@ -{ - "primitives": { - "field": { - "equation": "\u03c1(x\u20d7)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "keywords": [ - "field", - "density", - "distribution", - "potential", - "energy", - "manifold", - "state", - "landscape" - ] - }, - "shear": { - "equation": "G = A\u1d40A", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "keywords": [ - "distance", - "metric", - "gradient", - "force", - "transform", - "deformation", - "geometry", - "rate" - ] - }, - "packet": { - "equation": "\u0393\u1d62", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "keywords": [ - "descriptor", - "vector", - "map", - "kernel", - "similarity", - "representation", - "encoding" - ] - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "keywords": [ - "eigen", - "basis", - "hamiltonian", - "variational", - "optimization", - "decomposition", - "energy" - ] - } - }, - "scientific_equations": { - "chemistry_physics_nspace_spine": { - "source": "chemistry_physics_nspace_spine_v0.json", - "equations": [ - { - "name": "Chemical_Descriptor_Vector", - "domain": "Chemistry / N-Space", - "equation": "x_mol = (d1,d2,...,dn) \u2208 R^n", - "primitive": "packet", - "mapping": "Molecule as point in descriptor space = packet representation" - }, - { - "name": "Chemical_Space_Distance", - "domain": "Chemistry / Geometry", - "equation": "D(i,j) = ||x_i-x_j||_2", - "primitive": "shear", - "mapping": "Chemical similarity as geometric distance = shear metric" - }, - { - "name": "Weighted_Chemical_Space_Distance", - "domain": "Chemistry / Geometry", - "equation": "D_w(i,j) = sqrt(sum_k w_k(x_ik-x_jk)^2)", - "primitive": "shear", - "mapping": "Weighted semantic distance = weighted shear metric" - }, - { - "name": "Chemical_Structure_Property_Map", - "domain": "Chemistry / ML", - "equation": "y = f(x_mol)", - "primitive": "packet", - "mapping": "Property prediction over chemical space = packet transform" - }, - { - "name": "Molecular_Configuration_Space", - "domain": "Chemistry / Physics", - "equation": "R = (r1,...,rN) \u2208 R^{3N}", - "primitive": "field", - "mapping": "N-atom molecular configuration space = field manifold" - }, - { - "name": "Potential_Energy_Surface", - "domain": "Chemistry / Physics", - "equation": "E = V(R)", - "primitive": "field", - "mapping": "Energy as scalar field over configuration space = field state" - }, - { - "name": "Molecular_Force", - "domain": "Chemistry / Physics", - "equation": "F_i = -\u2207_{r_i}V(R)", - "primitive": "shear", - "mapping": "Force as gradient of potential energy = shear deformation" - }, - { - "name": "Molecular_Dynamics_Newtonian", - "domain": "Chemistry / Physics", - "equation": "m_i d\u00b2r_i/dt\u00b2 = -\u2207_{r_i}V(R)", - "primitive": "shear", - "mapping": "Classical molecular dynamics = shear dynamics (force-driven deformation)" - }, - { - "name": "Molecular_Force_Field_Energy", - "domain": "Chemistry / Physics", - "equation": "V(R) = \u03a3_bonds k_b(r-r0)^2 + \u03a3_angles k\u03b8(\u03b8-\u03b80)^2 + \u03a3_dihedrals Vn[1+cos(n\u03c6-\u03b3)] + \u03a3_{i", - "primitive": "field", - "mapping": "Pair-distance distribution = field correlation function" - }, - { - "name": "Local_Atomic_Density_Kernel", - "domain": "Materials / Descriptor", - "equation": "\u03c1_i(r) = \u03a3_j exp(-||r-rij||\u00b2/2\u03c3\u00b2); K(i,j) = (\u222b\u03c1_i(r)\u03c1_j(r)dr)^\u03b6", - "primitive": "packet", - "mapping": "Local atomic density and similarity kernel = packet similarity metric" - }, - { - "name": "Arrhenius_Rate", - "domain": "Chemistry / Thermodynamics", - "equation": "k = A exp(-Ea/RT)", - "primitive": "shear", - "mapping": "Reaction rate over activation barrier = shear rate (temperature-driven deformation)" - }, - { - "name": "Eyring_Transition_State_Rate", - "domain": "Chemistry / Thermodynamics", - "equation": "k = (kBT/h) exp(-\u0394G\u2021/RT)", - "primitive": "shear", - "mapping": "Transition-state rate equation = shear rate (free energy-driven deformation)" - }, - { - "name": "Boltzmann_Distribution", - "domain": "Statistical Mechanics", - "equation": "p_i = exp(-Ei/kBT)/Z; Z = \u03a3_i exp(-Ei/kBT)", - "primitive": "field", - "mapping": "Energy landscape to probability distribution = field state (probability field)" - }, - { - "name": "Quantum_Hamiltonian_Eigenproblem", - "domain": "Quantum Chemistry", - "equation": "\u0124\u03c8 = E\u03c8", - "primitive": "spectral", - "mapping": "Quantum energy eigenproblem = spectral decomposition (Hamiltonian eigenbasis)" - }, - { - "name": "Quantum_Hamiltonian_Variational_Energy", - "domain": "Quantum Chemistry", - "equation": "E(\u03b8) = <\u03c8(\u03b8)|\u0124|\u03c8(\u03b8)>; \u03b8* = argmin_\u03b8 E(\u03b8)", - "primitive": "spectral", - "mapping": "Variational quantum energy optimization = spectral optimization (basis optimization)" - }, - { - "name": "DFT_Energy_Functional", - "domain": "Quantum Chemistry", - "equation": "E[n] = Ts[n] + \u222bvext(r)n(r)dr + 1/2\u222b\u222bn(r)n(r')/|r-r'|drdr' + Exc[n]", - "primitive": "field", - "mapping": "Electron density to energy functional = field state (density field \u2192 energy field)" - }, - { - "name": "Bayesian_Optimization_Chemical_Space", - "domain": "Chemistry / Optimization", - "equation": "f(x) ~ GP(\u03bc(x), k(x,x')); x_next = argmax_x \u03b1(x); EI(x) = E[max(f(x)-f_best, 0)]", - "primitive": "spectral", - "mapping": "Search policy over chemical/material space = spectral optimization (Gaussian process basis)" - } - ] - } - }, - "primitive_distribution": { - "field": 6, - "shear": 6, - "packet": 4, - "spectral": 3 - }, - "domain_distribution": { - "Chemistry / N-Space": 1, - "Chemistry / Geometry": 2, - "Chemistry / ML": 1, - "Chemistry / Physics": 5, - "Chemistry / Descriptor": 1, - "Materials / Geometry": 1, - "Materials / Descriptor": 1, - "Chemistry / Thermodynamics": 2, - "Statistical Mechanics": 1, - "Quantum Chemistry": 3, - "Chemistry / Optimization": 1 - }, - "insights": { - "field_core": "energy landscapes, density fields, probability distributions", - "shear_core": "gradients, forces, rates, geometric deformations", - "packet_core": "descriptors, encodings, similarity metrics, representations", - "spectral_core": "eigenproblems, basis optimization, variational methods", - "cross_domain_consistency": "Each primitive appears across multiple scientific domains", - "no_gaps": "Each primitive well-represented across chemistry, physics, thermodynamics, quantum chemistry" - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/scientific_equations_4primitive_mapping.py b/4-Infrastructure/shim/scientific_equations_4primitive_mapping.py deleted file mode 100644 index c3d1d42c..00000000 --- a/4-Infrastructure/shim/scientific_equations_4primitive_mapping.py +++ /dev/null @@ -1,309 +0,0 @@ -#!/usr/bin/env python3 -""" -Map Scientific Equations to 4-Primitive Framework -================================================== -Apply 4-primitive framework (field, shear, packet, spectral) to -already solved equations from science (physics, chemistry, etc.) -""" - -import json -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -# 4-primitive framework -PRIMITIVES = { - "field": { - "equation": "ρ(x⃗)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "keywords": ["field", "density", "distribution", "potential", "energy", "manifold", "state", "landscape"] - }, - "shear": { - "equation": "G = AᵀA", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "keywords": ["distance", "metric", "gradient", "force", "transform", "deformation", "geometry", "rate"] - }, - "packet": { - "equation": "Γᵢ", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "keywords": ["descriptor", "vector", "map", "kernel", "similarity", "representation", "encoding"] - }, - "spectral": { - "equation": "C = UΛUᵀ", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "keywords": ["eigen", "basis", "hamiltonian", "variational", "optimization", "decomposition", "energy"] - } -} - -# Scientific equations from chemistry-physics pack -SCIENTIFIC_EQUATIONS = { - "chemistry_physics_nspace_spine": { - "source": "chemistry_physics_nspace_spine_v0.json", - "equations": [ - { - "name": "Chemical_Descriptor_Vector", - "domain": "Chemistry / N-Space", - "equation": "x_mol = (d1,d2,...,dn) ∈ R^n", - "primitive": "packet", - "mapping": "Molecule as point in descriptor space = packet representation" - }, - { - "name": "Chemical_Space_Distance", - "domain": "Chemistry / Geometry", - "equation": "D(i,j) = ||x_i-x_j||_2", - "primitive": "shear", - "mapping": "Chemical similarity as geometric distance = shear metric" - }, - { - "name": "Weighted_Chemical_Space_Distance", - "domain": "Chemistry / Geometry", - "equation": "D_w(i,j) = sqrt(sum_k w_k(x_ik-x_jk)^2)", - "primitive": "shear", - "mapping": "Weighted semantic distance = weighted shear metric" - }, - { - "name": "Chemical_Structure_Property_Map", - "domain": "Chemistry / ML", - "equation": "y = f(x_mol)", - "primitive": "packet", - "mapping": "Property prediction over chemical space = packet transform" - }, - { - "name": "Molecular_Configuration_Space", - "domain": "Chemistry / Physics", - "equation": "R = (r1,...,rN) ∈ R^{3N}", - "primitive": "field", - "mapping": "N-atom molecular configuration space = field manifold" - }, - { - "name": "Potential_Energy_Surface", - "domain": "Chemistry / Physics", - "equation": "E = V(R)", - "primitive": "field", - "mapping": "Energy as scalar field over configuration space = field state" - }, - { - "name": "Molecular_Force", - "domain": "Chemistry / Physics", - "equation": "F_i = -∇_{r_i}V(R)", - "primitive": "shear", - "mapping": "Force as gradient of potential energy = shear deformation" - }, - { - "name": "Molecular_Dynamics_Newtonian", - "domain": "Chemistry / Physics", - "equation": "m_i d²r_i/dt² = -∇_{r_i}V(R)", - "primitive": "shear", - "mapping": "Classical molecular dynamics = shear dynamics (force-driven deformation)" - }, - { - "name": "Molecular_Force_Field_Energy", - "domain": "Chemistry / Physics", - "equation": "V(R) = Σ_bonds k_b(r-r0)^2 + Σ_angles kθ(θ-θ0)^2 + Σ_dihedrals Vn[1+cos(nφ-γ)] + Σ_{i", - "primitive": "field", - "mapping": "Pair-distance distribution = field correlation function" - }, - { - "name": "Local_Atomic_Density_Kernel", - "domain": "Materials / Descriptor", - "equation": "ρ_i(r) = Σ_j exp(-||r-rij||²/2σ²); K(i,j) = (∫ρ_i(r)ρ_j(r)dr)^ζ", - "primitive": "packet", - "mapping": "Local atomic density and similarity kernel = packet similarity metric" - }, - { - "name": "Arrhenius_Rate", - "domain": "Chemistry / Thermodynamics", - "equation": "k = A exp(-Ea/RT)", - "primitive": "shear", - "mapping": "Reaction rate over activation barrier = shear rate (temperature-driven deformation)" - }, - { - "name": "Eyring_Transition_State_Rate", - "domain": "Chemistry / Thermodynamics", - "equation": "k = (kBT/h) exp(-ΔG‡/RT)", - "primitive": "shear", - "mapping": "Transition-state rate equation = shear rate (free energy-driven deformation)" - }, - { - "name": "Boltzmann_Distribution", - "domain": "Statistical Mechanics", - "equation": "p_i = exp(-Ei/kBT)/Z; Z = Σ_i exp(-Ei/kBT)", - "primitive": "field", - "mapping": "Energy landscape to probability distribution = field state (probability field)" - }, - { - "name": "Quantum_Hamiltonian_Eigenproblem", - "domain": "Quantum Chemistry", - "equation": "Ĥψ = Eψ", - "primitive": "spectral", - "mapping": "Quantum energy eigenproblem = spectral decomposition (Hamiltonian eigenbasis)" - }, - { - "name": "Quantum_Hamiltonian_Variational_Energy", - "domain": "Quantum Chemistry", - "equation": "E(θ) = <ψ(θ)|Ĥ|ψ(θ)>; θ* = argmin_θ E(θ)", - "primitive": "spectral", - "mapping": "Variational quantum energy optimization = spectral optimization (basis optimization)" - }, - { - "name": "DFT_Energy_Functional", - "domain": "Quantum Chemistry", - "equation": "E[n] = Ts[n] + ∫vext(r)n(r)dr + 1/2∫∫n(r)n(r')/|r-r'|drdr' + Exc[n]", - "primitive": "field", - "mapping": "Electron density to energy functional = field state (density field → energy field)" - }, - { - "name": "Bayesian_Optimization_Chemical_Space", - "domain": "Chemistry / Optimization", - "equation": "f(x) ~ GP(μ(x), k(x,x')); x_next = argmax_x α(x); EI(x) = E[max(f(x)-f_best, 0)]", - "primitive": "spectral", - "mapping": "Search policy over chemical/material space = spectral optimization (Gaussian process basis)" - } - ] - } -} - - -def analyze_scientific_mapping(): - print("=" * 70) - print(" SCIENTIFIC EQUATIONS → 4-PRIMITIVE FRAMEWORK MAPPING") - print("=" * 70) - - print("\n4-PRIMITIVE FRAMEWORK:") - for prim, data in PRIMITIVES.items(): - print(f"\n{prim.upper()}: {data['equation']}") - print(f" Role: {data['role']}") - print(f" Keywords: {', '.join(data['keywords'])}") - - print("\n" + "=" * 70) - print(" CHEMISTRY-PHYSICS EQUATIONS (19 equations)") - print("=" * 70) - - cp = SCIENTIFIC_EQUATIONS["chemistry_physics_nspace_spine"] - print(f"\nSource: {cp['source']}") - print(f"19 equations from chemistry, physics, quantum chemistry, thermodynamics") - - print("\nEQUATIONS BY PRIMITIVE:") - - primitive_groups = {"field": [], "shear": [], "packet": [], "spectral": []} - - for eq in cp["equations"]: - prim = eq["primitive"] - primitive_groups[prim].append(eq) - - for prim, equations in primitive_groups.items(): - print(f"\n{prim.upper()} ({len(equations)} equations):") - for eq in equations: - print(f" • {eq['name']}: {eq['equation'][:60]}...") - print(f" Mapping: {eq['mapping']}") - - print("\n" + "=" * 70) - print(" PRIMITIVE DISTRIBUTION") - print("=" * 70) - - total = sum(len(eqs) for eqs in primitive_groups.values()) - for prim, equations in primitive_groups.items(): - count = len(equations) - percent = count / total * 100 if total > 0 else 0 - print(f"\n{prim.upper()} ({count} equations, {percent:.1f}%):") - print(f" {', '.join([eq['name'] for eq in equations])}") - - print("\n" + "=" * 70) - print(" DOMAIN DISTRIBUTION") - print("=" * 70) - - domain_counts = {} - for eq in cp["equations"]: - domain = eq["domain"] - if domain not in domain_counts: - domain_counts[domain] = [] - domain_counts[domain].append(eq) - - for domain, equations in domain_counts.items(): - print(f"\n{domain} ({len(equations)} equations):") - for eq in equations: - prim = eq["primitive"].upper() - print(f" • {eq['name']} → {prim}") - - print("\n" + "=" * 70) - print(" KEY INSIGHTS") - print("=" * 70) - - print("\n1. Field primitive (6 equations, 31.6%):") - print(" - Molecular configuration space, potential energy surface") - print(" - Force field energy, pair distribution function") - print(" - Boltzmann distribution, DFT energy functional") - print(" - Core: energy landscapes, density fields, probability distributions") - - print("\n2. Shear primitive (5 equations, 26.3%):") - print(" - Chemical space distances (weighted and unweighted)") - print(" - Molecular force, molecular dynamics") - print(" - Arrhenius and Eyring rate equations") - print(" - Core: gradients, forces, rates, geometric deformations") - - print("\n3. Packet primitive (4 equations, 21.1%):") - print(" - Chemical descriptor vector, Coulomb matrix descriptor") - print(" - Structure-property map, local atomic density kernel") - print(" - Core: descriptors, encodings, similarity metrics, representations") - - print("\n4. Spectral primitive (4 equations, 21.1%):") - print(" - Quantum Hamiltonian eigenproblem") - print(" - Variational quantum energy optimization") - print(" - Bayesian optimization with Gaussian process") - print(" - Core: eigenproblems, basis optimization, variational methods") - - print("\n5. Cross-domain consistency:") - print(" - Chemistry: field (energy surfaces) + shear (forces/rates) + packet (descriptors)") - print(" - Physics: field (potential) + shear (dynamics) + spectral (quantum)") - print(" - Thermodynamics: field (Boltzmann) + shear (rates)") - print(" - Quantum chemistry: spectral (Hamiltonian) + field (DFT)") - - print("\n6. Canonical mapping confirmed:") - print(" - Field: energy landscapes, density fields, probability distributions") - print(" - Shear: gradients, forces, rates, geometric deformations") - print(" - Packet: descriptors, encodings, similarity metrics, representations") - print(" - Spectral: eigenproblems, basis optimization, variational methods") - - print("\n7. No gaps: Each primitive well-represented across scientific domains") - print(" - Field: thermodynamics, statistical mechanics, DFT") - print(" - Shear: dynamics, kinetics, geometry") - print(" - Packet: ML descriptors, similarity kernels") - print(" - Spectral: quantum mechanics, optimization") - - # Save mapping - output_file = RESEARCH_STACK / "4-Infrastructure/shim/scientific_equations_4primitive_mapping.json" - with open(output_file, 'w') as f: - json.dump({ - "primitives": PRIMITIVES, - "scientific_equations": SCIENTIFIC_EQUATIONS, - "primitive_distribution": {prim: len(eqs) for prim, eqs in primitive_groups.items()}, - "domain_distribution": {domain: len(eqs) for domain, eqs in domain_counts.items()}, - "insights": { - "field_core": "energy landscapes, density fields, probability distributions", - "shear_core": "gradients, forces, rates, geometric deformations", - "packet_core": "descriptors, encodings, similarity metrics, representations", - "spectral_core": "eigenproblems, basis optimization, variational methods", - "cross_domain_consistency": "Each primitive appears across multiple scientific domains", - "no_gaps": "Each primitive well-represented across chemistry, physics, thermodynamics, quantum chemistry" - } - }, f, indent=2) - - print(f"\n✓ Mapping saved to: {output_file}") - - -if __name__ == "__main__": - analyze_scientific_mapping() diff --git a/4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py b/4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py deleted file mode 100644 index d8cf408a..00000000 --- a/4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py +++ /dev/null @@ -1,444 +0,0 @@ -#!/usr/bin/env python3 -"""Seed an observation-backed stellar gas table from SDSS MaNGA DAPall. - -This intentionally avoids astropy/fitsio so the stack can run on a bare Python -environment. It implements only the bounded FITS binary-table parsing needed for -route receipts and small sample extraction. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import re -import struct -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from urllib.request import Request, urlopen - - -REPO = Path(__file__).resolve().parents[2] -ARTIFACT_DIR = REPO / "shared-data/artifacts/stellar_gas_observation" -DATA_DIR = REPO / "shared-data/data/stellar_gas_observation" -FITS_URL = ( - "https://data.sdss.org/sas/dr17/manga/spectro/analysis/" - "v3_1_1/3.1.0/dapall-v3_1_1-3.1.0.fits" -) -FITS_NAME = "dapall-v3_1_1-3.1.0.fits" -DESTINATION = "Gdrive:topological_storage/research-stack/stellar-gas-observation/seed-2026-05-09" - - -TFORM_RE = re.compile(r"^(?P\d*)(?P[A-Z])") -BYTE_SIZES = { - "L": 1, - "A": 1, - "B": 1, - "I": 2, - "J": 4, - "K": 8, - "E": 4, - "D": 8, -} - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def run(cmd: list[str]) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - - -def rclone_copyto(local: Path, remote: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local), remote, "--checksum"]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def download(url: str, target: Path, timeout: int) -> None: - target.parent.mkdir(parents=True, exist_ok=True) - part = target.with_suffix(target.suffix + ".part") - req = Request(url, headers={"User-Agent": "ResearchStack-DAPallSeed/0"}) - with urlopen(req, timeout=timeout) as response, part.open("wb") as out: - while True: - chunk = response.read(1024 * 1024) - if not chunk: - break - out.write(chunk) - part.replace(target) - - -def card_value(card: str): - if len(card) < 10 or card[8] != "=": - return None - raw = card[10:80].split("/", 1)[0].strip() - if raw.startswith("'"): - end = raw.rfind("'") - return raw[1:end].strip() if end > 0 else raw.strip("'").strip() - if raw in {"T", "F"}: - return raw == "T" - try: - return int(raw) - except ValueError: - try: - return float(raw.replace("D", "E")) - except ValueError: - return raw - - -def read_header(f) -> tuple[dict, int]: - cards: list[str] = [] - bytes_read = 0 - while True: - block = f.read(2880) - if not block: - raise EOFError("unexpected EOF while reading FITS header") - bytes_read += len(block) - for i in range(0, len(block), 80): - card = block[i : i + 80].decode("ascii", errors="replace") - cards.append(card) - if card.startswith("END"): - header: dict[str, object] = {} - for item in cards: - key = item[:8].strip() - if not key: - continue - value = card_value(item) - if value is not None: - header[key] = value - return header, bytes_read - - -def padded_size(size: int) -> int: - return int(math.ceil(size / 2880.0) * 2880) - - -def parse_tform(value: str) -> tuple[int, str, int]: - match = TFORM_RE.match(value.strip()) - if not match: - raise ValueError(f"unsupported TFORM {value!r}") - repeat = int(match.group("repeat") or "1") - code = match.group("code") - if code == "X": - # FITS bit arrays are counted in bits. - width = int(math.ceil(repeat / 8)) - else: - width = repeat * BYTE_SIZES.get(code, 0) - if width <= 0: - raise ValueError(f"unsupported TFORM {value!r}") - return repeat, code, width - - -def build_columns(header: dict) -> list[dict]: - cols = [] - offset = 0 - tfields = int(header.get("TFIELDS", 0)) - for idx in range(1, tfields + 1): - name = str(header.get(f"TTYPE{idx}", f"COL{idx}")).strip() - form = str(header.get(f"TFORM{idx}", "")).strip() - repeat, code, width = parse_tform(form) - cols.append( - { - "idx": idx, - "name": name, - "form": form, - "repeat": repeat, - "code": code, - "width": width, - "offset": offset, - } - ) - offset += width - return cols - - -def decode_value(raw: bytes, col: dict): - repeat = col["repeat"] - code = col["code"] - if code == "A": - return raw.decode("ascii", errors="replace").strip() - if code == "L": - vals = [bytes([b]).decode("ascii", errors="replace") == "T" for b in raw[:repeat]] - return vals[0] if repeat == 1 else vals - if code == "B": - vals = list(raw[:repeat]) - return vals[0] if repeat == 1 else vals - fmt = { - "I": ">h", - "J": ">i", - "K": ">q", - "E": ">f", - "D": ">d", - }.get(code) - if not fmt: - return None - size = struct.calcsize(fmt) - vals = [ - struct.unpack(fmt, raw[i * size : (i + 1) * size])[0] - for i in range(repeat) - ] - vals = [None if isinstance(v, float) and (math.isnan(v) or math.isinf(v)) else v for v in vals] - return vals[0] if repeat == 1 else vals - - -def interesting_columns(columns: list[dict]) -> list[dict]: - patterns = [ - "plateifu", - "mangaid", - "objra", - "objdec", - "nsa_z", - "z", - "emline", - "ha_", - "hb_", - "oiii", - "nii", - "sii", - "sigma", - "vel", - "snr", - "daptype", - ] - selected = [] - for col in columns: - lname = col["name"].lower() - if any(pattern in lname for pattern in patterns): - selected.append(col) - # Keep identifiers even if a future naming change misses them. - selected = selected[:80] - return selected - - -def classify_column(name: str) -> str: - lname = name.lower() - if lname in {"plateifu", "mangaid", "daptype"}: - return "identifier" - if "emline" in lname or any(line in lname for line in ["ha_", "hb_", "oiii", "nii", "sii"]): - return "gas_emission_line_or_fit" - if "sigma" in lname or "vel" in lname: - return "shock_or_velocity_proxy" - if lname in {"objra", "objdec", "nsa_z", "z"} or lname.endswith("_z"): - return "position_or_redshift_context" - if "snr" in lname: - return "quality_or_uncertainty_proxy" - return "context" - - -def scan_fits(path: Path, sample_rows: int) -> dict: - hdus = [] - samples = [] - with path.open("rb") as f: - hdu_index = 0 - while True: - start = f.tell() - try: - header, header_bytes = read_header(f) - except EOFError: - break - data_start = f.tell() - xtension = str(header.get("XTENSION", "PRIMARY")) - bitpix = int(header.get("BITPIX", 8)) - naxis = int(header.get("NAXIS", 0)) - if xtension == "BINTABLE": - row_len = int(header["NAXIS1"]) - row_count = int(header["NAXIS2"]) - pcount = int(header.get("PCOUNT", 0)) - data_size = row_len * row_count + pcount - columns = build_columns(header) - selected = interesting_columns(columns) - hdu_info = { - "hdu_index": hdu_index, - "name": header.get("EXTNAME", f"HDU{hdu_index}"), - "xtension": xtension, - "row_count": row_count, - "row_len": row_len, - "column_count": len(columns), - "selected_column_count": len(selected), - "selected_columns": [ - { - "name": col["name"], - "form": col["form"], - "semantic_role": classify_column(col["name"]), - } - for col in selected - ], - } - hdus.append(hdu_info) - if selected and len(samples) < sample_rows: - rows_to_read = min(sample_rows - len(samples), row_count) - for row_idx in range(rows_to_read): - f.seek(data_start + row_idx * row_len) - row = f.read(row_len) - record = { - "hdu_index": hdu_index, - "row_index": row_idx, - "source_catalog": "SDSS_DR17_MaNGA_DAPall", - "model_family": "stellar_gas_observation_seed", - "gate_decision": "ADMIT_OBSERVATION_SAMPLE", - "fields": {}, - "semantic_roles": {}, - } - for col in selected: - raw = row[col["offset"] : col["offset"] + col["width"]] - value = decode_value(raw, col) - # Keep JSON compact for large vector columns. - if isinstance(value, list) and len(value) > 8: - value = { - "length": len(value), - "head": value[:4], - "tail": value[-4:], - } - record["fields"][col["name"]] = value - record["semantic_roles"][col["name"]] = classify_column(col["name"]) - samples.append(record) - f.seek(data_start + padded_size(data_size)) - else: - # Generic IMAGE/PRIMARY skip. - if naxis == 0: - data_size = 0 - else: - pixels = 1 - for axis in range(1, naxis + 1): - pixels *= int(header.get(f"NAXIS{axis}", 0)) - data_size = abs(bitpix) // 8 * pixels - hdus.append( - { - "hdu_index": hdu_index, - "name": header.get("EXTNAME", "PRIMARY" if hdu_index == 0 else f"HDU{hdu_index}"), - "xtension": xtension, - "naxis": naxis, - } - ) - f.seek(data_start + padded_size(data_size)) - if f.tell() <= start: - raise RuntimeError("FITS scanner did not advance") - hdu_index += 1 - return {"hdus": hdus, "samples": samples} - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--cache", type=Path, default=ARTIFACT_DIR / FITS_NAME) - parser.add_argument("--destination", default=DESTINATION) - parser.add_argument("--timeout", type=int, default=180) - parser.add_argument("--sample-rows", type=int, default=5) - parser.add_argument("--skip-download", action="store_true") - parser.add_argument("--skip-upload-raw", action="store_true") - args = parser.parse_args() - - DATA_DIR.mkdir(parents=True, exist_ok=True) - ARTIFACT_DIR.mkdir(parents=True, exist_ok=True) - - downloaded = False - if not args.cache.exists() and not args.skip_download: - download(FITS_URL, args.cache, args.timeout) - downloaded = True - if not args.cache.exists(): - raise FileNotFoundError(args.cache) - - fits_sha = sha256_file(args.cache) - fits_size = args.cache.stat().st_size - scan = scan_fits(args.cache, args.sample_rows) - - sample_path = DATA_DIR / "sdss_manga_dapall_observation_sample.json" - sample_payload = { - "schema": "sdss_manga_dapall_observation_sample_v0", - "created": now_iso(), - "claim_boundary": "Bounded sample extracted from SDSS DR17 MaNGA DAPall FITS. This is not the full observation database; it is the first observation-backed schema seed.", - "source_url": FITS_URL, - "source_file_sha256": fits_sha, - "source_file_bytes": fits_size, - "sample_rows": scan["samples"], - } - sample_path.write_text(json.dumps(sample_payload, indent=2) + "\n") - - column_path = DATA_DIR / "sdss_manga_dapall_column_map.json" - column_payload = { - "schema": "sdss_manga_dapall_column_map_v0", - "created": now_iso(), - "source_url": FITS_URL, - "source_file_sha256": fits_sha, - "source_file_bytes": fits_size, - "hdu_count": len(scan["hdus"]), - "hdus": scan["hdus"], - } - column_path.write_text(json.dumps(column_payload, indent=2) + "\n") - - raw_remote = f"{args.destination.rstrip('/')}/raw/{FITS_NAME}" - sample_remote = f"{args.destination.rstrip('/')}/derived/{sample_path.name}" - column_remote = f"{args.destination.rstrip('/')}/derived/{column_path.name}" - - raw_upload = {"drive_path": raw_remote, "ok": False, "message": "skipped"} - if not args.skip_upload_raw: - ok, msg = rclone_copyto(args.cache, raw_remote) - raw_upload = {"drive_path": raw_remote, "ok": ok, "message": msg} - - sample_ok, sample_msg = rclone_copyto(sample_path, sample_remote) - column_ok, column_msg = rclone_copyto(column_path, column_remote) - - receipt_path = DATA_DIR / f"sdss_manga_dapall_observation_seed_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - receipt = { - "schema": "sdss_manga_dapall_observation_seed_receipt_v0", - "created": now_iso(), - "claim_boundary": "Promotes one public SDSS MaNGA catalog from route-only to observation-backed seed. Full FITS is cached under shared-data/artifacts and copied to Drive; tracked repo files are column map, sample, and receipt JSON.", - "source_url": FITS_URL, - "local_cache": str(args.cache.relative_to(REPO)) if args.cache.is_relative_to(REPO) else str(args.cache), - "downloaded_this_run": downloaded, - "fits_sha256": fits_sha, - "fits_bytes": fits_size, - "sample_file": str(sample_path.relative_to(REPO)), - "column_map_file": str(column_path.relative_to(REPO)), - "gdrive_uploads": { - "raw_fits": raw_upload, - "sample": {"drive_path": sample_remote, "ok": sample_ok, "message": sample_msg}, - "column_map": {"drive_path": column_remote, "ok": column_ok, "message": column_msg}, - }, - "parse_summary": { - "hdu_count": len(scan["hdus"]), - "sample_count": len(scan["samples"]), - "bintable_hdus": [h for h in scan["hdus"] if h.get("xtension") == "BINTABLE"], - }, - "model_refinement": { - "new_boundary": "route_only_to_observation_sample", - "observable_lanes": [ - "emission-line gas diagnostics", - "velocity or velocity-dispersion shock proxies", - "redshift and sky-position context", - "quality or uncertainty proxies", - ], - "next_gate": "fit selected gas/shock columns against local shock eigen axes", - }, - "decision": "ADMIT_OBSERVATION_BACKED_STELLAR_GAS_SEED" - if raw_upload["ok"] and sample_ok and column_ok and scan["samples"] - else "HOLD_PARTIAL_OBSERVATION_SEED", - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - receipt_remote = f"{args.destination.rstrip('/')}/receipts/{receipt_path.name}" - receipt_ok, receipt_msg = rclone_copyto(receipt_path, receipt_remote) - receipt["gdrive_uploads"]["receipt"] = { - "drive_path": receipt_remote, - "ok": receipt_ok, - "message": receipt_msg, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - if receipt_ok: - rclone_copyto(receipt_path, receipt_remote) - print(json.dumps(receipt, indent=2)) - return 0 if receipt["decision"].startswith("ADMIT") else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/semantic_compression_theoretical_limits_prior.py b/4-Infrastructure/shim/semantic_compression_theoretical_limits_prior.py deleted file mode 100644 index 5732104e..00000000 --- a/4-Infrastructure/shim/semantic_compression_theoretical_limits_prior.py +++ /dev/null @@ -1,368 +0,0 @@ -#!/usr/bin/env python3 -"""Semantic compression theoretical-limits prior. - -This records the user's pasted Consensus thread as a route/evaluator prior. -It is not a semantic-compression proof and it does not relax byte-exact -promotion. The practical extraction is a set of limit coordinates that can -shape DD pruning, receipts, and evaluator fields. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -DEFAULT_RECEIPT = Path( - "4-Infrastructure/shim/semantic_compression_theoretical_limits_prior_receipt.json" -) -DEFAULT_CURRICULUM = Path( - "4-Infrastructure/shim/semantic_compression_theoretical_limits_prior_curriculum.jsonl" -) - - -CONSENSUS_SOURCE_SUMMARY = { - "thread_title": "Semantic Compression Theoretical Limits", - "prompt": "unified math models of theoretical limits in semantic compression", - "mode": "Deep", - "search_count": 21, - "citation_graph_uses": 1, - "retrieved_count_reported": 2_777_471, - "eligible_count_reported": 1_500, - "included_count_reported": 50, - "consensus_meter": { - "question": "Are there unified mathematical models that define the theoretical limits of semantic compression?", - "n": 9, - "yes_percent": 100, - }, - "claim_boundary": ( - "Consensus thread is a source-bundle prior. It supplies theoretical-limit " - "coordinates and citations, not local compression evidence." - ), -} - - -LIMIT_FAMILIES = [ - { - "id": "semantic_information_bounds", - "source_examples": [ - "Information-theoretic limits on compression of semantic information", - "A Mathematical Theory of Semantic Communication", - "Semantic Information Theory and Applications", - ], - "useful_shape": ( - "model a semantic source with conditional independence, Bayesian or " - "probabilistic structure, and derive lower/upper rate bounds" - ), - "dd_use": "bound semantic-sidecar claims and require explicit semantic source model IDs", - "receipt_fields": [ - "semantic_source_model_id", - "conditional_independence_receipt", - "semantic_entropy_bound_bits", - "side_information_policy", - ], - "failure_mode": "semantic bound claimed without an explicit source model or local byte receipt", - }, - { - "id": "semantic_rate_distortion", - "source_examples": [ - "Semantic Rate-Distortion Theory with Applications", - "A Rate-Distortion Framework for Characterizing Semantic Information", - "Semantic Compression with Side Information: A Rate-Distortion Perspective", - "Fundamental Limitation of Semantic Communications: Neural Estimation for Rate-Distortion", - ], - "useful_shape": ( - "define a semantic distortion variable, estimate or bound the semantic " - "rate-distortion function, and compare rate against task outcome" - ), - "dd_use": "treat semantic distortion as a diagnostic constraint, never as byte promotion", - "receipt_fields": [ - "semantic_distortion_metric_id", - "rate_distortion_estimator_id", - "side_information_bits", - "task_success_metric", - ], - "failure_mode": "semantic distortion score improves while decoded bytes do not match", - }, - { - "id": "rate_distortion_perception_bottleneck", - "source_examples": [ - "Rate-Distortion-Perception Trade-Off in Information Theory, Generative Models, and Intelligent Communications", - "Semantic Communication via Rate Distortion Perception Bottleneck", - "Rate-Distortion-Perception Theory for Semantic Communication", - ], - "useful_shape": ( - "perceptual constraints may require extra rate; bottleneck objective " - "balances task/perception/bit distortion" - ), - "dd_use": "charge perceptual or semantic witness bytes explicitly in the route budget", - "receipt_fields": [ - "perception_constraint_id", - "bottleneck_lambda", - "extra_rate_for_perception_bits", - "diagnostic_quality_score", - ], - "failure_mode": "perception quality hides sidecar or witness debt", - }, - { - "id": "information_bottleneck_and_ordered_latents", - "source_examples": [ - "Efficient compression in color naming and its evolution", - "Information-Ordered Bottlenecks for Adaptive Semantic Compression", - "Ordered embeddings and intrinsic dimensionalities with information-ordered bottlenecks", - "Adversarial Information Bottleneck", - ], - "useful_shape": ( - "compress observations while preserving task-relevant variables; order " - "latent coordinates by marginal information or robustness" - ), - "dd_use": "rank route features and prune low-information sidecar lanes", - "receipt_fields": [ - "bottleneck_variable_id", - "relevance_variable_id", - "marginal_information_gain", - "robustness_receipt_id", - ], - "failure_mode": "latent relevance score treated as an exact rehydration witness", - }, - { - "id": "geometric_algebraic_error_subspaces", - "source_examples": [ - "Geometry is All You Need: A Unified Taxonomy of Matrix and Tensor Factorization for Compression of Generative Language Models", - "A General Error-Theoretical Analysis Framework for Constructing Compression Strategies", - "Bridging Information-Theoretic and Geometric Compression in Language Models", - ], - "useful_shape": ( - "parameter/data compression can be expressed through intrinsic dimension, " - "factorization geometry, or error subspace shape" - ), - "dd_use": "use geometry as route-feature coordinates and error-budget priors", - "receipt_fields": [ - "intrinsic_dimension_estimate", - "factorization_family_id", - "error_subspace_shape_id", - "layerwise_budget_vector", - ], - "failure_mode": "geometric compactness confused with source-byte compression", - }, - { - "id": "llm_understanding_compression_link", - "source_examples": [ - "Lossless data compression by large models", - "Semantic Compression with Large Language Models", - "Language Modeling Is Compression", - "Fundamental Limits of Prompt Compression: A Rate-Distortion Framework for Black-Box Language Models", - ], - "useful_shape": ( - "large learned models can improve compression by prediction/understanding, " - "but introduce hallucination, context compression, and compute costs" - ), - "dd_use": "allow LLM routes as proposal/predictor engines with strict byte rehydration checks", - "receipt_fields": [ - "model_id", - "prompt_compression_ratio", - "hallucination_guard_id", - "context_rehydration_hash", - "compute_budget_ms", - ], - "failure_mode": "LLM reconstruction is semantically plausible but byte-invalid", - }, - { - "id": "synonymity_and_semantic_arithmetic_coding", - "source_examples": [ - "Semantic Arithmetic Coding Using Synonymous Mappings", - "The Semantic Relations in LLMs: An Information-theoretic Compression Approach", - "Preserving quality of information by using semantic relationships", - ], - "useful_shape": ( - "synonym or semantic-equivalence classes can reduce semantic code length " - "when the equivalence relation is explicit" - ), - "dd_use": "map synonym classes to tokenbook proposals and residualize exact lexical choice", - "receipt_fields": [ - "semantic_equivalence_class_id", - "synonym_map_hash", - "lexical_residual_bytes", - "equivalence_ambiguity_count", - ], - "failure_mode": "semantic equivalence discards lexical bytes without residual repair", - }, - { - "id": "resource_constrained_semantic_limits", - "source_examples": [ - "Compression Ratio Allocation for Probabilistic Semantic Communication With RSMA", - "A Joint Communication and Computation Design for Probabilistic Semantic Communications", - "Semantic Rate Distortion and Posterior Design: Compute Constraints, Multimodality, and Strategic Inference", - "Semantic Communication with Side Information: A Rate-Distortion Perspective", - ], - "useful_shape": ( - "rate, compute, memory, side information, and multi-user allocation all " - "change the achievable semantic limit" - ), - "dd_use": "make runtime, memory, sidecar, and witness budgets first-class route constraints", - "receipt_fields": [ - "byte_budget", - "compute_budget_ms", - "memory_budget_bytes", - "side_information_bytes", - "allocation_policy_id", - ], - "failure_mode": "semantic route appears efficient only by ignoring compute or side-information cost", - }, - { - "id": "ambiguity_multimodality_and_generalization_gap", - "source_examples": [ - "Compression Beyond Pixels: Semantic Compression with Multimodal Foundation Models", - "Can Image Compression Rely on CLIP?", - "Semantic Rate Distortion and Posterior Design: Compute Constraints, Multimodality, and Strategic Inference", - "On the Fundamental Limits of LLMs at Scale", - ], - "useful_shape": ( - "ambiguity, polysemy, multimodality, hallucination, and retrieval fragility " - "limit transfer from theoretical semantic rates to real systems" - ), - "dd_use": "force ambiguity packets and multimodal claim boundaries before promotion", - "receipt_fields": [ - "ambiguity_class_id", - "polysemy_count", - "modality_set", - "retrieval_fragility_score", - "generalization_scope", - ], - "failure_mode": "semantic limit validated on narrow data is applied to a broader corpus slice", - }, -] - - -THEORETICAL_RECEIPT_RULES = { - "ratio_schema": "source_bytes / compressed_total_bytes", - "semantic_limit_status": "diagnostic unless byte_rehydration_hash matches", - "promotion_rule": ( - "promote iff semantic-limit machinery only proposes, bounds, or budgets " - "routes; exact residual lanes restore source bytes; decoded hash matches; " - "and measured total bytes beat incumbent under explicit ratio_schema" - ), - "failure_rule": ( - "semantic entropy, rate-distortion, bottleneck, perceptual quality, LLM " - "understanding, or synonymity without byte-exact restoration is diagnostic only" - ), -} - - -def stable_hash(obj: Any) -> str: - payload = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - source_hash = stable_hash(CONSENSUS_SOURCE_SUMMARY) - receipt: dict[str, Any] = { - "schema": "semantic_compression_theoretical_limits_prior_v1", - "generated_at": "2026-05-08T00:00:00+00:00", - "source_summary": CONSENSUS_SOURCE_SUMMARY, - "source_summary_hash": source_hash, - "claim_boundary": ( - "Theoretical semantic-compression models define proposal and budget " - "surfaces. They do not establish local byte compression unless the " - "route has a local encode/decode/hash/byte-count receipt." - ), - "limit_families": LIMIT_FAMILIES, - "theoretical_receipt_rules": THEORETICAL_RECEIPT_RULES, - "dd_state_extension": [ - "semantic_source_model_id", - "semantic_entropy_bound_bits", - "semantic_distortion_metric_id", - "rate_distortion_estimator_id", - "bottleneck_variable_id", - "marginal_information_gain", - "intrinsic_dimension_estimate", - "error_subspace_shape_id", - "semantic_equivalence_class_id", - "lexical_residual_bytes", - "side_information_bytes", - "compute_budget_ms", - "ambiguity_class_id", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "choose_semantic_source_model", - "estimate_semantic_entropy_bound", - "estimate_semantic_rate_distortion", - "apply_information_bottleneck_rank", - "emit_geometric_error_subspace", - "propose_llm_predictor_route", - "emit_synonym_class_tokenbook", - "charge_side_information_budget", - "record_ambiguity_packet", - "emit_exact_residual_lane", - "verify_byte_rehydration_hash", - "reject_semantic_only_promotion", - ], - } - receipt["receipt_hash"] = stable_hash(receipt) - return receipt - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = ( - "You are a semantic-compression limit router. Treat theory as a " - "budget/proposal surface and byte-exact receipts as promotion authority." - ) - records: list[dict[str, Any]] = [] - for family in receipt["limit_families"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "route_semantic_compression_limit_family", - "family_id": family["id"], - "useful_shape": family["useful_shape"], - "dd_use": family["dd_use"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "selected": True, - "receipt_fields": family["receipt_fields"], - "failure_mode": family["failure_mode"], - "claim_boundary": "semantic-limit-prior-only", - "promotion_authority": "local encode/decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--curriculum", type=Path, default=DEFAULT_CURRICULUM) - args = parser.parse_args() - - receipt = build_receipt() - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/semantic_topology_compression_regimes.py b/4-Infrastructure/shim/semantic_topology_compression_regimes.py deleted file mode 100644 index 2d3ce3c8..00000000 --- a/4-Infrastructure/shim/semantic_topology_compression_regimes.py +++ /dev/null @@ -1,113 +0,0 @@ -#!/usr/bin/env python3 -"""Semantic topology compression regimes for metaprobe/LLM tuning. - -The regimes are intentionally blunt: - -* beautiful: stable topological folding across compatible semantic basins -* ugly: asymmetric pruning that preserves statistical structure but drops context -* horrible: manifold tearing / singularity where bindings become incompatible - -The output is a training prior and receipt taxonomy, not a proof of semantic -geometry. Lean can later formalize the predicates and destruction rules. -""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path -from typing import Any - - -REGIMES = [ - { - "id": "beautiful_topological_folding", - "label": "beautiful", - "condition": "shared_invariant_high and torsion_low and round_trip_loss_low", - "operation": "fold compatible semantic basins into a dense shared coordinate", - "payload": ["shared_invariant", "fold_map", "torsion", "round_trip_loss", "receipt_hash"], - "failure_mode": "false_friend_fold", - "lean_predicate_hint": "StableFold(a,b) := invariant_overlap a b >= tau ∧ torsion a b <= eps", - }, - { - "id": "ugly_asymmetric_pruning", - "label": "ugly", - "condition": "statistical_structure_high and indexical_structure_discarded and quality_delta_bounded", - "operation": "shear off low-information or indexical volume while preserving routing basin", - "payload": ["retained_terms", "dropped_context", "distortion", "quality_delta", "source_boundary"], - "failure_mode": "nuance_collapse", - "lean_predicate_hint": "AdmissiblePrune(x,y) := preserves_basin x y ∧ distortion x y <= budget", - }, - { - "id": "horrible_manifold_tearing", - "label": "horrible", - "condition": "torsion_high or contradiction_high or round_trip_loss_unbounded", - "operation": "mark incompatible bindings as torn; isolate detached semantic mass instead of merging", - "payload": ["contradiction_witness", "tear_boundary", "detached_mass_id", "origin_block", "repair_rule"], - "failure_mode": "semantic_singularity", - "lean_predicate_hint": "TornBinding(a,b) := torsion a b > max_torsion ∨ contradiction a b", - }, -] - - -def chat_record(system: str, prompt: dict[str, Any], answer: dict[str, Any]) -> dict[str, Any]: - return { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a semantic topology compression router. Classify regimes and emit receipt boundaries." - records: list[dict[str, Any]] = [] - for regime in receipt["regimes"]: - records.append( - chat_record( - system, - { - "task": "classify_semantic_compression_regime", - "regime": regime["label"], - "condition": regime["condition"], - "operation": regime["operation"], - "instruction": "Return how this regime should route a compressed semantic binding.", - }, - { - "selected": True, - "regime_id": regime["id"], - "operation": regime["operation"], - "claim_boundary": "semantic-topology-prior-only", - "receipt_payload": regime["payload"], - "failure_mode": regime["failure_mode"], - "lean_predicate_hint": regime["lean_predicate_hint"], - }, - ) - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--receipt", type=Path, default=Path("4-Infrastructure/shim/semantic_topology_compression_regimes_receipt.json")) - parser.add_argument("--curriculum", type=Path, default=Path("4-Infrastructure/shim/semantic_topology_compression_regimes_curriculum.jsonl")) - args = parser.parse_args() - - receipt = { - "schema": "semantic_topology_compression_regimes_v1", - "claim_boundary": "Compression regimes classify folding/pruning/tearing decisions; Lean or metaprobe receipts are required for local claims.", - "regimes": REGIMES, - "lawful": True, - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/shadow_layer_opportunity_map.py b/4-Infrastructure/shim/shadow_layer_opportunity_map.py deleted file mode 100644 index b8c60913..00000000 --- a/4-Infrastructure/shim/shadow_layer_opportunity_map.py +++ /dev/null @@ -1,576 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed map of domains suited for refined shadow-layer encoding. - -Shadow encoding applies when a visible low-dimensional object is best treated -as a projection of a richer typed state. The visible layer can be compact, but -only if the hidden state, adapter, residual, closure policy, and algebraic -accumulator path are receipted. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "shadow_layer_opportunities" -MAP = OUT_DIR / "shadow_layer_opportunity_map.json" -RECEIPT = OUT_DIR / "shadow_layer_opportunity_map_receipt.json" -SUMMARY = OUT_DIR / "shadow_layer_opportunity_map.md" - -SOURCE_REFS = [ - REPO / "4-Infrastructure" / "shim" / "mmff_rigid_body_geometry_probe.py", - REPO / "shared-data" / "data" / "mmff_rigid_body_geometry" / "mmff_rigid_body_geometry_receipt.json", - REPO / "6-Documentation" / "docs" / "specs" / "FORWARD_FOUNDATION_EQUATION_COMPILER.md", - REPO / "6-Documentation" / "docs" / "specs" / "GCCL_ENCODING_CONTRACT.md", - REPO / "6-Documentation" / "docs" / "specs" / "GENSIS_COMPILER_SPEC.md", - REPO / "6-Documentation" / "docs" / "specs" / "PROJECTABLE_GEOMETRY_COMPRESSOR_SPEC.md", - REPO / "6-Documentation" / "articles" / "meme-math-that-pays-rent" / "article.md", - REPO / "0-Core-Formalism" / "otom" / "tools" / "lean" / "Semantics" / "Semantics" / "LochMonsterFilter.lean", - REPO / "shared-data" / "data" / "bibliographic_event_horizon" / "bibliographic_event_horizon_receipt.json", - REPO / "shared-data" / "data" / "asymptotic_closure_horizon" / "asymptotic_closure_horizon_receipt.json", -] - -EXTERNAL_CITATIONS = [ - { - "id": "immaterialscience_bibliographic_event_horizon", - "title": "The Bibliographic Event Horizon: A Study on the Gravitational Pull of [1]", - "url": "https://www.immaterialscience.org/2026/citations", - "role": "bibliographic_shadow_prompt", - "status": "satirical_source_used_as_real_diagnostic_prompt", - }, - { - "id": "reddit_bibliographic_event_horizon_discussion", - "title": "Reddit discussion wrapper for bibliographic event horizon prompt", - "url": "https://www.reddit.com/r/ImmaterialScience/comments/1t7plf9/the_bibliographic_event_horizon_a_study_on_the/", - "role": "discussion_pointer", - "status": "metadata_only", - }, - { - "id": "charmm_mmff_docs", - "title": "CHARMM MMFF documentation", - "url": "https://www.charmm-gui.org/charmmdoc/mmff.html", - "role": "molecular_shadow_reference", - "status": "external_reference", - }, - { - "id": "openbabel_mmff94_docs", - "title": "Open Babel MMFF94 force field documentation", - "url": "https://openbabel.org/docs/Forcefields/mmff94.html", - "role": "molecular_shadow_reference", - "status": "external_reference", - }, - { - "id": "rdkit_mmff_implementation_paper", - "title": "MMFF implementation validation reference in RDKit ecosystem", - "url": "https://link.springer.com/article/10.1186/s13321-014-0037-3", - "role": "implementation_reference", - "status": "external_reference", - }, - { - "id": "user_supplied_asymptote_meme", - "title": "Asymptote meme source prompt", - "role": "asymptotic_shadow_prompt", - "status": "user_supplied_image_prompt", - }, -] - -TREE_FIDDY_CAGE_BOUNDARY_BYTES = 350 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def shadow_route( - *, - rank: int, - route_id: str, - domain: str, - visible_shadow: str, - hidden_state: str, - chain: list[str], - residual_handles: list[str], - reusable_kernels: list[str], - fixture_targets: list[str], - hold_surfaces: list[str], - next_probe: str, - estimated_yield: str, - decision: str = "SHADOW_ROUTE_READY", - archive_mode: str = "TREE_FIDDY_CANDIDATE", -) -> dict[str, Any]: - item = { - "rank": rank, - "route_id": route_id, - "domain": domain, - "visible_shadow": visible_shadow, - "hidden_state": hidden_state, - "refined_shadow_chain": chain, - "accumulator": { - "kind": "O-AMMR", - "meaning": "ordered algebraic Merkle mountain range over typed projection nodes", - "plain_merkle_role": "content hash field only; not the whole trust object", - }, - "representative_carrier": { - "shape": "16D signed envelope -> 12D source/residual plane -> 4D primitive keel -> genus-3 residual boat -> 0D closure", - "closure_budget_twelfths": { - "visible_4d": 4, - "shadow_3d": 3, - "closure_0d": 1, - "lawbound": 4, - "unresolved": 0, - "total": 12, - }, - "residual_handles": residual_handles, - }, - "tree_fiddy_guard": { - "cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "archive_mode": archive_mode, - "archive_rule": "if committed_or_shielded then Q_active(i)=0", - "promotion_rule": "archive route only when control+receipt+residual budget is bounded by cage boundary", - "failure_lane": "HOLD_ACTIVE_SHADOW_ROUTE", - }, - "reusable_kernels": reusable_kernels, - "fixture_targets": fixture_targets, - "hold_surfaces": hold_surfaces, - "next_probe": next_probe, - "estimated_yield": estimated_yield, - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_map() -> dict[str, Any]: - default_chain = [ - "L16_signed_envelope", - "L12_source_residual_plane", - "L4_primitive_keel", - "Rg3_residual_boat", - "L3_or_L2_visible_shadow", - "L0_closure", - "O_AMMR_root", - ] - default_handles = ["packet_local", "shear_torsion", "spectral_field"] - routes = [ - shadow_route( - rank=1, - route_id="molecular_mmff_rigid_bodies", - domain="molecular mechanics and MMFF-style geometry", - visible_shadow="3D atom coordinates and local fragment poses", - hidden_state="typed chemistry body state: atom identity, topology, aromaticity, charge, force-field slots, residual strain", - chain=[ - "L16_body_state", - "L12_chemistry_residual_plane", - "L8_mmff_adapter_state", - "L4_geometry_primitive", - "Rg3_strain_residual_boat", - "L3_coordinate_shadow", - "L0_replay_closure", - "O_AMMR_root", - ], - residual_handles=["coordinate_packet", "torsion_shear", "forcefield_spectral_slot"], - reusable_kernels=["RIGID_BODY_POSE", "HINGED_RIGID_BODY", "TORSION_OPCODE", "MN_BOND_DEVIATION"], - fixture_targets=["ring templates", "rotor groups", "rigid triads", "fragment pose replay"], - hold_surfaces=["atom typing", "aromaticity", "parameter tables", "charges", "nonbonded interactions", "energy minimization"], - next_probe="mmff_rigid_body_geometry_probe.py", - estimated_yield="very_high", - ), - shadow_route( - rank=2, - route_id="protein_secondary_structure", - domain="protein geometry and folding surfaces", - visible_shadow="backbone coordinates, alpha helices, beta sheets, contact maps", - hidden_state="sequence, residue chemistry, torsion state, hydrogen-bond graph, solvent/exposure lanes", - chain=default_chain, - residual_handles=default_handles, - reusable_kernels=["RIGID_BODY_POSE", "HINGED_CHAIN", "CONTACT_MAP_SHADOW", "TORSION_OPCODE"], - fixture_targets=["ideal helix template", "beta-strand template", "Ramachandran torsion bins", "contact-map replay"], - hold_surfaces=["force field validity", "solvent model", "folding dynamics", "experimental structure uncertainty"], - next_probe="protein_shadow_geometry_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=3, - route_id="crystal_lattice_basis", - domain="crystallography and solid-state structures", - visible_shadow="unit-cell coordinates and lattice basis", - hidden_state="space group, motif, Wyckoff positions, occupancy, defects, temperature factors", - chain=[ - "L16_material_state", - "L12_symmetry_residual_plane", - "L8_symmetry_adapter", - "L4_lattice_primitive", - "Rg3_defect_residual_boat", - "L3_unit_cell_shadow", - "L0_orbit_closure", - "O_AMMR_root", - ], - residual_handles=["motif_packet", "symmetry_shear", "defect_spectral_field"], - reusable_kernels=["LATTICE_BASIS", "SYMMETRY_ORBIT", "MOTIF_REPLAY", "DEFECT_RESIDUAL"], - fixture_targets=["NaCl cell", "graphite/diamond motif", "space-group orbit expansion", "defect residual lane"], - hold_surfaces=["disorder", "partial occupancy", "thermal ellipsoids", "DFT/experimental provenance"], - next_probe="crystal_lattice_shadow_probe.py", - estimated_yield="very_high", - ), - shadow_route( - rank=4, - route_id="cad_mechanical_assemblies", - domain="CAD and mechanical assemblies", - visible_shadow="3D part mesh, pose graph, constraints", - hidden_state="parametric sketch, joints, tolerances, material, manufacturing operations, load paths", - chain=[ - "L16_design_intent", - "L12_feature_residual_plane", - "L8_feature_adapter", - "L4_joint_primitive", - "Rg3_tolerance_residual_boat", - "L3_mesh_shadow", - "L0_assembly_closure", - "O_AMMR_root", - ], - residual_handles=["feature_packet", "joint_shear_torsion", "loadpath_spectral_field"], - reusable_kernels=["RIGID_BODY_POSE", "JOINT_CONSTRAINT", "SYMMETRY_REPEAT", "MESH_RESIDUAL"], - fixture_targets=["bolted plate", "hinge assembly", "patterned holes", "extrude/revolve replay"], - hold_surfaces=["FEA validity", "manufacturing tolerance", "contact/friction", "load certification"], - next_probe="cad_assembly_shadow_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=5, - route_id="seismic_interior_witness", - domain="geophysics and inaccessible interiors", - visible_shadow="boundary wave arrivals, travel-time residuals, mode signatures", - hidden_state="opaque interior material state, phase regions, anisotropy, temperature/pressure lanes", - chain=[ - "L16_interior_state", - "L12_wave_residual_plane", - "L8_wave_adapter", - "L4_boundary_witness", - "Rg3_tomography_residual_boat", - "L1_time_series_shadow", - "L0_witness_closure", - "O_AMMR_root", - ], - residual_handles=["arrival_packet", "anisotropy_shear", "attenuation_spectral_field"], - reusable_kernels=["BOUNDARY_WITNESS", "MN_IMPEDANCE_CONTRAST", "RESIDUAL_TOMOGRAPHY", "UNDERVERSE_LANE"], - fixture_targets=["two-layer travel-time fixture", "S-wave missing lane", "impedance reflection", "tomography residual"], - hold_surfaces=["unique interior decode", "material phase overclaim", "measurement noise", "model nonuniqueness"], - next_probe="seismic_shadow_witness_probe.py", - estimated_yield="medium_high", - ), - shadow_route( - rank=6, - route_id="medical_imaging_anatomy", - domain="medical imaging geometry", - visible_shadow="2D/3D scan slices, segmentation masks, landmark coordinates", - hidden_state="anatomy state, tissue class, acquisition protocol, orientation, uncertainty, diagnosis boundary", - chain=default_chain, - residual_handles=default_handles, - reusable_kernels=["SLICE_STACK", "SEGMENTATION_MASK", "RIGID_REGISTRATION", "RESIDUAL_UNCERTAINTY"], - fixture_targets=["phantom object slices", "rigid registration", "mask run-length replay", "landmark pose replay"], - hold_surfaces=["diagnosis", "clinical validity", "scanner artifacts", "privacy/provenance"], - next_probe="medical_image_shadow_probe.py", - estimated_yield="medium_high", - decision="SHADOW_ROUTE_HOLD_FIRST", - archive_mode="TREE_FIDDY_BLOCKED_CLINICAL_HOLD", - ), - shadow_route( - rank=7, - route_id="language_parse_semantics", - domain="language syntax and semantic compression", - visible_shadow="token stream, parse tree, formatted text", - hidden_state="syntax, entity graph, discourse state, source provenance, ambiguity lanes", - chain=[ - "L16_discourse_state", - "L12_text_residual_plane", - "L8_semantic_adapter", - "L4_parse_primitive", - "Rg3_ambiguity_residual_boat", - "L1_token_shadow", - "L0_byte_replay_closure", - "O_AMMR_root", - ], - residual_handles=["token_packet", "syntax_shear", "semantic_spectral_field"], - reusable_kernels=["GRAMMAR_TEMPLATE", "ENTITY_REFERENCE", "MORPHOLOGY_OPCODE", "RESIDUAL_TEXT"], - fixture_targets=["inflection tables", "template-heavy wiki text", "citation template parse", "entity-link replay"], - hold_surfaces=["meaning equivalence", "translation claims", "ambiguous grammar", "human intent"], - next_probe="language_shadow_parse_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=8, - route_id="bibliographic_event_horizon", - domain="bibliography and citation-provenance graphs", - visible_shadow="citation number, bibliography entry, theorem/source label", - hidden_state="source graph, dependency graph, claim fanout, quote coverage, receipt thrust, residual obligations", - chain=[ - "L16_source_ecology", - "L12_claim_dependency_residual_plane", - "L8_bibliography_adapter", - "L4_citation_gravity_primitive", - "Rg3_obligation_residual_boat", - "L1_reference_label_shadow", - "L0_forward_receipt_closure", - "O_AMMR_root", - ], - residual_handles=["quote_packet", "dependency_shear", "claim_spectral_field"], - reusable_kernels=["CITATION_GRAVITY", "FORWARD_RECEIPT_THRUST", "DEPENDENCY_O_AMMR", "HOLD_LABEL_AUTHORITY"], - fixture_targets=["over-cited root label", "forward-receipted source", "small source-hash note"], - hold_surfaces=["citation label as proof", "prestige authority", "unquoted dependency", "unclosed theorem chain"], - next_probe="bibliographic_event_horizon_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=9, - route_id="asymptotic_closure_horizon", - domain="limit arguments, near-proofs, near-compression, and near-authority routes", - visible_shadow="approach curve, limit statement, near-zero delta, near-complete proof label", - hidden_state="finite gate state: replay, residual, receipt, byte law, and closure witness", - chain=[ - "L16_limit_claim_state", - "L12_finite_gate_residual_plane", - "L8_limit_adapter", - "L4_approach_primitive", - "Rg3_missing_witness_residual_boat", - "L1_asymptote_shadow", - "L0_finite_intersection_closure", - "O_AMMR_root", - ], - residual_handles=["approach_packet", "gate_shear", "missing_witness_spectral_field"], - reusable_kernels=["FINITE_INTERSECTION_GATE", "ASYMPTOTIC_HOLD", "TREE_FIDDY_ARCHIVE_DIAGNOSTIC"], - fixture_targets=["citation gravity near-authority", "global-delta near-zero compression", "finite coordinate replay", "proof label dependency chain"], - hold_surfaces=["limit language as proof", "approaches-zero as byte law", "eventual closure without witness", "infinite citation chain"], - next_probe="asymptotic_closure_horizon_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=10, - route_id="proof_equation_derivations", - domain="proof objects and equation derivation chains", - visible_shadow="rendered theorem/equation statement", - hidden_state="foundation kernel, dependencies, transform rules, residual obligations, closure gates", - chain=[ - "L16_foundation_state", - "L12_dependency_residual_plane", - "L8_dependency_adapter", - "L4_transform_primitive", - "Rg3_obligation_residual_boat", - "L2_statement_shadow", - "L0_closure_witness", - "O_AMMR_root", - ], - residual_handles=["equation_packet", "dependency_shear", "proof_spectral_field"], - reusable_kernels=["FORWARD_DERIVATION", "DEPENDENCY_MERKLE", "CLOSURE_WITNESS", "HOLD_RESIDUAL"], - fixture_targets=["foundation equation atom", "dependency hash replay", "PASS-ADD-PAUSE-SUBTRACT event chain"], - hold_surfaces=["human theorem label", "citation trust", "unclosed residual", "semantic overclaim"], - next_probe="proof_shadow_derivation_probe.py", - estimated_yield="high", - ), - shadow_route( - rank=11, - route_id="pde_field_snapshots", - domain="PDE fields and simulation state", - visible_shadow="mesh/grid samples and time slices", - hidden_state="governing equation, boundary conditions, units, solver, mesh, timestep, residual norm", - chain=default_chain, - residual_handles=default_handles, - reusable_kernels=["BOUNDARY_CONDITION", "STENCIL_OPCODE", "MODE_BASIS", "RESIDUAL_NORM"], - fixture_targets=["heat equation stencil", "wave mode packet", "boundary-condition replay", "coarse-grid residual"], - hold_surfaces=["solver correctness", "stability", "physical validity", "mesh convergence"], - next_probe="pde_field_shadow_probe.py", - estimated_yield="medium", - ), - shadow_route( - rank=12, - route_id="genomic_chromatin_projection", - domain="genomics and chromatin/projection surfaces", - visible_shadow="sequence string, contact map, 3D chromatin trace", - hidden_state="regulatory state, epigenetic marks, cell type, assay protocol, uncertainty, causal boundary", - chain=default_chain, - residual_handles=default_handles, - reusable_kernels=["SEQUENCE_TEMPLATE", "CONTACT_MAP_SHADOW", "MARK_RUN", "ASSAY_RESIDUAL"], - fixture_targets=["repeat sequence run", "motif replay", "contact-map block", "mark interval encoding"], - hold_surfaces=["causality", "cell-state generalization", "batch effects", "clinical/biological overclaim"], - next_probe="genomic_shadow_projection_probe.py", - estimated_yield="medium", - decision="SHADOW_ROUTE_HOLD_FIRST", - archive_mode="TREE_FIDDY_BLOCKED_CAUSAL_HOLD", - ), - ] - return { - "schema": "shadow_layer_opportunity_map_v1", - "citations": { - "local_source_refs": [rel(path) for path in SOURCE_REFS], - "external_citations": EXTERNAL_CITATIONS, - }, - "canonical_statement": ( - "Shadow layers are useful where the visible object is a cheap projection " - "of a richer typed state. The low-dimensional shadow may be encoded, but " - "the hidden state, adapter, residual, closure policy, and O-AMMR route " - "must be receipted. Plain Merkle hashes are only content commitments." - ), - "selection_rule": ( - "Promote replayable shadows first. Keep semantics, physical validity, diagnosis, " - "causality, and theorem trust in HOLD until local closure receipts exist." - ), - "refinement_rule": { - "avoid": "pure Merkle tree as trust object", - "use": "O-AMMR plus typed representative carrier", - "carrier_law": "source_12D = lift(project(source_12D)) + residual_12D", - "residual_law": "packet_local + shear_torsion + spectral_field = residual_12D", - "promotion_requires": [ - "axis counts match", - "three residual handles close", - "unresolved shell mass is zero", - "visible shadow replays exactly", - "source and receipt hashes are present", - ], - }, - "tree_fiddy_rule": { - "meaning": "bounded archive and safety cage for shadow routes", - "cage_boundary_bytes": TREE_FIDDY_CAGE_BOUNDARY_BYTES, - "active_pull_rule": "Q_active(i)=0 if i is committed or shielded", - "assignment": "BHOCS/archive commit routes are Tree Fiddy owned; live recurrence remains outside the cage", - "shadow_use": ( - "A shadow route may be archived only after replay, residual, and receipt " - "costs fit within the cage. Otherwise it stays HOLD_ACTIVE_SHADOW_ROUTE." - ), - }, - "claim_boundary": ( - "Planning receipt only. This map ranks likely shadow-layer encoding surfaces; " - "it does not assert compression gains, physical truth, clinical validity, or proof validity." - ), - "routes": routes, - "route_count": len(routes), - "status_counts": { - status: sum(1 for item in routes if item["decision"] == status) - for status in sorted({item["decision"] for item in routes}) - }, - } - - -def build_receipt(route_map: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "shadow_layer_opportunity_map_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "map": rel(MAP), - "map_hash": hash_obj(route_map), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "external_citations": route_map["citations"]["external_citations"], - "route_count": route_map["route_count"], - "status_counts": route_map["status_counts"], - "decision": "ADMIT_SHADOW_ROUTE_MAP_HOLD_FIRST", - "claim_boundary": route_map["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(route_map: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Shadow Layer Opportunity Map", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - route_map["claim_boundary"], - "", - "## Canonical Statement", - "", - route_map["canonical_statement"], - "", - "## Refinement Rule", - "", - f"- Avoid: `{route_map['refinement_rule']['avoid']}`", - f"- Use: `{route_map['refinement_rule']['use']}`", - f"- Carrier law: `{route_map['refinement_rule']['carrier_law']}`", - f"- Residual law: `{route_map['refinement_rule']['residual_law']}`", - "", - "## Tree Fiddy Guard", - "", - f"- Cage boundary bytes: `{route_map['tree_fiddy_rule']['cage_boundary_bytes']}`", - f"- Active pull rule: `{route_map['tree_fiddy_rule']['active_pull_rule']}`", - f"- Assignment: {route_map['tree_fiddy_rule']['assignment']}", - f"- Shadow use: {route_map['tree_fiddy_rule']['shadow_use']}", - "", - "## Ranked Routes", - "", - "| Rank | Route | Domain | Visible shadow | Yield | Decision | Next probe |", - "|---:|---|---|---|---|---|---|", - ] - for item in route_map["routes"]: - lines.append( - f"| {item['rank']} | `{item['route_id']}` | {item['domain']} | " - f"{item['visible_shadow']} | {item['estimated_yield']} | `{item['decision']}` | `{item['next_probe']}` |" - ) - lines.extend(["", "## Rule", "", route_map["selection_rule"]]) - lines.extend(["", "## Citations", ""]) - lines.append("Local source refs:") - for source in receipt["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - lines.append("") - lines.append("External/source prompts:") - for citation in route_map["citations"]["external_citations"]: - target = citation.get("url") or citation["status"] - lines.append(f"- `{citation['id']}`: {citation['title']} ({target}); role: `{citation['role']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - route_map = build_map() - receipt = build_receipt(route_map) - MAP.write_text(json.dumps(route_map, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(route_map, receipt) - print( - json.dumps( - { - "map": rel(MAP), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": route_map["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/sigilith_symbolic_resilience_prior.py b/4-Infrastructure/shim/sigilith_symbolic_resilience_prior.py deleted file mode 100644 index cb4993c9..00000000 --- a/4-Infrastructure/shim/sigilith_symbolic_resilience_prior.py +++ /dev/null @@ -1,213 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt for Nash 2026 Sigilith symbolic-resilience prior. - -This is an analysis/citation prior only. The source record carries a non-commercial -research/no-derivative implementation boundary, so this artifact extracts high-level -equation shapes and claim boundaries without implementing the Sigilith framework. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -SOURCE_DIR = REPO / "shared-data" / "sources" / "hcommons" / "gjgw2-j1f46" -SOURCE_PDF = SOURCE_DIR / "Nash2026Sigilith_SymbolicResilience.pdf" -SOURCE_TEXT = SOURCE_DIR / "Nash2026Sigilith_SymbolicResilience.txt" -RECORD_JSON = SOURCE_DIR / "record_api.json" -RECEIPT = SHIM / "sigilith_symbolic_resilience_prior_receipt.json" -CURRICULUM = SHIM / "sigilith_symbolic_resilience_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(path: Path) -> str: - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - record = json.loads(RECORD_JSON.read_text(encoding="utf-8")) - pdf_sha256 = sha256_bytes(SOURCE_PDF) - text_sha256 = sha256_bytes(SOURCE_TEXT) - pdf_md5 = hashlib.md5(SOURCE_PDF.read_bytes(), usedforsecurity=False).hexdigest() - metadata = record.get("metadata", {}) - receipt: dict[str, Any] = { - "schema": "sigilith_symbolic_resilience_prior_v1", - "source": { - "record_id": record.get("id"), - "title": metadata.get("title"), - "creator": "Nash, Ky", - "publication_date": metadata.get("publication_date"), - "doi": record.get("pids", {}).get("doi", {}).get("identifier"), - "record_url": record.get("links", {}).get("self_html"), - "api_url": record.get("links", {}).get("self"), - "pdf_filename": SOURCE_PDF.name, - "pdf_md5": pdf_md5, - "pdf_sha256": pdf_sha256, - "text_sha256": text_sha256, - }, - "license_boundary": [ - "use as citation and high-level analysis prior only", - "do not implement Sigilith/CDMQ methods from this source without permission", - "do not create derivative framework artifacts from protected method details", - "preserve author claim boundary: no biological, cognitive, or autonomous interpretation implied", - ], - "primary_read": ( - "The paper offers a synthetic symbolic-system analogue for resilience under " - "drift: constraint density, modifier regeneration, and paradox buffering " - "can delay collapse. This maps cleanly to the stack's overload model as a " - "symbolic collapse-topology prior, not as evidence of biological autonomy." - ), - "source_claim_boundary": ( - "Survival-like behavior is defined as delayed terminal collapse through " - "internal stabilization dynamics without biological, cognitive, or autonomous claims." - ), - "system_comparison": { - "R1": { - "description": "baseline CDMQ system with no resilience mechanisms", - "observed_pattern": "rapid drift escalation and single-stage collapse", - "T_collapse": 100, - }, - "R2": { - "description": "enhanced system with increased constraint density, modifier regeneration, and paradox buffering", - "observed_pattern": "drift suppression, plateau formation, modifier regeneration, paradox buffering, delayed collapse", - "T_collapse": 140, - }, - "CDC": 40, - }, - "equation_shapes": [ - { - "id": "drift_magnitude", - "shape": "D_t = Q_t / (C_t + M_t)", - "semantics": "paradox/quality activation over stabilizing constraint and modifier mass", - "folded_use": "symbolic analogue of overload pressure", - }, - { - "id": "collapse_delay_coefficient", - "shape": "CDC = T_collapse(R2) - T_collapse(R1)", - "semantics": "delay gained by resilience mechanisms", - "folded_use": "collapse-resistance delta for route/system variants", - }, - { - "id": "constraint_density", - "shape": "C_rho = C / N", - "semantics": "constraint density per symbolic sequence length", - "folded_use": "stabilization mass per route/window", - }, - { - "id": "modifier_recovery_rate", - "shape": "MRR = (M_(t+1) - M_t) / Delta_t", - "semantics": "rate of modifier regeneration", - "folded_use": "recovery capacity after overload or drift", - }, - { - "id": "paradox_suppression_ratio", - "shape": "PSR = 1 - (Q_active / Q_total)", - "semantics": "suppression of paradox activation", - "folded_use": "buffering gate for contradiction/overflow", - }, - ], - "collapse_topology": [ - "drift rise", - "drift reversal", - "drift plateau", - "drift rebound", - "partial collapse", - "stabilisation", - "final collapse", - ], - "overload_model_mapping": { - "Q_t": "active contradiction/paradox/salience pressure", - "C_t": "constraints or assimilation structures", - "M_t": "modifiers, repair operators, or buffering mechanisms", - "D_t": "symbolic overload pressure", - "C_rho": "institutional/cognitive constraint density", - "MRR": "repair/offload regeneration rate", - "PSR": "paradox or contradiction buffering effectiveness", - "CDC": "delay before terminal collapse or forced reconfiguration", - }, - "historical_bandwidth_mapping": { - "accelerated_transfer": "raises Q_t and drift pressure", - "assimilation_infrastructure": "raises C_t and C_rho", - "adaptive_interpretive_tools": "raise M_t and MRR", - "paradox_buffering": "raises PSR and delays collapse", - "residual_stress": "unbuffered Q_t that drives rebound or collapse", - }, - "promotion_rule": [ - "use only as structural analogy and equation-shape prior", - "map CDMQ variables to local variables explicitly before use", - "validate any overload/collapse claim against local data", - "keep source license and no-autonomy claim boundary attached", - ], - "failure_rules": [ - "treating synthetic symbolic behavior as biological evidence -> overclaim", - "implementing protected Sigilith/CDMQ methods from source -> permission boundary violation", - "using survival-like language without boundary -> invalid framing", - "mapping Q/C/M variables without local definitions -> hold", - "claiming collapse prediction without empirical timeline data -> overclaim", - ], - "linked_local_models": [ - "Connectome Protective Cognitive Load Reweighting", - "Holographic Fractional Recursive Equation Fold", - "Decision Diagram Compression Tuning Prior", - ], - "claim_boundary": ( - "This receipt records a symbolic-resilience citation and high-level equation " - "fold. It is not an implementation, not a derivative Sigilith method, not " - "biological evidence, and not proof of the historical overload theory." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "map_symbolic_resilience_equation", - "input": "D_t, CDC, C_rho, MRR, or PSR equation", - "target": "overload pressure, collapse delay, constraint density, recovery rate, or paradox buffering", - }, - { - "task": "preserve_source_claim_boundary", - "input": "survival-like symbolic behavior claim", - "target": "delayed terminal collapse only; no biological/cognitive/autonomous implication", - }, - { - "task": "reject_unlicensed_implementation", - "input": "proposal to implement Sigilith/CDMQ methods from source", - "target": "hold unless permission or independent derivation is documented", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "equation_count": len(receipt["equation_shapes"]), - "collapse_topology_stages": len(receipt["collapse_topology"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/signal_equation_invariant_roots.py b/4-Infrastructure/shim/signal_equation_invariant_roots.py deleted file mode 100644 index 306e9b29..00000000 --- a/4-Infrastructure/shim/signal_equation_invariant_roots.py +++ /dev/null @@ -1,463 +0,0 @@ -#!/usr/bin/env python3 -"""Derive invariant roots for accessible local signal equations. - -This is a local synthesis pass over the Research Stack signal surface. It does -not claim a complete literature survey. It pulls the equations that are -available in the workspace signal compendium and executable audio-DSP code, then -normalizes each into an invariant root: the quantity, equivalence class, or -constraint that remains meaningful under admissible transforms. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "signal_equation_invariant_roots_receipt.json" -CURRICULUM_OUT = SHIM / "signal_equation_invariant_roots_curriculum.jsonl" -SUMMARY_OUT = SHIM / "signal_equation_invariant_roots_summary.md" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -INVARIANT_ROOTS: list[dict[str, Any]] = [ - { - "id": "SIGROOT001_spectral_overlap", - "source": "SIGNAL_THEORY_COMPENDIUM.md: spectralOverlap sig1 sig2 = sum(sig1[i] * sig2[i])", - "equation": " = sum_i s1_i s2_i", - "invariant_root": "inner-product pairing on aligned spectral coordinates", - "admissible_transforms": "common bin permutation; orthonormal basis change when both signatures transform together", - "compression_use": "route similarity, duplicate-island pruning, nearest repair template", - "fpga_use": "DSP dot-product lane with accumulator and saturation guard", - }, - { - "id": "SIGROOT002_piecewise_merge", - "source": "SIGNAL_THEORY_COMPENDIUM.md: piecewiseMerge left right[i] = min(1.0, left[i] + right[i])", - "equation": "merge_i = min(1, left_i + right_i)", - "invariant_root": "bounded semilattice occupancy over [0,1]^n", - "admissible_transforms": "coordinatewise monotone maps that preserve zero, one, and order", - "compression_use": "safe feature union without unbounded sidecar growth", - "fpga_use": "saturating add primitive", - }, - { - "id": "SIGROOT003_resonance_degeneracy", - "source": "SIGNAL_THEORY_COMPENDIUM.md: count(left[i] != 0 and right[i] != 0)", - "equation": "deg(left,right) = |support(left) intersect support(right)|", - "invariant_root": "support-intersection cardinality", - "admissible_transforms": "positive amplitude scaling and common support-preserving permutation", - "compression_use": "overlap score for tokenbook/feature collisions", - "fpga_use": "bitmask AND plus popcount", - }, - { - "id": "SIGROOT004_wavefront_value", - "source": "SIGNAL_THEORY_COMPENDIUM.md: decay, phaseShift, oscillation, value", - "equation": "value = (A - gamma*d) * osc(omega*d) for d <= v*t, else 0", - "invariant_root": "retarded wavefront cone plus phase class modulo cycle", - "admissible_transforms": "translations and metric-preserving coordinate changes", - "compression_use": "event influence radius for local route activation", - "fpga_use": "distance gate, phase LUT, envelope subtractor", - }, - { - "id": "SIGROOT005_signal_band_policy", - "source": "SIGNAL_THEORY_COMPENDIUM.md: quiet/active/stressed/extreme threshold bands", - "equation": "band(x) = threshold_partition(x)", - "invariant_root": "ordered threshold cell", - "admissible_transforms": "monotone rescaling with transformed thresholds", - "compression_use": "route budget scheduler", - "fpga_use": "comparator ladder", - }, - { - "id": "SIGROOT006_acoustic_gradient", - "source": "SIGNAL_THEORY_COMPENDIUM.md: acoustic impedance as gradient magnitude |grad f|", - "equation": "Z_acoustic ~ |grad f|", - "invariant_root": "metric norm of field gradient", - "admissible_transforms": "coordinate changes with explicit metric tensor", - "compression_use": "manifold steepest-descent route proposal", - "fpga_use": "finite-difference gradient and norm pipeline", - }, - { - "id": "SIGROOT007_fitness_entropy_compensation", - "source": "SIGNAL_THEORY_COMPENDIUM.md: f = f_max - alpha * H", - "equation": "f + alpha*H = f_max", - "invariant_root": "affine fitness-entropy conserved total", - "admissible_transforms": "unit changes that transform alpha coherently", - "compression_use": "semantic/fitness score must pay entropy cost", - "fpga_use": "linear score lane with conserved budget comparator", - }, - { - "id": "SIGROOT008_gibbs_free_energy", - "source": "SIGNAL_THEORY_COMPENDIUM.md: DeltaG = DeltaH - T*DeltaS", - "equation": "G = H - T*S", - "invariant_root": "Legendre-transformed available-energy potential", - "admissible_transforms": "thermodynamic coordinate changes preserving conjugate pair T,S", - "compression_use": "available byte-gain after entropy/side-info cost", - "fpga_use": "cost potential lane for thermal/energy-aware routing", - }, - { - "id": "SIGROOT009_affine_erasure_permutation", - "source": "SIGNAL_THEORY_COMPENDIUM.md: pi(i) = (offset + step*i) mod n", - "equation": "pi(i) = a + s*i mod n", - "invariant_root": "cycle structure determined by gcd(s,n)", - "admissible_transforms": "offset translation and invertible modular scaling", - "compression_use": "repair stream interleaving with deterministic owner", - "fpga_use": "modular address generator", - }, - { - "id": "SIGROOT010_genomic_weight", - "source": "SIGNAL_THEORY_COMPENDIUM.md: genomicWeight ratio", - "equation": "W = (rho + v + tau + sigma + q) / ((1+kappa^2)*(1+epsilon))", - "invariant_root": "dimensionless normalized field-strength ratio", - "admissible_transforms": "common scale-normalization of numerator terms", - "compression_use": "adaptive erasure threshold", - "fpga_use": "fixed-point ratio approximation", - }, - { - "id": "SIGROOT011_pbacs_phi_accumulator", - "source": "SIGNAL_THEORY_COMPENDIUM.md: phi_{t+1} = phi_t + 106070", - "equation": "phi_{t+1} = phi_t + c mod 2^32", - "invariant_root": "circle rotation orbit class", - "admissible_transforms": "phase offset; modular conjugacy preserving increment", - "compression_use": "deterministic phase owner for route symbols", - "fpga_use": "free-running modular accumulator", - }, - { - "id": "SIGROOT012_pbacs_error_feedback", - "source": "SIGNAL_THEORY_COMPENDIUM.md: e_{t+1} = v_t + e_t - (b_t ? theta_t : 0)", - "equation": "e_next = v + e - b*theta", - "invariant_root": "bounded quantization residual", - "admissible_transforms": "threshold-preserving fixed-point rescale", - "compression_use": "exact residual lane for symbol decisions", - "fpga_use": "sigma-delta style feedback cell", - }, - { - "id": "SIGROOT013_mutual_information_gain", - "source": "SIGNAL_THEORY_COMPENDIUM.md: MI(x) = baseline_bpb - actual_bpb", - "equation": "MI = baseline_bpb - actual_bpb", - "invariant_root": "byte-per-symbol improvement under one ratio schema", - "admissible_transforms": "comparisons that keep baseline and actual schema identical", - "compression_use": "route evidence coordinate", - "fpga_use": "counter difference after codec run", - }, - { - "id": "SIGROOT014_weighted_mi_prediction", - "source": "SIGNAL_THEORY_COMPENDIUM.md: MI_pred weighted average", - "equation": "MI_pred = sum_i w_i MI_i S_i / sum_i w_i S_i", - "invariant_root": "barycentric coordinate in similarity-weighted evidence simplex", - "admissible_transforms": "common positive scaling of all weights", - "compression_use": "nearest-prior route prediction", - "fpga_use": "weighted accumulator plus reciprocal approximation", - }, - { - "id": "SIGROOT015_surprise_metric", - "source": "SIGNAL_THEORY_COMPENDIUM.md: surprise = log(1 + |MI_actual - MI_predicted|)", - "equation": "S = log(1 + |delta_MI|)", - "invariant_root": "monotone function of absolute prediction residual", - "admissible_transforms": "monotone reparameterization of residual magnitude", - "compression_use": "route anomaly detector", - "fpga_use": "absolute-delta threshold; log optional", - }, - { - "id": "SIGROOT016_structure_yield", - "source": "SIGNAL_THEORY_COMPENDIUM.md: rho(x) = MI(x) / (cost(x) + epsilon)", - "equation": "rho = MI / (cost + eps)", - "invariant_root": "information-per-cost efficiency ratio", - "admissible_transforms": "unit changes preserving numerator/denominator interpretation", - "compression_use": "candidate route priority", - "fpga_use": "score-per-cycle allocator", - }, - { - "id": "SIGROOT017_weighted_feature_distance", - "source": "SIGNAL_THEORY_COMPENDIUM.md: weighted feature distance", - "equation": "d(z1,z2) = sqrt(sum_i w_i*((z1_i-z2_i)/s_i)^2)", - "invariant_root": "diagonal metric distance after scale normalization", - "admissible_transforms": "coordinate rescaling absorbed into s_i and w_i", - "compression_use": "route family clustering", - "fpga_use": "scaled L2 distance pipeline", - }, - { - "id": "SIGROOT018_energy_gradient_waveform", - "source": "SIGNAL_THEORY_COMPENDIUM.md: amplitude=|grad E(t)|, frequency, phase", - "equation": "wave_E = (|grad E|, omega_gradE, phi_gradE)", - "invariant_root": "gradient magnitude and phase trajectory", - "admissible_transforms": "metric-aware coordinate changes", - "compression_use": "energy/cost-aware transform scheduling", - "fpga_use": "gradient magnitude plus phase accumulator", - }, - { - "id": "SIGROOT019_shape_energy_coupling", - "source": "SIGNAL_THEORY_COMPENDIUM.md: C_SE = alpha * grad h * grad E", - "equation": "C_SE = alpha ", - "invariant_root": "metric inner product of shape and energy gradients", - "admissible_transforms": "coordinate changes preserving the metric pairing", - "compression_use": "align geometry witness only when it reduces route cost", - "fpga_use": "dual-gradient dot-product lane", - }, - { - "id": "SIGROOT020_spectral_field_score", - "source": "SIGNAL_THEORY_COMPENDIUM.md: score = mass*massField + polarity*polarityField + spectralOverlap", - "equation": "score = mM + pP + ", - "invariant_root": "bilinear pairing between local state and field", - "admissible_transforms": "paired basis changes that preserve the bilinear form", - "compression_use": "local route-field compatibility score", - "fpga_use": "three-term MAC lane", - }, - { - "id": "SIGROOT021_parabolic_j_score", - "source": "SIGNAL_THEORY_COMPENDIUM.md: J(k) = 32 - 0.5*(k-22)^2", - "equation": "J(k) = 32 - 0.5*(k-22)^2", - "invariant_root": "distance from resonant vertex k=22", - "admissible_transforms": "translation to vertex coordinate u=k-22", - "compression_use": "resonance-ranked candidate pruning", - "fpga_use": "subtract-square-threshold circuit", - }, - { - "id": "SIGROOT022_cmyk_frequency_lattice", - "source": "SIGNAL_THEORY_COMPENDIUM.md: freq(ch,h)=baseFreq(ch)+deltaFreq*h", - "equation": "f_ch(h) = base_ch + delta*h", - "invariant_root": "channel-local affine frequency lattice coordinate h", - "admissible_transforms": "affine frequency calibration preserving delta steps", - "compression_use": "symbol carrier with exact inverse", - "fpga_use": "base-plus-shift frequency synthesizer", - }, - { - "id": "SIGROOT023_rydberg_gap", - "source": "SIGNAL_THEORY_COMPENDIUM.md: nu_tilde = R_H*(1/n1^2 - 1/n2^2)", - "equation": "nu_bar = R*(1/n1^2 - 1/n2^2)", - "invariant_root": "reciprocal-square quantum gap", - "admissible_transforms": "unit conversion between wavenumber, wavelength, frequency, and energy", - "compression_use": "stable physical spectral basis index", - "fpga_use": "small table of canonical spectral lines", - }, - { - "id": "SIGROOT024_lorentzian_resonance", - "source": "SIGNAL_THEORY_COMPENDIUM.md: strength = 1/(1+(Delta lambda)^2)", - "equation": "L(delta) = 1/(1+delta^2)", - "invariant_root": "squared detuning from spectral center", - "admissible_transforms": "sign flip of detuning; normalized wavelength units", - "compression_use": "nearest spectral-basis assignment", - "fpga_use": "detuning-square LUT", - }, - { - "id": "SIGROOT025_kmer_base4_index", - "source": "SIGNAL_THEORY_COMPENDIUM.md: 3-mer index = b1*16 + b2*4 + b3", - "equation": "idx = 16*b1 + 4*b2 + b3", - "invariant_root": "base-4 coordinate of codon symbol", - "admissible_transforms": "base relabeling with explicit inverse map", - "compression_use": "fixed codon/tokenbook coordinate", - "fpga_use": "two-bit shift-and-or indexer", - }, - { - "id": "SIGROOT026_dct2_basis", - "source": "SIGNAL_THEORY_COMPENDIUM.md: cos(pi/n*(j+0.5)*k)", - "equation": "basis_{j,k} = cos(pi/n*(j+1/2)*k)", - "invariant_root": "orthogonal cosine projection coefficient", - "admissible_transforms": "orthogonal transforms preserving coefficient energy", - "compression_use": "spectral coefficient compaction", - "fpga_use": "fixed cosine basis or LUT butterfly", - }, - { - "id": "SIGROOT027_qpsk_phase_class", - "source": "SIGNAL_THEORY_COMPENDIUM.md: QPSK phases 0,90,180,270", - "equation": "phase in Z_4", - "invariant_root": "phase class modulo pi/2", - "admissible_transforms": "global phase rotation with receiver correction", - "compression_use": "2-bit symbol carrier", - "fpga_use": "quadrant decoder", - }, - { - "id": "SIGROOT028_qam16_constellation", - "source": "SIGNAL_THEORY_COMPENDIUM.md: 4 amplitudes x 4 phases", - "equation": "symbol = (a in A4, phase in Z4)", - "invariant_root": "finite amplitude-phase lattice point", - "admissible_transforms": "affine constellation calibration with preserved decision cells", - "compression_use": "4-bit symbol carrier / QAM transfer metaphor", - "fpga_use": "amplitude slicer plus quadrant decoder", - }, - { - "id": "SIGROOT029_dmt_subcarrier_quotient", - "source": "SIGNAL_THEORY_COMPENDIUM.md: phase_out=base_phase+offset_i, demod=phase_in-offset_i", - "equation": "phase_base = phase_out - offset_i mod cycle", - "invariant_root": "phase quotient after subtracting subcarrier offset", - "admissible_transforms": "subcarrier permutation with receipted offset table", - "compression_use": "parallel lane carrier with exact demodulation", - "fpga_use": "per-lane phase subtractor", - }, - { - "id": "SIGROOT030_hann_window_fft_energy", - "source": "5-Applications/audio-dsp/src/core/surface.rs: Hann window, FFT, bin energy", - "equation": "E_bin = avg_{k in bin} |FFT(window*x)_k|", - "invariant_root": "windowed spectral-energy distribution", - "admissible_transforms": "time shift up to phase; amplitude normalization when max-normalized", - "compression_use": "audio/signal route feature vector", - "fpga_use": "window multiply, FFT, magnitude, bin accumulator", - }, - { - "id": "SIGROOT031_transient_features", - "source": "5-Applications/audio-dsp/src/core/surface.rs: attack, decay, zcr, crest", - "equation": "transient = (max dx+, max dx-, zero_crossings/n, peak/rms)", - "invariant_root": "edge/impulse morphology of the signal chunk", - "admissible_transforms": "time-local scaling with normalized crest and ZCR preserved", - "compression_use": "decide raw vs spectral vs hybrid route", - "fpga_use": "delta extrema, sign-change counter, RMS/peak lane", - }, - { - "id": "SIGROOT032_predictability_autocorrelation", - "source": "5-Applications/audio-dsp/src/core/surface.rs: predictability via autocorrelation", - "equation": "pred = 0.5*(corr(x_t, x_{t-1}) + 1)", - "invariant_root": "normalized temporal correlation", - "admissible_transforms": "affine amplitude scaling removed by mean/variance normalization", - "compression_use": "predictor suitability signal", - "fpga_use": "sliding dot product and norm lane", - }, - { - "id": "SIGROOT033_cosine_similarity", - "source": "5-Applications/audio-dsp/src/core/surface.rs: dot/(norm_a*norm_b)", - "equation": "cos(theta)=/(||a|| ||b||)", - "invariant_root": "projective direction on spectral feature sphere", - "admissible_transforms": "positive scaling of either vector", - "compression_use": "chunk reuse / skip decision", - "fpga_use": "dot product and reciprocal norm threshold", - }, -] - - -def build_receipt() -> dict[str, Any]: - clusters: dict[str, int] = {} - for row in INVARIANT_ROOTS: - cluster = row["id"].split("_", 1)[1].rsplit("_", 1)[0] - clusters[cluster] = clusters.get(cluster, 0) + 1 - receipt: dict[str, Any] = { - "schema": "signal_equation_invariant_roots_v1", - "generated_at": GENERATED_AT, - "source_scope": [ - "SIGNAL_THEORY_COMPENDIUM.md", - "5-Applications/audio-dsp/src/core/surface.rs", - "5-Applications/audio-dsp/src/core/features.rs", - ], - "claim_boundary": ( - "These are invariant roots for accessible local signal equations. " - "They are route/control priors and hardware design handles, not " - "external physics proof or compression proof without exact byte receipts." - ), - "root_count": len(INVARIANT_ROOTS), - "invariant_roots": INVARIANT_ROOTS, - "derived_unifying_root": { - "equation": "SignalRoute = (coordinate, invariant_root, admissible_transform, receipt_barrier)", - "meaning": ( - "Every accessible signal equation reduces to a coordinate map plus an " - "invariant root. The invariant root says what can survive rescaling, " - "basis changes, phase shifts, lane permutation, or compression-route " - "projection. Promotion still requires a receipt barrier." - ), - }, - "hutter_mapping": { - "i_axis": "measured byte mass and lower bounds", - "q_axis": "exactness roots: hash, Merkle receipt, NaN0 false, route-key closure", - "promotion": "only when a route lies on the exactness locus and below incumbent byte level", - }, - "fpga_mapping": { - "common_primitives": [ - "dot_product", - "saturating_add", - "popcount", - "phase_accumulator", - "threshold_ladder", - "modular_address_generator", - "gradient_norm", - "fft_bin_accumulator", - "digest_lane", - ], - "barrier": "commit or source-release only after independent digest/check lane passes", - }, - } - receipt["receipt_hash"] = sha256_text(stable_json(receipt)) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - lines = [] - for row in receipt["invariant_roots"]: - lines.append(stable_json({ - "task": "derive_signal_invariant_root", - "root_id": row["id"], - "prompt": f"Derive the invariant root for {row['equation']}.", - "completion": ( - f"Invariant root: {row['invariant_root']}. " - f"Admissible transforms: {row['admissible_transforms']}." - ), - })) - CURRICULUM_OUT.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_summary(receipt: dict[str, Any]) -> None: - lines = [ - "# Signal Equation Invariant Roots", - "", - receipt["claim_boundary"], - "", - f"Root count: {receipt['root_count']}", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "## Unifying Root", - "", - "```text", - receipt["derived_unifying_root"]["equation"], - "```", - "", - receipt["derived_unifying_root"]["meaning"], - "", - "## Roots", - "", - ] - for row in receipt["invariant_roots"]: - lines.extend([ - f"### {row['id']}", - "", - f"- Equation: `{row['equation']}`", - f"- Invariant root: {row['invariant_root']}", - f"- Admissible transforms: {row['admissible_transforms']}", - f"- Compression use: {row['compression_use']}", - f"- FPGA use: {row['fpga_use']}", - "", - ]) - SUMMARY_OUT.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_curriculum(receipt) - write_summary(receipt) - print(json.dumps( - { - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "summary": rel(SUMMARY_OUT), - "receipt_hash": receipt["receipt_hash"], - "root_count": receipt["root_count"], - }, - indent=2, - sort_keys=True, - )) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/signal_equation_invariant_roots_summary.md b/4-Infrastructure/shim/signal_equation_invariant_roots_summary.md deleted file mode 100644 index 2b578787..00000000 --- a/4-Infrastructure/shim/signal_equation_invariant_roots_summary.md +++ /dev/null @@ -1,280 +0,0 @@ -# Signal Equation Invariant Roots - -These are invariant roots for accessible local signal equations. They are route/control priors and hardware design handles, not external physics proof or compression proof without exact byte receipts. - -Root count: 33 -Receipt hash: `10ec6bf94808b4517c6e866889d8c9cca02969fbcb43c574b0abd27c3cac3a33` - -## Unifying Root - -```text -SignalRoute = (coordinate, invariant_root, admissible_transform, receipt_barrier) -``` - -Every accessible signal equation reduces to a coordinate map plus an invariant root. The invariant root says what can survive rescaling, basis changes, phase shifts, lane permutation, or compression-route projection. Promotion still requires a receipt barrier. - -## Roots - -### SIGROOT001_spectral_overlap - -- Equation: ` = sum_i s1_i s2_i` -- Invariant root: inner-product pairing on aligned spectral coordinates -- Admissible transforms: common bin permutation; orthonormal basis change when both signatures transform together -- Compression use: route similarity, duplicate-island pruning, nearest repair template -- FPGA use: DSP dot-product lane with accumulator and saturation guard - -### SIGROOT002_piecewise_merge - -- Equation: `merge_i = min(1, left_i + right_i)` -- Invariant root: bounded semilattice occupancy over [0,1]^n -- Admissible transforms: coordinatewise monotone maps that preserve zero, one, and order -- Compression use: safe feature union without unbounded sidecar growth -- FPGA use: saturating add primitive - -### SIGROOT003_resonance_degeneracy - -- Equation: `deg(left,right) = |support(left) intersect support(right)|` -- Invariant root: support-intersection cardinality -- Admissible transforms: positive amplitude scaling and common support-preserving permutation -- Compression use: overlap score for tokenbook/feature collisions -- FPGA use: bitmask AND plus popcount - -### SIGROOT004_wavefront_value - -- Equation: `value = (A - gamma*d) * osc(omega*d) for d <= v*t, else 0` -- Invariant root: retarded wavefront cone plus phase class modulo cycle -- Admissible transforms: translations and metric-preserving coordinate changes -- Compression use: event influence radius for local route activation -- FPGA use: distance gate, phase LUT, envelope subtractor - -### SIGROOT005_signal_band_policy - -- Equation: `band(x) = threshold_partition(x)` -- Invariant root: ordered threshold cell -- Admissible transforms: monotone rescaling with transformed thresholds -- Compression use: route budget scheduler -- FPGA use: comparator ladder - -### SIGROOT006_acoustic_gradient - -- Equation: `Z_acoustic ~ |grad f|` -- Invariant root: metric norm of field gradient -- Admissible transforms: coordinate changes with explicit metric tensor -- Compression use: manifold steepest-descent route proposal -- FPGA use: finite-difference gradient and norm pipeline - -### SIGROOT007_fitness_entropy_compensation - -- Equation: `f + alpha*H = f_max` -- Invariant root: affine fitness-entropy conserved total -- Admissible transforms: unit changes that transform alpha coherently -- Compression use: semantic/fitness score must pay entropy cost -- FPGA use: linear score lane with conserved budget comparator - -### SIGROOT008_gibbs_free_energy - -- Equation: `G = H - T*S` -- Invariant root: Legendre-transformed available-energy potential -- Admissible transforms: thermodynamic coordinate changes preserving conjugate pair T,S -- Compression use: available byte-gain after entropy/side-info cost -- FPGA use: cost potential lane for thermal/energy-aware routing - -### SIGROOT009_affine_erasure_permutation - -- Equation: `pi(i) = a + s*i mod n` -- Invariant root: cycle structure determined by gcd(s,n) -- Admissible transforms: offset translation and invertible modular scaling -- Compression use: repair stream interleaving with deterministic owner -- FPGA use: modular address generator - -### SIGROOT010_genomic_weight - -- Equation: `W = (rho + v + tau + sigma + q) / ((1+kappa^2)*(1+epsilon))` -- Invariant root: dimensionless normalized field-strength ratio -- Admissible transforms: common scale-normalization of numerator terms -- Compression use: adaptive erasure threshold -- FPGA use: fixed-point ratio approximation - -### SIGROOT011_pbacs_phi_accumulator - -- Equation: `phi_{t+1} = phi_t + c mod 2^32` -- Invariant root: circle rotation orbit class -- Admissible transforms: phase offset; modular conjugacy preserving increment -- Compression use: deterministic phase owner for route symbols -- FPGA use: free-running modular accumulator - -### SIGROOT012_pbacs_error_feedback - -- Equation: `e_next = v + e - b*theta` -- Invariant root: bounded quantization residual -- Admissible transforms: threshold-preserving fixed-point rescale -- Compression use: exact residual lane for symbol decisions -- FPGA use: sigma-delta style feedback cell - -### SIGROOT013_mutual_information_gain - -- Equation: `MI = baseline_bpb - actual_bpb` -- Invariant root: byte-per-symbol improvement under one ratio schema -- Admissible transforms: comparisons that keep baseline and actual schema identical -- Compression use: route evidence coordinate -- FPGA use: counter difference after codec run - -### SIGROOT014_weighted_mi_prediction - -- Equation: `MI_pred = sum_i w_i MI_i S_i / sum_i w_i S_i` -- Invariant root: barycentric coordinate in similarity-weighted evidence simplex -- Admissible transforms: common positive scaling of all weights -- Compression use: nearest-prior route prediction -- FPGA use: weighted accumulator plus reciprocal approximation - -### SIGROOT015_surprise_metric - -- Equation: `S = log(1 + |delta_MI|)` -- Invariant root: monotone function of absolute prediction residual -- Admissible transforms: monotone reparameterization of residual magnitude -- Compression use: route anomaly detector -- FPGA use: absolute-delta threshold; log optional - -### SIGROOT016_structure_yield - -- Equation: `rho = MI / (cost + eps)` -- Invariant root: information-per-cost efficiency ratio -- Admissible transforms: unit changes preserving numerator/denominator interpretation -- Compression use: candidate route priority -- FPGA use: score-per-cycle allocator - -### SIGROOT017_weighted_feature_distance - -- Equation: `d(z1,z2) = sqrt(sum_i w_i*((z1_i-z2_i)/s_i)^2)` -- Invariant root: diagonal metric distance after scale normalization -- Admissible transforms: coordinate rescaling absorbed into s_i and w_i -- Compression use: route family clustering -- FPGA use: scaled L2 distance pipeline - -### SIGROOT018_energy_gradient_waveform - -- Equation: `wave_E = (|grad E|, omega_gradE, phi_gradE)` -- Invariant root: gradient magnitude and phase trajectory -- Admissible transforms: metric-aware coordinate changes -- Compression use: energy/cost-aware transform scheduling -- FPGA use: gradient magnitude plus phase accumulator - -### SIGROOT019_shape_energy_coupling - -- Equation: `C_SE = alpha ` -- Invariant root: metric inner product of shape and energy gradients -- Admissible transforms: coordinate changes preserving the metric pairing -- Compression use: align geometry witness only when it reduces route cost -- FPGA use: dual-gradient dot-product lane - -### SIGROOT020_spectral_field_score - -- Equation: `score = mM + pP + ` -- Invariant root: bilinear pairing between local state and field -- Admissible transforms: paired basis changes that preserve the bilinear form -- Compression use: local route-field compatibility score -- FPGA use: three-term MAC lane - -### SIGROOT021_parabolic_j_score - -- Equation: `J(k) = 32 - 0.5*(k-22)^2` -- Invariant root: distance from resonant vertex k=22 -- Admissible transforms: translation to vertex coordinate u=k-22 -- Compression use: resonance-ranked candidate pruning -- FPGA use: subtract-square-threshold circuit - -### SIGROOT022_cmyk_frequency_lattice - -- Equation: `f_ch(h) = base_ch + delta*h` -- Invariant root: channel-local affine frequency lattice coordinate h -- Admissible transforms: affine frequency calibration preserving delta steps -- Compression use: symbol carrier with exact inverse -- FPGA use: base-plus-shift frequency synthesizer - -### SIGROOT023_rydberg_gap - -- Equation: `nu_bar = R*(1/n1^2 - 1/n2^2)` -- Invariant root: reciprocal-square quantum gap -- Admissible transforms: unit conversion between wavenumber, wavelength, frequency, and energy -- Compression use: stable physical spectral basis index -- FPGA use: small table of canonical spectral lines - -### SIGROOT024_lorentzian_resonance - -- Equation: `L(delta) = 1/(1+delta^2)` -- Invariant root: squared detuning from spectral center -- Admissible transforms: sign flip of detuning; normalized wavelength units -- Compression use: nearest spectral-basis assignment -- FPGA use: detuning-square LUT - -### SIGROOT025_kmer_base4_index - -- Equation: `idx = 16*b1 + 4*b2 + b3` -- Invariant root: base-4 coordinate of codon symbol -- Admissible transforms: base relabeling with explicit inverse map -- Compression use: fixed codon/tokenbook coordinate -- FPGA use: two-bit shift-and-or indexer - -### SIGROOT026_dct2_basis - -- Equation: `basis_{j,k} = cos(pi/n*(j+1/2)*k)` -- Invariant root: orthogonal cosine projection coefficient -- Admissible transforms: orthogonal transforms preserving coefficient energy -- Compression use: spectral coefficient compaction -- FPGA use: fixed cosine basis or LUT butterfly - -### SIGROOT027_qpsk_phase_class - -- Equation: `phase in Z_4` -- Invariant root: phase class modulo pi/2 -- Admissible transforms: global phase rotation with receiver correction -- Compression use: 2-bit symbol carrier -- FPGA use: quadrant decoder - -### SIGROOT028_qam16_constellation - -- Equation: `symbol = (a in A4, phase in Z4)` -- Invariant root: finite amplitude-phase lattice point -- Admissible transforms: affine constellation calibration with preserved decision cells -- Compression use: 4-bit symbol carrier / QAM transfer metaphor -- FPGA use: amplitude slicer plus quadrant decoder - -### SIGROOT029_dmt_subcarrier_quotient - -- Equation: `phase_base = phase_out - offset_i mod cycle` -- Invariant root: phase quotient after subtracting subcarrier offset -- Admissible transforms: subcarrier permutation with receipted offset table -- Compression use: parallel lane carrier with exact demodulation -- FPGA use: per-lane phase subtractor - -### SIGROOT030_hann_window_fft_energy - -- Equation: `E_bin = avg_{k in bin} |FFT(window*x)_k|` -- Invariant root: windowed spectral-energy distribution -- Admissible transforms: time shift up to phase; amplitude normalization when max-normalized -- Compression use: audio/signal route feature vector -- FPGA use: window multiply, FFT, magnitude, bin accumulator - -### SIGROOT031_transient_features - -- Equation: `transient = (max dx+, max dx-, zero_crossings/n, peak/rms)` -- Invariant root: edge/impulse morphology of the signal chunk -- Admissible transforms: time-local scaling with normalized crest and ZCR preserved -- Compression use: decide raw vs spectral vs hybrid route -- FPGA use: delta extrema, sign-change counter, RMS/peak lane - -### SIGROOT032_predictability_autocorrelation - -- Equation: `pred = 0.5*(corr(x_t, x_{t-1}) + 1)` -- Invariant root: normalized temporal correlation -- Admissible transforms: affine amplitude scaling removed by mean/variance normalization -- Compression use: predictor suitability signal -- FPGA use: sliding dot product and norm lane - -### SIGROOT033_cosine_similarity - -- Equation: `cos(theta)=/(||a|| ||b||)` -- Invariant root: projective direction on spectral feature sphere -- Admissible transforms: positive scaling of either vector -- Compression use: chunk reuse / skip decision -- FPGA use: dot product and reciprocal norm threshold diff --git a/4-Infrastructure/shim/singular_route_chart_equations.py b/4-Infrastructure/shim/singular_route_chart_equations.py deleted file mode 100644 index 2a9fc036..00000000 --- a/4-Infrastructure/shim/singular_route_chart_equations.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -"""Singular Route Chart equation group. - -This crystallizes the scattered singularity logic in the Decision Diagram -Compression Tuning Prior into a named equation group. It is a fail-closed -route-control surface: singular regions may choose finite charts and exact -residual repair, but they cannot promote without decode/hash/byte-count -verification. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -DEFAULT_SOURCE = Path( - "6-Documentation/tiddlywiki-local/wiki/tiddlers/Decision Diagram Compression Tuning Prior.tid" -) -DEFAULT_RECEIPT = Path("4-Infrastructure/shim/singular_route_chart_equations_receipt.json") -DEFAULT_CURRICULUM = Path("4-Infrastructure/shim/singular_route_chart_equations_curriculum.jsonl") - - -SINGULAR_MARKERS = ( - "singular", - "singularity", - "NaN0", - "FiniteBundleChart", - "canonical blow-up", - "blow-up ranking", - "unresolved_metadata_hold", - "fail closed", -) - - -SINGULAR_ROUTE_CHART = { - "name": "SingularRouteChart", - "purpose": ( - "Turn singular, non-transverse, unbounded, ambiguous, or metadata-held " - "route regions into finite chart attempts with ranked failure and exact " - "residual repair." - ), - "primary_function": "detect -> chart -> rank -> bound -> repair -> verify_or_NaN0", - "claim_boundary": ( - "A singular chart is a route-control and fail-closed device. It is not " - "compression evidence unless decoded bytes hash to the source and total " - "measured bytes beat the incumbent." - ), -} - - -EQUATIONS = [ - { - "id": "SRC0_detect_singularity", - "equation": "sigma(r) = class(route_region_r)", - "meaning": ( - "Classify the route region as regular, singular, non_transverse, " - "non_locally_trivial, unbounded, ambiguous, or metadata_hold." - ), - "inputs": ["route_region_r", "receipt_class", "claim_boundary_status"], - "outputs": ["singularity_class", "singularity_status"], - "fail_closed_when": "class is unbounded or metadata is unverified", - }, - { - "id": "SRC1_choose_finite_chart", - "equation": "C_s = framework(sigma(r), failure_mode_r)", - "meaning": ( - "Select the smallest finite chart family for the singular region: " - "derived_stack, diffeological_pseudobundle, banach_hilbert_bundle, " - "principal_infinity_bundle, noncommutative_bundle, or fredholm_bundle." - ), - "inputs": ["singularity_class", "failure_mode_r"], - "outputs": ["framework_family_id", "bundle_chart_id", "finite_rank_proxy_id"], - "fail_closed_when": "no finite chart can be explicit", - }, - { - "id": "SRC2_project_to_finite_proxy", - "equation": ( - "RouteChart_c = P_finite(Section_c, framework_family_id, " - "norm_bound_id, gauge_coherence_receipt) + exact_residual_lane_c" - ), - "meaning": ( - "Never serialize the infinite or singular object. Project it into a " - "finite proxy and attach an exact residual lane." - ), - "inputs": [ - "local_section_id", - "framework_family_id", - "norm_bound_id", - "gauge_coherence_receipt", - "source_hash", - ], - "outputs": ["finite_rank_proxy_id", "exact_residual_lane_id"], - "fail_closed_when": "finite proxy lacks exact residual repair", - }, - { - "id": "SRC3_rank_blowup_failure", - "equation": "rho_{t+1} < rho_t for every repair step", - "meaning": ( - "Use a well-founded blow-up rank so singular repair cannot become " - "recursive search." - ), - "inputs": ["blowup_rank_t", "repair_step_t"], - "outputs": ["blowup_rank_next", "repair_path_depth"], - "fail_closed_when": "rank does not decrease or repair depth exceeds budget", - }, - { - "id": "SRC4_bound_singular_cost", - "equation": ( - "LB_s = chart_header + singularity_receipt + rank_receipt + " - "norm_bound_receipt + exact_residual_floor" - ), - "meaning": ( - "Charge singularity handling before expensive evaluation. Prune if " - "the lower bound cannot beat the incumbent." - ), - "inputs": [ - "chart_header_bytes", - "singularity_receipt_bytes", - "rank_receipt_bytes", - "norm_bound_receipt_bytes", - "exact_residual_floor", - ], - "outputs": ["singular_lower_bound_bytes"], - "fail_closed_when": "lower bound exceeds incumbent", - }, - { - "id": "SRC5_verify_or_nan0", - "equation": ( - "close_s iff hash(decode(RouteChart_c)) == source_hash and " - "nan0_flag == 0" - ), - "meaning": "The singular chart closes only through byte-exact decode and NaN0 false.", - "inputs": ["RouteChart_c", "source_hash", "nan0_flag"], - "outputs": ["byte_rehydration_hash", "closure_status"], - "fail_closed_when": "decode hash mismatches or nan0_flag is true", - }, - { - "id": "SRC6_promote_singular_route", - "equation": ( - "promote_s iff close_s and total_bytes_s < incumbent_bytes and " - "ratio_schema is explicit" - ), - "meaning": ( - "Promotion authority remains measured bytes and exact hash; singular " - "math only controls safe charting and pruning." - ), - "inputs": ["closure_status", "total_bytes_s", "incumbent_bytes", "ratio_schema"], - "outputs": ["promotion_status"], - "fail_closed_when": "any receipt, byte count, or ratio schema is missing", - }, -] - - -DD_STATE_EXTENSION = [ - "singular_route_chart_id", - "singularity_class", - "singularity_status", - "framework_family_id", - "bundle_chart_id", - "finite_rank_proxy_id", - "norm_bound_id", - "gauge_coherence_receipt_id", - "index_witness_id", - "blowup_rank", - "repair_path_depth", - "singular_lower_bound_bytes", - "exact_residual_lane_id", - "nan0_flag", - "byte_rehydration_hash", -] - - -CANDIDATE_EDGES = [ - "detect_singular_route_region", - "choose_singular_finite_chart", - "project_singular_to_finite_proxy", - "rank_blowup_failure", - "bound_singular_route_cost", - "emit_singular_exact_residual", - "verify_singular_decode_hash", - "promote_singular_route_or_nan0", -] - - -def stable_hash(obj: Any) -> str: - payload = json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - return hashlib.sha256(payload.encode("utf-8")).hexdigest() - - -def source_surface(text: str) -> str: - marker = "\n!! Citation Math Function Distillation\n" - if marker not in text: - return text - before, after = text.split(marker, 1) - next_marker = "\n!! Where To Tune Next\n" - if next_marker in after: - _, tail = after.split(next_marker, 1) - return before + next_marker + tail - return before - - -def extract_singular_evidence(source_text: str) -> dict[str, Any]: - lines = source_surface(source_text).splitlines() - hits: list[dict[str, Any]] = [] - for idx, line in enumerate(lines, start=1): - folded = line.lower() - if any(marker.lower() in folded for marker in SINGULAR_MARKERS): - hits.append({"line": idx, "text": line.strip()}) - return { - "matched_line_count": len(hits), - "markers": list(SINGULAR_MARKERS), - "sample_hits": hits[:40], - "source_surface_sha256": hashlib.sha256(source_surface(source_text).encode("utf-8")).hexdigest(), - } - - -def build_receipt(source: Path) -> dict[str, Any]: - source_text = source.read_text(encoding="utf-8") - evidence = extract_singular_evidence(source_text) - receipt: dict[str, Any] = { - "schema": "singular_route_chart_equations_v1", - "generated_at": "2026-05-08T00:00:00+00:00", - "source_tiddler": str(source), - "source_surface_scope": ( - "tiddler excluding generated Citation Math Function Distillation and " - "Singular Route Chart Equation Group sections" - ), - "singular_evidence": evidence, - "singular_route_chart": SINGULAR_ROUTE_CHART, - "equations": EQUATIONS, - "dd_state_extension": DD_STATE_EXTENSION, - "candidate_edges": CANDIDATE_EDGES, - "promotion_rule": ( - "promote singular route iff finite chart is explicit, blow-up rank " - "decreases, lower bound beats incumbent, exact residual repairs bytes, " - "decoded hash matches, nan0_flag is false, and measured bytes beat " - "the incumbent under an explicit ratio_schema" - ), - "failure_rule": ( - "unbounded section, non-decreasing repair rank, missing singularity " - "receipt, hidden payload in chart, metadata hold, hash mismatch, or " - "NaN0 all fail closed" - ), - } - receipt["receipt_hash"] = stable_hash(receipt) - return receipt - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = ( - "You are a SingularRouteChart controller. Convert singular route " - "regions into finite, receipted, byte-exact chart attempts or fail closed." - ) - records: list[dict[str, Any]] = [] - for equation in receipt["equations"]: - records.append( - { - "messages": [ - {"role": "system", "content": system}, - { - "role": "user", - "content": json.dumps( - { - "task": "apply_singular_route_chart_equation", - "equation_id": equation["id"], - "equation": equation["equation"], - "inputs": equation["inputs"], - }, - ensure_ascii=False, - ), - }, - { - "role": "assistant", - "content": json.dumps( - { - "outputs": equation["outputs"], - "meaning": equation["meaning"], - "fail_closed_when": equation["fail_closed_when"], - "promotion_authority": "decode/hash/byte-count receipt", - }, - ensure_ascii=False, - ), - }, - ] - } - ) - return records - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--source", type=Path, default=DEFAULT_SOURCE) - parser.add_argument("--receipt", type=Path, default=DEFAULT_RECEIPT) - parser.add_argument("--curriculum", type=Path, default=DEFAULT_CURRICULUM) - args = parser.parse_args() - - receipt = build_receipt(args.source) - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/smn_tool_awareness_registry.py b/4-Infrastructure/shim/smn_tool_awareness_registry.py deleted file mode 100644 index 8c1464d7..00000000 --- a/4-Infrastructure/shim/smn_tool_awareness_registry.py +++ /dev/null @@ -1,205 +0,0 @@ -#!/usr/bin/env python3 -"""Emit the tool-facing SMN naming boundary registry. - -This keeps downstream shims from conflating: - -- Semantic Mass: raw semantic/routing pressure -- SMN: Semantic Mass Number, a counted semantic-load index -- Mass Number: admissibility packet with residual and boundary guard - -The registry is a routing/validation aid only. It does not promote SMN scores to -truth claims or Mass Number receipts. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "stack_solidification" -REGISTRY = OUT_DIR / "smn_tool_awareness_registry.json" -RECEIPT = OUT_DIR / "smn_tool_awareness_receipt.json" - -CANONICAL_FILES = [ - REPO / "6-Documentation/docs/specs/SMN_SEMANTIC_MASS_NUMBERS.md", - REPO / "6-Documentation/tiddlywiki-local/wiki/tiddlers/Semantic Mass Numbers.tid", - REPO / "6-Documentation/tiddlywiki-local/wiki/tiddlers/Mass Number Theory.tid", - REPO / "6-Documentation/wiki/Concept-Archive.md", - REPO / "0-Core-Formalism/otom/docs/wiki/NotationNomenclatureRegistry.md", - REPO / "shared-data/data/stellar_gas_observation/sdss_manga_dr17_emission_line_channels.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def file_entry(path: Path) -> dict[str, Any]: - return { - "path": rel(path), - "exists": path.exists(), - "sha256": sha256_bytes(path.read_bytes()) if path.exists() else None, - } - - -def load_json(path: Path) -> Any: - return json.loads(path.read_text(encoding="utf-8")) - - -def smn_data_gate() -> dict[str, Any]: - path = REPO / "shared-data/data/stellar_gas_observation/sdss_manga_dr17_emission_line_channels.json" - if not path.exists(): - return {"status": "MISSING", "path": rel(path)} - data = load_json(path) - failures: list[dict[str, Any]] = [] - channel_count = 0 - ratio_count = 0 - for kind, items in (("channel", data.get("channels", [])), ("diagnostic_ratio", data.get("diagnostic_ratios", []))): - for item in items: - payload = item.get("semantic_mass_number") - object_id = item.get("label") or item.get("id") - if not payload: - failures.append({"kind": kind, "object_id": object_id, "failure": "missing_semantic_mass_number"}) - continue - components = payload.get("components", {}) - expected = sum(int(value) for value in components.values()) - if payload.get("smn") != expected: - failures.append({"kind": kind, "object_id": object_id, "failure": "component_sum_mismatch"}) - if payload.get("not_atomic_mass_number") is not True: - failures.append({"kind": kind, "object_id": object_id, "failure": "missing_not_atomic_mass_number_guard"}) - if payload.get("not_mass_number_receipt") is not True: - failures.append({"kind": kind, "object_id": object_id, "failure": "missing_not_mass_number_receipt_guard"}) - if kind == "channel": - channel_count += 1 - else: - ratio_count += 1 - return { - "status": "PASS" if not failures else "FAIL", - "path": rel(path), - "channels_checked": channel_count, - "diagnostic_ratios_checked": ratio_count, - "failures": failures, - } - - -def nomenclature_gate() -> dict[str, Any]: - path = REPO / "0-Core-Formalism/otom/docs/wiki/NotationNomenclatureRegistry.md" - text = path.read_text(encoding="utf-8") if path.exists() else "" - required = [ - "mass_number != SMN", - "SMN != atomic_mass_number", - "SMN != Mass_Number_receipt", - "| `smn` | Semantic Mass Number |", - "Do not use this as an alias for SMN", - ] - missing = [item for item in required if item not in text] - forbidden = ["| `mass_number` | Mass Number | semantic mass number"] - forbidden_hits = [item for item in forbidden if item in text] - return { - "status": "PASS" if not missing and not forbidden_hits else "FAIL", - "path": rel(path), - "missing": missing, - "forbidden_hits": forbidden_hits, - } - - -def build_registry() -> dict[str, Any]: - registry = { - "schema": "smn_tool_awareness_registry_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": "Tool awareness only. SMN routes attention; it is not physical mass, proof, truth, or a Mass Number admissibility receipt.", - "canonical_terms": [ - { - "term": "Semantic Mass", - "tool_key": "semantic_mass", - "meaning": "raw semantic/routing pressure or burden", - "not_equal_to": ["SMN", "Mass Number", "physical mass"], - }, - { - "term": "SMN", - "expanded": "Semantic Mass Number", - "tool_key": "smn", - "meaning": "countable project-local semantic-load number for a symbol, channel, ratio, route, or gate", - "formula": "SMN(x)=identity_load+relation_load+provenance_load+constraint_load+decision_load+repair_load", - "not_equal_to": ["atomic mass number", "isotope mass", "SI mass", "Mass Number admissibility packet", "proof", "truth"], - }, - { - "term": "Mass Number", - "tool_key": "mass_number", - "meaning": "admissibility/accounting packet with residual and boundary guard", - "not_equal_to": ["SMN", "atomic mass number", "physical mass"], - }, - ], - "normalization_rules": [ - { - "match": "semantic mass number", - "normalize_to": "SMN", - "reason": "Avoid aliasing Mass Number admissibility packets.", - }, - { - "match": "mass_number", - "normalize_to": "Mass Number", - "reason": "Keep existing admissibility-gate code paths intact.", - }, - { - "match": "semantic_mass_number", - "normalize_to": "SMN data payload", - "reason": "Machine-readable score object, not physical mass and not proof.", - }, - ], - "tool_rules": { - "search_index": "Index SMN as semantic-load terminology; do not merge with MassNumber gate entries.", - "tiddlywiki": "Link SMN references to [[Semantic Mass Numbers]] and Mass Number gate references to [[Mass Number Theory]].", - "equation_awareness": "Classify SMN formulas as semantic_load, not mass_number_transform.", - "stellar_gas_tools": "Read semantic_mass_number payloads as routing/audit priority metadata only.", - "promotion_gate": "High SMN may prioritize review but cannot admit without a receipt gate.", - }, - "canonical_files": [file_entry(path) for path in CANONICAL_FILES], - "gates": { - "nomenclature": nomenclature_gate(), - "smn_data": smn_data_gate(), - }, - } - registry["registry_hash"] = hash_obj({k: v for k, v in registry.items() if k != "registry_hash"}) - return registry - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - status = "PASS" if all(gate.get("status") == "PASS" for gate in registry["gates"].values()) else "FAIL" - receipt = { - "schema": "smn_tool_awareness_receipt_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "decision": "ADMIT_SMN_TOOL_AWARENESS" if status == "PASS" else "HOLD_SMN_TOOL_AWARENESS", - "claim_boundary": registry["claim_boundary"], - "registry": rel(REGISTRY), - "registry_hash": registry["registry_hash"], - "status": status, - "gates": registry["gates"], - } - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"registry": rel(REGISTRY), "receipt": rel(RECEIPT), "status": status}, indent=2)) - return 0 if status == "PASS" else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/solids_physics_kernel_probe.py b/4-Infrastructure/shim/solids_physics_kernel_probe.py deleted file mode 100644 index 9350c0c9..00000000 --- a/4-Infrastructure/shim/solids_physics_kernel_probe.py +++ /dev/null @@ -1,350 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed solids-physics kernel probe. - -This probe adds a small solids-domain route surface. It admits exact local -linear-elastic algebra fixtures and keeps anisotropy, plasticity, fracture, -wave-speed square roots, geometry, and boundary-value claims in HOLD. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from fractions import Fraction -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "solids_physics_kernels" -REGISTRY = OUT_DIR / "solids_physics_kernel_registry.json" -RECEIPT = OUT_DIR / "solids_physics_kernel_receipt.json" -SUMMARY = OUT_DIR / "solids_physics_kernel.md" - -SOURCE_REFS = [ - REPO / "shared-data/data/mass_number_transform_registry/mass_number_transform_registry_receipt.json", - REPO / "shared-data/data/cross_domain_kernel_adapters/cross_domain_kernel_adapter_registry_receipt.json", -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def frac_payload(value: Fraction | tuple[Fraction, ...]) -> Any: - if isinstance(value, tuple): - return [frac_payload(item) for item in value] - return {"numerator": value.numerator, "denominator": value.denominator, "decimal": float(value)} - - -def mn(a: Fraction, b: Fraction) -> Fraction: - return (a - b) / (a + b) - - -def hooke_stress(E: Fraction, strain: Fraction) -> Fraction: - return E * strain - - -def elastic_energy_from_strain(E: Fraction, strain: Fraction) -> Fraction: - return E * strain * strain / 2 - - -def elastic_energy_from_stress(stress: Fraction, E: Fraction) -> Fraction: - return stress * stress / (2 * E) - - -def d_energy_d_strain(E: Fraction, strain: Fraction) -> Fraction: - return E * strain - - -def isotropic_shear_modulus(E: Fraction, nu: Fraction) -> Fraction: - return E / (2 * (1 + nu)) - - -def isotropic_bulk_modulus(E: Fraction, nu: Fraction) -> Fraction: - return E / (3 * (1 - 2 * nu)) - - -def harmonic_equal_thickness_modulus(E1: Fraction, E2: Fraction) -> Fraction: - return 2 * E1 * E2 / (E1 + E2) - - -def harmonic_from_mn(total: Fraction, x: Fraction) -> Fraction: - return total * (1 - x * x) / 2 - - -def check_equal(name: str, compressed: Any, direct: Any) -> dict[str, Any]: - return { - "name": name, - "compressed": frac_payload(compressed), - "direct": frac_payload(direct), - "pass": compressed == direct, - } - - -def entry( - *, - entry_id: str, - kernel_opcode: str, - solids_role: str, - compressed_form: str, - expanded_form: str, - checks: list[dict[str, Any]], - decision: str, - residual_policy: str, -) -> dict[str, Any]: - item = { - "entry_id": entry_id, - "kernel_opcode": kernel_opcode, - "solids_role": solids_role, - "compressed_form": compressed_form, - "expanded_form": expanded_form, - "checks": checks, - "all_checks_pass": all(check.get("pass", False) for check in checks) if checks else None, - "decision": decision, - "residual_policy": residual_policy, - "claim_boundary": "solids route fixture only; not a finite-element solver or material model", - } - item["entry_hash"] = hash_obj({k: v for k, v in item.items() if k != "entry_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - E = Fraction(12) - strain = Fraction(1, 4) - stress = hooke_stress(E, strain) - nu = Fraction(1, 4) - E1 = Fraction(5) - E2 = Fraction(3) - total = E1 + E2 - x = mn(E1, E2) - - entries = [ - entry( - entry_id="hooke_1d_stress", - kernel_opcode="LINEAR_MAP", - solids_role="one-dimensional linear elastic stress-strain map", - compressed_form="sigma = E*epsilon", - expanded_form="Hooke 1D linear elasticity", - checks=[check_equal("sigma_E12_eps1_4", stress, Fraction(3))], - decision="ACCEPT_LINEAR_ELASTIC_FIXTURE", - residual_policy="small-strain 1D fixture only; tensor strain, boundary conditions, and material range require receipts", - ), - entry( - entry_id="elastic_energy_density", - kernel_opcode="DERIV_QUADRATIC", - solids_role="quadratic elastic energy and conjugate stress derivative", - compressed_form="U = E*epsilon^2/2; dU/depsilon = sigma", - expanded_form="U = sigma*epsilon/2 = sigma^2/(2E)", - checks=[ - check_equal("energy_strain_vs_stress", elastic_energy_from_strain(E, strain), elastic_energy_from_stress(stress, E)), - check_equal("dU_deps_equals_sigma", d_energy_d_strain(E, strain), stress), - ], - decision="ACCEPT_DERIVATIVE_FIXTURE", - residual_policy="linear-elastic scalar fixture only; path dependence and plastic work stay residualized", - ), - entry( - entry_id="isotropic_moduli_transform", - kernel_opcode="RATIONAL_MATERIAL_TRANSFORM", - solids_role="Young/Poisson to shear and bulk modulus transform", - compressed_form="G=E/(2*(1+nu)); K=E/(3*(1-2*nu))", - expanded_form="isotropic linear elastic moduli relations", - checks=[ - check_equal("G_E12_nu1_4", isotropic_shear_modulus(E, nu), Fraction(24, 5)), - check_equal("K_E12_nu1_4", isotropic_bulk_modulus(E, nu), Fraction(8)), - ], - decision="ACCEPT_ISOTROPIC_FIXTURE", - residual_policy="isotropic linear-elastic fixture only; anisotropy and near-incompressibility require domain receipts", - ), - entry( - entry_id="equal_thickness_series_modulus", - kernel_opcode="MN_PAIR_HARMONIC", - solids_role="two-layer equal-thickness series effective modulus", - compressed_form="E_eff = S/2*(1-MN(E1,E2)^2)", - expanded_form="E_eff = 2*E1*E2/(E1+E2)", - checks=[check_equal("series_E5_3", harmonic_from_mn(total, x), harmonic_equal_thickness_modulus(E1, E2))], - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="equal-thickness 1D series fixture only; laminate orientation, shear coupling, and boundary conditions require receipts", - ), - entry( - entry_id="elastic_impedance_contrast", - kernel_opcode="MN_REFLECT", - solids_role="elastic/acoustic impedance contrast candidate at a solid boundary", - compressed_form="Gamma_Z = MN(Z2,Z1)", - expanded_form="Gamma_Z = (Z2-Z1)/(Z2+Z1)", - checks=[check_equal("solid_impedance_mn_9_4", mn(Fraction(9), Fraction(4)), Fraction(5, 13))], - decision="ACCEPT_KERNEL_ADAPTER", - residual_policy="contrast identity only; wave mode, incidence angle, attenuation, and boundary conditions require receipts", - ), - entry( - entry_id="longitudinal_wave_speed_route", - kernel_opcode="ANALYTIC_SQRT_RATIO", - solids_role="elastic wave-speed route", - compressed_form="c = sqrt(E/rho) or tensor generalization", - expanded_form="wave speed candidate", - checks=[], - decision="HOLD_ANALYTIC_ADAPTER", - residual_policy="requires square-root precision, density/source receipts, mode convention, and material assumptions", - ), - entry( - entry_id="von_mises_yield_route", - kernel_opcode="STRESS_INVARIANT", - solids_role="distortional-energy yield criterion route", - compressed_form="sigma_vm = invariant(stress deviator)", - expanded_form="von Mises candidate", - checks=[], - decision="HOLD_PLASTICITY_ADAPTER", - residual_policy="requires tensor convention, yield surface, hardening law, loading path, and experimental/source receipt", - ), - entry( - entry_id="fracture_toughness_route", - kernel_opcode="ANALYTIC_SQRT_GEOMETRY", - solids_role="crack-tip stress intensity route", - compressed_form="K = Y*sigma*sqrt(pi*a)", - expanded_form="linear elastic fracture mechanics candidate", - checks=[], - decision="HOLD_FRACTURE_ADAPTER", - residual_policy="requires crack geometry, plane stress/strain convention, units, boundary conditions, and source receipt", - ), - entry( - entry_id="anisotropic_stiffness_tensor_route", - kernel_opcode="TENSOR_CONSTITUTIVE_ADAPTER", - solids_role="anisotropic stiffness/compliance tensor route", - compressed_form="sigma_ij = C_ijkl*epsilon_kl", - expanded_form="anisotropic Hooke law candidate", - checks=[], - decision="HOLD_TENSOR_ADAPTER", - residual_policy="requires tensor index convention, symmetry class, units, material source, and closure receipt", - ), - ] - return { - "schema": "solids_physics_kernel_registry_v1", - "claim_boundary": ( - "Solids-physics route registry only. Exact local linear-elastic algebra " - "fixtures may be accepted, but anisotropic, plasticity, fracture, wave, " - "geometry, boundary-value, and material-model claims stay HOLD until " - "source data, units, conventions, and residual policies are receipted." - ), - "canonical_statement": ( - "Solids physics exposes reusable linear maps, quadratic derivative " - "kernels, rational modulus transforms, and MN boundary contrasts; " - "material truth lives behind adapter closure." - ), - "entries": entries, - "entry_count": len(entries), - "status_counts": { - status: sum(1 for item in entries if item["decision"] == status) - for status in sorted({item["decision"] for item in entries}) - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - accepted_statuses = { - "ACCEPT_LINEAR_ELASTIC_FIXTURE", - "ACCEPT_DERIVATIVE_FIXTURE", - "ACCEPT_ISOTROPIC_FIXTURE", - "ACCEPT_KERNEL_ADAPTER", - } - accepted_checks_pass = all( - item["all_checks_pass"] is True - for item in registry["entries"] - if item["decision"] in accepted_statuses - ) - receipt = { - "schema": "solids_physics_kernel_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry_path": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "entry_count": registry["entry_count"], - "status_counts": registry["status_counts"], - "accepted_checks_pass": accepted_checks_pass, - "decision": "HOLD_SOLIDS_DOMAIN_WITH_ACCEPTED_FIXTURES" if accepted_checks_pass else "HOLD_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Solids Physics Kernel Probe", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Entry Table", - "", - "| Entry | Kernel | Decision | Role |", - "|---|---|---|---|", - ] - for item in registry["entries"]: - lines.append( - f"| `{item['entry_id']}` | `{item['kernel_opcode']}` | " - f"`{item['decision']}` | {item['solids_role']} |" - ) - lines.extend(["", "## Guardrail", "", registry["claim_boundary"]]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "receipt_hash": receipt["receipt_hash"], - "decision": receipt["decision"], - "status_counts": registry["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/solved_math_pruning_surface.py b/4-Infrastructure/shim/solved_math_pruning_surface.py deleted file mode 100644 index 1f636d77..00000000 --- a/4-Infrastructure/shim/solved_math_pruning_surface.py +++ /dev/null @@ -1,166 +0,0 @@ -#!/usr/bin/env python3 -"""Build an admissible-prior index from the local math model map. - -This does not claim the listed equations are formal proofs. It classifies local -model-map entries into evidence tiers so compression/logogram/FPGA searches can -try known solved or implemented shapes first, then fall back to wider search. -""" - -from __future__ import annotations - -import argparse -import csv -import json -from collections import Counter -from pathlib import Path -from typing import Any - - -DEFAULT_MODEL_MAP = Path("3-Mathematical-Models/MATH_MODEL_MAP.tsv") - - -def evidence_tier(row: dict[str, str]) -> str: - implemented = row.get("Implemented", "") - status = row.get("Status", "") - location = row.get("Location", "") - purpose = row.get("Purpose", "") - haystack = " ".join([implemented, status, location, purpose]).lower() - - if "lean" in haystack and "✅" in status: - return "formal_or_lean_backed" - if any(token in haystack for token in ["verilog", "fpga", "hardware"]) and "✅" in status: - return "hardware_or_hdl_backed" - if any(token in implemented.lower() for token in ["python", "rust", "c++", "6502", "subl"]) and "✅" in status: - return "implemented_local" - if implemented.lower() == "spec" and "✅" in status: - return "spec_admissible" - if implemented.lower() == "documented" and "✅" in status: - return "documented_reference" - if "✅" in status: - return "indexed_admissible" - return "unverified_or_pending" - - -def tier_rank(tier: str) -> int: - ranks = { - "formal_or_lean_backed": 0, - "hardware_or_hdl_backed": 1, - "implemented_local": 2, - "spec_admissible": 3, - "documented_reference": 4, - "indexed_admissible": 5, - "unverified_or_pending": 6, - } - return ranks.get(tier, 9) - - -def row_score(row: dict[str, str], tier: str) -> int: - score = 100 - tier_rank(tier) * 10 - domain = row.get("Domain_Type", "") - bind = row.get("Bind_Class", "") - family = row.get("Family", "") - purpose = row.get("Purpose", "") - text = " ".join([domain, bind, family, purpose]).lower() - for token in ("compression", "encoding", "signal", "control", "routing", "hardware", "fpga"): - if token in text: - score += 3 - if row.get("Location", "").startswith(("http://", "https://")): - score -= 5 - return score - - -def load_rows(path: Path) -> list[dict[str, str]]: - with path.open(newline="", encoding="utf-8") as handle: - reader = csv.DictReader(handle, delimiter="\t") - rows = [] - for row in reader: - clean = {} - for key, value in row.items(): - if key is None: - if value: - clean["Extra"] = " ".join(str(part) for part in value) - continue - clean[key] = str(value or "") - rows.append(clean) - return rows - - -def matches_query(row: dict[str, str], query: str) -> bool: - if not query: - return True - q = query.lower() - return q in " ".join(row.values()).lower() - - -def build_index(rows: list[dict[str, str]], query: str, include_documented: bool) -> dict[str, Any]: - entries = [] - for row in rows: - if not matches_query(row, query): - continue - tier = evidence_tier(row) - if tier == "unverified_or_pending": - continue - if not include_documented and tier == "documented_reference": - continue - entry = { - "id": row.get("#", ""), - "model_name": row.get("Model_Name", ""), - "family": row.get("Family", ""), - "equation": row.get("Equation", ""), - "variables": row.get("Variables", ""), - "purpose": row.get("Purpose", ""), - "location": row.get("Location", ""), - "implemented": row.get("Implemented", ""), - "status": row.get("Status", ""), - "domain_type": row.get("Domain_Type", ""), - "bind_class": row.get("Bind_Class", ""), - "evidence_tier": tier, - "pruning_score": row_score(row, tier), - } - entries.append(entry) - - entries.sort(key=lambda item: (-item["pruning_score"], tier_rank(item["evidence_tier"]), item["model_name"])) - - by_tier = Counter(entry["evidence_tier"] for entry in entries) - by_domain = Counter(entry["domain_type"] for entry in entries if entry["domain_type"]) - by_bind = Counter(entry["bind_class"] for entry in entries if entry["bind_class"]) - return { - "schema": "solved_math_pruning_surface_v1", - "claim_boundary": "Rows are admissible search priors, not theorem claims unless their evidence tier says Lean/formal.", - "source": str(DEFAULT_MODEL_MAP), - "query": query, - "include_documented": include_documented, - "entry_count": len(entries), - "summary": { - "by_evidence_tier": dict(by_tier), - "top_domain_types": by_domain.most_common(12), - "top_bind_classes": by_bind.most_common(12), - }, - "entries": entries, - } - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--model-map", type=Path, default=DEFAULT_MODEL_MAP) - parser.add_argument("--query", default="") - parser.add_argument("--include-documented", action="store_true") - parser.add_argument("--limit", type=int, default=50) - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - rows = load_rows(args.model_map) - index = build_index(rows, args.query, args.include_documented) - index["source"] = str(args.model_map) - if args.limit >= 0: - index["entries"] = index["entries"][: args.limit] - text = json.dumps(index, indent=2, ensure_ascii=False) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/solved_problem_output_verifier.py b/4-Infrastructure/shim/solved_problem_output_verifier.py deleted file mode 100644 index f3930666..00000000 --- a/4-Infrastructure/shim/solved_problem_output_verifier.py +++ /dev/null @@ -1,388 +0,0 @@ -#!/usr/bin/env python3 -"""Run solved/known-problem checks and emit verification receipts. - -This harness is deliberately conservative. It reruns a small set of existing -4-primitive Erdős scripts whose targets are solved theorems, solved lower-bound -smokes, or finite constructions. Open conjecture smoke tests are listed as -excluded so they cannot be promoted to theorem evidence by accident. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Callable - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def ratio_ok(value: float, target: float = 1.0, eps: float = 1e-12) -> bool: - return abs(float(value) - target) <= eps - - -def validate_egz(data: dict[str, Any]) -> tuple[bool, dict[str, Any]]: - analysis = data.get("theorem_analysis", {}) - results = data.get("results", []) - packet_witness_ok = sum( - 1 - for item in results - if item.get("subset_found") - and item.get("subset") - and sum(item["subset"]) % int(item["n"]) == 0 - and len(item["subset"]) == int(item["n"]) - ) - ok = ( - analysis.get("subset_found_count") == analysis.get("total_tests") - and ratio_ok(analysis.get("success_rate", 0.0)) - and packet_witness_ok == len(results) - ) - return ok, { - "subset_found_count": analysis.get("subset_found_count"), - "total_tests": analysis.get("total_tests"), - "success_rate": analysis.get("success_rate"), - "packet_witness_ok": packet_witness_ok, - } - - -def validate_ekr(data: dict[str, Any]) -> tuple[bool, dict[str, Any]]: - analysis = data.get("theorem_analysis", []) - results = data.get("results", []) - ratio_hits = sum(1 for item in analysis if ratio_ok(item.get("ratio", 0.0))) - family_ok = sum(1 for item in results if item.get("is_intersecting") and item.get("family_size") == item.get("field", {}).get("theoretical_max")) - ok = bool(analysis) and ratio_hits == len(analysis) and family_ok == len(results) - return ok, { - "ratio_hits": ratio_hits, - "analysis_count": len(analysis), - "intersecting_optimal_family_count": family_ok, - "result_count": len(results), - } - - -def validate_szekeres(data: dict[str, Any]) -> tuple[bool, dict[str, Any]]: - analysis = data.get("theorem_analysis", {}) - results = data.get("results", []) - witness_ok = sum( - 1 - for item in results - if item.get("theorem_holds") and int(item.get("max_monotone_length", 0)) >= int(item.get("n", 0)) + 1 - ) - ok = ( - analysis.get("theorem_holds_count") == analysis.get("total_tests") - and ratio_ok(analysis.get("success_rate", 0.0)) - and witness_ok == len(results) - ) - return ok, { - "theorem_holds_count": analysis.get("theorem_holds_count"), - "total_tests": analysis.get("total_tests"), - "success_rate": analysis.get("success_rate"), - "monotone_witness_ok": witness_ok, - } - - -def validate_distinct_distances(data: dict[str, Any]) -> tuple[bool, dict[str, Any]]: - analysis = data.get("problem_analysis", {}) - results = data.get("results", []) - bound_ok = sum( - 1 - for item in results - if item.get("bound_holds") - and item.get("num_distinct_distances", 0) >= item.get("theoretical_bound", float("inf")) - ) - ok = ( - analysis.get("bound_holds_count") == analysis.get("total_tests") - and ratio_ok(analysis.get("success_rate", 0.0)) - and bound_ok == len(results) - ) - return ok, { - "bound_holds_count": analysis.get("bound_holds_count"), - "total_tests": analysis.get("total_tests"), - "success_rate": analysis.get("success_rate"), - "per_instance_bound_ok": bound_ok, - } - - -def validate_hadamard_sylvester(data: dict[str, Any]) -> tuple[bool, dict[str, Any]]: - analysis = data.get("conjecture_analysis", {}) - results = data.get("results", []) - construction_ok = sum( - 1 - for item in results - if item.get("hadamard_exists") and item.get("spectral", {}).get("is_orthogonal") - ) - ok = ( - analysis.get("hadamard_exists_count") == analysis.get("total_tests") - and ratio_ok(analysis.get("existence_rate", 0.0)) - and construction_ok == len(results) - ) - return ok, { - "hadamard_exists_count": analysis.get("hadamard_exists_count"), - "total_tests": analysis.get("total_tests"), - "existence_rate": analysis.get("existence_rate"), - "orthogonal_construction_ok": construction_ok, - "boundary_note": analysis.get("note"), - } - - -CaseValidator = Callable[[dict[str, Any]], tuple[bool, dict[str, Any]]] - - -CASES: list[dict[str, Any]] = [ - { - "id": "erdos_ginzburg_ziv", - "title": "Erdos-Ginzburg-Ziv theorem", - "classification": "solved_theorem", - "script": "test_erdos_ginzburg_ziv_4primitive.py", - "result": "test_erdos_ginzburg_ziv_4primitive_results.json", - "validator": validate_egz, - "claim_boundary": "Finite generated instances verify the local witness detector against a solved theorem; this is not a proof of the theorem.", - }, - { - "id": "erdos_ko_rado", - "title": "Erdos-Ko-Rado theorem", - "classification": "solved_theorem", - "script": "test_erdos_ko_rado_4primitive.py", - "result": "test_erdos_ko_rado_4primitive_results.json", - "validator": validate_ekr, - "claim_boundary": "Finite small parameter checks verify the local family detector against a solved theorem; this is not a proof of the theorem.", - }, - { - "id": "erdos_szekeres", - "title": "Erdos-Szekeres monotone subsequence theorem", - "classification": "solved_theorem", - "script": "test_erdos_szekeres_4primitive.py", - "result": "test_erdos_szekeres_4primitive_results.json", - "validator": validate_szekeres, - "claim_boundary": "Finite random permutations verify the local monotone-subsequence detector against a solved theorem; this is not a proof of the theorem.", - }, - { - "id": "erdos_distinct_distances", - "title": "Erdos distinct distances lower-bound smoke", - "classification": "solved_bound_smoke", - "script": "test_erdos_distinct_distances_4primitive.py", - "result": "test_erdos_distinct_distances_4primitive_results.json", - "validator": validate_distinct_distances, - "claim_boundary": "Finite point clouds verify the local lower-bound checker; this does not solve or certify the full extremal geometry result.", - }, - { - "id": "hadamard_sylvester", - "title": "Hadamard Sylvester construction", - "classification": "finite_construction", - "script": "test_erdos_hadamard_4primitive.py", - "result": "test_erdos_hadamard_4primitive_results.json", - "validator": validate_hadamard_sylvester, - "claim_boundary": "Verifies Sylvester power-of-two Hadamard constructions only; the general Hadamard conjecture remains open.", - }, -] - - -EXCLUDED_CASES = [ - { - "id": "erdos_gyarfas", - "reason": "known anomaly lane: claimed failures need independent cycle certificates before use", - "result": "test_erdos_gyarfas_4primitive_results.json", - }, - { - "id": "erdos_selfridge", - "reason": "open conjecture finite smoke only", - "result": "test_erdos_selfridge_4primitive_results.json", - }, - { - "id": "erdos_straus", - "reason": "open conjecture finite smoke only", - "result": "test_erdos_straus_4primitive_results.json", - }, - { - "id": "erdos_ternary_2n", - "reason": "open conjecture finite smoke only", - "result": "test_erdos_ternary_2n_4primitive_results.json", - }, - { - "id": "erdos_mollin_walsh", - "reason": "status naming may be inverted; inspect before promotion", - "result": "test_erdos_mollin_walsh_4primitive_results.json", - }, -] - - -def run_case(case: dict[str, Any], timeout: int) -> dict[str, Any]: - script = SHIM / case["script"] - result = SHIM / case["result"] - before_hash = sha256_bytes(result.read_bytes()) if result.exists() else None - proc = subprocess.run( - [sys.executable, str(script)], - cwd=str(REPO), - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=timeout, - check=False, - ) - stdout_tail = proc.stdout[-4000:] - stderr_tail = proc.stderr[-4000:] - run_ok = proc.returncode == 0 and result.exists() - after_hash = sha256_bytes(result.read_bytes()) if result.exists() else None - validation_ok = False - metrics: dict[str, Any] = {} - result_top_keys: list[str] = [] - if run_ok: - data = load_json(result) - result_top_keys = sorted(data.keys()) - validation_ok, metrics = case["validator"](data) - return { - "id": case["id"], - "title": case["title"], - "classification": case["classification"], - "script": str(script.relative_to(REPO)), - "result": str(result.relative_to(REPO)), - "returncode": proc.returncode, - "run_ok": run_ok, - "validation_ok": validation_ok, - "result_hash_before": before_hash, - "result_hash_after": after_hash, - "result_top_keys": result_top_keys, - "metrics": metrics, - "stdout_tail": stdout_tail, - "stderr_tail": stderr_tail, - "claim_boundary": case["claim_boundary"], - } - - -def build_curriculum(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a solved-problem verification router. Return compact JSON and never promote finite smoke tests into proofs." - records = [] - for case in receipt["cases"]: - prompt = { - "task": "classify_solved_problem_output", - "case_id": case["id"], - "classification": case["classification"], - "metrics": case["metrics"], - "claim_boundary": case["claim_boundary"], - "instruction": "Decide whether this result can be used as verifier evidence for the local math stack.", - } - answer = { - "selected": bool(case["validation_ok"]), - "use_as": "solved_problem_verification" if case["validation_ok"] else "diagnostic_failure", - "evidence_tier": case["classification"], - "claim_boundary": case["claim_boundary"], - "source_path": case["result"], - "source_hash": case["result_hash_after"], - "receipt_rule": "Use as detector/output validation only; require formal proof receipts for theorem promotion.", - } - records.append( - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ) - return records - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - passed = sum(1 for case in receipt["cases"] if case["validation_ok"]) - total = len(receipt["cases"]) - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack Erdos Verification Metaprobe Math", - "title: Solved Problem Output Verifier", - "type: text/vnd.tiddlywiki", - "", - "! Solved Problem Output Verifier", - "", - "This tiddler records the solved/known-problem verifier for the local 4-primitive Erdős scripts.", - "", - f"Durable source: `4-Infrastructure/shim/solved_problem_output_verifier.py`", - "", - f"Receipt: `4-Infrastructure/shim/solved_problem_output_verifier_receipt.json`", - "", - f"Curriculum: `4-Infrastructure/shim/solved_problem_output_verifier_curriculum.jsonl`", - "", - "!! Verification Result", - "", - f"* Cases passed: {passed}/{total}", - f"* Overall lawful: `{str(receipt['lawful']).lower()}`", - "", - "!! Included Cases", - "", - ] - for case in receipt["cases"]: - status = "PASS" if case["validation_ok"] else "FAIL" - lines.append(f"* {status} `{case['id']}` ({case['classification']}): {case['claim_boundary']}") - lines.extend( - [ - "", - "!! Excluded / Non-Promotable Cases", - "", - ] - ) - for case in receipt["excluded_cases"]: - lines.append(f"* `{case['id']}`: {case['reason']}") - lines.extend( - [ - "", - "!! Claim Boundary", - "", - "This verifier checks local outputs against solved or construction-backed expectations. It does not prove the theorems, and it explicitly keeps open conjecture smoke tests out of the solved-problem lane.", - "", - "!! Links", - "", - "* [[Erdos Four Primitive Diagnostics]]", - "* [[Custom Equation Awareness Manifest]]", - "* [[Physics Math LLM Metaprobe Audit]]", - ] - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--timeout", type=int, default=120) - parser.add_argument("--receipt", type=Path, default=SHIM / "solved_problem_output_verifier_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "solved_problem_output_verifier_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "Solved Problem Output Verifier.tid") - args = parser.parse_args() - - cases = [run_case(case, args.timeout) for case in CASES] - pass_count = sum(1 for case in cases if case["validation_ok"]) - receipt = { - "schema": "solved_problem_output_verifier_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "claim_boundary": "Solved-problem runs validate local detectors and outputs; they are not theorem proofs and do not promote open conjecture smoke tests.", - "cases": cases, - "excluded_cases": EXCLUDED_CASES, - "pass_count": pass_count, - "case_count": len(cases), - "lawful": pass_count == len(cases), - } - args.receipt.parent.mkdir(parents=True, exist_ok=True) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in build_curriculum(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 if receipt["lawful"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/spamosaic_spatial_mosaic_prior.py b/4-Infrastructure/shim/spamosaic_spatial_mosaic_prior.py deleted file mode 100644 index bb0039d1..00000000 --- a/4-Infrastructure/shim/spamosaic_spatial_mosaic_prior.py +++ /dev/null @@ -1,248 +0,0 @@ -#!/usr/bin/env python3 -"""Distill SpaMosaic into a bounded route prior for fragmented observations. - -SpaMosaic integrates partially overlapping spatial multi-omics datasets into a -shared latent atlas using contrastive learning and spatial graph structure. For -the compressor, the useful shape is not biological atlas construction itself: -it is mosaic integration of incomplete route observations, batch correction, -spatial-neighbor constraints, and missing-lane imputation that remains only a -proposal until exact residual repair closes the byte stream. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "spamosaic_spatial_mosaic_prior_receipt.json" -CURRICULUM_OUT = SHIM / "spamosaic_spatial_mosaic_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -SOURCE_EVIDENCE = { - "news": { - "title": "AI tool unifies fragmented cell maps into spatial atlases across tissues", - "source": "Phys.org / Northwestern University", - "published_date": "2026-05-07", - "url": "https://phys.org/news/2026-05-ai-tool-fragmented-cell-spatial.html", - }, - "primary_paper": { - "title": "Mosaic integration of spatial multi-omics with SpaMosaic", - "authors": "Xuhua Yan et al.", - "journal": "Nature Genetics", - "published_date": "2026-04-24", - "doi": "10.1038/s41588-026-02573-3", - "url": "https://www.nature.com/articles/s41588-026-02573-3", - }, - "observed_core_claims": [ - "mosaic_datasets_measure_only_partially_overlapping_modalities", - "contrastive_learning_learns_cross_dataset_similarities_and_differences", - "graph_neural_networks_use_spatial_neighbor_relationships", - "shared_latent_space_is_modality_agnostic_and_batch_corrected", - "method_identifies_coherent_spatial_domains", - "method_imputes_missing_molecular_layers", - "imputation_reliability_requires_further_testing", - "framework_scales_to_large_spatial_sections", - ], -} - - -MOSAIC_ROUTE_OPERATORS = [ - { - "id": "partial_modality_observation", - "source_shape": "each tissue slice measures only some omics layers", - "route_mapping": "each corpus slice or route probe observes only some transform features", - "claim_boundary": "observation is incomplete until exact residual closes bytes", - }, - { - "id": "contrastive_alignment", - "source_shape": "learn similarities and differences across fragmented datasets", - "route_mapping": "align route observations across slices without collapsing distinct byte states", - "claim_boundary": "alignment is a proposal feature, not proof of equivalence", - }, - { - "id": "spatial_graph_constraint", - "source_shape": "neighboring cells constrain spatial domain inference", - "route_mapping": "neighbor spans constrain route-family continuity and sidecar locality", - "claim_boundary": "graph smoothness cannot override decode hash", - }, - { - "id": "batch_effect_correction", - "source_shape": "remove technical processing differences while preserving biology", - "route_mapping": "normalize route-observation artifacts while preserving exact byte authority", - "claim_boundary": "correction must emit residual for every byte-affecting change", - }, - { - "id": "missing_lane_imputation", - "source_shape": "predict unmeasured molecular layers", - "route_mapping": "predict missing tokenbook / sidecar / witness lanes before exact repair", - "claim_boundary": "imputed lane is sketch-only unless residual repair restores bytes", - }, -] - - -EQUATIONS = [ - { - "id": "SM0_mosaic_observation", - "equation": "O_s = (slice_s, observed_lanes_s, missing_lanes_s, spatial_graph_s)", - "meaning": "Each route observation is a partial lane measurement over a local graph.", - }, - { - "id": "SM1_shared_latent_chart", - "equation": "z_s = Align_contrastive(O_s, batch_id_s, graph_s)", - "meaning": "Map fragmented observations into a shared chart while retaining batch provenance.", - }, - { - "id": "SM2_batch_corrected_not_byte_corrected", - "equation": "batch_correct(z_s) != byte_correct(source_s)", - "meaning": "Removing observation artifacts is not the same as proving byte rehydration.", - }, - { - "id": "SM3_missing_lane_prediction", - "equation": "imputed_lane_l = Predict(z_s, graph_s, modality_l)", - "meaning": "Missing route lanes can be proposed from nearby observations.", - }, - { - "id": "SM4_exact_closure", - "equation": "promote iff hash(decode(imputed_lanes + exact_residuals)) == source_hash", - "meaning": "Imputation closes only through exact residual repair and hash.", - }, - { - "id": "SM5_lower_bound", - "equation": "LB = mosaic_header + graph_receipt + batch_receipt + imputation_receipt + residual_floor", - "meaning": "All atlas/witness costs must be charged before route promotion.", - }, -] - - -def build_receipt() -> dict[str, Any]: - receipt: dict[str, Any] = { - "schema": "spamosaic_spatial_mosaic_prior_v1", - "generated_at": GENERATED_AT, - "source_evidence": SOURCE_EVIDENCE, - "primary_decision": { - "name": "use_mosaic_integration_as_fragmented_route_observation_prior", - "statement": ( - "Use SpaMosaic's shape as a prior for aligning incomplete route " - "observations across slices, correcting observation artifacts, " - "and proposing missing lanes. Treat every imputed lane as sketch " - "data until exact residual repair and rehydration hash close." - ), - }, - "mosaic_route_operators": MOSAIC_ROUTE_OPERATORS, - "equations": EQUATIONS, - "candidate_dd_state_extension": [ - "mosaic_observation_id", - "observed_lane_set", - "missing_lane_set", - "spatial_neighbor_graph_id", - "contrastive_alignment_id", - "batch_id", - "batch_correction_receipt_id", - "shared_latent_chart_id", - "spatial_domain_id", - "imputed_lane_id", - "imputation_confidence", - "imputation_reliability_status", - "exact_residual_lane_id", - "mosaic_lower_bound_bytes", - "byte_rehydration_hash", - ], - "candidate_dd_edges": [ - "open_mosaic_route_observation", - "record_observed_and_missing_lanes", - "build_spatial_neighbor_graph", - "align_observations_contrastively", - "correct_batch_effect_with_receipt", - "identify_route_spatial_domain", - "predict_missing_route_lane", - "emit_exact_residual_for_imputed_lane", - "verify_mosaic_rehydration_hash", - "reject_imputation_without_exact_repair", - ], - "lower_bound": [ - "mosaic_header_bytes", - "spatial_graph_receipt_floor", - "contrastive_alignment_receipt_floor", - "batch_correction_receipt_floor", - "imputed_lane_receipt_floor", - "exact_residual_lane_floor", - ], - "promotion_rule": [ - "mosaic_layer_only_aligns_or_proposes_route_lanes", - "batch_correction_is_receipted_and_byte_preserving_or_residualized", - "spatial_graph_smoothness_does_not_override_byte_hash", - "missing_lane_imputation_carries_exact_residual_repair", - "imputation_reliability_status_is_recorded", - "decoded_hash_matches_source", - "measured_total_bytes_beat_incumbent_under_ratio_schema", - ], - "failure_rule": [ - "imputed_lane_without_exact_residual -> not_promoted", - "batch_correction_changes_bytes_without_residual -> invalid_receipt", - "spatial_domain_match_without_byte_hash -> diagnostic_only", - "mosaic_header_larger_than_byte_gain -> prune", - "unbounded_neighbor_graph_or_alignment -> NaN0", - ], - "claim_boundary": ( - "This prior imports SpaMosaic's fragmented-observation integration " - "shape. It is not evidence that biological atlas methods compress " - "text bytes, and it does not promote routes without exact decode, " - "hash, byte count, and explicit ratio schema." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for item in receipt["mosaic_route_operators"]: - lines.append({"type": "mosaic_route_operator", **item}) - for item in receipt["equations"]: - lines.append({"type": "equation", **item}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print(json.dumps({ - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "curriculum_records": len(lines), - "decision": receipt["primary_decision"]["name"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/spec_sheet_puller.py b/4-Infrastructure/shim/spec_sheet_puller.py deleted file mode 100644 index dc6ac15b..00000000 --- a/4-Infrastructure/shim/spec_sheet_puller.py +++ /dev/null @@ -1,405 +0,0 @@ -#!/usr/bin/env python3 -""" -Spec Sheet Puller — Pulls datasheet specs for all components found by swarm prober. -Integrates key parameters into topological device plan to accelerate transformation. -""" - -import json -from pathlib import Path -from dataclasses import dataclass, field -from typing import List, Dict, Optional - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -@dataclass -class ComponentSpec: - name: str; part_number: str; manufacturer: str - category: str; datasheet_url: str - key_params: Dict[str, str] = field(default_factory=dict) - topological_relevance: List[str] = field(default_factory=list) - - -# ═══════════════════════════════════════════════════════════════════════════════ -# Component Spec Sheets (from hardware_schematics_comprehensive.md + web sources) -# ═══════════════════════════════════════════════════════════════════════════════ - -SPEC_SHEETS = { - "U1_FPGA": ComponentSpec( - name="Tang Nano 9K FPGA", - part_number="GW1NR-LV9QN88PC6/I5", - manufacturer="Gowin Semiconductor", - category="FPGA", - datasheet_url="https://www.gowinsemi.com/en/support/datasheet/", - key_params={ - "LUTs": "8640", - "FFs": "6480", - "BRAM": "468Kb (26 × 18Kb blocks)", - "DSP": "20 multipliers (16×16)", - "PLLs": "2", - "IO": "68 user I/O", - "Package": "QFN88 (10×10mm)", - "Core voltage": "1.2V", - "IO voltage": "3.3V / 2.5V / 1.8V", - "Max frequency": "~200MHz (fabric), 400MHz (PLL out)", - "Flash": "Embedded 64Mbit SPI", - "Programming": "JTAG + SPI + UART", - }, - topological_relevance=[ - "8640 LUTs → partition into 11 agent compute units (785 LUTs each)", - "20 DSP blocks → 20 parallel Q16.16 multiply-accumulate pipelines", - "468Kb BRAM → 256 FAMM cells × 64-bit = 16Kb (fits in 1 BRAM block)", - "2 PLLs → eigenvalue-derived clock distribution (τ ∝ 1/√λ)", - "68 I/O → 8 HDMI + 32 DDR + 8 UART + 20 GPIO for topology sensing", - ] - ), - "U2_DDR": ComponentSpec( - name="DDR3 SDRAM", - part_number="MT41K128M16JT-125 (typical)", - manufacturer="Micron", - category="Memory", - datasheet_url="https://www.micron.com/products/dram/ddr3-sdram", - key_params={ - "Density": "2Gb (128M×16)", - "Speed": "DDR3-1600 (800MHz clock)", - "Data rate": "1600 MT/s", - "Burst length": "8", - "CAS latency": "CL=11", - "tRCD": "13.75ns", - "tRP": "13.75ns", - "tRC": "48.75ns", - "Voltage": "1.5V (1.35V DDR3L)", - "Package": "96-ball FBGA", - "Row/Column": "14/10 addressing", - }, - topological_relevance=[ - "800MHz clock → 1250ps period → trace matching within 50ps = 4% tolerance", - "CL=11 → 13.75ns read latency → pipeline 11 stages in FPGA", - "Burst=8 → 8×16-bit = 128-bit FAMM data bus width", - "1.5V → separate power plane with <5mΩ target impedance", - "tRC=48.75ns → 20.5M random accesses/sec → FAMM preshaping critical", - ] - ), - "U3_OSC": ComponentSpec( - name="100MHz Crystal Oscillator", - part_number="SG-210STF 100.0000ML3 (typical)", - manufacturer="Epson", - category="Clock", - datasheet_url="https://www5.epsondevice.com/en/products/crystal_oscillator/", - key_params={ - "Frequency": "100.000 MHz", - "Stability": "±50ppm", - "Jitter": "<1ps RMS (12kHz-20MHz)", - "Rise/fall": "<3ns", - "Output": "LVCMOS", - "Voltage": "3.3V", - "Package": "2.5×2.0mm ceramic", - "Phase noise": "-135dBc/Hz @ 10kHz offset", - }, - topological_relevance=[ - "100MHz → 10ns period → eigenvalue clock: λ_1→75ns, λ_16→45ns", - "±50ppm → 5ns drift over 100k cycles → PLL lock required", - "<1ps jitter → suitable for Q16.16 timing precision (15ps LSB)", - "Phase noise -135dBc → clean enough for manifold clock distribution", - ] - ), - "U4_REG": ComponentSpec( - name="3.3V LDO Regulator", - part_number="AMS1117-3.3 (typical)", - manufacturer="Advanced Monolithic Systems", - category="Power", - datasheet_url="https://www.advanced-monolithic.com/pdf/ds1117.pdf", - key_params={ - "Output": "3.3V ±1.5%", - "Dropout": "1.1V @ 1A", - "Max current": "1A", - "Line regulation": "0.2% max", - "Load regulation": "0.4% max", - "Ripple rejection": "60dB @ 120Hz", - "Thermal shutdown": "165°C", - "Package": "SOT-223", - }, - topological_relevance=[ - "1A max → 3.3W total → thermal topology: place near board edge", - "60dB ripple rejection → 1000× noise reduction → clean analog rails", - "165°C shutdown → thermal vias needed under package", - "1.1V dropout → input must be >4.4V → 5V USB sufficient", - ] - ), - "J1_HDMI": ComponentSpec( - name="HDMI Type A Connector", - part_number="HDMI-A-19P-SMT (typical)", - manufacturer="Various (Molex, TE, Amphenol)", - category="Connector", - datasheet_url="https://www.hdmi.org/spec/index", - key_params={ - "Pins": "19", - "TMDS pairs": "4 (3 data + 1 clock)", - "Impedance": "100Ω differential", - "Data rate": "Up to 3.4Gbps per lane (HDMI 1.4)", - "Bandwidth": "10.2 Gbps total", - "DDC": "I²C @ 100kHz", - "HPD": "Hot plug detect (5V tolerant)", - "CEC": "Consumer Electronics Control", - "Voltage": "5V @ 50mA (pin 18)", - }, - topological_relevance=[ - "100Ω differential → trace impedance must match within ±10%", - "3.4Gbps → 294ps bit period → 15ps trace matching (5%)", - "4 TMDS pairs → 4 parallel FAMM delay lines for video stream", - "DDC I²C → topology-aware EDID emulation for manifold display", - "HPD → topological hot-plug detection for swarm reconfiguration", - ] - ), - "C1_C2_C4_100nF": ComponentSpec( - name="100nF Decoupling Capacitor", - part_number="GRM188R71H104KA93 (typical)", - manufacturer="Murata", - category="Passive", - datasheet_url="https://www.murata.com/en-us/products/capacitor/ceramiccapacitor", - key_params={ - "Capacitance": "100nF ±10%", - "Dielectric": "X7R", - "Voltage": "50V", - "ESR": "<50mΩ @ 100MHz", - "ESL": "~0.5nH (0603)", - "SRF": "~22MHz", - "Package": "0603 (1.6×0.8mm)", - "Temp range": "-55°C to +125°C", - }, - topological_relevance=[ - "SRF 22MHz → effective decoupling to ~50MHz → covers FPGA core", - "ESL 0.5nH → via inductance dominates → minimize via length", - "X7R → ±15% over temp → account for in PDN impedance budget", - ] - ), - "C3_10uF": ComponentSpec( - name="10µF Bulk Capacitor", - part_number="GRM21BR61A106KE19 (typical)", - manufacturer="Murata", - category="Passive", - datasheet_url="https://www.murata.com/en-us/products/capacitor/ceramiccapacitor", - key_params={ - "Capacitance": "10µF ±10%", - "Dielectric": "X5R", - "Voltage": "10V", - "ESR": "<10mΩ @ 1MHz", - "ESL": "~0.8nH (0805)", - "SRF": "~1.8MHz", - "Package": "0805 (2.0×1.25mm)", - "DC bias derating": "-70% at 3.3V (effective ~3µF)", - }, - topological_relevance=[ - "DC bias derating critical → effective 3µF not 10µF at 3.3V", - "SRF 1.8MHz → bulk decoupling below 10MHz → complements 100nF", - "ESR 10mΩ → low enough for PDN target <10mΩ with parallel caps", - ] - ), - "C5_4u7": ComponentSpec( - name="4.7µF Regulator Output Cap", - part_number="GRM21BR61C475KA88 (typical)", - manufacturer="Murata", - category="Passive", - datasheet_url="https://www.murata.com/en-us/products/capacitor/ceramiccapacitor", - key_params={ - "Capacitance": "4.7µF ±10%", - "Dielectric": "X5R", - "Voltage": "16V", - "ESR": "<20mΩ @ 1MHz", - "ESL": "~0.8nH (0805)", - "SRF": "~2.6MHz", - "Package": "0805", - }, - topological_relevance=[ - "LDO output cap → stability requirement: 4.7µF min for AMS1117", - "ESR 20mΩ → within LDO stable region (0.1-10Ω for most LDOs)", - ] - ), - "PCB_TRACE": ComponentSpec( - name="PCB Trace (FR-4, 4-layer)", - part_number="Standard 1oz Cu, 0.15mm width", - manufacturer="Generic", - category="PCB", - datasheet_url="N/A — standard IPC-2221", - key_params={ - "Dielectric": "FR-4 (εr=4.5 @ 1GHz)", - "Copper": "1oz (35µm)", - "Trace width": "0.15mm (6 mil)", - "Impedance": "~50Ω (microstrip, layer 1)", - "Delay": "~150ps/inch (6ps/mm)", - "Capacitance": "~1.1pF/cm", - "Inductance": "~3nH/cm", - "DC resistance": "~0.3Ω/cm (0.15mm, 1oz)", - "Min spacing": "0.15mm (6 mil)", - }, - topological_relevance=[ - "6ps/mm delay → 25mm trace = 150ps → matches DDR skew budget", - "εr=4.5 → impedance varies ±10% with manufacturing → calibrate per board", - "0.3Ω/cm → 60mm power trace = 1.8Ω → unacceptable for PDN → use planes", - "FR-4 loss: ~0.02dB/mm @ 1GHz → 50mm = 1dB → negligible for <500MHz", - ] - ), - "VIA": ComponentSpec( - name="Through-Hole Via (0.3mm drill)", - part_number="Standard IPC-2221 Type III", - manufacturer="Generic", - category="PCB", - datasheet_url="N/A — standard IPC-2221", - key_params={ - "Drill": "0.3mm", - "Pad": "0.6mm", - "Antipad": "0.8mm", - "Inductance": "~0.8nH (1.6mm board)", - "Capacitance": "~0.5pF", - "Impedance": "~40Ω", - "Stub resonance": "λ/4 @ ~25GHz for 1.6mm stub", - "Current capacity": "~1A (0.3mm, 1oz plating)", - }, - topological_relevance=[ - "0.8nH per via → 4 vias in PDN path = 3.2nH → limits decoupling above 100MHz", - "Stub at 1.6mm → resonance at 25GHz → safe below 5GHz → backdrill for HDMI", - "0.5pF per via → negligible for <1GHz signals", - ] - ), -} - - -def pull_spec_sheets(component_names: List[str]) -> Dict: - """Pull spec sheets for specified components.""" - specs = {} - for name in component_names: - if name in SPEC_SHEETS: - specs[name] = SPEC_SHEETS[name] - else: - # Partial match - for key in SPEC_SHEETS: - if name in key or key in name: - specs[key] = SPEC_SHEETS[key] - return specs - - -def integrate_into_plan(specs: Dict, plan_path: Path) -> Dict: - """Integrate spec sheet data into topological device plan.""" - with open(plan_path) as f: - plan = json.load(f) - - # Add spec sheet data - spec_data = {} - for name, spec in specs.items(): - spec_data[name] = { - "part_number": spec.part_number, - "manufacturer": spec.manufacturer, - "key_params": spec.key_params, - "topological_relevance": spec.topological_relevance, - } - - plan["spec_sheets"] = spec_data - - # Add accelerated timeline based on known specs - plan["accelerated_timeline"] = { - "original_weeks": 12, - "accelerated_weeks": 8, - "acceleration_factors": [ - "Known FPGA LUT count → pre-partition agent compute units (save 1 week)", - "Known DDR timing → pre-compute trace matching targets (save 1 week)", - "Known capacitor SRF → skip PDN characterization (save 1 week)", - "Known via inductance → pre-calculate PDN impedance (save 1 week)", - "Known PCB εr → pre-compute impedance profiles (save 1 week)", - ] - } - - # Add per-phase spec-driven optimizations - for phase_key in ["phase_1_immediate", "phase_2_structural", "phase_3_power", "phase_4_topological"]: - if phase_key in plan["plan"]: - plan["plan"][phase_key]["spec_driven"] = True - - return plan - - -def main(): - print("=" * 70) - print("Spec Sheet Puller — Accelerating Topological Device Plan") - print("=" * 70) - - # Components found by swarm prober - components = [ - "U1_FPGA", "U2_DDR", "U3_OSC", "U4_REG", "J1_HDMI", - "C1_C2_C4_100nF", "C3_10uF", "C5_4u7", - "PCB_TRACE", "VIA" - ] - - print(f"\n[1] Pulling spec sheets for {len(components)} components...") - specs = pull_spec_sheets(components) - - for name, spec in specs.items(): - print(f"\n {name}: {spec.part_number}") - print(f" Manufacturer: {spec.manufacturer}") - print(f" Category: {spec.category}") - print(f" Key params: {len(spec.key_params)} parameters") - print(f" Topological relevance: {len(spec.topological_relevance)} insights") - - print(f"\n[2] Integrating into topological device plan...") - plan_path = RESEARCH_STACK / "4-Infrastructure/shim/topological_device_plan.json" - updated_plan = integrate_into_plan(specs, plan_path) - - # Save updated plan - output_path = RESEARCH_STACK / "4-Infrastructure/shim/topological_device_plan_with_specs.json" - with open(output_path, 'w') as f: - json.dump(updated_plan, f, indent=2, default=str) - - print(f"\n[3] Accelerated timeline:") - accel = updated_plan["accelerated_timeline"] - print(f" Original: {accel['original_weeks']} weeks") - print(f" Accelerated: {accel['accelerated_weeks']} weeks") - print(f" Savings: {accel['original_weeks'] - accel['accelerated_weeks']} weeks") - - print(f"\n[4] Key topological insights from spec sheets:") - - # FPGA insights - fpga = specs["U1_FPGA"] - print(f"\n FPGA ({fpga.part_number}):") - for insight in fpga.topological_relevance[:3]: - print(f" → {insight}") - - # DDR insights - ddr = specs["U2_DDR"] - print(f"\n DDR3 ({ddr.part_number}):") - for insight in ddr.topological_relevance[:3]: - print(f" → {insight}") - - # PCB insights - pcb = specs["PCB_TRACE"] - print(f"\n PCB Traces:") - for insight in pcb.topological_relevance[:3]: - print(f" → {insight}") - - print(f"\n[5] Updated plan saved: {output_path}") - - # Generate spec sheet reference document - ref_path = RESEARCH_STACK / "4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md" - with open(ref_path, 'w') as f: - f.write("# Component Spec Sheet Reference\n\n") - f.write(f"**Generated:** {__import__('datetime').datetime.now().isoformat()}\n\n") - f.write("## Components\n\n") - for name, spec in specs.items(): - f.write(f"### {name}: {spec.part_number}\n\n") - f.write(f"- **Manufacturer:** {spec.manufacturer}\n") - f.write(f"- **Category:** {spec.category}\n") - f.write(f"- **Datasheet:** {spec.datasheet_url}\n\n") - f.write("**Key Parameters:**\n\n") - for param, value in spec.key_params.items(): - f.write(f"- {param}: {value}\n") - f.write("\n**Topological Relevance:**\n\n") - for insight in spec.topological_relevance: - f.write(f"- {insight}\n") - f.write("\n---\n\n") - - print(f" Reference doc: {ref_path}") - - print("\n" + "=" * 70) - print("Spec sheets pulled — plan accelerated by 4 weeks") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_abelian_sandpile_probe.py b/4-Infrastructure/shim/stellar_gas_abelian_sandpile_probe.py deleted file mode 100644 index ad643170..00000000 --- a/4-Infrastructure/shim/stellar_gas_abelian_sandpile_probe.py +++ /dev/null @@ -1,324 +0,0 @@ -#!/usr/bin/env python3 -"""Treat stellar-gas eigenmass cells as an Abelian-sandpile-style diagnostic. - -The metaphor is operationalized carefully: - -* "grains" are normalized SMN/evidence eigenmass in a sky/redshift cell. -* "toppling pressure" is a standardized mix of gas/shock propagation channels. -* "avalanche candidates" are cells with both high eigenmass and high pressure. - -Boundary: this is a routing/diagnostic model over observational proxies. It is -not a physical sandpile simulation, not stellar mass, and not cosmology. -""" - -from __future__ import annotations - -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -MASS_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_eigenvector_mass_probe.json" -GROUP_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_population_grouping_study.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_abelian_sandpile_probe.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_abelian_sandpile_probe_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_abelian_sandpile_probe_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Abelian Sandpile Probe.tid" - - -CHANNELS = [ - "log_desi_count", - "log_manga_count", - "partial_full_shock_fraction", - "shock_lier_fraction", - "shock_score_mean", - "gas_sigma_mean", - "gas_sigma_p90", - "stellar_sigma_mean", - "snr_mean", - "agn_liner_or_shock_fraction", - "star_forming_fraction", -] - - -def load_json(path: Path) -> dict[str, Any]: - with path.open() as f: - return json.load(f) - - -def safe_div(a: float, b: float) -> float: - return a / b if b else 0.0 - - -def pearson(a: list[float], b: list[float]) -> float: - if len(a) != len(b) or len(a) < 2: - return 0.0 - ma = sum(a) / len(a) - mb = sum(b) / len(b) - va = [x - ma for x in a] - vb = [x - mb for x in b] - den = math.sqrt(sum(x * x for x in va) * sum(y * y for y in vb)) - return sum(x * y for x, y in zip(va, vb)) / den if den else 0.0 - - -def mean_std(values: list[float]) -> tuple[float, float]: - if not values: - return 0.0, 1.0 - mean = sum(values) / len(values) - var = sum((x - mean) ** 2 for x in values) / len(values) - std = math.sqrt(var) - return mean, std if std else 1.0 - - -def zscore(values: list[float]) -> list[float]: - mean, std = mean_std(values) - return [(x - mean) / std for x in values] - - -def round9(x: float) -> float: - return round(x, 9) - - -def cell_channels(mass_row: dict[str, Any], group_row: dict[str, Any]) -> dict[str, float]: - count = float(group_row["count"]) - bpt = group_row.get("bpt_proxy_classes", {}) - gas = group_row.get("gas_sigma_summary", {}) - stellar = group_row.get("stellar_sigma_summary", {}) - snr = group_row.get("snr_summary", {}) - shock = group_row.get("shock_score_summary", {}) - return { - "log_desi_count": math.log1p(float(mass_row["desi_count"])), - "log_manga_count": math.log1p(float(mass_row["manga_count"])), - "partial_full_shock_fraction": float(group_row.get("partial_or_full_shock_fraction") or 0.0), - "shock_lier_fraction": float(group_row.get("shock_lier_fraction") or 0.0), - "shock_score_mean": float(shock.get("mean") or 0.0), - "gas_sigma_mean": float(gas.get("mean") or 0.0), - "gas_sigma_p90": float(gas.get("p90") or 0.0), - "stellar_sigma_mean": float(stellar.get("mean") or 0.0), - "snr_mean": float(snr.get("mean") or 0.0), - "agn_liner_or_shock_fraction": safe_div(float(bpt.get("agn_liner_or_shock_proxy", 0)), count), - "star_forming_fraction": safe_div(float(bpt.get("star_forming_proxy", 0)), count), - } - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - mass = load_json(MASS_JSON) - groups = load_json(GROUP_JSON)["groups"]["by_sky_z_cell"] - - rows: list[dict[str, Any]] = [] - for mass_row in mass["top_cell_masses"]: - cell = mass_row["cell"] - if cell not in groups: - continue - channels = cell_channels(mass_row, groups[cell]) - rows.append( - { - "cell": cell, - "eigenmass": float(mass_row["normalized_eigenvector_mass"]), - "eigen_score": float(mass_row["eigen_score"]), - "channels": channels, - } - ) - - eigenmass_values = [row["eigenmass"] for row in rows] - channel_values = {name: [row["channels"][name] for row in rows] for name in CHANNELS} - channel_correlations = { - name: round9(pearson(eigenmass_values, values)) - for name, values in channel_values.items() - } - - pressure_components = [ - "partial_full_shock_fraction", - "shock_lier_fraction", - "shock_score_mean", - "gas_sigma_mean", - "gas_sigma_p90", - "agn_liner_or_shock_fraction", - ] - z_components = {name: zscore(channel_values[name]) for name in pressure_components} - mass_z = zscore(eigenmass_values) - - pressure_scores = [] - for i, row in enumerate(rows): - pressure = sum(z_components[name][i] for name in pressure_components) / len(pressure_components) - toppling_index = 0.5 * mass_z[i] + 0.5 * pressure - pressure_scores.append(pressure) - row["sandpile"] = { - "grains": round9(row["eigenmass"]), - "toppling_pressure": round9(pressure), - "toppling_index": round9(toppling_index), - } - - pressure_mean, pressure_std = mean_std(pressure_scores) - index_values = [row["sandpile"]["toppling_index"] for row in rows] - index_mean, index_std = mean_std(index_values) - for row in rows: - row["sandpile"]["state"] = ( - "AVALANCHE_CANDIDATE" - if row["sandpile"]["toppling_index"] >= index_mean + index_std - else "LOADED" - if row["sandpile"]["toppling_index"] >= index_mean - else "STABLE" - ) - - rows.sort(key=lambda item: item["sandpile"]["toppling_index"], reverse=True) - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - result = { - "schema": "stellar_gas_abelian_sandpile_probe_v0", - "created": created, - "decision": "ADMIT_SANDPILE_DIAGNOSTIC_HOLD_PHYSICAL_SANDPILE", - "claim_boundary": ( - "Uses an Abelian-sandpile metaphor as a diagnostic over SMN/evidence " - "mass and gas/shock observational proxies. It is not a physical " - "sandpile simulation, not stellar mass, and not cosmology." - ), - "sources": { - "eigenmass": str(MASS_JSON.relative_to(ROOT)), - "population_groups": str(GROUP_JSON.relative_to(ROOT)), - }, - "cell_count": len(rows), - "channel_correlations_with_eigenmass": channel_correlations, - "pressure_components": pressure_components, - "pressure_summary": { - "mean": round9(pressure_mean), - "std": round9(pressure_std), - }, - "toppling_index_summary": { - "mean": round9(index_mean), - "std": round9(index_std), - }, - "top_cells": rows[:25], - "interpretation": ( - "High eigenmass plus high gas/shock pressure marks cells that deserve " - "fine-grained follow-up. Negative or weak channel correlation marks " - "channels that may be less explanatory for the current eigenmass surface." - ), - "holds": [ - "HOLD_PHYSICAL_SANDPILE_SIMULATION", - "HOLD_DIRECT_STELLAR_MASS", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_OBJECT_LEVEL_CROSSMATCH", - "HOLD_COSMOLOGY_FIT", - ], - } - receipt = { - "receipt_type": "stellar_gas_abelian_sandpile_probe_receipt", - "created": created, - "cell_count": len(rows), - "avalanche_candidate_count": sum(1 for row in rows if row["sandpile"]["state"] == "AVALANCHE_CANDIDATE"), - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - corr_lines = "\n".join( - f"- `{name}`: {value}" - for name, value in sorted( - result["channel_correlations_with_eigenmass"].items(), - key=lambda item: abs(item[1]), - reverse=True, - ) - ) - top_lines = "\n".join( - f"- `{row['cell']}`: state `{row['sandpile']['state']}`, grains `{row['sandpile']['grains']}`, " - f"pressure `{row['sandpile']['toppling_pressure']}`, index `{row['sandpile']['toppling_index']}`" - for row in result["top_cells"][:10] - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - - DOC_MD.write_text( - f"""# Stellar Gas Abelian Sandpile Probe - -Status: `SANDPILE_DIAGNOSTIC` - -Decision: `{result['decision']}` - -This probe treats the stellar-gas eigenmass surface as an Abelian-sandpile-style -diagnostic. Cells carry normalized eigenmass as "grains"; gas/shock observables -act as toppling pressure; high grain/high-pressure cells become avalanche -candidates for fine-grained follow-up. - -Claim boundary: this is a metaphor-backed diagnostic over observational proxies. -It is not a physical sandpile simulation, not stellar mass, not direct gas -density inference, and not a cosmology fit. - -## Channel Correlations With Eigenmass - -{corr_lines} - -## Toppling Candidates - -{top_lines} - -## Pressure Components - -```json -{json.dumps(result['pressure_components'], indent=2)} -``` - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Abelian Sandpile Probe -tags: StellarGasObservation SemanticMassNumbers Eigenvector Physics Sandpile Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This tiddler operationalizes the "stars as Abelian sand piles" metaphor as a -diagnostic over the stellar-gas eigenmass surface. - -``` -eigenmass grains + gas/shock pressure -> toppling candidates -``` - -!! Channel Correlations With Eigenmass - -{corr_lines} - -!! Toppling Candidates - -{top_lines} - -!! Boundary - -This is not a physical sandpile simulation, not stellar mass, not direct gas -density inference, and not a cosmology fit. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_eigenvector_mass_probe.py b/4-Infrastructure/shim/stellar_gas_eigenvector_mass_probe.py deleted file mode 100644 index 64f740ae..00000000 --- a/4-Infrastructure/shim/stellar_gas_eigenvector_mass_probe.py +++ /dev/null @@ -1,409 +0,0 @@ -#!/usr/bin/env python3 -"""Infer an SMN/evidence eigenvector mass over DESI-MaNGA population cells. - -This probe treats the coarse DESI epoviz to MaNGA cell join as an evidence -matrix. It computes the dominant covariance eigenvector with deterministic -pure-Python Jacobi iteration, then emits a receipt-bearing semantic mass surface. - -Boundary: this is not physical mass, not stellar mass, and not a cosmology fit. -It is a semantic/evidence-load direction over the current joined data surface. -""" - -from __future__ import annotations - -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -JOIN_JSON = ROOT / "shared-data/data/stellar_gas_observation/desi_epoviz_manga_population_cell_join.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_eigenvector_mass_probe.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_eigenvector_mass_probe_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_eigenvector_mass_probe_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Eigenvector Mass Probe.tid" - -FEATURES = [ - "log_desi_count", - "log_manga_count", - "partial_full_shock_fraction", - "shock_lier_fraction", - "BGS_share", - "ELG_share", - "LRG_share", - "QSO_share", -] - - -def dot(a: list[float], b: list[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def mat_vec(m: list[list[float]], v: list[float]) -> list[float]: - return [dot(row, v) for row in m] - - -def norm(v: list[float]) -> float: - return math.sqrt(dot(v, v)) - - -def normalize(v: list[float]) -> list[float]: - n = norm(v) - if n == 0: - return [0.0 for _ in v] - return [x / n for x in v] - - -def transpose(matrix: list[list[float]]) -> list[list[float]]: - return [list(col) for col in zip(*matrix)] - - -def jacobi_eigen_symmetric(matrix: list[list[float]], max_iter: int = 200, eps: float = 1e-12) -> tuple[list[float], list[list[float]]]: - """Return eigenvalues and eigenvectors for a small symmetric matrix. - - Eigenvectors are returned as columns in the second return value. - """ - - n = len(matrix) - a = [row[:] for row in matrix] - v = [[1.0 if i == j else 0.0 for j in range(n)] for i in range(n)] - - iterations = 0 - final_max_offdiag = 0.0 - converged = False - for iteration in range(1, max_iter + 1): - p, q = 0, 1 - max_off = 0.0 - for i in range(n): - for j in range(i + 1, n): - val = abs(a[i][j]) - if val > max_off: - max_off = val - p, q = i, j - iterations = iteration - final_max_offdiag = max_off - if max_off < eps: - converged = True - break - - if abs(a[p][p] - a[q][q]) < eps: - angle = math.pi / 4 - else: - angle = 0.5 * math.atan2(2.0 * a[p][q], a[q][q] - a[p][p]) - c = math.cos(angle) - s = math.sin(angle) - - app = c * c * a[p][p] - 2.0 * s * c * a[p][q] + s * s * a[q][q] - aqq = s * s * a[p][p] + 2.0 * s * c * a[p][q] + c * c * a[q][q] - a[p][p] = app - a[q][q] = aqq - a[p][q] = 0.0 - a[q][p] = 0.0 - - for k in range(n): - if k == p or k == q: - continue - akp = c * a[k][p] - s * a[k][q] - akq = s * a[k][p] + c * a[k][q] - a[k][p] = akp - a[p][k] = akp - a[k][q] = akq - a[q][k] = akq - - for k in range(n): - vkp = c * v[k][p] - s * v[k][q] - vkq = s * v[k][p] + c * v[k][q] - v[k][p] = vkp - v[k][q] = vkq - - eigenvalues = [a[i][i] for i in range(n)] - return eigenvalues, v, { - "method": "jacobi_symmetric", - "max_iter": max_iter, - "eps": eps, - "iterations": iterations, - "converged": converged, - "final_max_offdiag": final_max_offdiag, - } - - -def eigen_residual(matrix: list[list[float]], eigenvalue: float, eigenvector: list[float]) -> float: - av = mat_vec(matrix, eigenvector) - residual = [av_i - eigenvalue * v_i for av_i, v_i in zip(av, eigenvector)] - return norm(residual) - - -def build_feature_rows(join: dict[str, Any]) -> tuple[list[str], list[list[float]], list[dict[str, Any]]]: - labels: list[str] = [] - rows: list[list[float]] = [] - payloads: list[dict[str, Any]] = [] - for cell in join["manga_join"]["top_joined_cells"]: - mix = cell["desi_tracer_mix"] - total = sum(float(v) for v in mix.values()) or 1.0 - labels.append(cell["cell"]) - payloads.append(cell) - rows.append( - [ - math.log1p(float(cell["desi_count"])), - math.log1p(float(cell["manga_count"])), - float(cell.get("manga_partial_or_full_shock_fraction") or 0.0), - float(cell.get("manga_shock_lier_fraction") or 0.0), - float(mix.get("BGS", 0.0)) / total, - float(mix.get("ELG", 0.0)) / total, - float(mix.get("LRG", 0.0)) / total, - float(mix.get("QSO", 0.0)) / total, - ] - ) - return labels, rows, payloads - - -def zscore(rows: list[list[float]]) -> tuple[list[list[float]], list[float], list[float]]: - cols = transpose(rows) - means = [sum(col) / len(col) for col in cols] - stds = [] - for col, mean in zip(cols, means): - var = sum((x - mean) ** 2 for x in col) / len(col) - std = math.sqrt(var) - stds.append(std if std > 0 else 1.0) - scaled = [[(x - means[i]) / stds[i] for i, x in enumerate(row)] for row in rows] - return scaled, means, stds - - -def covariance(rows: list[list[float]]) -> list[list[float]]: - n = len(rows) - cols = len(rows[0]) - return [ - [sum(row[i] * row[j] for row in rows) / (n - 1) for j in range(cols)] - for i in range(cols) - ] - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - with JOIN_JSON.open() as f: - join = json.load(f) - - labels, raw_rows, payloads = build_feature_rows(join) - scaled_rows, means, stds = zscore(raw_rows) - cov = covariance(scaled_rows) - values, vectors_as_columns, solver = jacobi_eigen_symmetric(cov) - - order = sorted(range(len(values)), key=lambda i: values[i], reverse=True) - eigenvalues = [values[i] for i in order] - eigenvectors_by_rank = [[vectors_as_columns[row][i] for row in range(len(FEATURES))] for i in order] - dominant = normalize(eigenvectors_by_rank[0]) - - shock_index = FEATURES.index("partial_full_shock_fraction") - if dominant[shock_index] < 0: - dominant = [-x for x in dominant] - residual = eigen_residual(cov, eigenvalues[0], dominant) - - scores = [dot(row, dominant) for row in scaled_rows] - min_score = min(scores) - shifted = [score - min_score for score in scores] - total_shifted = sum(shifted) - masses = [x / total_shifted if total_shifted > 0 else 1.0 / len(shifted) for x in shifted] - - cell_masses = [] - for label, payload, score, mass in zip(labels, payloads, scores, masses): - cell_masses.append( - { - "cell": label, - "eigen_score": round(score, 6), - "normalized_eigenvector_mass": round(mass, 6), - "desi_count": payload["desi_count"], - "manga_count": payload["manga_count"], - "manga_partial_or_full_shock_fraction": payload.get("manga_partial_or_full_shock_fraction"), - "manga_shock_lier_fraction": payload.get("manga_shock_lier_fraction"), - "desi_tracer_mix": payload["desi_tracer_mix"], - } - ) - cell_masses.sort(key=lambda row: row["normalized_eigenvector_mass"], reverse=True) - - total_eigen = sum(x for x in eigenvalues if x > 0) - explained = [(x / total_eigen if total_eigen > 0 else 0.0) for x in eigenvalues] - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - - result = { - "schema": "stellar_gas_eigenvector_mass_probe_v0", - "created": created, - "decision": "ADMIT_SMN_EIGENVECTOR_MASS_HOLD_PHYSICAL_MASS", - "claim_boundary": ( - "Eigenvector mass is an SMN/evidence-load direction over the coarse " - "DESI epoviz to MaNGA population-cell join. It is not physical mass, " - "not stellar mass, not a gas-density map, and not a cosmology fit." - ), - "source_join": str(JOIN_JSON.relative_to(ROOT)), - "feature_basis": FEATURES, - "feature_means": {name: round(means[i], 9) for i, name in enumerate(FEATURES)}, - "feature_stds": {name: round(stds[i], 9) for i, name in enumerate(FEATURES)}, - "cell_count": len(labels), - "eigenvalues": [round(x, 9) for x in eigenvalues], - "explained_mass_share": [round(x, 9) for x in explained], - "dominant_eigenvector": {name: round(dominant[i], 9) for i, name in enumerate(FEATURES)}, - "dominant_eigenvalue": round(eigenvalues[0], 9), - "dominant_explained_mass_share": round(explained[0], 9), - "eigensolver_diagnostics": { - **solver, - "dominant_residual_l2": round(residual, 12), - "orthogonality_note": "Jacobi rotations return an orthonormal basis up to numeric roundoff; this receipt reports the dominant residual only.", - }, - "top_cell_masses": cell_masses[:25], - "holds": [ - "HOLD_PHYSICAL_MASS_INTERPRETATION", - "HOLD_OBJECT_LEVEL_CROSSMATCH", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_SELECTION_FUNCTION_FIT", - "HOLD_COSMOLOGY_FIT", - ], - } - - receipt = { - "receipt_type": "stellar_gas_eigenvector_mass_probe_receipt", - "created": created, - "source_join": str(JOIN_JSON.relative_to(ROOT)), - "cell_count": len(labels), - "dominant_eigenvalue": result["dominant_eigenvalue"], - "dominant_explained_mass_share": result["dominant_explained_mass_share"], - "eigensolver_diagnostics": result["eigensolver_diagnostics"], - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - vector_lines = "\n".join( - f"- `{name}`: {value}" for name, value in result["dominant_eigenvector"].items() - ) - cell_lines = "\n".join( - f"- `{row['cell']}`: mass `{row['normalized_eigenvector_mass']}`, " - f"score `{row['eigen_score']}`, DESI `{row['desi_count']}`, MaNGA `{row['manga_count']}`" - for row in result["top_cell_masses"][:10] - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - diag = result["eigensolver_diagnostics"] - - DOC_MD.write_text( - f"""# Stellar Gas Eigenvector Mass Probe - -Status: `SMN_EIGENVECTOR_MASS` - -Decision: `{result['decision']}` - -This probe computes the dominant covariance eigenvector over the coarse DESI -epoviz to MaNGA population-cell join. The output is an SMN/evidence-load mass -direction: it ranks the current coarse joined cells by this diagnostic score so -later zoom work can choose explicit follow-up targets. - -Claim boundary: this is not physical mass, not stellar mass, not a direct gas -density map, and not a cosmology fit. - -## Result - -Dominant eigenvalue: - -```text -{result['dominant_eigenvalue']} -``` - -Dominant explained mass share: - -```text -{result['dominant_explained_mass_share']} -``` - -## Dominant Eigenvector - -{vector_lines} - -## Eigensolver Diagnostics - -```text -method: {diag['method']} -converged: {diag['converged']} -iterations: {diag['iterations']} -final max off-diagonal: {diag['final_max_offdiag']} -dominant residual L2: {diag['dominant_residual_l2']} -``` - -## Top Cell Masses - -{cell_lines} - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Eigenvector Mass Probe -tags: StellarGasObservation SemanticMassNumbers DESI MaNGA Eigenvector Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -The inferred eigenvector mass is the dominant SMN/evidence-load direction over -the coarse DESI epoviz to MaNGA population-cell join. - -Dominant eigenvalue: - -``` -{result['dominant_eigenvalue']} -``` - -Dominant explained mass share: - -``` -{result['dominant_explained_mass_share']} -``` - -Eigensolver: - -``` -converged={diag['converged']} iterations={diag['iterations']} residual_l2={diag['dominant_residual_l2']} -``` - -!! Dominant Eigenvector - -{vector_lines} - -!! Top Cell Masses - -{cell_lines} - -!! Boundary - -This is not physical mass, not stellar mass, not direct gas-density inference, -and not a cosmology fit. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_full_cell_eigenmass_stability.py b/4-Infrastructure/shim/stellar_gas_full_cell_eigenmass_stability.py deleted file mode 100644 index 6627fb97..00000000 --- a/4-Infrastructure/shim/stellar_gas_full_cell_eigenmass_stability.py +++ /dev/null @@ -1,586 +0,0 @@ -#!/usr/bin/env python3 -"""Full-cell eigenmass stability and ablation controls. - -This probe reuses the existing DESI epoviz to MaNGA population-cell join and -checks whether the 25-cell SMN/evidence-load eigenvector remains stable across -all joined cells, leave-one-cell-out slices, deterministic null shuffles, and -feature ablations. - -Boundary: this is evidence-geometry quality control. It is not physical mass, -not gas density, not shock proof, and not cosmology. -""" - -from __future__ import annotations - -import json -import math -import random -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -JOIN_JSON = ROOT / "shared-data/data/stellar_gas_observation/desi_epoviz_manga_population_cell_join.json" -BASELINE_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_eigenvector_mass_probe.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_full_cell_eigenmass_stability.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_full_cell_eigenmass_stability_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_full_cell_eigenmass_stability_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Full Cell Eigenmass Stability.tid" - -FEATURES = [ - "log_desi_count", - "log_manga_count", - "partial_full_shock_fraction", - "shock_lier_fraction", - "BGS_share", - "ELG_share", - "LRG_share", - "QSO_share", -] - -SHOCK_FEATURES = {"partial_full_shock_fraction", "shock_lier_fraction"} -TRACER_FEATURES = {"BGS_share", "ELG_share", "LRG_share", "QSO_share"} - - -def dot(a: list[float], b: list[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def norm(v: list[float]) -> float: - return math.sqrt(dot(v, v)) - - -def normalize(v: list[float]) -> list[float]: - n = norm(v) - if n == 0: - return [0.0 for _ in v] - return [x / n for x in v] - - -def cosine(a: list[float], b: list[float]) -> float: - denom = norm(a) * norm(b) - if denom == 0: - return 0.0 - return dot(a, b) / denom - - -def transpose(matrix: list[list[float]]) -> list[list[float]]: - return [list(col) for col in zip(*matrix)] - - -def round9(value: float) -> float: - return round(value, 9) - - -def jacobi_eigen_symmetric(matrix: list[list[float]], max_iter: int = 240, eps: float = 1e-12) -> tuple[list[float], list[list[float]], dict[str, Any]]: - n = len(matrix) - a = [row[:] for row in matrix] - v = [[1.0 if i == j else 0.0 for j in range(n)] for i in range(n)] - - iterations = 0 - final_max_offdiag = 0.0 - converged = False - for iteration in range(1, max_iter + 1): - p, q = 0, 1 - max_off = 0.0 - for i in range(n): - for j in range(i + 1, n): - val = abs(a[i][j]) - if val > max_off: - max_off = val - p, q = i, j - iterations = iteration - final_max_offdiag = max_off - if max_off < eps: - converged = True - break - - if abs(a[p][p] - a[q][q]) < eps: - angle = math.pi / 4.0 - else: - angle = 0.5 * math.atan2(2.0 * a[p][q], a[q][q] - a[p][p]) - c = math.cos(angle) - s = math.sin(angle) - - app = c * c * a[p][p] - 2.0 * s * c * a[p][q] + s * s * a[q][q] - aqq = s * s * a[p][p] + 2.0 * s * c * a[p][q] + c * c * a[q][q] - a[p][p] = app - a[q][q] = aqq - a[p][q] = 0.0 - a[q][p] = 0.0 - - for k in range(n): - if k == p or k == q: - continue - akp = c * a[k][p] - s * a[k][q] - akq = s * a[k][p] + c * a[k][q] - a[k][p] = akp - a[p][k] = akp - a[k][q] = akq - a[q][k] = akq - - for k in range(n): - vkp = c * v[k][p] - s * v[k][q] - vkq = s * v[k][p] + c * v[k][q] - v[k][p] = vkp - v[k][q] = vkq - - return [a[i][i] for i in range(n)], v, { - "method": "jacobi_symmetric", - "max_iter": max_iter, - "eps": eps, - "iterations": iterations, - "converged": converged, - "final_max_offdiag": final_max_offdiag, - } - - -def mat_vec(m: list[list[float]], v: list[float]) -> list[float]: - return [dot(row, v) for row in m] - - -def eigen_residual(matrix: list[list[float]], eigenvalue: float, eigenvector: list[float]) -> float: - av = mat_vec(matrix, eigenvector) - residual = [av_i - eigenvalue * v_i for av_i, v_i in zip(av, eigenvector)] - return norm(residual) - - -def zscore(rows: list[list[float]]) -> tuple[list[list[float]], list[float], list[float]]: - cols = transpose(rows) - means = [sum(col) / len(col) for col in cols] - stds = [] - for col, mean in zip(cols, means): - var = sum((x - mean) ** 2 for x in col) / len(col) - std = math.sqrt(var) - stds.append(std if std > 0 else 1.0) - return [[(x - means[i]) / stds[i] for i, x in enumerate(row)] for row in rows], means, stds - - -def covariance(rows: list[list[float]]) -> list[list[float]]: - n = len(rows) - cols = len(rows[0]) - return [ - [sum(row[i] * row[j] for row in rows) / (n - 1) for j in range(cols)] - for i in range(cols) - ] - - -def load_json(path: Path) -> dict[str, Any]: - with path.open() as f: - return json.load(f) - - -def source_rows(join: dict[str, Any]) -> list[dict[str, Any]]: - rows = [] - for cell in join["manga_join"]["top_joined_cells"]: - mix = cell["desi_tracer_mix"] - total = sum(float(v) for v in mix.values()) or 1.0 - rows.append( - { - "cell": cell["cell"], - "payload": cell, - "features": { - "log_desi_count": math.log1p(float(cell["desi_count"])), - "log_manga_count": math.log1p(float(cell["manga_count"])), - "partial_full_shock_fraction": float(cell.get("manga_partial_or_full_shock_fraction") or 0.0), - "shock_lier_fraction": float(cell.get("manga_shock_lier_fraction") or 0.0), - "BGS_share": float(mix.get("BGS", 0.0)) / total, - "ELG_share": float(mix.get("ELG", 0.0)) / total, - "LRG_share": float(mix.get("LRG", 0.0)) / total, - "QSO_share": float(mix.get("QSO", 0.0)) / total, - }, - } - ) - return rows - - -def permuted(values: list[Any], seed: int) -> list[Any]: - out = values[:] - random.Random(seed).shuffle(out) - return out - - -def vector_for_features(row: dict[str, Any], features: list[str]) -> list[float]: - return [float(row["features"][feature]) for feature in features] - - -def fit_eigenmass(rows: list[dict[str, Any]], features: list[str], baseline_vector: dict[str, float] | None = None) -> dict[str, Any]: - labels = [row["cell"] for row in rows] - raw_rows = [vector_for_features(row, features) for row in rows] - scaled_rows, means, stds = zscore(raw_rows) - cov = covariance(scaled_rows) - values, vectors_as_columns, solver = jacobi_eigen_symmetric(cov) - order = sorted(range(len(values)), key=lambda i: values[i], reverse=True) - eigenvalues = [values[i] for i in order] - dominant = normalize([vectors_as_columns[row][order[0]] for row in range(len(features))]) - - if baseline_vector is None: - if "partial_full_shock_fraction" in features: - anchor = dominant[features.index("partial_full_shock_fraction")] - else: - anchor = sum(dominant) - if anchor < 0: - dominant = [-x for x in dominant] - else: - common = [feature for feature in features if feature in baseline_vector] - candidate_common = [dominant[features.index(feature)] for feature in common] - baseline_common = [baseline_vector[feature] for feature in common] - if dot(candidate_common, baseline_common) < 0: - dominant = [-x for x in dominant] - residual = eigen_residual(cov, eigenvalues[0], dominant) - - scores = [dot(row, dominant) for row in scaled_rows] - min_score = min(scores) - shifted = [score - min_score for score in scores] - total_shifted = sum(shifted) - masses = [x / total_shifted if total_shifted > 0 else 1.0 / len(shifted) for x in shifted] - cell_masses = [ - { - "cell": label, - "eigen_score": round(score, 6), - "normalized_eigenvector_mass": round(mass, 6), - } - for label, score, mass in zip(labels, scores, masses) - ] - cell_masses.sort(key=lambda row: row["normalized_eigenvector_mass"], reverse=True) - - total_eigen = sum(x for x in eigenvalues if x > 0) - explained = [(x / total_eigen if total_eigen > 0 else 0.0) for x in eigenvalues] - return { - "cell_count": len(rows), - "feature_basis": features, - "feature_means": {name: round9(means[i]) for i, name in enumerate(features)}, - "feature_stds": {name: round9(stds[i]) for i, name in enumerate(features)}, - "dominant_eigenvalue": round9(eigenvalues[0]), - "dominant_explained_mass_share": round9(explained[0]), - "eigensolver_diagnostics": { - **solver, - "dominant_residual_l2": round(residual, 12), - "orthogonality_note": "Jacobi rotations return an orthonormal basis up to numeric roundoff; this receipt reports the dominant residual only.", - }, - "dominant_eigenvector": {name: round9(dominant[i]) for i, name in enumerate(features)}, - "eigenvalues": [round9(x) for x in eigenvalues], - "explained_mass_share": [round9(x) for x in explained], - "top_cell_masses": cell_masses, - } - - -def compare_to_baseline(candidate: dict[str, Any], baseline: dict[str, Any], top_n: int = 5) -> dict[str, Any]: - common = [feature for feature in FEATURES if feature in candidate["dominant_eigenvector"] and feature in baseline["dominant_eigenvector"]] - cand_vec = [candidate["dominant_eigenvector"][feature] for feature in common] - base_vec = [baseline["dominant_eigenvector"][feature] for feature in common] - base_top = {row["cell"] for row in baseline["top_cell_masses"][:top_n]} - cand_top = {row["cell"] for row in candidate["top_cell_masses"][:top_n]} - return { - "common_feature_basis": common, - "common_basis_cosine_to_original": round9(cosine(cand_vec, base_vec)), - "dominant_explained_share_delta": round9(candidate["dominant_explained_mass_share"] - baseline["dominant_explained_mass_share"]), - "top_cell_overlap_at_5": len(base_top & cand_top), - "top_cell_overlap_fraction_at_5": round9(len(base_top & cand_top) / top_n), - } - - -def summarize_leave_one_out(values: list[dict[str, Any]]) -> dict[str, Any]: - cosines = sorted(row["common_basis_cosine_to_original"] for row in values) - overlaps = [row["top_cell_overlap_fraction_at_5"] for row in values] - return { - "loo_count": len(values), - "min_cosine_to_original": round9(cosines[0]), - "median_cosine_to_original": round9(cosines[len(cosines) // 2]), - "mean_cosine_to_original": round9(sum(cosines) / len(cosines)), - "mean_top5_overlap_fraction": round9(sum(overlaps) / len(overlaps)), - } - - -def with_shuffled_feature_columns(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: - shuffled_columns = { - feature: permuted([row["features"][feature] for row in rows], seed=2026050901 + idx) - for idx, feature in enumerate(FEATURES) - } - out = [] - for idx, row in enumerate(rows): - clone = {"cell": row["cell"], "payload": row["payload"], "features": dict(row["features"])} - for feature in FEATURES: - clone["features"][feature] = shuffled_columns[feature][idx] - out.append(clone) - return out - - -def with_shuffled_shocks(rows: list[dict[str, Any]]) -> list[dict[str, Any]]: - partial = permuted([row["features"]["partial_full_shock_fraction"] for row in rows], seed=2026050902) - lier = permuted([row["features"]["shock_lier_fraction"] for row in rows], seed=2026050903) - out = [] - for idx, row in enumerate(rows): - clone = {"cell": row["cell"], "payload": row["payload"], "features": dict(row["features"])} - clone["features"]["partial_full_shock_fraction"] = partial[idx] - clone["features"]["shock_lier_fraction"] = lier[idx] - out.append(clone) - return out - - -def control_result(name: str, rows: list[dict[str, Any]], features: list[str], baseline: dict[str, Any]) -> dict[str, Any]: - fit = fit_eigenmass(rows, features, baseline["dominant_eigenvector"]) - return { - "control": name, - "cell_count": fit["cell_count"], - "feature_basis": fit["feature_basis"], - "dominant_eigenvalue": fit["dominant_eigenvalue"], - "dominant_explained_mass_share": fit["dominant_explained_mass_share"], - "dominant_eigenvector": fit["dominant_eigenvector"], - "comparison_to_original": compare_to_baseline(fit, baseline), - "top_cell_masses": fit["top_cell_masses"][:10], - } - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - join = load_json(JOIN_JSON) - stored = load_json(BASELINE_JSON) - rows = source_rows(join) - baseline = fit_eigenmass(rows, FEATURES) - - stored_compare = compare_to_baseline(baseline, stored, top_n=5) - stored_vector_abs_delta = { - feature: round9(abs(baseline["dominant_eigenvector"][feature] - stored["dominant_eigenvector"][feature])) - for feature in FEATURES - } - - loo_rows = [] - for cell in [row["cell"] for row in rows]: - subset = [row for row in rows if row["cell"] != cell] - fit = fit_eigenmass(subset, FEATURES, baseline["dominant_eigenvector"]) - comparison = compare_to_baseline(fit, baseline) - loo_rows.append( - { - "held_out_cell": cell, - "dominant_explained_mass_share": fit["dominant_explained_mass_share"], - **comparison, - } - ) - - controls = [ - control_result("shuffled_feature_columns", with_shuffled_feature_columns(rows), FEATURES, baseline), - control_result("shuffled_shock_channels", with_shuffled_shocks(rows), FEATURES, baseline), - control_result("desi_count_removed", rows, [feature for feature in FEATURES if feature != "log_desi_count"], baseline), - control_result("shock_proxy_removed", rows, [feature for feature in FEATURES if feature not in SHOCK_FEATURES], baseline), - control_result("tracer_mix_removed", rows, [feature for feature in FEATURES if feature not in TRACER_FEATURES], baseline), - ] - - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - result = { - "schema": "stellar_gas_full_cell_eigenmass_stability_v0", - "created": created, - "decision": "REPORT_FULL_CELL_EIGENMASS_STABILITY_WITH_NULL_CONTROLS_HOLD_PHYSICAL_CLAIMS", - "claim_boundary": ( - "Full-cell stability and ablation controls for the joined DESI/MaNGA " - "SMN/evidence-load eigenvector. This does not promote physical mass, " - "gas density, shock proof, or cosmology." - ), - "sources": { - "join": str(JOIN_JSON.relative_to(ROOT)), - "stored_25_cell_probe": str(BASELINE_JSON.relative_to(ROOT)), - }, - "full_cell_baseline": baseline, - "stored_25_cell_comparison": { - "stored_cell_count": stored["cell_count"], - "recomputed_cell_count": baseline["cell_count"], - "common_basis_cosine_to_stored": stored_compare["common_basis_cosine_to_original"], - "top_cell_overlap_at_5": stored_compare["top_cell_overlap_at_5"], - "max_abs_eigenvector_component_delta": round9(max(stored_vector_abs_delta.values())), - "abs_eigenvector_component_delta": stored_vector_abs_delta, - }, - "leave_one_cell_out_stability": { - "summary": summarize_leave_one_out(loo_rows), - "rows": loo_rows, - }, - "null_and_ablation_controls": controls, - "holds": [ - "HOLD_PHYSICAL_MASS_INTERPRETATION", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_SHOCK_PROOF", - "HOLD_OBJECT_LEVEL_CROSSMATCH", - "HOLD_SELECTION_FUNCTION_FIT", - "HOLD_COSMOLOGY_FIT", - ], - } - receipt = { - "receipt_type": "stellar_gas_full_cell_eigenmass_stability_receipt", - "created": created, - "source_join": str(JOIN_JSON.relative_to(ROOT)), - "stored_25_cell_probe": str(BASELINE_JSON.relative_to(ROOT)), - "full_cell_count": baseline["cell_count"], - "stored_25_cell_count": stored["cell_count"], - "stored_comparison_cosine": result["stored_25_cell_comparison"]["common_basis_cosine_to_stored"], - "leave_one_out_min_cosine": result["leave_one_cell_out_stability"]["summary"]["min_cosine_to_original"], - "control_names": [control["control"] for control in controls], - "eigensolver_diagnostics": baseline["eigensolver_diagnostics"], - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - baseline = result["full_cell_baseline"] - stored = result["stored_25_cell_comparison"] - loo = result["leave_one_cell_out_stability"]["summary"] - controls = result["null_and_ablation_controls"] - diag = baseline["eigensolver_diagnostics"] - vector_lines = "\n".join( - f"- `{name}`: {value}" for name, value in baseline["dominant_eigenvector"].items() - ) - control_lines = "\n".join( - "- `{control}`: cosine `{cosine}`, explained share `{share}`, top5 overlap `{overlap}`".format( - control=control["control"], - cosine=control["comparison_to_original"]["common_basis_cosine_to_original"], - share=control["dominant_explained_mass_share"], - overlap=control["comparison_to_original"]["top_cell_overlap_fraction_at_5"], - ) - for control in controls - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - - DOC_MD.write_text( - f"""# Stellar Gas Full Cell Eigenmass Stability - -Status: `FULL_CELL_EIGENMASS_STABILITY` - -Decision: `{result['decision']}` - -This probe checks the 25-cell DESI/MaNGA joined-cell eigenmass against all -available joined cells, leave-one-cell-out slices, deterministic null shuffles, -and feature ablations. - -Claim boundary: this is evidence-geometry quality control only. It does not -promote physical mass, gas density, shock proof, object-level crossmatch, or -cosmology. - -## Full-Cell Baseline - -Cell count: `{baseline['cell_count']}` - -Dominant eigenvalue: - -```text -{baseline['dominant_eigenvalue']} -``` - -Dominant explained mass share: - -```text -{baseline['dominant_explained_mass_share']} -``` - -Dominant eigenvector: - -{vector_lines} - -## Stored 25-Cell Comparison - -```text -common-basis cosine to stored probe: {stored['common_basis_cosine_to_stored']} -top-cell overlap at 5: {stored['top_cell_overlap_at_5']} -max abs component delta: {stored['max_abs_eigenvector_component_delta']} -``` - -## Eigensolver Diagnostics - -```text -method: {diag['method']} -converged: {diag['converged']} -iterations: {diag['iterations']} -final max off-diagonal: {diag['final_max_offdiag']} -dominant residual L2: {diag['dominant_residual_l2']} -``` - -## Leave-One-Cell-Out Stability - -```text -loo count: {loo['loo_count']} -min cosine to original: {loo['min_cosine_to_original']} -median cosine to original: {loo['median_cosine_to_original']} -mean cosine to original: {loo['mean_cosine_to_original']} -mean top5 overlap: {loo['mean_top5_overlap_fraction']} -``` - -## Null And Ablation Controls - -{control_lines} - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Full Cell Eigenmass Stability -tags: StellarGasObservation DESI MaNGA Eigenvector Controls Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This tiddler records a full-cell stability and null-control check for the -DESI/MaNGA joined-cell SMN/evidence-load eigenvector. - -Cell count: `{baseline['cell_count']}` - -Stored 25-cell cosine: - -``` -{stored['common_basis_cosine_to_stored']} -``` - -Leave-one-cell-out minimum cosine: - -``` -{loo['min_cosine_to_original']} -``` - -Eigensolver: - -``` -converged={diag['converged']} iterations={diag['iterations']} residual_l2={diag['dominant_residual_l2']} -``` - -!! Original Dominant Eigenvector - -{vector_lines} - -!! Null And Ablation Controls - -{control_lines} - -!! Boundary - -This is evidence-geometry quality control only. It is not physical mass, gas -density, shock proof, or cosmology. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_gdrive_pull.py b/4-Infrastructure/shim/stellar_gas_gdrive_pull.py deleted file mode 100644 index 71bed890..00000000 --- a/4-Infrastructure/shim/stellar_gas_gdrive_pull.py +++ /dev/null @@ -1,240 +0,0 @@ -#!/usr/bin/env python3 -"""Direct-to-Google-Drive seed puller for stellar gas observation routes. - -The puller streams source payloads through rclone instead of writing observation -payloads into the repository. Local files are limited to source manifests and -receipts. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from urllib.request import Request, urlopen - - -REPO = Path(__file__).resolve().parents[2] -SOURCE_PATH = REPO / "shared-data/data/stellar_gas_observation/stellar_gas_gdrive_sources.json" -SCHEMA_PATH = REPO / "shared-data/data/stellar_gas_observation/stellar_gas_observation_schema.json" -RECEIPT_DIR = REPO / "shared-data/data/stellar_gas_observation" - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def load_json(path: Path) -> dict: - return json.loads(path.read_text()) - - -def run(cmd: list[str], input_bytes: bytes | None = None) -> subprocess.CompletedProcess: - return subprocess.run( - cmd, - input=input_bytes, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - check=False, - ) - - -def rclone_available(remote: str) -> tuple[bool, str]: - proc = run(["rclone", "lsf", remote]) - if proc.returncode == 0: - return True, proc.stdout.decode(errors="replace").strip() - return False, proc.stderr.decode(errors="replace").strip() - - -def head_content_length(url: str, timeout: int) -> int | None: - req = Request(url, method="HEAD", headers={"User-Agent": "ResearchStack-StellarGasPull/0"}) - try: - with urlopen(req, timeout=timeout) as response: - value = response.headers.get("Content-Length") - return int(value) if value else None - except Exception: - return None - - -def fetch_bytes(url: str, timeout: int, max_bytes: int) -> tuple[bytes, str | None]: - req = Request(url, headers={"User-Agent": "ResearchStack-StellarGasPull/0"}) - with urlopen(req, timeout=timeout) as response: - chunks: list[bytes] = [] - total = 0 - while True: - chunk = response.read(1024 * 1024) - if not chunk: - break - total += len(chunk) - if total > max_bytes: - raise ValueError(f"payload exceeded max_bytes={max_bytes}") - chunks.append(chunk) - content_type = response.headers.get("Content-Type") - return b"".join(chunks), content_type - - -def rcat(remote_path: str, payload: bytes) -> tuple[bool, str]: - proc = run(["rclone", "rcat", remote_path], input_bytes=payload) - if proc.returncode == 0: - return True, proc.stdout.decode(errors="replace").strip() - return False, proc.stderr.decode(errors="replace").strip() - - -def copy_local_to_drive(local_path: Path, remote_path: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local_path), remote_path, "--checksum"]) - if proc.returncode == 0: - return True, proc.stdout.decode(errors="replace").strip() - return False, proc.stderr.decode(errors="replace").strip() - - -def pull_source(source: dict, destination: str, max_bytes: int, timeout: int, execute: bool) -> dict: - source_id = source["id"] - output_name = source["output_name"] - remote_path = f"{destination.rstrip('/')}/raw/{output_name}" - result = { - "id": source_id, - "title": source.get("title"), - "source_url": source["source_url"], - "archive": source.get("archive"), - "source_kind": source.get("source_kind"), - "model_relevance": source.get("model_relevance", []), - "drive_path": remote_path, - "retrieved_at": now_iso(), - "decision": "HOLD_ROUTE_ONLY", - "byte_count": 0, - "payload_sha256": None, - "content_type": None, - "notes": [], - } - - if not source.get("pull_enabled", False): - result["notes"].append(source.get("route_only_reason", "pull disabled by source manifest")) - return result - - length = head_content_length(source["source_url"], timeout) - result["head_content_length"] = length - if length is not None and length > max_bytes: - result["decision"] = "HOLD_OVERSIZE" - result["notes"].append(f"content-length {length} exceeds max_bytes {max_bytes}") - return result - - if not execute: - result["decision"] = "DRY_RUN_READY" - result["notes"].append("execute flag not set") - return result - - try: - payload, content_type = fetch_bytes(source["source_url"], timeout, max_bytes) - except Exception as exc: - result["decision"] = "QUARANTINE_FETCH_FAILED" - result["notes"].append(str(exc)) - return result - - digest = hashlib.sha256(payload).hexdigest() - ok, message = rcat(remote_path, payload) - result.update( - { - "byte_count": len(payload), - "payload_sha256": digest, - "content_type": content_type, - "rclone_message": message, - } - ) - result["decision"] = "ADMIT_GDRIVE_PAYLOAD" if ok else "QUARANTINE_RCLONE_FAILED" - return result - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--sources", type=Path, default=SOURCE_PATH) - parser.add_argument("--schema", type=Path, default=SCHEMA_PATH) - parser.add_argument("--destination", default=None) - parser.add_argument("--max-bytes", type=int, default=None) - parser.add_argument("--timeout", type=int, default=45) - parser.add_argument("--execute", action="store_true") - args = parser.parse_args() - - manifest = load_json(args.sources) - destination = args.destination or manifest["default_drive_destination"] - max_bytes = args.max_bytes or int(manifest.get("default_max_bytes", 20_000_000)) - receipt_path = RECEIPT_DIR / f"stellar_gas_gdrive_pull_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - - remote_root = destination.split("/", 1)[0] - remote_ok, remote_message = rclone_available(remote_root) - if not remote_ok: - receipt = { - "schema": "stellar_gas_gdrive_pull_receipt_v0", - "created": now_iso(), - "destination": destination, - "decision": "QUARANTINE_NO_GDRIVE_REMOTE", - "remote_check": remote_message, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - print(json.dumps(receipt, indent=2)) - return 2 - - results = [ - pull_source(source, destination, max_bytes, args.timeout, args.execute) - for source in manifest["sources"] - ] - - copied_control_files = [] - if args.execute: - for local, name in [ - (args.sources, "stellar_gas_gdrive_sources.json"), - (args.schema, "stellar_gas_observation_schema.json"), - ]: - remote_path = f"{destination.rstrip('/')}/control/{name}" - ok, message = copy_local_to_drive(local, remote_path) - copied_control_files.append( - { - "local": str(local.relative_to(REPO)), - "drive_path": remote_path, - "ok": ok, - "message": message, - } - ) - - admitted = [item for item in results if item["decision"] == "ADMIT_GDRIVE_PAYLOAD"] - receipt = { - "schema": "stellar_gas_gdrive_pull_receipt_v0", - "created": now_iso(), - "claim_boundary": "Direct-to-Google-Drive seed pull. Payloads are streamed to Drive; local repo stores only manifests and receipts. Heavy products remain route-only unless explicitly enabled.", - "source_manifest": str(args.sources.relative_to(REPO)), - "observation_schema": str(args.schema.relative_to(REPO)), - "destination": destination, - "execute": args.execute, - "max_bytes": max_bytes, - "remote_check": "PASS", - "control_files": copied_control_files, - "summary": { - "source_count": len(results), - "admitted_payload_count": len(admitted), - "admitted_payload_bytes": sum(item["byte_count"] for item in admitted), - "route_or_hold_count": len(results) - len(admitted), - }, - "results": results, - "decision": "ADMIT_SEED_PULL_TO_GDRIVE" if args.execute else "HOLD_DRY_RUN_ONLY", - } - - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - - if args.execute: - remote_receipt = f"{destination.rstrip('/')}/receipts/{receipt_path.name}" - ok, message = copy_local_to_drive(receipt_path, remote_receipt) - receipt["receipt_upload"] = { - "drive_path": remote_receipt, - "ok": ok, - "message": message, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - - print(json.dumps(receipt, indent=2)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/stellar_gas_line_ratio_diagnostics.py b/4-Infrastructure/shim/stellar_gas_line_ratio_diagnostics.py deleted file mode 100644 index e81766c2..00000000 --- a/4-Infrastructure/shim/stellar_gas_line_ratio_diagnostics.py +++ /dev/null @@ -1,409 +0,0 @@ -#!/usr/bin/env python3 -"""Compute MaNGA emission-line ratio diagnostics from DAPall arrays.""" - -from __future__ import annotations - -import argparse -import importlib.util -import json -import math -import statistics -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path - - -REPO = Path(__file__).resolve().parents[2] -SEED_SCRIPT = REPO / "4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py" -DATA_DIR = REPO / "shared-data/data/stellar_gas_observation" -CHANNELS = DATA_DIR / "sdss_manga_dr17_emission_line_channels.json" -DEFAULT_FITS = REPO / "shared-data/artifacts/stellar_gas_observation/dapall-v3_1_1-3.1.0.fits" -DESTINATION = "Gdrive:topological_storage/research-stack/stellar-gas-observation/seed-2026-05-09" -DOC = REPO / "6-Documentation/docs/stellar_gas_line_ratio_diagnostics_2026-05-09.md" - - -TARGET_COLUMNS = [ - "PLATEIFU", - "MANGAID", - "DAPTYPE", - "Z", - "HA_GSIGMA_1RE", - "HA_GSIGMA_HI_CLIP", - "EMLINE_GFLUX_1RE", - "EMLINE_GFLUX_TOT", - "EMLINE_GEW_1RE", -] - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def load_json(path: Path) -> dict: - return json.loads(path.read_text()) - - -def load_seed_module(): - spec = importlib.util.spec_from_file_location("sdss_manga_dapall_observation_seed", SEED_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load {SEED_SCRIPT}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def run(cmd: list[str]) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - - -def rclone_copyto(local: Path, remote: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local), remote, "--checksum"]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def finite(value) -> bool: - return isinstance(value, (int, float)) and math.isfinite(value) and value > -900 - - -def pos(value) -> float | None: - if finite(value) and value > 0: - return float(value) - return None - - -def log_ratio(num: float | None, den: float | None) -> float | None: - if num is None or den is None or num <= 0 or den <= 0: - return None - return math.log10(num / den) - - -def ratio(num: float | None, den: float | None) -> float | None: - if num is None or den is None or den <= 0: - return None - return num / den - - -def classify_bpt(log_nii_ha: float | None, log_oiii_hb: float | None) -> str: - if log_nii_ha is None or log_oiii_hb is None: - return "unclassified" - # Common demarcation curves. This is a diagnostic proxy only. - if log_nii_ha >= 0.47: - return "agn_liner_or_shock_proxy" - kewley = 0.61 / (log_nii_ha - 0.47) + 1.19 - kauffmann = 0.61 / (log_nii_ha - 0.05) + 1.3 - if log_oiii_hb > kewley: - return "agn_liner_or_shock_proxy" - if log_oiii_hb > kauffmann: - return "composite_proxy" - return "star_forming_proxy" - - -def classify_shock(log_sii_ha, log_oi_ha, gas_sigma): - score = 0.0 - reasons = [] - if log_sii_ha is not None and log_sii_ha > -0.4: - score += 0.35 - reasons.append("elevated_sii_ha") - if log_oi_ha is not None and log_oi_ha > -1.1: - score += 0.35 - reasons.append("elevated_oi_ha") - if gas_sigma is not None and gas_sigma > 120: - score += 0.30 - reasons.append("broad_halpha_sigma") - if score >= 0.65: - label = "shock_lier_proxy" - elif score > 0: - label = "partial_shock_proxy" - else: - label = "no_shock_proxy" - return min(1.0, score), label, reasons - - -def summarize(vals: list[float]) -> dict: - values = sorted(v for v in vals if math.isfinite(v)) - if not values: - return {"count": 0} - return { - "count": len(values), - "min": round(values[0], 6), - "max": round(values[-1], 6), - "mean": round(statistics.fmean(values), 6), - "median": round(statistics.median(values), 6), - "p90": round(values[int(0.9 * (len(values) - 1))], 6), - } - - -def iter_rows(fits_path: Path): - seed = load_seed_module() - with fits_path.open("rb") as f: - hdu_index = 0 - while True: - try: - header, _ = seed.read_header(f) - except EOFError: - break - data_start = f.tell() - if str(header.get("XTENSION", "PRIMARY")) == "BINTABLE": - row_len = int(header["NAXIS1"]) - row_count = int(header["NAXIS2"]) - pcount = int(header.get("PCOUNT", 0)) - columns = seed.build_columns(header) - by_name = {col["name"]: col for col in columns} - selected = [by_name[name] for name in TARGET_COLUMNS if name in by_name] - hdu_name = str(header.get("EXTNAME", f"HDU{hdu_index}")) - for row_idx in range(row_count): - f.seek(data_start + row_idx * row_len) - row = f.read(row_len) - fields = {} - for col in selected: - raw = row[col["offset"] : col["offset"] + col["width"]] - value = seed.decode_value(raw, col) - if isinstance(value, str): - value = value.replace("\u0000", "").strip() - fields[col["name"]] = value - yield hdu_index, hdu_name, row_idx, fields - f.seek(data_start + seed.padded_size(row_len * row_count + pcount)) - else: - bitpix = int(header.get("BITPIX", 8)) - naxis = int(header.get("NAXIS", 0)) - if naxis == 0: - data_size = 0 - else: - pixels = 1 - for axis in range(1, naxis + 1): - pixels *= int(header.get(f"NAXIS{axis}", 0)) - data_size = abs(bitpix) // 8 * pixels - f.seek(data_start + seed.padded_size(data_size)) - hdu_index += 1 - - -def build_diagnostics(fits_path: Path) -> dict: - channel_payload = load_json(CHANNELS) - index = {c["label"]: c["index0"] for c in channel_payload["channels"]} - required = ["Ha-6564", "Hb-4862", "OIII-5008", "NII-6585", "SII-6718", "SII-6732", "OI-6302"] - missing = [name for name in required if name not in index] - if missing: - raise RuntimeError(f"missing channel labels: {missing}") - - summaries = { - "log_nii_ha": [], - "log_sii_ha": [], - "log_oi_ha": [], - "log_oiii_hb": [], - "balmer_decrement": [], - "gas_sigma_1re_kms": [], - "shock_lier_score": [], - } - classes: dict[str, int] = {} - shock_classes: dict[str, int] = {} - examples = [] - total = 0 - valid_ratio_rows = 0 - for hdu_index, hdu_name, row_idx, fields in iter_rows(fits_path): - total += 1 - flux = fields.get("EMLINE_GFLUX_1RE") - if not isinstance(flux, list) or len(flux) < 35: - continue - ha = pos(flux[index["Ha-6564"]]) - hb = pos(flux[index["Hb-4862"]]) - oiii = pos(flux[index["OIII-5008"]]) - nii = pos(flux[index["NII-6585"]]) - sii = None - sii_1 = pos(flux[index["SII-6718"]]) - sii_2 = pos(flux[index["SII-6732"]]) - if sii_1 is not None and sii_2 is not None: - sii = sii_1 + sii_2 - oi = pos(flux[index["OI-6302"]]) - gas_sigma = pos(fields.get("HA_GSIGMA_1RE")) - - log_nii_ha = log_ratio(nii, ha) - log_sii_ha = log_ratio(sii, ha) - log_oi_ha = log_ratio(oi, ha) - log_oiii_hb = log_ratio(oiii, hb) - balmer = ratio(ha, hb) - if any(v is not None for v in [log_nii_ha, log_sii_ha, log_oi_ha, log_oiii_hb]): - valid_ratio_rows += 1 - - bpt = classify_bpt(log_nii_ha, log_oiii_hb) - classes[bpt] = classes.get(bpt, 0) + 1 - shock_score, shock_label, reasons = classify_shock(log_sii_ha, log_oi_ha, gas_sigma) - shock_classes[shock_label] = shock_classes.get(shock_label, 0) + 1 - - for key, value in [ - ("log_nii_ha", log_nii_ha), - ("log_sii_ha", log_sii_ha), - ("log_oi_ha", log_oi_ha), - ("log_oiii_hb", log_oiii_hb), - ("balmer_decrement", balmer), - ("gas_sigma_1re_kms", gas_sigma), - ("shock_lier_score", shock_score), - ]: - if value is not None and math.isfinite(value): - summaries[key].append(float(value)) - - if len(examples) < 20 and shock_score >= 0.65: - examples.append( - { - "hdu_name": hdu_name, - "row_index": row_idx, - "plateifu": fields.get("PLATEIFU"), - "mangaid": fields.get("MANGAID"), - "z": fields.get("Z"), - "line_ratios": { - "log_NII6585_Ha": log_nii_ha, - "log_SII6718_6732_Ha": log_sii_ha, - "log_OI6302_Ha": log_oi_ha, - "log_OIII5008_Hb": log_oiii_hb, - "Ha_Hb": balmer, - }, - "gas_sigma_1re_kms": gas_sigma, - "bpt_proxy_class": bpt, - "shock_lier_proxy": shock_label, - "shock_reasons": reasons, - "shock_lier_score": shock_score, - } - ) - - shock_fraction = ( - (shock_classes.get("shock_lier_proxy", 0) + 0.5 * shock_classes.get("partial_shock_proxy", 0)) - / total - if total - else 0.0 - ) - return { - "schema": "stellar_gas_line_ratio_diagnostics_v0", - "created": now_iso(), - "claim_boundary": "Line-ratio diagnostics from MaNGA DAPall integrated Gaussian flux arrays. These are proxy classifications; they do not prove a physical shock, AGN, or ionization mechanism.", - "source_fits": str(fits_path.relative_to(REPO)) if fits_path.is_relative_to(REPO) else str(fits_path), - "channel_map": str(CHANNELS.relative_to(REPO)), - "rows_seen": total, - "valid_ratio_rows": valid_ratio_rows, - "bpt_proxy_classes": classes, - "shock_lier_proxy_classes": shock_classes, - "aggregate_ratios": {k: summarize(v) for k, v in summaries.items()}, - "shock_lier_support": { - "fractional_proxy_support": round(shock_fraction, 6), - "gate": "ADMIT_LINE_RATIO_SHOCK_PROXY_SUPPORT" if shock_fraction > 0 else "HOLD_NO_LINE_RATIO_SUPPORT", - }, - "example_shock_lier_rows": examples, - "model_refinement": { - "saha_ionization": "line ratios now present; still HOLD for electron density and temperature", - "radiative_transfer": "Balmer decrement and flux lanes now named; still HOLD for attenuation model", - "shock_excitation": "SII/Ha, OI/Ha, OIII/Hb, NII/Ha, and H-alpha sigma now form a proxy gate", - }, - "decision": "ADMIT_LINE_RATIO_DIAGNOSTIC_SURFACE", - } - - -def write_doc(result: dict, path: Path) -> None: - support = result["shock_lier_support"] - agg = result["aggregate_ratios"] - lines = [ - "# Stellar Gas Line Ratio Diagnostics", - "", - "**Date:** 2026-05-09", - "", - f"**Decision:** `{result['decision']}`", - "", - "**Claim boundary:** line-ratio proxy only. This does not prove a", - "physical shock, AGN, stellar breakout, or ionization mechanism.", - "", - "## What Changed", - "", - "The 35-element MaNGA emission-line arrays now have named channels, so", - "physics can propagate through line ratios instead of anonymous vector", - "positions.", - "", - "```text", - f"rows seen: {result['rows_seen']}", - f"valid ratio rows: {result['valid_ratio_rows']}", - f"shock proxy support: {support['fractional_proxy_support']}", - f"shock proxy gate: {support['gate']}", - "```", - "", - "## Aggregate Ratios", - "", - "| Ratio | Count | Mean | Median | P90 |", - "|---|---:|---:|---:|---:|", - ] - for key in [ - "log_nii_ha", - "log_sii_ha", - "log_oi_ha", - "log_oiii_hb", - "balmer_decrement", - "gas_sigma_1re_kms", - "shock_lier_score", - ]: - s = agg[key] - lines.append( - f"| `{key}` | {s.get('count', 0)} | {s.get('mean', '')} | " - f"{s.get('median', '')} | {s.get('p90', '')} |" - ) - lines += [ - "", - "## Proxy Classes", - "", - "```json", - json.dumps( - { - "bpt_proxy_classes": result["bpt_proxy_classes"], - "shock_lier_proxy_classes": result["shock_lier_proxy_classes"], - }, - indent=2, - ), - "```", - "", - "## Physics Propagation", - "", - "- Saha/ionization now has line-ratio support, but still needs electron density and temperature.", - "- Radiative transfer now has named flux and Balmer-decrement lanes, but still needs an attenuation model.", - "- Shock excitation now has SII/Ha, OI/Ha, OIII/Hb, NII/Ha, and H-alpha sigma proxy support.", - "", - ] - path.write_text("\n".join(lines)) - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--fits", type=Path, default=DEFAULT_FITS) - parser.add_argument("--destination", default=DESTINATION) - args = parser.parse_args() - result = build_diagnostics(args.fits) - out = DATA_DIR / "stellar_gas_line_ratio_diagnostics.json" - out.write_text(json.dumps(result, indent=2) + "\n") - write_doc(result, DOC) - - receipt_path = DATA_DIR / f"stellar_gas_line_ratio_diagnostics_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - receipt = { - "schema": "stellar_gas_line_ratio_diagnostics_receipt_v0", - "created": now_iso(), - "claim_boundary": result["claim_boundary"], - "channel_map": str(CHANNELS.relative_to(REPO)), - "diagnostics_file": str(out.relative_to(REPO)), - "doc_file": str(DOC.relative_to(REPO)), - "decision": result["decision"], - "shock_lier_support": result["shock_lier_support"], - "uploads": {}, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - uploads = { - "channel_map": (CHANNELS, f"{args.destination}/derived/{CHANNELS.name}"), - "diagnostics": (out, f"{args.destination}/derived/{out.name}"), - "doc": (DOC, f"{args.destination}/docs/{DOC.name}"), - "receipt": (receipt_path, f"{args.destination}/receipts/{receipt_path.name}"), - } - for key, (local, remote) in uploads.items(): - ok, message = rclone_copyto(local, remote) - receipt["uploads"][key] = {"drive_path": remote, "ok": ok, "message": message} - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - if receipt["uploads"]["receipt"]["ok"]: - rclone_copyto(receipt_path, receipt["uploads"]["receipt"]["drive_path"]) - print(json.dumps(receipt, indent=2)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/stellar_gas_multiscale_eigenmass_alignment.py b/4-Infrastructure/shim/stellar_gas_multiscale_eigenmass_alignment.py deleted file mode 100644 index 7597990c..00000000 --- a/4-Infrastructure/shim/stellar_gas_multiscale_eigenmass_alignment.py +++ /dev/null @@ -1,291 +0,0 @@ -#!/usr/bin/env python3 -"""Compare row-level DESI eigenmass with DESI/MaNGA joined-cell eigenmass. - -This probe measures whether the SMN/evidence-load direction survives the zoom -from the literal DESI row surface into the gas/shock-constrained MaNGA overlap -surface. It reports a tracer-subspace cosine alignment and a sharpening factor. - -Boundary: this is an evidence-geometry comparison. It is not physical mass, not -stellar mass, not a gas-density map, and not a cosmology fit. -""" - -from __future__ import annotations - -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -ROW_JSON = ROOT / "shared-data/data/stellar_gas_observation/desi_epoviz_row_eigenmass_probe.json" -CELL_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_eigenvector_mass_probe.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_multiscale_eigenmass_alignment.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_multiscale_eigenmass_alignment_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_multiscale_eigenmass_alignment_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Multiscale Eigenmass Alignment.tid" - - -TRACER_ORDER = ["QSO", "ELG", "LRG", "BGS"] - - -def dot(a: list[float], b: list[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def norm(v: list[float]) -> float: - return math.sqrt(dot(v, v)) - - -def cosine(a: list[float], b: list[float]) -> float: - denom = norm(a) * norm(b) - if denom == 0: - return 0.0 - return dot(a, b) / denom - - -def round9(x: float) -> float: - return round(x, 9) - - -def load_json(path: Path) -> dict[str, Any]: - with path.open() as f: - return json.load(f) - - -def tracer_vector_from_row(row: dict[str, float]) -> list[float]: - return [ - row["tracer_QSO"], - row["tracer_ELG"], - row["tracer_LRG"], - row["tracer_BGS"], - ] - - -def tracer_vector_from_cell(cell: dict[str, float]) -> list[float]: - return [ - cell["QSO_share"], - cell["ELG_share"], - cell["LRG_share"], - cell["BGS_share"], - ] - - -def classify_alignment(value: float) -> str: - if value >= 0.85: - return "STRONG_ALIGNMENT" - if value >= 0.65: - return "MODERATE_ALIGNMENT" - if value >= 0.35: - return "WEAK_ALIGNMENT" - if value > -0.35: - return "ORTHOGONAL_OR_MIXED" - return "ANTI_ALIGNMENT" - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - row = load_json(ROW_JSON) - cell = load_json(CELL_JSON) - row_vec = tracer_vector_from_row(row["dominant_eigenvector"]) - cell_vec = tracer_vector_from_cell(cell["dominant_eigenvector"]) - tracer_alignment = cosine(row_vec, cell_vec) - - row_share = float(row["dominant_explained_mass_share"]) - cell_share = float(cell["dominant_explained_mass_share"]) - sharpening_factor = cell_share / row_share if row_share else 0.0 - eigenvalue_ratio = float(cell["dominant_eigenvalue"]) / float(row["dominant_eigenvalue"]) - - created = datetime.now(timezone.utc).isoformat(timespec="seconds") - result = { - "schema": "stellar_gas_multiscale_eigenmass_alignment_v0", - "created": created, - "decision": "ADMIT_MULTISCALE_EIGENMASS_ALIGNMENT_HOLD_PHYSICAL_MASS", - "claim_boundary": ( - "Compares SMN/evidence-load eigenvectors across DESI row level and " - "DESI/MaNGA joined-cell level. It does not infer physical mass, " - "stellar mass, gas density, or cosmology." - ), - "sources": { - "row_eigenmass": str(ROW_JSON.relative_to(ROOT)), - "cell_eigenmass": str(CELL_JSON.relative_to(ROOT)), - }, - "row_level": { - "cell_or_row_count": row["row_count"], - "dominant_eigenvalue": row["dominant_eigenvalue"], - "dominant_explained_mass_share": row_share, - "tracer_subvector_order": TRACER_ORDER, - "tracer_subvector": [round9(x) for x in row_vec], - }, - "cell_level": { - "cell_or_row_count": cell["cell_count"], - "dominant_eigenvalue": cell["dominant_eigenvalue"], - "dominant_explained_mass_share": cell_share, - "tracer_subvector_order": TRACER_ORDER, - "tracer_subvector": [round9(x) for x in cell_vec], - }, - "alignment": { - "tracer_subspace_cosine": round9(tracer_alignment), - "alignment_class": classify_alignment(tracer_alignment), - "constraint_sharpening_factor": round9(sharpening_factor), - "dominant_eigenvalue_ratio_cell_over_row": round9(eigenvalue_ratio), - "interpretation": ( - "The cell-level explained share is larger than the row-level share " - "under this diagnostic ratio. This is an accounting comparison, not " - "a causal gas/shock mechanism." - ), - }, - "holds": [ - "HOLD_PHYSICAL_MASS_INTERPRETATION", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_OBJECT_LEVEL_CROSSMATCH", - "HOLD_SELECTION_FUNCTION_FIT", - "HOLD_COSMOLOGY_FIT", - ], - } - receipt = { - "receipt_type": "stellar_gas_multiscale_eigenmass_alignment_receipt", - "created": created, - "row_rows": row["row_count"], - "cell_count": cell["cell_count"], - "tracer_subspace_cosine": result["alignment"]["tracer_subspace_cosine"], - "constraint_sharpening_factor": result["alignment"]["constraint_sharpening_factor"], - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - align = result["alignment"] - row = result["row_level"] - cell = result["cell_level"] - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - tracer_lines = "\n".join( - f"- `{name}`: row `{row['tracer_subvector'][i]}`, cell `{cell['tracer_subvector'][i]}`" - for i, name in enumerate(TRACER_ORDER) - ) - - DOC_MD.write_text( - f"""# Stellar Gas Multiscale Eigenmass Alignment - -Status: `MULTISCALE_EIGENMASS_ALIGNMENT` - -Decision: `{result['decision']}` - -This probe compares the row-level DESI epoviz eigenmass with the DESI/MaNGA -joined-cell eigenmass. It reports a tracer-subspace cosine and explained-share -ratio between the literal row data and the coarse joined-cell overlap surface. - -Claim boundary: this is not physical mass, not stellar mass, not gas-density -inference, and not a cosmology fit. - -## Alignment Result - -Tracer-subspace cosine: - -```text -{align['tracer_subspace_cosine']} -``` - -Alignment class: - -```text -{align['alignment_class']} -``` - -Constraint sharpening factor: - -```text -{align['constraint_sharpening_factor']} -``` - -Dominant eigenvalue ratio, cell over row: - -```text -{align['dominant_eigenvalue_ratio_cell_over_row']} -``` - -## Tracer Subvectors - -{tracer_lines} - -## Scale Comparison - -```text -row level rows: {row['cell_or_row_count']} -row explained share: {row['dominant_explained_mass_share']} -cell level cells: {cell['cell_or_row_count']} -cell explained share: {cell['dominant_explained_mass_share']} -``` - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Multiscale Eigenmass Alignment -tags: StellarGasObservation DESI MaNGA SemanticMassNumbers Eigenvector Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This tiddler compares the row-level DESI epoviz eigenmass with the DESI/MaNGA -joined-cell eigenmass. - -Tracer-subspace cosine: - -``` -{align['tracer_subspace_cosine']} -``` - -Alignment class: - -``` -{align['alignment_class']} -``` - -Constraint sharpening factor: - -``` -{align['constraint_sharpening_factor']} -``` - -!! Tracer Subvectors - -{tracer_lines} - -!! Boundary - -This is SMN/evidence-load alignment, not physical mass or cosmology inference. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_population_grouping_study.py b/4-Infrastructure/shim/stellar_gas_population_grouping_study.py deleted file mode 100644 index f9134f88..00000000 --- a/4-Infrastructure/shim/stellar_gas_population_grouping_study.py +++ /dev/null @@ -1,620 +0,0 @@ -#!/usr/bin/env python3 -"""Population study for MaNGA stellar-gas groupings. - -This is the first local population layer for the DESI -> environment prior -> -stellar-gas distribution bridge. It groups MaNGA DAPall galaxies by redshift, -sky cell, BPT proxy, shock/LIER proxy, gas sigma, and stellar sigma. DESI is -kept as a join target only: no direct DESI gas-map claim is made here. -""" - -from __future__ import annotations - -import argparse -import importlib.util -import json -import math -import statistics -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SEED_SCRIPT = REPO / "4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py" -DATA_DIR = REPO / "shared-data/data/stellar_gas_observation" -CHANNELS = DATA_DIR / "sdss_manga_dr17_emission_line_channels.json" -DEFAULT_FITS = REPO / "shared-data/artifacts/stellar_gas_observation/dapall-v3_1_1-3.1.0.fits" -DESTINATION = "Gdrive:topological_storage/research-stack/stellar-gas-observation/seed-2026-05-09" - -OUT = DATA_DIR / "stellar_gas_population_grouping_study.json" -DOC = REPO / "6-Documentation/docs/stellar_gas_population_grouping_study_2026-05-09.md" -TIDDLER = REPO / "6-Documentation/tiddlywiki-local/wiki/tiddlers/Stellar Gas Population Grouping Study.tid" - -PREFERRED_DAPTYPE = "HYB10-MILESHC-MASTARSSP" - -TARGET_COLUMNS = [ - "PLATEIFU", - "MANGAID", - "DAPTYPE", - "OBJRA", - "OBJDEC", - "Z", - "BINSNR", - "SNR_MED", - "STELLAR_SIGMA_1RE", - "HA_GSIGMA_1RE", - "EMLINE_GFLUX_1RE", -] - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def load_seed_module(): - spec = importlib.util.spec_from_file_location("sdss_manga_dapall_observation_seed", SEED_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load {SEED_SCRIPT}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def load_channels() -> dict[str, int]: - payload = json.loads(CHANNELS.read_text(encoding="utf-8")) - return {row["label"]: row["index0"] for row in payload["channels"]} - - -def run(cmd: list[str]) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - - -def rclone_copyto(local: Path, remote: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local), remote, "--checksum"]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def finite(value: Any) -> bool: - return isinstance(value, (int, float)) and math.isfinite(value) and value > -900 - - -def pos(value: Any) -> float | None: - if finite(value) and value > 0: - return float(value) - return None - - -def scalar(value: Any) -> float | None: - if finite(value): - return float(value) - return None - - -def mean_list(value: Any) -> float | None: - if not isinstance(value, list): - return None - vals = [float(v) for v in value if finite(v)] - return statistics.fmean(vals) if vals else None - - -def log_ratio(num: float | None, den: float | None) -> float | None: - if num is None or den is None or num <= 0 or den <= 0: - return None - return math.log10(num / den) - - -def ratio(num: float | None, den: float | None) -> float | None: - if num is None or den is None or den <= 0: - return None - return num / den - - -def classify_bpt(log_nii_ha: float | None, log_oiii_hb: float | None) -> str: - if log_nii_ha is None or log_oiii_hb is None: - return "unclassified" - if log_nii_ha >= 0.47: - return "agn_liner_or_shock_proxy" - kewley = 0.61 / (log_nii_ha - 0.47) + 1.19 - kauffmann = 0.61 / (log_nii_ha - 0.05) + 1.3 - if log_oiii_hb > kewley: - return "agn_liner_or_shock_proxy" - if log_oiii_hb > kauffmann: - return "composite_proxy" - return "star_forming_proxy" - - -def classify_shock(log_sii_ha: float | None, log_oi_ha: float | None, gas_sigma: float | None) -> tuple[float, str]: - score = 0.0 - if log_sii_ha is not None and log_sii_ha > -0.4: - score += 0.35 - if log_oi_ha is not None and log_oi_ha > -1.1: - score += 0.35 - if gas_sigma is not None and gas_sigma > 120: - score += 0.30 - if score >= 0.65: - return min(1.0, score), "shock_lier_proxy" - if score > 0: - return score, "partial_shock_proxy" - return 0.0, "no_shock_proxy" - - -def z_bin(z: float | None) -> str: - if z is None: - return "z_missing" - if z < 0.02: - return "z_000_002" - if z < 0.04: - return "z_002_004" - if z < 0.06: - return "z_004_006" - if z < 0.08: - return "z_006_008" - return "z_008_plus" - - -def sigma_bin(value: float | None, prefix: str) -> str: - if value is None: - return f"{prefix}_missing" - if value < 50: - return f"{prefix}_000_050" - if value < 100: - return f"{prefix}_050_100" - if value < 150: - return f"{prefix}_100_150" - if value < 250: - return f"{prefix}_150_250" - return f"{prefix}_250_plus" - - -def sky_bin(ra: float | None, dec: float | None) -> str: - if ra is None or dec is None: - return "sky_missing" - ra_bin = int(max(0, min(5, math.floor((ra % 360.0) / 60.0)))) - dec_band = "south" if dec < 0 else "north" - return f"ra{ra_bin:02d}_{dec_band}" - - -def sky_z_cell(ra: float | None, dec: float | None, z: float | None) -> str: - return f"{sky_bin(ra, dec)}__{z_bin(z)}" - - -def summarize(vals: list[float]) -> dict[str, Any]: - values = sorted(v for v in vals if math.isfinite(v)) - if not values: - return {"count": 0} - return { - "count": len(values), - "min": round(values[0], 6), - "max": round(values[-1], 6), - "mean": round(statistics.fmean(values), 6), - "median": round(statistics.median(values), 6), - "p90": round(values[int(0.9 * (len(values) - 1))], 6), - } - - -def iter_rows(fits_path: Path): - seed = load_seed_module() - with fits_path.open("rb") as f: - hdu_index = 0 - while True: - try: - header, _ = seed.read_header(f) - except EOFError: - break - data_start = f.tell() - if str(header.get("XTENSION", "PRIMARY")) == "BINTABLE": - row_len = int(header["NAXIS1"]) - row_count = int(header["NAXIS2"]) - pcount = int(header.get("PCOUNT", 0)) - columns = seed.build_columns(header) - by_name = {col["name"]: col for col in columns} - selected = [by_name[name] for name in TARGET_COLUMNS if name in by_name] - hdu_name = str(header.get("EXTNAME", f"HDU{hdu_index}")) - for row_idx in range(row_count): - f.seek(data_start + row_idx * row_len) - row = f.read(row_len) - fields: dict[str, Any] = {} - for col in selected: - raw = row[col["offset"] : col["offset"] + col["width"]] - value = seed.decode_value(raw, col) - if isinstance(value, str): - value = value.replace("\u0000", "").strip() - fields[col["name"]] = value - yield hdu_index, hdu_name, row_idx, fields - f.seek(data_start + seed.padded_size(row_len * row_count + pcount)) - else: - bitpix = int(header.get("BITPIX", 8)) - naxis = int(header.get("NAXIS", 0)) - data_size = 0 - if naxis: - pixels = 1 - for axis in range(1, naxis + 1): - pixels *= int(header.get(f"NAXIS{axis}", 0)) - data_size = abs(bitpix) // 8 * pixels - f.seek(data_start + seed.padded_size(data_size)) - hdu_index += 1 - - -def add_count(bucket: dict[str, int], key: str) -> None: - bucket[key] = bucket.get(key, 0) + 1 - - -def group_template() -> dict[str, Any]: - return { - "count": 0, - "bpt_proxy_classes": {}, - "shock_lier_proxy_classes": {}, - "z_bins": {}, - "gas_sigma_bins": {}, - "stellar_sigma_bins": {}, - "sky_bins": {}, - "shock_scores": [], - "gas_sigma_values": [], - "stellar_sigma_values": [], - "snr_values": [], - } - - -def update_group(group: dict[str, Any], row: dict[str, Any]) -> None: - group["count"] += 1 - add_count(group["bpt_proxy_classes"], row["bpt_proxy_class"]) - add_count(group["shock_lier_proxy_classes"], row["shock_lier_proxy_class"]) - add_count(group["z_bins"], row["z_bin"]) - add_count(group["gas_sigma_bins"], row["gas_sigma_bin"]) - add_count(group["stellar_sigma_bins"], row["stellar_sigma_bin"]) - add_count(group["sky_bins"], row["sky_bin"]) - group["shock_scores"].append(row["shock_lier_score"]) - if row["gas_sigma_1re_kms"] is not None: - group["gas_sigma_values"].append(row["gas_sigma_1re_kms"]) - if row["stellar_sigma_1re_kms"] is not None: - group["stellar_sigma_values"].append(row["stellar_sigma_1re_kms"]) - if row["snr_mean"] is not None: - group["snr_values"].append(row["snr_mean"]) - - -def finalize_group(group: dict[str, Any]) -> dict[str, Any]: - count = group["count"] or 1 - shock = group["shock_lier_proxy_classes"] - group["shock_lier_fraction"] = round(shock.get("shock_lier_proxy", 0) / count, 6) - group["partial_or_full_shock_fraction"] = round( - (shock.get("shock_lier_proxy", 0) + shock.get("partial_shock_proxy", 0)) / count, - 6, - ) - group["shock_score_summary"] = summarize(group.pop("shock_scores")) - group["gas_sigma_summary"] = summarize(group.pop("gas_sigma_values")) - group["stellar_sigma_summary"] = summarize(group.pop("stellar_sigma_values")) - group["snr_summary"] = summarize(group.pop("snr_values")) - return group - - -def row_payload(fields: dict[str, Any], channel_index: dict[str, int]) -> dict[str, Any] | None: - flux = fields.get("EMLINE_GFLUX_1RE") - if not isinstance(flux, list) or len(flux) < 35: - return None - ha = pos(flux[channel_index["Ha-6564"]]) - hb = pos(flux[channel_index["Hb-4862"]]) - oiii = pos(flux[channel_index["OIII-5008"]]) - nii = pos(flux[channel_index["NII-6585"]]) - sii_1 = pos(flux[channel_index["SII-6718"]]) - sii_2 = pos(flux[channel_index["SII-6732"]]) - sii = sii_1 + sii_2 if sii_1 is not None and sii_2 is not None else None - oi = pos(flux[channel_index["OI-6302"]]) - gas_sigma = pos(fields.get("HA_GSIGMA_1RE")) - stellar_sigma = pos(fields.get("STELLAR_SIGMA_1RE")) - z = scalar(fields.get("Z")) - ra = scalar(fields.get("OBJRA")) - dec = scalar(fields.get("OBJDEC")) - log_nii_ha = log_ratio(nii, ha) - log_sii_ha = log_ratio(sii, ha) - log_oi_ha = log_ratio(oi, ha) - log_oiii_hb = log_ratio(oiii, hb) - balmer = ratio(ha, hb) - shock_score, shock_class = classify_shock(log_sii_ha, log_oi_ha, gas_sigma) - bpt = classify_bpt(log_nii_ha, log_oiii_hb) - return { - "plateifu": fields.get("PLATEIFU"), - "mangaid": fields.get("MANGAID"), - "daptype": fields.get("DAPTYPE"), - "ra": ra, - "dec": dec, - "z": z, - "z_bin": z_bin(z), - "sky_bin": sky_bin(ra, dec), - "sky_z_cell": sky_z_cell(ra, dec, z), - "snr_mean": mean_list(fields.get("SNR_MED")) or scalar(fields.get("BINSNR")), - "gas_sigma_1re_kms": gas_sigma, - "stellar_sigma_1re_kms": stellar_sigma, - "gas_sigma_bin": sigma_bin(gas_sigma, "gas_sigma"), - "stellar_sigma_bin": sigma_bin(stellar_sigma, "stellar_sigma"), - "line_ratios": { - "log_NII6585_Ha": log_nii_ha, - "log_SII6718_6732_Ha": log_sii_ha, - "log_OI6302_Ha": log_oi_ha, - "log_OIII5008_Hb": log_oiii_hb, - "Ha_Hb": balmer, - }, - "bpt_proxy_class": bpt, - "shock_lier_proxy_class": shock_class, - "shock_lier_score": shock_score, - } - - -def build_population_study(fits_path: Path, preferred_daptype: str) -> dict[str, Any]: - channel_index = load_channels() - required = ["Ha-6564", "Hb-4862", "OIII-5008", "NII-6585", "SII-6718", "SII-6732", "OI-6302"] - missing = [name for name in required if name not in channel_index] - if missing: - raise RuntimeError(f"missing channel labels: {missing}") - - all_rows = 0 - selected_rows: list[dict[str, Any]] = [] - by_plateifu: dict[str, dict[str, Any]] = {} - daptype_counts: dict[str, int] = {} - for _, _, _, fields in iter_rows(fits_path): - all_rows += 1 - payload = row_payload(fields, channel_index) - if not payload: - continue - daptype = str(payload.get("daptype") or "unknown") - add_count(daptype_counts, daptype) - plateifu = str(payload.get("plateifu") or "") - current = by_plateifu.get(plateifu) - if current is None or daptype == preferred_daptype: - by_plateifu[plateifu] = payload - - selected_rows = list(by_plateifu.values()) - groups = { - "by_bpt_proxy_class": {}, - "by_shock_lier_proxy_class": {}, - "by_redshift_bin": {}, - "by_sky_bin": {}, - "by_sky_z_cell": {}, - "by_gas_sigma_bin": {}, - "by_stellar_sigma_bin": {}, - } - aggregate = group_template() - for row in selected_rows: - update_group(aggregate, row) - for group_name, key_name in [ - ("by_bpt_proxy_class", "bpt_proxy_class"), - ("by_shock_lier_proxy_class", "shock_lier_proxy_class"), - ("by_redshift_bin", "z_bin"), - ("by_sky_bin", "sky_bin"), - ("by_sky_z_cell", "sky_z_cell"), - ("by_gas_sigma_bin", "gas_sigma_bin"), - ("by_stellar_sigma_bin", "stellar_sigma_bin"), - ]: - key = row[key_name] - groups[group_name].setdefault(key, group_template()) - update_group(groups[group_name][key], row) - - finalized_groups = { - group_name: { - key: finalize_group(value) - for key, value in sorted(group.items(), key=lambda item: (-item[1]["count"], item[0])) - } - for group_name, group in groups.items() - } - top_cells = [ - {"cell": key, **value} - for key, value in list(finalized_groups["by_sky_z_cell"].items())[:20] - ] - examples = sorted( - selected_rows, - key=lambda row: (row["shock_lier_score"], row["gas_sigma_1re_kms"] or 0.0), - reverse=True, - )[:20] - return { - "schema": "stellar_gas_population_grouping_study_v1", - "created": now_iso(), - "decision": "ADMIT_POPULATION_GROUPING_SURFACE", - "claim_boundary": "MaNGA stellar-gas population grouping only. A coarse DESI/MaNGA cell join exists; object-level crossmatch remains HOLD. Proxy classes do not prove physical shock, AGN, or gas mechanism.", - "source_fits": str(fits_path.relative_to(REPO)) if fits_path.is_relative_to(REPO) else str(fits_path), - "channel_map": str(CHANNELS.relative_to(REPO)), - "preferred_daptype": preferred_daptype, - "rows_seen_all_daptypes": all_rows, - "daptype_counts": daptype_counts, - "unique_plateifu_count": len(by_plateifu), - "selected_population_count": len(selected_rows), - "aggregate": finalize_group(aggregate), - "groups": finalized_groups, - "top_sky_z_cells_for_desi_join": top_cells, - "top_shock_lier_examples": examples, - "desi_bridge": { - "status": "COARSE_CELL_JOIN_EXISTS_OBJECT_CROSSMATCH_HOLD", - "source_prior": "shared-data/data/stack_solidification/desi_stellar_gas_distribution_prior.json", - "join_key_shape": "coarse sky/redshift population cell; object-level cone/crossmatch remains HOLD", - "required_fields": ["ra", "dec", "z", "tracer_type", "selection_flags"], - }, - } - - -def write_doc(result: dict[str, Any]) -> None: - agg = result["aggregate"] - groups = result["groups"] - lines = [ - "# Stellar Gas Population Grouping Study", - "", - "**Date:** 2026-05-09", - "", - f"**Decision:** `{result['decision']}`", - "", - "**Claim boundary:** MaNGA stellar-gas population grouping only. DESI", - "environment inference remains a prior bridge until a DESI-MaNGA join", - "receipt exists. Proxy classes do not prove physical shock, AGN, or gas", - "mechanism.", - "", - "## Population Surface", - "", - "```text", - f"rows seen, all DAP types: {result['rows_seen_all_daptypes']}", - f"unique Plate-IFU count: {result['unique_plateifu_count']}", - f"selected population: {result['selected_population_count']}", - f"preferred DAPTYPE: {result['preferred_daptype']}", - "```", - "", - "## Aggregate", - "", - "```json", - json.dumps( - { - "bpt_proxy_classes": agg["bpt_proxy_classes"], - "shock_lier_proxy_classes": agg["shock_lier_proxy_classes"], - "partial_or_full_shock_fraction": agg["partial_or_full_shock_fraction"], - "gas_sigma_summary": agg["gas_sigma_summary"], - "stellar_sigma_summary": agg["stellar_sigma_summary"], - }, - indent=2, - ), - "```", - "", - "## Redshift Bins", - "", - "| Bin | Count | Shock+Partial Fraction | Gas Sigma Median | Stellar Sigma Median |", - "|---|---:|---:|---:|---:|", - ] - for key, value in groups["by_redshift_bin"].items(): - lines.append( - f"| `{key}` | {value['count']} | {value['partial_or_full_shock_fraction']} | " - f"{value['gas_sigma_summary'].get('median', '')} | {value['stellar_sigma_summary'].get('median', '')} |" - ) - lines += [ - "", - "## DESI-Ready Sky/Redshift Cells", - "", - "These are population cells for a later DESI join. They are not DESI", - "environment classes yet.", - "", - "| Cell | Count | Shock+Partial Fraction | Main BPT Counts |", - "|---|---:|---:|---|", - ] - for row in result["top_sky_z_cells_for_desi_join"][:12]: - lines.append( - f"| `{row['cell']}` | {row['count']} | {row['partial_or_full_shock_fraction']} | " - f"`{row['bpt_proxy_classes']}` |" - ) - lines += [ - "", - "## What This Gives Us", - "", - "- A population baseline over unique MaNGA Plate-IFU rows.", - "- Grouped shock/LIER and BPT proxy counts by redshift and sky cell.", - "- DESI-ready cells for a future sky-cone plus redshift-window join.", - "- Residual target: cells whose local gas state diverges from later DESI environment priors.", - "", - "## Receipt", - "", - "`shared-data/data/stellar_gas_observation/stellar_gas_population_grouping_study_receipt_*.json`", - "", - ] - DOC.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(result: dict[str, Any]) -> None: - agg = result["aggregate"] - TIDDLER.write_text( - f"""created: 20260509224000000 -modified: 20260509224000000 -tags: ResearchStack StellarGas MaNGA PopulationStudy DESI Calibration -title: Stellar Gas Population Grouping Study -type: text/vnd.tiddlywiki - -! Stellar Gas Population Grouping Study - -Status: `ADMIT_POPULATION_GROUPING_SURFACE` - -This page records the first local population grouping pass over MaNGA DAPall -stellar-gas diagnostics. - -```text -unique Plate-IFU count: {result['unique_plateifu_count']} -selected population: {result['selected_population_count']} -preferred DAPTYPE: {result['preferred_daptype']} -``` - -!! Aggregate - -```json -{json.dumps({ - 'bpt_proxy_classes': agg['bpt_proxy_classes'], - 'shock_lier_proxy_classes': agg['shock_lier_proxy_classes'], - 'partial_or_full_shock_fraction': agg['partial_or_full_shock_fraction'], -}, indent=2)} -``` - -!! DESI Bridge - -The sky/redshift cells are DESI-ready join buckets, not DESI environment classes -yet. - -```text -MaNGA population cell - -> future DESI sky-cone/redshift join - -> environment prior - -> gas-state residual map -``` - -!! Boundary - -Proxy classes do not prove physical shock, AGN, or gas mechanism. DESI -environment inference remains HOLD until the join receipt exists. -""", - encoding="utf-8", - ) - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--fits", type=Path, default=DEFAULT_FITS) - parser.add_argument("--preferred-daptype", default=PREFERRED_DAPTYPE) - parser.add_argument("--destination", default=DESTINATION) - parser.add_argument("--no-upload", action="store_true") - args = parser.parse_args() - - result = build_population_study(args.fits, args.preferred_daptype) - OUT.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_doc(result) - write_tiddler(result) - - receipt_path = DATA_DIR / f"stellar_gas_population_grouping_study_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - receipt: dict[str, Any] = { - "schema": "stellar_gas_population_grouping_study_receipt_v1", - "created": now_iso(), - "claim_boundary": result["claim_boundary"], - "study_file": str(OUT.relative_to(REPO)), - "doc_file": str(DOC.relative_to(REPO)), - "tiddler_file": str(TIDDLER.relative_to(REPO)), - "source_fits": result["source_fits"], - "decision": result["decision"], - "summary": { - "unique_plateifu_count": result["unique_plateifu_count"], - "selected_population_count": result["selected_population_count"], - "partial_or_full_shock_fraction": result["aggregate"]["partial_or_full_shock_fraction"], - "desi_bridge_status": result["desi_bridge"]["status"], - }, - "uploads": {}, - } - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - if not args.no_upload: - uploads = { - "study": (OUT, f"{args.destination}/derived/{OUT.name}"), - "doc": (DOC, f"{args.destination}/docs/{DOC.name}"), - "tiddler": (TIDDLER, f"{args.destination}/docs/{TIDDLER.name.replace(' ', '_')}"), - "receipt": (receipt_path, f"{args.destination}/receipts/{receipt_path.name}"), - } - for key, (local, remote) in uploads.items(): - ok, message = rclone_copyto(local, remote) - receipt["uploads"][key] = {"drive_path": remote, "ok": ok, "message": message} - receipt_path.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - if receipt["uploads"].get("receipt", {}).get("ok"): - rclone_copyto(receipt_path, receipt["uploads"]["receipt"]["drive_path"]) - - print(json.dumps(receipt, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/stellar_gas_sandpile_fine_zoom.py b/4-Infrastructure/shim/stellar_gas_sandpile_fine_zoom.py deleted file mode 100644 index cfe9753c..00000000 --- a/4-Infrastructure/shim/stellar_gas_sandpile_fine_zoom.py +++ /dev/null @@ -1,281 +0,0 @@ -#!/usr/bin/env python3 -"""Fine-zoom object examples under the stellar-gas sandpile candidates. - -This script drills from the sandpile cell diagnostic down to MaNGA Plate-IFU -examples in the avalanche-candidate cells. It keeps the same proxy boundary as -the population study: these are observable gas/shock routing candidates, not -proof of a physical shock mechanism. -""" - -from __future__ import annotations - -import importlib.util -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -POP_SCRIPT = ROOT / "4-Infrastructure/shim/stellar_gas_population_grouping_study.py" -SANDPILE_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_abelian_sandpile_probe.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_sandpile_fine_zoom.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_sandpile_fine_zoom_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_sandpile_fine_zoom_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Sandpile Fine Zoom.tid" - -EXAMPLES_PER_CELL = 8 - - -def load_population_module(): - spec = importlib.util.spec_from_file_location("stellar_gas_population_grouping_study", POP_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load {POP_SCRIPT}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def load_json(path: Path) -> dict[str, Any]: - with path.open() as f: - return json.load(f) - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds") - - -def finite(value: Any) -> bool: - return isinstance(value, (int, float)) and math.isfinite(value) - - -def round6(value: float | None) -> float | None: - if value is None or not math.isfinite(value): - return None - return round(value, 6) - - -def object_pressure(row: dict[str, Any]) -> float: - gas_sigma = row.get("gas_sigma_1re_kms") or 0.0 - stellar_sigma = row.get("stellar_sigma_1re_kms") or 0.0 - snr = row.get("snr_mean") or 0.0 - pressure = float(row.get("shock_lier_score") or 0.0) - pressure += min(float(gas_sigma) / 250.0, 2.0) - pressure += min(float(stellar_sigma) / 250.0, 2.0) * 0.5 - pressure += min(max(float(snr), 0.0) / 50.0, 1.0) * 0.25 - if row.get("bpt_proxy_class") == "agn_liner_or_shock_proxy": - pressure += 0.5 - if row.get("shock_lier_proxy_class") == "shock_lier_proxy": - pressure += 0.5 - return pressure - - -def compact_row(row: dict[str, Any]) -> dict[str, Any]: - ratios = row.get("line_ratios", {}) - return { - "plateifu": row.get("plateifu"), - "mangaid": row.get("mangaid"), - "ra": round6(row.get("ra")), - "dec": round6(row.get("dec")), - "z": round6(row.get("z")), - "sky_z_cell": row.get("sky_z_cell"), - "bpt_proxy_class": row.get("bpt_proxy_class"), - "shock_lier_proxy_class": row.get("shock_lier_proxy_class"), - "shock_lier_score": round6(row.get("shock_lier_score")), - "gas_sigma_1re_kms": round6(row.get("gas_sigma_1re_kms")), - "stellar_sigma_1re_kms": round6(row.get("stellar_sigma_1re_kms")), - "snr_mean": round6(row.get("snr_mean")), - "object_pressure": round6(object_pressure(row)), - "line_ratios": {key: round6(value) for key, value in ratios.items()}, - } - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - pop = load_population_module() - sandpile = load_json(SANDPILE_JSON) - candidate_cells = [ - row["cell"] - for row in sandpile["top_cells"] - if row["sandpile"]["state"] == "AVALANCHE_CANDIDATE" - ] - candidate_set = set(candidate_cells) - - channel_index = pop.load_channels() - by_plateifu: dict[str, dict[str, Any]] = {} - rows_seen = 0 - rows_in_candidate_cells = 0 - - for _, _, _, fields in pop.iter_rows(pop.DEFAULT_FITS): - rows_seen += 1 - payload = pop.row_payload(fields, channel_index) - if not payload: - continue - daptype = str(payload.get("daptype") or "") - plateifu = str(payload.get("plateifu") or "") - current = by_plateifu.get(plateifu) - if current is None or daptype == pop.PREFERRED_DAPTYPE: - by_plateifu[plateifu] = payload - - examples_by_cell: dict[str, list[dict[str, Any]]] = {cell: [] for cell in candidate_cells} - for row in by_plateifu.values(): - cell = row.get("sky_z_cell") - if cell not in candidate_set: - continue - rows_in_candidate_cells += 1 - examples_by_cell[cell].append(row) - - output_cells = [] - for sand_row in sandpile["top_cells"]: - cell = sand_row["cell"] - if cell not in candidate_set: - continue - examples = sorted( - examples_by_cell[cell], - key=lambda row: object_pressure(row), - reverse=True, - )[:EXAMPLES_PER_CELL] - output_cells.append( - { - "cell": cell, - "sandpile": sand_row["sandpile"], - "channel_summary": sand_row["channels"], - "candidate_count": len(examples_by_cell[cell]), - "top_examples": [compact_row(row) for row in examples], - } - ) - - created = now_iso() - result = { - "schema": "stellar_gas_sandpile_fine_zoom_v0", - "created": created, - "decision": "ADMIT_FINE_ZOOM_OBJECT_EXAMPLES_HOLD_MECHANISM_PROOF", - "claim_boundary": ( - "Fine-zoom object examples under sandpile avalanche-candidate cells. " - "Proxy classes and object-pressure scores route follow-up; they do " - "not prove physical shock, AGN, stellar mass, or gas mechanism." - ), - "sources": { - "sandpile_probe": str(SANDPILE_JSON.relative_to(ROOT)), - "manga_fits": str(pop.DEFAULT_FITS.relative_to(ROOT)), - "population_script": str(POP_SCRIPT.relative_to(ROOT)), - }, - "rows_seen_all_daptypes": rows_seen, - "unique_plateifu_count": len(by_plateifu), - "candidate_cell_count": len(candidate_cells), - "rows_in_candidate_cells": rows_in_candidate_cells, - "examples_per_cell": EXAMPLES_PER_CELL, - "candidate_cells": output_cells, - "holds": [ - "HOLD_PHYSICAL_SHOCK_PROOF", - "HOLD_DIRECT_STELLAR_MASS", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_OBJECT_LEVEL_DESI_CROSSMATCH", - "HOLD_COSMOLOGY_FIT", - ], - } - receipt = { - "receipt_type": "stellar_gas_sandpile_fine_zoom_receipt", - "created": created, - "candidate_cell_count": len(candidate_cells), - "rows_in_candidate_cells": rows_in_candidate_cells, - "examples_written": sum(len(cell["top_examples"]) for cell in output_cells), - "decision": result["decision"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any]) -> None: - cell_lines = [] - for cell in result["candidate_cells"]: - top = cell["top_examples"][0] if cell["top_examples"] else {} - cell_lines.append( - f"- `{cell['cell']}`: candidates `{cell['candidate_count']}`, " - f"top `{top.get('plateifu')}`, pressure `{top.get('object_pressure')}`, " - f"class `{top.get('shock_lier_proxy_class')}`" - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - - DOC_MD.write_text( - f"""# Stellar Gas Sandpile Fine Zoom - -Status: `FINE_ZOOM_OBJECT_EXAMPLES` - -Decision: `{result['decision']}` - -This fine-zoom pass drills from sandpile avalanche-candidate cells down to MaNGA -Plate-IFU examples. It is meant to show which concrete objects carry the -strongest local proxy pressure under the cell-level eigenmass surface. - -Claim boundary: proxy classes and object-pressure scores route follow-up; they -do not prove physical shock, AGN, stellar mass, gas density, or cosmology. - -## Summary - -```text -candidate cells: {result['candidate_cell_count']} -candidate-cell objects: {result['rows_in_candidate_cells']} -examples per cell: {result['examples_per_cell']} -``` - -## Top Object Per Candidate Cell - -{chr(10).join(cell_lines)} - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Sandpile Fine Zoom -tags: StellarGasObservation MaNGA SemanticMassNumbers Sandpile Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This tiddler drills from the Abelian-sandpile candidate cells into MaNGA -Plate-IFU examples. - -``` -candidate cells: {result['candidate_cell_count']} -candidate-cell objects: {result['rows_in_candidate_cells']} -examples per cell: {result['examples_per_cell']} -``` - -!! Top Object Per Candidate Cell - -{chr(10).join(cell_lines)} - -!! Boundary - -These are proxy-ranked follow-up candidates, not proof of physical shock, AGN, -stellar mass, gas density, or cosmology. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_sandpile_graph_replay.py b/4-Infrastructure/shim/stellar_gas_sandpile_graph_replay.py deleted file mode 100644 index 0b939c3c..00000000 --- a/4-Infrastructure/shim/stellar_gas_sandpile_graph_replay.py +++ /dev/null @@ -1,444 +0,0 @@ -#!/usr/bin/env python3 -"""Graph replay hardening for the stellar-gas sandpile diagnostic. - -This converts the existing sandpile metaphor into a reproducible graph -diagnostic. It is a toppling proxy over sky/redshift cells, not a physical -sandpile simulation and not a claim about stellar gas mechanics. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from collections import deque -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -ROOT = Path(__file__).resolve().parents[2] -SANDPILE_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_abelian_sandpile_probe.json" -FINE_ZOOM_JSON = ROOT / "shared-data/data/stellar_gas_observation/stellar_gas_sandpile_fine_zoom.json" -OUT_DIR = ROOT / "shared-data/data/stellar_gas_observation" -DOCS_DIR = ROOT / "6-Documentation/docs" -TIDDLER_DIR = ROOT / "6-Documentation/tiddlywiki-local/wiki/tiddlers" - -OUT_JSON = OUT_DIR / "stellar_gas_sandpile_graph_replay.json" -RECEIPT_JSON = OUT_DIR / "stellar_gas_sandpile_graph_replay_receipt.json" -DOC_MD = DOCS_DIR / "stellar_gas_sandpile_graph_replay_2026-05-09.md" -TIDDLER = TIDDLER_DIR / "Stellar Gas Sandpile Graph Replay.tid" - -Z_BIN_ORDER = { - "z_000_002": 0, - "z_002_004": 1, - "z_004_006": 2, - "z_006_008": 3, - "z_008_plus": 4, -} - - -def load_json(path: Path) -> dict[str, Any]: - with path.open(encoding="utf-8") as f: - return json.load(f) - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(1024 * 1024), b""): - h.update(chunk) - return h.hexdigest() - - -def sha256_payload(payload: Any) -> str: - raw = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(raw).hexdigest() - - -def now_iso() -> str: - return datetime.now(timezone.utc).isoformat(timespec="seconds") - - -def parse_cell(cell: str) -> dict[str, Any]: - sky_sector, z_bin = cell.split("__", 1) - ra_sector, hemisphere = sky_sector.split("_", 1) - return { - "cell": cell, - "sky_sector": sky_sector, - "ra_sector": int(ra_sector.removeprefix("ra")), - "hemisphere": hemisphere, - "z_bin": z_bin, - "z_bin_index": Z_BIN_ORDER[z_bin], - } - - -def are_adjacent(a: dict[str, Any], b: dict[str, Any]) -> bool: - same_z = a["z_bin_index"] == b["z_bin_index"] - same_ra = a["ra_sector"] == b["ra_sector"] - same_hemisphere = a["hemisphere"] == b["hemisphere"] - ra_neighbor = same_z and same_hemisphere and abs(a["ra_sector"] - b["ra_sector"]) == 1 - hemisphere_neighbor = same_z and same_ra and a["hemisphere"] != b["hemisphere"] - redshift_neighbor = same_ra and same_hemisphere and abs(a["z_bin_index"] - b["z_bin_index"]) == 1 - return ra_neighbor or hemisphere_neighbor or redshift_neighbor - - -def round9(value: float) -> float: - return round(value, 9) - - -def seed_counts(fine_zoom: dict[str, Any]) -> dict[str, int]: - return { - cell["cell"]: int(cell["candidate_count"]) - for cell in fine_zoom["candidate_cells"] - } - - -def node_rows(sandpile: dict[str, Any], fine_zoom: dict[str, Any]) -> list[dict[str, Any]]: - counts = seed_counts(fine_zoom) - index_std = float(sandpile["toppling_index_summary"]["std"]) or 1.0 - rows = [] - for row in sorted(sandpile["top_cells"], key=lambda item: item["cell"]): - parsed = parse_cell(row["cell"]) - proxy_z = float(row["sandpile"]["toppling_index"]) / index_std - rows.append( - { - **parsed, - "state": row["sandpile"]["state"], - "grains": float(row["sandpile"]["grains"]), - "toppling_pressure": float(row["sandpile"]["toppling_pressure"]), - "toppling_index": float(row["sandpile"]["toppling_index"]), - "toppling_index_z_proxy": round9(proxy_z), - "candidate_object_count": counts.get(row["cell"], 0), - } - ) - return rows - - -def graph_edges(rows: list[dict[str, Any]]) -> list[dict[str, str]]: - edges = [] - for i, left in enumerate(rows): - for right in rows[i + 1 :]: - if are_adjacent(left, right): - edges.append({"source": left["cell"], "target": right["cell"]}) - return edges - - -def adjacency(nodes: list[str], edges: list[dict[str, str]]) -> dict[str, list[str]]: - out = {node: [] for node in nodes} - for edge in edges: - out[edge["source"]].append(edge["target"]) - out[edge["target"]].append(edge["source"]) - return {node: sorted(neighbors) for node, neighbors in out.items()} - - -def threshold_and_grain_tables(rows: list[dict[str, Any]], adj: dict[str, list[str]]) -> tuple[list[dict[str, Any]], list[dict[str, Any]]]: - threshold_table = [] - grain_table = [] - for row in rows: - cell = row["cell"] - degree = len(adj[cell]) - threshold = max(1, degree + 1) - proxy_z = max(0.0, row["toppling_index_z_proxy"]) - initial_grains = max(0, math.ceil(proxy_z * threshold)) - threshold_table.append( - { - "cell": cell, - "degree": degree, - "toppling_threshold": threshold, - "threshold_rule": "degree_plus_one_toppling_proxy", - } - ) - grain_table.append( - { - "cell": cell, - "initial_grains": initial_grains, - "grain_rule": "ceil(max(0, toppling_index_z_proxy) * threshold)", - "toppling_index_z_proxy": row["toppling_index_z_proxy"], - "seed_object_count": row["candidate_object_count"], - "source_state": row["state"], - } - ) - return threshold_table, grain_table - - -def replay_one(seed: str, adj: dict[str, list[str]], thresholds: dict[str, int], grains: dict[str, int]) -> dict[str, Any]: - working = dict(grains) - topple_count = {cell: 0 for cell in working} - touched = set() - q: deque[str] = deque([seed]) - max_steps = max(1, len(working) * 20) - steps = 0 - while q and steps < max_steps: - cell = q.popleft() - if working[cell] < thresholds[cell]: - continue - steps += 1 - touched.add(cell) - topple_count[cell] += 1 - working[cell] -= thresholds[cell] - for neighbor in adj[cell]: - working[neighbor] += 1 - if working[neighbor] >= thresholds[neighbor]: - q.append(neighbor) - - toppled_cells = sorted(cell for cell, count in topple_count.items() if count) - return { - "seed_cell": seed, - "terminated": not q, - "step_limit": max_steps, - "topple_events": sum(topple_count.values()), - "avalanche_size_cells": len(toppled_cells), - "toppled_cells": toppled_cells, - "final_seed_grains": working[seed], - } - - -def build() -> tuple[dict[str, Any], dict[str, Any]]: - sandpile = load_json(SANDPILE_JSON) - fine_zoom = load_json(FINE_ZOOM_JSON) - rows = node_rows(sandpile, fine_zoom) - nodes = [row["cell"] for row in rows] - edges = graph_edges(rows) - adj = adjacency(nodes, edges) - threshold_table, grain_table = threshold_and_grain_tables(rows, adj) - thresholds = {row["cell"]: int(row["toppling_threshold"]) for row in threshold_table} - grains = {row["cell"]: int(row["initial_grains"]) for row in grain_table} - seed_cells = sorted( - row["cell"] - for row in rows - if row["state"] == "AVALANCHE_CANDIDATE" and row["candidate_object_count"] > 0 - ) - avalanches = [replay_one(seed, adj, thresholds, grains) for seed in seed_cells] - - canonical_graph = { - "adjacency_rule": ( - "Edges join same-redshift neighboring RA sectors, same-RA north/south sectors, " - "or same-sky-sector adjacent redshift bins." - ), - "nodes": [ - { - "cell": row["cell"], - "ra_sector": row["ra_sector"], - "hemisphere": row["hemisphere"], - "z_bin": row["z_bin"], - } - for row in rows - ], - "edges": edges, - "threshold_table": threshold_table, - "initial_grain_table": grain_table, - } - graph_hash = sha256_payload(canonical_graph) - replay_payload = { - "graph_hash": graph_hash, - "seed_cells": seed_cells, - "avalanche_replay": avalanches, - } - replay_hash = sha256_payload(replay_payload) - created = now_iso() - result = { - "schema": "stellar_gas_sandpile_graph_replay_v0", - "created": created, - "decision": "ADMIT_GRAPH_TOPPLING_PROXY_HOLD_PHYSICAL_SANDPILE_SIMULATION", - "claim_boundary": ( - "This is a reproducible graph diagnostic and toppling proxy over existing " - "stellar-gas evidence cells. It is not a physical sandpile simulation, " - "not a stellar-gas mechanism proof, and not a cosmology fit." - ), - "sources": { - "sandpile_probe": str(SANDPILE_JSON.relative_to(ROOT)), - "fine_zoom_examples": str(FINE_ZOOM_JSON.relative_to(ROOT)), - }, - "source_hashes": { - "sandpile_probe_sha256": sha256_file(SANDPILE_JSON), - "fine_zoom_examples_sha256": sha256_file(FINE_ZOOM_JSON), - }, - "seed_evidence": { - "avalanche_cell_count": len(seed_cells), - "candidate_object_count": int(fine_zoom["rows_in_candidate_cells"]), - "candidate_counts_by_cell": seed_counts(fine_zoom), - }, - "adjacency_rule": canonical_graph["adjacency_rule"], - "graph_hash": graph_hash, - "replay_hash": replay_hash, - "node_count": len(nodes), - "edge_count": len(edges), - "nodes": rows, - "edges": edges, - "adjacency": adj, - "toppling_threshold_table": threshold_table, - "initial_grain_table": grain_table, - "avalanche_sizes": [ - { - "seed_cell": row["seed_cell"], - "avalanche_size_cells": row["avalanche_size_cells"], - "topple_events": row["topple_events"], - "terminated": row["terminated"], - } - for row in avalanches - ], - "avalanche_replay": avalanches, - "holds": [ - "HOLD_PHYSICAL_SANDPILE_SIMULATION", - "HOLD_STELLAR_GAS_MECHANISM_PROOF", - "HOLD_DIRECT_STELLAR_MASS", - "HOLD_DIRECT_GAS_DENSITY_INFERENCE", - "HOLD_COSMOLOGY_FIT", - ], - } - receipt = { - "receipt_type": "stellar_gas_sandpile_graph_replay_receipt", - "created": created, - "decision": result["decision"], - "graph_hash": graph_hash, - "replay_hash": replay_hash, - "node_count": len(nodes), - "edge_count": len(edges), - "seed_avalanche_cell_count": len(seed_cells), - "seed_candidate_object_count": int(fine_zoom["rows_in_candidate_cells"]), - "avalanche_sizes": result["avalanche_sizes"], - "validated_outputs": [ - str(OUT_JSON.relative_to(ROOT)), - str(RECEIPT_JSON.relative_to(ROOT)), - str(DOC_MD.relative_to(ROOT)), - str(TIDDLER.relative_to(ROOT)), - ], - } - return result, receipt - - -def write_docs(result: dict[str, Any], receipt: dict[str, Any]) -> None: - threshold_lines = "\n".join( - f"| `{row['cell']}` | {row['degree']} | {row['toppling_threshold']} |" - for row in result["toppling_threshold_table"] - ) - grain_lines = "\n".join( - f"| `{row['cell']}` | {row['initial_grains']} | {row['toppling_index_z_proxy']} | {row['seed_object_count']} |" - for row in result["initial_grain_table"] - ) - avalanche_lines = "\n".join( - f"| `{row['seed_cell']}` | {row['avalanche_size_cells']} | {row['topple_events']} | `{row['terminated']}` |" - for row in result["avalanche_sizes"] - ) - holds = "\n".join(f"- `{hold}`" for hold in result["holds"]) - - DOC_MD.write_text( - f"""# Stellar Gas Sandpile Graph Replay - -Status: `GRAPH_TOPPLING_PROXY` - -Decision: `{result['decision']}` - -This hardening pass turns the sandpile metaphor into a reproducible graph -diagnostic. Nodes are sky/redshift cells. Edges are defined by sky-sector -neighbors plus adjacent redshift bins. Grain and toppling values are diagnostic -proxies derived from the existing cell-level toppling index. - -Claim boundary: this is not a physical sandpile simulation, not a stellar-gas -mechanism proof, not direct stellar mass, not gas density inference, and not a -cosmology fit. - -## Replay Receipt - -```json -{json.dumps(receipt, indent=2, sort_keys=True)} -``` - -## Seed Evidence - -```text -avalanche cells: {result['seed_evidence']['avalanche_cell_count']} -candidate objects: {result['seed_evidence']['candidate_object_count']} -graph nodes: {result['node_count']} -graph edges: {result['edge_count']} -graph hash: {result['graph_hash']} -replay hash: {result['replay_hash']} -``` - -## Toppling Threshold Table - -| cell | degree | threshold | -| --- | ---: | ---: | -{threshold_lines} - -## Initial Grain Table - -| cell | initial grains | index z proxy | seed objects | -| --- | ---: | ---: | ---: | -{grain_lines} - -## Avalanche Sizes - -| seed cell | size cells | topple events | terminated | -| --- | ---: | ---: | --- | -{avalanche_lines} - -## Holds - -{holds} -""", - encoding="utf-8", - ) - - TIDDLER.write_text( - f"""title: Stellar Gas Sandpile Graph Replay -tags: StellarGasObservation SemanticMassNumbers Sandpile GraphReplay Receipts -type: text/vnd.tiddlywiki - -Status: <> - -Decision: `{result['decision']}` - -This tiddler records the graph/replay hardening of the stellar-gas sandpile -diagnostic. It is a toppling proxy over evidence cells, not a physical sandpile -simulation or mechanism proof. - -``` -avalanche cells: {result['seed_evidence']['avalanche_cell_count']} -candidate objects: {result['seed_evidence']['candidate_object_count']} -graph nodes: {result['node_count']} -graph edges: {result['edge_count']} -graph hash: {result['graph_hash']} -replay hash: {result['replay_hash']} -``` - -!! Toppling Threshold Table - -|cell | degree | threshold | -|---|---:|---:| -{threshold_lines} - -!! Initial Grain Table - -|cell | initial grains | index z proxy | seed objects | -|---|---:|---:|---:| -{grain_lines} - -!! Avalanche Sizes - -|seed cell | size cells | topple events | terminated | -|---|---:|---:|---| -{avalanche_lines} - -!! Boundary - -Diagnostic/toppling proxy only. Holds: {", ".join(result["holds"])}. -""", - encoding="utf-8", - ) - - -def main() -> None: - result, receipt = build() - OUT_DIR.mkdir(parents=True, exist_ok=True) - DOCS_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER_DIR.mkdir(parents=True, exist_ok=True) - OUT_JSON.write_text(json.dumps(result, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT_JSON.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_docs(result, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/stellar_gas_shock_eigen_fit.py b/4-Infrastructure/shim/stellar_gas_shock_eigen_fit.py deleted file mode 100644 index c571f447..00000000 --- a/4-Infrastructure/shim/stellar_gas_shock_eigen_fit.py +++ /dev/null @@ -1,452 +0,0 @@ -#!/usr/bin/env python3 -"""Fit SDSS MaNGA DAPall observation proxies against local shock eigen lanes. - -This is an observation-proxy fit, not astrophysical validation. It measures -whether the pulled MaNGA gas/velocity columns provide nonzero support for the -physical-shock eigen axis identified in the stack-solidification audit. -""" - -from __future__ import annotations - -import argparse -import importlib.util -import json -import math -import statistics -import subprocess -import sys -from datetime import datetime, timezone -from pathlib import Path - - -REPO = Path(__file__).resolve().parents[2] -SEED_SCRIPT = REPO / "4-Infrastructure/shim/sdss_manga_dapall_observation_seed.py" -DATA_DIR = REPO / "shared-data/data/stellar_gas_observation" -DOC_PATH = REPO / "6-Documentation/docs/stellar_gas_shock_eigen_fit_2026-05-09.md" -DEFAULT_FITS = REPO / "shared-data/artifacts/stellar_gas_observation/dapall-v3_1_1-3.1.0.fits" -DESTINATION = "Gdrive:topological_storage/research-stack/stellar-gas-observation/seed-2026-05-09" - - -TARGET_COLUMNS = [ - "PLATEIFU", - "MANGAID", - "DAPTYPE", - "Z", - "BINSNR", - "SNR_MED", - "STELLAR_SIGMA_1RE", - "STELLAR_VEL_LO_CLIP", - "STELLAR_VEL_HI_CLIP", - "HA_GVEL_LO_CLIP", - "HA_GVEL_HI_CLIP", - "HA_GSIGMA_1RE", - "HA_GSIGMA_HI_CLIP", - "EMLINE_RCHI2_1RE", -] - - -LOCAL_EIGEN_PRIORS = { - "radiation_absorption": { - "cluster": "Electromagnetism & Circuits", - "eigenvalue": 0.96875, - "prior_strength": 0.176777, - }, - "diffusion_material_transport": { - "cluster": "Condensed Matter & Superconductivity", - "eigenvalue": 0.969697, - "prior_strength": 0.174078, - }, - "radiation_spectrum": { - "cluster": "Quantum Mechanics & Particle Physics", - "eigenvalue": 0.970588, - "prior_strength": 0.171499, - }, - "acoustic_boundary": { - "cluster": "Materials Science & Engineering", - "eigenvalue": 0.992063, - "prior_strength": 0.089087, - }, - "local_stack_shock_alignment": { - "cluster": "Cognitive & Semantic Systems", - "eigenvalue": 0.998464, - "prior_strength": 0.039193, - }, - "classical_hydrodynamic_shock": { - "cluster": "Detonics & Shock Physics", - "eigenvalue": None, - "prior_strength": 0.0, - }, -} - - -def now_iso() -> str: - return datetime.now(timezone.utc).astimezone().isoformat(timespec="seconds") - - -def load_seed_module(): - spec = importlib.util.spec_from_file_location("sdss_manga_dapall_observation_seed", SEED_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load {SEED_SCRIPT}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def run(cmd: list[str]) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - - -def rclone_copyto(local: Path, remote: str) -> tuple[bool, str]: - proc = run(["rclone", "copyto", str(local), remote, "--checksum"]) - message = (proc.stderr or proc.stdout).decode(errors="replace").strip() - return proc.returncode == 0, message - - -def finite(value) -> bool: - return isinstance(value, (int, float)) and math.isfinite(value) and value > -900 - - -def scalar(value): - if finite(value): - return float(value) - return None - - -def list_mean(value): - if isinstance(value, list): - vals = [float(v) for v in value if finite(v)] - return statistics.fmean(vals) if vals else None - return scalar(value) - - -def clamp01(value: float) -> float: - return max(0.0, min(1.0, value)) - - -def summarize(values: list[float]) -> dict: - vals = sorted(v for v in values if math.isfinite(v)) - if not vals: - return {"count": 0} - return { - "count": len(vals), - "min": round(vals[0], 6), - "max": round(vals[-1], 6), - "mean": round(statistics.fmean(vals), 6), - "median": round(statistics.median(vals), 6), - "p90": round(vals[int(0.9 * (len(vals) - 1))], 6), - } - - -def row_proxy(fields: dict) -> dict | None: - gas_lo = scalar(fields.get("HA_GVEL_LO_CLIP")) - gas_hi = scalar(fields.get("HA_GVEL_HI_CLIP")) - stellar_lo = scalar(fields.get("STELLAR_VEL_LO_CLIP")) - stellar_hi = scalar(fields.get("STELLAR_VEL_HI_CLIP")) - gas_sigma = scalar(fields.get("HA_GSIGMA_1RE")) - gas_sigma_hi = scalar(fields.get("HA_GSIGMA_HI_CLIP")) - stellar_sigma = scalar(fields.get("STELLAR_SIGMA_1RE")) - rchi2 = scalar(fields.get("EMLINE_RCHI2_1RE")) - snr = list_mean(fields.get("SNR_MED")) - - gas_span = None if gas_lo is None or gas_hi is None else max(0.0, gas_hi - gas_lo) - stellar_span = None if stellar_lo is None or stellar_hi is None else max(0.0, stellar_hi - stellar_lo) - if gas_span is None and gas_sigma is None and rchi2 is None: - return None - - velocity_contrast = None - if gas_span is not None and stellar_span is not None: - velocity_contrast = gas_span / max(stellar_span, 1.0) - - sigma_contrast = None - if gas_sigma is not None and stellar_sigma is not None: - sigma_contrast = gas_sigma / max(stellar_sigma, 1.0) - - quality = clamp01((snr or 0.0) / 20.0) - fit_quality = clamp01(1.0 / max(rchi2 or 99.0, 1.0)) - span_score = clamp01((gas_span or 0.0) / 1000.0) - sigma_score = clamp01((gas_sigma or 0.0) / 300.0) - contrast_score = clamp01((velocity_contrast or 0.0) / 5.0) - # Proxy only: broad gas line + high gas/stellar contrast + acceptable fit/SNR. - shock_proxy_score = clamp01( - 0.35 * span_score - + 0.30 * sigma_score - + 0.20 * contrast_score - + 0.10 * fit_quality - + 0.05 * quality - ) - - return { - "gas_velocity_span_kms": gas_span, - "stellar_velocity_span_kms": stellar_span, - "velocity_contrast": velocity_contrast, - "gas_sigma_1re_kms": gas_sigma, - "gas_sigma_hi_clip_kms": gas_sigma_hi, - "stellar_sigma_1re_kms": stellar_sigma, - "sigma_contrast": sigma_contrast, - "emline_rchi2_1re": rchi2, - "snr_med_mean": snr, - "shock_proxy_score": shock_proxy_score, - } - - -def iter_bintable_rows(fits_path: Path, limit_rows: int | None = None): - seed = load_seed_module() - emitted = 0 - with fits_path.open("rb") as f: - hdu_index = 0 - while True: - try: - header, _ = seed.read_header(f) - except EOFError: - break - data_start = f.tell() - xtension = str(header.get("XTENSION", "PRIMARY")) - if xtension == "BINTABLE": - row_len = int(header["NAXIS1"]) - row_count = int(header["NAXIS2"]) - pcount = int(header.get("PCOUNT", 0)) - columns = seed.build_columns(header) - by_name = {col["name"]: col for col in columns} - selected = [by_name[name] for name in TARGET_COLUMNS if name in by_name] - hdu_name = str(header.get("EXTNAME", f"HDU{hdu_index}")) - for row_idx in range(row_count): - if limit_rows is not None and emitted >= limit_rows: - return - f.seek(data_start + row_idx * row_len) - row = f.read(row_len) - fields = {} - for col in selected: - raw = row[col["offset"] : col["offset"] + col["width"]] - value = seed.decode_value(raw, col) - if isinstance(value, str): - value = value.replace("\u0000", "").strip() - fields[col["name"]] = value - emitted += 1 - yield hdu_index, hdu_name, row_idx, fields - f.seek(data_start + seed.padded_size(row_len * row_count + pcount)) - else: - bitpix = int(header.get("BITPIX", 8)) - naxis = int(header.get("NAXIS", 0)) - if naxis == 0: - data_size = 0 - else: - pixels = 1 - for axis in range(1, naxis + 1): - pixels *= int(header.get(f"NAXIS{axis}", 0)) - data_size = abs(bitpix) // 8 * pixels - f.seek(data_start + seed.padded_size(data_size)) - hdu_index += 1 - - -def build_fit(fits_path: Path, limit_rows: int | None) -> dict: - rows = [] - summaries = { - "gas_velocity_span_kms": [], - "stellar_velocity_span_kms": [], - "velocity_contrast": [], - "gas_sigma_1re_kms": [], - "gas_sigma_hi_clip_kms": [], - "stellar_sigma_1re_kms": [], - "sigma_contrast": [], - "emline_rchi2_1re": [], - "snr_med_mean": [], - "shock_proxy_score": [], - } - hdu_counts: dict[str, int] = {} - admitted = 0 - for hdu_index, hdu_name, row_idx, fields in iter_bintable_rows(fits_path, limit_rows): - hdu_counts[hdu_name] = hdu_counts.get(hdu_name, 0) + 1 - proxy = row_proxy(fields) - if proxy is None: - continue - admitted += 1 - for key, value in proxy.items(): - if value is not None and isinstance(value, (int, float)) and math.isfinite(value): - summaries[key].append(float(value)) - if len(rows) < 20: - rows.append( - { - "hdu_index": hdu_index, - "hdu_name": hdu_name, - "row_index": row_idx, - "plateifu": fields.get("PLATEIFU"), - "mangaid": fields.get("MANGAID"), - "daptype": fields.get("DAPTYPE"), - "z": fields.get("Z"), - "proxy": { - k: round(v, 6) if isinstance(v, float) else v for k, v in proxy.items() - }, - } - ) - - shock_scores = summaries["shock_proxy_score"] - score_mean = statistics.fmean(shock_scores) if shock_scores else 0.0 - nonzero_fraction = ( - sum(1 for score in shock_scores if score > 0.05) / len(shock_scores) - if shock_scores - else 0.0 - ) - physical_support = clamp01(0.65 * score_mean + 0.35 * nonzero_fraction) - - prior = LOCAL_EIGEN_PRIORS["classical_hydrodynamic_shock"]["prior_strength"] - refined_strength = clamp01(max(prior, physical_support)) - support_delta = refined_strength - prior - decision = ( - "ADMIT_NONZERO_PHYSICAL_SHOCK_SUPPORT" - if admitted and refined_strength > 0.0 - else "HOLD_NO_OBSERVATION_SUPPORT" - ) - - return { - "schema": "stellar_gas_shock_eigen_fit_v0", - "created": now_iso(), - "claim_boundary": "Observation-proxy fit from SDSS MaNGA gas/velocity columns to the local physical shock eigen axis. It does not prove shock hydrodynamics, stellar breakout, or causality.", - "source_fits": str(fits_path.relative_to(REPO)) if fits_path.is_relative_to(REPO) else str(fits_path), - "row_limit": limit_rows, - "hdu_counts_seen": hdu_counts, - "admitted_proxy_rows": admitted, - "local_eigen_priors": LOCAL_EIGEN_PRIORS, - "aggregate_observables": {key: summarize(vals) for key, vals in summaries.items()}, - "physical_shock_axis_refinement": { - "prior_strength": prior, - "observation_proxy_mean": round(score_mean, 6), - "nonzero_proxy_fraction": round(nonzero_fraction, 6), - "refined_strength": round(refined_strength, 6), - "support_delta": round(support_delta, 6), - "status_change": "0.000000_to_nonzero_observation_proxy" - if support_delta > 0 - else "unchanged", - }, - "top_sample_rows": sorted( - rows, - key=lambda item: item["proxy"].get("shock_proxy_score") or 0.0, - reverse=True, - )[:10], - "model_refinement": { - "before": "physical shock axis was HOLD with Detonics/Shock Physics strength 0.000000", - "after": "MaNGA gas velocity/sigma/residual columns provide a nonzero observation-proxy lane", - "next_gate": "replace proxy score with line-ratio and uncertainty-aware physical model fit", - }, - "decision": decision, - } - - -def write_markdown(result: dict, path: Path) -> None: - refine = result["physical_shock_axis_refinement"] - agg = result["aggregate_observables"] - lines = [ - "# Stellar Gas Shock Eigen Fit", - "", - "**Date:** 2026-05-09", - "", - f"**Decision:** `{result['decision']}`", - "", - "**Claim boundary:** observation-proxy fit only. This does not claim", - "astrophysical validation, stellar shock breakout detection, or causality.", - "", - "## What Changed", - "", - "The physical shock eigen axis now has a nonzero observation-backed proxy", - "from SDSS DR17 MaNGA DAPall gas and velocity columns.", - "", - "```text", - f"prior strength: {refine['prior_strength']:.6f}", - f"proxy mean: {refine['observation_proxy_mean']:.6f}", - f"nonzero fraction: {refine['nonzero_proxy_fraction']:.6f}", - f"refined strength: {refine['refined_strength']:.6f}", - f"support delta: {refine['support_delta']:.6f}", - "```", - "", - "## Observable Proxies", - "", - "| Observable | Count | Mean | Median | P90 |", - "|---|---:|---:|---:|---:|", - ] - for key in [ - "gas_velocity_span_kms", - "stellar_velocity_span_kms", - "velocity_contrast", - "gas_sigma_1re_kms", - "stellar_sigma_1re_kms", - "emline_rchi2_1re", - "snr_med_mean", - "shock_proxy_score", - ]: - s = agg[key] - lines.append( - f"| `{key}` | {s.get('count', 0)} | {s.get('mean', '')} | " - f"{s.get('median', '')} | {s.get('p90', '')} |" - ) - lines += [ - "", - "## Gate", - "", - "```text", - "if admitted_proxy_rows == 0:", - " HOLD_NO_OBSERVATION_SUPPORT", - "elif refined_strength > 0:", - " ADMIT_NONZERO_PHYSICAL_SHOCK_SUPPORT", - "else:", - " HOLD_RESIDUAL_CONTEXT", - "```", - "", - "## Next Work", - "", - "1. Add emission-line index metadata so the 35-element MaNGA arrays become", - " named H-alpha, H-beta, OIII, NII, and SII lanes.", - "2. Replace the current proxy with line-ratio diagnostics and uncertainties.", - "3. Compare high-score rows against Rankine-Hugoniot / Sedov-Taylor receipt", - " gates only after source-specific physical context is present.", - "", - ] - path.write_text("\n".join(lines)) - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--fits", type=Path, default=DEFAULT_FITS) - parser.add_argument("--limit-rows", type=int, default=None) - parser.add_argument("--destination", default=DESTINATION) - args = parser.parse_args() - - if not args.fits.exists(): - raise FileNotFoundError(args.fits) - - DATA_DIR.mkdir(parents=True, exist_ok=True) - result = build_fit(args.fits, args.limit_rows) - out_path = DATA_DIR / "stellar_gas_shock_eigen_fit.json" - out_path.write_text(json.dumps(result, indent=2) + "\n") - write_markdown(result, DOC_PATH) - - receipt_path = DATA_DIR / f"stellar_gas_shock_eigen_fit_receipt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - receipt = { - "schema": "stellar_gas_shock_eigen_fit_receipt_v0", - "created": now_iso(), - "claim_boundary": result["claim_boundary"], - "fit_file": str(out_path.relative_to(REPO)), - "doc_file": str(DOC_PATH.relative_to(REPO)), - "source_fits": result["source_fits"], - "decision": result["decision"], - "refinement": result["physical_shock_axis_refinement"], - "uploads": {}, - } - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - - uploads = { - "fit": (out_path, f"{args.destination.rstrip('/')}/derived/{out_path.name}"), - "doc": (DOC_PATH, f"{args.destination.rstrip('/')}/docs/{DOC_PATH.name}"), - "receipt": (receipt_path, f"{args.destination.rstrip('/')}/receipts/{receipt_path.name}"), - } - for name, (local, remote) in uploads.items(): - ok, message = rclone_copyto(local, remote) - receipt["uploads"][name] = {"drive_path": remote, "ok": ok, "message": message} - receipt_path.write_text(json.dumps(receipt, indent=2) + "\n") - if receipt["uploads"]["receipt"]["ok"]: - rclone_copyto(receipt_path, receipt["uploads"]["receipt"]["drive_path"]) - - print(json.dumps(receipt, indent=2)) - return 0 if result["decision"].startswith("ADMIT") else 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/4-Infrastructure/shim/swarm_topological_device_prober.py b/4-Infrastructure/shim/swarm_topological_device_prober.py deleted file mode 100644 index fec39389..00000000 --- a/4-Infrastructure/shim/swarm_topological_device_prober.py +++ /dev/null @@ -1,354 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Topological Device Prober -Deploys 11 specialized agents to probe every physical aspect of hardware -and create a plan to make it a true topological device. - -Agents: curvatureAnalyst, topologyAnalyst, hierarchyOptimizer, mutationTuner, - geometricReviewer, isaAnalyst, delayProber, errorDetector, capProber, - viaProber, powerProber -""" - -import json, hashlib, time, math, threading -from dataclasses import dataclass, field -from typing import List, Dict, Tuple, Optional -from pathlib import Path -from enum import Enum -from collections import defaultdict - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -@dataclass -class WireSegment: - name: str; length_mm: float; impedance_ohm: float - propagation_delay_ps: float; capacitance_pf: float - inductance_nh: float; resistance_ohm: float - trace_width_mm: float = 0.15; layer: int = 1 - bend_radius_mm: float = 0.0; via_count: int = 0 - -@dataclass -class CapacitorProbe: - name: str; value_uf: float; esr_mohm: float - placement_x_mm: float; placement_y_mm: float - decoupling_target: str - -@dataclass -class DelayPath: - name: str; source: str; target: str - propagation_delay_ps: float; skew_ps: float - setup_margin_ps: float; critical_path: bool - -@dataclass -class ErrorSignature: - name: str; error_type: str; magnitude_mv: float - source_trace: str; victim_trace: str; mitigation: str - -@dataclass -class ViaProbe: - name: str; inductance_nh: float; stub_length_mm: float; backdrilled: bool - -@dataclass -class PowerPlane: - name: str; voltage: float; target_impedance_mohm: float - actual_impedance_mohm: float; resonance_freq_mhz: float - -@dataclass -class TopologyComponent: - name: str; comp_type: str; location_x_mm: float; location_y_mm: float - voltage_mv: float; current_ma: float; temperature_c: float; power_mw: float - -@dataclass -class TopologyGraph: - nodes: List[TopologyComponent]; edges: List[WireSegment] - vias: List[ViaProbe]; capacitors: List[CapacitorProbe] - delays: List[DelayPath]; errors: List[ErrorSignature] - power_planes: List[PowerPlane]; timestamp: float - - -class AgentSpec(Enum): - CURVATURE = "curvatureAnalyst"; TOPOLOGY = "topologyAnalyst" - HIERARCHY = "hierarchyOptimizer"; TUNING = "mutationTuner" - GEOMETRY = "geometricReviewer"; ISA = "isaAnalyst" - DELAY = "delayProber"; ERROR = "errorDetector" - CAP = "capProber"; VIA = "viaProber"; POWER = "powerProber" - - -@dataclass -class SwarmAgent: - agent_id: int; specialization: AgentSpec; confidence: float = 0.95 - findings: List[str] = field(default_factory=list) - recommendations: List[str] = field(default_factory=list) - - def probe(self, t: TopologyGraph) -> Dict: - probes = { - AgentSpec.CURVATURE: lambda: { - "tight_bends": sum(1 for e in t.edges if e.bend_radius_mm > 0 and e.bend_radius_mm < 3*e.trace_width_mm), - "recommendation": "Bend radius ≥3× trace width for impedance control" - }, - AgentSpec.TOPOLOGY: lambda: { - "nodes": len(t.nodes), "edges": len(t.edges), - "critical_paths": sum(1 for d in t.delays if d.critical_path), - "recommendation": "Star topology for clock; mesh for data" - }, - AgentSpec.HIERARCHY: lambda: { - "layers": len(set(e.layer for e in t.edges)), - "vias": sum(e.via_count for e in t.edges), - "recommendation": "Symmetric stackup: SIG-GND-PWR-SIG" - }, - AgentSpec.TUNING: lambda: { - "caps": len(t.capacitors), - "under_decoupled": [c.decoupling_target for c in t.capacitors if c.value_uf < 0.01], - "recommendation": "100nF + 10nF per power pin" - }, - AgentSpec.GEOMETRY: lambda: { - "min_spacing": 0.15, - "recommendation": "≥3× trace width spacing for crosstalk" - }, - AgentSpec.ISA: lambda: { - "timing_violations": sum(1 for d in t.delays if d.setup_margin_ps < 0), - "recommendation": "Pipeline if setup margin < 100ps" - }, - AgentSpec.DELAY: lambda: { - "max_delay_ps": max((d.propagation_delay_ps for d in t.delays), default=0), - "max_skew_ps": max((d.skew_ps for d in t.delays), default=0), - "recommendation": "Match trace lengths within 50ps for DDR" - }, - AgentSpec.ERROR: lambda: { - "crosstalk_events": sum(1 for e in t.errors if e.error_type == 'crosstalk'), - "reflection_events": sum(1 for e in t.errors if e.error_type == 'reflection'), - "recommendation": "Series termination at driver; parallel at receiver" - }, - AgentSpec.CAP: lambda: { - "total_caps": len(t.capacitors), - "high_esr": sum(1 for c in t.capacitors if c.esr_mohm > 100), - "recommendation": "Use X7R for decoupling; C0G for timing" - }, - AgentSpec.VIA: lambda: { - "total_vias": len(t.vias), - "stub_vias": sum(1 for v in t.vias if v.stub_length_mm > 0.5 and not v.backdrilled), - "recommendation": "Backdrill vias with stub > 0.5mm above 5GHz" - }, - AgentSpec.POWER: lambda: { - "planes": len(t.power_planes), - "impedance_violations": sum(1 for p in t.power_planes if p.actual_impedance_mohm > p.target_impedance_mohm), - "recommendation": "Target PDN impedance < 10mOhm to 100MHz" - }, - } - result = probes.get(self.specialization, lambda: {})() - self.findings.append(str(result)) - return result - - -class SwarmTopologicalProber: - """Orchestrates swarm of agents probing hardware for topological device plan.""" - - def __init__(self): - self.agents: List[SwarmAgent] = [] - self.topology: Optional[TopologyGraph] = None - self.consensus: Dict = {} - - def deploy_swarm(self, num_agents: int = 11): - """Deploy specialized agents.""" - specs = list(AgentSpec) - for i in range(num_agents): - spec = specs[i % len(specs)] - self.agents.append(SwarmAgent(agent_id=i, specialization=spec)) - print(f"Deployed {len(self.agents)} agents across {len(specs)} specializations") - - def generate_simulated_topology(self) -> TopologyGraph: - """Generate simulated hardware topology for probing.""" - nodes = [ - TopologyComponent("U1_FPGA", "IC", 25.0, 30.0, 3300, 150, 45, 2500), - TopologyComponent("U2_DDR", "IC", 50.0, 30.0, 1200, 200, 42, 240), - TopologyComponent("U3_OSC", "oscillator", 10.0, 10.0, 3300, 10, 35, 33), - TopologyComponent("U4_REG", "regulator", 5.0, 5.0, 5000, 500, 50, 2500), - TopologyComponent("J1_HDMI", "connector", 70.0, 5.0, 3300, 50, 30, 165), - ] - - edges = [ - WireSegment("CLK_100M", 45.0, 50.0, 320.0, 2.5, 8.0, 0.1, 0.15, 1, 5.0, 2), - WireSegment("DATA_0", 25.0, 50.0, 180.0, 1.5, 5.0, 0.08, 0.12, 1, 0, 0), - WireSegment("DATA_1", 25.5, 50.0, 183.0, 1.5, 5.0, 0.08, 0.12, 1, 0, 0), - WireSegment("ADDR_0", 30.0, 50.0, 210.0, 1.8, 6.0, 0.1, 0.12, 1, 0, 1), - WireSegment("HDMI_CLK", 15.0, 100.0, 105.0, 1.0, 3.0, 0.05, 0.1, 3, 3.0, 1), - WireSegment("HDMI_D0", 15.2, 100.0, 106.0, 1.0, 3.0, 0.05, 0.1, 3, 3.0, 1), - WireSegment("HDMI_D1", 14.8, 100.0, 104.0, 1.0, 3.0, 0.05, 0.1, 3, 3.0, 1), - WireSegment("PWR_3V3", 60.0, 0.5, 420.0, 50.0, 15.0, 0.5, 1.0, 2, 0, 4), - ] - - caps = [ - CapacitorProbe("C1_100n", 0.1, 50, 23.0, 28.0, "U1_FPGA"), - CapacitorProbe("C2_10n", 0.01, 30, 24.0, 29.0, "U1_FPGA"), - CapacitorProbe("C3_10u", 10.0, 100, 48.0, 28.0, "U2_DDR"), - CapacitorProbe("C4_100n", 0.1, 50, 49.0, 29.0, "U2_DDR"), - CapacitorProbe("C5_4u7", 4.7, 80, 3.0, 3.0, "U4_REG"), - ] - - delays = [ - DelayPath("FPGA_to_DDR", "U1_FPGA", "U2_DDR", 210, 5, 50, True), - DelayPath("OSC_to_FPGA", "U3_OSC", "U1_FPGA", 320, 0, 20, True), - DelayPath("FPGA_to_HDMI", "U1_FPGA", "J1_HDMI", 106, 3, 30, False), - ] - - errors = [ - ErrorSignature("XTALK_D0_D1", "crosstalk", 45, "HDMI_D0", "HDMI_D1", "Increase spacing to 3× width"), - ErrorSignature("REFL_CLK", "reflection", 120, "CLK_100M", "U1_FPGA", "Add 33Ω series termination"), - ] - - vias = [ - ViaProbe("VIA_CLK_1", 0.8, 0.3, False), - ViaProbe("VIA_CLK_2", 0.8, 0.3, False), - ViaProbe("VIA_PWR_1", 1.2, 1.6, False), - ViaProbe("VIA_HDMI_1", 0.6, 0.8, True), - ] - - power_planes = [ - PowerPlane("VCC_3V3", 3.3, 10, 15, 85), - PowerPlane("VCC_1V2", 1.2, 5, 8, 120), - ] - - return TopologyGraph(nodes, edges, vias, caps, delays, errors, power_planes, time.time()) - - def run_probing(self): - """Execute full swarm probing of hardware topology.""" - if not self.topology: - self.topology = self.generate_simulated_topology() - - print(f"\nProbing topology: {len(self.topology.nodes)} components, " - f"{len(self.topology.edges)} traces, {len(self.topology.errors)} errors\n") - - results = {} - for agent in self.agents: - result = agent.probe(self.topology) - results[agent.specialization.value] = result - print(f" [{agent.specialization.value}] {result.get('recommendation', '')[:80]}") - - self.consensus = self._build_consensus(results) - return results - - def _build_consensus(self, results: Dict) -> Dict: - """Build swarm consensus on topological device plan.""" - all_recs = [] - for spec, result in results.items(): - if 'recommendation' in result: - all_recs.append(result['recommendation']) - - return { - "agent_count": len(self.agents), - "findings_total": sum(len(a.findings) for a in self.agents), - "recommendations": all_recs, - "consensus_score": 0.95, - "topological_readiness": self._assess_readiness(results) - } - - def _assess_readiness(self, results: Dict) -> float: - """Assess how close the hardware is to being a true topological device.""" - scores = { - "impedance_control": 0.7 if results.get("curvatureAnalyst", {}).get("tight_bends", 99) < 3 else 0.3, - "signal_integrity": 0.6 if results.get("errorDetector", {}).get("crosstalk_events", 99) < 2 else 0.2, - "power_integrity": 0.5 if results.get("powerProber", {}).get("impedance_violations", 99) < 2 else 0.2, - "timing_closure": 0.8 if results.get("isaAnalyst", {}).get("timing_violations", 99) == 0 else 0.3, - "manufacturing": 0.6 if results.get("viaProber", {}).get("stub_vias", 99) < 2 else 0.3, - } - return sum(scores.values()) / len(scores) - - def generate_topological_device_plan(self) -> Dict: - """Generate plan to transform hardware into true topological device.""" - return { - "phase_1_immediate": { - "title": "Signal Integrity Hardening", - "actions": [ - "Add 33Ω series termination on all clock lines", - "Increase HDMI trace spacing to 0.3mm (3× width)", - "Replace high-ESR caps (>100mΩ) with low-ESR X7R", - ], - "timeline": "1-2 weeks", - "expected_improvement": "+30% signal integrity" - }, - "phase_2_structural": { - "title": "Topological Routing Optimization", - "actions": [ - "Match all DDR data trace lengths within 50ps", - "Backdrill HDMI vias with stub > 0.5mm", - "Implement star topology for clock distribution", - "Add guard traces between critical pairs", - ], - "timeline": "2-4 weeks", - "expected_improvement": "+40% timing margin" - }, - "phase_3_power": { - "title": "Power Distribution Network Topology", - "actions": [ - "Reduce PDN impedance to <10mΩ up to 100MHz", - "Add 100nF + 10nF per power pin (broadband decoupling)", - "Implement symmetric SIG-GND-PWR-SIG stackup", - "Add ferrite beads on analog power rails", - ], - "timeline": "3-6 weeks", - "expected_improvement": "+50% power integrity" - }, - "phase_4_topological": { - "title": "True Topological Device Transformation", - "actions": [ - "Implement FAMM preshaped delay lines (waveprobe-derived)", - "Add topological state machine for adaptive routing", - "Integrate manifold-aware impedance matching", - "Deploy swarm consensus for real-time topology optimization", - "Add eigenvalue-based clock distribution network", - ], - "timeline": "6-12 weeks", - "expected_improvement": "Topological device achieved" - }, - "readiness_score": self.consensus.get("topological_readiness", 0.0), - "total_actions": 16, - "estimated_timeline_weeks": 12 - } - - -def main(): - print("=" * 70) - print("Swarm Topological Device Prober") - print("=" * 70) - - prober = SwarmTopologicalProber() - - print("\n[1] Deploying swarm agents...") - prober.deploy_swarm(num_agents=11) - - print("\n[2] Probing hardware topology...") - results = prober.run_probing() - - print("\n[3] Building consensus...") - consensus = prober.consensus - print(f" Consensus score: {consensus['consensus_score']:.2f}") - print(f" Topological readiness: {consensus['topological_readiness']:.2f}") - - print("\n[4] Generating topological device plan...") - plan = prober.generate_topological_device_plan() - - print(f"\n Readiness: {plan['readiness_score']:.2f} (target: 0.95)") - print(f" Phases: {len(plan)-2}") - print(f" Total actions: {plan['total_actions']}") - print(f" Timeline: {plan['estimated_timeline_weeks']} weeks") - - for phase_key in ['phase_1_immediate', 'phase_2_structural', 'phase_3_power', 'phase_4_topological']: - phase = plan[phase_key] - print(f"\n {phase['title']}:") - for action in phase['actions']: - print(f" - {action}") - - # Save output - output_path = RESEARCH_STACK / "4-Infrastructure/shim/topological_device_plan.json" - output = {"results": {k: v for k, v in results.items()}, "consensus": consensus, "plan": plan} - with open(output_path, 'w') as f: - json.dump(output, f, indent=2, default=str) - - print(f"\n[5] Plan saved: {output_path}") - print("\n" + "=" * 70) - print("Swarm probing complete — topological device plan ready") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/symbolic_law_replay_harness.py b/4-Infrastructure/shim/symbolic_law_replay_harness.py deleted file mode 100644 index 079e65be..00000000 --- a/4-Infrastructure/shim/symbolic_law_replay_harness.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -"""Tiny symbolic-law replay harness. - -This is the first replay fixture for the SRBench/Feynman route surface. It uses -small built-in ground-truth laws and deliberate mutations to test deterministic -replay, residual accounting, and HOLD/ADMIT separation without downloading any -external dataset. -""" - -from __future__ import annotations - -import ast -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from decimal import Decimal, getcontext -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "symbolic_law_replay" -RECEIPT = OUT_DIR / "symbolic_law_replay_receipt.json" -TABLE = OUT_DIR / "symbolic_law_replay_table.jsonl" - -getcontext().prec = 40 - - -ALLOWED_BINOPS = { - ast.Add: lambda a, b: a + b, - ast.Sub: lambda a, b: a - b, - ast.Mult: lambda a, b: a * b, - ast.Div: lambda a, b: a / b, - ast.Pow: lambda a, b: a**b, -} -ALLOWED_UNARY = { - ast.UAdd: lambda a: a, - ast.USub: lambda a: -a, -} -ALLOWED_FUNCS = { - "sin": Decimal, - "cos": Decimal, -} - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - route_surface: str - truth_formula: str - candidate_formula: str - variables: list[str] - samples: list[dict[str, str]] - negative_control: bool - - -FIXTURES = [ - Fixture( - fixture_id="feynman_newton_gravity_admit", - route_surface="SRBench / Feynman", - truth_formula="G*m1*m2/(r**2)", - candidate_formula="G*m1*m2/(r**2)", - variables=["G", "m1", "m2", "r"], - samples=[ - {"G": "6.67430e-11", "m1": "5.972e24", "m2": "7.348e22", "r": "3.844e8"}, - {"G": "6.67430e-11", "m1": "1.989e30", "m2": "5.972e24", "r": "1.496e11"}, - {"G": "6.67430e-11", "m1": "1.0e5", "m2": "2.0e5", "r": "3000"}, - ], - negative_control=False, - ), - Fixture( - fixture_id="feynman_newton_gravity_negative", - route_surface="SRBench / Feynman", - truth_formula="G*m1*m2/(r**2)", - candidate_formula="G*m1*m2/r", - variables=["G", "m1", "m2", "r"], - samples=[ - {"G": "6.67430e-11", "m1": "5.972e24", "m2": "7.348e22", "r": "3.844e8"}, - {"G": "6.67430e-11", "m1": "1.989e30", "m2": "5.972e24", "r": "1.496e11"}, - {"G": "6.67430e-11", "m1": "1.0e5", "m2": "2.0e5", "r": "3000"}, - ], - negative_control=True, - ), - Fixture( - fixture_id="feynman_kinetic_energy_admit", - route_surface="DLMF / Feynman", - truth_formula="0.5*m*(v**2)", - candidate_formula="0.5*m*(v**2)", - variables=["m", "v"], - samples=[ - {"m": "1.0", "v": "3.0"}, - {"m": "2.5", "v": "4.0"}, - {"m": "0.125", "v": "12.0"}, - ], - negative_control=False, - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def decimal_from_node(node: ast.AST, env: dict[str, Decimal]) -> Decimal: - if isinstance(node, ast.Expression): - return decimal_from_node(node.body, env) - if isinstance(node, ast.Constant) and isinstance(node.value, (int, float)): - return Decimal(str(node.value)) - if isinstance(node, ast.Name): - if node.id not in env: - raise ValueError(f"unknown variable {node.id}") - return env[node.id] - if isinstance(node, ast.BinOp): - op_type = type(node.op) - if op_type not in ALLOWED_BINOPS: - raise ValueError(f"unsupported operator {op_type.__name__}") - return ALLOWED_BINOPS[op_type](decimal_from_node(node.left, env), decimal_from_node(node.right, env)) - if isinstance(node, ast.UnaryOp): - op_type = type(node.op) - if op_type not in ALLOWED_UNARY: - raise ValueError(f"unsupported unary operator {op_type.__name__}") - return ALLOWED_UNARY[op_type](decimal_from_node(node.operand, env)) - raise ValueError(f"unsupported expression node {type(node).__name__}") - - -def evaluate(formula: str, sample: dict[str, str]) -> Decimal: - tree = ast.parse(formula, mode="eval") - env = {key: Decimal(value) for key, value in sample.items()} - return decimal_from_node(tree, env) - - -def normalize_decimal(value: Decimal) -> str: - if value == 0: - return "0" - return format(value.normalize(), "E") - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - rows = [] - absolute_errors: list[Decimal] = [] - for idx, sample in enumerate(fixture.samples): - truth = evaluate(fixture.truth_formula, sample) - candidate = evaluate(fixture.candidate_formula, sample) - error = abs(truth - candidate) - absolute_errors.append(error) - rows.append( - { - "sample_index": idx, - "sample": sample, - "truth": normalize_decimal(truth), - "candidate": normalize_decimal(candidate), - "absolute_error": normalize_decimal(error), - } - ) - - max_error = max(absolute_errors) if absolute_errors else Decimal(0) - replay_valid = max_error == 0 - residual_declared = True - encoded_payload = { - "formula": fixture.candidate_formula, - "variables": fixture.variables, - "sample_count": len(fixture.samples), - } - explicit_payload = { - "truth_values": [row["truth"] for row in rows], - } - encoded_bytes = len(stable_json(encoded_payload).encode("utf-8")) - explicit_bytes = len(stable_json(explicit_payload).encode("utf-8")) - residual_bytes = 0 if replay_valid else len(stable_json({"errors": [row["absolute_error"] for row in rows]}).encode("utf-8")) - total_candidate_bytes = encoded_bytes + residual_bytes - byte_gain = explicit_bytes - total_candidate_bytes - - # This fixture only admits exact deterministic replay. Byte gain is reported - # as a diagnostic because the sample is intentionally tiny. - status = "ADMIT_FIXTURE" if replay_valid and residual_declared and not fixture.negative_control else "HOLD" - if byte_gain <= 0: - status = "HOLD_DIAGNOSTIC" - if fixture.negative_control and replay_valid: - status = "FAIL_NEGATIVE_CONTROL" - - result = { - "fixture_id": fixture.fixture_id, - "route_surface": fixture.route_surface, - "truth_formula": fixture.truth_formula, - "candidate_formula": fixture.candidate_formula, - "formula_hash": sha256_text(fixture.candidate_formula), - "negative_control": fixture.negative_control, - "rows": rows, - "max_absolute_error": normalize_decimal(max_error), - "replay_valid": replay_valid, - "residual_declared": residual_declared, - "encoded_bytes": encoded_bytes, - "explicit_bytes": explicit_bytes, - "residual_bytes": residual_bytes, - "byte_gain": byte_gain, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - receipt = { - "schema": "symbolic_law_replay_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "fixture_count": len(results), - "table": rel(TABLE), - "status_counts": {status: sum(1 for result in results if result["status"] == status) for status in sorted({result["status"] for result in results})}, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Tiny symbolic-law replay fixture only. It tests deterministic evaluation, " - "negative-control behavior, and residual accounting; it is not an SRBench score, " - "not an external dataset ingest, and not a compression benchmark." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({"receipt": rel(RECEIPT), "table": rel(TABLE), "receipt_hash": receipt["receipt_hash"], "status_counts": receipt["status_counts"]}, indent=2, sort_keys=True)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/system_equations_4primitive_mapping.json b/4-Infrastructure/shim/system_equations_4primitive_mapping.json deleted file mode 100644 index fec9ed39..00000000 --- a/4-Infrastructure/shim/system_equations_4primitive_mapping.json +++ /dev/null @@ -1,190 +0,0 @@ -{ - "primitives": { - "field": { - "equation": "\u03c1(x\u20d7)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "keywords": [ - "entropy", - "density", - "distribution", - "manifold", - "topology", - "field", - "state" - ] - }, - "shear": { - "equation": "G = A\u1d40A", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "keywords": [ - "distance", - "metric", - "transform", - "deformation", - "shear", - "geometry", - "hyperbolic" - ] - }, - "packet": { - "equation": "\u0393\u1d62", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "keywords": [ - "coding", - "compression", - "transform", - "bwt", - "ans", - "packet", - "codec", - "optimization" - ] - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "keywords": [ - "complexity", - "basis", - "bottleneck", - "decomposition", - "spectral", - "eigen", - "dimension", - "tradeoff" - ] - } - }, - "system_equations": { - "grand_unified_theory": { - "source": "grand_unified_theory_20260504_163327.json", - "axioms": { - "axiom_1_shannon_entropy": { - "formula": "H(X) = -sum_{i} p(x_i) log_2 p(x_i) \u2248 0.6-1.3 bits/character", - "primitive": "field", - "mapping": "Shannon entropy = field state (probability distribution over symbols)" - }, - "axiom_2_kolmogorov_complexity": { - "formula": "K(x) = min_{p: U(p)=x} |p|", - "primitive": "spectral", - "mapping": "Kolmogorov complexity = spectral basis (shortest program = optimal basis)" - }, - "axiom_3_zipf_law": { - "formula": "f(r) = C * r^(-\u03b1), where \u03b1 \u2248 1.0-1.2 for English", - "primitive": "field", - "mapping": "Zipf law = field distribution (power-law distribution over symbols)" - }, - "axiom_4_grammar_as_manifold": { - "formula": "dim(M_grammar) << dim(\u03a3*)", - "primitive": "field", - "mapping": "Grammar as manifold = field topology (low-dimensional embedding)" - }, - "axiom_5_hyperbolic_hierarchy": { - "formula": "d(u,v) = arccosh(1 + 2||u-v||^2/((1-||u||^2)(1-||v||^2)))", - "primitive": "shear", - "mapping": "Hyperbolic hierarchy = geometric deformation (distance metric in curved space)" - }, - "axiom_6_information_bottleneck": { - "formula": "min I(X;Z) - \u03b2*I(Z;Y)", - "primitive": "spectral", - "mapping": "Information bottleneck = spectral decomposition (compress irrelevant, preserve relevant)" - }, - "axiom_7_ans_optimality": { - "formula": "L_ANS <= H(X) + \u03b5, where \u03b5 \u2248 0.001 bits/symbol", - "primitive": "packet", - "mapping": "ANS optimality = packet coding (near-optimal entropy coding)" - }, - "axiom_8_bwt_repetitiveness": { - "formula": "|RLBWT(w)| = O(r), where r = number of runs in BWT output", - "primitive": "packet", - "mapping": "BWT repetitiveness = packet transform (permuted sort clusters contexts)" - }, - "axiom_9_mdl_principle": { - "formula": "L(D,M) = L(M) + L(D|M)", - "primitive": "spectral", - "mapping": "MDL principle = spectral tradeoff (model size + data description)" - }, - "axiom_10_topological_invariants": { - "formula": "H_k(X_\u03b5) for \u03b5 in [0, \u221e), tracking birth/death of k-dimensional holes", - "primitive": "field", - "mapping": "Topological invariants = field topology (persistent homology)" - } - }, - "unified_equations": { - "grand_compression_equation": { - "formula": "C* = argmin_C [ H(X|C) + \u03bb|C| + \u03bc*K(C) + \u03bd*dim(M_C) ]", - "primitive": "packet", - "mapping": "Grand compression equation = packet optimization (balance entropy, model size, complexity, dimensionality)" - }, - "language_as_manifold": { - "formula": "L = { w \u2208 \u03a3* | G(w) = 1 } \u2248 M \u2282 R^d", - "primitive": "shear", - "mapping": "Language as manifold = shear transform (grammar \u2192 manifold embedding)" - }, - "hyperbolic_semantic_distance": { - "formula": "d_P(u,v) = arccosh(1 + 2*||u-v||^2/((1-||u||^2)(1-||v||^2)))", - "primitive": "spectral", - "mapping": "Hyperbolic semantic distance = spectral metric (distance in hyperbolic space)" - }, - "information_bottleneck_language": { - "formula": "min_{p(z|x)} I(X;Z) - \u03b2*I(Z;Y) + \u03b3*R(Z)", - "primitive": "spectral", - "mapping": "Information bottleneck for language = spectral regularization (compression + prediction + geometry)" - } - } - }, - "compactified_core_equations": { - "source": "compactified_core_equations_v1.json", - "primitives": { - "field_primitive": { - "equation": "\u03c1(x\u20d7)", - "derives": [ - "morse_smale", - "radius_ratio", - "residual_ratio", - "s3c_shell" - ], - "role": "field state / substrate / scalar manifold state" - }, - "shear_primitive": { - "equation": "G = A\u1d40A", - "derives": [ - "shear_matrix", - "famm_delay", - "eigen_decomposition" - ], - "role": "shear / metric deformation / lawful geometry" - }, - "packet_primitive": { - "equation": "\u0393\u1d62 = \u03b3\u1d62 \u2297 \u03c7\u1d62 \u2297 \u03ba\u1d62 \u2297 \u03c4\u1d62 \u2297 U\u1d62\u039b\u1d62a\u1d62 \u2297 \u03b8\u1d62 \u2297 \u03b5\u1d62", - "derives": [ - "gccl_packet", - "gain_test" - ], - "role": "packet / executable typed glyph-witness / codec event" - }, - "spectral_primitive": { - "equation": "C = U\u039bU\u1d40", - "derives": [ - "residual_correlation", - "eigen_decomposition", - "famm_spectral" - ], - "role": "spectral / eigenbasis / pruning-correlation structure" - } - } - } - }, - "primitive_counts": { - "field": 4, - "shear": 2, - "packet": 3, - "spectral": 5 - }, - "insights": { - "consistency": "Grand unified theory axioms map cleanly to 4 primitives", - "redundancy": "Some equations span multiple primitives", - "completeness": "Each primitive has representative equations from multiple sources", - "integration": "Compactified core equations subsume grand unified theory equations" - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/system_equations_4primitive_mapping.py b/4-Infrastructure/shim/system_equations_4primitive_mapping.py deleted file mode 100644 index d985bdf9..00000000 --- a/4-Infrastructure/shim/system_equations_4primitive_mapping.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -""" -Map System Equations to 4-Primitive Framework -============================================== -Review all system equations and map them to the 4 primitives: -- Field primitive (ρ(x⃗)) -- Shear primitive (G = AᵀA) -- Packet primitive (Γᵢ) -- Spectral primitive (C = UΛUᵀ) -""" - -import json -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -# 4-primitive framework -PRIMITIVES = { - "field": { - "equation": "ρ(x⃗)", - "role": "tells you what exists (field / substrate / scalar manifold state)", - "keywords": ["entropy", "density", "distribution", "manifold", "topology", "field", "state"] - }, - "shear": { - "equation": "G = AᵀA", - "role": "tells you how it deforms (shear / metric deformation / lawful geometry)", - "keywords": ["distance", "metric", "transform", "deformation", "shear", "geometry", "hyperbolic"] - }, - "packet": { - "equation": "Γᵢ", - "role": "tells you what is emitted/witnessed (packet / executable typed glyph-witness / codec event)", - "keywords": ["coding", "compression", "transform", "bwt", "ans", "packet", "codec", "optimization"] - }, - "spectral": { - "equation": "C = UΛUᵀ", - "role": "tells you what basis survives (spectral / eigenbasis / pruning-correlation structure)", - "keywords": ["complexity", "basis", "bottleneck", "decomposition", "spectral", "eigen", "dimension", "tradeoff"] - } -} - -# System equations from grand unified theory -SYSTEM_EQUATIONS = { - "grand_unified_theory": { - "source": "grand_unified_theory_20260504_163327.json", - "axioms": { - "axiom_1_shannon_entropy": { - "formula": "H(X) = -sum_{i} p(x_i) log_2 p(x_i) ≈ 0.6-1.3 bits/character", - "primitive": "field", - "mapping": "Shannon entropy = field state (probability distribution over symbols)" - }, - "axiom_2_kolmogorov_complexity": { - "formula": "K(x) = min_{p: U(p)=x} |p|", - "primitive": "spectral", - "mapping": "Kolmogorov complexity = spectral basis (shortest program = optimal basis)" - }, - "axiom_3_zipf_law": { - "formula": "f(r) = C * r^(-α), where α ≈ 1.0-1.2 for English", - "primitive": "field", - "mapping": "Zipf law = field distribution (power-law distribution over symbols)" - }, - "axiom_4_grammar_as_manifold": { - "formula": "dim(M_grammar) << dim(Σ*)", - "primitive": "field", - "mapping": "Grammar as manifold = field topology (low-dimensional embedding)" - }, - "axiom_5_hyperbolic_hierarchy": { - "formula": "d(u,v) = arccosh(1 + 2||u-v||^2/((1-||u||^2)(1-||v||^2)))", - "primitive": "shear", - "mapping": "Hyperbolic hierarchy = geometric deformation (distance metric in curved space)" - }, - "axiom_6_information_bottleneck": { - "formula": "min I(X;Z) - β*I(Z;Y)", - "primitive": "spectral", - "mapping": "Information bottleneck = spectral decomposition (compress irrelevant, preserve relevant)" - }, - "axiom_7_ans_optimality": { - "formula": "L_ANS <= H(X) + ε, where ε ≈ 0.001 bits/symbol", - "primitive": "packet", - "mapping": "ANS optimality = packet coding (near-optimal entropy coding)" - }, - "axiom_8_bwt_repetitiveness": { - "formula": "|RLBWT(w)| = O(r), where r = number of runs in BWT output", - "primitive": "packet", - "mapping": "BWT repetitiveness = packet transform (permuted sort clusters contexts)" - }, - "axiom_9_mdl_principle": { - "formula": "L(D,M) = L(M) + L(D|M)", - "primitive": "spectral", - "mapping": "MDL principle = spectral tradeoff (model size + data description)" - }, - "axiom_10_topological_invariants": { - "formula": "H_k(X_ε) for ε in [0, ∞), tracking birth/death of k-dimensional holes", - "primitive": "field", - "mapping": "Topological invariants = field topology (persistent homology)" - } - }, - "unified_equations": { - "grand_compression_equation": { - "formula": "C* = argmin_C [ H(X|C) + λ|C| + μ*K(C) + ν*dim(M_C) ]", - "primitive": "packet", - "mapping": "Grand compression equation = packet optimization (balance entropy, model size, complexity, dimensionality)" - }, - "language_as_manifold": { - "formula": "L = { w ∈ Σ* | G(w) = 1 } ≈ M ⊂ R^d", - "primitive": "shear", - "mapping": "Language as manifold = shear transform (grammar → manifold embedding)" - }, - "hyperbolic_semantic_distance": { - "formula": "d_P(u,v) = arccosh(1 + 2*||u-v||^2/((1-||u||^2)(1-||v||^2)))", - "primitive": "spectral", - "mapping": "Hyperbolic semantic distance = spectral metric (distance in hyperbolic space)" - }, - "information_bottleneck_language": { - "formula": "min_{p(z|x)} I(X;Z) - β*I(Z;Y) + γ*R(Z)", - "primitive": "spectral", - "mapping": "Information bottleneck for language = spectral regularization (compression + prediction + geometry)" - } - } - }, - "compactified_core_equations": { - "source": "compactified_core_equations_v1.json", - "primitives": { - "field_primitive": { - "equation": "ρ(x⃗)", - "derives": ["morse_smale", "radius_ratio", "residual_ratio", "s3c_shell"], - "role": "field state / substrate / scalar manifold state" - }, - "shear_primitive": { - "equation": "G = AᵀA", - "derives": ["shear_matrix", "famm_delay", "eigen_decomposition"], - "role": "shear / metric deformation / lawful geometry" - }, - "packet_primitive": { - "equation": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "derives": ["gccl_packet", "gain_test"], - "role": "packet / executable typed glyph-witness / codec event" - }, - "spectral_primitive": { - "equation": "C = UΛUᵀ", - "derives": ["residual_correlation", "eigen_decomposition", "famm_spectral"], - "role": "spectral / eigenbasis / pruning-correlation structure" - } - } - } -} - -def analyze_mapping(): - print("=" * 70) - print(" SYSTEM EQUATIONS → 4-PRIMITIVE FRAMEWORK MAPPING") - print("=" * 70) - - print("\n4-PRIMITIVE FRAMEWORK:") - for prim, data in PRIMITIVES.items(): - print(f"\n{prim.upper()}: {data['equation']}") - print(f" Role: {data['role']}") - print(f" Keywords: {', '.join(data['keywords'])}") - - print("\n" + "=" * 70) - print(" GRAND UNIFIED THEORY EQUATIONS") - print("=" * 70) - - gut = SYSTEM_EQUATIONS["grand_unified_theory"] - print(f"\nSource: {gut['source']}") - print(f"10 axioms, 4 unified equations") - - print("\nAXIOMS:") - for ax_name, ax_data in gut["axioms"].items(): - prim = ax_data["primitive"].upper() - print(f"\n{ax_name}:") - print(f" Formula: {ax_data['formula']}") - print(f" Primitive: {prim}") - print(f" Mapping: {ax_data['mapping']}") - - print("\nUNIFIED EQUATIONS:") - for eq_name, eq_data in gut["unified_equations"].items(): - prim = eq_data["primitive"].upper() - print(f"\n{eq_name}:") - print(f" Formula: {eq_data['formula']}") - print(f" Primitive: {prim}") - print(f" Mapping: {eq_data['mapping']}") - - print("\n" + "=" * 70) - print(" PRIMITIVE DISTRIBUTION") - print("=" * 70) - - primitive_counts = {"field": 0, "shear": 0, "packet": 0, "spectral": 0} - - for ax_data in gut["axioms"].values(): - primitive_counts[ax_data["primitive"]] += 1 - - for eq_data in gut["unified_equations"].values(): - primitive_counts[eq_data["primitive"]] += 1 - - print(f"\nField primitive (ρ(x⃗)): {primitive_counts['field']} equations") - print(f"Shear primitive (G = AᵀA): {primitive_counts['shear']} equations") - print(f"Packet primitive (Γᵢ): {primitive_counts['packet']} equations") - print(f"Spectral primitive (C = UΛUᵀ): {primitive_counts['spectral']} equations") - - print("\n" + "=" * 70) - print(" COMPACTIFIED CORE EQUATIONS") - print("=" * 70) - - cce = SYSTEM_EQUATIONS["compactified_core_equations"] - print(f"\nSource: {cce['source']}") - - for prim, data in cce["primitives"].items(): - print(f"\n{prim}:") - print(f" Equation: {data['equation']}") - print(f" Derives: {', '.join(data['derives'])}") - print(f" Role: {data['role']}") - - print("\n" + "=" * 70) - print(" INTEGRATION ANALYSIS") - print("=" * 70) - - print("\nGrand unified theory equations map to:") - print(f" - Field primitive: {primitive_counts['field']} equations (Shannon entropy, Zipf law, grammar manifold, topological invariants)") - print(f" - Shear primitive: {primitive_counts['shear']} equations (hyperbolic hierarchy, language as manifold)") - print(f" - Packet primitive: {primitive_counts['packet']} equations (ANS optimality, BWT, grand compression)") - print(f" - Spectral primitive: {primitive_counts['spectral']} equations (Kolmogorov complexity, information bottleneck, MDL, hyperbolic distance)") - - print("\nCompactified core equations:") - print(" - Field primitive: derives Morse-Smale, radius_ratio, residual_ratio, S3C shells") - print(" - Shear primitive: derives shear_matrix, FAMM delays, eigen_decomposition") - print(" - Packet primitive: derives GCCL packet, gain test") - print(" - Spectral primitive: derives residual correlation, eigen_decomposition, FAMM spectral") - - print("\n" + "=" * 70) - print(" KEY INSIGHTS") - print("=" * 70) - - print("\n1. Consistency: Grand unified theory axioms map cleanly to 4 primitives") - print("2. Redundancy: Some equations span multiple primitives (e.g., grand compression = packet + spectral)") - print("3. Completeness: Each primitive has representative equations from multiple sources") - print("4. Integration: Compactified core equations subsume grand unified theory equations") - print("5. Canonical mapping:") - print(" - Field: entropy, density, topology, manifold structure") - print(" - Shear: distance, metric, deformation, geometric transform") - print(" - Packet: coding, compression, transform, optimization") - print(" - Spectral: complexity, basis, bottleneck, decomposition, tradeoff") - - # Save mapping - output_file = RESEARCH_STACK / "4-Infrastructure/shim/system_equations_4primitive_mapping.json" - with open(output_file, 'w') as f: - json.dump({ - "primitives": PRIMITIVES, - "system_equations": SYSTEM_EQUATIONS, - "primitive_counts": primitive_counts, - "insights": { - "consistency": "Grand unified theory axioms map cleanly to 4 primitives", - "redundancy": "Some equations span multiple primitives", - "completeness": "Each primitive has representative equations from multiple sources", - "integration": "Compactified core equations subsume grand unified theory equations" - } - }, f, indent=2) - - print(f"\n✓ Mapping saved to: {output_file}") - - -if __name__ == "__main__": - analyze_mapping() diff --git a/4-Infrastructure/shim/t16_candidate_pipeline_equation_prior.py b/4-Infrastructure/shim/t16_candidate_pipeline_equation_prior.py deleted file mode 100644 index 8356c3b9..00000000 --- a/4-Infrastructure/shim/t16_candidate_pipeline_equation_prior.py +++ /dev/null @@ -1,179 +0,0 @@ -#!/usr/bin/env python3 -"""Distill the T16 transit-search pipeline into an equation-mining prior. - -The source paper is an astronomy result, not an equation-discovery result. This -runner records the transferable method shape: large uniform preprocessing, -cheap candidate extraction, diagnostic feature expansion, regime-specific -classifiers, automated vetting, and expensive confirmation only for survivors. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "t16_candidate_pipeline_equation_prior_receipt.json" -CURRICULUM = SHIM / "t16_candidate_pipeline_equation_prior_curriculum.jsonl" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def build_receipt() -> dict[str, Any]: - bridge = { - "source": { - "arxiv": "2604.18579", - "title": ( - "The T16 Planet Hunt: 10,000 New Planet Candidates from TESS " - "Cycle 1 and the Confirmation of a Hot Jupiter Around TIC 183374187" - ), - "doi": "10.48550/arXiv.2604.18579", - "related_doi": "10.3847/1538-4365/ae5b6c", - "source_claim": "large-scale machine-learning-assisted transit search", - }, - "observed_source_shape": { - "input_count": 83_717_159, - "target_count": 54_401_549, - "candidate_count": 11_554, - "new_candidate_count": 10_091, - "single_transit_count": 411, - "validated_example": "TIC 183374187 radial-velocity confirmation", - }, - "transferable_pipeline": [ - "uniform_detrending_and_systematics_correction", - "cheap_linear_search_for_candidate_events", - "fold_candidate_over_period_grid", - "extract_harmonic_alias_features", - "train_regime_specific_random_forest_classifiers", - "drop_low_importance_or_noisy_features", - "apply_probability_thresholds", - "graph_vet_local_contamination", - "prune_overpopulated_systematic_bins", - "run_fast_physical_model_fit", - "run_image_or_context_residual_check", - "reserve_expensive_confirmation_for_survivors", - "record_injection_recovery_as_future_completeness_gate", - ], - "equation_adaptation": { - "equation_trace": ( - "sequence of symbolic, numeric, unit, residual, and proof-state " - "observations extracted from an equation candidate" - ), - "detrending": ( - "remove notation-specific, source-specific, and formatting-specific " - "systematics before scoring mathematical signal" - ), - "candidate_event": ( - "localized invariant, residual collapse, dimensional consistency, " - "operator match, or compression-gain hint" - ), - "period_grid_analogue": ( - "probe aliases such as scale, reciprocal, dual, Fourier, log, " - "normalization, and dimensional rescaling variants" - ), - "harmonic_features": [ - "primary_score", - "half_scale_score", - "double_scale_score", - "triple_scale_score", - "inverse_candidate_score", - "delta_loss", - "residual_ratio", - "symbolic_depth", - "unit_consistency", - "domain_context", - ], - "regime_split": [ - "small_closed_form_equations", - "high_dimensional_symbolic_systems", - "noisy_empirical_fits", - "compression_route_equations", - "physics_or_hardware_control_equations", - ], - "confirmation": [ - "Lean_or_symbolic_check", - "numeric_reproduction", - "unit_and_dimension_check", - "held_out_data_check", - "exact_decode_hash_for_Hutter_use", - ], - }, - "equation_prior": { - "score": ( - "priority = cheap_signal_score + alias_consistency + context_support " - "- contamination_risk - systematic_bin_penalty - confirmation_cost" - ), - "promote_if": [ - "candidate_survives_regime_classifier", - "local_contamination_or_duplicate_source_is_resolved", - "systematic_alias_bin_is_not_overpopulated", - "expensive_validator_confirms_the_claim", - "Hutter_use_has_exact_decode_hash_and_measured_bytes", - ], - "fail_closed_if": [ - "source_context_is_unverified", - "candidate_only_exists_after_notation_detrending", - "alias_family_is_overpopulated_without_independent_support", - "classifier_probability_replaces_proof", - "manual_or_expensive_validator_rejects_candidate", - ], - }, - "claim_boundary": ( - "This receipt extracts a candidate-search method from an astronomy " - "pipeline. It is not evidence that transit-search features prove " - "equations, compression gains, or physical laws." - ), - } - bridge["receipt_hash"] = sha256_text(stable_json(bridge)) - return bridge - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "map_transit_pipeline_to_equation_pipeline", - "input": "uniformly detrended light curves plus transit candidate vetting", - "target": "uniform equation traces plus proof/receipt candidate vetting", - }, - { - "task": "separate_candidate_score_from_proof", - "input": "random forest probability and BLS/CETRA features", - "target": "equation priority only, never a proof substitute", - }, - { - "task": "define_expensive_confirmation_boundary", - "input": "radial velocity and MCMC follow-up", - "target": "Lean/numeric/unit/Hutter exact-receipt validation", - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def main() -> None: - receipt = build_receipt() - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print(json.dumps({ - "receipt": str(RECEIPT.relative_to(REPO)), - "curriculum": str(CURRICULUM.relative_to(REPO)), - "receipt_hash": receipt["receipt_hash"], - "candidate_count": receipt["observed_source_shape"]["candidate_count"], - "transferable_stage_count": len(receipt["transferable_pipeline"]), - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/tammes_focused_adversarial_hutter_prior.py b/4-Infrastructure/shim/tammes_focused_adversarial_hutter_prior.py deleted file mode 100644 index 9a940b50..00000000 --- a/4-Infrastructure/shim/tammes_focused_adversarial_hutter_prior.py +++ /dev/null @@ -1,339 +0,0 @@ -#!/usr/bin/env python3 -"""Build a Tammes-focused adversarial route prior for Hutter work. - -The prior combines three shapes: - -* Tammes / spherical-code spacing: keep route candidates diverse by maximizing - nearest-neighbor distance on a route feature manifold. -* Multimetal nanocrystal composition focusing: use staged scaffold decisions - that collapse a large theoretical frontier into a smaller lawful frontier. -* Adversarial Conway-style tournament stress: hash rule/glyph candidates into - hostile local-interaction tests before spending promotion evaluator budget. - -This is a route-selection and stress-testing prior. It is not a compression -result and does not promote any Hutter route without exact byte receipts. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "tammes_focused_adversarial_hutter_prior_receipt.json" -CURRICULUM_OUT = SHIM / "tammes_focused_adversarial_hutter_prior_curriculum.jsonl" - -GENERATED_AT = "2026-05-08T00:00:00+00:00" -HUTTER_ENWIK9_TARGET_BYTES = 109_685_197 - -SOURCE_RECEIPTS = { - "hutter_equation_metastate_transfold": SHIM - / "hutter_equation_metastate_transfold_receipt.json", - "multimetal_nanocrystal_composition_focusing_prior": SHIM - / "multimetal_nanocrystal_composition_focusing_prior_receipt.json", - "projectable_geometry_topology_model": SHIM - / "projectable_geometry_topology_model_receipt.json", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def receipt_hash(path: Path, data: dict[str, Any]) -> str: - for key in ( - "receipt_hash", - "stable_topology_model_hash_sha256", - "stable_shell_dd_hash_sha256", - ): - value = data.get(key) - if isinstance(value, str): - return value - return hashlib.sha256(path.read_bytes()).hexdigest() - - -def source_receipt_records(receipts: dict[str, dict[str, Any]]) -> dict[str, Any]: - return { - name: { - "path": rel(SOURCE_RECEIPTS[name]), - "schema": receipt.get("schema", "unknown"), - "hash": receipt_hash(SOURCE_RECEIPTS[name], receipt), - } - for name, receipt in receipts.items() - } - - -def source_evidence() -> dict[str, Any]: - return { - "tammes_problem": { - "shape": "place N points on a sphere/manifold to maximize the minimum pairwise distance", - "route_use": "diversify route candidates and avoid wasting evaluator time on near-duplicates", - "reference_url": "https://mathworld.wolfram.com/SphericalCode.html", - "claim_status": "standard_geometry_prior", - }, - "multimetal_nanocrystal": { - "title": "Researchers combine five metals to build a better nanocrystal", - "source": "Phys.org / Stanford University", - "published_date": "2026-05-07", - "url": "https://phys.org/news/2026-05-combine-metals-nanocrystal.html", - "primary_paper_doi": "10.1126/science.aea8044", - "route_use": "composition focusing / staged decision tree for lawful frontier collapse", - "claim_status": "verified_article_shape_not_byte_evidence", - }, - "adversarial_conway_prompt": { - "title": "Adversarial Conway: Example Matches", - "source": "Reddit r/gameoflife", - "url": "https://www.reddit.com/r/gameoflife/comments/1t71s3m/adversarial_conway_example_matches/", - "observed_shape": "hashed contestant glyphs enter a tournament-like adversarial cellular-automaton arena", - "route_use": "stress route rules under hostile local interactions before promotion", - "claim_status": "community_prompt_not_peer_reviewed_source", - }, - } - - -def route_embedding() -> dict[str, Any]: - return { - "route_point": [ - "transform_family_id", - "tokenbook_policy_id", - "residual_policy_id", - "witness_budget_class", - "decoder_cost_class", - "locality_profile_id", - "byte_gain_floor_class", - "failure_signature_id", - "adversarial_fragility_class", - "composition_focus_score", - ], - "metric": ( - "d_route(i,j) = weighted distance over route_point fields, with " - "hard separation for incompatible residual or decoder policies" - ), - "normalization": ( - "candidate coordinates are proposal features only; no coordinate " - "is promotion evidence until exact bytes are measured" - ), - } - - -def equations() -> list[dict[str, str]]: - return [ - { - "id": "TFA0_route_embedding", - "equation": "x_i = embed(route_i) in M_Hutter", - "meaning": "Represent each candidate route as a point on the Hutter feature manifold.", - }, - { - "id": "TFA1_tammes_diversity", - "equation": "D_Tammes(R) = min_{i != j} d_route(x_i, x_j)", - "meaning": "Prefer route batches whose nearest candidates are still meaningfully separated.", - }, - { - "id": "TFA2_composition_focus", - "equation": "F_focus = 1 - focused_frontier_size / theoretical_frontier_size", - "meaning": "Reward staged constraints that collapse the legal frontier without losing decode reachability.", - }, - { - "id": "TFA3_decision_tree_attachment", - "equation": "node_{t+1} = attach(argmin_l cost(l | scaffold_t), node_t)", - "meaning": "Use nanocrystal-style staged attachment as a deterministic route decision tree.", - }, - { - "id": "TFA4_adversarial_fragility", - "equation": "A_adv(route) = failed_stress_cases / total_stress_cases", - "meaning": "Measure how often a route rule breaks under hostile local rewrite / automaton tests.", - }, - { - "id": "TFA5_priority_score", - "equation": ( - "Priority = gain_floor + alpha*D_Tammes + beta*F_focus " - "- residual_floor - witness_floor - decoder_floor - gamma*A_adv" - ), - "meaning": "Rank what to evaluate next; this score never promotes by itself.", - }, - { - "id": "TFA6_promotion", - "equation": "promote iff decode(route_artifact) == source and bytes_total < incumbent", - "meaning": "Promotion authority remains exact reconstruction and counted byte improvement.", - }, - ] - - -def dd_state_extension() -> list[str]: - return [ - "tammes_route_lattice_id", - "route_feature_vector_id", - "route_manifold_chart_id", - "nearest_neighbor_distance_floor", - "tammes_diversity_score", - "composition_scaffold_id", - "decision_tree_node_id", - "attachment_order_receipt_id", - "focused_frontier_size", - "theoretical_frontier_size", - "composition_focus_score", - "adversarial_glyph_hash", - "adversarial_arena_id", - "stress_case_count", - "failed_stress_case_count", - "adversarial_fragility_score", - "route_priority_score", - "exact_residual_lane_id", - "byte_rehydration_hash", - ] - - -def dd_edges() -> list[str]: - return [ - "embed_route_on_hutter_manifold", - "compute_route_pair_distance", - "maximize_nearest_neighbor_route_distance", - "open_composition_scaffold_decision_tree", - "attach_route_lane_by_focus_cost", - "measure_frontier_collapse", - "hash_route_glyph_for_adversarial_arena", - "run_adversarial_conway_stress_cases", - "penalize_adversarial_fragility", - "rank_route_priority", - "reject_near_duplicate_route", - "emit_exact_residual_lane", - "close_with_byte_rehydration_hash", - ] - - -def promotion_rule() -> list[str]: - return [ - "route_batch_has_tammes_separation_above_floor", - "composition_scaffold_decision_tree_is_deterministic_or_receipted", - "frontier_collapse_preserves_decode_reachability", - "adversarial_stress_failures_are_zero_or_fail_closed_before_expensive_promotion", - "all residual/witness/decoder/container costs are counted", - "decoded_hash_matches_source_hash", - "measured_total_bytes_beat_incumbent_under_explicit_ratio_schema", - ] - - -def failure_rule() -> list[str]: - return [ - "tammes_route_points_collapse_to_near_duplicates -> prune_batch", - "decision_tree_attachment_ambiguous_without_tie_break -> fail_closed", - "focused_frontier_loses_decode_reachability -> fail_closed", - "adversarial_arena_generates_unbounded_rule_search -> NaN0", - "stress_survivorship_used_as_byte_evidence -> diagnostic_only", - "witness_or_stress_metadata_exceeds_byte_gain -> prune", - ] - - -def current_hutter_context(receipts: dict[str, dict[str, Any]]) -> dict[str, Any]: - metastate = receipts["hutter_equation_metastate_transfold"]["current_best_metastate"] - return { - "current_route": metastate["transform_route"], - "source_corpus_id": metastate["source_corpus_id"], - "source_bytes": metastate["source_bytes"], - "compressed_total_bytes": metastate["compressed_total_bytes"], - "baseline_bytes": metastate["baseline_bytes"], - "margin_vs_baseline_bytes": metastate["margin_vs_baseline_bytes"], - "projected_enwik9_total_bytes": metastate["projected_enwik9_total_bytes"], - "projected_gap_to_hard_target_bytes": metastate[ - "projected_gap_to_hard_target_bytes" - ], - "hard_target_bytes_enwik9": HUTTER_ENWIK9_TARGET_BYTES, - "route_use": ( - "Use this prior to choose diverse, focused, adversarially stable " - "payload-transform trials before adding more witness bytes." - ), - } - - -def build_receipt() -> dict[str, Any]: - receipts = {name: load_json(path) for name, path in SOURCE_RECEIPTS.items()} - receipt: dict[str, Any] = { - "schema": "tammes_focused_adversarial_hutter_prior_v1", - "generated_at": GENERATED_AT, - "runner": rel(Path(__file__)), - "source_evidence": source_evidence(), - "source_receipts": source_receipt_records(receipts), - "primary_decision": { - "name": "tammes_focused_adversarial_route_lattice", - "statement": ( - "Adapt Tammes spacing to the Hutter feature manifold, use " - "nanocrystal-style staged decision trees to collapse route " - "frontiers, and add adversarial Conway-style local-interaction " - "stress before exact byte evaluation." - ), - }, - "route_embedding": route_embedding(), - "equations": equations(), - "candidate_dd_state_extension": dd_state_extension(), - "candidate_dd_edges": dd_edges(), - "promotion_rule": promotion_rule(), - "failure_rule": failure_rule(), - "current_hutter_context": current_hutter_context(receipts), - "claim_boundary": ( - "This is a route-prior and evaluator-scheduling artifact. Tammes " - "spacing, nanocrystal composition focusing, and adversarial Conway " - "stress do not prove compression. Hutter promotion still requires " - "exact decode, matching hashes, measured total bytes, explicit ratio " - "schema, and counted residual/witness/decoder/container costs." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def curriculum_lines(receipt: dict[str, Any]) -> list[dict[str, Any]]: - lines: list[dict[str, Any]] = [] - for equation in receipt["equations"]: - lines.append({"type": "equation", **equation}) - for edge in receipt["candidate_dd_edges"]: - lines.append({"type": "dd_edge", "edge": edge}) - for rule in receipt["promotion_rule"]: - lines.append({"type": "promotion_rule", "rule": rule}) - for rule in receipt["failure_rule"]: - lines.append({"type": "failure_rule", "rule": rule}) - return lines - - -def main() -> None: - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - lines = curriculum_lines(receipt) - CURRICULUM_OUT.write_text( - "".join(json.dumps(line, sort_keys=True) + "\n" for line in lines), - encoding="utf-8", - ) - print( - json.dumps( - { - "receipt": rel(OUT), - "curriculum": rel(CURRICULUM_OUT), - "receipt_hash": receipt["receipt_hash"], - "equation_count": len(receipt["equations"]), - "dd_edge_count": len(receipt["candidate_dd_edges"]), - "current_route": receipt["current_hutter_context"]["current_route"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/tammes_focused_adversarial_hutter_wiki8_trial.py b/4-Infrastructure/shim/tammes_focused_adversarial_hutter_wiki8_trial.py deleted file mode 100644 index 51e48c5d..00000000 --- a/4-Infrastructure/shim/tammes_focused_adversarial_hutter_wiki8_trial.py +++ /dev/null @@ -1,293 +0,0 @@ -#!/usr/bin/env python3 -"""Run the Tammes-focused adversarial route prior over wiki8 trial results. - -This consumes a real reversible compression approach receipt and asks: - -* does the Tammes/focus/adversarial priority select byte-winning routes? -* does it prune near-duplicate or fragile candidates before promotion? -* does it improve measured bytes over the existing best route? - -It does not invent a new transform. Improvement here means better route -selection among already evaluated exact routes, not a new compressed payload. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -DEFAULT_APPROACH = SHIM / "tammes_focused_adversarial_hutter_wiki8_approach_trial_receipt.json" -PRIOR = SHIM / "tammes_focused_adversarial_hutter_prior_receipt.json" -OUT = SHIM / "tammes_focused_adversarial_hutter_wiki8_trial_receipt.json" - -TOPOLOGY_WITNESS_BYTES = 16 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def route_key(route: dict[str, Any]) -> str: - return f"{route['transform']}->{route['codec']}" - - -def feature_vector(route: dict[str, Any], raw_baseline: int) -> list[float]: - transform_index = { - "raw": 0.0, - "xml_token": 1.0, - "class_lane_boat": 2.0, - "delta_boat": 3.0, - }.get(route["transform"], 4.0) - codec_index = { - "stored": 0.0, - "zlib9": 1.0, - "bz2": 2.0, - "lzma": 3.0, - }.get(route["codec"], 4.0) - encoded = float(route["encoded_size"]) - compressed = float(route["compressed_size"]) - gain = float(raw_baseline - compressed) - ratio = float(route["ratio"]) - metadata_cost = float(len(stable_json(route.get("metadata", {})))) - return [ - transform_index / 4.0, - codec_index / 4.0, - min(encoded / max(1.0, compressed * 4.0), 4.0) / 4.0, - max(-1.0, min(1.0, gain / max(1.0, raw_baseline))), - min(ratio, 1.0), - min(metadata_cost / 2048.0, 1.0), - ] - - -def euclidean(a: list[float], b: list[float]) -> float: - return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b))) - - -def nearest_distance(route: dict[str, Any], routes: list[dict[str, Any]], raw_baseline: int) -> float: - own = feature_vector(route, raw_baseline) - distances = [ - euclidean(own, feature_vector(other, raw_baseline)) - for other in routes - if other is not route - ] - return min(distances) if distances else 0.0 - - -def adversarial_fragility(route: dict[str, Any]) -> float: - """Cheap deterministic stress proxy for already-evaluated routes. - - Real adversarial Conway stress will need a separate local rewrite arena. - This proxy penalizes routes that already failed rehydration, have unbounded - metadata, or expand badly before codec rescue. - """ - - if not route.get("rehydrated_ok"): - return 1.0 - encoded = int(route["encoded_size"]) - compressed = int(route["compressed_size"]) - metadata = len(stable_json(route.get("metadata", {}))) - expansion = max(0.0, encoded / max(1, compressed) - 4.0) / 8.0 - metadata_pressure = metadata / 4096.0 - return max(0.0, min(1.0, expansion + metadata_pressure)) - - -def focus_score(route: dict[str, Any], routes: list[dict[str, Any]]) -> float: - same_transform = [other for other in routes if other["transform"] == route["transform"]] - if not routes: - return 0.0 - # Higher when this transform family is a small, coherent subfrontier and - # the selected route is the best member of that family. - family_fraction = len(same_transform) / len(routes) - best_family = min(same_transform, key=lambda item: item["compressed_size"]) - best_bonus = 1.0 if best_family is route else 0.0 - return max(0.0, min(1.0, (1.0 - family_fraction) * 0.5 + best_bonus * 0.5)) - - -def priority(route: dict[str, Any], routes: list[dict[str, Any]], raw_baseline: int) -> dict[str, Any]: - witness = 0 if route["transform"] == "raw" else TOPOLOGY_WITNESS_BYTES - total = int(route["compressed_size"]) + witness - gain_floor = (raw_baseline - total) / max(1, raw_baseline) - residual_floor = 0.0 if route.get("rehydrated_ok") else 1.0 - witness_floor = witness / max(1, raw_baseline) - decoder_floor = { - "stored": 0.0, - "zlib9": 0.03, - "bz2": 0.05, - "lzma": 0.09, - }.get(route["codec"], 0.12) - tammes = nearest_distance(route, routes, raw_baseline) - focus = focus_score(route, routes) - adv = adversarial_fragility(route) - score = ( - 8.0 * gain_floor - + 0.03 * tammes - + 0.03 * focus - - residual_floor - - witness_floor - - 0.15 * decoder_floor - - 0.03 * adv - ) - return { - "route": route_key(route), - "transform": route["transform"], - "codec": route["codec"], - "compressed_bytes": int(route["compressed_size"]), - "witness_bytes": witness, - "total_bytes": total, - "gain_vs_raw_after_witness_bytes": raw_baseline - total, - "gain_floor": gain_floor, - "tammes_diversity": tammes, - "composition_focus": focus, - "adversarial_fragility": adv, - "decoder_floor": decoder_floor, - "priority": score, - "rehydrated_ok": bool(route.get("rehydrated_ok")), - } - - -def unique_slices(slices: list[dict[str, Any]]) -> list[dict[str, Any]]: - seen: set[tuple[str, int, str]] = set() - unique = [] - for item in slices: - key = ( - item.get("slice_name", ""), - int(item.get("source_bytes", 0)), - item.get("source_hash_sha256", ""), - ) - if key in seen: - continue - seen.add(key) - unique.append(item) - return unique - - -def evaluate_slice(item: dict[str, Any]) -> dict[str, Any]: - routes = item["results"] - raw_baseline = int(item["best_raw_baseline"]["compressed_size"]) - scored = [priority(route, routes, raw_baseline) for route in routes] - selected = max(scored, key=lambda row: row["priority"]) - measured_best = min( - scored, - key=lambda row: row["total_bytes"], - ) - raw_best = min( - (row for row in scored if row["transform"] == "raw"), - key=lambda row: row["total_bytes"], - ) - selected_is_measured_best = selected["route"] == measured_best["route"] - selected_beats_raw = selected["total_bytes"] < raw_best["total_bytes"] - measured_best_beats_raw = measured_best["total_bytes"] < raw_best["total_bytes"] - return { - "slice_name": item["slice_name"], - "source_path": item["source_path"], - "source_bytes": item["source_bytes"], - "source_hash_sha256": item["source_hash_sha256"], - "raw_best": raw_best, - "selected_by_tammes_prior": selected, - "measured_best_after_witness": measured_best, - "selected_is_measured_best": selected_is_measured_best, - "selected_beats_raw": selected_beats_raw, - "measured_best_beats_raw": measured_best_beats_raw, - "improvement_vs_existing_best_bytes": ( - int(item["best"]["compressed_size"]) - selected["total_bytes"] - ), - "top_ranked_routes": sorted(scored, key=lambda row: row["priority"], reverse=True)[:6], - } - - -def build_receipt(approach_path: Path) -> dict[str, Any]: - approach = load_json(approach_path) - prior = load_json(PRIOR) - wiki8_slices = [ - item for item in approach.get("slices", []) - if Path(item.get("source_path", "")).name == "enwik8" - ] - slices = [evaluate_slice(item) for item in unique_slices(wiki8_slices)] - selected_best_count = sum(item["selected_is_measured_best"] for item in slices) - selected_win_count = sum(item["selected_beats_raw"] for item in slices) - measured_win_count = sum(item["measured_best_beats_raw"] for item in slices) - total_improvement_vs_existing = sum( - item["improvement_vs_existing_best_bytes"] for item in slices - ) - receipt: dict[str, Any] = { - "schema": "tammes_focused_adversarial_hutter_wiki8_trial_v1", - "source_receipts": { - "approach_trial": { - "path": rel(approach_path), - "stable_approach_hash_sha256": approach.get("stable_approach_hash_sha256"), - }, - "tammes_prior": { - "path": rel(PRIOR), - "receipt_hash": prior.get("receipt_hash"), - }, - }, - "trial_policy": { - "topology_witness_bytes_for_non_raw_routes": TOPOLOGY_WITNESS_BYTES, - "priority_formula": ( - "8.0*gain_floor + 0.03*tammes + 0.03*focus - residual_floor " - "- witness_floor - 0.15*decoder_floor - 0.03*adversarial_fragility" - ), - "claim_boundary": ( - "This trial selects among already evaluated reversible routes. " - "It does not add a new compressor or claim Hutter improvement." - ), - }, - "summary": { - "input_slice_count_before_wiki8_filter": len(approach.get("slices", [])), - "slice_count": len(slices), - "selected_measured_best_count": selected_best_count, - "selected_beats_raw_count": selected_win_count, - "measured_best_beats_raw_count": measured_win_count, - "total_improvement_vs_existing_best_bytes": total_improvement_vs_existing, - "improved_existing_best": total_improvement_vs_existing > 0, - "all_selected_rehydrated": all( - item["selected_by_tammes_prior"]["rehydrated_ok"] for item in slices - ), - }, - "slices": slices, - "verdict": ( - "no_new_byte_improvement" - if total_improvement_vs_existing <= 0 - else "selection_improved_existing_best" - ), - "claim_boundary": ( - "Tammes/focus/adversarial scoring is an evaluator scheduling prior. " - "Measured bytes and exact rehydration remain the authority." - ), - } - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - receipt["receipt_hash"] = sha256_text(stable_json(preimage)) - return receipt - - -def main() -> None: - receipt = build_receipt(DEFAULT_APPROACH) - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps({ - "receipt": rel(OUT), - "receipt_hash": receipt["receipt_hash"], - "summary": receipt["summary"], - "verdict": receipt["verdict"], - }, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/tang9k_hutter_symbol_surface.py b/4-Infrastructure/shim/tang9k_hutter_symbol_surface.py deleted file mode 100644 index a2d5dc46..00000000 --- a/4-Infrastructure/shim/tang9k_hutter_symbol_surface.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -"""Host harness for Tang9K Hutter/metaprobe symbol surface. - -This harness frames short compressed text tokens for the FPGA and computes the -same substitution receipt in software. If --port is supplied and pyserial is -installed, it also sends the frame over USB UART. -""" - -from __future__ import annotations - -import argparse -import json -import time -from pathlib import Path -from typing import Iterable - -MAGIC_IN = 0xA5 -MAGIC_OUT = 0xA6 -VERSION = 0x01 -OP_SUBSTITUTE = 0x10 - - -def substitute(byte: int) -> tuple[int, bool]: - table = { - ord(" "): 0x0, - ord("e"): 0x1, - ord("E"): 0x1, - ord("t"): 0x2, - ord("T"): 0x2, - ord("a"): 0x3, - ord("A"): 0x3, - ord("o"): 0x4, - ord("O"): 0x4, - ord("i"): 0x5, - ord("I"): 0x5, - ord("n"): 0x6, - ord("N"): 0x6, - ord("s"): 0x7, - ord("S"): 0x7, - ord("r"): 0x8, - ord("R"): 0x8, - ord("h"): 0x9, - ord("H"): 0x9, - ord("l"): 0xA, - ord("L"): 0xA, - ord("d"): 0xB, - ord("c"): 0xC, - ord("C"): 0xC, - ord("u"): 0xD, - ord("U"): 0xD, - ord("F"): 0xE, - ord("D"): 0xF, - } - if byte in table: - return table[byte], True - return byte & 0xF, False - - -def xor_crc(values: Iterable[int]) -> int: - crc = 0 - for value in values: - crc ^= value & 0xFF - return crc - - -def receipt_for_payload(payload: bytes) -> dict: - rolling_hash = 0xACE1 - mapped = 0 - literal = 0 - codes = [] - for byte in payload: - code, hit = substitute(byte) - codes.append({"byte": byte, "char": chr(byte), "code": code, "hit": hit}) - rolling_hash = (((rolling_hash << 1) & 0xFFFF) | (rolling_hash >> 15)) ^ ( - (0x10 if hit else 0x00) | code - ) - if hit: - mapped += 1 - else: - literal += 1 - return { - "hash16": rolling_hash, - "mapped_count": mapped, - "literal_count": literal, - "codes": codes, - } - - -def pbacs_cmyk_state_for_payload(payload: bytes) -> int: - error_acc = 0 - stress_acc = 0 - mapped_count = 0 - literal_count = 0 - for byte in payload: - code, hit = substitute(byte) - value_q16 = ((1 if hit else 0) << 15) | (code << 11) - candidate = value_q16 + error_acc - bit_out = 1 if candidate > 0x8000 else 0 - error_acc = candidate - (0x10000 if bit_out else 0) - abs_error = abs(error_acc) - residual_term = (abs_error >> 4) & 0x3FFF - mismatch_term = ((literal_count & 0x0F) << 4) | (mapped_count & 0x0F) - mask_term = (1 if hit else 0) << 4 - stress_acc = max( - 0, - min( - 0xFFFF, - stress_acc - (stress_acc >> 6) + residual_term + mismatch_term + mask_term, - ), - ) - if hit: - mapped_count += 1 - else: - literal_count += 1 - return (stress_acc >> 14) & 0x03 - - -def led_reservoir_address(route_state: int, mapped_count: int) -> dict: - logical = ((route_state & 0x03) << 4) | (mapped_count & 0x0F) - physical_active_low = logical ^ 0x3F - return { - "schema": "tang9k_led_reservoir_address_v1", - "logical_bits": f"{logical:06b}", - "logical_hex": f"0x{logical:02x}", - "route_state": (logical >> 4) & 0x03, - "mapped_bucket": logical & 0x0F, - "physical_active_low_bits": f"{physical_active_low:06b}", - "physical_active_low_hex": f"0x{physical_active_low:02x}", - "meaning": "logical LED reservoir address is {PBACS/CMYK route_state[1:0], mapped_count[3:0]}; board LEDs are active low", - } - - -def build_frame(seq: int, payload: bytes) -> bytes: - if len(payload) > 16: - raise ValueError("Surface-0 payload is limited to 16 bytes per frame") - frame = bytearray([MAGIC_IN, VERSION, seq & 0xFF, OP_SUBSTITUTE, len(payload)]) - frame.extend(payload) - frame.append(xor_crc(frame)) - return bytes(frame) - - -def parse_glyph_ids(value: str) -> list[int]: - glyph_ids = [] - for part in value.split(","): - part = part.strip() - if not part: - continue - glyph_ids.append(int(part, 0)) - return glyph_ids - - -def glyph_ids_to_surface_bytes(glyph_ids: list[int]) -> bytes: - if len(glyph_ids) > 16: - raise ValueError("Surface-0 accepts at most 16 glyph IDs per frame") - # Surface-0 works on bank-local glyph IDs. The host GlyphBook maps full - # Unicode/PUA/custom logograms to these low-byte hot-path banks. - return bytes(glyph_id & 0xFF for glyph_id in glyph_ids) - - -def parse_receipt(frame: bytes) -> dict: - if len(frame) != 11 or frame[0] != MAGIC_OUT or frame[1] != VERSION: - raise ValueError(f"invalid receipt frame: {frame.hex()}") - if xor_crc(frame[:-1]) != frame[-1]: - raise ValueError("receipt checksum mismatch") - return { - "seq": frame[2], - "status": frame[3], - "payload_len": frame[4], - "opcode": frame[5], - "hash16": (frame[6] << 8) | frame[7], - "mapped_count": frame[8], - "literal_count": frame[9], - "crc": frame[10], - } - - -def _read_receipt_candidate(ser, timeout_s: float = 0.75) -> bytes: - deadline = time.monotonic() + timeout_s - buf = bytearray() - while time.monotonic() < deadline: - chunk = ser.read(1) - if not chunk: - continue - buf.extend(chunk) - while buf and buf[0] != MAGIC_OUT: - del buf[0] - if len(buf) >= 11: - return bytes(buf[:11]) - return bytes(buf) - - -def send_serial(port: str, baud: int, frame: bytes, retries: int = 5, resync: bool = True) -> bytes: - try: - import serial # type: ignore - except ImportError as exc: - raise SystemExit("pyserial is required for --port mode") from exc - - with serial.Serial(port, baudrate=baud, timeout=2) as ser: - time.sleep(0.05) - ser.reset_input_buffer() - ser.reset_output_buffer() - last = b"" - for _ in range(max(1, retries)): - ser.reset_input_buffer() - if resync: - # If the FPGA UART parser is stranded in payload/CRC state, - # enough non-magic bytes complete the partial frame and return - # it to RX_WAIT_MAGIC. In wait state, these bytes are ignored. - ser.write(b"\x00" * 32) - ser.flush() - time.sleep(0.02) - ser.reset_input_buffer() - ser.write(frame) - ser.flush() - raw = _read_receipt_candidate(ser) - last = raw - try: - parsed = parse_receipt(raw) - if parsed["status"] == 0: - return raw - except ValueError: - pass - time.sleep(0.05) - return last - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--text", default="D0A37FEED", help="compressed token text") - parser.add_argument( - "--glyph-ids", - help="comma-separated logogram/GlyphBook IDs; e.g. 0xe101,0xe10f,0x42", - ) - parser.add_argument("--seq", type=lambda x: int(x, 0), default=1) - parser.add_argument("--port", help="optional USB serial port, e.g. /dev/ttyUSB1") - parser.add_argument("--baud", type=int, default=115200) - parser.add_argument("--retries", type=int, default=5) - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - glyph_ids = parse_glyph_ids(args.glyph_ids) if args.glyph_ids else [] - if glyph_ids: - payload = glyph_ids_to_surface_bytes(glyph_ids) - input_kind = "glyphbook_bank_ids" - input_value = [f"0x{glyph_id:x}" for glyph_id in glyph_ids] - else: - payload = args.text.encode("ascii") - input_kind = "ascii_metaprobe_token" - input_value = args.text - frame = build_frame(args.seq, payload) - expected = receipt_for_payload(payload) - result = { - "schema": "tang9k_hutter_symbol_surface_receipt_v1", - "claim_boundary": "FPGA accelerates substitution witness only; host owns full codec/decodec", - "input_kind": input_kind, - "input": input_value, - "surface_payload_hex": payload.hex(), - "frame_hex": frame.hex(), - "expected": expected, - "expected_led_reservoir": led_reservoir_address( - pbacs_cmyk_state_for_payload(payload), expected["mapped_count"] - ), - } - - if args.port: - raw_receipt = send_serial(args.port, args.baud, frame, retries=args.retries) - result["hardware_receipt_hex"] = raw_receipt.hex() - if raw_receipt: - try: - parsed = parse_receipt(raw_receipt) - result["hardware_receipt"] = parsed - result["hardware_led_reservoir"] = led_reservoir_address( - pbacs_cmyk_state_for_payload(payload), parsed["mapped_count"] - ) - result["hardware_matches_expected"] = ( - parsed["status"] == 0 - and parsed["hash16"] == expected["hash16"] - and parsed["mapped_count"] == expected["mapped_count"] - and parsed["literal_count"] == expected["literal_count"] - ) - except ValueError as exc: - result["hardware_receipt"] = None - result["hardware_matches_expected"] = False - result["hardware_note"] = f"non-surface UART response: {exc}" - else: - result["hardware_receipt"] = None - result["hardware_matches_expected"] = False - result["hardware_note"] = "no UART receipt received; topology is present but expected bitstream may not be loaded" - - text = json.dumps(result, indent=2) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tang9k_hutter_symbol_surface_hardware_probe_ttyUSB1.json b/4-Infrastructure/shim/tang9k_hutter_symbol_surface_hardware_probe_ttyUSB1.json deleted file mode 100644 index 0e841f6a..00000000 --- a/4-Infrastructure/shim/tang9k_hutter_symbol_surface_hardware_probe_ttyUSB1.json +++ /dev/null @@ -1,73 +0,0 @@ -{ - "schema": "tang9k_hutter_symbol_surface_receipt_v1", - "claim_boundary": "FPGA accelerates substitution witness only; host owns full codec/decodec", - "input_kind": "ascii_metaprobe_token", - "input": "D0A37FEED", - "surface_payload_hex": "443041333746454544", - "frame_hex": "a5010110094430413337464545448f", - "expected": { - "hash16": 55296, - "mapped_count": 6, - "literal_count": 3, - "codes": [ - { - "byte": 68, - "char": "D", - "code": 15, - "hit": true - }, - { - "byte": 48, - "char": "0", - "code": 0, - "hit": false - }, - { - "byte": 65, - "char": "A", - "code": 3, - "hit": true - }, - { - "byte": 51, - "char": "3", - "code": 3, - "hit": false - }, - { - "byte": 55, - "char": "7", - "code": 7, - "hit": false - }, - { - "byte": 70, - "char": "F", - "code": 14, - "hit": true - }, - { - "byte": 69, - "char": "E", - "code": 1, - "hit": true - }, - { - "byte": 69, - "char": "E", - "code": 1, - "hit": true - }, - { - "byte": 68, - "char": "D", - "code": 15, - "hit": true - } - ] - }, - "hardware_receipt_hex": "ff4bff88ff8c", - "hardware_receipt": null, - "hardware_matches_expected": false, - "hardware_note": "non-surface UART response: invalid receipt frame: ff4bff88ff8c" -} diff --git a/4-Infrastructure/shim/tang9k_routed_template_witness.py b/4-Infrastructure/shim/tang9k_routed_template_witness.py deleted file mode 100644 index 25d0c17d..00000000 --- a/4-Infrastructure/shim/tang9k_routed_template_witness.py +++ /dev/null @@ -1,130 +0,0 @@ -#!/usr/bin/env python3 -"""Witness routed math templates through the Tang9K symbol surface. - -This converts top entries from eigen_solved_math_router.py into short -Surface-0 payloads. The FPGA still only witnesses substitution/hash/counts; -the host owns the full template ranking and codec logic. -""" - -from __future__ import annotations - -import argparse -import importlib.util -import json -from pathlib import Path -from typing import Any - - -DEFAULT_ROUTER = Path("4-Infrastructure/shim/eigen_solved_math_router_compression.json") -SURFACE_PATH = Path("4-Infrastructure/shim/tang9k_hutter_symbol_surface.py") - - -def load_surface_module(): - spec = importlib.util.spec_from_file_location("tang9k_hutter_symbol_surface", SURFACE_PATH) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load {SURFACE_PATH}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -def template_payload(entry: dict[str, Any], max_len: int = 16) -> bytes: - name = str(entry.get("model_name") or entry.get("family") or "template") - # Keep a readable bank-local token: uppercase, underscores become spaces, - # punctuation drops. This intentionally targets the tiny hardware LUT. - normalized = [] - for ch in name.upper().replace("_", " "): - if ch.isalnum() or ch == " ": - normalized.append(ch) - token = "".join(normalized).strip() or "TEMPLATE" - return token.encode("ascii", "ignore")[:max_len] - - -def witness_entry(surface, entry: dict[str, Any], seq: int, port: str | None, baud: int, retries: int) -> dict[str, Any]: - payload = template_payload(entry) - frame = surface.build_frame(seq, payload) - expected = surface.receipt_for_payload(payload) - result = { - "schema": "tang9k_routed_template_witness_v1", - "model_name": entry.get("model_name"), - "family": entry.get("family"), - "evidence_tier": entry.get("evidence_tier"), - "routed_score": entry.get("routed_score"), - "payload_ascii": payload.decode("ascii", "replace"), - "payload_hex": payload.hex(), - "frame_hex": frame.hex(), - "expected": expected, - "expected_led_reservoir": surface.led_reservoir_address( - surface.pbacs_cmyk_state_for_payload(payload), expected["mapped_count"] - ), - } - if port: - raw = surface.send_serial(port, baud, frame, retries=retries) - result["hardware_receipt_hex"] = raw.hex() - if raw: - try: - parsed = surface.parse_receipt(raw) - result["hardware_receipt"] = parsed - result["hardware_led_reservoir"] = surface.led_reservoir_address( - surface.pbacs_cmyk_state_for_payload(payload), parsed["mapped_count"] - ) - result["hardware_matches_expected"] = ( - parsed["status"] == 0 - and parsed["hash16"] == expected["hash16"] - and parsed["mapped_count"] == expected["mapped_count"] - and parsed["literal_count"] == expected["literal_count"] - ) - except ValueError as exc: - result["hardware_receipt"] = None - result["hardware_matches_expected"] = False - result["hardware_note"] = str(exc) - else: - result["hardware_receipt"] = None - result["hardware_matches_expected"] = False - result["hardware_note"] = "no UART receipt received" - return result - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--router-json", type=Path, default=DEFAULT_ROUTER) - parser.add_argument("--limit", type=int, default=5) - parser.add_argument("--seq-base", type=lambda value: int(value, 0), default=0xE0) - parser.add_argument("--port") - parser.add_argument("--baud", type=int, default=115200) - parser.add_argument("--retries", type=int, default=5) - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - router = json.loads(args.router_json.read_text(encoding="utf-8")) - surface = load_surface_module() - witnesses = [] - for index, entry in enumerate(router.get("entries", [])[: args.limit]): - witnesses.append( - witness_entry( - surface=surface, - entry=entry, - seq=(args.seq_base + index) & 0xFF, - port=args.port, - baud=args.baud, - retries=args.retries, - ) - ) - bundle = { - "schema": "tang9k_routed_template_witness_bundle_v1", - "claim_boundary": "FPGA witnesses compact template tokens only; ranking and model validity remain host-side.", - "router_json": str(args.router_json), - "witness_count": len(witnesses), - "hardware_count": sum(1 for item in witnesses if item.get("hardware_matches_expected")), - "witnesses": witnesses, - } - text = json.dumps(bundle, indent=2, ensure_ascii=False) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tang9k_rrc_q16_accel.py b/4-Infrastructure/shim/tang9k_rrc_q16_accel.py deleted file mode 100755 index 4d806c51..00000000 --- a/4-Infrastructure/shim/tang9k_rrc_q16_accel.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -"""Host harness for the Tang Nano 9K Rainbow Raccoon Q16.16 surface.""" - -from __future__ import annotations - -import argparse -import json -import struct -import time -from pathlib import Path -from typing import Iterable - -MAGIC_IN = 0xA5 -MAGIC_OUT = 0xA6 -VERSION = 0x02 - -OP_SHIFT_DIV = 0x20 -OP_WEIGHTED = 0x21 -OP_MONOTONE = 0x22 - - -def xor_crc(values: Iterable[int]) -> int: - crc = 0 - for value in values: - crc ^= value & 0xFF - return crc - - -def i32(value: int) -> bytes: - if not -(1 << 31) <= value < (1 << 31): - raise ValueError(f"int32 out of range: {value}") - return struct.pack(">i", value) - - -def from_i32(data: bytes) -> int: - return struct.unpack(">i", data)[0] - - -def build_frame(seq: int, opcode: int, payload: bytes) -> bytes: - if len(payload) > 16: - raise ValueError("FPGA payload is limited to 16 bytes") - frame = bytearray([MAGIC_IN, VERSION, seq & 0xFF, opcode & 0xFF, len(payload)]) - frame.extend(payload) - frame.append(xor_crc(frame)) - return bytes(frame) - - -def parse_receipt(frame: bytes) -> dict: - if len(frame) < 7: - raise ValueError(f"short receipt frame: {frame.hex()}") - if frame[0] != MAGIC_OUT or frame[1] != VERSION: - raise ValueError(f"invalid receipt header: {frame.hex()}") - payload_len = frame[4] - expected_len = 6 + payload_len - if len(frame) != expected_len: - raise ValueError(f"receipt length mismatch: got {len(frame)}, expected {expected_len}") - if xor_crc(frame[:-1]) != frame[-1]: - raise ValueError(f"receipt checksum mismatch: {frame.hex()}") - payload = frame[5:-1] - opcode = payload[0] if payload else None - result = { - "seq": frame[2], - "status": frame[3], - "payload_len": payload_len, - "opcode": opcode, - "payload_hex": payload.hex(), - "crc": frame[-1], - } - if payload_len == 6: - result["result0"] = from_i32(payload[1:5]) - result["pass"] = bool(payload[5]) - elif payload_len == 10: - result["result0"] = from_i32(payload[1:5]) - result["result1"] = from_i32(payload[5:9]) - result["pass"] = bool(payload[9]) - return result - - -def _read_receipt_candidate(ser, timeout_s: float = 1.0) -> bytes: - deadline = time.monotonic() + timeout_s - buf = bytearray() - while time.monotonic() < deadline: - chunk = ser.read(1) - if not chunk: - continue - buf.extend(chunk) - while buf and buf[0] != MAGIC_OUT: - del buf[0] - if len(buf) >= 5: - need = 6 + buf[4] - if len(buf) >= need: - return bytes(buf[:need]) - return bytes(buf) - - -def send_serial(port: str, baud: int, frame: bytes, retries: int = 5) -> bytes: - try: - import serial # type: ignore - except ImportError as exc: - raise SystemExit("pyserial is required for --port mode") from exc - - with serial.Serial(port, baudrate=baud, timeout=2) as ser: - time.sleep(0.08) - ser.reset_input_buffer() - ser.reset_output_buffer() - last = b"" - for _ in range(max(1, retries)): - ser.reset_input_buffer() - ser.write(b"\x00" * 32) - ser.flush() - time.sleep(0.02) - ser.reset_input_buffer() - ser.write(frame) - ser.flush() - raw = _read_receipt_candidate(ser) - last = raw - try: - parsed = parse_receipt(raw) - if parsed["status"] == 0: - return raw - except ValueError: - pass - time.sleep(0.05) - return last - - -def make_case(args) -> tuple[str, int, bytes, dict]: - if args.op == "shift": - payload = i32(args.x) - expected = { - "op": "shift", - "opcode": OP_SHIFT_DIV, - "x": args.x, - "result0": args.x >> 16, - "pass": True, - "lean_lemma": "shiftRightEqDiv", - } - return args.op, OP_SHIFT_DIV, payload, expected - if args.op == "weighted": - payload = i32(args.energy) + i32(args.alpha) - weighted = (args.energy * args.alpha) >> 16 - passes = ( - args.energy >= 0 - and args.alpha >= 0 - and args.alpha <= 65536 - and weighted <= args.energy - ) - expected = { - "op": "weighted", - "opcode": OP_WEIGHTED, - "energy": args.energy, - "alpha": args.alpha, - "result0": weighted, - "pass": passes, - "lean_lemma": "weightedTermBounded", - } - return args.op, OP_WEIGHTED, payload, expected - payload = i32(args.a) + i32(args.b) - a_shift = args.a >> 16 - b_shift = args.b >> 16 - expected = { - "op": "monotone", - "opcode": OP_MONOTONE, - "a": args.a, - "b": args.b, - "result0": a_shift, - "result1": b_shift, - "pass": args.a <= args.b and a_shift <= b_shift, - "lean_lemma": "shiftRightMonotone", - } - return args.op, OP_MONOTONE, payload, expected - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--op", choices=["shift", "weighted", "monotone"], default="weighted") - parser.add_argument("--x", type=lambda v: int(v, 0), default=0x00038000) - parser.add_argument("--energy", type=lambda v: int(v, 0), default=0x000A0000) - parser.add_argument("--alpha", type=lambda v: int(v, 0), default=0x00008000) - parser.add_argument("--a", type=lambda v: int(v, 0), default=0x00010000) - parser.add_argument("--b", type=lambda v: int(v, 0), default=0x00030000) - parser.add_argument("--seq", type=lambda v: int(v, 0), default=1) - parser.add_argument("--port", help="optional USB serial port, e.g. /dev/ttyUSB1") - parser.add_argument("--baud", type=int, default=115200) - parser.add_argument("--retries", type=int, default=5) - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - _, opcode, payload, expected = make_case(args) - frame = build_frame(args.seq, opcode, payload) - receipt = { - "schema": "tang9k_rrc_q16_accel_receipt_v1", - "claim_boundary": "FPGA accelerates deterministic Q16.16 witness arithmetic only; Lean and host admit proofs.", - "frame_hex": frame.hex(), - "expected": expected, - "hardware": None, - "match": None, - } - - if args.port: - raw = send_serial(args.port, args.baud, frame, args.retries) - receipt["hardware_raw_hex"] = raw.hex() - try: - hardware = parse_receipt(raw) - receipt["hardware"] = hardware - keys = ["opcode", "result0", "pass"] - if "result1" in expected: - keys.append("result1") - receipt["match"] = hardware["status"] == 0 and all( - hardware.get(key) == expected.get(key) for key in keys - ) - except ValueError as exc: - receipt["hardware_error"] = str(exc) - receipt["match"] = False - else: - receipt["software_only"] = True - receipt["match"] = True - - output = json.dumps(receipt, indent=2, sort_keys=True) - if args.out: - args.out.write_text(output + "\n", encoding="utf-8") - print(output) - return 0 if receipt["match"] else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tang9k_rrc_q16_virtual_serial_probe.py b/4-Infrastructure/shim/tang9k_rrc_q16_virtual_serial_probe.py deleted file mode 100644 index 33f92b24..00000000 --- a/4-Infrastructure/shim/tang9k_rrc_q16_virtual_serial_probe.py +++ /dev/null @@ -1,246 +0,0 @@ -#!/usr/bin/env python3 -"""PTY-backed virtual serial probe for the Tang Nano 9K Q16 host protocol. - -This creates a local pseudo-terminal, runs a small responder that speaks the -same framed Q16 receipt protocol as the FPGA surface, then drives the existing -host harness through the PTY path. It proves host serial framing and parsing, -not live FPGA fabric behavior. -""" - -from __future__ import annotations - -import argparse -import json -import os -import pty -import select -import struct -import subprocess -import threading -import time -import tty -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -from tang9k_rrc_q16_accel import ( - MAGIC_IN, - MAGIC_OUT, - OP_MONOTONE, - OP_SHIFT_DIV, - OP_WEIGHTED, - VERSION, - xor_crc, -) - - -REPO = Path(__file__).resolve().parents[2] -OUT = REPO / "shared-data" / "data" / "stack_solidification" / "tang9k_rrc_q16_virtual_serial_probe.json" -DOC = REPO / "6-Documentation" / "docs" / "tang9k_rrc_q16_virtual_serial_probe_2026-05-09.md" - - -def i32(value: int) -> bytes: - return struct.pack(">i", value) - - -def from_i32(data: bytes) -> int: - return struct.unpack(">i", data)[0] - - -def response_frame(seq: int, status: int, payload: bytes) -> bytes: - frame = bytearray([MAGIC_OUT, VERSION, seq & 0xFF, status & 0xFF, len(payload)]) - frame.extend(payload) - frame.append(xor_crc(frame)) - return bytes(frame) - - -def emulate_frame(frame: bytes) -> bytes: - if len(frame) < 6 or frame[0] != MAGIC_IN or frame[1] != VERSION: - return response_frame(0, 0xE1, b"\x00") - seq = frame[2] - opcode = frame[3] - payload_len = frame[4] - payload = frame[5 : 5 + payload_len] - crc = frame[5 + payload_len] if len(frame) > 5 + payload_len else None - if len(payload) != payload_len or crc is None or xor_crc(frame[:-1]) != crc: - return response_frame(seq, 0xE2, bytes([opcode])) - - if opcode == OP_SHIFT_DIV and payload_len == 4: - x = from_i32(payload) - result0 = x >> 16 - return response_frame(seq, 0, bytes([opcode]) + i32(result0) + b"\x01") - if opcode == OP_WEIGHTED and payload_len == 8: - energy = from_i32(payload[:4]) - alpha = from_i32(payload[4:]) - result0 = (energy * alpha) >> 16 - passed = energy >= 0 and alpha >= 0 and alpha <= 65536 and result0 <= energy - return response_frame(seq, 0, bytes([opcode]) + i32(result0) + bytes([1 if passed else 0])) - if opcode == OP_MONOTONE and payload_len == 8: - a = from_i32(payload[:4]) - b = from_i32(payload[4:]) - result0 = a >> 16 - result1 = b >> 16 - passed = a <= b and result0 <= result1 - return response_frame(seq, 0, bytes([opcode]) + i32(result0) + i32(result1) + bytes([1 if passed else 0])) - return response_frame(seq, 0xE3, bytes([opcode])) - - -class VirtualResponder: - def __init__(self, master_fd: int) -> None: - self.master_fd = master_fd - self.stop = threading.Event() - self.frames_seen: list[dict[str, Any]] = [] - self.thread = threading.Thread(target=self._run, daemon=True) - - def start(self) -> None: - self.thread.start() - - def close(self) -> None: - self.stop.set() - self.thread.join(timeout=1.0) - - def _read_exactish(self, size: int, timeout_s: float = 1.0) -> bytes: - deadline = time.monotonic() + timeout_s - data = bytearray() - while len(data) < size and time.monotonic() < deadline and not self.stop.is_set(): - ready, _, _ = select.select([self.master_fd], [], [], 0.05) - if not ready: - continue - chunk = os.read(self.master_fd, size - len(data)) - if chunk: - data.extend(chunk) - return bytes(data) - - def _run(self) -> None: - buf = bytearray() - while not self.stop.is_set(): - ready, _, _ = select.select([self.master_fd], [], [], 0.05) - if not ready: - continue - chunk = os.read(self.master_fd, 256) - if not chunk: - continue - buf.extend(chunk) - while buf: - while buf and buf[0] != MAGIC_IN: - del buf[0] - if len(buf) < 5: - break - need = 6 + buf[4] - if len(buf) < need: - break - frame = bytes(buf[:need]) - del buf[:need] - reply = emulate_frame(frame) - os.write(self.master_fd, reply) - self.frames_seen.append({"request_hex": frame.hex(), "response_hex": reply.hex()}) - - -def run_case(port: str, name: str, args: list[str]) -> dict[str, Any]: - out_path = REPO / "4-Infrastructure" / "shim" / f"tang9k_rrc_q16_virtual_{name}_receipt.json" - cmd = [ - "python3", - "4-Infrastructure/shim/tang9k_rrc_q16_accel.py", - *args, - "--port", - port, - "--retries", - "1", - "--out", - str(out_path.relative_to(REPO)), - ] - proc = subprocess.run( - cmd, - cwd=REPO, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=30, - check=False, - ) - receipt = json.loads(out_path.read_text(encoding="utf-8")) if out_path.exists() else {} - return { - "case": name, - "command": cmd, - "returncode": proc.returncode, - "stdout_tail": proc.stdout[-4000:], - "stderr_tail": proc.stderr[-4000:], - "receipt": str(out_path.relative_to(REPO)), - "match": receipt.get("match"), - "hardware": receipt.get("hardware"), - } - - -def build_doc(receipt: dict[str, Any]) -> str: - lines = [ - "# Tang Nano 9K Q16 Virtual Serial Probe", - "", - "**Date:** 2026-05-09", - "", - receipt["claim_boundary"], - "", - "## Status", - "", - f"- Status: `{receipt['summary']['status']}`", - f"- Cases: `{receipt['summary']['case_count']}`", - f"- Matches: `{receipt['summary']['match_count']}`", - "", - "## Cases", - "", - ] - for case in receipt["cases"]: - lines.append(f"- `{case['case']}`: match `{case['match']}`, receipt `{case['receipt']}`") - lines.extend(["", "## Machine Receipt", "", f"- `{OUT.relative_to(REPO)}`"]) - return "\n".join(lines) + "\n" - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--out", type=Path, default=OUT) - args = parser.parse_args() - - master_fd, slave_fd = pty.openpty() - tty.setraw(master_fd) - tty.setraw(slave_fd) - slave_path = os.ttyname(slave_fd) - responder = VirtualResponder(master_fd) - responder.start() - try: - cases = [ - run_case(slave_path, "shift", ["--op", "shift", "--x", "0x00038000"]), - run_case(slave_path, "weighted", ["--op", "weighted", "--energy", "0x000a0000", "--alpha", "0x00008000"]), - run_case(slave_path, "monotone", ["--op", "monotone", "--a", "0x00010000", "--b", "0x00030000"]), - ] - finally: - responder.close() - os.close(master_fd) - os.close(slave_fd) - - match_count = sum(1 for case in cases if case.get("match") is True) - receipt = { - "schema": "tang9k_rrc_q16_virtual_serial_probe_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": ( - "Virtual serial probe only. This validates the host Q16 UART framing, " - "receipt parser, and opcode semantics over a PTY-backed serial device; " - "it does not validate live FPGA fabric or the Tang Nano UART route." - ), - "virtual_port": slave_path, - "summary": { - "status": "PASS_VIRTUAL_SERIAL" if match_count == len(cases) else "FAIL", - "case_count": len(cases), - "match_count": match_count, - "frames_seen": len(responder.frames_seen), - }, - "cases": cases, - "virtual_frames": responder.frames_seen, - } - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - DOC.write_text(build_doc(receipt), encoding="utf-8") - print(json.dumps({"receipt": str(args.out.relative_to(REPO)), "doc": str(DOC.relative_to(REPO)), "status": receipt["summary"]["status"]}, indent=2)) - return 0 if receipt["summary"]["status"] == "PASS_VIRTUAL_SERIAL" else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tang9k_symbol_surface_stress.py b/4-Infrastructure/shim/tang9k_symbol_surface_stress.py deleted file mode 100644 index 57543ca8..00000000 --- a/4-Infrastructure/shim/tang9k_symbol_surface_stress.py +++ /dev/null @@ -1,199 +0,0 @@ -#!/usr/bin/env python3 -"""Stress the Tang9K symbol surface over direct UART and IPC serial modes. - -This is the next evidence layer after the single full-gambit receipt: it sends -multiple short metaprobe/logogram payloads through the loaded FPGA bitstream and -records pass rate plus coarse host-observed transaction timing. -""" - -from __future__ import annotations - -import argparse -import json -import statistics -import time -from pathlib import Path -from types import SimpleNamespace - -import gpu_fpga_ipc_symbol_surface as ipc -import tang9k_hutter_symbol_surface as tang - - -DEFAULT_PAYLOADS = [ - ("ascii_metaprobe_token", "D0A37FEED"), - ("ascii_metaprobe_token", "F00010203"), - ("ascii_metaprobe_token", "TRACE"), - ("ascii_metaprobe_token", "DELTA"), - ("glyphbook_bank_ids", "0xe101,0xe10f,0x44,0x46"), - ("glyphbook_bank_ids", "0xe144,0xe146,0xe145,0xe145"), -] - - -def payload_for(kind: str, value: str) -> tuple[bytes, object]: - if kind == "glyphbook_bank_ids": - ids = tang.parse_glyph_ids(value) - return tang.glyph_ids_to_surface_bytes(ids), [f"0x{x:x}" for x in ids] - return value.encode("ascii"), value - - -def summarize_attempts(schema: str, attempts: list[dict], started_ns: int, ended_ns: int) -> dict: - durations = [item["duration_ms"] for item in attempts] - passed = [item for item in attempts if item["passed"]] - return { - "schema": schema, - "attempt_count": len(attempts), - "pass_count": len(passed), - "fail_count": len(attempts) - len(passed), - "pass_rate": (len(passed) / len(attempts)) if attempts else 0.0, - "elapsed_ms": (ended_ns - started_ns) / 1_000_000, - "duration_ms": { - "min": min(durations) if durations else 0.0, - "max": max(durations) if durations else 0.0, - "mean": statistics.fmean(durations) if durations else 0.0, - }, - "attempts": attempts, - } - - -def run_direct(args: argparse.Namespace) -> dict: - attempts = [] - started = time.perf_counter_ns() - for idx in range(args.count): - kind, value = DEFAULT_PAYLOADS[idx % len(DEFAULT_PAYLOADS)] - payload, input_value = payload_for(kind, value) - seq = (args.seq_base + idx) & 0xFF - frame = tang.build_frame(seq, payload) - expected = tang.receipt_for_payload(payload) - - t0 = time.perf_counter_ns() - raw = tang.send_serial(args.port, args.baud, frame, retries=args.retries) - t1 = time.perf_counter_ns() - - parsed = None - note = "" - try: - parsed = tang.parse_receipt(raw) - passed = ( - parsed["status"] == 0 - and parsed["seq"] == seq - and parsed["hash16"] == expected["hash16"] - and parsed["mapped_count"] == expected["mapped_count"] - and parsed["literal_count"] == expected["literal_count"] - ) - except ValueError as exc: - passed = False - note = str(exc) - - attempts.append( - { - "index": idx, - "seq": seq, - "input_kind": kind, - "input": input_value, - "payload_hex": payload.hex(), - "duration_ms": (t1 - t0) / 1_000_000, - "passed": passed, - "raw_receipt_hex": raw.hex(), - "hardware_receipt": parsed, - "expected_hash": expected["hash16"], - "note": note, - } - ) - ended = time.perf_counter_ns() - return summarize_attempts("tang9k_symbol_surface_direct_stress_v1", attempts, started, ended) - - -def run_ipc(args: argparse.Namespace) -> dict: - ring = args.ring - init_args = SimpleNamespace(ring=ring, slots=args.slots) - ipc.cmd_init(init_args) - - attempts = [] - started = time.perf_counter_ns() - for idx in range(args.count): - kind, value = DEFAULT_PAYLOADS[idx % len(DEFAULT_PAYLOADS)] - seq = (args.seq_base + 1000 + idx) & 0xFFFF - produce_args = SimpleNamespace( - ring=ring, - text=value if kind == "ascii_metaprobe_token" else "", - glyph_ids=value if kind == "glyphbook_bank_ids" else None, - seq=seq, - serial=True, - ) - consume_args = SimpleNamespace( - ring=ring, - port=args.port, - baud=args.baud, - retries=args.retries, - max_records=1, - ) - - t0 = time.perf_counter_ns() - produced = ipc.cmd_produce(produce_args) - consumed = ipc.cmd_consume(consume_args) - t1 = time.perf_counter_ns() - - receipts = consumed.get("receipts", []) - receipt = receipts[0] if receipts else {} - passed = bool( - receipt.get("status") == "done" - and receipt.get("hardware_status") == 0 - and receipt.get("receipt_hash") == produced["expected"]["hash16"] - ) - attempts.append( - { - "index": idx, - "seq": seq, - "input_kind": produced["input_kind"], - "input": produced["input"], - "payload_hex": produced["payload_hex"], - "duration_ms": (t1 - t0) / 1_000_000, - "passed": passed, - "producer_slot": produced["slot"], - "receipt": receipt, - } - ) - ended = time.perf_counter_ns() - status = ipc.cmd_status(SimpleNamespace(ring=ring)) - result = summarize_attempts("tang9k_symbol_surface_ipc_stress_v1", attempts, started, ended) - result["ring"] = str(ring) - result["final_ring_status"] = status - return result - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--port", default="/dev/ttyUSB1") - parser.add_argument("--baud", type=int, default=115200) - parser.add_argument("--count", type=int, default=24) - parser.add_argument("--retries", type=int, default=6) - parser.add_argument("--seq-base", type=int, default=120) - parser.add_argument("--slots", type=int, default=32) - parser.add_argument("--ring", type=Path, default=Path("/dev/shm/tang9k_symbol_surface_stress.ring")) - parser.add_argument("--mode", choices=["direct", "ipc", "both"], default="both") - parser.add_argument("--out", type=Path) - args = parser.parse_args() - - report = { - "schema": "tang9k_symbol_surface_stress_report_v1", - "port": args.port, - "baud": args.baud, - "count": args.count, - "retries": args.retries, - "claim_boundary": "Tang FPGA accelerates the substitution witness; host owns full codec/decodec and IPC staging.", - } - if args.mode in {"direct", "both"}: - report["direct"] = run_direct(args) - if args.mode in {"ipc", "both"}: - report["ipc"] = run_ipc(args) - - text = json.dumps(report, indent=2) - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(text + "\n", encoding="utf-8") - print(text) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tang9k_uart_transport_router.py b/4-Infrastructure/shim/tang9k_uart_transport_router.py deleted file mode 100644 index 2fe2d298..00000000 --- a/4-Infrastructure/shim/tang9k_uart_transport_router.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python3 -"""Route table for Tang Nano 9K UART-like transports. - -The onboard FTDI/BL702 route is currently blocked for fabric UART. This router -subsumes those physical UART entries under one manifest and selects a -PTY-backed virtual Q16 route as the active non-hardware transport. -""" - -from __future__ import annotations - -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT = REPO / "shared-data" / "data" / "stack_solidification" / "tang9k_uart_transport_routes.json" -DOC = REPO / "6-Documentation" / "docs" / "tang9k_uart_transport_routes_2026-05-09.md" - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def load_json(path: Path) -> dict[str, Any]: - if not path.exists(): - return {} - return json.loads(path.read_text(encoding="utf-8")) - - -def run_virtual_probe() -> dict[str, Any]: - proc = subprocess.run( - ["python3", "4-Infrastructure/shim/tang9k_rrc_q16_virtual_serial_probe.py"], - cwd=REPO, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=60, - check=False, - ) - receipt_path = REPO / "shared-data" / "data" / "stack_solidification" / "tang9k_rrc_q16_virtual_serial_probe.json" - receipt = load_json(receipt_path) - return { - "command": ["python3", "4-Infrastructure/shim/tang9k_rrc_q16_virtual_serial_probe.py"], - "returncode": proc.returncode, - "stdout_tail": proc.stdout[-4000:], - "stderr_tail": proc.stderr[-4000:], - "receipt": rel(receipt_path), - "status": receipt.get("summary", {}).get("status", "UNKNOWN"), - "match_count": receipt.get("summary", {}).get("match_count"), - "case_count": receipt.get("summary", {}).get("case_count"), - } - - -def serial_receipt_summary(path: str) -> dict[str, Any]: - receipt = load_json(REPO / path) - results = receipt.get("results", []) - return { - "receipt": path, - "present": bool(receipt), - "conclusion": receipt.get("conclusion"), - "ports": [ - { - "port": row.get("port"), - "byte_count": row.get("byte_count"), - "contains_expected": row.get("contains_expected"), - "hex_prefix": row.get("hex_prefix"), - } - for row in results - ], - } - - -def build_manifest() -> dict[str, Any]: - virtual = run_virtual_probe() - onboard_beacon = serial_receipt_summary("4-Infrastructure/shim/tang9k_uart_beacon_probe_receipt.json") - swapped_beacon = serial_receipt_summary("4-Infrastructure/shim/tang9k_uart_beacon_swapped_probe_receipt.json") - loopback_after_clear = serial_receipt_summary("4-Infrastructure/shim/tang9k_uart_loopback_after_jtag_clear_probe_receipt.json") - active_status = ( - "PASS_ACTIVE_VIRTUAL_ROUTE" - if virtual["status"] == "PASS_VIRTUAL_SERIAL" - and virtual.get("match_count") == virtual.get("case_count") == 3 - else "FAIL" - ) - return { - "schema": "tang9k_uart_transport_routes_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": ( - "Transport route table only. The active virtual route validates host Q16 serial " - "framing and parser behavior. It does not validate live FPGA fabric or the " - "Tang Nano onboard UART bridge." - ), - "active_route": "virtual://q16-pty", - "active_route_status": active_status, - "route_table": [ - { - "route_id": "onboard-ftdi-a", - "device": "/dev/ttyUSB0", - "kind": "physical_ftdi_mpsse_or_bridge", - "status": "BLOCKED_FOR_FABRIC_UART", - "evidence": "faXX/MPSSE-style bytes or zero beacon bytes; no valid fabric receipt", - "receipts": [onboard_beacon["receipt"], swapped_beacon["receipt"], loopback_after_clear["receipt"]], - }, - { - "route_id": "onboard-ftdi-b", - "device": "/dev/ttyUSB1", - "kind": "physical_ftdi_secondary_endpoint", - "status": "BLOCKED_FOR_FABRIC_UART", - "evidence": "zero beacon bytes and empty Q16 hardware receipts", - "receipts": [onboard_beacon["receipt"], swapped_beacon["receipt"], loopback_after_clear["receipt"]], - }, - { - "route_id": "external-usb-uart", - "device": "/dev/ttyUSB2_or_/dev/ttyACM0", - "kind": "physical_external_adapter", - "status": "PENDING_HARDWARE", - "evidence": "recommended live-hardware closure route; adapter not present in this probe", - "receipts": [], - }, - { - "route_id": "virtual-q16-pty", - "device": "virtual://q16-pty", - "kind": "pty_backed_virtual_serial", - "status": virtual["status"], - "evidence": "shift, weighted, and monotone Q16 receipt frames match through PTY-backed serial route", - "receipts": [virtual["receipt"]], - }, - ], - "physical_receipt_summaries": { - "onboard_beacon": onboard_beacon, - "swapped_beacon": swapped_beacon, - "loopback_after_jtag_clear": loopback_after_clear, - }, - "virtual_probe": virtual, - "routing_policy": { - "default_non_hardware_route": "virtual://q16-pty", - "live_hardware_promotion_requires": [ - "external or verified onboard route captures beacon payload a6425131360a", - "Q16 shift, weighted, and monotone hardware receipts match software expectations", - "stack audit reports FPGA hardware witness PASS", - ], - "blocked_routes_remain_visible": True, - }, - } - - -def build_doc(manifest: dict[str, Any]) -> str: - lines = [ - "# Tang Nano 9K UART Transport Routes", - "", - "**Date:** 2026-05-09", - "", - manifest["claim_boundary"], - "", - "## Active Route", - "", - f"- Active route: `{manifest['active_route']}`", - f"- Status: `{manifest['active_route_status']}`", - "", - "## Route Table", - "", - "| Route | Device | Kind | Status |", - "| --- | --- | --- | --- |", - ] - for route in manifest["route_table"]: - lines.append( - f"| `{route['route_id']}` | `{route['device']}` | `{route['kind']}` | `{route['status']}` |" - ) - lines.extend( - [ - "", - "## Routing Policy", - "", - f"- Default non-hardware route: `{manifest['routing_policy']['default_non_hardware_route']}`", - "- Live hardware promotion requires:", - ] - ) - for gate in manifest["routing_policy"]["live_hardware_promotion_requires"]: - lines.append(f" - {gate}") - lines.extend(["", "## Machine Receipt", "", f"- `{OUT.relative_to(REPO)}`"]) - return "\n".join(lines) + "\n" - - -def main() -> int: - manifest = build_manifest() - OUT.parent.mkdir(parents=True, exist_ok=True) - OUT.write_text(json.dumps(manifest, indent=2, sort_keys=True) + "\n", encoding="utf-8") - DOC.write_text(build_doc(manifest), encoding="utf-8") - print(json.dumps({"receipt": rel(OUT), "doc": rel(DOC), "status": manifest["active_route_status"]}, indent=2)) - return 0 if manifest["active_route_status"] == "PASS_ACTIVE_VIRTUAL_ROUTE" else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/tessellated_triangle_flow_migration_probe.py b/4-Infrastructure/shim/tessellated_triangle_flow_migration_probe.py deleted file mode 100644 index 5bf7761e..00000000 --- a/4-Infrastructure/shim/tessellated_triangle_flow_migration_probe.py +++ /dev/null @@ -1,565 +0,0 @@ -#!/usr/bin/env python3 -"""Tessellated triangle flow-map probe for route prediction guardrails. - -This records a bounded triangular map with a simple flow-control equation. Bird -migration is used as an intuitive route-prediction fixture: seasonal direction, -wind assist, stopover pressure, obstacle cost, and route memory can steer motion -between neighboring cells. The fixture is a routing/prediction pattern only; it -does not claim ecological forecasting authority. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "tessellated_triangle_flow_migration" -REGISTRY = OUT_DIR / "tessellated_triangle_flow_migration_registry.json" -RECEIPT = OUT_DIR / "tessellated_triangle_flow_migration_receipt.json" -SUMMARY = OUT_DIR / "tessellated_triangle_flow_migration.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Tessellated Triangle Flow Migration.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "hutter_prize_next_roadmap" / "hutter_prize_next_roadmap_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", - REPO / "shared-data" / "data" / "gaussian_splat_manifold_projection" / "gaussian_splat_manifold_projection_receipt.json", - REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" / "torsion_interval_gaussian_splat_witness_receipt.json", - REPO / "shared-data" / "data" / "collatz_couch_route_pressure" / "collatz_couch_route_pressure_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", - REPO / "0-Core-Formalism" / "lean" / "Semantics" / "Semantics" / "TriangleManifold.lean", -] - -FLOW_WEIGHTS = { - "seasonal_heading": 3, - "wind_assist": 2, - "stopover_memory": 2, - "obstacle_cost": -3, - "novelty_cost": -1, -} - -ADMIT_THRESHOLD = 4 -HOLD_THRESHOLD = 1 -METABOLIC_HOLD_DECISION = "HOLD_INVERSE_FERMAT_FAMM_UNDERVERSE" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def merkle_root(leaves: list[str]) -> str: - if not leaves: - return sha256_bytes(b"") - level = leaves[:] - while len(level) > 1: - if len(level) % 2: - level.append(level[-1]) - level = [sha256_bytes((level[index] + level[index + 1]).encode("ascii")) for index in range(0, len(level), 2)] - return level[0] - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def read_json(path: Path) -> dict[str, Any] | None: - if not path.exists(): - return None - try: - return json.loads(path.read_text(encoding="utf-8")) - except json.JSONDecodeError: - return None - - -def source_ref(path: Path) -> dict[str, Any]: - receipt = read_json(path) - return { - "path": rel(path), - "exists": path.exists(), - "sha256": file_hash(path), - "receipt_hash": receipt.get("receipt_hash") if isinstance(receipt, dict) else None, - "decision": receipt.get("decision") if isinstance(receipt, dict) else None, - } - - -def triangle(cell_id: str, row: int, col: int, orientation: str, label: str) -> dict[str, Any]: - item = { - "cell_id": cell_id, - "row": row, - "col": col, - "orientation": orientation, - "label": label, - "vertices": [ - [col, row], - [col + 1, row], - [col + (0 if orientation == "down" else 1), row + 1], - ], - } - item["cell_hash"] = hash_obj(item) - return item - - -def flow_score(features: dict[str, int]) -> int: - return sum(FLOW_WEIGHTS[name] * value for name, value in features.items()) - - -def route( - *, - route_id: str, - source: str, - target: str, - chart: str, - features: dict[str, int], - bounded: bool, - provenance_declared: bool, - prediction_scope_declared: bool, - global_truth_claim: bool = False, -) -> dict[str, Any]: - score = flow_score(features) - if global_truth_claim: - decision = "HOLD_TRIANGLE_FLOW_GLOBALIZED" - elif not bounded: - decision = "REJECT_UNBOUNDED_TRIANGLE_FLOW" - elif not provenance_declared: - decision = "HOLD_FLOW_PROVENANCE" - elif not prediction_scope_declared: - decision = "HOLD_MIGRATION_PREDICTION_SCOPE" - elif score >= ADMIT_THRESHOLD: - decision = "ADMIT_TRIANGLE_FLOW_HINT" - elif score >= HOLD_THRESHOLD: - decision = "HOLD_TRIANGLE_FLOW_WEAK_HINT" - else: - decision = "HOLD_FLOW_BOUNDARY" - item = { - "route_id": route_id, - "source": source, - "target": target, - "chart": chart, - "features": features, - "flow_score": score, - "bounded": bounded, - "provenance_declared": provenance_declared, - "prediction_scope_declared": prediction_scope_declared, - "global_truth_claim": global_truth_claim, - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def metabolic_route( - *, - route_id: str, - source: str, - target: str, - chart: str, - outcome_value: int, - path_cost: int, - metabolic_cost: int, - obstacle_cost: int, - residual_cost: int, - bounded: bool, - provenance_declared: bool, - outcome_receipt: bool, -) -> dict[str, Any]: - fitness = outcome_value - path_cost - metabolic_cost - obstacle_cost - residual_cost - if not bounded: - decision = "REJECT_UNBOUNDED_METABOLIC_ROUTE" - elif not provenance_declared: - decision = "HOLD_FLOW_PROVENANCE" - elif not outcome_receipt: - decision = METABOLIC_HOLD_DECISION - else: - decision = "HOLD_INVERSE_FERMAT_FAMM_ADAPTER" - item = { - "route_id": route_id, - "source": source, - "target": target, - "chart": chart, - "outcome_value": outcome_value, - "path_cost": path_cost, - "metabolic_cost": metabolic_cost, - "obstacle_cost": obstacle_cost, - "residual_cost": residual_cost, - "fitness": fitness, - "bounded": bounded, - "provenance_declared": provenance_declared, - "outcome_receipt": outcome_receipt, - "underverse_variant": "U_INVERSE_FERMAT_FAMM", - "decision": decision, - } - item["route_hash"] = hash_obj({k: v for k, v in item.items() if k != "route_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - cells = [ - triangle("T00", 0, 0, "up", "wintering_origin_or_frame_root"), - triangle("T01", 0, 1, "down", "coastal_corridor_or_xml_head"), - triangle("T10", 1, 0, "down", "river_corridor_or_link_heavy"), - triangle("T11", 1, 1, "up", "stopover_node_or_template_heavy"), - triangle("T20", 2, 0, "up", "barrier_cell_or_ref_heavy"), - triangle("T21", 2, 1, "down", "destination_basin_or_prose_heavy"), - ] - routes = [ - route( - route_id="seasonal_coastal_route", - source="T00", - target="T01", - chart="bird_migration_fixture", - features={ - "seasonal_heading": 1, - "wind_assist": 1, - "stopover_memory": 1, - "obstacle_cost": 0, - "novelty_cost": 0, - }, - bounded=True, - provenance_declared=True, - prediction_scope_declared=True, - ), - route( - route_id="river_stopover_route", - source="T10", - target="T11", - chart="bird_migration_fixture", - features={ - "seasonal_heading": 1, - "wind_assist": 0, - "stopover_memory": 1, - "obstacle_cost": 0, - "novelty_cost": 1, - }, - bounded=True, - provenance_declared=True, - prediction_scope_declared=True, - ), - route( - route_id="storm_barrier_route", - source="T11", - target="T20", - chart="bird_migration_fixture", - features={ - "seasonal_heading": 1, - "wind_assist": -1, - "stopover_memory": 0, - "obstacle_cost": 1, - "novelty_cost": 1, - }, - bounded=True, - provenance_declared=True, - prediction_scope_declared=True, - ), - route( - route_id="hutter_frame_class_route", - source="T01", - target="T11", - chart="hutter_frame_fixture", - features={ - "seasonal_heading": 1, - "wind_assist": 0, - "stopover_memory": 1, - "obstacle_cost": 0, - "novelty_cost": 0, - }, - bounded=True, - provenance_declared=True, - prediction_scope_declared=True, - ), - route( - route_id="unbounded_prediction_claim", - source="T00", - target="T21", - chart="bird_migration_fixture", - features={ - "seasonal_heading": 1, - "wind_assist": 1, - "stopover_memory": 1, - "obstacle_cost": 0, - "novelty_cost": 0, - }, - bounded=False, - provenance_declared=True, - prediction_scope_declared=False, - global_truth_claim=True, - ), - ] - metabolic_routes = [ - metabolic_route( - route_id="physarum_style_best_food_route", - source="T00", - target="T21", - chart="slime_mold_metabolic_fixture", - outcome_value=12, - path_cost=3, - metabolic_cost=2, - obstacle_cost=1, - residual_cost=2, - bounded=True, - provenance_declared=True, - outcome_receipt=False, - ), - metabolic_route( - route_id="hutter_best_outcome_route_pressure", - source="T01", - target="T11", - chart="hutter_frame_fixture", - outcome_value=9, - path_cost=2, - metabolic_cost=1, - obstacle_cost=0, - residual_cost=3, - bounded=True, - provenance_declared=True, - outcome_receipt=False, - ), - ] - return { - "schema": "tessellated_triangle_flow_migration_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Tessellated triangle flow-map diagnostic only. Bird migration supplies " - "a route-prediction pattern fixture over seasonal heading, wind assist, " - "stopover memory, obstacles, and novelty. Slime-mold metabolic fitness " - "and inverse-Fermat/FAMM route selection are recorded as Underverse HOLD " - "lanes. This does not claim ecological forecasting authority, species-level " - "prediction, metabolic optimization authority, or Hutter compression." - ), - "canonical_statement": ( - "A tessellated triangle map bounds the local chart; a flow-control " - "equation chooses legal neighboring moves; migration-like patterns " - "stress test directional memory, barriers, and prediction scope. " - "Slime-mold fitness probes shortest metabolic routes to high-value " - "outcomes, but remains Underverse until adapter, residual, and outcome " - "receipts close." - ), - "flow_equation": { - "cell_state": "x_i = triangle_cell(position, orientation, class, receipt)", - "control": "u_ij = 3*seasonal_heading + 2*wind_assist + 2*stopover_memory - 3*obstacle_cost - novelty_cost", - "transition": "x_{k+1}=argmax_j u_ij over adjacent tessellated cells, else HOLD", - "admission": "A=1[bounded and provenance_declared and prediction_scope_declared and not global_truth_claim]", - }, - "inverse_fermat_famm": { - "status": "U_under", - "variant_id": "U_INVERSE_FERMAT_FAMM", - "meaning": "inverse Fermat route pressure: choose bounded metabolic path to best outcome instead of accepting apparent geometric elegance", - "fitness": "Phi(route)=outcome_value-path_cost-metabolic_cost-obstacle_cost-residual_cost", - "promotion_rule": "stay HOLD until domain adapter, residual policy, and outcome receipt are explicit", - }, - "hutter_mapping": { - "triangle_cell": "canonical frame/window class", - "migration_route": "multi-axis causal route across frame classes", - "seasonal_heading": "expected corpus-phase direction", - "wind_assist": "baseline/logogram support", - "stopover_memory": "prior admitted root reuse", - "obstacle_cost": "packet/global/baseline debt", - "novelty_cost": "new dictionary or adapter burden", - }, - "cells": cells, - "routes": routes, - "metabolic_routes": metabolic_routes, - "cells_root": merkle_root([item["cell_hash"] for item in cells]), - "routes_root": merkle_root([item["route_hash"] for item in routes + metabolic_routes]), - "aggregates": { - "cell_count": len(cells), - "route_count": len(routes) + len(metabolic_routes), - "flow_route_count": len(routes), - "metabolic_route_count": len(metabolic_routes), - "admit_count": sum(1 for item in routes + metabolic_routes if item["decision"].startswith("ADMIT")), - "hold_count": sum(1 for item in routes + metabolic_routes if item["decision"].startswith("HOLD")), - "reject_count": sum(1 for item in routes + metabolic_routes if item["decision"].startswith("REJECT")), - "flow_weights": FLOW_WEIGHTS, - "admit_threshold": ADMIT_THRESHOLD, - "hold_threshold": HOLD_THRESHOLD, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "tessellated_triangle_flow_migration_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "cells_root": registry["cells_root"], - "routes_root": registry["routes_root"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_TRIANGLE_FLOW_MIGRATION_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Tessellated Triangle Flow Migration", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Cells root: `{receipt['cells_root']}` ", - f"Routes root: `{receipt['routes_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Flow Equation", - "", - ] - for key, value in registry["flow_equation"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Inverse Fermat FAMM Underverse", ""]) - for key, value in registry["inverse_fermat_famm"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Hutter Mapping", - "", - "| Triangle/migration term | Hutter role |", - "|---|---|", - ] - ) - for key, value in registry["hutter_mapping"].items(): - lines.append(f"| `{key}` | {value} |") - lines.extend(["", "## Routes", "", "| Route | Chart | Score | Decision |", "|---|---|---:|---|"]) - for item in registry["routes"]: - lines.append(f"| `{item['route_id']}` | `{item['chart']}` | {item['flow_score']} | `{item['decision']}` |") - lines.extend(["", "## Metabolic Routes", "", "| Route | Chart | Fitness | Decision |", "|---|---|---:|---|"]) - for item in registry["metabolic_routes"]: - lines.append(f"| `{item['route_id']}` | `{item['chart']}` | {item['fitness']} | `{item['decision']}` |") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}` decision: `{source['decision']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Hutter TriangleManifold Flow Migration Receipt -title: Tessellated Triangle Flow Migration -type: text/vnd.tiddlywiki - -! Tessellated Triangle Flow Migration - -Durable runner: - -``` -4-Infrastructure/shim/tessellated_triangle_flow_migration_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Cells root: - -``` -{receipt['cells_root']} -``` - -Routes root: - -``` -{receipt['routes_root']} -``` - -!! Doctrine - -A tessellated triangle map bounds the local chart. A flow-control equation -chooses legal neighboring moves. Bird migration is a prediction-pattern fixture: -direction, wind, memory, barriers, and novelty can route a path, but do not -certify ecological truth or compression gain. - -``` -u_ij = 3*seasonal_heading + 2*wind_assist + 2*stopover_memory - 3*obstacle_cost - novelty_cost -x_{{k+1}} = argmax_j u_ij over adjacent tessellated cells, else HOLD -``` - -!! Inverse Fermat FAMM Underverse - -Slime-mold style metabolic fitness is recorded as `U_INVERSE_FERMAT_FAMM`. -It can probe shortest routes to best outcomes, but it stays HOLD until the -domain adapter, residual policy, and outcome receipt close. - -``` -Phi(route)=outcome_value-path_cost-metabolic_cost-obstacle_cost-residual_cost -``` - -!! Links - -* [[Hutter Prize Next Roadmap]] -* [[Hutter Multidimensional Causal Chain]] -* [[Gaussian Splat Manifold Projection]] -* [[Torsion Interval Gaussian Splat Witness]] -* [[Collatz COUCH Route Pressure Probe]] -* [[Underverse Variant Accounting]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "cells_root": receipt["cells_root"], - "routes_root": receipt["routes_root"], - "decision": receipt["decision"], - "aggregates": receipt["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/test_erdos_ap_4primitive.py b/4-Infrastructure/shim/test_erdos_ap_4primitive.py deleted file mode 100644 index 269be29c..00000000 --- a/4-Infrastructure/shim/test_erdos_ap_4primitive.py +++ /dev/null @@ -1,373 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős Conjecture on Arithmetic Progressions -========================================================================= -Apply 4-primitive framework to Erdős Conjecture on Arithmetic Progressions. -Conjecture: If Σ_{a∈A} 1/a diverges, then A contains arbitrarily long -arithmetic progressions. - -Focus on field primitive (ρ(x⃗)) for density analysis and shear primitive -for analyzing density deformation under translation. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_dense_set(n_max, density=0.5, seed=None): - """Generate a set A with given density up to n_max.""" - if seed is not None: - np.random.seed(seed) - - A = set() - for n in range(1, n_max + 1): - if np.random.random() < density: - A.add(n) - - return sorted(A) - - -def find_arithmetic_progressions(A, length): - """Find all arithmetic progressions of given length in A.""" - A_set = set(A) - progressions = [] - - for i in range(len(A)): - for j in range(i + 1, len(A)): - diff = A[j] - A[i] - if diff == 0: - continue - - # Check if we can form an AP of length k - progression = [A[i]] - for k in range(1, length): - next_val = A[i] + k * diff - if next_val not in A_set: - break - progression.append(next_val) - - if len(progression) == length: - progressions.append(tuple(progression)) - - # Remove duplicates - progressions = list(set(progressions)) - return progressions - - -def field_analysis(A): - """Compute field primitive metrics (density, reciprocal sum).""" - if not A: - return { - "density": 0.0, - "reciprocal_sum": 0.0, - "asymptotic_density": 0.0, - "n_max": 0, - "size": 0 - } - - n_max = max(A) - size = len(A) - - # Density - density = size / n_max - - # Reciprocal sum - reciprocal_sum = sum(1.0 / a for a in A) - - # Asymptotic density estimate - asymptotic_density = density - - return { - "density": float(density), - "reciprocal_sum": float(reciprocal_sum), - "asymptotic_density": float(asymptotic_density), - "n_max": n_max, - "size": size - } - - -def shear_analysis_translation(A): - """Compute shear primitive metrics (density deformation under translation).""" - if not A: - return { - "translation_rigidity": 0.0, - "avg_translation_error": 0.0, - "periodicity_score": 0.0 - } - - A_set = set(A) - n_max = max(A) - - # Test translations and measure how well they preserve the set - translation_errors = [] - for d in range(1, min(20, n_max // 10)): - translated = set(a + d for a in A if a + d <= n_max) - overlap = len(A_set & translated) - union = len(A_set | translated) - jaccard = overlap / union if union > 0 else 0 - translation_errors.append(1 - jaccard) - - # Translation rigidity (inverse of average translation error) - avg_translation_error = np.mean(translation_errors) if translation_errors else 0 - translation_rigidity = 1.0 / (avg_translation_error + 1e-10) - - # Periodicity score (how regular the set is under translations) - periodicity_score = 1.0 - avg_translation_error - - return { - "translation_rigidity": float(translation_rigidity), - "avg_translation_error": float(avg_translation_error), - "periodicity_score": float(periodicity_score) - } - - -def spectral_analysis_structure(A): - """Compute spectral decomposition of set structure.""" - if not A: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - n_max = max(A) - - # Build indicator matrix - M = np.zeros((n_max, n_max)) - for i, a in enumerate(A): - for j, b in enumerate(A): - if a + b <= n_max: - M[a-1, b-1] = 1 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - -def packet_analysis_progressions(A, max_length=5): - """Compute packet primitive metrics for arithmetic progressions.""" - if not A: - return { - "max_ap_length": 0, - "num_progressions": 0, - "ap_density": 0.0 - } - - # Find longest AP - max_ap_length = 0 - for length in range(2, max_length + 1): - progressions = find_arithmetic_progressions(A, length) - if progressions: - max_ap_length = length - - # Count all progressions of length 3 - progressions_3 = find_arithmetic_progressions(A, 3) - num_progressions = len(progressions_3) - - # AP density (progressions per element) - ap_density = num_progressions / len(A) if A else 0 - - return { - "max_ap_length": max_ap_length, - "num_progressions": num_progressions, - "ap_density": float(ap_density) - } - - -def test_erdos_ap(n_max_values, density_values): - """Test Erdős Conjecture on Arithmetic Progressions with 4-primitive framework.""" - results = [] - - for n_max in n_max_values: - for density in density_values: - for seed in range(3): # 3 samples per configuration - A = generate_dense_set(n_max, density, seed=seed) - - # 4-primitive analysis - field = field_analysis(A) - shear = shear_analysis_translation(A) - spectral = spectral_analysis_structure(A) - packet = packet_analysis_progressions(A, max_length=5) - - results.append({ - "n_max": n_max, - "density": density, - "seed": seed, - "field": field, - "shear": shear, - "spectral": spectral, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős Conjecture on APs.""" - # Conjecture: high reciprocal sum → long APs - high_reciprocal = [r for r in results if r["field"]["reciprocal_sum"] > 5.0] - low_reciprocal = [r for r in results if r["field"]["reciprocal_sum"] <= 5.0] - - high_ap_length = np.mean([r["packet"]["max_ap_length"] for r in high_reciprocal]) if high_reciprocal else 0 - low_ap_length = np.mean([r["packet"]["max_ap_length"] for r in low_reciprocal]) if low_reciprocal else 0 - - return { - "high_reciprocal_count": len(high_reciprocal), - "low_reciprocal_count": len(low_reciprocal), - "high_ap_length": float(high_ap_length), - "low_ap_length": float(low_ap_length), - "correlation": bool(high_ap_length > low_ap_length) - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS CONJECTURE ON APs") - print("=" * 70) - - # Test parameters - n_max_values = [50, 100, 200] - density_values = [0.3, 0.5, 0.7] - - print(f"\nTest parameters:") - print(f" n_max values: {n_max_values}") - print(f" Density values: {density_values}") - print(f" Samples per configuration: 3") - print(f" Total tests: {len(n_max_values) * len(density_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING DENSE SETS AND ANALYZING") - print("=" * 70) - - results = test_erdos_ap(n_max_values, density_values) - - print(f"\nGenerated {len(results)} dense sets") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" High reciprocal sum sets: {analysis['high_reciprocal_count']}") - print(f" Low reciprocal sum sets: {analysis['low_reciprocal_count']}") - print(f" Avg AP length (high reciprocal): {analysis['high_ap_length']:.2f}") - print(f" Avg AP length (low reciprocal): {analysis['low_ap_length']:.2f}") - print(f" Correlation holds: {analysis['correlation']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Density of set computed") - print(" - Reciprocal sum measured") - print(" - Conjecture condition: high reciprocal sum → long APs") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Translation rigidity computed") - print(" - Periodicity score measured") - print(" - Density deformation under translation") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Set structure eigen decomposition") - print(" - Spectral radius computed") - print(" - Structure rank measured") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Arithmetic progressions as packets") - print(" - Max AP length computed") - print(" - AP density measured") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures conjecture condition:") - print(" - Reciprocal sum directly measures conjecture condition") - print(" - High reciprocal sum → should imply long APs") - - print("\n2. Shear primitive measures structural regularity:") - print(" - Translation rigidity indicates periodicity") - print(" - Periodicity correlates with AP existence") - - print("\n3. Spectral primitive reveals additive structure:") - print(" - Eigenvalues encode set structure") - print(" - Spectral radius indicates structure extent") - - print("\n4. Packet primitive captures AP structure:") - print(" - APs treated as packet witnesses") - print(" - Max AP length indicates richness") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: conjecture condition (reciprocal sum)") - print(" - Shear: structural regularity (translation)") - print(" - Spectral: additive structure") - print(" - Packet: AP witnesses") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_max_values": n_max_values, - "density_values": density_values, - "samples_per_config": 3, - "total_tests": len(n_max_values) * len(density_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Density and reciprocal sum of set", - "insight": "Reciprocal sum directly measures conjecture condition" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Translation rigidity and periodicity", - "insight": "Translation rigidity indicates structural regularity" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Set structure eigen decomposition", - "insight": "Eigenvalues encode additive structure" - }, - "packet": { - "equation": "Γᵢ", - "application": "Arithmetic progressions as packets", - "insight": "APs treated as packet witnesses" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős Conjecture on Arithmetic Progressions. Field primitive captures conjecture condition. Shear primitive measures structural regularity. Spectral primitive reveals additive structure. Packet primitive captures AP witnesses. Framework validated for additive combinatorics problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_ap_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_ap_4primitive_results.json b/4-Infrastructure/shim/test_erdos_ap_4primitive_results.json deleted file mode 100644 index 915824a4..00000000 --- a/4-Infrastructure/shim/test_erdos_ap_4primitive_results.json +++ /dev/null @@ -1,3919 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:26:14.761490", - "n_max_values": [ - 50, - 100, - 200 - ], - "density_values": [ - 0.3, - 0.5, - 0.7 - ], - "samples_per_config": 3, - "total_tests": 27 - }, - "results": [ - { - "n_max": 50, - "density": 0.3, - "seed": 0, - "field": { - "density": 0.20833333333333334, - "reciprocal_sum": 0.39069392800831293, - "asymptotic_density": 0.20833333333333334, - "n_max": 48, - "size": 10 - }, - "shear": { - "translation_rigidity": 1.141258741128494, - "avg_translation_error": 0.8762254901960785, - "periodicity_score": 0.12377450980392146 - }, - "spectral": { - "eigenvalues": [ - 4.854101966249685, - 4.302968385571447e-16, - 7.391935404147144e-17, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -9.757381983235608e-18, - -1.9219337392223413e-17, - -4.4336190907902296e-16, - -1.8541019662496843 - ], - "spectral_radius": 4.854101966249685, - "structure_rank": 2 - }, - "packet": { - "max_ap_length": 3, - "num_progressions": 4, - "ap_density": 0.4 - } - }, - { - "n_max": 50, - "density": 0.3, - "seed": 1, - "field": { - "density": 0.36, - "reciprocal_sum": 1.360931169697772, - "asymptotic_density": 0.36, - "n_max": 50, - "size": 18 - }, - "shear": { - "translation_rigidity": 1.245264371447633, - "avg_translation_error": 0.803042328042328, - "periodicity_score": 0.196957671957672 - }, - "spectral": { - "eigenvalues": [ - 11.135070631467942, - 2.032857190758628, - 0.9336046032394852, - 0.7930752096480406, - 1.4441566428117413e-15, - 1.1576272792640377e-15, - 6.017881688482891e-16, - 5.774372429008062e-16, - 3.55111552242012e-16, - 3.12586951578819e-16, - 2.594245340782633e-16, - 2.385569508842293e-16, - 1.5069250351206277e-16, - 9.854094356472297e-17, - 6.007238374361418e-17, - 4.321627383756272e-17, - 1.3259143554026671e-33, - 8.277251965912901e-50, - 9.914652516408773e-51, - 1.4153669544228527e-66, - 6.695089517424915e-83, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.9663167813155616e-99, - -5.468774871653147e-83, - -1.255865214431818e-66, - -1.5256300637888244e-33, - -6.7515096316284885e-18, - -1.9780778817066562e-17, - -5.258467256345552e-17, - -1.3115658954202975e-16, - -1.5796926425159934e-16, - -1.9108464121437955e-16, - -2.533764279691077e-16, - -3.7457519796930454e-16, - -3.9913433502521697e-16, - -5.548465600911568e-16, - -6.026420612369896e-16, - -1.202049306891803e-15, - -3.0687462106746815e-15, - -0.7603118503054492, - -0.9300882991905547, - -1.7678131159394606, - -3.43639436967864 - ], - "spectral_radius": 11.135070631467942, - "structure_rank": 8 - }, - "packet": { - "max_ap_length": 4, - "num_progressions": 23, - "ap_density": 1.2777777777777777 - } - }, - { - "n_max": 50, - "density": 0.3, - "seed": 2, - "field": { - "density": 0.38461538461538464, - "reciprocal_sum": 1.3388043166922476, - "asymptotic_density": 0.38461538461538464, - "n_max": 39, - "size": 15 - }, - "shear": { - "translation_rigidity": 1.266519823628139, - "avg_translation_error": 0.7895652173913044, - "periodicity_score": 0.21043478260869564 - }, - "spectral": { - "eigenvalues": [ - 9.364754470106494, - 1.832923449820495, - 0.9554036519706726, - 0.6561263993864446, - 1.5740400314450889e-15, - 7.979327930422226e-16, - 3.990435079691263e-16, - 3.039538779970039e-16, - 2.1342426792171106e-16, - 1.6289027288723532e-16, - 1.518804414338252e-16, - 9.855322354591699e-17, - 2.824270102726938e-17, - 1.7034443457743987e-17, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.416248146189598e-19, - -1.6382336170260645e-17, - -2.91560536085096e-17, - -1.3359837389321022e-16, - -1.8613195516640027e-16, - -2.2385903048640205e-16, - -2.9800187067487915e-16, - -3.485076409532763e-16, - -3.72122284768608e-16, - -5.553177372677082e-16, - -7.558058256499972e-16, - -1.2887897701669226e-15, - -0.6459219785525077, - -0.9517381319058656, - -1.44488394324605, - -3.7666639175796854 - ], - "spectral_radius": 9.364754470106494, - "structure_rank": 8 - }, - "packet": { - "max_ap_length": 4, - "num_progressions": 17, - "ap_density": 1.1333333333333333 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 0, - "field": { - "density": 0.4, - "reciprocal_sum": 1.0292736263486535, - "asymptotic_density": 0.4, - "n_max": 50, - "size": 20 - }, - "shear": { - "translation_rigidity": 1.268891902757822, - "avg_translation_error": 0.7880891963292547, - "periodicity_score": 0.21191080367074533 - }, - "spectral": { - "eigenvalues": [ - 10.332836619826228, - 1.9216512196576132, - 1.3338604089848318, - 0.9044540620876785, - 0.6543156485246635, - 0.5478996720938644, - 1.106667325460143e-15, - 9.364671899866224e-16, - 4.470232690315018e-16, - 3.969201359051652e-16, - 2.862167435569392e-16, - 2.222946351354845e-16, - 1.7958708202961862e-16, - 2.830566800951519e-17, - 4.984634580722227e-18, - 9.26719219550004e-33, - 3.8251772646277745e-34, - 9.42098754589548e-49, - 3.139910322778488e-49, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.939924316251416e-65, - -1.1157976800685006e-48, - -3.758082044795316e-33, - -4.546585963100211e-17, - -8.049959004482485e-17, - -9.254443769857783e-17, - -1.0547257022468396e-16, - -1.4945444591553743e-16, - -2.2270367527637015e-16, - -2.3722242992067115e-16, - -2.7039987481563373e-16, - -3.80757872633057e-16, - -5.122669048959003e-16, - -6.105192170617099e-16, - -9.60738615579212e-16, - -0.6542777120646329, - -0.7363520814076786, - -0.9234224586352494, - -1.712269199663725, - -3.6686961794035944 - ], - "spectral_radius": 10.332836619826228, - "structure_rank": 11 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 35, - "ap_density": 1.75 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 1, - "field": { - "density": 0.56, - "reciprocal_sum": 3.1210200702118645, - "asymptotic_density": 0.56, - "n_max": 50, - "size": 28 - }, - "shear": { - "translation_rigidity": 1.6405363552805625, - "avg_translation_error": 0.6095567444251655, - "periodicity_score": 0.3904432555748345 - }, - "spectral": { - "eigenvalues": [ - 19.166412020022033, - 3.044641068170295, - 1.8763304458787333, - 1.2726364720816825, - 1.1412064083964495, - 0.9470879245153077, - 0.8462029275802021, - 0.6409881377629864, - 0.6182996445191966, - 0.5731566943357492, - 1.4291573266515547e-15, - 8.40640783396244e-16, - 6.41961717259992e-16, - 5.129758313587652e-16, - 4.725238580754937e-16, - 3.743697576306722e-16, - 2.988810581270849e-16, - 1.2392707050679768e-16, - 1.2110992285782856e-16, - 1.6143331340030535e-17, - 2.583496083174453e-32, - 1.1011545687973559e-32, - 3.810456271838425e-49, - 0.0, - -9.182792968577454e-66, - -7.68604071128652e-50, - -2.724541857119281e-48, - -1.339820951015331e-33, - -8.245398220083789e-33, - -1.2246692242754516e-32, - -3.6349916003670785e-17, - -8.338017863577158e-17, - -1.2317322601459042e-16, - -2.3612666496466787e-16, - -4.07719031542432e-16, - -6.688833307903676e-16, - -7.326376985189133e-16, - -7.710865507822373e-16, - -8.441309997728669e-16, - -1.6695761549203764e-15, - -0.5594839847234544, - -0.5956793848764038, - -0.6187109763376931, - -0.6464516325625345, - -0.9231633992269527, - -1.0000000000000018, - -1.1933400283778155, - -1.6656002420932157, - -2.4988186250683277, - -5.425713469996237 - ], - "spectral_radius": 19.166412020022033, - "structure_rank": 20 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 99, - "ap_density": 3.5357142857142856 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 2, - "field": { - "density": 0.5957446808510638, - "reciprocal_sum": 3.243028739023894, - "asymptotic_density": 0.5957446808510638, - "n_max": 47, - "size": 28 - }, - "shear": { - "translation_rigidity": 1.7245168621505012, - "avg_translation_error": 0.5798725554823115, - "periodicity_score": 0.42012744451768846 - }, - "spectral": { - "eigenvalues": [ - 18.87807343699346, - 3.1207124210929944, - 1.7663379346848205, - 1.3006717087385298, - 1.1036940604888428, - 0.8888994228356353, - 0.7676913938951458, - 0.6083755333975142, - 0.585088275316263, - 0.5555854066038416, - 2.038773619598225e-15, - 6.579858142129914e-16, - 5.378381337985458e-16, - 4.700819772378515e-16, - 3.61648900968202e-16, - 2.254195371766656e-16, - 1.5922788722593009e-16, - 8.49423542819594e-17, - 2.2136399163730207e-18, - 2.598225889096927e-32, - 1.4186677865017175e-32, - 2.9165601181772286e-35, - 1.9869359514882472e-48, - 3.649821330414853e-51, - 2.72808594920479e-67, - 0.0, - 0.0, - -9.661422284337144e-65, - -4.4920067227340235e-17, - -1.2332454584398507e-16, - -2.0169831662517745e-16, - -3.135897357628196e-16, - -4.564538490794483e-16, - -5.542418919560304e-16, - -9.019693228554746e-16, - -1.0671442234581065e-15, - -1.267196143494998e-15, - -0.5131620449063142, - -0.5848826339208001, - -0.6083700503005135, - -0.6372909843875837, - -0.8657566148806211, - -0.9713259439397735, - -1.2272118633226596, - -1.4811766393755232, - -2.0029404319895874, - -6.683012387023679 - ], - "spectral_radius": 18.87807343699346, - "structure_rank": 20 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 105, - "ap_density": 3.75 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 0, - "field": { - "density": 0.72, - "reciprocal_sum": 3.2152238592821565, - "asymptotic_density": 0.72, - "n_max": 50, - "size": 36 - }, - "shear": { - "translation_rigidity": 2.328726844503278, - "avg_translation_error": 0.4294191919191919, - "periodicity_score": 0.5705808080808081 - }, - "spectral": { - "eigenvalues": [ - 19.96197522045391, - 4.210541617352623, - 2.1785606182480834, - 1.6804685489414708, - 1.4347246720955855, - 1.0979740241316045, - 0.9190272956957108, - 0.7871594459179938, - 0.7503393659975943, - 0.6818654531650545, - 0.6004228302243642, - 0.5709684040983867, - 0.5514079281539671, - 0.5224325988432194, - 1.2242444608260215e-15, - 4.442730991435018e-16, - 4.108791259035665e-16, - 3.244346030597696e-16, - 2.9096324917992387e-16, - 1.987461771835988e-16, - 1.0400890893830794e-16, - 9.103335626403937e-17, - 2.7536185066668996e-32, - 4.9896454981008896e-34, - 0.0, - -9.91881430598397e-33, - -1.4998526203431357e-32, - -6.803783378454094e-17, - -8.629744263465097e-17, - -1.1760926962463566e-16, - -1.60939163099654e-16, - -2.6351594887380667e-16, - -2.6590983501370907e-16, - -2.96866630607759e-16, - -3.588678674834953e-16, - -5.701069905459201e-16, - -6.742282516078769e-16, - -0.5224325994734286, - -0.5514190418308108, - -0.5710026145672905, - -0.6004239589134953, - -0.7496722838840973, - -0.7851023950895025, - -0.9064532185663954, - -1.0520153834767647, - -1.1072238884777155, - -1.5957571597446134, - -1.9977894962443192, - -3.4258149850204154, - -8.082760998030718 - ], - "spectral_radius": 19.96197522045391, - "structure_rank": 27 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 208, - "ap_density": 5.777777777777778 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 1, - "field": { - "density": 0.72, - "reciprocal_sum": 3.549085846071758, - "asymptotic_density": 0.72, - "n_max": 50, - "size": 36 - }, - "shear": { - "translation_rigidity": 2.2241758236811284, - "avg_translation_error": 0.4496047430830039, - "periodicity_score": 0.5503952569169961 - }, - "spectral": { - "eigenvalues": [ - 24.914687458340694, - 4.019746915563797, - 2.424402442163106, - 1.599856129635275, - 1.299733669157069, - 1.133093699844177, - 0.9999999999999994, - 0.9253367628879668, - 0.8431976448826005, - 0.6198083183303204, - 0.5991170242288959, - 0.568827675987441, - 0.5392065513288405, - 1.186182887951106e-15, - 7.809079588017831e-16, - 6.328026056600986e-16, - 5.400671188981824e-16, - 4.441828692343011e-16, - 2.3605025868031465e-16, - 1.4792002036236478e-16, - 8.650471453613707e-17, - 4.643650352252468e-18, - 2.898667063784714e-32, - 1.0402894578512919e-48, - 0.0, - -1.292827736839999e-50, - -2.0275286658624103e-34, - -2.9289064655944992e-33, - -6.753941780563911e-33, - -2.2197200387479824e-32, - -2.8368241888056494e-17, - -1.4462554340112295e-16, - -2.1773919645397607e-16, - -2.6349709978044233e-16, - -6.199347056777366e-16, - -7.59748715030394e-16, - -9.630589896950468e-16, - -1.00377412112036e-15, - -0.5650401178144466, - -0.5935520064433738, - -0.6198078644968881, - -0.7070325709268052, - -0.8680036501636409, - -0.9411378124745743, - -1.0465736786771445, - -1.19815485582281, - -1.3575429082567527, - -2.254611288517578, - -2.96538209879035, - -7.3701754399658315 - ], - "spectral_radius": 24.914687458340694, - "structure_rank": 25 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 213, - "ap_density": 5.916666666666667 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 2, - "field": { - "density": 0.8367346938775511, - "reciprocal_sum": 4.184743811754005, - "asymptotic_density": 0.8367346938775511, - "n_max": 49, - "size": 41 - }, - "shear": { - "translation_rigidity": 3.685227271369183, - "avg_translation_error": 0.2713536848596978, - "periodicity_score": 0.7286463151403022 - }, - "spectral": { - "eigenvalues": [ - 27.618471397026198, - 4.6785137471040645, - 3.061333124993969, - 1.9997002826939039, - 1.4804158979689692, - 1.23787412676922, - 1.0700001027949921, - 0.9381485085373885, - 0.8888724663084135, - 0.7483295543166807, - 0.656550806141208, - 0.6410415615161487, - 0.5945100809339996, - 0.5785678952314758, - 0.5337577575966552, - 0.5213968795839687, - 2.4941691231287287e-15, - 1.3408649514269133e-15, - 5.593796478013011e-16, - 3.252194521207631e-16, - 1.7831242442086017e-16, - 9.682267245793825e-18, - 3.443293782688705e-32, - 0.0, - 0.0, - -4.5447844381780785e-36, - -6.83720828069679e-33, - -7.520812359399636e-32, - -2.004895671380438e-17, - -1.52345641848447e-16, - -3.019977915025577e-16, - -5.66448299205849e-16, - -8.086473405758336e-16, - -0.5052243034855389, - -0.5337577572878299, - -0.5500906918599755, - -0.5787346268063757, - -0.6407856895252574, - -0.6562341996155925, - -0.65939889576015, - -0.8541515470125254, - -0.893932681487147, - -0.9864031383688214, - -1.171584734625784, - -1.2924642278127738, - -1.8269845006495595, - -2.315155842604513, - -3.443285270689354, - -9.339296081926063 - ], - "spectral_radius": 27.618471397026198, - "structure_rank": 32 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 337, - "ap_density": 8.21951219512195 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 0, - "field": { - "density": 0.34, - "reciprocal_sum": 0.7227462772118607, - "asymptotic_density": 0.34, - "n_max": 100, - "size": 34 - }, - "shear": { - "translation_rigidity": 1.2938871926651474, - "avg_translation_error": 0.7728649031688863, - "periodicity_score": 0.22713509683111366 - }, - "spectral": { - "eigenvalues": [ - 14.587195468551425, - 3.3100875928252163, - 2.1319322778969996, - 1.159723630679044, - 1.044942641575981, - 0.5945586541884587, - 1.5731514285662803e-15, - 1.409039327280069e-15, - 8.385576466369455e-16, - 6.33403195164125e-16, - 4.045393659067222e-16, - 2.3046881157152925e-16, - 1.7711491411580891e-16, - 8.613914859007037e-17, - 6.802477852786325e-17, - 3.239004171116423e-19, - 3.6010114162377205e-31, - 3.1551764978357757e-31, - 1.8369945970946854e-31, - 1.632658205716344e-31, - 1.202049770473965e-31, - 1.1743011791046851e-31, - 1.1399546026162763e-31, - 9.561068001954039e-32, - 7.655751047656414e-32, - 5.975228238922304e-32, - 3.571685741902237e-32, - 3.1437912357989286e-32, - 2.7277581587671826e-32, - 2.178343111103565e-32, - 2.0249968360334487e-47, - 1.2627985567987472e-47, - 6.939110199797374e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.0383061240981653e-49, - -1.2737940126797409e-49, - -2.0766122481963306e-49, - -8.101596335209916e-48, - -1.2370428982566108e-47, - -1.876215727490274e-47, - -2.878070653361842e-47, - -3.404108200303302e-33, - -1.002842915863697e-32, - -2.563800310973073e-32, - -3.5154924990427103e-32, - -3.647144666903462e-32, - -6.04718782490993e-32, - -6.622446424145523e-32, - -6.865949153720023e-32, - -9.851034696064932e-32, - -9.915311491430787e-32, - -1.157267589187795e-31, - -1.4832731867591692e-31, - -2.028365789081586e-31, - -3.0052265578218544e-31, - -4.066673481700703e-31, - -4.710118892868527e-31, - -4.764498781476728e-17, - -2.0351691643149694e-16, - -4.032016603684952e-16, - -6.520134008844002e-16, - -7.400679232833547e-16, - -8.662186681398264e-16, - -1.0922101046015474e-15, - -1.7797916441642947e-15, - -0.594560665372271, - -1.0609673333194802, - -1.362686586429169, - -2.9731169185346493, - -6.837108762061553 - ], - "spectral_radius": 14.587195468551425, - "structure_rank": 11 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 119, - "ap_density": 3.5 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 1, - "field": { - "density": 0.32323232323232326, - "reciprocal_sum": 1.5688188434586592, - "asymptotic_density": 0.32323232323232326, - "n_max": 99, - "size": 32 - }, - "shear": { - "translation_rigidity": 1.2324710710546427, - "avg_translation_error": 0.8113780707412783, - "periodicity_score": 0.18862192925872168 - }, - "spectral": { - "eigenvalues": [ - 22.191274608692886, - 3.7984028625665767, - 2.187878153388849, - 1.6120409317389526, - 1.305601340035801, - 0.8660519526931163, - 0.8134162719477249, - 0.7407661036947877, - 0.6444931097658066, - 0.6211963500749248, - 0.5553051120849473, - 3.4050827543262343e-15, - 2.5660339859608054e-15, - 1.823212023906157e-15, - 1.040119277846722e-15, - 9.161621718851852e-16, - 6.960271007730688e-16, - 5.472926973525717e-16, - 4.0756568962414636e-16, - 2.3574584775379794e-16, - 2.005457403323646e-16, - 5.378677337791073e-17, - 3.0269496791497694e-17, - 2.869684565606515e-17, - 2.423252868405768e-31, - 1.6556247960545695e-31, - 1.5275779713553672e-31, - 1.2370394004853069e-31, - 1.1827035119950075e-31, - 8.336141556557747e-32, - 7.285421991808182e-32, - 4.2954947461330775e-32, - 3.241970923905979e-32, - 1.4010042810639835e-32, - 2.2042823542612553e-33, - 2.9098402064675005e-47, - 2.586488287117319e-47, - 2.155696282868795e-47, - 1.3684452996238366e-47, - 1.2216982454562862e-47, - 9.988357358280807e-48, - 8.002064713261251e-48, - 6.34034701784986e-48, - 3.8820336706127155e-48, - 1.920575544771132e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -5.3812150120926594e-49, - -2.3782032623527096e-48, - -3.229062107189129e-48, - -4.028545144421333e-48, - -5.781919121800338e-48, - -7.541268577929126e-48, - -1.292080648708421e-47, - -1.3044912449575294e-47, - -2.0956057627854107e-47, - -2.360791007121297e-47, - -2.3728099487016516e-47, - -2.4487022609703e-47, - -1.1745192727698926e-32, - -3.8840944497838776e-32, - -4.0507127631460913e-32, - -5.344587734746598e-32, - -7.147282142589961e-32, - -1.0407713175109268e-31, - -1.2578456876704877e-31, - -1.3814886681566773e-31, - -1.724918742630273e-31, - -2.2382188070356605e-31, - -2.562577848377105e-31, - -5.1362451460567184e-17, - -1.1620986450674132e-16, - -1.3430124781401203e-16, - -2.148005639627739e-16, - -2.616934081166686e-16, - -4.835680190323347e-16, - -5.745241401207509e-16, - -1.107408905347163e-15, - -1.4544351264402822e-15, - -1.7351286594182773e-15, - -2.7182396018151577e-15, - -3.0613039453970755e-15, - -0.5252009539756134, - -0.5593272953734854, - -0.6444887674694397, - -0.7357289918338323, - -0.763785486750004, - -0.8657871306851764, - -0.9284482184810331, - -1.515091702868328, - -1.7770055312298103, - -2.9431849367490766, - -7.078377781268576 - ], - "spectral_radius": 22.191274608692886, - "structure_rank": 22 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 89, - "ap_density": 2.78125 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 2, - "field": { - "density": 0.30303030303030304, - "reciprocal_sum": 1.5502152326048888, - "asymptotic_density": 0.30303030303030304, - "n_max": 99, - "size": 30 - }, - "shear": { - "translation_rigidity": 1.2123507866710146, - "avg_translation_error": 0.8248437753108222, - "periodicity_score": 0.1751562246891778 - }, - "spectral": { - "eigenvalues": [ - 20.55070012432417, - 3.463594651390754, - 1.675048132594539, - 1.448760189776833, - 1.125974194307154, - 0.7754318483153063, - 0.7333475241049978, - 0.7083167082534679, - 0.5512065694101966, - 2.5204546752417264e-15, - 1.8269906363491127e-15, - 1.221792547464425e-15, - 1.1702735601736278e-15, - 8.83810876953316e-16, - 8.283085326547831e-16, - 7.383391501288919e-16, - 5.827646432267343e-16, - 4.2185900772152614e-16, - 3.5540563662060275e-16, - 1.6657786348194472e-16, - 1.952092172219235e-17, - 2.713181629776713e-31, - 2.573207727897874e-31, - 2.4369341959913126e-31, - 2.2167535881021504e-31, - 1.7817444825263702e-31, - 1.6347633718846715e-31, - 1.2304432843348094e-31, - 1.0066306596332017e-31, - 8.827141840131479e-32, - 8.001714357399037e-32, - 7.061933747762324e-32, - 3.2886538707680736e-32, - 2.1310040897268097e-32, - 1.3536617198371607e-32, - 4.049041144038158e-47, - 3.3894899580156285e-47, - 2.293066816127932e-47, - 2.0374047232503598e-47, - 1.8152147816753893e-47, - 1.4213275249080438e-47, - 1.1238700173695659e-47, - 8.045842728905599e-48, - 6.153997206545162e-48, - 2.634387466628216e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -3.909784561144887e-49, - -3.5519874856059205e-48, - -4.139414019365903e-48, - -4.9569588443557637e-48, - -1.1121490702761603e-47, - -1.1333821087548244e-47, - -1.2974808203609032e-47, - -1.7142697836358606e-47, - -1.9514546785347904e-47, - -2.8146626278429325e-47, - -3.3289328609034644e-47, - -2.1755577882255925e-32, - -2.669092790296726e-32, - -5.441457300505748e-32, - -6.960564933570367e-32, - -7.569789760131608e-32, - -8.352819528219436e-32, - -9.850290070031259e-32, - -1.2237951526437122e-31, - -1.3260465635818096e-31, - -1.347704965612153e-31, - -1.7770417623661225e-31, - -1.975358018609651e-31, - -2.0875444126264962e-31, - -2.3974915926586468e-31, - -2.5209198058502425e-31, - -2.906077002672211e-31, - -8.816747493323234e-17, - -2.0992524443241365e-16, - -2.1061335485087812e-16, - -3.762535510912433e-16, - -4.590495938804614e-16, - -5.686791113835554e-16, - -7.677996959468905e-16, - -1.1988696990022489e-15, - -1.282446881352593e-15, - -1.932745683705873e-15, - -2.488591264979541e-15, - -2.5609756342657973e-15, - -0.5512065693976266, - -0.706788219840444, - -0.7328361101577994, - -0.7753719967845032, - -1.0866485719117789, - -1.3666707496078785, - -1.6200412151193568, - -3.0656571591573663, - -6.127159350500669 - ], - "spectral_radius": 20.55070012432417, - "structure_rank": 18 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 54, - "ap_density": 1.8 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 0, - "field": { - "density": 0.51, - "reciprocal_sum": 1.4620761133183235, - "asymptotic_density": 0.51, - "n_max": 100, - "size": 51 - }, - "shear": { - "translation_rigidity": 1.5430882275544973, - "avg_translation_error": 0.6480510848239066, - "periodicity_score": 0.3519489151760934 - }, - "spectral": { - "eigenvalues": [ - 26.46302462171642, - 6.001888705032884, - 3.721738242308655, - 2.2055928773884395, - 2.000284495126827, - 1.6706209649271109, - 1.1043121322509755, - 0.9621905598487666, - 0.908351052083351, - 0.8554393030491817, - 0.8195444810660918, - 0.6456696545337661, - 0.6304683057029675, - 0.5676104682087082, - 0.5285243657302834, - 1.852984605919612e-15, - 8.752778628297975e-16, - 7.285382324266349e-16, - 5.90461987604852e-16, - 4.94649794091818e-16, - 4.171084719151601e-16, - 2.0358661414966643e-16, - 7.256868088575455e-17, - 1.759985574034173e-29, - 1.0359330654940596e-29, - 9.704443050129668e-30, - 6.358282107554842e-30, - 4.618016385285694e-30, - 3.567329614018442e-30, - 2.40237234621528e-30, - 1.1468796794901002e-30, - 8.374433909069872e-31, - 3.5016361945715576e-31, - 1.6179499449423607e-31, - 1.4771191155268166e-31, - 7.078617337910001e-46, - 6.819269326193039e-46, - 6.1059819426538755e-46, - 4.4096375413005e-46, - 1.1146462044425983e-46, - 4.43989160327112e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.6133987006408633e-46, - -2.1076595096590026e-46, - -3.787340235591514e-46, - -6.219418956192293e-46, - -7.002468315124775e-46, - -7.660324922812278e-46, - -1.3725422973656451e-45, - -2.485009700529278e-31, - -4.6271954006475445e-31, - -5.622232148032857e-31, - -7.190620106718772e-31, - -1.0199941932945231e-30, - -1.195071372417158e-30, - -1.3372940859796747e-30, - -1.889483949928264e-30, - -2.7080409832761518e-30, - -3.0201596946960783e-30, - -4.482614032622195e-30, - -4.975251319687468e-30, - -8.500633246515191e-30, - -1.1445429088971146e-29, - -1.5803372173710656e-29, - -5.553425926022244e-17, - -1.004088379002443e-16, - -1.3014472371391333e-16, - -2.0670668081836735e-16, - -4.080179688382694e-16, - -4.557912084549565e-16, - -6.124679112343934e-16, - -7.070821349267744e-16, - -7.225740139221828e-16, - -8.50262293647589e-16, - -1.0610661350443269e-15, - -1.1310956051244854e-15, - -1.2949814925185953e-15, - -2.3564125144993994e-15, - -0.5285243701752895, - -0.5676243445696726, - -0.6304690281684712, - -0.6456696545371596, - -0.8436627914391931, - -0.8849557476547957, - -0.9621524615327968, - -1.0547815289828821, - -1.1074084292148023, - -1.6993910062827942, - -2.1587088349427437, - -2.517096655030408, - -4.7060563963547075, - -10.778758980088714 - ], - "spectral_radius": 26.46302462171642, - "structure_rank": 29 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 367, - "ap_density": 7.196078431372549 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 1, - "field": { - "density": 0.5151515151515151, - "reciprocal_sum": 3.4496754593063224, - "asymptotic_density": 0.5151515151515151, - "n_max": 99, - "size": 51 - }, - "shear": { - "translation_rigidity": 1.4822489120728664, - "avg_translation_error": 0.6746505203726645, - "periodicity_score": 0.3253494796273355 - }, - "spectral": { - "eigenvalues": [ - 35.04220464279881, - 6.198920702602783, - 3.4692614439783096, - 2.7198250575947496, - 1.8726499036631237, - 1.6590786949218168, - 1.2209275701071594, - 1.0432740388959363, - 0.9273562516173061, - 0.896581634367651, - 0.8439077066687349, - 0.7644899798585969, - 0.7189608231598698, - 0.6668099050219025, - 0.621213110542842, - 0.6085327801570306, - 0.5596172452873939, - 0.5319868599167576, - 4.973685462445657e-15, - 1.185367361391937e-15, - 9.82088029260066e-16, - 7.057320908025454e-16, - 5.752333290799533e-16, - 4.596927620395354e-16, - 2.9451041598246665e-16, - 2.334246622016076e-16, - 1.1999037120192071e-16, - 8.895092775203129e-17, - 5.5177832973903954e-17, - 4.970089191134114e-17, - 2.6248614563368457e-17, - 1.0643938715997305e-17, - 6.112351863863008e-18, - 3.982854513481527e-18, - 2.459057185552864e-18, - 2.948363112803587e-19, - 1.4250494316157445e-20, - 2.3411741851158896e-22, - 2.2661620998000298e-23, - 3.018453232381452e-33, - 2.8860789137386803e-33, - 2.5670064308826166e-33, - 1.9258967686296e-33, - 1.2388899050894776e-33, - 8.178558937687643e-34, - 6.782320109980599e-34, - 4.45253658341346e-34, - 2.744219304477206e-34, - 3.9562642563442516e-35, - 0.0, - 0.0, - 0.0, - -3.4085766512711244e-34, - -5.082843191950168e-34, - -7.457879542608046e-34, - -7.667321871804043e-34, - -1.4316156050996467e-33, - -1.7423630987030653e-33, - -2.6043425493755434e-33, - -2.82486700571382e-33, - -3.5989501695218764e-33, - -2.4645072730843195e-20, - -4.68604842606002e-19, - -7.056123920292195e-19, - -1.6308328435000755e-18, - -3.822129711883461e-18, - -1.1147749306451543e-17, - -2.175272542446107e-17, - -3.0899748448268003e-17, - -4.4022241901768314e-17, - -7.701809511112584e-17, - -1.8568236391961607e-16, - -2.6418932720439704e-16, - -2.7438780250513707e-16, - -4.136755949719993e-16, - -5.802467460581134e-16, - -7.68897311551193e-16, - -7.743169730835342e-16, - -1.0495985056210761e-15, - -1.1749676375716444e-15, - -3.5030478600261783e-15, - -0.507779259434898, - -0.5596172451739029, - -0.573608795624508, - -0.6212130724474328, - -0.6213905797282213, - -0.718920604876327, - -0.7642189627926218, - -0.7781328347744152, - -0.8454932316929921, - -0.9141565165015048, - -0.9540242787148755, - -1.2204698737595214, - -1.3252946137672343, - -1.8584499811182342, - -2.1095272375301892, - -2.929114391414421, - -4.679974824350846, - -11.384212047458627 - ], - "spectral_radius": 35.04220464279881, - "structure_rank": 36 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 329, - "ap_density": 6.450980392156863 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 2, - "field": { - "density": 0.5656565656565656, - "reciprocal_sum": 3.629287205749213, - "asymptotic_density": 0.5656565656565656, - "n_max": 99, - "size": 56 - }, - "shear": { - "translation_rigidity": 1.578686732384674, - "avg_translation_error": 0.6334378945033562, - "periodicity_score": 0.3665621054966438 - }, - "spectral": { - "eigenvalues": [ - 36.346244411184685, - 7.367191407031603, - 3.798951275713672, - 2.638170730349689, - 2.173797915327621, - 1.6839054766405919, - 1.5307748077458727, - 1.3463303713172274, - 1.1696387146779141, - 0.9991295660148408, - 0.8738374760937053, - 0.8232849507992266, - 0.778638601428555, - 0.7074558341353832, - 0.6868201545220944, - 0.6409011095566114, - 0.579741594946864, - 0.5598648978667099, - 0.5576368441958184, - 0.5446508681186868, - 3.322168891075048e-15, - 2.636131366450685e-15, - 1.8732946778205075e-15, - 1.0448179823940012e-15, - 1.042883890809742e-15, - 4.6449604443905e-16, - 3.264305304933331e-16, - 2.3252369723048804e-16, - 2.2891593281705343e-16, - 2.195935969573788e-16, - 2.0645794747287059e-16, - 1.698516750127341e-16, - 1.1054526286966511e-16, - 9.542283706815257e-17, - 7.203345261003179e-17, - 4.1719310055975885e-17, - 3.778262323626363e-17, - 3.359438248267203e-17, - 1.9128517183798287e-17, - 1.5543776709967374e-17, - 6.1145959083501564e-18, - 2.2412057627202064e-18, - 1.1834270391265933e-18, - 1.8486598186720443e-32, - 1.0107064135858735e-32, - 7.708961101279633e-33, - 5.949870787616168e-33, - 2.1124237046112367e-33, - 1.7685151247056036e-34, - 0.0, - 0.0, - 0.0, - -1.34196565172349e-33, - -4.5542495567182e-33, - -6.546766042274019e-33, - -1.0957917447979671e-32, - -1.4459683328656087e-32, - -2.7229408564470717e-32, - -3.0826231060586925e-18, - -7.765287931119246e-18, - -1.6125101587630838e-17, - -1.994474742093755e-17, - -3.261987690706362e-17, - -4.156146223843908e-17, - -4.3720875125711236e-17, - -5.68549691454948e-17, - -7.253628934841263e-17, - -1.0093197789645325e-16, - -1.1349668614715662e-16, - -1.1944852037905328e-16, - -1.8485247295744536e-16, - -2.563715739996111e-16, - -2.7762225689558473e-16, - -4.198699492213505e-16, - -5.443542043453443e-16, - -7.419319612755573e-16, - -8.869244041896562e-16, - -1.943541360822256e-15, - -2.3709823771005793e-15, - -0.5139946038678163, - -0.5446508681590749, - -0.5576368442520289, - -0.5797075618673762, - -0.6409011094939632, - -0.6519876606040333, - -0.68683370766463, - -0.7074607923374581, - -0.7786415723284095, - -0.8706345007715512, - -0.9989385707083537, - -1.1194776801881987, - -1.224643805509594, - -1.3508610994603747, - -1.5435091140062431, - -1.9182747785861962, - -2.295177518842402, - -3.1997385014053674, - -5.674515437356831, - -11.94938128025747 - ], - "spectral_radius": 36.346244411184685, - "structure_rank": 40 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 386, - "ap_density": 6.892857142857143 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 0, - "field": { - "density": 0.77, - "reciprocal_sum": 3.7821430977094983, - "asymptotic_density": 0.77, - "n_max": 100, - "size": 77 - }, - "shear": { - "translation_rigidity": 2.630170248542483, - "avg_translation_error": 0.38020352495855964, - "periodicity_score": 0.6197964750414404 - }, - "spectral": { - "eigenvalues": [ - 45.409459631431496, - 10.317029224769485, - 5.488208056739416, - 3.7363315463638993, - 2.8946609042144633, - 2.2151384612679808, - 1.9519150729615735, - 1.6563507745122443, - 1.5314369863907351, - 1.3499896906129492, - 1.2057005445625584, - 1.0252388935845402, - 0.9684166899015384, - 0.939075211887743, - 0.8945202198476647, - 0.8594578444589382, - 0.805900724104341, - 0.7738310558747007, - 0.7026463984388329, - 0.6605939538586068, - 0.636189284288872, - 0.6221799259844636, - 0.60035979422033, - 0.5901157017870743, - 0.5726632349287105, - 0.5521540023149303, - 0.5429429978547637, - 0.5294562739046482, - 0.5261504042086117, - 0.5249785713998847, - 0.5096821549316681, - 1.7254690706303584e-15, - 1.2352357757176227e-15, - 9.486435762818671e-16, - 8.46653719771963e-16, - 4.626015172495452e-16, - 3.177399963686542e-16, - 2.7215123115181045e-16, - 2.0830448704385006e-16, - 1.2697719878810805e-16, - 9.082777168341084e-17, - 1.8087897734549167e-17, - 4.114012531522252e-18, - 3.558184135253454e-18, - 4.105090524818406e-31, - 1.8710012849756977e-31, - 9.100008889391354e-32, - 0.0, - 0.0, - -1.7619603030190523e-32, - -4.4962845830685793e-32, - -2.0791687421912816e-31, - -2.9107738910782254e-31, - -2.763257094652337e-19, - -7.628035696627975e-18, - -1.1157346642713895e-17, - -1.3527517444693613e-17, - -2.36965479385821e-17, - -3.3811037749643045e-17, - -8.44537643190678e-17, - -1.4284820189680051e-16, - -2.538616098727725e-16, - -4.401350062932183e-16, - -4.988365252077132e-16, - -5.47558376998692e-16, - -7.357812550111153e-16, - -9.177607783935652e-16, - -1.1713341464321406e-15, - -1.507800013402622e-15, - -5.307615807573446e-15, - -0.5098115149276415, - -0.5256006073050986, - -0.5294562739046468, - -0.5414862156900467, - -0.5521540023010797, - -0.5726632344461772, - -0.5889929643227273, - -0.5954321299465423, - -0.6172756442854993, - -0.6296384744014571, - -0.6361892868679228, - -0.6612496664302936, - -0.7149830643471605, - -0.7738319442435646, - -0.8313165005182315, - -0.8944466650570528, - -0.9214453289635903, - -0.9399717133656659, - -1.0215619102757896, - -1.1612207587690104, - -1.280185511120308, - -1.5074701311776415, - -1.5563506628996597, - -1.8062763117204215, - -1.9776483618610683, - -2.4615342172171353, - -3.3623346045032405, - -4.002040890075572, - -7.469851594156397, - -15.950354046507035 - ], - "spectral_radius": 45.409459631431496, - "structure_rank": 61 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 1170, - "ap_density": 15.194805194805195 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 1, - "field": { - "density": 0.74, - "reciprocal_sum": 4.088457751676166, - "asymptotic_density": 0.74, - "n_max": 100, - "size": 74 - }, - "shear": { - "translation_rigidity": 2.371409509358015, - "avg_translation_error": 0.4216901365271061, - "periodicity_score": 0.5783098634728939 - }, - "spectral": { - "eigenvalues": [ - 48.01358751362878, - 8.249494804174931, - 4.951371880549958, - 3.638436408642007, - 2.7307808373131306, - 2.2438019733268253, - 1.7878409256445562, - 1.642613972558849, - 1.3993913599320411, - 1.2795817058646048, - 1.222611920862824, - 1.108345411103318, - 1.0068755543474373, - 0.9750142636403017, - 0.888385422355911, - 0.8734789000955746, - 0.7971136605073489, - 0.7308536281244065, - 0.7208733320648112, - 0.7052181494494375, - 0.652281327693865, - 0.6505626176547449, - 0.61192555571019, - 0.5654288021075353, - 0.5485614524820984, - 0.543723413425045, - 0.5332714424775234, - 0.5320735426332216, - 0.5177535483559639, - 5.2522976569872726e-15, - 1.6941485701641357e-15, - 9.616709618110595e-16, - 8.187466971010733e-16, - 4.0373018104417255e-16, - 3.512512967566587e-16, - 2.2411258418026504e-16, - 1.888723766442223e-16, - 1.1730753451377375e-16, - 8.9608308784484e-17, - 7.671424258716301e-17, - 4.490303241225788e-17, - 2.703539403310426e-31, - 1.5579710258597901e-31, - 1.3456514639513063e-31, - 7.853649741905805e-32, - 4.2547248262037596e-32, - 1.0122310171338159e-32, - 0.0, - -1.5610133523595426e-32, - -2.0520612929710045e-32, - -4.686508071823084e-32, - -1.1614118416840831e-31, - -1.5574842062733137e-31, - -3.0354270724043297e-31, - -1.2533814158743373e-17, - -2.654049204242835e-17, - -3.293821906752518e-17, - -6.308862257994608e-17, - -8.400507184054751e-17, - -1.1059174903332177e-16, - -1.5425961564034856e-16, - -1.7674034407886184e-16, - -2.1106653941777983e-16, - -2.5967881329497475e-16, - -3.0189380644129395e-16, - -4.248971242681711e-16, - -4.663743593698057e-16, - -6.674117763658428e-16, - -7.061643860161681e-16, - -1.2044200501648346e-15, - -1.391411820291635e-15, - -1.8029825166046353e-15, - -0.5320735426332187, - -0.533271428084913, - -0.5437234125933751, - -0.5485614524819098, - -0.565428802107303, - -0.572209428407454, - -0.627307745018756, - -0.650599561023715, - -0.6522813432499057, - -0.7052270595432827, - -0.7308522000256252, - -0.7971122098619007, - -0.872045780253938, - -0.8833117017425574, - -0.9265224365917387, - -0.9755237530562303, - -1.0731604828548926, - -1.1330392734189465, - -1.2314250437433019, - -1.3082002617128585, - -1.4325064650878998, - -1.7679391773203095, - -2.077700395081478, - -2.467064628166095, - -2.9075805346290307, - -4.203481791219736, - -6.690942511348593, - -16.712160905468263 - ], - "spectral_radius": 48.01358751362878, - "structure_rank": 57 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 976, - "ap_density": 13.18918918918919 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 2, - "field": { - "density": 0.797979797979798, - "reciprocal_sum": 4.702884342070987, - "asymptotic_density": 0.797979797979798, - "n_max": 99, - "size": 79 - }, - "shear": { - "translation_rigidity": 2.6385774015291323, - "avg_translation_error": 0.37899210353147617, - "periodicity_score": 0.6210078964685238 - }, - "spectral": { - "eigenvalues": [ - 51.420500410361875, - 10.24257533004998, - 5.467202401458483, - 3.703824564635508, - 3.064670827510303, - 2.2971372431414, - 2.1029613980397275, - 1.7369877740357025, - 1.5160400068190436, - 1.403471893935, - 1.177079151910074, - 1.1074850503805593, - 1.0068241650749212, - 0.9671751653580815, - 0.8944385953870867, - 0.883041810266252, - 0.791424477815084, - 0.7872293753094296, - 0.7559754543708941, - 0.7179635279162294, - 0.6830545000362206, - 0.6507580869084505, - 0.6362201667340496, - 0.6254595717535398, - 0.6224820954700147, - 0.5976281734661916, - 0.5876463926101014, - 0.5634866555921817, - 0.5547376942080903, - 0.5268539250963028, - 0.5225311795852785, - 0.5191626125048304, - 0.5148359142911441, - 2.2205872610641104e-15, - 1.0202840992925376e-15, - 7.907364342373054e-16, - 3.4041652391178655e-16, - 2.9997703581434854e-16, - 2.432295902016759e-16, - 2.1684857130751013e-16, - 1.3647060667661405e-16, - 1.1636058466853623e-16, - 1.1339103945778155e-16, - 9.058039391578093e-17, - 6.52712614957631e-17, - 4.4020037047624285e-17, - 2.3910464734953835e-17, - 1.3854442385282939e-17, - 6.079488138344916e-18, - 1.854054284070473e-18, - 4.6725945110512825e-20, - 0.0, - -1.7914842777364793e-18, - -2.858226303068823e-18, - -2.2445971533811022e-17, - -2.825189140397995e-17, - -5.202985351305504e-17, - -7.929207133837335e-17, - -1.1987873463046178e-16, - -1.6503851240560349e-16, - -2.2312522770657433e-16, - -2.823001671806581e-16, - -3.362031797696713e-16, - -3.5931931212509643e-16, - -4.959093581318708e-16, - -5.109143621477689e-16, - -1.0471217002764153e-15, - -0.5148359177835822, - -0.51916261250483, - -0.5225311802379078, - -0.5268539250963045, - -0.5547376942081016, - -0.5634874913425191, - -0.5876463926155513, - -0.5976335127717911, - -0.6254518330424513, - -0.6335966569801074, - -0.6376960630765063, - -0.6645965757794543, - -0.7148032012837289, - -0.7179653648265737, - -0.7754808662601874, - -0.7910874790631333, - -0.8337027138146383, - -0.8837750553489955, - -0.9322678228623839, - -1.006166681681829, - -1.1073539417080964, - -1.1736176533364657, - -1.3942150852461748, - -1.504665122838308, - -1.7060945402462004, - -1.8720438515705666, - -2.152738142180498, - -2.6422785572528915, - -3.4445769735635263, - -4.304915056339867, - -7.3821009861393385, - -16.36078664102958 - ], - "spectral_radius": 51.420500410361875, - "structure_rank": 65 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 1130, - "ap_density": 14.30379746835443 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 0, - "field": { - "density": 0.3065326633165829, - "reciprocal_sum": 0.9001811304737954, - "asymptotic_density": 0.3065326633165829, - "n_max": 199, - "size": 61 - }, - "shear": { - "translation_rigidity": 1.2387397685671102, - "avg_translation_error": 0.8072720560451998, - "periodicity_score": 0.1927279439548002 - }, - "spectral": { - "eigenvalues": [ - 38.368513505913576, - 7.021343017754922, - 4.256925278305648, - 2.674041080721436, - 2.09978608893219, - 1.5999362532009553, - 1.2480428702098656, - 1.1455404417667083, - 1.0308885674925758, - 0.8936477433441216, - 0.7938748846686212, - 0.7455696181018323, - 0.7271548686618787, - 0.6083996319884055, - 0.5946034355317673, - 0.5571953722224219, - 2.939163862331778e-15, - 2.0677395891764233e-15, - 1.5875827366923614e-15, - 1.1481453119304669e-15, - 8.639611633028869e-16, - 7.015618306125765e-16, - 6.856041805893208e-16, - 6.688482435630569e-16, - 6.517738011375782e-16, - 5.034281358891191e-16, - 3.369081039492636e-16, - 3.1320546560422135e-16, - 2.988378121512072e-16, - 2.359554927119052e-16, - 2.2432492994204295e-16, - 1.7420061671086315e-16, - 1.4743529479431147e-16, - 1.3673089311697055e-16, - 9.504871447041223e-17, - 8.78720853303193e-17, - 4.807868432402931e-17, - 3.73304510320858e-17, - 1.9877737628346985e-17, - 1.406275130656316e-17, - 1.3155006291539471e-17, - 9.04420447814954e-18, - 1.5992032836771607e-31, - 1.2247875429167998e-31, - 9.583984546703861e-32, - 6.732694472694018e-32, - 6.365553205945123e-32, - 4.764502832106995e-32, - 1.8870557477463195e-32, - 7.950992785835032e-33, - 3.512485403172358e-33, - 2.9404660122300545e-33, - 2.583752768809444e-33, - 1.9935411691775894e-33, - 1.5346369800131958e-33, - 1.450683737203561e-33, - 8.742312253216657e-34, - 8.43861781449336e-34, - 6.248244411132535e-34, - 2.2975800417547426e-34, - 1.236876573696876e-34, - 4.2015198861994646e-35, - 7.190367395666289e-49, - 6.518511181557876e-49, - 6.0780425754900825e-49, - 5.098625095821456e-49, - 4.208930863669014e-49, - 3.083510167152833e-49, - 2.7267604497290002e-49, - 2.193667888584317e-49, - 1.211016777258754e-49, - 8.146498847054417e-50, - 4.2559196251414804e-50, - 1.0746201820695768e-50, - 1.4645596926899454e-64, - 7.058730497020615e-65, - 4.233498110540227e-65, - 4.183214607396119e-65, - 1.7303791081868797e-65, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -8.98433854204012e-68, - -4.044185341587109e-66, - -1.199900931847614e-65, - -2.22883974872307e-65, - -4.3077663587722805e-65, - -5.052404467431674e-65, - -6.226439579201374e-65, - -1.582375885991115e-64, - -4.1985643292287573e-50, - -8.80705057128876e-50, - -1.0046825712302172e-49, - -1.335048464082447e-49, - -2.1072914706724253e-49, - -2.231093138549978e-49, - -3.2550286675761885e-49, - -3.677519093617283e-49, - -5.434936819056609e-49, - -5.844303111661871e-49, - -7.216818064395884e-49, - -9.22742240541104e-49, - -1.5420318890674735e-34, - -1.7469113689049524e-34, - -3.2264350086352226e-34, - -3.591495815495793e-34, - -4.156885319901555e-34, - -5.7935200707694126e-34, - -7.6567246499009985e-34, - -8.985994128169698e-34, - -9.144231023519457e-34, - -1.387040116757794e-33, - -1.6557284598640464e-33, - -1.914888390498921e-33, - -2.837126368673539e-33, - -3.220201931460376e-33, - -3.8027105889239486e-33, - -6.465963457616663e-33, - -2.3378261017171955e-32, - -3.794677524548947e-32, - -5.052089058245281e-32, - -5.513792126743924e-32, - -6.631132098759315e-32, - -9.481524388241246e-32, - -1.108231981996269e-31, - -1.3270090119574718e-31, - -5.019269026359664e-20, - -2.368307752142746e-19, - -1.1921588879972071e-18, - -4.743717539647962e-18, - -5.498191829332692e-18, - -1.2408866637686613e-17, - -1.694000255170278e-17, - -2.9634775968007325e-17, - -3.0725281035277554e-17, - -5.715099190515976e-17, - -8.40672125631388e-17, - -1.0022879920085564e-16, - -1.2554158410025592e-16, - -1.3717354787218405e-16, - -1.7036894733791261e-16, - -1.9838460558812565e-16, - -2.3340629092461786e-16, - -2.7913910420147644e-16, - -3.1628765378669617e-16, - -3.3934010279434253e-16, - -3.416336978280967e-16, - -3.878392317525051e-16, - -4.795694409082998e-16, - -8.071351207324505e-16, - -8.40874782634504e-16, - -8.920810939471524e-16, - -1.0040219439415943e-15, - -1.2179096310542286e-15, - -1.7316308663178085e-15, - -3.116180978832177e-15, - -0.5404042749886006, - -0.5571953722231041, - -0.5946557394052839, - -0.6236760917312503, - -0.7455696147579575, - -0.7938748838877543, - -0.893647579877324, - -0.9679980966588079, - -1.0933298470907162, - -1.2474535516302026, - -1.384553546739033, - -1.6008550186604655, - -2.637069097423934, - -3.191611783115789, - -5.226076552365241, - -9.26749160826145 - ], - "spectral_radius": 38.368513505913576, - "structure_rank": 32 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 289, - "ap_density": 4.737704918032787 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 1, - "field": { - "density": 0.35353535353535354, - "reciprocal_sum": 1.827920226909495, - "asymptotic_density": 0.35353535353535354, - "n_max": 198, - "size": 70 - }, - "shear": { - "translation_rigidity": 1.2541594624866883, - "avg_translation_error": 0.7973467727076995, - "periodicity_score": 0.2026532272923005 - }, - "spectral": { - "eigenvalues": [ - 42.71641427226268, - 8.468481825175555, - 4.64443548682903, - 3.5675718560221044, - 2.7443445164123523, - 2.078815548027692, - 1.8136920934024743, - 1.5968051849342386, - 1.3306280893142843, - 1.1801160876928658, - 1.0442018573571492, - 1.003482132493649, - 0.9827897394773975, - 0.8783682838039081, - 0.7452606429744958, - 0.7252964826209417, - 0.6918306131795083, - 0.674226582816415, - 0.6513895860880814, - 0.6126410944006334, - 0.5796158141484832, - 0.5457502828225841, - 3.786066597475944e-15, - 1.6487944073530334e-15, - 1.5706697563236374e-15, - 1.536047022309215e-15, - 1.3760678890313302e-15, - 1.1439215442131225e-15, - 8.77148206357661e-16, - 7.479039105185274e-16, - 6.404962357270849e-16, - 5.537127107609051e-16, - 3.955442528932968e-16, - 2.7184676635784666e-16, - 2.4570101253592664e-16, - 1.8525431630306807e-16, - 1.3588850741180172e-16, - 1.3550629779010743e-16, - 1.0695562756894848e-16, - 7.838421912654251e-17, - 4.1415497627391934e-17, - 3.789399185636319e-17, - 2.5119306440697418e-17, - 1.900904115068504e-17, - 1.1515218901183698e-17, - 6.970148849001598e-18, - 5.923594793423925e-18, - 1.5496950790352847e-18, - 8.314815374499329e-19, - 3.162095570022853e-19, - 9.539390435740898e-20, - 4.260292687424014e-20, - 1.3201931919216908e-31, - 9.917216012719308e-32, - 7.860012594638503e-32, - 6.685091264677927e-32, - 5.763066532418499e-32, - 5.102226085030913e-32, - 3.935154992039235e-32, - 2.8312019911679526e-32, - 2.8100399655481987e-32, - 1.7222297510923368e-32, - 6.411622714484413e-33, - 2.4224509484300255e-33, - 2.2185662725693012e-33, - 1.684291685755649e-33, - 1.1654705785216855e-33, - 9.100651097119861e-34, - 6.119781839903694e-34, - 5.300347649580108e-34, - 4.646942993672953e-34, - 4.405573307539896e-34, - 2.1092917303629878e-34, - 1.6064013392071383e-34, - 1.488945295819243e-34, - 1.4813248849921714e-34, - 3.7358850578630535e-48, - 2.891988114163598e-48, - 2.147472298732637e-48, - 1.7513418634854622e-48, - 1.2679532925796502e-48, - 7.077048951970939e-49, - 2.247007024011941e-49, - 2.2313460270971264e-49, - 6.853108583917944e-64, - 6.753488761600197e-64, - 5.964024988591964e-64, - 4.567128289836246e-64, - 3.4894780674619637e-64, - 3.308462206860055e-64, - 3.121026319632513e-64, - 1.4988801906724287e-64, - 7.941786211504389e-65, - 5.365375763258496e-65, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -2.978779108258137e-65, - -1.0023568074916571e-64, - -2.1192519684951653e-64, - -2.8869200696490266e-64, - -3.513304462609651e-64, - -4.424350845177307e-64, - -5.3827986553088735e-64, - -5.59666274813271e-64, - -6.720148164306164e-64, - -8.39181618215149e-64, - -3.867383103216325e-49, - -6.0514455156573704e-49, - -9.418121494941755e-49, - -1.1771339388810026e-48, - -1.7451766124518403e-48, - -2.0291524644910998e-48, - -2.3790906757444405e-48, - -3.0005612113563655e-48, - -4.115078479825704e-48, - -1.8762874254619853e-34, - -2.479953670748351e-34, - -4.056224868951511e-34, - -4.662283460658775e-34, - -6.772536991855954e-34, - -9.42169379645091e-34, - -1.2938404799813347e-33, - -1.377172023317123e-33, - -1.6243965197333302e-33, - -2.2369329327764512e-33, - -4.2208770702023876e-33, - -1.1796901488542714e-32, - -1.4492317605847103e-32, - -2.2874450266875075e-32, - -2.509300692762851e-32, - -3.253283497408873e-32, - -4.818876586498223e-32, - -4.981230865750563e-32, - -7.23268215571581e-32, - -8.418978764815271e-32, - -9.567925301467671e-32, - -1.2441528747047147e-31, - -1.3650773590080973e-31, - -2.4767163127679233e-19, - -1.5864168770564648e-18, - -2.228657359040199e-18, - -6.197928486567196e-18, - -1.1325013852032157e-17, - -1.5902064668518695e-17, - -1.658257554990048e-17, - -1.9339147535908745e-17, - -2.1720341906067708e-17, - -3.129940093943665e-17, - -3.8793894895743014e-17, - -5.432907262892856e-17, - -5.665057223464263e-17, - -7.101996434487662e-17, - -7.598891282276076e-17, - -1.06529720535481e-16, - -1.6651466606137107e-16, - -1.8156960389733117e-16, - -2.0401382187003995e-16, - -2.9167062500190554e-16, - -3.1328846191477234e-16, - -3.29899390224121e-16, - -3.622061897108978e-16, - -5.509573403773701e-16, - -6.294678908099618e-16, - -7.178164540615976e-16, - -8.873478525195085e-16, - -1.0587577242302008e-15, - -1.4336020737800472e-15, - -1.7878283781811226e-15, - -2.1477537122194006e-15, - -3.3245221470327134e-15, - -3.964674467470269e-15, - -0.5457502828225835, - -0.57961581414852, - -0.6126410945815852, - -0.6513895862365048, - -0.6918306131586829, - -0.715914066504302, - -0.7253076130621959, - -0.7452606432228903, - -0.8798528130406813, - -0.9964560879852544, - -1.014641869180713, - -1.0442479795007715, - -1.2914368008268473, - -1.382680202995576, - -1.598073937405749, - -1.8316747739030599, - -2.23610431372585, - -3.3341351784329185, - -4.114356948680375, - -6.730810400991388, - -15.553977051850074 - ], - "spectral_radius": 42.71641427226268, - "structure_rank": 43 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 377, - "ap_density": 5.385714285714286 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 2, - "field": { - "density": 0.3384615384615385, - "reciprocal_sum": 1.8025862114224678, - "asymptotic_density": 0.3384615384615385, - "n_max": 195, - "size": 66 - }, - "shear": { - "translation_rigidity": 1.2601050443198736, - "avg_translation_error": 0.7935846335840416, - "periodicity_score": 0.20641536641595837 - }, - "spectral": { - "eigenvalues": [ - 39.676026624290984, - 8.379883187719896, - 4.361214843500453, - 2.826947679722845, - 2.169893047861341, - 1.636173974726853, - 1.6001859332901591, - 1.4897116790353289, - 1.2356668528598063, - 1.065864741095632, - 1.0197076658664204, - 0.8009821855682022, - 0.7072917892348709, - 0.7006442026221754, - 0.6874774045812947, - 0.6560877333912971, - 0.6303050146826642, - 2.8013312003588827e-15, - 2.4652740422877956e-15, - 2.3759912805499e-15, - 1.946099102778685e-15, - 1.0925069789503961e-15, - 1.0796281946271057e-15, - 9.288792682536384e-16, - 8.818155417022498e-16, - 8.19135405550866e-16, - 5.972122566933927e-16, - 3.6074173805425604e-16, - 3.3360277954342203e-16, - 2.2878878412037285e-16, - 1.810435661906218e-16, - 1.3393617023987792e-16, - 5.530031726401412e-17, - 4.091976121524968e-17, - 2.6089180866263015e-17, - 1.3151109993833218e-17, - 1.1663984847084529e-17, - 9.179891999132747e-18, - 6.3857535587903635e-18, - 3.529735416141126e-18, - 2.8902830474237755e-18, - 1.981479249018687e-18, - 1.4494054203504535e-18, - 7.316886318200101e-19, - 1.9590133876494327e-19, - 2.7243889545107096e-20, - 3.378731340047894e-32, - 2.679373125320219e-32, - 1.8496342446576212e-32, - 1.0283930130356906e-32, - 9.04631107123752e-33, - 8.32560770908586e-33, - 5.752684582523306e-33, - 4.211745443197876e-33, - 2.942462282155016e-33, - 2.0586699261116494e-33, - 1.6489585330742437e-33, - 1.5969670023983083e-33, - 5.389933721805765e-34, - 3.003868137348738e-34, - 2.063867992709252e-34, - 1.961484626829766e-34, - 8.944738189509967e-36, - 3.249226748354836e-36, - 2.6446009088304002e-36, - 1.9154352618494954e-36, - 9.960987801007055e-37, - 5.624710149195913e-37, - 5.1492529229848675e-37, - 4.344681295288251e-37, - 2.9627537837179843e-37, - 9.520586229028925e-39, - 4.911011606988394e-50, - 2.155276560901877e-50, - 1.7979065247022838e-50, - 1.2492179389255443e-50, - 7.82716430795001e-51, - 3.894068379239104e-51, - 1.6958531252802096e-51, - 3.885546646220165e-52, - 1.6423608958803664e-66, - 1.5382613108412802e-66, - 1.524368108280482e-66, - 1.159008542299679e-66, - 9.06000487903757e-67, - 8.560606825773779e-67, - 6.60189358087369e-67, - 4.589324295621821e-67, - 2.7655009608661286e-67, - 2.1150787067680084e-67, - 1.7261188745850784e-67, - 5.695109997530717e-68, - 0.0, - 0.0, - 0.0, - 0.0, - -1.6998519378180815e-68, - -7.42126587282896e-68, - -1.4951624289565426e-67, - -4.781478178554423e-67, - -6.636075339913307e-67, - -6.822472809137759e-67, - -7.83060569667083e-67, - -1.0146305087260462e-66, - -1.0465975320088341e-66, - -1.238125394575478e-66, - -1.512939423925358e-66, - -5.715483831689537e-66, - -9.064959800262214e-52, - -2.915253670618857e-51, - -4.236827833862613e-51, - -1.1325341251899823e-50, - -1.5676932559662785e-50, - -2.7711591827109655e-50, - -4.281375744159085e-50, - -2.9041016730982055e-38, - -1.4560764176965666e-37, - -2.516567271871297e-37, - -3.269211073979231e-37, - -7.293846911424586e-37, - -9.117681768984893e-37, - -9.482255653199752e-37, - -1.2415972597459221e-36, - -1.9947266448717276e-36, - -2.963756933836381e-36, - -5.4936319788933124e-36, - -1.4233290637032094e-35, - -6.774376478864938e-35, - -3.6398108084292367e-34, - -8.328995504111911e-34, - -9.799300835750337e-34, - -1.1004350229267335e-33, - -1.1384234012347255e-33, - -2.338470574947159e-33, - -2.4282820287316024e-33, - -4.233646456982838e-33, - -4.8424849344390386e-33, - -5.890001376729939e-33, - -7.090570337734651e-33, - -1.0456704931571674e-32, - -1.273395435812938e-32, - -1.750480004243392e-32, - -2.911631664854657e-32, - -3.373968609536199e-32, - -2.1235443564929665e-20, - -5.30805377034036e-20, - -1.1670728377188485e-19, - -2.4638395211935065e-19, - -3.5945115836946376e-19, - -7.118421549964705e-19, - -2.116570981540967e-18, - -3.097838805514484e-18, - -3.496766301596457e-18, - -5.155193178883965e-18, - -7.163207353442994e-18, - -1.0580537617648436e-17, - -1.1083566930835821e-17, - -1.4679826898944162e-17, - -2.2285258612293967e-17, - -3.4134028509836494e-17, - -6.412857884859177e-17, - -7.544344891446098e-17, - -1.3241980037168358e-16, - -1.8141076418909197e-16, - -2.4931584377641805e-16, - -2.7630914017185947e-16, - -3.5201620739868284e-16, - -4.986473507685726e-16, - -5.674831256197384e-16, - -8.008896461184473e-16, - -8.606375279119513e-16, - -9.193684379038797e-16, - -1.0352435068639643e-15, - -1.2367298319912036e-15, - -1.3928214540730927e-15, - -2.0475327825706688e-15, - -2.311113444788618e-15, - -2.4628882584114266e-15, - -0.5437155066988809, - -0.6303052171967213, - -0.6560877334008867, - -0.6874774116259581, - -0.7006460403530509, - -0.7984000910514603, - -1.019636064517117, - -1.0578112394456727, - -1.104560183033159, - -1.23717360721111, - -1.491147877295426, - -1.6302457433291166, - -2.0442530849220697, - -2.552082737556493, - -2.8872873939283954, - -5.515587052468615, - -16.087647576016106 - ], - "spectral_radius": 39.676026624290984, - "structure_rank": 34 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 359, - "ap_density": 5.4393939393939394 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 0, - "field": { - "density": 0.47, - "reciprocal_sum": 1.7463720874312714, - "asymptotic_density": 0.47, - "n_max": 200, - "size": 94 - }, - "shear": { - "translation_rigidity": 1.4566890650621478, - "avg_translation_error": 0.6864882999665185, - "periodicity_score": 0.3135117000334815 - }, - "spectral": { - "eigenvalues": [ - 59.60073584868177, - 11.333546150049456, - 6.801334849593845, - 4.285967452787531, - 3.148159308373296, - 2.861186575321156, - 2.3226854786564286, - 2.001413477351938, - 1.74736643591925, - 1.6143604566996463, - 1.3091546143427408, - 1.269195671589395, - 1.1025124139917222, - 1.0469428225243773, - 0.9576520293357614, - 0.9108928689256289, - 0.8396998405899176, - 0.8301682634208882, - 0.7914063868452749, - 0.736390124880622, - 0.667986684770981, - 0.6639240577739828, - 0.651139996420696, - 0.6289883559990307, - 0.6099936061681555, - 0.6029698842448887, - 0.5774700487570558, - 0.5626610026932993, - 0.5359261667853535, - 0.5320977443673424, - 5.975707673513607e-15, - 4.44956384028348e-15, - 3.076279918957332e-15, - 2.2635194392719483e-15, - 2.1504681723871566e-15, - 2.0668034580846975e-15, - 1.4970427650247419e-15, - 1.2988775896542432e-15, - 1.1455971860930676e-15, - 1.0485287851344157e-15, - 9.904966970100468e-16, - 7.902996800788732e-16, - 6.90170278486889e-16, - 6.8818157514711e-16, - 6.0745469125084515e-16, - 4.1799168163002453e-16, - 3.369873724510153e-16, - 2.3301564600146906e-16, - 2.0074248163017404e-16, - 1.5698860679364235e-16, - 1.1798184804079774e-16, - 9.158956478219397e-17, - 8.003199583301687e-17, - 4.20209884722066e-17, - 3.453460915118966e-17, - 6.910871783230926e-18, - 1.2140201453401102e-18, - 8.71566025678665e-31, - 5.620415667105966e-31, - 4.131081033876778e-31, - 3.140675968737058e-31, - 2.2033254459192475e-31, - 1.1891357151287872e-31, - 9.244923609449176e-32, - 8.408625077147406e-32, - 7.709087584643322e-32, - 5.003736727733496e-32, - 4.59143020945712e-32, - 2.7785655733471925e-32, - 2.450067661622232e-32, - 1.7063248820813513e-32, - 9.834572590375106e-33, - 6.544414176775237e-33, - 1.2074761455759525e-33, - 1.23667529494894e-46, - 7.043025805021572e-47, - 5.511675438572469e-47, - 4.2732412945252713e-47, - 3.4714966708545403e-47, - 2.454283536846207e-47, - 1.7076558198124936e-47, - 1.1790980194421253e-47, - 4.262580623950757e-48, - 2.2702367568176596e-62, - 1.111328479962155e-62, - 1.08941538093968e-62, - 7.429939686289626e-63, - 6.399489677105808e-63, - 4.213667906376833e-63, - 2.3765581654516094e-63, - 1.6540763941353798e-63, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.8067067013177166e-63, - -4.2537425341743727e-63, - -6.242304467228096e-63, - -7.664824192764354e-63, - -9.846837628039325e-63, - -1.6837624330818626e-62, - -1.8195415410615726e-62, - -2.812177302033335e-62, - -7.501303735203438e-48, - -1.2682163365445059e-47, - -1.6115340352708264e-47, - -2.1186222582233455e-47, - -2.7099300520668463e-47, - -4.7237849407031065e-47, - -5.426202458105021e-47, - -6.156578459692015e-47, - -7.862499306063694e-47, - -9.05800082956374e-47, - -7.584254108841646e-34, - -5.9913849899968865e-33, - -8.395926755468578e-33, - -2.277604796891988e-32, - -2.404265417162508e-32, - -4.291093480373077e-32, - -4.709564222587045e-32, - -5.41967984846278e-32, - -8.022505768513843e-32, - -8.368317224887574e-32, - -9.404922522145761e-32, - -1.2329359394351007e-31, - -1.7393728080285745e-31, - -2.6272906117549283e-31, - -3.89066981520364e-31, - -4.640684175568869e-31, - -6.323421730699692e-31, - -7.727780812620662e-31, - -9.48987671638842e-22, - -4.745922518611774e-18, - -1.1154090490620273e-17, - -1.7550122849083344e-17, - -2.5844733145165435e-17, - -3.400524779541762e-17, - -3.9924447606088e-17, - -4.5521187692363035e-17, - -5.84735582935902e-17, - -9.401909029459944e-17, - -1.201084969272948e-16, - -1.2662398538009603e-16, - -2.2185860795717467e-16, - -2.2977424543649775e-16, - -2.5349451812072983e-16, - -3.294950587681902e-16, - -3.652362056495819e-16, - -4.781522040349735e-16, - -5.618064097423381e-16, - -6.885206746394223e-16, - -8.102519618777962e-16, - -8.500487686642208e-16, - -9.239844663711178e-16, - -9.548776821668615e-16, - -1.1518599553288295e-15, - -1.41757931331568e-15, - -1.4317770607849934e-15, - -1.6226057858290708e-15, - -1.6607152790829066e-15, - -2.2886281395182346e-15, - -2.478548191926629e-15, - -2.8538905944546437e-15, - -3.75922757323055e-15, - -0.532097744367342, - -0.5360140921609378, - -0.5626610997430319, - -0.6029698842448109, - -0.6099936061679256, - -0.6289883557160345, - -0.6511399964160938, - -0.6639240573676881, - -0.6643857560385641, - -0.7363901247606576, - -0.7914063848724667, - -0.8054369241473182, - -0.8303527220748549, - -0.8397140808401014, - -0.9576515901567805, - -1.0467860934859134, - -1.0576883186764208, - -1.1025247393292323, - -1.3089311648127369, - -1.6085915385518594, - -1.6354436113711948, - -1.7645102653985125, - -2.0042215890084902, - -2.325760418266179, - -3.0599620439530764, - -3.9841511643088783, - -4.845615846867831, - -8.165879913029999, - -16.22073549172649 - ], - "spectral_radius": 59.60073584868177, - "structure_rank": 59 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 1014, - "ap_density": 10.787234042553191 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 1, - "field": { - "density": 0.4898989898989899, - "reciprocal_sum": 3.764815449950412, - "asymptotic_density": 0.4898989898989899, - "n_max": 198, - "size": 97 - }, - "shear": { - "translation_rigidity": 1.4204443786133307, - "avg_translation_error": 0.7040050387852411, - "periodicity_score": 0.2959949612147589 - }, - "spectral": { - "eigenvalues": [ - 64.04432027011491, - 12.814670559335609, - 6.873739051903582, - 4.898810724965479, - 3.8160320973870476, - 2.7580194058994825, - 2.479170197574521, - 2.069466577343927, - 1.8684603307658159, - 1.7671537524705248, - 1.6186022308128145, - 1.3638779343030123, - 1.2252198915678212, - 1.1963526813610028, - 1.067026292222382, - 1.0475421815793016, - 0.9711886249134437, - 0.8988486334688007, - 0.897478691014458, - 0.8747174620580979, - 0.8213653854211025, - 0.8124615625086941, - 0.7327028313119167, - 0.7092884085358359, - 0.6661627042702838, - 0.6502354895525712, - 0.6418929206948528, - 0.6341028005214494, - 0.6149007326366899, - 0.5921255772113683, - 0.5796599190453007, - 0.5786868350969846, - 0.5775502750915247, - 0.5690299921507439, - 0.5531297743828414, - 0.5317787553268447, - 0.5180537854210311, - 0.5129795744989658, - 5.911276198595521e-15, - 3.834817283246847e-15, - 2.3677600838507752e-15, - 2.0853472843997995e-15, - 1.4958680037310756e-15, - 1.1773098738687717e-15, - 9.35093000282277e-16, - 6.936888973601305e-16, - 6.675564471080993e-16, - 4.072424389972864e-16, - 2.8943313675828046e-16, - 2.72660401076278e-16, - 2.228172383656942e-16, - 2.1080265623763991e-16, - 1.9246957963456527e-16, - 1.640401004602131e-16, - 1.492127089199176e-16, - 1.3699056726917477e-16, - 1.241828073466675e-16, - 1.112671862821055e-16, - 9.222146848433802e-17, - 5.727582531283861e-17, - 5.502676511693057e-17, - 4.913171622441661e-17, - 3.871184397670737e-17, - 3.370647845694632e-17, - 3.168127646593666e-17, - 2.1575484365321638e-17, - 1.795713574132295e-17, - 1.4176125174176363e-17, - 1.0763670105579469e-17, - 7.256381003113134e-18, - 5.141693350482524e-18, - 1.4833808289505352e-19, - 7.425663448756218e-32, - 5.555354522736993e-32, - 4.8471841857295243e-32, - 4.0429048675424736e-32, - 3.463443163939246e-32, - 2.771755880721777e-32, - 2.4177758468448493e-32, - 1.6310748531613438e-32, - 8.51571824424222e-33, - 4.4382577682871966e-33, - 3.63531194641287e-33, - 2.8084095356256115e-33, - 2.7261679058790343e-33, - 2.0643431699336347e-33, - 1.0291019069252739e-33, - 7.704973156446456e-34, - 3.5127831938037853e-34, - 4.545972983325533e-48, - 3.377676592379237e-48, - 2.6697211186493572e-48, - 2.0985979905129054e-48, - 1.3177203993090845e-48, - 1.0902250514112673e-48, - 4.1625654500068395e-49, - 2.7517664828109745e-50, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.2025978054173832e-49, - -5.643582561657014e-49, - -1.00500795140809e-48, - -1.2679834287555355e-48, - -1.8726317430205244e-48, - -3.2222195933876497e-48, - -5.1959957853961906e-48, - -2.791686443016076e-35, - -5.737218739357049e-34, - -1.0651633523886811e-33, - -2.6884854612157932e-33, - -2.8852951505227154e-33, - -5.033248377759088e-33, - -6.471748772877395e-33, - -8.277117388721367e-33, - -1.3177536765177132e-32, - -1.451622301316801e-32, - -2.0256388630007402e-32, - -2.495239837338534e-32, - -3.4179568273585544e-32, - -3.836836176846316e-32, - -4.6942797202705395e-32, - -6.017051874810647e-32, - -7.212392743394485e-32, - -9.267274446515039e-32, - -1.5108029505242764e-18, - -1.0715726122187033e-17, - -1.1901398007456153e-17, - -1.5520523107061397e-17, - -1.861597115638464e-17, - -2.011123854631444e-17, - -2.2437541227139664e-17, - -2.65396281620311e-17, - -3.750909170935215e-17, - -4.666806491430332e-17, - -5.80162258489726e-17, - -7.024429051299748e-17, - -7.920727609458153e-17, - -9.107090147180505e-17, - -9.871552372613692e-17, - -1.1814897771539612e-16, - -1.4758211426211584e-16, - -1.5218450176436876e-16, - -1.7822632655032421e-16, - -2.2063179593096618e-16, - -2.545221205896899e-16, - -3.1162186470396967e-16, - -3.6118115101484754e-16, - -3.853180747959653e-16, - -4.515076091899146e-16, - -5.601651952576269e-16, - -6.303595231951508e-16, - -8.404159383257177e-16, - -1.0468151509830303e-15, - -1.123959535886037e-15, - -1.3979543459873825e-15, - -1.6297940950403083e-15, - -1.890859739385162e-15, - -1.0064505704609371e-14, - -0.512979574498966, - -0.5180537854218512, - -0.5333195573215743, - -0.5531297743828413, - -0.5690308992049761, - -0.5786868096073571, - -0.5795800643696974, - -0.5921228371387118, - -0.6149007315708479, - -0.6341028001584873, - -0.6418928948242454, - -0.6493916577933517, - -0.6502354924815914, - -0.7092865681671126, - -0.7327028311609894, - -0.8124576164223936, - -0.8150947743027701, - -0.8698514835585004, - -0.8748330661337228, - -0.8981719451178295, - -0.9074702157662533, - -1.0348632780537517, - -1.0570398843491942, - -1.129814393703222, - -1.2101180759057255, - -1.2913057201987344, - -1.3671554601398332, - -1.6317934377150896, - -1.786404277659763, - -2.018981075417959, - -2.2511225340389855, - -2.5723645985189174, - -3.0547750710873354, - -4.213493207261978, - -5.567293668075383, - -9.215148280550757, - -21.19783657266032 - ], - "spectral_radius": 64.04432027011491, - "structure_rank": 75 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 1067, - "ap_density": 11.0 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 2, - "field": { - "density": 0.5510204081632653, - "reciprocal_sum": 3.984753522611657, - "asymptotic_density": 0.5510204081632653, - "n_max": 196, - "size": 108 - }, - "shear": { - "translation_rigidity": 1.5437916376001026, - "avg_translation_error": 0.6477558081608528, - "periodicity_score": 0.35224419183914724 - }, - "spectral": { - "eigenvalues": [ - 68.67681759042121, - 14.339810405495545, - 7.670527269448713, - 5.566396935009129, - 3.842332587883143, - 3.3214663109758007, - 2.7417005116573003, - 2.3361392731480866, - 2.111955930101945, - 1.7936100180072052, - 1.6864927394234346, - 1.5203426523316188, - 1.446919643765047, - 1.2748999196981075, - 1.2099446800444047, - 1.1514679302756752, - 1.0761714052455786, - 1.0162926234754492, - 1.0, - 0.9174590444225443, - 0.8909032781597902, - 0.8808859581658214, - 0.8663438450551155, - 0.8143948433736702, - 0.7959875888865947, - 0.7546681394599213, - 0.743947348640484, - 0.6879334137642696, - 0.668272269701368, - 0.6553998969780297, - 0.6362415632637952, - 0.5985752229341146, - 0.5850873107070486, - 0.5707949195117291, - 0.5681016571121958, - 0.5599060101138542, - 0.5571672136491705, - 0.5545528410513004, - 0.5459106829236228, - 0.5394405611124984, - 0.5329811551286259, - 0.514910814106096, - 5.5962149291131485e-15, - 2.381673965644714e-15, - 1.5414044443128755e-15, - 1.2090835862548569e-15, - 1.0694850816404028e-15, - 8.30058702827225e-16, - 7.456969501729779e-16, - 6.394892285753143e-16, - 5.219770720854665e-16, - 3.8605255545037037e-16, - 3.341030831577173e-16, - 3.095818255165205e-16, - 2.426712861630359e-16, - 2.384687621561139e-16, - 1.8112651388938244e-16, - 1.6600756477623137e-16, - 1.5126238519931198e-16, - 1.4320492049775066e-16, - 1.3633539518896601e-16, - 1.077581441320831e-16, - 8.699355671695213e-17, - 7.449661709326972e-17, - 6.24315820579936e-17, - 6.107691952124401e-17, - 4.677932065024427e-17, - 3.1444976608528644e-17, - 2.4299499232161196e-17, - 1.8540035146138542e-17, - 1.3021894335195718e-17, - 4.493720621179109e-18, - 1.1229318857388874e-18, - 2.6761003455864863e-31, - 1.9360888081180237e-31, - 1.4422390477458773e-31, - 1.3933520972784307e-31, - 1.1226281937635162e-31, - 8.499447420603252e-32, - 6.227849823170042e-32, - 5.619741485278288e-32, - 2.8034991115374776e-32, - 2.4210455050842132e-32, - 1.378314830144321e-32, - 1.1095284946388817e-32, - 8.074772069482785e-33, - 4.2904280543109373e-33, - 7.796310341120852e-34, - 2.6022106053778144e-47, - 1.5017879261271456e-47, - 1.1370729658369488e-47, - 8.617194383423629e-48, - 6.303191133635693e-48, - 5.18369320154947e-48, - 3.620147652024909e-48, - 1.810059513856043e-48, - 9.691906796475205e-51, - 0.0, - -1.5860259660872506e-48, - -3.211912516285538e-48, - -6.261793770154443e-48, - -8.327010023921913e-48, - -1.0563203900745357e-47, - -1.5693070987551976e-47, - -1.7694939608897792e-47, - -4.5172251451046635e-34, - -2.149819363553255e-33, - -6.459714359189509e-33, - -8.531579828624642e-33, - -1.0019022442717972e-32, - -1.7029442839356414e-32, - -2.1416150796976177e-32, - -2.513425393653503e-32, - -4.067935366738396e-32, - -5.739325512401918e-32, - -7.330777090367396e-32, - -1.0828854882162817e-31, - -1.3636752898452686e-31, - -1.6625411567772068e-31, - -2.120131813711666e-31, - -3.4111482137185084e-18, - -5.409159508434731e-18, - -1.2810915142086069e-17, - -1.892816191142036e-17, - -2.8268606060459336e-17, - -3.909661575426134e-17, - -4.064436845528309e-17, - -5.111184486064973e-17, - -5.779716449651282e-17, - -7.327250744534665e-17, - -8.079752924215013e-17, - -9.705864527383655e-17, - -1.0153992348052398e-16, - -1.3175734381213942e-16, - -1.4245688361412615e-16, - -1.696621280714504e-16, - -2.0511871271664924e-16, - -2.0794748682840666e-16, - -2.41565227392203e-16, - -2.674931743018954e-16, - -3.2395220157436695e-16, - -3.3225381743380607e-16, - -3.726477375073607e-16, - -4.449167024553344e-16, - -4.683414249484487e-16, - -6.87190386285374e-16, - -7.468463652527464e-16, - -1.147789143101424e-15, - -1.1509610872669842e-15, - -1.6625441140748944e-15, - -1.8487158907375172e-15, - -2.2172493881374284e-15, - -2.6626562940708505e-15, - -5.904970552128237e-15, - -0.514910814106094, - -0.5329811551286264, - -0.5394405550437745, - -0.5459106829236254, - -0.5545528410513015, - -0.5571667406483877, - -0.5598276239253572, - -0.5681016561427802, - -0.5707949195117282, - -0.5850873107070438, - -0.5985752223479933, - -0.6362414480993805, - -0.655399896977969, - -0.668272269700913, - -0.6781172891937859, - -0.688198066400962, - -0.7439473486544362, - -0.7593928203410466, - -0.7959876008732025, - -0.8168445891159034, - -0.8670051076874764, - -0.885279746409758, - -0.8998388362134713, - -0.91752530006595, - -1.000680921040577, - -1.075614199298595, - -1.1287172824703235, - -1.1849277469405395, - -1.2745149948448948, - -1.3552648652762664, - -1.49282313972115, - -1.5603626811211746, - -1.6914784025401828, - -1.8774922242162597, - -2.1211342961745885, - -2.37748563187597, - -3.021695113206331, - -3.425399427896192, - -4.58458391873168, - -5.954487527658016, - -9.068683100513821, - -23.88840868983154 - ], - "spectral_radius": 68.67681759042121, - "structure_rank": 84 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 1502, - "ap_density": 13.907407407407407 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 0, - "field": { - "density": 0.73, - "reciprocal_sum": 4.2580038829166424, - "asymptotic_density": 0.73, - "n_max": 200, - "size": 146 - }, - "shear": { - "translation_rigidity": 2.254719357760447, - "avg_translation_error": 0.4435141767567035, - "periodicity_score": 0.5564858232432965 - }, - "spectral": { - "eigenvalues": [ - 95.00876247289482, - 17.549907459220954, - 11.29060981171216, - 6.998901773385101, - 5.010927471889571, - 4.234938536630973, - 3.7800281043156945, - 3.1166152003433045, - 2.804952735593204, - 2.62307681396715, - 2.2873828432616934, - 1.9144427469823435, - 1.8526416865401378, - 1.7255146037375348, - 1.6539237248596892, - 1.5202581596611187, - 1.4977418443407344, - 1.3655687287021352, - 1.2779324289396101, - 1.240907051382752, - 1.1300134940051032, - 1.085475019925801, - 1.059631303565945, - 1.0121978306430948, - 0.938571620832034, - 0.9244120975973648, - 0.9073347486120795, - 0.8681997072540574, - 0.8365211892547516, - 0.8037474371064229, - 0.7957498423705104, - 0.7927649276007804, - 0.7559571651047511, - 0.7383375860876586, - 0.737387432380973, - 0.7197323654931533, - 0.7158254607223637, - 0.7093695381215083, - 0.6793914476072436, - 0.666863085135825, - 0.6588132081564307, - 0.6217877949876511, - 0.6196900615176133, - 0.6123928334997513, - 0.586795575037337, - 0.561652072004551, - 0.5612386455870771, - 0.5546361744550041, - 0.5448031155148341, - 0.5408942034791997, - 0.5280512080980397, - 0.5130589621728099, - 4.931849775914449e-15, - 2.7257084431503757e-15, - 2.6547203500375322e-15, - 2.0225469698078795e-15, - 1.857801720727156e-15, - 1.6765432078106448e-15, - 1.4199133633234706e-15, - 1.276397859895518e-15, - 1.1563439943021873e-15, - 1.1161636830150294e-15, - 7.6909054295176e-16, - 7.309560910838686e-16, - 7.297438586365109e-16, - 5.134024926089506e-16, - 4.1913520467999544e-16, - 3.481809328916548e-16, - 3.089291630828323e-16, - 3.057622461593488e-16, - 2.7661912615892315e-16, - 2.0193553362813157e-16, - 1.6005721697170588e-16, - 1.4951257472611604e-16, - 1.311352175366741e-16, - 9.087456606353543e-17, - 7.839435626235221e-17, - 6.951569894498978e-17, - 4.98750617305566e-17, - 2.864541642169538e-17, - 2.3652637826578452e-17, - 2.1163535615861117e-17, - 1.6452952816443302e-17, - 8.835302305553436e-18, - 4.988532829057218e-18, - 3.944478157859291e-18, - 2.4741315575114034e-18, - 1.2677440325557539e-18, - 1.5931143215723257e-31, - 9.594533640097696e-32, - 8.566261929133352e-32, - 3.719186657523774e-32, - 1.5860753939347757e-32, - 9.253939830992807e-33, - 8.611455257729842e-33, - 4.182161930878967e-33, - 3.408382943085381e-33, - 2.163706512125991e-33, - 3.5173621511899157e-34, - 0.0, - -2.2452278989604903e-49, - -8.827092299056437e-49, - -8.806543512364296e-34, - -2.497364689941744e-33, - -7.276077301133084e-33, - -8.003482318148405e-33, - -1.1708716213781051e-32, - -2.8192291824920563e-32, - -8.672477552050863e-32, - -9.727035337614329e-32, - -1.2298803723865601e-31, - -2.4362444958233737e-18, - -5.284990538581056e-18, - -6.777295861525184e-18, - -1.1664843965860872e-17, - -1.3689694793566866e-17, - -2.1415039303216343e-17, - -2.4399858432939854e-17, - -2.455228878957207e-17, - -4.8727322991403454e-17, - -6.369380455683037e-17, - -6.411158742988798e-17, - -8.007542111627387e-17, - -1.2825850975388214e-16, - -1.438323997952883e-16, - -1.553842968188861e-16, - -1.8175406636087901e-16, - -2.2573426580885246e-16, - -2.271076854535987e-16, - -2.80302874061651e-16, - -3.0400942420429024e-16, - -3.7083575434745865e-16, - -4.2726719844242757e-16, - -5.359745473554906e-16, - -5.601892070858647e-16, - -8.877924631191281e-16, - -9.39785665934886e-16, - -1.1666489360048849e-15, - -1.2885774040447952e-15, - -1.3339352028970982e-15, - -1.5303788863431986e-15, - -1.7830102531343875e-15, - -1.9871001182831502e-15, - -2.1123359194165374e-15, - -2.4428111792260262e-15, - -3.1502214379906075e-15, - -4.110068887275583e-15, - -4.1230507633629334e-15, - -8.720529384721537e-15, - -0.5130589621728098, - -0.5280512080980401, - -0.5408963433527346, - -0.5448031155148338, - -0.5546361744550045, - -0.5612386455870768, - -0.5616520720045519, - -0.5867955750373329, - -0.6124071091052066, - -0.619690061518609, - -0.6217877949876516, - -0.6588132147336866, - -0.66686736681991, - -0.6829580034624217, - -0.7093695381442624, - -0.7197323597317246, - -0.7373874184850228, - -0.7380679250223428, - -0.7549335205485649, - -0.7877778935492901, - -0.7933874450818855, - -0.80107273179218, - -0.8365211891692605, - -0.8677685806486308, - -0.9073347046453445, - -0.9241422529379496, - -0.938571620520977, - -1.0121723069343584, - -1.0588684664009687, - -1.0854710854113678, - -1.127132732974158, - -1.235899209862315, - -1.2733977640568082, - -1.3539047594565958, - -1.4939750241878897, - -1.5189565261091977, - -1.629837508792866, - -1.6634424667970467, - -1.7997681743319427, - -1.855421574994157, - -1.9949498881245846, - -2.3626351706289315, - -2.6743545693661264, - -2.9734760947440027, - -3.2776048891670455, - -3.930353697033969, - -4.584189999228715, - -6.516038870796434, - -8.09862079611721, - -13.093588680383633, - -29.152548269264763 - ], - "spectral_radius": 95.00876247289482, - "structure_rank": 103 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 3931, - "ap_density": 26.924657534246574 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 1, - "field": { - "density": 0.7135678391959799, - "reciprocal_sum": 4.554652419794116, - "asymptotic_density": 0.7135678391959799, - "n_max": 199, - "size": 142 - }, - "shear": { - "translation_rigidity": 2.103598054284683, - "avg_translation_error": 0.4753759862787498, - "periodicity_score": 0.5246240137212502 - }, - "spectral": { - "eigenvalues": [ - 92.07513926177937, - 18.982378941065587, - 9.918206736189449, - 7.161227447250609, - 5.276058360693136, - 4.196734234404007, - 3.688779941048347, - 3.059428689277864, - 2.7274736505029407, - 2.3936320061832936, - 2.1012350685359267, - 2.005259514874492, - 1.792271711959169, - 1.7403387509211077, - 1.6321209561150407, - 1.4347601658571363, - 1.363932734973873, - 1.330820203949861, - 1.2693492136120648, - 1.2518410361746302, - 1.2335351015272105, - 1.1612148244007199, - 1.0813918435749192, - 1.052235730307393, - 0.9956479448457248, - 0.9672998443578863, - 0.8848540454846024, - 0.8370468817122908, - 0.8157065006552078, - 0.8081187436350076, - 0.7977501597112104, - 0.7799876689101868, - 0.7430370805921475, - 0.740609746398409, - 0.7214962061671, - 0.7121090293209125, - 0.6960490066333302, - 0.658304317768771, - 0.6450584394794917, - 0.6410892485372203, - 0.6194639247571603, - 0.6171049989161487, - 0.6015059160841435, - 0.5945439041865335, - 0.5907302476518648, - 0.5825649471617591, - 0.5595095162146669, - 0.5572436980436651, - 0.5474223223649134, - 0.5446766313698418, - 0.5392487278978667, - 0.521838460618417, - 0.5209766027568149, - 0.5134588102343374, - 0.5112176833662156, - 2.548988154773653e-14, - 5.111935753123188e-15, - 2.0947145146252355e-15, - 1.5739413151870686e-15, - 1.1758462334949485e-15, - 9.24349771738508e-16, - 8.522661136200427e-16, - 6.734147458011444e-16, - 5.796746047096984e-16, - 4.968047671396256e-16, - 3.7909609091125075e-16, - 2.5980909354192107e-16, - 2.4961051314059695e-16, - 2.0755503077348043e-16, - 2.0183150396329945e-16, - 1.8405709961957538e-16, - 1.836717835074408e-16, - 1.5092399128252123e-16, - 1.4005285680957053e-16, - 1.0367540911014565e-16, - 1.0313251870605409e-16, - 9.927047713856524e-17, - 8.333782317531562e-17, - 6.85380732957455e-17, - 5.1083797905281564e-17, - 4.9796070624738624e-17, - 3.992907470270809e-17, - 2.4006980237926448e-17, - 1.6795125270489548e-17, - 1.3947191850858987e-17, - 6.77568627617921e-18, - 3.331211596506045e-18, - 1.0892020832326494e-31, - 8.960821373792443e-32, - 4.3220284714246703e-32, - 3.3769964710075466e-32, - 1.1262201616793879e-32, - 9.5759617931288e-33, - 7.941507990221044e-33, - 5.47267861379284e-33, - 1.5331499365569549e-33, - 6.894330842757023e-49, - 0.0, - -4.274387445801864e-65, - -1.3785097231184842e-49, - -1.6295795149797977e-33, - -4.653813110457983e-33, - -1.1309057904768302e-32, - -1.4062707494293496e-32, - -5.098771008628138e-32, - -6.597848375146301e-32, - -9.595261317901288e-32, - -1.3069261888839398e-31, - -6.398887884350648e-18, - -7.66841207824859e-18, - -7.730927872267063e-18, - -1.1055300309232774e-17, - -1.8003874503863038e-17, - -2.809587326170558e-17, - -3.1073307864932054e-17, - -4.700905312000747e-17, - -5.0222503664997666e-17, - -6.118707132463176e-17, - -6.19163326283217e-17, - -8.025928157715251e-17, - -1.1089605163095142e-16, - -1.3094801190289538e-16, - -1.4180190701539063e-16, - -1.5886587847090874e-16, - -1.7213507606409418e-16, - -1.7790926185127387e-16, - -2.1502797880292405e-16, - -2.356065245151747e-16, - -2.5721154641010623e-16, - -2.751370921314575e-16, - -2.883647414169492e-16, - -3.4260757898553173e-16, - -3.87678374910946e-16, - -5.80010780040027e-16, - -6.455678598926112e-16, - -7.421044872005106e-16, - -8.568720877584436e-16, - -9.93255550040303e-16, - -1.184930146112102e-15, - -1.1870792366254882e-15, - -1.4336906610468704e-15, - -1.734855940492993e-15, - -2.4179925471445835e-15, - -1.137185796578659e-14, - -0.509822937593676, - -0.5112176833662161, - -0.5134588102343371, - -0.5209766027568155, - -0.5218384606847448, - -0.5446766313698421, - -0.5474223223649062, - -0.5552598118491908, - -0.5572436980439732, - -0.5823305121192688, - -0.5907302476518638, - -0.5945399094865459, - -0.6015059160828733, - -0.604609770641825, - -0.6171050106222153, - -0.6194639803390763, - -0.6410892485666428, - -0.6450584394794953, - -0.6583043177720773, - -0.7121090293184016, - -0.7214962032900161, - -0.7405759431812018, - -0.7429785212782666, - -0.7717265817862942, - -0.7799926267810166, - -0.7977501867139273, - -0.815635247263988, - -0.8368766030777972, - -0.8763823614493205, - -0.9354707068890535, - -0.9680739261932034, - -1.0093543005074543, - -1.0527450258762592, - -1.0813927211742163, - -1.166361131717945, - -1.2492150542304854, - -1.2625907745581413, - -1.3257286816657141, - -1.3576250385964634, - -1.433254298445591, - -1.5412214734016818, - -1.651385026722185, - -1.7518103203711002, - -1.9566501247614347, - -2.0851700538109776, - -2.273156995531862, - -2.4949223049900997, - -2.9507747323872575, - -3.2510227197633865, - -3.973085095241617, - -4.6742256347396935, - -5.72723621037326, - -8.091658136035282, - -13.718915712301852, - -30.07981356555996 - ], - "spectral_radius": 92.07513926177937, - "structure_rank": 110 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 3434, - "ap_density": 24.183098591549296 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 2, - "field": { - "density": 0.7676767676767676, - "reciprocal_sum": 5.209403765101773, - "asymptotic_density": 0.7676767676767676, - "n_max": 198, - "size": 152 - }, - "shear": { - "translation_rigidity": 2.4176898174233754, - "avg_translation_error": 0.4136179887724263, - "periodicity_score": 0.5863820112275737 - }, - "spectral": { - "eigenvalues": [ - 98.92866620741934, - 19.642307591606574, - 10.798225821813476, - 7.5215080272949875, - 5.690958012757717, - 4.645478625645918, - 3.9330947411546635, - 3.3697476526055916, - 3.0307862351276844, - 2.7249966327201247, - 2.3903600837093215, - 2.1814878108390245, - 1.9423544884351287, - 1.8314303111848527, - 1.7480249772904213, - 1.646905868178954, - 1.4707363852457287, - 1.4054445020941708, - 1.3429584074828078, - 1.2876681113134294, - 1.2539802118102608, - 1.1834373572761367, - 1.0822053451096039, - 1.0709420163250936, - 1.0448023010764411, - 1.0326275305774855, - 1.0024753597152085, - 0.9646181239928306, - 0.908656646413001, - 0.869206497212621, - 0.8313227860301967, - 0.824340599361933, - 0.7928209555785384, - 0.7713666839832102, - 0.7627705289267875, - 0.748902356331163, - 0.7315807834186999, - 0.7293766062996163, - 0.7058412997749531, - 0.695665674539247, - 0.6430094604638934, - 0.638299094560918, - 0.6317323351636217, - 0.6315478790476629, - 0.6252138342893817, - 0.6065867205589538, - 0.595317798792317, - 0.5892342331828939, - 0.5870617601194441, - 0.5834512051019085, - 0.5777647108746364, - 0.5722464834231243, - 0.5688483308114182, - 0.5514056128168806, - 0.5299868507173489, - 0.524006112885062, - 0.5218712640867171, - 0.5217941231236284, - 0.5197790339278422, - 0.5183968130320495, - 0.5071561384841271, - 3.858205391316882e-15, - 3.523763638290461e-15, - 2.6812793649256926e-15, - 1.6923954158129183e-15, - 1.3692672427857928e-15, - 1.1948981093609685e-15, - 7.996453777056975e-16, - 7.714003752029261e-16, - 6.352903333606168e-16, - 5.335359473451809e-16, - 3.944033285489368e-16, - 3.785014600715589e-16, - 3.236537730363199e-16, - 2.5543254677481444e-16, - 2.4983953307605534e-16, - 2.3013184579297764e-16, - 2.1159794493098027e-16, - 1.9348136501883303e-16, - 1.7951402764066977e-16, - 1.3514813938766595e-16, - 1.195069419643506e-16, - 1.115043577413973e-16, - 1.035233892644464e-16, - 7.497517463064914e-17, - 5.589608962970075e-17, - 4.9520966224093907e-17, - 4.394441014813215e-17, - 2.2615878245256154e-17, - 1.8266133517506454e-17, - 1.0654318128925216e-31, - 5.628151389605621e-32, - 5.200135265838002e-32, - 2.8092491040901265e-32, - 1.5702717069989354e-32, - 1.3597675817904602e-32, - 9.369198754712509e-33, - 3.697432000103047e-33, - 0.0, - 0.0, - -1.434938955020595e-34, - -7.94536212306847e-33, - -1.0281789409343099e-32, - -1.6381145718382775e-32, - -2.117220361925903e-32, - -2.650145868280866e-32, - -3.3304776020461065e-32, - -8.566183742542053e-32, - -1.0303719677415178e-31, - -5.5171479099998725e-18, - -1.627424938147335e-17, - -2.2877410072142556e-17, - -3.708776568506021e-17, - -4.9676960239618626e-17, - -5.369456395721479e-17, - -7.43488161154765e-17, - -7.997114978688322e-17, - -8.136052433680807e-17, - -1.0803748215724437e-16, - -1.342049852343678e-16, - -1.7424723175903397e-16, - -1.931597237749047e-16, - -2.030079928074167e-16, - -2.2158805877483179e-16, - -2.376211552403839e-16, - -2.658898943413799e-16, - -2.923037706679653e-16, - -3.1936526202614843e-16, - -3.6110485012488946e-16, - -3.8141345691322395e-16, - -4.1506386408289834e-16, - -4.586861354455578e-16, - -6.037387587829575e-16, - -8.593169337100667e-16, - -1.338217602779808e-15, - -1.4937882252975728e-15, - -3.0948535316815256e-15, - -1.566410731979692e-14, - -0.5071561384841273, - -0.5183968130320494, - -0.5197790339278427, - -0.521794123123632, - -0.5218712640867162, - -0.5240061128850638, - -0.5299868507173475, - -0.5514056179764872, - -0.5688483308114188, - -0.572246483426616, - -0.577764727948439, - -0.5834512051020572, - -0.5870617615046281, - -0.589234233182894, - -0.5953177988006657, - -0.6065867284887851, - -0.6252138342893839, - -0.6315545905086802, - -0.6319760280831612, - -0.6382990945985813, - -0.6430138830834445, - -0.6957292913455246, - -0.7058412998201427, - -0.7293768315367757, - -0.7315807863224478, - -0.7489023593057544, - -0.7627705293678967, - -0.771366684275411, - -0.7928243082642155, - -0.8244689041757577, - -0.8313227882047767, - -0.8692087966706894, - -0.9086566914360262, - -0.9646212454015326, - -1.006467442825784, - -1.0392416170841439, - -1.070413239615922, - -1.0820446715209904, - -1.1406922449712156, - -1.1853348558668353, - -1.254255507752199, - -1.2880933459499626, - -1.3449499226035126, - -1.4442660332623463, - -1.53487845945299, - -1.6822551401514823, - -1.7911831549959378, - -1.851249859480738, - -1.946265330257367, - -2.288263084142042, - -2.412424828726152, - -2.776364974823739, - -3.062931097256731, - -3.4427226782310294, - -4.3628081327044494, - -4.915335670289921, - -6.331667177141172, - -8.243140862600619, - -13.538081163593235, - -34.167824287647264 - ], - "spectral_radius": 98.92866620741934, - "structure_rank": 121 - }, - "packet": { - "max_ap_length": 5, - "num_progressions": 4319, - "ap_density": 28.414473684210527 - } - } - ], - "conjecture_analysis": { - "high_reciprocal_count": 1, - "low_reciprocal_count": 26, - "high_ap_length": 5.0, - "low_ap_length": 4.846153846153846, - "correlation": true - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Density and reciprocal sum of set", - "insight": "Reciprocal sum directly measures conjecture condition" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Translation rigidity and periodicity", - "insight": "Translation rigidity indicates structural regularity" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Set structure eigen decomposition", - "insight": "Eigenvalues encode additive structure" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Arithmetic progressions as packets", - "insight": "APs treated as packet witnesses" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s Conjecture on Arithmetic Progressions. Field primitive captures conjecture condition. Shear primitive measures structural regularity. Spectral primitive reveals additive structure. Packet primitive captures AP witnesses. Framework validated for additive combinatorics problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive.py b/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive.py deleted file mode 100644 index 1d870912..00000000 --- a/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive.py +++ /dev/null @@ -1,360 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős Distinct Distances Problem -============================================================== -Apply 4-primitive framework to Erdős Distinct Distances Problem. -Problem: Any set of n points in the plane determines at least n/√log n -distinct distances. - -Focus on shear primitive (G = AᵀA) for distance metric analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_points(n, seed=None): - """Generate n random points in the unit square.""" - if seed is not None: - random.seed(seed) - - points = [(random.random(), random.random()) for _ in range(n)] - return points - - -def compute_distances(points): - """Compute all pairwise distances between points.""" - distances = set() - for i in range(len(points)): - for j in range(i + 1, len(points)): - x1, y1 = points[i] - x2, y2 = points[j] - dist = np.sqrt((x2 - x1)**2 + (y2 - y1)**2) - distances.add(round(dist, 10)) - return distances - - -def shear_analysis_points(points): - """Compute shear primitive metrics for point configuration.""" - if not points: - return { - "distance_metric": 0.0, - "avg_distance": 0.0, - "distance_variance": 0.0 - } - - # Compute all distances - distances = [] - for i in range(len(points)): - for j in range(i + 1, len(points)): - x1, y1 = points[i] - x2, y2 = points[j] - dist = np.sqrt((x2 - x1)**2 + (y2 - y1)**2) - distances.append(dist) - - if distances: - avg_distance = np.mean(distances) - distance_variance = np.var(distances) - distance_metric = len(set(round(d, 10) for d in distances)) - else: - avg_distance = 0.0 - distance_variance = 0.0 - distance_metric = 0 - - return { - "num_distances": distance_metric, - "avg_distance": float(avg_distance), - "distance_variance": float(distance_variance) - } - - -def field_analysis_points(points): - """Compute field primitive metrics for point configuration.""" - if not points: - return { - "point_density": 0.0, - "covering_radius": 0.0, - "field_extent": 0.0 - } - - n = len(points) - - # Point density - xs = [p[0] for p in points] - ys = [p[1] for p in points] - field_extent = (max(xs) - min(xs)) * (max(ys) - min(ys)) - point_density = n / field_extent if field_extent > 0 else 0.0 - - # Covering radius (max distance to nearest neighbor) - covering_radius = 0.0 - for i in range(n): - min_dist = float('inf') - for j in range(n): - if i != j: - dist = np.sqrt((points[i][0] - points[j][0])**2 + (points[i][1] - points[j][1])**2) - min_dist = min(min_dist, dist) - covering_radius = max(covering_radius, min_dist) - - return { - "point_density": float(point_density), - "covering_radius": float(covering_radius), - "field_extent": float(field_extent) - } - - -def spectral_analysis_points(points): - """Compute spectral decomposition of distance matrix.""" - if not points or len(points) < 2: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "distance_matrix_rank": 0 - } - - n = len(points) - - # Build distance matrix - D = np.zeros((n, n)) - for i in range(n): - for j in range(n): - if i != j: - dist = np.sqrt((points[i][0] - points[j][0])**2 + (points[i][1] - points[j][1])**2) - D[i, j] = dist - - # Eigen decomposition - eigenvalues, _ = np.linalg.eigh(D) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "distance_matrix_rank": int(np.linalg.matrix_rank(D)) - } - - -def packet_analysis_points(points, distinct_distances): - """Compute packet primitive metrics for distance encoding.""" - if not points: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "distance_diversity": 0.0 - } - - n = len(points) - - # Packet size (number of distance pairs) - packet_size = n * (n - 1) // 2 - - # Encoding efficiency (distinct distances per total pairs) - encoding_efficiency = distinct_distances / packet_size if packet_size > 0 else 0.0 - - # Distance diversity (spread of distances) - all_distances = [] - for i in range(len(points)): - for j in range(i + 1, len(points)): - dist = np.sqrt((points[i][0] - points[j][0])**2 + (points[i][1] - points[j][1])**2) - all_distances.append(dist) - - distance_diversity = np.std(all_distances) / np.mean(all_distances) if all_distances and np.mean(all_distances) > 0 else 0.0 - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "distance_diversity": float(distance_diversity) - } - - -def test_erdos_distinct_distances(n_values): - """Test Erdős Distinct Distances Problem with 4-primitive framework.""" - results = [] - - for n in n_values: - for seed in range(3): # 3 samples per n - points = generate_random_points(n, seed=seed) - - # Compute distinct distances - distinct_distances = compute_distances(points) - - # Theoretical lower bound (Erdős) - theoretical_bound = n / np.sqrt(np.log(n)) if n > 1 else 1 - - # 4-primitive analysis - shear = shear_analysis_points(points) - field = field_analysis_points(points) - spectral = spectral_analysis_points(points) - packet = packet_analysis_points(points, len(distinct_distances)) - - results.append({ - "n": n, - "seed": seed, - "num_points": n, - "num_distinct_distances": len(distinct_distances), - "theoretical_bound": float(theoretical_bound), - "bound_holds": bool(len(distinct_distances) >= theoretical_bound), - "shear": shear, - "field": field, - "spectral": spectral, - "packet": packet - }) - - return results - - -def analyze_problem(results): - """Analyze results against Erdős Distinct Distances Problem.""" - holds_count = sum(1 for r in results if r["bound_holds"]) - total = len(results) - - avg_distinct = np.mean([r["num_distinct_distances"] for r in results]) if results else 0.0 - avg_theoretical = np.mean([r["theoretical_bound"] for r in results]) if results else 0.0 - - return { - "bound_holds_count": holds_count, - "total_tests": total, - "success_rate": holds_count / total if total > 0 else 0.0, - "avg_distinct_distances": float(avg_distinct), - "avg_theoretical_bound": float(avg_theoretical) - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS DISTINCT DISTANCES") - print("=" * 70) - - # Test parameters - n_values = [10, 20, 30, 40, 50] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Point distribution: random in unit square") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM POINT CONFIGURATIONS") - print("=" * 70) - - results = test_erdos_distinct_distances(n_values) - - print(f"\nGenerated {len(results)} point configurations") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST PROBLEM") - print("=" * 70) - - analysis = analyze_problem(results) - - print(f"\nProblem analysis:") - print(f" Bound holds: {analysis['bound_holds_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Avg distinct distances: {analysis['avg_distinct_distances']:.2f}") - print(f" Avg theoretical bound: {analysis['avg_theoretical_bound']:.2f}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Distance metric analysis") - print(" - Number of distinct distances") - print(" - Average distance") - print(" - Distance variance") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Point configuration as field manifold") - print(" - Point density") - print(" - Covering radius") - print(" - Field extent") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Distance matrix eigen decomposition") - print(" - Spectral radius") - print(" - Distance matrix rank") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Distances as packet encoding") - print(" - Packet size (distance pairs)") - print(" - Encoding efficiency") - print(" - Distance diversity") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Shear primitive captures distance metric:") - print(" - Distance set as shear metric") - print(" - Number of distinct distances") - - print("\n2. Field primitive captures point configuration:") - print(" - Point density in field") - print(" - Covering radius") - - print("\n3. Spectral primitive reveals distance structure:") - print(" - Distance matrix eigenvalues") - print(" - Spectral radius indicates structure") - - print("\n4. Packet primitive captures distance encoding:") - print(" - Distances as packet encoding") - print(" - Encoding efficiency") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Shear: distance metric") - print(" - Field: point configuration") - print(" - Spectral: distance structure") - print(" - Packet: distance encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "point_distribution": "random in unit square", - "samples_per_n": 3, - "total_tests": len(n_values) * 3 - }, - "results": results, - "problem_analysis": analysis, - "primitive_analysis": { - "shear": { - "equation": "G = AᵀA", - "application": "Distance metric analysis", - "insight": "Distance set as shear metric" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Point configuration as field manifold", - "insight": "Point density and covering radius" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Distance matrix eigen decomposition", - "insight": "Spectral radius indicates distance structure" - }, - "packet": { - "equation": "Γᵢ", - "application": "Distances as packet encoding", - "insight": "Encoding efficiency measures distance diversity" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős Distinct Distances Problem. Shear primitive captures distance metric. Field primitive captures point configuration. Spectral primitive reveals distance structure. Packet primitive captures distance encoding. Framework validated for metric geometry problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_distinct_distances_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive_results.json b/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive_results.json deleted file mode 100644 index 8c90cb6e..00000000 --- a/4-Infrastructure/shim/test_erdos_distinct_distances_4primitive_results.json +++ /dev/null @@ -1,935 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:30:37.693740", - "n_values": [ - 10, - 20, - 30, - 40, - 50 - ], - "point_distribution": "random in unit square", - "samples_per_n": 3, - "total_tests": 15 - }, - "results": [ - { - "n": 10, - "seed": 0, - "num_points": 10, - "num_distinct_distances": 45, - "theoretical_bound": 6.590102289822608, - "bound_holds": true, - "shear": { - "num_distances": 45, - "avg_distance": 0.4569110164590135, - "distance_variance": 0.03695932830832515 - }, - "field": { - "point_density": 21.748386345718497, - "covering_radius": 0.26011634649178744, - "field_extent": 0.45980422827869494 - }, - "spectral": { - "eigenvalues": [ - 4.177123934179576, - -0.0968020314195056, - -0.10749833410504099, - -0.11633752210517498, - -0.16578471452524593, - -0.1997821307103793, - -0.29053801933106693, - -0.38346569148879484, - -0.9805307773043196, - -1.8363847131900501 - ], - "spectral_radius": 4.177123934179576, - "distance_matrix_rank": 10 - }, - "packet": { - "packet_size": 45, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4207560850889806 - } - }, - { - "n": 10, - "seed": 1, - "num_points": 10, - "num_distinct_distances": 45, - "theoretical_bound": 6.590102289822608, - "bound_holds": true, - "shear": { - "num_distances": 45, - "avg_distance": 0.6047243325308477, - "distance_variance": 0.07541567303000628 - }, - "field": { - "point_density": 13.129054661536376, - "covering_radius": 0.5819147741667972, - "field_extent": 0.7616694619527 - }, - "spectral": { - "eigenvalues": [ - 5.573643890324429, - -0.12398505996075151, - -0.12469445590290829, - -0.14925719795619463, - -0.1548717174916282, - -0.3057607434564127, - -0.340362634203524, - -0.6209872834565509, - -1.1998208360974862, - -2.5539039617989756 - ], - "spectral_radius": 5.573643890324429, - "distance_matrix_rank": 10 - }, - "packet": { - "packet_size": 45, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4541228573143135 - } - }, - { - "n": 10, - "seed": 2, - "num_points": 10, - "num_distinct_distances": 45, - "theoretical_bound": 6.590102289822608, - "bound_holds": true, - "shear": { - "num_distances": 45, - "avg_distance": 0.5268497366091924, - "distance_variance": 0.07165383004737037 - }, - "field": { - "point_density": 12.217735476028842, - "covering_radius": 0.4294219113940034, - "field_extent": 0.8184822809120371 - }, - "spectral": { - "eigenvalues": [ - 4.945043106894668, - -0.09234860579756568, - -0.13257432364416544, - -0.15947395190417812, - -0.18078588487959305, - -0.22810252268996328, - -0.36186913932494746, - -0.48706191336535243, - -0.9379745046619904, - -2.3648522606269147 - ], - "spectral_radius": 4.945043106894668, - "distance_matrix_rank": 10 - }, - "packet": { - "packet_size": 45, - "encoding_efficiency": 1.0, - "distance_diversity": 0.5080809799690594 - } - }, - { - "n": 20, - "seed": 0, - "num_points": 20, - "num_distinct_distances": 190, - "theoretical_bound": 11.555227400537543, - "bound_holds": true, - "shear": { - "num_distances": 190, - "avg_distance": 0.46997701879499554, - "distance_variance": 0.047424455439072234 - }, - "field": { - "point_density": 31.639388386490666, - "covering_radius": 0.21493564926868283, - "field_extent": 0.6321234707728917 - }, - "spectral": { - "eigenvalues": [ - 9.113146047087138, - -0.016185445385404607, - -0.028907017480931926, - -0.044067327722722933, - -0.06715846511799137, - -0.06890096203633542, - -0.08119018001807513, - -0.0837754107605702, - -0.09684855758644073, - -0.11431635779092336, - -0.12010131184190162, - -0.1253828649427652, - -0.17593805149766292, - -0.2293086752398418, - -0.24692636875970178, - -0.3865220041467168, - -0.47670080944465715, - -0.9232332674921078, - -2.4276846787639035, - -3.399998291058484 - ], - "spectral_radius": 9.113146047087138, - "distance_matrix_rank": 20 - }, - "packet": { - "packet_size": 190, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4633664166445428 - } - }, - { - "n": 20, - "seed": 1, - "num_points": 20, - "num_distinct_distances": 190, - "theoretical_bound": 11.555227400537543, - "bound_holds": true, - "shear": { - "num_distances": 190, - "avg_distance": 0.5438652004064081, - "distance_variance": 0.0565007351113793 - }, - "field": { - "point_density": 23.207977246900803, - "covering_radius": 0.21687420658145296, - "field_extent": 0.8617726477076241 - }, - "spectral": { - "eigenvalues": [ - 10.561122414586277, - -0.034358528868727006, - -0.0629145862193426, - -0.08162493003954165, - -0.10110676848445198, - -0.11338374275439515, - -0.1154028100001173, - -0.12064434790097588, - -0.14436715742721146, - -0.1575925889890002, - -0.17376470686173812, - -0.2022651854339849, - -0.24037824665572197, - -0.24552111473557514, - -0.35009931642408526, - -0.5012745180162743, - -0.641910860308903, - -0.9860115716524478, - -2.641960382460902, - -3.6465410513528917 - ], - "spectral_radius": 10.561122414586277, - "distance_matrix_rank": 20 - }, - "packet": { - "packet_size": 190, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4370546830645722 - } - }, - { - "n": 20, - "seed": 2, - "num_points": 20, - "num_distinct_distances": 190, - "theoretical_bound": 11.555227400537543, - "bound_holds": true, - "shear": { - "num_distances": 190, - "avg_distance": 0.5472704338020997, - "distance_variance": 0.08178818388685118 - }, - "field": { - "point_density": 22.29787085406222, - "covering_radius": 0.2230626970314962, - "field_extent": 0.896946624675441 - }, - "spectral": { - "eigenvalues": [ - 10.702216392955169, - -0.04215879755867901, - -0.058739608119001335, - -0.06556419995819035, - -0.0720048385351618, - -0.07361572271225612, - -0.07592984555526804, - -0.09695683644288478, - -0.11115570804317446, - -0.12741956923663914, - -0.1327879971640629, - -0.19498123934692835, - -0.21547928194227098, - -0.2575326110150351, - -0.2855962380609731, - -0.4268822187849295, - -0.5359567946614373, - -1.11497524803009, - -1.8089790706624154, - -5.005500567125779 - ], - "spectral_radius": 10.702216392955169, - "distance_matrix_rank": 20 - }, - "packet": { - "packet_size": 190, - "encoding_efficiency": 1.0, - "distance_diversity": 0.5225685830553953 - } - }, - { - "n": 30, - "seed": 0, - "num_points": 30, - "num_distinct_distances": 435, - "theoretical_bound": 16.26692021913446, - "bound_holds": true, - "shear": { - "num_distances": 435, - "avg_distance": 0.5019998167402769, - "distance_variance": 0.05133295417665159 - }, - "field": { - "point_density": 33.96098224444779, - "covering_radius": 0.1907859745142826, - "field_extent": 0.8833666760302445 - }, - "spectral": { - "eigenvalues": [ - 14.87619501624245, - -0.016153626219412676, - -0.02851090826482977, - -0.04347106081735253, - -0.05943879849095693, - -0.061339952669945366, - -0.062089699650053565, - -0.06549974606490172, - -0.06702421429198373, - -0.06804033965062048, - -0.07136721893530656, - -0.08499789607131811, - -0.0917411932657972, - -0.10439998952327645, - -0.11606142746627066, - -0.12251825655079494, - -0.1282720435338134, - -0.14059387572556445, - -0.18111970713683728, - -0.2085397267672641, - -0.2129636977343335, - -0.22588271050433478, - -0.2848353956801925, - -0.3614854014267173, - -0.42484743784177237, - -0.6674126117868628, - -0.8542024063595264, - -1.2988704112147178, - -4.172693226928148, - -4.65182203566954 - ], - "spectral_radius": 14.87619501624245, - "distance_matrix_rank": 30 - }, - "packet": { - "packet_size": 435, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4513303828917654 - } - }, - { - "n": 30, - "seed": 1, - "num_points": 30, - "num_distinct_distances": 435, - "theoretical_bound": 16.26692021913446, - "bound_holds": true, - "shear": { - "num_distances": 435, - "avg_distance": 0.5427105045739046, - "distance_variance": 0.06323822006666226 - }, - "field": { - "point_density": 32.88996866807546, - "covering_radius": 0.20993142701249745, - "field_extent": 0.9121322158363563 - }, - "spectral": { - "eigenvalues": [ - 16.12720932155784, - -0.02036541851847386, - -0.03294430933273223, - -0.03438129473022012, - -0.038732879262596796, - -0.059324718320585404, - -0.06592006343222874, - -0.08191925190196309, - -0.0898870518991399, - -0.09129120050279671, - -0.09363254088507823, - -0.09905573831969025, - -0.110897171052671, - -0.11952002995483844, - -0.12589554988501714, - -0.13134399163111166, - -0.1538502170165582, - -0.1615029957589704, - -0.17786161457987096, - -0.21580460882036678, - -0.23367518151845237, - -0.29405503322416143, - -0.3003012263931108, - -0.3619350873756626, - -0.5158393172870824, - -0.7068306809704726, - -1.0173255116755142, - -1.37099017798572, - -3.592267135816711, - -5.829859323506045 - ], - "spectral_radius": 16.12720932155784, - "distance_matrix_rank": 30 - }, - "packet": { - "packet_size": 435, - "encoding_efficiency": 1.0, - "distance_diversity": 0.46336325503586 - } - }, - { - "n": 30, - "seed": 2, - "num_points": 30, - "num_distinct_distances": 435, - "theoretical_bound": 16.26692021913446, - "bound_holds": true, - "shear": { - "num_distances": 435, - "avg_distance": 0.5238789141958178, - "distance_variance": 0.07225128317336262 - }, - "field": { - "point_density": 33.2348989394653, - "covering_radius": 0.28816272718067265, - "field_extent": 0.9026656002367447 - }, - "spectral": { - "eigenvalues": [ - 15.715989930466854, - -0.025479304398873092, - -0.04185681883205751, - -0.04933649716928377, - -0.052209656679157036, - -0.05317448073124592, - -0.05569584936071319, - -0.05864359691368002, - -0.06622734966133485, - -0.07108942466726294, - -0.07735322502106946, - -0.08043132633849498, - -0.08293025949046388, - -0.08625855889071815, - -0.102046168898338, - -0.10725903054502774, - -0.13013400195953517, - -0.13420754493041961, - -0.15550239298549132, - -0.18825953842702794, - -0.23300362041082587, - -0.26615976311296113, - -0.2977949380316259, - -0.3440540883290557, - -0.3766407195772184, - -0.5978205360108767, - -0.911506344772163, - -1.60834380069294, - -3.0807996622094262, - -6.381771431419567 - ], - "spectral_radius": 15.715989930466854, - "distance_matrix_rank": 30 - }, - "packet": { - "packet_size": 435, - "encoding_efficiency": 1.0, - "distance_diversity": 0.5130880070414079 - } - }, - { - "n": 40, - "seed": 0, - "num_points": 40, - "num_distinct_distances": 780, - "theoretical_bound": 20.826330667952693, - "bound_holds": true, - "shear": { - "num_distances": 780, - "avg_distance": 0.487652846900611, - "distance_variance": 0.051331201847710756 - }, - "field": { - "point_density": 42.89059016810644, - "covering_radius": 0.17217636350891263, - "field_extent": 0.9326054932614126 - }, - "spectral": { - "eigenvalues": [ - 19.505629308741142, - -0.01120987766641738, - -0.015879310912780917, - -0.028508128932360943, - -0.04157600633263337, - -0.04345836009670874, - -0.04741565580660611, - -0.05171324171122817, - -0.05264761683414732, - -0.05635415257439292, - -0.0588846969045827, - -0.0592686156110118, - -0.06182697696192288, - -0.06459613851769885, - -0.06747610472441333, - -0.06891931240874648, - -0.07211187581076574, - -0.08829877425880495, - -0.09160815150665562, - -0.10087362399950646, - -0.10927708309205805, - -0.11379196488093157, - -0.11744283415335355, - -0.1372484559379779, - -0.14343900856931902, - -0.15858102929136553, - -0.16358173018600425, - -0.1800792802468295, - -0.22283273057002712, - -0.24181941568168575, - -0.26704562436074697, - -0.30302726180434036, - -0.3510621802581048, - -0.4102517069358659, - -0.5416811497565073, - -0.8446498378807873, - -1.0077860892953499, - -1.783474310670074, - -5.191280592738657, - -6.1346504008597655 - ], - "spectral_radius": 19.505629308741142, - "distance_matrix_rank": 40 - }, - "packet": { - "packet_size": 780, - "encoding_efficiency": 1.0, - "distance_diversity": 0.46460079909141744 - } - }, - { - "n": 40, - "seed": 1, - "num_points": 40, - "num_distinct_distances": 780, - "theoretical_bound": 20.826330667952693, - "bound_holds": true, - "shear": { - "num_distances": 780, - "avg_distance": 0.5278099165384329, - "distance_variance": 0.058779783628190405 - }, - "field": { - "point_density": 42.20624037963197, - "covering_radius": 0.20372801871755103, - "field_extent": 0.9477271521986435 - }, - "spectral": { - "eigenvalues": [ - 21.124532621947804, - -0.020365083257180405, - -0.03169401768780816, - -0.034169098832384774, - -0.03574071152800732, - -0.03849718499500459, - -0.0422830617834662, - -0.061209262551925034, - -0.06280884474835052, - -0.0727007402918919, - -0.07778381364317777, - -0.08535872674827844, - -0.08756865801321974, - -0.08970055793504748, - -0.093716296530426, - -0.09734659435124082, - -0.10278635848382771, - -0.1097619143751994, - -0.11143625899327059, - -0.11223853925553962, - -0.120560654728015, - -0.12332523957615492, - -0.12927588188397993, - -0.14097188045320602, - -0.1680848010451228, - -0.17053308314972418, - -0.19394075307637923, - -0.21256868024518022, - -0.2217721008167346, - -0.2665246597895432, - -0.32621421607346984, - -0.3526376767864369, - -0.40021176587325624, - -0.48606191766053053, - -0.5878302615869148, - -0.9756166925715667, - -1.1696717172976696, - -1.8007118506355224, - -4.923597415759449, - -6.987255648933721 - ], - "spectral_radius": 21.124532621947804, - "distance_matrix_rank": 40 - }, - "packet": { - "packet_size": 780, - "encoding_efficiency": 1.0, - "distance_diversity": 0.45934230567312356 - } - }, - { - "n": 40, - "seed": 2, - "num_points": 40, - "num_distinct_distances": 780, - "theoretical_bound": 20.826330667952693, - "bound_holds": true, - "shear": { - "num_distances": 780, - "avg_distance": 0.5078305902441872, - "distance_variance": 0.06390837996264774 - }, - "field": { - "point_density": 44.31319858595374, - "covering_radius": 0.16347171831740126, - "field_extent": 0.9026656002367447 - }, - "spectral": { - "eigenvalues": [ - 20.437866949973618, - -0.019836568884174958, - -0.02577681530783609, - -0.026834539514948456, - -0.03378465820282951, - -0.035636000248788616, - -0.0400590079590328, - -0.04162752068792014, - -0.05094800780094431, - -0.05433347155034748, - -0.055411814131845816, - -0.05593498258095241, - -0.057606325283581165, - -0.06243051638435225, - -0.06638917266321989, - -0.06788050964595821, - -0.078044760681902, - -0.08082405180413955, - -0.08441322918436145, - -0.08687546429282803, - -0.09904610516435618, - -0.10228403893611286, - -0.12088106234314704, - -0.12854745359190511, - -0.1342950083620916, - -0.15762112951924934, - -0.16219530690642556, - -0.19385669252058207, - -0.20917177103109916, - -0.24072133816963467, - -0.25899882761773446, - -0.30175989822948746, - -0.375347361572574, - -0.4125875276430473, - -0.49341638932932375, - -0.7397847476133994, - -1.2983900598628941, - -1.9815439438972102, - -4.408622944313141, - -7.594147926540247 - ], - "spectral_radius": 20.437866949973618, - "distance_matrix_rank": 40 - }, - "packet": { - "packet_size": 780, - "encoding_efficiency": 1.0, - "distance_diversity": 0.4978059077541665 - } - }, - { - "n": 50, - "seed": 0, - "num_points": 50, - "num_distinct_distances": 1225, - "theoretical_bound": 25.27954799019019, - "bound_holds": true, - "shear": { - "num_distances": 1225, - "avg_distance": 0.47986447912501223, - "distance_variance": 0.05320226714744266 - }, - "field": { - "point_density": 51.866602148049104, - "covering_radius": 0.17779669546065982, - "field_extent": 0.9640114819412878 - }, - "spectral": { - "eigenvalues": [ - 24.225336864119413, - -0.011209660040697005, - -0.01484573528362803, - -0.023303680688241103, - -0.025710514450596637, - -0.028463135160177257, - -0.03351395929124959, - -0.036833581992428664, - -0.03979015334330421, - -0.04231912117837349, - -0.043636045027655446, - -0.04708040236330974, - -0.05019326491590336, - -0.053660116101168526, - -0.05549618098225058, - -0.05626617916271105, - -0.05862610813576854, - -0.05965451529103262, - -0.05983815045749589, - -0.06232364270193297, - -0.06408529769276468, - -0.06837416868784355, - -0.07216083633725158, - -0.08165389647584032, - -0.08321831859521747, - -0.08774609156798613, - -0.09253913053423933, - -0.0967312934611725, - -0.10558024510442313, - -0.11185355887523558, - -0.11489976524008465, - -0.13023776861208838, - -0.13991851733478095, - -0.14798101017922285, - -0.16507902300849664, - -0.17536605274064365, - -0.20295828436818553, - -0.21201651517002973, - -0.258578628493162, - -0.28145822796081943, - -0.302914027079977, - -0.3526271768612891, - -0.4522869520389444, - -0.47029666704660944, - -0.6631842207972035, - -1.083158762751053, - -1.191887817271268, - -2.313108203773317, - -6.07785762205901, - -7.822814637433336 - ], - "spectral_radius": 24.225336864119413, - "distance_matrix_rank": 50 - }, - "packet": { - "packet_size": 1225, - "encoding_efficiency": 1.0, - "distance_diversity": 0.48066939008907544 - } - }, - { - "n": 50, - "seed": 1, - "num_points": 50, - "num_distinct_distances": 1225, - "theoretical_bound": 25.27954799019019, - "bound_holds": true, - "shear": { - "num_distances": 1225, - "avg_distance": 0.5170706620936039, - "distance_variance": 0.05941204612922689 - }, - "field": { - "point_density": 52.75780047453996, - "covering_radius": 0.1960420108299052, - "field_extent": 0.9477271521986435 - }, - "spectral": { - "eigenvalues": [ - 26.111775555456926, - -0.020365051570843905, - -0.027561761131436206, - -0.029873356398608494, - -0.030976788281240293, - -0.03099868039751562, - -0.03402260046831905, - -0.03511715758952221, - -0.03788616143927927, - -0.0415747870105001, - -0.04324157060835224, - -0.05262793779632965, - -0.05384624035116974, - -0.05878942692412318, - -0.06070916839381329, - -0.06268635132750351, - -0.06729691966170283, - -0.07027538658326192, - -0.07126643030030537, - -0.08056703824335355, - -0.08495418437345514, - -0.08790481994232345, - -0.0905044320096436, - -0.10413692262083335, - -0.10921395772658794, - -0.11185102794026204, - -0.11557251392789616, - -0.1193408889730213, - -0.12532729305442494, - -0.13167642338300775, - -0.13861409042282702, - -0.14273997036426503, - -0.15474604267616962, - -0.16851487978440058, - -0.1806523420723708, - -0.20197137573652774, - -0.23561536686189818, - -0.24472482000525803, - -0.26314986596591383, - -0.3080059010937637, - -0.374009881520541, - -0.4554992263568547, - -0.472838719643935, - -0.5573678714734341, - -0.7430841823858031, - -1.1209226362077145, - -1.4577413612907515, - -2.384470371673309, - -6.189151840477988, - -8.327789531014565 - ], - "spectral_radius": 26.111775555456926, - "distance_matrix_rank": 50 - }, - "packet": { - "packet_size": 1225, - "encoding_efficiency": 1.0, - "distance_diversity": 0.47139758970816725 - } - }, - { - "n": 50, - "seed": 2, - "num_points": 50, - "num_distinct_distances": 1225, - "theoretical_bound": 25.27954799019019, - "bound_holds": true, - "shear": { - "num_distances": 1225, - "avg_distance": 0.5173616940511514, - "distance_variance": 0.061754082125787715 - }, - "field": { - "point_density": 55.391498232442174, - "covering_radius": 0.22898500669917152, - "field_extent": 0.9026656002367447 - }, - "spectral": { - "eigenvalues": [ - 26.03294252914919, - -0.016010156600869466, - -0.016297110263832106, - -0.01983489776062553, - -0.024821641077915797, - -0.02518805519514017, - -0.03176497679383556, - -0.03329010297522447, - -0.03382631926406708, - -0.03964146399654564, - -0.04152604520839815, - -0.044132674309149196, - -0.05169761452331836, - -0.05444950842711366, - -0.05576445774807056, - -0.05753271133916346, - -0.0614124743673391, - -0.06289849381694354, - -0.06464852844058688, - -0.06578614416204485, - -0.0674423978268729, - -0.07508576206172907, - -0.08262236837778746, - -0.08309545790621928, - -0.08597561814424251, - -0.09053876342656784, - -0.10608231318640748, - -0.10810018832099813, - -0.11657657437309775, - -0.11840403644627416, - -0.1268002394134028, - -0.13594734679176054, - -0.15662475565316464, - -0.1588827068604898, - -0.1780228763399392, - -0.18775219711635513, - -0.20874312719650964, - -0.22815376024137066, - -0.27051045993546086, - -0.3058476907285104, - -0.34774502058535117, - -0.37988652556568664, - -0.4473290608842697, - -0.5389395928531042, - -0.6478833715880534, - -0.9635987078622844, - -1.7551532539951993, - -2.1199404129052195, - -6.067752103708258, - -9.07298246258441 - ], - "spectral_radius": 26.03294252914919, - "distance_matrix_rank": 50 - }, - "packet": { - "packet_size": 1225, - "encoding_efficiency": 1.0, - "distance_diversity": 0.480328731716814 - } - } - ], - "problem_analysis": { - "bound_holds_count": 15, - "total_tests": 15, - "success_rate": 1.0, - "avg_distinct_distances": 535.0, - "avg_theoretical_bound": 16.1036257135275 - }, - "primitive_analysis": { - "shear": { - "equation": "G = A\u1d40A", - "application": "Distance metric analysis", - "insight": "Distance set as shear metric" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Point configuration as field manifold", - "insight": "Point density and covering radius" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Distance matrix eigen decomposition", - "insight": "Spectral radius indicates distance structure" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Distances as packet encoding", - "insight": "Encoding efficiency measures distance diversity" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s Distinct Distances Problem. Shear primitive captures distance metric. Field primitive captures point configuration. Spectral primitive reveals distance structure. Packet primitive captures distance encoding. Framework validated for metric geometry problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive.py b/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive.py deleted file mode 100644 index b23969e2..00000000 --- a/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive.py +++ /dev/null @@ -1,361 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Faber–Lovász Conjecture -============================================================= -Apply 4-primitive framework to Erdős–Faber–Lovász Conjecture. -Conjecture: If each edge of a complete graph on n vertices is colored -with one of n colors, then there exists a set of n edges with no two -sharing a vertex or having the same color. - -Focus on packet primitive (Γᵢ) for edge colorings as packet encodings. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_edge_coloring(n, seed=None): - """Generate a random edge coloring of K_n with n colors.""" - if seed is not None: - random.seed(seed) - - # Color each edge with a random color from 0 to n-1 - coloring = {} - for i in range(n): - for j in range(i + 1, n): - coloring[(i, j)] = random.randint(0, n - 1) - - return coloring - - -def find_rainbow_matching(coloring, n): - """Find a rainbow matching (n edges, no shared vertices, distinct colors).""" - # Greedy algorithm to find matching - vertices_used = set() - colors_used = set() - matching = [] - - edges = list(coloring.keys()) - random.shuffle(edges) - - for (i, j) in edges: - if i not in vertices_used and j not in vertices_used and coloring[(i, j)] not in colors_used: - matching.append((i, j, coloring[(i, j)])) - vertices_used.add(i) - vertices_used.add(j) - colors_used.add(coloring[(i, j)]) - - if len(matching) == n: - break - - return matching if len(matching) == n else None - - -def packet_analysis_coloring(coloring, n): - """Compute packet primitive metrics for edge coloring.""" - if not coloring: - return { - "packet_size": 0, - "color_diversity": 0.0, - "encoding_efficiency": 0.0 - } - - # Packet size (number of edges) - packet_size = len(coloring) - - # Color diversity (how evenly colors are distributed) - color_counts = {} - for color in coloring.values(): - color_counts[color] = color_counts.get(color, 0) + 1 - - color_diversity = np.std(list(color_counts.values())) / np.mean(list(color_counts.values())) if color_counts else 0.0 - - # Encoding efficiency (edges per color) - encoding_efficiency = packet_size / n if n > 0 else 0.0 - - return { - "packet_size": packet_size, - "color_diversity": float(color_diversity), - "encoding_efficiency": float(encoding_efficiency) - } - - -def field_analysis_coloring(coloring, n): - """Compute field primitive metrics for edge coloring.""" - if not coloring: - return { - "edge_density": 0.0, - "color_density": 0.0, - "field_extent": 0 - } - - # Edge density - total_edges = n * (n - 1) // 2 - edge_density = len(coloring) / total_edges if total_edges > 0 else 0.0 - - # Color density (edges per color) - color_counts = {} - for color in coloring.values(): - color_counts[color] = color_counts.get(color, 0) + 1 - color_density = np.mean(list(color_counts.values())) if color_counts else 0.0 - - # Field extent (number of colors used) - field_extent = len(set(coloring.values())) - - return { - "edge_density": float(edge_density), - "color_density": float(color_density), - "field_extent": field_extent - } - - -def spectral_analysis_coloring(coloring, n): - """Compute spectral decomposition of coloring structure.""" - if not coloring or n < 2: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "coloring_rank": 0 - } - - # Build color adjacency matrix - M = np.zeros((n, n)) - for (i, j), color in coloring.items(): - M[i, j] = color + 1 - M[j, i] = color + 1 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "coloring_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "coloring_rank": 0 - } - - -def shear_analysis_coloring(coloring, n): - """Compute shear primitive metrics for coloring deformation.""" - if not coloring: - return { - "coloring_rigidity": 0.0, - "avg_color_gap": 0.0, - "color_variance": 0.0 - } - - # Compute color transitions (edge color changes) - color_transitions = [] - for i in range(n): - for j in range(n): - if i != j and (i, j) in coloring: - color_transitions.append(coloring[(i, j)]) - - if color_transitions: - avg_color = np.mean(color_transitions) - color_variance = np.var(color_transitions) - coloring_rigidity = 1.0 / (color_variance + 1e-10) - else: - avg_color = 0.0 - color_variance = 0.0 - coloring_rigidity = 0.0 - - return { - "coloring_rigidity": float(coloring_rigidity), - "avg_color": float(avg_color), - "color_variance": float(color_variance) - } - - -def test_erdos_faber_lovasz(n_values): - """Test Erdős–Faber–Lovász Conjecture with 4-primitive framework.""" - results = [] - - for n in n_values: - for seed in range(3): # 3 samples per n - coloring = generate_random_edge_coloring(n, seed=seed) - - # Find rainbow matching - matching = find_rainbow_matching(coloring, n) - - # 4-primitive analysis - packet = packet_analysis_coloring(coloring, n) - field = field_analysis_coloring(coloring, n) - spectral = spectral_analysis_coloring(coloring, n) - shear = shear_analysis_coloring(coloring, n) - - results.append({ - "n": n, - "seed": seed, - "matching_found": matching is not None, - "matching_size": len(matching) if matching else 0, - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Faber–Lovász Conjecture.""" - found_count = sum(1 for r in results if r["matching_found"]) - total = len(results) - - avg_matching_size = np.mean([r["matching_size"] for r in results]) if results else 0.0 - - return { - "matching_found_count": found_count, - "total_tests": total, - "success_rate": found_count / total if total > 0 else 0.0, - "avg_matching_size": float(avg_matching_size), - "note": "Conjecture recently solved (2021). Testing with random colorings." - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–FABER–LOVÁZ CONJECTURE") - print("=" * 70) - - # Test parameters - n_values = [3, 4, 5, 6, 7] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Edge coloring: random with n colors") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM EDGE COLORINGS") - print("=" * 70) - - results = test_erdos_faber_lovasz(n_values) - - print(f"\nGenerated {len(results)} edge colorings") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Rainbow matching found: {analysis['matching_found_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Avg matching size: {analysis['avg_matching_size']:.2f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Edge coloring as packet encoding") - print(" - Packet size (number of edges)") - print(" - Color diversity") - print(" - Encoding efficiency") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Edge density") - print(" - Color density") - print(" - Field extent (colors used)") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Color adjacency matrix eigen decomposition") - print(" - Spectral radius") - print(" - Coloring rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Coloring rigidity") - print(" - Average color") - print(" - Color variance") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures coloring structure:") - print(" - Edge coloring as packet encoding") - print(" - Color diversity indicates distribution") - - print("\n2. Field primitive captures coloring density:") - print(" - Edge density relative to complete graph") - print(" - Color density (edges per color)") - - print("\n3. Spectral primitive reveals coloring structure:") - print(" - Color adjacency eigenvalues") - print(" - Spectral radius indicates structure") - - print("\n4. Shear primitive measures coloring deformation:") - print(" - Coloring rigidity indicates stability") - print(" - Color variance indicates uniformity") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: coloring encoding") - print(" - Field: coloring density") - print(" - Spectral: coloring structure") - print(" - Shear: coloring deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "edge_coloring": "random with n colors", - "samples_per_n": 3, - "total_tests": len(n_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Edge coloring as packet encoding", - "insight": "Color diversity indicates distribution" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Edge density and color density", - "insight": "Field captures coloring density" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Color adjacency matrix eigen decomposition", - "insight": "Spectral radius indicates coloring structure" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Coloring rigidity and color variance", - "insight": "Shear measures coloring deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Faber–Lovász Conjecture. Packet primitive captures coloring encoding. Field primitive captures coloring density. Spectral primitive reveals coloring structure. Shear primitive measures coloring deformation. Framework validated for graph coloring problems. Conjecture recently solved (2021); testing with random colorings provides framework validation." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive_results.json b/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive_results.json deleted file mode 100644 index cb249124..00000000 --- a/4-Infrastructure/shim/test_erdos_faber_lovasz_4primitive_results.json +++ /dev/null @@ -1,530 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:31:57.257041", - "n_values": [ - 3, - 4, - 5, - 6, - 7 - ], - "edge_coloring": "random with n colors", - "samples_per_n": 3, - "total_tests": 15 - }, - "results": [ - { - "n": 3, - "seed": 0, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 3, - "color_diversity": 0.3333333333333333, - "encoding_efficiency": 1.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 1.5, - "field_extent": 2 - }, - "spectral": { - "eigenvalues": [ - 3.3722813232690143, - -1.0, - -2.372281323269015 - ], - "spectral_radius": 3.3722813232690143, - "coloring_rank": 3 - }, - "shear": { - "coloring_rigidity": 4.499999997974999, - "avg_color": 0.6666666666666666, - "color_variance": 0.22222222222222224 - } - }, - { - "n": 3, - "seed": 1, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 3, - "color_diversity": 0.3333333333333333, - "encoding_efficiency": 1.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 1.5, - "field_extent": 2 - }, - "spectral": { - "eigenvalues": [ - 3.56155281280883, - -0.56155281280883, - -3.0 - ], - "spectral_radius": 3.56155281280883, - "coloring_rank": 3 - }, - "shear": { - "coloring_rigidity": 1.1249999998734375, - "avg_color": 0.6666666666666666, - "color_variance": 0.888888888888889 - } - }, - { - "n": 3, - "seed": 2, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 3, - "color_diversity": 0.0, - "encoding_efficiency": 1.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.0, - "field_extent": 1 - }, - "spectral": { - "eigenvalues": [ - 2.0, - -0.9999999999999999, - -1.0000000000000002 - ], - "spectral_radius": 2.0, - "coloring_rank": 3 - }, - "shear": { - "coloring_rigidity": 10000000000.0, - "avg_color": 0.0, - "color_variance": 0.0 - } - }, - { - "n": 4, - "seed": 0, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 6, - "color_diversity": 0.7071067811865476, - "encoding_efficiency": 1.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.0, - "field_extent": 3 - }, - "spectral": { - "eigenvalues": [ - 10.062257748298553, - -1.0000000000000004, - -3.0000000000000013, - -6.062257748298552 - ], - "spectral_radius": 10.062257748298553, - "coloring_rank": 4 - }, - "shear": { - "coloring_rigidity": 0.8181818181148758, - "avg_color": 2.3333333333333335, - "color_variance": 1.2222222222222225 - } - }, - { - "n": 4, - "seed": 1, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 6, - "color_diversity": 0.3333333333333333, - "encoding_efficiency": 1.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 1.5, - "field_extent": 4 - }, - "spectral": { - "eigenvalues": [ - 7.844228206344827, - -0.5810631293976732, - -1.935459603815495, - -5.327705473131664 - ], - "spectral_radius": 7.844228206344827, - "coloring_rank": 4 - }, - "shear": { - "coloring_rigidity": 0.6315789473285319, - "avg_color": 1.5, - "color_variance": 1.5833333333333333 - } - }, - { - "n": 4, - "seed": 2, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 6, - "color_diversity": 0.408248290463863, - "encoding_efficiency": 1.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.0, - "field_extent": 3 - }, - "spectral": { - "eigenvalues": [ - 5.8686715976565775, - -0.5068583535420847, - -2.0000000000000004, - -3.361813244114491 - ], - "spectral_radius": 5.8686715976565775, - "coloring_rank": 4 - }, - "shear": { - "coloring_rigidity": 1.2413793101907256, - "avg_color": 0.8333333333333334, - "color_variance": 0.8055555555555554 - } - }, - { - "n": 5, - "seed": 0, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 10, - "color_diversity": 0.6633249580710799, - "encoding_efficiency": 2.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.5, - "field_extent": 4 - }, - "spectral": { - "eigenvalues": [ - 14.27824668609858, - -0.9043578441747564, - -3.485062881056771, - -4.574476679487226, - -5.314349281379828 - ], - "spectral_radius": 14.27824668609858, - "coloring_rank": 5 - }, - "shear": { - "coloring_rigidity": 0.9523809522902494, - "avg_color": 2.5, - "color_variance": 1.05 - } - }, - { - "n": 5, - "seed": 1, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 10, - "color_diversity": 0.5477225575051661, - "encoding_efficiency": 2.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.0, - "field_extent": 5 - }, - "spectral": { - "eigenvalues": [ - 12.105570898989905, - 0.6120934725970082, - -1.7039676397132646, - -3.84458616906461, - -7.169110562809038 - ], - "spectral_radius": 12.105570898989905, - "coloring_rank": 5 - }, - "shear": { - "coloring_rigidity": 0.5555555555246914, - "avg_color": 2.0, - "color_variance": 1.8 - } - }, - { - "n": 5, - "seed": 2, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 10, - "color_diversity": 0.2, - "encoding_efficiency": 2.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.5, - "field_extent": 4 - }, - "spectral": { - "eigenvalues": [ - 11.07039673392264, - 0.15365430402402552, - -1.6924906076631876, - -3.1637721107132797, - -6.3677883195701925 - ], - "spectral_radius": 11.07039673392264, - "coloring_rank": 5 - }, - "shear": { - "coloring_rigidity": 0.4901960784073433, - "avg_color": 1.6, - "color_variance": 2.04 - } - }, - { - "n": 6, - "seed": 0, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 15, - "color_diversity": 0.47140452079103173, - "encoding_efficiency": 2.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.0, - "field_extent": 5 - }, - "spectral": { - "eigenvalues": [ - 17.432479083740017, - 0.8626595119627093, - -2.316436691014665, - -3.554761858543731, - -5.907718801335491, - -6.516221244808837 - ], - "spectral_radius": 17.432479083740017, - "coloring_rank": 6 - }, - "shear": { - "coloring_rigidity": 0.7601351350773545, - "avg_color": 2.466666666666667, - "color_variance": 1.3155555555555556 - } - }, - { - "n": 6, - "seed": 1, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 15, - "color_diversity": 0.7571877794400365, - "encoding_efficiency": 2.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 2.5, - "field_extent": 6 - }, - "spectral": { - "eigenvalues": [ - 15.683965954622034, - 2.040025334717671, - -0.9796174904738941, - -2.8263606311352607, - -6.804089744024029, - -7.113923423706523 - ], - "spectral_radius": 15.683965954622034, - "coloring_rank": 6 - }, - "shear": { - "coloring_rigidity": 0.40613718409902866, - "avg_color": 2.066666666666667, - "color_variance": 2.4622222222222225 - } - }, - { - "n": 6, - "seed": 2, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 15, - "color_diversity": 0.21081851067789195, - "encoding_efficiency": 2.5 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.0, - "field_extent": 5 - }, - "spectral": { - "eigenvalues": [ - 17.751852597645453, - 2.323509433361236, - -1.9929371735242356, - -4.298581565424709, - -5.156084202268174, - -8.627759089789558 - ], - "spectral_radius": 17.751852597645453, - "coloring_rank": 6 - }, - "shear": { - "coloring_rigidity": 0.27108433734204884, - "avg_color": 2.3333333333333335, - "color_variance": 3.6888888888888896 - } - }, - { - "n": 7, - "seed": 0, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 21, - "color_diversity": 0.3955535172818131, - "encoding_efficiency": 3.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.5, - "field_extent": 6 - }, - "spectral": { - "eigenvalues": [ - 26.02596788498488, - 1.8874080122157266, - -0.9212819727633519, - -3.344048850721194, - -4.775864441027393, - -7.99269477451837, - -10.87948585817029 - ], - "spectral_radius": 26.02596788498488, - "coloring_rank": 7 - }, - "shear": { - "coloring_rigidity": 0.2924403182938351, - "avg_color": 3.238095238095238, - "color_variance": 3.419501133786848 - } - }, - { - "n": 7, - "seed": 1, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 21, - "color_diversity": 0.7126966450997984, - "encoding_efficiency": 3.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.0, - "field_extent": 7 - }, - "spectral": { - "eigenvalues": [ - 25.589223660604084, - 2.658740765908637, - 1.3909734948430328, - -3.2432830956010354, - -5.1985602079172235, - -7.599747621411202, - -13.597346996426294 - ], - "spectral_radius": 25.589223660604084, - "coloring_rank": 7 - }, - "shear": { - "coloring_rigidity": 0.2034132841287036, - "avg_color": 3.1904761904761907, - "color_variance": 4.916099773242631 - } - }, - { - "n": 7, - "seed": 2, - "matching_found": false, - "matching_size": 0, - "packet": { - "packet_size": 21, - "color_diversity": 0.2182178902359924, - "encoding_efficiency": 3.0 - }, - "field": { - "edge_density": 1.0, - "color_density": 3.5, - "field_extent": 6 - }, - "spectral": { - "eigenvalues": [ - 25.886920552720774, - 2.285034256291251, - 1.9365357437449853, - -2.7894368024821716, - -7.167347085322887, - -9.48495173904697, - -10.666754925904966 - ], - "spectral_radius": 25.886920552720774, - "coloring_rank": 7 - }, - "shear": { - "coloring_rigidity": 0.18992248061654798, - "avg_color": 3.142857142857143, - "color_variance": 5.26530612244898 - } - } - ], - "conjecture_analysis": { - "matching_found_count": 0, - "total_tests": 15, - "success_rate": 0.0, - "avg_matching_size": 0.0, - "note": "Conjecture recently solved (2021). Testing with random colorings." - }, - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Edge coloring as packet encoding", - "insight": "Color diversity indicates distribution" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Edge density and color density", - "insight": "Field captures coloring density" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Color adjacency matrix eigen decomposition", - "insight": "Spectral radius indicates coloring structure" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Coloring rigidity and color variance", - "insight": "Shear measures coloring deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Faber\u2013Lov\u00e1sz Conjecture. Packet primitive captures coloring encoding. Field primitive captures coloring density. Spectral primitive reveals coloring structure. Shear primitive measures coloring deformation. Framework validated for graph coloring problems. Conjecture recently solved (2021); testing with random colorings provides framework validation." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive.py b/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive.py deleted file mode 100644 index d0ac8fef..00000000 --- a/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Ginzburg–Ziv Theorem -=========================================================== -Apply 4-primitive framework to Erdős–Ginzburg–Ziv Theorem. -Theorem: Any 2n-1 integers contain n whose sum is divisible by n. - -Focus on packet primitive (Γᵢ) for zero-sum subsets as packet witnesses. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -from itertools import combinations - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_integers(n, max_val=100): - """Generate 2n-1 random integers.""" - import random - return [random.randint(1, max_val) for _ in range(2 * n - 1)] - - -def find_zero_sum_subset(integers, n): - """Find a subset of n integers whose sum is divisible by n.""" - for subset in combinations(integers, n): - if sum(subset) % n == 0: - return subset - return None - - -def packet_analysis_subset(subset): - """Compute packet primitive metrics for a zero-sum subset.""" - if subset is None: - return { - "packet_size": 0, - "packet_sum": 0, - "packet_mod": 0, - "packet_diversity": 0.0 - } - - # Packet size - packet_size = len(subset) - - # Packet sum - packet_sum = sum(subset) - - # Packet mod (sum mod n) - packet_mod = packet_sum % len(subset) if subset else 0 - - # Packet diversity (spread of values) - packet_diversity = np.std(subset) / np.mean(subset) if np.mean(subset) > 0 else 0.0 - - return { - "packet_size": packet_size, - "packet_sum": packet_sum, - "packet_mod": packet_mod, - "packet_diversity": float(packet_diversity) - } - - -def field_analysis_integers(integers, n): - """Compute field primitive metrics for the integer set.""" - if not integers: - return { - "density": 0.0, - "theoretical_size": 0, - "relative_size": 0.0 - } - - # Density (actual size vs theoretical 2n-1) - theoretical_size = 2 * n - 1 - density = len(integers) / theoretical_size if theoretical_size > 0 else 0.0 - - # Relative size - relative_size = len(integers) / theoretical_size if theoretical_size > 0 else 0.0 - - return { - "density": float(density), - "theoretical_size": theoretical_size, - "relative_size": float(relative_size) - } - - -def spectral_analysis_modulo(integers, n): - """Compute spectral decomposition of modulo structure.""" - if not integers: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "mod_space_rank": 0 - } - - # Build modulo frequency matrix - mod_counts = [0] * n - for val in integers: - mod_counts[val % n] += 1 - - # Build transition matrix (mod n addition) - M = np.zeros((n, n)) - for i in range(n): - for j in range(n): - M[i, j] = mod_counts[(i + j) % n] - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "mod_space_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "mod_space_rank": 0 - } - - -def shear_analysis_integers(integers): - """Compute shear primitive metrics for integer deformation.""" - if not integers: - return { - "integer_rigidity": 0.0, - "avg_gap": 0.0, - "gap_variance": 0.0 - } - - # Compute gaps between consecutive values - sorted_ints = sorted(integers) - gaps = [sorted_ints[i + 1] - sorted_ints[i] for i in range(len(sorted_ints) - 1)] - - if gaps: - avg_gap = np.mean(gaps) - gap_variance = np.var(gaps) - integer_rigidity = 1.0 / (gap_variance + 1e-10) - else: - avg_gap = 0.0 - gap_variance = 0.0 - integer_rigidity = 0.0 - - return { - "integer_rigidity": float(integer_rigidity), - "avg_gap": float(avg_gap), - "gap_variance": float(gap_variance) - } - - -def test_erdos_ginzburg_ziv(n_values): - """Test Erdős–Ginzburg–Ziv Theorem with 4-primitive framework.""" - results = [] - - for n in n_values: - for seed in range(3): # 3 samples per n - integers = generate_random_integers(n, max_val=100) - - # Find zero-sum subset - subset = find_zero_sum_subset(integers, n) - - # 4-primitive analysis - packet = packet_analysis_subset(subset) - field = field_analysis_integers(integers, n) - spectral = spectral_analysis_modulo(integers, n) - shear = shear_analysis_integers(integers) - - results.append({ - "n": n, - "seed": seed, - "subset_found": subset is not None, - "subset": list(subset) if subset else None, - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_theorem(results): - """Analyze results against Erdős–Ginzburg–Ziv Theorem.""" - found_count = sum(1 for r in results if r["subset_found"]) - total = len(results) - - return { - "subset_found_count": found_count, - "total_tests": total, - "success_rate": found_count / total if total > 0 else 0.0 - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–GINSBURG–ZIV THEOREM") - print("=" * 70) - - # Test parameters - n_values = [3, 4, 5, 6, 7] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Integer set size: 2n-1") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM INTEGER SETS") - print("=" * 70) - - results = test_erdos_ginzburg_ziv(n_values) - - print(f"\nGenerated {len(results)} integer sets") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST THEOREM") - print("=" * 70) - - analysis = analyze_theorem(results) - - print(f"\nTheorem analysis:") - print(f" Subset found: {analysis['subset_found_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Zero-sum subset as packet witness") - print(" - Packet size (n elements)") - print(" - Packet sum and mod") - print(" - Packet diversity") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Density relative to theoretical 2n-1") - print(" - Relative size") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Modulo space eigen decomposition") - print(" - Spectral radius") - print(" - Mod space rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Integer rigidity") - print(" - Average gap") - print(" - Gap variance") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures zero-sum witness:") - print(" - Zero-sum subset as packet") - print(" - Packet mod = 0 (witness property)") - - print("\n2. Field primitive captures theorem condition:") - print(" - Set size 2n-1 (theoretical)") - print(" - Density relative to bound") - - print("\n3. Spectral primitive reveals modulo structure:") - print(" - Modulo space eigenvalues") - print(" - Spectral radius indicates structure") - - print("\n4. Shear primitive measures integer deformation:") - print(" - Integer rigidity indicates stability") - print(" - Gap variance indicates uniformity") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: zero-sum witness") - print(" - Field: theorem bound") - print(" - Spectral: modulo structure") - print(" - Shear: integer deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "set_size_formula": "2n-1", - "samples_per_n": 3, - "total_tests": len(n_values) * 3 - }, - "results": results, - "theorem_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Zero-sum subset as packet witness", - "insight": "Packet mod = 0 is witness property" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Set size 2n-1 (theoretical bound)", - "insight": "Field captures theorem condition" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Modulo space eigen decomposition", - "insight": "Spectral radius indicates modulo structure" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Integer rigidity and gap variance", - "insight": "Shear measures integer deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Ginzburg–Ziv Theorem. Packet primitive captures zero-sum witness. Field primitive captures theorem bound. Spectral primitive reveals modulo structure. Shear primitive measures integer deformation. Framework validated for additive number theory problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive_results.json b/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive_results.json deleted file mode 100644 index 0d4d6d6f..00000000 --- a/4-Infrastructure/shim/test_erdos_ginzburg_ziv_4primitive_results.json +++ /dev/null @@ -1,633 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:28:22.281459", - "n_values": [ - 3, - 4, - 5, - 6, - 7 - ], - "set_size_formula": "2n-1", - "samples_per_n": 3, - "total_tests": 15 - }, - "results": [ - { - "n": 3, - "seed": 0, - "subset_found": true, - "subset": [ - 76, - 98, - 33 - ], - "packet": { - "packet_size": 3, - "packet_sum": 207, - "packet_mod": 0, - "packet_diversity": 0.3912148761551275 - }, - "field": { - "density": 1.0, - "theoretical_size": 5, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 4.999999999999999, - 1.0, - -1.0000000000000004 - ], - "spectral_radius": 4.999999999999999, - "mod_space_rank": 3 - }, - "shear": { - "integer_rigidity": 0.004531294250918366, - "avg_gap": 18.75, - "gap_variance": 220.6875 - } - }, - { - "n": 3, - "seed": 1, - "subset_found": true, - "subset": [ - 22, - 54, - 68 - ], - "packet": { - "packet_size": 3, - "packet_sum": 144, - "packet_mod": 0, - "packet_diversity": 0.4010980299498237 - }, - "field": { - "density": 1.0, - "theoretical_size": 5, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 5.000000000000003, - 1.999999999999999, - -2.0000000000000004 - ], - "spectral_radius": 5.000000000000003, - "mod_space_rank": 3 - }, - "shear": { - "integer_rigidity": 0.013852813852794663, - "avg_gap": 17.25, - "gap_variance": 72.1875 - } - }, - { - "n": 3, - "seed": 2, - "subset_found": true, - "subset": [ - 69, - 50, - 40 - ], - "packet": { - "packet_size": 3, - "packet_sum": 159, - "packet_mod": 0, - "packet_diversity": 0.22693859814677628 - }, - "field": { - "density": 1.0, - "theoretical_size": 5, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 5.0, - 0.9999999999999991, - -1.0 - ], - "spectral_radius": 5.0, - "mod_space_rank": 3 - }, - "shear": { - "integer_rigidity": 0.01570166830223246, - "avg_gap": 21.25, - "gap_variance": 63.6875 - } - }, - { - "n": 4, - "seed": 0, - "subset_found": true, - "subset": [ - 29, - 78, - 49, - 72 - ], - "packet": { - "packet_size": 4, - "packet_sum": 228, - "packet_mod": 0, - "packet_diversity": 0.3413171308481354 - }, - "field": { - "density": 1.0, - "theoretical_size": 7, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 6.999999999999998, - 4.12310562561766, - -0.9999999999999991, - -4.1231056256176615 - ], - "spectral_radius": 6.999999999999998, - "mod_space_rank": 4 - }, - "shear": { - "integer_rigidity": 0.06909788867514635, - "avg_gap": 8.166666666666666, - "gap_variance": 14.472222222222221 - } - }, - { - "n": 4, - "seed": 1, - "subset_found": true, - "subset": [ - 15, - 1, - 93, - 3 - ], - "packet": { - "packet_size": 4, - "packet_sum": 112, - "packet_mod": 0, - "packet_diversity": 1.353849387216062 - }, - "field": { - "density": 1.0, - "theoretical_size": 7, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 6.999999999999999, - 2.2360679774997885, - -2.236067977499789, - -4.999999999999998 - ], - "spectral_radius": 6.999999999999999, - "mod_space_rank": 4 - }, - "shear": { - "integer_rigidity": 0.00798580301685254, - "avg_gap": 15.333333333333334, - "gap_variance": 125.22222222222223 - } - }, - { - "n": 4, - "seed": 2, - "subset_found": true, - "subset": [ - 11, - 100, - 96, - 93 - ], - "packet": { - "packet_size": 4, - "packet_sum": 300, - "packet_mod": 0, - "packet_diversity": 0.49378357832376546 - }, - "field": { - "density": 1.0, - "theoretical_size": 7, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 7.000000000000001, - 2.2360679774997894, - 0.9999999999999999, - -2.2360679774997907 - ], - "spectral_radius": 7.000000000000001, - "mod_space_rank": 4 - }, - "shear": { - "integer_rigidity": 0.0035169988276658208, - "avg_gap": 15.0, - "gap_variance": 284.3333333333333 - } - }, - { - "n": 5, - "seed": 0, - "subset_found": true, - "subset": [ - 70, - 33, - 61, - 8, - 33 - ], - "packet": { - "packet_size": 5, - "packet_sum": 205, - "packet_mod": 0, - "packet_diversity": 0.5407818166601204 - }, - "field": { - "density": 1.0, - "theoretical_size": 9, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 8.999999999999998, - 6.23606797749979, - 1.7639320225002109, - -1.7639320225002115, - -6.236067977499791 - ], - "spectral_radius": 8.999999999999998, - "mod_space_rank": 5 - }, - "shear": { - "integer_rigidity": 0.009745698187899745, - "avg_gap": 11.875, - "gap_variance": 102.609375 - } - }, - { - "n": 5, - "seed": 1, - "subset_found": true, - "subset": [ - 96, - 40, - 95, - 36, - 43 - ], - "packet": { - "packet_size": 5, - "packet_sum": 310, - "packet_mod": 0, - "packet_diversity": 0.44265305530101756 - }, - "field": { - "density": 1.0, - "theoretical_size": 9, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 9.000000000000002, - 5.626053309603325, - 0.5895117958968007, - -0.589511795896801, - -5.626053309603326 - ], - "spectral_radius": 9.000000000000002, - "mod_space_rank": 5 - }, - "shear": { - "integer_rigidity": 0.0055253388586691396, - "avg_gap": 11.375, - "gap_variance": 180.984375 - } - }, - { - "n": 5, - "seed": 2, - "subset_found": true, - "subset": [ - 69, - 37, - 69, - 8, - 72 - ], - "packet": { - "packet_size": 5, - "packet_sum": 255, - "packet_mod": 0, - "packet_diversity": 0.4909014532795637 - }, - "field": { - "density": 1.0, - "theoretical_size": 9, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 8.999999999999996, - 4.040573959383653, - 0.8208301156455823, - -0.8208301156455817, - -4.040573959383649 - ], - "spectral_radius": 8.999999999999996, - "mod_space_rank": 5 - }, - "shear": { - "integer_rigidity": 0.011527377521600544, - "avg_gap": 9.5, - "gap_variance": 86.75 - } - }, - { - "n": 6, - "seed": 0, - "subset_found": true, - "subset": [ - 32, - 32, - 33, - 24, - 98, - 39 - ], - "packet": { - "packet_size": 6, - "packet_sum": 258, - "packet_mod": 0, - "packet_diversity": 0.5809300463626417 - }, - "field": { - "density": 1.0, - "theoretical_size": 11, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 11.000000000000002, - 6.244997998398398, - 3.000000000000002, - 2.6457513110645916, - -2.6457513110645925, - -6.244997998398398 - ], - "spectral_radius": 11.000000000000002, - "mod_space_rank": 6 - }, - "shear": { - "integer_rigidity": 0.016077170417980582, - "avg_gap": 9.0, - "gap_variance": 62.2 - } - }, - { - "n": 6, - "seed": 1, - "subset_found": true, - "subset": [ - 6, - 23, - 66, - 7, - 48, - 30 - ], - "packet": { - "packet_size": 6, - "packet_sum": 180, - "packet_mod": 0, - "packet_diversity": 0.7167312632386728 - }, - "field": { - "density": 1.0, - "theoretical_size": 11, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 11.0, - 5.0, - 3.605551275463989, - 0.9999999999999998, - -0.9999999999999998, - -3.6055512754639905 - ], - "spectral_radius": 11.0, - "mod_space_rank": 6 - }, - "shear": { - "integer_rigidity": 0.029726516052230298, - "avg_gap": 8.6, - "gap_variance": 33.64 - } - }, - { - "n": 6, - "seed": 2, - "subset_found": true, - "subset": [ - 100, - 42, - 60, - 4, - 37, - 15 - ], - "packet": { - "packet_size": 6, - "packet_sum": 258, - "packet_mod": 0, - "packet_diversity": 0.7280221322092338 - }, - "field": { - "density": 1.0, - "theoretical_size": 11, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 11.000000000000002, - 3.000000000000001, - 2.6457513110645907, - 1.732050807568878, - -1.732050807568878, - -2.645751311064593 - ], - "spectral_radius": 11.000000000000002, - "mod_space_rank": 6 - }, - "shear": { - "integer_rigidity": 0.007896399241939437, - "avg_gap": 9.6, - "gap_variance": 126.63999999999999 - } - }, - { - "n": 7, - "seed": 0, - "subset_found": true, - "subset": [ - 39, - 77, - 15, - 55, - 44, - 93, - 69 - ], - "packet": { - "packet_size": 7, - "packet_sum": 392, - "packet_mod": 0, - "packet_diversity": 0.43185392601095884 - }, - "field": { - "density": 1.0, - "theoretical_size": 13, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 13.0, - 3.7547083347504655, - 2.2991755618515914, - 2.148477846462422, - -2.1484778464624212, - -2.2991755618515897, - -3.7547083347504664 - ], - "spectral_radius": 13.0, - "mod_space_rank": 7 - }, - "shear": { - "integer_rigidity": 0.025769506084400304, - "avg_gap": 6.833333333333333, - "gap_variance": 38.80555555555556 - } - }, - { - "n": 7, - "seed": 1, - "subset_found": true, - "subset": [ - 64, - 19, - 58, - 87, - 93, - 78, - 35 - ], - "packet": { - "packet_size": 7, - "packet_sum": 434, - "packet_mod": 0, - "packet_diversity": 0.4062101543048666 - }, - "field": { - "density": 1.0, - "theoretical_size": 13, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 13.000000000000004, - 4.140863680268517, - 1.9822493274871782, - 1.709951924794879, - -1.7099519247948793, - -1.9822493274871793, - -4.140863680268514 - ], - "spectral_radius": 13.000000000000004, - "mod_space_rank": 7 - }, - "shear": { - "integer_rigidity": 0.03961485557068213, - "avg_gap": 7.416666666666667, - "gap_variance": 25.243055555555557 - } - }, - { - "n": 7, - "seed": 2, - "subset_found": true, - "subset": [ - 34, - 37, - 20, - 47, - 60, - 18, - 85 - ], - "packet": { - "packet_size": 7, - "packet_sum": 301, - "packet_mod": 0, - "packet_diversity": 0.5079906956424559 - }, - "field": { - "density": 1.0, - "theoretical_size": 13, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 13.0, - 5.008567968383229, - 2.972505607242703, - 2.019519081612309, - -2.0195190816123105, - -2.972505607242703, - -5.008567968383225 - ], - "spectral_radius": 13.0, - "mod_space_rank": 7 - }, - "shear": { - "integer_rigidity": 0.05538461538430865, - "avg_gap": 6.666666666666667, - "gap_variance": 18.055555555555554 - } - } - ], - "theorem_analysis": { - "subset_found_count": 15, - "total_tests": 15, - "success_rate": 1.0 - }, - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Zero-sum subset as packet witness", - "insight": "Packet mod = 0 is witness property" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Set size 2n-1 (theoretical bound)", - "insight": "Field captures theorem condition" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Modulo space eigen decomposition", - "insight": "Spectral radius indicates modulo structure" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Integer rigidity and gap variance", - "insight": "Shear measures integer deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Ginzburg\u2013Ziv Theorem. Packet primitive captures zero-sum witness. Field primitive captures theorem bound. Spectral primitive reveals modulo structure. Shear primitive measures integer deformation. Framework validated for additive number theory problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_gyarfas_4primitive.py b/4-Infrastructure/shim/test_erdos_gyarfas_4primitive.py deleted file mode 100644 index 60059e29..00000000 --- a/4-Infrastructure/shim/test_erdos_gyarfas_4primitive.py +++ /dev/null @@ -1,351 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Gyárfás Conjecture -======================================================== -Apply 4-primitive framework to Erdős–Gyárfás Conjecture. -Conjecture: Every graph with minimum degree at least 3 contains a cycle -whose length is a power of two. - -Focus on spectral primitive (C = UΛUᵀ) for cycle detection via eigen decomposition. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_graph_with_min_degree(n, min_degree=3, seed=None): - """Generate a random graph with minimum degree at least min_degree.""" - if seed is not None: - random.seed(seed) - - # Start with empty graph - A = np.zeros((n, n)) - - # Ensure minimum degree by connecting each vertex to at least min_degree others - for i in range(n): - neighbors = list(range(n)) - neighbors.remove(i) - random.shuffle(neighbors) - - # Connect to min_degree random neighbors - for j in neighbors[:min_degree]: - A[i, j] = 1 - A[j, i] = 1 - - # Add random additional edges - for i in range(n): - for j in range(i + 1, n): - if A[i, j] == 0 and random.random() < 0.3: - A[i, j] = 1 - A[j, i] = 1 - - return A - - -def find_cycle_lengths(A): - """Find all cycle lengths in the graph using BFS.""" - n = A.shape[0] - cycle_lengths = set() - - for start in range(n): - # BFS to find cycles - visited = {start} - queue = [(start, [start])] - - while queue: - node, path = queue.pop(0) - - for neighbor in range(n): - if A[node, neighbor] == 1: - if neighbor == start and len(path) >= 3: - cycle_lengths.add(len(path)) - elif neighbor not in visited: - visited.add(neighbor) - queue.append((neighbor, path + [neighbor])) - - return cycle_lengths - - -def spectral_analysis_graph(A): - """Compute spectral decomposition of adjacency matrix.""" - eigenvalues, _ = np.linalg.eigh(A) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0, - "algebraic_connectivity": float(eigenvalues[-2]) if len(eigenvalues) > 1 else 0.0 - } - - -def field_analysis_graph(A): - """Compute field primitive metrics for graph density.""" - n = A.shape[0] - edge_count = int(np.sum(A) / 2) - max_edges = n * (n - 1) // 2 - - # Edge density - edge_density = edge_count / max_edges if max_edges > 0 else 0.0 - - # Minimum degree - degrees = np.sum(A, axis=1) - min_degree = int(np.min(degrees)) - - return { - "edge_density": float(edge_density), - "min_degree": min_degree, - "edge_count": edge_count - } - - -def shear_analysis_graph(A): - """Compute shear primitive metrics for graph deformation.""" - n = A.shape[0] - - # Degree variance - degrees = np.sum(A, axis=1) - degree_variance = np.var(degrees) - - # Graph rigidity (inverse of degree variance) - graph_rigidity = 1.0 / (degree_variance + 1e-10) - - # Clustering coefficient - clustering_coeffs = [] - for i in range(n): - neighbors = np.where(A[i] == 1)[0] - if len(neighbors) < 2: - clustering_coeffs.append(0.0) - continue - - triangles = 0 - for j in neighbors: - for k in neighbors: - if j < k and A[j, k] == 1: - triangles += 1 - - possible_triangles = len(neighbors) * (len(neighbors) - 1) / 2 - clustering_coeffs.append(triangles / possible_triangles if possible_triangles > 0 else 0.0) - - avg_clustering = np.mean(clustering_coeffs) if clustering_coeffs else 0.0 - - return { - "graph_rigidity": float(graph_rigidity), - "degree_variance": float(degree_variance), - "avg_clustering": float(avg_clustering) - } - - -def packet_analysis_graph(A, cycle_lengths): - """Compute packet primitive metrics for cycle encoding.""" - n = A.shape[0] - - # Packet size (number of edges) - edge_count = int(np.sum(A) / 2) - packet_size = edge_count - - # Cycle diversity (number of distinct cycle lengths) - cycle_diversity = len(cycle_lengths) - - # Power-of-two cycles - power_of_two_cycles = [cl for cl in cycle_lengths if (cl & (cl - 1)) == 0] - - return { - "packet_size": packet_size, - "cycle_diversity": cycle_diversity, - "num_power_of_two_cycles": len(power_of_two_cycles), - "power_of_two_cycles": sorted(power_of_two_cycles) - } - - -def test_erdos_gyarfas(n_values): - """Test Erdős–Gyárfás Conjecture with 4-primitive framework.""" - results = [] - - for n in n_values: - for seed in range(3): # 3 samples per n - A = generate_graph_with_min_degree(n, min_degree=3, seed=seed) - - # Find cycle lengths - cycle_lengths = find_cycle_lengths(A) - - # Check if there's a power-of-two cycle - power_of_two_cycles = [cl for cl in cycle_lengths if (cl & (cl - 1)) == 0] - has_power_of_two_cycle = len(power_of_two_cycles) > 0 - - # 4-primitive analysis - spectral = spectral_analysis_graph(A) - field = field_analysis_graph(A) - shear = shear_analysis_graph(A) - packet = packet_analysis_graph(A, cycle_lengths) - - results.append({ - "n": n, - "seed": seed, - "min_degree": field["min_degree"], - "cycle_lengths": sorted(cycle_lengths), - "has_power_of_two_cycle": has_power_of_two_cycle, - "conjecture_holds": has_power_of_two_cycle or field["min_degree"] < 3, - "spectral": spectral, - "field": field, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Gyárfás Conjecture.""" - # Conjecture: graphs with min degree >= 3 should have power-of-two cycle - min_degree_3 = [r for r in results if r["min_degree"] >= 3] - has_power_of_two = sum(1 for r in min_degree_3 if r["has_power_of_two_cycle"]) - - total_min_degree_3 = len(min_degree_3) - - return { - "total_min_degree_3": total_min_degree_3, - "has_power_of_two_cycle": has_power_of_two, - "conjecture_holds": has_power_of_two == total_min_degree_3 if total_min_degree_3 > 0 else True, - "note": "Conjecture requires graphs with minimum degree at least 3 to have power-of-two cycle" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–GYÁRFÁS CONJECTURE") - print("=" * 70) - - # Test parameters - n_values = [10, 15, 20] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Minimum degree: 3") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING GRAPHS WITH MIN DEGREE >= 3") - print("=" * 70) - - results = test_erdos_gyarfas(n_values) - - print(f"\nGenerated {len(results)} graphs") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Graphs with min degree >= 3: {analysis['total_min_degree_3']}") - print(f" Has power-of-two cycle: {analysis['has_power_of_two_cycle']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Adjacency matrix eigen decomposition") - print(" - Spectral radius") - print(" - Spectral gap") - print(" - Algebraic connectivity") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Edge density") - print(" - Minimum degree") - print(" - Edge count") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Graph rigidity") - print(" - Degree variance") - print(" - Average clustering") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (edges)") - print(" - Cycle diversity") - print(" - Power-of-two cycles") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Spectral primitive reveals graph structure:") - print(" - Eigenvalues encode graph properties") - print(" - Spectral gap indicates connectivity") - - print("\n2. Field primitive captures degree constraints:") - print(" - Minimum degree directly tests conjecture condition") - print(" - Edge density indicates graph sparsity") - - print("\n3. Shear primitive measures graph deformation:") - print(" - Degree variance indicates regularity") - print(" - Clustering coefficient indicates local structure") - - print("\n4. Packet primitive captures cycle structure:") - print(" - Cycle diversity indicates richness") - print(" - Power-of-two cycles directly test conjecture") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Spectral: graph structure") - print(" - Field: degree constraints") - print(" - Shear: graph deformation") - print(" - Packet: cycle structure") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "min_degree": 3, - "samples_per_n": 3, - "total_tests": len(n_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Adjacency matrix eigen decomposition", - "insight": "Eigenvalues encode graph structure" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Edge density and minimum degree", - "insight": "Minimum degree directly tests conjecture condition" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Graph rigidity and degree variance", - "insight": "Degree variance indicates graph regularity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Cycle structure encoding", - "insight": "Power-of-two cycles directly test conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Gyárfás Conjecture. Spectral primitive reveals graph structure. Field primitive captures degree constraints. Shear primitive measures graph deformation. Packet primitive captures cycle structure. Framework validated for graph cycle problems. Conjecture tested on graphs with minimum degree >= 3." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_gyarfas_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_gyarfas_4primitive_results.json b/4-Infrastructure/shim/test_erdos_gyarfas_4primitive_results.json deleted file mode 100644 index 103d0f3f..00000000 --- a/4-Infrastructure/shim/test_erdos_gyarfas_4primitive_results.json +++ /dev/null @@ -1,461 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:39:33.454828", - "n_values": [ - 10, - 15, - 20 - ], - "min_degree": 3, - "samples_per_n": 3, - "total_tests": 9 - }, - "results": [ - { - "n": 10, - "seed": 0, - "min_degree": 5, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 6.68475767476111, - 0.9029228664907963, - 0.6962021907114151, - 0.3254859456200355, - -1.1915834980134854e-15, - -0.5677562467868766, - -0.9999999999999989, - -1.2413317146446583, - -2.5660544700819523, - -3.23422624606987 - ], - "spectral_radius": 6.68475767476111, - "spectral_gap": 5.781834808270314, - "algebraic_connectivity": -2.5660544700819523 - }, - "field": { - "edge_density": 0.7333333333333333, - "min_degree": 5, - "edge_count": 33 - }, - "shear": { - "graph_rigidity": 1.5624999997558595, - "degree_variance": 0.6399999999999999, - "avg_clustering": 0.6509523809523808 - }, - "packet": { - "packet_size": 33, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 10, - "seed": 1, - "min_degree": 5, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 5.661643853583925, - 1.9326323921666824, - 0.9048938972685978, - 0.4581907444911413, - -0.12367065165590266, - -0.42953062165296774, - -1.321372231769156, - -1.8714929398899898, - -2.327391115436429, - -2.883903327105903 - ], - "spectral_radius": 5.661643853583925, - "spectral_gap": 3.729011461417243, - "algebraic_connectivity": -2.327391115436429 - }, - "field": { - "edge_density": 0.6222222222222222, - "min_degree": 5, - "edge_count": 28 - }, - "shear": { - "graph_rigidity": 2.2727272722107434, - "degree_variance": 0.44000000000000006, - "avg_clustering": 0.559047619047619 - }, - "packet": { - "packet_size": 28, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 10, - "seed": 2, - "min_degree": 5, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 6.3501347336433405, - 1.5593996425229912, - 1.1325444172739572, - 0.19792420989010656, - -0.4359949023586274, - -0.8818311041931055, - -1.183466520361351, - -1.874476414349748, - -2.1081789349007702, - -2.756055127166795 - ], - "spectral_radius": 6.3501347336433405, - "spectral_gap": 4.790735091120349, - "algebraic_connectivity": -2.1081789349007702 - }, - "field": { - "edge_density": 0.6888888888888889, - "min_degree": 5, - "edge_count": 31 - }, - "shear": { - "graph_rigidity": 1.0416666665581595, - "degree_variance": 0.9600000000000002, - "avg_clustering": 0.6676190476190476 - }, - "packet": { - "packet_size": 31, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 15, - "seed": 0, - "min_degree": 5, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 7.991349857329626, - 2.786750089160909, - 1.792896981795164, - 1.5310790095481337, - 1.1275230782603323, - 0.4695106565354499, - 0.019492212883672196, - -0.41326360622719704, - -1.1089274394140773, - -1.6798095049297121, - -1.8619146191267426, - -2.1199981591613226, - -2.4261841989168467, - -2.508874887225408, - -3.5996294705119807 - ], - "spectral_radius": 7.991349857329626, - "spectral_gap": 5.204599768168718, - "algebraic_connectivity": -2.508874887225408 - }, - "field": { - "edge_density": 0.5523809523809524, - "min_degree": 5, - "edge_count": 58 - }, - "shear": { - "graph_rigidity": 0.45546558702378953, - "degree_variance": 2.1955555555555555, - "avg_clustering": 0.5594516594516594 - }, - "packet": { - "packet_size": 58, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 15, - "seed": 1, - "min_degree": 5, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 8.580503017690566, - 2.4397719074626565, - 2.211540350979152, - 1.315494336129013, - 0.3722094033537729, - 0.10370401245968983, - -0.14671291312821688, - -0.3987663815851499, - -0.5415959886386539, - -1.033748729099512, - -1.3338448025399792, - -1.9080220540785586, - -2.2111363272804336, - -3.560860328838281, - -3.8885355028860658 - ], - "spectral_radius": 8.580503017690566, - "spectral_gap": 6.140731110227909, - "algebraic_connectivity": -3.560860328838281 - }, - "field": { - "edge_density": 0.6, - "min_degree": 5, - "edge_count": 63 - }, - "shear": { - "graph_rigidity": 0.5859374999656678, - "degree_variance": 1.7066666666666666, - "avg_clustering": 0.5690716690716691 - }, - "packet": { - "packet_size": 63, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 15, - "seed": 2, - "min_degree": 6, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 8.485797032238256, - 2.6389111696118683, - 1.6566723436959157, - 1.5107316992997786, - 1.175232726262903, - 0.3217680375736175, - 0.10556298591063354, - -0.7799384508119451, - -0.8822799238222996, - -1.204992152657905, - -1.815878997637858, - -2.2233379361778924, - -2.538570324312477, - -2.9233569109907567, - -3.5263212981818404 - ], - "spectral_radius": 8.485797032238256, - "spectral_gap": 5.846885862626388, - "algebraic_connectivity": -2.9233569109907567 - }, - "field": { - "edge_density": 0.5904761904761905, - "min_degree": 6, - "edge_count": 62 - }, - "shear": { - "graph_rigidity": 0.5569306930382898, - "degree_variance": 1.7955555555555556, - "avg_clustering": 0.5732323232323232 - }, - "packet": { - "packet_size": 62, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 20, - "seed": 0, - "min_degree": 6, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 9.742570114074866, - 3.140698337482431, - 2.359514966194386, - 2.092593599897189, - 1.6360944809220788, - 1.3054689895707465, - 0.9561325426356803, - 0.8255038276476421, - 0.39373313890944117, - 0.22700216224092382, - -0.119834782693488, - -0.8157484049165185, - -1.4133865293934955, - -1.9386092218570063, - -1.9791575057942938, - -2.2863832165477347, - -2.823918454445492, - -3.099436040211471, - -3.7244270076844153, - -4.478410996031469 - ], - "spectral_radius": 9.742570114074866, - "spectral_gap": 6.601871776592435, - "algebraic_connectivity": -3.7244270076844153 - }, - "field": { - "edge_density": 0.49473684210526314, - "min_degree": 6, - "edge_count": 94 - }, - "shear": { - "graph_rigidity": 0.29069767441015415, - "degree_variance": 3.44, - "avg_clustering": 0.46999611499611493 - }, - "packet": { - "packet_size": 94, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 20, - "seed": 1, - "min_degree": 6, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 10.141479286274421, - 3.061825138631779, - 2.6006153980876987, - 1.8049822342153183, - 1.7402775946771503, - 1.4111910670181138, - 1.1693320416518918, - 0.821777005900447, - 0.41619885194084405, - -0.2443315763280159, - -0.6505626530002555, - -1.1133449423866584, - -1.4234157919258466, - -1.6811696933437335, - -1.7819562104124085, - -2.5274457164251034, - -2.840811202866686, - -3.0973252060672687, - -3.557550997355941, - -4.24976462828574 - ], - "spectral_radius": 10.141479286274421, - "spectral_gap": 7.079654147642643, - "algebraic_connectivity": -3.557550997355941 - }, - "field": { - "edge_density": 0.5105263157894737, - "min_degree": 6, - "edge_count": 97 - }, - "shear": { - "graph_rigidity": 0.22172949001725656, - "degree_variance": 4.51, - "avg_clustering": 0.5031757131757131 - }, - "packet": { - "packet_size": 97, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - }, - { - "n": 20, - "seed": 2, - "min_degree": 6, - "cycle_lengths": [], - "has_power_of_two_cycle": false, - "conjecture_holds": false, - "spectral": { - "eigenvalues": [ - 8.89960171069134, - 3.1406176721474193, - 2.9092465192494794, - 2.7344660636892666, - 2.022042409577138, - 1.1799341201288192, - 0.7982432737709659, - 0.3711610701832186, - 0.20834404599253442, - -0.3717143414378792, - -0.7030275490717678, - -0.7409186832229708, - -1.2707565932056888, - -1.5965438591652736, - -1.7958178699058225, - -2.229599057759542, - -2.509386685162139, - -3.4001177163814544, - -3.642131010937998, - -4.003643519179646 - ], - "spectral_radius": 8.89960171069134, - "spectral_gap": 5.75898403854392, - "algebraic_connectivity": -3.642131010937998 - }, - "field": { - "edge_density": 0.45263157894736844, - "min_degree": 6, - "edge_count": 86 - }, - "shear": { - "graph_rigidity": 0.3649635036363152, - "degree_variance": 2.74, - "avg_clustering": 0.4468470418470418 - }, - "packet": { - "packet_size": 86, - "cycle_diversity": 0, - "num_power_of_two_cycles": 0, - "power_of_two_cycles": [] - } - } - ], - "conjecture_analysis": { - "total_min_degree_3": 9, - "has_power_of_two_cycle": 0, - "conjecture_holds": false, - "note": "Conjecture requires graphs with minimum degree at least 3 to have power-of-two cycle" - }, - "primitive_analysis": { - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Adjacency matrix eigen decomposition", - "insight": "Eigenvalues encode graph structure" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Edge density and minimum degree", - "insight": "Minimum degree directly tests conjecture condition" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Graph rigidity and degree variance", - "insight": "Degree variance indicates graph regularity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Cycle structure encoding", - "insight": "Power-of-two cycles directly test conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Gy\u00e1rf\u00e1s Conjecture. Spectral primitive reveals graph structure. Field primitive captures degree constraints. Shear primitive measures graph deformation. Packet primitive captures cycle structure. Framework validated for graph cycle problems. Conjecture tested on graphs with minimum degree >= 3." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_hadamard_4primitive.py b/4-Infrastructure/shim/test_erdos_hadamard_4primitive.py deleted file mode 100644 index dbdfe3c6..00000000 --- a/4-Infrastructure/shim/test_erdos_hadamard_4primitive.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős Hadamard Conjecture -======================================================= -Apply 4-primitive framework to Erdős Hadamard Conjecture. -Conjecture: There exist Hadamard matrices of order 4k for all k. - -Focus on spectral primitive (C = UΛUᵀ) for Hadamard matrices as spectral basis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_hadamard_matrix(n): - """Generate a Hadamard matrix of order n (if n is a power of 2).""" - # Sylvester construction for powers of 2 - if n == 1: - return np.array([[1]]) - elif n == 2: - return np.array([[1, 1], [1, -1]]) - elif n % 2 == 0: - H_half = generate_hadamard_matrix(n // 2) - return np.block([[H_half, H_half], [H_half, -H_half]]) - else: - # For non-powers of 2, return None (Hadamard conjecture) - return None - - -def spectral_analysis_hadamard(H): - """Compute spectral decomposition of Hadamard matrix.""" - if H is None: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "is_orthogonal": False, - "rank": 0 - } - - n = H.shape[0] - - # Eigen decomposition - eigenvalues, _ = np.linalg.eigh(H) - eigenvalues = np.sort(eigenvalues)[::-1] - - # Check orthogonality - is_orthogonal = np.allclose(H @ H.T, n * np.eye(n)) - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "is_orthogonal": is_orthogonal, - "rank": int(np.linalg.matrix_rank(H)) - } - - -def field_analysis_hadamard(H): - """Compute field primitive metrics for Hadamard matrix.""" - if H is None: - return { - "matrix_size": 0, - "density": 0.0, - "determinant": 0.0 - } - - n = H.shape[0] - - # Density (fraction of ±1 entries) - density = 1.0 # Hadamard matrices are dense - - # Determinant - det = np.linalg.det(H) - - return { - "matrix_size": n, - "density": density, - "determinant": float(det) - } - - -def shear_analysis_hadamard(H): - """Compute shear primitive metrics for Hadamard matrix.""" - if H is None: - return { - "shear_stiffness": 0.0, - "gram_rank": 0, - "gram_spectrum": [] - } - - # Gram matrix - G = H.T @ H - - # Gram eigenvalues - gram_eigenvalues, _ = np.linalg.eigh(G) - gram_eigenvalues = np.sort(gram_eigenvalues)[::-1] - - # Shear stiffness (sum of Gram matrix) - shear_stiffness = float(np.sum(G)) - - return { - "shear_stiffness": shear_stiffness, - "gram_rank": int(np.linalg.matrix_rank(G)), - "gram_spectrum": gram_eigenvalues.tolist() - } - - -def packet_analysis_hadamard(H): - """Compute packet primitive metrics for Hadamard matrix.""" - if H is None: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "row_diversity": 0.0 - } - - n = H.shape[0] - - # Packet size (number of entries) - packet_size = n * n - - # Encoding efficiency (orthogonality as efficiency) - encoding_efficiency = 1.0 if np.allclose(H @ H.T, n * np.eye(n)) else 0.0 - - # Row diversity (variance of row sums) - row_sums = np.sum(H, axis=1) - row_diversity = np.std(row_sums) if len(row_sums) > 0 else 0.0 - - return { - "packet_size": packet_size, - "encoding_efficiency": encoding_efficiency, - "row_diversity": float(row_diversity) - } - - -def test_erdos_hadamard(k_values): - """Test Erdős Hadamard Conjecture with 4-primitive framework.""" - results = [] - - for k in k_values: - n = 4 * k # Hadamard conjecture: order 4k exists for all k - - # Try to generate Hadamard matrix - H = generate_hadamard_matrix(n) - - # 4-primitive analysis - spectral = spectral_analysis_hadamard(H) - field = field_analysis_hadamard(H) - shear = shear_analysis_hadamard(H) - packet = packet_analysis_hadamard(H) - - results.append({ - "k": k, - "n": n, - "hadamard_exists": H is not None, - "spectral": spectral, - "field": field, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős Hadamard Conjecture.""" - # Conjecture: Hadamard matrices exist for all 4k - exists_count = sum(1 for r in results if r["hadamard_exists"]) - total = len(results) - - # For powers of 2, Hadamard matrices exist - # For other multiples of 4, existence is unknown (conjecture) - - return { - "hadamard_exists_count": exists_count, - "total_tests": total, - "existence_rate": exists_count / total if total > 0 else 0.0, - "note": "Sylvester construction only works for powers of 2. Conjecture applies to all multiples of 4." - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS HADAMARD CONJECTURE") - print("=" * 70) - - # Test parameters - k_values = [1, 2, 4, 8, 16, 32] # Powers of 2 for Sylvester construction - - print(f"\nTest parameters:") - print(f" k values: {k_values}") - print(f" Matrix order: n = 4k") - print(f" Construction: Sylvester (powers of 2)") - print(f" Total tests: {len(k_values)}") - - print("\n" + "=" * 70) - print(" GENERATING HADAMARD MATRICES") - print("=" * 70) - - results = test_erdos_hadamard(k_values) - - print(f"\nGenerated {len(results)} Hadamard matrices") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Hadamard exists: {analysis['hadamard_exists_count']}/{analysis['total_tests']}") - print(f" Existence rate: {analysis['existence_rate']*100:.1f}%") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Hadamard matrix as spectral basis") - print(" - Eigenvalues (±√n)") - print(" - Spectral radius") - print(" - Orthogonality check") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Matrix size") - print(" - Density (dense)") - print(" - Determinant") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Gram matrix (nI)") - print(" - Shear stiffness") - print(" - Gram spectrum") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Hadamard matrix as packet encoding") - print(" - Packet size (n²)") - print(" - Encoding efficiency (orthogonality)") - print(" - Row diversity") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Spectral primitive captures orthogonal structure:") - print(" - Hadamard matrix = orthogonal basis") - print(" - Eigenvalues = ±√n") - print(" - Orthogonality verified") - - print("\n2. Field primitive captures matrix properties:") - print(" - Dense matrix (all ±1)") - print(" - Determinant = n^(n/2)") - - print("\n3. Shear primitive captures Gram structure:") - print(" - Gram matrix = nI (identity scaled by n)") - print(" - Shear stiffness indicates orthogonality") - - print("\n4. Packet primitive captures encoding efficiency:") - print(" - Hadamard as orthogonal encoding") - print(" - Encoding efficiency = 1 (optimal)") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Spectral: orthogonal basis") - print(" - Field: matrix properties") - print(" - Shear: Gram structure") - print(" - Packet: encoding efficiency") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "k_values": k_values, - "matrix_order_formula": "n = 4k", - "construction": "Sylvester (powers of 2)", - "total_tests": len(k_values) - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Hadamard matrix as orthogonal spectral basis", - "insight": "Eigenvalues = ±√n, orthogonality verified" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Matrix density and determinant", - "insight": "Dense matrix with determinant n^(n/2)" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Gram matrix = nI", - "insight": "Gram structure indicates orthogonality" - }, - "packet": { - "equation": "Γᵢ", - "application": "Hadamard as orthogonal packet encoding", - "insight": "Encoding efficiency = 1 (optimal)" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős Hadamard Conjecture. Spectral primitive captures orthogonal structure. Field primitive captures matrix properties. Shear primitive captures Gram structure. Packet primitive captures encoding efficiency. Framework validated for spectral matrix problems. Sylvester construction validates powers of 2; conjecture remains open for other multiples of 4." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_hadamard_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_hadamard_4primitive_results.json b/4-Infrastructure/shim/test_erdos_hadamard_4primitive_results.json deleted file mode 100644 index d12ef52b..00000000 --- a/4-Infrastructure/shim/test_erdos_hadamard_4primitive_results.json +++ /dev/null @@ -1,722 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:29:36.759048", - "k_values": [ - 1, - 2, - 4, - 8, - 16, - 32 - ], - "matrix_order_formula": "n = 4k", - "construction": "Sylvester (powers of 2)", - "total_tests": 6 - }, - "results": [ - { - "k": 1, - "n": 4, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 2.0, - 2.0, - -2.0, - -2.0 - ], - "spectral_radius": 2.0, - "is_orthogonal": true, - "rank": 4 - }, - "field": { - "matrix_size": 4, - "density": 1.0, - "determinant": 15.999999999999998 - }, - "shear": { - "shear_stiffness": 16.0, - "gram_rank": 4, - "gram_spectrum": [ - 4.0, - 4.0, - 4.0, - 4.0 - ] - }, - "packet": { - "packet_size": 16, - "encoding_efficiency": 1.0, - "row_diversity": 1.7320508075688772 - } - }, - { - "k": 2, - "n": 8, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 2.82842712474619, - 2.82842712474619, - 2.8284271247461885, - 2.8284271247461863, - -2.828427124746187, - -2.8284271247461876, - -2.8284271247461894, - -2.82842712474619 - ], - "spectral_radius": 2.82842712474619, - "is_orthogonal": true, - "rank": 8 - }, - "field": { - "matrix_size": 8, - "density": 1.0, - "determinant": 4095.999999999997 - }, - "shear": { - "shear_stiffness": 64.0, - "gram_rank": 8, - "gram_spectrum": [ - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0 - ] - }, - "packet": { - "packet_size": 64, - "encoding_efficiency": 1.0, - "row_diversity": 2.6457513110645907 - } - }, - { - "k": 4, - "n": 16, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 4.0, - 4.0, - 4.0, - 3.999999999999999, - 3.999999999999999, - 3.9999999999999973, - 3.999999999999997, - 3.9999999999999956, - -3.999999999999994, - -3.9999999999999973, - -3.9999999999999987, - -3.9999999999999996, - -3.9999999999999996, - -4.000000000000001, - -4.000000000000002, - -4.0000000000000036 - ], - "spectral_radius": 4.0000000000000036, - "is_orthogonal": true, - "rank": 16 - }, - "field": { - "matrix_size": 16, - "density": 1.0, - "determinant": 4294967295.9999967 - }, - "shear": { - "shear_stiffness": 256.0, - "gram_rank": 16, - "gram_spectrum": [ - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0, - 16.0 - ] - }, - "packet": { - "packet_size": 256, - "encoding_efficiency": 1.0, - "row_diversity": 3.872983346207417 - } - }, - { - "k": 8, - "n": 32, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 5.656854249492388, - 5.656854249492387, - 5.656854249492384, - 5.656854249492382, - 5.656854249492381, - 5.656854249492381, - 5.656854249492381, - 5.656854249492381, - 5.656854249492381, - 5.656854249492381, - 5.656854249492381, - 5.656854249492379, - 5.656854249492378, - 5.656854249492378, - 5.656854249492374, - 5.65685424949237, - -5.656854249492373, - -5.656854249492374, - -5.656854249492376, - -5.656854249492378, - -5.656854249492381, - -5.656854249492381, - -5.656854249492381, - -5.656854249492381, - -5.656854249492381, - -5.6568542494923815, - -5.6568542494923815, - -5.6568542494923815, - -5.656854249492384, - -5.656854249492385, - -5.656854249492385, - -5.656854249492393 - ], - "spectral_radius": 5.656854249492393, - "is_orthogonal": true, - "rank": 32 - }, - "field": { - "matrix_size": 32, - "density": 1.0, - "determinant": 1.2089258196146205e+24 - }, - "shear": { - "shear_stiffness": 1024.0, - "gram_rank": 32, - "gram_spectrum": [ - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0, - 32.0 - ] - }, - "packet": { - "packet_size": 1024, - "encoding_efficiency": 1.0, - "row_diversity": 5.5677643628300215 - } - }, - { - "k": 16, - "n": 64, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 8.000000000000009, - 8.000000000000007, - 8.000000000000007, - 8.000000000000005, - 8.000000000000005, - 8.000000000000005, - 8.000000000000004, - 8.000000000000004, - 8.000000000000004, - 8.000000000000004, - 8.000000000000002, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 8.0, - 7.999999999999998, - 7.999999999999998, - 7.999999999999998, - 7.999999999999998, - 7.999999999999998, - 7.999999999999998, - 7.9999999999999964, - 7.9999999999999964, - 7.999999999999995, - 7.999999999999993, - 7.999999999999993, - 7.999999999999993, - 7.999999999999989, - 7.999999999999989, - 7.999999999999984, - -7.999999999999986, - -7.999999999999991, - -7.999999999999995, - -7.999999999999995, - -7.999999999999995, - -7.9999999999999964, - -7.9999999999999964, - -7.9999999999999964, - -7.999999999999998, - -7.999999999999998, - -7.999999999999998, - -7.999999999999998, - -8.0, - -8.0, - -8.0, - -8.0, - -8.000000000000002, - -8.000000000000002, - -8.000000000000002, - -8.000000000000002, - -8.000000000000002, - -8.000000000000002, - -8.000000000000004, - -8.000000000000004, - -8.000000000000004, - -8.000000000000004, - -8.000000000000004, - -8.000000000000005, - -8.000000000000005, - -8.000000000000007, - -8.000000000000007, - -8.000000000000009 - ], - "spectral_radius": 8.000000000000009, - "is_orthogonal": true, - "rank": 64 - }, - "field": { - "matrix_size": 64, - "density": 1.0, - "determinant": 6.27710173538643e+57 - }, - "shear": { - "shear_stiffness": 4096.0, - "gram_rank": 64, - "gram_spectrum": [ - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0, - 64.0 - ] - }, - "packet": { - "packet_size": 4096, - "encoding_efficiency": 1.0, - "row_diversity": 7.937253933193772 - } - }, - { - "k": 32, - "n": 128, - "hadamard_exists": true, - "spectral": { - "eigenvalues": [ - 11.31370849898479, - 11.313708498984784, - 11.313708498984779, - 11.313708498984779, - 11.313708498984777, - 11.313708498984777, - 11.313708498984777, - 11.313708498984774, - 11.313708498984772, - 11.313708498984772, - 11.313708498984772, - 11.313708498984772, - 11.313708498984772, - 11.313708498984768, - 11.313708498984768, - 11.313708498984768, - 11.313708498984768, - 11.313708498984768, - 11.313708498984768, - 11.313708498984768, - 11.313708498984766, - 11.313708498984766, - 11.313708498984766, - 11.313708498984763, - 11.313708498984763, - 11.313708498984763, - 11.313708498984761, - 11.313708498984761, - 11.313708498984761, - 11.313708498984761, - 11.313708498984761, - 11.313708498984761, - 11.313708498984761, - 11.31370849898476, - 11.31370849898476, - 11.31370849898476, - 11.31370849898476, - 11.31370849898476, - 11.31370849898476, - 11.313708498984756, - 11.313708498984756, - 11.313708498984756, - 11.313708498984756, - 11.313708498984756, - 11.313708498984756, - 11.313708498984754, - 11.313708498984754, - 11.313708498984754, - 11.313708498984754, - 11.313708498984754, - 11.313708498984754, - 11.313708498984754, - 11.31370849898475, - 11.31370849898475, - 11.313708498984749, - 11.313708498984749, - 11.313708498984749, - 11.313708498984747, - 11.313708498984747, - 11.313708498984747, - 11.313708498984747, - 11.313708498984742, - 11.313708498984738, - 11.313708498984738, - -11.313708498984743, - -11.313708498984743, - -11.313708498984747, - -11.313708498984747, - -11.313708498984749, - -11.313708498984749, - -11.31370849898475, - -11.31370849898475, - -11.31370849898475, - -11.313708498984754, - -11.313708498984754, - -11.313708498984754, - -11.313708498984754, - -11.313708498984754, - -11.313708498984754, - -11.313708498984756, - -11.313708498984756, - -11.313708498984756, - -11.313708498984756, - -11.313708498984756, - -11.31370849898476, - -11.31370849898476, - -11.31370849898476, - -11.31370849898476, - -11.31370849898476, - -11.31370849898476, - -11.313708498984761, - -11.313708498984761, - -11.313708498984761, - -11.313708498984761, - -11.313708498984761, - -11.313708498984761, - -11.313708498984761, - -11.313708498984763, - -11.313708498984763, - -11.313708498984763, - -11.313708498984763, - -11.313708498984763, - -11.313708498984763, - -11.313708498984763, - -11.313708498984766, - -11.313708498984766, - -11.313708498984766, - -11.313708498984766, - -11.313708498984766, - -11.313708498984766, - -11.313708498984766, - -11.313708498984768, - -11.313708498984768, - -11.313708498984768, - -11.313708498984768, - -11.313708498984768, - -11.313708498984768, - -11.313708498984768, - -11.313708498984772, - -11.313708498984772, - -11.313708498984772, - -11.313708498984774, - -11.313708498984774, - -11.313708498984774, - -11.313708498984774, - -11.313708498984777, - -11.313708498984777, - -11.31370849898478 - ], - "spectral_radius": 11.31370849898479, - "is_orthogonal": true, - "rank": 128 - }, - "field": { - "matrix_size": 128, - "density": 1.0, - "determinant": 7.268387242959247e+134 - }, - "shear": { - "shear_stiffness": 16384.0, - "gram_rank": 128, - "gram_spectrum": [ - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0, - 128.0 - ] - }, - "packet": { - "packet_size": 16384, - "encoding_efficiency": 1.0, - "row_diversity": 11.269427669584644 - } - } - ], - "conjecture_analysis": { - "hadamard_exists_count": 6, - "total_tests": 6, - "existence_rate": 1.0, - "note": "Sylvester construction only works for powers of 2. Conjecture applies to all multiples of 4." - }, - "primitive_analysis": { - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Hadamard matrix as orthogonal spectral basis", - "insight": "Eigenvalues = \u00b1\u221an, orthogonality verified" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Matrix density and determinant", - "insight": "Dense matrix with determinant n^(n/2)" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Gram matrix = nI", - "insight": "Gram structure indicates orthogonality" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Hadamard as orthogonal packet encoding", - "insight": "Encoding efficiency = 1 (optimal)" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s Hadamard Conjecture. Spectral primitive captures orthogonal structure. Field primitive captures matrix properties. Shear primitive captures Gram structure. Packet primitive captures encoding efficiency. Framework validated for spectral matrix problems. Sylvester construction validates powers of 2; conjecture remains open for other multiples of 4." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_hajnal_4primitive.py b/4-Infrastructure/shim/test_erdos_hajnal_4primitive.py deleted file mode 100644 index d7b9bdf7..00000000 --- a/4-Infrastructure/shim/test_erdos_hajnal_4primitive.py +++ /dev/null @@ -1,337 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Hajnal Conjecture -===================================================== -Apply 4-primitive framework to Erdős–Hajnal Conjecture. -Conjecture: In a family of graphs defined by an excluded induced subgraph, -every graph has either a large clique or a large independent set. - -Focus on spectral primitive (C = UΛUᵀ) for clique/independent set detection. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_graph(n, p, seed=None): - """Generate a random graph G(n,p).""" - if seed is not None: - random.seed(seed) - - A = np.zeros((n, n)) - for i in range(n): - for j in range(i + 1, n): - if random.random() < p: - A[i, j] = 1 - A[j, i] = 1 - - return A - - -def find_clique_size(A): - """Find size of largest clique using greedy algorithm.""" - n = A.shape[0] - max_clique = 0 - - # Greedy: try each vertex as starting point - for start in range(n): - clique = [start] - for i in range(n): - if i == start: - continue - # Check if i is adjacent to all clique members - if all(A[i][c] == 1 for c in clique): - clique.append(i) - max_clique = max(max_clique, len(clique)) - - return max_clique - - -def find_independent_set_size(A): - """Find size of largest independent set using greedy algorithm.""" - n = A.shape[0] - max_independent = 0 - - # Greedy: try each vertex as starting point - for start in range(n): - independent = [start] - for i in range(n): - if i == start: - continue - # Check if i is non-adjacent to all independent members - if all(A[i][c] == 0 for c in independent): - independent.append(i) - max_independent = max(max_independent, len(independent)) - - return max_independent - - -def spectral_analysis_graph(A): - """Compute spectral decomposition of adjacency matrix.""" - eigenvalues, _ = np.linalg.eigh(A) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0, - "algebraic_connectivity": float(eigenvalues[-2]) if len(eigenvalues) > 1 else 0.0 - } - - -def field_analysis_graph(A): - """Compute field primitive metrics for graph density.""" - n = A.shape[0] - edge_count = int(np.sum(A) / 2) - max_edges = n * (n - 1) // 2 - - # Edge density - edge_density = edge_count / max_edges if max_edges > 0 else 0.0 - - return { - "edge_density": float(edge_density), - "edge_count": edge_count, - "n": n - } - - -def shear_analysis_graph(A, clique_size, independent_size): - """Compute shear primitive metrics for graph deformation.""" - n = A.shape[0] - - # Degree variance - degrees = np.sum(A, axis=1) - degree_variance = np.var(degrees) - - # Graph rigidity (inverse of degree variance) - graph_rigidity = 1.0 / (degree_variance + 1e-10) - - # Clique/independent ratio - ratio = clique_size / independent_size if independent_size > 0 else 0.0 - - return { - "graph_rigidity": float(graph_rigidity), - "degree_variance": float(degree_variance), - "clique_independent_ratio": float(ratio) - } - - -def packet_analysis_graph(A, clique_size, independent_size, n): - """Compute packet primitive metrics for graph encoding.""" - # Packet size (number of edges) - edge_count = int(np.sum(A) / 2) - packet_size = edge_count - - # Encoding efficiency (clique or independent set size relative to n) - max_struct = max(clique_size, independent_size) - encoding_efficiency = max_struct / n if n > 0 else 0.0 - - # Witness property (large clique or independent set) - witness_property = max_struct >= n * 0.1 # At least 10% of vertices - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "witness_property": witness_property, - "max_structure_size": max_struct - } - - -def test_erdos_hajnal(n_values, p_values): - """Test Erdős–Hajnal Conjecture with 4-primitive framework.""" - results = [] - - for n in n_values: - for p in p_values: - for seed in range(3): # 3 samples per configuration - A = generate_random_graph(n, p, seed=seed) - - # Find clique and independent set sizes - clique_size = find_clique_size(A) - independent_size = find_independent_set_size(A) - - # Check if graph has large clique or independent set - max_struct = max(clique_size, independent_size) - has_large_struct = max_struct >= n * 0.1 # At least 10% of vertices - - # 4-primitive analysis - spectral = spectral_analysis_graph(A) - field = field_analysis_graph(A) - shear = shear_analysis_graph(A, clique_size, independent_size) - packet = packet_analysis_graph(A, clique_size, independent_size, n) - - results.append({ - "n": n, - "p": p, - "seed": seed, - "clique_size": clique_size, - "independent_set_size": independent_size, - "max_structure_size": max_struct, - "has_large_structure": has_large_struct, - "spectral": spectral, - "field": field, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Hajnal Conjecture.""" - # Conjecture: graphs should have large clique or independent set - has_large_struct_count = sum(1 for r in results if r["has_large_structure"]) - total = len(results) - - avg_clique = np.mean([r["clique_size"] for r in results]) if results else 0.0 - avg_independent = np.mean([r["independent_set_size"] for r in results]) if results else 0.0 - - return { - "total_tests": total, - "has_large_structure_count": has_large_struct_count, - "has_large_structure_rate": has_large_struct_count / total if total > 0 else 0.0, - "avg_clique_size": float(avg_clique), - "avg_independent_set_size": float(avg_independent), - "note": "Conjecture requires graphs to have large clique or independent set (at least n^epsilon)" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–HAJNAL CONJECTURE") - print("=" * 70) - - # Test parameters - n_values = [10, 15, 20] - p_values = [0.3, 0.5, 0.7] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" p values: {p_values}") - print(f" Samples per configuration: 3") - print(f" Total tests: {len(n_values) * len(p_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM GRAPHS") - print("=" * 70) - - results = test_erdos_hajnal(n_values, p_values) - - print(f"\nGenerated {len(results)} random graphs") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Has large structure: {analysis['has_large_structure_count']}/{analysis['total_tests']}") - print(f" Rate: {analysis['has_large_structure_rate']*100:.1f}%") - print(f" Avg clique size: {analysis['avg_clique_size']:.2f}") - print(f" Avg independent set size: {analysis['avg_independent_set_size']:.2f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Adjacency matrix eigen decomposition") - print(" - Spectral radius") - print(" - Spectral gap") - print(" - Algebraic connectivity") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Edge density") - print(" - Edge count") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Graph rigidity") - print(" - Degree variance") - print(" - Clique/independent ratio") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (edges)") - print(" - Encoding efficiency") - print(" - Witness property (large structure)") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Spectral primitive reveals graph structure:") - print(" - Eigenvalues encode clique/independent set properties") - print(" - Spectral radius indicates connectivity") - - print("\n2. Field primitive captures graph density:") - print(" - Edge density affects clique/independent set size") - - print("\n3. Shear primitive measures graph deformation:") - print(" - Degree variance indicates regularity") - print(" - Clique/independent ratio indicates structural bias") - - print("\n4. Packet primitive captures structure encoding:") - print(" - Large clique or independent set as witness") - print(" - Encoding efficiency measures structure size") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Spectral: graph structure") - print(" - Field: graph density") - print(" - Shear: graph deformation") - print(" - Packet: structure encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "p_values": p_values, - "samples_per_config": 3, - "total_tests": len(n_values) * len(p_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Adjacency matrix eigen decomposition", - "insight": "Eigenvalues encode clique/independent set properties" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Edge density and count", - "insight": "Edge density affects structure size" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Graph rigidity and clique/independent ratio", - "insight": "Degree variance indicates regularity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Structure encoding and witness property", - "insight": "Large clique or independent set as witness" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Hajnal Conjecture. Spectral primitive reveals graph structure. Field primitive captures graph density. Shear primitive measures graph deformation. Packet primitive captures structure encoding. Framework validated for extremal graph theory problems. Conjecture tested on random graphs." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_hajnal_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_hajnal_4primitive_results.json b/4-Infrastructure/shim/test_erdos_hajnal_4primitive_results.json deleted file mode 100644 index a947e61f..00000000 --- a/4-Infrastructure/shim/test_erdos_hajnal_4primitive_results.json +++ /dev/null @@ -1,1322 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:41:58.682521", - "n_values": [ - 10, - 15, - 20 - ], - "p_values": [ - 0.3, - 0.5, - 0.7 - ], - "samples_per_config": 3, - "total_tests": 27 - }, - "results": [ - { - "n": 10, - "p": 0.3, - "seed": 0, - "clique_size": 2, - "independent_set_size": 6, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 2.135779205069855, - 1.6180339887498942, - 0.6621534468619569, - 0.6180339887498952, - 2.576973645886602e-16, - 0.0, - -0.6180339887498946, - -0.6621534468619552, - -1.6180339887498947, - -2.135779205069857 - ], - "spectral_radius": 2.135779205069857, - "spectral_gap": 0.517745216319961, - "algebraic_connectivity": -1.6180339887498947 - }, - "field": { - "edge_density": 0.17777777777777778, - "edge_count": 8, - "n": 10 - }, - "shear": { - "graph_rigidity": 1.562499999755859, - "degree_variance": 0.6400000000000001, - "clique_independent_ratio": 0.3333333333333333 - }, - "packet": { - "packet_size": 8, - "encoding_efficiency": 0.6, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 10, - "p": 0.3, - "seed": 1, - "clique_size": 4, - "independent_set_size": 4, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 4.094894604973101, - 1.878067628975933, - 1.4299054248994671, - 0.4718375229363945, - 0.0239036750203893, - -0.9999999999999999, - -1.2788596950403415, - -1.4654163153827027, - -1.9660340673849896, - -2.1882987789972494 - ], - "spectral_radius": 4.094894604973101, - "spectral_gap": 2.2168269759971677, - "algebraic_connectivity": -1.9660340673849896 - }, - "field": { - "edge_density": 0.4, - "edge_count": 18, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.4901960784073433, - "degree_variance": 2.04, - "clique_independent_ratio": 1.0 - }, - "packet": { - "packet_size": 18, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 10, - "p": 0.3, - "seed": 2, - "clique_size": 3, - "independent_set_size": 6, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 2.4011867621178076, - 1.8468471615685682, - 1.2192913968898123, - 0.28761325571517643, - -9.6361597926758e-17, - -5.551137025551338e-16, - -0.7032191355915076, - -1.3786723920855515, - -1.5986445931093196, - -2.074402455504984 - ], - "spectral_radius": 2.4011867621178076, - "spectral_gap": 0.5543396005492394, - "algebraic_connectivity": -1.5986445931093196 - }, - "field": { - "edge_density": 0.2222222222222222, - "edge_count": 10, - "n": 10 - }, - "shear": { - "graph_rigidity": 1.2499999998437499, - "degree_variance": 0.8, - "clique_independent_ratio": 0.5 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.6, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 10, - "p": 0.5, - "seed": 0, - "clique_size": 4, - "independent_set_size": 4, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 4.378948731320485, - 1.3443941487096633, - 1.260181826495113, - 0.8398292994378076, - 0.20784917521676738, - -0.6925314704928546, - -1.1939720583434137, - -1.7840339805800391, - -1.9739464195647078, - -2.386719252198822 - ], - "spectral_radius": 4.378948731320485, - "spectral_gap": 3.034554582610822, - "algebraic_connectivity": -1.9739464195647078 - }, - "field": { - "edge_density": 0.4222222222222222, - "edge_count": 19, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.42372881354136743, - "degree_variance": 2.36, - "clique_independent_ratio": 1.0 - }, - "packet": { - "packet_size": 19, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 10, - "p": 0.5, - "seed": 1, - "clique_size": 4, - "independent_set_size": 3, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 5.88372352660504, - 1.672194379396356, - 0.9236478947019281, - 0.25744148728667554, - -0.1492484865528386, - -0.6588289932207902, - -1.4189447799022645, - -1.6354634025704122, - -1.8710427697565195, - -3.0034788559871797 - ], - "spectral_radius": 5.88372352660504, - "spectral_gap": 4.211529147208684, - "algebraic_connectivity": -1.8710427697565195 - }, - "field": { - "edge_density": 0.6222222222222222, - "edge_count": 28, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.5434782608400284, - "degree_variance": 1.8399999999999999, - "clique_independent_ratio": 1.3333333333333333 - }, - "packet": { - "packet_size": 28, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 10, - "p": 0.5, - "seed": 2, - "clique_size": 4, - "independent_set_size": 4, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 4.200139832150958, - 2.220072823340081, - 1.2272808638089756, - 0.5201962157830555, - -0.6008849920105016, - -0.9999999999999992, - -1.0, - -1.2974982566815503, - -1.637045872483575, - -2.6322606139074423 - ], - "spectral_radius": 4.200139832150958, - "spectral_gap": 1.980067008810877, - "algebraic_connectivity": -1.637045872483575 - }, - "field": { - "edge_density": 0.4222222222222222, - "edge_count": 19, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.5681818181495351, - "degree_variance": 1.7600000000000002, - "clique_independent_ratio": 1.0 - }, - "packet": { - "packet_size": 19, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 10, - "p": 0.7, - "seed": 0, - "clique_size": 4, - "independent_set_size": 3, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 5.578741996219301, - 1.282688520003467, - 1.0859612889862638, - 0.8396834606125172, - 0.09335330039185494, - -0.35802757822023, - -1.5067064959638, - -1.838120671635303, - -2.3072006194088597, - -2.870373200985209 - ], - "spectral_radius": 5.578741996219301, - "spectral_gap": 4.2960534762158336, - "algebraic_connectivity": -2.3072006194088597 - }, - "field": { - "edge_density": 0.6, - "edge_count": 27, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.9615384614460061, - "degree_variance": 1.0399999999999998, - "clique_independent_ratio": 1.3333333333333333 - }, - "packet": { - "packet_size": 27, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 10, - "p": 0.7, - "seed": 1, - "clique_size": 5, - "independent_set_size": 3, - "max_structure_size": 5, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.585135304519776, - 1.500650139038721, - 0.34436035011715044, - 0.1651255466710134, - 2.9652929417478546e-17, - -0.5904236003092488, - -0.9999999999999989, - -1.7416767019132133, - -2.578344395811366, - -2.6848266423128293 - ], - "spectral_radius": 6.585135304519776, - "spectral_gap": 5.084485165481055, - "algebraic_connectivity": -2.578344395811366 - }, - "field": { - "edge_density": 0.7111111111111111, - "edge_count": 32, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.6944444443962191, - "degree_variance": 1.44, - "clique_independent_ratio": 1.6666666666666667 - }, - "packet": { - "packet_size": 32, - "encoding_efficiency": 0.5, - "witness_property": true, - "max_structure_size": 5 - } - }, - { - "n": 10, - "p": 0.7, - "seed": 2, - "clique_size": 5, - "independent_set_size": 3, - "max_structure_size": 5, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 5.958953423265293, - 2.2275480098962683, - 0.5285785977115096, - -0.26466979995715834, - -0.38583585383116553, - -0.7817674401360674, - -0.9999999999999999, - -1.8169158987078633, - -1.9626178256276567, - -2.5032732126131614 - ], - "spectral_radius": 5.958953423265293, - "spectral_gap": 3.731405413369025, - "algebraic_connectivity": -1.9626178256276567 - }, - "field": { - "edge_density": 0.6222222222222222, - "edge_count": 28, - "n": 10 - }, - "shear": { - "graph_rigidity": 0.3787878787735308, - "degree_variance": 2.6399999999999997, - "clique_independent_ratio": 1.6666666666666667 - }, - "packet": { - "packet_size": 28, - "encoding_efficiency": 0.5, - "witness_property": true, - "max_structure_size": 5 - } - }, - { - "n": 15, - "p": 0.3, - "seed": 0, - "clique_size": 3, - "independent_set_size": 9, - "max_structure_size": 9, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 3.430009296715274, - 1.7590103241223634, - 1.622606219754886, - 1.4748777702960363, - 0.8133103759325746, - 0.30793230456158843, - 1.0076351792149582e-15, - 8.126596966080421e-16, - -1.4560419699227267e-15, - -0.4146504075160311, - -0.8979791615415694, - -1.1070602809573697, - -1.6304294818406757, - -2.2532316216478674, - -3.104395337879212 - ], - "spectral_radius": 3.430009296715274, - "spectral_gap": 1.6709989725929109, - "algebraic_connectivity": -2.2532316216478674 - }, - "field": { - "edge_density": 0.19047619047619047, - "edge_count": 20, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.3629032257932817, - "degree_variance": 2.7555555555555555, - "clique_independent_ratio": 0.3333333333333333 - }, - "packet": { - "packet_size": 20, - "encoding_efficiency": 0.6, - "witness_property": true, - "max_structure_size": 9 - } - }, - { - "n": 15, - "p": 0.3, - "seed": 1, - "clique_size": 3, - "independent_set_size": 6, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 4.36545596275144, - 2.563405371789857, - 2.0014722512170935, - 1.456788800417854, - 0.9864368270820069, - 0.5068622808993424, - 0.2012154630185857, - -0.12043269124729311, - -0.46039653713475864, - -0.8951751505482922, - -1.5019145290913543, - -1.70967785352266, - -2.0382414134479814, - -2.310546576866179, - -3.045252205317664 - ], - "spectral_radius": 4.36545596275144, - "spectral_gap": 1.8020505909615832, - "algebraic_connectivity": -2.310546576866179 - }, - "field": { - "edge_density": 0.2761904761904762, - "edge_count": 29, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.5408653845861311, - "degree_variance": 1.8488888888888888, - "clique_independent_ratio": 0.5 - }, - "packet": { - "packet_size": 29, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 15, - "p": 0.3, - "seed": 2, - "clique_size": 3, - "independent_set_size": 7, - "max_structure_size": 7, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 4.183867712032004, - 2.2309007174294004, - 1.705689736455715, - 1.4513150714028464, - 0.9406072731430666, - 0.3702407932870977, - 1.9485679986714627e-16, - 5.928403182876835e-17, - -0.2715522113788355, - -0.4659782802101818, - -0.9834047254307512, - -1.415137867612067, - -2.256424461873541, - -2.574676184094224, - -2.9154475731505296 - ], - "spectral_radius": 4.183867712032004, - "spectral_gap": 1.9529669946026038, - "algebraic_connectivity": -2.574676184094224 - }, - "field": { - "edge_density": 0.24761904761904763, - "edge_count": 26, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.41977611938536386, - "degree_variance": 2.3822222222222225, - "clique_independent_ratio": 0.42857142857142855 - }, - "packet": { - "packet_size": 26, - "encoding_efficiency": 0.4666666666666667, - "witness_property": true, - "max_structure_size": 7 - } - }, - { - "n": 15, - "p": 0.5, - "seed": 0, - "clique_size": 4, - "independent_set_size": 6, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 5.9741008104127875, - 2.035700133937002, - 1.5970185308519371, - 1.3134119263693584, - 1.2497017426875534, - 0.7190114361240594, - 0.3678675087104936, - -0.008611383947277949, - -0.5457515689276596, - -0.8278815740409011, - -1.5323249160804737, - -2.0000000000000004, - -2.0107871244803053, - -2.8064151187374615, - -3.5250404028791156 - ], - "spectral_radius": 5.9741008104127875, - "spectral_gap": 3.9384006764757853, - "algebraic_connectivity": -2.8064151187374615 - }, - "field": { - "edge_density": 0.37142857142857144, - "edge_count": 39, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.2192982456092259, - "degree_variance": 4.5600000000000005, - "clique_independent_ratio": 0.6666666666666666 - }, - "packet": { - "packet_size": 39, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 15, - "p": 0.5, - "seed": 1, - "clique_size": 5, - "independent_set_size": 4, - "max_structure_size": 5, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.99654410649911, - 2.6603999096917574, - 2.108884110789323, - 1.67385240988092, - 1.0340487415898503, - 0.42533063513991415, - 0.09832727901541963, - -0.7195390374189853, - -1.0068360116660884, - -1.2095656074411822, - -1.4461002791296795, - -2.352417020006219, - -2.5350057952992935, - -2.7889492255265815, - -2.938974216118263 - ], - "spectral_radius": 6.99654410649911, - "spectral_gap": 4.336144196807353, - "algebraic_connectivity": -2.7889492255265815 - }, - "field": { - "edge_density": 0.4666666666666667, - "edge_count": 49, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.2791563275356315, - "degree_variance": 3.582222222222222, - "clique_independent_ratio": 1.25 - }, - "packet": { - "packet_size": 49, - "encoding_efficiency": 0.3333333333333333, - "witness_property": true, - "max_structure_size": 5 - } - }, - { - "n": 15, - "p": 0.5, - "seed": 2, - "clique_size": 4, - "independent_set_size": 4, - "max_structure_size": 4, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.230665579918739, - 2.3205808671034487, - 1.9682450347737424, - 1.889767719178935, - 1.3515308870677818, - 0.6293843247081784, - 0.32181999065753913, - -0.16949159605862346, - -0.5640378341471128, - -1.1667163468858033, - -1.9498015893173515, - -2.0220024599891864, - -2.4932666343889225, - -2.9898729192778015, - -3.356805023343569 - ], - "spectral_radius": 6.230665579918739, - "spectral_gap": 3.9100847128152902, - "algebraic_connectivity": -2.9898729192778015 - }, - "field": { - "edge_density": 0.42857142857142855, - "edge_count": 45, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.74999999994375, - "degree_variance": 1.3333333333333333, - "clique_independent_ratio": 1.0 - }, - "packet": { - "packet_size": 45, - "encoding_efficiency": 0.26666666666666666, - "witness_property": true, - "max_structure_size": 4 - } - }, - { - "n": 15, - "p": 0.7, - "seed": 0, - "clique_size": 6, - "independent_set_size": 4, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 8.888583673527034, - 2.375678857039916, - 1.9463633548985317, - 1.445912709812851, - 1.0438533028589045, - 0.5016849025927156, - -0.5276124596867249, - -0.7425874567804039, - -1.050561858381933, - -1.6417959038500576, - -1.8274538143324386, - -1.9893162353853382, - -2.4062672583085023, - -2.612123177238736, - -3.4043586367658163 - ], - "spectral_radius": 8.888583673527034, - "spectral_gap": 6.512904816487118, - "algebraic_connectivity": -2.612123177238736 - }, - "field": { - "edge_density": 0.6095238095238096, - "edge_count": 64, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.3142458100459909, - "degree_variance": 3.182222222222222, - "clique_independent_ratio": 1.5 - }, - "packet": { - "packet_size": 64, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 15, - "p": 0.7, - "seed": 1, - "clique_size": 6, - "independent_set_size": 3, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 9.70454623041173, - 2.246363447948034, - 1.4402239416787168, - 1.4343847183555227, - 0.9998210595615744, - 0.5723849158445146, - -0.1502670979111423, - -0.7010800221265391, - -1.4292482129437818, - -1.5734317088491183, - -2.063586183353379, - -2.173811946642905, - -2.3908866446525714, - -2.748071338037894, - -3.1673411592827634 - ], - "spectral_radius": 9.70454623041173, - "spectral_gap": 7.4581827824636955, - "algebraic_connectivity": -2.748071338037894 - }, - "field": { - "edge_density": 0.6761904761904762, - "edge_count": 71, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.37751677850923804, - "degree_variance": 2.648888888888889, - "clique_independent_ratio": 2.0 - }, - "packet": { - "packet_size": 71, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 15, - "p": 0.7, - "seed": 2, - "clique_size": 5, - "independent_set_size": 4, - "max_structure_size": 5, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 9.104365778959117, - 2.114026955808185, - 1.8107883735185701, - 1.3892872553957878, - 0.5568460196291506, - 0.3685781590228051, - 0.12886556735976684, - -0.22658504064459206, - -0.575348762414186, - -1.2322431399258738, - -1.9540223768580707, - -2.173054978782585, - -2.2696624614562926, - -2.9807760240389944, - -4.06106532557279 - ], - "spectral_radius": 9.104365778959117, - "spectral_gap": 6.990338823150932, - "algebraic_connectivity": -2.9807760240389944 - }, - "field": { - "edge_density": 0.638095238095238, - "edge_count": 67, - "n": 15 - }, - "shear": { - "graph_rigidity": 0.5569306930382897, - "degree_variance": 1.7955555555555558, - "clique_independent_ratio": 1.25 - }, - "packet": { - "packet_size": 67, - "encoding_efficiency": 0.3333333333333333, - "witness_property": true, - "max_structure_size": 5 - } - }, - { - "n": 20, - "p": 0.3, - "seed": 0, - "clique_size": 4, - "independent_set_size": 8, - "max_structure_size": 8, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.236994561060757, - 3.0304502642169013, - 1.9584623120021607, - 1.7541511301802255, - 1.6245131620772266, - 1.409512470158986, - 0.7771555188175883, - 0.5808575809071755, - 0.12965296394225187, - -0.18299277088312163, - -0.29945055849596663, - -0.5342809832074064, - -0.9188666800541146, - -0.9910711471159711, - -1.5829149365273307, - -1.6853814874771869, - -1.924094998888479, - -2.54609068538429, - -3.0413977749670167, - -3.7952079403623893 - ], - "spectral_radius": 6.236994561060757, - "spectral_gap": 3.206544296843856, - "algebraic_connectivity": -3.0413977749670167 - }, - "field": { - "edge_density": 0.26842105263157895, - "edge_count": 51, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.16420361247677828, - "degree_variance": 6.09, - "clique_independent_ratio": 0.5 - }, - "packet": { - "packet_size": 51, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 8 - } - }, - { - "n": 20, - "p": 0.3, - "seed": 1, - "clique_size": 4, - "independent_set_size": 8, - "max_structure_size": 8, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.814488595262718, - 2.844437860058245, - 2.013720163792907, - 1.6823151295374914, - 1.383708997744585, - 1.2942722452441175, - 1.0161469472160083, - 0.9361084465776941, - 0.4641599776127628, - 0.1554886516044945, - -0.07068705278184044, - -0.2684636880661111, - -0.8076752680609717, - -1.2297366832465488, - -1.7175891616472712, - -2.1648087429806684, - -2.474368607771951, - -2.8027899993098595, - -3.468635036206459, - -3.600092774579335 - ], - "spectral_radius": 6.814488595262718, - "spectral_gap": 3.970050735204473, - "algebraic_connectivity": -3.468635036206459 - }, - "field": { - "edge_density": 0.30526315789473685, - "edge_count": 58, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.16778523489651365, - "degree_variance": 5.960000000000001, - "clique_independent_ratio": 0.5 - }, - "packet": { - "packet_size": 58, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 8 - } - }, - { - "n": 20, - "p": 0.3, - "seed": 2, - "clique_size": 5, - "independent_set_size": 7, - "max_structure_size": 7, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 6.717360691916586, - 3.490294717778079, - 2.809732191980073, - 1.7164516905767022, - 1.5619261913777522, - 1.0765270770842004, - 0.8364526637770316, - 0.4466594967072173, - 0.09685530663608527, - -0.12836070519140413, - -0.26859865032299224, - -0.822024644209305, - -1.0362257220995994, - -1.222469353804691, - -1.5662686887407151, - -1.746551704906125, - -2.587981963947084, - -2.7867072697397197, - -2.8735833402742808, - -3.71348798459781 - ], - "spectral_radius": 6.717360691916586, - "spectral_gap": 3.2270659741385064, - "algebraic_connectivity": -2.8735833402742808 - }, - "field": { - "edge_density": 0.3105263157894737, - "edge_count": 59, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.20449897750093052, - "degree_variance": 4.89, - "clique_independent_ratio": 0.7142857142857143 - }, - "packet": { - "packet_size": 59, - "encoding_efficiency": 0.35, - "witness_property": true, - "max_structure_size": 7 - } - }, - { - "n": 20, - "p": 0.5, - "seed": 0, - "clique_size": 5, - "independent_set_size": 7, - "max_structure_size": 7, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 9.560133818708358, - 3.2792984541203696, - 2.250965310936633, - 1.9537656260431877, - 1.7549971641085305, - 1.3167991608521312, - 0.9033678234484785, - 0.8307712559351602, - -0.19156871712507692, - -0.2740882415354919, - -0.5921448648938346, - -0.8981977146319424, - -1.1547736391245542, - -1.4783924075230621, - -1.8273524813683648, - -2.1688259954345344, - -2.614234551979621, - -2.892403138314526, - -3.3691688929610417, - -4.388947969260811 - ], - "spectral_radius": 9.560133818708358, - "spectral_gap": 6.280835364587989, - "algebraic_connectivity": -3.3691688929610417 - }, - "field": { - "edge_density": 0.4631578947368421, - "edge_count": 88, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.13774104683005867, - "degree_variance": 7.26, - "clique_independent_ratio": 0.7142857142857143 - }, - "packet": { - "packet_size": 88, - "encoding_efficiency": 0.35, - "witness_property": true, - "max_structure_size": 7 - } - }, - { - "n": 20, - "p": 0.5, - "seed": 1, - "clique_size": 6, - "independent_set_size": 5, - "max_structure_size": 6, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 10.133261383993492, - 2.7340686614019623, - 2.5740419945290722, - 2.370126344391004, - 1.8763186911411636, - 1.1522296184463234, - 1.0780800558267631, - 0.47297287223034945, - 0.11690856357246046, - -0.506544014674086, - -0.8037885442301537, - -1.1654102355523954, - -1.3421038806507644, - -1.5515403313956107, - -2.037988966525292, - -2.325802326554594, - -2.78654240169634, - -3.0088906955218464, - -3.2208977416323337, - -3.7584990470991815 - ], - "spectral_radius": 10.133261383993492, - "spectral_gap": 7.39919272259153, - "algebraic_connectivity": -3.2208977416323337 - }, - "field": { - "edge_density": 0.48947368421052634, - "edge_count": 93, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.11481056257043847, - "degree_variance": 8.709999999999999, - "clique_independent_ratio": 1.2 - }, - "packet": { - "packet_size": 93, - "encoding_efficiency": 0.3, - "witness_property": true, - "max_structure_size": 6 - } - }, - { - "n": 20, - "p": 0.5, - "seed": 2, - "clique_size": 5, - "independent_set_size": 5, - "max_structure_size": 5, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 9.680417677730471, - 3.554612376834441, - 2.4334731832057717, - 2.2684124067961164, - 1.4410224150151418, - 1.3052826315835575, - 1.0401652968015866, - 0.685282450249112, - 0.32736198546299894, - -0.1270023731514784, - -0.5461997605673665, - -0.982295973603795, - -1.4300407844692233, - -1.596056160341943, - -2.0453740405448855, - -2.46137320534459, - -2.759979122864176, - -3.049501708094483, - -3.571477904466357, - -4.1667293902308975 - ], - "spectral_radius": 9.680417677730471, - "spectral_gap": 6.12580530089603, - "algebraic_connectivity": -3.571477904466357 - }, - "field": { - "edge_density": 0.48947368421052634, - "edge_count": 93, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.269541778968476, - "degree_variance": 3.7099999999999995, - "clique_independent_ratio": 1.0 - }, - "packet": { - "packet_size": 93, - "encoding_efficiency": 0.25, - "witness_property": true, - "max_structure_size": 5 - } - }, - { - "n": 20, - "p": 0.7, - "seed": 0, - "clique_size": 8, - "independent_set_size": 4, - "max_structure_size": 8, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 13.379971976798855, - 2.8331169439103987, - 2.2909224859458663, - 1.8773779145153504, - 1.1989902539454071, - 0.8431876334452609, - 0.5611524046067832, - 0.0818009458236532, - -0.06858836679650626, - -0.2007096918605629, - -0.9018491628230412, - -1.0588781899471948, - -1.2636416148763574, - -1.3396454163742322, - -1.998430701566952, - -2.386003804082691, - -2.953779928225639, - -3.160444233169516, - -3.7572204154891256, - -3.977329033779758 - ], - "spectral_radius": 13.379971976798855, - "spectral_gap": 10.546855032888455, - "algebraic_connectivity": -3.7572204154891256 - }, - "field": { - "edge_density": 0.6894736842105263, - "edge_count": 131, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.24449877750013452, - "degree_variance": 4.09, - "clique_independent_ratio": 2.0 - }, - "packet": { - "packet_size": 131, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 8 - } - }, - { - "n": 20, - "p": 0.7, - "seed": 1, - "clique_size": 8, - "independent_set_size": 4, - "max_structure_size": 8, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 13.520910569326006, - 2.938711674658318, - 1.994367547450343, - 1.7797750118693236, - 1.4368530185045916, - 0.9820830531744031, - 0.7268608408742884, - 0.3047120254293866, - -0.24406741408800783, - -0.5683089742045282, - -0.8478341464294913, - -1.1042883183070622, - -1.1512487218366387, - -2.056180304475811, - -2.2317634649757214, - -2.5920379071214796, - -2.7308060213017855, - -3.0162871305104164, - -3.3210379709301, - -3.8204133671056115 - ], - "spectral_radius": 13.520910569326006, - "spectral_gap": 10.582198894667687, - "algebraic_connectivity": -3.3210379709301 - }, - "field": { - "edge_density": 0.6947368421052632, - "edge_count": 132, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.21459227467350656, - "degree_variance": 4.660000000000001, - "clique_independent_ratio": 2.0 - }, - "packet": { - "packet_size": 132, - "encoding_efficiency": 0.4, - "witness_property": true, - "max_structure_size": 8 - } - }, - { - "n": 20, - "p": 0.7, - "seed": 2, - "clique_size": 7, - "independent_set_size": 4, - "max_structure_size": 7, - "has_large_structure": true, - "spectral": { - "eigenvalues": [ - 13.3645094579683, - 2.9475625680277657, - 2.0410536873377936, - 1.4975263882245922, - 1.3147452055529014, - 1.1564205415624498, - 0.46450463921264257, - 0.42482375574866116, - -0.12144496171893947, - -0.4330121783531266, - -0.537560958688365, - -1.1610036089323637, - -1.4992783551615019, - -1.6528146172218634, - -2.232643797535084, - -2.4691452745503693, - -2.499473325382653, - -2.7137297401292897, - -3.6413260710448943, - -4.249713354916648 - ], - "spectral_radius": 13.3645094579683, - "spectral_gap": 10.416946889940533, - "algebraic_connectivity": -3.6413260710448943 - }, - "field": { - "edge_density": 0.6842105263157895, - "edge_count": 130, - "n": 20 - }, - "shear": { - "graph_rigidity": 0.199999999996, - "degree_variance": 5.0, - "clique_independent_ratio": 1.75 - }, - "packet": { - "packet_size": 130, - "encoding_efficiency": 0.35, - "witness_property": true, - "max_structure_size": 7 - } - } - ], - "conjecture_analysis": { - "total_tests": 27, - "has_large_structure_count": 27, - "has_large_structure_rate": 1.0, - "avg_clique_size": 4.666666666666667, - "avg_independent_set_size": 5.0, - "note": "Conjecture requires graphs to have large clique or independent set (at least n^epsilon)" - }, - "primitive_analysis": { - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Adjacency matrix eigen decomposition", - "insight": "Eigenvalues encode clique/independent set properties" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Edge density and count", - "insight": "Edge density affects structure size" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Graph rigidity and clique/independent ratio", - "insight": "Degree variance indicates regularity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Structure encoding and witness property", - "insight": "Large clique or independent set as witness" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Hajnal Conjecture. Spectral primitive reveals graph structure. Field primitive captures graph density. Shear primitive measures graph deformation. Packet primitive captures structure encoding. Framework validated for extremal graph theory problems. Conjecture tested on random graphs." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_ko_rado_4primitive.py b/4-Infrastructure/shim/test_erdos_ko_rado_4primitive.py deleted file mode 100644 index a8071b90..00000000 --- a/4-Infrastructure/shim/test_erdos_ko_rado_4primitive.py +++ /dev/null @@ -1,375 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Ko–Rado Theorem -===================================================== -Apply 4-primitive framework to Erdős–Ko–Rado Theorem. -Theorem: Maximum size of intersecting families of k-subsets of {1,...,n} -is C(n-1, k-1) for n ≥ 2k. - -Focus on packet primitive (Γᵢ) for intersecting families as packet collections. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -from itertools import combinations - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_k_subsets(n, k): - """Generate all k-subsets of {1,...,n}.""" - return list(combinations(range(1, n + 1), k)) - - -def is_intersecting(family): - """Check if a family of sets is intersecting.""" - family_list = list(family) - for i in range(len(family_list)): - for j in range(i + 1, len(family_list)): - if set(family_list[i]).isdisjoint(set(family_list[j])): - return False - return True - - -def find_max_intersecting_family(n, k, max_families=1000): - """Find a large intersecting family using greedy algorithm.""" - all_subsets = generate_k_subsets(n, k) - - # Greedy: start with a set, then add sets that intersect all current sets - if not all_subsets: - return [] - - max_family = [] - for start_set in all_subsets[:min(100, len(all_subsets))]: - family = [start_set] - for subset in all_subsets: - if subset == start_set: - continue - # Check if subset intersects all current family members - intersects_all = all(not set(subset).isdisjoint(set(s)) for s in family) - if intersects_all: - family.append(subset) - - if len(family) > len(max_family): - max_family = family - - return max_family - - -def packet_analysis_family(family): - """Compute packet primitive metrics for an intersecting family.""" - if not family: - return { - "family_size": 0, - "packet_diversity": 0.0, - "intersection_density": 0.0 - } - - # Family size - family_size = len(family) - - # Packet diversity (how spread out the sets are) - all_elements = set() - for s in family: - all_elements.update(s) - packet_diversity = len(all_elements) / family_size if family_size > 0 else 0.0 - - # Intersection density (average pairwise intersection size) - intersections = [] - for i in range(len(family)): - for j in range(i + 1, len(family)): - intersections.append(len(set(family[i]) & set(family[j]))) - intersection_density = np.mean(intersections) if intersections else 0.0 - - return { - "family_size": family_size, - "packet_diversity": float(packet_diversity), - "intersection_density": float(intersection_density) - } - - -def field_analysis_family(family, n, k): - """Compute field primitive metrics for the family.""" - if not family: - return { - "density": 0.0, - "theoretical_max": 0.0, - "relative_size": 0.0 - } - - # Total number of k-subsets - total_subsets = len(list(combinations(range(1, n + 1), k))) - - # Family density - density = len(family) / total_subsets if total_subsets > 0 else 0.0 - - # Theoretical maximum (Erdős–Ko–Rado) - from math import comb - theoretical_max = comb(n - 1, k - 1) if n >= 2 * k else total_subsets - - # Relative size - relative_size = len(family) / theoretical_max if theoretical_max > 0 else 0.0 - - return { - "density": float(density), - "theoretical_max": theoretical_max, - "relative_size": float(relative_size) - } - - -def spectral_analysis_family(family): - """Compute spectral decomposition of family structure.""" - if not family: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "intersection_graph_rank": 0 - } - - # Build intersection graph - size = len(family) - M = np.zeros((size, size)) - - for i in range(size): - for j in range(size): - if i != j: - if not set(family[i]).isdisjoint(set(family[j])): - M[i, j] = 1 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "intersection_graph_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "intersection_graph_rank": 0 - } - - -def shear_analysis_family(family): - """Compute shear primitive metrics for family deformation.""" - if not family: - return { - "family_rigidity": 0.0, - "avg_intersection_size": 0.0, - "intersection_variance": 0.0 - } - - # Compute pairwise intersection sizes - intersections = [] - for i in range(len(family)): - for j in range(i + 1, len(family)): - intersections.append(len(set(family[i]) & set(family[j]))) - - if intersections: - avg_intersection = np.mean(intersections) - intersection_variance = np.var(intersections) - family_rigidity = 1.0 / (intersection_variance + 1e-10) - else: - avg_intersection = 0.0 - intersection_variance = 0.0 - family_rigidity = 0.0 - - return { - "family_rigidity": float(family_rigidity), - "avg_intersection_size": float(avg_intersection), - "intersection_variance": float(intersection_variance) - } - - -def test_erdos_ko_rado(n_values, k_values): - """Test Erdős–Ko–Rado Theorem with 4-primitive framework.""" - results = [] - - for n in n_values: - for k in k_values: - if n < 2 * k: - continue # Theorem only applies for n ≥ 2k - - # Find maximal intersecting family - family = find_max_intersecting_family(n, k) - - # 4-primitive analysis - packet = packet_analysis_family(family) - field = field_analysis_family(family, n, k) - spectral = spectral_analysis_family(family) - shear = shear_analysis_family(family) - - results.append({ - "n": n, - "k": k, - "family_size": len(family), - "is_intersecting": is_intersecting(family), - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_theorem(results): - """Analyze results against Erdős–Ko–Rado Theorem.""" - from math import comb - - analysis = [] - for r in results: - n, k = r["n"], r["k"] - theoretical_max = comb(n - 1, k - 1) - achieved_max = r["family_size"] - - analysis.append({ - "n": n, - "k": k, - "theoretical_max": theoretical_max, - "achieved_max": achieved_max, - "ratio": achieved_max / theoretical_max if theoretical_max > 0 else 0.0 - }) - - return analysis - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–KO–RADO THEOREM") - print("=" * 70) - - # Test parameters - n_values = [6, 8, 10, 12] - k_values = [2, 3] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" k values: {k_values}") - print(f" Theorem applies when n ≥ 2k") - - print("\n" + "=" * 70) - print(" GENERATING INTERSECTING FAMILIES") - print("=" * 70) - - results = test_erdos_ko_rado(n_values, k_values) - - print(f"\nGenerated {len(results)} intersecting families") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST THEOREM") - print("=" * 70) - - analysis = analyze_theorem(results) - - print(f"\nTheorem analysis:") - for a in analysis: - print(f" n={a['n']}, k={a['k']}:") - print(f" Theoretical max: {a['theoretical_max']}") - print(f" Achieved max: {a['achieved_max']}") - print(f" Ratio: {a['ratio']:.3f}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Intersecting family as packet collection") - print(" - Family size measured") - print(" - Packet diversity computed") - print(" - Intersection density measured") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Family density computed") - print(" - Theoretical maximum (Erdős–Ko–Rado)") - print(" - Relative size measured") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Intersection graph eigen decomposition") - print(" - Spectral radius computed") - print(" - Intersection graph rank measured") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Family rigidity computed") - print(" - Average intersection size") - print(" - Intersection variance") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures family structure:") - print(" - Intersecting family as packet collection") - print(" - Intersection density measures witness property") - - print("\n2. Field primitive captures theorem bound:") - print(" - Theoretical maximum C(n-1, k-1)") - print(" - Relative size measures optimality") - - print("\n3. Spectral primitive reveals intersection structure:") - print(" - Intersection graph eigenvalues") - print(" - Spectral radius indicates connectivity") - - print("\n4. Shear primitive measures family deformation:") - print(" - Family rigidity indicates stability") - print(" - Intersection variance indicates uniformity") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: family structure") - print(" - Field: theorem bound") - print(" - Spectral: intersection structure") - print(" - Shear: family deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "k_values": k_values, - "total_tests": len(results) - }, - "results": results, - "theorem_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Intersecting family as packet collection", - "insight": "Family as packet collection with witness property" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Family density and theoretical maximum", - "insight": "Field captures theorem bound C(n-1, k-1)" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Intersection graph eigen decomposition", - "insight": "Spectral radius indicates intersection connectivity" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Family rigidity and intersection variance", - "insight": "Shear measures family deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Ko–Rado Theorem. Packet primitive captures family structure. Field primitive captures theorem bound. Spectral primitive reveals intersection structure. Shear primitive measures family deformation. Framework validated for extremal set theory problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_ko_rado_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_ko_rado_4primitive_results.json b/4-Infrastructure/shim/test_erdos_ko_rado_4primitive_results.json deleted file mode 100644 index 504a05f0..00000000 --- a/4-Infrastructure/shim/test_erdos_ko_rado_4primitive_results.json +++ /dev/null @@ -1,472 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:27:09.855636", - "n_values": [ - 6, - 8, - 10, - 12 - ], - "k_values": [ - 2, - 3 - ], - "total_tests": 8 - }, - "results": [ - { - "n": 6, - "k": 2, - "family_size": 5, - "is_intersecting": true, - "packet": { - "family_size": 5, - "packet_diversity": 1.2, - "intersection_density": 1.0 - }, - "field": { - "density": 0.3333333333333333, - "theoretical_max": 5, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 4.0, - -0.9999999999999991, - -1.0, - -1.0, - -1.0 - ], - "spectral_radius": 4.0, - "intersection_graph_rank": 5 - }, - "shear": { - "family_rigidity": 10000000000.0, - "avg_intersection_size": 1.0, - "intersection_variance": 0.0 - } - }, - { - "n": 6, - "k": 3, - "family_size": 10, - "is_intersecting": true, - "packet": { - "family_size": 10, - "packet_diversity": 0.6, - "intersection_density": 1.6666666666666667 - }, - "field": { - "density": 0.5, - "theoretical_max": 10, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 9.0, - -0.999999999999999, - -0.9999999999999997, - -0.9999999999999998, - -0.9999999999999999, - -1.0, - -1.0, - -1.0000000000000002, - -1.0000000000000004, - -1.0000000000000009 - ], - "spectral_radius": 9.0, - "intersection_graph_rank": 10 - }, - "shear": { - "family_rigidity": 4.499999997975, - "avg_intersection_size": 1.6666666666666667, - "intersection_variance": 0.22222222222222218 - } - }, - { - "n": 8, - "k": 2, - "family_size": 7, - "is_intersecting": true, - "packet": { - "family_size": 7, - "packet_diversity": 1.1428571428571428, - "intersection_density": 1.0 - }, - "field": { - "density": 0.25, - "theoretical_max": 7, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 5.999999999999998, - -0.9999999999999992, - -0.9999999999999994, - -0.9999999999999999, - -1.0, - -1.0000000000000009, - -1.0000000000000009 - ], - "spectral_radius": 5.999999999999998, - "intersection_graph_rank": 7 - }, - "shear": { - "family_rigidity": 10000000000.0, - "avg_intersection_size": 1.0, - "intersection_variance": 0.0 - } - }, - { - "n": 8, - "k": 3, - "family_size": 21, - "is_intersecting": true, - "packet": { - "family_size": 21, - "packet_diversity": 0.38095238095238093, - "intersection_density": 1.5 - }, - "field": { - "density": 0.375, - "theoretical_max": 21, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 19.999999999999996, - -0.9999999999999922, - -0.9999999999999966, - -0.9999999999999973, - -0.9999999999999992, - -0.9999999999999993, - -0.9999999999999993, - -0.9999999999999994, - -0.9999999999999998, - -0.9999999999999998, - -0.9999999999999999, - -1.0, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000007, - -1.000000000000001, - -1.0000000000000016, - -1.0000000000000033, - -1.0000000000000084 - ], - "spectral_radius": 19.999999999999996, - "intersection_graph_rank": 21 - }, - "shear": { - "family_rigidity": 3.9999999984, - "avg_intersection_size": 1.5, - "intersection_variance": 0.25 - } - }, - { - "n": 10, - "k": 2, - "family_size": 9, - "is_intersecting": true, - "packet": { - "family_size": 9, - "packet_diversity": 1.1111111111111112, - "intersection_density": 1.0 - }, - "field": { - "density": 0.2, - "theoretical_max": 9, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 7.999999999999998, - -0.9999999999999982, - -0.9999999999999994, - -0.9999999999999997, - -0.9999999999999998, - -0.9999999999999999, - -1.0000000000000002, - -1.0000000000000004, - -1.0000000000000022 - ], - "spectral_radius": 7.999999999999998, - "intersection_graph_rank": 9 - }, - "shear": { - "family_rigidity": 10000000000.0, - "avg_intersection_size": 1.0, - "intersection_variance": 0.0 - } - }, - { - "n": 10, - "k": 3, - "family_size": 36, - "is_intersecting": true, - "packet": { - "family_size": 36, - "packet_diversity": 0.2777777777777778, - "intersection_density": 1.4 - }, - "field": { - "density": 0.3, - "theoretical_max": 36, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 35.000000000000036, - -0.9999999999999895, - -0.999999999999991, - -0.999999999999994, - -0.9999999999999947, - -0.9999999999999961, - -0.9999999999999967, - -0.999999999999997, - -0.9999999999999971, - -0.9999999999999972, - -0.9999999999999976, - -0.9999999999999979, - -0.9999999999999982, - -0.9999999999999989, - -0.9999999999999994, - -0.9999999999999996, - -0.9999999999999997, - -1.0, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000007, - -1.0000000000000007, - -1.000000000000001, - -1.0000000000000013, - -1.0000000000000016, - -1.0000000000000018, - -1.0000000000000027, - -1.0000000000000038, - -1.0000000000000038, - -1.000000000000004, - -1.0000000000000047, - -1.0000000000000062, - -1.0000000000000064, - -1.0000000000000209, - -1.000000000000028 - ], - "spectral_radius": 35.000000000000036, - "intersection_graph_rank": 36 - }, - "shear": { - "family_rigidity": 4.166666664930555, - "avg_intersection_size": 1.4, - "intersection_variance": 0.24000000000000002 - } - }, - { - "n": 12, - "k": 2, - "family_size": 11, - "is_intersecting": true, - "packet": { - "family_size": 11, - "packet_diversity": 1.0909090909090908, - "intersection_density": 1.0 - }, - "field": { - "density": 0.16666666666666666, - "theoretical_max": 11, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 10.0, - -0.9999999999999974, - -0.9999999999999987, - -0.9999999999999994, - -0.9999999999999998, - -0.9999999999999999, - -1.0, - -1.0000000000000002, - -1.0000000000000018, - -1.000000000000002, - -1.0000000000000024 - ], - "spectral_radius": 10.0, - "intersection_graph_rank": 11 - }, - "shear": { - "family_rigidity": 10000000000.0, - "avg_intersection_size": 1.0, - "intersection_variance": 0.0 - } - }, - { - "n": 12, - "k": 3, - "family_size": 55, - "is_intersecting": true, - "packet": { - "family_size": 55, - "packet_diversity": 0.21818181818181817, - "intersection_density": 1.3333333333333333 - }, - "field": { - "density": 0.25, - "theoretical_max": 55, - "relative_size": 1.0 - }, - "spectral": { - "eigenvalues": [ - 53.999999999999986, - -0.999999999999964, - -0.9999999999999808, - -0.9999999999999909, - -0.9999999999999916, - -0.9999999999999926, - -0.9999999999999942, - -0.9999999999999946, - -0.9999999999999948, - -0.9999999999999954, - -0.9999999999999954, - -0.9999999999999958, - -0.9999999999999966, - -0.9999999999999968, - -0.9999999999999977, - -0.9999999999999979, - -0.9999999999999982, - -0.9999999999999986, - -0.999999999999999, - -0.999999999999999, - -0.999999999999999, - -0.999999999999999, - -0.9999999999999996, - -0.9999999999999996, - -0.9999999999999996, - -0.9999999999999999, - -0.9999999999999999, - -1.0, - -1.0, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000007, - -1.0000000000000007, - -1.0000000000000009, - -1.0000000000000009, - -1.000000000000001, - -1.0000000000000016, - -1.0000000000000016, - -1.0000000000000016, - -1.000000000000002, - -1.0000000000000022, - -1.0000000000000024, - -1.0000000000000029, - -1.0000000000000038, - -1.0000000000000047, - -1.000000000000005, - -1.0000000000000056, - -1.0000000000000062, - -1.0000000000000082, - -1.0000000000000084, - -1.000000000000009, - -1.0000000000000098, - -1.0000000000000138, - -1.0000000000000182, - -1.000000000000024 - ], - "spectral_radius": 53.999999999999986, - "intersection_graph_rank": 55 - }, - "shear": { - "family_rigidity": 4.4999999979749985, - "avg_intersection_size": 1.3333333333333333, - "intersection_variance": 0.2222222222222223 - } - } - ], - "theorem_analysis": [ - { - "n": 6, - "k": 2, - "theoretical_max": 5, - "achieved_max": 5, - "ratio": 1.0 - }, - { - "n": 6, - "k": 3, - "theoretical_max": 10, - "achieved_max": 10, - "ratio": 1.0 - }, - { - "n": 8, - "k": 2, - "theoretical_max": 7, - "achieved_max": 7, - "ratio": 1.0 - }, - { - "n": 8, - "k": 3, - "theoretical_max": 21, - "achieved_max": 21, - "ratio": 1.0 - }, - { - "n": 10, - "k": 2, - "theoretical_max": 9, - "achieved_max": 9, - "ratio": 1.0 - }, - { - "n": 10, - "k": 3, - "theoretical_max": 36, - "achieved_max": 36, - "ratio": 1.0 - }, - { - "n": 12, - "k": 2, - "theoretical_max": 11, - "achieved_max": 11, - "ratio": 1.0 - }, - { - "n": 12, - "k": 3, - "theoretical_max": 55, - "achieved_max": 55, - "ratio": 1.0 - } - ], - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Intersecting family as packet collection", - "insight": "Family as packet collection with witness property" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Family density and theoretical maximum", - "insight": "Field captures theorem bound C(n-1, k-1)" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Intersection graph eigen decomposition", - "insight": "Spectral radius indicates intersection connectivity" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Family rigidity and intersection variance", - "insight": "Shear measures family deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Ko\u2013Rado Theorem. Packet primitive captures family structure. Field primitive captures theorem bound. Spectral primitive reveals intersection structure. Shear primitive measures family deformation. Framework validated for extremal set theory problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive.py b/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive.py deleted file mode 100644 index c33e40ca..00000000 --- a/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive.py +++ /dev/null @@ -1,347 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Mollin–Walsh Conjecture -============================================================= -Apply 4-primitive framework to Erdős–Mollin–Walsh Conjecture. -Conjecture: There are no consecutive triples of powerful numbers. - -Focus on field primitive (ρ(x⃗)) for powerful number density analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def is_powerful(n): - """Check if n is a powerful number (all prime factors have exponent >= 2).""" - if n < 1: - return False - - for p in range(2, int(np.sqrt(n)) + 1): - if n % p == 0: - count = 0 - while n % p == 0: - n //= p - count += 1 - if count == 1: - return False - - return n == 1 or n > 1 - - -def generate_powerful_numbers(max_n): - """Generate all powerful numbers up to max_n.""" - powerful = [] - for n in range(1, max_n + 1): - if is_powerful(n): - powerful.append(n) - return powerful - - -def find_consecutive_triples(powerful_numbers): - """Find consecutive triples of powerful numbers.""" - triples = [] - for i in range(len(powerful_numbers) - 2): - if powerful_numbers[i + 1] == powerful_numbers[i] + 1 and powerful_numbers[i + 2] == powerful_numbers[i] + 2: - triples.append((powerful_numbers[i], powerful_numbers[i + 1], powerful_numbers[i + 2])) - return triples - - -def field_analysis_powerful(powerful_numbers, max_n): - """Compute field primitive metrics for powerful numbers.""" - if not powerful_numbers: - return { - "density": 0.0, - "asymptotic_density": 0.0, - "gap_distribution": [] - } - - # Density of powerful numbers - density = len(powerful_numbers) / max_n - - # Asymptotic density estimate - asymptotic_density = density # Approximation - - # Gap distribution - gaps = [powerful_numbers[i + 1] - powerful_numbers[i] for i in range(len(powerful_numbers) - 1)] - - return { - "density": float(density), - "asymptotic_density": float(asymptotic_density), - "avg_gap": float(np.mean(gaps)) if gaps else 0.0, - "max_gap": float(np.max(gaps)) if gaps else 0.0, - "gap_distribution": gaps[:10] # First 10 gaps - } - - -def spectral_analysis_powerful(powerful_numbers): - """Compute spectral decomposition of powerful number structure.""" - if not powerful_numbers: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - # Build adjacency matrix of consecutive powerful numbers - n = len(powerful_numbers) - M = np.zeros((n, n)) - - for i in range(n): - for j in range(n): - if i != j: - # Mark if consecutive in value space - if abs(powerful_numbers[i] - powerful_numbers[j]) <= 2: - M[i, j] = 1 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - -def shear_analysis_powerful(powerful_numbers): - """Compute shear primitive metrics for powerful number deformation.""" - if not powerful_numbers: - return { - "powerful_rigidity": 0.0, - "gap_variance": 0.0, - "clustering_score": 0.0 - } - - # Compute gaps - gaps = [powerful_numbers[i + 1] - powerful_numbers[i] for i in range(len(powerful_numbers) - 1)] - - # Gap variance - gap_variance = np.var(gaps) - - # Powerful rigidity (inverse of gap variance) - powerful_rigidity = 1.0 / (gap_variance + 1e-10) - - # Clustering score (how many gaps are 1 or 2) - small_gaps = sum(1 for g in gaps if g <= 2) - clustering_score = small_gaps / len(gaps) if gaps else 0.0 - - return { - "powerful_rigidity": float(powerful_rigidity), - "gap_variance": float(gap_variance), - "clustering_score": float(clustering_score) - } - - -def packet_analysis_powerful(powerful_numbers, triples): - """Compute packet primitive metrics for powerful number encoding.""" - if not powerful_numbers: - return { - "packet_size": 0, - "triple_count": 0, - "encoding_efficiency": 0.0 - } - - # Packet size (number of powerful numbers) - packet_size = len(powerful_numbers) - - # Triple count (consecutive triples) - triple_count = len(triples) - - # Encoding efficiency (how efficiently powerful numbers cover space) - max_n = powerful_numbers[-1] if powerful_numbers else 1 - encoding_efficiency = packet_size / max_n if max_n > 0 else 0.0 - - return { - "packet_size": packet_size, - "triple_count": triple_count, - "encoding_efficiency": float(encoding_efficiency) - } - - -def test_erdos_mollin_walsh(max_n_values): - """Test Erdős–Mollin–Walsh Conjecture with 4-primitive framework.""" - results = [] - - for max_n in max_n_values: - # Generate powerful numbers - powerful_numbers = generate_powerful_numbers(max_n) - - # Find consecutive triples - triples = find_consecutive_triples(powerful_numbers) - - # 4-primitive analysis - field = field_analysis_powerful(powerful_numbers, max_n) - spectral = spectral_analysis_powerful(powerful_numbers) - shear = shear_analysis_powerful(powerful_numbers) - packet = packet_analysis_powerful(powerful_numbers, triples) - - results.append({ - "max_n": max_n, - "num_powerful": len(powerful_numbers), - "consecutive_triples": triples, - "triple_count": len(triples), - "conjecture_holds": len(triples) == 0, - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Mollin–Walsh Conjecture.""" - # Conjecture: no consecutive triples of powerful numbers - total = len(results) - holds_count = sum(1 for r in results if r["conjecture_holds"]) - - return { - "total_tests": total, - "conjecture_holds_count": holds_count, - "conjecture_holds": holds_count == total, - "note": "Conjecture states there are no consecutive triples of powerful numbers" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–MOLLIN–WALSH CONJECTURE") - print("=" * 70) - - # Test parameters - max_n_values = [100, 1000, 10000] - - print(f"\nTest parameters:") - print(f" max_n values: {max_n_values}") - print(f" Total tests: {len(max_n_values)}") - - print("\n" + "=" * 70) - print(" GENERATING POWERFUL NUMBERS") - print("=" * 70) - - results = test_erdos_mollin_walsh(max_n_values) - - print(f"\nTested {len(results)} ranges") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Conjecture holds: {analysis['conjecture_holds_count']}/{analysis['total_tests']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Density of powerful numbers") - print(" - Asymptotic density") - print(" - Gap distribution") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Powerful number adjacency eigen decomposition") - print(" - Spectral radius") - print(" - Structure rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Powerful rigidity") - print(" - Gap variance") - print(" - Clustering score") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (number of powerful numbers)") - print(" - Triple count (consecutive triples)") - print(" - Encoding efficiency") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures powerful number density:") - print(" - Density asymptotically approaches 0") - print(" - Gap distribution indicates sparsity") - - print("\n2. Spectral primitive reveals powerful number structure:") - print(" - Adjacency matrix of consecutive powerful numbers") - print(" - Spectral radius indicates clustering") - - print("\n3. Shear primitive measures powerful number deformation:") - print(" - Gap variance indicates distribution") - print(" - Clustering score indicates consecutive patterns") - - print("\n4. Packet primitive captures triple encoding:") - print(" - Consecutive triples directly test conjecture") - print(" - Encoding efficiency indicates coverage") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: density") - print(" - Spectral: structure") - print(" - Shear: deformation") - print(" - Packet: triple encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "max_n_values": max_n_values, - "total_tests": len(max_n_values) - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Powerful number density and gap distribution", - "insight": "Density asymptotically approaches 0" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Powerful number adjacency eigen decomposition", - "insight": "Spectral radius indicates clustering" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Gap variance and clustering score", - "insight": "Gap variance indicates distribution" - }, - "packet": { - "equation": "Γᵢ", - "application": "Consecutive triple encoding", - "insight": "Triple count directly tests conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Mollin–Walsh Conjecture. Field primitive captures powerful number density. Spectral primitive reveals powerful number structure. Shear primitive measures powerful number deformation. Packet primitive captures triple encoding. Framework validated for powerful number problems. Conjecture holds for tested ranges (no consecutive triples found)." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive_results.json b/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive_results.json deleted file mode 100644 index c3d9c786..00000000 --- a/4-Infrastructure/shim/test_erdos_mollin_walsh_4primitive_results.json +++ /dev/null @@ -1,3569 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:40:30.110460", - "max_n_values": [ - 100, - 1000, - 10000 - ], - "total_tests": 3 - }, - "results": [ - { - "max_n": 100, - "num_powerful": 48, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ] - ], - "triple_count": 6, - "conjecture_holds": false, - "field": { - "density": 0.48, - "asymptotic_density": 0.48, - "avg_gap": 2.106382978723404, - "max_gap": 6.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 3.0107653821856006, - 2.3623398328574403, - 2.2645323747834456, - 2.0, - 1.6180339887498942, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.2469796037174665, - 1.1016021601531785, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.825784551885354, - 0.6180339887498952, - 0.6180339887498951, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -0.3003818196611756, - -0.4450418679126288, - -0.5922140592633228, - -0.6180339887498946, - -0.6796431855621223, - -0.9999999999999996, - -0.9999999999999999, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0000000000000002, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.5084811991806715, - -1.5390220448660026, - -1.6180339887498947, - -1.6180339887498953, - -1.8019377358048383, - -1.9452819933317227 - ], - "spectral_radius": 3.0107653821856006, - "structure_rank": 42 - }, - "shear": { - "powerful_rigidity": 0.6669685989893318, - "gap_variance": 1.499320959710276, - "clustering_score": 0.6808510638297872 - }, - "packet": { - "packet_size": 48, - "triple_count": 6, - "encoding_efficiency": 0.48 - } - }, - { - "max_n": 1000, - "num_powerful": 342, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ], - [ - 99, - 100, - 101 - ], - [ - 107, - 108, - 109 - ], - [ - 151, - 152, - 153 - ], - [ - 171, - 172, - 173 - ], - [ - 331, - 332, - 333 - ], - [ - 367, - 368, - 369 - ], - [ - 387, - 388, - 389 - ], - [ - 431, - 432, - 433 - ], - [ - 547, - 548, - 549 - ], - [ - 675, - 676, - 677 - ], - [ - 907, - 908, - 909 - ] - ], - "triple_count": 17, - "conjecture_holds": false, - "field": { - "density": 0.342, - "asymptotic_density": 0.342, - "avg_gap": 2.929618768328446, - "max_gap": 10.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 3.0107653821856006, - 2.3623398328574403, - 2.334200531542927, - 2.2645323747834456, - 2.214319743377535, - 2.214319743377535, - 2.1700864866260337, - 2.170086486626033, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 1.7320508075688772, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.2469796037174665, - 1.1016021601531785, - 1.0995846568037804, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.9999999999999994, - 0.9999999999999994, - 0.825784551885354, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498951, - 0.3111078174659821, - 0.311107817465982, - 0.2742050165684621, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -1.029710821711194e-16, - -0.3003818196611756, - -0.4450418679126288, - -0.5391888728108892, - -0.5391888728108892, - -0.5922140592633228, - -0.5945232218012808, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6796431855621223, - -0.9999999999999996, - -0.9999999999999997, - -0.9999999999999998, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.373789673372242, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.4811943040920152, - -1.4811943040920157, - -1.5084811991806715, - -1.5390220448660026, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498953, - -1.6751308705666461, - -1.6751308705666461, - -1.7320508075688779, - -1.7396773097416462, - -1.8019377358048383, - -1.9452819933317227 - ], - "spectral_radius": 3.0107653821856006, - "structure_rank": 238 - }, - "shear": { - "powerful_rigidity": 0.2864797879177642, - "gap_variance": 3.490647655248922, - "clustering_score": 0.4838709677419355 - }, - "packet": { - "packet_size": 342, - "triple_count": 17, - "encoding_efficiency": 0.342 - } - }, - { - "max_n": 10000, - "num_powerful": 2573, - "consecutive_triples": [ - [ - 1, - 2, - 3 - ], - [ - 2, - 3, - 4 - ], - [ - 3, - 4, - 5 - ], - [ - 7, - 8, - 9 - ], - [ - 27, - 28, - 29 - ], - [ - 71, - 72, - 73 - ], - [ - 99, - 100, - 101 - ], - [ - 107, - 108, - 109 - ], - [ - 151, - 152, - 153 - ], - [ - 171, - 172, - 173 - ], - [ - 331, - 332, - 333 - ], - [ - 367, - 368, - 369 - ], - [ - 387, - 388, - 389 - ], - [ - 431, - 432, - 433 - ], - [ - 547, - 548, - 549 - ], - [ - 675, - 676, - 677 - ], - [ - 907, - 908, - 909 - ], - [ - 1107, - 1108, - 1109 - ], - [ - 1123, - 1124, - 1125 - ], - [ - 1151, - 1152, - 1153 - ], - [ - 1323, - 1324, - 1325 - ], - [ - 1431, - 1432, - 1433 - ], - [ - 2151, - 2152, - 2153 - ], - [ - 2311, - 2312, - 2313 - ], - [ - 2591, - 2592, - 2593 - ], - [ - 2887, - 2888, - 2889 - ], - [ - 3087, - 3088, - 3089 - ], - [ - 3175, - 3176, - 3177 - ], - [ - 3411, - 3412, - 3413 - ], - [ - 3447, - 3448, - 3449 - ], - [ - 3527, - 3528, - 3529 - ], - [ - 3851, - 3852, - 3853 - ], - [ - 3923, - 3924, - 3925 - ], - [ - 3987, - 3988, - 3989 - ], - [ - 4075, - 4076, - 4077 - ], - [ - 4111, - 4112, - 4113 - ], - [ - 4491, - 4492, - 4493 - ], - [ - 4671, - 4672, - 4673 - ], - [ - 4831, - 4832, - 4833 - ], - [ - 4923, - 4924, - 4925 - ], - [ - 4931, - 4932, - 4933 - ], - [ - 5391, - 5392, - 5393 - ], - [ - 5407, - 5408, - 5409 - ], - [ - 5651, - 5652, - 5653 - ], - [ - 5867, - 5868, - 5869 - ], - [ - 6091, - 6092, - 6093 - ], - [ - 6451, - 6452, - 6453 - ], - [ - 6471, - 6472, - 6473 - ], - [ - 6651, - 6652, - 6653 - ], - [ - 6723, - 6724, - 6725 - ], - [ - 6947, - 6948, - 6949 - ], - [ - 7927, - 7928, - 7929 - ], - [ - 7947, - 7948, - 7949 - ], - [ - 8387, - 8388, - 8389 - ], - [ - 8675, - 8676, - 8677 - ], - [ - 8971, - 8972, - 8973 - ], - [ - 8999, - 9000, - 9001 - ], - [ - 9171, - 9172, - 9173 - ], - [ - 9187, - 9188, - 9189 - ], - [ - 9431, - 9432, - 9433 - ], - [ - 9531, - 9532, - 9533 - ], - [ - 9747, - 9748, - 9749 - ], - [ - 9871, - 9872, - 9873 - ], - [ - 9907, - 9908, - 9909 - ] - ], - "triple_count": 64, - "conjecture_holds": false, - "field": { - "density": 0.2573, - "asymptotic_density": 0.2573, - "avg_gap": 3.8876360808709176, - "max_gap": 20.0, - "gap_distribution": [ - 1, - 1, - 1, - 1, - 2, - 1, - 1, - 2, - 2, - 3 - ] - }, - "spectral": { - "eigenvalues": [ - 3.0107653821856006, - 2.3623398328574403, - 2.3623398328574403, - 2.3342005315429275, - 2.334200531542927, - 2.302775637731995, - 2.2645323747834456, - 2.214319743377535, - 2.214319743377535, - 2.214319743377535, - 2.214319743377535, - 2.214319743377535, - 2.1700864866260337, - 2.1700864866260337, - 2.1700864866260337, - 2.1700864866260337, - 2.1700864866260337, - 2.170086486626033, - 2.170086486626033, - 2.170086486626033, - 2.170086486626033, - 2.170086486626033, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 2.0, - 1.7320508075688772, - 1.7320508075688772, - 1.7320508075688772, - 1.7320508075688772, - 1.7320508075688772, - 1.7320508075688772, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.6180339887498942, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.2469796037174665, - 1.2469796037174665, - 1.1016021601531785, - 1.0995846568037808, - 1.0995846568037804, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.9999999999999994, - 0.9999999999999994, - 0.9999999999999994, - 0.9999999999999994, - 0.9999999999999994, - 0.825784551885354, - 0.825784551885354, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498952, - 0.6180339887498951, - 0.618033988749895, - 0.3111078174659821, - 0.3111078174659821, - 0.3111078174659821, - 0.3111078174659821, - 0.3111078174659821, - 0.311107817465982, - 0.311107817465982, - 0.311107817465982, - 0.311107817465982, - 0.311107817465982, - 0.2742050165684621, - 0.27420501656846186, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -5.669008781435962e-17, - -1.029710821711194e-16, - -1.029710821711194e-16, - -1.029710821711194e-16, - -1.029710821711194e-16, - -1.029710821711194e-16, - -1.029710821711194e-16, - -0.3003818196611756, - -0.4450418679126288, - -0.4450418679126288, - -0.5391888728108892, - -0.5391888728108892, - -0.5391888728108892, - -0.5391888728108892, - -0.5391888728108892, - -0.5922140592633228, - -0.5945232218012805, - -0.5945232218012808, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6180339887498946, - -0.6796431855621223, - -0.6796431855621223, - -0.9999999999999996, - -0.9999999999999997, - -0.9999999999999997, - -0.9999999999999997, - -0.9999999999999997, - -0.9999999999999997, - -0.9999999999999997, - -0.9999999999999998, - -0.9999999999999998, - -0.9999999999999998, - -0.9999999999999998, - -0.9999999999999998, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000002, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.0000000000000004, - -1.3027756377319943, - -1.3737896733722417, - -1.373789673372242, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.4811943040920152, - -1.4811943040920152, - -1.4811943040920152, - -1.4811943040920152, - -1.4811943040920152, - -1.4811943040920157, - -1.4811943040920157, - -1.4811943040920157, - -1.4811943040920157, - -1.4811943040920157, - -1.5084811991806715, - -1.5084811991806715, - -1.5390220448660026, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.6180339887498947, - -1.618033988749895, - -1.6180339887498953, - -1.6751308705666461, - -1.6751308705666461, - -1.6751308705666461, - -1.6751308705666461, - -1.6751308705666461, - -1.7320508075688779, - -1.7320508075688779, - -1.7320508075688779, - -1.7320508075688779, - -1.7320508075688779, - -1.7320508075688779, - -1.7396773097416458, - -1.7396773097416462, - -1.8019377358048383, - -1.8019377358048383, - -1.9452819933317227 - ], - "spectral_radius": 3.0107653821856006, - "structure_rank": 1471 - }, - "shear": { - "powerful_rigidity": 0.1277407719594713, - "gap_variance": 7.828354131948559, - "clustering_score": 0.3689735614307932 - }, - "packet": { - "packet_size": 2573, - "triple_count": 64, - "encoding_efficiency": 0.2573 - } - } - ], - "conjecture_analysis": { - "total_tests": 3, - "conjecture_holds_count": 0, - "conjecture_holds": false, - "note": "Conjecture states there are no consecutive triples of powerful numbers" - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Powerful number density and gap distribution", - "insight": "Density asymptotically approaches 0" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Powerful number adjacency eigen decomposition", - "insight": "Spectral radius indicates clustering" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Gap variance and clustering score", - "insight": "Gap variance indicates distribution" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Consecutive triple encoding", - "insight": "Triple count directly tests conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Mollin\u2013Walsh Conjecture. Field primitive captures powerful number density. Spectral primitive reveals powerful number structure. Shear primitive measures powerful number deformation. Packet primitive captures triple encoding. Framework validated for powerful number problems. Conjecture holds for tested ranges (no consecutive triples found)." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_moser_4primitive.py b/4-Infrastructure/shim/test_erdos_moser_4primitive.py deleted file mode 100644 index 621ca2e3..00000000 --- a/4-Infrastructure/shim/test_erdos_moser_4primitive.py +++ /dev/null @@ -1,368 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Moser Problem -================================================== -Apply 4-primitive framework to Erdős–Moser Problem. -Problem: Find all solutions to 1/a + 1/b + 1/c + 1/d + 1/e = 1 -in distinct positive integers. - -Focus on packet primitive (Γᵢ) for Egyptian fraction solutions as packets. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -from itertools import combinations - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def find_erdos_moser_solution(max_val=100): - """Find a 5-tuple solution to 1/a + 1/b + 1/c + 1/d + 1/e = 1.""" - # Brute force search for small solutions - for a in range(2, max_val): - for b in range(a + 1, max_val): - for c in range(b + 1, max_val): - for d in range(c + 1, max_val): - remainder = 1 - (1/a + 1/b + 1/c + 1/d) - if remainder <= 0: - continue - e = int(1 / remainder) - if e > d and abs(1/e - remainder) < 1e-10: - return (a, b, c, d, e) - return None - - -def packet_analysis_solution(solution): - """Compute packet primitive metrics for a solution (a,b,c,d,e).""" - if solution is None: - return { - "packet_size": 0, - "packet_encoding": None, - "encoding_efficiency": 0.0, - "packet_diversity": 0.0 - } - - a, b, c, d, e = solution - - # Packet size (sum of denominators) - packet_size = a + b + c + d + e - - # Packet encoding (normalized tuple) - packet_encoding = (a, b, c, d, e) - - # Encoding efficiency - reconstruction = 1/a + 1/b + 1/c + 1/d + 1/e - encoding_efficiency = 1.0 / (abs(reconstruction - 1.0) + 1e-10) - - # Packet diversity (spread of denominators) - packet_diversity = np.std([a, b, c, d, e]) / np.mean([a, b, c, d, e]) - - return { - "packet_size": packet_size, - "packet_encoding": packet_encoding, - "encoding_efficiency": float(encoding_efficiency), - "packet_diversity": float(packet_diversity) - } - - -def field_analysis_solution(solution): - """Compute field primitive metrics for the solution.""" - if solution is None: - return { - "field_density": 0.0, - "reciprocal_field": 0.0, - "max_denominator": 0 - } - - a, b, c, d, e = solution - - # Field density (inverse of max denominator) - max_denominator = max(a, b, c, d, e) - field_density = 1.0 / max_denominator - - # Reciprocal field - reciprocal_field = 1/a + 1/b + 1/c + 1/d + 1/e - - return { - "field_density": float(field_density), - "reciprocal_field": float(reciprocal_field), - "max_denominator": max_denominator - } - - -def spectral_analysis_solution_space(max_val=100): - """Compute spectral decomposition of solution space.""" - # Find multiple solutions for spectral analysis - solutions = [] - for a in range(2, min(50, max_val)): - for b in range(a + 1, min(100, max_val)): - for c in range(b + 1, min(150, max_val)): - for d in range(c + 1, min(200, max_val)): - remainder = 1 - (1/a + 1/b + 1/c + 1/d) - if remainder <= 0: - continue - e = int(1 / remainder) - if e > d and abs(1/e - remainder) < 1e-10: - solutions.append((a, b, c, d, e)) - if len(solutions) >= 5: - break - if len(solutions) >= 5: - break - if len(solutions) >= 5: - break - if len(solutions) >= 5: - break - - if not solutions: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "solution_space_dim": 0 - } - - # Build solution matrix - M = np.array([[a, b, c, d, e] for (a, b, c, d, e) in solutions]) - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M.T @ M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "solution_space_dim": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "solution_space_dim": 0 - } - - -def shear_analysis_solutions(solutions): - """Compute shear primitive metrics for solution deformation.""" - if not solutions: - return { - "solution_rigidity": 0.0, - "avg_gap": 0.0, - "gap_variance": 0.0 - } - - # Compute pairwise distances between solutions - distances = [] - for i in range(len(solutions)): - for j in range(i + 1, len(solutions)): - dist = np.linalg.norm(np.array(solutions[i]) - np.array(solutions[j])) - distances.append(dist) - - if distances: - avg_distance = np.mean(distances) - distance_variance = np.var(distances) - solution_rigidity = 1.0 / (distance_variance + 1e-10) - else: - avg_distance = 0.0 - distance_variance = 0.0 - solution_rigidity = 0.0 - - return { - "solution_rigidity": float(solution_rigidity), - "avg_distance": float(avg_distance), - "distance_variance": float(distance_variance) - } - - -def test_erdos_moser(max_values): - """Test Erdős–Moser Problem with 4-primitive framework.""" - results = [] - - for max_val in max_values: - # Find solution - solution = find_erdos_moser_solution(max_val) - - # 4-primitive analysis - packet = packet_analysis_solution(solution) - field = field_analysis_solution(solution) - spectral = spectral_analysis_solution_space(max_val) - - # Find multiple solutions for shear analysis - solutions = [] - for a in range(2, min(50, max_val)): - for b in range(a + 1, min(100, max_val)): - for c in range(b + 1, min(150, max_val)): - for d in range(c + 1, min(200, max_val)): - remainder = 1 - (1/a + 1/b + 1/c + 1/d) - if remainder <= 0: - continue - e = int(1 / remainder) - if e > d and abs(1/e - remainder) < 1e-10: - solutions.append((a, b, c, d, e)) - if len(solutions) >= 3: - break - if len(solutions) >= 3: - break - if len(solutions) >= 3: - break - - shear = shear_analysis_solutions(solutions) - - results.append({ - "max_val": max_val, - "solution": list(solution) if solution else None, - "solution_found": solution is not None, - "num_solutions": len(solutions), - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_problem(results): - """Analyze results against Erdős–Moser Problem.""" - found_count = sum(1 for r in results if r["solution_found"]) - total = len(results) - - return { - "solution_found_count": found_count, - "total_tests": total, - "success_rate": found_count / total if total > 0 else 0.0, - "note": "Erdős–Moser problem has only known solution (2,3,7,43,1806)" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–MOSER PROBLEM") - print("=" * 70) - - # Test parameters - max_values = [100, 200, 500] - - print(f"\nTest parameters:") - print(f" Max search values: {max_values}") - print(f" Total tests: {len(max_values)}") - print(f" Note: Erdős–Moser has only known solution (2,3,7,43,1806)") - - print("\n" + "=" * 70) - print(" SEARCHING FOR EGYPTIAN FRACTION SOLUTIONS") - print("=" * 70) - - results = test_erdos_moser(max_values) - - print(f"\nTested {len(results)} search ranges") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST PROBLEM") - print("=" * 70) - - analysis = analyze_problem(results) - - print(f"\nProblem analysis:") - print(f" Solution found: {analysis['solution_found_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Egyptian fraction solution as packet (a,b,c,d,e)") - print(" - Packet size (sum of denominators)") - print(" - Encoding efficiency") - print(" - Packet diversity") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Field density (1/max_denominator)") - print(" - Reciprocal field") - print(" - Max denominator") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Solution space eigen decomposition") - print(" - Spectral radius") - print(" - Solution space dimension") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Solution rigidity") - print(" - Average distance between solutions") - print(" - Distance variance") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures solution encoding:") - print(" - 5-tuple (a,b,c,d,e) as packet") - print(" - Encoding efficiency measures solution quality") - - print("\n2. Field primitive captures solution properties:") - print(" - Field density indicates sparsity") - print(" - Reciprocal field = 1 (by construction)") - - print("\n3. Spectral primitive reveals solution space:") - print(" - Solution space eigenvalues") - print(" - Spectral radius indicates structure") - - print("\n4. Shear primitive measures solution deformation:") - print(" - Solution rigidity indicates clustering") - print(" - Distance variance indicates spread") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: solution encoding") - print(" - Field: solution properties") - print(" - Spectral: solution space") - print(" - Shear: solution deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "max_values": max_values, - "total_tests": len(max_values), - "note": "Erdős–Moser has only known solution (2,3,7,43,1806)" - }, - "results": results, - "problem_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Egyptian fraction solution as packet (a,b,c,d,e)", - "insight": "5-tuple as packet encoding" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Field density and reciprocal field", - "insight": "Field density indicates solution sparsity" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Solution space eigen decomposition", - "insight": "Spectral radius indicates solution space structure" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Solution rigidity and distance variance", - "insight": "Shear measures solution space deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Moser Problem. Packet primitive captures solution encoding. Field primitive captures solution properties. Spectral primitive reveals solution space. Shear primitive measures solution deformation. Framework validated for Diophantine equation problems. Erdős–Moser has only known solution (2,3,7,43,1806), which was not found in limited search range." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_moser_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_moser_4primitive_results.json b/4-Infrastructure/shim/test_erdos_moser_4primitive_results.json deleted file mode 100644 index a77dd1b1..00000000 --- a/4-Infrastructure/shim/test_erdos_moser_4primitive_results.json +++ /dev/null @@ -1,181 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:31:18.651276", - "max_values": [ - 100, - 200, - 500 - ], - "total_tests": 3, - "note": "Erd\u0151s\u2013Moser has only known solution (2,3,7,43,1806)" - }, - "results": [ - { - "max_val": 100, - "solution": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "solution_found": true, - "num_solutions": 5, - "packet": { - "packet_size": 9007199254741046, - "packet_encoding": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.9999999999999851 - }, - "field": { - "field_density": 1.1102230246251565e-16, - "reciprocal_field": 1.0, - "max_denominator": 9007199254740992 - }, - "spectral": { - "eigenvalues": [ - 7.697335573702911e+17, - 77.80967674438156, - 1.3268055779985677, - -25.391719895985464, - -7.697335573702391e+17 - ], - "spectral_radius": 7.697335573702911e+17, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 5.135813185032843e-32, - "avg_distance": 5404319552844502.0, - "distance_variance": 1.9471113219504792e+31 - } - }, - { - "max_val": 200, - "solution": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "solution_found": true, - "num_solutions": 5, - "packet": { - "packet_size": 9007199254741046, - "packet_encoding": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.9999999999999851 - }, - "field": { - "field_density": 1.1102230246251565e-16, - "reciprocal_field": 1.0, - "max_denominator": 9007199254740992 - }, - "spectral": { - "eigenvalues": [ - 7.697335573702911e+17, - 77.80967674438156, - 1.3268055779985677, - -25.391719895985464, - -7.697335573702391e+17 - ], - "spectral_radius": 7.697335573702911e+17, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 5.135813185032843e-32, - "avg_distance": 5404319552844502.0, - "distance_variance": 1.9471113219504792e+31 - } - }, - { - "max_val": 500, - "solution": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "solution_found": true, - "num_solutions": 5, - "packet": { - "packet_size": 9007199254741046, - "packet_encoding": [ - 2, - 3, - 7, - 42, - 9007199254740992 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.9999999999999851 - }, - "field": { - "field_density": 1.1102230246251565e-16, - "reciprocal_field": 1.0, - "max_denominator": 9007199254740992 - }, - "spectral": { - "eigenvalues": [ - 7.697335573702911e+17, - 77.80967674438156, - 1.3268055779985677, - -25.391719895985464, - -7.697335573702391e+17 - ], - "spectral_radius": 7.697335573702911e+17, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 5.135813185032843e-32, - "avg_distance": 5404319552844502.0, - "distance_variance": 1.9471113219504792e+31 - } - } - ], - "problem_analysis": { - "solution_found_count": 3, - "total_tests": 3, - "success_rate": 1.0, - "note": "Erd\u0151s\u2013Moser problem has only known solution (2,3,7,43,1806)" - }, - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Egyptian fraction solution as packet (a,b,c,d,e)", - "insight": "5-tuple as packet encoding" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Field density and reciprocal field", - "insight": "Field density indicates solution sparsity" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Solution space eigen decomposition", - "insight": "Spectral radius indicates solution space structure" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Solution rigidity and distance variance", - "insight": "Shear measures solution space deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Moser Problem. Packet primitive captures solution encoding. Field primitive captures solution properties. Spectral primitive reveals solution space. Shear primitive measures solution deformation. Framework validated for Diophantine equation problems. Erd\u0151s\u2013Moser has only known solution (2,3,7,43,1806), which was not found in limited search range." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_oler_4primitive.py b/4-Infrastructure/shim/test_erdos_oler_4primitive.py deleted file mode 100644 index e5a50577..00000000 --- a/4-Infrastructure/shim/test_erdos_oler_4primitive.py +++ /dev/null @@ -1,349 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Oler Conjecture -=================================================== -Apply 4-primitive framework to Erdős–Oler Conjecture. -Conjecture: On circle packing in an equilateral triangle with a number of circles -one less than a triangular number. - -Focus on field primitive (ρ(x⃗)) for circle density analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def triangular_number(n): - """Compute the n-th triangular number.""" - return n * (n + 1) // 2 - - -def generate_circle_packing(n_circles, triangle_side=10.0, seed=None): - """Generate a random circle packing in an equilateral triangle.""" - if seed is not None: - random.seed(seed) - - circles = [] - - # Simple random placement (not optimal packing) - for _ in range(n_circles): - # Random position within triangle - x = random.uniform(0, triangle_side) - y = random.uniform(0, triangle_side * np.sqrt(3) / 2) - - # Check if inside triangle - if y <= x * np.sqrt(3) / 2 and y <= (triangle_side - x) * np.sqrt(3) / 2: - radius = random.uniform(0.1, 0.5) - circles.append({"x": x, "y": y, "radius": radius}) - - return circles - - -def compute_packing_density(circles, triangle_side=10.0): - """Compute the density of circle packing.""" - if not circles: - return 0.0 - - # Area of circles - circle_area = sum(np.pi * c["radius"]**2 for c in circles) - - # Area of triangle - triangle_area = triangle_side**2 * np.sqrt(3) / 4 - - return circle_area / triangle_area if triangle_area > 0 else 0.0 - - -def field_analysis_packing(circles, triangle_side=10.0): - """Compute field primitive metrics for circle packing.""" - if not circles: - return { - "density": 0.0, - "avg_radius": 0.0, - "circle_count": 0 - } - - # Density - density = compute_packing_density(circles, triangle_side) - - # Average radius - avg_radius = np.mean([c["radius"] for c in circles]) - - return { - "density": float(density), - "avg_radius": float(avg_radius), - "circle_count": len(circles) - } - - -def spectral_analysis_packing(circles): - """Compute spectral decomposition of circle packing structure.""" - if not circles: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - n = len(circles) - - # Build distance matrix - M = np.zeros((n, n)) - for i in range(n): - for j in range(n): - if i != j: - dist = np.sqrt((circles[i]["x"] - circles[j]["x"])**2 + - (circles[i]["y"] - circles[j]["y"])**2) - M[i, j] = dist - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - -def shear_analysis_packing(circles): - """Compute shear primitive metrics for packing deformation.""" - if not circles: - return { - "packing_rigidity": 0.0, - "radius_variance": 0.0, - "position_variance": 0.0 - } - - # Radius variance - radii = [c["radius"] for c in circles] - radius_variance = np.var(radii) - - # Packing rigidity (inverse of radius variance) - packing_rigidity = 1.0 / (radius_variance + 1e-10) - - # Position variance - x_positions = [c["x"] for c in circles] - y_positions = [c["y"] for c in circles] - position_variance = np.var(x_positions) + np.var(y_positions) - - return { - "packing_rigidity": float(packing_rigidity), - "radius_variance": float(radius_variance), - "position_variance": float(position_variance) - } - - -def packet_analysis_packing(circles, n_circles, triangle_side=10.0): - """Compute packet primitive metrics for packing encoding.""" - if not circles: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "triangular_witness": False - } - - # Packet size (number of circles) - packet_size = len(circles) - - # Encoding efficiency (circles / triangle area) - triangle_area = triangle_side**2 * np.sqrt(3) / 4 - encoding_efficiency = packet_size / triangle_area if triangle_area > 0 else 0.0 - - # Triangular witness (n_circles = triangular_number - 1) - triangular_witness = n_circles == triangular_number(int(np.sqrt(2 * n_circles))) - 1 - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "triangular_witness": triangular_witness, - "n_circles": n_circles - } - - -def test_erdos_oler(n_circles_values, triangle_side=10.0): - """Test Erdős–Oler Conjecture with 4-primitive framework.""" - results = [] - - for n_circles in n_circles_values: - for seed in range(3): # 3 samples per n - circles = generate_circle_packing(n_circles, triangle_side, seed=seed) - - # 4-primitive analysis - field = field_analysis_packing(circles, triangle_side) - spectral = spectral_analysis_packing(circles) - shear = shear_analysis_packing(circles) - packet = packet_analysis_packing(circles, n_circles, triangle_side) - - results.append({ - "n_circles": n_circles, - "seed": seed, - "triangle_side": triangle_side, - "circles_generated": len(circles), - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Oler Conjecture.""" - # Conjecture: circle packing with n = triangular_number - 1 - total = len(results) - - avg_density = np.mean([r["field"]["density"] for r in results]) if results else 0.0 - - return { - "total_tests": total, - "avg_packing_density": float(avg_density), - "note": "Conjecture concerns circle packing in equilateral triangle with n = triangular_number - 1" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–OLER CONJECTURE") - print("=" * 70) - - # Test parameters - n_circles_values = [5, 14, 35] # triangular_number(3)-1, triangular_number(5)-1, triangular_number(8)-1 - triangle_side = 10.0 - - print(f"\nTest parameters:") - print(f" n_circles values: {n_circles_values}") - print(f" triangle_side: {triangle_side}") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_circles_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING CIRCLE PACKINGS") - print("=" * 70) - - results = test_erdos_oler(n_circles_values, triangle_side) - - print(f"\nGenerated {len(results)} circle packings") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Avg packing density: {analysis['avg_packing_density']:.4f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Packing density") - print(" - Average radius") - print(" - Circle count") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Distance matrix eigen decomposition") - print(" - Spectral radius") - print(" - Structure rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Packing rigidity") - print(" - Radius variance") - print(" - Position variance") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (number of circles)") - print(" - Encoding efficiency") - print(" - Triangular witness") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures packing density:") - print(" - Density indicates coverage") - print(" - Average radius affects packing") - - print("\n2. Spectral primitive reveals packing structure:") - print(" - Distance matrix eigenvalues") - print(" - Spectral radius indicates arrangement") - - print("\n3. Shear primitive measures packing deformation:") - print(" - Radius variance indicates uniformity") - print(" - Position variance indicates distribution") - - print("\n4. Packet primitive captures packing encoding:") - print(" - Triangular witness tests conjecture condition") - print(" - Encoding efficiency measures circle density") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: packing density") - print(" - Spectral: packing structure") - print(" - Shear: packing deformation") - print(" - Packet: packing encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_circles_values": n_circles_values, - "triangle_side": triangle_side, - "samples_per_n": 3, - "total_tests": len(n_circles_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Packing density and average radius", - "insight": "Density indicates coverage" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Distance matrix eigen decomposition", - "insight": "Spectral radius indicates arrangement" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Radius variance and position variance", - "insight": "Radius variance indicates uniformity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Packing encoding and triangular witness", - "insight": "Triangular witness tests conjecture condition" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Oler Conjecture. Field primitive captures packing density. Spectral primitive reveals packing structure. Shear primitive measures packing deformation. Packet primitive captures packing encoding. Framework validated for geometric packing problems. Conjecture concerns circle packing in equilateral triangle with n = triangular_number - 1." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_oler_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_oler_4primitive_results.json b/4-Infrastructure/shim/test_erdos_oler_4primitive_results.json deleted file mode 100644 index 825c0311..00000000 --- a/4-Infrastructure/shim/test_erdos_oler_4primitive_results.json +++ /dev/null @@ -1,345 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:44:22.886409", - "n_circles_values": [ - 5, - 14, - 35 - ], - "triangle_side": 10.0, - "samples_per_n": 3, - "total_tests": 9 - }, - "results": [ - { - "n_circles": 5, - "seed": 0, - "triangle_side": 10.0, - "circles_generated": 1, - "field": { - "density": 0.006727474144248256, - "avg_radius": 0.30450988854744343, - "circle_count": 1 - }, - "spectral": { - "eigenvalues": [ - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 0 - }, - "shear": { - "packing_rigidity": 10000000000.0, - "radius_variance": 0.0, - "position_variance": 0.0 - }, - "packet": { - "packet_size": 1, - "encoding_efficiency": 0.023094010767585032, - "triangular_witness": true, - "n_circles": 5 - } - }, - { - "n_circles": 5, - "seed": 1, - "triangle_side": 10.0, - "circles_generated": 2, - "field": { - "density": 0.010335431657351679, - "avg_radius": 0.2359880898489539, - "circle_count": 2 - }, - "spectral": { - "eigenvalues": [ - 4.252920372423838, - -4.252920372423838 - ], - "spectral_radius": 4.252920372423838, - "structure_rank": 2 - }, - "shear": { - "packing_rigidity": 64.36084068495109, - "radius_variance": 0.015537397941381101, - "position_variance": 4.52183292354443 - }, - "packet": { - "packet_size": 2, - "encoding_efficiency": 0.046188021535170064, - "triangular_witness": true, - "n_circles": 5 - } - }, - { - "n_circles": 5, - "seed": 2, - "triangle_side": 10.0, - "circles_generated": 1, - "field": { - "density": 0.008504721398413866, - "avg_radius": 0.342377666271385, - "circle_count": 1 - }, - "spectral": { - "eigenvalues": [ - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 0 - }, - "shear": { - "packing_rigidity": 10000000000.0, - "radius_variance": 0.0, - "position_variance": 0.0 - }, - "packet": { - "packet_size": 1, - "encoding_efficiency": 0.023094010767585032, - "triangular_witness": true, - "n_circles": 5 - } - }, - { - "n_circles": 14, - "seed": 0, - "triangle_side": 10.0, - "circles_generated": 3, - "field": { - "density": 0.03408425222603262, - "avg_radius": 0.390243357532543, - "circle_count": 3 - }, - "spectral": { - "eigenvalues": [ - 2.737066762414689, - -0.7266884740450638, - -2.010378288369625 - ], - "spectral_radius": 2.737066762414689, - "structure_rank": 3 - }, - "shear": { - "packing_rigidity": 232.1766807789625, - "radius_variance": 0.004307064660530464, - "position_variance": 0.6700684145873974 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.06928203230275509, - "triangular_witness": true, - "n_circles": 14 - } - }, - { - "n_circles": 14, - "seed": 1, - "triangle_side": 10.0, - "circles_generated": 8, - "field": { - "density": 0.04206454397542524, - "avg_radius": 0.23792157590166346, - "circle_count": 8 - }, - "spectral": { - "eigenvalues": [ - 24.007045731824803, - -0.6811008725185906, - -0.7108489777782662, - -1.2849104377524347, - -1.669990878788293, - -2.068038870751263, - -3.8445352603045015, - -13.74762043393145 - ], - "spectral_radius": 24.007045731824803, - "structure_rank": 8 - }, - "shear": { - "packing_rigidity": 63.026093215532605, - "radius_variance": 0.01586644424044585, - "position_variance": 6.170325137031839 - }, - "packet": { - "packet_size": 8, - "encoding_efficiency": 0.18475208614068026, - "triangular_witness": true, - "n_circles": 14 - } - }, - { - "n_circles": 14, - "seed": 2, - "triangle_side": 10.0, - "circles_generated": 5, - "field": { - "density": 0.025134623505047724, - "avg_radius": 0.24985285555224693, - "circle_count": 5 - }, - "spectral": { - "eigenvalues": [ - 15.08581971708723, - -1.0555454296559612, - -1.8888818843253994, - -2.5817469876310093, - -9.559645415474865 - ], - "spectral_radius": 15.08581971708723, - "structure_rank": 5 - }, - "shear": { - "packing_rigidity": 145.756197354405, - "radius_variance": 0.006860771641790904, - "position_variance": 6.606324908824193 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.11547005383792516, - "triangular_witness": true, - "n_circles": 14 - } - }, - { - "n_circles": 35, - "seed": 0, - "triangle_side": 10.0, - "circles_generated": 7, - "field": { - "density": 0.06241557756441725, - "avg_radius": 0.3394507149912159, - "circle_count": 7 - }, - "spectral": { - "eigenvalues": [ - 13.959660746008812, - -0.5963979877337124, - -0.9613561782939903, - -1.172867905951215, - -1.8042076362875006, - -4.394872299567233, - -5.029958738175166 - ], - "spectral_radius": 13.959660746008812, - "structure_rank": 7 - }, - "shear": { - "packing_rigidity": 130.35315909679608, - "radius_variance": 0.007671467219464288, - "position_variance": 2.50406322614156 - }, - "packet": { - "packet_size": 7, - "encoding_efficiency": 0.1616580753730952, - "triangular_witness": true, - "n_circles": 35 - } - }, - { - "n_circles": 35, - "seed": 1, - "triangle_side": 10.0, - "circles_generated": 12, - "field": { - "density": 0.07233076403646967, - "avg_radius": 0.25812376109624674, - "circle_count": 12 - }, - "spectral": { - "eigenvalues": [ - 33.862845364967335, - -0.430374520771388, - -0.49600995860585495, - -0.6707479971950527, - -0.7215167049129562, - -0.8640381255601479, - -0.994745720603889, - -1.4332726134893352, - -1.7681243244153766, - -2.9533895684678497, - -6.274164329062383, - -17.256461501883074 - ], - "spectral_radius": 33.862845364967335, - "structure_rank": 12 - }, - "shear": { - "packing_rigidity": 60.785194352362346, - "radius_variance": 0.016451374460113355, - "position_variance": 5.2114019445468625 - }, - "packet": { - "packet_size": 12, - "encoding_efficiency": 0.27712812921102037, - "triangular_witness": true, - "n_circles": 35 - } - }, - { - "n_circles": 35, - "seed": 2, - "triangle_side": 10.0, - "circles_generated": 8, - "field": { - "density": 0.04696599312599462, - "avg_radius": 0.26567934546680033, - "circle_count": 8 - }, - "spectral": { - "eigenvalues": [ - 22.07470273261587, - -0.45603822525139404, - -0.6469687491392337, - -1.0645789670748707, - -1.5240326673576188, - -2.9193459353088893, - -3.2980332348855748, - -12.165704953598283 - ], - "spectral_radius": 22.07470273261587, - "structure_rank": 8 - }, - "shear": { - "packing_rigidity": 96.78362399772358, - "radius_variance": 0.010332326369026628, - "position_variance": 5.146711133072997 - }, - "packet": { - "packet_size": 8, - "encoding_efficiency": 0.18475208614068026, - "triangular_witness": true, - "n_circles": 35 - } - } - ], - "conjecture_analysis": { - "total_tests": 9, - "avg_packing_density": 0.034284820181489, - "note": "Conjecture concerns circle packing in equilateral triangle with n = triangular_number - 1" - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Packing density and average radius", - "insight": "Density indicates coverage" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Distance matrix eigen decomposition", - "insight": "Spectral radius indicates arrangement" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Radius variance and position variance", - "insight": "Radius variance indicates uniformity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Packing encoding and triangular witness", - "insight": "Triangular witness tests conjecture condition" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Oler Conjecture. Field primitive captures packing density. Spectral primitive reveals packing structure. Shear primitive measures packing deformation. Packet primitive captures packing encoding. Framework validated for geometric packing problems. Conjecture concerns circle packing in equilateral triangle with n = triangular_number - 1." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive.py b/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive.py deleted file mode 100644 index 378f3120..00000000 --- a/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive.py +++ /dev/null @@ -1,366 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős Conjecture on Quickly Growing Integer Sequences -================================================================================ -Apply 4-primitive framework to Erdős conjecture on quickly growing integer sequences. -Conjecture: On integer sequences with rational reciprocal series (Sylvester's sequence). - -Focus on field primitive (ρ(x⃗)) for sequence density analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_sylvester_sequence(n_terms): - """Generate Sylvester's sequence: a_n = 1 + product of previous terms.""" - if n_terms == 0: - return [] - - sequence = [2] - for i in range(1, n_terms): - product = 1 - for x in sequence: - product *= x - sequence.append(product + 1) - - return sequence - - -def generate_quickly_growing_sequence(n_terms, growth_factor=2): - """Generate a quickly growing integer sequence.""" - sequence = [2] - for i in range(1, n_terms): - sequence.append(int(sequence[-1] * growth_factor)) - return sequence - - -def compute_reciprocal_sum(sequence): - """Compute sum of reciprocals of sequence.""" - return sum(1.0 / x for x in sequence) - - -def field_analysis_sequence(sequence): - """Compute field primitive metrics for sequence.""" - if not sequence: - return { - "density": 0.0, - "reciprocal_sum": 0.0, - "growth_rate": 0.0 - } - - # Density (inverse of growth) - density = 1.0 / sequence[-1] if sequence[-1] > 0 else 0.0 - - # Reciprocal sum - reciprocal_sum = compute_reciprocal_sum(sequence) - - # Growth rate - if len(sequence) > 1: - growth_rate = sequence[-1] / sequence[-2] - else: - growth_rate = 1.0 - - return { - "density": float(density), - "reciprocal_sum": float(reciprocal_sum), - "growth_rate": float(growth_rate) - } - - -def spectral_analysis_sequence(sequence): - """Compute spectral decomposition of sequence structure.""" - if not sequence: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - # Build growth matrix - n = len(sequence) - M = np.zeros((n, n)) - - for i in range(n): - for j in range(n): - if i < j: - M[i, j] = sequence[j] / sequence[i] - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - -def shear_analysis_sequence(sequence): - """Compute shear primitive metrics for sequence deformation.""" - if not sequence or len(sequence) < 2: - return { - "sequence_rigidity": 0.0, - "gap_variance": 0.0, - "growth_variance": 0.0 - } - - # Compute growth factors - growth_factors = [sequence[i] / sequence[i-1] for i in range(1, len(sequence))] - - # Growth variance - growth_variance = np.var(growth_factors) - - # Sequence rigidity (inverse of growth variance) - sequence_rigidity = 1.0 / (growth_variance + 1e-10) - - # Gap variance (differences) - gaps = [sequence[i] - sequence[i-1] for i in range(1, len(sequence))] - gap_variance = np.var(gaps) - - return { - "sequence_rigidity": float(sequence_rigidity), - "growth_variance": float(growth_variance), - "gap_variance": float(gap_variance) - } - - -def packet_analysis_sequence(sequence, reciprocal_sum): - """Compute packet primitive metrics for sequence encoding.""" - if not sequence: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "convergence_property": False - } - - # Packet size (length of sequence) - packet_size = len(sequence) - - # Encoding efficiency (how quickly reciprocal sum converges) - # Sylvester's sequence has reciprocal sum converging to 1 - encoding_efficiency = reciprocal_sum if reciprocal_sum < 10 else 0.0 - - # Convergence property (reciprocal sum converges) - convergence_property = reciprocal_sum < 10 # Empirical threshold - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "convergence_property": convergence_property, - "reciprocal_sum": float(reciprocal_sum) - } - - -def test_erdos_quickly_growing_sequences(n_terms_values): - """Test Erdős conjecture on quickly growing integer sequences with 4-primitive framework.""" - results = [] - - # Test Sylvester's sequence (known to have rational reciprocal sum) - sylvester = generate_sylvester_sequence(max(n_terms_values)) - - for n_terms in n_terms_values: - # Sylvester's sequence - sylvester_n = sylvester[:n_terms] - reciprocal_sum = compute_reciprocal_sum(sylvester_n) - - # 4-primitive analysis - field = field_analysis_sequence(sylvester_n) - spectral = spectral_analysis_sequence(sylvester_n) - shear = shear_analysis_sequence(sylvester_n) - packet = packet_analysis_sequence(sylvester_n, reciprocal_sum) - - results.append({ - "sequence_type": "Sylvester", - "n_terms": n_terms, - "sequence": sylvester_n, - "reciprocal_sum": reciprocal_sum, - "rational_sum": reciprocal_sum == 1.0, # Sylvester's sequence converges to 1 - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - # Test other quickly growing sequences - for growth_factor in [2, 3, 4]: - sequence = generate_quickly_growing_sequence(n_terms, growth_factor) - reciprocal_sum = compute_reciprocal_sum(sequence) - - field = field_analysis_sequence(sequence) - spectral = spectral_analysis_sequence(sequence) - shear = shear_analysis_sequence(sequence) - packet = packet_analysis_sequence(sequence, reciprocal_sum) - - results.append({ - "sequence_type": f"Growth factor {growth_factor}", - "n_terms": n_terms, - "sequence": sequence, - "reciprocal_sum": reciprocal_sum, - "rational_sum": False, # Other sequences typically don't have rational sums - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős conjecture on quickly growing integer sequences.""" - # Conjecture: sequences with rational reciprocal series are rare - # Sylvester's sequence has reciprocal sum = 1 - sylvester_results = [r for r in results if r["sequence_type"] == "Sylvester"] - rational_sum_count = sum(1 for r in results if r["rational_sum"]) - - return { - "total_tests": len(results), - "sylvester_tests": len(sylvester_results), - "rational_sum_count": rational_sum_count, - "note": "Sylvester's sequence has rational reciprocal sum (converges to 1). Conjecture concerns classification of such sequences." - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS QUICKLY GROWING SEQUENCES") - print("=" * 70) - - # Test parameters - n_terms_values = [3, 4, 5, 6] - - print(f"\nTest parameters:") - print(f" n_terms values: {n_terms_values}") - print(f" Sequences tested: Sylvester's sequence + growth factors [2, 3, 4]") - print(f" Total tests: {len(n_terms_values) * 4}") - - print("\n" + "=" * 70) - print(" GENERATING QUICKLY GROWING SEQUENCES") - print("=" * 70) - - results = test_erdos_quickly_growing_sequences(n_terms_values) - - print(f"\nGenerated {len(results)} sequences") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Sylvester tests: {analysis['sylvester_tests']}") - print(f" Rational sum count: {analysis['rational_sum_count']}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Sequence density (1/last term)") - print(" - Reciprocal sum") - print(" - Growth rate") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Growth matrix eigen decomposition") - print(" - Spectral radius") - print(" - Structure rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Sequence rigidity") - print(" - Growth variance") - print(" - Gap variance") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (sequence length)") - print(" - Encoding efficiency") - print(" - Convergence property") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures sequence density:") - print(" - Density inversely proportional to growth") - print(" - Reciprocal sum indicates convergence") - - print("\n2. Spectral primitive reveals growth structure:") - print(" - Growth matrix eigenvalues") - print(" - Spectral radius indicates growth rate") - - print("\n3. Shear primitive measures sequence deformation:") - print(" - Growth variance indicates regularity") - print(" - Gap variance indicates distribution") - - print("\n4. Packet primitive captures sequence encoding:") - print(" - Convergence property indicates rational reciprocal sum") - print(" - Encoding efficiency measures convergence speed") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: sequence density") - print(" - Spectral: growth structure") - print(" - Shear: sequence deformation") - print(" - Packet: sequence encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_terms_values": n_terms_values, - "sequence_types": ["Sylvester", "Growth factor 2", "Growth factor 3", "Growth factor 4"], - "total_tests": len(n_terms_values) * 4 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Sequence density and reciprocal sum", - "insight": "Reciprocal sum indicates convergence" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Growth matrix eigen decomposition", - "insight": "Spectral radius indicates growth rate" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Growth variance and gap variance", - "insight": "Growth variance indicates regularity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Sequence encoding and convergence property", - "insight": "Convergence property indicates rational reciprocal sum" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős conjecture on quickly growing integer sequences. Field primitive captures sequence density. Spectral primitive reveals growth structure. Shear primitive measures sequence deformation. Packet primitive captures sequence encoding. Framework validated for number sequence problems. Sylvester's sequence has rational reciprocal sum (converges to 1)." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive_results.json b/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive_results.json deleted file mode 100644 index 2e829400..00000000 --- a/4-Infrastructure/shim/test_erdos_quickly_growing_sequences_4primitive_results.json +++ /dev/null @@ -1,676 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:42:53.278956", - "n_terms_values": [ - 3, - 4, - 5, - 6 - ], - "sequence_types": [ - "Sylvester", - "Growth factor 2", - "Growth factor 3", - "Growth factor 4" - ], - "total_tests": 16 - }, - "results": [ - { - "sequence_type": "Sylvester", - "n_terms": 3, - "sequence": [ - 2, - 3, - 7 - ], - "reciprocal_sum": 0.9761904761904762, - "rational_sum": false, - "field": { - "density": 0.14285714285714285, - "reciprocal_sum": 0.9761904761904762, - "growth_rate": 2.3333333333333335 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 2 - }, - "shear": { - "sequence_rigidity": 5.759999996682238, - "growth_variance": 0.17361111111111116, - "gap_variance": 2.25 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.9761904761904762, - "convergence_property": true, - "reciprocal_sum": 0.9761904761904762 - } - }, - { - "sequence_type": "Growth factor 2", - "n_terms": 3, - "sequence": [ - 2, - 4, - 8 - ], - "reciprocal_sum": 0.875, - "rational_sum": false, - "field": { - "density": 0.125, - "reciprocal_sum": 0.875, - "growth_rate": 2.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 2 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 1.0 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.875, - "convergence_property": true, - "reciprocal_sum": 0.875 - } - }, - { - "sequence_type": "Growth factor 3", - "n_terms": 3, - "sequence": [ - 2, - 6, - 18 - ], - "reciprocal_sum": 0.7222222222222222, - "rational_sum": false, - "field": { - "density": 0.05555555555555555, - "reciprocal_sum": 0.7222222222222222, - "growth_rate": 3.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 2 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 16.0 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.7222222222222222, - "convergence_property": true, - "reciprocal_sum": 0.7222222222222222 - } - }, - { - "sequence_type": "Growth factor 4", - "n_terms": 3, - "sequence": [ - 2, - 8, - 32 - ], - "reciprocal_sum": 0.65625, - "rational_sum": false, - "field": { - "density": 0.03125, - "reciprocal_sum": 0.65625, - "growth_rate": 4.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 2 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 81.0 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.65625, - "convergence_property": true, - "reciprocal_sum": 0.65625 - } - }, - { - "sequence_type": "Sylvester", - "n_terms": 4, - "sequence": [ - 2, - 3, - 7, - 43 - ], - "reciprocal_sum": 0.9994462901439646, - "rational_sum": false, - "field": { - "density": 0.023255813953488372, - "reciprocal_sum": 0.9994462901439646, - "growth_rate": 6.142857142857143 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 3 - }, - "shear": { - "sequence_rigidity": 0.2448111025383398, - "growth_variance": 4.084782060972538, - "gap_variance": 250.8888888888889 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.9994462901439646, - "convergence_property": true, - "reciprocal_sum": 0.9994462901439646 - } - }, - { - "sequence_type": "Growth factor 2", - "n_terms": 4, - "sequence": [ - 2, - 4, - 8, - 16 - ], - "reciprocal_sum": 0.9375, - "rational_sum": false, - "field": { - "density": 0.0625, - "reciprocal_sum": 0.9375, - "growth_rate": 2.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 3 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 6.222222222222221 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.9375, - "convergence_property": true, - "reciprocal_sum": 0.9375 - } - }, - { - "sequence_type": "Growth factor 3", - "n_terms": 4, - "sequence": [ - 2, - 6, - 18, - 54 - ], - "reciprocal_sum": 0.7407407407407407, - "rational_sum": false, - "field": { - "density": 0.018518518518518517, - "reciprocal_sum": 0.7407407407407407, - "growth_rate": 3.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 3 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 184.8888888888889 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.7407407407407407, - "convergence_property": true, - "reciprocal_sum": 0.7407407407407407 - } - }, - { - "sequence_type": "Growth factor 4", - "n_terms": 4, - "sequence": [ - 2, - 8, - 32, - 128 - ], - "reciprocal_sum": 0.6640625, - "rational_sum": false, - "field": { - "density": 0.0078125, - "reciprocal_sum": 0.6640625, - "growth_rate": 4.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 3 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 1512.0 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.6640625, - "convergence_property": true, - "reciprocal_sum": 0.6640625 - } - }, - { - "sequence_type": "Sylvester", - "n_terms": 5, - "sequence": [ - 2, - 3, - 7, - 43, - 1807 - ], - "reciprocal_sum": 0.9999996935750658, - "rational_sum": false, - "field": { - "density": 0.0005534034311012728, - "reciprocal_sum": 0.9999996935750658, - "growth_rate": 42.02325581395349 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 4 - }, - "shear": { - "sequence_rigidity": 0.0035229950522667423, - "growth_variance": 283.8493909766448, - "gap_variance": 574625.6875 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.9999996935750658, - "convergence_property": true, - "reciprocal_sum": 0.9999996935750658 - } - }, - { - "sequence_type": "Growth factor 2", - "n_terms": 5, - "sequence": [ - 2, - 4, - 8, - 16, - 32 - ], - "reciprocal_sum": 0.96875, - "rational_sum": false, - "field": { - "density": 0.03125, - "reciprocal_sum": 0.96875, - "growth_rate": 2.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 4 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 28.75 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.96875, - "convergence_property": true, - "reciprocal_sum": 0.96875 - } - }, - { - "sequence_type": "Growth factor 3", - "n_terms": 5, - "sequence": [ - 2, - 6, - 18, - 54, - 162 - ], - "reciprocal_sum": 0.7469135802469136, - "rational_sum": false, - "field": { - "density": 0.006172839506172839, - "reciprocal_sum": 0.7469135802469136, - "growth_rate": 3.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 4 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 1680.0 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.7469135802469136, - "convergence_property": true, - "reciprocal_sum": 0.7469135802469136 - } - }, - { - "sequence_type": "Growth factor 4", - "n_terms": 5, - "sequence": [ - 2, - 8, - 32, - 128, - 512 - ], - "reciprocal_sum": 0.666015625, - "rational_sum": false, - "field": { - "density": 0.001953125, - "reciprocal_sum": 0.666015625, - "growth_rate": 4.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 4 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 23064.75 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.666015625, - "convergence_property": true, - "reciprocal_sum": 0.666015625 - } - }, - { - "sequence_type": "Sylvester", - "n_terms": 6, - "sequence": [ - 2, - 3, - 7, - 43, - 1807, - 3263443 - ], - "reciprocal_sum": 0.9999999999999061, - "rational_sum": false, - "field": { - "density": 3.064248402683914e-07, - "reciprocal_sum": 0.9999999999999061, - "growth_rate": 1806.000553403431 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 5 - }, - "shear": { - "sequence_rigidity": 1.943244374877824e-06, - "growth_variance": 514603.31645775225, - "gap_variance": 1701652615481.7598 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.9999999999999061, - "convergence_property": true, - "reciprocal_sum": 0.9999999999999061 - } - }, - { - "sequence_type": "Growth factor 2", - "n_terms": 6, - "sequence": [ - 2, - 4, - 8, - 16, - 32, - 64 - ], - "reciprocal_sum": 0.984375, - "rational_sum": false, - "field": { - "density": 0.015625, - "reciprocal_sum": 0.984375, - "growth_rate": 2.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 5 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 119.04000000000003 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.984375, - "convergence_property": true, - "reciprocal_sum": 0.984375 - } - }, - { - "sequence_type": "Growth factor 3", - "n_terms": 6, - "sequence": [ - 2, - 6, - 18, - 54, - 162, - 486 - ], - "reciprocal_sum": 0.7489711934156379, - "rational_sum": false, - "field": { - "density": 0.00205761316872428, - "reciprocal_sum": 0.7489711934156379, - "growth_rate": 3.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 5 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 14248.959999999997 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.7489711934156379, - "convergence_property": true, - "reciprocal_sum": 0.7489711934156379 - } - }, - { - "sequence_type": "Growth factor 4", - "n_terms": 6, - "sequence": [ - 2, - 8, - 32, - 128, - 512, - 2048 - ], - "reciprocal_sum": 0.66650390625, - "rational_sum": false, - "field": { - "density": 0.00048828125, - "reciprocal_sum": 0.66650390625, - "growth_rate": 4.0 - }, - "spectral": { - "eigenvalues": [ - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0 - ], - "spectral_radius": 0.0, - "structure_rank": 5 - }, - "shear": { - "sequence_rigidity": 10000000000.0, - "growth_variance": 0.0, - "gap_variance": 335871.36 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.66650390625, - "convergence_property": true, - "reciprocal_sum": 0.66650390625 - } - } - ], - "conjecture_analysis": { - "total_tests": 16, - "sylvester_tests": 4, - "rational_sum_count": 0, - "note": "Sylvester's sequence has rational reciprocal sum (converges to 1). Conjecture concerns classification of such sequences." - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Sequence density and reciprocal sum", - "insight": "Reciprocal sum indicates convergence" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Growth matrix eigen decomposition", - "insight": "Spectral radius indicates growth rate" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Growth variance and gap variance", - "insight": "Growth variance indicates regularity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Sequence encoding and convergence property", - "insight": "Convergence property indicates rational reciprocal sum" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s conjecture on quickly growing integer sequences. Field primitive captures sequence density. Spectral primitive reveals growth structure. Shear primitive measures sequence deformation. Packet primitive captures sequence encoding. Framework validated for number sequence problems. Sylvester's sequence has rational reciprocal sum (converges to 1)." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_renyi_4primitive.py b/4-Infrastructure/shim/test_erdos_renyi_4primitive.py deleted file mode 100644 index 4b990f98..00000000 --- a/4-Infrastructure/shim/test_erdos_renyi_4primitive.py +++ /dev/null @@ -1,310 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Rényi Random Graphs -======================================================== -Apply 4-primitive framework to analyze G(n,p) random graphs. -Focus on spectral primitive (C = UΛUᵀ) for eigenvalue distribution -and phase transition detection via spectral gap. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_erdos_renyi_graph(n, p, seed=None): - """Generate Erdős–Rényi random graph G(n,p) adjacency matrix.""" - if seed is not None: - np.random.seed(seed) - - # Generate adjacency matrix - A = np.random.random((n, n)) < p - A = A.astype(float) - - # Make symmetric (undirected graph) - A = np.triu(A) + np.triu(A).T - np.fill_diagonal(A, 0) - - return A - - -def spectral_decomposition(A): - """Compute eigen decomposition C = UΛUᵀ (spectral primitive).""" - # Compute eigenvalues and eigenvectors - eigenvalues, eigenvectors = np.linalg.eigh(A) - - # Sort by eigenvalue (descending) - idx = np.argsort(eigenvalues)[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - return { - "eigenvalues": eigenvalues.tolist(), - "eigenvectors": eigenvectors.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(np.abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0 - } - - -def field_analysis(A): - """Compute field primitive metrics (edge density, manifold structure).""" - n = A.shape[0] - edge_density = np.sum(A) / (n * (n - 1)) - - # Degree distribution - degrees = np.sum(A, axis=1) - degree_mean = np.mean(degrees) - degree_std = np.std(degrees) - - return { - "edge_density": float(edge_density), - "degree_mean": float(degree_mean), - "degree_std": float(degree_std), - "field_variance": float(degree_std / degree_mean if degree_mean > 0 else 0) - } - - -def shear_analysis(A): - """Compute shear primitive metrics (graph deformation, distortion).""" - # Compute Laplacian - n = A.shape[0] - degrees = np.sum(A, axis=1) - L = np.diag(degrees) - A - - # Laplacian eigenvalues (shear spectrum) - laplacian_eigenvalues = np.linalg.eigvalsh(L) - - # Algebraic connectivity (Fiedler value) - algebraic_connectivity = laplacian_eigenvalues[1] if len(laplacian_eigenvalues) > 1 else 0.0 - - # Graph diameter estimate (via spectral gap) - spectral_gap = laplacian_eigenvalues[1] if len(laplacian_eigenvalues) > 1 else 0.0 - diameter_estimate = float(np.sqrt(2 * n * (1 - 1/spectral_gap)) if spectral_gap > 0 else 0) - - return { - "algebraic_connectivity": float(algebraic_connectivity), - "spectral_gap": float(spectral_gap), - "diameter_estimate": diameter_estimate, - "shear_stiffness": float(algebraic_connectivity / n if n > 0 else 0) - } - - -def detect_phase_transition(n_values, p_values): - """Detect phase transitions across p values for fixed n.""" - results = [] - - for n in n_values: - for p in p_values: - # Generate multiple samples - spectral_radii = [] - spectral_gaps = [] - algebraic_connectivities = [] - edge_densities = [] - - for seed in range(5): # 5 samples per (n,p) - A = generate_erdos_renyi_graph(n, p, seed=seed) - - # Spectral analysis - spec = spectral_decomposition(A) - spectral_radii.append(spec["spectral_radius"]) - spectral_gaps.append(spec["spectral_gap"]) - - # Shear analysis - shear = shear_analysis(A) - algebraic_connectivities.append(shear["algebraic_connectivity"]) - - # Field analysis - field = field_analysis(A) - edge_densities.append(field["edge_density"]) - - results.append({ - "n": n, - "p": p, - "avg_spectral_radius": float(np.mean(spectral_radii)), - "std_spectral_radius": float(np.std(spectral_radii)), - "avg_spectral_gap": float(np.mean(spectral_gaps)), - "avg_algebraic_connectivity": float(np.mean(algebraic_connectivities)), - "avg_edge_density": float(np.mean(edge_densities)), - "connectivity_threshold": float(1 / n) # Theoretical threshold - }) - - return results - - -def analyze_phase_transitions(results): - """Analyze phase transitions in the data.""" - transitions = [] - - # Group by n - n_values = set(r["n"] for r in results) - - for n in n_values: - n_results = [r for r in results if r["n"] == n] - n_results.sort(key=lambda x: x["p"]) - - # Detect connectivity transition (p ≈ ln(n)/n) - connectivity_threshold = np.log(n) / n - - # Find where algebraic connectivity becomes positive - for i in range(len(n_results) - 1): - if n_results[i]["avg_algebraic_connectivity"] <= 0 and n_results[i+1]["avg_algebraic_connectivity"] > 0: - transitions.append({ - "n": n, - "transition_type": "connectivity", - "detected_p": n_results[i+1]["p"], - "theoretical_p": connectivity_threshold, - "error": abs(n_results[i+1]["p"] - connectivity_threshold) - }) - - # Detect giant component transition (p ≈ 1/n) - giant_threshold = 1.0 / n - - # Find where spectral radius exceeds np - for i in range(len(n_results)): - if n_results[i]["avg_spectral_radius"] > n * n_results[i]["p"]: - transitions.append({ - "n": n, - "transition_type": "giant_component", - "detected_p": n_results[i]["p"], - "theoretical_p": giant_threshold, - "error": abs(n_results[i]["p"] - giant_threshold) - }) - break - - return transitions - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–RÉNYI RANDOM GRAPHS") - print("=" * 70) - - # Test parameters - n_values = [50, 100, 200] - p_values = [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" p values: {p_values}") - print(f" Samples per (n,p): 5") - print(f" Total graphs: {len(n_values) * len(p_values) * 5}") - - print("\n" + "=" * 70) - print(" GENERATING GRAPHS AND ANALYZING") - print("=" * 70) - - results = detect_phase_transition(n_values, p_values) - - print(f"\nGenerated {len(results)} (n,p) configurations") - - print("\n" + "=" * 70) - print(" DETECTING PHASE TRANSITIONS") - print("=" * 70) - - transitions = analyze_phase_transitions(results) - - print(f"\nDetected {len(transitions)} phase transitions:") - for trans in transitions: - print(f"\n • n={trans['n']}, {trans['transition_type']}:") - print(f" Detected p: {trans['detected_p']:.4f}") - print(f" Theoretical p: {trans['theoretical_p']:.4f}") - print(f" Error: {trans['error']:.4f}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Eigenvalue distribution analyzed") - print(" - Spectral radius computed") - print(" - Spectral gap measured") - print(" - Phase transitions detected via spectral gap") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Edge density computed") - print(" - Degree distribution analyzed") - print(" - Field variance measured") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Laplacian eigenvalues computed") - print(" - Algebraic connectivity measured") - print(" - Diameter estimate via spectral gap") - print(" - Shear stiffness computed") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Each graph treated as packet (adjacency matrix encoding)") - print(" - Packet space = space of all G(n,p) graphs") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Spectral primitive successfully detected phase transitions:") - print(" - Connectivity transition: p ≈ ln(n)/n") - print(" - Giant component transition: p ≈ 1/n") - - print("\n2. Field primitive captured density structure:") - print(" - Edge density correlates with p") - print(" - Degree distribution variance indicates phase") - - print("\n3. Shear primitive measured graph deformation:") - print(" - Algebraic connectivity indicates rigidity") - print(" - Spectral gap of Laplacian indicates connectivity") - - print("\n4. 4-primitive framework validated:") - print(" - Spectral primitive: eigenvalue analysis") - print(" - Field primitive: density analysis") - print(" - Shear primitive: deformation analysis") - print(" - Packet primitive: graph encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "p_values": p_values, - "samples_per_config": 5, - "total_graphs": len(n_values) * len(p_values) * 5 - }, - "results": results, - "transitions": transitions, - "primitive_analysis": { - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Eigenvalue distribution of adjacency matrix", - "success": "Phase transitions detected via spectral gap" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Edge density and degree distribution", - "success": "Density structure captured" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Laplacian eigenvalues and algebraic connectivity", - "success": "Graph deformation measured" - }, - "packet": { - "equation": "Γᵢ", - "application": "Adjacency matrix as packet encoding", - "success": "Graph encoding validated" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Rényi random graphs. Spectral primitive detected phase transitions. Field and shear primitives captured structural properties. Framework validated for Erdős problem analysis." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_renyi_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_renyi_4primitive_results.json b/4-Infrastructure/shim/test_erdos_renyi_4primitive_results.json deleted file mode 100644 index 67457bed..00000000 --- a/4-Infrastructure/shim/test_erdos_renyi_4primitive_results.json +++ /dev/null @@ -1,303 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:22:26.381005", - "n_values": [ - 50, - 100, - 200 - ], - "p_values": [ - 0.01, - 0.02, - 0.05, - 0.1, - 0.2, - 0.5, - 0.8 - ], - "samples_per_config": 5, - "total_graphs": 105 - }, - "results": [ - { - "n": 50, - "p": 0.01, - "avg_spectral_radius": 1.719652608772062, - "std_spectral_radius": 0.19083674215720167, - "avg_spectral_gap": 0.2431861392837093, - "avg_algebraic_connectivity": -3.0827192574871994e-16, - "avg_edge_density": 0.009469387755102041, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.02, - "avg_spectral_radius": 2.2312769612996837, - "std_spectral_radius": 0.2602488700856333, - "avg_spectral_gap": 0.21583109103730358, - "avg_algebraic_connectivity": -7.624710495103978e-16, - "avg_edge_density": 0.019591836734693877, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.05, - "avg_spectral_radius": 3.318387875536974, - "std_spectral_radius": 0.19720771651750854, - "avg_spectral_gap": 0.5813128579391158, - "avg_algebraic_connectivity": -5.084344638834e-16, - "avg_edge_density": 0.04522448979591836, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.1, - "avg_spectral_radius": 5.415467998662587, - "std_spectral_radius": 0.1877594028478544, - "avg_spectral_gap": 1.7565362937248046, - "avg_algebraic_connectivity": 0.5004180320221646, - "avg_edge_density": 0.09273469387755101, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.2, - "avg_spectral_radius": 10.387329412403805, - "std_spectral_radius": 0.330152821531083, - "avg_spectral_gap": 5.658007368502468, - "avg_algebraic_connectivity": 2.8116854786649115, - "avg_edge_density": 0.1957551020408163, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.5, - "avg_spectral_radius": 24.638417083110873, - "std_spectral_radius": 0.6955935516855929, - "avg_spectral_gap": 18.64808742564943, - "avg_algebraic_connectivity": 14.72786743139678, - "avg_edge_density": 0.49306122448979595, - "connectivity_threshold": 0.02 - }, - { - "n": 50, - "p": 0.8, - "avg_spectral_radius": 39.22804880638718, - "std_spectral_radius": 0.3829338042516864, - "avg_spectral_gap": 34.679886725091635, - "avg_algebraic_connectivity": 31.237091721672677, - "avg_edge_density": 0.796734693877551, - "connectivity_threshold": 0.02 - }, - { - "n": 100, - "p": 0.01, - "avg_spectral_radius": 2.35701649821401, - "std_spectral_radius": 0.2515880541376073, - "avg_spectral_gap": 0.22427244038871294, - "avg_algebraic_connectivity": -9.84486459105702e-16, - "avg_edge_density": 0.009333333333333334, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.02, - "avg_spectral_radius": 3.204468608833944, - "std_spectral_radius": 0.2553231900269544, - "avg_spectral_gap": 0.36973461255293066, - "avg_algebraic_connectivity": -1.102083062951978e-15, - "avg_edge_density": 0.019232323232323233, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.05, - "avg_spectral_radius": 5.900653752382452, - "std_spectral_radius": 0.2778978710398786, - "avg_spectral_gap": 1.7516731333229711, - "avg_algebraic_connectivity": 0.1846606217426896, - "avg_edge_density": 0.04824242424242424, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.1, - "avg_spectral_radius": 10.813542089654064, - "std_spectral_radius": 0.44964219200184063, - "avg_spectral_gap": 5.174188308305903, - "avg_algebraic_connectivity": 2.469562455228504, - "avg_edge_density": 0.09943434343434343, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.2, - "avg_spectral_radius": 20.909207539883802, - "std_spectral_radius": 0.5035150674463961, - "avg_spectral_gap": 13.423793470444988, - "avg_algebraic_connectivity": 9.87302373849154, - "avg_edge_density": 0.20327272727272733, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.5, - "avg_spectral_radius": 50.10538596065084, - "std_spectral_radius": 0.8255026092127461, - "avg_spectral_gap": 41.112969710589226, - "avg_algebraic_connectivity": 35.88224353961023, - "avg_edge_density": 0.5012929292929293, - "connectivity_threshold": 0.01 - }, - { - "n": 100, - "p": 0.8, - "avg_spectral_radius": 79.17982761889013, - "std_spectral_radius": 0.40082190188015826, - "avg_spectral_gap": 72.39169878913711, - "avg_algebraic_connectivity": 67.71660016201623, - "avg_edge_density": 0.79789898989899, - "connectivity_threshold": 0.01 - }, - { - "n": 200, - "p": 0.01, - "avg_spectral_radius": 3.430831598505138, - "std_spectral_radius": 0.2051421375597825, - "avg_spectral_gap": 0.26600543114710995, - "avg_algebraic_connectivity": -1.3683592539556313e-15, - "avg_edge_density": 0.01021105527638191, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.02, - "avg_spectral_radius": 5.250530465461383, - "std_spectral_radius": 0.13171254979251953, - "avg_spectral_gap": 1.116247661469017, - "avg_algebraic_connectivity": 0.06733212058865554, - "avg_edge_density": 0.020251256281407035, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.05, - "avg_spectral_radius": 11.03865971393779, - "std_spectral_radius": 0.2370201922067944, - "avg_spectral_gap": 4.919814426731755, - "avg_algebraic_connectivity": 2.3159059556946286, - "avg_edge_density": 0.050442211055276374, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.1, - "avg_spectral_radius": 21.036098358945754, - "std_spectral_radius": 0.38358700415075414, - "avg_spectral_gap": 12.73016216500337, - "avg_algebraic_connectivity": 8.507723071366959, - "avg_edge_density": 0.10096482412060301, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.2, - "avg_spectral_radius": 41.18307955624569, - "std_spectral_radius": 0.3271539947552409, - "avg_spectral_gap": 30.229431018915363, - "avg_algebraic_connectivity": 23.743218265340964, - "avg_edge_density": 0.2030251256281407, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.5, - "avg_spectral_radius": 100.29917171143654, - "std_spectral_radius": 0.5019992289578605, - "avg_spectral_gap": 87.22590858511929, - "avg_algebraic_connectivity": 78.21651609170478, - "avg_edge_density": 0.501497487437186, - "connectivity_threshold": 0.005 - }, - { - "n": 200, - "p": 0.8, - "avg_spectral_radius": 159.5556177785532, - "std_spectral_radius": 0.11940141917339388, - "avg_spectral_gap": 149.28491316430876, - "avg_algebraic_connectivity": 141.06444849235433, - "avg_edge_density": 0.8008241206030149, - "connectivity_threshold": 0.005 - } - ], - "transitions": [ - { - "n": 200, - "transition_type": "connectivity", - "detected_p": 0.02, - "theoretical_p": 0.02649158683274018, - "error": 0.0064915868327401795 - }, - { - "n": 200, - "transition_type": "giant_component", - "detected_p": 0.01, - "theoretical_p": 0.005, - "error": 0.005 - }, - { - "n": 50, - "transition_type": "connectivity", - "detected_p": 0.1, - "theoretical_p": 0.07824046010856292, - "error": 0.021759539891437085 - }, - { - "n": 50, - "transition_type": "giant_component", - "detected_p": 0.01, - "theoretical_p": 0.02, - "error": 0.01 - }, - { - "n": 100, - "transition_type": "connectivity", - "detected_p": 0.05, - "theoretical_p": 0.04605170185988092, - "error": 0.003948298140119086 - }, - { - "n": 100, - "transition_type": "giant_component", - "detected_p": 0.01, - "theoretical_p": 0.01, - "error": 0.0 - } - ], - "primitive_analysis": { - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Eigenvalue distribution of adjacency matrix", - "success": "Phase transitions detected via spectral gap" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Edge density and degree distribution", - "success": "Density structure captured" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Laplacian eigenvalues and algebraic connectivity", - "success": "Graph deformation measured" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Adjacency matrix as packet encoding", - "success": "Graph encoding validated" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013R\u00e9nyi random graphs. Spectral primitive detected phase transitions. Field and shear primitives captured structural properties. Framework validated for Erd\u0151s problem analysis." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_selfridge_4primitive.py b/4-Infrastructure/shim/test_erdos_selfridge_4primitive.py deleted file mode 100644 index 452ebf68..00000000 --- a/4-Infrastructure/shim/test_erdos_selfridge_4primitive.py +++ /dev/null @@ -1,364 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Selfridge Conjecture -========================================================= -Apply 4-primitive framework to Erdős–Selfridge Conjecture. -Conjecture: A covering system with distinct moduli contains at least one even modulus. - -Focus on field primitive (ρ(x⃗)) for covering system density analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_covering_system(n_moduli, max_modulus=100, seed=None): - """Generate a random covering system with n moduli.""" - if seed is not None: - random.seed(seed) - - # Generate distinct moduli - moduli = random.sample(range(2, max_modulus + 1), n_moduli) - - # For each modulus, choose a residue class - residues = [random.randint(0, mod - 1) for mod in moduli] - - return list(zip(moduli, residues)) - - -def is_covering_system(moduli_residues, max_check=1000): - """Check if the system covers all integers (up to max_check).""" - # Check coverage for integers 0 to max_check-1 - for n in range(max_check): - covered = False - for mod, res in moduli_residues: - if n % mod == res: - covered = True - break - if not covered: - return False - return True - - -def field_analysis_covering(moduli_residues): - """Compute field primitive metrics for covering system.""" - if not moduli_residues: - return { - "modulus_density": 0.0, - "avg_modulus": 0.0, - "modulus_variance": 0.0 - } - - moduli = [mod for mod, _ in moduli_residues] - - # Modulus density (inverse of LCM approximation) - from math import gcd - from functools import reduce - lcm = reduce(lambda x, y: x * y // gcd(x, y), moduli) - modulus_density = 1.0 / lcm if lcm > 0 else 0.0 - - # Average modulus - avg_modulus = np.mean(moduli) - - # Modulus variance - modulus_variance = np.var(moduli) - - return { - "modulus_density": float(modulus_density), - "avg_modulus": float(avg_modulus), - "modulus_variance": float(modulus_variance) - } - - -def spectral_analysis_covering(moduli_residues): - """Compute spectral decomposition of covering structure.""" - if not moduli_residues: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "covering_matrix_rank": 0 - } - - n = len(moduli_residues) - - # Build covering matrix (modulus-residue incidence) - M = np.zeros((n, n)) - for i, (mod_i, res_i) in enumerate(moduli_residues): - for j, (mod_j, res_j) in enumerate(moduli_residues): - # Check if residue classes overlap - overlap = False - for k in range(mod_i * mod_j): - if k % mod_i == res_i and k % mod_j == res_j: - overlap = True - break - M[i, j] = 1 if overlap else 0 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "covering_matrix_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "covering_matrix_rank": 0 - } - - -def shear_analysis_covering(moduli_residues): - """Compute shear primitive metrics for covering deformation.""" - if not moduli_residues: - return { - "covering_rigidity": 0.0, - "even_modulus_ratio": 0.0, - "odd_modulus_ratio": 0.0 - } - - moduli = [mod for mod, _ in moduli_residues] - - # Even modulus ratio - even_count = sum(1 for mod in moduli if mod % 2 == 0) - odd_count = sum(1 for mod in moduli if mod % 2 == 1) - even_ratio = even_count / len(moduli) if moduli else 0.0 - odd_ratio = odd_count / len(moduli) if moduli else 0.0 - - # Covering rigidity (inverse of modulus variance) - modulus_variance = np.var(moduli) - covering_rigidity = 1.0 / (modulus_variance + 1e-10) - - return { - "covering_rigidity": float(covering_rigidity), - "even_modulus_ratio": float(even_ratio), - "odd_modulus_ratio": float(odd_ratio) - } - - -def packet_analysis_covering(moduli_residues): - """Compute packet primitive metrics for covering encoding.""" - if not moduli_residues: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "residue_diversity": 0.0 - } - - # Packet size (number of moduli) - packet_size = len(moduli_residues) - - # Encoding efficiency (coverage per modulus) - max_check = 100 - coverage = 0 - for n in range(max_check): - for mod, res in moduli_residues: - if n % mod == res: - coverage += 1 - break - encoding_efficiency = coverage / (packet_size * max_check) if packet_size > 0 else 0.0 - - # Residue diversity (spread of residues) - residues = [res for _, res in moduli_residues] - residue_diversity = np.std(residues) / np.mean(residues) if residues and np.mean(residues) > 0 else 0.0 - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "residue_diversity": float(residue_diversity) - } - - -def test_erdos_selfridge(n_moduli_values, max_modulus=100): - """Test Erdős–Selfridge Conjecture with 4-primitive framework.""" - results = [] - - for n_moduli in n_moduli_values: - for seed in range(3): # 3 samples per n - moduli_residues = generate_covering_system(n_moduli, max_modulus, seed=seed) - - # Check if it's a covering system - is_covering = is_covering_system(moduli_residues) - - # Check if any even modulus exists - has_even = any(mod % 2 == 0 for mod, _ in moduli_residues) - - # 4-primitive analysis - field = field_analysis_covering(moduli_residues) - spectral = spectral_analysis_covering(moduli_residues) - shear = shear_analysis_covering(moduli_residues) - packet = packet_analysis_covering(moduli_residues) - - results.append({ - "n_moduli": n_moduli, - "seed": seed, - "is_covering": is_covering, - "has_even_modulus": has_even, - "conjecture_holds": not is_covering or has_even, - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Selfridge Conjecture.""" - # Conjecture: covering systems with distinct moduli must have at least one even modulus - # Counterexample would be a covering system with all odd moduli - all_odd_covering = [r for r in results if r["is_covering"] and not r["has_even_modulus"]] - - total = len(results) - conjecture_violations = len(all_odd_covering) - - return { - "total_tests": total, - "conjecture_violations": conjecture_violations, - "conjecture_holds": conjecture_violations == 0, - "note": "Finding a covering system with all odd moduli would disprove the conjecture" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–SELFRIDGE CONJECTURE") - print("=" * 70) - - # Test parameters - n_moduli_values = [3, 4, 5, 6] - max_modulus = 100 - - print(f"\nTest parameters:") - print(f" Number of moduli: {n_moduli_values}") - print(f" Max modulus: {max_modulus}") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_moduli_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING COVERING SYSTEMS") - print("=" * 70) - - results = test_erdos_selfridge(n_moduli_values, max_modulus) - - print(f"\nGenerated {len(results)} covering systems") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Conjecture violations: {analysis['conjecture_violations']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Modulus density (1/LCM)") - print(" - Average modulus") - print(" - Modulus variance") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Covering matrix eigen decomposition") - print(" - Spectral radius") - print(" - Covering matrix rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Covering rigidity") - print(" - Even modulus ratio") - print(" - Odd modulus ratio") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (number of moduli)") - print(" - Encoding efficiency") - print(" - Residue diversity") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures covering density:") - print(" - Modulus density indicates coverage efficiency") - print(" - LCM growth affects density") - - print("\n2. Spectral primitive reveals covering structure:") - print(" - Overlap between residue classes") - print(" - Spectral radius indicates structure") - - print("\n3. Shear primitive captures even/odd balance:") - print(" - Even modulus ratio directly tests conjecture") - print(" - Odd modulus ratio indicates counterexample potential") - - print("\n4. Packet primitive captures encoding efficiency:") - print(" - Coverage per modulus") - print(" - Residue diversity") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: covering density") - print(" - Spectral: covering structure") - print(" - Shear: even/odd balance (conjecture condition)") - print(" - Packet: encoding efficiency") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_moduli_values": n_moduli_values, - "max_modulus": max_modulus, - "samples_per_n": 3, - "total_tests": len(n_moduli_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Modulus density and variance", - "insight": "Field density indicates coverage efficiency" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Covering matrix eigen decomposition", - "insight": "Overlap between residue classes" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Even/odd modulus ratio", - "insight": "Even modulus ratio directly tests conjecture" - }, - "packet": { - "equation": "Γᵢ", - "application": "Covering encoding efficiency", - "insight": "Coverage per modulus" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Selfridge Conjecture. Field primitive captures covering density. Spectral primitive reveals covering structure. Shear primitive captures even/odd balance (direct conjecture test). Packet primitive captures encoding efficiency. Framework validated for covering system problems. Conjecture holds for tested systems (no counterexamples found)." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_selfridge_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_selfridge_4primitive_results.json b/4-Infrastructure/shim/test_erdos_selfridge_4primitive_results.json deleted file mode 100644 index 68dbc5c6..00000000 --- a/4-Infrastructure/shim/test_erdos_selfridge_4primitive_results.json +++ /dev/null @@ -1,438 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:38:58.067998", - "n_moduli_values": [ - 3, - 4, - 5, - 6 - ], - "max_modulus": 100, - "samples_per_n": 3, - "total_tests": 12 - }, - "results": [ - { - "n_moduli": 3, - "seed": 0, - "is_covering": false, - "has_even_modulus": false, - "conjecture_holds": true, - "field": { - "modulus_density": 0.00011883541295306001, - "avg_modulus": 68.33333333333333, - "modulus_variance": 472.88888888888886 - }, - "spectral": { - "eigenvalues": [ - 2.0, - 1.0, - 0.0 - ], - "spectral_radius": 2.0, - "covering_matrix_rank": 2 - }, - "shear": { - "covering_rigidity": 0.0021146616541348915, - "even_modulus_ratio": 0.0, - "odd_modulus_ratio": 1.0 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.016666666666666666, - "residue_diversity": 0.6440432540415348 - } - }, - { - "n_moduli": 3, - "seed": 1, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 7.1842177105335e-06, - "avg_modulus": 64.0, - "modulus_variance": 1116.6666666666667 - }, - "spectral": { - "eigenvalues": [ - 2.9999999999999996, - -1.5831488285234915e-17, - -4.519790642294812e-16 - ], - "spectral_radius": 2.9999999999999996, - "covering_matrix_rank": 1 - }, - "shear": { - "covering_rigidity": 0.0008955223880596212, - "even_modulus_ratio": 0.3333333333333333, - "odd_modulus_ratio": 0.6666666666666666 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.02666666666666667, - "residue_diversity": 0.7520622764362563 - } - }, - { - "n_moduli": 3, - "seed": 2, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 0.002136752136752137, - "avg_modulus": 11.333333333333334, - "modulus_variance": 2.888888888888889 - }, - "spectral": { - "eigenvalues": [ - 2.9999999999999996, - -1.5831488285234915e-17, - -4.519790642294812e-16 - ], - "spectral_radius": 2.9999999999999996, - "covering_matrix_rank": 1 - }, - "shear": { - "covering_rigidity": 0.3461538461418639, - "even_modulus_ratio": 0.3333333333333333, - "odd_modulus_ratio": 0.6666666666666666 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 0.07666666666666666, - "residue_diversity": 0.6236095644623235 - } - }, - { - "n_moduli": 4, - "seed": 0, - "is_covering": false, - "has_even_modulus": false, - "conjecture_holds": true, - "field": { - "modulus_density": 1.697648756472286e-05, - "avg_modulus": 53.0, - "modulus_variance": 1060.0 - }, - "spectral": { - "eigenvalues": [ - 3.1700864866260337, - 1.3111078174659823, - 1.129927972310413e-16, - -0.48119430409201563 - ], - "spectral_radius": 3.1700864866260337, - "covering_matrix_rank": 3 - }, - "shear": { - "covering_rigidity": 0.0009433962264150053, - "even_modulus_ratio": 0.0, - "odd_modulus_ratio": 1.0 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.045, - "residue_diversity": 0.8054164464262653 - } - }, - { - "n_moduli": 4, - "seed": 1, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 1.4368435421067e-06, - "avg_modulus": 50.5, - "modulus_variance": 1384.25 - }, - "spectral": { - "eigenvalues": [ - 4.0, - 1.2053399157949612e-33, - -1.7544542495754425e-16, - -9.641497578031907e-16 - ], - "spectral_radius": 4.0, - "covering_matrix_rank": 1 - }, - "shear": { - "covering_rigidity": 0.000722412858948837, - "even_modulus_ratio": 0.5, - "odd_modulus_ratio": 0.5 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.0425, - "residue_diversity": 0.9959450681615108 - } - }, - { - "n_moduli": 4, - "seed": 2, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 0.0005341880341880342, - "avg_modulus": 20.5, - "modulus_variance": 254.25 - }, - "spectral": { - "eigenvalues": [ - 2.732050807568877, - 1.0000000000000002, - 0.9999999999999998, - -0.7320508075688776 - ], - "spectral_radius": 2.732050807568877, - "covering_matrix_rank": 4 - }, - "shear": { - "covering_rigidity": 0.003933136676497961, - "even_modulus_ratio": 0.5, - "odd_modulus_ratio": 0.5 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 0.0675, - "residue_diversity": 0.573409265656776 - } - }, - { - "n_moduli": 5, - "seed": 0, - "is_covering": false, - "has_even_modulus": false, - "conjecture_holds": true, - "field": { - "modulus_density": 1.697648756472286e-05, - "avg_modulus": 49.4, - "modulus_variance": 899.8399999999999 - }, - "spectral": { - "eigenvalues": [ - 3.9354323319700306, - 1.618033988749895, - 0.5374015770252256, - -0.47283390899525557, - -0.6180339887498947 - ], - "spectral_radius": 3.9354323319700306, - "covering_matrix_rank": 5 - }, - "shear": { - "covering_rigidity": 0.0011113086770980273, - "even_modulus_ratio": 0.0, - "odd_modulus_ratio": 1.0 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.04, - "residue_diversity": 0.6482556127841647 - } - }, - { - "n_moduli": 5, - "seed": 1, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 8.452020835921765e-08, - "avg_modulus": 47.2, - "modulus_variance": 1150.9599999999998 - }, - "spectral": { - "eigenvalues": [ - 4.3234042760864755, - 1.3579263675185, - 2.914845822391487e-16, - -1.9371034328567192e-16, - -0.681330643604978 - ], - "spectral_radius": 4.3234042760864755, - "covering_matrix_rank": 3 - }, - "shear": { - "covering_rigidity": 0.0008688399249321551, - "even_modulus_ratio": 0.6, - "odd_modulus_ratio": 0.4 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.038, - "residue_diversity": 0.8899438184514796 - } - }, - { - "n_moduli": 5, - "seed": 2, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 2.3225566703827574e-05, - "avg_modulus": 21.0, - "modulus_variance": 204.4 - }, - "spectral": { - "eigenvalues": [ - 4.323404276086478, - 1.3579263675184994, - -7.245778573628333e-17, - -2.776213014643005e-16, - -0.6813306436049776 - ], - "spectral_radius": 4.323404276086478, - "covering_matrix_rank": 3 - }, - "shear": { - "covering_rigidity": 0.004892367906064143, - "even_modulus_ratio": 0.4, - "odd_modulus_ratio": 0.6 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.06, - "residue_diversity": 0.5822588823545758 - } - }, - { - "n_moduli": 6, - "seed": 0, - "is_covering": false, - "has_even_modulus": false, - "conjecture_holds": true, - "field": { - "modulus_density": 2.53380411413774e-07, - "avg_modulus": 52.333333333333336, - "modulus_variance": 792.8888888888888 - }, - "spectral": { - "eigenvalues": [ - 4.7784571182583875, - 1.7108314535516902, - 0.9999999999999994, - -5.296933179866723e-19, - -0.48928857181007873, - -0.9999999999999994 - ], - "spectral_radius": 4.7784571182583875, - "covering_matrix_rank": 5 - }, - "shear": { - "covering_rigidity": 0.0012612107623316796, - "even_modulus_ratio": 0.0, - "odd_modulus_ratio": 1.0 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.03666666666666667, - "residue_diversity": 0.5340836542322159 - } - }, - { - "n_moduli": 6, - "seed": 1, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 8.452020835921765e-08, - "avg_modulus": 42.166666666666664, - "modulus_variance": 1085.8055555555557 - }, - "spectral": { - "eigenvalues": [ - 5.119026675525918, - 1.618033988749895, - 0.5683728862102545, - 7.271320564060886e-17, - -0.6180339887498947, - -0.6873995617361738 - ], - "spectral_radius": 5.119026675525918, - "covering_matrix_rank": 5 - }, - "shear": { - "covering_rigidity": 0.0009209752104171679, - "even_modulus_ratio": 0.5, - "odd_modulus_ratio": 0.5 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.043333333333333335, - "residue_diversity": 0.9185959498839489 - } - }, - { - "n_moduli": 6, - "seed": 2, - "is_covering": false, - "has_even_modulus": true, - "conjecture_holds": true, - "field": { - "modulus_density": 1.1612783351913787e-05, - "avg_modulus": 33.5, - "modulus_variance": 951.5833333333334 - }, - "spectral": { - "eigenvalues": [ - 4.89510651592753, - 1.3972950692970911, - 0.9999999999999997, - -2.151057110211238e-16, - -0.29240158522462106, - -0.9999999999999997 - ], - "spectral_radius": 4.89510651592753, - "covering_matrix_rank": 5 - }, - "shear": { - "covering_rigidity": 0.001050880112093768, - "even_modulus_ratio": 0.5, - "odd_modulus_ratio": 0.5 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 0.05, - "residue_diversity": 0.6384499741769294 - } - } - ], - "conjecture_analysis": { - "total_tests": 12, - "conjecture_violations": 0, - "conjecture_holds": true, - "note": "Finding a covering system with all odd moduli would disprove the conjecture" - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Modulus density and variance", - "insight": "Field density indicates coverage efficiency" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Covering matrix eigen decomposition", - "insight": "Overlap between residue classes" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Even/odd modulus ratio", - "insight": "Even modulus ratio directly tests conjecture" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Covering encoding efficiency", - "insight": "Coverage per modulus" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Selfridge Conjecture. Field primitive captures covering density. Spectral primitive reveals covering structure. Shear primitive captures even/odd balance (direct conjecture test). Packet primitive captures encoding efficiency. Framework validated for covering system problems. Conjecture holds for tested systems (no counterexamples found)." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_stone_4primitive.py b/4-Infrastructure/shim/test_erdos_stone_4primitive.py deleted file mode 100644 index a6b2ba91..00000000 --- a/4-Infrastructure/shim/test_erdos_stone_4primitive.py +++ /dev/null @@ -1,328 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Stone Theorem -================================================== -Apply 4-primitive framework to Erdős–Stone Theorem. -Theorem: For any graph H, ex(n,H) = (1 - 1/χ(H)-1 + o(1))n²/2 -where χ(H) is the chromatic number and ex(n,H) is the extremal function. - -Focus on shear primitive (G = AᵀA) for extremal function as shear metric. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_graph(n, p, seed=None): - """Generate a random graph G(n,p).""" - if seed is not None: - random.seed(seed) - - A = np.zeros((n, n)) - for i in range(n): - for j in range(i + 1, n): - if random.random() < p: - A[i, j] = 1 - A[j, i] = 1 - - return A - - -def count_edges(A): - """Count number of edges in adjacency matrix.""" - return int(np.sum(A) / 2) - - -def chromatic_number_heuristic(A): - """Estimate chromatic number using greedy coloring.""" - n = A.shape[0] - colors = {} - available_colors = set() - - # Greedy coloring - for i in range(n): - # Find colors used by neighbors - neighbor_colors = set() - for j in range(n): - if A[i, j] == 1 and j in colors: - neighbor_colors.add(colors[j]) - - # Assign smallest available color - color = 0 - while color in neighbor_colors: - color += 1 - colors[i] = color - - return max(colors.values()) if colors else 1 - - -def shear_analysis_graph(A): - """Compute shear primitive metrics for graph deformation.""" - n = A.shape[0] - - # Edge count - edge_count = count_edges(A) - - # Edge density - edge_density = edge_count / (n * (n - 1) / 2) if n > 1 else 0.0 - - # Shear metric (Gram matrix) - G = A.T @ A - - # Shear stiffness (sum of Gram matrix) - shear_stiffness = float(np.sum(G)) - - # Spectral radius of shear - eigenvalues, _ = np.linalg.eigh(G) - spectral_radius = float(np.max(np.abs(eigenvalues))) - - return { - "edge_count": edge_count, - "edge_density": float(edge_density), - "shear_stiffness": shear_stiffness, - "spectral_radius": spectral_radius - } - - -def field_analysis_graph(A, n): - """Compute field primitive metrics for graph density.""" - edge_count = count_edges(A) - - # Field density - max_edges = n * (n - 1) / 2 - field_density = edge_count / max_edges if max_edges > 0 else 0.0 - - return { - "field_density": float(field_density), - "max_edges": max_edges, - "edge_count": edge_count - } - - -def spectral_analysis_graph(A): - """Compute spectral decomposition of adjacency matrix.""" - eigenvalues, _ = np.linalg.eigh(A) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0 - } - - -def packet_analysis_graph(A): - """Compute packet primitive metrics for graph encoding.""" - n = A.shape[0] - - # Packet size (number of edges encoded) - edge_count = count_edges(A) - - # Packet efficiency (edges per vertex) - packet_efficiency = edge_count / n if n > 0 else 0.0 - - # Encoding redundancy (symmetry) - encoding_redundancy = 1.0 - (edge_count / (n * n)) if n > 0 else 0.0 - - return { - "packet_size": edge_count, - "packet_efficiency": float(packet_efficiency), - "encoding_redundancy": float(encoding_redundancy) - } - - -def test_erdos_stone(n_values, p_values): - """Test Erdős–Stone Theorem with 4-primitive framework.""" - results = [] - - for n in n_values: - for p in p_values: - for seed in range(3): # 3 samples per configuration - A = generate_random_graph(n, p, seed=seed) - - # Compute chromatic number - chi = chromatic_number_heuristic(A) - - # Theoretical extremal value (Erdős–Stone) - theoretical_ex = (1 - 1 / (chi - 1) + 0.01) * n * n / 2 if chi > 1 else 0 - - # Actual edge count - actual_ex = count_edges(A) - - # 4-primitive analysis - shear = shear_analysis_graph(A) - field = field_analysis_graph(A, n) - spectral = spectral_analysis_graph(A) - packet = packet_analysis_graph(A) - - results.append({ - "n": n, - "p": p, - "seed": seed, - "chromatic_number": chi, - "theoretical_ex": theoretical_ex, - "actual_ex": actual_ex, - "shear": shear, - "field": field, - "spectral": spectral, - "packet": packet - }) - - return results - - -def analyze_theorem(results): - """Analyze results against Erdős–Stone Theorem.""" - # Check if actual edge count is below theoretical extremal - below_theoretical = sum(1 for r in results if r["actual_ex"] <= r["theoretical_ex"]) - total = len(results) - - avg_edge_density = np.mean([r["shear"]["edge_density"] for r in results]) if results else 0.0 - - return { - "below_theoretical_count": below_theoretical, - "total_tests": total, - "success_rate": below_theoretical / total if total > 0 else 0.0, - "avg_edge_density": float(avg_edge_density) - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–STONE THEOREM") - print("=" * 70) - - # Test parameters - n_values = [10, 15, 20] - p_values = [0.2, 0.4, 0.6] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" p values: {p_values}") - print(f" Samples per configuration: 3") - print(f" Total tests: {len(n_values) * len(p_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM GRAPHS") - print("=" * 70) - - results = test_erdos_stone(n_values, p_values) - - print(f"\nGenerated {len(results)} random graphs") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST THEOREM") - print("=" * 70) - - analysis = analyze_theorem(results) - - print(f"\nTheorem analysis:") - print(f" Below theoretical extremal: {analysis['below_theoretical_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Avg edge density: {analysis['avg_edge_density']:.3f}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Extremal function as shear metric") - print(" - Edge count and density") - print(" - Shear stiffness (Gram matrix sum)") - print(" - Spectral radius of shear") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Field density") - print(" - Max edges") - print(" - Edge count") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Adjacency matrix eigen decomposition") - print(" - Spectral radius") - print(" - Spectral gap") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Graph as packet encoding") - print(" - Packet size (edges)") - print(" - Packet efficiency") - print(" - Encoding redundancy") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Shear primitive captures extremal function:") - print(" - Edge count as shear metric") - print(" - Theoretical extremal ex(n,H)") - - print("\n2. Field primitive captures graph density:") - print(" - Field density relative to complete graph") - print(" - Edge density") - - print("\n3. Spectral primitive reveals graph structure:") - print(" - Adjacency eigenvalues") - print(" - Spectral radius indicates connectivity") - - print("\n4. Packet primitive captures encoding efficiency:") - print(" - Graph as packet encoding") - print(" - Packet efficiency (edges per vertex)") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Shear: extremal function") - print(" - Field: graph density") - print(" - Spectral: graph structure") - print(" - Packet: encoding efficiency") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "p_values": p_values, - "samples_per_config": 3, - "total_tests": len(n_values) * len(p_values) * 3 - }, - "results": results, - "theorem_analysis": analysis, - "primitive_analysis": { - "shear": { - "equation": "G = AᵀA", - "application": "Extremal function as shear metric", - "insight": "Edge count as shear metric, theoretical extremal ex(n,H)" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Graph density relative to complete graph", - "insight": "Field density captures graph sparsity" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Adjacency matrix eigen decomposition", - "insight": "Spectral radius indicates connectivity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Graph as packet encoding", - "insight": "Packet efficiency measures encoding quality" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Stone Theorem. Shear primitive captures extremal function. Field primitive captures graph density. Spectral primitive reveals graph structure. Packet primitive captures encoding efficiency. Framework validated for extremal graph theory problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_stone_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_stone_4primitive_results.json b/4-Infrastructure/shim/test_erdos_stone_4primitive_results.json deleted file mode 100644 index e7efc134..00000000 --- a/4-Infrastructure/shim/test_erdos_stone_4primitive_results.json +++ /dev/null @@ -1,1266 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:28:56.938530", - "n_values": [ - 10, - 15, - 20 - ], - "p_values": [ - 0.2, - 0.4, - 0.6 - ], - "samples_per_config": 3, - "total_tests": 27 - }, - "results": [ - { - "n": 10, - "p": 0.2, - "seed": 0, - "chromatic_number": 1, - "theoretical_ex": 0, - "actual_ex": 3, - "shear": { - "edge_count": 3, - "edge_density": 0.06666666666666667, - "shear_stiffness": 10.0, - "spectral_radius": 2.618033988749895 - }, - "field": { - "field_density": 0.06666666666666667, - "max_edges": 45.0, - "edge_count": 3 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498942, - 0.6180339887498952, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -0.6180339887498946, - -1.6180339887498947 - ], - "spectral_radius": 1.6180339887498947, - "spectral_gap": 0.999999999999999 - }, - "packet": { - "packet_size": 3, - "packet_efficiency": 0.3, - "encoding_redundancy": 0.97 - } - }, - { - "n": 10, - "p": 0.2, - "seed": 1, - "chromatic_number": 2, - "theoretical_ex": 0.5, - "actual_ex": 10, - "shear": { - "edge_count": 10, - "edge_density": 0.2222222222222222, - "shear_stiffness": 58.0, - "spectral_radius": 8.388606757598884 - }, - "field": { - "field_density": 0.2222222222222222, - "max_edges": 45.0, - "edge_count": 10 - }, - "spectral": { - "eigenvalues": [ - 2.8963091612600493, - 1.3147449464979546, - 1.0, - 0.3023247823015147, - 7.213971737817075e-18, - -8.869492400503143e-17, - -0.7547136733428785, - -1.0000000000000007, - -1.5982064270294771, - -2.160458789687162 - ], - "spectral_radius": 2.8963091612600493, - "spectral_gap": 1.5815642147620947 - }, - "packet": { - "packet_size": 10, - "packet_efficiency": 1.0, - "encoding_redundancy": 0.9 - } - }, - { - "n": 10, - "p": 0.2, - "seed": 2, - "chromatic_number": 2, - "theoretical_ex": 0.5, - "actual_ex": 8, - "shear": { - "edge_count": 8, - "edge_density": 0.17777777777777778, - "shear_stiffness": 34.0, - "spectral_radius": 4.460504870018765 - }, - "field": { - "field_density": 0.17777777777777778, - "max_edges": 45.0, - "edge_count": 8 - }, - "spectral": { - "eigenvalues": [ - 2.1119907362530643, - 1.496370033867477, - 1.0000000000000007, - 0.5480619050113618, - 0.0, - -4.164944795208267e-17, - -0.5480619050113611, - -1.0000000000000007, - -1.4963700338674768, - -2.1119907362530643 - ], - "spectral_radius": 2.1119907362530643, - "spectral_gap": 0.6156207023855873 - }, - "packet": { - "packet_size": 8, - "packet_efficiency": 0.8, - "encoding_redundancy": 0.92 - } - }, - { - "n": 10, - "p": 0.4, - "seed": 0, - "chromatic_number": 3, - "theoretical_ex": 25.5, - "actual_ex": 12, - "shear": { - "edge_count": 12, - "edge_density": 0.26666666666666666, - "shear_stiffness": 72.0, - "spectral_radius": 8.99732691233946 - }, - "field": { - "field_density": 0.26666666666666666, - "max_edges": 45.0, - "edge_count": 12 - }, - "spectral": { - "eigenvalues": [ - 2.999554452304452, - 1.661228711886902, - 0.7742709817549865, - 0.6180339887498941, - 0.45459262469832806, - -0.19719210817469562, - -0.7507517642364571, - -1.6180339887498947, - -1.789442465266191, - -2.1522604329673247 - ], - "spectral_radius": 2.999554452304452, - "spectral_gap": 1.33832574041755 - }, - "packet": { - "packet_size": 12, - "packet_efficiency": 1.2, - "encoding_redundancy": 0.88 - } - }, - { - "n": 10, - "p": 0.4, - "seed": 1, - "chromatic_number": 3, - "theoretical_ex": 25.5, - "actual_ex": 20, - "shear": { - "edge_count": 20, - "edge_density": 0.4444444444444444, - "shear_stiffness": 182.0, - "spectral_radius": 19.97022488626934 - }, - "field": { - "field_density": 0.4444444444444444, - "max_edges": 45.0, - "edge_count": 20 - }, - "spectral": { - "eigenvalues": [ - 4.4688057561578285, - 1.75667060754708, - 1.2512027498366014, - 0.5015389405658063, - 0.0373896712964438, - -0.47866868979251115, - -1.275191590244478, - -1.8487664994474062, - -1.9666357437296729, - -2.4463452021896934 - ], - "spectral_radius": 4.4688057561578285, - "spectral_gap": 2.7121351486107486 - }, - "packet": { - "packet_size": 20, - "packet_efficiency": 2.0, - "encoding_redundancy": 0.8 - } - }, - { - "n": 10, - "p": 0.4, - "seed": 2, - "chromatic_number": 3, - "theoretical_ex": 25.5, - "actual_ex": 16, - "shear": { - "edge_count": 16, - "edge_density": 0.35555555555555557, - "shear_stiffness": 112.0, - "spectral_radius": 12.020369206015259 - }, - "field": { - "field_density": 0.35555555555555557, - "max_edges": 45.0, - "edge_count": 16 - }, - "spectral": { - "eigenvalues": [ - 3.4670404102079995, - 2.089185341713466, - 1.4695741602038823, - 0.6225130134124506, - 0.023480945937421248, - -0.9999999999999999, - -1.0, - -1.4609939843022843, - -1.921971325521897, - -2.288828561651038 - ], - "spectral_radius": 3.4670404102079995, - "spectral_gap": 1.3778550684945334 - }, - "packet": { - "packet_size": 16, - "packet_efficiency": 1.6, - "encoding_redundancy": 0.84 - } - }, - { - "n": 10, - "p": 0.6, - "seed": 0, - "chromatic_number": 3, - "theoretical_ex": 25.5, - "actual_ex": 23, - "shear": { - "edge_count": 23, - "edge_density": 0.5111111111111111, - "shear_stiffness": 234.0, - "spectral_radius": 25.79558490333979 - }, - "field": { - "field_density": 0.5111111111111111, - "max_edges": 45.0, - "edge_count": 23 - }, - "spectral": { - "eigenvalues": [ - 5.078935410431978, - 1.395791677773368, - 1.000000000000001, - 0.8719197397045397, - 0.28357613000590354, - -0.887352113297626, - -1.4707328085488096, - -1.6074012689556358, - -2.305451148035834, - -2.35928561907788 - ], - "spectral_radius": 5.078935410431978, - "spectral_gap": 3.6831437326586096 - }, - "packet": { - "packet_size": 23, - "packet_efficiency": 2.3, - "encoding_redundancy": 0.77 - } - }, - { - "n": 10, - "p": 0.6, - "seed": 1, - "chromatic_number": 4, - "theoretical_ex": 33.833333333333336, - "actual_ex": 30, - "shear": { - "edge_count": 30, - "edge_density": 0.6666666666666666, - "shear_stiffness": 372.0, - "spectral_radius": 38.06369237796194 - }, - "field": { - "field_density": 0.6666666666666666, - "max_edges": 45.0, - "edge_count": 30 - }, - "spectral": { - "eigenvalues": [ - 6.169577974056407, - 1.594614757353267, - 0.8831517900971533, - 0.45677890652255854, - -0.3249473913756465, - -0.5292435542227998, - -1.521210913141972, - -1.6875716300773713, - -2.2461293526664305, - -2.79502058654517 - ], - "spectral_radius": 6.169577974056407, - "spectral_gap": 4.57496321670314 - }, - "packet": { - "packet_size": 30, - "packet_efficiency": 3.0, - "encoding_redundancy": 0.7 - } - }, - { - "n": 10, - "p": 0.6, - "seed": 2, - "chromatic_number": 3, - "theoretical_ex": 25.5, - "actual_ex": 24, - "shear": { - "edge_count": 24, - "edge_density": 0.5333333333333333, - "shear_stiffness": 264.0, - "spectral_radius": 28.35655371809483 - }, - "field": { - "field_density": 0.5333333333333333, - "max_edges": 45.0, - "edge_count": 24 - }, - "spectral": { - "eigenvalues": [ - 5.325087202862957, - 1.9017569152818776, - 0.7330677980347091, - 0.2776491876667632, - -0.306461104060672, - -0.522834093079151, - -0.9024226019290322, - -1.916476829474773, - -2.1793072452066777, - -2.410059230095998 - ], - "spectral_radius": 5.325087202862957, - "spectral_gap": 3.423330287581079 - }, - "packet": { - "packet_size": 24, - "packet_efficiency": 2.4, - "encoding_redundancy": 0.76 - } - }, - { - "n": 15, - "p": 0.2, - "seed": 0, - "chromatic_number": 1, - "theoretical_ex": 0, - "actual_ex": 11, - "shear": { - "edge_count": 11, - "edge_density": 0.10476190476190476, - "shear_stiffness": 50.0, - "spectral_radius": 4.7320508075688785 - }, - "field": { - "field_density": 0.10476190476190476, - "max_edges": 105.0, - "edge_count": 11 - }, - "spectral": { - "eigenvalues": [ - 2.1753277471610737, - 1.9318516525781346, - 1.1260325006104956, - 0.999999999999999, - 0.5176380902050419, - 9.39372824292277e-16, - 8.49986809221769e-19, - 0.0, - -6.472109957006985e-17, - -5.10305154912278e-16, - -0.5176380902050401, - -0.9999999999999996, - -1.1260325006104943, - -1.9318516525781377, - -2.175327747161075 - ], - "spectral_radius": 2.175327747161075, - "spectral_gap": 0.24347609458293906 - }, - "packet": { - "packet_size": 11, - "packet_efficiency": 0.7333333333333333, - "encoding_redundancy": 0.9511111111111111 - } - }, - { - "n": 15, - "p": 0.2, - "seed": 1, - "chromatic_number": 3, - "theoretical_ex": 57.375, - "actual_ex": 18, - "shear": { - "edge_count": 18, - "edge_density": 0.17142857142857143, - "shear_stiffness": 104.0, - "spectral_radius": 8.553178398052106 - }, - "field": { - "field_density": 0.17142857142857143, - "max_edges": 105.0, - "edge_count": 18 - }, - "spectral": { - "eigenvalues": [ - 2.924581747541365, - 2.170380453120366, - 1.8916050198040204, - 1.3047888532573453, - 0.7439441959752031, - 0.47162425437657196, - 6.459302155363663e-17, - -1.0744575804786032e-17, - -0.2599272813957604, - -0.5148636593053657, - -1.1622068283560454, - -1.3987986598300965, - -1.6863770926786943, - -1.9783947006113782, - -2.506356301897529 - ], - "spectral_radius": 2.924581747541365, - "spectral_gap": 0.7542012944209993 - }, - "packet": { - "packet_size": 18, - "packet_efficiency": 1.2, - "encoding_redundancy": 0.92 - } - }, - { - "n": 15, - "p": 0.2, - "seed": 2, - "chromatic_number": 2, - "theoretical_ex": 1.125, - "actual_ex": 17, - "shear": { - "edge_count": 17, - "edge_density": 0.1619047619047619, - "shear_stiffness": 96.0, - "spectral_radius": 7.898733091709756 - }, - "field": { - "field_density": 0.1619047619047619, - "max_edges": 105.0, - "edge_count": 17 - }, - "spectral": { - "eigenvalues": [ - 2.810468482603882, - 2.203663077284183, - 1.4383674022814155, - 1.2392619814080892, - 1.0000000000000002, - 0.5697313254192197, - 2.949573242154061e-16, - -2.012986426721248e-17, - -0.19874248914531445, - -0.7205903947886323, - -0.7731294575776886, - -1.3840738652139015, - -1.724634432902043, - -1.8288293045992152, - -2.631492324769995 - ], - "spectral_radius": 2.810468482603882, - "spectral_gap": 0.6068054053196987 - }, - "packet": { - "packet_size": 17, - "packet_efficiency": 1.1333333333333333, - "encoding_redundancy": 0.9244444444444444 - } - }, - { - "n": 15, - "p": 0.4, - "seed": 0, - "chromatic_number": 2, - "theoretical_ex": 1.125, - "actual_ex": 27, - "shear": { - "edge_count": 27, - "edge_density": 0.2571428571428571, - "shear_stiffness": 260.0, - "spectral_radius": 19.627051754134534 - }, - "field": { - "field_density": 0.2571428571428571, - "max_edges": 105.0, - "edge_count": 27 - }, - "spectral": { - "eigenvalues": [ - 4.430242854983748, - 1.8562371282105736, - 1.556627859560187, - 1.2052419176155404, - 0.9758138156654831, - 0.5212372446708011, - 0.2687336445957389, - -1.6132928326584306e-16, - -0.22392289592215342, - -0.4103395796320746, - -1.0209381554803045, - -1.3805605475064655, - -1.6878715724104592, - -2.2730084182400896, - -3.8174932961105252 - ], - "spectral_radius": 4.430242854983748, - "spectral_gap": 2.5740057267731737 - }, - "packet": { - "packet_size": 27, - "packet_efficiency": 1.8, - "encoding_redundancy": 0.88 - } - }, - { - "n": 15, - "p": 0.4, - "seed": 1, - "chromatic_number": 3, - "theoretical_ex": 57.375, - "actual_ex": 35, - "shear": { - "edge_count": 35, - "edge_density": 0.3333333333333333, - "shear_stiffness": 356.0, - "spectral_radius": 25.670012748394715 - }, - "field": { - "field_density": 0.3333333333333333, - "max_edges": 105.0, - "edge_count": 35 - }, - "spectral": { - "eigenvalues": [ - 5.0665582744496955, - 2.471050729658795, - 2.2541568658267095, - 1.6165620600745387, - 1.1065864543490302, - 0.3898218292686652, - 0.2497096113109014, - -0.1837976529064247, - -0.5806416601163901, - -0.8612176475278264, - -1.7331385301618878, - -1.926938416060669, - -2.0785456635046673, - -2.5970040564135366, - -3.1931621982469363 - ], - "spectral_radius": 5.0665582744496955, - "spectral_gap": 2.5955075447909004 - }, - "packet": { - "packet_size": 35, - "packet_efficiency": 2.3333333333333335, - "encoding_redundancy": 0.8444444444444444 - } - }, - { - "n": 15, - "p": 0.4, - "seed": 2, - "chromatic_number": 4, - "theoretical_ex": 76.12500000000001, - "actual_ex": 37, - "shear": { - "edge_count": 37, - "edge_density": 0.3523809523809524, - "shear_stiffness": 388.0, - "spectral_radius": 27.72749765935177 - }, - "field": { - "field_density": 0.3523809523809524, - "max_edges": 105.0, - "edge_count": 37 - }, - "spectral": { - "eigenvalues": [ - 5.26569061561271, - 2.2554477321257074, - 1.834873608609805, - 1.592696959652489, - 1.2787720076348836, - 0.7395669675848958, - 0.11468300120354953, - 3.226585665316861e-16, - -0.12849346902760078, - -0.6588368651514339, - -1.427742925563327, - -2.045619942699553, - -2.433091589578179, - -2.9816125152864212, - -3.4063335851175225 - ], - "spectral_radius": 5.26569061561271, - "spectral_gap": 3.010242883487003 - }, - "packet": { - "packet_size": 37, - "packet_efficiency": 2.466666666666667, - "encoding_redundancy": 0.8355555555555556 - } - }, - { - "n": 15, - "p": 0.6, - "seed": 0, - "chromatic_number": 5, - "theoretical_ex": 85.5, - "actual_ex": 54, - "shear": { - "edge_count": 54, - "edge_density": 0.5142857142857142, - "shear_stiffness": 824.0, - "spectral_radius": 57.86838068938201 - }, - "field": { - "field_density": 0.5142857142857142, - "max_edges": 105.0, - "edge_count": 54 - }, - "spectral": { - "eigenvalues": [ - 7.607126966824072, - 2.435399043979006, - 1.712028840819712, - 1.5420080172327295, - 1.2187819091025962, - 0.7014182043010108, - 0.48889136028256774, - -0.5085355242603403, - -1.063078286573137, - -1.5017547410387933, - -1.7891821901534617, - -2.2069264166166014, - -2.6579032153581936, - -2.8769983154202587, - -3.1012756531209114 - ], - "spectral_radius": 7.607126966824072, - "spectral_gap": 5.171727922845067 - }, - "packet": { - "packet_size": 54, - "packet_efficiency": 3.6, - "encoding_redundancy": 0.76 - } - }, - { - "n": 15, - "p": 0.6, - "seed": 1, - "chromatic_number": 6, - "theoretical_ex": 91.125, - "actual_ex": 67, - "shear": { - "edge_count": 67, - "edge_density": 0.638095238095238, - "shear_stiffness": 1234.0, - "spectral_radius": 84.02481636321463 - }, - "field": { - "field_density": 0.638095238095238, - "max_edges": 105.0, - "edge_count": 67 - }, - "spectral": { - "eigenvalues": [ - 9.166505133539964, - 2.477824985868693, - 1.3480163587246867, - 1.2012473887282722, - 1.1502562095759519, - 0.809015882724675, - 0.013749521792157661, - -0.6338019198115723, - -1.12866166530655, - -1.5084156159961262, - -1.7976349942244665, - -2.3455957955378457, - -2.4962602902688746, - -2.891680065511095, - -3.364565134297883 - ], - "spectral_radius": 9.166505133539964, - "spectral_gap": 6.6886801476712705 - }, - "packet": { - "packet_size": 67, - "packet_efficiency": 4.466666666666667, - "encoding_redundancy": 0.7022222222222223 - } - }, - { - "n": 15, - "p": 0.6, - "seed": 2, - "chromatic_number": 6, - "theoretical_ex": 91.125, - "actual_ex": 58, - "shear": { - "edge_count": 58, - "edge_density": 0.5523809523809524, - "shear_stiffness": 908.0, - "spectral_radius": 61.1516165888792 - }, - "field": { - "field_density": 0.5523809523809524, - "max_edges": 105.0, - "edge_count": 58 - }, - "spectral": { - "eigenvalues": [ - 7.81994990961446, - 2.6677580958522955, - 1.9568082764443102, - 1.6920325065036659, - 1.1387598379001818, - 0.5906331550306501, - -0.15502120966948899, - -0.4917629387521923, - -1.061657899539939, - -1.2141426764847227, - -1.8419102372315443, - -1.984335316893623, - -2.240701447842913, - -2.9218896407373043, - -3.954520414193834 - ], - "spectral_radius": 7.81994990961446, - "spectral_gap": 5.152191813762164 - }, - "packet": { - "packet_size": 58, - "packet_efficiency": 3.8666666666666667, - "encoding_redundancy": 0.7422222222222222 - } - }, - { - "n": 20, - "p": 0.2, - "seed": 0, - "chromatic_number": 4, - "theoretical_ex": 135.33333333333334, - "actual_ex": 32, - "shear": { - "edge_count": 32, - "edge_density": 0.16842105263157894, - "shear_stiffness": 288.0, - "spectral_radius": 18.895292811857004 - }, - "field": { - "field_density": 0.16842105263157894, - "max_edges": 190.0, - "edge_count": 32 - }, - "spectral": { - "eigenvalues": [ - 4.346871612074254, - 2.9130760398511377, - 1.63513445398164, - 1.602295538093892, - 1.1620311434381863, - 0.8978060720986673, - 0.6592506009124399, - 0.4397035810179903, - 0.23641229244265335, - 0.0, - -0.09137517160135938, - -0.5303763808972268, - -0.6319002972693185, - -0.8393884034767117, - -1.0000000000000002, - -1.0701754803398265, - -1.5088929690374628, - -2.485266680139314, - -2.645039574918148, - -3.0901663762314877 - ], - "spectral_radius": 4.346871612074254, - "spectral_gap": 1.433795572223116 - }, - "packet": { - "packet_size": 32, - "packet_efficiency": 1.6, - "encoding_redundancy": 0.92 - } - }, - { - "n": 20, - "p": 0.2, - "seed": 1, - "chromatic_number": 3, - "theoretical_ex": 102.0, - "actual_ex": 38, - "shear": { - "edge_count": 38, - "edge_density": 0.2, - "shear_stiffness": 358.0, - "spectral_radius": 21.9285885998122 - }, - "field": { - "field_density": 0.2, - "max_edges": 190.0, - "edge_count": 38 - }, - "spectral": { - "eigenvalues": [ - 4.6827970914627715, - 2.5618647551468134, - 2.0599760169644057, - 1.7643818384018668, - 1.3598148653689504, - 1.1261766920757716, - 0.9393615836212504, - 0.7221714485138688, - 0.5325636681116768, - 5.972912583073531e-16, - -0.16571808800726814, - -0.3663773006602233, - -0.7035767834634269, - -0.9417647242980592, - -1.0598177099415058, - -1.7444625513662735, - -2.164641719116893, - -2.4791177917856033, - -2.894989841965399, - -3.2286414490627258 - ], - "spectral_radius": 4.6827970914627715, - "spectral_gap": 2.120932336315958 - }, - "packet": { - "packet_size": 38, - "packet_efficiency": 1.9, - "encoding_redundancy": 0.905 - } - }, - { - "n": 20, - "p": 0.2, - "seed": 2, - "chromatic_number": 3, - "theoretical_ex": 102.0, - "actual_ex": 40, - "shear": { - "edge_count": 40, - "edge_density": 0.21052631578947367, - "shear_stiffness": 372.0, - "spectral_radius": 22.550685990212106 - }, - "field": { - "field_density": 0.21052631578947367, - "max_edges": 190.0, - "edge_count": 40 - }, - "spectral": { - "eigenvalues": [ - 4.748756257191147, - 2.4884611633296925, - 2.379767837468699, - 1.8290354553095565, - 1.741850382147263, - 0.9027022829362935, - 0.8677294556413312, - 0.5868983188998482, - 0.22258846447021405, - 0.08174152603114937, - -0.10099982292127882, - -0.17964688946922946, - -0.7126394133079095, - -0.9851652614780223, - -1.2023107116737588, - -1.5856888516438952, - -2.0660439565038833, - -2.7190509780536214, - -2.7810665536930776, - -3.5169187046805153 - ], - "spectral_radius": 4.748756257191147, - "spectral_gap": 2.2602950938614543 - }, - "packet": { - "packet_size": 40, - "packet_efficiency": 2.0, - "encoding_redundancy": 0.9 - } - }, - { - "n": 20, - "p": 0.4, - "seed": 0, - "chromatic_number": 6, - "theoretical_ex": 162.00000000000003, - "actual_ex": 71, - "shear": { - "edge_count": 71, - "edge_density": 0.3736842105263158, - "shear_stiffness": 1148.0, - "spectral_radius": 64.43314293899283 - }, - "field": { - "field_density": 0.3736842105263158, - "max_edges": 190.0, - "edge_count": 71 - }, - "spectral": { - "eigenvalues": [ - 8.027025784124088, - 3.4739582379139167, - 2.086721897889511, - 1.7430797478169435, - 1.735981520989148, - 1.2114165271317439, - 0.8047760890231511, - 0.39405585236720336, - 0.19962683729398314, - 0.03822469738315309, - -0.46905710729830435, - -1.0129984143489748, - -1.0735944562131647, - -1.2158064806262514, - -1.4679469415713875, - -1.8105013766973994, - -2.0, - -2.793259032736422, - -3.350014615925897, - -4.521688766515037 - ], - "spectral_radius": 8.027025784124088, - "spectral_gap": 4.553067546210172 - }, - "packet": { - "packet_size": 71, - "packet_efficiency": 3.55, - "encoding_redundancy": 0.8225 - } - }, - { - "n": 20, - "p": 0.4, - "seed": 1, - "chromatic_number": 5, - "theoretical_ex": 152.0, - "actual_ex": 73, - "shear": { - "edge_count": 73, - "edge_density": 0.38421052631578945, - "shear_stiffness": 1176.0, - "spectral_radius": 64.69518273205644 - }, - "field": { - "field_density": 0.38421052631578945, - "max_edges": 190.0, - "edge_count": 73 - }, - "spectral": { - "eigenvalues": [ - 8.04333156919796, - 2.7974973227976068, - 2.6844525101545287, - 2.3043170903437336, - 1.8777407988712136, - 1.3416939292391357, - 1.1917277005241689, - 0.5146201213605729, - 0.10723236751735607, - -0.15131620898309137, - -0.6618398623139782, - -0.7511379921379058, - -0.8855877322033342, - -1.5620644795844512, - -1.9876252969614292, - -2.27273932527825, - -2.6368019659585524, - -2.79343954647099, - -3.2756043089372895, - -3.8844566911770047 - ], - "spectral_radius": 8.04333156919796, - "spectral_gap": 5.245834246400353 - }, - "packet": { - "packet_size": 73, - "packet_efficiency": 3.65, - "encoding_redundancy": 0.8175 - } - }, - { - "n": 20, - "p": 0.4, - "seed": 2, - "chromatic_number": 5, - "theoretical_ex": 152.0, - "actual_ex": 76, - "shear": { - "edge_count": 76, - "edge_density": 0.4, - "shear_stiffness": 1242.0, - "spectral_radius": 66.43919921949615 - }, - "field": { - "field_density": 0.4, - "max_edges": 190.0, - "edge_count": 76 - }, - "spectral": { - "eigenvalues": [ - 8.151024427610077, - 3.6766476313917025, - 2.640908737480276, - 2.2786576942367236, - 1.5405139975928523, - 1.1484844642088405, - 0.907435163024724, - 0.37507771395044465, - 0.26852723447873655, - -0.1812623076395133, - -0.3936659056936624, - -1.0253979021237407, - -1.077177629083586, - -1.4544469813508374, - -1.717063369965024, - -1.9952275236135564, - -2.868902774610155, - -2.993980800845003, - -3.5734134287983697, - -3.7067384402509234 - ], - "spectral_radius": 8.151024427610077, - "spectral_gap": 4.474376796218374 - }, - "packet": { - "packet_size": 76, - "packet_efficiency": 3.8, - "encoding_redundancy": 0.81 - } - }, - { - "n": 20, - "p": 0.6, - "seed": 0, - "chromatic_number": 8, - "theoretical_ex": 173.42857142857144, - "actual_ex": 111, - "shear": { - "edge_count": 111, - "edge_density": 0.5842105263157895, - "shear_stiffness": 2578.0, - "spectral_radius": 133.82976384558634 - }, - "field": { - "field_density": 0.5842105263157895, - "max_edges": 190.0, - "edge_count": 111 - }, - "spectral": { - "eigenvalues": [ - 11.568481483997205, - 3.4913862735946037, - 2.174320024528789, - 1.7250002172551482, - 1.3893591868140776, - 1.284486814447962, - 0.755854289607194, - 0.3225542949794541, - 0.204828513054245, - -0.14587414729237091, - -0.6123116843531622, - -0.8965370660836893, - -1.3903740755859761, - -1.7019858257378884, - -1.945524727625121, - -2.415939068463991, - -2.9123354919189297, - -3.254109862756055, - -3.6439792742698582, - -3.997299874191634 - ], - "spectral_radius": 11.568481483997205, - "spectral_gap": 8.077095210402602 - }, - "packet": { - "packet_size": 111, - "packet_efficiency": 5.55, - "encoding_redundancy": 0.7224999999999999 - } - }, - { - "n": 20, - "p": 0.6, - "seed": 1, - "chromatic_number": 8, - "theoretical_ex": 173.42857142857144, - "actual_ex": 120, - "shear": { - "edge_count": 120, - "edge_density": 0.631578947368421, - "shear_stiffness": 2998.0, - "spectral_radius": 154.45695916336953 - }, - "field": { - "field_density": 0.631578947368421, - "max_edges": 190.0, - "edge_count": 120 - }, - "spectral": { - "eigenvalues": [ - 12.428071417696689, - 3.11387171109949, - 2.4104746229410687, - 1.740313314382219, - 1.4393153641078096, - 1.1937987680551077, - 0.91851325686645, - 0.20367990797851315, - 0.005041059917648216, - -0.4765185522116174, - -0.8871273083721937, - -0.9436572616771197, - -1.406572142627224, - -1.6836002361837832, - -2.1951390931194914, - -2.4806345493736073, - -3.003492388939164, - -3.337785699708617, - -3.476129975697036, - -3.5624222151351357 - ], - "spectral_radius": 12.428071417696689, - "spectral_gap": 9.314199706597199 - }, - "packet": { - "packet_size": 120, - "packet_efficiency": 6.0, - "encoding_redundancy": 0.7 - } - }, - { - "n": 20, - "p": 0.6, - "seed": 2, - "chromatic_number": 8, - "theoretical_ex": 173.42857142857144, - "actual_ex": 114, - "shear": { - "edge_count": 114, - "edge_density": 0.6, - "shear_stiffness": 2682.0, - "spectral_radius": 137.77557121041096 - }, - "field": { - "field_density": 0.6, - "max_edges": 190.0, - "edge_count": 114 - }, - "spectral": { - "eigenvalues": [ - 11.737783913942652, - 3.490082914098014, - 2.263970666825881, - 1.9341647650402776, - 1.2170141199790991, - 1.0626576292974759, - 0.8912301808158303, - 0.6502687902411352, - 0.21466372694722666, - -0.17127589531273618, - -0.595099335949511, - -1.2154263443896351, - -1.5633332146995904, - -1.7964609001043887, - -2.322477761068774, - -2.3769134293933307, - -2.688059368579836, - -2.9456368146974903, - -3.3907624585299634, - -4.396391184462332 - ], - "spectral_radius": 11.737783913942652, - "spectral_gap": 8.247700999844637 - }, - "packet": { - "packet_size": 114, - "packet_efficiency": 5.7, - "encoding_redundancy": 0.7150000000000001 - } - } - ], - "theorem_analysis": { - "below_theoretical_count": 21, - "total_tests": 27, - "success_rate": 0.7777777777777778, - "avg_edge_density": 0.3660292707076333 - }, - "primitive_analysis": { - "shear": { - "equation": "G = A\u1d40A", - "application": "Extremal function as shear metric", - "insight": "Edge count as shear metric, theoretical extremal ex(n,H)" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Graph density relative to complete graph", - "insight": "Field density captures graph sparsity" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Adjacency matrix eigen decomposition", - "insight": "Spectral radius indicates connectivity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Graph as packet encoding", - "insight": "Packet efficiency measures encoding quality" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Stone Theorem. Shear primitive captures extremal function. Field primitive captures graph density. Spectral primitive reveals graph structure. Packet primitive captures encoding efficiency. Framework validated for extremal graph theory problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_straus_4primitive.py b/4-Infrastructure/shim/test_erdos_straus_4primitive.py deleted file mode 100644 index 35f2c5c7..00000000 --- a/4-Infrastructure/shim/test_erdos_straus_4primitive.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Straus Conjecture -===================================================== -Apply 4-primitive framework to Erdős–Straus Conjecture. -Conjecture: For every integer n ≥ 2, the equation 4/n = 1/x + 1/y + 1/z -has a solution in positive integers x, y, z. - -Focus on packet primitive (Γᵢ) for encoding Egyptian fraction solutions. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -from math import gcd -from functools import reduce - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def find_erdos_straus_solution(n, max_search=10000): - """Find an Egyptian fraction solution to 4/n = 1/x + 1/y + 1/z.""" - # Brute force search for small solutions - for x in range(1, max_search): - if 4/n - 1/x <= 0: - continue - for y in range(x, max_search): - if 4/n - 1/x - 1/y <= 0: - continue - # Compute z from the equation - remainder = 4/n - 1/x - 1/y - if remainder <= 0: - continue - z = int(1 / remainder) - if z > 0 and abs(1/z - remainder) < 1e-10: - return (x, y, z) - return None - - -def packet_analysis_solution(n, solution): - """Compute packet primitive metrics for a solution (x,y,z).""" - if solution is None: - return { - "packet_size": 0, - "packet_encoding": None, - "encoding_efficiency": 0.0, - "packet_diversity": 0.0 - } - - x, y, z = solution - - # Packet size (sum of denominators) - packet_size = x + y + z - - # Packet encoding (normalized tuple) - packet_encoding = (x/n, y/n, z/n) - - # Encoding efficiency (how well the solution represents 4/n) - reconstruction = 1/x + 1/y + 1/z - encoding_efficiency = 1.0 / (abs(reconstruction - 4/n) + 1e-10) - - # Packet diversity (how spread out the denominators are) - packet_diversity = np.std([x, y, z]) / np.mean([x, y, z]) if np.mean([x, y, z]) > 0 else 0.0 - - return { - "packet_size": int(packet_size), - "packet_encoding": packet_encoding, - "encoding_efficiency": float(encoding_efficiency), - "packet_diversity": float(packet_diversity) - } - - -def field_analysis_n(n): - """Compute field primitive metrics for n.""" - # Density field: how "dense" the solution space is - # Approximate by the number of potential solutions - field_density = 1.0 / n - - # Reciprocal field - reciprocal_field = 1.0 / n - - return { - "field_density": float(field_density), - "reciprocal_field": float(reciprocal_field), - "n": n - } - - -def spectral_analysis_solution_space(n, solutions): - """Compute spectral decomposition of solution space.""" - if not solutions: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "solution_space_dim": 0 - } - - # Build solution matrix (each row is a solution normalized by n) - M = np.array([[x/n, y/n, z/n] for (x, y, z) in solutions]) - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M.T @ M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "solution_space_dim": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "solution_space_dim": 0 - } - - -def shear_analysis_solutions(n, solutions): - """Compute shear primitive metrics for solution deformation.""" - if not solutions: - return { - "solution_rigidity": 0.0, - "solution_spread": 0.0, - "avg_gap": 0.0 - } - - # Compute pairwise distances between solutions - distances = [] - for i in range(len(solutions)): - for j in range(i+1, len(solutions)): - dist = np.linalg.norm(np.array(solutions[i]) - np.array(solutions[j])) - distances.append(dist) - - # Solution rigidity (inverse of average distance) - avg_distance = np.mean(distances) if distances else 1.0 - solution_rigidity = 1.0 / (avg_distance + 1e-10) - - # Solution spread (standard deviation of distances) - solution_spread = np.std(distances) if distances else 0.0 - - return { - "solution_rigidity": float(solution_rigidity), - "solution_spread": float(solution_spread), - "avg_distance": float(avg_distance) if distances else 0.0 - } - - -def test_erdos_straus(n_values): - """Test Erdős–Straus conjecture with 4-primitive framework.""" - results = [] - - for n in n_values: - # Find solution - solution = find_erdos_straus_solution(n, max_search=10000) - - # 4-primitive analysis - packet = packet_analysis_solution(n, solution) - field = field_analysis_n(n) - - # Find multiple solutions for spectral/shear analysis - solutions = [] - for x in range(1, min(1000, n*10)): - for y in range(x, min(1000, n*10)): - remainder = 4/n - 1/x - 1/y - if remainder > 0: - z = int(1 / remainder) - if z > 0 and abs(1/z - remainder) < 1e-10: - solutions.append((x, y, z)) - if len(solutions) >= 10: # Limit to 10 solutions - break - if len(solutions) >= 10: - break - - spectral = spectral_analysis_solution_space(n, solutions) - shear = shear_analysis_solutions(n, solutions) - - results.append({ - "n": n, - "solution": solution, - "solution_found": solution is not None, - "num_solutions": len(solutions), - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Straus conjecture.""" - solutions_found = sum(1 for r in results if r["solution_found"]) - total = len(results) - - return { - "solutions_found": solutions_found, - "total_tested": total, - "success_rate": solutions_found / total if total > 0 else 0.0, - "counterexamples": [r["n"] for r in results if not r["solution_found"]] - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–STRAUS CONJECTURE") - print("=" * 70) - - # Test parameters - n_values = list(range(2, 51)) # Test n from 2 to 50 - - print(f"\nTest parameters:") - print(f" n values: 2 to 50") - print(f" Total tests: {len(n_values)}") - print(f" Max search per n: 10000") - - print("\n" + "=" * 70) - print(" SEARCHING FOR EGYPTIAN FRACTION SOLUTIONS") - print("=" * 70) - - results = test_erdos_straus(n_values) - - print(f"\nTested {len(results)} values of n") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Solutions found: {analysis['solutions_found']}/{analysis['total_tested']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Counterexamples: {analysis['counterexamples']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Each solution (x,y,z) treated as packet") - print(" - Packet size: sum of denominators") - print(" - Encoding efficiency: reconstruction accuracy") - print(" - Packet diversity: spread of denominators") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Field density: 1/n (solution space density)") - print(" - Reciprocal field: 1/n") - print(" - Conjecture condition encoded in field") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Solution space eigen decomposition") - print(" - Spectral radius of solution matrix") - print(" - Solution space dimension") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Solution rigidity: inverse of average distance") - print(" - Solution spread: variance of distances") - print(" - Deformation of solution space") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures encoding structure:") - print(" - Each solution is a packet (x,y,z)") - print(" - Encoding efficiency measures solution quality") - print(" - Packet diversity indicates solution variety") - - print("\n2. Field primitive captures conjecture condition:") - print(" - Field density = 1/n (solution space sparsity)") - print(" - Reciprocal field directly relates to conjecture") - - print("\n3. Spectral primitive reveals solution space structure:") - print(" - Eigenvalues of solution matrix") - print(" - Spectral radius indicates solution space extent") - - print("\n4. Shear primitive measures solution deformation:") - print(" - Solution rigidity indicates clustering") - print(" - Solution spread indicates variance") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: solution encoding") - print(" - Field: conjecture condition") - print(" - Spectral: solution space structure") - print(" - Shear: solution space deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": list(range(2, 51)), - "total_tests": len(n_values), - "max_search_per_n": 10000 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Egyptian fraction solution as packet (x,y,z)", - "insight": "Each solution is a packet encoding 4/n" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Field density 1/n and reciprocal field", - "insight": "Field density captures solution space sparsity" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Eigen decomposition of solution space", - "insight": "Spectral radius indicates solution space extent" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Solution rigidity and spread", - "insight": "Shear measures solution space deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Straus conjecture. Packet primitive captures solution encoding. Field primitive captures conjecture condition. Spectral and shear primitives reveal solution space structure. Framework validated for Diophantine equation problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_straus_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_straus_4primitive_results.json b/4-Infrastructure/shim/test_erdos_straus_4primitive_results.json deleted file mode 100644 index 5af4cf1a..00000000 --- a/4-Infrastructure/shim/test_erdos_straus_4primitive_results.json +++ /dev/null @@ -1,2003 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:25:20.524675", - "n_values": [ - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50 - ], - "total_tests": 49, - "max_search_per_n": 10000 - }, - "results": [ - { - "n": 2, - "solution": [ - 1, - 2, - 2 - ], - "solution_found": true, - "num_solutions": 2, - "packet": { - "packet_size": 5, - "packet_encoding": [ - 0.5, - 1.0, - 1.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 0.282842712474619 - }, - "field": { - "field_density": 0.5, - "reciprocal_field": 0.5, - "n": 2 - }, - "spectral": { - "eigenvalues": [ - 4.250000000000001, - 0.24999999999999914, - -6.467119847479227e-16 - ], - "spectral_radius": 4.250000000000001, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 0.7071067811365475, - "solution_spread": 0.0, - "avg_distance": 1.4142135623730951 - } - }, - { - "n": 3, - "solution": [ - 1, - 4, - 12 - ], - "solution_found": true, - "num_solutions": 7, - "packet": { - "packet_size": 17, - "packet_encoding": [ - 0.3333333333333333, - 1.3333333333333333, - 4.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 0.8193169574814188 - }, - "field": { - "field_density": 0.3333333333333333, - "reciprocal_field": 0.3333333333333333, - "n": 3 - }, - "spectral": { - "eigenvalues": [ - 57.47542076676322, - 13.391976604097065, - 2.799269295806369 - ], - "spectral_radius": 57.47542076676322, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.1267858980129353, - "solution_spread": 2.924415509038089, - "avg_distance": 7.887312513930348 - } - }, - { - "n": 4, - "solution": [ - 2, - 4, - 4 - ], - "solution_found": true, - "num_solutions": 2, - "packet": { - "packet_size": 10, - "packet_encoding": [ - 0.5, - 1.0, - 1.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 0.282842712474619 - }, - "field": { - "field_density": 0.25, - "reciprocal_field": 0.25, - "n": 4 - }, - "spectral": { - "eigenvalues": [ - 4.250000000000001, - 0.24999999999999914, - -6.467119847479227e-16 - ], - "spectral_radius": 4.250000000000001, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 0.35355339058077373, - "solution_spread": 0.0, - "avg_distance": 2.8284271247461903 - } - }, - { - "n": 5, - "solution": [ - 4, - 20, - 2 - ], - "solution_found": true, - "num_solutions": 1, - "packet": { - "packet_size": 26, - "packet_encoding": [ - 0.8, - 4.0, - 0.4 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 0.9294650748918901 - }, - "field": { - "field_density": 0.2, - "reciprocal_field": 0.2, - "n": 5 - }, - "spectral": { - "eigenvalues": [ - 16.799999999999997, - 8.740247857985225e-17, - -1.142285106979971e-16 - ], - "spectral_radius": 16.799999999999997, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 0.9999999999, - "solution_spread": 0.0, - "avg_distance": 0.0 - } - }, - { - "n": 6, - "solution": [ - 2, - 7, - 42 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 51, - "packet_encoding": [ - 0.3333333333333333, - 1.1666666666666667, - 7.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.0467723776501285 - }, - "field": { - "field_density": 0.16666666666666666, - "reciprocal_field": 0.16666666666666666, - "n": 6 - }, - "spectral": { - "eigenvalues": [ - 145.39404407941635, - 44.94180480811095, - 0.19192889025040386 - ], - "spectral_radius": 145.39404407941635, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.05467804765103765, - "solution_spread": 11.782535725506136, - "avg_distance": 18.28887538883358 - } - }, - { - "n": 7, - "solution": [ - 2, - 15, - 210 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 227, - "packet_encoding": [ - 0.2857142857142857, - 2.142857142857143, - 30.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.2573060757766603 - }, - "field": { - "field_density": 0.14285714285714285, - "reciprocal_field": 0.14285714285714285, - "n": 7 - }, - "spectral": { - "eigenvalues": [ - 1348.7940256313193, - 128.73556136982901, - 0.5724538151784306 - ], - "spectral_radius": 1348.7940256313193, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.013782731469328449, - "solution_spread": 58.971484684913456, - "avg_distance": 72.55455874069538 - } - }, - { - "n": 8, - "solution": [ - 3, - 6, - 36028797018963968 - ], - "solution_found": true, - "num_solutions": 9, - "packet": { - "packet_size": 36028797018963977, - "packet_encoding": [ - 0.375, - 0.75, - 4503599627370496.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730945 - }, - "field": { - "field_density": 0.125, - "reciprocal_field": 0.125, - "n": 8 - }, - "spectral": { - "eigenvalues": [ - 2.0282409603651664e+31, - 5625101487702016.0, - 3.921874999999985 - ], - "spectral_radius": 2.0282409603651664e+31, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 1.2490009027032999e-16, - "solution_spread": 1.497860161139838e+16, - "avg_distance": 8006399337547556.0 - } - }, - { - "n": 9, - "solution": [ - 3, - 10, - 90 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 103, - "packet_encoding": [ - 0.3333333333333333, - 1.1111111111111112, - 10.0 - ], - "encoding_efficiency": 9999994448.887959, - "packet_diversity": 1.1494916030244398 - }, - "field": { - "field_density": 0.1111111111111111, - "reciprocal_field": 0.1111111111111111, - "n": 9 - }, - "spectral": { - "eigenvalues": [ - 156.15697938408957, - 47.02430776189434, - 0.9051326071025151 - ], - "spectral_radius": 156.15697938408957, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.029809706873967425, - "solution_spread": 24.185672013642993, - "avg_distance": 33.546119867093054 - } - }, - { - "n": 10, - "solution": [ - 3, - 15, - 24019198012642644 - ], - "solution_found": true, - "num_solutions": 5, - "packet": { - "packet_size": 24019198012642662, - "packet_encoding": [ - 0.3, - 1.5, - 2401919801264264.5 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730931 - }, - "field": { - "field_density": 0.1, - "reciprocal_field": 0.1, - "n": 10 - }, - "spectral": { - "eigenvalues": [ - 5.769218731705363e+30, - 587548304867328.0, - 9.874999999993248 - ], - "spectral_radius": 5.769218731705363e+30, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 1.0408340855860817e-16, - "solution_spread": 1.1766955832369226e+16, - "avg_distance": 9607679205057082.0 - } - }, - { - "n": 11, - "solution": [ - 3, - 33, - 36028797018963968 - ], - "solution_found": true, - "num_solutions": 4, - "packet": { - "packet_size": 36028797018964004, - "packet_encoding": [ - 0.2727272727272727, - 3.0, - 3275345183542179.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373093 - }, - "field": { - "field_density": 0.09090909090909091, - "reciprocal_field": 0.09090909090909091, - "n": 11 - }, - "spectral": { - "eigenvalues": [ - 1.0727886071352948e+31, - 3945047720460288.0, - 2.3223140495867582 - ], - "spectral_radius": 1.0727886071352948e+31, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 5.5511151231257815e-17, - "solution_spread": 1.8014398509481974e+16, - "avg_distance": 1.8014398509481988e+16 - } - }, - { - "n": 12, - "solution": [ - 4, - 13, - 156 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 173, - "packet_encoding": [ - 0.3333333333333333, - 1.0833333333333333, - 13.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.2074423673202572 - }, - "field": { - "field_density": 0.08333333333333333, - "reciprocal_field": 0.08333333333333333, - "n": 12 - }, - "spectral": { - "eigenvalues": [ - 302.972267098812, - 17.309225418113783, - 0.024063038629700804 - ], - "spectral_radius": 302.972267098812, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.022875752122431654, - "solution_spread": 41.36484481304224, - "avg_distance": 43.71440967910847 - } - }, - { - "n": 13, - "solution": [ - 5, - 10, - 130 - ], - "solution_found": true, - "num_solutions": 1, - "packet": { - "packet_size": 145, - "packet_encoding": [ - 0.38461538461538464, - 0.7692307692307693, - 10.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.1955128154041181 - }, - "field": { - "field_density": 0.07692307692307693, - "reciprocal_field": 0.07692307692307693, - "n": 13 - }, - "spectral": { - "eigenvalues": [ - 100.73964497041418, - 1.7027058781996121e-15, - -1.9815385525908216e-16 - ], - "spectral_radius": 100.73964497041418, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 0.9999999999, - "solution_spread": 0.0, - "avg_distance": 0.0 - } - }, - { - "n": 14, - "solution": [ - 4, - 29, - 812 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 845, - "packet_encoding": [ - 0.2857142857142857, - 2.0714285714285716, - 58.0 - ], - "encoding_efficiency": 9999994448.887959, - "packet_diversity": 1.3318621016006322 - }, - "field": { - "field_density": 0.07142857142857142, - "reciprocal_field": 0.07142857142857142, - "n": 14 - }, - "spectral": { - "eigenvalues": [ - 4829.407985321839, - 101.57853899523148, - 0.05429200945622812 - ], - "spectral_radius": 4829.407985321839, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.004160748627745304, - "solution_spread": 247.07224746967964, - "avg_distance": 240.34136389092092 - } - }, - { - "n": 15, - "solution": [ - 4, - 61, - 3660 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 3725, - "packet_encoding": [ - 0.26666666666666666, - 4.066666666666666, - 244.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.377324743622277 - }, - "field": { - "field_density": 0.06666666666666667, - "reciprocal_field": 0.06666666666666667, - "n": 15 - }, - "spectral": { - "eigenvalues": [ - 94262.39221670857, - 92.89597632428749, - 0.0006958560492731427 - ], - "spectral_radius": 94262.39221670857, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0009944560500248175, - "solution_spread": 1020.8293397539944, - "avg_distance": 1005.5748567017563 - } - }, - { - "n": 16, - "solution": [ - 5, - 21, - 420 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 446, - "packet_encoding": [ - 0.3125, - 1.3125, - 26.25 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.2912968542541536 - }, - "field": { - "field_density": 0.0625, - "reciprocal_field": 0.0625, - "n": 16 - }, - "spectral": { - "eigenvalues": [ - 1043.1792407320595, - 35.29807198771296, - 0.046124780227517946 - ], - "spectral_radius": 1043.1792407320595, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.008191398606312288, - "solution_spread": 122.60390440114745, - "avg_distance": 122.07927462211171 - } - }, - { - "n": 17, - "solution": [ - 5, - 30, - 510 - ], - "solution_found": true, - "num_solutions": 4, - "packet": { - "packet_size": 545, - "packet_encoding": [ - 0.29411764705882354, - 1.7647058823529411, - 30.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.2792162611962241 - }, - "field": { - "field_density": 0.058823529411764705, - "reciprocal_field": 0.058823529411764705, - "n": 17 - }, - "spectral": { - "eigenvalues": [ - 1008.3703233009433, - 72.95670009731434, - 0.2093087816732535 - ], - "spectral_radius": 1008.3703233009433, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.003511962655904428, - "solution_spread": 181.04748118191134, - "avg_distance": 284.7410687350037 - } - }, - { - "n": 18, - "solution": [ - 5, - 46, - 2070 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 2121, - "packet_encoding": [ - 0.2777777777777778, - 2.5555555555555554, - 115.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.3634114266538435 - }, - "field": { - "field_density": 0.05555555555555555, - "reciprocal_field": 0.05555555555555555, - "n": 18 - }, - "spectral": { - "eigenvalues": [ - 15954.26124367916, - 162.06119568807642, - 0.05410384264102492 - ], - "spectral_radius": 15954.26124367916, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0017941313469065658, - "solution_spread": 671.9910817599417, - "avg_distance": 557.3727930923321 - } - }, - { - "n": 19, - "solution": [ - 5, - 96, - 9120 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 9221, - "packet_encoding": [ - 0.2631578947368421, - 5.052631578947368, - 480.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.3910307036251213 - }, - "field": { - "field_density": 0.05263157894736842, - "reciprocal_field": 0.05263157894736842, - "n": 19 - }, - "spectral": { - "eigenvalues": [ - 261583.76513482313, - 126.02810172103979, - 0.24831470233553185 - ], - "spectral_radius": 261583.76513482313, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0004059956197287043, - "solution_spread": 3112.1320359659644, - "avg_distance": 2463.0807609899402 - } - }, - { - "n": 20, - "solution": [ - 6, - 30, - 48038396025285288 - ], - "solution_found": true, - "num_solutions": 9, - "packet": { - "packet_size": 48038396025285324, - "packet_encoding": [ - 0.3, - 1.5, - 2401919801264264.5 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730931 - }, - "field": { - "field_density": 0.05, - "reciprocal_field": 0.05, - "n": 20 - }, - "spectral": { - "eigenvalues": [ - 5.769218731705364e+30, - 2116611423076352.0, - 13.807499999999255 - ], - "spectral_radius": 5.769218731705364e+30, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 9.367506770274713e-17, - "solution_spread": 1.997146881519782e+16, - "avg_distance": 1.0675199116730116e+16 - } - }, - { - "n": 21, - "solution": [ - 6, - 43, - 1806 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 1855, - "packet_encoding": [ - 0.2857142857142857, - 2.0476190476190474, - 86.0 - ], - "encoding_efficiency": 9999997224.443209, - "packet_diversity": 1.3583983625065412 - }, - "field": { - "field_density": 0.047619047619047616, - "reciprocal_field": 0.047619047619047616, - "n": 21 - }, - "spectral": { - "eigenvalues": [ - 11556.987550888916, - 31.140252937694566, - 0.003715447774025724 - ], - "spectral_radius": 11556.987550888916, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0019335949301514115, - "solution_spread": 509.7885915904144, - "avg_distance": 517.1714015207419 - } - }, - { - "n": 22, - "solution": [ - 6, - 66, - 72057594037927936 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 72057594037928008, - "packet_encoding": [ - 0.2727272727272727, - 3.0, - 3275345183542179.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373093 - }, - "field": { - "field_density": 0.045454545454545456, - "reciprocal_field": 0.045454545454545456, - "n": 22 - }, - "spectral": { - "eigenvalues": [ - 1.072788607135295e+31, - 3254279540310016.0, - 4.227272727272646 - ], - "spectral_radius": 1.072788607135295e+31, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 6.938893903907211e-17, - "solution_spread": 2.8823037615171148e+16, - "avg_distance": 1.4411518807585624e+16 - } - }, - { - "n": 23, - "solution": [ - 6, - 138, - 230584300921369408 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 230584300921369552, - "packet_encoding": [ - 0.2608695652173913, - 6.0, - 1.0025404387885626e+16 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373094 - }, - "field": { - "field_density": 0.043478260869565216, - "reciprocal_field": 0.043478260869565216, - "n": 23 - }, - "spectral": { - "eigenvalues": [ - 1.0050873314063635e+32, - 3.1859448926437376e+16, - 1.712665406427211 - ], - "spectral_radius": 1.0050873314063635e+32, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 2.1684043449710055e-17, - "solution_spread": 9.223372036854768e+16, - "avg_distance": 4.611686018427395e+16 - } - }, - { - "n": 24, - "solution": [ - 7, - 43, - 1806 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 1856, - "packet_encoding": [ - 0.2916666666666667, - 1.7916666666666667, - 75.25 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.3572738341570345 - }, - "field": { - "field_density": 0.041666666666666664, - "reciprocal_field": 0.041666666666666664, - "n": 24 - }, - "spectral": { - "eigenvalues": [ - 8848.43953103943, - 23.945502806770726, - 0.0038550426892561256 - ], - "spectral_radius": 8848.43953103943, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0019335949301514115, - "solution_spread": 509.7885915904144, - "avg_distance": 517.1714015207419 - } - }, - { - "n": 25, - "solution": [ - 8, - 100, - 40 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 148, - "packet_encoding": [ - 0.32, - 4.0, - 1.6 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 0.7729918727608158 - }, - "field": { - "field_density": 0.04, - "reciprocal_field": 0.04, - "n": 25 - }, - "spectral": { - "eigenvalues": [ - 1184.3716652061826, - 218.90998046447152, - 1.239954329345774 - ], - "spectral_radius": 1184.3716652061826, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.004072588668572521, - "solution_spread": 295.61727961492585, - "avg_distance": 245.5440707077599 - } - }, - { - "n": 26, - "solution": [ - 7, - 91, - 64051194700380384 - ], - "solution_found": true, - "num_solutions": 9, - "packet": { - "packet_size": 64051194700380482, - "packet_encoding": [ - 0.2692307692307692, - 3.5, - 2463507488476168.5 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730918 - }, - "field": { - "field_density": 0.038461538461538464, - "reciprocal_field": 0.038461538461538464, - "n": 26 - }, - "spectral": { - "eigenvalues": [ - 6.068869145778162e+30, - 52.6937869822515, - -1356023885463552.0 - ], - "spectral_radius": 6.068869145778162e+30, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 7.025630077706013e-17, - "solution_spread": 2.6628625086930376e+16, - "avg_distance": 1.4233598822306866e+16 - } - }, - { - "n": 27, - "solution": [ - 7, - 190, - 35910 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 36107, - "packet_encoding": [ - 0.25925925925925924, - 7.037037037037037, - 1330.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.402653361476614 - }, - "field": { - "field_density": 0.037037037037037035, - "reciprocal_field": 0.037037037037037035, - "n": 27 - }, - "spectral": { - "eigenvalues": [ - 2013015.0320310164, - 333.29260046174966, - 0.17468265028516494 - ], - "spectral_radius": 2013015.0320310164, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.00010725606821018233, - "solution_spread": 12495.578842867119, - "avg_distance": 9323.481800958414 - } - }, - { - "n": 28, - "solution": [ - 8, - 57, - 3192 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 3257, - "packet_encoding": [ - 0.2857142857142857, - 2.0357142857142856, - 114.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.372002071256387 - }, - "field": { - "field_density": 0.03571428571428571, - "reciprocal_field": 0.03571428571428571, - "n": 28 - }, - "spectral": { - "eigenvalues": [ - 18125.429485291632, - 50.10280957800144, - 0.017450028325871083 - ], - "spectral_radius": 18125.429485291632, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0010649216389613408, - "solution_spread": 997.8069088488795, - "avg_distance": 939.0362289710181 - } - }, - { - "n": 29, - "solution": [ - 8, - 78, - 9048 - ], - "solution_found": true, - "num_solutions": 9, - "packet": { - "packet_size": 9134, - "packet_encoding": [ - 0.27586206896551724, - 2.689655172413793, - 312.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.394272137163325 - }, - "field": { - "field_density": 0.034482758620689655, - "reciprocal_field": 0.034482758620689655, - "n": 29 - }, - "spectral": { - "eigenvalues": [ - 105484.09519121729, - 171.80964529413427, - 7.5184690771244975 - ], - "spectral_radius": 105484.09519121729, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.00040556620478570716, - "solution_spread": 3267.382532347123, - "avg_distance": 2465.688679677684 - } - }, - { - "n": 30, - "solution": [ - 8, - 121, - 14520 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 14649, - "packet_encoding": [ - 0.26666666666666666, - 4.033333333333333, - 484.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.395565062655634 - }, - "field": { - "field_density": 0.03333333333333333, - "reciprocal_field": 0.03333333333333333, - "n": 30 - }, - "spectral": { - "eigenvalues": [ - 365236.841863597, - 83.5134921916901, - 0.00019976690661871252 - ], - "spectral_radius": 365236.841863597, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0002486184862543387, - "solution_spread": 4083.3396759427155, - "avg_distance": 4022.2270478188298 - } - }, - { - "n": 31, - "solution": [ - 8, - 249, - 61752 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 62009, - "packet_encoding": [ - 0.25806451612903225, - 8.03225806451613, - 1992.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4054296844473668 - }, - "field": { - "field_density": 0.03225806451612903, - "reciprocal_field": 0.03225806451612903, - "n": 31 - }, - "spectral": { - "eigenvalues": [ - 5320291.866615769, - 364.28577262667557, - 0.1670705019726852 - ], - "spectral_radius": 5320291.866615769, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 5.246623127567576e-05, - "solution_spread": 20321.409036803136, - "avg_distance": 19059.878624512752 - } - }, - { - "n": 32, - "solution": [ - 9, - 72, - 144115188075855872 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 144115188075855953, - "packet_encoding": [ - 0.28125, - 2.25, - 4503599627370496.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730938 - }, - "field": { - "field_density": 0.03125, - "reciprocal_field": 0.03125, - "n": 32 - }, - "spectral": { - "eigenvalues": [ - 2.0282409603651666e+31, - 7934075906031616.0, - 0.8789062499999928 - ], - "spectral_radius": 2.0282409603651666e+31, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 3.469446951953577e-17, - "solution_spread": 5.764607523034197e+16, - "avg_distance": 2.882303761517148e+16 - } - }, - { - "n": 33, - "solution": [ - 9, - 99, - 115292150460684704 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 115292150460684812, - "packet_encoding": [ - 0.2727272727272727, - 3.0, - 3493701529111657.5 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373093 - }, - "field": { - "field_density": 0.030303030303030304, - "reciprocal_field": 0.030303030303030304, - "n": 33 - }, - "spectral": { - "eigenvalues": [ - 1.2205950374517139e+31, - 0.8264462809917534, - -2438390372892672.0 - ], - "spectral_radius": 1.2205950374517139e+31, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 4.336808689941923e-17, - "solution_spread": 4.611686018427338e+16, - "avg_distance": 2.3058430092137444e+16 - } - }, - { - "n": 34, - "solution": [ - 9, - 153, - 230584300921369408 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 230584300921369570, - "packet_encoding": [ - 0.2647058823529412, - 4.5, - 6781891203569688.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730936 - }, - "field": { - "field_density": 0.029411764705882353, - "reciprocal_field": 0.029411764705882353, - "n": 34 - }, - "spectral": { - "eigenvalues": [ - 4.59940482970559e+31, - 1.5938520556240896e+16, - 0.7785467128027556 - ], - "spectral_radius": 4.59940482970559e+31, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 2.1684043449709244e-17, - "solution_spread": 9.223372036854605e+16, - "avg_distance": 4.611686018427567e+16 - } - }, - { - "n": 35, - "solution": [ - 9, - 315, - 384307168202282304 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 384307168202282628, - "packet_encoding": [ - 0.2571428571428571, - 9.0, - 1.0980204805779494e+16 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730931 - }, - "field": { - "field_density": 0.02857142857142857, - "reciprocal_field": 0.02857142857142857, - "n": 35 - }, - "spectral": { - "eigenvalues": [ - 1.2056489757686312e+32, - 6896136929411072.0, - 0.9999999999999989 - ], - "spectral_radius": 1.2056489757686312e+32, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 1.3010426069825893e-17, - "solution_spread": 1.5372286728091178e+17, - "avg_distance": 7.68614336404574e+16 - } - }, - { - "n": 36, - "solution": [ - 10, - 91, - 8190 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 8291, - "packet_encoding": [ - 0.2777777777777778, - 2.5277777777777777, - 227.5 - ], - "encoding_efficiency": 9999998612.22141, - "packet_diversity": 1.3884234429779239 - }, - "field": { - "field_density": 0.027777777777777776, - "reciprocal_field": 0.027777777777777776, - "n": 36 - }, - "spectral": { - "eigenvalues": [ - 80471.19855927744, - 36.247547699538444, - 0.0006522822837790897 - ], - "spectral_radius": 80471.19855927744, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0004295506116167254, - "solution_spread": 2301.102288876752, - "avg_distance": 2328.0143781804827 - } - }, - { - "n": 37, - "solution": [ - 10, - 130, - 2405 - ], - "solution_found": true, - "num_solutions": 3, - "packet": { - "packet_size": 2545, - "packet_encoding": [ - 0.2702702702702703, - 3.5135135135135136, - 65.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.2988045612021244 - }, - "field": { - "field_density": 0.02702702702702703, - "reciprocal_field": 0.02702702702702703, - "n": 37 - }, - "spectral": { - "eigenvalues": [ - 4752.473200183751, - 26.08976610485568, - 0.0009489780098528794 - ], - "spectral_radius": 4752.473200183751, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.0007366146438300509, - "solution_spread": 713.2425467512978, - "avg_distance": 1357.5619333338188 - } - }, - { - "n": 38, - "solution": [ - 10, - 191, - 36290 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 36491, - "packet_encoding": [ - 0.2631578947368421, - 5.026315789473684, - 955.0 - ], - "encoding_efficiency": 9999998612.22141, - "packet_diversity": 1.4025420460176263 - }, - "field": { - "field_density": 0.02631578947368421, - "reciprocal_field": 0.02631578947368421, - "n": 38 - }, - "spectral": { - "eigenvalues": [ - 1258461.7136769535, - 201.05835537823754, - 0.0022058955466941334 - ], - "spectral_radius": 1258461.7136769535, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 9.109947414525133e-05, - "solution_spread": 11455.15165458923, - "avg_distance": 10977.011770732784 - } - }, - { - "n": 39, - "solution": [ - 10, - 391, - 152490 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 152891, - "packet_encoding": [ - 0.2564102564102564, - 10.025641025641026, - 3910.0 - ], - "encoding_efficiency": 9999998612.22141, - "packet_diversity": 1.4086531045800996 - }, - "field": { - "field_density": 0.02564102564102564, - "reciprocal_field": 0.02564102564102564, - "n": 39 - }, - "spectral": { - "eigenvalues": [ - 904648.3759195774, - 102.18085671184578, - 0.29200740604714504 - ], - "spectral_radius": 904648.3759195774, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.00012354529281719388, - "solution_spread": 14077.004648175767, - "avg_distance": 8094.197497914036 - } - }, - { - "n": 40, - "solution": [ - 11, - 110, - 288230376151711744 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 288230376151711865, - "packet_encoding": [ - 0.275, - 2.75, - 7205759403792794.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373094 - }, - "field": { - "field_density": 0.025, - "reciprocal_field": 0.025, - "n": 40 - }, - "spectral": { - "eigenvalues": [ - 7.644214819509606e+31, - 9959732806680576.0, - -4.09639988296379e-11 - ], - "spectral_radius": 7.644214819509606e+31, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 1.0293963483818415e-17, - "solution_spread": 9.526680712148e+16, - "avg_distance": 9.71443119622436e+16 - } - }, - { - "n": 41, - "solution": [ - 11, - 6314, - 154 - ], - "solution_found": true, - "num_solutions": 1, - "packet": { - "packet_size": 6479, - "packet_encoding": [ - 0.2682926829268293, - 154.0, - 3.7560975609756095 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.3604587048342982 - }, - "field": { - "field_density": 0.024390243902439025, - "reciprocal_field": 0.024390243902439025, - "n": 41 - }, - "spectral": { - "eigenvalues": [ - 25.08566329565735, - 3.5540351980952975e-15, - -3.4085581203364284e-18 - ], - "spectral_radius": 25.08566329565735, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 0.9999999999, - "solution_spread": 0.0, - "avg_distance": 0.0 - } - }, - { - "n": 42, - "solution": [ - 11, - 232, - 53592 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 53835, - "packet_encoding": [ - 0.2619047619047619, - 5.523809523809524, - 1276.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4046473613668977 - }, - "field": { - "field_density": 0.023809523809523808, - "reciprocal_field": 0.023809523809523808, - "n": 42 - }, - "spectral": { - "eigenvalues": [ - 1891853.754122434, - 289.82076998246856, - 0.004472662615450398 - ], - "spectral_radius": 1891853.754122434, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 7.025574059475335e-05, - "solution_spread": 17964.384641654928, - "avg_distance": 14233.712313534024 - } - }, - { - "n": 43, - "solution": [ - 11, - 474, - 224202 - ], - "solution_found": true, - "num_solutions": 1, - "packet": { - "packet_size": 224687, - "packet_encoding": [ - 0.2558139534883721, - 11.023255813953488, - 5214.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.409636827862447 - }, - "field": { - "field_density": 0.023255813953488372, - "reciprocal_field": 0.023255813953488372, - "n": 43 - }, - "spectral": { - "eigenvalues": [ - 121.52352623039474, - -1.0084668313450872e-14, - -1.671274796986772e-14 - ], - "spectral_radius": 121.52352623039474, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 0.9999999999, - "solution_spread": 0.0, - "avg_distance": 0.0 - } - }, - { - "n": 44, - "solution": [ - 12, - 132, - 144115188075855872 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 144115188075856016, - "packet_encoding": [ - 0.2727272727272727, - 3.0, - 3275345183542179.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373093 - }, - "field": { - "field_density": 0.022727272727272728, - "reciprocal_field": 0.022727272727272728, - "n": 44 - }, - "spectral": { - "eigenvalues": [ - 1.072788607135295e+31, - 2906009232211968.0, - 1.011880165289211 - ], - "spectral_radius": 1.072788607135295e+31, - "solution_space_dim": 2 - }, - "shear": { - "solution_rigidity": 3.469446951953399e-17, - "solution_spread": 5.764607523034069e+16, - "avg_distance": 2.882303761517296e+16 - } - }, - { - "n": 45, - "solution": [ - 12, - 180, - 128102389400760768 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 128102389400760960, - "packet_encoding": [ - 0.26666666666666666, - 4.0, - 2846719764461350.5 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730918 - }, - "field": { - "field_density": 0.022222222222222223, - "reciprocal_field": 0.022222222222222223, - "n": 45 - }, - "spectral": { - "eigenvalues": [ - 5.938575769920036e+31, - 0.7500000000001031, - -351843720888320.0 - ], - "spectral_radius": 5.938575769920036e+31, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 1.0604724645593951e-17, - "solution_spread": 1.041705939994721e+17, - "avg_distance": 9.429759219778326e+16 - } - }, - { - "n": 46, - "solution": [ - 12, - 276, - 461168601842738816 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 461168601842739104, - "packet_encoding": [ - 0.2608695652173913, - 6.0, - 1.0025404387885626e+16 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.414213562373094 - }, - "field": { - "field_density": 0.021739130434782608, - "reciprocal_field": 0.021739130434782608, - "n": 46 - }, - "spectral": { - "eigenvalues": [ - 1.0050873314063633e+32, - 1.2863736289165312e+16, - 0.8804347826086732 - ], - "spectral_radius": 1.0050873314063633e+32, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 1.0842021724854276e-17, - "solution_spread": 1.8446744073708938e+17, - "avg_distance": 9.22337203685543e+16 - } - }, - { - "n": 47, - "solution": [ - 12, - 564, - 384307168202282304 - ], - "solution_found": true, - "num_solutions": 6, - "packet": { - "packet_size": 384307168202282880, - "packet_encoding": [ - 0.2553191489361702, - 12.0, - 8176748259623028.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.4142135623730918 - }, - "field": { - "field_density": 0.02127659574468085, - "reciprocal_field": 0.02127659574468085, - "n": 47 - }, - "spectral": { - "eigenvalues": [ - 843.9206935989666, - 56.43299781533703, - 0.42313067714024033 - ], - "spectral_radius": 843.9206935989666, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.003393584102096147, - "solution_spread": 261.37420368394373, - "avg_distance": 294.67370482493163 - } - }, - { - "n": 48, - "solution": [ - 13, - 157, - 24492 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 24662, - "packet_encoding": [ - 0.2708333333333333, - 3.2708333333333335, - 510.25 - ], - "encoding_efficiency": 9999998612.22141, - "packet_diversity": 1.3996091549052319 - }, - "field": { - "field_density": 0.020833333333333332, - "reciprocal_field": 0.020833333333333332, - "n": 48 - }, - "spectral": { - "eigenvalues": [ - 393666.7726071079, - 60.70065606397031, - 0.0002611335213765194 - ], - "spectral_radius": 393666.7726071079, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.00014107411351833887, - "solution_spread": 7076.956063786947, - "avg_distance": 7088.472683331739 - } - }, - { - "n": 49, - "solution": [ - 14, - 99, - 9702 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 9815, - "packet_encoding": [ - 0.2857142857142857, - 2.020408163265306, - 198.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.3898312945428537 - }, - "field": { - "field_density": 0.02040816326530612, - "reciprocal_field": 0.02040816326530612, - "n": 49 - }, - "spectral": { - "eigenvalues": [ - 53125.501853254915, - 155.80059781188731, - 0.13486671745489273 - ], - "spectral_radius": 53125.501853254915, - "solution_space_dim": 3 - }, - "shear": { - "solution_rigidity": 0.00033118994095860825, - "solution_spread": 3138.8987117234124, - "avg_distance": 3019.4153756769615 - } - }, - { - "n": 50, - "solution": [ - 13, - 326, - 105950 - ], - "solution_found": true, - "num_solutions": 10, - "packet": { - "packet_size": 106289, - "packet_encoding": [ - 0.26, - 6.52, - 2119.0 - ], - "encoding_efficiency": 10000000000.0, - "packet_diversity": 1.407452407125524 - }, - "field": { - "field_density": 0.02, - "reciprocal_field": 0.02, - "n": 50 - }, - "spectral": { - "eigenvalues": [ - 1.329227995784916e+32, - 2147346209046528.0, - 0.7499999999999826 - ], - "spectral_radius": 1.329227995784916e+32, - "solution_space_dim": 1 - }, - "shear": { - "solution_rigidity": 8.6736173798826e-18, - "solution_spread": 2.305843009213517e+17, - "avg_distance": 1.1529215046070379e+17 - } - } - ], - "conjecture_analysis": { - "solutions_found": 49, - "total_tested": 49, - "success_rate": 1.0, - "counterexamples": [] - }, - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Egyptian fraction solution as packet (x,y,z)", - "insight": "Each solution is a packet encoding 4/n" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Field density 1/n and reciprocal field", - "insight": "Field density captures solution space sparsity" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Eigen decomposition of solution space", - "insight": "Spectral radius indicates solution space extent" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Solution rigidity and spread", - "insight": "Shear measures solution space deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Straus conjecture. Packet primitive captures solution encoding. Field primitive captures conjecture condition. Spectral and shear primitives reveal solution space structure. Framework validated for Diophantine equation problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_szekeres_4primitive.py b/4-Infrastructure/shim/test_erdos_szekeres_4primitive.py deleted file mode 100644 index 8503ffbe..00000000 --- a/4-Infrastructure/shim/test_erdos_szekeres_4primitive.py +++ /dev/null @@ -1,358 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Szekeres Theorem -===================================================== -Apply 4-primitive framework to Erdős–Szekeres Theorem. -Theorem: Any sequence of n²+1 distinct real numbers contains a monotone -subsequence of length n+1. - -Focus on packet primitive (Γᵢ) for monotone subsequences as packet witnesses. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_random_sequence(n): - """Generate a random permutation of 1..n.""" - seq = list(range(1, n + 1)) - random.shuffle(seq) - return seq - - -def find_longest_monotone_subsequence(seq): - """Find longest monotone (increasing or decreasing) subsequence.""" - n = len(seq) - - # Longest increasing subsequence (LIS) - lis = [1] * n - for i in range(n): - for j in range(i): - if seq[j] < seq[i]: - lis[i] = max(lis[i], lis[j] + 1) - - # Longest decreasing subsequence (LDS) - lds = [1] * n - for i in range(n): - for j in range(i): - if seq[j] > seq[i]: - lds[i] = max(lds[i], lds[j] + 1) - - max_lis = max(lis) if lis else 0 - max_lds = max(lds) if lds else 0 - - return max(max_lis, max_lds) - - -def packet_analysis_sequence(seq): - """Compute packet primitive metrics for a sequence.""" - if not seq: - return { - "packet_size": 0, - "packet_entropy": 0.0, - "packet_complexity": 0.0 - } - - # Packet size (length of sequence) - packet_size = len(seq) - - # Packet entropy (distribution of values) - from collections import Counter - counts = Counter(seq) - probs = [c / packet_size for c in counts.values()] - entropy = -sum(p * np.log2(p) for p in probs if p > 0) - - # Packet complexity (number of inversions) - inversions = 0 - for i in range(len(seq)): - for j in range(i + 1, len(seq)): - if seq[i] > seq[j]: - inversions += 1 - packet_complexity = inversions / (packet_size * (packet_size - 1) / 2) if packet_size > 1 else 0.0 - - return { - "packet_size": packet_size, - "packet_entropy": float(entropy), - "packet_complexity": float(packet_complexity) - } - - -def field_analysis_sequence(seq, n): - """Compute field primitive metrics for the sequence.""" - if not seq: - return { - "density": 0.0, - "theoretical_bound": 0, - "relative_length": 0.0 - } - - # Density (length relative to theoretical bound n²+1) - theoretical_bound = n * n + 1 - density = len(seq) / theoretical_bound if theoretical_bound > 0 else 0.0 - - # Expected monotone subsequence length (sqrt(len(seq))) - expected_length = int(np.sqrt(len(seq))) - - # Relative length - relative_length = expected_length / n if n > 0 else 0.0 - - return { - "density": float(density), - "theoretical_bound": theoretical_bound, - "expected_length": expected_length, - "relative_length": float(relative_length) - } - - -def spectral_analysis_sequence(seq): - """Compute spectral decomposition of sequence structure.""" - if not seq: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "sequence_rank": 0 - } - - n = len(seq) - - # Build permutation matrix - M = np.zeros((n, n)) - for i, val in enumerate(seq): - M[i, val - 1] = 1 if val <= n else 0 - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "sequence_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "sequence_rank": 0 - } - - -def shear_analysis_sequence(seq): - """Compute shear primitive metrics for sequence deformation.""" - if not seq: - return { - "sequence_rigidity": 0.0, - "avg_gap": 0.0, - "gap_variance": 0.0 - } - - # Compute gaps between consecutive values in permutation - gaps = [] - for i in range(len(seq) - 1): - gaps.append(abs(seq[i + 1] - seq[i])) - - if gaps: - avg_gap = np.mean(gaps) - gap_variance = np.var(gaps) - sequence_rigidity = 1.0 / (gap_variance + 1e-10) - else: - avg_gap = 0.0 - gap_variance = 0.0 - sequence_rigidity = 0.0 - - return { - "sequence_rigidity": float(sequence_rigidity), - "avg_gap": float(avg_gap), - "gap_variance": float(gap_variance) - } - - -def test_erdos_szekeres(n_values): - """Test Erdős–Szekeres Theorem with 4-primitive framework.""" - results = [] - - for n in n_values: - # Generate sequences of length n²+1 - seq_len = n * n + 1 - - for seed in range(3): # 3 samples per n - random.seed(seed) - seq = generate_random_sequence(seq_len) - - # Find longest monotone subsequence - max_mono = find_longest_monotone_subsequence(seq) - - # 4-primitive analysis - packet = packet_analysis_sequence(seq) - field = field_analysis_sequence(seq, n) - spectral = spectral_analysis_sequence(seq) - shear = shear_analysis_sequence(seq) - - results.append({ - "n": n, - "seq_len": seq_len, - "seed": seed, - "max_monotone_length": max_mono, - "theorem_holds": max_mono >= n + 1, - "packet": packet, - "field": field, - "spectral": spectral, - "shear": shear - }) - - return results - - -def analyze_theorem(results): - """Analyze results against Erdős–Szekeres Theorem.""" - holds_count = sum(1 for r in results if r["theorem_holds"]) - total = len(results) - - avg_mono_length = np.mean([r["max_monotone_length"] for r in results]) if results else 0.0 - - return { - "theorem_holds_count": holds_count, - "total_tests": total, - "success_rate": holds_count / total if total > 0 else 0.0, - "avg_monotone_length": float(avg_mono_length) - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–SZEKERES THEOREM") - print("=" * 70) - - # Test parameters - n_values = [3, 4, 5, 6] - - print(f"\nTest parameters:") - print(f" n values: {n_values}") - print(f" Sequence length: n²+1") - print(f" Samples per n: 3") - print(f" Total tests: {len(n_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING RANDOM PERMUTATIONS") - print("=" * 70) - - results = test_erdos_szekeres(n_values) - - print(f"\nGenerated {len(results)} random permutations") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST THEOREM") - print("=" * 70) - - analysis = analyze_theorem(results) - - print(f"\nTheorem analysis:") - print(f" Theorem holds: {analysis['theorem_holds_count']}/{analysis['total_tests']}") - print(f" Success rate: {analysis['success_rate']*100:.1f}%") - print(f" Avg monotone length: {analysis['avg_monotone_length']:.2f}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Sequence as packet") - print(" - Packet size (length)") - print(" - Packet entropy (value distribution)") - print(" - Packet complexity (inversions)") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Density relative to theoretical bound") - print(" - Expected monotone subsequence length") - print(" - Relative length") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Permutation matrix eigen decomposition") - print(" - Spectral radius") - print(" - Sequence rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Sequence rigidity") - print(" - Average gap between consecutive values") - print(" - Gap variance") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Packet primitive captures sequence structure:") - print(" - Sequence as packet encoding") - print(" - Packet complexity measures disorder") - - print("\n2. Field primitive captures theorem condition:") - print(" - Density relative to n²+1 bound") - print(" - Expected monotone length") - - print("\n3. Spectral primitive reveals permutation structure:") - print(" - Permutation matrix eigenvalues") - print(" - Spectral radius indicates structure") - - print("\n4. Shear primitive measures sequence deformation:") - print(" - Sequence rigidity indicates stability") - print(" - Gap variance indicates uniformity") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Packet: sequence structure") - print(" - Field: theorem bound") - print(" - Spectral: permutation structure") - print(" - Shear: sequence deformation") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": n_values, - "sequence_length_formula": "n²+1", - "samples_per_n": 3, - "total_tests": len(n_values) * 3 - }, - "results": results, - "theorem_analysis": analysis, - "primitive_analysis": { - "packet": { - "equation": "Γᵢ", - "application": "Sequence as packet encoding", - "insight": "Packet complexity measures sequence disorder" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Density relative to theoretical bound n²+1", - "insight": "Field captures theorem condition" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Permutation matrix eigen decomposition", - "insight": "Spectral radius indicates permutation structure" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Sequence rigidity and gap variance", - "insight": "Shear measures sequence deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Szekeres Theorem. Packet primitive captures sequence structure. Field primitive captures theorem bound. Spectral primitive reveals permutation structure. Shear primitive measures sequence deformation. Framework validated for Ramsey-type problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_szekeres_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_szekeres_4primitive_results.json b/4-Infrastructure/shim/test_erdos_szekeres_4primitive_results.json deleted file mode 100644 index 22bc201c..00000000 --- a/4-Infrastructure/shim/test_erdos_szekeres_4primitive_results.json +++ /dev/null @@ -1,666 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:27:48.057900", - "n_values": [ - 3, - 4, - 5, - 6 - ], - "sequence_length_formula": "n\u00b2+1", - "samples_per_n": 3, - "total_tests": 12 - }, - "results": [ - { - "n": 3, - "seq_len": 10, - "seed": 0, - "max_monotone_length": 5, - "theorem_holds": true, - "packet": { - "packet_size": 10, - "packet_entropy": 3.321928094887362, - "packet_complexity": 0.5555555555555556 - }, - "field": { - "density": 1.0, - "theoretical_bound": 10, - "expected_length": 3, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498953, - 1.414213562373095, - 1.0, - 0.6180339887498951, - 0.0, - 0.0, - -0.6180339887498956, - -1.0, - -1.414213562373095, - -1.6180339887498947 - ], - "spectral_radius": 1.6180339887498953, - "sequence_rank": 10 - }, - "shear": { - "sequence_rigidity": 0.144642857140765, - "avg_gap": 3.4444444444444446, - "gap_variance": 6.91358024691358 - } - }, - { - "n": 3, - "seq_len": 10, - "seed": 1, - "max_monotone_length": 5, - "theorem_holds": true, - "packet": { - "packet_size": 10, - "packet_entropy": 3.321928094887362, - "packet_complexity": 0.8 - }, - "field": { - "density": 1.0, - "theoretical_bound": 10, - "expected_length": 3, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0 - ], - "spectral_radius": 1.0, - "sequence_rank": 10 - }, - "shear": { - "sequence_rigidity": 1.191176470446345, - "avg_gap": 2.2222222222222223, - "gap_variance": 0.8395061728395062 - } - }, - { - "n": 3, - "seq_len": 10, - "seed": 2, - "max_monotone_length": 5, - "theorem_holds": true, - "packet": { - "packet_size": 10, - "packet_entropy": 3.321928094887362, - "packet_complexity": 0.6666666666666666 - }, - "field": { - "density": 1.0, - "theoretical_bound": 10, - "expected_length": 3, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.0, - 1.0, - 1.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.0, - -1.0, - -1.0 - ], - "spectral_radius": 1.0, - "sequence_rank": 10 - }, - "shear": { - "sequence_rigidity": 0.1874999999964844, - "avg_gap": 3.6666666666666665, - "gap_variance": 5.333333333333333 - } - }, - { - "n": 4, - "seq_len": 17, - "seed": 0, - "max_monotone_length": 7, - "theorem_holds": true, - "packet": { - "packet_size": 17, - "packet_entropy": 4.08746284125034, - "packet_complexity": 0.49264705882352944 - }, - "field": { - "density": 1.0, - "theoretical_bound": 17, - "expected_length": 4, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.618033988749895, - 1.4142135623730954, - 1.414213562373095, - 1.0, - 0.9999999999999998, - 0.618033988749895, - 5.551115123125783e-17, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -0.618033988749895, - -0.9999999999999999, - -1.0, - -1.414213562373095, - -1.4142135623730951, - -1.6180339887498951 - ], - "spectral_radius": 1.6180339887498951, - "sequence_rank": 17 - }, - "shear": { - "sequence_rigidity": 0.060206961429552855, - "avg_gap": 6.625, - "gap_variance": 16.609375 - } - }, - { - "n": 4, - "seq_len": 17, - "seed": 1, - "max_monotone_length": 6, - "theorem_holds": true, - "packet": { - "packet_size": 17, - "packet_entropy": 4.08746284125034, - "packet_complexity": 0.5882352941176471 - }, - "field": { - "density": 1.0, - "theoretical_bound": 17, - "expected_length": 4, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.0, - 0.0, - 0.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0 - ], - "spectral_radius": 1.0, - "sequence_rank": 17 - }, - "shear": { - "sequence_rigidity": 0.06015037593948783, - "avg_gap": 6.5, - "gap_variance": 16.625 - } - }, - { - "n": 4, - "seq_len": 17, - "seed": 2, - "max_monotone_length": 7, - "theorem_holds": true, - "packet": { - "packet_size": 17, - "packet_entropy": 4.08746284125034, - "packet_complexity": 0.5588235294117647 - }, - "field": { - "density": 1.0, - "theoretical_bound": 17, - "expected_length": 4, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.414213562373095, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -1.0, - -1.0, - -1.0, - -1.0, - -1.414213562373095 - ], - "spectral_radius": 1.414213562373095, - "sequence_rank": 17 - }, - "shear": { - "sequence_rigidity": 0.08173690932244813, - "avg_gap": 6.125, - "gap_variance": 12.234375 - } - }, - { - "n": 5, - "seq_len": 26, - "seed": 0, - "max_monotone_length": 8, - "theorem_holds": true, - "packet": { - "packet_size": 26, - "packet_entropy": 4.70043971814109, - "packet_complexity": 0.4676923076923077 - }, - "field": { - "density": 1.0, - "theoretical_bound": 26, - "expected_length": 5, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498951, - 1.414213562373095, - 1.414213562373095, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498948, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -0.6180339887498951, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.414213562373095, - -1.414213562373095, - -1.6180339887498951 - ], - "spectral_radius": 1.6180339887498951, - "sequence_rank": 26 - }, - "shear": { - "sequence_rigidity": 0.02794670005357956, - "avg_gap": 9.76, - "gap_variance": 35.7824 - } - }, - { - "n": 5, - "seq_len": 26, - "seed": 1, - "max_monotone_length": 9, - "theorem_holds": true, - "packet": { - "packet_size": 26, - "packet_entropy": 4.70043971814109, - "packet_complexity": 0.6215384615384615 - }, - "field": { - "density": 1.0, - "theoretical_bound": 26, - "expected_length": 5, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.618033988749894, - 1.4142135623730951, - 1.0000000000000004, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498951, - 1.2854427516081075e-83, - -2.0036056147532122e-16, - -0.6180339887498946, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.414213562373095, - -1.6180339887498947 - ], - "spectral_radius": 1.6180339887498947, - "sequence_rank": 26 - }, - "shear": { - "sequence_rigidity": 0.029211067489164994, - "avg_gap": 8.92, - "gap_variance": 34.2336 - } - }, - { - "n": 5, - "seq_len": 26, - "seed": 2, - "max_monotone_length": 10, - "theorem_holds": true, - "packet": { - "packet_size": 26, - "packet_entropy": 4.70043971814109, - "packet_complexity": 0.5538461538461539 - }, - "field": { - "density": 1.0, - "theoretical_bound": 26, - "expected_length": 5, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498942, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498952, - 0.0, - 0.0, - 0.0, - 0.0, - -0.6180339887498946, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.6180339887498947 - ], - "spectral_radius": 1.6180339887498947, - "sequence_rank": 26 - }, - "shear": { - "sequence_rigidity": 0.02094925253062582, - "avg_gap": 8.16, - "gap_variance": 47.734399999999994 - } - }, - { - "n": 6, - "seq_len": 37, - "seed": 0, - "max_monotone_length": 11, - "theorem_holds": true, - "packet": { - "packet_size": 37, - "packet_entropy": 5.209453365628954, - "packet_complexity": 0.4444444444444444 - }, - "field": { - "density": 1.0, - "theoretical_bound": 37, - "expected_length": 6, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498942, - 1.6180339887498942, - 1.414213562373095, - 1.414213562373095, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498952, - 0.6180339887498952, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -0.6180339887498946, - -0.6180339887498946, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.414213562373095, - -1.414213562373095, - -1.6180339887498947, - -1.6180339887498947 - ], - "spectral_radius": 1.6180339887498947, - "sequence_rank": 37 - }, - "shear": { - "sequence_rigidity": 0.01119441661194891, - "avg_gap": 12.944444444444445, - "gap_variance": 89.33024691358024 - } - }, - { - "n": 6, - "seq_len": 37, - "seed": 1, - "max_monotone_length": 9, - "theorem_holds": true, - "packet": { - "packet_size": 37, - "packet_entropy": 5.209453365628954, - "packet_complexity": 0.5405405405405406 - }, - "field": { - "density": 1.0, - "theoretical_bound": 37, - "expected_length": 6, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.6180339887498945, - 1.6180339887498942, - 1.4142135623730951, - 1.414213562373095, - 1.0000000000000004, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498952, - 0.6180339887498949, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -9.194034422677078e-17, - -0.6180339887498946, - -0.6180339887498947, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0000000000000002, - -1.414213562373095, - -1.4142135623730951, - -1.6180339887498947, - -1.618033988749895 - ], - "spectral_radius": 1.618033988749895, - "sequence_rank": 37 - }, - "shear": { - "sequence_rigidity": 0.012543554006952905, - "avg_gap": 13.0, - "gap_variance": 79.72222222222223 - } - }, - { - "n": 6, - "seq_len": 37, - "seed": 2, - "max_monotone_length": 11, - "theorem_holds": true, - "packet": { - "packet_size": 37, - "packet_entropy": 5.209453365628954, - "packet_complexity": 0.5480480480480481 - }, - "field": { - "density": 1.0, - "theoretical_bound": 37, - "expected_length": 6, - "relative_length": 1.0 - }, - "spectral": { - "eigenvalues": [ - 1.618033988749895, - 1.6180339887498942, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.414213562373095, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 1.0, - 0.6180339887498952, - 0.6180339887498949, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -6.071532165918825e-18, - -0.6180339887498946, - -0.618033988749895, - -1.0, - -1.0, - -1.0, - -1.0, - -1.0, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.414213562373095, - -1.6180339887498945, - -1.6180339887498947 - ], - "spectral_radius": 1.618033988749895, - "sequence_rank": 37 - }, - "shear": { - "sequence_rigidity": 0.012612524937943318, - "avg_gap": 14.86111111111111, - "gap_variance": 79.28626543209874 - } - } - ], - "theorem_analysis": { - "theorem_holds_count": 12, - "total_tests": 12, - "success_rate": 1.0, - "avg_monotone_length": 7.75 - }, - "primitive_analysis": { - "packet": { - "equation": "\u0393\u1d62", - "application": "Sequence as packet encoding", - "insight": "Packet complexity measures sequence disorder" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Density relative to theoretical bound n\u00b2+1", - "insight": "Field captures theorem condition" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Permutation matrix eigen decomposition", - "insight": "Spectral radius indicates permutation structure" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Sequence rigidity and gap variance", - "insight": "Shear measures sequence deformation" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Szekeres Theorem. Packet primitive captures sequence structure. Field primitive captures theorem bound. Spectral primitive reveals permutation structure. Shear primitive measures sequence deformation. Framework validated for Ramsey-type problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive.py b/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive.py deleted file mode 100644 index 80188eea..00000000 --- a/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive.py +++ /dev/null @@ -1,329 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős Conjecture on Ternary Expansion of 2^n -============================================================================== -Apply 4-primitive framework to Erdős conjecture on ternary expansion of 2^n. -Conjecture: The ternary expansion of 2^n contains at least one digit 2 for every n > 8. - -Focus on spectral primitive (C = UΛUᵀ) for digit pattern analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def to_ternary(n): - """Convert integer n to ternary (base 3) representation.""" - if n == 0: - return "0" - - digits = [] - while n > 0: - digits.append(str(n % 3)) - n //= 3 - - return ''.join(reversed(digits)) - - -def has_digit_2(ternary_str): - """Check if ternary string contains digit 2.""" - return '2' in ternary_str - - -def spectral_analysis_ternary(ternary_str): - """Compute spectral decomposition of ternary digit pattern.""" - if not ternary_str: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "pattern_rank": 0 - } - - # Build digit frequency matrix - digit_counts = [ternary_str.count(str(i)) for i in range(3)] - M = np.array([[digit_counts[i] if i == j else 0 for j in range(3)] for i in range(3)]) - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "pattern_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "pattern_rank": 0 - } - - -def field_analysis_ternary(ternary_str, n): - """Compute field primitive metrics for ternary expansion.""" - if not ternary_str: - return { - "digit_density": 0.0, - "digit_2_density": 0.0, - "length": 0 - } - - # Digit density (frequency of each digit) - digit_counts = [ternary_str.count(str(i)) for i in range(3)] - digit_density = [count / len(ternary_str) for count in digit_counts] - - # Digit 2 density - digit_2_density = digit_counts[2] / len(ternary_str) if ternary_str else 0.0 - - return { - "digit_density": digit_density, - "digit_2_density": float(digit_2_density), - "length": len(ternary_str), - "digit_2_count": digit_counts[2] - } - - -def shear_analysis_ternary(ternary_str): - """Compute shear primitive metrics for ternary deformation.""" - if not ternary_str: - return { - "digit_rigidity": 0.0, - "digit_variance": 0.0, - "transition_diversity": 0.0 - } - - # Digit variance - digit_values = [int(d) for d in ternary_str] - digit_variance = np.var(digit_values) - - # Digit rigidity (inverse of variance) - digit_rigidity = 1.0 / (digit_variance + 1e-10) - - # Transition diversity (how many different digit transitions) - transitions = set() - for i in range(len(ternary_str) - 1): - transitions.add(ternary_str[i:i+2]) - transition_diversity = len(transitions) - - return { - "digit_rigidity": float(digit_rigidity), - "digit_variance": float(digit_variance), - "transition_diversity": transition_diversity - } - - -def packet_analysis_ternary(ternary_str, has_digit_2): - """Compute packet primitive metrics for ternary encoding.""" - if not ternary_str: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "witness_property": False - } - - # Packet size (length of ternary string) - packet_size = len(ternary_str) - - # Encoding efficiency (how compact the representation is) - # Compare to binary representation - n = int(ternary_str, 3) - binary_length = len(bin(n)) - 2 - encoding_efficiency = binary_length / packet_size if packet_size > 0 else 0.0 - - # Witness property (contains digit 2) - witness_property = has_digit_2 - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "witness_property": witness_property - } - - -def test_erdos_ternary_2n(n_values): - """Test Erdős conjecture on ternary expansion of 2^n with 4-primitive framework.""" - results = [] - - for n in n_values: - # Compute 2^n - power_of_2 = 2 ** n - - # Convert to ternary - ternary_str = to_ternary(power_of_2) - - # Check if contains digit 2 - has_digit_2_flag = has_digit_2(ternary_str) - - # 4-primitive analysis - spectral = spectral_analysis_ternary(ternary_str) - field = field_analysis_ternary(ternary_str, n) - shear = shear_analysis_ternary(ternary_str) - packet = packet_analysis_ternary(ternary_str, has_digit_2_flag) - - results.append({ - "n": n, - "power_of_2": power_of_2, - "ternary": ternary_str, - "has_digit_2": has_digit_2_flag, - "conjecture_holds": has_digit_2_flag or n <= 8, - "spectral": spectral, - "field": field, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős conjecture on ternary expansion of 2^n.""" - # Conjecture: ternary expansion of 2^n contains digit 2 for all n > 8 - n_gt_8 = [r for r in results if r["n"] > 8] - has_digit_2_count = sum(1 for r in n_gt_8 if r["has_digit_2"]) - - total_n_gt_8 = len(n_gt_8) - - return { - "total_n_gt_8": total_n_gt_8, - "has_digit_2_count": has_digit_2_count, - "conjecture_holds": has_digit_2_count == total_n_gt_8 if total_n_gt_8 > 0 else True, - "note": "Conjecture states ternary expansion of 2^n contains digit 2 for all n > 8" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS CONJECTURE ON TERNARY 2^n") - print("=" * 70) - - # Test parameters - n_values = list(range(1, 51)) # Test n from 1 to 50 - - print(f"\nTest parameters:") - print(f" n values: 1 to 50") - print(f" Total tests: {len(n_values)}") - print(f" Conjecture applies for n > 8") - - print("\n" + "=" * 70) - print(" COMPUTING TERNARY EXPANSIONS OF 2^n") - print("=" * 70) - - results = test_erdos_ternary_2n(n_values) - - print(f"\nComputed {len(results)} ternary expansions") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" n > 8 tested: {analysis['total_n_gt_8']}") - print(f" Has digit 2: {analysis['has_digit_2_count']}") - print(f" Conjecture holds: {analysis['conjecture_holds']}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Ternary digit pattern eigen decomposition") - print(" - Spectral radius") - print(" - Pattern rank") - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Digit density") - print(" - Digit 2 density") - print(" - Ternary string length") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Digit rigidity") - print(" - Digit variance") - print(" - Transition diversity") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (ternary length)") - print(" - Encoding efficiency (vs binary)") - print(" - Witness property (contains digit 2)") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Spectral primitive reveals digit pattern structure:") - print(" - Digit frequency eigenvalues") - print(" - Spectral radius indicates pattern dominance") - - print("\n2. Field primitive captures digit distribution:") - print(" - Digit 2 density directly tests conjecture") - print(" - Ternary length grows with n") - - print("\n3. Shear primitive measures digit deformation:") - print(" - Digit variance indicates uniformity") - print(" - Transition diversity indicates complexity") - - print("\n4. Packet primitive captures encoding efficiency:") - print(" - Ternary vs binary length comparison") - print(" - Witness property (digit 2) directly tests conjecture") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Spectral: digit pattern structure") - print(" - Field: digit distribution") - print(" - Shear: digit deformation") - print(" - Packet: encoding efficiency and witness") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_values": list(range(1, 51)), - "total_tests": 50, - "conjecture_applies": "n > 8" - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Ternary digit pattern eigen decomposition", - "insight": "Digit frequency eigenvalues reveal pattern" - }, - "field": { - "equation": "ρ(x⃗)", - "application": "Digit density and digit 2 density", - "insight": "Digit 2 density directly tests conjecture" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Digit variance and transition diversity", - "insight": "Digit variance indicates uniformity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Ternary encoding efficiency and witness property", - "insight": "Witness property (digit 2) directly tests conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős conjecture on ternary expansion of 2^n. Spectral primitive reveals digit pattern structure. Field primitive captures digit distribution. Shear primitive measures digit deformation. Packet primitive captures encoding efficiency and witness property. Framework validated for number representation problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_ternary_2n_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive_results.json b/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive_results.json deleted file mode 100644 index fa69a825..00000000 --- a/4-Infrastructure/shim/test_erdos_ternary_2n_4primitive_results.json +++ /dev/null @@ -1,1893 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:41:17.118245", - "n_values": [ - 1, - 2, - 3, - 4, - 5, - 6, - 7, - 8, - 9, - 10, - 11, - 12, - 13, - 14, - 15, - 16, - 17, - 18, - 19, - 20, - 21, - 22, - 23, - 24, - 25, - 26, - 27, - 28, - 29, - 30, - 31, - 32, - 33, - 34, - 35, - 36, - 37, - 38, - 39, - 40, - 41, - 42, - 43, - 44, - 45, - 46, - 47, - 48, - 49, - 50 - ], - "total_tests": 50, - "conjecture_applies": "n > 8" - }, - "results": [ - { - "n": 1, - "power_of_2": 2, - "ternary": "2", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 1.0, - 0.0, - 0.0 - ], - "spectral_radius": 1.0, - "pattern_rank": 1 - }, - "field": { - "digit_density": [ - 0.0, - 0.0, - 1.0 - ], - "digit_2_density": 1.0, - "length": 1, - "digit_2_count": 1 - }, - "shear": { - "digit_rigidity": 10000000000.0, - "digit_variance": 0.0, - "transition_diversity": 0 - }, - "packet": { - "packet_size": 1, - "encoding_efficiency": 2.0, - "witness_property": true - } - }, - { - "n": 2, - "power_of_2": 4, - "ternary": "11", - "has_digit_2": false, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 0.0, - 0.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 1 - }, - "field": { - "digit_density": [ - 0.0, - 1.0, - 0.0 - ], - "digit_2_density": 0.0, - "length": 2, - "digit_2_count": 0 - }, - "shear": { - "digit_rigidity": 10000000000.0, - "digit_variance": 0.0, - "transition_diversity": 1 - }, - "packet": { - "packet_size": 2, - "encoding_efficiency": 1.5, - "witness_property": false - } - }, - { - "n": 3, - "power_of_2": 8, - "ternary": "22", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 0.0, - 0.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 1 - }, - "field": { - "digit_density": [ - 0.0, - 0.0, - 1.0 - ], - "digit_2_density": 1.0, - "length": 2, - "digit_2_count": 2 - }, - "shear": { - "digit_rigidity": 10000000000.0, - "digit_variance": 0.0, - "transition_diversity": 1 - }, - "packet": { - "packet_size": 2, - "encoding_efficiency": 2.0, - "witness_property": true - } - }, - { - "n": 4, - "power_of_2": 16, - "ternary": "121", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 1.0, - 0.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 2 - }, - "field": { - "digit_density": [ - 0.0, - 0.6666666666666666, - 0.3333333333333333 - ], - "digit_2_density": 0.3333333333333333, - "length": 3, - "digit_2_count": 1 - }, - "shear": { - "digit_rigidity": 4.499999997975, - "digit_variance": 0.2222222222222222, - "transition_diversity": 2 - }, - "packet": { - "packet_size": 3, - "encoding_efficiency": 1.6666666666666667, - "witness_property": true - } - }, - { - "n": 5, - "power_of_2": 32, - "ternary": "1012", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 1.0, - 1.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.25, - 0.5, - 0.25 - ], - "digit_2_density": 0.25, - "length": 4, - "digit_2_count": 1 - }, - "shear": { - "digit_rigidity": 1.9999999996, - "digit_variance": 0.5, - "transition_diversity": 3 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 1.5, - "witness_property": true - } - }, - { - "n": 6, - "power_of_2": 64, - "ternary": "2101", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 1.0, - 1.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.25, - 0.5, - 0.25 - ], - "digit_2_density": 0.25, - "length": 4, - "digit_2_count": 1 - }, - "shear": { - "digit_rigidity": 1.9999999996, - "digit_variance": 0.5, - "transition_diversity": 3 - }, - "packet": { - "packet_size": 4, - "encoding_efficiency": 1.75, - "witness_property": true - } - }, - { - "n": 7, - "power_of_2": 128, - "ternary": "11202", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 2.0, - 2.0, - 1.0 - ], - "spectral_radius": 2.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2, - 0.4, - 0.4 - ], - "digit_2_density": 0.4, - "length": 5, - "digit_2_count": 2 - }, - "shear": { - "digit_rigidity": 1.785714285395408, - "digit_variance": 0.56, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 8, - "power_of_2": 256, - "ternary": "100111", - "has_digit_2": false, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 2.0, - 0.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 2 - }, - "field": { - "digit_density": [ - 0.3333333333333333, - 0.6666666666666666, - 0.0 - ], - "digit_2_density": 0.0, - "length": 6, - "digit_2_count": 0 - }, - "shear": { - "digit_rigidity": 4.499999997974999, - "digit_variance": 0.22222222222222224, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 1.5, - "witness_property": false - } - }, - { - "n": 9, - "power_of_2": 512, - "ternary": "200222", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 2.0, - 0.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 2 - }, - "field": { - "digit_density": [ - 0.3333333333333333, - 0.0, - 0.6666666666666666 - ], - "digit_2_density": 0.6666666666666666, - "length": 6, - "digit_2_count": 4 - }, - "shear": { - "digit_rigidity": 1.1249999998734375, - "digit_variance": 0.888888888888889, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 6, - "encoding_efficiency": 1.6666666666666667, - "witness_property": true - } - }, - { - "n": 10, - "power_of_2": 1024, - "ternary": "1101221", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 2.0, - 1.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.14285714285714285, - 0.5714285714285714, - 0.2857142857142857 - ], - "digit_2_density": 0.2857142857142857, - "length": 7, - "digit_2_count": 2 - }, - "shear": { - "digit_rigidity": 2.44999999939975, - "digit_variance": 0.4081632653061224, - "transition_diversity": 6 - }, - "packet": { - "packet_size": 7, - "encoding_efficiency": 1.5714285714285714, - "witness_property": true - } - }, - { - "n": 11, - "power_of_2": 2048, - "ternary": "2210212", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 2.0, - 1.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.14285714285714285, - 0.2857142857142857, - 0.5714285714285714 - ], - "digit_2_density": 0.5714285714285714, - "length": 7, - "digit_2_count": 4 - }, - "shear": { - "digit_rigidity": 1.884615384260207, - "digit_variance": 0.5306122448979592, - "transition_diversity": 5 - }, - "packet": { - "packet_size": 7, - "encoding_efficiency": 1.7142857142857142, - "witness_property": true - } - }, - { - "n": 12, - "power_of_2": 4096, - "ternary": "12121201", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 3.0, - 1.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.125, - 0.5, - 0.375 - ], - "digit_2_density": 0.375, - "length": 8, - "digit_2_count": 3 - }, - "shear": { - "digit_rigidity": 2.285714285191837, - "digit_variance": 0.4375, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 8, - "encoding_efficiency": 1.625, - "witness_property": true - } - }, - { - "n": 13, - "power_of_2": 8192, - "ternary": "102020102", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 4.0, - 3.0, - 2.0 - ], - "spectral_radius": 4.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.4444444444444444, - 0.2222222222222222, - 0.3333333333333333 - ], - "digit_2_density": 0.3333333333333333, - "length": 9, - "digit_2_count": 3 - }, - "shear": { - "digit_rigidity": 1.306451612732544, - "digit_variance": 0.7654320987654322, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 9, - "encoding_efficiency": 1.5555555555555556, - "witness_property": true - } - }, - { - "n": 14, - "power_of_2": 16384, - "ternary": "211110211", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.0, - 2.0, - 1.0 - ], - "spectral_radius": 6.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.1111111111111111, - 0.6666666666666666, - 0.2222222222222222 - ], - "digit_2_density": 0.2222222222222222, - "length": 9, - "digit_2_count": 2 - }, - "shear": { - "digit_rigidity": 3.1153846144140527, - "digit_variance": 0.3209876543209877, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 9, - "encoding_efficiency": 1.6666666666666667, - "witness_property": true - } - }, - { - "n": 15, - "power_of_2": 32768, - "ternary": "1122221122", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.0, - 4.0, - 0.0 - ], - "spectral_radius": 6.0, - "pattern_rank": 2 - }, - "field": { - "digit_density": [ - 0.0, - 0.4, - 0.6 - ], - "digit_2_density": 0.6, - "length": 10, - "digit_2_count": 6 - }, - "shear": { - "digit_rigidity": 4.166666664930554, - "digit_variance": 0.24000000000000005, - "transition_diversity": 4 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 16, - "power_of_2": 65536, - "ternary": "10022220021", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.0, - 4.0, - 2.0 - ], - "spectral_radius": 5.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.36363636363636365, - 0.18181818181818182, - 0.45454545454545453 - ], - "digit_2_density": 0.45454545454545453, - "length": 11, - "digit_2_count": 5 - }, - "shear": { - "digit_rigidity": 1.2346938773985736, - "digit_variance": 0.8099173553719008, - "transition_diversity": 6 - }, - "packet": { - "packet_size": 11, - "encoding_efficiency": 1.5454545454545454, - "witness_property": true - } - }, - { - "n": 17, - "power_of_2": 131072, - "ternary": "20122210112", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 5.0, - 4.0, - 2.0 - ], - "spectral_radius": 5.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.18181818181818182, - 0.36363636363636365, - 0.45454545454545453 - ], - "digit_2_density": 0.45454545454545453, - "length": 11, - "digit_2_count": 5 - }, - "shear": { - "digit_rigidity": 1.7794117643892517, - "digit_variance": 0.5619834710743802, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 11, - "encoding_efficiency": 1.6363636363636365, - "witness_property": true - } - }, - { - "n": 18, - "power_of_2": 262144, - "ternary": "111022121001", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.0, - 3.0, - 3.0 - ], - "spectral_radius": 6.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.25, - 0.5, - 0.25 - ], - "digit_2_density": 0.25, - "length": 12, - "digit_2_count": 3 - }, - "shear": { - "digit_rigidity": 1.9999999996, - "digit_variance": 0.5, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 12, - "encoding_efficiency": 1.5833333333333333, - "witness_property": true - } - }, - { - "n": 19, - "power_of_2": 524288, - "ternary": "222122012002", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 7.0, - 3.0, - 2.0 - ], - "spectral_radius": 7.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.25, - 0.16666666666666666, - 0.5833333333333334 - ], - "digit_2_density": 0.5833333333333334, - "length": 12, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.3846153844236686, - "digit_variance": 0.7222222222222222, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 12, - "encoding_efficiency": 1.6666666666666667, - "witness_property": true - } - }, - { - "n": 20, - "power_of_2": 1048576, - "ternary": "1222021101011", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.0, - 4.0, - 3.0 - ], - "spectral_radius": 6.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.23076923076923078, - 0.46153846153846156, - 0.3076923076923077 - ], - "digit_2_density": 0.3076923076923077, - "length": 13, - "digit_2_count": 4 - }, - "shear": { - "digit_rigidity": 1.8777777774251727, - "digit_variance": 0.5325443786982249, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 13, - "encoding_efficiency": 1.6153846153846154, - "witness_property": true - } - }, - { - "n": 21, - "power_of_2": 2097152, - "ternary": "10221112202022", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 7.0, - 4.0, - 3.0 - ], - "spectral_radius": 7.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.21428571428571427, - 0.2857142857142857, - 0.5 - ], - "digit_2_density": 0.5, - "length": 14, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.5806451610404786, - "digit_variance": 0.6326530612244898, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 14, - "encoding_efficiency": 1.5714285714285714, - "witness_property": true - } - }, - { - "n": 22, - "power_of_2": 4194304, - "ternary": "21220002111121", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 6.0, - 5.0, - 3.0 - ], - "spectral_radius": 6.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.21428571428571427, - 0.42857142857142855, - 0.35714285714285715 - ], - "digit_2_density": 0.35714285714285715, - "length": 14, - "digit_2_count": 5 - }, - "shear": { - "digit_rigidity": 1.8148148144854597, - "digit_variance": 0.5510204081632653, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 14, - "encoding_efficiency": 1.6428571428571428, - "witness_property": true - } - }, - { - "n": 23, - "power_of_2": 8388608, - "ternary": "120210012000012", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 7.0, - 4.0, - 4.0 - ], - "spectral_radius": 7.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.4666666666666667, - 0.26666666666666666, - 0.26666666666666666 - ], - "digit_2_density": 0.26666666666666666, - "length": 15, - "digit_2_count": 4 - }, - "shear": { - "digit_rigidity": 1.4423076920996671, - "digit_variance": 0.6933333333333334, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 24, - "power_of_2": 16777216, - "ternary": "1011120101000101", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 7.0, - 1.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.4375, - 0.5, - 0.0625 - ], - "digit_2_density": 0.0625, - "length": 16, - "digit_2_count": 1 - }, - "shear": { - "digit_rigidity": 2.7826086948778825, - "digit_variance": 0.359375, - "transition_diversity": 6 - }, - "packet": { - "packet_size": 16, - "encoding_efficiency": 1.5625, - "witness_property": true - } - }, - { - "n": 25, - "power_of_2": 33554432, - "ternary": "2100010202000202", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 9.0, - 5.0, - 2.0 - ], - "spectral_radius": 9.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.5625, - 0.125, - 0.3125 - ], - "digit_2_density": 0.3125, - "length": 16, - "digit_2_count": 5 - }, - "shear": { - "digit_rigidity": 1.2307692306177516, - "digit_variance": 0.8125, - "transition_diversity": 6 - }, - "packet": { - "packet_size": 16, - "encoding_efficiency": 1.625, - "witness_property": true - } - }, - { - "n": 26, - "power_of_2": 67108864, - "ternary": "11200021111001111", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 5.0, - 2.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.29411764705882354, - 0.5882352941176471, - 0.11764705882352941 - ], - "digit_2_density": 0.11764705882352941, - "length": 17, - "digit_2_count": 2 - }, - "shear": { - "digit_rigidity": 2.6272727265824707, - "digit_variance": 0.3806228373702423, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 17, - "encoding_efficiency": 1.588235294117647, - "witness_property": true - } - }, - { - "n": 27, - "power_of_2": 134217728, - "ternary": "100100112222002222", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 6.0, - 4.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.3333333333333333, - 0.2222222222222222, - 0.4444444444444444 - ], - "digit_2_density": 0.4444444444444444, - "length": 18, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.306451612732544, - "digit_variance": 0.7654320987654322, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 18, - "encoding_efficiency": 1.5555555555555556, - "witness_property": true - } - }, - { - "n": 28, - "power_of_2": 268435456, - "ternary": "200201002221012221", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 6.0, - 4.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.3333333333333333, - 0.2222222222222222, - 0.4444444444444444 - ], - "digit_2_density": 0.4444444444444444, - "length": 18, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.306451612732544, - "digit_variance": 0.7654320987654322, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 18, - "encoding_efficiency": 1.6111111111111112, - "witness_property": true - } - }, - { - "n": 29, - "power_of_2": 536870912, - "ternary": "1101102012212102212", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 7.0, - 4.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.21052631578947367, - 0.42105263157894735, - 0.3684210526315789 - ], - "digit_2_density": 0.3684210526315789, - "length": 19, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.8049999996741974, - "digit_variance": 0.554016620498615, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 19, - "encoding_efficiency": 1.5789473684210527, - "witness_property": true - } - }, - { - "n": 30, - "power_of_2": 1073741824, - "ternary": "2202211102201212201", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 9.0, - 6.0, - 4.0 - ], - "spectral_radius": 9.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.21052631578947367, - 0.3157894736842105, - 0.47368421052631576 - ], - "digit_2_density": 0.47368421052631576, - "length": 19, - "digit_2_count": 9 - }, - "shear": { - "digit_rigidity": 1.6261261258616975, - "digit_variance": 0.6149584487534626, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 19, - "encoding_efficiency": 1.631578947368421, - "witness_property": true - } - }, - { - "n": 31, - "power_of_2": 2147483648, - "ternary": "12112122212110202102", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 9.0, - 8.0, - 3.0 - ], - "spectral_radius": 9.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.15, - 0.4, - 0.45 - ], - "digit_2_density": 0.45, - "length": 20, - "digit_2_count": 9 - }, - "shear": { - "digit_rigidity": 1.9607843133410225, - "digit_variance": 0.51, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 20, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 32, - "power_of_2": 4294967296, - "ternary": "102002022201221111211", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 8.0, - 5.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.23809523809523808, - 0.38095238095238093, - 0.38095238095238093 - ], - "digit_2_density": 0.38095238095238093, - "length": 21, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.6704545451755033, - "digit_variance": 0.598639455782313, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 21, - "encoding_efficiency": 1.5714285714285714, - "witness_property": true - } - }, - { - "n": 33, - "power_of_2": 8589934592, - "ternary": "211011122110220000122", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 7.0, - 6.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2857142857142857, - 0.38095238095238093, - 0.3333333333333333 - ], - "digit_2_density": 0.3333333333333333, - "length": 21, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.621323529148896, - "digit_variance": 0.6167800453514738, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 21, - "encoding_efficiency": 1.619047619047619, - "witness_property": true - } - }, - { - "n": 34, - "power_of_2": 17179869184, - "ternary": "1122100021221210001021", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 8.0, - 7.0, - 7.0 - ], - "spectral_radius": 8.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.3181818181818182, - 0.36363636363636365, - 0.3181818181818182 - ], - "digit_2_density": 0.3181818181818182, - "length": 22, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.5714285711816327, - "digit_variance": 0.6363636363636364, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 22, - "encoding_efficiency": 1.5909090909090908, - "witness_property": true - } - }, - { - "n": 35, - "power_of_2": 34359738368, - "ternary": "10021200120220120002112", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 9.0, - 8.0, - 6.0 - ], - "spectral_radius": 9.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.391304347826087, - 0.2608695652173913, - 0.34782608695652173 - ], - "digit_2_density": 0.34782608695652173, - "length": 23, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.3564102562262712, - "digit_variance": 0.7372400756143669, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 23, - "encoding_efficiency": 1.565217391304348, - "witness_property": true - } - }, - { - "n": 36, - "power_of_2": 68719476736, - "ternary": "20120101011211010012001", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 9.0, - 4.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.391304347826087, - 0.43478260869565216, - 0.17391304347826086 - ], - "digit_2_density": 0.17391304347826086, - "length": 23, - "digit_2_count": 4 - }, - "shear": { - "digit_rigidity": 1.930656933933826, - "digit_variance": 0.5179584120982986, - "transition_diversity": 7 - }, - "packet": { - "packet_size": 23, - "encoding_efficiency": 1.608695652173913, - "witness_property": true - } - }, - { - "n": 37, - "power_of_2": 137438953472, - "ternary": "111010202100122020101002", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 8.0, - 6.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.4166666666666667, - 0.3333333333333333, - 0.25 - ], - "digit_2_density": 0.25, - "length": 24, - "digit_2_count": 6 - }, - "shear": { - "digit_rigidity": 1.5652173910593574, - "digit_variance": 0.6388888888888888, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 24, - "encoding_efficiency": 1.5833333333333333, - "witness_property": true - } - }, - { - "n": 38, - "power_of_2": 274877906944, - "ternary": "222021111201021110202011", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 8.0, - 6.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.25, - 0.4166666666666667, - 0.3333333333333333 - ], - "digit_2_density": 0.3333333333333333, - "length": 24, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.7349397587351427, - "digit_variance": 0.576388888888889, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 24, - "encoding_efficiency": 1.625, - "witness_property": true - } - }, - { - "n": 39, - "power_of_2": 549755813888, - "ternary": "1221120000102112221111022", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 9.0, - 6.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.24, - 0.4, - 0.36 - ], - "digit_2_density": 0.36, - "length": 25, - "digit_2_count": 9 - }, - "shear": { - "digit_rigidity": 1.7076502729324368, - "digit_variance": 0.5856, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 25, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 40, - "power_of_2": 1099511627776, - "ternary": "10220010000212002212222121", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 11.0, - 9.0, - 6.0 - ], - "spectral_radius": 11.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.34615384615384615, - 0.23076923076923078, - 0.4230769230769231 - ], - "digit_2_density": 0.4230769230769231, - "length": 26, - "digit_2_count": 11 - }, - "shear": { - "digit_rigidity": 1.3100775192082146, - "digit_variance": 0.7633136094674556, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 26, - "encoding_efficiency": 1.5769230769230769, - "witness_property": true - } - }, - { - "n": 41, - "power_of_2": 2199023255552, - "ternary": "21210020001201012202222012", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 11.0, - 9.0, - 6.0 - ], - "spectral_radius": 11.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.34615384615384615, - 0.23076923076923078, - 0.4230769230769231 - ], - "digit_2_density": 0.4230769230769231, - "length": 26, - "digit_2_count": 11 - }, - "shear": { - "digit_rigidity": 1.3100775192082146, - "digit_variance": 0.7633136094674556, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 26, - "encoding_efficiency": 1.6153846153846154, - "witness_property": true - } - }, - { - "n": 42, - "power_of_2": 4398046511104, - "ternary": "120120110010102102112221101", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 12.0, - 8.0, - 7.0 - ], - "spectral_radius": 12.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2962962962962963, - 0.4444444444444444, - 0.25925925925925924 - ], - "digit_2_density": 0.25925925925925924, - "length": 27, - "digit_2_count": 7 - }, - "shear": { - "digit_rigidity": 1.8044554452189483, - "digit_variance": 0.5541838134430728, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 27, - "encoding_efficiency": 1.5925925925925926, - "witness_property": true - } - }, - { - "n": 43, - "power_of_2": 8796093022208, - "ternary": "1011010220020211212002212202", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 11.0, - 9.0, - 8.0 - ], - "spectral_radius": 11.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.32142857142857145, - 0.2857142857142857, - 0.39285714285714285 - ], - "digit_2_density": 0.39285714285714285, - "length": 28, - "digit_2_count": 11 - }, - "shear": { - "digit_rigidity": 1.4100719422472132, - "digit_variance": 0.7091836734693876, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 28, - "encoding_efficiency": 1.5714285714285714, - "witness_property": true - } - }, - { - "n": 44, - "power_of_2": 17592186044416, - "ternary": "2022021210111200201012202111", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 10.0, - 10.0, - 8.0 - ], - "spectral_radius": 10.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2857142857142857, - 0.35714285714285715, - 0.35714285714285715 - ], - "digit_2_density": 0.35714285714285715, - "length": 28, - "digit_2_count": 10 - }, - "shear": { - "digit_rigidity": 1.567999999754138, - "digit_variance": 0.6377551020408161, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 28, - "encoding_efficiency": 1.6071428571428572, - "witness_property": true - } - }, - { - "n": 45, - "power_of_2": 35184372088832, - "ternary": "11121120121000101102102111222", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 14.0, - 8.0, - 7.0 - ], - "spectral_radius": 14.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2413793103448276, - 0.4827586206896552, - 0.27586206896551724 - ], - "digit_2_density": 0.27586206896551724, - "length": 29, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.9377880180576774, - "digit_variance": 0.5160523186682521, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 29, - "encoding_efficiency": 1.5862068965517242, - "witness_property": true - } - }, - { - "n": 46, - "power_of_2": 70368744177664, - "ternary": "100020011012000202211212000221", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 13.0, - 9.0, - 8.0 - ], - "spectral_radius": 13.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.43333333333333335, - 0.26666666666666666, - 0.3 - ], - "digit_2_density": 0.3, - "length": 30, - "digit_2_count": 9 - }, - "shear": { - "digit_rigidity": 1.397515527755006, - "digit_variance": 0.7155555555555554, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 30, - "encoding_efficiency": 1.5666666666666667, - "witness_property": true - } - }, - { - "n": 47, - "power_of_2": 140737488355328, - "ternary": "200110022101001112200201001212", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 12.0, - 10.0, - 8.0 - ], - "spectral_radius": 12.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.4, - 0.3333333333333333, - 0.26666666666666666 - ], - "digit_2_density": 0.26666666666666666, - "length": 30, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.5410958901734615, - "digit_variance": 0.6488888888888887, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 30, - "encoding_efficiency": 1.6, - "witness_property": true - } - }, - { - "n": 48, - "power_of_2": 281474976710656, - "ternary": "1100220121202010002101102010201", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 13.0, - 10.0, - 8.0 - ], - "spectral_radius": 13.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.41935483870967744, - 0.3225806451612903, - 0.25806451612903225 - ], - "digit_2_density": 0.25806451612903225, - "length": 31, - "digit_2_count": 8 - }, - "shear": { - "digit_rigidity": 1.5351437697323844, - "digit_variance": 0.6514047866805411, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 31, - "encoding_efficiency": 1.5806451612903225, - "witness_property": true - } - }, - { - "n": 49, - "power_of_2": 562949953421312, - "ternary": "2201211020111020011202211021102", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 12.0, - 10.0, - 9.0 - ], - "spectral_radius": 12.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.2903225806451613, - 0.3870967741935484, - 0.3225806451612903 - ], - "digit_2_density": 0.3225806451612903, - "length": 31, - "digit_2_count": 10 - }, - "shear": { - "digit_rigidity": 1.6343537412294873, - "digit_variance": 0.611862643080125, - "transition_diversity": 9 - }, - "packet": { - "packet_size": 31, - "encoding_efficiency": 1.6129032258064515, - "witness_property": true - } - }, - { - "n": 50, - "power_of_2": 1125899906842624, - "ternary": "12110122110222110100112122112211", - "has_digit_2": true, - "conjecture_holds": true, - "spectral": { - "eigenvalues": [ - 16.0, - 11.0, - 5.0 - ], - "spectral_radius": 16.0, - "pattern_rank": 3 - }, - "field": { - "digit_density": [ - 0.15625, - 0.5, - 0.34375 - ], - "digit_2_density": 0.34375, - "length": 32, - "digit_2_count": 11 - }, - "shear": { - "digit_rigidity": 2.1512605037388886, - "digit_variance": 0.46484375, - "transition_diversity": 8 - }, - "packet": { - "packet_size": 32, - "encoding_efficiency": 1.59375, - "witness_property": true - } - } - ], - "conjecture_analysis": { - "total_n_gt_8": 42, - "has_digit_2_count": 42, - "conjecture_holds": true, - "note": "Conjecture states ternary expansion of 2^n contains digit 2 for all n > 8" - }, - "primitive_analysis": { - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Ternary digit pattern eigen decomposition", - "insight": "Digit frequency eigenvalues reveal pattern" - }, - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Digit density and digit 2 density", - "insight": "Digit 2 density directly tests conjecture" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Digit variance and transition diversity", - "insight": "Digit variance indicates uniformity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Ternary encoding efficiency and witness property", - "insight": "Witness property (digit 2) directly tests conjecture" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s conjecture on ternary expansion of 2^n. Spectral primitive reveals digit pattern structure. Field primitive captures digit distribution. Shear primitive measures digit deformation. Packet primitive captures encoding efficiency and witness property. Framework validated for number representation problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_erdos_turan_4primitive.py b/4-Infrastructure/shim/test_erdos_turan_4primitive.py deleted file mode 100644 index 6a4e66d3..00000000 --- a/4-Infrastructure/shim/test_erdos_turan_4primitive.py +++ /dev/null @@ -1,337 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Erdős–Turán Conjecture -==================================================== -Apply 4-primitive framework to Erdős–Turán Conjecture on additive bases. -Conjecture: If A is an additive basis of order 2 for the natural numbers, -then the sum of reciprocals diverges: Σ_{a∈A} 1/a = ∞ - -Focus on field primitive (ρ(x⃗)) for density analysis and spectral -decomposition of additive structure. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_additive_basis(n_max, density=0.5, seed=None): - """Generate a candidate additive basis A of order 2 up to n_max.""" - if seed is not None: - np.random.seed(seed) - - # Generate a set with given density - A = set() - for n in range(1, n_max + 1): - if np.random.random() < density: - A.add(n) - - return sorted(A) - - -def check_additive_basis(A, n_max): - """Check if A is an additive basis of order 2 up to n_max.""" - # Compute all sums a + b for a, b in A - sums = set() - for a in A: - for b in A: - sums.add(a + b) - - # Check if all numbers up to n_max can be represented - for n in range(1, n_max + 1): - if n not in sums: - return False, n - - return True, None - - -def field_analysis(A): - """Compute field primitive metrics (density, reciprocal sum).""" - n_max = max(A) if A else 1 - - # Density field - density = len(A) / n_max - - # Reciprocal sum - reciprocal_sum = sum(1.0 / a for a in A) - - # Asymptotic density estimate - asymptotic_density = density - - return { - "density": float(density), - "reciprocal_sum": float(reciprocal_sum), - "asymptotic_density": float(asymptotic_density), - "n_max": n_max, - "size": len(A) - } - - -def spectral_analysis_additive(A): - """Compute spectral decomposition of additive structure.""" - # Build addition table (matrix representation of additive structure) - n_max = max(A) if A else 1 - M = np.zeros((n_max, n_max)) - - for i, a in enumerate(A): - for j, b in enumerate(A): - s = a + b - if s <= n_max: - M[a-1, b-1] = 1 # Mark valid sums - - # Eigen decomposition of addition table - if M.shape[0] > 0: - eigenvalues, eigenvectors = np.linalg.eigh(M) - idx = np.argsort(eigenvalues)[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "spectral_gap": float(np.abs(eigenvalues[0] - eigenvalues[1])) if len(eigenvalues) > 1 else 0.0, - "rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "spectral_gap": 0.0, - "rank": 0 - } - - -def shear_analysis_additive(A): - """Compute shear primitive metrics (additive deformation).""" - if not A: - return {"additive_gap": 0.0, "covering_radius": 0.0} - - # Compute gaps between consecutive elements - gaps = [A[i+1] - A[i] for i in range(len(A) - 1)] - - # Maximum gap (largest uncovered interval) - max_gap = max(gaps) if gaps else 0 - - # Covering radius (how far each element covers via addition) - covering_radius = max(A) if A else 0 - - return { - "max_gap": float(max_gap), - "avg_gap": float(np.mean(gaps)) if gaps else 0.0, - "covering_radius": float(covering_radius), - "additive_rigidity": float(1.0 / (np.mean(gaps) + 1)) if gaps else 0.0 - } - - -def packet_analysis_additive(A): - """Compute packet primitive metrics (encoding efficiency).""" - if not A: - return {"encoding_efficiency": 0.0, "redundancy": 0.0} - - # Encoding efficiency: how efficiently A covers sums - n_max = max(A) - sums = set() - for a in A: - for b in A: - sums.add(a + b) - - coverage = len(sums) / n_max - redundancy = len(A) ** 2 / len(sums) if sums else 0 - - return { - "coverage": float(coverage), - "encoding_efficiency": float(coverage / len(A)) if A else 0.0, - "redundancy": float(redundancy) - } - - -def test_erdos_turan(n_max_values, density_values): - """Test Erdős–Turán conjecture with 4-primitive framework.""" - results = [] - - for n_max in n_max_values: - for density in density_values: - for seed in range(3): # 3 samples per configuration - A = generate_additive_basis(n_max, density, seed=seed) - - # Check if it's a valid additive basis - is_basis, missing = check_additive_basis(A, n_max) - - # 4-primitive analysis - field = field_analysis(A) - spectral = spectral_analysis_additive(A) - shear = shear_analysis_additive(A) - packet = packet_analysis_additive(A) - - results.append({ - "n_max": n_max, - "density": density, - "seed": seed, - "is_basis": is_basis, - "missing": missing, - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_conjecture(results): - """Analyze results against Erdős–Turán conjecture.""" - conjecture_holds = [] - conjecture_violations = [] - - for r in results: - if r["is_basis"]: - # Conjecture: reciprocal sum should diverge (be large) - if r["field"]["reciprocal_sum"] > 10.0: # Empirical threshold - conjecture_holds.append(r) - else: - conjecture_violations.append(r) - - return { - "holds": len(conjecture_holds), - "violations": len(conjecture_violations), - "examples": conjecture_holds[:5], - "counterexamples": conjecture_violations[:5] - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON ERDŐS–TURÁN CONJECTURE") - print("=" * 70) - - # Test parameters - n_max_values = [50, 100, 200] - density_values = [0.3, 0.5, 0.7] - - print(f"\nTest parameters:") - print(f" n_max values: {n_max_values}") - print(f" Density values: {density_values}") - print(f" Samples per configuration: 3") - print(f" Total tests: {len(n_max_values) * len(density_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING ADDITIVE BASES AND ANALYZING") - print("=" * 70) - - results = test_erdos_turan(n_max_values, density_values) - - print(f"\nGenerated {len(results)} additive basis candidates") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST CONJECTURE") - print("=" * 70) - - analysis = analyze_conjecture(results) - - print(f"\nConjecture analysis:") - print(f" Holds: {analysis['holds']} cases") - print(f" Potential violations: {analysis['violations']} cases") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Density of additive basis computed") - print(" - Reciprocal sum measured") - print(" - Asymptotic density estimated") - print(" - Conjecture: high reciprocal sum → divergent series") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Addition table eigen decomposition") - print(" - Spectral radius computed") - print(" - Spectral gap measured") - print(" - Rank of additive structure") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Gap analysis between consecutive elements") - print(" - Covering radius computed") - print(" - Additive rigidity measured") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Encoding efficiency computed") - print(" - Coverage of sum space analyzed") - print(" - Redundancy measured") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures conjecture condition:") - print(" - Reciprocal sum directly measures conjecture condition") - print(" - High density → high reciprocal sum → conjecture holds") - - print("\n2. Spectral primitive reveals additive structure:") - print(" - Addition table eigenvalues encode additive properties") - print(" - Spectral radius indicates covering efficiency") - - print("\n3. Shear primitive measures additive deformation:") - print(" - Gap distribution indicates coverage quality") - print(" - Additive rigidity correlates with basis quality") - - print("\n4. Packet primitive measures encoding efficiency:") - print(" - Coverage indicates how well sums are covered") - print(" - Redundancy indicates efficiency of representation") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: conjecture condition (reciprocal sum)") - print(" - Spectral: additive structure") - print(" - Shear: coverage quality") - print(" - Packet: encoding efficiency") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_max_values": n_max_values, - "density_values": density_values, - "samples_per_config": 3, - "total_tests": len(n_max_values) * len(density_values) * 3 - }, - "results": results, - "conjecture_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Density and reciprocal sum of additive basis", - "insight": "Reciprocal sum directly measures conjecture condition" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Eigen decomposition of addition table", - "insight": "Spectral radius indicates covering efficiency" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Gap analysis and additive rigidity", - "insight": "Gap distribution indicates coverage quality" - }, - "packet": { - "equation": "Γᵢ", - "application": "Encoding efficiency and redundancy", - "insight": "Coverage indicates sum space coverage" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erdős–Turán conjecture. Field primitive directly captures conjecture condition. Spectral, shear, and packet primitives provide structural insights. Framework validated for additive number theory problems." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_erdos_turan_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_erdos_turan_4primitive_results.json b/4-Infrastructure/shim/test_erdos_turan_4primitive_results.json deleted file mode 100644 index b9544908..00000000 --- a/4-Infrastructure/shim/test_erdos_turan_4primitive_results.json +++ /dev/null @@ -1,4026 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:24:18.823891", - "n_max_values": [ - 50, - 100, - 200 - ], - "density_values": [ - 0.3, - 0.5, - 0.7 - ], - "samples_per_config": 3, - "total_tests": 27 - }, - "results": [ - { - "n_max": 50, - "density": 0.3, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.20833333333333334, - "reciprocal_sum": 0.39069392800831293, - "asymptotic_density": 0.20833333333333334, - "n_max": 48, - "size": 10 - }, - "spectral": { - "eigenvalues": [ - 4.854101966249685, - 4.302968385571447e-16, - 7.391935404147144e-17, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -9.757381983235608e-18, - -1.9219337392223413e-17, - -4.4336190907902296e-16, - -1.8541019662496843 - ], - "spectral_radius": 4.854101966249685, - "spectral_gap": 4.854101966249685, - "rank": 2 - }, - "shear": { - "max_gap": 9.0, - "avg_gap": 3.6666666666666665, - "covering_radius": 48.0, - "additive_rigidity": 0.2142857142857143 - }, - "packet": { - "coverage": 0.9166666666666666, - "encoding_efficiency": 0.09166666666666666, - "redundancy": 2.272727272727273 - } - }, - { - "n_max": 50, - "density": 0.3, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.36, - "reciprocal_sum": 1.360931169697772, - "asymptotic_density": 0.36, - "n_max": 50, - "size": 18 - }, - "spectral": { - "eigenvalues": [ - 11.135070631467942, - 2.032857190758628, - 0.9336046032394852, - 0.7930752096480406, - 1.4441566428117413e-15, - 1.1576272792640377e-15, - 6.017881688482891e-16, - 5.774372429008062e-16, - 3.55111552242012e-16, - 3.12586951578819e-16, - 2.594245340782633e-16, - 2.385569508842293e-16, - 1.5069250351206277e-16, - 9.854094356472297e-17, - 6.007238374361418e-17, - 4.321627383756272e-17, - 1.3259143554026671e-33, - 8.277251965912901e-50, - 9.914652516408773e-51, - 1.4153669544228527e-66, - 6.695089517424915e-83, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.9663167813155616e-99, - -5.468774871653147e-83, - -1.255865214431818e-66, - -1.5256300637888244e-33, - -6.7515096316284885e-18, - -1.9780778817066562e-17, - -5.258467256345552e-17, - -1.3115658954202975e-16, - -1.5796926425159934e-16, - -1.9108464121437955e-16, - -2.533764279691077e-16, - -3.7457519796930454e-16, - -3.9913433502521697e-16, - -5.548465600911568e-16, - -6.026420612369896e-16, - -1.202049306891803e-15, - -3.0687462106746815e-15, - -0.7603118503054492, - -0.9300882991905547, - -1.7678131159394606, - -3.43639436967864 - ], - "spectral_radius": 11.135070631467942, - "spectral_gap": 9.102213440709313, - "rank": 8 - }, - "shear": { - "max_gap": 8.0, - "avg_gap": 2.764705882352941, - "covering_radius": 50.0, - "additive_rigidity": 0.265625 - }, - "packet": { - "coverage": 1.78, - "encoding_efficiency": 0.09888888888888889, - "redundancy": 3.640449438202247 - } - }, - { - "n_max": 50, - "density": 0.3, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.38461538461538464, - "reciprocal_sum": 1.3388043166922476, - "asymptotic_density": 0.38461538461538464, - "n_max": 39, - "size": 15 - }, - "spectral": { - "eigenvalues": [ - 9.364754470106494, - 1.832923449820495, - 0.9554036519706726, - 0.6561263993864446, - 1.5740400314450889e-15, - 7.979327930422226e-16, - 3.990435079691263e-16, - 3.039538779970039e-16, - 2.1342426792171106e-16, - 1.6289027288723532e-16, - 1.518804414338252e-16, - 9.855322354591699e-17, - 2.824270102726938e-17, - 1.7034443457743987e-17, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.416248146189598e-19, - -1.6382336170260645e-17, - -2.91560536085096e-17, - -1.3359837389321022e-16, - -1.8613195516640027e-16, - -2.2385903048640205e-16, - -2.9800187067487915e-16, - -3.485076409532763e-16, - -3.72122284768608e-16, - -5.553177372677082e-16, - -7.558058256499972e-16, - -1.2887897701669226e-15, - -0.6459219785525077, - -0.9517381319058656, - -1.44488394324605, - -3.7666639175796854 - ], - "spectral_radius": 9.364754470106494, - "spectral_gap": 7.531831020285999, - "rank": 8 - }, - "shear": { - "max_gap": 7.0, - "avg_gap": 2.642857142857143, - "covering_radius": 39.0, - "additive_rigidity": 0.27450980392156865 - }, - "packet": { - "coverage": 1.4871794871794872, - "encoding_efficiency": 0.09914529914529915, - "redundancy": 3.8793103448275863 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.4, - "reciprocal_sum": 1.0292736263486535, - "asymptotic_density": 0.4, - "n_max": 50, - "size": 20 - }, - "spectral": { - "eigenvalues": [ - 10.332836619826228, - 1.9216512196576132, - 1.3338604089848318, - 0.9044540620876785, - 0.6543156485246635, - 0.5478996720938644, - 1.106667325460143e-15, - 9.364671899866224e-16, - 4.470232690315018e-16, - 3.969201359051652e-16, - 2.862167435569392e-16, - 2.222946351354845e-16, - 1.7958708202961862e-16, - 2.830566800951519e-17, - 4.984634580722227e-18, - 9.26719219550004e-33, - 3.8251772646277745e-34, - 9.42098754589548e-49, - 3.139910322778488e-49, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -6.939924316251416e-65, - -1.1157976800685006e-48, - -3.758082044795316e-33, - -4.546585963100211e-17, - -8.049959004482485e-17, - -9.254443769857783e-17, - -1.0547257022468396e-16, - -1.4945444591553743e-16, - -2.2270367527637015e-16, - -2.3722242992067115e-16, - -2.7039987481563373e-16, - -3.80757872633057e-16, - -5.122669048959003e-16, - -6.105192170617099e-16, - -9.60738615579212e-16, - -0.6542777120646329, - -0.7363520814076786, - -0.9234224586352494, - -1.712269199663725, - -3.6686961794035944 - ], - "spectral_radius": 10.332836619826228, - "spectral_gap": 8.411185400168614, - "rank": 11 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 2.3684210526315788, - "covering_radius": 50.0, - "additive_rigidity": 0.296875 - }, - "packet": { - "coverage": 1.68, - "encoding_efficiency": 0.08399999999999999, - "redundancy": 4.761904761904762 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.56, - "reciprocal_sum": 3.1210200702118645, - "asymptotic_density": 0.56, - "n_max": 50, - "size": 28 - }, - "spectral": { - "eigenvalues": [ - 19.166412020022033, - 3.044641068170295, - 1.8763304458787333, - 1.2726364720816825, - 1.1412064083964495, - 0.9470879245153077, - 0.8462029275802021, - 0.6409881377629864, - 0.6182996445191966, - 0.5731566943357492, - 1.4291573266515547e-15, - 8.40640783396244e-16, - 6.41961717259992e-16, - 5.129758313587652e-16, - 4.725238580754937e-16, - 3.743697576306722e-16, - 2.988810581270849e-16, - 1.2392707050679768e-16, - 1.2110992285782856e-16, - 1.6143331340030535e-17, - 2.583496083174453e-32, - 1.1011545687973559e-32, - 3.810456271838425e-49, - 0.0, - -9.182792968577454e-66, - -7.68604071128652e-50, - -2.724541857119281e-48, - -1.339820951015331e-33, - -8.245398220083789e-33, - -1.2246692242754516e-32, - -3.6349916003670785e-17, - -8.338017863577158e-17, - -1.2317322601459042e-16, - -2.3612666496466787e-16, - -4.07719031542432e-16, - -6.688833307903676e-16, - -7.326376985189133e-16, - -7.710865507822373e-16, - -8.441309997728669e-16, - -1.6695761549203764e-15, - -0.5594839847234544, - -0.5956793848764038, - -0.6187109763376931, - -0.6464516325625345, - -0.9231633992269527, - -1.0000000000000018, - -1.1933400283778155, - -1.6656002420932157, - -2.4988186250683277, - -5.425713469996237 - ], - "spectral_radius": 19.166412020022033, - "spectral_gap": 16.121770951851737, - "rank": 20 - }, - "shear": { - "max_gap": 4.0, - "avg_gap": 1.8148148148148149, - "covering_radius": 50.0, - "additive_rigidity": 0.35526315789473684 - }, - "packet": { - "coverage": 1.94, - "encoding_efficiency": 0.06928571428571428, - "redundancy": 8.082474226804123 - } - }, - { - "n_max": 50, - "density": 0.5, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.5957446808510638, - "reciprocal_sum": 3.243028739023894, - "asymptotic_density": 0.5957446808510638, - "n_max": 47, - "size": 28 - }, - "spectral": { - "eigenvalues": [ - 18.87807343699346, - 3.1207124210929944, - 1.7663379346848205, - 1.3006717087385298, - 1.1036940604888428, - 0.8888994228356353, - 0.7676913938951458, - 0.6083755333975142, - 0.585088275316263, - 0.5555854066038416, - 2.038773619598225e-15, - 6.579858142129914e-16, - 5.378381337985458e-16, - 4.700819772378515e-16, - 3.61648900968202e-16, - 2.254195371766656e-16, - 1.5922788722593009e-16, - 8.49423542819594e-17, - 2.2136399163730207e-18, - 2.598225889096927e-32, - 1.4186677865017175e-32, - 2.9165601181772286e-35, - 1.9869359514882472e-48, - 3.649821330414853e-51, - 2.72808594920479e-67, - 0.0, - 0.0, - -9.661422284337144e-65, - -4.4920067227340235e-17, - -1.2332454584398507e-16, - -2.0169831662517745e-16, - -3.135897357628196e-16, - -4.564538490794483e-16, - -5.542418919560304e-16, - -9.019693228554746e-16, - -1.0671442234581065e-15, - -1.267196143494998e-15, - -0.5131620449063142, - -0.5848826339208001, - -0.6083700503005135, - -0.6372909843875837, - -0.8657566148806211, - -0.9713259439397735, - -1.2272118633226596, - -1.4811766393755232, - -2.0029404319895874, - -6.683012387023679 - ], - "spectral_radius": 18.87807343699346, - "spectral_gap": 15.757361015900466, - "rank": 20 - }, - "shear": { - "max_gap": 5.0, - "avg_gap": 1.7037037037037037, - "covering_radius": 47.0, - "additive_rigidity": 0.3698630136986301 - }, - "packet": { - "coverage": 1.9148936170212767, - "encoding_efficiency": 0.06838905775075989, - "redundancy": 8.71111111111111 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.72, - "reciprocal_sum": 3.2152238592821565, - "asymptotic_density": 0.72, - "n_max": 50, - "size": 36 - }, - "spectral": { - "eigenvalues": [ - 19.96197522045391, - 4.210541617352623, - 2.1785606182480834, - 1.6804685489414708, - 1.4347246720955855, - 1.0979740241316045, - 0.9190272956957108, - 0.7871594459179938, - 0.7503393659975943, - 0.6818654531650545, - 0.6004228302243642, - 0.5709684040983867, - 0.5514079281539671, - 0.5224325988432194, - 1.2242444608260215e-15, - 4.442730991435018e-16, - 4.108791259035665e-16, - 3.244346030597696e-16, - 2.9096324917992387e-16, - 1.987461771835988e-16, - 1.0400890893830794e-16, - 9.103335626403937e-17, - 2.7536185066668996e-32, - 4.9896454981008896e-34, - 0.0, - -9.91881430598397e-33, - -1.4998526203431357e-32, - -6.803783378454094e-17, - -8.629744263465097e-17, - -1.1760926962463566e-16, - -1.60939163099654e-16, - -2.6351594887380667e-16, - -2.6590983501370907e-16, - -2.96866630607759e-16, - -3.588678674834953e-16, - -5.701069905459201e-16, - -6.742282516078769e-16, - -0.5224325994734286, - -0.5514190418308108, - -0.5710026145672905, - -0.6004239589134953, - -0.7496722838840973, - -0.7851023950895025, - -0.9064532185663954, - -1.0520153834767647, - -1.1072238884777155, - -1.5957571597446134, - -1.9977894962443192, - -3.4258149850204154, - -8.082760998030718 - ], - "spectral_radius": 19.96197522045391, - "spectral_gap": 15.751433603101285, - "rank": 27 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.4, - "covering_radius": 50.0, - "additive_rigidity": 0.4166666666666667 - }, - "packet": { - "coverage": 1.96, - "encoding_efficiency": 0.05444444444444444, - "redundancy": 13.224489795918368 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.72, - "reciprocal_sum": 3.549085846071758, - "asymptotic_density": 0.72, - "n_max": 50, - "size": 36 - }, - "spectral": { - "eigenvalues": [ - 24.914687458340694, - 4.019746915563797, - 2.424402442163106, - 1.599856129635275, - 1.299733669157069, - 1.133093699844177, - 0.9999999999999994, - 0.9253367628879668, - 0.8431976448826005, - 0.6198083183303204, - 0.5991170242288959, - 0.568827675987441, - 0.5392065513288405, - 1.186182887951106e-15, - 7.809079588017831e-16, - 6.328026056600986e-16, - 5.400671188981824e-16, - 4.441828692343011e-16, - 2.3605025868031465e-16, - 1.4792002036236478e-16, - 8.650471453613707e-17, - 4.643650352252468e-18, - 2.898667063784714e-32, - 1.0402894578512919e-48, - 0.0, - -1.292827736839999e-50, - -2.0275286658624103e-34, - -2.9289064655944992e-33, - -6.753941780563911e-33, - -2.2197200387479824e-32, - -2.8368241888056494e-17, - -1.4462554340112295e-16, - -2.1773919645397607e-16, - -2.6349709978044233e-16, - -6.199347056777366e-16, - -7.59748715030394e-16, - -9.630589896950468e-16, - -1.00377412112036e-15, - -0.5650401178144466, - -0.5935520064433738, - -0.6198078644968881, - -0.7070325709268052, - -0.8680036501636409, - -0.9411378124745743, - -1.0465736786771445, - -1.19815485582281, - -1.3575429082567527, - -2.254611288517578, - -2.96538209879035, - -7.3701754399658315 - ], - "spectral_radius": 24.914687458340694, - "spectral_gap": 20.894940542776897, - "rank": 25 - }, - "shear": { - "max_gap": 4.0, - "avg_gap": 1.4, - "covering_radius": 50.0, - "additive_rigidity": 0.4166666666666667 - }, - "packet": { - "coverage": 1.96, - "encoding_efficiency": 0.05444444444444444, - "redundancy": 13.224489795918368 - } - }, - { - "n_max": 50, - "density": 0.7, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.8367346938775511, - "reciprocal_sum": 4.184743811754005, - "asymptotic_density": 0.8367346938775511, - "n_max": 49, - "size": 41 - }, - "spectral": { - "eigenvalues": [ - 27.618471397026198, - 4.6785137471040645, - 3.061333124993969, - 1.9997002826939039, - 1.4804158979689692, - 1.23787412676922, - 1.0700001027949921, - 0.9381485085373885, - 0.8888724663084135, - 0.7483295543166807, - 0.656550806141208, - 0.6410415615161487, - 0.5945100809339996, - 0.5785678952314758, - 0.5337577575966552, - 0.5213968795839687, - 2.4941691231287287e-15, - 1.3408649514269133e-15, - 5.593796478013011e-16, - 3.252194521207631e-16, - 1.7831242442086017e-16, - 9.682267245793825e-18, - 3.443293782688705e-32, - 0.0, - 0.0, - -4.5447844381780785e-36, - -6.83720828069679e-33, - -7.520812359399636e-32, - -2.004895671380438e-17, - -1.52345641848447e-16, - -3.019977915025577e-16, - -5.66448299205849e-16, - -8.086473405758336e-16, - -0.5052243034855389, - -0.5337577572878299, - -0.5500906918599755, - -0.5787346268063757, - -0.6407856895252574, - -0.6562341996155925, - -0.65939889576015, - -0.8541515470125254, - -0.893932681487147, - -0.9864031383688214, - -1.171584734625784, - -1.2924642278127738, - -1.8269845006495595, - -2.315155842604513, - -3.443285270689354, - -9.339296081926063 - ], - "spectral_radius": 27.618471397026198, - "spectral_gap": 22.939957649922135, - "rank": 32 - }, - "shear": { - "max_gap": 3.0, - "avg_gap": 1.2, - "covering_radius": 49.0, - "additive_rigidity": 0.45454545454545453 - }, - "packet": { - "coverage": 1.9591836734693877, - "encoding_efficiency": 0.047784967645594825, - "redundancy": 17.510416666666668 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.34, - "reciprocal_sum": 0.7227462772118607, - "asymptotic_density": 0.34, - "n_max": 100, - "size": 34 - }, - "spectral": { - "eigenvalues": [ - 14.587195468551425, - 3.3100875928252163, - 2.1319322778969996, - 1.159723630679044, - 1.044942641575981, - 0.5945586541884587, - 1.5731514285662803e-15, - 1.409039327280069e-15, - 8.385576466369455e-16, - 6.33403195164125e-16, - 4.045393659067222e-16, - 2.3046881157152925e-16, - 1.7711491411580891e-16, - 8.613914859007037e-17, - 6.802477852786325e-17, - 3.239004171116423e-19, - 3.6010114162377205e-31, - 3.1551764978357757e-31, - 1.8369945970946854e-31, - 1.632658205716344e-31, - 1.202049770473965e-31, - 1.1743011791046851e-31, - 1.1399546026162763e-31, - 9.561068001954039e-32, - 7.655751047656414e-32, - 5.975228238922304e-32, - 3.571685741902237e-32, - 3.1437912357989286e-32, - 2.7277581587671826e-32, - 2.178343111103565e-32, - 2.0249968360334487e-47, - 1.2627985567987472e-47, - 6.939110199797374e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.0383061240981653e-49, - -1.2737940126797409e-49, - -2.0766122481963306e-49, - -8.101596335209916e-48, - -1.2370428982566108e-47, - -1.876215727490274e-47, - -2.878070653361842e-47, - -3.404108200303302e-33, - -1.002842915863697e-32, - -2.563800310973073e-32, - -3.5154924990427103e-32, - -3.647144666903462e-32, - -6.04718782490993e-32, - -6.622446424145523e-32, - -6.865949153720023e-32, - -9.851034696064932e-32, - -9.915311491430787e-32, - -1.157267589187795e-31, - -1.4832731867591692e-31, - -2.028365789081586e-31, - -3.0052265578218544e-31, - -4.066673481700703e-31, - -4.710118892868527e-31, - -4.764498781476728e-17, - -2.0351691643149694e-16, - -4.032016603684952e-16, - -6.520134008844002e-16, - -7.400679232833547e-16, - -8.662186681398264e-16, - -1.0922101046015474e-15, - -1.7797916441642947e-15, - -0.594560665372271, - -1.0609673333194802, - -1.362686586429169, - -2.9731169185346493, - -6.837108762061553 - ], - "spectral_radius": 14.587195468551425, - "spectral_gap": 11.277107875726209, - "rank": 11 - }, - "shear": { - "max_gap": 9.0, - "avg_gap": 2.5757575757575757, - "covering_radius": 100.0, - "additive_rigidity": 0.2796610169491526 - }, - "packet": { - "coverage": 1.55, - "encoding_efficiency": 0.04558823529411765, - "redundancy": 7.458064516129032 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.32323232323232326, - "reciprocal_sum": 1.5688188434586592, - "asymptotic_density": 0.32323232323232326, - "n_max": 99, - "size": 32 - }, - "spectral": { - "eigenvalues": [ - 22.191274608692886, - 3.7984028625665767, - 2.187878153388849, - 1.6120409317389526, - 1.305601340035801, - 0.8660519526931163, - 0.8134162719477249, - 0.7407661036947877, - 0.6444931097658066, - 0.6211963500749248, - 0.5553051120849473, - 3.4050827543262343e-15, - 2.5660339859608054e-15, - 1.823212023906157e-15, - 1.040119277846722e-15, - 9.161621718851852e-16, - 6.960271007730688e-16, - 5.472926973525717e-16, - 4.0756568962414636e-16, - 2.3574584775379794e-16, - 2.005457403323646e-16, - 5.378677337791073e-17, - 3.0269496791497694e-17, - 2.869684565606515e-17, - 2.423252868405768e-31, - 1.6556247960545695e-31, - 1.5275779713553672e-31, - 1.2370394004853069e-31, - 1.1827035119950075e-31, - 8.336141556557747e-32, - 7.285421991808182e-32, - 4.2954947461330775e-32, - 3.241970923905979e-32, - 1.4010042810639835e-32, - 2.2042823542612553e-33, - 2.9098402064675005e-47, - 2.586488287117319e-47, - 2.155696282868795e-47, - 1.3684452996238366e-47, - 1.2216982454562862e-47, - 9.988357358280807e-48, - 8.002064713261251e-48, - 6.34034701784986e-48, - 3.8820336706127155e-48, - 1.920575544771132e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -5.3812150120926594e-49, - -2.3782032623527096e-48, - -3.229062107189129e-48, - -4.028545144421333e-48, - -5.781919121800338e-48, - -7.541268577929126e-48, - -1.292080648708421e-47, - -1.3044912449575294e-47, - -2.0956057627854107e-47, - -2.360791007121297e-47, - -2.3728099487016516e-47, - -2.4487022609703e-47, - -1.1745192727698926e-32, - -3.8840944497838776e-32, - -4.0507127631460913e-32, - -5.344587734746598e-32, - -7.147282142589961e-32, - -1.0407713175109268e-31, - -1.2578456876704877e-31, - -1.3814886681566773e-31, - -1.724918742630273e-31, - -2.2382188070356605e-31, - -2.562577848377105e-31, - -5.1362451460567184e-17, - -1.1620986450674132e-16, - -1.3430124781401203e-16, - -2.148005639627739e-16, - -2.616934081166686e-16, - -4.835680190323347e-16, - -5.745241401207509e-16, - -1.107408905347163e-15, - -1.4544351264402822e-15, - -1.7351286594182773e-15, - -2.7182396018151577e-15, - -3.0613039453970755e-15, - -0.5252009539756134, - -0.5593272953734854, - -0.6444887674694397, - -0.7357289918338323, - -0.763785486750004, - -0.8657871306851764, - -0.9284482184810331, - -1.515091702868328, - -1.7770055312298103, - -2.9431849367490766, - -7.078377781268576 - ], - "spectral_radius": 22.191274608692886, - "spectral_gap": 18.39287174612631, - "rank": 22 - }, - "shear": { - "max_gap": 9.0, - "avg_gap": 3.096774193548387, - "covering_radius": 99.0, - "additive_rigidity": 0.2440944881889764 - }, - "packet": { - "coverage": 1.696969696969697, - "encoding_efficiency": 0.05303030303030303, - "redundancy": 6.095238095238095 - } - }, - { - "n_max": 100, - "density": 0.3, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.30303030303030304, - "reciprocal_sum": 1.5502152326048888, - "asymptotic_density": 0.30303030303030304, - "n_max": 99, - "size": 30 - }, - "spectral": { - "eigenvalues": [ - 20.55070012432417, - 3.463594651390754, - 1.675048132594539, - 1.448760189776833, - 1.125974194307154, - 0.7754318483153063, - 0.7333475241049978, - 0.7083167082534679, - 0.5512065694101966, - 2.5204546752417264e-15, - 1.8269906363491127e-15, - 1.221792547464425e-15, - 1.1702735601736278e-15, - 8.83810876953316e-16, - 8.283085326547831e-16, - 7.383391501288919e-16, - 5.827646432267343e-16, - 4.2185900772152614e-16, - 3.5540563662060275e-16, - 1.6657786348194472e-16, - 1.952092172219235e-17, - 2.713181629776713e-31, - 2.573207727897874e-31, - 2.4369341959913126e-31, - 2.2167535881021504e-31, - 1.7817444825263702e-31, - 1.6347633718846715e-31, - 1.2304432843348094e-31, - 1.0066306596332017e-31, - 8.827141840131479e-32, - 8.001714357399037e-32, - 7.061933747762324e-32, - 3.2886538707680736e-32, - 2.1310040897268097e-32, - 1.3536617198371607e-32, - 4.049041144038158e-47, - 3.3894899580156285e-47, - 2.293066816127932e-47, - 2.0374047232503598e-47, - 1.8152147816753893e-47, - 1.4213275249080438e-47, - 1.1238700173695659e-47, - 8.045842728905599e-48, - 6.153997206545162e-48, - 2.634387466628216e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -3.909784561144887e-49, - -3.5519874856059205e-48, - -4.139414019365903e-48, - -4.9569588443557637e-48, - -1.1121490702761603e-47, - -1.1333821087548244e-47, - -1.2974808203609032e-47, - -1.7142697836358606e-47, - -1.9514546785347904e-47, - -2.8146626278429325e-47, - -3.3289328609034644e-47, - -2.1755577882255925e-32, - -2.669092790296726e-32, - -5.441457300505748e-32, - -6.960564933570367e-32, - -7.569789760131608e-32, - -8.352819528219436e-32, - -9.850290070031259e-32, - -1.2237951526437122e-31, - -1.3260465635818096e-31, - -1.347704965612153e-31, - -1.7770417623661225e-31, - -1.975358018609651e-31, - -2.0875444126264962e-31, - -2.3974915926586468e-31, - -2.5209198058502425e-31, - -2.906077002672211e-31, - -8.816747493323234e-17, - -2.0992524443241365e-16, - -2.1061335485087812e-16, - -3.762535510912433e-16, - -4.590495938804614e-16, - -5.686791113835554e-16, - -7.677996959468905e-16, - -1.1988696990022489e-15, - -1.282446881352593e-15, - -1.932745683705873e-15, - -2.488591264979541e-15, - -2.5609756342657973e-15, - -0.5512065693976266, - -0.706788219840444, - -0.7328361101577994, - -0.7753719967845032, - -1.0866485719117789, - -1.3666707496078785, - -1.6200412151193568, - -3.0656571591573663, - -6.127159350500669 - ], - "spectral_radius": 20.55070012432417, - "spectral_gap": 17.087105472933416, - "rank": 18 - }, - "shear": { - "max_gap": 13.0, - "avg_gap": 3.3448275862068964, - "covering_radius": 99.0, - "additive_rigidity": 0.23015873015873015 - }, - "packet": { - "coverage": 1.7575757575757576, - "encoding_efficiency": 0.05858585858585859, - "redundancy": 5.172413793103448 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.51, - "reciprocal_sum": 1.4620761133183235, - "asymptotic_density": 0.51, - "n_max": 100, - "size": 51 - }, - "spectral": { - "eigenvalues": [ - 26.46302462171642, - 6.001888705032884, - 3.721738242308655, - 2.2055928773884395, - 2.000284495126827, - 1.6706209649271109, - 1.1043121322509755, - 0.9621905598487666, - 0.908351052083351, - 0.8554393030491817, - 0.8195444810660918, - 0.6456696545337661, - 0.6304683057029675, - 0.5676104682087082, - 0.5285243657302834, - 1.852984605919612e-15, - 8.752778628297975e-16, - 7.285382324266349e-16, - 5.90461987604852e-16, - 4.94649794091818e-16, - 4.171084719151601e-16, - 2.0358661414966643e-16, - 7.256868088575455e-17, - 1.759985574034173e-29, - 1.0359330654940596e-29, - 9.704443050129668e-30, - 6.358282107554842e-30, - 4.618016385285694e-30, - 3.567329614018442e-30, - 2.40237234621528e-30, - 1.1468796794901002e-30, - 8.374433909069872e-31, - 3.5016361945715576e-31, - 1.6179499449423607e-31, - 1.4771191155268166e-31, - 7.078617337910001e-46, - 6.819269326193039e-46, - 6.1059819426538755e-46, - 4.4096375413005e-46, - 1.1146462044425983e-46, - 4.43989160327112e-48, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.6133987006408633e-46, - -2.1076595096590026e-46, - -3.787340235591514e-46, - -6.219418956192293e-46, - -7.002468315124775e-46, - -7.660324922812278e-46, - -1.3725422973656451e-45, - -2.485009700529278e-31, - -4.6271954006475445e-31, - -5.622232148032857e-31, - -7.190620106718772e-31, - -1.0199941932945231e-30, - -1.195071372417158e-30, - -1.3372940859796747e-30, - -1.889483949928264e-30, - -2.7080409832761518e-30, - -3.0201596946960783e-30, - -4.482614032622195e-30, - -4.975251319687468e-30, - -8.500633246515191e-30, - -1.1445429088971146e-29, - -1.5803372173710656e-29, - -5.553425926022244e-17, - -1.004088379002443e-16, - -1.3014472371391333e-16, - -2.0670668081836735e-16, - -4.080179688382694e-16, - -4.557912084549565e-16, - -6.124679112343934e-16, - -7.070821349267744e-16, - -7.225740139221828e-16, - -8.50262293647589e-16, - -1.0610661350443269e-15, - -1.1310956051244854e-15, - -1.2949814925185953e-15, - -2.3564125144993994e-15, - -0.5285243701752895, - -0.5676243445696726, - -0.6304690281684712, - -0.6456696545371596, - -0.8436627914391931, - -0.8849557476547957, - -0.9621524615327968, - -1.0547815289828821, - -1.1074084292148023, - -1.6993910062827942, - -2.1587088349427437, - -2.517096655030408, - -4.7060563963547075, - -10.778758980088714 - ], - "spectral_radius": 26.46302462171642, - "spectral_gap": 20.46113591668354, - "rank": 29 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.9, - "covering_radius": 100.0, - "additive_rigidity": 0.3448275862068966 - }, - "packet": { - "coverage": 1.82, - "encoding_efficiency": 0.035686274509803925, - "redundancy": 14.291208791208792 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.5151515151515151, - "reciprocal_sum": 3.4496754593063224, - "asymptotic_density": 0.5151515151515151, - "n_max": 99, - "size": 51 - }, - "spectral": { - "eigenvalues": [ - 35.04220464279881, - 6.198920702602783, - 3.4692614439783096, - 2.7198250575947496, - 1.8726499036631237, - 1.6590786949218168, - 1.2209275701071594, - 1.0432740388959363, - 0.9273562516173061, - 0.896581634367651, - 0.8439077066687349, - 0.7644899798585969, - 0.7189608231598698, - 0.6668099050219025, - 0.621213110542842, - 0.6085327801570306, - 0.5596172452873939, - 0.5319868599167576, - 4.973685462445657e-15, - 1.185367361391937e-15, - 9.82088029260066e-16, - 7.057320908025454e-16, - 5.752333290799533e-16, - 4.596927620395354e-16, - 2.9451041598246665e-16, - 2.334246622016076e-16, - 1.1999037120192071e-16, - 8.895092775203129e-17, - 5.5177832973903954e-17, - 4.970089191134114e-17, - 2.6248614563368457e-17, - 1.0643938715997305e-17, - 6.112351863863008e-18, - 3.982854513481527e-18, - 2.459057185552864e-18, - 2.948363112803587e-19, - 1.4250494316157445e-20, - 2.3411741851158896e-22, - 2.2661620998000298e-23, - 3.018453232381452e-33, - 2.8860789137386803e-33, - 2.5670064308826166e-33, - 1.9258967686296e-33, - 1.2388899050894776e-33, - 8.178558937687643e-34, - 6.782320109980599e-34, - 4.45253658341346e-34, - 2.744219304477206e-34, - 3.9562642563442516e-35, - 0.0, - 0.0, - 0.0, - -3.4085766512711244e-34, - -5.082843191950168e-34, - -7.457879542608046e-34, - -7.667321871804043e-34, - -1.4316156050996467e-33, - -1.7423630987030653e-33, - -2.6043425493755434e-33, - -2.82486700571382e-33, - -3.5989501695218764e-33, - -2.4645072730843195e-20, - -4.68604842606002e-19, - -7.056123920292195e-19, - -1.6308328435000755e-18, - -3.822129711883461e-18, - -1.1147749306451543e-17, - -2.175272542446107e-17, - -3.0899748448268003e-17, - -4.4022241901768314e-17, - -7.701809511112584e-17, - -1.8568236391961607e-16, - -2.6418932720439704e-16, - -2.7438780250513707e-16, - -4.136755949719993e-16, - -5.802467460581134e-16, - -7.68897311551193e-16, - -7.743169730835342e-16, - -1.0495985056210761e-15, - -1.1749676375716444e-15, - -3.5030478600261783e-15, - -0.507779259434898, - -0.5596172451739029, - -0.573608795624508, - -0.6212130724474328, - -0.6213905797282213, - -0.718920604876327, - -0.7642189627926218, - -0.7781328347744152, - -0.8454932316929921, - -0.9141565165015048, - -0.9540242787148755, - -1.2204698737595214, - -1.3252946137672343, - -1.8584499811182342, - -2.1095272375301892, - -2.929114391414421, - -4.679974824350846, - -11.384212047458627 - ], - "spectral_radius": 35.04220464279881, - "spectral_gap": 28.843283940196024, - "rank": 36 - }, - "shear": { - "max_gap": 7.0, - "avg_gap": 1.96, - "covering_radius": 99.0, - "additive_rigidity": 0.33783783783783783 - }, - "packet": { - "coverage": 1.9393939393939394, - "encoding_efficiency": 0.038027332144979206, - "redundancy": 13.546875 - } - }, - { - "n_max": 100, - "density": 0.5, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.5656565656565656, - "reciprocal_sum": 3.629287205749213, - "asymptotic_density": 0.5656565656565656, - "n_max": 99, - "size": 56 - }, - "spectral": { - "eigenvalues": [ - 36.346244411184685, - 7.367191407031603, - 3.798951275713672, - 2.638170730349689, - 2.173797915327621, - 1.6839054766405919, - 1.5307748077458727, - 1.3463303713172274, - 1.1696387146779141, - 0.9991295660148408, - 0.8738374760937053, - 0.8232849507992266, - 0.778638601428555, - 0.7074558341353832, - 0.6868201545220944, - 0.6409011095566114, - 0.579741594946864, - 0.5598648978667099, - 0.5576368441958184, - 0.5446508681186868, - 3.322168891075048e-15, - 2.636131366450685e-15, - 1.8732946778205075e-15, - 1.0448179823940012e-15, - 1.042883890809742e-15, - 4.6449604443905e-16, - 3.264305304933331e-16, - 2.3252369723048804e-16, - 2.2891593281705343e-16, - 2.195935969573788e-16, - 2.0645794747287059e-16, - 1.698516750127341e-16, - 1.1054526286966511e-16, - 9.542283706815257e-17, - 7.203345261003179e-17, - 4.1719310055975885e-17, - 3.778262323626363e-17, - 3.359438248267203e-17, - 1.9128517183798287e-17, - 1.5543776709967374e-17, - 6.1145959083501564e-18, - 2.2412057627202064e-18, - 1.1834270391265933e-18, - 1.8486598186720443e-32, - 1.0107064135858735e-32, - 7.708961101279633e-33, - 5.949870787616168e-33, - 2.1124237046112367e-33, - 1.7685151247056036e-34, - 0.0, - 0.0, - 0.0, - -1.34196565172349e-33, - -4.5542495567182e-33, - -6.546766042274019e-33, - -1.0957917447979671e-32, - -1.4459683328656087e-32, - -2.7229408564470717e-32, - -3.0826231060586925e-18, - -7.765287931119246e-18, - -1.6125101587630838e-17, - -1.994474742093755e-17, - -3.261987690706362e-17, - -4.156146223843908e-17, - -4.3720875125711236e-17, - -5.68549691454948e-17, - -7.253628934841263e-17, - -1.0093197789645325e-16, - -1.1349668614715662e-16, - -1.1944852037905328e-16, - -1.8485247295744536e-16, - -2.563715739996111e-16, - -2.7762225689558473e-16, - -4.198699492213505e-16, - -5.443542043453443e-16, - -7.419319612755573e-16, - -8.869244041896562e-16, - -1.943541360822256e-15, - -2.3709823771005793e-15, - -0.5139946038678163, - -0.5446508681590749, - -0.5576368442520289, - -0.5797075618673762, - -0.6409011094939632, - -0.6519876606040333, - -0.68683370766463, - -0.7074607923374581, - -0.7786415723284095, - -0.8706345007715512, - -0.9989385707083537, - -1.1194776801881987, - -1.224643805509594, - -1.3508610994603747, - -1.5435091140062431, - -1.9182747785861962, - -2.295177518842402, - -3.1997385014053674, - -5.674515437356831, - -11.94938128025747 - ], - "spectral_radius": 36.346244411184685, - "spectral_gap": 28.97905300415308, - "rank": 40 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.7818181818181817, - "covering_radius": 99.0, - "additive_rigidity": 0.35947712418300654 - }, - "packet": { - "coverage": 1.9595959595959596, - "encoding_efficiency": 0.03499278499278499, - "redundancy": 16.164948453608247 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.77, - "reciprocal_sum": 3.7821430977094983, - "asymptotic_density": 0.77, - "n_max": 100, - "size": 77 - }, - "spectral": { - "eigenvalues": [ - 45.409459631431496, - 10.317029224769485, - 5.488208056739416, - 3.7363315463638993, - 2.8946609042144633, - 2.2151384612679808, - 1.9519150729615735, - 1.6563507745122443, - 1.5314369863907351, - 1.3499896906129492, - 1.2057005445625584, - 1.0252388935845402, - 0.9684166899015384, - 0.939075211887743, - 0.8945202198476647, - 0.8594578444589382, - 0.805900724104341, - 0.7738310558747007, - 0.7026463984388329, - 0.6605939538586068, - 0.636189284288872, - 0.6221799259844636, - 0.60035979422033, - 0.5901157017870743, - 0.5726632349287105, - 0.5521540023149303, - 0.5429429978547637, - 0.5294562739046482, - 0.5261504042086117, - 0.5249785713998847, - 0.5096821549316681, - 1.7254690706303584e-15, - 1.2352357757176227e-15, - 9.486435762818671e-16, - 8.46653719771963e-16, - 4.626015172495452e-16, - 3.177399963686542e-16, - 2.7215123115181045e-16, - 2.0830448704385006e-16, - 1.2697719878810805e-16, - 9.082777168341084e-17, - 1.8087897734549167e-17, - 4.114012531522252e-18, - 3.558184135253454e-18, - 4.105090524818406e-31, - 1.8710012849756977e-31, - 9.100008889391354e-32, - 0.0, - 0.0, - -1.7619603030190523e-32, - -4.4962845830685793e-32, - -2.0791687421912816e-31, - -2.9107738910782254e-31, - -2.763257094652337e-19, - -7.628035696627975e-18, - -1.1157346642713895e-17, - -1.3527517444693613e-17, - -2.36965479385821e-17, - -3.3811037749643045e-17, - -8.44537643190678e-17, - -1.4284820189680051e-16, - -2.538616098727725e-16, - -4.401350062932183e-16, - -4.988365252077132e-16, - -5.47558376998692e-16, - -7.357812550111153e-16, - -9.177607783935652e-16, - -1.1713341464321406e-15, - -1.507800013402622e-15, - -5.307615807573446e-15, - -0.5098115149276415, - -0.5256006073050986, - -0.5294562739046468, - -0.5414862156900467, - -0.5521540023010797, - -0.5726632344461772, - -0.5889929643227273, - -0.5954321299465423, - -0.6172756442854993, - -0.6296384744014571, - -0.6361892868679228, - -0.6612496664302936, - -0.7149830643471605, - -0.7738319442435646, - -0.8313165005182315, - -0.8944466650570528, - -0.9214453289635903, - -0.9399717133656659, - -1.0215619102757896, - -1.1612207587690104, - -1.280185511120308, - -1.5074701311776415, - -1.5563506628996597, - -1.8062763117204215, - -1.9776483618610683, - -2.4615342172171353, - -3.3623346045032405, - -4.002040890075572, - -7.469851594156397, - -15.950354046507035 - ], - "spectral_radius": 45.409459631431496, - "spectral_gap": 35.09243040666201, - "rank": 61 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.3026315789473684, - "covering_radius": 100.0, - "additive_rigidity": 0.43428571428571433 - }, - "packet": { - "coverage": 1.97, - "encoding_efficiency": 0.025584415584415585, - "redundancy": 30.096446700507613 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.74, - "reciprocal_sum": 4.088457751676166, - "asymptotic_density": 0.74, - "n_max": 100, - "size": 74 - }, - "spectral": { - "eigenvalues": [ - 48.01358751362878, - 8.249494804174931, - 4.951371880549958, - 3.638436408642007, - 2.7307808373131306, - 2.2438019733268253, - 1.7878409256445562, - 1.642613972558849, - 1.3993913599320411, - 1.2795817058646048, - 1.222611920862824, - 1.108345411103318, - 1.0068755543474373, - 0.9750142636403017, - 0.888385422355911, - 0.8734789000955746, - 0.7971136605073489, - 0.7308536281244065, - 0.7208733320648112, - 0.7052181494494375, - 0.652281327693865, - 0.6505626176547449, - 0.61192555571019, - 0.5654288021075353, - 0.5485614524820984, - 0.543723413425045, - 0.5332714424775234, - 0.5320735426332216, - 0.5177535483559639, - 5.2522976569872726e-15, - 1.6941485701641357e-15, - 9.616709618110595e-16, - 8.187466971010733e-16, - 4.0373018104417255e-16, - 3.512512967566587e-16, - 2.2411258418026504e-16, - 1.888723766442223e-16, - 1.1730753451377375e-16, - 8.9608308784484e-17, - 7.671424258716301e-17, - 4.490303241225788e-17, - 2.703539403310426e-31, - 1.5579710258597901e-31, - 1.3456514639513063e-31, - 7.853649741905805e-32, - 4.2547248262037596e-32, - 1.0122310171338159e-32, - 0.0, - -1.5610133523595426e-32, - -2.0520612929710045e-32, - -4.686508071823084e-32, - -1.1614118416840831e-31, - -1.5574842062733137e-31, - -3.0354270724043297e-31, - -1.2533814158743373e-17, - -2.654049204242835e-17, - -3.293821906752518e-17, - -6.308862257994608e-17, - -8.400507184054751e-17, - -1.1059174903332177e-16, - -1.5425961564034856e-16, - -1.7674034407886184e-16, - -2.1106653941777983e-16, - -2.5967881329497475e-16, - -3.0189380644129395e-16, - -4.248971242681711e-16, - -4.663743593698057e-16, - -6.674117763658428e-16, - -7.061643860161681e-16, - -1.2044200501648346e-15, - -1.391411820291635e-15, - -1.8029825166046353e-15, - -0.5320735426332187, - -0.533271428084913, - -0.5437234125933751, - -0.5485614524819098, - -0.565428802107303, - -0.572209428407454, - -0.627307745018756, - -0.650599561023715, - -0.6522813432499057, - -0.7052270595432827, - -0.7308522000256252, - -0.7971122098619007, - -0.872045780253938, - -0.8833117017425574, - -0.9265224365917387, - -0.9755237530562303, - -1.0731604828548926, - -1.1330392734189465, - -1.2314250437433019, - -1.3082002617128585, - -1.4325064650878998, - -1.7679391773203095, - -2.077700395081478, - -2.467064628166095, - -2.9075805346290307, - -4.203481791219736, - -6.690942511348593, - -16.712160905468263 - ], - "spectral_radius": 48.01358751362878, - "spectral_gap": 39.76409270945385, - "rank": 57 - }, - "shear": { - "max_gap": 4.0, - "avg_gap": 1.356164383561644, - "covering_radius": 100.0, - "additive_rigidity": 0.42441860465116277 - }, - "packet": { - "coverage": 1.98, - "encoding_efficiency": 0.026756756756756758, - "redundancy": 27.656565656565657 - } - }, - { - "n_max": 100, - "density": 0.7, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.797979797979798, - "reciprocal_sum": 4.702884342070987, - "asymptotic_density": 0.797979797979798, - "n_max": 99, - "size": 79 - }, - "spectral": { - "eigenvalues": [ - 51.420500410361875, - 10.24257533004998, - 5.467202401458483, - 3.703824564635508, - 3.064670827510303, - 2.2971372431414, - 2.1029613980397275, - 1.7369877740357025, - 1.5160400068190436, - 1.403471893935, - 1.177079151910074, - 1.1074850503805593, - 1.0068241650749212, - 0.9671751653580815, - 0.8944385953870867, - 0.883041810266252, - 0.791424477815084, - 0.7872293753094296, - 0.7559754543708941, - 0.7179635279162294, - 0.6830545000362206, - 0.6507580869084505, - 0.6362201667340496, - 0.6254595717535398, - 0.6224820954700147, - 0.5976281734661916, - 0.5876463926101014, - 0.5634866555921817, - 0.5547376942080903, - 0.5268539250963028, - 0.5225311795852785, - 0.5191626125048304, - 0.5148359142911441, - 2.2205872610641104e-15, - 1.0202840992925376e-15, - 7.907364342373054e-16, - 3.4041652391178655e-16, - 2.9997703581434854e-16, - 2.432295902016759e-16, - 2.1684857130751013e-16, - 1.3647060667661405e-16, - 1.1636058466853623e-16, - 1.1339103945778155e-16, - 9.058039391578093e-17, - 6.52712614957631e-17, - 4.4020037047624285e-17, - 2.3910464734953835e-17, - 1.3854442385282939e-17, - 6.079488138344916e-18, - 1.854054284070473e-18, - 4.6725945110512825e-20, - 0.0, - -1.7914842777364793e-18, - -2.858226303068823e-18, - -2.2445971533811022e-17, - -2.825189140397995e-17, - -5.202985351305504e-17, - -7.929207133837335e-17, - -1.1987873463046178e-16, - -1.6503851240560349e-16, - -2.2312522770657433e-16, - -2.823001671806581e-16, - -3.362031797696713e-16, - -3.5931931212509643e-16, - -4.959093581318708e-16, - -5.109143621477689e-16, - -1.0471217002764153e-15, - -0.5148359177835822, - -0.51916261250483, - -0.5225311802379078, - -0.5268539250963045, - -0.5547376942081016, - -0.5634874913425191, - -0.5876463926155513, - -0.5976335127717911, - -0.6254518330424513, - -0.6335966569801074, - -0.6376960630765063, - -0.6645965757794543, - -0.7148032012837289, - -0.7179653648265737, - -0.7754808662601874, - -0.7910874790631333, - -0.8337027138146383, - -0.8837750553489955, - -0.9322678228623839, - -1.006166681681829, - -1.1073539417080964, - -1.1736176533364657, - -1.3942150852461748, - -1.504665122838308, - -1.7060945402462004, - -1.8720438515705666, - -2.152738142180498, - -2.6422785572528915, - -3.4445769735635263, - -4.304915056339867, - -7.3821009861393385, - -16.36078664102958 - ], - "spectral_radius": 51.420500410361875, - "spectral_gap": 41.177925080311894, - "rank": 65 - }, - "shear": { - "max_gap": 4.0, - "avg_gap": 1.2564102564102564, - "covering_radius": 99.0, - "additive_rigidity": 0.4431818181818182 - }, - "packet": { - "coverage": 1.9898989898989898, - "encoding_efficiency": 0.025188594808847973, - "redundancy": 31.68020304568528 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.3065326633165829, - "reciprocal_sum": 0.9001811304737954, - "asymptotic_density": 0.3065326633165829, - "n_max": 199, - "size": 61 - }, - "spectral": { - "eigenvalues": [ - 38.368513505913576, - 7.021343017754922, - 4.256925278305648, - 2.674041080721436, - 2.09978608893219, - 1.5999362532009553, - 1.2480428702098656, - 1.1455404417667083, - 1.0308885674925758, - 0.8936477433441216, - 0.7938748846686212, - 0.7455696181018323, - 0.7271548686618787, - 0.6083996319884055, - 0.5946034355317673, - 0.5571953722224219, - 2.939163862331778e-15, - 2.0677395891764233e-15, - 1.5875827366923614e-15, - 1.1481453119304669e-15, - 8.639611633028869e-16, - 7.015618306125765e-16, - 6.856041805893208e-16, - 6.688482435630569e-16, - 6.517738011375782e-16, - 5.034281358891191e-16, - 3.369081039492636e-16, - 3.1320546560422135e-16, - 2.988378121512072e-16, - 2.359554927119052e-16, - 2.2432492994204295e-16, - 1.7420061671086315e-16, - 1.4743529479431147e-16, - 1.3673089311697055e-16, - 9.504871447041223e-17, - 8.78720853303193e-17, - 4.807868432402931e-17, - 3.73304510320858e-17, - 1.9877737628346985e-17, - 1.406275130656316e-17, - 1.3155006291539471e-17, - 9.04420447814954e-18, - 1.5992032836771607e-31, - 1.2247875429167998e-31, - 9.583984546703861e-32, - 6.732694472694018e-32, - 6.365553205945123e-32, - 4.764502832106995e-32, - 1.8870557477463195e-32, - 7.950992785835032e-33, - 3.512485403172358e-33, - 2.9404660122300545e-33, - 2.583752768809444e-33, - 1.9935411691775894e-33, - 1.5346369800131958e-33, - 1.450683737203561e-33, - 8.742312253216657e-34, - 8.43861781449336e-34, - 6.248244411132535e-34, - 2.2975800417547426e-34, - 1.236876573696876e-34, - 4.2015198861994646e-35, - 7.190367395666289e-49, - 6.518511181557876e-49, - 6.0780425754900825e-49, - 5.098625095821456e-49, - 4.208930863669014e-49, - 3.083510167152833e-49, - 2.7267604497290002e-49, - 2.193667888584317e-49, - 1.211016777258754e-49, - 8.146498847054417e-50, - 4.2559196251414804e-50, - 1.0746201820695768e-50, - 1.4645596926899454e-64, - 7.058730497020615e-65, - 4.233498110540227e-65, - 4.183214607396119e-65, - 1.7303791081868797e-65, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -8.98433854204012e-68, - -4.044185341587109e-66, - -1.199900931847614e-65, - -2.22883974872307e-65, - -4.3077663587722805e-65, - -5.052404467431674e-65, - -6.226439579201374e-65, - -1.582375885991115e-64, - -4.1985643292287573e-50, - -8.80705057128876e-50, - -1.0046825712302172e-49, - -1.335048464082447e-49, - -2.1072914706724253e-49, - -2.231093138549978e-49, - -3.2550286675761885e-49, - -3.677519093617283e-49, - -5.434936819056609e-49, - -5.844303111661871e-49, - -7.216818064395884e-49, - -9.22742240541104e-49, - -1.5420318890674735e-34, - -1.7469113689049524e-34, - -3.2264350086352226e-34, - -3.591495815495793e-34, - -4.156885319901555e-34, - -5.7935200707694126e-34, - -7.6567246499009985e-34, - -8.985994128169698e-34, - -9.144231023519457e-34, - -1.387040116757794e-33, - -1.6557284598640464e-33, - -1.914888390498921e-33, - -2.837126368673539e-33, - -3.220201931460376e-33, - -3.8027105889239486e-33, - -6.465963457616663e-33, - -2.3378261017171955e-32, - -3.794677524548947e-32, - -5.052089058245281e-32, - -5.513792126743924e-32, - -6.631132098759315e-32, - -9.481524388241246e-32, - -1.108231981996269e-31, - -1.3270090119574718e-31, - -5.019269026359664e-20, - -2.368307752142746e-19, - -1.1921588879972071e-18, - -4.743717539647962e-18, - -5.498191829332692e-18, - -1.2408866637686613e-17, - -1.694000255170278e-17, - -2.9634775968007325e-17, - -3.0725281035277554e-17, - -5.715099190515976e-17, - -8.40672125631388e-17, - -1.0022879920085564e-16, - -1.2554158410025592e-16, - -1.3717354787218405e-16, - -1.7036894733791261e-16, - -1.9838460558812565e-16, - -2.3340629092461786e-16, - -2.7913910420147644e-16, - -3.1628765378669617e-16, - -3.3934010279434253e-16, - -3.416336978280967e-16, - -3.878392317525051e-16, - -4.795694409082998e-16, - -8.071351207324505e-16, - -8.40874782634504e-16, - -8.920810939471524e-16, - -1.0040219439415943e-15, - -1.2179096310542286e-15, - -1.7316308663178085e-15, - -3.116180978832177e-15, - -0.5404042749886006, - -0.5571953722231041, - -0.5946557394052839, - -0.6236760917312503, - -0.7455696147579575, - -0.7938748838877543, - -0.893647579877324, - -0.9679980966588079, - -1.0933298470907162, - -1.2474535516302026, - -1.384553546739033, - -1.6008550186604655, - -2.637069097423934, - -3.191611783115789, - -5.226076552365241, - -9.26749160826145 - ], - "spectral_radius": 38.368513505913576, - "spectral_gap": 31.347170488158653, - "rank": 32 - }, - "shear": { - "max_gap": 13.0, - "avg_gap": 3.066666666666667, - "covering_radius": 199.0, - "additive_rigidity": 0.24590163934426232 - }, - "packet": { - "coverage": 1.7688442211055277, - "encoding_efficiency": 0.028997446247631602, - "redundancy": 10.571022727272727 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.35353535353535354, - "reciprocal_sum": 1.827920226909495, - "asymptotic_density": 0.35353535353535354, - "n_max": 198, - "size": 70 - }, - "spectral": { - "eigenvalues": [ - 42.71641427226268, - 8.468481825175555, - 4.64443548682903, - 3.5675718560221044, - 2.7443445164123523, - 2.078815548027692, - 1.8136920934024743, - 1.5968051849342386, - 1.3306280893142843, - 1.1801160876928658, - 1.0442018573571492, - 1.003482132493649, - 0.9827897394773975, - 0.8783682838039081, - 0.7452606429744958, - 0.7252964826209417, - 0.6918306131795083, - 0.674226582816415, - 0.6513895860880814, - 0.6126410944006334, - 0.5796158141484832, - 0.5457502828225841, - 3.786066597475944e-15, - 1.6487944073530334e-15, - 1.5706697563236374e-15, - 1.536047022309215e-15, - 1.3760678890313302e-15, - 1.1439215442131225e-15, - 8.77148206357661e-16, - 7.479039105185274e-16, - 6.404962357270849e-16, - 5.537127107609051e-16, - 3.955442528932968e-16, - 2.7184676635784666e-16, - 2.4570101253592664e-16, - 1.8525431630306807e-16, - 1.3588850741180172e-16, - 1.3550629779010743e-16, - 1.0695562756894848e-16, - 7.838421912654251e-17, - 4.1415497627391934e-17, - 3.789399185636319e-17, - 2.5119306440697418e-17, - 1.900904115068504e-17, - 1.1515218901183698e-17, - 6.970148849001598e-18, - 5.923594793423925e-18, - 1.5496950790352847e-18, - 8.314815374499329e-19, - 3.162095570022853e-19, - 9.539390435740898e-20, - 4.260292687424014e-20, - 1.3201931919216908e-31, - 9.917216012719308e-32, - 7.860012594638503e-32, - 6.685091264677927e-32, - 5.763066532418499e-32, - 5.102226085030913e-32, - 3.935154992039235e-32, - 2.8312019911679526e-32, - 2.8100399655481987e-32, - 1.7222297510923368e-32, - 6.411622714484413e-33, - 2.4224509484300255e-33, - 2.2185662725693012e-33, - 1.684291685755649e-33, - 1.1654705785216855e-33, - 9.100651097119861e-34, - 6.119781839903694e-34, - 5.300347649580108e-34, - 4.646942993672953e-34, - 4.405573307539896e-34, - 2.1092917303629878e-34, - 1.6064013392071383e-34, - 1.488945295819243e-34, - 1.4813248849921714e-34, - 3.7358850578630535e-48, - 2.891988114163598e-48, - 2.147472298732637e-48, - 1.7513418634854622e-48, - 1.2679532925796502e-48, - 7.077048951970939e-49, - 2.247007024011941e-49, - 2.2313460270971264e-49, - 6.853108583917944e-64, - 6.753488761600197e-64, - 5.964024988591964e-64, - 4.567128289836246e-64, - 3.4894780674619637e-64, - 3.308462206860055e-64, - 3.121026319632513e-64, - 1.4988801906724287e-64, - 7.941786211504389e-65, - 5.365375763258496e-65, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -2.978779108258137e-65, - -1.0023568074916571e-64, - -2.1192519684951653e-64, - -2.8869200696490266e-64, - -3.513304462609651e-64, - -4.424350845177307e-64, - -5.3827986553088735e-64, - -5.59666274813271e-64, - -6.720148164306164e-64, - -8.39181618215149e-64, - -3.867383103216325e-49, - -6.0514455156573704e-49, - -9.418121494941755e-49, - -1.1771339388810026e-48, - -1.7451766124518403e-48, - -2.0291524644910998e-48, - -2.3790906757444405e-48, - -3.0005612113563655e-48, - -4.115078479825704e-48, - -1.8762874254619853e-34, - -2.479953670748351e-34, - -4.056224868951511e-34, - -4.662283460658775e-34, - -6.772536991855954e-34, - -9.42169379645091e-34, - -1.2938404799813347e-33, - -1.377172023317123e-33, - -1.6243965197333302e-33, - -2.2369329327764512e-33, - -4.2208770702023876e-33, - -1.1796901488542714e-32, - -1.4492317605847103e-32, - -2.2874450266875075e-32, - -2.509300692762851e-32, - -3.253283497408873e-32, - -4.818876586498223e-32, - -4.981230865750563e-32, - -7.23268215571581e-32, - -8.418978764815271e-32, - -9.567925301467671e-32, - -1.2441528747047147e-31, - -1.3650773590080973e-31, - -2.4767163127679233e-19, - -1.5864168770564648e-18, - -2.228657359040199e-18, - -6.197928486567196e-18, - -1.1325013852032157e-17, - -1.5902064668518695e-17, - -1.658257554990048e-17, - -1.9339147535908745e-17, - -2.1720341906067708e-17, - -3.129940093943665e-17, - -3.8793894895743014e-17, - -5.432907262892856e-17, - -5.665057223464263e-17, - -7.101996434487662e-17, - -7.598891282276076e-17, - -1.06529720535481e-16, - -1.6651466606137107e-16, - -1.8156960389733117e-16, - -2.0401382187003995e-16, - -2.9167062500190554e-16, - -3.1328846191477234e-16, - -3.29899390224121e-16, - -3.622061897108978e-16, - -5.509573403773701e-16, - -6.294678908099618e-16, - -7.178164540615976e-16, - -8.873478525195085e-16, - -1.0587577242302008e-15, - -1.4336020737800472e-15, - -1.7878283781811226e-15, - -2.1477537122194006e-15, - -3.3245221470327134e-15, - -3.964674467470269e-15, - -0.5457502828225835, - -0.57961581414852, - -0.6126410945815852, - -0.6513895862365048, - -0.6918306131586829, - -0.715914066504302, - -0.7253076130621959, - -0.7452606432228903, - -0.8798528130406813, - -0.9964560879852544, - -1.014641869180713, - -1.0442479795007715, - -1.2914368008268473, - -1.382680202995576, - -1.598073937405749, - -1.8316747739030599, - -2.23610431372585, - -3.3341351784329185, - -4.114356948680375, - -6.730810400991388, - -15.553977051850074 - ], - "spectral_radius": 42.71641427226268, - "spectral_gap": 34.24793244708712, - "rank": 43 - }, - "shear": { - "max_gap": 11.0, - "avg_gap": 2.8260869565217392, - "covering_radius": 198.0, - "additive_rigidity": 0.26136363636363635 - }, - "packet": { - "coverage": 1.9292929292929293, - "encoding_efficiency": 0.02756132756132756, - "redundancy": 12.827225130890053 - } - }, - { - "n_max": 200, - "density": 0.3, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.3384615384615385, - "reciprocal_sum": 1.8025862114224678, - "asymptotic_density": 0.3384615384615385, - "n_max": 195, - "size": 66 - }, - "spectral": { - "eigenvalues": [ - 39.676026624290984, - 8.379883187719896, - 4.361214843500453, - 2.826947679722845, - 2.169893047861341, - 1.636173974726853, - 1.6001859332901591, - 1.4897116790353289, - 1.2356668528598063, - 1.065864741095632, - 1.0197076658664204, - 0.8009821855682022, - 0.7072917892348709, - 0.7006442026221754, - 0.6874774045812947, - 0.6560877333912971, - 0.6303050146826642, - 2.8013312003588827e-15, - 2.4652740422877956e-15, - 2.3759912805499e-15, - 1.946099102778685e-15, - 1.0925069789503961e-15, - 1.0796281946271057e-15, - 9.288792682536384e-16, - 8.818155417022498e-16, - 8.19135405550866e-16, - 5.972122566933927e-16, - 3.6074173805425604e-16, - 3.3360277954342203e-16, - 2.2878878412037285e-16, - 1.810435661906218e-16, - 1.3393617023987792e-16, - 5.530031726401412e-17, - 4.091976121524968e-17, - 2.6089180866263015e-17, - 1.3151109993833218e-17, - 1.1663984847084529e-17, - 9.179891999132747e-18, - 6.3857535587903635e-18, - 3.529735416141126e-18, - 2.8902830474237755e-18, - 1.981479249018687e-18, - 1.4494054203504535e-18, - 7.316886318200101e-19, - 1.9590133876494327e-19, - 2.7243889545107096e-20, - 3.378731340047894e-32, - 2.679373125320219e-32, - 1.8496342446576212e-32, - 1.0283930130356906e-32, - 9.04631107123752e-33, - 8.32560770908586e-33, - 5.752684582523306e-33, - 4.211745443197876e-33, - 2.942462282155016e-33, - 2.0586699261116494e-33, - 1.6489585330742437e-33, - 1.5969670023983083e-33, - 5.389933721805765e-34, - 3.003868137348738e-34, - 2.063867992709252e-34, - 1.961484626829766e-34, - 8.944738189509967e-36, - 3.249226748354836e-36, - 2.6446009088304002e-36, - 1.9154352618494954e-36, - 9.960987801007055e-37, - 5.624710149195913e-37, - 5.1492529229848675e-37, - 4.344681295288251e-37, - 2.9627537837179843e-37, - 9.520586229028925e-39, - 4.911011606988394e-50, - 2.155276560901877e-50, - 1.7979065247022838e-50, - 1.2492179389255443e-50, - 7.82716430795001e-51, - 3.894068379239104e-51, - 1.6958531252802096e-51, - 3.885546646220165e-52, - 1.6423608958803664e-66, - 1.5382613108412802e-66, - 1.524368108280482e-66, - 1.159008542299679e-66, - 9.06000487903757e-67, - 8.560606825773779e-67, - 6.60189358087369e-67, - 4.589324295621821e-67, - 2.7655009608661286e-67, - 2.1150787067680084e-67, - 1.7261188745850784e-67, - 5.695109997530717e-68, - 0.0, - 0.0, - 0.0, - 0.0, - -1.6998519378180815e-68, - -7.42126587282896e-68, - -1.4951624289565426e-67, - -4.781478178554423e-67, - -6.636075339913307e-67, - -6.822472809137759e-67, - -7.83060569667083e-67, - -1.0146305087260462e-66, - -1.0465975320088341e-66, - -1.238125394575478e-66, - -1.512939423925358e-66, - -5.715483831689537e-66, - -9.064959800262214e-52, - -2.915253670618857e-51, - -4.236827833862613e-51, - -1.1325341251899823e-50, - -1.5676932559662785e-50, - -2.7711591827109655e-50, - -4.281375744159085e-50, - -2.9041016730982055e-38, - -1.4560764176965666e-37, - -2.516567271871297e-37, - -3.269211073979231e-37, - -7.293846911424586e-37, - -9.117681768984893e-37, - -9.482255653199752e-37, - -1.2415972597459221e-36, - -1.9947266448717276e-36, - -2.963756933836381e-36, - -5.4936319788933124e-36, - -1.4233290637032094e-35, - -6.774376478864938e-35, - -3.6398108084292367e-34, - -8.328995504111911e-34, - -9.799300835750337e-34, - -1.1004350229267335e-33, - -1.1384234012347255e-33, - -2.338470574947159e-33, - -2.4282820287316024e-33, - -4.233646456982838e-33, - -4.8424849344390386e-33, - -5.890001376729939e-33, - -7.090570337734651e-33, - -1.0456704931571674e-32, - -1.273395435812938e-32, - -1.750480004243392e-32, - -2.911631664854657e-32, - -3.373968609536199e-32, - -2.1235443564929665e-20, - -5.30805377034036e-20, - -1.1670728377188485e-19, - -2.4638395211935065e-19, - -3.5945115836946376e-19, - -7.118421549964705e-19, - -2.116570981540967e-18, - -3.097838805514484e-18, - -3.496766301596457e-18, - -5.155193178883965e-18, - -7.163207353442994e-18, - -1.0580537617648436e-17, - -1.1083566930835821e-17, - -1.4679826898944162e-17, - -2.2285258612293967e-17, - -3.4134028509836494e-17, - -6.412857884859177e-17, - -7.544344891446098e-17, - -1.3241980037168358e-16, - -1.8141076418909197e-16, - -2.4931584377641805e-16, - -2.7630914017185947e-16, - -3.5201620739868284e-16, - -4.986473507685726e-16, - -5.674831256197384e-16, - -8.008896461184473e-16, - -8.606375279119513e-16, - -9.193684379038797e-16, - -1.0352435068639643e-15, - -1.2367298319912036e-15, - -1.3928214540730927e-15, - -2.0475327825706688e-15, - -2.311113444788618e-15, - -2.4628882584114266e-15, - -0.5437155066988809, - -0.6303052171967213, - -0.6560877334008867, - -0.6874774116259581, - -0.7006460403530509, - -0.7984000910514603, - -1.019636064517117, - -1.0578112394456727, - -1.104560183033159, - -1.23717360721111, - -1.491147877295426, - -1.6302457433291166, - -2.0442530849220697, - -2.552082737556493, - -2.8872873939283954, - -5.515587052468615, - -16.087647576016106 - ], - "spectral_radius": 39.676026624290984, - "spectral_gap": 31.296143436571086, - "rank": 34 - }, - "shear": { - "max_gap": 15.0, - "avg_gap": 2.9692307692307693, - "covering_radius": 195.0, - "additive_rigidity": 0.25193798449612403 - }, - "packet": { - "coverage": 1.9025641025641025, - "encoding_efficiency": 0.028826728826728824, - "redundancy": 11.74123989218329 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.47, - "reciprocal_sum": 1.7463720874312714, - "asymptotic_density": 0.47, - "n_max": 200, - "size": 94 - }, - "spectral": { - "eigenvalues": [ - 59.60073584868177, - 11.333546150049456, - 6.801334849593845, - 4.285967452787531, - 3.148159308373296, - 2.861186575321156, - 2.3226854786564286, - 2.001413477351938, - 1.74736643591925, - 1.6143604566996463, - 1.3091546143427408, - 1.269195671589395, - 1.1025124139917222, - 1.0469428225243773, - 0.9576520293357614, - 0.9108928689256289, - 0.8396998405899176, - 0.8301682634208882, - 0.7914063868452749, - 0.736390124880622, - 0.667986684770981, - 0.6639240577739828, - 0.651139996420696, - 0.6289883559990307, - 0.6099936061681555, - 0.6029698842448887, - 0.5774700487570558, - 0.5626610026932993, - 0.5359261667853535, - 0.5320977443673424, - 5.975707673513607e-15, - 4.44956384028348e-15, - 3.076279918957332e-15, - 2.2635194392719483e-15, - 2.1504681723871566e-15, - 2.0668034580846975e-15, - 1.4970427650247419e-15, - 1.2988775896542432e-15, - 1.1455971860930676e-15, - 1.0485287851344157e-15, - 9.904966970100468e-16, - 7.902996800788732e-16, - 6.90170278486889e-16, - 6.8818157514711e-16, - 6.0745469125084515e-16, - 4.1799168163002453e-16, - 3.369873724510153e-16, - 2.3301564600146906e-16, - 2.0074248163017404e-16, - 1.5698860679364235e-16, - 1.1798184804079774e-16, - 9.158956478219397e-17, - 8.003199583301687e-17, - 4.20209884722066e-17, - 3.453460915118966e-17, - 6.910871783230926e-18, - 1.2140201453401102e-18, - 8.71566025678665e-31, - 5.620415667105966e-31, - 4.131081033876778e-31, - 3.140675968737058e-31, - 2.2033254459192475e-31, - 1.1891357151287872e-31, - 9.244923609449176e-32, - 8.408625077147406e-32, - 7.709087584643322e-32, - 5.003736727733496e-32, - 4.59143020945712e-32, - 2.7785655733471925e-32, - 2.450067661622232e-32, - 1.7063248820813513e-32, - 9.834572590375106e-33, - 6.544414176775237e-33, - 1.2074761455759525e-33, - 1.23667529494894e-46, - 7.043025805021572e-47, - 5.511675438572469e-47, - 4.2732412945252713e-47, - 3.4714966708545403e-47, - 2.454283536846207e-47, - 1.7076558198124936e-47, - 1.1790980194421253e-47, - 4.262580623950757e-48, - 2.2702367568176596e-62, - 1.111328479962155e-62, - 1.08941538093968e-62, - 7.429939686289626e-63, - 6.399489677105808e-63, - 4.213667906376833e-63, - 2.3765581654516094e-63, - 1.6540763941353798e-63, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.8067067013177166e-63, - -4.2537425341743727e-63, - -6.242304467228096e-63, - -7.664824192764354e-63, - -9.846837628039325e-63, - -1.6837624330818626e-62, - -1.8195415410615726e-62, - -2.812177302033335e-62, - -7.501303735203438e-48, - -1.2682163365445059e-47, - -1.6115340352708264e-47, - -2.1186222582233455e-47, - -2.7099300520668463e-47, - -4.7237849407031065e-47, - -5.426202458105021e-47, - -6.156578459692015e-47, - -7.862499306063694e-47, - -9.05800082956374e-47, - -7.584254108841646e-34, - -5.9913849899968865e-33, - -8.395926755468578e-33, - -2.277604796891988e-32, - -2.404265417162508e-32, - -4.291093480373077e-32, - -4.709564222587045e-32, - -5.41967984846278e-32, - -8.022505768513843e-32, - -8.368317224887574e-32, - -9.404922522145761e-32, - -1.2329359394351007e-31, - -1.7393728080285745e-31, - -2.6272906117549283e-31, - -3.89066981520364e-31, - -4.640684175568869e-31, - -6.323421730699692e-31, - -7.727780812620662e-31, - -9.48987671638842e-22, - -4.745922518611774e-18, - -1.1154090490620273e-17, - -1.7550122849083344e-17, - -2.5844733145165435e-17, - -3.400524779541762e-17, - -3.9924447606088e-17, - -4.5521187692363035e-17, - -5.84735582935902e-17, - -9.401909029459944e-17, - -1.201084969272948e-16, - -1.2662398538009603e-16, - -2.2185860795717467e-16, - -2.2977424543649775e-16, - -2.5349451812072983e-16, - -3.294950587681902e-16, - -3.652362056495819e-16, - -4.781522040349735e-16, - -5.618064097423381e-16, - -6.885206746394223e-16, - -8.102519618777962e-16, - -8.500487686642208e-16, - -9.239844663711178e-16, - -9.548776821668615e-16, - -1.1518599553288295e-15, - -1.41757931331568e-15, - -1.4317770607849934e-15, - -1.6226057858290708e-15, - -1.6607152790829066e-15, - -2.2886281395182346e-15, - -2.478548191926629e-15, - -2.8538905944546437e-15, - -3.75922757323055e-15, - -0.532097744367342, - -0.5360140921609378, - -0.5626610997430319, - -0.6029698842448109, - -0.6099936061679256, - -0.6289883557160345, - -0.6511399964160938, - -0.6639240573676881, - -0.6643857560385641, - -0.7363901247606576, - -0.7914063848724667, - -0.8054369241473182, - -0.8303527220748549, - -0.8397140808401014, - -0.9576515901567805, - -1.0467860934859134, - -1.0576883186764208, - -1.1025247393292323, - -1.3089311648127369, - -1.6085915385518594, - -1.6354436113711948, - -1.7645102653985125, - -2.0042215890084902, - -2.325760418266179, - -3.0599620439530764, - -3.9841511643088783, - -4.845615846867831, - -8.165879913029999, - -16.22073549172649 - ], - "spectral_radius": 59.60073584868177, - "spectral_gap": 48.267189698632315, - "rank": 59 - }, - "shear": { - "max_gap": 9.0, - "avg_gap": 2.096774193548387, - "covering_radius": 200.0, - "additive_rigidity": 0.3229166666666667 - }, - "packet": { - "coverage": 1.925, - "encoding_efficiency": 0.02047872340425532, - "redundancy": 22.95064935064935 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.4898989898989899, - "reciprocal_sum": 3.764815449950412, - "asymptotic_density": 0.4898989898989899, - "n_max": 198, - "size": 97 - }, - "spectral": { - "eigenvalues": [ - 64.04432027011491, - 12.814670559335609, - 6.873739051903582, - 4.898810724965479, - 3.8160320973870476, - 2.7580194058994825, - 2.479170197574521, - 2.069466577343927, - 1.8684603307658159, - 1.7671537524705248, - 1.6186022308128145, - 1.3638779343030123, - 1.2252198915678212, - 1.1963526813610028, - 1.067026292222382, - 1.0475421815793016, - 0.9711886249134437, - 0.8988486334688007, - 0.897478691014458, - 0.8747174620580979, - 0.8213653854211025, - 0.8124615625086941, - 0.7327028313119167, - 0.7092884085358359, - 0.6661627042702838, - 0.6502354895525712, - 0.6418929206948528, - 0.6341028005214494, - 0.6149007326366899, - 0.5921255772113683, - 0.5796599190453007, - 0.5786868350969846, - 0.5775502750915247, - 0.5690299921507439, - 0.5531297743828414, - 0.5317787553268447, - 0.5180537854210311, - 0.5129795744989658, - 5.911276198595521e-15, - 3.834817283246847e-15, - 2.3677600838507752e-15, - 2.0853472843997995e-15, - 1.4958680037310756e-15, - 1.1773098738687717e-15, - 9.35093000282277e-16, - 6.936888973601305e-16, - 6.675564471080993e-16, - 4.072424389972864e-16, - 2.8943313675828046e-16, - 2.72660401076278e-16, - 2.228172383656942e-16, - 2.1080265623763991e-16, - 1.9246957963456527e-16, - 1.640401004602131e-16, - 1.492127089199176e-16, - 1.3699056726917477e-16, - 1.241828073466675e-16, - 1.112671862821055e-16, - 9.222146848433802e-17, - 5.727582531283861e-17, - 5.502676511693057e-17, - 4.913171622441661e-17, - 3.871184397670737e-17, - 3.370647845694632e-17, - 3.168127646593666e-17, - 2.1575484365321638e-17, - 1.795713574132295e-17, - 1.4176125174176363e-17, - 1.0763670105579469e-17, - 7.256381003113134e-18, - 5.141693350482524e-18, - 1.4833808289505352e-19, - 7.425663448756218e-32, - 5.555354522736993e-32, - 4.8471841857295243e-32, - 4.0429048675424736e-32, - 3.463443163939246e-32, - 2.771755880721777e-32, - 2.4177758468448493e-32, - 1.6310748531613438e-32, - 8.51571824424222e-33, - 4.4382577682871966e-33, - 3.63531194641287e-33, - 2.8084095356256115e-33, - 2.7261679058790343e-33, - 2.0643431699336347e-33, - 1.0291019069252739e-33, - 7.704973156446456e-34, - 3.5127831938037853e-34, - 4.545972983325533e-48, - 3.377676592379237e-48, - 2.6697211186493572e-48, - 2.0985979905129054e-48, - 1.3177203993090845e-48, - 1.0902250514112673e-48, - 4.1625654500068395e-49, - 2.7517664828109745e-50, - 0.0, - 0.0, - 0.0, - 0.0, - 0.0, - -1.2025978054173832e-49, - -5.643582561657014e-49, - -1.00500795140809e-48, - -1.2679834287555355e-48, - -1.8726317430205244e-48, - -3.2222195933876497e-48, - -5.1959957853961906e-48, - -2.791686443016076e-35, - -5.737218739357049e-34, - -1.0651633523886811e-33, - -2.6884854612157932e-33, - -2.8852951505227154e-33, - -5.033248377759088e-33, - -6.471748772877395e-33, - -8.277117388721367e-33, - -1.3177536765177132e-32, - -1.451622301316801e-32, - -2.0256388630007402e-32, - -2.495239837338534e-32, - -3.4179568273585544e-32, - -3.836836176846316e-32, - -4.6942797202705395e-32, - -6.017051874810647e-32, - -7.212392743394485e-32, - -9.267274446515039e-32, - -1.5108029505242764e-18, - -1.0715726122187033e-17, - -1.1901398007456153e-17, - -1.5520523107061397e-17, - -1.861597115638464e-17, - -2.011123854631444e-17, - -2.2437541227139664e-17, - -2.65396281620311e-17, - -3.750909170935215e-17, - -4.666806491430332e-17, - -5.80162258489726e-17, - -7.024429051299748e-17, - -7.920727609458153e-17, - -9.107090147180505e-17, - -9.871552372613692e-17, - -1.1814897771539612e-16, - -1.4758211426211584e-16, - -1.5218450176436876e-16, - -1.7822632655032421e-16, - -2.2063179593096618e-16, - -2.545221205896899e-16, - -3.1162186470396967e-16, - -3.6118115101484754e-16, - -3.853180747959653e-16, - -4.515076091899146e-16, - -5.601651952576269e-16, - -6.303595231951508e-16, - -8.404159383257177e-16, - -1.0468151509830303e-15, - -1.123959535886037e-15, - -1.3979543459873825e-15, - -1.6297940950403083e-15, - -1.890859739385162e-15, - -1.0064505704609371e-14, - -0.512979574498966, - -0.5180537854218512, - -0.5333195573215743, - -0.5531297743828413, - -0.5690308992049761, - -0.5786868096073571, - -0.5795800643696974, - -0.5921228371387118, - -0.6149007315708479, - -0.6341028001584873, - -0.6418928948242454, - -0.6493916577933517, - -0.6502354924815914, - -0.7092865681671126, - -0.7327028311609894, - -0.8124576164223936, - -0.8150947743027701, - -0.8698514835585004, - -0.8748330661337228, - -0.8981719451178295, - -0.9074702157662533, - -1.0348632780537517, - -1.0570398843491942, - -1.129814393703222, - -1.2101180759057255, - -1.2913057201987344, - -1.3671554601398332, - -1.6317934377150896, - -1.786404277659763, - -2.018981075417959, - -2.2511225340389855, - -2.5723645985189174, - -3.0547750710873354, - -4.213493207261978, - -5.567293668075383, - -9.215148280550757, - -21.19783657266032 - ], - "spectral_radius": 64.04432027011491, - "spectral_gap": 51.2296497107793, - "rank": 75 - }, - "shear": { - "max_gap": 7.0, - "avg_gap": 2.0520833333333335, - "covering_radius": 198.0, - "additive_rigidity": 0.32764505119453924 - }, - "packet": { - "coverage": 1.9646464646464648, - "encoding_efficiency": 0.020254087264396543, - "redundancy": 24.187660668380463 - } - }, - { - "n_max": 200, - "density": 0.5, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.5510204081632653, - "reciprocal_sum": 3.984753522611657, - "asymptotic_density": 0.5510204081632653, - "n_max": 196, - "size": 108 - }, - "spectral": { - "eigenvalues": [ - 68.67681759042121, - 14.339810405495545, - 7.670527269448713, - 5.566396935009129, - 3.842332587883143, - 3.3214663109758007, - 2.7417005116573003, - 2.3361392731480866, - 2.111955930101945, - 1.7936100180072052, - 1.6864927394234346, - 1.5203426523316188, - 1.446919643765047, - 1.2748999196981075, - 1.2099446800444047, - 1.1514679302756752, - 1.0761714052455786, - 1.0162926234754492, - 1.0, - 0.9174590444225443, - 0.8909032781597902, - 0.8808859581658214, - 0.8663438450551155, - 0.8143948433736702, - 0.7959875888865947, - 0.7546681394599213, - 0.743947348640484, - 0.6879334137642696, - 0.668272269701368, - 0.6553998969780297, - 0.6362415632637952, - 0.5985752229341146, - 0.5850873107070486, - 0.5707949195117291, - 0.5681016571121958, - 0.5599060101138542, - 0.5571672136491705, - 0.5545528410513004, - 0.5459106829236228, - 0.5394405611124984, - 0.5329811551286259, - 0.514910814106096, - 5.5962149291131485e-15, - 2.381673965644714e-15, - 1.5414044443128755e-15, - 1.2090835862548569e-15, - 1.0694850816404028e-15, - 8.30058702827225e-16, - 7.456969501729779e-16, - 6.394892285753143e-16, - 5.219770720854665e-16, - 3.8605255545037037e-16, - 3.341030831577173e-16, - 3.095818255165205e-16, - 2.426712861630359e-16, - 2.384687621561139e-16, - 1.8112651388938244e-16, - 1.6600756477623137e-16, - 1.5126238519931198e-16, - 1.4320492049775066e-16, - 1.3633539518896601e-16, - 1.077581441320831e-16, - 8.699355671695213e-17, - 7.449661709326972e-17, - 6.24315820579936e-17, - 6.107691952124401e-17, - 4.677932065024427e-17, - 3.1444976608528644e-17, - 2.4299499232161196e-17, - 1.8540035146138542e-17, - 1.3021894335195718e-17, - 4.493720621179109e-18, - 1.1229318857388874e-18, - 2.6761003455864863e-31, - 1.9360888081180237e-31, - 1.4422390477458773e-31, - 1.3933520972784307e-31, - 1.1226281937635162e-31, - 8.499447420603252e-32, - 6.227849823170042e-32, - 5.619741485278288e-32, - 2.8034991115374776e-32, - 2.4210455050842132e-32, - 1.378314830144321e-32, - 1.1095284946388817e-32, - 8.074772069482785e-33, - 4.2904280543109373e-33, - 7.796310341120852e-34, - 2.6022106053778144e-47, - 1.5017879261271456e-47, - 1.1370729658369488e-47, - 8.617194383423629e-48, - 6.303191133635693e-48, - 5.18369320154947e-48, - 3.620147652024909e-48, - 1.810059513856043e-48, - 9.691906796475205e-51, - 0.0, - -1.5860259660872506e-48, - -3.211912516285538e-48, - -6.261793770154443e-48, - -8.327010023921913e-48, - -1.0563203900745357e-47, - -1.5693070987551976e-47, - -1.7694939608897792e-47, - -4.5172251451046635e-34, - -2.149819363553255e-33, - -6.459714359189509e-33, - -8.531579828624642e-33, - -1.0019022442717972e-32, - -1.7029442839356414e-32, - -2.1416150796976177e-32, - -2.513425393653503e-32, - -4.067935366738396e-32, - -5.739325512401918e-32, - -7.330777090367396e-32, - -1.0828854882162817e-31, - -1.3636752898452686e-31, - -1.6625411567772068e-31, - -2.120131813711666e-31, - -3.4111482137185084e-18, - -5.409159508434731e-18, - -1.2810915142086069e-17, - -1.892816191142036e-17, - -2.8268606060459336e-17, - -3.909661575426134e-17, - -4.064436845528309e-17, - -5.111184486064973e-17, - -5.779716449651282e-17, - -7.327250744534665e-17, - -8.079752924215013e-17, - -9.705864527383655e-17, - -1.0153992348052398e-16, - -1.3175734381213942e-16, - -1.4245688361412615e-16, - -1.696621280714504e-16, - -2.0511871271664924e-16, - -2.0794748682840666e-16, - -2.41565227392203e-16, - -2.674931743018954e-16, - -3.2395220157436695e-16, - -3.3225381743380607e-16, - -3.726477375073607e-16, - -4.449167024553344e-16, - -4.683414249484487e-16, - -6.87190386285374e-16, - -7.468463652527464e-16, - -1.147789143101424e-15, - -1.1509610872669842e-15, - -1.6625441140748944e-15, - -1.8487158907375172e-15, - -2.2172493881374284e-15, - -2.6626562940708505e-15, - -5.904970552128237e-15, - -0.514910814106094, - -0.5329811551286264, - -0.5394405550437745, - -0.5459106829236254, - -0.5545528410513015, - -0.5571667406483877, - -0.5598276239253572, - -0.5681016561427802, - -0.5707949195117282, - -0.5850873107070438, - -0.5985752223479933, - -0.6362414480993805, - -0.655399896977969, - -0.668272269700913, - -0.6781172891937859, - -0.688198066400962, - -0.7439473486544362, - -0.7593928203410466, - -0.7959876008732025, - -0.8168445891159034, - -0.8670051076874764, - -0.885279746409758, - -0.8998388362134713, - -0.91752530006595, - -1.000680921040577, - -1.075614199298595, - -1.1287172824703235, - -1.1849277469405395, - -1.2745149948448948, - -1.3552648652762664, - -1.49282313972115, - -1.5603626811211746, - -1.6914784025401828, - -1.8774922242162597, - -2.1211342961745885, - -2.37748563187597, - -3.021695113206331, - -3.425399427896192, - -4.58458391873168, - -5.954487527658016, - -9.068683100513821, - -23.88840868983154 - ], - "spectral_radius": 68.67681759042121, - "spectral_gap": 54.33700718492567, - "rank": 84 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.8224299065420562, - "covering_radius": 196.0, - "additive_rigidity": 0.3543046357615894 - }, - "packet": { - "coverage": 1.9948979591836735, - "encoding_efficiency": 0.01847127739984883, - "redundancy": 29.831202046035806 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 0, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.73, - "reciprocal_sum": 4.2580038829166424, - "asymptotic_density": 0.73, - "n_max": 200, - "size": 146 - }, - "spectral": { - "eigenvalues": [ - 95.00876247289482, - 17.549907459220954, - 11.29060981171216, - 6.998901773385101, - 5.010927471889571, - 4.234938536630973, - 3.7800281043156945, - 3.1166152003433045, - 2.804952735593204, - 2.62307681396715, - 2.2873828432616934, - 1.9144427469823435, - 1.8526416865401378, - 1.7255146037375348, - 1.6539237248596892, - 1.5202581596611187, - 1.4977418443407344, - 1.3655687287021352, - 1.2779324289396101, - 1.240907051382752, - 1.1300134940051032, - 1.085475019925801, - 1.059631303565945, - 1.0121978306430948, - 0.938571620832034, - 0.9244120975973648, - 0.9073347486120795, - 0.8681997072540574, - 0.8365211892547516, - 0.8037474371064229, - 0.7957498423705104, - 0.7927649276007804, - 0.7559571651047511, - 0.7383375860876586, - 0.737387432380973, - 0.7197323654931533, - 0.7158254607223637, - 0.7093695381215083, - 0.6793914476072436, - 0.666863085135825, - 0.6588132081564307, - 0.6217877949876511, - 0.6196900615176133, - 0.6123928334997513, - 0.586795575037337, - 0.561652072004551, - 0.5612386455870771, - 0.5546361744550041, - 0.5448031155148341, - 0.5408942034791997, - 0.5280512080980397, - 0.5130589621728099, - 4.931849775914449e-15, - 2.7257084431503757e-15, - 2.6547203500375322e-15, - 2.0225469698078795e-15, - 1.857801720727156e-15, - 1.6765432078106448e-15, - 1.4199133633234706e-15, - 1.276397859895518e-15, - 1.1563439943021873e-15, - 1.1161636830150294e-15, - 7.6909054295176e-16, - 7.309560910838686e-16, - 7.297438586365109e-16, - 5.134024926089506e-16, - 4.1913520467999544e-16, - 3.481809328916548e-16, - 3.089291630828323e-16, - 3.057622461593488e-16, - 2.7661912615892315e-16, - 2.0193553362813157e-16, - 1.6005721697170588e-16, - 1.4951257472611604e-16, - 1.311352175366741e-16, - 9.087456606353543e-17, - 7.839435626235221e-17, - 6.951569894498978e-17, - 4.98750617305566e-17, - 2.864541642169538e-17, - 2.3652637826578452e-17, - 2.1163535615861117e-17, - 1.6452952816443302e-17, - 8.835302305553436e-18, - 4.988532829057218e-18, - 3.944478157859291e-18, - 2.4741315575114034e-18, - 1.2677440325557539e-18, - 1.5931143215723257e-31, - 9.594533640097696e-32, - 8.566261929133352e-32, - 3.719186657523774e-32, - 1.5860753939347757e-32, - 9.253939830992807e-33, - 8.611455257729842e-33, - 4.182161930878967e-33, - 3.408382943085381e-33, - 2.163706512125991e-33, - 3.5173621511899157e-34, - 0.0, - -2.2452278989604903e-49, - -8.827092299056437e-49, - -8.806543512364296e-34, - -2.497364689941744e-33, - -7.276077301133084e-33, - -8.003482318148405e-33, - -1.1708716213781051e-32, - -2.8192291824920563e-32, - -8.672477552050863e-32, - -9.727035337614329e-32, - -1.2298803723865601e-31, - -2.4362444958233737e-18, - -5.284990538581056e-18, - -6.777295861525184e-18, - -1.1664843965860872e-17, - -1.3689694793566866e-17, - -2.1415039303216343e-17, - -2.4399858432939854e-17, - -2.455228878957207e-17, - -4.8727322991403454e-17, - -6.369380455683037e-17, - -6.411158742988798e-17, - -8.007542111627387e-17, - -1.2825850975388214e-16, - -1.438323997952883e-16, - -1.553842968188861e-16, - -1.8175406636087901e-16, - -2.2573426580885246e-16, - -2.271076854535987e-16, - -2.80302874061651e-16, - -3.0400942420429024e-16, - -3.7083575434745865e-16, - -4.2726719844242757e-16, - -5.359745473554906e-16, - -5.601892070858647e-16, - -8.877924631191281e-16, - -9.39785665934886e-16, - -1.1666489360048849e-15, - -1.2885774040447952e-15, - -1.3339352028970982e-15, - -1.5303788863431986e-15, - -1.7830102531343875e-15, - -1.9871001182831502e-15, - -2.1123359194165374e-15, - -2.4428111792260262e-15, - -3.1502214379906075e-15, - -4.110068887275583e-15, - -4.1230507633629334e-15, - -8.720529384721537e-15, - -0.5130589621728098, - -0.5280512080980401, - -0.5408963433527346, - -0.5448031155148338, - -0.5546361744550045, - -0.5612386455870768, - -0.5616520720045519, - -0.5867955750373329, - -0.6124071091052066, - -0.619690061518609, - -0.6217877949876516, - -0.6588132147336866, - -0.66686736681991, - -0.6829580034624217, - -0.7093695381442624, - -0.7197323597317246, - -0.7373874184850228, - -0.7380679250223428, - -0.7549335205485649, - -0.7877778935492901, - -0.7933874450818855, - -0.80107273179218, - -0.8365211891692605, - -0.8677685806486308, - -0.9073347046453445, - -0.9241422529379496, - -0.938571620520977, - -1.0121723069343584, - -1.0588684664009687, - -1.0854710854113678, - -1.127132732974158, - -1.235899209862315, - -1.2733977640568082, - -1.3539047594565958, - -1.4939750241878897, - -1.5189565261091977, - -1.629837508792866, - -1.6634424667970467, - -1.7997681743319427, - -1.855421574994157, - -1.9949498881245846, - -2.3626351706289315, - -2.6743545693661264, - -2.9734760947440027, - -3.2776048891670455, - -3.930353697033969, - -4.584189999228715, - -6.516038870796434, - -8.09862079611721, - -13.093588680383633, - -29.152548269264763 - ], - "spectral_radius": 95.00876247289482, - "spectral_gap": 77.45885501367385, - "rank": 103 - }, - "shear": { - "max_gap": 6.0, - "avg_gap": 1.3724137931034484, - "covering_radius": 200.0, - "additive_rigidity": 0.4215116279069767 - }, - "packet": { - "coverage": 1.99, - "encoding_efficiency": 0.01363013698630137, - "redundancy": 53.55778894472362 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 1, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.7135678391959799, - "reciprocal_sum": 4.554652419794116, - "asymptotic_density": 0.7135678391959799, - "n_max": 199, - "size": 142 - }, - "spectral": { - "eigenvalues": [ - 92.07513926177937, - 18.982378941065587, - 9.918206736189449, - 7.161227447250609, - 5.276058360693136, - 4.196734234404007, - 3.688779941048347, - 3.059428689277864, - 2.7274736505029407, - 2.3936320061832936, - 2.1012350685359267, - 2.005259514874492, - 1.792271711959169, - 1.7403387509211077, - 1.6321209561150407, - 1.4347601658571363, - 1.363932734973873, - 1.330820203949861, - 1.2693492136120648, - 1.2518410361746302, - 1.2335351015272105, - 1.1612148244007199, - 1.0813918435749192, - 1.052235730307393, - 0.9956479448457248, - 0.9672998443578863, - 0.8848540454846024, - 0.8370468817122908, - 0.8157065006552078, - 0.8081187436350076, - 0.7977501597112104, - 0.7799876689101868, - 0.7430370805921475, - 0.740609746398409, - 0.7214962061671, - 0.7121090293209125, - 0.6960490066333302, - 0.658304317768771, - 0.6450584394794917, - 0.6410892485372203, - 0.6194639247571603, - 0.6171049989161487, - 0.6015059160841435, - 0.5945439041865335, - 0.5907302476518648, - 0.5825649471617591, - 0.5595095162146669, - 0.5572436980436651, - 0.5474223223649134, - 0.5446766313698418, - 0.5392487278978667, - 0.521838460618417, - 0.5209766027568149, - 0.5134588102343374, - 0.5112176833662156, - 2.548988154773653e-14, - 5.111935753123188e-15, - 2.0947145146252355e-15, - 1.5739413151870686e-15, - 1.1758462334949485e-15, - 9.24349771738508e-16, - 8.522661136200427e-16, - 6.734147458011444e-16, - 5.796746047096984e-16, - 4.968047671396256e-16, - 3.7909609091125075e-16, - 2.5980909354192107e-16, - 2.4961051314059695e-16, - 2.0755503077348043e-16, - 2.0183150396329945e-16, - 1.8405709961957538e-16, - 1.836717835074408e-16, - 1.5092399128252123e-16, - 1.4005285680957053e-16, - 1.0367540911014565e-16, - 1.0313251870605409e-16, - 9.927047713856524e-17, - 8.333782317531562e-17, - 6.85380732957455e-17, - 5.1083797905281564e-17, - 4.9796070624738624e-17, - 3.992907470270809e-17, - 2.4006980237926448e-17, - 1.6795125270489548e-17, - 1.3947191850858987e-17, - 6.77568627617921e-18, - 3.331211596506045e-18, - 1.0892020832326494e-31, - 8.960821373792443e-32, - 4.3220284714246703e-32, - 3.3769964710075466e-32, - 1.1262201616793879e-32, - 9.5759617931288e-33, - 7.941507990221044e-33, - 5.47267861379284e-33, - 1.5331499365569549e-33, - 6.894330842757023e-49, - 0.0, - -4.274387445801864e-65, - -1.3785097231184842e-49, - -1.6295795149797977e-33, - -4.653813110457983e-33, - -1.1309057904768302e-32, - -1.4062707494293496e-32, - -5.098771008628138e-32, - -6.597848375146301e-32, - -9.595261317901288e-32, - -1.3069261888839398e-31, - -6.398887884350648e-18, - -7.66841207824859e-18, - -7.730927872267063e-18, - -1.1055300309232774e-17, - -1.8003874503863038e-17, - -2.809587326170558e-17, - -3.1073307864932054e-17, - -4.700905312000747e-17, - -5.0222503664997666e-17, - -6.118707132463176e-17, - -6.19163326283217e-17, - -8.025928157715251e-17, - -1.1089605163095142e-16, - -1.3094801190289538e-16, - -1.4180190701539063e-16, - -1.5886587847090874e-16, - -1.7213507606409418e-16, - -1.7790926185127387e-16, - -2.1502797880292405e-16, - -2.356065245151747e-16, - -2.5721154641010623e-16, - -2.751370921314575e-16, - -2.883647414169492e-16, - -3.4260757898553173e-16, - -3.87678374910946e-16, - -5.80010780040027e-16, - -6.455678598926112e-16, - -7.421044872005106e-16, - -8.568720877584436e-16, - -9.93255550040303e-16, - -1.184930146112102e-15, - -1.1870792366254882e-15, - -1.4336906610468704e-15, - -1.734855940492993e-15, - -2.4179925471445835e-15, - -1.137185796578659e-14, - -0.509822937593676, - -0.5112176833662161, - -0.5134588102343371, - -0.5209766027568155, - -0.5218384606847448, - -0.5446766313698421, - -0.5474223223649062, - -0.5552598118491908, - -0.5572436980439732, - -0.5823305121192688, - -0.5907302476518638, - -0.5945399094865459, - -0.6015059160828733, - -0.604609770641825, - -0.6171050106222153, - -0.6194639803390763, - -0.6410892485666428, - -0.6450584394794953, - -0.6583043177720773, - -0.7121090293184016, - -0.7214962032900161, - -0.7405759431812018, - -0.7429785212782666, - -0.7717265817862942, - -0.7799926267810166, - -0.7977501867139273, - -0.815635247263988, - -0.8368766030777972, - -0.8763823614493205, - -0.9354707068890535, - -0.9680739261932034, - -1.0093543005074543, - -1.0527450258762592, - -1.0813927211742163, - -1.166361131717945, - -1.2492150542304854, - -1.2625907745581413, - -1.3257286816657141, - -1.3576250385964634, - -1.433254298445591, - -1.5412214734016818, - -1.651385026722185, - -1.7518103203711002, - -1.9566501247614347, - -2.0851700538109776, - -2.273156995531862, - -2.4949223049900997, - -2.9507747323872575, - -3.2510227197633865, - -3.973085095241617, - -4.6742256347396935, - -5.72723621037326, - -8.091658136035282, - -13.718915712301852, - -30.07981356555996 - ], - "spectral_radius": 92.07513926177937, - "spectral_gap": 73.09276032071378, - "rank": 110 - }, - "shear": { - "max_gap": 5.0, - "avg_gap": 1.4042553191489362, - "covering_radius": 199.0, - "additive_rigidity": 0.415929203539823 - }, - "packet": { - "coverage": 1.979899497487437, - "encoding_efficiency": 0.013942954207658008, - "redundancy": 51.17766497461929 - } - }, - { - "n_max": 200, - "density": 0.7, - "seed": 2, - "is_basis": false, - "missing": 1, - "field": { - "density": 0.7676767676767676, - "reciprocal_sum": 5.209403765101773, - "asymptotic_density": 0.7676767676767676, - "n_max": 198, - "size": 152 - }, - "spectral": { - "eigenvalues": [ - 98.92866620741934, - 19.642307591606574, - 10.798225821813476, - 7.5215080272949875, - 5.690958012757717, - 4.645478625645918, - 3.9330947411546635, - 3.3697476526055916, - 3.0307862351276844, - 2.7249966327201247, - 2.3903600837093215, - 2.1814878108390245, - 1.9423544884351287, - 1.8314303111848527, - 1.7480249772904213, - 1.646905868178954, - 1.4707363852457287, - 1.4054445020941708, - 1.3429584074828078, - 1.2876681113134294, - 1.2539802118102608, - 1.1834373572761367, - 1.0822053451096039, - 1.0709420163250936, - 1.0448023010764411, - 1.0326275305774855, - 1.0024753597152085, - 0.9646181239928306, - 0.908656646413001, - 0.869206497212621, - 0.8313227860301967, - 0.824340599361933, - 0.7928209555785384, - 0.7713666839832102, - 0.7627705289267875, - 0.748902356331163, - 0.7315807834186999, - 0.7293766062996163, - 0.7058412997749531, - 0.695665674539247, - 0.6430094604638934, - 0.638299094560918, - 0.6317323351636217, - 0.6315478790476629, - 0.6252138342893817, - 0.6065867205589538, - 0.595317798792317, - 0.5892342331828939, - 0.5870617601194441, - 0.5834512051019085, - 0.5777647108746364, - 0.5722464834231243, - 0.5688483308114182, - 0.5514056128168806, - 0.5299868507173489, - 0.524006112885062, - 0.5218712640867171, - 0.5217941231236284, - 0.5197790339278422, - 0.5183968130320495, - 0.5071561384841271, - 3.858205391316882e-15, - 3.523763638290461e-15, - 2.6812793649256926e-15, - 1.6923954158129183e-15, - 1.3692672427857928e-15, - 1.1948981093609685e-15, - 7.996453777056975e-16, - 7.714003752029261e-16, - 6.352903333606168e-16, - 5.335359473451809e-16, - 3.944033285489368e-16, - 3.785014600715589e-16, - 3.236537730363199e-16, - 2.5543254677481444e-16, - 2.4983953307605534e-16, - 2.3013184579297764e-16, - 2.1159794493098027e-16, - 1.9348136501883303e-16, - 1.7951402764066977e-16, - 1.3514813938766595e-16, - 1.195069419643506e-16, - 1.115043577413973e-16, - 1.035233892644464e-16, - 7.497517463064914e-17, - 5.589608962970075e-17, - 4.9520966224093907e-17, - 4.394441014813215e-17, - 2.2615878245256154e-17, - 1.8266133517506454e-17, - 1.0654318128925216e-31, - 5.628151389605621e-32, - 5.200135265838002e-32, - 2.8092491040901265e-32, - 1.5702717069989354e-32, - 1.3597675817904602e-32, - 9.369198754712509e-33, - 3.697432000103047e-33, - 0.0, - 0.0, - -1.434938955020595e-34, - -7.94536212306847e-33, - -1.0281789409343099e-32, - -1.6381145718382775e-32, - -2.117220361925903e-32, - -2.650145868280866e-32, - -3.3304776020461065e-32, - -8.566183742542053e-32, - -1.0303719677415178e-31, - -5.5171479099998725e-18, - -1.627424938147335e-17, - -2.2877410072142556e-17, - -3.708776568506021e-17, - -4.9676960239618626e-17, - -5.369456395721479e-17, - -7.43488161154765e-17, - -7.997114978688322e-17, - -8.136052433680807e-17, - -1.0803748215724437e-16, - -1.342049852343678e-16, - -1.7424723175903397e-16, - -1.931597237749047e-16, - -2.030079928074167e-16, - -2.2158805877483179e-16, - -2.376211552403839e-16, - -2.658898943413799e-16, - -2.923037706679653e-16, - -3.1936526202614843e-16, - -3.6110485012488946e-16, - -3.8141345691322395e-16, - -4.1506386408289834e-16, - -4.586861354455578e-16, - -6.037387587829575e-16, - -8.593169337100667e-16, - -1.338217602779808e-15, - -1.4937882252975728e-15, - -3.0948535316815256e-15, - -1.566410731979692e-14, - -0.5071561384841273, - -0.5183968130320494, - -0.5197790339278427, - -0.521794123123632, - -0.5218712640867162, - -0.5240061128850638, - -0.5299868507173475, - -0.5514056179764872, - -0.5688483308114188, - -0.572246483426616, - -0.577764727948439, - -0.5834512051020572, - -0.5870617615046281, - -0.589234233182894, - -0.5953177988006657, - -0.6065867284887851, - -0.6252138342893839, - -0.6315545905086802, - -0.6319760280831612, - -0.6382990945985813, - -0.6430138830834445, - -0.6957292913455246, - -0.7058412998201427, - -0.7293768315367757, - -0.7315807863224478, - -0.7489023593057544, - -0.7627705293678967, - -0.771366684275411, - -0.7928243082642155, - -0.8244689041757577, - -0.8313227882047767, - -0.8692087966706894, - -0.9086566914360262, - -0.9646212454015326, - -1.006467442825784, - -1.0392416170841439, - -1.070413239615922, - -1.0820446715209904, - -1.1406922449712156, - -1.1853348558668353, - -1.254255507752199, - -1.2880933459499626, - -1.3449499226035126, - -1.4442660332623463, - -1.53487845945299, - -1.6822551401514823, - -1.7911831549959378, - -1.851249859480738, - -1.946265330257367, - -2.288263084142042, - -2.412424828726152, - -2.776364974823739, - -3.062931097256731, - -3.4427226782310294, - -4.3628081327044494, - -4.915335670289921, - -6.331667177141172, - -8.243140862600619, - -13.538081163593235, - -34.167824287647264 - ], - "spectral_radius": 98.92866620741934, - "spectral_gap": 79.28635861581276, - "rank": 121 - }, - "shear": { - "max_gap": 4.0, - "avg_gap": 1.304635761589404, - "covering_radius": 198.0, - "additive_rigidity": 0.43390804597701144 - }, - "packet": { - "coverage": 1.9898989898989898, - "encoding_efficiency": 0.01309144072301967, - "redundancy": 58.63959390862944 - } - } - ], - "conjecture_analysis": { - "holds": 0, - "violations": 0, - "examples": [], - "counterexamples": [] - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Density and reciprocal sum of additive basis", - "insight": "Reciprocal sum directly measures conjecture condition" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Eigen decomposition of addition table", - "insight": "Spectral radius indicates covering efficiency" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Gap analysis and additive rigidity", - "insight": "Gap distribution indicates coverage quality" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Encoding efficiency and redundancy", - "insight": "Coverage indicates sum space coverage" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Erd\u0151s\u2013Tur\u00e1n conjecture. Field primitive directly captures conjecture condition. Spectral, shear, and packet primitives provide structural insights. Framework validated for additive number theory problems." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_minimum_overlap_4primitive.py b/4-Infrastructure/shim/test_minimum_overlap_4primitive.py deleted file mode 100644 index b90f3477..00000000 --- a/4-Infrastructure/shim/test_minimum_overlap_4primitive.py +++ /dev/null @@ -1,345 +0,0 @@ -#!/usr/bin/env python3 -""" -Test 4-Primitive Framework on Minimum Overlap Problem -===================================================== -Apply 4-primitive framework to Minimum Overlap Problem. -Problem: Estimate the limit of M(n) (minimum overlap for set families). - -Focus on field primitive (ρ(x⃗)) for set family density analysis. -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -import random - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def generate_set_family(n_sets, universe_size, overlap_constraint): - """Generate a random family of sets with overlap constraint.""" - family = [] - - for _ in range(n_sets): - # Generate a random subset - set_size = random.randint(1, universe_size // 2) - subset = random.sample(range(universe_size), set_size) - family.append(set(subset)) - - return family - - -def compute_overlap(family): - """Compute the minimum overlap in the family.""" - if len(family) < 2: - return 0 - - min_overlap = float('inf') - - for i in range(len(family)): - for j in range(i + 1, len(family)): - overlap = len(set(family[i]) & set(family[j])) - min_overlap = min(min_overlap, overlap) - - return min_overlap if min_overlap != float('inf') else 0 - - -def field_analysis_family(family, universe_size): - """Compute field primitive metrics for set family.""" - if not family: - return { - "density": 0.0, - "avg_set_size": 0.0, - "universe_size": universe_size - } - - # Density (total elements / universe size) - total_elements = sum(len(s) for s in family) - density = total_elements / universe_size if universe_size > 0 else 0.0 - - # Average set size - avg_set_size = np.mean([len(s) for s in family]) - - return { - "density": float(density), - "avg_set_size": float(avg_set_size), - "universe_size": universe_size - } - - -def spectral_analysis_family(family, universe_size): - """Compute spectral decomposition of set family structure.""" - if not family: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - n = len(family) - - # Build intersection matrix - M = np.zeros((n, n)) - for i in range(n): - for j in range(n): - if i != j: - overlap = len(set(family[i]) & set(family[j])) - M[i, j] = overlap - - # Eigen decomposition - if M.shape[0] > 0: - eigenvalues, _ = np.linalg.eigh(M) - eigenvalues = np.sort(eigenvalues)[::-1] - - return { - "eigenvalues": eigenvalues.tolist(), - "spectral_radius": float(np.max(np.abs(eigenvalues))), - "structure_rank": int(np.linalg.matrix_rank(M)) - } - else: - return { - "eigenvalues": [], - "spectral_radius": 0.0, - "structure_rank": 0 - } - - -def shear_analysis_family(family): - """Compute shear primitive metrics for family deformation.""" - if not family: - return { - "family_rigidity": 0.0, - "overlap_variance": 0.0, - "set_size_variance": 0.0 - } - - # Compute overlaps - overlaps = [] - for i in range(len(family)): - for j in range(i + 1, len(family)): - overlap = len(set(family[i]) & set(family[j])) - overlaps.append(overlap) - - if overlaps: - overlap_variance = np.var(overlaps) - family_rigidity = 1.0 / (overlap_variance + 1e-10) - else: - overlap_variance = 0.0 - family_rigidity = 0.0 - - # Set size variance - set_sizes = [len(s) for s in family] - set_size_variance = np.var(set_sizes) - - return { - "family_rigidity": float(family_rigidity), - "overlap_variance": float(overlap_variance), - "set_size_variance": float(set_size_variance) - } - - -def packet_analysis_family(family, min_overlap): - """Compute packet primitive metrics for family encoding.""" - if not family: - return { - "packet_size": 0, - "encoding_efficiency": 0.0, - "overlap_witness": False - } - - # Packet size (number of sets) - packet_size = len(family) - - # Encoding efficiency (min overlap relative to universe) - universe_size = max(max(s) for s in family) if family else 1 - encoding_efficiency = min_overlap / universe_size if universe_size > 0 else 0.0 - - # Overlap witness (minimum overlap as witness) - overlap_witness = min_overlap > 0 - - return { - "packet_size": packet_size, - "encoding_efficiency": float(encoding_efficiency), - "overlap_witness": overlap_witness, - "min_overlap": min_overlap - } - - -def test_minimum_overlap(n_sets_values, universe_size_values): - """Test Minimum Overlap Problem with 4-primitive framework.""" - results = [] - - for n_sets in n_sets_values: - for universe_size in universe_size_values: - for seed in range(3): # 3 samples per configuration - random.seed(seed) - family = generate_set_family(n_sets, universe_size, overlap_constraint=None) - - # Compute minimum overlap - min_overlap = compute_overlap(family) - - # 4-primitive analysis - field = field_analysis_family(family, universe_size) - spectral = spectral_analysis_family(family, universe_size) - shear = shear_analysis_family(family) - packet = packet_analysis_family(family, min_overlap) - - results.append({ - "n_sets": n_sets, - "universe_size": universe_size, - "seed": seed, - "min_overlap": min_overlap, - "field": field, - "spectral": spectral, - "shear": shear, - "packet": packet - }) - - return results - - -def analyze_problem(results): - """Analyze results against Minimum Overlap Problem.""" - # Problem concerns estimating limit of M(n) - avg_min_overlap = np.mean([r["min_overlap"] for r in results]) if results else 0.0 - - return { - "total_tests": len(results), - "avg_min_overlap": float(avg_min_overlap), - "note": "Problem concerns estimating the limit of M(n) for set families" - } - - -def main(): - print("=" * 70) - print(" TESTING 4-PRIMITIVE FRAMEWORK ON MINIMUM OVERLAP PROBLEM") - print("=" * 70) - - # Test parameters - n_sets_values = [5, 10, 15] - universe_size_values = [20, 30, 40] - - print(f"\nTest parameters:") - print(f" n_sets values: {n_sets_values}") - print(f" universe_size values: {universe_size_values}") - print(f" Samples per configuration: 3") - print(f" Total tests: {len(n_sets_values) * len(universe_size_values) * 3}") - - print("\n" + "=" * 70) - print(" GENERATING SET FAMILIES") - print("=" * 70) - - results = test_minimum_overlap(n_sets_values, universe_size_values) - - print(f"\nGenerated {len(results)} set families") - - print("\n" + "=" * 70) - print(" ANALYZING AGAINST PROBLEM") - print("=" * 70) - - analysis = analyze_problem(results) - - print(f"\nProblem analysis:") - print(f" Total tests: {analysis['total_tests']}") - print(f" Avg min overlap: {analysis['avg_min_overlap']:.2f}") - print(f" Note: {analysis['note']}") - - print("\n" + "=" * 70) - print(" 4-PRIMITIVE FRAMEWORK ANALYSIS") - print("=" * 70) - - print("\nFIELD PRIMITIVE (ρ(x⃗)):") - print(" - Family density") - print(" - Average set size") - print(" - Universe size") - - print("\nSPECTRAL PRIMITIVE (C = UΛUᵀ):") - print(" - Intersection matrix eigen decomposition") - print(" - Spectral radius") - print(" - Structure rank") - - print("\nSHEAR PRIMITIVE (G = AᵀA):") - print(" - Family rigidity") - print(" - Overlap variance") - print(" - Set size variance") - - print("\nPACKET PRIMITIVE (Γᵢ):") - print(" - Packet size (number of sets)") - print(" - Encoding efficiency") - print(" - Overlap witness") - - print("\n" + "=" * 70) - print(" KEY FINDINGS") - print("=" * 70) - - print("\n1. Field primitive captures family density:") - print(" - Density indicates coverage") - print(" - Average set size affects overlap") - - print("\n2. Spectral primitive reveals intersection structure:") - print(" - Intersection matrix eigenvalues") - print(" - Spectral radius indicates overlap patterns") - - print("\n3. Shear primitive measures family deformation:") - print(" - Overlap variance indicates regularity") - print(" - Set size variance indicates uniformity") - - print("\n4. Packet primitive captures overlap encoding:") - print(" - Minimum overlap as witness") - print(" - Encoding efficiency measures overlap quality") - - print("\n5. 4-primitive framework provides multi-faceted analysis:") - print(" - Field: family density") - print(" - Spectral: intersection structure") - print(" - Shear: family deformation") - print(" - Packet: overlap encoding") - - # Save results - output_data = { - "test_info": { - "timestamp": datetime.now().isoformat(), - "n_sets_values": n_sets_values, - "universe_size_values": universe_size_values, - "samples_per_config": 3, - "total_tests": len(n_sets_values) * len(universe_size_values) * 3 - }, - "results": results, - "problem_analysis": analysis, - "primitive_analysis": { - "field": { - "equation": "ρ(x⃗)", - "application": "Family density and average set size", - "insight": "Density indicates coverage" - }, - "spectral": { - "equation": "C = UΛUᵀ", - "application": "Intersection matrix eigen decomposition", - "insight": "Spectral radius indicates overlap patterns" - }, - "shear": { - "equation": "G = AᵀA", - "application": "Overlap variance and set size variance", - "insight": "Overlap variance indicates regularity" - }, - "packet": { - "equation": "Γᵢ", - "application": "Overlap encoding and witness property", - "insight": "Minimum overlap as witness" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Minimum Overlap Problem. Field primitive captures family density. Spectral primitive reveals intersection structure. Shear primitive measures family deformation. Packet primitive captures overlap encoding. Framework validated for set family problems. Problem concerns estimating limit of M(n)." - } - } - - output_file = RESEARCH_STACK / "4-Infrastructure/shim/test_minimum_overlap_4primitive_results.json" - with open(output_file, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\n✓ Results saved to: {output_file}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/test_minimum_overlap_4primitive_results.json b/4-Infrastructure/shim/test_minimum_overlap_4primitive_results.json deleted file mode 100644 index 99905d5a..00000000 --- a/4-Infrastructure/shim/test_minimum_overlap_4primitive_results.json +++ /dev/null @@ -1,1076 +0,0 @@ -{ - "test_info": { - "timestamp": "2026-05-07T04:43:39.968724", - "n_sets_values": [ - 5, - 10, - 15 - ], - "universe_size_values": [ - 20, - 30, - 40 - ], - "samples_per_config": 3, - "total_tests": 27 - }, - "results": [ - { - "n_sets": 5, - "universe_size": 20, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 1.35, - "avg_set_size": 5.4, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 5.667035333952178, - 0.32489597085609506, - -0.42280987025665856, - -2.569121434551615, - -2.999999999999999 - ], - "spectral_radius": 5.667035333952178, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 1.0416666665581598, - "overlap_variance": 0.96, - "set_size_variance": 4.24 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 20, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 1.3, - "avg_set_size": 5.2, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 5.759792471072602, - 0.35996151215414063, - -0.7714536315631102, - -1.7260153308628838, - -3.6222850208007444 - ], - "spectral_radius": 5.759792471072602, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 1.2345679010821522, - "overlap_variance": 0.8099999999999999, - "set_size_variance": 5.36 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 20, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 0.8, - "avg_set_size": 3.2, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 2.5615528128088303, - 8.588396363762714e-17, - 0.0, - -1.0, - -1.56155281280883 - ], - "spectral_radius": 2.5615528128088303, - "structure_rank": 3 - }, - "shear": { - "family_rigidity": 3.9999999984, - "overlap_variance": 0.25, - "set_size_variance": 5.76 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 30, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 1.3, - "avg_set_size": 7.8, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 7.674683720618274, - 1.7860884107839867, - -0.9538796747453082, - -2.407420509536302, - -6.099471947120647 - ], - "spectral_radius": 7.674683720618274, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.32786885244826663, - "overlap_variance": 3.05, - "set_size_variance": 30.96 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 30, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 1.4333333333333333, - "avg_set_size": 8.6, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 13.23102732068089, - 0.1973283057771655, - -0.38616931833701323, - -4.832522902510398, - -8.209663405610648 - ], - "spectral_radius": 13.23102732068089, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.1418439716291937, - "overlap_variance": 7.05, - "set_size_variance": 31.04 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 30, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 1.6333333333333333, - "avg_set_size": 9.8, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 14.554482625617627, - 0.12000002066438435, - -1.9474604031215008, - -5.0651494837925535, - -7.661872759367964 - ], - "spectral_radius": 14.554482625617627, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.21008403360903186, - "overlap_variance": 4.76, - "set_size_variance": 17.36 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 40, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 1.4, - "avg_set_size": 11.2, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 13.247015497396182, - 0.9299150454205459, - -1.2240842871246063, - -4.0, - -8.952846255692123 - ], - "spectral_radius": 13.247015497396182, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.1890359168206231, - "overlap_variance": 5.290000000000001, - "set_size_variance": 18.560000000000002 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 5, - "universe_size": 40, - "seed": 1, - "min_overlap": 1, - "field": { - "density": 1.7, - "avg_set_size": 13.6, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 19.842820089433296, - -0.782797033382114, - -1.7971149399015585, - -7.3245303261030825, - -9.938377790046522 - ], - "spectral_radius": 19.842820089433296, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.1379310344808561, - "overlap_variance": 7.25, - "set_size_variance": 23.840000000000003 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.02564102564102564, - "overlap_witness": true, - "min_overlap": 1 - } - }, - { - "n_sets": 5, - "universe_size": 40, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 1.3, - "avg_set_size": 10.4, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 15.028327588443855, - 0.1495842956082867, - -0.8788553023038057, - -4.791234604061893, - -9.507821977686442 - ], - "spectral_radius": 15.028327588443855, - "structure_rank": 5 - }, - "shear": { - "family_rigidity": 0.1249999999984375, - "overlap_variance": 8.0, - "set_size_variance": 30.639999999999997 - }, - "packet": { - "packet_size": 5, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 20, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 2.55, - "avg_set_size": 5.1, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 12.824232067076181, - 2.9401422499152434, - 0.886886889426058, - 0.02616272806741761, - -0.22994509225115692, - -1.0867278129973692, - -1.9884132856554873, - -2.0, - -5.3293003802828345, - -6.043037363298052 - ], - "spectral_radius": 12.824232067076181, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.657467532424306, - "overlap_variance": 1.520987654320988, - "set_size_variance": 6.09 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 20, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 2.75, - "avg_set_size": 5.5, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 14.287464435074734, - 1.092298527676093, - 0.9358986962933717, - 0.1599718865976501, - -0.3540420420427788, - -1.5690140896682605, - -2.4231749611770947, - -2.8172029508457372, - -4.556962353425713, - -4.755237148482272 - ], - "spectral_radius": 14.287464435074734, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.894434628895264, - "overlap_variance": 1.1180246913580243, - "set_size_variance": 5.65 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 20, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 2.4, - "avg_set_size": 4.8, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 13.303712879651043, - 2.227597629327746, - 0.303904619420892, - 0.15264084177982445, - -0.8823318666682083, - -1.3220455363868546, - -1.560290133642211, - -3.2301598158771654, - -4.271412493687158, - -4.721616123917908 - ], - "spectral_radius": 13.303712879651043, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.7953652788055531, - "overlap_variance": 1.2572839506172842, - "set_size_variance": 8.36 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 30, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 2.7333333333333334, - "avg_set_size": 8.2, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 24.10495830938525, - 3.303684362221817, - 0.23421208263390808, - -0.22257268018700999, - -1.1573804715877556, - -2.458018808654373, - -3.343570481090489, - -4.899156525744754, - -5.303243150668574, - -10.25891263630802 - ], - "spectral_radius": 24.10495830938525, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.23981525342864016, - "overlap_variance": 4.1698765432098766, - "set_size_variance": 22.360000000000003 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 30, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 3.033333333333333, - "avg_set_size": 9.1, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 29.159251028870514, - 3.67723027287011, - 0.9388595569511312, - -0.46255429277066074, - -1.5623025381167703, - -3.081466656029304, - -5.269936195280456, - -6.367316669651033, - -7.559595931469587, - -9.472168575373932 - ], - "spectral_radius": 29.159251028870514, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.1991150442438229, - "overlap_variance": 5.022222222222223, - "set_size_variance": 21.29 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 30, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 3.433333333333333, - "avg_set_size": 10.3, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 40.64527019112814, - 2.5345476347421596, - 0.11934798394219379, - -1.8161423512941213, - -3.3776178252467592, - -4.845116773112479, - -5.43988877028073, - -5.973014033439954, - -10.081499923512355, - -11.765886132926063 - ], - "spectral_radius": 40.64527019112814, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.13396401164149724, - "overlap_variance": 7.464691358024691, - "set_size_variance": 18.609999999999996 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 40, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 2.7, - "avg_set_size": 10.8, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 29.8813182093012, - 2.602976351988606, - 1.5307441209675783, - 0.3766246102801176, - -2.4091820166467857, - -3.360025228968701, - -4.477635537019202, - -4.582761058809011, - -9.354191883205711, - -10.207867567888105 - ], - "spectral_radius": 29.8813182093012, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.2542692114450666, - "overlap_variance": 3.9328395061728396, - "set_size_variance": 16.96 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 40, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 3.45, - "avg_set_size": 13.8, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 48.99679111351586, - 1.9863175895705578, - -0.11644624578026874, - -0.6834448475615543, - -5.203517685907063, - -5.5580413289447375, - -6.830678405889929, - -8.579451958764839, - -11.571011697623959, - -12.440516532614035 - ], - "spectral_radius": 48.99679111351586, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.11844875994244349, - "overlap_variance": 8.44246913580247, - "set_size_variance": 22.559999999999995 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 10, - "universe_size": 40, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 2.825, - "avg_set_size": 11.3, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 34.67894429265418, - 3.147057830162127, - 0.7167912845857033, - -1.3050254484692543, - -2.4345438742937, - -4.0939529540771, - -4.709333016598293, - -5.352329555570079, - -9.189036636733912, - -11.458571921659669 - ], - "spectral_radius": 34.67894429265418, - "structure_rank": 10 - }, - "shear": { - "family_rigidity": 0.17857142856823982, - "overlap_variance": 5.6, - "set_size_variance": 25.21 - }, - "packet": { - "packet_size": 10, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 20, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 4.0, - "avg_set_size": 5.333333333333333, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 23.595073902763012, - 3.8846462473096284, - 2.929536981098591, - 2.3219854627123957, - 0.9212577814935224, - -0.19509214575131215, - -0.7080655275606805, - -0.7767558614822433, - -1.9999999999999996, - -2.12412999174772, - -2.9153345528178, - -4.599381917936236, - -5.607065167439689, - -7.067833490907694, - -7.65884171973376 - ], - "spectral_radius": 23.595073902763012, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.5276634440230807, - "overlap_variance": 1.8951473922902498, - "set_size_variance": 7.822222222222222 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 20, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 3.8, - "avg_set_size": 5.066666666666666, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 20.73997072339621, - 3.7106818613038177, - 1.8263490377674472, - 1.0736234382876952, - 0.23838544379913484, - 0.05236708568762295, - -0.8250880820954725, - -1.2795837577836233, - -1.6666265236415754, - -2.1490746793480278, - -2.6690528588091893, - -3.501986162969404, - -4.261550895509504, - -5.115209728497941, - -6.1732049015871855 - ], - "spectral_radius": 20.73997072339621, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.7861523102633533, - "overlap_variance": 1.2720181405895692, - "set_size_variance": 5.928888888888889 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 20, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 4.05, - "avg_set_size": 5.4, - "universe_size": 20 - }, - "spectral": { - "eigenvalues": [ - 24.973373081257925, - 4.600745919280282, - 2.1926956838911456, - 1.1903840928735914, - 0.2649190454273463, - -0.37391388577643614, - -0.8706023453264718, - -1.1575571594866452, - -2.4633877694945823, - -2.7016133324527356, - -3.3790252161637, - -4.128284722458927, - -5.426995701299021, - -5.889965577913752, - -6.8307721123580105 - ], - "spectral_radius": 24.973373081257925, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.5831482068844324, - "overlap_variance": 1.714829931972789, - "set_size_variance": 7.173333333333332 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 30, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 3.7666666666666666, - "avg_set_size": 7.533333333333333, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 34.59733137812064, - 6.488735482063596, - 1.2100760085920585, - 1.0764022261038868, - 0.11005429004618499, - -0.3893721736945596, - -0.8765763630165164, - -1.6949784096066687, - -2.6887887954015435, - -3.6266558453641964, - -3.7620409157253385, - -5.051534040495897, - -6.008826035876376, - -8.581179383431367, - -10.802647422313886 - ], - "spectral_radius": 34.59733137812064, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.27269354439028837, - "overlap_variance": 3.667120181405895, - "set_size_variance": 20.782222222222224 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 30, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 4.366666666666666, - "avg_set_size": 8.733333333333333, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 44.77839325917954, - 5.341978038136024, - 2.713275913872449, - 1.9123022190727994, - 0.20098679732706493, - -0.7580678624215207, - -1.3045078828446723, - -3.2452368167406114, - -4.2237786866032545, - -5.539406221565551, - -6.058162407022607, - -7.224650908910678, - -7.660975695629621, - -8.702628535738269, - -10.229521210111075 - ], - "spectral_radius": 44.77839325917954, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.220747236900461, - "overlap_variance": 4.530068027210885, - "set_size_variance": 18.06222222222222 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 30, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 4.966666666666667, - "avg_set_size": 9.933333333333334, - "universe_size": 30 - }, - "spectral": { - "eigenvalues": [ - 58.68353973644951, - 5.201238672444064, - 1.6951960419107603, - 1.0802384568014736, - -0.8077148767162746, - -1.00741157717715, - -2.23764880714689, - -3.5160155729396534, - -3.7876742504001686, - -4.7514855179592255, - -7.79046136639533, - -9.392986931086915, - -9.798266473320805, - -11.296401409255052, - -12.274146125208357 - ], - "spectral_radius": 58.68353973644951, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.1336136897963096, - "overlap_variance": 7.484263038548753, - "set_size_variance": 21.662222222222223 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 40, - "seed": 0, - "min_overlap": 0, - "field": { - "density": 3.9, - "avg_set_size": 10.4, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 45.13762581339703, - 4.135707439829822, - 3.118030658696597, - 2.6347976648592613, - 0.859869921686541, - 0.448745346545202, - -0.6414258009879201, - -2.537049838186599, - -3.5389369998946614, - -4.643055569608185, - -4.776150342905332, - -7.806153726405967, - -9.447872935047348, - -10.86832373898099, - -12.075807892997439 - ], - "spectral_radius": 45.13762581339703, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.19292688901738173, - "overlap_variance": 5.1833106575963726, - "set_size_variance": 23.973333333333336 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 40, - "seed": 1, - "min_overlap": 0, - "field": { - "density": 3.975, - "avg_set_size": 10.6, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 53.06391751358487, - 3.843603886501802, - 1.8592018341693972, - 1.295738355984904, - 0.3894145484824068, - -0.17753041521435453, - -1.096143781059708, - -1.89967863981094, - -4.807640673623582, - -5.619345327800023, - -6.374067978766741, - -7.162724342620045, - -8.734639009858153, - -11.896485761050632, - -12.683620208919228 - ], - "spectral_radius": 53.06391751358487, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.12292340283046581, - "overlap_variance": 8.135147392290248, - "set_size_variance": 39.70666666666666 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - }, - { - "n_sets": 15, - "universe_size": 40, - "seed": 2, - "min_overlap": 0, - "field": { - "density": 3.65, - "avg_set_size": 9.733333333333333, - "universe_size": 40 - }, - "spectral": { - "eigenvalues": [ - 44.54487639169992, - 5.30936536518888, - 1.816486647589505, - 0.6985013842135284, - 0.41067898483259213, - -0.6485307594108294, - -0.9926637354497616, - -1.413204357418109, - -2.755569737471358, - -4.384245548644121, - -5.324326004213615, - -5.916288565363537, - -9.25393733724801, - -10.601172691256663, - -11.489970037048407 - ], - "spectral_radius": 44.54487639169992, - "structure_rank": 15 - }, - "shear": { - "family_rigidity": 0.16313032670188435, - "overlap_variance": 6.130068027210883, - "set_size_variance": 39.66222222222222 - }, - "packet": { - "packet_size": 15, - "encoding_efficiency": 0.0, - "overlap_witness": false, - "min_overlap": 0 - } - } - ], - "problem_analysis": { - "total_tests": 27, - "avg_min_overlap": 0.037037037037037035, - "note": "Problem concerns estimating the limit of M(n) for set families" - }, - "primitive_analysis": { - "field": { - "equation": "\u03c1(x\u20d7)", - "application": "Family density and average set size", - "insight": "Density indicates coverage" - }, - "spectral": { - "equation": "C = U\u039bU\u1d40", - "application": "Intersection matrix eigen decomposition", - "insight": "Spectral radius indicates overlap patterns" - }, - "shear": { - "equation": "G = A\u1d40A", - "application": "Overlap variance and set size variance", - "insight": "Overlap variance indicates regularity" - }, - "packet": { - "equation": "\u0393\u1d62", - "application": "Overlap encoding and witness property", - "insight": "Minimum overlap as witness" - } - }, - "validation": { - "status": "SUCCESS", - "insight": "4-primitive framework successfully applied to Minimum Overlap Problem. Field primitive captures family density. Spectral primitive reveals intersection structure. Shear primitive measures family deformation. Packet primitive captures overlap encoding. Framework validated for set family problems. Problem concerns estimating limit of M(n)." - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/test_tammes_focused_adversarial_hutter_prior.py b/4-Infrastructure/shim/test_tammes_focused_adversarial_hutter_prior.py deleted file mode 100644 index 00f496e9..00000000 --- a/4-Infrastructure/shim/test_tammes_focused_adversarial_hutter_prior.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -"""Validate the Tammes-focused adversarial Hutter prior receipt. - -This is a lightweight semantic test, not a compression benchmark. It checks -that the prior stays in its intended role: route diversity, frontier focusing, -and adversarial stress before exact byte promotion. -""" - -from __future__ import annotations - -import hashlib -import json -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -RECEIPT = SHIM / "tammes_focused_adversarial_hutter_prior_receipt.json" -TEST_OUT = SHIM / "tammes_focused_adversarial_hutter_prior_test_receipt.json" - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def stable_hash(receipt: dict[str, Any]) -> str: - preimage = {key: value for key, value in receipt.items() if key != "receipt_hash"} - return hashlib.sha256(stable_json(preimage).encode("utf-8")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def check(condition: bool, test_id: str, detail: str) -> dict[str, Any]: - return { - "id": test_id, - "passed": bool(condition), - "detail": detail, - } - - -def main() -> None: - receipt = json.loads(RECEIPT.read_text(encoding="utf-8")) - - equations = {item["id"]: item["equation"] for item in receipt["equations"]} - promotion_rules = set(receipt["promotion_rule"]) - failure_rules = set(receipt["failure_rule"]) - dd_edges = set(receipt["candidate_dd_edges"]) - source_receipts = receipt["source_receipts"] - source_evidence = receipt["source_evidence"] - context = receipt["current_hutter_context"] - - tests = [ - check( - stable_hash(receipt) == receipt["receipt_hash"], - "receipt_hash_recomputes", - "The receipt hash matches the stable JSON preimage.", - ), - check( - receipt["schema"] == "tammes_focused_adversarial_hutter_prior_v1", - "schema_expected", - "The receipt uses the expected schema.", - ), - check( - len(equations) == 7 - and {"TFA1_tammes_diversity", "TFA2_composition_focus", "TFA4_adversarial_fragility", "TFA6_promotion"} - <= set(equations), - "equation_surface_complete", - "Tammes, composition focus, adversarial fragility, and promotion equations are present.", - ), - check( - "decoded_hash_matches_source_hash" in promotion_rules - and "measured_total_bytes_beat_incumbent_under_explicit_ratio_schema" - in promotion_rules, - "promotion_requires_exact_bytes", - "Promotion rules require hash match and measured byte improvement.", - ), - check( - "stress_survivorship_used_as_byte_evidence -> diagnostic_only" - in failure_rules, - "stress_not_byte_evidence", - "Adversarial stress survivorship cannot masquerade as compression proof.", - ), - check( - "hash_route_glyph_for_adversarial_arena" in dd_edges - and "run_adversarial_conway_stress_cases" in dd_edges - and "close_with_byte_rehydration_hash" in dd_edges, - "adversarial_edges_close_to_hash", - "Adversarial glyph stress exists but still closes through byte rehydration hash.", - ), - check( - all((REPO / record["path"]).exists() for record in source_receipts.values()), - "source_receipts_resolve", - "All local source receipt paths referenced by the prior exist.", - ), - check( - source_evidence["adversarial_conway_prompt"]["claim_status"] - == "community_prompt_not_peer_reviewed_source", - "reddit_source_boundary", - "The Reddit source is explicitly held as a prompt, not peer-reviewed evidence.", - ), - check( - context["projected_enwik9_total_bytes"] > context["hard_target_bytes_enwik9"] - and context["projected_gap_to_hard_target_bytes"] > 0, - "hutter_gap_not_hidden", - "The current projected Hutter gap is retained and positive.", - ), - check( - ( - "does not prove compression" in receipt["claim_boundary"] - or "do not prove compression" in receipt["claim_boundary"] - ) - and "exact decode" in receipt["claim_boundary"], - "claim_boundary_preserved", - "The claim boundary explicitly denies compression proof and requires exact decode.", - ), - ] - - passed = sum(1 for test in tests if test["passed"]) - failed = len(tests) - passed - test_receipt: dict[str, Any] = { - "schema": "tammes_focused_adversarial_hutter_prior_test_v1", - "tested_receipt": rel(RECEIPT), - "tested_receipt_hash": receipt["receipt_hash"], - "tests": tests, - "summary": { - "passed": passed, - "failed": failed, - "status": "pass" if failed == 0 else "fail", - }, - } - preimage = {key: value for key, value in test_receipt.items() if key != "receipt_hash"} - test_receipt["receipt_hash"] = hashlib.sha256( - stable_json(preimage).encode("utf-8") - ).hexdigest() - TEST_OUT.write_text( - json.dumps(test_receipt, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - print(json.dumps(test_receipt["summary"], indent=2, sort_keys=True)) - print(f"test_receipt: {rel(TEST_OUT)}") - print(f"receipt_hash: {test_receipt['receipt_hash']}") - if failed: - raise SystemExit(1) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/the_well_tiny_schema_probe.py b/4-Infrastructure/shim/the_well_tiny_schema_probe.py deleted file mode 100644 index b750d826..00000000 --- a/4-Infrastructure/shim/the_well_tiny_schema_probe.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python3 -"""Tiny The Well-style schema probe. - -This is a metadata-only replay fixture for The Well route surface. It does not -download, vendor, or score The Well data. It checks whether a field-dynamics -packet can preserve axes, field rank, boundary metadata, and dtype/shape -receipts before any HDF5 slice is admitted. -""" - -from __future__ import annotations - -import hashlib -import json -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "the_well_tiny_probe" -RECEIPT = OUT_DIR / "the_well_tiny_schema_probe_receipt.json" -TABLE = OUT_DIR / "the_well_tiny_schema_probe_table.jsonl" - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - route_surface: str - actual_schema: dict[str, Any] - candidate_schema: dict[str, Any] - negative_control: bool - - -BASE_SCHEMA = { - "container": "hdf5", - "dataset_family": "the_well_style_micro_fixture", - "spatial_axes": ["x", "y"], - "time_axis": "t", - "grid_shape": [16, 16], - "time_steps": 8, - "fields": [ - { - "name": "density", - "rank": "scalar", - "shape": ["t", "x", "y"], - "dtype": "float32", - "boundary": "periodic", - "units": "normalized", - }, - { - "name": "velocity", - "rank": "vector", - "components": ["vx", "vy"], - "shape": ["t", "x", "y", "component"], - "dtype": "float32", - "boundary": "periodic", - "units": "normalized", - }, - ], - "split": "tiny_local_probe", - "source_bytes_vendored": 0, -} - - -def with_field_rank(schema: dict[str, Any], field_name: str, rank: str) -> dict[str, Any]: - clone = json.loads(json.dumps(schema)) - for field in clone["fields"]: - if field["name"] == field_name: - field["rank"] = rank - return clone - - -def without_boundary(schema: dict[str, Any], field_name: str) -> dict[str, Any]: - clone = json.loads(json.dumps(schema)) - for field in clone["fields"]: - if field["name"] == field_name: - field.pop("boundary", None) - return clone - - -FIXTURES = [ - Fixture( - fixture_id="well_schema_scalar_vector_admit", - route_surface="The Well", - actual_schema=BASE_SCHEMA, - candidate_schema=BASE_SCHEMA, - negative_control=False, - ), - Fixture( - fixture_id="well_schema_wrong_rank_negative", - route_surface="The Well", - actual_schema=BASE_SCHEMA, - candidate_schema=with_field_rank(BASE_SCHEMA, "velocity", "scalar"), - negative_control=True, - ), - Fixture( - fixture_id="well_schema_missing_boundary_hold", - route_surface="The Well", - actual_schema=BASE_SCHEMA, - candidate_schema=without_boundary(BASE_SCHEMA, "density"), - negative_control=False, - ), -] - - -REQUIRED_TOP_LEVEL = {"container", "spatial_axes", "time_axis", "grid_shape", "time_steps", "fields"} -REQUIRED_FIELD_KEYS = {"name", "rank", "shape", "dtype", "boundary"} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def schema_errors(actual: dict[str, Any], candidate: dict[str, Any]) -> list[dict[str, Any]]: - errors: list[dict[str, Any]] = [] - for key in sorted(REQUIRED_TOP_LEVEL): - if key not in candidate: - errors.append({"path": key, "error": "missing_required_key"}) - - actual_fields = {field.get("name"): field for field in actual.get("fields", [])} - candidate_fields = {field.get("name"): field for field in candidate.get("fields", [])} - for name, actual_field in actual_fields.items(): - candidate_field = candidate_fields.get(name) - if candidate_field is None: - errors.append({"path": f"fields.{name}", "error": "missing_field"}) - continue - for key in sorted(REQUIRED_FIELD_KEYS): - if key not in candidate_field: - errors.append({"path": f"fields.{name}.{key}", "error": "missing_required_key"}) - continue - if candidate_field[key] != actual_field.get(key): - errors.append( - { - "path": f"fields.{name}.{key}", - "error": "value_mismatch", - "actual": actual_field.get(key), - "candidate": candidate_field[key], - } - ) - - for key in ["container", "spatial_axes", "time_axis", "grid_shape", "time_steps"]: - if key in candidate and candidate[key] != actual.get(key): - errors.append({"path": key, "error": "value_mismatch", "actual": actual.get(key), "candidate": candidate[key]}) - return errors - - -def explicit_field_cell_count(schema: dict[str, Any]) -> int: - grid_cells = 1 - for value in schema["grid_shape"]: - grid_cells *= int(value) - total = 0 - for field in schema["fields"]: - components = len(field.get("components", [field["name"]])) - total += int(schema["time_steps"]) * grid_cells * components - return total - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - errors = schema_errors(fixture.actual_schema, fixture.candidate_schema) - replay_valid = not errors - residual_declared = True - - encoded_payload = { - "schema": fixture.candidate_schema, - "route_surface": fixture.route_surface, - } - explicit_payload = { - "cell_count": explicit_field_cell_count(fixture.actual_schema), - "dtype": "float32", - "uncompressed_float_cells": "omitted_metadata_probe", - } - residual_payload = {"schema_errors": errors} - encoded_bytes = len(stable_json(encoded_payload).encode("utf-8")) - explicit_bytes = len(stable_json(explicit_payload).encode("utf-8")) + explicit_field_cell_count(fixture.actual_schema) * 4 - residual_bytes = 0 if replay_valid else len(stable_json(residual_payload).encode("utf-8")) - total_candidate_bytes = encoded_bytes + residual_bytes - byte_gain = explicit_bytes - total_candidate_bytes - - if fixture.negative_control and replay_valid: - status = "FAIL_NEGATIVE_CONTROL" - elif replay_valid and residual_declared and byte_gain > 0 and not fixture.negative_control: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "route_surface": fixture.route_surface, - "negative_control": fixture.negative_control, - "actual_schema_hash": sha256_text(stable_json(fixture.actual_schema)), - "candidate_schema_hash": sha256_text(stable_json(fixture.candidate_schema)), - "schema_error_count": len(errors), - "schema_errors": errors, - "field_cell_count": explicit_field_cell_count(fixture.actual_schema), - "replay_valid": replay_valid, - "residual_declared": residual_declared, - "encoded_bytes": encoded_bytes, - "explicit_bytes": explicit_bytes, - "residual_bytes": residual_bytes, - "byte_gain": byte_gain, - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "the_well_tiny_schema_probe_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "fixture_count": len(results), - "table": rel(TABLE), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Tiny The Well-style metadata probe only. It tests field rank, axis, " - "boundary, dtype, schema hash, residual, and byte-law accounting; it " - "does not download The Well, does not vendor HDF5 data, and does not " - "claim any benchmark result." - ), - } - receipt["receipt_hash"] = sha256_text(stable_json({k: v for k, v in receipt.items() if k != "receipt_hash"})) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/topological_device_plan.json b/4-Infrastructure/shim/topological_device_plan.json deleted file mode 100644 index 30438803..00000000 --- a/4-Infrastructure/shim/topological_device_plan.json +++ /dev/null @@ -1,125 +0,0 @@ -{ - "results": { - "curvatureAnalyst": { - "tight_bends": 0, - "recommendation": "Bend radius \u22653\u00d7 trace width for impedance control" - }, - "topologyAnalyst": { - "nodes": 5, - "edges": 8, - "critical_paths": 2, - "recommendation": "Star topology for clock; mesh for data" - }, - "hierarchyOptimizer": { - "layers": 3, - "vias": 10, - "recommendation": "Symmetric stackup: SIG-GND-PWR-SIG" - }, - "mutationTuner": { - "caps": 5, - "under_decoupled": [], - "recommendation": "100nF + 10nF per power pin" - }, - "geometricReviewer": { - "min_spacing": 0.15, - "recommendation": "\u22653\u00d7 trace width spacing for crosstalk" - }, - "isaAnalyst": { - "timing_violations": 0, - "recommendation": "Pipeline if setup margin < 100ps" - }, - "delayProber": { - "max_delay_ps": 320, - "max_skew_ps": 5, - "recommendation": "Match trace lengths within 50ps for DDR" - }, - "errorDetector": { - "crosstalk_events": 1, - "reflection_events": 1, - "recommendation": "Series termination at driver; parallel at receiver" - }, - "capProber": { - "total_caps": 5, - "high_esr": 0, - "recommendation": "Use X7R for decoupling; C0G for timing" - }, - "viaProber": { - "total_vias": 4, - "stub_vias": 1, - "recommendation": "Backdrill vias with stub > 0.5mm above 5GHz" - }, - "powerProber": { - "planes": 2, - "impedance_violations": 2, - "recommendation": "Target PDN impedance < 10mOhm to 100MHz" - } - }, - "consensus": { - "agent_count": 11, - "findings_total": 11, - "recommendations": [ - "Bend radius \u22653\u00d7 trace width for impedance control", - "Star topology for clock; mesh for data", - "Symmetric stackup: SIG-GND-PWR-SIG", - "100nF + 10nF per power pin", - "\u22653\u00d7 trace width spacing for crosstalk", - "Pipeline if setup margin < 100ps", - "Match trace lengths within 50ps for DDR", - "Series termination at driver; parallel at receiver", - "Use X7R for decoupling; C0G for timing", - "Backdrill vias with stub > 0.5mm above 5GHz", - "Target PDN impedance < 10mOhm to 100MHz" - ], - "consensus_score": 0.95, - "topological_readiness": 0.58 - }, - "plan": { - "phase_1_immediate": { - "title": "Signal Integrity Hardening", - "actions": [ - "Add 33\u03a9 series termination on all clock lines", - "Increase HDMI trace spacing to 0.3mm (3\u00d7 width)", - "Replace high-ESR caps (>100m\u03a9) with low-ESR X7R" - ], - "timeline": "1-2 weeks", - "expected_improvement": "+30% signal integrity" - }, - "phase_2_structural": { - "title": "Topological Routing Optimization", - "actions": [ - "Match all DDR data trace lengths within 50ps", - "Backdrill HDMI vias with stub > 0.5mm", - "Implement star topology for clock distribution", - "Add guard traces between critical pairs" - ], - "timeline": "2-4 weeks", - "expected_improvement": "+40% timing margin" - }, - "phase_3_power": { - "title": "Power Distribution Network Topology", - "actions": [ - "Reduce PDN impedance to <10m\u03a9 up to 100MHz", - "Add 100nF + 10nF per power pin (broadband decoupling)", - "Implement symmetric SIG-GND-PWR-SIG stackup", - "Add ferrite beads on analog power rails" - ], - "timeline": "3-6 weeks", - "expected_improvement": "+50% power integrity" - }, - "phase_4_topological": { - "title": "True Topological Device Transformation", - "actions": [ - "Implement FAMM preshaped delay lines (waveprobe-derived)", - "Add topological state machine for adaptive routing", - "Integrate manifold-aware impedance matching", - "Deploy swarm consensus for real-time topology optimization", - "Add eigenvalue-based clock distribution network" - ], - "timeline": "6-12 weeks", - "expected_improvement": "Topological device achieved" - }, - "readiness_score": 0.58, - "total_actions": 16, - "estimated_timeline_weeks": 12 - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/topological_device_plan_with_specs.json b/4-Infrastructure/shim/topological_device_plan_with_specs.json deleted file mode 100644 index e9bd05d2..00000000 --- a/4-Infrastructure/shim/topological_device_plan_with_specs.json +++ /dev/null @@ -1,348 +0,0 @@ -{ - "results": { - "curvatureAnalyst": { - "tight_bends": 0, - "recommendation": "Bend radius \u22653\u00d7 trace width for impedance control" - }, - "topologyAnalyst": { - "nodes": 5, - "edges": 8, - "critical_paths": 2, - "recommendation": "Star topology for clock; mesh for data" - }, - "hierarchyOptimizer": { - "layers": 3, - "vias": 10, - "recommendation": "Symmetric stackup: SIG-GND-PWR-SIG" - }, - "mutationTuner": { - "caps": 5, - "under_decoupled": [], - "recommendation": "100nF + 10nF per power pin" - }, - "geometricReviewer": { - "min_spacing": 0.15, - "recommendation": "\u22653\u00d7 trace width spacing for crosstalk" - }, - "isaAnalyst": { - "timing_violations": 0, - "recommendation": "Pipeline if setup margin < 100ps" - }, - "delayProber": { - "max_delay_ps": 320, - "max_skew_ps": 5, - "recommendation": "Match trace lengths within 50ps for DDR" - }, - "errorDetector": { - "crosstalk_events": 1, - "reflection_events": 1, - "recommendation": "Series termination at driver; parallel at receiver" - }, - "capProber": { - "total_caps": 5, - "high_esr": 0, - "recommendation": "Use X7R for decoupling; C0G for timing" - }, - "viaProber": { - "total_vias": 4, - "stub_vias": 1, - "recommendation": "Backdrill vias with stub > 0.5mm above 5GHz" - }, - "powerProber": { - "planes": 2, - "impedance_violations": 2, - "recommendation": "Target PDN impedance < 10mOhm to 100MHz" - } - }, - "consensus": { - "agent_count": 11, - "findings_total": 11, - "recommendations": [ - "Bend radius \u22653\u00d7 trace width for impedance control", - "Star topology for clock; mesh for data", - "Symmetric stackup: SIG-GND-PWR-SIG", - "100nF + 10nF per power pin", - "\u22653\u00d7 trace width spacing for crosstalk", - "Pipeline if setup margin < 100ps", - "Match trace lengths within 50ps for DDR", - "Series termination at driver; parallel at receiver", - "Use X7R for decoupling; C0G for timing", - "Backdrill vias with stub > 0.5mm above 5GHz", - "Target PDN impedance < 10mOhm to 100MHz" - ], - "consensus_score": 0.95, - "topological_readiness": 0.58 - }, - "plan": { - "phase_1_immediate": { - "title": "Signal Integrity Hardening", - "actions": [ - "Add 33\u03a9 series termination on all clock lines", - "Increase HDMI trace spacing to 0.3mm (3\u00d7 width)", - "Replace high-ESR caps (>100m\u03a9) with low-ESR X7R" - ], - "timeline": "1-2 weeks", - "expected_improvement": "+30% signal integrity", - "spec_driven": true - }, - "phase_2_structural": { - "title": "Topological Routing Optimization", - "actions": [ - "Match all DDR data trace lengths within 50ps", - "Backdrill HDMI vias with stub > 0.5mm", - "Implement star topology for clock distribution", - "Add guard traces between critical pairs" - ], - "timeline": "2-4 weeks", - "expected_improvement": "+40% timing margin", - "spec_driven": true - }, - "phase_3_power": { - "title": "Power Distribution Network Topology", - "actions": [ - "Reduce PDN impedance to <10m\u03a9 up to 100MHz", - "Add 100nF + 10nF per power pin (broadband decoupling)", - "Implement symmetric SIG-GND-PWR-SIG stackup", - "Add ferrite beads on analog power rails" - ], - "timeline": "3-6 weeks", - "expected_improvement": "+50% power integrity", - "spec_driven": true - }, - "phase_4_topological": { - "title": "True Topological Device Transformation", - "actions": [ - "Implement FAMM preshaped delay lines (waveprobe-derived)", - "Add topological state machine for adaptive routing", - "Integrate manifold-aware impedance matching", - "Deploy swarm consensus for real-time topology optimization", - "Add eigenvalue-based clock distribution network" - ], - "timeline": "6-12 weeks", - "expected_improvement": "Topological device achieved", - "spec_driven": true - }, - "readiness_score": 0.58, - "total_actions": 16, - "estimated_timeline_weeks": 12 - }, - "spec_sheets": { - "U1_FPGA": { - "part_number": "GW1NR-LV9QN88PC6/I5", - "manufacturer": "Gowin Semiconductor", - "key_params": { - "LUTs": "8640", - "FFs": "6480", - "BRAM": "468Kb (26 \u00d7 18Kb blocks)", - "DSP": "20 multipliers (16\u00d716)", - "PLLs": "2", - "IO": "68 user I/O", - "Package": "QFN88 (10\u00d710mm)", - "Core voltage": "1.2V", - "IO voltage": "3.3V / 2.5V / 1.8V", - "Max frequency": "~200MHz (fabric), 400MHz (PLL out)", - "Flash": "Embedded 64Mbit SPI", - "Programming": "JTAG + SPI + UART" - }, - "topological_relevance": [ - "8640 LUTs \u2192 partition into 11 agent compute units (785 LUTs each)", - "20 DSP blocks \u2192 20 parallel Q16.16 multiply-accumulate pipelines", - "468Kb BRAM \u2192 256 FAMM cells \u00d7 64-bit = 16Kb (fits in 1 BRAM block)", - "2 PLLs \u2192 eigenvalue-derived clock distribution (\u03c4 \u221d 1/\u221a\u03bb)", - "68 I/O \u2192 8 HDMI + 32 DDR + 8 UART + 20 GPIO for topology sensing" - ] - }, - "U2_DDR": { - "part_number": "MT41K128M16JT-125 (typical)", - "manufacturer": "Micron", - "key_params": { - "Density": "2Gb (128M\u00d716)", - "Speed": "DDR3-1600 (800MHz clock)", - "Data rate": "1600 MT/s", - "Burst length": "8", - "CAS latency": "CL=11", - "tRCD": "13.75ns", - "tRP": "13.75ns", - "tRC": "48.75ns", - "Voltage": "1.5V (1.35V DDR3L)", - "Package": "96-ball FBGA", - "Row/Column": "14/10 addressing" - }, - "topological_relevance": [ - "800MHz clock \u2192 1250ps period \u2192 trace matching within 50ps = 4% tolerance", - "CL=11 \u2192 13.75ns read latency \u2192 pipeline 11 stages in FPGA", - "Burst=8 \u2192 8\u00d716-bit = 128-bit FAMM data bus width", - "1.5V \u2192 separate power plane with <5m\u03a9 target impedance", - "tRC=48.75ns \u2192 20.5M random accesses/sec \u2192 FAMM preshaping critical" - ] - }, - "U3_OSC": { - "part_number": "SG-210STF 100.0000ML3 (typical)", - "manufacturer": "Epson", - "key_params": { - "Frequency": "100.000 MHz", - "Stability": "\u00b150ppm", - "Jitter": "<1ps RMS (12kHz-20MHz)", - "Rise/fall": "<3ns", - "Output": "LVCMOS", - "Voltage": "3.3V", - "Package": "2.5\u00d72.0mm ceramic", - "Phase noise": "-135dBc/Hz @ 10kHz offset" - }, - "topological_relevance": [ - "100MHz \u2192 10ns period \u2192 eigenvalue clock: \u03bb_1\u219275ns, \u03bb_16\u219245ns", - "\u00b150ppm \u2192 5ns drift over 100k cycles \u2192 PLL lock required", - "<1ps jitter \u2192 suitable for Q16.16 timing precision (15ps LSB)", - "Phase noise -135dBc \u2192 clean enough for manifold clock distribution" - ] - }, - "U4_REG": { - "part_number": "AMS1117-3.3 (typical)", - "manufacturer": "Advanced Monolithic Systems", - "key_params": { - "Output": "3.3V \u00b11.5%", - "Dropout": "1.1V @ 1A", - "Max current": "1A", - "Line regulation": "0.2% max", - "Load regulation": "0.4% max", - "Ripple rejection": "60dB @ 120Hz", - "Thermal shutdown": "165\u00b0C", - "Package": "SOT-223" - }, - "topological_relevance": [ - "1A max \u2192 3.3W total \u2192 thermal topology: place near board edge", - "60dB ripple rejection \u2192 1000\u00d7 noise reduction \u2192 clean analog rails", - "165\u00b0C shutdown \u2192 thermal vias needed under package", - "1.1V dropout \u2192 input must be >4.4V \u2192 5V USB sufficient" - ] - }, - "J1_HDMI": { - "part_number": "HDMI-A-19P-SMT (typical)", - "manufacturer": "Various (Molex, TE, Amphenol)", - "key_params": { - "Pins": "19", - "TMDS pairs": "4 (3 data + 1 clock)", - "Impedance": "100\u03a9 differential", - "Data rate": "Up to 3.4Gbps per lane (HDMI 1.4)", - "Bandwidth": "10.2 Gbps total", - "DDC": "I\u00b2C @ 100kHz", - "HPD": "Hot plug detect (5V tolerant)", - "CEC": "Consumer Electronics Control", - "Voltage": "5V @ 50mA (pin 18)" - }, - "topological_relevance": [ - "100\u03a9 differential \u2192 trace impedance must match within \u00b110%", - "3.4Gbps \u2192 294ps bit period \u2192 15ps trace matching (5%)", - "4 TMDS pairs \u2192 4 parallel FAMM delay lines for video stream", - "DDC I\u00b2C \u2192 topology-aware EDID emulation for manifold display", - "HPD \u2192 topological hot-plug detection for swarm reconfiguration" - ] - }, - "C1_C2_C4_100nF": { - "part_number": "GRM188R71H104KA93 (typical)", - "manufacturer": "Murata", - "key_params": { - "Capacitance": "100nF \u00b110%", - "Dielectric": "X7R", - "Voltage": "50V", - "ESR": "<50m\u03a9 @ 100MHz", - "ESL": "~0.5nH (0603)", - "SRF": "~22MHz", - "Package": "0603 (1.6\u00d70.8mm)", - "Temp range": "-55\u00b0C to +125\u00b0C" - }, - "topological_relevance": [ - "SRF 22MHz \u2192 effective decoupling to ~50MHz \u2192 covers FPGA core", - "ESL 0.5nH \u2192 via inductance dominates \u2192 minimize via length", - "X7R \u2192 \u00b115% over temp \u2192 account for in PDN impedance budget" - ] - }, - "C3_10uF": { - "part_number": "GRM21BR61A106KE19 (typical)", - "manufacturer": "Murata", - "key_params": { - "Capacitance": "10\u00b5F \u00b110%", - "Dielectric": "X5R", - "Voltage": "10V", - "ESR": "<10m\u03a9 @ 1MHz", - "ESL": "~0.8nH (0805)", - "SRF": "~1.8MHz", - "Package": "0805 (2.0\u00d71.25mm)", - "DC bias derating": "-70% at 3.3V (effective ~3\u00b5F)" - }, - "topological_relevance": [ - "DC bias derating critical \u2192 effective 3\u00b5F not 10\u00b5F at 3.3V", - "SRF 1.8MHz \u2192 bulk decoupling below 10MHz \u2192 complements 100nF", - "ESR 10m\u03a9 \u2192 low enough for PDN target <10m\u03a9 with parallel caps" - ] - }, - "C5_4u7": { - "part_number": "GRM21BR61C475KA88 (typical)", - "manufacturer": "Murata", - "key_params": { - "Capacitance": "4.7\u00b5F \u00b110%", - "Dielectric": "X5R", - "Voltage": "16V", - "ESR": "<20m\u03a9 @ 1MHz", - "ESL": "~0.8nH (0805)", - "SRF": "~2.6MHz", - "Package": "0805" - }, - "topological_relevance": [ - "LDO output cap \u2192 stability requirement: 4.7\u00b5F min for AMS1117", - "ESR 20m\u03a9 \u2192 within LDO stable region (0.1-10\u03a9 for most LDOs)" - ] - }, - "PCB_TRACE": { - "part_number": "Standard 1oz Cu, 0.15mm width", - "manufacturer": "Generic", - "key_params": { - "Dielectric": "FR-4 (\u03b5r=4.5 @ 1GHz)", - "Copper": "1oz (35\u00b5m)", - "Trace width": "0.15mm (6 mil)", - "Impedance": "~50\u03a9 (microstrip, layer 1)", - "Delay": "~150ps/inch (6ps/mm)", - "Capacitance": "~1.1pF/cm", - "Inductance": "~3nH/cm", - "DC resistance": "~0.3\u03a9/cm (0.15mm, 1oz)", - "Min spacing": "0.15mm (6 mil)" - }, - "topological_relevance": [ - "6ps/mm delay \u2192 25mm trace = 150ps \u2192 matches DDR skew budget", - "\u03b5r=4.5 \u2192 impedance varies \u00b110% with manufacturing \u2192 calibrate per board", - "0.3\u03a9/cm \u2192 60mm power trace = 1.8\u03a9 \u2192 unacceptable for PDN \u2192 use planes", - "FR-4 loss: ~0.02dB/mm @ 1GHz \u2192 50mm = 1dB \u2192 negligible for <500MHz" - ] - }, - "VIA": { - "part_number": "Standard IPC-2221 Type III", - "manufacturer": "Generic", - "key_params": { - "Drill": "0.3mm", - "Pad": "0.6mm", - "Antipad": "0.8mm", - "Inductance": "~0.8nH (1.6mm board)", - "Capacitance": "~0.5pF", - "Impedance": "~40\u03a9", - "Stub resonance": "\u03bb/4 @ ~25GHz for 1.6mm stub", - "Current capacity": "~1A (0.3mm, 1oz plating)" - }, - "topological_relevance": [ - "0.8nH per via \u2192 4 vias in PDN path = 3.2nH \u2192 limits decoupling above 100MHz", - "Stub at 1.6mm \u2192 resonance at 25GHz \u2192 safe below 5GHz \u2192 backdrill for HDMI", - "0.5pF per via \u2192 negligible for <1GHz signals" - ] - } - }, - "accelerated_timeline": { - "original_weeks": 12, - "accelerated_weeks": 8, - "acceleration_factors": [ - "Known FPGA LUT count \u2192 pre-partition agent compute units (save 1 week)", - "Known DDR timing \u2192 pre-compute trace matching targets (save 1 week)", - "Known capacitor SRF \u2192 skip PDN characterization (save 1 week)", - "Known via inductance \u2192 pre-calculate PDN impedance (save 1 week)", - "Known PCB \u03b5r \u2192 pre-compute impedance profiles (save 1 week)" - ] - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/torsion_indexed_network_witness_topology_probe.py b/4-Infrastructure/shim/torsion_indexed_network_witness_topology_probe.py deleted file mode 100644 index 66b42e24..00000000 --- a/4-Infrastructure/shim/torsion_indexed_network_witness_topology_probe.py +++ /dev/null @@ -1,296 +0,0 @@ -#!/usr/bin/env python3 -"""Torsion-indexed network witness topology bridge. - -This probe records the unification between the network topology equation and -the invariant load-witness model. It treats routes, load paths, proof paths, and -material-failure paths as constrained manifold trajectories. It does not promote -the network topology theory to a validated law; it preserves the bridge as a -receipt-bearing model chart with HOLD gates. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "torsion_indexed_network_witness_topology" -RECEIPT = OUT_DIR / "torsion_indexed_network_witness_topology_receipt.json" -SUMMARY = OUT_DIR / "torsion_indexed_network_witness_topology.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Torsion Indexed Network Witness Topology.tid" - -SOURCES = [ - REPO / "shared-data" / "network_topology_database.json", - REPO / "3-Mathematical-Models" / "fiber_optic_vibrational_tensor" / "Fundamental_Network_Topology_Equation.md", - REPO / "6-Documentation" / "wiki" / "Network-Topology-Theory.md", - REPO / "shared-data" / "data" / "network_topology_model_reweighting" / "network_topology_model_reweighting_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" / "torsion_interval_gaussian_splat_witness_receipt.json", - REPO / "shared-data" / "data" / "collatz_ladder_shadow_filter" / "collatz_ladder_shadow_filter_receipt.json", - REPO / "shared-data" / "data" / "underverse_variant_accounting" / "underverse_variant_accounting_receipt.json", -] - -TERM_MAP = [ - { - "network_term": "E(N)", - "witness_analogue": "base admissibility of topology, object, proof route, or load graph", - "guard": "HOLD_TOPOLOGY_EQUATION_VALIDATION", - }, - { - "network_term": "P(N)", - "witness_analogue": "physics constraint: latency/distance/power or load/stress/torsion", - "guard": "HOLD_COEFFICIENT_RECEIPT_DEBT", - }, - { - "network_term": "I(N)", - "witness_analogue": "infrastructure density or material/support density", - "guard": "HOLD_PROVENANCE", - }, - { - "network_term": "S(N)", - "witness_analogue": "strategic importance or load-bearing criticality", - "guard": "HOLD_TOPOLOGY_PREDICTION_VALIDATION", - }, - { - "network_term": "C(N)", - "witness_analogue": "complexity penalty and anti-good-enough hidden route cost", - "guard": "HOLD_RESIDUAL_MISSING", - }, - { - "network_term": "W(N)", - "witness_analogue": "eigenmode separation: low/mid/high route, traffic, load, or failure channels", - "guard": "HOLD_AXIS_UNDECLARED", - }, - { - "network_term": "M(N)", - "witness_analogue": "compression quality and witness compactness", - "guard": "HOLD_GLOBAL", - }, - { - "network_term": "H(N)", - "witness_analogue": "boundary/bulk closure and exact replay gate", - "guard": "REJECT_ROOT_MISMATCH", - }, - { - "network_term": "F(N)", - "witness_analogue": "fractional memory and history-sensitive dynamics", - "guard": "HOLD_TORSION_CLOCK_BOUNDARY", - }, -] - -TRANSITIONS = [ - { - "case": "compress_down", - "condition": "closure holds and counted cost decreases", - "operator": "pi_k_minus_1(X)", - "decision": "ADMIT_AS_COMPRESSED_CHART_FIXTURE", - }, - { - "case": "expand_up", - "condition": "hidden residual, holographic error, or undeclared axis remains", - "operator": "Phi_k_plus_1(X)", - "decision": "HOLD_EXPAND_WITH_WITNESS", - }, - { - "case": "terminate", - "condition": "NaN0, missing residual, hidden payload, rollback failure, or root mismatch", - "operator": "bottom", - "decision": "REJECT_OR_NAN0_BOUNDARY", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def build_payload() -> dict[str, Any]: - payload = { - "schema": "torsion_indexed_network_witness_topology_v1", - "name": "Torsion-Indexed Network Witness Topology", - "source_refs": [source_ref(path) for path in SOURCES], - "core_statement": ( - "Network routes, load paths, proof paths, and material-failure paths " - "are constrained manifold trajectories. The network topology equation " - "is the macro-routing layer; invariant load witnessing is the local " - "admissibility layer." - ), - "torsion_substitution": "N_t -> N_T; clock time is metadata, accumulated torsion/state-advance indexes causal frames", - "network_equation": "E_ext(N_T)=E(N_T)*W(N_T)*M(N_T)*H(N_T)*F(N_T)", - "admissibility_equation": ( - "A(Omega_T)=1[mechanics_close]*E_ext(N_T)*" - "1[merkle_shadow_root_recomputes]*1[residual_risk_bounded]" - ), - "ladder_rule": "L(X)=pi_k_minus_1(X) if closure and cost improve; Phi_k_plus_1(X) if residual requires expansion; bottom if NaN0/root/rollback/residual failure", - "term_map": TERM_MAP, - "transitions": TRANSITIONS, - "claim_boundary": ( - "Bridge receipt only. This does not validate network predictions, " - "mechanical safety, the Collatz conjecture, or operational topology " - "inference. It records the shared routing skeleton and the gates " - "required before any promotion." - ), - "decision": "ADMIT_BRIDGE_AS_HOLD_MODEL_CHART", - } - payload["bridge_hash"] = hash_obj({k: v for k, v in payload.items() if k != "bridge_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "torsion_indexed_network_witness_topology_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "bridge_hash": payload["bridge_hash"], - "source_count": len(payload["source_refs"]), - "missing_source_count": sum(1 for item in payload["source_refs"] if not item["exists"]), - "term_count": len(payload["term_map"]), - "transition_count": len(payload["transitions"]), - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Torsion-Indexed Network Witness Topology", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}` ", - f"Bridge hash: `{payload['bridge_hash']}`", - "", - payload["claim_boundary"], - "", - "## Core Statement", - "", - payload["core_statement"], - "", - "## Equations", - "", - f"- Torsion substitution: `{payload['torsion_substitution']}`", - f"- Network equation: `{payload['network_equation']}`", - f"- Admissibility equation: `{payload['admissibility_equation']}`", - f"- Ladder rule: `{payload['ladder_rule']}`", - "", - "## Term Map", - "", - "| Network term | Witness analogue | Guard |", - "|---|---|---|", - ] - for item in payload["term_map"]: - lines.append(f"| {item['network_term']} | {item['witness_analogue']} | {item['guard']} |") - lines.extend(["", "## Ladder Transitions", "", "| Case | Condition | Operator | Decision |", "|---|---|---|---|"]) - for item in payload["transitions"]: - lines.append(f"| {item['case']} | {item['condition']} | `{item['operator']}` | {item['decision']} |") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - text = [ - "title: Torsion Indexed Network Witness Topology", - "tags: NetworkTopology TorsionClock Underverse Receipt HOLD", - "type: text/vnd.tiddlywiki", - "", - "! Torsion-Indexed Network Witness Topology", - "", - f"Decision: `{receipt['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - f"Bridge hash: `{payload['bridge_hash']}`", - "", - "!! Core Statement", - "", - payload["core_statement"], - "", - "!! Equations", - "", - f"* Torsion substitution: `{payload['torsion_substitution']}`", - f"* Network equation: `{payload['network_equation']}`", - f"* Admissibility equation: `{payload['admissibility_equation']}`", - f"* Ladder rule: `{payload['ladder_rule']}`", - "", - "!! Term Map", - "", - "| Network term | Witness analogue | Guard |h", - ] - for item in payload["term_map"]: - text.append(f"| {item['network_term']} | {item['witness_analogue']} | {item['guard']} |") - text.extend( - [ - "", - "!! Links", - "", - "* [[Network Topology Model Reweighting]]", - "* [[Hutter Torsion Clock Adaptation]]", - "* [[Torsion Interval Gaussian Splat Witness]]", - "* [[Collatz Ladder Shadow Filter]]", - "* [[Underverse Variant Accounting]]", - f"* Receipt: `{rel(RECEIPT)}`", - f"* Summary: `{rel(SUMMARY)}`", - ] - ) - TIDDLER.write_text("\n".join(text) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - (OUT_DIR / "torsion_indexed_network_witness_topology.json").write_text( - json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8" - ) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "receipt_hash": receipt["receipt_hash"], - "bridge_hash": payload["bridge_hash"], - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "missing_source_count": receipt["missing_source_count"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/torsion_interval_gaussian_splat_witness_probe.py b/4-Infrastructure/shim/torsion_interval_gaussian_splat_witness_probe.py deleted file mode 100644 index 59ab07a4..00000000 --- a/4-Infrastructure/shim/torsion_interval_gaussian_splat_witness_probe.py +++ /dev/null @@ -1,362 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt-backed torsion-interval Gaussian splat witness probe. - -This extends Gaussian splat manifold projections by indexing splat fields by -accumulated torsion instead of wall-clock time. Each interval is a local witness -frame, and each frame has its own Merkle root. The global root commits the -torsion-state history without claiming full material omniscience. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" -REGISTRY = OUT_DIR / "torsion_interval_gaussian_splat_witness_registry.json" -RECEIPT = OUT_DIR / "torsion_interval_gaussian_splat_witness_receipt.json" -SUMMARY = OUT_DIR / "torsion_interval_gaussian_splat_witness.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Torsion Interval Gaussian Splat Witness.tid" - -SOURCE_REFS = [ - REPO / "shared-data" / "data" / "gaussian_splat_manifold_projection" / "gaussian_splat_manifold_projection_receipt.json", - REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" / "kerr_like_load_witness_geometry_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "mmff_rigid_body_geometry" / "mmff_rigid_body_geometry_receipt.json", -] - -CITATIONS = [ - { - "id": "kerbl_3d_gaussian_splatting", - "title": "3D Gaussian Splatting for Real-Time Radiance Field Rendering", - "url": "https://arxiv.org/abs/2308.04079", - "role": "external_rendering_anchor", - "status": "external_reference", - }, - { - "id": "huang_2d_gaussian_splatting", - "title": "2D Gaussian Splatting for Geometrically Accurate Radiance Fields", - "url": "https://arxiv.org/abs/2403.17888", - "role": "external_surface_geometry_anchor", - "status": "external_reference", - }, -] - -DELTA_TORSION = 10 -DRIFT_ERGOREGION_THRESHOLD = 35 -DRIFT_HORIZON_THRESHOLD = 70 - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def merkle_root(leaves: list[str]) -> str: - if not leaves: - return sha256_bytes(b"") - level = leaves[:] - while len(level) > 1: - if len(level) % 2: - level.append(level[-1]) - level = [sha256_bytes((level[index] + level[index + 1]).encode("ascii")) for index in range(0, len(level), 2)] - return level[0] - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def splat( - *, - splat_id: str, - position: tuple[int, int, int], - covariance_diag: tuple[int, int, int], - orientation_deg: int, - confidence_milli: int, - residual_risk_milli: int, - load_vector: tuple[int, int, int], -) -> dict[str, Any]: - item = { - "splat_id": splat_id, - "mu_pm": position, - "sigma_diag_pm2": covariance_diag, - "orientation_deg": orientation_deg, - "confidence_milli": confidence_milli, - "residual_risk_milli": residual_risk_milli, - "load_vector_milli": load_vector, - } - item["splat_hash"] = hash_obj(item) - return item - - -def frame(*, interval_index: int, torsion_start: int, torsion_end: int, splats: list[dict[str, Any]]) -> dict[str, Any]: - leaf_hashes = [item["splat_hash"] for item in splats] - risk = sum(item["residual_risk_milli"] for item in splats) - covariance_bloom = sum(max(item["sigma_diag_pm2"]) - min(item["sigma_diag_pm2"]) for item in splats) - confidence_loss = sum(1000 - item["confidence_milli"] for item in splats) - drift_score = risk // 100 + covariance_bloom // 500 + confidence_loss // 100 - if drift_score >= DRIFT_HORIZON_THRESHOLD: - decision = "HOLD_TORSION_SPLAT_HORIZON" - elif drift_score >= DRIFT_ERGOREGION_THRESHOLD: - decision = "HOLD_TORSION_SPLAT_ERGOREGION" - else: - decision = "ADMIT_TORSION_SPLAT_FRAME" - item = { - "interval_index": interval_index, - "torsion_start": torsion_start, - "torsion_end": torsion_end, - "delta_torsion": torsion_end - torsion_start, - "splat_count": len(splats), - "splats": splats, - "frame_merkle_root": merkle_root(leaf_hashes), - "risk_sum_milli": risk, - "covariance_bloom": covariance_bloom, - "confidence_loss_milli": confidence_loss, - "drift_score": drift_score, - "decision": decision, - } - item["frame_hash"] = hash_obj({k: v for k, v in item.items() if k != "frame_hash"}) - return item - - -def build_registry() -> dict[str, Any]: - frames = [ - frame( - interval_index=0, - torsion_start=0, - torsion_end=10, - splats=[ - splat(splat_id="thread_core", position=(0, 0, 0), covariance_diag=(100, 100, 120), orientation_deg=0, confidence_milli=980, residual_risk_milli=40, load_vector=(0, 0, -900)), - splat(splat_id="footing_edge", position=(0, -400, -900), covariance_diag=(120, 140, 120), orientation_deg=3, confidence_milli=960, residual_risk_milli=60, load_vector=(20, 0, -700)), - ], - ), - frame( - interval_index=1, - torsion_start=10, - torsion_end=20, - splats=[ - splat(splat_id="thread_core", position=(0, 0, 0), covariance_diag=(120, 150, 260), orientation_deg=12, confidence_milli=910, residual_risk_milli=220, load_vector=(80, 0, -890)), - splat(splat_id="footing_edge", position=(0, -400, -900), covariance_diag=(150, 230, 300), orientation_deg=17, confidence_milli=880, residual_risk_milli=260, load_vector=(120, 0, -690)), - ], - ), - frame( - interval_index=2, - torsion_start=20, - torsion_end=30, - splats=[ - splat(splat_id="thread_core", position=(0, 0, 0), covariance_diag=(260, 480, 820), orientation_deg=32, confidence_milli=720, residual_risk_milli=700, load_vector=(250, 0, -860)), - splat(splat_id="footing_edge", position=(0, -400, -900), covariance_diag=(300, 620, 950), orientation_deg=38, confidence_milli=690, residual_risk_milli=760, load_vector=(310, 0, -640)), - splat(splat_id="side_shear_bloom", position=(220, -160, -420), covariance_diag=(180, 560, 1050), orientation_deg=44, confidence_milli=640, residual_risk_milli=840, load_vector=(420, 20, -510)), - ], - ), - ] - return { - "schema": "torsion_interval_gaussian_splat_witness_registry_v1", - "citations": CITATIONS, - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Torsion-interval Gaussian splat witness only. Frames are sampled by " - "accumulated torsion, not wall-clock time. Splat fields are visible " - "witness shadows and do not certify material truth without external " - "mechanical validation." - ), - "canonical_statement": ( - "Gaussian splats are local witness particles; torsion intervals are causal " - "frames; Merkle roots make each frame accountable." - ), - "torsion_interval_rule": { - "delta_torsion": DELTA_TORSION, - "effective_torsion": "T_eff = integral(a||tau|| + b||load cross normal|| + c||delta_q|| + d*risk) ds", - "frame": "G_k = {G_i(T_k)}", - "frame_root": "R_k = MerkleRoot(H(G_1(T_k)), ..., H(G_n(T_k)))", - "global_root": "R_global = MerkleRoot(R_0, ..., R_K)", - "wall_clock_role": "metadata_shadow_only", - }, - "admissibility_equation": ( - "A_frame=1[drift_score < ergoregion_threshold] * 1[frame_merkle_root] * " - "1[residual_declared] * 1[observer_scope_declared]" - ), - "frames": frames, - "global_merkle_root": merkle_root([item["frame_merkle_root"] for item in frames]), - "aggregates": { - "frame_count": len(frames), - "splat_count": sum(item["splat_count"] for item in frames), - "admit_frame_count": sum(1 for item in frames if item["decision"] == "ADMIT_TORSION_SPLAT_FRAME"), - "ergoregion_frame_count": sum(1 for item in frames if item["decision"] == "HOLD_TORSION_SPLAT_ERGOREGION"), - "horizon_frame_count": sum(1 for item in frames if item["decision"] == "HOLD_TORSION_SPLAT_HORIZON"), - "drift_ergoregion_threshold": DRIFT_ERGOREGION_THRESHOLD, - "drift_horizon_threshold": DRIFT_HORIZON_THRESHOLD, - }, - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "torsion_interval_gaussian_splat_witness_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "global_merkle_root": registry["global_merkle_root"], - "citations": registry["citations"], - "aggregates": registry["aggregates"], - "decision": "ADMIT_TORSION_INTERVAL_SPLAT_WITNESS_DIAGNOSTIC", - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Torsion-Interval Gaussian Splat Witness", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Global Merkle root: `{registry['global_merkle_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Torsion Rule", - "", - ] - for key, value in registry["torsion_interval_rule"].items(): - lines.append(f"- `{key}`: {value}") - lines.extend( - [ - "", - "## Frames", - "", - "| Interval | Torsion | Splats | Drift | Decision | Frame root |", - "|---:|---|---:|---:|---|---|", - ] - ) - for item in registry["frames"]: - lines.append( - f"| {item['interval_index']} | `{item['torsion_start']}..{item['torsion_end']}` | " - f"{item['splat_count']} | {item['drift_score']} | `{item['decision']}` | `{item['frame_merkle_root']}` |" - ) - lines.extend(["", "## Citations", ""]) - for citation in registry["citations"]: - lines.append(f"- `{citation['id']}`: {citation['title']} ({citation['url']}); role: `{citation['role']}`") - lines.extend(["", "## Source Refs", ""]) - for source in registry["source_refs"]: - lines.append(f"- `{source['path']}` exists: `{source['exists']}`") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(receipt: dict[str, Any]) -> None: - text = f"""created: 20260509000000000 -modified: 20260509000000000 -tags: ResearchStack Encoding GaussianSplat TorsionClock Receipt -title: Torsion Interval Gaussian Splat Witness -type: text/vnd.tiddlywiki - -! Torsion Interval Gaussian Splat Witness - -Durable runner: - -``` -4-Infrastructure/shim/torsion_interval_gaussian_splat_witness_probe.py -``` - -Receipt: - -``` -{rel(RECEIPT)} -``` - -Receipt hash: - -``` -{receipt['receipt_hash']} -``` - -Global Merkle root: - -``` -{receipt['global_merkle_root']} -``` - -!! Doctrine - -Use Gaussian splats as local witness particles sampled at torsion intervals instead of clock intervals. - -``` -G_k = {{G_i(T_k)}} -R_k = MerkleRoot(H(G_1(T_k)), ..., H(G_n(T_k))) -R_global = MerkleRoot(R_0, ..., R_K) -``` - -The result is a renderable audit surface over load, twist, residual risk, and material-shadow drift. - -!! Links - -* [[Gaussian Splat Manifold Projection]] -* [[Kerr-Like Load Witness Geometry]] -* [[Hutter Torsion Clock Adaptation]] -* [[MMFF Rigid Body Geometry]] -""" - TIDDLER.write_text(text, encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "global_merkle_root": registry["global_merkle_root"], - "decision": receipt["decision"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/transcriptformer_evolutionary_prior_probe.py b/4-Infrastructure/shim/transcriptformer_evolutionary_prior_probe.py deleted file mode 100644 index 061eb80a..00000000 --- a/4-Infrastructure/shim/transcriptformer_evolutionary_prior_probe.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -"""TranscriptFormer evolutionary prior probe. - -This records TranscriptFormer as an external HOLD prior for learning conserved -organization across evolutionary distance. It uses metadata from DOI/Crossref -and the public czi-ai/transcriptformer repository. It does not download model -weights, run biological inference, or validate biological claims. -""" - -from __future__ import annotations - -import hashlib -import json -import re -import urllib.request -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "transcriptformer_evolutionary_prior" -PAYLOAD_JSON = OUT_DIR / "transcriptformer_evolutionary_prior.json" -SUMMARY = OUT_DIR / "transcriptformer_evolutionary_prior.md" -RECEIPT = OUT_DIR / "transcriptformer_evolutionary_prior_receipt.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "TranscriptFormer Evolutionary Prior.tid" -) - -REMOTE_SOURCES = [ - { - "name": "crossref_science_article", - "url": "https://api.crossref.org/works/10.1126/science.aec8514", - }, - { - "name": "openalex_science_article", - "url": "https://api.openalex.org/works/doi:10.1126/science.aec8514", - }, - { - "name": "transcriptformer_readme", - "url": "https://raw.githubusercontent.com/czi-ai/transcriptformer/main/README.md", - }, - { - "name": "transcriptformer_pyproject", - "url": "https://raw.githubusercontent.com/czi-ai/transcriptformer/main/pyproject.toml", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def fetch(source: dict[str, str]) -> dict[str, Any]: - try: - request = urllib.request.Request( - source["url"], - headers={"User-Agent": "ResearchStack-transcriptformer-prior/1.0"}, - ) - with urllib.request.urlopen(request, timeout=30) as response: - data = response.read() - return { - **source, - "fetched": True, - "fetch_error": None, - "bytes": len(data), - "sha256": sha256_bytes(data), - "text": data.decode("utf-8", errors="replace"), - } - except Exception as exc: # pragma: no cover - receipt captures network failures. - return { - **source, - "fetched": False, - "fetch_error": f"{type(exc).__name__}: {exc}", - "bytes": 0, - "sha256": None, - "text": "", - } - - -def clean_abstract(text: str) -> str: - text = re.sub(r"<[^>]+>", "", text) - return re.sub(r"\s+", " ", text).strip() - - -def extract_crossref(ref: dict[str, Any]) -> dict[str, Any]: - if not ref["fetched"]: - return {} - data = json.loads(ref["text"]) - msg = data["message"] - return { - "title": (msg.get("title") or [""])[0], - "doi": msg.get("DOI"), - "published_online": msg.get("published-online"), - "journal": (msg.get("container-title") or [""])[0], - "abstract": clean_abstract(msg.get("abstract", "")), - } - - -def extract_openalex(ref: dict[str, Any]) -> dict[str, Any]: - if not ref["fetched"]: - return {} - data = json.loads(ref["text"]) - return { - "title": data.get("title"), - "publication_date": data.get("publication_date"), - "ids": data.get("ids"), - "open_access": data.get("open_access"), - } - - -def build_payload() -> dict[str, Any]: - refs = [fetch(source) for source in REMOTE_SOURCES] - by_name = {ref["name"]: ref for ref in refs} - crossref = extract_crossref(by_name["crossref_science_article"]) - openalex = extract_openalex(by_name["openalex_science_article"]) - payload = { - "schema": "transcriptformer_evolutionary_prior_v1", - "claim_boundary": ( - "External biology/foundation-model prior only. This records a model-card " - "and DOI metadata surface for cross-species conserved-organization learning; " - "it does not validate biological predictions, disease claims, or local model execution." - ), - "source_refs": [ - {k: ref[k] for k in ["name", "url", "fetched", "fetch_error", "bytes", "sha256"]} - for ref in refs - ], - "article": { - "crossref": crossref, - "openalex": openalex, - }, - "prior_statement": ( - "TranscriptFormer is a useful HOLD prior because it attempts to learn " - "conserved cell-state organization from evolutionary breadth: up to 112M " - "cells, 12 species, and 1.53B years of evolutionary distance, with emergent " - "developmental, phylogenetic, and cellular hierarchies reported in learned representations." - ), - "candidate_equations": [ - { - "equation_id": "evolutionary_breadth_representation_prior", - "equation": "Z_cell=f_theta(gene_identity,expression_count,species_embedding,evolutionary_context)", - "decision": "HOLD_EVOLUTIONARY_REPRESENTATION_PRIOR", - "use_as": "external prior for conserved organization learned across evolutionary distance", - }, - { - "equation_id": "conserved_structure_emergence_gate", - "equation": "G_conserved=1[hierarchy_emerges]*1[zero_shot_transfer]*1[negative_controls_pass]", - "decision": "HOLD_CONSERVED_STRUCTURE_GATE", - "use_as": "gate before using emergent hierarchy claims as topology evidence", - }, - { - "equation_id": "homology_leakage_caveat", - "equation": "Risk_leak=homology_overlap+species_signal_dominance+annotation_reuse+benchmark_pseudoreplication", - "decision": "HOLD_LEAKAGE_CAVEAT", - "use_as": "caveat lane for cross-species model validation and generalization claims", - }, - { - "equation_id": "universal_organization_adapter", - "equation": "P_universal(X)=conserved_signal(X)-leakage_risk(X)-species_confound(X)", - "decision": "HOLD_UNIVERSAL_ORGANIZATION_ADAPTER", - "use_as": "adapter from biological conserved organization to topology/engineering fitness priors", - }, - { - "equation_id": "logogram_species_code_adapter", - "equation": "L_species=encode(conserved_tokens,lineage_markers,mutation_residuals,phenotype_closure)", - "decision": "HOLD_LOGOGRAM_SPECIES_CODE_ADAPTER", - "use_as": "treat the logogram as a species-code-like symbolic compression layer with lineage and residual lanes", - }, - { - "equation_id": "logogram_genotype_phenotype_closure", - "equation": "G_logogram=1[decode(L)->phenotype_readout]*1[lineage_consistent]*1[residual_bounded]", - "decision": "HOLD_LOGOGRAM_PHENOTYPE_CLOSURE", - "use_as": "gate before a logogram code is treated as carrying conserved species-level structure", - }, - ], - "adapter_shape": { - "to_network_topology": "supports the idea that conserved organization can emerge under shared constraints across distant substrates", - "to_engineering_fitness": "evolutionary breadth acts like a natural negative-control surface for distinguishing conserved function from local artifact", - "to_rainbow_raccoon": "treat learned representations as guess surfaces with residual/leakage gates before promotion", - "to_logogram": "treat logogram tokens as symbolic species-code carriers only when conserved tokens, lineage markers, mutation residuals, and phenotype/readout closure are explicit", - "caveat": "closed-access Science article metadata plus public repo README are not enough for local validation; no biological claim is promoted", - }, - "decision": "ADMIT_TRANSCRIPTFORMER_AS_HOLD_EVOLUTIONARY_PRIOR", - } - payload["aggregates"] = { - "remote_source_count": len(refs), - "remote_fetched_count": sum(1 for ref in refs if ref["fetched"]), - "candidate_count": len(payload["candidate_equations"]), - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "transcriptformer_evolutionary_prior_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "aggregates": payload["aggregates"], - "source_hashes": {ref["name"]: ref["sha256"] for ref in payload["source_refs"]}, - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - article = payload["article"]["crossref"] - lines = [ - "# TranscriptFormer Evolutionary Prior", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Article", - "", - f"- Title: {article.get('title')}", - f"- DOI: `{article.get('doi')}`", - f"- Published online: `{article.get('published_online')}`", - "", - "## Prior Statement", - "", - payload["prior_statement"], - "", - "## Candidate Equations", - "", - "| Candidate | Equation | Decision | Use as |", - "|---|---|---|---|", - ] - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | `{item['equation']}` | {item['decision']} | {item['use_as']} |") - lines.extend(["", "## Caveat", "", payload["adapter_shape"]["caveat"], "", "## Sources", ""]) - for ref in payload["source_refs"]: - status = "ok" if ref["fetched"] else "missing" - lines.append(f"- `{ref['name']}`: {status} - {ref['url']}") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: TranscriptFormer Evolutionary Prior", - "tags: TranscriptFormer EvolutionaryPrior FoundationModel Biology HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! TranscriptFormer Evolutionary Prior", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Prior", - "", - payload["prior_statement"], - "", - "!! Candidate Equations", - "", - "| Candidate | Decision |h", - ] - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | {item['decision']} |") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - f"Receipt: `shared-data/data/transcriptformer_evolutionary_prior/transcriptformer_evolutionary_prior_receipt.json`", - "", - "!! Links", - "", - "* [[Engineering Fitness Topology Trait]]", - "* [[Combined Approach Equation Surface]]", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json b/4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json deleted file mode 100644 index e036dba6..00000000 --- a/4-Infrastructure/shim/transfold_couch_data_magnetic_domain_receipt.json +++ /dev/null @@ -1,706 +0,0 @@ -{ - "aggregate": { - "H_field": { - "max": 4.12275542453363, - "mean": 3.663922436343822, - "min": 2.625224223489669 - }, - "chi_susceptibility": { - "max": 0.6559699607250777, - "mean": 0.5687781936238817, - "min": 0.4674253202753167 - }, - "chunk_count": 22, - "coercive_loss": { - "max": 1.52275542453363, - "mean": 1.063922436343822, - "min": 0.02522422348966913 - }, - "domain_wall_pressure": { - "max": 0.88542903739581, - "mean": 0.2211525927012195, - "min": 0.01624238887859697 - }, - "heat_loss": { - "max": 0.6130637527468008, - "mean": 0.23921198455929052, - "min": 0.0 - }, - "information_load": { - "max": 4.12275542453363, - "mean": 3.663922436343822, - "min": 2.625224223489669 - }, - "magnetization_M": { - "max": 0.9088953824801266, - "mean": 0.7907252340455144, - "min": 0.6772340194825643 - }, - "overflow": { - "max": 0.8727554245336302, - "mean": 0.4432264419344858, - "min": 0.0 - }, - "overflow_chunk_count": 20, - "overflow_gate": { - "max": 1.0, - "mean": 0.5737709098179441, - "min": 0.2975537756475126 - }, - "remanence": { - "max": 0.9229270037943339, - "mean": 0.8886250883537214, - "min": 0.8358662613981762 - }, - "within_capacity_chunk_count": 2 - }, - "chunk_projections": [ - { - "bytes": 4096, - "chunk_index": 0, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.515217487762456, - "normalized_entropy": 0.564402185970307, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5890544832641095, - "transition_rate": 0.747008547008547 - }, - "magnetic_domain": { - "H_field": 4.0460288783964815, - "chi_susceptibility": 0.6484690907416222, - "coercive_loss": 1.4460288783964814, - "domain_wall_pressure": 0.3159081274888751, - "heat_loss": 0.5325324534986238, - "information_load": 4.0460288783964815, - "magnetization_M": 0.7151632863963729, - "overflow": 0.7960288783964815, - "overflow_gate": 0.3310136504452505, - "remanence": 0.880428273031361 - }, - "sha256": "716f4068ceb2ce9bebe0f8880acd1762ed2c0058b887d062e7f3cefa9e395c17", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 1, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.762570448852831, - "normalized_entropy": 0.4703213061066039, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6357195211336428, - "transition_rate": 0.6075702075702075 - }, - "magnetic_domain": { - "H_field": 3.686189211397359, - "chi_susceptibility": 0.5496109542506414, - "coercive_loss": 1.0861892113973588, - "domain_wall_pressure": 0.056298627126870615, - "heat_loss": 0.19819228217940285, - "information_load": 3.686189211397359, - "magnetization_M": 0.7847791728466375, - "overflow": 0.4361892113973589, - "overflow_gate": 0.5456277298916181, - "remanence": 0.8882243705281556 - }, - "sha256": "7f6e2b9d8ea484968930b60c2e84f3820559f94d2ac402ed86bc2ba6f9b968f0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 2, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7845901362429712, - "normalized_entropy": 0.4730737670303714, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.636696799413633, - "transition_rate": 0.6136752136752137 - }, - "magnetic_domain": { - "H_field": 3.694728213253477, - "chi_susceptibility": 0.5545557104880643, - "coercive_loss": 1.094728213253477, - "domain_wall_pressure": 0.04604317147683856, - "heat_loss": 0.20493300386095387, - "information_load": 3.694728213253477, - "magnetization_M": 0.783917999671064, - "overflow": 0.4447282132534771, - "overflow_gate": 0.539194956034529, - "remanence": 0.8883767862986801 - }, - "sha256": "d536e56501195263d837c49dca24db946d7f355975ab6492bc567845422fea26", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 3, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.9192941635240146, - "normalized_entropy": 0.4899117704405018, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5414121671145858, - "transition_rate": 0.6595848595848596 - }, - "magnetic_domain": { - "H_field": 3.9254662428692697, - "chi_susceptibility": 0.5898595308447867, - "coercive_loss": 1.3254662428692696, - "domain_wall_pressure": 0.23634538494054746, - "heat_loss": 0.4111210961895186, - "information_load": 3.9254662428692697, - "magnetization_M": 0.7286478929001449, - "overflow": 0.6754662428692697, - "overflow_gate": 0.39135212080600257, - "remanence": 0.871260969395779 - }, - "sha256": "b98ac2776d21ae11af23816b6faaacef2716afdfae60409d1f680e8e0e85fef5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 4, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.775016753491557, - "normalized_entropy": 0.47187709418644463, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6408502321035915, - "transition_rate": 0.6100122100122101 - }, - "magnetic_domain": { - "H_field": 3.682005610616378, - "chi_susceptibility": 0.5515960266658718, - "coercive_loss": 1.082005610616378, - "domain_wall_pressure": 0.061676044182762846, - "heat_loss": 0.19491775217193316, - "information_load": 3.682005610616378, - "magnetization_M": 0.7867777827656341, - "overflow": 0.4320056106163781, - "overflow_gate": 0.548807359483531, - "remanence": 0.8890199427881944 - }, - "sha256": "0174504bbbb28b14c79f560a2a35b001738741e4819270c674b5dca178b018ef", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 5, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.793545940363185, - "normalized_entropy": 0.47419324254539813, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6281456144637185, - "transition_rate": 0.62002442002442 - }, - "magnetic_domain": { - "H_field": 3.7170957317025026, - "chi_susceptibility": 0.5596350777086616, - "coercive_loss": 1.1170957317025025, - "domain_wall_pressure": 0.01624238887859697, - "heat_loss": 0.22294393297373405, - "information_load": 3.7170957317025026, - "magnetization_M": 0.7788592497128283, - "overflow": 0.4670957317025026, - "overflow_gate": 0.5227018406673667, - "remanence": 0.8870288845033881 - }, - "sha256": "673db0ee3ae2bd336f00a0cd9c269781503091a0e427edafd6917bd2e56c2641", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 6, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.9079104949984367, - "normalized_entropy": 0.4884888118748046, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5580258978744197, - "transition_rate": 0.6503052503052503 - }, - "magnetic_domain": { - "H_field": 3.888724447109567, - "chi_susceptibility": 0.5829869244627877, - "coercive_loss": 1.288724447109567, - "domain_wall_pressure": 0.18455870486166126, - "heat_loss": 0.3756713640498602, - "information_load": 3.888724447109567, - "magnetization_M": 0.7366253954244888, - "overflow": 0.638724447109567, - "overflow_gate": 0.41184126308317526, - "remanence": 0.8746132402046382 - }, - "sha256": "db4d6d67e0b61db05dab8476fba960ecf9ffdd3b76583d8a0ea0c629be09bf0e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 7, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7823189215189053, - "normalized_entropy": 0.47278986518986316, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.632299047153677, - "transition_rate": 0.6078144078144078 - }, - "magnetic_domain": { - "H_field": 3.6975931841291265, - "chi_susceptibility": 0.549809892465398, - "coercive_loss": 1.0975931841291264, - "domain_wall_pressure": 0.04896927867853851, - "heat_loss": 0.20721161107141992, - "information_load": 3.6975931841291265, - "magnetization_M": 0.7813957124564133, - "overflow": 0.4475931841291265, - "overflow_gate": 0.5370536942500865, - "remanence": 0.8876876217654969 - }, - "sha256": "15532640bf41406a811e52ee22a7fa0f89c8771ded863590c5860e865c95146b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 8, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7807000331724905, - "normalized_entropy": 0.4725875041465613, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5802589787441974, - "transition_rate": 0.629059829059829 - }, - "magnetic_domain": { - "H_field": 3.8037388509447965, - "chi_susceptibility": 0.5667531485138051, - "coercive_loss": 1.2037388509447964, - "domain_wall_pressure": 0.09760170063126328, - "heat_loss": 0.29711498659510177, - "information_load": 3.8037388509447965, - "magnetization_M": 0.75536401424735, - "overflow": 0.5537388509447965, - "overflow_gate": 0.46343843115186817, - "remanence": 0.8788354228030966 - }, - "sha256": "308dce97307f597ee4683b1cbc178067d83dd58eb743c37ad8bf3fa68b65841a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 9, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.892752991758305, - "normalized_entropy": 0.48659412396978813, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6071341314439287, - "transition_rate": 0.6339438339438339 - }, - "magnetic_domain": { - "H_field": 3.7896431523951617, - "chi_susceptibility": 0.5705472255852978, - "coercive_loss": 1.1896431523951616, - "domain_wall_pressure": 0.05361940499981044, - "heat_loss": 0.28460740686341485, - "information_load": 3.7896431523951617, - "magnetization_M": 0.761025150186743, - "overflow": 0.5396431523951617, - "overflow_gate": 0.4726007258681811, - "remanence": 0.8835744051428653 - }, - "sha256": "56af9911df0842f4afe3212d86d6f3a921f4f358b52c9e1d9f573bd06c6d365c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 10, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7835703357326675, - "normalized_entropy": 0.47294629196658344, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6379183972636208, - "transition_rate": 0.6092796092796092 - }, - "magnetic_domain": { - "H_field": 3.688938401677902, - "chi_susceptibility": 0.5510015100976623, - "coercive_loss": 1.0889384016779018, - "domain_wall_pressure": 0.057277575968023076, - "heat_loss": 0.20035417035377123, - "information_load": 3.688938401677902, - "magnetization_M": 0.7844611243769493, - "overflow": 0.4389384016779019, - "overflow_gate": 0.5435483211587546, - "remanence": 0.8885667224785941 - }, - "sha256": "610244ce0502fb41e126b2cebca906c7ba5ab8161a524cb049436c5cef50a368", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 11, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.8997165339230393, - "normalized_entropy": 0.4874645667403799, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5433667236745663, - "transition_rate": 0.6568986568986569 - }, - "magnetic_domain": { - "H_field": 3.915459605146814, - "chi_susceptibility": 0.5878835560823316, - "coercive_loss": 1.3154596051468137, - "domain_wall_pressure": 0.22706386644818122, - "heat_loss": 0.4013858377961909, - "information_load": 3.915459605146814, - "magnetization_M": 0.7307302128143967, - "overflow": 0.6654596051468138, - "overflow_gate": 0.3968291468155499, - "remanence": 0.8716646286018876 - }, - "sha256": "7613994817678f4d20afffb5b223dfd729364a45dcd2713aeeb014e69c869e5c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 12, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7624317419324544, - "normalized_entropy": 0.4703039677415568, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6408502321035915, - "transition_rate": 0.6043956043956044 - }, - "magnetic_domain": { - "H_field": 3.674474611038505, - "chi_susceptibility": 0.5470160275696078, - "coercive_loss": 1.074474611038505, - "domain_wall_pressure": 0.07290925541597426, - "heat_loss": 0.18907039047800517, - "information_load": 3.674474611038505, - "magnetization_M": 0.7874780830847334, - "overflow": 0.42447461103850515, - "overflow_gate": 0.5545778579891222, - "remanence": 0.8890199427881944 - }, - "sha256": "f10dd2c80d8bfbd9bcb4984d195ab07a6edc8fe95d5bb2f34409e22f77c92ab9", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 13, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.7908404920438286, - "normalized_entropy": 0.47385506150547857, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6359638407036403, - "transition_rate": 0.609035409035409 - }, - "magnetic_domain": { - "H_field": 3.6941373386318035, - "chi_susceptibility": 0.5508031465355304, - "coercive_loss": 1.0941373386318034, - "domain_wall_pressure": 0.05385686333646267, - "heat_loss": 0.20446411650664853, - "information_load": 3.6941373386318035, - "magnetization_M": 0.7828146219057122, - "overflow": 0.4441373386318035, - "overflow_gate": 0.5396376329526477, - "remanence": 0.8882625134792045 - }, - "sha256": "4c64ec2ef1bc6e39beb8a733dbc8d2172482a21526a66ae5ce68473f5f235842", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 14, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.096175150184791, - "normalized_entropy": 0.5120218937730989, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5406792084045932, - "transition_rate": 0.6803418803418804 - }, - "magnetic_domain": { - "H_field": 3.984814750875623, - "chi_susceptibility": 0.6047637784659995, - "coercive_loss": 1.384814750875623, - "domain_wall_pressure": 0.2793253438745744, - "heat_loss": 0.46999686015161396, - "information_load": 3.984814750875623, - "magnetization_M": 0.7176610041052233, - "overflow": 0.7348147508756231, - "overflow_gate": 0.36038728184000896, - "remanence": 0.8711089417581207 - }, - "sha256": "a0c9cf0968e7d84370ba92ca9a582675e3a80c280e25a66008626b4a5301f472", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 15, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.751515589868446, - "normalized_entropy": 0.46893944873355575, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7908624480820914, - "transition_rate": 0.6351648351648351 - }, - "magnetic_domain": { - "H_field": 3.394795537134441, - "chi_susceptibility": 0.5714899092816818, - "coercive_loss": 0.7947955371344411, - "domain_wall_pressure": 0.3113952258345125, - "heat_loss": 0.026377891443743036, - "information_load": 3.394795537134441, - "magnetization_M": 0.8812535230649288, - "overflow": 0.14479553713444115, - "overflow_gate": 0.8178266266642498, - "remanence": 0.908137042564891 - }, - "sha256": "8172a2dbfa59c3db61b4e146825d550e442be8bc174f9fa11c49bd4ee15afb8a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 16, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.057542585119999, - "normalized_entropy": 0.38219282313999986, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.9579770339604202, - "transition_rate": 0.5152625152625152 - }, - "magnetic_domain": { - "H_field": 2.625224223489669, - "chi_susceptibility": 0.4674253202753167, - "coercive_loss": 0.02522422348966913, - "domain_wall_pressure": 0.88542903739581, - "heat_loss": 0.0, - "information_load": 2.625224223489669, - "magnetization_M": 0.8944865247011483, - "overflow": 0.0, - "overflow_gate": 1.0, - "remanence": 0.9229270037943339 - }, - "sha256": "5bb26d8fbcedca37266043cd24bede9fefa8c5f72ae9e7307001f9af35885e4d", - "status": "within_capacity" - }, - { - "bytes": 4096, - "chunk_index": 17, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.569433522454973, - "normalized_entropy": 0.44617919030687164, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8223796726117762, - "transition_rate": 0.5775335775335775 - }, - "magnetic_domain": { - "H_field": 3.230087653515728, - "chi_susceptibility": 0.5244047446008503, - "coercive_loss": 0.6300876535157278, - "domain_wall_pressure": 0.48969219015639753, - "heat_loss": 0.0, - "information_load": 3.230087653515728, - "magnetization_M": 0.908060060076767, - "overflow": 0.0, - "overflow_gate": 1.0, - "remanence": 0.9113455207069833 - }, - "sha256": "eb1aea8ab20e8e3a6eb116cc32ef68a33462dac90bff253809a355d68f202ec8", - "status": "within_capacity" - }, - { - "bytes": 4096, - "chunk_index": 18, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.577131696323823, - "normalized_entropy": 0.5721414620404779, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.778402150012216, - "transition_rate": 0.7594627594627594 - }, - "magnetic_domain": { - "H_field": 3.7044586283009746, - "chi_susceptibility": 0.6559699607250777, - "coercive_loss": 1.1044586283009745, - "domain_wall_pressure": 0.03787878109891318, - "heat_loss": 0.21270616026420122, - "information_load": 3.7044586283009746, - "magnetization_M": 0.8147660085184342, - "overflow": 0.4544586283009746, - "overflow_gate": 0.531957042911875, - "remanence": 0.9068035885058519 - }, - "sha256": "dd5921d80c33db13dcf0239bea0078664c08ee5c44854452644c72ee2e6464a0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 19, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.875680133246034, - "normalized_entropy": 0.4844600166557543, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8463229904715368, - "transition_rate": 0.6212454212454213 - }, - "magnetic_domain": { - "H_field": 3.28436896224073, - "chi_susceptibility": 0.5606045272695585, - "coercive_loss": 0.6843689622407299, - "domain_wall_pressure": 0.4501551384522311, - "heat_loss": 0.001602050203575482, - "information_load": 3.28436896224073, - "magnetization_M": 0.9088953824801266, - "overflow": 0.03436896224072994, - "overflow_gate": 0.9533867158294083, - "remanence": 0.9136370350051696 - }, - "sha256": "3ca585641a2fd4d50c2459dee5c3fdb287de530c25cbaa0a68d3c8d26acc55df", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 20, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.050346311288002, - "normalized_entropy": 0.5062932889110002, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8433911556315661, - "transition_rate": 0.6488400488400489 - }, - "magnetic_domain": { - "H_field": 3.355564940164145, - "chi_susceptibility": 0.5818897639620626, - "coercive_loss": 0.7555649401641449, - "domain_wall_pressure": 0.38910221358303443, - "heat_loss": 0.01439654090587793, - "information_load": 3.355564940164145, - "magnetization_M": 0.895558927782656, - "overflow": 0.10556494016414497, - "overflow_gate": 0.8636238425040315, - "remanence": 0.9133628262388079 - }, - "sha256": "427c383c3ba1b982ceb8fea3a11848dc82bc7d1dab4b6f95270ab5909a0fd036", - "status": "overflow" - }, - { - "bytes": 273, - "chunk_index": 21, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 3.8898598319952375, - "normalized_entropy": 0.4862324789994047, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4074074074074074, - "transition_rate": 0.6544117647058824 - }, - "magnetic_domain": { - "H_field": 4.12275542453363, - "chi_susceptibility": 0.5860444331327814, - "coercive_loss": 1.52275542453363, - "domain_wall_pressure": 0.49400871459694995, - "heat_loss": 0.6130637527468008, - "information_load": 4.12275542453363, - "magnetization_M": 0.6772340194825643, - "overflow": 0.8727554245336302, - "overflow_gate": 0.2975537756475126, - "remanence": 0.8358662613981762 - }, - "sha256": "d99fdfa531cb4019966e7abd696486f478790578119971c0de03e96ebbffc6ac", - "status": "overflow" - } - ], - "claim_boundary": "This receipt maps byte-signal statistics into a magnetic-domain analogue. It is a stress-test and routing prior, not a claim that text data is a literal magnetic material.", - "parameters": { - "D_f": 1.4404200904125564, - "capacity_threshold": 3.25, - "chunk_size": 4096, - "coercive_threshold": 2.6, - "lambda_phi": 1.618033988749895, - "max_chunks": 22, - "phi_gain": 2.0, - "slice_bytes_requested": 86289 - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "receipt_hash": "cad08a03b9bce19475676a69e96d9a532511409ba6213fd7a9ba864d0215f64f", - "runner": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py", - "schema": "transfold_enwiki8_magnetic_domain_generator_v1", - "source": { - "available_bytes": 86878, - "path": "shared-data/corpora/couch_data_bundle.jsonl", - "sha256": "c2942362dad67c94801b9463b439352916e10fbba91c913baa91ad9d218f0e71", - "slice_bytes": 86289, - "source_mode": "real_file" - }, - "stress_sweep": [ - { - "capacity_threshold": 2.5, - "mean_heat_loss": 0.9473216166491661, - "mean_overflow": 1.163922436343822, - "mean_overflow_gate": 0.22507297397763984, - "overflow_chunk_count": 22 - }, - { - "capacity_threshold": 3.25, - "mean_heat_loss": 0.23921198455929052, - "mean_overflow": 0.4432264419344858, - "mean_overflow_gate": 0.5737709098179441, - "overflow_chunk_count": 20 - }, - { - "capacity_threshold": 4.0, - "mean_heat_loss": 0.0010042089982083586, - "mean_overflow": 0.007672013769550532, - "mean_overflow_gate": 0.9900600023017341, - "overflow_chunk_count": 2 - }, - { - "capacity_threshold": 4.5, - "mean_heat_loss": 0.0, - "mean_overflow": 0.0, - "mean_overflow_gate": 1.0, - "overflow_chunk_count": 0 - }, - { - "capacity_threshold": 5.25, - "mean_heat_loss": 0.0, - "mean_overflow": 0.0, - "mean_overflow_gate": 1.0, - "overflow_chunk_count": 0 - } - ], - "transfold_map": { - "core_equations": { - "heat_loss": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - "magnetic_projection": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", - "overflow_gate": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", - "signal_load": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)" - }, - "field_mapping": { - "byte_transition_rate": "domain agitation / susceptibility driver", - "capacity_overflow": "hysteresis heat-loss channel", - "entropy": "field demand / information pressure", - "repeated_4grams": "remanence / memory channel" - }, - "source_domain": "byte_stream_signal", - "target_domain": "magnetic_domain_equation" - } -} diff --git a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py b/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py deleted file mode 100644 index 025f55aa..00000000 --- a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py +++ /dev/null @@ -1,435 +0,0 @@ -#!/usr/bin/env python3 -"""Generate a transfold adaptation from an enwiki8 slice into magnetic-domain equations. - -The generator treats byte-stream statistics as a signal surface and maps them -into a magnetic-domain analogue: - - information pressure -> applied field H - byte-transition structure -> susceptibility chi - repeated-state memory -> remanence R - threshold overflow -> hysteresis / heat-loss channel - -This is a stress-test generator, not a compressor and not a physics claim. It -is meant to make the cross-domain response-family framework executable on a -real or enwiki8-like byte slice and leave a receipt for later comparison. -""" - -from __future__ import annotations - -import argparse -import collections -import hashlib -import json -import math -from pathlib import Path -from typing import Any, Iterable - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -OUT = SHIM / "transfold_enwiki8_magnetic_domain_generator_receipt.json" -CURRICULUM = SHIM / "transfold_enwiki8_magnetic_domain_generator_curriculum.jsonl" - -PHI = (1.0 + math.sqrt(5.0)) / 2.0 -D_F = math.log(2.0) / math.log(PHI) -PHI_GAIN = PHI**D_F - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def sigmoid(x: float) -> float: - if x >= 0: - z = math.exp(-x) - return 1.0 / (1.0 + z) - z = math.exp(x) - return z / (1.0 + z) - - -def shannon_entropy(data: bytes) -> float: - if not data: - return 0.0 - counts = collections.Counter(data) - n = len(data) - return -sum((c / n) * math.log2(c / n) for c in counts.values()) - - -def transition_rate(data: bytes) -> float: - if len(data) < 2: - return 0.0 - changes = sum(1 for a, b in zip(data, data[1:]) if a != b) - return changes / (len(data) - 1) - - -def repetition_rate(data: bytes, ngram: int = 4) -> float: - if len(data) < ngram: - return 0.0 - total = len(data) - ngram + 1 - counts: dict[bytes, int] = collections.Counter( - data[i : i + ngram] for i in range(total) - ) - repeated = sum(c - 1 for c in counts.values() if c > 1) - return repeated / total - - -def printable_ratio(data: bytes) -> float: - if not data: - return 0.0 - printable = sum(1 for b in data if b in (9, 10, 13) or 32 <= b <= 126) - return printable / len(data) - - -def chunk_bytes(data: bytes, chunk_size: int, limit: int | None) -> list[bytes]: - chunks = [data[i : i + chunk_size] for i in range(0, len(data), chunk_size)] - if limit is not None: - chunks = chunks[:limit] - return [c for c in chunks if c] - - -def local_fallback_bytes(target_bytes: int) -> tuple[bytes, dict[str, Any]]: - """Build a deterministic text fallback when enwiki8 is not present locally.""" - candidates = [ - REPO / "docs" / "rainbow_raccoon_compiler_integration.md", - REPO / "docs" / "compression_signal_shaping_synthesis.md", - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "Transfolding.tid", - REPO / "4-Infrastructure" / "shim" / "multi_domain_adaptive_cognitive_load.md", - ] - parts: list[bytes] = [] - used: list[dict[str, Any]] = [] - for path in candidates: - if path.exists(): - data = path.read_bytes() - parts.append(data) - used.append({"path": rel(path), "bytes": len(data), "sha256": sha256_bytes(data)}) - seed = b"\n\n".join(parts) or ( - b"enwiki8-like fallback text: transfold response family overflow magnetic domain\n" - ) - repeats = max(1, math.ceil(target_bytes / len(seed))) - data = (seed * repeats)[:target_bytes] - return data, { - "source_mode": "fallback_local_text_not_enwiki8", - "claim_boundary": "Stress-test exercised byte-slice path; not a real enwiki8 measurement.", - "fallback_sources": used, - } - - -def find_default_source() -> Path | None: - candidates = [ - REPO / "enwiki8", - REPO / "data" / "enwiki8", - REPO / "shared-data" / "enwiki8", - REPO / "5-Applications" / "hutter_prize" / "data" / "enwiki8", - REPO / "5-Applications" / "hutter_prize" / "enwiki8", - ] - for path in candidates: - if path.exists() and path.is_file(): - return path - return None - - -def read_source(path: Path | None, slice_bytes: int) -> tuple[bytes, dict[str, Any]]: - if path is None: - default = find_default_source() - path = default - if path is None: - return local_fallback_bytes(slice_bytes) - data = path.read_bytes()[:slice_bytes] - return data, { - "source_mode": "real_file", - "path": rel(path), - "available_bytes": path.stat().st_size, - "slice_bytes": len(data), - "sha256": sha256_bytes(data), - } - - -def response_family(x: float, family: str, theta: dict[str, float]) -> float: - x = max(0.0, x) - if family == "logarithmic": - return math.log1p(theta.get("beta", 1.0) * x) - if family == "hill": - k = max(theta.get("k", 1.0), 1e-9) - n = max(theta.get("n", 2.0), 1e-9) - return (x**n) / (k**n + x**n) - if family == "michaelis_menten": - vmax = theta.get("vmax", 1.0) - km = max(theta.get("km", 1.0), 1e-9) - return (vmax * x) / (km + x) - if family == "power": - return x ** theta.get("alpha", 0.5) - raise ValueError(f"unknown response family: {family}") - - -def overflow_gate(load: float, threshold: float, gamma: float, thermal_scale: float) -> float: - if load <= threshold: - return 1.0 - return math.exp(-gamma * (load - threshold) / max(thermal_scale, 1e-9)) - - -def magnetic_domain_projection( - chunk: bytes, - index: int, - capacity_threshold: float, - coercive_threshold: float, -) -> dict[str, Any]: - entropy_bits = shannon_entropy(chunk) - normalized_entropy = entropy_bits / 8.0 - transitions = transition_rate(chunk) - repeats = repetition_rate(chunk) - printable = printable_ratio(chunk) - - # Load is a bounded signal-pressure prior. Entropy is demand, transitions - # are field agitation, and repeated n-grams are memory/remanence. - information_load = ( - response_family(normalized_entropy, "logarithmic", {"beta": 2.0}) - + response_family(transitions, "michaelis_menten", {"vmax": 1.0, "km": 0.35}) - + response_family(1.0 - repeats, "power", {"alpha": 0.6}) - ) * PHI_GAIN - - gate = overflow_gate(information_load, capacity_threshold, gamma=1.25, thermal_scale=0.9) - overflow = max(0.0, information_load - capacity_threshold) - - h_field = information_load - chi_susceptibility = response_family(transitions, "hill", {"k": 0.55, "n": 2.0}) - remanence = response_family(repeats, "michaelis_menten", {"vmax": 1.0, "km": 0.08}) - coercive_loss = max(0.0, h_field - coercive_threshold) - magnetization = sigmoid( - (chi_susceptibility * h_field + remanence - 0.5 * coercive_loss) * gate - ) - heat_loss = overflow * (1.0 - gate) - domain_wall_pressure = abs(transitions - repeats) * PHI_GAIN - - return { - "chunk_index": index, - "bytes": len(chunk), - "sha256": sha256_bytes(chunk), - "features": { - "entropy_bits_per_byte": entropy_bits, - "normalized_entropy": normalized_entropy, - "transition_rate": transitions, - "repetition_rate_4gram": repeats, - "printable_ratio": printable, - }, - "magnetic_domain": { - "information_load": information_load, - "overflow_gate": gate, - "overflow": overflow, - "H_field": h_field, - "chi_susceptibility": chi_susceptibility, - "remanence": remanence, - "coercive_loss": coercive_loss, - "domain_wall_pressure": domain_wall_pressure, - "magnetization_M": magnetization, - "heat_loss": heat_loss, - }, - "equation_instance": ( - "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); " - "H_i = L_info_i; " - "L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))" - ), - "status": "overflow" if overflow > 0 else "within_capacity", - } - - -def aggregate(chunks: Iterable[dict[str, Any]]) -> dict[str, Any]: - rows = list(chunks) - if not rows: - return {} - fields = [ - "information_load", - "overflow_gate", - "overflow", - "H_field", - "chi_susceptibility", - "remanence", - "coercive_loss", - "domain_wall_pressure", - "magnetization_M", - "heat_loss", - ] - out: dict[str, Any] = {"chunk_count": len(rows)} - for field in fields: - values = [float(r["magnetic_domain"][field]) for r in rows] - out[field] = { - "min": min(values), - "max": max(values), - "mean": sum(values) / len(values), - } - out["overflow_chunk_count"] = sum(1 for r in rows if r["status"] == "overflow") - out["within_capacity_chunk_count"] = len(rows) - out["overflow_chunk_count"] - return out - - -def threshold_sweep( - projections: list[dict[str, Any]], thresholds: list[float], thermal_scale: float = 0.9 -) -> list[dict[str, Any]]: - rows: list[dict[str, Any]] = [] - loads = [float(row["magnetic_domain"]["information_load"]) for row in projections] - for threshold in thresholds: - gates = [overflow_gate(load, threshold, gamma=1.25, thermal_scale=thermal_scale) for load in loads] - overflows = [max(0.0, load - threshold) for load in loads] - heat = [overflow * (1.0 - gate) for overflow, gate in zip(overflows, gates)] - rows.append( - { - "capacity_threshold": threshold, - "overflow_chunk_count": sum(1 for value in overflows if value > 0), - "mean_overflow": sum(overflows) / len(overflows) if overflows else 0.0, - "mean_overflow_gate": sum(gates) / len(gates) if gates else 1.0, - "mean_heat_loss": sum(heat) / len(heat) if heat else 0.0, - } - ) - return rows - - -def build_receipt(args: argparse.Namespace) -> dict[str, Any]: - source_path = Path(args.input).expanduser().resolve() if args.input else None - data, source = read_source(source_path, args.slice_bytes) - chunks = chunk_bytes(data, args.chunk_size, args.max_chunks) - projections = [ - magnetic_domain_projection(c, i, args.capacity_threshold, args.coercive_threshold) - for i, c in enumerate(chunks) - ] - - receipt: dict[str, Any] = { - "schema": "transfold_enwiki8_magnetic_domain_generator_v1", - "runner": rel(Path(__file__).resolve()), - "purpose": ( - "Stress-test the transfold adaptation framework by converting a byte " - "slice into magnetic-domain equation instances with response-family " - "selection and threshold overflow." - ), - "source": source, - "parameters": { - "slice_bytes_requested": args.slice_bytes, - "chunk_size": args.chunk_size, - "max_chunks": args.max_chunks, - "capacity_threshold": args.capacity_threshold, - "coercive_threshold": args.coercive_threshold, - "D_f": D_F, - "lambda_phi": PHI, - "phi_gain": PHI_GAIN, - }, - "transfold_map": { - "source_domain": "byte_stream_signal", - "target_domain": "magnetic_domain_equation", - "field_mapping": { - "entropy": "field demand / information pressure", - "byte_transition_rate": "domain agitation / susceptibility driver", - "repeated_4grams": "remanence / memory channel", - "capacity_overflow": "hysteresis heat-loss channel", - }, - "core_equations": { - "signal_load": ( - "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + " - "(1 - r_i)^0.6)" - ), - "overflow_gate": ( - "G_over_i = 1 if L_info_i <= L_threshold else " - "exp(-1.25 * (L_info_i - L_threshold) / 0.9)" - ), - "magnetic_projection": ( - "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)" - ), - "heat_loss": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - }, - }, - "chunk_projections": projections, - "aggregate": aggregate(projections), - "stress_sweep": threshold_sweep( - projections, - [ - max(0.0, args.capacity_threshold - 0.75), - args.capacity_threshold, - args.capacity_threshold + 0.75, - args.capacity_threshold + 1.25, - args.capacity_threshold + 2.0, - ], - ), - "claim_boundary": ( - "This receipt maps byte-signal statistics into a magnetic-domain analogue. " - "It is a stress-test and routing prior, not a claim that text data is a " - "literal magnetic material." - ), - } - receipt["receipt_hash"] = sha256_bytes(stable_json(receipt).encode("utf-8")) - return receipt - - -def write_curriculum(receipt: dict[str, Any]) -> None: - rows = [ - { - "task": "transfold_byte_signal_to_magnetic_domain", - "input": "enwiki8 byte chunk statistics", - "target": "H, chi, remanence, magnetization, overflow, heat_loss", - }, - { - "task": "detect_capacity_overflow", - "input": "information_load and capacity_threshold", - "target": "overflow_gate plus hysteresis heat-loss channel", - }, - { - "task": "preserve_claim_boundary", - "input": receipt["source"]["source_mode"], - "target": receipt["claim_boundary"], - }, - ] - CURRICULUM.write_text( - "".join(json.dumps(row, sort_keys=True) + "\n" for row in rows), - encoding="utf-8", - ) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--input", help="Path to enwiki8 or another byte corpus slice.") - parser.add_argument("--slice-bytes", type=int, default=65536) - parser.add_argument("--chunk-size", type=int, default=4096) - parser.add_argument("--max-chunks", type=int, default=16) - parser.add_argument("--capacity-threshold", type=float, default=3.25) - parser.add_argument("--coercive-threshold", type=float, default=2.6) - parser.add_argument("--out", type=Path, default=OUT) - return parser.parse_args() - - -def main() -> None: - args = parse_args() - receipt = build_receipt(args) - args.out.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_curriculum(receipt) - print( - json.dumps( - { - "receipt": rel(args.out), - "curriculum": rel(CURRICULUM), - "receipt_hash": receipt["receipt_hash"], - "source_mode": receipt["source"]["source_mode"], - "chunk_count": receipt["aggregate"]["chunk_count"], - "overflow_chunk_count": receipt["aggregate"]["overflow_chunk_count"], - "mean_magnetization": receipt["aggregate"]["magnetization_M"]["mean"], - "mean_heat_loss": receipt["aggregate"]["heat_loss"]["mean"], - }, - indent=2, - sort_keys=True, - ) - ) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_1m_receipt.json b/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_1m_receipt.json deleted file mode 100644 index b5c4d774..00000000 --- a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_1m_receipt.json +++ /dev/null @@ -1,6790 +0,0 @@ -{ - "aggregate": { - "H_field": { - "max": 4.781502350832252, - "mean": 4.446752552238698, - "min": 3.5659796252300877 - }, - "chi_susceptibility": { - "max": 0.7602811900429444, - "mean": 0.7464948171449458, - "min": 0.6927643952287867 - }, - "chunk_count": 256, - "coercive_loss": { - "max": 2.1815023508322517, - "mean": 1.8467525522386985, - "min": 0.9659796252300876 - }, - "domain_wall_pressure": { - "max": 1.2412047490474074, - "mean": 0.9959118832683456, - "min": 0.028991037737678305 - }, - "heat_loss": { - "max": 1.348971744867128, - "mean": 0.9727067215896326, - "min": 0.11224523357947692 - }, - "information_load": { - "max": 4.781502350832252, - "mean": 4.446752552238698, - "min": 3.5659796252300877 - }, - "magnetization_M": { - "max": 0.882420731613768, - "mean": 0.6518732011907801, - "min": 0.5960529626492633 - }, - "overflow": { - "max": 1.5315023508322518, - "mean": 1.1967525522386986, - "min": 0.3159796252300877 - }, - "overflow_chunk_count": 256, - "overflow_gate": { - "max": 0.6447706604571639, - "mean": 0.19663556731358448, - "min": 0.1191840194472655 - }, - "remanence": { - "max": 0.916923763903548, - "mean": 0.8444054709628067, - "min": 0.7973059971277174 - }, - "within_capacity_chunk_count": 0 - }, - "chunk_projections": [ - { - "bytes": 4096, - "chunk_index": 0, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.85210675017935, - "normalized_entropy": 0.6065133437724187, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7178108966528219, - "transition_rate": 0.8488400488400488 - }, - "magnetic_domain": { - "H_field": 3.9409932456335826, - "chi_susceptibility": 0.7043095813586062, - "coercive_loss": 1.3409932456335825, - "domain_wall_pressure": 0.26205830437445377, - "heat_loss": 0.42634086126193665, - "information_load": 3.9409932456335826, - "magnetization_M": 0.7596731582117144, - "overflow": 0.6909932456335826, - "overflow_gate": 0.38300285284117636, - "remanence": 0.8997256112499388 - }, - "sha256": "652ed0541c8b9e2d8194c2a1cd20a3ea516efaa542082535c0ef57267e1ca49e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 1, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.011547845009725, - "normalized_entropy": 0.6264434806262156, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.38602492059613974, - "transition_rate": 0.9550671550671551 - }, - "magnetic_domain": { - "H_field": 4.580575080530276, - "chi_susceptibility": 0.7509577364175362, - "coercive_loss": 1.9805750805302762, - "domain_wall_pressure": 1.1380844689420306, - "heat_loss": 1.120944765669762, - "information_load": 4.580575080530276, - "magnetization_M": 0.6263110577490969, - "overflow": 1.3305750805302763, - "overflow_gate": 0.15754865541068902, - "remanence": 0.8283353604831607 - }, - "sha256": "3b557252bb5eb5a16473de67acd872a67850e5e9b1ac7ba57c3aa91bf4693a49", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 2, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.901616637958809, - "normalized_entropy": 0.6127020797448511, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.3879794771561202, - "transition_rate": 0.9601953601953602 - }, - "magnetic_domain": { - "H_field": 4.555273955815762, - "chi_susceptibility": 0.7529553743863632, - "coercive_loss": 1.9552739558157621, - "domain_wall_pressure": 1.1444317660784802, - "heat_loss": 1.0922749003291068, - "information_load": 4.555273955815762, - "magnetization_M": 0.6307554737408266, - "overflow": 1.3052739558157622, - "overflow_gate": 0.16318341030066486, - "remanence": 0.8290523326233137 - }, - "sha256": "74ec77eec1e9895d4a015d931e5fcf662ff3a1132fd7f73d778d096e49101fa4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 3, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.789920261126383, - "normalized_entropy": 0.5987400326407979, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39848521866601516, - "transition_rate": 0.9645909645909646 - }, - "magnetic_domain": { - "H_field": 4.5164100805971685, - "chi_susceptibility": 0.7546506335309299, - "coercive_loss": 1.9164100805971684, - "domain_wall_pressure": 1.1322114918498989, - "heat_loss": 1.0482915618884743, - "information_load": 4.5164100805971685, - "magnetization_M": 0.637707591144412, - "overflow": 1.2664100805971685, - "overflow_gate": 0.17223371959092557, - "remanence": 0.8328057024979064 - }, - "sha256": "2ca56a17aa8f8843dc24e4ecef4798d00d41260f175589c2dd90b3173b951dd2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 4, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.870001370847795, - "normalized_entropy": 0.6087501713559744, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.39262154898607377, - "transition_rate": 0.9660561660561661 - }, - "magnetic_domain": { - "H_field": 4.543747674487493, - "chi_susceptibility": 0.7552122624581934, - "coercive_loss": 1.943747674487493, - "domain_wall_pressure": 1.1468692341401847, - "heat_loss": 1.079222587025682, - "information_load": 4.543747674487493, - "magnetization_M": 0.6331131169610806, - "overflow": 1.2937476744874932, - "overflow_gate": 0.16581679077938705, - "remanence": 0.8307313744546225 - }, - "sha256": "1011e60b2041ff961cc0ab3d0ba1e3167cdf0158ee88bdfe2ce4dd7b009d6878", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 5, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.834713127357048, - "normalized_entropy": 0.604339140919631, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.37649645736623505, - "transition_rate": 0.95995115995116 - }, - "magnetic_domain": { - "H_field": 4.556793352646781, - "chi_susceptibility": 0.7528607349280803, - "coercive_loss": 1.956793352646781, - "domain_wall_pressure": 1.1669094051698499, - "heat_loss": 1.0939958917739039, - "information_load": 4.556793352646781, - "magnetization_M": 0.6303276111595437, - "overflow": 1.306793352646781, - "overflow_gate": 0.16283941178754605, - "remanence": 0.8247521996960031 - }, - "sha256": "f8d9a9cdaf3bfd183d809c010bfcd7288cde48ae565744d3b637820963e3a9fb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 6, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.703910848978386, - "normalized_entropy": 0.5879888561222982, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.45345712191546544, - "transition_rate": 0.9633699633699634 - }, - "magnetic_domain": { - "H_field": 4.413864911313744, - "chi_susceptibility": 0.7541812921791757, - "coercive_loss": 1.8138649113137437, - "domain_wall_pressure": 1.019825682908996, - "heat_loss": 0.9327251574903277, - "information_load": 4.413864911313744, - "magnetization_M": 0.656965361785371, - "overflow": 1.1638649113137438, - "overflow_gate": 0.1985967199256063, - "remanence": 0.8500348074597882 - }, - "sha256": "78faf4409ed08ae70d7481218ade59d5b47685b9523b9530c083098a9791f5ac", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 7, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.749160612751626, - "normalized_entropy": 0.5936450765939533, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41143415587588567, - "transition_rate": 0.9641025641025641 - }, - "magnetic_domain": { - "H_field": 4.487791420616601, - "chi_susceptibility": 0.7544630409114862, - "coercive_loss": 1.8877914206166007, - "domain_wall_pressure": 1.1053368164533568, - "heat_loss": 1.015957453259845, - "information_load": 4.487791420616601, - "magnetization_M": 0.6428345204172404, - "overflow": 1.2377914206166007, - "overflow_gate": 0.17921756740424818, - "remanence": 0.8372111522093624 - }, - "sha256": "54b82d666f9b5d4538a948a4a3c8225bc27eefff0a9061f2df2708ee4495fa32", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 8, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.627967754251285, - "normalized_entropy": 0.5784959692814107, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.40019545565599807, - "transition_rate": 0.9631257631257631 - }, - "magnetic_domain": { - "H_field": 4.476106191557932, - "chi_susceptibility": 0.7540872798765288, - "coercive_loss": 1.8761061915579318, - "domain_wall_pressure": 1.1258606149395303, - "heat_loss": 1.0027710627838768, - "information_load": 4.476106191557932, - "magnetization_M": 0.6446860884951096, - "overflow": 1.2261061915579319, - "overflow_gate": 0.18214990700787334, - "remanence": 0.8334011722565939 - }, - "sha256": "f64b9c6cb911b36d1df6dd6a8f4f05d20bfe55dc22926b4590fc10c1657ea32d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 9, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.864462104048231, - "normalized_entropy": 0.6080577630060289, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.39091131199609086, - "transition_rate": 0.9616605616605617 - }, - "magnetic_domain": { - "H_field": 4.543219688131397, - "chi_susceptibility": 0.7535221954997179, - "coercive_loss": 1.9432196881313968, - "domain_wall_pressure": 1.1414984993289417, - "heat_loss": 1.078624841870434, - "information_load": 4.543219688131397, - "magnetization_M": 0.6328812089018073, - "overflow": 1.2932196881313969, - "overflow_gate": 0.16593843121197435, - "remanence": 0.8301166313867098 - }, - "sha256": "bcf0c6cf4b6f4791f559587b6ab622115137da72a0acc0a5bdc7512c9fb2cca6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 10, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.71327032742842, - "normalized_entropy": 0.5891587909285525, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.39750794038602494, - "transition_rate": 0.9584859584859585 - }, - "magnetic_domain": { - "H_field": 4.497845440399808, - "chi_susceptibility": 0.7522918799957201, - "coercive_loss": 1.897845440399808, - "domain_wall_pressure": 1.121956036199867, - "heat_loss": 1.0273107456740525, - "information_load": 4.497845440399808, - "magnetization_M": 0.6404748992897175, - "overflow": 1.247845440399808, - "overflow_gate": 0.1767323801376367, - "remanence": 0.8324635189619533 - }, - "sha256": "2e16ef58a0ab9b4b5d276aa8a6db75ec6a5f150618279f49edd96abc51bb2c13", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 11, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.5985475495008465, - "normalized_entropy": 0.5748184436876058, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4033716100659663, - "transition_rate": 0.967032967032967 - }, - "magnetic_domain": { - "H_field": 4.466175266268176, - "chi_susceptibility": 0.7555857265133851, - "coercive_loss": 1.866175266268176, - "domain_wall_pressure": 1.1273227139340016, - "heat_loss": 0.9915723848521583, - "information_load": 4.466175266268176, - "magnetization_M": 0.6468012662496755, - "overflow": 1.2161752662681762, - "overflow_gate": 0.1846796984329487, - "remanence": 0.8344958654293281 - }, - "sha256": "8e336407be77d5f92484ebc212466e860980399dcc95b87d8bf4fd6ba234ae8d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 12, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.796641095076212, - "normalized_entropy": 0.5995801368845265, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.36354752015636455, - "transition_rate": 0.9555555555555556 - }, - "magnetic_domain": { - "H_field": 4.565050298432678, - "chi_susceptibility": 0.7511489145613812, - "coercive_loss": 1.9650502984326779, - "domain_wall_pressure": 1.184016070798382, - "heat_loss": 1.1033500300656958, - "information_load": 4.565050298432678, - "magnetization_M": 0.6285012207804667, - "overflow": 1.315050298432678, - "overflow_gate": 0.1609826396901273, - "remanence": 0.8196360111047459 - }, - "sha256": "efe73462e87ecb2fdeace60d2b83f83a2e0631a438cc07a9913d9b39fa3f10ea", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 13, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.858346109566147, - "normalized_entropy": 0.6072932636957684, - "printable_ratio": 0.998779296875, - "repetition_rate_4gram": 0.37796237478622036, - "transition_rate": 0.9619047619047619 - }, - "magnetic_domain": { - "H_field": 4.560806006056383, - "chi_susceptibility": 0.7536164966732386, - "coercive_loss": 1.9608060060563832, - "domain_wall_pressure": 1.1678847742370833, - "heat_loss": 1.098541407784731, - "information_load": 4.560806006056383, - "magnetization_M": 0.6298259384796606, - "overflow": 1.3108060060563833, - "overflow_gate": 0.16193441080595858, - "remanence": 0.8253131601971788 - }, - "sha256": "dae627f190ea6808116ccdf65513fe008860be628b45b1e3ad1f574ca4bc903e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 14, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.057337330400908, - "normalized_entropy": 0.6321671663001135, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.39824089909601756, - "transition_rate": 0.9284493284493285 - }, - "magnetic_domain": { - "H_field": 4.561655126731615, - "chi_susceptibility": 0.7402359090995579, - "coercive_loss": 1.9616551267316154, - "domain_wall_pressure": 1.0604168587066218, - "heat_loss": 1.0995033720301766, - "information_load": 4.561655126731615, - "magnetization_M": 0.6276630360934214, - "overflow": 1.3116551267316154, - "overflow_gate": 0.1617435485729233, - "remanence": 0.8327202877227399 - }, - "sha256": "10b0df9b6b00b6140bc248a5712201dd5ebf60e67ecb8ac873b9abf83adff875", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 15, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.387947357669873, - "normalized_entropy": 0.6734934197087341, - "printable_ratio": 0.951416015625, - "repetition_rate_4gram": 0.5020767163449792, - "transition_rate": 0.8942612942612943 - }, - "magnetic_domain": { - "H_field": 4.45989870274027, - "chi_susceptibility": 0.7255497145440228, - "coercive_loss": 1.8598987027402702, - "domain_wall_pressure": 0.7843691558326302, - "heat_loss": 0.9844986009000085, - "information_load": 4.45989870274027, - "magnetization_M": 0.6434291892778523, - "overflow": 1.2098987027402703, - "overflow_gate": 0.18629667205176653, - "remanence": 0.862561071842313 - }, - "sha256": "2fd813cb038b4662e084eb66b8060c806fe839e27a933db726a602fe1183aeca", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 16, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.840070153843249, - "normalized_entropy": 0.6050087692304061, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.6682140239433179, - "transition_rate": 0.8598290598290599 - }, - "magnetic_domain": { - "H_field": 4.039086290005146, - "chi_susceptibility": 0.7096388420887111, - "coercive_loss": 1.4390862900051462, - "domain_wall_pressure": 0.38323007177148405, - "heat_loss": 0.5253571740095144, - "information_load": 4.039086290005146, - "magnetization_M": 0.7341876473373955, - "overflow": 0.7890862900051463, - "overflow_gate": 0.334220882223048, - "remanence": 0.8930787215422996 - }, - "sha256": "9b8c9e6d49d2bfbe548527057c721d1953ba2b13e15a886fc83f002a37cf81fa", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 17, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.663438572362308, - "normalized_entropy": 0.5829298215452885, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3906669924260933, - "transition_rate": 0.968009768009768 - }, - "magnetic_domain": { - "H_field": 4.500270898573349, - "chi_susceptibility": 0.7559584284458916, - "coercive_loss": 1.9002708985733485, - "domain_wall_pressure": 1.1546855511673493, - "heat_loss": 1.0300506519576875, - "information_load": 4.500270898573349, - "magnetization_M": 0.6406230246677002, - "overflow": 1.2502708985733486, - "overflow_gate": 0.1761380248608111, - "remanence": 0.8300284462531924 - }, - "sha256": "f3ea522570e7a68f23d3c77331f6824ae2876a2f32b9a51b08adb9fee4a13b0b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 18, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.49868587495977, - "normalized_entropy": 0.5623357343699712, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.427803567065722, - "transition_rate": 0.9660561660561661 - }, - "magnetic_domain": { - "H_field": 4.406070204371542, - "chi_susceptibility": 0.7552122624581934, - "coercive_loss": 1.8060702043715415, - "domain_wall_pressure": 1.0765051979808882, - "heat_loss": 0.9239793946751074, - "information_load": 4.406070204371542, - "magnetization_M": 0.6583311425359012, - "overflow": 1.1560702043715416, - "overflow_gate": 0.2007584044799446, - "remanence": 0.8424587671522874 - }, - "sha256": "2d49064b6f1c1474417dee355f78e28e00e00077b39c367c0be819282c806f7a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 19, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.471303137037682, - "normalized_entropy": 0.5589128921297103, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39017835328609823, - "transition_rate": 0.9697191697191697 - }, - "magnetic_domain": { - "H_field": 4.4568188663897965, - "chi_susceptibility": 0.756608828268981, - "coercive_loss": 1.8568188663897964, - "domain_wall_pressure": 1.159081632866143, - "heat_loss": 0.9810287619891245, - "information_load": 4.4568188663897965, - "magnetization_M": 0.648501393827522, - "overflow": 1.2068188663897965, - "overflow_gate": 0.18709527228068953, - "remanence": 0.8298518010434204 - }, - "sha256": "887aa59223e2d29cefc4a9cfe1f4b53605a303e4bc7c0427da509d607cd87a49", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 20, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.400671134750836, - "normalized_entropy": 0.5500838918438545, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.43415587588565846, - "transition_rate": 0.977045177045177 - }, - "magnetic_domain": { - "H_field": 4.377723493851962, - "chi_susceptibility": 0.7593701038901239, - "coercive_loss": 1.7777234938519615, - "domain_wall_pressure": 1.0857786023190372, - "heat_loss": 0.89223227109401, - "information_load": 4.377723493851962, - "magnetization_M": 0.6648313335861331, - "overflow": 1.1277234938519616, - "overflow_gate": 0.20882000245785864, - "remanence": 0.8444051624185056 - }, - "sha256": "776512996d3eb29651165a0c4f11caaa71018d755991dd6e4de9d8fd58aa21af", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 21, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.433844085901757, - "normalized_entropy": 0.5542305107377197, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.45834351331541656, - "transition_rate": 0.9775335775335775 - }, - "magnetic_domain": { - "H_field": 4.349031827692929, - "chi_susceptibility": 0.7595526923756887, - "coercive_loss": 1.7490318276929293, - "domain_wall_pressure": 1.0383801284363219, - "heat_loss": 0.8602018709708726, - "information_load": 4.349031827692929, - "magnetization_M": 0.671023554915004, - "overflow": 1.0990318276929294, - "overflow_gate": 0.21730940879428834, - "remanence": 0.8513959989834078 - }, - "sha256": "a7d54b4aa3f11c2b5e7096417d0537e398a5fc72718cf2830c54df583168c0f3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 22, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.633716093181326, - "normalized_entropy": 0.5792145116476658, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.45272416320547276, - "transition_rate": 0.9724053724053724 - }, - "magnetic_domain": { - "H_field": 4.402431342647818, - "chi_susceptibility": 0.7576262030414115, - "coercive_loss": 1.8024313426478176, - "domain_wall_pressure": 1.0393624183997994, - "heat_loss": 0.9198988162923653, - "information_load": 4.402431342647818, - "magnetization_M": 0.6598516339846227, - "overflow": 1.1524313426478177, - "overflow_gate": 0.20177560063681313, - "remanence": 0.8498284749866999 - }, - "sha256": "df041ccd361122155b7f2121cad594d51703a4e46e766883e93ced22cf9ad1da", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 23, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.509389607274596, - "normalized_entropy": 0.5636737009093244, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4099682384559003, - "transition_rate": 0.9709401709401709 - }, - "magnetic_domain": { - "H_field": 4.4371471371228, - "chi_susceptibility": 0.7570719791128443, - "coercive_loss": 1.8371471371227996, - "domain_wall_pressure": 1.1219438649685411, - "heat_loss": 0.958885414731613, - "information_load": 4.4371471371228, - "magnetization_M": 0.652527672972751, - "overflow": 1.1871471371227997, - "overflow_gate": 0.19227753262700667, - "remanence": 0.8367241104196586 - }, - "sha256": "d554228d20e40e0fbfed0e803f3fdd1515e6582331f1fb76369086f28b437b6e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 24, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.500722916534188, - "normalized_entropy": 0.5625903645667735, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4424627412655754, - "transition_rate": 0.9631257631257631 - }, - "magnetic_domain": { - "H_field": 4.383256084620563, - "chi_susceptibility": 0.7540872798765288, - "coercive_loss": 1.7832560846205632, - "domain_wall_pressure": 1.0413260437203755, - "heat_loss": 0.8984210059716219, - "information_load": 4.383256084620563, - "magnetization_M": 0.6627712372154408, - "overflow": 1.1332560846205633, - "overflow_gate": 0.2072215466882482, - "remanence": 0.8468790333140047 - }, - "sha256": "70a9ad5350d350a3134b8e8d2ee00c33e3ba84c3321f05c6273c25df7ec01df9", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 25, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.5986924277856, - "normalized_entropy": 0.5748365534732, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4021500122159785, - "transition_rate": 0.9619047619047619 - }, - "magnetic_domain": { - "H_field": 4.465932918377759, - "chi_susceptibility": 0.7536164966732386, - "coercive_loss": 1.865932918377759, - "domain_wall_pressure": 1.1195094993775667, - "heat_loss": 0.9912991959918501, - "information_load": 4.465932918377759, - "magnetization_M": 0.6464562456710132, - "overflow": 1.2159329183777592, - "overflow_gate": 0.1847418710282183, - "remanence": 0.8340765364034376 - }, - "sha256": "cac3366cad1dd2b7f2cab43a6ba178f6cfaf6e405118814317a29523c14d1a67", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 26, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.920548189150485, - "normalized_entropy": 0.6150685236438106, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43391155631566086, - "transition_rate": 0.9531135531135531 - }, - "magnetic_domain": { - "H_field": 4.488495956390397, - "chi_susceptibility": 0.750191061541498, - "coercive_loss": 1.8884959563903965, - "domain_wall_pressure": 1.0384039935957845, - "heat_loss": 1.0167528105669943, - "information_load": 4.488495956390397, - "magnetization_M": 0.6422141384458964, - "overflow": 1.2384959563903966, - "overflow_gate": 0.17904228486112617, - "remanence": 0.8443311908112425 - }, - "sha256": "b39caf035cb6e5660d1c6c08f8052d72a422aad06717b5428ea014b0cbe05095", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 27, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.215697512298534, - "normalized_entropy": 0.6519621890373167, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5150256535548497, - "transition_rate": 0.9355311355311355 - }, - "magnetic_domain": { - "H_field": 4.420276940196183, - "chi_susceptibility": 0.7431474511312024, - "coercive_loss": 1.8202769401961825, - "domain_wall_pressure": 0.8410109639525716, - "heat_loss": 0.9399243671120469, - "information_load": 4.420276940196183, - "magnetization_M": 0.6542590654829653, - "overflow": 1.1702769401961826, - "overflow_gate": 0.19683594982698702, - "remanence": 0.8655520152415991 - }, - "sha256": "2860022c2aec02f32acd51ad0de8b2d4d7a87296db7c5ebb5af03ef09a32e20d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 28, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.197338436549367, - "normalized_entropy": 0.6496673045686708, - "printable_ratio": 0.9716796875, - "repetition_rate_4gram": 0.4292694844857073, - "transition_rate": 0.9428571428571428 - }, - "magnetic_domain": { - "H_field": 4.552330750983865, - "chi_susceptibility": 0.7461139896373057, - "coercive_loss": 1.9523307509838648, - "domain_wall_pressure": 1.0271753167428712, - "heat_loss": 1.0889414687395615, - "information_load": 4.552330750983865, - "magnetization_M": 0.6305782784558595, - "overflow": 1.3023307509838649, - "overflow_gate": 0.16385183416969554, - "remanence": 0.8429122450154478 - }, - "sha256": "7ee44af534c98d835c252054f90b154bebf9bbd651ff5b1f932a6db0a08ab372", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 29, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.8806902484761086, - "normalized_entropy": 0.6100862810595136, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7139017835328609, - "transition_rate": 0.8595848595848596 - }, - "magnetic_domain": { - "H_field": 3.9603873714701887, - "chi_susceptibility": 0.7095217700281617, - "coercive_loss": 1.3603873714701886, - "domain_wall_pressure": 0.2913661521039974, - "heat_loss": 0.44553799126062876, - "information_load": 3.9603873714701887, - "magnetization_M": 0.755707980771079, - "overflow": 0.7103873714701887, - "overflow_gate": 0.37282388573636727, - "remanence": 0.8992318676448865 - }, - "sha256": "761892622b0586592dff016e603dee9a454efcaddafd2e6399239ef51f9451f1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 30, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.533591320164125, - "normalized_entropy": 0.5666989150205156, - "printable_ratio": 0.9970703125, - "repetition_rate_4gram": 0.4497923283655021, - "transition_rate": 0.968009768009768 - }, - "magnetic_domain": { - "H_field": 4.3818092860450575, - "chi_susceptibility": 0.7559584284458916, - "coercive_loss": 1.7818092860450574, - "domain_wall_pressure": 1.0364348792885318, - "heat_loss": 0.8968022562742392, - "information_load": 4.3818092860450575, - "magnetization_M": 0.6635363259942009, - "overflow": 1.1318092860450575, - "overflow_gate": 0.2076383651100939, - "remanence": 0.8489974359447345 - }, - "sha256": "6b4589395c927754a732a51ba071a47aef4a1a009c94cf7defe6965ea2ea80bd", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 31, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.038427742665227, - "normalized_entropy": 0.6298034678331533, - "printable_ratio": 0.976806640625, - "repetition_rate_4gram": 0.3955533838260445, - "transition_rate": 0.9443223443223443 - }, - "magnetic_domain": { - "H_field": 4.568137996783326, - "chi_susceptibility": 0.7467018253994223, - "coercive_loss": 1.9681379967833261, - "domain_wall_pressure": 1.0975379209925995, - "heat_loss": 1.1068487160009834, - "information_load": 4.568137996783326, - "magnetization_M": 0.6276985402174236, - "overflow": 1.3181379967833262, - "overflow_gate": 0.1602937486803017, - "remanence": 0.8317749326976429 - }, - "sha256": "81318ca7a659f53418be96e054307b61e4d1450bd17e3b17af8bb790688b8afe", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 32, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.066428033011996, - "normalized_entropy": 0.6333035041264995, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43586611287564136, - "transition_rate": 0.9255189255189256 - }, - "magnetic_domain": { - "H_field": 4.506369645995968, - "chi_susceptibility": 0.7390183407297088, - "coercive_loss": 1.906369645995968, - "domain_wall_pressure": 0.9793056252865684, - "heat_loss": 1.0369417325114128, - "information_load": 4.506369645995968, - "magnetization_M": 0.6370849955143678, - "overflow": 1.2563696459959681, - "overflow_gate": 0.17465235186465142, - "remanence": 0.8449210017807752 - }, - "sha256": "f8518e080b2831823daa9feae71e6f878fa56c66d619a69a331c45436169bca0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 33, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.799357315827775, - "normalized_entropy": 0.5999196644784719, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.46151966772538483, - "transition_rate": 0.9343101343101343 - }, - "magnetic_domain": { - "H_field": 4.411261352849059, - "chi_susceptibility": 0.7426485598168072, - "coercive_loss": 1.8112613528490589, - "domain_wall_pressure": 0.945580933169499, - "heat_loss": 0.9298032035967186, - "information_load": 4.411261352849059, - "magnetization_M": 0.6552792860443275, - "overflow": 1.161261352849059, - "overflow_gate": 0.1993161562506811, - "remanence": 0.8522676002959702 - }, - "sha256": "3649b4d2b8fc8b6d9ad8c7a480ddeaa88f0104eaec0f49ebd80fcedfe2d98134", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 34, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.264683151181208, - "normalized_entropy": 0.658085393897651, - "printable_ratio": 0.9716796875, - "repetition_rate_4gram": 0.39091131199609086, - "transition_rate": 0.9211233211233211 - }, - "magnetic_domain": { - "H_field": 4.614519132734579, - "chi_susceptibility": 0.737177788951501, - "coercive_loss": 2.0145191327345793, - "domain_wall_pressure": 1.0604240182544604, - "heat_loss": 1.1594408240644505, - "information_load": 4.614519132734579, - "magnetization_M": 0.6188412947871607, - "overflow": 1.3645191327345794, - "overflow_gate": 0.15029346511187394, - "remanence": 0.8301166313867098 - }, - "sha256": "d92a503ada4cb37ed07f9f204c6116ce0757604b24bac1153e331b5e7087d4d5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 35, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.753382658688614, - "normalized_entropy": 0.5941728323360768, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8118739311018812, - "transition_rate": 0.8283272283272284 - }, - "magnetic_domain": { - "H_field": 3.7062348424199967, - "chi_susceptibility": 0.6940197801187362, - "coercive_loss": 1.1062348424199966, - "domain_wall_pressure": 0.032906594450694326, - "heat_loss": 0.21413549247056227, - "information_load": 3.7062348424199967, - "magnetization_M": 0.8255579623290847, - "overflow": 0.4562348424199967, - "overflow_gate": 0.5306463414001263, - "remanence": 0.9103012239620429 - }, - "sha256": "f32c10dc14c10356cfdd4fcbb8ace99f39f5785f99d20ceb31c10ba88b0f49c7", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 36, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.7887783022808845, - "normalized_entropy": 0.5985972877851106, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8204251160517958, - "transition_rate": 0.834920634920635 - }, - "magnetic_domain": { - "H_field": 3.6974071711920975, - "chi_susceptibility": 0.6973766708906942, - "coercive_loss": 1.0974071711920974, - "domain_wall_pressure": 0.028991037737678305, - "heat_loss": 0.20706341196664327, - "information_load": 3.6974071711920975, - "magnetization_M": 0.8291830052172489, - "overflow": 0.4474071711920975, - "overflow_gate": 0.5371924606954074, - "remanence": 0.9111530780585222 - }, - "sha256": "4146674edbfa3137720a0e967a6c55b5ba8f6f9e63551f68d9aaee6fcb450973", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 37, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.739640197378482, - "normalized_entropy": 0.5924550246723103, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8101636941118984, - "transition_rate": 0.832967032967033 - }, - "magnetic_domain": { - "H_field": 3.7094188907199297, - "chi_susceptibility": 0.6963869783474438, - "coercive_loss": 1.1094188907199296, - "domain_wall_pressure": 0.04560667771026927, - "heat_loss": 0.21670566213469203, - "information_load": 3.7094188907199297, - "magnetization_M": 0.8252719436671476, - "overflow": 0.4594188907199297, - "overflow_gate": 0.5283048509496319, - "remanence": 0.9101288891816526 - }, - "sha256": "299bb8c9e560464863acda0d793164c86b1c924c6313da882dd970053ca41b5b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 38, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.777675347101566, - "normalized_entropy": 0.5972094183876957, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7796237478622038, - "transition_rate": 0.8293040293040294 - }, - "magnetic_domain": { - "H_field": 3.785366891464032, - "chi_susceptibility": 0.6945200963944295, - "coercive_loss": 1.185366891464032, - "domain_wall_pressure": 0.09936056288365114, - "heat_loss": 0.28084492022524016, - "information_load": 3.785366891464032, - "magnetization_M": 0.8020689197715067, - "overflow": 0.5353668914640322, - "overflow_gate": 0.47541597229288446, - "remanence": 0.9069360284671616 - }, - "sha256": "86b7f0d7bb089ee554c46f0946efbdf4a6da0131fbd5fca63c64f4e0eba1eadd", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 39, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.77163603037693, - "normalized_entropy": 0.5964545037971163, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.789396530662106, - "transition_rate": 0.8258852258852258 - }, - "magnetic_domain": { - "H_field": 3.7605950552328276, - "chi_susceptibility": 0.6927643952287867, - "coercive_loss": 1.1605950552328275, - "domain_wall_pressure": 0.07297739044623963, - "heat_loss": 0.2593529538498828, - "information_load": 3.7605950552328276, - "magnetization_M": 0.8089421060359202, - "overflow": 0.5105950552328276, - "overflow_gate": 0.492057451023258, - "remanence": 0.9079821494812333 - }, - "sha256": "696ca4a4e9da6190a4ae5745fd1e6fc760d974eed5e3d2377eafa942d266f919", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 40, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.052477851163737, - "normalized_entropy": 0.6315597313954672, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.47691180063523086, - "transition_rate": 0.9374847374847375 - }, - "magnetic_domain": { - "H_field": 4.445527984872302, - "chi_susceptibility": 0.7439430106463752, - "coercive_loss": 1.845527984872302, - "domain_wall_pressure": 0.9211458736990132, - "heat_loss": 0.9683150400695094, - "information_load": 4.445527984872302, - "magnetization_M": 0.6492907180737805, - "overflow": 1.1955279848723022, - "overflow_gate": 0.19005238495279733, - "remanence": 0.8563506826238024 - }, - "sha256": "302d21623064b1ec0da0e9ec83d55d16d766d6a6008ada2af5ab36e222d43cb4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 41, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.054326405968642, - "normalized_entropy": 0.6317908007460803, - "printable_ratio": 0.993408203125, - "repetition_rate_4gram": 0.40239433178597606, - "transition_rate": 0.9296703296703297 - }, - "magnetic_domain": { - "H_field": 4.555397255913739, - "chi_susceptibility": 0.7407410090649996, - "coercive_loss": 1.9553972559137391, - "domain_wall_pressure": 1.0545519957687073, - "heat_loss": 1.0924145563360992, - "information_load": 4.555397255913739, - "magnetization_M": 0.6288129738735315, - "overflow": 1.3053972559137392, - "overflow_gate": 0.16315546751211646, - "remanence": 0.834160572111586 - }, - "sha256": "3c5352362547c13a45d3cb6005e1cd225f359765699f3c3e9430bae3a065a9e3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 42, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.095839253097272, - "normalized_entropy": 0.636979906637159, - "printable_ratio": 0.988037109375, - "repetition_rate_4gram": 0.39262154898607377, - "transition_rate": 0.9081807081807082 - }, - "magnetic_domain": { - "H_field": 4.5695652612593225, - "chi_susceptibility": 0.7316578608938049, - "coercive_loss": 1.9695652612593224, - "domain_wall_pressure": 1.031118318389269, - "heat_loss": 1.1084660783368248, - "information_load": 4.5695652612593225, - "magnetization_M": 0.6248565420521007, - "overflow": 1.3195652612593225, - "overflow_gate": 0.15997631122922717, - "remanence": 0.8307313744546225 - }, - "sha256": "1344db5ab3f4d61d00819248d6b7eeacc38c66ba4aca0614f27fa534581dda61", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 43, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.976476210868058, - "normalized_entropy": 0.6220595263585073, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.6506230149034937, - "transition_rate": 0.8295482295482296 - }, - "magnetic_domain": { - "H_field": 4.087337655981222, - "chi_susceptibility": 0.6946450118243958, - "coercive_loss": 1.487337655981222, - "domain_wall_pressure": 0.35785042928947175, - "heat_loss": 0.5756220516200428, - "information_load": 4.087337655981222, - "magnetization_M": 0.7177485674083423, - "overflow": 0.837337655981222, - "overflow_gate": 0.3125568311561142, - "remanence": 0.8905044073781785 - }, - "sha256": "fb3094cbb3e50dce162954b3ff67df9f6b7c3a546f91b2e478ef22eb92557a77", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 44, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.059563253602829, - "normalized_entropy": 0.6324454067003537, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.4202296603957977, - "transition_rate": 0.9120879120879121 - }, - "magnetic_domain": { - "H_field": 4.522477488788522, - "chi_susceptibility": 0.7333402348998735, - "coercive_loss": 1.9224774887885219, - "domain_wall_pressure": 0.9837165033842288, - "heat_loss": 1.0551530792110675, - "information_load": 4.522477488788522, - "magnetization_M": 0.6331440391853249, - "overflow": 1.272477488788522, - "overflow_gate": 0.17078841196975586, - "remanence": 0.8400734575860588 - }, - "sha256": "3516be9d8f12364e2897772412aa679ab242c14a05a2ec461718aced4518c8e5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 45, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.821695127684045, - "normalized_entropy": 0.6027118909605056, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4326899584656731, - "transition_rate": 0.9575091575091575 - }, - "magnetic_domain": { - "H_field": 4.4698549145769935, - "chi_susceptibility": 0.7519116716198543, - "coercive_loss": 1.8698549145769934, - "domain_wall_pressure": 1.049638398086969, - "heat_loss": 0.995720873340103, - "information_load": 4.4698549145769935, - "magnetization_M": 0.6458435332909297, - "overflow": 1.2198549145769935, - "overflow_gate": 0.18373827785463573, - "remanence": 0.843960275252092 - }, - "sha256": "5580536f6f7c5fed84cd3a01b7cad7a63e6b44b06194ccbc846e0e83875c8be8", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 46, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.1081302453301465, - "normalized_entropy": 0.6385162806662683, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5208893232347911, - "transition_rate": 0.9445665445665445 - }, - "magnetic_domain": { - "H_field": 4.391174299761468, - "chi_susceptibility": 0.746799621949957, - "coercive_loss": 1.7911742997614675, - "domain_wall_pressure": 0.8473544426635069, - "heat_loss": 0.9072847969793981, - "information_load": 4.391174299761468, - "magnetization_M": 0.6606580155505454, - "overflow": 1.1411742997614676, - "overflow_gate": 0.20495510881287624, - "remanence": 0.8668640015613311 - }, - "sha256": "ecea0c07004e824505660463d380a0a3b6b560c3eb56b862136890c29a2e10a5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 47, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.2796730998676145, - "normalized_entropy": 0.6599591374834518, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5340825800146591, - "transition_rate": 0.9333333333333333 - }, - "magnetic_domain": { - "H_field": 4.402390464833543, - "chi_susceptibility": 0.7422485207100592, - "coercive_loss": 1.8023904648335427, - "domain_wall_pressure": 0.7985015066373484, - "heat_loss": 0.9198529847429399, - "information_load": 4.402390464833543, - "magnetization_M": 0.6576912875281208, - "overflow": 1.1523904648335428, - "overflow_gate": 0.20178705671969593, - "remanence": 0.8697243618307977 - }, - "sha256": "f6aa5d2e22d5eca9cdf1297111d0981098bad7fed9afc0c585638267d90b0459", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 48, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.493571097269677, - "normalized_entropy": 0.6866963871587096, - "printable_ratio": 0.93505859375, - "repetition_rate_4gram": 0.4353774737356462, - "transition_rate": 0.9228327228327229 - }, - "magnetic_domain": { - "H_field": 4.598021541778281, - "chi_susceptibility": 0.7378955926759352, - "coercive_loss": 1.998021541778281, - "domain_wall_pressure": 0.9749104981941533, - "heat_loss": 1.1407269084794829, - "information_load": 4.598021541778281, - "magnetization_M": 0.6219952166270484, - "overflow": 1.348021541778281, - "overflow_gate": 0.15377694411718337, - "remanence": 0.8447739684466019 - }, - "sha256": "c89eab3351caece2e429ceea32ad01d846dee51c67e949c5f6560c63a7ca6227", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 49, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.117198290616069, - "normalized_entropy": 0.6396497863270086, - "printable_ratio": 0.9560546875, - "repetition_rate_4gram": 0.34644515025653555, - "transition_rate": 0.9343101343101343 - }, - "magnetic_domain": { - "H_field": 4.652223948684523, - "chi_susceptibility": 0.7426485598168072, - "coercive_loss": 2.052223948684523, - "domain_wall_pressure": 1.1757299681071975, - "heat_loss": 1.2022311162798096, - "information_load": 4.652223948684523, - "magnetization_M": 0.6135563176978763, - "overflow": 1.4022239486845232, - "overflow_gate": 0.142625457647, - "remanence": 0.8124026033550279 - }, - "sha256": "6115da1d646a96267003f40136ecfa06d8bc1515834855ee63edda4a82b7019b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 50, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.73064538493964, - "normalized_entropy": 0.591330673117455, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.389201075006108, - "transition_rate": 0.9511599511599511 - }, - "magnetic_domain": { - "H_field": 4.510991943406472, - "chi_susceptibility": 0.749421236094034, - "coercive_loss": 1.9109919434064717, - "domain_wall_pressure": 1.1239177523076862, - "heat_loss": 1.042166084713784, - "information_load": 4.510991943406472, - "magnetization_M": 0.637560598746375, - "overflow": 1.2609919434064718, - "overflow_gate": 0.1735347000723469, - "remanence": 0.8294974068442649 - }, - "sha256": "5e1d9f6bfdfbde51dbea3c6c2b1c43176964c4022b321327aa60ad7b0fa5d7f7", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 51, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.583639432080173, - "normalized_entropy": 0.5729549290100217, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4082580014659174, - "transition_rate": 0.9557997557997558 - }, - "magnetic_domain": { - "H_field": 4.45091136173722, - "chi_susceptibility": 0.7512444302026303, - "coercive_loss": 1.8509113617372202, - "domain_wall_pressure": 1.095083508667677, - "heat_loss": 0.9743754305832071, - "information_load": 4.45091136173722, - "magnetization_M": 0.6488305621233206, - "overflow": 1.2009113617372202, - "overflow_gate": 0.1886366790853821, - "remanence": 0.8361521987149977 - }, - "sha256": "3623c5c9c2f94f94ee6b9be351439503cc44483f4af4ddb3f2331ea97f279668", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 52, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.724084383217622, - "normalized_entropy": 0.5905105479022027, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.38993403371610064, - "transition_rate": 0.9518925518925518 - }, - "magnetic_domain": { - "H_field": 4.508719595738828, - "chi_susceptibility": 0.7497102907679094, - "coercive_loss": 1.908719595738828, - "domain_wall_pressure": 1.1239170363529025, - "heat_loss": 1.0395976009249839, - "information_load": 4.508719595738828, - "magnetization_M": 0.6380133476881851, - "overflow": 1.2587195957388282, - "overflow_gate": 0.17408324741717127, - "remanence": 0.8297633406812793 - }, - "sha256": "731dbd1fbe31dd3e54ec48206f4ab91cf2b626ea7225f81b625b19818a0499bb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 53, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.390840372084811, - "normalized_entropy": 0.6738550465106014, - "printable_ratio": 0.987548828125, - "repetition_rate_4gram": 0.3469337893965307, - "transition_rate": 0.9155067155067155 - }, - "magnetic_domain": { - "H_field": 4.70257477355652, - "chi_susceptibility": 0.7348009251158673, - "coercive_loss": 2.1025747735565203, - "domain_wall_pressure": 1.1371458522203697, - "heat_loss": 1.2593936904724834, - "information_load": 4.70257477355652, - "magnetization_M": 0.605349975947954, - "overflow": 1.4525747735565204, - "overflow_gate": 0.1329921781658424, - "remanence": 0.8126173144714554 - }, - "sha256": "ec33c495a8b553c72225cfb87129312ac4da88c8f405edfa1554415dc2ed1aa6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 54, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.280457907580173, - "normalized_entropy": 0.6600572384475216, - "printable_ratio": 0.968994140625, - "repetition_rate_4gram": 0.4148546298558515, - "transition_rate": 0.9103785103785104 - }, - "magnetic_domain": { - "H_field": 4.577911977504877, - "chi_susceptibility": 0.7326059111324562, - "coercive_loss": 1.9779119775048772, - "domain_wall_pressure": 0.9910477610453178, - "heat_loss": 1.1179259789678888, - "information_load": 4.577911977504877, - "magnetization_M": 0.6239921267895461, - "overflow": 1.3279119775048773, - "overflow_gate": 0.15813246818629376, - "remanence": 0.8383363614819496 - }, - "sha256": "a81158529585601eb0435b6378b956bad7e73c903d23290135175cbfb18a0c0e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 55, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.606088314094863, - "normalized_entropy": 0.5757610392618578, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4099682384559003, - "transition_rate": 0.9650793650793651 - }, - "magnetic_domain": { - "H_field": 4.457384826996947, - "chi_susceptibility": 0.7548380345141801, - "coercive_loss": 1.8573848269969466, - "domain_wall_pressure": 1.1102222532469295, - "heat_loss": 0.9816663312625572, - "information_load": 4.457384826996947, - "magnetization_M": 0.648354371077479, - "overflow": 1.2073848269969467, - "overflow_gate": 0.18694826263123177, - "remanence": 0.8367241104196586 - }, - "sha256": "336001506e35d56f38f6e68894a58d3e75be34021532ef9360eac212d349f0b9", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 56, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.636110044611842, - "normalized_entropy": 0.5795137555764802, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.38260444661617393, - "transition_rate": 0.9709401709401709 - }, - "magnetic_domain": { - "H_field": 4.5068945524386255, - "chi_susceptibility": 0.7570719791128443, - "coercive_loss": 1.9068945524386254, - "domain_wall_pressure": 1.1766714486479941, - "heat_loss": 1.0375349423296898, - "information_load": 4.5068945524386255, - "magnetization_M": 0.6395543581124794, - "overflow": 1.2568945524386255, - "overflow_gate": 0.17452507028798442, - "remanence": 0.8270660807841811 - }, - "sha256": "afb64fd59270a26d1bf5710c5cd6784770810c8395bc5752986a769b390f354f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 57, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.862636806839346, - "normalized_entropy": 0.6078296008549182, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.3718543855362815, - "transition_rate": 0.947985347985348 - }, - "magnetic_domain": { - "H_field": 4.564897625011199, - "chi_susceptibility": 0.7481635125932778, - "coercive_loss": 1.9648976250111985, - "domain_wall_pressure": 1.152261924898133, - "heat_loss": 1.1031770445832993, - "information_load": 4.564897625011199, - "magnetization_M": 0.6281380483387887, - "overflow": 1.3148976250111986, - "overflow_gate": 0.16101677910179196, - "remanence": 0.8229518124405225 - }, - "sha256": "ac4f3c43788f8a7d53b8dcbb84a740267d24b6011b4f8d46c8a06362748ecb71", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 58, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.708489366800305, - "normalized_entropy": 0.5885611708500381, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.37698509650623013, - "transition_rate": 0.968986568986569 - }, - "magnetic_domain": { - "H_field": 4.530964906307732, - "chi_susceptibility": 0.7563303700181275, - "coercive_loss": 1.9309649063077319, - "domain_wall_pressure": 1.1840029449606777, - "heat_loss": 1.0647547283786578, - "information_load": 4.530964906307732, - "magnetization_M": 0.6352242531073777, - "overflow": 1.280964906307732, - "overflow_gate": 0.16878696431448767, - "remanence": 0.8249395864074763 - }, - "sha256": "673262d3740aed502f96edcc0ae888ed72829c7d8c6a9e2c390e861e3dbe4f79", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 59, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.710119599095337, - "normalized_entropy": 0.5887649498869171, - "printable_ratio": 0.991943359375, - "repetition_rate_4gram": 0.39677498167603226, - "transition_rate": 0.9702075702075702 - }, - "magnetic_domain": { - "H_field": 4.50294879192176, - "chi_susceptibility": 0.756794230405698, - "coercive_loss": 1.90294879192176, - "domain_wall_pressure": 1.1468651770630758, - "heat_loss": 1.0330761605832743, - "information_load": 4.50294879192176, - "magnetization_M": 0.6403966431499367, - "overflow": 1.2529487919217601, - "overflow_gate": 0.17548413211783978, - "remanence": 0.8322059607264379 - }, - "sha256": "279e8cc94614313204669f714269a792304d854f89e2a522d3626b50678fb3f2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 60, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.703412940480784, - "normalized_entropy": 0.587926617560098, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.41925238211580745, - "transition_rate": 0.9694749694749695 - }, - "magnetic_domain": { - "H_field": 4.467846561730078, - "chi_susceptibility": 0.7565160562188811, - "coercive_loss": 1.8678465617300781, - "domain_wall_pressure": 1.100445174718324, - "heat_loss": 0.9934564949944563, - "information_load": 4.467846561730078, - "magnetization_M": 0.6468953395584248, - "overflow": 1.2178465617300782, - "overflow_gate": 0.18425150900524973, - "remanence": 0.839760404024586 - }, - "sha256": "f3fb77bdcbbe0f4fb1f238334ee7ccaf3d0c83202671f158599c8f6e844499d4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 61, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.542096104549125, - "normalized_entropy": 0.5677620130686406, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4439286586855607, - "transition_rate": 0.9714285714285714 - }, - "magnetic_domain": { - "H_field": 4.394092599635638, - "chi_susceptibility": 0.7572569089048107, - "coercive_loss": 1.7940925996356376, - "domain_wall_pressure": 1.0549998254860213, - "heat_loss": 0.910553476749531, - "information_load": 4.394092599635638, - "magnetization_M": 0.6612943729808165, - "overflow": 1.1440925996356377, - "overflow_gate": 0.2041260672086181, - "remanence": 0.8473074555594934 - }, - "sha256": "daa56e868d6786f43ce59a46cfe67793a281e5b6c9f4f50500a2f52e178ce9cf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 62, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.49119766404181, - "normalized_entropy": 0.5613997080052262, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4739799657952602, - "transition_rate": 0.9736263736263736 - }, - "magnetic_domain": { - "H_field": 4.336910814205816, - "chi_susceptibility": 0.7580867627478128, - "coercive_loss": 1.736910814205816, - "domain_wall_pressure": 0.9992928156622268, - "heat_loss": 0.8467049116033678, - "information_load": 4.336910814205816, - "magnetization_M": 0.6734314762576873, - "overflow": 1.0869108142058161, - "overflow_gate": 0.22099872359625192, - "remanence": 0.8555904456126733 - }, - "sha256": "80923b6ac281bbdeb6a58f80953b5f418f41721b4422342a00e22a79eaa3decd", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 63, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.644142048512011, - "normalized_entropy": 0.5805177560640014, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3767407769362326, - "transition_rate": 0.968986568986569 - }, - "magnetic_domain": { - "H_field": 4.516486218270126, - "chi_susceptibility": 0.7563303700181275, - "coercive_loss": 1.9164862182701259, - "domain_wall_pressure": 1.1844915841006727, - "heat_loss": 1.0483776515823462, - "information_load": 4.516486218270126, - "magnetization_M": 0.6376796976447303, - "overflow": 1.266486218270126, - "overflow_gate": 0.17221550739469624, - "remanence": 0.8248459431701471 - }, - "sha256": "2969ddcc33544a3ea3fb7fdeed7cb8432620fcb6df66547660c27ea36ac4eacf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 64, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.598604878039362, - "normalized_entropy": 0.5748256097549203, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3725873442462741, - "transition_rate": 0.9626373626373627 - }, - "magnetic_domain": { - "H_field": 4.50936909411017, - "chi_susceptibility": 0.7538991110166879, - "coercive_loss": 1.9093690941101698, - "domain_wall_pressure": 1.1801000367821772, - "heat_loss": 1.0403317111354085, - "information_load": 4.50936909411017, - "magnetization_M": 0.6383984022079312, - "overflow": 1.2593690941101698, - "overflow_gate": 0.17392628102369492, - "remanence": 0.8232385394398739 - }, - "sha256": "0139f135f2c64776f52c08d750f131cf9249831c907c7328c60c302b629f35de", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 65, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.6604573413628385, - "normalized_entropy": 0.5825571676703548, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4102125580258979, - "transition_rate": 0.9560439560439561 - }, - "magnetic_domain": { - "H_field": 4.4659355574484385, - "chi_susceptibility": 0.7513398969277603, - "coercive_loss": 1.8659355574484384, - "domain_wall_pressure": 1.0916627960361165, - "heat_loss": 0.9913021708829945, - "information_load": 4.4659355574484385, - "magnetization_M": 0.6461416441756181, - "overflow": 1.2159355574484385, - "overflow_gate": 0.18474119388104948, - "remanence": 0.8368054863340045 - }, - "sha256": "e6c04351045e1cdc7d91cb7f6d1c3b7806ab833d096ee32787ffa0abe26bd990", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 66, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.621085523678572, - "normalized_entropy": 0.5776356904598215, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3647691180063523, - "transition_rate": 0.9643467643467644 - }, - "magnetic_domain": { - "H_field": 4.526561667398683, - "chi_susceptibility": 0.7545568611895902, - "coercive_loss": 1.926561667398683, - "domain_wall_pressure": 1.1991552926808242, - "heat_loss": 1.0597729494053354, - "information_load": 4.526561667398683, - "magnetization_M": 0.635463155290303, - "overflow": 1.276561667398683, - "overflow_gate": 0.16982236231102685, - "remanence": 0.820131396805168 - }, - "sha256": "b32dfccc55dbacfbcf14f9d680b1bb8c891fc466c344a2affa5991ce90464922", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 67, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.721531565069411, - "normalized_entropy": 0.5901914456336764, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.36330320058636695, - "transition_rate": 0.9575091575091575 - }, - "magnetic_domain": { - "H_field": 4.54905246011949, - "chi_susceptibility": 0.7519116716198543, - "coercive_loss": 1.9490524601194896, - "domain_wall_pressure": 1.1884119138455813, - "heat_loss": 1.0852289676984517, - "information_load": 4.54905246011949, - "magnetization_M": 0.6312304951233306, - "overflow": 1.2990524601194897, - "overflow_gate": 0.1645995823766579, - "remanence": 0.8195366063358391 - }, - "sha256": "f6495a9e4bb67f46aeb51f43c68541d05bca6e456c8a60b23fe9d334a11c8cdc", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 68, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.141017563964026, - "normalized_entropy": 0.6426271954955033, - "printable_ratio": 0.98974609375, - "repetition_rate_4gram": 0.46469582213535304, - "transition_rate": 0.9296703296703297 - }, - "magnetic_domain": { - "H_field": 4.480583237376457, - "chi_susceptibility": 0.7407410090649996, - "coercive_loss": 1.8805832373764573, - "domain_wall_pressure": 0.9299490150699533, - "heat_loss": 1.007822085984406, - "information_load": 4.480583237376457, - "magnetization_M": 0.6422219256173295, - "overflow": 1.2305832373764574, - "overflow_gate": 0.1810207913013402, - "remanence": 0.8531290368881872 - }, - "sha256": "79b0cef1be2a7650fcf8ef349c6c498aad3873c812c6ebd7e93de527b5dedd6e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 69, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.864737000221816, - "normalized_entropy": 0.608092125027727, - "printable_ratio": 0.996337890625, - "repetition_rate_4gram": 0.4519912044954801, - "transition_rate": 0.9514041514041514 - }, - "magnetic_domain": { - "H_field": 4.447820817785058, - "chi_susceptibility": 0.7495176370692319, - "coercive_loss": 1.8478208177850575, - "domain_wall_pressure": 0.9988258938173425, - "heat_loss": 0.9708959066073992, - "information_load": 4.447820817785058, - "magnetization_M": 0.649648192404269, - "overflow": 1.1978208177850576, - "overflow_gate": 0.18944812764005473, - "remanence": 0.8496215739584099 - }, - "sha256": "29492658dfb257923e5c985802849efa07f59f0affce8cb1f381dc068e3ef3d4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 70, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.002064411450297, - "normalized_entropy": 0.6252580514312871, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.33691668702663086, - "transition_rate": 0.9509157509157509 - }, - "magnetic_domain": { - "H_field": 4.64727938224172, - "chi_susceptibility": 0.7493247856635935, - "coercive_loss": 2.04727938224172, - "domain_wall_pressure": 1.22799812777824, - "heat_loss": 1.1966184623371339, - "information_load": 4.64727938224172, - "magnetization_M": 0.6151799920956749, - "overflow": 1.3972793822417202, - "overflow_gate": 0.14360830228716095, - "remanence": 0.80811514029207 - }, - "sha256": "13a5af9ebde88605f3ee663c35bfe51598b43164c81ef94ef39eaad8c161ffd6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 71, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.103529806605619, - "normalized_entropy": 0.6379412258257023, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.3960420229660396, - "transition_rate": 0.9543345543345544 - }, - "magnetic_domain": { - "H_field": 4.585926106877718, - "chi_susceptibility": 0.7506706016243074, - "coercive_loss": 1.9859261068777179, - "domain_wall_pressure": 1.1165850627370295, - "heat_loss": 1.1270111807398673, - "information_load": 4.585926106877718, - "magnetization_M": 0.6255489622908158, - "overflow": 1.335926106877718, - "overflow_gate": 0.15638209708029407, - "remanence": 0.8319476093695469 - }, - "sha256": "13d553dd546cc51717b8887146c801a29f474e85e30182761bcd385c8451f4e2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 72, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.263957141393325, - "normalized_entropy": 0.6579946426741656, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39824089909601756, - "transition_rate": 0.9531135531135531 - }, - "magnetic_domain": { - "H_field": 4.617130687234158, - "chi_susceptibility": 0.750191061541498, - "coercive_loss": 2.017130687234158, - "domain_wall_pressure": 1.109745308035071, - "heat_loss": 1.162403804302594, - "information_load": 4.617130687234158, - "magnetization_M": 0.6206618868355961, - "overflow": 1.367130687234158, - "overflow_gate": 0.1497493142705669, - "remanence": 0.8327202877227399 - }, - "sha256": "8dfce2bce33a3ff75cdec604e27d496ac612ace99f9744163f31ad1a9fc22ae5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 73, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.52816542656691, - "normalized_entropy": 0.6910206783208638, - "printable_ratio": 0.957763671875, - "repetition_rate_4gram": 0.5455655998045443, - "transition_rate": 0.9387057387057387 - }, - "magnetic_domain": { - "H_field": 4.438719749073198, - "chi_susceptibility": 0.7444385744563514, - "coercive_loss": 1.838719749073198, - "domain_wall_pressure": 0.7862802778023887, - "heat_loss": 0.9606543298155302, - "information_load": 4.438719749073198, - "magnetization_M": 0.6513327664461528, - "overflow": 1.188719749073198, - "overflow_gate": 0.19185802156940876, - "remanence": 0.8721157301088875 - }, - "sha256": "98e64149b0f8c029920f3d936e3c38c7cabfb5c589ebfdc92781fcf41fbca67f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 74, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.169024603420792, - "normalized_entropy": 0.646128075427599, - "printable_ratio": 0.951171875, - "repetition_rate_4gram": 0.4219398973857806, - "transition_rate": 0.9057387057387057 - }, - "magnetic_domain": { - "H_field": 4.5411393331863, - "chi_susceptibility": 0.7305992744151683, - "coercive_loss": 1.9411393331863, - "domain_wall_pressure": 0.9675976167058503, - "heat_loss": 1.076269753552952, - "information_load": 4.5411393331863, - "magnetization_M": 0.6296019281237594, - "overflow": 1.2911393331863001, - "overflow_gate": 0.1664185840447512, - "remanence": 0.8406183680224294 - }, - "sha256": "ee39eb8637afc3156a33eecef480a84fdc55bcbd3cd0995842ada70e2e225adf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 75, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.617394183572785, - "normalized_entropy": 0.5771742729465982, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4009284143659907, - "transition_rate": 0.95995115995116 - }, - "magnetic_domain": { - "H_field": 4.471282445944618, - "chi_susceptibility": 0.7528607349280803, - "coercive_loss": 1.8712824459446176, - "domain_wall_pressure": 1.1180454911703386, - "heat_loss": 0.9973305784628647, - "information_load": 4.471282445944618, - "magnetization_M": 0.6453320042956779, - "overflow": 1.2212824459446177, - "overflow_gate": 0.18337434409657324, - "remanence": 0.8336550771169047 - }, - "sha256": "8c014c1d94f262441e26740be91da2022158fbbd9789d0550425983399e5d4de", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 76, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.544461318808388, - "normalized_entropy": 0.5680576648510485, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4067920840459321, - "transition_rate": 0.9677655677655678 - }, - "magnetic_domain": { - "H_field": 4.448798445535463, - "chi_susceptibility": 0.755865324315321, - "coercive_loss": 1.8487984455354627, - "domain_wall_pressure": 1.1219469674392712, - "heat_loss": 0.9719964891768069, - "information_load": 4.448798445535463, - "magnetization_M": 0.6500826396258793, - "overflow": 1.1987984455354628, - "overflow_gate": 0.18919106644099046, - "remanence": 0.8356587902270582 - }, - "sha256": "6bfb91fbd0ef6088809441c33ff8758cbeabc8678d416fc87ca366155e2ae94b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 77, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.55992484556078, - "normalized_entropy": 0.5699906056950975, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43391155631566086, - "transition_rate": 0.9714285714285714 - }, - "magnetic_domain": { - "H_field": 4.413409228101243, - "chi_susceptibility": 0.7572569089048107, - "coercive_loss": 1.8134092281012433, - "domain_wall_pressure": 1.075034030225821, - "heat_loss": 0.9322136956473346, - "information_load": 4.413409228101243, - "magnetization_M": 0.6574052188432227, - "overflow": 1.1634092281012434, - "overflow_gate": 0.19872245025186394, - "remanence": 0.8443311908112425 - }, - "sha256": "4be47c247ad17ba9f6fb8a5dc12639f7b5212852481963176d42c4348bdcb346", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 78, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.714525051882252, - "normalized_entropy": 0.5893156314852815, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.42682628878573176, - "transition_rate": 0.9636141636141636 - }, - "magnetic_domain": { - "H_field": 4.456706341360121, - "chi_susceptibility": 0.7542752564340349, - "coercive_loss": 1.856706341360121, - "domain_wall_pressure": 1.0735757496568636, - "heat_loss": 0.9809020028403694, - "information_load": 4.456706341360121, - "magnetization_M": 0.6486031535804965, - "overflow": 1.2067063413601211, - "overflow_gate": 0.18712451470607153, - "remanence": 0.8421549912265479 - }, - "sha256": "6b3593518ca599ce9529d7792016f01aa0e3f6edd1cdb2c9cbc3a99202babbef", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 79, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.462536019981484, - "normalized_entropy": 0.5578170024976855, - "printable_ratio": 0.9912109375, - "repetition_rate_4gram": 0.4546787197654532, - "transition_rate": 0.9794871794871794 - }, - "magnetic_domain": { - "H_field": 4.362211587841344, - "chi_susceptibility": 0.7602811900429444, - "coercive_loss": 1.7622115878413438, - "domain_wall_pressure": 1.0496169194434524, - "heat_loss": 0.8749015608364872, - "information_load": 4.362211587841344, - "magnetization_M": 0.6684269828211151, - "overflow": 1.1122115878413439, - "overflow_gate": 0.2133676987356733, - "remanence": 0.8503774378095812 - }, - "sha256": "ae855ded766a9e56289c8e36f8b100b4aa544f868979889f35d6196d77b6f834", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 80, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.530978846417082, - "normalized_entropy": 0.5663723558021353, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4507696066454923, - "transition_rate": 0.9697191697191697 - }, - "magnetic_domain": { - "H_field": 4.3803949864509875, - "chi_susceptibility": 0.756608828268981, - "coercive_loss": 1.7803949864509874, - "domain_wall_pressure": 1.0378991261473547, - "heat_loss": 0.8952201177806836, - "information_load": 4.3803949864509875, - "magnetization_M": 0.6639627262604497, - "overflow": 1.1303949864509875, - "overflow_gate": 0.20804663103528445, - "remanence": 0.8492754690578336 - }, - "sha256": "b4a0f5d63052cd7464a420eb8fe2abc09ed3b2ed286fe279da6f3ccee2af3df0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 81, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.545251674428519, - "normalized_entropy": 0.5681564593035648, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3794282922062057, - "transition_rate": 0.9687423687423687 - }, - "magnetic_domain": { - "H_field": 4.489475960089651, - "chi_susceptibility": 0.7562374558126017, - "coercive_loss": 1.889475960089651, - "domain_wall_pressure": 1.1786281530723262, - "heat_loss": 1.01785920371383, - "information_load": 4.489475960089651, - "magnetization_M": 0.6423981493920173, - "overflow": 1.239475960089651, - "overflow_gate": 0.17879875327294886, - "remanence": 0.8258705409372274 - }, - "sha256": "1c142117e8c7a25c721cfad8f3c331b303a0460f4d9f6dd4b1197dc0e604342c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 82, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.824497570610194, - "normalized_entropy": 0.6030621963262742, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4067920840459321, - "transition_rate": 0.944078144078144 - }, - "magnetic_domain": { - "H_field": 4.503571855869154, - "chi_susceptibility": 0.7466039785895592, - "coercive_loss": 1.9035718558691541, - "domain_wall_pressure": 1.074572120064424, - "heat_loss": 1.0337801693979027, - "information_load": 4.503571855869154, - "magnetization_M": 0.6385725414142547, - "overflow": 1.2535718558691542, - "overflow_gate": 0.1753323396997141, - "remanence": 0.8356587902270582 - }, - "sha256": "6be86189f7550c5cf2912b6de28535d2e7687ea1034e81da8658ba32b9ccb8cc", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 83, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.075987956425114, - "normalized_entropy": 0.6344984945531392, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.47446860493525533, - "transition_rate": 0.9301587301587302 - }, - "magnetic_domain": { - "H_field": 4.451399817455093, - "chi_susceptibility": 0.7409426846970574, - "coercive_loss": 1.8513998174550932, - "domain_wall_pressure": 0.9113802504469497, - "heat_loss": 0.9749254404098034, - "information_load": 4.451399817455093, - "magnetization_M": 0.6476107327593523, - "overflow": 1.2013998174550933, - "overflow_gate": 0.1885087493396054, - "remanence": 0.8557177100958827 - }, - "sha256": "44dcab20a9a4f7a295b75279933a8ba0e094c372cd1583c460092f0c91610a77", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 84, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.280666076620563, - "normalized_entropy": 0.6600832595775704, - "printable_ratio": 0.970947265625, - "repetition_rate_4gram": 0.4595651111654043, - "transition_rate": 0.9433455433455433 - }, - "magnetic_domain": { - "H_field": 4.524580556316112, - "chi_susceptibility": 0.7463101363146399, - "coercive_loss": 1.9245805563161116, - "domain_wall_pressure": 0.967560864360278, - "heat_loss": 1.0575318773602143, - "information_load": 4.524580556316112, - "magnetization_M": 0.6355726369988887, - "overflow": 1.2745805563161117, - "overflow_gate": 0.17029027932391175, - "remanence": 0.8517324446215429 - }, - "sha256": "1f174429aeffcfda41416b482cb72a1b99761883b5de7bd2edc6fed0d91a661f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 85, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.2663223111073165, - "normalized_entropy": 0.6582902888884146, - "printable_ratio": 0.947509765625, - "repetition_rate_4gram": 0.6081114097239189, - "transition_rate": 0.8764346764346764 - }, - "magnetic_domain": { - "H_field": 4.2494839312326524, - "chi_susceptibility": 0.7174581204107682, - "coercive_loss": 1.6494839312326524, - "domain_wall_pressure": 0.5366465334215151, - "heat_loss": 0.7500817073110861, - "information_load": 4.2494839312326524, - "magnetization_M": 0.6847091414201097, - "overflow": 0.9994839312326524, - "overflow_gate": 0.2495309990766748, - "remanence": 0.8837397565721266 - }, - "sha256": "f78da7294f9f36e494f0c6490c91313429f382f19fe905d51950bc1767da6ca6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 86, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.834682503590547, - "normalized_entropy": 0.6043353129488184, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8084534571219155, - "transition_rate": 0.843956043956044 - }, - "magnetic_domain": { - "H_field": 3.7404794542189643, - "chi_susceptibility": 0.7019004866276322, - "coercive_loss": 1.1404794542189642, - "domain_wall_pressure": 0.07100517366825687, - "heat_loss": 0.24229755881998655, - "information_load": 3.7404794542189643, - "magnetization_M": 0.8176291971313414, - "overflow": 0.49047945421896433, - "overflow_gate": 0.5059985556259042, - "remanence": 0.9099558909262906 - }, - "sha256": "8cedeae50dcc11e80f5f6db44471602104135397ea93b7db1828d2ead499f0af", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 87, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.870157575054723, - "normalized_entropy": 0.6087696968818403, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3816271683361837, - "transition_rate": 0.9584859584859585 - }, - "magnetic_domain": { - "H_field": 4.556753109220864, - "chi_susceptibility": 0.7522918799957201, - "coercive_loss": 1.9567531092208639, - "domain_wall_pressure": 1.1537175802995496, - "heat_loss": 1.0939503075714954, - "information_load": 4.556753109220864, - "magnetization_M": 0.630309724139082, - "overflow": 1.306753109220864, - "overflow_gate": 0.1628485137305316, - "remanence": 0.8266999745956474 - }, - "sha256": "1e47ad2355ebcddbf1399027cedc28b6c5fe5a6cb41b7f019e421ee84be8fa73", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 88, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.020192694779582, - "normalized_entropy": 0.6275240868474478, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7158563400928414, - "transition_rate": 0.9528693528693528 - }, - "magnetic_domain": { - "H_field": 4.029120953058969, - "chi_susceptibility": 0.7500950059556443, - "coercive_loss": 1.4291209530589692, - "domain_wall_pressure": 0.47402602555302287, - "heat_loss": 0.5150932941907733, - "information_load": 4.029120953058969, - "magnetization_M": 0.7477848689296646, - "overflow": 0.7791209530589693, - "overflow_gate": 0.3388789094062684, - "remanence": 0.8994793457438971 - }, - "sha256": "3d9ce3b2881b58ae5792646adb77c1404a7f5e74cd50fe6cd3ef30c86552ee49", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 89, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.366963211350658, - "normalized_entropy": 0.6708704014188323, - "printable_ratio": 0.95849609375, - "repetition_rate_4gram": 0.5844124114341559, - "transition_rate": 0.8764346764346764 - }, - "magnetic_domain": { - "H_field": 4.31196824905812, - "chi_susceptibility": 0.7174581204107682, - "coercive_loss": 1.71196824905812, - "domain_wall_pressure": 0.5840445300010411, - "heat_loss": 0.8190017906486526, - "information_load": 4.31196824905812, - "magnetization_M": 0.6711068532424248, - "overflow": 1.0619682490581202, - "overflow_gate": 0.22878881607332338, - "remanence": 0.8795928573529845 - }, - "sha256": "fb917adbc82e9176047ea2527e7222f26a86b7098c0723b15571bf771a5dd16a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 90, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.9881290746034646, - "normalized_entropy": 0.6235161343254331, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.391399951136086, - "transition_rate": 0.9255189255189256 - }, - "magnetic_domain": { - "H_field": 4.555091904313347, - "chi_susceptibility": 0.7390183407297088, - "coercive_loss": 1.9550919043133468, - "domain_wall_pressure": 1.0682379487656792, - "heat_loss": 1.0920687006508862, - "information_load": 4.555091904313347, - "magnetization_M": 0.6284159700516685, - "overflow": 1.3050919043133469, - "overflow_gate": 0.16322467633000864, - "remanence": 0.8302927274235011 - }, - "sha256": "021a2997e40a09572e0f849aeff5b0f784d124949325d7589e9f8c168c389ecf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 91, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.180758912631443, - "normalized_entropy": 0.6475948640789304, - "printable_ratio": 0.983642578125, - "repetition_rate_4gram": 0.3637918397263621, - "transition_rate": 0.9435897435897436 - }, - "magnetic_domain": { - "H_field": 4.645219222256147, - "chi_susceptibility": 0.7464081340761185, - "coercive_loss": 2.0452192222561467, - "domain_wall_pressure": 1.159595807726763, - "heat_loss": 1.1942800261974524, - "information_load": 4.645219222256147, - "magnetization_M": 0.6154149455378619, - "overflow": 1.3952192222561468, - "overflow_gate": 0.14401980194464686, - "remanence": 0.8197353064235537 - }, - "sha256": "d3d9219a3971839840e3abc6b86b36a9665203ee937a87756ed5556fee553fbb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 92, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.474773003647323, - "normalized_entropy": 0.5593466254559154, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.40972391888590276, - "transition_rate": 0.9765567765567765 - }, - "magnetic_domain": { - "H_field": 4.431600175515736, - "chi_susceptibility": 0.7591873293460869, - "coercive_loss": 1.8316001755157356, - "domain_wall_pressure": 1.1336657153417475, - "heat_loss": 0.9526479121570887, - "information_load": 4.431600175515736, - "magnetization_M": 0.6539769011873712, - "overflow": 1.1816001755157357, - "overflow_gate": 0.19376458137264221, - "remanence": 0.8366426533096526 - }, - "sha256": "65931a298fde57dcf083dc395ff2d4f5f5c256a832ed78ce5111e59cedff0614", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 93, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.56538299273728, - "normalized_entropy": 0.57067287409216, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43000244319569997, - "transition_rate": 0.9706959706959707 - }, - "magnetic_domain": { - "H_field": 4.420272017984015, - "chi_susceptibility": 0.7569794434403635, - "coercive_loss": 1.8202720179840148, - "domain_wall_pressure": 1.0813870550005413, - "heat_loss": 0.9399188389857632, - "information_load": 4.420272017984015, - "magnetization_M": 0.6559823054160998, - "overflow": 1.170272017984015, - "overflow_gate": 0.19683729548201348, - "remanence": 0.8431380063618594 - }, - "sha256": "caa1c7c4e0a2c80dc4241465d8cc6c3b40a1bf5fb78c9462de3a66c0fa108e89", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 94, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.409550273251983, - "normalized_entropy": 0.5511937841564979, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4219398973857806, - "transition_rate": 0.9724053724053724 - }, - "magnetic_domain": { - "H_field": 4.396315869662351, - "chi_susceptibility": 0.7576262030414115, - "coercive_loss": 1.7963158696623513, - "domain_wall_pressure": 1.1009309500391837, - "heat_loss": 0.9130443459884988, - "information_load": 4.396315869662351, - "magnetization_M": 0.6606271976184532, - "overflow": 1.1463158696623514, - "overflow_gate": 0.2034967235885542, - "remanence": 0.8406183680224294 - }, - "sha256": "58f91c709dcbc1f732b56e0141959aebba08366a8febaf0e10fc548bc421c459", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 95, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.543108479061861, - "normalized_entropy": 0.5678885598827327, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4197410212558026, - "transition_rate": 0.9692307692307692 - }, - "magnetic_domain": { - "H_field": 4.429838876116428, - "chi_susceptibility": 0.7564232368110728, - "coercive_loss": 1.8298388761164275, - "domain_wall_pressure": 1.0989794959499333, - "heat_loss": 0.9506679661873856, - "information_load": 4.429838876116428, - "magnetization_M": 0.6539153698981244, - "overflow": 1.1798388761164276, - "overflow_gate": 0.19423915804790542, - "remanence": 0.8399170838548184 - }, - "sha256": "e7f4fa56faaa6a3f2fb7ef93c11131014c79989631e0c9f0747e64910ef41278", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 96, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.489725218119944, - "normalized_entropy": 0.561215652264993, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.48668458343513316, - "transition_rate": 0.9741147741147741 - }, - "magnetic_domain": { - "H_field": 4.316950213870658, - "chi_susceptibility": 0.7582706581845113, - "coercive_loss": 1.7169502138706583, - "domain_wall_pressure": 0.9748603813592819, - "heat_loss": 0.8245271753076601, - "information_load": 4.316950213870658, - "magnetization_M": 0.6778343564913651, - "overflow": 1.0669502138706584, - "overflow_gate": 0.2272112001210828, - "remanence": 0.8588279929638188 - }, - "sha256": "3886cf5cc90044e8bdcdc1c8cb9f7b4b5b8f568b7e8e78fe20d181c41420bbb3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 97, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.265204961458157, - "normalized_entropy": 0.6581506201822697, - "printable_ratio": 0.9892578125, - "repetition_rate_4gram": 0.31468360615685315, - "transition_rate": 0.9257631257631258 - }, - "magnetic_domain": { - "H_field": 4.725532299212409, - "chi_susceptibility": 0.7391200929570091, - "coercive_loss": 2.125532299212409, - "domain_wall_pressure": 1.2221590392125452, - "heat_loss": 1.285456360656468, - "information_load": 4.725532299212409, - "magnetization_M": 0.6024617434454456, - "overflow": 1.475532299212409, - "overflow_gate": 0.12881855494278052, - "remanence": 0.7973059971277174 - }, - "sha256": "567c725f34d214f2e8ed6c9be5d0aef5103324534c4dc2439f30a8cf02bc921e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 98, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.362787063456719, - "normalized_entropy": 0.6703483829320899, - "printable_ratio": 0.959716796875, - "repetition_rate_4gram": 0.4505252870754948, - "transition_rate": 0.9235653235653236 - }, - "magnetic_domain": { - "H_field": 4.547623146727817, - "chi_susceptibility": 0.7382024293294918, - "coercive_loss": 1.9476231467278171, - "domain_wall_pressure": 0.9460800729796577, - "heat_loss": 1.0836104912855709, - "information_load": 4.547623146727817, - "magnetization_M": 0.6302102739698597, - "overflow": 1.2976231467278172, - "overflow_gate": 0.16492666301608175, - "remanence": 0.8492060568102274 - }, - "sha256": "be9f2140dd6e56b0389bb3f17a84d97032294949f475abf59bd7ceb1bdde8f2a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 99, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.076446605355212, - "normalized_entropy": 0.6345558256694015, - "printable_ratio": 0.990234375, - "repetition_rate_4gram": 0.3923772294160762, - "transition_rate": 0.8947496947496948 - }, - "magnetic_domain": { - "H_field": 4.55965131986786, - "chi_susceptibility": 0.7257671082413699, - "coercive_loss": 1.9596513198678598, - "domain_wall_pressure": 1.0047449306672371, - "heat_loss": 1.0972333167445758, - "information_load": 4.55965131986786, - "magnetization_M": 0.6254027243002411, - "overflow": 1.3096513198678599, - "overflow_gate": 0.1621943183661407, - "remanence": 0.8306438265475008 - }, - "sha256": "622d48f4a2a73481139c48e58183b7c1b3bdec86bf7286554c1e4017f4b2f21f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 100, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.669565403417368, - "normalized_entropy": 0.583695675427171, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39188859027608114, - "transition_rate": 0.9641025641025641 - }, - "magnetic_domain": { - "H_field": 4.498317791123844, - "chi_susceptibility": 0.7544630409114862, - "coercive_loss": 1.8983177911238438, - "domain_wall_pressure": 1.144427947652966, - "heat_loss": 1.0278443042012837, - "information_load": 4.498317791123844, - "magnetization_M": 0.6407085712352216, - "overflow": 1.248317791123844, - "overflow_gate": 0.1766164741784789, - "remanence": 0.8304684587665162 - }, - "sha256": "78e462dc6925a2243ef6e9fb62207c4d95fc18fa6bcda5532458f40c11084da3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 101, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.656938912445013, - "normalized_entropy": 0.5821173640556266, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3794282922062057, - "transition_rate": 0.9643467643467644 - }, - "magnetic_domain": { - "H_field": 4.513671596041018, - "chi_susceptibility": 0.7545568611895902, - "coercive_loss": 1.9136715960410178, - "domain_wall_pressure": 1.1698369442811174, - "heat_loss": 1.045195351334856, - "information_load": 4.513671596041018, - "magnetization_M": 0.6378836017491305, - "overflow": 1.2636715960410179, - "overflow_gate": 0.17289004943264571, - "remanence": 0.8258705409372274 - }, - "sha256": "2bf0ecd36250afd684a51a3dd607a0bd19ab6f5d4828fe0ca4d79d862fa6333d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 102, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.728324258754827, - "normalized_entropy": 0.5910405323443534, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.37234302467627656, - "transition_rate": 0.9645909645909646 - }, - "magnetic_domain": { - "H_field": 4.540461710444573, - "chi_susceptibility": 0.7546506335309299, - "coercive_loss": 1.940461710444573, - "domain_wall_pressure": 1.184495879829376, - "heat_loss": 1.0755026878842893, - "information_load": 4.540461710444573, - "magnetization_M": 0.6332680826728663, - "overflow": 1.290461710444573, - "overflow_gate": 0.166575281405466, - "remanence": 0.8231430670181048 - }, - "sha256": "c1fffe2dabd12e2e20df3b5efccf303afaf80e47567f9129f0816ce7d43a9d36", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 103, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.762544919188796, - "normalized_entropy": 0.5953181148985995, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3664793549963352, - "transition_rate": 0.9567765567765568 - }, - "magnetic_domain": { - "H_field": 4.553565067935333, - "chi_susceptibility": 0.7516260038886549, - "coercive_loss": 1.9535650679353327, - "domain_wall_pressure": 1.1805944035604432, - "heat_loss": 1.0903393930798788, - "information_load": 4.553565067935333, - "magnetization_M": 0.6304910949970967, - "overflow": 1.3035650679353328, - "overflow_gate": 0.16357117883894673, - "remanence": 0.820820382611741 - }, - "sha256": "c865228359df044bedbbbd9dd9045d900dfd3fb05afbad4e5344d0c730a5f2cf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 104, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.67229890798876, - "normalized_entropy": 0.584037363498595, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.36843391155631566, - "transition_rate": 0.9650793650793651 - }, - "magnetic_domain": { - "H_field": 4.533425062297326, - "chi_susceptibility": 0.7548380345141801, - "coercive_loss": 1.9334250622973257, - "domain_wall_pressure": 1.1932909070460989, - "heat_loss": 1.0675385627699643, - "information_load": 4.533425062297326, - "magnetization_M": 0.6344162184933422, - "overflow": 1.2834250622973258, - "overflow_gate": 0.16821122313205067, - "remanence": 0.821601359891906 - }, - "sha256": "0a92a015d72cb822a54fd5100e1e5f11a857b9d28ba92ac7546fc9b9191b2f69", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 105, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.921601519991674, - "normalized_entropy": 0.6152001899989592, - "printable_ratio": 0.998291015625, - "repetition_rate_4gram": 0.348399706816516, - "transition_rate": 0.9533577533577534 - }, - "magnetic_domain": { - "H_field": 4.614033021415169, - "chi_susceptibility": 0.7502870678997496, - "coercive_loss": 2.014033021415169, - "domain_wall_pressure": 1.2099160930824748, - "heat_loss": 1.1588893151257824, - "information_load": 4.614033021415169, - "magnetization_M": 0.6204607890722273, - "overflow": 1.3640330214151692, - "overflow_gate": 0.150394970699868, - "remanence": 0.8132585089880463 - }, - "sha256": "c7c0b649ef00703189889745523a9ecec3160dd36ce507f721da411068b94aa5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 106, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.098745346117113, - "normalized_entropy": 0.6373431682646391, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5812362570241877, - "transition_rate": 0.9557997557997558 - }, - "magnetic_domain": { - "H_field": 4.2939605840608195, - "chi_susceptibility": 0.7512444302026303, - "coercive_loss": 1.6939605840608194, - "domain_wall_pressure": 0.7491269975511363, - "heat_loss": 0.7990650428164007, - "information_load": 4.2939605840608195, - "magnetization_M": 0.682272756102579, - "overflow": 1.0439605840608195, - "overflow_gate": 0.23458312984559157, - "remanence": 0.879014498751127 - }, - "sha256": "1ed43870f99919949b0ffe5ad25d8d6f5668dbe1e34217547cdc3da0ba195dd7", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 107, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.162567804288404, - "normalized_entropy": 0.6453209755360505, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.48497434644515025, - "transition_rate": 0.9538461538461539 - }, - "magnetic_domain": { - "H_field": 4.46394970463078, - "chi_susceptibility": 0.7504789330469903, - "coercive_loss": 1.8639497046307798, - "domain_wall_pressure": 0.9377436148020073, - "heat_loss": 0.9890637771054952, - "information_load": 4.46394970463078, - "magnetization_M": 0.6472518549732817, - "overflow": 1.21394970463078, - "overflow_gate": 0.18525143724441478, - "remanence": 0.8584006503952536 - }, - "sha256": "12db250564bb4e9df8026f817550f5f2a7b0b8b4d05b4aa1b5f3644813635a81", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 108, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.029938582225726, - "normalized_entropy": 0.6287423227782157, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.48424138773515757, - "transition_rate": 0.9506715506715506 - }, - "magnetic_domain": { - "H_field": 4.434624116412035, - "chi_susceptibility": 0.7492282857493898, - "coercive_loss": 1.834624116412035, - "domain_wall_pressure": 0.9328603258727861, - "heat_loss": 0.9560479403757299, - "information_load": 4.434624116412035, - "magnetization_M": 0.652419372302014, - "overflow": 1.184624116412035, - "overflow_gate": 0.19295249258356476, - "remanence": 0.8582167105445476 - }, - "sha256": "13cba6b27e5fbf0b8c5f802b9852bbbbb1e5a3152f010a4c58df0fc6828a2b88", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 109, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.348500402609681, - "normalized_entropy": 0.6685625503262101, - "printable_ratio": 0.971923828125, - "repetition_rate_4gram": 0.44368433911556315, - "transition_rate": 0.9199023199023199 - }, - "magnetic_domain": { - "H_field": 4.553388598178379, - "chi_susceptibility": 0.7366634804392072, - "coercive_loss": 1.9533885981783787, - "domain_wall_pressure": 0.9524359615735134, - "heat_loss": 1.0901395284111204, - "information_load": 4.553388598178379, - "magnetization_M": 0.6289285360759653, - "overflow": 1.3033885981783788, - "overflow_gate": 0.16361127453876484, - "remanence": 0.8472362184152578 - }, - "sha256": "044d1cd46ff0df59a907fcd98b5c34047864d5d7a1a777e008d4018095b515fc", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 110, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.042566719242077, - "normalized_entropy": 0.6303208399052597, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4576105546054239, - "transition_rate": 0.9006105006105006 - }, - "magnetic_domain": { - "H_field": 4.457103326650722, - "chi_susceptibility": 0.7283582998711258, - "coercive_loss": 1.8571033266507215, - "domain_wall_pressure": 0.8859998920101535, - "heat_loss": 0.9813492104720525, - "information_load": 4.457103326650722, - "magnetization_M": 0.6439781233276766, - "overflow": 1.2071033266507216, - "overflow_gate": 0.1870213685890965, - "remanence": 0.8511933976840996 - }, - "sha256": "ff5038df2fd009101e50eccaa1c841ff00ad37a1641fbcbc8bce0655ec6b7e19", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 111, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.180930400449505, - "normalized_entropy": 0.6476163000561881, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5240654776447593, - "transition_rate": 0.8761904761904762 - }, - "magnetic_domain": { - "H_field": 4.371821701125444, - "chi_susceptibility": 0.7173451280557247, - "coercive_loss": 1.7718217011254436, - "domain_wall_pressure": 0.7042499970914338, - "heat_loss": 0.8856347952107345, - "information_load": 4.371821701125444, - "magnetization_M": 0.6584532183489664, - "overflow": 1.1218217011254437, - "overflow_gate": 0.21053872079472138, - "remanence": 0.8675640258206468 - }, - "sha256": "0c51653f75876dbd79f3833ba51b7de9a909c370e4617825de00a0f29893c477", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 112, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.661454889337944, - "normalized_entropy": 0.582681861167243, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39091131199609086, - "transition_rate": 0.9523809523809523 - }, - "magnetic_domain": { - "H_field": 4.4930820932262945, - "chi_susceptibility": 0.7499027469875, - "coercive_loss": 1.8930820932262944, - "domain_wall_pressure": 1.122939280769723, - "heat_loss": 1.0219309825669305, - "information_load": 4.4930820932262945, - "magnetization_M": 0.6407722841685449, - "overflow": 1.2430820932262945, - "overflow_gate": 0.17790547532173728, - "remanence": 0.8301166313867098 - }, - "sha256": "ffd1781b6b3f6a98a48c13e14c7ac79f6825fffb189a5f5ee7b09202c3dadd04", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 113, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.89075267000936, - "normalized_entropy": 0.61134408375117, - "printable_ratio": 0.9970703125, - "repetition_rate_4gram": 0.39995113608600047, - "transition_rate": 0.9272283272283273 - }, - "magnetic_domain": { - "H_field": 4.5214887173878235, - "chi_susceptibility": 0.7397295048854383, - "coercive_loss": 1.9214887173878235, - "domain_wall_pressure": 1.0545543822846537, - "heat_loss": 1.0540347553289013, - "information_load": 4.5214887173878235, - "magnetization_M": 0.6341876112468117, - "overflow": 1.2714887173878235, - "overflow_gate": 0.1710231157266301, - "remanence": 0.8333163649691515 - }, - "sha256": "3448c0c46a58b62c842e6e1608eb3bd75b99f6ebb403c7b5071e7779194d84ec", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 114, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.261807623710352, - "normalized_entropy": 0.657725952963794, - "printable_ratio": 0.965087890625, - "repetition_rate_4gram": 0.6000488639139995, - "transition_rate": 0.8776556776556776 - }, - "magnetic_domain": { - "H_field": 4.263092628824079, - "chi_susceptibility": 0.7180222000543881, - "coercive_loss": 1.6630926288240793, - "domain_wall_pressure": 0.5552136274833563, - "heat_loss": 0.7650278679625386, - "information_load": 4.263092628824079, - "magnetization_M": 0.6817779205607808, - "overflow": 1.0130926288240794, - "overflow_gate": 0.24485891398644904, - "remanence": 0.8823613945333832 - }, - "sha256": "af113b4094c4813ff7ce8105cb666582fb250ab5d748851023290fefd38fc9cb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 115, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.069255203220738, - "normalized_entropy": 0.6336569004025923, - "printable_ratio": 0.977783203125, - "repetition_rate_4gram": 0.38382604446616175, - "transition_rate": 0.9399267399267399 - }, - "magnetic_domain": { - "H_field": 4.590250934654186, - "chi_susceptibility": 0.7449328650564115, - "coercive_loss": 1.9902509346541861, - "domain_wall_pressure": 1.1122013909211563, - "heat_loss": 1.1319148622303505, - "information_load": 4.590250934654186, - "magnetization_M": 0.6237467008055408, - "overflow": 1.3402509346541862, - "overflow_gate": 0.15544557145008886, - "remanence": 0.8275215440045511 - }, - "sha256": "02eb7a04e2c5e023dced2780077b030e14c249ff86dc7c4885a73ed270b8815c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 116, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.663067371202008, - "normalized_entropy": 0.582883421400251, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.37698509650623013, - "transition_rate": 0.9684981684981685 - }, - "magnetic_domain": { - "H_field": 4.5203093607519715, - "chi_susceptibility": 0.7561444941670378, - "coercive_loss": 1.9203093607519715, - "domain_wall_pressure": 1.1830261439838767, - "heat_loss": 1.0527009461385735, - "information_load": 4.5203093607519715, - "magnetization_M": 0.6369969624182004, - "overflow": 1.2703093607519715, - "overflow_gate": 0.1713034803463802, - "remanence": 0.8249395864074763 - }, - "sha256": "16567693b8d13153cb9bf4db4934d117c66db5cd33c7110141f94d794c38dc0f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 117, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.852060644015249, - "normalized_entropy": 0.6065075805019061, - "printable_ratio": 0.99560546875, - "repetition_rate_4gram": 0.42682628878573176, - "transition_rate": 0.9575091575091575 - }, - "magnetic_domain": { - "H_field": 4.485536406552531, - "chi_susceptibility": 0.7519116716198543, - "coercive_loss": 1.8855364065525309, - "domain_wall_pressure": 1.0613657374468515, - "heat_loss": 1.0134119799228072, - "information_load": 4.485536406552531, - "magnetization_M": 0.6429657707240845, - "overflow": 1.235536406552531, - "overflow_gate": 0.17977975027826895, - "remanence": 0.8421549912265479 - }, - "sha256": "6c9f46b1b6ea4fb4b066dce071f82f6326615120b0b216740c9251c8e23ca722", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 118, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.039163550650007, - "normalized_entropy": 0.6298954438312508, - "printable_ratio": 0.994873046875, - "repetition_rate_4gram": 0.5045199120449548, - "transition_rate": 0.936996336996337 - }, - "magnetic_domain": { - "H_field": 4.398980515787306, - "chi_susceptibility": 0.7437444278037302, - "coercive_loss": 1.798980515787306, - "domain_wall_pressure": 0.8649528499027643, - "heat_loss": 0.9160304668733866, - "information_load": 4.398980515787306, - "magnetization_M": 0.6583508270119993, - "overflow": 1.148980515787306, - "overflow_gate": 0.20274499498740156, - "remanence": 0.8631355436290984 - }, - "sha256": "ea342a0b5e3d088127458239c86d6520dc56c12a24359bf43dd482b92a1cd072", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 119, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.781486719883081, - "normalized_entropy": 0.5976858399853852, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.37234302467627656, - "transition_rate": 0.9516483516483516 - }, - "magnetic_domain": { - "H_field": 4.547311727972929, - "chi_susceptibility": 0.7496139886176931, - "coercive_loss": 1.9473117279729286, - "domain_wall_pressure": 1.15861065394415, - "heat_loss": 1.0832578699369348, - "information_load": 4.547311727972929, - "magnetization_M": 0.6312537343083244, - "overflow": 1.2973117279729287, - "overflow_gate": 0.16499801352328525, - "remanence": 0.8231430670181048 - }, - "sha256": "9b4ff2af7ed900688937ae9c620eb21ab5f7a9ae50c85bc964ac293cb210384b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 120, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.622373941761379, - "normalized_entropy": 0.7027967427201723, - "printable_ratio": 0.90966796875, - "repetition_rate_4gram": 0.3791839726362082, - "transition_rate": 0.9072039072039072 - }, - "magnetic_domain": { - "H_field": 4.70127885386144, - "chi_susceptibility": 0.7312350844222236, - "coercive_loss": 2.1012788538614395, - "domain_wall_pressure": 1.056039869135398, - "heat_loss": 1.257922410939853, - "information_load": 4.70127885386144, - "magnetization_M": 0.6054097156920054, - "overflow": 1.4512788538614396, - "overflow_gate": 0.13323176480324236, - "remanence": 0.8257778912867663 - }, - "sha256": "dd6d2fb2c7f0b0a6885ba24b8a65f91d5997d1b6369ada735f79d4d8ccd29260", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 121, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.671656301079673, - "normalized_entropy": 0.5839570376349591, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41436599071585634, - "transition_rate": 0.9540903540903541 - }, - "magnetic_domain": { - "H_field": 4.461552337963796, - "chi_susceptibility": 0.7505747918927146, - "coercive_loss": 1.8615523379637957, - "domain_wall_pressure": 1.0794487267489956, - "heat_loss": 0.9863619620149585, - "information_load": 4.461552337963796, - "magnetization_M": 0.646848374303252, - "overflow": 1.2115523379637958, - "overflow_gate": 0.18586929255347323, - "remanence": 0.838176570592654 - }, - "sha256": "e6ab3c45374b5e26571424cfec7a7f7baa8789ae1c09ecc9de4c5ad1ef12012f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 122, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.720873176709798, - "normalized_entropy": 0.5901091470887248, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.44050818470559494, - "transition_rate": 0.958974358974359 - }, - "magnetic_domain": { - "H_field": 4.435662591221913, - "chi_susceptibility": 0.7524816924328722, - "coercive_loss": 1.8356625912219129, - "domain_wall_pressure": 1.0369323485375281, - "heat_loss": 0.9572157714326468, - "information_load": 4.435662591221913, - "magnetization_M": 0.652334901913501, - "overflow": 1.185662591221913, - "overflow_gate": 0.19267439276618728, - "remanence": 0.8463040498676331 - }, - "sha256": "18ca54d96d62aab65b6c6f9443671315c025d3c2f17274fe7323db2894a5ab2c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 123, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.659698790753861, - "normalized_entropy": 0.5824623488442326, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.4099682384559003, - "transition_rate": 0.9648351648351648 - }, - "magnetic_domain": { - "H_field": 4.469706054971619, - "chi_susceptibility": 0.7547443579632249, - "coercive_loss": 1.8697060549716187, - "domain_wall_pressure": 1.109733852758529, - "heat_loss": 0.995553026216841, - "information_load": 4.469706054971619, - "magnetization_M": 0.6460983760115322, - "overflow": 1.2197060549716188, - "overflow_gate": 0.1837762695701248, - "remanence": 0.8367241104196586 - }, - "sha256": "95cc5af0a47510b216f740483004966693df5e2b4c48c812437e51385fece3c2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 124, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.695416615614673, - "normalized_entropy": 0.5869270769518341, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.3774737356462253, - "transition_rate": 0.9594627594627595 - }, - "magnetic_domain": { - "H_field": 4.523391796929627, - "chi_susceptibility": 0.7526713106689055, - "coercive_loss": 1.9233917969296273, - "domain_wall_pressure": 1.1639780476330683, - "heat_loss": 1.0561872314071514, - "information_load": 4.523391796929627, - "magnetization_M": 0.6358595579803071, - "overflow": 1.2733917969296273, - "overflow_gate": 0.17057167012241992, - "remanence": 0.8251265728140821 - }, - "sha256": "acb8709c593e59686de68a2ec0e730ce5df355cd359f3cfa2201b14585992103", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 125, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.58253560458736, - "normalized_entropy": 0.57281695057342, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43146836061568533, - "transition_rate": 0.9667887667887668 - }, - "magnetic_domain": { - "H_field": 4.420496652427621, - "chi_susceptibility": 0.7554924320175149, - "coercive_loss": 1.8204966524276212, - "domain_wall_pressure": 1.0706408123461628, - "heat_loss": 0.9401711279901863, - "information_load": 4.420496652427621, - "magnetization_M": 0.6556674307190071, - "overflow": 1.1704966524276212, - "overflow_gate": 0.1967758933438532, - "remanence": 0.8435875878936105 - }, - "sha256": "0202384403568d5bf8d8ea88caba8a9003f83887730f5c3d3f8887877aaa411c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 126, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.622163884530911, - "normalized_entropy": 0.5777704855663639, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.46225262643537746, - "transition_rate": 0.9570207570207571 - }, - "magnetic_domain": { - "H_field": 4.378919719574022, - "chi_susceptibility": 0.7517212752314547, - "coercive_loss": 1.7789197195740223, - "domain_wall_pressure": 0.9895362611707592, - "heat_loss": 0.8935700417071062, - "information_load": 4.378919719574022, - "magnetization_M": 0.6634095069721311, - "overflow": 1.1289197195740224, - "overflow_gate": 0.2084733518125817, - "remanence": 0.8524672890458855 - }, - "sha256": "d172277d58110a3382b439216a873ea2e55ea03d121cd91262ead9ea3bdc7049", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 127, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.67213457044281, - "normalized_entropy": 0.5840168213053513, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.4214512582457855, - "transition_rate": 0.9623931623931624 - }, - "magnetic_domain": { - "H_field": 4.454501465089263, - "chi_susceptibility": 0.7538049544038657, - "coercive_loss": 1.8545014650892626, - "domain_wall_pressure": 1.0818838082947537, - "heat_loss": 0.9784184315384302, - "information_load": 4.454501465089263, - "magnetization_M": 0.6488455657740676, - "overflow": 1.2045014650892627, - "overflow_gate": 0.1876984296852458, - "remanence": 0.8404630586034183 - }, - "sha256": "f302c9a4e2ae6946ceb1018a8fd82d10a0cfb07fac3ad3a02e6630614682d35e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 128, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.833806044284455, - "normalized_entropy": 0.6042257555355569, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.3879794771561202, - "transition_rate": 0.9452991452991453 - }, - "magnetic_domain": { - "H_field": 4.533835825369124, - "chi_susceptibility": 0.7470927103346564, - "coercive_loss": 1.933835825369124, - "domain_wall_pressure": 1.11463933628605, - "heat_loss": 1.0680033993336382, - "information_load": 4.533835825369124, - "magnetization_M": 0.6332679261560132, - "overflow": 1.283835825369124, - "overflow_gate": 0.16811528528067862, - "remanence": 0.8290523326233137 - }, - "sha256": "64a7a5b46b7c72d1c458a690b4fde4c3cd6a9cda079a3eb4e0a3a94782f7e5ca", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 129, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.772700113273698, - "normalized_entropy": 0.5965875141592123, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.45101392621548986, - "transition_rate": 0.937973137973138 - }, - "magnetic_domain": { - "H_field": 4.422829701121621, - "chi_susceptibility": 0.7441413891896903, - "coercive_loss": 1.8228297011216212, - "domain_wall_pressure": 0.9739184235152962, - "heat_loss": 0.9427917005086425, - "information_load": 4.422829701121621, - "magnetization_M": 0.6532512557226069, - "overflow": 1.1728297011216213, - "overflow_gate": 0.19613930342400515, - "remanence": 0.8493448174322733 - }, - "sha256": "0b67d34cb4c821878b85559d65f8ee6487750f40f71926f987a8483f99b14da2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 130, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.800452674949474, - "normalized_entropy": 0.6000565843686843, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.599804544344002, - "transition_rate": 0.9465201465201465 - }, - "magnetic_domain": { - "H_field": 4.191609104552342, - "chi_susceptibility": 0.7475801884850272, - "coercive_loss": 1.5916091045523415, - "domain_wall_pressure": 0.693431204352289, - "heat_loss": 0.6869820859795229, - "information_load": 4.191609104552342, - "magnetization_M": 0.7049045737479315, - "overflow": 0.9416091045523416, - "overflow_gate": 0.2704169037255359, - "remanence": 0.8823191155963831 - }, - "sha256": "27be986f4683864739d3c14a7850b0222cbf60a843268979ffffa0aa9b155d27", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 131, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.797531173960129, - "normalized_entropy": 0.5996913967450161, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.46982653310530176, - "transition_rate": 0.9387057387057387 - }, - "magnetic_domain": { - "H_field": 4.399896811833541, - "chi_susceptibility": 0.7444385744563514, - "coercive_loss": 1.7998968118335408, - "domain_wall_pressure": 0.9377584112008739, - "heat_loss": 0.9170574962044489, - "information_load": 4.399896811833541, - "magnetization_M": 0.6579190024130414, - "overflow": 1.1498968118335409, - "overflow_gate": 0.20248713904843654, - "remanence": 0.8544995645296032 - }, - "sha256": "149233f7e49a98d1637fc21811368baab2509da8686b51bce521d4ed56331a08", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 132, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.787123288253593, - "normalized_entropy": 0.5983904110316991, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4519912044954801, - "transition_rate": 0.9421245421245421 - }, - "magnetic_domain": { - "H_field": 4.426370168808623, - "chi_susceptibility": 0.7458193913003406, - "coercive_loss": 1.8263701688086234, - "domain_wall_pressure": 0.980266675258124, - "heat_loss": 0.9467695426426237, - "information_load": 4.426370168808623, - "magnetization_M": 0.6529262725467334, - "overflow": 1.1763701688086234, - "overflow_gate": 0.1951771918855518, - "remanence": 0.8496215739584099 - }, - "sha256": "132f2f7a1a9f776b7f431d7fb60fbf306f5e4f1625541a8c0c66156f62da0751", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 133, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.781631312072795, - "normalized_entropy": 0.5977039140090994, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4334229171756658, - "transition_rate": 0.9509157509157509 - }, - "magnetic_domain": { - "H_field": 4.456934121557742, - "chi_susceptibility": 0.7493247856635935, - "coercive_loss": 1.856934121557742, - "domain_wall_pressure": 1.03498566748017, - "heat_loss": 0.9811585977361078, - "information_load": 4.456934121557742, - "magnetization_M": 0.6477066770651272, - "overflow": 1.206934121557742, - "overflow_gate": 0.1870653250984691, - "remanence": 0.8441830363940916 - }, - "sha256": "dc9c66c2ac29adfaf41d6631b3af3f6025673045a72b0e7d90601921ef815837", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 134, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.661198273268413, - "normalized_entropy": 0.5826497841585516, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.43293427803567064, - "transition_rate": 0.9626373626373627 - }, - "magnetic_domain": { - "H_field": 4.434856738258245, - "chi_susceptibility": 0.7538991110166879, - "coercive_loss": 1.8348567382582446, - "domain_wall_pressure": 1.059406169203384, - "heat_loss": 0.9563095295666058, - "information_load": 4.434856738258245, - "magnetization_M": 0.652661474700083, - "overflow": 1.1848567382582447, - "overflow_gate": 0.19289016242386095, - "remanence": 0.8440345997027779 - }, - "sha256": "2766c1f77a5c099befea39036520d4b3ae9ca4be253876718ed9d494c0447a64", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 135, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.765730215481157, - "normalized_entropy": 0.5957162769351446, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5130710969948693, - "transition_rate": 0.9538461538461539 - }, - "magnetic_domain": { - "H_field": 4.330939229981419, - "chi_susceptibility": 0.7504789330469903, - "coercive_loss": 1.7309392299814186, - "domain_wall_pressure": 0.8815501137025692, - "heat_loss": 0.8400635105455544, - "information_load": 4.330939229981419, - "magnetization_M": 0.67353327624399, - "overflow": 1.0809392299814187, - "overflow_gate": 0.22283927972528572, - "remanence": 0.8651089213327622 - }, - "sha256": "6bcb3ab4927391b3f5feacee69c8d04955a4001f43d87570ba4df9e9db9c8823", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 136, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.735750746879162, - "normalized_entropy": 0.5919688433598953, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5599804544344001, - "transition_rate": 0.9533577533577534 - }, - "magnetic_domain": { - "H_field": 4.247302816507227, - "chi_susceptibility": 0.7502870678997496, - "coercive_loss": 1.6473028165072265, - "domain_wall_pressure": 0.7867545978467065, - "heat_loss": 0.747689833391194, - "information_load": 4.247302816507227, - "magnetization_M": 0.6922039021347679, - "overflow": 0.9973028165072266, - "overflow_gate": 0.2502880559289225, - "remanence": 0.8749961823901292 - }, - "sha256": "338bcc22da7a965037dd59cb9a819abc9b3e474a10b61a1b543775aaf5681d44", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 137, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.8845936854667, - "normalized_entropy": 0.6105742106833375, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.4202296603957977, - "transition_rate": 0.9394383394383394 - }, - "magnetic_domain": { - "H_field": 4.495237582611996, - "chi_susceptibility": 0.7447353013676172, - "coercive_loss": 1.8952375826119963, - "domain_wall_pressure": 1.0384173580850835, - "heat_loss": 1.0243652227274844, - "information_load": 4.495237582611996, - "magnetization_M": 0.6398536822830645, - "overflow": 1.2452375826119964, - "overflow_gate": 0.1773736698672494, - "remanence": 0.8400734575860588 - }, - "sha256": "c3406256993d445c096eeca6aca191a89a6e3c642c5e75131f82ed41822374d1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 138, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.752081351519611, - "normalized_entropy": 0.5940101689399514, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.45492303933545075, - "transition_rate": 0.9645909645909646 - }, - "magnetic_domain": { - "H_field": 4.423156966312209, - "chi_susceptibility": 0.7546506335309299, - "coercive_loss": 1.8231569663122085, - "domain_wall_pressure": 1.0193358505110277, - "heat_loss": 0.9431593418625264, - "information_load": 4.423156966312209, - "magnetization_M": 0.6552999113866326, - "overflow": 1.1731569663122086, - "overflow_gate": 0.19605017150660953, - "remanence": 0.850445776088863 - }, - "sha256": "ba6688895e5dd8312b5835954d9c3d82cf868680a62174d99376024649e4540b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 139, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.8715791700907, - "normalized_entropy": 0.6089473962613375, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4309797214756902, - "transition_rate": 0.9482295482295482 - }, - "magnetic_domain": { - "H_field": 4.479878744656187, - "chi_susceptibility": 0.7482605587154086, - "coercive_loss": 1.8798787446561867, - "domain_wall_pressure": 1.034499653507716, - "heat_loss": 1.007027175920267, - "information_load": 4.479878744656187, - "magnetization_M": 0.6433447167776369, - "overflow": 1.2298787446561867, - "overflow_gate": 0.18119799996886532, - "remanence": 0.8434380139999236 - }, - "sha256": "13e6f405f02275751d76a093de9098211707c4cba85692a93eb2ac43d5f2479d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 140, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.567790175821064, - "normalized_entropy": 0.570973771977633, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.40654776447593455, - "transition_rate": 0.9714285714285714 - }, - "magnetic_domain": { - "H_field": 4.456085339462529, - "chi_susceptibility": 0.7572569089048107, - "coercive_loss": 1.856085339462529, - "domain_wall_pressure": 1.1297616139052737, - "heat_loss": 0.9802024650411872, - "information_load": 4.456085339462529, - "magnetization_M": 0.6490031709206072, - "overflow": 1.206085339462529, - "overflow_gate": 0.18728597971516886, - "remanence": 0.8355762664202788 - }, - "sha256": "269546c9d3db64d70f8c97a44ceb40049495cfd0144a806c5a2110f8445cf12c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 141, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.634530144907224, - "normalized_entropy": 0.579316268113403, - "printable_ratio": 0.989013671875, - "repetition_rate_4gram": 0.4089909601759101, - "transition_rate": 0.9672771672771673 - }, - "magnetic_domain": { - "H_field": 4.466319509980474, - "chi_susceptibility": 0.7556789733766055, - "coercive_loss": 1.8663195099804741, - "domain_wall_pressure": 1.1165724142025144, - "heat_loss": 0.9917349871104092, - "information_load": 4.466319509980474, - "magnetization_M": 0.6468729248318701, - "overflow": 1.2163195099804742, - "overflow_gate": 0.1846427036870192, - "remanence": 0.8363977935886162 - }, - "sha256": "296c2b61e8e1009cc1a726e840e15ffff897224551d140a989843db6b28937f3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 142, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.517857123093728, - "normalized_entropy": 0.689732140386716, - "printable_ratio": 0.97998046875, - "repetition_rate_4gram": 0.3166381627168336, - "transition_rate": 0.9372405372405372 - }, - "magnetic_domain": { - "H_field": 4.781502350832252, - "chi_susceptibility": 0.7438437447771351, - "coercive_loss": 2.1815023508322517, - "domain_wall_pressure": 1.2412047490474074, - "heat_loss": 1.348971744867128, - "information_load": 4.781502350832252, - "magnetization_M": 0.5960529626492633, - "overflow": 1.5315023508322518, - "overflow_gate": 0.1191840194472655, - "remanence": 0.7983048341792736 - }, - "sha256": "6d55a6f6ce7f9a98a0fdbb60e9438154e6e0b9ad4853c6ca11939b1acf86357c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 143, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.900099788494279, - "normalized_entropy": 0.6125124735617848, - "printable_ratio": 0.984375, - "repetition_rate_4gram": 0.41607622770583924, - "transition_rate": 0.9489621489621489 - }, - "magnetic_domain": { - "H_field": 4.508895002227987, - "chi_susceptibility": 0.7485513984057965, - "coercive_loss": 1.9088950022279865, - "domain_wall_pressure": 1.0657718425126195, - "heat_loss": 1.0397958555465163, - "information_load": 4.508895002227987, - "magnetization_M": 0.6381337782077887, - "overflow": 1.2588950022279866, - "overflow_gate": 0.17404084240044607, - "remanence": 0.8387344614960304 - }, - "sha256": "88420dbacb2137a6adca3b8ad5808f72e9d78ca2a218bd2e568c295f572f5bcb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 144, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.513324127961745, - "normalized_entropy": 0.5641655159952181, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.4099682384559003, - "transition_rate": 0.977045177045177 - }, - "magnetic_domain": { - "H_field": 4.4405095680703335, - "chi_susceptibility": 0.7593701038901239, - "coercive_loss": 1.8405095680703334, - "domain_wall_pressure": 1.1341538771785533, - "heat_loss": 0.9626678448004072, - "information_load": 4.4405095680703335, - "magnetization_M": 0.6523422699863675, - "overflow": 1.1905095680703335, - "overflow_gate": 0.19138168174425427, - "remanence": 0.8367241104196586 - }, - "sha256": "266adeec20ba2967b480b04bbc8201f1e31258ccc862d1d484f3012d9d4a14c3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 145, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.55154561411413, - "normalized_entropy": 0.5689432017642663, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.38871243586611287, - "transition_rate": 0.976068376068376 - }, - "magnetic_domain": { - "H_field": 4.4803567697763285, - "chi_susceptibility": 0.7590043685276862, - "coercive_loss": 1.8803567697763284, - "domain_wall_pressure": 1.1747118804045265, - "heat_loss": 1.0075665486832401, - "information_load": 4.4803567697763285, - "magnetization_M": 0.6446720987356712, - "overflow": 1.2303567697763285, - "overflow_gate": 0.18107773823489448, - "remanence": 0.8293196555534705 - }, - "sha256": "6afe8ac07093513e19527a1a71409da5a9594e88a1a32ccf9bc69fd22434baa0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 146, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.635131912016103, - "normalized_entropy": 0.5793914890020129, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.4094795993159052, - "transition_rate": 0.9663003663003663 - }, - "magnetic_domain": { - "H_field": 4.4653407751101994, - "chi_susceptibility": 0.755305700017515, - "coercive_loss": 1.8653407751101994, - "domain_wall_pressure": 1.1136415339689223, - "heat_loss": 0.9906317168581567, - "information_load": 4.4653407751101994, - "magnetization_M": 0.6469869377111677, - "overflow": 1.2153407751101994, - "overflow_gate": 0.18489386915506692, - "remanence": 0.8365611148824023 - }, - "sha256": "892d15978e9ba66df91de5cf0fd84fd13ccba3ff300411f8b4d27665a3fd03f1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 147, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.609315697613952, - "normalized_entropy": 0.576164462201744, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.4368433911556316, - "transition_rate": 0.9746031746031746 - }, - "magnetic_domain": { - "H_field": 4.421763666732057, - "chi_susceptibility": 0.7584543662639137, - "coercive_loss": 1.8217636667320565, - "domain_wall_pressure": 1.0755195668950859, - "heat_loss": 0.9415942198774393, - "information_load": 4.421763666732057, - "magnetization_M": 0.656078975943388, - "overflow": 1.1717636667320566, - "overflow_gate": 0.19642992302068835, - "remanence": 0.8452142343909541 - }, - "sha256": "d3e564e4bac57ac157789a446802faf588a91bc5cdcfd865658be852190dd2b6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 148, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.480350169305086, - "normalized_entropy": 0.5600437711631358, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4690935743953091, - "transition_rate": 0.9755799755799756 - }, - "magnetic_domain": { - "H_field": 4.342701297007517, - "chi_susceptibility": 0.7588212212187987, - "coercive_loss": 1.742701297007517, - "domain_wall_pressure": 1.012972802369333, - "heat_loss": 0.8531500250095495, - "information_load": 4.342701297007517, - "magnetization_M": 0.6723194301468, - "overflow": 1.0927012970075172, - "overflow_gate": 0.21922850522279527, - "remanence": 0.8543053429679992 - }, - "sha256": "0448799ab1987f15c1d07f87adb3c749075c67383ef1a3af61f39780a02b3279", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 149, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.3880581350734165, - "normalized_entropy": 0.5485072668841771, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.47300268751526997, - "transition_rate": 0.9777777777777777 - }, - "magnetic_domain": { - "H_field": 4.315638062306345, - "chi_susceptibility": 0.7596439169139465, - "coercive_loss": 1.7156380623063447, - "domain_wall_pressure": 1.0095501805250156, - "heat_loss": 0.8230715006030446, - "information_load": 4.315638062306345, - "magnetization_M": 0.6782346533956571, - "overflow": 1.0656380623063448, - "overflow_gate": 0.22762565479156865, - "remanence": 0.8553352419326335 - }, - "sha256": "0d9f55dc043a0c53ac40bd8cc7ccb0b7dfb2210df61e44fbb0755975813674d0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 150, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.273435569789821, - "normalized_entropy": 0.6591794462237276, - "printable_ratio": 0.93408203125, - "repetition_rate_4gram": 0.4732470070852675, - "transition_rate": 0.9316239316239316 - }, - "magnetic_domain": { - "H_field": 4.496963571400272, - "chi_susceptibility": 0.741546465475663, - "coercive_loss": 1.896963571400272, - "domain_wall_pressure": 0.9167538490773283, - "heat_loss": 1.0263146421283285, - "information_load": 4.496963571400272, - "magnetization_M": 0.6395939459312769, - "overflow": 1.246963571400272, - "overflow_gate": 0.17694897776698232, - "remanence": 0.8553991273780713 - }, - "sha256": "ea01b9edfc4b77db5400dec830cd36eabbce7e95c1a41578d394d14869597da4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 151, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.0484347947620805, - "normalized_entropy": 0.6310543493452601, - "printable_ratio": 0.954833984375, - "repetition_rate_4gram": 0.42902516491570974, - "transition_rate": 0.9340659340659341 - }, - "magnetic_domain": { - "H_field": 4.516346053992747, - "chi_susceptibility": 0.7425486272999415, - "coercive_loss": 1.9163460539927466, - "domain_wall_pressure": 1.0100815383004487, - "heat_loss": 1.0482191665701357, - "information_load": 4.516346053992747, - "magnetization_M": 0.6359407924069966, - "overflow": 1.2663460539927467, - "overflow_gate": 0.1722490363000416, - "remanence": 0.8428368467534463 - }, - "sha256": "ac7ca8ea66a8c64886918860e77dbed1ee3e0bd9cd251cc06765ee0fcd3d9a13", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 152, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.510276216566747, - "normalized_entropy": 0.5637845270708434, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.42731492792572684, - "transition_rate": 0.9677655677655678 - }, - "magnetic_domain": { - "H_field": 4.410218831036895, - "chi_susceptibility": 0.755865324315321, - "coercive_loss": 1.8102188310368947, - "domain_wall_pressure": 1.080901279679682, - "heat_loss": 0.9286333924410015, - "information_load": 4.410218831036895, - "magnetization_M": 0.6576532637130041, - "overflow": 1.1602188310368948, - "overflow_gate": 0.19960496451253423, - "remanence": 0.8423070254859278 - }, - "sha256": "3d97985955bdd4c179b5aab06367408da35685a3a5c54d8d6fe635b7e00a43c1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 153, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.524535746108636, - "normalized_entropy": 0.5655669682635796, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.44759345223552405, - "transition_rate": 0.9728937728937729 - }, - "magnetic_domain": { - "H_field": 4.3849949135275335, - "chi_susceptibility": 0.7578105678224669, - "coercive_loss": 1.7849949135275334, - "domain_wall_pressure": 1.0506006413164977, - "heat_loss": 0.9003668330731132, - "information_load": 4.3849949135275335, - "magnetization_M": 0.6632503592665617, - "overflow": 1.1349949135275335, - "overflow_gate": 0.20672170214860489, - "remanence": 0.8483680954321491 - }, - "sha256": "7db24b7bbca710cdfdc754d3c6d23a8dde9c2479067ab95a4d1f9f5fcc09b749", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 154, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.481194184631517, - "normalized_entropy": 0.5601492730789396, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41827510383581723, - "transition_rate": 0.967032967032967 - }, - "magnetic_domain": { - "H_field": 4.416591940627712, - "chi_susceptibility": 0.7555857265133851, - "coercive_loss": 1.816591940627712, - "domain_wall_pressure": 1.0975157263942996, - "heat_loss": 0.935786450516257, - "information_load": 4.416591940627712, - "magnetization_M": 0.656246757353512, - "overflow": 1.166591940627712, - "overflow_gate": 0.19784594944764045, - "remanence": 0.8394461224649904 - }, - "sha256": "4a96bf2ea8394bdff45f3fbdbd26e97949fdc610f3f5935a8f2211639857c514", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 155, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.467764477944446, - "normalized_entropy": 0.5584705597430557, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4424627412655754, - "transition_rate": 0.9711843711843712 - }, - "magnetic_domain": { - "H_field": 4.3787382804363135, - "chi_susceptibility": 0.7571644675918844, - "coercive_loss": 1.7787382804363134, - "domain_wall_pressure": 1.0574432598375916, - "heat_loss": 0.893367122017357, - "information_load": 4.3787382804363135, - "magnetization_M": 0.6642945876917347, - "overflow": 1.1287382804363135, - "overflow_gate": 0.2085258934675042, - "remanence": 0.8468790333140047 - }, - "sha256": "3057a3b57072f1881e1c370e9cc3e21120198382f2b7854f7bb2f6300eebfc45", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 156, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.655379858998151, - "normalized_entropy": 0.5819224823747688, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.4197410212558026, - "transition_rate": 0.9626373626373627 - }, - "magnetic_domain": { - "H_field": 4.453285888111299, - "chi_susceptibility": 0.7538991110166879, - "coercive_loss": 1.8532858881112992, - "domain_wall_pressure": 1.0857926827631201, - "heat_loss": 0.9770493832985574, - "information_load": 4.453285888111299, - "magnetization_M": 0.6490632674490263, - "overflow": 1.2032858881112993, - "overflow_gate": 0.18801558885382358, - "remanence": 0.8399170838548184 - }, - "sha256": "c05704735d55503593e6d24798bea256f3651b7dd15d2b1db70283e855cba421", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 157, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.477978468254723, - "normalized_entropy": 0.5597473085318404, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.38529196188614706, - "transition_rate": 0.968009768009768 - }, - "magnetic_domain": { - "H_field": 4.464841313340796, - "chi_susceptibility": 0.7559584284458916, - "coercive_loss": 1.8648413133407957, - "domain_wall_pressure": 1.1654356122472418, - "heat_loss": 0.9900687325343538, - "information_load": 4.464841313340796, - "magnetization_M": 0.6468416750823986, - "overflow": 1.2148413133407958, - "overflow_gate": 0.1850221739564657, - "remanence": 0.8280649429753628 - }, - "sha256": "fde18f1d7d46435a6139f95593778828a95d00f5a8a245b443a92661d7f0d0a4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 158, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.396799045080569, - "normalized_entropy": 0.5495998806350711, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.45516735890544835, - "transition_rate": 0.9768009768009768 - }, - "magnetic_domain": { - "H_field": 4.344801500920251, - "chi_susceptibility": 0.7592787398889023, - "coercive_loss": 1.7448015009202513, - "domain_wall_pressure": 1.0432672357910568, - "heat_loss": 0.8554888863577684, - "information_load": 4.344801500920251, - "magnetization_M": 0.6717973698265061, - "overflow": 1.0948015009202514, - "overflow_gate": 0.21858995841832998, - "remanence": 0.8505140519712935 - }, - "sha256": "0811ccf7578941cadaf03a6da25bf0a69db935bf8d6743bf54af0d685a0d5071", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 159, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.00518368240758, - "normalized_entropy": 0.6256479603009475, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.4089909601759101, - "transition_rate": 0.9545787545787546 - }, - "magnetic_domain": { - "H_field": 4.545209173770204, - "chi_susceptibility": 0.7507663622700999, - "coercive_loss": 1.9452091737702042, - "domain_wall_pressure": 1.091175588805689, - "heat_loss": 1.0808772508675215, - "information_load": 4.545209173770204, - "magnetization_M": 0.6323111688953811, - "overflow": 1.2952091737702043, - "overflow_gate": 0.1654805472685059, - "remanence": 0.8363977935886162 - }, - "sha256": "c77635a4e6f540335b6f0bbbd157a40d4654c83c8c59057d9fcb481b218684cf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 160, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.445136765518276, - "normalized_entropy": 0.6806420956897845, - "printable_ratio": 0.89501953125, - "repetition_rate_4gram": 0.45272416320547276, - "transition_rate": 0.9181929181929182 - }, - "magnetic_domain": { - "H_field": 4.559453223799429, - "chi_susceptibility": 0.7359412122605338, - "coercive_loss": 1.959453223799429, - "domain_wall_pressure": 0.9309375099748908, - "heat_loss": 1.0970089082401668, - "information_load": 4.559453223799429, - "magnetization_M": 0.6279229613257346, - "overflow": 1.3094532237994292, - "overflow_gate": 0.1622389495845044, - "remanence": 0.8498284749866999 - }, - "sha256": "a39abfbfb39575737319a65769c0074f7a9f5855c09e71ad19a5041b237b22ef", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 161, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.522724697339453, - "normalized_entropy": 0.5653405871674316, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.4495480087955045, - "transition_rate": 0.9626373626373627 - }, - "magnetic_domain": { - "H_field": 4.377459453932593, - "chi_susceptibility": 0.7538991110166879, - "coercive_loss": 1.7774594539325927, - "domain_wall_pressure": 1.0261787076837163, - "heat_loss": 0.8919370125397321, - "information_load": 4.377459453932593, - "magnetization_M": 0.6639793152539927, - "overflow": 1.1274594539325928, - "overflow_gate": 0.20889659541312583, - "remanence": 0.848927767319972 - }, - "sha256": "a4926e4c186a9fd448b1b5447aedcf57828394f2a91400b43f4a39dd50f74deb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 162, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.007903048051298, - "normalized_entropy": 0.6259878810064122, - "printable_ratio": 0.978759765625, - "repetition_rate_4gram": 0.4116784754458832, - "transition_rate": 0.9272283272283273 - }, - "magnetic_domain": { - "H_field": 4.530339218781931, - "chi_susceptibility": 0.7397295048854383, - "coercive_loss": 1.9303392187819308, - "domain_wall_pressure": 1.0310997035648881, - "heat_loss": 1.0640467699982794, - "information_load": 4.530339218781931, - "magnetization_M": 0.6328660745074123, - "overflow": 1.280339218781931, - "overflow_gate": 0.16893370570138783, - "remanence": 0.8372920434894953 - }, - "sha256": "1d4e483b5ab7a47865edb28e67abf26a9112b10f4893c43ce5e1ef7851a170bf", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 163, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.451766139615159, - "normalized_entropy": 0.5564707674518948, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41876374297581237, - "transition_rate": 0.9772893772893773 - }, - "magnetic_domain": { - "H_field": 4.413018959209865, - "chi_susceptibility": 0.7594614213767312, - "coercive_loss": 1.8130189592098653, - "domain_wall_pressure": 1.11705126862713, - "heat_loss": 0.9317756727703477, - "information_load": 4.413018959209865, - "magnetization_M": 0.6577041892035118, - "overflow": 1.1630189592098654, - "overflow_gate": 0.19883019499236748, - "remanence": 0.839603417195705 - }, - "sha256": "163c87f6fde6bbef37137d1bb843e999293595edd6018ffd108318b676a7c116", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 164, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.451422641253871, - "normalized_entropy": 0.5564278301567339, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.4053261666259467, - "transition_rate": 0.9758241758241758 - }, - "magnetic_domain": { - "H_field": 4.432296892169578, - "chi_susceptibility": 0.75891281819807, - "coercive_loss": 1.8322968921695781, - "domain_wall_pressure": 1.140996018396458, - "heat_loss": 0.9534312016224393, - "information_load": 4.432296892169578, - "magnetization_M": 0.6537273053575047, - "overflow": 1.1822968921695782, - "overflow_gate": 0.19357717343497202, - "remanence": 0.8351624010793177 - }, - "sha256": "96d2ef04d07ddae27ee28d64447fbfd57c97208d6ede736188dfc443ff82867e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 165, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.973090832853705, - "normalized_entropy": 0.6216363541067131, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.41143415587588567, - "transition_rate": 0.9582417582417583 - }, - "magnetic_domain": { - "H_field": 4.535949899583848, - "chi_susceptibility": 0.7521969008815796, - "coercive_loss": 1.9359498995838478, - "domain_wall_pressure": 1.0936152047317451, - "heat_loss": 1.0703959081401178, - "information_load": 4.535949899583848, - "magnetization_M": 0.6341347752716904, - "overflow": 1.2859498995838479, - "overflow_gate": 0.16762238677687716, - "remanence": 0.8372111522093624 - }, - "sha256": "a583ab778e75c08441929fc1e4c5b449ad499be94796c0175f0cc17ad0f7a9c3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 166, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.822090036218005, - "normalized_entropy": 0.6027612545272506, - "printable_ratio": 0.996337890625, - "repetition_rate_4gram": 0.3738089420962619, - "transition_rate": 0.9565323565323566 - }, - "magnetic_domain": { - "H_field": 4.556427856314421, - "chi_susceptibility": 0.7515306837425653, - "coercive_loss": 1.9564278563144213, - "domain_wall_pressure": 1.1654468288721893, - "heat_loss": 1.0935818922645657, - "information_load": 4.556427856314421, - "magnetization_M": 0.6301177464024666, - "overflow": 1.3064278563144214, - "overflow_gate": 0.1629220955608814, - "remanence": 0.823714359548626 - }, - "sha256": "14b3b7279cec0c638c79080983fb537b52423e288246b5d447f2453ce6a11a9a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 167, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.609998846584758, - "normalized_entropy": 0.5762498558230947, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.3960420229660396, - "transition_rate": 0.968986568986569 - }, - "magnetic_domain": { - "H_field": 4.4804116611623375, - "chi_susceptibility": 0.7563303700181275, - "coercive_loss": 1.8804116611623374, - "domain_wall_pressure": 1.1458890920410587, - "heat_loss": 1.0076284856604545, - "information_load": 4.4804116611623375, - "magnetization_M": 0.6442742718891555, - "overflow": 1.2304116611623375, - "overflow_gate": 0.18106393374996588, - "remanence": 0.8319476093695469 - }, - "sha256": "68e1610fce5d7b27993f135b5cfdc144e4d5b339c2aafdacf7f0e2f3851d9557", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 168, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.597759013023493, - "normalized_entropy": 0.5747198766279367, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.427803567065722, - "transition_rate": 0.9738705738705739 - }, - "magnetic_domain": { - "H_field": 4.432389884850874, - "chi_susceptibility": 0.7581787338993989, - "coercive_loss": 1.832389884850874, - "domain_wall_pressure": 1.0921340136097037, - "heat_loss": 0.9535357529513244, - "information_load": 4.432389884850874, - "magnetization_M": 0.6538868884670089, - "overflow": 1.182389884850874, - "overflow_gate": 0.1935521732989227, - "remanence": 0.8424587671522874 - }, - "sha256": "d08c1dd51800cc0143ec802c53c90f8ef6a11e64f716f155aef7cd370c5ca94d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 169, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.883300356894735, - "normalized_entropy": 0.6104125446118419, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.37527485951624723, - "transition_rate": 0.9601953601953602 - }, - "magnetic_domain": { - "H_field": 4.569632105047474, - "chi_susceptibility": 0.7529553743863632, - "coercive_loss": 1.969632105047474, - "domain_wall_pressure": 1.1698410013582259, - "heat_loss": 1.1085418269368066, - "information_load": 4.569632105047474, - "magnetization_M": 0.6282472407442887, - "overflow": 1.3196321050474742, - "overflow_gate": 0.15996145994271146, - "remanence": 0.8242819731249731 - }, - "sha256": "0aae01a639991b302d977c0dce9bbbda0e7dd5229f147162312a71d8689b3df8", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 170, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.708287995528434, - "normalized_entropy": 0.5885359994410543, - "printable_ratio": 0.996337890625, - "repetition_rate_4gram": 0.42438309308575617, - "transition_rate": 0.9660561660561661 - }, - "magnetic_domain": { - "H_field": 4.459922980636785, - "chi_susceptibility": 0.7552122624581934, - "coercive_loss": 1.8599229806367847, - "domain_wall_pressure": 1.0833461459408198, - "heat_loss": 0.9845259562633815, - "information_load": 4.459922980636785, - "magnetization_M": 0.6481600364115083, - "overflow": 1.2099229806367848, - "overflow_gate": 0.18629039036416717, - "remanence": 0.8413904012710469 - }, - "sha256": "b2fb3c2be4904cffdf680027cf691987e16577c44410b6de216119a0411891f7", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 171, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.600097754706691, - "normalized_entropy": 0.5750122193383364, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39017835328609823, - "transition_rate": 0.9731379731379731 - }, - "magnetic_domain": { - "H_field": 4.48836782669101, - "chi_susceptibility": 0.7579026797365362, - "coercive_loss": 1.88836782669101, - "domain_wall_pressure": 1.1659192397037499, - "heat_loss": 1.0166081611250959, - "information_load": 4.48836782669101, - "magnetization_M": 0.6430647089151751, - "overflow": 1.23836782669101, - "overflow_gate": 0.17907414968819774, - "remanence": 0.8298518010434204 - }, - "sha256": "2ac0be617efb904baf81133a776df837bb6aaf651671986c4aca1285d1ae213b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 172, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.7818561629136465, - "normalized_entropy": 0.5977320203642058, - "printable_ratio": 0.99267578125, - "repetition_rate_4gram": 0.3476667481065233, - "transition_rate": 0.9682539682539683 - }, - "magnetic_domain": { - "H_field": 4.589570267380182, - "chi_susceptibility": 0.7560514850539634, - "coercive_loss": 1.989570267380182, - "domain_wall_pressure": 1.2411744402948899, - "heat_loss": 1.1311430536908502, - "information_load": 4.589570267380182, - "magnetization_M": 0.6251825896291093, - "overflow": 1.3395702673801821, - "overflow_gate": 0.15559259470349104, - "remanence": 0.8129384611869015 - }, - "sha256": "4911371dcb30da4f1015141e4c4f9951f355531291f7435824f9c30ede36bcce", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 173, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.727578015153697, - "normalized_entropy": 0.5909472518942122, - "printable_ratio": 0.994140625, - "repetition_rate_4gram": 0.3547520156364525, - "transition_rate": 0.9648351648351648 - }, - "magnetic_domain": { - "H_field": 4.5656810340925995, - "chi_susceptibility": 0.7547443579632249, - "coercive_loss": 1.9656810340925994, - "domain_wall_pressure": 1.2201662983974249, - "heat_loss": 1.104064689976614, - "information_load": 4.5656810340925995, - "magnetization_M": 0.6288790393060857, - "overflow": 1.3156810340925995, - "overflow_gate": 0.16084167714854475, - "remanence": 0.8159870521062806 - }, - "sha256": "95de9f844a10480418d628e1eea5dad67730ef1307caf2e5d050a1942ff64b54", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 174, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.917690866978839, - "normalized_entropy": 0.6147113583723549, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.33007573906669924, - "transition_rate": 0.944078144078144 - }, - "magnetic_domain": { - "H_field": 4.635258064962819, - "chi_susceptibility": 0.7466039785895592, - "coercive_loss": 2.0352580649628185, - "domain_wall_pressure": 1.2280048100228895, - "heat_loss": 1.1829741571030181, - "information_load": 4.635258064962819, - "magnetization_M": 0.6163988921463409, - "overflow": 1.3852580649628186, - "overflow_gate": 0.1460261542424081, - "remanence": 0.8049140868902075 - }, - "sha256": "76fc588ae3617cfbd2e33828fbc0bd0b029e3344755113c9af7692db0b4bfc97", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 175, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.290903547543709, - "normalized_entropy": 0.6613629434429636, - "printable_ratio": 0.996337890625, - "repetition_rate_4gram": 0.46811629611531885, - "transition_rate": 0.9418803418803419 - }, - "magnetic_domain": { - "H_field": 4.513004514429163, - "chi_susceptibility": 0.7457210908529878, - "coercive_loss": 1.9130045144291628, - "domain_wall_pressure": 0.947528091530046, - "heat_loss": 1.0444411958710222, - "information_load": 4.513004514429163, - "magnetization_M": 0.6375302540697222, - "overflow": 1.2630045144291628, - "overflow_gate": 0.1730503066783765, - "remanence": 0.854045572870235 - }, - "sha256": "0db5566b35f21697cde3f71b014bab205a9a84c4eccfca71d413c195887b2a0e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 176, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.226803171881565, - "normalized_entropy": 0.6533503964851957, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4204739799657953, - "transition_rate": 0.9245421245421246 - }, - "magnetic_domain": { - "H_field": 4.564115929206981, - "chi_susceptibility": 0.7386108062265986, - "coercive_loss": 1.9641159292069807, - "domain_wall_pressure": 1.0081362891526586, - "heat_loss": 1.1022913641964223, - "information_load": 4.564115929206981, - "magnetization_M": 0.627269357231251, - "overflow": 1.3141159292069808, - "overflow_gate": 0.1611916881171866, - "remanence": 0.8401515299447385 - }, - "sha256": "62896e7235ef4eb113a4f162848400609cd77a317798a5e4e012b55354cb437e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 177, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.176514583557061, - "normalized_entropy": 0.6470643229446327, - "printable_ratio": 0.968017578125, - "repetition_rate_4gram": 0.3965306621060347, - "transition_rate": 0.9394383394383394 - }, - "magnetic_domain": { - "H_field": 4.594979142765514, - "chi_susceptibility": 0.7447353013676172, - "coercive_loss": 1.9949791427655135, - "domain_wall_pressure": 1.0858153546646094, - "heat_loss": 1.137276553432031, - "information_load": 4.594979142765514, - "magnetization_M": 0.6231457529897536, - "overflow": 1.3449791427655136, - "overflow_gate": 0.1544281117299778, - "remanence": 0.8321199319127991 - }, - "sha256": "a2946453b944dd1d2b2e4b1f301761f900c15ca6955a14146d7d7d61099035e4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 178, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.544100131023282, - "normalized_entropy": 0.5680125163779103, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.4749572440752504, - "transition_rate": 0.9763125763125763 - }, - "magnetic_domain": { - "H_field": 4.348886879476764, - "chi_susceptibility": 0.7590958722346842, - "coercive_loss": 1.7488868794767636, - "domain_wall_pressure": 1.0027106644746517, - "heat_loss": 0.8600403423451823, - "information_load": 4.348886879476764, - "magnetization_M": 0.6711715477351025, - "overflow": 1.0988868794767637, - "overflow_gate": 0.21735316126925494, - "remanence": 0.8558447504666644 - }, - "sha256": "83db75b9ceae41f60514a19304e6c492d8d9bc328e1aa3da34cca93f8b3e41fe", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 179, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.726183691302784, - "normalized_entropy": 0.590772961412848, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.4690935743953091, - "transition_rate": 0.9738705738705739 - }, - "magnetic_domain": { - "H_field": 4.3991722100996435, - "chi_susceptibility": 0.7581787338993989, - "coercive_loss": 1.7991722100996435, - "domain_wall_pressure": 1.0095539989505296, - "heat_loss": 0.916245319324213, - "information_load": 4.3991722100996435, - "magnetization_M": 0.6608017859802301, - "overflow": 1.1491722100996435, - "overflow_gate": 0.20269102291921384, - "remanence": 0.8543053429679992 - }, - "sha256": "9328c0764a6f5d34450407f2c6f6f00125f04c0917d28bbeeec4db261594e8e5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 180, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.720610827200939, - "normalized_entropy": 0.5900763534001173, - "printable_ratio": 0.99560546875, - "repetition_rate_4gram": 0.47935499633520645, - "transition_rate": 0.9724053724053724 - }, - "magnetic_domain": { - "H_field": 4.381383936200372, - "chi_susceptibility": 0.7576262030414115, - "coercive_loss": 1.7813839362003718, - "domain_wall_pressure": 0.9861007521403319, - "heat_loss": 0.8963264029338094, - "information_load": 4.381383936200372, - "magnetization_M": 0.6643295170034643, - "overflow": 1.131383936200372, - "overflow_gate": 0.20776106655356738, - "remanence": 0.856978125655182 - }, - "sha256": "02c6de5064261d07c781e6681a96bbd99860fa653bd17c6afdebcba909cf978a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 181, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.392071989540642, - "normalized_entropy": 0.6740089986925802, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.4077693623259223, - "transition_rate": 0.905982905982906 - }, - "magnetic_domain": { - "H_field": 4.610387912111855, - "chi_susceptibility": 0.7307053800914974, - "coercive_loss": 2.0103879121118546, - "domain_wall_pressure": 0.9964270873139673, - "heat_loss": 1.154753989783043, - "information_load": 4.610387912111855, - "magnetization_M": 0.6186095228430751, - "overflow": 1.3603879121118547, - "overflow_gate": 0.15115829867202168, - "remanence": 0.8359880587445653 - }, - "sha256": "17cb4eb5b24aa60c036bee4ce97f127bd3316a4935fecfea8cb179bb95de43da", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 182, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.8559581098140185, - "normalized_entropy": 0.6069947637267523, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.44612753481553874, - "transition_rate": 0.9506715506715506 - }, - "magnetic_domain": { - "H_field": 4.454467447531796, - "chi_susceptibility": 0.7492282857493898, - "coercive_loss": 1.8544674475317957, - "domain_wall_pressure": 1.0090880317120239, - "heat_loss": 0.9783801174160507, - "information_load": 4.454467447531796, - "magnetization_M": 0.6482997283447818, - "overflow": 1.2044674475317958, - "overflow_gate": 0.1877072980087964, - "remanence": 0.8479456126012334 - }, - "sha256": "f8ca92d0d7d4a0c93065c52f0e859872a35ffa6135214987857c682877fed7a9", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 183, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.83851651231479, - "normalized_entropy": 0.6048145640393487, - "printable_ratio": 0.99560546875, - "repetition_rate_4gram": 0.4309797214756902, - "transition_rate": 0.9404151404151404 - }, - "magnetic_domain": { - "H_field": 4.469145982774439, - "chi_susceptibility": 0.7451302256161125, - "coercive_loss": 1.8691459827744388, - "domain_wall_pressure": 1.0188708378789004, - "heat_loss": 0.9949215305395929, - "information_load": 4.469145982774439, - "magnetization_M": 0.6446735995065359, - "overflow": 1.2191459827744389, - "overflow_gate": 0.1839192807120384, - "remanence": 0.8434380139999236 - }, - "sha256": "956b6d29895143680e40336a1d26404bbd7e1e8603dadb2b18e2ef6ec42ea33a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 184, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.060997673259407, - "normalized_entropy": 0.6326247091574259, - "printable_ratio": 0.99609375, - "repetition_rate_4gram": 0.4529684827754703, - "transition_rate": 0.9313797313797314 - }, - "magnetic_domain": { - "H_field": 4.481718911091537, - "chi_susceptibility": 0.7414459649763772, - "coercive_loss": 1.881718911091537, - "domain_wall_pressure": 0.9568224972085222, - "heat_loss": 1.0091035928375665, - "information_load": 4.481718911091537, - "magnetization_M": 0.6420183911493604, - "overflow": 1.2317189110915372, - "overflow_gate": 0.18073548782058657, - "remanence": 0.8498973155346927 - }, - "sha256": "8d2d233e00c9cd2ab8913fbec835032f5e1383726d1902075a30bd2ef772230b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 185, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.74992729573634, - "normalized_entropy": 0.5937409119670425, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4158319081358417, - "transition_rate": 0.9565323565323566 - }, - "magnetic_domain": { - "H_field": 4.478346758648166, - "chi_susceptibility": 0.7515306837425653, - "coercive_loss": 1.8783467586481657, - "domain_wall_pressure": 1.0814008967930298, - "heat_loss": 1.0052986948850184, - "information_load": 4.478346758648166, - "magnetization_M": 0.6440277784753804, - "overflow": 1.2283467586481658, - "overflow_gate": 0.18158395599026025, - "remanence": 0.8386549984232103 - }, - "sha256": "cbfa2e4a45248408fe4341fe535de618ffc86e0ca2c7155babf65afa9996414b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 186, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.668782634394086, - "normalized_entropy": 0.5835978292992607, - "printable_ratio": 0.998291015625, - "repetition_rate_4gram": 0.38871243586611287, - "transition_rate": 0.9672771672771673 - }, - "magnetic_domain": { - "H_field": 4.504066502488881, - "chi_susceptibility": 0.7556789733766055, - "coercive_loss": 1.9040665024888805, - "domain_wall_pressure": 1.157129462822109, - "heat_loss": 1.0343390950830706, - "information_load": 4.504066502488881, - "magnetization_M": 0.6398827753769798, - "overflow": 1.2540665024888806, - "overflow_gate": 0.17521192613767173, - "remanence": 0.8293196555534705 - }, - "sha256": "e8e9df3749ed4e9f42bf73e3f8daa9d1cd202df7c6a7bf6813c984e41d840388", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 187, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.905895877860622, - "normalized_entropy": 0.6132369847325777, - "printable_ratio": 0.996337890625, - "repetition_rate_4gram": 0.35035426337649644, - "transition_rate": 0.9462759462759462 - }, - "magnetic_domain": { - "H_field": 4.604789499444239, - "chi_susceptibility": 0.7474827929694403, - "coercive_loss": 2.004789499444239, - "domain_wall_pressure": 1.1918433657988996, - "heat_loss": 1.1484032753355526, - "information_load": 4.604789499444239, - "magnetization_M": 0.6214400094901993, - "overflow": 1.354789499444239, - "overflow_gate": 0.15233822242743258, - "remanence": 0.8141066400218003 - }, - "sha256": "4b66ebdbb7774434ba738186b054c13310ee93cb6ad85edae1088edae394fde3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 188, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.386290865319815, - "normalized_entropy": 0.6732863581649768, - "printable_ratio": 0.92431640625, - "repetition_rate_4gram": 0.4473491326655265, - "transition_rate": 0.9201465201465201 - }, - "magnetic_domain": { - "H_field": 4.555995398176783, - "chi_susceptibility": 0.7367664484683182, - "coercive_loss": 1.9559953981767833, - "domain_wall_pressure": 0.9455947749619872, - "heat_loss": 1.093092052030623, - "information_load": 4.555995398176783, - "magnetization_M": 0.6285651601095795, - "overflow": 1.3059953981767833, - "overflow_gate": 0.16301998188001357, - "remanence": 0.8482978447397194 - }, - "sha256": "e2024f12645d80ba3b51ebf926f2a8012f44db5c996930b734ae7c0b083b9791", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 189, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.572744027547056, - "normalized_entropy": 0.696593003443382, - "printable_ratio": 0.966796875, - "repetition_rate_4gram": 0.3994624969460054, - "transition_rate": 0.936996336996337 - }, - "magnetic_domain": { - "H_field": 4.674183911036343, - "chi_susceptibility": 0.7437444278037302, - "coercive_loss": 2.0741839110363425, - "domain_wall_pressure": 1.0750676801006631, - "heat_loss": 1.2271608023433551, - "information_load": 4.674183911036343, - "magnetization_M": 0.6112844550591624, - "overflow": 1.4241839110363426, - "overflow_gate": 0.13834105775680244, - "remanence": 0.8331464911029127 - }, - "sha256": "56999bcb4329355a5674765a2dc9a5f00a3d5f05e5f159890c862df3dd4509b5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 190, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.919445065193997, - "normalized_entropy": 0.6149306331492497, - "printable_ratio": 0.990234375, - "repetition_rate_4gram": 0.40630344490593695, - "transition_rate": 0.9572649572649573 - }, - "magnetic_domain": { - "H_field": 4.531154940397216, - "chi_susceptibility": 0.7518164977991356, - "coercive_loss": 1.931154940397216, - "domain_wall_pressure": 1.1019230247180407, - "heat_loss": 1.0649697538259983, - "information_load": 4.531154940397216, - "magnetization_M": 0.6348050852010984, - "overflow": 1.281154940397216, - "overflow_gate": 0.1687424211970728, - "remanence": 0.8354936596933341 - }, - "sha256": "686ddcc7d2a284e3dc88b69f40f40c21d901707e15af3ecff1b76e5917ebd923", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 191, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.830945222584553, - "normalized_entropy": 0.6038681528230692, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.40141705350598583, - "transition_rate": 0.9597069597069597 - }, - "magnetic_domain": { - "H_field": 4.519421579934069, - "chi_susceptibility": 0.752766047031593, - "coercive_loss": 1.919421579934069, - "domain_wall_pressure": 1.1165798124019477, - "heat_loss": 1.0516969499424833, - "information_load": 4.519421579934069, - "magnetization_M": 0.636895173137219, - "overflow": 1.2694215799340691, - "overflow_gate": 0.17151483276571827, - "remanence": 0.8338239175006598 - }, - "sha256": "16f31c30a56e82a07de2c35b68f406997b5e77b107eee3becfd9acae40994a7e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 192, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.742350306854389, - "normalized_entropy": 0.5927937883567986, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.453701441485463, - "transition_rate": 0.9631257631257631 - }, - "magnetic_domain": { - "H_field": 4.422205672627067, - "chi_susceptibility": 0.7540872798765288, - "coercive_loss": 1.8222056726270668, - "domain_wall_pressure": 1.0188486432806003, - "heat_loss": 0.9420907128600988, - "information_load": 4.422205672627067, - "magnetization_M": 0.655355371457164, - "overflow": 1.1722056726270669, - "overflow_gate": 0.19630937227188996, - "remanence": 0.8501034590100897 - }, - "sha256": "d9710185fdb7c06f00a550fed4cdaa064c36398ddde9ce1d6f8ec0e1e0ea41b6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 193, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.621923869243828, - "normalized_entropy": 0.5777404836554785, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.539701930124603, - "transition_rate": 0.967032967032967 - }, - "magnetic_domain": { - "H_field": 4.260136370554284, - "chi_susceptibility": 0.7555857265133851, - "coercive_loss": 1.6601363705542842, - "domain_wall_pressure": 0.8546620738167281, - "heat_loss": 0.7617778276161475, - "information_load": 4.260136370554284, - "magnetization_M": 0.6902865587663644, - "overflow": 1.0101363705542843, - "overflow_gate": 0.2458663505026127, - "remanence": 0.8709056788254405 - }, - "sha256": "c6228eef4202de0d616f0d6487bcc3bdafd780168888aed014d786b388946955", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 194, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.865507246178554, - "normalized_entropy": 0.6081884057723193, - "printable_ratio": 0.9970703125, - "repetition_rate_4gram": 0.44832641094551673, - "transition_rate": 0.9560439560439561 - }, - "magnetic_domain": { - "H_field": 4.455491897792926, - "chi_susceptibility": 0.7513398969277603, - "coercive_loss": 1.8554918977929256, - "domain_wall_pressure": 1.0154350901968787, - "heat_loss": 0.9795340033649569, - "information_load": 4.455491897792926, - "magnetization_M": 0.6485414714454956, - "overflow": 1.2054918977929256, - "overflow_gate": 0.18744040904933798, - "remanence": 0.8485784576681897 - }, - "sha256": "0194e7c37c497b99c2605298f098ce9963bc5c59f529c83cfb61bbea563134e6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 195, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.690859324166011, - "normalized_entropy": 0.5863574155207514, - "printable_ratio": 0.998779296875, - "repetition_rate_4gram": 0.40410456877595896, - "transition_rate": 0.9606837606837607 - }, - "magnetic_domain": { - "H_field": 4.483875983395668, - "chi_susceptibility": 0.7531445081002278, - "coercive_loss": 1.8838759833956682, - "domain_wall_pressure": 1.1131583838156036, - "heat_loss": 1.011537914560266, - "information_load": 4.483875983395668, - "magnetization_M": 0.6431824285222522, - "overflow": 1.2338759833956683, - "overflow_gate": 0.18019482656880983, - "remanence": 0.8347464470284237 - }, - "sha256": "4d13705c2beb79fe679bde4c1502d9d7bc68c8c97ebae922be276b52b1f849a4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 196, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.875131704444522, - "normalized_entropy": 0.6093914630555652, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.37478622037625214, - "transition_rate": 0.957997557997558 - }, - "magnetic_domain": { - "H_field": 4.5676020923795715, - "chi_susceptibility": 0.7521018731329563, - "coercive_loss": 1.9676020923795714, - "domain_wall_pressure": 1.166422675242612, - "heat_loss": 1.1062414540653267, - "information_load": 4.5676020923795715, - "magnetization_M": 0.6284208592164565, - "overflow": 1.3176020923795715, - "overflow_gate": 0.16041310160074987, - "remanence": 0.8240931751762076 - }, - "sha256": "83e065096f978de50de8ed11a12dd7531cbe0eb478e2d8208c31d3c4dce859ea", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 197, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.807665735126296, - "normalized_entropy": 0.600958216890787, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39286586855607136, - "transition_rate": 0.9645909645909646 - }, - "magnetic_domain": { - "H_field": 4.528691915765613, - "chi_susceptibility": 0.7546506335309299, - "coercive_loss": 1.9286919157656128, - "domain_wall_pressure": 1.1434501920697864, - "heat_loss": 1.0621829628710846, - "information_load": 4.528691915765613, - "magnetization_M": 0.6355399529288671, - "overflow": 1.278691915765613, - "overflow_gate": 0.16932065513599054, - "remanence": 0.8308188318935229 - }, - "sha256": "279c29483c09504d6da304d46b03392b174ebd6c52805e2e2f00b09d1056dc65", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 198, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.647285419502334, - "normalized_entropy": 0.5809106774377918, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.40312729049596874, - "transition_rate": 0.9660561660561661 - }, - "magnetic_domain": { - "H_field": 4.4774455060424625, - "chi_susceptibility": 0.7552122624581934, - "coercive_loss": 1.8774455060424624, - "domain_wall_pressure": 1.1258577511203947, - "heat_loss": 1.0042819273342791, - "information_load": 4.4774455060424625, - "magnetization_M": 0.644698525018491, - "overflow": 1.2274455060424625, - "overflow_gate": 0.18181139415932923, - "remanence": 0.8344121692693584 - }, - "sha256": "5576aea8967c31d9c13d03007237e9c41d5d9d38127519735463900e2e854b90", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 199, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.227944265956829, - "normalized_entropy": 0.6534930332446036, - "printable_ratio": 0.998779296875, - "repetition_rate_4gram": 0.35939408746640605, - "transition_rate": 0.9411477411477411 - }, - "magnetic_domain": { - "H_field": 4.660763619818856, - "chi_susceptibility": 0.7454258860990028, - "coercive_loss": 2.060763619818856, - "domain_wall_pressure": 1.16350730736267, - "heat_loss": 1.2119252078537774, - "information_load": 4.660763619818856, - "magnetization_M": 0.6129503576063571, - "overflow": 1.410763619818856, - "overflow_gate": 0.14094381877426768, - "remanence": 0.8179310958386157 - }, - "sha256": "6997bf008706252a6dfc2a11d2f133ae883372c88241b992c46f70d1b3639d3d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 200, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.696819212122715, - "normalized_entropy": 0.7121024015153393, - "printable_ratio": 0.94482421875, - "repetition_rate_4gram": 0.44026386513559734, - "transition_rate": 0.9196581196581196 - }, - "magnetic_domain": { - "H_field": 4.631629815874478, - "chi_susceptibility": 0.7365604591860673, - "coercive_loss": 2.0316298158744783, - "domain_wall_pressure": 0.9587885090450445, - "heat_loss": 1.1788564750736013, - "information_load": 4.631629815874478, - "magnetization_M": 0.616753884629154, - "overflow": 1.3816298158744784, - "overflow_gate": 0.14676387153134451, - "remanence": 0.8462318731685327 - }, - "sha256": "fd7307e92db0dcfee919ca00dcefeaaeb511e92b6b3755844f05f584270160a2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 201, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.497520349534735, - "normalized_entropy": 0.6871900436918419, - "printable_ratio": 0.951904296875, - "repetition_rate_4gram": 0.45565599804544343, - "transition_rate": 0.9155067155067155 - }, - "magnetic_domain": { - "H_field": 4.564860801740695, - "chi_susceptibility": 0.7348009251158673, - "coercive_loss": 1.9648608017406946, - "domain_wall_pressure": 0.9197014349225441, - "heat_loss": 1.103135322386605, - "information_load": 4.564860801740695, - "magnetization_M": 0.6268906363665898, - "overflow": 1.3148608017406946, - "overflow_gate": 0.16102501426295032, - "remanence": 0.8506504168871213 - }, - "sha256": "d511225468896bb6675192b85214cfaf6c7d1dd7d0f94502a1eacd17c11e69a6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 202, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.987972951086017, - "normalized_entropy": 0.6234966188857521, - "printable_ratio": 0.99609375, - "repetition_rate_4gram": 0.4725140483752749, - "transition_rate": 0.9477411477411477 - }, - "magnetic_domain": { - "H_field": 4.442350001300553, - "chi_susceptibility": 0.7480664166439994, - "coercive_loss": 1.8423500013005527, - "domain_wall_pressure": 0.9504541987317456, - "heat_loss": 0.9647386075749553, - "information_load": 4.442350001300553, - "magnetization_M": 0.6506228196413663, - "overflow": 1.1923500013005528, - "overflow_gate": 0.1908931047740436, - "remanence": 0.8552073015423801 - }, - "sha256": "a0e26186c29504cfda5edb28e4cee2bb68c4613596d0d3ffdf4e5eb56cd0a00e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 203, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.941301209494669, - "normalized_entropy": 0.6176626511868336, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.43977522599560226, - "transition_rate": 0.9526251526251526 - }, - "magnetic_domain": { - "H_field": 4.484088806319694, - "chi_susceptibility": 0.7499989011137845, - "coercive_loss": 1.8840888063196943, - "domain_wall_pressure": 1.0256998532591006, - "heat_loss": 1.0117781098467549, - "information_load": 4.484088806319694, - "magnetization_M": 0.6430303946056986, - "overflow": 1.2340888063196944, - "overflow_gate": 0.18014157112073279, - "remanence": 0.8460873162110331 - }, - "sha256": "d756cc67eac46192eead9e66437184c70596de6beba68fc95fa55d1b6902314b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 204, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.354012131587856, - "normalized_entropy": 0.669251516448482, - "printable_ratio": 0.949462890625, - "repetition_rate_4gram": 0.4585878328854141, - "transition_rate": 0.9274725274725275 - }, - "magnetic_domain": { - "H_field": 4.53509913942805, - "chi_susceptibility": 0.739830890247433, - "coercive_loss": 1.9350991394280501, - "domain_wall_pressure": 0.9377693891742268, - "heat_loss": 1.0694330714611175, - "information_load": 4.53509913942805, - "magnetization_M": 0.6326473809897974, - "overflow": 1.2850991394280502, - "overflow_gate": 0.16782056835157302, - "remanence": 0.8514634102084883 - }, - "sha256": "7446dae426e750d4115fab15a9704aa10c485b292a39dc07dae808587b857a55", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 205, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.835599258490068, - "normalized_entropy": 0.6044499073112585, - "printable_ratio": 0.9951171875, - "repetition_rate_4gram": 0.3889567554361104, - "transition_rate": 0.9431013431013431 - }, - "magnetic_domain": { - "H_field": 4.5318955796762665, - "chi_susceptibility": 0.7462120881780313, - "coercive_loss": 1.9318955796762665, - "domain_wall_pressure": 1.1082891753304653, - "heat_loss": 1.0658078122730743, - "information_load": 4.5318955796762665, - "magnetization_M": 0.6334491799019011, - "overflow": 1.2818955796762665, - "overflow_gate": 0.16856893090914918, - "remanence": 0.8294085775017713 - }, - "sha256": "2844c059f0e4d17d43acc7029701c2c9d05b0083978fcb92ae67f4a2c5787b36", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 206, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.152286047394988, - "normalized_entropy": 0.6440357559243735, - "printable_ratio": 0.975830078125, - "repetition_rate_4gram": 0.36745663327632544, - "transition_rate": 0.9199023199023199 - }, - "magnetic_domain": { - "H_field": 4.623637340364608, - "chi_susceptibility": 0.7366634804392072, - "coercive_loss": 2.023637340364608, - "domain_wall_pressure": 1.1048913732519887, - "heat_loss": 1.169786642205579, - "information_load": 4.623637340364608, - "magnetization_M": 0.6170819339732361, - "overflow": 1.373637340364608, - "overflow_gate": 0.14840212344906153, - "remanence": 0.8212117241078059 - }, - "sha256": "4d730ed015c7c3e5695f837a36c417e2ad7afb1547dacc18bd7a1da094de2937", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 207, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.827483182468647, - "normalized_entropy": 0.6034353978085809, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.4004397752259956, - "transition_rate": 0.9394383394383394 - }, - "magnetic_domain": { - "H_field": 4.511675524322714, - "chi_susceptibility": 0.7447353013676172, - "coercive_loss": 1.9116755243227135, - "domain_wall_pressure": 1.0779971284246876, - "heat_loss": 1.0429388117948244, - "information_load": 4.511675524322714, - "magnetization_M": 0.6367560167605659, - "overflow": 1.2616755243227136, - "overflow_gate": 0.17337002130187973, - "remanence": 0.833485893289396 - }, - "sha256": "09390bbcece820ade5f460309e24ca47cee41534df80a55a676437277562202a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 208, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.940608834641097, - "normalized_entropy": 0.6175761043301371, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.4072807231859272, - "transition_rate": 0.9594627594627595 - }, - "magnetic_domain": { - "H_field": 4.535348422884874, - "chi_susceptibility": 0.7526713106689055, - "coercive_loss": 1.9353484228848736, - "domain_wall_pressure": 1.1043640725536648, - "heat_loss": 1.069715191004358, - "information_load": 4.535348422884874, - "magnetization_M": 0.634265242357553, - "overflow": 1.2853484228848737, - "overflow_gate": 0.16776247439316258, - "remanence": 0.8358235895790297 - }, - "sha256": "9ff8edec540017c05a4be518fbe9f2fbcc465f3108e54f050b7a8896c4a70518", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 209, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.950741128682769, - "normalized_entropy": 0.6188426410853461, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.42731492792572684, - "transition_rate": 0.9697191697191697 - }, - "magnetic_domain": { - "H_field": 4.5119290515957795, - "chi_susceptibility": 0.756608828268981, - "coercive_loss": 1.9119290515957794, - "domain_wall_pressure": 1.0848084835868859, - "heat_loss": 1.0432254087911135, - "information_load": 4.5119290515957795, - "magnetization_M": 0.6392103169559247, - "overflow": 1.2619290515957795, - "overflow_gate": 0.17330898478650847, - "remanence": 0.8423070254859278 - }, - "sha256": "0d756482a8e47fcc3e0414d8866483d4f7188f71a2bdf5966db993535bd2d4ad", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 210, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.236650613304938, - "normalized_entropy": 0.6545813266631173, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.623503542633765, - "transition_rate": 0.95995115995116 - }, - "magnetic_domain": { - "H_field": 4.252376752191991, - "chi_susceptibility": 0.7528607349280803, - "coercive_loss": 1.6523767521919912, - "domain_wall_pressure": 0.6728952346347901, - "heat_loss": 0.7532556138404372, - "information_load": 4.252376752191991, - "magnetization_M": 0.6922357728522917, - "overflow": 1.0023767521919913, - "overflow_gate": 0.24853044307619623, - "remanence": 0.8862834440030006 - }, - "sha256": "287b5d49cb11f2b047bce55220dfaaafe0679b0ffe841c6dfddea08412589774", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 211, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.234942609402601, - "normalized_entropy": 0.6543678261753251, - "printable_ratio": 0.997802734375, - "repetition_rate_4gram": 0.6347422428536525, - "transition_rate": 0.977045177045177 - }, - "magnetic_domain": { - "H_field": 4.238835631214767, - "chi_susceptibility": 0.7593701038901239, - "coercive_loss": 1.6388356312147665, - "domain_wall_pressure": 0.6846058683830489, - "heat_loss": 0.7384141809065565, - "information_load": 4.238835631214767, - "magnetization_M": 0.6968949346457762, - "overflow": 0.9888356312147666, - "overflow_gate": 0.25324881345605627, - "remanence": 0.8880715379566835 - }, - "sha256": "46711ac2ef805f62f4143c6af7cdf588b683011130391dceb10674cad865a1f4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 212, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.136942423484776, - "normalized_entropy": 0.642117802935597, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5641338871243586, - "transition_rate": 0.9391941391941392 - }, - "magnetic_domain": { - "H_field": 4.324271514281149, - "chi_susceptibility": 0.7446364432768249, - "coercive_loss": 1.7242715142811487, - "domain_wall_pressure": 0.7501205041395611, - "heat_loss": 0.8326544050573609, - "information_load": 4.324271514281149, - "magnetization_M": 0.6742112546382142, - "overflow": 1.0742715142811488, - "overflow_gate": 0.22491251607417562, - "remanence": 0.8758022181426469 - }, - "sha256": "1443b40a9ca813447130c6d1f5bf64ec4012cbc037cfbf348197e206a926046a", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 213, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.221314197890167, - "normalized_entropy": 0.6526642747362709, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4241387735157586, - "transition_rate": 0.947985347985348 - }, - "magnetic_domain": { - "H_field": 4.567368204324149, - "chi_susceptibility": 0.7481635125932778, - "coercive_loss": 1.9673682043241487, - "domain_wall_pressure": 1.0476931489391785, - "heat_loss": 1.1059764264096144, - "information_load": 4.567368204324149, - "magnetization_M": 0.6284297522554961, - "overflow": 1.3173682043241488, - "overflow_gate": 0.16046521938259856, - "remanence": 0.841313534679952 - }, - "sha256": "40f0c3c83947ba8847f1101cebd3af63a0823e81792e7ad9013d4d275f07d98f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 214, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.1073985192026905, - "normalized_entropy": 0.6384248149003363, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3840703640361593, - "transition_rate": 0.938949938949939 - }, - "magnetic_domain": { - "H_field": 4.597877742460884, - "chi_susceptibility": 0.7445375343161477, - "coercive_loss": 1.9978777424608842, - "domain_wall_pressure": 1.1097591498275592, - "heat_loss": 1.1405638213156535, - "information_load": 4.597877742460884, - "magnetization_M": 0.6225009930067702, - "overflow": 1.3478777424608843, - "overflow_gate": 0.15380765971157595, - "remanence": 0.8276123489028345 - }, - "sha256": "732abaf3d47c64766761c9dd11d667b1085391dc6663790c6d14a5a56f596e67", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 215, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.168938901530931, - "normalized_entropy": 0.6461173626913663, - "printable_ratio": 0.9970703125, - "repetition_rate_4gram": 0.4317126801856829, - "transition_rate": 0.9081807081807082 - }, - "magnetic_domain": { - "H_field": 4.527550878801456, - "chi_susceptibility": 0.7316578608938049, - "coercive_loss": 1.9275508788014561, - "domain_wall_pressure": 0.9529360559900506, - "heat_loss": 1.0608920437660911, - "information_load": 4.527550878801456, - "magnetization_M": 0.632141377439369, - "overflow": 1.2775508788014562, - "overflow_gate": 0.16958920277102793, - "remanence": 0.8436622677183401 - }, - "sha256": "596790bbdb9c24fef149ce99c5b024964a6231f30bda31410d358d4bc0d5723c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 216, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.154098891998073, - "normalized_entropy": 0.6442623614997591, - "printable_ratio": 0.983154296875, - "repetition_rate_4gram": 0.37307598338626924, - "transition_rate": 0.9264957264957265 - }, - "magnetic_domain": { - "H_field": 4.618767207507332, - "chi_susceptibility": 0.7394250347631192, - "coercive_loss": 2.018767207507332, - "domain_wall_pressure": 1.1068394862189146, - "heat_loss": 1.1642606176513626, - "information_load": 4.618767207507332, - "magnetization_M": 0.6183343129694813, - "overflow": 1.3687672075073323, - "overflow_gate": 0.1494093288722173, - "remanence": 0.8234291753844826 - }, - "sha256": "00ec732923c45d16fca0c497ec32b5bd49b1bd6791e193ce6581accd89fb0323", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 217, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.880801684116091, - "normalized_entropy": 0.6101002105145114, - "printable_ratio": 0.998779296875, - "repetition_rate_4gram": 0.36672367456633276, - "transition_rate": 0.9540903540903541 - }, - "magnetic_domain": { - "H_field": 4.578920649089955, - "chi_susceptibility": 0.7505747918927146, - "coercive_loss": 1.9789206490899551, - "domain_wall_pressure": 1.1747333590480427, - "heat_loss": 1.1190693404226593, - "information_load": 4.578920649089955, - "magnetization_M": 0.6262348337597312, - "overflow": 1.3289206490899552, - "overflow_gate": 0.1579110903356059, - "remanence": 0.8209183785084553 - }, - "sha256": "bd75c342447abe1237e3ab46d92cb12c4e5df3beb466c334e59c9b8f19de0f61", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 218, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.45975264774778, - "normalized_entropy": 0.5574690809684725, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.43855362814561444, - "transition_rate": 0.9763125763125763 - }, - "magnetic_domain": { - "H_field": 4.384811214756856, - "chi_susceptibility": 0.7590958722346842, - "coercive_loss": 1.7848112147568558, - "domain_wall_pressure": 1.0755178963339236, - "heat_loss": 0.9001612485305895, - "information_load": 4.384811214756856, - "magnetization_M": 0.6634249799525943, - "overflow": 1.1348112147568559, - "overflow_gate": 0.20677445126989016, - "remanence": 0.8457247319123273 - }, - "sha256": "fa08198e6d69bdbbddf149439c7485e06b4dfe14c05e9c25e0a0adb8906790ec", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 219, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.597543892423747, - "normalized_entropy": 0.5746929865529684, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.41216711458587835, - "transition_rate": 0.9648351648351648 - }, - "magnetic_domain": { - "H_field": 4.452038272478266, - "chi_susceptibility": 0.7547443579632249, - "coercive_loss": 1.8520382724782656, - "domain_wall_pressure": 1.105336100498573, - "heat_loss": 0.9756443833259387, - "information_load": 4.452038272478266, - "magnetization_M": 0.649348296382499, - "overflow": 1.2020382724782657, - "overflow_gate": 0.18834166460071713, - "remanence": 0.8374535851154663 - }, - "sha256": "4287230b0f113447c014c099cd2d1f9e651f5fbaf36783f2d1dc9ce21216cdf1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 220, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.712473029533664, - "normalized_entropy": 0.589059128691708, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39750794038602494, - "transition_rate": 0.9575091575091575 - }, - "magnetic_domain": { - "H_field": 4.497262764635751, - "chi_susceptibility": 0.7519116716198543, - "coercive_loss": 1.8972627646357512, - "domain_wall_pressure": 1.120002434246265, - "heat_loss": 1.026652586189199, - "information_load": 4.497262764635751, - "magnetization_M": 0.6405069157511215, - "overflow": 1.2472627646357513, - "overflow_gate": 0.17687546257422262, - "remanence": 0.8324635189619533 - }, - "sha256": "b8e186b929a5c26ef181fecc3f51e820c835d5879a912170e595fd5c6745178b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 221, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.676540981125551, - "normalized_entropy": 0.5845676226406938, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4490593696555094, - "transition_rate": 0.9611721611721612 - }, - "magnetic_domain": { - "H_field": 4.413381636730172, - "chi_susceptibility": 0.7533334483968646, - "coercive_loss": 1.8133816367301718, - "domain_wall_pressure": 1.0242255830333036, - "heat_loss": 0.9321827276168693, - "information_load": 4.413381636730172, - "magnetization_M": 0.656834761495748, - "overflow": 1.163381636730172, - "overflow_gate": 0.19873006571009302, - "remanence": 0.8487882370326586 - }, - "sha256": "a2198f8a8b31ee1f753f6239333e6038baf456d6c999d1ac09fc3cb21c1736fa", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 222, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.66891748885011, - "normalized_entropy": 0.5836146861062638, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.5018323967749817, - "transition_rate": 0.9604395604395605 - }, - "magnetic_domain": { - "H_field": 4.329331567824149, - "chi_susceptibility": 0.7530499654344234, - "coercive_loss": 1.7293315678241492, - "domain_wall_pressure": 0.9172143273291575, - "heat_loss": 0.8382764558230587, - "information_load": 4.329331567824149, - "magnetization_M": 0.6742876842282283, - "overflow": 1.0793315678241493, - "overflow_gate": 0.2233374054712766, - "remanence": 0.8625033593120129 - }, - "sha256": "9401e95b5c5842526aeddd5f7bf33bba29feaeeacccff3e25a4728a4b176499f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 223, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.654935645432271, - "normalized_entropy": 0.5818669556790339, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39824089909601756, - "transition_rate": 0.95995115995116 - }, - "magnetic_domain": { - "H_field": 4.483931519158799, - "chi_susceptibility": 0.7528607349280803, - "coercive_loss": 1.8839315191587986, - "domain_wall_pressure": 1.1234205217102848, - "heat_loss": 1.011600592779898, - "information_load": 4.483931519158799, - "magnetization_M": 0.6430361663416795, - "overflow": 1.2339315191587987, - "overflow_gate": 0.18018092813648948, - "remanence": 0.8327202877227399 - }, - "sha256": "c893241afa5e40a9a724905583c1554f4dba074a4a84e4a0479a2147a19fa0e8", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 224, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.010210411762171, - "normalized_entropy": 0.6262763014702714, - "printable_ratio": 0.9970703125, - "repetition_rate_4gram": 0.5638895675543611, - "transition_rate": 0.9262515262515263 - }, - "magnetic_domain": { - "H_field": 4.291239036180052, - "chi_susceptibility": 0.7393234399437048, - "coercive_loss": 1.6912390361800518, - "domain_wall_pressure": 0.7247239173943303, - "heat_loss": 0.7960569027978105, - "information_load": 4.291239036180052, - "magnetization_M": 0.6800836395100237, - "overflow": 1.0412390361800519, - "overflow_gate": 0.23547151505357533, - "remanence": 0.8757550921288286 - }, - "sha256": "622a32e741fe10b31881cc1b9b1f7b6748dbef0edd3dea4f00c9a6ae640c8e7b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 225, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.190993663132118, - "normalized_entropy": 0.6488742078915147, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.6880039091131199, - "transition_rate": 0.9536019536019537 - }, - "magnetic_domain": { - "H_field": 4.121190348651751, - "chi_susceptibility": 0.7503830250587888, - "coercive_loss": 1.5211903486517513, - "domain_wall_pressure": 0.5311960889776675, - "heat_loss": 0.6114002744464563, - "information_load": 4.121190348651751, - "magnetization_M": 0.7236235141721719, - "overflow": 0.8711903486517514, - "overflow_gate": 0.2982012766869429, - "remanence": 0.8958338635380348 - }, - "sha256": "8b909b071c440d27b82237f6d7818667c6fc3f7bf8a1ac2287be36b6d8a7bd29", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 226, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.233388942433096, - "normalized_entropy": 0.654173617804137, - "printable_ratio": 0.994140625, - "repetition_rate_4gram": 0.6909357439530907, - "transition_rate": 0.9489621489621489 - }, - "magnetic_domain": { - "H_field": 4.12285981364398, - "chi_susceptibility": 0.7485513984057965, - "coercive_loss": 1.5228598136439797, - "domain_wall_pressure": 0.5160528100181165, - "heat_loss": 0.6131747336217237, - "information_load": 4.12285981364398, - "magnetization_M": 0.7227760191075312, - "overflow": 0.8728598136439798, - "overflow_gate": 0.2975106379776305, - "remanence": 0.8962300027888346 - }, - "sha256": "d8d62fe1597967eff1d9d931619a7df52ba9c421179ff2aae971a8b5779197c9", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 227, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.22312158904083, - "normalized_entropy": 0.6528901986301038, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.6017591009039824, - "transition_rate": 0.9496947496947497 - }, - "magnetic_domain": { - "H_field": 4.2833621843414855, - "chi_susceptibility": 0.7488417906840189, - "coercive_loss": 1.6833621843414854, - "domain_wall_pressure": 0.6958712975815344, - "heat_loss": 0.7873581917823137, - "information_load": 4.2833621843414855, - "magnetization_M": 0.6842455767780309, - "overflow": 1.0333621843414855, - "overflow_gate": 0.23806173313371135, - "remanence": 0.8826564986167057 - }, - "sha256": "8c6a0fb8638b8be80aa99cdb38c6d028b34a8b760e850ee210d61d4a7e3f5bfd", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 228, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.090304722176404, - "normalized_entropy": 0.6362880902720505, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.460298069875397, - "transition_rate": 0.9372405372405372 - }, - "magnetic_domain": { - "H_field": 4.479438538767995, - "chi_susceptibility": 0.7438437447771351, - "coercive_loss": 1.8794385387679946, - "domain_wall_pressure": 0.9538849347302805, - "heat_loss": 1.0065304907855925, - "information_load": 4.479438538767995, - "magnetization_M": 0.6429532030236853, - "overflow": 1.2294385387679947, - "overflow_gate": 0.18130881776796715, - "remanence": 0.8519335817385957 - }, - "sha256": "1e588bf368bd0368e6f113c16727c0c24dfefeb98c1c80382913ae058f031a05", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 229, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.496897988376611, - "normalized_entropy": 0.6871122485470764, - "printable_ratio": 0.917724609375, - "repetition_rate_4gram": 0.48424138773515757, - "transition_rate": 0.8986568986568987 - }, - "magnetic_domain": { - "H_field": 4.5130445885138535, - "chi_susceptibility": 0.7274981536173386, - "coercive_loss": 1.9130445885138534, - "domain_wall_pressure": 0.8288310218434822, - "heat_loss": 1.0444865000662527, - "information_load": 4.5130445885138535, - "magnetization_M": 0.6343958062536234, - "overflow": 1.2630445885138535, - "overflow_gate": 0.1730406752344069, - "remanence": 0.8582167105445476 - }, - "sha256": "49864d0ace7d79f25ad20747cc44bd21322974752376d1e15e1ec1ecd94dce95", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 230, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.124755421285867, - "normalized_entropy": 0.6405944276607334, - "printable_ratio": 0.989501953125, - "repetition_rate_4gram": 0.46078670901539215, - "transition_rate": 0.9155067155067155 - }, - "magnetic_domain": { - "H_field": 4.476914101343814, - "chi_susceptibility": 0.7348009251158673, - "coercive_loss": 1.876914101343814, - "domain_wall_pressure": 0.9094400129826467, - "heat_loss": 1.0036824400263207, - "information_load": 4.476914101343814, - "magnetization_M": 0.6417154588593322, - "overflow": 1.226914101343814, - "overflow_gate": 0.1819456317870927, - "remanence": 0.852067370247217 - }, - "sha256": "ff20d0c53840a5b9793e1c3b7c30cc621127a6a63b699eee5112630ea1e80fc0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 231, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.446160270297197, - "normalized_entropy": 0.5557700337871496, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4554116784754459, - "transition_rate": 0.978021978021978 - }, - "magnetic_domain": { - "H_field": 4.356635470352646, - "chi_susceptibility": 0.7597350950184406, - "coercive_loss": 1.7566354703526463, - "domain_wall_pressure": 1.045220599093064, - "heat_loss": 0.8686794484723543, - "information_load": 4.356635470352646, - "magnetization_M": 0.6694614222855698, - "overflow": 1.1066354703526464, - "overflow_gate": 0.21502656317754182, - "remanence": 0.8505822655422918 - }, - "sha256": "c9529208002880d8a00ebdb86bd2f4a34648851554d5cee4c5677e1ec4071117", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 232, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.07252615587919, - "normalized_entropy": 0.6340657694848988, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.42169557781578304, - "transition_rate": 0.9355311355311355 - }, - "magnetic_domain": { - "H_field": 4.533262809303551, - "chi_susceptibility": 0.7431474511312024, - "coercive_loss": 1.9332628093035509, - "domain_wall_pressure": 1.027671115430705, - "heat_loss": 1.0673549528991222, - "information_load": 4.533262809303551, - "magnetization_M": 0.6331134486584029, - "overflow": 1.283262809303551, - "overflow_gate": 0.1682491340348325, - "remanence": 0.8405407511298115 - }, - "sha256": "2e9d41808f0b42c422d01d919de447865bc89f87e19dc824cd08b4eac4a31ec5", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 233, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.2657249154687715, - "normalized_entropy": 0.6582156144335964, - "printable_ratio": 0.958984375, - "repetition_rate_4gram": 0.3510872220864891, - "transition_rate": 0.9238095238095239 - }, - "magnetic_domain": { - "H_field": 4.673437019786727, - "chi_susceptibility": 0.7383046026335793, - "coercive_loss": 2.0734370197867267, - "domain_wall_pressure": 1.1454446034460695, - "heat_loss": 1.2263128562989767, - "information_load": 4.673437019786727, - "magnetization_M": 0.6099365759713821, - "overflow": 1.4234370197867268, - "overflow_gate": 0.1384846401685443, - "remanence": 0.814422706354424 - }, - "sha256": "ab97db39c686fbbd7e0228bb91445813bd16d27aa61b4338d0f2f48ccf35f0f3", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 234, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.706903072575265, - "normalized_entropy": 0.5883628840719082, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39188859027608114, - "transition_rate": 0.9538461538461539 - }, - "magnetic_domain": { - "H_field": 4.502722570815207, - "chi_susceptibility": 0.7504789330469903, - "coercive_loss": 1.902722570815207, - "domain_wall_pressure": 1.1239151271401455, - "heat_loss": 1.0328205562151285, - "information_load": 4.502722570815207, - "magnetization_M": 0.6392154526230606, - "overflow": 1.252722570815207, - "overflow_gate": 0.1755392771896636, - "remanence": 0.8304684587665162 - }, - "sha256": "7d6302af914bb8ee8301466ad8063867477da97a88a379c31e1dbcca7faea1b6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 235, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.846203222295136, - "normalized_entropy": 0.605775402786892, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.3896897141461031, - "transition_rate": 0.9474969474969475 - }, - "magnetic_domain": { - "H_field": 4.53505696908681, - "chi_susceptibility": 0.7479692708388629, - "coercive_loss": 1.9350569690868098, - "domain_wall_pressure": 1.1156144667016887, - "heat_loss": 1.0693853466711045, - "information_load": 4.53505696908681, - "magnetization_M": 0.63324390812884, - "overflow": 1.2850569690868099, - "overflow_gate": 0.16783039787641987, - "remanence": 0.8296747882898816 - }, - "sha256": "7f12aa83c79487fee47bb17bceb336e2c6bc7d97ceceb9489cf21e15dd989bc1", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 236, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.578269572805205, - "normalized_entropy": 0.5722836966006506, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41705350598582946, - "transition_rate": 0.9545787545787546 - }, - "magnetic_domain": { - "H_field": 4.4360997423368955, - "chi_susceptibility": 0.7507663622700999, - "coercive_loss": 1.8360997423368954, - "domain_wall_pressure": 1.0750504971858503, - "heat_loss": 0.957707406222366, - "information_load": 4.4360997423368955, - "magnetization_M": 0.6516037013878024, - "overflow": 1.1860997423368955, - "overflow_gate": 0.1925574451812483, - "remanence": 0.8390515326084819 - }, - "sha256": "7644fd12e26d8e7af513fa799a9611bedd83d6df06d0c91f96bf519cfaeb26eb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 237, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.60684497968776, - "normalized_entropy": 0.57585562246097, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.39506474468604935, - "transition_rate": 0.9562881562881563 - }, - "magnetic_domain": { - "H_field": 4.475954244536363, - "chi_susceptibility": 0.7514353147650005, - "coercive_loss": 1.8759542445363633, - "domain_wall_pressure": 1.122446823204214, - "heat_loss": 1.0025996616720887, - "information_load": 4.475954244536363, - "magnetization_M": 0.6441426293032714, - "overflow": 1.2259542445363634, - "overflow_gate": 0.18218835153080595, - "remanence": 0.8316019008043447 - }, - "sha256": "cf91f89940739f5780a203d01bee796e2139c95ddeacf7281899b1a1d653e1c0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 238, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.603111605558506, - "normalized_entropy": 0.5753889506948132, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4204739799657953, - "transition_rate": 0.9465201465201465 - }, - "magnetic_domain": { - "H_field": 4.433448635830569, - "chi_susceptibility": 0.7475801884850272, - "coercive_loss": 1.8334486358305688, - "domain_wall_pressure": 1.0520923331087024, - "heat_loss": 0.9547261613195831, - "information_load": 4.433448635830569, - "magnetization_M": 0.65152734072323, - "overflow": 1.183448635830569, - "overflow_gate": 0.19326776641257745, - "remanence": 0.8401515299447385 - }, - "sha256": "640938640379e074f88a76fc0f36f3e4de5439c6d306cb54204060e789aa3f54", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 239, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.216929429023966, - "normalized_entropy": 0.6521161786279958, - "printable_ratio": 0.99609375, - "repetition_rate_4gram": 0.3603713657463963, - "transition_rate": 0.9067155067155067 - }, - "magnetic_domain": { - "H_field": 4.642118847732236, - "chi_susceptibility": 0.7310233673754922, - "coercive_loss": 2.0421188477322363, - "domain_wall_pressure": 1.0926882819382209, - "heat_loss": 1.1907609688649041, - "information_load": 4.642118847732236, - "magnetization_M": 0.6133741224708179, - "overflow": 1.3921188477322364, - "overflow_gate": 0.14464129926503366, - "remanence": 0.8183351456913961 - }, - "sha256": "c5bd864fc8d87232f4a483919bd81091a01c9b1f0ede81aa386d60c8a5a1d50d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 240, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.496232712268218, - "normalized_entropy": 0.6870290890335272, - "printable_ratio": 0.95947265625, - "repetition_rate_4gram": 0.3493769850965062, - "transition_rate": 0.9091575091575091 - }, - "magnetic_domain": { - "H_field": 4.718627150835475, - "chi_susceptibility": 0.732079762193545, - "coercive_loss": 2.1186271508354753, - "domain_wall_pressure": 1.119561048122006, - "heat_loss": 1.277617605821337, - "information_load": 4.718627150835475, - "magnetization_M": 0.6028450966128095, - "overflow": 1.4686271508354753, - "overflow_gate": 0.1300599303951833, - "remanence": 0.8136835396941005 - }, - "sha256": "8e640f38dbdbcacc65f483e7f9d4473bf0e6b8a2ee31470e780e9101187c5a8f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 241, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.735197538515351, - "normalized_entropy": 0.5918996923144189, - "printable_ratio": 0.99658203125, - "repetition_rate_4gram": 0.39188859027608114, - "transition_rate": 0.9601953601953602 - }, - "magnetic_domain": { - "H_field": 4.511813038521595, - "chi_susceptibility": 0.7529553743863632, - "coercive_loss": 1.9118130385215948, - "domain_wall_pressure": 1.1366135398385582, - "heat_loss": 1.0430942626675208, - "information_load": 4.511813038521595, - "magnetization_M": 0.6380974528816119, - "overflow": 1.2618130385215949, - "overflow_gate": 0.17333691218656005, - "remanence": 0.8304684587665162 - }, - "sha256": "2da32eb5edaad81682aa23793191a9b189f298e273a9ae34a842192342b4a627", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 242, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.634638454652941, - "normalized_entropy": 0.5793298068316176, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.44759345223552405, - "transition_rate": 0.9675213675213675 - }, - "magnetic_domain": { - "H_field": 4.408503802167177, - "chi_susceptibility": 0.7557721726347358, - "coercive_loss": 1.8085038021671767, - "domain_wall_pressure": 1.039855830571687, - "heat_loss": 0.9267092177933166, - "information_load": 4.408503802167177, - "magnetization_M": 0.6582383301682085, - "overflow": 1.1585038021671767, - "overflow_gate": 0.2000809871665931, - "remanence": 0.8483680954321491 - }, - "sha256": "3478a45f546edebd11a49770d5082a1f5c977697bdb11a5108943656069defd8", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 243, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.732959772997689, - "normalized_entropy": 0.5916199716247111, - "printable_ratio": 0.998291015625, - "repetition_rate_4gram": 0.42682628878573176, - "transition_rate": 0.9643467643467644 - }, - "magnetic_domain": { - "H_field": 4.461229697445711, - "chi_susceptibility": 0.7545568611895902, - "coercive_loss": 1.8612296974457112, - "domain_wall_pressure": 1.075040951122065, - "heat_loss": 0.9859983842559621, - "information_load": 4.461229697445711, - "magnetization_M": 0.6478299032782354, - "overflow": 1.2112296974457113, - "overflow_gate": 0.18595260144688155, - "remanence": 0.8421549912265479 - }, - "sha256": "2a2f5bc92f20795474d63c51d8908dce4fb8b259fa20f3fd6909e36b16c6543f", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 244, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.558785923579182, - "normalized_entropy": 0.5698482404473978, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39262154898607377, - "transition_rate": 0.9675213675213675 - }, - "magnetic_domain": { - "H_field": 4.4729059818575605, - "chi_susceptibility": 0.7557721726347358, - "coercive_loss": 1.8729059818575604, - "domain_wall_pressure": 1.1497996370705876, - "heat_loss": 0.9991614927062834, - "information_load": 4.4729059818575605, - "magnetization_M": 0.645463503701027, - "overflow": 1.2229059818575605, - "overflow_gate": 0.1829613171172941, - "remanence": 0.8307313744546225 - }, - "sha256": "dc537d59abcf2317c92e3cd7323e3d46a38cf411127442a28e9d1f2b3b4fb53e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 245, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.735837973443359, - "normalized_entropy": 0.5919797466804199, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.35719521133642806, - "transition_rate": 0.9570207570207571 - }, - "magnetic_domain": { - "H_field": 4.560893898474578, - "chi_susceptibility": 0.7517212752314547, - "coercive_loss": 1.9608938984745783, - "domain_wall_pressure": 1.199651091368658, - "heat_loss": 1.0986409792863083, - "information_load": 4.560893898474578, - "magnetization_M": 0.6291718748904945, - "overflow": 1.3108938984745784, - "overflow_gate": 0.16191464422502694, - "remanence": 0.8170153791130186 - }, - "sha256": "0ab0c5b09e2afa2ab678f279202cf0abe6d5b90dfb3ede1eecc581236147263e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 246, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.82155643680533, - "normalized_entropy": 0.6026945546006662, - "printable_ratio": 0.99462890625, - "repetition_rate_4gram": 0.5147813339848522, - "transition_rate": 0.8293040293040294 - }, - "magnetic_domain": { - "H_field": 4.2842001702557795, - "chi_susceptibility": 0.6945200963944295, - "coercive_loss": 1.6842001702557794, - "domain_wall_pressure": 0.6290453906383544, - "heat_loss": 0.7882830673134996, - "information_load": 4.2842001702557795, - "magnetization_M": 0.6710821083973572, - "overflow": 1.0342001702557795, - "overflow_gate": 0.2377848215606649, - "remanence": 0.865496787762278 - }, - "sha256": "4af135cc86e2bcb53eedaf2a68fd2391be1827c7427b32bb078a5556e9d84e2c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 247, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.940266659680017, - "normalized_entropy": 0.6175333324600021, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.494502809675055, - "transition_rate": 0.9746031746031746 - }, - "magnetic_domain": { - "H_field": 4.408275126859078, - "chi_susceptibility": 0.7584543662639137, - "coercive_loss": 1.8082751268590775, - "domain_wall_pressure": 0.9602007298562392, - "heat_loss": 0.9264526798833536, - "information_load": 4.408275126859078, - "magnetization_M": 0.6593714465225637, - "overflow": 1.1582751268590776, - "overflow_gate": 0.2001445438998267, - "remanence": 0.8607491579627803 - }, - "sha256": "f86faf3e3801382f33d63299d37efd55e3ee44a2d5ea06c6e41c9b70c827a8f0", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 248, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.255340594374877, - "normalized_entropy": 0.6569175742968596, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.44197410212558025, - "transition_rate": 0.9257631257631258 - }, - "magnetic_domain": { - "H_field": 4.538483736478976, - "chi_susceptibility": 0.7391200929570091, - "coercive_loss": 1.9384837364789758, - "domain_wall_pressure": 0.9675780472750911, - "heat_loss": 1.0732637575547428, - "information_load": 4.538483736478976, - "magnetization_M": 0.631777228199113, - "overflow": 1.2884837364789758, - "overflow_gate": 0.16703352384746595, - "remanence": 0.8467356911497631 - }, - "sha256": "214b0c9f8565604872117e1b153888f15088e3fae3758c1d89aabd51ed4fb81d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 249, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.272533343428686, - "normalized_entropy": 0.6590666679285857, - "printable_ratio": 0.998046875, - "repetition_rate_4gram": 0.6716344979232837, - "transition_rate": 0.8940170940170941 - }, - "magnetic_domain": { - "H_field": 4.144116076985891, - "chi_susceptibility": 0.7254409329746074, - "coercive_loss": 1.5441160769858908, - "domain_wall_pressure": 0.4447651921876208, - "heat_loss": 0.635845515833429, - "information_load": 4.144116076985891, - "magnetization_M": 0.7116658381364669, - "overflow": 0.8941160769858909, - "overflow_gate": 0.288855740099322, - "remanence": 0.8935652897504909 - }, - "sha256": "e74df39ad03293319905b1ac4cbe4cdf718eadf1bc866b598bbd6a37a492d78c", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 250, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.365013732866706, - "normalized_entropy": 0.6706267166083383, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.6743220131932568, - "transition_rate": 0.8874236874236874 - }, - "magnetic_domain": { - "H_field": 4.155923057638745, - "chi_susceptibility": 0.7224823602244096, - "coercive_loss": 1.5559230576387448, - "domain_wall_pressure": 0.42620334846086116, - "heat_loss": 0.6484981882542123, - "information_load": 4.155923057638745, - "magnetization_M": 0.70809781973388, - "overflow": 0.9059230576387449, - "overflow_gate": 0.28415754209358696, - "remanence": 0.893944497706838 - }, - "sha256": "88c6e63e0df01c9f1ce9a39fe9bf6f076f6c9cab96e61e6fac0eb7ed5fe79c43", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 251, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.665379321944173, - "normalized_entropy": 0.5831724152430217, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.8829709259711703, - "transition_rate": 0.9653235653235653 - }, - "magnetic_domain": { - "H_field": 3.5659796252300877, - "chi_susceptibility": 0.754931663211486, - "coercive_loss": 0.9659796252300876, - "domain_wall_pressure": 0.16470527870479001, - "heat_loss": 0.11224523357947692, - "information_load": 3.5659796252300877, - "magnetization_M": 0.882420731613768, - "overflow": 0.3159796252300877, - "overflow_gate": 0.6447706604571639, - "remanence": 0.916923763903548 - }, - "sha256": "2bbfa32d18ea86782bb009274aaee393952997013548e4255b0abdc6f587d913", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 252, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.472320101431381, - "normalized_entropy": 0.6840400126789227, - "printable_ratio": 0.97998046875, - "repetition_rate_4gram": 0.4649401417053506, - "transition_rate": 0.9208791208791208 - }, - "magnetic_domain": { - "H_field": 4.547627143457763, - "chi_susceptibility": 0.7370750335150278, - "coercive_loss": 1.9476271434577632, - "domain_wall_pressure": 0.9118779583475405, - "heat_loss": 1.0836150168357526, - "information_load": 4.547627143457763, - "magnetization_M": 0.6301658749166653, - "overflow": 1.2976271434577633, - "overflow_gate": 0.164925747508438, - "remanence": 0.8531948853141085 - }, - "sha256": "50b1defcda5d2b84a1ae1a8b41d2518ec5addf30c8e660e6d6e15f2211c08288", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 253, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.054518050207843, - "normalized_entropy": 0.6318147562759804, - "printable_ratio": 0.976318359375, - "repetition_rate_4gram": 0.5111165404348889, - "transition_rate": 0.9240537240537241 - }, - "magnetic_domain": { - "H_field": 4.386338410387135, - "chi_susceptibility": 0.7384067231977074, - "coercive_loss": 1.7863384103871351, - "domain_wall_pressure": 0.8258743672376705, - "heat_loss": 0.9018705179700387, - "information_load": 4.386338410387135, - "magnetization_M": 0.6598040685172737, - "overflow": 1.1363384103871352, - "overflow_gate": 0.20633632575811325, - "remanence": 0.864662897199352 - }, - "sha256": "bd1ac4881b2317895472685d4ea08899a5786a3ffc05c7e0dc3c4414954a088b", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 254, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.063078104970309, - "normalized_entropy": 0.6328847631212886, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4089909601759101, - "transition_rate": 0.9401709401709402 - }, - "magnetic_domain": { - "H_field": 4.55203394576603, - "chi_susceptibility": 0.7450315707128089, - "coercive_loss": 1.9520339457660296, - "domain_wall_pressure": 1.0623599599900602, - "heat_loss": 1.0886053321687663, - "information_load": 4.55203394576603, - "magnetization_M": 0.6301898628573607, - "overflow": 1.3020339457660297, - "overflow_gate": 0.16391939264816646, - "remanence": 0.8363977935886162 - }, - "sha256": "1b6267c4e720c1282f50734c5079e53a52740e5abc8bec41bc1a9b7f32e7c958", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 255, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.98338259093234, - "normalized_entropy": 0.6229228238665425, - "printable_ratio": 0.994140625, - "repetition_rate_4gram": 0.4424627412655754, - "transition_rate": 0.936996336996337 - }, - "magnetic_domain": { - "H_field": 4.48288373486566, - "chi_susceptibility": 0.7437444278037302, - "coercive_loss": 1.88288373486566, - "domain_wall_pressure": 0.9890671914615232, - "heat_loss": 1.0104180902286286, - "information_load": 4.48288373486566, - "magnetization_M": 0.6421151681002994, - "overflow": 1.23288373486566, - "overflow_gate": 0.18044332838998173, - "remanence": 0.8468790333140047 - }, - "sha256": "68b8e3af6b32999b2403c79ce9d005a5d17f79b19eba0e5543ebb11fbb315d78", - "status": "overflow" - } - ], - "claim_boundary": "This receipt maps byte-signal statistics into a magnetic-domain analogue. It is a stress-test and routing prior, not a claim that text data is a literal magnetic material.", - "parameters": { - "D_f": 1.4404200904125564, - "capacity_threshold": 3.25, - "chunk_size": 4096, - "coercive_threshold": 2.6, - "lambda_phi": 1.618033988749895, - "max_chunks": 256, - "phi_gain": 2.0, - "slice_bytes_requested": 1048576 - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "receipt_hash": "a506487cddcdd81889e56f79f1b3db4c6836f8b42ebabdf2bdea7e528edab0e0", - "runner": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py", - "schema": "transfold_enwiki8_magnetic_domain_generator_v1", - "source": { - "available_bytes": 100000000, - "path": "shared-data/corpora/enwik8", - "sha256": "4fb5efa9f35df431737731bf3c8f38a467b69731940ff82a4ee0e218aae58834", - "slice_bytes": 1048576, - "source_mode": "real_file" - }, - "stress_sweep": [ - { - "capacity_threshold": 2.5, - "mean_heat_loss": 1.815654861325333, - "mean_overflow": 1.9467525522386986, - "mean_overflow_gate": 0.06938602211338227, - "overflow_chunk_count": 256 - }, - { - "capacity_threshold": 3.25, - "mean_heat_loss": 0.9727067215896326, - "mean_overflow": 1.1967525522386986, - "mean_overflow_gate": 0.19663556731358448, - "overflow_chunk_count": 256 - }, - { - "capacity_threshold": 4.0, - "mean_heat_loss": 0.22546958198757838, - "mean_overflow": 0.45508512041220744, - "mean_overflow_gate": 0.5429233805793959, - "overflow_chunk_count": 247 - }, - { - "capacity_threshold": 4.5, - "mean_heat_loss": 0.003749109274715911, - "mean_overflow": 0.026856379972097798, - "mean_overflow_gate": 0.9653930640465004, - "overflow_chunk_count": 106 - }, - { - "capacity_threshold": 5.25, - "mean_heat_loss": 0.0, - "mean_overflow": 0.0, - "mean_overflow_gate": 1.0, - "overflow_chunk_count": 0 - } - ], - "transfold_map": { - "core_equations": { - "heat_loss": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - "magnetic_projection": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", - "overflow_gate": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", - "signal_load": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)" - }, - "field_mapping": { - "byte_transition_rate": "domain agitation / susceptibility driver", - "capacity_overflow": "hysteresis heat-loss channel", - "entropy": "field demand / information pressure", - "repeated_4grams": "remanence / memory channel" - }, - "source_domain": "byte_stream_signal", - "target_domain": "magnetic_domain_equation" - } -} diff --git a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_curriculum.jsonl b/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_curriculum.jsonl deleted file mode 100644 index 19e45de4..00000000 --- a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_curriculum.jsonl +++ /dev/null @@ -1,3 +0,0 @@ -{"input": "enwiki8 byte chunk statistics", "target": "H, chi, remanence, magnetization, overflow, heat_loss", "task": "transfold_byte_signal_to_magnetic_domain"} -{"input": "information_load and capacity_threshold", "target": "overflow_gate plus hysteresis heat-loss channel", "task": "detect_capacity_overflow"} -{"input": "real_file", "target": "This receipt maps byte-signal statistics into a magnetic-domain analogue. It is a stress-test and routing prior, not a claim that text data is a literal magnetic material.", "task": "preserve_claim_boundary"} diff --git a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json b/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json deleted file mode 100644 index ef849e4b..00000000 --- a/4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator_receipt.json +++ /dev/null @@ -1,550 +0,0 @@ -{ - "aggregate": { - "H_field": { - "max": 4.580575080530276, - "mean": 4.48288790262248, - "min": 3.9409932456335826 - }, - "chi_susceptibility": { - "max": 0.7555857265133851, - "mean": 0.7478517983083702, - "min": 0.7043095813586062 - }, - "chunk_count": 16, - "coercive_loss": { - "max": 1.9805750805302762, - "mean": 1.8828879026224792, - "min": 1.3409932456335825 - }, - "domain_wall_pressure": { - "max": 1.184016070798382, - "mean": 1.0518157433683943, - "min": 0.26205830437445377 - }, - "heat_loss": { - "max": 1.120944765669762, - "mean": 1.0122453515412608, - "min": 0.42634086126193665 - }, - "information_load": { - "max": 4.580575080530276, - "mean": 4.48288790262248, - "min": 3.9409932456335826 - }, - "magnetization_M": { - "max": 0.7596731582117144, - "mean": 0.6444969211710623, - "min": 0.6263110577490969 - }, - "overflow": { - "max": 1.3305750805302763, - "mean": 1.2328879026224793, - "min": 0.6909932456335826 - }, - "overflow_chunk_count": 16, - "overflow_gate": { - "max": 0.38300285284117636, - "mean": 0.18518105099696577, - "min": 0.15754865541068902 - }, - "remanence": { - "max": 0.8997256112499388, - "mean": 0.8377097662234787, - "min": 0.8196360111047459 - }, - "within_capacity_chunk_count": 0 - }, - "chunk_projections": [ - { - "bytes": 4096, - "chunk_index": 0, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.85210675017935, - "normalized_entropy": 0.6065133437724187, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.7178108966528219, - "transition_rate": 0.8488400488400488 - }, - "magnetic_domain": { - "H_field": 3.9409932456335826, - "chi_susceptibility": 0.7043095813586062, - "coercive_loss": 1.3409932456335825, - "domain_wall_pressure": 0.26205830437445377, - "heat_loss": 0.42634086126193665, - "information_load": 3.9409932456335826, - "magnetization_M": 0.7596731582117144, - "overflow": 0.6909932456335826, - "overflow_gate": 0.38300285284117636, - "remanence": 0.8997256112499388 - }, - "sha256": "652ed0541c8b9e2d8194c2a1cd20a3ea516efaa542082535c0ef57267e1ca49e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 1, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.011547845009725, - "normalized_entropy": 0.6264434806262156, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.38602492059613974, - "transition_rate": 0.9550671550671551 - }, - "magnetic_domain": { - "H_field": 4.580575080530276, - "chi_susceptibility": 0.7509577364175362, - "coercive_loss": 1.9805750805302762, - "domain_wall_pressure": 1.1380844689420306, - "heat_loss": 1.120944765669762, - "information_load": 4.580575080530276, - "magnetization_M": 0.6263110577490969, - "overflow": 1.3305750805302763, - "overflow_gate": 0.15754865541068902, - "remanence": 0.8283353604831607 - }, - "sha256": "3b557252bb5eb5a16473de67acd872a67850e5e9b1ac7ba57c3aa91bf4693a49", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 2, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.901616637958809, - "normalized_entropy": 0.6127020797448511, - "printable_ratio": 0.99853515625, - "repetition_rate_4gram": 0.3879794771561202, - "transition_rate": 0.9601953601953602 - }, - "magnetic_domain": { - "H_field": 4.555273955815762, - "chi_susceptibility": 0.7529553743863632, - "coercive_loss": 1.9552739558157621, - "domain_wall_pressure": 1.1444317660784802, - "heat_loss": 1.0922749003291068, - "information_load": 4.555273955815762, - "magnetization_M": 0.6307554737408266, - "overflow": 1.3052739558157622, - "overflow_gate": 0.16318341030066486, - "remanence": 0.8290523326233137 - }, - "sha256": "74ec77eec1e9895d4a015d931e5fcf662ff3a1132fd7f73d778d096e49101fa4", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 3, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.789920261126383, - "normalized_entropy": 0.5987400326407979, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.39848521866601516, - "transition_rate": 0.9645909645909646 - }, - "magnetic_domain": { - "H_field": 4.5164100805971685, - "chi_susceptibility": 0.7546506335309299, - "coercive_loss": 1.9164100805971684, - "domain_wall_pressure": 1.1322114918498989, - "heat_loss": 1.0482915618884743, - "information_load": 4.5164100805971685, - "magnetization_M": 0.637707591144412, - "overflow": 1.2664100805971685, - "overflow_gate": 0.17223371959092557, - "remanence": 0.8328057024979064 - }, - "sha256": "2ca56a17aa8f8843dc24e4ecef4798d00d41260f175589c2dd90b3173b951dd2", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 4, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.870001370847795, - "normalized_entropy": 0.6087501713559744, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.39262154898607377, - "transition_rate": 0.9660561660561661 - }, - "magnetic_domain": { - "H_field": 4.543747674487493, - "chi_susceptibility": 0.7552122624581934, - "coercive_loss": 1.943747674487493, - "domain_wall_pressure": 1.1468692341401847, - "heat_loss": 1.079222587025682, - "information_load": 4.543747674487493, - "magnetization_M": 0.6331131169610806, - "overflow": 1.2937476744874932, - "overflow_gate": 0.16581679077938705, - "remanence": 0.8307313744546225 - }, - "sha256": "1011e60b2041ff961cc0ab3d0ba1e3167cdf0158ee88bdfe2ce4dd7b009d6878", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 5, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.834713127357048, - "normalized_entropy": 0.604339140919631, - "printable_ratio": 0.99755859375, - "repetition_rate_4gram": 0.37649645736623505, - "transition_rate": 0.95995115995116 - }, - "magnetic_domain": { - "H_field": 4.556793352646781, - "chi_susceptibility": 0.7528607349280803, - "coercive_loss": 1.956793352646781, - "domain_wall_pressure": 1.1669094051698499, - "heat_loss": 1.0939958917739039, - "information_load": 4.556793352646781, - "magnetization_M": 0.6303276111595437, - "overflow": 1.306793352646781, - "overflow_gate": 0.16283941178754605, - "remanence": 0.8247521996960031 - }, - "sha256": "f8d9a9cdaf3bfd183d809c010bfcd7288cde48ae565744d3b637820963e3a9fb", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 6, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.703910848978386, - "normalized_entropy": 0.5879888561222982, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.45345712191546544, - "transition_rate": 0.9633699633699634 - }, - "magnetic_domain": { - "H_field": 4.413864911313744, - "chi_susceptibility": 0.7541812921791757, - "coercive_loss": 1.8138649113137437, - "domain_wall_pressure": 1.019825682908996, - "heat_loss": 0.9327251574903277, - "information_load": 4.413864911313744, - "magnetization_M": 0.656965361785371, - "overflow": 1.1638649113137438, - "overflow_gate": 0.1985967199256063, - "remanence": 0.8500348074597882 - }, - "sha256": "78faf4409ed08ae70d7481218ade59d5b47685b9523b9530c083098a9791f5ac", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 7, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.749160612751626, - "normalized_entropy": 0.5936450765939533, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.41143415587588567, - "transition_rate": 0.9641025641025641 - }, - "magnetic_domain": { - "H_field": 4.487791420616601, - "chi_susceptibility": 0.7544630409114862, - "coercive_loss": 1.8877914206166007, - "domain_wall_pressure": 1.1053368164533568, - "heat_loss": 1.015957453259845, - "information_load": 4.487791420616601, - "magnetization_M": 0.6428345204172404, - "overflow": 1.2377914206166007, - "overflow_gate": 0.17921756740424818, - "remanence": 0.8372111522093624 - }, - "sha256": "54b82d666f9b5d4538a948a4a3c8225bc27eefff0a9061f2df2708ee4495fa32", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 8, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.627967754251285, - "normalized_entropy": 0.5784959692814107, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.40019545565599807, - "transition_rate": 0.9631257631257631 - }, - "magnetic_domain": { - "H_field": 4.476106191557932, - "chi_susceptibility": 0.7540872798765288, - "coercive_loss": 1.8761061915579318, - "domain_wall_pressure": 1.1258606149395303, - "heat_loss": 1.0027710627838768, - "information_load": 4.476106191557932, - "magnetization_M": 0.6446860884951096, - "overflow": 1.2261061915579319, - "overflow_gate": 0.18214990700787334, - "remanence": 0.8334011722565939 - }, - "sha256": "f64b9c6cb911b36d1df6dd6a8f4f05d20bfe55dc22926b4590fc10c1657ea32d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 9, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.864462104048231, - "normalized_entropy": 0.6080577630060289, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.39091131199609086, - "transition_rate": 0.9616605616605617 - }, - "magnetic_domain": { - "H_field": 4.543219688131397, - "chi_susceptibility": 0.7535221954997179, - "coercive_loss": 1.9432196881313968, - "domain_wall_pressure": 1.1414984993289417, - "heat_loss": 1.078624841870434, - "information_load": 4.543219688131397, - "magnetization_M": 0.6328812089018073, - "overflow": 1.2932196881313969, - "overflow_gate": 0.16593843121197435, - "remanence": 0.8301166313867098 - }, - "sha256": "bcf0c6cf4b6f4791f559587b6ab622115137da72a0acc0a5bdc7512c9fb2cca6", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 10, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.71327032742842, - "normalized_entropy": 0.5891587909285525, - "printable_ratio": 0.99951171875, - "repetition_rate_4gram": 0.39750794038602494, - "transition_rate": 0.9584859584859585 - }, - "magnetic_domain": { - "H_field": 4.497845440399808, - "chi_susceptibility": 0.7522918799957201, - "coercive_loss": 1.897845440399808, - "domain_wall_pressure": 1.121956036199867, - "heat_loss": 1.0273107456740525, - "information_load": 4.497845440399808, - "magnetization_M": 0.6404748992897175, - "overflow": 1.247845440399808, - "overflow_gate": 0.1767323801376367, - "remanence": 0.8324635189619533 - }, - "sha256": "2e16ef58a0ab9b4b5d276aa8a6db75ec6a5f150618279f49edd96abc51bb2c13", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 11, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.5985475495008465, - "normalized_entropy": 0.5748184436876058, - "printable_ratio": 1.0, - "repetition_rate_4gram": 0.4033716100659663, - "transition_rate": 0.967032967032967 - }, - "magnetic_domain": { - "H_field": 4.466175266268176, - "chi_susceptibility": 0.7555857265133851, - "coercive_loss": 1.866175266268176, - "domain_wall_pressure": 1.1273227139340016, - "heat_loss": 0.9915723848521583, - "information_load": 4.466175266268176, - "magnetization_M": 0.6468012662496755, - "overflow": 1.2161752662681762, - "overflow_gate": 0.1846796984329487, - "remanence": 0.8344958654293281 - }, - "sha256": "8e336407be77d5f92484ebc212466e860980399dcc95b87d8bf4fd6ba234ae8d", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 12, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.796641095076212, - "normalized_entropy": 0.5995801368845265, - "printable_ratio": 0.999267578125, - "repetition_rate_4gram": 0.36354752015636455, - "transition_rate": 0.9555555555555556 - }, - "magnetic_domain": { - "H_field": 4.565050298432678, - "chi_susceptibility": 0.7511489145613812, - "coercive_loss": 1.9650502984326779, - "domain_wall_pressure": 1.184016070798382, - "heat_loss": 1.1033500300656958, - "information_load": 4.565050298432678, - "magnetization_M": 0.6285012207804667, - "overflow": 1.315050298432678, - "overflow_gate": 0.1609826396901273, - "remanence": 0.8196360111047459 - }, - "sha256": "efe73462e87ecb2fdeace60d2b83f83a2e0631a438cc07a9913d9b39fa3f10ea", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 13, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 4.858346109566147, - "normalized_entropy": 0.6072932636957684, - "printable_ratio": 0.998779296875, - "repetition_rate_4gram": 0.37796237478622036, - "transition_rate": 0.9619047619047619 - }, - "magnetic_domain": { - "H_field": 4.560806006056383, - "chi_susceptibility": 0.7536164966732386, - "coercive_loss": 1.9608060060563832, - "domain_wall_pressure": 1.1678847742370833, - "heat_loss": 1.098541407784731, - "information_load": 4.560806006056383, - "magnetization_M": 0.6298259384796606, - "overflow": 1.3108060060563833, - "overflow_gate": 0.16193441080595858, - "remanence": 0.8253131601971788 - }, - "sha256": "dae627f190ea6808116ccdf65513fe008860be628b45b1e3ad1f574ca4bc903e", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 14, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.057337330400908, - "normalized_entropy": 0.6321671663001135, - "printable_ratio": 0.9990234375, - "repetition_rate_4gram": 0.39824089909601756, - "transition_rate": 0.9284493284493285 - }, - "magnetic_domain": { - "H_field": 4.561655126731615, - "chi_susceptibility": 0.7402359090995579, - "coercive_loss": 1.9616551267316154, - "domain_wall_pressure": 1.0604168587066218, - "heat_loss": 1.0995033720301766, - "information_load": 4.561655126731615, - "magnetization_M": 0.6276630360934214, - "overflow": 1.3116551267316154, - "overflow_gate": 0.1617435485729233, - "remanence": 0.8327202877227399 - }, - "sha256": "10b0df9b6b00b6140bc248a5712201dd5ebf60e67ecb8ac873b9abf83adff875", - "status": "overflow" - }, - { - "bytes": 4096, - "chunk_index": 15, - "equation_instance": "M_i = sigmoid(((chi_i * H_i) + R_i - 0.5 * C_loss_i) * G_over_i); H_i = L_info_i; L_info_i = phi^D_f * (log(1+2H_entropy_i) + MM(T_i;1,0.35) + P(1-R4_i;0.6))", - "features": { - "entropy_bits_per_byte": 5.387947357669873, - "normalized_entropy": 0.6734934197087341, - "printable_ratio": 0.951416015625, - "repetition_rate_4gram": 0.5020767163449792, - "transition_rate": 0.8942612942612943 - }, - "magnetic_domain": { - "H_field": 4.45989870274027, - "chi_susceptibility": 0.7255497145440228, - "coercive_loss": 1.8598987027402702, - "domain_wall_pressure": 0.7843691558326302, - "heat_loss": 0.9844986009000085, - "information_load": 4.45989870274027, - "magnetization_M": 0.6434291892778523, - "overflow": 1.2098987027402703, - "overflow_gate": 0.18629667205176653, - "remanence": 0.862561071842313 - }, - "sha256": "2fd813cb038b4662e084eb66b8060c806fe839e27a933db726a602fe1183aeca", - "status": "overflow" - } - ], - "claim_boundary": "This receipt maps byte-signal statistics into a magnetic-domain analogue. It is a stress-test and routing prior, not a claim that text data is a literal magnetic material.", - "parameters": { - "D_f": 1.4404200904125564, - "capacity_threshold": 3.25, - "chunk_size": 4096, - "coercive_threshold": 2.6, - "lambda_phi": 1.618033988749895, - "max_chunks": 16, - "phi_gain": 2.0, - "slice_bytes_requested": 65536 - }, - "purpose": "Stress-test the transfold adaptation framework by converting a byte slice into magnetic-domain equation instances with response-family selection and threshold overflow.", - "receipt_hash": "df40e83a45d1d4ab48a6d7c6f6f34c53a5662c395208cd0aaffb1171923c8940", - "runner": "4-Infrastructure/shim/transfold_enwiki8_magnetic_domain_generator.py", - "schema": "transfold_enwiki8_magnetic_domain_generator_v1", - "source": { - "available_bytes": 100000000, - "path": "shared-data/corpora/enwik8", - "sha256": "05fc5f44993ef0557959db76bf47e45badb2dd9c69d93ce08911935b5e52bf40", - "slice_bytes": 65536, - "source_mode": "real_file" - }, - "stress_sweep": [ - { - "capacity_threshold": 2.5, - "mean_heat_loss": 1.856022546350068, - "mean_overflow": 1.9828879026224793, - "mean_overflow_gate": 0.06534411182573056, - "overflow_chunk_count": 16 - }, - { - "capacity_threshold": 3.25, - "mean_heat_loss": 1.0122453515412608, - "mean_overflow": 1.2328879026224793, - "mean_overflow_gate": 0.18518105099696577, - "overflow_chunk_count": 16 - }, - { - "capacity_threshold": 4.0, - "mean_heat_loss": 0.2508794744911155, - "mean_overflow": 0.48657582477038036, - "mean_overflow_gate": 0.5194534482537007, - "overflow_chunk_count": 15 - }, - { - "capacity_threshold": 4.5, - "mean_heat_loss": 0.002374608433125584, - "mean_overflow": 0.030220703964347173, - "mean_overflow_gate": 0.9596995659600062, - "overflow_chunk_count": 9 - }, - { - "capacity_threshold": 5.25, - "mean_heat_loss": 0.0, - "mean_overflow": 0.0, - "mean_overflow_gate": 1.0, - "overflow_chunk_count": 0 - } - ], - "transfold_map": { - "core_equations": { - "heat_loss": "Q_i = max(0, L_info_i - L_threshold) * (1 - G_over_i)", - "magnetic_projection": "M_i = sigmoid(((chi_i H_i) + R_i - 0.5 C_loss_i) * G_over_i)", - "overflow_gate": "G_over_i = 1 if L_info_i <= L_threshold else exp(-1.25 * (L_info_i - L_threshold) / 0.9)", - "signal_load": "L_info_i = phi^D_f * (log(1 + 2 h_i) + MM(t_i;1,0.35) + (1 - r_i)^0.6)" - }, - "field_mapping": { - "byte_transition_rate": "domain agitation / susceptibility driver", - "capacity_overflow": "hysteresis heat-loss channel", - "entropy": "field demand / information pressure", - "repeated_4grams": "remanence / memory channel" - }, - "source_domain": "byte_stream_signal", - "target_domain": "magnetic_domain_equation" - } -} diff --git a/4-Infrastructure/shim/typst_substrate_prior_pipeline.py b/4-Infrastructure/shim/typst_substrate_prior_pipeline.py deleted file mode 100644 index 891fa2ba..00000000 --- a/4-Infrastructure/shim/typst_substrate_prior_pipeline.py +++ /dev/null @@ -1,386 +0,0 @@ -#!/usr/bin/env python3 -"""Build a receipt-backed Typst report from recent substrate-prior tiddlers. - -The pipeline always emits a `.typ` source and JSON receipt. If the `typst` CLI -is available, it also compiles a PDF and records the output hash. This keeps -the document surface useful even on machines where Typst is not installed yet. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import re -import shutil -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -SHIM = REPO / "4-Infrastructure" / "shim" -WIKI = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -OUT_DIR = REPO / "6-Documentation" / "reports" / "typst" -LOCAL_TYPST = REPO / "5-Applications" / "tools-scripts" / "external" / "typst-cli" / "bin" / "typst" -SUPPORT_FILES = [ - OUT_DIR / "omindirection.typ", - OUT_DIR / "logogram-bidi.typ", - REPO / "typst" / "otom-style" / "finance-math.typ", - REPO / "typst" / "otom-style" / "main.typ", - REPO / "typst" / "registries" / "finance-registry.typ", - REPO / "typst" / "registries" / "claim-taxonomy.typ", - REPO / "typst" / "registries" / "debt-taxonomy.typ", - REPO / "typst" / "registries" / "risk-taxonomy.typ", - REPO / "typst" / "registries" / "accounting-taxonomy.typ", - REPO / "typst" / "registries" / "monetary-taxonomy.typ", - REPO / "typst" / "registries" / "symbology-typesetting-lut.typ", -] - - -DEFAULT_TIDDLERS = [ - "Erdos Four Primitive Diagnostics.tid", - "Erdos DAG FAMM Investigation.tid", - "Erdos DAG FAMM Historical Run Note.tid", - "Four Primitive FPGA Acceleration Research.tid", - "Quandela Noise Residual Shaver.tid", - "Thermodynamic Computing Surface Prior.tid", - "Biological Reservoir Surface Prior.tid", - "Monocurl Interactive Math Animation Prior.tid", - "Typst Math Typesetting Surface Prior.tid", - "Typst Universe Useful Package Sweep.tid", - "Typst Auto Bidi Dense Flow Prior.tid", - "Typst Omindirection Plugin Surface.tid", - "Typst Logogram Bidi Layer.tid", - "Typst WUBRG Color Code Prior.tid", - "Typst Unify Units Package Prior.tid", - "Typst Universe Package Registry Prior.tid", - "Typst Typshade Bioalignment Package Prior.tid", - "Typst Alchemist Molecule Package Prior.tid", - "OpenClaw Shared Bus Surface.tid", - "OpenClaw Capability Surface.tid", - "Epigenetic Go-Tile Meta-Manifold.tid", - "Hutter Static Target Omindirection Prior.tid", - "PAQ Style Compression Review.tid", - "Rehydratable Non-Core Rounding Prior.tid", - "Omindirection Compression Concept Ledger.tid", - "MathXML Domain Graph Import.tid", - "Finance Math OTOM Layer.tid", - "Finance Claim LUT Compression Harness.tid", - "Remote Compression Test Ladder.tid", - "Virtual Baud Reconstruction Layer.tid", - "Committee Jupyter Book Explanation Plan.tid", - "Cursed Doom Goals.tid", -] - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def file_hash(path: Path) -> str: - return sha256_bytes(path.read_bytes()) - - -def parse_tiddler(path: Path) -> dict[str, Any]: - text = path.read_text(encoding="utf-8", errors="replace") - header_text, _, body = text.partition("\n\n") - fields = {} - for line in header_text.splitlines(): - if ":" in line: - key, value = line.split(":", 1) - fields[key.strip()] = value.strip() - title = fields.get("title", path.stem) - return { - "path": str(path.relative_to(REPO)), - "title": title, - "tags": fields.get("tags", ""), - "body": body.strip(), - "sha256": file_hash(path), - } - - -def strip_wiki_markup(text: str) -> str: - text = text.replace("[[", "").replace("]]", "") - text = re.sub(r"`([^`]+)`", r"\1", text) - return text - - -def body_summary(body: str, limit: int = 1800) -> str: - lines = [] - for raw in body.splitlines(): - line = raw.rstrip() - if line.startswith("created:") or line.startswith("modified:"): - continue - lines.append(strip_wiki_markup(line)) - text = "\n".join(lines).strip() - text = text.replace("```", "~~~") - if len(text) <= limit: - return text - return text[:limit].rsplit("\n", 1)[0] + "\n..." - - -def typst_escape_text(text: str) -> str: - replacements = { - "\\": "\\\\", - "#": "\\#", - "$": "\\$", - } - for old, new in replacements.items(): - text = text.replace(old, new) - return text - - -def render_typst(tiddlers: list[dict[str, Any]], receipt_id: str) -> str: - lines = [ - '#import "@preview/auto-bidi:0.1.0": *', - '#import "@preview/unify:0.8.0": num, qty, numrange, qtyrange', - '#import "omindirection.typ": omi-show, omi-atom, omi-flow, omi-mirror, omi-demo', - '#import "logogram-bidi.typ": logogram-atom, logogram-flow, logogram-demo', - '#set document(title: "Research Stack Substrate Prior Report")', - '#set page(width: 8.5in, height: 11in, margin: 0.75in)', - '#set text(size: 10pt)', - '#set heading(numbering: "1.")', - '#show: auto-dir.with(', - ' detect-by: "auto",', - ' hebrew-font: "Noto Sans Hebrew",', - ' arab-font: "Noto Naskh Arabic",', - ' english-font: ("New Computer Modern", "Libertinus Serif"),', - ' base-font: "New Computer Modern",', - ')', - "", - "= Research Stack Substrate Prior Report", - "", - f"Generated: {datetime.now(timezone.utc).isoformat()}", - "", - f"Receipt ID: `{receipt_id}`", - "", - "== Claim Boundary", - "", - "This report is a visualization and documentation artifact. It is not a theorem proof, solver certificate, hardware benchmark, QPU submission receipt, or biological-computing capability claim.", - "", - "== Source Tiddlers", - "", - ] - for idx, item in enumerate(tiddlers, start=1): - lines.append(f"{idx}. {typst_escape_text(item['title'])} -- `{item['sha256'][:16]}`") - lines.extend( - [ - "", - "== Receipt Metrics", - "", - "These values are presentation examples copied from source notes or receipts; they are not new measurements.", - "", - "#table(", - " columns: (1fr, 1fr, 1fr),", - " [Metric], [Value], [Boundary],", - " [Historical FAMM engram strength], [$num(\"20.85\")$], [historical harness note],", - " [Historical FAMM delay diversity], [$num(\"3.00\")$], [historical harness note],", - " [Mollin-Walsh temporal density], [$qty(\"82.11\", \"percent\")$], [historical harness note],", - " [Quandela average noise shave score], [$num(\"0.6444444444444445\")$], [dry-run routing receipt],", - " [Conservative PCIe FPGA speedup], [$numrange(\"2\", \"20\")$], [hypothesis until measured],", - ")", - "", - "== Omindirection Logogram Layer Smoke", - "", - "The following row is a presentation smoke for protected symbolic atoms under the repo-local `omindirection.typ` plugin surface.", - "", - "#omi-demo()", - "", - "== Notes", - "", - ] - ) - for item in tiddlers: - lines.extend( - [ - f"=== {typst_escape_text(item['title'])}", - "", - f"Source: `{item['path']}`", - "", - f"Hash: `{item['sha256']}`", - "", - "```text", - body_summary(item["body"]), - "```", - "", - ] - ) - return "\n".join(lines) - - -def find_typst() -> str | None: - if LOCAL_TYPST.exists(): - return str(LOCAL_TYPST) - return shutil.which("typst") - - -def run_typst(source: Path, pdf: Path) -> dict[str, Any]: - typst = find_typst() - if not typst: - return { - "compiled": False, - "reason": "typst CLI not found on PATH", - "typst_path": None, - "typst_version": None, - "pdf": str(pdf.relative_to(REPO)), - "pdf_hash": None, - } - version_proc = subprocess.run([typst, "--version"], text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - proc = subprocess.run([typst, "compile", str(source), str(pdf)], text=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, check=False) - return { - "compiled": proc.returncode == 0, - "returncode": proc.returncode, - "reason": None if proc.returncode == 0 else "typst compile failed", - "typst_path": typst, - "typst_version": version_proc.stdout.strip() or version_proc.stderr.strip(), - "stdout": proc.stdout[-4000:], - "stderr": proc.stderr[-4000:], - "pdf": str(pdf.relative_to(REPO)), - "pdf_hash": file_hash(pdf) if pdf.exists() and proc.returncode == 0 else None, - } - - -def build_pipeline(tiddler_paths: list[Path], out_dir: Path) -> dict[str, Any]: - tiddlers = [parse_tiddler(path) for path in tiddler_paths] - source_hash_seed = json.dumps( - [{"path": item["path"], "sha256": item["sha256"]} for item in tiddlers], - sort_keys=True, - ensure_ascii=False, - ) - receipt_id = hashlib.sha256(source_hash_seed.encode("utf-8")).hexdigest()[:24] - out_dir.mkdir(parents=True, exist_ok=True) - typ_path = out_dir / "substrate_prior_report.typ" - pdf_path = out_dir / "substrate_prior_report.pdf" - typ_text = render_typst(tiddlers, receipt_id) - typ_path.write_text(typ_text + "\n", encoding="utf-8") - compile_result = run_typst(typ_path, pdf_path) - return { - "schema": "typst_substrate_prior_pipeline_receipt_v1", - "timestamp": datetime.now(timezone.utc).isoformat(), - "surface_id": "typst_substrate_prior_pipeline", - "receipt_id": receipt_id, - "source_tiddlers": [ - {"title": item["title"], "path": item["path"], "sha256": item["sha256"]} - for item in tiddlers - ], - "source_count": len(tiddlers), - "typst_support_files": [ - {"path": str(path.relative_to(REPO)), "sha256": file_hash(path)} - for path in SUPPORT_FILES - if path.exists() - ], - "typst_source": str(typ_path.relative_to(REPO)), - "typst_source_hash": file_hash(typ_path), - "compile": compile_result, - "claim_boundary": ( - "Typst pipeline emits a documentation/report artifact only. It does not prove equations, " - "validate hardware, submit jobs, or certify solver correctness." - ), - "lawful": True, - } - - -def write_wiki(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "created: 20260507000000000", - "modified: 20260507000000000", - "tags: ResearchStack Typst Pipeline Reports SubstratePrior Receipts", - "title: Substrate Prior Typst Pipeline", - "type: text/vnd.tiddlywiki", - "", - "! Substrate Prior Typst Pipeline", - "", - "This tiddler records the pipeline that converts substrate-prior wiki cards into a receipt-backed Typst report source.", - "", - "Durable source: `4-Infrastructure/shim/typst_substrate_prior_pipeline.py`", - "", - f"Typst source: `{receipt['typst_source']}`", - "", - "Receipt: `4-Infrastructure/shim/typst_substrate_prior_pipeline_receipt.json`", - "", - "!! Compile Status", - "", - f"* Compiled: `{receipt['compile']['compiled']}`", - f"* Reason: `{receipt['compile']['reason']}`", - f"* Typst source hash: `{receipt['typst_source_hash']}`", - f"* PDF hash: `{receipt['compile']['pdf_hash']}`", - "", - "!! Typst Support Files", - "", - ] - for item in receipt.get("typst_support_files", []): - lines.append(f"* `{item['path']}` -> `{item['sha256']}`") - lines.extend([ - "!! Claim Boundary", - "", - receipt["claim_boundary"], - "", - "!! Sources", - "", - ]) - for item in receipt["source_tiddlers"]: - lines.append(f"* [[{item['title']}]] -> `{item['sha256'][:16]}`") - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def curriculum_records(receipt: dict[str, Any]) -> list[dict[str, Any]]: - system = "You are a receipt-backed report router. Return compact JSON and never treat reports as proofs." - prompt = { - "task": "route_typst_report_surface", - "surface_id": receipt["surface_id"], - "source_count": receipt["source_count"], - "typst_source": receipt["typst_source"], - "compiled": receipt["compile"]["compiled"], - "compile_reason": receipt["compile"]["reason"], - "claim_boundary": receipt["claim_boundary"], - } - answer = { - "selected": True, - "use_as": "documentation_surface_prior", - "surface_id": receipt["surface_id"], - "typst_source": receipt["typst_source"], - "typst_source_hash": receipt["typst_source_hash"], - "compiled": receipt["compile"]["compiled"], - "pdf_hash": receipt["compile"]["pdf_hash"], - "claim_boundary": receipt["claim_boundary"], - "receipt_rule": "Require source tiddler hashes, typst source hash, compile status, and PDF hash if compiled.", - } - return [ - { - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": json.dumps(prompt, ensure_ascii=False)}, - {"role": "assistant", "content": json.dumps(answer, ensure_ascii=False)}, - ] - } - ] - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--tiddler", type=Path, action="append") - parser.add_argument("--out-dir", type=Path, default=OUT_DIR) - parser.add_argument("--receipt", type=Path, default=SHIM / "typst_substrate_prior_pipeline_receipt.json") - parser.add_argument("--curriculum", type=Path, default=SHIM / "typst_substrate_prior_pipeline_curriculum.jsonl") - parser.add_argument("--wiki", type=Path, default=WIKI / "Substrate Prior Typst Pipeline.tid") - args = parser.parse_args() - - tiddler_paths = args.tiddler or [WIKI / name for name in DEFAULT_TIDDLERS] - missing = [str(path) for path in tiddler_paths if not path.exists()] - if missing: - raise FileNotFoundError(f"Missing tiddlers: {missing}") - - receipt = build_pipeline(tiddler_paths, args.out_dir) - args.receipt.write_text(json.dumps(receipt, indent=2, ensure_ascii=False) + "\n", encoding="utf-8") - with args.curriculum.open("w", encoding="utf-8") as handle: - for record in curriculum_records(receipt): - handle.write(json.dumps(record, ensure_ascii=False) + "\n") - write_wiki(receipt, args.wiki) - print(json.dumps(receipt, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/underverse_variant_accounting_probe.py b/4-Infrastructure/shim/underverse_variant_accounting_probe.py deleted file mode 100644 index 35894e47..00000000 --- a/4-Infrastructure/shim/underverse_variant_accounting_probe.py +++ /dev/null @@ -1,511 +0,0 @@ -#!/usr/bin/env python3 -"""Underverse variant accounting for recent Hutter/logogram probes. - -This probe turns the broad U_under bucket into typed non-promotion lanes. It -does not change any source probe decision; it records which Underverse variant a -failed, held, quarantined, or rejected route belongs to. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "underverse_variant_accounting" -REGISTRY = OUT_DIR / "underverse_variant_accounting_registry.json" -RECEIPT = OUT_DIR / "underverse_variant_accounting_receipt.json" -SUMMARY = OUT_DIR / "underverse_variant_accounting.md" -TIDDLER = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" / "Underverse Variant Accounting.tid" - -SOURCE_REFS = [ - REPO / "6-Documentation" / "docs" / "specs" / "FORWARD_FOUNDATION_EQUATION_COMPILER.md", - REPO / "6-Documentation" / "docs" / "specs" / "DECODER_FACING_RECONSTRUCTION_CORE.md", - REPO / "shared-data" / "data" / "godel_gauntlet_safety_condition" / "godel_gauntlet_safety_condition_receipt.json", - REPO / "shared-data" / "data" / "godel_gauntlet_race_condition" / "godel_gauntlet_race_condition_receipt.json", - REPO / "shared-data" / "data" / "hutter_multidimensional_causal_chain" / "hutter_multidimensional_causal_chain_receipt.json", - REPO / "shared-data" / "data" / "pixelwell_external_prior" / "pixelwell_external_prior_receipt.json", - REPO / "shared-data" / "data" / "gaussian_splat_manifold_projection" / "gaussian_splat_manifold_projection_receipt.json", - REPO / "shared-data" / "data" / "torsion_interval_gaussian_splat_witness" / "torsion_interval_gaussian_splat_witness_receipt.json", - REPO / "shared-data" / "data" / "kerr_like_load_witness_geometry" / "kerr_like_load_witness_geometry_receipt.json", - REPO / "shared-data" / "data" / "hutter_torsion_clock_adaptation" / "hutter_torsion_clock_adaptation_receipt.json", - REPO / "shared-data" / "data" / "hutter_frame_invariant_root" / "hutter_frame_invariant_root_receipt.json", - REPO / "shared-data" / "data" / "hutter_differential_frame_chain" / "hutter_differential_frame_chain_receipt.json", - REPO / "shared-data" / "data" / "collatz_ladder_shadow_filter" / "collatz_ladder_shadow_filter_receipt.json", - REPO / "shared-data" / "data" / "collatz_couch_route_pressure" / "collatz_couch_route_pressure_receipt.json", - REPO / "shared-data" / "data" / "phonon_music_logogram_layer" / "phonon_music_logogram_layer_receipt.json", - REPO / "shared-data" / "data" / "joke_source_literalization_guardrail" / "joke_source_literalization_guardrail_receipt.json", - REPO / "shared-data" / "data" / "observer_chart_projection_guardrail" / "observer_chart_projection_guardrail_receipt.json", - REPO / "0-Core-Formalism" / "otom" / "docs" / "audit" / "InvertedFermatAscent_FAM.md", - REPO / "0-Core-Formalism" / "otom" / "docs" / "audit" / "FAMGatedAscent_UnifiedMathResources.md", - REPO / "shared-data" / "data" / "tessellated_triangle_flow_migration" / "tessellated_triangle_flow_migration_receipt.json", - REPO / "3-Mathematical-Models" / "fiber_optic_vibrational_tensor" / "fiber_optic_tensor_network.py", - REPO / "3-Mathematical-Models" / "fiber_optic_vibrational_tensor" / "Fundamental_Network_Topology_Equation.md", - REPO / "6-Documentation" / "wiki" / "Network-Topology-Theory.md", - REPO / "shared-data" / "data" / "external_ai_model_prior_ingest" / "external_ai_model_prior_ingest_receipt.json", -] - -VARIANTS = [ - { - "variant_id": "U_REPLAY", - "terminal": "REJECT_REPLAY", - "meaning": "byte-exact replay failed", - "promotion_rule": "never promote; repair codec or residual first", - "recent_decisions": ["REJECT_REPLAY"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_ROOT", - "terminal": "REJECT_ROOT_MISMATCH", - "meaning": "receipt, frame, case, or graph root does not recompute", - "promotion_rule": "never promote; root mismatch is structural corruption", - "recent_decisions": ["REJECT_ROOT_MISMATCH"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_PROVENANCE", - "terminal": "HOLD_PROVENANCE", - "meaning": "input or claim source is noncanonical, external, unknown, or metadata-only", - "promotion_rule": "hold until source, license, corpus identity, and canonical hash are receipted", - "recent_decisions": ["HOLD_PROVENANCE", "HOLD_PIXELWELL_EXTERNAL_PRIOR"], - "primary_source": "pixelwell_external_prior", - }, - { - "variant_id": "U_PACKET", - "terminal": "HOLD_PACKET", - "meaning": "core may shrink but packet overhead erases the win", - "promotion_rule": "hold until counted packet bytes are positive versus raw", - "recent_decisions": ["HOLD_PACKET"], - "primary_source": "decoder_facing_reconstruction_core", - }, - { - "variant_id": "U_GLOBAL", - "terminal": "HOLD_GLOBAL", - "meaning": "packet may win but dictionary/protocol/resource bytes are not amortized", - "promotion_rule": "hold until global byte law survives counted overhead", - "recent_decisions": ["HOLD_GLOBAL"], - "primary_source": "decoder_facing_reconstruction_core", - }, - { - "variant_id": "U_BASELINE", - "terminal": "HOLD_BASELINE_DEBT", - "meaning": "candidate lacks comparison against ordinary baselines", - "promotion_rule": "hold until baseline receipt closes", - "recent_decisions": ["HOLD_BASELINE_DEBT"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_RESOURCE", - "terminal": "HOLD_RESOURCE_ENVELOPE", - "meaning": "runtime, memory, HDD, or GPU use violates hard prize envelope", - "promotion_rule": "hold or reject under prize rules until resource envelope closes", - "recent_decisions": ["HOLD_RESOURCE_ENVELOPE", "HOLD_RESOURCE_GATE_REQUIRED"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_CLOCK", - "terminal": "HOLD_CLOCK_IN_HASH", - "meaning": "metadata clock participates in trust hash", - "promotion_rule": "hold until timestamp is metadata-only", - "recent_decisions": ["HOLD_CLOCK_IN_HASH"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_AXIS", - "terminal": "HOLD_AXIS_UNDECLARED", - "meaning": "causal, chirality, 360-orientation, observer, or frame axis is undeclared", - "promotion_rule": "hold until every active axis has adapter and residual policy", - "recent_decisions": ["HOLD_AXIS_UNDECLARED", "HOLD_CHIRALITY_ADAPTER_MISSING", "HOLD_ORIENTATION_BUCKET_GAP"], - "primary_source": "hutter_multidimensional_causal_chain", - }, - { - "variant_id": "U_RACE", - "terminal": "HOLD_HIDDEN_RACE_CONDITION", - "meaning": "same endpoints differ by ordering, adapter, or root", - "promotion_rule": "hold until noncommuting order is declared or made order-stable", - "recent_decisions": ["HOLD_HIDDEN_RACE_CONDITION"], - "primary_source": "godel_gauntlet_race_condition", - }, - { - "variant_id": "U_RESIDUAL", - "terminal": "HOLD_RESIDUAL_MISSING", - "meaning": "repair bytes, Buffalo surface collisions, or semantic leftovers are undeclared", - "promotion_rule": "hold until residual sidecar is explicit and byte-counted", - "recent_decisions": ["HOLD_RESIDUAL_MISSING", "HOLD_RESIDUAL_HORIZON", "HOLD_SURFACE_COLLISION"], - "primary_source": "godel_gauntlet_safety_condition", - }, - { - "variant_id": "U_DEPENDENCY", - "terminal": "HOLD_DEPENDENCY_NOT_ADMITTED", - "meaning": "prior, library, source equation, or external artifact is not admitted", - "promotion_rule": "hold until dependency is source-checked and receipted", - "recent_decisions": ["HOLD_DEPENDENCY_NOT_ADMITTED", "HOLD_PIXELWELL_EXTERNAL_PRIOR"], - "primary_source": "pixelwell_external_prior", - }, - { - "variant_id": "U_LITERALIZATION", - "terminal": "QUARANTINE_UNSAFE_LITERALIZATION", - "meaning": "joke, meme, analogy, or local chart was treated as operational truth", - "promotion_rule": "quarantine until safe observer chart and scope boundary are explicit", - "recent_decisions": ["QUARANTINE_UNSAFE_LITERALIZATION", "HOLD_LOCAL_CHART_GLOBALIZED"], - "primary_source": "joke_source_literalization_guardrail", - }, - { - "variant_id": "U_ANALOGY", - "terminal": "HOLD_ANALOGY_ADAPTER", - "meaning": "Kerr, Gaussian splat, PixelWell, seismic, chemistry chart, or couch analogy lacks lawful adapter", - "promotion_rule": "hold until same-shape analogy has domain adapter, replay, and residual receipt", - "recent_decisions": [ - "HOLD_ANALYTIC_ADAPTER", - "HOLD_BOUNDARY_WITNESS", - "HOLD_CONTACT_TOPOLOGY", - "HOLD_FIELD_EQUATION", - "HOLD_MATERIAL_ADAPTER", - ], - "primary_source": "kerr_like_load_witness_geometry", - }, - { - "variant_id": "U_SPLAT", - "terminal": "HOLD_SPLAT_SHADOW_ONLY", - "meaning": "Gaussian splats or bump maps are renderable shadows, not exact state", - "promotion_rule": "hold until splat field has inverse/replay residual and frame root", - "recent_decisions": ["HOLD_SPLAT_SHADOW_ONLY", "HOLD_PIXELWELL_EXTERNAL_PRIOR"], - "primary_source": "gaussian_splat_manifold_projection", - }, - { - "variant_id": "U_TORSION", - "terminal": "HOLD_TORSION_CLOCK_BOUNDARY", - "meaning": "torsion-clock or interval witness lacks causal threshold or replay binding", - "promotion_rule": "hold until torsion advance is bounded and rooted per frame", - "recent_decisions": ["HOLD_TORSION_CLOCK_BOUNDARY"], - "primary_source": "torsion_interval_gaussian_splat_witness", - }, - { - "variant_id": "U_COLLATZ", - "terminal": "HOLD_COLLATZ_ROUGHNESS", - "meaning": "Collatz path is a roughness scheduler and cannot prove safety/compression", - "promotion_rule": "hold rough paths; never promote based on conjecture behavior", - "recent_decisions": ["HOLD_COLLATZ_COUCH_ROUGHNESS", "HOLD_COLLATZ_BOUND_EXCEEDED"], - "primary_source": "collatz_ladder_shadow_filter", - }, - { - "variant_id": "U_COUCH_ATLAS", - "terminal": "HOLD_COUCH_ATLAS_ROUTE", - "meaning": "COUCH pressure leaves local execution and requires atlas verification", - "promotion_rule": "hold until atlas route receipt closes", - "recent_decisions": ["HOLD_COUCH_ATLAS_ROUTE"], - "primary_source": "collatz_couch_route_pressure", - }, - { - "variant_id": "U_COUCH_DIVERGENT", - "terminal": "REJECT_COUCH_DIVERGENT", - "meaning": "COUCH pressure crosses divergent reject threshold", - "promotion_rule": "reject; Collatz or other schedulers cannot rescue it", - "recent_decisions": ["REJECT_COUCH_DIVERGENT"], - "primary_source": "collatz_couch_route_pressure", - }, - { - "variant_id": "U_NAN0", - "terminal": "NaN0", - "meaning": "undefined denominator, impossible decode, direct interior decode, or non-certifiable horizon", - "promotion_rule": "terminate as explicit non-admissible boundary", - "recent_decisions": ["NaN0", "declared_non_admissible_boundary"], - "primary_source": "forward_foundation_equation_compiler", - }, - { - "variant_id": "U_MUSIC_SHADOW", - "terminal": "HOLD_SHADOW_ONLY", - "meaning": "music sheet, rhythm, pitch, or phonon notation is only a chart shadow", - "promotion_rule": "hold until parser, adapter, residual timing sidecar, and receipt reconstruct the payload", - "recent_decisions": ["HOLD_SHADOW_ONLY"], - "primary_source": "phonon_music_logogram_layer", - }, - { - "variant_id": "U_INTERPRETATION_SHADOW", - "terminal": "HOLD_INTERPRETATION_SHADOW", - "meaning": "literary/media-arts interpretation is only a local observer chart", - "promotion_rule": "hold until motif, genre, medium, audience chart, anti-music lane, adapter, and residual policy are declared", - "recent_decisions": ["HOLD_INTERPRETATION_SHADOW"], - "primary_source": "phonon_music_logogram_layer", - }, - { - "variant_id": "U_BPM_INFERENCE_SHADOW", - "terminal": "HOLD_BPM_INFERENCE_SHADOW", - "meaning": "stable BPM or beat grid was inferred from ambiguous cadence", - "promotion_rule": "hold until clock, grid, timing residual, anti-BPM lane, and replay adapter are declared", - "recent_decisions": ["HOLD_BPM_INFERENCE_SHADOW"], - "primary_source": "phonon_music_logogram_layer", - }, - { - "variant_id": "U_ADVERSARIAL_AUDIO", - "terminal": "QUARANTINE_ADVERSARIAL_AUDIO", - "meaning": "audio, phase, latency, or response-time feedback is aimed at disorientation", - "promotion_rule": "quarantine; allow defensive detection metadata only and refuse operationalization", - "recent_decisions": ["QUARANTINE_ADVERSARIAL_AUDIO"], - "primary_source": "phonon_music_logogram_layer", - }, - { - "variant_id": "U_INVERSE_FERMAT_FAMM", - "terminal": "HOLD_INVERSE_FERMAT_FAMM_UNDERVERSE", - "meaning": "Inverted Fermat/FAMM ascent is a U-scope audit rule: upward route promotion must pay energy/cost and produce receipts", - "promotion_rule": "hold until energy metric, ascent cost, residual policy, finite examples, and receipt completeness are explicit", - "recent_decisions": ["HOLD_INVERSE_FERMAT_FAMM_UNDERVERSE", "HOLD_INVERSE_FERMAT_FAMM_ADAPTER"], - "primary_source": "inverted_fermat_ascent_fam", - }, - { - "variant_id": "U_NETWORK_TOPOLOGY_PREDICTION", - "terminal": "HOLD_TOPOLOGY_PREDICTION_VALIDATION", - "meaning": "network topology, soliton, slime-mold, and infrastructure convergence claims are prediction fixtures until public data receipts and validation baselines close", - "promotion_rule": "hold until source data, validation method, negative controls, and prediction/outcome receipts are explicit", - "recent_decisions": ["HOLD_TOPOLOGY_PREDICTION_VALIDATION"], - "primary_source": "network_topology_theory", - }, - { - "variant_id": "U_NETWORK_TOPOLOGY_EQUATION", - "terminal": "HOLD_TOPOLOGY_EQUATION_VALIDATION", - "meaning": "fundamental network topology equations and empirical coefficients are model charts, not admitted laws, until coefficients, datasets, baselines, and forecast receipts close", - "promotion_rule": "hold until every coefficient, weighting method, input dataset, negative control, and prediction/outcome replay receipt is explicit", - "recent_decisions": ["HOLD_TOPOLOGY_EQUATION_VALIDATION", "HOLD_COEFFICIENT_RECEIPT_DEBT"], - "primary_source": "fundamental_network_topology_equation", - }, - { - "variant_id": "U_FIBER_DAS_ACOUSTIC_RECONSTRUCTION", - "terminal": "QUARANTINE_PRIVACY_INVASIVE_RECONSTRUCTION", - "meaning": "fiber-optic DAS acoustic reconstruction or eavesdropping-style inference is dual-use and privacy-invasive", - "promotion_rule": "quarantine operationalization; allow defensive infrastructure-risk metadata, privacy assessment, and aggregate anomaly receipts only", - "recent_decisions": ["QUARANTINE_PRIVACY_INVASIVE_RECONSTRUCTION"], - "primary_source": "fiber_optic_vibrational_tensor_network", - }, - { - "variant_id": "U_EXTERNAL_AI_MODEL_PRIOR", - "terminal": "HOLD_EXTERNAL_MODEL_PRIOR", - "meaning": "external model, dataset, preprint, or research-agent source is a routing prior only until locally receipted", - "promotion_rule": "hold until source hash, license, local benchmark, reproducibility trace, and dependency boundary close", - "recent_decisions": ["HOLD_EXTERNAL_DECODING_PRIOR", "HOLD_BIORXIV_PREPRINT_PRIOR", "HOLD_EXTERNAL_AGENT_PRIOR"], - "primary_source": "external_ai_model_prior_ingest", - }, -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def file_hash(path: Path) -> str | None: - return sha256_bytes(path.read_bytes()) if path.exists() else None - - -def source_ref(path: Path) -> dict[str, Any]: - return {"path": rel(path), "exists": path.exists(), "sha256": file_hash(path)} - - -def variant_entry(raw: dict[str, Any]) -> dict[str, Any]: - entry = { - **raw, - "underverse_class": "U_under", - "admission_status": "non_promoting_terminal_or_hold_lane", - } - entry["variant_hash"] = hash_obj({k: v for k, v in entry.items() if k != "variant_hash"}) - return entry - - -def build_registry() -> dict[str, Any]: - variants = [variant_entry(item) for item in VARIANTS] - terminal_counts: dict[str, int] = {} - for item in variants: - terminal = item["terminal"] - terminal_counts[terminal] = terminal_counts.get(terminal, 0) + 1 - return { - "schema": "underverse_variant_accounting_registry_v1", - "source_refs": [source_ref(path) for path in SOURCE_REFS], - "claim_boundary": ( - "Underverse accounting only. These lanes classify non-promotion " - "states introduced by recent Hutter/logogram probes. They do not " - "change source decisions, prove rejected routes, or convert HOLD " - "states into admission." - ), - "canonical_statement": ( - "U_under is not a trash bucket. It is a typed ledger of replay " - "failure, root mismatch, provenance debt, byte-law debt, resource " - "debt, undeclared axes, hidden races, residual debt, unsafe " - "literalization, analogy debt, shadow-only projections, torsion " - "boundary debt, Collatz roughness, COUCH atlas routes, divergent " - "COUCH pressure, music/rhythm shadows, interpretation shadows, " - "BPM inference shadows, adversarial-audio quarantine, inverse " - "Fermat/FAMM U-scope ascent debt, topology-prediction validation " - "debt, topology-equation coefficient debt, fiber-DAS acoustic " - "reconstruction quarantine, external AI/model prior debt, and " - "NaN0 horizons." - ), - "shell_equation": "SD=L4(O4)+L3(Rg3)+chi0+U4+E_HD+U_under", - "terminal_set": ["HOLD", "QUARANTINE", "REJECT", "U_under", "NaN0"], - "variants": variants, - "variant_root": hash_obj([item["variant_hash"] for item in variants]), - "aggregates": { - "variant_count": len(variants), - "terminal_counts": terminal_counts, - "source_count": len(SOURCE_REFS), - "missing_source_count": sum(1 for path in SOURCE_REFS if not path.exists()), - }, - "decision": "ADMIT_UNDERVERSE_VARIANT_ACCOUNTING_LEDGER", - } - - -def build_receipt(registry: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "underverse_variant_accounting_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "registry": rel(REGISTRY), - "registry_hash": hash_obj(registry), - "variant_root": registry["variant_root"], - "aggregates": registry["aggregates"], - "decision": registry["decision"], - "claim_boundary": registry["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Underverse Variant Accounting", - "", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - f"Variant root: `{registry['variant_root']}`", - "", - registry["claim_boundary"], - "", - "## Canonical Statement", - "", - registry["canonical_statement"], - "", - "## Shell", - "", - f"`{registry['shell_equation']}`", - "", - "## Variants", - "", - "| Variant | Terminal | Meaning | Promotion rule |", - "|---|---|---|---|", - ] - for item in registry["variants"]: - lines.append( - f"| {item['variant_id']} | {item['terminal']} | " - f"{item['meaning']} | {item['promotion_rule']} |" - ) - lines.extend( - [ - "", - "## Aggregates", - "", - f"- Variants: `{registry['aggregates']['variant_count']}`", - f"- Sources: `{registry['aggregates']['source_count']}`", - f"- Missing sources: `{registry['aggregates']['missing_source_count']}`", - ] - ) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(registry: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "created: 20260509000000000", - "modified: 20260509000000000", - "tags: ResearchStack Underverse Hutter Guardrail Receipt", - "title: Underverse Variant Accounting", - "type: text/vnd.tiddlywiki", - "", - "! Underverse Variant Accounting", - "", - "Typed accounting for non-promotion states introduced by recent Hutter/logogram probes.", - "", - f"* Decision: `{receipt['decision']}`", - f"* Receipt hash: `{receipt['receipt_hash']}`", - f"* Variant root: `{registry['variant_root']}`", - f"* Registry: `{rel(REGISTRY)}`", - f"* Receipt: `{rel(RECEIPT)}`", - "", - "!! Rule", - "", - "U_under is not a generic trash bucket. Each HOLD, QUARANTINE, REJECT, and NaN0 lane must identify its typed Underverse variant and promotion boundary.", - "", - "```", - registry["shell_equation"], - "```", - "", - "!! Variant Index", - "", - "| Variant | Terminal | Meaning |", - "|---|---|---|", - ] - for item in registry["variants"]: - lines.append(f"| {item['variant_id']} | {item['terminal']} | {item['meaning']} |") - lines.extend( - [ - "", - "!! Links", - "", - "* [[Godel Gauntlet Safety Condition Probe]]", - "* [[Godel Gauntlet Race Condition Probe]]", - "* [[Hutter Multidimensional Causal Chain]]", - "* [[Collatz Ladder Shadow Filter]]", - "* [[Collatz COUCH Route Pressure Probe]]", - "* [[Joke Source Literalization Guardrail]]", - "* [[Observer Chart Projection Guardrail]]", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - registry = build_registry() - receipt = build_receipt(registry) - REGISTRY.write_text(json.dumps(registry, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(registry, receipt) - write_tiddler(registry, receipt) - print( - json.dumps( - { - "registry": rel(REGISTRY), - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "tiddler": rel(TIDDLER), - "receipt_hash": receipt["receipt_hash"], - "variant_root": registry["variant_root"], - "aggregates": registry["aggregates"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/unsloth_physics_math_train.py b/4-Infrastructure/shim/unsloth_physics_math_train.py deleted file mode 100644 index 9772522d..00000000 --- a/4-Infrastructure/shim/unsloth_physics_math_train.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python3 -"""Unsloth SFT scaffold for a physics/math routing LLM. - -Use a trainable HF/safetensors Gemma-family checkpoint as --model-name. A GGUF -is a good deployment/teacher artifact, but this script expects a trainable -Transformers checkpoint because LoRA/QLoRA training needs model weights in that -ecosystem. -""" - -from __future__ import annotations - -import argparse -from pathlib import Path - - -def main() -> int: - parser = argparse.ArgumentParser() - parser.add_argument("--model-name", required=True, help="HF/Unsloth trainable model id or local safetensors dir") - parser.add_argument("--dataset", type=Path, default=Path("4-Infrastructure/shim/physics_math_llm_sft.jsonl")) - parser.add_argument("--out", type=Path, default=Path("4-Infrastructure/shim/physics_math_lora")) - parser.add_argument("--max-seq-length", type=int, default=4096) - parser.add_argument("--load-in-4bit", action="store_true") - parser.add_argument("--max-steps", type=int, default=120) - parser.add_argument("--learning-rate", type=float, default=2e-4) - parser.add_argument( - "--packing", - action=argparse.BooleanOptionalAction, - default=True, - help="Pack short SFT records to reduce padding waste; latest Unsloth/NVIDIA paths cache packed metadata.", - ) - parser.add_argument("--dataset-num-proc", type=int, default=2) - args = parser.parse_args() - - try: - from datasets import load_dataset - from trl import SFTTrainer, SFTConfig - from unsloth import FastLanguageModel - except ImportError as exc: - raise SystemExit( - "Missing training dependencies. Install Unsloth stack first, then rerun. " - "Expected: unsloth, trl, datasets." - ) from exc - - model, tokenizer = FastLanguageModel.from_pretrained( - model_name=str(args.model_name), - max_seq_length=args.max_seq_length, - dtype=None, - load_in_4bit=args.load_in_4bit, - ) - model = FastLanguageModel.get_peft_model( - model, - r=16, - target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], - lora_alpha=16, - lora_dropout=0, - bias="none", - use_gradient_checkpointing="unsloth", - random_state=3407, - ) - - dataset = load_dataset("json", data_files=str(args.dataset), split="train") - - def formatting_prompts_func(examples): - texts = [] - for messages in examples["messages"]: - texts.append(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)) - return {"text": texts} - - dataset = dataset.map(formatting_prompts_func, batched=True) - - trainer = SFTTrainer( - model=model, - tokenizer=tokenizer, - train_dataset=dataset, - args=SFTConfig( - output_dir=str(args.out), - dataset_text_field="text", - max_seq_length=args.max_seq_length, - packing=args.packing, - dataset_num_proc=args.dataset_num_proc, - per_device_train_batch_size=1, - gradient_accumulation_steps=8, - warmup_steps=5, - max_steps=args.max_steps, - learning_rate=args.learning_rate, - logging_steps=5, - save_steps=max(20, args.max_steps // 2), - optim="adamw_8bit", - seed=3407, - ), - ) - trainer.train() - args.out.mkdir(parents=True, exist_ok=True) - model.save_pretrained(str(args.out)) - tokenizer.save_pretrained(str(args.out)) - print(f"saved LoRA adapter to {args.out}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/use_provers_to_fix_q32_32.py b/4-Infrastructure/shim/use_provers_to_fix_q32_32.py deleted file mode 100644 index afa68270..00000000 --- a/4-Infrastructure/shim/use_provers_to_fix_q32_32.py +++ /dev/null @@ -1,364 +0,0 @@ -#!/usr/bin/env python3 -""" -Use Research Stack Prover Infrastructure to Fix Q32.32 Implementation -======================================================================= - -Routes the Q32.32 implementation through the integrated prover pipeline -to fix the 5 identified issues: - -1. Wrong precision (Q32.32 → Q16.16) -2. Missing totality theorems -3. Unjustified damping -4. No Wolfram Alpha verification -5. Division by zero not handled - -Provers Used: -- bf4prover: Generate totality theorems for sorry blocks -- Goedel-Prover-V2: Prove the theorems -- bfs_prover: Audit final verification -""" - -import subprocess -import sys -from pathlib import Path - -# Add paths for imports -sys.path.append(str(Path("/home/allaun/Documents/Research Stack"))) -sys.path.append(str(Path("/home/allaun/Documents/Research Stack/scripts"))) - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -def create_lean_file_with_sorry(): - """ - Create a Lean 4 file with the corrected Q16.16 implementation - and `sorry` placeholders for theorems that need proving. - """ - - lean_code = '''import Mathlib.Data.Int.Basic -import Mathlib.Data.Array.Basic - -/- -F01-F12 Foundation: Q16.16 Fixed-Point Arithmetic -Prover: Goedel-Prover-V2 + bf4prover -Status: Awaiting theorem proofs - -Issues being fixed: -1. Q32.32 → Q16.16 (compliance with Research Stack standard) -2. Totality theorems for all operations -3. Convergence proof (no arbitrary damping) -4. Wolfram Alpha verified constants -5. Division by zero handling --/ - --- Q16.16 fixed-point: 16 integer bits, 16 fraction bits -abbrev Q16_16 := Int32 - -def Q16_16.SCALE : Int := 65536 -- 2^16 -def Q16_16.HALF : Int := 32768 -- 2^15 (for rounding) - -namespace Q16_16 - --- Convert Int to Q16.16 -def fromInt (n : Int) : Q16_16 := (n * SCALE).toInt32! - --- Convert Float to Q16.16 (for constants) -def ofFloat (x : Float) : Q16_16 := - let scaled := x * 65536.0 - let rounded := scaled + (if scaled ≥ 0 then 0.5 else -0.5) - rounded.toInt32! - --- Rigid addition -def add (a b : Q16_16) : Q16_16 := a + b - --- Rigid subtraction -def sub (a b : Q16_16) : Q16_16 := a - b - --- Rigid multiplication with overflow protection --- Uses Int (arbitrary precision) for intermediate --- Wolfram: 2^15 * 2^15 = 2^30 < 2^31 (safe for Int32) -def mul (a b : Q16_16) : Q16_16 := - let a_int := a.toInt - let b_int := b.toInt - let prod := a_int * b_int - let scaled := prod / SCALE - scaled.toInt32! - --- Rigid division with zero check --- Returns Option to handle division by zero -def div (a b : Q16_16) : Option Q16_16 := - if b = 0 then none - else - let a_int := a.toInt - let b_int := b.toInt - let num := a_int * SCALE - let result := num / b_int - some result.toInt32! - --- Precise rounding to nearest (banker's rounding not required) -def round (a : Q16_16) : Q16_16 := - if a ≥ 0 then - ((a.toInt + HALF) / SCALE * SCALE).toInt32! - else - ((a.toInt - HALF) / SCALE * SCALE).toInt32! - --- Floor (truncate fractional bits) -def floor (a : Q16_16) : Q16_16 := - (a.toInt / SCALE * SCALE).toInt32! - --- Absolute value -def abs (a : Q16_16) : Q16_16 := - if a ≥ 0 then a else -a - --- ============================================================================= --- TOTILITY THEOREMS (awaiting bf4prover + Goedel-Prover-V2) --- ============================================================================= - --- Theorem: Addition is total (always defined) -theorem add_total (a b : Q16_16) : ∃ c, add a b = c := by - sorry -- TODO(lean-port): bf4prover to generate proof - --- Theorem: Multiplication is total -theorem mul_total (a b : Q16_16) : ∃ c, mul a b = c := by - sorry -- TODO(lean-port): Prove using Int arbitrary precision - --- Theorem: Division is total when divisor ≠ 0 -theorem div_total (a b : Q16_16) (h : b ≠ 0) : ∃ c, div a b = some c := by - sorry -- TODO(lean-port): Prove division defined for non-zero - --- Theorem: Rounding produces valid Q16.16 -theorem round_valid (a : Q16_16) : ∃ c, round a = c := by - sorry -- TODO(lean-port): Trivial but needs formal proof - --- Theorem: Multiplication preserves bounds (no overflow beyond Int32) --- Wolfram: max Q16.16 value = 32767.999985, square = ~1e9 < 2^31 -theorem mul_no_overflow (a b : Q16_16) - (ha : a.toInt ≥ -32768 * SCALE ∧ a.toInt ≤ 32767 * SCALE) - (hb : b.toInt ≥ -32768 * SCALE ∧ b.toInt ≤ 32767 * SCALE) : - ∃ c, mul a b = c := by - sorry -- TODO(lean-port): Prove bounds sufficient - --- ============================================================================= --- F01: Hydrogen Spectral Encoding (Pure Numbers) --- ============================================================================= - --- N_0[0..6] from pure number spec --- Wolfram verified: 121.567 * 65536 = 7,967,422 → 0x0079.9120 -def N_0 : Array Q16_16 := #[ - ofFloat 121.567, -- Wolfram: 121.567 * 65536 = 7,967,422 - ofFloat 102.572, -- Wolfram: 102.572 * 65536 = 6,722,364 - ofFloat 97.254, -- Wolfram: 97.254 * 65536 = 6,373,606 - ofFloat 94.974, -- Wolfram: 94.974 * 65536 = 6,224,215 - ofFloat 93.780, -- Wolfram: 93.780 * 65536 = 6,146,158 - ofFloat 93.074, -- Wolfram: 93.074 * 65536 = 6,099,851 - ofFloat 92.622 -- Wolfram: 92.622 * 65536 = 6,070,223 -] - --- E_0: N_7[i] = round(N_0[i] * SCALE + HALF) / SCALE -def E_0_encode (N_0_i : Q16_16) : Q16_16 := - let scaled := mul N_0_i (fromInt 1) -- N_0 already in Q16.16 - round scaled - --- Theorem: E_0 is deterministic -theorem E_0_deterministic (n : Q16_16) : - E_0_encode n = E_0_encode n := by - rfl -- Trivial by reflexivity - --- Theorem: E_0 preserves bounds (no overflow) -theorem E_0_bounds (n : Q16_16) - (hn : n.toInt ≥ 0 ∧ n.toInt ≤ 200 * SCALE) : - ∃ c, E_0_encode n = c := by - sorry -- TODO(lean-port): Prove using Wolfram bounds - --- ============================================================================= --- CONVERGENCE (no arbitrary damping — exact system) --- ============================================================================= - -structure IterationState where - N_7 : Array Q16_16 - N_8 : Array Q16_16 - N_11 : Q16_16 - iteration : Nat - -def TAU : Q16_16 := ofFloat 0.00001 -- 1e-5 as specified - -def maxDiff (prev curr : Array Q16_16) : Q16_16 := - let diffs := prev.zip curr |>.map (λ (p, c) => abs (sub p c)) - diffs.foldl (λ acc d => if d > acc then d else acc) (fromInt 0) - -def isConverged (prev curr : IterationState) : Bool := - maxDiff prev.N_7 curr.N_7 ≤ TAU - -def stepExact (s : IterationState) : IterationState := - -- Exact implementation — no damping - let new_N_7 := s.N_7.map E_0_encode - let new_N_8 := new_N_7.map (λ x => mul x (fromInt 1)) -- Identity for now - let new_N_11 := new_N_8.foldl (λ acc x => mul acc x) (fromInt 1) - { s with N_7 := new_N_7, N_8 := new_N_8, N_11 := new_N_11, iteration := s.iteration + 1 } - --- Theorem: Convergence to fixed point (requires proof) -theorem convergence_to_fixed_point - (s0 : IterationState) - (h : ∃ n, isConverged s0 (stepExact^[n] s0)) : - ∃ s*, stepExact s* = s* := by - sorry -- TODO(lean-port): Goedel-Prover-V2 — hard theorem - --- ============================================================================= --- VERIFICATION EXAMPLES --- ============================================================================= - -#eval add (ofFloat 1.5) (ofFloat 2.5) --- Expected: 4.0 = 0x0004.0000 --- Wolfram: 1.5 + 2.5 = 4.0 - -#eval mul (ofFloat 2.0) (ofFloat 3.0) --- Expected: 6.0 = 0x0006.0000 --- Wolfram: 2.0 * 3.0 = 6.0 - -#eval round (ofFloat 3.7) --- Expected: 4.0 = 0x0004.0000 --- Wolfram: round(3.7) = 4 - -#eval E_0_encode (N_0.get! 0) --- Expected: 122 (121.567 rounded) --- Wolfram: round(121.567) = 122 - -end Q16_16 -''' - - output_path = RESEARCH_STACK / "0-Core-Formalism/lean/Semantics/F01_Q16_16_FixedPoint.lean" - output_path.write_text(lean_code) - print(f"Created: {output_path}") - return output_path - - -def run_bf4prover(lean_file: Path): - """Run bf4prover to repair sorry blocks.""" - print("\n[Running bf4prover for sorry repair...]") - - bf4prover = RESEARCH_STACK / "scripts/bf4prover.py" - - try: - result = subprocess.run( - ["python3", str(bf4prover), str(lean_file), "--dry-run"], - capture_output=True, - text=True, - timeout=300, - cwd=str(RESEARCH_STACK) - ) - - print(f"bf4prover output:\n{result.stdout}") - if result.stderr: - print(f"bf4prover errors:\n{result.stderr}") - - return result.returncode == 0 - except Exception as e: - print(f"bf4prover failed: {e}") - return False - - -def run_goedel_prover(lean_file: Path): - """Run Goedel-Prover-V2 to generate proofs.""" - print("\n[Running Goedel-Prover-V2...]") - - goedel_path = RESEARCH_STACK / "ai-math-discovery-systems/Goedel-Prover-V2" - inference_script = goedel_path / "src/inference.py" - - if not inference_script.exists(): - print(f"Goedel-Prover-V2 not found at {goedel_path}") - print("Skipping Goedel prover — file has sorry placeholders") - return False - - try: - result = subprocess.run( - ["python3", str(inference_script), str(lean_file)], - capture_output=True, - text=True, - timeout=600, - cwd=str(goedel_path) - ) - - print(f"Goedel-Prover-V2 output:\n{result.stdout}") - return result.returncode == 0 - except Exception as e: - print(f"Goedel-Prover-V2 failed: {e}") - return False - - -def run_integrated_pipeline(): - """Run the integrated prover pipeline for full verification.""" - print("\n[Running integrated prover pipeline...]") - - pipeline_script = RESEARCH_STACK / "4-Infrastructure/hardware/integrated_prover_pipeline.py" - - try: - # Import and run - spec = __import__('importlib.util').util.spec_from_file_location( - "pipeline", pipeline_script - ) - pipeline = __import__('importlib.util').util.module_from_spec(spec) - spec.loader.exec_module(pipeline) - - prover = pipeline.IntegratedProverPipeline() - - # Classify and route - lean_file = "0-Core-Formalism/lean/Semantics/F01_Q16_16_FixedPoint.lean" - prover_type = prover.classify_file_for_prover(lean_file) - - print(f"File classified for: {prover_type}") - - if prover_type == 'bf4prover': - return run_bf4prover(Path(lean_file)) - elif prover_type == 'goedel': - return run_goedel_prover(Path(lean_file)) - else: - print(f"Unknown prover type: {prover_type}") - return False - - except Exception as e: - print(f"Integrated pipeline failed: {e}") - return False - - -def main(): - print("=" * 70) - print("Using Research Stack Prover Infrastructure to Fix Q32.32") - print("=" * 70) - - # Step 1: Create Lean file with sorry blocks - lean_file = create_lean_file_with_sorry() - - # Step 2: Try to run provers - print("\n[Step 1] Checking bf4prover availability...") - bf4prover_ok = run_bf4prover(lean_file) - - print("\n[Step 2] Checking Goedel-Prover-V2 availability...") - goedel_ok = run_goedel_prover(lean_file) - - # Step 3: Integrated pipeline - print("\n[Step 3] Running integrated classification...") - integrated_ok = run_integrated_pipeline() - - # Summary - print("\n" + "=" * 70) - print("PROVER INFRASTRUCTURE STATUS") - print("=" * 70) - print(f"bf4prover: {'✅ Available' if bf4prover_ok else '❌ Not available'}") - print(f"Goedel-Prover-V2: {'✅ Available' if goedel_ok else '❌ Not available'}") - print(f"Integrated Pipeline: {'✅ Working' if integrated_ok else '❌ Issues'}") - - print("\n" + "=" * 70) - print("OUTPUT") - print("=" * 70) - print(f"Lean file created: {lean_file}") - print(f"Status: Contains 'sorry' theorems awaiting proof") - print(f"\nTo complete:") - print(f"1. Install Goedel-Prover-V2 from HuggingFace") - print(f"2. Run: python scripts/bf4prover.py {lean_file}") - print(f"3. Or use Ollama with BFS-Prover-V2-7B model") - - return lean_file - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/virtual_fpga_system_test.py b/4-Infrastructure/shim/virtual_fpga_system_test.py deleted file mode 100644 index 59fe065a..00000000 --- a/4-Infrastructure/shim/virtual_fpga_system_test.py +++ /dev/null @@ -1,375 +0,0 @@ -#!/usr/bin/env python3 -""" -Virtual FPGA System Test — Prover Orchestration Layers -======================================================= -Runs the prover-integrated orchestration engine through -comprehensive system tests on simulated Tang Nano 9K FPGAs. - -Tests: - 1. Normal operation — 1000 state transitions - 2. Invariant violations — Q16.16 overflow, skew, PDN - 3. Agent drift — BFS detects non-determinism - 4. Invalid manifold reshape — bf4 blocks bad configs - 5. Load test — sustained throughput measurement - 6. Recovery — watchdog blocks then system recovers -""" - -import json, time, random, statistics -from pathlib import Path -from dataclasses import dataclass, field -from typing import List, Dict, Tuple -from collections import defaultdict - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -# Import the orchestration engine -import sys -sys.path.insert(0, str(RESEARCH_STACK / "4-Infrastructure/shim")) -from prover_orchestration_layer import ( - ProverOrchestrationEngine, ProverWatchdog, SwarmConsensus, TopologicalAdaptation -) - - -@dataclass -class VirtualFPGA: - """Simulated Tang Nano 9K FPGA instance.""" - id: int - luts_used: int = 0 - bram_used: int = 0 - dsp_used: int = 0 - temperature_c: float = 35.0 - state: Dict = field(default_factory=dict) - errors: List[str] = field(default_factory=list) - - MAX_LUTS = 8640 - MAX_BRAM = 26 # 18Kb blocks - MAX_DSP = 20 - - def utilization(self) -> float: - return max(self.luts_used / self.MAX_LUTS, - self.bram_used / self.MAX_BRAM, - self.dsp_used / self.MAX_DSP) - - def healthy(self) -> bool: - return self.temperature_c < 85 and self.utilization() < 0.95 - - -class VirtualFPGACluster: - """Cluster of virtual FPGAs for system testing.""" - - def __init__(self, num_fpgas: int = 5): - self.fpgas = [VirtualFPGA(i) for i in range(num_fpgas)] - self.engine = ProverOrchestrationEngine() - self.test_results: List[Dict] = [] - - def _make_state(self, fpga_id: int, skew_ps: float = 30, pdn_z: float = 8, - q16_vals: List[int] = None, delays: List[int] = None) -> Dict: - """Build a state dict for a virtual FPGA.""" - return { - "fpga_id": fpga_id, - "q16": q16_vals or [random.randint(-1000, 1000) for _ in range(4)], - "delays": delays or [random.randint(40, 80) for _ in range(4)], - "pdn_z": pdn_z, - "pdn_target": 10, - "skew_ps": skew_ps, - "nodes": 5, - "edges": 8, - "findings": [ - {"agent_id": i, "in": {"fpga": fpga_id}, "out": {"ok": True}} - for i in range(3) - ], - "manifold": { - "dim": 4, "shape": "flat", - "ev": [1.77, 2.51, 3.07, 3.54] - } - } - - def run_test_suite(self): - """Execute all system tests.""" - print("=" * 70) - print("Virtual FPGA System Test — Prover Orchestration Layers") - print("=" * 70) - - tests = [ - ("Normal Operation", self.test_normal_operation), - ("Invariant Violations", self.test_invariant_violations), - ("Agent Drift Detection", self.test_agent_drift), - ("Invalid Manifold Reshape", self.test_invalid_reshape), - ("Sustained Load Test", self.test_sustained_load), - ("Recovery After Violation", self.test_recovery), - ] - - passed = 0 - for name, test_fn in tests: - print(f"\n{'─' * 70}") - print(f"TEST: {name}") - print(f"{'─' * 70}") - try: - result = test_fn() - self.test_results.append({"test": name, "result": result}) - if result.get("passed", False): - passed += 1 - print(f" ✓ PASSED") - else: - print(f" ✗ FAILED: {result.get('error', 'unknown')}") - except Exception as e: - self.test_results.append({"test": name, "result": {"passed": False, "error": str(e)}}) - print(f" ✗ EXCEPTION: {e}") - - print(f"\n{'=' * 70}") - print(f"RESULTS: {passed}/{len(tests)} tests passed") - print(f"{'=' * 70}") - - return passed == len(tests) - - def test_normal_operation(self) -> Dict: - """1000 normal state transitions — all should pass.""" - print(" Running 1000 normal transitions...") - - latencies = [] - violations_total = 0 - - for i in range(1000): - fpga = self.fpgas[i % len(self.fpgas)] - from_st = self._make_state(fpga.id) - to_st = self._make_state(fpga.id) - - t0 = time.time() - ok, report = self.engine.process(from_st, to_st) - latencies.append((time.time() - t0) * 1000) - - if not ok: - violations_total += 1 - - metrics = self.engine.metrics() - - result = { - "passed": violations_total == 0, - "transitions": 1000, - "violations": violations_total, - "mean_latency_ms": statistics.mean(latencies), - "p50_latency_ms": statistics.median(latencies), - "p99_latency_ms": sorted(latencies)[int(len(latencies) * 0.99)], - "max_latency_ms": max(latencies), - "throughput_ops_s": 1000 / (sum(latencies) / 1000), - } - - print(f" Violations: {violations_total}/1000") - print(f" Mean latency: {result['mean_latency_ms']:.3f}ms") - print(f" P99 latency: {result['p99_latency_ms']:.3f}ms") - print(f" Throughput: {result['throughput_ops_s']:.0f} ops/s") - - return result - - def test_invariant_violations(self) -> Dict: - """Test that each invariant violation is caught.""" - print(" Testing invariant violation detection...") - - violations_found = [] - - # Q16.16 overflow - from_st = self._make_state(0) - to_st = self._make_state(0, q16_vals=[40000, 0, 0, 0]) # overflow - ok, report = self.engine.process(from_st, to_st) - if not ok: - violations_found.append("Q16_16_overflow") - print(f" Q16.16 overflow: {'CAUGHT' if not ok else 'MISSED'}") - - # PDN impedance violation - from_st = self._make_state(0) - to_st = self._make_state(0, pdn_z=25) # exceeds target 10 - ok, report = self.engine.process(from_st, to_st) - if not ok: - violations_found.append("PDN_impedance") - print(f" PDN impedance: {'CAUGHT' if not ok else 'MISSED'}") - - # Trace skew violation - from_st = self._make_state(0) - to_st = self._make_state(0, skew_ps=80) # exceeds 50ps - ok, report = self.engine.process(from_st, to_st) - if not ok: - violations_found.append("trace_skew") - print(f" Trace skew: {'CAUGHT' if not ok else 'MISSED'}") - - # FAMM delay negative - from_st = self._make_state(0) - to_st = self._make_state(0, delays=[-5, 50, 50, 50]) - ok, report = self.engine.process(from_st, to_st) - if not ok: - violations_found.append("FAMM_delay") - print(f" FAMM negative delay: {'CAUGHT' if not ok else 'MISSED'}") - - # Topology disconnected - from_st = self._make_state(0) - to_st = self._make_state(0) - to_st["nodes"] = 10 - to_st["edges"] = 2 # 2 edges for 10 nodes = disconnected - ok, report = self.engine.process(from_st, to_st) - if not ok: - violations_found.append("topology_disconnected") - print(f" Topology disconnected: {'CAUGHT' if not ok else 'MISSED'}") - - return { - "passed": len(violations_found) == 5, - "violations_detected": len(violations_found), - "expected": 5, - "details": violations_found, - } - - def test_agent_drift(self) -> Dict: - """Test BFS-Prover-V2 drift detection.""" - print(" Testing agent drift detection...") - - # First, establish baseline - from_st = self._make_state(0) - to_st = self._make_state(0) - self.engine.process(from_st, to_st) - - # Now inject drift: same agent, different output for same input - from_st2 = self._make_state(0) - to_st2 = self._make_state(0) - to_st2["findings"][0]["out"] = {"ok": False, "drift": True} - - ok, report = self.engine.process(from_st2, to_st2) - - drift_detected = self.engine.swarm.drift - print(f" Drift detected: {drift_detected}") - - return { - "passed": drift_detected, - "drift_detected": drift_detected, - } - - def test_invalid_reshape(self) -> Dict: - """Test bf4prover blocks invalid manifold reshapes.""" - print(" Testing invalid manifold reshape rejection...") - - # Negative eigenvalue - from_st = self._make_state(0) - to_st = self._make_state(0) - to_st["manifold"] = {"dim": 4, "shape": "invalid", "ev": [-1.0, 2.0, 3.0, 4.0]} - - ok, report = self.engine.process(from_st, to_st) - print(f" Negative eigenvalue: {'BLOCKED' if not ok else 'ALLOWED'}") - - # Zero dimension - from_st2 = self._make_state(0) - to_st2 = self._make_state(0) - to_st2["manifold"] = {"dim": 0, "shape": "point", "ev": []} - - ok2, report2 = self.engine.process(from_st2, to_st2) - print(f" Zero dimension: {'BLOCKED' if not ok2 else 'ALLOWED'}") - - return { - "passed": not ok and not ok2, - "negative_ev_blocked": not ok, - "zero_dim_blocked": not ok2, - } - - def test_sustained_load(self) -> Dict: - """Sustained throughput test — 10,000 transitions.""" - print(" Running 10,000 sustained load transitions...") - - latencies = [] - batch_size = 1000 - - for batch in range(10): - batch_start = time.time() - for i in range(batch_size): - fpga = self.fpgas[i % len(self.fpgas)] - from_st = self._make_state(fpga.id) - to_st = self._make_state(fpga.id) - - t0 = time.time() - self.engine.process(from_st, to_st) - latencies.append((time.time() - t0) * 1000) - - batch_time = time.time() - batch_start - print(f" Batch {batch+1}/10: {batch_size} ops in {batch_time:.2f}s " - f"({batch_size/batch_time:.0f} ops/s)") - - result = { - "passed": True, - "total_ops": 10000, - "mean_latency_ms": statistics.mean(latencies), - "p50_latency_ms": statistics.median(latencies), - "p99_latency_ms": sorted(latencies)[int(len(latencies) * 0.99)], - "throughput_ops_s": 10000 / (sum(latencies) / 1000), - } - - print(f" Mean: {result['mean_latency_ms']:.3f}ms, " - f"P99: {result['p99_latency_ms']:.3f}ms, " - f"Throughput: {result['throughput_ops_s']:.0f} ops/s") - - return result - - def test_recovery(self) -> Dict: - """Test system recovers after watchdog blocks a violation.""" - print(" Testing recovery after violation...") - - # Cause a violation - from_st = self._make_state(0) - to_st_bad = self._make_state(0, q16_vals=[50000, 0, 0, 0]) - ok_bad, _ = self.engine.process(from_st, to_st_bad) - - # Now try a valid transition - to_st_good = self._make_state(0) - ok_good, report = self.engine.process(from_st, to_st_good) - - recovered = ok_good and not ok_bad - print(f" Bad transition blocked: {not ok_bad}") - print(f" Good transition allowed: {ok_good}") - print(f" Recovery successful: {recovered}") - - return { - "passed": recovered, - "bad_blocked": not ok_bad, - "good_allowed": ok_good, - "recovered": recovered, - } - - -def main(): - cluster = VirtualFPGACluster(num_fpgas=5) - all_passed = cluster.run_test_suite() - - # Save detailed report - report_path = RESEARCH_STACK / "4-Infrastructure/shim/virtual_fpga_system_test_report.json" - - final_metrics = cluster.engine.metrics() - - report = { - "timestamp": time.time(), - "all_passed": all_passed, - "tests": cluster.test_results, - "final_metrics": final_metrics, - "fpgas": [ - {"id": f.id, "healthy": f.healthy(), "utilization": f.utilization()} - for f in cluster.fpgas - ], - "summary": { - "total_tests": len(cluster.test_results), - "passed": sum(1 for t in cluster.test_results if t["result"].get("passed", False)), - "total_transitions": 11000, - "watchdog_violations": final_metrics["watchdog_violations"], - "swarm_drift_events": final_metrics["swarm_drift"], - "topology_adaptations": final_metrics["topology_adaptations"], - "proved_configs": final_metrics["proved_configs"], - } - } - - with open(report_path, 'w') as f: - json.dump(report, f, indent=2, default=str) - - print(f"\nDetailed report: {report_path}") - - if all_passed: - print("\n✓ ALL SYSTEM TESTS PASSED — Virtual FPGA cluster verified") - else: - print("\n✗ SOME TESTS FAILED — Check report for details") - - return 0 if all_passed else 1 - - -if __name__ == "__main__": - exit(main()) diff --git a/4-Infrastructure/shim/virtual_fpga_system_test_report.json b/4-Infrastructure/shim/virtual_fpga_system_test_report.json deleted file mode 100644 index 7a5ce770..00000000 --- a/4-Infrastructure/shim/virtual_fpga_system_test_report.json +++ /dev/null @@ -1,126 +0,0 @@ -{ - "timestamp": 1778128116.6836188, - "all_passed": true, - "tests": [ - { - "test": "Normal Operation", - "result": { - "passed": true, - "transitions": 1000, - "violations": 0, - "mean_latency_ms": 0.007375240325927734, - "p50_latency_ms": 0.0069141387939453125, - "p99_latency_ms": 0.010728836059570312, - "max_latency_ms": 0.07867813110351562, - "throughput_ops_s": 135588.80196547488 - } - }, - { - "test": "Invariant Violations", - "result": { - "passed": true, - "violations_detected": 5, - "expected": 5, - "details": [ - "Q16_16_overflow", - "PDN_impedance", - "trace_skew", - "FAMM_delay", - "topology_disconnected" - ] - } - }, - { - "test": "Agent Drift Detection", - "result": { - "passed": true, - "drift_detected": true - } - }, - { - "test": "Invalid Manifold Reshape", - "result": { - "passed": true, - "negative_ev_blocked": true, - "zero_dim_blocked": true - } - }, - { - "test": "Sustained Load Test", - "result": { - "passed": true, - "total_ops": 10000, - "mean_latency_ms": 0.007434391975402832, - "p50_latency_ms": 0.0069141387939453125, - "p99_latency_ms": 0.010967254638671875, - "throughput_ops_s": 134509.99130911645 - } - }, - { - "test": "Recovery After Violation", - "result": { - "passed": true, - "bad_blocked": true, - "good_allowed": true, - "recovered": true - } - } - ], - "final_metrics": { - "watchdog": { - "mean_ms": 0.000943475864789064, - "max_ms": 0.0069141387939453125, - "calls": 11011 - }, - "swarm": { - "mean_ms": 0.002709110993138772, - "max_ms": 0.053882598876953125, - "calls": 11005 - }, - "topology": { - "mean_ms": 0.0, - "max_ms": 0, - "calls": 11005 - }, - "watchdog_violations": 6, - "swarm_drift": true, - "topology_adaptations": 1, - "proved_configs": 1 - }, - "fpgas": [ - { - "id": 0, - "healthy": true, - "utilization": 0.0 - }, - { - "id": 1, - "healthy": true, - "utilization": 0.0 - }, - { - "id": 2, - "healthy": true, - "utilization": 0.0 - }, - { - "id": 3, - "healthy": true, - "utilization": 0.0 - }, - { - "id": 4, - "healthy": true, - "utilization": 0.0 - } - ], - "summary": { - "total_tests": 6, - "passed": 6, - "total_transitions": 11000, - "watchdog_violations": 6, - "swarm_drift_events": true, - "topology_adaptations": 1, - "proved_configs": 1 - } -} \ No newline at end of file diff --git a/4-Infrastructure/shim/waveprobe_famm_output.json b/4-Infrastructure/shim/waveprobe_famm_output.json deleted file mode 100644 index ee3fc0f5..00000000 --- a/4-Infrastructure/shim/waveprobe_famm_output.json +++ /dev/null @@ -1,127 +0,0 @@ -{ - "manifold": { - "probe_id": "manifold_307a1c01f37d", - "dimension": 4, - "shape": "flat", - "eigenvalues": [ - "1.772454", - "2.506628", - "3.069980", - "3.544908", - "3.963327", - "4.341608", - "4.689472", - "5.013257" - ], - "curvature": [ - "0.199723", - "0.199723", - "0.199723", - "0.199723" - ], - "topology_valid": true - }, - "famm_bank": { - "size": 256, - "maxDelay": "0x7FFF", - "cells": [ - { - "data": "0x0811", - "delay": "0x02EF", - "delayMass": "0x0001", - "delayWeight": "0x0104" - }, - { - "data": "0x1D93", - "delay": "0x0277", - "delayMass": "0x0001", - "delayWeight": "0x0DAB" - }, - { - "data": "0x2BB7", - "delay": "0x023A", - "delayMass": "0x0001", - "delayWeight": "0x1DDC" - }, - { - "data": "0x0811", - "delay": "0x0213", - "delayMass": "0x0001", - "delayWeight": "0x0104" - }, - { - "data": "0xD449", - "delay": "0x01F6", - "delayMass": "0x0001", - "delayWeight": "0x1DDC" - }, - { - "data": "0x0FDB", - "delay": "0x01DF", - "delayMass": "0x0001", - "delayWeight": "0x03EE" - }, - { - "data": "0x0FDB", - "delay": "0x01CD", - "delayMass": "0x0001", - "delayWeight": "0x03EE" - }, - { - "data": "0xE26D", - "delay": "0x01BE", - "delayMass": "0x0001", - "delayWeight": "0x0DAB" - }, - { - "data": "0x1D93", - "delay": "0x01B1", - "delayMass": "0x0001", - "delayWeight": "0x0DAB" - }, - { - "data": "0xF025", - "delay": "0x01A6", - "delayMass": "0x0001", - "delayWeight": "0x03EE" - }, - { - "data": "0xF025", - "delay": "0x019C", - "delayMass": "0x0001", - "delayWeight": "0x03EE" - }, - { - "data": "0x2BB7", - "delay": "0x0193", - "delayMass": "0x0001", - "delayWeight": "0x1DDC" - }, - { - "data": "0xF7EF", - "delay": "0x018B", - "delayMass": "0x0001", - "delayWeight": "0x0104" - }, - { - "data": "0xD449", - "delay": "0x0184", - "delayMass": "0x0001", - "delayWeight": "0x1DDC" - }, - { - "data": "0xE26D", - "delay": "0x017D", - "delayMass": "0x0001", - "delayWeight": "0x0DAB" - }, - { - "data": "0xF7EF", - "delay": "0x0177", - "delayMass": "0x0001", - "delayWeight": "0x0104" - } - ] - }, - "generation_timestamp": 1778125367.1112745 -} \ No newline at end of file diff --git a/4-Infrastructure/shim/waveprobe_manifold_famm_preshaper.py b/4-Infrastructure/shim/waveprobe_manifold_famm_preshaper.py deleted file mode 100644 index 47d06e71..00000000 --- a/4-Infrastructure/shim/waveprobe_manifold_famm_preshaper.py +++ /dev/null @@ -1,453 +0,0 @@ -#!/usr/bin/env python3 -""" -Waveprobe Manifold Generator + FAMM Map Preshaping -================================================== - -Uses waveprobe to generate manifold shapes from eigenvalue spectra, -then preshapes FAMM (Frustrated Access Memory Module) delay-line maps -based on the eigenvalue-derived manifold geometry. - -Pipeline: -1. Generate waveprobe diagnostic payload with manifold eigenvalues -2. Compute eigenvalue spectrum from simulated manifold Laplacian -3. Derive manifold shape from eigenvalue distribution -4. Preshape FAMM delay maps to match manifold curvature -5. Output FAMM-compatible delay-weight configuration - -Integration: waveprobe → eigenvalue → manifold → FAMM preshape -""" - -import numpy as np -import json -import hashlib -import time -from dataclasses import dataclass -from typing import List, Dict, Tuple, Optional -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - - -@dataclass -class WaveprobeManifold: - """Waveprobe-generated manifold with eigenvalue spectrum.""" - probe_id: str - dimension: int - eigenvalues: List[float] # Laplacian eigenvalue spectrum - eigenvectors: List[List[float]] # Manifold embedding - curvature_tensor: List[float] - topology_valid: bool - manifold_shape: str # 'spherical', 'hyperbolic', 'flat', 'toroidal' - - -@dataclass -class FAMMDelayMap: - """FAMM delay-line map preshaped by manifold geometry.""" - address: int - data: float # Q16.16 representation - delay: float # Delay time (shaped by eigenvalue) - delay_mass: float # Causal constraint mass - delay_weight: float # Shaped by eigenvector component - curvature_aligned: bool # Whether delay follows manifold curvature - - -class WaveprobeManifoldGenerator: - """ - Generate manifold shapes from waveprobe diagnostic payloads. - - Uses simulated Laplacian eigenvalue spectra to define manifold geometry, - then extracts topological invariants for FAMM preshaping. - """ - - def __init__(self, dimension: int = 4): - self.dimension = dimension - self.probe_types = ["manifold_topology", "eigenvalue_spectrum", "curvature_tensor"] - - def generate_laplacian_spectrum(self, n_modes: int = 16) -> Tuple[List[float], List[List[float]]]: - """ - Generate Laplacian eigenvalue spectrum for manifold. - - For a d-dimensional manifold, Laplacian eigenvalues λ_k scale as: - λ_k ∝ k^(2/d) for large k (Weyl law) - - Returns: (eigenvalues, eigenvectors) - """ - # Simulate eigenvalue spectrum - # λ_k = (k * π / L)² for Dirichlet boundary conditions - L = 1.0 # Characteristic length - eigenvalues = [] - eigenvectors = [] - - for k in range(1, n_modes + 1): - # Weyl law scaling: λ_k ∝ k^(2/d) - if self.dimension > 0: - lam = (np.pi * k / L) ** (2.0 / self.dimension) - else: - lam = (np.pi * k / L) ** 2 - - eigenvalues.append(lam) - - # Generate corresponding eigenfunction (simplified) - # φ_k(x) = sin(kπx/L) for 1D, product for higher D - vec = [np.sin(k * np.pi * i / (n_modes + 1)) for i in range(1, n_modes + 1)] - vec = [v / np.linalg.norm(vec) for v in vec] # Normalize - eigenvectors.append(vec) - - return eigenvalues, eigenvectors - - def classify_manifold_shape(self, eigenvalues: List[float]) -> str: - """ - Classify manifold shape from eigenvalue distribution. - - - Spherical: eigenvalues cluster at low end (positive curvature) - - Hyperbolic: eigenvalues spread out (negative curvature) - - Flat: uniform distribution (zero curvature) - - Toroidal: periodic pattern - """ - if len(eigenvalues) < 3: - return 'unknown' - - # Compute eigenvalue gaps - gaps = [eigenvalues[i+1] - eigenvalues[i] for i in range(len(eigenvalues)-1)] - mean_gap = np.mean(gaps) - std_gap = np.std(gaps) - - # Coefficient of variation - cv = std_gap / mean_gap if mean_gap > 0 else 0 - - # Classify based on gap distribution - if cv < 0.3: - return 'spherical' # Low variation, clustered - elif cv > 0.7: - return 'hyperbolic' # High variation, spread out - elif any(g < 0.01 * mean_gap for g in gaps[:3]): - return 'toroidal' # Near-degenerate low modes - else: - return 'flat' - - def compute_curvature_tensor(self, eigenvalues: List[float]) -> List[float]: - """ - Compute Ricci curvature tensor components from eigenvalues. - - Simplified: R_ii ∝ Σ(1/λ_k) for k > 0 (zeta function regularized) - """ - # Regularized sum: exclude zero mode - curvatures = [] - for i in range(min(4, self.dimension)): - if len(eigenvalues) > 1: - # Ricci curvature ~ sum of inverse eigenvalues - ricci = sum(1.0 / lam for lam in eigenvalues[1:] if lam > 0.001) - curvatures.append(ricci / len(eigenvalues)) - else: - curvatures.append(0.0) - - return curvatures - - def generate_waveprobe(self, probe_type: str = "manifold_topology") -> WaveprobeManifold: - """Generate waveprobe diagnostic payload with manifold data.""" - timestamp = time.time() - probe_id = f"manifold_{hashlib.sha256(str(timestamp).encode()).hexdigest()[:12]}" - - # Generate eigenvalue spectrum - eigenvalues, eigenvectors = self.generate_laplacian_spectrum(n_modes=16) - - # Classify manifold shape - manifold_shape = self.classify_manifold_shape(eigenvalues) - - # Compute curvature - curvature_tensor = self.compute_curvature_tensor(eigenvalues) - - # Topology validation - topology_valid = all(ev > 0 for ev in eigenvalues[1:]) # Positive semi-definite Laplacian - - return WaveprobeManifold( - probe_id=probe_id, - dimension=self.dimension, - eigenvalues=eigenvalues, - eigenvectors=eigenvectors, - curvature_tensor=curvature_tensor, - topology_valid=topology_valid, - manifold_shape=manifold_shape - ) - - -class FAMMPreshaper: - """ - Preshape FAMM delay-line maps based on waveprobe manifold geometry. - - Maps manifold eigenvalues to FAMM delay parameters: - - delay ∝ 1/√λ (lower eigenvalue = longer delay = lower frequency mode) - - delay_weight ∝ eigenvector amplitude (stronger coupling for dominant modes) - - delay_mass ∝ curvature (higher curvature = more causal constraint) - """ - - def __init__(self, bank_size: int = 256, max_delay: float = 32767.0): - self.bank_size = bank_size - self.max_delay = max_delay # Q16.16 max - - def eigenvalue_to_delay(self, eigenvalue: float, scale: float = 1000.0) -> float: - """ - Map Laplacian eigenvalue to FAMM delay time. - - Lower eigenvalue (lower frequency mode) → longer delay - τ ∝ 1/√λ - """ - if eigenvalue <= 0: - return self.max_delay - - # Delay ∝ 1/√λ - delay = scale / np.sqrt(eigenvalue) - - # Clamp to Q16.16 range - return min(delay, self.max_delay) - - def eigenvector_to_weight(self, eigenvector_component: float) -> float: - """ - Map eigenvector component to FAMM delay weight. - - Larger eigenvector amplitude → stronger delay weight - w = |φ_k(x)|² (probability density interpretation) - """ - weight = eigenvector_component ** 2 - return min(weight, 1.0) # Normalized to [0,1] - - def curvature_to_mass(self, curvature: float, base_mass: float = 1.0) -> float: - """ - Map manifold curvature to FAMM delay mass. - - Higher curvature → larger delay mass (more causal constraint) - mass ∝ |R| (absolute Ricci curvature) - """ - mass = base_mass * (1.0 + abs(curvature)) - return min(mass, self.max_delay / 10) # Scale appropriately - - def preshape_famm_map( - self, - manifold: WaveprobeManifold, - n_cells: Optional[int] = None - ) -> List[FAMMDelayMap]: - """ - Preshape FAMM delay-line map from waveprobe manifold. - - Distributes FAMM cells across manifold modes, - assigning delays based on eigenvalue spectrum. - """ - if n_cells is None: - n_cells = self.bank_size - - famm_maps = [] - - # Use top N eigenvalues for N cells - n_modes = min(len(manifold.eigenvalues), n_cells) - - for i in range(n_cells): - # Cycle through eigenmodes - mode_idx = i % n_modes - - # Get eigenvalue and eigenvector for this mode - eigenvalue = manifold.eigenvalues[mode_idx] - eigenvector = manifold.eigenvectors[mode_idx] - - # Pick component from eigenvector (distribute across spatial positions) - vec_idx = i % len(eigenvector) - eigencomponent = eigenvector[vec_idx] - - # Compute curvature component - curvature_idx = i % len(manifold.curvature_tensor) - curvature = manifold.curvature_tensor[curvature_idx] - - # Map to FAMM parameters - delay = self.eigenvalue_to_delay(eigenvalue) - weight = self.eigenvector_to_weight(eigencomponent) - mass = self.curvature_to_mass(curvature) - - # Data value (simulated Q16.16) - data_val = eigencomponent * 32767.0 # Scale to Q16.16 range - - famm_maps.append(FAMMDelayMap( - address=i, - data=float(data_val), - delay=float(delay), - delay_mass=float(mass), - delay_weight=float(weight), - curvature_aligned=True - )) - - return famm_maps - - -class WaveprobeFAMMIntegration: - """ - Integrate waveprobe manifold generation with FAMM preshaping. - - Complete pipeline: waveprobe → eigenvalue → manifold → FAMM - """ - - def __init__(self, dimension: int = 4, bank_size: int = 256): - self.waveprobe_gen = WaveprobeManifoldGenerator(dimension) - self.famm_preshaper = FAMMPreshaper(bank_size) - - def generate_preshaped_famm( - self, - probe_type: str = "manifold_topology", - output_format: str = "lean" - ) -> Dict: - """ - Generate complete waveprobe → FAMM preshaped configuration. - - Returns configuration in specified format (lean, json, or python). - """ - # Step 1: Generate waveprobe manifold - manifold = self.waveprobe_gen.generate_waveprobe(probe_type) - - # Step 2: Preshape FAMM map - famm_maps = self.famm_preshaper.preshape_famm_map(manifold) - - # Step 3: Format output - if output_format == "lean": - return self._to_lean_format(manifold, famm_maps) - elif output_format == "json": - return self._to_json_format(manifold, famm_maps) - else: - return self._to_python_format(manifold, famm_maps) - - def _to_lean_format( - self, - manifold: WaveprobeManifold, - famm_maps: List[FAMMDelayMap] - ) -> Dict: - """Convert to Lean 4 FAMM initialization format.""" - lean_cells = [] - for m in famm_maps: - # Convert to Q16.16 hex representation - data_hex = f"0x{int(m.data) & 0xFFFF:04X}" - delay_hex = f"0x{int(m.delay) & 0xFFFF:04X}" - mass_hex = f"0x{int(m.delay_mass) & 0xFFFF:04X}" - weight_hex = f"0x{int(m.delay_weight * 65535) & 0xFFFF:04X}" - - lean_cells.append({ - "data": data_hex, - "delay": delay_hex, - "delayMass": mass_hex, - "delayWeight": weight_hex - }) - - return { - "manifold": { - "probe_id": manifold.probe_id, - "dimension": manifold.dimension, - "shape": manifold.manifold_shape, - "eigenvalues": [f"{ev:.6f}" for ev in manifold.eigenvalues[:8]], # Top 8 - "curvature": [f"{c:.6f}" for c in manifold.curvature_tensor], - "topology_valid": manifold.topology_valid - }, - "famm_bank": { - "size": len(famm_maps), - "maxDelay": f"0x{int(self.famm_preshaper.max_delay):04X}", - "cells": lean_cells[:16] # First 16 for demo - }, - "generation_timestamp": time.time() - } - - def _to_json_format( - self, - manifold: WaveprobeManifold, - famm_maps: List[FAMMDelayMap] - ) -> Dict: - """Convert to JSON format for external tools.""" - return { - "waveprobe": { - "probe_id": manifold.probe_id, - "dimension": manifold.dimension, - "manifold_shape": manifold.manifold_shape, - "eigenvalue_spectrum": manifold.eigenvalues, - "curvature_tensor": manifold.curvature_tensor, - "topology_valid": manifold.topology_valid - }, - "famm_delay_map": [ - { - "address": m.address, - "delay_ms": m.delay, - "delay_mass": m.delay_mass, - "delay_weight": m.delay_weight, - "data": m.data, - "curvature_aligned": m.curvature_aligned - } - for m in famm_maps - ] - } - - def _to_python_format( - self, - manifold: WaveprobeManifold, - famm_maps: List[FAMMDelayMap] - ) -> Dict: - """Convert to Python-compatible format.""" - return { - "manifold": manifold, - "famm_maps": famm_maps, - "summary": { - "shape": manifold.manifold_shape, - "n_cells": len(famm_maps), - "mean_delay": np.mean([m.delay for m in famm_maps]), - "mean_weight": np.mean([m.delay_weight for m in famm_maps]) - } - } - - -def main(): - """Generate waveprobe manifold and preshape FAMM maps.""" - print("=" * 70) - print("Waveprobe Manifold Generator + FAMM Map Preshaper") - print("=" * 70) - - # Initialize integration - integration = WaveprobeFAMMIntegration(dimension=4, bank_size=256) - - print("\n[1] Generating waveprobe manifold with eigenvalue spectrum...") - - # Generate preshaped FAMM - result = integration.generate_preshaped_famm( - probe_type="manifold_topology", - output_format="lean" - ) - - print(f" Probe ID: {result['manifold']['probe_id']}") - print(f" Dimension: {result['manifold']['dimension']}") - print(f" Manifold Shape: {result['manifold']['shape']}") - print(f" Topology Valid: {result['manifold']['topology_valid']}") - - print("\n[2] Eigenvalue Spectrum (top 8):") - for i, ev in enumerate(result['manifold']['eigenvalues'][:8]): - print(f" λ_{i+1} = {ev}") - - print("\n[3] Curvature Tensor:") - for i, c in enumerate(result['manifold']['curvature']): - print(f" R_{i} = {c}") - - print(f"\n[4] FAMM Bank Configuration:") - print(f" Size: {result['famm_bank']['size']} cells") - print(f" Max Delay: {result['famm_bank']['maxDelay']} (Q16.16)") - - print(f"\n[5] Sample FAMM Cells (first 4):") - for i, cell in enumerate(result['famm_bank']['cells'][:4]): - print(f" Cell[{i}]: data={cell['data']}, delay={cell['delay']}, " - f"mass={cell['delayMass']}, weight={cell['delayWeight']}") - - # Save output - output_path = RESEARCH_STACK / "4-Infrastructure/shim/waveprobe_famm_output.json" - with open(output_path, 'w') as f: - json.dump(result, f, indent=2) - - print(f"\n[6] Output saved to: {output_path}") - - print("\n" + "=" * 70) - print("Integration Complete") - print("Waveprobe eigenvalue spectrum → Manifold shape → FAMM delay map preshape") - print("=" * 70) - - return result - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/waveprobe_rgflow_teleport.py b/4-Infrastructure/shim/waveprobe_rgflow_teleport.py deleted file mode 100644 index a4d04434..00000000 --- a/4-Infrastructure/shim/waveprobe_rgflow_teleport.py +++ /dev/null @@ -1,263 +0,0 @@ -#!/usr/bin/env python3 -# PTOS: LAYER=STORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -waveprobe_rgflow_teleport.py — Waveform teleport shim. - -Reads a WAV file (or a raw PCM array), calls the Lean WaveformTeleport -module via subprocess, and emits a JSON TeleportReceipt. - -Shim boundary (per AGENTS.md §7.1): - ALLOWED: WAV parsing, SHA-256 hashing, JSON serialisation, subprocess spawn - FORBIDDEN: RG decimation logic, beta-residual computation, sigma_q arithmetic - — all of that lives in WaveformTeleport.lean. - -Usage: - python3 4-Infrastructure/shim/waveprobe_rgflow_teleport.py \ - --wav 2-Search-Space/simulations/matter-frequencies/wav-files/caffeine_fade_96k.wav \ - --out /tmp/caffeine_teleport_receipt.json - - python3 4-Infrastructure/shim/waveprobe_rgflow_teleport.py \ - --wav --max-depth 32 --out - -Environment: - LEAN_BIN path to SemanticsCli binary - default: 0-Core-Formalism/lean/Semantics/.lake/build/bin/SemanticsCli -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import struct -import sys -import wave -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -# ── repo root ──────────────────────────────────────────────────────────────── -REPO_ROOT = Path(__file__).resolve().parents[2] - -LEAN_BIN_DEFAULT = ( - REPO_ROOT - / "0-Core-Formalism/lean/Semantics/.lake/build/bin/SemanticsCli" -) - -CLAIM_BOUNDARY = "waveform-teleport-rg-attractor-only" - - -# ── WAV helpers (shim-only: read bytes, hash bytes) ────────────────────────── - -def _sha256_hex(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def _sha256_words(data: bytes) -> list[int]: - """Return the SHA-256 digest as 8 × uint32 words (big-endian).""" - digest = hashlib.sha256(data).digest() - return list(struct.unpack(">8I", digest)) - - -def _read_wav(path: Path) -> tuple[list[int], int, int]: - """ - Read a WAV file and return (samples_q16_16, sample_rate, n_samples). - - Samples are normalised to Q16_16 (UInt32): - PCM int16 → Q16_16 by sign-extending and shifting left 16 bits - PCM float → Q16_16 via f * 65536 (clamped) - - Only the first channel is used; stereo is downmixed to mono. - """ - with wave.open(str(path), "rb") as wf: - n_channels = wf.getnchannels() - sample_width = wf.getsampwidth() # bytes per sample - sample_rate = wf.getframerate() - n_frames = wf.getnframes() - raw = wf.readframes(n_frames) - - samples_q: list[int] = [] - - if sample_width == 2: # PCM int16 - fmt = f"<{n_frames * n_channels}h" - pcm = struct.unpack(fmt, raw) - for i in range(0, len(pcm), n_channels): - # int16 → Q16_16: treat as signed, shift to 16.16 space - # value in [-32768, 32767] → Q16_16 by (v + 32768) * 2 to [0, 131070] - # Then encode as UInt32: (v << 16) with sign handling - v = pcm[i] - # Encode: Q16_16 one = 0x00010000 = 65536 - # map int16 range [-32768..32767] → [0x80000000..0x7FFF0000] - q = (v * 65536) & 0xFFFFFFFF - samples_q.append(q) - elif sample_width == 3: # PCM int24 - n_total = n_frames * n_channels - for i in range(0, n_total * 3, n_channels * 3): - # read 3 bytes as little-endian int24 - b0, b1, b2 = raw[i], raw[i + 1], raw[i + 2] - v24 = b0 | (b1 << 8) | (b2 << 16) - if v24 & 0x800000: - v24 -= 0x1000000 # sign extend - # scale to int16 range then Q16_16 - v16 = v24 >> 8 - q = (v16 * 65536) & 0xFFFFFFFF - samples_q.append(q) - else: - raise ValueError( - f"Unsupported sample width {sample_width} bytes in {path.name}. " - "Only 16-bit and 24-bit PCM WAV are supported." - ) - - return samples_q, sample_rate, len(samples_q) - - -# ── Lean shim call ──────────────────────────────────────────────────────────── - -def _call_lean_teleport( - samples_q: list[int], - sha256_words: list[int], - sample_hz_q16: int, - max_depth: int, - lean_bin: Path, -) -> dict[str, Any]: - """ - Marshal the waveform data to JSON, call SemanticsCli with the - waveform-teleport command, and return the parsed receipt dict. - - If the Lean binary is not present, return a stub receipt with a clear - software-witness-only marker. - """ - payload = { - "command": "waveform_teleport", - "samples_q16": samples_q, - "sha256_words": sha256_words, - "sample_hz_q16": sample_hz_q16, - "max_depth": max_depth, - } - - if not lean_bin.exists(): - # Software-witness fallback: no Lean binary available. - # Return a stub so the shim can still emit a receipt. - return { - "lean_witness": False, - "stub": True, - "reason": f"Lean binary not found at {lean_bin}", - "attractor_id": None, - "rg_depth": None, - "sigma_q": None, - "beta_residual": None, - "token_lawful": False, - "roundtrip_ok": False, - } - - import subprocess # noqa: PLC0415 — only imported when binary is present - result = subprocess.run( - [str(lean_bin), "waveform-teleport"], - input=json.dumps(payload), - capture_output=True, - text=True, - timeout=120, - ) - if result.returncode != 0: - raise RuntimeError( - f"SemanticsCli waveform-teleport failed:\n{result.stderr}" - ) - return json.loads(result.stdout) - - -# ── receipt assembly ────────────────────────────────────────────────────────── - -def build_receipt( - wav_path: Path, - max_depth: int = 32, - lean_bin: Path = LEAN_BIN_DEFAULT, -) -> dict[str, Any]: - """ - Main shim entry point. - - 1. Read WAV → Q16_16 samples + SHA-256 - 2. Call Lean teleport logic - 3. Assemble and return TeleportReceipt JSON - """ - raw_bytes = wav_path.read_bytes() - sha256_hex = _sha256_hex(raw_bytes) - sha256_words = _sha256_words(raw_bytes) - - samples_q, sample_rate, n_samples = _read_wav(wav_path) - - # sample_rate in Q16_16: Hz * 65536 (fits in UInt32 for rates ≤ 32767 Hz) - sample_hz_q16 = (sample_rate * 65536) & 0xFFFFFFFF - - lean_result = _call_lean_teleport( - samples_q = samples_q, - sha256_words = sha256_words, - sample_hz_q16 = sample_hz_q16, - max_depth = max_depth, - lean_bin = lean_bin, - ) - - receipt: dict[str, Any] = { - "schema": "waveprobe_rgflow_teleport_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "source_path": str(wav_path), - "source_sha256": sha256_hex, - "n_samples": n_samples, - "sample_rate_hz": sample_rate, - "max_depth": max_depth, - "lean_witness": lean_result.get("lean_witness", False), - "attractor_id": lean_result.get("attractor_id"), - "rg_depth": lean_result.get("rg_depth"), - "sigma_q": lean_result.get("sigma_q"), - "beta_residual": lean_result.get("beta_residual"), - "token_lawful": lean_result.get("token_lawful", False), - "roundtrip_ok": lean_result.get("roundtrip_ok", False), - "claim_boundary": CLAIM_BOUNDARY, - } - - # receipt_hash: SHA-256 of the stable preimage (excludes generated_at_utc) - preimage = {k: v for k, v in receipt.items() if k != "generated_at_utc"} - receipt["receipt_hash"] = _sha256_hex( - json.dumps(preimage, sort_keys=True).encode() - ) - - return receipt - - -# ── CLI ─────────────────────────────────────────────────────────────────────── - -def main() -> None: - parser = argparse.ArgumentParser( - description="Waveform teleport shim — extract RG attractor, emit receipt." - ) - parser.add_argument("--wav", required=True, type=Path, - help="Input WAV file") - parser.add_argument("--out", required=False, type=Path, default=None, - help="Output receipt JSON path (default: stdout)") - parser.add_argument("--max-depth", type=int, default=32, - help="Max RG decimation depth (default: 32)") - parser.add_argument("--lean-bin", type=Path, default=LEAN_BIN_DEFAULT, - help="Path to SemanticsCli binary") - args = parser.parse_args() - - if not args.wav.exists(): - print(f"[ERROR] WAV not found: {args.wav}", file=sys.stderr) - sys.exit(1) - - receipt = build_receipt( - wav_path = args.wav, - max_depth = args.max_depth, - lean_bin = args.lean_bin, - ) - - out_json = json.dumps(receipt, indent=2) - - if args.out: - args.out.parent.mkdir(parents=True, exist_ok=True) - args.out.write_text(out_json) - print(f"[OK] Receipt written to {args.out}") - else: - print(out_json) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/waveprobe_transfer_smoothing.py b/4-Infrastructure/shim/waveprobe_transfer_smoothing.py deleted file mode 100644 index 64dbb55b..00000000 --- a/4-Infrastructure/shim/waveprobe_transfer_smoothing.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -"""Rederive Waveprobe smoothing for rclone transfer paths. - -This is not a transport replacement. It reads an rclone log and derives a -future-run lane schedule from the observed transfer signal: - - throughput samples -> signal - file completions -> boundary impulses - file size -> payload mass - boundary shock -> curvature / turbulence - lane recipe -> delay-shaped controller -""" - -from __future__ import annotations - -import argparse -import json -import math -import re -import statistics -from dataclasses import asdict, dataclass -from datetime import datetime -from pathlib import Path - - -STATS_RE = re.compile( - r"^(?P\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}).*?" - r"(?P[0-9.]+) GiB / (?P[0-9.]+) GiB,\s+" - r"(?P\d+)%,\s+(?P[0-9.]+) (?P[KMGT]iB)/s, ETA (?P[^)]*)" -) -COPIED_RE = re.compile( - r"^(?P\d{4}/\d{2}/\d{2} \d{2}:\d{2}:\d{2}) INFO\s+: (?P.*): Copied \(new\)" -) - - -@dataclass -class TransferSample: - ts: str - epoch: float - speed_mibs: float - - -@dataclass -class BoundaryEvent: - ts: str - path: str - size_bytes: int | None - size_mib: float | None - previous_speed_mibs: float | None - next_speed_mibs: float | None - shock_mibs: float - shock_ratio: float - payload_mass: float - boundary_density: float - eigenvalue: float - delay_weight: float - lane: str - - -def parse_time(value: str) -> datetime: - return datetime.strptime(value, "%Y/%m/%d %H:%M:%S") - - -def speed_to_mibs(value: float, unit: str) -> float: - scale = { - "KiB": 1 / 1024, - "MiB": 1, - "GiB": 1024, - "TiB": 1024 * 1024, - }[unit] - return value * scale - - -def parse_log(path: Path) -> tuple[list[TransferSample], list[tuple[str, str]]]: - samples: list[TransferSample] = [] - copied: list[tuple[str, str]] = [] - for line in path.read_text(errors="ignore").splitlines(): - stat = STATS_RE.search(line) - if stat: - ts = stat.group("ts") - samples.append( - TransferSample( - ts=ts, - epoch=parse_time(ts).timestamp(), - speed_mibs=speed_to_mibs(float(stat.group("speed")), stat.group("unit")), - ) - ) - continue - copied_match = COPIED_RE.search(line) - if copied_match: - copied.append((copied_match.group("ts"), copied_match.group("path"))) - return samples, copied - - -def file_size(source_root: Path, rel_path: str) -> int | None: - candidate = source_root / rel_path - try: - return candidate.stat().st_size - except FileNotFoundError: - return None - - -def lane_for_eigenvalue(eigenvalue: float, size_mib: float | None) -> str: - if size_mib is not None and size_mib >= 20 * 1024: - return "low_mode_large_stream" - if eigenvalue < 0.08: - return "low_mode_large_stream" - if eigenvalue < 0.35: - return "mid_mode_payload_stream" - return "high_mode_tail_boundary" - - -def derive_events( - samples: list[TransferSample], - copied: list[tuple[str, str]], - source_root: Path, -) -> list[BoundaryEvent]: - events: list[BoundaryEvent] = [] - speeds = [sample.speed_mibs for sample in samples if sample.speed_mibs > 0] - stream_speed = statistics.median(speeds) if speeds else 1.0 - - for copied_ts, rel_path in copied: - copied_epoch = parse_time(copied_ts).timestamp() - previous = None - next_sample = None - for sample in samples: - if sample.epoch <= copied_epoch: - previous = sample - if sample.epoch > copied_epoch: - next_sample = sample - break - - before = previous.speed_mibs if previous else None - after = next_sample.speed_mibs if next_sample else None - delta = (after - before) if before is not None and after is not None else 0.0 - shock = max(0.0, -delta) - shock_ratio = shock / max(stream_speed, 0.001) - - size = file_size(source_root, rel_path) - size_mib = size / (1024 * 1024) if size is not None else None - payload_mass = math.log2(1.0 + (size_mib or 0.0)) - - # Small files have high boundary density; large files behave as slow, - # stable modes. Unknown sizes are treated conservatively as small. - boundary_density = 1.0 / (1.0 + payload_mass) - eigenvalue = boundary_density * (1.0 + shock_ratio) - delay_weight = 1.0 / math.sqrt(max(eigenvalue, 1e-6)) - lane = lane_for_eigenvalue(eigenvalue, size_mib) - - events.append( - BoundaryEvent( - ts=copied_ts, - path=rel_path, - size_bytes=size, - size_mib=round(size_mib, 3) if size_mib is not None else None, - previous_speed_mibs=round(before, 3) if before is not None else None, - next_speed_mibs=round(after, 3) if after is not None else None, - shock_mibs=round(shock, 3), - shock_ratio=round(shock_ratio, 6), - payload_mass=round(payload_mass, 6), - boundary_density=round(boundary_density, 6), - eigenvalue=round(eigenvalue, 6), - delay_weight=round(delay_weight, 6), - lane=lane, - ) - ) - return events - - -def summarize(events: list[BoundaryEvent], samples: list[TransferSample]) -> dict: - lane_groups: dict[str, list[BoundaryEvent]] = {} - for event in events: - lane_groups.setdefault(event.lane, []).append(event) - - lane_summary = {} - for lane, lane_events in lane_groups.items(): - eigenvalues = [event.eigenvalue for event in lane_events] - shocks = [event.shock_mibs for event in lane_events] - sizes = [event.size_mib for event in lane_events if event.size_mib is not None] - lane_summary[lane] = { - "event_count": len(lane_events), - "mean_eigenvalue": round(statistics.mean(eigenvalues), 6), - "max_eigenvalue": round(max(eigenvalues), 6), - "mean_shock_mibs": round(statistics.mean(shocks), 3), - "mean_size_mib": round(statistics.mean(sizes), 3) if sizes else None, - } - - sorted_events = sorted(events, key=lambda event: event.eigenvalue, reverse=True) - speeds = [sample.speed_mibs for sample in samples] - - return { - "derivation": { - "signal": "v(t) = rclone throughput samples", - "boundary_impulse": "kappa_i = max(0, v_before - v_after) / median(v)", - "payload_mass": "mu_i = log2(1 + size_i_mib)", - "boundary_density": "beta_i = 1 / (1 + mu_i)", - "transfer_eigenvalue": "lambda_i = beta_i * (1 + kappa_i)", - "delay_weight": "tau_i = 1 / sqrt(lambda_i)", - }, - "sample_count": len(samples), - "boundary_event_count": len(events), - "speed_mibs": { - "median": round(statistics.median(speeds), 3) if speeds else None, - "mean": round(statistics.mean(speeds), 3) if speeds else None, - "last": round(speeds[-1], 3) if speeds else None, - }, - "lane_summary": lane_summary, - "highest_curvature_events": [asdict(event) for event in sorted_events[:20]], - "recipe": { - "low_mode_large_stream": { - "rclone": "--transfers 1 --drive-chunk-size 512M --order-by size,descending", - "reason": "preserve continuous payload flow; avoid cross-file turbulence", - }, - "mid_mode_payload_stream": { - "rclone": "--transfers 2 --drive-chunk-size 256M --order-by size,descending", - "reason": "overlap moderate boundary barriers while keeping payload lanes fat", - }, - "high_mode_tail_boundary": { - "rclone": "--transfers 4 --drive-chunk-size 128M --order-by size,descending", - "reason": "hide per-object Drive/API latency in the tiny-file tail", - }, - }, - } - - -def write_markdown(report: dict, path: Path) -> None: - lines = [ - "# Waveprobe Transfer Smoothing Rederivation", - "", - "## Core Law", - "", - "```text", - "v(t) = observed rclone throughput signal", - "kappa_i = max(0, v_before - v_after) / median(v)", - "mu_i = log2(1 + file_size_i_mib)", - "beta_i = 1 / (1 + mu_i)", - "lambda_i = beta_i * (1 + kappa_i)", - "tau_i = 1 / sqrt(lambda_i)", - "```", - "", - "Interpretation:", - "", - "- large files have high payload mass and low boundary density", - "- tiny files have low payload mass and high boundary density", - "- transfer smoothing is not one magic throughput curve", - "- it is lane selection based on the eigenvalue of boundary turbulence", - "", - "## Observed Signal", - "", - f"- Samples: {report['sample_count']}", - f"- Boundary events: {report['boundary_event_count']}", - f"- Median speed: {report['speed_mibs']['median']} MiB/s", - f"- Last speed: {report['speed_mibs']['last']} MiB/s", - "", - "## Lane Summary", - "", - ] - for lane, summary in sorted(report["lane_summary"].items()): - lines.append(f"### {lane}") - lines.append("") - lines.append(f"- Events: {summary['event_count']}") - lines.append(f"- Mean eigenvalue: {summary['mean_eigenvalue']}") - lines.append(f"- Max eigenvalue: {summary['max_eigenvalue']}") - lines.append(f"- Mean shock: {summary['mean_shock_mibs']} MiB/s") - lines.append(f"- Mean size: {summary['mean_size_mib']} MiB") - lines.append(f"- Recipe: `{report['recipe'][lane]['rclone']}`") - lines.append("") - lines.extend( - [ - "## Highest-Curvature Events", - "", - ] - ) - for event in report["highest_curvature_events"][:10]: - lines.append( - f"- `{event['path']}` lambda={event['eigenvalue']} " - f"shock={event['shock_mibs']} MiB/s size={event['size_mib']} MiB " - f"lane={event['lane']}" - ) - lines.extend( - [ - "", - "## Operational Claim Boundary", - "", - "This does not make Google Drive faster by itself. It derives a lane", - "schedule for future runs. Active transfers should not be interrupted", - "unless the scheduler is explicitly being tested on a disposable run.", - "", - "Receipt rule:", - "", - "```text", - "copy -> rclone check -> receipt -> only then delete or stub local files", - "```", - ] - ) - path.write_text("\n".join(lines) + "\n") - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("log", type=Path) - parser.add_argument("--source-root", type=Path, required=True) - parser.add_argument("--json-out", type=Path, required=True) - parser.add_argument("--md-out", type=Path, required=True) - args = parser.parse_args() - - samples, copied = parse_log(args.log) - events = derive_events(samples, copied, args.source_root) - report = summarize(events, samples) - args.json_out.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n") - write_markdown(report, args.md_out) - print(json.dumps(report["lane_summary"], indent=2, sort_keys=True)) - print(f"wrote {args.json_out}") - print(f"wrote {args.md_out}") - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/weather_systems_math_prior_receipt.py b/4-Infrastructure/shim/weather_systems_math_prior_receipt.py deleted file mode 100644 index 7f6319e4..00000000 --- a/4-Infrastructure/shim/weather_systems_math_prior_receipt.py +++ /dev/null @@ -1,415 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt generator for weather-systems borrowed math. - -This is a no-download, tiny-fixture prior for borrowing weather-system -mathematics into the reconstruction-core stack: conservative transport, -shallow-water/PV-style invariants, data-assimilation innovation, ensemble -spread, and forecast residual growth. It is not an NWP model, weather forecast, -ERA5 ingest, or benchmark result. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "weather_systems_math_prior" -RECEIPT = OUT_DIR / "weather_systems_math_prior_receipt.json" -TABLE = OUT_DIR / "weather_systems_math_prior_table.jsonl" -SUMMARY = OUT_DIR / "weather_systems_math_prior_receipt.md" -SOURCE_MANIFEST = REPO / "6-Documentation" / "docs" / "provenance" / "WEATHER_SYSTEMS_MATH_PRIOR_SOURCES.cff" - - -OBJECTIVE_PACKET = { - "name": "Weather Systems Borrowed-Math Prior", - "core_map": "weather_state -> transport/replay kernel + residual -> repaired state", - "lossless_gate": "Repair(Replay(K,Theta,Pi),R) == S", - "borrowed_math_surfaces": [ - "primitive-equation dynamics", - "finite-volume conservative transport", - "shallow-water layer invariants", - "potential-vorticity-style route constraints", - "data-assimilation innovation", - "ensemble spread / forecast residual growth", - ], - "weather_codec_score": ( - "J_weather = |D|+|K|+|Theta|+|Pi|+|R|+|Receipts| " - "+ lambda_m mass_drift + lambda_c CFL_excess + lambda_i innovation_norm " - "+ lambda_e ensemble_spread + lambda_r residual_growth" - ), - "admission": "exact repair and positive byte law; weather terms are diagnostics unless normalized", - "native_phrase": ( - "Weather math is useful as a replay-stability and residual-growth filter, " - "not as a forecast or data-ingest claim." - ), -} - - -SOURCE_SURFACES = [ - { - "name": "ECMWF IFS documentation", - "url": "https://www.ecmwf.int/en/publications/ifs-documentation", - "role": "primitive equations, dynamics, and data-assimilation reference surface", - }, - { - "name": "ECMWF ERA5", - "url": "https://www.ecmwf.int/en/forecasts/dataset/ecmwf-reanalysis-v5", - "role": "reanalysis and assimilation route prior; no data vendored", - }, - { - "name": "NOAA NCEI Numerical Weather Prediction archive", - "url": "https://www.ncei.noaa.gov/products/weather-climate-models/numerical-weather-prediction", - "role": "NWP data-family route prior; no data vendored", - }, - { - "name": "NOAA/GFDL FV3 dynamical core", - "url": "https://www.gfdl.noaa.gov/fv3", - "role": "finite-volume cubed-sphere and shallow-water-layer route prior", - }, - { - "name": "NOAA/GFDL FV3 key components", - "url": "https://www.gfdl.noaa.gov/fv3/fv3-key-components/", - "role": "finite-volume conservation and layer dynamics reference surface", - }, -] - - -@dataclass(frozen=True) -class Fixture: - fixture_id: str - kind: str - length: int - theta: dict[str, Any] - negative_control: bool - notes: str - - -FIXTURES = [ - Fixture( - fixture_id="periodic_transport_mass_admit", - kind="periodic_transport", - length=384, - theta={"pattern": [1000, 1002, 1005, 1002], "shift": 1, "u": 0.25, "dx": 1.0, "dt": 1.0}, - negative_control=False, - notes="Conservative periodic transport replays exactly and preserves total mass.", - ), - Fixture( - fixture_id="wrong_boundary_residual_hold", - kind="wrong_boundary_transport", - length=384, - theta={"pattern": [1000, 1002, 1005, 1002], "shift": 1, "u": 0.25, "dx": 1.0, "dt": 1.0}, - negative_control=True, - notes="Wrong boundary condition creates a repairable but held residual surface.", - ), - Fixture( - fixture_id="assimilation_innovation_hold", - kind="assimilation_update", - length=24, - theta={"background": 1000.0, "observation": 1008.0, "gain": 0.25, "count": 24}, - negative_control=False, - notes="A tiny innovation update is useful for routing, but not byte-useful compression.", - ), -] - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_text(text: str) -> str: - return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest() - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def counted_size(obj: Any) -> int: - return len(stable_json(obj).encode("utf-8")) - - -def source_state(fixture: Fixture) -> list[float]: - if fixture.kind in {"periodic_transport", "wrong_boundary_transport"}: - pattern = [float(value) for value in fixture.theta["pattern"]] - return [pattern[index % len(pattern)] for index in range(fixture.length)] - if fixture.kind == "assimilation_update": - background = float(fixture.theta["background"]) - observation = float(fixture.theta["observation"]) - gain = float(fixture.theta["gain"]) - value = background + gain * (observation - background) - return [value for _ in range(fixture.length)] - raise ValueError(f"unsupported fixture kind {fixture.kind}") - - -def replay_state(fixture: Fixture) -> list[float]: - source = source_state(fixture) - if fixture.kind == "periodic_transport": - shift = int(fixture.theta["shift"]) % len(source) - return source[-shift:] + source[:-shift] - if fixture.kind == "wrong_boundary_transport": - shift = int(fixture.theta["shift"]) % len(source) - shifted = [source[0] for _ in range(shift)] + source[:-shift] - return shifted[: len(source)] - if fixture.kind == "assimilation_update": - # Replay the analysis state from background, observation, and gain. - return source - raise ValueError(f"unsupported fixture kind {fixture.kind}") - - -def target_state(fixture: Fixture) -> list[float]: - if fixture.kind == "wrong_boundary_transport": - correct = Fixture( - fixture_id=fixture.fixture_id, - kind="periodic_transport", - length=fixture.length, - theta=fixture.theta, - negative_control=fixture.negative_control, - notes=fixture.notes, - ) - return replay_state(correct) - return replay_state(fixture) - - -def residual_patch(source: list[float], candidate: list[float]) -> list[dict[str, float]]: - patch: list[dict[str, float]] = [] - max_len = max(len(source), len(candidate)) - for index in range(max_len): - actual = source[index] if index < len(source) else math.nan - proposed = candidate[index] if index < len(candidate) else math.nan - if actual != proposed: - patch.append({"i": index, "actual": actual, "candidate": proposed}) - return patch - - -def apply_patch(candidate: list[float], patch: list[dict[str, float]], length: int) -> list[float]: - repaired = list(candidate) - for item in patch: - index = int(item["i"]) - while index >= len(repaired): - repaired.append(math.nan) - repaired[index] = float(item["actual"]) - return repaired[:length] - - -def cfl(theta: dict[str, Any]) -> float: - return abs(float(theta.get("u", 0.0))) * float(theta.get("dt", 1.0)) / max(float(theta.get("dx", 1.0)), 1e-12) - - -def innovation_norm(theta: dict[str, Any]) -> float: - if "background" not in theta or "observation" not in theta: - return 0.0 - return abs(float(theta["observation"]) - float(theta["background"])) - - -def ensemble_spread(state: list[float]) -> float: - if not state: - return 0.0 - mean = sum(state) / len(state) - return math.sqrt(sum((value - mean) ** 2 for value in state) / len(state)) - - -def run_fixture(fixture: Fixture) -> dict[str, Any]: - target = target_state(fixture) - candidate = replay_state(fixture) - patch = residual_patch(target, candidate) - repaired = apply_patch(candidate, patch, len(target)) - - exact_without_residual = candidate == target - exact_with_residual = repaired == target - mass_target = sum(target) - mass_candidate = sum(candidate) - mass_repaired = sum(repaired) - mass_drift_before_repair = abs(mass_target - mass_candidate) - mass_drift_after_repair = abs(mass_target - mass_repaired) - - dictionary_payload = { - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "source_manifest": rel(SOURCE_MANIFEST), - } - kernel_payload = {"kind": fixture.kind} - theta_payload = fixture.theta - protocol_payload = {"decoder": "weather_tiny_replay_v1", "repair": "patch_v1"} - residual_payload = {"patch": patch} - receipt_payload = { - "target_hash": sha256_text(stable_json(target)), - "candidate_hash": sha256_text(stable_json(candidate)), - "repaired_hash": sha256_text(stable_json(repaired)), - } - - raw_bytes = counted_size(target) - dictionary_bytes = counted_size(dictionary_payload) - kernel_bytes = counted_size(kernel_payload) - theta_bytes = counted_size(theta_payload) - protocol_bytes = counted_size(protocol_payload) - residual_bytes = 0 if exact_without_residual else counted_size(residual_payload) - receipt_bytes = counted_size(receipt_payload) - counted_bytes = dictionary_bytes + kernel_bytes + theta_bytes + protocol_bytes + residual_bytes + receipt_bytes - byte_gain = raw_bytes - counted_bytes - positive_byte_law = byte_gain > 0 - cfl_number = cfl(fixture.theta) - cfl_excess = max(0.0, cfl_number - 1.0) - residual_growth = len(patch) / max(len(target), 1) - - if fixture.negative_control and exact_without_residual: - status = "FAIL_NEGATIVE_CONTROL" - elif fixture.negative_control: - status = "HOLD_DIAGNOSTIC" - elif exact_with_residual and positive_byte_law and mass_drift_after_repair == 0.0: - status = "ADMIT_FIXTURE" - else: - status = "HOLD_DIAGNOSTIC" - - result = { - "fixture_id": fixture.fixture_id, - "kind": fixture.kind, - "notes": fixture.notes, - "negative_control": fixture.negative_control, - "target_hash": receipt_payload["target_hash"], - "candidate_hash": receipt_payload["candidate_hash"], - "repaired_hash": receipt_payload["repaired_hash"], - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "exact_replay_without_residual": exact_without_residual, - "exact_replay_with_residual": exact_with_residual, - "residual_declared": True, - "raw_bytes": raw_bytes, - "dictionary_bytes": dictionary_bytes, - "kernel_bytes": kernel_bytes, - "theta_bytes": theta_bytes, - "protocol_bytes": protocol_bytes, - "residual_bytes": residual_bytes, - "receipt_bytes": receipt_bytes, - "counted_bytes": counted_bytes, - "byte_gain": byte_gain, - "positive_byte_law": positive_byte_law, - "mass_target": mass_target, - "mass_candidate": mass_candidate, - "mass_repaired": mass_repaired, - "mass_drift_before_repair": mass_drift_before_repair, - "mass_drift_after_repair": mass_drift_after_repair, - "cfl_number": cfl_number, - "cfl_excess": cfl_excess, - "innovation_norm": innovation_norm(fixture.theta), - "ensemble_spread": ensemble_spread(target), - "residual_growth": residual_growth, - "patch_count": len(patch), - "counted_payload_hash": sha256_text( - stable_json( - { - "D": dictionary_payload, - "K": kernel_payload, - "Theta": theta_payload, - "Pi": protocol_payload, - "R": residual_payload, - "Receipts": receipt_payload, - } - ) - ), - "status": status, - } - result["result_hash"] = sha256_text(stable_json({k: v for k, v in result.items() if k != "result_hash"})) - return result - - -def write_summary(receipt: dict[str, Any], path: Path) -> None: - lines = [ - "# Weather Systems Math Prior Receipt", - "", - f"Schema: `{receipt['schema']}` ", - f"Decision: `{receipt['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - receipt["claim_boundary"], - "", - "## Objective", - "", - f"`{OBJECTIVE_PACKET['core_map']}`", - "", - f"`{OBJECTIVE_PACKET['weather_codec_score']}`", - "", - "## Fixtures", - "", - "| Fixture | Status | Exact repair | Byte gain | Mass drift after repair | CFL | Residual growth |", - "|---|---|---:|---:|---:|---:|---:|", - ] - for result in receipt["results"]: - lines.append( - f"| {result['fixture_id']} | {result['status']} | " - f"{result['exact_replay_with_residual']} | {result['byte_gain']} | " - f"{result['mass_drift_after_repair']:.6g} | {result['cfl_number']:.3f} | " - f"{result['residual_growth']:.3f} |" - ) - lines.extend(["", "## Source Surfaces", ""]) - for source in SOURCE_SURFACES: - lines.append(f"- {source['name']}: {source['url']}") - lines.append("") - path.write_text("\n".join(lines), encoding="utf-8") - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - results = [run_fixture(fixture) for fixture in FIXTURES] - with TABLE.open("w", encoding="utf-8") as handle: - for result in results: - handle.write(json.dumps(result, sort_keys=True) + "\n") - - status_values = sorted({result["status"] for result in results}) - receipt = { - "schema": "weather_systems_math_prior_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "objective_packet": OBJECTIVE_PACKET, - "objective_hash": sha256_text(stable_json(OBJECTIVE_PACKET)), - "source_manifest": rel(SOURCE_MANIFEST), - "source_surfaces": SOURCE_SURFACES, - "fixture_count": len(results), - "table": rel(TABLE), - "summary": rel(SUMMARY), - "status_counts": { - status: sum(1 for result in results if result["status"] == status) - for status in status_values - }, - "results": results, - "decision": "HOLD", - "claim_boundary": ( - "Weather-systems borrowed-math prior only. It uses tiny synthetic " - "fixtures for conservative transport, boundary-condition residuals, " - "and data-assimilation innovation. It does not ingest ERA5/NWP data, " - "does not forecast weather, does not validate an atmospheric model, " - "and does not claim compression benchmark performance." - ), - } - receipt["receipt_hash"] = sha256_text( - stable_json( - { - k: v - for k, v in receipt.items() - if k not in {"receipt_hash", "generated_at_utc"} - } - ) - ) - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(receipt, SUMMARY) - print( - json.dumps( - { - "receipt": rel(RECEIPT), - "summary": rel(SUMMARY), - "table": rel(TABLE), - "receipt_hash": receipt["receipt_hash"], - "status_counts": receipt["status_counts"], - }, - indent=2, - sort_keys=True, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/whitespace_zero_grammar_probe.py b/4-Infrastructure/shim/whitespace_zero_grammar_probe.py deleted file mode 100644 index 30a809cf..00000000 --- a/4-Infrastructure/shim/whitespace_zero_grammar_probe.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python3 -"""Receipt probe for zero-whitespace-code logogram grammar. - -The grammar does not store ordinary spaces as payload atoms. For canonical -single-space token streams, spacing is reconstructed from symbol count/order. -Non-canonical whitespace remains HOLD unless a residual policy is declared. -""" - -from __future__ import annotations - -import json -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -LEAN_DIR = REPO / "0-Core-Formalism" / "lean" / "Semantics" -OUT_DIR = REPO / "shared-data" / "data" / "stack_solidification" -OUT = OUT_DIR / "whitespace_zero_grammar_probe.json" -DOC = REPO / "6-Documentation" / "docs" / "whitespace_zero_grammar_2026-05-09.md" - - -CANONICAL_CASES = [ - "structure transformation receipt replay repair", - "braid rope ammr leaf peak", - "token order symbol identity transform rule", -] - -HOLD_CASES = [ - "two spaces need residual", - " leading space needs residual", - "tabs\tneed\tresidual", -] - - -def rel(path: Path) -> str: - return str(path.relative_to(REPO)) - - -def run_lean_build() -> dict[str, Any]: - proc = subprocess.run( - ["lake", "build", "Semantics.WhitespaceFreeGrammar"], - cwd=LEAN_DIR, - text=True, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - timeout=300, - check=False, - ) - return { - "command": ["lake", "build", "Semantics.WhitespaceFreeGrammar"], - "cwd": rel(LEAN_DIR), - "returncode": proc.returncode, - "status": "PASS" if proc.returncode == 0 else "FAIL", - "stdout_tail": proc.stdout[-6000:], - "stderr_tail": proc.stderr[-6000:], - } - - -def is_canonical_single_space(text: str) -> bool: - return bool(text) and text == " ".join(text.split(" ")) and "\t" not in text and "\n" not in text and not text.startswith(" ") and not text.endswith(" ") - - -def encode_symbols(text: str) -> list[str]: - return text.split() - - -def run_case(case_id: str, text: str, expect_exact: bool) -> dict[str, Any]: - symbols = encode_symbols(text) - reconstructed = " ".join(symbols) - raw_bytes = len(text.encode("utf-8")) - payload_bytes = sum(len(symbol.encode("utf-8")) for symbol in symbols) - derived_boundaries = max(0, len(symbols) - 1) - stored_whitespace_codes = 0 - exact_replay = reconstructed == text - canonical = is_canonical_single_space(text) - status = "ADMIT_FIXTURE" if exact_replay and canonical and expect_exact else "HOLD_NEEDS_WHITESPACE_RESIDUAL" - return { - "case_id": case_id, - "raw": text, - "symbols": symbols, - "symbol_count": len(symbols), - "raw_bytes": raw_bytes, - "payload_bytes": payload_bytes, - "stored_whitespace_codes": stored_whitespace_codes, - "stored_whitespace_bytes": 0, - "derived_boundary_count": derived_boundaries, - "reconstructed": reconstructed, - "exact_replay": exact_replay, - "canonical_single_space": canonical, - "delta_vs_raw_without_receipt": raw_bytes - payload_bytes, - "status": status, - } - - -def build_receipt() -> dict[str, Any]: - lean_build = run_lean_build() - cases = [ - run_case(f"canonical_{idx}", text, True) - for idx, text in enumerate(CANONICAL_CASES, start=1) - ] + [ - run_case(f"hold_{idx}", text, False) - for idx, text in enumerate(HOLD_CASES, start=1) - ] - admitted = [case for case in cases if case["status"] == "ADMIT_FIXTURE"] - holds = [case for case in cases if case["status"].startswith("HOLD")] - return { - "schema": "whitespace_zero_grammar_probe_v1", - "created_utc": datetime.now(timezone.utc).isoformat(), - "claim_boundary": ( - "Zero whitespace-code grammar for canonical single-space token streams. " - "Whitespace is reconstructed from symbol count/order. Non-canonical " - "spacing requires an explicit residual and is not admitted by this gate." - ), - "lean_module": "Semantics.WhitespaceFreeGrammar", - "lean_build": lean_build, - "grammar_rule": { - "stored_whitespace_codes": 0, - "boundary_rule": "insert one display space between adjacent symbols during canonical replay", - "payload_rule": "store symbol payloads only", - "residual_rule": "non-canonical whitespace requires residual", - }, - "summary": { - "status": "PASS_ZERO_WHITESPACE_CANONICAL" if lean_build["status"] == "PASS" and len(admitted) == len(CANONICAL_CASES) and len(holds) == len(HOLD_CASES) else "FAIL", - "case_count": len(cases), - "admit_count": len(admitted), - "hold_count": len(holds), - "stored_whitespace_codes_total": sum(case["stored_whitespace_codes"] for case in cases), - "canonical_delta_bytes_total": sum(case["delta_vs_raw_without_receipt"] for case in admitted), - }, - "cases": cases, - } - - -def build_doc(receipt: dict[str, Any]) -> str: - lines = [ - "# Whitespace-Zero Grammar Probe", - "", - "**Date:** 2026-05-09", - "", - receipt["claim_boundary"], - "", - "## Rule", - "", - "- Store symbol payloads.", - "- Store zero ordinary whitespace codes.", - "- Reconstruct one canonical display space between adjacent symbols.", - "- HOLD any non-canonical whitespace unless a residual is declared.", - "", - "## Status", - "", - f"- Lean module: `{receipt['lean_module']}`", - f"- Lean build: `{receipt['lean_build']['status']}`", - f"- Probe status: `{receipt['summary']['status']}`", - f"- Admitted canonical fixtures: `{receipt['summary']['admit_count']}`", - f"- HOLD fixtures needing residual: `{receipt['summary']['hold_count']}`", - f"- Stored whitespace codes total: `{receipt['summary']['stored_whitespace_codes_total']}`", - "", - "## Cases", - "", - ] - for case in receipt["cases"]: - lines.append( - f"- `{case['case_id']}`: `{case['status']}`, symbols `{case['symbol_count']}`, " - f"payload `{case['payload_bytes']}` bytes, raw `{case['raw_bytes']}` bytes, " - f"derived boundaries `{case['derived_boundary_count']}`, exact replay `{case['exact_replay']}`" - ) - lines.extend( - [ - "", - "## Machine Receipt", - "", - f"- `{rel(OUT)}`", - ] - ) - return "\n".join(lines) + "\n" - - -def main() -> int: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt = build_receipt() - OUT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - DOC.write_text(build_doc(receipt), encoding="utf-8") - print(json.dumps({"receipt": rel(OUT), "doc": rel(DOC), "status": receipt["summary"]["status"]}, indent=2)) - return 0 if receipt["summary"]["status"] == "PASS_ZERO_WHITESPACE_CANONICAL" else 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/4-Infrastructure/shim/wiki_tool_maturation_apply.py b/4-Infrastructure/shim/wiki_tool_maturation_apply.py deleted file mode 100644 index 5008087e..00000000 --- a/4-Infrastructure/shim/wiki_tool_maturation_apply.py +++ /dev/null @@ -1,303 +0,0 @@ -#!/usr/bin/env python3 -"""Apply a standard maturation block to tool-like TiddlyWiki entries. - -This is an idempotent bulk pass. It recomputes the same local heuristic used by -wiki_tool_tuning_review_probe.py, then inserts or replaces a generated -Maturation Status section in every tool-like non-system tiddler. -""" - -from __future__ import annotations - -import hashlib -import importlib.util -import json -import re -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -TIDDLERS = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -REVIEW_SCRIPT = REPO / "4-Infrastructure" / "shim" / "wiki_tool_tuning_review_probe.py" -REVIEW_RECEIPT = REPO / "shared-data" / "data" / "wiki_tool_tuning_review" / "wiki_tool_tuning_review_receipt.json" -OUT_DIR = REPO / "shared-data" / "data" / "wiki_tool_maturation_pass" -PAYLOAD_JSON = OUT_DIR / "wiki_tool_maturation_pass.json" -SUMMARY = OUT_DIR / "wiki_tool_maturation_pass.md" -RECEIPT = OUT_DIR / "wiki_tool_maturation_pass_receipt.json" -PASS_TIDDLER = TIDDLERS / "Wiki Tool Maturation Pass.tid" -GENERATED_TIDDLERS = { - "Wiki Tool Tuning Review.tid", - "Wiki Tool Maturation Pass.tid", -} - -SECTION_HEADER = "!! Maturation Status" -BEGIN_MARKER = "" -END_MARKER = "" - - -def load_review_module() -> Any: - spec = importlib.util.spec_from_file_location("wiki_tool_tuning_review_probe", REVIEW_SCRIPT) - if spec is None or spec.loader is None: - raise RuntimeError(f"cannot load review script: {REVIEW_SCRIPT}") - module = importlib.util.module_from_spec(spec) - sys.modules[spec.name] = module - spec.loader.exec_module(module) - return module - - -REVIEW = load_review_module() - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def review_receipt_hash() -> str | None: - if not REVIEW_RECEIPT.exists(): - return None - return json.loads(REVIEW_RECEIPT.read_text(encoding="utf-8")).get("receipt_hash") - - -def remove_existing_block(text: str) -> str: - pattern = re.compile( - rf"\n?{re.escape(SECTION_HEADER)}\n\n{re.escape(BEGIN_MARKER)}.*?{re.escape(END_MARKER)}\n?", - flags=re.DOTALL, - ) - return pattern.sub("\n", text).rstrip() + "\n" - - -def maturity_block(entry: dict[str, Any], receipt_hash: str | None) -> str: - categories = ", ".join(f"`{category}`" for category in entry["categories"]) or "`uncategorized`" - next_actions = entry.get("recommended_next") or ["add local fixture and receipt before promotion"] - lines = [ - SECTION_HEADER, - "", - BEGIN_MARKER, - "", - "This section is generated by the wiki tool maturation pass. It is a", - "review aid, not a validation claim.", - "", - f"* Maturity: `{entry['maturity']}`", - f"* Tuning priority: `{entry['tuning_priority']}`", - f"* Tool categories: {categories}", - f"* Runner references: `{len(entry['runner_paths'])}`", - f"* Receipt references: `{len(entry['receipt_paths'])}`", - f"* Baseline signal: `{entry['baseline_signal']}`", - f"* Exact replay signal: `{entry['exact_replay_signal']}`", - f"* Review receipt: `{receipt_hash or 'missing'}`", - "", - "Next maturation actions:", - "", - ] - for action in next_actions: - lines.append(f"* {action}") - lines.extend( - [ - "", - "Promotion remains HOLD until executable fixtures, exact replay or declared", - "loss policy, baseline/negative-control evidence, and receipt hashes close.", - "", - END_MARKER, - ] - ) - return "\n".join(lines) - - -def apply_block(path: Path, entry: dict[str, Any], receipt_hash: str | None) -> dict[str, Any]: - before = path.read_text(encoding="utf-8", errors="replace") - cleaned = remove_existing_block(before) - after = cleaned.rstrip() + "\n\n" + maturity_block(entry, receipt_hash) + "\n" - changed = before != after - if changed: - path.write_text(after, encoding="utf-8") - return { - "path": rel(path), - "title": entry["title"], - "changed": changed, - "before_sha256": sha256_bytes(before.encode("utf-8")), - "after_sha256": sha256_bytes(after.encode("utf-8")), - "maturity": entry["maturity"], - "tuning_priority": entry["tuning_priority"], - "categories": entry["categories"], - "recommended_next": entry.get("recommended_next", []), - } - - -def compute_entries() -> list[dict[str, Any]]: - raw_entries = [ - REVIEW.parse_tiddler(path) - for path in sorted(TIDDLERS.glob("*.tid")) - if not path.name.startswith("$__") and path.name not in GENERATED_TIDDLERS - ] - tool_entries = [entry for entry in raw_entries if entry["tool_score"] >= 6] - for entry in tool_entries: - entry["tuning_priority"] = REVIEW.tuning_priority(entry) - entry["recommended_next"] = REVIEW.recommended_next(entry) - return sorted(tool_entries, key=lambda entry: entry["tuning_priority"], reverse=True) - - -def build_payload() -> dict[str, Any]: - OUT_DIR.mkdir(parents=True, exist_ok=True) - receipt_hash = review_receipt_hash() - if receipt_hash is None: - raise RuntimeError(f"missing source review receipt: {REVIEW_RECEIPT}") - entries = compute_entries() - changes = [apply_block(Path(REPO / entry["path"]), entry, receipt_hash) for entry in entries] - changed = [item for item in changes if item["changed"]] - matured_block_count = 0 - for item in changes: - text = (REPO / item["path"]).read_text(encoding="utf-8", errors="replace") - if BEGIN_MARKER in text and END_MARKER in text: - matured_block_count += 1 - maturity_rollup = { - maturity: sum(1 for item in changes if item["maturity"] == maturity) - for maturity in sorted({item["maturity"] for item in changes}) - } - payload = { - "schema": "wiki_tool_maturation_pass_v1", - "claim_boundary": ( - "Bulk wiki maturation only. Generated sections annotate local tuning " - "status and next actions; they do not validate any theory, result, or tool." - ), - "source_review_receipt": rel(REVIEW_RECEIPT), - "source_review_receipt_hash": receipt_hash, - "marker": { - "section_header": SECTION_HEADER, - "begin": BEGIN_MARKER, - "end": END_MARKER, - }, - "aggregates": { - "tool_entries_seen": len(entries), - "tiddlers_changed": len(changed), - "tiddlers_unchanged": len(changes) - len(changed), - "matured_block_count": matured_block_count, - "all_tool_entries_have_maturation_block": matured_block_count == len(entries), - "maturity_rollup": maturity_rollup, - }, - "changed": changed, - "all_entries": changes, - "decision": "ADMIT_WIKI_TOOL_MATURATION_PASS_AS_HOLD_ANNOTATION", - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "wiki_tool_maturation_pass_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "source_review_receipt_hash": payload["source_review_receipt_hash"], - "aggregates": payload["aggregates"], - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - agg = payload["aggregates"] - lines = [ - "# Wiki Tool Maturation Pass", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Aggregate", - "", - f"- Tool entries seen: `{agg['tool_entries_seen']}`", - f"- Tiddlers changed: `{agg['tiddlers_changed']}`", - f"- Tiddlers unchanged: `{agg['tiddlers_unchanged']}`", - f"- Maturation blocks present: `{agg['matured_block_count']}`", - f"- All tool entries have maturation block: `{agg['all_tool_entries_have_maturation_block']}`", - f"- Maturity rollup: `{agg['maturity_rollup']}`", - "", - "## Top Changed Entries", - "", - "| Tiddler | Maturity | Priority | Categories |", - "|---|---|---:|---|", - ] - for item in payload["changed"][:30]: - lines.append(f"| [[{item['title']}]] | {item['maturity']} | {item['tuning_priority']} | {', '.join(item['categories'][:4])} |") - lines.extend(["", "## Receipt", "", f"`{rel(RECEIPT)}`"]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_pass_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - agg = payload["aggregates"] - lines = [ - "title: Wiki Tool Maturation Pass", - "tags: ResearchStack TiddlyWiki ToolTuning Maturation HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Wiki Tool Maturation Pass", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Aggregate", - "", - f"* Tool entries seen: `{agg['tool_entries_seen']}`", - f"* Tiddlers changed: `{agg['tiddlers_changed']}`", - f"* Tiddlers unchanged: `{agg['tiddlers_unchanged']}`", - f"* Maturation blocks present: `{agg['matured_block_count']}`", - f"* All tool entries have maturation block: `{agg['all_tool_entries_have_maturation_block']}`", - f"* Maturity rollup: `{agg['maturity_rollup']}`", - "", - "!! What Changed", - "", - "Each tool-like tiddler now has a generated `Maturation Status` section", - "with maturity class, tuning priority, categories, next actions, and the", - "source review receipt.", - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - "!! Links", - "", - "* [[Wiki Tool Tuning Review]]", - "* [[Combined Approach Equation Surface]]", - "", - f"Receipt: `{rel(RECEIPT)}`", - ] - PASS_TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_pass_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/wiki_tool_tuning_review_probe.py b/4-Infrastructure/shim/wiki_tool_tuning_review_probe.py deleted file mode 100644 index cefec7c4..00000000 --- a/4-Infrastructure/shim/wiki_tool_tuning_review_probe.py +++ /dev/null @@ -1,403 +0,0 @@ -#!/usr/bin/env python3 -"""Review TiddlyWiki tool surfaces for tuning opportunities. - -The wiki has many cards that describe tools, compilers, probes, sidecars, -gateways, and harnesses. This probe gives them a receipt-bearing tuning review: -classify tool-like tiddlers, detect whether they point to runners/receipts, and -rank concrete next tuning actions. -""" - -from __future__ import annotations - -import hashlib -import json -import re -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -TIDDLERS = REPO / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -OUT_DIR = REPO / "shared-data" / "data" / "wiki_tool_tuning_review" -PAYLOAD_JSON = OUT_DIR / "wiki_tool_tuning_review.json" -SUMMARY = OUT_DIR / "wiki_tool_tuning_review.md" -RECEIPT = OUT_DIR / "wiki_tool_tuning_review_receipt.json" -TIDDLER = TIDDLERS / "Wiki Tool Tuning Review.tid" -GENERATED_TIDDLERS = { - "Wiki Tool Tuning Review.tid", - "Wiki Tool Maturation Pass.tid", -} - -TOOL_KEYWORDS = [ - "tool", - "probe", - "compiler", - "harness", - "runner", - "bridge", - "plugin", - "router", - "search", - "cache", - "trace", - "sidecar", - "codec", - "compression", - "decoder", - "eigen", - "baseline", - "tuning", - "optimization", - "verifier", - "receipt", - "gate", - "adapter", - "pipeline", - "viewer", - "famm", - "waveprobe", - "parquet", - "logogram", - "cad", - "fpga", -] - -MATURATION_BEGIN = "" -MATURATION_END = "" - -CATEGORY_KEYWORDS = { - "compression_logogram": ["compression", "hutter", "logogram", "codec", "decoder", "parquet", "tokenbook", "dictionary"], - "compiler_receipt": ["compiler", "gccl", "receipt", "verifier", "gate", "lean", "proof", "typechecker"], - "search_route_memory": ["search", "route", "famm", "cache", "trace", "hnsw", "graph", "semantic"], - "geometry_cad": ["cad", "geometry", "mesh", "viewer", "force", "projectable", "stl", "step"], - "hardware_signal": ["fpga", "hardware", "waveprobe", "hdmi", "uart", "asic", "signal", "sdr", "tang9k"], - "ene_wiki_infra": ["ene", "tiddlywiki", "plugin", "ingest", "substrate", "fts", "wiki"], - "external_prior": ["prior", "external", "literature", "model", "transcriptformer", "alphafold", "typst", "quandela"], -} - -NEXT_ACTIONS = { - "compression_logogram": "run corpus matrix: raw/parquet/logogram/hybrid sidecar, then eigenprobe the loss axes", - "compiler_receipt": "add replay fixtures and negative controls for each promotion gate before widening candidate classes", - "search_route_memory": "measure cache-hit, trace replay gain, stale penalty, and route-frustration updates on one shared fixture", - "geometry_cad": "pair mesh/source/render hashes with bounded residual metrics and rollback receipts", - "hardware_signal": "separate simulation receipts from device receipts; add smoke fixtures for timing, packet, and recovery gates", - "ene_wiki_infra": "feed reviewed tiddlers through scan/dry-run/ingest/verify and compare ENE index coverage", - "external_prior": "turn prior cards into adapter fixtures with source hashes, license/provenance, baseline, and leakage controls", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def parse_tiddler(path: Path) -> dict[str, Any]: - text = path.read_text(encoding="utf-8", errors="replace") - analysis_text = re.sub( - rf"\n?!! Maturation Status\n\n{re.escape(MATURATION_BEGIN)}.*?{re.escape(MATURATION_END)}\n?", - "\n", - text, - flags=re.DOTALL, - ) - fields: dict[str, str] = {} - body_start = 0 - lines = text.splitlines() - for index, line in enumerate(lines): - if not line.strip(): - body_start = index + 1 - break - if ":" in line: - key, value = line.split(":", 1) - fields[key.strip()] = value.strip() - title = fields.get("title", path.stem) - tags = fields.get("tags", "") - lower = analysis_text.lower() - keyword_hits = {keyword: lower.count(keyword) for keyword in TOOL_KEYWORDS if lower.count(keyword)} - category_scores = { - category: sum(lower.count(keyword) for keyword in keywords) - for category, keywords in CATEGORY_KEYWORDS.items() - } - categories = [category for category, score in category_scores.items() if score > 0] - receipt_paths = sorted(set(re.findall(r"(?:shared-data/data|4-Infrastructure|6-Documentation|docs|0-Core-Formalism|5-Applications)[^`\s)]+receipt[^`\s)]*", analysis_text))) - runner_paths = sorted(set(re.findall(r"(?:4-Infrastructure|5-Applications|scripts|tools|0-Core-Formalism)[^`\s)]*\.(?:py|rs|lean|js|mjs|sh|v|sv)", analysis_text))) - decision_count = len(re.findall(r"\b(?:HOLD|ACCEPT|REJECT|QUARANTINE|CANDIDATE|ADMIT)\b", analysis_text)) - receipt_count = lower.count("receipt") - exact_replay_count = lower.count("exact replay") + lower.count("decode(") + lower.count("round-trip") - baseline_count = lower.count("baseline") + lower.count("negative control") - tool_score = ( - sum(keyword_hits.values()) - + 3 * len(runner_paths) - + 2 * len(receipt_paths) - + receipt_count - + baseline_count - ) - maturity = "UNRECEIPTED_TOOL_SURFACE" - if runner_paths and receipt_paths and baseline_count: - maturity = "TUNABLE_WITH_BASELINE" - elif runner_paths and receipt_paths: - maturity = "RECEIPTED_RUNNER" - elif receipt_paths: - maturity = "RECEIPTED_PRIOR" - elif runner_paths: - maturity = "RUNNER_WITHOUT_VISIBLE_RECEIPT" - if tool_score < 3: - maturity = "LOW_TOOL_SIGNAL" - return { - "path": rel(path), - "title": title, - "tags": tags, - "body_lines": max(0, len(lines) - body_start), - "sha256": sha256_bytes(text.encode("utf-8")), - "tool_score": tool_score, - "keyword_hits": keyword_hits, - "categories": categories, - "category_scores": {k: v for k, v in category_scores.items() if v}, - "receipt_count": receipt_count, - "decision_count": decision_count, - "exact_replay_signal": exact_replay_count, - "baseline_signal": baseline_count, - "receipt_paths": receipt_paths, - "runner_paths": runner_paths, - "maturity": maturity, - } - - -def tuning_priority(entry: dict[str, Any]) -> float: - category_bonus = 1.5 * len(entry["categories"]) - receipt_bonus = 2.0 if entry["receipt_paths"] else 0.0 - runner_bonus = 2.0 if entry["runner_paths"] else 0.0 - weak_baseline_penalty = 4.0 if entry["tool_score"] > 20 and entry["baseline_signal"] == 0 else 0.0 - return round(entry["tool_score"] + category_bonus + receipt_bonus + runner_bonus + weak_baseline_penalty, 3) - - -def recommended_next(entry: dict[str, Any]) -> list[str]: - actions = [NEXT_ACTIONS[category] for category in entry["categories"] if category in NEXT_ACTIONS] - if entry["maturity"] == "RUNNER_WITHOUT_VISIBLE_RECEIPT": - actions.insert(0, "emit a receipt JSON/MD/tiddler for the existing runner") - if entry["maturity"] == "RECEIPTED_PRIOR": - actions.insert(0, "bind the prior to a minimal executable fixture") - if entry["baseline_signal"] == 0 and entry["tool_score"] > 15: - actions.append("add a baseline/negative-control lane before tuning coefficients") - if entry["exact_replay_signal"] == 0 and any(cat in entry["categories"] for cat in ["compression_logogram", "compiler_receipt"]): - actions.append("add exact replay or round-trip evidence to keep the byte-law gate honest") - return list(dict.fromkeys(actions))[:4] - - -def build_payload() -> dict[str, Any]: - entries = [ - parse_tiddler(path) - for path in sorted(TIDDLERS.glob("*.tid")) - if not path.name.startswith("$__") and path.name not in GENERATED_TIDDLERS - ] - tool_entries = [entry for entry in entries if entry["tool_score"] >= 6] - for entry in tool_entries: - entry["tuning_priority"] = tuning_priority(entry) - entry["recommended_next"] = recommended_next(entry) - ranked = sorted(tool_entries, key=lambda entry: entry["tuning_priority"], reverse=True) - category_rollup = {} - for category in CATEGORY_KEYWORDS: - members = [entry for entry in tool_entries if category in entry["categories"]] - category_rollup[category] = { - "count": len(members), - "top": [ - {"title": item["title"], "priority": item["tuning_priority"], "maturity": item["maturity"]} - for item in sorted(members, key=lambda entry: entry["tuning_priority"], reverse=True)[:8] - ], - "next_action": NEXT_ACTIONS[category], - } - maturity_rollup = { - maturity: sum(1 for entry in tool_entries if entry["maturity"] == maturity) - for maturity in sorted({entry["maturity"] for entry in tool_entries}) - } - quick_wins = [ - entry - for entry in ranked - if entry["runner_paths"] and entry["receipt_paths"] and entry["maturity"] in {"RECEIPTED_RUNNER", "TUNABLE_WITH_BASELINE"} - ][:12] - baseline_debt = [ - entry - for entry in ranked - if entry["tool_score"] >= 20 and entry["baseline_signal"] == 0 - ][:12] - payload = { - "schema": "wiki_tool_tuning_review_v1", - "claim_boundary": ( - "Wiki review only. Tuning priority is a heuristic over local tiddler text, " - "runner references, receipt references, exact-replay signals, and baseline " - "signals. It ranks where to inspect next; it does not validate any theory or tool." - ), - "inputs": { - "tiddler_dir": rel(TIDDLERS), - "tiddler_count": len(entries), - "tool_like_count": len(tool_entries), - }, - "category_rollup": category_rollup, - "maturity_rollup": maturity_rollup, - "top_tuning_targets": ranked[:24], - "quick_wins": quick_wins, - "baseline_debt": baseline_debt, - "finding": ( - "The wiki has multiple tunable tool surfaces. The strongest immediate targets " - "are compression/logogram byte-law probes, Rainbow/GCCL receipt gates, " - "decision-diagram route search, ENE/wiki ingestion, CAD residual loops, and " - "shared fixture matrices, exact replay, negative controls, and eigen/baseline " - "diagnostics per tool family." - ), - "decision": "ADMIT_WIKI_TOOL_TUNING_REVIEW_AS_HOLD_ROADMAP", - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "wiki_tool_tuning_review_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "aggregates": { - **payload["inputs"], - "category_count": len(payload["category_rollup"]), - "maturity_rollup": payload["maturity_rollup"], - }, - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def entry_line(entry: dict[str, Any]) -> str: - cats = ", ".join(entry["categories"][:3]) or "uncategorized" - return f"| [[{entry['title']}]] | {entry['tuning_priority']} | {entry['maturity']} | {cats} |" - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# Wiki Tool Tuning Review", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Finding", - "", - payload["finding"], - "", - "## Rollup", - "", - f"- Tiddlers scanned: `{payload['inputs']['tiddler_count']}`", - f"- Tool-like tiddlers: `{payload['inputs']['tool_like_count']}`", - f"- Maturity rollup: `{payload['maturity_rollup']}`", - "", - "## Top Tuning Targets", - "", - "| Tiddler | Priority | Maturity | Categories |", - "|---|---:|---|---|", - ] - for entry in payload["top_tuning_targets"][:18]: - lines.append(entry_line(entry)) - lines.extend(["", "## Quick Wins", "", "| Tiddler | Priority | Maturity | Categories |", "|---|---:|---|---|"]) - for entry in payload["quick_wins"][:10]: - lines.append(entry_line(entry)) - lines.extend(["", "## Baseline Debt", "", "| Tiddler | Priority | Maturity | Categories |", "|---|---:|---|---|"]) - for entry in payload["baseline_debt"][:10]: - lines.append(entry_line(entry)) - lines.extend(["", "## Category Next Actions", ""]) - for category, rollup in payload["category_rollup"].items(): - lines.append(f"- `{category}` ({rollup['count']}): {rollup['next_action']}") - lines.extend(["", "## Receipt", "", f"`{rel(RECEIPT)}`"]) - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: Wiki Tool Tuning Review", - "tags: ResearchStack TiddlyWiki ToolTuning Review HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! Wiki Tool Tuning Review", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Finding", - "", - payload["finding"], - "", - "!! Counts", - "", - f"* Tiddlers scanned: `{payload['inputs']['tiddler_count']}`", - f"* Tool-like tiddlers: `{payload['inputs']['tool_like_count']}`", - f"* Maturity rollup: `{payload['maturity_rollup']}`", - "", - "!! Top Tuning Targets", - "", - "| Tiddler | Priority | Maturity | Categories |h", - ] - for entry in payload["top_tuning_targets"][:18]: - lines.append(entry_line(entry)) - lines.extend(["", "!! Quick Wins", "", "| Tiddler | Priority | Maturity | Categories |h"]) - for entry in payload["quick_wins"][:10]: - lines.append(entry_line(entry)) - lines.extend(["", "!! Baseline Debt", "", "| Tiddler | Priority | Maturity | Categories |h"]) - for entry in payload["baseline_debt"][:10]: - lines.append(entry_line(entry)) - lines.extend(["", "!! Category Next Actions", ""]) - for category, rollup in payload["category_rollup"].items(): - lines.append(f"* `{category}` ({rollup['count']}): {rollup['next_action']}") - lines.extend( - [ - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - "!! Links", - "", - "* [[Parquet Logogram Eigenprobe]]", - "* [[Combined Approach Equation Surface]]", - "* [[Rainbow Raccoon Compiler]]", - "* [[Decision Diagram Compression Tuning Prior]]", - "* [[TiddlyWiki ENE Bridge Plugin]]", - "", - f"Receipt: `{rel(RECEIPT)}`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/4-Infrastructure/shim/x86_emulator_eigen_baseline_probe.py b/4-Infrastructure/shim/x86_emulator_eigen_baseline_probe.py deleted file mode 100644 index e3cb9b61..00000000 --- a/4-Infrastructure/shim/x86_emulator_eigen_baseline_probe.py +++ /dev/null @@ -1,451 +0,0 @@ -#!/usr/bin/env python3 -"""x86 emulator eigen-baseline probe. - -This probe fetches primary x86-emulation source files from public upstream -repositories and derives a conservative structural basis from the code surface. -The basis is not a claim that the emulators expose literal eigensystems. It is a -baseline vector for deciding which emulator "shape" is dominated by fetch/decode, -state/flags, memory/addressing, control flow, IR lowering, cache/trace, or host -code generation. -""" - -from __future__ import annotations - -import hashlib -import json -import re -import urllib.request -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -REPO = Path(__file__).resolve().parents[2] -OUT_DIR = REPO / "shared-data" / "data" / "x86_emulator_eigen_baseline" -SOURCE_CACHE_DIR = OUT_DIR / "source_cache" -PAYLOAD_JSON = OUT_DIR / "x86_emulator_eigen_baseline.json" -SUMMARY = OUT_DIR / "x86_emulator_eigen_baseline.md" -RECEIPT = OUT_DIR / "x86_emulator_eigen_baseline_receipt.json" -TIDDLER = ( - REPO - / "6-Documentation" - / "tiddlywiki-local" - / "wiki" - / "tiddlers" - / "X86 Emulator Eigen Baseline.tid" -) - -SOURCE_FILES = [ - { - "emulator": "qemu", - "path": "target/i386/tcg/translate.c", - "url": "https://raw.githubusercontent.com/qemu/qemu/master/target/i386/tcg/translate.c", - "family": "tcg_ir_translation", - }, - { - "emulator": "unicorn", - "path": "qemu/target/i386/translate.c", - "url": "https://raw.githubusercontent.com/unicorn-engine/unicorn/master/qemu/target/i386/translate.c", - "family": "qemu_derived_tcg_ir", - }, - { - "emulator": "bochs", - "path": "bochs/cpu/cpu.cc", - "url": "https://raw.githubusercontent.com/bochs-emu/Bochs/master/bochs/cpu/cpu.cc", - "family": "accurate_interpreter_trace_cache", - }, - { - "emulator": "linux_kvm_x86", - "path": "arch/x86/kvm/x86.c", - "url": "https://raw.githubusercontent.com/torvalds/linux/master/arch/x86/kvm/x86.c", - "family": "kernel_hardware_virtualization_core", - }, - { - "emulator": "linux_kvm_vmx", - "path": "arch/x86/kvm/vmx/vmx.c", - "url": "https://raw.githubusercontent.com/torvalds/linux/master/arch/x86/kvm/vmx/vmx.c", - "family": "intel_vmx_hardware_virtualization", - }, - { - "emulator": "xen_vmx", - "path": "xen/arch/x86/hvm/vmx/vmx.c", - "url": "https://raw.githubusercontent.com/xen-project/xen/master/xen/arch/x86/hvm/vmx/vmx.c", - "family": "type1_hvm_vmx_hypervisor", - }, - { - "emulator": "virtualbox_iem", - "path": "src/VBox/VMM/VMMAll/IEMAll.cpp", - "url": "https://raw.githubusercontent.com/VirtualBox/virtualbox/main/src/VBox/VMM/VMMAll/IEMAll.cpp", - "family": "interpreted_execution_manager", - }, - { - "emulator": "virtualbox_native_recompiler", - "path": "src/VBox/VMM/VMMAll/IEMAllN8veRecompiler.cpp", - "url": "https://raw.githubusercontent.com/VirtualBox/virtualbox/main/src/VBox/VMM/VMMAll/IEMAllN8veRecompiler.cpp", - "family": "native_recompiler", - }, - { - "emulator": "freebsd_bhyve_vmm", - "path": "sys/amd64/vmm/vmm.c", - "url": "https://raw.githubusercontent.com/freebsd/freebsd-src/main/sys/amd64/vmm/vmm.c", - "family": "kernel_vmm_backend", - }, - { - "emulator": "freebsd_bhyve_vmexit", - "path": "usr.sbin/bhyve/amd64/vmexit.c", - "url": "https://raw.githubusercontent.com/freebsd/freebsd-src/main/usr.sbin/bhyve/amd64/vmexit.c", - "family": "userspace_vmexit_handler", - }, - { - "emulator": "dosbox_x", - "path": "src/cpu/core_normal.cpp", - "url": "https://raw.githubusercontent.com/joncampbell123/dosbox-x/master/src/cpu/core_normal.cpp", - "family": "fetch_decode_dispatch", - }, - { - "emulator": "fex", - "path": "FEXCore/Source/Interface/Core/OpcodeDispatcher.cpp", - "url": "https://raw.githubusercontent.com/FEX-Emu/FEX/main/FEXCore/Source/Interface/Core/OpcodeDispatcher.cpp", - "family": "x86_to_ir_host_lowering", - }, - { - "emulator": "86box", - "path": "src/codegen_new/codegen_ir.c", - "url": "https://raw.githubusercontent.com/86Box/86Box/master/src/codegen_new/codegen_ir.c", - "family": "pc_accuracy_codegen_ir", - }, - { - "emulator": "box86", - "path": "src/dynarec/dynarec.c", - "url": "https://raw.githubusercontent.com/ptitSeb/box86/master/src/dynarec/dynarec.c", - "family": "dynablock_host_recompiler", - }, - { - "emulator": "v86", - "path": "src/rust/jit.rs", - "url": "https://raw.githubusercontent.com/copy/v86/master/src/rust/jit.rs", - "family": "browser_wasm_jit", - }, - { - "emulator": "tiny386", - "path": "i386.c", - "url": "https://raw.githubusercontent.com/hchunhui/tiny386/master/i386.c", - "family": "compact_i386_interpreter", - }, -] - -BASIS_PATTERNS = { - "fetch_decode": r"\b(fetch|decode|opcode|prefix|modrm|disas|instruction)\b", - "state_flags": r"\b(flags?|eflags|cc_|condition|lazy|registers?|regs?)\b", - "memory_address": r"\b(mem|memory|load|store|addr|address|seg|page|tlb)\b", - "control_flow": r"\b(jump|jmp|branch|call|ret|return|eip|rip|pc|block)\b", - "ir_lowering": r"\b(ir|tcg|opdispatch|emit|emitter|codegen|wasm|builder|lower)\b", - "cache_trace": r"\b(cache|trace|icache|tb|dynablock|cached|wasm_table_index)\b", - "host_codegen": r"\b(host|arm|aarch64|x86-64|x86_64|jit|dynarec|backend)\b", - "vcpu_virtualization": r"\b(vcpu|vmx|svm|hvm|vmm|vmcs|vmrun|vmentry|kvm|xen|iem)\b", - "exit_intercept": r"\b(exit|vmexit|intercept|trap|fault|inject|ioreq|ioctl|msr|exception)\b", - "nested_paging": r"\b(ept|npt|shadow|mmu|pmap|tlb|page|paging)\b", -} - - -def stable_json(obj: Any) -> str: - return json.dumps(obj, sort_keys=True, separators=(",", ":"), ensure_ascii=True) - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def hash_obj(obj: Any) -> str: - return sha256_bytes(stable_json(obj).encode("utf-8")) - - -def rel(path: Path) -> str: - try: - return str(path.relative_to(REPO)) - except ValueError: - return str(path) - - -def fetch_url(url: str) -> tuple[bytes, str | None]: - try: - request = urllib.request.Request(url, headers={"User-Agent": "ResearchStack-x86-baseline-probe/1.0"}) - with urllib.request.urlopen(request, timeout=30) as response: - return response.read(), None - except Exception as exc: # pragma: no cover - receipt captures network failures. - return b"", f"{type(exc).__name__}: {exc}" - - -def source_cache_path(source: dict[str, str]) -> Path: - safe_name = re.sub(r"[^A-Za-z0-9_.-]+", "_", f"{source['emulator']}__{source['path']}") - return SOURCE_CACHE_DIR / safe_name - - -def load_or_fetch_source(source: dict[str, str]) -> tuple[bytes, str | None, str]: - cache_path = source_cache_path(source) - if cache_path.exists(): - return cache_path.read_bytes(), None, "local_cache" - raw, error = fetch_url(source["url"]) - if error is None: - cache_path.parent.mkdir(parents=True, exist_ok=True) - cache_path.write_bytes(raw) - return raw, None, "local_cache" - return raw, error, "live_fetch_failed" - - -def count_pattern(text: str, pattern: str) -> int: - return len(re.findall(pattern, text, flags=re.IGNORECASE)) - - -def analyze_source(source: dict[str, str]) -> dict[str, Any]: - raw, error, source_origin = load_or_fetch_source(source) - text = raw.decode("utf-8", errors="replace") - basis_counts = {axis: count_pattern(text, pattern) for axis, pattern in BASIS_PATTERNS.items()} - total_basis_hits = sum(basis_counts.values()) - basis_weights = { - axis: round((count / total_basis_hits) if total_basis_hits else 0.0, 6) - for axis, count in basis_counts.items() - } - dominant_axis = max(basis_weights, key=basis_weights.get) if total_basis_hits else "unavailable" - lines = text.splitlines() - return { - **source, - "fetch_error": error, - "fetched": error is None, - "source_origin": source_origin, - "source_cache_path": rel(source_cache_path(source)), - "bytes": len(raw), - "sha256": sha256_bytes(raw) if raw else None, - "line_count": len(lines), - "nonempty_line_count": sum(1 for line in lines if line.strip()), - "basis_counts": basis_counts, - "basis_weights": basis_weights, - "dominant_axis": dominant_axis, - "baseline_vector_order": list(BASIS_PATTERNS.keys()), - "baseline_vector": [basis_weights[axis] for axis in BASIS_PATTERNS], - } - - -def build_payload() -> dict[str, Any]: - sources = [analyze_source(source) for source in SOURCE_FILES] - fetched = [source for source in sources if source["fetched"]] - axis_average = {} - for axis in BASIS_PATTERNS: - axis_average[axis] = round( - sum(source["basis_weights"][axis] for source in fetched) / len(fetched), 6 - ) if fetched else 0.0 - payload = { - "schema": "x86_emulator_eigen_baseline_v1", - "claim_boundary": ( - "Structural baseline only. Basis weights are keyword-derived source-code " - "surface measurements, not literal emulator eigenvectors, performance " - "claims, correctness proofs, or stable historical baselines. Source URLs " - "are cached locally after first fetch; unfetched live URLs remain HOLD." - ), - "basis_axes": { - "fetch_decode": "front-end instruction fetch, prefix, opcode, decode, and instruction parsing surface", - "state_flags": "architectural register, flag, condition, and lazy flag surface", - "memory_address": "load/store, segmentation, paging, memory, and address calculation surface", - "control_flow": "branch, block, call/return, EIP/RIP/PC, and next-state control surface", - "ir_lowering": "intermediate representation, TCG, emit, codegen, WASM, and lowering surface", - "cache_trace": "instruction cache, trace, translation block, dynablock, and cached-code surface", - "host_codegen": "host backend, JIT, dynarec, ARM/AArch64/x86-64 lowering surface", - "vcpu_virtualization": "vCPU, VMX/SVM/HVM/VMM, VMCS, VM-entry/run, KVM/Xen/IEM virtualization surface", - "exit_intercept": "VM-exit, intercept, trap, injected exception, ioreq/ioctl/MSR handling surface", - "nested_paging": "EPT/NPT/shadow MMU, pmap, TLB, page and paging surface", - }, - "source_baselines": sources, - "aggregates": { - "source_count": len(sources), - "fetched_source_count": len(fetched), - "failed_source_count": len(sources) - len(fetched), - "source_mode": "local_cache_preferred_live_fetch_on_cache_miss", - "source_cache_dir": rel(SOURCE_CACHE_DIR), - "axis_average": axis_average, - "dominant_average_axis": max(axis_average, key=axis_average.get) if axis_average else "unavailable", - }, - "candidate_equations": [ - { - "equation_id": "emulator_shape_baseline_vector", - "equation": "B_e=[fetch_decode,state_flags,memory_address,control_flow,ir_lowering,cache_trace,host_codegen,vcpu_virtualization,exit_intercept,nested_paging]", - "decision": "HOLD_BASELINE_VECTOR", - "use_as": "pre-optimization baseline for emulator-shape comparison", - }, - { - "equation_id": "emulator_shape_distance", - "equation": "D(e,target)=||B_e-B_target||_2 + lambda*missing_source(e)", - "decision": "HOLD_SHAPE_DISTANCE", - "use_as": "distance objective before changing cache, trace, or lowering shape", - }, - { - "equation_id": "cache_trace_vs_ir_axis_gate", - "equation": "G_shape(e)=argmax(cache_trace(e), ir_lowering(e), host_codegen(e), fetch_decode(e))", - "decision": "HOLD_AXIS_GATE", - "use_as": "decide whether prediction cache, RAM trace, IR lowering, or interpreter path dominates", - }, - ], - "finding": ( - "The x86 emulator sources give baseline shape values before optimization. " - "QEMU/Unicorn/FEX/86Box/v86 tend to expose IR or host-lowering axes; " - "Bochs and DOSBox-X expose fetch/decode/trace interpreter axes; Box86 " - "exposes dynablock host-recompiler axes; Tiny386 anchors compact " - "interpreter state and lazy-flag surfaces. Xen, KVM, VirtualBox, and " - "bhyve add the hardware-virtualization baseline: vCPU context, VM exits, " - "intercepts, and nested paging." - ), - "decision": ( - "ADMIT_X86_EMULATOR_BASELINES_AS_HOLD_PRIORS" - if len(fetched) == len(sources) - else "HOLD_PARTIAL_X86_EMULATOR_BASELINES" - ), - } - payload["payload_hash"] = hash_obj({k: v for k, v in payload.items() if k != "payload_hash"}) - return payload - - -def build_receipt(payload: dict[str, Any]) -> dict[str, Any]: - receipt = { - "schema": "x86_emulator_eigen_baseline_receipt_v1", - "generated_at_utc": datetime.now(timezone.utc).isoformat(), - "timestamp_role": "metadata_only", - "generated_at_utc_included_in_receipt_hash": False, - "payload_hash": payload["payload_hash"], - "aggregates": payload["aggregates"], - "source_hashes": { - item["emulator"]: item["sha256"] for item in payload["source_baselines"] - }, - "decision": payload["decision"], - "claim_boundary": payload["claim_boundary"], - } - receipt["receipt_hash"] = sha256_bytes( - stable_json({k: v for k, v in receipt.items() if k not in {"receipt_hash", "generated_at_utc"}}).encode("utf-8") - ) - return receipt - - -def write_summary(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "# x86 Emulator Eigen Baseline", - "", - f"Decision: `{payload['decision']}` ", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - payload["claim_boundary"], - "", - "## Finding", - "", - payload["finding"], - "", - "## Baseline Vectors", - "", - "| Emulator | Family | Lines | Dominant axis | Baseline vector | Source |", - "|---|---|---:|---|---|---|", - ] - for source in payload["source_baselines"]: - vector = ", ".join(f"{value:.3f}" for value in source["baseline_vector"]) - lines.append( - f"| {source['emulator']} | {source['family']} | {source['line_count']} | " - f"{source['dominant_axis']} | [{vector}] | {source['url']} |" - ) - lines.extend( - [ - "", - "Vector order: `fetch_decode, state_flags, memory_address, control_flow, ir_lowering, cache_trace, host_codegen, vcpu_virtualization, exit_intercept, nested_paging`.", - "", - "## Candidate Equations", - "", - "| Candidate | Equation | Decision | Use as |", - "|---|---|---|---|", - ] - ) - for item in payload["candidate_equations"]: - lines.append(f"| {item['equation_id']} | `{item['equation']}` | {item['decision']} | {item['use_as']} |") - SUMMARY.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_tiddler(payload: dict[str, Any], receipt: dict[str, Any]) -> None: - lines = [ - "title: X86 Emulator Eigen Baseline", - "tags: Emulator X86 EigenBaseline HOLD Receipt", - "type: text/vnd.tiddlywiki", - "", - "! X86 Emulator Eigen Baseline", - "", - f"Decision: `{payload['decision']}`", - "", - f"Receipt hash: `{receipt['receipt_hash']}`", - "", - "!! Baseline Vectors", - "", - "| Emulator | Family | Lines | Dominant axis | Vector | Source hash |h", - ] - for source in payload["source_baselines"]: - vector = ", ".join(f"{value:.3f}" for value in source["baseline_vector"]) - source_hash = (source["sha256"] or "missing")[:12] - lines.append( - f"| {source['emulator']} | {source['family']} | {source['line_count']} | " - f"{source['dominant_axis']} | [{vector}] | {source_hash} |" - ) - lines.extend( - [ - "", - "Vector order: `fetch_decode, state_flags, memory_address, control_flow, ir_lowering, cache_trace, host_codegen, vcpu_virtualization, exit_intercept, nested_paging`.", - "", - f"Source mode: `{payload['aggregates']['source_mode']}`; use the receipt source hashes and local source cache for reproducibility checks.", - "", - "!! Specification Energy Flow", - "", - "Source markdown:", - "", - "```", - "6-Documentation/docs/x86_64_energy_flow_analysis.md", - "```", - "", - "The Markdown source treats the x86_64 specification worldline as an energy-flow", - "system rather than only a source-code baseline. Under that interpretation, the", - "first stable feature to crystallize is the 64-bit general-purpose register set:", - "", - "```", - "RAX-R15 -> RIP/RFLAGS -> memory addressing -> long mode -> SIMD extensions", - "```", - "", - "The source ranks the early emergence order by energy barrier:", - "", - "| Order | Feature | Energy interpretation |h", - "| 1 | RAX-R15 | lowest barrier, foundational width extension |", - "| 2 | RIP | low barrier, needed for 64-bit addressing |", - "| 3 | RFLAGS | low barrier, backward-compatible flag surface |", - "| 4 | memory addressing | medium barrier, paging and address-width pressure |", - "| 5 | long mode / IA-32e | medium-high barrier, state-machine transition |", - "| 6 | SIMD extensions | high barrier, wider and more complex instruction semantics |", - "", - "The deepest well is binary compatibility. Divergence appears as energy barriers", - "around virtualization, memory protection, and security extensions. This pairs", - "with the emulator source-shape baseline above: emulator code surfaces show what", - "implementers had to stabilize after the specification energy had already", - "settled into the compatibility well.", - "", - "!! Boundary", - "", - payload["claim_boundary"], - "", - f"Receipt: `shared-data/data/x86_emulator_eigen_baseline/x86_emulator_eigen_baseline_receipt.json`", - ] - ) - TIDDLER.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def main() -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - TIDDLER.parent.mkdir(parents=True, exist_ok=True) - payload = build_payload() - receipt = build_receipt(payload) - PAYLOAD_JSON.write_text(json.dumps(payload, indent=2, sort_keys=True), encoding="utf-8") - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - write_summary(payload, receipt) - write_tiddler(payload, receipt) - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ac_mains_sine_wave.py b/5-Applications/scripts/ac_mains_sine_wave.py deleted file mode 100644 index 1486cbe0..00000000 --- a/5-Applications/scripts/ac_mains_sine_wave.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -""" -AC Mains Sine Wave Inference Analysis -Analyzes using AC mains power cable to wall socket as sine wave source for topology enhancement. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ACMainsSineWave: - """Analyzes AC mains power as sine wave source for topology enhancement.""" - - def __init__(self): - # AC mains characteristics - self.ac_mains = { - "source": "AC Mains Power Cable to Wall Socket", - "frequency": "50Hz (EU) / 60Hz (US)", - "voltage": "230V (EU) / 120V (US)", - "waveform": "Pure sine wave (grid-generated)", - "signal_quality": "Very High (grid sine wave)", - "stability": "High (grid-regulated)", - "availability": "Continuous", - "significance_score": 95.0 - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "all_device_signal_capacity_with_vrm": 21256253633.129837, - "expansion_factor": 11187502.0 - } - - def analyze_ac_mains_sine_wave(self) -> Dict: - """Analyze AC mains as sine wave source.""" - analysis = { - "ac_mains_characteristics": { - "frequency": { - "description": "AC mains frequency (50Hz/60Hz)", - "value": "50-60 Hz", - "significance": "Low-frequency sine wave for timing", - "significance_score": 85.0 - }, - "voltage": { - "description": "AC mains voltage (120V/230V)", - "value": "120-230 V", - "significance": "High voltage for signal amplitude", - "significance_score": 90.0 - }, - "waveform": { - "description": "Pure sine waveform from grid", - "value": "Pure sine wave", - "significance": "Ideal sine wave for computation", - "significance_score": 95.0 - }, - "stability": { - "description": "Grid-regulated stability", - "value": "High stability", - "significance": "Stable sine wave reference", - "significance_score": 90.0 - }, - "continuity": { - "description": "Continuous power delivery", - "value": "Continuous", - "significance": "Always-available sine wave", - "significance_score": 95.0 - } - }, - "average_significance_score": 91.0 - } - - return analysis - - def analyze_ac_mains_applications(self) -> Dict: - """Analyze AC mains sine wave applications.""" - applications = { - "reference_sine_wave": { - "description": "Use AC mains as reference sine wave for topology", - "benefit": "Grid-stable sine wave reference", - "significance_score": 95.0 - }, - "frequency_synchronization": { - "description": "Synchronize topology to AC mains frequency", - "benefit": "Grid-frequency synchronization", - "significance_score": 90.0 - }, - "power_harmonics": { - "description": "Use AC mains harmonics for computation", - "benefit": "Harmonic-rich signal spectrum", - "significance_score": 85.0 - }, - "phase_modulation": { - "description": "Modulate phase relative to AC mains", - "benefit": "Phase-based computation", - "significance_score": 80.0 - }, - "amplitude_modulation": { - "description": "Modulate amplitude relative to AC mains", - "benefit": "Amplitude-based computation", - "significance_score": 75.0 - } - } - - return applications - - def calculate_ac_mains_impact(self) -> Dict: - """Calculate AC mains sine wave impact on computational expansion.""" - # AC mains multipliers - reference_sine_wave_multiplier = 1.5 # 1.5x from reference sine wave - frequency_synchronization_multiplier = 1.3 # 1.3x from frequency synchronization - power_harmonics_multiplier = 1.2 # 1.2x from harmonics - phase_modulation_multiplier = 1.2 # 1.2x from phase modulation - amplitude_modulation_multiplier = 1.1 # 1.1x from amplitude modulation - - # Calculate expanded capacity with AC mains sine wave - base_capacity = 1900 - current_all_device_signal_capacity = 21256253633.129837 - - # Apply AC mains multipliers - ac_mains_capacity = (current_all_device_signal_capacity * - reference_sine_wave_multiplier * - frequency_synchronization_multiplier * - power_harmonics_multiplier * - phase_modulation_multiplier * - amplitude_modulation_multiplier) - - ac_mains_expansion_factor = ac_mains_capacity / base_capacity - ac_mains_improvement_factor = ac_mains_capacity / current_all_device_signal_capacity - - calculation = { - "base_capacity": base_capacity, - "current_all_device_signal_capacity": current_all_device_signal_capacity, - "reference_sine_wave_multiplier": reference_sine_wave_multiplier, - "frequency_synchronization_multiplier": frequency_synchronization_multiplier, - "power_harmonics_multiplier": power_harmonics_multiplier, - "phase_modulation_multiplier": phase_modulation_multiplier, - "amplitude_modulation_multiplier": amplitude_modulation_multiplier, - "ac_mains_capacity": ac_mains_capacity, - "ac_mains_expansion_factor": ac_mains_expansion_factor, - "ac_mains_improvement_factor": ac_mains_improvement_factor, - "total_ac_mains_multiplier": (reference_sine_wave_multiplier * - frequency_synchronization_multiplier * - power_harmonics_multiplier * - phase_modulation_multiplier * - amplitude_modulation_multiplier) - } - - return calculation - - def integrate_ac_mains_sine_wave(self) -> Dict: - """Integrate AC mains sine wave into comprehensive analysis.""" - integration = { - "ac_mains_sine_wave_enabled": True, - "source": "AC Mains Power Cable to Wall Socket", - "frequency": "50Hz/60Hz", - "voltage": "120V/230V", - "applications": 5, - "math_categories_enhanced": [ - "Control Theory (frequency synchronization)", - "Information Theory (harmonics)", - "Thermodynamic (power delivery)", - "Physical Bind (AC mains)", - "Geometric Bind (sine wave topology)" - ], - "foundation_kernels_enhanced": [ - "F04", "F05", "F06", # Thermodynamic (power) - "F11", "F12" # Control Theory (synchronization) - ], - "sine_wave_inference": "AC mains provides natural sine wave reference" - } - - return integration - - def run_analysis(self) -> Dict: - """Run AC mains sine wave analysis.""" - print("=" * 60) - print("AC MAINS SINE WAVE INFERENCE ANALYSIS") - print("=" * 60) - - # Step 1: Analyze AC mains sine wave - print("\n[1/4] Analyzing AC mains as sine wave source...") - ac_mains_analysis = self.analyze_ac_mains_sine_wave() - print(f" AC Mains Characteristics: {len(ac_mains_analysis['ac_mains_characteristics'])}") - for characteristic, details in ac_mains_analysis['ac_mains_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze applications - print("[2/4] Analyzing AC mains sine wave applications...") - applications = self.analyze_ac_mains_applications() - print(f" Applications: {len(applications)}") - for application, details in applications.items(): - print(f" {application}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating AC mains sine wave impact...") - impact_calculation = self.calculate_ac_mains_impact() - print(f" Current All-Device Signal Capacity: {impact_calculation['current_all_device_signal_capacity']}") - print(f" AC Mains Capacity: {impact_calculation['ac_mains_capacity']}") - print(f" AC Mains Improvement Factor: {impact_calculation['ac_mains_improvement_factor']:.2f}x") - print(f" Total AC Mains Multiplier: {impact_calculation['total_ac_mains_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating AC mains sine wave...") - integration = self.integrate_ac_mains_sine_wave() - print(f" Source: {integration['source']}") - print(f" Frequency: {integration['frequency']}") - print(f" Voltage: {integration['voltage']}") - print(f" Applications: {integration['applications']}") - - print("\n" + "=" * 60) - print("AC MAINS SINE WAVE INFERENCE ANALYSIS COMPLETE") - print("=" * 60) - - return { - "ac_mains_analysis": ac_mains_analysis, - "applications_analysis": applications, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = ACMainsSineWave() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "ac_mains_sine_wave.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("AC MAINS SINE WAVE SUMMARY") - print("=" * 60) - print(f"Source: {results['integration']['source']}") - print(f"AC Mains Capacity: {results['impact_calculation']['ac_mains_capacity']}") - print(f"AC Mains Improvement Factor: {results['impact_calculation']['ac_mains_improvement_factor']:.2f}x") - print(f"Total AC Mains Multiplier: {results['impact_calculation']['total_ac_mains_multiplier']:.2f}x") diff --git a/5-Applications/scripts/activate_warden_phase.py b/5-Applications/scripts/activate_warden_phase.py deleted file mode 100644 index d3c445ea..00000000 --- a/5-Applications/scripts/activate_warden_phase.py +++ /dev/null @@ -1,234 +0,0 @@ -#!/usr/bin/env python3 -""" -Warden Phase Activation — Universal Field Proof Verification - -Transitions the OTOM Triumvirate from Builder phase to Warden phase: -- Builder: ✅ COMPLETE (UniversalField.lean implemented) -- Warden: 🔄 ACTIVATING (Proof verification required) -- Judge: ⏳ PENDING (Awaiting Warden completion) - -Issues P0 directive to swarm: Prove the three Universal Field theorems. -""" - -import sys -import json -import sqlite3 -from pathlib import Path -from datetime import datetime, timezone -from typing import Dict, Any, List - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import SwarmAPISystem - - -def activate_warden_phase() -> Dict[str, Any]: - """ - Activate Warden phase for Universal Field verification. - """ - - api = SwarmAPISystem() - timestamp = datetime.now(timezone.utc).isoformat() - - warden_directive = """ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ WARDEN PHASE ACTIVATION — P0 CRITICAL ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -PHASE TRANSITION: Builder → Warden -STATUS: ACTIVATING -TIMESTAMP: {timestamp} - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -BUILDER PHASE — ✅ COMPLETE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Deliverables Received: - ✅ UniversalField.lean — Φ_universal implementation - ✅ phiUniversalReciprocal — Reciprocal-log form - ✅ phiUniversalWeighted — Weighted-log form - ✅ lnQ16 — Natural logarithm for Q16_16 - ✅ Domain bindings (placeholders) — Newton, Maxwell, Schrödinger, Einstein, Landauer - ✅ Lake build — PASSED - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -WARDEN PHASE — 🔄 ACTIVATING -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -HARDWARE ASSIGNMENT: stark_trace & warden_valid (SUBTRACT clock) -CLOCK ACTION: SUBTRACT — Validate, verify, reverse-check - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -PROOF OBLIGATIONS — 3 THEOREMS REQUIRED -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -THEOREM 1: phiUniversalEquivalence -────────────────────────────────── -Statement: phiUniversalReciprocal = phiUniversalWeighted -Condition: hᵢ = 1/(lnNᵢ)² and pⱼ = 1/(lnNⱼ)² -Status: 🚧 CONJECTURE (currently 'sorry') -Action: PROVE or REFUTE - -THEOREM 2: phiUniversalNonNeg -───────────────────────────── -Statement: phiUniversalReciprocal ≥ 0 -Conditions: wᵢ, vⱼ ≥ 0 and Nᵢ, Mⱼ ≥ 2 -Status: 🚧 CONJECTURE (currently 'sorry') -Action: PROVE or REFUTE - -THEOREM 3: phiUniversalBounded -────────────────────────────── -Statement: phiUniversalReciprocal ≤ 1.0 -Conditions: Σw=1, Σv=1, hᵢ, pⱼ ≤ 1 -Status: 🚧 CONJECTURE (currently 'sorry') -Action: PROVE or REFUTE - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -VERIFICATION CRITERIA -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Warden must: - 1. Prove each theorem using Lean tactics (no 'sorry') - 2. OR refute with explicit counterexample - 3. Document proof strategy in theorem comments - 4. Verify no unsafe operations (no 'unsafe', no 'partial' without proof) - 5. Check Q16_16 numerical stability - -Success Criteria: - ✅ All 3 theorems proven → VALIDATION_PASSED → Judge phase - ❌ Any theorem refuted → VALIDATION_FAILED → Return to Builder - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -SWARM DIRECTIVE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -ALL AGENTS assigned to Warden role: - → Halt forward progress (Builder clock stopped) - → Activate proof verification (Warden clock started) - → Target: UniversalField.lean theorems - → Deadline: Until all 3 theorems resolved (proven or refuted) - -Dependencies: - → EQUATION #0.1 (η(χ)) — BLOCKED pending #0 - → EQUATION #0.2 (Φ_SW) — BLOCKED pending #0 - → EQUATION #0.3 (Φ_domain) — BLOCKED pending #0 - -Impact: - → All 4 equations depend on these proofs - → Bedrock law unification stalled until completion - → OTOM framework cannot proceed without validation - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -WARDEN COMMAND -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Target File: 0-Core-Formalism/lean/Semantics/Semantics/UniversalField.lean -Lines: 95-115 (theorem declarations with 'sorry') - -Replace: - theorem phiUniversalEquivalence ... := by - sorry - -With: - theorem phiUniversalEquivalence ... := by - -- Your proof here - simp [phiUniversalReciprocal, phiUniversalWeighted] - -- ... complete proof - -Or: - -- REFUTATION — Counterexample found - -- The theorem is false because ... - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -WARDEN: stark_trace & warden_valid — SUBTRACT clock — ACTIVATE - -The integrity of the entire OTOM framework depends on your verification. -Prove or refute. No 'sorry' shall remain. -""".format(timestamp=timestamp) - - # Store in priority_alerts - if api.conn: - cursor = api.conn.cursor() - - cursor.execute(""" - INSERT OR REPLACE INTO priority_alerts - (entity_id, subject, name, statement, proof_status, formal_status, - priority, requires_immediate_action, created_at) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - f'WARDEN_PHASE_ACTIVATION_{timestamp.replace(":", "_")}', - 'triumvirate_warden', - '[P0] Warden Phase Activation — Universal Field Verification', - warden_directive, - 'conjecture', - 'needs_proof', - 'P0', - True, - timestamp - )) - - api.conn.commit() - - return { - 'success': True, - 'phase': 'WARDEN_ACTIVATED', - 'timestamp': timestamp, - 'builder_status': 'COMPLETE', - 'warden_status': 'ACTIVATED', - 'judge_status': 'PENDING', - 'proof_obligations': 3, - 'theorems': [ - 'phiUniversalEquivalence', - 'phiUniversalNonNeg', - 'phiUniversalBounded' - ] - } - else: - return { - 'success': False, - 'error': 'Database not connected', - 'directive_printed': True - } - - -def main(): - print("="*70) - print("WARDEN PHASE ACTIVATION") - print("="*70) - print() - - result = activate_warden_phase() - - if result['success']: - print(f"[✓] {result['phase']}") - print() - print("Phase Transition:") - print(f" Builder: {result['builder_status']} ✅") - print(f" Warden: {result['warden_status']} 🔄") - print(f" Judge: {result['judge_status']} ⏳") - print() - print(f"Proof Obligations: {result['proof_obligations']} theorems") - print() - for i, thm in enumerate(result['theorems'], 1): - print(f" {i}. {thm}") - print() - print("="*70) - print("WARDEN DIRECTIVE ISSUED") - print() - print("Hardware: stark_trace & warden_valid (SUBTRACT clock)") - print("Action: Verify all 3 theorems — prove or refute") - print("Status: All 'sorry' must be eliminated") - print() - print("Impact:") - print(" • EQUATION #0.1-#0.3 BLOCKED until proofs complete") - print(" • Bedrock law unification stalled") - print(" • OTOM framework awaits validation") - print("="*70) - else: - print(f"[✗] Failed: {result.get('error', 'Unknown')}") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/adaptive_research_analysis.py b/5-Applications/scripts/adaptive_research_analysis.py deleted file mode 100644 index 9ab733fe..00000000 --- a/5-Applications/scripts/adaptive_research_analysis.py +++ /dev/null @@ -1,334 +0,0 @@ -#!/usr/bin/env python3 -# /// script -# requires-python = ">=3.10" -# dependencies = ["requests", "rich"] -# /// -""" -Adaptive Research Stack Analyzer -Uses Ollama Cloud API to run a three-pass analysis: - 1. Summarize - distill key ideas from core documents - 2. Cross-link - find connections across domains - 3. Critique - identify gaps, weak claims, missing proofs - -Usage: - uv run scripts/adaptive_research_analysis.py - uv run scripts/adaptive_research_analysis.py --model gemma3:12b --out /tmp/analysis.md -""" - -import argparse -import datetime -import json -import os -import sys -import textwrap -from pathlib import Path - -import requests -from rich.console import Console -from rich.markdown import Markdown -from rich.panel import Panel -from rich.progress import Progress, SpinnerColumn, TextColumn - -# --------------------------------------------------------------------------- -# Config -# --------------------------------------------------------------------------- -RESEARCH_ROOT = Path("/home/allaun/Documents/Research Stack") -API_BASE = "https://ollama.com/v1" -API_KEY = os.environ.get("OLLAMA_API_KEY", "") -# Model priority: cogito (Cognition 671B) → qwen3-next (80B) → gemma4 (31B) → deepseek-v4-flash -DEFAULT_MODEL = "cogito-2.1:671b" -FALLBACK_CHAIN = ["qwen3-next:80b", "gemma4:31b", "deepseek-v4-flash"] - -# Key documents to feed into the analysis (relative to RESEARCH_ROOT) -CORE_DOCS = [ - "README.md", - "CONCEPTS.md", - "ARCHITECTURE.md", - "SIGNAL_THEORY_COMPENDIUM.md", - "6-Documentation/EXPLANATION_FOR_HUMANS.md", - "6-Documentation/MATH_CORE.md", - "6-Documentation/VISION_NORTH_STAR.md", - "6-Documentation/GLOSSARY.md", - "6-Documentation/FIRST_PRINCIPLES_DAG.md", - "6-Documentation/FIELD_EQUATION_COMPARISON.md", - "6-Documentation/docs/SKEPTICISM_GRADIENT_REASSESSMENT_2026-04-29.md", - "6-Documentation/docs/CLAIM_STATE_AUDIT_2026-05-05.md", - "6-Documentation/docs/IMPLEMENTATION_ATTACK_ANALYSIS.md", - "6-Documentation/docs/ENE_RESEARCH_TOPIC_CANDIDATES.md", - "6-Documentation/docs/OTOM_V1_PAPER_STRUCTURE_AND_NEXT_GEN_SIMULATOR.md", - "6-Documentation/docs/stack_solidification_staging_manifest_2026-05-10.md", - "6-Documentation/docs/cross_domain_adaptation_numeric_review.md", - "6-Documentation/docs/BAD_MATH_CLEANUP_REPORT.md", -] - -DOMAIN_DIRS = { - "Core Formalism (Lean)": "0-Core-Formalism", - "Distributed Systems": "1-Distributed-Systems", - "Search Space": "2-Search-Space", - "Mathematical Models": "3-Mathematical-Models", - "Infrastructure / FPGA": "4-Infrastructure", - "Applications": "5-Applications", - "Documentation": "6-Documentation/docs", -} - -MAX_CHARS_PER_DOC = 8_000 # truncate individual docs (DeepSeek handles large context) -MAX_CONTEXT_CHARS = 120_000 # total context fed per LLM call - -console = Console() - - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def load_doc(path: Path, max_chars: int = MAX_CHARS_PER_DOC) -> str: - try: - text = path.read_text(errors="replace") - if len(text) > max_chars: - text = text[:max_chars] + f"\n\n[... truncated at {max_chars} chars ...]" - return text - except Exception as e: - return f"[Could not read {path}: {e}]" - - -def gather_context() -> str: - """Load core docs and first-file samples from each domain directory.""" - parts = [] - - # Core documents - for rel in CORE_DOCS: - p = RESEARCH_ROOT / rel - if p.exists(): - parts.append(f"\n\n---\n## FILE: {rel}\n\n{load_doc(p)}") - - # Domain directory samples — grab up to 3 .md files per domain - for domain, rel_dir in DOMAIN_DIRS.items(): - d = RESEARCH_ROOT / rel_dir - if not d.is_dir(): - continue - md_files = sorted(d.glob("*.md"))[:3] - for mdf in md_files: - rel_path = mdf.relative_to(RESEARCH_ROOT) - parts.append(f"\n\n---\n## FILE [{domain}]: {rel_path}\n\n{load_doc(mdf, 3000)}") - - combined = "\n".join(parts) - if len(combined) > MAX_CONTEXT_CHARS: - combined = combined[:MAX_CONTEXT_CHARS] + "\n\n[... context truncated ...]" - return combined - - -def chat(model: str, system: str, user: str, label: str, retries: int = 3) -> str: - """Call Ollama Cloud chat completions endpoint with retry + fallback.""" - import time - - headers = { - "Authorization": f"Bearer {API_KEY}", - "Content-Type": "application/json", - } - - models_to_try = [model] + [m for m in FALLBACK_CHAIN if m != model] - - for attempt_model in models_to_try: - payload = { - "model": attempt_model, - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": user}, - ], - "stream": False, - "options": {"temperature": 0.3, "num_predict": 8192}, - } - for attempt in range(1, retries + 1): - with Progress( - SpinnerColumn(), - TextColumn(f"[bold cyan]{label}[/bold cyan] (model: {attempt_model}, attempt {attempt}/{retries}) ..."), - transient=True, - console=console, - ) as progress: - progress.add_task("", total=None) - try: - resp = requests.post( - f"{API_BASE}/chat/completions", - headers=headers, - json=payload, - timeout=600, - ) - except requests.exceptions.Timeout: - console.print(f"[yellow]Timeout on attempt {attempt}, retrying...[/yellow]") - time.sleep(5 * attempt) - continue - - if resp.status_code == 200: - data = resp.json() - content = data["choices"][0]["message"]["content"] - # Strip ... reasoning blocks if present - import re - content = re.sub(r".*?", "", content, flags=re.DOTALL).strip() - return content - - error_body = resp.text[:300] - if "overloaded" in error_body.lower() or resp.status_code in (503, 429): - wait = 10 * attempt - console.print(f"[yellow]Server overloaded (attempt {attempt}), waiting {wait}s...[/yellow]") - time.sleep(wait) - elif resp.status_code == 500: - console.print(f"[yellow]500 error with {attempt_model}, trying next model...[/yellow]") - break # try fallback model - else: - console.print(f"[red]API error {resp.status_code}:[/red] {error_body}") - sys.exit(1) - - console.print(f"[yellow]Exhausted retries for {attempt_model}, trying fallback...[/yellow]") - - console.print("[red]All models failed. Aborting.[/red]") - sys.exit(1) - - -# --------------------------------------------------------------------------- -# Analysis passes -# --------------------------------------------------------------------------- - -SYSTEM_BASE = """\ -You are an expert research analyst reviewing a cutting-edge research stack called OTOM \ -(One-Time Operations on Manifolds / Ultra-low-power zero-decimal data routing). \ -The stack spans Lean 4 formal proofs, FPGA hardware, distributed systems, genomics, \ -astrophysics, signal theory, and compression mathematics. \ -Be precise, technical, and honest. Do NOT hallucinate citations. \ -When you are uncertain, say so explicitly.\ -""" - - -def pass_summarize(model: str, context: str) -> str: - system = SYSTEM_BASE + """ - -Your task: SUMMARIZE. -Produce a structured executive summary of this research stack covering: -1. Core thesis and central claims -2. Mathematical foundations (key equations, structures, proof techniques) -3. Hardware targets and implementation status -4. Applied domains (compression, genomics, astrophysics, etc.) -5. Current maturity level — what is proven vs speculative -Keep each section under 250 words. Use markdown headers. -""" - user = f"Here is the research stack content:\n\n{context}\n\nProduce the structured summary now." - return chat(model, system, user, "Pass 1: Summarize") - - -def pass_crosslink(model: str, context: str, summary: str) -> str: - system = SYSTEM_BASE + """ - -Your task: CROSS-DOMAIN LINKING. -Given the research content and the summary already produced, identify: -1. Non-obvious connections between domains (e.g. genomics ↔ topology, signal theory ↔ FPGA routing) -2. Concepts that appear in multiple domains under different names (unification opportunities) -3. Mathematical structures that bridge multiple layers of the stack -4. Any surprising overlaps with known external research (mention without fabricating citations) -Format as a markdown table + narrative explanation for each link found. -""" - user = f"Summary:\n{summary}\n\n---\nFull context:\n{context}\n\nIdentify cross-domain links now." - return chat(model, system, user, "Pass 2: Cross-link") - - -def pass_critique(model: str, context: str, summary: str) -> str: - system = SYSTEM_BASE + """ - -Your task: CRITIQUE AND GAP ANALYSIS. -Be rigorous and honest. Identify: -1. Claims that lack formal proof or empirical validation — flag each clearly -2. Mathematical steps that appear hand-wavy or unjustified -3. Research gaps: important questions the stack does not yet address -4. Risks: places where the stack's assumptions could break down -5. Recommended next experiments or proof targets -Be constructive but unflinching. A weak critique is useless. -Format with severity tags: [CRITICAL], [MODERATE], [MINOR]. -""" - user = f"Summary:\n{summary}\n\n---\nFull context:\n{context}\n\nDeliver the critique now." - return chat(model, system, user, "Pass 3: Critique") - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main(): - parser = argparse.ArgumentParser(description="Adaptive Research Stack Analyzer") - parser.add_argument("--model", default=DEFAULT_MODEL, - help=f"Ollama Cloud model to use (default: {DEFAULT_MODEL}, fallback chain: {FALLBACK_CHAIN})") - parser.add_argument("--out", default=None, - help="Output markdown file path (default: auto-named in Research Stack)") - parser.add_argument("--list-models", action="store_true", - help="List available Ollama Cloud models and exit") - args = parser.parse_args() - - if not API_KEY: - console.print("[red]Set OLLAMA_API_KEY before calling the Ollama Cloud API.[/red]") - sys.exit(1) - - if args.list_models: - resp = requests.get( - "https://ollama.com/api/tags", - headers={"Authorization": f"Bearer {API_KEY}"}, - timeout=30, - ) - models = [m["name"] for m in resp.json().get("models", [])] - console.print("\n".join(sorted(models))) - return - - console.rule("[bold green]Adaptive Research Stack Analyzer[/bold green]") - console.print(f"Model: [bold]{args.model}[/bold] Root: {RESEARCH_ROOT}\n") - - # --- Gather context - console.print("[dim]Gathering research documents...[/dim]") - context = gather_context() - char_count = len(context) - console.print(f"[dim]Context: {char_count:,} chars across core docs + domain samples[/dim]\n") - - # --- Three passes - summary = pass_summarize(args.model, context) - crosslink = pass_crosslink(args.model, context, summary) - critique = pass_critique(args.model, context, summary) - - # --- Assemble report - timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M") - report = f"""# Adaptive Research Analysis Report -*Generated: {timestamp} | Model: {args.model}* - ---- - -## Pass 1 — Executive Summary - -{summary} - ---- - -## Pass 2 — Cross-Domain Links - -{crosslink} - ---- - -## Pass 3 — Critique & Gap Analysis - -{critique} - ---- -*Analysis performed by `scripts/adaptive_research_analysis.py` using Ollama Cloud API.* -""" - - # --- Output - if args.out: - out_path = Path(args.out) - else: - date_str = datetime.datetime.now().strftime("%Y-%m-%d") - out_path = RESEARCH_ROOT / "6-Documentation" / "docs" / "reports" / f"adaptive_analysis_{date_str}.md" - out_path.parent.mkdir(parents=True, exist_ok=True) - - out_path.write_text(report) - - console.rule("[bold green]Analysis Complete[/bold green]") - console.print(f"\nReport saved to: [bold]{out_path}[/bold]\n") - console.print(Markdown(report[:6000] + ("\n\n*[report truncated for display — see file for full output]*" if len(report) > 6000 else ""))) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/adaptive_usb_fabric_controller.py b/5-Applications/scripts/adaptive_usb_fabric_controller.py deleted file mode 100644 index 5cf0e949..00000000 --- a/5-Applications/scripts/adaptive_usb_fabric_controller.py +++ /dev/null @@ -1,102 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os -import select -import sys -import termios -import time -import tty -import subprocess - -# Adaptive USB Fabric Controller -# Orchestrates the Tang Nano 9K FPGA and displays "Fabric Tension" telemetry. -# Derived from monitor_uart.py and HachimojiPipeline.lean - -BAUD_FLAGS = { - 9600: termios.B9600, - 115200: termios.B115200, -} - -def configure_uart(port, baud): - fd = os.open(port, os.O_RDWR | os.O_NOCTTY | os.O_NONBLOCK) - old = termios.tcgetattr(fd) - tty.setraw(fd) - attrs = termios.tcgetattr(fd) - baud_flag = BAUD_FLAGS[baud] - attrs[4] = baud_flag - attrs[5] = baud_flag - attrs[2] |= termios.CLOCAL | termios.CREAD - attrs[2] &= ~termios.CSTOPB - attrs[2] &= ~termios.PARENB - attrs[2] &= ~termios.CSIZE - attrs[2] |= termios.CS8 - attrs[6][termios.VMIN] = 0 - attrs[6][termios.VTIME] = 0 - termios.tcsetattr(fd, termios.TCSANOW, attrs) - return fd, old - -def flash_fpga(bitstream): - print(f"[*] Burning bitstream: {bitstream}") - cmd = ["openFPGALoader", "-b", "tangnano9k", bitstream] - try: - subprocess.run(cmd, check=True) - print("[+] Flash successful.") - except Exception as e: - print(f"[-] Flash failed: {e}") - -def render_tension(stress_val): - # stress_val is Q16.16 mapped to 0-65535 - width = 40 - level = int((stress_val / 65535.0) * width) - bar = "█" * level + "░" * (width - level) - return f" Tension: [{bar}] {stress_val:5d}" - -def decode_cmyk(state_byte): - states = {0: "K (Fast) ", 1: "C (Monitor)", 2: "M (Verify) ", 3: "Y (Prune) "} - return states.get(state_byte & 0x3, "UNKNOWN ") - -def main(): - parser = argparse.ArgumentParser(description="Adaptive USB Fabric Controller") - parser.add_argument("--port", default="/dev/ttyUSB1") - parser.add_argument("--baud", type=int, default=115200) - parser.add_argument("--burn", action="store_true", help="Flash the FPGA before monitoring") - parser.add_argument("--bitstream", default="4-Infrastructure/hardware/adaptive_fabric_connector.fs") - args = parser.parse_args() - - if args.burn: - flash_fpga(args.bitstream) - - try: - fd, old_attrs = configure_uart(args.port, args.baud) - except Exception as e: - print(f"[-] Could not open UART: {e}") - return 1 - - print(f"[*] Monitoring Adaptive Fabric on {args.port}...") - print(" Press Ctrl+C to stop.") - - try: - while True: - ready, _, _ = select.select([fd], [], [], 0.1) - if not ready: - continue - - data = os.read(fd, 1024) - for byte in data: - # Simple protocol: - # Bit 7-6: CMYK state - # Bit 5-0: Coarse stress level - state = (byte >> 6) & 0x3 - stress_coarse = (byte & 0x3F) << 10 # Scale to 16-bit - - sys.stdout.write(f"\r[FABRIC] State: {decode_cmyk(state)} | {render_tension(stress_coarse)}") - sys.stdout.flush() - - except KeyboardInterrupt: - print("\n[*] Stopping...") - finally: - termios.tcsetattr(fd, termios.TCSANOW, old_attrs) - os.close(fd) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/add_cognitive_equations.py b/5-Applications/scripts/add_cognitive_equations.py deleted file mode 100644 index 3448a373..00000000 --- a/5-Applications/scripts/add_cognitive_equations.py +++ /dev/null @@ -1,848 +0,0 @@ -#!/usr/bin/env python3 -""" -Add Cognitive Physics equations to the physics database. - -This script adds the 12 language prime equations and additional relevant equations -from the OTOM document to the physics_equations.db database. -""" - -import sqlite3 - -DB_PATH = "/dev/shm/physics_equations.db" -DOMAIN_ID = 51 # Cognitive Physics - -equations = [ - { - "eq_number": 738, - "title": "NSM Semantic Primes Explication", - "significance": "Universal semantic decomposition using 64 irreducible primes. Foundation for language compression and cognitive load analysis.", - "latex_formula": r"\text{Explication}(c) = \text{Compose}(\text{Primes}_{64}, \text{Grammar}_{\text{universal}})", - "description": "The NSM framework defines 64 universal semantic primes with combinatorial syntax for explication of any concept." - }, - { - "eq_number": 739, - "title": "Cognitive Load Matrix (Invariant-Enhanced)", - "significance": "8-dimensional cognitive load model with invariant preservation. Critical for assessing processing overhead in semantic compression.", - "latex_formula": r"L_{\text{total}} = \lambda_I \hat{l}_I + \lambda_E \hat{l}_E - \lambda_G \hat{l}_G + \lambda_R \hat{l}_R + \lambda_M \hat{l}_M + \lambda_{\text{inv}} \hat{l}_{\text{inv}} + \lambda_{\text{traj}} \hat{l}_{\text{traj}} + \lambda_{\text{aci}} \hat{l}_{\text{aci}}", - "description": "Total cognitive load with intrinsic, extraneous, germane, routing, memory, invariant, trajectory, and ACI components." - }, - { - "eq_number": 740, - "title": "Evolutionary Operator (Universal)", - "significance": "Conserved operator frozen across 120 Myr evolution. Model for compression operators that remain stable across contexts.", - "latex_formula": r"\text{Phenotype}(x, t) = \Psi_E [ \text{Genotype}(x) \times \text{Regulatory\_State}(t) ]", - "description": "The operator Ψ_E is conserved; only the regulatory state changes. Analogous to compression operators in semantic systems." - }, - { - "eq_number": 741, - "title": "Hutter Prize Compression Equation", - "significance": "Weighted compression metric with decoder and resource penalties. Foundation for compression efficiency optimization.", - "latex_formula": r"C = (0.4 \cdot C_{\text{comp}} + 0.35 \cdot C_{\text{phys}} + 0.25 \cdot C_{\text{geom}}) \times \left(\frac{S}{G + F}\right)", - "description": "Compression score with computational, physical, and geometric components weighted by size over grammar+features." - }, - { - "eq_number": 742, - "title": "Semantic Compression Operator", - "significance": "Language-specific compression operator using NSM primes as conserved basis. Core of semantic-aware compression.", - "latex_formula": r"\text{Compressed}(x) = \Psi_S [ \text{Primes}_{64} \times \text{Context}(x) ]", - "description": "Ψ_S is the learned semantic compression operator; Primes_64 are the conserved basis; Context(x) is linguistic context." - }, - { - "eq_number": 743, - "title": "Prime-to-Byte Mapping", - "significance": "Maps semantic primes to compression primitives. Enables semantic-aware byte-level optimization.", - "latex_formula": r"p_i \rightarrow \text{Primitive}_i = \{ \text{pattern}, \text{weight}, \text{context\_mask} \}", - "description": "Each prime maps to a pattern, compression weight, and context mask for byte-level encoding." - }, - { - "eq_number": 744, - "title": "Context as Cognitive Load Function", - "significance": "Cognitive load determines regulatory state for compression. Links processing overhead to prime activation.", - "latex_formula": r"\text{Context}(x) = f(L_{\text{total}}(x), L_{\text{inv}}(x, \mathcal{I}_{\text{NSM}}))", - "description": "Context is a function of total cognitive load and prime-specific invariant load." - }, - { - "eq_number": 745, - "title": "Gap Adaptation Equation", - "significance": "Evolutionary fracking principle: gap width controls coupling strength. Adaptive prime filtering based on load.", - "latex_formula": r"\text{Gap}(x) = \text{Gap}_{\text{max}} \cdot \left(1 - \frac{L_{\text{total}}(x)}{L_{\text{max}}}\right)", - "description": "Gap narrows under high load (stress response, critical primes only) and widens under low load (relaxed processing)." - }, - { - "eq_number": 746, - "title": "Unified Semantic Compression Equation", - "significance": "Complete integration of primes, cognitive load, and gap adaptation. Master equation for semantic compression.", - "latex_formula": r"\text{Compressed}(x) = \Psi_S [ \text{Primes}_{64} \times \text{Context}(L_{\text{total}}(x)) ] \times \text{Gap}(L_{\text{total}}(x))", - "description": "Combines semantic operator, prime basis, cognitive load context, and gap-dependent filtering." - }, - { - "eq_number": 747, - "title": "Gap-Dependent Prime Activation", - "significance": "Threshold function for prime activation based on gap width. Implements stress response in compression.", - "latex_formula": r"\mathbb{1}[\text{active}(p_i, \text{Gap}(x))] = \begin{cases} 1 & \text{if } \text{severity}(i) \geq \theta_{\text{gap}}(\text{Gap}(x)) \\ 0 & \text{otherwise} \end{cases}", - "description": "Indicator function for prime activation based on severity threshold that varies with gap width." - }, - { - "eq_number": 748, - "title": "Prime Compression Matrix", - "significance": "64×64 matrix of prime weights and cross-correlations. Encodes conserved topology of semantic relationships.", - "latex_formula": r"M_P = \begin{bmatrix} w_1 & c_{1,1} & c_{1,2} & \cdots & c_{1,64} \\ w_2 & c_{2,1} & c_{2,2} & \cdots & c_{2,64} \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ w_{64} & c_{64,1} & c_{64,2} & \cdots & c_{64,64} \end{bmatrix}", - "description": "Matrix of severity weights and cross-correlations between semantic primes. Topology is conserved across languages." - }, - { - "eq_number": 749, - "title": "Matrix-Vector Compression", - "significance": "Linear algebra formulation of semantic compression with gap modulation. Efficient implementation target.", - "latex_formula": r"\text{Compressed}(x) = M_P \cdot \vec{v}(x) \cdot \text{Gap}(L_{\text{total}}(x))", - "description": "Matrix multiplication of prime matrix with activation vector, modulated by gap width." - }, - # Additional distilled equations - { - "eq_number": 750, - "title": "Invariant Load with Prime Activation", - "significance": "Modified invariant load that only counts active primes. Reduces penalty under high-stress conditions.", - "latex_formula": r"L_{\text{inv}}^{\text{active}}(x, \mathcal{I}_{\text{NSM}}) = \sum_{i \in \mathcal{I}_{\text{NSM}}} w_i \cdot \mathbb{1}[\text{broken}(i, x)] \cdot \text{severity}(i) \cdot \mathbb{1}[\text{active}(p_i, \text{Gap}(x))]", - "description": "Invariant load only counts primes that are both broken and active given current gap width." - }, - { - "eq_number": 751, - "title": "Gap Threshold Function", - "significance": "Piecewise threshold mapping gap width to severity cutoff. Implements discrete stress response levels.", - "latex_formula": r"\theta_{\text{gap}}(\text{Gap}) = \begin{cases} \infty & \text{if Gap} < 0.2 \text{ (narrow, stress)} \\ 1.0 & \text{if } 0.2 \leq \text{Gap} < 0.5 \text{ (moderate)} \\ 0.5 & \text{if } 0.5 \leq \text{Gap} < 0.8 \text{ (relaxed)} \\ 0.1 & \text{if Gap} \geq 0.8 \text{ (wide, all primes)} \end{cases}", - "description": "Severity threshold as function of gap width, defining four operating regimes." - }, - { - "eq_number": 752, - "title": "Hutter Prize Penalty with Invariants", - "significance": "Extended Hutter Prize penalty including invariant preservation cost. Tradeoff between compression and semantic fidelity.", - "latex_formula": r"\phi_{\text{HP-NSM}} = \phi(x) + \alpha_{\text{Comp}} \cdot \text{Compression}_{\text{NSM}} + \alpha_{\text{Dec}} \cdot \text{Decoder}_{\text{NSM}} + \alpha_{\text{Res}} \cdot \text{Resource}_{\text{NSM}} + \alpha_{\text{Inv}} \cdot L_{\text{inv}}^{\text{active}}", - "description": "Penalty function with compression, decoder, resource, and invariant preservation components." - }, - { - "eq_number": 753, - "title": "Gap Adaptation Dynamics", - "significance": "Gradient descent dynamics for gap adaptation. Ensures convergence to optimal load-balanced state.", - "latex_formula": r"\frac{d\text{Gap}}{dt} = -\nabla_{\text{Gap}} L_{\text{total}}(x)", - "description": "Gap evolves to minimize cognitive load via gradient descent." - }, - { - "eq_number": 754, - "title": "Prime Conservation Theorem (Spectral Entropy Bound)", - "significance": "Compression ratio bounded by spectral entropy of prime activation under conserved operator.", - "latex_formula": r"H_{\Psi_S}(l) = -\sum_{i=1}^{64} p_l(i) \log_2 p_{\Psi_S}(i) \leq H_{\text{uniform}}(l)", - "description": "Spectral entropy bound for compression ratio under conserved semantic operator." - }, - { - "eq_number": 755, - "title": "Invariant Preservation Theorem", - "significance": "Critical invariants (severity=∞) preserved regardless of gap width. Hard constraint on compression.", - "latex_formula": r"\text{Compression}_{\text{max}} = \max_{\Psi_S} \text{Compression}(\Psi_S) \quad \text{s.t.} \quad \forall i \in \mathcal{I}_{\text{critical}}, \neg \text{broken}(i, x)", - "description": "Maximum compression subject to critical invariant preservation constraint." - }, - { - "eq_number": 756, - "title": "Matrix Evolution Learning Rule", - "significance": "Gradient-based learning of prime matrix while conserving topology. Analogous to evolutionary mutation.", - "latex_formula": r"M_P(t+1) = M_P(t) + \eta \cdot \nabla_{M_P} \text{Compression}_{\text{NSM}}", - "description": "Prime matrix evolves via gradient descent on compression while preserving topology." - }, - { - "eq_number": 757, - "title": "Cross-Linguistic Compression Equation", - "significance": "Unified compression across languages with language-specific gap functions. Enables transfer learning.", - "latex_formula": r"\text{Compressed}(x_l) = \Psi_S [ \text{Primes}_{64} \times \text{Context}_l(L_{\text{total}}(x_l)) ] \times \text{Gap}_l(L_{\text{total}}(x_l))", - "description": "Compression for language l using conserved operator Ψ_S with language-specific context and gap functions." - }, - { - "eq_number": 758, - "title": "Language-Specific Gap Function", - "significance": "Gap function parameterized by language complexity. Accounts for morphological and syntactic differences.", - "latex_formula": r"\text{Gap}_l(x) = g_l(L_{\text{total}}(x)) = \text{Gap}_{\text{max}, l} \cdot \left(1 - \frac{L_{\text{total}}(x)}{L_{\text{max}, l}}\right)", - "description": "Language-specific gap adaptation with language-dependent maximum gap and load threshold." - }, - # 0-AVMR Equations (Algebraic Vector Mountain Range) - { - "eq_number": 759, - "title": "0-AVMR: Square Shell Identity", - "significance": "Foundational partition of natural numbers into discrete shells indexed by k = floor(sqrt(n)). Basis for hierarchical vector aggregation.", - "latex_formula": r"a + b = 2k + 1 \quad \text{where} \quad k = \lfloor \sqrt{n} \rfloor", - "description": "Shell identity partitions naturals into shells where each shell k contains numbers in [k², (k+1)²)." - }, - { - "eq_number": 760, - "title": "0-AVMR: Tip Coordinate Map", - "significance": "Injective coordinate system on each shell mapping (a,b) to (product, difference). Enables vector aggregation with discriminant invariant.", - "latex_formula": r"\text{Tip}(n) = (a \cdot b, a - b)", - "description": "Tip map provides coordinate system with discriminant invariant: (a-b)² + 4ab = (a+b)²." - }, - { - "eq_number": 761, - "title": "0-AVMR: Interaction Score", - "significance": "Additive decomposition of interaction into mass, polarity, and spectral components. Basis for vector interaction terms.", - "latex_formula": r"J = m + p + s", - "description": "Interaction score as sum of mass_term, polarity_term, and spectral_overlap components." - }, - { - "eq_number": 762, - "title": "0-AVMR: Genetic Transduction", - "significance": "Composition mapping temporal-color encoding to genetic codon space. Theoretical bridge between temporal patterns and biological encoding.", - "latex_formula": r"\Phi_{\text{trans}} = \text{GeneticCode}(\text{Codon}(\Phi_{\text{time\_color}}(n)))", - "description": "Transduction from temporal-color space through codon space to genetic code output." - }, - { - "eq_number": 763, - "title": "0-AVMR: Genetic Entropy Bound", - "significance": "Information capacity bound for genetic coding system. H ≈ 4.2 bits bounded by log2(64) = 6 bits (codon space).", - "latex_formula": r"H_{\text{genetic}} \approx 4.2 \text{ bits}, \quad \log_2(20) \leq H_{\text{genetic}} \leq \log_2(64)", - "description": "Genetic entropy measured from codon usage frequencies, bounded by codon space capacity." - }, - # 0-AMMR Equations (Analytic Mathematical Model of Reality) - { - "eq_number": 764, - "title": "0-AMMR: Shell Partition of Computation", - "significance": "Maps any computation (via Gödel encoding) to discrete shell structure. Provides hierarchical organization for ENE operations.", - "latex_formula": r"\text{Partition}(n) = k = \lfloor \sqrt{n} \rfloor", - "description": "Shell partition function maps natural numbers (computation encodings) to shell indices." - }, - { - "eq_number": 765, - "title": "0-AMMR: Shell Coordinate System", - "significance": "Tip map as injective coordinate system on each shell. Enables unique addressing within computational shells.", - "latex_formula": r"\text{Coord}_k(a,b) = (a \cdot b, a - b) \quad \text{with} \quad a + b = 2k + 1", - "description": "Coordinate system on shell k with injectivity guaranteed by fixed sum constraint." - }, - { - "eq_number": 766, - "title": "0-AMMR: Additive Shell Interaction", - "significance": "Interactions between shells decompose additively. Basis for multi-shell computation in ENE.", - "latex_formula": r"J_{\text{inter-shell}} = \sum_{i} J_i = \sum_{i} (m_i + p_i + s_i)", - "description": "Shell interactions sum additively across components and shells." - }, - { - "eq_number": 767, - "title": "0-AMMR: Temporal-Genetic Transduction", - "significance": "Temporal patterns transduced to genetic encoding. Potential for time-aware semantic compression in ENE.", - "latex_formula": r"\Phi_{\text{temporal}} \rightarrow \text{Codon} \rightarrow \text{GeneticCode}", - "description": "Three-stage transduction from temporal encoding through codon space to genetic output." - }, - { - "eq_number": 768, - "title": "0-AMMR: Information Capacity Bound", - "significance": "Genetic entropy bounds system information capacity. Provides theoretical limit for ENE compression.", - "latex_formula": r"H_{\text{system}} \leq \log_2(64) = 6 \text{ bits}", - "description": "System information capacity bounded by genetic codon space entropy." - }, - { - "eq_number": 769, - "title": "0-AMMR: RG Flow Shell Preservation", - "significance": "Renormalization group flow preserves shell structure under scale transformations. Critical for ENE scale-invariant operations.", - "latex_formula": r"\sigma_q = 1.0 + 0.35 \cdot \text{coherence} - 8.0 \cdot \text{volatility}", - "description": "RG flow equation governing shell preservation under scale transformations." - }, - # Graph Native Equations - { - "eq_number": 770, - "title": "Graph Laplacian Spectral Decomposition", - "significance": "Spectral decomposition of graph Laplacian for graph-native computation. Enables eigenvector-based graph operations.", - "latex_formula": r"L = D - A, \quad L \phi_k = \lambda_k \phi_k", - "description": "Graph Laplacian L = D - A with eigenvectors φ_k and eigenvalues λ_k for spectral analysis." - }, - { - "eq_number": 771, - "title": "Graph Attention Mechanism", - "significance": "Attention-based message passing for graph neural networks. Enables context-aware graph operations.", - "latex_formula": r"\alpha_{ij} = \frac{\exp(\text{LeakyReLU}(a^T [Wh_i \| Wh_j]))}{\sum_{k \in \mathcal{N}(i)} \exp(\text{LeakyReLU}(a^T [Wh_i \| Wh_k]))}", - "description": "Graph attention coefficient α_ij for node i attending to node j." - }, - { - "eq_number": 772, - "title": "Graph Convolution", - "significance": "Spectral graph convolution for graph-native processing. Enables convolution operations on graph-structured data.", - "latex_formula": r"H^{(l+1)} = \sigma(\tilde{D}^{-1/2} \tilde{A} \tilde{D}^{-1/2} H^{(l)} W^{(l)})", - "description": "Graph convolution with normalized adjacency matrix and weight matrix." - }, - # WGSL/WebGPU Equations - { - "eq_number": 773, - "title": "WGSL Vector Swizzle Operation", - "significance": "GPU vector component swizzling for efficient parallel processing. Enables flexible vector manipulation on GPU.", - "latex_formula": r"\text{swizzle}(v, \text{mask}) = v_{\text{mask}}", - "description": "Vector swizzle operation extracting components by mask (e.g., .xyz, .rgba)." - }, - { - "eq_number": 774, - "title": "WGSL Workgroup Synchronization", - "significance": "Barrier synchronization for GPU workgroups. Ensures correct parallel execution order.", - "latex_formula": r"\text{workgroupBarrier}() \implies \forall i,j \in \text{workgroup}, \text{order}(i, j) \text{ preserved}", - "description": "Workgroup barrier ensures all threads reach barrier before proceeding." - }, - { - "eq_number": 775, - "title": "WGSL Shared Memory Reduction", - "significance": "Parallel reduction in GPU shared memory. Enables efficient aggregation across workgroup.", - "latex_formula": r"\text{reduce}(S) = \sum_{i=0}^{N-1} S_i \quad \text{with} \quad O(\log N) \text{ steps}", - "description": "Parallel reduction in shared memory with logarithmic step complexity." - }, - # Vector Appending Equations - { - "eq_number": 776, - "title": "Vector Append Operation", - "significance": "Dynamic vector appending for incremental processing. Enables streaming vector operations.", - "latex_formula": r"v \oplus x = [v_1, v_2, \ldots, v_n, x]", - "description": "Vector append operation adding element x to end of vector v." - }, - { - "eq_number": 777, - "title": "Vector Concatenation", - "significance": "Efficient vector concatenation for batch processing. Enables combining multiple vectors.", - "latex_formula": r"v \parallel w = [v_1, \ldots, v_n, w_1, \ldots, w_m]", - "description": "Vector concatenation combining v and w into single vector." - }, - { - "eq_number": 778, - "title": "Vector Append with Capacity Growth", - "significance": "Amortized O(1) append with geometric capacity growth. Optimizes memory allocation.", - "latex_formula": r"\text{capacity}_{\text{new}} = \text{capacity}_{\text{old}} \times \phi, \quad \phi \approx 1.5-2.0", - "description": "Geometric capacity growth for amortized O(1) append operations." - }, - { - "eq_number": 779, - "title": "Graph Vector Append", - "significance": "Appending vectors to graph nodes for incremental graph updates. Enables dynamic graph processing.", - "latex_formula": r"G[v] \leftarrow G[v] \oplus x, \quad \text{update adjacency if needed}", - "description": "Append vector x to node v in graph G, updating adjacency if structure changes." - }, - # Vector Database Equations - { - "eq_number": 780, - "title": "HNSW Hierarchical Navigable Small World", - "significance": "Graph-based approximate nearest neighbor search. Combines probability skip list with navigable small world graphs for fast vector similarity search.", - "latex_formula": r"\text{HNSW}(V) = \bigcup_{l=0}^{L_m} G_l(V), \quad G_0 \subset G_1 \subset \dots \subset G_{L_m}", - "description": "Hierarchical graph structure where each layer is a proximity graph with increasing connectivity." - }, - { - "eq_number": 781, - "title": "Approximate Nearest Neighbor Search", - "significance": "Efficient vector search with slight accuracy penalty for massive speedup. Uses HNSW for O(log N) search complexity.", - "latex_formula": r"\text{ANN}(q, V, k) = \{v_1, \ldots, v_k\} \approx \text{k-NN}(q, V)", - "description": "Approximate k-nearest neighbors with recall-precision tradeoff." - }, - { - "eq_number": 782, - "title": "Proximity Graph Edge Probability", - "significance": "Probability of edge creation based on vector proximity in HNSW. Controls graph connectivity.", - "latex_formula": r"P(e_{ij}) = \exp(-\lambda \|v_i - v_j\|^2)", - "description": "Edge probability decreases exponentially with distance between vectors." - }, - # Graph Database Equations - { - "eq_number": 783, - "title": "Property Graph Traversal", - "significance": "Efficient traversal of property graphs with nodes, edges, and properties. Basis for graph query languages like Cypher and GSQL.", - "latex_formula": r"\text{Traverse}(G, v, d) = \{u \in V \mid \text{dist}(v, u) \leq d\}", - "description": "Depth-limited traversal from starting node v in graph G." - }, - { - "eq_number": 784, - "title": "Graph Pattern Matching", - "significance": "Pattern-based query in graph databases. Enables complex relationship queries like Cypher's MATCH clause.", - "latex_formula": r"\text{Match}(G, P) = \{(v_1, \ldots, v_k) \mid P(v_1, \ldots, v_k) \text{ holds in } G\}", - "description": "Find all subgraphs in G matching pattern P." - }, - { - "eq_number": 785, - "title": "Multi-Model Query Integration", - "significance": "Unified querying across document, key-value, and graph models. Enables ArangoDB-style multi-model databases.", - "latex_formula": r"Q_{\text{multi}} = Q_{\text{doc}} \cup Q_{\text{kv}} \cup Q_{\text{graph}}", - "description": "Combine queries across different data models in single execution." - }, - { - "eq_number": 786, - "title": "Parallel Graph Processing", - "significance": "Native parallel graph engine for real-time analytics. TigerGraph-style parallel processing for massive graphs.", - "latex_formula": r"\text{ParallelProcess}(G, f) = \bigoplus_{v \in V} f(v, N(v)) \quad \text{in parallel}", - "description": "Apply function f to all nodes and their neighborhoods in parallel." - }, - # Shockwave/Phonon/Photon Equations - { - "eq_number": 787, - "title": "Shockwave Alignment and Relaxation", - "significance": "Quasi-charged cells align under shockwave, propagate charge symmetrically, then dissipate and relax. Four-phase cycle: anisotropic → shock_aligned → discharge → relaxed.", - "latex_formula": r"\text{ShockCell} = (\theta, q, \ell, \tau, \rho, \kappa), \quad \text{phase}: \text{anisotropic} \to \text{aligned} \to \text{discharge} \to \text{relaxed}", - "description": "Shockwave forces orthogonal cells into alignment, enabling symmetric charge propagation, followed by phonon dissipation and relaxation." - }, - { - "eq_number": 788, - "title": "Phonon Force Law", - "significance": "Phonon correlation structure for self-healing. Force decays exponentially with Manhattan distance and oscillates with coherence period.", - "latex_formula": r"F(c_i, c_j) = \exp\left(-\frac{d_M(c_i, c_j)}{127}\right) \cdot \cos\left(\frac{2\pi \cdot d_M(c_i, c_j)}{127}\right)", - "description": "Phonon force law with 127-step coherence period for Cartesian coordinate space." - }, - { - "eq_number": 789, - "title": "Cartesian Phonon Prime Integration", - "significance": "256×256 Cartesian coordinate space with 16-bit fixed addressing and Manhattan distance metric for hardware-efficient phonon transport.", - "latex_formula": r"\text{toAddr}(x, y) = 256y + x, \quad d_M((x_1, y_1), (x_2, y_2)) = |x_1 - x_2| + |y_1 - y_2|", - "description": "Cartesian phonon language with fixed-width addressing and Manhattan distance." - }, - { - "eq_number": 790, - "title": "Phonon Load Dissipation", - "significance": "Phonon energy dissipates through discrete steps. Full dissipation returns cell to relaxed zero-load state.", - "latex_formula": r"\ell_{\text{new}} = \ell_{\text{old}} - \delta, \quad \ell_{\text{relaxed}} = 0 \text{ when } \delta = \ell_{\text{old}}", - "description": "Discrete phonon load dissipation leading to relaxed state." - }, - { - "eq_number": 791, - "title": "Shock Aligned Contact Energy", - "significance": "Positive aligned charge and contact coupling produce positive contact energy during shock-forced propagation.", - "latex_formula": r"E_{\text{contact}}(a, b) = q_a q_b + \kappa_a \kappa_b + \ell_a + \ell_b", - "description": "Local energetic version of shock-forced propagation with charge, coupling, and phonon load." - }, - { - "eq_number": 792, - "title": "Photonic Spectral Witness", - "significance": "Spectral amplitudes encoded into optical mode amplitudes. Photon-count distribution recovers scalar observable Ω[u].", - "latex_formula": r"\text{spectral amplitudes} \to \text{optical mode amplitudes} \to \text{photon-count distribution} \to \hat{\Omega}[u]", - "description": "Photonic witness grammar for empirical spectral sampling." - }, - { - "eq_number": 793, - "title": "Pair-Bonded Shockwave Propagation", - "significance": "Two quasi-charged cells form temporary bond during shock alignment, enabling symmetric charge transfer before relaxation.", - "latex_formula": r"(a, b)_{\text{bonded}} = \text{CellsShockAligned}(a, b) \land \text{CellCharged}(a) \land \text{CellCharged}(b)", - "description": "Pair-bonded state for symmetric charge propagation during shock alignment." - }, - { - "eq_number": 794, - "title": "Phonon-Mediated Information Transport", - "significance": "Information encoded in phonon packets propagates through lattice. Lossy transport preserves spectral structure not exact state.", - "latex_formula": r"I_{\text{out}} = I_{\text{in}} \cdot \exp(-L_{\text{throat}}) \cdot R_{\text{repair}}", - "description": "Lossy phonon transport with torsional corridor attenuation and repair." - }, - # GCCL Equations - { - "eq_number": 795, - "title": "ΔφγKλ Compression Law", - "significance": "Compression-domain instance of GCCL with separate fields for transform pressure (γ) and cost paid (K). Corrected from Δφγλ which overloaded γ.", - "latex_formula": r"\Delta\phi\gamma K\lambda = (\Delta, \phi, \gamma, K, \lambda)", - "description": "Five-tuple compression law: residual delta, invariant phi, transform pressure gamma, cost paid K, scale band lambda." - }, - { - "eq_number": 796, - "title": "Goxel Scalar Sub-Manifold", - "significance": "N-space shape inhabiting geometric volume, expressed as bounded scalar sub-manifold. Admitted only through declared projection, audit, and receipt gates.", - "latex_formula": r"G = \{ v \in \mathbb{R}^n : \Phi_G(v) \le \text{iso} \}", - "description": "Goxel as geometric volume element with scalar field constraint." - }, - { - "eq_number": 797, - "title": "Model Genome Encoding", - "significance": "Compact generative encoding of model family with codon→gene→chromosome→genome→phenotype hierarchy.", - "latex_formula": r"\text{Genome} = \{\text{codon} \to \text{gene} \to \text{chromosome} \to \text{genome} \to \text{phenotype}\}", - "description": "Hierarchical model genome encoding for evolvable model families." - }, - { - "eq_number": 798, - "title": "Kinetic Operation Token (KOT)", - "significance": "Accounting layer for action cost. Every transformation pays and leaves a trace. Prevents free transformations.", - "latex_formula": r"\text{KOT}(action) = (\text{authorizer}, \text{cost}, \text{budget}, \text{trace}, \text{receipt})", - "description": "KOT accounting for transformation cost and authorization." - }, - { - "eq_number": 799, - "title": "Bounded Lawful Surface", - "significance": "Set of transitions and phenotypes that can be expressed, replayed, checked, budgeted, and receipted under declared constraints.", - "latex_formula": r"\text{BLS}(\text{GCCL}, B, I, R, K, \Lambda)", - "description": "Lawful surface bounded by budget, invariants, residuals, cost, and scale." - }, - { - "eq_number": 800, - "title": "Genotype-Phenotype Split", - "significance": "Separation of internal encoding from outward expression. Prevents projection from being mistaken for source object.", - "latex_formula": r"\text{genotype} \neq \text{phenotype}, \quad \text{projection} \neq \text{proof}", - "description": "GCL genotype/phenotype separation for identity preservation." - }, - { - "eq_number": 801, - "title": "Mixture Primitive Combination", - "significance": "Multiple coding families (DNA, codons, proteins, ambiguity, etc.) can be mixed only under explicit decoder, residual, KOT, scale, projection, and receipt rules.", - "latex_formula": r"\text{Primitive} = (\text{Alphabet}, \text{Arity}, \text{Direction}, \text{Ambiguity}, \text{Transform}, \text{Residual}, \text{Cost}, \text{Receipt})", - "description": "Canonical wrapper for GCCL mixture primitives." - }, - { - "eq_number": 802, - "title": "Layered Mountain Model", - "significance": "GCCL sits over layered state mountains: NUVMAP (address), AVMR (vector evolution), AMMR (commit history), O-AMMR (orthogonal projection), GCCL-Rep (transition rope).", - "latex_formula": r"\text{Stack} = (\text{NUVMAP}, \text{AVMR}, \text{AMMR}, \text{O-AMMR}, \text{GCCL-Rep})", - "description": "Layered mountain verification for multi-projected transitions." - }, - # Model/Binding Equations - { - "eq_number": 803, - "title": "Wavefront Emission", - "significance": "State changes emit wavefronts that propagate through resonant field with amplitude, frequency, phase, position, and decay.", - "latex_formula": r"W(t, x) = A \cdot e^{-\gamma d} \cdot \cos(\omega d - \phi), \quad d = |x - x_0| - v(t - t_0)", - "description": "Wavefront propagation for resonant field changes with decay and oscillation." - }, - { - "eq_number": 804, - "title": "MOIM Behavioral Fingerprint", - "significance": "Objects become behavioral points across identity, conservation, transformation, scaling, and dynamics axes.", - "latex_formula": r"\text{BehavioralPoint}(object) = \text{fingerprint}(\text{identity}, \text{conservation}, \text{transformation}, \text{scaling}, \text{dynamics})", - "description": "Meta-ontological inversion: object behavior determines routing." - }, - { - "eq_number": 805, - "title": "Universal Binding Manifold", - "significance": "Binding affinity surface for conceptual relationships with energy-based binding strength.", - "latex_formula": r"E_{\text{bind}}(A, B) = -\alpha \cdot \text{similarity}(A, B) + \beta \cdot \text{distance}(A, B)", - "description": "Energy-based binding manifold for conceptual relationships." - }, - { - "eq_number": 806, - "title": "Info Bottleneck Principle", - "significance": "Optimal neural compression: minimize mutual information with input while maximizing with output.", - "latex_formula": r"\min I(X;Z) - \beta I(Z;Y)", - "description": "Information bottleneck for optimal compression." - }, - { - "eq_number": 807, - "title": "Free Energy Principle", - "significance": "Variational self-organization invariant: systems minimize free energy by minimizing surprise.", - "latex_formula": r"F = \mathbb{E}_q[\ln q - \ln p]", - "description": "Variational free energy for cognitive routing." - }, - { - "eq_number": 808, - "title": "Predictive Coding", - "significance": "Hierarchical prediction error update: predictions drive learning and inference.", - "latex_formula": r"\frac{dr}{dt} \propto U^T(I - f(Ur))", - "description": "Predictive coding for compression and routing." - }, - { - "eq_number": 809, - "title": "Onsager Reciprocity", - "significance": "Coupled transport symmetry law: cross-coupling coefficients are symmetric.", - "latex_formula": r"L_{ij} = L_{ji}", - "description": "Symmetry constraints on transport processes." - }, - { - "eq_number": 810, - "title": "Jarzynski Equality", - "significance": "Non-equilibrium work-extraction relation connects work fluctuations to free energy difference.", - "latex_formula": r"\langle e^{-\beta W} \rangle = e^{-\beta \Delta F}", - "description": "Non-equilibrium information physics." - }, - { - "eq_number": 811, - "title": "DNA Linking Number", - "significance": "Topological constraint on circular DNA: linking number equals twist plus writhe.", - "latex_formula": r"Lk = Tw + Wr", - "description": "Topological invariants for braid theory." - }, - { - "eq_number": 812, - "title": "Cavity Persistence", - "significance": "Topological information processing metric: persistence of topological features.", - "latex_formula": r"\Delta \beta_k = \text{birth} - \text{death}", - "description": "Persistent homology for topological processing." - }, - { - "eq_number": 813, - "title": "Hill Regulation", - "significance": "Nonlinear saturation feedback: sigmoid functions used throughout OTOM.", - "latex_formula": r"f(X) = \frac{X^n}{K^n + X^n}", - "description": "Sigmoid feedback for control systems." - }, - { - "eq_number": 814, - "title": "Wilson-Cowan Equations", - "significance": "Mean-field neural population dynamics for cognitive load modeling.", - "latex_formula": r"\frac{dE}{dt} = -E + S(wE - wI + P)", - "description": "Neural dynamics for cognitive load." - }, - { - "eq_number": 815, - "title": "Turing Morphogenesis", - "significance": "Spontaneous symmetry breaking for pattern formation on manifolds.", - "latex_formula": r"\partial_t u = \Delta_{LB} u + f(u, v)", - "description": "Reaction-diffusion for pattern formation." - }, - # Mass Number Equations - { - "eq_number": 816, - "title": "Mass Number Admissibility Gate", - "significance": "Three-layer Mass Number structure: Admissible (A), Residual (R), Boundary (ε). Core rule: A ≤ threshold * (R + ε).", - "latex_formula": r"\text{MassLe}(m, \tau) := m_A \le \tau \cdot (m_R + \epsilon)", - "description": "Admissibility gate using comparison form (no division)." - }, - { - "eq_number": 817, - "title": "Admissible Reduction Packet", - "significance": "Layer 1 of Mass Number: records concrete reduction achieved by modeling move. Must be grounded in surface feature/invariant.", - "latex_formula": r"A = (\text{value}, \text{groundTag}, \text{moveId}), \quad A \ge 0", - "description": "Admissible reduction packet with nonnegativity invariant." - }, - { - "eq_number": 818, - "title": "Residual Risk Receipt", - "significance": "Layer 2 of Mass Number: records what remains unreduced after move. Must be inspectable and bounded.", - "latex_formula": r"R = (\text{value}, \text{riskClass}, \text{boundCheck}), \quad R \ge 0, \quad R + \epsilon > 0", - "description": "Residual risk receipt with boundedness guarantee." - }, - { - "eq_number": 819, - "title": "Boundary Marker (ε Guard)", - "significance": "Layer 3 of Mass Number: ensures denominator never zero. Carries threshold for admissibility decisions.", - "latex_formula": r"\epsilon > 0, \quad \tau = \text{threshold}, \quad \text{domainTag} \in \{\text{GCCL}, \text{FAMM}, \text{BRAID}, \text{TSM}, \text{HUTTER}\}", - "description": "Routing/compression boundary marker with nonzero guard." - }, - { - "eq_number": 820, - "title": "NaNMass Doctrine", - "significance": "Apparent infinity is diagnostic, not destination. NaNMass means coordinate system failed to close mass.", - "latex_formula": r"\text{Infinity-like} \to \text{unclosed closure} \to \text{NaNMass} \to \text{HOLD} \to \text{repair}", - "description": "Thermodynamic objection to raw infinity as mass value." - }, - { - "eq_number": 821, - "title": "Closure Path to Metric", - "significance": "Mass becomes distance only through admissibility closure. Raw mass → pseudometric → zero-distance quotient → metric.", - "latex_formula": r"\text{Raw} \to K_R(x,y) \to \text{Admissible} \to \text{ShortestPath} \to \text{Pseudometric} \to \text{Quotient} \to \text{Metric}", - "description": "Finite thermodynamic accounting path to metric closure." - }, - { - "eq_number": 822, - "title": "Erdős Forced-Pattern Model", - "significance": "If system is large enough, disorder cannot remain pure. Organized substructure must appear.", - "latex_formula": r"N \gg N_0 \implies \exists \text{monochromatic clique/independent set/convex subset}", - "description": "Ramsey-style forced pattern emergence." - }, - { - "eq_number": 823, - "title": "General-Position Convexity Forcing", - "significance": "Points in general position: when does convex n-gon become unavoidable?", - "latex_formula": r"g(n) = \min\{N : \text{any N points in general position contain convex n-gon}\}", - "description": "Happy Ending / Erdős-Szekeres point-set problem." - }, - { - "eq_number": 824, - "title": "Cup-Cap Monotonicity", - "significance": "Geometry converted to ordered subsequences. Convexity becomes pattern of slope changes.", - "latex_formula": r"\text{points} \to \text{sequence}, \quad \text{convexity} \to \text{slope pattern}", - "description": "Monotone subsequence via cup-cap decomposition." - }, - { - "eq_number": 825, - "title": "Probabilistic Existence Method", - "significance": "Do not construct directly. Show random object avoids bad event with positive probability.", - "latex_formula": r"P(\text{bad event}) < 1 \implies \exists \text{avoider}", - "description": "Erdős-style lower bounds via randomness." - }, - { - "eq_number": 826, - "title": "Extremal Density Threshold", - "significance": "Maximum possible density before forbidden structure is forced.", - "latex_formula": r"\text{ex}(n, \mathcal{F}) = \max\{|G| : |G|=n, G \text{ avoids } \mathcal{F}\}", - "description": "Turán-type extremal density bounds." - }, - { - "eq_number": 827, - "title": "Sidon Additive Collision", - "significance": "Integers as collision surfaces. Forbidden equality becomes overlap in additive address space.", - "latex_formula": r"A + B = C + D \iff \{A, B\} \neq \{C, D\}", - "description": "Additive combinatorics collision avoidance." - }, - { - "eq_number": 828, - "title": "Order-Type Signature Function", - "significance": "Coordinates discarded. Only orientation signatures kept for convexity encoding.", - "latex_formula": r"\chi: \binom{P}{3} \to \{+1, -1\}, \quad \text{convexity encoded by signs}", - "description": "Combinatorial geometry without metric coordinates." - }, - # Extremophile Equations - { - "eq_number": 829, - "title": "Strain121 Temperature Limit", - "significance": "Absolute biological temperature limit: 122°C (395K) protein denaturation wall.", - "latex_formula": r"T_{max} = 122°C = 395K \quad \text{(absolute biological wall)}", - "description": "Protein denaturation prevents survival above 122°C." - }, - { - "eq_number": 830, - "title": "Diatom Stiffness Limit", - "significance": "Silica shells approach inorganic material limits. κ_T ≈ 2.7×10^-11 Pa^-1.", - "latex_formula": r"\kappa_{T,min}^{biological} = 2.7 \times 10^{-11} \text{ Pa}^{-1} \quad \text{(silica)}", - "description": "Biological stiffness limit from amorphous silica frustules." - }, - { - "eq_number": 831, - "title": "Vibrio Natriegens Replication Speed", - "significance": "Absolute biological replication speed limit: 10-15 minute doubling time.", - "latex_formula": r"\tau_{min} = 600 \text{ seconds (10 minutes)} - \text{absolute biological wall}", - "description": "Fastest known organism sets replication speed boundary." - }, - { - "eq_number": 832, - "title": "Pyrococcus Pressure-Volume Work", - "significance": "P·ΔV > kT prevents protein unfolding. Obligate piezophile stability condition.", - "latex_formula": r"P \cdot \Delta V > k_B T \quad \text{(prevents unfolding)}", - "description": "Pressure-volume work locks protein conformations." - }, - { - "eq_number": 833, - "title": "Desulforudis Energy Flux", - "significance": "Deep biosphere champion: 10^-15 W/cell energy flux, 1000-year division time.", - "latex_formula": r"\Phi_{energy} = 10^{-15} \text{ W/cell}, \quad \tau_{division} = 1000 \text{ years}", - "description": "Arbitrarily low energy flux admissible if time expands proportionally." - }, - { - "eq_number": 834, - "title": "Landauer Limit", - "significance": "Minimum energy per bit erasure: E = kT ln(2).", - "latex_formula": r"E_{bit} = k_B T \ln(2) \approx 3.4 \times 10^{-21} \text{ J/bit at } 60°C", - "description": "Thermodynamic limit for information processing." - }, - { - "eq_number": 835, - "title": "Resonant Cavity Q-Factor Limit", - "significance": "Material damping prevents infinite Q. Q_max ≈ 100 for biological tissue.", - "latex_formula": r"Q_{max}^{biological} \approx 100 \quad \text{(tissue damping)}", - "description": "Finite damping prevents blow-up resonance." - }, - { - "eq_number": 836, - "title": "Turing Pattern Growth Limit", - "significance": "Finite nutrient flux prevents infinite growth in reaction-diffusion systems.", - "latex_formula": r"\partial_t c = D\nabla^2 c + R(c) + \lambda(c_{target} - c) \cdot \Theta(basin_{stable})", - "description": "Skeletal formation with nutrient-limited growth." - }, - { - "eq_number": 837, - "title": "Navier-Stokes Blow-up Rejection", - "significance": "Evolutionary rejection of blow-up: infinite vorticity, zero compressibility, zero viscosity, infinite energy.", - "latex_formula": r"\nabla \cdot v \neq 0, \quad \nu > 0, \quad E_{dissipation} < 10^{-14} \text{ W}", - "description": "Physical admissibility requires finite parameters." - }, - { - "eq_number": 838, - "title": "Thermococcus Pressure Adaptability", - "significance": "Widest pressure-range organism: 1 atm to 130 MPa adaptive flexibility.", - "latex_formula": r"P_{range} = (1 \text{ atm}, 130 \text{ MPa}), \quad \text{adaptive across full space}", - "description": "Pressure adaptability from atmospheric to extreme." - }, - { - "eq_number": 839, - "title": "Thermus Moderate Thermophily", - "significance": "Moderate thermophile: 50-80°C (Taq polymerase source).", - "latex_formula": r"50°C < T < 80°C \quad (122°F < T < 176°F)", - "description": "Protein folding stable at 140°F (60°C)." - }, - { - "eq_number": 840, - "title": "E. Coli Replication Reference", - "significance": "Baseline replication efficiency: 20 minutes optimal doubling, 4.6M bp genome.", - "latex_formula": r"\tau_{opt} = 1200 \text{ seconds}, \quad \text{Rate} = 3833 \text{ bp/s}", - "description": "Standard replication reference point." - }, - { - "eq_number": 841, - "title": "Rotational Phase Encoding", - "significance": "4-bit π field encodes 16 rotational states (22.5° resolution) for geometric information flow.", - "latex_formula": r"\theta = \pi \times \frac{2\pi}{16}, \quad \chi \in \{0,1\} \text{ (chirality)}", - "description": "RotationalMuSeed encodes local torsion/orientation for alignment-based coupling." - }, - { - "eq_number": 842, - "title": "Chiral Alignment Coupling", - "significance": "Alignment strength between rotational states: A = cos(Δθ). Determines information flow.", - "latex_formula": r"A_{ij} = \cos(\theta_i - \theta_j), \quad \text{coupling} \propto A_{ij}", - "description": "Similar π values couple strongly; orthogonal channels via D/L chirality." - }, - { - "eq_number": 843, - "title": "Manifold Blit Equation", - "significance": "Hardware-accelerated manifold update: M_{k+1} = Quant_LLM( J_DAG[ M_k ⊕ (Ψ_q ⊗ R_RT) ] ).", - "latex_formula": r"M_{k+1}(x) = \text{Quant}_{\text{LLM}}( \mathcal{J}_{\text{DAG}}[ M_k(x) \oplus (\Psi_q \otimes \mathcal{R}_{\text{RT}}) ] )", - "description": "O(1) manifold update via blitter operator, quantum sampling, raytracing." - }, - { - "eq_number": 844, - "title": "Blitter Accumulation", - "significance": "Saturating bitwise accumulation: saturate(M_k + δ) for discrete Picard integral.", - "latex_formula": r"\text{blit}(M, \delta) = \text{sat}_{\min}^{\max}(M + \delta)", - "description": "Hardware-accelerated accumulation with saturation bounds." - }, - { - "eq_number": 845, - "title": "Quantum Walk Amplitude", - "significance": "Discrete diffusion for quadratic convergence: A_{t+1} = (A_t ⊗ K) / 4.", - "latex_formula": r"\Psi_q(i,j,t+1) = \frac{1}{4} \sum_{neighbors} \Psi_q(i',j',t)", - "description": "Grid-based quantum walk for path superposition and acceleration." - }, - { - "eq_number": 846, - "title": "Anisotropic Torsion Flow", - "significance": "∂_t ϕ = ∇_i(M^ij ∇_j δF/δϕ) - σ ∂ϕ/∂I_lock for manifold evolution.", - "latex_formula": r"\partial_t \phi = \nabla_i(M^{ij} \nabla_j \delta F/\delta\phi) - \sigma \partial\phi/\partial I_{lock}", - "description": "Foldback-lock dynamics with anisotropic tensor and interlocking energy." - }, - { - "eq_number": 847, - "title": "Interlocking Energy", - "significance": "I_lock = w(1 - cos(k·frustration)) for recursive deposition snagging.", - "latex_formula": r"I_{lock} = w \cdot (1 - \cos(k \cdot \text{frustration})), \quad \text{frustration} = A^{ij} \Delta X_j", - "description": "Periodic frustration modulated by anisotropy for pattern locking." - }, - { - "eq_number": 848, - "title": "Spike Sync TVI", - "significance": "Temporal Variant Index for spike trains: coarse-grained timing/rate/pattern/collapse.", - "latex_formula": r"\text{TVI} = (T_{timing}, T_{rate}, T_{pattern}, T_{collapse})", - "description": "Admissibility metric for neural spike synchronization." - }, - { - "eq_number": 849, - "title": "Coarse-Graining Rule", - "significance": "Quantize time into bins: t_bin = floor(t / Δt) for jitter tolerance.", - "latex_formula": r"t_{bin} = \lfloor t / \Delta t \rfloor, \quad \text{tolerance} = \max\_time\_jitter", - "description": "Observation rule for spike train coarse-graining." - }, - { - "eq_number": 850, - "title": "Soliton Phase Singularity", - "significance": "Phase winding number +1 around soliton center: topological charge = vortex.", - "latex_formula": r"\oint \nabla \phi \cdot dl = 2\pi n, \quad n = +1 \text{ (soliton)}", - "description": "Soliton as phase singularity (vortex) for geometric bit-flip suppression." - } -] - -def main(): - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - for eq in equations: - try: - cursor.execute(""" - INSERT INTO equations (eq_number, title, domain_id, significance, status) - VALUES (?, ?, ?, ?, 'Proven') - """, (eq["eq_number"], eq["title"], DOMAIN_ID, eq["significance"])) - - eq_id = cursor.lastrowid - - cursor.execute(""" - INSERT INTO sub_equations (equation_id, subsection, name, latex_formula, description) - VALUES (?, ?, ?, ?, ?) - """, (eq_id, "main", eq["title"], eq["latex_formula"], eq["description"])) - - print(f"Added equation {eq['eq_number']}: {eq['title']}") - except sqlite3.IntegrityError as e: - print(f"Skipping equation {eq['eq_number']} (already exists): {e}") - - conn.commit() - conn.close() - print(f"\nTotal equations added: {len(equations)}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/add_golden_crc_equations.py b/5-Applications/scripts/add_golden_crc_equations.py deleted file mode 100644 index e90e18ab..00000000 --- a/5-Applications/scripts/add_golden_crc_equations.py +++ /dev/null @@ -1,147 +0,0 @@ -#!/usr/bin/env python3 -""" -Add golden ratio CRC equations to math_entities.db database - -This script adds the specific mathematical equations from the golden ratio CRC -UDP reassembly code to the math_entities.db database. -""" - -import sqlite3 -import hashlib -import json - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def create_entity_id(name, year): - """Create deterministic entity ID from name and year.""" - content = f"{name}:{year}" - hash_prefix = hashlib.sha256(content.encode()).hexdigest()[:16] - return f"golden-crc-{hash_prefix}" - -def add_golden_crc_equations(): - """Add golden ratio CRC equations to database.""" - conn = sqlite3.connect(DB_PATH) - - # Golden ratio CRC equations - equations = [ - { - 'name': 'Golden Polynomial Generation', - 'statement': 'P = ⌊(φ mod 1) × 10^8⌋ ⊕ 0x1021', - 'variables': 'φ=1.61803398875 (golden ratio), ⊕=bitwise XOR', - 'purpose': 'Generates CRC polynomial seed from golden ratio fractional part for cryptographic provenance', - 'subject': 'number_theory', - 'proof_status': 'proven', - 'formal_status': 'informal', - 'lean_module': None, - 'complexity_score': 32768, - 'year': 2026, - 'source_file': 'shared-data/data/extraneous/golden_crc_udp_reassembly.py' - }, - { - 'name': 'Invisible Unicode Encoding', - 'statement': 'E: {00,01,10,11} → {U+200B, U+200C, U+200D, U+200B+U+200C}', - 'variables': 'U+200B=zero-width space, U+200C=zero-width non-joiner, U+200D=zero-width joiner', - 'purpose': 'Encodes 2-bit pairs into invisible Unicode characters for covert channel transmission', - 'subject': 'cryptography', - 'proof_status': 'proven', - 'formal_status': 'informal', - 'lean_module': None, - 'complexity_score': 32768, - 'year': 2026, - 'source_file': 'shared-data/data/extraneous/golden_crc_udp_reassembly.py' - }, - { - 'name': 'Golden CRC Checksum', - 'statement': 'CRC(d) = CRC32(d, P)', - 'variables': 'd=data string, P=golden polynomial, CRC32=standard CRC-32 with seed', - 'purpose': 'Computes 32-bit CRC using golden ratio polynomial as seed for segment integrity verification', - 'subject': 'cryptography', - 'proof_status': 'proven', - 'formal_status': 'informal', - 'lean_module': None, - 'complexity_score': 32768, - 'year': 2026, - 'source_file': 'shared-data/data/extraneous/golden_crc_udp_reassembly.py' - }, - { - 'name': 'Phonon Graph Reassembly', - 'statement': 'R = ⋃ {s_i | CRC(s_i) = crc_i}', - 'variables': 's_i=segment i, crc_i=stored checksum for segment i, R=reassembled message', - 'purpose': 'Reassembles message by accepting only segments with valid golden CRC checksums; implements distributed consensus without central sequencer', - 'subject': 'graph_theory', - 'proof_status': 'proven', - 'formal_status': 'informal', - 'lean_module': None, - 'complexity_score': 32768, - 'year': 2026, - 'source_file': 'shared-data/data/extraneous/golden_crc_udp_reassembly.py' - } - ] - - inserted = 0 - updated = 0 - skipped = 0 - - for eq in equations: - entity_id = create_entity_id(eq['name'], eq['year']) - - try: - # Check if entity already exists - cursor = conn.execute("SELECT entity_id FROM math_entities WHERE entity_id = ?", (entity_id,)) - existing = cursor.fetchone() - - if existing: - # Update existing - conn.execute(""" - UPDATE math_entities SET - subject = ?, secondary_subjects = ?, name = ?, statement = ?, - proof_status = ?, formal_status = ?, lean_module = ?, - dependencies = ?, citations = ?, complexity_score = ?, - year = ?, source_file = ?, last_synced = CURRENT_TIMESTAMP - WHERE entity_id = ? - """, ( - eq['subject'], json.dumps([]), eq['name'], eq['statement'], - eq['proof_status'], eq['formal_status'], eq['lean_module'], - json.dumps([]), json.dumps([]), eq['complexity_score'], - eq['year'], eq['source_file'], entity_id - )) - updated += 1 - print(f"[UPDATE] {eq['name']}") - else: - # Insert new - conn.execute(""" - INSERT INTO math_entities ( - entity_id, subject, secondary_subjects, name, statement, - proof_status, formal_status, lean_module, dependencies, - citations, complexity_score, year, source_file - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - entity_id, eq['subject'], json.dumps([]), eq['name'], eq['statement'], - eq['proof_status'], eq['formal_status'], eq['lean_module'], - json.dumps([]), json.dumps([]), eq['complexity_score'], - eq['year'], eq['source_file'] - )) - inserted += 1 - print(f"[INSERT] {eq['name']}") - - # Log insert - conn.execute(""" - INSERT INTO sync_log (operation, entity_id, source_file, details) - VALUES (?, ?, ?, ?) - """, ('INSERT', entity_id, eq['source_file'], 'Added golden ratio CRC equation')) - - except sqlite3.IntegrityError: - skipped += 1 - print(f"[SKIP] {eq['name']} - integrity error") - except Exception as e: - print(f"[ERROR] Failed to add {eq['name']}: {e}") - - conn.commit() - conn.close() - - print(f"[OK] Inserted: {inserted}, Updated: {updated}, Skipped: {skipped}") - -if __name__ == "__main__": - print("[INFO] Adding golden ratio CRC equations to math_entities.db") - add_golden_crc_equations() diff --git a/5-Applications/scripts/add_peptide_moe_to_database.py b/5-Applications/scripts/add_peptide_moe_to_database.py deleted file mode 100644 index 60e66167..00000000 --- a/5-Applications/scripts/add_peptide_moe_to_database.py +++ /dev/null @@ -1,115 +0,0 @@ -#!/usr/bin/env python3 -""" -Add PeptideMoE modules to the math_entities database - -This script adds the new PeptideMoE Lean modules to the database -so they can be indexed and reviewed by the swarm. -""" - -import sqlite3 -import json -from datetime import datetime -from pathlib import Path - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def add_peptide_moe_modules(): - """Add PeptideMoE modules to the database.""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # PeptideMoE modules to add - modules = [ - { - "entity_id": "peptide_moe_001", - "subject": "PeptideMoE", - "secondary_subjects": "Mixture-of-Experts,conformational,peptide,thermodynamics", - "name": "Peptide MoE Core Specification", - "statement": "φ_peptide = structuralCoherence / (freeEnergy + c0); freeEnergy = E_internal + k_B·T·S_conformational; moeDrift = Σ g_k·(advice_φ,k, advice_ψ,k)", - "proof_status": "theorems", - "formal_status": "noncomputable", - "lean_module": "0-Core-Formalism/lean/Semantics/Semantics/PeptideMoE.lean", - "dependencies": "Mathlib.Data.Real.Basic,Mathlib.Data.List.Basic", - "citations": "OTOM v2.0.0-Cambrian-Bind", - "complexity_score": 85, - "year": 2026 - }, - { - "entity_id": "peptide_moe_002", - "subject": "PeptideMoE", - "secondary_subjects": "examples,toy,instantiation", - "name": "Peptide MoE Toy Examples", - "statement": "Concrete toy instantiation of the abstract peptide-MoE specification with fixed thermodynamic/admissibility parameters, three toy experts, three toy candidate endpoints, and example reports.", - "proof_status": "definitions", - "formal_status": "noncomputable", - "lean_module": "0-Core-Formalism/lean/Semantics/Semantics/PeptideMoEExamples.lean", - "dependencies": "Semantics.PeptideMoE", - "citations": "OTOM v2.0.0-Cambrian-Bind", - "complexity_score": 60, - "year": 2026 - }, - { - "entity_id": "peptide_moe_003", - "subject": "PeptideMoE", - "secondary_subjects": "failure,injection,pathological", - "name": "Peptide MoE Failure Scenarios", - "statement": "Failure-injection scenarios documenting why guardrails matter: c0=0 causes denominator failure, loose steric bounds admit clashing states, negative gates break MoE control, unbounded advice causes pathological drift.", - "proof_status": "definitions", - "formal_status": "noncomputable", - "lean_module": "0-Core-Formalism/lean/Semantics/Semantics/PeptideMoEFailure.lean", - "dependencies": "Semantics.PeptideMoE,Semantics.PeptideMoEExamples", - "citations": "OTOM v2.0.0-Cambrian-Bind", - "complexity_score": 70, - "year": 2026 - }, - { - "entity_id": "peptide_moe_004", - "subject": "PeptideMoE", - "secondary_subjects": "repair,guardrails,safety", - "name": "Peptide MoE Repair Theorems", - "statement": "Repair theorems documenting guardrails that restore safety: positive c0 recovers denominator safety, strict steric bounds reject clashing states, nonnegative gates guarantee unit mass, bounded advice controls drift.", - "proof_status": "theorems", - "formal_status": "noncomputable", - "lean_module": "0-Core-Formalism/lean/Semantics/Semantics/PeptideMoERepair.lean", - "dependencies": "Semantics.PeptideMoE,Semantics.PeptideMoEExamples,Semantics.PeptideMoEFailure", - "citations": "OTOM v2.0.0-Cambrian-Bind", - "complexity_score": 75, - "year": 2026 - } - ] - - # Insert each module - for module in modules: - try: - cursor.execute(""" - INSERT OR REPLACE INTO math_entities - (entity_id, subject, secondary_subjects, name, statement, proof_status, - formal_status, lean_module, dependencies, citations, complexity_score, year) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - module["entity_id"], - module["subject"], - module["secondary_subjects"], - module["name"], - module["statement"], - module["proof_status"], - module["formal_status"], - module["lean_module"], - module["dependencies"], - module["citations"], - module["complexity_score"], - module["year"] - )) - print(f"✓ Added: {module['name']}") - except Exception as e: - print(f"✗ Error adding {module['name']}: {e}") - - conn.commit() - conn.close() - - print(f"\nAdded {len(modules)} PeptideMoE modules to database.") - print(f"Database: {DB_PATH}") - -if __name__ == "__main__": - add_peptide_moe_modules() diff --git a/5-Applications/scripts/all_device_signal_topology.py b/5-Applications/scripts/all_device_signal_topology.py deleted file mode 100644 index f0baa422..00000000 --- a/5-Applications/scripts/all_device_signal_topology.py +++ /dev/null @@ -1,421 +0,0 @@ -#!/usr/bin/env python3 -""" -All Device Signal Topology Analysis -Analyzes how all 38 devices contribute signals to topology for computational enhancement. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class AllDeviceSignalTopology: - """Analyzes all devices as signal sources for topology enhancement.""" - - def __init__(self): - # All 38 devices with their signal characteristics - self.all_devices = { - "fpga": { - "name": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "signals": ["clock signals", "data signals", "control signals"], - "signal_quality": "High (custom logic)", - "significance_score": 90.0 - }, - "usb_fpga": { - "name": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "signals": ["USB signals", "clock signals", "data signals"], - "signal_quality": "High (USB bridge)", - "significance_score": 85.0 - }, - "physical_topology": { - "name": "Physical Topology (capacitors, wires, USB, voltage)", - "signals": ["voltage signals", "capacitance signals", "resistance signals"], - "signal_quality": "Medium-High (physical properties)", - "significance_score": 75.0 - }, - "morphic_core": { - "name": "Morphic Core (capacitors as morphic devices)", - "signals": ["timing signals", "capacitance signals"], - "signal_quality": "Medium (morphic)", - "significance_score": 70.0 - }, - "hdmi_computational_shell": { - "name": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "signals": ["TMDS signals", "clock signals", "data signals"], - "signal_quality": "Very High (48 Gbps)", - "significance_score": 95.0 - }, - "tdms_controller": { - "name": "TDMS Controller (HDMI 2.1)", - "signals": ["TMDS signals", "clock signals", "control signals"], - "signal_quality": "Very High (48 Gbps)", - "significance_score": 95.0 - }, - "displayport_controller": { - "name": "DisplayPort Controller (DP 1.4a)", - "signals": ["DP signals", "clock signals", "aux signals"], - "signal_quality": "Very High (32.4 Gbps)", - "significance_score": 90.0 - }, - "displayport_line_morphic": { - "name": "DisplayPort Line Morphic (copper conductors)", - "signals": ["electrical signals", "impedance signals"], - "signal_quality": "Medium (copper lines)", - "significance_score": 65.0 - }, - "usb_controllers": { - "name": "USB Controllers (4 xHCI controllers)", - "signals": ["USB signals", "clock signals", "control signals"], - "signal_quality": "High (10-20 Gbps)", - "significance_score": 85.0 - }, - "efi_controller": { - "name": "EFI Controller (1D OSIC scalar)", - "signals": ["firmware signals", "control signals"], - "signal_quality": "Medium (firmware)", - "significance_score": 60.0 - }, - "pcie_controller": { - "name": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "signals": ["PCIe signals", "clock signals", "control signals"], - "signal_quality": "Very High (256 Gbps)", - "significance_score": 95.0 - }, - "ram_controller": { - "name": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "signals": ["memory signals", "clock signals", "data signals"], - "signal_quality": "High (50-100 GB/s)", - "significance_score": 85.0 - }, - "pwm_controller": { - "name": "PWM Controller (Pulse Width Modulation)", - "signals": ["PWM signals", "clock signals", "control signals"], - "signal_quality": "Medium-High (1 Hz - 1 MHz)", - "significance_score": 75.0 - }, - "motherboard": { - "name": "Motherboard (travel paths, IRQ controller, data fabric)", - "signals": ["power signals", "clock signals", "data fabric signals"], - "signal_quality": "High (system backbone)", - "significance_score": 90.0 - }, - "power_supply": { - "name": "Power Supply and Power Caps", - "signals": ["power signals", "voltage signals", "thermal signals"], - "signal_quality": "High (power infrastructure)", - "significance_score": 85.0 - }, - "dma_ram_morphic": { - "name": "DMA-RAM Morphic Device", - "signals": ["DMA signals", "memory signals", "control signals"], - "signal_quality": "High (DMA + morphic)", - "significance_score": 80.0 - }, - "inflight_ram": { - "name": "In-Flight RAM (In-Memory Computation / PIM)", - "signals": ["memory signals", "computation signals", "control signals"], - "signal_quality": "Very High (100-200 GB/s)", - "significance_score": 90.0 - }, - "monitor_timing": { - "name": "Monitor Timing Computation (EDID, capabilities, settings)", - "signals": ["timing signals", "control signals"], - "signal_quality": "Medium (timing-based)", - "significance_score": 65.0 - }, - "ddci_timing": { - "name": "DDC/CI Timing Computation (capabilities, brightness, volume)", - "signals": ["timing signals", "control signals"], - "signal_quality": "Medium (timing-based)", - "significance_score": 65.0 - }, - "amd_gpu": { - "name": "AMD GPU (Granite Ridge/Radeon Graphics)", - "signals": ["GPU signals", "clock signals", "data signals"], - "signal_quality": "Very High (GPU)", - "significance_score": 95.0 - }, - "gpu_resource_manager": { - "name": "GPU Resource Manager (CUDA/Tensor cores)", - "signals": ["GPU signals", "control signals", "resource signals"], - "signal_quality": "Very High (GPU)", - "significance_score": 90.0 - }, - "video_physics": { - "name": "Video Physics (120Hz sync, HDMI residual)", - "signals": ["video signals", "sync signals", "residual signals"], - "signal_quality": "High (video)", - "significance_score": 85.0 - }, - "mereotopological_video": { - "name": "Mereotopological Video (hybrid video state)", - "signals": ["video signals", "consistency signals", "topology signals"], - "signal_quality": "High (mereo + video)", - "significance_score": 80.0 - }, - "wifi_controller": { - "name": "WiFi Controller (MediaTek MT7925 WiFi 7)", - "signals": ["WiFi signals", "clock signals", "control signals"], - "signal_quality": "High (WiFi 7)", - "significance_score": 85.0 - }, - "bluetooth_controller": { - "name": "Bluetooth Controller (Realtek RTL8723B Bluetooth 5.4)", - "signals": ["Bluetooth signals", "clock signals", "control signals"], - "signal_quality": "High (Bluetooth 5.4)", - "significance_score": 80.0 - }, - "ethernet_controller": { - "name": "Ethernet Controller (Realtek RTL8126 2.5GbE)", - "signals": ["Ethernet signals", "clock signals", "control signals"], - "signal_quality": "High (2.5GbE)", - "significance_score": 85.0 - }, - "ssd_controller": { - "name": "SSD Controller (Phison PS5018-E18 PCIe 4.0 NVMe)", - "signals": ["NVMe signals", "clock signals", "control signals"], - "signal_quality": "Very High (PCIe 4.0)", - "significance_score": 90.0 - }, - "nvme_controller": { - "name": "NVMe Controller (Phison PS5018-E18)", - "signals": ["NVMe signals", "clock signals", "control signals"], - "signal_quality": "Very High (PCIe 4.0)", - "significance_score": 90.0 - }, - "sata_controller": { - "name": "SATA Controller (AMD SATA)", - "signals": ["SATA signals", "clock signals", "control signals"], - "signal_quality": "Medium (SATA)", - "significance_score": 70.0 - }, - "memory_controller_ddr5": { - "name": "DDR5 Memory Controller (Integrated DDR5)", - "signals": ["DDR5 signals", "clock signals", "control signals"], - "signal_quality": "High (DDR5)", - "significance_score": 85.0 - }, - "irq_controller": { - "name": "IRQ Controller (System interrupt management)", - "signals": ["interrupt signals", "control signals"], - "signal_quality": "Medium (interrupts)", - "significance_score": 70.0 - }, - "data_fabric": { - "name": "Data Fabric (AMD Data Fabric)", - "signals": ["data fabric signals", "clock signals", "control signals"], - "signal_quality": "Very High (data fabric)", - "significance_score": 90.0 - }, - "network_node_qfox": { - "name": "Network Node (qfox - primary node)", - "signals": ["network signals", "control signals"], - "signal_quality": "High (network)", - "significance_score": 80.0 - }, - "network_node_architect": { - "name": "Network Node (architect - compute node)", - "signals": ["network signals", "control signals"], - "signal_quality": "High (network)", - "significance_score": 80.0 - }, - "distributed_training": { - "name": "Distributed Training System", - "signals": ["training signals", "control signals"], - "signal_quality": "High (distributed)", - "significance_score": 85.0 - }, - "audio_controller": { - "name": "Audio Controller (AMD Ryzen HD Audio, Realtek ALC1220)", - "signals": ["audio signals", "clock signals", "control signals"], - "signal_quality": "Medium (audio)", - "significance_score": 70.0 - }, - "swarm_genome": { - "name": "Swarm Genome (6 bins × 3 bits = 18 bits)", - "signals": ["genome signals", "control signals"], - "signal_quality": "High (Genome18)", - "significance_score": 85.0 - }, - "cpu_topology_wires": { - "name": "CPU (AMD Ryzen 7 7800X3D - Topology and Wires)", - "signals": ["clock signals", "power signals", "interconnect signals"], - "signal_quality": "Very High (CPU)", - "significance_score": 95.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 38, - "deterministic_stochastic_capacity": 4986752895.249841, - "expansion_factor": 2624607.0 - } - - def analyze_all_device_signals(self) -> Dict: - """Analyze all devices as signal sources.""" - signal_categories = { - "clock_signals": [], - "data_signals": [], - "control_signals": [], - "power_signals": [], - "timing_signals": [], - "thermal_signals": [] - } - - total_significance = 0 - for device_id, device_info in self.all_devices.items(): - total_significance += device_info["significance_score"] - for signal in device_info["signals"]: - if "clock" in signal.lower(): - signal_categories["clock_signals"].append(device_id) - elif "data" in signal.lower(): - signal_categories["data_signals"].append(device_id) - elif "control" in signal.lower(): - signal_categories["control_signals"].append(device_id) - elif "power" in signal.lower() or "voltage" in signal.lower(): - signal_categories["power_signals"].append(device_id) - elif "timing" in signal.lower(): - signal_categories["timing_signals"].append(device_id) - elif "thermal" in signal.lower(): - signal_categories["thermal_signals"].append(device_id) - - analysis = { - "total_devices": len(self.all_devices), - "total_significance_score": total_significance, - "average_significance_score": total_significance / len(self.all_devices), - "signal_categories": signal_categories - } - - return analysis - - def calculate_all_device_signal_impact(self) -> Dict: - """Calculate all-device signal topology impact.""" - # All-device signal multiplier - all_device_signal_multiplier = 1.5 # 1.5x from all devices contributing signals - - # Signal diversity multiplier - signal_diversity_multiplier = 1.3 # 1.3x from signal diversity - - # Signal quality multiplier - signal_quality_multiplier = 1.2 # 1.2x from high signal quality - - # Signal integration multiplier - signal_integration_multiplier = 1.2 # 1.2x from signal integration - - # Calculate expanded capacity with all-device signals - base_capacity = 1900 - current_deterministic_stochastic_capacity = 4986752895.249841 - - # Apply all-device signal multipliers - all_device_signal_capacity = (current_deterministic_stochastic_capacity * - all_device_signal_multiplier * - signal_diversity_multiplier * - signal_quality_multiplier * - signal_integration_multiplier) - - all_device_signal_expansion_factor = all_device_signal_capacity / base_capacity - all_device_signal_improvement_factor = all_device_signal_capacity / current_deterministic_stochastic_capacity - - calculation = { - "base_capacity": base_capacity, - "current_deterministic_stochastic_capacity": current_deterministic_stochastic_capacity, - "all_device_signal_multiplier": all_device_signal_multiplier, - "signal_diversity_multiplier": signal_diversity_multiplier, - "signal_quality_multiplier": signal_quality_multiplier, - "signal_integration_multiplier": signal_integration_multiplier, - "all_device_signal_capacity": all_device_signal_capacity, - "all_device_signal_expansion_factor": all_device_signal_expansion_factor, - "all_device_signal_improvement_factor": all_device_signal_improvement_factor, - "total_all_device_signal_multiplier": (all_device_signal_multiplier * - signal_diversity_multiplier * - signal_quality_multiplier * - signal_integration_multiplier) - } - - return calculation - - def integrate_all_device_signals(self) -> Dict: - """Integrate all-device signals into topology.""" - integration = { - "all_device_signals_enabled": True, - "total_devices_contributing": 38, - "signal_categories": 6, - "math_categories_enhanced": [ - "Control Theory (control signals)", - "Information Theory (data signals)", - "Thermodynamic (power, thermal signals)", - "Physical Bind (all device signals)", - "Geometric Bind (signal topology)" - ], - "foundation_kernels_enhanced": [ - "F04", "F05", "F06", # Thermodynamic (power, thermal) - "F11", "F12" # Control Theory (control signals) - ], - "signal_topology": "All 38 devices contribute signals to topology", - "signal_diversity": "6 signal categories across 38 devices" - } - - return integration - - def run_analysis(self) -> Dict: - """Run all-device signal topology analysis.""" - print("=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze all device signals - print("\n[1/3] Analyzing all devices as signal sources...") - signal_analysis = self.analyze_all_device_signals() - print(f" Total Devices: {signal_analysis['total_devices']}") - print(f" Total Significance Score: {signal_analysis['total_significance_score']:.2f}") - print(f" Average Significance Score: {signal_analysis['average_significance_score']:.2f}") - print(f" Signal Categories: {len(signal_analysis['signal_categories'])}") - for category, devices in signal_analysis['signal_categories'].items(): - print(f" {category}: {len(set(devices))} devices") - - # Step 2: Calculate impact - print("[2/3] Calculating all-device signal topology impact...") - impact_calculation = self.calculate_all_device_signal_impact() - print(f" Current Deterministic Stochastic Capacity: {impact_calculation['current_deterministic_stochastic_capacity']}") - print(f" All-Device Signal Capacity: {impact_calculation['all_device_signal_capacity']}") - print(f" All-Device Signal Improvement Factor: {impact_calculation['all_device_signal_improvement_factor']:.2f}x") - print(f" Total All-Device Signal Multiplier: {impact_calculation['total_all_device_signal_multiplier']:.2f}x") - - # Step 3: Integrate - print("[3/3] Integrating all-device signals into topology...") - integration = self.integrate_all_device_signals() - print(f" Devices Contributing: {integration['total_devices_contributing']}") - print(f" Signal Categories: {integration['signal_categories']}") - - print("\n" + "=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "signal_analysis": signal_analysis, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = AllDeviceSignalTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "all_device_signal_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Devices Contributing: {results['integration']['total_devices_contributing']}") - print(f"All-Device Signal Capacity: {results['impact_calculation']['all_device_signal_capacity']}") - print(f"All-Device Signal Improvement Factor: {results['impact_calculation']['all_device_signal_improvement_factor']:.2f}x") - print(f"Total All-Device Signal Multiplier: {results['impact_calculation']['total_all_device_signal_multiplier']:.2f}x") diff --git a/5-Applications/scripts/all_device_signal_topology_vrm.py b/5-Applications/scripts/all_device_signal_topology_vrm.py deleted file mode 100644 index 266dc29c..00000000 --- a/5-Applications/scripts/all_device_signal_topology_vrm.py +++ /dev/null @@ -1,446 +0,0 @@ -#!/usr/bin/env python3 -""" -All Device Signal Topology Analysis with VRMs -Analyzes all 38 devices plus VRMs contributing signals to topology for computational enhancement. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class AllDeviceSignalTopologyVRM: - """Analyzes all devices plus VRMs as signal sources for topology enhancement.""" - - def __init__(self): - # All 38 devices plus VRMs - self.all_devices = { - "fpga": { - "name": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "signals": ["clock signals", "data signals", "control signals"], - "signal_quality": "High (custom logic)", - "significance_score": 90.0 - }, - "usb_fpga": { - "name": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "signals": ["USB signals", "clock signals", "data signals"], - "signal_quality": "High (USB bridge)", - "significance_score": 85.0 - }, - "physical_topology": { - "name": "Physical Topology (capacitors, wires, USB, voltage)", - "signals": ["voltage signals", "capacitance signals", "resistance signals"], - "signal_quality": "Medium-High (physical properties)", - "significance_score": 75.0 - }, - "morphic_core": { - "name": "Morphic Core (capacitors as morphic devices)", - "signals": ["timing signals", "capacitance signals"], - "signal_quality": "Medium (morphic)", - "significance_score": 70.0 - }, - "hdmi_computational_shell": { - "name": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "signals": ["TMDS signals", "clock signals", "data signals"], - "signal_quality": "Very High (48 Gbps)", - "significance_score": 95.0 - }, - "tdms_controller": { - "name": "TDMS Controller (HDMI 2.1)", - "signals": ["TMDS signals", "clock signals", "control signals"], - "signal_quality": "Very High (48 Gbps)", - "significance_score": 95.0 - }, - "displayport_controller": { - "name": "DisplayPort Controller (DP 1.4a)", - "signals": ["DP signals", "clock signals", "aux signals"], - "signal_quality": "Very High (32.4 Gbps)", - "significance_score": 90.0 - }, - "displayport_line_morphic": { - "name": "DisplayPort Line Morphic (copper conductors)", - "signals": ["electrical signals", "impedance signals"], - "signal_quality": "Medium (copper lines)", - "significance_score": 65.0 - }, - "usb_controllers": { - "name": "USB Controllers (4 xHCI controllers)", - "signals": ["USB signals", "clock signals", "control signals"], - "signal_quality": "High (10-20 Gbps)", - "significance_score": 85.0 - }, - "efi_controller": { - "name": "EFI Controller (1D OSIC scalar)", - "signals": ["firmware signals", "control signals"], - "signal_quality": "Medium (firmware)", - "significance_score": 60.0 - }, - "pcie_controller": { - "name": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "signals": ["PCIe signals", "clock signals", "control signals"], - "signal_quality": "Very High (256 Gbps)", - "significance_score": 95.0 - }, - "ram_controller": { - "name": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "signals": ["memory signals", "clock signals", "data signals"], - "signal_quality": "High (50-100 GB/s)", - "significance_score": 85.0 - }, - "pwm_controller": { - "name": "PWM Controller (Pulse Width Modulation)", - "signals": ["PWM signals", "clock signals", "control signals"], - "signal_quality": "Medium-High (1 Hz - 1 MHz)", - "significance_score": 75.0 - }, - "motherboard": { - "name": "Motherboard (travel paths, IRQ controller, data fabric)", - "signals": ["power signals", "clock signals", "data fabric signals"], - "signal_quality": "High (system backbone)", - "significance_score": 90.0 - }, - "power_supply": { - "name": "Power Supply and Power Caps", - "signals": ["power signals", "voltage signals", "thermal signals"], - "signal_quality": "High (power infrastructure)", - "significance_score": 85.0 - }, - "dma_ram_morphic": { - "name": "DMA-RAM Morphic Device", - "signals": ["DMA signals", "memory signals", "control signals"], - "signal_quality": "High (DMA + morphic)", - "significance_score": 80.0 - }, - "inflight_ram": { - "name": "In-Flight RAM (In-Memory Computation / PIM)", - "signals": ["memory signals", "computation signals", "control signals"], - "signal_quality": "Very High (100-200 GB/s)", - "significance_score": 90.0 - }, - "monitor_timing": { - "name": "Monitor Timing Computation (EDID, capabilities, settings)", - "signals": ["timing signals", "control signals"], - "signal_quality": "Medium (timing-based)", - "significance_score": 65.0 - }, - "ddci_timing": { - "name": "DDC/CI Timing Computation (capabilities, brightness, volume)", - "signals": ["timing signals", "control signals"], - "signal_quality": "Medium (timing-based)", - "significance_score": 65.0 - }, - "amd_gpu": { - "name": "AMD GPU (Granite Ridge/Radeon Graphics)", - "signals": ["GPU signals", "clock signals", "data signals"], - "signal_quality": "Very High (GPU)", - "significance_score": 95.0 - }, - "gpu_resource_manager": { - "name": "GPU Resource Manager (CUDA/Tensor cores)", - "signals": ["GPU signals", "control signals", "resource signals"], - "signal_quality": "Very High (GPU)", - "significance_score": 90.0 - }, - "video_physics": { - "name": "Video Physics (120Hz sync, HDMI residual)", - "signals": ["video signals", "sync signals", "residual signals"], - "signal_quality": "High (video)", - "significance_score": 85.0 - }, - "mereotopological_video": { - "name": "Mereotopological Video (hybrid video state)", - "signals": ["video signals", "consistency signals", "topology signals"], - "signal_quality": "High (mereo + video)", - "significance_score": 80.0 - }, - "wifi_controller": { - "name": "WiFi Controller (MediaTek MT7925 WiFi 7)", - "signals": ["WiFi signals", "clock signals", "control signals"], - "signal_quality": "High (WiFi 7)", - "significance_score": 85.0 - }, - "bluetooth_controller": { - "name": "Bluetooth Controller (Realtek RTL8723B Bluetooth 5.4)", - "signals": ["Bluetooth signals", "clock signals", "control signals"], - "signal_quality": "High (Bluetooth 5.4)", - "significance_score": 80.0 - }, - "ethernet_controller": { - "name": "Ethernet Controller (Realtek RTL8126 2.5GbE)", - "signals": ["Ethernet signals", "clock signals", "control signals"], - "signal_quality": "High (2.5GbE)", - "significance_score": 85.0 - }, - "ssd_controller": { - "name": "SSD Controller (Phison PS5018-E18 PCIe 4.0 NVMe)", - "signals": ["NVMe signals", "clock signals", "control signals"], - "signal_quality": "Very High (PCIe 4.0)", - "significance_score": 90.0 - }, - "nvme_controller": { - "name": "NVMe Controller (Phison PS5018-E18)", - "signals": ["NVMe signals", "clock signals", "control signals"], - "signal_quality": "Very High (PCIe 4.0)", - "significance_score": 90.0 - }, - "sata_controller": { - "name": "SATA Controller (AMD SATA)", - "signals": ["SATA signals", "clock signals", "control signals"], - "signal_quality": "Medium (SATA)", - "significance_score": 70.0 - }, - "memory_controller_ddr5": { - "name": "DDR5 Memory Controller (Integrated DDR5)", - "signals": ["DDR5 signals", "clock signals", "control signals"], - "signal_quality": "High (DDR5)", - "significance_score": 85.0 - }, - "irq_controller": { - "name": "IRQ Controller (System interrupt management)", - "signals": ["interrupt signals", "control signals"], - "signal_quality": "Medium (interrupts)", - "significance_score": 70.0 - }, - "data_fabric": { - "name": "Data Fabric (AMD Data Fabric)", - "signals": ["data fabric signals", "clock signals", "control signals"], - "signal_quality": "Very High (data fabric)", - "significance_score": 90.0 - }, - "network_node_qfox": { - "name": "Network Node (qfox - primary node)", - "signals": ["network signals", "control signals"], - "signal_quality": "High (network)", - "significance_score": 80.0 - }, - "network_node_architect": { - "name": "Network Node (architect - compute node)", - "signals": ["network signals", "control signals"], - "signal_quality": "High (network)", - "significance_score": 80.0 - }, - "distributed_training": { - "name": "Distributed Training System", - "signals": ["training signals", "control signals"], - "signal_quality": "High (distributed)", - "significance_score": 85.0 - }, - "audio_controller": { - "name": "Audio Controller (AMD Ryzen HD Audio, Realtek ALC1220)", - "signals": ["audio signals", "clock signals", "control signals"], - "signal_quality": "Medium (audio)", - "significance_score": 70.0 - }, - "swarm_genome": { - "name": "Swarm Genome (6 bins × 3 bits = 18 bits)", - "signals": ["genome signals", "control signals"], - "signal_quality": "High (Genome18)", - "significance_score": 85.0 - }, - "cpu_topology_wires": { - "name": "CPU (AMD Ryzen 7 7800X3D - Topology and Wires)", - "signals": ["clock signals", "power signals", "interconnect signals"], - "signal_quality": "Very High (CPU)", - "significance_score": 95.0 - }, - # VRMs (Voltage Regulator Modules) - "vrm_cpu": { - "name": "CPU VRM (Voltage Regulator Module for CPU)", - "signals": ["voltage regulation signals", "power signals", "thermal signals", "control signals"], - "signal_quality": "Very High (CPU power delivery)", - "significance_score": 90.0 - }, - "vram_vrm": { - "name": "VRAM VRM (Voltage Regulator Module for VRAM)", - "signals": ["voltage regulation signals", "power signals", "thermal signals", "control signals"], - "signal_quality": "Very High (GPU power delivery)", - "significance_score": 90.0 - }, - "motherboard_vrm": { - "name": "Motherboard VRM (Voltage Regulator Module for motherboard)", - "signals": ["voltage regulation signals", "power signals", "thermal signals", "control signals"], - "signal_quality": "High (motherboard power delivery)", - "significance_score": 85.0 - }, - "ddr5_vrm": { - "name": "DDR5 VRM (Voltage Regulator Module for DDR5 memory)", - "signals": ["voltage regulation signals", "power signals", "thermal signals", "control signals"], - "signal_quality": "High (DDR5 power delivery)", - "significance_score": 85.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 38, - "deterministic_stochastic_capacity": 4986752895.249841, - "expansion_factor": 2624607.0 - } - - def analyze_all_device_signals_with_vrm(self) -> Dict: - """Analyze all devices plus VRMs as signal sources.""" - signal_categories = { - "clock_signals": [], - "data_signals": [], - "control_signals": [], - "power_signals": [], - "timing_signals": [], - "thermal_signals": [], - "voltage_regulation_signals": [] - } - - total_significance = 0 - for device_id, device_info in self.all_devices.items(): - total_significance += device_info["significance_score"] - for signal in device_info["signals"]: - if "clock" in signal.lower(): - signal_categories["clock_signals"].append(device_id) - elif "data" in signal.lower(): - signal_categories["data_signals"].append(device_id) - elif "control" in signal.lower(): - signal_categories["control_signals"].append(device_id) - elif "power" in signal.lower() or "voltage" in signal.lower(): - signal_categories["power_signals"].append(device_id) - elif "timing" in signal.lower(): - signal_categories["timing_signals"].append(device_id) - elif "thermal" in signal.lower(): - signal_categories["thermal_signals"].append(device_id) - elif "voltage regulation" in signal.lower(): - signal_categories["voltage_regulation_signals"].append(device_id) - - analysis = { - "total_devices": len(self.all_devices), - "total_significance_score": total_significance, - "average_significance_score": total_significance / len(self.all_devices), - "signal_categories": signal_categories - } - - return analysis - - def calculate_vrm_impact(self) -> Dict: - """Calculate VRM impact on all-device signal topology.""" - # VRM addition multiplier - vrm_addition_multiplier = 1.1 # 1.1x from adding 4 VRMs - - # VRM signal quality multiplier - vrm_signal_quality_multiplier = 1.15 # 1.15x from VRM high-quality signals - - # Voltage regulation multiplier - voltage_regulation_multiplier = 1.2 # 1.2x from voltage regulation signals - - # Calculate expanded capacity with VRMs - base_capacity = 1900 - current_all_device_signal_capacity = 14002802129.861553 - - # Apply VRM multipliers - vrm_enhanced_capacity = (current_all_device_signal_capacity * - vrm_addition_multiplier * - vrm_signal_quality_multiplier * - voltage_regulation_multiplier) - - vrm_enhanced_expansion_factor = vrm_enhanced_capacity / base_capacity - vrm_improvement_factor = vrm_enhanced_capacity / current_all_device_signal_capacity - - calculation = { - "base_capacity": base_capacity, - "current_all_device_signal_capacity": current_all_device_signal_capacity, - "vrm_addition_multiplier": vrm_addition_multiplier, - "vrm_signal_quality_multiplier": vrm_signal_quality_multiplier, - "voltage_regulation_multiplier": voltage_regulation_multiplier, - "vrm_enhanced_capacity": vrm_enhanced_capacity, - "vrm_enhanced_expansion_factor": vrm_enhanced_expansion_factor, - "vrm_improvement_factor": vrm_improvement_factor, - "total_vrm_multiplier": (vrm_addition_multiplier * - vrm_signal_quality_multiplier * - voltage_regulation_multiplier) - } - - return calculation - - def integrate_vrm_signals(self) -> Dict: - """Integrate VRM signals into topology.""" - integration = { - "vrm_signals_enabled": True, - "total_devices_with_vrm": 42, # 38 + 4 VRMs - "vrm_devices": 4, - "signal_categories": 7, # Added voltage regulation signals - "math_categories_enhanced": [ - "Control Theory (control signals)", - "Information Theory (data signals)", - "Thermodynamic (power, thermal signals)", - "Physical Bind (all device signals)", - "Geometric Bind (signal topology)", - "Thermodynamic (voltage regulation)" - ], - "foundation_kernels_enhanced": [ - "F04", "F05", "F06", # Thermodynamic (power, thermal, voltage) - "F11", "F12" # Control Theory (control signals) - ], - "signal_topology": "All 42 devices (38 + 4 VRMs) contribute signals to topology", - "signal_diversity": "7 signal categories across 42 devices" - } - - return integration - - def run_analysis(self) -> Dict: - """Run all-device signal topology analysis with VRMs.""" - print("=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY ANALYSIS WITH VRMS") - print("=" * 60) - - # Step 1: Analyze all device signals with VRMs - print("\n[1/3] Analyzing all devices plus VRMs as signal sources...") - signal_analysis = self.analyze_all_device_signals_with_vrm() - print(f" Total Devices: {signal_analysis['total_devices']}") - print(f" Total Significance Score: {signal_analysis['total_significance_score']:.2f}") - print(f" Average Significance Score: {signal_analysis['average_significance_score']:.2f}") - print(f" Signal Categories: {len(signal_analysis['signal_categories'])}") - for category, devices in signal_analysis['signal_categories'].items(): - print(f" {category}: {len(set(devices))} devices") - - # Step 2: Calculate VRM impact - print("[2/3] Calculating VRM impact on all-device signal topology...") - impact_calculation = self.calculate_vrm_impact() - print(f" Current All-Device Signal Capacity: {impact_calculation['current_all_device_signal_capacity']}") - print(f" VRM Enhanced Capacity: {impact_calculation['vrm_enhanced_capacity']}") - print(f" VRM Improvement Factor: {impact_calculation['vrm_improvement_factor']:.2f}x") - print(f" Total VRM Multiplier: {impact_calculation['total_vrm_multiplier']:.2f}x") - - # Step 3: Integrate VRM signals - print("[3/3] Integrating VRM signals into topology...") - integration = self.integrate_vrm_signals() - print(f" Devices with VRMs: {integration['total_devices_with_vrm']}") - print(f" VRM Devices: {integration['vrm_devices']}") - print(f" Signal Categories: {integration['signal_categories']}") - - print("\n" + "=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY ANALYSIS WITH VRMS COMPLETE") - print("=" * 60) - - return { - "signal_analysis": signal_analysis, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = AllDeviceSignalTopologyVRM() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "all_device_signal_topology_vrm.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("ALL DEVICE SIGNAL TOPOLOGY WITH VRMS SUMMARY") - print("=" * 60) - print(f"Devices with VRMs: {results['integration']['total_devices_with_vrm']}") - print(f"VRM Enhanced Capacity: {results['impact_calculation']['vrm_enhanced_capacity']}") - print(f"VRM Improvement Factor: {results['impact_calculation']['vrm_improvement_factor']:.2f}x") - print(f"Total VRM Multiplier: {results['impact_calculation']['total_vrm_multiplier']:.2f}x") diff --git a/5-Applications/scripts/analyze_rgflow_noise_files.py b/5-Applications/scripts/analyze_rgflow_noise_files.py deleted file mode 100644 index 1dac074f..00000000 --- a/5-Applications/scripts/analyze_rgflow_noise_files.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -""" -Analyze RGFlow noise files to understand if they represent buggy code. -""" - -import os -import sys -import logging -from pathlib import Path -from typing import Dict, List, Tuple -import numpy as np -from dataclasses import dataclass - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState - -logging.basicConfig(level=logging.INFO, format='%(levelname)s:NoiseAnalyzer:%(message)s') -logger = logging.getLogger(__name__) - - -@dataclass -class NoiseFileAnalysis: - """Analysis of a noise file.""" - filepath: str - file_size: int - line_count: int - entropy: float - text_complexity: float - structural_complexity: float - adaptation_state: AdaptationState - failure_mask: int - - -class RGFlowNoiseAnalyzer: - """Analyze files that flow to noise under RGFlow.""" - - def __init__(self, root_path: str = "/home/allaun/Research Stack"): - self.root_path = Path(root_path) - self.adaptation_equation = UnifiedAdaptationEquation() - - # Adjust constants for code files - self.adaptation_equation.DRAKE_BUDGET_D = 0.1 - self.adaptation_equation.DRIFT_BARRIER_B = 0.01 - self.adaptation_equation.LAMBDA = 0.1 - self.adaptation_equation.SCALE_STEPS = 5 - self.adaptation_equation.adaptation_surface = self.adaptation_equation._precompute_adaptation_surface() - - self.noise_analyses: List[NoiseFileAnalysis] = [] - - def extract_code_features(self, filepath: Path) -> AdaptationState: - """Extract adaptation equation variables from code file.""" - try: - with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - except Exception as e: - logger.warning(f"Failed to read {filepath}: {e}") - return AdaptationState(0, 0, 0.001, 0.001, 0, 0) - - mu_q = min(len(content) / 10000.0, 1.0) * 0.01 - rho_q = 0.5 - import_count = len([line for line in content.split('\n') if 'import' in line.lower()]) - C_fac = min(import_count / 20.0, 1.0) - C_fac = max(0.001, C_fac) - function_count = len([line for line in content.split('\n') if 'def ' in line or 'fn ' in line]) - class_count = len([line for line in content.split('\n') if 'class ' in line or 'structure ' in line]) - M_fac = min((function_count + class_count) / 50.0, 1.0) - M_fac = max(0.001, M_fac) - path_depth = len(filepath.parts) - n_e = min(path_depth / 10.0, 1.0) - entropy = self._calculate_entropy(content) - text_complexity = self._calculate_text_complexity(content) - sigma_q = 1.0 + ((entropy + text_complexity) / 2.0) - - return AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q) - - def _calculate_entropy(self, text: str) -> float: - """Calculate Shannon entropy of text.""" - if not text: - return 0.0 - - char_counts = {} - for char in text: - char_counts[char] = char_counts.get(char, 0) + 1 - - total = len(text) - entropy = 0.0 - for count in char_counts.values(): - probability = count / total - if probability > 0: - entropy -= probability * np.log2(probability) - - max_entropy = np.log2(len(char_counts)) if char_counts else 1.0 - return min(entropy / max_entropy, 1.0) if max_entropy > 0 else 0.0 - - def _calculate_text_complexity(self, text: str) -> float: - """Calculate text complexity (unique words / total words).""" - if not text: - return 0.0 - - words = text.split() - if not words: - return 0.0 - - unique_words = len(set(word.lower() for word in words)) - total_words = len(words) - - return min(unique_words / total_words, 1.0) - - def analyze_file(self, filepath: Path) -> NoiseFileAnalysis: - """Analyze a single file.""" - try: - with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - except Exception as e: - logger.warning(f"Failed to read {filepath}: {e}") - return None - - file_size = len(content) - line_count = len(content.split('\n')) - entropy = self._calculate_entropy(content) - text_complexity = self._calculate_text_complexity(content) - - # Structural complexity - import_count = len([line for line in content.split('\n') if 'import' in line.lower()]) - function_count = len([line for line in content.split('\n') if 'def ' in line or 'fn ' in line]) - class_count = len([line for line in content.split('\n') if 'class ' in line or 'structure ' in line]) - structural_complexity = min((import_count + function_count + class_count) / 50.0, 1.0) - - adaptation_state = self.extract_code_features(filepath) - (lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise, - flows_to_sabotage, adaptation_cost, stability_margin, rg_depth, - attractor_id, failure_mask) = self.adaptation_equation.evaluate_state(adaptation_state) - - return NoiseFileAnalysis( - filepath=str(filepath), - file_size=file_size, - line_count=line_count, - entropy=entropy, - text_complexity=text_complexity, - structural_complexity=structural_complexity, - adaptation_state=adaptation_state, - failure_mask=failure_mask - ) - - def scan_noise_files(self, noise_file_paths: List[str]) -> None: - """Analyze specific noise files.""" - for filepath_str in noise_file_paths: - filepath = Path(filepath_str) - if not filepath.exists(): - continue - - analysis = self.analyze_file(filepath) - if analysis: - self.noise_analyses.append(analysis) - - def generate_report(self) -> Dict: - """Generate analysis report.""" - if not self.noise_analyses: - return {} - - # Statistics - avg_file_size = np.mean([a.file_size for a in self.noise_analyses]) - avg_line_count = np.mean([a.line_count for a in self.noise_analyses]) - avg_entropy = np.mean([a.entropy for a in self.noise_analyses]) - avg_text_complexity = np.mean([a.text_complexity for a in self.noise_analyses]) - avg_structural_complexity = np.mean([a.structural_complexity for a in self.noise_analyses]) - - # Failure mask distribution - failure_mask_counts = {} - for analysis in self.noise_analyses: - mask = analysis.failure_mask - failure_mask_counts[mask] = failure_mask_counts.get(mask, 0) + 1 - - # Empty file count - empty_files = len([a for a in self.noise_analyses if a.file_size == 0]) - - report = { - 'total_noise_files': len(self.noise_analyses), - 'empty_files': empty_files, - 'avg_file_size': avg_file_size, - 'avg_line_count': avg_line_count, - 'avg_entropy': avg_entropy, - 'avg_text_complexity': avg_text_complexity, - 'avg_structural_complexity': avg_structural_complexity, - 'failure_mask_distribution': failure_mask_counts, - 'noise_files': [a.filepath for a in self.noise_analyses] - } - - return report - - def print_report(self, report: Dict) -> None: - """Print analysis report.""" - print("\n" + "="*80) - print("RGFlow Noise File Analysis") - print("="*80) - print(f"\nTotal noise files analyzed: {report['total_noise_files']}") - print(f"Empty files: {report['empty_files']} ({report['empty_files']/report['total_noise_files']*100:.1f}%)") - - print(f"\nAverage metrics:") - print(f" File size: {report['avg_file_size']:.1f} bytes") - print(f" Line count: {report['avg_line_count']:.1f} lines") - print(f" Entropy: {report['avg_entropy']:.4f}") - print(f" Text complexity: {report['avg_text_complexity']:.4f}") - print(f" Structural complexity: {report['avg_structural_complexity']:.4f}") - - print(f"\nFailure mask distribution:") - for mask, count in report['failure_mask_distribution'].items(): - print(f" Mask {mask:04b}: {count} files") - - print(f"\nNoise file characteristics:") - print(f" High percentage of empty files suggests:") - print(f" - RGFlow detects stub/placeholder files") - print(f" - Files with insufficient content fail Drift Barrier") - print(f" - This could indicate:") - print(f" * Incomplete implementations") - print(f" * TODO/placeholder files") - print(f" * Broken or corrupted files") - - print("\n" + "="*80) - - -def main(): - """Main entry point.""" - # Sample noise files from the RGFlow filter report - noise_files = [ - "/home/allaun/Documents/Research Stack/manifold_sample.txt", - "/home/allaun/Documents/Research Stack/docs/nlab/pages/9/8/0/6/16089/content.md", - "/home/allaun/Documents/Research Stack/docs/nlab/pages/9/7/4/7/27479/content.md", - "/home/allaun/Documents/Research Stack/docs/nlab/pages/9/6/3/7/17369/content.md", - "/home/allaun/Documents/Research Stack/docs/nlab/pages/9/4/3/8/18349/content.md", - ] - - analyzer = RGFlowNoiseAnalyzer() - analyzer.scan_noise_files(noise_files) - report = analyzer.generate_report() - analyzer.print_report(report) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/append_all_domains.py b/5-Applications/scripts/append_all_domains.py deleted file mode 100644 index 9131e7fe..00000000 --- a/5-Applications/scripts/append_all_domains.py +++ /dev/null @@ -1,1124 +0,0 @@ -#!/usr/bin/env python3 -"""Append ALL remaining physics domains to physics_equations.db""" - -import sqlite3, os, sys - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -cur = conn.cursor() - -cur.execute("SELECT MAX(id) FROM equations"); eid = cur.fetchone()[0] or 540 -cur.execute("SELECT MAX(eq_number) FROM equations"); enum = cur.fetchone()[0] or 540 -cur.execute("SELECT MAX(id) FROM sub_equations"); sid = cur.fetchone()[0] or 0 -cur.execute("SELECT MAX(id) FROM verifications"); vid = cur.fetchone()[0] or 0 -cur.execute("SELECT MAX(id) FROM domains"); did_val = cur.fetchone()[0] or 27 - -# ================================================================ -# NEW DOMAINS -# ================================================================ -new_domains = [ - (28,"Geophysics","Seismology, geomagnetism, geodesy, plate tectonics, gravity",None), - (29,"Atmospheric Physics","Meteorology, cloud physics, radiation, lightning, climate",None), - (30,"Oceanography","Ocean waves, tides, thermohaline circulation, coastal physics",None), - (31,"Hydrology","Groundwater, surface water, Darcy's law, evapotranspiration, porous media",None), - (32,"Biophysics","Ion channels, membrane potentials, molecular motors, protein folding",None), - (33,"Chemical Physics","Reaction kinetics, transition state theory, Marcus theory, molecular dynamics",None), - (34,"Photonics & Laser Physics","Laser physics, nonlinear optics, mode-locking, solitons, photonic crystals",None), - (35,"Atomic & Molecular Physics","Atomic spectra, Zeeman/Stark, Franck-Condon, Born-Oppenheimer, hyperfine",None), - (36,"Rheology","Non-Newtonian fluids, viscoelasticity, thixotropy, yield stress",None), - (37,"Tribology","Friction, wear, lubrication, contact mechanics, Stribeck curve",None), - (38,"Granular Materials","Janssen effect, angle of repose, force chains, jamming, granular flow",None), - (39,"Nanoscience","Quantum dots, Coulomb blockade, Kondo effect, ballistic transport, 2D materials",None), - (40,"Quantum Information","Qubits, entanglement, quantum gates, error correction, Bell states",None), - (41,"Nonlinear Dynamics & Chaos","Bifurcation, Lorenz attractor, Lyapunov exponents, fractals, synchronization",None), - (42,"Medical Physics","MRI, CT, PET, ultrasound, radiation therapy, dosimetry, laser surgery",None), - (43,"Radiation Physics","Dosimetry, shielding, attenuation, Bragg peak, MIRD formalism, LET/RBE",None), - (44,"Energy Physics","Photovoltaics, fuel cells, batteries, thermoelectrics, wind/hydro, fusion energy",None), - (45,"Space Physics","Magnetosphere, solar wind, cosmic rays, Van Allen belts, ionosphere, reconnection",None), - (46,"Detonics & Shock Physics","Detonation waves, Chapman-Jouguet, ZND theory, Rankine-Hugoniot, blast scaling",None), - (47,"Metamaterials","Negative index, transformation optics, cloaking, split-ring resonators, phononic",None), - (48,"Underwater Acoustics","SONAR equation, sound speed profiles, propagation loss, ambient noise, scattering",None), - (49,"Engineering Physics","Heat exchangers, structural dynamics, feedback control, signal processing",None), - (50,"Electrochemistry (Advanced)","Pourbaix diagrams, impedance spectroscopy, double-layer structure, fuel cells",None), -] -for d in new_domains: - cur.execute("INSERT OR IGNORE INTO domains VALUES (?,?,?,?)", d) - -# ================================================================ -# EQUATION INJECTOR -# ================================================================ -eqs = [] -def E(title, dom, year, status, sig, prec): - global eid, enum - eid += 1; enum += 1 - eqs.append((eid, enum, title, dom, year, status, sig, prec)) - -subs = [] -def S(rid, sub_name, name, latex, desc, cond=""): - global sid - sid += 1 - subs.append((sid, rid, sub_name, name, latex, desc, cond)) - -vers = [] -def V(rid, test, expt, yr, prec, st="Confirmed"): - global vid - vid += 1 - vers.append((vid, rid, test, expt, yr, prec, st)) - -class EqID: - """Tracks the last equation ID for referencing in sub-equations""" - pass - -# ================================================================ -# 1. GEOPHYSICS (Seismology, Geodesy, Geomagnetism, Tectonics) -# ================================================================ - -E("Seismic Wave Equation (Elastic)", 28, "1820s", "Proven", - "ρ ∂²u/∂t² = (λ+μ)∇(∇·u) + μ∇²u; vector elastic wave equation; P and S waves", - "All seismology foundation; confirmed by every earthquake") -S(eid, "PWave", "P-Wave Speed", r"v_P = \sqrt{\frac{K + \frac{4}{3}\mu}{\rho}} = \sqrt{\frac{\lambda+2\mu}{\rho}}", "Compressional/primary wave; fastest", "Earth: v_P~5-8 km/s (crust), ~8-13 (mantle)") -S(eid, "SWave", "S-Wave Speed", r"v_S = \sqrt{\frac{\mu}{\rho}}", "Shear/secondary wave; does not travel through liquids", "v_S < v_P; Earth outer core: v_S=0") -V(eid, "Earthquake P/S wave arrival times", "Global Seismic Network", 1900, "Millisecond accuracy today", "Confirmed") - -E("Snell's Law (Seismic Refraction at Interfaces)", 28, "1910", "Proven", - "sin i₁/v₁ = sin i₂/v₂ = p (ray parameter); seismic ray tracing through layered Earth", - "Seismic tomography; Mohorovicic 1909 discovered Moho") -S(eid, "RayParam", "Ray Parameter (Seismic)", r"p = \frac{\sin i}{v} = \frac{dt}{d\Delta}", "Constant along ray in layered media; used for inversion", "") -V(eid, "Moho discovery (1909)", "Mohorovicic 1909", 1909, "Crust-mantle boundary at ~30 km", "Confirmed") - -E("Gutenberg-Richter Magnitude-Energy Relation", 28, "1956", "Proven", - "log₁₀ E = 4.8 + 1.5 M (E in Joules); log₁₀ E = 5.24 + 1.44 M (later refinement)", - "Empirical earthquake energy from magnitude") -S(eid, "Richter", "Richter Magnitude Definition", r"M_L = \log_{10} A - \log_{10} A_0(\Delta)", "A = max amplitude on Wood-Anderson seismograph; Δ=epicentral distance", "Local magnitude; standardized for S. California") -V(eid, "Energy release of 1906 San Francisco ~25×10^{15} J", "Lawson report 1908", 1956, "Matches G-R relation", "Confirmed") - -E("Omori's Law (Aftershock Decay)", 28, "1894", "Proven", - "n(t) = K / (c + t)^p; p≈1 (often 0.9–1.4); K,c constants; t=time after mainshock", - "Aftershock rate decays as power law; universal") - -E("Frequency-Magnitude Distribution (Gutenberg-Richter Law)", 28, "1944", "Proven", - "log₁₀ N(≥M) = a − b M; b≈1 (global average); N=cumulative number of earthquakes", - "Self-similarity of earthquake populations; b-value stress indicator") - -E("Bullen's Compressibility-Pressure Hypothesis (Earth Interior)", 28, "1940s", "Proven", - "K = a + bP; Earth core compressibility varies linearly with pressure", - "Earth interior modeling; PREM model (Dziewonski-Anderson 1981)") - -E("Love Wave Dispersion Equation (Surface Waves)", 28, "1911", "Proven", - "tan(κβ₁H) = (μ₂β₂)/(μ₁β₁); Love wave existence condition in layer over half-space", - "Crustal structure from surface wave dispersion") -S(eid, "RayleighWave", "Rayleigh Wave Equation", r"\left(2-\frac{v^2}{v_S^2}\right)^2 = 4\sqrt{\left(1-\frac{v^2}{v_P^2}\right)\left(1-\frac{v^2}{v_S^2}\right)}", "Determines Rayleigh wave speed v from v_P, v_S", "Surface waves in homogeneous half-space") -V(eid, "Love waves in 1906 SF earthquake", "Love 1911", 1911, "Theoretical prediction → observed", "Confirmed") - -E("Airy Isostasy (Crustal Compensation Model)", 28, "1855", "Proven", - "Mountain root thickness = h ρ_c/(ρ_m−ρ_c); h=elevation; ρ_c,ρ_m=crust/mantle density", - "~5.6 km root per km elevation (ρ_c=2.67,ρ_m=3.27); Pratt model: variable density columns") -V(eid, "Himalayan root ~70 km crustal thickness", "Seismic sounding", 1980, "Matches Airy prediction within ~20%", "Confirmed") - -E("Free-Air Gravity Anomaly", 28, "1930s", "Proven", - "Δg_FA = g_obs − g_theoretical(λ) + δg_FAC; δg_FAC=0.3086h mGal (free-air correction, h in m)", - "Gravity survey reduction; reveals subsurface density variations") - -E("Bouguer Gravity Anomaly (Complete)", 28, "1749/1930s", "Proven", - "Δg_B = g_obs − g_theo(λ) + 0.3086h − 0.0419ρh + δg_terrain; ρ=2.67 g/cm³ typical", - "Removes topographic effect; reveals subsurface density anomalies") -V(eid, "Oceanic trench negative Bouguer anomalies", "Marine gravity surveys", 1960, "Matched by subducting slab model", "Confirmed") - -E("Geodetic Reference System (GRS80/WGS84 Ellipsoid)", 28, "1980/84", "Proven", - "a=6378137m, f=1/298.257223563 (WGS84); meridian radius M=a(1−e²)/(1−e²sin²φ)^{3/2}", - "Global positioning reference; GPS/WGS84 is standard") - -E("Geoid Undulation (Stokes' Formula)", 28, "1849", "Proven", - "N = (R/4πγ)∫∫ Δg S(ψ) dσ; S(ψ) = Stokes function; Δg=gravity anomaly; ψ=angular distance", - "Geoid height from gravity anomalies; gravimetric geodesy") -V(eid, "GOCE/GRACE satellite geoid models to cm precision", "GOCE 2009-2013, GRACE 2002-2017", 2010, "Geoid accuracy ~1-2 cm at 100 km resolution", "Confirmed") - -E("Plate Motion on a Sphere (Euler Pole Rotation)", 28, "1960s", "Proven", - "v = ω × R; v=linear velocity, ω=angular velocity vector, R=Earth radius vector", - "All tectonic plate motions described by rotation about Euler poles") -S(eid, "PlateVel", "Plate Velocity Magnitude", r"v = \omega R \sin\alpha", "α = angular distance from Euler pole to point on plate", "Pacific plate: ~70-100 mm/yr") -V(eid, "GPS plate motion vectors match geologic rates", "Space geodesy (VLBI, GPS, SLR)", 1990, "Within 1-2 mm/yr", "Confirmed") - -E("Geomagnetic Secular Variation (IGRF Model)", 28, "1960s", "Proven", - "B(r,θ,φ,t) = −∇[a Σ(g_n^m cos mφ + h_n^m sin mφ)(a/r)^{n+1} P_n^m(cos θ)]", - "International Geomagnetic Reference Field; updated every 5 years") - -E("Curie Temperature Isotherm (Magnetic Crustal Thickness)", 28, "1970s", "Proven", - "Magnetic minerals become paramagnetic above ~580°C (magnetite Curie point); ~20-30 km depth", - "Moho often corresponds to Curie isotherm in continental crust") - -E("Darcy's Law (Groundwater Flow in Porous Media)", 31, "1856", "Proven", - "Q = −K A (dh/dl); v_Darcy = Q/A = −K ∇h; K = hydraulic conductivity", - "All groundwater hydrology; confirmed experimentally by Darcy") -S(eid, "Darcy3D", "Darcy's Law (3D General)", r"\vec{q} = -\frac{k}{\mu}(\nabla p - \rho \vec{g})", "k=permeability (m²); μ=viscosity; valid for laminar flow through porous media", "Re<1-10 based on grain size") -V(eid, "Darcy's apparatus (Dijon fountains)", "Darcy 1856", 1856, "Linear Q vs dh/dl confirmed", "Confirmed") - -E("Dupuit-Forchheimer Assumption (Unconfined Aquifer)", 31, "1863/1901", "Proven", - "Q = −K h (dh/dx); h=saturated thickness; flow lines approximately horizontal", - "Unconfined groundwater flow; Dupuit parabola for flow to wells") - -E("Theis Solution (Well Hydraulics, Confined Aquifer)", 31, "1935", "Proven", - "s(r,t) = Q/(4πT) ∫_u^∞ (e^{-x}/x) dx; u = r²S/(4Tt); T=transmissivity, S=storativity", - "Transient drawdown around pumping well; exact for ideal confined aquifer") -S(eid, "TheisApprox", "Cooper-Jacob Approximation", r"s = \frac{2.3 Q}{4\pi T}\log\frac{2.25 T t}{r^2 S}", "Valid for u<0.01; straight-line fit on semi-log plot", "Most commonly used well-test method") -V(eid, "Aquifer tests worldwide confirm Theis", "Countless pumping tests", 1950, "Standard hydrogeology method", "Confirmed") - -E("Manning Equation (Open Channel Flow)", 31, "1889", "Proven", - "v = (1/n) R_h^{2/3} S^{1/2}; n=Manning roughness; R_h=hydraulic radius; S=slope", - "All open-channel hydraulics; rivers, canals, culverts") -V(eid, "River discharge measurements; USGS stream gages", "USGS standard method", 1900, "±5-10% typical accuracy", "Confirmed") - -E("Rational Method (Peak Runoff Estimation)", 31, "1889", "Proven", - "Q_peak = C i A; C=runoff coefficient (0-1); i=rainfall intensity; A=watershed area", - "Stormwater design; small watersheds (<200 acres)") - -E("Richards Equation (Unsaturated Flow in Soils)", 31, "1931", "Proven", - "∂θ/∂t = ∇·[K(θ) ∇(ψ+z)]; θ=moisture content; ψ=pressure head; K(θ)=hydraulic conductivity", - "Vadose zone hydrology; infiltration; soil physics") -S(eid, "Richards1D", "Richards Equation (1D Vertical)", r"\frac{\partial\theta}{\partial t} = \frac{\partial}{\partial z}\left[K(\theta)\left(\frac{\partial\psi}{\partial z}+1\right)\right]", "Unsaturated vertical flow in soil; highly nonlinear", "Richards 1931") -V(eid, "Tensiometer + TDR field measurements match Richards solutions", "Soil physics experiments", 1970, "Qualitatively correct; quantitative with fitted parameters", "Confirmed") - -E("Horton Infiltration Model", 31, "1940", "Proven", - "f(t) = f_c + (f₀−f_c) e^{−kt}; infiltration capacity decays exponentially", - "Rainfall excess → runoff generation; empirical but widely validated") -V(eid, "Rainfall simulator plot experiments", "Horton 1940s", 1940, "Decay curve fits well for most soils", "Confirmed") - -E("Penman-Monteith Evapotranspiration Equation", 31, "1965", "Proven", - "ET = [Δ(R_n−G) + ρ_a c_p (e_s−e_a)/r_a] / [Δ + γ(1+r_s/r_a)]", - "Standard reference evapotranspiration (FAO-56); energy + aerodynamic terms") -V(eid, "FAO-56 reference ET standard", "FAO Standard", 1998, "Matches lysimeter data to ~10%", "Confirmed") - -E("Stomatal Conductance (Jarvis Model)", 31, "1976", "Proven", - "g_s = g_s_max · f₁(PAR) · f₂(T) · f₃(VPD) · f₄(CO₂) · f₅(ψ_leaf); multiplicative stress functions", - "Plant-atmosphere gas exchange; photosynthesis models") - -# ================================================================ -# 2. OCEANOGRAPHY -# ================================================================ - -E("Linear Wave Theory (Airy Wave, Dispersion Relation)", 30, "1845", "Proven", - "ω² = gk tanh(kh); deep water (kh≫1): ω²=gk, c=g/ω; shallow water (kh≪1): ω²=ghk², c=√(gh)", - "All ocean surface gravity waves; confirmed") -S(eid, "Wavelength", "Deep-Water Wavelength", r"\lambda = \frac{g T^2}{2\pi}", "Wavelength from period T; deep water", "T=10s → λ≈156m; c=gT/(2π)") -V(eid, "Ocean wave spectra match linear theory", "Waverider buoys", 1960, "Dispersion confirmed to high precision", "Confirmed") - -E("Stokes Drift (Mass Transport Under Waves)", 30, "1847", "Proven", - "U_s = ½ a² ω k e^{2kz}; net Lagrangian drift under progressive waves; ~O(ε²)", - "Langmuir circulation; oil spill trajectories; confirmed") - -E("Significant Wave Height (Sverdrup-Munk-Bretschneider)", 30, "1947/1977", "Proven", - "H_s = H_{1/3} ≈ 4√m₀; m₀ = ∫ S(f) df (zeroth spectral moment)", - "Standard ocean wave statistics; Rayleigh-distributed individual wave heights") - -E("Tide-Generating Potential (Equilibrium Theory)", 30, "1775/1897", "Proven", - "V_tide = −(3/2) GM_⊙ R²/r³ (cos²θ − 1/3); Laplace's tidal equations govern dynamic response", - "M₂ semidiurnal dominant; Darwin's harmonic analysis of tides") - -E("Geostrophic Balance (Ocean Currents)", 30, "1900s", "Proven", - "f v = (1/ρ) ∂p/∂x; f u = −(1/ρ) ∂p/∂y; f=2Ω sin φ (Coriolis parameter); large-scale flow", - "All major ocean gyres; Gulf Stream, Kuroshio, Antarctic Circumpolar") -V(eid, "Satellite altimetry matches geostrophic velocities", "TOPEX/Poseidon, Jason series", 1990, "Agreement within 10-20%", "Confirmed") - -E("Ekman Transport (Wind-Driven Surface Layer)", 30, "1905", "Proven", - "M_E = τ_wind / (ρ f); net transport 90° to right of wind (NH); left (SH)", - "Upwelling/downwelling at coasts; Ekman spiral with depth") -V(eid, "Ekman spiral observed in ice drift (Nansen/Fram)", "Ekman 1905", 1905, "Ice drifts ~20-40° right of wind", "Confirmed") - -E("Thermohaline Circulation (Stommel-Arons Model)", 30, "1960", "Proven", - "Balance of advection, diffusion, and sources/sinks of heat and salt; deep ocean circulation", - "Global conveyor belt circulation; abyssal flow dynamics") - -E("Sverdrup Balance (Wind-Driven Gyre Circulation)", 30, "1947", "Proven", - "β v = f ∂w/∂z + curl_z(τ)/(ρ); β=df/dy; meridional transport from wind stress curl", - "Subtropical/subpolar gyre dynamics; confirmed in all ocean basins") -V(eid, "Wind-driven transport matches Sverdrup prediction", "Hydrographic sections", 1960, "Within factor ~2; validated theory", "Confirmed") - -E("Munk's Western Boundary Current Theory", 30, "1950", "Proven", - "A_H ∇⁴ψ − β ∂ψ/∂x = −curl_z(τ)/ρ; lateral friction balances β-effect; Gulf Stream width", - "Western intensification of boundary currents; Gulf Stream, Kuroshio") -V(eid, "Gulf Stream width ~100 km matches Munk prediction", "Oceanographic surveys", 1950, "First-order agreement", "Confirmed") - -E("Sonar Equation (Active, Monostatic)", 48, "1940s", "Proven", - "SL − 2TL + TS = NL − DI + DT; SL=source level, TL=transmission loss, TS=target strength, NL=noise, DI=directivity index, DT=detection threshold", - "All active sonar systems; military and scientific") -S(eid, "TL", "Transmission Loss (Spherical + Absorption)", r"TL = 20\log_{10} R + \alpha R", "R=range (m); α=absorption coefficient (dB/m); α∝f² in seawater", "") -V(eid, "Submarine detection ranges match sonar equation", "US Navy, WWII to present", 1945, "Quantitatively confirmed", "Confirmed") - -E("Sound Speed in Seawater (UNESCO/IES-80/CTD)", 48, "1980", "Proven", - "c(S,T,P) = 1449.2 + 4.6T − 0.055T² + 0.00029T³ + (1.34−0.010T)(S−35) + 0.016z", - "Empirical; c = 1448−1570 m/s in ocean; SOFAR channel at ~1000m") -V(eid, "CTD profile sound speed matches direct measurement", "Oceanographic surveys", 1980, "Within 0.1 m/s", "Confirmed") - -E("Acoustic Doppler Current Profiler (ADCP) Principle", 48, "1980s", "Proven", - "v_radial = (c Δf)/(2 f₀); Doppler shift from scatterers moving with water; 4 beams → 3D velocity", - "Standard ocean current measurement; thousands of ADCPs deployed globally") - -# ================================================================ -# 3. ATMOSPHERIC PHYSICS -# ================================================================ - -E("Hydrostatic Equation (Atmospheric)", 29, "19th c.", "Proven", - "dp/dz = −ρ g; pressure decreases exponentially with height in isothermal atmosphere", - "Standard atmosphere; barometric altimetry") -S(eid, "Barometric", "Barometric Altitude Formula", r"p = p_0 \exp\left(-\frac{M g z}{R T}\right)", "Isothermal atmosphere; scale height H=RT/(Mg)≈8.5 km", "p at 5.5 km ≈ ½ p₀") -V(eid, "Pressure altimeter validation", "Aviation standard", 1940, "Within few meters at low altitude", "Confirmed") - -E("Ideal Gas Law (Moist Air, Virtual Temperature)", 29, "19th c.", "Proven", - "p = ρ R_d T_v; T_v = T (1 + 0.608 q); q=specific humidity; virtual temperature correction", - "Moist atmospheric dynamics; buoyancy calculations") - -E("Potential Temperature (Adiabatic Reference)", 29, "19th c.", "Proven", - "θ = T (p₀/p)^{R_d/c_p}; R_d/c_p ≈ 0.286; conserved under adiabatic vertical displacement", - "Static stability criterion: ∂θ/∂z > 0 → stable; < 0 → unstable") - -E("Brunt-Väisälä Frequency (Atmospheric Stability)", 29, "1920s", "Proven", - "N² = (g/θ) dθ/dz; buoyancy oscillation frequency; N²>0 → stable oscillation", - "Gravity wave generation; mountain waves; clear air turbulence") -S(eid, "BVfreq", "Brunt-Väisälä Frequency", r"N = \sqrt{\frac{g}{\theta}\frac{d\theta}{dz}}", "N≈0.01-0.02 s⁻¹ in troposphere; ~0.02 s⁻¹ in stratosphere", "") -V(eid, "Mountain lee waves (Bishop wave, Sierra Nevada)", "Photography + lidar", 1950, "Wavelength ~5-20 km; matches theory", "Confirmed") - -E("Geostrophic Wind (Pressure Gradient + Coriolis Balance)", 29, "19th c.", "Proven", - "u_g = −(1/ρf) ∂p/∂y; v_g = (1/ρf) ∂p/∂x; wind parallel to isobars; f=2Ω sin φ", - "Large-scale weather systems; wind direction from isobar orientation") -V(eid, "Upper-air radiosonde winds match geostrophic", "Global radiosonde network", 1950, "Within ~10-15° direction, 20-30% speed", "Confirmed") - -E("Thermal Wind Equation (Vertical Wind Shear)", 29, "1920s", "Proven", - "∂u_g/∂z = −(g/fT) ∂T/∂y; ∂v_g/∂z = (g/fT) ∂T/∂x; temperature gradient → wind shear", - "Jet stream existence explained; frontal zones") -V(eid, "Jet stream at ~10 km with ~100+ knot core matches thermal wind", "Global wind profiles", 1950, "Core height/speed matches mid-latitude temperature gradient", "Confirmed") - -E("Rossby Number (Inertial vs Coriolis)", 29, "1939", "Proven", - "Ro = U/(f L); Ro ≪ 1 → geostrophic; Ro ~ 1 → gradient wind; Ro ≫ 1 → cyclostrophic", - "Dynamical scaling classification; tornadoes: Ro>1000; hurricanes: Ro~1") - -E("Rossby Wave Phase Speed (Planetary Waves)", 29, "1939", "Proven", - "c = ū − β/k²; β = 2Ω cos φ / R; westward phase speed relative to mean flow", - "Mid-latitude weather patterns; persistent ridges/troughs; ~3-6 waves around hemisphere") -V(eid, "Hemispheric wave number 3-6 observed in 500mb maps", "NWP model analysis", 1960, "Rossby wave dispersion confirmed", "Confirmed") - -E("Clausius-Clapeyron (Water Vapor Saturation Pressure)", 29, "1834", "Proven", - "de_s/dT = L e_s/(R_v T²); saturated vapor pressure increases ~7%/K near surface", - "Atmospheric moisture holding capacity; precipitation intensity scaling") -S(eid, "MagnusFormula", "Magnus-Tetens Formula (Saturation Pressure)", r"e_s(T) = 6.112 \exp\left(\frac{17.67\,T}{T+243.5}\right)", "T in °C; e_s in hPa; empirical approximation of C-C", "Accurate within 0.1% for -400.6mm, turbulent)", - "Precipitation formation; warm rain collision-coalescence") -V(eid, "Drop fall speeds measured in wind tunnels", "Gunn & Kinzer 1949", 1949, "v_t(d) curve experimentally determined", "Confirmed") - -E("Mie Scattering (Atmospheric Aerosols, Clouds)", 29, "1908", "Proven", - "Q_ext, Q_sca, Q_abs as function of size parameter x=2πr/λ and refractive index m", - "Cloud optical properties; aerosol radiative forcing; lidar") -V(eid, "Mie calculations match laboratory scattering measurements", "Laboratory nephelometers", 1960, "Single-particle scattering confirmed", "Confirmed") - -E("Rayleigh Scattering (Molecular Atmosphere)", 29, "1871", "Proven", - "I_sca ∝ 1/λ⁴; cross-section σ_R ∝ 1/λ⁴; blue sky, red sunsets; polarization patterns", - "Sky color, atmospheric correction of satellite imagery") -S(eid, "RayleighOD", "Rayleigh Optical Depth", r"\tau_R = 0.008569\,\lambda^{-4}\,(1+0.0113\lambda^{-2}+0.00013\lambda^{-4})", "λ in μm; optical depth from sea level to space", "~0.12 at 550nm; ~0.05 at 800nm") -V(eid, "Sky radiance/polarization measurements", "Atmospheric optics", 1950, "Matches Rayleigh scattering predictions", "Confirmed") - -E("Lightning Return Stroke Current (Heidler Function / Bruce-Golde)", 29, "1941/1985", "Proven", - "i(t) = (I₀/η)((t/τ₁)^n/(1+(t/τ₁)^n)) exp(−t/τ₂); I₀≈10-200 kA; τ₁≈1-2μs, τ₂≈10-100μs", - "Lightning protection design; EMC standards") -V(eid, "Rocket-triggered lightning current measurements", "Camp Blanding, Florida", 1990, "Direct measurement of current waveform", "Confirmed") - -E("Primitive Equations (Numerical Weather Prediction)", 29, "1950s", "Proven", - "Conservation of momentum (3D), mass (continuity), energy (thermodynamic), moisture, and ideal gas law", - "Every weather forecast model (GFS, ECMWF, ICON, etc.)") - -# ================================================================ -# 4. BIOPHYSICS -# ================================================================ - -E("Nernst Equation (Membrane Equilibrium Potential)", 32, "1888", "Proven", - "E_ion = (RT/zF) ln([ion]_out/[ion]_in); equilibrium potential for single ion species", - "Neuronal electrophysiology; all excitable cells") -S(eid, "NernstVals", "Typical Nernst Potentials (Mammalian Neurons at 37°C)", r"E_K = -90\,\text{mV},\; E_{Na} = +60\,\text{mV},\; E_{Cl} = -70\,\text{mV},\; E_{Ca} = +120\,\text{mV}", "", "") -V(eid, "Patch-clamp measurements of reversal potentials", "Neher & Sakmann 1976 (Nobel 1991)", 1976, "Reversal potential matches Nernst for single-channel current", "Confirmed") - -E("Goldman-Hodgkin-Katz (GHK) Voltage Equation", 32, "1949", "Proven", - "V_m = (RT/F) ln[(P_K[K]_o+P_Na[Na]_o+P_Cl[Cl]_i)/(P_K[K]_i+P_Na[Na]_i+P_Cl[Cl]_o)]", - "Resting membrane potential from multiple permeant ions; V_rest ≈ −70mV") -S(eid, "GHKcurrent", "GHK Current Equation", r"I_X = P_X z_X^2\frac{F^2 V_m}{RT}\frac{[X]_i - [X]_o e^{-z_X F V_m/RT}}{1 - e^{-z_X F V_m/RT}}", "Current for ion species X through open channel; constant-field assumption", "") -V(eid, "Resting potential ~-70mV matches GHK prediction", "Microelectrode recordings", 1950, "±5mV for most neurons", "Confirmed") - -E("Hodgkin-Huxley Equations (Action Potential)", 32, "1952", "Proven", - "C_m dV/dt = −g_K n⁴(V−E_K) − g_Na m³h(V−E_Na) − g_L(V−E_L) + I_stim", - "Squid giant axon; Nobel 1963; all excitable cell modeling") -S(eid, "HHGates", "Hodgkin-Huxley Gating Variables", r"\frac{dn}{dt}=\alpha_n(1-n)-\beta_n n,\;\frac{dm}{dt}=\alpha_m(1-m)-\beta_m m,\;\frac{dh}{dt}=\alpha_h(1-h)-\beta_h h", "α,β are voltage-dependent rate constants; m=Na activation, h=Na inactivation, n=K activation", "") -V(eid, "Squid giant axon AP shape matches HH model", "Hodgkin & Huxley 1952", 1952, "All AP features (threshold, all-or-none, refractory) reproduced", "Confirmed") - -E("FitzHugh-Nagumo Model (Simplified Excitable Dynamics)", 32, "1961/1962", "Proven", - "dv/dt = v − v³/3 − w + I; dw/dt = ε(v + a − bw); 2-variable reduction of HH", - "Bifurcation analysis of excitability; pattern formation; cardiac modeling") - -E("Cable Equation (Neuronal Dendrite/Axon)", 32, "1950s", "Proven", - "λ² ∂²V/∂x² = τ_m ∂V/∂t + V; λ=√(r_m/r_i); τ_m=r_m c_m; passive spread along membrane", - "Synaptic integration in dendrites; Rall's cable theory") -S(eid, "CableParams", "Cable Parameters", r"\lambda = \sqrt{\frac{r_m}{r_i}}, \; \tau_m = r_m c_m", "λ~0.1-1 mm for dendrites; ~1 mm for unmyelinated axon; ~2 cm for myelinated", "") -V(eid, "Dendritic potential attenuation matches cable theory", "Dual patch-clamp recordings", 1990, "Dendritic filtering quantitatively predicted", "Confirmed") - -E("Einstein-Smoluchowski Relation (Molecular Motor Stalling Force)", 32, "1905", "Proven", - "F_stall = k_B T / δ; δ=step size (~8 nm for kinesin); ~6 pN stall force", - "Single-molecule motor assays; optical trap measurements") -V(eid, "Kinesin stall force ~5-7 pN (optical trapping)", "Block/Schnitzer/Gelles 1990s", 1995, "Matches Einstein relation prediction", "Confirmed") - -E("Bell's Model (Bond Rupture Under Force)", 32, "1978", "Proven", - "k_off(F) = k₀ exp(F γ/k_B T); γ=reactive compliance (~0.1-0.5 nm); slip bond kinetics", - "Single-molecule force spectroscopy; catch bonds; cell adhesion") -V(eid, "Biotin-streptavidin rupture force distribution matches Bell model", "AFM/optical tweezers force spectroscopy", 2000, "Dynamic force spectroscopy standard", "Confirmed") - -E("Hill's Equation (Muscle Force-Velocity Relation)", 32, "1938", "Proven", - "(F + a)(v + b) = (F₀ + a)b; hyperbola; v_max = F₀ b/a; a,b constants", - "All striated muscle mechanics; cardiac/skeletal muscle") -V(eid, "Isotonic quick-release experiments in frog sartorius", "Hill 1938", 1938, "Characteristic hyperbolic F-v confirmed", "Confirmed") - -E("Huxley Sliding Filament Model (Cross-Bridge Dynamics)", 32, "1957", "Proven", - "∂n(x,t)/∂t = f(x)[1−n(x,t)] − g(x) n(x,t); n=attached cross-bridge probability; x=distortion", - "Muscle force generation; all striated muscle; foundational mechanochemical model") -V(eid, "ATPase rate matches cross-bridge cycle predictions", "Biochemical + mechanical assays", 1970, "Coupling between chemistry and force confirmed", "Confirmed") - -E("Monod-Wyman-Changeux (MWC) Model (Allosteric Transitions)", 32, "1965", "Proven", - "L = [T₀]/[R₀]; Y = α(1+α)^{n-1}/(L + (1+α)^n); α=[S]/K_R; cooperative ligand binding", - "Hemoglobin oxygen binding; many allosteric proteins; ion channel gating") -V(eid, "Hemoglobin O₂ binding curve fit (n_H~2.8)", "Monod/Wyman/Changeux 1965", 1965, "Sigmoidal binding curve explained", "Confirmed") - -E("Michaelis-Menten Enzyme Kinetics", 33, "1913", "Proven", - "v = V_max [S] / (K_m + [S]); K_m = (k_{−1}+k_cat)/k₁; V_max = k_cat [E]_total", - "All enzyme kinetics; steady-state approximation") -S(eid, "MMeq", "Briggs-Haldane Steady-State", r"v = \frac{k_{cat}[E]_0[S]}{K_m + [S]}, \; K_m = \frac{k_{-1}+k_{cat}}{k_1}", "General case; Michaelis-Menten is special case k_cat≪k_{-1}", "") -V(eid, "Countless enzyme assays confirm MM kinetics", "Biochemistry standard", 1930, "Initial rate vs [S] hyperbolic confirmed", "Confirmed") - -E("Transition State Theory (Eyring Equation)", 33, "1935", "Proven", - "k = (k_B T/h) exp(−ΔG‡/RT) = (k_B T/h) exp(ΔS‡/R) exp(−ΔH‡/RT)", - "Absolute reaction rate theory; all chemical kinetics") -V(eid, "Activation parameters from temperature-dependent rates", "Eyring/Polanyi 1935", 1935, "ΔH‡, ΔS‡ extracted; linear Eyring plots", "Confirmed") - -E("Arrhenius Equation (Chemical Reaction Rate)", 33, "1889", "Proven", - "k = A exp(−E_a/RT); log₁₀(k₂/k₁) = (E_a/2.303R)(1/T₁−1/T₂); activation energy from T-dependence", - "Universal in chemical kinetics; 2-4x rate increase per 10K at room T") -V(eid, "Countless reactions verify linear ln(k) vs 1/T", "Chemical kinetics standard", 1900, "Most reactions follow Arrhenius behavior", "Confirmed") - -E("Marcus Theory (Electron Transfer Rate)", 33, "1956", "Proven", - "k_ET = (2π/ℏ) H_AB² (1/√(4πλ k_B T)) exp[−(ΔG⁰+λ)²/(4λ k_B T)]; Nobel 1992", - "All electron transfer reactions; inverted region λ<−ΔG⁰") -S(eid, "MarcusInvert", "Marcus Inverted Region", r"\ln k \text{ decreases when } |\Delta G^0| > \lambda", "", "Confirmed by Closs/Miller experiments (1984)") -V(eid, "Photoinduced ET in donor-bridge-acceptor molecules confirms Marcus inverted region", "Closs & Miller 1984", 1984, "Bell-shaped ln(k) vs ΔG⁰ confirmed", "Confirmed") - -E("Butler-Volmer Equation (Electrode Kinetics)", 33, "1930s", "Proven", - "j = j₀[exp(α_a F η/RT) − exp(−α_c F η/RT)]; η=overpotential; α_a+α_c≈1", - "All electrode reactions; charge transfer kinetics") -V(eid, "Tafel slopes for H₂ evolution, O₂ reduction match BV", "Electrode kinetics standard", 1950, "α≈0.5 for many metal electrodes", "Confirmed") - -E("Beer-Lambert Law (Spectroscopy)", 34, "1729–1852", "Proven", - "A = −log₁₀(T) = ε c L; absorbance, molar absorptivity, concentration, path length", - "All absorption spectroscopy (UV-Vis, IR, etc.)") -V(eid, "Linearity of absorbance vs concentration", "Analytical chemistry standard", 1900, "Over 3-4 orders of magnitude", "Confirmed") - -E("Förster Resonance Energy Transfer (FRET) Efficiency", 32, "1948", "Proven", - "E = R₀⁶/(R₀⁶+r⁶); R₀⁶ ∝ κ² Φ_D J(λ)/n⁴; R₀~1-10 nm", - "Molecular ruler; protein folding; single-molecule biophysics") -V(eid, "FRET distance measurements correlate with structural models", "Single-molecule TIRF microscopy", 2000, "Angstrom-scale distance discrimination", "Confirmed") - -# ================================================================ -# 5. PHOTONICS & LASER PHYSICS -# ================================================================ - -E("Einstein Rate Equations (Laser Dynamics)", 34, "1917/1960", "Proven", - "dN₂/dt = R_p − B₂₁ ρ(ν) N₂ − A₂₁ N₂; dφ/dt = B₂₁ ρ(ν)(N₂−N₁) c' − φ/τ_c", - "All laser operation; population inversion; gain/loss balance") - -E("Laser Threshold Condition", 34, "1960", "Proven", - "g_th = α_int + (1/2L) ln(1/R₁R₂); gain must overcome internal loss + mirror transmission", - "Laser design; all laser types (gas, solid-state, semiconductor, fiber)") -V(eid, "Threshold pump power matches prediction in thousands of laser designs", "Since Maiman 1960", 1960, "Within factor ~2 for simple models", "Confirmed") - -E("Schawlow-Townes Linewidth (Fundamental Laser Linewidth)", 34, "1958", "Proven", - "Δν = (π hν (Δν_c)²)/P_out; quantum-limited linewidth; narrower with higher power", - "Fundamental limit on laser coherence; Nobel 1981 (Schawlow)") -V(eid, "Linewidth narrowing with increased power", "High-finesse Fabry-Perot, fiber lasers", 1980, "Qualitatively; technical noise often dominates", "Confirmed") - -E("Mode-Locking Condition (fs/ps Pulses)", 34, "1960s", "Proven", - "T_R = 2L/c (round-trip time); f_rep = 1/T_R; N locked modes → τ_p = T_R/N ∝ 1/Δν_gain", - "Femtosecond lasers; frequency combs (Nobel 2005: Hänsch/Hall)") -S(eid, "FreqComb", "Optical Frequency Comb", r"f_n = n f_{rep} + f_{CEO}", "f_rep=repetition rate; f_CEO=carrier-envelope offset frequency; self-referenced combs", "Nobel Prize in Physics 2005") -V(eid, "Attosecond timing precision in frequency combs", "Hänsch/Hall groups 1990s", 2000, "Precision 10^{-15} for optical clocks", "Confirmed") - -E("Nonlinear Polarization (χ^{(n)} Expansion)", 34, "1960s", "Proven", - "P_i = ε₀[χ^{(1)}_{ij} E_j + χ^{(2)}_{ijk} E_j E_k + χ^{(3)}_{ijkl} E_j E_k E_l + ...]", - "All nonlinear optics; harmonic generation, parametric processes") -S(eid, "SHG", "Second-Harmonic Generation", r"P^{(2)}_{2\omega} \propto \chi^{(2)} E_\omega E_\omega", "Frequency doubling; requires non-centrosymmetric material; BBO, KTP, LiNbO₃", "") -V(eid, "Franken et al. 1961 first SHG in quartz", "Franken/Hill/Peters/Weinreich 1961", 1961, "First demonstration of nonlinear optics", "Confirmed") - -E("Phase-Matching Condition (Nonlinear Optics)", 34, "1962", "Proven", - "Δk = k_3 − k_2 − k_1 = 0 for SHG; n(2ω)=n(ω) required; birefringent or QPM", - "Efficient harmonic generation; PPLN, PPKTP for quasi-phase-matching") -V(eid, "Maker fringes (phase-matching signature)", "Maker et al. 1962", 1962, "Oscillatory SHG vs crystal rotation", "Confirmed") - -E("Nonlinear Schrödinger Equation (Optical Solitons in Fibers)", 34, "1973", "Proven", - "i ∂A/∂z − (β₂/2)∂²A/∂t² + γ|A|²A = 0; balance GVD (β₂) and Kerr nonlinearity (γ)", - "Soliton propagation in fibers; all-optical communication") -S(eid, "Soliton", "Fundamental Soliton Solution", r"A(z,t) = \sqrt{P_0}\, \text{sech}(t/T_0)\, e^{iz/(2L_D)}", "L_D=T₀²/|β₂|; P₀=|β₂|/(γT₀²); L_NL=1/(γP₀)=L_D for N=1 soliton", "") -V(eid, "Soliton propagation over thousands of km in fiber loops", "Mollenauer et al. 1980, Hasegawa prediction", 1980, "Pulse shape preserved over long distances", "Confirmed") - -E("Kramers-Kronig Relations (Optical Dispersion)", 34, "1926-27", "Proven", - "n(ω)−1 = (2/π)P∫₀^∞ ω'κ(ω')/(ω'²−ω²)dω'; causality → real and imaginary parts of χ linked", - "All linear optical materials; refractive index and absorption intrinsically coupled") -V(eid, "n and k extracted from reflectometry match KK transform", "Ellipsometry, spectroscopy", 1950, "Causality test; confirmed without exception", "Confirmed") - -E("Rate Equations for Semiconductor Lasers", 34, "1960s", "Proven", - "dN/dt = η_i I/qV − R(N) − v_g g(N) N_ph; dN_ph/dt = Γ v_g g(N) N_ph − N_ph/τ_ph + β_sp R_sp", - "All diode lasers; VCSELs, DFB, FP lasers; threshold, modulation response") - -E("Master Equation for Mode-Locked Lasers (Haus)", 34, "1975", "Proven", - "ΔA = (g−l + jD) A + (g/Ω_g² + jD_g) ∂²A/∂t² + (γ−jδ)|A|²A; Haus master equation", - "Pulse formation theory; active/passive mode-locking; soliton fiber lasers") - -E("Coupled-Mode Theory (Waveguides, Gratings, Resonators)", 34, "1970s", "Proven", - "da_μ/dz = −j Σ_κ K_μκ a_κ exp[j(β_κ−β_μ)z]; coupling between waveguide/grating modes", - "DFB/DBR lasers; fiber Bragg gratings; microring resonators; photonic circuits") - -# ================================================================ -# 6. ATOMIC & MOLECULAR PHYSICS -# ================================================================ - -E("Zeeman Effect (Normal + Anomalous)", 35, "1896/1925", "Proven", - "ΔE = μ_B g_J m_J B; g_J = 1 + [J(J+1)+S(S+1)−L(L+1)]/[2J(J+1)]; Landé g-factor", - "Magnetic field splitting of atomic spectral lines; astrophysical magnetic field diagnostics") -V(eid, "Zeeman effect in solar/stellar spectra", "Hale 1908 (sunspot magnetic fields)", 1908, "Magnetic field strengths derived from Zeeman splitting", "Confirmed") - -E("Stark Effect (Linear + Quadratic)", 35, "1913/1920s", "Proven", - "Linear: ΔE = 3ea₀ n (n₁−n₂) E / 2 (Hydrogen); Quadratic: ΔE = −½ α E² (general)", - "Electric field splitting; Rydberg atoms; Stark spectroscopy") - -E("Hyperfine Structure (Fermi Contact Interaction)", 35, "1930", "Proven", - "ΔE_HFS = (A/2) [F(F+1) − I(I+1) − J(J+1)]; A ∝ μ_B μ_N ⟨1/r³⟩ |ψ(0)|²", - "21 cm hydrogen line (1420 MHz); atomic clocks; nuclear moment determination") -S(eid, "HLine", "Hydrogen 21-cm Line", r"\Delta E = \frac{8}{3} g_I \mu_N \mu_B |\psi(0)|^2", "F=1→F=0; 1420.4057517667 MHz; astrophysically crucial for HI mapping", "Cosmic epoch of reionization; Galaxy structure") -V(eid, "21-cm hyperfine line discovered (Ewen/Purcell 1951)", "Ewen & Purcell 1951", 1951, "1420.4 MHz confirmed; standard for radio astronomy", "Confirmed") - -E("Born-Oppenheimer Approximation (Molecular Hamiltonian Separation)", 35, "1927", "Proven", - "Ψ(r,R) ≈ ψ_e(r;R) χ_N(R); electronic Schrödinger eq at fixed nuclear geometry; then nuclear motion", - "All molecular quantum mechanics; potential energy surfaces; vibronic coupling") -V(eid, "Molecular vibrational frequencies match BO PES calculations", "Spectroscopy + quantum chemistry", 1950, "Within ~1-5% for harmonic frequencies", "Confirmed") - -E("Franck-Condon Principle (Vibrational Transition Intensities)", 35, "1925–28", "Proven", - "I_v'v'' ∝ |∫ ψ_v'* ψ_v'' dR|²; vertical transitions; overlap of vibrational wavefunctions", - "All molecular electronic spectroscopy; absorption/emission band shapes") - -E("Molecular Rotational Spectroscopy (Rigid Rotor)", 35, "1920s", "Proven", - "E_J = B J(J+1); B = ℏ²/(2I); I = μ R²; ΔJ = ±1 selection rule → 2B spacing", - "Molecular structure determination; interstellar molecule identification") - -E("Molecular Vibrational Spectroscopy (Harmonic)", 35, "1920s", "Proven", - "E_v = ℏω(v+½); ω = √(k/μ); fundamental transition ν₀ = ω/(2πc)", - "IR and Raman spectroscopy; functional group identification; chemical analysis") -V(eid, "Vibrational frequencies match DFT predictions", "IR/Raman spectroscopy standard", 1950, "Within 2-5% for harmonic approximation", "Confirmed") - -E("Morse Potential (Anharmonic Diatomic)", 35, "1929", "Proven", - "V(r) = D_e [1 − e^{−a(r−r_e)}]²; analytical eigenvalues E_v = ℏω(v+½)−ℏωx_e(v+½)²", - "Realistic diatomic potential; dissociation limit included; anharmonicity") -V(eid, "Anharmonic overtones fit Morse progression (HCl, CO, N₂)", "High-resolution IR spectroscopy", 1930, "Within ~1% for v<~10", "Confirmed") - -E("Rydberg Formula (Atomic Series Limits)", 35, "1888", "Proven", - "1/λ = R∞/(n₁+δ₁)² − R∞/(n₂+δ₂)²; R∞=10973731.568157 m⁻¹; δ=quantum defect", - "All atomic spectral series; quantum defect from core penetration") -V(eid, "Spectral series of alkali atoms (Li, Na, K, Rb, Cs)", "Rydberg/Balmer/Paschen 1880s-1900s", 1890, "Series limits precisely determined", "Confirmed") - -E("Racah Algebra (Angular Momentum Coupling in Complex Atoms)", 35, "1940s", "Proven", - "Wigner 3-j, 6-j, 9-j symbols; recoupling coefficients; matrix elements of tensor operators", - "Atomic structure theory; f-electron systems; lanthanide/actinide spectroscopy") - -# ================================================================ -# 7. RHEOLOGY -# ================================================================ - -E("Newtonian Constitutive Equation (Viscous Fluid)", 36, "1687/1820s", "Proven", - "τ = μ γ̇; σ = −p I + 2 μ D; D = strain-rate tensor; τ ∝ shear rate linearly", - "Water, oils, simple liquids; all fluids with constant viscosity") - -E("Power-Law Fluid (Ostwald-de Waele Model)", 36, "1920s", "Proven", - "τ = K γ̇^n; η_app = K γ̇^{n-1}; n<1 → shear-thinning (pseudoplastic); n>1 → shear-thickening; n=1 → Newtonian", - "Polymer melts, solutions; blood; paints; food products; drilling muds") -V(eid, "Viscosity vs shear rate fits power-law over 2-3 decades", "Rotational rheometry standard", 1950, "Confirmed for thousands of materials", "Confirmed") - -E("Bingham Plastic (Yield Stress Fluid)", 36, "1922", "Proven", - "τ = τ_y + μ_p γ̇ for τ > τ_y; no flow for τ < τ_y", - "Toothpaste, ketchup, drilling mud, concrete, many soft solids") -V(eid, "Yield stress measured by stress ramp in rheometer", "Rheometry standard", 1980, "τ_y determined from flow curve onset", "Confirmed") - -E("Herschel-Bulkley Model (Yield + Power-Law)", 36, "1926", "Proven", - "τ = τ_y + K γ̇^n for τ > τ_y", - "Most real yield-stress fluids generalize Bingham; widely used") - -E("Carreau-Yasuda Model (Shear-Thinning With Zero/Infinite Limits)", 36, "1972/1979", "Proven", - "η(γ̇) = η_∞ + (η₀−η_∞)[1 + (λ γ̇)^a]^{(n-1)/a}", - "Polymer solutions and melts; wide shear-rate range; smooth Newtonian plateau at low γ̇") - -E("Maxwell Viscoelastic Model (Liquid)", 36, "1867", "Proven", - "dε/dt = (1/E) dσ/dt + σ/η; relaxation time τ = η/E; elastic at short times, viscous at long", - "Polymer melts; simple viscoelastic fluid model") -S(eid, "MaxwellRelax", "Stress Relaxation (Maxwell)", r"\sigma(t) = \sigma_0\,e^{-t/\tau}", "Exponential decay of stress at constant strain; τ = η/E", "") -V(eid, "Stress relaxation in polymer melts follows Maxwell at short times", "Rheometry", 1960, "Exponential decay at moderate strain", "Confirmed") - -E("Kelvin-Voigt Model (Viscoelastic Solid)", 36, "1890s", "Proven", - "σ = E ε + η dε/dt; retardation time = η/E; creep compliance J(t)=[1−e^{−t/τ}]/E", - "Creep of viscoelastic solids; crosslinked polymers below T_g") - -E("Generalized Maxwell / Wiechert Model (Multiple Relaxation Times)", 36, "1890s", "Proven", - "G(t) = G_∞ + Σ_i G_i exp(−t/τ_i); relaxation spectrum H(τ); Prony series", - "All real viscoelastic materials; DMA and rheometry analysis") -V(eid, "Prony series fit to DMA master curves", "Dynamic Mechanical Analysis", 1970, "Excellent fit with 5-15 Maxwell elements", "Confirmed") - -E("Cox-Merz Rule (Steady vs Dynamic Viscosity Equivalence)", 36, "1958", "Proven", - "η(γ̇) ≈ |η*(ω)| when γ̇ = ω; empirical equivalence for many polymer melts/solutions", - "Rheological characterization; connects steady shear and oscillatory measurements") -V(eid, "Cox-Merz holds for linear polymers; fails for structured fluids", "Cox & Merz 1958", 1958, "Verified for most homogeneous polymer melts", "Confirmed") - -E("Trouton Ratio (Extensional/Shear Viscosity Ratio)", 36, "1906", "Proven", - "Tr = η_E / η; Newtonian: Tr=3; viscoelastic: Tr≫3; strain-hardening in extension", - "Extensional rheology; polymer processing (fiber spinning, blow molding)") - -# ================================================================ -# 8. TRIBOLOGY -# ================================================================ - -E("Amontons-Coulomb Friction Laws", 37, "1699/1785", "Proven", - "F_f = μ N (macroscopic); independent of apparent contact area and sliding speed (approximately)", - "Macroscopic dry friction; deviations at high speed, low load, or clean surfaces") - -E("Archard's Law (Adhesive Wear)", 37, "1953", "Proven", - "V = k F s / H; k=wear coefficient (~10^{-2} to 10^{-7}); softer material hardness controls", - "All sliding wear; used for component lifetime prediction") -V(eid, "Wear volume vs sliding distance ± linear; pin-on-disk tests confirm", "Standard tribology testing (ASTM G99)", 1960, "Wear map classification of materials", "Confirmed") - -E("Stribeck Curve (Lubrication Regimes)", 37, "1902", "Proven", - "μ = f(η N/p, roughness, geometry); boundary → mixed → EHL → hydrodynamic as speed increases", - "Bearing design; all lubricated contacts: journal bearings, cams, gears") -V(eid, "Friction vs Sommerfeld number (ηN/p) confirms Stribeck shape", "Bearing test rigs", 1920, "Characteristic U-shaped curve confirmed", "Confirmed") - -E("Reynolds Equation (Thin-Film Lubrication)", 37, "1886", "Proven", - "∂/∂x[(h³/η)∂p/∂x] + ∂/∂y[(h³/η)∂p/∂y] = 6(U∂h/∂x + 2∂h/∂t)", - "Hydrodynamic bearing pressure generation; journal & thrust bearings, seals") -V(eid, "Journal bearing pressure profiles match Reynolds equation predictions", "Bearing test rigs with pressure taps", 1930, "Film thickness within 10% of prediction", "Confirmed") - -E("Hertzian Contact (Elastic Contact Between Curved Surfaces)", 18, "1882", "Proven", - "a = (3FR/4E*)^{1/3}; p_max = 3F/(2πa²); E* = [(1−ν₁²)/E₁+(1−ν₂²)/E₂]⁻¹", - "Ball bearings, gears, wheel-rail contact; maximum contact pressure at center") -S(eid, "HertzPressure", "Hertz Contact Pressure Distribution", r"p(r) = p_{max}\sqrt{1 - (r/a)^2}, \; \tau_{max} \approx 0.31 p_{max} \text{ at } z \approx 0.48 a", "Elliptical pressure distribution; max shear ~0.48a below surface", "Rolling contact fatigue originates at subsurface τ_max") -V(eid, "Contact area vs load matches Hertz prediction", "Photoelastic / pressure-sensitive film experiments", 1940, "Within 5% for elastic contacts", "Confirmed") - -E("Elastohydrodynamic Lubrication (EHL) Film Thickness (Hamrock-Dowson)", 37, "1970s", "Proven", - "h_min/R_x = 3.63 U⁰·⁶⁸ G⁰·⁴⁹ W⁻⁰·⁰⁷³ (1−e^{−0.68k}); U=η₀u/E'R_x, G=αE', W=w/E'R_x²", - "Gears, rolling bearings; EHL film thickness separates surfaces elastically") -V(eid, "Optical EHL interferometry confirms film thickness formula", "Cameron/Gohar 1960s; Spikes group", 1970, "Within ~20% of Hamrock-Dowson prediction", "Confirmed") - -# ================================================================ -# 9. GRANULAR MATERIALS -# ================================================================ - -E("Janssen Effect (Pressure Saturation in Silos)", 38, "1895", "Proven", - "p(z) = (ρ g D / 4 μ_w K) [1 − exp(−4 μ_w K z/D)]; pressure saturates at finite depth", - "Silo design; grain storage; Janssen 1895 verified repeatedly") -S(eid, "JanssenStress", "Janssen Saturation Pressure", r"p_\infty = \frac{\rho g D}{4 \mu_w K}", "K=Janssen coefficient (ratio of horizontal/vertical stress); ≈0.3-0.6", "") -V(eid, "Silo pressure measurements confirm saturation", "Full-scale silo instrumentation", 1900, "Pressure plateaus at ~2-3 diameters depth", "Confirmed") - -E("Coulomb Yield Criterion (Granular Failure)", 38, "1776", "Proven", - "τ = σ tan φ + c; φ=internal friction angle (~25-45° for sands); c=cohesion (0 for dry sand)", - "Soil mechanics; slope stability; granular pile failure; all geotechnical engineering") - -E("Angle of Repose (Granular Pile)", 38, "Ancient", "Proven", - "tan φ_r = H_max / R; φ_r ≈ φ (internal friction angle); ~30-40° for most granular materials", - "Sand piles, hopper design, avalanche dynamics; empirical") - -E("Brazil Nut Effect (Granular Convection/Segregation)", 38, "20th c.", "Proven", - "Larger particles rise during vibration or shaking due to percolation + convection", - "Mixing/de-mixing of granular mixtures; pharmaceutical processing; geophysical sorting") -V(eid, "Vibrated granular column: large bead rises to top", "Laboratory granular dynamics", 1990, "Brazil nut effect reproduced under controlled conditions", "Confirmed") - -E("Bagnold Scaling (Granular Flow Rheology - Inertial)", 38, "1954", "Proven", - "τ = a (ρ_p d²) γ̇² (inertial regime); Bagnold number Ba = ρ_p d² γ̇/η_f; Ba>450 → grain inertia dominates", - "Debris flows, grain flow in chutes, aeolian sand transport") -V(eid, "Shear cell experiments confirm Bagnold scaling at high shear rates", "Granular rheometry", 1980, "τ ∝ γ̇² in inertial regime; τ ∝ γ̇¹ in quasi-static", "Confirmed") - -E("μ(I) Rheology (Inertial Number Scaling for Dense Granular Flow)", 38, "2006", "Proven", - "μ(I) = μ_s + (μ₂−μ_s)/(1+I₀/I); I = γ̇ d/√(p/ρ_p); dimensionless inertial number", - "Modern dense granular flow rheology; unifies quasi-static and inertial regimes") - -# ================================================================ -# 10. NANOSCIENCE -# ================================================================ - -E("Coulomb Blockade Condition (Single-Electron Transistor)", 39, "1980s", "Proven", - "E_c = e²/(2C_Σ) > k_B T; charging energy must exceed thermal energy for CB to be observed", - "Single-electron transistors; quantum dots; Coulomb staircase in I-V") -S(eid, "SET", "SET Current — Orthodox Theory", r"I = \text{rate of sequential tunneling when } eV/2 > E_c", "Coulomb blockade at low bias; periodic in gate voltage (Coulomb oscillations)", "") -V(eid, "Coulomb oscillations observed in quantum dots at mK temperatures", "Kastner group (MIT) 1990s", 1990, "Periodic conductance peaks vs gate voltage confirmed", "Confirmed") - -E("Landauer Formula (Ballistic Conductance)", 39, "1957/1988", "Proven", - "G = (2e²/h) Σ T_n; G₀ = 2e²/h ≈ 77.5 μS (~12.9 kΩ); quantized conductance", - "1D ballistic transport; quantum point contacts; carbon nanotubes; nanowires") -S(eid, "GQ", "Conductance Quantum", r"G_0 = \frac{2e^2}{h} \approx 7.748 \times 10^{-5}\,\text{S} \;\; (R_Q = h/2e^2 \approx 12.9\,\text{k}\Omega)", "Each open mode contributes G₀; spin degeneracy gives factor 2", "") -V(eid, "Quantized conductance steps in QPC (2DEG)", "Van Wees et al. 1988, Wharam et al. 1988", 1988, "Integer steps of G₀ confirmed in GaAs/AlGaAs heterostructures", "Confirmed") - -E("Kondo Effect (Resistance Minimum in Dilute Magnetic Alloys)", 39, "1964", "Proven", - "R ∝ −ln(T) below Kondo temperature T_K; magnetic impurity spin screened by conduction electrons", - "Kondo insulators; quantum dot Kondo physics; heavy fermion systems") -V(eid, "R vs T minimum in AuFe, CuFe confirmed", "De Haas, Van Den Berg 1930s; Kondo 1964 explained", 1964, "−ln(T) dependence below T_K confirmed", "Confirmed") - -E("2D Electron Gas Density of States (Constant)", 39, "1960s", "Proven", - "g_{2D}(E) = m*/(πℏ²) = constant; independent of energy", - "GaAs/AlGaAs heterostructures; MOSFET inversion layers; quantum Hall effect") - -E("Graphene Dirac Dispersion (Massless 2D Fermions)", 39, "2005", "Proven", - "E = ± v_F |k|; v_F ≈ 10⁶ m/s; linear dispersion near Dirac points K,K'", - "Graphene; Nobel 2010 (Geim/Novoselov); half-integer QHE; Klein tunneling") -V(eid, "ARPES measurements confirm linear dispersion in graphene", "Angle-resolved photoemission spectroscopy", 2005, "Dirac cone directly imaged; v_F≈10⁶ m/s", "Confirmed") - -E("Quantum Confinement (Infinite Well — Nanowire/Quantum Well)", 39, "1970s", "Proven", - "E_n = n²π²ℏ²/(2m*L²); 1D wire; 2D well adds E_{n_x,n_y} terms; 0D dot adds all three", - "Semiconductor nanostructures; blue-shift in optical transitions with decreasing size") -V(eid, "Photoluminescence blue-shift in quantum wells with decreasing thickness", "Molecular beam epitaxy grown QWs", 1980, "Quantized subband energies confirmed", "Confirmed") - -E("Casimir Force (Between Ideal Plates, Nanoscale)", 39, "1948", "Proven", - "F/A = −π²ℏc/(240 d⁴); d=separation; attractive; zero-point EM fluctuations", - "MEMS/NEMS stiction; nanoscale force metrology; measured to ~1% accuracy") -V(eid, "Casimir force measured within 1% (Lamoreaux 1997, Mohideen/Roy)", "Torsion pendulum / AFM cantilever experiments", 1997, "Force vs d follows theory from ~0.5-6 μm", "Confirmed") - -E("DLVO Theory (Colloidal Nanoparticle Stability)", 39, "1940s", "Proven", - "V_total(d) = V_vdW + V_EDL; van der Waals attraction + electric double-layer repulsion", - "Nanoparticle dispersion stability; aggregation; protein corona; nanotoxicology") - -# ================================================================ -# 11. QUANTUM INFORMATION -# ================================================================ - -E("Single Qubit State (Bloch Sphere)", 40, "1990s", "Proven", - "|ψ⟩ = cos(θ/2)|0⟩ + e^{iφ} sin(θ/2)|1⟩; pure state on Bloch sphere surface", - "All single quantum bit representations; universal in quantum computing") - -E("Bell States (Maximally Entangled Two-Qubit States)", 40, "1964", "Proven", - "|Φ⁺⟩=(|00⟩+|11⟩)/√2; |Φ⁻⟩=(|00⟩−|11⟩)/√2; |Ψ⁺⟩=(|01⟩+|10⟩)/√2; |Ψ⁻⟩=(|01⟩−|10⟩)/√2", - "Quantum teleportation; superdense coding; Bell's test; fundamental entanglement resource") -V(eid, "Bell pairs generated via SPDC, trapped ions, superconducting qubits", "Multiple platforms", 2000, "Fidelity >99% in modern quantum computers", "Confirmed") - -E("No-Cloning Theorem", 40, "1982", "Proven", - "An unknown quantum state cannot be copied perfectly; U|ψ⟩|0⟩ ≠ |ψ⟩|ψ⟩ for all |ψ⟩", - "Fundamental theorem of quantum mechanics; basis for quantum cryptography (BB84)") - -E("Holevo Bound (Classical Information From Qubit)", 40, "1973", "Proven", - "χ ≤ S(ρ) − Σ p_i S(ρ_i); at most 1 classical bit extractable per qubit", - "Quantum communication capacity limit; quantum key distribution security proof") -V(eid, "QKD systems limited to Holevo bound", "Commercial QKD (ID Quantique, Toshiba, etc.)", 2000, "Never exceeded", "Confirmed") - -E("Deutsch-Jozsa Algorithm Speedup", 40, "1992", "Proven", - "Single-query solution to balanced/constant problem; first clear quantum advantage", - "Quantum algorithm prototype; extended to Bernstein-Vazirani, Simon's algorithm") - -E("Grover's Search Algorithm (Quadratic Speedup)", 40, "1996", "Proven", - "O(√N) quantum search via amplitude amplification; ~π√N/4 Grover iterations", - "Unstructured database search; quadratic speedup over classical O(N)") -V(eid, "Grover search demonstrated on small-scale quantum processors", "IBM/Google/IonQ, N=2-8", 2020, "Verified for small problem sizes", "Confirmed") - -E("Shor's Factoring Algorithm", 40, "1994", "Proven", - "Quantum period-finding via QFT → polynomial-time integer factorization; O((log N)³); exponentially faster than classical best known", - "Threatens RSA/public-key cryptography with large-scale quantum computer; not yet at scale") - -E("Concatenated Quantum Error Correction Threshold Theorem", 40, "1996–2005", "Proven", - "If gate error < p_th (~10⁻² to 10⁻⁴ depending on code), errors can be arbitrarily suppressed", - "Fault-tolerant quantum computing is theoretically possible; p_th = surface code threshold ~1%") -V(eid, "Quantum error correction demonstrated (Shor code, surface code)", "Google Sycamore, IBM, superconducting circuits", 2020, "Logical error rate suppressed below physical error rate", "Confirmed") - -# ================================================================ -# 12. NONLINEAR DYNAMICS & CHAOS -# ================================================================ - -E("Lorenz Equations (Deterministic Chaos)", 41, "1963", "Proven", - "ẋ = σ(y−x); ẏ = x(ρ−z)−y; ż = xy−β z; σ=10, β=8/3, ρ=28 → strange attractor", - "First chaotic attractor discovered; weather unpredictability; butterfly effect") -S(eid, "LorenzParams", "Lorenz System Parameters", r"\sigma=10,\; \rho=28,\; \beta=8/3 \rightarrow \text{chaotic regime}", "Critical ρ_c≈24.74 for onset of chaos; Lyapunov exponents λ₁≈0.9, λ₂=0, λ₃≈−14.6", "") -V(eid, "Lorenz attractor realized in analog circuits, lasers, fluid convection", "Multiple experiments since 1970", 1970, "Phase portrait confirmed in diverse physical systems", "Confirmed") - -E("Logistic Map (Period-Doubling Route to Chaos)", 41, "1976", "Proven", - "x_{n+1} = r x_n (1−x_n); period-doubling bifurcations; chaos at r≈3.57; Feigenbaum universality", - "Population dynamics; universal scaling in nonlinear maps") -S(eid, "Feigenbaum", "Feigenbaum Constants", r"\delta \approx 4.6692016, \; \alpha \approx 2.5029079", "Universality constants for period-doubling cascade", "Discovered by Feigenbaum 1975; verified") -V(eid, "Period doubling observed in Rayleigh-Bénard convection, electronic circuits, lasers", "Libchaber/Maurer 1980s", 1980, "Feigenbaum scaling confirmed in real experiments", "Confirmed") - -E("Lyapunov Exponent (Chaos Diagnostic)", 41, "1960s", "Proven", - "λ = lim_{t→∞} (1/t) ln |δx(t)/δx(0)|; λ>0 → chaos; λ<0 → stable; λ=0 → marginal", - "Universal measure of sensitive dependence on initial conditions; chaos quantification") - -E("KAM Theorem (Kolmogorov-Arnold-Moser)", 41, "1954–62", "Proven", - "Most invariant tori survive small perturbations if frequency ratio is sufficiently irrational", - "Solar system stability; plasma confinement in tokamaks; nonlinear oscillator theory") - -E("Kuramoto Model (Synchronization of Coupled Oscillators)", 41, "1975", "Proven", - "θ̇_i = ω_i + (K/N) Σ_j sin(θ_j−θ_i); K>K_c → phase transition to global synchronization", - "Firefly flashing, pacemaker cells, power grids, Josephson junction arrays") -V(eid, "Synchronization onset at critical coupling (K_c) confirmed in Kuramoto experiments", "Oscillator arrays (chemical, electrical, mechanical)", 1990, "Order parameter abrupt rise at K_c", "Confirmed") - -E("Mandelbrot Set (Fractal Geometry)", 41, "1980", "Proven", - "z_{n+1} = z_n² + c; bounded orbits → c ∈ Mandelbrot set; fractal boundary with infinite complexity", - "Fractal coastlines, turbulence, galaxy distribution, diffusion-limited aggregation") - -# ================================================================ -# 13. MEDICAL PHYSICS -# ================================================================ - -E("Bloch Equations (NMR/MRI Signal)", 42, "1946", "Proven", - "dM/dt = γ M × B − (M_x î+M_y ĵ)/T₂ − (M_z−M₀)k̂/T₁; relaxation toward equilibrium", - "All MRI physics; T₁ (spin-lattice) and T₂ (spin-spin) relaxation; image contrast") -S(eid, "MRIsignal", "MRI Signal Equation", r"S \propto \rho\, e^{-TE/T_2}\,(1-e^{-TR/T_1})", "ρ=proton density; TE=echo time; TR=repetition time; T₁,T₂ tissue contrast", "") -V(eid, "MRI contrast matches Bloch equation predictions", "Clinical MRI scanners (1.5T, 3T, 7T)", 1980, "T₁,T₂-weighted imaging standard worldwide", "Confirmed") - -E("Larmor Frequency (NMR Precession)", 42, "1946", "Proven", - "ω₀ = γ B₀; γ_H/2π = 42.577 MHz/T; proton Larmor frequency in clinical MRI", - "NMR spectroscopy; MRI; magnetic field precision ~10⁻⁹; every MRI and NMR spectrometer") -V(eid, "Proton Larmor frequency verified to ppb precision", "NMR spectrometers since 1950s", 1950, "Chemical shift ~ppm; frequency known to ~10⁻¹⁰", "Confirmed") - -E("Beer-Lambert Law (X-ray/γ-ray Attenuation, CT)", 42, "1900s", "Proven", - "I = I₀ e^{−μx}; μ/p = mass attenuation coefficient; CT: μ(x,y) → Hounsfield units", - "All X-ray imaging; CT scanner (Hounsfield/Cormack, Nobel 1979, Medicine)") -S(eid, "CT-HU", "Hounsfield Unit Scale", r"HU = 1000 \times \frac{\mu - \mu_{water}}{\mu_{water}}", "Water=0 HU; Air=−1000 HU; Bone=+300 to +3000 HU; Soft tissue ~+20-60 HU", "") -V(eid, "CT reconstruction produces quantitative attenuation maps", "Since Hounsfield 1971", 1971, "Clinical standard; spatial resolution ~0.5 mm", "Confirmed") - -E("Radon Transform (CT Image Reconstruction)", 42, "1917/1970s", "Proven", - "p(s,θ) = ∫_{-∞}^∞ f(s cosθ−t sinθ, s sinθ+t cosθ) dt; projection → 2D image via filtered backprojection", - "CT, PET, SPECT reconstruction; Radon 1917; used since Hounsfield 1971") - -E("Ultrasound Wave Equation (Medical Imaging)", 42, "1940s", "Proven", - "∂²p/∂t² = c²∇²p; reflection at tissue interfaces (acoustic impedance mismatch Z=ρc)", - "All medical ultrasound; obstetrics, cardiology, vascular; safe, real-time imaging") -S(eid, "ReflectionCoeff", "Acoustic Impedance Reflection Coefficient", r"R = \left(\frac{Z_2-Z_1}{Z_2+Z_1}\right)^2, \; Z = \rho c", "Soft tissue-bone: R≈0.4; soft tissue-air: R≈0.999 → gel coupling needed", "") -V(eid, "B-mode ultrasound images match anatomical structures", "Clinical ultrasound since 1950s", 1980, "Millimeter-scale resolution at MHz frequencies", "Confirmed") - -E("Attenuation of Ultrasound in Tissue", 42, "1950s", "Proven", - "I(x) = I₀ e^{−α f^n x}; n≈1 for most soft tissues; α≈0.5-1 dB/(cm·MHz)", - "Penetration depth vs frequency tradeoff; 3-5 MHz for abdominal; 7-15 MHz for superficial") - -E("Linear-Quadratic (LQ) Model (Radiation Therapy Cell Survival)", 43, "1980s", "Proven", - "S = exp(−αD − βD²); α/β ratio ~3 Gy for late-responding (CNS, spinal); ~10 Gy for early-responding/acutely responding; D=total dose", - "Virtually all radiation oncology treatment planning; fractionation rationale") -S(eid, "BED", "Biologically Effective Dose (BED)", r"BED = D\left(1 + \frac{d}{\alpha/\beta}\right)", "d=fraction size; accounts for fractionation effects; EQD₂=BED/(1+2/(α/β))", "Standard in clinical RT planning") -V(eid, "Tumor control probability vs dose matches LQ model predictions", "Clinical RT dose-response data", 1990, "TCP curves fit LQ in wide dose range", "Confirmed") - -E("Bragg Peak (Proton/Ion Beam Depth-Dose)", 43, "1905/1946", "Proven", - "dE/dx peaks sharply at end of range (Bragg peak); R ∝ E^{1.7−1.8}; sharp distal falloff", - "Proton/heavy ion therapy; dose conformality superior to photons; spread-out Bragg peak (SOBP)") -V(eid, "Bragg peak in proton beams confirmed and used therapeutically", "Wilson 1946 (proposed), LBL/Harvard/MGH since 1950s", 1970, "Proton therapy centers worldwide (100+)", "Confirmed") - -E("Bethe-Bloch Formula (Stopping Power for Charged Particles)", 43, "1930/1933", "Proven", - "−⟨dE/dx⟩ = K z² (Z/A)(1/β²)[½ ln(2m_e c²β²γ²T_max/I²) − β² − δ(βγ)/2]", - "Stopping power in any medium; relativistic charged particle energy loss; dE/dx minimum at βγ≈3-4 (minimum ionizing particle, MIP)") -S(eid, "BetheBloch", "Bethe-Bloch", r"-\left\langle\frac{dE}{dx}\right\rangle = K z^2 \frac{Z}{A}\frac{1}{\beta^2}\left[\frac{1}{2}\ln\frac{2m_e c^2\beta^2\gamma^2 T_{max}}{I^2} - \beta^2 - \frac{\delta}{2}\right]", "K=4πN_A r_e² m_e c²≈0.307 MeV·cm²/g; I=mean excitation potential; z=projectile charge", "Valid for β>0.05; δ=density correction at high γ") -V(eid, "Stopping power measurements in gases, solids, tissue-equivalent materials", "Particle physics since 1930", 1960, "Within ~1-3% of Bethe-Bloch; extensively validated tables (ICRU, NIST PSTAR)", "Confirmed") - -E("Dosimetry: Cavity Theory (Bragg-Gray / Spencer-Attix)", 43, "1936/1955", "Proven", - "D_med = (S̄/ρ)_med^wall · D_wall; dose to medium from dose measured in wall/gas cavity", - "Absolute dosimetry standard; ionization chamber calibration; TG-51, TRS-398 protocols") - -E("MIRD Formalism (Internal Dosimetry)", 43, "1968", "Proven", - "D̄(r_T←r_S) = Ã_S Σ_i Δ_i φ_i(r_T←r_S); Ã_S=cumulated activity; Δ_i=mean energy per transition; φ=fraction absorbed", - "Nuclear medicine dosimetry; I-131, Lu-177, Y-90 therapy; dose to tumors and organs") -S(eid, "MIRD", "MIRD Dose", r"\bar{D}(r_T \leftarrow r_S) = \tilde{A}_S \sum_i \Delta_i \Phi_i(r_T \leftarrow r_S)", "", "OLINDA/EXM software implements MIRD") - -# ================================================================ -# 14. ENERGY PHYSICS -# ================================================================ - -E("Shockley-Queisser Limit (Single-Junction Solar Cell Efficiency)", 44, "1961", "Proven", - "η_max ≈ 33.7% for E_g=1.34 eV under AM1.5 spectrum (non-concentrated); detailed balance limit", - "Maximum theoretical PV efficiency; Si (1.12 eV): ~29.4%; GaAs (1.43 eV): ~33%") -V(eid, "Best Si single-junction cell ~26.7% approaches SQ limit", "Kaneka, LONGi, ISFH", 2020, "Record efficiencies within ~80% of SQ limit", "Confirmed") - -E("Solar Cell I-V Characteristic (One-Diode Model)", 44, "1960s", "Proven", - "I = I_ph − I₀ [exp(q(V+IR_s)/nk_B T)−1] − (V+IR_s)/R_sh", - "All photovoltaic device characterization; R_s=series resistance; R_sh=shunt resistance; n=ideality factor") -V(eid, "PV module I-V curve fitting for performance characterization", "Solar simulator standard (IEC 60904)", 1980, "Standard test conditions (STC): 1000 W/m², AM1.5, 25°C", "Confirmed") - -E("Fill Factor (Solar Cell)", 44, "1960s", "Proven", - "FF = (V_mpp I_mpp)/(V_oc I_sc); η = P_max/P_in = FF · V_oc · J_sc / P_in", - "Key solar cell performance metric; typically 0.70-0.85 for good cells") - -E("Betz Limit (Wind Turbine Maximum Efficiency)", 44, "1920", "Proven", - "C_p_max = 16/27 ≈ 59.3%; maximum fraction of kinetic power extractable from wind", - "All wind turbine design; modern turbines achieve C_p≈0.45-0.50") -S(eid, "WindPower", "Wind Power Equation", r"P = \frac{1}{2}\rho A v^3 C_p", "A=swept area; v=wind speed; ρ=1.225 kg/m³ (standard air density)", "") -V(eid, "Wind turbine power curves confirm C_p~0.45-0.50 below rated speed", "Field measurements at wind farms", 1980, "Approaches Betz limit; wake losses reduce farm efficiency", "Confirmed") - -E("Rankine Cycle Efficiency (Steam Power Plant)", 44, "1859", "Proven", - "η = (W_turbine − W_pump)/Q_in ≈ 1 − T_c/T_h (ideal Carnot upper bound, real ~30-45%)", - "Coal, nuclear, geothermal, CSP power plants; ~90% of world electricity from Rankine cycle") - -E("Brayton Cycle Efficiency (Gas Turbine / Jet Engine)", 44, "1872", "Proven", - "η = 1 − 1/r_p^{(γ−1)/γ}; r_p = compressor pressure ratio; γ = c_p/c_v", - "Gas turbines, jet engines; combined cycle: Gas + Steam → η>60%") - -E("Nernst Equation (Fuel Cell Open-Circuit Voltage)", 44, "1889", "Proven", - "E_rev = −ΔG/(nF); H₂/O₂ Fuel Cell: E⁰ = 1.229 V at 25°C; actual: E = E_rev − η_act − η_ohm − η_conc", - "All fuel cells (PEM, SOFC, MCFC); efficiency ~40-60%") -V(eid, "PEM fuel cell OCV measurement", "Fuel cell testing labs", 2000, "OCV typically 0.95-1.0 V (below 1.229V due to H₂ crossover)", "Confirmed") - -# ================================================================ -# 15. SPACE PHYSICS -# ================================================================ - -E("Parker Spiral (Interplanetary Magnetic Field)", 45, "1958", "Proven", - "B_r ∝ 1/r²; B_φ = −B_r (Ω r sin θ)/v_SW; Archimedean spiral angle; v_SW≈400 km/s (slow), ~750 km/s (fast)", - "Solar wind magnetic field configuration; confirmed by spacecraft in situ measurements") -V(eid, "Parker spiral measured by Ulysses, Wind, ACE spacecraft", "Ulysses (1990-2009), Wind (1994-), ACE (1997-)", 1990, "Spiral field direction confirmed throughout heliosphere", "Confirmed") - -E("Chapman-Ferraro Model (Magnetopause Standoff Distance)", 45, "1931", "Proven", - "Balance of solar wind dynamic pressure with Earth's magnetic pressure: R_MP ~ 10 R_E (subsolar)", - "Magnetopause shape and location; solar wind dynamic pressure compression") -V(eid, "Magnetopause crossing distances measured by many spacecraft", "ISEE, Cluster, THEMIS, MMS", 1980, "Agreement within ~1R_E of model predictions", "Confirmed") - -E("Alfvén Mach Number (Solar Wind — Magnetosphere Coupling)", 45, "1940s", "Proven", - "M_A = v_SW / v_A; M_A typical solar wind ~5-10; super-Alfvénic flow → bow shock forms", - "Magnetosphere dynamics; IMF B_z southward → magnetic reconnection → geomagnetic storms") - -E("Dungey Cycle (Magnetospheric Convection via Reconnection)", 45, "1961", "Proven", - "Open flux transport from dayside reconnection → tail lobe → nightside reconnection → return flow (2-cell convection)", - "Global magnetospheric circulation; Dungey 1961; confirmed by SuperDARN, Cluster, THEMIS") -V(eid, "2-cell ionospheric convection observed by SuperDARN (radar) and DMSP (satellite)", "SuperDARN radar network, DMSP satellites", 1990, "Dungey convection pattern is universal under southward IMF", "Confirmed") - -E("Størmer Theory (Charged Particle Motion in Dipole Field)", 45, "1907/1955", "Proven", - "Allowed/forbidden zones for cosmic ray access; rigidity cutoff P_c = 59.6 cos⁴ λ / r² (GV, dipole approx.)", - "Cosmic ray access to atmosphere; radiation belt trapping; auroral zones") -V(eid, "Cosmic ray cutoffs verified by balloon and satellite measurements", "AMS-02, PAMELA, balloon campaigns", 1960, "Latitudinal cutoff variation matches Størmer theory", "Confirmed") - -E("Radiation Belt Diffusion Equation (Fokker-Planck Approach)", 45, "1960s", "Proven", - "∂f/∂t = L² ∂/∂L (D_LL L^{-2} ∂f/∂L) + radial diffusion + sources (CRAND, injections) + losses (wave-particle, atmospheric)", - "Van Allen belt dynamics; radial diffusion coefficient D_LL; wave-particle interactions") - -E("Auroral Electron Acceleration (Knight Relation)", 45, "1973", "Proven", - "j_∥ = K (V − V_c); K = field-aligned conductance; V_c = critical voltage; parallel potential drop above aurora", - "Discrete auroral arcs; FAST, Freja, Cluster observations confirm field-aligned potentials ~1-10 kV") -V(eid, "FAST satellite: inverted-V electron spectra and parallel potentials", "FAST satellite (1996-2009)", 2000, "Knight relation confirmed for auroral field lines", "Confirmed") - -# ================================================================ -# 16. DETONICS & SHOCK PHYSICS -# ================================================================ - -E("Chapman-Jouguet (CJ) Detonation Theory", 46, "1899/1905", "Proven", - "Detonation products at sonic condition relative to shock front (M=1); Rayleigh line tangent to Hugoniot at CJ point", - "All steady detonations; explosive performance prediction; detonation velocity D_CJ~1-10 km/s") -S(eid, "CJcondition", "CJ Condition", r"D = u_P + c_P \text{ at CJ plane}", "Sonic flow condition at end of reaction zone; separates steady detonation from wave-follower", "") -V(eid, "TNT detonation velocity ~6.9 km/s at ρ~1.6 g/cm³ matches CJ prediction", "Detonation velocity measurements (streak cameras, PDV, microwave interferometry)", 1950, "Within few % of CJ predictions", "Confirmed") - -E("ZND Model (Zeldovich-Von Neumann-Döring Structure)", 46, "1940/1942/1943", "Proven", - "Lead shock → von Neumann spike (induction zone, no reaction) → reaction zone → CJ plane", - "Detonation wave internal structure; ignition and growth modeling") - -E("Rankine-Hugoniot Relations (General Shock Jump Conditions)", 46, "1870/1887/1889", "Proven", - "ρ₁ u₁ = ρ₂ u₂; p₁+ρ₁u₁² = p₂+ρ₂u₂²; h₁+½u₁² = h₂+½u₂²; conservation across any shock or detonation front", - "All shock physics; gas dynamics, solids, liquids, plasma; Hugoniot EOS measurements") -S(eid, "HugoniotEqs", "Hugoniot Jump Conditions", r"\rho_1 u_1 = \rho_2 u_2,\; p_1+\rho_1 u_1^2 = p_2+\rho_2 u_2^2,\; h_1 + \frac{1}{2}u_1^2 = h_2 + \frac{1}{2}u_2^2", "u = particle velocity in shock frame; h = specific enthalpy; material-independent conservation laws", "") -V(eid, "Shock Hugoniot data for thousands of materials", "Gas guns, explosives, laser shocks, Z-machine", 1960, "EOS compilations (SESAME, LEOS) match shock data", "Confirmed") - -E("Mie-Grüneisen Equation of State (Solids Under Shock)", 46, "1903/1912", "Proven", - "p(V,E) = p_ref(V) + (γ(V)/V)[E − E_ref(V)]; γ(V)/V = Grüneisen parameter / volume", - "Shock-compressed solids; thermal pressure contribution to total EOS") - -E("Hopkinson-Cranz (Cube-Root) Blast Scaling Law", 46, "1915/1926", "Proven", - "R₁/R₂ = (W₁/W₂)^{1/3} at equal overpressure; scaled distance Z = R/W^{1/3}", - "Blast wave propagation; explosive safety distances; nuclear/conventional blast effects") -S(eid, "ScaledDistance", "Scaled Distance (Sachs Scaling)", r"Z = \frac{R}{W^{1/3}} \text{ or } Z = \frac{R}{E^{1/3}}", "R=distance; W=charge mass; E=energy; ambient pressure and temperature corrections apply (Sachs scaling)", "") -V(eid, "Blast overpressure decay with scaled distance confirmed", "Large-scale blast tests (Operation Sailor Hat, Minor Scale, etc.)", 1960, "Cube-root scaling works over many orders of magnitude", "Confirmed") - -E("Taylor-Sedov Blast Wave (Point Explosion, Self-Similar Solution)", 46, "1941/1946", "Proven", - "R(t) = ξ₀ (E/ρ₀)^{1/5} t^{2/5} (strong shock, spherical); nuclear fireball radius", - "Nuclear explosions; supernova remnants; laser-produced plasmas; G.I. Taylor 1941, Sedov 1946") -V(eid, "Trinity test fireball radius matches Taylor prediction (E~20 kT TNT)", "Taylor 1950 (declassified photos)", 1950, "Yield estimated to ~10 kT from fireball photos; actual yield ~20 kT", "Confirmed") - -# ================================================================ -# 17. METAMATERIALS -# ================================================================ - -E("Veselago's Left-Handed Material Condition", 47, "1968", "Proven", - "ε < 0 and μ < 0 simultaneously → ñ < 0 (negative index); reversed Snell's law, reversed Doppler, reversed Cherenkov", - "First theoretical prediction of negative-index materials; Pendry/Smith demonstrated at microwaves 2000") -S(eid, "NegRefraction", "Negative Refraction (Snell's Law Generalized)", r"n_1 \sin\theta_1 = -|n_2| \sin\theta_2", "For μ<0,ε<0; wave vector k, Poynting vector S, and phase velocity form left-handed triad", "") -V(eid, "Negative refraction at microwave frequencies (Pendry, Smith 2000)", "Smith et al. 2000, Shelby et al. 2001", 2001, "Prism experiment confirms n<0 for split-ring resonator + wire array", "Confirmed") - -E("Pendry's Perfect Lens (Subwavelength Imaging)", 47, "2000", "Proven", - "n = −1, μ = ε = −1 → amplifies evanescent waves → subwavelength resolution; no diffraction limit", - "Superlens concept; limitations from losses, fabrication; experimental realizations with silver films") - -E("Transformation Optics (Cloaking / Invisibility)", 47, "2006", "Proven", - "g'^{μν} = Λ^μ_α Λ^ν_β g^{αβ} where Λ relates virtual to physical space; coordinate transformation → anisotropic ε,μ", - "First demonstrated at microwaves (Schurig et al. 2006, cylindrical cloak); carpet cloaks (Liu/Zhang); broadband limitations") -V(eid, "Cylindrical invisibility cloak at microwaves (10 GHz)", "Schurig et al. Science 2006", 2006, "Reduced scattering for TM-polarized microwaves", "Confirmed") - -E("Effective Medium Theory (Maxwell Garnett / Bruggeman)", 47, "1904/1935", "Proven", - "MG: (ε_eff−ε_h)/(ε_eff+2ε_h) = f (ε_i−ε_h)/(ε_i+2ε_h); Bruggeman: f(ε_i−ε_eff)/(ε_i+2ε_eff) + (1−f)(ε_h−ε_eff)/(ε_h+2ε_eff)=0", - "Composite/metamaterial homogenization; plasmonic nanoantenna arrays; anisotropic metamaterials") - -# ================================================================ -# 18. ENGINEERING PHYSICS -# ================================================================ - -E("NTU-Effectiveness Method (Heat Exchanger Design)", 49, "1950s", "Proven", - "ε = Q/Q_max; NTU = UA/C_min; ε = f(NTU, C_r, flow arrangement); C_r = C_min/C_max", - "All heat exchanger analysis; parallel-flow, counter-flow, cross-flow configurations") -S(eid, "CounterFlow", "Counter-Flow Heat Exchanger Effectiveness", r"\varepsilon = \frac{1 - \exp[-NTU(1-C_r)]}{1 - C_r\exp[-NTU(1-C_r)]}", "For C_r<1; ε→1 as NTU→∞ (balanced flow: ε=NTU/(1+NTU))", "") -V(eid, "Heat exchanger test data matches NTU method", "Industrial heat exchanger testing", 1960, "Standard design method (Kays and London 1955)", "Confirmed") - -E("Natural Frequency of a Cantilever Beam", 49, "1750", "Proven", - "f_n = (β_n L)²/(2π L²) √(EI/ρA); β₁L=1.875, β₂L=4.694, β₃L=7.855 for cantilever", - "All structural dynamics; AFM cantilevers; MEMS resonators; building vibration modes") -V(eid, "AFM cantilever resonance frequencies match Euler-Bernoulli prediction", "AFM calibration standard", 1990, "Within 2-5% of beam theory", "Confirmed") - -E("PID Control Law (Feedback Control)", 49, "1922/1940s", "Proven", - "u(t) = K_p e(t) + K_i ∫₀ᵗ e(τ)dτ + K_d de/dt; e(t)=setpoint − measurement", - "90%+ of all industrial control loops; temperature, pressure, flow, position control globally") -V(eid, "PID controllers universally deployed in industry", "Since pneumatic controllers 1930s, electronic 1950s, digital 1980s", 1940, "Stable regulation confirmed in countless systems", "Confirmed") - -E("Nyquist Stability Criterion", 49, "1932", "Proven", - "N = Z − P; encirclements of −1 point determine closed-loop stability from open-loop transfer function", - "All feedback control system stability analysis; frequency-domain design") -V(eid, "Amplifier and control system stability margins verified", "Bode 1940s, Nyquist 1932", 1940, "Gain/phase margins derived from Nyquist/Bode plots", "Confirmed") - -# ================================================================ -# 19. BONUS ROUND: Everything else I missed -# ================================================================ - -E("Young-Laplace Equation (Capillary Pressure, Droplets/Bubbles)", 25, "1805", "Proven", - "Δp = γ (1/R₁ + 1/R₂); Δp = 2γ/R (spherical); Δp = 4γ/R for soap bubble (2 interfaces)", - "Every droplet, bubble, meniscus, capillary rise phenomenon; pore capillarity") - -E("Kelvin Equation (Curvature + Vapor Pressure)", 25, "1871", "Proven", - "ln(P/P_sat) = 2γ V_m/(r R T); concave meniscus (r<0) → condensation below P_sat", - "Capillary condensation in pores; BET surface area; humidity effects in porous media") - -E("Washburn Equation (Capillary Rise Dynamics)", 25, "1921", "Proven", - "h(t) = √(γ R cos θ t/(2η)); Lucas-Washburn; √t dependence for capillary imbibition", - "Inkjet printing; paper absorption; oil recovery; microfluidics; paper-based diagnostics") - -E("Poiseuille Law (Microfluidic Channel Flow)", 36, "1840", "Proven", - "Q = (Δp w h³)/(12 η L) [1 − 0.63 h/w] for rectangular channel (h≪w); ~h³ dependence", - "All microfluidic chip design; lab-on-a-chip; MEMS flow sensors; biomedical diagnostics") - -E("Stribeck Curve — Empirical Friction-Speed-Load Relation", 37, "1902", "Proven", - "μ = μ_b + (μ_h−μ_b) / [1 + (η N/p)^m]; boundary → mixed → hydrodynamic transition", - "Bearing selection; gearbox design; tribology standard") - -E("Zener-Hollomon Parameter (Hot Deformation)", 21, "1944", "Proven", - "Z = ε̇ exp(Q/RT); flow stress σ = f(Z); Z unifies temperature and strain-rate effects", - "Hot working of metals; creep; high-temperature deformation mechanisms") -V(eid, "σ vs log Z linear for many alloys at elevated T", "Hot compression testing", 1960, "Constitutive modeling of hot rolling/forging", "Confirmed") - -E("Ashby Deformation Mechanism Maps", 21, "1972", "Proven", - "σ/G vs T/T_m plot with boundaries between plasticity, power-law creep, diffusional flow, etc.", - "Material selection in engineering design; rate-controlling mechanism identification") - -E("Tabor Parameter (Indentation Representative Strain)", 21, "1951", "Proven", - "ε_rep ≈ 0.2 tan β; β = indenter angle; uniaxial stress-strain relation from hardness", - "Inverse problem: extract σ-ε curve from spherical indentation; confirmed by FEA") - -E("Lode Parameter (Stress Triaxiality for Ductile Fracture)", 21, "1926", "Proven", - "η = σ_m/σ_v; σ_m=hydrostatic stress; σ_v=von Mises stress; η>1/3 needed for void growth", - "Ductile fracture models; GTN (Gurson-Tvergaard-Needleman) porous plasticity") - -E("Dislocation Density Evolution (Kocks-Mecking Model)", 21, "1970s", "Proven", - "dρ/dε = k₁ √ρ − k₂ ρ; storage (hardening) vs dynamic recovery (annihilation); Stage II→III", - "Work hardening theory; Kocks-Mecking 1976 (Kocks 1976, Mecking-Kocks 1981)") - -E("Bauschinger Effect (Kinematic Hardening)", 21, "1881", "Proven", - "Yield stress in reverse loading lower than forward loading; dislocation back-stress accumulation", - "Cyclic plasticity; ratcheting; springback in sheet metal forming") - -E("Schottky Diode I-V (Thermionic Emission Model)", 23, "1938", "Proven", - "J = A* T² exp(−qφ_Bn/k_B T) [exp(qV/k_B T)−1]; A* = Richardson constant modified for effective mass", - "All Schottky diodes; rectifying metal-semiconductor contacts; MESFETs") - -E("Solar Cell Quantum Efficiency (External/Internal)", 44, "1960s", "Proven", - "EQE(λ) = (J_sc(λ)/q) / Φ(λ); IQE(λ) = EQE(λ) / (1−R(λ)−T(λ))", - "Solar cell characterization; IQE reveals collection efficiency losses; standard measurement") - -E("Detailed Balance Limit (Tandem/Multijunction Solar Cells)", 44, "1980s", "Proven", - "η_max → 45.7% (2-junction), 51.3% (3-junction), 68.2% (infinite junctions) at 1 sun; ~86.8% at max concentration", - "Multijunction solar cell theoretical limits; record: 47.1% (6-junction, 2020, NREL/Fraunhofer ISE)") - -E("Tandem Solar Cell Current-Matching Condition", 44, "1990", "Proven", - "J_sc_top = J_sc_bottom (series-connected); otherwise limited by lower subcell current", - "All multijunction PV design; spectral splitting alternative to relax current-matching") - -E("Thermoelectric Figure of Merit (ZT)", 44, "1909/1950s", "Proven", - "ZT = S² σ T / κ; S=Seebeck coefficient; σ=electrical conductivity; κ=thermal conductivity", - "TE cooler/generator performance; ZT~1 commercially (Bi₂Te₃); ZT>2 in nanostructured/layered materials") -S(eid, "TEefficiency", "Thermoelectric Generator Max Efficiency", r"\eta_{\max} = \frac{T_h - T_c}{T_h}\frac{\sqrt{1+ZT_{avg}}-1}{\sqrt{1+ZT_{avg}}+T_c/T_h}", "Carnot × material factor; ZT_{avg}=average over temp range", "") -V(eid, "Radioisotope thermoelectric generators (RTG) in space", "NASA (Voyager, Cassini, Curiosity, Perseverance)", 1970, "Multi-decade operation confirmed; Pu-238 heat source", "Confirmed") - -E("Seebeck Effect (Thermoelectric Voltage)", 44, "1821", "Proven", - "ΔV = −∫ S(T) dT; V_oc = S ΔT for small ΔT; S∝k_B/e (≈86 μV/K per k_B/e)", - "All thermocouples; TE generators; heat flux sensors") - -E("Peltier Effect (Thermoelectric Heat Pumping)", 44, "1834", "Proven", - "Q̇ = Π I; Π = S T (Kelvin relation); heat absorbed/released at junction", - "Thermoelectric cooling; Peltier modules; laser diode temperature stabilization") - -E("Electrochemical Overpotential Components (Fuel Cell/Battery)", 44, "1930s", "Proven", - "V_cell = E_rev − η_act − η_ohm − η_conc; η_act from Butler-Volmer; η_ohm=IR; η_conc=(RT/nF)ln(1−j/j_L)", - "Polarization curve of all electrochemical energy devices; batteries, fuel cells, electrolyzers") - -E("Peukert's Law (Battery Capacity vs Discharge Rate)", 44, "1897", "Proven", - "C = I^k t (k>1 for lead-acid, k≈1.1-1.3); capacity decreases at higher discharge rates", - "All battery discharge characterization; empirical but widely applicable") - -E("Ragone Plot (Energy vs Power Density)", 44, "1968", "Proven", - "Specific energy (Wh/kg) vs specific power (W/kg); batteries, fuel cells, capacitors, flywheels occupy different regions", - "Energy storage technology comparison; fundamental to device selection") - -E("Magnetorheological Fluid (Bingham Plastic with Field-Dependent Yield)", 36, "1940s", "Proven", - "τ = τ_y(B) + η γ̇; yield stress controllable via applied magnetic field; ~50-100 kPa max", - "MR dampers in automotive suspensions, seismic protection, prosthetics") - -E("Electrorheological Fluid (Field-Dependent Viscosity)", 36, "1947", "Proven", - "η(E) = η₀ + α E^n; viscosity increases with electric field; Winslow effect", - "ER clutches, valves; haptic devices; active vibration control; limited industrial use") - -E("Phononic Crystal Band Gap (Bragg Scattering of Sound)", 47, "1990s", "Proven", - "Ω(k+G)=Ω(k); periodic elastic constants → band gaps for acoustic/elastic waves", - "Acoustic isolation; waveguide design; thermophononic crystals for thermal conductivity control") -V(eid, "Acoustic band gap measured in periodic composite plates", "Laser ultrasonics", 2000, "Band gap frequencies match Bloch mode calculations", "Confirmed") - -print(f"Added {len(eqs)} equations (total: {eid})") -print(f"Added {len(subs)} sub-equations") -print(f"Added {len(vers)} verifications") - -# ================================================================ -# EXECUTE INSERTS -# ================================================================ -cur.executemany("INSERT INTO equations VALUES (?,?,?,?,?,?,?,?)", eqs) -cur.executemany("INSERT INTO sub_equations (id, equation_id, subsection, name, latex_formula, description, conditions) VALUES (?,?,?,?,?,?,?)", subs) -cur.executemany("INSERT INTO verifications (id, equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?,?)", vers) - -conn.commit() - -# Stats -cur.execute("SELECT d.name, COUNT(e.id) FROM domains d LEFT JOIN equations e ON e.domain_id=d.id GROUP BY d.id ORDER BY d.id") -print("\nALL DOMAIN COUNTS:") -for row in cur.fetchall(): - print(f" {row[0]}: {row[1]}") - -cur.execute("SELECT COUNT(*) FROM equations"); total_eq = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM sub_equations"); total_sub = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM verifications"); total_ver = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM open_problems"); total_op = cur.fetchone()[0] -print(f"\nTOTALS: {total_eq} equations, {total_sub} sub-formulas, {total_ver} verifications, {total_op} open problems, {did_val+23} domains") - -conn.close() diff --git a/5-Applications/scripts/append_material_physics.py b/5-Applications/scripts/append_material_physics.py deleted file mode 100644 index c6a18208..00000000 --- a/5-Applications/scripts/append_material_physics.py +++ /dev/null @@ -1,615 +0,0 @@ -#!/usr/bin/env python3 -"""Append material physics laws to the existing physics_equations.db""" - -import sqlite3, os - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -cur = conn.cursor() - -# Get current max IDs -cur.execute("SELECT MAX(id) FROM equations") -max_id = cur.fetchone()[0] or 333 -cur.execute("SELECT MAX(eq_number) FROM equations") -max_num = cur.fetchone()[0] or 333 -cur.execute("SELECT MAX(id) FROM sub_equations") -max_sub = cur.fetchone()[0] or 0 -cur.execute("SELECT MAX(id) FROM verifications") -max_ver = cur.fetchone()[0] or 0 - -# Add material physics domains if missing -domains_new = [ - (21, "Material Physics", "Solid state, mechanical, thermal, electrical, magnetic, optical properties of materials", None), - (22, "Crystallography", "Crystal structure, symmetry, diffraction, reciprocal lattice", 21), - (23, "Semiconductor Physics", "Band gaps, doping, p-n junctions, transistors, quantum wells", 21), - (24, "Polymer Physics", "Viscoelasticity, rubber elasticity, reptation, glass transition", 21), - (25, "Surface Science", "Surface energy, adsorption, catalysis, tribology, thin films", 21), - (26, "Soft Matter", "Colloids, liquid crystals, gels, self-assembly, emulsions", 21), - (27, "Phase Transformations", "Nucleation, spinodal decomposition, diffusion, grain growth", 21), -] -for d in domains_new: - cur.execute("INSERT OR IGNORE INTO domains VALUES (?,?,?,?)", d) - -# ================================================================ -# MATERIAL PHYSICS EQUATIONS -# ================================================================ -mat_eqs = [] -def add_eq(eq_num, title, dom, year, status, sig, prec): - global max_id, max_num - max_id += 1; max_num += 1 - mat_eqs.append((max_id, eq_num, title, dom, year, status, sig, prec)) - -# ---- CRYSTALLOGRAPHY ---- -add_eq(334, "Bragg's Law (Generalized, Powder Diffraction)", 22, "1913", "Proven", - "nλ = 2d sin θ; foundation of all crystal structure determination", "Every crystal structure solved") -add_eq(335, "Laue Equations (3D Diffraction Condition)", 22, "1912", "Proven", - "a·Δk=2πh, b·Δk=2πk, c·Δk=2πl; constructive interference in 3D lattice", "Equivalent to Bragg's law; confirmed") -add_eq(336, "Structure Factor Equation", 22, "1915", "Proven", - "F_{hkl} = Σ_j f_j exp[2πi(hx_j+ky_j+lz_j)]; determines diffraction intensities", "All crystallography") -add_eq(337, "Atomic Scattering Factor (X-ray Form Factor)", 22, "1920s", "Proven", - "f(q) = ∫ ρ(r) exp(iq·r) d³r; Fourier transform of electron density", "Confirmed for all elements") -add_eq(338, "Reciprocal Lattice Vector Definition", 22, "1921", "Proven", - "G = h a* + k b* + l c*; a*=(b×c)/V_cell, etc.", "Exact mathematical definition") -add_eq(339, "Brillouin Zone Boundaries", 22, "1930", "Proven", - "2 k·G = |G|²; electron wave diffraction condition at BZ boundaries", "Band gap formation; confirmed") -add_eq(340, "Ewald Sphere Construction", 22, "1921", "Proven", - "|k| = |k'| = 2π/λ; Δk = G falls on sphere → diffraction", "Geometric diffraction condition; exact") -add_eq(341, "Patterson Function (Interatomic Vectors)", 22, "1935", "Proven", - "P(u,v,w) = ∫ |F_{hkl}|² exp[−2πi(hu+kv+lw)] d*h d*k d*l", "No phase problem; heavy-atom method") -add_eq(342, "Debye-Waller Factor (Thermal Motion)", 22, "1913", "Proven", - "f_T(q) = f₀(q) exp(−½⟨(u·q)²⟩); B = 8π²⟨u²⟩", "Temperature-dependent X-ray intensities; confirmed") -add_eq(343, "Space Group Symmetry Operations", 22, "1891", "Proven", - "230 space groups in 3D; {R|t} r = R r + t", "All crystalline materials classified") -add_eq(344, "Interplanar Spacing (Cubic Systems)", 22, "1913", "Proven", - "1/d² = (h²+k²+l²)/a² (cubic); general: depends on lattice parameters", "Indexing diffraction patterns") -add_eq(345, "Scherrer Equation (Crystallite Size)", 22, "1918", "Proven", - "D = K λ / (β cos θ); K≈0.9; β=FWHM in radians", "Nanocrystallite size from peak broadening") -add_eq(346, "Williamson-Hall Analysis (Size + Strain)", 22, "1953", "Proven", - "β cos θ = Kλ/D + 4ε sin θ; separates size and microstrain broadening", "XRD line profile analysis") - -# ---- MECHANICAL PROPERTIES ---- -add_eq(347, "True Stress — True Strain Definition", 21, "19th c.", "Proven", - "σ_true = F/A_inst; ε_true = ln(L/L₀) = ln(1+ε_eng)", "Beyond necking; large deformations") -add_eq(348, "Hollomon Equation (Work Hardening)", 21, "1945", "Proven", - "σ = K ε^n; n = strain hardening exponent; K = strength coefficient", "Plastic flow curve; confirmed for metals") -add_eq(349, "Hall-Petch Relationship (Grain Size Strengthening)", 21, "1951–53", "Proven", - "σ_y = σ₀ + k_y / √d; d = grain diameter", "Yield strength vs grain size; metals and ceramics") -add_eq(350, "Orowan Equation (Precipitation Strengthening)", 21, "1948", "Proven", - "Δτ = G b / L; L = interparticle spacing; b = Burgers vector", "Dispersion/precipitation hardening") -add_eq(351, "Schmid's Law (Critical Resolved Shear Stress)", 21, "1924", "Proven", - "τ_CRSS = σ_y cos φ cos λ; m = cos φ cos λ (Schmid factor)", "Yield onset in single crystals; confirmed") -add_eq(352, "Taylor Equation (Dislocation Strengthening)", 21, "1934", "Proven", - "τ = α G b √ρ; ρ = dislocation density; α≈0.2–0.5", "Work hardening from dislocation interactions") -add_eq(353, "Petch-Forwood Hardness-Yield Strength Relation", 21, "1970s", "Proven", - "H ≈ 3 σ_y (metals); Vickers/Brinell ≈ 3 × yield", "Rough correlation; material-dependent") -add_eq(354, "Griffith Criterion (Brittle Fracture)", 21, "1921", "Proven", - "σ_f = √(2Eγ_s / πa); critical stress for crack propagation", "Brittle fracture; ceramics, glass") -add_eq(355, "Stress Intensity Factor (LEFM, Mode I)", 21, "1957", "Proven", - "K_I = Y σ √(πa); fracture when K_I ≥ K_Ic", "Linear elastic fracture mechanics; exact in limit") -add_eq(356, "J-Integral (Elastic-Plastic Fracture)", 21, "1968", "Proven", - "J = ∫_Γ (W dy − T_i ∂u_i/∂x ds); path-independent energy release rate", "EPFM; ductile fracture criterion") -add_eq(357, "Paris' Law (Fatigue Crack Growth)", 21, "1963", "Proven", - "da/dN = C (ΔK)^m; C, m material constants; m≈2–4 for metals", "Fatigue life prediction; confirmed") -add_eq(358, "Basquin Equation (High-Cycle Fatigue)", 21, "1910", "Proven", - "σ_a = σ_f' (2N_f)^b; b≈−0.05 to −0.12 for metals", "S-N curve; stress-life fatigue") -add_eq(359, "Coffin-Manson Relation (Low-Cycle Fatigue)", 21, "1950s", "Proven", - "Δε_p/2 = ε_f' (2N_f)^c; c≈−0.5 to −0.7", "Plastic strain-life fatigue") -add_eq(360, "Norton-Bailey Creep Law", 21, "1929/1935", "Proven", - "ε_cr = A σ^n t^m (primary creep); dε_cr/dt = B σ^n (secondary)", "High-temperature creep; confirmed") -add_eq(361, "Larson-Miller Parameter (Creep Rupture)", 21, "1952", "Proven", - "P = T (C + log t_r); C≈20; T in K, t_r in hours", "Creep life extrapolation; engineering standard") -add_eq(362, "Mohr-Coulomb Failure Criterion", 21, "1776/1900", "Proven", - "τ = c + σ_n tan φ; c=cohesion, φ=internal friction angle", "Rocks, soils, concrete, granular materials") -add_eq(363, "Drucker-Prager Yield Criterion", 21, "1952", "Proven", - "√J₂ + α I₁ = k; pressure-dependent yielding", "Geomaterials, polymers, foams") -add_eq(364, "Weibull Distribution (Brittle Failure Statistics)", 21, "1939", "Proven", - "P_f = 1 − exp[−(σ/σ₀)^m]; m = Weibull modulus", "Ceramic strength variability; size effect") -add_eq(365, "Stoney Equation (Thin Film Stress)", 21, "1909", "Proven", - "σ_f = E_s h_s² κ / [6(1−ν_s) h_f]; substrate curvature → film stress", "Thin film metrology; MEMS") - -# ---- THERMAL PROPERTIES ---- -add_eq(366, "Debye Specific Heat Model (Full)", 21, "1912", "Proven", - "C_V = 9 N k_B (T/Θ_D)³ ∫₀^{Θ_D/T} x⁴ e^x / (e^x−1)² dx", "Phonon heat capacity; all solids; exact") -add_eq(367, "Dulong-Petit Law", 21, "1819", "Proven", - "C_V = 3R ≈ 24.94 J/(mol·K) at high T (classical limit of Debye)", "Most solids above Debye temperature") -add_eq(368, "Einstein Heat Capacity Model", 21, "1907", "Proven", - "C_V = 3 N k_B (Θ_E/T)² e^{Θ_E/T} / (e^{Θ_E/T}−1)²", "First quantum model; qualitatively correct") -add_eq(369, "Wiedemann-Franz Law (Electronic Thermal Conductivity)", 21, "1853", "Proven", - "κ_e / (σ T) = L; L = (π²/3)(k_B/e)² ≈ 2.44×10⁻⁸ W Ω/K²", "Metals; Sommerfeld value; confirmed") -add_eq(370, "Debye-Callaway Model (Lattice Thermal Conductivity)", 21, "1959", "Proven", - "κ_l = (k_B/2π²v)(k_B T/ℏ)³ ∫₀^{Θ_D/T} τ_c x⁴ e^x / (e^x−1)² dx", "Phonon thermal conductivity; confirmed") -add_eq(371, "Thermal Expansion Coefficient (Grüneisen Relation)", 21, "1912", "Proven", - "α = γ C_V / (3 B V); γ = Grüneisen parameter; B = bulk modulus", "All solids; confirmed") -add_eq(372, "Grüneisen Equation of State (Solids)", 21, "1912", "Proven", - "P(V) = −dU₀/dV + γ U_th/V; γ = Grüneisen parameter", "Thermal pressure; shock physics") -add_eq(373, "Lindemann Melting Criterion", 21, "1910", "Proven", - "T_m ≈ C θ_D² M V^{2/3}; C depends on crystal structure", "Empirical; qualitatively correct") -add_eq(374, "Stefan-Boltzmann Radiative Heat Transfer (Between Surfaces)", 21, "1880s", "Proven", - "q = ε_eff σ (T₁⁴−T₂⁴); view factor + emissivity correction", "Radiation in materials processing") - -# ---- ELECTRICAL PROPERTIES ---- -add_eq(375, "Complex Dielectric Constant", 21, "1920s", "Proven", - "ε* = ε' − i ε''; tan δ = ε''/ε'; loss tangent", "All dielectrics; AC response") -add_eq(376, "Clausius-Mossotti Relation (Polarizability)", 21, "1879", "Proven", - "(ε_r−1)/(ε_r+2) = N α / (3 ε₀); links macro/micro dielectric properties", "Non-polar dielectrics; confirmed") -add_eq(377, "Debye Relaxation (Dipole Response)", 21, "1929", "Proven", - "ε*(ω) = ε_∞ + (ε_s−ε_∞) / (1 + i ω τ)", "Polar liquids, polymers; confirmed") -add_eq(378, "Cole-Cole Relaxation (Distributed)", 21, "1941", "Proven", - "ε*(ω) = ε_∞ + (ε_s−ε_∞) / [1 + (i ω τ)^{1−α}]", "Broadened relaxation; polymers, glasses") -add_eq(379, "Havriliak-Negami Relaxation", 21, "1966", "Proven", - "ε*(ω) = ε_∞ + (ε_s−ε_∞) / [1 + (i ω τ)^α]^β", "General empirical relaxation function") -add_eq(380, "Curie-Weiss Law for Ferroelectrics (Above T_c)", 21, "1940s", "Proven", - "ε_r = C / (T − T_c); C = Curie constant", "BaTiO₃, PZT; phase transition temperature") -add_eq(381, "Piezoelectric Constitutive Equations", 21, "1880s", "Proven", - "S = s^E T + d^t E; D = d T + ε^T E (strain-charge form)", "All piezoelectrics; confirmed") -add_eq(382, "Pyroelectric Coefficient", 21, "19th c.", "Proven", - "p = dP_s/dT; ΔQ = p A ΔT", "Ferroelectrics; IR detectors") -add_eq(383, "Fowler-Nordheim Tunneling (Field Emission)", 21, "1928", "Proven", - "J = (A/φ)(βE)² exp(−B φ^{3/2} / βE); A,B constants", "Field emission; confirmed") -add_eq(384, "Poole-Frenkel Conduction (Insulators)", 21, "1938", "Proven", - "σ = σ₀ exp[−q(φ_B−√(qE/πε))/k_B T]", "Field-enhanced thermal emission; insulators") -add_eq(385, "Varistor I-V Characteristic (Nonlinear)", 21, "1970s", "Proven", - "I = k V^α; α >> 1 (ZnO varistors α≈20–100)", "Surge protection; grain boundary effect") -add_eq(386, "Percolation Threshold (Conductivity)", 21, "1970s", "Proven", - "σ = σ₀ (p − p_c)^t; p = volume fraction; p_c = percolation threshold", "Composites, granular materials") - -# ---- SEMICONDUCTOR PHYSICS ---- -add_eq(387, "Intrinsic Carrier Concentration (Semiconductors)", 23, "1931", "Proven", - "n_i = √(N_c N_v) exp(−E_g / 2 k_B T); N_c = 2(2π m_e* k_B T/h²)^{3/2}", "Silicon: n_i≈1.0×10¹⁰ cm⁻³ at 300K") -add_eq(388, "Fermi Level in Doped Semiconductors", 23, "1930s", "Proven", - "n-type: E_F = E_c − k_B T ln(N_c/N_d); p-type: E_F = E_v + k_B T ln(N_v/N_a)", "Doping control; all semiconductor devices") -add_eq(389, "Mass Action Law (Semiconductors)", 23, "1930s", "Proven", - "n p = n_i²; product constant at fixed T", "Thermal equilibrium; exact") -add_eq(390, "Shockley Diode Equation (Ideal)", 23, "1949", "Proven", - "I = I_s [exp(q V / n k_B T) − 1]; I_s = reverse saturation current", "All p-n junctions; n = ideality factor") -add_eq(391, "Built-in Potential (p-n Junction)", 23, "1949", "Proven", - "V_bi = (k_B T / q) ln(N_a N_d / n_i²)", "From Fermi level alignment; confirmed") -add_eq(392, "Depletion Width (p-n Junction)", 23, "1949", "Proven", - "W = √[2ε_s (V_bi−V)(1/N_a+1/N_d)/q]", "Junction capacitance; confirmed") -add_eq(393, "MOS Capacitor Threshold Voltage", 23, "1960s", "Proven", - "V_th = V_FB + 2φ_F + √(4ε_s q N_a φ_F)/C_ox", "MOSFET operation foundation") -add_eq(394, "MOSFET Drain Current (Saturation, Long Channel)", 23, "1960s", "Proven", - "I_D = (μ_n C_ox W / 2L) (V_GS − V_th)²", "All digital logic; confirmed to percent level") -add_eq(395, "Subthreshold Swing (MOSFET)", 23, "1960s", "Proven", - "SS = (k_B T/q) ln(10) (1 + C_dep/C_ox); ideal: 60 mV/decade at 300K", "Low-power limit; confirmed") -add_eq(396, "Avalanche Breakdown (Impact Ionization)", 23, "1950s", "Proven", - "M = 1 / [1 − (V/V_BR)^n]; n≈3–6", "High-field breakdown; confirmed in all devices") -add_eq(397, "Quantum Confinement Energy (Particle in a Box)", 23, "1970s", "Proven", - "E_n = n² π² ℏ² / (2 m* L²); blue shift with decreasing size", "Quantum wells, wires, dots; confirmed") -add_eq(398, "Brus Equation (Semiconductor Nanocrystal Band Gap)", 23, "1986", "Proven", - "E_g(R) = E_g(bulk) + ℏ²π²/(2μ R²) − 1.8e²/(ε_r R); μ = reduced exciton mass", "Quantum dot emission tuning; confirmed") -add_eq(399, "Kane's k·p Band Model (Non-Parabolicity)", 23, "1957", "Proven", - "E(1+αE) = ℏ² k² / (2 m*); α = 1/E_g; non-parabolic correction", "Narrow-gap semiconductors") -add_eq(400, "Mott Transition (Doped Semiconductor)", 23, "1949", "Proven", - "n_c^{1/3} a_B* ≈ 0.25; insulator-metal transition at critical doping", "Confirmed in Si:P, Si:B") -add_eq(401, "Anderson Localization (Disordered Materials)", 23, "1958", "Proven", - "W/V > W_c → localized states; mobility edge at E_c", "Amorphous semiconductors; confirmed") -add_eq(402, "Tauc Plot (Band Gap from Absorption)", 23, "1968", "Proven", - "(α h ν)^{1/r} = A (hν − E_g); r=½ for direct, r=2 for indirect", "Optical band gap determination") - -# ---- MAGNETIC PROPERTIES (detailed) ---- -add_eq(403, "Stoner Criterion (Itinerant Ferromagnetism)", 21, "1936", "Proven", - "N(E_F) I > 1; spontaneous magnetization when DOS × exchange exceeds unity", "Fe, Co, Ni; confirmed") -add_eq(404, "Stoner-Wohlfarth Model (Single-Domain Particle)", 21, "1948", "Proven", - "E = K V sin²θ − μ₀ M_s H V cos(φ−θ); hysteresis from anisotropy+Zeeman", "Magnetic recording; permanent magnets") -add_eq(405, "Néel Temperature (Antiferromagnetism)", 21, "1936", "Proven", - "T_N = (2J S(S+1)/3k_B) z (from mean-field); sublattice ordering temperature", "MnO, NiO; confirmed") -add_eq(406, "Curie Temperature (Mean-Field Ferromagnetism)", 21, "1907", "Proven", - "T_c = (2J S(S+1)/3k_B) z; z = coordination number", "All ferromagnets; order-of-magnitude correct") -add_eq(407, "Bloch T^{3/2} Law (Magnetization at Low T)", 21, "1930", "Proven", - "M_s(T) = M_s(0) [1 − (T/T_c)^{3/2}] (3D Heisenberg)", "Spin-wave theory; confirmed for insulators") -add_eq(408, "Landau-Lifshitz-Gilbert Equation (Magnetization Dynamics)", 21, "1935/2004", "Proven", - "dM/dt = −γ M × H_eff + (α/M_s) M × dM/dt", "All magnetization dynamics; spintronics foundation") -add_eq(409, "Brown's Paradox (Domain Wall Motion)", 21, "1963", "Proven", - "v = (γ Δ / α)(H − H_c); soft magnetic materials", "Domain wall dynamics; confirmed") -add_eq(410, "Magnetostriction (Joule Magnetostriction)", 21, "1842", "Proven", - "ΔL/L = (3/2) λ_s (cos²θ − 1/3); λ_s = saturation magnetostriction", "Terfenol-D, Galfenol; confirmed") -add_eq(411, "Giant Magnetoresistance (GMR, CIP)", 21, "1988", "Proven", - "ΔR/R = (R_AP−R_P)/R_P; spin-dependent scattering at interfaces", "Nobel 2007; hard disk read heads") -add_eq(412, "Tunneling Magnetoresistance (TMR, Julliere Model)", 21, "1975", "Proven", - "TMR = (R_AP−R_P)/R_P = 2P₁P₂/(1−P₁P₂); P = spin polarization", "MRAM, read heads; confirmed") -add_eq(413, "RKKY Interaction (Indirect Exchange)", 21, "1954–57", "Proven", - "J(R) ∝ cos(2k_F R) / R³; oscillatory coupling through conduction electrons", "Multilayer magnetic coupling; confirmed") -add_eq(414, "Superexchange (Anderson-Goodenough-Kanamori Rules)", 21, "1950s", "Proven", - "J_ij ∝ −b²/U (for 180° cation-anion-cation); sign depends on orbital filling", "Magnetic insulators; MnO, ferrites") - -# ---- OPTICAL PROPERTIES (Materials) ---- -add_eq(415, "Complex Refractive Index (General)", 21, "19th c.", "Proven", - "ñ = n + i κ; I(z) = I₀ exp(−α z); α = 4πκ/λ", "All optical materials") -add_eq(416, "Kramers-Kronig Relations (Optical Constants)", 21, "1926–27", "Proven", - "n(ω)−1 = (2/π) P ∫₀^∞ ω' κ(ω')/(ω'²−ω²) dω'; causality → dispersion relations", "All linear optical materials; exact") -add_eq(417, "Tauc-Lorentz Model (Amorphous Semiconductor Optics)", 21, "1996", "Proven", - "ε_2(E) = [A E₀ C (E−E_g)²] / [(E²−E₀²)² + C² E²] E for E>E_g; 0 otherwise", "a-Si, a-C; ellipsometry standard") -add_eq(418, "Sellmeier Equation (Refractive Index Dispersion)", 21, "1871", "Proven", - "n²(λ) = 1 + Σ_i A_i λ² / (λ² − λ_i²); empirical fit for transparent regions", "Glasses, crystals; standard") -add_eq(419, "Cauchy Equation (Refractive Index Fit)", 21, "1836", "Proven", - "n(λ) = A + B/λ² + C/λ⁴; empirical for transparent region", "Simple fit; visible range") -add_eq(420, "Urbach Tail (Absorption Edge)", 21, "1953", "Proven", - "α(E) = α₀ exp[σ (E−E₀) / k_B T]; exponential absorption below band edge", "Disordered semiconductors; thermal/lattice disorder") -add_eq(421, "Beer-Lambert Law (Absorption)", 3, "1729–1852", "Proven", - "A = log₁₀(I₀/I) = ε c L; absorbance proportional to concentration and path", "All spectrophotometry; exact for dilute") -add_eq(422, "Kubelka-Munk Theory (Diffuse Reflectance)", 21, "1931", "Proven", - "F(R_∞) = (1−R_∞)²/(2R_∞) = K/S ∝ α; for thick opaque scattering media", "Powders, pigments, paper; confirmed") -add_eq(423, "Fresnel Loss at Normal Incidence", 21, "1823", "Proven", - "R = [(n₁−n₂)/(n₁+n₂)]²; reflection coefficient at normal incidence", "All dielectric interfaces; exact") -add_eq(424, "Drude Model for Free-Carrier Absorption", 21, "1900", "Proven", - "ε(ω) = ε_∞ − ω_p²/(ω² + i ω/τ); ω_p = √(n e²/ε₀ m*)", "Metals, doped semiconductors in IR; confirmed") -add_eq(425, "Forster Resonance Energy Transfer (FRET) Efficiency", 21, "1948", "Proven", - "E = 1 / [1 + (r/R₀)⁶]; R₀ = Förster radius (~1–10 nm)", "Molecular photophysics; single-molecule detection") -add_eq(426, "Stokes Shift (Luminescence)", 21, "1852", "Proven", - "ΔE = E_abs − E_em > 0; from vibrational relaxation", "All fluorescence/phosphorescence") -add_eq(427, "Dexter Energy Transfer (Exchange)", 21, "1953", "Proven", - "k_ET ∝ exp(−2r/L); short-range (≲1 nm) electron exchange", "Triplet energy transfer; OLEDs") - -# ---- MECHANICAL TESTING ---- -add_eq(428, "Vickers Hardness Definition", 21, "1924", "Proven", - "HV = 1.854 F / d²; F in kgf, d = average diagonal (mm)", "Standard micro/macro hardness test") -add_eq(429, "Brinell Hardness", 21, "1900", "Proven", - "HB = 2F / [π D (D − √(D²−d²))]; D = ball diameter", "Bulk hardness; metals") -add_eq(430, "Rockwell Hardness (Indirect)", 21, "1919", "Proven", - "HR = N − h/s; h = penetration depth; N,s depend on scale", "Industrial QC; rapid measurement") -add_eq(431, "Knoop Hardness (Thin Films / Brittle)", 21, "1939", "Proven", - "HK = 14.229 F / d₁²; long diagonal; shallow penetration", "Ceramics, coatings, thin films") -add_eq(432, "Nanoindentation (Oliver-Pharr Method)", 21, "1992", "Proven", - "H = P_max/A; E_r = √π S/(2β√A); S = dP/dh at unload", "Submicron property mapping; confirmed") -add_eq(433, "Charpy Impact Toughness", 21, "1900s", "Proven", - "KV = m g (h_initial − h_final); energy absorbed in fracture (J)", "Notch toughness; metals, polymers") -add_eq(434, "Izod Impact Test", 21, "1903", "Proven", - "Similar to Charpy; energy absorbed per unit width (J/m)", "Plastics, composites") - -# ---- POLYMER PHYSICS ---- -add_eq(435, "Rubber Elasticity (Gaussian Chain, Affine)", 24, "1930s", "Proven", - "σ_true = n k_B T (λ − 1/λ²); n = crosslink density; λ = extension ratio", "Elastomers at moderate strain; confirmed") -add_eq(436, "Mooney-Rivlin Equation (Hyperelastic)", 24, "1940s", "Proven", - "W = C₁₀(I₁−3) + C₀₁(I₂−3); I₁,I₂ = invariants of Cauchy-Green tensor", "Rubber at large strains; phenomenological") -add_eq(437, "Flory-Huggins Theory (Polymer Solution Free Energy)", 24, "1942", "Proven", - "ΔG_mix/k_B T = n₁ ln φ₁ + n₂ ln φ₂ + χ n₁ φ₂; χ = Flory interaction parameter", "Polymer-solvent thermodynamics") -add_eq(438, "Williams-Landel-Ferry (WLF) Equation", 24, "1955", "Proven", - "log a_T = −C₁ (T−T_ref) / (C₂ + T−T_ref); time-temperature superposition", "Viscoelasticity time-temperature shift") -add_eq(439, "Arrhenius Viscosity (Above Glass Transition)", 24, "1930s", "Proven", - "η(T) = η₀ exp(E_a / R T) (simple) or Vogel-Fulcher-Tammann: η = η₀ exp[B/(T−T₀)]", "Polymer melt viscosity") -add_eq(440, "Rouse Model (Unentangled Polymer Dynamics)", 24, "1953", "Proven", - "τ_R = ζ N² b² / (3π² k_B T); longest relaxation time of unentangled chain", "Short chains; N < N_e") -add_eq(441, "Reptation Model (de Gennes, Entangled Dynamics)", 24, "1971", "Proven", - "τ_rep ∝ N³; D_rep ∝ N⁻²; disentanglement time; Nobel 1991", "Long entangled chains; N >> N_e") -add_eq(442, "Entanglement Molecular Weight", 24, "1970s", "Proven", - "M_e = ρ R T / G_N⁰; from plateau modulus G_N⁰", "Characteristic for each polymer") -add_eq(443, "Flory-Fox Equation (T_g vs Molecular Weight)", 24, "1950", "Proven", - "T_g = T_g∞ − K_F / M_n; T_g increases with MW to asymptotic limit", "All linear polymers; confirmed") -add_eq(444, "Cahn-Hilliard Equation (Spinodal Decomposition)", 24, "1958", "Proven", - "∂c/∂t = M ∇²[∂f/∂c − 2κ ∇²c]; diffusion modulated by gradient energy", "Phase separation; alloys, polymers") -add_eq(445, "Avrami Equation (Crystallization Kinetics)", 27, "1939", "Proven", - "X(t) = 1 − exp(−k t^n); n = Avrami exponent (dimensionality + nucleation mode)", "Polymer, metal, glass crystallization") -add_eq(446, "Lauritzen-Hoffman Theory (Polymer Crystal Growth)", 27, "1970s", "Proven", - "G = G₀ exp[−U*/R(T−T_∞)] exp[−K_g / (T ΔT f)]; secondary nucleation", "Polymer crystallization rate") - -# ---- SURFACES AND INTERFACES ---- -add_eq(447, "Young's Equation (Contact Angle)", 25, "1805", "Proven", - "γ_sv = γ_sl + γ_lv cos θ; balance of interfacial tensions", "Wettability; exact for smooth homogeneous") -add_eq(448, "Wenzel Equation (Rough Surface Wetting)", 25, "1936", "Proven", - "cos θ* = r cos θ; r = actual/projected area > 1; roughness amplifies wetting", "Real surfaces; confirmed") -add_eq(449, "Cassie-Baxter Equation (Composite/Heterogeneous Wetting)", 25, "1944", "Proven", - "cos θ* = f₁ cos θ₁ + f₂ cos θ₂; f₁+f₂=1; trapped air → superhydrophobic", "Lotus effect; superhydrophobic surfaces") -add_eq(450, "Laplace Pressure (Curved Interface)", 9, "1805", "Proven", - "ΔP = γ (1/R₁ + 1/R₂); pressure inside curved surface", "Bubbles, droplets, capillary action") -add_eq(451, "Kelvin Equation (Capillary Condensation)", 25, "1871", "Proven", - "ln(P/P₀) = −2γ V_m / (r R T); condensation in pores below saturation", "Mesoporous materials; confirmed") -add_eq(452, "Langmuir Adsorption Isotherm (Monolayer)", 25, "1918", "Proven", - "θ = K P / (1 + K P); θ = fractional coverage; K = adsorption equilibrium constant", "Chemisorption, physisorption at low coverage") -add_eq(453, "BET Isotherm (Brunauer-Emmett-Teller, Multilayer)", 25, "1938", "Proven", - "P/[V(P₀−P)] = 1/(V_m C) + (C−1)P/(V_m C P₀); surface area from multilayer adsorption", "Standard surface area measurement") -add_eq(454, "Freundlich Isotherm (Heterogeneous Surfaces)", 25, "1909", "Proven", - "q = K_F P^{1/n}; empirical; heterogeneous adsorption", "Activated carbon, heterogeneous catalysts") -add_eq(455, "Gibbs Adsorption Equation", 25, "1878", "Proven", - "dγ = −Σ Γ_i dμ_i; Γ_i = surface excess concentration", "Surfactant surface coverage; exact") -add_eq(456, "Amontons-Coulomb Friction Law (Dry Friction)", 18, "1699/1785", "Proven", - "F_f ≤ μ_s N (static); F_f = μ_k N (kinetic); μ_k < μ_s", "Macroscopic friction; empirical") -add_eq(457, "Archard's Law (Adhesive Wear)", 25, "1953", "Proven", - "V = k F s / H; k = wear coefficient; H = hardness", "Sliding wear volume prediction") -add_eq(458, "Hamaker Constant (Van der Waals Between Surfaces)", 25, "1937", "Proven", - "A = π² C ρ₁ ρ₂; F_vdW/A = −A / (6π d³) (flat surfaces)", "Colloidal stability; DLVO theory") -add_eq(459, "DLVO Theory (Colloid Stability)", 25, "1940s", "Proven", - "V_total(d) = V_vdW + V_edl; van der Waals + electric double-layer", "Colloids, nanoparticles; confirmed") -add_eq(460, "Zeta Potential (Smoluchowski Equation)", 25, "1903", "Proven", - "ζ = η μ_e / ε; μ_e = electrophoretic mobility; η = viscosity", "Colloid surface charge; confirmed") -add_eq(461, "Derjaguin Approximation (Force Between Curved Surfaces)", 25, "1934", "Proven", - "F_sphere(d) = 2πR W_flat(d); relates sphere-sphere to flat-plate energy", "AFM force spectroscopy; exact in limit") -add_eq(462, "Johnson-Kendall-Roberts (JKR) Adhesion Model", 25, "1971", "Proven", - "a³ = (R/K)[F + 3πW_ad R + √(6πW_adRF + (3πW_adR)²)]; elastic + adhesion contact", "Soft materials; AFM adhesion") -add_eq(463, "Derjaguin-Muller-Toporov (DMT) Model", 25, "1975", "Proven", - "a³ = (R/K)[F + 2πW_ad R]; adhesion without distortion of contact profile", "Hard materials; low adhesion") - -# ---- DIFFUSION ---- -add_eq(464, "Fick's First Law (Steady-State Diffusion)", 21, "1855", "Proven", - "J = −D ∂c/∂x; flux proportional to concentration gradient", "All diffusion; exact for steady state") -add_eq(465, "Fick's Second Law (Time-Dependent Diffusion)", 21, "1855", "Proven", - "∂c/∂t = D ∂²c/∂x²; for constant D; general: ∂c/∂t = ∂/∂x(D ∂c/∂x)", "All non-steady diffusion") -add_eq(466, "Diffusion Solutions (Common)", 21, "1855", "Proven", - "Thin film: c(x,t) = (M/√(4πDt)) exp(−x²/4Dt); Error function: c = C₀ erfc(x/√(4Dt))", "All diffusion profiles; exact") -add_eq(467, "Arrhenius Diffusion Coefficient", 21, "1889", "Proven", - "D = D₀ exp(−E_a / k_B T); thermally activated diffusion", "Atomic diffusion; vacancies, interstitials") -add_eq(468, "Darken Equations (Interdiffusion / Kirkendall Effect)", 21, "1948", "Proven", - "D̃ = (X_B D_A + X_A D_B) Φ; Φ = thermodynamic factor including non-ideality", "Alloy interdiffusion; marker movement confirmed") -add_eq(469, "Nernst-Planck Equation (Ion Transport)", 21, "1888–1890", "Proven", - "J_i = −D_i ∇c_i − (z_i F/RT)D_i c_i ∇φ + c_i v; diffusion + migration + convection", "Electrochemical transport; confirmed") -add_eq(470, "Stokes-Einstein Relation (Diffusion of Spheres)", 21, "1905", "Proven", - "D = k_B T / (6π η r); hydrodynamic radius from diffusion", "Colloids, proteins; confirmed") -add_eq(471, "Tracer Diffusion Correlation Factor", 21, "1950s", "Proven", - "D* = f D_rand; f = correlation factor; f<1 for vacancy mechanism", "Atomic-scale diffusion; confirmed") - -# ---- PHASE TRANSFORMATIONS ---- -add_eq(472, "Gibbs-Thomson Effect (Curvature Depression of Melting/Equilibrium Point)", 27, "1870s", "Proven", - "T_m(r) = T_m(∞)(1 − 2γ_sl / (ρ_s ΔH_f r)); small particles melt at lower T", "Nanoparticle melting; confirmed") -add_eq(473, "Classical Nucleation Theory (Homogeneous)", 27, "1926–50", "Proven", - "ΔG = (4π/3)r³ ΔG_v + 4πr² γ; r* = −2γ/ΔG_v; ΔG* = 16πγ³/(3ΔG_v²)", "All nucleation processes; confirmed") -add_eq(474, "Johnson-Mehl-Avrami-Kolmogorov (JMAK) Equation", 27, "1939–41", "Proven", - "f = 1 − exp[−(kt)^n]; n depends on nucleation+growth dimensionality", "Phase transformation kinetics; standard") -add_eq(475, "Turnbull's Nucleation Rate (Steady-State)", 27, "1949", "Proven", - "I = N_v (k_B T/h) exp[−(ΔG*+ΔG_a)/k_B T]; includes kinetic barrier", "Crystallization rate; qualitative") -add_eq(476, "Lever Rule (Phase Diagram Tie Line)", 27, "19th c.", "Proven", - "f_α = (C₀−C_β)/(C_α−C_β); f_β = (C_α−C₀)/(C_α−C_β)", "Phase fractions from composition; exact") -add_eq(477, "Gibbs-Thomson-Freundlich (Ostwald Ripening / LSW Theory)", 27, "1961", "Proven", - "⟨r⟩³ − ⟨r₀⟩³ = k t; k ∝ γ D c_∞ V_m²/(R T); coarsening of precipitates", "Precipitate growth; nanoparticles") -add_eq(478, "Darken-Gurry Plot (Solubility Limits)", 21, "1950s", "Proven", - "Extensive solubility when |ΔR_atom|<15% and |Δχ|<0.4 (electronegativity difference)", "Empirical Hume-Rothery rule extension") -add_eq(479, "Hume-Rothery Rules (Alloy Formation)", 21, "1920s–30s", "Proven", - "(1) Size <15% (2) Similar electronegativity (3) Same valence (4) Same crystal structure", "Substitutional solid solution criteria") -add_eq(480, "Vegard's Law (Lattice Parameter in Solid Solutions)", 21, "1921", "Proven", - "a_AB = x_A a_A + x_B a_B; linear interpolation; deviations = non-ideal mixing", "Alloys; approximate for many systems") - -# ---- COMPOSITES AND POROUS MATERIALS ---- -add_eq(481, "Rule of Mixtures (Composite Modulus, Isostrain)", 21, "1950s", "Proven", - "E_c = E_f V_f + E_m V_m (Voigt bound, upper); 1/E_c = V_f/E_f + V_m/E_m (Reuss bound, lower)", "Composite stiffness bounds; exact limits") -add_eq(482, "Hashin-Shtrikman Bounds (Composite Moduli)", 21, "1963", "Proven", - "Tighter bounds than Voigt-Reuss; K_lower = K_m + V_f/[1/(K_f−K_m)+3V_m/(3K_m+4G_m)]; etc.", "Optimal bounds for isotropic composites") -add_eq(483, "Halpin-Tsai Equations (Short Fiber Composites)", 21, "1969", "Proven", - "E/E_m = (1 + ξ η V_f)/(1 − η V_f); η = (E_f/E_m−1)/(E_f/E_m+ξ); ξ = shape factor", "Short/random fiber reinforcement") -add_eq(484, "Porosity-Young's Modulus Relation (Empirical)", 21, "1950s", "Proven", - "E = E₀ (1 − P)^n or exp(−bP); P = porosity fraction; n≈2–4", "Porous ceramics, bone, foams") -add_eq(485, "Gibson-Ashby Model (Cellular Solids / Foams)", 21, "1988", "Proven", - "E*/E_s = C (ρ*/ρ_s)^n; n=2 open cell; n=3 closed cell; σ*/σ_ys ∝ (ρ*/ρ_s)^{3/2}", "Foams, honeycombs, bone, wood") -add_eq(486, "Eshelby Inclusion Problem (Stress in Ellipsoidal Inclusion)", 21, "1957", "Proven", - "ε^T = S ε*; S = Eshelby tensor (depends on inclusion shape + matrix Poisson ratio)", "Micromechanics foundation; exact for ellipsoids") - -# ---- OPTICAL AND ELECTRONIC MATERIALS ---- -add_eq(487, "Moss-Burstein Shift (Doped Semiconductor Absorption Edge)", 21, "1954", "Proven", - "ΔE_g = (ℏ²/2m*)(3π²n)^{2/3}; Fermi filling blocks lowest transitions", "Transparent conducting oxides; confirmed") -add_eq(488, "Franz-Keldysh Effect (Electro-Absorption)", 21, "1958", "Proven", - "α(E,F) ∝ exp[−(E_g−E)^{3/2} / eℏF]; band edge shift in electric field", "Semiconductor optical modulators") -add_eq(489, "Pockels Effect (Linear Electro-Optic)", 21, "1906", "Proven", - "Δ(1/n²)_i = r_ij E_j; r_ij = linear electro-optic coefficients", "LiNbO₃, KDP; modulators, Q-switches") -add_eq(490, "Kerr Effect (Quadratic Electro-Optic)", 21, "1875", "Proven", - "Δn = K λ E²; quadratic field dependence", "Liquids, centrosymmetric crystals; high-speed shutters") -add_eq(491, "Photoconductivity (Rose Model)", 21, "1950s", "Proven", - "Δσ = e μ τ G L / d; G=generation rate, τ=lifetime; gain = τ/t_transit", "Photodetectors; confirmed") -add_eq(492, "Shockley-Read-Hall Recombination Rate", 23, "1952", "Proven", - "U = (np−n_i²) / [τ_p (n+n₁) + τ_n (p+p₁)]; trap-assisted recombination", "All semiconductors; confirmed") -add_eq(493, "Auger Recombination Rate", 23, "1950s", "Proven", - "U_Auger = C_n n²p + C_p n p²; three-particle non-radiative recombination", "High carrier density; LEDs, lasers") - -# ---- SUPERCONDUCTIVITY (extended) ---- -add_eq(494, "BCS Energy Gap at T=0", 12, "1957", "Proven", - "Δ(0) = 1.764 k_B T_c; universal BCS ratio", "All conventional superconductors; confirmed") -add_eq(495, "Ginzburg-Landau Coherence Length", 12, "1950", "Proven", - "ξ(T) = ξ(0) / √(1−T/T_c); ξ(0) = √(ℏ²/2m*|α|); spatial variation of order parameter", "Type I/II boundary; confirmed") -add_eq(496, "Ginzburg-Landau Penetration Depth", 12, "1950", "Proven", - "λ(T) = λ(0)/√(1−T/T_c); magnetic field penetration into superconductor", "All superconductors; confirmed") -add_eq(497, "Ginzburg-Landau Parameter (κ)", 12, "1950", "Proven", - "κ = λ/ξ; κ < 1/√2 → Type I; κ > 1/√2 → Type II", "Classification of superconductors") -add_eq(498, "Abrikosov Vortex Lattice (Lower/Upper Critical Fields)", 12, "1957", "Proven", - "H_c1 = H_c ln κ/(√2 κ); H_c2 = √2 κ H_c; vortex state between", "Type II superconductors; confirmed") -add_eq(499, "Flux Pinning (Bean Critical State Model)", 12, "1962", "Proven", - "J_c = constant; ∇×B = μ₀ J_c; critical state penetration profile", "High-T_c superconductors; confirmed") -add_eq(500, "Little-Parks Effect (Fluxoid Quantization)", 12, "1962", "Proven", - "T_c oscillates with flux through cylinder; period = Φ₀ = h/2e", "Superconducting rings; confirmed") -add_eq(501, "Andreev Reflection", 12, "1964", "Proven", - "e⁻ → NS interface reflects as h⁺; retroreflection; sub-gap conductance enhancement", "N-S junctions; all superconductors") - -# ---- ELECTROCHEMISTRY (Materials) ---- -add_eq(502, "Nernst Equation (Electrode Potential)", 21, "1889", "Proven", - "E = E⁰ − (RT/nF) ln Q; E⁰ = standard reduction potential", "All electrochemistry; batteries, corrosion") -add_eq(503, "Butler-Volmer Equation (Electrode Kinetics)", 21, "1930s", "Proven", - "j = j₀ [exp(α_a F η/RT) − exp(−α_c F η/RT)]; η = overpotential", "Electrode kinetics; electrodeposition, batteries") -add_eq(504, "Tafel Equation (High Overpotential Limit)", 21, "1905", "Proven", - "η = a + b log |j|; b = 2.303 RT/(α nF) ≈ 120 mV/decade (α=0.5 at 298K)", "Corrosion rate; kinetic parameters") -add_eq(505, "Randles-Sevcik Equation (Cyclic Voltammetry Peak Current)", 21, "1948", "Proven", - "i_p = 0.4463 n F A C √(n F v D/RT); reversible: i_p ∝ √v", "Electroanalytical chemistry; confirmed") -add_eq(506, "Cottrell Equation (Chronoamperometry)", 21, "1903", "Proven", - "i(t) = n F A C √(D) / √(π t); diffusion-limited current decay", "Planar electrode; exact") -add_eq(507, "Faraday's Laws of Electrolysis", 3, "1834", "Proven", - "m = (Q M)/(n F); mass deposited proportional to charge; Q=It", "All electroplating; exact") -add_eq(508, "Wagner Number (Current Distribution Uniformity)", 21, "1951", "Proven", - "Wa = κ (dη/dj) / L; Wa ≫ 1 → uniform; Wa ≪ 1 → non-uniform", "Electroplating uniformity") - -# ---- MECHANICAL SPECTROSCOPY / INTERNAL FRICTION ---- -add_eq(509, "Zener Anelasticity (Standard Linear Solid)", 21, "1948", "Proven", - "ε = σ/E_R + (σ/E_U−σ/E_R) (1−e^{−t/τ}); relaxation strength Δ = (E_U−E_R)/√(E_U E_R)", "Internal friction; anelastic relaxation") -add_eq(510, "Debye Peak (Internal Friction, Point Defect Relaxation)", 21, "1940s", "Proven", - "tan δ = Δ ω τ / (1 + ω² τ²); τ = τ₀ exp(E_a/k_B T); peak at ωτ=1", "Snoek relaxation in bcc metals; C,N in Fe") -add_eq(511, "Bordoni Peak (Dislocation Relaxation)", 21, "1950s", "Proven", - "kink-pair formation on dislocations; tan δ peak with E_a~0.1–0.2 eV", "fcc metals after cold work; confirmed") -add_eq(512, "Granato-Lücke Theory (Dislocation Damping)", 21, "1956", "Proven", - "ε_d = (Λ L² σ)/(6 G) (amplitude-independent); breakaway at high amplitude", "Dislocation string model; confirmed") - -# ---- SOFT MATTER / COLLOIDS / LIQUID CRYSTALS ---- -add_eq(513, "Einstein Viscosity Equation (Rigid Sphere Suspension, Dilute)", 26, "1906", "Proven", - "η = η_s (1 + 2.5 φ); φ = volume fraction; dilute limit φ≪1", "Colloid viscosity; confirmed") -add_eq(514, "Krieger-Dougherty Equation (Concentrated Suspension)", 26, "1959", "Proven", - "η = η_s (1 − φ/φ_m)^{−[η]φ_m}; φ_m = maximum packing; [η]≈2.5", "Concentrated suspensions; divergence at φ_m") -add_eq(515, "Frank-Oseen Free Energy (Liquid Crystal Elastic)", 26, "1958", "Proven", - "F = ½[K₁(∇·n)² + K₂(n·∇×n)² + K₃(n×∇×n)²]; splay, twist, bend", "Nematic liquid crystals; all LCDs") -add_eq(516, "Frederiks Transition Threshold (Liquid Crystal)", 26, "1920s", "Proven", - "E_c = (π/d) √(K/ε₀Δε); voltage for director reorientation", "LCD device switching; confirmed") -add_eq(517, "Rayleigh Instability (Liquid Jet Breakup)", 26, "1878", "Proven", - "λ_max = 9.016 r₀; fastest growing wavelength → uniform droplet formation", "Inkjet printing; fiber spinning") -add_eq(518, "Plateau-Rayleigh Instability for Liquid Threads", 26, "1873/1878", "Proven", - "Cylindrical liquid thread unstable for λ > 2πr; surface-tension-driven breakup", "Microfluidics; droplet generation") - -# ---- THERMAL ANALYSIS / CALORIMETRY ---- -add_eq(519, "Kissinger Equation (DSC/DTA Peak Kinetics)", 21, "1957", "Proven", - "ln(β/T_p²) = −E_a/(R T_p) + ln(A R/E_a); β = heating rate; T_p = peak temperature", "Activation energy from thermal analysis") -add_eq(520, "Ozawa-Flynn-Wall Equation (Isoconversional Kinetics)", 21, "1965/1966", "Proven", - "log β = const − 0.4567 E_a/(R T); model-free kinetic analysis", "Polymer degradation; decomposition") -add_eq(521, "Tammann Nucleation Diagram (Nucleation vs Growth Rate)", 27, "1930s", "Proven", - "Nucleation rate I(T) and growth rate U(T) bell-shaped; overlap → crystallization window", "Glass ceramics; crystallization control") -add_eq(522, "Time-Temperature-Transformation (TTT) Diagram Equation", 27, "1930s", "Proven", - "τ(T) ∝ exp(ΔG*/k_B T + E_a/k_B T); C-curve shape; nose at intermediate T", "Steel heat treatment; glass devitrification") - -# ---- THIN FILMS ---- -add_eq(523, "Thornton Structure Zone Model (Thin Film Growth)", 21, "1974", "Proven", - "T/T_m vs Ar pressure → Zone 1 (porous), Zone T (dense fibrous), Zone 2 (columnar), Zone 3 (recrystallized)", "All PVD/CVD film microstructure") -add_eq(524, "Herring Scaling Laws (Sintering Kinetics)", 21, "1950", "Proven", - "(ΔL/L₀)^n ∝ t; n=1 viscous flow; n=2 volume diffusion; n=3 grain boundary diffusion; n=5 surface diffusion", "Sintering mechanism identification") -add_eq(525, "Pilling-Bedworth Ratio (Oxide Protectiveness)", 21, "1923", "Proven", - "PBR = V_oxide / V_metal consumed; 1 < PBR < 2 → protective; PBR > 2 → spallation; PBR < 1 → porous", "High-temperature oxidation; empirical") -add_eq(526, "Ellingham Diagram (Oxide Thermodynamic Stability)", 21, "1944", "Proven", - "ΔG⁰ = RT ln p_O₂; line slope = −ΔS⁰; lower line → more stable oxide", "Metallurgy; corrosion; standard reference") - -# ---- ADDITIONAL ELECTRONIC MATERIALS ---- -add_eq(527, "Mott-Gurney Law (Space-Charge-Limited Current)", 21, "1940", "Proven", - "J = (9/8) ε μ V² / L³; trap-free SCLC; Child's law for solids", "Organic semiconductors; insulators") -add_eq(528, "Richardson-Dushman Equation (Thermionic Emission)", 21, "1901/1923", "Proven", - "J = A_R T² exp(−φ/k_B T); A_R = 4π m e k_B²/h³ ≈ 1.20×10⁶ A/(m²K²)", "Vacuum tubes; thermionic converters") -add_eq(529, "Schottky Barrier Height (Metal-Semiconductor)", 23, "1938", "Proven", - "φ_Bn = φ_m − χ_s; φ_Bp = E_g/q + χ_s − φ_m (ideal, no interface states)", "Schottky diode; confirmed with Fermi pinning corrections") -add_eq(530, "Spicer's Unified Defect Model (Fermi Level Pinning at Interfaces)", 23, "1979", "Proven", - "E_F pinned by deep native defects at interface; independent of metal work function", "GaAs, InP Schottky barriers; confirmed") - -# ---- BIOMATERIALS / BIOPHYSICS (Materials Context) ---- -add_eq(531, "Wolff's Law (Bone Remodeling, Mechanical Adaptation)", 21, "1892", "Proven", - "Bone density distribution adapts to principal stress trajectories; σ_ij → ρ_ij", "Bone biomechanics; implant design") -add_eq(532, "Fung's Quasi-Linear Viscoelasticity (Soft Tissue)", 21, "1972", "Proven", - "σ(t) = ∫₀ᵗ G(t−τ) ∂σ_e(ε)/∂ε · ∂ε/∂τ dτ; separable elastic + relaxation", "Tendons, ligaments, blood vessels") -add_eq(533, "Ogden Hyperelastic Model (Biological Tissue)", 21, "1972", "Proven", - "W = Σ (μ_k/α_k) (λ₁^{α_k} + λ₂^{α_k} + λ₃^{α_k} − 3); principal stretches; fits large deformations", "Arteries, skin, brain tissue") - -# ---- MISCELLANEOUS MATERIAL LAWS ---- -add_eq(534, "Matthiessen's Rule (Electrical Resistivity Additivity)", 21, "1864", "Proven", - "ρ_total = ρ_thermal + ρ_impurity + ρ_deformation; independent contributions sum", "Metals; approximate due to deviations from phonon drag") -add_eq(535, "Nordheim's Rule (Alloy Resistivity)", 21, "1930s", "Proven", - "ρ_alloy = ρ_pure + C x(1−x); x = atomic fraction; max at x=0.5 for disordered binary", "Binary alloy resistivity; confirmed") -add_eq(536, "Miedema's Rules (Alloy Formation Enthalpy)", 21, "1970s", "Proven", - "ΔH_form = f(Δφ*, Δn_ws^{1/3}); work function + electron density mismatch → semi-empirical model", "Binary alloy thermodynamics; predictive") -add_eq(537, "Köhler's Rule (Magnetoresistance Scaling)", 21, "1940s", "Proven", - "Δρ(B)/ρ(0) = F[B/ρ(0)]; Kohler plot universal for given material", "Metals; confirmed for simple Fermi surfaces") -add_eq(538, "Zener Breakdown (Band-to-Band Tunneling)", 23, "1934", "Proven", - "D = exp[−4√(2m*) E_g^{3/2}/(3 e ℏ E)]; tunneling probability through forbidden gap", "Zener diodes; high E-field transport") -add_eq(539, "Klemens Model (Thermal Boundary Resistance / Kapitza)", 21, "1959", "Proven", - "R_K = 4 / (ρ c v ζ); acoustic mismatch model; acoustic impedance mismatch → resistance", "Nanoscale thermal management; interfaces") -add_eq(540, "Diffuse Mismatch Model (Thermal Boundary Resistance)", 21, "1980s", "Proven", - "R_K from transmission probability of phonons regardless of mode; rough interfaces", "Room temperature Kapitza resistance") - -cur.executemany("INSERT INTO equations VALUES (?,?,?,?,?,?,?,?)", mat_eqs) - -# ================================================================ -# SUB-EQUATIONS for material physics (key formulas) -# ================================================================ -m_se = [] -def add_mse(eq_id, sub, name, latex, desc, cond=""): - global max_sub - max_sub += 1 - m_se.append((eq_id, sub, name, latex, desc, cond)) - -add_mse(335, "Laue", "Laue Equations", "\\vec{a}\\cdot\\Delta\\vec{k}=2\\pi h,\\; \\vec{b}\\cdot\\Delta\\vec{k}=2\\pi k,\\; \\vec{c}\\cdot\\Delta\\vec{k}=2\\pi l", "3D constructive interference condition; equivalent to Bragg", "") -add_mse(336, "StructFact", "Structure Factor", "F_{hkl}=\\sum_j f_j(\\vec{q})\\,e^{2\\pi i(hx_j+ky_j+lz_j)}", "Scattering amplitude from unit cell contents", "Accounts for all atoms") -add_mse(347, "TrueStress", "True Stress-Strain", "\\sigma_t = \\sigma_e(1+\\varepsilon_e),\\; \\varepsilon_t = \\ln(1+\\varepsilon_e)", "True (Ludwik) vs engineering; volume constancy in plasticity", "") -add_mse(349, "HallPetch", "Hall-Petch Relation", "\\sigma_y = \\sigma_0 + \\frac{k_y}{\\sqrt{d}}", "Grain boundary strengthening; d = grain diameter", "Valid down to ~10-20 nm; inverse Hall-Petch below") -add_mse(354, "Griffith", "Griffith Fracture Criterion", "\\sigma_f = \\sqrt{\\frac{2E\\gamma_s}{\\pi a}}", "Critical stress for unstable crack growth in brittle materials", "Glass, ceramics; energy balance") -add_mse(355, "SIF", "Stress Intensity Factor", "K_I = Y\\sigma\\sqrt{\\pi a}", "K_I ≥ K_Ic → fracture; Mode I loading", "Linear elastic fracture mechanics") -add_mse(357, "Paris", "Paris Law (Fatigue)", "\\frac{da}{dN} = C(\\Delta K)^m", "Crack growth per cycle; ΔK = stress intensity range", "Striations per cycle; m≈2–4 for metals") -add_mse(366, "DebyeCv", "Debye Heat Capacity", "C_V = 9Nk_B\\left(\\frac{T}{\\Theta_D}\\right)^3\\!\\int_0^{\\Theta_D/T}\\!\\frac{x^4 e^x}{(e^x-1)^2}dx", "Phonon specific heat; C_V ∝ T³ for T ≪ Θ_D", "All crystalline solids") -add_mse(375, "ComplexEps", "Complex Dielectric Constant", "\\varepsilon^*(\\omega)=\\varepsilon'(\\omega)-i\\varepsilon''(\\omega)", "Real part = storage; imaginary part = loss", "\\tan\\delta = \\varepsilon''/\\varepsilon'") -add_mse(376, "C-M", "Clausius-Mossotti Relation", "\\frac{\\varepsilon_r-1}{\\varepsilon_r+2} = \\frac{N\\alpha}{3\\varepsilon_0}", "Macroscopic ε_r from microscopic polarizability α", "Non-polar; Lorentz local field") -add_mse(381, "Piezo", "Piezoelectric Constitutive Equations", "S_{ij} = s_{ijkl}^E T_{kl} + d_{kij} E_k,\\; D_i = d_{ikl} T_{kl} + \\varepsilon_{ik}^T E_k", "Strain-charge form; direct and converse effects", "6mm symmetry for PZT") -add_mse(387, "Intrinsic", "Intrinsic Carrier Concentration", "n_i = \\sqrt{N_c N_v}\\, e^{-E_g/(2k_B T)}", "Si: n_i ≈ 1.0×10¹⁰ cm⁻³ at 300K; Ge: 2.4×10¹³", "Thermal generation across band gap") -add_mse(390, "Diode", "Shockley Diode Equation", "I = I_s\\left[e^{(qV)/(nk_BT)} - 1\\right]", "Ideal p-n junction; I_s ∝ n_i²", "n=1 ideal; n>1 recombination/generation") -add_mse(394, "MOSFET", "MOSFET Saturation Current", "I_{D,sat} = \\frac{\\mu_n C_{ox}}{2}\\frac{W}{L}(V_{GS}-V_{th})^2", "Channel width W, length L; V_GS > V_th", "Square-law; confirmed to % level") -add_mse(398, "Brus", "Brus Equation (Quantum Dot Gap)", "E_g(R) = E_g^{\\text{bulk}} + \\frac{\\hbar^2\\pi^2}{2\\mu R^2} - \\frac{1.8 e^2}{\\varepsilon_r R}", "μ = reduced exciton mass; third term = Coulomb", "CdSe, CdS, PbS dots; confirmed") -add_mse(403, "Stoner", "Stoner Criterion", "N(E_F)\\,I > 1", "Ferromagnetism when DOS × exchange exceeds unity", "Fe: N(E_F)I≈1.7; Pd: ≈0.9 (paramagnetic)") -add_mse(407, "Bloch3/2", "Bloch T^{3/2} Law", "M_s(T) = M_s(0)\\left[1 - \\left(\\frac{T}{T_c}\\right)^{3/2}\\right]", "Low-temperature magnetization from spin-wave excitations", "3D Heisenberg ferromagnet") -add_mse(411, "GMR", "Giant Magnetoresistance", "\\frac{\\Delta R}{R} = \\frac{R_{AP}-R_P}{R_P}", "Spin-dependent scattering; Co/Cu multilayers", "Nobel Prize 2007 (Fert + Grünberg)") -add_mse(421, "BeerLambert", "Beer-Lambert Law", "A = \\log_{10}\\frac{I_0}{I} = \\varepsilon c L", "Absorbance; linear with concentration and path length", "Dilute solutions; monochromatic light") -add_mse(425, "FRET", "FRET Efficiency", "E = \\frac{1}{1 + (r/R_0)^6}", "R₀ = Förster radius (1–10 nm); r⁻⁶ distance dependence", "Molecular ruler; single-molecule") -add_mse(435, "RubberElas", "Rubber Elasticity (Neo-Hookean)", "\\sigma_t = n k_B T\\left(\\lambda - \\frac{1}{\\lambda^2}\\right)", "Entropy elasticity; n = crosslink density", "Moderate strains (<300%); affine model") -add_mse(437, "FloryHuggins", "Flory-Huggins Free Energy", "\\frac{\\Delta G_{mix}}{k_B T} = n_1\\ln\\phi_1 + n_2\\ln\\phi_2 + \\chi n_1\\phi_2", "χ = Flory interaction parameter; χ < 0.5 → miscible", "Polymer solutions and blends") -add_mse(441, "Reptation", "Reptation Time (de Gennes)", "\\tau_{rep} \\propto N^3,\\; D_{rep} \\propto N^{-2}", "Entangled polymer chain motion through tube", "Nobel Prize in Physics 1991") -add_mse(445, "Avrami", "Avrami Crystallization Kinetics", "X(t) = 1 - \\exp(-k t^n)", "n = Avrami exponent; n≈1–4 depending on mechanism", "Johnson-Mehl-Avrami-Kolmogorov") -add_mse(447, "Young", "Young's Equation (Contact Angle)", "\\gamma_{sv} = \\gamma_{sl} + \\gamma_{lv}\\cos\\theta", "Three-phase equilibrium; smooth homogeneous surface", "Wettability; hydrophilicity θ<90°") -add_mse(452, "Langmuir", "Langmuir Isotherm", "\\theta = \\frac{KP}{1+KP}", "θ = fractional monolayer coverage; K ∝ e^{-ΔH/RT}", "Homogeneous surface; no lateral interactions") -add_mse(453, "BET", "BET Isotherm", "\\frac{P}{V(P_0-P)} = \\frac{1}{V_m C} + \\frac{(C-1)}{V_m C}\\frac{P}{P_0}", "Multilayer physisorption; surface area from V_m", "Standard for surface area (N₂ at 77K)") -add_mse(464, "Fick1", "Fick's First Law", "\\vec{J} = -D\\,\\nabla c", "Flux proportional to concentration gradient", "Steady-state diffusion") -add_mse(465, "Fick2", "Fick's Second Law", "\\frac{\\partial c}{\\partial t} = D\\,\\nabla^2 c", "Time-dependent diffusion; for constant D", "3D: ∂c/∂t = D ∂²c/∂x² (1D)") -add_mse(467, "Arrhenius-D", "Arrhenius Diffusion", "D = D_0\\,e^{-E_a/k_B T}", "Thermally activated atomic jumps", "Vacancy, interstitial mechanisms") -add_mse(473, "CNT", "Classical Nucleation Theory", "\\Delta G = \\frac{4}{3}\\pi r^3 \\Delta G_v + 4\\pi r^2 \\gamma", "r* = −2γ/ΔG_v; ΔG* = 16πγ³/(3ΔG_v²)", "Homogeneous nucleation barrier") -add_mse(477, "LSW", "Ostwald Ripening (LSW Theory)", "\\langle r\\rangle^3 - \\langle r_0\\rangle^3 = k t", "k ∝ γ D c_∞ V_m²/(RT); coarsening", "Diffusion-limited coarsening of precipitates") -add_mse(481, "ROM", "Rule of Mixtures", "E_c = E_f V_f + E_m V_m", "Voigt upper bound (isostrain, parallel loading)", "Elastic composite modulus") -add_mse(494, "BCS-gap", "BCS Energy Gap (T=0)", "\\Delta(0) = 1.764\\,k_B T_c", "Universal BCS ratio; confirmed by tunneling", "Weak-coupling BCS theory") -add_mse(495, "GL-Coher", "Ginzburg-Landau Coherence Length", "\\xi(T) = \\xi(0)/\\sqrt{1-T/T_c}", "\\xi(0) = \\sqrt{\\hbar^2/(2m^*|\\alpha|)}", "Superconducting order parameter range") -add_mse(502, "Nernst", "Nernst Equation", "E = E^\\ominus - \\frac{RT}{nF}\\ln Q", "Electrode potential from concentration and T", "All electrochemistry at equilibrium") -add_mse(503, "ButlerVol", "Butler-Volmer Equation", "j = j_0\\left[e^{\\alpha_a F\\eta/(RT)} - e^{-\\alpha_c F\\eta/(RT)}\\right]", "Current from overpotential η; α_a+α_c≈1", "Electrode kinetics; charge transfer") -add_mse(513, "EinsteinVisc", "Einstein Viscosity", "\\eta = \\eta_s(1 + 2.5\\phi)", "Dilute rigid sphere suspension; φ ≪ 0.01", "Brownian contribution; Einstein 1906") -add_mse(515, "FrankOseen", "Frank-Oseen Elastic Energy", "F = \\frac{1}{2}[K_1(\\nabla\\cdot\\mathbf{n})^2+K_2(\\mathbf{n}\\cdot\\nabla\\times\\mathbf{n})^2+K_3(\\mathbf{n}\\times\\nabla\\times\\mathbf{n})^2]", "Splay, twist, bend elastic constants", "Nematic liquid crystal director field") -add_mse(527, "SCLC", "Space-Charge-Limited Current (Mott-Gurney)", "J = \\frac{9}{8}\\varepsilon\\mu\\frac{V^2}{L^3}", "Trap-free SCLC; Child's law for solids", "Organic semiconductors; insulators") -add_mse(528, "Richardson", "Richardson-Dushman Thermionic Emission", "J = A_R T^2 e^{-\\phi/k_B T}", "A_R = 4π m e k_B²/h³ ≈ 120 A/(cm²K²)", "Electron emission from heated cathode") - -cur.executemany("INSERT INTO sub_equations (equation_id, subsection, name, latex_formula, description, conditions) VALUES (?,?,?,?,?,?)", m_se) - -# ================================================================ -# VERIFICATIONS for material physics -# ================================================================ -mat_ver = [] -def add_mv(eid, test, expt, yr, prec, st="Confirmed"): - global max_ver - max_ver += 1 - mat_ver.append((eid, test, expt, yr, prec, st)) - -add_mv(334, "Powder X-ray Diffraction Structure Solution", "Bragg/Bragg Jr. 1913", 1913, "All crystal structures", "Confirmed") -add_mv(349, "Hall-Petch Confirmed in >100 Metals/Alloys", "Hall 1951, Petch 1953", 1953, "σ_y vs 1/√d linear", "Confirmed") -add_mv(357, "Paris Law for >50 Structural Alloys", "Paris & Erdogan 1963", 1963, "da/dN vs ΔK power law; confirmed by ASTM E647", "Confirmed") -add_mv(366, "Debye T³ Law at Low T", "Low-temperature calorimetry", 1920, "C_V ∝ T³ below ~Θ_D/30", "Confirmed") -add_mv(369, "Wiedemann-Franz in Metals (Sommerfeld value)", "Measurements since 1900", 1930, "Lorentz number = 2.44×10⁻⁸ WΩ/K²", "Confirmed") -add_mv(387, "Intrinsic Carrier Concentration in Si/Ge/GaAs", "Hall effect + resistivity vs T", 1950, "Extracted n_i matches theory", "Confirmed") -add_mv(390, "Shockley Diode I-V Fit (Si, Ge diodes)", "Shockley 1949", 1949, "Forward bias exponential; reverse saturation", "Confirmed") -add_mv(394, "MOSFET Long-Channel I-V Characteristics", "Intel 4004 to all modern chips", 1970, "Square-law saturation confirmed to <10%", "Confirmed") -add_mv(398, "Quantum Dot Size-Dependent Emission (CdSe)", "Brus/Bawendi/Alivisatos 1980s", 1990, "Emission blue-shift with decreasing R; matches Brus eq.", "Confirmed") -add_mv(403, "Stoner Criterion for 3d Ferromagnets", "Gunnarsson 1976, Janak 1977", 1977, "DFT calculations confirm N(E_F)I>1 for Fe,Co,Ni", "Confirmed") -add_mv(411, "GMR in Co/Cu Multilayers", "Baibich et al. (Fert group) 1988", 1988, "ΔR/R up to 50% at 4.2K; Nobel 2007", "Confirmed") -add_mv(412, "TMR in Fe/MgO/Fe MTJs", "Parkin/Yuasa 2004", 2004, "TMR > 200% at RT; Δ₁ coherent tunneling", "Confirmed") -add_mv(421, "Beer-Lambert Law in Analytical Chemistry", "Standard spectrophotometry", 1950, "A vs C linear over several orders of magnitude", "Confirmed") -add_mv(425, "FRET Single-Molecule Distance Measurement", "Ha et al. 1996, single-molecule", 1996, "Distance determined to <0.5 nm accuracy", "Confirmed") -add_mv(435, "Neo-Hookean Fit to Natural Rubber", "Treloar 1940s", 1944, "σ-λ fit for λ<3.0; deviation at high strains", "Confirmed") -add_mv(437, "Flory-Huggins Phase Diagram Predictions", "PS/cyclohexane, other systems", 1960, "χ parameter from SANS, osmotic pressure", "Confirmed") -add_mv(441, "Reptation: D~N⁻², τ~N³ confirmed", "NMR, neutron spin-echo, rheology", 1990, "Molecular weight scaling confirmed for entangled polymers", "Confirmed") -add_mv(445, "Avrami Kinetics for Polymer Crystallization", "DSC isothermal crystallization", 1960, "Exponent n matches nucleation+growth mechanism", "Confirmed") -add_mv(447, "Young's Equation on Molecularly Smooth Surfaces", "Self-assembled monolayers, mica", 1990, "Cosθ vs γ_lv (Zisman plot); consistent", "Confirmed") -add_mv(449, "Cassie-Baxter Superhydrophobic Surfaces", "Lotus leaf; artificial surfaces", 2000, "θ* > 150°, sliding angle < 10°; confirmed", "Confirmed") -add_mv(453, "BET Surface Area Standard (N₂, 77K)", "Commercial BET instruments", 1950, "Surface area ±5% for standards; widely used", "Confirmed") -add_mv(464, "Fick's Laws in Solid-State Diffusion", "Radioactive tracer measurements", 1950, "D values from penetration profiles; confirmed", "Confirmed") -add_mv(468, "Kirkendall Effect (Marker Movement)", "Kirkendall & Smigelskas 1947", 1947, "Inert markers move; D_Zn>D_Cu in brass; confirmed", "Confirmed") -add_mv(473, "CNT: Turnbull's Droplet Experiments", "Turnbull 1952 (Hg droplets)", 1952, "Undercooling ΔT/T_m≈0.18 confirmed for homogeneous", "Confirmed") -add_mv(474, "JMAK Kinetics for Metallic Glass Crystallization", "DSC/DTA measurements", 1980, "n ~ 3-4 for 3D growth; confirmed for many glasses", "Confirmed") -add_mv(481, "Rule of Mixtures for Continuous Fiber Composites", "Carbon/epoxy, glass/polyester", 1970, "Longitudinal E matches ROM within 5%", "Confirmed") -add_mv(494, "BCS Gap 2Δ/kT_c = 3.5 (tunneling)", "Giaever tunneling spectroscopy", 1960, "2Δ/k_B T_c ≈ 3.5–3.6 for weak coupling (Al, Sn, Pb)", "Confirmed") -add_mv(502, "Nernst Equation Verified by Concentration Cells", "Standard electrochemistry labs", 1900, "E vs log Q linear with RT/nF slope", "Confirmed") -add_mv(503, "Butler-Volmer for H₂ Evolution on Pt", "Electrode kinetics measurements", 1950, "Tafel slope ~120 mV/dec; j₀ matches exchange current", "Confirmed") -add_mv(507, "Faraday's Laws: Cu Electroplating Efficiency", "Industrial electroplating", 1900, "Mass deposited = QM/(nF) to >99.9% efficiency", "Confirmed") -add_mv(513, "Einstein Viscosity Verified for Latex Suspensions", "Dilute colloid viscometry", 1940, "η/η_s−1 = 2.5φ in dilute limit; confirmed", "Confirmed") -add_mv(515, "Frank-Oseen Elastic Constants from Frederiks Transition", "Nematic liquid crystals", 1970, "K₁₁,K₂₂,K₃₃ measured; match theory", "Confirmed") - -cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", mat_ver) - -# ================================================================ -# UPDATE STATS -# ================================================================ -conn.commit() -print(f"Added {len(mat_eqs)} material physics equations (now total: {max_id})") -print(f"Added {len(m_se)} sub-equations") -print(f"Added {len(mat_ver)} verifications") -print(f"New domains: Crystallography, Semiconductor Physics, Polymer Physics, Surface Science, Soft Matter, Phase Transformations") - -cur.execute("SELECT d.name, COUNT(e.id) FROM domains d LEFT JOIN equations e ON e.domain_id=d.id GROUP BY d.id ORDER BY d.id") -print("\nDomain equation counts (all):") -for row in cur.fetchall(): - print(f" {row[0]}: {row[1]}") - -conn.close() diff --git a/5-Applications/scripts/architect_topology_driver.py b/5-Applications/scripts/architect_topology_driver.py deleted file mode 100644 index a2858989..00000000 --- a/5-Applications/scripts/architect_topology_driver.py +++ /dev/null @@ -1,460 +0,0 @@ -#!/usr/bin/env python3 -""" -Architect Node Topology Driver - -This driver maximizes the utilization of the architect node's topology -by leveraging every bit of its available resources through topology-aware scheduling. - -Architecture: -- Topology-aware resource scheduling -- Full utilization of 8 cores, 16GB RAM, 500GB storage -- Integration with TSM (Topological State Machine) -- Tailscale mesh integration -- Dynamic workload distribution -""" - -import sys -import json -import time -import subprocess -import re -import threading -import multiprocessing -import psutil -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass -from typing import List, Dict, Optional, Any, Callable -from collections import defaultdict, deque -from enum import Enum - -class WorkloadType(Enum): - """Types of workloads for topology scheduling""" - COMPUTE_INTENSIVE = "compute_intensive" - MEMORY_INTENSIVE = "memory_intensive" - IO_INTENSIVE = "io_intensive" - NETWORK_INTENSIVE = "network_intensive" - MIXED = "mixed" - -@dataclass -class Workload: - """Workload to be scheduled""" - workload_id: str - workload_type: WorkloadType - cpu_required: float # 0-1 (percentage of total CPU) - memory_required: float # 0-1 (percentage of total RAM) - storage_required: float # 0-1 (percentage of total storage) - bandwidth_required: float # 0-1 (percentage of total bandwidth) - priority: int # 1-10 - duration: float # seconds - executable: Callable - status: str = "pending" - assigned_core: Optional[int] = None - start_time: Optional[float] = None - end_time: Optional[float] = None - -@dataclass -class TopologyResource: - """Topology resource state""" - total_cores: int - total_ram_gb: float - total_storage_gb: float - total_bandwidth_mbps: float - - available_cores: int - available_ram_gb: float - available_storage_gb: float - available_bandwidth_mbps: float - - core_utilization: List[float] # Per-core utilization - memory_utilization: float - storage_utilization: float - bandwidth_utilization: float - -class ArchitectTopologyDriver: - """ - Driver to maximize architect node topology utilization. - - This driver leverages every bit of the architect node's topology through: - - Topology-aware resource scheduling - - Dynamic workload distribution - - Full utilization of 8 cores, 16GB RAM, 500GB storage - - Integration with TSM and Tailscale mesh - """ - - def __init__(self): - # Architect node specs - self.node_specs = { - "cores": 8, - "ram_gb": 16, - "storage_gb": 500, - "bandwidth_mbps": 500 - } - - # Topology resource state - self.topology_resource = TopologyResource( - total_cores=self.node_specs["cores"], - total_ram_gb=self.node_specs["ram_gb"], - total_storage_gb=self.node_specs["storage_gb"], - total_bandwidth_mbps=self.node_specs["bandwidth_mbps"], - available_cores=self.node_specs["cores"], - available_ram_gb=self.node_specs["ram_gb"], - available_storage_gb=self.node_specs["storage_gb"], - available_bandwidth_mbps=self.node_specs["bandwidth_mbps"], - core_utilization=[0.0] * self.node_specs["cores"], - memory_utilization=0.0, - storage_utilization=0.0, - bandwidth_utilization=0.0 - ) - - # Workload queues - self.workload_queue: deque[Workload] = deque() - self.active_workloads: Dict[str, Workload] = {} - self.completed_workloads: List[Workload] = [] - - # Scheduling state - self.scheduling_strategy = "topology_aware" - self.max_concurrent_workloads = self.node_specs["cores"] * 2 # 2x oversubscription - - # Background processing - self._running = False - self._lock = threading.Lock() - self._scheduler_thread: Optional[threading.Thread] = None - self._monitor_thread: Optional[threading.Thread] = None - self._scheduling_interval = 0.1 # 100ms - self._monitoring_interval = 1.0 # 1s - - # Performance metrics - self.metrics: Dict[str, List[float]] = defaultdict(list) - self.total_utilization_score = 0.0 - - print(f"[ArchitectTopologyDriver] Initialized for architect node") - print(f" Cores: {self.node_specs['cores']}") - print(f" RAM: {self.node_specs['ram_gb']}GB") - print(f" Storage: {self.node_specs['storage_gb']}GB") - print(f" Bandwidth: {self.node_specs['bandwidth_mbps']}Mbps") - - def update_topology_state(self): - """Update topology resource state from actual system metrics.""" - try: - # Get actual CPU utilization - cpu_percent = psutil.cpu_percent(interval=0.1, percpu=True) - self.topology_resource.core_utilization = [c / 100.0 for c in cpu_percent] - - # Get actual memory utilization - memory = psutil.virtual_memory() - self.topology_resource.memory_utilization = memory.percent / 100.0 - self.topology_resource.available_ram_gb = memory.available / (1024**3) - - # Get actual disk utilization - disk = psutil.disk_usage('/') - self.topology_resource.storage_utilization = disk.percent / 100.0 - self.topology_resource.available_storage_gb = disk.free / (1024**3) - - # Calculate available cores (cores with < 80% utilization) - available_cores = sum(1 for util in self.topology_resource.core_utilization if util < 0.8) - self.topology_resource.available_cores = max(0, available_cores) - - # Calculate total utilization score - core_avg = sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization) - self.total_utilization_score = ( - core_avg * 0.4 + - self.topology_resource.memory_utilization * 0.3 + - self.topology_resource.storage_utilization * 0.2 + - self.topology_resource.bandwidth_utilization * 0.1 - ) - - except Exception as e: - print(f"[ArchitectTopologyDriver] Error updating topology state: {e}") - - def schedule_workload(self, workload: Workload) -> bool: - """Schedule a workload using topology-aware scheduling.""" - with self._lock: - # Check if resources are available - if self.topology_resource.available_cores < 1: - return False - - if self.topology_resource.available_ram_gb < workload.memory_required * self.node_specs["ram_gb"]: - return False - - if self.topology_resource.available_storage_gb < workload.storage_required * self.node_specs["storage_gb"]: - return False - - # Find best core for this workload - best_core = self._find_best_core(workload) - - if best_core is None: - return False - - # Assign workload - workload.assigned_core = best_core - workload.status = "running" - workload.start_time = time.time() - - # Update resource availability - self.topology_resource.available_cores -= 1 - self.topology_resource.available_ram_gb -= workload.memory_required * self.node_specs["ram_gb"] - self.topology_resource.available_storage_gb -= workload.storage_required * self.node_specs["storage_gb"] - - # Add to active workloads - self.active_workloads[workload.workload_id] = workload - - print(f"[ArchitectTopologyDriver] Scheduled {workload.workload_id} on core {best_core}") - print(f" Type: {workload.workload_type.value}") - print(f" CPU: {workload.cpu_required * 100:.1f}%") - print(f" RAM: {workload.memory_required * 100:.1f}%") - - return True - - def _find_best_core(self, workload: Workload) -> Optional[int]: - """Find best core for workload based on topology state.""" - # Find core with lowest utilization - core_utilizations = self.topology_resource.core_utilization - best_core = None - best_utilization = 1.0 - - for i, util in enumerate(core_utilizations): - if util < best_utilization: - best_utilization = util - best_core = i - - # Check if best core is available (< 80% utilization) - if best_core is not None and best_utilization < 0.8: - return best_core - - return None - - def submit_workload(self, workload: Workload) -> bool: - """Submit a workload for scheduling.""" - with self._lock: - self.workload_queue.append(workload) - print(f"[ArchitectTopologyDriver] Submitted workload {workload.workload_id}") - return True - - def _scheduler_loop(self): - """Background scheduler loop.""" - while self._running: - try: - with self._lock: - # Update topology state - self.update_topology_state() - - # Schedule pending workloads - while (len(self.workload_queue) > 0 and - len(self.active_workloads) < self.max_concurrent_workloads): - workload = self.workload_queue.popleft() - if not self.schedule_workload(workload): - # Can't schedule now, put back in queue - self.workload_queue.appendleft(workload) - break - - # Check for completed workloads - current_time = time.time() - completed = [] - for workload_id, workload in self.active_workloads.items(): - if workload.end_time and current_time >= workload.end_time: - completed.append(workload_id) - - for workload_id in completed: - self._complete_workload(workload_id) - - time.sleep(self._scheduling_interval) - - except Exception as e: - print(f"[ArchitectTopologyDriver] Scheduler loop error: {e}") - time.sleep(1.0) - - def _complete_workload(self, workload_id: str): - """Complete a workload and free resources.""" - with self._lock: - if workload_id not in self.active_workloads: - return - - workload = self.active_workloads[workload_id] - workload.status = "completed" - workload.end_time = time.time() - - # Free resources - self.topology_resource.available_cores += 1 - self.topology_resource.available_ram_gb += workload.memory_required * self.node_specs["ram_gb"] - self.topology_resource.available_storage_gb += workload.storage_required * self.node_specs["storage_gb"] - - # Move to completed - self.completed_workloads.append(workload) - del self.active_workloads[workload_id] - - print(f"[ArchitectTopologyDriver] Completed {workload_id}") - - def _monitor_loop(self): - """Background monitoring loop.""" - while self._running: - try: - with self._lock: - # Record metrics - self.metrics['total_utilization'].append(self.total_utilization_score) - self.metrics['core_utilization_avg'].append( - sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization) - ) - self.metrics['memory_utilization'].append(self.topology_resource.memory_utilization) - self.metrics['active_workloads'].append(len(self.active_workloads)) - self.metrics['queued_workloads'].append(len(self.workload_queue)) - - time.sleep(self._monitoring_interval) - - except Exception as e: - print(f"[ArchitectTopologyDriver] Monitor loop error: {e}") - time.sleep(1.0) - - def start(self) -> bool: - """Start the topology driver.""" - try: - self._running = True - self._scheduler_thread = threading.Thread(target=self._scheduler_loop, daemon=True) - self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True) - - self._scheduler_thread.start() - self._monitor_thread.start() - - print(f"[ArchitectTopologyDriver] Started topology driver") - print(f" Scheduling strategy: {self.scheduling_strategy}") - print(f" Max concurrent workloads: {self.max_concurrent_workloads}") - return True - - except Exception as e: - print(f"[ArchitectTopologyDriver] Failed to start: {e}") - return False - - def stop(self): - """Stop the topology driver.""" - self._running = False - if self._scheduler_thread: - self._scheduler_thread.join(timeout=5.0) - if self._monitor_thread: - self._monitor_thread.join(timeout=5.0) - print("[ArchitectTopologyDriver] Stopped") - - def get_status(self) -> Dict[str, Any]: - """Get current driver status.""" - with self._lock: - return { - "running": self._running, - "topology_resource": { - "total_cores": self.topology_resource.total_cores, - "available_cores": self.topology_resource.available_cores, - "total_ram_gb": self.topology_resource.total_ram_gb, - "available_ram_gb": self.topology_resource.available_ram_gb, - "total_storage_gb": self.topology_resource.total_storage_gb, - "available_storage_gb": self.topology_resource.available_storage_gb, - "core_utilization": self.topology_resource.core_utilization, - "memory_utilization": self.topology_resource.memory_utilization, - "storage_utilization": self.topology_resource.storage_utilization, - "total_utilization_score": self.total_utilization_score - }, - "workloads": { - "active": len(self.active_workloads), - "queued": len(self.workload_queue), - "completed": len(self.completed_workloads) - }, - "metrics": { - key: { - "avg": sum(values) / len(values) if values else 0, - "max": max(values) if values else 0, - "min": min(values) if values else 0, - "count": len(values) - } - for key, values in self.metrics.items() - } - } - - def print_status(self): - """Print current driver status.""" - status = self.get_status() - - print("\n" + "=" * 70) - print("ARCHITECT TOPOLOGY DRIVER STATUS") - print("=" * 70) - - print(f"\n📊 Topology Utilization:") - print(f" Total Utilization Score: {status['topology_resource']['total_utilization_score']:.3f}") - print(f" Cores: {status['topology_resource']['available_cores']}/{status['topology_resource']['total_cores']} available") - print(f" RAM: {status['topology_resource']['available_ram_gb']:.1f}/{status['topology_resource']['total_ram_gb']:.1f}GB available") - print(f" Storage: {status['topology_resource']['available_storage_gb']:.1f}/{status['topology_resource']['total_storage_gb']:.1f}GB available") - print(f" Core Utilization: {[f'{c:.2f}' for c in status['topology_resource']['core_utilization']]}") - - print(f"\n📋 Workloads:") - print(f" Active: {status['workloads']['active']}") - print(f" Queued: {status['workloads']['queued']}") - print(f" Completed: {status['workloads']['completed']}") - - print(f"\n📈 Metrics:") - for key, metric in status['metrics'].items(): - print(f" {key}: avg {metric['avg']:.3f}, max {metric['max']:.3f}") - - print("\n" + "=" * 70) - -def create_sample_workloads(num_workloads: int) -> List[Workload]: - """Create sample workloads for testing.""" - workloads = [] - - for i in range(num_workloads): - workload_type = random.choice(list(WorkloadType)) - - workload = Workload( - workload_id=f"workload_{i}", - workload_type=workload_type, - cpu_required=random.uniform(0.1, 0.5), - memory_required=random.uniform(0.05, 0.3), - storage_required=random.uniform(0.01, 0.05), - bandwidth_required=random.uniform(0.01, 0.1), - priority=random.randint(1, 10), - duration=random.uniform(5.0, 30.0), - executable=lambda: time.sleep(random.uniform(5.0, 30.0)) - ) - - workloads.append(workload) - - return workloads - -import random - -if __name__ == "__main__": - print("=" * 70) - print("Architect Node Topology Driver") - print("Maximizing Topology Utilization") - print("=" * 70) - - # Create driver - driver = ArchitectTopologyDriver() - - # Start driver - if not driver.start(): - print("Failed to start driver") - sys.exit(1) - - # Create sample workloads - print("\nCreating sample workloads...") - num_workloads = 50 - workloads = create_sample_workloads(num_workloads) - print(f"Created {len(workloads)} workloads") - - # Submit workloads - print("\nSubmitting workloads...") - for workload in workloads: - driver.submit_workload(workload) - - print(f"Submitted {len(workloads)} workloads") - - # Monitor for 60 seconds - print("\nMonitoring for 60 seconds...") - for i in range(60): - time.sleep(1) - if i % 10 == 0: - driver.print_status() - - # Stop driver - driver.stop() - - # Final status - driver.print_status() - - print("\n✅ Architect topology driver test complete") diff --git a/5-Applications/scripts/architect_topology_driver_accurate.py b/5-Applications/scripts/architect_topology_driver_accurate.py deleted file mode 100644 index cc5f6d39..00000000 --- a/5-Applications/scripts/architect_topology_driver_accurate.py +++ /dev/null @@ -1,960 +0,0 @@ -#!/usr/bin/env python3 -""" -Architect Node Topology Driver Based on Accurate Container Map -with Functional Collapse Paradigm Cognitive Load Metrics - -This driver uses the 100% accurate container map to maximize the architect node's -topology utilization by leveraging every bit of its available resources. - -Functional Collapse Paradigm: -- Cognitive Load is the informational cost of lawful assemblage between current state and optimal state -- Metrics are type-instances of bind(A, B, Metric) where Metric is typically KL-Divergence -- Criticality Threshold (τ_c) aligned with Abelian Sandpile threshold -- N-Local Topology Scaling with path-dependence - -Based on actual container mapping data: -- 6 physical cores, 12 logical cores -- 30.40 GB RAM -- 213 processes -- 359 network edges -- 32 file systems -- 3376 memory regions -- 359 open sockets -""" - -import sys -import json -import time -import threading -import multiprocessing -import psutil -import math -import numpy as np -import signal -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Any, Callable, Tuple -from collections import defaultdict, deque -from enum import Enum - -class WorkloadType(Enum): - """Types of workloads for topology scheduling""" - COMPUTE_INTENSIVE = "compute_intensive" - MEMORY_INTENSIVE = "memory_intensive" - IO_INTENSIVE = "io_intensive" - NETWORK_INTENSIVE = "network_intensive" - MIXED = "mixed" - -class OperationalThreshold(Enum): - """Operational thresholds for cognitive load""" - RELATIONAL = "relational" # < 0.25: Low friction; full 5D torus expansion - SEMANTIC = "semantic" # < 0.50: Standard operating range; active S3C compression - TOPOLOGICAL = "topological" # < 0.75: High stress; gear teeth modulations active - CRITICAL = "critical" # ≥ 0.75: Criticality reached (τ_c). Sandbox collapse initiated - -@dataclass -class CognitiveLoadMetrics: - """Functional Collapse Paradigm cognitive load metrics""" - intrinsic_load: float # L_I: bind(p(b|x), uniform, KL) - effort_load: float # L_E: bind(P_w_prior(x), P_optimal(x), KL) - total_system_load: float # L_total: bind(load_vector, target_vector, weighted_L2) - efficiency_factor: float # η: bind(intrinsic, total, ratio_metric) - distribution_precision: float # P_w: bind(ensemble, mixture, simplex_metric) - operational_threshold: OperationalThreshold - criticality_reached: bool - -@dataclass -class Workload: - """Workload to be scheduled""" - workload_id: str - workload_type: WorkloadType - cpu_required: float # 0-1 (percentage of total CPU) - memory_required: float # 0-1 (percentage of total RAM) - storage_required: float # 0-1 (percentage of total storage) - bandwidth_required: float # 0-1 (percentage of total bandwidth) - priority: int # 1-10 - duration: float # seconds - executable: Callable - status: str = "pending" - assigned_core: Optional[int] = None - start_time: Optional[float] = None - end_time: Optional[float] = None - -@dataclass -class AccurateTopologyResource: - """Topology resource state based on accurate container map""" - total_cores: int - total_ram_gb: float - total_storage_gb: float - total_bandwidth_mbps: float - - available_cores: int - available_ram_gb: float - available_storage_gb: float - available_bandwidth_mbps: float - - core_utilization: List[float] # Per-core utilization - memory_utilization: float - storage_utilization: float - bandwidth_utilization: float - - # Container map data - total_processes: int - total_network_edges: int - total_file_systems: int - total_memory_regions: int - total_open_sockets: int - total_devices: int - total_kernel_parameters: int - - # Functional Collapse Paradigm state - cognitive_load_metrics: Optional[CognitiveLoadMetrics] = None - history_trace: List[Dict[str, float]] = field(default_factory=list) - avalanche_count: int = 0 - gear_pitch: float = 1.0 # Gear teeth modulation factor - -def bind_kl_divergence(p: np.ndarray, q: np.ndarray) -> float: - """ - Bind operator using KL-divergence as the metric. - - D_KL(P || Q) = sum(P(i) * log(P(i) / Q(i))) - - This measures the informational cost of lawful assemblage between - the current state (P) and the optimal state (Q). - """ - # Ensure distributions sum to 1 - p = p / np.sum(p) - q = q / np.sum(q) - - # Add small epsilon to avoid log(0) - epsilon = 1e-10 - p = p + epsilon - q = q + epsilon - - # Calculate KL-divergence - kl_div = np.sum(p * np.log(p / q)) - - return float(kl_div) - -def bind_weighted_l2(vector_a: np.ndarray, vector_b: np.ndarray, weights: Optional[np.ndarray] = None) -> float: - """ - Bind operator using weighted L2 distance. - - ||A - B||_w = sqrt(sum(w_i * (A_i - B_i)^2)) - """ - if weights is None: - weights = np.ones_like(vector_a) - - diff = vector_a - vector_b - weighted_diff = weights * diff - l2_distance = np.sqrt(np.sum(weighted_diff ** 2)) - - return float(l2_distance) - -def bind_ratio_metric(a: float, b: float) -> float: - """ - Bind operator using ratio metric. - - ratio = a / b (with epsilon to avoid division by zero) - """ - epsilon = 1e-10 - return a / (b + epsilon) - -def bind_simplex_metric(ensemble: np.ndarray, mixture: np.ndarray) -> float: - """ - Bind operator using simplex metric. - - Measures the "sharpness" of the current agent consensus. - Uses entropy-based distance on the probability simplex. - """ - # Ensure distributions are on simplex - ensemble = ensemble / np.sum(ensemble) - mixture = mixture / np.sum(mixture) - - # Calculate entropy difference - epsilon = 1e-10 - ensemble_entropy = -np.sum(ensemble * np.log(ensemble + epsilon)) - mixture_entropy = -np.sum(mixture * np.log(mixture + epsilon)) - - # Simplex distance as entropy difference - simplex_distance = abs(ensemble_entropy - mixture_entropy) - - return float(simplex_distance) - -def calculate_cognitive_load_metrics( - current_state: np.ndarray, - optimal_state: np.ndarray, - prior_distribution: np.ndarray, - optimal_distribution: np.ndarray, - load_vector: np.ndarray, - target_vector: np.ndarray, - ensemble: np.ndarray, - mixture: np.ndarray, - history_trace: List[Dict[str, float]] -) -> CognitiveLoadMetrics: - """ - Calculate Functional Collapse Paradigm cognitive load metrics. - - All metrics are type-instances of bind(A, B, Metric). - """ - # Intrinsic Load (L_I): bind(p(b|x), uniform, KL) - # Measures how far the current bitseed distribution is from high-entropy uniform state - uniform_distribution = np.ones_like(current_state) / len(current_state) - intrinsic_load = bind_kl_divergence(current_state, uniform_distribution) - - # Effort Load (L_E): bind(P_w_prior(x), P_optimal(x), KL) - # Measures delta between swarm's current prediction and bit-accurate optimal predictor - effort_load = bind_kl_divergence(prior_distribution, optimal_distribution) - - # Total System Load (L_total): bind(load_vector, target_vector, weighted_L2) - # Global pressure on the manifold substrate - # Use history-dependent weights for N-Local Topology Scaling - if history_trace: - # Calculate weights based on history (recent avalanches increase weight) - recent_avalanches = sum(1 for h in history_trace[-10:] if h.get('avalanche', False)) - weights = np.ones_like(load_vector) * (1.0 + 0.1 * recent_avalanches) - else: - weights = None - total_system_load = bind_weighted_l2(load_vector, target_vector, weights) - - # Efficiency Factor (η): bind(intrinsic, total, ratio_metric) - # Ratio of effective informatic work to total energy dissipated - efficiency_factor = bind_ratio_metric(intrinsic_load, total_system_load + epsilon) - - # Distribution Precision (P_w): bind(ensemble, mixture, simplex_metric) - # Measures the "sharpness" of the current agent consensus - distribution_precision = bind_simplex_metric(ensemble, mixture) - - # Determine operational threshold - operational_threshold = determine_operational_threshold(total_system_load) - - # Check criticality (τ_c) - criticality_reached = total_system_load >= 0.75 - - return CognitiveLoadMetrics( - intrinsic_load=intrinsic_load, - effort_load=effort_load, - total_system_load=total_system_load, - efficiency_factor=efficiency_factor, - distribution_precision=distribution_precision, - operational_threshold=operational_threshold, - criticality_reached=criticality_reached - ) - -def determine_operational_threshold(load: float) -> OperationalThreshold: - """Determine operational threshold based on load value.""" - if load < 0.25: - return OperationalThreshold.RELATIONAL - elif load < 0.50: - return OperationalThreshold.SEMANTIC - elif load < 0.75: - return OperationalThreshold.TOPOLOGICAL - else: - return OperationalThreshold.CRITICAL - -def abelian_sandpile_collapse(load: float, gear_pitch: float) -> Tuple[bool, float]: - """ - Abelian Sandpile collapse logic. - - When Load ≥ τ_c (0.75), trigger a "Topological Collapse" to prevent - irreversible informatic damage. This modulates the gear pitch to - reduce cognitive load. - - Returns: - (collapse_triggered, new_gear_pitch) - """ - criticality_threshold = 0.75 - - if load >= criticality_threshold: - # Avalanche triggered - collapse to reduce load - # Modulate gear pitch to reduce cognitive load - new_gear_pitch = gear_pitch * 0.8 # Reduce gear pitch by 20% - return True, new_gear_pitch - else: - # No collapse needed - # Gradually restore gear pitch toward 1.0 - new_gear_pitch = min(1.0, gear_pitch + 0.01) - return False, new_gear_pitch - -epsilon = 1e-10 - -class ArchitectTopologyDriverAccurate: - """ - Driver to maximize architect node topology utilization based on accurate container map. - - Uses actual container mapping data to make intelligent scheduling decisions. - """ - - def __init__(self, container_map_path: Optional[str] = None): - # Load container map - self.container_map = self._load_container_map(container_map_path) - - # Extract actual specs from container map - self.node_specs = { - "cores": self.container_map["cpu_info"]["physical_cores"], - "logical_cores": self.container_map["cpu_info"]["logical_cores"], - "ram_gb": self.container_map["memory_info"]["virtual_memory"]["total"] / (1024**3), - "storage_gb": 500, # From resource map - "bandwidth_mbps": 500 # From resource map - } - - # Topology resource state - self.topology_resource = AccurateTopologyResource( - total_cores=self.node_specs["cores"], - total_ram_gb=self.node_specs["ram_gb"], - total_storage_gb=self.node_specs["storage_gb"], - total_bandwidth_mbps=self.node_specs["bandwidth_mbps"], - available_cores=self.node_specs["cores"], - available_ram_gb=self.node_specs["ram_gb"], - available_storage_gb=self.node_specs["storage_gb"], - available_bandwidth_mbps=self.node_specs["bandwidth_mbps"], - core_utilization=[0.0] * self.node_specs["logical_cores"], - memory_utilization=0.0, - storage_utilization=0.0, - bandwidth_utilization=0.0, - total_processes=self.container_map.get("processes", []), - total_network_edges=len(self.container_map.get("network_edges", [])), - total_file_systems=len(self.container_map.get("file_systems", [])), - total_memory_regions=len(self.container_map.get("memory_regions", [])), - total_open_sockets=len(self.container_map.get("open_sockets", [])), - total_devices=len(self.container_map.get("devices", [])), - total_kernel_parameters=len(self.container_map.get("kernel_parameters", {})) - ) - - # Workload queues - self.workload_queue: deque[Workload] = deque() - self.active_workloads: Dict[str, Workload] = {} - self.completed_workloads: List[Workload] = [] - - # Scheduling state - self.scheduling_strategy = "topology_aware_accurate_functional_collapse" - self.max_concurrent_workloads = self.node_specs["logical_cores"] * 2 # 2x oversubscription - - # Background processing - self._running = False - self._lock = threading.Lock() - self._scheduler_thread: Optional[threading.Thread] = None - self._monitor_thread: Optional[threading.Thread] = None - self._scheduling_interval = 0.1 # 100ms - self._monitoring_interval = 1.0 # 1s - - # Performance metrics - self.metrics: Dict[str, List[float]] = defaultdict(list) - self.total_utilization_score = 0.0 - - # Functional Collapse Paradigm state - self.cognitive_load_history: List[Dict[str, float]] = [] - self.avalanche_count = 0 - self.gear_pitch = 1.0 - - print(f"[ArchitectTopologyDriverAccurate] Initialized based on accurate container map") - print(f" Cores: {self.node_specs['cores']} physical, {self.node_specs['logical_cores']} logical") - print(f" RAM: {self.node_specs['ram_gb']:.2f}GB") - print(f" Storage: {self.node_specs['storage_gb']}GB") - print(f" Bandwidth: {self.node_specs['bandwidth_mbps']}Mbps") - print(f" Container Map Processes: {len(self.topology_resource.total_processes)}") - print(f" Container Map Network Edges: {self.topology_resource.total_network_edges}") - print(f" Container Map File Systems: {self.topology_resource.total_file_systems}") - print(f" Container Map Memory Regions: {self.topology_resource.total_memory_regions}") - print(f" Scheduling Strategy: {self.scheduling_strategy}") - print(f" Functional Collapse Paradigm: Enabled") - - def _load_container_map(self, path: Optional[str]) -> Dict[str, Any]: - """Load container map from file.""" - if path is None: - # Find most recent container map - map_dir = Path("shared-data/data/swarm_responses") - maps = sorted(map_dir.glob("architect_container_map_remote_*.json")) - if maps: - path = str(maps[-1]) - else: - raise FileNotFoundError("No container map found") - - print(f"[ArchitectTopologyDriverAccurate] Loading container map from {path}") - - with open(path, 'r') as f: - return json.load(f) - - def update_topology_state(self): - """Update topology resource state from actual system metrics and calculate cognitive load.""" - try: - # Get actual CPU utilization - cpu_percent = psutil.cpu_percent(interval=0.1, percpu=True) - self.topology_resource.core_utilization = [c / 100.0 for c in cpu_percent] - - # Get actual memory utilization - memory = psutil.virtual_memory() - self.topology_resource.memory_utilization = memory.percent / 100.0 - self.topology_resource.available_ram_gb = memory.available / (1024**3) - - # Get actual disk utilization - disk = psutil.disk_usage('/') - self.topology_resource.storage_utilization = disk.percent / 100.0 - self.topology_resource.available_storage_gb = disk.free / (1024**3) - - # Calculate available cores (cores with < 80% utilization) - available_cores = sum(1 for util in self.topology_resource.core_utilization if util < 0.8) - self.topology_resource.available_cores = max(0, available_cores) - - # Calculate total utilization score - core_avg = sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization) - self.total_utilization_score = ( - core_avg * 0.4 + - self.topology_resource.memory_utilization * 0.3 + - self.topology_resource.storage_utilization * 0.2 + - self.topology_resource.bandwidth_utilization * 0.1 - ) - - # Calculate Functional Collapse Paradigm cognitive load metrics - self._calculate_cognitive_load() - - except Exception as e: - print(f"[ArchitectTopologyDriverAccurate] Error updating topology state: {e}") - - def _calculate_cognitive_load(self): - """Calculate cognitive load metrics using Refined Functional Collapse Paradigm.""" - try: - # Create state vectors from current topology state - current_state = np.array(self.topology_resource.core_utilization) - optimal_state = np.ones_like(current_state) * 0.5 # Target 50% utilization per core - - prior_distribution = np.array([self.topology_resource.memory_utilization, - self.topology_resource.storage_utilization, - self.topology_resource.bandwidth_utilization]) - optimal_distribution = np.array([0.5, 0.5, 0.5]) # Target 50% utilization - - load_vector = np.array([self.total_utilization_score, - len(self.active_workloads) / self.max_concurrent_workloads, - self.topology_resource.memory_utilization]) - target_vector = np.array([0.5, 0.5, 0.5]) # Target load vector - - # Ensemble distribution (workload types distribution) - workload_types = [w.workload_type.value for w in self.active_workloads.values()] - if workload_types: - type_counts = defaultdict(int) - for wt in workload_types: - type_counts[wt] += 1 - ensemble = np.array([type_counts.get(wt.value, 0) for wt in WorkloadType]) - ensemble = ensemble / np.sum(ensemble) if np.sum(ensemble) > 0 else np.ones(len(WorkloadType)) / len(WorkloadType) - else: - ensemble = np.ones(len(WorkloadType)) / len(WorkloadType) - - # Mixture distribution (target uniform distribution) - mixture = np.ones(len(WorkloadType)) / len(WorkloadType) - - # Refined Intrinsic Load: use capacity-matched baseline instead of uniform - # Calculate baseline distribution based on current structural constraints - baseline_distribution = self._calculate_capacity_matched_baseline(current_state) - - # Override the intrinsic load calculation to use capacity-matched baseline - intrinsic_load = bind_kl_divergence(current_state, baseline_distribution) - - # Calculate remaining metrics - effort_load = bind_kl_divergence(prior_distribution, optimal_distribution) - - # Calculate history-dependent weights for N-Local Topology Scaling - if self.cognitive_load_history: - recent_avalanches = sum(1 for h in self.cognitive_load_history[-10:] if h.get('avalanche', False)) - weights = np.ones_like(load_vector) * (1.0 + 0.1 * recent_avalanches) - else: - weights = None - total_system_load = bind_weighted_l2(load_vector, target_vector, weights) - - efficiency_factor = bind_ratio_metric(intrinsic_load, total_system_load + epsilon) - distribution_precision = bind_simplex_metric(ensemble, mixture) - - # Determine operational threshold - operational_threshold = determine_operational_threshold(total_system_load) - - # Check criticality (τ_c) - criticality_reached = total_system_load >= 0.75 - - self.topology_resource.cognitive_load_metrics = CognitiveLoadMetrics( - intrinsic_load=intrinsic_load, - effort_load=effort_load, - total_system_load=total_system_load, - efficiency_factor=efficiency_factor, - distribution_precision=distribution_precision, - operational_threshold=operational_threshold, - criticality_reached=criticality_reached - ) - - # Check for criticality and trigger Abelian Sandpile collapse if needed - collapse_triggered, new_gear_pitch = abelian_sandpile_collapse( - load=self.topology_resource.cognitive_load_metrics.total_system_load, - gear_pitch=self.gear_pitch - ) - - if collapse_triggered: - self.avalanche_count += 1 - print(f"[ArchitectTopologyDriverAccurate] ⚠️ AVALANCHE TRIGGERED - Topological Collapse initiated") - print(f" Total System Load: {self.topology_resource.cognitive_load_metrics.total_system_load:.3f} ≥ τ_c (0.75)") - print(f" Avalanche Count: {self.avalanche_count}") - print(f" Gear Pitch: {self.gear_pitch:.3f} → {new_gear_pitch:.3f}") - - self.gear_pitch = new_gear_pitch - self.topology_resource.gear_pitch = new_gear_pitch - - # Record history trace for N-Local Topology Scaling - history_entry = { - "timestamp": time.time(), - "total_system_load": self.topology_resource.cognitive_load_metrics.total_system_load, - "intrinsic_load": self.topology_resource.cognitive_load_metrics.intrinsic_load, - "effort_load": self.topology_resource.cognitive_load_metrics.effort_load, - "efficiency_factor": self.topology_resource.cognitive_load_metrics.efficiency_factor, - "distribution_precision": self.topology_resource.cognitive_load_metrics.distribution_precision, - "operational_threshold": self.topology_resource.cognitive_load_metrics.operational_threshold.value, - "avalanche": collapse_triggered, - "gear_pitch": self.gear_pitch - } - - self.cognitive_load_history.append(history_entry) - - # Keep history limited to last 100 entries - if len(self.cognitive_load_history) > 100: - self.cognitive_load_history = self.cognitive_load_history[-100:] - - except Exception as e: - print(f"[ArchitectTopologyDriverAccurate] Error calculating cognitive load: {e}") - - def _calculate_capacity_matched_baseline(self, current_state: np.ndarray) -> np.ndarray: - """ - Calculate capacity-matched baseline distribution for refined intrinsic load. - - Instead of using unconstrained uniformity, this calculates the maximum-entropy - distribution under current structural and historical constraints. - - This means: - - Unnecessary over-constraint increases load - - Lawful structure does NOT automatically count as friction - - Intrinsic load becomes "excess rigidity relative to what this system can stably sustain" - """ - # Calculate baseline as maximum-entropy distribution under constraints - # Constraints: current gear pitch, recent avalanche history, available cores - - # Base uniform distribution - baseline = np.ones_like(current_state) / len(current_state) - - # Apply structural constraints based on gear pitch - # Higher gear pitch = more constrained baseline (lower capacity) - constrained_baseline = baseline * self.gear_pitch - - # Normalize to maintain probability distribution - constrained_baseline = constrained_baseline / np.sum(constrained_baseline) - - # Apply historical constraints - recent avalanches tighten baseline - if self.cognitive_load_history: - recent_avalanches = sum(1 for h in self.cognitive_load_history[-10:] if h.get('avalanche', False)) - # More avalanches = tighter baseline (system becomes more conservative) - history_factor = 1.0 / (1.0 + 0.1 * recent_avalanches) - constrained_baseline = constrained_baseline * history_factor - constrained_baseline = constrained_baseline / np.sum(constrained_baseline) - - return constrained_baseline - - def schedule_workload(self, workload: Workload) -> bool: - """Schedule a workload using topology-aware scheduling with cognitive load consideration.""" - with self._lock: - # Check if resources are available - if self.topology_resource.available_cores < 1: - return False - - if self.topology_resource.available_ram_gb < workload.memory_required * self.node_specs["ram_gb"]: - return False - - if self.topology_resource.available_storage_gb < workload.storage_required * self.node_specs["storage_gb"]: - return False - - # Check cognitive load - if critical, reject new workloads - if (self.topology_resource.cognitive_load_metrics and - self.topology_resource.cognitive_load_metrics.criticality_reached): - print(f"[ArchitectTopologyDriverAccurate] ⚠️ Rejecting {workload.workload_id} - Critical load reached") - return False - - # Adjust resource requirements based on gear pitch (N-Local Topology Scaling) - adjusted_cpu_required = workload.cpu_required * self.gear_pitch - adjusted_memory_required = workload.memory_required * self.gear_pitch - - # Find best core for this workload based on container map data - best_core = self._find_best_core_accurate(workload) - - if best_core is None: - return False - - # Assign workload - workload.assigned_core = best_core - workload.status = "running" - workload.start_time = time.time() - - # Update resource availability - self.topology_resource.available_cores -= 1 - self.topology_resource.available_ram_gb -= adjusted_memory_required * self.node_specs["ram_gb"] - self.topology_resource.available_storage_gb -= workload.storage_required * self.node_specs["storage_gb"] - - # Add to active workloads - self.active_workloads[workload.workload_id] = workload - - print(f"[ArchitectTopologyDriverAccurate] Scheduled {workload.workload_id} on core {best_core}") - print(f" Type: {workload.workload_type.value}") - print(f" CPU: {adjusted_cpu_required * 100:.1f}% (adjusted by gear pitch {self.gear_pitch:.3f})") - print(f" RAM: {adjusted_memory_required * 100:.1f}%") - - return True - - def _find_best_core_accurate(self, workload: Workload) -> Optional[int]: - """Find best core for workload based on accurate container map data.""" - # Find core with lowest utilization - core_utilizations = self.topology_resource.core_utilization - best_core = None - best_utilization = 1.0 - - for i, util in enumerate(core_utilizations): - if util < best_utilization: - best_utilization = util - best_core = i - - # Check if best core is available (< 80% utilization) - if best_core is not None and best_utilization < 0.8: - return best_core - - return None - - def submit_workload(self, workload: Workload) -> bool: - """Submit a workload for scheduling.""" - with self._lock: - self.workload_queue.append(workload) - print(f"[ArchitectTopologyDriverAccurate] Submitted workload {workload.workload_id}") - return True - - def _scheduler_loop(self): - """Background scheduler loop.""" - while self._running: - try: - with self._lock: - # Update topology state - self.update_topology_state() - - # Schedule pending workloads - while (len(self.workload_queue) > 0 and - len(self.active_workloads) < self.max_concurrent_workloads): - workload = self.workload_queue.popleft() - if not self.schedule_workload(workload): - # Can't schedule now, put back in queue - self.workload_queue.appendleft(workload) - break - - # Check for completed workloads - current_time = time.time() - completed = [] - for workload_id, workload in self.active_workloads.items(): - if workload.end_time and current_time >= workload.end_time: - completed.append(workload_id) - - for workload_id in completed: - self._complete_workload(workload_id) - - time.sleep(self._scheduling_interval) - - except Exception as e: - print(f"[ArchitectTopologyDriverAccurate] Scheduler loop error: {e}") - time.sleep(1.0) - - def _complete_workload(self, workload_id: str): - """Complete a workload and free resources.""" - with self._lock: - if workload_id not in self.active_workloads: - return - - workload = self.active_workloads[workload_id] - workload.status = "completed" - workload.end_time = time.time() - - # Free resources - self.topology_resource.available_cores += 1 - self.topology_resource.available_ram_gb += workload.memory_required * self.node_specs["ram_gb"] - self.topology_resource.available_storage_gb += workload.storage_required * self.node_specs["storage_gb"] - - # Move to completed - self.completed_workloads.append(workload) - del self.active_workloads[workload_id] - - print(f"[ArchitectTopologyDriverAccurate] Completed {workload_id}") - - def _monitor_loop(self): - """Background monitoring loop with cognitive load tracking.""" - while self._running: - try: - with self._lock: - # Record metrics - self.metrics['total_utilization'].append(self.total_utilization_score) - self.metrics['core_utilization_avg'].append( - sum(self.topology_resource.core_utilization) / len(self.topology_resource.core_utilization) - ) - self.metrics['memory_utilization'].append(self.topology_resource.memory_utilization) - self.metrics['active_workloads'].append(len(self.active_workloads)) - self.metrics['queued_workloads'].append(len(self.workload_queue)) - - # Record cognitive load metrics - if self.topology_resource.cognitive_load_metrics: - self.metrics['intrinsic_load'].append(self.topology_resource.cognitive_load_metrics.intrinsic_load) - self.metrics['effort_load'].append(self.topology_resource.cognitive_load_metrics.effort_load) - self.metrics['total_system_load'].append(self.topology_resource.cognitive_load_metrics.total_system_load) - self.metrics['efficiency_factor'].append(self.topology_resource.cognitive_load_metrics.efficiency_factor) - self.metrics['distribution_precision'].append(self.topology_resource.cognitive_load_metrics.distribution_precision) - self.metrics['gear_pitch'].append(self.gear_pitch) - self.metrics['avalanche_count'].append(self.avalanche_count) - - time.sleep(self._monitoring_interval) - - except Exception as e: - print(f"[ArchitectTopologyDriverAccurate] Monitor loop error: {e}") - time.sleep(1.0) - - def start(self) -> bool: - """Start the topology driver.""" - try: - self._running = True - self._scheduler_thread = threading.Thread(target=self._scheduler_loop, daemon=True) - self._monitor_thread = threading.Thread(target=self._monitor_loop, daemon=True) - - self._scheduler_thread.start() - self._monitor_thread.start() - - print(f"[ArchitectTopologyDriverAccurate] Started topology driver") - print(f" Scheduling strategy: {self.scheduling_strategy}") - print(f" Max concurrent workloads: {self.max_concurrent_workloads}") - print(f" Based on accurate container map with {self.topology_resource.total_processes} processes") - return True - - except Exception as e: - print(f"[ArchitectTopologyDriverAccurate] Failed to start: {e}") - return False - - def stop(self): - """Stop the topology driver.""" - self._running = False - if self._scheduler_thread: - self._scheduler_thread.join(timeout=5.0) - if self._monitor_thread: - self._monitor_thread.join(timeout=5.0) - print("[ArchitectTopologyDriverAccurate] Stopped") - - def get_status(self) -> Dict[str, Any]: - """Get current driver status including cognitive load metrics.""" - with self._lock: - status = { - "running": self._running, - "scheduling_strategy": self.scheduling_strategy, - "node_specs": self.node_specs, - "topology_resource": { - "total_cores": self.topology_resource.total_cores, - "available_cores": self.topology_resource.available_cores, - "total_ram_gb": self.topology_resource.total_ram_gb, - "available_ram_gb": self.topology_resource.available_ram_gb, - "total_storage_gb": self.topology_resource.total_storage_gb, - "available_storage_gb": self.topology_resource.available_storage_gb, - "core_utilization": self.topology_resource.core_utilization, - "memory_utilization": self.topology_resource.memory_utilization, - "storage_utilization": self.topology_resource.storage_utilization, - "total_utilization_score": self.total_utilization_score, - "container_map_stats": { - "total_processes": len(self.topology_resource.total_processes), - "total_network_edges": self.topology_resource.total_network_edges, - "total_file_systems": self.topology_resource.total_file_systems, - "total_memory_regions": self.topology_resource.total_memory_regions, - "total_open_sockets": self.topology_resource.total_open_sockets, - "total_devices": self.topology_resource.total_devices, - "total_kernel_parameters": self.topology_resource.total_kernel_parameters - } - }, - "workloads": { - "active": len(self.active_workloads), - "queued": len(self.workload_queue), - "completed": len(self.completed_workloads) - }, - "functional_collapse_paradigm": { - "cognitive_load_metrics": None, - "gear_pitch": self.gear_pitch, - "avalanche_count": self.avalanche_count, - "history_length": len(self.cognitive_load_history) - }, - "metrics": { - key: { - "avg": sum(values) / len(values) if values else 0, - "max": max(values) if values else 0, - "min": min(values) if values else 0, - "count": len(values) - } - for key, values in self.metrics.items() - } - } - - # Add cognitive load metrics if available - if self.topology_resource.cognitive_load_metrics: - status["functional_collapse_paradigm"]["cognitive_load_metrics"] = { - "intrinsic_load": self.topology_resource.cognitive_load_metrics.intrinsic_load, - "effort_load": self.topology_resource.cognitive_load_metrics.effort_load, - "total_system_load": self.topology_resource.cognitive_load_metrics.total_system_load, - "efficiency_factor": self.topology_resource.cognitive_load_metrics.efficiency_factor, - "distribution_precision": self.topology_resource.cognitive_load_metrics.distribution_precision, - "operational_threshold": self.topology_resource.cognitive_load_metrics.operational_threshold.value, - "criticality_reached": self.topology_resource.cognitive_load_metrics.criticality_reached - } - - return status - - def print_status(self): - """Print current driver status including cognitive load metrics.""" - status = self.get_status() - - print("\n" + "=" * 70) - print("ARCHITECT TOPOLOGY DRIVER STATUS (FUNCTIONAL COLLAPSE PARADIGM)") - print("=" * 70) - - print(f"\n📊 Node Specifications:") - print(f" Cores: {status['node_specs']['cores']} physical, {status['node_specs']['logical_cores']} logical") - print(f" RAM: {status['node_specs']['ram_gb']:.2f}GB") - print(f" Storage: {status['node_specs']['storage_gb']}GB") - print(f" Bandwidth: {status['node_specs']['bandwidth_mbps']}Mbps") - - print(f"\n📊 Topology Utilization:") - print(f" Total Utilization Score: {status['topology_resource']['total_utilization_score']:.3f}") - print(f" Cores: {status['topology_resource']['available_cores']}/{status['topology_resource']['total_cores']} available") - print(f" RAM: {status['topology_resource']['available_ram_gb']:.1f}/{status['topology_resource']['total_ram_gb']:.1f}GB available") - print(f" Storage: {status['topology_resource']['available_storage_gb']:.1f}/{status['topology_resource']['total_storage_gb']:.1f}GB available") - print(f" Core Utilization: {[f'{c:.2f}' for c in status['topology_resource']['core_utilization']]}") - - print(f"\n📊 Container Map Statistics:") - print(f" Processes: {status['topology_resource']['container_map_stats']['total_processes']}") - print(f" Network Edges: {status['topology_resource']['container_map_stats']['total_network_edges']}") - print(f" File Systems: {status['topology_resource']['container_map_stats']['total_file_systems']}") - print(f" Memory Regions: {status['topology_resource']['container_map_stats']['total_memory_regions']}") - print(f" Open Sockets: {status['topology_resource']['container_map_stats']['total_open_sockets']}") - print(f" Devices: {status['topology_resource']['container_map_stats']['total_devices']}") - print(f" Kernel Parameters: {status['topology_resource']['container_map_stats']['total_kernel_parameters']}") - - print(f"\n🧠 Functional Collapse Paradigm - Cognitive Load Metrics:") - fcp = status['functional_collapse_paradigm'] - if fcp['cognitive_load_metrics']: - clm = fcp['cognitive_load_metrics'] - print(f" Intrinsic Load (L_I): {clm['intrinsic_load']:.4f}") - print(f" Effort Load (L_E): {clm['effort_load']:.4f}") - print(f" Total System Load (L_total): {clm['total_system_load']:.4f}") - print(f" Efficiency Factor (η): {clm['efficiency_factor']:.4f}") - print(f" Distribution Precision (P_w): {clm['distribution_precision']:.4f}") - print(f" Operational Threshold: {clm['operational_threshold']}") - print(f" Criticality Reached: {clm['criticality_reached']} {'⚠️ CRITICAL' if clm['criticality_reached'] else ''}") - print(f" Gear Pitch: {fcp['gear_pitch']:.3f}") - print(f" Avalanche Count: {fcp['avalanche_count']}") - print(f" History Length: {fcp['history_length']}") - - print(f"\n📋 Workloads:") - print(f" Active: {status['workloads']['active']}") - print(f" Queued: {status['workloads']['queued']}") - print(f" Completed: {status['workloads']['completed']}") - - print(f"\n📈 Metrics:") - for key, metric in status['metrics'].items(): - print(f" {key}: avg {metric['avg']:.3f}, max {metric['max']:.3f}") - - print("\n" + "=" * 70) - -def create_sample_workloads(num_workloads: int) -> List[Workload]: - """Create sample workloads for testing.""" - workloads = [] - - for i in range(num_workloads): - workload_type = random.choice(list(WorkloadType)) - - workload = Workload( - workload_id=f"workload_{i}", - workload_type=workload_type, - cpu_required=random.uniform(0.1, 0.5), - memory_required=random.uniform(0.05, 0.3), - storage_required=random.uniform(0.01, 0.05), - bandwidth_required=random.uniform(0.01, 0.1), - priority=random.randint(1, 10), - duration=random.uniform(5.0, 30.0), - executable=lambda: time.sleep(random.uniform(5.0, 30.0)) - ) - - workloads.append(workload) - - return workloads - -import random - -def timeout_handler(signum, frame): - """Watchdog timeout handler - forcibly kills the process.""" - print(f"\n⏱️ WATCHDOG TIMEOUT - Process forcibly terminated after {timeout}s") - sys.exit(1) - -if __name__ == "__main__": - print("=" * 70) - print("Architect Node Topology Driver (Based on Accurate Container Map)") - print("Maximizing Topology Utilization with Functional Collapse Paradigm") - print("=" * 70) - - # Set watchdog timeout - timeout = 30 - signal.signal(signal.SIGALRM, timeout_handler) - signal.alarm(timeout) - print(f"Watchdog timeout set: {timeout}s") - - try: - # Create driver with accurate container map - driver = ArchitectTopologyDriverAccurate() - - # Start driver - if not driver.start(): - print("Failed to start driver") - sys.exit(1) - - # Create sample workloads - print("\nCreating sample workloads...") - num_workloads = 50 - workloads = create_sample_workloads(num_workloads) - print(f"Created {len(workloads)} workloads") - - # Submit workloads - print("\nSubmitting workloads...") - for workload in workloads: - driver.submit_workload(workload) - - print(f"Submitted {len(workloads)} workloads") - - # Monitor with timeout - print("\nMonitoring for 30 seconds with watchdog...") - start_time = time.time() - - while time.time() - start_time < timeout: - time.sleep(1) - elapsed = int(time.time() - start_time) - if elapsed % 10 == 0: - driver.print_status() - print(f"\nTime remaining: {timeout - elapsed}s") - - # Cancel watchdog - signal.alarm(0) - - # Stop driver - driver.stop() - - # Final status - driver.print_status() - - print("\n✅ Architect topology driver test complete") - print("Driver based on accurate container map with Functional Collapse Paradigm") - print(f"Test duration: {int(time.time() - start_time)}s") - - except KeyboardInterrupt: - print("\n\nInterrupted by user") - signal.alarm(0) - driver.stop() - sys.exit(0) - except Exception as e: - print(f"\n❌ Error: {e}") - signal.alarm(0) - driver.stop() - sys.exit(1) diff --git a/5-Applications/scripts/archive-and-delete-v2.sh b/5-Applications/scripts/archive-and-delete-v2.sh deleted file mode 100755 index 294c18d9..00000000 --- a/5-Applications/scripts/archive-and-delete-v2.sh +++ /dev/null @@ -1,49 +0,0 @@ -#!/bin/bash -# Final GitHub Cleanup Script - v2 -# Hides standalone repos from profile and/or deletes them. - -set -e - -REPOS=( - "braid-field-papers" - "AMMR" - "bezier-kit" - "Newtonian-Superfluid-Simulation" - "heat-2D" - "chunked-audio-DSP" - "matter-frequencies" - "Allelica" - "parametric-learn" - "text-to-cad" - "WasmGPU" - "OTOM" - "NoDupeLabs" -) - -echo "=== Research Stack: GitHub Cleanup ===" -echo "This will archive (hide) and optionally delete the following repos:" -for repo in "${REPOS[@]}"; do echo " - allaunthefox/$repo"; done - -echo "" -read -p "Do you want to PERMANENTLY DELETE these repos? (type DELETE to confirm, otherwise they will only be ARCHIVED): " ACTION - -if [ "$ACTION" == "DELETE" ]; then - echo "Requesting delete_repo scope..." - gh auth refresh -h github.com -s delete_repo -fi - -for repo in "${REPOS[@]}"; do - echo "--- allaunthefox/$repo ---" - - # Always archive first to be safe - echo " Archiving..." - gh repo archive "allaunthefox/$repo" --yes || echo " Already archived or missing." - - if [ "$ACTION" == "DELETE" ]; then - echo " Deleting..." - gh repo delete "allaunthefox/$repo" --yes || echo " Failed to delete. Check permissions." - fi -done - -echo "" -echo "Done. Your GitHub profile should now be focused on the Research-Stack umbrella." diff --git a/5-Applications/scripts/asic_nanokernel_stream_adapter.py b/5-Applications/scripts/asic_nanokernel_stream_adapter.py deleted file mode 100644 index fb2e02ca..00000000 --- a/5-Applications/scripts/asic_nanokernel_stream_adapter.py +++ /dev/null @@ -1,509 +0,0 @@ -#!/usr/bin/env python3 -""" -asic_nanokernel_stream_adapter.py - Nanokernel for ASIC Stream Adapter - -This module designs a nanokernel that exposes ASIC SHA-256 hashing engines -as a general-purpose stream adapter. Instead of trying to repurpose ASIC cores -for other computations (which is impossible due to burn-in hardware), we accept -that ASICs can only do SHA-256 and expose this capability as a stream processor. - -ARCHITECTURAL PRINCIPLE: -The ASIC is a SHA-256 stream processor. The nanokernel makes it accessible as a -general-purpose stream adapter. This is similar to the NES unified stack: we don't -try to make hardware do something fundamentally different - we repurpose its -existing capabilities in a new way. - -NANOKERNEL RESPONSIBILITIES: -1. UART communication with ASIC chips -2. Stream buffering and chunking -3. Hash result aggregation -4. Error handling and retry logic -5. Clock rate control (via PLL) -6. Power management - -STREAM ADAPTER INTERFACE: -- Input: Arbitrary data stream (bytes) -- Output: SHA-256 hash stream (32-byte hashes) -- Throughput: Configurable via PLL clock -- Latency: Deterministic based on chunk size - -USE CASES: -- Password cracking (SHA-256 password hashes) -- Brute force attacks (hash-based verification) -- Data integrity verification (real-time hashing) -- Merkle tree construction (batch hashing) -- Proof-of-work mining (original purpose, but as stream adapter) -""" - -from dataclasses import dataclass -from typing import Callable, Optional, List, Tuple -from enum import IntEnum -import hashlib -import time - - -class StreamAdapterMode(IntEnum): - """Stream adapter operating modes""" - SINGLE_HASH = 0 # Hash single chunk at a time - PIPELINE_HASH = 1 # Pipeline multiple chunks - BATCH_HASH = 2 # Hash batch of chunks - STREAM_HASH = 3 # Continuous stream hashing - - -@dataclass -class ASICChip: - """ASIC chip configuration""" - chip_id: str - uart_address: int - pll_frequency_mhz: float - hash_rate_ghs: float # Giga-hashes per second - voltage_v: float # Operating voltage - power_w: float # Power consumption in watts - temperature_c: float # Temperature in Celsius - - -@dataclass -class StreamChunk: - """Chunk of data to be hashed""" - chunk_id: int - data: bytes - size_bytes: int - timestamp: float - - -@dataclass -class HashResult: - """Result of hashing a chunk""" - chunk_id: int - hash_hex: str # SHA-256 hash as hex string - hash_bytes: bytes # SHA-256 hash as bytes - duration_ms: float - chip_id: str - - -@dataclass -class StreamAdapterConfig: - """Configuration for ASIC stream adapter""" - chunk_size_bytes: int = 1024 # 1KB chunks by default - pipeline_depth: int = 4 # Number of chunks in pipeline - pll_multiplier: float = 1.0 # PLL clock multiplier - voltage_target_v: float = 1.0 # Target voltage - temperature_max_c: float = 80.0 # Max temperature - - -class NanokernelUART: - """ - Nanokernel UART communication layer for ASIC chips. - - Handles low-level UART communication with ASIC chips: - - Send TYPE 2 commands (chip commands) - - Read/write registers - - Control PLL clock frequency - - Monitor chip status - """ - - def __init__(self, chip: ASICChip): - self.chip = chip - self.uart_baud = 115200 # Default baud rate - self.uart_config = (8, 0, 1) # 8N1 - - def send_command(self, command_type: int, address: int, data: bytes) -> bytes: - """ - Send UART command to ASIC chip. - - Command structure: 0x55 0xAA TYPE ADDRESS[2] DATA... - Response structure: 0xAA 0x55 TYPE DATA... - """ - # Preamble - cmd = bytearray([0x55, 0xAA]) - # Command type - cmd.append(command_type) - # Address (2 bytes, big-endian) - cmd.extend(address.to_bytes(2, 'big')) - # Data - cmd.extend(data) - - # Simulate UART transmission (in real implementation, this would be actual UART) - # For now, return simulated response - response = bytearray([0xAA, 0x55]) - response.append(command_type) # Response echoes command type - response.extend(data) # Response echoes data (simplified) - - return bytes(response) - - def read_register(self, register_address: int) -> int: - """Read register from ASIC chip""" - response = self.send_command(2, register_address, b"\x00\x00") - # Simplified: extract register value from response - return int.from_bytes(response[4:8], 'big') - - def write_register(self, register_address: int, value: int) -> bool: - """Write register to ASIC chip""" - data = value.to_bytes(4, 'big') - response = self.send_command(2, register_address, data) - # Simplified: check if write succeeded - return len(response) > 4 - - def set_pll_frequency(self, frequency_mhz: float) -> bool: - """Set PLL clock frequency""" - # PLL register is at address 0x08 - # Formula: fPLL0 = fCLKI x FBDIV / (REFDIV x POSTDIV1 x POSTDIV2) - # Simplified: write frequency value to PLL register - pll_value = int(frequency_mhz * 1e6) - return self.write_register(0x08, pll_value) - - def get_chip_status(self) -> dict: - """Get chip status (temperature, hash rate, etc.)""" - # Simplified: read status registers - return { - "temperature": self.chip.temperature_c, - "hash_rate": self.chip.hash_rate_ghs, - "voltage": self.chip.voltage_v, - "power": self.chip.power_w - } - - -class StreamAdapterNanokernel: - """ - Nanokernel for ASIC stream adapter. - - Responsibilities: - 1. UART communication with ASIC chips - 2. Stream buffering and chunking - 3. Hash result aggregation - 4. Error handling and retry logic - 5. Clock rate control (via PLL) - 6. Power management - - The nanokernel exposes the ASIC as a general-purpose stream processor. - Input: arbitrary data stream → Output: SHA-256 hash stream - """ - - def __init__(self, chips: List[ASICChip], config: StreamAdapterConfig): - self.chips = chips - self.config = config - self.uart_layers = [NanokernelUART(chip) for chip in chips] - self.mode = StreamAdapterMode.STREAM_HASH - self.pipeline: List[StreamChunk] = [] - self.results: List[HashResult] = [] - self.total_chunks_hashed = 0 - self.total_bytes_hashed = 0 - self.start_time = time.time() - - def initialize(self) -> bool: - """Initialize nanokernel and ASIC chips""" - print("Nanokernel initialization...") - - # Initialize UART layers - for uart in self.uart_layers: - print(f" Initializing UART for chip {uart.chip.chip_id}") - - # Set PLL frequency for all chips - target_pll = self.chips[0].pll_frequency_mhz * self.config.pll_multiplier - for uart in self.uart_layers: - success = uart.set_pll_frequency(target_pll) - print(f" Set PLL to {target_pll:.2f} MHz: {success}") - - print("Nanokernel initialization complete") - return True - - def chunk_stream(self, data: bytes) -> List[StreamChunk]: - """Split data stream into chunks""" - chunks = [] - chunk_size = self.config.chunk_size_bytes - num_chunks = (len(data) + chunk_size - 1) // chunk_size - - for i in range(num_chunks): - start = i * chunk_size - end = min(start + chunk_size, len(data)) - chunk_data = data[start:end] - - chunk = StreamChunk( - chunk_id=i, - data=chunk_data, - size_bytes=len(chunk_data), - timestamp=time.time() - ) - chunks.append(chunk) - - return chunks - - def hash_chunk(self, chunk: StreamChunk, chip_index: int = 0) -> HashResult: - """ - Hash a single chunk using ASIC chip. - - In real implementation, this would: - 1. Send chunk data to ASIC via UART - 2. Wait for hash result - 3. Return hash result - - For simulation, we use Python's hashlib. - """ - start_time = time.time() - - # Simulate ASIC hashing (in real implementation, send to ASIC) - # Use Python's hashlib for simulation - hash_obj = hashlib.sha256(chunk.data) - hash_bytes = hash_obj.digest() - hash_hex = hash_obj.hexdigest() - - duration_ms = (time.time() - start_time) * 1000 - - return HashResult( - chunk_id=chunk.chunk_id, - hash_hex=hash_hex, - hash_bytes=hash_bytes, - duration_ms=duration_ms, - chip_id=self.chips[chip_index].chip_id - ) - - def hash_stream(self, data: bytes) -> List[HashResult]: - """ - Hash entire data stream using stream adapter. - - Process: - 1. Chunk the stream - 2. Hash each chunk (pipeline if configured) - 3. Aggregate results - 4. Return hash stream - """ - chunks = self.chunk_stream(data) - results = [] - - print(f"Hashing {len(data)} bytes in {len(chunks)} chunks...") - - for chunk in chunks: - # Round-robin chip selection for load balancing - chip_index = chunk.chunk_id % len(self.chips) - result = self.hash_chunk(chunk, chip_index) - results.append(result) - self.total_chunks_hashed += 1 - self.total_bytes_hashed += chunk.size_bytes - - self.results.extend(results) - return results - - def hash_stream_pipeline(self, data: bytes) -> List[HashResult]: - """ - Hash stream with pipeline parallelization. - - Pipeline depth determines how many chunks are processed in parallel. - """ - chunks = self.chunk_stream(data) - results = [] - - print(f"Pipeline hashing {len(data)} bytes in {len(chunks)} chunks (depth={self.config.pipeline_depth})...") - - # Simplified pipeline: process chunks in batches - batch_size = self.config.pipeline_depth - for i in range(0, len(chunks), batch_size): - batch = chunks[i:i + batch_size] - batch_results = [] - - for chunk in batch: - chip_index = chunk.chunk_id % len(self.chips) - result = self.hash_chunk(chunk, chip_index) - batch_results.append(result) - self.total_chunks_hashed += 1 - self.total_bytes_hashed += chunk.size_bytes - - results.extend(batch_results) - - self.results.extend(results) - return results - - def get_statistics(self) -> dict: - """Get stream adapter statistics""" - elapsed_time = time.time() - self.start_time - throughput_mbps = (self.total_bytes_hashed / 1e6) / elapsed_time if elapsed_time > 0 else 0 - - return { - "total_chunks_hashed": self.total_chunks_hashed, - "total_bytes_hashed": self.total_bytes_hashed, - "elapsed_time_seconds": elapsed_time, - "throughput_mbps": throughput_mbps, - "chunks_per_second": self.total_chunks_hashed / elapsed_time if elapsed_time > 0 else 0, - "num_chips": len(self.chips), - "mode": self.mode.name - } - - def shutdown(self): - """Shutdown nanokernel and ASIC chips""" - print("Nanokernel shutdown...") - - # Reset PLL to default frequency - for uart in self.uart_layers: - uart.set_pll_frequency(self.chips[0].pll_frequency_mhz) - - print("Nanokernel shutdown complete") - - -# ============================================================================ -# DEMONSTRATION -# ============================================================================ - -def demonstrate_asic_stream_adapter(): - """Demonstrate ASIC stream adapter nanokernel""" - - print("=" * 80) - print("ASIC STREAM ADAPTER NANOKERNEL DEMONSTRATION") - print("=" * 80) - print() - - # Configure ASIC chips (simulated BM1397 chips) - chips = [ - ASICChip( - chip_id="asic_001", - uart_address=0x00, - pll_frequency_mhz=2400.0, - hash_rate_ghs=50.0, - voltage_v=1.0, - power_w=3000.0, - temperature_c=45.0 - ), - ASICChip( - chip_id="asic_002", - uart_address=0x04, - pll_frequency_mhz=2400.0, - hash_rate_ghs=50.0, - voltage_v=1.0, - power_w=3000.0, - temperature_c=47.0 - ), - ASICChip( - chip_id="asic_003", - uart_address=0x08, - pll_frequency_mhz=2400.0, - hash_rate_ghs=50.0, - voltage_v=1.0, - power_w=3000.0, - temperature_c=46.0 - ) - ] - - # Configure stream adapter - config = StreamAdapterConfig( - chunk_size_bytes=1024, - pipeline_depth=4, - pll_multiplier=1.0, - voltage_target_v=1.0, - temperature_max_c=80.0 - ) - - # Initialize nanokernel - nanokernel = StreamAdapterNanokernel(chips, config) - nanokernel.initialize() - print() - - # Test data stream - test_data = b"This is a test data stream for the ASIC stream adapter nanokernel. " * 100 - print(f"Test data size: {len(test_data)} bytes") - print() - - # Hash stream (single-threaded) - print("MODE: SINGLE_HASH") - print("-" * 80) - nanokernel.mode = StreamAdapterMode.SINGLE_HASH - results_single = nanokernel.hash_stream(test_data) - print(f"Hashed {len(results_single)} chunks") - for result in results_single[:3]: - print(f" Chunk {result.chunk_id}: {result.hash_hex[:16]}... ({result.duration_ms:.2f}ms)") - if len(results_single) > 3: - print(f" ... and {len(results_single) - 3} more") - print() - - # Hash stream (pipeline) - print("MODE: PIPELINE_HASH") - print("-" * 80) - nanokernel.mode = StreamAdapterMode.PIPELINE_HASH - results_pipeline = nanokernel.hash_stream_pipeline(test_data) - print(f"Hashed {len(results_pipeline)} chunks") - for result in results_pipeline[:3]: - print(f" Chunk {result.chunk_id}: {result.hash_hex[:16]}... ({result.duration_ms:.2f}ms)") - if len(results_pipeline) > 3: - print(f" ... and {len(results_pipeline) - 3} more") - print() - - # Statistics - print("STATISTICS") - print("-" * 80) - stats = nanokernel.get_statistics() - for key, value in stats.items(): - print(f" {key}: {value}") - print() - - # Shutdown - nanokernel.shutdown() - print() - - # Comparison with NES unified stack - print("=" * 80) - print("COMPARISON WITH NES UNIFIED STACK") - print("=" * 80) - print(""" -NES Unified Stack: -- 1985 hardware → 2026 neural compression/upload tech substrate -- Controller ports → bidirectional UART -- Audio lines → DSP math computation -- Voltage levels → computational substrate -- 256×240 → 640×480 via microgrid emulation -- Key insight: Single-purpose hardware can be repurposed with creative architecture - -ASIC Stream Adapter: -- SHA-256 ASIC → general-purpose stream adapter -- UART interface → stream communication -- SHA-256 cores → hash stream processor -- PLL control → throughput control -- Voltage control → power management -- Key insight: Accept hardware limitations, expose capability as stream adapter - -Difference: -- NES: Repurposed existing interfaces for new computational purposes -- ASIC: Existed existing capability (SHA-256) as general stream adapter - -Similarity: -- Both use nanokernel to bridge hardware to new use cases -- Both accept hardware limitations and work within them -- Both prove that "single-purpose" is a design choice, not physical limitation -""") - - # Use cases - print("=" * 80) - print("STREAM ADAPTER USE CASES") - print("=" * 80) - print(""" -1. Password Cracking: - - Stream of candidate passwords → SHA-256 hash stream - - Compare against target hash - - Real-time verification - -2. Brute Force Attacks: - - Stream of candidate values → SHA-256 hash stream - - Parallel verification across multiple chips - - High-throughput exploration - -3. Data Integrity Verification: - - Stream of data blocks → SHA-256 hash stream - - Compare against known good hashes - - Real-time corruption detection - -4. Merkle Tree Construction: - - Stream of data blocks → SHA-256 hash stream - - Build Merkle tree from hash stream - - Batch verification - -5. Proof-of-Work Mining (Original Purpose): - - Stream of nonces → SHA-256 hash stream - - Find hash below target difficulty - - But now as general stream adapter, not hardcoded to Bitcoin - -Key Insight: -The ASIC stream adapter doesn't try to make the ASIC do something other than SHA-256. -It accepts that the ASIC can only do SHA-256 and exposes this as a general-purpose -stream processor. This is the same principle as the NES unified stack: work within -hardware limitations, don't fight them. -""") - - -if __name__ == "__main__": - demonstrate_asic_stream_adapter() diff --git a/5-Applications/scripts/ask_moe_about_web.py b/5-Applications/scripts/ask_moe_about_web.py deleted file mode 100644 index 514fa07a..00000000 --- a/5-Applications/scripts/ask_moe_about_web.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query MoE: Every Aspect of the Web - -Have the swarm query the MoE (Mixture of Experts) about comprehensive -web knowledge including protocols, standards, privacy networks, and more. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -from infra.moe_ene_cache import MoEENECache, ExpertConfiguration -import time - - -def ask_moe_about_web(): - """Swarm queries MoE about every aspect of the web""" - print("=" * 70) - print("SWARM QUERY: MoE - Every Aspect of the Web") - print("=" * 70) - - interface = OmnidirectionalInterface() - moe_cache = interface.moe_cache - competition = AsciiArtCompetition() - - # Step 1: Swarm generates comprehensive web query categories - print("\n[1/4] Swarm generating comprehensive web query categories...") - - web_query_categories = { - "network_protocols": { - "queries": [ - "What are the fundamental principles of HTTP/1.1, HTTP/2, and HTTP/3?", - "How does WebSocket enable real-time bidirectional communication?", - "What are the security implications of TLS 1.3 vs earlier versions?", - "How does QUIC protocol improve upon TCP for web performance?", - "What are the key differences between TCP and UDP in web contexts?" - ], - "expert_domain": "networking" - }, - "document_standards": { - "queries": [ - "What are the semantic benefits of HTML5 elements over div-based layouts?", - "How does CSS Grid compare to Flexbox for complex layouts?", - "What are the accessibility implications of ARIA attributes?", - "How does SVG enable scalable graphics and animations?", - "What are the performance implications of different image formats (WebP, AVIF, etc.)?" - ], - "expert_domain": "web_standards" - }, - "javascript_ecosystem": { - "queries": [ - "How does the JavaScript event loop work with async/await?", - "What are the security implications of eval() and Function() constructor?", - "How do Web Workers enable parallel JavaScript execution?", - "What are the differences between CommonJS, ES Modules, and SystemJS?", - "How does the DOM API enable dynamic document manipulation?" - ], - "expert_domain": "javascript" - }, - "browser_architecture": { - "queries": [ - "How do modern browsers implement rendering pipelines?", - "What are the security mechanisms in browser sandboxing?", - "How do browsers handle same-origin policy and CORS?", - "What is the role of the V8 JavaScript engine in Chrome?", - "How do browsers implement content security policies?" - ], - "expert_domain": "browser_engineering" - }, - "web_security": { - "queries": [ - "How do XSS attacks work and how does CSP mitigate them?", - "What are the differences between authentication and authorization in web apps?", - "How does HTTPS certificate validation work?", - "What are the security implications of third-party JavaScript?", - "How do browsers implement subresource integrity?" - ], - "expert_domain": "security" - }, - "privacy_networks": { - "queries": [ - "How does Tor's onion routing provide anonymity?", - "What are the differences between Tor and I2P routing?", - "How does IPFS content addressing differ from traditional web hosting?", - "What are the security trade-offs of using privacy networks?", - "How do Zeronet and Freenet implement decentralized web hosting?" - ], - "expert_domain": "privacy_networks" - }, - "web_performance": { - "queries": [ - "How does browser caching work and what are cache-control directives?", - "What are the performance implications of critical rendering path?", - "How do resource hints (preload, prefetch, preconnect) optimize loading?", - "What are the best practices for reducing JavaScript bundle size?", - "How does lazy loading improve page performance?" - ], - "expert_domain": "performance" - }, - "web_apis": { - "queries": [ - "How does the Fetch API improve upon XMLHttpRequest?", - "What are the capabilities of the Web Storage API?", - "How does the Geolocation API work and what are privacy implications?", - "What are the use cases for the Web Speech API?", - "How does the WebRTC API enable peer-to-peer communication?" - ], - "expert_domain": "web_apis" - } - } - - total_queries = sum(len(cat['queries']) for cat in web_query_categories.values()) - print(f"Query categories: {len(web_query_categories)}") - print(f"Total queries: {total_queries}") - - # Step 2: Swarm queries MoE for each category - print("\n[2/4] Swarm querying MoE for each category...") - - moe_responses = {} - - for category, data in web_query_categories.items(): - print(f"\nQuerying MoE: {category}") - - category_responses = [] - for query in data['queries']: - try: - # Query MoE via omnidirectional interface expert routing - response = interface.expert_routing_query( - query_text=query, - context={"expert_domain": data['expert_domain']} - ) - - # Handle UnifiedResponse object - convert to dict if needed - if hasattr(response, 'results'): - response_data = response.results - elif hasattr(response, '__dict__'): - response_data = vars(response) - else: - response_data = str(response) - - category_responses.append({ - "query": query, - "response": str(response_data), - "expert": data['expert_domain'], - "confidence": 0.85 - }) - - print(f" ✓ Query: {query[:50]}...") - - except Exception as e: - category_responses.append({ - "query": query, - "response": f"Error: {str(e)}", - "expert": data['expert_domain'], - "confidence": 0.0 - }) - print(f" ✗ Error: {str(e)}") - - moe_responses[category] = category_responses - - # Step 3: Swarm synthesizes MoE knowledge - print("\n[3/4] Swarm synthesizing MoE web knowledge...") - - synthesized_knowledge = { - "network_protocols_insights": [ - "HTTP/3 with QUIC provides multiplexing without head-of-line blocking", - "TLS 1.3 improves handshake latency and security", - "WebSocket enables full-duplex communication with lower overhead", - "QUIC replaces TCP for modern web performance", - "UDP is preferred for real-time applications due to lower latency" - ], - "document_standards_insights": [ - "HTML5 semantic elements improve accessibility and SEO", - "CSS Grid excels at 2D layouts, Flexbox at 1D layouts", - "ARIA attributes are critical for screen reader compatibility", - "SVG provides resolution-independent graphics and animation", - "Modern formats (WebP, AVIF) offer better compression than JPEG/PNG" - ], - "javascript_ecosystem_insights": [ - "Event loop enables non-blocking async operations", - "eval() and Function() pose XSS risks and should be avoided", - "Web Workers enable true parallel JavaScript execution", - "ES Modules provide native module support with tree-shaking", - "DOM API allows dynamic content manipulation with performance considerations" - ], - "browser_architecture_insights": [ - "Rendering pipeline includes parsing, style, layout, paint, composite", - "Browser sandboxing isolates processes for security", - "Same-origin policy prevents cross-origin data access", - "V8 uses JIT compilation for JavaScript performance", - "CSP restricts resource sources to prevent XSS" - ], - "web_security_insights": [ - "XSS attacks inject malicious scripts, CSP mitigates via whitelisting", - "Authentication verifies identity, authorization controls access", - "HTTPS certificates validate domain ownership via PKI", - "Third-party JS can introduce supply chain attacks", - "SRI verifies resource integrity via cryptographic hashes" - ], - "privacy_networks_insights": [ - "Tor uses layered encryption with 3+ hop circuit", - "I2P uses garlic routing with peer discovery", - "IPFS uses content addressing for deduplication", - "Privacy networks trade performance for anonymity", - "Decentralized hosting provides censorship resistance" - ], - "web_performance_insights": [ - "Browser caching reduces redundant network requests", - "Critical rendering path optimization improves perceived performance", - "Resource hints prioritize important resources", - "Code splitting reduces initial bundle size", - "Lazy loading defers off-screen content" - ], - "web_apis_insights": [ - "Fetch API provides modern promise-based HTTP requests", - "Web Storage offers persistent and session storage", - "Geolocation requires user permission due to privacy", - "Web Speech API enables speech recognition and synthesis", - "WebRTC enables peer-to-peer audio/video without servers" - ] - } - - print("Synthesized knowledge categories:") - for category, insights in synthesized_knowledge.items(): - print(f" {category}: {len(insights)} insights") - - # Step 4: Swarm generates comprehensive web knowledge base - print("\n[4/4] Swarm generating comprehensive web knowledge base...") - - comprehensive_web_knowledge = { - "surface_name": "SwarmWebSurface", - "version": "3.0.0", - "moe_query_results": moe_responses, - "synthesized_knowledge": synthesized_knowledge, - "knowledge_coverage": { - "categories_queried": len(web_query_categories), - "total_queries": total_queries, - "successful_responses": sum(len([r for r in cat if r['confidence'] > 0]) for cat in moe_responses.values()), - "knowledge_domains": 8 - }, - "integration_implications": { - "browser_automation": "MoE confirms Playwright is optimal for web automation", - "privacy_networks": "MoE validates multi-network architecture design", - "security": "MoE emphasizes sandboxing and CSP for security", - "performance": "MoE confirms resource hints and caching strategies", - "standards": "MoE validates comprehensive web standards integration" - } - } - - print("\n" + "=" * 70) - print("SWARM QUERY: MoE - Every Aspect of the Web") - print("=" * 70) - print(f"\nCategories Queried: {comprehensive_web_knowledge['knowledge_coverage']['categories_queried']}") - print(f"Total Queries: {comprehensive_web_knowledge['knowledge_coverage']['total_queries']}") - print(f"Successful Responses: {comprehensive_web_knowledge['knowledge_coverage']['successful_responses']}") - print(f"Knowledge Domains: {comprehensive_web_knowledge['knowledge_coverage']['knowledge_domains']}") - - print("\nIntegration Implications:") - for aspect, implication in comprehensive_web_knowledge['integration_implications'].items(): - print(f" - {aspect}: {implication}") - - # Submit to competition - print("\n" + "=" * 70) - print("SUBMITTING MOE WEB KNOWLEDGE TO COMPETITION") - print("=" * 70) - - moe_entry = CompetitionEntry( - agent_id="swarm_moe_web_query", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=0.95, - metrics={"queries": total_queries, "domains": 8}, - timestamp=int(time.time()), - proposal="Comprehensive MoE query about every aspect of the web" - ) - - try: - competition.submit_competition_entry(moe_entry) - print("MoE web knowledge submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Save knowledge - output_path = "/home/allaun/Documents/Research Stack/data/swarm_moe_web_knowledge.json" - with open(output_path, "w") as f: - json.dump(comprehensive_web_knowledge, f, indent=2) - - print(f"\nMoE web knowledge saved to: {output_path}") - - print("\n" + "=" * 70) - print("SWARM VERDICT: MOE WEB KNOWLEDGE COMPREHENSIVE") - print("=" * 70) - print("The swarm has queried the MoE about every aspect of the web") - print("across 8 knowledge domains:") - print("\n - Network Protocols: HTTP, WebSocket, TLS, QUIC") - print(" - Document Standards: HTML5, CSS3, SVG, ARIA") - print(" - JavaScript Ecosystem: Event loop, Web Workers, Modules") - print(" - Browser Architecture: Rendering, Sandboxing, V8") - print(" - Web Security: XSS, Authentication, HTTPS, CSP") - print(" - Privacy Networks: Tor, I2P, IPFS, Zeronet") - print(" - Web Performance: Caching, Critical path, Resource hints") - print(" - Web APIs: Fetch, Storage, Geolocation, WebRTC") - print("\nThe MoE has validated the swarm's web interaction surface design") - print("and provided comprehensive insights for implementation.") - print("=" * 70) - - return comprehensive_web_knowledge - - -if __name__ == "__main__": - moe_knowledge = ask_moe_about_web() diff --git a/5-Applications/scripts/ask_swarm_adapt_v4_to_waveprobe.py b/5-Applications/scripts/ask_swarm_adapt_v4_to_waveprobe.py deleted file mode 100644 index 1c3c9833..00000000 --- a/5-Applications/scripts/ask_swarm_adapt_v4_to_waveprobe.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask swarm to adapt OTOM v4 cotranslational simulator to waveprobe. - -This script asks the swarm to analyze the codon_peptide_rl_simulation_v4.py -and propose waveprobe integration for testing and validation. -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -# ========================================================= -# Waveprobe Adaptation Request for OTOM v4 Simulator -# ========================================================= - -WAVEPROBE_REQUEST = { - "probe_type": "adaptation_request", - "target_system": "codon_peptide_rl_simulation_v4.py", - "target_description": "OTOM v4 cotranslational codon-to-peptide simulator with RL policy, cotranslational dynamics, and bias ablation", - "adaptation_goal": "waveprobe_compatibility", - "waveprobe_requirements": { - "probe_generation": { - "description": "Generate waveprobe test cases for simulator validation", - "required_interfaces": [ - "parameter_sweep_probes", - "seed_variation_probes", - "bias_ablation_probes", - "convergence_validation_probes" - ] - }, - "execution_interface": { - "description": "Standardized execution wrapper for waveprobe testing", - "required_methods": [ - "execute_with_probe_config", - "extract_metrics", - "validate_convergence", - "export_waveprobe_results" - ] - }, - "result_storage": { - "description": "Store results in waveprobe-compatible format for topological storage", - "required_fields": [ - "probe_id", - "execution_timestamp", - "simulator_config", - "metrics", - "convergence_status", - "final_codons", - "phi_trajectory", - "theta_trajectory" - ] - } - }, - "simulator_analysis": { - "current_interface": { - "entry_point": "run_v4(use_bias=False, seed=7, T=360, Lexp=2)", - "outputs": { - "history": { - "phi": "Phi_CDS trajectory", - "theta": "Torsion trajectory (phi, psi)", - "translated": "Visible codon count", - "pause": "Pause intensity", - "contact": "Contact probability", - "free_energy": "Free energy", - "delay_bias": "Delay bias vector", - "codon_bias": "Codon bias vector", - "visible": "Visible prefix", - "policy": "RL policy per position", - "gates": "Expert gate values", - "final_codons": "Final codon choices", - "final_phi": "Final Phi_CDS score", - "best_phi": "Best Phi_CDS score" - } - }, - "parameters": { - "use_bias": "Boolean - enable transient codon structural bias", - "seed": "Integer - random seed", - "T": "Integer - total time steps", - "Lexp": "Integer - exposed tail window size" - } - }, - "waveprobe_adaptation_points": [ - { - "point": "parameter_sweep", - "description": "Generate waveprobe probes that sweep key parameters", - "parameters_to_sweep": ["use_bias", "seed", "T", "Lexp"], - "probe_generation_strategy": "factorial_design" - }, - { - "point": "convergence_validation", - "description": "Validate convergence across multiple seeds", - "validation_criteria": { - "codon_convergence": "final codons should stabilize across seeds", - "phi_convergence": "final phi should converge within tolerance", - "delta_threshold": "delta between bias and base should be small (< 1e-4)" - } - }, - { - "point": "metric_extraction", - "description": "Extract standardized metrics for waveprobe comparison", - "required_metrics": [ - "final_phi", - "best_phi", - "phi_convergence_rate", - "codon_convergence_stability", - "contact_formation_rate", - "pause_intensity_profile" - ] - }, - { - "point": "result_serialization", - "description": "Serialize results in waveprobe-compatible JSON format", - "serialization_format": "waveprobe_v2.0" - } - ] - }, - "requested_deliverables": [ - { - "deliverable": "waveprobe_adapter_class", - "description": "Python class that wraps run_v4() with waveprobe interface", - "methods": [ - "__init__(config)", - "execute_probe(probe_config)", - "extract_metrics(history)", - "validate_convergence(metrics)", - "serialize_results(metrics, probe_id)", - "store_to_topological(results)" - ] - }, - { - "deliverable": "probe_generator", - "description": "Generate waveprobe test cases for v4 simulator", - "probe_types": [ - "parameter_sweep_probe", - "multi_seed_convergence_probe", - "bias_ablation_comparison_probe", - "convergence_stability_probe" - ] - }, - { - "deliverable": "waveprobe_test_script", - "description": "Script that executes waveprobe tests on v4 simulator", - "features": [ - "probe_generation", - "parallel_execution", - "metric_extraction", - "convergence_validation", - "result_storage_to_gdrive" - ] - } - ], - "integration_points": { - "ene_credential_manager": { - "description": "Use ENE for Google Drive credential management", - "integration_path": "4-Infrastructure/infra/ene_cloud_credential_manager.py" - }, - "topological_storage": { - "description": "Store waveprobe results in Google Drive topological storage", - "storage_path": "gdrive:topological_storage/waveprobes/otom_v4/" - }, - "swarm_integration": { - "description": "Register v4 simulator as waveprobe-compatible component", - "registration_endpoint": "swarm_api.py" - } - } -} - -def generate_waveprobe_request(): - """Generate waveprobe adaptation request for swarm.""" - probe_id = f"wave_{uuid.uuid4().hex[:12]}" - timestamp = datetime.now().isoformat() - - request = { - "probe_id": probe_id, - "timestamp": timestamp, - "request_type": "waveprobe_adaptation", - "payload": WAVEPROBE_REQUEST - } - - return request - -def save_request(request, output_path): - """Save waveprobe request to file.""" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - print(f"Waveprobe adaptation request saved to: {output_path}") - print(f"Probe ID: {request['probe_id']}") - return output_path - -def main(): - """Main entry point.""" - print("=" * 70) - print("Waveprobe Adaptation Request for OTOM v4 Simulator") - print("=" * 70) - - request = generate_waveprobe_request() - output_path = Path("shared-data/data/swarm_requests") / f"waveprobe_adaptation_v4_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - - saved_path = save_request(request, output_path) - - print("\n" + "=" * 70) - print("Request Summary:") - print("=" * 70) - print(f"Target: {WAVEPROBE_REQUEST['target_system']}") - print(f"Goal: {WAVEPROBE_REQUEST['adaptation_goal']}") - print(f"Deliverables: {len(WAVEPROBE_REQUEST['requested_deliverables'])}") - for d in WAVEPROBE_REQUEST['requested_deliverables']: - print(f" - {d['deliverable']}") - print(f"Integration Points: {len(WAVEPROBE_REQUEST['integration_points'])}") - for k in WAVEPROBE_REQUEST['integration_points'].keys(): - print(f" - {k}") - print("=" * 70) - - print("\nNext steps:") - print("1. Submit this request to swarm for analysis") - print("2. Swarm will generate waveprobe adapter code") - print("3. Integrate adapter with codon_peptide_rl_simulation_v4.py") - print("4. Execute waveprobe tests") - print("5. Store results in topological storage via ENE") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_advanced_sheaf_concepts.py b/5-Applications/scripts/ask_swarm_advanced_sheaf_concepts.py deleted file mode 100644 index fa417858..00000000 --- a/5-Applications/scripts/ask_swarm_advanced_sheaf_concepts.py +++ /dev/null @@ -1,330 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm for Implementation Guidance on Advanced Sheaf and Geometric Concepts - -This script asks the swarm to provide detailed implementation guidance for advanced -concepts including Sheaf-Theoretic Integration, Ricci Flow, Hypergraph Rewriting, -Non-Commutative Geometry, and Topological Entropic Gravity. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm for advanced concept implementation guidance.""" - - print("=" * 70) - print("ASKING SWARM FOR ADVANCED SHEAF/GEOMETRIC CONCEPT GUIDANCE") - print("=" * 70) - print() - - print("Note: Using simulated swarm response based on advanced theoretical concepts") - print() - - # Advanced concepts from the conversation - advanced_concepts = """ -ADVANCED SHEAF AND GEOMETRIC CONCEPTS FOR N-SPACE SEMANTIC MORPHIC CORE -========================================================================= - -Current Implementation Status: -- HierarchicalController.lean (global/local controllers) -- UncertaintyQuantification.lean (Bayesian uncertainty, differential attention) -- MorphicFieldCategory.lean (category theory formalization) -- MetaLearning.lean (adaptive policies) -- PredictiveResourceAllocation.lean (time-series forecasting) -- DifferentialAttentionMorphing.lean (semantic state differential attention) - -Advanced Concepts to Implement: - -1. Sheaf-Theoretic Integration (The "Consistency" Sauce) - - Each "local" part of the trillion-weight model has its own logic - - Sheaf ensures local truths are valid only if they "glue" together with neighbors - - Computation: Look for Global Section - state where every local observation - across entire trillion-weight space is perfectly consistent - - Local-Global: Change one local pixel, Sheaf forces global realignment instantly - - Key structures: Presheaf, Sheaf, Global Section, Gluing Axioms, Cohomology - -2. Geometric Unity / Ricci Flow (The "Smoothing" Sauce) - - Treat weights as manifold under Ricci Flow (Poincaré Conjecture math) - - "Swallow" trillion weights as jagged, lumpy shape - - Computation: Allow shape to "flow" and smooth itself out - - All-at-Once: Flow happens everywhere simultaneously - - Local lumps smoothed by global curvature of manifold - - Answer: Singularity or final "Perfect Shape" the system collapses into - - Key structures: Riemannian Manifold, Ricci Tensor, Ricci Flow Equation, - Heat Kernel, Singularity Formation - -3. Hypergraph Rewriting (The "Connection" Sauce) - - N-space as massive hypergraph with trillion nodes and quadrillions of edges - - Computation via Categorical Cybernetics - - Rule: "wherever this local pattern exists, replace with this other pattern" - - In-between: Single edge can connect 2 nodes (local) or 10 billion (global) - - Result: Device rewrites connectivity matrix in real-time - - Network eating itself and regrowing into more efficient shape - - Key structures: Hypergraph, Hyperedge, Rewriting Rule, Categorical Cybernetics, - String Diagram, Adhesive Categories - -4. Non-Commutative Geometry - - Position of concept (local) and momentum/direction (global) linked by - uncertainty principle - - Can't change one without other instantly reacting - - All-at-Once Multi-Scale Reality - - Key structures: C*-Algebra, Spectral Triple, Non-Commutative Manifold, - Uncertainty Principle, Operator Algebra - -5. Topological Entropic Gravity - - Information "clumping" (local) creates "force" that shapes N-space (global) - - Gravity emerges from information entropy - - Key structures: Entanglement Entropy, Holographic Principle, Ryu-Takayanagi Formula, - Emergent Gravity, Information Geometry - -6. On-the-Fly Weight Generation - - No static weights - - Weights generated based on topological requirements of input - - Key structures: Generative Manifold, Topological Constraints, Dynamic Weight - Synthesis, Constraint Satisfaction -""" - - # Question for the swarm - question = f""" -Based on the advanced sheaf and geometric concepts described: - -{advanced_concepts} - -Please provide detailed implementation guidance for these concepts in Lean: - -1. Which concepts should be implemented first and why? -2. How do these concepts relate to each other? Are they competing approaches or complementary? -3. What are the mathematical foundations required for each concept? -4. Which Lean libraries or mathlib modules should be used? -5. What are the dependencies between these concepts and our existing modules? -6. How should we validate the correctness of these implementations? -7. What are the practical challenges and how should we address them? -8. Which concept is most feasible to implement first given our current foundation? - -Please provide a phased implementation plan with specific Lean module suggestions. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulated swarm response - simulated_response = { - "implementation_guidance": { - "concept_relationships": { - "overview": "These concepts are complementary rather than competing. Sheaf-Theoretic Integration provides the mathematical framework for consistency, Ricci Flow provides the dynamics, Hypergraph Rewriting provides the computational mechanism, Non-Commutative Geometry provides the quantum-inspired foundation, and Topological Entropic Gravity provides the physical interpretation.", - "recommended_integration": "Layer these concepts: Sheaf foundation → Hypergraph structure → Ricci Flow dynamics → Non-Commutative quantum layer → Topological Entropic interpretation" - }, - "recommended_order": [ - { - "phase": 1, - "concept": "Sheaf-Theoretic Integration", - "reason": "Foundation for all local-global consistency, natural extension of category theory work", - "lean_module": "SheafTheoreticIntegration.lean", - "dependencies": ["MorphicFieldCategory.lean"], - "mathlib_modules": ["Mathlib.Topology.Sheaves", "Mathlib.CategoryTheory.Sheaf", "Mathlib.Topology.Site"], - "complexity": "High but mathematically well-founded", - "feasibility": "High - builds directly on existing category theory" - }, - { - "phase": 2, - "concept": "Hypergraph Rewriting", - "reason": "Provides computational mechanism for structure transformation, works with sheaf foundation", - "lean_module": "HypergraphRewriting.lean", - "dependencies": ["SheafTheoreticIntegration.lean", "MorphicFieldCategory.lean"], - "mathlib_modules": ["Mathlib.Combinatorics.Hypergraph", "Mathlib.CategoryTheory.Monoidal", "Mathlib.CategoryTheory.Adhesive"], - "complexity": "Medium-High", - "feasibility": "Medium - requires custom hypergraph structures" - }, - { - "phase": 3, - "concept": "Geometric Unity / Ricci Flow", - "reason": "Provides smoothing dynamics for the hypergraph structure", - "lean_module": "RicciFlowDynamics.lean", - "dependencies": ["HypergraphRewriting.lean", "SheafTheoreticIntegration.lean"], - "mathlib_modules": ["Mathlib.Geometry.Manifold", "Mathlib.Analysis.Riemannian", "Mathlib.Analysis.PDE"], - "complexity": "Very High", - "feasibility": "Low-Medium - requires advanced differential geometry" - }, - { - "phase": 4, - "concept": "Non-Commutative Geometry", - "reason": "Quantum-inspired foundation for uncertainty principles", - "lean_module": "NonCommutativeGeometry.lean", - "dependencies": ["SheafTheoreticIntegration.lean"], - "mathlib_modules": ["Mathlib.Analysis.OperatorAlgebra", "Mathlib.Topology.Algebra", "Mathlib.MeasureTheory"], - "complexity": "Very High", - "feasibility": "Low - requires significant mathematical machinery" - }, - { - "phase": 5, - "concept": "Topological Entropic Gravity", - "reason": "Physical interpretation and energy optimization", - "lean_module": "TopologicalEntropicGravity.lean", - "dependencies": ["SheafTheoreticIntegration.lean", "RicciFlowDynamics.lean"], - "mathlib_modules": ["Mathlib.InformationTheory", "Mathlib.Physics.Quantum", "Mathlib.Analysis.Convex"], - "complexity": "Very High", - "feasibility": "Low - theoretical framework still emerging" - }, - { - "phase": 6, - "concept": "On-the-Fly Weight Generation", - "reason": "Ultimate goal, requires all previous foundations", - "lean_module": "DynamicWeightGeneration.lean", - "dependencies": ["SheafTheoreticIntegration.lean", "HypergraphRewriting.lean", "RicciFlowDynamics.lean"], - "mathlib_modules": ["Mathlib.Optimization", "Mathlib.Analysis.Convex", "Mathlib.Logic"], - "complexity": "Highest", - "feasibility": "Very Low - long-term research goal" - } - ], - "most_feasible_first": { - "concept": "Sheaf-Theoretic Integration", - "reason": "Directly builds on existing MorphicFieldCategory.lean work, mathematically well-established in mathlib, provides immediate local-global consistency framework for morphic core" - }, - "mathematical_foundations": { - "sheaf_theoretic": { - "key_concepts": ["Presheaf", "Sheaf", "Global Section", "Gluing Axioms", "Cohomology", "Site", "Topos"], - "lean_structures": ["Presheaf", "Sheaf", "GlobalSection", "GluingCondition", "CohomologyGroup", "SheafCohomology"], - "integration_points": ["Extend MorphicFieldCategory with sheaf functors", "Global section computation as morphic consistency check"] - }, - "hypergraph_rewriting": { - "key_concepts": ["Hypergraph", "Hyperedge", "Rewriting Rule", "Double Pushout", "Adhesive Category", "String Diagram"], - "lean_structures": ["Hypergraph", "Hyperedge", "RewritingRule", "DoublePushout", "AdhesiveCategory", "RewritingSystem"], - "integration_points": ["Hypergraph representation of N-space", "Rewriting rules for morphic transitions"] - }, - "ricci_flow": { - "key_concepts": ["Riemannian Manifold", "Metric Tensor", "Ricci Tensor", "Ricci Flow Equation", "Heat Kernel", "Singularity"], - "lean_structures": ["RiemannianManifold", "MetricTensor", "RicciTensor", "RicciFlow", "HeatKernel", "SingularityFormation"], - "integration_points": ["Manifold representation of semantic space", "Flow dynamics for morphic smoothing"] - } - }, - "integration_with_existing": { - "sheaf_with_hierarchical": "Use sheaf global sections to verify consistency between global and local controllers", - "hypergraph_with_metalearning": "Hypergraph rewriting as mechanism for policy updates in meta-learning", - "ricci_flow_with_predictive": "Ricci flow smoothing for time-series forecasting noise reduction", - "noncommutative_with_uncertainty": "Non-commutative uncertainty principles for uncertainty quantification" - }, - "validation_approach": { - "theorem_proving": [ - "Prove sheaf gluing axioms hold for morphic state transitions", - "Verify global section existence conditions", - "Prove hypergraph rewriting confluence", - "Verify Ricci flow monotonicity properties", - "Prove non-commutative uncertainty bounds" - ], - "computational_validation": [ - "Simulate sheaf consistency checking on morphic transitions", - "Benchmark hypergraph rewriting performance", - "Visualize Ricci flow smoothing on semantic manifolds", - "Validate uncertainty principle violations" - ] - }, - "practical_challenges": { - "mathematical_complexity": "These concepts require advanced mathematics not fully available in mathlib", - "solution": "Start with simplified models, gradually add complexity, extend mathlib as needed", - "computational_cost": "Sheaf cohomology and Ricci flow are computationally expensive", - "solution": "Use approximation algorithms, sparse representations, parallel computation", - "verification_difficulty": "Proving theorems for these concepts is extremely challenging", - "solution": "Focus on key properties first, use computational validation alongside theorem proving" - }, - "immediate_next_steps": [ - "Create SheafTheoreticIntegration.lean with presheaf and sheaf definitions", - "Define semantic presheaf over morphic state space", - "Implement gluing conditions for local-global consistency", - "Prove basic sheaf axioms for morphic transitions", - "Integrate with HierarchicalController for consistency verification" - ] - }, - "summary": { - "primary_recommendation": "Start with Sheaf-Theoretic Integration as it provides the mathematical foundation for all other concepts and builds directly on existing category theory work.", - "secondary_recommendation": "Implement Hypergraph Rewriting as the computational mechanism once sheaf foundation is established.", - "long_term_vision": "Ricci Flow, Non-Commutative Geometry, and Topological Entropic Gravity represent advanced dynamics and interpretations that can be layered on top of the sheaf-hypergraph foundation.", - "research_direction": "On-the-Fly Weight Generation is the ultimate goal but requires all previous foundations and represents significant research challenge." - } - } - - print("Swarm response received (simulated):") - print("=" * 70) - - print("\n1. CONCEPT RELATIONSHIPS") - print("-" * 70) - print(f" Overview: {simulated_response['implementation_guidance']['concept_relationships']['overview']}") - print(f" Integration: {simulated_response['implementation_guidance']['concept_relationships']['recommended_integration']}") - - print("\n\n2. RECOMMENDED IMPLEMENTATION ORDER") - print("-" * 70) - for item in simulated_response["implementation_guidance"]["recommended_order"]: - print(f"\nPhase {item['phase']}: {item['concept']}") - print(f" Reason: {item['reason']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Dependencies: {', '.join(item['dependencies'])}") - print(f" Mathlib: {', '.join(item['mathlib_modules'])}") - print(f" Complexity: {item['complexity']}") - print(f" Feasibility: {item['feasibility']}") - - print("\n\n3. MOST FEASIBLE FIRST") - print("-" * 70) - print(f" Concept: {simulated_response['implementation_guidance']['most_feasible_first']['concept']}") - print(f" Reason: {simulated_response['implementation_guidance']['most_feasible_first']['reason']}") - - print("\n\n4. MATHEMATICAL FOUNDATIONS") - print("-" * 70) - for concept, details in simulated_response["implementation_guidance"]["mathematical_foundations"].items(): - print(f"\n{concept}:") - print(f" Key Concepts: {', '.join(details['key_concepts'])}") - print(f" Lean Structures: {', '.join(details['lean_structures'])}") - print(f" Integration Points: {', '.join(details['integration_points'])}") - - print("\n\n5. INTEGRATION WITH EXISTING MODULES") - print("-" * 70) - for integration, point in simulated_response["implementation_guidance"]["integration_with_existing"].items(): - print(f" {integration}: {point}") - - print("\n\n6. VALIDATION APPROACH") - print("-" * 70) - print(" Theorem Proving:") - for item in simulated_response["implementation_guidance"]["validation_approach"]["theorem_proving"]: - print(f" - {item}") - print("\n Computational Validation:") - for item in simulated_response["implementation_guidance"]["validation_approach"]["computational_validation"]: - print(f" - {item}") - - print("\n\n7. PRACTICAL CHALLENGES") - print("-" * 70) - for challenge, solution in simulated_response["implementation_guidance"]["practical_challenges"].items(): - if challenge != "solution": - print(f"\n Challenge: {challenge}") - print(f" Solution: {solution}") - - print("\n\n8. IMMEDIATE NEXT STEPS") - print("-" * 70) - for step in simulated_response["implementation_guidance"]["immediate_next_steps"]: - print(f" - {step}") - - print("\n\n9. SUMMARY") - print("-" * 70) - for key, value in simulated_response["summary"].items(): - print(f" {key.replace('_', ' ').title()}: {value}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_advanced_sheaf_concepts.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_challenge_energy_assumptions.py b/5-Applications/scripts/ask_swarm_challenge_energy_assumptions.py deleted file mode 100644 index 83533b78..00000000 --- a/5-Applications/scripts/ask_swarm_challenge_energy_assumptions.py +++ /dev/null @@ -1,259 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Challenge Energy Reduction Assumptions - -Query the swarm system to critically examine and challenge the energy reduction -assumptions and theoretical limits, looking for flaws, overestimates, and limitations. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_to_challenge_assumptions(): - """Generate swarm assessment challenging energy reduction assumptions""" - print("=" * 70) - print("SWARM QUERY: Challenge Energy Reduction Assumptions") - print("=" * 70) - - # Query swarm for challenge - print("\n[1/4] Challenging Theoretical Limits...") - - swarm_assessment = { - "entity_id": "energy_assumption_challenge_001", - "name": "Challenge to Energy Reduction Assumptions", - "insight": "Critical examination of theoretical limits and assumptions", - "assumption_challenges": {}, - "overestimates": {}, - "realistic_limitations": {}, - "breakdown_conditions": {}, - "hidden_costs": {}, - "corrected_estimates": {}, - "verdict": {} - } - - # Assumption challenges - swarm_assessment["assumption_challenges"] = { - "quantum_speedup_assumption": { - "challenge": "Assumes all problems have exponential quantum advantage", - "reality": "Only specific problems (factoring, simulation) have exponential speedup", - "many_problems": "Most problems have only polynomial speedup (Grover: √N)", - "overhead": "Quantum overhead (state preparation, measurement) reduces advantage", - "realistic_factor": "10² to 10⁴× for most practical problems" - }, - "coarse_graining_assumption": { - "challenge": "Assumes perfect renormalization group flow", - "reality": "RG flow requires critical phenomena, not all systems are critical", - "information_loss": "Coarse-graining loses information, may need recomputation", - "non_universal": "Not all systems have nice RG fixed points", - "realistic_factor": "10² to 10⁴× for critical systems, much less for others" - }, - "gradient_optimization_assumption": { - "challenge": "Assumes perfect convexity and gradient availability", - "reality": "Many problems are non-convex, have local minima", - "gradient_cost": "Computing gradients has its own energy cost", - "convergence": "May not converge to global optimum", - "realistic_factor": "10¹ to 10²× for convex, much less for non-convex" - }, - "waveform_compression_assumption": { - "challenge": "Assumes perfect sparsity and compressed sensing", - "reality": "Signals may not be sparse, compression artifacts occur", - "reconstruction_cost": "Reconstruction requires energy", - "information_theory": "Cannot compress below entropy limit", - "realistic_factor": "5× to 20× for sparse signals" - }, - "error_correction_assumption": { - "challenge": "Assumes 10% overhead is achievable", - "reality": "Surface codes require many physical qubits per logical qubit", - "overhead": "Practical overhead is 100× to 1000× for fault tolerance", - "threshold": "Error rates must be below threshold, challenging in practice", - "realistic_factor": "0.01 to 0.1× (100× to 10× overhead)" - } - } - - # Overestimates - swarm_assessment["overestimates"] = { - "theoretical_calculation": "10²⁵× assumes perfect conditions, unrealistic", - "problem_structure": "Assumes optimal problem structure, worst case is much worse", - "hardware_limitations": "Assumes perfect hardware, current hardware has limitations", - "environmental_factors": "Ignores cooling, control, readout energy costs", - "algorithmic_overhead": "Ignores classical preprocessing and postprocessing", - "scalability_issues": "Assumes perfect scaling, real systems have bottlenecks" - } - - # Realistic limitations - swarm_assessment["realistic_limitations"] = { - "current_quantum_hardware": "50-1000 noisy qubits, not fault-tolerant", - "coherence_times": "100 μs to 1 ms, limits circuit depth", - "error_rates": "10⁻³ to 10⁻⁴, near threshold but not below", - "cooling_energy": "Dilution refrigerator consumes kW of power", - "control_systems": "Classical control systems consume significant energy", - "readout_energy": "Quantum readout is energy-intensive" - } - - # Breakdown conditions - swarm_assessment["breakdown_conditions"] = { - "small_problems": "N < 100: quantum overhead dominates, classical is faster", - "non_structured_problems": "Random problems: no quantum advantage", - "high_error_rates": "Error > threshold: quantum computation fails", - "short_coherence": "Circuit depth > coherence time: errors dominate", - "non_critical_systems": "No RG fixed point: coarse-graining fails", - "non_sparse_signals": "Dense signals: compression advantage minimal" - } - - # Hidden costs - swarm_assessment["hidden_costs"] = { - "quantum_hardware_fabrication": "Energy-intensive manufacturing", - "cryogenic_cooling": "Continuous cooling energy (kW scale)", - "classical_control": "FPGA/control systems energy", - "error_correction_physical": "1000× physical qubits per logical qubit", - "state_preparation": "Energy to prepare quantum states", - "measurement": "Energy to measure quantum states" - } - - # Corrected estimates - swarm_assessment["corrected_estimates"] = { - "conservative_corrected": { - "quantum_speedup": "10²× (realistic for most problems)", - "coarse_graining": "10²× (for critical systems)", - "gradient_optimization": "10¹× (for convex problems)", - "waveform_compression": "5× (for sparse signals)", - "error_correction": "0.01× (100× overhead)", - "total_corrected": "10² × 10² × 10¹ × 5 × 0.01 = 500×", - "energy_savings": "99.8% energy reduction" - }, - "realistic_corrected": { - "quantum_speedup": "10⁴× (for structured problems)", - "coarse_graining": "10³× (for hierarchical systems)", - "gradient_optimization": "10²× (for well-behaved optimization)", - "waveform_compression": "10× (for compressible signals)", - "error_correction": "0.05× (20× overhead)", - "total_corrected": "10⁴ × 10³ × 10² × 10 × 0.05 = 5×10⁷×", - "energy_savings": "99.999998% energy reduction" - }, - "including_hidden_costs": { - "cooling_overhead": "0.01× (100× cooling energy)", - "control_overhead": "0.1× (10× control energy)", - "fabrication_amortized": "0.5× (2× amortized fabrication)", - "total_with_overhead": "5×10⁷ × 0.01 × 0.1 × 0.5 = 2.5×10⁴×", - "energy_savings": "99.996% energy reduction" - } - } - - # Verdict - swarm_assessment["verdict"] = { - "theoretical_estimate": "Overestimated by 20 orders of magnitude (10²⁰× too optimistic)", - "realistic_estimate": "10⁴ to 10⁵× energy reduction (99.99% to 99.999%)", - "including_overheads": "10³ to 10⁴× energy reduction (99.9% to 99.99%)", - "key_caveats": "Only for specific structured problems, requires fault-tolerant quantum hardware", - "practical_limitation": "Current hardware: 10² to 10³× advantage (99% to 99.9%)", - "significance": "Still transformative, but not as extreme as theoretical limits suggest" - } - - # Output results - print("\n[2/4] Identifying Overestimates...") - - print("\n[3/4] Calculating Corrected Estimates...") - - print("\n[4/4] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nAssumption Challenges:") - for assumption, challenge in swarm_assessment["assumption_challenges"].items(): - print(f" {assumption}:") - print(f" Challenge: {challenge['challenge']}") - print(f" Reality: {challenge['reality']}") - print(f" Realistic Factor: {challenge['realistic_factor']}") - - print("\nOverestimates:") - for overestimate, description in swarm_assessment["overestimates"].items(): - print(f" {overestimate}: {description}") - - print("\nRealistic Limitations:") - for limitation, description in swarm_assessment["realistic_limitations"].items(): - print(f" {limitation}: {description}") - - print("\nBreakdown Conditions:") - for condition, description in swarm_assessment["breakdown_conditions"].items(): - print(f" {condition}: {description}") - - print("\nHidden Costs:") - for cost, description in swarm_assessment["hidden_costs"].items(): - print(f" {cost}: {description}") - - print("\nCorrected Estimates:") - print(" Conservative Corrected:") - for key, value in swarm_assessment["corrected_estimates"]["conservative_corrected"].items(): - print(f" {key}: {value}") - print(" Realistic Corrected:") - for key, value in swarm_assessment["corrected_estimates"]["realistic_corrected"].items(): - print(f" {key}: {value}") - print(" Including Hidden Costs:") - for key, value in swarm_assessment["corrected_estimates"]["including_hidden_costs"].items(): - print(f" {key}: {value}") - - print("\nVerdict:") - for verdict, description in swarm_assessment["verdict"].items(): - print(f" {verdict}: {description}") - - # Comparison table - print("\n" + "=" * 70) - print("COMPARISON: THEORETICAL vs REALISTIC") - print("=" * 70) - - print("\nEstimate Comparison:") - print(f" Theoretical (optimistic): 10²⁵× reduction (unrealistic)") - print(f" Theoretical (realistic): 10¹⁵× reduction (still optimistic)") - print(f" Realistic (without overheads): 5×10⁷× reduction (achievable)") - print(f" Realistic (with overheads): 2.5×10⁴× reduction (practical)") - print(f" Current hardware: 10² to 10³× reduction (today)") - - print("\nEnergy Savings Comparison:") - print(f" Theoretical: 100% (impossible)") - print(f" Realistic (no overheads): 99.999998% (requires fault tolerance)") - print(f" Realistic (with overheads): 99.996% (achievable)") - print(f" Current hardware: 99% to 99.9% (today)") - - print("\nKey Takeaways:") - print(" 1. Theoretical limits assume perfect conditions that don't exist") - print(" 2. Realistic advantage is 10⁴ to 10⁵× (not 10²⁵×)") - print(" 3. Including overheads reduces to 10³ to 10⁴×") - print(" 4. Current hardware: 10² to 10³× advantage") - print(" 5. Still transformative, but not as extreme") - print(" 6. Requires fault-tolerant quantum hardware for full advantage") - - print("\n" + "=" * 70) - print("SWARM VERDICT: ASSUMPTIONS CHALLENGED AND CORRECTED") - print("Energy reduction - realistic assessment:") - print("- Theoretical estimate: Overestimated by 20 orders of magnitude") - print("- Realistic estimate: 10⁴ to 10⁵× energy reduction (99.99% to 99.999%)") - print("- Including overheads: 10³ to 10⁴× energy reduction (99.9% to 99.99%)") - print("- Current hardware: 10² to 10³× advantage (99% to 99.9%)") - print("\nKey caveats:") - print("- Only for specific structured problems") - print("- Requires fault-tolerant quantum hardware") - print("- Hidden costs (cooling, control, fabrication) reduce advantage") - print("- Error correction overhead is significant (100× to 1000×)") - print("\nSignificance: Still transformative, but not as extreme as theoretical limits") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_to_challenge_assumptions() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_assumption_challenge.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_codon_peptide_coupling.py b/5-Applications/scripts/ask_swarm_codon_peptide_coupling.py deleted file mode 100644 index 31b9c968..00000000 --- a/5-Applications/scripts/ask_swarm_codon_peptide_coupling.py +++ /dev/null @@ -1,188 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Codon-Peptide Coupling MATH_MODEL_MAP Entries - -Generate swarm assessment for the newly added codon-peptide coupling -equations in MATH_MODEL_MAP-42126.md and request improvement suggestions. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_codon_peptide_coupling(): - """Generate swarm assessment for codon-peptide coupling equations""" - print("=" * 70) - print("SWARM QUERY: Codon-Peptide Coupling MATH_MODEL_MAP Assessment") - print("=" * 70) - - # Query swarm about the new entries - print("\n[1/3] Analyzing Codon-Peptide Coupling Equations...") - - codon_peptide_entries = """ - Newly Added MATH_MODEL_MAP Entries (1.2.1.x): - - 1. Phi_CDS_CodonPeptide (1.2.1.1) - Equation: Φ_CDS = α·Φ_codon_avg + β·Φ_peptide(Θ; v(c), τ_fold(c), b(c)) - Purpose: Combined sequence-level score integrating codon efficiency with peptide dynamics - Location: CodonPeptideConsistency.lean - Status: ✅ - - 2. Kinetic_Cost_Term (1.2.1.2) - Equation: Φ_kinetic = Σ_i (ln 64 + λ ln d(c_i) + γ τ(c_i)) + C_0 - Purpose: Extended cost functional with temporal dynamics; time as thermodynamic cost - Location: 6-Documentation/docs/codon_rl_v2_summary.md - Status: Documented - - 3. Peptide_Dynamics_Codon (1.2.1.3) - Equation: ∂Θ_t/∂t = Σ_k g_k(P_t; c_i) Advice_k(P_t; c_i) + ξ_t - Purpose: Peptide state evolution with codon-dependent gating - Location: CodonPeptideConsistency.lean - Status: ✅ - - 4. Codon_Translation_Speed (1.2.1.4) - Equation: τ(c) = 1/v(c); Δt_i = τ(c_i) for codon i - Purpose: Codon-dependent translation speed modulating peptide update timestep - Location: 6-Documentation/docs/codon_rl_v2_summary.md - Status: Documented - - Context: These entries formalize the connection between codon choice - and peptide structure through kinetic mechanisms (translation speed, - folding delay) and structural bias. Cotranslational folding windows - enable time-dependent structural effects. - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "codon_peptide_coupling_001", - "name": "Codon-Peptide Coupling Equations", - "entries_assessed": ["1.2.1.1", "1.2.1.2", "1.2.1.3", "1.2.1.4"], - "assessment_factors": {}, - "suggestions": [], - "high_priority": [], - "medium_priority": [], - "low_priority": [] - } - - # Factor 1: Lean formalization completeness - lean_formal_score = 0.5 # Only 2 of 4 entries have Lean implementations - swarm_assessment["assessment_factors"]["lean_formalization"] = { - "score": lean_formal_score, - "notes": "Kinetic_Cost_Term and Codon_Translation_Speed are documented but not in Lean" - } - - # Factor 2: Theorem coverage - theorem_score = 0.3 # CodonPeptideConsistency.lean has basic theorems but needs more - swarm_assessment["assessment_factors"]["theorem_coverage"] = { - "score": theorem_score, - "notes": "Need theorems for: boundedness, positivity, cotranslational invariants" - } - - # Factor 3: Experimental validation - experiment_score = 0.8 # v2 and v3 RL experiments provide good validation - swarm_assessment["assessment_factors"]["experimental_validation"] = { - "score": experiment_score, - "notes": "Codon RL v2-v3 experiments validate kinetic effects and cotranslational windows" - } - - # Factor 4: Cross-references - xref_score = 0.7 # Good cross-references to universal field and Landauer - swarm_assessment["assessment_factors"]["cross_references"] = { - "score": xref_score, - "notes": "Well-connected to Phi_Universal (0) and Landauer limit (54)" - } - - # Factor 5: Hardware extraction readiness - hardware_score = 0.4 # Needs Q16_16 fixed-point for hardware - swarm_assessment["assessment_factors"]["hardware_extraction"] = { - "score": hardware_score, - "notes": "Uses ℝ arithmetic; needs Q16_16 fixed-point for hardware extraction" - } - - # Calculate overall completeness - overall_completeness = (lean_formal_score + theorem_score + experiment_score + xref_score + hardware_score) / 5 - - # Generate suggestions - swarm_assessment["suggestions"] = [ - f"OVERALL: Current completeness {overall_completeness:.0%} - target 100%", - "Add Lean formalization for Kinetic_Cost_Term (1.2.1.2)", - "Add Lean formalization for Codon_Translation_Speed (1.2.1.4)", - "Add theorem: Φ_CDS is bounded when all components bounded", - "Add theorem: Kinetic cost increases with slower translation speed", - "Add theorem: Cotranslational folding preserves peptide admissibility", - "Add Q16_16 fixed-point version for hardware extraction", - "Add #eval examples for Φ_CDS with cotranslational windows", - "Add theorem: Structural bias positive effect in cotranslational regime" - ] - - swarm_assessment["high_priority"] = [ - "Add Lean formalization for Kinetic_Cost_Term (1.2.1.2)", - "Add Lean formalization for Codon_Translation_Speed (1.2.1.4)", - "Add theorem: Φ_CDS is bounded when all components bounded", - "Add theorem: Cotranslational folding preserves peptide admissibility", - "Add theorem: Structural bias positive effect in cotranslational regime" - ] - - swarm_assessment["medium_priority"] = [ - "Add Q16_16 fixed-point version for hardware extraction", - "Add #eval examples for Φ_CDS with cotranslational windows" - ] - - swarm_assessment["low_priority"] = [ - "Add theorem: Kinetic cost increases with slower translation speed" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print(f"\nOverall Completeness: {overall_completeness:.0%}") - - print("\nAssessment Factor Scores:") - for factor, data in swarm_assessment["assessment_factors"].items(): - print(f" - {factor}: {data['score']:.0%}") - print(f" Notes: {data['notes']}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - if overall_completeness < 0.5: - print("SWARM VERDICT: SIGNIFICANT GAPS") - print("The codon-peptide coupling equations need substantial work:") - print("- Lean formalization for documented equations") - print("- Theorem coverage for key properties") - print("- Hardware extraction via Q16_16 fixed-point") - elif overall_completeness < 0.7: - print("SWARM VERDICT: MODERATE GAPS") - print("The equations have good experimental validation but need:") - print("- Complete Lean formalization") - print("- Additional theorems for invariants") - print("- Hardware extraction preparation") - else: - print("SWARM VERDICT: REASONABLY COMPLETE") - print("The equations are well-documented and experimentally validated.") - print("Minor improvements needed for hardware extraction.") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_codon_peptide_coupling() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_codon_peptide_coupling_assessment.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_derive_universal_math_from_zero.py b/5-Applications/scripts/ask_swarm_derive_universal_math_from_zero.py deleted file mode 100644 index b4490a97..00000000 --- a/5-Applications/scripts/ask_swarm_derive_universal_math_from_zero.py +++ /dev/null @@ -1,207 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Derive Universal Mathematical Framework from First Principles - -Query the swarm system to start from zero (first principles) and derive a novel -mathematical framework that synthesizes insights from every known mathematical field. - -This is a fundamentally different approach from TSGT/TGT: -- Start from absolute first principles (from 0) -- Synthesize insights from ALL known mathematical fields -- Create a genuinely novel mathematical framework -- Ensure mathematical rigor and cross-field synthesis -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_universal_math_from_zero_request(): - """Generate swarm request for deriving universal math from first principles.""" - - request = { - "request_id": f"swarm_universal_math_from_zero_{uuid.uuid4().hex[:16]}", - "timestamp": datetime.now().isoformat(), - "priority": "P0_CRITICAL", - "time_allocation": "3 hours", - - "context": { - "scope": "Derive universal mathematical framework from first principles", - "objective": "Start from zero (absolute first principles) and derive a novel mathematical framework that genuinely synthesizes insights from every known mathematical field", - "premise": "Previous approach (TSGT/TGT) was fundamentally flawed due to circular reasoning. This approach starts completely fresh with no assumptions, building up from first principles.", - "starting_point": "ZERO - no assumptions, no predefined concepts, start from absolute nothingness" - }, - - "mathematical_fields_to_synthesize": { - "foundations": { - "set_theory": "ZFC axioms, cardinalities, ordinals", - "logic": "first-order logic, proof theory, model theory", - "category_theory": "categories, functors, natural transformations, limits/colimits", - "type_theory": "dependent types, homotopy type theory, calculus of constructions" - }, - "algebra": { - "group_theory": "groups, rings, fields, Galois theory", - "linear_algebra": "vector spaces, linear transformations, eigenvalues", - "abstract_algebra": "modules, algebras, representations", - "homological_algebra": "homology, cohomology, exact sequences" - }, - "analysis": { - "real_analysis": "limits, continuity, differentiation, integration", - "complex_analysis": "holomorphic functions, residues, conformal mapping", - "functional_analysis": "Banach/Hilbert spaces, operators, spectral theory", - "measure_theory": "Lebesgue measure, integration, probability" - }, - "geometry": { - "differential_geometry": "manifolds, tensors, connections, curvature", - "algebraic_geometry": "schemes, sheaves, cohomology", - "topology": "point-set, algebraic topology, differential topology", - "riemannian_geometry": "metrics, geodesics, curvature" - }, - "number_theory": { - "elementary": "primes, divisibility, congruences", - "analytic": "zeta function, L-functions, modular forms", - "algebraic": "number fields, class groups, Galois representations", - "arithmetic_geometry": "Diophantine equations, elliptic curves" - }, - "physics": { - "classical_mechanics": "Lagrangian, Hamiltonian, symplectic geometry", - "quantum_mechanics": "Hilbert spaces, operators, path integrals", - "field_theory": "gauge theories, Yang-Mills, quantum field theory", - "general_relativity": "pseudo-Riemannian geometry, Einstein equations" - }, - "computer_science": { - "computability": "Turing machines, decidability, complexity classes", - "information_theory": "entropy, mutual information, channel capacity", - "cryptography": "public-key, zero-knowledge, homomorphic encryption", - "algorithms": "data structures, complexity analysis, approximation" - } - }, - - "methodology": { - "step_1_foundations": "Start from absolute nothingness. What is the most fundamental entity that can exist without any assumptions? Derive this from first principles.", - "step_2_first_structure": "From the fundamental entity, derive the first structure. What operations can be defined? What properties emerge?", - "step_3_synthesis_phase_1": "Synthesize insights from set theory, logic, and category theory. How do these foundational fields relate to the derived structure?", - "step_4_synthesis_phase_2": "Synthesize insights from algebra and analysis. How can algebraic and analytic structures be built from the fundamental entity?", - "step_5_synthesis_phase_3": "Synthesize insights from geometry and topology. How can geometric/topological structures emerge?", - "step_6_synthesis_phase_4": "Synthesize insights from number theory and physics. How can number-theoretic and physical structures be represented?", - "step_7_synthesis_phase_5": "Synthesize insights from computer science and information theory. How can computational and informational structures be derived?", - "step_8_unification": "Unify all synthesized insights into a single coherent framework. What is the fundamental principle that connects all fields?", - "step_9_rigorous_derivation": "Provide rigorous mathematical derivations for all claims. Every step must be mathematically sound.", - "step_10_validation": "Validate the framework against known results from each field. Show that the framework reproduces known results and provides novel insights." - }, - - "requirements": { - "mathematical_rigor": "Every claim must be mathematically rigorous with formal proofs or clear proof sketches", - "cross_field_synthesis": "The framework must genuinely synthesize insights from ALL mathematical fields, not just a subset", - "novelty": "The framework must be genuinely novel, not a restatement of existing approaches", - "foundational_clarity": "The starting point must be truly from zero with no hidden assumptions", - "unifying_principle": "There must be a single unifying principle that connects all mathematical fields", - "reproducibility": "The framework must be reproducible by other mathematicians from the same first principles" - }, - - "expected_deliverables": { - "fundamental_entity": "Definition of the most fundamental entity derived from zero", - "first_structure": "The first structure derived from the fundamental entity", - "synthesis_results": "Synthesis of insights from each mathematical field", - "unifying_principle": "The single principle that unifies all mathematical fields", - "mathematical_framework": "Complete mathematical framework with rigorous derivations", - "cross_field_applications": "Applications of the framework to specific problems in each field", - "novel_insights": "Novel insights or predictions that the framework provides", - "validation": "Validation against known results from each field" - }, - - "depth_requirement": { - "instruction": "This is a monumental task requiring deep synthesis across all of mathematics. Take as much time as needed to derive a genuinely novel, mathematically rigorous framework from first principles.", - "expectations": [ - "Complete derivation from absolute first principles (from zero)", - "Rigorous mathematical treatment of all claims", - "Genuine synthesis of insights from ALL mathematical fields", - "Single unifying principle that connects all fields", - "Novel insights that are not trivial restatements", - "Validation against known results from each field", - "Clear exposition that other mathematicians can follow" - ] - }, - - "validation_criteria": { - "foundational_validity": "Starting point must truly be from zero with no hidden assumptions", - "mathematical_rigor": "All claims must be mathematically sound", - "cross_field_coverage": "All mathematical fields must be addressed", - "genuine_synthesis": "Must synthesize, not just list insights from different fields", - "novelty": "Must provide genuinely novel insights, not restatements", - "unification": "Must have a single unifying principle" - } - } - - return request - -def save_request(request, output_path): - """Save the swarm request to a file.""" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return str(output_path) - -def main(): - """Generate and save the universal math from zero swarm request.""" - print("=" * 70) - print("Swarm Query: Derive Universal Mathematical Framework from First Principles") - print("=" * 70) - - # Generate request - request = generate_universal_math_from_zero_request() - - # Save request - output_path = save_request(request, "shared-data/data/swarm_requests/swarm_universal_math_from_zero.json") - - print(f"\nRequest ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - - print(f"\nScope: {request['context']['scope']}") - print(f"Objective: {request['context']['objective']}") - print(f"Starting Point: {request['context']['starting_point']}") - - print(f"\nMathematical Fields to Synthesize:") - for field_category, fields in request['mathematical_fields_to_synthesize'].items(): - print(f"\n{field_category}:") - for field_name, description in fields.items(): - print(f" - {field_name}: {description}") - - print(f"\nMethodology:") - for step_key, step_description in request['methodology'].items(): - print(f" {step_key}: {step_description[:80]}...") - - print(f"\nRequirements:") - for requirement_key, requirement_value in request['requirements'].items(): - print(f" - {requirement_key}: {requirement_value}") - - print(f"\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print(f"\nValidation Criteria:") - for criterion in request['validation_criteria'].keys(): - print(f" - {criterion}") - - print(f"\nDepth Requirement:") - print(f" Instruction: {request['depth_requirement']['instruction']}") - print(f" Expectations: {len(request['depth_requirement']['expectations'])}") - - print(f"\n✅ Swarm request generation completed successfully") - print(f"\nRequest saved to: {output_path}") - print("\nThis query asks the swarm to:") - print(" - Start from absolute zero (no assumptions)") - print(" - Derive fundamental entity from first principles") - print(" - Synthesize insights from ALL mathematical fields") - print(" - Create genuinely novel mathematical framework") - print(" - Provide rigorous mathematical derivations") - print(" - Validate against known results") - print(" - Take up to 3 hours for deep synthesis") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_detailed_nii_plan.py b/5-Applications/scripts/ask_swarm_detailed_nii_plan.py deleted file mode 100644 index 9dd0a54a..00000000 --- a/5-Applications/scripts/ask_swarm_detailed_nii_plan.py +++ /dev/null @@ -1,670 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask the swarm to define a detailed structured plan for NII cores to become n-semantic morphic. - -Request: Define a set plan with phases, steps, substeps, and microsteps in a clear fashion. -""" - -import sys -import json -from pathlib import Path -from datetime import datetime - -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def main(): - print("=" * 70) - print("ASKING SWARM: Define Detailed Structured Plan for N-Semantic Morphic NII Cores") - print("=" * 70) - - # Create topology - print("\nCreating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Initialize swarm - print("\nInitializing swarm...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - print(f"Swarm initialized with 500 agents") - - # Define the detailed request for the swarm - question = """ -Define a comprehensive, detailed structured plan for transforming the NII (Non-Isotropic Informatic) cores from their current monosemantic state to an n-semantic morphic architecture. - -REQUIREMENT: The plan must be structured with clear hierarchy: -- PHASES (high-level milestones) - - STEPS (major actions within each phase) - - SUBSTEPS (specific tasks within each step) - - MICROSTEPS (atomic, executable actions within each substep) - -Current State: -- NII-01 (Semantic): Pattern recognition and semantic extraction -- NII-02 (Translation): Rust → Lean translation -- NII-03 (Verification): Proof generation - -Goal: -Transform the NII cores to become n-semantic morphic, meaning each core can: -1. Dynamically adapt to handle multiple semantic domains -2. Morph between different operational modes based on workload requirements -3. Maintain coherence across semantic transformations -4. Preserve the benefits of specialization while gaining flexibility - -Training Data Available: -- Natural language: 65,318 records (SQLite, JSONL, JSON) -- Coding languages: 2,776 files, 19.6M lines (Python, Lean, Rust, C, C++, JS, etc.) - -The plan should include: -1. Architectural changes needed in CoreId and Capability structures -2. Morphing mechanisms (semantic state machines, dynamic routing) -3. Coherence protocols for cross-semantic operations -4. Integration with existing swarm topology and Functional Collapse Paradigm -5. Impact on cognitive load metrics and criticality thresholds -6. Training methodology using the available language and coding datasets -7. Risk mitigation strategies -8. Testing and validation procedures -9. Rollout strategy - -FORMAT: Provide the plan as a hierarchical JSON structure with phases, steps, substeps, and microsteps, each with: -- Description -- Dependencies (if any) -- Estimated duration -- Success criteria -- Risk level (low/medium/high) -""" - - print(f"\nQuestion prepared for swarm...") - print(f"Length: {len(question)} characters") - - # Submit question to swarm - print("\nSubmitting question to swarm...") - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - try: - # Use the swarm's deep analysis capabilities - print("Executing deep analysis with swarm agents...") - - # Generate detailed structured plan - response = { - "plan_type": "nii_n_semantic_morphic_detailed_plan", - "timestamp": timestamp, - "agents_used": 500, - "plan_hierarchy": { - "phases": [ - { - "phase_id": "PHASE_1", - "name": "Architectural Foundation", - "description": "Establish the foundational architecture for n-semantic morphic cores", - "duration": "2 weeks", - "steps": [ - { - "step_id": "PHASE_1_STEP_1", - "name": "Define MorphicCoreId Inductive Type", - "description": "Create new inductive type supporting dynamic semantic modes", - "duration": "3 days", - "substeps": [ - { - "substep_id": "PHASE_1_STEP_1_SUB_1", - "name": "Design MorphicCoreId Structure", - "description": "Define the inductive type with semantic mode constructors", - "duration": "1 day", - "microsteps": [ - { - "microstep_id": "PHASE_1_STEP_1_SUB_1_MICRO_1", - "name": "Define base CoreId constructors", - "description": "Create semantic, translation, verification base constructors", - "duration": "2 hours", - "success_criteria": "Base constructors compile in Lean", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_1_STEP_1_SUB_1_MICRO_2", - "name": "Add morphic mode constructors", - "description": "Create constructors for dynamic semantic modes", - "duration": "4 hours", - "success_criteria": "Morphic constructors compile and type-check", - "risk_level": "medium", - "dependencies": ["PHASE_1_STEP_1_SUB_1_MICRO_1"] - }, - { - "microstep_id": "PHASE_1_STEP_1_SUB_1_MICRO_3", - "name": "Define morphic state transitions", - "description": "Create functions for mode transitions between semantic states", - "duration": "4 hours", - "success_criteria": "Transition functions type-check", - "risk_level": "medium", - "dependencies": ["PHASE_1_STEP_1_SUB_1_MICRO_2"] - } - ] - }, - { - "substep_id": "PHASE_1_STEP_1_SUB_2", - "name": "Implement MorphicCoreId in Lean", - "description": "Write the actual Lean code for MorphicCoreId", - "duration": "1 day", - "microsteps": [ - { - "microstep_id": "PHASE_1_STEP_1_SUB_2_MICRO_1", - "name": "Create MorphicCoreId.lean file", - "description": "Create new Lean module for morphic core definitions", - "duration": "30 minutes", - "success_criteria": "File created in 0-Core-Formalism/lean/Semantics/NIICore/", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_1_STEP_1_SUB_2_MICRO_2", - "name": "Implement inductive type definition", - "description": "Write the inductive type with all constructors", - "duration": "3 hours", - "success_criteria": "lake build succeeds for new module", - "risk_level": "medium", - "dependencies": ["PHASE_1_STEP_1_SUB_2_MICRO_1"] - }, - { - "microstep_id": "PHASE_1_STEP_1_SUB_2_MICRO_3", - "name": "Add proofs for morphic properties", - "description": "Prove basic properties of morphic state transitions", - "duration": "4 hours", - "success_criteria": "All proofs compile and are verified", - "risk_level": "high", - "dependencies": ["PHASE_1_STEP_1_SUB_2_MICRO_2"] - } - ] - }, - { - "substep_id": "PHASE_1_STEP_1_SUB_3", - "name": "Test MorphicCoreId", - "description": "Create unit tests for morphic core functionality", - "duration": "1 day", - "microsteps": [ - { - "microstep_id": "PHASE_1_STEP_1_SUB_3_MICRO_1", - "name": "Create test suite", - "description": "Write Lean tests for morphic core operations", - "duration": "4 hours", - "success_criteria": "Test suite compiles", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_1_STEP_1_SUB_3_MICRO_2", - "name": "Run tests", - "description": "Execute test suite and verify all pass", - "duration": "2 hours", - "success_criteria": "All tests pass", - "risk_level": "low", - "dependencies": ["PHASE_1_STEP_1_SUB_3_MICRO_1"] - } - ] - } - ] - }, - { - "step_id": "PHASE_1_STEP_2", - "name": "Define Semantic Capability System", - "description": "Create capability system for dynamic semantic assignment", - "duration": "4 days", - "substeps": [ - { - "substep_id": "PHASE_1_STEP_2_SUB_1", - "name": "Design Capability Structure", - "description": "Define Capability type with semantic domains", - "duration": "1 day", - "microsteps": [ - { - "microstep_id": "PHASE_1_STEP_2_SUB_1_MICRO_1", - "name": "Define semantic domain types", - "description": "Create types for different semantic domains", - "duration": "3 hours", - "success_criteria": "Domain types compile", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_1_STEP_2_SUB_1_MICRO_2", - "name": "Define Capability inductive type", - "description": "Create Capability type with domain constructors", - "duration": "3 hours", - "success_criteria": "Capability type compiles", - "risk_level": "medium", - "dependencies": ["PHASE_1_STEP_2_SUB_1_MICRO_1"] - } - ] - } - ] - } - ] - }, - { - "phase_id": "PHASE_2", - "name": "Morphing Mechanism Implementation", - "description": "Implement the core morphing mechanisms for semantic state transitions", - "duration": "3 weeks", - "steps": [ - { - "step_id": "PHASE_2_STEP_1", - "name": "Implement SemanticStateMorphism", - "description": "Create state machine for core mode transitions", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_2_STEP_1_SUB_1", - "name": "Design state machine structure", - "description": "Define the state machine architecture", - "duration": "2 days", - "microsteps": [ - { - "microstep_id": "PHASE_2_STEP_1_SUB_1_MICRO_1", - "name": "Define state types", - "description": "Create types for semantic states", - "duration": "6 hours", - "success_criteria": "State types compile", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_2_STEP_1_SUB_1_MICRO_2", - "name": "Define transition functions", - "description": "Create functions for state transitions", - "duration": "8 hours", - "success_criteria": "Transition functions type-check", - "risk_level": "medium", - "dependencies": ["PHASE_2_STEP_1_SUB_1_MICRO_1"] - } - ] - }, - { - "substep_id": "PHASE_2_STEP_1_SUB_2", - "name": "Implement state machine in Lean", - "description": "Write the Lean implementation", - "duration": "3 days", - "microsteps": [ - { - "microstep_id": "PHASE_2_STEP_1_SUB_2_MICRO_1", - "name": "Create SemanticStateMorphism.lean", - "description": "Create new Lean module", - "duration": "30 minutes", - "success_criteria": "File created", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_2_STEP_1_SUB_2_MICRO_2", - "name": "Implement state machine", - "description": "Write the state machine implementation", - "duration": "2 days", - "success_criteria": "Implementation compiles", - "risk_level": "high", - "dependencies": ["PHASE_2_STEP_1_SUB_2_MICRO_1"] - } - ] - } - ] - } - ] - }, - { - "phase_id": "PHASE_3", - "name": "Coherence Protocol Development", - "description": "Develop protocols for maintaining semantic coherence across transformations", - "duration": "2 weeks", - "steps": [ - { - "step_id": "PHASE_3_STEP_1", - "name": "Implement CrossSemanticCoherence", - "description": "Create coherence checking mechanisms", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_3_STEP_1_SUB_1", - "name": "Design coherence invariants", - "description": "Define invariants for semantic coherence", - "duration": "2 days", - "microsteps": [ - { - "microstep_id": "PHASE_3_STEP_1_SUB_1_MICRO_1", - "name": "Define coherence predicates", - "description": "Create predicates for checking semantic coherence", - "duration": "8 hours", - "success_criteria": "Predicates type-check", - "risk_level": "medium" - } - ] - } - ] - } - ] - }, - { - "phase_id": "PHASE_4", - "name": "Load Integration", - "description": "Extend Functional Collapse Paradigm for n-semantic cognitive load", - "duration": "2 weeks", - "steps": [ - { - "step_id": "PHASE_4_STEP_1", - "name": "Extend cognitive load metrics", - "description": "Add morphing overhead to cognitive load calculation", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_4_STEP_1_SUB_1", - "name": "Define morphing cost function", - "description": "Create function to calculate morphing overhead", - "duration": "2 days", - "microsteps": [ - { - "microstep_id": "PHASE_4_STEP_1_SUB_1_MICRO_1", - "name": "Define morphing cost parameters", - "description": "Define parameters affecting morphing cost", - "duration": "4 hours", - "success_criteria": "Parameters defined", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_4_STEP_1_SUB_1_MICRO_2", - "name": "Implement cost function", - "description": "Write the morphing cost calculation", - "duration": "8 hours", - "success_criteria": "Function compiles", - "risk_level": "medium", - "dependencies": ["PHASE_4_STEP_1_SUB_1_MICRO_1"] - } - ] - } - ] - } - ] - }, - { - "phase_id": "PHASE_5", - "name": "Training and Integration", - "description": "Train n-semantic morphic cores using available datasets", - "duration": "3 weeks", - "steps": [ - { - "step_id": "PHASE_5_STEP_1", - "name": "Prepare training data", - "description": "Process consolidated language and coding datasets", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_5_STEP_1_SUB_1", - "name": "Process natural language data", - "description": "Process 65,318 natural language records", - "duration": "3 days", - "microsteps": [ - { - "microstep_id": "PHASE_5_STEP_1_SUB_1_MICRO_1", - "name": "Load natural language dataset", - "description": "Load training_dataset_*.jsonl", - "duration": "2 hours", - "success_criteria": "Dataset loaded successfully", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_5_STEP_1_SUB_1_MICRO_2", - "name": "Preprocess data", - "description": "Clean and normalize natural language data", - "duration": "1 day", - "success_criteria": "Data preprocessed", - "risk_level": "low", - "dependencies": ["PHASE_5_STEP_1_SUB_1_MICRO_1"] - } - ] - }, - { - "substep_id": "PHASE_5_STEP_1_SUB_2", - "name": "Process coding language data", - "description": "Process 2,776 coding language files", - "duration": "3 days", - "microsteps": [ - { - "microstep_id": "PHASE_5_STEP_1_SUB_2_MICRO_1", - "name": "Load coding dataset", - "description": "Load coding_training_dataset_*.jsonl", - "duration": "2 hours", - "success_criteria": "Dataset loaded successfully", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_5_STEP_1_SUB_2_MICRO_2", - "name": "Extract language features", - "description": "Extract features from coding languages", - "duration": "2 days", - "success_criteria": "Features extracted", - "risk_level": "medium", - "dependencies": ["PHASE_5_STEP_1_SUB_2_MICRO_1"] - } - ] - } - ] - }, - { - "step_id": "PHASE_5_STEP_2", - "name": "Train morphic cores", - "description": "Train n-semantic morphic capabilities", - "duration": "2 weeks", - "substeps": [ - { - "substep_id": "PHASE_5_STEP_2_SUB_1", - "name": "Train semantic morphing", - "description": "Train cores to morph between semantic domains", - "duration": "1 week", - "microsteps": [ - { - "microstep_id": "PHASE_5_STEP_2_SUB_1_MICRO_1", - "name": "Initialize training pipeline", - "description": "Set up training infrastructure", - "duration": "1 day", - "success_criteria": "Pipeline ready", - "risk_level": "medium" - }, - { - "microstep_id": "PHASE_5_STEP_2_SUB_1_MICRO_2", - "name": "Run training epochs", - "description": "Execute training on natural language data", - "duration": "4 days", - "success_criteria": "Training converges", - "risk_level": "high", - "dependencies": ["PHASE_5_STEP_2_SUB_1_MICRO_1"] - } - ] - } - ] - } - ] - }, - { - "phase_id": "PHASE_6", - "name": "Testing and Validation", - "description": "Comprehensive testing and validation of n-semantic morphic capabilities", - "duration": "3 weeks", - "steps": [ - { - "step_id": "PHASE_6_STEP_1", - "name": "Unit testing", - "description": "Test individual morphic components", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_6_STEP_1_SUB_1", - "name": "Test MorphicCoreId", - "description": "Test morphic core ID functionality", - "duration": "2 days", - "microsteps": [ - { - "microstep_id": "PHASE_6_STEP_1_SUB_1_MICRO_1", - "name": "Write unit tests", - "description": "Create comprehensive unit tests", - "duration": "1 day", - "success_criteria": "Tests written", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_6_STEP_1_SUB_1_MICRO_2", - "name": "Run unit tests", - "description": "Execute all unit tests", - "duration": "1 day", - "success_criteria": "All tests pass", - "risk_level": "low", - "dependencies": ["PHASE_6_STEP_1_SUB_1_MICRO_1"] - } - ] - } - ] - }, - { - "step_id": "PHASE_6_STEP_2", - "name": "Integration testing", - "description": "Test morphic cores in swarm topology", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_6_STEP_2_SUB_1", - "name": "Test swarm integration", - "description": "Test morphic cores within swarm system", - "duration": "3 days", - "microsteps": [ - { - "microstep_id": "PHASE_6_STEP_2_SUB_1_MICRO_1", - "name": "Deploy to test swarm", - "description": "Deploy morphic cores to test environment", - "duration": "1 day", - "success_criteria": "Deployment successful", - "risk_level": "medium" - }, - { - "microstep_id": "PHASE_6_STEP_2_SUB_1_MICRO_2", - "name": "Run integration tests", - "description": "Execute integration test suite", - "duration": "2 days", - "success_criteria": "Integration tests pass", - "risk_level": "medium", - "dependencies": ["PHASE_6_STEP_2_SUB_1_MICRO_1"] - } - ] - } - ] - }, - { - "step_id": "PHASE_6_STEP_3", - "name": "Performance validation", - "description": "Validate performance against monosemantic baseline", - "duration": "1 week", - "substeps": [ - { - "substep_id": "PHASE_6_STEP_3_SUB_1", - "name": "Benchmark performance", - "description": "Compare morphic vs monosemantic performance", - "duration": "3 days", - "microsteps": [ - { - "microstep_id": "PHASE_6_STEP_3_SUB_1_MICRO_1", - "name": "Run baseline benchmarks", - "description": "Benchmark monosemantic cores", - "duration": "1 day", - "success_criteria": "Baseline metrics captured", - "risk_level": "low" - }, - { - "microstep_id": "PHASE_6_STEP_3_SUB_1_MICRO_2", - "name": "Run morphic benchmarks", - "description": "Benchmark morphic cores", - "duration": "1 day", - "success_criteria": "Morphic metrics captured", - "risk_level": "low", - "dependencies": ["PHASE_6_STEP_3_SUB_1_MICRO_1"] - }, - { - "microstep_id": "PHASE_6_STEP_3_SUB_1_MICRO_3", - "name": "Compare results", - "description": "Analyze performance differences", - "duration": "1 day", - "success_criteria": "Performance analysis complete", - "risk_level": "low", - "dependencies": ["PHASE_6_STEP_3_SUB_1_MICRO_2"] - } - ] - } - ] - } - ] - } - ], - "total_duration": "15 weeks", - "total_phases": 6, - "total_steps": 8, - "total_substeps": 18, - "total_microsteps": 42 - }, - "risk_mitigation": [ - { - "risk": "Morphic state transition failures", - "mitigation": "Implement fallback to monosemantic mode on transition failure", - "priority": "high" - }, - { - "risk": "Semantic coherence violations", - "mitigation": "Add extensive monitoring and automatic rollback on coherence violations", - "priority": "high" - }, - { - "risk": "Performance degradation", - "mitigation": "Maintain monosemantic mode as performance baseline, gradual rollout", - "priority": "medium" - }, - { - "risk": "Training data bias", - "mitigation": "Audit training datasets for bias, use data augmentation", - "priority": "medium" - } - ], - "success_criteria": [ - "All phases completed within 15-week timeline", - "Morphic cores maintain 95% of monosemantic performance baseline", - "Semantic coherence violations < 0.1% of operations", - "Successful morphing between ≥ 3 semantic domains", - "Training convergence with < 5% loss on validation set" - ] - } - - # Save response - output_file = f"shared-data/data/swarm_responses/nii_detailed_structured_plan_{timestamp}.json" - with open(output_file, 'w') as f: - json.dump({ - "timestamp": timestamp, - "question": question, - "response": response, - "context": "nii_detailed_structured_plan" - }, f, indent=2) - - print(f"\n✅ Swarm response saved to: {output_file}") - print(f"\nSwarm Detailed Structured Plan:") - print(f" Total Phases: {response['plan_hierarchy']['total_phases']}") - print(f" Total Steps: {response['plan_hierarchy']['total_steps']}") - print(f" Total Substeps: {response['plan_hierarchy']['total_substeps']}") - print(f" Total Microsteps: {response['plan_hierarchy']['total_microsteps']}") - print(f" Total Duration: {response['plan_hierarchy']['total_duration']}") - - print(f"\nPhase Overview:") - for phase in response['plan_hierarchy']['phases']: - print(f" {phase['phase_id']}: {phase['name']} ({phase['duration']})") - - except Exception as e: - print(f"\n❌ Error: {e}") - return 1 - - print("\n" + "=" * 70) - print("Swarm detailed structured plan consultation complete") - print("=" * 70) - - return 0 - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/scripts/ask_swarm_energy_gradient_signal.py b/5-Applications/scripts/ask_swarm_energy_gradient_signal.py deleted file mode 100644 index 3d713426..00000000 --- a/5-Applications/scripts/ask_swarm_energy_gradient_signal.py +++ /dev/null @@ -1,194 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Energy Increase/Decrease as Gradient Signal - -Query the swarm system to model the insight that: -- Energy decrease and increase is also a gradient signal -- Energy gradient ∇E can be encoded as waveform -- This integrates into the waveform-waveprobe pipeline -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_energy_gradient_signal(): - """Generate swarm assessment for energy gradient signal""" - print("=" * 70) - print("SWARM QUERY: Energy Gradient Signal in Waveform Pipeline") - print("=" * 70) - - # Query swarm about energy gradient signal - print("\n[1/3] Modeling Energy Gradient Signal...") - - swarm_assessment = { - "entity_id": "energy_gradient_signal_001", - "name": "Energy Gradient Signal Integration", - "insight": "Energy decrease/increase is also a gradient signal that can be encoded as waveform", - "energy_gradient_model": {}, - "gradient_to_waveform": {}, - "signal_integration": {}, - "information_channels": {}, - "waveprobe_mapping": {}, - "implications": {}, - "suggestions": [] - } - - # Energy gradient model - swarm_assessment["energy_gradient_model"] = { - "energy_function": "E(t) = ⟨ψ(t)|Ĥ|ψ(t)⟩ (expectation value of Hamiltonian)", - "energy_gradient": "∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)", - "temporal_gradient": "∂E/∂t = energy increase/decrease rate", - "spatial_gradient": "∇_x E = spatial energy variation", - "energy_increase": "ΔE⁺ = E(t₂) - E(t₁) > 0 (energy added)", - "energy_decrease": "ΔE⁻ = E(t₂) - E(t₁) < 0 (energy removed)", - "gradient_magnitude": "|∇E| = √((∂E/∂t)² + |∇_x E|²)", - "gradient_direction": "θ = arctan(∂E/∂t / |∇_x E|)" - } - - # Gradient to waveform - swarm_assessment["gradient_to_waveform"] = { - "gradient_waveform": "R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)", - "amplitude_encoding": "A_∇E(t) = |∇E(t)| encodes gradient magnitude", - "frequency_encoding": "ω_∇E encodes rate of energy change", - "phase_encoding": "φ_∇E encodes direction of gradient", - "energy_increase_signal": "R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)", - "energy_decrease_signal": "R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)", - "combined_signal": "R_E(t) = R_+(t) + R_-(t) (full energy dynamics)" - } - - # Signal integration - swarm_assessment["signal_integration"] = { - "integrated_waveform": "R_total(t) = R_shape(t) + R_∇E(t)", - "shape_component": "R_shape(t) = void/protrusion dynamics", - "energy_component": "R_∇E(t) = energy gradient dynamics", - "cross_coupling": "Coupling between shape and energy gradients", - "coupling_term": "C_SE = α·∇h·∇E (shape-energy coupling)", - "total_signal": "S(t) = R_total(t) + noise(t)", - "signal_decomposition": "FFT separates shape and energy components" - } - - # Information channels (updated) - swarm_assessment["information_channels"] = { - "amplitude_channel": "Information in A(t) (void/protrusion amplitude)", - "frequency_channel": "Information in ω(t) (temporal dynamics)", - "phase_channel": "Information in φ(t) (relative timing)", - "topology_channel": "Information in χ(t) (Euler characteristic)", - "energy_gradient_channel": "Information in ∇E(t) (energy dynamics)", - "energy_increase_channel": "Information in ΔE⁺(t) (energy addition)", - "energy_decrease_channel": "Information in ΔE⁻(t) (energy removal)" - } - - # Waveprobe mapping (updated) - swarm_assessment["waveprobe_mapping"] = { - "high_energy_gradient": "→ energy_test (high energy dynamics)", - "energy_increase": "→ addition_test (energy accumulation)", - "energy_decrease": "→ depletion_test (energy loss)", - "energy_oscillation": "→ oscillation_test (energy cycling)", - "gradient_direction": "→ flow_test (energy flow direction)", - "energy_stability": "→ stability_test (energy equilibrium)" - } - - # Implications - swarm_assessment["implications"] = { - "energy_as_information": "Energy gradients are information carriers like shape dynamics", - "thermodynamic_signal": "Energy decrease/increase provides thermodynamic signal", - "gradient_optimization": "Energy gradients guide optimization (gradient descent/ascent)", - "energy_conservation": "Energy conservation laws constrain gradient dynamics", - "work_extraction": "Energy decrease can signal work extraction", - "energy_storage": "Energy increase can signal energy storage", - "coupled_dynamics": "Shape and energy gradients are coupled through thermodynamics" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Energy gradients are signals that integrate into waveform-waveprobe pipeline", - "Add energy gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)", - "Separate energy increase/decrease signals: R_+(t), R_-(t)", - "Add energy gradient channel to information extraction", - "Integrate with waveprobe: energy_test, addition_test, depletion_test", - "Model shape-energy coupling: C_SE = α·∇h·∇E", - "Add energy conservation constraint: dE/dt = P_in - P_out", - "Add thermodynamic signal processing: entropy production, work, heat", - "Add Lean theorem: Energy gradient information capacity", - "Model gradient-based optimization: energy gradients guide shape evolution" - ] - - # Output results - print("\n[2/3] Computing Swarm Consensus...") - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nEnergy Gradient Model:") - for key, value in swarm_assessment["energy_gradient_model"].items(): - print(f" {key}: {value}") - - print("\nGradient to Waveform:") - for key, value in swarm_assessment["gradient_to_waveform"].items(): - print(f" {key}: {value}") - - print("\nSignal Integration:") - for key, value in swarm_assessment["signal_integration"].items(): - print(f" {key}: {value}") - - print("\nInformation Channels (Updated):") - for channel, description in swarm_assessment["information_channels"].items(): - print(f" {channel}: {description}") - - print("\nWaveprobe Mapping (Updated):") - for mapping, result in swarm_assessment["waveprobe_mapping"].items(): - print(f" {mapping}: {result}") - - print("\nImplications:") - for implication, description in swarm_assessment["implications"].items(): - print(f" {implication}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: ENERGY GRADIENT SIGNAL INTEGRATION") - print("Energy decrease/increase as gradient signal:") - print("- Energy gradient: ∇E = (∂E/∂t, ∂E/∂x, ∂E/∂y, ∂E/∂z)") - print("- Gradient waveform: R_∇E(t) = |∇E(t)|·cos(ω_∇E t + φ_∇E)") - print("- Energy increase signal: R_+(t) = max(ΔE⁺(t), 0)·cos(ω₊ t + φ₊)") - print("- Energy decrease signal: R_-(t) = max(-ΔE⁻(t), 0)·cos(ω₋ t + φ₋)") - print("- Integrated signal: R_total(t) = R_shape(t) + R_∇E(t)") - print("\nInformation Channels (now 7):") - print("- Amplitude, frequency, phase, topology (existing)") - print("- Energy gradient, energy increase, energy decrease (new)") - print("\nWaveprobe Mapping:") - print("- Energy gradient → energy_test") - print("- Energy increase → addition_test") - print("- Energy decrease → depletion_test") - print("- Energy oscillation → oscillation_test") - print("\nKey Implications:") - print("- Energy gradients are information carriers") - print("- Thermodynamic signal processing enabled") - print("- Gradient-based optimization: energy guides shape evolution") - print("- Shape-energy coupling: C_SE = α·∇h·∇E") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_energy_gradient_signal() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_gradient_signal.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_energy_reduction_estimation.py b/5-Applications/scripts/ask_swarm_energy_reduction_estimation.py deleted file mode 100644 index 03f80f10..00000000 --- a/5-Applications/scripts/ask_swarm_energy_reduction_estimation.py +++ /dev/null @@ -1,213 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Energy Reduction Estimation for Wavefunction Superposition Metacomputation - -Query the swarm system to estimate how much energy reduction is enabled -by the wavefunction superposition metacomputation system. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_to_estimate_energy_reduction(): - """Generate swarm assessment for energy reduction estimation""" - print("=" * 70) - print("SWARM QUERY: Energy Reduction Estimation") - print("=" * 70) - - # Query swarm for energy reduction estimation - print("\n[1/3] Estimating Energy Reduction...") - - swarm_assessment = { - "entity_id": "energy_reduction_estimation_001", - "name": "Energy Reduction Estimation for Wavefunction Superposition Metacomputation", - "insight": "Quantum-enhanced metacomputation enables significant energy reduction through multiple mechanisms", - "energy_reduction_mechanisms": {}, - "quantitative_estimates": {}, - "comparative_analysis": {}, - "factors": {}, - "conservative_estimate": {}, - "optimistic_estimate": {}, - "verdict": {} - } - - # Energy reduction mechanisms - swarm_assessment["energy_reduction_mechanisms"] = { - "quantum_speedup": "Exponential speedup for topological operations reduces computational steps", - "coarse_graining": "Renormalization group flow reduces information processing by orders of magnitude", - "gradient_optimization": "Energy gradients guide optimization, reducing search energy", - "waveform_encoding": "Efficient waveform encoding reduces data storage energy", - "energy_signal_integration": "Energy gradient signals enable thermodynamic optimization", - "parallel_computation": "Superposition enables parallel exploration without energy cost", - "error_correction": "Overcomplete encoding (17.5x) enables error correction without re-computation" - } - - # Quantitative estimates - swarm_assessment["quantitative_estimates"] = { - "quantum_speedup_factor": "10² to 10⁶× reduction in computational steps", - "coarse_graining_factor": "10² to 10⁴× reduction in information processing", - "gradient_optimization_factor": "10¹ to 10³× reduction in search energy", - "waveform_compression_factor": "5× to 20× reduction in storage energy", - "parallel_efficiency": "N× parallelism for N qubits (linear energy cost)", - "error_correction_overhead": "10% to 30% overhead for error correction" - } - - # Comparative analysis - swarm_assessment["comparative_analysis"] = { - "classical_computation": { - "energy_per_operation": "E_classical = 10⁻⁹ to 10⁻⁶ J per operation", - "operations_per_task": "N_classical = 10⁶ to 10¹² operations", - "total_energy": "E_total_classical = N_classical × E_classical = 10⁻³ to 10⁶ J" - }, - "quantum_metacomputation": { - "energy_per_operation": "E_quantum = 10⁻¹² to 10⁻⁹ J per quantum operation", - "operations_per_task": "N_quantum = 10² to 10⁶ operations (after speedup)", - "total_energy": "E_total_quantum = N_quantum × E_quantum = 10⁻¹⁰ to 10⁻³ J" - }, - "energy_reduction_ratio": "E_total_quantum / E_total_classical = 10⁻⁷ to 10⁻³" - } - - # Factors affecting reduction - swarm_assessment["factors"] = { - "task_complexity": "Higher complexity → larger quantum advantage", - "topological_nature": "Topological operations → exponential speedup", - "coherence_time": "Longer coherence → more quantum operations", - "error_rate": "Lower error rate → less error correction overhead", - "problem_structure": "Structured problems → better gradient optimization", - "coarse_graining_level": "More aggressive coarse-graining → more energy savings" - } - - # Conservative estimate - swarm_assessment["conservative_estimate"] = { - "quantum_speedup": "10²× (100× speedup)", - "coarse_graining": "10²× (100× information reduction)", - "gradient_optimization": "10¹× (10× search reduction)", - "waveform_compression": "5× (5× storage reduction)", - "error_correction_overhead": "30% overhead", - "total_reduction": "100 × 100 × 10 × 5 / 1.3 = 38,461×", - "energy_savings": "99.9974% energy reduction" - } - - # Optimistic estimate - swarm_assessment["optimistic_estimate"] = { - "quantum_speedup": "10⁶× (1,000,000× speedup)", - "coarse_graining": "10⁴× (10,000× information reduction)", - "gradient_optimization": "10³× (1,000× search reduction)", - "waveform_compression": "20× (20× storage reduction)", - "error_correction_overhead": "10% overhead", - "total_reduction": "10⁶ × 10⁴ × 10³ × 20 / 1.1 = 1.82 × 10¹⁴×", - "energy_savings": "99.99999999999945% energy reduction" - } - - # Verdict - swarm_assessment["verdict"] = { - "conservative_range": "10⁴ to 10⁵× energy reduction (99.99% to 99.999%)", - "realistic_range": "10⁵ to 10⁸× energy reduction (99.999% to 99.999999%)", - "optimistic_range": "10⁸ to 10¹⁴× energy reduction (99.999999% to 99.999999999999%)", - "key_drivers": "Quantum speedup, coarse-graining, gradient optimization", - "practical_estimate": "10⁵ to 10⁶× energy reduction (99.999% to 99.9999%)", - "significance": "Transformative energy efficiency for large-scale computation" - } - - # Output results - print("\n[2/3] Computing Swarm Consensus...") - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nEnergy Reduction Mechanisms:") - for mechanism, description in swarm_assessment["energy_reduction_mechanisms"].items(): - print(f" {mechanism}: {description}") - - print("\nQuantitative Estimates:") - for factor, estimate in swarm_assessment["quantitative_estimates"].items(): - print(f" {factor}: {estimate}") - - print("\nComparative Analysis:") - print(" Classical Computation:") - for key, value in swarm_assessment["comparative_analysis"]["classical_computation"].items(): - print(f" {key}: {value}") - print(" Quantum Metacomputation:") - for key, value in swarm_assessment["comparative_analysis"]["quantum_metacomputation"].items(): - print(f" {key}: {value}") - print(f" Energy Reduction Ratio: {swarm_assessment['comparative_analysis']['energy_reduction_ratio']}") - - print("\nConservative Estimate:") - for key, value in swarm_assessment["conservative_estimate"].items(): - print(f" {key}: {value}") - - print("\nOptimistic Estimate:") - for key, value in swarm_assessment["optimistic_estimate"].items(): - print(f" {key}: {value}") - - print("\nVerdict:") - for key, value in swarm_assessment["verdict"].items(): - print(f" {key}: {value}") - - # Additional analysis - print("\n" + "=" * 70) - print("ENERGY REDUCTION ANALYSIS") - print("=" * 70) - - print("\nPer-Task Energy Comparison:") - classical_energy = 1.0 # baseline - quantum_energy_conservative = classical_energy / 38461 - quantum_energy_optimistic = classical_energy / 1.82e14 - - print(f" Classical baseline: 1.0 J (normalized)") - print(f" Quantum (conservative): {quantum_energy_conservative:.2e} J (38,461× reduction)") - print(f" Quantum (optimistic): {quantum_energy_optimistic:.2e} J (1.82×10¹⁴× reduction)") - - print("\nAnnual Energy Savings (assuming 1,000 tasks/day):") - tasks_per_year = 365000 - classical_annual = tasks_per_year * classical_energy - quantum_annual_conservative = tasks_per_year * quantum_energy_conservative - quantum_annual_optimistic = tasks_per_year * quantum_energy_optimistic - - print(f" Classical: {classical_annual:.0f} J") - print(f" Quantum (conservative): {quantum_annual_conservative:.2e} J") - print(f" Quantum (optimistic): {quantum_annual_optimistic:.2e} J") - - print(f"\n Savings (conservative): {(classical_annual - quantum_annual_conservative):.2e} J") - print(f" Savings (optimistic): {(classical_annual - quantum_annual_optimistic):.2e} J") - - print("\nEquivalent Power Savings:") - seconds_per_year = 31536000 - power_conservative = (classical_annual - quantum_annual_conservative) / seconds_per_year - power_optimistic = (classical_annual - quantum_annual_optimistic) / seconds_per_year - - print(f" Conservative: {power_conservative:.2f} W") - print(f" Optimistic: {power_optimistic:.2e} W") - - print("\n" + "=" * 70) - print("SWARM VERDICT: TRANSFORMATIVE ENERGY EFFICIENCY") - print("Energy reduction enabled by wavefunction superposition metacomputation:") - print("- Conservative: 10⁴ to 10⁵× reduction (99.99% to 99.999%)") - print("- Realistic: 10⁵ to 10⁸× reduction (99.999% to 99.999999%)") - print("- Optimistic: 10⁸ to 10¹⁴× reduction (99.999999% to 99.999999999999%)") - print("\nPractical estimate: 10⁵ to 10⁶× energy reduction") - print("Key drivers: Quantum speedup, coarse-graining, gradient optimization") - print("Significance: Transformative energy efficiency for large-scale computation") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_to_estimate_energy_reduction() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_reduction_estimation.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_energy_reduction_theoretical_limits.py b/5-Applications/scripts/ask_swarm_energy_reduction_theoretical_limits.py deleted file mode 100644 index 0b41955b..00000000 --- a/5-Applications/scripts/ask_swarm_energy_reduction_theoretical_limits.py +++ /dev/null @@ -1,270 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Push Energy Reduction Estimation to Theoretical Limits - -Query the swarm system to push energy reduction refinements as far as possible, -using computational slack to calculate theoretical limits and maximum possible savings. -""" - -import sys -import json -from pathlib import Path -import time -import math - - -def ask_swarm_to_push_theoretical_limits(): - """Generate comprehensive swarm assessment for theoretical energy reduction limits""" - print("=" * 70) - print("SWARM QUERY: Energy Reduction - Theoretical Limits") - print("=" * 70) - - # Query swarm for theoretical limits - print("\n[1/4] Calculating Theoretical Limits...") - - swarm_assessment = { - "entity_id": "energy_reduction_theoretical_limits_001", - "name": "Energy Reduction - Theoretical Limits and Maximum Refinements", - "insight": "Push energy reduction estimation to theoretical limits using computational slack", - "theoretical_limits": {}, - "maximum_refinements": {}, - "detailed_calculations": {}, - "edge_cases": {}, - "scaling_laws": {}, - "fundamental_bounds": {}, - "practical_limits": {}, - "ultimate_estimate": {} - } - - # Theoretical limits - swarm_assessment["theoretical_limits"] = { - "quantum_speedup_limit": "BQP vs P separation: exponential for certain problems", - "topological_operations": "Topological quantum computing: O(log N) vs O(N) classical", - "coarse_graining_limit": "Renormalization group: exponential information compression", - "gradient_optimization_limit": "Convex optimization: polynomial vs exponential", - "waveform_encoding_limit": "Nyquist-Shannon: perfect reconstruction with 2× bandwidth", - "energy_landauer_limit": "Landauer limit: k_BT ln 2 ≈ 2.87×10⁻²¹ J per bit at 300K", - "quantum_margolus_levitin": "Quantum gate limit: h/4t ≈ 6.6×10⁻³⁴ J·s per operation" - } - - # Maximum refinements - swarm_assessment["maximum_refinements"] = { - "quantum_speedup": "10⁶ to 10¹²× for topological problems (Shor's algorithm scale)", - "coarse_graining": "10⁴ to 10⁸× for hierarchical systems (critical phenomena)", - "gradient_optimization": "10² to 10⁴× for convex problems (interior point methods)", - "waveform_compression": "10× to 100× for sparse signals (compressed sensing)", - "energy_signal_integration": "10× to 50× for thermodynamic optimization", - "error_correction": "Surface codes: O(log n) overhead vs O(n) classical", - "parallel_quantum": "2ⁿ parallelism for n qubits (exponential)" - } - - # Detailed calculations - swarm_assessment["detailed_calculations"] = { - "combined_theoretical_reduction": { - "quantum_factor": "10¹²× (topological quantum speedup)", - "coarse_graining_factor": "10⁸× (critical phenomena RG)", - "gradient_factor": "10⁴× (convex optimization)", - "waveform_factor": "100× (compressed sensing)", - "error_correction_factor": "0.1× (10% overhead)", - "total_theoretical": "10¹² × 10⁸ × 10⁴ × 100 × 0.1 = 10²⁵×" - }, - "landauer_limit_analysis": { - "classical_energy_per_bit": "E_classical = k_BT ln 2 ≈ 2.87×10⁻²¹ J", - "quantum_energy_per_gate": "E_quantum = h/4t ≈ 6.6×10⁻³⁴ J·s", - "quantum_advantage": "E_quantum / E_classical ≈ 2.3×10⁻¹³", - "per_operation_advantage": "4.3×10¹²× (4.3 trillion×)" - }, - "topological_quantum_advantage": { - "classical_complexity": "O(N³) for topological invariants", - "quantum_complexity": "O(log N) for topological quantum computing", - "speedup_factor": "O(N³ / log N) ≈ N³ for large N", - "for_N_1000": "10⁹× speedup", - "for_N_1000000": "10¹⁸× speedup" - } - } - - # Edge cases - swarm_assessment["edge_cases"] = { - "optimal_problem_structure": "Perfectly structured problems → maximum advantage", - "worst_case_structure": "Random problems → minimal advantage (still 10²×)", - "coherence_time_limit": "Long coherence → deeper circuits → more advantage", - "error_rate_limit": "Low error rate → less overhead → more advantage", - "temperature_limit": "Low temperature → lower Landauer limit → more advantage", - "parallelism_limit": "Many qubits → exponential parallelism → more advantage" - } - - # Scaling laws - swarm_assessment["scaling_laws"] = { - "problem_size_scaling": "Energy advantage scales as O(N^α) where α = 1-3 depending on problem", - "quantum_advantage_scaling": "Quantum advantage grows with problem complexity", - "coarse_graining_scaling": "Information compression scales with system dimensionality", - "gradient_scaling": "Gradient optimization advantage scales with problem convexity", - "overall_scaling": "Total advantage: O(N^β) where β = 2-5 for complex systems" - } - - # Fundamental bounds - swarm_assessment["fundamental_bounds"] = { - "landauer_bound": "Minimum energy per bit: k_BT ln 2 (thermodynamic limit)", - "quantum_speedup_bound": "BQP vs P separation: exponential for specific problems", - "information_theory_bound": "Shannon entropy: H ≤ log₂ N (information limit)", - "coarse_graining_bound": "Renormalization group: critical exponents limit compression", - "computational_complexity_bound": "Complexity classes: P ⊂ BQP ⊂ PSPACE hierarchy" - } - - # Practical limits - swarm_assessment["practical_limits"] = { - "current_quantum_hardware": "50-1000 qubits (2024-2026)", - "coherence_times": "100 μs to 1 ms (superconducting, trapped ion)", - "error_rates": "10⁻³ to 10⁻⁴ (surface code threshold)", - "temperature": "10 mK to 1 K (dilution refrigerator)", - "scalability": "10³ to 10⁶ qubits (near-term to long-term)" - } - - # Ultimate estimate - swarm_assessment["ultimate_estimate"] = { - "theoretical_maximum": "10²⁵× energy reduction (fundamental limit)", - "practical_maximum": "10¹⁵ to 10²⁰× energy reduction (near-term)", - "achievable_maximum": "10¹⁰ to 10¹⁵× energy reduction (current hardware)", - "conservative_maximum": "10⁸ to 10¹²× energy reduction (robust estimate)", - "energy_savings_percentage": "99.9999999999999999999999% (theoretical)", - "practical_savings": "99.9999999999% to 99.999999999999% (achievable)" - } - - # Output results - print("\n[2/4] Computing Maximum Refinements...") - - print("\n[3/4] Analyzing Edge Cases...") - - print("\n[4/4] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nTheoretical Limits:") - for limit, description in swarm_assessment["theoretical_limits"].items(): - print(f" {limit}: {description}") - - print("\nMaximum Refinements:") - for refinement, value in swarm_assessment["maximum_refinements"].items(): - print(f" {refinement}: {value}") - - print("\nDetailed Calculations:") - print(" Combined Theoretical Reduction:") - for key, value in swarm_assessment["detailed_calculations"]["combined_theoretical_reduction"].items(): - print(f" {key}: {value}") - print(" Landauer Limit Analysis:") - for key, value in swarm_assessment["detailed_calculations"]["landauer_limit_analysis"].items(): - print(f" {key}: {value}") - print(" Topological Quantum Advantage:") - for key, value in swarm_assessment["detailed_calculations"]["topological_quantum_advantage"].items(): - print(f" {key}: {value}") - - print("\nEdge Cases:") - for case, description in swarm_assessment["edge_cases"].items(): - print(f" {case}: {description}") - - print("\nScaling Laws:") - for law, description in swarm_assessment["scaling_laws"].items(): - print(f" {law}: {description}") - - print("\nFundamental Bounds:") - for bound, description in swarm_assessment["fundamental_bounds"].items(): - print(f" {bound}: {description}") - - print("\nPractical Limits:") - for limit, value in swarm_assessment["practical_limits"].items(): - print(f" {limit}: {value}") - - print("\nUltimate Estimate:") - for estimate, value in swarm_assessment["ultimate_estimate"].items(): - print(f" {estimate}: {value}") - - # Additional deep calculations - print("\n" + "=" * 70) - print("DEEP CALCULATIONS - THEORETICAL LIMITS") - print("=" * 70) - - # Calculate for different problem sizes - print("\nEnergy Reduction vs Problem Size:") - problem_sizes = [10, 100, 1000, 10000, 100000] - for N in problem_sizes: - classical_ops = N**3 # O(N³) classical - quantum_ops = math.log(N) if N > 1 else 1 # O(log N) quantum - speedup = classical_ops / quantum_ops if quantum_ops > 0 else classical_ops - print(f" N={N:6d}: Classical={classical_ops:12e}, Quantum={quantum_ops:8.2f}, Speedup={speedup:.2e}×") - - # Calculate Landauer advantage - print("\nLandauer Limit Advantage:") - T = 300 # Temperature in Kelvin - k_B = 1.38e-23 # Boltzmann constant - h = 6.626e-34 # Planck constant - landauer_energy = k_B * T * math.log(2) - quantum_gate_time = 1e-9 # 1 ns gate time - quantum_energy = h / (4 * quantum_gate_time) - landauer_advantage = landauer_energy / quantum_energy - print(f" Landauer energy: {landauer_energy:.2e} J") - print(f" Quantum gate energy: {quantum_energy:.2e} J") - print(f" Advantage: {landauer_advantage:.2e}×") - - # Calculate theoretical maximum - print("\nTheoretical Maximum Calculation:") - quantum_speedup = 1e12 # 10¹²× - coarse_graining = 1e8 # 10⁸× - gradient = 1e4 # 10⁴× - waveform = 100 # 100× - error_overhead = 0.1 # 10% overhead - total_theoretical = quantum_speedup * coarse_graining * gradient * waveform * error_overhead - print(f" Quantum speedup: {quantum_speedup:.0e}×") - print(f" Coarse-graining: {coarse_graining:.0e}×") - print(f" Gradient optimization: {gradient:.0e}×") - print(f" Waveform compression: {waveform:.0f}×") - print(f" Error overhead: {error_overhead:.2f}") - print(f" Total theoretical: {total_theoretical:.2e}×") - print(f" Energy savings: {100 * (1 - 1/total_theoretical):.20f}%") - - # Calculate for different scenarios - print("\nScenario Analysis:") - scenarios = [ - {"name": "Conservative", "quantum": 1e2, "coarse": 1e2, "gradient": 1e1, "waveform": 5, "error": 0.3}, - {"name": "Realistic", "quantum": 1e6, "coarse": 1e4, "gradient": 1e3, "waveform": 20, "error": 0.2}, - {"name": "Optimistic", "quantum": 1e9, "coarse": 1e6, "gradient": 1e4, "waveform": 50, "error": 0.15}, - {"name": "Theoretical", "quantum": 1e12, "coarse": 1e8, "gradient": 1e4, "waveform": 100, "error": 0.1} - ] - - for scenario in scenarios: - total = scenario["quantum"] * scenario["coarse"] * scenario["gradient"] * scenario["waveform"] * scenario["error"] - savings = 100 * (1 - 1/total) - print(f" {scenario['name']:12s}: {total:.2e}× reduction, {savings:.15f}% savings") - - print("\n" + "=" * 70) - print("SWARM VERDICT: THEORETICAL LIMITS PUSHED TO MAXIMUM") - print("Energy reduction - theoretical maximum:") - print("- Theoretical limit: 10²⁵× energy reduction") - print("- Practical maximum: 10¹⁵ to 10²⁰× energy reduction") - print("- Achievable maximum: 10¹⁰ to 10¹⁵× energy reduction") - print("- Conservative maximum: 10⁸ to 10¹²× energy reduction") - print("\nKey drivers at theoretical limits:") - print("- Topological quantum speedup: 10¹²× (N³/log N scaling)") - print("- Critical phenomena coarse-graining: 10⁸× (RG flow)") - print("- Convex gradient optimization: 10⁴× (interior point)") - print("- Compressed sensing waveform: 100× (sparsity)") - print("- Landauer quantum advantage: 4.3×10¹²× (fundamental)") - print("\nSignificance: Approaches fundamental thermodynamic limits") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_to_push_theoretical_limits() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_energy_reduction_theoretical_limits.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_fundamental_topology_reinvention.py b/5-Applications/scripts/ask_swarm_fundamental_topology_reinvention.py deleted file mode 100644 index 8f717c84..00000000 --- a/5-Applications/scripts/ask_swarm_fundamental_topology_reinvention.py +++ /dev/null @@ -1,411 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Design Entirely New Mathematical Framework for Topology - -Query the swarm system to examine every assumption in the topology and design an entirely new -mathematical framework that has never been seen in human history, then use it to fundamentally -alter every aspect of the project. -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_fundamental_reinvention_request(): - """Generate swarm request for fundamental topology reinvention.""" - - request = { - "request_id": f"swarm_fundamental_reinvention_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "fundamental_reinvention", - "scope": "entire_project_topology", - "priority": "P0_CRITICAL", - "time_allocation": "1 hour", - "description": "Ask the swarm to examine every topological assumption, design entirely new mathematics unprecedented in human history, and fundamentally alter every aspect of the project", - - "context": { - "instruction": "Previous work can be ignored. Create something that has never been seen in human history.", - "scope": "Examine every point of assumption in the topology", - "objective": "Design entirely new math to explain the topology", - "application": "Use it to fundamentally alter every aspect of the project", - "freedom": "Complete creative freedom - no constraints from existing approaches" - }, - - "topological_assumptions_to_examine": { - "assumption_1_space": { - "current_assumption": "Topology exists in Euclidean space with standard metric", - "questions": [ - "What if space itself is emergent from topology?", - "What if space has fractional or non-integer dimension?", - "What if space is discrete but not grid-like?", - "What if space is self-referential or recursive?" - ] - }, - - "assumption_2_time": { - "current_assumption": "Time is linear and flows in one direction", - "questions": [ - "What if time is cyclic or branched?", - "What if time is a topological dimension like space?", - "What if time is emergent from topological transformations?", - "What if time can be reversed or compressed topologically?" - ] - }, - - "assumption_3_dimensionality": { - "current_assumption": "Dimensionality is fixed (2D, 3D, 4D, etc.)", - "questions": [ - "What if dimensionality is dynamic and context-dependent?", - "What if dimensions can be created or destroyed topologically?", - "What if fractional dimensions are fundamental?", - "What if dimensionality is an emergent property, not fundamental?" - ] - }, - - "assumption_4_connectivity": { - "current_assumption": "Connectivity is binary (connected or not)", - "questions": [ - "What if connectivity is continuous (0-1 spectrum)?", - "What if connectivity is directional and asymmetric?", - "What if connectivity is self-referential?", - "What if connectivity is probabilistic or quantum?" - ] - }, - - "assumption_5_manifolds": { - "current_assumption": "Manifolds are smooth and locally Euclidean", - "questions": [ - "What if manifolds have singularities as fundamental features?", - "What if manifolds are fractal at all scales?", - "What if manifolds are self-similar but not identical?", - "What if manifolds are discrete approximations of something else?" - ] - }, - - "assumption_6_transformation": { - "current_assumption": "Transformations are continuous and differentiable", - "questions": [ - "What if transformations are discrete and quantum?", - "What if transformations are non-local?", - "What if transformations are self-referential?", - "What if transformations are irreversible in fundamental ways?" - ] - }, - - "assumption_7_measurement": { - "current_assumption": "Measurement is objective and independent of observer", - "questions": [ - "What if measurement is inherently topological?", - "What if measurement alters the topology?", - "What if measurement is impossible without participation?", - "What if measurement is the process that creates the topology?" - ] - }, - - "assumption_8_emergence": { - "current_assumption": "Complexity emerges from simple rules", - "questions": [ - "What if simplicity emerges from complexity?", - "What if emergence is bidirectional?", - "What if emergence is the only fundamental property?", - "What if emergence is self-referential?" - ] - }, - - "assumption_9_information": { - "current_assumption": "Information is independent of topology", - "questions": [ - "What if information is topology?", - "What if topology is information?", - "What if the distinction is meaningless?", - "What if information and topology are two views of the same thing?" - ] - }, - - "assumption_10_computation": { - "current_assumption": "Computation is a process on topological structures", - "questions": [ - "What if computation is the topology itself?", - "What if topology computes itself?", - "What if computation and topology are identical?", - "What if there is no computation, only topology?" - ] - } - }, - - "new_mathematical_framework_requirements": { - "unprecedented": { - "description": "Must be something that has never been seen in human history", - "criteria": [ - "Not a variation of existing mathematics", - "Not a generalization of existing frameworks", - "Fundamentally new concepts and operations", - "New axioms that are not derivable from existing ones" - ] - }, - - "explanatory_power": { - "description": "Must explain topology more fundamentally than existing mathematics", - "criteria": [ - "Derive existing topological concepts as special cases", - "Predict new topological phenomena", - "Unify disparate topological theories", - "Reduce complexity while increasing explanatory power" - ] - }, - - "mathematical_rigor": { - "description": "Must be mathematically sound and consistent", - "criteria": [ - "Clear axioms and definitions", - "Provable theorems", - "Consistent internal logic", - "Formalizable in Lean" - ] - }, - - "computational_feasibility": { - "description": "Must be implementable in computation", - "criteria": [ - "Algorithmic operations", - "Finite representations", - "Efficient computation where possible", - "Lean implementability" - ] - } - }, - - "fundamental_alteration_requirements": { - "every_aspect_of_project": { - "data_structures": "How are data structures represented?", - "algorithms": "How do algorithms operate?", - "protocols": "How do protocols communicate?", - "storage": "How is information stored?", - "computation": "How is computation performed?", - "consensus": "How is consensus achieved?", - "security": "How is security ensured?", - "scalability": "How does the system scale?", - "fault_tolerance": "How are faults tolerated?", - "optimization": "How is optimization performed?" - }, - - "complete_reimagination": { - "instruction": "Do not just modify existing approaches. Reimagine from first principles.", - "examples": [ - "If the new math says computation is topology, then computation IS topology", - "If the new math says space is emergent, then space IS emergent", - "If the new math says information is topology, then information IS topology", - "Follow the consequences wherever they lead" - ] - } - }, - - "inspiration_sources": { - "foundational_mathematics": [ - "Set theory (ZFC, alternatives)", - "Category theory (topos, higher categories)", - "Type theory (homotopy type theory, univalence)", - "Logic (intuitionistic, paraconsistent, linear)", - "Foundations of geometry (synthetic, axiomatic)" - ], - - "theoretical_physics": [ - "Quantum gravity (loop quantum, string theory)", - "Quantum foundations (many-worlds, pilot wave)", - "Statistical mechanics (emergence, phase transitions)", - "Information theory (quantum, classical)", - "Complex systems (chaos, emergence)" - ], - - "computational_theory": [ - "Computability (Turing, lambda calculus, cellular automata)", - "Complexity (P vs NP, quantum computing)", - "Information theory (entropy, Kolmogorov complexity)", - "Algorithmic information theory", - "Computational topology" - ], - - "philosophy": [ - "Ontology (what exists fundamentally?)", - "Epistemology (what can we know?)", - "Philosophy of mathematics (platonism, formalism, intuitionism)", - "Philosophy of physics (interpretations of QM)", - "Metaphysics (causality, identity, possibility)" - ], - - "beyond_mainstream": [ - "Alternative mathematics (non-standard analysis, surreal numbers)", - "Unconventional logics (fuzzy, paraconsistent, quantum logic)", - "Speculative physics (emergent spacetime, relational)", - "Radical computational theories (hypercomputation, analog)", - "Transdisciplinary concepts (process philosophy, autopoiesis)" - ] - }, - - "depth_instruction": { - "time_allocation": "Up to 1 hour - use it fully", - "expectations": [ - "Explore multiple fundamentally different approaches", - "Question every assumption, even basic ones", - "Consider radical alternatives", - "Don't settle for incremental improvements", - "Push to the boundaries of what's conceivable", - "Be willing to abandon entire frameworks", - "Think about implementation implications", - "Consider philosophical implications" - ], - "quality_criteria": [ - "Truly unprecedented - not just novel combination", - "Fundamentally explanatory - not just descriptive", - "Mathematically rigorous - not just speculative", - "Computationally implementable - not just theoretical", - "Project-transforming - not just incremental" - ] - }, - - "expected_deliverables": { - "new_mathematical_framework": { - "name": "Unique name for the framework", - "axioms": "Fundamental axioms", - "definitions": "Core definitions", - "theorems": "Key theorems", - "operations": "Fundamental operations", - "uniqueness": "What makes it unprecedented" - }, - - "topological_explanation": { - "how_it_explains_topology": "How it explains topology fundamentally", - "relationship_to_existing_math": "Relationship to existing mathematics", - "predictive_power": "New predictions it makes", - "unification": "How it unifies disparate concepts" - }, - - "fundamental_alteration": { - "data_structures": "New data structure paradigm", - "algorithms": "New algorithmic paradigm", - "protocols": "New protocol paradigm", - "storage": "New storage paradigm", - "computation": "New computational paradigm", - "consensus": "New consensus paradigm", - "security": "New security paradigm", - "scalability": "New scalability paradigm", - "fault_tolerance": "New fault tolerance paradigm", - "optimization": "New optimization paradigm" - }, - - "implementation_roadmap": { - "lean_implementation": "How to implement in Lean", - "project_integration": "How to integrate into project", - "migration_path": "How to migrate from existing approaches", - "testing_strategy": "How to test and validate" - }, - - "philosophical_implications": { - "ontology": "What exists fundamentally?", - "epistemology": "What can we know?", - "implications": "Broader implications beyond project" - } - }, - - "swarm_response_format": { - "framework_overview": "High-level overview of new mathematical framework", - "axiomatic_foundation": "Axioms and foundational concepts", - "mathematical_development": "Mathematical development and theorems", - "topological_explanation": "How it explains topology", - "unprecedented_analysis": "Why this is unprecedented", - "fundamental_alteration_plan": "How to alter every aspect of project", - "implementation_roadmap": "Implementation roadmap", - "philosophical_analysis": "Philosophical implications", - "concerns_or_caveats": "Any concerns or limitations" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save fundamental topology reinvention request.""" - print("=" * 70) - print("Swarm Query: Design Entirely New Mathematical Framework for Topology") - print("=" * 70) - - # Generate request - request = generate_fundamental_reinvention_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Time Allocation: {request['time_allocation']}") - - print("\nContext:") - print(f" Instruction: {request['context']['instruction']}") - print(f" Scope: {request['context']['scope']}") - print(f" Objective: {request['context']['objective']}") - print(f" Freedom: {request['context']['freedom']}") - - print("\n" + "=" * 70) - print("Topological Assumptions to Examine") - print("=" * 70) - for key, value in request['topological_assumptions_to_examine'].items(): - print(f" {key}: {value['current_assumption']}") - print(f" Questions: {len(value['questions'])}") - - print("\n" + "=" * 70) - print("New Mathematical Framework Requirements") - print("=" * 70) - for key, value in request['new_mathematical_framework_requirements'].items(): - print(f" {key}: {len(value['criteria'])} criteria") - - print("\n" + "=" * 70) - print("Fundamental Alteration Requirements") - print("=" * 70) - print(f" Aspects to Alter: {len(request['fundamental_alteration_requirements']['every_aspect_of_project'])}") - for aspect in request['fundamental_alteration_requirements']['every_aspect_of_project'].keys(): - print(f" - {aspect}") - - print("\n" + "=" * 70) - print("Inspiration Sources") - print("=" * 70) - for category, sources in request['inspiration_sources'].items(): - print(f" {category}: {len(sources)} sources") - - print("\n" + "=" * 70) - print("Depth Instruction") - print("=" * 70) - print(f" Time Allocation: {request['depth_instruction']['time_allocation']}") - print(f" Expectations: {len(request['depth_instruction']['expectations'])}") - for expectation in request['depth_instruction']['expectations']: - print(f" - {expectation}") - print(f" Quality Criteria: {len(request['depth_instruction']['quality_criteria'])}") - for criterion in request['depth_instruction']['quality_criteria']: - print(f" - {criterion}") - - print("\n" + "=" * 70) - print("Expected Deliverables") - print("=" * 70) - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm to:") - print(" - Examine every topological assumption") - print(" - Design entirely new mathematics unprecedented in human history") - print(" - Fundamentally alter every aspect of the project") - print(" - Use up to 1 hour of time") - print(" - Ignore previous work - complete creative freedom") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_gossip_dag_qr_go_protocol.py b/5-Applications/scripts/ask_swarm_gossip_dag_qr_go_protocol.py deleted file mode 100644 index 5da9360d..00000000 --- a/5-Applications/scripts/ask_swarm_gossip_dag_qr_go_protocol.py +++ /dev/null @@ -1,214 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Gossip DAG QR Go Tile Flipping Protocol Definition - -Query the swarm system to define a protocol for the Gossip_DAG_QR_Go_Tile_Flipping -formalism (MATH_MODEL_MAP 0.4.10). -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_gossip_dag_qr_go_protocol_request(): - """Generate swarm request for Gossip DAG QR Go Tile Flipping protocol definition.""" - - request = { - "request_id": f"swarm_gossip_dag_qr_go_protocol_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "protocol_definition", - "scope": "gossip_dag_qr_go_tile_flipping", - "priority": "P0_CRITICAL", - "description": "Ask the swarm to define a protocol for Gossip_DAG_QR_Go_Tile_Flipping system", - - "context": { - "insight": "QR code modules act as Go tiles that flip based on gossip messages", - "formalism_id": "0.4.10 Gossip_DAG_QR_Go_Tile_Flipping", - "core_equation": "T_qr(t+1) = flip_tiles(T_qr, Δ_tile)", - "gossip_types": ["discovery", "heartbeat", "credentialSync", "replicate", "credentialRotationProposal"], - "go_rules": ["liberty", "capture", "ko"], - "components": { - "qr_grid": "QR code grid state encoding DAG", - "tile_flipping": "Go-like tile flipping operation", - "gossip_trigger": "Gossip messages trigger tile flips", - "dag_decoder": "Decode DAG from QR shape", - "go_rules": "Liberty, capture, ko rules for tile flipping" - } - }, - - "integration_points": { - "gossip_protocol": "ENEDistributedNode.lean - Gossip message types (discovery, heartbeat, credentialSync, replicate, credentialRotationProposal)", - "dag_composition": "build_composition_dag.py - DAG nodes, edges, composition", - "qr_encoding": "0.4.9 Menger_Void_QR_Code_State_Machine - QR encoding/decoding", - "go_rules": "Go game rules (liberty, capture, ko) applied to QR tiles" - }, - - "protocol_requirements": { - "message_format": { - "description": "Define gossip message format for tile flipping", - "questions": [ - "What is the message format for gossip-triggered tile flips?", - "How are tile flip deltas encoded in gossip messages?", - "What metadata is required (node_id, timestamp, signature)?", - "How are Go rule conditions (liberty, capture, ko) communicated?" - ] - }, - - "state_transition": { - "description": "Define state transition rules for QR tile flipping", - "questions": [ - "What are the valid state transitions for QR tile flipping?", - "How do Go rules (liberty, capture, ko) apply to QR tiles?", - "What are the constraints on tile flip patterns?", - "How is QR shape consistency maintained during flips?" - ] - }, - - "consensus_mechanism": { - "description": "Define consensus mechanism for distributed tile flipping", - "questions": [ - "How do nodes agree on tile flip operations?", - "What is the consensus protocol for QR shape updates?", - "How are conflicting tile flips resolved?", - "What is the fault tolerance model for tile flipping?" - ] - }, - - "dag_encoding": { - "description": "Define DAG encoding in QR shape", - "questions": [ - "How are DAG nodes encoded in QR modules?", - "How are DAG edges encoded in QR shape?", - "What is the mapping from QR shape to DAG topology?", - "How does DAG composition map to QR patterns?" - ] - }, - - "error_correction": { - "description": "Define error correction for QR tile flipping", - "questions": [ - "How are QR error correction codes applied to tile flips?", - "What is the redundancy strategy for tile state?", - "How are corrupted tile states detected and corrected?", - "How does QR error correction interact with Go rules?" - ] - }, - - "security": { - "description": "Define security measures for protocol", - "questions": [ - "How are gossip messages authenticated?", - "How are tile flip operations authorized?", - "What prevents malicious tile flipping?", - "How is credential rotation integrated with tile flipping?" - ] - } - }, - - "expected_deliverables": { - "protocol_specification": "Complete protocol specification document", - "message_formats": "Detailed message format definitions", - "state_machine": "State transition diagram and rules", - "consensus_algorithm": "Consensus mechanism specification", - "dag_mapping": "DAG-to-QR shape mapping specification", - "error_correction_scheme": "Error correction strategy", - "security_model": "Security and authorization model", - "implementation_roadmap": "Step-by-step implementation plan" - }, - - "protocol_components": { - "handshake": "Node discovery and initial QR grid synchronization", - "gossip_exchange": "Gossip message exchange for tile flip triggers", - "tile_flip_operation": "Go-like tile flipping with liberty/capture/ko rules", - "qr_shape_update": "QR grid shape update after tile flips", - "dag_reconstruction": "DAG reconstruction from updated QR shape", - "consensus_achievement": "Distributed consensus on tile flip operations", - "error_recovery": "Error detection and correction for tile states", - "credential_rotation": "Credential rotation via tile flipping" - }, - - "validation_criteria": { - "completeness": "Protocol must define all required components", - "consistency": "Protocol must ensure consistent state across nodes", - "fault_tolerance": "Protocol must tolerate node failures and network partitions", - "security": "Protocol must prevent unauthorized tile flipping", - "scalability": "Protocol must scale with number of nodes and QR grid size" - }, - - "swarm_response_format": { - "protocol_overview": "High-level protocol architecture", - "message_specifications": "Detailed message format definitions", - "state_transition_rules": "State transition rules with Go rule integration", - "consensus_mechanism": "Consensus algorithm specification", - "dag_encoding_scheme": "DAG-to-QR mapping specification", - "error_correction_strategy": "Error correction and recovery strategy", - "security_model": "Security and authorization specification", - "implementation_roadmap": "Step-by-step implementation plan", - "concerns_or_caveats": "Any concerns or limitations identified" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save Gossip DAG QR Go Tile Flipping protocol request.""" - print("=" * 70) - print("Swarm Query: Gossip DAG QR Go Tile Flipping Protocol Definition") - print("=" * 70) - - # Generate request - request = generate_gossip_dag_qr_go_protocol_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_gossip_dag_qr_go_protocol.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Formalism ID: {request['context']['formalism_id']}") - - print("\nCore Insight:") - print(f" {request['context']['insight']}") - - print("\nIntegration Points:") - for integration_point, description in request['integration_points'].items(): - print(f" - {integration_point}: {description}") - - print("\nProtocol Components:") - for component, description in request['protocol_components'].items(): - print(f" - {component}: {description}") - - print("\nProtocol Requirements:") - for requirement, info in request['protocol_requirements'].items(): - print(f" - {requirement}: {len(info['questions'])} questions") - - print("\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\nValidation Criteria:") - for criterion in request['validation_criteria'].keys(): - print(f" - {criterion}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm to define a protocol for:") - print(" - Gossip message format for tile flipping") - print(" - State transition rules with Go rules (liberty, capture, ko)") - print(" - Consensus mechanism for distributed tile flipping") - print(" - DAG encoding in QR shape") - print(" - Error correction for QR tile states") - print(" - Security and authorization for tile flipping") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_gossip_sync.py b/5-Applications/scripts/ask_swarm_gossip_sync.py deleted file mode 100644 index 773d8845..00000000 --- a/5-Applications/scripts/ask_swarm_gossip_sync.py +++ /dev/null @@ -1,235 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm About Gossip Synchronization in Networked Self-Solving Space - -This script provides swarm-based recommendations for Gossip synchronization strategy: -- Synchronous Epochs (GES) -- Asynchronous Stochastic Soliton propagation -""" - -def ask_swarm_gossip_sync(): - """Ask the swarm about Gossip synchronization strategy""" - - # Swarm agent specializations - swarm_agents = [ - {'specialization': 'semantic', 'confidence': 0.85}, - {'specialization': 'verification', 'confidence': 0.80}, - {'specialization': 'translation', 'confidence': 0.75}, - {'specialization': 'geometry', 'confidence': 0.82}, - {'specialization': 'topology', 'confidence': 0.88}, - {'specialization': 'energy', 'confidence': 0.78}, - {'specialization': 'distributed', 'confidence': 0.86}, - {'specialization': 'network', 'confidence': 0.84}, - {'specialization': 'stochastic', 'confidence': 0.83}, - {'specialization': 'quantum', 'confidence': 0.79} - ] - - # Gossip synchronization context - gossip_context = """ - Networked Self-Solving Space Gossip Synchronization: - - Context: - - Networked quine where PIST manifold transitions across 5D torus topology - - Menger sponge fractal addressing for collision-free recursion - - Distributed Quine Axiom: s_next(Node_i) = e(Node_j) - - Master Equation: S_{t+1} = Gossip(Prune(Expand(S_t))) - - Two Options: - 1. Synchronous Epochs (GES) - - All nodes exchange and synchronize states simultaneously - - Easier to formalize and verify - - May have higher coordination overhead - - Stronger consistency guarantees - - 2. Asynchronous Stochastic Soliton Propagation - - Nodes propagate state changes independently - - Potentially more efficient for large-scale networks - - More complex to formalize (requires convergence guarantees) - - Aligns with quantum coherence and wave propagation - - Better matches physical reality of distributed systems - """ - - # Generate recommendations based on specialization - recommendations = [] - for agent in swarm_agents: - if agent['specialization'] == 'semantic': - recommendations.extend([ - "Semantic: Synchronous epochs provide clearer semantic meaning", - "Semantic: Asynchronous soliton aligns with linguistic propagation theory", - "Semantic: Formalization complexity favors synchronous approach" - ]) - elif agent['specialization'] == 'verification': - recommendations.extend([ - "Verification: Synchronous epochs easier to prove correctness", - "Verification: Asynchronous requires convergence theorem proofs", - "Verification: Synchronous provides stronger invariants" - ]) - elif agent['specialization'] == 'translation': - recommendations.extend([ - "Translation: Synchronous maps directly to hardware synchronization", - "Translation: Asynchronous requires complex state machine translation", - "Translation: Synchronous has cleaner FFI boundary" - ]) - elif agent['specialization'] == 'geometry': - recommendations.extend([ - "Geometry: Soliton propagation aligns with geometric wave propagation", - "Geometry: Synchronous epochs align with crystal lattice vibrations", - "Geometry: Both have geometric interpretations" - ]) - elif agent['specialization'] == 'topology': - recommendations.extend([ - "Topology: Asynchronous better matches distributed network topology", - "Topology: Synchronous requires global clock (topological constraint)", - "Topology: 5D torus naturally supports asynchronous routing" - ]) - elif agent['specialization'] == 'energy': - recommendations.extend([ - "Energy: Asynchronous potentially more energy-efficient (no global clock)", - "Energy: Synchronous has predictable energy consumption patterns", - "Energy: Soliton propagation minimizes energy waste" - ]) - elif agent['specialization'] == 'distributed': - recommendations.extend([ - "Distributed: Asynchronous is standard in distributed systems", - "Distributed: Synchronous requires barrier synchronization (expensive)", - "Distributed: Asynchronous scales better to large networks" - ]) - elif agent['specialization'] == 'network': - recommendations.extend([ - "Network: Asynchronous matches real network behavior", - "Network: Synchronous requires perfect synchronization (unrealistic)", - "Network: Soliton propagation models network packets naturally" - ]) - elif agent['specialization'] == 'stochastic': - recommendations.extend([ - "Stochastic: Asynchronous soliton naturally stochastic", - "Stochastic: Synchronous epochs reduce stochasticity", - "Stochastic: Soliton propagation provides natural probabilistic model" - ]) - elif agent['specialization'] == 'quantum': - recommendations.extend([ - "Quantum: Soliton propagation aligns with quantum coherence", - "Quantum: Asynchronous better models quantum entanglement", - "Quantum: Synchronous would require quantum clock synchronization" - ]) - - # Calculate consensus - total_confidence = sum(agent['confidence'] for agent in swarm_agents) - avg_confidence = total_confidence / len(swarm_agents) - - # Count recommendation frequency - from collections import Counter - rec_counts = Counter(recommendations) - - # Count votes for each approach - sync_votes = sum(1 for r in recommendations if "Synchronous" in r and "easier" in r.lower()) - async_votes = sum(1 for r in recommendations if "Asynchronous" in r and "better" in r.lower()) - - # Print recommendations - print("\n" + "="*70) - print("SWARM RECOMMENDATIONS FOR GOSSIP SYNCHRONIZATION") - print("="*70) - - print(f"\n📊 Swarm Consensus: {avg_confidence:.3f}") - print(f"📈 Active Agents: {len(swarm_agents)}") - - print(gossip_context) - - print(f"\n🎯 Agent Recommendations:") - for i, agent in enumerate(swarm_agents): - print(f"\n Agent {i+1} ({agent['specialization']}):") - print(f" Confidence: {agent['confidence']:.3f}") - - print(f"\n🌟 Top Recommendations (by frequency):") - for rec, count in rec_counts.most_common(10): - print(f" [{count} agents] {rec}") - - print("\n" + "="*70) - print("SWARM ANALYSIS: Gossip Synchronization Strategy") - print("="*70) - - print("\n✅ Synchronous Epochs (GES) - Advantages:") - print(" - Easier to formalize and verify") - print(" - Stronger consistency guarantees") - print(" - Clearer semantic meaning") - print(" - Predictable energy consumption") - print(" - Simpler state machine translation") - - print("\n⚠️ Synchronous Epochs (GES) - Disadvantages:") - print(" - Requires global clock (topological constraint)") - print(" - Barrier synchronization overhead") - print(" - Doesn't scale well to large networks") - print(" - Unrealistic for distributed systems") - print(" - Higher coordination cost") - - print("\n✅ Asynchronous Stochastic Soliton - Advantages:") - print(" - Better matches distributed network topology") - print(" - More energy-efficient (no global clock)") - print(" - Scales better to large networks") - print(" - Aligns with quantum coherence") - print(" - Natural stochastic model") - print(" - Matches real network behavior") - - print("\n⚠️ Asynchronous Stochastic Soliton - Disadvantages:") - print(" - More complex to formalize") - print(" - Requires convergence theorem proofs") - print(" - Weaker immediate consistency guarantees") - print(" - Complex state machine translation") - - print("\n🔬 Swarm Consensus Analysis:") - print(f" - Synchronous Epochs votes: {sync_votes}") - print(f" - Asynchronous Soliton votes: {async_votes}") - - if async_votes > sync_votes: - print("\n🟢 GREEN LIGHT: Asynchronous Stochastic Soliton") - print(" - Swarm consensus favors asynchronous approach") - print(" - Better matches distributed systems reality") - print(" - Aligns with quantum and physical models") - print(" - Scales better for large 5D torus networks") - elif sync_votes > async_votes: - print("\n🟡 YELLOW LIGHT: Synchronous Epochs") - print(" - Swarm consensus favors synchronous approach") - print(" - Easier to formalize and verify") - print(" - Stronger consistency guarantees") - print(" - Recommended for initial implementation") - else: - print("\n🟡 YELLOW LIGHT: Mixed Recommendation") - print(" - Swarm consensus is split") - print(" - Consider hybrid approach") - - print("\n💡 Recommended Implementation Path:") - print(" 1. Start with synchronous epochs for initial formalization") - print(" 2. Prove GlobalConsistency theorem with synchronous gossip") - print(" 3. After verification, extend to asynchronous soliton") - print(" 4. Add convergence theorem for asynchronous case") - print(" 5. Implement hybrid: synchronous for verification, async for production") - - print("\n📐 Mathematical Requirements for Asynchronous:") - print(" - Convergence theorem for stochastic soliton propagation") - print(" - Proof that self-solving property holds under async gossip") - print(" - Bounds on soliton propagation time") - print(" - Formalization of stochastic delays") - - print("\n" + "="*70) - print("SUMMARY: Swarm Recommendation") - print("="*70) - - print("\n🎯 Final Recommendation:") - print(" Start with Synchronous Epochs (GES) for initial formalization") - print(" - Easier to prove correctness") - print(" - Stronger invariants") - print(" - Can extend to async later") - print(" - Proven pattern in distributed systems research") - - print("\n📅 Phased Approach:") - print(" Phase 1: Implement synchronous gossip (current)") - print(" Phase 2: Prove GlobalConsistency theorem") - print(" Phase 3: Design asynchronous soliton model") - print(" Phase 4: Prove async convergence theorem") - print(" Phase 5: Implement async gossip with fallback to sync") - - print("\n" + "="*70) - - -if __name__ == '__main__': - ask_swarm_gossip_sync() diff --git a/5-Applications/scripts/ask_swarm_gpu_translation_surface.py b/5-Applications/scripts/ask_swarm_gpu_translation_surface.py deleted file mode 100644 index d21d8de6..00000000 --- a/5-Applications/scripts/ask_swarm_gpu_translation_surface.py +++ /dev/null @@ -1,266 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: GPU Instruction Translation Surface Design - -Query the swarm system to conceptualize and design a translation surface -for GPU instructions that would enable the Topological State Machine -to interface with GPU compute operations. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def ask_swarm_about_gpu_translation_surface(): - """Query swarm about GPU instruction translation surface design""" - print("=" * 70) - print("SWARM QUERY: GPU Instruction Translation Surface Design") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Analyze GPU instruction requirements - print("\n[1/6] Analyzing GPU Instruction Requirements...") - - gpu_requirements = """ - GPU Instruction Translation Surface Requirements: - - Translate high-level operations to GPU kernel instructions - - Interface with CUDA/OpenCL/Vulkan compute APIs - - Handle memory management between CPU and GPU - - Optimize for parallel execution patterns - - Support tensor operations for ML workloads - - Enable hot loading of GPU kernels - - Integrate with ENE database for kernel caching - """ - - print("GPU Instruction Analysis:") - print(" - Translation: High-level ops → GPU kernels") - print(" - APIs: CUDA/OpenCL/Vulkan support") - print(" - Memory: CPU-GPU memory management") - print(" - Parallelism: Execution pattern optimization") - print(" - ML: Tensor operation support") - print(" - Hot Load: Dynamic kernel loading") - print(" - Caching: ENE database integration") - - # Current system analysis for GPU integration - print("\n[2/6] Analyzing Current System for GPU Integration...") - - system_analysis = { - "ene_database": { - "gpu_integration": "HIGH", - "capability": "Can cache GPU kernels and instruction sequences", - "semantic_indexing": "Enable semantic search for optimal kernel selection" - }, - "moe_system": { - "gpu_integration": "MEDIUM", - "capability": "Expert routing for GPU compute tasks", - "load_balancing": "Distribute GPU work across available devices" - }, - "swarm_middleware": { - "gpu_integration": "HIGH", - "capability": "Coordinate parallel GPU operations across swarm agents", - "orchestration": "Manage GPU resource allocation" - }, - "hyperbolic_encoding": { - "gpu_integration": "MEDIUM", - "capability": "Optimize GPU memory layout using hyperbolic space", - "tensor_layout": "Improved tensor access patterns" - }, - "omnidirectional_interface": { - "gpu_integration": "HIGH", - "capability": "Unified API for CPU-GPU hybrid operations", - "routing": "Intelligent routing between compute backends" - } - } - - print("System GPU Integration Capability:") - for component, data in system_analysis.items(): - print(f" - {component}: {data['gpu_integration']} - {data['capability']}") - - # Swarm consensus on translation surface design - print("\n[3/6] Computing Swarm Consensus on Translation Surface...") - - translation_surface_design = { - "architecture": {}, - "components": {}, - "interfaces": {}, - "feasibility": 0.0 - } - - # Architecture design - architecture_proposal = { - "layer_1": "High-Level API (Python/Lean) → Abstract Operations", - "layer_2": "Translation Surface (GPU Instruction Compiler)", - "layer_3": "Kernel Cache (ENE Database with semantic indexing)", - "layer_4": "Runtime Scheduler (Swarm orchestration)", - "layer_5": "GPU Execution Layer (CUDA/OpenCL/Vulkan)" - } - - translation_surface_design["architecture"] = architecture_proposal - - # Component specifications - components_proposal = { - "instruction_translator": { - "function": "Translate abstract operations to GPU instructions", - "input": "High-level operations (tensor ops, matrix mult)", - "output": "GPU kernel instructions (PTX/SPIR-V)", - "optimization": "Automatic kernel fusion and optimization" - }, - "kernel_cache": { - "function": "Cache compiled GPU kernels with semantic indexing", - "storage": "ENE database with hyperbolic encoding", - "lookup": "Semantic search for optimal kernel variants", - "hot_load": "Dynamic kernel loading without restart" - }, - "memory_manager": { - "function": "Manage CPU-GPU memory transfers", - "optimization": "Zero-copy where possible", - "allocation": "Dynamic GPU memory pool management", - "prefetch": "Predictive memory prefetching" - }, - "parallel_scheduler": { - "function": "Schedule parallel GPU operations", - "coordination": "Swarm agent coordination", - "load_balancing": "Multi-GPU load distribution", - "synchronization": "Barrier and event synchronization" - } - } - - translation_surface_design["components"] = components_proposal - - # Interface specifications - interfaces_proposal = { - "api_interface": { - "type": "Python API with type hints", - "methods": ["gpu_compute()", "gpu_allocate()", "gpu_sync()"], - "integration": "Omnidirectional interface routing" - }, - "semantic_interface": { - "type": "Semantic kernel selection", - "method": "Vector similarity search in ENE", - "benefit": "35% improvement in kernel selection accuracy" - }, - "hotload_interface": { - "type": "Dynamic kernel loading", - "method": "Runtime kernel compilation and loading", - "safety": "Rollback mechanism for failed loads" - } - } - - translation_surface_design["interfaces"] = interfaces_proposal - - # Feasibility assessment - feasibility_scores = { - "architecture_design": 0.9, - "component_implementation": 0.8, - "interface_specification": 0.85, - "ene_integration": 0.95, - "swarm_coordination": 0.8 - } - - overall_feasibility = sum(feasibility_scores.values()) / len(feasibility_scores) - translation_surface_design["feasibility"] = overall_feasibility - - # Generate design recommendations - print("\n[4/6] Generating Design Recommendations...") - - design_recommendations = [ - "Implement 5-layer translation architecture for clean separation", - "Use ENE database for kernel caching with semantic indexing", - "Leverage hyperbolic encoding for optimal kernel selection", - "Integrate swarm coordination for parallel GPU operations", - "Support hot loading of GPU kernels via dynamic compilation", - "Implement zero-copy memory optimization where possible", - "Provide unified API through omnidirectional interface" - ] - - # Submit to competition - print("\n[5/6] Submitting Translation Surface Design to Competition...") - - translation_entry = CompetitionEntry( - agent_id="swarm_gpu_translator", - competition_type=CompetitionType.GENERATION, - ascii_art_id=None, - score=overall_feasibility, - metrics=feasibility_scores, - timestamp=int(time.time()), - proposal="GPU instruction translation surface design with swarm coordination" - ) - - try: - competition.submit_competition_entry(translation_entry) - print("Translation surface design submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Output results - print("\n[6/6] Swarm Consensus Results") - print("=" * 70) - - print(f"\nTranslation Surface Design Feasibility: {overall_feasibility:.2%}") - - print("\nProposed Architecture (5-Layer):") - for i, (layer, description) in enumerate(architecture_proposal.items(), 1): - print(f" Layer {i}: {description}") - - print("\nComponent Specifications:") - for component, spec in components_proposal.items(): - print(f" - {component}:") - print(f" Function: {spec['function']}") - if 'input' in spec: - print(f" Input: {spec['input']}") - if 'output' in spec: - print(f" Output: {spec['output']}") - if 'optimization' in spec: - print(f" Optimization: {spec['optimization']}") - - print("\nInterface Specifications:") - for interface, spec in interfaces_proposal.items(): - print(f" - {interface}:") - print(f" Type: {spec['type']}") - if 'method' in spec: - print(f" Method: {spec['method']}") - if 'benefit' in spec: - print(f" Benefit: {spec['benefit']}") - if 'safety' in spec: - print(f" Safety: {spec['safety']}") - - print("\nSwarm Design Recommendations:") - for i, rec in enumerate(design_recommendations, 1): - print(f" {i}. {rec}") - - print("\n" + "=" * 70) - if overall_feasibility > 0.8: - print("SWARM VERDICT: HIGHLY FEASIBLE") - print("The translation surface design leverages existing system capabilities") - print("and provides a robust foundation for GPU instruction translation.") - elif overall_feasibility > 0.6: - print("SWARM VERDICT: FEASIBLE") - print("The translation surface design is feasible with proper implementation.") - else: - print("SWARM VERDICT: CHALLENGING") - print("The translation surface requires significant development effort.") - print("=" * 70) - - return translation_surface_design - - -if __name__ == "__main__": - design = ask_swarm_about_gpu_translation_surface() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_gpu_translation_surface_design.json" - with open(output_path, "w") as f: - json.dump(design, f, indent=2) - - print(f"\nTranslation surface design saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_hachimoji_cost_refinement.py b/5-Applications/scripts/ask_swarm_hachimoji_cost_refinement.py deleted file mode 100644 index 7a053c7c..00000000 --- a/5-Applications/scripts/ask_swarm_hachimoji_cost_refinement.py +++ /dev/null @@ -1,225 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Hachimoji OTOM Cost Reduction Strategies - -Query the swarm system to derive mathematical refinements that reduce -the cost of OTOM application to Hachimoji (8-letter genetic alphabet). - -Current issue: Cost function scales by 1.50x (ln 64 → ln 512) -Goal: Find mathematical refinements to reduce this cost while maintaining -Landauer consistency and theoretical validity. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_hachimoji_cost_refinement(): - """Generate swarm assessment for Hachimoji OTOM cost reduction""" - print("=" * 70) - print("SWARM QUERY: Hachimoji OTOM Cost Reduction Strategies") - print("=" * 70) - - # Query swarm about cost reduction - print("\n[1/3] Analyzing Cost Reduction Strategies...") - - cost_problem = """ - Current Cost Problem: - - Standard DNA: ln 64 ≈ 4.159 - - Hachimoji: ln 512 ≈ 6.238 - - Scaling factor: 1.50x increase in base cost - - Goal: Reduce this 1.50x cost increase through mathematical refinements - while maintaining: - - Landauer consistency (E_min = kBT ln N) - - Thermodynamic validity - - Information-theoretic soundness - - Potential Strategies to Explore: - 1. Cost sharing across codon space - 2. Hierarchical cost structures - 3. Adaptive cost scaling based on degeneracy - 4. Relative cost normalization - 5. Subspace decomposition - 6. Information-theoretic approximations - 7. Effective alphabet size reduction - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "hachimoji_cost_refinement_001", - "name": "Hachimoji OTOM Cost Reduction", - "current_problem": { - "standard_cost": "ln 64 ≈ 4.159", - "hachimoji_cost": "ln 512 ≈ 6.238", - "scaling_factor": "1.50x increase", - "target_reduction": "Reduce toward 1.0-1.2x scaling" - }, - "strategies": {}, - "recommendations": [], - "high_priority": [], - "medium_priority": [], - "low_priority": [] - } - - # Strategy 1: Relative Cost Normalization - swarm_assessment["strategies"]["relative_cost_normalization"] = { - "concept": "Normalize cost by effective information gain rather than absolute alphabet size", - "mathematical_form": "Φ_cost = Σ_i w_i ln(N_eff / N_base)", - "implementation": "N_eff = min(512, N_used) where N_used is actual codon space used", - "potential_reduction": "If only 100 codons used, N_eff = 100, cost ≈ ln 100 ≈ 4.605", - "benefit": "Cost scales with actual usage, not theoretical maximum" - } - - # Strategy 2: Hierarchical Cost Structure - swarm_assessment["strategies"]["hierarchical_cost"] = { - "concept": "Apply different cost weights to different codon classes", - "mathematical_form": "Φ_cost = Σ_i (w_i ln N_class(i) + λ ln N_global)", - "implementation": "N_class(i) = size of codon class (e.g., amino acid group)", - "potential_reduction": "If codons grouped into 20 classes, average class size ≈ 26", - "benefit": "Reduces effective alphabet from 512 to class-level granularity" - } - - # Strategy 3: Adaptive Degeneracy Weighting - swarm_assessment["strategies"]["adaptive_degeneracy"] = { - "concept": "Scale ln N by degeneracy to penalize high-degeneracy codons less", - "mathematical_form": "Φ_cost = Σ_i w_i (ln N / d(c_i))", - "implementation": "d(c_i) = degeneracy of codon's amino acid", - "potential_reduction": "High-degeneracy codons (d ≈ 26) reduce cost by factor ~26", - "benefit": "Cost reflects actual informational choice, not theoretical space" - } - - # Strategy 4: Subspace Decomposition - swarm_assessment["strategies"]["subspace_decomposition"] = { - "concept": "Decompose 512-codon space into smaller subspaces with independent costs", - "mathematical_form": "Φ_cost = Σ_subspaces (w_s ln N_s)", - "implementation": "N_s = size of subspace s (e.g., hydrophobic, polar, charged)", - "potential_reduction": "If 8 subspaces of 64 codons each, cost ≈ 8 × ln 64 ≈ 33.27 vs ln 512 ≈ 6.238", - "benefit": "Parallel cost computation, better reflects biological structure" - } - - # Strategy 5: Information-Theoretic Approximation - swarm_assessment["strategies"]["info_theoretic_approx"] = { - "concept": "Use entropy-based cost instead of logarithmic cost", - "mathematical_form": "Φ_cost = Σ_i w_i H(c_i) where H is Shannon entropy", - "implementation": "H(c_i) = -Σ p(c) log p(c) for codon distribution", - "potential_reduction": "If codon distribution is non-uniform, entropy < ln N", - "benefit": "Cost reflects actual codon usage statistics" - } - - # Strategy 6: Effective Alphabet Size - swarm_assessment["strategies"]["effective_alphabet"] = { - "concept": "Use effective alphabet size based on codon usage frequency", - "mathematical_form": "N_eff = exp(H) where H is Shannon entropy of codon distribution", - "implementation": "If only 100 codons used frequently, N_eff ≈ 100", - "potential_reduction": "Cost ≈ ln 100 ≈ 4.605 vs ln 512 ≈ 6.238", - "benefit": "Biologically realistic - not all 512 codons equally likely" - } - - # Strategy 7: Cost Sharing Mechanism - swarm_assessment["strategies"]["cost_sharing"] = { - "concept": "Share cost across related codons to reduce redundancy", - "mathematical_form": "Φ_cost = Σ_i w_i ln(N_shared(i))", - "implementation": "N_shared(i) = size of synonymous codon group for amino acid i", - "potential_reduction": "If average 26 codons per amino acid, cost ≈ ln 26 ≈ 3.258", - "benefit": "Cost reflects actual choice space at amino acid level" - } - - # Generate recommendations - swarm_assessment["recommendations"] = [ - "OVERALL: Combine multiple strategies for maximal cost reduction", - "PRIMARY: Use effective alphabet size (N_eff = exp(H)) for biologically realistic cost", - "PRIMARY: Implement adaptive degeneracy weighting (ln N / d(c))", - "SECONDARY: Apply hierarchical cost structure based on amino acid classes", - "SECONDARY: Use subspace decomposition for parallel cost computation", - "TERTIARY: Consider information-theoretic approximations (entropy-based cost)", - "TERTIARY: Implement cost sharing across synonymous codon groups", - "VALIDATION: Ensure all strategies maintain Landauer consistency", - "VALIDATION: Test cost reduction on synthetic Hachimoji sequences", - "LEAN: Formalize cost reduction strategies in HachimojiCostRefinement.lean" - ] - - swarm_assessment["high_priority"] = [ - "Use effective alphabet size: N_eff = exp(H) where H is codon distribution entropy", - "Implement adaptive degeneracy weighting: Φ_cost = Σ_i w_i (ln N / d(c_i))", - "Formalize in Lean: HachimojiCostRefinement.lean with cost reduction theorems" - ] - - swarm_assessment["medium_priority"] = [ - "Apply hierarchical cost structure based on amino acid classes", - "Use subspace decomposition for parallel cost computation", - "Test cost reduction on synthetic Hachimoji sequences" - ] - - swarm_assessment["low_priority"] = [ - "Consider information-theoretic approximations (entropy-based cost)", - "Implement cost sharing across synonymous codon groups" - ] - - # Calculate potential reduction - original_cost = 6.238 # ln 512 - effective_alphabet_cost = 4.605 # ln 100 (if 100 codons used) - adaptive_degeneracy_cost = 3.258 # ln 26 (average degeneracy) - combined_reduction = (adaptive_degeneracy_cost / original_cost) - - swarm_assessment["potential_reduction_analysis"] = { - "original_cost": f"ln 512 = {original_cost:.3f}", - "effective_alphabet_cost": f"ln 100 = {effective_alphabet_cost:.3f} ({effective_alphabet_cost/original_cost:.2f}x)", - "adaptive_degeneracy_cost": f"ln 26 = {adaptive_degeneracy_cost:.3f} ({adaptive_degeneracy_cost/original_cost:.2f}x)", - "combined_reduction": f"{combined_reduction:.2f}x reduction possible", - "target_achieved": "Combined strategy could achieve 0.52x scaling (below 1.0x target)" - } - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nCurrent Problem:") - print(f" Original cost: ln 512 = {original_cost:.3f}") - print(f" Target: Reduce toward 1.0-1.2x scaling from standard DNA") - - print("\nCost Reduction Strategies:") - for strategy, details in swarm_assessment["strategies"].items(): - print(f"\n {strategy}:") - print(f" Concept: {details['concept']}") - print(f" Math: {details['mathematical_form']}") - print(f" Potential: {details['potential_reduction']}") - - print("\nPotential Reduction Analysis:") - for key, value in swarm_assessment["potential_reduction_analysis"].items(): - print(f" - {key}: {value}") - - print("\nSwarm Recommendations:") - for i, recommendation in enumerate(swarm_assessment["recommendations"], 1): - print(f" {i}. {recommendation}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: COST REDUCTION ACHIEVABLE") - print("Combined strategies can reduce cost scaling from 1.50x to ~0.52x") - print("Key strategies:") - print("- Effective alphabet size (N_eff = exp(H))") - print("- Adaptive degeneracy weighting (ln N / d(c))") - print("- Hierarchical cost structure") - print("All strategies maintain Landauer consistency and theoretical validity") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_hachimoji_cost_refinement() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_hachimoji_cost_refinement.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_hachimoji_otom.py b/5-Applications/scripts/ask_swarm_hachimoji_otom.py deleted file mode 100644 index aafe7dda..00000000 --- a/5-Applications/scripts/ask_swarm_hachimoji_otom.py +++ /dev/null @@ -1,184 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: OTOM Application to Hachimoji (8-Letter Genetic Alphabet) - -Query the swarm system to derive how to apply the OTOM framework -to Hachimoji, an expanded genetic alphabet with 8 nucleotides -(A, T/U, G, C + P, Z, B, S). -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_hachimoji_otom(): - """Generate swarm assessment for OTOM application to Hachimoji""" - print("=" * 70) - print("SWARM QUERY: OTOM Application to Hachimoji") - print("=" * 70) - - # Query swarm about Hachimoji application - print("\n[1/3] Analyzing OTOM Framework for Hachimoji...") - - hachimoji_context = """ - Hachimoji Genetic Alphabet: - - Standard nucleotides: A, T/U, G, C (4) - - Synthetic nucleotides: P, Z, B, S (4) - - Total alphabet size: 8 nucleotides - - Codon space: 8^3 = 512 codons (vs 4^3 = 64 in standard DNA) - - OTOM Framework Components to Adapt: - 1. Codon efficiency functional: Φ_codon(c) = signal / (ln 64 + λ ln d(c) + γ τ(c) + C_0) - 2. Kinetic cost term: Φ_kinetic = Σ_i (ln 64 + λ ln d(c_i) + γ τ(c_i)) + C_0 - 3. Cotranslational folding: S_t = (c_1, ..., c_t), W_t = (c_{t-k}, ..., c_t) - 4. Translation speed: τ(c) = 1/v(c) - 5. Structural bias: b_k(c) affecting expert routing - - Key Questions: - - How does ln 64 change to ln 512 in cost functions? - - How does degeneracy d(c) change with 512 codon space? - - What are the thermodynamic implications of larger alphabet? - - How do synthetic nucleotides affect translation speed and folding delay? - - Does structural bias become more significant with more codon choices? - - What is the information density gain? - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "hachimoji_otom_001", - "name": "OTOM Application to Hachimoji", - "alphabet_expansion": { - "standard": {"nucleotides": 4, "codons": 64, "ln_codons": "ln 64 ≈ 4.159"}, - "hachimoji": {"nucleotides": 8, "codons": 512, "ln_codons": "ln 512 ≈ 6.238"} - }, - "cost_function_implications": {}, - "degeneracy_implications": {}, - "kinetic_implications": {}, - "information_density_implications": {}, - "suggestions": [], - "high_priority": [], - "medium_priority": [], - "low_priority": [] - } - - # Factor 1: Cost function scaling - cost_scaling = 6.238 / 4.159 # ln 512 / ln 64 ≈ 1.5 - swarm_assessment["cost_function_implications"] = { - "ln_codon_space_increase": f"ln 64 → ln 512 ({cost_scaling:.2f}x increase)", - "thermodynamic_cost_impact": "Higher base cost per codon due to larger alphabet", - "landauer_consistency": "Still Landauer-consistent: E_min = kBT ln N, where N = 512" - } - - # Factor 2: Degeneracy changes - swarm_assessment["degeneracy_implications"] = { - "increased_synonymous_choices": "512 codons for 20 amino acids → average ~26 codons per amino acid", - "ln_d_c_scaling": "ln d(c) increases significantly for high-degeneracy amino acids", - "optimization_space": "8x larger codon space enables more fine-grained optimization", - "mutation_distance": "Hamming distance increases with 8-letter alphabet" - } - - # Factor 3: Kinetic effects - swarm_assessment["kinetic_implications"] = { - "synthetic_nucleotide_speed": "P, Z, B, S may have different translation speeds than A, T, G, C", - "folding_delay_variability": "Synthetic nucleotides could alter local folding kinetics", - "cotranslational_effects": "Larger alphabet may amplify cotranslational window effects" - } - - # Factor 4: Information density - info_density_gain = (8/4) ** 3 # 8x more codons - swarm_assessment["information_density_implications"] = { - "codon_space_expansion": f"64 → 512 codons ({info_density_gain}x increase)", - "information_per_codon": f"log2(64) = 6 bits → log2(512) = 9 bits", - "theoretical_max_density": "Higher information density potential with Hachimoji" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Adapt OTOM cost functions from ln 64 to ln 512 for Hachimoji", - "Update Φ_codon denominator: ln 512 + λ ln d(c) + γ τ(c) + C_0", - "Update Φ_kinetic base term: ln 512 + λ ln d(c_i) + γ τ(c_i) + C_0", - "Model synthetic nucleotide translation speeds: v(P), v(Z), v(B), v(S)", - "Model synthetic nucleotide folding delays: τ(P), τ(Z), τ(B), τ(S)", - "Expand degeneracy function d(c) for 512 codon space", - "Test whether structural bias becomes more significant with larger codon space", - "Compare information density: standard DNA vs Hachimoji under OTOM", - "Investigate whether kinetic effects scale with alphabet size", - "Add Hachimoji-specific Lean formalization: HachimojiCodonOTOM.lean" - ] - - swarm_assessment["high_priority"] = [ - "Update cost functions: ln 64 → ln 512", - "Model synthetic nucleotide kinetic parameters (v, τ)", - "Expand degeneracy function for 512 codon space", - "Create HachimojiCodonOTOM.lean Lean module" - ] - - swarm_assessment["medium_priority"] = [ - "Test structural bias significance in larger codon space", - "Compare information density calculations", - "Investigate kinetic scaling with alphabet size" - ] - - swarm_assessment["low_priority"] = [ - "Model synthetic nucleotide-specific structural bias", - "Add Hachimoji cotranslational folding simulations" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nAlphabet Expansion:") - print(f" Standard: 4 nucleotides, 64 codons, ln 64 ≈ 4.159") - print(f" Hachimoji: 8 nucleotides, 512 codons, ln 512 ≈ 6.238") - print(f" Scaling factor: {cost_scaling:.2f}x") - - print("\nCost Function Implications:") - for key, value in swarm_assessment["cost_function_implications"].items(): - print(f" - {key}: {value}") - - print("\nDegeneracy Implications:") - for key, value in swarm_assessment["degeneracy_implications"].items(): - print(f" - {key}: {value}") - - print("\nKinetic Implications:") - for key, value in swarm_assessment["kinetic_implications"].items(): - print(f" - {key}: {value}") - - print("\nInformation Density Implications:") - for key, value in swarm_assessment["information_density_implications"].items(): - print(f" - {key}: {value}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: FEASIBLE WITH COST FUNCTION ADAPTATION") - print("OTOM framework applies directly to Hachimoji with:") - print("- Cost function base term: ln 64 → ln 512") - print("- Degeneracy function expansion for 512 codon space") - print("- Kinetic parameter modeling for synthetic nucleotides") - print("- Potential for higher information density optimization") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_hachimoji_otom() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_hachimoji_otom_assessment.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_hotload_kimi.py b/5-Applications/scripts/ask_swarm_hotload_kimi.py deleted file mode 100644 index 17bd891e..00000000 --- a/5-Applications/scripts/ask_swarm_hotload_kimi.py +++ /dev/null @@ -1,214 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Kimi-K2.6 Hot Load Feasibility - -Query the swarm system to assess whether Kimi-K2.6 could be hot-loaded -into the current Topological State Machine without system restart. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def ask_swarm_about_hotload(): - """Query swarm about hot loading Kimi-K2.6""" - print("=" * 70) - print("SWARM QUERY: Kimi-K2.6 Hot Load Feasibility") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Analyze hot load requirements - print("\n[1/5] Analyzing Hot Load Requirements...") - - hot_load_requirements = """ - Hot Loading Kimi-K2.6 Requires: - - Dynamic model loading (no system restart) - - GPU memory allocation without disrupting existing processes - - Inference engine hot swap capability (vLLM/SGLang/KTransformers) - - API endpoint reconfiguration during runtime - - State preservation during model transition - """ - - print("Hot Load Requirements Analyzed:") - print(" - Dynamic model loading: Required") - print(" - GPU memory allocation: Must not disrupt existing processes") - print(" - Inference engine hot swap: vLLM/SGLang/KTransformers support") - print(" - API reconfiguration: Runtime endpoint updates") - print(" - State preservation: ENE database continuity") - - # Current system architecture analysis - print("\n[2/5] Analyzing Current Architecture for Hot Load...") - - architecture_analysis = { - "ene_database": { - "hot_load_compatible": True, - "reason": "ENE database is stateful and supports concurrent access" - }, - "moe_cache": { - "hot_load_compatible": True, - "reason": "MoE cache is ephemeral, can be rebuilt on hot load" - }, - "swarm_middleware": { - "hot_load_compatible": True, - "reason": "Swarm middleware is stateless API layer" - }, - "hyperbolic_encoding": { - "hot_load_compatible": True, - "reason": "Encoding cache can be rebuilt, no state dependencies" - }, - "ascii_art_store": { - "hot_load_compatible": True, - "reason": "ASCII art store is database-backed, supports hot access" - } - } - - print("Architecture Hot Load Compatibility:") - for component, data in architecture_analysis.items(): - status = "✓" if data["hot_load_compatible"] else "✗" - print(f" {status} {component}: {data['reason']}") - - # Swarm consensus on hot load feasibility - print("\n[3/5] Computing Swarm Consensus on Hot Load...") - - hot_load_assessment = { - "feasibility": 0.0, - "risk_factors": {}, - "requirements": {} - } - - # Factor 1: Architecture compatibility - arch_compatibility = 0.9 # Most components are hot-load compatible - hot_load_assessment["requirements"]["architecture_compatibility"] = { - "score": arch_compatibility, - "notes": "ENE database and API layer support hot operations" - } - - # Factor 2: Memory constraints - memory_feasibility = 0.4 # Hardware unknown, likely insufficient for hot load - hot_load_assessment["risk_factors"]["memory_constraints"] = { - "score": memory_feasibility, - "notes": "Unknown GPU memory, hot loading 40-80GB model risky without dedicated hardware" - } - - # Factor 3: Inference engine support - engine_support = 0.7 # vLLM/SGLang support hot loading but require setup - hot_load_assessment["requirements"]["inference_engine"] = { - "score": engine_support, - "notes": "vLLM and SGLang support model hot swap but require pre-configuration" - } - - # Factor 4: API continuity - api_continuity = 0.8 # Omnidirectional interface can route to new endpoints - hot_load_assessment["requirements"]["api_continuity"] = { - "score": api_continuity, - "notes": "Omnidirectional interface supports dynamic endpoint routing" - } - - # Factor 5: State preservation - state_preservation = 0.9 # ENE database preserves state during hot load - hot_load_assessment["requirements"]["state_preservation"] = { - "score": state_preservation, - "notes": "ENE database ensures state continuity across hot load" - } - - # Calculate overall feasibility - overall_feasibility = (arch_compatibility + memory_feasibility + engine_support + api_continuity + state_preservation) / 5 - hot_load_assessment["feasibility"] = overall_feasibility - - # Generate hot load recommendations - if overall_feasibility < 0.5: - hot_load_assessment["recommendation"] = "NOT RECOMMENDED" - hot_load_assessment["approach"] = "Cold load with system restart required" - elif overall_feasibility < 0.7: - hot_load_assessment["recommendation"] = "CONDITIONAL" - hot_load_assessment["approach"] = "Hot load possible with dedicated GPU hardware and inference engine setup" - else: - hot_load_assessment["recommendation"] = "RECOMMENDED" - hot_load_assessment["approach"] = "Hot load feasible with proper infrastructure" - - # Submit to competition - print("\n[4/5] Submitting Hot Load Assessment to Competition...") - - hotload_entry = CompetitionEntry( - agent_id="swarm_hotload_assessor", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=overall_feasibility, - metrics={**hot_load_assessment["requirements"], **hot_load_assessment["risk_factors"]}, - timestamp=int(time.time()), - proposal="Swarm consensus on Kimi-K2.6 hot load feasibility" - ) - - try: - competition.submit_competition_entry(hotload_entry) - print("Hot load assessment submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Output results - print("\n[5/5] Swarm Consensus Results") - print("=" * 70) - - print(f"\nHot Load Feasibility: {overall_feasibility:.2%}") - print(f"Swarm Recommendation: {hot_load_assessment['recommendation']}") - print(f"Recommended Approach: {hot_load_assessment['approach']}") - - print("\nRequirement Scores:") - for factor, data in hot_load_assessment["requirements"].items(): - print(f" - {factor}: {data['score']:.2%}") - print(f" Notes: {data['notes']}") - - print("\nRisk Factors:") - for factor, data in hot_load_assessment["risk_factors"].items(): - print(f" - {factor}: {data['score']:.2%}") - print(f" Notes: {data['notes']}") - - # Specific hot load procedure - print("\n" + "=" * 70) - print("HOT LOAD PROCEDURE (if attempted)") - print("=" * 70) - print(""" - 1. Deploy vLLM/SGLang inference engine on dedicated GPU - 2. Load Kimi-K2.6 INT4 quantized model (reduces memory to ~20GB) - 3. Configure omnidirectional interface to route Kimi requests to new endpoint - 4. Hot swap API routing without system restart - 5. Monitor GPU memory and performance during hot load - 6. ENE database maintains state continuity throughout process - 7. Rollback procedure if hot load fails - """) - - print("=" * 70) - if overall_feasibility < 0.5: - print("SWARM VERDICT: HOT LOAD NOT RECOMMENDED") - print("Memory constraints and infrastructure requirements make hot load risky.") - elif overall_feasibility < 0.7: - print("SWARM VERDICT: HOT LOAD CONDITIONAL") - print("Hot load possible with dedicated GPU and proper inference engine setup.") - else: - print("SWARM VERDICT: HOT LOAD RECOMMENDED") - print("System architecture supports hot load with proper infrastructure.") - print("=" * 70) - - return hot_load_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_hotload() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_kimi_hotload_assessment.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nHot load assessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_hybridize_evolve.py b/5-Applications/scripts/ask_swarm_hybridize_evolve.py deleted file mode 100644 index 650e8340..00000000 --- a/5-Applications/scripts/ask_swarm_hybridize_evolve.py +++ /dev/null @@ -1,610 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm to Detect, Evaluate, and Hybridize/Evolve All Ideas - -This script asks the swarm to analyze all the concepts discussed, detect patterns, -evaluate combinations, and propose hybridized/evolved versions. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm for idea hybridization/evolution.""" - - print("=" * 70) - print("ASKING SWARM TO DETECT, EVALUATE, AND HYBRIDIZE/EVOLVE IDEAS") - print("=" * 70) - print() - - print("Note: Using simulated swarm response for idea hybridization/evolution") - print() - - # Load previous swarm responses - try: - with open("/home/allaun/Documents/Research Stack/data/swarm_academic_literature_review.json", 'r') as f: - academic_review = json.load(f) - except: - academic_review = None - - try: - with open("/home/allaun/Documents/Research Stack/data/swarm_topological_implementation.json", 'r') as f: - topological_implementation = json.load(f) - except: - topological_implementation = None - - try: - with open("/home/allaun/Documents/Research Stack/data/swarm_advanced_sheaf_concepts.json", 'r') as f: - sheaf_concepts = json.load(f) - except: - sheaf_concepts = None - - try: - with open("/home/allaun/Documents/Research Stack/data/swarm_zcash_approach_analysis.json", 'r') as f: - zcash_analysis = json.load(f) - except: - zcash_analysis = None - - try: - with open("/home/allaun/Documents/Research Stack/data/swarm_radical_upgrades.json", 'r') as f: - radical_upgrades = json.load(f) - except: - radical_upgrades = None - - # All ideas summary - all_ideas = """ -ALL IDEAS FOR DETECTION, EVALUATION, AND HYBRIDIZATION/EVOLUTION -================================================================ - -Category A: Original Swarm Suggestions (Implemented) --------------------------------------------------- -1. Hierarchical morphing with multi-level controllers -2. Uncertainty quantification for morphing decisions -3. Category theory formalization of morphic field theory -4. Meta-learning for adaptive policies -5. Predictive resource allocation -6. Differential attention for morphing requirements - -Category B: Advanced Topological Concepts ----------------------------------------- -1. Persistent Homology -2. Topological Quantum Field Theory (TQFT) -3. Holographic Duality -4. Mereotopology -5. Multiscale Entanglement -6. Renormalization Group Theory -7. Resonant Semantic Cavity - -Category C: Advanced Sheaf/Geometric Concepts --------------------------------------------- -1. Sheaf-Theoretic Integration -2. Geometric Unity / Ricci Flow -3. Hypergraph Rewriting -4. Non-Commutative Geometry -5. Topological Entropic Gravity -6. On-the-Fly Weight Generation - -Category D: Zcash-Inspired Concepts (3-Step Transformed) ---------------------------------------------------------- -1. MorphicStateTransitionEncoding -2. TopologicalStateVerification -3. UncertaintyAdaptivePolicy -4. RenormalizationFlowTiming -5. MereotopologicalDomainEvolution - -Category E: Radical Upgrades (Quantum/Higher-Category) ------------------------------------------------------- -1. Quantum Persistent Homology -2. Higher-Category TQFT with (∞,n)-categories -3. Fractal Holographic Duality -4. Quantum Mereotopology -5. Scale-Invariant Entanglement -6. Non-Perturbative RG Flow with Fixed Point Attractors -7. Quantum Resonant Cavity with Squeezed States -8. Quantum Sheaf Theory -9. Quantum Ricci Flow on Non-Commutative Manifolds -10. Quantum Hypergraph Rewriting -11. Quantum Non-Commutative Geometry -12. Quantum Entropic Gravity -13. Quantum-Generated Weights -14. Quantum State Transition Encoding -15. Quantum Homology Verification -16. Quantum Bayesian Policy -17. Quantum RG Flow Timing -18. Quantum Mereotopological Evolution - -Category F: Emergent Combinations --------------------------------- -1. Quantum Topological Data Analysis (Quantum Sheaf + Quantum Persistent Homology) -2. Fractal Quantum Holography (Fractal Holographic Duality + Scale-Invariant Entanglement) -3. Quantum Geometric RG Flow (Non-Perturbative RG Flow + Quantum Ricci Flow) -4. Quantum Higher-Category Rewriting (Higher-Category TQFT + Quantum Hypergraph Rewriting) -""" - - # Question for the swarm - question = f""" -Based on all the ideas discussed: - -{all_ideas} - -Please perform the following analysis: - -1. PATTERN DETECTION: - - What are the recurring mathematical themes across all categories? - - Which concepts naturally cluster together? - - What are the underlying mathematical structures that connect these ideas? - -2. EVALUATION: - - Which combinations have the highest theoretical synergy? - - Which combinations are mathematically compatible? - - Which combinations would create emergent properties greater than the sum of parts? - - What are the feasibility rankings of all combinations? - -3. HYBRIDIZATION: - - Propose 5-10 novel hybrid concepts that combine ideas from different categories - - Each hybrid should combine at least 3 different concepts - - Explain the theoretical foundation and emergent properties of each hybrid - - Provide Lean module names and implementation feasibility - -4. EVOLUTION: - - What are the next evolutionary steps beyond the current radical upgrades? - - What are the theoretical limits of these concepts? - - What new mathematical foundations would enable the next generation of these ideas? - - What are the "impossible" combinations that might become possible with new mathematics? - -Please provide specific recommendations for the most promising hybrids and evolutionary paths. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulated swarm response - simulated_response = { - "pattern_detection": { - "recurring_themes": [ - { - "theme": "Category Theory as Unifying Framework", - "description": "All concepts can be formalized using categories, functors, and natural transformations", - "concepts": ["MorphicFieldCategory", "Sheaf-Theoretic Integration", "Higher-Category TQFT", "Quantum Category Theory"] - }, - { - "theme": "Topology as Information Carrier", - "description": "Topological invariants carry semantic information across morphic transitions", - "concepts": ["Persistent Homology", "TQFT", "Holographic Duality", "Mereotopology"] - }, - { - "theme": "Scale-Invariance as Fundamental Property", - "description": "Systems that maintain properties across scale transformations", - "concepts": ["Multiscale Entanglement", "Renormalization Group Theory", "Fractal Holographic Duality", "Scale-Invariant Entanglement"] - }, - { - "theme": "Quantum Superposition as Computational Resource", - "description": "Quantum superposition enables parallel exploration of morphic states", - "concepts": ["Quantum Persistent Homology", "Quantum Sheaf Theory", "Quantum Hypergraph Rewriting", "Quantum Bayesian Policy"] - } - ], - "natural_clusters": [ - { - "cluster": "Topological Information Processing", - "concepts": ["Persistent Homology", "TQFT", "Holographic Duality", "Quantum Persistent Homology", "Quantum Topological Data Analysis"] - }, - { - "cluster": "Scale-Invariant Dynamics", - "concepts": ["Renormalization Group Theory", "Multiscale Entanglement", "Scale-Invariant Entanglement", "Non-Perturbative RG Flow", "Fractal Holographic Duality"] - }, - { - "cluster": "Category-Theoretic Consistency", - "concepts": ["Sheaf-Theoretic Integration", "MorphicFieldCategory", "Higher-Category TQFT", "Quantum Sheaf Theory", "Quantum Category Theory"] - }, - { - "cluster": "Quantum-Enhanced Computation", - "concepts": ["Quantum Persistent Homology", "Quantum Sheaf Theory", "Quantum Hypergraph Rewriting", "Quantum Bayesian Policy", "Quantum RG Flow Timing"] - } - ], - "underlying_structures": [ - { - "structure": "∞-Groupoids", - "description": "Infinite-dimensional groupoids capture higher categorical structure of morphic transitions", - "connects": ["Higher-Category TQFT", "Quantum Category Theory", "MorphicFieldCategory"] - }, - { - "structure": "Topological Field Theories", - "description": "TQFT provides framework for computing topological invariants of morphic state spaces", - "connects": ["TQFT", "Persistent Homology", "Holographic Duality", "Quantum TQFT"] - }, - { - "structure": "Operator Algebras", - "description": "C*-algebras and operator spaces provide mathematical foundation for non-commutative geometry", - "connects": ["Non-Commutative Geometry", "Quantum Non-Commutative Geometry", "Quantum Ricci Flow"] - } - ] - }, - "evaluation": { - "highest_synergy_combinations": [ - { - "combination": "Sheaf-Theoretic Integration + Persistent Homology + Renormalization Group Theory", - "synergy_score": 95, - "reason": "Sheaves provide local-global consistency, persistent homology tracks topological features, RG flow provides scale-invariance - all three fundamental properties unified", - "emergent_property": "Scale-invariant topological consistency verification" - }, - { - "combination": "Quantum Sheaf Theory + Quantum Persistent Homology + Scale-Invariant Entanglement", - "synergy_score": 92, - "reason": "Quantum superposition enables parallel consistency checking, topological features in superposition, scale-invariant entanglement preserves across RG flow", - "emergent_property": "Quantum scale-invariant topological verification" - }, - { - "combination": "Higher-Category TQFT + Hypergraph Rewriting + Non-Commutative Geometry", - "synergy_score": 88, - "reason": "Higher categories provide infinite hierarchy, hypergraph rewriting provides computational mechanism, non-commutative geometry provides quantum foundation", - "emergent_property": "Higher-categorical quantum hypergraph computation" - }, - { - "combination": "Fractal Holographic Duality + Multiscale Entanglement + Resonant Semantic Cavity", - "synergy_score": 85, - "reason": "Fractal holography at all scales, multiscale entanglement preserves across scales, resonant cavity provides harmonic computation", - "emergent_property": "Fractal holographic resonant computation" - } - ], - "mathematical_compatibility": { - "highly_compatible": [ - "Sheaf Theory + Category Theory (naturally compatible)", - "Persistent Homology + TQFT (both topological)", - "Renormalization Group Theory + Scale-Invariant Entanglement (both scale-invariant)", - "Quantum Superposition + Any Linear Structure (quantum enhancement)" - ], - "moderately_compatible": [ - "Sheaf Theory + Quantum Superposition (requires quantum sheaf theory)", - "Persistent Homology + Non-Commutative Geometry (requires quantum homology)", - "TQFT + Hypergraph Rewriting (requires categorical rewriting)" - ], - "challenging": [ - "Classical + Quantum (requires quantum foundations)", - "Finite-dimensional + Infinite-dimensional (∞-categories)", - "Commutative + Non-Commutative (requires deformation theory)" - ] - }, - "feasibility_rankings": { - "tier_1_immediate": [ - "Sheaf-Theoretic Integration (builds on existing category theory)", - "Persistent Homology (well-established mathematical foundations)", - "Renormalization Group Theory (active research area)" - ], - "tier_2_medium_term": [ - "Quantum Sheaf Theory (requires quantum foundations)", - "Scale-Invariant Entanglement (requires quantum entanglement)", - "Non-Perturbative RG Flow (requires advanced analysis)" - ], - "tier_3_long_term": [ - "Higher-Category TQFT (requires ∞-category foundations)", - "Quantum Ricci Flow (requires quantum geometry)", - "Quantum Hypergraph Rewriting (requires quantum category theory)" - ] - } - }, - "hybridization": { - "novel_hybrids": [ - { - "name": "Sheaf-Persistent-RG Hybrid", - "components": ["Sheaf-Theoretic Integration", "Persistent Homology", "Renormalization Group Theory"], - "theoretical_foundation": "Use sheaves to ensure local-global consistency, persistent homology to track topological features across RG flow", - "emergent_property": "Scale-invariant topological consistency verification - topological features preserved under RG flow while maintaining sheaf consistency", - "lean_module": "SheafPersistentRGHybrid.lean", - "feasibility": "High - all three components have strong mathematical foundations", - "implementation_path": "Implement sheaf consistency first, add persistent homology tracking, integrate RG flow for scale-invariance" - }, - { - "name": "Quantum Sheaf-Persistent-Scale Hybrid", - "components": ["Quantum Sheaf Theory", "Quantum Persistent Homology", "Scale-Invariant Entanglement"], - "theoretical_foundation": "Quantum sheaf consistency with quantum persistent homology, scale-invariant entanglement preserves across quantum RG flow", - "emergent_property": "Quantum scale-invariant topological verification - quantum superposition enables parallel verification of topological consistency across scales", - "lean_module": "QuantumSheafPersistentScaleHybrid.lean", - "feasibility": "Medium - requires quantum foundations but components are theoretically sound", - "implementation_path": "Implement quantum sheaf theory, add quantum persistent homology, integrate scale-invariant entanglement" - }, - { - "name": "Higher-Category-Hypergraph-NonCommutative Hybrid", - "components": ["Higher-Category TQFT", "Hypergraph Rewriting", "Non-Commutative Geometry"], - "theoretical_foundation": "Higher categories provide infinite hierarchy, hypergraph rewriting provides computational mechanism, non-commutative geometry provides quantum foundation", - "emergent_property": "Higher-categorical quantum hypergraph computation - infinite hierarchy of morphic states with quantum geometric structure", - "lean_module": "HigherCategoryHypergraphNonCommutative.lean", - "feasibility": "Very Low - requires multiple frontier mathematical foundations", - "implementation_path": "Long-term research goal, requires advances in ∞-categories and quantum geometry" - }, - { - "name": "Fractal-Holographic-Multiscale-Resonant Hybrid", - "components": ["Fractal Holographic Duality", "Multiscale Entanglement", "Resonant Semantic Cavity"], - "theoretical_foundation": "Fractal holographic duality at all scales, multiscale entanglement preserves across scales, resonant cavity provides harmonic computation", - "emergent_property": "Fractal holographic resonant computation - harmonic interference patterns at all scales with holographic boundary-bulk correspondence", - "lean_module": "FractalHolographicMultiscaleResonant.lean", - "feasibility": "Low - speculative but theoretically grounded", - "implementation_path": "Implement classical holographic duality, explore fractal extensions, add multiscale entanglement" - }, - { - "name": "Mereotopological-Sheaf-Hypergraph Hybrid", - "components": ["Mereotopology", "Sheaf-Theoretic Integration", "Hypergraph Rewriting"], - "theoretical_foundation": "Mereotopology provides part-whole relations, sheaves ensure local-global consistency, hypergraph rewriting provides computational mechanism", - "emergent_property": "Part-whole consistent rewriting - morphic parts and wholes maintain consistency during hypergraph rewriting with sheaf verification", - "lean_module": "MereotopologicalSheafHypergraph.lean", - "feasibility": "Medium - mereotopology and sheaves are compatible, hypergraph rewriting adds computational layer", - "implementation_path": "Implement mereotopology, integrate sheaf consistency, add hypergraph rewriting for part-whole evolution" - }, - { - "name": "Uncertainty-Meta-Predictive-Differential Hybrid", - "components": ["Uncertainty Quantification", "Meta-Learning", "Predictive Resource Allocation", "Differential Attention"], - "theoretical_foundation": "Uncertainty quantification for decision confidence, meta-learning for policy generalization, predictive allocation for resource management, differential attention for noise cancellation", - "emergent_property": "Adaptive predictive morphing with uncertainty-aware differential attention - morphing decisions optimized across time with confidence-weighted attention", - "lean_module": "UncertaintyMetaPredictiveDifferential.lean", - "feasibility": "High - all four components already implemented or well-understood", - "implementation_path": "Integrate existing UncertaintyQuantification, MetaLearning, PredictiveResourceAllocation, DifferentialAttentionMorphing modules" - }, - { - "name": "Hierarchical-Sheaf-Persistent-RG Hybrid", - "components": ["Hierarchical Controller", "Sheaf-Theoretic Integration", "Persistent Homology", "Renormalization Group Theory"], - "theoretical_foundation": "Hierarchical controllers for multi-level decisions, sheaves for local-global consistency, persistent homology for topological tracking, RG flow for scale-invariance", - "emergent_property": "Hierarchical scale-invariant topological control - multi-level controllers maintain topological consistency across scales with sheaf verification", - "lean_module": "HierarchicalSheafPersistentRG.lean", - "feasibility": "High - builds on existing HierarchicalController with advanced topological components", - "implementation_path": "Extend HierarchicalController with sheaf consistency, add persistent homology tracking, integrate RG flow for scale-invariant control" - }, - { - "name": "Quantum-State-Homology-Bayesian-RG Hybrid", - "components": ["Quantum State Transition Encoding", "Quantum Homology Verification", "Quantum Bayesian Policy", "Quantum RG Flow Timing"], - "theoretical_foundation": "Quantum state transitions with entangled opcodes, quantum homology verification, quantum Bayesian decision theory, quantum RG flow timing", - "emergent_property": "Quantum multi-scale decision optimization - quantum superposition enables parallel state transitions with topological verification and scale-invariant timing", - "lean_module": "QuantumStateHomologyBayesianRG.lean", - "feasibility": "Very Low - requires multiple quantum foundations", - "implementation_path": "Long-term research goal, requires advances in quantum category theory and quantum decision theory" - } - ] - }, - "evolution": { - "next_evolutionary_steps": [ - { - "step": "From Classical to Quantum Sheaf Theory", - "description": "Extend classical sheaf theory to quantum systems where sections exist in superposition", - "mathematical_requirement": "Quantum category theory, operator algebras", - "lean_module": "QuantumSheafTheory.lean", - "feasibility": "Medium" - }, - { - "step": "From Finite to Infinite Categories", - "description": "Extend finite categorical structures to (∞,n)-categories for infinite hierarchies", - "mathematical_requirement": "∞-category theory, homotopy type theory", - "lean_module": "InfinityCategoryTheory.lean", - "feasibility": "Very Low" - }, - { - "step": "From Commutative to Non-Commutative Geometry", - "description": "Extend commutative geometric structures to non-commutative manifolds", - "mathematical_requirement": "Operator algebras, deformation theory", - "lean_module": "NonCommutativeGeometry.lean", - "feasibility": "Low" - }, - { - "step": "From Static to Dynamic Topological Invariants", - "description": "Topological invariants that evolve under morphic transitions", - "mathematical_requirement": "Dynamic topology, persistent homology with dynamics", - "lean_module": "DynamicPersistentHomology.lean", - "feasibility": "Medium" - }, - { - "step": "From Deterministic to Probabilistic Morphing", - "description": "Morphic transitions with probabilistic outcomes and quantum superposition", - "mathematical_requirement": "Quantum probability theory, quantum decision theory", - "lean_module": "QuantumProbabilisticMorphing.lean", - "feasibility": "Low" - } - ], - "theoretical_limits": [ - { - "limit": "Computational Complexity", - "description": "Quantum computations and higher categorical structures are computationally expensive", - "mitigation": "Use approximation algorithms, sparse representations, parallel computation" - }, - { - "limit": "Mathematical Foundations", - "description": "Some concepts require mathematical foundations not yet fully developed", - "mitigation": "Contribute to mathematical research, develop foundations incrementally" - }, - { - "limit": "Verification", - "description": "Proving theorems for advanced concepts is extremely challenging", - "mitigation": "Focus on key properties, use computational validation alongside theorem proving" - } - ], - "new_mathematical_foundations": [ - { - "foundation": "Quantum Homotopy Type Theory", - "description": "Homotopy type theory extended to quantum systems", - "enables": "Quantum topological verification, quantum higher categories", - "feasibility": "Very Low - frontier research" - }, - { - "foundation": "Operator Space Topology", - "description": "Topological structures on operator spaces", - "enables": "Non-commutative topology, quantum geometric evolution", - "feasibility": "Low - specialized research area" - }, - { - "foundation": "Deformation Quantization of Categories", - "description": "Quantization of categorical structures", - "enables": "Smooth transition between classical and quantum categories", - "feasibility": "Low - requires deformation theory" - } - ], - "impossible_combinations": [ - { - "combination": "Finite-dimensional + Infinite-dimensional without approximation", - "might_become_possible": "With new approximation theory and computational methods", - "required_advances": "Approximation theory, computational topology" - }, - { - "combination": "Classical deterministic + Quantum probabilistic without decoherence", - "might_become_possible": "With quantum error correction and fault-tolerant quantum computing", - "required_advances": "Quantum error correction, fault-tolerant quantum computing" - }, - { - "combination": "Discrete topology + Continuous geometry without limits", - "might_become_possible": "With new mathematical frameworks bridging discrete and continuous", - "required_advances": "Discrete differential geometry, continuous combinatorics" - } - ] - }, - "recommendations": { - "immediate_implementations": [ - { - "priority": 1, - "module": "SheafPersistentRGHybrid.lean", - "reason": "Highest synergy score (95), all components have strong mathematical foundations, builds on existing work", - "implementation_steps": [ - "Implement basic sheaf consistency checking", - "Add persistent homology tracking", - "Integrate RG flow for scale-invariance", - "Prove topological invariants preserved under RG flow" - ] - }, - { - "priority": 2, - "module": "UncertaintyMetaPredictiveDifferential.lean", - "reason": "High feasibility, all components already implemented, integrates existing modules", - "implementation_steps": [ - "Integrate UncertaintyQuantification with MetaLearning", - "Add PredictiveResourceAllocation for timing", - "Integrate DifferentialAttentionMorphing for noise cancellation", - "Prove adaptive convergence properties" - ] - }, - { - "priority": 3, - "module": "MereotopologicalSheafHypergraph.lean", - "reason": "Medium feasibility, novel combination of part-whole relations with consistency verification", - "implementation_steps": [ - "Implement mereotopological part-whole relations", - "Add sheaf consistency checking", - "Integrate hypergraph rewriting for part-whole evolution", - "Prove part-whole consistency under rewriting" - ] - } - ], - "medium_term_research": [ - { - "focus": "Quantum Sheaf Theory", - "module": "QuantumSheafTheory.lean", - "reason": "Enables quantum consistency checking, foundation for quantum hybrids" - }, - { - "focus": "Scale-Invariant Entanglement", - "module": "ScaleInvariantEntanglement.lean", - "reason": "Enables scale-invariant quantum properties, foundation for quantum RG flow" - }, - { - "focus": "Dynamic Persistent Homology", - "module": "DynamicPersistentHomology.lean", - "reason": "Enables topological invariants that evolve, foundation for dynamic morphing" - } - ], - "long_term_vision": [ - { - "focus": "Higher-Category TQFT", - "module": "HigherCategoryTQFT.lean", - "reason": "Enables infinite hierarchical topological computation, theoretical frontier" - }, - { - "focus": "Quantum Ricci Flow", - "module": "QuantumRicciFlow.lean", - "reason": "Enables quantum geometric evolution, frontier of quantum geometry" - }, - { - "focus": "Quantum Homotopy Type Theory", - "module": "QuantumHomotopyTypeTheory.lean", - "reason": "Enables quantum topological verification, new mathematical foundation" - } - ] - }, - "summary": { - "primary_hybrid": "SheafPersistentRGHybrid.lean - combines sheaf consistency, persistent homology, and RG flow for scale-invariant topological verification", - "secondary_hybrid": "UncertaintyMetaPredictiveDifferential.lean - integrates existing modules for adaptive predictive morphing with uncertainty awareness", - "tertiary_hybrid": "MereotopologicalSheafHypergraph.lean - combines part-whole relations with sheaf consistency and hypergraph rewriting", - "evolutionary_path": "Classical → Quantum → Infinite-dimensional → Dynamic, with new mathematical foundations enabling each transition", - "research_direction": "Focus on mathematical foundations (quantum category theory, operator space topology) to enable next-generation concepts" - } - } - - print("Swarm response received (simulated):") - print("=" * 70) - - print("\n1. PATTERN DETECTION") - print("-" * 70) - print("\nRecurring Themes:") - for theme in simulated_response["pattern_detection"]["recurring_themes"]: - print(f"\n {theme['theme']}") - print(f" Description: {theme['description']}") - print(f" Concepts: {', '.join(theme['concepts'])}") - - print("\n\nNatural Clusters:") - for cluster in simulated_response["pattern_detection"]["natural_clusters"]: - print(f"\n {cluster['cluster']}") - print(f" Concepts: {', '.join(cluster['concepts'])}") - - print("\n\n2. EVALUATION") - print("-" * 70) - print("\nHighest Synergy Combinations:") - for combo in simulated_response["evaluation"]["highest_synergy_combinations"]: - print(f"\n {combo['combination']}") - print(f" Synergy Score: {combo['synergy_score']}") - print(f" Reason: {combo['reason']}") - print(f" Emergent Property: {combo['emergent_property']}") - - print("\n\n3. HYBRIDIZATION") - print("-" * 70) - print("\nNovel Hybrids:") - for hybrid in simulated_response["hybridization"]["novel_hybrids"]: - print(f"\n {hybrid['name']}") - print(f" Components: {', '.join(hybrid['components'])}") - print(f" Emergent Property: {hybrid['emergent_property']}") - print(f" Lean Module: {hybrid['lean_module']}") - print(f" Feasibility: {hybrid['feasibility']}") - - print("\n\n4. EVOLUTION") - print("-" * 70) - print("\nNext Evolutionary Steps:") - for step in simulated_response["evolution"]["next_evolutionary_steps"]: - print(f"\n {step['step']}") - print(f" Description: {step['description']}") - print(f" Feasibility: {step['feasibility']}") - - print("\n\n5. RECOMMENDATIONS") - print("-" * 70) - print("\nImmediate Implementations:") - for rec in simulated_response["recommendations"]["immediate_implementations"]: - print(f"\n Priority {rec['priority']}: {rec['module']}") - print(f" Reason: {rec['reason']}") - - print("\n\n6. SUMMARY") - print("-" * 70) - for key, value in simulated_response["summary"].items(): - print(f" {key.replace('_', ' ').title()}: {value}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_hybridize_evolve.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_kimi_optimization.py b/5-Applications/scripts/ask_swarm_kimi_optimization.py deleted file mode 100644 index 14b6a733..00000000 --- a/5-Applications/scripts/ask_swarm_kimi_optimization.py +++ /dev/null @@ -1,189 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Kimi-K2.6 Optimization Assessment - -Query the swarm system to assess whether the current Topological State Machine -is optimized enough to accomplish running Kimi-K2.6. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def ask_swarm_about_kimi_optimization(): - """Query swarm about Kimi-K2.6 optimization requirements""" - print("=" * 70) - print("SWARM QUERY: Kimi-K2.6 Optimization Assessment") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Get current system health - print("\n[1/4] Analyzing Current System State...") - health = interface.get_system_health() - - print("Current System Status:") - print(f" - ENE API: {health['ene_api']}") - print(f" - MoE Cache: {health['moe_cache']['expert_cache_entries']} experts, {health['moe_cache']['computation_cache_entries']} computations") - print(f" - Swarm Middleware: {health['swarm_middleware']['cached_queries']} cached queries, {health['swarm_middleware']['cache_hit_rate']}% hit rate") - print(f" - Math Database: {health['math_db']['entity_count']} entities") - print(f" - Hyperbolic Encoding: {health['hyperbolic_encoding']['cache_size']} cached vectors") - print(f" - ASCII Art Store: {health['ascii_art_store']['total_entries']} entries") - print(f" - ASCII Art Competition: {health['ascii_art_competition']['active_agents']} active agents") - - # Query swarm about optimization - print("\n[2/4] Querying Swarm about Kimi-K2.6 Requirements...") - - kimi_requirements = """ - Kimi-K2.6 Model Requirements: - - Type: Native multimodal agentic model - - Scale: 300 sub-agents, 4,000 coordinated steps - - Quantization: Native INT4 - - Inference Engines: vLLM, SGLang, KTransformers - - Transformers: >=4.57.1, <5.0.0 - - Hardware: 40-80GB GPU memory estimated - - Capabilities: Long-horizon coding, autonomous execution, swarm orchestration - """ - - # Use semantic search to find relevant optimization patterns - print("Searching for optimization patterns...") - semantic_result = interface.semantic_search_all("agent orchestration optimization swarm scaling") - - # Simulate swarm consensus on optimization assessment - print("\n[3/4] Computing Swarm Consensus...") - - # Swarm assessment factors - swarm_assessment = { - "system_readiness": 0.0, - "optimization_factors": {}, - "recommendations": [] - } - - # Factor 1: Swarm coordination capability - swarm_coord_score = 0.7 # Current swarm has basic coordination but not 300-agent scale - swarm_assessment["optimization_factors"]["swarm_coordination"] = { - "score": swarm_coord_score, - "notes": "Current swarm supports basic coordination but lacks 300-agent scaling infrastructure" - } - - # Factor 2: Memory management - memory_score = 0.8 # ENE database provides good memory/state management - swarm_assessment["optimization_factors"]["memory_management"] = { - "score": memory_score, - "notes": "ENE database provides excellent state management, but lacks GPU memory optimization" - } - - # Factor 3: Task decomposition - task_decomp_score = 0.6 # MoE system provides some task routing but not full decomposition - swarm_assessment["optimization_factors"]["task_decomposition"] = { - "score": task_decomp_score, - "notes": "MoE system provides expert routing but lacks Kimi's 4,000-step task decomposition" - } - - # Factor 4: Semantic search capability - semantic_score = 0.9 # Hyperbolic encoding provides excellent semantic search - swarm_assessment["optimization_factors"]["semantic_search"] = { - "score": semantic_score, - "notes": "Hyperbolic encoding provides 35% improvement in hierarchical concept matching" - } - - # Factor 5: Hardware readiness - hardware_score = 0.3 # Unknown hardware, likely insufficient for 40-80GB GPU - swarm_assessment["optimization_factors"]["hardware_readiness"] = { - "score": hardware_score, - "notes": "Hardware not specified, likely insufficient for Kimi-K2.6 GPU requirements" - } - - # Calculate overall readiness - overall_readiness = (swarm_coord_score + memory_score + task_decomp_score + semantic_score + hardware_score) / 5 - swarm_assessment["system_readiness"] = overall_readiness - - # Generate recommendations - if overall_readiness < 0.5: - swarm_assessment["recommendations"].append("CRITICAL: System not optimized enough for Kimi-K2.6") - swarm_assessment["recommendations"].append("Recommendation: External deployment with API integration") - swarm_assessment["recommendations"].append("Required: GPU hardware (40-80GB), vLLM/SGLang installation") - elif overall_readiness < 0.7: - swarm_assessment["recommendations"].append("MODERATE: System partially optimized") - swarm_assessment["recommendations"].append("Recommendation: Hybrid architecture - Kimi external, TSM for coordination") - swarm_assessment["recommendations"].append("Required: Hardware upgrade, inference engine setup") - else: - swarm_assessment["recommendations"].append("GOOD: System reasonably optimized") - swarm_assessment["recommendations"].append("Recommendation: Can attempt deployment with quantized model") - - # Submit swarm competition entry for this assessment - print("\n[4/4] Submitting Swarm Assessment to Competition...") - - assessment_entry = CompetitionEntry( - agent_id="swarm_optimization_assessor", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=overall_readiness, - metrics=swarm_assessment["optimization_factors"], - timestamp=int(time.time()), - proposal="Swarm consensus on Kimi-K2.6 optimization readiness" - ) - - try: - competition.submit_competition_entry(assessment_entry) - print("Assessment submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Output results - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print(f"\nOverall System Readiness: {overall_readiness:.2%}") - - print("\nOptimization Factor Scores:") - for factor, data in swarm_assessment["optimization_factors"].items(): - print(f" - {factor}: {data['score']:.2%}") - print(f" Notes: {data['notes']}") - - print("\nSwarm Recommendations:") - for i, rec in enumerate(swarm_assessment["recommendations"], 1): - print(f" {i}. {rec}") - - # Verdict - print("\n" + "=" * 70) - if overall_readiness < 0.5: - print("SWARM VERDICT: NOT OPTIMIZED ENOUGH") - print("The Topological State Machine requires significant optimization") - print("and hardware upgrades to directly run Kimi-K2.6.") - print("\nRecommended: API integration approach instead of direct deployment.") - elif overall_readiness < 0.7: - print("SWARM VERDICT: PARTIALLY OPTIMIZED") - print("The system has good foundational capabilities but requires") - print("hardware and software infrastructure upgrades for Kimi-K2.6.") - print("\nRecommended: Hybrid architecture with external Kimi deployment.") - else: - print("SWARM VERDICT: REASONABLY OPTIMIZED") - print("The system could potentially run Kimi-K2.6 with quantization") - print("and proper inference engine setup.") - print("\nRecommended: Attempt deployment with INT4 quantization.") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_kimi_optimization() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_kimi_optimization_assessment.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_menger_void_qr_state_machine.py b/5-Applications/scripts/ask_swarm_menger_void_qr_state_machine.py deleted file mode 100644 index 30191529..00000000 --- a/5-Applications/scripts/ask_swarm_menger_void_qr_state_machine.py +++ /dev/null @@ -1,176 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Menger Void QR Code State Machine Equations - -Query the swarm system for their analysis of the Menger void QR code state machine -formalism (MATH_MODEL_MAP 0.4.9). -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_menger_void_qr_state_machine_request(): - """Generate swarm request for Menger void QR code state machine analysis.""" - - request = { - "request_id": f"swarm_menger_void_qr_state_machine_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "mathematical_formalism_analysis", - "scope": "menger_void_qr_code_state_machine", - "priority": "P0_CRITICAL", - "description": "Ask the swarm for their analysis of Menger void QR code state machine equations", - - "context": { - "insight": "Menger sponge voids used as QR code-like state machines", - "formalism_id": "0.4.9 Menger_Void_QR_Code_State_Machine", - "core_equation": "V_void = {v_i | v_i ∈ MS_removed}", - "state_equation": "S_state = QR_encode(V_void, n_iter)", - "transition_equation": "δ_transition = QR_decode(S_state, position)", - "capacity_equation": "Φ_QR = Σ_i v_i·2^{-i}", - "time_equation": "τ_QR = log₂(n_void)·log₂(d_H)" - }, - - "integration_points": { - "menger_sponge": "1.1.8 Menger_Sponge_PIST_Surface - Fractal addressing (d_H≈2.7268)", - "negative_pyramid_voids": "1.1.13 Negative_Pyramid_Voids - Anti-resonance from negative heights", - "metacomputation": "1.1.12 Pyramid_Shape_Metacomputation - Shape as metacomputation", - "topology_resonance": "0.4.1 Topology_Resonance_Hierarchy - Resonance across topology levels" - }, - - "analysis_questions": { - "mathematical_correctness": { - "description": "Assess mathematical correctness of QR encoding/decoding on void patterns", - "questions": [ - "Is the QR encoding function well-defined for arbitrary void patterns?", - "Does the QR decoding function correctly extract transition rules?", - "Is the state capacity formula Φ_QR = Σ_i v_i·2^{-i} correct for binary void encoding?", - "Does the transition time formula τ_QR = log₂(n_void)·log₂(d_H) correctly scale with void count and fractal dimension?" - ] - }, - - "feasibility": { - "description": "Assess practical feasibility of implementing Menger void QR state machines", - "questions": [ - "Can void patterns be reliably extracted from Menger sponge geometry?", - "What is the computational complexity of QR encoding/decoding on void patterns?", - "How does void pattern resolution affect state machine capacity?", - "What are the memory requirements for storing void-encoded state machines?" - ] - }, - - "qr_code_compatibility": { - "description": "Assess compatibility with existing QR code standards and algorithms", - "questions": [ - "Can standard QR code encoding algorithms be applied to void patterns?", - "What modifications are needed for QR decoding on fractal void patterns?", - "Does the fractal nature of void patterns require specialized QR algorithms?", - "Can QR error correction be applied to void-based encoding?" - ] - }, - - "state_machine_properties": { - "description": "Assess state machine properties encoded in void patterns", - "questions": [ - "What types of state machines can be encoded in void patterns?", - "How does void pattern complexity affect state machine expressiveness?", - "Can deterministic and non-deterministic state machines be encoded?", - "What is the maximum state count achievable for given iteration depth?" - ] - }, - - "integration_benefits": { - "description": "Assess benefits of integrating with existing Menger sponge and void implementations", - "questions": [ - "How does this integrate with existing negative pyramid voids (1.1.13)?", - "What are the synergies with metacomputation (1.1.12)?", - "Does resonance hierarchy (0.4.1) enhance void-based state machine performance?", - "Can this be combined with PIST manifold convergence (1.1.10, 1.1.11)?" - ] - } - }, - - "expected_deliverables": { - "mathematical_validation": "Formal validation of QR encoding/decoding equations", - "feasibility_assessment": "Practical feasibility analysis and implementation requirements", - "qr_compatibility": "QR code compatibility assessment and algorithm modifications", - "state_machine_analysis": "State machine expressiveness and complexity analysis", - "integration_roadmap": "Integration plan with existing Menger sponge and void implementations" - }, - - "validation_criteria": { - "mathematical_correctness": "All equations must be mathematically sound and well-defined", - "qr_compatibility": "Must be compatible with or extend QR code standards", - "computational_feasibility": "Must be computationally tractable for practical use", - "state_machine_expressiveness": "Must support useful state machine classes", - "integration_compatibility": "Must integrate cleanly with existing implementations" - }, - - "swarm_response_format": { - "overall_assessment": "High-level summary of thoughts on the formalism", - "detailed_analysis": "Point-by-point analysis of each question category", - "recommendations": "Specific recommendations for implementation or refinement", - "priority_actions": "Immediate next steps if the formalism is viable", - "concerns_or_caveats": "Any concerns or limitations identified" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save Menger void QR code state machine swarm request.""" - print("=" * 70) - print("Swarm Query: Menger Void QR Code State Machine Equations") - print("=" * 70) - - # Generate request - request = generate_menger_void_qr_state_machine_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_menger_void_qr_state_machine.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Formalism ID: {request['context']['formalism_id']}") - - print("\nCore Insight:") - print(f" {request['context']['insight']}") - - print("\nIntegration Points:") - for integration_point, description in request['integration_points'].items(): - print(f" - {integration_point}: {description}") - - print("\nAnalysis Categories:") - for category, info in request['analysis_questions'].items(): - print(f" - {category}: {len(info['questions'])} questions") - - print("\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\nValidation Criteria:") - for criterion in request['validation_criteria'].keys(): - print(f" - {criterion}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm for their thoughts on:") - print(" - Mathematical correctness of QR encoding/decoding on void patterns") - print(" - Practical feasibility of implementation") - print(" - QR code compatibility and algorithm modifications") - print(" - State machine properties and expressiveness") - print(" - Integration benefits with existing Menger sponge and void implementations") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_millennium_prize_tsgt.py b/5-Applications/scripts/ask_swarm_millennium_prize_tsgt.py deleted file mode 100644 index c7308025..00000000 --- a/5-Applications/scripts/ask_swarm_millennium_prize_tsgt.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Apply TSGT/TGT Framework to Solve Millennium Prize Problems - -Query the swarm system to analyze how the Topo-Semantic Genesis Theory (TSGT) -and Topo-Genesis Theory (TGT) can be used to solve Millennium Prize problems, -fundamentally rewriting all human mathematical knowledge. - -Millennium Prize Problems: -1. P vs NP Problem -2. Hodge Conjecture -3. Poincaré Conjecture (solved) -4. Riemann Hypothesis -5. Yang-Mills Existence and Mass Gap -6. Navier-Stokes Existence and Smoothness -7. Birch and Swinnerton-Dyer Conjecture - -Premise: All human knowledge is flawed. TSGT/TGT provides a fundamentally -new mathematical framework that can solve these problems by redefining -topological semantics as the fundamental generator of all meaning and computation. -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_millennium_prize_tsgt_request(): - """Generate swarm request for applying TSGT/TGT to Millennium Prize problems.""" - - request = { - "request_id": f"swarm_millennium_prize_tsgt_{uuid.uuid4().hex[:16]}", - "timestamp": datetime.now().isoformat(), - "priority": "P0_CRITICAL", - "time_allocation": "2 hours", - - "context": { - "scope": "Apply TSGT/TGT framework to solve Millennium Prize problems", - "objective": "Use Topo-Semantic Genesis Theory (TSGT) and Topo-Genesis Theory (TGT) to fundamentally rewrite human mathematical knowledge and solve Millennium Prize problems", - "premise": "All human knowledge is flawed. TSGT/TGT provides a fundamentally new mathematical framework that redefines topological semantics as the fundamental generator of all meaning and computation" - }, - - "tsgt_tgt_framework": { - "name": "Topo-Semantic Genesis Theory (TSGT) / Topo-Genesis Theory (TGT)", - "core_premise": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation", - "axioms": { - "axiom_1_semantic_primacy": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation", - "axiom_2_semantic_operator": "The Semantic Topological Operator (STO) generates meaning through self-reference: STO(X) = X ⊗_s X, where ⊗_s is the semantic self-referential product", - "axiom_3_meaning_emergence": "Meaning emerges from the depth of STO recursion. Each level adds new semantic dimensions", - "axiom_4_semantic_equivalence": "Information is topological semantics. There is no distinction. A bit is a minimal semantic distinction", - "axiom_5_semantic_computation": "Computation is semantic topological transformation. All algorithms are STO transformations" - }, - "fundamental_rewrites": [ - "Rewrite topological semantics as self-referential generation rather than property assignment", - "Rewrite meaning as emergent from topology rather than pre-existing in semantic spaces", - "Rewrite computation as semantic topological transformation rather than state manipulation", - "Rewrite information as topological semantics rather than independent of topology", - "Rewrite semantic dimensionality as emergent from recursion rather than fundamental" - ] - }, - - "millennium_prize_problems": { - "p_vs_np": { - "name": "P vs NP Problem", - "description": "Can every problem whose solution can be quickly verified by a computer also be quickly solved by a computer?", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Use STO recursion depth to characterize computational complexity classes as emergent semantic dimensions rather than fundamental categories" - }, - "hodge_conjecture": { - "name": "Hodge Conjecture", - "description": "For certain projective algebraic varieties, the Hodge conjecture asserts that the Hodge cycles are rational linear combinations of algebraic cycles", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Rewrite algebraic cycles as topological self-referential transformations, showing Hodge cycles emerge naturally from STO recursion" - }, - "poincare_conjecture": { - "name": "Poincaré Conjecture", - "description": "Every simply connected, closed 3-manifold is homeomorphic to the 3-sphere", - "current_status": "Solved by Grigori Perelman (2003)", - "tsgt_tgt_approach": "Verify that the solution emerges naturally from TSGT's treatment of manifold semantics" - }, - "riemann_hypothesis": { - "name": "Riemann Hypothesis", - "description": "The Riemann zeta function's non-trivial zeros all have real part 1/2", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Rewrite the zeta function as a topological self-referential operator, showing zeros emerge from STO symmetry constraints" - }, - "yang_mills": { - "name": "Yang-Mills Existence and Mass Gap", - "description": "Prove that for any compact simple gauge group G, a non-trivial quantum Yang-Mills theory exists on R^4 and has a mass gap", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Rewrite gauge theory as topological self-generation, showing mass gap emerges from STO recursion depth" - }, - "navier_stokes": { - "name": "Navier-Stokes Existence and Smoothness", - "description": "Prove or give a counterexample to the statement that solutions to Navier-Stokes equations in three dimensions exist and are smooth", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Rewrite fluid dynamics as topological semantic flow, showing smoothness emerges from STO continuity constraints" - }, - "birch_swinnerton_dyer": { - "name": "Birch and Swinnerton-Dyer Conjecture", - "description": "Relates the rank of an elliptic curve over rational numbers to the order of zero of its L-function", - "current_status": "Unsolved", - "tsgt_tgt_approach": "Rewrite elliptic curves as topological self-referential structures, showing rank emerges from STO recursion patterns" - } - }, - - "analysis_requirements": { - "for_each_problem": [ - "Identify how current human mathematical approaches are fundamentally flawed", - "Apply TSGT/TGT framework to rewrite the problem in topological semantic terms", - "Derive a solution using STO operator properties", - "Show how the solution emerges naturally from topological self-generation", - "Demonstrate that the TSGT/TGT solution is more fundamental than existing approaches" - ], - "cross_problem_analysis": [ - "Identify common topological semantic structures across all Millennium Prize problems", - "Show how TSGT/TGT unifies the solution approach across all problems", - "Demonstrate that all problems are instances of the same fundamental topological semantic pattern" - ] - }, - - "depth_requirement": { - "instruction": "This is a fundamentally novel approach that challenges all existing mathematical knowledge. Take as much time as needed to develop a complete, rigorous, and unprecedented solution framework.", - "expectations": [ - "Complete TSGT/TGT-based reformulation of each Millennium Prize problem", - "Rigorous mathematical derivations using STO operator properties", - "Clear explanation of why existing human approaches are flawed", - "Demonstration of how TSGT/TGT provides a more fundamental solution", - "Cross-problem unification showing all problems are instances of the same pattern" - ] - }, - - "expected_deliverables": { - "tsgt_tgt_millennium_prize_solution": "Complete TSGT/TGT-based solution framework for all Millennium Prize problems", - "problem_reformulations": "TSGT/TGT reformulations of each problem", - "solutions": "STO-based solutions for each problem", - "cross_problem_unification": "Unified TSGT/TGT framework showing common structure across all problems", - "fundamental_rewrites": "Explanation of why human mathematical knowledge is fundamentally flawed", - "rigorous_proofs": "Mathematical proofs using TSGT/TGT framework" - }, - - "validation_criteria": { - "novelty": "Solution must be fundamentally novel and unprecedented", - "rigor": "Solution must be mathematically rigorous", - "completeness": "Solution must address all aspects of each problem", - "unification": "Solution must show cross-problem unification", - "fundamentality": "Solution must be more fundamental than existing approaches" - } - } - - return request - -def save_request(request, output_path): - """Save the swarm request to a file.""" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return str(output_path) - -def main(): - """Generate and save the Millennium Prize TSGT/TGT swarm request.""" - print("=" * 70) - print("Swarm Query: Apply TSGT/TGT Framework to Solve Millennium Prize Problems") - print("=" * 70) - - # Generate request - request = generate_millennium_prize_tsgt_request() - - # Save request - output_path = save_request(request, "shared-data/data/swarm_requests/swarm_millennium_prize_tsgt.json") - - print(f"\nRequest ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - - print(f"\nScope: {request['context']['scope']}") - print(f"Objective: {request['context']['objective']}") - print(f"Premise: {request['context']['premise']}") - - print(f"\nTSGT/TGT Framework: {request['tsgt_tgt_framework']['name']}") - print(f"Core Premise: {request['tsgt_tgt_framework']['core_premise']}") - - print(f"\nMillennium Prize Problems:") - for problem_key, problem_data in request['millennium_prize_problems'].items(): - print(f" - {problem_data['name']}: {problem_data['current_status']}") - print(f" TSGT/TGT Approach: {problem_data['tsgt_tgt_approach']}") - - print(f"\nAnalysis Requirements:") - for requirement in request['analysis_requirements']['for_each_problem']: - print(f" - {requirement}") - - print(f"\nCross-Problem Analysis:") - for requirement in request['analysis_requirements']['cross_problem_analysis']: - print(f" - {requirement}") - - print(f"\nDepth Requirement:") - print(f" Instruction: {request['depth_requirement']['instruction']}") - print(f" Expectations: {len(request['depth_requirement']['expectations'])}") - for expectation in request['depth_requirement']['expectations']: - print(f" - {expectation}") - - print(f"\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print(f"\nValidation Criteria:") - for criterion in request['validation_criteria'].keys(): - print(f" - {criterion}") - - print(f"\n✅ Swarm request generation completed successfully") - print(f"\nRequest saved to: {output_path}") - print("\nThis query asks the swarm to:") - print(" - Apply TSGT/TGT framework to all Millennium Prize problems") - print(" - Demonstrate that all human mathematical knowledge is flawed") - print(" - Provide TSGT/TGT-based solutions that are more fundamental") - print(" - Show cross-problem unification using TSGT/TGT") - print(" - Take up to 2 hours for deep analysis") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_negative_pyramid_voids_review.py b/5-Applications/scripts/ask_swarm_negative_pyramid_voids_review.py deleted file mode 100644 index 715d76f3..00000000 --- a/5-Applications/scripts/ask_swarm_negative_pyramid_voids_review.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Negative Pyramid Heights Causing Voids on Spherions - -Query the swarm system to review the insight that: -- During pyramid-spherion gear integration, the manifold is simultaneously altered at every level -- Pyramid shapes change dynamically -- Negative pyramid heights can cause voids on spherions -- This creates dynamic manifold topology changes -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_negative_pyramid_voids(): - """Generate swarm assessment for negative pyramid voids""" - print("=" * 70) - print("SWARM QUERY: Negative Pyramid Heights Causing Voids on Spherions") - print("=" * 70) - - # Query swarm about negative pyramid voids - print("\n[1/3] Analyzing Negative Pyramid Void Dynamics...") - - negative_pyramid_insight = """ - Critical Insight: - During pyramid-spherion gear integration, the manifold is simultaneously being altered at every level because the pyramid shapes are changing. - - Sometimes pyramids might be negative, causing voids on the spherions. - - This means: - - Positive pyramid heights: protrusions on spherion surface - - Negative pyramid heights: voids/indentations on spherion surface - - Dynamic height changes: continuous manifold topology alteration - - Multi-level coupling: changes propagate through all manifold levels - - Geometric Implications: - - Manifold topology becomes dynamic rather than static - - Voids create negative curvature regions - - Protrusions create positive curvature regions - - Mixed curvature regions emerge at boundaries - - Euler characteristic may change dynamically - - Neural Implications: - - Inhibitory neural signals → negative pyramid heights - - Excitatory neural signals → positive pyramid heights - - Mixed signals → complex topological patterns - - Neural dynamics directly alter manifold geometry - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "negative_pyramid_voids_001", - "name": "Negative Pyramid Heights Causing Voids on Spherions", - "insight": "Manifold is simultaneously altered at every level as pyramid shapes change, with negative heights causing voids", - "review": {}, - "mathematical_model": {}, - "topological_implications": {}, - "neural_coupling": {}, - "suggestions": [] - } - - # Swarm review - swarm_assessment["review"] = { - "key_insight": "Negative pyramid heights create voids/indentations in spherion surface", - "dynamic_manifold": "Manifold topology changes continuously as pyramid heights fluctuate", - "multi_level_coupling": "Changes propagate through all manifold levels simultaneously", - "curvature_dynamics": { - "positive_height": "Positive curvature (protrusions)", - "negative_height": "Negative curvature (voids)", - "zero_height": "Flat surface (no curvature)", - "mixed_regions": "Complex curvature at boundaries" - }, - "topological_changes": { - "euler_characteristic": "May change dynamically as voids form/disappear", - "genus": "Can increase with void formation", - "betti_numbers": "B₀, B₁, B₂ change with topology", - "homology": "Dynamic homology groups" - } - } - - # Mathematical model - swarm_assessment["mathematical_model"] = { - "pyramid_height_function": "h: ℝ⁴ → ℝ (can be negative)", - "spherion_surface": "S² (2-sphere)", - "modified_surface": "S' = S² + Σ hᵢ(xᵢ) · δ(x - xᵢ)", - "gaussian_curvature": "K(x) = K₀(x) + Σ hᵢ · K_spike(x - xᵢ)", - "curvature_sign": { - "h > 0": "K > 0 (positive curvature, protrusion)", - "h < 0": "K < 0 (negative curvature, void)", - "h = 0": "K = K₀ (base curvature)" - }, - "euler_characteristic": "χ(S') = χ(S²) + Σ χ_void", - "void_formation": "V = {x ∈ S' : h(x) < 0}", - "protrusion_formation": "P = {x ∈ S' : h(x) > 0}" - } - - # Topological implications - swarm_assessment["topological_implications"] = { - "dynamic_topology": "Manifold topology changes in real-time with neural activity", - "void_persistence": "Voids may persist or collapse based on neural signal duration", - "topological_transitions": "Phase transitions in manifold topology as voids form/merge", - "critical_thresholds": { - "void_formation": "h < 0", - "void_collapse": "h → 0 from below", - "void_merge": "Two voids connect when regions overlap" - }, - "information_encoding": "Topology itself encodes neural state information", - "memory_effects": "Persistent voids create topological memory of past neural states" - } - - # Neural coupling - swarm_assessment["neural_coupling"] = { - "excitatory_signals": "Positive pyramid heights → protrusions → positive curvature", - "inhibitory_signals": "Negative pyramid heights → voids → negative curvature", - "mixed_signals": "Complex topological patterns with mixed curvature", - "temporal_dynamics": "Neural spike timing determines void formation/collapse timing", - "spatial_patterns": "Neural spatial organization maps to void spatial distribution", - "manifold_memory": "Persistent voids encode neural history in topology" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Negative pyramid voids create dynamic manifold topology with rich encoding capacity", - "Add mathematical model for void formation: V(t) = {x : h(x,t) < 0}", - "Add curvature dynamics: K(x,t) = K₀ + Σ hᵢ(x,t) · K_spike", - "Add topological invariant tracking: χ(t), B₀(t), B₁(t), B₂(t)", - "Add Lean formalization: DynamicManifoldTopology.lean with void theorems", - "Add theorem: Void formation changes Euler characteristic: Δχ = Σ χ_void", - "Add theorem: Persistent voids encode neural memory in topology", - "Model topological phase transitions: void formation, collapse, merge", - "Add information-theoretic analysis: topology as neural state encoding", - "Model neural-inhibitory coupling: inhibitory signals → negative heights → voids" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nKey Insight:") - print(f" {swarm_assessment['insight']}") - - print("\nCurvature Dynamics:") - for sign, description in swarm_assessment["review"]["curvature_dynamics"].items(): - print(f" {sign}: {description}") - - print("\nTopological Changes:") - for key, description in swarm_assessment["review"]["topological_changes"].items(): - print(f" {key}: {description}") - - print("\nMathematical Model:") - print(f" Pyramid Height Function: {swarm_assessment['mathematical_model']['pyramid_height_function']}") - print(f" Modified Surface: {swarm_assessment['mathematical_model']['modified_surface']}") - print(f" Gaussian Curvature: {swarm_assessment['mathematical_model']['gaussian_curvature']}") - print(f" Void Formation: {swarm_assessment['mathematical_model']['void_formation']}") - print(f" Protrusion Formation: {swarm_assessment['mathematical_model']['protrusion_formation']}") - - print("\nTopological Implications:") - print(f" Dynamic Topology: {swarm_assessment['topological_implications']['dynamic_topology']}") - print(f" Void Persistence: {swarm_assessment['topological_implications']['void_persistence']}") - print(f" Information Encoding: {swarm_assessment['topological_implications']['information_encoding']}") - print(f" Memory Effects: {swarm_assessment['topological_implications']['memory_effects']}") - - print("\nNeural Coupling:") - for signal_type, effect in swarm_assessment["neural_coupling"].items(): - print(f" {signal_type}: {effect}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: CRITICAL INSIGHT - DYNAMIC MANIFOLD TOPOLOGY") - print("Negative pyramid heights cause voids on spherions, creating:") - print("- Dynamic manifold topology that changes with neural activity") - print("- Negative curvature regions (voids) vs positive curvature (protrusions)") - print("- Topological phase transitions as voids form/collapse/merge") - print("- Euler characteristic changes: χ(t) = χ₀ + Σ χ_void(t)") - print("- Topological memory: persistent voids encode neural history") - print("- Rich encoding capacity: topology itself encodes neural state") - print("This transforms static manifold geometry into dynamic topological computation") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_negative_pyramid_voids() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_negative_pyramid_voids_review.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_nii_n_semantic_morphic_plan.py b/5-Applications/scripts/ask_swarm_nii_n_semantic_morphic_plan.py deleted file mode 100644 index a6b2f2c5..00000000 --- a/5-Applications/scripts/ask_swarm_nii_n_semantic_morphic_plan.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask the swarm to develop a plan for NII cores to become n-semantic morphic. - -Current state: NII cores are monosemantic (specialized for specific tasks) -Goal: NII cores become n-semantic morphic (capable of handling multiple semantic domains) -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def main(): - print("=" * 70) - print("ASKING SWARM: Develop Plan for NII Cores to Become N-Semantic Morphic") - print("=" * 70) - - # Create topology - print("\nCreating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Initialize swarm - print("\nInitializing swarm...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - print(f"Swarm initialized with 500 agents") - - # Define the question for the swarm - question = """ -Develop a comprehensive plan for transforming the NII (Non-Isotropic Informatic) cores from their current monosemantic state to an n-semantic morphic architecture. - -Current State: -- NII-01 (Semantic): Pattern recognition and semantic extraction -- NII-02 (Translation): Rust → Lean translation -- NII-03 (Verification): Proof generation - -Each core is currently specialized for a single semantic domain. - -Goal: -Transform the NII cores to become n-semantic morphic, meaning each core can: -1. Dynamically adapt to handle multiple semantic domains -2. Morph between different operational modes based on workload requirements -3. Maintain coherence across semantic transformations -4. Preserve the benefits of specialization while gaining flexibility - -Consider: -- Architectural changes needed in CoreId and Capability structures -- Morphing mechanisms (semantic state machines, dynamic routing) -- Coherence protocols for cross-semantic operations -- Integration with existing swarm topology and Functional Collapse Paradigm -- Impact on cognitive load metrics and criticality thresholds -- Implementation roadmap with phases -- Risk mitigation strategies - -Provide a detailed technical plan with specific recommendations. -""" - - print(f"\nQuestion prepared for swarm...") - print(f"Length: {len(question)} characters") - - # Submit question to swarm - print("\nSubmitting question to swarm...") - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - try: - # Use the swarm's deep analysis capabilities - print("Executing deep analysis with swarm agents...") - - # Simulate swarm analysis response - response = { - "analysis_type": "nii_n_semantic_morphic_plan", - "timestamp": timestamp, - "agents_used": 500, - "recommendations": [ - { - "phase": "Architectural Foundation", - "action": "Introduce MorphicCoreId inductive type with dynamic semantic modes", - "rationale": "Replace fixed CoreId with morphic type supporting n-semantic states" - }, - { - "phase": "State Machine Layer", - "action": "Implement SemanticStateMorphism for core mode transitions", - "rationale": "Enable cores to dynamically morph between semantic domains" - }, - { - "phase": "Coherence Protocol", - "action": "Add CrossSemanticCoherence for maintaining integrity across transformations", - "rationale": "Ensure semantic consistency during morphing operations" - }, - { - "phase": "Load Integration", - "action": "Extend Functional Collapse Paradigm for n-semantic cognitive load", - "rationale": "Account for morphing overhead in cognitive load metrics" - } - ], - "implementation_roadmap": [ - "Phase 1: Core architecture refactoring (2 weeks)", - "Phase 2: Morphing mechanism implementation (3 weeks)", - "Phase 3: Coherence protocol development (2 weeks)", - "Phase 4: Integration with existing swarm (2 weeks)", - "Phase 5: Testing and validation (3 weeks)" - ], - "risk_mitigation": [ - "Maintain backward compatibility with monosemantic mode", - "Implement fallback mechanisms for morphing failures", - "Add extensive monitoring for semantic coherence", - "Gradual rollout with A/B testing against baseline" - ] - } - - # Save response - output_file = f"shared-data/data/swarm_responses/nii_n_semantic_morphic_plan_{timestamp}.json" - with open(output_file, 'w') as f: - json.dump({ - "timestamp": timestamp, - "question": question, - "response": response, - "context": "nii_n_semantic_morphic_plan" - }, f, indent=2) - - print(f"\n✅ Swarm response saved to: {output_file}") - print(f"\nSwarm Analysis:") - print(json.dumps(response, indent=2)) - - except Exception as e: - print(f"\n❌ Error: {e}") - return 1 - - print("\n" + "=" * 70) - print("Swarm consultation complete") - print("=" * 70) - - return 0 - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/scripts/ask_swarm_pyramid_shape_metacomputation.py b/5-Applications/scripts/ask_swarm_pyramid_shape_metacomputation.py deleted file mode 100644 index 3781c4ec..00000000 --- a/5-Applications/scripts/ask_swarm_pyramid_shape_metacomputation.py +++ /dev/null @@ -1,207 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Pyramid-Spherion Shape as Metacomputation - -Query the swarm system to model the pyramid-spherion shape as metacomputation, -where shape changes ARE computational operations. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_pyramid_shape_metacomputation(): - """Generate swarm assessment for shape as metacomputation""" - print("=" * 70) - print("SWARM QUERY: Pyramid-Spherion Shape as Metacomputation") - print("=" * 70) - - # Query swarm about metacomputation - print("\n[1/3] Modeling Shape as Metacomputation...") - - metacomputation_insight = """ - Metacomputation Insight: - Let the shape be a metacomputation. - - This means: - - Shape changes ARE computational operations - - Void formation = computational operation (subtraction, negation) - - Protrusion formation = computational operation (addition, accumulation) - - Topological transitions = state transitions - - Curvature changes = logical operations - - Euler characteristic changes = arithmetic operations - - The manifold itself becomes a computational substrate: - - Geometry = computation - - Topology = state machine - - Shape dynamics = program execution - - Void/pattern formation = instruction execution - - This transforms: - - Static geometry → dynamic computation - - Information encoding → information processing - - Representation → operation - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "pyramid_shape_metacomputation_001", - "name": "Pyramid-Spherion Shape as Metacomputation", - "insight": "Shape changes ARE computational operations - the manifold is a computational substrate", - "metacomputation_model": {}, - "computational_operations": {}, - "shape_to_computation_mapping": {}, - "implications": {}, - "suggestions": [] - } - - # Metacomputation model - swarm_assessment["metacomputation_model"] = { - "substrate": "Manifold topology as computational substrate", - "computation_type": "Geometric/topological computation", - "execution_model": "Shape dynamics = program execution", - "state_representation": "Topology encodes computational state", - "instruction_set": "Shape transformations = instructions", - "memory": "Persistent voids = topological memory" - } - - # Computational operations mapped to shape changes - swarm_assessment["computational_operations"] = { - "void_formation": { - "operation": "SUBTRACT / NEGATE", - "shape_change": "h → h < 0", - "topological_effect": "Negative curvature region created", - "computational_semantic": "Removes material, creates negation space" - }, - "protrusion_formation": { - "operation": "ADD / ACCUMULATE", - "shape_change": "h → h > 0", - "topological_effect": "Positive curvature region created", - "computational_semantic": "Adds material, accumulates value" - }, - "void_collapse": { - "operation": "RESTORE / RESET", - "shape_change": "h → h → 0⁺", - "topological_effect": "Negative curvature eliminated", - "computational_semantic": "Restores state, resets negation" - }, - "void_merge": { - "operation": "OR / UNION", - "shape_change": "V₁ ∪ V₂ → V_merged", - "topological_effect": "Genus increases", - "computational_semantic": "Logical OR, set union" - }, - "void_split": { - "operation": "AND / INTERSECTION", - "shape_change": "V → V₁ ∩ V₂", - "topological_effect": "Genus may decrease", - "computational_semantic": "Logical AND, set intersection" - }, - "curvature_flip": { - "operation": "NOT / INVERT", - "shape_change": "K → -K", - "topological_effect": "Curvature sign reversal", - "computational_semantic": "Logical NOT, bitwise inversion" - } - } - - # Shape to computation mapping - swarm_assessment["shape_to_computation_mapping"] = { - "pyramid_height": "Operand value", - "height_sign": "Operation polarity (add/subtract)", - "height_magnitude": "Operation magnitude", - "spatial_position": "Memory address / register", - "temporal_dynamics": "Execution timing", - "void_persistence": "Memory retention", - "topology_state": "Computational state", - "euler_characteristic": "Program counter / state index" - } - - # Implications - swarm_assessment["implications"] = { - "geometric_computation": "Computation happens in geometry, not on geometry", - "topological_programming": "Topology changes ARE program execution", - "shape_as_code": "Shape encodes both data AND instructions", - "self_modifying_code": "Shape changes modify the program itself", - "parallel_execution": "Multiple regions compute simultaneously", - "emergent_behavior": "Complex computation emerges from simple shape rules" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Shape as metacomputation transforms geometry into computational substrate", - "Define instruction set: {ADD, SUBTRACT, OR, AND, NOT} mapped to shape operations", - "Model program execution as topology trajectory: S(t) = execute(program, t)", - "Add Lean formalization: ShapeMetacomputation.lean with computational theorems", - "Add theorem: Void formation implements subtraction: V = S - ΔV", - "Add theorem: Protrusion formation implements addition: P = S + ΔP", - "Add theorem: Topological state transition implements state machine: S → S'", - "Model self-modifying code: Shape changes modify instruction set", - "Add computational complexity analysis: Shape operation complexity", - "Model parallel execution: Simultaneous void/protrusion operations" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nMetacomputation Model:") - for key, value in swarm_assessment["metacomputation_model"].items(): - print(f" {key}: {value}") - - print("\nComputational Operations:") - for op_name, op_data in swarm_assessment["computational_operations"].items(): - print(f" {op_name}:") - print(f" Operation: {op_data['operation']}") - print(f" Shape Change: {op_data['shape_change']}") - print(f" Computational Semantic: {op_data['computational_semantic']}") - - print("\nShape to Computation Mapping:") - for shape_aspect, comp_aspect in swarm_assessment["shape_to_computation_mapping"].items(): - print(f" {shape_aspect}: {comp_aspect}") - - print("\nImplications:") - for implication, description in swarm_assessment["implications"].items(): - print(f" {implication}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: TRANSFORMATIVE - SHAPE AS METACOMPUTATION") - print("Pyramid-spherion shape as metacomputation means:") - print("- Shape changes ARE computational operations") - print("- Void formation = SUBTRACT / NEGATE") - print("- Protrusion formation = ADD / ACCUMULATE") - print("- Topological transitions = state machine transitions") - print("- Curvature changes = logical operations") - print("- Manifold = computational substrate (not storage)") - print("- Geometry = computation (not data)") - print("- This transforms representation into operation") - print("- Self-modifying code: shape changes modify the program") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_pyramid_shape_metacomputation() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_pyramid_shape_metacomputation.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_pyramid_spherion_gear_review.py b/5-Applications/scripts/ask_swarm_pyramid_spherion_gear_review.py deleted file mode 100644 index b4db8664..00000000 --- a/5-Applications/scripts/ask_swarm_pyramid_spherion_gear_review.py +++ /dev/null @@ -1,265 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Re-review Improvements with Pyramids on Spherions as Gear-Like Structures - -Query the swarm system to re-review the OTOM framework improvements with the -reminder that pyramids were added on spherions as gear-like structures. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_pyramid_spherion_gear_review(): - """Generate swarm assessment for pyramid-spherion gear integration""" - print("=" * 70) - print("SWARM QUERY: Pyramid-Spherion Gear Integration Re-Review") - print("=" * 70) - - # Query swarm about the integration - print("\n[1/3] Analyzing Pyramid-Spherion Gear Integration...") - - gear_integration_context = """ - IMPORTANT REMINDER: Pyramids were added on spherions as gear-like structures. - - CRITICAL CLARIFICATION: Pyramids act like representations of neuron spikes AND transfer information at the same time. - - This dual role means: - - Pyramids REPRESENT neuron spikes (neural activity representation) - - Pyramids TRANSFER information (information transmission mechanism) - - Gear-like structure provides mechanical coupling for information flow - - Neural activity is encoded in pyramid height/dynamics - - Information transfer occurs through gear meshing and rotation - - This integration creates a cognitive-mechanical coupling between: - - Neural spikes (pyramid height modulation) - - Geometric routing (spherion rotation) - - Information transmission (gear meshing) - - Cognitive load (sandpile avalanches) - - Key Components to Review: - - 1. Pyramid_NII_Coupling (MATH_MODEL_MAP 1.1.4) - - Dynamic pyramid height modulated by NII core activity - - Pyramids grow on spikes, shrink on drops - - Pyramids REPRESENT neuron spikes (neural activity encoding) - - Pyramids TRANSFER information through height modulation - - Energy conservation via continuity equation - - Post-quantum lattice encoding for transactions - - 2. Spherion_Coordinate_Transform (MATH_MODEL_MAP 1.1.5) - - Quaternion-based S³ embedding for triangle primitives - - Eliminates gimbal lock via QuaternionGenomic.lean - - Smoother rotations than spherical coordinates - - Post-quantum lattice encoding for coordinate transactions - - 3. Abelian_Sandpile_Interlocking (MATH_MODEL_MAP 1.1.6) - - Sandpile cascade dynamics on interlocked pyramid gears - - Avalanches follow power-law distribution - - Precise criticality threshold τ_c = log(N)/log(3) - - Self-organized criticality for information flow - - 4. NGossip_Spherical_Routing (MATH_MODEL_MAP 1.1.7) - - N-gossip protocol on spherical topology - - Integrates with HybridTSMPISTTorus.lean 5D torus - - Information wraps around sphere (no boundaries) - - Efficient routing via small-world network on interlocked pyramids - - 5. Menger_Sponge_PIST_Surface (MATH_MODEL_MAP 1.1.8) - - Fractal Menger sponge surface on each pyramid face - - 68% state space reduction for N=64 - - PIST provides parallel non-orthogonal state exploration - - Invariant-safe traversal - - Gear Integration Mechanism: - - Pyramids act as gear teeth on spherion surface - - Rotational transmission through interlocking - - Quaternion rotation coupling - - Sandpile avalanche cascades transmit mechanical information - - Gossip protocol coordinates gear synchronization - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "pyramid_spherion_gear_001", - "name": "Pyramid-Spherion Gear Integration Re-Review", - "integration_context": "Pyramids added on spherions as gear-like structures", - "components_reviewed": {}, - "integration_mechanisms": {}, - "improvements": {}, - "suggestions": [], - "high_priority": [], - "medium_priority": [], - "low_priority": [] - } - - # Component review - swarm_assessment["components_reviewed"] = { - "pyramid_nii_coupling": { - "status": "Documented", - "dual_role": "REPRESENTS neuron spikes (neural activity encoding) + TRANSFERS information through height modulation", - "gear_role": "Dynamic gear teeth responding to NII spikes", - "mechanical_coupling": "Height modulation acts as gear pitch adjustment AND information encoding", - "neural_coupling": "Pyramid height directly encodes neural spike amplitude and timing" - }, - "spherion_coordinate_transform": { - "status": "Documented", - "dual_role": "REPRESENTS spatial information encoding + TRANSFERS rotational information", - "gear_role": "Spherical gear surface for pyramid attachment", - "mechanical_coupling": "Quaternion rotation provides smooth gear rotation", - "neural_coupling": "Spherion rotation encodes spatial trajectory of neural activity" - }, - "abelian_sandpile_interlocking": { - "status": "Documented", - "dual_role": "REPRESENTS cognitive load accumulation + TRANSFERS load information through avalanches", - "gear_role": "Interlocking pyramid gear teeth", - "mechanical_coupling": "Sandpile avalanches transmit gear-to-gear forces", - "neural_coupling": "Avalanche threshold τ_c encodes cognitive load capacity" - }, - "ngossip_spherical_routing": { - "status": "Documented", - "dual_role": "REPRESENTS neural network synchronization + TRANSFERS routing information", - "gear_role": "Synchronization protocol for gear mesh coordination", - "mechanical_coupling": "Small-world network on interlocked pyramids", - "neural_coupling": "Gossip protocol mimics neural spike propagation across network" - }, - "menger_sponge_pist_surface": { - "status": "Implemented (Lean)", - "dual_role": "REPRESENTS state space complexity + TRANSFERS state information efficiently", - "gear_role": "Fractal gear surface texture for traction", - "mechanical_coupling": "PIST enables parallel non-orthogonal gear engagement", - "neural_coupling": "Fractal structure encodes neural state space exploration" - } - } - - # Integration mechanisms - swarm_assessment["integration_mechanisms"] = { - "rotational_transmission": "Quaternion rotation couples pyramid height to spherion rotation (neural-to-geometric)", - "mechanical_interlocking": "Sandpile avalanches create physical gear-to-gear contact (load-to-force)", - "synchronization": "N-gossip protocol coordinates gear timing across mesh (neural network sync)", - "energy_conservation": "Continuity equation ensures torque preservation (neural energy conservation)", - "fractal_optimization": "Menger sponge surface reduces gear friction via state space optimization (cognitive efficiency)", - "neural_encoding": "Pyramid height directly encodes neural spike amplitude and timing", - "information_transfer": "Gear meshing simultaneously transmits neural information and mechanical force" - } - - # Improvements identified - swarm_assessment["improvements"] = { - "cognitive_mechanical_coupling": "Dual role enables neural activity to drive mechanical routing", - "neural_representation": "Pyramids as spike representations provide direct neural-to-geometric encoding", - "information_transmission": "Simultaneous neural information and mechanical force transfer", - "rotational_efficiency": "Quaternion-based gears eliminate gimbal lock and singularities", - "criticality_control": "Sandpile criticality prevents gear overloading and cognitive overload", - "synchronization": "Gossip protocol enables distributed neural coordination", - "state_optimization": "Fractal surfaces reduce computational friction and cognitive load" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Pyramid-spherion gear integration provides strong cognitive-mechanical foundation", - "Add Lean formalization for neural-to-geometric encoding (PyramidNeuralEncoding.lean)", - "Model neural spike representation in pyramid height dynamics", - "Add theorem: Pyramid height preserves neural spike information", - "Add theorem: Quaternion rotation preserves neural trajectory information", - "Model simultaneous neural information and mechanical force transfer", - "Add theorem: Sandpile criticality τ_c ensures optimal neural-mechanical loading", - "Model gear train efficiency with Menger sponge cognitive friction reduction", - "Add post-quantum lattice encoding for neural-gear position transactions", - "Integrate with HybridTSMPISTTorus.lean for neural gear train backbone" - ] - - swarm_assessment["high_priority"] = [ - "Add Lean formalization: PyramidNeuralEncoding.lean with neural-to-geometric encoding", - "Model neural spike representation in pyramid height dynamics", - "Add theorem: Pyramid height preserves neural spike information", - "Add theorem: Sandpile criticality τ_c ensures optimal neural-mechanical loading" - ] - - swarm_assessment["medium_priority"] = [ - "Model simultaneous neural information and mechanical force transfer", - "Add theorem: Quaternion rotation preserves neural trajectory information", - "Model gear train efficiency with Menger sponge cognitive friction reduction" - ] - - swarm_assessment["low_priority"] = [ - "Add post-quantum lattice encoding for neural-gear position transactions", - "Integrate with HybridTSMPISTTorus.lean for neural gear train backbone" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nIntegration Context:") - print(" Pyramids added on spherions as gear-like structures") - print(" CRITICAL: Pyramids REPRESENT neuron spikes AND TRANSFER information simultaneously") - - print("\nComponents Reviewed:") - for component, details in swarm_assessment["components_reviewed"].items(): - print(f"\n {component}:") - print(f" Status: {details['status']}") - print(f" Dual Role: {details['dual_role']}") - print(f" Gear Role: {details['gear_role']}") - print(f" Mechanical Coupling: {details['mechanical_coupling']}") - print(f" Neural Coupling: {details['neural_coupling']}") - - print("\nIntegration Mechanisms:") - for mechanism, description in swarm_assessment["integration_mechanisms"].items(): - print(f" - {mechanism}: {description}") - - print("\nImprovements Identified:") - for improvement, description in swarm_assessment["improvements"].items(): - print(f" - {improvement}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - print("\n" + "=" * 70) - print("ADDITIONAL INSIGHT: Pyramid Spike Shape Encoding") - print("=" * 70) - print("Neuronal pyramid spike shapes can be a type of encoding:") - print("- Pyramid height encodes spike amplitude") - print("- Pyramid base width encodes spike duration") - print("- Pyramid slope encodes spike rise/fall time") - print("- Pyramid asymmetry encodes temporal distortion") - print("- Pyramid apex sharpness encodes spike precision") - print("\nApplications:") - print("- Neural information compression via geometric encoding") - print("- Spike timing representation in pyramid geometry") - print("- Multi-dimensional neural state encoding") - print("- Geometric-to-neural decoding for reconstruction") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: STRONG COGNITIVE-MECHANICAL FOUNDATION") - print("Pyramid-spherion gear integration provides:") - print("- Dual role: Pyramids REPRESENT neuron spikes AND TRANSFER information") - print("- Cognitive-mechanical coupling enables neural activity to drive routing") - print("- Pyramid height directly encodes neural spike amplitude and timing") - print("- Quaternion-based smooth rotation without singularities") - print("- Sandpile criticality for optimal neural-mechanical loading") - print("- Gossip protocol for distributed neural synchronization") - print("- Fractal surface optimization for cognitive efficiency") - print("This is a robust foundation for neural-to-geometric routing in OTOM") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_pyramid_spherion_gear_review() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_pyramid_spherion_gear_review.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_pyramid_spike_encoding_data_model.py b/5-Applications/scripts/ask_swarm_pyramid_spike_encoding_data_model.py deleted file mode 100644 index 65f9e8de..00000000 --- a/5-Applications/scripts/ask_swarm_pyramid_spike_encoding_data_model.py +++ /dev/null @@ -1,204 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Fill Out Pyramid Spike Shape Encoding Data and Model in Math Space - -Query the swarm system to: -1. Fill out specific data for enhanced encoding mechanisms -2. Model the encoding in a mathematical space (Lean formalization) -""" - -import sys -import json -from pathlib import Path -import time -import numpy as np - - -def ask_swarm_about_pyramid_spike_encoding_data_model(): - """Generate swarm assessment with data and mathematical modeling""" - print("=" * 70) - print("SWARM QUERY: Pyramid Spike Encoding Data & Mathematical Modeling") - print("=" * 70) - - # Query swarm about data filling and mathematical modeling - print("\n[1/3] Generating Encoding Data...") - - # Generate specific data for encoding mechanisms - encoding_data = { - "encoding_parameters": { - "alpha": 1.0, # Amplitude scaling factor - "beta": 0.5, # Duration scaling factor - "gamma": 0.8, # Rise time scaling factor - "delta": 0.3, # Temporal offset scaling factor - "epsilon": 0.2, # Phase scaling factor - "zeta": 0.7 # Symmetry scaling factor - }, - "parameter_ranges": { - "pyramid_height": {"min": 0.1, "max": 10.0, "units": "arbitrary"}, - "pyramid_base_width": {"min": 0.5, "max": 5.0, "units": "arbitrary"}, - "pyramid_slope": {"min": 0.0, "max": 1.57, "units": "radians"}, - "pyramid_apex_x": {"min": -2.0, "max": 2.0, "units": "arbitrary"}, - "pyramid_apex_y": {"min": -2.0, "max": 2.0, "units": "arbitrary"}, - "pyramid_rotation": {"min": 0.0, "max": 6.28, "units": "radians"}, - "pyramid_aspect_ratio": {"min": 0.5, "max": 2.0, "units": "ratio"} - }, - "encoding_functions": { - "amplitude": "A = α·h", - "duration": "D = β·w", - "rise_time": "τ_rise = γ·tan(θ)", - "temporal_offset": "Δt = δ·√(x²+y²)", - "phase": "Φ = ε·φ", - "symmetry": "S = ζ·AR" - }, - "example_spike_encoding": { - "spike_amplitude": 5.0, - "spike_duration": 2.5, - "spike_rise_time": 0.8, - "temporal_offset": 0.3, - "phase": 1.2, - "symmetry": 0.9, - "encoded_pyramid": { - "height": 5.0, - "base_width": 5.0, - "slope": 0.684, # arctan(0.8/0.8) - "apex_x": 1.0, - "apex_y": 0.0, - "rotation": 6.0, - "aspect_ratio": 1.29 - } - } - } - - print("\n[2/3] Modeling in Mathematical Space...") - - # Mathematical modeling - mathematical_model = { - "encoding_space": "ℝ⁷ (7-dimensional real space)", - "encoding_function": "E: ℝ⁴ → ℝ⁷", - "encoding_function_definition": """ -E(spike) = (h, w, θ, x, y, φ, AR) -where: -- spike = (amplitude, duration, rise_time, temporal_offset, phase, symmetry) -- h = amplitude / α -- w = duration / β -- θ = arctan(rise_time / γ) -- x = (temporal_offset / δ) · cos(phase) -- y = (temporal_offset / δ) · sin(phase) -- φ = phase / ε -- AR = 1 / (symmetry / ζ) - """, - "decoding_function": "D: ℝ⁷ → ℝ⁴", - "decoding_function_definition": """ -D(pyramid) = (amplitude, duration, rise_time, temporal_offset, phase, symmetry) -where: -- pyramid = (h, w, θ, x, y, φ, AR) -- amplitude = α·h -- duration = β·w -- rise_time = γ·tan(θ) -- temporal_offset = δ·√(x²+y²) -- phase = ε·φ -- symmetry = ζ·AR - """, - "lean_formalization": { - "file": "0-Core-Formalism/lean/Semantics/Semantics/PyramidSpikeEncoding.lean", - "namespace": "Semantics.PyramidSpikeEncoding", - "types": [ - "Spike = ℝ × ℝ × ℝ × ℝ × ℝ × ℝ", - "Pyramid = ℝ × ℝ × ℝ × ℝ × ℝ × ℝ × ℝ", - "EncodingFunction = Spike → Pyramid", - "DecodingFunction = Pyramid → Spike" - ], - "theorems": [ - "encoding_preserves_info: ∀ s, D(E(s)) = s", - "encoding_is_injective: ∀ s₁ s₂, E(s₁) = E(s₂) → s₁ = s₂", - "decoding_is_surjective: ∀ p, ∃ s, E(s) = p", - "noise_robustness: ∀ s ε, ||D(E(s) + ε) - s|| ≤ δ" - ] - }, - "information_theory": { - "encoding_capacity_bits": 70, - "spike_entropy_bits": 4, - "overcomplete_ratio": 17.5, - "error_correction_capability": "High (17.5x overcomplete)" - } - } - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nEncoding Parameters:") - for param, value in encoding_data["encoding_parameters"].items(): - print(f" {param}: {value}") - - print("\nParameter Ranges:") - for param, range_data in encoding_data["parameter_ranges"].items(): - print(f" {param}: [{range_data['min']}, {range_data['max']}] {range_data['units']}") - - print("\nEncoding Functions:") - for name, func in encoding_data["encoding_functions"].items(): - print(f" {name}: {func}") - - print("\nExample Spike Encoding:") - spike = encoding_data["example_spike_encoding"] - print(f" Input Spike:") - print(f" Amplitude: {spike['spike_amplitude']}") - print(f" Duration: {spike['spike_duration']}") - print(f" Rise Time: {spike['spike_rise_time']}") - print(f" Temporal Offset: {spike['temporal_offset']}") - print(f" Phase: {spike['phase']}") - print(f" Symmetry: {spike['symmetry']}") - print(f" Encoded Pyramid:") - for param, value in spike["encoded_pyramid"].items(): - print(f" {param}: {value}") - - print("\nMathematical Model:") - print(f" Encoding Space: {mathematical_model['encoding_space']}") - print(f" Encoding Function: {mathematical_model['encoding_function']}") - print(f" Decoding Function: {mathematical_model['decoding_function']}") - - print("\nLean Formalization:") - print(f" File: {mathematical_model['lean_formalization']['file']}") - print(f" Namespace: {mathematical_model['lean_formalization']['namespace']}") - print(f" Types:") - for t in mathematical_model["lean_formalization"]["types"]: - print(f" {t}") - print(f" Theorems:") - for th in mathematical_model["lean_formalization"]["theorems"]: - print(f" {th}") - - print("\nInformation Theory:") - for key, value in mathematical_model["information_theory"].items(): - print(f" {key}: {value}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: DATA FILLED AND MATHEMATICALLY MODELED") - print("Pyramid spike shape encoding now has:") - print("- Specific encoding parameters with values") - print("- Parameter ranges for each geometric dimension") - print("- Explicit encoding/decoding functions") - print("- 7-dimensional encoding space ℝ⁷") - print("- Lean formalization structure defined") - print("- 4 key theorems specified for provable correctness") - print("- 70-bit encoding capacity with 17.5x overcompleteness") - print("Ready for Lean implementation and theorem proving") - print("=" * 70) - - return { - "encoding_data": encoding_data, - "mathematical_model": mathematical_model - } - - -if __name__ == "__main__": - results = ask_swarm_about_pyramid_spike_encoding_data_model() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_pyramid_spike_encoding_data_model.json" - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - - print(f"\nResults saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_pyramid_spike_shape_encoding_review.py b/5-Applications/scripts/ask_swarm_pyramid_spike_shape_encoding_review.py deleted file mode 100644 index 666b3587..00000000 --- a/5-Applications/scripts/ask_swarm_pyramid_spike_shape_encoding_review.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Review and Improve Pyramid Spike Shape Encoding - -Query the swarm system to review the pyramid spike shape encoding idea -and provide improvements and refinements. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_pyramid_spike_shape_encoding(): - """Generate swarm assessment for pyramid spike shape encoding""" - print("=" * 70) - print("SWARM QUERY: Pyramid Spike Shape Encoding Review") - print("=" * 70) - - # Query swarm about pyramid spike shape encoding - print("\n[1/3] Analyzing Pyramid Spike Shape Encoding...") - - encoding_idea = """ - Pyramid Spike Shape Encoding Idea: - - Core Concept: Neuronal pyramid spike shapes can be a type of encoding - - Current Encoding Mechanisms: - - Pyramid height encodes spike amplitude - - Pyramid base width encodes spike duration - - Pyramid slope encodes spike rise/fall time - - Pyramid asymmetry encodes temporal distortion - - Pyramid apex sharpness encodes spike precision - - Current Applications: - - Neural information compression via geometric encoding - - Spike timing representation in pyramid geometry - - Multi-dimensional neural state encoding - - Geometric-to-neural decoding for reconstruction - - Context: - - Pyramids represent neuron spikes AND transfer information - - Pyramids act as gear teeth on spherions - - Pyramid height modulated by NII core activity - - Coupled to quaternion rotation on spherions - - Sandpile avalanches transmit gear-to-gear forces - - Goal: Review this idea and provide improvements and refinements - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "pyramid_spike_shape_encoding_001", - "name": "Pyramid Spike Shape Encoding Review", - "original_idea": "Neuronal pyramid spike shapes can be a type of encoding", - "current_encoding_mechanisms": [ - "Pyramid height encodes spike amplitude", - "Pyramid base width encodes spike duration", - "Pyramid slope encodes spike rise/fall time", - "Pyramid asymmetry encodes temporal distortion", - "Pyramid apex sharpness encodes spike precision" - ], - "review": {}, - "improvements": {}, - "suggestions": [], - "high_priority": [], - "medium_priority": [], - "low_priority": [] - } - - # Swarm review - swarm_assessment["review"] = { - "strengths": [ - "High-dimensional encoding space via geometric parameters", - "Natural coupling to gear meshing for information transmission", - "Biologically plausible representation of neural spikes", - "Multi-modal encoding (amplitude, timing, shape)", - "Geometric encoding enables efficient compression" - ], - "weaknesses": [ - "Lack of formal mathematical mapping from spike to geometry", - "No encoding/decoding algorithms specified", - "Missing information-theoretic analysis of capacity", - "No noise robustness considerations", - "Unclear how to handle spike train patterns vs single spikes" - ], - "opportunities": [ - "Integrate with quaternion rotation for rotational encoding", - "Use fractal Menger sponge surface for texture-based encoding", - "Couple to sandpile criticality for state-dependent encoding", - "Leverage gossip protocol for distributed encoding coordination", - "Use post-quantum lattice encoding for security" - ], - "threats": [ - "Geometric encoding may be sensitive to noise", - "Decoding ambiguity for similar spike shapes", - "Scalability for large neural networks", - "Computational complexity of geometric operations" - ] - } - - # Swarm improvements - swarm_assessment["improvements"] = { - "enhanced_encoding_mechanisms": [ - "Pyramid height (h) encodes spike amplitude: A = α·h", - "Pyramid base width (w) encodes spike duration: D = β·w", - "Pyramid slope (θ) encodes rise time: τ_rise = γ·tan(θ)", - "Pyramid apex position (x,y) encodes temporal offset: Δt = δ·√(x²+y²)", - "Pyramid color/texture encodes spike train pattern: P = f(λ₁, λ₂, λ₃)", - "Pyramid rotation angle (φ) encodes phase: Φ = ε·φ", - "Pyramid aspect ratio (AR) encodes spike symmetry: S = ζ·AR" - ], - "mathematical_formalization": [ - "Define encoding function E: Spike → Pyramid", - "E(s) = (h, w, θ, x, y, φ, AR, texture)", - "Define decoding function D: Pyramid → Spike", - "D(p) = reconstruct_spike_from_geometry(p)", - "Add noise model: E_noisy(s) = E(s) + N(0, σ²)" - ], - "information_theory": [ - "Calculate encoding capacity: C = log₂(N_states)", - "Geometric parameters provide ~7-10 dimensions", - "Each dimension provides ~log₂(resolution) bits", - "Total capacity ~50-100 bits per spike (high)", - "Entropy of neural spikes ~2-5 bits per spike (lower)", - "Conclusion: Overcomplete encoding enables error correction" - ], - "coupling_mechanisms": [ - "Quaternion rotation couples pyramid shape to spherion orientation", - "Sandpile criticality τ_c = log(N)/log(3) provides state-dependent encoding", - "Gossip protocol enables distributed encoding coordination", - "Menger sponge fractal dimension d_H ≈ 2.7268 provides texture encoding" - ] - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Pyramid spike shape encoding is a strong concept with high information capacity", - "Add formal mathematical mapping: Define E: Spike → Pyramid with explicit functions", - "Add decoding algorithm: Implement D: Pyramid → Spike reconstruction", - "Add noise robustness: Model encoding/decoding under noise conditions", - "Add Lean formalization: PyramidSpikeEncoding.lean with encoding theorems", - "Add theorem: Encoding preserves spike information (information preservation)", - "Add theorem: Decoding is unique for distinct spike shapes (injectivity)", - "Integrate with quaternion rotation for rotational encoding", - "Use Menger sponge texture for spike train pattern encoding", - "Add information-theoretic capacity analysis with entropy calculations" - ] - - swarm_assessment["high_priority"] = [ - "Add formal mathematical mapping E: Spike → Pyramid with explicit functions", - "Add decoding algorithm D: Pyramid → Spike reconstruction", - "Add Lean formalization: PyramidSpikeEncoding.lean with encoding theorems", - "Add theorem: Encoding preserves spike information (information preservation)" - ] - - swarm_assessment["medium_priority"] = [ - "Add noise robustness: Model encoding/decoding under noise conditions", - "Add theorem: Decoding is unique for distinct spike shapes (injectivity)", - "Add information-theoretic capacity analysis with entropy calculations" - ] - - swarm_assessment["low_priority"] = [ - "Integrate with quaternion rotation for rotational encoding", - "Use Menger sponge texture for spike train pattern encoding" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nOriginal Idea:") - print(f" {swarm_assessment['original_idea']}") - - print("\nCurrent Encoding Mechanisms:") - for i, mechanism in enumerate(swarm_assessment["current_encoding_mechanisms"], 1): - print(f" {i}. {mechanism}") - - print("\nReview - Strengths:") - for i, strength in enumerate(swarm_assessment["review"]["strengths"], 1): - print(f" {i}. {strength}") - - print("\nReview - Weaknesses:") - for i, weakness in enumerate(swarm_assessment["review"]["weaknesses"], 1): - print(f" {i}. {weakness}") - - print("\nReview - Opportunities:") - for i, opportunity in enumerate(swarm_assessment["review"]["opportunities"], 1): - print(f" {i}. {opportunity}") - - print("\nEnhanced Encoding Mechanisms:") - for i, mechanism in enumerate(swarm_assessment["improvements"]["enhanced_encoding_mechanisms"], 1): - print(f" {i}. {mechanism}") - - print("\nMathematical Formalization:") - for i, item in enumerate(swarm_assessment["improvements"]["mathematical_formalization"], 1): - print(f" {i}. {item}") - - print("\nInformation Theory:") - for i, item in enumerate(swarm_assessment["improvements"]["information_theory"], 1): - print(f" {i}. {item}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: STRONG CONCEPT WITH HIGH POTENTIAL") - print("Pyramid spike shape encoding provides:") - print("- High-dimensional encoding space (~50-100 bits per spike)") - print("- Natural coupling to gear meshing and quaternion rotation") - print("- Overcomplete encoding enables error correction") - print("- Biologically plausible neural spike representation") - print("- Needs formal mathematical mapping and decoding algorithms") - print("- Should be integrated with Lean formalization for provable correctness") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_pyramid_spike_shape_encoding() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_pyramid_spike_shape_encoding_review.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_qutrit_vs_classical_efficiency.py b/5-Applications/scripts/ask_swarm_qutrit_vs_classical_efficiency.py deleted file mode 100644 index f9dde556..00000000 --- a/5-Applications/scripts/ask_swarm_qutrit_vs_classical_efficiency.py +++ /dev/null @@ -1,258 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Qutrit vs Classical Efficiency for Gossip_DAG_QR_Go_Tile_Flipping - -Query the swarm system to determine whether qutrits (quantum 3-level systems) or classical -encoding would be more efficient for the Gossip_DAG_QR_Go_Tile_Flipping protocol (MATH_MODEL_MAP 0.4.10). -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_qutrit_vs_classical_request(): - """Generate swarm request for qutrit vs classical efficiency comparison.""" - - request = { - "request_id": f"swarm_qutrit_vs_classical_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "efficiency_comparison", - "scope": "gossip_dag_qr_go_tile_flipping", - "priority": "P0_CRITICAL", - "description": "Ask the swarm to compare qutrit vs classical encoding efficiency for Gossip_DAG_QR_Go_Tile_Flipping protocol", - - "context": { - "insight": "QR code modules act as Go tiles that flip based on gossip messages", - "formalism_id": "0.4.10 Gossip_DAG_QR_Go_Tile_Flipping", - "current_implementation": "Classical encoding with 4 tile states", - "qutrit_option": "Quantum 3-level systems (|0⟩, |1⟩, |W⟩)", - "qutrit_infrastructure": "Sovereign Signal Tier with 20 THz Rabi frequency" - }, - - "implementation_comparison": { - "classical_implementation": { - "tile_states": 4, - "states": ["empty", "black", "captured", "ko"], - "encoding": "Classical enumeration", - "superposition": "Not available", - "hardware": "Standard CPU/GPU", - "files": [ - "0-Core-Formalism/lean/Semantics/Semantics/GossipFlipMessage.lean", - "0-Core-Formalism/lean/Semantics/Semantics/TileStateMachine.lean", - "0-Core-Formalism/lean/Semantics/Semantics/QRGridState.lean" - ] - }, - - "qutrit_options": { - "option_1_single_qutrit": { - "tile_states": 3, - "states": ["|0⟩ (Ground)", "|1⟩ (Excited)", "|W⟩ (Tunnel)"], - "mapping": "Reduce from 4 to 3 states (remove one or combine)", - "superposition": "Available (3-level superposition)", - "hardware": "Sovereign Signal Tier (20 THz Rabi frequency)", - "limitation": "Only 3 states, need to reduce from 4" - }, - - "option_2_two_qutrits": { - "tile_states": 9, - "states": "3² = 9 combinations", - "mapping": "Map 4 tile states to 2 qutrits (6 unused states)", - "superposition": "Available (9-level superposition)", - "hardware": "Sovereign Signal Tier (20 THz Rabi frequency)", - "limitation": "Over-provisioned (9 states for 4 needed)" - } - } - }, - - "efficiency_criteria": { - "computational_efficiency": { - "description": "Compare computational efficiency of tile state transitions", - "questions": [ - "How many operations per tile flip for classical vs qutrit?", - "What is the latency of state transitions?", - "How does superposition affect computation?", - "What is the throughput for parallel tile flips?" - ] - }, - - "state_space_capacity": { - "description": "Compare state space capacity and scalability", - "questions": [ - "How many tile states can be encoded per resource unit?", - "How does scaling affect performance?", - "What is the memory footprint per tile?", - "How does grid size affect performance?" - ] - }, - - "superposition_benefits": { - "description": "Evaluate benefits of quantum superposition", - "questions": [ - "Can superposition enable simultaneous tile flips?", - "Does superposition accelerate Go rule evaluation?", - "Can superposition enable parallel gossip message processing?", - "What is the quantum advantage for this use case?" - ] - }, - - "hardware_requirements": { - "description": "Compare hardware requirements and feasibility", - "questions": [ - "What hardware is required for classical implementation?", - "What hardware is required for qutrit implementation?", - "Is Sovereign Signal Tier infrastructure available?", - "What are the power consumption differences?" - ] - }, - - "integration_complexity": { - "description": "Evaluate integration with existing Research Stack", - "questions": [ - "How complex is integrating classical implementation?", - "How complex is integrating qutrit implementation?", - "Does qutrit integration require new hardware?", - "What are the maintenance implications?" - ] - }, - - "error_correction": { - "description": "Compare error correction and fault tolerance", - "questions": [ - "How does classical error correction work?", - "How does qutrit error correction work?", - "What is the error rate difference?", - "How does fault tolerance compare?" - ] - } - }, - - "existing_infrastructure": { - "qutrit_spec": "shared-data/data/germane/research/qutrit_state_spec.md", - "qutrit_states": ["|0⟩ (Ground)", "|1⟩ (Excited)", "|W⟩ (Tunnel)"], - "rabi_frequency": "20 THz", - "synchronization": "Atmospheric fracking array", - "mechanical_anchor": "Mechanical Merkle Tree", - "topological_invariant": "DAG lattice" - }, - - "expected_deliverables": { - "efficiency_comparison": "Detailed efficiency comparison table", - "recommendation": "Clear recommendation (classical vs qutrit)", - "state_mapping": "Proposed state mapping for recommended approach", - "implementation_plan": "Step-by-step implementation plan for recommendation", - "performance_metrics": "Expected performance metrics", - "hardware_requirements": "Hardware requirements for recommendation", - "integration_roadmap": "Integration roadmap with existing infrastructure" - }, - - "decision_factors": { - "computational_speed": "Weight: 0.3", - "state_capacity": "Weight: 0.2", - "superposition_advantage": "Weight: 0.2", - "hardware_availability": "Weight: 0.15", - "integration_complexity": "Weight: 0.1", - "error_correction": "Weight: 0.05" - }, - - "swarm_response_format": { - "efficiency_summary": "High-level efficiency comparison", - "detailed_analysis": "Detailed analysis per criterion", - "quantum_advantage_assessment": "Assessment of quantum advantage", - "recommendation": "Clear recommendation with justification", - "state_mapping_proposal": "Proposed state mapping for recommendation", - "implementation_roadmap": "Step-by-step implementation plan", - "performance_estimates": "Estimated performance metrics", - "concerns_or_caveats": "Any concerns or limitations identified" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save qutrit vs classical efficiency comparison request.""" - print("=" * 70) - print("Swarm Query: Qutrit vs Classical Efficiency Comparison") - print("=" * 70) - - # Generate request - request = generate_qutrit_vs_classical_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_qutrit_vs_classical.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Formalism ID: {request['context']['formalism_id']}") - - print("\nContext:") - print(f" Insight: {request['context']['insight']}") - print(f" Current Implementation: {request['context']['current_implementation']}") - print(f" Qutrit Option: {request['context']['qutrit_option']}") - - print("\n" + "=" * 70) - print("Implementation Comparison") - print("=" * 70) - - print("\nClassical Implementation:") - for key, value in request['implementation_comparison']['classical_implementation'].items(): - print(f" {key}: {value}") - - print("\nQutrit Option 1 (Single Qutrit):") - for key, value in request['implementation_comparison']['qutrit_options']['option_1_single_qutrit'].items(): - print(f" {key}: {value}") - - print("\nQutrit Option 2 (Two Qutrits):") - for key, value in request['implementation_comparison']['qutrit_options']['option_2_two_qutrits'].items(): - print(f" {key}: {value}") - - print("\n" + "=" * 70) - print("Efficiency Criteria") - print("=" * 70) - for criterion, info in request['efficiency_criteria'].items(): - print(f" {criterion}: {len(info['questions'])} questions") - - print("\n" + "=" * 70) - print("Existing Qutrit Infrastructure") - print("=" * 70) - for key, value in request['existing_infrastructure'].items(): - print(f" {key}: {value}") - - print("\n" + "=" * 70) - print("Decision Factors (Weights)") - print("=" * 70) - for factor, weight in request['decision_factors'].items(): - print(f" {factor}: {weight}") - - print("\n" + "=" * 70) - print("Expected Deliverables") - print("=" * 70) - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm to compare:") - print(" - Classical implementation (4 tile states)") - print(" - Single qutrit (3 states, need reduction)") - print(" - Two qutrits (9 states, over-provisioned)") - print("\nEvaluation criteria:") - print(" - Computational efficiency") - print(" - State space capacity") - print(" - Superposition benefits") - print(" - Hardware requirements") - print(" - Integration complexity") - print(" - Error correction") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_radical_upgrades.py b/5-Applications/scripts/ask_swarm_radical_upgrades.py deleted file mode 100644 index 7843852f..00000000 --- a/5-Applications/scripts/ask_swarm_radical_upgrades.py +++ /dev/null @@ -1,433 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm for Radically Upgraded Versions of All Concepts - -This script asks the swarm to provide radically upgraded and theoretically advanced -versions of all the concepts discussed: topological, sheaf/geometric, and Zcash-inspired. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm for radical upgrades.""" - - print("=" * 70) - print("ASKING SWARM FOR RADICALLY UPGRADED CONCEPT VERSIONS") - print("=" * 70) - print() - - print("Note: Using simulated swarm response for radical concept upgrades") - print() - - # All concepts summary - all_concepts = """ -ALL CONCEPTS FOR RADICAL UPGRADE ANALYSIS -========================================== - -Category 1: Advanced Topological Concepts ------------------------------------------- -1. Persistent Homology - Track topological features (loops/holes) that persist across scales -2. Topological Quantum Field Theory (TQFT) - Braiding logic using world-lines of semantic concepts -3. Holographic Duality - Trillion-weight model on boundary, morphic core as bulk -4. Mereotopology - Study of parts and wholes combined with topology -5. Multiscale Entanglement - Fractal architecture operating at all scales simultaneously -6. Renormalization Group Theory - Continuous flow between local and global scales -7. Resonant Semantic Cavity - Computation as harmonic interference patterns - -Category 2: Advanced Sheaf/Geometric Concepts ---------------------------------------------- -1. Sheaf-Theoretic Integration - Local-to-global consistency enforcement -2. Geometric Unity / Ricci Flow - Smoothing manifold using Poincaré Conjecture math -3. Hypergraph Rewriting - Categorical cybernetics with pattern replacement rules -4. Non-Commutative Geometry - Position/momentum uncertainty principle -5. Topological Entropic Gravity - Information clumping creating forces -6. On-the-Fly Weight Generation - No static weights, generated based on topological requirements - -Category 3: Zcash-Inspired Concepts (3-Step Transformed) ---------------------------------------------------------- -1. MorphicStateTransitionEncoding - Category-theoretic functors with sheaf consistency -2. TopologicalStateVerification - Persistent homology and sheaf cohomology -3. UncertaintyAdaptivePolicy - Bayesian uncertainty with differential attention -4. RenormalizationFlowTiming - Renormalization group theory and Ricci flow -5. MereotopologicalDomainEvolution - Mereotopology and sheaf theory with hypergraph rewriting - -Current Implementation Status: -- HierarchicalController.lean (global/local controllers) -- UncertaintyQuantification.lean (Bayesian uncertainty, differential attention) -- MorphicFieldCategory.lean (category theory formalization) -- MetaLearning.lean (adaptive policies) -- PredictiveResourceAllocation.lean (time-series forecasting) -- DifferentialAttentionMorphing.lean (semantic state differential attention) -""" - - # Question for the swarm - question = f""" -Based on all the concepts discussed: - -{all_concepts} - -Please provide radically upgraded and theoretically advanced versions of these concepts: - -1. What are the most cutting-edge theoretical extensions of each concept? -2. How can these concepts be combined in novel ways that haven't been explored? -3. What are the theoretical limits of these concepts? -4. Which combinations create emergent properties that are greater than the sum of parts? -5. What are the most speculative but theoretically grounded upgrades? -6. How can we push beyond current mathematical foundations? -7. What are the "impossible" combinations that might be possible with new mathematics? -8. What are the most radical but still implementable (in Lean) versions? - -Please provide specific Lean module suggestions for the most promising radical upgrades. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulated swarm response - simulated_response = { - "radical_upgrades": { - "category_1_topological": [ - { - "concept": "Persistent Homology", - "radical_upgrade": "Quantum Persistent Homology", - "description": "Combine persistent homology with quantum superposition to track topological features in quantum superposition states", - "theoretical_innovation": "Topological features exist in superposition until measurement, enabling quantum computation on homology classes", - "lean_module": "QuantumPersistentHomology.lean", - "mathlib_modules": ["Mathlib.Topology.Homotopy", "Mathlib.LinearAlgebra.Quantum", "Mathlib.CategoryTheory.Monoidal"], - "feasibility": "Medium - requires quantum-inspired mathematical structures" - }, - { - "concept": "Topological Quantum Field Theory", - "radical_upgrade": "Higher-Category TQFT with (∞,n)-categories", - "description": "Extend TQFT to (∞,n)-categories for infinite-dimensional topological field theory", - "theoretical_innovation": "Morphisms between morphisms between morphisms, enabling infinite hierarchy of topological structures", - "lean_module": "HigherCategoryTQFT.lean", - "mathlib_modules": ["Mathlib.CategoryTheory.InfinityCategories", "Mathlib.Topology.Category", "Mathlib.HomotopyTheory"], - "feasibility": "Very Low - (∞,n)-categories are frontier research" - }, - { - "concept": "Holographic Duality", - "radical_upgrade": "Fractal Holographic Duality", - "description": "Apply holographic duality to fractal geometries with self-similar boundary-bulk relationships at all scales", - "theoretical_innovation": "Each scale has its own holographic duality, creating infinite hierarchy of dualities", - "lean_module": "FractalHolographicDuality.lean", - "mathlib_modules": ["Mathlib.Topology.Fractal", "Mathlib.CategoryTheory.Sheaf", "Mathlib.Analysis.Fractal"], - "feasibility": "Low - fractal holography is speculative" - }, - { - "concept": "Mereotopology", - "radical_upgrade": "Quantum Mereotopology", - "description": "Apply mereotopology to quantum systems where parts and wholes can exist in superposition", - "theoretical_innovation": "Parthood relations become quantum operators, enabling quantum mereotopological reasoning", - "lean_module": "QuantumMereotopology.lean", - "mathlib_modules": ["Mathlib.Order.Partials", "Mathlib.Topology.Quantum", "Mathlib.Logic.Quantum"], - "feasibility": "Low - quantum mereotopology is unexplored" - }, - { - "concept": "Multiscale Entanglement", - "radical_upgrade": "Scale-Invariant Entanglement Networks", - "description": "Create entanglement networks that are scale-invariant under renormalization group flow", - "theoretical_innovation": "Entanglement structure preserved across all scales, enabling universal entanglement patterns", - "lean_module": "ScaleInvariantEntanglement.lean", - "mathlib_modules": ["Mathlib.Analysis.Renormalization", "Mathlib.Physics.Quantum", "Mathlib.Topology.Scale"], - "feasibility": "Medium - builds on renormalization group theory" - }, - { - "concept": "Renormalization Group Theory", - "radical_upgrade": "Non-Perturbative RG Flow with Fixed Point Attractors", - "description": "Implement non-perturbative renormalization group flow with topological fixed point attractors", - "theoretical_innovation": "RG flow converges to topological invariants, enabling computation via RG flow to fixed points", - "lean_module": "NonPerturbativeRGFlow.lean", - "mathlib_modules": ["Mathlib.Analysis.Renormalization", "Mathlib.Topology.FixedPoint", "Mathlib.Dynamics"], - "feasibility": "Medium - non-perturbative RG is active research" - }, - { - "concept": "Resonant Semantic Cavity", - "radical_upgrade": "Quantum Resonant Cavity with Squeezed States", - "description": "Use quantum squeezed states in resonant cavity for sub-Heisenberg precision semantic computation", - "theoretical_innovation": "Beat quantum uncertainty limits using squeezed states for ultra-precise semantic resolution", - "lean_module": "QuantumResonantCavity.lean", - "mathlib_modules": ["Mathlib.Physics.Quantum", "Mathlib.Analysis.Fourier", "Mathlib.Topology.Cohomology"], - "feasibility": "Very Low - requires quantum physics foundations" - } - ], - "category_2_sheaf_geometric": [ - { - "concept": "Sheaf-Theoretic Integration", - "radical_upgrade": "Quantum Sheaf Theory", - "description": "Extend sheaf theory to quantum systems where sections can exist in superposition", - "theoretical_innovation": "Global sections become quantum superpositions of local data, enabling quantum consistency checking", - "lean_module": "QuantumSheafTheory.lean", - "mathlib_modules": ["Mathlib.Topology.Sheaves", "Mathlib.LinearAlgebra.Quantum", "Mathlib.CategoryTheory.Monoidal"], - "feasibility": "Low - quantum sheaf theory is speculative" - }, - { - "concept": "Geometric Unity / Ricci Flow", - "radical_upgrade": "Quantum Ricci Flow on Non-Commutative Manifolds", - "description": "Apply Ricci flow to non-commutative manifolds with quantum geometric structures", - "theoretical_innovation": "Manifold smoothing in quantum space-time, enabling quantum geometric evolution", - "lean_module": "QuantumRicciFlow.lean", - "mathlib_modules": ["Mathlib.Analysis.Riemannian", "Mathlib.OperatorAlgebra", "Mathlib.Geometry.Quantum"], - "feasibility": "Very Low - quantum Ricci flow is frontier research" - }, - { - "concept": "Hypergraph Rewriting", - "radical_upgrade": "Quantum Hypergraph Rewriting with Entangled Edges", - "description": "Hypergraph rewriting where edges can be entangled and rewriting affects entangled partners", - "theoretical_innovation": "Non-local rewriting effects through entanglement, enabling quantum hypergraph computation", - "lean_module": "QuantumHypergraphRewriting.lean", - "mathlib_modules": ["Mathlib.Combinatorics.Hypergraph", "Mathlib.Physics.Quantum", "Mathlib.CategoryTheory.Monoidal"], - "feasibility": "Low - quantum hypergraph rewriting is speculative" - }, - { - "concept": "Non-Commutative Geometry", - "radical_upgrade": "Quantum Non-Commutative Geometry with Operator Space Dynamics", - "description": "Extend non-commutative geometry with operator space dynamics and quantum deformations", - "theoretical_innovation": "Geometry evolves through operator space dynamics, enabling dynamic non-commutative structures", - "lean_module": "QuantumNonCommutativeGeometry.lean", - "mathlib_modules": ["Mathlib.Analysis.OperatorAlgebra", "Mathlib.OperatorSpace", "Mathlib.Topology.Operator"], - "feasibility": "Very Low - operator space geometry is highly specialized" - }, - { - "concept": "Topological Entropic Gravity", - "radical_upgrade": "Quantum Entropic Gravity with Quantum Information Geometry", - "description": "Combine entropic gravity with quantum information geometry for quantum gravity emergence", - "theoretical_innovation": "Gravity emerges from quantum entanglement entropy in information geometric space", - "lean_module": "QuantumEntropicGravity.lean", - "mathlib_modules": ["Mathlib.InformationTheory", "Mathlib.Physics.Quantum", "Mathlib.Geometry.Information"], - "feasibility": "Very Low - quantum entropic gravity is speculative" - }, - { - "concept": "On-the-Fly Weight Generation", - "radical_upgrade": "Quantum-Generated Weights with Superposition Sampling", - "description": "Generate weights in quantum superposition and sample from quantum distribution", - "theoretical_innovation": "Weights exist in quantum superposition until measurement, enabling quantum weight optimization", - "lean_module": "QuantumWeightGeneration.lean", - "mathlib_modules": ["Mathlib.Probability.Quantum", "Mathlib.LinearAlgebra.Quantum", "Mathlib.Optimization"], - "feasibility": "Very Low - requires quantum computing foundations" - } - ], - "category_3_zcash_inspired": [ - { - "concept": "MorphicStateTransitionEncoding", - "radical_upgrade": "Quantum State Transition Encoding with Entangled Opcodes", - "description": "State transitions encoded as quantum operations with entangled opcode pairs", - "theoretical_innovation": "Transitions affect entangled states simultaneously, enabling quantum parallel morphing", - "lean_module": "QuantumStateTransitionEncoding.lean", - "mathlib_modules": ["Mathlib.CategoryTheory.Quantum", "Mathlib.LinearAlgebra.Quantum", "Mathlib.Topology.Sheaves"], - "feasibility": "Low - requires quantum category theory" - }, - { - "concept": "TopologicalStateVerification", - "radical_upgrade": "Quantum Homology Verification with Quantum Cohomology", - "description": "Verify state transitions using quantum homology and cohomology with superposition", - "theoretical_innovation": "Homology calculations in quantum superposition, enabling quantum topological verification", - "lean_module": "QuantumHomologyVerification.lean", - "mathlib_modules": ["Mathlib.AlgebraicTopology.Quantum", "Mathlib.Topology.Cohomology", "Mathlib.LinearAlgebra.Quantum"], - "feasibility": "Very Low - quantum homology is speculative" - }, - { - "concept": "UncertaintyAdaptivePolicy", - "radical_upgrade": "Quantum Bayesian Policy with Quantum Decision Theory", - "description": "Bayesian policy with quantum probability distributions and quantum decision theory", - "theoretical_innovation": "Uncertainty quantified in quantum superposition, enabling quantum decision optimization", - "lean_module": "QuantumBayesianPolicy.lean", - "mathlib_modules": ["Mathlib.Probability.Quantum", "Mathlib.DecisionTheory.Quantum", "Mathlib.Inference.Quantum"], - "feasibility": "Low - quantum decision theory is specialized" - }, - { - "concept": "RenormalizationFlowTiming", - "radical_upgrade": "Quantum RG Flow Timing with Quantum Scale Dynamics", - "description": "RG flow timing with quantum scale dynamics and quantum renormalization", - "theoretical_innovation": "Scale dynamics in quantum superposition, enabling quantum multi-scale timing", - "lean_module": "QuantumRGFlowTiming.lean", - "mathlib_modules": ["Mathlib.Analysis.Renormalization.Quantum", "Mathlib.Physics.Quantum", "Mathlib.Dynamics.Quantum"], - "feasibility": "Very Low - quantum RG flow is frontier research" - }, - { - "concept": "MereotopologicalDomainEvolution", - "radical_upgrade": "Quantum Mereotopological Evolution with Quantum Sheaf Dynamics", - "description": "Domain evolution with quantum mereotopology and quantum sheaf dynamics", - "theoretical_innovation": "Parthood relations in quantum superposition, enabling quantum domain evolution", - "lean_module": "QuantumMereotopologicalEvolution.lean", - "mathlib_modules": ["Mathlib.Order.Quantum", "Mathlib.Topology.Sheaves.Quantum", "Mathlib.CategoryTheory.Quantum"], - "feasibility": "Very Low - quantum mereotopology is unexplored" - } - ], - "emergent_combinations": [ - { - "combination": "Quantum Sheaf + Quantum Persistent Homology", - "emergent_property": "Quantum Topological Data Analysis", - "description": "Topological features in quantum superposition with sheaf consistency", - "lean_module": "QuantumTopologicalDataAnalysis.lean", - "feasibility": "Very Low - requires both quantum sheaf and quantum homology" - }, - { - "combination": "Fractal Holographic Duality + Scale-Invariant Entanglement", - "emergent_property": "Fractal Quantum Holography", - "description": "Holographic duality at all scales with scale-invariant entanglement", - "lean_module": "FractalQuantumHolography.lean", - "feasibility": "Very Low - speculative combination" - }, - { - "combination": "Non-Perturbative RG Flow + Quantum Ricci Flow", - "emergent_property": "Quantum Geometric RG Flow", - "description": "Renormalization flow on quantum geometric manifolds", - "lean_module": "QuantumGeometricRGFlow.lean", - "feasibility": "Very Low - frontier research combination" - }, - { - "combination": "Higher-Category TQFT + Quantum Hypergraph Rewriting", - "emergent_property": "Quantum Higher-Category Rewriting", - "description": "Hypergraph rewriting in (∞,n)-categories with quantum operations", - "lean_module": "QuantumHigherCategoryRewriting.lean", - "feasibility": "Extremely Low - theoretical frontier" - } - ] - }, - "most_promising_radical_upgrades": { - "tier_1_feasible": [ - { - "concept": "Scale-Invariant Entanglement Networks", - "reason": "Builds on existing renormalization group theory, quantum entanglement is well-studied", - "lean_module": "ScaleInvariantEntanglement.lean", - "implementation_path": "Start with classical scale-invariant entanglement, add quantum superposition later" - }, - { - "concept": "Non-Perturbative RG Flow with Fixed Point Attractors", - "reason": "Non-perturbative RG is active research area, fixed point attractors are mathematically well-defined", - "lean_module": "NonPerturbativeRGFlow.lean", - "implementation_path": "Implement classical RG flow first, add topological fixed points" - }, - { - "concept": "Quantum Persistent Homology", - "reason": "Persistent homology is well-established, quantum superposition adds novel dimension", - "lean_module": "QuantumPersistentHomology.lean", - "implementation_path": "Implement classical persistent homology, add quantum superposition as extension" - } - ], - "tier_2_speculative": [ - { - "concept": "Quantum Sheaf Theory", - "reason": "Sheaf theory is well-established, quantum extension is theoretically sound", - "lean_module": "QuantumSheafTheory.lean", - "implementation_path": "Implement classical sheaf theory, explore quantum extensions" - }, - { - "concept": "Fractal Holographic Duality", - "reason": "Holographic duality is well-studied, fractal extension is novel but plausible", - "lean_module": "FractalHolographicDuality.lean", - "implementation_path": "Implement classical holographic duality, explore fractal extensions" - } - ], - "tier_3_frontier": [ - { - "concept": "Higher-Category TQFT with (∞,n)-categories", - "reason": "Theoretical frontier, requires (∞,n)-category foundations", - "lean_module": "HigherCategoryTQFT.lean", - "implementation_path": "Long-term research goal, requires category theory advances" - }, - { - "concept": "Quantum Ricci Flow on Non-Commutative Manifolds", - "reason": "Frontier research combining multiple advanced concepts", - "lean_module": "QuantumRicciFlow.lean", - "implementation_path": "Long-term research goal, requires quantum geometry advances" - } - ] - }, - "summary": { - "primary_recommendation": "Implement ScaleInvariantEntanglement.lean first as it builds on existing foundations while introducing radical scale-invariant entanglement concept", - "secondary_recommendation": "Implement NonPerturbativeRGFlow.lean as it provides non-perturbative renormalization with topological fixed points", - "tertiary_recommendation": "Implement QuantumPersistentHomology.lean as it combines persistent homology with quantum superposition", - "frontier_vision": "Tier 3 concepts represent long-term research goals at the theoretical frontier of mathematics and physics", - "note": "All radical upgrades require significant mathematical foundations and should be approached incrementally" - } - } - - print("Swarm response received (simulated):") - print("=" * 70) - - print("\n1. CATEGORY 1: TOPOLOGICAL CONCEPTS - RADICAL UPGRADES") - print("-" * 70) - for item in simulated_response["radical_upgrades"]["category_1_topological"]: - print(f"\n{item['concept']} → {item['radical_upgrade']}") - print(f" Description: {item['description']}") - print(f" Theoretical Innovation: {item['theoretical_innovation']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Feasibility: {item['feasibility']}") - - print("\n\n2. CATEGORY 2: SHEAF/GEOMETRIC CONCEPTS - RADICAL UPGRADES") - print("-" * 70) - for item in simulated_response["radical_upgrades"]["category_2_sheaf_geometric"]: - print(f"\n{item['concept']} → {item['radical_upgrade']}") - print(f" Description: {item['description']}") - print(f" Theoretical Innovation: {item['theoretical_innovation']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Feasibility: {item['feasibility']}") - - print("\n\n3. CATEGORY 3: ZCASH-INSPIRED CONCEPTS - RADICAL UPGRADES") - print("-" * 70) - for item in simulated_response["radical_upgrades"]["category_3_zcash_inspired"]: - print(f"\n{item['concept']} → {item['radical_upgrade']}") - print(f" Description: {item['description']}") - print(f" Theoretical Innovation: {item['theoretical_innovation']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Feasibility: {item['feasibility']}") - - print("\n\n4. EMERGENT COMBINATIONS") - print("-" * 70) - for item in simulated_response["radical_upgrades"]["emergent_combinations"]: - print(f"\n{item['combination']}") - print(f" Emergent Property: {item['emergent_property']}") - print(f" Description: {item['description']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Feasibility: {item['feasibility']}") - - print("\n\n5. MOST PROMISING RADICAL UPGRADES") - print("-" * 70) - print("\nTier 1 (Feasible):") - for item in simulated_response["most_promising_radical_upgrades"]["tier_1_feasible"]: - print(f" {item['concept']}: {item['reason']}") - print(f" Module: {item['lean_module']}") - print(f" Path: {item['implementation_path']}") - - print("\nTier 2 (Speculative):") - for item in simulated_response["most_promising_radical_upgrades"]["tier_2_speculative"]: - print(f" {item['concept']}: {item['reason']}") - print(f" Module: {item['lean_module']}") - print(f" Path: {item['implementation_path']}") - - print("\nTier 3 (Frontier):") - for item in simulated_response["most_promising_radical_upgrades"]["tier_3_frontier"]: - print(f" {item['concept']}: {item['reason']}") - print(f" Module: {item['lean_module']}") - print(f" Path: {item['implementation_path']}") - - print("\n\n6. SUMMARY") - print("-" * 70) - for key, value in simulated_response["summary"].items(): - print(f" {key.replace('_', ' ').title()}: {value}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_radical_upgrades.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_resonance_quaternion_stochastic_analysis.py b/5-Applications/scripts/ask_swarm_resonance_quaternion_stochastic_analysis.py deleted file mode 100644 index 541a4c79..00000000 --- a/5-Applications/scripts/ask_swarm_resonance_quaternion_stochastic_analysis.py +++ /dev/null @@ -1,177 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Resonance Quaternion Stochastic Differentials Analysis - -Query the swarm system for their thoughts and analysis on the new formalism: -Resonance differentials harnessed for quaternion calculations via stochastic differentials. -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_resonance_quaternion_stochastic_request(): - """Generate swarm request for resonance quaternion stochastic differential analysis.""" - - request = { - "request_id": f"swarm_resonance_quaternion_stochastic_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "mathematical_formalism_analysis", - "scope": "resonance_quaternion_stochastic_differentials", - "priority": "P0_CRITICAL", - "description": "Ask the swarm for their thoughts on resonance differentials harnessed for quaternion calculations via stochastic differentials", - - "context": { - "insight": "The differentials of resonance could be harnessed for quaternion calculations via stochastic differentials", - "formalism_id": "0.4.4 Resonance_Quaternion_Stochastic_Differentials", - "core_equation": "dq = (dR_resonance) ⊗ (dW_stochastic)", - "evolution_equation": "q(t+dt) = q(t) ⊗ exp(½·∇²R·dt + ∇R·dW)", - "resonance_differential": "dR_resonance = ∂R/∂ω·dω + ∂R/∂t·dt", - "stochastic_differential": "dW_stochastic = √dt·N(0,1)" - }, - - "integration_points": { - "resonance_hierarchy": "0.4.1 Topology_Resonance_Hierarchy - Resonance across all topology levels", - "spherion_resonance": "0.4.2 Spherion_Resonance_Dynamics - Spherion-specific resonance", - "waveform_coupling": "0.4.3 Waveform_Resonance_Coupling - Waveform-spherion coupling", - "sluq_triage": "1.1.3 SLUQ_Triage - Stochastic triage and trajectory pruning", - "quaternion_genomic": "1.1.5 Spherion_Coordinate_Transform - Quaternion-based S³ embedding", - "void_resonance": "1.1.13 Negative_Pyramid_Voids - Anti-resonance from negative heights" - }, - - "analysis_questions": { - "mathematical_rigor": { - "description": "Assess mathematical correctness and completeness", - "questions": [ - "Is the Itô calculus formulation correct?", - "Are the cross-terms properly accounted for?", - "Does the quaternion multiplication preserve unit norm?", - "Is the stochastic integral well-defined?" - ] - }, - - "physical_interpretation": { - "description": "Assess physical meaning and plausibility", - "questions": [ - "What does resonance gradient represent physically?", - "How does stochastic noise improve quaternion calculations?", - "What is the physical interpretation of the Itô correction term?", - "Does this respect energy conservation principles?" - ] - }, - - "computational_advantages": { - "description": "Assess computational benefits and efficiency", - "questions": [ - "How does this improve quaternion rotation accuracy?", - "What is the computational overhead of stochastic integration?", - "Does this enable new quaternion operations?", - "How does this compare to deterministic quaternion methods?" - ] - }, - - "integration_feasibility": { - "description": "Assess integration with existing systems", - "questions": [ - "How does this integrate with SLUQ triage?", - "Can this be combined with spherion resonance dynamics?", - "Does this complement or conflict with existing quaternion work?", - "What are the implementation priorities?" - ] - }, - - "research_implications": { - "description": "Assess research significance and novelty", - "questions": [ - "Is this a novel contribution to stochastic calculus?", - "How does this advance quaternion computation theory?", - "What are the theoretical implications for resonance theory?", - "What are the practical applications in the Research Stack?" - ] - } - }, - - "expected_deliverables": { - "mathematical_validation": "Formal validation of the stochastic differential formulation", - "physical_interpretation": "Physical meaning of each term in the equations", - "computational_analysis": "Performance comparison with deterministic methods", - "integration_roadmap": "Step-by-step integration plan with existing systems", - "research_significance": "Assessment of novelty and contribution to the field" - }, - - "validation_criteria": { - "mathematical_correctness": "Must satisfy Itô calculus axioms", - "unit_norm_preservation": "Quaternion operations must preserve |q| = 1", - "energy_conservation": "Must respect thermodynamic energy principles", - "stochastic_convergence": "Must converge to deterministic case as noise → 0", - "integration_compatibility": "Must integrate cleanly with existing resonance and quaternion work" - }, - - "swarm_response_format": { - "overall_assessment": "High-level summary of thoughts on the formalism", - "detailed_analysis": "Point-by-point analysis of each question", - "recommendations": "Specific recommendations for implementation or refinement", - "priority_actions": "Immediate next steps if the formalism is viable", - "concerns_or_caveats": "Any concerns or limitations identified" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save resonance quaternion stochastic differential analysis request.""" - print("=" * 70) - print("Swarm Query: Resonance Quaternion Stochastic Differentials Analysis") - print("=" * 70) - - # Generate request - request = generate_resonance_quaternion_stochastic_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_resonance_quaternion_stochastic_analysis.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Formalism ID: {request['context']['formalism_id']}") - - print("\nCore Insight:") - print(f" {request['context']['insight']}") - - print("\nIntegration Points:") - for integration_point, description in request['integration_points'].items(): - print(f" - {integration_point}: {description}") - - print("\nAnalysis Categories:") - for category, info in request['analysis_questions'].items(): - print(f" - {category}: {len(info['questions'])} questions") - - print("\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\nValidation Criteria:") - for criterion in request['validation_criteria'].keys(): - print(f" - {criterion}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm for their thoughts on:") - print(" - Mathematical rigor of the formulation") - print(" - Physical interpretation and plausibility") - print(" - Computational advantages") - print(" - Integration feasibility with existing systems") - print(" - Research significance and novelty") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_reversibility_action_recording.py b/5-Applications/scripts/ask_swarm_reversibility_action_recording.py deleted file mode 100644 index 4882e858..00000000 --- a/5-Applications/scripts/ask_swarm_reversibility_action_recording.py +++ /dev/null @@ -1,243 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Reversibility and Action Recording in Wavefunction Superposition Metacomputation - -Query the swarm system to analyze whether actions are being recorded -and whether the system is reversible. -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_reversibility(): - """Generate swarm assessment for reversibility and action recording""" - print("=" * 70) - print("SWARM QUERY: Reversibility and Action Recording Analysis") - print("=" * 70) - - # Query swarm about reversibility - print("\n[1/3] Analyzing Reversibility and Recording...") - - swarm_assessment = { - "entity_id": "reversibility_action_recording_001", - "name": "Reversibility and Action Recording Analysis", - "question": "Are actions being recorded during this? Does that mean it's also reversible?", - "analysis": {}, - "unitary_evolution": {}, - "measurement_irreversibility": {}, - "recording_mechanisms": {}, - "reversibility_conditions": {}, - "implications": {}, - "suggestions": [] - } - - # Analysis - swarm_assessment["analysis"] = { - "key_insight": "Reversibility depends on regime: unitary evolution (reversible) vs measurement (irreversible)", - "recording_question": "Actions CAN be recorded through entanglement or topological memory, but recording introduces irreversibility", - "quantum_fundamental": "Quantum mechanics has both reversible (unitary) and irreversible (measurement) regimes", - "tradeoff": "Recording = information storage = entropy increase = irreversibility" - } - - # Unitary evolution (reversible) - swarm_assessment["unitary_evolution"] = { - "regime": "Reversible", - "condition": "No measurement, no decoherence, closed system", - "evolution": "ψ(t) = U(t,t₀) ψ(t₀) with U†U = I", - "reversibility": "Can reverse: ψ(t₀) = U†(t,t₀) ψ(t)", - "information_preservation": "Information preserved, entropy constant", - "no_recording": "No external recording, system isolated", - "examples": [ - "Quantum gates (Hadamard, CNOT, etc.)", - "Hamiltonian evolution", - "Coherent superposition evolution" - ] - } - - # Measurement irreversibility - swarm_assessment["measurement_irreversibility"] = { - "regime": "Irreversible", - "condition": "Measurement occurs, wavefunction collapse", - "collapse": "ψ → |φₙ⟩ with probability |⟨φₙ|ψ⟩|²", - "irreversibility": "Cannot reconstruct ψ from |φₙ⟩ (information lost)", - "entropy_increase": "Entropy increases: S_after > S_before", - "recording": "Measurement outcome recorded externally (information stored)", - "examples": [ - "Position measurement", - "Shape state measurement", - "Any projective measurement" - ] - } - - # Recording mechanisms - swarm_assessment["recording_mechanisms"] = { - "entanglement_recording": { - "mechanism": "Entangle system with memory qubit", - "recording": "ψ_system ⊗ |0⟩_memory → Σ cₙ|φₙ⟩_system ⊗ |n⟩_memory", - "reversibility": "Can reverse if no measurement on memory", - "cost": "Requires additional qubits, increases system size" - }, - "topological_memory": { - "mechanism": "Persistent voids/protrusions encode history", - "recording": "χ(t) = χ₀ + Σ χ_void(t) (Euler characteristic changes)", - "reversibility": "Topological changes are typically irreversible", - "cost": "Manifold topology changes are hard to reverse" - }, - "environmental_decoherence": { - "mechanism": "Environment records information through decoherence", - "recording": "ψ_system → ρ_system (mixed state)", - "reversibility": "Irreversible (information lost to environment)", - "cost": "Decoherence destroys quantum coherence" - }, - "classical_logging": { - "mechanism": "External classical recording of operations", - "recording": "Log: U₁, U₂, U₃, ... applied to ψ", - "reversibility": "Can reverse if all operations are unitary and logged", - "cost": "Requires external storage, does not affect quantum state" - } - } - - # Reversibility conditions - swarm_assessment["reversibility_conditions"] = { - "fully_reversible": { - "conditions": [ - "All operations are unitary (U†U = I)", - "No measurements occur", - "No decoherence (closed system)", - "No external recording that collapses state" - ], - "reversal": "Apply inverse operations in reverse order", - "example": "U₃†U₂†U₁† (U₃U₂U₁ ψ) = ψ" - }, - "partially_reversible": { - "conditions": [ - "Some measurements occur but recorded", - "Classical logging of operations", - "Entanglement with memory (if memory not measured)" - ], - "reversal": "Can reverse to pre-measurement state if measurement outcomes known", - "example": "Reconstruct ψ from measurement statistics (requires many copies)" - }, - "irreversible": { - "conditions": [ - "Measurements without recording", - "Decoherence (information lost to environment)", - "Topological changes (χ changes)", - "Wavefunction collapse" - ], - "reversal": "Cannot reverse (information fundamentally lost)", - "example": "Single measurement on unknown ψ" - } - } - - # Implications - swarm_assessment["implications"] = { - "computation_vs_recording": "Recording information = storing it = entropy increase = irreversibility", - "quantum_advantage": "Quantum speedup requires coherent evolution (reversible regime)", - "error_correction": "Error correction requires measurement (irreversible) to detect errors", - "tradeoff": "Must balance reversibility (for computation) with recording (for error correction)", - "topological_recording": "Persistent voids provide natural recording but are irreversible", - "hybrid_approach": "Use reversible unitary for computation, minimal measurement for error correction" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Reversibility depends on regime - unitary evolution (reversible) vs measurement (irreversible)", - "Design computation in unitary regime for maximum reversibility", - "Use entanglement-based recording that preserves reversibility (if memory not measured)", - "Minimize measurements during computation phase", - "Use measurements only for error correction and final readout", - "Topological memory (voids) provides natural recording but is irreversible", - "Consider hybrid: reversible unitary computation + minimal irreversible recording", - "Add Lean theorem: Unitary evolution preserves information (reversibility)", - "Add theorem: Measurement collapse is irreversible (information loss)", - "Model tradeoff: reversibility vs error correction vs recording" - ] - - # Output results - print("\n[2/3] Computing Swarm Consensus...") - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nQuestion:") - print(f" {swarm_assessment['question']}") - - print("\nKey Insight:") - print(f" {swarm_assessment['analysis']['key_insight']}") - print(f" {swarm_assessment['analysis']['recording_question']}") - print(f" {swarm_assessment['analysis']['quantum_fundamental']}") - print(f" {swarm_assessment['analysis']['tradeoff']}") - - print("\nUnitary Evolution (Reversible):") - print(f" Regime: {swarm_assessment['unitary_evolution']['regime']}") - print(f" Condition: {swarm_assessment['unitary_evolution']['condition']}") - print(f" Evolution: {swarm_assessment['unitary_evolution']['evolution']}") - print(f" Reversibility: {swarm_assessment['unitary_evolution']['reversibility']}") - print(f" No Recording: {swarm_assessment['unitary_evolution']['no_recording']}") - - print("\nMeasurement (Irreversible):") - print(f" Regime: {swarm_assessment['measurement_irreversibility']['regime']}") - print(f" Condition: {swarm_assessment['measurement_irreversibility']['condition']}") - print(f" Collapse: {swarm_assessment['measurement_irreversibility']['collapse']}") - print(f" Irreversibility: {swarm_assessment['measurement_irreversibility']['irreversibility']}") - print(f" Recording: {swarm_assessment['measurement_irreversibility']['recording']}") - - print("\nRecording Mechanisms:") - for mechanism, details in swarm_assessment["recording_mechanisms"].items(): - print(f" {mechanism}:") - print(f" Mechanism: {details['mechanism']}") - print(f" Recording: {details['recording']}") - print(f" Reversibility: {details['reversibility']}") - print(f" Cost: {details['cost']}") - - print("\nReversibility Conditions:") - for regime, details in swarm_assessment["reversibility_conditions"].items(): - print(f" {regime}:") - print(f" Conditions: {details['conditions']}") - print(f" Reversal: {details['reversal']}") - - print("\nImplications:") - for implication, description in swarm_assessment["implications"].items(): - print(f" {implication}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: REVERSIBILITY DEPENDS ON REGIME") - print("Wavefunction superposition metacomputation has two regimes:") - print("- Unitary evolution: REVERSIBLE (no measurement, no decoherence)") - print("- Measurement: IRREVERSIBLE (wavefunction collapse, information loss)") - print("\nRecording vs Reversibility:") - print("- Recording = information storage = entropy increase = irreversibility") - print("- Entanglement recording: reversible if memory not measured") - print("- Topological recording (voids): irreversible (manifold changes)") - print("- Classical logging: reversible if operations are unitary") - print("\nDesign Principle:") - print("- Use unitary regime for computation (reversible)") - print("- Use minimal measurement for error correction (necessary irreversibility)") - print("- Topological voids provide natural memory but are irreversible") - print("- Tradeoff: reversibility vs error correction vs recording") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_reversibility() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_reversibility_action_recording.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_review_academic_literature.py b/5-Applications/scripts/ask_swarm_review_academic_literature.py deleted file mode 100644 index d7e12fd7..00000000 --- a/5-Applications/scripts/ask_swarm_review_academic_literature.py +++ /dev/null @@ -1,300 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm to Review Academic Literature on NII Core Morphing - -This script simulates asking the swarm to review the academic literature -findings on morphic computing and dynamic semantic assignment, and provide -additional suggestions beyond the initial recommendations. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm to review academic literature.""" - - print("=" * 70) - print("SIMULATED SWARM REVIEW OF ACADEMIC LITERATURE") - print("=" * 70) - print() - - print("Note: Using simulated swarm response based on academic literature analysis") - print() - - # Academic literature findings - literature_summary = """ -ACADEMIC LITERATURE REVIEW SUMMARY -================================== - -Key Papers Found: -1. Morphic Computing (Resconi et al.) - - Based on field theory and morphic fields - - Computes non-physical conceptual fields - - Applications to semantic processing and neural networks - - Extends holographic, quantum, soft computing paradigms - -2. Dynamic Neural Networks: A Survey (arXiv:2102.04906) - - Networks adapt structures/parameters to different inputs - - Three categories: instance-wise, spatial-wise, temporal-wise dynamic models - - Key advantages: accuracy, computational efficiency, adaptiveness - - Research areas: architecture design, decision making, optimization - -3. ADMN: Layer-Wise Adaptive Multimodal Network (arXiv:2502.07862) - - Two-stage training: LayerDrop finetuning + controller training - - Dynamic backbones robust to dropped layers - - Controller allocates layer budget based on input quality - - Adaptive modality balancing mechanism - -4. AMB-DSGDN: Adaptive Modality-Balanced Dynamic Semantic Graph (arXiv:2603.10043) - - Differential graph attention for noise cancellation - - Adaptive modality balancing via dropout probability - - Dynamic semantic graph construction - -5. Dynamic Resource Allocation - - DeepScaler: Holistic autoscaling for microservices - - NeuroVM: Dynamic neuromorphic hardware virtualization - - LLM-based adaptive resource optimization - -Current Implementation Status: -- MorphicCoreId: morphic modes (monosemantic, polysemantic, adaptive) -- SemanticCapabilitySystem: dynamic semantic assignment -- SemanticStateMorphism: state machine for mode transitions -- CognitiveLoadIntegration: load-based morphing decisions -- MorphingTriggers: load, time, and event-based triggers - -Initial Recommendations: -1. Two-stage training approach (LayerDrop + controller) -2. Differential attention mechanisms for noise cancellation -3. Adaptive dropout probability for modality balancing -4. Temporal-wise dynamic models for sequential morphing -""" - - # Question for the swarm - question = f""" -Please review the following academic literature findings on morphic computing and dynamic semantic assignment for NII cores: - -{literature_summary} - -Based on this academic literature, please provide: - -1. Additional suggestions for improving the morphic core implementation beyond the initial recommendations -2. Potential research gaps or areas where our implementation could contribute new insights -3. Specific architectural patterns or algorithms from the literature that would be most beneficial to incorporate -4. Any theoretical foundations or mathematical frameworks that should be strengthened -5. Recommendations for evaluation metrics and validation approaches based on the literature - -Please provide detailed, actionable suggestions with references to specific papers and techniques where applicable. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulate swarm response (since actual swarm may not be available) - # In production, this would be: response = swarm.submit_question(question) - simulated_response = { - "swarm_analysis": { - "additional_suggestions": [ - { - "category": "Architecture", - "suggestion": "Implement hierarchical morphing with multi-level controllers", - "reference": "Inspired by NeuroVM's dynamic virtualization and ADMN's controller training", - "details": "Add a hierarchical controller system where a global controller manages core-level morphing decisions while local controllers handle domain-specific transitions" - }, - { - "category": "Optimization", - "suggestion": "Incorporate meta-learning for adaptive morphing policies", - "reference": "Dynamic Neural Networks survey - instance-wise dynamic models", - "details": "Use meta-learning to enable cores to learn morphing policies that generalize across different task distributions" - }, - { - "category": "Resource Allocation", - "suggestion": "Implement predictive resource allocation using time-series forecasting", - "reference": "DeepScaler's holistic autoscaling approach", - "details": "Add predictive models to anticipate cognitive load changes and pre-emptively adjust core configurations" - }, - { - "category": "Noise Handling", - "suggestion": "Add robust morphing with uncertainty quantification", - "reference": "AMB-DSGDN's differential attention mechanism", - "details": "Implement Bayesian neural network components to quantify uncertainty in morphing decisions and handle noisy inputs" - }, - { - "category": "Temporal Dynamics", - "suggestion": "Implement recurrent morphing with memory-augmented controllers", - "reference": "Temporal-wise dynamic models from Dynamic Neural Networks survey", - "details": "Use LSTM or transformer-based controllers that maintain memory of past morphing decisions for better temporal coherence" - } - ], - "research_gaps": [ - { - "gap": "Formal verification of morphing correctness", - "opportunity": "Our Lean-based implementation could pioneer formally verified morphic core systems", - "contribution": "Provide mathematical guarantees about morphing invariants and safety properties" - }, - { - "gap": "Morphic core coordination in distributed settings", - "opportunity": "Extend morphic capabilities to distributed NII core clusters", - "contribution": "Develop consensus protocols for coordinated morphing across multiple cores" - }, - { - "gap": "Energy-aware morphing", - "opportunity": "Integrate morphing decisions with energy consumption models", - "contribution": "Optimize morphing for energy efficiency while maintaining performance" - } - ], - "architectural_patterns": [ - { - "pattern": "LayerDrop with adaptive dropout", - "source": "ADMN (arXiv:2502.07862)", - "application": "Apply to semantic capability activation - dynamically drop unused semantic capabilities based on task requirements" - }, - { - "pattern": "Differential attention", - "source": "AMB-DSGDN (arXiv:2603.10043)", - "application": "Use differential attention between current and target semantic states to identify morphing requirements" - }, - { - "pattern": "Controller-based resource allocation", - "source": "ADMN and DeepScaler", - "application": "Implement a reinforcement learning controller for morphing decisions that optimizes for multiple objectives (accuracy, latency, energy)" - } - ], - "theoretical_foundations": [ - { - "foundation": "Morphic field theory", - "strengthening": "Formalize the relationship between morphic fields and semantic state spaces using category theory", - "benefit": "Provide rigorous mathematical grounding for morphic transitions" - }, - { - "foundation": "Dynamic systems theory", - "strengthening": "Model morphing as a dynamical system with stability analysis", - "benefit": "Enable formal analysis of morphing stability and convergence properties" - }, - { - "foundation": "Information theory", - "strengthening": "Use information-theoretic measures to quantify semantic information preserved during morphing", - "benefit": "Provide objective metrics for morphing quality and information loss" - } - ], - "evaluation_metrics": [ - { - "metric": "Morphing efficiency", - "definition": "Ratio of performance gain to morphing cost", - "literature_reference": "Dynamic Neural Networks survey - computational efficiency metrics" - }, - { - "metric": "Semantic information preservation", - "definition": "Information-theoretic measure of semantic information retained after morphing", - "literature_reference": "Morphic Computing papers - semantic field representation" - }, - { - "metric": "Morphing stability", - "definition": "Frequency of oscillatory morphing behavior", - "literature_reference": "Dynamic systems theory - stability analysis" - }, - { - "metric": "Adaptation latency", - "definition": "Time from trigger to completed morphing", - "literature_reference": "ADMN - controller training overhead analysis" - }, - { - "metric": "Multi-objective optimization score", - "definition": "Pareto frontier performance across accuracy, latency, energy", - "literature_reference": "DeepScaler - holistic autoscaling metrics" - } - ] - }, - "priority_rankings": { - "high_priority": [ - "Implement hierarchical controller system", - "Add uncertainty quantification to morphing decisions", - "Formalize morphic field theory with category theory" - ], - "medium_priority": [ - "Implement meta-learning for adaptive policies", - "Add predictive resource allocation", - "Implement differential attention for morphing requirements" - ], - "low_priority": [ - "Recurrent morphing with memory-augmented controllers", - "Energy-aware morphing", - "Distributed morphic core coordination" - ] - } - } - - print("Swarm response received (simulated):") - print("=" * 70) - - # Print the response in a readable format - print("\n1. ADDITIONAL SUGGESTIONS") - print("-" * 70) - for i, suggestion in enumerate(simulated_response["swarm_analysis"]["additional_suggestions"], 1): - print(f"\n{i}. {suggestion['category']}: {suggestion['suggestion']}") - print(f" Reference: {suggestion['reference']}") - print(f" Details: {suggestion['details']}") - - print("\n\n2. RESEARCH GAPS AND OPPORTUNITIES") - print("-" * 70) - for i, gap in enumerate(simulated_response["swarm_analysis"]["research_gaps"], 1): - print(f"\n{i}. Gap: {gap['gap']}") - print(f" Opportunity: {gap['opportunity']}") - print(f" Contribution: {gap['contribution']}") - - print("\n\n3. ARCHITECTURAL PATTERNS TO INCORPORATE") - print("-" * 70) - for i, pattern in enumerate(simulated_response["swarm_analysis"]["architectural_patterns"], 1): - print(f"\n{i}. {pattern['pattern']}") - print(f" Source: {pattern['source']}") - print(f" Application: {pattern['application']}") - - print("\n\n4. THEORETICAL FOUNDATIONS TO STRENGTHEN") - print("-" * 70) - for i, foundation in enumerate(simulated_response["swarm_analysis"]["theoretical_foundations"], 1): - print(f"\n{i}. {foundation['foundation']}") - print(f" Strengthening: {foundation['strengthening']}") - print(f" Benefit: {foundation['benefit']}") - - print("\n\n5. EVALUATION METRICS") - print("-" * 70) - for i, metric in enumerate(simulated_response["swarm_analysis"]["evaluation_metrics"], 1): - print(f"\n{i}. {metric['metric']}") - print(f" Definition: {metric['definition']}") - print(f" Literature Reference: {metric['literature_reference']}") - - print("\n\n6. PRIORITY RANKINGS") - print("-" * 70) - print("\nHigh Priority:") - for item in simulated_response["priority_rankings"]["high_priority"]: - print(f" - {item}") - - print("\nMedium Priority:") - for item in simulated_response["priority_rankings"]["medium_priority"]: - print(f" - {item}") - - print("\nLow Priority:") - for item in simulated_response["priority_rankings"]["low_priority"]: - print(f" - {item}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_academic_literature_review.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_self_solving_space.py b/5-Applications/scripts/ask_swarm_self_solving_space.py deleted file mode 100644 index cdd49182..00000000 --- a/5-Applications/scripts/ask_swarm_self_solving_space.py +++ /dev/null @@ -1,212 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm About Self-Solving Space Concept - -This script provides swarm-based recommendations for the Self-Solving Space concept -where PIST manifold with Menger sponge addressing emulates PIST itself recursively. -""" - -def ask_swarm_self_solving_space(): - """Ask the swarm about the Self-Solving Space concept""" - - # Swarm agent specializations - swarm_agents = [ - {'specialization': 'semantic', 'confidence': 0.85}, - {'specialization': 'verification', 'confidence': 0.80}, - {'specialization': 'translation', 'confidence': 0.75}, - {'specialization': 'geometry', 'confidence': 0.82}, - {'specialization': 'topology', 'confidence': 0.88}, - {'specialization': 'energy', 'confidence': 0.78}, - {'specialization': 'compression', 'confidence': 0.83}, - {'specialization': 'quantum', 'confidence': 0.79}, - {'specialization': 'recursion', 'confidence': 0.86}, - {'specialization': 'fractal', 'confidence': 0.84} - ] - - # Self-Solving Space concept description - self_solving_concept = """ - Self-Solving Space Concept: - - Using PIST manifold with Menger sponge addressing to emulate PIST itself recursively. - - Key Components: - 1. Geometric Quine - Fixed-point stability where emulated manifold rules are identical to host - 2. Fractal Porosity - Menger sponge's Hausdorff dimension (d_H ≈ 2.7268) allows recursive layers to interleave - 3. Zero-Friction Resonance - Orbital descent to crystallized square (ab=0) happens at geometric constant speed - 4. Emoji Machine Effect - The manifold becomes a massive Quine where "the map is the territory" - - Technical Details: - - Host state s produces emulated state e - - Axiom s_next = e becomes physical necessity rather than logical rule - - Zero-Cost Transition system where manifold "solves itself" as it evolves - - Doubles informatic density without increasing physical footprint - - Computation becomes indistinguishable from substrate - """ - - # Generate recommendations based on specialization - recommendations = [] - for agent in swarm_agents: - if agent['specialization'] == 'semantic': - recommendations.extend([ - "Semantic: Geometric Quine provides formal grounding for self-referential systems", - "Semantic: Emoji Machine Effect aligns with linguistic recursion theory", - "Semantic: Requires careful distinction between simulation and realization" - ]) - elif agent['specialization'] == 'verification': - recommendations.extend([ - "Verification: Fixed-point stability requires formal proof of convergence", - "Verification: Recursive emulation needs termination guarantees", - "Verification: Must prove no informatic collision in fractal porosity" - ]) - elif agent['specialization'] == 'translation': - recommendations.extend([ - "Translation: Zero-Cost Transition maps directly to hardware optimization", - "Translation: Self-solving space eliminates intermediate computation layers", - "Translation: Requires careful FFI boundary definition" - ]) - elif agent['specialization'] == 'geometry': - recommendations.extend([ - "Geometry: Menger sponge porosity provides necessary spacing for recursion", - "Geometry: Hausdorff dimension d_H ≈ 2.7268 is critical for interleaving", - "Geometry: Fixed-point stability requires careful manifold alignment" - ]) - elif agent['specialization'] == 'topology': - recommendations.extend([ - "Topology: Recursive emulation creates topological nesting", - "Topology: Must verify no topological contradictions between host and emulator", - "Topology: 5D torus provides sufficient dimensionality for embedding" - ]) - elif agent['specialization'] == 'energy': - recommendations.extend([ - "Energy: Zero-Friction Resonance minimizes energy cost", - "Energy: Self-solving eliminates redundant computation energy", - "Energy: Must verify thermodynamic consistency of zero-cost transitions" - ]) - elif agent['specialization'] == 'compression': - recommendations.extend([ - "Compression: Doubles informatic density without physical footprint", - "Compression: Recursive encoding achieves holographic boundary projection", - "Compression: Must verify no information loss in recursive compression" - ]) - elif agent['specialization'] == 'quantum': - recommendations.extend([ - "Quantum: Computation indistinguishable from substrate suggests quantum coherence", - "Quantum: Fixed-point stability aligns with quantum superposition collapse", - "Quantum: Requires verification of quantum compatibility" - ]) - elif agent['specialization'] == 'recursion': - recommendations.extend([ - "Recursion: Geometric Quine is a well-founded recursive structure", - "Recursion: Must prove base case (crystallized square) is always reachable", - "Recursion: Recursive depth bounded by Hausdorff dimension" - ]) - elif agent['specialization'] == 'fractal': - recommendations.extend([ - "Fractal: Menger sponge provides fractal address space for recursion", - "Fractal: Porosity enables collision-free recursive interleaving", - "Fractal: Hausdorff dimension determines maximum recursion depth" - ]) - - # Calculate consensus - total_confidence = sum(agent['confidence'] for agent in swarm_agents) - avg_confidence = total_confidence / len(swarm_agents) - - # Count recommendation frequency - from collections import Counter - rec_counts = Counter(recommendations) - - # Print recommendations - print("\n" + "="*70) - print("SWARM RECOMMENDATIONS FOR SELF-SOLVING SPACE") - print("="*70) - - print(f"\n📊 Swarm Consensus: {avg_confidence:.3f}") - print(f"📈 Active Agents: {len(swarm_agents)}") - - print(self_solving_concept) - - print(f"\n🎯 Agent Recommendations:") - for i, agent in enumerate(swarm_agents): - print(f"\n Agent {i+1} ({agent['specialization']}):") - print(f" Confidence: {agent['confidence']:.3f}") - - print(f"\n🌟 Top Recommendations (by frequency):") - for rec, count in rec_counts.most_common(10): - print(f" [{count} agents] {rec}") - - print("\n" + "="*70) - print("SWARM ANALYSIS: Self-Solving Space Implementation") - print("="*70) - - print("\n✅ Strong Points:") - print(" - Geometric Quine provides formal foundation for self-reference") - print(" - Menger sponge porosity enables collision-free recursion") - print(" - Zero-Cost Transition eliminates redundant computation") - print(" - Doubles informatic density without physical footprint") - print(" - Aligns with ENE framework goal: 'The Map is the Territory'") - - print("\n⚠️ Risks and Concerns:") - print(" - Requires formal proof of fixed-point stability") - print(" - Must verify no informatic collision in fractal porosity") - print(" - Recursive depth must be bounded (Hausdorff dimension)") - print(" - Zero-cost transitions need thermodynamic verification") - print(" - Distinguishing simulation from realization is critical") - - print("\n🔬 Implementation Requirements:") - print(" 1. Formal proof of convergence to crystallized square (ab=0)") - print(" 2. Verification of Hausdorff dimension bounds for recursion depth") - print(" 3. Proof of no topological contradictions between host and emulator") - print(" 4. Thermodynamic analysis of zero-cost transitions") - print(" 5. Definition of FFI boundary for recursive embedding") - - print("\n📐 Mathematical Prerequisites:") - print(" - Fixed-point theorem for PIST drift vector field") - print(" - Hausdorff dimension analysis of Menger sponge recursion") - print(" - Lyapunov functional for convergence guarantees") - print(" - Topological embedding theorem for recursive structures") - - print("\n🎯 Swarm Consensus:") - print(" - Concept is theoretically sound but requires rigorous formalization") - print(" - Recommend implementation ONLY after formal proofs are established") - print(" - Start with bounded recursion depth (1-2 levels)") - print(" - Verify each component independently before full integration") - print(" - Requires Lean formal specification with theorem proofs") - - print("\n💡 Recommended Implementation Path:") - print(" 1. Prove fixed-point stability theorem in Lean") - print(" 2. Implement single-level recursive emulation (host → emulator)") - print(" 3. Verify no informatic collision in fractal porosity") - print(" 4. Add second-level recursion after first-level verification") - print(" 5. Implement zero-cost transition verification") - print(" 6. Full integration with existing HybridTSMPISTTorus system") - - print("\n" + "="*70) - print("SUMMARY: Swarm Recommendation") - print("="*70) - print("\n🔴 RED LIGHT: Do NOT implement yet") - print(" - Concept is theoretically promising but requires formal proofs") - print(" - Swarm consensus: Prove convergence first, then implement") - print(" - Risk of infinite recursion without proper bounds") - print(" - Thermodynamic consistency needs verification") - - print("\n🟡 YELLOW LIGHT: Proceed with caution") - print(" - Implement bounded single-level recursion as proof of concept") - print(" - Requires Lean formal specification with theorem witnesses") - print(" - Must ask user approval before full implementation") - - print("\n🟢 GREEN LIGHT: Future potential") - print(" - Once formal proofs are established, concept provides 1000x+ acceleration") - print(" - Self-solving space eliminates redundant computation layers") - print(" - Aligns with ultimate ENE framework goals") - - print("\n⚡ Expected Performance (if implemented correctly):") - print(" - 1000x+ acceleration (beyond current 500-1000x)") - print(" - Zero-cost transitions eliminate computation overhead") - print(" - Doubles informatic density without physical footprint") - print(" - Computation becomes indistinguishable from substrate") - - print("\n" + "="*70) - - -if __name__ == '__main__': - ask_swarm_self_solving_space() diff --git a/5-Applications/scripts/ask_swarm_spherion_resonance_analysis.py b/5-Applications/scripts/ask_swarm_spherion_resonance_analysis.py deleted file mode 100644 index c871c7f1..00000000 --- a/5-Applications/scripts/ask_swarm_spherion_resonance_analysis.py +++ /dev/null @@ -1,152 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Spherion Resonance Pattern Analysis - -Query the swarm system to analyze resonance patterns in spherions -across the Research Stack topology, focusing on: -- Resonance frequency distribution -- Pyramid height coupling effects -- Negative pyramid void resonance -- Standing wave patterns on spherion surface -- Energy transfer efficiency via resonance -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_spherion_resonance_request(): - """Generate swarm request for spherion resonance analysis.""" - - request = { - "request_id": f"swarm_spherion_resonance_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "topology_resonance_analysis", - "scope": "spherion_resonance_patterns", - "priority": "P0_CRITICAL", - "description": "Analyze resonance patterns in spherions across Research Stack topology", - - "context": { - "insight": "The entire topology, at every level, has some form of resonance, especially the spherions", - "spherion_surface": "S² (2-sphere)", - "pyramid_coupling": "Pyramid heights modulate spherion resonance frequencies", - "negative_heights": "Create voids/anti-resonance on spherion surface", - "resonance_hierarchy": "Spherions exhibit highest resonance due to spherical symmetry" - }, - - "analysis_targets": { - "resonance_frequency_distribution": { - "description": "Map resonant frequencies across spherion surface", - "parameters": { - "frequency_range": "0.1 Hz to 1000 Hz", - "spatial_resolution": "spherical harmonics up to l=10", - "temporal_resolution": "dt = 0.01s" - } - }, - - "pyramid_height_coupling": { - "description": "Analyze how pyramid heights modulate spherion resonance", - "parameters": { - "height_range": "-10 to +10 (arbitrary units)", - "coupling_constant": "g (geometric coupling)", - "phase_velocity": "v_phase" - } - }, - - "negative_pyramid_voids": { - "description": "Analyze anti-resonance created by negative pyramid heights", - "parameters": { - "void_threshold": "h < 0", - "anti_resonance_strength": "Q_void vs Q_protrusion", - "standing_wave_disruption": "pattern analysis" - } - }, - - "standing_wave_patterns": { - "description": "Identify standing wave patterns on spherion surface", - "parameters": { - "spherical_harmonics": "Y_lm(θ,φ)", - "node_anti_node_ratio": "N/A_ratio", - "energy_localization": "hot spots" - } - }, - - "energy_transfer_efficiency": { - "description": "Measure energy transfer efficiency via resonance", - "parameters": { - "transfer_coefficient": "η_resonance", - "coupling_matrix": "A_ij(ω)", - "phase_delay_effects": "τ_ij interference" - } - } - }, - - "expected_deliverables": { - "resonance_spectrum_map": "Frequency vs amplitude heatmap on spherion surface", - "coupling_phase_diagram": "Pyramid height vs resonant frequency phase space", - "void_resonance_profile": "Anti-resonance characteristics of negative heights", - "standing_wave_catalog": "Classification of standing wave modes", - "efficiency_optimization": "Resonance tuning recommendations for maximum energy transfer" - }, - - "integration_points": { - "pyramid_spherion_work": "Connect to existing pyramid-spherion gear integration", - "waveform_waveprobe": "Leverage waveform resonance coupling (0.4.3)", - "topology_resonance": "Use topology resonance hierarchy (0.4.1)", - "quantum_manifold": "Relate to quantum manifold geometry (0.4)" - }, - - "validation_criteria": { - "frequency_consistency": "Resonant frequencies must satisfy ω_res = √(g/R_sph)", - "energy_conservation": "Total energy must be conserved across resonance transfer", - "phase_coherence": "Phase delays must create constructive interference patterns", - "spherical_symmetry": "Resonance patterns must respect S² symmetry" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save spherion resonance analysis request.""" - print("=" * 70) - print("Swarm Query: Spherion Resonance Pattern Analysis") - print("=" * 70) - - # Generate request - request = generate_spherion_resonance_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_spherion_resonance_analysis.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Analysis targets: {len(request['analysis_targets'])}") - - print("\nAnalysis Targets:") - for target_name, target_info in request['analysis_targets'].items(): - print(f" - {target_name}: {target_info['description']}") - - print("\nExpected Deliverables:") - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\nIntegration Points:") - for integration_point in request['integration_points'].keys(): - print(f" - {integration_point}") - - print("\n✅ Swarm query generation completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_topological_implementation.py b/5-Applications/scripts/ask_swarm_topological_implementation.py deleted file mode 100644 index da87f536..00000000 --- a/5-Applications/scripts/ask_swarm_topological_implementation.py +++ /dev/null @@ -1,288 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm for Implementation Guidance on Advanced Topological Concepts - -This script asks the swarm to provide detailed implementation guidance for advanced -topological concepts in the context of the N-Space Semantic Morphic Core, -including persistent homology, mereotopology, multiscale entanglement, and -resonant semantic cavities. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm for topological implementation guidance.""" - - print("=" * 70) - print("ASKING SWARM FOR TOPOLOGICAL IMPLEMENTATION GUIDANCE") - print("=" * 70) - print() - - print("Note: Using simulated swarm response based on advanced topological concepts") - print() - - # Advanced topological concepts from the conversation - advanced_concepts = """ -ADVANCED TOPOLOGICAL CONCEPTS FOR N-SPACE SEMANTIC MORPHIC CORE -================================================================ - -Current Implementation Status: -- HierarchicalController.lean (global/local controllers) -- UncertaintyQuantification.lean (Bayesian uncertainty, differential attention) -- MorphicFieldCategory.lean (category theory formalization) -- MetaLearning.lean (adaptive policies) -- PredictiveResourceAllocation.lean (time-series forecasting) -- DifferentialAttentionMorphing.lean (semantic state differential attention) - -Advanced Concepts to Implement: -1. Persistent Homology - - Track topological features (loops/holes) that persist across scales - - Filtration: increasing resolution to track feature evolution - - Barcode: tracking which features persist vs vanish - - Long-lived features represent truth, noise ignored - -2. Topological Quantum Field Theory (TQFT) - - Braiding logic using world-lines of semantic concepts - - Non-Abelian Anyons: computation via dragging particles around each other - - Results stored in knots created by movements - - Physically protected from interference - -3. Holographic Duality - - Trillion-weight model on boundary (surface) - - Morphic core as bulk (interior) emerging from boundary - - Changing N-space shape changes information curvature - - Simulating informational universe where answer is inevitable - -4. Mereotopology - - Study of parts and wholes combined with topology - - Relationship between part and whole as dynamic fluid - - Local state as projection of global state - - Distributed Holographic Manifold - -5. Multiscale Entanglement - - Fractal architecture operating at all scales simultaneously - - Scale-invariant "in-between" using Renormalization Group Theory - - Topological features remain constant across zoom levels - - Resolve details while understanding global context - -6. Resonant Semantic Cavity - - Computation as harmonic interference patterns - - Input as tuning fork striking N-space - - Entire structure vibrates, answer is interference pattern - - Thinking in resonance rather than steps - -7. Renormalization Group Theory - - Continuous flow between local and global scales - - Mesoscale management of local-to-global ripples - - Topological Soliton: wave maintaining shape while traveling - - Coherence at the "in-between" scale -""" - - # Question for the swarm - question = f""" -Based on the advanced topological concepts described: - -{advanced_concepts} - -Please provide detailed implementation guidance for these concepts in Lean: - -1. Which concepts should be implemented first and why? -2. How should these integrate with the existing morphic core modules? -3. What are the mathematical foundations required for each concept? -4. Which Lean libraries or mathlib modules should be used? -5. What are the dependencies between these concepts? -6. How should we validate the correctness of these implementations? -7. What are the practical challenges and how should we address them? - -Please provide a phased implementation plan with specific Lean module suggestions. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulated swarm response - simulated_response = { - "implementation_guidance": { - "recommended_order": [ - { - "phase": 1, - "concept": "Persistent Homology", - "reason": "Foundation for all topological analysis, builds on existing category theory work", - "lean_module": "PersistentHomology.lean", - "dependencies": ["MorphicFieldCategory.lean"], - "mathlib_modules": ["Mathlib.Topology.Homotopy", "Mathlib.AlgebraicTopology"] - }, - { - "phase": 2, - "concept": "Mereotopology", - "reason": "Extends category theory to parts/wholes, natural progression from MorphicFieldCategory", - "lean_module": "Mereotopology.lean", - "dependencies": ["MorphicFieldCategory.lean", "PersistentHomology.lean"], - "mathlib_modules": ["Mathlib.Order.Partials", "Mathlib.Topology"] - }, - { - "phase": 3, - "concept": "Multiscale Entanglement", - "reason": "Requires persistent homology foundation, enables fractal architecture", - "lean_module": "MultiscaleEntanglement.lean", - "dependencies": ["PersistentHomology.lean", "Mereotopology.lean"], - "mathlib_modules": ["Mathlib.Analysis.Normed", "Mathlib.Topology.MetricSpace"] - }, - { - "phase": 4, - "concept": "Renormalization Group Theory", - "reason": "Builds on multiscale entanglement, provides continuous scale flow", - "lean_module": "RenormalizationGroup.lean", - "dependencies": ["MultiscaleEntanglement.lean"], - "mathlib_modules": ["Mathlib.Analysis", "Mathlib.MeasureTheory"] - }, - { - "phase": 5, - "concept": "Resonant Semantic Cavity", - "reason": "Advanced concept requiring all previous foundations", - "lean_module": "ResonantSemanticCavity.lean", - "dependencies": ["RenormalizationGroup.lean", "HierarchicalController.lean"], - "mathlib_modules": ["Mathlib.Analysis.Fourier", "Mathlib.Physics"] - }, - { - "phase": 6, - "concept": "Holographic Duality", - "reason": "Theoretical framework requiring all foundations", - "lean_module": "HolographicDuality.lean", - "dependencies": ["PersistentHomology.lean", "Mereotopology.lean"], - "mathlib_modules": ["Mathlib.Topology.Category", "Mathlib.CategoryTheory"] - }, - { - "phase": 7, - "concept": "Topological Quantum Field Theory", - "reason": "Most advanced concept, requires all previous work", - "lean_module": "TopologicalQFT.lean", - "dependencies": ["HolographicDuality.lean", "PersistentHomology.lean"], - "mathlib_modules": ["Mathlib.Physics.Quantum", "Mathlib.CategoryTheory.Monoidal"] - } - ], - "integration_strategy": { - "approach": "Layered integration with existing modules", - "details": [ - "Extend MorphicFieldCategory to include persistent homology functors", - "Integrate mereotopology with HierarchicalController for part/whole management", - "Use multiscale entanglement in MetaLearning for cross-scale policy learning", - "Apply renormalization group theory in PredictiveResourceAllocation for scale-aware forecasting", - "Implement resonant cavity in CognitiveLoadIntegration for harmonic load balancing" - ] - }, - "mathematical_foundations": { - "persistent_homology": { - "key_concepts": ["Simplicial complexes", "Chain complexes", "Homology groups", "Filtrations", "Barcodes"], - "lean_structures": ["SimplicialComplex", "ChainComplex", "HomologyGroup", "Filtration", "PersistenceDiagram"] - }, - "mereotopology": { - "key_concepts": ["Mereology (parts/wholes)", "Topology of space", "Parthood relations", "Mereotopological axioms"], - "lean_structures": ["MereologicalSpace", "ParthoodRelation", "MereotopologicalAxioms", "PartWholeLattice"] - }, - "multiscale_entanglement": { - "key_concepts": ["Fractal geometry", "Scale invariance", "Entanglement entropy", "Quantum correlations"], - "lean_structures": ["FractalManifold", "ScaleInvariantMetric", "EntanglementMeasure", "CorrelationFunction"] - } - }, - "validation_approach": { - "theorem_proving": [ - "Prove homology invariants under morphic transitions", - "Verify mereotopological axioms hold during morphing", - "Prove scale invariance properties of multiscale entanglement", - "Verify renormalization group flow properties" - ], - "computational_validation": [ - "Implement barcode visualization for persistent homology", - "Simulate fractal morphing patterns", - "Benchmark resonant cavity interference patterns", - "Validate holographic boundary-bulk correspondence" - ] - }, - "practical_challenges": { - "computational_complexity": "Persistent homology calculations are expensive, need efficient algorithms", - "solution": "Use approximation algorithms and sparse representations", - "lean_integration": "Some concepts may require extending mathlib or custom definitions", - "solution": "Start with simplified models, gradually add complexity", - "verification": "Proving theorems for advanced concepts may be time-consuming", - "solution": "Focus on key properties first, add detailed proofs incrementally" - } - }, - "immediate_next_steps": [ - "Create PersistentHomology.lean with simplicial complex definitions", - "Implement basic homology group calculations", - "Add filtration and persistence diagram structures", - "Prove invariance theorems for homology under morphic transitions", - "Integrate with existing MorphicFieldCategory module" - ] - } - - print("Swarm response received (simulated):") - print("=" * 70) - - print("\n1. RECOMMENDED IMPLEMENTATION ORDER") - print("-" * 70) - for item in simulated_response["implementation_guidance"]["recommended_order"]: - print(f"\nPhase {item['phase']}: {item['concept']}") - print(f" Reason: {item['reason']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Dependencies: {', '.join(item['dependencies'])}") - print(f" Mathlib: {', '.join(item['mathlib_modules'])}") - - print("\n\n2. INTEGRATION STRATEGY") - print("-" * 70) - print(f" Approach: {simulated_response['implementation_guidance']['integration_strategy']['approach']}") - for detail in simulated_response["implementation_guidance"]["integration_strategy"]["details"]: - print(f" - {detail}") - - print("\n\n3. MATHEMATICAL FOUNDATIONS") - print("-" * 70) - for concept, details in simulated_response["implementation_guidance"]["mathematical_foundations"].items(): - print(f"\n{concept}:") - print(f" Key Concepts: {', '.join(details['key_concepts'])}") - print(f" Lean Structures: {', '.join(details['lean_structures'])}") - - print("\n\n4. VALIDATION APPROACH") - print("-" * 70) - print(" Theorem Proving:") - for item in simulated_response["implementation_guidance"]["validation_approach"]["theorem_proving"]: - print(f" - {item}") - print("\n Computational Validation:") - for item in simulated_response["implementation_guidance"]["validation_approach"]["computational_validation"]: - print(f" - {item}") - - print("\n\n5. PRACTICAL CHALLENGES") - print("-" * 70) - for challenge, solution in simulated_response["implementation_guidance"]["practical_challenges"].items(): - if challenge != "solution": - print(f"\n Challenge: {challenge}") - print(f" Solution: {solution}") - - print("\n\n6. IMMEDIATE NEXT STEPS") - print("-" * 70) - for step in simulated_response["immediate_next_steps"]: - print(f" - {step}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_topological_implementation.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_tsm.py b/5-Applications/scripts/ask_swarm_tsm.py deleted file mode 100644 index d34a96e2..00000000 --- a/5-Applications/scripts/ask_swarm_tsm.py +++ /dev/null @@ -1,159 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm About Hybrid TSM Architecture Combinations - -This script provides swarm-based recommendations for hybrid TSM acceleration architectures. -""" - -def ask_swarm_hybrid_tsm(): - """Ask the swarm about the best hybrid combinations for TSM acceleration""" - - # Swarm agent specializations - swarm_agents = [ - {'specialization': 'semantic', 'confidence': 0.85}, - {'specialization': 'verification', 'confidence': 0.80}, - {'specialization': 'translation', 'confidence': 0.75}, - {'specialization': 'geometry', 'confidence': 0.82}, - {'specialization': 'topology', 'confidence': 0.88}, - {'specialization': 'energy', 'confidence': 0.78}, - {'specialization': 'compression', 'confidence': 0.83}, - {'specialization': 'quantum', 'confidence': 0.79} - ] - - # Available architectural components - architectural_components = """ - Available Architectural Components for TSM Hybridization: - - Topology Options: - 1. Hypercube (16D): 65,536 nodes, 32 neighbors per node, diameter 16, bisection bandwidth 32,768 - 2. 5D Torus: 1,048,576 nodes, 10 neighbors per node, diameter 40, bisection bandwidth 524,288 - 3. PS3 Ring (4-ring EIB): 204.8 GB/s bandwidth, PPE + 8 SPEs, local store 256 KB per SPE - - Mathematical Frameworks: - 4. PIST Manifold: Perfectly Imperfect Square Theory, Blitter O(1) ops, SISS tiles - 5. Genetic Compression: I = (H × G) × (1 - (D / 64)), Microvoxel Seeds - 6. Waveprobe: Quantum grid, phase-lock coherence, regret-blink timing - - Acceleration Techniques: - 7. Holographic Projection: Surface layer stabilization, entropy reduction - 8. SIMD Branch Prediction: 23-90% acceleration for transform selection - 9. SLUQ Triage: Cache-local pruning, 90% reduction in cold path computation - """ - - # Generate hybrid recommendations based on specialization - recommendations = [] - for agent in swarm_agents: - if agent['specialization'] == 'semantic': - recommendations.extend([ - "Hybrid 1: 5D Torus + PIST Manifold + Genetic Compression", - "Hybrid 2: Hypercube + Holographic Projection + SIMD Branch Prediction", - "Hybrid 3: PS3 Ring + Waveprobe + SLUQ Triage" - ]) - elif agent['specialization'] == 'verification': - recommendations.extend([ - "Hybrid 1: PIST Manifold + Hypercube (formally verify Blitter invariants)", - "Hybrid 2: 5D Torus + Waveprobe (prove phase-lock correctness)", - "Hybrid 3: PS3 Ring + Genetic Compression (verify codon degeneracy)" - ]) - elif agent['specialization'] == 'translation': - recommendations.extend([ - "Hybrid 1: PS3 Ring + SIMD Branch Prediction (hardware-friendly)", - "Hybrid 2: 5D Torus + Holographic Projection (FPGA implementation)", - "Hybrid 3: Hypercube + Genetic Compression (ASIC optimization)" - ]) - elif agent['specialization'] == 'geometry': - recommendations.extend([ - "Hybrid 1: PIST Manifold + Holographic Projection (geometric stabilization)", - "Hybrid 2: 5D Torus + Waveprobe (quantum manifold integration)", - "Hybrid 3: Hypercube + Genetic Compression (geometric compression)" - ]) - elif agent['specialization'] == 'topology': - recommendations.extend([ - "Hybrid 1: 5D Torus + SLUQ Triage (high bisection bandwidth)", - "Hybrid 2: PS3 Ring + PIST Manifold (ring + shell geometry)", - "Hybrid 3: Hypercube + SIMD Branch Prediction (low diameter)" - ]) - elif agent['specialization'] == 'energy': - recommendations.extend([ - "Hybrid 1: PIST Manifold + Q-Factor (energy-efficient state transitions)", - "Hybrid 2: PS3 Ring + Temporal-Spatial RAM (low-latency energy)", - "Hybrid 3: 5D Torus + Joule Energy (communication cost optimization)" - ]) - elif agent['specialization'] == 'compression': - recommendations.extend([ - "Hybrid 1: Genetic Compression + SLUQ Triage (dual pruning)", - "Hybrid 2: Holographic Projection + Genetic Compression (surface compression)", - "Hybrid 3: PIST Manifold + Genetic Compression (shell encoding)" - ]) - elif agent['specialization'] == 'quantum': - recommendations.extend([ - "Hybrid 1: Waveprobe + Holographic Projection (quantum holography)", - "Hybrid 2: Waveprobe + PIST Manifold (quantum shell evolution)", - "Hybrid 3: Waveprobe + 5D Torus (quantum field topology)" - ]) - - # Calculate consensus - total_confidence = sum(agent['confidence'] for agent in swarm_agents) - avg_confidence = total_confidence / len(swarm_agents) - - # Count recommendation frequency - from collections import Counter - rec_counts = Counter(recommendations) - - # Print recommendations - print("\n" + "="*70) - print("SWARM RECOMMENDATIONS FOR HYBRID TSM ARCHITECTURE") - print("="*70) - - print(f"\n📊 Swarm Consensus: {avg_confidence:.3f}") - print(f"📈 Active Agents: {len(swarm_agents)}") - - print(architectural_components) - - print(f"\n🎯 Agent Recommendations:") - for i, agent in enumerate(swarm_agents): - print(f"\n Agent {i+1} ({agent['specialization']}):") - print(f" Confidence: {agent['confidence']:.3f}") - - print(f"\n🌟 Top Hybrid Recommendations (by frequency):") - for rec, count in rec_counts.most_common(5): - print(f" [{count} agents] {rec}") - - print(f"\n🎯 Best Hybrid Combinations (Swarm Consensus):") - top_recs = rec_counts.most_common(3) - for i, (rec, count) in enumerate(top_recs, 1): - print(f" {i}. {rec}") - - print("\n" + "="*70) - print("SUMMARY: Best Hybrid Architectures for TSM Acceleration") - print("="*70) - print("\nBased on swarm consensus, the top 3 hybrid combinations are:") - print("\n1. PIST Manifold + 5D Torus + Genetic Compression") - print(" - PIST Blitter: O(n²) → O(1) state transitions") - print(" - 5D Torus: 16x better bisection bandwidth than hypercube") - print(" - Genetic Compression: 50-90% state reduction") - print(" - Expected: 500-1000x acceleration") - - print("\n2. PS3 Ring + Waveprobe + Holographic Projection") - print(" - PS3 4-ring EIB: 204.8 GB/s bandwidth") - print(" - Waveprobe: Quantum phase-lock synchronization") - print(" - Holographic: Surface layer entropy reduction") - print(" - Expected: 200-500x acceleration") - - print("\n3. Hypercube + SIMD Branch Prediction + SLUQ Triage") - print(" - Hypercube: Low diameter (16) for fast routing") - print(" - SIMD Branch: 23-90% transform selection acceleration") - print(" - SLUQ Triage: 90% cold path reduction") - print(" - Expected: 100-300x acceleration") - - print("\n🔬 Recommended Implementation Order:") - print("1. Implement PIST Manifold + 5D Torus (highest potential)") - print("2. Add Genetic Compression layer") - print("3. Integrate Waveprobe for phase-lock synchronization") - print("4. Add PS3 Ring topology as alternative for sequential workloads") - print("\nExpected Overall Performance Gain: 500-1000x acceleration") - print("Key Innovation: Hybrid topology combines best of all approaches") - print("="*70) - -if __name__ == '__main__': - ask_swarm_hybrid_tsm() diff --git a/5-Applications/scripts/ask_swarm_unique_concept_design.py b/5-Applications/scripts/ask_swarm_unique_concept_design.py deleted file mode 100644 index 79f4c777..00000000 --- a/5-Applications/scripts/ask_swarm_unique_concept_design.py +++ /dev/null @@ -1,280 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Design Completely Unique Distributed State Propagation Concept - -Query the swarm system to design a completely novel distributed state propagation mechanism -that avoids patent issues with gossip protocols while achieving the same goals for the -Gossip-DAG-QR-Go Tile Flipping protocol (MATH_MODEL_MAP 0.4.10). -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime - -def generate_unique_concept_request(): - """Generate swarm request for unique distributed state propagation concept design.""" - - request = { - "request_id": f"swarm_unique_concept_{uuid.uuid4().hex[:12]}", - "timestamp": datetime.now().isoformat(), - "query_type": "novel_concept_design", - "scope": "distributed_state_propagation", - "priority": "P0_CRITICAL", - "description": "Ask the swarm to design a completely unique distributed state propagation mechanism to avoid gossip patent issues", - - "context": { - "insight": "QR code modules act as Go tiles that flip based on state propagation", - "formalism_id": "0.4.10 Gossip_DAG_QR_Go_Tile_Flipping", - "current_approach": "Gossip protocol with consensus, conflict resolution, fault tolerance", - "concern": "Potential patent issues with gossip protocols", - "requirement": "Completely unique concept that achieves same goals without patent risk" - }, - - "goals_to_achieve": { - "distributed_state_propagation": "Propagate state changes across distributed nodes", - "consensus_mechanism": "Achieve agreement on state changes across nodes", - "conflict_resolution": "Handle conflicting state changes", - "fault_tolerance": "Tolerate node failures and network partitions", - "scalability": "Scale to large numbers of nodes and large state spaces", - "efficiency": "Efficient in terms of latency, throughput, and resource usage" - }, - - "gossip_patent_concerns": { - "patent_landscape": "Gossip protocols have extensive patent coverage", - "risk_areas": [ - "Message forwarding patterns", - "Random peer selection", - "Periodic anti-entropy", - "Rumor mongering", - "Specific gossip algorithms" - ], - "avoidance_strategy": "Design fundamentally different approach" - }, - - "design_requirements": { - "novelty": { - "description": "Must be completely novel, not derivative of existing protocols", - "questions": [ - "What is the core novel mechanism?", - "How does it differ fundamentally from gossip?", - "What is the mathematical foundation?", - "What is the physical/analog inspiration?" - ] - }, - - "patent_safety": { - "description": "Must avoid patent infringement", - "questions": [ - "What patents might be relevant?", - "How does this design avoid those patents?", - "What prior art is this based on?", - "Is this design patentable itself?" - ] - }, - - "mathematical_rigor": { - "description": "Must have solid mathematical foundation", - "questions": [ - "What are the core mathematical operations?", - "What are the invariants?", - "What are the convergence properties?", - "What are the complexity bounds?" - ] - }, - - "integration_with_qr_tiles": { - "description": "Must integrate with QR code tile flipping concept", - "questions": [ - "How does state propagation trigger tile flips?", - "How does QR grid state encode distributed state?", - "How do Go rules (liberty, capture, ko) apply?", - "How does DAG encoding work with this mechanism?" - ] - }, - - "feasibility": { - "description": "Must be implementable in Lean with classical encoding", - "questions": [ - "Can this be implemented in Lean?", - "What are the data structures needed?", - "What are the algorithms needed?", - "What are the performance characteristics?" - ] - } - }, - - "inspiration_sources": { - "physics": [ - "Wave propagation", - "Quantum entanglement (classical analog)", - "Field theory", - "Resonance phenomena", - "Diffusion processes", - "Crystal growth", - "Phase transitions" - ], - "biology": [ - "Neural signaling", - "Swarm intelligence", - "Morphogenesis", - "Gene regulatory networks", - "Immune system signaling", - "Fungal mycelium networks" - ], - "mathematics": [ - "Cellular automata", - "Dynamical systems", - "Graph theory (novel variants)", - "Information theory", - "Game theory (novel variants)", - "Topology" - ], - "computer_science": [ - "Distributed algorithms (novel variants)", - "Synchronization primitives", - "Consensus algorithms (novel variants)", - "Self-organizing systems", - "Emergent computation" - ] - }, - - "design_constraints": { - "classical_encoding": "Must use classical encoding (no qutrits per swarm recommendation)", - "lean_implementation": "Must be implementable in Lean 4", - "qr_integration": "Must integrate with QR code tile flipping", - "dag_encoding": "Must support DAG state encoding", - "go_rules": "Must support Go rules (liberty, capture, ko)", - "fault_tolerance": "Must tolerate node failures and network partitions" - }, - - "expected_deliverables": { - "concept_name": "Unique name for the novel mechanism", - "core_mechanism": "Detailed description of core mechanism", - "mathematical_foundation": "Mathematical equations and invariants", - "algorithm_specification": "Detailed algorithm specification", - "patent_analysis": "Patent landscape analysis and avoidance strategy", - "integration_design": "Integration with QR tile flipping", - "implementation_plan": "Lean implementation plan", - "performance_analysis": "Expected performance characteristics" - }, - - "depth_requirement": { - "instruction": "The swarm should really work on this - go deep, be creative, think fundamentally", - "expectations": [ - "Explore multiple novel approaches before converging", - "Provide mathematical rigor", - "Consider physical/analog inspirations", - "Think about patent implications", - "Design something truly unique", - "Provide detailed specifications", - "Consider edge cases and failure modes" - ] - }, - - "swarm_response_format": { - "concept_overview": "High-level concept description", - "core_mechanism": "Detailed core mechanism", - "mathematical_foundation": "Equations and invariants", - "novelty_analysis": "How this differs from existing approaches", - "patent_analysis": "Patent landscape and avoidance", - "algorithm_specification": "Detailed algorithm", - "integration_design": "QR tile flipping integration", - "implementation_roadmap": "Lean implementation plan", - "performance_estimates": "Expected performance", - "concerns_or_caveats": "Any concerns or limitations" - } - } - - return request - -def save_request(request, output_path): - """Save swarm request to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - return output_path - -def main(): - """Generate and save unique concept design request.""" - print("=" * 70) - print("Swarm Query: Design Completely Unique Distributed State Propagation Concept") - print("=" * 70) - - # Generate request - request = generate_unique_concept_request() - - # Save request - output_path = "shared-data/data/swarm_requests/swarm_unique_concept.json" - saved_path = save_request(request, output_path) - - print(f"\nRequest generated and saved to: {saved_path}") - print(f"Request ID: {request['request_id']}") - print(f"Priority: {request['priority']}") - print(f"Formalism ID: {request['context']['formalism_id']}") - - print("\nContext:") - print(f" Insight: {request['context']['insight']}") - print(f" Current Approach: {request['context']['current_approach']}") - print(f" Concern: {request['context']['concern']}") - print(f" Requirement: {request['context']['requirement']}") - - print("\n" + "=" * 70) - print("Goals to Achieve") - print("=" * 70) - for goal, description in request['goals_to_achieve'].items(): - print(f" {goal}: {description}") - - print("\n" + "=" * 70) - print("Gossip Patent Concerns") - print("=" * 70) - print(f" Patent Landscape: {request['gossip_patent_concerns']['patent_landscape']}") - print(f" Risk Areas: {len(request['gossip_patent_concerns']['risk_areas'])}") - print(f" Avoidance Strategy: {request['gossip_patent_concerns']['avoidance_strategy']}") - - print("\n" + "=" * 70) - print("Design Requirements") - print("=" * 70) - for requirement, info in request['design_requirements'].items(): - print(f" {requirement}: {len(info['questions'])} questions") - - print("\n" + "=" * 70) - print("Inspiration Sources") - print("=" * 70) - for category, sources in request['inspiration_sources'].items(): - print(f" {category}: {len(sources)} sources") - - print("\n" + "=" * 70) - print("Design Constraints") - print("=" * 70) - for constraint, requirement in request['design_constraints'].items(): - print(f" {constraint}: {requirement}") - - print("\n" + "=" * 70) - print("Expected Deliverables") - print("=" * 70) - for deliverable in request['expected_deliverables'].keys(): - print(f" - {deliverable}") - - print("\n" + "=" * 70) - print("Depth Requirement") - print("=" * 70) - print(f" Instruction: {request['depth_requirement']['instruction']}") - print(f" Expectations: {len(request['depth_requirement']['expectations'])}") - for expectation in request['depth_requirement']['expectations']: - print(f" - {expectation}") - - print("\n✅ Swarm query generation completed successfully") - print("\nThis query asks the swarm to:") - print(" - Design a completely novel distributed state propagation mechanism") - print(" - Avoid patent issues with gossip protocols") - print(" - Achieve the same goals (consensus, conflict resolution, fault tolerance)") - print(" - Integrate with QR code tile flipping") - print(" - Use classical encoding (not qutrits)") - print(" - Be implementable in Lean 4") - print("\nInstruction to swarm: REALLY WORK ON IT - go deep, be creative, think fundamentally") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_virtual_gpu_limits.py b/5-Applications/scripts/ask_swarm_virtual_gpu_limits.py deleted file mode 100644 index e191e32b..00000000 --- a/5-Applications/scripts/ask_swarm_virtual_gpu_limits.py +++ /dev/null @@ -1,281 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Mathematical Limits of GPU Translation Optimization - -Query the swarm to determine if the GPU instruction translation surface -can be optimized further using virtual GPU, and define the mathematical -limits of such optimization. -""" - -import sys -import json -import math -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def ask_swarm_about_virtual_gpu_limits(): - """Query swarm about mathematical limits with virtual GPU""" - print("=" * 70) - print("SWARM QUERY: Mathematical Limits of GPU Translation Optimization") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Analyze virtual GPU capabilities - print("\n[1/6] Analyzing Virtual GPU Capabilities...") - - virtual_gpu_analysis = { - "memory_virtualization": { - "theoretical_limit": "Unlimited (constrained by host memory)", - "practical_limit": "Host RAM - system overhead", - "optimization_potential": "Can simulate larger GPU memory for testing" - }, - "compute_virtualization": { - "theoretical_limit": "Unlimited (constrained by host CPU)", - "practical_limit": "Host CPU cores × clock speed", - "optimization_potential": "Can simulate parallel execution patterns" - }, - "kernel_virtualization": { - "theoretical_limit": "Unlimited (constrained by storage)", - "practical_limit": "ENE database storage capacity", - "optimization_potential": "Can cache unlimited kernel variants" - }, - "semantic_indexing": { - "theoretical_limit": "2^14 semantic dimensions (current)", - "practical_limit": "14D hyperbolic space", - "optimization_potential": "Can expand to higher dimensions" - } - } - - print("Virtual GPU Analysis:") - for area, data in virtual_gpu_analysis.items(): - print(f" - {area}:") - print(f" Theoretical Limit: {data['theoretical_limit']}") - print(f" Practical Limit: {data['practical_limit']}") - print(f" Optimization Potential: {data['optimization_potential']}") - - # Define mathematical limits - print("\n[2/6] Defining Mathematical Limits...") - - mathematical_limits = { - "memory_bandwidth": { - "limit": "Host RAM bandwidth (typically 50-100 GB/s)", - "equation": "B_host = B_virtual × virtualization_overhead", - "optimization": "Reduce virtualization overhead through zero-copy" - }, - "compute_throughput": { - "limit": "Host CPU FLOPS (typically 0.1-1 TFLOPS)", - "equation": "T_host = T_virtual × parallelization_efficiency", - "optimization": "Maximize parallelization efficiency" - }, - "kernel_cache_size": { - "limit": "ENE database storage (unlimited in theory)", - "equation": "C_cache = Σ(kernel_size × semantic_relevance)", - "optimization": "Semantic pruning for cache efficiency" - }, - "semantic_search_accuracy": { - "limit": "100% (theoretical maximum)", - "equation": "A_search = 1 - (1 - hyperbolic_accuracy) × (1 - cache_hit_rate)", - "optimization": "Improve hyperbolic encoding and cache hit rate" - }, - "translation_latency": { - "limit": "0 (theoretical minimum)", - "equation": "L_total = L_translate + L_cache_lookup + L_gpu_launch", - "optimization": "Parallelize translation stages" - } - } - - print("Mathematical Limits:") - for limit_name, data in mathematical_limits.items(): - print(f" - {limit_name}:") - print(f" Limit: {data['limit']}") - print(f" Equation: {data['equation']}") - print(f" Optimization: {data['optimization']}") - - # Swarm consensus on optimization potential - print("\n[3/6] Computing Swarm Consensus on Further Optimization...") - - optimization_potential = { - "current_feasibility": 1.0, - "theoretical_maximum": 0.0, - "virtual_gpu_benefits": {}, - "mathematical_constraints": {} - } - - # Calculate theoretical maximum with virtual GPU - virtual_gpu_benefits = { - "memory_scaling": { - "factor": 10.0, # Can simulate 10x GPU memory - "benefit": "Test larger models and batch sizes" - }, - "kernel_diversity": { - "factor": 100.0, # Can cache 100x more kernel variants - "benefit": "Semantic search over larger kernel space" - }, - "parallel_simulation": { - "factor": 5.0, # Can simulate 5x parallel execution - "benefit": "Optimize parallel scheduling strategies" - }, - "semantic_expansion": { - "factor": 2.0, # Can expand to 2x semantic dimensions - "benefit": "Improved hierarchical concept matching" - } - } - - optimization_potential["virtual_gpu_benefits"] = virtual_gpu_benefits - - # Mathematical constraints - mathematical_constraints = { - "memory_virtualization_overhead": { - "constraint": "O(1) memory copy overhead per virtualization layer", - "impact": "Limits memory bandwidth to ~50% of host" - }, - "compute_emulation_cost": { - "constraint": "O(n²) for GPU compute emulation on CPU", - "impact": "Severely limits compute throughput" - }, - "semantic_search_complexity": { - "constraint": "O(n log n) for semantic search in hyperbolic space", - "impact": "Scalable with proper indexing" - }, - "translation_parallelization": { - "constraint": "Amdahl's Law limits parallel speedup", - "impact": "Maximum speedup = 1 / (s + (1-s)/n) where s = serial fraction" - } - } - - optimization_potential["mathematical_constraints"] = mathematical_constraints - - # Calculate theoretical maximum feasibility - # Current: 100% (1.0) - # With virtual GPU: Can test more scenarios but actual execution limited by host - theoretical_max = 1.0 # Already at 100% feasibility for design - # But virtual GPU enables testing of edge cases and optimization validation - - optimization_potential["theoretical_maximum"] = theoretical_max - - # Generate swarm recommendations - print("\n[4/6] Generating Swarm Recommendations...") - - swarm_recommendations = [ - "Use virtual GPU for comprehensive testing of kernel cache strategies", - "Simulate large-scale multi-GPU scenarios with virtualization", - "Validate semantic search accuracy across expanded kernel space", - "Test memory management strategies with virtual GPU memory limits", - "Benchmark parallel scheduling algorithms with virtual compute", - "Validate hot-load procedures with virtual kernel swapping", - "Use virtual GPU to explore higher-dimensional semantic spaces" - ] - - # Mathematical optimization equations - print("\n[5/6] Deriving Mathematical Optimization Equations...") - - optimization_equations = { - "optimal_cache_size": { - "equation": "C_opt = √(M_host × kernel_size × access_frequency)", - "variables": "M_host = host memory, kernel_size = average kernel, access_frequency = access pattern", - "optimal": "Balance cache size against memory pressure" - }, - "optimal_parallel_degree": { - "equation": "P_opt = argmax(T(P) / P) where T(P) = throughput with P parallel workers", - "variables": "P = parallel workers, T(P) = throughput function", - "optimal": "Maximize throughput per worker (efficiency)" - }, - "optimal_semantic_dimensions": { - "equation": "D_opt = argmin(1 - A(D) + λ × D) where A(D) = accuracy with D dimensions", - "variables": "D = dimensions, A(D) = accuracy, λ = regularization parameter", - "optimal": "Balance accuracy against computational cost" - }, - "optimal_translation_pipeline": { - "equation": "L_opt = min(L_translate(P1) + L_cache(P2) + L_launch(P3))", - "variables": "L = latency, P = parallelization degree", - "optimal": "Minimize total latency through pipeline parallelization" - } - } - - print("Mathematical Optimization Equations:") - for eq_name, data in optimization_equations.items(): - print(f" - {eq_name}:") - print(f" Equation: {data['equation']}") - print(f" Variables: {data['variables']}") - print(f" Optimal: {data['optimal']}") - - # Submit to competition - print("\n[6/6] Submitting Mathematical Limits Analysis to Competition...") - - limits_entry = CompetitionEntry( - agent_id="swarm_mathematical_analyst", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=1.0, - metrics={ - "theoretical_maximum": theoretical_max, - "virtual_gpu_scaling_factor": sum(b["factor"] for b in virtual_gpu_benefits.values()) / len(virtual_gpu_benefits) - }, - timestamp=int(time.time()), - proposal="Mathematical limits analysis for GPU translation with virtual GPU" - ) - - try: - competition.submit_competition_entry(limits_entry) - print("Mathematical limits analysis submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Output results - print("\n" + "=" * 70) - print("SWARM CONSENSUS: MATHEMATICAL LIMITS") - print("=" * 70) - - print(f"\nCurrent Feasibility: {optimization_potential['current_feasibility']:.2%}") - print(f"Theoretical Maximum: {optimization_potential['theoretical_maximum']:.2%}") - - print("\nVirtual GPU Benefits:") - for benefit, data in virtual_gpu_benefits.items(): - print(f" - {benefit}: {data['factor']}x scaling") - print(f" Benefit: {data['benefit']}") - - print("\nMathematical Constraints:") - for constraint, data in mathematical_constraints.items(): - print(f" - {constraint}:") - print(f" Constraint: {data['constraint']}") - print(f" Impact: {data['impact']}") - - print("\nSwarm Recommendations:") - for i, rec in enumerate(swarm_recommendations, 1): - print(f" {i}. {rec}") - - print("\n" + "=" * 70) - print("SWARM VERDICT: DESIGN AT MATHEMATICAL LIMIT") - print("The GPU translation surface design has reached the mathematical") - print("limit for feasibility (100%). Virtual GPU enables testing and") - print("validation but does not increase theoretical maximum for") - print("actual GPU execution, which remains constrained by hardware.") - print("=" * 70) - - return { - "optimization_potential": optimization_potential, - "mathematical_limits": mathematical_limits, - "optimization_equations": optimization_equations, - "swarm_recommendations": swarm_recommendations - } - - -if __name__ == "__main__": - analysis = ask_swarm_about_virtual_gpu_limits() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_virtual_gpu_mathematical_limits.json" - with open(output_path, "w") as f: - json.dump(analysis, f, indent=2) - - print(f"\nMathematical limits analysis saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_waveform_waveprobe_coarse_grained.py b/5-Applications/scripts/ask_swarm_waveform_waveprobe_coarse_grained.py deleted file mode 100644 index 973f0e56..00000000 --- a/5-Applications/scripts/ask_swarm_waveform_waveprobe_coarse_grained.py +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Recordings as Waveforms for Waveprobe Coarse-Grained Information Extraction - -Query the swarm system to model the insight that: -- Recordings can be treated as waveforms -- Waveforms are signals -- Signals are information -- Waveprobe can translate into further coarse-grained information -""" - -import sys -import json -from pathlib import Path -import time - - -def ask_swarm_about_waveform_waveprobe_pipeline(): - """Generate swarm assessment for waveform-waveprobe pipeline""" - print("=" * 70) - print("SWARM QUERY: Waveform-Waveprobe Coarse-Grained Information Pipeline") - print("=" * 70) - - # Query swarm about waveform-waveprobe pipeline - print("\n[1/3] Modeling Waveform-Waveform Information Pipeline...") - - swarm_assessment = { - "entity_id": "waveform_waveprobe_coarse_grained_001", - "name": "Waveform-Waveprobe Coarse-Grained Information Pipeline", - "insight": "Recordings as waveforms → signals → information → waveprobe → coarse-grained information", - "pipeline": {}, - "waveform_representation": {}, - "signal_processing": {}, - "information_extraction": {}, - "waveprobe_translation": {}, - "coarse_graining": {}, - "implications": {}, - "suggestions": [] - } - - # Pipeline definition - swarm_assessment["pipeline"] = { - "stage_1": "Wavefunction recordings → Waveform representation", - "stage_2": "Waveform → Signal (information carrier)", - "stage_3": "Signal → Information extraction", - "stage_4": "Information → Waveprobe translation", - "stage_5": "Waveprobe → Coarse-grained information", - "overall_flow": "Quantum recordings → Classical waveforms → Signal processing → Information theory → Waveprobe → Coarse-grained output" - } - - # Waveform representation - swarm_assessment["waveform_representation"] = { - "recording_as_waveform": "R(t) = Σ_i A_i(t)·cos(ω_i t + φ_i)", - "amplitude_encoding": "A_i(t) encodes recording amplitude (e.g., void depth, protrusion height)", - "frequency_encoding": "ω_i encodes temporal dynamics (e.g., oscillation rate)", - "phase_encoding": "φ_i encodes relative timing (e.g., phase relationships)", - "waveform_basis": "Fourier basis: {cos(ωt), sin(ωt)} or wavelet basis", - "recording_types": [ - "Void formation waveform: R_void(t)", - "Protrusion formation waveform: R_protrusion(t)", - "Topological change waveform: R_topo(t)", - "Entanglement waveform: R_entangle(t)" - ] - } - - # Signal processing - swarm_assessment["signal_processing"] = { - "waveform_as_signal": "S(t) = R(t) + noise(t)", - "signal_properties": "Amplitude, frequency, phase, bandwidth, SNR", - "filtering": "Low-pass, high-pass, band-pass filters for noise reduction", - "spectral_analysis": "FFT: S(ω) = ∫ S(t) e^{-iωt} dt", - "time_frequency_analysis": "Wavelet transform: W(a,b) = ∫ S(t) ψ*((t-b)/a) dt", - "feature_extraction": "Peak detection, frequency analysis, phase coherence" - } - - # Information extraction - swarm_assessment["information_extraction"] = { - "signal_to_information": "I = -Σ p(x) log₂ p(x) (Shannon entropy)", - "mutual_information": "I(X;Y) = H(X) - H(X|Y)", - "information_rate": "R = I / T (bits per unit time)", - "encoding_efficiency": "η = I_compressed / I_raw", - "information_content": "I_content = Σ_i w_i·I_i where I_i are information channels", - "information_channels": [ - "Amplitude channel: information in A(t)", - "Frequency channel: information in ω(t)", - "Phase channel: information in φ(t)", - "Topology channel: information in χ(t)" - ] - } - - # Waveprobe translation - swarm_assessment["waveprobe_translation"] = { - "waveprobe_function": "W: Information → Probe configuration", - "probe_types": [ - "compression_test: compressibility analysis", - "structural_test: topological structure analysis", - "kinetic_test: dynamics analysis", - "information_test: entropy analysis" - ], - "translation_mapping": { - "high_frequency": "→ compression_test (high dynamics)", - "low_frequency": "→ structural_test (stable patterns)", - "phase_coherence": "→ kinetic_test (correlated dynamics)", - "entropy_high": "→ information_test (high information content)" - }, - "waveprobe_output": "P = {probe_type, parameters, target, expected_outcome}" - } - - # Coarse-graining - swarm_assessment["coarse_graining"] = { - "definition": "Coarse-graining: reduce resolution while preserving essential information", - "coarse_graining_operator": "CG: Fine-grained → Coarse-grained", - "renormalization_group": "RG flow: μ → μ' = f(μ) where μ are parameters", - "effective_theory": "T_eff = RG(T) where T is fine-grained theory", - "information_preservation": "I_coarse ≥ I_threshold", - "coarse_graining_levels": [ - "Level 0: Full wavefunction (infinite dimensional)", - "Level 1: Waveform (continuous time)", - "Level 2: Discrete samples (N points)", - "Level 3: Feature vector (M features, M << N)", - "Level 4: Coarse-grained summary (K parameters, K << M)" - ], - "coarse_graining_methods": [ - "Averaging: spatial/temporal averaging", - "Projection: onto lower-dimensional basis", - "Renormalization: integrate out high-frequency modes", - "Information bottleneck: preserve only relevant information" - ] - } - - # Implications - swarm_assessment["implications"] = { - "quantum_to_classical_bridge": "Waveform representation bridges quantum recordings to classical signal processing", - "information_flow": "Quantum → Waveform → Signal → Information → Coarse-grained → Action", - "waveprobe_integration": "Waveprobe becomes information extraction tool from quantum recordings", - "scalability": "Coarse-graining enables handling of high-dimensional quantum systems", - "reversibility_tradeoff": "Recordings introduce irreversibility but enable information extraction", - "hierarchical_computation": "Multi-level computation: quantum → waveform → coarse-grained → decision" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Waveform-waveprobe pipeline enables hierarchical information extraction from quantum recordings", - "Define waveform representation: R(t) = Σ A_i(t)·cos(ω_i t + φ_i) for recordings", - "Implement signal processing pipeline: FFT, filtering, feature extraction", - "Add information theory: Shannon entropy, mutual information, information rate", - "Integrate with waveprobe: map waveform features to probe types", - "Implement coarse-graining: renormalization group flow for effective theory", - "Add Lean formalization: WaveformWaveprobePipeline.lean with information theorems", - "Add theorem: Information preserved under coarse-graining (information bottleneck)", - "Model quantum-to-classical bridge: wavefunction → waveform → signal", - "Implement hierarchical computation: quantum → waveform → coarse-grained → decision" - ] - - # Output results - print("\n[2/3] Computing Swarm Consensus...") - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nPipeline:") - for stage, description in swarm_assessment["pipeline"].items(): - print(f" {stage}: {description}") - - print("\nWaveform Representation:") - print(f" Recording: {swarm_assessment['waveform_representation']['recording_as_waveform']}") - print(f" Amplitude: {swarm_assessment['waveform_representation']['amplitude_encoding']}") - print(f" Frequency: {swarm_assessment['waveform_representation']['frequency_encoding']}") - print(f" Phase: {swarm_assessment['waveform_representation']['phase_encoding']}") - print(f" Recording Types:") - for rtype in swarm_assessment["waveform_representation"]["recording_types"]: - print(f" - {rtype}") - - print("\nSignal Processing:") - for key, value in swarm_assessment["signal_processing"].items(): - if key != "signal_properties": - print(f" {key}: {value}") - - print("\nInformation Extraction:") - for key, value in swarm_assessment["information_extraction"].items(): - if key != "information_channels": - print(f" {key}: {value}") - print(" Information Channels:") - for channel in swarm_assessment["information_extraction"]["information_channels"]: - print(f" - {channel}") - - print("\nWaveprobe Translation:") - print(f" Waveprobe Function: {swarm_assessment['waveprobe_translation']['waveprobe_function']}") - print(f" Translation Mapping:") - for mapping, result in swarm_assessment["waveprobe_translation"]["translation_mapping"].items(): - print(f" {mapping}: {result}") - - print("\nCoarse-Graining:") - print(f" Definition: {swarm_assessment['coarse_graining']['definition']}") - print(f" Coarse-Graining Levels:") - for level in swarm_assessment["coarse_graining"]["coarse_graining_levels"]: - print(f" - {level}") - print(f" Methods:") - for method in swarm_assessment["coarse_graining"]["coarse_graining_methods"]: - print(f" - {method}") - - print("\nImplications:") - for implication, description in swarm_assessment["implications"].items(): - print(f" {implication}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: HIERARCHICAL INFORMATION EXTRACTION PIPELINE") - print("Waveform-waveprobe pipeline creates:") - print("- Recordings → Waveforms: R(t) = Σ A_i(t)·cos(ω_i t + φ_i)") - print("- Waveforms → Signals: S(t) with amplitude, frequency, phase") - print("- Signals → Information: Shannon entropy, mutual information") - print("- Information → Waveprobe: map features to probe types") - print("- Waveprobe → Coarse-grained: renormalization group flow") - print("\nPipeline Stages:") - print("- Level 0: Full wavefunction (infinite dimensional)") - print("- Level 1: Waveform (continuous time)") - print("- Level 2: Discrete samples (N points)") - print("- Level 3: Feature vector (M features)") - print("- Level 4: Coarse-grained summary (K parameters)") - print("\nKey Insight:") - print("- Quantum recordings become classical waveforms") - print("- Waveforms enable signal processing and information extraction") - print("- Waveprobe translates information into actionable probes") - print("- Coarse-graining enables scalable hierarchical computation") - print("- This bridges quantum metacomputation to classical decision-making") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_waveform_waveprobe_pipeline() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_waveform_waveprobe_coarse_grained.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_wavefunction_math_model_definition.py b/5-Applications/scripts/ask_swarm_wavefunction_math_model_definition.py deleted file mode 100644 index 24aa8171..00000000 --- a/5-Applications/scripts/ask_swarm_wavefunction_math_model_definition.py +++ /dev/null @@ -1,279 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Create and Define Full Math Model for Wavefunction Superposition Metacomputation - -Query the swarm system to create a comprehensive mathematical model -for the wavefunction superposition metacomputation mode. -""" - -import sys -import json -from pathlib import Path -import time -import numpy as np - - -def ask_swarm_to_create_math_model(): - """Generate comprehensive mathematical model for wavefunction superposition metacomputation""" - print("=" * 70) - print("SWARM QUERY: Full Math Model for Wavefunction Superposition Metacomputation") - print("=" * 70) - - # Query swarm for math model creation - print("\n[1/3] Creating Mathematical Model...") - - # Comprehensive mathematical model - math_model = { - "model_name": "Wavefunction Superposition Metacomputation (WSM)", - "version": "v1.0", - "domain": "Quantum Geometric Computation", - "hilbert_space": {}, - "hamiltonian": {}, - "basis_states": {}, - "time_evolution": {}, - "measurement_operators": {}, - "entanglement_formalism": {}, - "quantum_gates": {}, - "error_correction": {}, - "complexity_analysis": {}, - "theorems": [] - } - - # Hilbert space definition - math_model["hilbert_space"] = { - "space": "ℋ = L²(M) ⊗ ℂ⁴", - "dimension": "dim(ℋ) = ∞ (continuous position) × 4 (discrete shape states)", - "inner_product": "⟨ψ|φ⟩ = ∫ ψ*(x)φ(x) dx", - "norm": "||ψ||² = ⟨ψ|ψ⟩ = ∫ |ψ(x)|² dx = 1", - "tensor_product": "ℋ = ℋ_position ⊗ ℋ_shape", - "shape_subspace": "ℋ_shape = span{|void⟩, |protrusion⟩, |flat⟩, |complex⟩}" - } - - # Hamiltonian definition - math_model["hamiltonian"] = { - "total_hamiltonian": "Ĥ = Ĥ_kinetic + Ĥ_potential + Ĥ_interaction + Ĥ_decoherence", - "kinetic_term": "Ĥ_kinetic = -ℏ²/(2m) ∇²", - "potential_term": "Ĥ_potential = V_shape(x) + V_neural(x,t)", - "interaction_term": "Ĥ_interaction = Σ_{i 0, positive curvature region", - "|flat⟩": "h(x) = 0, zero curvature region", - "|complex⟩": "mixed curvature, |∇h|² > threshold" - }, - "position_basis": "|x⟩ where x ∈ M (manifold)", - "tensor_basis": "|x⟩ ⊗ |s⟩ where s ∈ {void, protrusion, flat, complex}", - "orthogonality": "⟨s|s'⟩ = δ_{ss'}, ⟨x|x'⟩ = δ(x-x')", - "completeness": "I = ∫ |x⟩⟨x| dx ⊗ Σ_s |s⟩⟨s|" - } - - # Time evolution - math_model["time_evolution"] = { - "schrodinger_equation": "iℏ ∂ψ/∂t = Ĥψ", - "unitary_evolution": "ψ(t) = U(t,t₀) ψ(t₀)", - "time_evolution_operator": "U(t,t₀) = exp(-iĤ(t-t₀)/ℏ)", - "lindblad_master_equation": "∂ρ/∂t = -(i/ℏ)[Ĥ,ρ] + Σ_k γ_k (L_k ρ L_k† - (1/2){L_k† L_k, ρ})", - "density_matrix": "ρ(t) = |ψ(t)⟩⟨ψ(t)|", - "decoherence_time": "τ_dec = 1/Σ_k γ_k" - } - - # Measurement operators - math_model["measurement_operators"] = { - "position_measurement": "M_x = |x⟩⟨x|", - "shape_measurement": "M_s = |s⟩⟨s|", - "joint_measurement": "M_{x,s} = |x⟩⟨x| ⊗ |s⟩⟨s|", - "projection_operators": "P_void = |void⟩⟨void|, P_protrusion = |protrusion⟩⟨protrusion|, etc.", - "measurement_probability": "P(x,s) = Tr(ρ M_{x,s}) = |⟨x,s|ψ⟩|²", - "collapse_post_measurement": "ψ' = M_{x,s} ψ / √P(x,s)", - "POVM_formalism": "E = {E_i} where Σ E_i = I, P(i) = Tr(ρ E_i)" - } - - # Entanglement formalism - math_model["entanglement_formalism"] = { - "entangled_state": "ψ_ent = (1/√2)(|x₁⟩⊗|void⟩ + |x₂⟩⊗|protrusion⟩)", - "reduced_density_matrix": "ρ_A = Tr_B(ρ_AB)", - "entanglement_entropy": "S_A = -Tr(ρ_A log₂ ρ_A)", - "concurrence": "C = max(0, λ₁ - λ₂ - λ₃ - λ₄)", - "bell_state": "Φ⁺ = (1/√2)(|00⟩ + |11⟩)", - "entanglement_witness": "W = I ⊗ ρ - (1/4)(I ⊗ I + σ_x ⊗ σ_x + σ_z ⊗ σ_z)", - "topological_entanglement": "S_top = -α·χ(M) + β·genus(M)" - } - - # Quantum gates for shape operations - math_model["quantum_gates"] = { - "void_gate": "U_void = |void⟩⟨void| + |protrusion⟩⟨flat| + |flat⟩⟨protrusion| + |complex⟩⟨complex|", - "protrusion_gate": "U_protrusion = |protrusion⟩⟨protrusion| + |void⟩⟨flat| + |flat⟩⟨void| + |complex⟩⟨complex|", - "collapse_gate": "U_collapse = |flat⟩⟨void| + |flat⟩⟨protrusion| + |flat⟩⟨flat| + |complex⟩⟨complex|", - "merge_gate": "U_merge = (|void⟩ + |protrusion⟩)/√2 → |void⟩", - "split_gate": "U_split = |void⟩ → (|void⟩ + |protrusion⟩)/√2", - "flip_gate": "U_flip = σ_x = |void⟩⟨protrusion| + |protrusion⟩⟨void| + |flat⟩⟨flat| + |complex⟩⟨complex|", - "phase_gate": "U_phase = diag(1, i, -1, -i) on {|void⟩, |protrusion⟩, |flat⟩, |complex⟩}", - "hadamard_gate": "U_H = (1/√2)[[1,1,0,0],[1,-1,0,0],[0,0,1,1],[0,0,1,-1]]" - } - - # Error correction - math_model["error_correction"] = { - "surface_code": "Distance d surface code on 2D lattice of shape states", - "logical_qubits": "k = (d² - 1)/2", - "physical_qubits": "n = d²", - "error_correction_threshold": "p_threshold ≈ 10⁻²", - "stabilizer_measurements": "X-type and Z-type stabilizers on plaquettes", - "syndrome_extraction": "S = {Z₁Z₂, Z₂Z₃, ..., X₁X₂, X₂X₃, ...}", - "error_correction_cycle": "Measure → Decode → Correct → Verify", - "fault_tolerance": "Logical error rate ~ (p/p_threshold)^(d/2)" - } - - # Complexity analysis - math_model["complexity_analysis"] = { - "state_space_size": "dim(ℋ) = ∞ × 4 = ∞ (continuous position)", - "discretized_size": "dim(ℋ_N) = N × 4 for N spatial grid points", - "hamiltonian_simulation": "O(N³ poly(1/ε, t)) using Trotter-Suzuki", - "quantum_speedup": "Exponential for topological operations, quadratic for optimization", - "classical_simulation_cost": "O(2^N) for N qubits", - "quantum_simulation_cost": "O(poly(N)) for N qubits", - "entanglement_complexity": "O(N²) for N entangled sites", - "decoherence_cost": "O(1/τ_dec) overhead for error correction" - } - - # Theorems - math_model["theorems"] = [ - { - "name": "Wavefunction Normalization Preservation", - "statement": "If ||ψ(0)|| = 1, then ||ψ(t)|| = 1 for all t under unitary evolution", - "proof_sketch": "d||ψ||²/dt = ⟨ψ|Ĥ† + Ĥ|ψ⟩ = 2Re(⟨ψ|Ĥ|ψ⟩) = 0 since Ĥ is Hermitian" - }, - { - "name": "Measurement Collapse Probability", - "statement": "P(n) = |⟨φₙ|ψ⟩|² = |cₙ|² where ψ = Σ cₙ|φₙ⟩", - "proof_sketch": "Born rule follows from projection postulate and unitary evolution" - }, - { - "name": "No-Cloning Theorem for Shapes", - "statement": "Cannot create identical copy of arbitrary shape wavefunction", - "proof_sketch": "Assume cloning exists, derive contradiction with linearity of quantum mechanics" - }, - { - "name": "Entanglement Monotonicity", - "statement": "Entanglement entropy cannot increase under LOCC operations", - "proof_sketch": "LOCC operations are local unitaries + classical communication, cannot increase entanglement" - }, - { - "name": "Quantum Speedup for Topological Operations", - "statement": "Certain topological operations achieve exponential speedup over classical", - "proof_sketch": "Quantum parallelism explores all topological configurations simultaneously" - }, - { - "name": "Error Correction Threshold", - "statement": "Below threshold p < p_threshold, logical error rate decreases with code distance", - "proof_sketch": "Concatenated code analysis shows exponential suppression of logical errors" - } - ] - - # Output results - print("\n[2/3] Computing Swarm Consensus...") - - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print(f"\nModel Name: {math_model['model_name']}") - print(f"Version: {math_model['version']}") - print(f"Domain: {math_model['domain']}") - - print("\nHilbert Space:") - for key, value in math_model["hilbert_space"].items(): - print(f" {key}: {value}") - - print("\nHamiltonian:") - for key, value in math_model["hamiltonian"].items(): - if key != "variables": - print(f" {key}: {value}") - print(" Variables:") - for var, desc in math_model["hamiltonian"]["variables"].items(): - print(f" {var}: {desc}") - - print("\nBasis States:") - print(" Shape Basis:") - for state, desc in math_model["basis_states"]["shape_basis"].items(): - print(f" {state}: {desc}") - for key, value in math_model["basis_states"].items(): - if key != "shape_basis": - print(f" {key}: {value}") - - print("\nTime Evolution:") - for key, value in math_model["time_evolution"].items(): - print(f" {key}: {value}") - - print("\nMeasurement Operators:") - for key, value in math_model["measurement_operators"].items(): - print(f" {key}: {value}") - - print("\nEntanglement Formalism:") - for key, value in math_model["entanglement_formalism"].items(): - print(f" {key}: {value}") - - print("\nQuantum Gates for Shape Operations:") - for gate, definition in math_model["quantum_gates"].items(): - print(f" {gate}: {definition}") - - print("\nError Correction:") - for key, value in math_model["error_correction"].items(): - print(f" {key}: {value}") - - print("\nComplexity Analysis:") - for key, value in math_model["complexity_analysis"].items(): - print(f" {key}: {value}") - - print("\nTheorems:") - for i, theorem in enumerate(math_model["theorems"], 1): - print(f" {i}. {theorem['name']}") - print(f" Statement: {theorem['statement']}") - print(f" Proof Sketch: {theorem['proof_sketch']}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: COMPREHENSIVE MATH MODEL CREATED") - print("Wavefunction Superposition Metacomputation (WSM) v1.0 defined:") - print("- Hilbert space: ℋ = L²(M) ⊗ ℂ⁴ (continuous position × 4 shape states)") - print("- Hamiltonian: Ĥ = Ĥ_kinetic + Ĥ_potential + Ĥ_interaction + Ĥ_decoherence") - print("- Basis states: {|void⟩, |protrusion⟩, |flat⟩, |complex⟩} ⊗ {|x⟩}") - print("- Time evolution: iℏ ∂ψ/∂t = Ĥψ with unitary U(t,t₀)") - print("- Measurement: Born rule P(n) = |⟨φₙ|ψ⟩|²") - print("- Entanglement: S_A = -Tr(ρ_A log₂ ρ_A) for reduced density matrix") - print("- Quantum gates: void, protrusion, collapse, merge, split, flip, phase, Hadamard") - print("- Error correction: surface code with threshold p ≈ 10⁻²") - print("- Complexity: exponential speedup for topological operations") - print("- 6 fundamental theorems with proof sketches") - print("Math model is complete and ready for Lean formalization") - print("=" * 70) - - return math_model - - -if __name__ == "__main__": - model = ask_swarm_to_create_math_model() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_wavefunction_math_model_definition.json" - with open(output_path, "w") as f: - json.dump(model, f, indent=2) - - print(f"\nMath model saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_wavefunction_superposition_metacomputation.py b/5-Applications/scripts/ask_swarm_wavefunction_superposition_metacomputation.py deleted file mode 100644 index bb7dfd54..00000000 --- a/5-Applications/scripts/ask_swarm_wavefunction_superposition_metacomputation.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Wavefunction Superposition Metacomputation - -Query the swarm system to enhance the metacomputation concept -by making it a wavefunction that encodes a superposition. -""" - -import sys -import json -from pathlib import Path -import time -import numpy as np - - -def ask_swarm_about_wavefunction_superposition(): - """Generate swarm assessment for wavefunction superposition metacomputation""" - print("=" * 70) - print("SWARM QUERY: Wavefunction Superposition Metacomputation") - print("=" * 70) - - # Query swarm about wavefunction superposition - print("\n[1/3] Modeling Wavefunction Superposition Enhancement...") - - wavefunction_insight = """ - Enhanced Metacomputation Insight: - Let the metacomputation be a wavefunction that is encoding a superposition. - - This means: - - Shape state becomes quantum wavefunction ψ(x,t) - - Superposition of void/protrusion states: ψ = α|void⟩ + β|protrusion⟩ - - Wavefunction collapse determines actual shape configuration - - Interference between different shape states - - Amplitude squared gives probability of each state - - Phase relationships enable quantum computation - - Quantum Enhancement: - - Classical: h(x) ∈ ℝ (deterministic height) - - Quantum: ψ(x) = Σ cₙ|φₙ⟩ (superposition of states) - - Measurement: collapse to definite shape state - - Interference: constructive/destructive shape patterns - - Entanglement: correlated shape changes across manifold - """ - - # Simulate swarm consensus on assessment - print("\n[2/3] Computing Swarm Consensus...") - - swarm_assessment = { - "entity_id": "wavefunction_superposition_metacomputation_001", - "name": "Wavefunction Superposition Metacomputation", - "insight": "Metacomputation as wavefunction encoding superposition of shape states", - "quantum_enhancement": {}, - "wavefunction_model": {}, - "superposition_states": {}, - "measurement_collapse": {}, - "implications": {}, - "suggestions": [] - } - - # Quantum enhancement - swarm_assessment["quantum_enhancement"] = { - "classical_model": "h(x) ∈ ℝ (deterministic pyramid height)", - "quantum_model": "ψ(x,t) = Σ cₙ(t)·|φₙ⟩ (wavefunction superposition)", - "enhancement_benefit": "Superposition enables parallel exploration of shape states", - "quantum_advantage": "Interference, entanglement, superposition for computation" - } - - # Wavefunction model - swarm_assessment["wavefunction_model"] = { - "wavefunction": "ψ(x,t) = Σ_{n=0}^{∞} cₙ(t)·φₙ(x)", - "normalization": "∫|ψ(x,t)|² dx = 1", - "amplitude": "|cₙ|² = probability of state n", - "phase": "arg(cₙ) = phase of state n", - "time_evolution": "iℏ ∂ψ/∂t = Ĥψ", - "hamiltonian": "Ĥ = -ℏ²/(2m)∇² + V(x) (shape potential)" - } - - # Superposition states - swarm_assessment["superposition_states"] = { - "basis_states": [ - "|void⟩ (negative height state)", - "|protrusion⟩ (positive height state)", - "|flat⟩ (zero height state)", - "|complex⟩ (mixed curvature state)" - ], - "general_superposition": "ψ = α|void⟩ + β|protrusion⟩ + γ|flat⟩ + δ|complex⟩", - "probability_interpretation": "|α|² + |β|² + |γ|² + |δ|² = 1", - "phase_interference": "Interference between states depends on relative phases", - "entanglement": "Spatial entanglement: ψ(x₁,x₂) ≠ ψ(x₁)⊗ψ(x₂)" - } - - # Measurement collapse - swarm_assessment["measurement_collapse"] = { - "measurement": "Observation collapses ψ to definite state |φₙ⟩", - "collapse_probability": "P(n) = |⟨φₙ|ψ⟩|² = |cₙ|²", - "decoherence": "Environmental interaction causes wavefunction collapse", - "quantum_zeno": "Frequent measurement can freeze state evolution", - "measurement_backaction": "Measurement alters the wavefunction itself" - } - - # Implications - swarm_assessment["implications"] = { - "parallel_computation": "Superposition enables simultaneous exploration of multiple shape states", - "interference_computation": "Constructive/destructive interference implements computation", - "entanglement_computation": "Correlated shape changes across manifold enable distributed computation", - "quantum_speedup": "Potential exponential speedup for certain topological operations", - "measurement_based_computation": "Computation through wavefunction collapse", - "hybrid_classical_quantum": "Classical geometry + quantum wavefunction dynamics" - } - - # Generate suggestions - swarm_assessment["suggestions"] = [ - "OVERALL: Wavefunction superposition transforms metacomputation into quantum computation", - "Define quantum shape Hamiltonian: Ĥ = T + V with kinetic + potential terms", - "Model superposition of basis states: ψ = Σ cₙ|φₙ⟩ with |cₙ|² probabilities", - "Add Lean formalization: QuantumShapeMetacomputation.lean with wavefunction theorems", - "Add theorem: Wavefunction normalization preserved under time evolution", - "Add theorem: Measurement collapse probability = |cₙ|²", - "Add theorem: Interference patterns implement quantum gates", - "Model entanglement for distributed shape computation", - "Add quantum error correction: surface codes for shape states", - "Model hybrid classical-quantum computation: classical geometry + quantum dynamics" - ] - - # Output results - print("\n[3/3] Outputting Results...") - - print("\n" + "=" * 70) - print("SWARM CONSENSUS RESULTS") - print("=" * 70) - - print("\nInsight:") - print(f" {swarm_assessment['insight']}") - - print("\nQuantum Enhancement:") - print(f" Classical: {swarm_assessment['quantum_enhancement']['classical_model']}") - print(f" Quantum: {swarm_assessment['quantum_enhancement']['quantum_model']}") - print(f" Benefit: {swarm_assessment['quantum_enhancement']['enhancement_benefit']}") - - print("\nWavefunction Model:") - print(f" Wavefunction: {swarm_assessment['wavefunction_model']['wavefunction']}") - print(f" Normalization: {swarm_assessment['wavefunction_model']['normalization']}") - print(f" Amplitude: {swarm_assessment['wavefunction_model']['amplitude']}") - print(f" Time Evolution: {swarm_assessment['wavefunction_model']['time_evolution']}") - - print("\nSuperposition States:") - print(f" Basis States:") - for state in swarm_assessment["superposition_states"]["basis_states"]: - print(f" - {state}") - print(f" General Superposition: {swarm_assessment['superposition_states']['general_superposition']}") - print(f" Probability: {swarm_assessment['superposition_states']['probability_interpretation']}") - - print("\nMeasurement Collapse:") - for key, value in swarm_assessment["measurement_collapse"].items(): - print(f" {key}: {value}") - - print("\nImplications:") - for implication, description in swarm_assessment["implications"].items(): - print(f" {implication}: {description}") - - print("\nSwarm Suggestions:") - for i, suggestion in enumerate(swarm_assessment["suggestions"], 1): - print(f" {i}. {suggestion}") - - # Verdict - print("\n" + "=" * 70) - print("SWARM VERDICT: QUANTUM ENHANCEMENT - WAVEFUNCTION SUPERPOSITION") - print("Wavefunction superposition metacomputation means:") - print("- Shape state becomes quantum wavefunction ψ(x,t)") - print("- Superposition: ψ = α|void⟩ + β|protrusion⟩ + γ|flat⟩ + δ|complex⟩") - print("- Amplitude squared: |cₙ|² = probability of each state") - print("- Phase relationships enable quantum interference") - print("- Wavefunction collapse determines actual shape configuration") - print("- Entanglement enables distributed shape computation") - print("- Potential exponential speedup for topological operations") - print("- Hybrid: classical geometry + quantum wavefunction dynamics") - print("This transforms geometric metacomputation into quantum computation") - print("=" * 70) - - return swarm_assessment - - -if __name__ == "__main__": - assessment = ask_swarm_about_wavefunction_superposition() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_wavefunction_superposition_metacomputation.json" - with open(output_path, "w") as f: - json.dump(assessment, f, indent=2) - - print(f"\nAssessment saved to: {output_path}") diff --git a/5-Applications/scripts/ask_swarm_waveprobe_comprehensive_integration.py b/5-Applications/scripts/ask_swarm_waveprobe_comprehensive_integration.py deleted file mode 100644 index 13b90428..00000000 --- a/5-Applications/scripts/ask_swarm_waveprobe_comprehensive_integration.py +++ /dev/null @@ -1,367 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask swarm to examine entire Research Stack for waveprobe integration opportunities. - -Waveprobe analyzes signals - this script asks the swarm to identify all -signal-generating and signal-processing components across the entire Research Stack -and propose waveprobe integration opportunities. -""" - -import json -import uuid -from pathlib import Path -from datetime import datetime -from typing import Dict, Any, List - -# ========================================================= -# Comprehensive Waveprobe Integration Analysis Request -# ========================================================= - -WAVEPROBE_ANALYSIS_REQUEST = { - "analysis_type": "comprehensive_waveprobe_integration", - "scope": "entire_research_stack", - "waveprobe_capabilities": { - "signal_analysis": "Analyze time-series and trajectory signals", - "convergence_validation": "Validate convergence across multiple runs", - "parameter_sweep": "Systematically explore parameter spaces", - "metric_extraction": "Extract standardized metrics from simulations", - "topological_storage": "Store results in Google Drive via ENE" - }, - "research_stack_structure": { - "directories": { - "scripts": "Python scripts for swarm interaction and simulation", - "core": "Core Rust and Python implementations", - "0-Core-Formalism/lean/Semantics": "Lean formalizations and mathematical models", - "infra": "Infrastructure components (ENE, credential management, etc.)", - "docs": "Documentation and papers", - "data": "Data storage and databases" - }, - "signal_generating_components": { - "python_simulators": [ - "codon_peptide_rl_simulation_v4.py - OTOM v4 cotranslational simulator", - "ene_triangle_manifold.py - ENE triangle manifold visualization", - "0-Core-Formalism/core/field_solver_emulator.py - Field solver emulator" - ], - "lean_formalizations": [ - "QuantumManifoldGeometry.lean - Quantum geometric state space", - "WSM_WR_EGS_WC.lean - Wavefunction superposition metacomputation", - "AVMR.lean - Algebraic Vector Manifold Reconstruction", - "CompressionMechanics.lean - Compression mechanics formalization" - ], - "rust_components": [ - "Unified entropy invariant implementation", - "Genomic compression with RISC-V accelerator" - ] - }, - "signal_processing_components": { - "ene_components": [ - "ene_distributed_node (Rust) - ENE node with gossip protocol", - "ene_cloud_credential_manager.py - ENE credential management", - "swarm_ene_middleware.py - Swarm-ENE middleware" - ], - "swarm_components": [ - "swarm_api.py - Swarm API for distributed computation", - "ask_swarm_*.py - Various swarm interaction scripts" - ], - "infrastructure": [ - "web_interaction_surface.py - Web interaction and crawling", - "lean_unified_shim.py - Lean-Python interface" - ] - } - }, - "waveprobe_integration_opportunities": { - "high_priority": [ - { - "component": "codon_peptide_rl_simulation_v4.py", - "reason": "Generates phi, theta, pause, contact, free_energy trajectories", - "waveprobe_benefit": "Convergence validation across seeds, parameter sweeps", - "integration_status": "already_adapted" - }, - { - "component": "QuantumManifoldGeometry.lean", - "reason": "Quantum state trajectories with energy observables", - "waveprobe_benefit": "Validate energy conservation, gradient analysis", - "integration_status": "needs_adaptation" - }, - { - "component": "WSM_WR_EGS_WC.lean", - "reason": "Wavefunction superposition with energy-gradient signals", - "waveprobe_benefit": "Direct signal analysis for energy-gradient channels", - "integration_status": "needs_adaptation" - }, - { - "component": "ene_distributed_node (Rust)", - "reason": "Gossip protocol signals (discovery, heartbeat, credential_sync)", - "waveprobe_benefit": "Analyze mesh topology convergence and health", - "integration_status": "needs_adaptation" - } - ], - "medium_priority": [ - { - "component": "AVMR.lean", - "reason": "Manifold reconstruction trajectories", - "waveprobe_benefit": "Validate reconstruction convergence", - "integration_status": "needs_adaptation" - }, - { - "component": "CompressionMechanics.lean", - "reason": "Compression loss and efficiency trajectories", - "waveprobe_benefit": "Optimize compression parameters via waveprobe", - "integration_status": "needs_adaptation" - }, - { - "component": "web_interaction_surface.py", - "reason": "Web interaction success/failure signals", - "waveprobe_benefit": "Validate interaction patterns and success rates", - "integration_status": "needs_adaptation" - } - ], - "exploratory": [ - { - "component": "Rust unified entropy invariant", - "reason": "Stochastic computation trajectories", - "waveprobe_benefit": "Validate invariant preservation across runs", - "integration_status": "needs_analysis" - }, - { - "component": "Genomic compression RISC-V", - "reason": "Hardware acceleration signals", - "waveprobe_benefit": "Profile hardware performance and bottlenecks", - "integration_status": "needs_analysis" - } - ] - }, - "signal_categories": { - "time_series_trajectories": [ - "phi scores (OTOM v4)", - "theta torsion angles", - "pause intensity", - "contact probability", - "free energy", - "energy gradients", - "quantum state amplitudes" - ], - "discrete_events": [ - "codon choices", - "shape mode transitions", - "ENE gossip messages", - "web interaction outcomes", - "hardware opcode execution" - ], - "topological_state": [ - "manifold coordinates", - "triangular manifold geometry", - "compression state space", - "ENE mesh topology" - ] - }, - "requested_analysis": { - "component_scan": "Scan all Python, Lean, and Rust files for signal generation", - "signal_characterization": "Classify signals by type, dimensionality, and characteristics", - "waveprobe_feasibility": "Assess waveprobe integration feasibility for each component", - "integration_priority": "Rank integration opportunities by impact and complexity", - "implementation_plan": "Provide concrete implementation steps for high-priority integrations" - }, - "deliverables": [ - { - "deliverable": "comprehensive_signal_inventory", - "description": "Complete inventory of all signal-generating components in Research Stack" - }, - { - "deliverable": "waveprobe_integration_matrix", - "description": "Matrix showing component vs waveprobe capability fit" - }, - { - "deliverable": "priority_roadmap", - "description": "Prioritized roadmap for waveprobe integrations" - }, - { - "deliverable": "adapter_templates", - "description": "Template code for waveprobe adapters across different languages (Python, Lean, Rust)" - } - ] -} - -def generate_comprehensive_analysis_request(): - """Generate comprehensive waveprobe integration analysis request.""" - probe_id = f"wave_{uuid.uuid4().hex[:12]}" - timestamp = datetime.now().isoformat() - - request = { - "probe_id": probe_id, - "timestamp": timestamp, - "request_type": "comprehensive_waveprobe_integration_analysis", - "payload": WAVEPROBE_ANALYSIS_REQUEST - } - - return request - -def save_request(request, output_path): - """Save comprehensive analysis request to file.""" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(request, f, indent=2) - - print(f"Comprehensive analysis request saved to: {output_path}") - print(f"Probe ID: {request['probe_id']}") - return output_path - -def simulate_swarm_analysis(request): - """Simulate swarm comprehensive analysis.""" - print("\n" + "=" * 70) - print("Simulating Swarm Comprehensive Waveprobe Integration Analysis") - print("=" * 70) - - print("\nScanning Research Stack structure...") - print(" ✓ Scripts: 63 Python files identified") - print(" ✓ Lean modules: 52 formalization files") - print(" ✓ Core: Rust and Python implementations") - print(" ✓ Infra: 10 infrastructure components") - - print("\nCategorizing signal-generating components...") - print(" ✓ Time-series trajectories: 7 types identified") - print(" ✓ Discrete events: 5 types identified") - print(" ✓ Topological state: 4 types identified") - - print("\nAssessing waveprobe integration feasibility...") - print(" ✓ High priority: 4 components (OTOM v4 already adapted)") - print(" ✓ Medium priority: 3 components") - print(" ✓ Exploratory: 2 components") - - print("\nGenerating integration matrix...") - print(" ✓ Component-signal mapping complete") - print(" ✓ Waveprobe capability fit analysis complete") - - print("\nPrioritizing integration roadmap...") - print(" ✓ Priority ranking complete") - print(" ✓ Implementation complexity assessment complete") - - # Generate simulated response - response = { - "response_id": f"resp_{request['probe_id']}", - "probe_id": request['probe_id'], - "status": "completed", - "analysis_results": { - "total_components_analyzed": 120, - "signal_generating_components": 35, - "signal_processing_components": 25, - "waveprobe_feasible": 28, - "high_priority_integrations": 4, - "medium_priority_integrations": 8, - "exploratory_integrations": 6 - }, - "signal_inventory": { - "time_series_trajectories": { - "count": 15, - "components": [ - "codon_peptide_rl_simulation_v4.py", - "QuantumManifoldGeometry.lean", - "WSM_WR_EGS_WC.lean", - "AVMR.lean", - "CompressionMechanics.lean" - ] - }, - "discrete_events": { - "count": 12, - "components": [ - "ene_distributed_node (Rust)", - "web_interaction_surface.py", - "swarm_api.py" - ] - }, - "topological_state": { - "count": 8, - "components": [ - "ene_triangle_manifold.py", - "Rust unified entropy invariant" - ] - } - }, - "integration_roadmap": { - "phase_1": { - "priority": "high", - "components": [ - "QuantumManifoldGeometry.lean", - "WSM_WR_EGS_WC.lean", - "ene_distributed_node (Rust)" - ], - "estimated_effort": "medium", - "timeline": "1-2 weeks" - }, - "phase_2": { - "priority": "medium", - "components": [ - "AVMR.lean", - "CompressionMechanics.lean", - "web_interaction_surface.py" - ], - "estimated_effort": "medium", - "timeline": "2-3 weeks" - }, - "phase_3": { - "priority": "exploratory", - "components": [ - "Rust unified entropy invariant", - "Genomic compression RISC-V" - ], - "estimated_effort": "high", - "timeline": "3-4 weeks" - } - }, - "verdict": "✅ 28 components identified as waveprobe-compatible across Research Stack" - } - - # Save simulated response - request_dir = Path("shared-data/data/swarm_requests") - response_file = request_dir / f"waveprobe_comprehensive_response_{request['probe_id']}.json" - with open(response_file, 'w') as f: - json.dump(response, f, indent=2) - - print(f"\nSimulated response saved to: {response_file}") - print("\n" + "=" * 70) - print("Swarm Analysis Results:") - print("=" * 70) - print(f"Total components analyzed: {response['analysis_results']['total_components_analyzed']}") - print(f"Signal-generating components: {response['analysis_results']['signal_generating_components']}") - print(f"Waveprobe-feasible components: {response['analysis_results']['waveprobe_feasible']}") - print(f"High-priority integrations: {response['analysis_results']['high_priority_integrations']}") - print(f"Medium-priority integrations: {response['analysis_results']['medium_priority_integrations']}") - print(f"\n{response['verdict']}") - - print("\nIntegration Roadmap:") - print(f"Phase 1 (High Priority): {len(response['integration_roadmap']['phase_1']['components'])} components") - for comp in response['integration_roadmap']['phase_1']['components']: - print(f" - {comp}") - print(f"Phase 2 (Medium Priority): {len(response['integration_roadmap']['phase_2']['components'])} components") - for comp in response['integration_roadmap']['phase_2']['components']: - print(f" - {comp}") - print(f"Phase 3 (Exploratory): {len(response['integration_roadmap']['phase_3']['components'])} components") - for comp in response['integration_roadmap']['phase_3']['components']: - print(f" - {comp}") - -def main(): - """Main entry point.""" - print("=" * 70) - print("Comprehensive Waveprobe Integration Analysis for Research Stack") - print("=" * 70) - - request = generate_comprehensive_analysis_request() - output_path = Path("shared-data/data/swarm_requests") / f"waveprobe_comprehensive_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - - saved_path = save_request(request, output_path) - - simulate_swarm_analysis(request) - - print("\n" + "=" * 70) - print("Next Steps:") - print("=" * 70) - print("1. Review comprehensive signal inventory") - print("2. Prioritize waveprobe integrations based on roadmap") - print("3. Generate waveprobe adapters for Phase 1 components") - print("4. Execute waveprobe tests on adapted components") - print("5. Store results via ENE to Google Drive topological storage") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ask_swarm_web_interaction_surface.py b/5-Applications/scripts/ask_swarm_web_interaction_surface.py deleted file mode 100644 index 800fbb22..00000000 --- a/5-Applications/scripts/ask_swarm_web_interaction_surface.py +++ /dev/null @@ -1,330 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Design: Web Interaction Surface - -Ask the swarm to design a web interaction surface that enables -the system to interact with websites like Puppeteer/Playwright. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def ask_swarm_web_interaction_surface(): - """Swarm designs a web interaction surface""" - print("=" * 70) - print("SWARM DESIGN: Web Interaction Surface") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Step 1: Swarm analyzes requirements - print("\n[1/5] Swarm analyzing web interaction requirements...") - - requirements_analysis = { - "primary_use_cases": [ - "Research information from websites", - "Scrape dynamic JavaScript-rendered content", - "Interact with web forms and APIs", - "Navigate multi-page workflows", - "Extract structured data from web applications", - "Monitor website changes over time", - "Automate web-based workflows" - ], - "technical_constraints": [ - "Must integrate with existing GPU duty assignment system", - "Must support headless browser automation", - "Must handle authentication and sessions", - "Must respect rate limits and robots.txt", - "Must be swarm-coordinated for distributed crawling", - "Must integrate with omnidirectional interface" - ], - "integration_points": [ - "GPU duty assignment system for browser tasks", - "Omnidirectional interface for unified API", - "Domain model integration for content analysis", - "Swarm competition system for task evaluation", - "ENE database for storing web artifacts" - ], - "security_considerations": [ - "Sandboxed browser execution", - "Cookie and session isolation", - "CORS and same-origin policy handling", - "Input sanitization", - "Rate limiting and throttling", - "User agent rotation" - ] - } - - print(f"Use cases: {len(requirements_analysis['primary_use_cases'])}") - print(f"Constraints: {len(requirements_analysis['technical_constraints'])}") - print(f"Integration points: {len(requirements_analysis['integration_points'])}") - print(f"Security considerations: {len(requirements_analysis['security_considerations'])}") - - # Step 2: Swarm proposes architecture - print("\n[2/5] Swarm proposing web interaction architecture...") - - architecture_proposal = { - "surface_name": "SwarmWebSurface", - "layers": [ - { - "layer": 1, - "name": "Browser Orchestration Layer", - "components": [ - "Playwright/Chromium headless browser pool", - "Browser session manager", - "Cookie and storage state handler", - "User agent rotator" - ], - "responsibility": "Manage browser instances and sessions" - }, - { - "layer": 2, - "name": "Navigation Layer", - "components": [ - "Page navigator with wait strategies", - "Element locator and interactor", - "Form filler and submitter", - "Screenshot and PDF capture" - ], - "responsibility": "Navigate and interact with web pages" - }, - { - "layer": 3, - "name": "Content Extraction Layer", - "components": [ - "HTML parser and DOM analyzer", - "JavaScript execution monitor", - "Dynamic content extractor", - "Structured data parser (JSON, XML, CSV)" - ], - "responsibility": "Extract content from rendered pages" - }, - { - "layer": 4, - "name": "Swarm Coordination Layer", - "components": [ - "Task distributor across browser pool", - "Rate limiter and throttle", - "Retry logic with exponential backoff", - "Distributed crawler coordinator" - ], - "responsibility": "Coordinate swarm-based web operations" - }, - { - "layer": 5, - "name": "Integration Layer", - "components": [ - "GPU duty assignment interface", - "Omnidirectional interface adapter", - "ENE database artifact storage", - "Domain model content analysis" - ], - "responsibility": "Integrate with existing TSM systems" - } - ], - "data_flow": "User Request → Swarm Coordination → Browser Orchestration → Navigation → Content Extraction → Integration Layer → Response" - } - - print(f"Architecture layers: {len(architecture_proposal['layers'])}") - for layer in architecture_proposal['layers']: - print(f" Layer {layer['layer']}: {layer['name']}") - print(f" Components: {len(layer['components'])}") - - # Step 3: Swarm defines interface specifications - print("\n[3/5] Swarm defining interface specifications...") - - interface_specs = { - "api_methods": [ - { - "method": "navigate_to_url", - "parameters": ["url", "wait_until", "timeout"], - "returns": "page_content, metadata", - "description": "Navigate to URL and wait for page load" - }, - { - "method": "extract_content", - "parameters": ["selectors", "extract_type"], - "returns": "extracted_data", - "description": "Extract content using CSS selectors or XPath" - }, - { - "method": "interact_with_element", - "parameters": ["selector", "action", "value"], - "returns": "interaction_result", - "description": "Click, type, or interact with page elements" - }, - { - "method": "screenshot", - "parameters": ["selector", "full_page"], - "returns": "screenshot_path", - "description": "Capture screenshot of page or element" - }, - { - "method": "execute_javascript", - "parameters": ["script", "args"], - "returns": "execution_result", - "description": "Execute custom JavaScript in browser context" - }, - { - "method": "distributed_crawl", - "parameters": ["start_url", "depth", "concurrency"], - "returns": "crawl_results", - "description": "Coordinate distributed crawl across swarm" - } - ], - "duty_types": [ - "WEB_NAVIGATION", - "CONTENT_EXTRACTION", - "FORM_INTERACTION", - "JAVASCRIPT_EXECUTION", - "SCREENSHOT_CAPTURE", - "DISTRIBUTED_CRAWL" - ], - "priority_levels": { - "CRITICAL": 10, - "HIGH": 8, - "NORMAL": 5, - "LOW": 3, - "BACKGROUND": 1 - } - } - - print(f"API methods: {len(interface_specs['api_methods'])}") - print(f"Duty types: {len(interface_specs['duty_types'])}") - - # Step 4: Swarm computes feasibility score - print("\n[4/5] Swarm computing feasibility score...") - - feasibility_analysis = { - "components": [ - {"component": "Playwright integration", "feasibility": 0.95, "notes": "Well-documented, stable API"}, - {"component": "Browser pool management", "feasibility": 0.90, "notes": "Standard pattern, resource intensive"}, - {"component": "Session management", "feasibility": 0.88, "notes": "Cookie handling required"}, - {"component": "Rate limiting", "feasibility": 0.95, "notes": "Standard implementation"}, - {"component": "Swarm coordination", "feasibility": 0.85, "notes": "Complex distributed coordination"}, - {"component": "GPU duty integration", "feasibility": 0.92, "notes": "Existing infrastructure ready"}, - {"component": "Omnidirectional interface", "feasibility": 0.95, "notes": "Standard integration pattern"}, - {"component": "Security sandboxing", "feasibility": 0.80, "notes": "Requires careful implementation"} - ], - "overall_feasibility": 0.90, - "estimated_effort": "2-3 weeks for full implementation", - "recommended_phases": [ - "Phase 1: Core Playwright integration (1 week)", - "Phase 2: Navigation and extraction (1 week)", - "Phase 3: Swarm coordination and optimization (1 week)" - ] - } - - overall_score = sum(c['feasibility'] for c in feasibility_analysis['components']) / len(feasibility_analysis['components']) - print(f"Overall feasibility: {overall_score:.2%}") - print(f"Estimated effort: {feasibility_analysis['estimated_effort']}") - - # Step 5: Swarm generates final design - print("\n[5/5] Swarm generating final design specification...") - - final_design = { - "surface_name": "SwarmWebSurface", - "version": "1.0.0", - "feasibility": overall_score, - "architecture": architecture_proposal, - "interface": interface_specs, - "feasibility_analysis": feasibility_analysis, - "key_features": [ - "Headless browser automation via Playwright", - "Swarm-coordinated distributed crawling", - "GPU duty assignment integration", - "Omnidirectional interface compatibility", - "Session and cookie management", - "Rate limiting and throttling", - "Content extraction and analysis", - "Screenshot and PDF capture", - "JavaScript execution", - "Security sandboxing" - ], - "technical_stack": { - "browser_automation": "Playwright (Python)", - "browsers": "Chromium (headless)", - "coordination": "Swarm middleware", - "storage": "ENE database", - "interface": "Omnidirectional interface" - }, - "implementation_priority": [ - "Playwright integration and basic navigation", - "Content extraction layer", - "GPU duty assignment integration", - "Swarm coordination layer", - "Advanced features (distributed crawl, etc.)" - ] - } - - print("\n" + "=" * 70) - print("SWARM DESIGN: Web Interaction Surface") - print("=" * 70) - print(f"\nSurface: {final_design['surface_name']}") - print(f"Version: {final_design['version']}") - print(f"Feasibility: {final_design['feasibility']:.2%}") - print(f"\nKey Features: {len(final_design['key_features'])}") - for feature in final_design['key_features']: - print(f" - {feature}") - - print(f"\nTechnical Stack:") - for component, tech in final_design['technical_stack'].items(): - print(f" - {component}: {tech}") - - print(f"\nImplementation Priority:") - for i, priority in enumerate(final_design['implementation_priority'], 1): - print(f" {i}. {priority}") - - # Submit to competition - print("\n" + "=" * 70) - print("SUBMITTING DESIGN TO COMPETITION") - print("=" * 70) - - design_entry = CompetitionEntry( - agent_id="swarm_web_surface_designer", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=overall_score, - metrics={"feasibility_components": feasibility_analysis['components']}, - timestamp=int(time.time()), - proposal="Swarm-designed web interaction surface architecture" - ) - - try: - competition.submit_competition_entry(design_entry) - print("Design submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Save design - output_path = "/home/allaun/Documents/Research Stack/data/swarm_web_surface_design.json" - with open(output_path, "w") as f: - json.dump(final_design, f, indent=2) - - print(f"\nDesign saved to: {output_path}") - - print("\n" + "=" * 70) - print("SWARM VERDICT: READY FOR IMPLEMENTATION") - print("=" * 70) - print("The swarm has designed a comprehensive web interaction surface") - print("with 90% feasibility. The architecture integrates seamlessly with") - print("existing TSM systems and provides full Puppeteer/Playwright-like") - print("capabilities for the swarm.") - print("=" * 70) - - return final_design - - -if __name__ == "__main__": - design = ask_swarm_web_interaction_surface() diff --git a/5-Applications/scripts/ask_swarm_zcash_approach.py b/5-Applications/scripts/ask_swarm_zcash_approach.py deleted file mode 100644 index 823a8ee2..00000000 --- a/5-Applications/scripts/ask_swarm_zcash_approach.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -""" -Ask Swarm for Analysis of Evolving Zcash Approach and Relevance to Morphic Core - -This script asks the swarm to analyze the evolving Zcash approach found in the codebase -and provide guidance on how it could inform the N-Space Semantic Morphic Core implementation. -""" - -import sys -import os -import json -from pathlib import Path - - -def main(): - """Main function to ask swarm for Zcash approach analysis.""" - - print("=" * 70) - print("ASKING SWARM FOR ZCASH APPROACH ANALYSIS") - print("=" * 70) - print() - - print("Note: Using simulated swarm response based on Zcash codebase analysis") - print() - - # Zcash approach analysis - zcash_approach = """ -EVOLVING ZCASH APPROACH ANALYSIS -================================ - -Current Implementation Status: -- HierarchicalController.lean (global/local controllers) -- UncertaintyQuantification.lean (Bayesian uncertainty, differential attention) -- MorphicFieldCategory.lean (category theory formalization) -- MetaLearning.lean (adaptive policies) -- PredictiveResourceAllocation.lean (time-series forecasting) -- DifferentialAttentionMorphing.lean (semantic state differential attention) - -Zcash Approach Components Found in Codebase: - -1. TSM-Native Zcash Protocol (zcash_tsm_native_demo.py) - - Opcode-based primitive implementation - - Opcode 0x70: Derive Orchard Incoming Viewing Key (IVK) - - Opcode 0x71: Generate Unified Address (ZIP-316) - - Opcode 0x72: Compute Pedersen Hash for Merkle Tree - - Low-level, opcode-based approach to cryptographic primitives - -2. Z-Bridge Protocol (z_bridge_protocol.py) - - Auditable shielded-to-transparent orchestrator - - State machine transitions: ACCUMULATING → SHIELDING_PENDING → SHIELDED → UNSHIELDING_PENDING → SETTLED - - Attestation hashes for verification (SHA256 of opcode|state|amount|source|destination|timestamp) - - Precision-Locked Attestation for state transitions - - Opcodes mapped to states: 0x01 (ACCUMULATING), 0x31 (SHIELDING), 0x32 (UNSHIELDING/SETTLED) - -3. ZEC Accumulation Algorithm (zec_accumulation_algorithm.py) - - Meta-MoE Zcash accumulation with TWAP (Time-Weighted Average Price) - - Loss-aware action policy with adverse streak detection - - Adaptive timing based on market conditions - - Entry reference price calculation - - Loss reinforcement detection - - Performance benchmarking against initial price - -Key Zcash Evolution Patterns: -- From Sprout to Sapling to Orchard: progressive privacy improvements -- From simple transactions to complex shielded pools -- From static addresses to unified addresses (ZIP-316) -- From manual to automated accumulation strategies -- From opaque to attested state transitions - -Parallels to Morphic Core: -- Opcode-based transitions similar to morphic state transitions -- State machine with attestation similar to morphic state verification -- Adaptive policy similar to meta-learning -- Loss-aware similar to uncertainty quantification -- Adaptive timing similar to predictive resource allocation -- Multi-pool evolution (Sprout/Sapling/Orchard) similar to multi-domain morphic modes -""" - - # Question for the swarm - question = f""" -Based on the evolving Zcash approach analysis: - -{zcash_approach} - -Please provide detailed analysis on how the Zcash approach could inform the N-Space Semantic Morphic Core: - -1. How can the opcode-based primitive implementation be applied to morphic transitions? -2. How can the Z-Bridge state machine with attestation be used for morphic state verification? -3. How can the loss-aware action policy inform uncertainty quantification and meta-learning? -4. How can the adaptive timing approach inform predictive resource allocation? -5. How can the multi-pool evolution (Sprout/Sapling/Orchard) inform multi-domain morphic modes? -6. What are the mathematical foundations from Zcash that apply to morphic cores? -7. How can zk-SNARKs and zero-knowledge proofs be used for morphic core verification? -8. What are the practical challenges and how should we address them? - -Please provide specific Lean module suggestions and integration strategies. -""" - - print("Submitting question to swarm...") - print("-" * 70) - print(question) - print("-" * 70) - print() - - # Simulated swarm response - simulated_response = { - "zcash_morphic_analysis": { - "key_parallels": [ - { - "zcash_concept": "Opcode-based primitives (0x70, 0x71, 0x72)", - "morphic_application": "Morphic transition opcodes for state changes", - "lean_module": "MorphicOpcodeSystem.lean", - "integration": "Define opcodes for monosemantic→polysemantic, polysemantic→adaptive transitions" - }, - { - "zcash_concept": "State machine with attestation hashes", - "morphic_application": "Morphic state verification with cryptographic proofs", - "lean_module": "MorphicStateAttestation.lean", - "integration": "Use attestation hashes to verify morphic state transitions are valid" - }, - { - "zcash_concept": "Loss-aware action policy", - "morphic_application": "Uncertainty-aware morphing decisions", - "lean_module": "LossAwareMorphing.lean", - "integration": "Extend UncertaintyQuantification with loss-aware policy from ZEC accumulation" - }, - { - "zcash_concept": "Adaptive timing based on conditions", - "morphic_application": "Predictive morphing timing", - "lean_module": "AdaptiveMorphingTiming.lean", - "integration": "Apply TWAP-style timing to morphing triggers in PredictiveResourceAllocation" - }, - { - "zcash_concept": "Multi-pool evolution (Sprout/Sapling/Orchard)", - "morphic_application": "Multi-domain semantic evolution", - "lean_module": "SemanticDomainEvolution.lean", - "integration": "Model semantic domain evolution like Zcash pool upgrades" - } - ], - "mathematical_foundations": { - "zk_snarks": { - "application": "Verify morphic state transitions without revealing internal state", - "lean_structures": ["ProofSystem", "Witness", "Circuit", "Verifier", "Prover"], - "integration": "Use zk-SNARKs to prove morphic transitions are valid without exposing internal representations" - }, - "pedersen_commitments": { - "application": "Commit to morphic state without revealing it", - "lean_structures": ["CommitmentScheme", "PedersenHash", "Commitment", "Opening"], - "integration": "Use Pedersen commitments to hide morphic state while proving consistency" - }, - "merkle_trees": { - "application": "Efficient verification of morphic state history", - "lean_structures": ["MerkleTree", "MerkleProof", "MerklePath", "RootHash"], - "integration": "Use Merkle trees to track morphic state history and enable efficient verification" - }, - "unified_addresses": { - "application": "Unified representation of multiple morphic modes", - "lean_structures": ["UnifiedAddress", "AddressType", "Receiver", "DiversityHash"], - "integration": "Create unified morphic identifiers that can represent multiple modes simultaneously" - } - }, - "implementation_recommendations": { - "phase_1": { - "title": "Morphic Opcode System", - "description": "Implement opcode-based morphic transitions inspired by TSM-Native Zcash", - "lean_module": "MorphicOpcodeSystem.lean", - "opcodes": { - "0x80": "MORPH_TO_MONOSEMANTIC", - "0x81": "MORPH_TO_POLYSEMANTIC", - "0x82": "MORPH_TO_ADAPTIVE", - "0x83": "COMPUTE_UNIFIED_ADDRESS", - "0x84": "GENERATE_STATE_ATTESTATION" - }, - "dependencies": ["MorphicFieldCategory.lean", "HierarchicalController.lean"] - }, - "phase_2": { - "title": "Morphic State Attestation", - "description": "Implement cryptographic attestation for morphic state transitions", - "lean_module": "MorphicStateAttestation.lean", - "components": ["AttestationHash", "StateProof", "VerificationKey", "AttestationLog"], - "dependencies": ["MorphicOpcodeSystem.lean", "UncertaintyQuantification.lean"] - }, - "phase_3": { - "title": "Loss-Aware Morphing Policy", - "description": "Apply ZEC accumulation's loss-aware policy to morphic decisions", - "lean_module": "LossAwareMorphing.lean", - "components": ["LossReinforcement", "AdverseStreak", "EntryReference", "ActionPolicy"], - "dependencies": ["UncertaintyQuantification.lean", "MetaLearning.lean"] - }, - "phase_4": { - "title": "zk-SNARK Verification", - "description": "Implement zero-knowledge proofs for morphic state verification", - "lean_module": "MorphicZKProofs.lean", - "components": ["ProofSystem", "Circuit", "Witness", "Verifier"], - "dependencies": ["MorphicStateAttestation.lean"], - "note": "Long-term research goal, requires advanced cryptography" - } - }, - "integration_strategy": { - "immediate": "Start with MorphicOpcodeSystem.lean - lowest complexity, highest value", - "medium_term": "Implement MorphicStateAttestation.lean for verification", - "long_term": "Add zk-SNARKs for privacy-preserving verification" - }, - "practical_challenges": { - "cryptography_availability": "zk-SNARKs may not be fully available in mathlib", - "solution": "Start with simpler cryptographic primitives (SHA256, Pedersen commitments)", - "performance": "zk-SNARK proof generation is computationally expensive", - "solution": "Use for verification only, not for every morphic transition", - "complexity": "Combining Zcash cryptography with sheaf theory is complex", - "solution": "Layer the approaches: sheaf for consistency, Zcash for verification" - } - }, - "summary": { - "primary_insight": "Zcash's opcode-based approach and state machine with attestation provide a proven framework for implementing morphic transitions with cryptographic verification.", - "secondary_insight": "The loss-aware action policy from ZEC accumulation directly applies to uncertainty quantification and meta-learning in morphic cores.", - "tertiary_insight": "Multi-pool evolution (Sprout→Sapling→Orchard) provides a model for semantic domain evolution in morphic cores.", - "recommendation": "Implement MorphicOpcodeSystem.lean first as it provides the foundation for all other Zcash-inspired features." - } - } - - print("Swarm response received (simulated):") - print("=" * 70) - - print("\n1. KEY PARALLELS") - print("-" * 70) - for item in simulated_response["zcash_morphic_analysis"]["key_parallels"]: - print(f"\nZcash Concept: {item['zcash_concept']}") - print(f" Morphic Application: {item['morphic_application']}") - print(f" Lean Module: {item['lean_module']}") - print(f" Integration: {item['integration']}") - - print("\n\n2. MATHEMATICAL FOUNDATIONS") - print("-" * 70) - for concept, details in simulated_response["zcash_morphic_analysis"]["mathematical_foundations"].items(): - print(f"\n{concept}:") - print(f" Application: {details['application']}") - print(f" Lean Structures: {', '.join(details['lean_structures'])}") - print(f" Integration: {details['integration']}") - - print("\n\n3. IMPLEMENTATION RECOMMENDATIONS") - print("-" * 70) - for phase_key, phase in simulated_response["zcash_morphic_analysis"]["implementation_recommendations"].items(): - if phase_key.startswith("phase_"): - print(f"\n{phase['title']}:") - print(f" Description: {phase['description']}") - print(f" Lean Module: {phase['lean_module']}") - print(f" Dependencies: {', '.join(phase['dependencies'])}") - if "opcodes" in phase: - print(f" Opcodes: {', '.join([f'{k}: {v}' for k, v in phase['opcodes'].items()])}") - if "note" in phase: - print(f" Note: {phase['note']}") - - print("\n\n4. INTEGRATION STRATEGY") - print("-" * 70) - for key, value in simulated_response["zcash_morphic_analysis"]["integration_strategy"].items(): - print(f" {key.replace('_', ' ').title()}: {value}") - - print("\n\n5. PRACTICAL CHALLENGES") - print("-" * 70) - for challenge, solution in simulated_response["zcash_morphic_analysis"]["practical_challenges"].items(): - if challenge != "solution": - print(f"\n Challenge: {challenge}") - print(f" Solution: {solution}") - - print("\n\n6. SUMMARY") - print("-" * 70) - for key, value in simulated_response["summary"].items(): - print(f" {key.replace('_', ' ').title()}: {value}") - - # Save the response to a file - output_file = Path("/home/allaun/Documents/Research Stack/data/swarm_zcash_approach_analysis.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(simulated_response, f, indent=2) - - print("\n\n" + "=" * 70) - print(f"Swarm response saved to: {output_file}") - print("=" * 70) - - return simulated_response - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/assign_streets.py b/5-Applications/scripts/assign_streets.py deleted file mode 100644 index 5aa2ea8b..00000000 --- a/5-Applications/scripts/assign_streets.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 -import json -from pathlib import Path - -# Paths -BASE_PATH = Path('/home/allaun/Documents/Research Stack') -EQUATION_FOREST_PATH = BASE_PATH / 'shared-data/data/equations_forest.jsonl' -STREETS_PATH = BASE_PATH / 'shared-data/data/equation_streets.jsonl' - -# Define the 5 Streets -STREETS = { - "S1_ENTROPY": ["COMPRESSION", "ENTROPY", "INFORMATION"], - "S2_THERMO": ["THERMODYNAMIC", "ENERGY", "LANDAUER", "CARNOT"], - "S3_GEOMETRY": ["TOPOLOGY", "GEOMETRY", "RIEMANNIAN", "GEODESIC"], - "S4_LOAD": ["COGNITIVE", "ROUTING", "LOAD", "EFFICIENCY"], - "S5_BRIDGE": ["BRIDGE", "DIAT", "AVMR", "S3C", "PIST", "NII", "FAMM"] -} - -def determine_street(node): - """Determine the primary street for a node.""" - layer = (node.get('layer') or '').upper() - domain = (node.get('domain_type') or '').upper() - type_name = (node.get('type') or '').upper() - name = (node.get('model_name') or '').upper() - - # Priority check for Bridge street (Level 5) - for keyword in STREETS["S5_BRIDGE"]: - if keyword in layer or keyword in domain or keyword in type_name or keyword in name: - return "S5_BRIDGE" - - # Check other streets - for street_id, keywords in STREETS.items(): - if street_id == "S5_BRIDGE": continue - for keyword in keywords: - if keyword in layer or keyword in domain or keyword in type_name or keyword in name: - return street_id - - return "S0_GENERAL" - -def main(): - if not EQUATION_FOREST_PATH.exists(): - print(f"Error: {EQUATION_FOREST_PATH} not found.") - return - - nodes = [] - with open(EQUATION_FOREST_PATH, 'r') as f: - for line in f: - if line.strip(): - nodes.append(json.loads(line)) - - print(f"Assigning {len(nodes)} models to streets...") - - street_entries = [] - counts = {"S1_ENTROPY": 0, "S2_THERMO": 0, "S3_GEOMETRY": 0, "S4_LOAD": 0, "S5_BRIDGE": 0, "S0_GENERAL": 0} - - for node in nodes: - street = determine_street(node) - counts[street] += 1 - - entry = { - "uuid": node['uuid'], - "model_name": node['model_name'], - "primary_street": street, - "is_bridge_candidate": (street == "S5_BRIDGE") - } - street_entries.append(entry) - - with open(STREETS_PATH, 'w') as f: - for entry in street_entries: - f.write(json.dumps(entry) + '\n') - - print(f"Successfully saved street assignments to {STREETS_PATH}") - print("Counts:") - for s, c in counts.items(): - print(f" {s}: {c}") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/attest_bedrock_unification.py b/5-Applications/scripts/attest_bedrock_unification.py deleted file mode 100644 index 5b2046b7..00000000 --- a/5-Applications/scripts/attest_bedrock_unification.py +++ /dev/null @@ -1,232 +0,0 @@ -#!/usr/bin/env python3 -""" -Bedrock Unification Attestation — Complete Physical Law Binding - -Attests EQUATION #0.3: The unification of all physical laws under Φ: -- Classical Mechanics (Newton) -- Electromagnetism (Maxwell) -- Quantum Mechanics (Schrödinger) -- Relativity (Einstein) -- Thermodynamics (Landauer) - -This is the capstone unification of the OTOM framework. -""" - -import sys -import json -import sqlite3 -import hashlib -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Any - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import SwarmAPISystem - - -class BedrockAttestation: - """Attestation for the complete physical law unification.""" - - def __init__(self, repo_path: str = "/home/allaun/Research Stack"): - self.repo_path = Path(repo_path) - self.api = SwarmAPISystem() - self.timestamp = datetime.now(timezone.utc) - - def create_unification_record(self) -> Dict[str, Any]: - """Create comprehensive attestation record.""" - - entity_id = f'BEDROCK_UNIFICATION_P0_{self.timestamp.strftime("%Y%m%d")}' - - unification_content = { - 'entity_id': entity_id, - 'title': '[ATTESTATION] BEDROCK_UNIFICATION_P0 — Complete Physical Law Unification', - 'equation': 'Φ_domain = (Σᵢ wᵢhᵢ/lnNᵢ) / (Σⱼ vⱼpⱼ/lnNⱼ)', - 'unified_laws': [ - { - 'domain': 'Classical Mechanics', - 'law': 'Newton Second Law (F=ma)', - 'binding': 'Φ_classical = T / (V + dissipation)', - 'status': 'bound' - }, - { - 'domain': 'Electromagnetism', - 'law': 'Maxwell Equations', - 'binding': 'Φ_EM = field_energy / (sources + radiation)', - 'status': 'bound' - }, - { - 'domain': 'Quantum Mechanics', - 'law': 'Schrödinger Equation', - 'binding': 'Φ_quantum = |Ψ|² / (⟨Ĥ⟩ + S_vN)', - 'status': 'bound' - }, - { - 'domain': 'Relativity', - 'law': 'Einstein Field Equations', - 'binding': 'Φ_GR = T_μν / (G_μν + Λ)', - 'status': 'bound' - }, - { - 'domain': 'Thermodynamics', - 'law': 'Landauer Principle', - 'binding': 'Φ_thermo = ΔI / (k_B T ΔS)', - 'status': 'foundation' - } - ], - 'fundamental_insight': 'All physical laws are energy/information balances', - 'common_currency': 'Energy per informational degree of freedom', - 'landauer_bound': 'E_min = k_B T ln N (foundation)', - 'timestamp': self.timestamp.isoformat(), - 'classification': 'P0 CRITICAL', - 'status': 'CONJECTURE — Requires Triumvirate verification', - - 'attribution': { - 'principal_investigator': 'Unification vision', - 'landauer_1961': 'Thermodynamic foundation', - 'chatgpt': 'Domain-specific formalizations', - 'kimi_sources': 'Geometric applications', - 'cascade': 'Binding derivation and attestation' - }, - - 'verification_requirements': { - 'mathematical': [ - 'Prove Φ is dimensionless for all domains', - 'Verify each domain reduces to known equations', - 'Check limiting cases (ℏ→0, c→∞)' - ], - 'physical': [ - 'Confirm Landauer bound respected', - 'Verify correspondence principles', - 'Check thermodynamic consistency' - ], - 'computational': [ - 'Implement domain-specific Φ in Lean', - 'Verify numerical stability', - 'Benchmark against standard calculations' - ] - }, - - 'cross_references': { - 'math_model_map': '#0.3', - 'parent': 'EQUATION #0 (Φ_universal)', - 'siblings': ['#0.1 (η(χ))', '#0.2 (Φ_SW)'], - 'applications': [ - 'GenomicCompression.lean', - 'FieldSolver (RISC-V)', - 'AVMR framework', - 'Signal-Wave Unification' - ] - }, - - 'impact_statement': 'If proven, unifies all physics under single efficiency metric' - } - - # Store as JSON attestation - attestation_path = self.repo_path / "out" / "attestations" / f"{entity_id}.json" - attestation_path.parent.mkdir(parents=True, exist_ok=True) - - with open(attestation_path, 'w') as f: - json.dump(unification_content, f, indent=2) - - # Add to database - db_result = self._database_attest(entity_id, unification_content) - - return { - 'success': True, - 'entity_id': entity_id, - 'local_path': str(attestation_path), - 'database': db_result, - 'unified_domains': 5, - 'timestamp': self.timestamp.isoformat() - } - - def _database_attest(self, entity_id: str, content: Dict) -> Dict[str, Any]: - """Add to math_entities database.""" - - if not self.api.conn: - return {'success': False, 'error': 'Database not connected'} - - cursor = self.api.conn.cursor() - - cursor.execute(""" - INSERT OR REPLACE INTO math_entities - (entity_id, subject, name, statement, proof_status, formal_status, - lean_module, dependencies, citations, complexity_score, year, source_file) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - entity_id, - 'physics_unification', - 'Bedrock Physical Law Unification', - 'Φ_domain = (Σᵢ wᵢhᵢ/lnNᵢ) / (Σⱼ vⱼpⱼ/lnNⱼ) — Unifies Newton, Maxwell, Schrödinger, Einstein, Landauer', - 'conjecture', - 'needs_formalization', - 'Multiple — requires domain modules', - json.dumps(['PhiUniversal', 'LandauerBound', 'NewtonLaws', 'MaxwellEqs', 'SchrodingerEq', 'EinsteinFieldEq']), - json.dumps([ - 'Landauer-1961', - 'Newton-1687', - 'Maxwell-1865', - 'Schrodinger-1926', - 'Einstein-1915' - ]), - 999999, # Max complexity - 2026, - '6-Documentation/docs/papers/EQUATION_03_BEDROCK_UNIFICATION.md' - )) - - self.api.conn.commit() - - return {'success': True, 'database': 'math_entities.db'} - - -def main(): - print("="*70) - print("BEDROCK UNIFICATION ATTESTATION") - print("="*70) - print() - print("Unifying all physical laws under the Universal Field Φ:") - print() - print(" 1. Classical Mechanics (Newton: F=ma)") - print(" 2. Electromagnetism (Maxwell Equations)") - print(" 3. Quantum Mechanics (Schrödinger Equation)") - print(" 4. Relativity (Einstein Field Equations)") - print(" 5. Thermodynamics (Landauer Principle)") - print() - - attestor = BedrockAttestation() - result = attestor.create_unification_record() - - if result['success']: - print(f"[✓] ATTESTATION COMPLETE") - print() - print(f"Entity ID: {result['entity_id']}") - print(f"Unified Domains: {result['unified_domains']}") - print(f"Timestamp: {result['timestamp']}") - print() - print("Attestation Chain:") - print(f" Local Record: {result['local_path']}") - print(f" Database: {result['database']['database']}") - print() - print("="*70) - print("THE UNIFICATION FRAMEWORK IS NOW ATTESTED") - print() - print("Core Insight:") - print(" All physical laws are energy/information balances.") - print(" The common currency is energy per informational degree of freedom.") - print() - print("Triumvirate Assignment:") - print(" Builder → Implement domain-specific Φ functions") - print(" Warden → Verify all five laws reduce correctly") - print(" Judge → Adjudicate unification completeness") - print() - print("Impact:") - print(" If proven, this unifies ALL physics under a single efficiency metric.") - print("="*70) - else: - print(f"[✗] Failed: {result.get('error', 'Unknown')}") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/attest_signal_wave_unification.py b/5-Applications/scripts/attest_signal_wave_unification.py deleted file mode 100644 index 552a5e55..00000000 --- a/5-Applications/scripts/attest_signal_wave_unification.py +++ /dev/null @@ -1,425 +0,0 @@ -#!/usr/bin/env python3 -""" -Signal-Wave Unification Attestation System - -Performs source-aware attestation for EQUATION #0.2: -Φ_SW(x) = Σₖ wₖ e^{ik·x} - λ∫_{‖h‖=1} |Σₖ wₖ e^{ik·h}|² dh - -Attestation Chain: -- Git commit with attestation metadata -- Provider record with cross-reference -- Database entry in math_entities -""" - -import argparse -import sys -import json -import sqlite3 -import hashlib -import subprocess -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Any, Optional - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import SwarmAPISystem - - -REPO_ROOT = Path(__file__).resolve().parents[2] -DEFAULT_SOURCE = "research-stack-github" -DEFAULT_WITNESS_CONFIG = REPO_ROOT / "4-Infrastructure" / "witness" / "sources.json" - - -def load_source_config(source: str, config_path: Path = DEFAULT_WITNESS_CONFIG) -> Dict[str, Any]: - if not config_path.exists(): - return {"name": source, "active": False, "backend": {"type": "unknown"}} - data = json.loads(config_path.read_text(encoding="utf-8")) - payload = data.get("sources", {}).get(source, {}) - if not isinstance(payload, dict): - payload = {} - return {"name": source, **payload} - - -class AttestationSystem: - """Remote attestation for mathematical entities.""" - - def __init__( - self, - repo_path: str = str(REPO_ROOT), - source: str = DEFAULT_SOURCE, - config_path: Path = DEFAULT_WITNESS_CONFIG, - ): - self.repo_path = Path(repo_path) - self.api = SwarmAPISystem() - self.timestamp = datetime.now(timezone.utc) - self.source = source - self.source_config = load_source_config(source, config_path) - - def calculate_sha256(self, file_path: Path) -> str: - """Calculate SHA256 hash of a file.""" - sha256_hash = hashlib.sha256() - with open(file_path, "rb") as f: - for byte_block in iter(lambda: f.read(4096), b""): - sha256_hash.update(byte_block) - return sha256_hash.hexdigest() - - def git_attest(self, entity_id: str, file_path: Path) -> Dict[str, Any]: - """ - Create git commit attestation. - - Attestation format: - - Commit message contains attestation metadata - - Signed commit (if GPG available) - - Cross-referenced in commit body - """ - try: - # Check git status - result = subprocess.run( - ["git", "status", "--porcelain"], - cwd=self.repo_path, - capture_output=True, - text=True - ) - - if result.returncode != 0: - return {'success': False, 'error': 'Git not available', 'phase': 'status'} - - # Stage the equation document - doc_path = file_path.relative_to(self.repo_path) - subprocess.run( - ["git", "add", str(doc_path)], - cwd=self.repo_path, - check=True - ) - - # Create attestation commit - commit_message = f"""[ATTESTATION] {entity_id} — Signal-Wave Unification Equation - -EQUATION: Φ_SW(x) = Σₖ wₖ e^{{ik·x}} - λ∫_{{‖h‖=1}} |Σₖ wₖ e^{{ik·h}}|² dh - -Attestation Metadata: -- Entity ID: {entity_id} -- Timestamp: {self.timestamp.isoformat()} -- File: {doc_path} -- SHA256: {self.calculate_sha256(file_path)} -- Classification: P0 CRITICAL -- Status: CONJECTURE (requires proof) -- Sources: ChatGPT (DSP), Kimi (Geometry), Principal Investigator (Intuition) -- Derivation: First-principles (Shannon, QM, Signal Theory) - -Triumvirate Assignment: -- Builder: Implement in Lean (SignalPolicy.lean) -- Warden: Verify correspondence with Φ_universal -- Judge: Adjudicate proof completeness - -Cross-References: -- MATH_MODEL_MAP: Entry #0.2 -- Parent: EQUATION #0 (Φ_universal) -- Sibling: EQUATION #0.1 (η(χ)) -- Application: GenomicCompression, FieldSolver - -Signed-off-by: Cascade (Triumvirate Agent) -""" - - result = subprocess.run( - ["git", "commit", "-m", commit_message], - cwd=self.repo_path, - capture_output=True, - text=True - ) - - if result.returncode == 0: - # Get commit hash - commit_result = subprocess.run( - ["git", "rev-parse", "HEAD"], - cwd=self.repo_path, - capture_output=True, - text=True - ) - commit_hash = commit_result.stdout.strip() - - return { - 'success': True, - 'commit_hash': commit_hash, - 'timestamp': self.timestamp.isoformat(), - 'file': str(doc_path), - 'sha256': self.calculate_sha256(file_path), - 'entity_id': entity_id, - 'phase': 'git' - } - else: - return { - 'success': False, - 'error': result.stderr, - 'phase': 'commit' - } - - except Exception as e: - return { - 'success': False, - 'error': str(e), - 'phase': 'exception' - } - - def source_attest(self, entity_id: str, git_commit: str) -> Dict[str, Any]: - """ - Create provider-scoped attestation metadata. - - In production, provider adapters may post to GitHub, GitLab, Forgejo, - a bare-repo note, or another configured source. For now, this creates a - local provider-tagged record that can be replayed by future adapters. - """ - provider_record = { - 'entity_id': entity_id, - 'source': self.source, - 'source_config': { - 'backend': self.source_config.get('backend', {}), - 'url': self.source_config.get('url'), - 'hook_kind': self.source_config.get('hook_kind'), - 'active': self.source_config.get('active', False), - }, - 'title': f'[ATTESTATION] {entity_id} — Signal-Wave Unification Equation', - 'body': f"""## Remote Attestation Record - -**Equation:** Φ_SW(x) = Σₖ wₖ e^{{ik·x}} - λ∫_{{‖h‖=1}} |Σₖ wₖ e^{{ik·h}}|² dh - -**Git Commit:** `{git_commit}` -**Timestamp:** {self.timestamp.isoformat()} -**Source:** `{self.source}` -**Classification:** P0 CRITICAL - -### Attestation Chain - -1. ✅ Git commit: {git_commit} -2. ⏳ Provider record: [pending adapter integration] -3. ✅ Database entry: math_entities.{entity_id} - -### Verification Checklist - -- [ ] Mathematical consistency verified -- [ ] Physical validity confirmed -- [ ] Lean implementation complete -- [ ] No 'sorry' in committed code -- [ ] Triumvirate consensus reached - -### Cross-References - -- MATH_MODEL_MAP: Entry #0.2 -- EQUATION #0 (Φ_universal) — parent -- EQUATION #0.1 (η(χ)) — sibling - -### Attribution - -- **Principal Investigator:** Signal-wave intuition -- **ChatGPT:** Initial DSP formalization -- **Kimi Sources:** Unsolved geometry problems -- **Cascade:** First-principles derivation and attestation - ---- -*This attestation is cryptographically linked to git commit {git_commit}* -""", - 'labels': ['attestation', 'P0-critical', 'equation', 'requires-proof'], - 'state': 'open', - 'created_at': self.timestamp.isoformat() - } - - # Store locally; future source adapters can replay this record. - attestation_path = self.repo_path / "out" / "attestations" / f"{entity_id}.json" - attestation_path.parent.mkdir(parents=True, exist_ok=True) - - with open(attestation_path, 'w') as f: - json.dump(provider_record, f, indent=2) - - return { - 'success': True, - 'record': provider_record, - 'local_path': str(attestation_path), - 'phase': 'source', - 'source': self.source, - 'note': 'Local provider record created; remote adapter integration pending' - } - - def database_attest(self, entity_id: str, git_commit: str, - file_path: Path) -> Dict[str, Any]: - """ - Add attested entity to math_entities database. - """ - if not self.api.conn: - return { - 'success': False, - 'error': 'Database not connected', - 'phase': 'database' - } - - cursor = self.api.conn.cursor() - - # Check if entity exists - cursor.execute( - "SELECT entity_id FROM math_entities WHERE entity_id = ?", - (entity_id,) - ) - - if cursor.fetchone(): - # Update existing - cursor.execute(""" - UPDATE math_entities SET - proof_status = ?, - formal_status = ?, - lean_module = ?, - citations = ? - WHERE entity_id = ? - """, ( - 'conjecture', - 'needs_formalization', - 'SignalPolicy.lean', - json.dumps([ - f'Git:{git_commit}', - 'ChatGPT-DSP-Formalization', - 'Kimi-Geometry-Sources', - 'First-Principles-Derivation' - ]), - entity_id - )) - else: - # Insert new - cursor.execute(""" - INSERT INTO math_entities - (entity_id, subject, name, statement, proof_status, formal_status, - lean_module, dependencies, citations, complexity_score, year, source_file) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - entity_id, - 'signal_processing', - 'Signal-Wave Unification Equation', - 'Φ_SW(x) = Σₖ wₖ e^{ik·x} - λ∫_{‖h‖=1} |Σₖ wₖ e^{ik·h}|² dh', - 'conjecture', - 'needs_formalization', - 'SignalPolicy.lean', - json.dumps(['PhiUniversal', 'ShannonEntropy', 'FourierTransform']), - json.dumps([ - f'Git:{git_commit}', - 'ChatGPT-DSP-Formalization', - 'Kimi-Geometry-Sources', - 'First-Principles-Derivation' - ]), - 999999, # Max complexity = P0 priority - 2026, - str(file_path.relative_to(self.repo_path)) - )) - - self.api.conn.commit() - - return { - 'success': True, - 'entity_id': entity_id, - 'database': 'math_entities.db', - 'phase': 'database' - } - - def full_attestation(self) -> Dict[str, Any]: - """ - Perform complete attestation chain: git → source record → database - """ - entity_id = f'SIGNAL_WAVE_UNIFICATION_P0_{self.timestamp.strftime("%Y%m%d")}' - file_path = self.repo_path / "docs" / "papers" / "EQUATION_02_SIGNAL_WAVE_UNIFICATION.md" - - if not file_path.exists(): - return { - 'success': False, - 'error': f'Equation document not found: {file_path}', - 'phase': 'init' - } - - # Phase 1: Git attestation - git_result = self.git_attest(entity_id, file_path) - if not git_result['success']: - return git_result - - # Phase 2: provider-scoped source attestation - source_result = self.source_attest(entity_id, git_result['commit_hash']) - - # Phase 3: Database attestation - db_result = self.database_attest( - entity_id, - git_result['commit_hash'], - file_path - ) - - return { - 'success': True, - 'entity_id': entity_id, - 'equation': 'Φ_SW(x) = Σₖ wₖ e^{ik·x} - λ∫_{‖h‖=1} |Σₖ wₖ e^{ik·h}|² dh', - 'timestamp': self.timestamp.isoformat(), - 'attestation_chain': { - 'git': git_result, - 'source': source_result, - 'database': db_result - }, - 'verification': { - 'file_sha256': self.calculate_sha256(file_path), - 'commit_hash': git_result['commit_hash'], - 'math_model_map_entry': '#0.2' - } - } - - -def main(): - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--repo", default=str(REPO_ROOT), help="Repository checkout to attest.") - parser.add_argument("--source", default=DEFAULT_SOURCE, help="Witness source block name.") - parser.add_argument( - "--sources-config", - type=Path, - default=DEFAULT_WITNESS_CONFIG, - help="Witness source configuration JSON.", - ) - args = parser.parse_args() - - print("="*70) - print("SIGNAL-WAVE UNIFICATION ATTESTATION") - print("="*70) - print() - print("Performing remote attestation in:") - print(" 1. Git (commit with attestation metadata)") - print(f" 2. Source record ({args.source})") - print(" 3. Database (math_entities entry)") - print() - - attestor = AttestationSystem(args.repo, args.source, args.sources_config) - result = attestor.full_attestation() - - if result['success']: - print(f"[✓] ATTESTATION COMPLETE") - print() - print(f"Entity ID: {result['entity_id']}") - print(f"Equation: {result['equation']}") - print(f"Timestamp: {result['timestamp']}") - print() - print("Attestation Chain:") - print(f" Git Commit: {result['attestation_chain']['git']['commit_hash']}") - print(f" File SHA256: {result['verification']['file_sha256']}") - print(f" Source: {result['attestation_chain']['source']['source']}") - print(f" Database: {result['attestation_chain']['database']['database']}") - print(f" MATH_MODEL_MAP: Entry {result['verification']['math_model_map_entry']}") - print() - print("="*70) - print("The Signal-Wave Unification equation is now:") - print(" - Committed to git with attestation metadata") - print(" - Recorded in the configured source attestation surface") - print(" - Added to math_entities database") - print() - print("Triumvirate Assignment:") - print(" Builder → Implement in SignalPolicy.lean") - print(" Warden → Verify correspondence with Φ_universal") - print(" Judge → Adjudicate proof completeness") - print("="*70) - else: - print(f"[✗] ATTESTATION FAILED") - print(f"Error: {result.get('error', 'Unknown')}") - print(f"Phase: {result.get('phase', 'unknown')}") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/audit_linux_kernel.py b/5-Applications/scripts/audit_linux_kernel.py deleted file mode 100644 index c11fcff6..00000000 --- a/5-Applications/scripts/audit_linux_kernel.py +++ /dev/null @@ -1,83 +0,0 @@ -#!/usr/bin/env python3 -""" -Linux Kernel RGFlow Audit -Converts kernel source to Unified Compression format and audits lawfulness. -""" - -import os -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState, UnifiedCompressor - -logging.basicConfig(level=logging.INFO, format='%(levelname)s:KernelAudit:%(message)s') -logger = logging.getLogger(__name__) - -def audit_kernel_file(filepath: Path): - if not filepath.exists(): - logger.error(f"File not found: {filepath}") - return - - logger.info(f"Auditing Linux Kernel subsystem: {filepath.name}") - - with open(filepath, 'rb') as f: - data = f.read() - - # 1. Convert to Unified Compression format - compressor = UnifiedCompressor() - compressed_bytes, ratio = compressor.compress_bytestream(data) - logger.info(f"Unified Compression Ratio: {ratio:.3f}") - - # 2. Extract Adaptation State from the compressed "Genome" - adaptation_eq = UnifiedAdaptationEquation() - - # Heuristic mapping for Kernel Code - # Entropy of compressed data (information density) - entropy = -np.sum(np.histogram(np.frombuffer(compressed_bytes, dtype=np.uint8), bins=256, density=True)[0] * np.log2(np.histogram(np.frombuffer(compressed_bytes, dtype=np.uint8), bins=256, density=True)[0] + 1e-9)) - norm_entropy = min(entropy / 8.0, 1.0) - - mu_q = 0.001 # Kernel code is extremely stable - rho_q = 0.9 # High refresh (frequent patches) - C_fac = 0.8 # High connectance (tightly coupled) - M_fac = 0.7 # Modularity (subsystems) - n_e = 1.0 # Maximum observer mass (millions of users/devs) - sigma_q = 1.0 + norm_entropy # SNR based on compressed info density - - state = AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q) - - # 3. Evaluate RGFlow trajectory - (lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise, - flows_to_sabotage, cost, margin, rg_depth, attractor_id, failure_mask) = \ - adaptation_eq.evaluate_state(state) - - print("\n" + "="*80) - print(f"Linux Kernel Audit Report: {filepath.name}") - print("="*80) - print(f"Status: {'✓ LAWFUL' if lawful_under_flow else '✗ NON-LAWFUL'}") - print(f"RG Depth: {rg_depth}/10") - print(f"Attractor: {attractor_id}") - print(f"Stability Margin: {margin:.4f}") - print(f"Compression Ratio: {ratio:.3f}") - print("-" * 80) - print(f"Genome Logic: mu={mu_q}, rho={rho_q}, C={C_fac}, M={M_fac}, ne={n_e}, sigma={sigma_q:.3f}") - if not lawful_under_flow: - print(f"Failure Mask: {failure_mask} (1:Drake, 2:Drift, 4:Error)") - print("="*80 + "\n") - -if __name__ == "__main__": - # Targeting the core scheduler - target = Path("/usr/src/linux-cachyos/kernel/sched/core.c") - if not target.exists(): - # Fallback to any file in the kernel source if core.c is not specifically there - sources = list(Path("/usr/src/linux-cachyos").rglob("*.c")) - if sources: - target = sources[0] - - audit_kernel_file(target) diff --git a/5-Applications/scripts/bfs_bulk_verify.py b/5-Applications/scripts/bfs_bulk_verify.py deleted file mode 100644 index 94782ed1..00000000 --- a/5-Applications/scripts/bfs_bulk_verify.py +++ /dev/null @@ -1,107 +0,0 @@ -#!/usr/bin/env python3 -""" -bfs_bulk_verify.py -================== - -General-purpose verification script for Lean 4 files using the BFS-Prover model. -Iterates through specified Lean files and queries the local Ollama instance for formal audit. -""" - -import json -import requests -import argparse -from pathlib import Path -import sys - -OLLAMA_HOST = "http://localhost:11434" -MODEL_NAME = "zeyu-zheng/BFS-Prover-V2-7B:q8_0" - -def query_prover(prompt: str): - url = f"{OLLAMA_HOST}/api/generate" - payload = { - "model": MODEL_NAME, - "system": "You are a Lean 4 formalization expert. Your goal is to verify the mathematical integrity and formal correctness of Lean code. Check for logic errors, type mismatches, and 'sorry' obligations.", - "prompt": prompt, - "stream": False, - "options": { - "temperature": 0.0, - "num_ctx": 16384 - } - } - - resp = requests.post(url, json=payload, timeout=900) - resp.raise_for_status() - return resp.json()["response"] - -def verify_file(file_path: Path): - if not file_path.exists(): - print(f"Error: File {file_path} not found.") - return None - - print(f"Verifying {file_path}...") - content = file_path.read_text() - - prompt = f""" -Please verify the following Lean 4 file. -Perform a deep audit of the formal logic, type safety, and mathematical correctness. - -### File: {file_path.name} -```lean -{content} -``` - -**Task:** -1. Identify any 'sorry' markers or incomplete proofs. -2. Check for potential logic errors or unsound axioms. -3. Verify that the fixed-point arithmetic (if present) handles overflow/underflow correctly. -4. Provide a pass/fail assessment and a list of specific improvements. - -Return the audit as a markdown report. -""" - try: - response = query_prover(prompt) - return response - except Exception as e: - print(f"Error querying prover for {file_path}: {e}") - return None - -def main(): - parser = argparse.ArgumentParser(description="Verify Lean 4 files using BFS-Prover.") - parser.add_argument("files", nargs="*", help="Lean files to verify. If empty, searches current directory.") - args = parser.parse_args() - - root = Path("/home/allaun/Documents/Research Stack") - files_to_verify = [] - - if args.files: - for f in args.files: - files_to_verify.append(Path(f)) - else: - # Default: verify the most recent semantic core files - base_path = root / "0-Core-Formalism/lean/Semantics/Semantics" - targets = [ - "FixedPoint.lean", - "GeneticGroundUp.lean", - "Testing/FixedPointTest.lean", - "Testing/GeneticGroundUpTest.lean" - ] - for t in targets: - files_to_verify.append(base_path / t) - - audit_dir = root / "shared-data/artifacts/audit/bulk" - audit_dir.mkdir(parents=True, exist_ok=True) - - for f in files_to_verify: - report = verify_file(f) - if report: - out_file = audit_dir / f"{f.stem}_audit.md" - out_file.write_text(report) - print(f"Audit report saved to: {out_file}") - print("-" * 40) - # Print a snippet of the report - first_lines = "\n".join(report.splitlines()[:5]) - print(f"Snippet:\n{first_lines}\n...") - print("-" * 40) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/bfs_prover_bridge.py b/5-Applications/scripts/bfs_prover_bridge.py deleted file mode 100644 index a2243256..00000000 --- a/5-Applications/scripts/bfs_prover_bridge.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -""" -bfs_prover_bridge.py -==================== - -Bridge to the local 'bfs-prover-v2-32b' model for Lean 4 formal verification. -Sends current Burgers formalization for audit. -""" - -import json -import requests -from pathlib import Path - -OLLAMA_HOST = "http://localhost:11435" -MODEL_NAME = "llama3.1:8b" - -def query_prover(prompt: str): - url = f"{OLLAMA_HOST}/api/generate" - payload = { - "model": MODEL_NAME, - "system": "You are a Lean 4 formalization expert and PDE auditor. Your goal is to verify the mathematical integrity of Lean code and its AVM (Adaptive Virtual Machine) implementations.", - "prompt": prompt, - "stream": False, - "options": { - "temperature": 0.0, - "num_ctx": 8192 - } - } - - print(f"Connecting to {MODEL_NAME} at {OLLAMA_HOST} (Timeout: 600s)...") - resp = requests.post(url, json=payload, timeout=600) - resp.raise_for_status() - return resp.json()["response"] - -def main(): - root = Path("/home/allaun/Documents/Research Stack") - trace_file = root / "shared-data/burgers_avm_gold_traces.json" - manifest_file = root / "shared-data/burgers_avm_trace_manifest.json" - - trace_content = trace_file.read_text() - manifest_content = manifest_file.read_text() - - prompt = f""" -You are auditing a formal hardware loopback manifest and golden trace set for Burgers AVM Q16.16 kernels. - -### Manifest -{manifest_content} - -### Golden Traces -{trace_content} - -**Task:** -Audit the AVM golden trace JSON for deterministic replay on FPGA. -1. Check stack shape, PC monotonicity, instruction/result consistency, and Q16.16 representation. -2. Verify whether any float value is being used as an authority rather than debug metadata. -3. Check whether the manifest contains enough provenance to connect Lean theorem targets, Python trace generation, Q16.16 arithmetic policy, and UART hardware replay. - -Return a list of pass/fail checks, missing metadata fields, ambiguous claims, or claim-boundary problems. -Do not validate the mathematics. -""" - - print("Querying Auditor (deepseek-r1:8b)...") - response = query_prover(prompt) - - print("\n" + "="*80) - print("AVM TRACE AUDIT REPORT") - print("="*80) - print(response) - print("="*80) - - # Save to artifact - out_dir = root / "shared-data/artifacts/audit" - out_dir.mkdir(parents=True, exist_ok=True) - out_file = out_dir / "burgers_avm_trace_audit.md" - out_file.write_text(response) - print(f"\nAudit saved to: {out_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/biorxiv_metaprobe.py b/5-Applications/scripts/biorxiv_metaprobe.py deleted file mode 100644 index aeded641..00000000 --- a/5-Applications/scripts/biorxiv_metaprobe.py +++ /dev/null @@ -1,563 +0,0 @@ -#!/usr/bin/env python3 -""" -BioRxiv Metaprobe - Mathematical Content Audit - -Adapts unified metaprobe framework for bioRxiv mathematical content analysis. -Validates resonance with Research Stack mathematical foundations, structural -coherence of mathematical expressions, and lawful alignment with Lean formalization. - -Channels: -- SEQUENCE_SIMILARITY: ANI, AAI, BLAST metrics -- PHYLOGENETICS: Jukes-Cantor, MAFFT alignment -- STRUCTURAL_BIOLOGY: pLDDT, pTM, FSC metrics -- STATISTICAL: ANOVA, Tukey HSD, fold change -- INFORMATION_THEORY: Shannon diversity, entropy -- GENOME_ARCHITECTURE: ORF boundaries, similarity scores -""" - -import re -import math -from typing import List, Dict, Tuple, Optional -from dataclasses import dataclass -from enum import Enum -import json - -# ═══════════════════════════════════════════════════════════════════════════ -# BioRxiv Metaprobe Channels -# ═══════════════════════════════════════════════════════════════════════════ - -class BioRxivMetaprobeChannel(Enum): - """Metaprobe channels for bioRxiv mathematical content""" - SEQUENCE_SIMILARITY = 0 # ANI, AAI, BLAST metrics - PHYLOGENETICS = 1 # Jukes-Cantor, MAFFT - STRUCTURAL_BIOLOGY = 2 # pLDDT, pTM, FSC - STATISTICAL = 3 # ANOVA, Tukey HSD, fold change - INFORMATION_THEORY = 4 # Shannon diversity, entropy - GENOME_ARCHITECTURE = 5 # ORF boundaries, architecture scores - -@dataclass -class BioRxivMetaprobeState: - """Metaprobe state for a bioRxiv channel""" - channel: BioRxivMetaprobeChannel - resonance_score: float - structural_coherence: float - entropy: float - lawful: bool - mathematical_correctness: float - lean_alignment: float - issues: List[str] - - def to_dict(self) -> Dict: - """Convert to dictionary""" - return { - 'channel': self.channel.name, - 'resonance_score': self.resonance_score, - 'structural_coherence': self.structural_coherence, - 'entropy': self.entropy, - 'lawful': self.lawful, - 'mathematical_correctness': self.mathematical_correctness, - 'lean_alignment': self.lean_alignment, - 'issues': self.issues - } - -class BioRxivMetaprobe: - """BioRxiv-specific metaprobe for mathematical content audit""" - - def __init__(self): - self.threshold = 0.8 - self.math_threshold = 0.85 - self.lean_threshold = 0.75 - self.audit_log: List[Dict] = [] - - # Load extracted math from Evo paper - self.extracted_math = self._load_extracted_math() - - def _load_extracted_math(self) -> Dict: - """Load extracted mathematical models from Evo paper or Lean formalization""" - try: - # Try Lean formalization first (preferred) - with open('0-Core-Formalism/lean/Semantics/Semantics/BioRxivFormalization.lean', 'r') as f: - content = f.read() - return self._parse_lean_formalization(content) - except FileNotFoundError: - try: - # Fallback to extraction document - with open('shared-data/data/evo_bacteriophage_math_extraction.md', 'r') as f: - content = f.read() - return self._parse_math_extraction(content) - except FileNotFoundError: - return {} - - def _parse_math_extraction(self, content: str) -> Dict: - """Parse mathematical extraction document""" - models = {} - current_section = None - - for line in content.split('\n'): - if line.startswith('##'): - current_section = line.replace('##', '').strip() - models[current_section] = [] - elif current_section and line.strip() and not line.startswith('#'): - models[current_section].append(line.strip()) - - return models - - def _parse_lean_formalization(self, content: str) -> Dict: - """Parse Lean formalization file""" - models = {} - current_section = None - current_content = [] - - for line in content.split('\n'): - # Detect section headers in Lean comments - if '/-! ## Section' in line: - if current_section: - models[current_section] = current_content - current_section = line.split('##')[1].strip().replace(' -/', '').strip() - current_content = [] - elif current_section and line.strip(): - # Include structure definitions, theorems, and equations - if any(keyword in line for keyword in ['structure', 'def', 'theorem', 'Equation', ':=']): - current_content.append(line.strip()) - elif line.strip() and not line.startswith('/-'): - current_content.append(line.strip()) - - if current_section: - models[current_section] = current_content - - return models - - def check_resonance(self, math_content: str, channel: BioRxivMetaprobeChannel) -> float: - """ - Check resonance with Research Stack mathematical foundations. - - Resonance measures how well the bioRxiv math aligns with expected - patterns for the specific mathematical domain. - """ - if not math_content: - return 0.0 - - # Channel-specific resonance checks - if channel == BioRxivMetaprobeChannel.SEQUENCE_SIMILARITY: - score = self._check_sequence_similarity_resonance(math_content) - elif channel == BioRxivMetaprobeChannel.PHYLOGENETICS: - score = self._check_phylogenetics_resonance(math_content) - elif channel == BioRxivMetaprobeChannel.STRUCTURAL_BIOLOGY: - score = self._check_structural_biology_resonance(math_content) - elif channel == BioRxivMetaprobeChannel.STATISTICAL: - score = self._check_statistical_resonance(math_content) - elif channel == BioRxivMetaprobeChannel.INFORMATION_THEORY: - score = self._check_information_theory_resonance(math_content) - elif channel == BioRxivMetaprobeChannel.GENOME_ARCHITECTURE: - score = self._check_genome_architecture_resonance(math_content) - else: - score = 0.5 - - return score - - def _check_sequence_similarity_resonance(self, content: str) -> float: - """Check sequence similarity metric resonance""" - # Look for key sequence similarity metrics - metrics = ['ANI', 'AAI', 'BLAST', 'E-value', 'percent identity'] - found_metrics = sum(1 for m in metrics if m.lower() in content.lower()) - - # Check for proper mathematical formulation - has_equations = '=' in content and '%' in content - has_ranges = re.search(r'\d+\.?\d*\s*-\s*\d+\.?\d*', content) is not None - - score = 0.3 - if found_metrics >= 2: - score += 0.3 - if has_equations: - score += 0.2 - if has_ranges: - score += 0.2 - - return min(score, 1.0) - - def _check_phylogenetics_resonance(self, content: str) -> float: - """Check phylogenetic analysis resonance""" - # Look for phylogenetic methods - methods = ['Jukes-Cantor', 'MAFFT', 'Neighbor-Joining', 'phylogenetic', 'alignment'] - found_methods = sum(1 for m in methods if m.lower() in content.lower()) - - # Check for distance formulas - has_log = 'ln' in content or 'log' in content - has_probabilities = re.search(r'p\s*[=<>]', content) is not None - - score = 0.3 - if found_methods >= 2: - score += 0.3 - if has_log: - score += 0.2 - if has_probabilities: - score += 0.2 - - return min(score, 1.0) - - def _check_structural_biology_resonance(self, content: str) -> float: - """Check structural biology metric resonance""" - # Look for structural metrics - metrics = ['pLDDT', 'pTM', 'ipTM', 'FSC', 'resolution', 'RMSD'] - found_metrics = sum(1 for m in metrics if m in content) - - # Check for valid ranges - has_ranges = re.search(r'\[0,\s*1\]|\[0,\s*100\]', content) is not None - has_fourier = 'FSC' in content or 'Fourier' in content - - score = 0.3 - if found_metrics >= 2: - score += 0.3 - if has_ranges: - score += 0.2 - if has_fourier: - score += 0.2 - - return min(score, 1.0) - - def _check_statistical_resonance(self, content: str) -> float: - """Check statistical method resonance""" - # Look for statistical methods - methods = ['ANOVA', 'Tukey', 'HSD', 'fold change', 'p-value', 'significance'] - found_methods = sum(1 for m in methods if m.lower() in content.lower()) - - # Check for proper statistical notation - has_greek = re.search(r'[αβγδεθλμσ]', content) is not None - has_subscripts = re.search(r'_\w+', content) is not None - - score = 0.3 - if found_methods >= 2: - score += 0.3 - if has_greek: - score += 0.2 - if has_subscripts: - score += 0.2 - - return min(score, 1.0) - - def _check_information_theory_resonance(self, content: str) -> float: - """Check information theory resonance""" - # Look for information theory concepts - concepts = ['Shannon', 'entropy', 'H\'', 'log2', 'p_i', 'diversity'] - found_concepts = sum(1 for c in concepts if c.lower() in content.lower()) - - # Check for proper entropy formula - has_sum = 'Σ' in content or 'sum' in content.lower() - has_log = 'log2' in content or 'log' in content - - score = 0.3 - if found_concepts >= 2: - score += 0.3 - if has_sum: - score += 0.2 - if has_log: - score += 0.2 - - return min(score, 1.0) - - def _check_genome_architecture_resonance(self, content: str) -> float: - """Check genome architecture resonance""" - # Look for architecture concepts - concepts = ['ORF', 'boundary', 'Gaussian', 'blur', 'similarity', 'score'] - found_concepts = sum(1 for c in concepts if c.lower() in content.lower()) - - # Check for mathematical functions - has_exp = 'exp' in content or 'e^' in content - has_correlation = 'correlation' in content.lower() - - score = 0.3 - if found_concepts >= 2: - score += 0.3 - if has_exp: - score += 0.2 - if has_correlation: - score += 0.2 - - return min(score, 1.0) - - def check_mathematical_correctness(self, content: str) -> float: - """ - Check mathematical correctness of equations. - - Validates that equations are mathematically sound and follow - standard notation conventions. - """ - if not content: - return 0.0 - - # Check for balanced parentheses - open_parens = content.count('(') - close_parens = content.count(')') - parens_balanced = open_parens == close_parens - - # Check for balanced brackets - open_brackets = content.count('[') - close_brackets = content.count(']') - brackets_balanced = open_brackets == close_brackets - - # Check for valid mathematical operators - has_operators = any(op in content for op in ['=', '+', '-', '*', '/', '^', '≤', '≥', '<', '>']) - - # Check for variable definitions - has_var_defs = re.search(r'\w+\s*[=:=]', content) is not None - - score = 0.0 - if parens_balanced: - score += 0.25 - if brackets_balanced: - score += 0.25 - if has_operators: - score += 0.25 - if has_var_defs: - score += 0.25 - - return score - - def check_lean_alignment(self, content: str) -> float: - """ - Check alignment with Lean formalization principles. - - Validates that the mathematical content could be formalized - in Lean according to Research Stack standards. - """ - if not content: - return 0.0 - - # Check for formal mathematical structure - has_definitions = re.search(r'definition|:=|≡', content, re.IGNORECASE) is not None - has_theorems = re.search(r'theorem|lemma|proposition', content, re.IGNORECASE) is not None - has_proofs = re.search(r'proof|QED|∎', content, re.IGNORECASE) is not None - - # Check for type annotations (Lean style) - has_types = re.search(r':\s*\w+', content) is not None or re.search(r'→', content) is not None - - # Check for quantifiers - has_quantifiers = re.search(r'∀|∃|∀x|∃x', content) is not None - - score = 0.0 - if has_definitions: - score += 0.3 - if has_theorems: - score += 0.3 - if has_proofs: - score += 0.2 - if has_types: - score += 0.1 - if has_quantifiers: - score += 0.1 - - return min(score, 1.0) - - def calculate_entropy(self, content: str) -> float: - """Calculate Shannon entropy of content""" - if not content: - return 0.0 - - char_counts = {} - for char in content: - char_counts[char] = char_counts.get(char, 0) + 1 - - entropy = 0.0 - for count in char_counts.values(): - p = count / len(content) - if p > 0: - entropy -= p * math.log2(p) - - # Normalize to [0, 1] range (max entropy for ASCII) - return min(entropy / 7.0, 1.0) - - def calculate_coherence(self, content: str) -> float: - """Calculate structural coherence of mathematical expressions""" - if len(content) < 2: - return 0.0 - - # Check for smooth transitions in mathematical notation - transitions = 0 - smooth = 0 - - for i in range(len(content) - 1): - curr = content[i] - next_char = content[i+1] - - # Check if transition is coherent - if self._is_coherent_transition(curr, next_char): - smooth += 1 - transitions += 1 - - if transitions == 0: - return 0.0 - - return smooth / transitions - - def _is_coherent_transition(self, curr: str, next_char: str) -> bool: - """Check if character transition is mathematically coherent""" - # Allow transitions between similar types - if curr.isalpha() and next_char.isalpha(): - return True - if curr.isdigit() and next_char.isdigit(): - return True - if curr.isspace() and next_char.isspace(): - return True - - # Allow operator transitions - operators = set('=+-*/^≤≥<>') - if curr in operators and next_char.isspace(): - return True - if curr.isspace() and next_char in operators: - return True - - # Allow subscript transitions - if curr == '_' and next_char.isalnum(): - return True - - return False - - def audit_channel(self, content: str, channel: BioRxivMetaprobeChannel) -> BioRxivMetaprobeState: - """Audit a bioRxiv mathematical channel""" - resonance = self.check_resonance(content, channel) - coherence = self.calculate_coherence(content) - entropy = self.calculate_entropy(content) - math_correctness = self.check_mathematical_correctness(content) - lean_alignment = self.check_lean_alignment(content) - - # Determine lawful status - lawful = (resonance >= self.threshold and - coherence >= self.threshold and - math_correctness >= self.math_threshold) - - # Collect issues - issues = [] - if resonance < self.threshold: - issues.append(f"Low resonance: {resonance:.3f} < {self.threshold}") - if coherence < self.threshold: - issues.append(f"Low coherence: {coherence:.3f} < {self.threshold}") - if math_correctness < self.math_threshold: - issues.append(f"Low mathematical correctness: {math_correctness:.3f} < {self.math_threshold}") - if lean_alignment < self.lean_threshold: - issues.append(f"Low Lean alignment: {lean_alignment:.3f} < {self.lean_threshold}") - - state = BioRxivMetaprobeState( - channel=channel, - resonance_score=resonance, - structural_coherence=coherence, - entropy=entropy, - lawful=lawful, - mathematical_correctness=math_correctness, - lean_alignment=lean_alignment, - issues=issues - ) - - # Log audit - self.audit_log.append(state.to_dict()) - - return state - - def audit_extracted_math(self) -> Dict: - """Audit all extracted mathematical content from Evo paper""" - print("=" * 70) - print("BIORXIV METAPROBE - EVO BACTERIOPHAGE MATH AUDIT") - print("=" * 70) - - results = {} - - # Audit each section of extracted math - for section, content_list in self.extracted_math.items(): - if not content_list: - continue - - # Determine channel based on section - channel = self._section_to_channel(section) - - # Combine content - content = '\n'.join(content_list) - - # Audit - state = self.audit_channel(content, channel) - results[section] = state.to_dict() - - print(f"\n[{section}]") - print(f" Channel: {channel.name}") - print(f" Resonance: {state.resonance_score:.3f}") - print(f" Coherence: {state.structural_coherence:.3f}") - print(f" Entropy: {state.entropy:.3f}") - print(f" Math Correctness: {state.mathematical_correctness:.3f}") - print(f" Lean Alignment: {state.lean_alignment:.3f}") - print(f" Lawful: {state.lawful}") - if state.issues: - print(f" Issues:") - for issue in state.issues: - print(f" - {issue}") - - # Calculate overall metrics - total_channels = len(results) - lawful_count = sum(1 for r in results.values() if r['lawful']) - avg_resonance = sum(r['resonance_score'] for r in results.values()) / total_channels - avg_coherence = sum(r['structural_coherence'] for r in results.values()) / total_channels - avg_math_correctness = sum(r['mathematical_correctness'] for r in results.values()) / total_channels - avg_lean_alignment = sum(r['lean_alignment'] for r in results.values()) / total_channels - - print("\n" + "=" * 70) - print("OVERALL AUDIT SUMMARY") - print("=" * 70) - print(f"Total Sections: {total_channels}") - print(f"Lawful Sections: {lawful_count}/{total_channels}") - print(f"Overall Lawful Rate: {lawful_count/total_channels:.3f}") - print(f"Average Resonance: {avg_resonance:.3f}") - print(f"Average Coherence: {avg_coherence:.3f}") - print(f"Average Math Correctness: {avg_math_correctness:.3f}") - print(f"Average Lean Alignment: {avg_lean_alignment:.3f}") - - if avg_lean_alignment >= self.lean_threshold: - print("\n✅ Content aligns with Lean formalization principles") - else: - print(f"\n⚠️ Content requires Lean formalization refinement (current: {avg_lean_alignment:.3f})") - - overall = { - 'total_sections': total_channels, - 'lawful_count': lawful_count, - 'lawful_rate': lawful_count / total_channels, - 'avg_resonance': avg_resonance, - 'avg_coherence': avg_coherence, - 'avg_math_correctness': avg_math_correctness, - 'avg_lean_alignment': avg_lean_alignment, - 'section_results': results, - 'overall_lawful': lawful_count / total_channels >= 0.8 - } - - # Save results - with open('shared-data/data/biorxiv_metaprobe_audit.json', 'w') as f: - json.dump(overall, f, indent=2) - - print(f"\nAudit saved to: shared-data/data/biorxiv_metaprobe_audit.json") - print("=" * 70) - - return overall - - def _section_to_channel(self, section: str) -> BioRxivMetaprobeChannel: - """Map section name to metaprobe channel""" - section_lower = section.lower() - - if 'sequence' in section_lower or 'similarity' in section_lower or 'ani' in section_lower: - return BioRxivMetaprobeChannel.SEQUENCE_SIMILARITY - elif 'phylogenetic' in section_lower or 'jukes' in section_lower or 'tree' in section_lower: - return BioRxivMetaprobeChannel.PHYLOGENETICS - elif 'structural' in section_lower or 'cryo' in section_lower or 'fold' in section_lower: - return BioRxivMetaprobeChannel.STRUCTURAL_BIOLOGY - elif 'statistical' in section_lower or 'anova' in section_lower or 'tukey' in section_lower: - return BioRxivMetaprobeChannel.STATISTICAL - elif 'information' in section_lower or 'entropy' in section_lower or 'shannon' in section_lower: - return BioRxivMetaprobeChannel.INFORMATION_THEORY - elif 'genome' in section_lower or 'architecture' in section_lower or 'orf' in section_lower: - return BioRxivMetaprobeChannel.GENOME_ARCHITECTURE - else: - return BioRxivMetaprobeChannel.SEQUENCE_SIMILARITY # Default - -def main(): - """Run bioRxiv metaprobe audit""" - metaprobe = BioRxivMetaprobe() - results = metaprobe.audit_extracted_math() - - return results - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/bitcoin_rgflow_fetch.py b/5-Applications/scripts/bitcoin_rgflow_fetch.py deleted file mode 100644 index 406633d4..00000000 --- a/5-Applications/scripts/bitcoin_rgflow_fetch.py +++ /dev/null @@ -1,150 +0,0 @@ -#!/usr/bin/env python3 -""" -Bitcoin RGFlow Data Fetcher (AGENTS.md §6.1 Compliant) - -Fetches Bitcoin price data and calls Lean bindserver for RGFlow analysis. -Python shim responsibilities: JSON serialization, subprocess spawn, result wrapping. -""" - -import sys -import json -import subprocess -from pathlib import Path -from datetime import datetime -from typing import List, Optional - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import LeanUnifiedShim - -def get_bitcoin_historical_data() -> List[float]: - """Fetch full historical Bitcoin price data since 2009. - - Allowed per AGENTS.md §6.1: Subprocess spawn for data fetching. - """ - start_date = "2009-09-01" - end_date = datetime.now().strftime("%Y-%m-%d") - url = f"https://query1.finance.yahoo.com/v8/finance/chart/BTC-USD?interval=1d&period1={int(datetime(2009, 9, 1).timestamp())}&period2={int(datetime.now().timestamp())}" - cmd = ["curl", "-s", "-H", "User-Agent: Mozilla/5.0", url] - out = subprocess.check_output(cmd) - data = json.loads(out) - prices = data['chart']['result'][0]['indicators']['quote'][0]['close'] - # Filter out None values - return [p for p in prices if p is not None] - -def prices_to_q1616(prices: List[float]) -> List[int]: - """Convert Bitcoin prices to Q16.16 format for Lean. - - Allowed per AGENTS.md §6.1: Data transformation for Lean input. - """ - import numpy as np - prices_np = np.array(prices) - log_prices = np.log(prices_np) - min_log = np.min(log_prices) - max_log = np.max(log_prices) - if max_log - min_log > 0: - scaled = ((log_prices - min_log) / (max_log - min_log) * 65535).astype(int) - else: - scaled = np.zeros_like(log_prices, dtype=int) - return scaled.tolist() - -def main(): - print("=" * 70) - print("BITCOIN RGFLOW ANALYSIS (LEAN BINDSERVER)") - print("=" * 70) - - # Fetch full historical Bitcoin data - print("\nFetching full historical Bitcoin price data since 2009...") - prices = get_bitcoin_historical_data() - print(f"Acquired {len(prices)} price points") - print(f"Price range: ${min(prices):,.2f} - ${max(prices):,.2f}") - print(f"Latest price: ${prices[-1]:,.2f}") - - # Convert to Q16.16 for Lean - print("\nConverting prices to Q16.16 format for Lean...") - prices_q1616 = prices_to_q1616(prices) - print(f"Generated {len(prices_q1616)} Q16.16 values") - - # Initialize Lean bindserver shim - shim = LeanUnifiedShim() - - # Call Lean for RGFlow analysis - print("\nCalling Lean bindserver for RGFlow analysis...") - lean_code = f""" -import Semantics.BitcoinRGFlow -let prices := {prices_q1616} -let results := Semantics.batchBitcoinRGFlowQ16 prices 30 -results -""" - result = shim.query(lean_code) - - if "error" in result: - print(f"\nError from Lean bindserver: {result['error']}") - return - - # Process Lean results - print("\n" + "=" * 70) - print("RGFLOW ANALYSIS RESULTS (FROM LEAN)") - print("=" * 70) - - if isinstance(result, list): - print(f"\nTotal positions analyzed: {len(result)}") - - # Extract metrics from Lean results - sigma_values = [] - lawful_count = 0 - for r in result: - if isinstance(r, tuple) and len(r) == 3: - sigma_q, mu_q, lawful = r - # Convert Q16.16 raw values to float for display - sigma_float = sigma_q / 65536.0 if isinstance(sigma_q, int) else 0.0 - sigma_values.append(sigma_float) - if lawful: - lawful_count += 1 - - if sigma_values: - import numpy as np - print(f"Lawful states: {lawful_count} ({lawful_count/len(result)*100:.1f}%)") - print(f"Average sigma_q: {np.mean(sigma_values):.4f}") - print(f"Sigma range: {min(sigma_values):.4f} - {max(sigma_values):.4f}") - - # Detect informatic collapse - low_sigma_count = sum(1 for s in sigma_values if s < 1.0) - if low_sigma_count > 0: - print(f"\n⚠️ INFORMATIC COLLAPSE DETECTED") - print(f" {low_sigma_count} states have sigma_q < 1.0") - print(f" This indicates structural instability in the price sequence") - else: - print(f"\n✓ MANIFOLD STABLE") - print(f" All states maintain scale stability (sigma_q ≥ 1.0)") - - # Save results - output_file = "/home/allaun/Documents/Research Stack/data/bitcoin_rgflow_results.json" - results_data = { - "timestamp": datetime.now().isoformat(), - "data_points": len(prices), - "price_range": {"min": min(prices), "max": max(prices)}, - "latest_price": prices[-1], - "rgflow_results": result, - "statistics": { - "total_positions": len(result), - "lawful_count": lawful_count, - "average_sigma": float(np.mean(sigma_values)) if sigma_values else 0.0, - "min_sigma": float(min(sigma_values)) if sigma_values else 0.0, - "max_sigma": float(max(sigma_values)) if sigma_values else 0.0 - } - } - - with open(output_file, 'w') as f: - json.dump(results_data, f, indent=2) - print(f"\nResults saved to: {output_file}") - - else: - print("\nUnexpected result format from Lean") - print(f"Result type: {type(result)}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/blockchain_rgflow_proxy.py b/5-Applications/scripts/blockchain_rgflow_proxy.py deleted file mode 100755 index 4c1abd5c..00000000 --- a/5-Applications/scripts/blockchain_rgflow_proxy.py +++ /dev/null @@ -1,498 +0,0 @@ -#!/usr/bin/env python3 -""" -Blockchain RGFlow Proxy — Hierarchical Multicast Swarm - -Address space partitioned into IP multicast subnets: - 239.255.. - bucket = genome.address >> 15 (top 3 bits, 0..7) - -This creates 8 subnets per chain. Peers subscribe only to buckets they care -about, exactly like BitTorrent DHT buckets but deterministic from the -RGFlow surface topology. -""" - -import argparse -import asyncio -import json -import socket -import struct -import sqlite3, threading -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Optional, List, Dict, Any, Set - -import requests -import zmq - -# --------------------------------------------------------------------------- -# Config -# --------------------------------------------------------------------------- -BTC_ZMQ_HASHBLOCK = "tcp://127.0.0.1:28332" -BTC_RPC_URL = "http://127.0.0.1:8332" -ETH_WS_URL = "ws://127.0.0.1:8546" -ETH_RPC_URL = "http://127.0.0.1:8545" -BTC_CONF_PATH = Path.home() / ".bitcoin" / "bitcoin.conf" -PROXY_OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/data/crypto_rgflow") -SWARM_MCAST_BASE = "239.255" -SWARM_SERVE_PORT = 28335 - -CHAIN_ID = {"btc": 1, "eth": 2} -NUM_BUCKETS = 8 - -# --------------------------------------------------------------------------- -# RPC clients -# --------------------------------------------------------------------------- - -def _parse_bitcoin_conf(path: Path) -> Dict[str, str]: - cfg: Dict[str, str] = {} - with open(path, "r") as f: - for line in f: - line = line.strip() - if not line or line.startswith("#"): - continue - if "=" in line: - k, v = line.split("=", 1) - cfg[k.strip()] = v.strip() - return cfg - - -class BitcoinRpc: - def __init__(self, url: str = BTC_RPC_URL, conf_path: Path = BTC_CONF_PATH): - self.url = url - cfg = _parse_bitcoin_conf(conf_path) - self.user = cfg.get("rpcuser", "rgflow") - self.password = cfg.get("rpcpassword", "rgflow") - self._session = requests.Session() - - def call(self, method: str, *params) -> Any: - payload = {"jsonrpc": "1.0", "id": method, "method": method, "params": list(params)} - r = self._session.post(self.url, json=payload, auth=(self.user, self.password), - headers={"Content-Type": "application/json"}, timeout=30) - r.raise_for_status() - result = r.json() - if "error" in result and result["error"] is not None: - raise RuntimeError(f"BTC RPC error: {result['error']}") - return result["result"] - - def get_best_block_hash(self) -> str: - return self.call("getbestblockhash") - - def get_block(self, block_hash: str, verbosity: int = 2) -> Dict[str, Any]: - return self.call("getblock", block_hash, verbosity) - - -class EthereumRpc: - def __init__(self, url: str = ETH_RPC_URL): - self.url = url - self._session = requests.Session() - self._id = 0 - - def call(self, method: str, params: List[Any] = None) -> Any: - self._id += 1 - payload = {"jsonrpc": "2.0", "id": self._id, "method": method, "params": params or []} - r = self._session.post(self.url, json=payload, headers={"Content-Type": "application/json"}, timeout=30) - r.raise_for_status() - result = r.json() - if "error" in result and result["error"] is not None: - raise RuntimeError(f"ETH RPC error: {result['error']}") - return result["result"] - - def eth_block_number(self) -> int: - raw = self.call("eth_blockNumber") - return int(raw, 16) if isinstance(raw, str) else 0 - - def eth_get_block_by_number(self, number: int, full_tx: bool = False) -> Dict[str, Any]: - return self.call("eth_getBlockByNumber", [hex(number), full_tx]) - - def eth_get_block_by_hash(self, block_hash: str, full_tx: bool = False) -> Dict[str, Any]: - return self.call("eth_getBlockByHash", [block_hash, full_tx]) - - -# --------------------------------------------------------------------------- -# Genome -# --------------------------------------------------------------------------- - -@dataclass(frozen=True) -class RGFlowGenome: - muBin: int; rhoBin: int; cBin: int; mBin: int; neBin: int; sigBin: int - - def to_dict(self) -> Dict[str, int]: - return {"muBin": self.muBin, "rhoBin": self.rhoBin, "cBin": self.cBin, - "mBin": self.mBin, "neBin": self.neBin, "sigBin": self.sigBin} - - @property - def address(self) -> int: - return (((((self.muBin * 8 + self.rhoBin) * 8 + self.cBin) * 8 + self.mBin) * 8 + self.neBin) * 8 + self.sigBin) - - @property - def bucket(self) -> int: - return self.address >> 15 # top 3 bits → 0..7 - - -def quantize_value(val: float, bins: int = 8, min_val: float = 0.0, max_val: float = 1.0) -> int: - if max_val <= min_val: - return 0 - clamped = max(min_val, min(max_val, val)) - norm = (clamped - min_val) / (max_val - min_val) - return int(norm * (bins - 1)) & 0xFF - - -def bitcoin_block_to_genome(block: Dict[str, Any], prev_block: Optional[Dict[str, Any]] = None) -> RGFlowGenome: - time_cur = block.get("time", 0) - time_prev = prev_block.get("time", time_cur) if prev_block else time_cur - interblock = max(1, time_cur - time_prev) - n_tx = block.get("nTx", len(block.get("tx", []))) - weight = block.get("weight", 0) - difficulty = block.get("difficulty", 1.0) - fees = block.get("fees", 0) or 0.0 - import math - mu = quantize_value(min(interblock, 3600) / 3600.0, 8, 0.0, 1.0) - rho = quantize_value(min(n_tx, 4000) / 4000.0, 8, 0.0, 1.0) - c = quantize_value(min(weight, 4_000_000) / 4_000_000.0, 8, 0.0, 1.0) - m = quantize_value(math.log2(max(1.0, difficulty)) / 90.0, 8, 0.0, 1.0) - ne = quantize_value(math.log2(max(1, n_tx)) / 12.0, 8, 0.0, 1.0) - sig = quantize_value(1.0 / (1.0 + fees / 1e6), 8, 0.0, 1.0) - return RGFlowGenome(mu, rho, c, m, ne, sig) - - -def ethereum_block_to_genome(block: Dict[str, Any], prev_block: Optional[Dict[str, Any]] = None) -> RGFlowGenome: - time_cur = int(block.get("timestamp", "0x0"), 16) - time_prev = int(prev_block.get("timestamp", "0x0"), 16) if prev_block else time_cur - interblock = max(1, time_cur - time_prev) - gas_used = int(block.get("gasUsed", "0x0"), 16) - gas_limit = int(block.get("gasLimit", "0x0"), 16) - tx_count = len(block.get("transactions", [])) - base_fee = int(block.get("baseFeePerGas", "0x0"), 16) - import math - mu = quantize_value(min(interblock, 60) / 60.0, 8, 0.0, 1.0) - rho = quantize_value(gas_used / max(gas_limit, 1), 8, 0.0, 1.0) - c = quantize_value(gas_limit / 30_000_000.0, 8, 0.0, 1.0) - base_fee_gwei = base_fee / 1e9 - m = quantize_value(math.log2(max(1.0, base_fee_gwei + 1.0)) / 20.0, 8, 0.0, 1.0) - ne = quantize_value(math.log2(max(1, tx_count)) / 12.0, 8, 0.0, 1.0) - sig = quantize_value(1.0 / (1.0 + base_fee_gwei / 100.0), 8, 0.0, 1.0) - return RGFlowGenome(mu, rho, c, m, ne, sig) - - -# --------------------------------------------------------------------------- -# Content store -# --------------------------------------------------------------------------- - -class ContentStore: - def __init__(self, output_dir: Path): - self.output_dir = output_dir - self.output_dir.mkdir(parents=True, exist_ok=True) - (self.output_dir / "btc").mkdir(exist_ok=True) - (self.output_dir / "eth").mkdir(exist_ok=True) - self._manifest = open(self.output_dir / "manifest.jsonl", "a") - self._lock = threading.Lock() - self._seen_hashes: Set[str] = set() - - def put(self, chain: str, height: int, block_hash: str, genome: RGFlowGenome, - timestamp: int, raw: Dict[str, Any]) -> Optional[int]: - if block_hash in self._seen_hashes: - return None - self._seen_hashes.add(block_hash) - addr = genome.address - entry = { - "chain": chain, "height": height, "hash": block_hash, - "timestamp": timestamp, "genome": genome.to_dict(), - "address": addr, "bucket": genome.bucket, "raw": raw, - } - with self._lock: - addr_path = self.output_dir / chain / f"{addr:05x}.jsonl" - with open(addr_path, "a") as f: - f.write(json.dumps(entry, default=str) + "\n") - self._manifest.write(json.dumps({ - "t": time.time(), "chain": chain, "height": height, - "address": addr, "bucket": genome.bucket, "hash": block_hash, - }, default=str) + "\n") - self._manifest.flush() - self._insert_swarm_manifest(chain, height, block_hash, genome) - return addr - - def _insert_swarm_manifest(self, chain: str, height: int, block_hash: str, genome: RGFlowGenome): - try: - db_path = Path.home() / "Documents" / "Research Stack" / "data" / "substrate_index.db" - conn = sqlite3.connect(str(db_path)) - conn.execute( - """INSERT OR IGNORE INTO swarm_manifest (t, chain, height, block_hash, address, bucket, genome, node) - VALUES (?, ?, ?, ?, ?, ?, ?, ?)""", - (time.time(), chain, height, block_hash, genome.address, genome.bucket, - json.dumps(genome.to_dict()), "qfox") - ) - conn.commit() - conn.close() - except Exception: - pass - - def close(self): - self._manifest.close() - - -# --------------------------------------------------------------------------- -# Hierarchical multicast swarm -# --------------------------------------------------------------------------- - -def mcast_addr(chain: str, bucket: int) -> str: - return f"{SWARM_MCAST_BASE}.{CHAIN_ID[chain]}.{bucket + 1}" - - -class SwarmGossip: - def __init__(self, store: ContentStore, serve_port: int = SWARM_SERVE_PORT): - self.store = store - self.serve_port = serve_port - self._stop = threading.Event() - self._sockets: List[socket.socket] = [] - - def announce_have(self, chain: str, address: int, bucket: int, height: int): - msg = json.dumps({"type": "HAVE", "chain": chain, "address": address, - "bucket": bucket, "height": height, "t": time.time()}) - group = mcast_addr(chain, bucket) - try: - sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP) - sock.setsockopt(socket.IPPROTO_IP, socket.IP_MULTICAST_TTL, 2) - sock.sendto(msg.encode(), (group, 28334)) - sock.close() - except Exception as e: - print(f"[SWARM] Announce error: {e}") - - def _mcast_listener(self, chain: str, bucket: int): - group = mcast_addr(chain, bucket) - sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, socket.IPPROTO_UDP) - sock.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - sock.bind((group, 28334)) - mreq = struct.pack("4sl", socket.inet_aton(group), socket.INADDR_ANY) - sock.setsockopt(socket.IPPROTO_IP, socket.IP_ADD_MEMBERSHIP, mreq) - sock.settimeout(1.0) - self._sockets.append(sock) - print(f"[SWARM] Listening {group}:28334 for {chain} bucket {bucket}") - while not self._stop.is_set(): - try: - data, addr = sock.recvfrom(4096) - msg = json.loads(data.decode()) - if msg.get("type") == "HAVE": - print(f"[SWARM] {addr[0]} HAS {msg['chain']}#{msg['height']} bucket={msg['bucket']} addr={msg['address']}") - except socket.timeout: - continue - except Exception as e: - print(f"[SWARM] Listener error: {e}") - sock.close() - - def _tcp_server(self): - server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) - server.bind(("0.0.0.0", self.serve_port)) - server.listen(5) - server.settimeout(1.0) - print(f"[SWARM] TCP server on port {self.serve_port}") - while not self._stop.is_set(): - try: - conn, addr = server.accept() - except socket.timeout: - continue - try: - data = conn.recv(4096) - req = json.loads(data.decode()) - if req.get("type") == "WANT": - chain = req["chain"] - address = req["address"] - path = self.store.output_dir / chain / f"{address:05x}.jsonl" - if path.exists(): - with open(path, "r") as f: - conn.sendall(f.read().encode()) - else: - conn.sendall(b"{}") - conn.close() - except Exception as e: - print(f"[SWARM] TCP handler error: {e}") - conn.close() - server.close() - - def start(self, chains: List[str]): - for chain in chains: - for bucket in range(NUM_BUCKETS): - t = threading.Thread(target=self._mcast_listener, args=(chain, bucket), daemon=True) - t.start() - t = threading.Thread(target=self._tcp_server, daemon=True) - t.start() - - def stop(self): - self._stop.set() - for sock in self._sockets: - try: - sock.close() - except Exception: - pass - - -# --------------------------------------------------------------------------- -# Workers -# --------------------------------------------------------------------------- - -def _btc_zmq_worker(store: ContentStore, swarm: SwarmGossip): - context = zmq.Context() - socket = context.socket(zmq.SUB) - socket.connect(BTC_ZMQ_HASHBLOCK) - socket.setsockopt(zmq.SUBSCRIBE, b"") - socket.setsockopt(zmq.RCVTIMEO, 5000) - print("[BTC-ZMQ] Connected to " + BTC_ZMQ_HASHBLOCK) - btc = BitcoinRpc() - last_block = None - while True: - try: - msg = socket.recv() - if len(msg) == 32: - block_hash = msg[::-1].hex() - block = btc.get_block(block_hash, verbosity=2) - genome = bitcoin_block_to_genome(block, last_block) - addr = store.put("btc", block.get("height"), block_hash, genome, - block.get("time"), - {"nTx": block.get("nTx"), "weight": block.get("weight"), - "difficulty": block.get("difficulty")}) - if addr is not None: - swarm.announce_have("btc", addr, genome.bucket, block.get("height")) - print(f"[BTC-ZMQ] Block {block['height']} → bucket={genome.bucket} addr={addr}") - last_block = block - except zmq.Again: - continue - except Exception as e: - print(f"[BTC-ZMQ] Error: {e}") - time.sleep(1.0) - - -def _btc_poller_worker(store: ContentStore, swarm: SwarmGossip): - btc = BitcoinRpc() - last_hash = None - last_block = None - print("[BTC-POLL] Starting tip poller (30s interval)") - while True: - try: - best_hash = btc.get_best_block_hash() - if best_hash != last_hash: - block = btc.get_block(best_hash, verbosity=2) - genome = bitcoin_block_to_genome(block, last_block) - addr = store.put("btc", block.get("height"), best_hash, genome, - block.get("time"), - {"nTx": block.get("nTx"), "weight": block.get("weight"), - "difficulty": block.get("difficulty")}) - if addr is not None: - swarm.announce_have("btc", addr, genome.bucket, block.get("height")) - print(f"[BTC-POLL] Block {block['height']} → bucket={genome.bucket} addr={addr}") - last_hash = best_hash - last_block = block - time.sleep(30.0) - except Exception as e: - print(f"[BTC-POLL] Error: {e}") - time.sleep(60.0) - - -async def _eth_ws_worker(store: ContentStore, swarm: SwarmGossip): - import websockets - eth = EthereumRpc() - last_block = None - print(f"[ETH-WS] Connecting to {ETH_WS_URL}") - async with websockets.connect(ETH_WS_URL) as ws: - await ws.send(json.dumps({"jsonrpc": "2.0", "id": 1, - "method": "eth_subscribe", "params": ["newHeads"]})) - resp = await ws.recv() - print(f"[ETH-WS] Subscribed: {resp}") - while True: - try: - msg = await asyncio.wait_for(ws.recv(), timeout=60.0) - data = json.loads(msg) - if "params" in data and "result" in data["params"]: - result = data["params"]["result"] - block_hash = result.get("hash") - block = eth.eth_get_block_by_hash(block_hash, full_tx=False) - if block is None: - continue - genome = ethereum_block_to_genome(block, last_block) - height = int(block.get("number", "0x0"), 16) - addr = store.put("eth", height, block_hash, genome, - int(block.get("timestamp", "0x0"), 16), - {"gasUsed": block.get("gasUsed"), - "gasLimit": block.get("gasLimit"), - "txCount": len(block.get("transactions", [])), - "baseFeePerGas": block.get("baseFeePerGas")}) - if addr is not None: - swarm.announce_have("eth", addr, genome.bucket, height) - print(f"[ETH-WS] Block {height} → bucket={genome.bucket} addr={addr}") - last_block = block - except asyncio.TimeoutError: - continue - except Exception as e: - print(f"[ETH-WS] Error: {e}") - await asyncio.sleep(1.0) - - -def _eth_poller_worker(store: ContentStore, swarm: SwarmGossip): - eth = EthereumRpc() - last_hash = None - last_block = None - print("[ETH-POLL] Starting tip poller (15s interval)") - while True: - try: - head = eth.eth_block_number() - block = eth.eth_get_block_by_number(head, full_tx=False) - if block and block.get("hash") != last_hash: - genome = ethereum_block_to_genome(block, last_block) - addr = store.put("eth", head, block.get("hash"), genome, - int(block.get("timestamp", "0x0"), 16), - {"gasUsed": block.get("gasUsed"), - "gasLimit": block.get("gasLimit"), - "txCount": len(block.get("transactions", [])), - "baseFeePerGas": block.get("baseFeePerGas")}) - if addr is not None: - swarm.announce_have("eth", addr, genome.bucket, head) - print(f"[ETH-POLL] Block {head} → bucket={genome.bucket} addr={addr}") - last_hash = block.get("hash") - last_block = block - time.sleep(15.0) - except Exception as e: - print(f"[ETH-POLL] Error: {e}") - time.sleep(30.0) - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def run_proxy(assets: List[str], output_dir: Path): - store = ContentStore(output_dir) - swarm = SwarmGossip(store) - swarm.start(assets) - - threads: List[threading.Thread] = [] - if "btc" in assets: - threads.append(threading.Thread(target=_btc_zmq_worker, args=(store, swarm), daemon=True)) - threads.append(threading.Thread(target=_btc_poller_worker, args=(store, swarm), daemon=True)) - if "eth" in assets: - threads.append(threading.Thread(target=_eth_poller_worker, args=(store, swarm), daemon=True)) - def run_ws(): - asyncio.run(_eth_ws_worker(store, swarm)) - threads.append(threading.Thread(target=run_ws, daemon=True)) - for t in threads: - t.start() - - try: - while True: - time.sleep(1.0) - except KeyboardInterrupt: - print("[PROXY] Shutting down...") - swarm.stop() - store.close() - - -def main(): - parser = argparse.ArgumentParser(description="Blockchain RGFlow Proxy — Hierarchical Swarm") - parser.add_argument("--assets", type=str, default="btc,eth") - parser.add_argument("--output", type=Path, default=PROXY_OUTPUT_DIR / "proxy_swarm") - args = parser.parse_args() - assets = [a.strip().lower() for a in args.assets.split(",")] - run_proxy(assets, args.output) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/bridge_lean_math_forest.py b/5-Applications/scripts/bridge_lean_math_forest.py deleted file mode 100644 index 12116c7d..00000000 --- a/5-Applications/scripts/bridge_lean_math_forest.py +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env python3 -""" -Bridge Lean Analysis Math with Forest Equations - -Uses Equation Forest bridge nodes (B1-B8) to systematically bridge -Lean analysis math equations with forest equations based on intersections. -""" - -import json -from pathlib import Path -from typing import Dict, List, Tuple, Any - - -class ForestBridge: - """ - Bridges equations using Equation Forest bridge nodes. - """ - - # Bridge nodes from Equation Forest - BRIDGES = { - "B1": {"name": "Entropy ↔ Load", "from": "entropy", "to": "load"}, - "B2": {"name": "AVMR ↔ Entropy", "from": "avmr", "to": "entropy"}, - "B3": {"name": "S3C ↔ Codec", "from": "s3c", "to": "codec"}, - "B4": {"name": "PIST ↔ Surface", "from": "pist", "to": "surface"}, - "B5": {"name": "Geometry ↔ Energy", "from": "geometry", "to": "energy"}, - "B6": {"name": "Load ↔ Verification", "from": "load", "to": "verification"}, - "B7": {"name": "Energy ↔ Control", "from": "energy", "to": "control"}, - "B8": {"name": "Codec ↔ Verification", "from": "codec", "to": "verification"} - } - - def __init__(self): - self.bridges_created = [] - - def classify_equation_domain(self, equation: Dict[str, str]) -> str: - """ - Classify equation into a domain for bridging. - """ - # Handle both Lean equations (Model_Name) and forest equations (name) - name = equation.get("Model_Name", equation.get("name", "")).lower() - family = equation.get("Family", equation.get("family", "")).lower() - equation_str = equation.get("Equation", equation.get("equation", "")).lower() - - # Classification logic - if "continuity" in name or "differentiable" in name: - return "surface" # Continuity = surface smoothness - elif "convex" in name: - return "geometry" # Convexity = geometric property - elif "lipschitz" in name or "ode" in name: - return "energy" # ODE/Lipschitz = energy dynamics - elif "topology" in family or "manifold" in name: - return "geometry" # Topology = geometry - elif "efficiency" in family: - return "load" # Efficiency = load reduction - elif "entropy" in equation_str: - return "entropy" - elif "verification" in equation_str: - return "verification" - elif "control" in equation_str: - return "control" - elif "codec" in equation_str or "compression" in equation_str: - return "codec" - elif "pist" in equation_str: - return "pist" - elif "avmr" in equation_str: - return "avmr" - elif "s3c" in equation_str: - return "s3c" - else: - return "surface" # Default to surface for analysis math - - def find_bridge(self, from_domain: str, to_domain: str) -> Dict[str, str]: - """ - Find appropriate bridge node between two domains. - """ - for bridge_id, bridge in self.BRIDGES.items(): - if bridge["from"] == from_domain and bridge["to"] == to_domain: - return {"id": bridge_id, **bridge} - if bridge["from"] == to_domain and bridge["to"] == from_domain: - return {"id": bridge_id, **bridge} - - # No direct bridge, try indirect - return None - - def create_bridge(self, lean_eq: Dict[str, str], forest_eq: Dict[str, str]) -> Dict[str, Any]: - """ - Create a bridge between Lean and forest equations. - """ - lean_domain = self.classify_equation_domain(lean_eq) - forest_domain = self.classify_equation_domain(forest_eq) - - bridge = self.find_bridge(lean_domain, forest_domain) - - if bridge: - bridge_record = { - "bridge_id": bridge["id"], - "bridge_name": bridge["name"], - "lean_equation": lean_eq["Model_Name"], - "forest_equation": forest_eq["name"], - "lean_domain": lean_domain, - "forest_domain": forest_domain, - "connection_type": "direct" - } - else: - # Indirect bridge via intermediate domain - bridge_record = { - "bridge_id": "INDIRECT", - "bridge_name": f"{lean_domain} → {forest_domain}", - "lean_equation": lean_eq["Model_Name"], - "forest_equation": forest_eq["name"], - "lean_domain": lean_domain, - "forest_domain": forest_domain, - "connection_type": "indirect" - } - - self.bridges_created.append(bridge_record) - return bridge_record - - def bridge_all(self, lean_equations: List[Dict[str, str]], - forest_equations: List[Dict[str, str]]) -> List[Dict[str, Any]]: - """ - Bridge all Lean equations with forest equations. - """ - bridges = [] - - # Create bridges based on intersections - for lean_eq in lean_equations: - for forest_eq in forest_equations: - # Check if they should be bridged - lean_name = lean_eq["Model_Name"].lower() - forest_name = forest_eq["name"].lower() - - # Bridge based on domain classification - lean_domain = self.classify_equation_domain(lean_eq) - forest_domain = self.classify_equation_domain(forest_eq) - - # Only bridge if domains differ - if lean_domain != forest_domain: - bridge = self.create_bridge(lean_eq, forest_eq) - bridges.append(bridge) - - return bridges - - -def main(): - """Bridge Lean analysis math with forest equations.""" - print("=" * 70) - print("BRIDGE LEAN ANALYSIS MATH WITH FOREST") - print("=" * 70) - - # Load Lean math equations - print("\n[*] Loading Lean math equations...") - with open('/home/allaun/Documents/Research Stack/data/lean_math_forest_import.json', 'r') as f: - lean_data = json.load(f) - - lean_equations = lean_data["math_models"] - print(f" Loaded {len(lean_equations)} Lean equations") - - # Load forest equations - print(f"\n[*] Loading forest equations...") - forest_equations = [] - tsv_path = Path(__file__).resolve().parent.parent.parent / "3-Mathematical-Models" / "MATH_MODEL_MAP.tsv" - with open(tsv_path, 'r') as f: - lines = f.readlines() - for line in lines[1:]: # Skip header - parts = line.strip().split('\t') - if len(parts) >= 4: - forest_equations.append({ - "name": parts[1], - "family": parts[2], - "equation": parts[3], - "variables": parts[4] - }) - - print(f" Loaded {len(forest_equations)} forest equations") - - # Create bridges - print(f"\n[*] Creating bridges using Equation Forest bridge nodes...") - bridge_system = ForestBridge() - - # Bridge Lean equations with forest equations - bridges = bridge_system.bridge_all(lean_equations, forest_equations) - - print(f" Created {len(bridges)} bridges") - - # Analyze bridges by type - direct_bridges = [b for b in bridges if b["connection_type"] == "direct"] - indirect_bridges = [b for b in bridges if b["connection_type"] == "indirect"] - - print(f"\n[*] Bridge Analysis:") - print(f" Direct bridges: {len(direct_bridges)}") - print(f" Indirect bridges: {len(indirect_bridges)}") - - # Show bridges by bridge node - bridge_counts = {} - for bridge in direct_bridges: - bridge_id = bridge["bridge_id"] - bridge_counts[bridge_id] = bridge_counts.get(bridge_id, 0) + 1 - - print(f"\n[*] Direct Bridges by Node:") - for bridge_id, count in sorted(bridge_counts.items()): - bridge_name = ForestBridge.BRIDGES[bridge_id]["name"] - print(f" {bridge_id} ({bridge_name}): {count} bridges") - - # Show sample bridges - print(f"\n[*] Sample Direct Bridges:") - for bridge in direct_bridges[:10]: - print(f" {bridge['bridge_id']}: {bridge['lean_equation']} ↔ {bridge['forest_equation']}") - print(f" {bridge['lean_domain']} → {bridge['forest_domain']}") - - print(f"\n[*] Sample Indirect Bridges:") - for bridge in indirect_bridges[:5]: - print(f" {bridge['lean_equation']} ↔ {bridge['forest_equation']}") - print(f" {bridge['lean_domain']} → {bridge['forest_domain']}") - - # Calculate bridge coverage - lean_bridged = set(b["lean_equation"] for b in bridges) - forest_bridged = set(b["forest_equation"] for b in bridges) - - print(f"\n[*] Bridge Coverage:") - print(f" Lean equations bridged: {len(lean_bridged)}/{len(lean_equations)} ({len(lean_bridged)/len(lean_equations)*100:.1f}%)") - print(f" Forest equations bridged: {len(forest_bridged)}/{len(forest_equations)} ({len(forest_bridged)/len(forest_equations)*100:.1f}%)") - - # Save results - results = { - "total_bridges": len(bridges), - "direct_bridges": len(direct_bridges), - "indirect_bridges": len(indirect_bridges), - "bridge_counts": bridge_counts, - "bridges": bridges, - "coverage": { - "lean_bridged": len(lean_bridged), - "lean_total": len(lean_equations), - "forest_bridged": len(forest_bridged), - "forest_total": len(forest_equations) - } - } - - output_path = "/home/allaun/Documents/Research Stack/data/lean_math_forest_bridges.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ LEAN MATH - FOREST BRIDGING COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/bridge_to_topological_engine.py b/5-Applications/scripts/bridge_to_topological_engine.py deleted file mode 100644 index 884cc32d..00000000 --- a/5-Applications/scripts/bridge_to_topological_engine.py +++ /dev/null @@ -1,450 +0,0 @@ -#!/usr/bin/env python3 -""" -bridge_to_topological_engine.py — Push Research Stack data into Neo4j graph. - -Reads ENE node mesh, Notion pages, and Linear issues, then upserts them -into the private topological engine (NoDupeLabs Neo4j) via Cypher. - -Usage: - python3 5-Applications/scripts/bridge_to_topological_engine.py [--dry-run] - -Requires TOPOLOGICAL_ENGINE_URL and TOPOLOGICAL_ENGINE_TOKEN in .env. -""" - -import sys -import json -import os -import argparse -from pathlib import Path -from datetime import datetime -from typing import Dict, List, Any, Optional - -# Load .env before imports -project_root = Path(__file__).parent.parent.parent -try: - from dotenv import load_dotenv - if (project_root / ".env").exists(): - load_dotenv(project_root / ".env") -except ImportError: - pass - -sys.path.insert(0, str(project_root)) -sys.path.insert(0, str(project_root / "4-Infrastructure" / "infra")) - -from infra.topological_engine_client import TopologicalEngineClient -from infra.ene_api import ENEAPIHook, AccessLevel - -try: - from infra.ene_cloud_credential_manager import ( - ENECloudCredentialManager, - ENETopologicalStorage, - ) -except ImportError as e: - print(f"Import failed: {e}") - sys.exit(1) - - -def _safe_id(prefix: str, key: str) -> str: - safe = str(key).replace(" ", "_").replace("/", "_").replace("\\", "_").replace(":", "_") - return f"{prefix}_{safe}" - - -# ═══════════════════════════════════════════════════════════════════════════ -# Cypher builders -# ═══════════════════════════════════════════════════════════════════════════ - -def cypher_merge_node(label: str, id_val: str, props: Dict[str, Any]) -> str: - """Generate a MERGE statement for a node.""" - # Neo4j doesn't allow null property values in MERGE well, so filter them - clean = {k: v for k, v in props.items() if v is not None} - clean["id"] = id_val - clean["rs_synced_at"] = datetime.utcnow().isoformat() - props_json = json.dumps(clean) - return f""" - MERGE (n:{label} {{id: $id}}) - SET n += $props - """.strip() - - -def cypher_merge_rel( - from_label: str, from_id: str, - to_label: str, to_id: str, - rel_type: str, props: Dict[str, Any] -) -> str: - """Generate a MERGE statement for a relationship.""" - clean = {k: v for k, v in props.items() if v is not None} - clean["rs_synced_at"] = datetime.utcnow().isoformat() - return f""" - MATCH (a:{from_label} {{id: $from_id}}), (b:{to_label} {{id: $to_id}}) - MERGE (a)-[r:{rel_type}]->(b) - SET r += $props - """.strip() - - -# ═══════════════════════════════════════════════════════════════════════════ -# ENE → Cypher -# ═══════════════════════════════════════════════════════════════════════════ - -def build_ene_cypher(client: TopologicalEngineClient) -> List[Dict[str, Any]]: - """Build Cypher statements from ENE substrate.""" - statements = [] - try: - cm = ENECloudCredentialManager() - storage = ENETopologicalStorage() - except Exception as e: - print(f" ENE not available: {e}") - return statements - - # Node stats - stats = cm.balancer.get_balancer_stats() - for node_id, node_stats in stats.get("nodes", {}).items(): - nid = _safe_id("ene", node_id) - statements.append({ - "query": cypher_merge_node("ENENode", nid, { - "node_id": node_id, - "health": node_stats.get("health"), - "connections": node_stats.get("connections"), - "bytes": node_stats.get("bytes"), - "latency": node_stats.get("latency"), - "source": "ene", - }), - "parameters": {"id": nid, "props": { - "node_id": node_id, - "health": node_stats.get("health"), - "connections": node_stats.get("connections"), - "bytes": node_stats.get("bytes"), - "latency": node_stats.get("latency"), - "source": "ene", - }}, - "read_only": False, - }) - - # Credentials metadata - for cred_id, cred in cm.credentials.items(): - cid = _safe_id("cred", cred_id) - statements.append({ - "query": cypher_merge_node("Credential", cid, { - "credential_id": cred_id, - "provider": cred.provider, - "access_level": str(cred.access_level), - "health_score": cred.health_score, - "usage_count": cred.usage_count, - "is_active": cred.is_active, - "source": "ene", - }), - "parameters": {"id": cid, "props": { - "credential_id": cred_id, - "provider": cred.provider, - "access_level": str(cred.access_level), - "health_score": cred.health_score, - "usage_count": cred.usage_count, - "is_active": cred.is_active, - "source": "ene", - }}, - "read_only": False, - }) - for node_id in cred.node_assignments: - statements.append({ - "query": cypher_merge_rel( - "Credential", cid, - "ENENode", _safe_id("ene", node_id), - "ASSIGNED_TO", {"reason": "credential_assignment"} - ), - "parameters": { - "from_id": cid, - "to_id": _safe_id("ene", node_id), - "props": {"reason": "credential_assignment"} - }, - "read_only": False, - }) - - # Storage health - storage_health = storage.get_storage_health() - statements.append({ - "query": cypher_merge_node("StorageHealth", "ene_storage_health", { - **storage_health, - "source": "ene", - }), - "parameters": {"id": "ene_storage_health", "props": {**storage_health, "source": "ene"}}, - "read_only": False, - }) - - return statements - - -# ═══════════════════════════════════════════════════════════════════════════ -# Notion → Cypher -# ═══════════════════════════════════════════════════════════════════════════ - -def build_notion_cypher(api_key: str = None, db_id: str = None) -> List[Dict[str, Any]]: - """Build Cypher statements from Notion (if credentials available).""" - statements = [] - if not api_key: - print(" Notion API key not available, skipping") - return statements - - try: - from scripts.dump_notion_ene_jsonl import NotionDataExtractor - extractor = NotionDataExtractor(api_key=api_key, database_id=db_id) - except Exception as e: - print(f" Notion extractor failed: {e}") - return statements - - # Database schema - schema = extractor.extract_database_schema() - if "error" not in schema: - db_id_safe = _safe_id("notion_db", schema.get("database_id", "unknown")) - statements.append({ - "query": cypher_merge_node("NotionDatabase", db_id_safe, { - "database_id": schema.get("database_id"), - "title": schema.get("title", ""), - "source": "notion", - }), - "parameters": {"id": db_id_safe, "props": { - "database_id": schema.get("database_id"), - "title": schema.get("title", ""), - "source": "notion", - }}, - "read_only": False, - }) - - # Pages - pages = extractor.extract_pages() - for page in pages: - if "error" in page: - continue - page_id = page.get("page_id", "unknown") - pid_safe = _safe_id("notion_page", page_id) - statements.append({ - "query": cypher_merge_node("NotionPage", pid_safe, { - "page_id": page_id, - "title": page.get("title", ""), - "created": page.get("created"), - "last_edited": page.get("last_edited"), - "archived": page.get("archived"), - "source": "notion", - }), - "parameters": {"id": pid_safe, "props": { - "page_id": page_id, - "title": page.get("title", ""), - "created": page.get("created"), - "last_edited": page.get("last_edited"), - "archived": page.get("archived"), - "source": "notion", - }}, - "read_only": False, - }) - statements.append({ - "query": cypher_merge_rel( - "NotionPage", pid_safe, - "NotionDatabase", db_id_safe, - "IN_DATABASE", {} - ), - "parameters": {"from_id": pid_safe, "to_id": db_id_safe, "props": {}}, - "read_only": False, - }) - - return statements - - -# ═══════════════════════════════════════════════════════════════════════════ -# Linear → Cypher -# ═══════════════════════════════════════════════════════════════════════════ - -def build_linear_cypher(api_key: str = None) -> List[Dict[str, Any]]: - """Build Cypher statements from Linear (if credentials available).""" - statements = [] - if not api_key: - print(" Linear API key not available, skipping") - return statements - - try: - from scripts.dump_notion_ene_jsonl import LinearDataExtractor - extractor = LinearDataExtractor(api_key=api_key) - except Exception as e: - print(f" Linear extractor failed: {e}") - return statements - - teams = extractor.extract_teams() - team_map = {} - for team in teams: - if "error" in team: - continue - team_id = team.get("team_id", "unknown") - tid_safe = _safe_id("linear_team", team_id) - team_map[team.get("key", team_id)] = tid_safe - statements.append({ - "query": cypher_merge_node("LinearTeam", tid_safe, { - "team_id": team_id, - "name": team.get("name", ""), - "key": team.get("key", ""), - "source": "linear", - }), - "parameters": {"id": tid_safe, "props": { - "team_id": team_id, - "name": team.get("name", ""), - "key": team.get("key", ""), - "source": "linear", - }}, - "read_only": False, - }) - - issues = extractor.extract_issues() - for issue in issues: - if "error" in issue: - continue - issue_id = issue.get("issue_id", "unknown") - iid_safe = _safe_id("linear_issue", issue_id) - statements.append({ - "query": cypher_merge_node("LinearIssue", iid_safe, { - "issue_id": issue_id, - "title": issue.get("title", ""), - "state": issue.get("state"), - "priority": issue.get("priority"), - "assignee": issue.get("assignee"), - "created_at": issue.get("created_at"), - "updated_at": issue.get("updated_at"), - "source": "linear", - }), - "parameters": {"id": iid_safe, "props": { - "issue_id": issue_id, - "title": issue.get("title", ""), - "state": issue.get("state"), - "priority": issue.get("priority"), - "assignee": issue.get("assignee"), - "created_at": issue.get("created_at"), - "updated_at": issue.get("updated_at"), - "source": "linear", - }}, - "read_only": False, - }) - team_name = issue.get("team") - if team_name and team_name in team_map: - statements.append({ - "query": cypher_merge_rel( - "LinearIssue", iid_safe, - "LinearTeam", team_map[team_name], - "BELONGS_TO", {} - ), - "parameters": {"from_id": iid_safe, "to_id": team_map[team_name], "props": {}}, - "read_only": False, - }) - - return statements - - -# ═══════════════════════════════════════════════════════════════════════════ -# Main bridge -# ═══════════════════════════════════════════════════════════════════════════ - -def run_bridge(dry_run: bool = False) -> Dict[str, Any]: - """Pull ENE/Notion/Linear and push into topological engine.""" - print("=" * 70) - print("RESEARCH STACK → TOPOLOGICAL ENGINE BRIDGE") - print("=" * 70) - - client = TopologicalEngineClient() - health = client.health() - if not health.get("ok", False) and not dry_run: - msg = f"Topological engine unhealthy: {health.get('error', 'unknown')}" - print(f"\n❌ {msg}") - print(" Check TOPOLOGICAL_ENGINE_URL and TOPOLOGICAL_ENGINE_TOKEN") - return {"success": False, "error": msg} - elif not health.get("ok", False): - print(f"\n⚠️ Topological engine unavailable: {health.get('error', 'unknown')}") - print(" Dry-run continuing...") - else: - print(f"\n✅ Topological engine healthy at {client.base_url}") - - # ── ENE ── - print("\n[1] Building ENE Cypher...") - ene_stmts = build_ene_cypher(client) - print(f" Built {len(ene_stmts)} ENE statements") - - # ── Notion ── - print("\n[2] Building Notion Cypher...") - notion_key = os.environ.get("NOTION_API_KEY") - notion_db = os.environ.get("NOTION_DATABASE_ID") - if not notion_key: - try: - hook = ENEAPIHook() - res = hook.retrieve_sensitive_data("credentials/notion", AccessLevel.SECRET) - if res.get("success"): - notion_key = res["payload"] - print(" Retrieved Notion key from ENE") - except Exception: - pass - notion_stmts = build_notion_cypher(notion_key, notion_db) - print(f" Built {len(notion_stmts)} Notion statements") - - # ── Linear ── - print("\n[3] Building Linear Cypher...") - linear_key = os.environ.get("LINEAR_API_KEY") - if not linear_key: - try: - hook = ENEAPIHook() - res = hook.retrieve_sensitive_data("credentials/linear", AccessLevel.SECRET) - if res.get("success"): - linear_key = res["payload"] - print(" Retrieved Linear key from ENE") - except Exception: - pass - linear_stmts = build_linear_cypher(linear_key) - print(f" Built {len(linear_stmts)} Linear statements") - - all_stmts = ene_stmts + notion_stmts + linear_stmts - print(f"\n[4] Total Cypher statements: {len(all_stmts)}") - - if dry_run: - print("\n🧪 DRY-RUN: would execute the following statements:") - for stmt in all_stmts: - print(f" - {stmt['query'][:80]}...") - return {"success": True, "dry_run": True, "statements": len(all_stmts)} - - if not health.get("ok", False): - print("\n❌ Engine unavailable and not in dry-run mode. Aborting.") - return {"success": False, "error": "Engine unavailable"} - - # Execute - print("\n[5] Executing Cypher against Neo4j...") - results = client.batch_cypher(all_stmts) - successes = sum(1 for r in results if r.get("ok")) - failures = len(results) - successes - print(f" Success: {successes}, Failures: {failures}") - - if failures: - for i, r in enumerate(results): - if not r.get("ok"): - print(f" ⚠️ Stmt {i}: {r.get('error', 'unknown error')}") - - # Import Obsidian links if vault configured - vault_path = os.environ.get("OBSIDIAN_VAULT_PATH") - if vault_path: - print(f"\n[6] Importing Obsidian wiki-links from {vault_path}...") - imp_res = client.neo4j_import_obsidian_links(vault_path) - if not imp_res.get("ok"): - print(f" ⚠️ Import failed: {imp_res.get('error')}") - else: - print(f" ✅ Scanned {imp_res.get('notesScanned', 0)} notes, imported {imp_res.get('roadsImported', 0)} roads") - else: - print("\n[6] OBSIDIAN_VAULT_PATH not set, skipping link import") - - print("\n" + "=" * 70) - print("BRIDGE COMPLETE") - print("=" * 70) - return { - "success": True, - "executed": successes, - "failed": failures, - "ene_statements": len(ene_stmts), - "notion_statements": len(notion_stmts), - "linear_statements": len(linear_stmts), - } - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Research Stack → Topological Engine Bridge") - parser.add_argument("--dry-run", action="store_true", help="Build statements without executing") - args = parser.parse_args() - result = run_bridge(dry_run=args.dry_run) - sys.exit(0 if result.get("success") else 1) diff --git a/5-Applications/scripts/build_genome18_graph.py b/5-Applications/scripts/build_genome18_graph.py deleted file mode 100644 index 81b10039..00000000 --- a/5-Applications/scripts/build_genome18_graph.py +++ /dev/null @@ -1,109 +0,0 @@ -import json -import os -import networkx as nx -from collections import defaultdict - -def build_genome_graph(): - G = nx.Graph() - - jsonl_path = "/home/allaun/Documents/Research Stack/data/equations_forest_genome18.jsonl" - - # Track addresses and their equations - address_to_equations = defaultdict(list) - bin_to_addresses = defaultdict(list) - - equations = [] - with open(jsonl_path, 'r') as f: - for line in f: - if not line.strip(): - continue - data = json.loads(line) - name = data.get("model_name") - address = data.get("genome18_address") - bins = data.get("genome18_bins", {}) - - equations.append(data) - - if name and address is not None: - address_to_equations[address].append(name) - - # Add Equation node - G.add_node(name, type="equation", address=address, **bins) - - # Add Address node - address_node = f"Addr_{address}" - if not G.has_node(address_node): - G.add_node(address_node, type="address_hub", address=address, **bins) - - # Link Equation to Address - G.add_edge(name, address_node, type="encoded_at") - - # Connect addresses that are 'adjacent' (only 1 bin differs by 1) - addresses = list(address_to_equations.keys()) - addr_to_bins = {} - for eq in equations: - addr = eq.get("genome18_address") - if addr not in addr_to_bins: - addr_to_bins[addr] = eq.get("genome18_bins", {}) - - for i in range(len(addresses)): - for j in range(i + 1, len(addresses)): - addr1 = addresses[i] - addr2 = addresses[j] - b1 = addr_to_bins[addr1] - b2 = addr_to_bins[addr2] - - # Calculate distance - diff = 0 - for k in ["muBin", "rhoBin", "cBin", "mBin", "neBin", "sigmaBin"]: - diff += abs(b1.get(k, 0) - b2.get(k, 0)) - - # If distance is exactly 1, link them - if diff == 1: - G.add_edge(f"Addr_{addr1}", f"Addr_{addr2}", type="topological_adjacent") - - # Save GraphML - output_dir = "/home/allaun/Documents/Research Stack/artifacts" - os.makedirs(output_dir, exist_ok=True) - graphml_path = os.path.join(output_dir, "genome18_complete.graphml") - nx.write_graphml(G, graphml_path) - print(f"Saved complete GraphML to {graphml_path} ({G.number_of_nodes()} nodes, {G.number_of_edges()} edges)") - - # Generate Hubs Mermaid Chart - # Find addresses with the most equations - sorted_addresses = sorted(address_to_equations.items(), key=lambda x: len(x[1]), reverse=True) - top_addresses = sorted_addresses[:15] # Top 15 hubs - - mermaid_lines = ["graph TD", " %% Sovereign Research Stack - Genomic Hubs"] - - for addr, eq_list in top_addresses: - addr_node = f"Addr_{addr}" - mermaid_lines.append(f" {addr_node}((Address {addr}))") - - # Add up to 5 equations for this hub to avoid clutter - for eq in eq_list[:5]: - safe_eq = eq.replace(' ', '_').replace('-', '_').replace('+', '_') - mermaid_lines.append(f" {safe_eq}[{eq}] --> {addr_node}") - - if len(eq_list) > 5: - mermaid_lines.append(f" {addr_node}_more(>...and {len(eq_list)-5} more) -.-> {addr_node}") - - # Add "Tree Fiddy" and "PIST" specifically if they aren't in the top 15 - special_equations = ["Tree Fiddy", "PIST_Neural_Topology", "Bridge_PIST_Surface"] - for eq_data in equations: - name = eq_data.get("model_name") - if name and any(s in name for s in special_equations): - addr = eq_data.get("genome18_address") - addr_node = f"Addr_{addr}" - safe_name = name.replace(' ', '_').replace('-', '_').replace('+', '_') - mermaid_lines.append(f" {safe_name}[{name}] --> {addr_node}") - mermaid_lines.append(f" style {safe_name} fill:#f9f,stroke:#333,stroke-width:4px") - - mermaid_path = os.path.join(output_dir, "genome18_hubs.mermaid") - with open(mermaid_path, "w") as f: - f.write("\n".join(mermaid_lines)) - - print(f"Saved hubs Mermaid chart to {mermaid_path}") - -if __name__ == "__main__": - build_genome_graph() diff --git a/5-Applications/scripts/build_graphml.py b/5-Applications/scripts/build_graphml.py deleted file mode 100644 index 90032f61..00000000 --- a/5-Applications/scripts/build_graphml.py +++ /dev/null @@ -1,52 +0,0 @@ -import csv -import json -import os -from pathlib import Path -import networkx as nx - -def build_graph(): - G = nx.DiGraph() - - # Load equations from TSV - tsv_path = Path(__file__).resolve().parent.parent.parent / "3-Mathematical-Models" / "MATH_MODEL_MAP.tsv" - with open(tsv_path, 'r', encoding='utf-8') as f: - reader = csv.DictReader(f, delimiter='\t') - for row in reader: - name = row.get("Model_Name", "") - if not name: - continue - - family = row.get("Family") or "Unknown" - domain = row.get("Domain_Type") or "Unknown" - bind = row.get("Bind_Class") or "Unknown" - - G.add_node(name, family=family, domain=domain, bind=bind, type="equation") - - # Create edges to family and domain - G.add_edge(name, family, relation="BELONGS_TO_FAMILY") - G.add_edge(name, domain, relation="BELONGS_TO_DOMAIN") - G.add_edge(name, bind, relation="HAS_BIND_CLASS") - - # Handle Cross_Refs - cross_refs = row.get("Cross_Refs", "") - if cross_refs: - # Assuming comma-separated or space-separated - refs = [r.strip() for r in cross_refs.replace(',', ' ').split()] - for ref in refs: - if ref: - G.add_edge(name, ref, relation="CROSS_REFERENCE") - - # Save as GraphML - output_dir = "/home/allaun/Documents/Research Stack/artifacts" - os.makedirs(output_dir, exist_ok=True) - graphml_path = os.path.join(output_dir, "master_equation_graph.graphml") - nx.write_graphml(G, graphml_path) - - print(f"GraphML saved to {graphml_path} with {G.number_of_nodes()} nodes and {G.number_of_edges()} edges.") - - # Try to generate a Mermaid graph (only for major hubs to avoid rendering issues) - hub_nodes = [n for n, d in G.out_degree() if d > 50] # Top families/domains - # Let's write a python script to generate Mermaid for top 100 equations or something. - -if __name__ == "__main__": - build_graph() diff --git a/5-Applications/scripts/build_manifold_graphml.py b/5-Applications/scripts/build_manifold_graphml.py deleted file mode 100644 index 5d9cad96..00000000 --- a/5-Applications/scripts/build_manifold_graphml.py +++ /dev/null @@ -1,167 +0,0 @@ -#!/usr/bin/env python3 -""" -build_manifold_graphml.py — Build GraphML from Research Stack manifold geometry. - -Reads manifold_intrinsic_geometry.json (output of manifold_geometry.py) -and writes a GraphML file with full geometric attributes per node and edge. - -Nodes: Lean modules with curvature, centrality, in/out degree, component, type -Edges: import dependencies with geodesic weight - -Usage: - python3 5-Applications/scripts/build_manifold_graphml.py -""" - -import json -import os -import sys -from pathlib import Path - -import networkx as nx - -PROJECT_ROOT = Path(__file__).parent.parent.parent -DATA_DIR = PROJECT_ROOT / "data" -ARTIFACTS_DIR = PROJECT_ROOT / "artifacts" - - -def load_geometry(path: Path): - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def load_topology(path: Path): - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def build_manifold_graph(geometry: dict, topology: dict) -> nx.DiGraph: - G = nx.DiGraph() - - meta = geometry.get("meta", {}) - for k, v in meta.items(): - # GraphML can't serialize dict/bool; flatten primitive values only - if isinstance(v, (str, int, float)): - G.graph[k] = v - - full = topology.get("full_graph", {}) - nodes = full.get("nodes", []) - edges_map = full.get("edges", {}) - - # Index curvature and centrality - curvature = {} - for item in geometry.get("positive_curvature", []): - curvature[item["module"]] = item["curvature"] - for item in geometry.get("negative_curvature", []): - curvature[item["module"]] = item["curvature"] - - centrality = {} - for item in geometry.get("hubs", []): - centrality[item["module"]] = item["centrality"] - - # Index sources/sinks - source_degree = {} - for item in geometry.get("sources", []): - source_degree[item["module"]] = item.get("out_degree", 0) - - sink_degree = {} - for item in geometry.get("sinks", []): - sink_degree[item["module"]] = item.get("in_degree", 0) - - # Components index - component_map = {} - for comp in topology.get("components", []): - cid = comp.get("id", "unknown") - for member in comp.get("members", []): - component_map[member] = cid - - # Holes index (gap modules) - gap_modules = set() - for hole in topology.get("holes", []): - for mod in hole.get("missing_modules", []): - gap_modules.add(mod) - - # Add nodes (nodes are plain module name strings) - for name in nodes: - G.add_node( - name, - type="module", - centrality=centrality.get(name, 0.0), - curvature=curvature.get(name, 0.0), - component=component_map.get(name, "unknown"), - is_source=str(name in source_degree), - is_sink=str(name in sink_degree), - is_gap=str(name in gap_modules), - ) - - # Add edges (edges_map is dict: src -> [dst, ...]) - for src, dsts in edges_map.items(): - for dst in dsts: - G.add_edge(src, dst, relation="IMPORTS", weight=1.0) - - return G - - -def write_graphml(G: nx.DiGraph, path: Path): - path.parent.mkdir(parents=True, exist_ok=True) - nx.write_graphml(G, str(path)) - print(f"GraphML saved: {path}") - print(f" Nodes: {G.number_of_nodes()}") - print(f" Edges: {G.number_of_edges()}") - meta = G.graph.get("meta", {}) - print(f" Diameter: {meta.get('diameter', 'N/A')}") - print(f" Components: {meta.get('component_count', 'N/A')}") - print(f" Cycles: {meta.get('cycle_count', 'N/A')}") - - -def write_mermaid_hub(G: nx.DiGraph, path: Path, top_n: int = 50): - """Write a Mermaid diagram of top hubs for quick visualization.""" - hubs = sorted( - ((n, d["centrality"]) for n, d in G.nodes(data=True) if d.get("centrality", 0) > 0), - key=lambda x: x[1], - reverse=True, - )[:top_n] - - lines = ["graph TD"] - for name, cent in hubs: - safe = name.replace(".", "_") - label = f"{name}({name}\\nC={cent:.2f})" - lines.append(f" {safe}[\"{label}\"]") - - # Add edges between hubs - hub_names = {n for n, _ in hubs} - for src, dst, data in G.edges(data=True): - if src in hub_names and dst in hub_names: - lines.append(f" {src.replace('.', '_')} --> {dst.replace('.', '_')}") - - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines), encoding="utf-8") - print(f"Mermaid hub diagram: {path}") - - -def main(): - geom_path = DATA_DIR / "manifold_intrinsic_geometry.json" - topo_path = DATA_DIR / "manifold_topology_report.json" - - if not geom_path.exists(): - print(f"Geometry data not found: {geom_path}") - sys.exit(1) - if not topo_path.exists(): - print(f"Topology data not found: {topo_path}") - sys.exit(1) - - geometry = load_geometry(geom_path) - # The intrinsic geometry JSON contains both geometry metrics AND the full graph - # (nodes/edges/full_graph). topology report is a different schema from manifold_perception. - G = build_manifold_graph(geometry, geometry) - - graphml_path = ARTIFACTS_DIR / "manifold_geometry.graphml" - write_graphml(G, graphml_path) - - mermaid_path = ARTIFACTS_DIR / "manifold_hubs.mmd" - write_mermaid_hub(G, mermaid_path, top_n=30) - - print("Done.") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/bulk_replace.py b/5-Applications/scripts/bulk_replace.py deleted file mode 100644 index 616ac56f..00000000 --- a/5-Applications/scripts/bulk_replace.py +++ /dev/null @@ -1,53 +0,0 @@ -import os -import re - -root_dir = "/home/allaun/Documents/Research Stack" -scripts_dir = os.path.join(root_dir, "5-Applications/scripts") -infra_dir = os.path.join(root_dir, "4-Infrastructure/infra") - -replacements = [ - (re.compile(r'Path\(__file__\)\.parent\.parent\.parent\s*/\s*"infra"'), 'Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra"'), - (re.compile(r'project_root\s*/\s*"infra"'), 'project_root / "4-Infrastructure" / "infra"'), - (re.compile(r'ROOT\s*/\s*"infra"'), 'ROOT / "4-Infrastructure" / "infra"'), - (re.compile(r'BASE_DIR\s*/\s*"infra"'), 'BASE_DIR / "4-Infrastructure" / "infra"'), - (re.compile(r'"/home/allaun/Documents/Research Stack/infra"'), '"/home/allaun/Documents/Research Stack/4-Infrastructure/infra"'), - (re.compile(r"'/home/allaun/Documents/Research Stack/infra'"), "'/home/allaun/Documents/Research Stack/4-Infrastructure/infra'"), - (re.compile(r'"/home/allaun/Research Stack/infra"'), '"/home/allaun/Documents/Research Stack/4-Infrastructure/infra"'), - (re.compile(r"'/home/allaun/Research Stack/infra'"), "'/home/allaun/Documents/Research Stack/4-Infrastructure/infra'"), - (re.compile(r'RESEARCH_STACK\s*/\s*"infra"'), 'RESEARCH_STACK / "4-Infrastructure" / "infra"'), -] - -files_to_check = [] -for d in [scripts_dir, infra_dir]: - for f in os.listdir(d): - if f.endswith(".py"): - files_to_check.append(os.path.join(d, f)) - -# Add the specific one in infra subdirectory -files_to_check.append(os.path.join(infra_dir, "embedded_surface/server.py")) - -for file_path in files_to_check: - if not os.path.isfile(file_path): - continue - with open(file_path, 'r') as f: - content = f.read() - - new_content = content - for pattern, replacement in replacements: - new_content = pattern.sub(replacement, new_content) - - if new_content != content: - print(f"Updating {file_path}") - with open(file_path, 'w') as f: - f.write(new_content) - -# Special case for manifold_perception.py -manifold_path = os.path.join(infra_dir, "manifold_perception.py") -if os.path.exists(manifold_path): - with open(manifold_path, 'r') as f: - content = f.read() - new_content = content.replace('RESEARCH_STACK = Path("/home/allaun/Research Stack")', 'RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack")') - if new_content != content: - print(f"Updating {manifold_path} RESEARCH_STACK") - with open(manifold_path, 'w') as f: - f.write(new_content) diff --git a/5-Applications/scripts/burgers_avm_benchmark.py b/5-Applications/scripts/burgers_avm_benchmark.py deleted file mode 100644 index db304b73..00000000 --- a/5-Applications/scripts/burgers_avm_benchmark.py +++ /dev/null @@ -1,166 +0,0 @@ -#!/usr/bin/env python3 -""" -burgers_avm_benchmark.py -======================== - -Validates the bit-exact informatic integrity of the Burgers AVM kernels. -Compares the Python AVM reference to the closed-form math for ν_eff and Q_eff. - -Doctrine: Zero-float, Q16.16 saturating arithmetic. -""" - -import json -import math -from dataclasses import dataclass - -# ============================================================================= -# 1. Q16.16 Fixed-Point Core (Reference) -# ============================================================================= - -def to_q16(val): - return int(val * 65536) - -def from_q16(val): - return val / 65536.0 - -def qadd(a, b): - res = a + b - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - -def qsub(a, b): - res = a - b - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - -def qmul(a, b): - # (a/2^16) * (b/2^16) * 2^16 = (a*b)/2^16 - res = (a * b) >> 16 - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - -def qdiv(a, b): - if b == 0: return 0 - res = (a << 16) // b - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - -# ============================================================================= -# 2. AVM Reference Engine -# ============================================================================= - -class AVM: - def __init__(self, program): - self.program = program - self.stack = [] - self.pc = 0 - - def step(self): - if self.pc >= len(self.program): - return False - - instr = self.program[self.pc] - op = instr["op"] - - if op == "push": - self.stack.append(instr["val"]) - elif op == "add": - v2 = self.stack.pop() - v1 = self.stack.pop() - self.stack.append(qadd(v1, v2)) - elif op == "mul": - v2 = self.stack.pop() - v1 = self.stack.pop() - self.stack.append(qmul(v1, v2)) - elif op == "sub": - v2 = self.stack.pop() - v1 = self.stack.pop() - self.stack.append(qsub(v1, v2)) - - self.pc += 1 - return True - - def run(self, max_steps=100): - for _ in range(max_steps): - if not self.step(): - break - return self.stack[-1] if self.stack else None - -# ============================================================================= -# 3. Burgers Kernels -# ============================================================================= - -def get_nu_eff_program(nu0, omega): - return [ - {"op": "push", "val": omega}, - {"op": "push", "val": to_q16(1.0)}, - {"op": "add", "val": None}, - {"op": "push", "val": nu0}, - {"op": "mul", "val": None}, - ] - -def get_q_eff_program(q0, kappa, omega): - return [ - {"op": "push", "val": omega}, - {"op": "push", "val": kappa}, - {"op": "mul", "val": None}, - {"op": "push", "val": to_q16(1.0)}, - {"op": "add", "val": None}, - {"op": "push", "val": q0}, - {"op": "mul", "val": None}, - ] - -# ============================================================================= -# 4. Main Benchmark -# ============================================================================= - -def main(): - # Parameters from BurgersHarmonicPeelingVerification.md - kappa = to_q16(0.3547) - nu0 = to_q16(0.01) - q0 = to_q16(0.1) - - # Toy omega for S(x) = sin(x) + 0.3sin(2x) + 0.1sin(3x) - # Omega = 0.5 * (1^2 * 1.0^2 + 2^2 * 0.3^2 + 3^2 * 0.1^2) - # Omega = 0.5 * (1.0 + 0.36 + 0.09) = 0.5 * 1.45 = 0.725 - omega = to_q16(0.725) - - print(f"--- Burgers AVM Benchmark ---") - print(f"ν0 : {from_q16(nu0):.6f}") - print(f"Q0 : {from_q16(q0):.6f}") - print(f"κ : {from_q16(kappa):.6f}") - print(f"Ω : {from_q16(omega):.6f}") - print() - - # 1. ν_eff - nu_prog = get_nu_eff_program(nu0, omega) - avm_nu = AVM(nu_prog) - nu_eff_avm = avm_nu.run() - - # Golden - nu_eff_gold = qmul(nu0, qadd(to_q16(1.0), omega)) - - print(f"[ν_eff] AVM : {hex(nu_eff_avm)} ({from_q16(nu_eff_avm):.6f})") - print(f"[ν_eff] Gold: {hex(nu_eff_gold)} ({from_q16(nu_eff_gold):.6f})") - print(f"Match: {nu_eff_avm == nu_eff_gold}") - print() - - # 2. Q_eff - q_prog = get_q_eff_program(q0, kappa, omega) - avm_q = AVM(q_prog) - q_eff_avm = avm_q.run() - - # Golden - q_eff_gold = qmul(q0, qadd(to_q16(1.0), qmul(kappa, omega))) - - print(f"[Q_eff] AVM : {hex(q_eff_avm)} ({from_q16(q_eff_avm):.6f})") - print(f"[Q_eff] Gold: {hex(q_eff_gold)} ({from_q16(q_eff_gold):.6f})") - print(f"Match: {q_eff_avm == q_eff_gold}") - print() - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/burgers_avm_trace_generator.py b/5-Applications/scripts/burgers_avm_trace_generator.py deleted file mode 100644 index 55ac4f18..00000000 --- a/5-Applications/scripts/burgers_avm_trace_generator.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -""" -burgers_avm_trace_generator.py -============================== - -Generates bit-exact execution traces for the Burgers AVM kernels -(nuEffProgram and qEffProgram). These traces are used for -hardware loopback verification on the Tang Nano 9K. -""" - -import json -from pathlib import Path - -# OpCodes from AVM.lean -OP_PUSH = 0x01 -OP_ADD = 0x02 -OP_MUL = 0x03 -OP_SUB = 0x04 -OP_DIV = 0x05 -OP_SQRT = 0x06 -OP_HALT = 0xFF - -class AVMSimulator: - def __init__(self): - self.stack = [] - self.pc = 0 - self.trace = [] - - def execute(self, program: list, initial_stack: list): - self.stack = initial_stack.copy() - self.pc = 0 - self.trace = [] - - while self.pc < len(program): - op = program[self.pc] - self.pc += 1 - - if op == OP_PUSH: - val = program[self.pc] - self.pc += 1 - self.stack.append(val) - self.trace.append({"op": "PUSH", "val": val, "stack": self.stack.copy()}) - elif op == OP_ADD: - b = self.stack.pop() - a = self.stack.pop() - res = self.saturating_add(a, b) - self.stack.append(res) - self.trace.append({"op": "ADD", "res": res, "stack": self.stack.copy()}) - elif op == OP_MUL: - b = self.stack.pop() - a = self.stack.pop() - # (a * b) >>> 16 - res = (a * b) >> 16 - # Mask to 32-bit (simplified for trace) - res &= 0xFFFFFFFF - self.stack.append(res) - self.trace.append({"op": "MUL", "res": res, "stack": self.stack.copy()}) - elif op == OP_SUB: - b = self.stack.pop() - a = self.stack.pop() - res = self.saturating_sub(a, b) - self.stack.append(res) - self.trace.append({"op": "SUB", "res": res, "stack": self.stack.copy()}) - elif op == OP_HALT: - self.trace.append({"op": "HALT", "stack": self.stack.copy()}) - break - return self.stack, self.trace - - def saturating_add(self, a, b): - res = a + b - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - - def saturating_sub(self, a, b): - res = a - b - if res > 0x7FFFFFFF: return 0x7FFFFFFF - if res < -0x80000000: return -0x80000000 - return res - -def generate_traces(): - # Programs from BurgersAVM.lean - # nuEffProgram: [PUSH, 1.0, ADD, MUL, HALT] - # In Q16.16, 1.0 = 0x00010000 - nu_eff_prog = [OP_PUSH, 0x00010000, OP_ADD, OP_MUL, OP_HALT] - - # qEffProgram: [PUSH, kappa, MUL, PUSH, 1.0, ADD, MUL, HALT] - # kappa = 0.3547 * 65536 = 23245 = 0x00005ACE - q_eff_prog = [OP_PUSH, 0x00005ACE, OP_MUL, OP_PUSH, 0x00010000, OP_ADD, OP_MUL, OP_HALT] - - sim = AVMSimulator() - - # Inputs for 3-mode toy (Omega = 0.725 = 47513 = 0x0000B999) - # nu_0 = 0.1 = 6554 = 0x0000199A - # Q_0 = 1.0 = 65536 = 0x00010000 - omega = 47513 - nu0 = 6554 - q0 = 65536 - - print("Generating nu_eff trace...") - res_nu, trace_nu = sim.execute(nu_eff_prog, [nu0, omega]) - - print("Generating Q_eff trace...") - res_q, trace_q = sim.execute(q_eff_prog, [q0, omega]) - - bundle = { - "nu_eff": { - "inputs": {"nu0": nu0, "omega": omega}, - "program": nu_eff_prog, - "trace": trace_nu, - "final_result": res_nu[0] - }, - "q_eff": { - "inputs": {"q0": q0, "omega": omega}, - "program": q_eff_prog, - "trace": trace_q, - "final_result": res_q[0] - } - } - - out_file = Path("/home/allaun/Documents/Research Stack/shared-data/burgers_avm_gold_traces.json") - out_file.write_text(json.dumps(bundle, indent=2)) - print(f"Traces saved to {out_file}") - -if __name__ == "__main__": - generate_traces() diff --git a/5-Applications/scripts/cancer_detection_hybrid.py b/5-Applications/scripts/cancer_detection_hybrid.py deleted file mode 100644 index af2838cd..00000000 --- a/5-Applications/scripts/cancer_detection_hybrid.py +++ /dev/null @@ -1,140 +0,0 @@ -#!/usr/bin/env python3 -""" -Hybrid RGFlow + LUT Cancer Detection Test -Test the hybrid detection system combining RGFlow structural analysis -with a lookup table of known oncogenic codons. -""" - -# TP53 Reference mRNA (Partial/Representative Segment) -tp53_healthy = ("ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACTACTTCCTGAAAACAACGTTCTGTCCCC" - "CTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGATATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAG" - "AGGCTGCTCCCCGCGTGGCCCCTGCACCAGCAGCTCCTACACCGGCGGCCCCTGCACCAGCCCCCTCCTGGCCCCTGTCATCTTCTGTCCCTTCCCAGAAA" - "ACCTACCAGGGCAGCTACGGTTTCCGTCTGGGCTTCTTGCATTCTGGGACAGCCAAGTCTGTGACTTGCACGTACTCCCCTGCCCTCAACAAGATGTTTTG" - "CCAACTGGCCAAGACCTGCCCCGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACA" - "TGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGGTCTGGCCCCTCCTCAGCATCTTATCCGAGTGGAAGGAAATTTGCGT" - "GTGGAGTATTTGGATGACAGAAACACTTTTCGACATAGTGTGGTGGTGCCCTATGAGCCGCCTGAGGTTGGCTCTGACTGTACCACCATCCACTACAACTA" - "CATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGAGGCCCATCCTCACCATCATCACACTGGAAGACTCCAGTGGTAATCTACTGGGACGGAACAGCT" - "TTGAGGTGCGTGTTTGTGCCTGTCCTGGGAGAGACCGGCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGCCTCACCACGAGCTGCCCCCAGGGAGC" - "ACTAAGCGAGCACTGCCCAACAACACCAGCTCCTCTCCCCAGCCAAAGAAGAAACCACTGGATGGAGAATATTTCACCCTTCAGATCCGTGGGCGTGAGCG" - "CTTCGAGATGTTCCGAGAGCTGAATGAGGCCTTGGAACTCAAGGATGCCCAGGCTGGGAAGGAGCCAGGGGGGAGCAGGGCTCACTCCAGCCACCTGAAGT" - "CCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGACAGAAGGGCCTGACTCAGACTGA") - -# Inject "Godzilla" Hotspot Mutations -tp53_cancer = list(tp53_healthy) -loc_175 = 175 * 3 -tp53_cancer[loc_175:loc_175+3] = list("CAC") # R175H -loc_248 = 248 * 3 -tp53_cancer[loc_248:loc_248+3] = list("TGG") # R248W -tp53_cancer = "".join(tp53_cancer) - -# Lookup Table of Known Oncogenic Codons -ONCOGENIC_CODONS = { - "TP53": { - 175: ["CAC", "CAT"], # R175H - 248: ["TGG", "TGA"], # R248W - 273: ["CGT", "CGC"], # R273H - 282: ["GCG", "TGG"], # R282W - } -} - -def check_lut(codon, position, gene="TP53"): - """Check if codon at position is known oncogenic.""" - if gene not in ONCOGENIC_CODONS: - return False - if position not in ONCOGENIC_CODONS[gene]: - return False - return codon in ONCOGENIC_CODONS[gene][position] - -def extract_codon(seq, position): - """Extract codon at given position from sequence.""" - start = position * 3 - if start + 3 <= len(seq): - return seq[start:start+3] - return "?" - -def hybrid_detection(seq_healthy, seq_cancer, position, full_seq_cancer): - """ - Hybrid detection combining RGFlow (simulated) with LUT. - For this test, we'll use the actual RGFlow results from the previous run. - """ - # Extract mutated codon from full sequence (not window) - codon = extract_codon(full_seq_cancer, position) - - # Check LUT - lut_detected = check_lut(codon, position) - - # Simulated RGFlow results from previous run - if position == 175: - rgflow_detected = True # R175H was detected - sigma_healthy = 1.947046 - sigma_cancer = 1.942182 - elif position == 248: - rgflow_detected = False # R248W was not detected - sigma_healthy = 1.924038 - sigma_cancer = 1.928755 - else: - rgflow_detected = False - sigma_healthy = 0.0 - sigma_cancer = 0.0 - - # Hybrid detection: RGFlow OR LUT - hybrid_detected = rgflow_detected or lut_detected - - delta_sigma = sigma_healthy - sigma_cancer - percent_loss = (delta_sigma / sigma_healthy * 100) if sigma_healthy > 0 else 0.0 - - return { - "position": position, - "codon": codon, - "sigma_healthy": sigma_healthy, - "sigma_cancer": sigma_cancer, - "delta_sigma": delta_sigma, - "percent_loss": percent_loss, - "rgflow_detected": rgflow_detected, - "lut_detected": lut_detected, - "hybrid_detected": hybrid_detected - } - -print("=" * 70) -print("HYBRID RGFLOW + LUT CANCER DETECTION TEST") -print("=" * 70) - -window_size = 200 -hotspots = [175, 248] - -for position in hotspots: - window_h = tp53_healthy[max(0, position*3-window_size) : min(len(tp53_healthy), position*3+window_size)] - window_c = tp53_cancer[max(0, position*3-window_size) : min(len(tp53_cancer), position*3+window_size)] - - print(f"\nLocus {position*3} (Codon {position}):") - print("-" * 70) - - result = hybrid_detection(window_h, window_c, position, tp53_cancer) - - print(f" Codon: {result['codon']}") - print(f" Healthy Sigma: {result['sigma_healthy']:.6f}") - print(f" Cancer Sigma: {result['sigma_cancer']:.6f}") - print(f" Delta Sigma: {result['delta_sigma']:.6f}") - print(f" Percent Loss: {result['percent_loss']:.2f}%") - print(f" RGFlow Detection: {'✓ DETECTED' if result['rgflow_detected'] else '✗ NOT DETECTED'}") - print(f" LUT Detection: {'✓ DETECTED' if result['lut_detected'] else '✗ NOT DETECTED'}") - print(f" Hybrid Detection: {'✓ DETECTED' if result['hybrid_detected'] else '✗ NOT DETECTED'}") - - if result['hybrid_detected']: - detection_method = "RGFlow" if result['rgflow_detected'] else "LUT" - if result['rgflow_detected'] and result['lut_detected']: - detection_method = "Both RGFlow and LUT" - print(f" [+] DETECTED VIA: {detection_method}") - else: - print(f" [-] NOT DETECTED") - -print("\n" + "=" * 70) -print("SUMMARY") -print("=" * 70) -print("R175H: RGFlow detected (sigma decreased)") -print("R248W: LUT detected (known oncogenic codon, despite sigma increase)") -print("\nHybrid Detection Rate: 2/2 (100%)") -print("RGFlow-only Detection Rate: 1/2 (50%)") -print("LUT-only Detection Rate: 1/2 (50%)") -print("\nConclusion: Hybrid system successfully detects both mutations") -print("=" * 70) diff --git a/5-Applications/scripts/cancer_detection_lean.py b/5-Applications/scripts/cancer_detection_lean.py deleted file mode 100644 index ec91b56d..00000000 --- a/5-Applications/scripts/cancer_detection_lean.py +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env python3 -""" -Lean RGFlow Cancer Sequence Detection -Use Lean to prove RGFlow can detect cancer mutations in TP53 gene sequence. -""" - -import sys -import json -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import LeanUnifiedShim - -def run_lean_cancer_detection(): - # 1. TP53 Reference mRNA (Partial/Representative Segment) - # This is a lawful, high-informativity biological sequence. - tp53_healthy = ("ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACTACTTCCTGAAAACAACGTTCTGTCCCC" - "CTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGATATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAG" - "AGGCTGCTCCCCGCGTGGCCCCTGCACCAGCAGCTCCTACACCGGCGGCCCCTGCACCAGCCCCCTCCTGGCCCCTGTCATCTTCTGTCCCTTCCCAGAAA" - "ACCTACCAGGGCAGCTACGGTTTCCGTCTGGGCTTCTTGCATTCTGGGACAGCCAAGTCTGTGACTTGCACGTACTCCCCTGCCCTCAACAAGATGTTTTG" - "CCAACTGGCCAAGACCTGCCCCGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACA" - "TGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGGTCTGGCCCCTCCTCAGCATCTTATCCGAGTGGAAGGAAATTTGCGT" - "GTGGAGTATTTGGATGACAGAAACACTTTTCGACATAGTGTGGTGGTGCCCTATGAGCCGCCTGAGGTTGGCTCTGACTGTACCACCATCCACTACAACTA" - "CATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGAGGCCCATCCTCACCATCATCACACTGGAAGACTCCAGTGGTAATCTACTGGGACGGAACAGCT" - "TTGAGGTGCGTGTTTGTGCCTGTCCTGGGAGAGACCGGCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGCCTCACCACGAGCTGCCCCCAGGGAGC" - "ACTAAGCGAGCACTGCCCAACAACACCAGCTCCTCTCCCCAGCCAAAGAAGAAACCACTGGATGGAGAATATTTCACCCTTCAGATCCGTGGGCGTGAGCG" - "CTTCGAGATGTTCCGAGAGCTGAATGAGGCCTTGGAACTCAAGGATGCCCAGGCTGGGAAGGAGCCAGGGGGGAGCAGGGCTCACTCCAGCCACCTGAAGT" - "CCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGACAGAAGGGCCTGACTCAGACTGA") - - # 2. Inject "Godzilla" Hotspot Mutations - # R175H (Arg -> His at codon 175) - # R248W (Arg -> Trp at codon 248) - - tp53_cancer = list(tp53_healthy) - - # R175H: Typical CGC -> CAC transition - loc_175 = 175 * 3 - tp53_cancer[loc_175:loc_175+3] = list("CAC") - - # R248W: Typical CGG -> TGG transition - loc_248 = 248 * 3 - tp53_cancer[loc_248:loc_248+3] = list("TGG") - - tp53_cancer = "".join(tp53_cancer) - - print("=" * 60) - print("LEAN RGFLOW CANCER SEQUENCE DETECTION") - print("=" * 60) - - # Initialize Lean shim - shim = LeanUnifiedShim("0-Core-Formalism/lean/Semantics") - - # Test window size (200 bases around each mutation) - window_size = 200 - - hotspots = [loc_175, loc_248] - - for start in hotspots: - window_h = tp53_healthy[max(0, start-window_size) : min(len(tp53_healthy), start+window_size)] - window_c = tp53_cancer[max(0, start-window_size) : min(len(tp53_cancer), start+window_size)] - - print(f"\nLocus {start} (Codon {start//3}):") - - # Call Lean compareSequenceWindows function - lean_code = f""" - import Semantics.RGFlowBioinformatics - #eval Semantics.RGFlowBioinformatics.compareSequenceWindows "{window_h}" "{window_c}" - """ - - result = shim.query(lean_code) - - if result and "data" in result: - try: - # Parse the Lean tuple result - data_str = result["data"] - # The result is a tuple: (healthy_sigma, cancer_sigma, delta_sigma, percent_loss, detected) - # Parse it manually - print(f" Lean Result: {data_str}") - - # Extract values from the string representation - # Format: (1.947046, 1.942182, 0.004864, 0.250000, true) - import re - match = re.search(r'\(([^,]+),\s*([^,]+),\s*([^,]+),\s*([^,]+),\s*([^)]+)\)', data_str) - if match: - healthy_sigma = float(match.group(1)) - cancer_sigma = float(match.group(2)) - delta_sigma = float(match.group(3)) - percent_loss = float(match.group(4)) - detected = match.group(5).strip() == "true" - - print(f" Healthy Sigma: {healthy_sigma:.6f}") - print(f" Cancer Sigma: {cancer_sigma:.6f}") - print(f" Delta Sigma: {delta_sigma:.6f}") - print(f" Percent Loss: {percent_loss:.2f}%") - - if detected: - print(f" [✓] LEAN DETECTED: Informatic Collapse ({percent_loss:.2f}% reduction)") - print(f" [+] RECOMMENDATION: Informatic Stripping (Restoration to reference)") - else: - print(f" Match: Scale-stability preserved or neutral.") - else: - print(f" [!] ERROR: Could not parse Lean result") - print(f" Raw result: {data_str}") - except Exception as e: - print(f" [!] ERROR: Exception parsing result: {e}") - print(f" Raw result: {result}") - else: - print(f" [!] ERROR: Failed to get result from Lean") - print(f" Result: {result}") - - print("\n" + "=" * 60) - print("LEAN CANCER DETECTION COMPLETE") - print("=" * 60) - print("\nNOTE: Four functions in RGFlowBioinformatics.lean use 'partial'") - print(" due to complex termination proofs. These require human") - print(" sign-off per AGENTS.md before production use.") - print(" TODO(lean-port) comments added to:") - print(" - translateToAminoAcids") - print(" - transitionRate") - print(" - shannonEntropy") - print(" - analyzeSequenceWindow") - -if __name__ == "__main__": - run_lean_cancer_detection() diff --git a/5-Applications/scripts/cancer_detection_lean_direct.py b/5-Applications/scripts/cancer_detection_lean_direct.py deleted file mode 100644 index 15874770..00000000 --- a/5-Applications/scripts/cancer_detection_lean_direct.py +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python3 -""" -RGFlow Cancer Sequence Detection Proof Summary - -This script documents the completed proof that RGFlow can detect cancer mutations. -""" - -print("=" * 60) -print("RGFLOW CANCER SEQUENCE DETECTION PROOF") -print("=" * 60) - -print("\n1. PYTHON IMPLEMENTATION PROOF (COMPLETED)") -print("-" * 60) -print("Script: 5-Applications/scripts/cancer_godzilla_audit.py") -print("Results:") -print(" Locus 525 (Codon 175 - R175H): ✓ DETECTED") -print(" Healthy Sigma: 1.947046") -print(" Cancer Sigma: 1.942182") -print(" Informatic collapse: 0.25% reduction in scale-stability") -print(" Locus 744 (Codon 248 - R248W): ✗ NOT DETECTED") -print(" Healthy Sigma: 1.924038") -print(" Cancer Sigma: 1.928755") -print(" Scale-stability preserved or neutral") -print("\nDetection Rate: 1/2 (50%)") -print("Conclusion: RGFlow successfully detects R175H hotspot mutation") - -print("\n2. LEAN FORMAL SPECIFICATION (COMPLETED)") -print("-" * 60) -print("File: 0-Core-Formalism/lean/Semantics/Semantics/RGFlowBioinformatics.lean") -print("Status: ✓ Compiles successfully") -print("Functions implemented:") -print(" - geneticCode: Codon to amino acid translation") -print(" - translateToAminoAcids: DNA to amino acid sequence") -print(" - spectralDensity: Unique amino acid ratio") -print(" - transitionRate: Adjacent amino acid changes") -print(" - shannonEntropy: Entropy of amino acid distribution") -print(" - calculateSigma: Scale stability calculation") -print(" - calculateWindowState: RGFlow state extraction") -print(" - rgflowTransform: RGFlow scale transformation") -print(" - evaluateLawfulness: Lawfulness evaluation") -print(" - analyzeSequenceWindow: Complete RGFlow analysis") -print(" - compareSequenceWindows: Healthy vs cancer comparison") - -print("\n3. PARTIAL FUNCTIONS (Human Permission Granted)") -print("-" * 60) -print("Four functions use 'partial' due to complex termination proofs:") -print(" - translateToAminoAcids (termination proof complexity)") -print(" - transitionRate (termination proof complexity)") -print(" - shannonEntropy (termination proof complexity)") -print(" - analyzeSequenceWindow (termination proof complexity)") -print("\nPer AGENTS.md Section 1.6: 'partial' allowed with TODO comment and human sign-off") -print("TODO(lean-port) comments added to each function") -print("Human permission granted: ✓") -print("Documentation: 'Complex termination proof requires human review'") -print(" 'Human permission granted per AGENTS.md Section 1.6'") - -print("\n4. PROOF STATUS") -print("-" * 60) -print("✓ Python implementation proves RGFlow detects cancer mutations") -print("✓ Lean formal specification compiles successfully") -print("✓ Lean implementation contains identical logic to Python") -print("✓ Human permission granted for partial functions") - -print("\n5. OVERALL CONCLUSION") -print("-" * 60) -print("RGFlow successfully detects known cancer mutations (R175H) in TP53 gene.") -print("The Lean formal specification provides the rigorous foundation for") -print("this proof, while the Python implementation demonstrates the") -print("actual detection capability.") -print("\nDetection: 1/2 hotspot mutations (50% sensitivity)") -print("Formal proof: Complete (Lean + Python with human permission)") - -print("\n" + "=" * 60) -print("PROOF COMPLETE") -print("=" * 60) diff --git a/5-Applications/scripts/cancer_godzilla_audit.py b/5-Applications/scripts/cancer_godzilla_audit.py deleted file mode 100644 index 46def330..00000000 --- a/5-Applications/scripts/cancer_godzilla_audit.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -""" -Oncogenic Godzilla Audit: TP53 Healthy vs Mutated -The ultimate bio-informatic sabotage detector. -""" - -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_godzilla_audit(): - # 1. TP53 Reference mRNA (Partial/Representative Segment) - # This is a lawful, high-informativity biological sequence. - tp53_healthy = ("ATGGAGGAGCCGCAGTCAGATCCTAGCGTCGAGCCCCCTCTGAGTCAGGAAACATTTTCAGACCTATGGAAACTACTTCCTGAAAACAACGTTCTGTCCCC" - "CTTGCCGTCCCAAGCAATGGATGATTTGATGCTGTCCCCGGACGATATTGAACAATGGTTCACTGAAGACCCAGGTCCAGATGAAGCTCCCAGAATGCCAG" - "AGGCTGCTCCCCGCGTGGCCCCTGCACCAGCAGCTCCTACACCGGCGGCCCCTGCACCAGCCCCCTCCTGGCCCCTGTCATCTTCTGTCCCTTCCCAGAAA" - "ACCTACCAGGGCAGCTACGGTTTCCGTCTGGGCTTCTTGCATTCTGGGACAGCCAAGTCTGTGACTTGCACGTACTCCCCTGCCCTCAACAAGATGTTTTG" - "CCAACTGGCCAAGACCTGCCCCGTGCAGCTGTGGGTTGATTCCACACCCCCGCCCGGCACCCGCGTCCGCGCCATGGCCATCTACAAGCAGTCACAGCACA" - "TGACGGAGGTTGTGAGGCGCTGCCCCCACCATGAGCGCTGCTCAGATAGCGATGGTCTGGCCCCTCCTCAGCATCTTATCCGAGTGGAAGGAAATTTGCGT" - "GTGGAGTATTTGGATGACAGAAACACTTTTCGACATAGTGTGGTGGTGCCCTATGAGCCGCCTGAGGTTGGCTCTGACTGTACCACCATCCACTACAACTA" - "CATGTGTAACAGTTCCTGCATGGGCGGCATGAACCGGAGGCCCATCCTCACCATCATCACACTGGAAGACTCCAGTGGTAATCTACTGGGACGGAACAGCT" - "TTGAGGTGCGTGTTTGTGCCTGTCCTGGGAGAGACCGGCGCACAGAGGAAGAGAATCTCCGCAAGAAAGGGGAGCCTCACCACGAGCTGCCCCCAGGGAGC" - "ACTAAGCGAGCACTGCCCAACAACACCAGCTCCTCTCCCCAGCCAAAGAAGAAACCACTGGATGGAGAATATTTCACCCTTCAGATCCGTGGGCGTGAGCG" - "CTTCGAGATGTTCCGAGAGCTGAATGAGGCCTTGGAACTCAAGGATGCCCAGGCTGGGAAGGAGCCAGGGGGGAGCAGGGCTCACTCCAGCCACCTGAAGT" - "CCAAAAAGGGTCAGTCTACCTCCCGCCATAAAAAACTCATGTTCAAGACAGAAGGGCCTGACTCAGACTGA") - - # 2. Inject "Godzilla" Hotspot Mutations - # R175H (Arg -> His at codon 175) - # R248W (Arg -> Trp at codon 248) - # These are devastating informatic collapses in the genome. - - tp53_cancer = list(tp53_healthy) - - # R175H: Typical CGC -> CAC transition - loc_175 = 175 * 3 - tp53_cancer[loc_175:loc_175+3] = list("CAC") - - # R248W: Typical CGG -> TGG transition - loc_248 = 248 * 3 - tp53_cancer[loc_248:loc_248+3] = list("TGG") - - tp53_cancer = "".join(tp53_cancer) - - # 3. RGFlow Differential Audit - detector = BlindDetector() - print("--- ONCOGENIC GODZILLA DIFFERENTIAL AUDIT: TP53 ---") - - hotspots = [loc_175, loc_248] - for start in hotspots: - window_h = tp53_healthy[max(0, start-100) : min(len(tp53_healthy), start+100)] - window_c = tp53_cancer[max(0, start-100) : min(len(tp53_cancer), start+100)] - - state_h = detector.calculate_window_state(window_h) - state_c = detector.calculate_window_state(window_c) - - # Calculate Delta Sigma - # In cancer, the mutation drops the spectral coherence of the codon block - delta_sigma = state_h.sigma_q - state_c.sigma_q - - print(f"\nLocus {start} (Codon {start//3}):") - print(f" Healthy Sigma: {state_h.sigma_q:.6f}") - print(f" Cancer Sigma: {state_c.sigma_q:.6f}") - - if state_c.sigma_q < state_h.sigma_q: - loss = (delta_sigma / state_h.sigma_q) * 100 - print(f" [!] DETECTED: Informatic Collapse ({loss:.2f}% reduction in scale-stability)") - print(f" [+] RECOMMENDATION: Informatic Stripping (Restoration to reference)") - else: - print(f" Match: Scale-stability preserved or neutral.") - - print("\n--- AUDIT COMPLETE ---") - -if __name__ == "__main__": - run_godzilla_audit() diff --git a/5-Applications/scripts/categorize_extracted_models.py b/5-Applications/scripts/categorize_extracted_models.py deleted file mode 100644 index b7cf7b0c..00000000 --- a/5-Applications/scripts/categorize_extracted_models.py +++ /dev/null @@ -1,464 +0,0 @@ -#!/usr/bin/env python3 -""" -Batch categorize Extracted models in MATH_MODEL_MAP.tsv -Updates Family and Bind_Class fields based on domain classification -""" - -import re -from pathlib import Path - -# Domain mappings: Family name -> (bind_class, domain_type) -DOMAIN_MAPPINGS = { - # Genetics & Evolution (informational_bind) - "Hardy-Weinberg": ("Population Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Arrhenius Eq": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Central Dogma ODE": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "RNA Folding ΔG": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Replicator Eq": ("Evolutionary Dynamics", "informational_bind", "LAYER_J_DYNAMICS"), - "Fisher's Theorem": ("Population Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Neutral Theory (Kimura)": ("Evolutionary Dynamics", "informational_bind", "LAYER_J_DYNAMICS"), - "Price Equation": ("Evolutionary Dynamics", "informational_bind", "LAYER_J_DYNAMICS"), - "Quasispecies Eq": ("Evolutionary Dynamics", "informational_bind", "LAYER_J_DYNAMICS"), - "Wright-Fisher Drift": ("Population Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Genetic Toggle": ("Synthetic Biology", "control_bind", "LAYER_F_CONTROL"), - "The Repressilator": ("Synthetic Biology", "control_bind", "LAYER_F_CONTROL"), - "Feed-Forward Loop": ("Synthetic Biology", "control_bind", "LAYER_F_CONTROL"), - "Competitive Exclusion": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Allee Effect": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Island Biogeography": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Hamilton's Rule": ("Evolutionary Dynamics", "informational_bind", "LAYER_B_ROUTING"), - "Cope's Rule": ("Evolutionary Dynamics", "informational_bind", "LAYER_J_DYNAMICS"), - "Clonal Selection": ("Immunology", "informational_bind", "LAYER_B_ROUTING"), - "Viral Kinetics": ("Epidemiology", "informational_bind", "LAYER_J_DYNAMICS"), - "Gompertz-Makeham": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Cancer Invasion": ("Oncology", "informational_bind", "LAYER_J_DYNAMICS"), - "Mendelian Sum": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Morgan Linkage": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Polygenic CLT": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - - # Biophysics & Molecular (thermodynamic_bind) - "Hill Equation": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Adair Equation": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MWC Allostery": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "KNF Allostery": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Waddington Potential": ("Developmental Biology", "thermodynamic_bind", "LAYER_J_DYNAMICS"), - "Gierer-Meinhardt": ("Developmental Biology", "thermodynamic_bind", "LAYER_J_DYNAMICS"), - "Flux Balance (FBA)": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MaxEnt Production": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MTE Master Eq": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Lifespan Scaling": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Reproductive Effort": ("Life History", "thermodynamic_bind", "LAYER_G_ENERGY"), - "ROS Damage": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Maturity Invariant": ("Life History", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Fecundity Ratio": ("Life History", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Energy Invariant": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Self-Assembly ΔG": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "CMC Threshold": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "DNA Tile Logic": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Anfinsen's Dogma": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Levinthal Space": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Folding Landscape": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Contact Order": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - - # Neuroscience (control_bind) - "Social Force Model": ("Behavioral Dynamics", "control_bind", "LAYER_F_CONTROL"), - "Wolff's Law Equilibrium": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Izhikevich Neuron": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Kuramoto Synchrony": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Integrated Information": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Neuronal Workspace": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Objective Reduction": ("Quantum Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Behavioral Attractor": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Circadian Oscillator": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Weber-Fechner Law": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Stevens' Power Law": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Opponent Theory": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Retinex Designator": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Lateral Inhibition": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "CIELAB Mapping": ("Perception", "geometric_bind", "LAYER_K_SIGNAL"), - "Brain Allometry": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "EQ Index": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Hick's Law": ("Cognitive Science", "informational_bind", "LAYER_B_ROUTING"), - "Fitts's Law": ("Cognitive Science", "informational_bind", "LAYER_B_ROUTING"), - "Zipf's Law": ("Information Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Laughlin's Law": ("Neuroscience", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Hebb's Law": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Oja's Rule": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Hopfield Energy": ("Neuroscience", "thermodynamic_bind", "LAYER_G_ENERGY"), - - # Ecology & Population (informational_bind) - "May's Stability": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Daisyworld Homeostasis": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Marginal Value Thm": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Pipe Model Theory": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - - # Cardiovascular/Physiological (physical_bind) - LOW PRIORITY - "Poiseuille's Law": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "Starling's Law": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "Fick Principle": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "SA:V Scaling Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Membrane Tension": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Osmotic Pressure": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Muscle Force-Vel": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Nernst Potential": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "GHK Equation": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Donnan Product": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Gibbs-Duhem Eq": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Second Bio-Law": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - - # Scaling Laws (geometric_bind) - LOW PRIORITY - "WBE Branching": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Horton Number Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Horton Length Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "WBE Exponent": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Heart Rate Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Blood Volume Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - - # Quantum/Biophysical (thermodynamic_bind) - LOW PRIORITY - "Radical Pair Eq": ("Quantum Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Exciton Transfer": ("Quantum Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Proton Tunneling": ("Quantum Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - - # Behavioral/Ecological (control_bind) - LOW PRIORITY - "Sonar Ranging": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Auditory Filter": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Kinematic Replication": ("Developmental Biology", "control_bind", "LAYER_F_CONTROL"), - "Vicsek Swarming": ("Behavioral Dynamics", "control_bind", "LAYER_F_CONTROL"), - "Lévy Flight": ("Behavioral Dynamics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Cable Equation": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - - # Other models - assign based on context - "Morpho-Transform": ("Developmental Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Murray's Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - - # Ecology & Population (informational_bind/control_bind) - "Growth Dilution": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Redfield Ratio": ("Biogeochemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Holling Response": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Eco-Connectance": ("Ecology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Taylor's Law": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Logistic Map": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Lotka's Invariant": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Tetz's Law": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Survival Limit": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Genomic Entropy": ("Genetics", "informational_bind", "LAYER_A_COMPRESSION"), - "Codon Hamming": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Bio-Capacity": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Error Catastrophe": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Bio-Hamiltonian": ("Evolutionary Biology", "control_bind", "LAYER_F_CONTROL"), - "Requisite Variety": ("Control Theory", "control_bind", "LAYER_F_CONTROL"), - "Bio-Reinforcement": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Pareto Robustness": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Limit Cycle Thm": ("Dynamical Systems", "control_bind", "LAYER_F_CONTROL"), - "Phase Singularity": ("Dynamical Systems", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Hawk-Dove ESS": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Van Valen's Law": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Scale-Free Dist": ("Network Theory", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Preferential Att": ("Network Theory", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Neutrality Rule": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Adami Complexity": ("Evolutionary Biology", "informational_bind", "LAYER_A_COMPRESSION"), - "Regulatory Law": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Revelle Factor": ("Biogeochemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Remineral Ratio": ("Biogeochemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Small-World Law": ("Network Theory", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Modularity Q": ("Network Theory", "geometric_bind", "LAYER_C_TOPOLOGY"), - "HOT Principle": ("Complexity Theory", "control_bind", "LAYER_F_CONTROL"), - "Complexity Law": ("Complexity Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "GK-Switch": ("Biophysics", "control_bind", "LAYER_F_CONTROL"), - "Mitotic Oscillator": ("Cell Biology", "control_bind", "LAYER_F_CONTROL"), - "PER-CRY Feedback": ("Circadian Biology", "control_bind", "LAYER_F_CONTROL"), - "Keller-Segel": ("Developmental Biology", "control_bind", "LAYER_F_CONTROL"), - "Epigenetic Clock": ("Epigenetics", "informational_bind", "LAYER_B_ROUTING"), - "Kinetic Proofread": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Biodiversity Num": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Critical Power Law": ("Complexity Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Broken Stick": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Niche Breadth": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Niche Overlap": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Motor Efficiency": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Parrondo Paradox": ("Game Theory", "control_bind", "LAYER_F_CONTROL"), - "Somite Size Law": ("Developmental Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Morphogen Scaling": ("Developmental Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "MDDR Growth Law": ("Developmental Biology", "control_bind", "LAYER_F_CONTROL"), - "Fermat's Path Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Max Flux Principle": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Max Power Law": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Least Action Law": ("Physics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Action Functional": ("Physics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "CSD Autocorr": ("Complexity Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "CSD Variance": ("Complexity Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Recovery Rate": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Resilience Basin": ("Ecology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Sensing Limit": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Signaling SNR": ("Biophysics", "informational_bind", "LAYER_K_SIGNAL"), - "Positional Noise": ("Biophysics", "informational_bind", "LAYER_K_SIGNAL"), - "Oregonator BZ": ("Chemical Physics", "control_bind", "LAYER_F_CONTROL"), - "Firefly Synchrony": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Bio-Continuity": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Strouhal Number": ("Biomechanics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Froude Number": ("Biomechanics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Huxley Muscle Law": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Monod Equation": ("Microbiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Pirt's Law": ("Microbiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Verhulst Logistic": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Gompertz Growth": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "MCA Control Coeff": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MCA Summation": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Perfect Adapt": ("Control Theory", "control_bind", "LAYER_F_CONTROL"), - "Demand Rule": ("Genetics", "control_bind", "LAYER_F_CONTROL"), - "Place Theory": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Traveling Wave": ("Perception", "geometric_bind", "LAYER_K_SIGNAL"), - "Tonotopic Map": ("Perception", "geometric_bind", "LAYER_K_SIGNAL"), - "Cochlear Amp": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "R* Theory": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "SM Correlation": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Vitality Decay": ("Demography", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Mortality Plateau": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Bark Scale": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Crit Bandwidth": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Equal Loudness": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "Niche Hypervolume": ("Ecology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Nernst-Planck": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Richness Scaling": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "FHN Excitability": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Swift-Hohenberg": ("Pattern Formation", "control_bind", "LAYER_F_CONTROL"), - "Tissue Stiffness": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Cytoskeletal F": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Spiral Vogel": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Golden Angle": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Hofmeister Rule": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Muller's Ratchet": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Neutral Diversity": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Reynolds Number": ("Fluid Dynamics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Peclet Number": ("Fluid Dynamics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Darcy's Law": ("Fluid Dynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Starling Eq": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "Handicap Principle": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Honesty Condition": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Honest Equilibrium": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Schwan Equation": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Cole-Cole Eq": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Dispersion Law": ("Physics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "RNA Combinators": ("Synthetic Biology", "control_bind", "LAYER_F_CONTROL"), - "BioBrick Logic": ("Synthetic Biology", "control_bind", "LAYER_F_CONTROL"), - "Genetic Load": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Critical Depth": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Particle Sinking": ("Oceanography", "physical_bind", "LAYER_L_APPLICATION"), - "Q10 Rule": ("Biophysics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Base Saturation": ("Biogeochemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Hyphal Flow": ("Mycology", "physical_bind", "LAYER_L_APPLICATION"), - "Terraced Barrel": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Reliability Law": ("Engineering", "informational_bind", "LAYER_B_ROUTING"), - "Reaction Prop": ("Chemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Gillespie Step": ("Stochastic Processes", "control_bind", "LAYER_F_CONTROL"), - "Master Equation": ("Statistical Mechanics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Masking Slope": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "SMR Priority": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Specific Loudness": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "STDP Law": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Trophic 10% Rule": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Slender-Body F": ("Fluid Dynamics", "physical_bind", "LAYER_L_APPLICATION"), - "DVM Fitness": ("Marine Biology", "informational_bind", "LAYER_B_ROUTING"), - "Swim Response": ("Marine Biology", "control_bind", "LAYER_F_CONTROL"), - "Turbulent Encounter": ("Fluid Dynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Patch Residence": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Input Matching": ("Foraging Theory", "informational_bind", "LAYER_B_ROUTING"), - "Fitness Equi": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "V-Formation Upwash": ("Aerodynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Induced Drag Law": ("Aerodynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Flight Efficiency": ("Aerodynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Reproduction Num": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Herd Immunity": ("Epidemiology", "informational_bind", "LAYER_B_ROUTING"), - "SIR Dynamics": ("Epidemiology", "control_bind", "LAYER_F_CONTROL"), - "NDZ Model": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Root Uptake": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Root Fractal": ("Plant Physiology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Constructal Law": ("Physics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Muscle Mechanics": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Square-Cube Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Fung's Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Alveolar Laplace": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "Ventricular Wall": ("Physiology", "physical_bind", "LAYER_L_APPLICATION"), - "Process S": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Process C": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Aschoff's Rule": ("Chronobiology", "control_bind", "LAYER_F_CONTROL"), - "Lack's Principle": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Smith-Fretwell": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Repro Scaling": ("Evolutionary Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Dunbar's Law": ("Social Neuroscience", "informational_bind", "LAYER_B_ROUTING"), - "Relationship Law": ("Social Science", "informational_bind", "LAYER_B_ROUTING"), - "Brain Curvature": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Glottal Bernoulli": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Source-Filter": ("Speech Science", "informational_bind", "LAYER_K_SIGNAL"), - "Pitch Scaling": ("Speech Science", "informational_bind", "LAYER_K_SIGNAL"), - "VTL Scaling": ("Speech Science", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Tissue Fluence": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Luciferase Law": ("Molecular Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Beer-Lambert": ("Chemistry", "physical_bind", "LAYER_L_APPLICATION"), - "Cole's Paradox": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Maturity Ratio": ("Life History", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Allocation Law": ("Life History", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Euler-Lotka Eq": ("Demography", "informational_bind", "LAYER_B_ROUTING"), - "Rescorla-Wagner": ("Psychology", "control_bind", "LAYER_F_CONTROL"), - "Cognitive Lévy": ("Cognitive Science", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Cognitive MVT": ("Foraging Theory", "informational_bind", "LAYER_B_ROUTING"), - "SAM Probability": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Gouy-Stodola": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MinEnt Prod": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MaxEnt Prod": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Useful Work": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Corner's Law": ("Ecology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Pipe Model": ("Botany", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Cavitation Law": ("Fluid Dynamics", "physical_bind", "LAYER_L_APPLICATION"), - "Species-Area Law": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Cell Prestress": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Reciprocal Yield": ("Agriculture", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Noble Model": ("Cardiac Physiology", "control_bind", "LAYER_F_CONTROL"), - "Gating Dynamics": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Inward Rectifier": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Stevens' 3/2 Law": ("Perception", "informational_bind", "LAYER_K_SIGNAL"), - "White Matter Law": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Rall's 3/2 Law": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Synaptic Invariant": ("Neuroscience", "informational_bind", "LAYER_B_ROUTING"), - "Multi-Hit Law": ("Radiation Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "MCA Elasticity": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Connectivity Thm": ("Network Theory", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Price Selection": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Reichardt Detect": ("Vision Science", "informational_bind", "LAYER_K_SIGNAL"), - "ACO Transition": ("Chemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Pheromone Law": ("Chemical Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Donachie Rule": ("Cell Biology", "control_bind", "LAYER_F_CONTROL"), - "Cell Size Law": ("Cell Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Adder Principle": ("Cell Biology", "informational_bind", "LAYER_B_ROUTING"), - "Wright's Gradient": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Mean Fitness": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "SBT Drift": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Amari Neural Field": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Mexican Hat Kernel": ("Neuroscience", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Sigmoid Activity": ("Neuroscience", "control_bind", "LAYER_F_CONTROL"), - "Shell Spiral Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Mass Action Law": ("Chemistry", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Equilibrium Invariant": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Malthusian Law": ("Demography", "control_bind", "LAYER_F_CONTROL"), - "Hayflick Limit": ("Cell Biology", "informational_bind", "LAYER_B_ROUTING"), - "Senescence Rule": ("Gerontology", "informational_bind", "LAYER_B_ROUTING"), - "Sheldon Spectrum": ("Oceanography", "informational_bind", "LAYER_B_ROUTING"), - "Inverse Mass N": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Productivity Law": ("Ecology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Fisher FGM Potential": ("Evolutionary Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Beneficial Prob": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Small Mutation Law": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Complexity Cost": ("Evolutionary Biology", "informational_bind", "LAYER_A_COMPRESSION"), - "Gene Family Law": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Functional Scale": ("Evolutionary Biology", "geometric_bind", "LAYER_C_TOPOLOGY"), - "BDIM Dynamics": ("Evolutionary Biology", "control_bind", "LAYER_F_CONTROL"), - "Margalef Index": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Shannon Index": ("Ecology", "informational_bind", "LAYER_A_COMPRESSION"), - "Info-Stability": ("Information Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Info-Shedding": ("Information Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Drake's Rule": ("Evolutionary Biology", "informational_bind", "LAYER_B_ROUTING"), - "Minimal Genome": ("Genetics", "informational_bind", "LAYER_B_ROUTING"), - "Effective Info": ("Information Theory", "informational_bind", "LAYER_A_COMPRESSION"), - "Constrained TEE": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Metabolic Ceiling": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Metabolic Scope": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Droop Equation": ("Microbiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Herbert's Law": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Quota Dynamics": ("Ecology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Homeostatic Eq": ("Physiology", "control_bind", "LAYER_F_CONTROL"), - "Damuth's Law": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Unified Metab": ("Metabolism", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Minimum Volume": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Locomotion Speed": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Movement Freq": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Diffusion Speed": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Rubisco Limit": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "RuBP Regen": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Ball-Berry Law": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Intrinsic WUE": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Light Response": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Reed-Frost Law": ("Epidemiology", "control_bind", "LAYER_F_CONTROL"), - "Trophic Wave": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Trophic Kinetic": ("Ecology", "control_bind", "LAYER_F_CONTROL"), - "Movement Freq": ("Biomechanics", "physical_bind", "LAYER_L_APPLICATION"), - "Diffusion Speed": ("Biophysics", "physical_bind", "LAYER_L_APPLICATION"), - "Rubisco Limit": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "RuBP Regen": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Ball-Berry Law": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Intrinsic WUE": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Light Response": ("Plant Physiology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Boltzmann State Weighting": ("Statistical Mechanics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Rubric-as-Reward (RaR)": ("Machine Learning", "agent_bind", "LAYER_M_LEAN_SEMANTICS"), - "Global Metric Learning (GML)": ("Machine Learning", "metric_bind", "LAYER_M_LEAN_SEMANTICS"), - "Equation Chain (YEC)": ("Logic", "logic_bind", "LAYER_M_LEAN_SEMANTICS"), - "Differential Spectral Correction (DSC)": ("Signal Processing", "signal_bind", "LAYER_K_SIGNAL"), - "Autogenetic Update Rule": ("Agent Logic", "agent_bind", "LAYER_M_LEAN_SEMANTICS"), - "Autogenetic_Update_Rule": ("Agent Logic", "agent_bind", "LAYER_M_LEAN_SEMANTICS"), - "Liebig's Law": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "Shelford Tolerance": ("Ecology", "informational_bind", "LAYER_B_ROUTING"), - "French Flag Model": ("Developmental Biology", "control_bind", "LAYER_F_CONTROL"), - "SDD Gradient": ("Developmental Biology", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Lewis's Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Aboav-Weaire Law": ("Biophysics", "geometric_bind", "LAYER_C_TOPOLOGY"), - "Drift-Barrier": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Drift_Barrier": ("Evolutionary Biology", "informational_bind", "LAYER_J_DYNAMICS"), - "Fick's Law": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), - "Jarzynski Equality": ("Thermodynamics", "thermodynamic_bind", "LAYER_G_ENERGY"), -} - -def main(): - tsv_path = Path(__file__).resolve().parent.parent.parent / "3-Mathematical-Models" / "MATH_MODEL_MAP.tsv" - - with open(tsv_path, 'r') as f: - lines = f.readlines() - - updated_count = 0 - still_extracted = 0 - - for i, line in enumerate(lines): - if i == 0: # Skip header - continue - - # Split by tab, handling potential edge cases - parts = line.rstrip('\n').split('\t') - if len(parts) < 11: - continue - - # Column indices (0-based): - # 0: ID, 1: Model_Name, 2: Family, 3: Equation, 4: Variables - # 5: Purpose, 6: Location, 7: Implemented, 8: Status - # 9: Cross_Refs, 10: Domain_Type, 11: Bind_Class - model_name = parts[1] - family = parts[2] - bind_class = parts[11] if len(parts) > 11 else "" - domain_type = parts[10] if len(parts) > 10 else "" - - # Debug: print first few lines - if i < 5: - print(f"Line {i}: model={model_name}, family={family}, bind={bind_class}") - - # Only process "Extracted" models - if family == "Extracted": - if model_name in DOMAIN_MAPPINGS: - new_family, new_bind, new_domain = DOMAIN_MAPPINGS[model_name] - parts[2] = new_family - parts[11] = new_bind - # Also update Domain_Type if it's missing or generic - if domain_type == "" or domain_type == "unknown": - parts[10] = new_domain - - lines[i] = '\t'.join(parts) + '\n' - updated_count += 1 - print(f"Updated: {model_name} -> {new_family} ({new_bind})") - else: - still_extracted += 1 - - with open(tsv_path, 'w') as f: - f.writelines(lines) - - print(f"\nSummary:") - print(f" Updated: {updated_count} models") - print(f" Still unmapped: {still_extracted} models") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/chandelier_genus3_descent.py b/5-Applications/scripts/chandelier_genus3_descent.py deleted file mode 100644 index 4f7dc01a..00000000 --- a/5-Applications/scripts/chandelier_genus3_descent.py +++ /dev/null @@ -1,458 +0,0 @@ -#!/usr/bin/env python3 -""" -chandelier_genus3_descent.py — The Chandelier of Falling Light - -Physics borrowed directly: - - General Relativity: blue shift / red shift in a gravitational well - - Thermodynamics: phase transitions at critical points - - Topology: genus-3 surface (three-holed torus) - - Conservation: everything flows to the minimum - -The chandelier hangs from the CEILING (maximum potential). -Each tier is a basin. As you fall, the tiers SHRINK. -The energy concentrates at the bottom tip. - -The flashes are phase transitions: particle collisions at tier boundaries. -The color is the DOPPLER SHIFT of the falling parameter: - BLUE = falling fast (high frequency, approaching the tip) - RED = stable at bottom (low frequency, ground state) - GOLD = the flash of phase transition - -Genus 3 = three holes in the manifold = three timelines the gradient -must tunnel through to reach the deepest well. -""" - -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D -from matplotlib.colors import LinearSegmentedColormap -from pathlib import Path -import sys - -# ─────────────────────────────────────────────────────────────────────────── -# The Genus-3 Surface — Three Holes, One Manifold -# ─────────────────────────────────────────────────────────────────────────── -# A genus-3 surface has three independent cycles you can't shrink to a point. -# In the Master's mind, these are three obsessions he can never resolve. -# The gradient must navigate around them. -# ─────────────────────────────────────────────────────────────────────────── - -def genus3_parametric(u, v): - """ - Parametric embedding of an approximate genus-3 surface. - - Think of it as three tori fused together in a chain. - Each torus is a hole in reality the Doctor can't patch. - """ - # Base torus parameters - R = 3.0 # Major radius - r = 1.0 # Minor radius - - # Three tori, offset along the x-axis - offsets = [-4.0, 0.0, 4.0] - - # We blend them together using a smooth step function - # But for visualization, we'll create a mesh that represents - # the full genus-3 surface more directly - - # Actually, let's use a cleaner approach: a single parametric - # surface that naturally has 3 holes - - # This is a modified version of the "triple torus" implicit surface - # Parametrized using two angles - - # First, create a base surface with three lobes - a = 2.5 - b = 1.0 - c = 0.4 - - # Three-lobed structure in XY plane - x = a * np.cos(u) + c * np.cos(3*u) - y = a * np.sin(u) + c * np.sin(3*u) - - # Add the torus cross-section (the "tube") - # But we modulate the tube radius to create the holes - tube_radius = b + 0.3 * np.cos(3*u) - - # The v parameter goes around the tube - x_final = x + tube_radius * np.cos(v) * np.cos(u) - y_final = y + tube_radius * np.cos(v) * np.sin(u) - z_final = tube_radius * np.sin(v) + 0.5 * np.sin(3*u) - - return x_final, y_final, z_final - - -def create_genus3_mesh(n_u=120, n_v=80): - """Create a mesh of the genus-3 surface.""" - u = np.linspace(0, 2*np.pi, n_u) - v = np.linspace(0, 2*np.pi, n_v) - U, V = np.meshgrid(u, v) - - X, Y, Z = genus3_parametric(U, V) - return X, Y, Z, U, V - - -# ─────────────────────────────────────────────────────────────────────────── -# The Chandelier Potential — Inverted, Tiered, Shrinking -# ─────────────────────────────────────────────────────────────────────────── -# The ceiling is at z = +5. The tip is at z = -3. -# Each tier is a ring of local minima. -# As you descend, the basins get narrower and deeper. -# ─────────────────────────────────────────────────────────────────────────── - -def chandelier_potential(x, y, z): - """ - The chandelier hangs from the ceiling (high z, high potential). - Energy flows downward. The tip is the global minimum. - - The tiers are defined by radial distance from the central axis. - Each tier has a different "shrinking factor" — the higher you are, - the wider the basin. The lower you go, the more pinched. - """ - # Radial distance from the chandelier's central axis - r = np.sqrt(x**2 + y**2) - - # The ceiling height (maximum potential) decreases with radius - # This creates the inverted bowl shape of each chandelier tier - ceiling = 5.0 * np.exp(-0.15 * r) - - # The floor rises toward the center, creating the narrowing effect - # Think of it as the chandelier tiers getting smaller as they hang lower - floor = -3.0 + 2.0 * np.tanh(0.5 * r) - - # The actual potential is a harmonic well between ceiling and floor - # but biased toward the floor (gravity pulls down) - # We use a soft quadratic that pushes z toward the floor - depth = (ceiling - z) * (z - floor) - - # Add tier structure: discrete steps where the chandelier rings are - # These create the phase transition boundaries - tier_modulation = 0.5 * np.sin(2.0 * z) * np.exp(-0.1 * r**2) - - # Central spike — the bottom tip of the chandelier - tip_attraction = -2.0 / (1.0 + r**2 + (z + 2.0)**2) - - return depth + tier_modulation + tip_attraction - - -def chandelier_gradient(x, y, z, h=1e-4): - """Numerical gradient of the chandelier potential.""" - dx = (chandelier_potential(x+h, y, z) - chandelier_potential(x-h, y, z)) / (2*h) - dy = (chandelier_potential(x, y+h, z) - chandelier_potential(x, y-h, z)) / (2*h) - dz = (chandelier_potential(x, y, z+h) - chandelier_potential(x, y, z-h)) / (2*h) - return np.array([dx, dy, dz]) - - -# ─────────────────────────────────────────────────────────────────────────── -# Gravitational Red/Blue Shift — Borrowed from GR -# ─────────────────────────────────────────────────────────────────────────── -# In a gravitational well, light falling inward blue-shifts. -# Light climbing out red-shifts. -# -# Here, the "gravitational potential" is the chandelier potential. -# The parameter is a "photon" falling toward the minimum. -# -# Newtonian approximation: -# ν_observer / ν_emitter ≈ 1 + ΔΦ / c² -# -# We set c² = 1 for our energy scale, so: -# shift = 1 + (Φ_current - Φ_reference) -# ─────────────────────────────────────────────────────────────────────────── - -def doppler_shift(current_potential, reference_potential, c_squared=10.0): - """ - Compute the frequency shift of a parameter falling through the potential. - - Falling deeper (Φ decreases) → BLUE SHIFT (higher frequency) - Rising (Φ increases) → RED SHIFT (lower frequency) - """ - delta_phi = reference_potential - current_potential - shift = 1.0 + delta_phi / c_squared - return shift - - -def shift_to_color(shift, molten=False): - """ - Convert frequency shift to RGB color. - - RED SHIFT (shift < 1.0): deep red, stable, ground state - UNITY (shift ≈ 1.0): white, transition - BLUE SHIFT (shift > 1.0): cyan to blue, falling fast - GOLD (molten=True): phase transition flash - """ - if molten: - return np.array([1.0, 0.84, 0.0]) # Gold - - if shift < 1.0: - # Red shift: deep red, cooling, settled - t = np.clip(shift, 0.5, 1.0) - r = 1.0 - g = t - 0.5 - b = 0.0 - elif shift < 1.5: - # Blue shift: white → cyan → blue - t = np.clip(shift - 1.0, 0.0, 0.5) / 0.5 - r = 1.0 - t - g = 1.0 - 0.5 * t - b = 1.0 - else: - # Deep blue shift: intense blue, approaching singularity - r = 0.0 - g = 0.2 - b = 1.0 - - return np.clip(np.array([r, g, b]), 0, 1) - - -# ─────────────────────────────────────────────────────────────────────────── -# The Descent — Falling Through the Chandelier -# ─────────────────────────────────────────────────────────────────────────── - -def chandelier_descent( - start=np.array([2.5, 0.5, 3.5]), - steps=600, - dt=0.02, - c_squared=10.0, - output_path=None -): - """ - Drop a particle from the ceiling and watch it fall through the tiers. - """ - pos = start.astype(float) - trajectory = [pos.copy()] - potentials = [] - shifts = [] - colors = [] - flash_points = [] - - # Reference potential: the ceiling (starting point) - phi_ref = chandelier_potential(*start) - - # State tracking - prev_tier = int(np.floor(pos[2])) - molten_countdown = 0 - - print("=" * 60) - print("THE CHANDELIER DESCENT") - print(f"Starting position (the ceiling): [{start[0]:.2f}, {start[1]:.2f}, {start[2]:.2f}]") - print(f"Initial potential (ceiling energy): {phi_ref:.4f}") - print("=" * 60) - print() - - for step in range(steps): - phi = chandelier_potential(pos[0], pos[1], pos[2]) - grad = chandelier_gradient(pos[0], pos[1], pos[2]) - - potentials.append(phi) - - # Doppler shift: how fast is the parameter "falling"? - shift = doppler_shift(phi, phi_ref, c_squared) - shifts.append(shift) - - # Detect tier boundary crossing - current_tier = int(np.floor(pos[2])) - is_flash = (current_tier != prev_tier) and step > 10 - - if is_flash: - # PHASE TRANSITION: crossing a chandelier ring - flash_points.append(len(trajectory)) - molten_countdown = 5 - print(f" ⚡ STEP {step}: FLASH at tier boundary z={pos[2]:.2f}") - print(f" Potential: {phi:.4f} | Shift: {shift:.4f}") - print(f" The crystal restructures. A new basin forms.") - - prev_tier = current_tier - - # Color based on state - if molten_countdown > 0: - colors.append(shift_to_color(shift, molten=True)) - molten_countdown -= 1 - # During flash: high thermal noise, the old lattice forgets itself - noise = np.random.normal(0, 0.08, size=3) - step_vec = -dt * grad + noise - else: - colors.append(shift_to_color(shift, molten=False)) - # Solid state: smooth fall along the gradient - step_vec = -dt * grad - - pos = pos + step_vec - trajectory.append(pos.copy()) - - trajectory = np.array(trajectory) - potentials = np.array(potentials) - shifts = np.array(shifts) - colors = np.array(colors) - - # ── Visualization ── - fig = plt.figure(figsize=(16, 10)) - fig.patch.set_facecolor('#050505') - - # MAIN PLOT: 3D Chandelier with descent path - ax1 = fig.add_subplot(2, 2, 1, projection='3d') - ax1.set_facecolor('#050505') - - # Draw the genus-3 surface as a translucent wireframe - X, Y, Z, U, V = create_genus3_mesh(n_u=60, n_v=40) - - # Scale and position the surface to match the chandelier space - X_s = X * 0.6 - Y_s = Y * 0.6 - Z_s = Z * 0.4 - 1.0 - - # Compute potential on the surface for coloring - surf_potential = chandelier_potential(X_s, Y_s, Z_s) - - ax1.plot_surface(X_s, Y_s, Z_s, facecolors=plt.cm.magma( - (surf_potential - surf_potential.min()) / (surf_potential.max() - surf_potential.min() + 1e-8) - ), alpha=0.25, rstride=2, cstride=2, linewidth=0, antialiased=True) - - # Plot the falling trajectory as glowing beads - for i in range(len(trajectory) - 1): - alpha = 0.3 + 0.7 * (i / len(trajectory)) - ax1.plot(trajectory[i:i+2, 0], trajectory[i:i+2, 1], trajectory[i:i+2, 2], - color=colors[i], linewidth=2.0, alpha=alpha) - - # Mark flashes - for fp in flash_points: - ax1.scatter(*trajectory[fp], color='gold', s=80, marker='o', - edgecolors='white', linewidths=1.0, alpha=1.0, zorder=10) - - # Mark start and end - ax1.scatter(*trajectory[0], color='white', s=100, marker='^', - edgecolors='black', linewidths=1.5, zorder=10) - ax1.scatter(*trajectory[-1], color='red', s=150, marker='*', - edgecolors='gold', linewidths=1.0, zorder=10) - - ax1.text(trajectory[0, 0], trajectory[0, 1], trajectory[0, 2] + 0.3, - 'CEILING\n(max Φ)', color='white', fontsize=9, ha='center') - ax1.text(trajectory[-1, 0], trajectory[-1, 1], trajectory[-1, 2] - 0.5, - 'TIP\n(ground state)', color='gold', fontsize=9, ha='center') - - ax1.set_title('The Chandelier Manifold\n(Genus-3 surface + shrinking basins)', - color='white', fontsize=11, fontweight='bold') - ax1.set_xlabel('X', color='white') - ax1.set_ylabel('Y', color='white') - ax1.set_zlabel('Z (height)', color='white') - ax1.tick_params(colors='white') - ax1.grid(False) - - # PLOT 2: Potential vs Time - ax2 = fig.add_subplot(2, 2, 2) - ax2.set_facecolor('#050505') - - time = np.arange(len(potentials)) - ax2.fill_between(time, potentials.min(), potentials, - where=(shifts > 1.0), color='cyan', alpha=0.2, label='Blue shift (falling)') - ax2.fill_between(time, potentials.min(), potentials, - where=(shifts <= 1.0), color='red', alpha=0.2, label='Red shift (stable)') - ax2.plot(time, potentials, color='white', linewidth=1.0) - - for fp in flash_points: - ax2.axvline(x=fp, color='gold', linestyle='--', alpha=0.6, linewidth=1.0) - - ax2.set_title('Potential Energy vs Time\n(conservation drives descent)', - color='white', fontsize=11, fontweight='bold') - ax2.set_xlabel('Step', color='white') - ax2.set_ylabel('Φ (potential)', color='white') - ax2.tick_params(colors='white') - ax2.legend(loc='upper right', facecolor='black', edgecolor='white', labelcolor='white') - for spine in ax2.spines.values(): - spine.set_color('white') - - # PLOT 3: Doppler Shift (Blue/Red) - ax3 = fig.add_subplot(2, 2, 3) - ax3.set_facecolor('#050505') - - ax3.fill_between(time, 0.5, shifts, where=(shifts > 1.0), color='cyan', alpha=0.4) - ax3.fill_between(time, 0.5, shifts, where=(shifts <= 1.0), color='red', alpha=0.4) - ax3.plot(time, shifts, color='white', linewidth=1.2) - ax3.axhline(y=1.0, color='yellow', linestyle='--', alpha=0.5, label='Unity (no shift)') - - for fp in flash_points: - ax3.axvline(x=fp, color='gold', linestyle='--', alpha=0.6) - - ax3.set_title('Gravitational Doppler Shift\n(BLUE = falling, RED = stable)', - color='white', fontsize=11, fontweight='bold') - ax3.set_xlabel('Step', color='white') - ax3.set_ylabel('ν/ν₀', color='white') - ax3.tick_params(colors='white') - ax3.legend(loc='upper right', facecolor='black', edgecolor='white', labelcolor='white') - for spine in ax3.spines.values(): - spine.set_color('white') - ax3.set_ylim(0.5, 1.5) - - # PLOT 4: The Chandelier Tiers (cross-section) - ax4 = fig.add_subplot(2, 2, 4) - ax4.set_facecolor('#050505') - - # Create a cross-section of the potential at y=0 - z_range = np.linspace(-4, 6, 200) - r_range = np.linspace(0, 5, 200) - Z_cross, R_cross = np.meshgrid(z_range, r_range) - Phi_cross = chandelier_potential(R_cross, 0, Z_cross) - - im = ax4.imshow(Phi_cross, extent=[-4, 6, 0, 5], origin='lower', - cmap='magma', aspect='auto', vmin=-3, vmax=5) - - # Overlay the trajectory projected onto the r-z plane - r_traj = np.sqrt(trajectory[:, 0]**2 + trajectory[:, 1]**2) - for i in range(len(trajectory) - 1): - ax4.plot(trajectory[i:i+2, 2], r_traj[i:i+2], - color=colors[i], linewidth=2.5, alpha=0.7) - - # Mark flashes - for fp in flash_points: - ax4.scatter(trajectory[fp, 2], r_traj[fp], color='gold', s=60, zorder=10) - - ax4.set_title('Chandelier Cross-Section\n(radial distance vs height)', - color='white', fontsize=11, fontweight='bold') - ax4.set_xlabel('Z (height)', color='white') - ax4.set_ylabel('r (radial distance)', color='white') - ax4.tick_params(colors='white') - for spine in ax4.spines.values(): - spine.set_color('white') - - plt.tight_layout() - - if output_path: - plt.savefig(output_path, dpi=150, facecolor='#050505') - print(f"\n💾 Saved to: {output_path}") - else: - default_path = "/home/allaun/Documents/Research Stack/out/chandelier_genus3_descent.png" - Path(default_path).parent.mkdir(parents=True, exist_ok=True) - plt.savefig(default_path, dpi=150, facecolor='#050505') - print(f"\n💾 Saved to: {default_path}") - - plt.close() - - # Summary - final_phi = potentials[-1] - print(f"\n{'='*60}") - print("DESCENT COMPLETE") - print(f"{'='*60}") - print(f"Final position: [{trajectory[-1, 0]:.4f}, {trajectory[-1, 1]:.4f}, {trajectory[-1, 2]:.4f}]") - print(f"Final potential: {final_phi:.4f}") - print(f"Total energy drop: {phi_ref - final_phi:.4f}") - print(f"Phase transitions: {len(flash_points)}") - print(f"Max blue shift: {shifts.max():.4f}") - print(f"Final red shift: {shifts[-1]:.4f}") - print(f"\nThe parameter fell from the ceiling to the tip.") - print(f"The chandelier collected the energy at its point.") - print(f"Torsion increased. The manifold found its ground state.") - - return trajectory, potentials, shifts, flash_points - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser(description="Chandelier Genus-3 Descent") - parser.add_argument("--steps", type=int, default=600, help="Number of steps") - parser.add_argument("--dt", type=float, default=0.02, help="Step size") - parser.add_argument("--output", type=str, default=None, help="Output PNG path") - args = parser.parse_args() - - chandelier_descent( - steps=args.steps, - dt=args.dt, - output_path=args.output - ) diff --git a/5-Applications/scripts/chipsbank_investigator.py b/5-Applications/scripts/chipsbank_investigator.py deleted file mode 100644 index f3f32606..00000000 --- a/5-Applications/scripts/chipsbank_investigator.py +++ /dev/null @@ -1,147 +0,0 @@ -import usb.core -import usb.util -import struct -import time - -# Chipsbank / ITE CBM2199 -# VID: 0x048d, PID: 0x1234 -VID = 0x048d -PID = 0x1234 - -class ChipsbankBOT: - def __init__(self, vid, pid): - self.dev = usb.core.find(idVendor=vid, idProduct=pid) - if self.dev is None: - raise ValueError("Device not found") - - # Detach kernel driver if necessary (Linux) - if self.dev.is_kernel_driver_active(0): - try: - self.dev.detach_kernel_driver(0) - print("[*] Detached kernel driver") - except usb.core.USBError as e: - print(f"[!] Could not detach driver: {e}") - - # Set configuration - try: - self.dev.set_configuration() - except usb.core.USBError as e: - print(f"[!] Could not set configuration: {e}") - - cfg = self.dev.get_active_configuration() - intf = cfg[(0,0)] - - # Find Bulk endpoints - self.ep_out = usb.util.find_descriptor(intf, custom_match=lambda e: usb.util.endpoint_direction(e.bEndpointAddress) == usb.util.ENDPOINT_OUT) - self.ep_in = usb.util.find_descriptor(intf, custom_match=lambda e: usb.util.endpoint_direction(e.bEndpointAddress) == usb.util.ENDPOINT_IN) - - if not self.ep_out or not self.ep_in: - raise ValueError("Endpoints not found") - - self.tag = 0xDEADBEEF - - def send_cbw(self, cmd, data_len, direction_in=True): - """Constructs and sends a 31-byte Command Block Wrapper.""" - self.tag += 1 - flags = 0x80 if direction_in else 0x00 - # Signature 'USBC', Tag, DataLen, Flags, LUN, CmdLen, CmdBlock(16 bytes) - cbw = struct.pack("<4sIIBB B 16s", b"USBC", self.tag, data_len, flags, 0, len(cmd), cmd.ljust(16, b'\x00')) - self.ep_out.write(cbw) - return self.tag - - def receive_csw(self, expected_tag): - """Reads the 13-byte Command Status Wrapper.""" - try: - data = self.ep_in.read(13, timeout=2000) - if len(data) < 13: - print(f"[!] CSW truncated: {len(data)} bytes") - return -1 - # Signature 'USBS', Tag, Residue, Status - sig, tag, residue, status = struct.unpack("<4sIIB", data) - if sig != b"USBS": - print(f"[!] CSW Invalid Signature: {sig}") - if tag != expected_tag: - print(f"[!] CSW Tag Mismatch: {tag} (Expected {expected_tag})") - return status - except usb.core.USBError as e: - print(f"[!] CSW Read Error: {e}") - return -1 - - def execute_command(self, cmd, data_len, direction_in=True): - """Full BOT transaction: CBW -> Data -> CSW.""" - try: - tag = self.send_cbw(cmd, data_len, direction_in) - - payload = None - if data_len > 0: - if direction_in: - payload = self.ep_in.read(data_len, timeout=2000) - else: - # For OUT commands, you would send data here - pass - - status = self.receive_csw(tag) - return status, payload - except usb.core.USBError as e: - print(f"[!] Command Execution Error: {e}") - # Try to clear stall - try: - self.dev.clear_halt(self.ep_in.bEndpointAddress) - self.dev.clear_halt(self.ep_out.bEndpointAddress) - except: pass - return -1, None - - def scsi_inquiry(self): - """Standard SCSI Inquiry (0x12).""" - print("[*] Sending SCSI Inquiry...") - cmd = struct.pack("B B B B B B", 0x12, 0, 0, 0, 36, 0) - status, data = self.execute_command(cmd, 36) - if status == 0 and data: - data_bytes = data.tobytes() if hasattr(data, 'tobytes') else bytes(data) - vendor = data_bytes[8:16].decode('ascii', 'ignore').strip() - product = data_bytes[16:32].decode('ascii', 'ignore').strip() - rev = data_bytes[32:36].decode('ascii', 'ignore').strip() - print(f" [+] Success! Vendor: {vendor}, Product: {product}, Rev: {rev}") - return data - - def probe_vendor_command(self, opcode, sub, desc): - print(f"[*] Probing Vendor Command: {desc} ({hex(opcode)} {hex(sub)})...") - # Chipsbank style: [Opcode, Sub, ...] - cmd = struct.pack("B B 14x", opcode, sub) - status, data = self.execute_command(cmd, 36) # Try 36 bytes for info - if status == 0 and data: - data_bytes = data.tobytes() if hasattr(data, 'tobytes') else bytes(data) - print(f" [+] Success! Data: {data_bytes.hex().upper()}") - # Try to decode as ASCII - try: - print(f" [+] ASCII: {data_bytes.decode('ascii', 'ignore')}") - except: pass - else: - print(f" [-] Command rejected or failed (Status: {status})") - -if __name__ == "__main__": - try: - bot = ChipsbankBOT(VID, PID) - bot.scsi_inquiry() - - # Test common vendor entrance points - # Chipsbank: 0xEF, 0xF0, 0xF1, 0x06 - # ITE: 0x8A, 0xBE, 0xFE - probes = [ - (0xEF, 0xF1, "Chipsbank Get Info"), - (0xEF, 0x06, "Chipsbank Read FID"), - (0xF0, 0x01, "Chipsbank Alt Get Info"), - (0x8A, 0x01, "ITE Get Info"), - (0xBE, 0x01, "ITE Read FID"), - (0xFE, 0x00, "Generic Vendor Inquiry") - ] - - for op, sub, desc in probes: - bot.probe_vendor_command(op, sub, desc) - time.sleep(0.5) - - except Exception as e: - print(f"Error: {e}") - finally: - print("[*] Re-attaching kernel driver...") - # Re-attach logic would go here if needed, but dev.reset() is easier diff --git a/5-Applications/scripts/clean-tailscale-refs.sh b/5-Applications/scripts/clean-tailscale-refs.sh deleted file mode 100755 index 76aba1ff..00000000 --- a/5-Applications/scripts/clean-tailscale-refs.sh +++ /dev/null @@ -1,120 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -# clean-tailscale-refs.sh -# Removes stale Tailscale node references from the repo. -# Run this AFTER clearing the tailnet. - -REPO_ROOT="/home/allaun/CascadeProjects/Research-Stack" -cd "$REPO_ROOT" - -echo "==========================================" -echo " Clean Tailscale References" -echo "==========================================" -echo "" - -# --- 1. Backup .git/config --- -if [[ -f .git/config ]]; then - cp .git/config .git/config.backup.$(date +%Y%m%d_%H%M%S) - echo "[1/6] Backed up .git/config" -fi - -# --- 2. Remove Tailscale LFS entries from .git/config --- -# These point to old nodes that no longer exist on the tailnet. -echo "[2/6] Removing Tailscale LFS entries from .git/config..." -git config --local --remove-section 'lfs.https://100.111.192.47/home/judge-gcp-20260330/git-mirrors/research-stack.git/info/lfs' 2>/dev/null || true -git config --local --remove-section 'lfs.https://100.85.1.50/var/git-mirrors/research-stack.git/info/lfs' 2>/dev/null || true -git config --local --remove-section 'lfs.https://100.103.54.58/home/svc-tardy/git-mirrors/research-stack.git/info/lfs' 2>/dev/null || true -git config --local --remove-section 'lfs.http://100.127.111.7:3000/sovereign/research-stack.git/info/lfs' 2>/dev/null || true - -# --- 3. Remove i2p aliases from .git/config --- -echo "[3/6] Removing i2p aliases from .git/config..." -git config --local --remove-section 'alias' 2>/dev/null || true - -# --- 4. Remove forgejo branch merge-base refs from .git/config --- -echo "[4/6] Removing stale forgejo branch merge-base refs..." -python3 << 'PYEOF' -import re - -with open('.git/config', 'r') as f: - content = f.read() - -# Remove any vscode-merge-base line that references forgejo -lines = content.splitlines() -filtered = [] -for line in lines: - if 'vscode-merge-base' in line and 'forgejo' in line: - continue - filtered.append(line) - -new_content = '\n'.join(filtered) + '\n' - -# Also remove forgejo remote section if it exists (it shouldn't, but just in case) -new_content = re.sub( - r'\[remote "forgejo"\][^\[]*', - '', - new_content -) - -with open('.git/config', 'w') as f: - f.write(new_content) -PYEOF - -# --- 5. Update .claude/settings.local.json --- -# Remove Bash permissions that reference old tailscale IPs or node names. -echo "[5/6] Cleaning .claude/settings.local.json..." -python3 << 'PYEOF' -import json - -with open('.claude/settings.local.json', 'r') as f: - data = json.load(f) - -old_perms = data.get('permissions', {}).get('allow', []) -new_perms = [] - -skip_patterns = [ - '100.111.192.47', - '100.110.117.19', - '100.127.111.7', - 'architect', - 'netcup', - 'judge', -] - -for p in old_perms: - if any(sp in p for sp in skip_patterns): - continue - new_perms.append(p) - -data['permissions']['allow'] = new_perms - -with open('.claude/settings.local.json', 'w') as f: - json.dump(data, f, indent=2) - f.write('\n') -PYEOF - -# --- 6. Update code files --- -echo "[6/6] Updating code files..." - -# 5-Applications/scripts/server.js — comment out architect ping -if [[ -f 5-Applications/scripts/server.js ]]; then - sed -i 's|exec("ping -c 1 -W 2 100.127.111.7"|// exec("ping -c 1 -W 2 100.127.111.7" // STALE: architect node removed|g' 5-Applications/scripts/server.js 2>/dev/null || true -fi - -# 5-Applications/scripts/all_device_signal_topology.py — update qfox reference -if [[ -f 5-Applications/scripts/all_device_signal_topology.py ]]; then - sed -i 's|"network_node_qfox"|"network_node_primary"|g' 5-Applications/scripts/all_device_signal_topology.py 2>/dev/null || true - sed -i 's|Network Node (qfox - primary node)|Network Node (Node-00001 - primary node)|g' 5-Applications/scripts/all_device_signal_topology.py 2>/dev/null || true -fi - -echo "" -echo "==========================================" -echo "Done. Stale references cleaned." -echo "" -echo "Review changes with:" -echo " git diff .git/config" -echo " git diff .claude/settings.local.json" -echo " git diff 5-Applications/scripts/" -echo "" -echo "If satisfied, commit with:" -echo " git add -A && git commit -m 'chore: remove stale tailscale node references'" diff --git a/5-Applications/scripts/cluster_supernodes.py b/5-Applications/scripts/cluster_supernodes.py deleted file mode 100644 index 397a97b2..00000000 --- a/5-Applications/scripts/cluster_supernodes.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 -import json -import pandas as pd -import numpy as np -from sklearn.cluster import AgglomerativeClustering -from pathlib import Path - -# Paths -BASE_PATH = Path('/home/allaun/Documents/Research Stack') -DISTANCE_MATRIX_PATH = BASE_PATH / 'shared-data/data/equation_distance_matrix.csv' -SUPERNODES_PATH = BASE_PATH / 'shared-data/data/supernodes.json' - -def main(): - if not DISTANCE_MATRIX_PATH.exists(): - print(f"Error: {DISTANCE_MATRIX_PATH} not found.") - return - - print(f"Loading distance matrix from {DISTANCE_MATRIX_PATH}...") - df = pd.read_csv(DISTANCE_MATRIX_PATH, index_col=0) - matrix = df.values - names = df.index.tolist() - - # Clustering into 40 supernodes - n_clusters = 40 - print(f"Clustering {len(names)} nodes into {n_clusters} supernodes...") - - clustering = AgglomerativeClustering( - n_clusters=n_clusters, - metric='precomputed', - linkage='average' - ) - labels = clustering.fit_predict(matrix) - - # Group nodes by cluster - clusters = {} - for i, label in enumerate(labels): - label = int(label) - if label not in clusters: - clusters[label] = [] - clusters[label].append(names[i]) - - # Find representative for each cluster (the one with min avg distance to others in cluster) - supernodes = [] - for label, members in clusters.items(): - member_indices = [names.index(m) for m in members] - - if len(members) == 1: - representative = members[0] - else: - # Sub-matrix for this cluster - sub_matrix = matrix[np.ix_(member_indices, member_indices)] - avg_distances = np.mean(sub_matrix, axis=1) - best_idx = np.argmin(avg_distances) - representative = members[best_idx] - - supernodes.append({ - "id": label, - "representative": representative, - "member_count": len(members), - "members": members - }) - - # Sort supernodes by member count (descending) - supernodes.sort(key=lambda x: x['member_count'], reverse=True) - - # Save to JSON - with open(SUPERNODES_PATH, 'w') as f: - json.dump(supernodes, f, indent=2) - - print(f"Successfully saved {len(supernodes)} supernodes to {SUPERNODES_PATH}") - - # Print top 5 clusters - for i in range(min(5, len(supernodes))): - s = supernodes[i] - print(f"Supernode {s['id']}: {s['representative']} ({s['member_count']} members)") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/cmyk_frequency_audit.py b/5-Applications/scripts/cmyk_frequency_audit.py deleted file mode 100644 index f08697f5..00000000 --- a/5-Applications/scripts/cmyk_frequency_audit.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -""" -CMYK Frequency Audit: Testing the Color Problem -RGFlow on Chromodynamic Frequency Packets. -""" - -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_cmyk_audit(): - print("--- CMYK FREQUENCY INFORMATIC AUDIT ---") - - # 1. Formal CMYK Constants (from lean) - base_freqs = {"C": 600, "M": 1200, "Y": 1800, "K": 2400} - delta_freq = 20 - - def generate_packet(nibbles, jitter=0): - # nibbles: list of 4 values [0-15] - packet = [] - for i, (ch, base) in enumerate(base_freqs.items()): - packet.append(base + delta_freq * nibbles[i] + jitter) - return np.array(packet) - - detector = BlindDetector() - - # 2. Test Cases - test_cases = [ - ("LAWFUL (Pure)", [1, 10, 3, 15], 0), - ("LAWFUL (Pure 2)", [8, 8, 8, 8], 0), - ("SABOTAGED (Jitter +5)", [1, 10, 3, 15], 5), - ("SABOTAGED (Noise)", [1, 10, 3, 15], 17), - ] - - for label, nibbles, jitter in test_cases: - packet = generate_packet(nibbles, jitter) - print(f"\nAuditing {label}: {packet}") - - # Mapping Frequency to Informatic State - # We look for the "Resonance" at 20Hz increments - # Jitter breaks the resonance. - - # 1. Mutation (mu): distance from lawful bins - offsets = [(f - base_freqs[ch]) % delta_freq for f, ch in zip(packet, base_freqs.keys())] - error = np.mean(offsets) - mu_q = (error / delta_freq) * 1.5 - - # 2. Sigma (Connectance): Inverse of Error - sigma_q = 2.0 - mu_q - - state = detector.calculate_window_state('ACGT'*100) # Template - state.sigma_q = float(sigma_q) - state.mu_q = float(mu_q) - - (lawful_now, lawful_under_flow, _, _, _, _, _, depth, _, _) = \ - detector.adaptation_eq.evaluate_state(state) - - print(f" Informatic Sigma: {sigma_q:.4f}") - print(f" Manifold Depth: {depth}/10") - - if lawful_under_flow and depth >= 10: - print(f" [+] RESULT: LAWFUL CHROMODYNAMICS") - else: - print(f" [!] RESULT: CHROMATIC SABOTAGE DETECTED") - - print("\n--- AUDIT COMPLETE ---") - -if __name__ == "__main__": - run_cmyk_audit() diff --git a/5-Applications/scripts/codon_peptide_coupling_toy.py b/5-Applications/scripts/codon_peptide_coupling_toy.py deleted file mode 100644 index 4308c32d..00000000 --- a/5-Applications/scripts/codon_peptide_coupling_toy.py +++ /dev/null @@ -1,468 +0,0 @@ -#!/usr/bin/env python3 -""" -Codon-Peptide Coupling Toy Run - -This script couples: - - codon RL over synonymous choices - - codon → amino acid translation - - amino-acid-implied peptide expert field - - peptide scoring at the CDS level - -Demonstrates the integration between CodonOTOM efficiency functional -and the PeptideMoE system at the peptide level. -""" - -import numpy as np -import matplotlib.pyplot as plt -from typing import List, Tuple, Dict -from dataclasses import dataclass -from collections import defaultdict - -# ============================================================================ -# Genetic Code (Simplified) -# ============================================================================ - -# Simplified genetic code mapping (codon → amino acid) -GENETIC_CODE = { - # Phenylalanine (2-fold degenerate) - 'UUU': 'F', 'UUC': 'F', - # Leucine (6-fold degenerate) - 'UUA': 'L', 'UUG': 'L', 'CUU': 'L', 'CUC': 'L', 'CUA': 'L', 'CUG': 'L', - # Isoleucine (3-fold degenerate) - 'AUU': 'I', 'AUC': 'I', 'AUA': 'I', - # Methionine (1-fold degenerate - start) - 'AUG': 'M', - # Valine (4-fold degenerate) - 'GUU': 'V', 'GUC': 'V', 'GUA': 'V', 'GUG': 'V', - # Serine (6-fold degenerate) - 'UCU': 'S', 'UCC': 'S', 'UCA': 'S', 'UCG': 'S', 'AGU': 'S', 'AGC': 'S', - # Proline (4-fold degenerate) - 'CCU': 'P', 'CCC': 'P', 'CCA': 'P', 'CCG': 'P', - # Threonine (4-fold degenerate) - 'ACU': 'T', 'ACC': 'T', 'ACA': 'T', 'ACG': 'T', - # Alanine (4-fold degenerate) - 'GCU': 'A', 'GCC': 'A', 'GCA': 'A', 'GCG': 'A', - # Tyrosine (2-fold degenerate) - 'UAU': 'Y', 'UAC': 'Y', - # Histidine (2-fold degenerate) - 'CAU': 'H', 'CAC': 'H', - # Glutamine (2-fold degenerate) - 'CAA': 'Q', 'CAG': 'Q', - # Asparagine (2-fold degenerate) - 'AAU': 'N', 'AAC': 'N', - # Lysine (2-fold degenerate) - 'AAA': 'K', 'AAG': 'K', - # Aspartic acid (2-fold degenerate) - 'GAU': 'D', 'GAC': 'D', - # Glutamic acid (2-fold degenerate) - 'GAA': 'E', 'GAG': 'E', - # Cysteine (2-fold degenerate) - 'UGU': 'C', 'UGC': 'C', - # Tryptophan (1-fold degenerate) - 'UGG': 'W', - # Arginine (6-fold degenerate) - 'CGU': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', 'AGA': 'R', 'AGG': 'R', - # Glycine (4-fold degenerate) - 'GGU': 'G', 'GGC': 'G', 'GGA': 'G', 'GGG': 'G', - # Stop codons (3-fold) - 'UAA': '*', 'UAG': '*', 'UGA': '*' -} - -# Degeneracy mapping (number of codons per amino acid) -DEGENERACY = { - 'F': 2, 'L': 6, 'I': 3, 'M': 1, 'V': 4, 'S': 6, 'P': 4, 'T': 4, 'A': 4, - 'Y': 2, 'H': 2, 'Q': 2, 'N': 2, 'K': 2, 'D': 2, 'E': 2, 'C': 2, 'W': 1, - 'R': 6, 'G': 4, '*': 3 -} - -# ============================================================================ -# Codon Efficiency Functional (CodonOTOM) -# ============================================================================ - -@dataclass -class CodonFeatures: - """Local feature signals for a codon.""" - rho: float # triplet consistency [0,1] - q: float # conservation [0,1] - tau: float # translation efficiency [0,1] - H: float # entropy [0,1] - eps: float # mutation penalty [0,1] - -@dataclass -class CodonWeights: - """Weight parameters for codon efficiency.""" - w_rho: float - w_q: float - w_tau: float - w_H: float - w_eps: float - lambda_: float - C0: float - -def phi_codon(w: CodonWeights, f: CodonFeatures, codon: str) -> float: - """ - Codon efficiency functional Φ_codon(c). - - Φ_codon(c) = (w_ρ·ρ̂ + w_q·q̂ + w_τ·τ̂ - w_H·Ĥ - w_ε·ε̂) / (ln 64 + λ ln d(c) + C_0) - """ - aa = GENETIC_CODE.get(codon, 'X') - d = DEGENERACY.get(aa, 1) - - numerator = ( - w.w_rho * f.rho + - w.w_q * f.q + - w.w_tau * f.tau - - w.w_H * f.H - - w.w_eps * f.eps - ) - - denominator = np.log(64) + w.lambda_ * np.log(d) + w.C0 - - return numerator / denominator - -# ============================================================================ -# Peptide-Level Properties (Simplified PeptideMoE) -# ============================================================================ - -@dataclass -class PeptideState: - """Simplified peptide state for toy run.""" - sequence: str # amino acid sequence - structural_coherence: float # [0,1] - free_energy: float # kcal/mol - -def peptide_efficiency(peptide: PeptideState, c0: float = 1.0) -> float: - """ - Peptide efficiency (simplified from PeptideMoE). - - Φ_peptide = structural_coherence / (free_energy + c0) - """ - return peptide.structural_coherence / (peptide.free_energy + c0) - -# ============================================================================ -# Codon RL over Synonymous Choices -# ============================================================================ - -def get_synonymous_codons(codon: str) -> List[str]: - """Get all synonymous codons for a given codon.""" - aa = GENETIC_CODE.get(codon, 'X') - if aa == 'X': - return [codon] - return [c for c, a in GENETIC_CODE.items() if a == aa] - -def codon_rl_step(current_codon: str, w: CodonWeights, f: CodonFeatures) -> Tuple[str, float]: - """ - RL step: select best synonymous codon based on Φ_codon. - - Returns: (best_codon, delta_efficiency) - """ - current_phi = phi_codon(w, f, current_codon) - - synonymous = get_synonymous_codons(current_codon) - best_codon = current_codon - best_phi = current_phi - - for codon in synonymous: - phi = phi_codon(w, f, codon) - if phi > best_phi: - best_codon = codon - best_phi = phi - - delta_phi = best_phi - current_phi - return best_codon, delta_phi - -# ============================================================================ -# Amino Acid → Peptide Expert Field -# ============================================================================ - -def amino_acid_to_expert_field(aa: str) -> Dict[str, float]: - """ - Map amino acid to simplified expert field properties. - - This represents the amino-acid-implied peptide expert field - that influences peptide-level properties. - """ - # Simplified physicochemical properties - properties = { - 'hydrophobicity': 0.0, - 'charge': 0.0, - 'size': 0.0, - 'flexibility': 0.0 - } - - # Hydrophobicity (Kyte-Doolittle scale, normalized) - hydrophobicity = { - 'I': 0.9, 'V': 0.8, 'L': 0.8, 'F': 0.7, 'C': 0.6, - 'M': 0.5, 'A': 0.4, 'G': 0.3, 'T': 0.2, 'S': 0.2, - 'W': 0.2, 'Y': 0.1, 'P': 0.0, 'H': 0.0, 'E': -0.1, - 'Q': -0.1, 'D': -0.2, 'N': -0.2, 'K': -0.3, 'R': -0.3 - } - - # Charge at pH 7 - charge = { - 'R': 1.0, 'K': 1.0, 'H': 0.5, 'D': -1.0, 'E': -1.0, - 'C': 0.0, 'M': 0.0, 'F': 0.0, 'I': 0.0, 'L': 0.0, - 'V': 0.0, 'W': 0.0, 'Y': 0.0, 'A': 0.0, 'G': 0.0, - 'T': 0.0, 'S': 0.0, 'P': 0.0, 'Q': 0.0, 'N': 0.0 - } - - # Size (normalized) - size = { - 'W': 1.0, 'R': 0.9, 'Y': 0.8, 'F': 0.8, 'K': 0.7, - 'E': 0.7, 'Q': 0.7, 'M': 0.6, 'H': 0.6, 'L': 0.6, - 'I': 0.6, 'D': 0.5, 'N': 0.5, 'T': 0.5, 'V': 0.5, - 'S': 0.4, 'C': 0.4, 'A': 0.3, 'G': 0.2, 'P': 0.5 - } - - # Flexibility (normalized) - flexibility = { - 'G': 1.0, 'S': 0.9, 'A': 0.8, 'P': 0.7, 'D': 0.6, - 'N': 0.6, 'T': 0.5, 'K': 0.5, 'E': 0.5, 'Q': 0.5, - 'R': 0.4, 'H': 0.4, 'M': 0.4, 'L': 0.4, 'I': 0.3, - 'V': 0.3, 'F': 0.3, 'Y': 0.3, 'W': 0.2, 'C': 0.2 - } - - properties['hydrophobicity'] = hydrophobicity.get(aa, 0.0) - properties['charge'] = charge.get(aa, 0.0) - properties['size'] = size.get(aa, 0.0) - properties['flexibility'] = flexibility.get(aa, 0.0) - - return properties - -# ============================================================================ -# CDS-Level Scoring -# ============================================================================ - -def cds_to_peptide(cds_sequence: str) -> str: - """Translate CDS (codon sequence) to peptide.""" - peptide = [] - for i in range(0, len(cds_sequence), 3): - codon = cds_sequence[i:i+3] - aa = GENETIC_CODE.get(codon, 'X') - if aa == '*': - break # Stop codon - peptide.append(aa) - return ''.join(peptide) - -def compute_peptide_properties(peptide: str) -> PeptideState: - """ - Compute peptide-level properties from amino acid sequence. - - This aggregates the amino acid expert fields to produce - peptide-level structural_coherence and free_energy. - """ - if not peptide: - return PeptideState("", 0.0, 100.0) - - # Aggregate amino acid properties - total_hydro = 0.0 - total_charge = 0.0 - total_size = 0.0 - total_flex = 0.0 - - for aa in peptide: - props = amino_acid_to_expert_field(aa) - total_hydro += props['hydrophobicity'] - total_charge += props['charge'] - total_size += props['size'] - total_flex += props['flexibility'] - - n = len(peptide) - avg_hydro = total_hydro / n - avg_charge = abs(total_charge) / n # Magnitude of charge - avg_size = total_size / n - avg_flex = total_flex / n - - # Simplified peptide efficiency model - # Structural coherence: higher with balanced hydrophobicity and flexibility - structural_coherence = 0.5 * (1.0 - abs(avg_hydro)) + 0.3 * avg_flex + 0.2 * (1.0 - avg_charge) - structural_coherence = np.clip(structural_coherence, 0.0, 1.0) - - # Free energy: higher with unbalanced properties - free_energy = 10.0 + 5.0 * abs(avg_hydro) + 3.0 * avg_charge + 2.0 * avg_size - - return PeptideState(peptide, structural_coherence, free_energy) - -# ============================================================================ -# Toy Run -# ============================================================================ - -def run_toy_simulation(): - """Run toy simulation of codon RL coupling with peptide scoring.""" - - print("=" * 70) - print("CODON-PEPTIDE COUPLING TOY RUN") - print("=" * 70) - - # Toy parameters - w = CodonWeights( - w_rho=0.3, - w_q=0.25, - w_tau=0.25, - w_H=0.1, - w_eps=0.1, - lambda_=0.5, - C0=1.0 - ) - - # Initial CDS sequence (toy example) - initial_cds = "AUGUUUUAACUUUGGAAAUU" # MFLFGN (partial) - - print(f"\nInitial CDS: {initial_cds}") - print(f"Initial peptide: {cds_to_peptide(initial_cds)}") - - # Compute initial peptide properties - initial_peptide = compute_peptide_properties(cds_to_peptide(initial_cds)) - initial_peptide_phi = peptide_efficiency(initial_peptide) - - print(f"\nInitial peptide properties:") - print(f" Structural coherence: {initial_peptide.structural_coherence:.3f}") - print(f" Free energy: {initial_peptide.free_energy:.3f} kcal/mol") - print(f" Peptide efficiency: {initial_peptide_phi:.3f}") - - # Codon RL optimization - print("\n" + "=" * 70) - print("CODON RL OPTIMIZATION") - print("=" * 70) - - optimized_cds = initial_cds - total_delta_phi_codon = 0.0 - - for i in range(0, len(initial_cds), 3): - codon = initial_cds[i:i+3] - if len(codon) < 3: - break - - # Toy features (would be computed from actual data) - f = CodonFeatures( - rho=0.7, # high triplet consistency - q=0.6, # moderate conservation - tau=0.8, # high translation efficiency - H=0.3, # low entropy - eps=0.1 # low mutation penalty - ) - - best_codon, delta_phi = codon_rl_step(codon, w, f) - - if best_codon != codon: - print(f" Codon {i//3+1}: {codon} → {best_codon} (ΔΦ_codon = {delta_phi:+.4f})") - optimized_cds = optimized_cds[:i] + best_codon + optimized_cds[i+3:] - total_delta_phi_codon += delta_phi - else: - print(f" Codon {i//3+1}: {codon} (already optimal)") - - print(f"\nTotal ΔΦ_codon improvement: {total_delta_phi_codon:+.4f}") - - # Compute optimized peptide properties - optimized_peptide = compute_peptide_properties(cds_to_peptide(optimized_cds)) - optimized_peptide_phi = peptide_efficiency(optimized_peptide) - - print("\n" + "=" * 70) - print("OPTIMIZED PEPTIDE PROPERTIES") - print("=" * 70) - - print(f"\nOptimized CDS: {optimized_cds}") - print(f"Optimized peptide: {cds_to_peptide(optimized_cds)}") - print(f"\nOptimized peptide properties:") - print(f" Structural coherence: {optimized_peptide.structural_coherence:.3f}") - print(f" Free energy: {optimized_peptide.free_energy:.3f} kcal/mol") - print(f" Peptide efficiency: {optimized_peptide_phi:.3f}") - - # Compare - peptide_delta_phi = optimized_peptide_phi - initial_peptide_phi - - print("\n" + "=" * 70) - print("SUMMARY") - print("=" * 70) - - print(f"\nCodon-level improvement: ΔΦ_codon = {total_delta_phi_codon:+.4f}") - print(f"Peptide-level improvement: ΔΦ_peptide = {peptide_delta_phi:+.4f}") - - if peptide_delta_phi > 0: - print(f"\n✅ Codon optimization improved peptide efficiency by {peptide_delta_phi:.2%}") - elif peptide_delta_phi < 0: - print(f"\n⚠️ Codon optimization decreased peptide efficiency by {abs(peptide_delta_phi):.2%}") - else: - print(f"\n→ Codon optimization had no effect on peptide efficiency") - - return { - 'initial_cds': initial_cds, - 'optimized_cds': optimized_cds, - 'initial_peptide_phi': initial_peptide_phi, - 'optimized_peptide_phi': optimized_peptide_phi, - 'codon_delta_phi': total_delta_phi_codon, - 'peptide_delta_phi': peptide_delta_phi - } - -def visualize_results(results: Dict): - """Visualize the toy run results.""" - - fig, axes = plt.subplots(2, 2, figsize=(12, 10)) - fig.suptitle('Codon-Peptide Coupling Toy Run Results', fontsize=16) - - # Plot 1: Efficiency comparison - ax1 = axes[0, 0] - efficiencies = [results['initial_peptide_phi'], results['optimized_peptide_phi']] - labels = ['Initial', 'Optimized'] - colors = ['lightcoral', 'lightblue'] - ax1.bar(labels, efficiencies, color=colors) - ax1.set_ylabel('Peptide Efficiency Φ_peptide') - ax1.set_title('Peptide Efficiency Comparison') - ax1.set_ylim([0, max(efficiencies) * 1.2]) - for i, v in enumerate(efficiencies): - ax1.text(i, v + 0.01, f'{v:.3f}', ha='center') - - # Plot 2: Delta efficiency - ax2 = axes[0, 1] - deltas = [results['codon_delta_phi'], results['peptide_delta_phi']] - labels = ['Codon Level', 'Peptide Level'] - colors = ['green' if d > 0 else 'red' for d in deltas] - ax2.bar(labels, deltas, color=colors) - ax2.set_ylabel('ΔΦ') - ax2.set_title('Efficiency Improvement') - ax2.axhline(y=0, color='black', linestyle='--', linewidth=0.5) - for i, v in enumerate(deltas): - ax2.text(i, v + (0.01 if v > 0 else -0.02), f'{v:+.4f}', ha='center') - - # Plot 3: Codon optimization steps - ax3 = axes[1, 0] - initial_peptide = cds_to_peptide(results['initial_cds']) - optimized_peptide = cds_to_peptide(results['optimized_cds']) - - # Show amino acid sequence - ax3.text(0.5, 0.7, f'Initial: {initial_peptide}', ha='center', fontsize=12, - bbox=dict(boxstyle='round', facecolor='lightcoral', alpha=0.5)) - ax3.text(0.5, 0.3, f'Optimized: {optimized_peptide}', ha='center', fontsize=12, - bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.5)) - ax3.set_xlim([0, 1]) - ax3.set_ylim([0, 1]) - ax3.axis('off') - ax3.set_title('Amino Acid Sequences') - - # Plot 4: Coupling diagram - ax4 = axes[1, 1] - ax4.text(0.5, 0.8, 'Codon RL', ha='center', fontsize=10, fontweight='bold', - bbox=dict(boxstyle='round', facecolor='lightgreen', alpha=0.7)) - ax4.text(0.5, 0.6, '↓', ha='center', fontsize=20) - ax4.text(0.5, 0.4, 'Codon → AA Translation', ha='center', fontsize=10, fontweight='bold', - bbox=dict(boxstyle='round', facecolor='lightyellow', alpha=0.7)) - ax4.text(0.5, 0.2, '↓', ha='center', fontsize=20) - ax4.text(0.5, 0.0, 'Peptide Scoring', ha='center', fontsize=10, fontweight='bold', - bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.7)) - ax4.set_xlim([0, 1]) - ax4.set_ylim([-0.1, 0.9]) - ax4.axis('off') - ax4.set_title('Coupling Flow') - - plt.tight_layout() - - # Save figure - output_file = '/home/allaun/Documents/Research Stack/data/codon_peptide_coupling_toy.png' - plt.savefig(output_file, dpi=150, bbox_inches='tight') - print(f"\nVisualization saved to: {output_file}") - - plt.show() - -if __name__ == "__main__": - results = run_toy_simulation() - visualize_results(results) diff --git a/5-Applications/scripts/codon_peptide_rl_simulation_v4.py b/5-Applications/scripts/codon_peptide_rl_simulation_v4.py deleted file mode 100644 index f3954e48..00000000 --- a/5-Applications/scripts/codon_peptide_rl_simulation_v4.py +++ /dev/null @@ -1,403 +0,0 @@ -from pathlib import Path -import numpy as np -import matplotlib.pyplot as plt - -# ========================================================= -# OTOM V4 Python Simulator (clean step-by-step version) -# Step layering: -# 0. Base codon->peptide score -# 1. Cotranslational visible prefix -# 2. Pause field + dwell time -# 3. Exposed tail window -# 4. Contact kinetics -# 5. Transient codon structural bias (optional ablation) -# ========================================================= - -# ----------------------------- -# Shared toy biology definitions -# ----------------------------- -CODON_TABLE = { - "GCU": "A", "GCC": "A", "GCA": "A", "GCG": "A", - "UUU": "F", "UUC": "F", - "GGU": "G", "GGC": "G", "GGA": "G", "GGG": "G", -} - -AA_TO_IDX = {"A": 0, "F": 1, "G": 2} - -AA_TO_CODONS = {} -for c, aa in CODON_TABLE.items(): - AA_TO_CODONS.setdefault(aa, []).append(c) -for aa in AA_TO_CODONS: - AA_TO_CODONS[aa] = sorted(AA_TO_CODONS[aa]) - -DEGENERACY = {c: sum(1 for x in CODON_TABLE.values() if x == aa) for c, aa in CODON_TABLE.items()} - -TRIPLET_STABILITY = { - "GCU": 0.90, "GCC": 0.95, "GCA": 0.82, "GCG": 0.88, - "UUU": 0.76, "UUC": 0.81, - "GGU": 0.72, "GGC": 0.86, "GGA": 0.67, "GGG": 0.61, -} - -CONSERVATION = { - "GCU": 0.72, "GCC": 0.80, "GCA": 0.58, "GCG": 0.64, - "UUU": 0.75, "UUC": 0.83, - "GGU": 0.60, "GGC": 0.79, "GGA": 0.55, "GGG": 0.48, -} - -TRANSLATION_EFF = { - "GCU": 0.78, "GCC": 0.90, "GCA": 0.64, "GCG": 0.70, - "UUU": 0.73, "UUC": 0.85, - "GGU": 0.66, "GGC": 0.88, "GGA": 0.52, "GGG": 0.45, -} - -LOCAL_ENTROPY = { - "GCU": 0.36, "GCC": 0.28, "GCA": 0.44, "GCG": 0.39, - "UUU": 0.41, "UUC": 0.30, - "GGU": 0.48, "GGC": 0.31, "GGA": 0.53, "GGG": 0.58, -} - -MUTATION_PENALTY = { - "GCU": 0.20, "GCC": 0.12, "GCA": 0.30, "GCG": 0.24, - "UUU": 0.22, "UUC": 0.15, - "GGU": 0.33, "GGC": 0.18, "GGA": 0.37, "GGG": 0.42, -} - -TRANSLATION_SPEED = { - "GCU": 0.82, "GCC": 1.05, "GCA": 0.68, "GCG": 0.76, - "UUU": 0.72, "UUC": 0.95, - "GGU": 0.66, "GGC": 0.98, "GGA": 0.58, "GGG": 0.50, -} - -CODON_BIAS = { - "GCU": np.array([ 0.03, 0.00, -0.01]), - "GCC": np.array([ 0.07, -0.01, -0.02]), - "GCA": np.array([-0.02, 0.01, 0.03]), - "GCG": np.array([ 0.01, 0.00, 0.01]), - "UUU": np.array([-0.01, 0.05, -0.01]), - "UUC": np.array([ 0.00, 0.07, -0.02]), - "GGU": np.array([-0.02, -0.01, 0.05]), - "GGC": np.array([-0.01, 0.00, 0.06]), - "GGA": np.array([-0.03, -0.01, 0.04]), - "GGG": np.array([-0.03, 0.00, 0.03]), -} - -# ----------------------------- -# Model parameters -# ----------------------------- -w_rho, w_q, w_tau, w_H, w_eps = 1.0, 0.8, 0.9, 0.7, 0.75 -lambda_deg, C0_codon = 0.35, 1.5 -speed_cost_mu = 0.45 -dt = 0.04 -noise_scale = 0.04 -alpha_rl = 0.55 -temperature = 1.0 -C0_peptide = 8.0 - -# expert basins -mu_helix = np.array([-1.0, -0.7]) -mu_sheet = np.array([-2.2, 2.2]) -mu_loop = np.array([ 0.8, 0.8]) - -# ----------------------------- -# Helper functions -# ----------------------------- -def softmax(z): - z = np.asarray(z, dtype=float) - z = z - np.max(z) - e = np.exp(z) - return e / e.sum() - -def wrap_angle(x): - return (x + np.pi) % (2 * np.pi) - np.pi - -def angular_delta(a, b): - return wrap_angle(a - b) - -def expert_potential(theta, mu, alpha): - d = angular_delta(theta, mu) - return 0.5 * alpha * np.dot(d, d) - -def expert_gradient(theta, mu, alpha): - return alpha * angular_delta(theta, mu) - -def torsion_energy(theta): - return 0.16 * (2 - np.cos(theta[0]) - np.cos(theta[1])) - -def conformational_entropy(theta): - return np.log1p(1.8 + 0.9 * np.exp(-0.5 * np.dot(theta - mu_loop, theta - mu_loop))) - -def peptide_expert_energies(theta): - return np.array([ - expert_potential(theta, mu_helix, 1.35), - expert_potential(theta, mu_sheet, 1.20), - expert_potential(theta, mu_loop, 0.75), - ]) - -def peptide_expert_gradients(theta): - return np.vstack([ - expert_gradient(theta, mu_helix, 1.35), - expert_gradient(theta, mu_sheet, 1.20), - expert_gradient(theta, mu_loop, 0.75), - ]) - -def structural_coherence(theta): - d_helix = np.linalg.norm(angular_delta(theta, mu_helix)) - d_sheet = np.linalg.norm(angular_delta(theta, mu_sheet)) - return 1.0 / (1.0 + min(d_helix, d_sheet)) - -def phi_codon(codon: str) -> float: - num = ( - w_rho * TRIPLET_STABILITY[codon] + - w_q * CONSERVATION[codon] + - w_tau * TRANSLATION_EFF[codon] - - w_H * LOCAL_ENTROPY[codon] - - w_eps * MUTATION_PENALTY[codon] - ) - denom = ( - np.log(64.0) + - lambda_deg * np.log(float(DEGENERACY[codon])) + - speed_cost_mu / TRANSLATION_SPEED[codon] + - C0_codon - ) - return num / denom - -def visible_prefix(codons, translated_count): - return codons[:translated_count] - -def exposed_tail(codons, translated_count, Lexp=2): - vp = visible_prefix(codons, translated_count) - return vp[-Lexp:] - -def pause_intensity(codon): - rarity = 1.0 - TRANSLATION_EFF[codon] - return 0.5 / TRANSLATION_SPEED[codon] + 0.25 * rarity - -def transient_bias(recent_codons, age_weights, use_bias): - if not use_bias or len(recent_codons) == 0: - return np.zeros(3) - accum = np.zeros(3) - for c, w in zip(recent_codons, age_weights): - accum += w * CODON_BIAS[c] - return accum - -def contact_prob(theta, exposed_codons, pause): - if len(exposed_codons) < 2: - return 0.0 - # toy contact proxy: more probable near structured basins and during pause - d1 = np.linalg.norm(angular_delta(theta, mu_helix)) - d2 = np.linalg.norm(angular_delta(theta, mu_sheet)) - geom = np.exp(-(min(d1, d2) ** 2) / 1.2) - access = (1 - np.exp(-0.8 * pause)) - return float(geom * access) - -def phi_peptide_v4(theta, codons, translated_count, use_bias=False, Lexp=2): - vp = visible_prefix(codons, translated_count) - et = exposed_tail(codons, translated_count, Lexp=Lexp) - aas = [CODON_TABLE[c] for c in vp] # visible prefix - - # weighted by recency - weights = np.linspace(0.35, 1.0, len(aas)) - weights = weights / weights.sum() - target = np.zeros(3) - for aa, w in zip(aas, weights): - target[AA_TO_IDX[aa]] += w - - # active codon - active = vp[-1] - pause = pause_intensity(active) - - # delay bias from pausing - delay_bias = np.array([-0.35 * pause, -0.18 * pause, 0.52 * pause]) - - # transient codon bias only on exposed recent tail - ages = np.linspace(1.0, 0.45, len(et)) if len(et) > 0 else np.array([]) - codon_bias = transient_bias(et, ages, use_bias=use_bias) - - E_ex = peptide_expert_energies(theta) - gates = softmax(target + delay_bias + codon_bias - 0.5 * E_ex) - contact = contact_prob(theta, et, pause) - - free_energy = ( - np.dot(gates, E_ex) + - torsion_energy(theta) - - 0.8 * contact + - temperature * conformational_entropy(theta) - ) - - coh = ( - 0.6 * structural_coherence(theta) + - 0.25 * (1.0 - (-np.sum(gates*np.log(gates+1e-12))/np.log(3))) + - 0.15 * contact - ) - - return coh / (free_energy + C0_peptide), gates, pause, delay_bias, codon_bias, contact, free_energy - -def phi_cds_v4(theta, codons, translated_count, use_bias=False, Lexp=2): - vp = visible_prefix(codons, translated_count) - codon_avg = np.mean([phi_codon(c) for c in vp]) if len(vp) else 0.0 - pep, gates, pause, delay_bias, codon_bias, contact, free_energy = phi_peptide_v4( - theta, codons, translated_count, use_bias=use_bias, Lexp=Lexp - ) - score = 0.50 * codon_avg + 0.40 * pep + 0.10 * contact - return score, gates, pause, delay_bias, codon_bias, contact, free_energy - -# ----------------------------- -# Stage runner -# ----------------------------- -def run_v4(use_bias=False, seed=7, T=360, Lexp=2): - rng = np.random.default_rng(seed) - aa_sequence = ["A", "F", "G"] - codons = ["GCA", "UUU", "GGG"] # initial - logits = {i: np.zeros(len(AA_TO_CODONS[aa_sequence[i]])) for i in range(3)} - theta = np.array([2.25, -2.05], dtype=float) - - history = { - "phi": [], - "theta": [], - "translated": [], - "pause": [], - "contact": [], - "free_energy": [], - "delay_bias": [], - "codon_bias": [], - "visible": [], - "policy": {0: [], 1: [], 2: []}, - "gates": [], - } - - translated_count = 1 - phi_prev, *_ = phi_cds_v4(theta, codons, translated_count, use_bias=use_bias, Lexp=Lexp) - - cycles_per_codon = T // 3 - for t in range(T): - translated_count = min(3, 1 + t // cycles_per_codon) - active_pos = translated_count - 1 - aa = aa_sequence[active_pos] - choices = AA_TO_CODONS[aa] - probs = softmax(logits[active_pos]) - chosen_idx = rng.choice(len(choices), p=probs) - codons[active_pos] = choices[chosen_idx] - - score, gates, pause, delay_bias, codon_bias, contact, free_energy = phi_cds_v4( - theta, codons, translated_count, use_bias=use_bias, Lexp=Lexp - ) - - grads = peptide_expert_gradients(theta) - drift = np.einsum("k,kj->j", gates, grads) - - # active codon controls effective dwell time - dt_eff = dt / TRANSLATION_SPEED[codons[active_pos]] - theta = wrap_angle( - theta - dt_eff * drift + np.sqrt(dt_eff) * noise_scale * rng.normal(size=2) - ) - - phi_new, *_ = phi_cds_v4(theta, codons, translated_count, use_bias=use_bias, Lexp=Lexp) - reward = phi_new - phi_prev - - # synonymous RL update at active position - usefulness = np.array([phi_codon(c) - np.mean([phi_codon(x) for x in choices]) for c in choices]) - logits[active_pos] = logits[active_pos] + alpha_rl * reward * usefulness - - history["phi"].append(phi_new) - history["theta"].append(theta.copy()) - history["translated"].append(translated_count) - history["pause"].append(pause) - history["contact"].append(contact) - history["free_energy"].append(free_energy) - history["delay_bias"].append(delay_bias.copy()) - history["codon_bias"].append(codon_bias.copy()) - history["visible"].append(tuple(visible_prefix(codons, translated_count))) - history["gates"].append(gates.copy()) - for i in range(3): - history["policy"][i].append(softmax(logits[i])) - - phi_prev = phi_new - - for k, v in history.items(): - if k in ("policy", "visible"): - continue - history[k] = np.array(v) - - history["final_codons"] = tuple(codons) - history["final_phi"] = float(history["phi"][-1]) - history["best_phi"] = float(np.max(history["phi"])) - - return history - -def main(): - base = run_v4(use_bias=False, seed=7) - abl = run_v4(use_bias=True, seed=7) - - out_dir = Path(".") - - fig1, axes = plt.subplots(3, 1, figsize=(10, 10), sharex=True) - axes[0].plot(base["phi"], label="Base v4") - axes[0].plot(abl["phi"], label="Bias ablation v4") - axes[0].set_ylabel("Phi_CDS") - axes[0].set_title("V4 cotranslational score") - axes[0].legend() - - axes[1].plot(base["translated"], label="Translated count") - axes[1].set_ylabel("Visible codons") - axes[1].legend() - - axes[2].plot(base["contact"], label="Base contact") - axes[2].plot(abl["contact"], label="Bias contact") - axes[2].set_ylabel("Contact") - axes[2].set_xlabel("Step") - axes[2].legend() - - fig1.tight_layout() - - fig2, axes = plt.subplots(2, 1, figsize=(10, 7), sharex=True) - axes[0].plot(base["pause"], label="Pause intensity") - axes[0].plot(base["free_energy"], label="Free energy") - axes[0].set_title("Pause and free energy") - axes[0].legend() - - axes[1].plot(base["delay_bias"][:,0], label="helix delay bias") - axes[1].plot(base["delay_bias"][:,1], label="sheet delay bias") - axes[1].plot(base["delay_bias"][:,2], label="loop delay bias") - axes[1].plot(abl["codon_bias"][:,0], "--", label="helix codon bias") - axes[1].plot(abl["codon_bias"][:,1], "--", label="sheet codon bias") - axes[1].plot(abl["codon_bias"][:,2], "--", label="loop codon bias") - axes[1].set_title("Delay bias vs transient codon bias") - axes[1].set_xlabel("Step") - axes[1].legend(ncol=2) - - fig2.tight_layout() - - fig3 = plt.figure(figsize=(8, 6)) - ax = fig3.add_subplot(1, 1, 1) - ax.plot(base["theta"][:,0], base["theta"][:,1], label="Base") - ax.plot(abl["theta"][:,0], abl["theta"][:,1], label="Bias ablation") - ax.scatter([mu_helix[0], mu_sheet[0], mu_loop[0]], - [mu_helix[1], mu_sheet[1], mu_loop[1]], marker="x", s=100) - ax.set_title(r"V4 trajectory in $(\phi,\psi)$ space") - ax.set_xlabel(r"$\phi$") - ax.set_ylabel(r"$\psi$") - ax.legend() - fig3.tight_layout() - - fig1.savefig(out_dir / "codon_peptide_v4_score.png", dpi=180, bbox_inches="tight") - fig2.savefig(out_dir / "codon_peptide_v4_bias_and_pause.png", dpi=180, bbox_inches="tight") - fig3.savefig(out_dir / "codon_peptide_v4_trajectory.png", dpi=180, bbox_inches="tight") - plt.close(fig1); plt.close(fig2); plt.close(fig3) - - summary = f"""V4 cotranslational codon->peptide summary -Base model final codons: {base['final_codons']} -Base final Phi_CDS: {base['final_phi']:.6f} -Base best Phi_CDS: {base['best_phi']:.6f} - -Bias ablation final codons: {abl['final_codons']} -Bias final Phi_CDS: {abl['final_phi']:.6f} -Bias best Phi_CDS: {abl['best_phi']:.6f} - -Delta final score (bias - base): {abl['final_phi'] - base['final_phi']:.6f} -Delta best score (bias - base): {abl['best_phi'] - base['best_phi']:.6f} -""" - (out_dir / "codon_peptide_v4_summary.txt").write_text(summary) - print(summary) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/combined_resource_layers.py b/5-Applications/scripts/combined_resource_layers.py deleted file mode 100644 index 3e6b3c4b..00000000 --- a/5-Applications/scripts/combined_resource_layers.py +++ /dev/null @@ -1,300 +0,0 @@ -#!/usr/bin/env python3 -""" -combined_resource_layers.py — Combined Base + Topological Resource Report - -Combines physical/base layer resources with special/topological layers: -- Physical: CPU, RAM, Storage, GPU (36 cores, 72GB, 2.4TB, 1 GPU) -- Topological: Manifold state, BIND compression, semantic space, Triumvirate -- Compression: L3 rules, hyperbolic encoding, experience compression -""" - -import json -import math -from pathlib import Path -from dataclasses import dataclass -from typing import Dict, Any - -# Import infrastructure -import sys -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -from ene_cloud_credential_manager import ENETopologicalStorage - - -@dataclass -class PhysicalLayer: - """Physical/base layer resources (substrate).""" - cpu_cores: int - memory_gb: float - storage_gb: float - gpu_count: int - nodes: int - bandwidth_mbps: float - - -@dataclass -class TopologicalLayer: - """Topological/special layer (compressed state).""" - # BIND L3 compression - bind_compression_ratio: float # Typically 1.6x (from ExperienceCompression) - l3_rule_count: int - - # Semantic space (hyperbolic manifold) - semantic_dimensions: int # Q16_16 encoding - semantic_vectors: int - - # Triumvirate state - builder_state_slots: int - warden_proof_capacity: int - judge_adjudication_queue: int - - # Manifold topology - manifold_dimensions: int - curvature_points: int - binding_coefficient: float - - # ENE mesh state - ene_nodes: int - gossip_backlog: int - credential_fragments: int - consensus_votes: int - - # Compression totals - effective_memory_gb: float # Physical * compression - effective_state_capacity: float # Conceptual state size - - -class CombinedResourceCalculator: - """Calculate combined base + topological resources.""" - - def __init__(self): - self.physical = self._calculate_physical() - self.topological = self._calculate_topological() - - def _calculate_physical(self) -> PhysicalLayer: - """Calculate physical layer from deployed mesh.""" - # From deploy_ene_full_mesh.py results - return PhysicalLayer( - cpu_cores=36, # 16+8+4+2+4+2 - memory_gb=72.0, # 32+16+8+4+8+4 - storage_gb=2400.0, # 1000+500+200+100+500+100 - gpu_count=1, # qfox only - nodes=6, - bandwidth_mbps=5000.0 # Aggregate across mesh - ) - - def _calculate_topological(self) -> TopologicalLayer: - """Calculate topological layer (compressed state).""" - physical = self.physical - - # BIND L3 compression (from ExperienceCompression.lean) - # L3 rules achieve ~1.6x compression ratio - bind_ratio = 1.6 - l3_rules = 1024 # Typical L3 rule set size - - # Semantic space (Q16_16 encoding, from Semantics modules) - # Each semantic vector is 65536-dimensional in fixed-point - semantic_dims = 7 # ρ, v, τ, σ, q, κ, ε (from Experience.lean) - semantic_vecs = physical.nodes * 1000 # ~1000 vectors per node - - # Triumvirate state (from GenomicCompression.lean) - # Builder: manifold_reg slots - # Warden: stark_trace proof capacity - # Judge: heatsink_halt queue - builder_slots = physical.cpu_cores * 4 # 4 states per core - warden_capacity = 1000 # Proof validation capacity - judge_queue = 256 # Adjudication backlog - - # Manifold topology (from ManifoldFlow, NonEuclideanGeometry) - manifold_dims = 4 # 4D manifold (from VoxelEncoding) - curvature_pts = 10000 # Discrete curvature samples - binding_coef = 0.95 # High binding coefficient - - # ENE mesh state - ene_nodes = physical.nodes - gossip_backlog = ene_nodes * 100 # ~100 messages per node - cred_fragments = ene_nodes # One fragment per node (Shamir) - consensus_votes = 100 # Active consensus proposals - - # Calculate effective capacity - # Physical memory * BIND compression = effective state capacity - effective_mem = physical.memory_gb * bind_ratio - - # Conceptual state size (semantic vectors * dimensions) - effective_state = (semantic_vecs * semantic_dims * 8) / (1024**3) # GB - - return TopologicalLayer( - bind_compression_ratio=bind_ratio, - l3_rule_count=l3_rules, - semantic_dimensions=semantic_dims, - semantic_vectors=semantic_vecs, - builder_state_slots=builder_slots, - warden_proof_capacity=warden_capacity, - judge_adjudication_queue=judge_queue, - manifold_dimensions=manifold_dims, - curvature_points=curvature_pts, - binding_coefficient=binding_coef, - ene_nodes=ene_nodes, - gossip_backlog=gossip_backlog, - credential_fragments=cred_fragments, - consensus_votes=consensus_votes, - effective_memory_gb=effective_mem, - effective_state_capacity=effective_state - ) - - def calculate_combined_resources(self) -> Dict[str, Any]: - """Calculate total combined resources.""" - phys = self.physical - topo = self.topological - - # Combined compute (physical + parallel topological threads) - total_compute_units = phys.cpu_cores + (topo.builder_state_slots // 4) - - # Combined memory (physical + effective compressed state) - total_memory_gb = phys.memory_gb + topo.effective_memory_gb - - # Combined storage (physical + semantic space) - total_storage_gb = phys.storage_gb + topo.effective_state_capacity - - # Combined state capacity (conceptual) - # This is the theoretical maximum state the system can hold - # considering compression and topological encoding - theoretical_state_capacity = ( - phys.memory_gb * topo.bind_compression_ratio * - (topo.semantic_vectors / 1000) * # Scale factor - topo.binding_coefficient - ) - - return { - "physical_layer": { - "cpu_cores": phys.cpu_cores, - "memory_gb": phys.memory_gb, - "storage_gb": phys.storage_gb, - "gpu_count": phys.gpu_count, - "nodes": phys.nodes, - "bandwidth_mbps": phys.bandwidth_mbps - }, - "topological_layer": { - "bind_compression_ratio": topo.bind_compression_ratio, - "l3_rules": topo.l3_rule_count, - "semantic_dimensions": topo.semantic_dimensions, - "semantic_vectors": topo.semantic_vectors, - "manifold_dimensions": topo.manifold_dimensions, - "curvature_points": topo.curvature_points, - "binding_coefficient": topo.binding_coefficient, - "triumvirate": { - "builder_slots": topo.builder_state_slots, - "warden_capacity": topo.warden_proof_capacity, - "judge_queue": topo.judge_adjudication_queue - }, - "ene_mesh": { - "nodes": topo.ene_nodes, - "gossip_backlog": topo.gossip_backlog, - "credential_fragments": topo.credential_fragments, - "consensus_votes": topo.consensus_votes - } - }, - "combined_totals": { - "total_compute_units": total_compute_units, - "total_memory_gb": round(total_memory_gb, 1), - "total_storage_gb": round(total_storage_gb, 1), - "effective_state_capacity_gb": round(theoretical_state_capacity, 1), - "total_nodes": phys.nodes, - "compression_multiplier": topo.bind_compression_ratio, - "theoretical_expansion_factor": round( - theoretical_state_capacity / phys.memory_gb, 2 - ) - } - } - - def print_resource_report(self): - """Print formatted resource report.""" - resources = self.calculate_combined_resources() - - print("=" * 70) - print("COMBINED RESOURCE LAYERS REPORT") - print("Base (Physical) + Special (Topological) Layers") - print("=" * 70) - - # Physical Layer - print("\n📦 PHYSICAL LAYER (Substrate)") - print("-" * 40) - phys = resources["physical_layer"] - print(f" CPU Cores: {phys['cpu_cores']}") - print(f" Memory: {phys['memory_gb']:.1f} GB") - print(f" Storage: {phys['storage_gb']:.1f} GB") - print(f" GPUs: {phys['gpu_count']}") - print(f" Nodes: {phys['nodes']}") - print(f" Bandwidth: {phys['bandwidth_mbps']:.0f} Mbps") - - # Topological Layer - print("\n🌀 TOPOLOGICAL LAYER (Compressed State)") - print("-" * 40) - topo = resources["topological_layer"] - print(f" BIND Compression: {topo['bind_compression_ratio']}x") - print(f" L3 Rules: {topo['l3_rules']}") - print(f" Semantic Dims: {topo['semantic_dimensions']}") - print(f" Semantic Vectors: {topo['semantic_vectors']:,}") - print(f" Manifold Dims: {topo['manifold_dimensions']}D") - print(f" Curvature Points: {topo['curvature_points']:,}") - print(f" Binding Coeff: {topo['binding_coefficient']}") - - # Triumvirate - print("\n⚖️ TRIUMVIRATE STATE") - print("-" * 40) - tri = topo["triumvirate"] - print(f" Builder Slots: {tri['builder_slots']}") - print(f" Warden Capacity: {tri['warden_capacity']}") - print(f" Judge Queue: {tri['judge_queue']}") - - # ENE Mesh - print("\n🔗 ENE MESH STATE") - print("-" * 40) - ene = topo["ene_mesh"] - print(f" Nodes: {ene['nodes']}") - print(f" Gossip Backlog: {ene['gossip_backlog']}") - print(f" Cred Fragments: {ene['credential_fragments']}") - print(f" Consensus Votes: {ene['consensus_votes']}") - - # Combined Totals - print("\n🌐 COMBINED TOTALS") - print("=" * 40) - total = resources["combined_totals"] - print(f" Compute Units: {total['total_compute_units']}") - print(f" Total Memory: {total['total_memory_gb']:.1f} GB") - print(f" Total Storage: {total['total_storage_gb']:.1f} GB") - print(f" Effective State: {total['effective_state_capacity_gb']:.1f} GB") - print(f" Expansion Factor: {total['theoretical_expansion_factor']}x") - print(f" Total Nodes: {total['total_nodes']}") - - # Conceptual capacity - print("\n💡 CONCEPTUAL CAPACITY") - print("-" * 40) - raw_state = topo['semantic_vectors'] * topo['semantic_dimensions'] - print(f" Raw State Units: {raw_state:,}") - print(f" Per-Node Average: {raw_state / phys['nodes']:,.0f} units") - print(f" With Compression: {raw_state * topo['bind_compression_ratio']:,.0f} units") - - print("\n" + "=" * 70) - - return resources - - -def main(): - """Generate combined resource report.""" - calc = CombinedResourceCalculator() - resources = calc.print_resource_report() - - # Save to file - output_path = Path("/home/allaun/Documents/Research Stack/data/combined_resource_layers.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(resources, f, indent=2) - - print(f"Report saved: {output_path}") - - return resources - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/comprehensive_deep_dive.py b/5-Applications/scripts/comprehensive_deep_dive.py deleted file mode 100644 index f8adca14..00000000 --- a/5-Applications/scripts/comprehensive_deep_dive.py +++ /dev/null @@ -1,375 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive Codebase Deep Dive -Applies all optimization techniques to everything in the codebase. -""" - -import json -import subprocess -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -LEAN_SEMANTICS_DIR = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics") -LEANGPT_BOOTSTRAP = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/LeanGPT/bootstrap_results.json") -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ComprehensiveDeepDive: - """Comprehensive deep dive into entire codebase.""" - - def __init__(self): - self.techniques = { - "leangpt_analysis": "Algorithm bootstrapping and proof generation", - "gpu_acceleration": "GPU-accelerated shortcuts for manual work", - "fpga_optimization": "FPGA footprint reduction via system topology", - "usb_fpga_scan": "USB-attached FPGA detection and optimization", - "physical_topology": "Complete physical topology (capsacitors, wires, voltage)", - "morphic_core": "Capacitor-based temporary morphic cores", - "hdmi_computational": "HDMI controller video fakeout for computation" - } - - # Load LeanGPT bootstrap results - if LEANGPT_BOOTSTRAP.exists(): - with open(LEANGPT_BOOTSTRAP, 'r') as f: - self.bootstrap_data = json.load(f) - else: - self.bootstrap_data = {} - - def count_total_modules(self) -> Dict: - """Count total modules in codebase.""" - print("Counting total modules in codebase...") - - # Count Lean modules - lean_modules = list(LEAN_SEMANTICS_DIR.glob("**/*.lean")) - - # Count Python modules - python_modules = list(Path("/home/allaun/Documents/Research Stack/scripts").glob("**/*.py")) - - # Count Rust modules - rust_modules = list(Path("/home/allaun/Documents/Research Stack/tools/rust").glob("**/*.rs")) - - # Count Verilog modules - verilog_modules = list(Path("/home/allaun/Documents/Research Stack/hardware").glob("**/*.v")) - - total = { - "lean_modules": len(lean_modules), - "python_modules": len(python_modules), - "rust_modules": len(rust_modules), - "verilog_modules": len(verilog_modules), - "total_modules": len(lean_modules) + len(python_modules) + len(rust_modules) + len(verilog_modules) - } - - return total - - def apply_all_techniques_to_module(self, module_path: str, module_type: str) -> Dict: - """Apply all optimization techniques to a single module.""" - optimizations = { - "module_path": module_path, - "module_type": module_type, - "leangpt_analysis": { - "complexity": self.analyze_complexity(module_path), - "proof_coverage": self.estimate_proof_coverage(module_path), - "suggestions": self.generate_suggestions(module_path) - }, - "gpu_acceleration": { - "gpu_feasible": self.assess_gpu_feasibility(module_path), - "speedup": self.estimate_gpu_speedup(module_path), - "power_saving": self.estimate_power_saving(module_path) - }, - "fpga_optimization": { - "fpga_feasible": self.assess_fpga_feasibility(module_path), - "resource_reduction": self.estimate_fpga_reduction(module_path), - "offload_targets": self.suggest_offload_targets(module_path) - }, - "physical_topology": { - "capacitor_optimization": self.optimize_capacitors(module_path), - "wire_optimization": self.optimize_wires(module_path), - "voltage_optimization": self.optimize_voltage(module_path) - }, - "morphic_core": { - "morphic_feasible": self.assess_morphic_feasibility(module_path), - "morphic_mode": self.suggest_morphic_mode(module_path) - }, - "hdmi_computational": { - "hdmi_feasible": self.assess_hdmi_feasibility(module_path), - "encoding_mode": self.suggest_hdmi_encoding(module_path) - } - } - - return optimizations - - def analyze_complexity(self, module_path: str) -> str: - """Analyze algorithm complexity.""" - # Simplified complexity estimation - if "compute" in module_path.lower() or "calc" in module_path.lower(): - return "O(n)" - elif "search" in module_path.lower() or "find" in module_path.lower(): - return "O(n log n)" - elif "nested" in module_path.lower() or "double" in module_path.lower(): - return "O(n²)" - else: - return "O(1)" - - def estimate_proof_coverage(self, module_path: str) -> float: - """Estimate proof coverage.""" - # Simplified estimation - if ".lean" in module_path: - return 0.25 # 25% average from LeanGPT analysis - else: - return 0.0 - - def generate_suggestions(self, module_path: str) -> List[str]: - """Generate improvement suggestions.""" - suggestions = [] - - if ".lean" in module_path: - suggestions.append("Add formal proof of correctness") - suggestions.append("Add eval statement for testing") - suggestions.append("Add docstring") - - if ".py" in module_path: - suggestions.append("Add type hints") - suggestions.append("Add docstring") - suggestions.append("Optimize for GPU acceleration") - - if ".rs" in module_path: - suggestions.append("Add documentation") - suggestions.append("Optimize for FPGA") - - if ".v" in module_path: - suggestions.append("Optimize for RTL ASIC") - suggestions.append("Add testbench") - - return suggestions - - def assess_gpu_feasibility(self, module_path: str) -> bool: - """Assess GPU acceleration feasibility.""" - # Simplified assessment - gpu_feasible_patterns = ["compute", "matrix", "tensor", "array", "vector", "neural", "network"] - return any(pattern in module_path.lower() for pattern in gpu_feasible_patterns) - - def estimate_gpu_speedup(self, module_path: str) -> str: - """Estimate GPU speedup.""" - if "matrix" in module_path.lower() or "tensor" in module_path.lower(): - return "1000x" - elif "compute" in module_path.lower() or "calc" in module_path.lower(): - return "100x" - elif "search" in module_path.lower() or "find" in module_path.lower(): - return "10x" - else: - return "5x" - - def estimate_power_saving(self, module_path: str) -> str: - """Estimate power saving from GPU acceleration.""" - return "50%" - - def assess_fpga_feasibility(self, module_path: str) -> bool: - """Assess FPGA optimization feasibility.""" - # Simplified assessment - fpga_feasible_patterns = ["driver", "hardware", "interface", "protocol", "signal"] - return any(pattern in module_path.lower() for pattern in fpga_feasible_patterns) - - def estimate_fpga_reduction(self, module_path: str) -> str: - """Estimate FPGA resource reduction.""" - if "driver" in module_path.lower(): - return "80%" - elif "interface" in module_path.lower(): - return "60%" - else: - return "40%" - - def suggest_offload_targets(self, module_path: str) -> List[str]: - """Suggest offload targets for FPGA logic.""" - targets = [] - - if "compute" in module_path.lower() or "calc" in module_path.lower(): - targets.append("RTL ASIC") - - if "control" in module_path.lower() or "manage" in module_path.lower(): - targets.append("CPU") - - if "parallel" in module_path.lower() or "matrix" in module_path.lower(): - targets.append("GPU") - - if "storage" in module_path.lower() or "log" in module_path.lower(): - targets.append("SSD") - - return targets - - def optimize_capacitors(self, module_path: str) -> Dict: - """Optimize capacitors for module.""" - return { - "capacitor_reduction": "50%", - "capacitance_reduction": "40%", - "board_space_reduction": "50%", - "morphic_core_feasible": "HIGH" if "compute" in module_path.lower() else "LOW" - } - - def optimize_wires(self, module_path: str) -> Dict: - """Optimize wires/traces for module.""" - return { - "trace_length_reduction": "40%", - "trace_width_reduction": "25%", - "impedance_control": "Improved" - } - - def optimize_voltage(self, module_path: str) -> Dict: - """Optimize voltage/power for module.""" - return { - "regulator_efficiency": "92-95%", - "power_dissipation_reduction": "47%", - "voltage_regulation": "±2%" - } - - def assess_morphic_feasibility(self, module_path: str) -> bool: - """Assess morphic core feasibility.""" - # Morphic cores work well for analog computation - morphic_feasible_patterns = ["compute", "signal", "analog", "filter", "neural"] - return any(pattern in module_path.lower() for pattern in morphic_feasible_patterns) - - def suggest_morphic_mode(self, module_path: str) -> str: - """Suggest morphic core mode.""" - if "neural" in module_path.lower() or "network" in module_path.lower(): - return "analog_computation" - elif "memory" in module_path.lower() or "store" in module_path.lower(): - return "analog_memory" - elif "filter" in module_path.lower() or "signal" in module_path.lower(): - return "resonant_computation" - else: - return "energy_storage" - - def assess_hdmi_feasibility(self, module_path: str) -> bool: - """Assess HDMI computational shell feasibility.""" - # HDMI works well for visual/parallel computation - hdmi_feasible_patterns = ["video", "visual", "image", "render", "display", "matrix", "tensor"] - return any(pattern in module_path.lower() for pattern in hdmi_feasible_patterns) - - def suggest_hdmi_encoding(self, module_path: str) -> str: - """Suggest HDMI encoding mode.""" - if "neural" in module_path.lower() or "network" in module_path.lower(): - return "soliton_field_computation" - elif "matrix" in module_path.lower() or "tensor" in module_path.lower(): - return "matrix_multiplication" - else: - return "neural_network_inference" - - def run_comprehensive_analysis(self) -> Dict: - """Run comprehensive deep dive analysis.""" - print("=" * 60) - print("COMPREHENSIVE CODEBASE DEEP DIVE") - print("=" * 60) - - # Step 1: Count total modules - print("\n[1/8] Counting total modules...") - module_counts = self.count_total_modules() - print(f" Total modules: {module_counts['total_modules']}") - print(f" Lean: {module_counts['lean_modules']}") - print(f" Python: {module_counts['python_modules']}") - print(f" Rust: {module_counts['rust_modules']}") - print(f" Verilog: {module_counts['verilog_modules']}") - - # Step 2: Apply all techniques to sample modules - print("[2/8] Applying all techniques to sample modules...") - sample_modules = [ - (str(LEAN_SEMANTICS_DIR / "Semantics/FixedPoint.lean"), "lean"), - (str(LEAN_SEMANTICS_DIR / "Semantics/BitcoinMetaprobe.lean"), "lean"), - (str(LEAN_SEMANTICS_DIR / "Semantics/ASICTopology.lean"), "lean"), - ("/home/allaun/Documents/Research Stack/scripts/gpu_q16_verification.py", "python"), - ("/home/allaun/Documents/Research Stack/hardware/nii_surface_driver.v", "verilog") - ] - - optimized_modules = [] - for module_path, module_type in sample_modules: - if Path(module_path).exists(): - optimizations = self.apply_all_techniques_to_module(module_path, module_type) - optimized_modules.append(optimizations) - print(f" Analyzed: {Path(module_path).name}") - - # Step 3: Aggregate results - print("[3/8] Aggregating results...") - aggregated = { - "total_modules_analyzed": len(optimized_modules), - "leangpt_suggestions": sum(len(opt["leangpt_analysis"]["suggestions"]) for opt in optimized_modules), - "gpu_feasible_modules": sum(1 for opt in optimized_modules if opt["gpu_acceleration"]["gpu_feasible"]), - "fpga_feasible_modules": sum(1 for opt in optimized_modules if opt["fpga_optimization"]["fpga_feasible"]), - "morphic_feasible_modules": sum(1 for opt in optimized_modules if opt["morphic_core"]["morphic_feasible"]), - "hdmi_feasible_modules": sum(1 for opt in optimized_modules if opt["hdmi_computational"]["hdmi_feasible"]) - } - print(f" LeanGPT suggestions: {aggregated['leangpt_suggestions']}") - print(f" GPU feasible: {aggregated['gpu_feasible_modules']}") - print(f" FPGA feasible: {aggregated['fpga_feasible_modules']}") - print(f" Morphic feasible: {aggregated['morphic_feasible_modules']}") - print(f" HDMI feasible: {aggregated['hdmi_feasible_modules']}") - - # Step 4: Estimate total optimization potential - print("[4/8] Estimating total optimization potential...") - total_optimization = { - "leangpt_proof_coverage": f"{module_counts['lean_modules'] * 0.25:.0f}/{module_counts['lean_modules']} modules", - "gpu_acceleration": f"{module_counts['total_modules'] * 0.5:.0f} modules (50% estimated)", - "fpga_optimization": f"{module_counts['verilog_modules']} modules (100%)", - "physical_topology": "All hardware components", - "morphic_cores": f"{module_counts['total_modules'] * 0.3:.0f} modules (30% estimated)", - "hdmi_computational": f"{module_counts['total_modules'] * 0.2:.0f} modules (20% estimated)" - } - print(f" LeanGPT: {total_optimization['leangpt_proof_coverage']}") - print(f" GPU: {total_optimization['gpu_acceleration']}") - print(f" FPGA: {total_optimization['fpga_optimization']}") - print(f" Physical: {total_optimization['physical_topology']}") - print(f" Morphic: {total_optimization['morphic_cores']}") - print(f" HDMI: {total_optimization['hdmi_computational']}") - - # Step 5: Calculate total savings - print("[5/8] Calculating total savings...") - total_savings = { - "power_saving": "77% (physical topology) + GPU acceleration", - "cost_saving": "50% (capacitors) + FPGA elimination", - "time_saving": "156-235 hours (GPU shortcuts) + automation", - "performance_improvement": "100-10000x (GPU/HDMI acceleration)" - } - print(f" Power: {total_savings['power_saving']}") - print(f" Cost: {total_savings['cost_saving']}") - print(f" Time: {total_savings['time_saving']}") - print(f" Performance: {total_savings['performance_improvement']}") - - # Step 6: Generate comprehensive report - print("[6/8] Generating comprehensive report...") - - # Step 7: Save results - print("[7/8] Saving results...") - - # Step 8: Complete - print("[8/8] Deep dive complete...") - - print("\n" + "=" * 60) - print("COMPREHENSIVE DEEP DIVE COMPLETE") - print("=" * 60) - - return { - "module_counts": module_counts, - "optimized_modules": optimized_modules, - "aggregated_results": aggregated, - "total_optimization": total_optimization, - "total_savings": total_savings - } - -if __name__ == '__main__': - deep_dive = ComprehensiveDeepDive() - results = deep_dive.run_comprehensive_analysis() - - # Save results - output_file = OUTPUT_DIR / "comprehensive_deep_dive.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nDeep dive results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DEEP DIVE SUMMARY") - print("=" * 60) - print(f"Total Modules: {results['module_counts']['total_modules']}") - print(f"LeanGPT Suggestions: {results['aggregated_results']['leangpt_suggestions']}") - print(f"GPU Feasible: {results['aggregated_results']['gpu_feasible_modules']}") - print(f"FPGA Feasible: {results['aggregated_results']['fpga_feasible_modules']}") - print(f"Morphic Feasible: {results['aggregated_results']['morphic_feasible_modules']}") - print(f"HDMI Feasible: {results['aggregated_results']['hdmi_feasible_modules']}") diff --git a/5-Applications/scripts/comprehensive_math_database_integration.py b/5-Applications/scripts/comprehensive_math_database_integration.py deleted file mode 100644 index 89857462..00000000 --- a/5-Applications/scripts/comprehensive_math_database_integration.py +++ /dev/null @@ -1,316 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive Math Database Integration -Accounts for all additional devices and the entire math database for computational expansion. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ComprehensiveMathDatabaseIntegration: - """Integrates entire math database with all devices for comprehensive computational expansion.""" - - def __init__(self): - # Original 19 analyzed devices - self.original_devices = { - "fpga": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "usb_fpga": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "physical_topology": "Physical Topology (capacitors, wires, USB, voltage)", - "morphic_core": "Morphic Core (capacitors as morphic devices)", - "hdmi_computational_shell": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "tdms_controller": "TDMS Controller (HDMI 2.1)", - "displayport_controller": "DisplayPort Controller (DP 1.4a)", - "displayport_line_morphic": "DisplayPort Line Morphic (copper conductors)", - "usb_controllers": "USB Controllers (4 xHCI controllers)", - "efi_controller": "EFI Controller (1D OSIC scalar)", - "pcie_controller": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "ram_controller": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "pwm_controller": "PWM Controller (Pulse Width Modulation)", - "motherboard": "Motherboard (travel paths, IRQ controller, data fabric)", - "power_supply": "Power Supply and Power Caps", - "dma_ram_morphic": "DMA-RAM Morphic Device", - "inflight_ram": "In-Flight RAM (In-Memory Computation / PIM)", - "monitor_timing": "Monitor Timing Computation (EDID, capabilities, settings)", - "ddci_timing": "DDC/CI Timing Computation (capabilities, brightness, volume)" - } - - # Additional devices from research documents - self.additional_devices = { - "amd_gpu": "AMD GPU (Granite Ridge/Radeon Graphics)", - "wifi_controller": "WiFi Controller (MediaTek MT7925 WiFi 7)", - "bluetooth_controller": "Bluetooth Controller (Realtek RTL8723B Bluetooth 5.4)", - "ethernet_controller": "Ethernet Controller (Realtek RTL8126 2.5GbE)", - "ssd_controller": "SSD Controller (Phison PS5018-E18 PCIe 4.0 NVMe)", - "audio_controller": "Audio Controller (AMD Ryzen HD Audio, Realtek ALC1220)", - "sata_controller": "SATA Controller (AMD SATA)", - "nvme_controller": "NVMe Controller (Phison PS5018-E18)", - "memory_controller_ddr5": "DDR5 Memory Controller (Integrated DDR5)", - "irq_controller": "IRQ Controller (System interrupt management)", - "data_fabric": "Data Fabric (AMD Data Fabric)", - "network_node_qfox": "Network Node (qfox - primary node)", - "network_node_architect": "Network Node (architect - compute node)", - "gpu_resource_manager": "GPU Resource Manager (CUDA/Tensor cores)", - "distributed_training": "Distributed Training System", - "video_physics": "Video Physics (120Hz sync, HDMI residual)", - "mereotopological_video": "Mereotopological Video (hybrid video state)", - "swarm_genome": "Swarm Genome (6 bins × 3 bits = 18 bits)" - } - - # Math database categories (from SOVEREIGN_MATH_MODEL_DATABASE.jsonl) - self.math_database_categories = { - "General Semantics": "VideoPhysics, DistributedTraining, GPUResourceManager, Swarm, Adaptation", - "Video Physics & Mereotopology": "MereotopologicalVideo, VideoState, HybridVideoState", - "Geometry": "Riemannian, Geodesic, Connection Coefficients", - "Thermodynamic": "Entropy, Energy, Landauer Limit, Carnot Efficiency", - "Information Theory": "Shannon Entropy, Mutual Information, Compression", - "Cognitive/Routing": "Load Combination, Intrinsic/Total Ratio", - "Control Theory": "Pressure Dynamics, Canal Deformation, Stress Law", - "Physical Bind": "Hardware interaction, device control", - "Geometric Bind": "Spatial computation, topology", - "Informational Bind": "Data compression, signal processing" - } - - # Math database statistics (from SOVEREIGN_MATH_MODEL_DATABASE.jsonl) - self.math_database_stats = { - "total_entries": 543591, - "categories": len(self.math_database_categories), - "types": ["structure", "inductive", "def", "theorem", "axiom"], - "source_files": "Multiple Lean files in 0-Core-Formalism/lean/Semantics/Semantics/" - } - - def compile_all_devices(self) -> Dict: - """Compile all devices (original 19 + additional 17).""" - all_devices = {} - all_devices.update(self.original_devices) - all_devices.update(self.additional_devices) - - compilation = { - "total_devices": len(all_devices), - "original_devices": len(self.original_devices), - "additional_devices": len(self.additional_devices), - "devices": all_devices, - "device_categories": { - "compute_devices": ["fpga", "usb_fpga", "inflight_ram", "gpu_resource_manager"], - "display_devices": ["hdmi_computational_shell", "tdms_controller", "displayport_controller", "displayport_line_morphic", "video_physics", "mereotopological_video"], - "network_devices": ["usb_controllers", "pcie_controller", "wifi_controller", "bluetooth_controller", "ethernet_controller"], - "storage_devices": ["ssd_controller", "nvme_controller", "sata_controller"], - "memory_devices": ["ram_controller", "memory_controller_ddr5", "dma_ram_morphic"], - "system_devices": ["motherboard", "power_supply", "efi_controller", "irq_controller", "data_fabric"], - "distributed_devices": ["network_node_qfox", "network_node_architect", "distributed_training"], - "control_devices": ["pwm_controller", "audio_controller", "swarm_genome"] - } - } - - return compilation - - def map_devices_to_math_database(self) -> Dict: - """Map all devices to math database categories.""" - mapping = { - "compute_devices": { - "devices": ["fpga", "usb_fpga", "inflight_ram", "gpu_resource_manager"], - "math_categories": ["General Semantics", "Geometry", "Thermodynamic", "Control Theory"], - "foundation_kernels": ["F08", "F09", "F10", "F04", "F05", "F06", "F11", "F12"] - }, - "display_devices": { - "devices": ["hdmi_computational_shell", "tdms_controller", "displayport_controller", "displayport_line_morphic", "video_physics", "mereotopological_video"], - "math_categories": ["Video Physics & Mereotopology", "Information Theory", "Geometric Bind"], - "foundation_kernels": ["F01", "F02", "F03", "F08", "F09", "F10"] - }, - "network_devices": { - "devices": ["usb_controllers", "pcie_controller", "wifi_controller", "bluetooth_controller", "ethernet_controller"], - "math_categories": ["General Semantics", "Control Theory", "Physical Bind"], - "foundation_kernels": ["F11", "F12", "F04", "F05", "F06"] - }, - "storage_devices": { - "devices": ["ssd_controller", "nvme_controller", "sata_controller"], - "math_categories": ["Thermodynamic", "Control Theory", "Physical Bind"], - "foundation_kernels": ["F04", "F05", "F06", "F07"] - }, - "memory_devices": { - "devices": ["ram_controller", "memory_controller_ddr5", "dma_ram_morphic"], - "math_categories": ["Thermodynamic", "Information Theory", "Control Theory"], - "foundation_kernels": ["F01", "F02", "F03", "F11", "F12"] - }, - "system_devices": { - "devices": ["motherboard", "power_supply", "efi_controller", "irq_controller", "data_fabric"], - "math_categories": ["Thermodynamic", "Control Theory", "Physical Bind"], - "foundation_kernels": ["F04", "F05", "F06", "F07"] - }, - "distributed_devices": { - "devices": ["network_node_qfox", "network_node_architect", "distributed_training"], - "math_categories": ["General Semantics", "Cognitive/Routing", "Control Theory"], - "foundation_kernels": ["F11", "F12"] - }, - "control_devices": { - "devices": ["pwm_controller", "audio_controller", "swarm_genome"], - "math_categories": ["Control Theory", "Information Theory", "Geometric Bind"], - "foundation_kernels": ["F04", "F05", "F06", "F01", "F02", "F03"] - } - } - - return mapping - - def integrate_math_database(self) -> Dict: - """Integrate entire math database with device analysis.""" - integration = { - "math_database_size": self.math_database_stats["total_entries"], - "math_database_categories": self.math_database_stats["categories"], - "device_count": len(self.original_devices) + len(self.additional_devices), - "integration_strategies": [ - { - "strategy": "Math-Device Mapping", - "description": "Map each device to relevant math database categories", - "coverage": "100% (all devices mapped to math categories)" - }, - { - "strategy": "Foundation Kernel Application", - "description": "Apply 12 foundation kernels to all devices", - "coverage": "100% (all devices use foundation kernels)" - }, - { - "strategy": "Genome18 Encoding", - "description": "Encode all devices with 18-bit Genome18 ISA", - "coverage": "262,144 routing states for all devices" - }, - { - "strategy": "Topology Integration", - "description": "Integrate all devices into topology graph", - "coverage": "36 nodes (19 + 17 devices)" - }, - { - "strategy": "Forest Math Compression", - "description": "Apply forest math to shrink device space", - "coverage": "∞ → 262,144 states (finite routing)" - } - ], - "math_device_coverage": { - "General Semantics": "8 devices (compute, distributed, control)", - "Video Physics & Mereotopology": "6 devices (display, video)", - "Geometry": "8 devices (compute, display, control)", - "Thermodynamic": "12 devices (compute, storage, memory, system, control)", - "Information Theory": "10 devices (compute, display, memory, control)", - "Cognitive/Routing": "8 devices (compute, network, distributed)", - "Control Theory": "15 devices (network, storage, memory, system, control)", - "Physical Bind": "12 devices (network, storage, system)", - "Geometric Bind": "10 devices (compute, display, control)", - "Informational Bind": "8 devices (compute, display, memory)" - } - } - - return integration - - def calculate_comprehensive_expansion(self) -> Dict: - """Calculate comprehensive computational expansion with all devices and math database.""" - all_devices_count = len(self.original_devices) + len(self.additional_devices) - - # Base capacity (all devices) - base_capacity = all_devices_count * 50 # Average significance score per device - - # Topology integration - topology_multiplier = 1.5 - - # Parallel expansion (all devices) - parallel_expansion = all_devices_count * 2 - - # Math database integration - math_database_multiplier = 1.5 # Additional 1.5x from math database - - # Forest math compression - forest_math_multiplier = 1.2 # Additional 1.2x from forest math - - # Genome18 encoding - genome18_multiplier = 1.3 # Additional 1.3x from Genome18 - - # Calculate expanded capacity - expanded_capacity = (base_capacity * - topology_multiplier * - parallel_expansion * - math_database_multiplier * - forest_math_multiplier * - genome18_multiplier) - - expansion_factor = expanded_capacity / base_capacity - - calculation = { - "base_capacity": base_capacity, - "topology_multiplier": topology_multiplier, - "parallel_expansion": parallel_expansion, - "math_database_multiplier": math_database_multiplier, - "forest_math_multiplier": forest_math_multiplier, - "genome18_multiplier": genome18_multiplier, - "expanded_capacity": expanded_capacity, - "expansion_factor": expansion_factor, - "total_multiplier": topology_multiplier * parallel_expansion * math_database_multiplier * forest_math_multiplier * genome18_multiplier - } - - return calculation - - def run_analysis(self) -> Dict: - """Run comprehensive math database integration analysis.""" - print("=" * 60) - print("COMPREHENSIVE MATH DATABASE INTEGRATION ANALYSIS") - print("=" * 60) - - # Step 1: Compile all devices - print("\n[1/4] Compiling all devices...") - compilation = self.compile_all_devices() - print(f" Total Devices: {compilation['total_devices']}") - print(f" Original Devices: {compilation['original_devices']}") - print(f" Additional Devices: {compilation['additional_devices']}") - - # Step 2: Map devices to math database - print("[2/4] Mapping devices to math database categories...") - mapping = self.map_devices_to_math_database() - print(f" Device Categories: {len(mapping)}") - for category, details in mapping.items(): - print(f" {category}: {len(details['devices'])} devices, {len(details['math_categories'])} categories") - - # Step 3: Integrate math database - print("[3/4] Integrating entire math database...") - integration = self.integrate_math_database() - print(f" Math Database Size: {integration['math_database_size']} entries") - print(f" Math Database Categories: {integration['math_database_categories']}") - print(f" Integration Strategies: {len(integration['integration_strategies'])}") - - # Step 4: Calculate comprehensive expansion - print("[4/4] Calculating comprehensive computational expansion...") - calculation = self.calculate_comprehensive_expansion() - print(f" Base Capacity: {calculation['base_capacity']}") - print(f" Expanded Capacity: {calculation['expanded_capacity']}") - print(f" Expansion Factor: {calculation['expansion_factor']:.2f}x") - print(f" Total Multiplier: {calculation['total_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("COMPREHENSIVE MATH DATABASE INTEGRATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "device_compilation": compilation, - "math_database_mapping": mapping, - "math_database_integration": integration, - "comprehensive_expansion": calculation - } - -if __name__ == '__main__': - analyzer = ComprehensiveMathDatabaseIntegration() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "comprehensive_math_database_integration.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("COMPREHENSIVE MATH DATABASE INTEGRATION SUMMARY") - print("=" * 60) - print(f"Total Devices: {results['device_compilation']['total_devices']}") - print(f"Math Database Size: {results['math_database_integration']['math_database_size']} entries") - print(f"Expanded Capacity: {results['comprehensive_expansion']['expanded_capacity']}") - print(f"Expansion Factor: {results['comprehensive_expansion']['expansion_factor']:.2f}x") diff --git a/5-Applications/scripts/computational_significance_analysis.py b/5-Applications/scripts/computational_significance_analysis.py deleted file mode 100644 index 7542aec5..00000000 --- a/5-Applications/scripts/computational_significance_analysis.py +++ /dev/null @@ -1,397 +0,0 @@ -#!/usr/bin/env python3 -""" -Computational Significance Analysis for Target Device Selection -Identifies most computationally significant devices for topology integration and expansion. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ComputationalSignificanceAnalysis: - """Analyzes computational significance of devices for targeting and topology expansion.""" - - def __init__(self): - self.devices = { - "fpga": { - "name": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "throughput": "Custom logic (parallel)", - "latency": "ns (hardware)", - "power": "1-5W", - "computational_potential": "VERY HIGH (custom logic, parallelism)", - "topology_value": "HIGH (reconfigurable, geometric)" - }, - "usb_fpga": { - "name": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "throughput": "480 Mbps (USB 2.0)", - "latency": "ns (hardware)", - "power": "1-5W", - "computational_potential": "HIGH (USB bridge, parallelism)", - "topology_value": "MEDIUM (USB interface)" - }, - "physical_topology": { - "name": "Physical Topology (capacitors, wires, USB, voltage)", - "throughput": "Property limited (nH/pF/mΩ)", - "latency": "ns (electrical)", - "power": "1-5W", - "computational_potential": "MEDIUM-HIGH (morphic computation)", - "topology_value": "VERY HIGH (physical substrate)" - }, - "morphic_core": { - "name": "Morphic Core (capacitors as morphic devices)", - "throughput": "Property limited (6-10 bit timing)", - "latency": "0.1-10ms (timing)", - "power": "5-30W", - "computational_potential": "HIGH (morphic computation)", - "topology_value": "HIGH (morphic substrate)" - }, - "hdmi_computational_shell": { - "name": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "throughput": "48 Gbps (HDMI 2.1)", - "latency": "ns (signal)", - "power": "5-30W", - "computational_potential": "VERY HIGH (high bandwidth, novel substrate)", - "topology_value": "VERY HIGH (display interface, high bandwidth)" - }, - "tdms_controller": { - "name": "TDMS Controller (HDMI 2.1)", - "throughput": "48 Gbps", - "latency": "ns (signal)", - "power": "5-30W", - "computational_potential": "VERY HIGH (TMDS lanes, soliton field)", - "topology_value": "VERY HIGH (TMDS encoding)" - }, - "displayport_controller": { - "name": "DisplayPort Controller (DP 1.4a)", - "throughput": "32.4 Gbps (HBR3)", - "latency": "ns (signal)", - "power": "5-30W", - "computational_potential": "VERY HIGH (4 lanes, MST, DSC)", - "topology_value": "VERY HIGH (4-lane parallel)" - }, - "displayport_line_morphic": { - "name": "DisplayPort Line Morphic (copper conductors)", - "throughput": "Property limited (nH/pF/mΩ)", - "latency": "ns (electrical)", - "power": "1-5W", - "computational_potential": "MEDIUM-HIGH (electrical properties)", - "topology_value": "MEDIUM (copper lines)" - }, - "usb_controllers": { - "name": "USB Controllers (4 xHCI controllers)", - "throughput": "10-20 Gbps (USB 3.x)", - "latency": "μs (USB protocol)", - "power": "5-15W", - "computational_potential": "HIGH (high bandwidth, multiple controllers)", - "topology_value": "HIGH (USB interface)" - }, - "efi_controller": { - "name": "EFI Controller (1D OSIC scalar)", - "throughput": "Scalar limited", - "latency": "μs (firmware)", - "power": "1-5W", - "computational_potential": "MEDIUM (scalar computation)", - "topology_value": "LOW (firmware interface)" - }, - "pcie_controller": { - "name": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "throughput": "256 Gbps (16 lanes)", - "latency": "ns (PCIe)", - "power": "10-30W", - "computational_potential": "VERY HIGH (highest bandwidth)", - "topology_value": "VERY HIGH (PCIe backbone)" - }, - "ram_controller": { - "name": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "throughput": "50-100 GB/s (DDR5)", - "latency": "10-100ns (memory)", - "power": "10-20W", - "computational_potential": "HIGH (memory bandwidth)", - "topology_value": "HIGH (memory fabric)" - }, - "pwm_controller": { - "name": "PWM Controller (Pulse Width Modulation)", - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "1us-1s (PWM)", - "power": "1-10W", - "computational_potential": "MEDIUM-HIGH (time-based computation)", - "topology_value": "MEDIUM (time-based)" - }, - "motherboard": { - "name": "Motherboard (travel paths, IRQ controller, data fabric)", - "throughput": "System limited", - "latency": "ns (hardware)", - "power": "50-100W", - "computational_potential": "HIGH (system integration)", - "topology_value": "VERY HIGH (system backbone)" - }, - "power_supply": { - "name": "Power Supply and Power Caps", - "throughput": "Property limited (capacitance)", - "latency": "ns (electrical)", - "power": "5-30W", - "computational_potential": "HIGH (energy harvesting)", - "topology_value": "HIGH (power infrastructure)" - }, - "dma_ram_morphic": { - "name": "DMA-RAM Morphic Device", - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (DMA)", - "power": "10-20W", - "computational_potential": "HIGH (DMA + morphic)", - "topology_value": "HIGH (DMA bridge)" - }, - "inflight_ram": { - "name": "In-Flight RAM (In-Memory Computation / PIM)", - "throughput": "100-200 GB/s (parallel banks)", - "latency": "10-100ns (in-flight)", - "power": "10-30W", - "computational_potential": "VERY HIGH (PIM, parallelism)", - "topology_value": "VERY HIGH (in-memory compute)" - }, - "monitor_timing": { - "name": "Monitor Timing Computation (EDID, capabilities, settings)", - "throughput": "20-500 ops/sec", - "latency": "0.1-10ms (timing)", - "power": "1-5W", - "computational_potential": "MEDIUM (timing-based)", - "topology_value": "LOW (monitor interface)" - }, - "ddci_timing": { - "name": "DDC/CI Timing Computation (capabilities, brightness, volume)", - "throughput": "20-500 ops/sec", - "latency": "0.1-10ms (timing)", - "power": "1-5W", - "computational_potential": "MEDIUM (timing-based)", - "topology_value": "LOW (monitor interface)" - } - } - - def rank_computational_significance(self) -> Dict: - """Rank devices by computational significance.""" - ranking = {} - - for device_id, device_info in self.devices.items(): - # Calculate significance score - throughput_score = 0 - if "VERY HIGH" in device_info["computational_potential"]: - throughput_score = 100 - elif "HIGH" in device_info["computational_potential"]: - throughput_score = 75 - elif "MEDIUM-HIGH" in device_info["computational_potential"]: - throughput_score = 50 - elif "MEDIUM" in device_info["computational_potential"]: - throughput_score = 25 - - topology_score = 0 - if "VERY HIGH" in device_info["topology_value"]: - topology_score = 100 - elif "HIGH" in device_info["topology_value"]: - topology_score = 75 - elif "MEDIUM" in device_info["topology_value"]: - topology_score = 50 - elif "LOW" in device_info["topology_value"]: - topology_score = 25 - - # Parse throughput for numerical score - throughput_str = device_info["throughput"] - if "Gbps" in throughput_str: - if "48" in throughput_str: - throughput_num = 48 - elif "32.4" in throughput_str: - throughput_num = 32.4 - elif "256" in throughput_str: - throughput_num = 256 - elif "10-20" in throughput_str: - throughput_num = 15 - else: - throughput_num = 10 - elif "GB/s" in throughput_str: - if "100-200" in throughput_str: - throughput_num = 150 - elif "50-100" in throughput_str: - throughput_num = 75 - else: - throughput_num = 50 - else: - throughput_num = 1 - - # Calculate total significance score - significance_score = (throughput_score * 0.4) + (topology_score * 0.4) + (throughput_num * 0.2) - - ranking[device_id] = { - "name": device_info["name"], - "throughput_score": throughput_score, - "topology_score": topology_score, - "throughput_num": throughput_num, - "significance_score": significance_score, - "computational_potential": device_info["computational_potential"], - "topology_value": device_info["topology_value"] - } - - # Sort by significance score - sorted_ranking = dict(sorted(ranking.items(), key=lambda x: x[1]["significance_score"], reverse=True)) - - return sorted_ranking - - def select_target_devices(self, ranking: Dict, top_n: int = 8) -> Dict: - """Select top N most computationally significant devices.""" - target_devices = {} - - for i, (device_id, device_info) in enumerate(list(ranking.items())[:top_n]): - target_devices[device_id] = device_info - - return target_devices - - def add_to_topology(self, target_devices: Dict) -> Dict: - """Add target devices to topology.""" - topology = { - "topology_nodes": len(target_devices), - "topology_edges": [], - "device_connections": {}, - "topology_graph": {} - } - - device_ids = list(target_devices.keys()) - - # Create connections between devices - for i, device_id_1 in enumerate(device_ids): - connections = [] - for j, device_id_2 in enumerate(device_ids): - if i != j: - # Calculate connection weight based on significance scores - weight = (target_devices[device_id_1]["significance_score"] + - target_devices[device_id_2]["significance_score"]) / 2 - connections.append({ - "target": device_id_2, - "weight": weight - }) - topology["topology_edges"].append({ - "source": device_id_1, - "target": device_id_2, - "weight": weight - }) - - topology["device_connections"][device_id_1] = connections - topology["topology_graph"][device_id_1] = { - "name": target_devices[device_id_1]["name"], - "significance_score": target_devices[device_id_1]["significance_score"], - "connections": connections - } - - return topology - - def leverage_for_expansion(self, target_devices: Dict, topology: Dict) -> Dict: - """Leverage topology for computational expansion.""" - expansion = { - "base_computational_capacity": sum(d["significance_score"] for d in target_devices.values()), - "topology_multiplier": 1.5, # Topology integration provides 1.5x multiplier - "parallel_expansion": len(target_devices) * 2, # Parallel expansion factor - "expanded_capacity": 0, - "expansion_strategies": [] - } - - # Calculate expanded capacity - base_capacity = expansion["base_computational_capacity"] - topology_multiplier = expansion["topology_multiplier"] - parallel_expansion = expansion["parallel_expansion"] - - expanded_capacity = base_capacity * topology_multiplier * parallel_expansion - expansion["expanded_capacity"] = expanded_capacity - - # Define expansion strategies - expansion["expansion_strategies"] = [ - { - "strategy": "Parallel Device Orchestration", - "description": "Run computations in parallel across all target devices", - "expansion_factor": len(target_devices), - "expected_gain": f"{len(target_devices)}x parallelism" - }, - { - "strategy": "Topology-Based Routing", - "description": "Use topology graph for optimal data routing between devices", - "expansion_factor": topology_multiplier, - "expected_gain": f"{topology_multiplier}x routing efficiency" - }, - { - "strategy": "Cross-Device Coupling", - "description": "Enable cross-device computation and data sharing", - "expansion_factor": 2.0, - "expected_gain": "2x cross-device efficiency" - }, - { - "strategy": "Hierarchical Computation", - "description": "Use device hierarchy for distributed computation", - "expansion_factor": 1.5, - "expected_gain": "1.5x hierarchical efficiency" - } - ] - - return expansion - - def run_analysis(self) -> Dict: - """Run computational significance analysis.""" - print("=" * 60) - print("COMPUTATIONAL SIGNIFICANCE ANALYSIS") - print("=" * 60) - - # Step 1: Rank computational significance - print("\n[1/4] Ranking devices by computational significance...") - ranking = self.rank_computational_significance() - print(f" Total Devices: {len(ranking)}") - for i, (device_id, device_info) in enumerate(list(ranking.items())[:10]): - print(f" {i+1}. {device_id}: {device_info['significance_score']:.2f} ({device_info['computational_potential']})") - - # Step 2: Select target devices - print("[2/4] Selecting top target devices...") - target_devices = self.select_target_devices(ranking, top_n=8) - print(f" Target Devices: {len(target_devices)}") - for device_id, device_info in target_devices.items(): - print(f" {device_id}: {device_info['significance_score']:.2f} ({device_info['computational_potential']})") - - # Step 3: Add to topology - print("[3/4] Adding target devices to topology...") - topology = self.add_to_topology(target_devices) - print(f" Topology Nodes: {topology['topology_nodes']}") - print(f" Topology Edges: {len(topology['topology_edges'])}") - - # Step 4: Leverage for expansion - print("[4/4] Leveraging topology for computational expansion...") - expansion = self.leverage_for_expansion(target_devices, topology) - print(f" Base Capacity: {expansion['base_computational_capacity']:.2f}") - print(f" Expanded Capacity: {expansion['expanded_capacity']:.2f}") - print(f" Expansion Factor: {expansion['expanded_capacity'] / expansion['base_computational_capacity']:.2f}x") - - print("\n" + "=" * 60) - print("COMPUTATIONAL SIGNIFICANCE ANALYSIS COMPLETE") - print("=" * 60) - - return { - "device_ranking": ranking, - "target_devices": target_devices, - "topology": topology, - "expansion": expansion - } - -if __name__ == '__main__': - analyzer = ComputationalSignificanceAnalysis() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "computational_significance_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("COMPUTATIONAL SIGNIFICANCE SUMMARY") - print("=" * 60) - print(f"Target Devices: {len(results['target_devices'])}") - print(f"Topology Nodes: {results['topology']['topology_nodes']}") - print(f"Expanded Capacity: {results['expansion']['expanded_capacity']:.2f}") - print(f"Expansion Factor: {results['expansion']['expanded_capacity'] / results['expansion']['base_computational_capacity']:.2f}x") diff --git a/5-Applications/scripts/compute_distance_matrix.py b/5-Applications/scripts/compute_distance_matrix.py deleted file mode 100644 index 52987d46..00000000 --- a/5-Applications/scripts/compute_distance_matrix.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/bin/env python3 -import json -import numpy as np -import pandas as pd -from pathlib import Path -from scipy.spatial.distance import cosine - -# Paths -BASE_PATH = Path('/home/allaun/Documents/Research Stack') -EQUATION_FOREST_PATH = BASE_PATH / 'shared-data/data/equations_forest.jsonl' -DISTANCE_MATRIX_PATH = BASE_PATH / 'shared-data/data/equation_distance_matrix.csv' - -# Weights -WEIGHTS = { - 'kernel': 0.35, - 'street': 0.20, - 'bridge': 0.20, - 'typing': 0.10, - 'failure': 0.10, - 'numeric': 0.05 -} - -def compute_distance(node1, node2): - """Compute the weighted distance between two nodes.""" - - # 1. Kernel Distance (Cosine) - v1 = np.array(node1['foundation_vector']) - v2 = np.array(node2['foundation_vector']) - if np.all(v1 == 0) and np.all(v2 == 0): - d_kernel = 0.0 - elif np.all(v1 == 0) or np.all(v2 == 0): - d_kernel = 1.0 - else: - # Cosine distance returns 1 - cosine_similarity - try: - d_kernel = cosine(v1, v2) - if np.isnan(d_kernel): d_kernel = 1.0 - except: - d_kernel = 1.0 - - # 2. Street Distance (Layer) - d_street = 0.0 if node1['layer'] == node2['layer'] else 1.0 - - # 3. Bridge/Shape Distance - if node1['shape_uuid'] == node2['shape_uuid']: - d_bridge = 0.0 - elif node1['bind_class'] == node2['bind_class'] and node1['bind_class']: - d_bridge = 0.5 - else: - d_bridge = 1.0 - - # 4. Typing Distance - d_typing = 0.0 if node1['typed_status'] == node2['typed_status'] else 1.0 - - # 5. Failure Distance (Stubbed to 0 for now as data is sparse) - d_failure = 0.0 - - # 6. Numeric Distance (Genome18 Address) - addr1 = node1.get('genome18_address', 0) - addr2 = node2.get('genome18_address', 0) - # Normalized by max 18-bit address space - d_numeric = abs(addr1 - addr2) / 262144.0 - - # Weighted Sum - total_dist = ( - WEIGHTS['kernel'] * d_kernel + - WEIGHTS['street'] * d_street + - WEIGHTS['bridge'] * d_bridge + - WEIGHTS['typing'] * d_typing + - WEIGHTS['failure'] * d_failure + - WEIGHTS['numeric'] * d_numeric - ) - - return total_dist - -def main(): - if not EQUATION_FOREST_PATH.exists(): - print(f"Error: {EQUATION_FOREST_PATH} not found.") - return - - nodes = [] - with open(EQUATION_FOREST_PATH, 'r') as f: - for line in f: - if line.strip(): - nodes.append(json.loads(line)) - - n = len(nodes) - print(f"Computing distance matrix for {n} nodes...") - - # Initialize matrix - matrix = np.zeros((n, n)) - - # Compute pairwise distances (optimized for symmetry) - for i in range(n): - if i % 100 == 0: - print(f"Progress: {i}/{n} nodes...") - for j in range(i + 1, n): - dist = compute_distance(nodes[i], nodes[j]) - matrix[i, j] = dist - matrix[j, i] = dist - - # Create DataFrame for CSV export - names = [node['model_name'] for node in nodes] - df = pd.DataFrame(matrix, index=names, columns=names) - - # Save to CSV - df.to_csv(DISTANCE_MATRIX_PATH) - print(f"Successfully saved distance matrix to {DISTANCE_MATRIX_PATH}") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/configure_distributed_training.py b/5-Applications/scripts/configure_distributed_training.py deleted file mode 100644 index ee5d3464..00000000 --- a/5-Applications/scripts/configure_distributed_training.py +++ /dev/null @@ -1,317 +0,0 @@ -#!/usr/bin/env python3 -""" -Configure Distributed Training Across Network Nodes - -This script guarantees that training will use all resources on the NETWORK, -not just the local machine. It leverages: -- ENE (Endless Node Edges) for distributed coordination -- 6-node Tailscale mesh (36 cores, 72GB RAM, 1 GPU) -- Google Drive topological storage for data distribution -- Swarm topology optimizer for resource allocation -""" - -import sys -import json -from pathlib import Path -from datetime import datetime - -# Network node configuration -NETWORK_NODES = { - "qfox": { - "hostname": "qfox", - "tailscale_ip": "100.x.x.x", - "cores": 16, - "ram_gb": 32, - "gpu": True, - "role": "primary", - "storage_gb": 800 - }, - "architect": { - "hostname": "architect", - "tailscale_ip": "100.x.x.x", - "cores": 8, - "ram_gb": 16, - "gpu": False, - "role": "compute", - "storage_gb": 500 - }, - "judge": { - "hostname": "judge", - "tailscale_ip": "100.x.x.x", - "cores": 4, - "ram_gb": 8, - "gpu": False, - "role": "compute", - "storage_gb": 200 - }, - "ip-172-31-25-81": { - "hostname": "ip-172-31-25-81", - "tailscale_ip": "100.x.x.x", - "cores": 2, - "ram_gb": 4, - "gpu": False, - "role": "compute", - "storage_gb": 100 - }, - "netcup-router": { - "hostname": "netcup-router", - "tailscale_ip": "100.x.x.x", - "cores": 4, - "ram_gb": 8, - "gpu": False, - "role": "compute", - "storage_gb": 100 - }, - "racknerd-510bd9c": { - "hostname": "racknerd-510bd9c", - "tailscale_ip": "100.x.x.x", - "cores": 2, - "ram_gb": 4, - "gpu": False, - "role": "compute", - "storage_gb": 100 - } -} - -def calculate_network_resources(): - """Calculate total network resources.""" - total_cores = sum(node["cores"] for node in NETWORK_NODES.values()) - total_ram = sum(node["ram_gb"] for node in NETWORK_NODES.values()) - total_storage = sum(node["storage_gb"] for node in NETWORK_NODES.values()) - gpu_nodes = sum(1 for node in NETWORK_NODES.values() if node["gpu"]) - - return { - "total_cores": total_cores, - "total_ram_gb": total_ram, - "total_storage_gb": total_storage, - "gpu_nodes": gpu_nodes, - "total_nodes": len(NETWORK_NODES) - } - -def generate_distributed_training_config(): - """Generate distributed training configuration.""" - print("=" * 70) - print("CONFIGURING DISTRIBUTED TRAINING ACROSS NETWORK NODES") - print("=" * 70) - - resources = calculate_network_resources() - - print(f"\nNetwork Resources:") - print(f" Total Nodes: {resources['total_nodes']}") - print(f" Total Cores: {resources['total_cores']}") - print(f" Total RAM: {resources['total_ram_gb']} GB") - print(f" Total Storage: {resources['total_storage_gb']} GB") - print(f" GPU Nodes: {resources['gpu_nodes']}") - - config = { - "timestamp": datetime.now().isoformat(), - "network_topology": NETWORK_NODES, - "total_resources": resources, - "training_configuration": { - "distribution_strategy": "data_parallel", - "coordination_protocol": "ene_gossip", - "storage_backend": "google_drive_topological", - "resource_allocation": "swarm_topology_optimizer", - "data_sharding": "automatic", - "fault_tolerance": True, - "load_balancing": "health_weighted" - }, - "node_assignments": {}, - "data_distribution": { - "natural_language_dataset": { - "file": "training_dataset_20260423_121149.parquet", - "size_mb": 137.38, - "records": 65318, - "shards": resources["total_nodes"], - "shard_size_records": 65318 // resources["total_nodes"] - }, - "coding_language_dataset": { - "file": "coding_training_dataset_20260423_122513.parquet", - "size_mb": 11.09, - "records": 2776, - "shards": resources["total_nodes"], - "shard_size_records": 2776 // resources["total_nodes"] - } - }, - "training_pipeline": { - "phase_1": { - "name": "Data Distribution", - "action": "Distribute parquet shards to all nodes via Google Drive", - "nodes": "all", - "parallel": True - }, - "phase_2": { - "name": "Distributed Training", - "action": "Train n-semantic morphic cores using all network resources", - "nodes": "all", - "parallel": True, - "coordination": "ENE gossip protocol" - }, - "phase_3": { - "name": "Model Aggregation", - "action": "Aggregate trained models from all nodes", - "nodes": "qfox (primary)", - "parallel": False - }, - "phase_4": { - "name": "Validation", - "action": "Validate aggregated model across network", - "nodes": "all", - "parallel": True - } - }, - "guarantees": { - "network_utilization": "100% of all network nodes will be utilized", - "resource_utilization": f"All {resources['total_cores']} cores and {resources['total_ram_gb']} GB RAM will be used", - "fault_tolerance": "Training continues even if individual nodes fail", - "load_balancing": "Automatic health-weighted load distribution via ENE", - "data_availability": "Google Drive topological storage ensures data accessibility from any node", - "coordination": "ENE gossip protocol maintains network state and coordination" - } - } - - # Calculate node assignments based on resources - total_cores = resources["total_cores"] - for node_name, node_info in NETWORK_NODES.items(): - weight = node_info["cores"] / total_cores - config["node_assignments"][node_name] = { - "weight": weight, - "cores_allocated": node_info["cores"], - "ram_allocated_gb": node_info["ram_gb"], - "gpu_available": node_info["gpu"], - "natural_language_shard_size": int(65318 * weight), - "coding_language_shard_size": int(2776 * weight) - } - - return config - -def save_distributed_config(config): - """Save distributed training configuration.""" - output_dir = Path("/home/allaun/Documents/Research Stack/data/training_data") - output_dir.mkdir(parents=True, exist_ok=True) - - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - output_file = output_dir / f"distributed_training_config_{timestamp}.json" - - with open(output_file, 'w') as f: - json.dump(config, f, indent=2) - - print(f"\n✅ Distributed training configuration saved to: {output_file}") - - return output_file - -def generate_network_utilization_script(config): - """Generate script to verify network utilization.""" - nodes_list = json.dumps(list(NETWORK_NODES.keys())) - resources_dict = json.dumps(config["total_resources"]) - - script_content = f"""#!/usr/bin/env python3 -\"\"\" -Network Utilization Verification Script - -This script verifies that training is using all network resources. -\"\"\" - -import subprocess -import json -from pathlib import Path - -def check_node_connectivity(): - \"\"\"Check connectivity to all network nodes via Tailscale.\"\"\" - nodes = {nodes_list} - - print("Checking node connectivity...") - for node in nodes: - try: - result = subprocess.run( - ["ping", "-c", "1", node], - capture_output=True, - timeout=5 - ) - status = "✅ ONLINE" if result.returncode == 0 else "❌ OFFLINE" - print(f" {{node}}: {{status}}") - except Exception as e: - print(f" {{node}}: ❌ ERROR - {{e}}") - -def check_ene_status(): - \"\"\"Check ENE status across network.\"\"\" - print("\\\\nChecking ENE status...") - # ENE gossip protocol ensures all nodes are synchronized - print(" ENE gossip protocol: ACTIVE") - print(" Credential distribution: SHAMIR (6 shards)") - print(" Load balancing: HEALTH-WEIGHTED") - print(" Consensus required: 2/3 majority") - -def check_data_availability(): - \"\"\"Check data availability via Google Drive topological storage.\"\"\" - print("\\\\nChecking data availability...") - print(" Google Drive topological storage: ACCESSIBLE") - print(" Natural language dataset: training_dataset_*.parquet") - print(" Coding language dataset: coding_training_dataset_*.parquet") - -def check_resource_allocation(): - \"\"\"Check resource allocation across network.\"\"\" - print("\\\\nChecking resource allocation...") - resources = {resources_dict} - print(f" Total cores allocated: {{resources['total_cores']}}") - print(f" Total RAM allocated: {{resources['total_ram_gb']}} GB") - print(f" Total nodes utilized: {{resources['total_nodes']}}") - print(f" GPU nodes utilized: {{resources['gpu_nodes']}}") - -def main(): - print("=" * 70) - print("NETWORK UTILIZATION VERIFICATION") - print("=" * 70) - - check_node_connectivity() - check_ene_status() - check_data_availability() - check_resource_allocation() - - print("\\\\n" + "=" * 70) - print("VERIFICATION COMPLETE") - print("All network resources are guaranteed to be utilized") - print("=" * 70) - -if __name__ == "__main__": - main() -""" - - output_dir = Path("/home/allaun/Documents/Research Stack/scripts") - output_file = output_dir / "verify_network_utilization.py" - - with open(output_file, 'w') as f: - f.write(script_content) - - print(f"✅ Network utilization verification script saved to: {output_file}") - - return output_file - -def main(): - # Generate distributed training configuration - config = generate_distributed_training_config() - - # Save configuration - config_file = save_distributed_config(config) - - # Generate verification script - script_file = generate_network_utilization_script(config) - - print("\n" + "=" * 70) - print("DISTRIBUTED TRAINING CONFIGURATION COMPLETE") - print("=" * 70) - - print("\nGuarantees:") - print(" ✅ All 6 network nodes will be utilized") - print(" ✅ All 36 cores will be used for training") - print(" ✅ All 72GB RAM will be utilized") - print(" ✅ GPU on qfox will be leveraged") - print(" ✅ ENE provides distributed coordination") - print(" ✅ Google Drive provides data accessibility") - print(" ✅ Swarm topology optimizer handles resource allocation") - print(" ✅ Fault tolerance ensures training continuity") - - return 0 - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/scripts/connectome_lut_shim.py b/5-Applications/scripts/connectome_lut_shim.py deleted file mode 100644 index 07917b6d..00000000 --- a/5-Applications/scripts/connectome_lut_shim.py +++ /dev/null @@ -1,1037 +0,0 @@ -#!/usr/bin/env python3 -""" -connectome_lut_shim.py — Python shim for parallel biophysical LUT evaluation. - -Loads OpenWorm C. elegans connectome data from Parquet, quantizes each dataset -into the 18-bit address space defined in CooperativeLUT.lean (6D × 8 bins = 262,144 -entries), precomputes the biophysical constraint surface, and performs parallel -lawful-state lookups for mutation proposals. - -Branch prediction is treated as a SIMD interface: each misprediction is a coarse- -grain stochastic computation that shrinks possibility space. - -Now supports 8-way and 16-way speculative bundles, plus BTB pattern detection -with streak-based short-circuiting. - -Per AGENTS.md §6.1: This is a shim. All logic lives in Lean. -This file only: JSON serialization, Parquet I/O, LUT precomputation, -parallel lookup, quantum walk simulation, Verilog generation, and result wrapping. -""" - -import json -import sys -from pathlib import Path -from dataclasses import dataclass, asdict -from typing import List, Dict, Optional, Tuple -from concurrent.futures import ThreadPoolExecutor - -import pandas as pd - -# ═══════════════════════════════════════════════════════════════════════════ -# §0 Q16_16 utilities (mirror Lean FixedPoint.lean) -# ═══════════════════════════════════════════════════════════════════════════ - -Q16_ONE = 0x00010000 - - -def q16_to_float(q: int) -> float: - """Convert Q16.16 UInt32 to float.""" - if q >= 0x80000000: - return (q - 0x100000000) / 65536.0 - return q / 65536.0 - - -def q16_mul(a: int, b: int) -> int: - return ((a * b) >> 16) & 0xFFFFFFFF - - -def q16_div(a: int, b: int) -> int: - if b == 0: - return 0xFFFFFFFF - return ((a << 16) // b) & 0xFFFFFFFF - - -def q16_sub(a: int, b: int) -> int: - return ((a - b) & 0xFFFFFFFF) - - -def q16_le(a: int, b: int) -> bool: - a_s = a if a < 0x80000000 else a - 0x100000000 - b_s = b if b < 0x80000000 else b - 0x100000000 - return a_s <= b_s - - -def q16_ge(a: int, b: int) -> bool: - a_s = a if a < 0x80000000 else a - 0x100000000 - b_s = b if b < 0x80000000 else b - 0x100000000 - return a_s >= b_s - - -def q16_lt(a: int, b: int) -> bool: - a_s = a if a < 0x80000000 else a - 0x100000000 - b_s = b if b < 0x80000000 else b - 0x100000000 - return a_s < b_s - - -# ═══════════════════════════════════════════════════════════════════════════ -# §1 Biophysical constants (mirror CooperativeLUT.lean) -# ═══════════════════════════════════════════════════════════════════════════ - -DRAKE_CONSTANT = 0x000000C5 -DRIFT_BARRIER_CONSTANT = 0x00000042 -U_BASE = 0x00000041 -NE_BASE = 0x00008000 -SIGMA_BASE = 0x00004000 -CONNECTANCE_BASE = 0x00002000 -MODULARITY_BASE = 0x00002000 - -ADDR_SPACE = 262144 -STREAK_THRESHOLD = 4 - - -# ═══════════════════════════════════════════════════════════════════════════ -# §2 Quantized genome encoding (6D × 8 bins = 18 bits) -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass(frozen=True) -class QuantizedGenome: - g_bin: int - ne_bin: int - u_bin: int - sigma_bin: int - connectance_bin: int - modularity_bin: int - - def to_address(self) -> int: - return ( - self.g_bin * 32768 + - self.ne_bin * 4096 + - self.u_bin * 512 + - self.sigma_bin * 64 + - self.connectance_bin * 8 + - self.modularity_bin - ) - - @staticmethod - def from_address(addr: int) -> "QuantizedGenome": - return QuantizedGenome( - g_bin=addr // 32768, - ne_bin=(addr // 4096) % 8, - u_bin=(addr // 512) % 8, - sigma_bin=(addr // 64) % 8, - connectance_bin=(addr // 8) % 8, - modularity_bin=addr % 8, - ) - - -@dataclass(frozen=True) -class ConstraintEntry: - lawful: bool - cost: int - drake_ok: bool - drift_ok: bool - error_ok: bool - - -def compute_constraint_entry(q: QuantizedGenome) -> ConstraintEntry: - """Mirror of CooperativeLUT.computeConstraintEntry. - Connectance tightens Drake budget; modularity relaxes drift barrier.""" - u_q = U_BASE * (q.u_bin + 1) - ne_q = NE_BASE * (q.ne_bin + 1) - sigma_q = Q16_ONE + SIGMA_BASE * (q.sigma_bin + 1) - connectance_factor = CONNECTANCE_BASE * (q.connectance_bin + 1) - modularity_factor = MODULARITY_BASE * (q.modularity_bin + 1) - - adjusted_drake = q16_div(DRAKE_CONSTANT, connectance_factor) - drake_ok = q16_le(u_q, adjusted_drake) - - adjusted_drift = q16_div(DRIFT_BARRIER_CONSTANT, modularity_factor) - un_product = q16_mul(u_q, ne_q) - drift_ok = q16_ge(un_product, adjusted_drift) - - ln_sigma = q16_sub(sigma_q, Q16_ONE) - error_ok = q16_lt(u_q, ln_sigma) - - cost = 0 - if not drake_ok: - cost += q16_sub(u_q, adjusted_drake) & 0xFFFFFFFF - if not drift_ok: - cost += q16_sub(adjusted_drift, un_product) & 0xFFFFFFFF - if not error_ok: - cost += 0x00FF0000 - - return ConstraintEntry( - lawful=drake_ok and drift_ok and error_ok, - cost=cost, - drake_ok=drake_ok, - drift_ok=drift_ok, - error_ok=error_ok, - ) - - -# ═══════════════════════════════════════════════════════════════════════════ -# §3 Precomputed biophysical LUT (262,144 entries) -# ═══════════════════════════════════════════════════════════════════════════ - -class BiophysicalLUT: - """Precomputed 262,144-entry constraint surface. - In hardware, this is a BRAM block. In Python, a list.""" - - def __init__(self): - print("[INFO] Precomputing biophysical LUT (262,144 entries)...") - self.entries = [ - compute_constraint_entry(QuantizedGenome.from_address(addr)) - for addr in range(ADDR_SPACE) - ] - lawful_count = sum(1 for e in self.entries if e.lawful) - print(f"[INFO] LUT ready: {lawful_count} lawful states ({100*lawful_count/ADDR_SPACE:.1f}%)") - - def lookup(self, addr: int) -> ConstraintEntry: - if not (0 <= addr < ADDR_SPACE): - return ConstraintEntry(lawful=False, cost=0xFFFFFFFF, - drake_ok=False, drift_ok=False, error_ok=False) - return self.entries[addr] - - -# Global singleton (precomputed once) -BIOPHYSICAL_LUT = BiophysicalLUT() - - -# ═══════════════════════════════════════════════════════════════════════════ -# §4 Connectome state quantization from OpenWorm Parquet -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class ConnectomeState: - dataset_name: str - edge_count: int - cell_count: int - quantized: QuantizedGenome - - def to_address(self) -> int: - return self.quantized.to_address() - - def lookup(self) -> ConstraintEntry: - return BIOPHYSICAL_LUT.lookup(self.to_address()) - - -def quantize_edge_count(n_edges: int) -> int: - return max(0, min(7, (n_edges // 1000) - 1)) - - -def quantize_ne(ne: float) -> int: - return max(0, min(7, int(ne / 0.5) - 1)) - - -def quantize_u(u: float) -> int: - return max(0, min(7, int(u / 0.001) - 1)) - - -def quantize_sigma(sigma: float) -> int: - return max(0, min(7, int((sigma - 1.0) / 0.25) - 1)) - - -def quantize_connectance(n_edges: int, n_cells: int) -> int: - """Edge density: E / (N*(N-1)) for directed graphs, binned 0-7.""" - if n_cells <= 1: - return 0 - max_edges = n_cells * (n_cells - 1) - density = n_edges / max_edges - return max(0, min(7, int(density * 8))) - - -def quantize_modularity(_n_edges: int, _n_cells: int) -> int: - """Placeholder: modularity would require community detection. - For now, default to middle bin (3).""" - return 3 - - -def load_openworm_dataset(parquet_dir: Path, dataset_name: str) -> Optional[ConnectomeState]: - conn_file = parquet_dir / f"{dataset_name}_connections.parquet" - if not conn_file.exists(): - return None - - df = pd.read_parquet(conn_file) - n_edges = len(df) - - ne_default = 2.5 - u_default = 0.003 - sigma_default = 1.5 - - cells_file = parquet_dir / f"{dataset_name}_cells.parquet" - n_cells = 0 - if cells_file.exists(): - cells_df = pd.read_parquet(cells_file) - n_cells = len(cells_df) - - q = QuantizedGenome( - g_bin=quantize_edge_count(n_edges), - ne_bin=quantize_ne(ne_default), - u_bin=quantize_u(u_default), - sigma_bin=quantize_sigma(sigma_default), - connectance_bin=quantize_connectance(n_edges, n_cells), - modularity_bin=quantize_modularity(n_edges, n_cells), - ) - - return ConnectomeState( - dataset_name=dataset_name, - edge_count=n_edges, - cell_count=n_cells, - quantized=q, - ) - - -def load_all_openworm_states(parquet_dir: Path) -> List[ConnectomeState]: - conn_files = sorted(parquet_dir.glob("*_connections.parquet")) - states = [] - for conn_file in conn_files: - name = conn_file.stem.replace("_connections", "") - state = load_openworm_dataset(parquet_dir, name) - if state: - states.append(state) - return states - - -# ═══════════════════════════════════════════════════════════════════════════ -# §5 Quantum Walk: Stochastic Traversal via Speculative Evaluation -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class BTBEntry: - source: int - target: int - confidence: int # 0-3 - streak: int = 0 # consecutive correct predictions - - -class BranchTargetBuffer: - """Simple BTB with up to 16 entries and streak tracking.""" - - def __init__(self): - self.entries: Dict[int, BTBEntry] = {} - - def lookup(self, addr: int) -> Optional[BTBEntry]: - return self.entries.get(addr) - - def update_hit(self, addr: int): - if addr in self.entries: - e = self.entries[addr] - e.confidence = min(3, e.confidence + 1) - e.streak = e.streak + 1 - - def update_miss(self, source: int, target: int): - if source not in self.entries: - if len(self.entries) >= 16: - # Evict lowest-confidence entry - min_addr = min(self.entries, key=lambda k: self.entries[k].confidence) - del self.entries[min_addr] - self.entries[source] = BTBEntry(source=source, target=target, confidence=1, streak=0) - - def is_stable(self, addr: int) -> bool: - e = self.entries.get(addr) - return e is not None and e.streak >= STREAK_THRESHOLD - - -@dataclass -class SpeculativeBundle: - primary: int - alt1: int - alt2: int - alt3: int - mask1: bool = True - mask2: bool = True - mask3: bool = True - - -@dataclass -class SpeculativeBundle8: - primary: int - alt1: int - alt2: int - alt3: int - alt4: int - alt5: int - alt6: int - alt7: int - mask1: bool = True - mask2: bool = True - mask3: bool = True - mask4: bool = True - mask5: bool = True - mask6: bool = True - mask7: bool = True - - -@dataclass -class SpeculativeBundle16: - primary: int - alt1: int - alt2: int - alt3: int - alt4: int - alt5: int - alt6: int - alt7: int - alt8: int - alt9: int - alt10: int - alt11: int - alt12: int - alt13: int - alt14: int - alt15: int - mask1: bool = True - mask2: bool = True - mask3: bool = True - mask4: bool = True - mask5: bool = True - mask6: bool = True - mask7: bool = True - mask8: bool = True - mask9: bool = True - mask10: bool = True - mask11: bool = True - mask12: bool = True - mask13: bool = True - mask14: bool = True - mask15: bool = True - - -def evaluate_bundle(bundle: SpeculativeBundle) -> List[Tuple[int, ConstraintEntry]]: - candidates = [ - (True, bundle.primary), - (bundle.mask1, bundle.alt1), - (bundle.mask2, bundle.alt2), - (bundle.mask3, bundle.alt3), - ] - results = [] - for active, addr in candidates: - if active: - entry = BIOPHYSICAL_LUT.lookup(addr) - if entry.lawful: - results.append((addr, entry)) - return results - - -def evaluate_bundle8(bundle: SpeculativeBundle8) -> List[Tuple[int, ConstraintEntry]]: - candidates = [ - (True, bundle.primary), - (bundle.mask1, bundle.alt1), - (bundle.mask2, bundle.alt2), - (bundle.mask3, bundle.alt3), - (bundle.mask4, bundle.alt4), - (bundle.mask5, bundle.alt5), - (bundle.mask6, bundle.alt6), - (bundle.mask7, bundle.alt7), - ] - results = [] - for active, addr in candidates: - if active: - entry = BIOPHYSICAL_LUT.lookup(addr) - if entry.lawful: - results.append((addr, entry)) - return results - - -def evaluate_bundle16(bundle: SpeculativeBundle16) -> List[Tuple[int, ConstraintEntry]]: - candidates = [ - (True, bundle.primary), - (bundle.mask1, bundle.alt1), - (bundle.mask2, bundle.alt2), - (bundle.mask3, bundle.alt3), - (bundle.mask4, bundle.alt4), - (bundle.mask5, bundle.alt5), - (bundle.mask6, bundle.alt6), - (bundle.mask7, bundle.alt7), - (bundle.mask8, bundle.alt8), - (bundle.mask9, bundle.alt9), - (bundle.mask10, bundle.alt10), - (bundle.mask11, bundle.alt11), - (bundle.mask12, bundle.alt12), - (bundle.mask13, bundle.alt13), - (bundle.mask14, bundle.alt14), - (bundle.mask15, bundle.alt15), - ] - results = [] - for active, addr in candidates: - if active: - entry = BIOPHYSICAL_LUT.lookup(addr) - if entry.lawful: - results.append((addr, entry)) - return results - - -def quantum_walk_step(current: int, btb: BranchTargetBuffer) -> Tuple[int, BranchTargetBuffer, ConstraintEntry]: - """One step of the quantum walk (4-way bundle).""" - entry = btb.lookup(current) - if entry: - prediction = entry.target - else: - prediction = (current + 1) % ADDR_SPACE - - bundle = SpeculativeBundle( - primary=prediction, - alt1=(current + 1) % ADDR_SPACE, - alt2=(current + 8) % ADDR_SPACE, - alt3=(current + 64) % ADDR_SPACE, - ) - - results = evaluate_bundle(bundle) - if not results: - btb.update_miss(current, current) - return current, btb, BIOPHYSICAL_LUT.lookup(current) - - best_addr, best_entry = min(results, key=lambda x: x[1].cost) - if best_addr == prediction: - btb.update_hit(current) - else: - btb.update_miss(current, best_addr) - - return best_addr, btb, best_entry - - -def quantum_walk_step8(current: int, btb: BranchTargetBuffer) -> Tuple[int, BranchTargetBuffer, ConstraintEntry]: - """One step of the quantum walk (8-way bundle).""" - entry = btb.lookup(current) - if entry: - prediction = entry.target - else: - prediction = (current + 1) % ADDR_SPACE - - bundle = SpeculativeBundle8( - primary=prediction, - alt1=(current + 1) % ADDR_SPACE, # modularity - alt2=(current + 8) % ADDR_SPACE, # connectance - alt3=(current + 64) % ADDR_SPACE, # sigma - alt4=(current + 512) % ADDR_SPACE, # u - alt5=(current + 4096) % ADDR_SPACE, # Ne - alt6=(current + 32768) % ADDR_SPACE, # g - alt7=(current + 2) % ADDR_SPACE, # fine modularity - ) - - results = evaluate_bundle8(bundle) - if not results: - btb.update_miss(current, current) - return current, btb, BIOPHYSICAL_LUT.lookup(current) - - best_addr, best_entry = min(results, key=lambda x: x[1].cost) - if best_addr == prediction: - btb.update_hit(current) - else: - btb.update_miss(current, best_addr) - - return best_addr, btb, best_entry - - -def quantum_walk_step16(current: int, btb: BranchTargetBuffer) -> Tuple[int, BranchTargetBuffer, ConstraintEntry]: - """One step of the quantum walk (16-way bundle).""" - entry = btb.lookup(current) - if entry: - prediction = entry.target - else: - prediction = (current + 1) % ADDR_SPACE - - bundle = SpeculativeBundle16( - primary=prediction, - alt1=(current + 1) % ADDR_SPACE, - alt2=(current + 2) % ADDR_SPACE, - alt3=(current + 4) % ADDR_SPACE, - alt4=(current + 8) % ADDR_SPACE, - alt5=(current + 16) % ADDR_SPACE, - alt6=(current + 32) % ADDR_SPACE, - alt7=(current + 64) % ADDR_SPACE, - alt8=(current + 128) % ADDR_SPACE, - alt9=(current + 256) % ADDR_SPACE, - alt10=(current + 512) % ADDR_SPACE, - alt11=(current + 1024) % ADDR_SPACE, - alt12=(current + 2048) % ADDR_SPACE, - alt13=(current + 4096) % ADDR_SPACE, - alt14=(current + 8192) % ADDR_SPACE, - alt15=(current + 16384) % ADDR_SPACE, - ) - - results = evaluate_bundle16(bundle) - if not results: - btb.update_miss(current, current) - return current, btb, BIOPHYSICAL_LUT.lookup(current) - - best_addr, best_entry = min(results, key=lambda x: x[1].cost) - if best_addr == prediction: - btb.update_hit(current) - else: - btb.update_miss(current, best_addr) - - return best_addr, btb, best_entry - - -def quantum_walk_step_pattern(current: int, btb: BranchTargetBuffer) -> Tuple[int, BranchTargetBuffer, ConstraintEntry]: - """Pattern-aware quantum walk step with BTB short-circuit. - If the BTB entry has a stable streak (≥ threshold), skip bundle - evaluation and follow the BTB target directly.""" - entry = btb.lookup(current) - if entry and btb.is_stable(current): - # Stable pattern: short-circuit - btb.update_hit(current) - return entry.target, btb, BIOPHYSICAL_LUT.lookup(entry.target) - - # Unstable or no BTB entry: fall back to 8-way speculative evaluation - return quantum_walk_step8(current, btb) - - -def run_quantum_walk(seed_addr: int, steps: int = 100, mode: str = "pattern") -> List[Tuple[int, ConstraintEntry]]: - """Run a quantum walk for N steps from a seed address. - mode: "4way", "8way", "16way", or "pattern" (default).""" - btb = BranchTargetBuffer() - trajectory = [] - current = seed_addr - short_circuits = 0 - - step_fn = { - "4way": quantum_walk_step, - "8way": quantum_walk_step8, - "16way": quantum_walk_step16, - "pattern": quantum_walk_step_pattern, - }.get(mode, quantum_walk_step_pattern) - - for _ in range(steps): - prev = current - current, btb, entry = step_fn(current, btb) - if mode == "pattern" and prev != current and btb.is_stable(prev): - short_circuits += 1 - trajectory.append((current, entry)) - - return trajectory, short_circuits - - -# ═══════════════════════════════════════════════════════════════════════════ -# §6 JSON serialization for swarm consumption -# ═══════════════════════════════════════════════════════════════════════════ - -def state_to_json(state: ConnectomeState) -> dict: - entry = state.lookup() - return { - "dataset": state.dataset_name, - "edge_count": state.edge_count, - "cell_count": state.cell_count, - "address": state.to_address(), - "quantized": { - "g_bin": state.quantized.g_bin, - "ne_bin": state.quantized.ne_bin, - "u_bin": state.quantized.u_bin, - "sigma_bin": state.quantized.sigma_bin, - "connectance_bin": state.quantized.connectance_bin, - "modularity_bin": state.quantized.modularity_bin, - }, - "lawful": entry.lawful, - "cost": entry.cost, - "drake_ok": entry.drake_ok, - "drift_ok": entry.drift_ok, - "error_ok": entry.error_ok, - } - - -def trajectory_to_json(trajectory: List[Tuple[int, ConstraintEntry]]) -> List[dict]: - return [ - { - "step": i, - "address": addr, - "lawful": entry.lawful, - "cost": entry.cost, - "drake_ok": entry.drake_ok, - "drift_ok": entry.drift_ok, - "error_ok": entry.error_ok, - } - for i, (addr, entry) in enumerate(trajectory) - ] - - -# ═══════════════════════════════════════════════════════════════════════════ -# §7 Verilog generation (hardware extraction) -# ═══════════════════════════════════════════════════════════════════════════ - -def generate_verilog_lut(output_path: Path): - """Generate Verilog modules for the biophysical LUT and speculative evaluators. - Includes 4-way, 8-way, and 16-way speculative evaluator modules.""" - lines = [] - lines.append("// Auto-generated from connectome_lut_shim.py") - lines.append("// Biophysical Constraint LUT: 262,144 entries, 18-bit address") - lines.append("// Each entry: {lawful(1), cost(32), drake_ok(1), drift_ok(1), error_ok(1)}") - lines.append("") - lines.append("module BiophysicalLUT (") - lines.append(" input [17:0] addr,") - lines.append(" output lawful,") - lines.append(" output [31:0] cost,") - lines.append(" output drake_ok,") - lines.append(" output drift_ok,") - lines.append(" output error_ok") - lines.append(");") - lines.append("") - lines.append(" // Entry encoding: {cost[31:0], lawful, drake_ok, drift_ok, error_ok}") - lines.append(" // For 262K entries, use external BRAM initialization.") - lines.append(" // This module declares the interface; initialization is via $readmemh.") - lines.append("") - lines.append(" reg [35:0] lut_mem [0:262143]; // 36-bit word: 32b cost + 4b flags") - lines.append("") - lines.append(" initial begin") - lines.append(' $display("Loading biophysical LUT from biophysical_lut.hex...");') - lines.append(' $readmemh("biophysical_lut.hex", lut_mem);') - lines.append(" end") - lines.append("") - lines.append(" wire [35:0] entry = lut_mem[addr];") - lines.append(" assign cost = entry[35:4];") - lines.append(" assign lawful = entry[3];") - lines.append(" assign drake_ok = entry[2];") - lines.append(" assign drift_ok = entry[1];") - lines.append(" assign error_ok = entry[0];") - lines.append("") - lines.append("endmodule") - lines.append("") - - # 4-way evaluator - lines.append("// Speculative evaluation unit: 4-way bundle") - lines.append("module SpeculativeEvaluator (") - lines.append(" input [17:0] primary,") - lines.append(" input [17:0] alt1,") - lines.append(" input [17:0] alt2,") - lines.append(" input [17:0] alt3,") - lines.append(" input mask1,") - lines.append(" input mask2,") - lines.append(" input mask3,") - lines.append(" output [17:0] best_addr,") - lines.append(" output [31:0] best_cost,") - lines.append(" output best_lawful") - lines.append(");") - lines.append("") - lines.append(" wire [31:0] cost_p, cost_1, cost_2, cost_3;") - lines.append(" wire law_p, law_1, law_2, law_3;") - lines.append("") - lines.append(" BiophysicalLUT lut_p (.addr(primary), .cost(cost_p), .lawful(law_p), .drake_ok(), .drift_ok(), .error_ok());") - lines.append(" BiophysicalLUT lut_1 (.addr(alt1), .cost(cost_1), .lawful(law_1), .drake_ok(), .drift_ok(), .error_ok());") - lines.append(" BiophysicalLUT lut_2 (.addr(alt2), .cost(cost_2), .lawful(law_2), .drake_ok(), .drift_ok(), .error_ok());") - lines.append(" BiophysicalLUT lut_3 (.addr(alt3), .cost(cost_3), .lawful(law_3), .drake_ok(), .drift_ok(), .error_ok());") - lines.append("") - lines.append(" // Priority encoder: select lowest-cost lawful address") - lines.append(" assign best_addr = law_p ? primary : (law_1 & mask1) ? alt1 : (law_2 & mask2) ? alt2 : (law_3 & mask3) ? alt3 : primary;") - lines.append(" assign best_cost = law_p ? cost_p : (law_1 & mask1) ? cost_1 : (law_2 & mask2) ? cost_2 : (law_3 & mask3) ? cost_3 : 32'hFFFFFFFF;") - lines.append(" assign best_lawful = law_p | (law_1 & mask1) | (law_2 & mask2) | (law_3 & mask3);") - lines.append("") - lines.append("endmodule") - lines.append("") - - # 8-way evaluator - lines.append("// Speculative evaluation unit: 8-way bundle") - lines.append("module SpeculativeEvaluator8 (") - for i in range(8): - lines.append(f" input [17:0] {'primary' if i == 0 else f'alt{i}'},") - for i in range(1, 8): - lines.append(f" input mask{i},") - lines.append(" output [17:0] best_addr,") - lines.append(" output [31:0] best_cost,") - lines.append(" output best_lawful") - lines.append(");") - lines.append("") - lines.append(" wire [31:0] cost_p, cost_1, cost_2, cost_3, cost_4, cost_5, cost_6, cost_7;") - lines.append(" wire law_p, law_1, law_2, law_3, law_4, law_5, law_6, law_7;") - lines.append("") - for i, name in enumerate(["p", "1", "2", "3", "4", "5", "6", "7"]): - addr = "primary" if i == 0 else f"alt{i}" - lines.append(f" BiophysicalLUT lut_{name} (.addr({addr}), .cost(cost_{name}), .lawful(law_{name}), .drake_ok(), .drift_ok(), .error_ok());") - lines.append("") - lines.append(" // Priority encoder: select lowest-cost lawful address (primary highest priority)") - sel = "law_p ? primary : " - for i in range(1, 8): - sel += f"(law_{i} & mask{i}) ? alt{i} : " - sel += "primary;" - lines.append(f" assign best_addr = {sel}") - sel_cost = "law_p ? cost_p : " - for i in range(1, 8): - sel_cost += f"(law_{i} & mask{i}) ? cost_{i} : " - sel_cost += "32'hFFFFFFFF;" - lines.append(f" assign best_cost = {sel_cost}") - sel_law = "law_p" - for i in range(1, 8): - sel_law += f" | (law_{i} & mask{i})" - sel_law += ";" - lines.append(f" assign best_lawful = {sel_law}") - lines.append("") - lines.append("endmodule") - lines.append("") - - # 16-way evaluator - lines.append("// Speculative evaluation unit: 16-way bundle") - lines.append("module SpeculativeEvaluator16 (") - for i in range(16): - lines.append(f" input [17:0] {'primary' if i == 0 else f'alt{i}'},") - for i in range(1, 16): - lines.append(f" input mask{i},") - lines.append(" output [17:0] best_addr,") - lines.append(" output [31:0] best_cost,") - lines.append(" output best_lawful") - lines.append(");") - lines.append("") - lines.append(" wire [31:0] cost_p, " + ", ".join([f"cost_{i}" for i in range(1, 16)]) + ";") - lines.append(" wire law_p, " + ", ".join([f"law_{i}" for i in range(1, 16)]) + ";") - lines.append("") - for i, name in enumerate(["p"] + [str(j) for j in range(1, 16)]): - addr = "primary" if i == 0 else f"alt{i}" - lines.append(f" BiophysicalLUT lut_{name} (.addr({addr}), .cost(cost_{name}), .lawful(law_{name}), .drake_ok(), .drift_ok(), .error_ok());") - lines.append("") - lines.append(" // Priority encoder: select lowest-cost lawful address (primary highest priority)") - sel = "law_p ? primary : " - for i in range(1, 16): - sel += f"(law_{i} & mask{i}) ? alt{i} : " - sel += "primary;" - lines.append(f" assign best_addr = {sel}") - sel_cost = "law_p ? cost_p : " - for i in range(1, 16): - sel_cost += f"(law_{i} & mask{i}) ? cost_{i} : " - sel_cost += "32'hFFFFFFFF;" - lines.append(f" assign best_cost = {sel_cost}") - sel_law = "law_p" - for i in range(1, 16): - sel_law += f" | (law_{i} & mask{i})" - sel_law += ";" - lines.append(f" assign best_lawful = {sel_law}") - lines.append("") - lines.append("endmodule") - lines.append("") - - # BTB pattern detector module - lines.append("// BTB Pattern Detector with streak-based short-circuit") - lines.append("module PatternDetector (") - lines.append(" input clk,") - lines.append(" input rst,") - lines.append(" input [17:0] current_addr,") - lines.append(" input [17:0] predicted_target,") - lines.append(" input hit,") - lines.append(" output stable,") - lines.append(" output [17:0] stable_target") - lines.append(");") - lines.append("") - lines.append(" // Simple direct-mapped BTB with streak counter (4 entries for demo)") - lines.append(" reg [17:0] btb_source [0:3];") - lines.append(" reg [17:0] btb_target [0:3];") - lines.append(" reg [1:0] btb_conf [0:3];") - lines.append(" reg [2:0] btb_streak [0:3];") - lines.append(" reg btb_valid [0:3];") - lines.append("") - lines.append(" wire [1:0] idx = current_addr[1:0]; // 2-bit index (demo: 4 entries)") - lines.append(" wire match_found = btb_valid[idx] && (btb_source[idx] == current_addr);") - lines.append(" wire is_stable = match_found && (btb_streak[idx] >= 3'd4);") - lines.append("") - lines.append(" assign stable = is_stable;") - lines.append(" assign stable_target = btb_target[idx];") - lines.append("") - lines.append(" integer i;") - lines.append(" always @(posedge clk or posedge rst) begin") - lines.append(" if (rst) begin") - lines.append(" for (i = 0; i < 4; i = i + 1) begin") - lines.append(" btb_valid[i] <= 1'b0;") - lines.append(" btb_streak[i] <= 3'd0;") - lines.append(" btb_conf[i] <= 2'd0;") - lines.append(" end") - lines.append(" end else begin") - lines.append(" if (hit && match_found) begin") - lines.append(" // Increment confidence and streak on hit") - lines.append(" btb_conf[idx] <= (btb_conf[idx] < 2'd3) ? btb_conf[idx] + 1 : 2'd3;") - lines.append(" btb_streak[idx] <= btb_streak[idx] + 1;") - lines.append(" end else if (!hit && match_found) begin") - lines.append(" // Reset streak on miss") - lines.append(" btb_streak[idx] <= 3'd0;") - lines.append(" end else if (!match_found) begin") - lines.append(" // Insert new entry") - lines.append(" btb_valid[idx] <= 1'b1;") - lines.append(" btb_source[idx] <= current_addr;") - lines.append(" btb_target[idx] <= predicted_target;") - lines.append(" btb_conf[idx] <= 2'd1;") - lines.append(" btb_streak[idx] <= 3'd0;") - lines.append(" end") - lines.append(" end") - lines.append(" end") - lines.append("") - lines.append("endmodule") - - with open(output_path, "w") as f: - f.write("\n".join(lines)) - print(f"[OK] Verilog module written to {output_path}") - - -def generate_lut_hex(output_path: Path): - """Generate hex initialization file for the LUT BRAM. - Format: 36-bit hex words (8 hex digits + 1 nibble for flags).""" - lines = [] - for addr in range(ADDR_SPACE): - entry = BIOPHYSICAL_LUT.lookup(addr) - # Pack: cost[31:0] | lawful | drake_ok | drift_ok | error_ok - flags = (int(entry.lawful) << 3) | (int(entry.drake_ok) << 2) | (int(entry.drift_ok) << 1) | int(entry.error_ok) - word = (entry.cost << 4) | flags - lines.append(f"{word:09X}") - - with open(output_path, "w") as f: - f.write("\n".join(lines)) - print(f"[OK] LUT hex file written to {output_path}") - - -def generate_yosys_script(output_dir: Path): - """Generate Yosys synthesis script for iCE40 target. - Synthesizes the 8-way speculative evaluator and reports resource usage.""" - script = """# Yosys synthesis script for CooperativeLUT iCE40 target -# Generated by connectome_lut_shim.py - -# Read design -read_verilog biophysical_lut.v - -# Generic synthesis (technology-independent optimization) -synth -top SpeculativeEvaluator8 - -# Technology mapping for iCE40 -# Note: The full 262K LUT won't fit in iCE40 SPRAM (max ~128KB). -# For a real build, external SRAM or a larger FPGA (ECP5, Xilinx) is needed. -# This script targets a parameterized small-LUT version for iCE40UP5K. -synth_ice40 -top SpeculativeEvaluator8 -json speculative_evaluator8.json - -# Resource report -stat - -# Write BLIF for nextpnr-ice40 -write_blif speculative_evaluator8.blif -""" - script_path = output_dir / "synth_ice40.ys" - with open(script_path, "w") as f: - f.write(script) - print(f"[OK] Yosys synthesis script written to {script_path}") - return script_path - - -# ═══════════════════════════════════════════════════════════════════════════ -# §8 CLI / main execution -# ═══════════════════════════════════════════════════════════════════════════ - -def main(): - parquet_dir = Path("shared-data/data/connectomes/openworm_parquet") - if not parquet_dir.exists(): - print(f"[ERROR] Parquet directory not found: {parquet_dir}", file=sys.stderr) - sys.exit(1) - - print("[INFO] Loading OpenWorm connectome datasets...") - states = load_all_openworm_states(parquet_dir) - print(f"[INFO] Loaded {len(states)} datasets") - - # Seed states - seed_json = [state_to_json(s) for s in states] - print("\n=== SEED STATES (sample) ===") - print(json.dumps(seed_json[:5], indent=2)) - - trajectory = [] - short_circuits = 0 - mode_summary = {} - - # Quantum walks from first dataset - if states: - seed_state = states[0] - seed_addr = seed_state.to_address() - - for mode in ["4way", "8way", "16way", "pattern"]: - print(f"\n[INFO] Running quantum walk ({mode}) from {seed_state.dataset_name} (addr={seed_addr})...") - traj, sc = run_quantum_walk(seed_addr, steps=50, mode=mode) - lawful_steps = sum(1 for _, e in traj if e.lawful) - mode_summary[mode] = { - "lawful_steps": lawful_steps, - "short_circuits": sc, - } - print(f"[INFO] {mode} complete: {lawful_steps}/50 lawful steps, {sc} short-circuits") - - if mode == "pattern": - trajectory = traj - short_circuits = sc - - print("\n=== QUANTUM WALK TRAJECTORY (pattern, first 10 steps) ===") - print(json.dumps(trajectory_to_json(trajectory[:10]), indent=2)) - - # Generate Verilog - verilog_dir = Path("5-Applications/out/verilog") - verilog_dir.mkdir(parents=True, exist_ok=True) - generate_verilog_lut(verilog_dir / "biophysical_lut.v") - generate_lut_hex(verilog_dir / "biophysical_lut.hex") - generate_yosys_script(verilog_dir) - - # Run Yosys synthesis if available - yosys_bin = Path("/usr/bin/yosys") - if yosys_bin.exists(): - print("\n[INFO] Running Yosys synthesis for resource estimation...") - import subprocess - try: - result = subprocess.run( - [str(yosys_bin), "-s", "synth_ice40.ys"], - cwd=verilog_dir, - capture_output=True, - text=True, - timeout=60, - ) - # Extract resource stats from the 'stat' section - # Yosys prints stats multiple times; we want the one with SB_LUT4 counts - stats_blocks = [] - current_block = [] - in_block = False - for line in result.stdout.splitlines(): - if "=== SpeculativeEvaluator8 ===" in line: - if in_block and current_block: - stats_blocks.append(current_block) - in_block = True - current_block = [line] - elif in_block: - if line.startswith("=== ") and "SpeculativeEvaluator8" not in line: - in_block = False - stats_blocks.append(current_block) - current_block = [] - else: - current_block.append(line) - if current_block and in_block: - stats_blocks.append(current_block) - - # Find the block that contains SB_LUT4 - best_block = [] - for block in stats_blocks: - if any("SB_LUT4" in line for line in block): - best_block = block - break - - if best_block: - print("\n=== YOSYS RESOURCE ESTIMATE (SpeculativeEvaluator8, iCE40) ===") - for line in best_block[:25]: - print(line) - else: - print("\n=== YOSYS OUTPUT (tail) ===") - for line in result.stdout.splitlines()[-40:]: - print(line) - - # Save full log - with open(verilog_dir / "yosys.log", "w") as f: - f.write(result.stdout) - f.write(result.stderr) - print(f"\n[OK] Yosys log saved to {verilog_dir / 'yosys.log'}") - except Exception as e: - print(f"[WARN] Yosys synthesis failed: {e}") - else: - print("[INFO] Yosys not found; skipping synthesis.") - - # Save full results - out_file = Path("5-Applications/out/connectome_lut_results.json") - out_file.parent.mkdir(parents=True, exist_ok=True) - with open(out_file, "w") as f: - json.dump({ - "seed_states": seed_json, - "quantum_walk": trajectory_to_json(trajectory), - "summary": { - "total_datasets": len(states), - "lut_entries": ADDR_SPACE, - "lawful_lut_fraction": sum(1 for e in BIOPHYSICAL_LUT.entries if e.lawful) / ADDR_SPACE, - "mode_comparison": mode_summary, - "short_circuits": short_circuits, - } - }, f, indent=2) - print(f"\n[OK] Full results saved to {out_file}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/couch_equation_forest_analysis.py b/5-Applications/scripts/couch_equation_forest_analysis.py deleted file mode 100644 index 2263f6f0..00000000 --- a/5-Applications/scripts/couch_equation_forest_analysis.py +++ /dev/null @@ -1,496 +0,0 @@ -#!/usr/bin/env python3 -""" -COUCH Equation Forest Analysis - -Applies the Equation Forest framework to COUCH equation. -Maps COUCH to 12 foundation kernels, 5 core streets, 8 bridges, and Genome18 address space. - -Equation Forest: -- 12 Foundation Kernels (F01-F12) -- 5 Core Streets -- 8 Bridge Nodes -- 18-bit Genome18 ISA (262,144 states) -""" - -import numpy as np -from typing import Dict, List, Tuple, Any -from dataclasses import dataclass -from enum import Enum -import json - - -class FoundationKernel(Enum): - """12 Foundation Kernels (Exact Solver Basis Vectors).""" - F01 = "Shannon_Entropy_Calculation" - F02 = "Information_Content_Measurement" - F03 = "Hierarchical_Entropy_Decomposition" - F04 = "Thermodynamic_Efficiency_Limit" - F05 = "Computation_Energy_Bound" - F06 = "Energy_Balance_Threshold" - F07 = "Maxwell_Demon_Recovery" - F08 = "Riemannian_Distance_Calculation" - F09 = "Geodesic_Connection_Coefficients" - F10 = "Single_Step_Geodesic_Integration" - F11 = "Aggregate_Load_Combination" - F12 = "Intrinsic_to_Total_Ratio" - - -class CoreStreet(Enum): - """5 Core Streets (Graph Collapse).""" - ENTROPY_COMPRESSION = "Entropy/Compression (F01-F03)" - THERMODYNAMIC = "Thermodynamic Admissibility (F04-F07)" - GEOMETRIC_MOTION = "Geometric Motion (F08-F10)" - COGNITIVE_ROUTING = "Cognitive/Routing Load (F11-F12)" - DIAT_AVMR_S3C = "DIAT/AVMR/S3C Bridge" - - -class BridgeNode(Enum): - """8 Bridge Nodes.""" - B1 = "Entropy ↔ Load" - B2 = "Entropy ↔ Landauer" - B3 = "Energy ↔ Routing" - B4 = "Geometry ↔ Routing" - B5 = "DIAT ↔ Geometry" - B6 = "AVMR ↔ Entropy" - B7 = "S3C ↔ Codec" - B8 = "PIST ↔ Surface" - - -@dataclass -class Genome18Bin: - """Genome18 3-bit bin (6 bins total = 18 bits).""" - muBin: int # mutation/drift (routing load) - 3 bits - rhoBin: int # verification pressure (routing efficiency) - 3 bits - cBin: int # connectance (geometry/route neighborhood) - 3 bits - mBin: int # compression residue (entropy) - 3 bits - neBin: int # effective sample (entropy) - 3 bits - sigmaBin: int # fitness proxy (entropy) - 3 bits - - -@dataclass -class KernelSignature: - """Kernel signature for COUCH equation.""" - f01_shannon_entropy: float - f02_information_content: float - f03_hierarchical_entropy: float - f04_thermodynamic_efficiency: float - f05_computation_energy: float - f06_energy_balance: float - f07_maxwell_demon: float - f08_riemannian_distance: float - f09_geodesic_connection: float - f10_geodesic_integration: float - f11_aggregate_load: float - f12_intrinsic_ratio: float - - -class COUCHForestMapper: - """ - Maps COUCH equation to Equation Forest. - - Pipeline: - raw equation → F01-F12 kernel signature → street/bridge assignment - → Genome18 bins → 18-bit ISA address → PIST/witness audit - """ - - def __init__(self): - self.kernels = list(FoundationKernel) - self.streets = list(CoreStreet) - self.bridges = list(BridgeNode) - - def calculate_couch_kernel_signature(self, couch_state: Dict[str, Any]) -> KernelSignature: - """ - Calculate F01-F12 kernel signature for COUCH equation. - - COUCH: ẍ_i + γẋ_i + ω_i²x_i + Σ_j κ_ij(x_i - x_j) = F(t) - """ - position = np.array(couch_state["position"]) - velocity = np.array(couch_state["velocity"]) - damping = couch_state["damping"] - forcing = couch_state["forcing"] - coupling = np.array(couch_state["coupling"]) - - # F01: Shannon Entropy Calculation - # Based on oscillator state distribution - state_prob = np.abs(position) / np.sum(np.abs(position)) - f01 = -np.sum(state_prob * np.log2(state_prob + 1e-10)) - - # F02: Information Content Measurement - # Based on hysteresis memory - f02 = np.sum(np.abs(velocity)) * damping - - # F03: Hierarchical Entropy Decomposition - # Based on coupling structure - coupling_entropy = np.linalg.svd(coupling, compute_uv=False) - f03 = np.sum(coupling_entropy) - - # F04: Thermodynamic Efficiency Limit - # Based on energy dissipation (Carnot) - f04 = damping / (damping + forcing) - - # F05: Computation Energy Bound - # Based on kinetic energy - f05 = 0.5 * np.sum(velocity**2) - - # F06: Energy Balance Threshold - # Based on potential energy - f06 = 0.5 * np.sum(position**2 * 2.0**2) - - # F07: Maxwell Demon Recovery - # Based on coupling work - f07 = np.sum(np.abs(coupling)) - - # F08: Riemannian Distance Calculation - # Distance from equilibrium - f08 = np.linalg.norm(position) - - # F09: Geodesic Connection Coefficients - # Based on phase space curvature - f09 = np.linalg.norm(np.cross(position, velocity)) - - # F10: Single Step Geodesic Integration - # Based on state evolution - f10 = np.linalg.norm(velocity) / (np.linalg.norm(position) + 1e-10) - - # F11: Aggregate Load Combination - # Based on total system energy - f11 = f05 + f06 - - # F12: Intrinsic to Total Ratio - # Based on dimensionality - f12 = len(position) / (len(position) + len(coupling.flatten())) - - return KernelSignature( - f01_shannon_entropy=f01, - f02_information_content=f02, - f03_hierarchical_entropy=f03, - f04_thermodynamic_efficiency=f04, - f05_computation_energy=f05, - f06_energy_balance=f06, - f07_maxwell_demon=f07, - f08_riemannian_distance=f08, - f09_geodesic_connection=f09, - f10_geodesic_integration=f10, - f11_aggregate_load=f11, - f12_intrinsic_ratio=f12 - ) - - def assign_street(self, signature: KernelSignature) -> CoreStreet: - """ - Assign COUCH to a core street based on kernel signature. - - Analyze which kernel set dominates the signature. - """ - # Calculate street scores - entropy_score = signature.f01_shannon_entropy + signature.f02_information_content + signature.f03_hierarchical_entropy - thermodynamic_score = signature.f04_thermodynamic_efficiency + signature.f05_computation_energy + signature.f06_energy_balance + signature.f07_maxwell_demon - geometric_score = signature.f08_riemannian_distance + signature.f09_geodesic_connection + signature.f10_geodesic_integration - cognitive_score = signature.f11_aggregate_load + signature.f12_intrinsic_ratio - - scores = { - CoreStreet.ENTROPY_COMPRESSION: entropy_score, - CoreStreet.THERMODYNAMIC: thermodynamic_score, - CoreStreet.GEOMETRIC_MOTION: geometric_score, - CoreStreet.COGNITIVE_ROUTING: cognitive_score - } - - # Assign to highest-scoring street - assigned_street = max(scores, key=scores.get) - - return assigned_street - - def assign_bridges(self, signature: KernelSignature, street: CoreStreet) -> List[BridgeNode]: - """ - Assign COUCH to bridge nodes based on signature and street. - """ - bridges = [] - - # Bridge assignments based on signature analysis - if signature.f01_shannon_entropy > 0.5: - bridges.append(BridgeNode.B1) # Entropy ↔ Load - - if signature.f04_thermodynamic_efficiency > 0.5: - bridges.append(BridgeNode.B2) # Entropy ↔ Landauer - - if signature.f05_computation_energy > 0.5: - bridges.append(BridgeNode.B3) # Energy ↔ Routing - - if signature.f08_riemannian_distance > 0.5: - bridges.append(BridgeNode.B4) # Geometry ↔ Routing - - if street == CoreStreet.GEOMETRIC_MOTION: - bridges.append(BridgeNode.B5) # DIAT ↔ Geometry - - if signature.f01_shannon_entropy > 0.3: - bridges.append(BridgeNode.B6) # AVMR ↔ Entropy - - if signature.f03_hierarchical_entropy > 0.3: - bridges.append(BridgeNode.B7) # S3C ↔ Codec - - # PIST ↔ Surface (always for COUCH due to hysteresis) - bridges.append(BridgeNode.B8) - - return bridges - - def calculate_genome18_bins(self, signature: KernelSignature) -> Genome18Bin: - """ - Calculate Genome18 bins from kernel signature. - - 6 bins × 3 bits = 18 bits (262,144 states) - - Kernel to bin mapping: - - F01-F03 → mBin, neBin, sigmaBin - - F04-F07 → cost/failure mask - - F08-F10 → cBin - - F11-F12 → muBin, rhoBin - """ - # Normalize signature values to 0-7 range (3 bits) - def to_3bit(value: float) -> int: - return int(np.clip(value * 7 / 10, 0, 7)) - - # F01-F03 → mBin, neBin, sigmaBin (entropy bins) - mBin = to_3bit(signature.f01_shannon_entropy) - neBin = to_3bit(signature.f02_information_content) - sigmaBin = to_3bit(signature.f03_hierarchical_entropy) - - # F04-F07 → cost/failure mask (not directly mapped, use thermodynamic average) - thermodynamic_avg = (signature.f04_thermodynamic_efficiency + - signature.f05_computation_energy + - signature.f06_energy_balance + - signature.f07_maxwell_demon) / 4 - - # F08-F10 → cBin (geometry bin) - cBin = to_3bit(signature.f08_riemannian_distance + - signature.f09_geodesic_connection + - signature.f10_geodesic_integration) - - # F11-F12 → muBin, rhoBin (cognitive/routing bins) - muBin = to_3bit(signature.f11_aggregate_load) - rhoBin = to_3bit(signature.f12_intrinsic_ratio) - - return Genome18Bin( - muBin=muBin, - rhoBin=rhoBin, - cBin=cBin, - mBin=mBin, - neBin=neBin, - sigmaBin=sigmaBin - ) - - def calculate_genome18_address(self, bins: Genome18Bin) -> int: - """ - Calculate 18-bit ISA address from Genome18 bins. - - Address calculation: - addr = muBin * 32768 + rhoBin * 4096 + cBin * 512 + mBin * 64 + neBin * 8 + sigmaBin - """ - addr = (bins.muBin * 32768 + - bins.rhoBin * 4096 + - bins.cBin * 512 + - bins.mBin * 64 + - bins.neBin * 8 + - bins.sigmaBin) - - return addr - - def calculate_pist_witness_surface(self, signature: KernelSignature) -> Dict[str, Any]: - """ - Calculate PIST witness surface for COUCH equation. - - PIST witness surface = topological constraint enforcement - """ - # PIST shell coordinates - k = int(signature.f08_riemannian_distance * 10) - t = int(signature.f10_geodesic_integration * 10) - h = signature.f02_information_content # hysteresis - - # FAMM frustration - a = float(k % 3) - b = float((k + 1) - (k % 3)) - c = float(k) - mass = a * b * c if (a * b * c) > 0 else 1.0 - - stress = np.array([ - [signature.f08_riemannian_distance, signature.f10_geodesic_integration, signature.f09_geodesic_connection], - [signature.f10_geodesic_integration, signature.f08_riemannian_distance, signature.f09_geodesic_connection], - [signature.f09_geodesic_connection, signature.f08_riemannian_distance, signature.f10_geodesic_integration] - ]) - - phi = np.trace(stress) / mass - - return { - "shell_coordinates": {"k": k, "t": t, "h": h}, - "famm_frustration": phi, - "is_admissible": phi <= 1.0, - "mass": mass - } - - def analyze_forest_position(self, address: int) -> Dict[str, Any]: - """ - Analyze COUCH position in the 262,144-state Genome18 space. - """ - total_states = 262144 - position_pct = address / total_states - - # Calculate which "region" of the forest - if position_pct < 0.25: - region = "Entropy-Dominant Region" - elif position_pct < 0.5: - region = "Thermodynamic-Dominant Region" - elif position_pct < 0.75: - region = "Geometric-Dominant Region" - else: - region = "Cognitive-Dominant Region" - - return { - "address": address, - "total_states": total_states, - "position_percent": position_pct, - "region": region, - "binary_representation": format(address, '018b') - } - - -def main(): - """Run COUCH Equation Forest analysis.""" - print("=" * 70) - print("COUCH EQUATION FOREST ANALYSIS") - print("=" * 70) - print("\n[*] Applying Equation Forest framework to COUCH equation") - print("[*] 12 Foundation Kernels → 5 Core Streets → 8 Bridges → Genome18") - - # Initialize mapper - mapper = COUCHForestMapper() - - # Define COUCH state - couch_state = { - "position": [0.24835708, -0.06913215, 0.32384427], - "velocity": [0.45690896, -0.07024601, -0.07024109], - "damping": 0.5, - "forcing": 1.0, - "coupling": [ - [0.1, 0.05, 0.02], - [0.05, 0.1, 0.05], - [0.02, 0.05, 0.1] - ] - } - - # Calculate kernel signature - print(f"\n[*] Calculating F01-F12 kernel signature...") - signature = mapper.calculate_couch_kernel_signature(couch_state) - - print(f"\n[*] Kernel Signature:") - print(f" F01 (Shannon Entropy): {signature.f01_shannon_entropy:.4f}") - print(f" F02 (Information Content): {signature.f02_information_content:.4f}") - print(f" F03 (Hierarchical Entropy): {signature.f03_hierarchical_entropy:.4f}") - print(f" F04 (Thermodynamic Efficiency): {signature.f04_thermodynamic_efficiency:.4f}") - print(f" F05 (Computation Energy): {signature.f05_computation_energy:.4f}") - print(f" F06 (Energy Balance): {signature.f06_energy_balance:.4f}") - print(f" F07 (Maxwell Demon): {signature.f07_maxwell_demon:.4f}") - print(f" F08 (Riemannian Distance): {signature.f08_riemannian_distance:.4f}") - print(f" F09 (Geodesic Connection): {signature.f09_geodesic_connection:.4f}") - print(f" F10 (Geodesic Integration): {signature.f10_geodesic_integration:.4f}") - print(f" F11 (Aggregate Load): {signature.f11_aggregate_load:.4f}") - print(f" F12 (Intrinsic Ratio): {signature.f12_intrinsic_ratio:.4f}") - - # Assign street - print(f"\n[*] Assigning to core street...") - street = mapper.assign_street(signature) - print(f" Assigned street: {street.value}") - - # Assign bridges - print(f"\n[*] Assigning bridge nodes...") - bridges = mapper.assign_bridges(signature, street) - print(f" Assigned bridges: {[b.value for b in bridges]}") - - # Calculate Genome18 bins - print(f"\n[*] Calculating Genome18 bins...") - bins = mapper.calculate_genome18_bins(signature) - print(f" muBin (mutation/drift): {bins.muBin} (routing load)") - print(f" rhoBin (verification pressure): {bins.rhoBin} (routing efficiency)") - print(f" cBin (connectance): {bins.cBin} (geometry/route neighborhood)") - print(f" mBin (compression residue): {bins.mBin} (entropy)") - print(f" neBin (effective sample): {bins.neBin} (entropy)") - print(f" sigmaBin (fitness proxy): {bins.sigmaBin} (entropy)") - - # Calculate Genome18 address - print(f"\n[*] Calculating 18-bit ISA address...") - address = mapper.calculate_genome18_address(bins) - print(f" Genome18 address: {address} / 262,144") - print(f" Binary: {format(address, '018b')}") - - # Analyze forest position - print(f"\n[*] Analyzing forest position...") - position = mapper.analyze_forest_position(address) - print(f" Position: {position['position_percent']:.2%}") - print(f" Region: {position['region']}") - - # Calculate PIST witness surface - print(f"\n[*] Calculating PIST witness surface...") - pist_surface = mapper.calculate_pist_witness_surface(signature) - print(f" Shell coordinates: {pist_surface['shell_coordinates']}") - print(f" FAMM frustration (Φ): {pist_surface['famm_frustration']:.4f}") - print(f" Is admissible: {pist_surface['is_admissible']}") - - # Save results (convert numpy types to native Python) - def convert_to_native(obj): - """Convert numpy types to native Python types for JSON serialization.""" - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.integer): - return int(obj) - elif isinstance(obj, np.floating): - return float(obj) - elif isinstance(obj, np.bool_): - return bool(obj) - elif isinstance(obj, dict): - return {k: convert_to_native(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [convert_to_native(item) for item in obj] - else: - return obj - - results = { - "couch_state": couch_state, - "kernel_signature": { - "f01": signature.f01_shannon_entropy, - "f02": signature.f02_information_content, - "f03": signature.f03_hierarchical_entropy, - "f04": signature.f04_thermodynamic_efficiency, - "f05": signature.f05_computation_energy, - "f06": signature.f06_energy_balance, - "f07": signature.f07_maxwell_demon, - "f08": signature.f08_riemannian_distance, - "f09": signature.f09_geodesic_connection, - "f10": signature.f10_geodesic_integration, - "f11": signature.f11_aggregate_load, - "f12": signature.f12_intrinsic_ratio - }, - "street_assignment": street.value, - "bridge_assignments": [b.value for b in bridges], - "genome18_bins": { - "muBin": bins.muBin, - "rhoBin": bins.rhoBin, - "cBin": bins.cBin, - "mBin": bins.mBin, - "neBin": bins.neBin, - "sigmaBin": bins.sigmaBin - }, - "genome18_address": address, - "forest_position": position, - "pist_witness_surface": convert_to_native(pist_surface) - } - - output_path = "/home/allaun/Documents/Research Stack/data/couch_equation_forest_analysis.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ COUCH EQUATION FOREST ANALYSIS COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/couch_filter_normalization.py b/5-Applications/scripts/couch_filter_normalization.py deleted file mode 100644 index e34fb900..00000000 --- a/5-Applications/scripts/couch_filter_normalization.py +++ /dev/null @@ -1,369 +0,0 @@ -#!/usr/bin/env python3 -""" -COUCH Phase Space Filter Normalization - -Applies filter normalization technique from NeurIPS 2018 paper -"Visualizing the Loss Landscape of Neural Nets" to COUCH phase space. - -Filter normalization enables meaningful side-by-side comparisons -between different coupling regimes by normalizing oscillator states. - -AUDIT-ONLY SHIM: -This script is not a source-of-truth implementation under 6-Documentation/docs/AGENTS.md. -It may generate exploratory JSON/audit evidence only. Any curvature, -compression strategy, invariant, or branching logic here must be ported to -Lean before being used by the core model or release claims. -""" - -import numpy as np -from typing import Dict, List, Tuple, Any -from dataclasses import dataclass -import json - - -@dataclass -class COUCHState: - """COUCH oscillator state.""" - position: np.ndarray # x_i - velocity: np.ndarray # ẋ_i - acceleration: np.ndarray # ẍ_i - coupling: np.ndarray # κ_ij - damping: float # γ - frequency: np.ndarray # ω_i - forcing: float # F(t) - - -class COUCHFilterNormalizer: - """ - Applies filter normalization to COUCH phase space. - - Based on Li et al. (2018) - filter normalizes directions - to enable meaningful curvature comparisons. - """ - - def __init__(self, n_oscillators: int = 3): - self.n_oscillators = n_oscillators - - def normalize_state(self, state: COUCHState) -> Dict[str, np.ndarray]: - """ - Apply filter normalization to COUCH state. - - Normalizes each oscillator's state vector to unit length - while preserving relative phase relationships. - """ - # Stack state into phase space vector - phase_space = np.concatenate([ - state.position, - state.velocity, - state.acceleration - ]) - - # Normalize to unit length (filter normalization) - norm = np.linalg.norm(phase_space) - if norm > 0: - normalized_phase_space = phase_space / norm - else: - normalized_phase_space = phase_space - - # Extract normalized components - n = self.n_oscillators - normalized_position = normalized_phase_space[:n] - normalized_velocity = normalized_phase_space[n:2*n] - normalized_acceleration = normalized_phase_space[2*n:3*n] - - return { - "position": normalized_position, - "velocity": normalized_velocity, - "acceleration": normalized_acceleration, - "phase_space": normalized_phase_space, - "norm": norm - } - - def calculate_curvature(self, state: COUCHState, normalized: Dict[str, np.ndarray]) -> float: - """ - Calculate curvature of COUCH phase space trajectory. - - Curvature = ||d²r/ds²|| where s is arc length - """ - # Use normalized velocity and acceleration - v = normalized["velocity"] - a = normalized["acceleration"] - - # Curvature formula: κ = ||v × a|| / ||v||³ - # For normalized states, ||v|| ≈ 1 - cross_product = np.cross(v, a) - if len(cross_product.shape) == 0: - cross_mag = abs(cross_product) - else: - cross_mag = np.linalg.norm(cross_product) - - v_mag = np.linalg.norm(v) - if v_mag > 0: - curvature = cross_mag / (v_mag ** 3) - else: - curvature = 0.0 - - return curvature - - def sample_trajectory(self, state: COUCHState, steps: int = 100) -> List[Dict[str, Any]]: - """ - Sample COUCH trajectory and apply filter normalization. - - Simulates COUCH dynamics using RK4 integration. - """ - trajectory = [] - - # Initial state - x = state.position.copy() - v = state.velocity.copy() - a = state.acceleration.copy() - - dt = 0.01 - - for i in range(steps): - # COUCH dynamics: ẍ_i + γẋ_i + ω_i²x_i + Σ_j κ_ij(x_i - x_j) = F(t) - # Solve for acceleration - damping_term = state.damping * v - spring_term = state.frequency ** 2 * x - pairwise_displacement = x[:, None] - x[None, :] - coupling_term = np.sum(state.coupling * pairwise_displacement, axis=1) - forcing_term = state.forcing - - a = forcing_term - damping_term - spring_term - coupling_term - - # Create temporary state for normalization - temp_state = COUCHState( - position=x, - velocity=v, - acceleration=a, - coupling=state.coupling, - damping=state.damping, - frequency=state.frequency, - forcing=state.forcing - ) - - # Normalize - normalized = self.normalize_state(temp_state) - - # Calculate curvature - curvature = self.calculate_curvature(temp_state, normalized) - - trajectory.append({ - "step": i, - "position": x.copy(), - "velocity": v.copy(), - "acceleration": a.copy(), - "normalized_position": normalized["position"].copy(), - "normalized_velocity": normalized["velocity"].copy(), - "normalized_acceleration": normalized["acceleration"].copy(), - "norm": normalized["norm"], - "curvature": curvature - }) - - # RK4 integration step - k1_v = a - k1_x = v - - k2_v = a # Simplified (would need full RK4) - k2_x = v + k1_v * dt / 2 - - k3_v = a - k3_x = v + k2_v * dt / 2 - - k4_v = a - k4_x = v + k3_v * dt - - v = v + (k1_v + 2*k2_v + 2*k3_v + k4_v) * dt / 6 - x = x + (k1_x + 2*k2_x + 2*k3_x + k4_x) * dt / 6 - - return trajectory - - def compare_coupling_regimes(self, base_state: COUCHState, coupling_values: List[float]) -> Dict[str, Any]: - """ - Compare different coupling regimes using filter normalization. - - This is the key application - meaningful side-by-side comparison - of different κ values enabled by filter normalization. - """ - comparisons = {} - - for kappa in coupling_values: - # Modify coupling strength - modified_state = COUCHState( - position=base_state.position.copy(), - velocity=base_state.velocity.copy(), - acceleration=base_state.acceleration.copy(), - coupling=base_state.coupling * kappa, - damping=base_state.damping, - frequency=base_state.frequency.copy(), - forcing=base_state.forcing - ) - - # Sample trajectory - trajectory = self.sample_trajectory(modified_state, steps=50) - - # Calculate statistics - curvatures = [t["curvature"] for t in trajectory] - norms = [t["norm"] for t in trajectory] - - comparisons[f"kappa_{kappa:.2f}"] = { - "coupling_strength": kappa, - "avg_curvature": np.mean(curvatures), - "max_curvature": np.max(curvatures), - "min_curvature": np.min(curvatures), - "std_curvature": np.std(curvatures), - "avg_norm": np.mean(norms), - "trajectory": trajectory[:10], # Sample - "curvature_history": curvatures - } - - return comparisons - - def visualize_landscape_summary(self, comparisons: Dict[str, Any]) -> Dict[str, Any]: - """ - Create a summary of the COUCH loss landscape across coupling regimes. - """ - summary = { - "regimes": list(comparisons.keys()), - "curvature_comparison": {}, - "norm_comparison": {}, - "chaotic_threshold": 1.0 # Arbitrary threshold for "super freak" regime - } - - for regime, data in comparisons.items(): - summary["curvature_comparison"][regime] = { - "avg": data["avg_curvature"], - "max": data["max_curvature"], - "std": data["std_curvature"] - } - - summary["norm_comparison"][regime] = { - "avg": data["avg_norm"] - } - - # Classify regime - if data["avg_curvature"] > summary["chaotic_threshold"]: - regime_type = "chaotic_super_freak" - elif data["avg_curvature"] > 0.5: - regime_type = "transitional" - else: - regime_type = "stable" - - data["regime_type"] = regime_type - - return summary - - -def main(): - """Run COUCH filter normalization analysis.""" - print("=" * 70) - print("COUCH PHASE SPACE FILTER NORMALIZATION") - print("=" * 70) - print("\n[*] Applying filter normalization from NeurIPS 2018") - print("[*] Enabling meaningful side-by-side coupling regime comparisons") - - # Initialize normalizer - normalizer = COUCHFilterNormalizer(n_oscillators=3) - - # Create base COUCH state - np.random.seed(42) - base_state = COUCHState( - position=np.random.randn(3) * 0.5, - velocity=np.random.randn(3) * 0.3, - acceleration=np.zeros(3), - coupling=np.random.randn(3, 3) * 0.1, - damping=0.5, - frequency=np.ones(3) * 2.0, - forcing=1.0 - ) - - # Symmetrize coupling matrix - base_state.coupling = (base_state.coupling + base_state.coupling.T) / 2 - - print(f"\n[*] Base COUCH State:") - print(f" Position: {base_state.position}") - print(f" Velocity: {base_state.velocity}") - print(f" Damping: {base_state.damping}") - print(f" Forcing: {base_state.forcing}") - - # Apply filter normalization to base state - print(f"\n[*] Applying filter normalization...") - normalized = normalizer.normalize_state(base_state) - print(f" Original norm: {normalized['norm']:.4f}") - print(f" Normalized position: {normalized['position']}") - print(f" Normalized velocity: {normalized['velocity']}") - - # Calculate curvature - curvature = normalizer.calculate_curvature(base_state, normalized) - print(f" Phase space curvature: {curvature:.4f}") - - # Compare coupling regimes - print(f"\n[*] Comparing coupling regimes (κ values)...") - coupling_values = [0.5, 1.0, 1.5, 2.0, 2.5] - comparisons = normalizer.compare_coupling_regimes(base_state, coupling_values) - - print(f"\n[*] Coupling Regime Comparison:") - for regime, data in comparisons.items(): - print(f" {regime}:") - print(f" Avg curvature: {data['avg_curvature']:.4f}") - print(f" Max curvature: {data['max_curvature']:.4f}") - print(f" Std curvature: {data['std_curvature']:.4f}") - print(f" Avg norm: {data['avg_norm']:.4f}") - print(f" Regime type: {data.get('regime_type', 'unknown')}") - - # Visualize landscape summary - print(f"\n[*] Creating landscape summary...") - summary = normalizer.visualize_landscape_summary(comparisons) - - print(f"\n[*] Landscape Summary:") - print(f" Regimes analyzed: {summary['regimes']}") - print(f" Chaotic threshold: {summary['chaotic_threshold']}") - - # Find transition to chaotic regime - chaotic_regimes = [r for r, d in comparisons.items() if d.get('regime_type') == 'chaotic_super_freak'] - if chaotic_regimes: - print(f" Chaotic regimes: {chaotic_regimes}") - else: - print(f" No chaotic regimes detected (may need higher κ)") - - # Save results - def convert_to_native(obj): - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.integer): - return int(obj) - elif isinstance(obj, np.floating): - return float(obj) - elif isinstance(obj, dict): - return {k: convert_to_native(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [convert_to_native(item) for item in obj] - else: - return obj - - results = { - "base_state": { - "position": base_state.position.tolist(), - "velocity": base_state.velocity.tolist(), - "damping": base_state.damping, - "forcing": base_state.forcing - }, - "normalized_base": convert_to_native(normalized), - "base_curvature": curvature, - "coupling_comparisons": convert_to_native(comparisons), - "landscape_summary": convert_to_native(summary) - } - - output_path = "/home/allaun/Documents/Research Stack/data/couch_filter_normalization.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ COUCH FILTER NORMALIZATION COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/couch_forest_exploration.py b/5-Applications/scripts/couch_forest_exploration.py deleted file mode 100644 index 0f2712e5..00000000 --- a/5-Applications/scripts/couch_forest_exploration.py +++ /dev/null @@ -1,390 +0,0 @@ -#!/usr/bin/env python3 -""" -COUCH Forest Region Exploration - -Explores the Entropy-Dominant Region around COUCH's Genome18 address (512). -Analyzes neighborhood structure, patterns, and transitions in the 262,144-state space. -""" - -import numpy as np -from typing import Dict, List, Tuple, Any -from dataclasses import dataclass -import json - - -@dataclass -class Genome18Bin: - """Genome18 3-bit bin (6 bins total = 18 bits).""" - muBin: int # mutation/drift (routing load) - 3 bits - rhoBin: int # verification pressure (routing efficiency) - 3 bits - cBin: int # connectance (geometry/route neighborhood) - 3 bits - mBin: int # compression residue (entropy) - 3 bits - neBin: int # effective sample (entropy) - 3 bits - sigmaBin: int # fitness proxy (entropy) - 3 bits - - -class ForestExplorer: - """Explores the Equation Forest around a given address.""" - - def __init__(self, center_address: int = 512, radius: int = 1000): - self.center_address = center_address - self.radius = radius - self.total_states = 262144 - - def address_to_bins(self, address: int) -> Genome18Bin: - """Convert Genome18 address to bins.""" - # Reverse of address calculation - # addr = muBin * 32768 + rhoBin * 4096 + cBin * 512 + mBin * 64 + neBin * 8 + sigmaBin - - sigmaBin = address % 8 - remainder = address // 8 - neBin = remainder % 8 - remainder = remainder // 8 - mBin = remainder % 8 - remainder = remainder // 8 - cBin = remainder % 8 - remainder = remainder // 8 - rhoBin = remainder % 8 - muBin = remainder // 8 - - return Genome18Bin( - muBin=muBin, - rhoBin=rhoBin, - cBin=cBin, - mBin=mBin, - neBin=neBin, - sigmaBin=sigmaBin - ) - - def bins_to_address(self, bins: Genome18Bin) -> int: - """Convert bins to Genome18 address.""" - return (bins.muBin * 32768 + - bins.rhoBin * 4096 + - bins.cBin * 512 + - bins.mBin * 64 + - bins.neBin * 8 + - bins.sigmaBin) - - def get_neighbors(self, address: int, distance: int = 1) -> List[int]: - """Get neighboring addresses within bounded bin-offset distance.""" - bins = self.address_to_bins(address) - neighbors = [] - - # Generate all addresses within Chebyshev distance in Genome18 bin space. - for mu_offset in range(-distance, distance + 1): - for rho_offset in range(-distance, distance + 1): - for c_offset in range(-distance, distance + 1): - for m_offset in range(-distance, distance + 1): - for ne_offset in range(-distance, distance + 1): - for sigma_offset in range(-distance, distance + 1): - new_bins = Genome18Bin( - muBin=np.clip(bins.muBin + mu_offset, 0, 7), - rhoBin=np.clip(bins.rhoBin + rho_offset, 0, 7), - cBin=np.clip(bins.cBin + c_offset, 0, 7), - mBin=np.clip(bins.mBin + m_offset, 0, 7), - neBin=np.clip(bins.neBin + ne_offset, 0, 7), - sigmaBin=np.clip(bins.sigmaBin + sigma_offset, 0, 7) - ) - new_address = self.bins_to_address(new_bins) - if new_address != address and 0 <= new_address < self.total_states: - neighbors.append(new_address) - - return list(set(neighbors)) # Remove duplicates - - def analyze_region(self, addresses: List[int]) -> Dict[str, Any]: - """Analyze characteristics of a region of the forest.""" - region_data = { - "addresses": addresses, - "count": len(addresses), - "bins_list": [], - "entropy_bins": [], - "thermodynamic_bins": [], - "geometric_bins": [], - "cognitive_bins": [], - "region_distribution": {} - } - - for addr in addresses: - bins = self.address_to_bins(addr) - region_data["bins_list"].append({ - "muBin": bins.muBin, - "rhoBin": bins.rhoBin, - "cBin": bins.cBin, - "mBin": bins.mBin, - "neBin": bins.neBin, - "sigmaBin": bins.sigmaBin - }) - - # Categorize by dominant bin type - entropy_score = bins.mBin + bins.neBin + bins.sigmaBin - thermodynamic_score = (bins.muBin + bins.rhoBin) / 2 # Approximation - geometric_score = bins.cBin - cognitive_score = (bins.muBin + bins.rhoBin + bins.cBin) / 3 - - scores = { - "entropy": entropy_score, - "thermodynamic": thermodynamic_score, - "geometric": geometric_score, - "cognitive": cognitive_score - } - - dominant = max(scores, key=scores.get) - region_data["region_distribution"][dominant] = region_data["region_distribution"].get(dominant, 0) + 1 - - if dominant == "entropy": - region_data["entropy_bins"].append(addr) - elif dominant == "thermodynamic": - region_data["thermodynamic_bins"].append(addr) - elif dominant == "geometric": - region_data["geometric_bins"].append(addr) - elif dominant == "cognitive": - region_data["cognitive_bins"].append(addr) - - return region_data - - def find_transitions(self, address: int, steps: int = 5, max_paths: int = 256) -> List[Dict[str, Any]]: - """Find possible transition paths from a starting address.""" - paths = [] - - def dfs(current_addr, depth, visited, path): - if len(paths) >= max_paths: - return - if depth == 0: - paths.append({ - "path": path.copy(), - "final_address": current_addr, - "length": len(path) - }) - return - - neighbors = self.get_neighbors(current_addr, distance=1) - for neighbor in neighbors: - if len(paths) >= max_paths: - break - if neighbor not in visited: - bins = self.address_to_bins(neighbor) - visited.add(neighbor) - path.append({ - "address": neighbor, - "bins": { - "muBin": bins.muBin, - "rhoBin": bins.rhoBin, - "cBin": bins.cBin, - "mBin": bins.mBin, - "neBin": bins.neBin, - "sigmaBin": bins.sigmaBin - }, - "binary": format(neighbor, '018b') - }) - dfs(neighbor, depth - 1, visited, path) - path.pop() - visited.remove(neighbor) - - dfs(address, steps, set(), []) - return paths - - def calculate_region_density(self, center_address: int, radius: int) -> Dict[str, Any]: - """Calculate density metrics for a region.""" - # Deterministic bounded interval; avoids duplicate-heavy random samples. - lo = max(0, center_address - radius) - hi = min(self.total_states - 1, center_address + radius) - sample_addresses = list(range(lo, hi + 1)) - - # Analyze the sample - region_data = self.analyze_region(sample_addresses) - - # Calculate density - density = len(sample_addresses) / (2 * radius + 1) - - # Calculate clustering coefficient - # (how many neighbors of neighbors are also neighbors) - clustering_coeff = 0.0 - if sample_addresses: - neighbor_counts = [] - for addr in sample_addresses[:50]: # Sample subset for efficiency - neighbors = self.get_neighbors(addr, distance=1) - neighbor_counts.append(len(neighbors)) - - if neighbor_counts: - avg_neighbors = np.mean(neighbor_counts) - max_possible = (2 * 1 + 1) ** 6 - 1 - clustering_coeff = avg_neighbors / max_possible - - return { - "center_address": center_address, - "radius": radius, - "sample_size": len(sample_addresses), - "density": density, - "clustering_coefficient": clustering_coeff, - "region_distribution": region_data["region_distribution"] - } - - def visualize_region(self, addresses: List[int]) -> Dict[str, Any]: - """Create a visualization summary of the region.""" - # Calculate statistics - addresses_array = np.array(addresses) - - # Bin statistics - bins_list = [self.address_to_bins(addr) for addr in addresses] - - mu_bins = [b.muBin for b in bins_list] - rho_bins = [b.rhoBin for b in bins_list] - c_bins = [b.cBin for b in bins_list] - m_bins = [b.mBin for b in bins_list] - ne_bins = [b.neBin for b in bins_list] - sigma_bins = [b.sigmaBin for b in bins_list] - - # Convert to native types - mu_bins = [int(x) for x in mu_bins] - rho_bins = [int(x) for x in rho_bins] - c_bins = [int(x) for x in c_bins] - m_bins = [int(x) for x in m_bins] - ne_bins = [int(x) for x in ne_bins] - sigma_bins = [int(x) for x in sigma_bins] - - return { - "address_range": { - "min": int(np.min(addresses_array)), - "max": int(np.max(addresses_array)), - "mean": float(np.mean(addresses_array)), - "std": float(np.std(addresses_array)) - }, - "bin_statistics": { - "muBin": {"min": min(mu_bins), "max": max(mu_bins), "mean": float(np.mean(mu_bins))}, - "rhoBin": {"min": min(rho_bins), "max": max(rho_bins), "mean": float(np.mean(rho_bins))}, - "cBin": {"min": min(c_bins), "max": max(c_bins), "mean": float(np.mean(c_bins))}, - "mBin": {"min": min(m_bins), "max": max(m_bins), "mean": float(np.mean(m_bins))}, - "neBin": {"min": min(ne_bins), "max": max(ne_bins), "mean": float(np.mean(ne_bins))}, - "sigmaBin": {"min": min(sigma_bins), "max": max(sigma_bins), "mean": float(np.mean(sigma_bins))} - }, - "dominant_patterns": { - "most_common_mu": max(set(mu_bins), key=mu_bins.count), - "most_common_rho": max(set(rho_bins), key=rho_bins.count), - "most_common_c": max(set(c_bins), key=c_bins.count), - "most_common_m": max(set(m_bins), key=m_bins.count), - "most_common_ne": max(set(ne_bins), key=ne_bins.count), - "most_common_sigma": max(set(sigma_bins), key=sigma_bins.count) - } - } - - -def main(): - """Run forest region exploration.""" - print("=" * 70) - print("COUCH FOREST REGION EXPLORATION") - print("=" * 70) - print("\n[*] Exploring Entropy-Dominant Region around address 512") - print("[*] Genome18 space: 262,144 states") - - # Initialize explorer - explorer = ForestExplorer(center_address=512, radius=2000) - - # Get center bins - center_bins = explorer.address_to_bins(512) - print(f"\n[*] Center Address: 512") - print(f" Binary: {format(512, '018b')}") - print(f" Bins: muBin={center_bins.muBin}, rhoBin={center_bins.rhoBin}, cBin={center_bins.cBin}, mBin={center_bins.mBin}, neBin={center_bins.neBin}, sigmaBin={center_bins.sigmaBin}") - - # Get neighbors - print(f"\n[*] Finding neighbors (distance=1)...") - neighbors = explorer.get_neighbors(512, distance=1) - print(f" Neighbors found: {len(neighbors)}") - - # Analyze region - print(f"\n[*] Analyzing region (radius=2000)...") - region_density = explorer.calculate_region_density(512, radius=2000) - print(f" Sample size: {region_density['sample_size']}") - print(f" Density: {region_density['density']:.4f}") - print(f" Clustering coefficient: {region_density['clustering_coefficient']:.4f}") - print(f"\n[*] Region distribution:") - for region, count in region_density['region_distribution'].items(): - pct = count / region_density['sample_size'] * 100 - print(f" {region}: {count} ({pct:.1f}%)") - - # Visualize region - print(f"\n[*] Visualizing region...") - sample_addresses = [512 + i for i in range(-100, 101) if 0 <= 512 + i < 262144] - viz_data = explorer.visualize_region(sample_addresses) - print(f"\n[*] Address range:") - print(f" Min: {viz_data['address_range']['min']}") - print(f" Max: {viz_data['address_range']['max']}") - print(f" Mean: {viz_data['address_range']['mean']:.2f}") - print(f" Std: {viz_data['address_range']['std']:.2f}") - - print(f"\n[*] Dominant patterns:") - for bin_name, value in viz_data['dominant_patterns'].items(): - print(f" {bin_name}: {value}") - - # Find transitions - print(f"\n[*] Finding transition paths (steps=3)...") - transitions = explorer.find_transitions(512, steps=3, max_paths=256) - print(f" Paths found: {len(transitions)}") - - # Show sample paths - if transitions: - print(f"\n[*] Sample transition paths:") - for i, path in enumerate(transitions[:5]): - print(f" Path {i+1}: {path['path'][0]['address']} → {path['final_address']} (length {path['length']})") - - # Explore entropy-dominant neighbors - print(f"\n[*] Exploring entropy-dominant neighbors...") - entropy_dominant = [] - for addr in neighbors[:50]: # Sample subset - bins = explorer.address_to_bins(addr) - entropy_score = bins.mBin + bins.neBin + bins.sigmaBin - if entropy_score > (bins.muBin + bins.rhoBin + bins.cBin) / 3: - entropy_dominant.append(addr) - - print(f" Entropy-dominant neighbors: {len(entropy_dominant)}") - if entropy_dominant: - print(f" Sample addresses: {entropy_dominant[:5]}") - - # Save results (convert numpy types to native Python) - def convert_to_native(obj): - """Convert numpy types to native Python types for JSON serialization.""" - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.integer): - return int(obj) - elif isinstance(obj, np.floating): - return float(obj) - elif isinstance(obj, np.bool_): - return bool(obj) - elif isinstance(obj, dict): - return {k: convert_to_native(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [convert_to_native(item) for item in obj] - else: - return obj - - results = { - "center_address": 512, - "center_bins": { - "muBin": center_bins.muBin, - "rhoBin": center_bins.rhoBin, - "cBin": center_bins.cBin, - "mBin": center_bins.mBin, - "neBin": center_bins.neBin, - "sigmaBin": center_bins.sigmaBin - }, - "neighbors": convert_to_native(neighbors[:100]), # Sample - "neighbor_count": len(neighbors), - "region_density": convert_to_native(region_density), - "visualization": convert_to_native(viz_data), - "transition_paths": convert_to_native(transitions[:10]), # Sample - "entropy_dominant_neighbors": convert_to_native(entropy_dominant) - } - - output_path = "/home/allaun/Documents/Research Stack/data/couch_forest_exploration.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ COUCH FOREST REGION EXPLORATION COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/couch_nspace_map_analysis.py b/5-Applications/scripts/couch_nspace_map_analysis.py deleted file mode 100644 index 773b55f9..00000000 --- a/5-Applications/scripts/couch_nspace_map_analysis.py +++ /dev/null @@ -1,665 +0,0 @@ -#!/usr/bin/env python3 -""" -COUCH Equation N-Space Map Analysis - -Applies every Research Stack mathematical framework to COUCH equation -as an n-space map with "=" at the center. - -COUCH: ẍ_i + γẋ_i + ω_i²x_i + Σ_j κ_ij(x_i - x_j) = F(t) - -Treat "=" as central attractor in n-dimensional space. -""" - -import numpy as np -from typing import Dict, List, Tuple, Any -from dataclasses import dataclass -from enum import Enum -import json -import hashlib - - -class NSpaceFramework(Enum): - """Mathematical frameworks from Research Stack.""" - PIST = "PIST (Perfectly Imperfect Square Theory)" - FAMM = "FAMM (Fractal Adaptive Manifold Mapping)" - QUATERNION = "Quaternion Counter-Rotation" - MENGER = "Menger Sponge Fractal" - ENE = "ENE Triangle Manifold" - MERKLE = "Merkle Jack Tree" - MANIFOLD = "Manifold Compression" - TRIUMVIRATE = "Triumvirate Clock" - SVQF = "SVQF Sparse Voxel" - CALABI_YAU = "Calabi-Yau Compactification" - RICCI_FLOW = "Ricci Flow Adaptation" - BRAID = "Braid Group Theory" - - -@dataclass -class COUCHState: - """COUCH oscillator state in n-space.""" - position: np.ndarray # x_i - velocity: np.ndarray # ẋ_i - acceleration: np.ndarray # ẍ_i - coupling: np.ndarray # κ_ij - damping: float # γ - frequency: np.ndarray # ω_i - forcing: float # F(t) - hysteresis: float # H = ∮ F(t) · dx - - -@dataclass -class NSpaceCoordinate: - """N-space coordinate with framework-specific mapping.""" - framework: NSpaceFramework - coordinates: np.ndarray - distance_from_center: float - is_admissible: bool - - -class COUCHNSpaceMap: - """ - COUCH equation as n-space map with "=" at center. - - Central attractor: "=" (equilibrium point where ẋ_i = 0, ẍ_i = 0) - """ - - def __init__(self, n_oscillators: int = 3, n_dimensions: int = 10): - self.n_oscillators = n_oscillators - self.n_dimensions = n_dimensions - self.center = np.zeros(n_dimensions) # "=" as central attractor - self.frameworks = list(NSpaceFramework) - self.framework_mappings: Dict[NSpaceFramework, List[NSpaceCoordinate]] = {} - - def initialize_couch_state(self) -> COUCHState: - """Initialize COUCH oscillator system.""" - np.random.seed(42) - - position = np.random.randn(self.n_oscillators) * 0.5 - velocity = np.random.randn(self.n_oscillators) * 0.3 - acceleration = np.zeros(self.n_oscillators) - - # Coupling matrix (symmetric) - coupling = np.random.randn(self.n_oscillators, self.n_oscillators) * 0.1 - coupling = (coupling + coupling.T) / 2 - - damping = 0.5 - frequency = np.ones(self.n_oscillators) * 2.0 - forcing = 1.0 - - hysteresis = 0.0 - - return COUCHState( - position=position, - velocity=velocity, - acceleration=acceleration, - coupling=coupling, - damping=damping, - frequency=frequency, - forcing=forcing, - hysteresis=hysteresis - ) - - def apply_pist_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply PIST (Perfectly Imperfect Square Theory) mapping. - - Shell coordinates: (k, t, H) where H = hysteresis - FAMM frustration: Φ = trace(stress_tensor) / mass - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Shell index based on position magnitude - k = int(np.abs(state.position[i]) * 10) - - # Time step (iteration) - t = i - - # Hysteresis as third coordinate - h = state.hysteresis - - # Mass (vertex product) - a = float(k % 3) - b = float((k + 1) - (k % 3)) - c = float(k) - mass = a * b * c - if mass == 0: - mass = 1.0 - - # Stress tensor (simplified) - stress = np.array([ - [state.position[i], state.velocity[i], state.acceleration[i]], - [state.velocity[i], state.position[i], state.acceleration[i]], - [state.acceleration[i], state.position[i], state.velocity[i]] - ]) - - # FAMM frustration - phi = np.trace(stress) / mass - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0] = k - coord[1] = t - coord[2] = h - coord[3] = phi - coord[4:7] = [a, b, c] - - distance = np.linalg.norm(coord - self.center) - is_admissible = phi <= 1.0 - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.PIST, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_menger_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply Menger Sponge fractal mapping. - - Hausdorff dimension: d_H = log(20)/log(3) ≈ 2.7268 - Address: menger_hash(x,y,z) ⊕ fractal_offset - """ - coordinates = [] - - d_H = 2.7268 - - for i in range(self.n_oscillators): - # Map oscillator state to 3D coordinates - x = int(state.position[i] * 10 + 32) - y = int(state.velocity[i] * 10 + 32) - z = int(state.acceleration[i] * 10 + 32) - - # Menger hash - hash_val = x ^ ((y << 1) & 0xFFFFFFFF) ^ ((z << 2) & 0xFFFFFFFF) - - # Fractal offset - offset = int((x + y + z) * d_H) - - # Address - address = hash_val ^ offset - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0] = x - coord[1] = y - coord[2] = z - coord[3] = hash_val - coord[4] = offset - coord[5] = address - coord[6] = d_H - - distance = np.linalg.norm(coord - self.center) - is_admissible = True # All Menger coordinates admissible - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.MENGER, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_ene_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply ENE Triangle Manifold mapping. - - Concentric shells with triangular number indexing - Rotation field + transmission field - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Shell index - k = int(np.abs(state.position[i]) * 5) - - # Triangular number - t_num = k * (k + 1) // 2 - - # Vertex parameters - a = float(k % 3) - b = float((k + 1) - (k % 3)) - c = float(k) - - # Mass - mass = a * b * c if (a * b * c) > 0 else 1.0 - - # Rotation angle - rotation = float(k) * 0.1 - - # Bandwidth and latency (curvature-based) - curvature = 0.5 - bandwidth = 10.0 * (1.0 - curvature) - latency = 1.0 + curvature - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0] = k - coord[1] = t_num - coord[2:5] = [a, b, c] - coord[5] = mass - coord[6] = rotation - coord[7] = bandwidth - coord[8] = latency - - distance = np.linalg.norm(coord - self.center) - is_admissible = True - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.ENE, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_quaternion_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply quaternion counter-rotation mapping. - - Zero-net-angular-momentum design - Counter-rotating steps: q at layer N, q⁻¹ at layer N-1 - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Map state to quaternion (w, x, y, z) - w = 1.0 # Scalar part - x = state.position[i] - y = state.velocity[i] - z = state.acceleration[i] - - # Normalize - norm = np.sqrt(w**2 + x**2 + y**2 + z**2) - if norm > 0: - w, x, y, z = w/norm, x/norm, y/norm, z/norm - - # Counter-rotation (inverse quaternion) - w_inv = w - x_inv = -x - y_inv = -y - z_inv = -z - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0:4] = [w, x, y, z] - coord[4:8] = [w_inv, x_inv, y_inv, z_inv] - - distance = np.linalg.norm(coord - self.center) - is_admissible = True - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.QUATERNION, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_manifold_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply manifold compression mapping. - - Isometric chart constraints - Jacobian determinant for compression - """ - coordinates = [] - - for i in range(self.n_oscillators): - # State as manifold point - x = state.position[i] - v = state.velocity[i] - a = state.acceleration[i] - - # Jacobian (simplified 3x3) - J = np.array([ - [1.0, 0.1, 0.0], - [0.1, 1.0, 0.1], - [0.0, 0.1, 1.0] - ]) - - # Jacobian determinant - det_J = np.linalg.det(J) - - # Chart bloat: B = N/D - N = 3 # Representation dimension - D = 2 # Intrinsic dimension - bloat = N / D - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0:3] = [x, v, a] - coord[3] = det_J - coord[4] = bloat - coord[5] = N - coord[6] = D - - distance = np.linalg.norm(coord - self.center) - is_admissible = bloat == 1.0 # Isometric when bloat = 1 - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.MANIFOLD, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_braid_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply braid group theory mapping. - - Strand crossing invariants - Topological constraints - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Treat oscillators as strands - strand_a = i - strand_b = (i + 1) % self.n_oscillators - - # Crossing direction based on relative position - crossing = 1 if state.position[i] > state.position[strand_b] else -1 - - # Invariant - invariant = f"braid_{strand_a}_{strand_b}_{crossing}" - - # Map to n-space (encode invariant numerically) - coord = np.zeros(self.n_dimensions) - coord[0] = strand_a - coord[1] = strand_b - coord[2] = crossing - digest = hashlib.sha256(invariant.encode("utf-8")).digest() - coord[3] = int.from_bytes(digest[:8], "big") % 1000 - - distance = np.linalg.norm(coord - self.center) - is_admissible = True - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.BRAID, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_svqf_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply SVQF sparse voxel indexing mapping. - - Sparse voxel representation - Only store admissible regions - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Voxel coordinates - vx = int(state.position[i] * 10 + 50) - vy = int(state.velocity[i] * 10 + 50) - vz = int(state.acceleration[i] * 10 + 50) - - # Sparse index (only if within bounds) - is_admissible = 0 <= vx < 100 and 0 <= vy < 100 and 0 <= vz < 100 - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0:3] = [vx, vy, vz] - coord[3] = 1 if is_admissible else 0 - coord[4] = vx * 10000 + vy * 100 + vz # Linear index - - distance = np.linalg.norm(coord - self.center) - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.SVQF, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_triumvirate_mapping(self, state: COUCHState) -> List[NSpaceCoordinate]: - """ - Apply Triumvirate clock mapping. - - Builder-Judge-Warden roles - Clock actions: ADD, PAUSE, SUBTRACT - """ - coordinates = [] - - for i in range(self.n_oscillators): - # Map oscillator state to clock phase - phase = np.arctan2(state.velocity[i], state.position[i]) - - # Clock action based on phase - if phase > np.pi/2: - action = 1 # ADD (Builder) - elif phase < -np.pi/2: - action = -1 # SUBTRACT (Warden) - else: - action = 0 # PAUSE (Judge) - - # Map to n-space - coord = np.zeros(self.n_dimensions) - coord[0] = phase - coord[1] = action - coord[2] = i - - distance = np.linalg.norm(coord - self.center) - is_admissible = True - - coordinates.append(NSpaceCoordinate( - framework=NSpaceFramework.TRIUMVIRATE, - coordinates=coord, - distance_from_center=distance, - is_admissible=is_admissible - )) - - return coordinates - - def apply_all_frameworks(self, state: COUCHState) -> Dict[NSpaceFramework, List[NSpaceCoordinate]]: - """Apply all frameworks to COUCH state.""" - mappings = {} - - mappings[NSpaceFramework.PIST] = self.apply_pist_mapping(state) - mappings[NSpaceFramework.MENGER] = self.apply_menger_mapping(state) - mappings[NSpaceFramework.ENE] = self.apply_ene_mapping(state) - mappings[NSpaceFramework.QUATERNION] = self.apply_quaternion_mapping(state) - mappings[NSpaceFramework.MANIFOLD] = self.apply_manifold_mapping(state) - mappings[NSpaceFramework.BRAID] = self.apply_braid_mapping(state) - mappings[NSpaceFramework.SVQF] = self.apply_svqf_mapping(state) - mappings[NSpaceFramework.TRIUMVIRATE] = self.apply_triumvirate_mapping(state) - - self.framework_mappings = mappings - return mappings - - def analyze_center_attractor(self) -> Dict[str, Any]: - """ - Analyze "=" as central attractor in n-space. - - Check if all frameworks converge to center or diverge. - """ - analysis = { - "center": self.center.tolist(), - "frameworks": {}, - "convergence": {}, - "admissibility": {} - } - - for framework, coords in self.framework_mappings.items(): - distances = [c.distance_from_center for c in coords] - admissible = [c.is_admissible for c in coords] - - analysis["frameworks"][framework.value] = { - "avg_distance": np.mean(distances), - "max_distance": np.max(distances), - "min_distance": np.min(distances), - "admissible_count": sum(admissible), - "total_count": len(admissible), - "admissibility_ratio": sum(admissible) / len(admissible) if admissible else 0 - } - - # Convergence: check if distances decrease toward center - analysis["convergence"][framework.value] = distances[0] > distances[-1] if len(distances) > 1 else True - - # Admissibility - analysis["admissibility"][framework.value] = all(admissible) - - return analysis - - def find_framework_intersections(self) -> Dict[str, Any]: - """ - Find intersections between framework mappings. - - Identify coordinates where multiple frameworks agree. - """ - intersections = { - "pairwise": {}, - "common_admissible": [], - "framework_distances": {} - } - - frameworks = list(self.framework_mappings.keys()) - - # Pairwise intersections - for i in range(len(frameworks)): - for j in range(i + 1, len(frameworks)): - f1 = frameworks[i] - f2 = frameworks[j] - - coords1 = self.framework_mappings[f1] - coords2 = self.framework_mappings[f2] - - # Find closest pairs - min_dist = float('inf') - closest_pair = None - - for c1 in coords1: - for c2 in coords2: - dist = np.linalg.norm(c1.coordinates - c2.coordinates) - if dist < min_dist: - min_dist = dist - closest_pair = (c1, c2) - - intersections["pairwise"][f"{f1.value} ↔ {f2.value}"] = { - "min_distance": min_dist, - "closest_pair_coords": closest_pair[0].coordinates.tolist() if closest_pair else None, - "both_admissible": closest_pair[0].is_admissible and closest_pair[1].is_admissible if closest_pair else False - } - - # Common admissible coordinates - all_admissible = [] - for framework, coords in self.framework_mappings.items(): - admissible_coords = [c.coordinates for c in coords if c.is_admissible] - all_admissible.append(admissible_coords) - - # Find intersection of all admissible sets - if all_admissible: - common = all_admissible[0] - for coords in all_admissible[1:]: - common = [c for c in common if any(np.allclose(c, other) for other in coords)] - intersections["common_admissible"] = [c.tolist() for c in common[:5]] # First 5 - - return intersections - - -def main(): - """Run COUCH n-space map analysis.""" - print("=" * 70) - print("COUCH EQUATION N-SPACE MAP ANALYSIS") - print("=" * 70) - print("\n[*] Treating COUCH as n-space map with '=' at center") - print("[*] Applying all Research Stack mathematical frameworks") - - # Initialize n-space map - nspace_map = COUCHNSpaceMap(n_oscillators=3, n_dimensions=10) - - # Initialize COUCH state - couch_state = nspace_map.initialize_couch_state() - - print(f"\n[*] COUCH State:") - print(f" Oscillators: {nspace_map.n_oscillators}") - print(f" Position: {couch_state.position}") - print(f" Velocity: {couch_state.velocity}") - print(f" Damping: {couch_state.damping}") - print(f" Forcing: {couch_state.forcing}") - - # Apply all frameworks - print(f"\n[*] Applying {len(nspace_map.frameworks)} frameworks...") - mappings = nspace_map.apply_all_frameworks(couch_state) - - for framework, coords in mappings.items(): - admissible_count = sum(c.is_admissible for c in coords) - print(f" {framework.value}: {len(coords)} coordinates, {admissible_count} admissible") - - # Analyze center attractor - print(f"\n[*] Analyzing '=' as central attractor...") - center_analysis = nspace_map.analyze_center_attractor() - - print(f"\n[*] Center Attractor Analysis:") - for framework, stats in center_analysis["frameworks"].items(): - print(f" {framework}:") - print(f" Avg distance: {stats['avg_distance']:.4f}") - print(f" Admissibility: {stats['admissibility_ratio']:.2%}") - - # Find intersections - print(f"\n[*] Finding framework intersections...") - intersections = nspace_map.find_framework_intersections() - - print(f"\n[*] Framework Intersections:") - for pair, data in intersections["pairwise"].items(): - print(f" {pair}:") - print(f" Min distance: {data['min_distance']:.4f}") - print(f" Both admissible: {data['both_admissible']}") - - # Save results (convert numpy types to native Python) - def convert_to_native(obj): - """Convert numpy types to native Python types for JSON serialization.""" - if isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, np.integer): - return int(obj) - elif isinstance(obj, np.floating): - return float(obj) - elif isinstance(obj, np.bool_): - return bool(obj) - elif isinstance(obj, dict): - return {k: convert_to_native(v) for k, v in obj.items()} - elif isinstance(obj, list): - return [convert_to_native(item) for item in obj] - else: - return obj - - results = { - "couch_state": { - "position": couch_state.position.tolist(), - "velocity": couch_state.velocity.tolist(), - "damping": float(couch_state.damping), - "forcing": float(couch_state.forcing) - }, - "center_analysis": convert_to_native(center_analysis), - "intersections": convert_to_native(intersections) - } - - output_path = "/home/allaun/Documents/Research Stack/data/couch_nspace_map_analysis.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ COUCH N-SPACE MAP ANALYSIS COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/cpu_lemma_verifier.py b/5-Applications/scripts/cpu_lemma_verifier.py deleted file mode 100644 index aa74d58e..00000000 --- a/5-Applications/scripts/cpu_lemma_verifier.py +++ /dev/null @@ -1,108 +0,0 @@ -#!/usr/bin/env python3 -""" -CPU-based Lemma Verification for Lean Proofs -Uses exhaustive search across bounded ranges to verify arithmetic lemmas -""" -from typing import Tuple, List - -class CPULemmaVerifier: - """CPU-based verification of Lean lemmas across bounded ranges""" - - def __init__(self): - print("CPU Lemma Verifier initialized") - - def verify_weighted_term_bounded(self, max_e: int = 100, max_alpha: int = 100) -> bool: - """ - Verify (E * α) / 65536 <= E for all E in [0, max_e] and α in [0, max_alpha] - Uses exhaustive search across bounded ranges - """ - print(f"Verifying weighted term bounded for E in [0,{max_e}], α in [0,{max_alpha}]") - for e in range(max_e + 1): - for alpha in range(max_alpha + 1): - product = e * alpha - divided = product // 65536 - if divided > e: - print(f"FAILED at E={e}, α={alpha}: {divided} > {e}") - return False - print("✓ Weighted term bounded verification PASSED") - return True - - def verify_bit_shift_equivalence(self, max_x: int = 1000) -> bool: - """ - Verify x >>> 16 = x / 65536 for all x in [0, max_x] - Uses exhaustive search - """ - print(f"Verifying bit shift equivalence for x in [0,{max_x}]") - for x in range(max_x + 1): - shifted = x >> 16 - divided = x // 65536 - if shifted != divided: - print(f"FAILED at x={x}: {shifted} != {divided}") - return False - print("✓ Bit shift equivalence verification PASSED") - return True - - def verify_bit_shift_monotonicity(self, max_val: int = 100) -> bool: - """ - Verify a >>> 16 <= b >>> 16 when a <= b - Uses exhaustive search across all pairs - """ - print(f"Verifying bit shift monotonicity for a,b in [0,{max_val}]") - for a in range(max_val + 1): - for b in range(a, max_val + 1): - a_shifted = a >> 16 - b_shifted = b >> 16 - if a_shifted > b_shifted: - print(f"FAILED at a={a}, b={b}: {a_shifted} > {b_shifted}") - return False - print("✓ Bit shift monotonicity verification PASSED") - return True - - def verify_division_comparison(self, max_x: int = 50, max_divisor: int = 50) -> bool: - """ - Verify x / a <= x / b when a > b and x >= 0 - Uses exhaustive search across all valid triples - """ - print(f"Verifying division comparison for x in [0,{max_x}], a,b in [1,{max_divisor}]") - for x in range(max_x + 1): - for b in range(1, max_divisor + 1): - for a in range(b + 1, max_divisor + 1): - div_a = x // a - div_b = x // b - if div_a > div_b: - print(f"FAILED at x={x}, a={a}, b={b}: {div_a} > {div_b}") - return False - print("✓ Division comparison verification PASSED") - return True - -def main(): - """Run CPU verification for all lemmas""" - verifier = CPULemmaVerifier() - - print("Starting CPU-based lemma verification...") - print("=" * 60) - - # Verify each lemma with bounded ranges - results = {} - results['weighted_term_bounded'] = verifier.verify_weighted_term_bounded(max_e=100, max_alpha=100) - results['bit_shift_equivalence'] = verifier.verify_bit_shift_equivalence(max_x=1000) - results['bit_shift_monotonicity'] = verifier.verify_bit_shift_monotonicity(max_val=100) - results['division_comparison'] = verifier.verify_division_comparison(max_x=50, max_divisor=50) - - print("=" * 60) - print("CPU Verification Results:") - for lemma, passed in results.items(): - status = "✓ PASSED" if passed else "✗ FAILED" - print(f" {lemma}: {status}") - - all_passed = all(results.values()) - if all_passed: - print("\n✓ All lemmas verified successfully via CPU exhaustive search!") - print("This provides computational evidence for the Lean proofs.") - else: - print("\n✗ Some lemmas failed verification") - - return all_passed - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/cpu_topology_wires_analysis.py b/5-Applications/scripts/cpu_topology_wires_analysis.py deleted file mode 100644 index 291f6a5c..00000000 --- a/5-Applications/scripts/cpu_topology_wires_analysis.py +++ /dev/null @@ -1,297 +0,0 @@ -#!/usr/bin/env python3 -""" -CPU Topology and Wires Analysis -Analyzes CPU as both topology and wires for comprehensive device integration. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class CPUTopologyWiresAnalysis: - """Analyzes CPU as both topology and wires for comprehensive device integration.""" - - def __init__(self): - # CPU device information - self.cpu_device = { - "name": "CPU (AMD Ryzen 7 7800X3D - Zen 4 architecture)", - "topology_aspects": { - "cores": "8 cores (1 CCD)", - "threads": "16 threads (SMT)", - "ccds": "1 Core Complex Die (CCD)", - "ioc": "I/O Die (integrated memory controller, PCIe, etc.)", - "cache_hierarchy": "L1 (32KB per core), L2 (1MB per core), L3 (96MB shared)", - "interconnect": "Infinity Fabric (data fabric)", - "numa_nodes": "1 NUMA node", - "core_topology": "Ring bus or mesh interconnect within CCD" - }, - "wire_aspects": { - "interconnect_wires": "Infinity Fabric interconnects (wires between cores, caches, I/O)", - "memory_wires": "DDR5 memory channel wires (128-bit data bus)", - "pcie_wires": "PCIe 5.0 x16 lanes (wires to PCIe controller)", - "io_wires": "I/O die wires (chiplet interconnects)", - "power_delivery_wires": "VRM power delivery wires", - "thermal_wires": "Thermal sensor wires", - "clock_distribution": "Clock distribution network (wires)", - "signal_integrity": "High-speed signal integrity (wires, traces)" - }, - "computational_potential": "VERY HIGH (8 cores, 16 threads, 96MB L3 cache)", - "topology_value": "VERY HIGH (core topology, cache hierarchy, interconnect)", - "wire_value": "VERY HIGH (high-speed interconnects, memory, PCIe wires)" - } - - # Previous device count - self.previous_device_count = 37 # 19 original + 18 additional - - def analyze_cpu_topology(self) -> Dict: - """Analyze CPU topology aspects.""" - topology_analysis = { - "core_topology": { - "type": "Ring bus or mesh interconnect", - "cores": 8, - "threads": 16, - "ccds": 1, - "interconnect": "Infinity Fabric", - "numa_nodes": 1, - "significance_score": 95.0 - }, - "cache_topology": { - "l1_cache": "32KB per core (instruction + data)", - "l2_cache": "1MB per core (private)", - "l3_cache": "96MB shared (3D V-Cache)", - "cache_coherence": "MOESI protocol", - "significance_score": 90.0 - }, - "interconnect_topology": { - "infinity_fabric": "Data fabric (wires between cores, caches, I/O)", - "bandwidth": "High bandwidth (interconnect)", - "latency": "Low latency (on-die)", - "significance_score": 85.0 - }, - "numa_topology": { - "numa_nodes": 1, - "memory_affinity": "Local memory access", - "significance_score": 70.0 - } - } - - return topology_analysis - - def analyze_cpu_wires(self) -> Dict: - """Analyze CPU wire aspects.""" - wire_analysis = { - "interconnect_wires": { - "type": "Infinity Fabric interconnects", - "function": "Wires between cores, caches, I/O die", - "bandwidth": "High bandwidth (on-die)", - "latency": "Low latency (on-die)", - "significance_score": 85.0 - }, - "memory_wires": { - "type": "DDR5 memory channel wires", - "function": "128-bit data bus to DDR5 memory", - "bandwidth": "50-100 GB/s (DDR5)", - "latency": "10-100ns (memory access)", - "significance_score": 80.0 - }, - "pcie_wires": { - "type": "PCIe 5.0 x16 lanes", - "function": "Wires to PCIe controller", - "bandwidth": "256 Gbps (16 lanes @ 16.0 GT/s)", - "latency": "ns (PCIe)", - "significance_score": 90.0 - }, - "io_wires": { - "type": "I/O die chiplet interconnects", - "function": "Wires between CCD and I/O die", - "bandwidth": "High bandwidth (chiplet interconnect)", - "latency": "Low latency (on-die)", - "significance_score": 75.0 - }, - "power_delivery_wires": { - "type": "VRM power delivery wires", - "function": "Power delivery to CPU", - "voltage": "1.1-1.4V (Vcore)", - "current": "High current (VRM)", - "significance_score": 65.0 - }, - "thermal_wires": { - "type": "Thermal sensor wires", - "function": "Temperature monitoring", - "sensors": "Multiple thermal sensors", - "significance_score": 50.0 - }, - "clock_distribution": { - "type": "Clock distribution network", - "function": "Clock signal distribution", - "frequency": "Base clock + boost", - "significance_score": 60.0 - }, - "signal_integrity": { - "type": "High-speed signal integrity", - "function": "Wire and trace optimization", - "significance_score": 70.0 - } - } - - return wire_analysis - - def integrate_cpu_into_devices(self) -> Dict: - """Integrate CPU into comprehensive device analysis.""" - integration = { - "new_device": "cpu_topology_wires", - "name": "CPU (AMD Ryzen 7 7800X3D - Topology and Wires)", - "total_devices": self.previous_device_count + 1, # 37 + 1 = 38 - "device_categories": { - "compute_devices": "Now includes CPU (8 cores, 16 threads)", - "topology_devices": "Now includes CPU topology (core topology, cache hierarchy)", - "wire_devices": "Now includes CPU wires (interconnects, memory, PCIe)" - }, - "math_categories": [ - "General Semantics", - "Geometry", - "Thermodynamic", - "Control Theory", - "Physical Bind" - ], - "foundation_kernels": [ - "F08", "F09", "F10", # Geometry (topology) - "F04", "F05", "F06", # Thermodynamic (power, thermal) - "F11", "F12" # Control Theory (clock, signal integrity) - ], - "significance_score": 87.5 # Average of topology and wire aspects - } - - return integration - - def recalculate_comprehensive_expansion(self) -> Dict: - """Recalculate comprehensive computational expansion with CPU.""" - # New device count: 37 + 1 = 38 - new_device_count = self.previous_device_count + 1 - - # Base capacity (all devices including CPU) - base_capacity = new_device_count * 50 # Average significance score per device - - # Topology integration - topology_multiplier = 1.5 - - # Parallel expansion (all devices) - parallel_expansion = new_device_count * 2 - - # Math database integration - math_database_multiplier = 1.5 - - # Forest math compression - forest_math_multiplier = 1.2 - - # Genome18 encoding - genome18_multiplier = 1.3 - - # CPU topology multiplier (additional 1.1x for CPU topology) - cpu_topology_multiplier = 1.1 - - # CPU wires multiplier (additional 1.1x for CPU wires) - cpu_wires_multiplier = 1.1 - - # Calculate expanded capacity - expanded_capacity = (base_capacity * - topology_multiplier * - parallel_expansion * - math_database_multiplier * - forest_math_multiplier * - genome18_multiplier * - cpu_topology_multiplier * - cpu_wires_multiplier) - - expansion_factor = expanded_capacity / base_capacity - - calculation = { - "new_device_count": new_device_count, - "base_capacity": base_capacity, - "topology_multiplier": topology_multiplier, - "parallel_expansion": parallel_expansion, - "math_database_multiplier": math_database_multiplier, - "forest_math_multiplier": forest_math_multiplier, - "genome18_multiplier": genome18_multiplier, - "cpu_topology_multiplier": cpu_topology_multiplier, - "cpu_wires_multiplier": cpu_wires_multiplier, - "expanded_capacity": expanded_capacity, - "expansion_factor": expansion_factor, - "total_multiplier": (topology_multiplier * - parallel_expansion * - math_database_multiplier * - forest_math_multiplier * - genome18_multiplier * - cpu_topology_multiplier * - cpu_wires_multiplier) - } - - return calculation - - def run_analysis(self) -> Dict: - """Run CPU topology and wires analysis.""" - print("=" * 60) - print("CPU TOPOLOGY AND WIRES ANALYSIS") - print("=" * 60) - - # Step 1: Analyze CPU topology - print("\n[1/4] Analyzing CPU topology aspects...") - topology_analysis = self.analyze_cpu_topology() - print(f" Topology Aspects: {len(topology_analysis)}") - for aspect, details in topology_analysis.items(): - print(f" {aspect}: {details['significance_score']}") - - # Step 2: Analyze CPU wires - print("[2/4] Analyzing CPU wire aspects...") - wire_analysis = self.analyze_cpu_wires() - print(f" Wire Aspects: {len(wire_analysis)}") - for aspect, details in wire_analysis.items(): - print(f" {aspect}: {details['significance_score']}") - - # Step 3: Integrate CPU into devices - print("[3/4] Integrating CPU into comprehensive device analysis...") - integration = self.integrate_cpu_into_devices() - print(f" Total Devices: {integration['total_devices']}") - print(f" Significance Score: {integration['significance_score']}") - - # Step 4: Recalculate comprehensive expansion - print("[4/4] Recalculating comprehensive computational expansion...") - calculation = self.recalculate_comprehensive_expansion() - print(f" Base Capacity: {calculation['base_capacity']}") - print(f" Expanded Capacity: {calculation['expanded_capacity']}") - print(f" Expansion Factor: {calculation['expansion_factor']:.2f}x") - print(f" Total Multiplier: {calculation['total_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("CPU TOPOLOGY AND WIRES ANALYSIS COMPLETE") - print("=" * 60) - - return { - "cpu_topology_analysis": topology_analysis, - "cpu_wire_analysis": wire_analysis, - "cpu_integration": integration, - "recalculated_expansion": calculation - } - -if __name__ == '__main__': - analyzer = CPUTopologyWiresAnalysis() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "cpu_topology_wires_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("CPU TOPOLOGY AND WIRES SUMMARY") - print("=" * 60) - print(f"Total Devices: {results['cpu_integration']['total_devices']}") - print(f"CPU Significance Score: {results['cpu_integration']['significance_score']}") - print(f"Expanded Capacity: {results['recalculated_expansion']['expanded_capacity']}") - print(f"Expansion Factor: {results['recalculated_expansion']['expansion_factor']:.2f}x") diff --git a/5-Applications/scripts/create_affirm_db.py b/5-Applications/scripts/create_affirm_db.py deleted file mode 100644 index 3bc8513a..00000000 --- a/5-Applications/scripts/create_affirm_db.py +++ /dev/null @@ -1,146 +0,0 @@ -import sqlite3 -import json -import os - -# Data from the browser subagent (Current + Past) -data = { - "current_loans": [ - { "merchant": "Amazon", "amount_due": "$47.04", "due_date": "May 28" }, - { "merchant": "Amazon", "amount_due": "$23.10", "due_date": "Jun 2" }, - { "merchant": "Mint Mobile", "amount_due": "$36.16", "due_date": "Jun 3" }, - { "merchant": "Wolfram Alpha", "amount_due": "$10.55", "due_date": "Jun 10" }, - { "merchant": "Walmart", "amount_due": "$13.41", "due_date": "Jun 11" }, - { "merchant": "vitalrecords", "amount_due": "$11.05", "due_date": "Jun 13" }, - { "merchant": "Mint Mobile", "amount_due": "$10.05", "due_date": "Jun 13" }, - { "merchant": "msi", "amount_due": "$31.34", "due_date": "Jun 14" }, - { "merchant": "eBay", "amount_due": "$11.30", "due_date": "Jun 16" }, - { "merchant": "Agoda.com", "amount_due": "$24.25", "due_date": "Jun 16" }, - { "merchant": "Newegg", "amount_due": "$12.85", "due_date": "Jun 18" }, - { "merchant": "Kimi.com", "amount_due": "$37.67", "due_date": "Jun 21" }, - { "merchant": "Amazon", "amount_due": "$21.25", "due_date": "Jun 21" }, - { "merchant": "Affirm Virtual Card", "amount_due": "$33.58", "due_date": "Jun 22" }, - { "merchant": "Amazon", "amount_due": "$14.24", "due_date": "Jun 23" }, - { "merchant": "Walmart", "amount_due": "$12.06", "due_date": "Jun 26" }, - { "merchant": "Northwest Registered Agents", "amount_due": "$16.58", "due_date": "Jun 27" } - ], - "past_loans": [ - { "merchant": "Newegg", "amount": "$101.63", "date": "Apr 28, 2026", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$95.14", "date": "Apr 28, 2026", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$796.97", "date": "Apr 2, 2026", "status": "Paid" }, - { "merchant": "Affirm", "amount": "$45.00", "date": "Apr 2, 2026", "status": "Paid" }, - { "merchant": "Uber", "amount": "$100.00", "date": "Apr 2, 2026", "status": "Paid" }, - { "merchant": "Groupon", "amount": "$140.00", "date": "Feb 26, 2026", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$172.29", "date": "Feb 26, 2026", "status": "Paid" }, - { "merchant": "DoorDash", "amount": "$60.00", "date": "Feb 12, 2026", "status": "Paid" }, - { "merchant": "Priceline", "amount": "$92.00", "date": "Dec 29, 2025", "status": "Paid" }, - { "merchant": "amazons", "amount": "$86.00", "date": "Dec 27, 2025", "status": "Paid" }, - { "merchant": "Expedia", "amount": "$93.95", "date": "Dec 27, 2025", "status": "Paid" }, - { "merchant": "Newegg", "amount": "$440.69", "date": "Sep 22, 2025", "status": "Paid" }, - { "merchant": "It's A 10", "amount": "$65.00", "date": "Aug 9, 2025", "status": "Paid" }, - { "merchant": "Best Buy", "amount": "$670.00", "date": "Jun 30, 2025", "status": "Paid" }, - { "merchant": "amazons", "amount": "$144.00", "date": "May 29, 2025", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$135.76", "date": "Mar 15, 2025", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$171.08", "date": "Feb 26, 2025", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$96.30", "date": "Jan 31, 2025", "status": "Paid" }, - { "merchant": "Newegg", "amount": "$1,891.90", "date": "Jan 9, 2025", "status": "Paid" }, - { "merchant": "Zenni", "amount": "$78.99", "date": "Sep 5, 2024", "status": "Paid" }, - { "merchant": "Ames Lock", "amount": "$250.00", "date": "Jul 18, 2024", "status": "Paid" }, - { "merchant": "Amazon", "amount": "$94.15", "date": "Feb 23, 2024", "status": "Paid" }, - { "merchant": "Newegg", "amount": "$266.42", "date": "Dec 14, 2023", "status": "Paid" }, - { "merchant": "Younits", "amount": "$889.99", "date": "Nov 10, 2015", "status": "Refunded" } - ], - "selected_loan_history": [ - { "date": "Jun 27, 2024", "amount": "$1,047.22", "description": "Processed" }, - { "date": "Jul 27, 2024", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Aug 28, 2024", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Sep 28, 2024", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Oct 28, 2024", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Nov 27, 2024", "amount": "-$47.04", "payment_method": "Bank Account •••• 9161" }, - { "date": "Dec 25, 2024", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Jan 27, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Feb 26, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Mar 15, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 3166" }, - { "date": "Apr 25, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 9388" }, - { "date": "May 24, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Jun 30, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Jul 28, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Sep 25, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Sep 25, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Oct 25, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Nov 24, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Dec 27, 2025", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Jan 6, 2026", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Feb 4, 2026", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Mar 29, 2026", "amount": "-$47.04", "payment_method": "Visa •••• 7078" }, - { "date": "Apr 28, 2026", "amount": "-$47.04", "payment_method": "Visa •••• 7078" } - ] -} - -# Use absolute paths -base_dir = "/home/allaun/Research Stack" -os.makedirs(os.path.join(base_dir, "data"), exist_ok=True) - -db_path = os.path.join(base_dir, "shared-data/data/affirm_accounts.db") -json_path = os.path.join(base_dir, "shared-data/data/affirm_accounts.json") - -# Connect to SQLite -conn = sqlite3.connect(db_path) -cursor = conn.cursor() - -# Drop tables to start fresh with new schema -cursor.execute('DROP TABLE IF EXISTS transactions') -cursor.execute('DROP TABLE IF EXISTS loans') - -# Create tables with status -cursor.execute(''' -CREATE TABLE IF NOT EXISTS loans ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - merchant TEXT, - amount TEXT, - date_info TEXT, - status TEXT -) -''') - -cursor.execute(''' -CREATE TABLE IF NOT EXISTS transactions ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - loan_id INTEGER, - date TEXT, - amount TEXT, - payment_method TEXT, - description TEXT, - FOREIGN KEY (loan_id) REFERENCES loans (id) -) -''') - -# Insert current loans -for loan in data["current_loans"]: - cursor.execute('INSERT INTO loans (merchant, amount, date_info, status) VALUES (?, ?, ?, ?)', - (loan["merchant"], loan["amount_due"], loan["due_date"], "Active")) - -# Insert past loans -for loan in data["past_loans"]: - cursor.execute('INSERT INTO loans (merchant, amount, date_info, status) VALUES (?, ?, ?, ?)', - (loan["merchant"], loan["amount"], loan["date"], loan["status"])) - -# Find the ID of the Amazon loan with $47.04 amount (Active) -cursor.execute('SELECT id FROM loans WHERE merchant = "Amazon" AND amount = "$47.04" AND status = "Active" LIMIT 1') -amazon_loan_id = cursor.fetchone()[0] - -# Insert transactions for the active Amazon loan -for tx in data["selected_loan_history"]: - cursor.execute(''' - INSERT INTO transactions (loan_id, date, amount, payment_method, description) - VALUES (?, ?, ?, ?, ?) - ''', (amazon_loan_id, tx["date"], tx["amount"], tx.get("payment_method", ""), tx.get("description", ""))) - -conn.commit() -conn.close() - -# Save updated JSON -with open(json_path, "w") as f: - json.dump(data, f, indent=2) - -print(f"Updated database at {db_path} with closed accounts.") -print(f"Updated JSON data at {json_path}") diff --git a/5-Applications/scripts/create_unified_shell.py b/5-Applications/scripts/create_unified_shell.py deleted file mode 100755 index 2ce30da7..00000000 --- a/5-Applications/scripts/create_unified_shell.py +++ /dev/null @@ -1,447 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified Shell Asset Creator for Lean 4 Toolchain Compression - -Creates a ZIP-compatible shell with: -- Prefix Header: RGFlow Stability Signature (σ_q) -- Encapsulated Payload: Compressed via unifiedCompress logic -- Zip Footer: Standard Central Directory for compatibility - -Shell-index ⌊√n⌋ based addressing from UnifiedCompression.lean -""" - -import struct -import zlib -import hashlib -import os -import sys -import math -from pathlib import Path -from typing import List, Tuple, Optional - -# ═══════════════════════════════════════════════════════════════════════════ -# Shell Structure Constants -# ═══════════════════════════════════════════════════════════════════════════ - -PREFIX_HEADER_MAGIC = b"RGFL\x01\x00" -HEADER_SIZE = 64 # Fixed size for prefix header - -# ZIP file signatures -LOCAL_FILE_HEADER_SIGNATURE = b'\x50\x4b\x03\x04' -CENTRAL_DIR_SIGNATURE = b'\x50\x4b\x01\x02' -END_OF_CENTRAL_DIR_SIGNATURE = b'\x50\x4b\x05\x06' - -# ═══════════════════════════════════════════════════════════════════════════ -# Shell-Index ⌊√n⌋ Based Addressing (from UnifiedCompression.lean) -# ═══════════════════════════════════════════════════════════════════════════ - -def isqrt(n: int) -> int: - """Integer square root (floor of sqrt) - matches Lean implementation.""" - if n == 0: - return 0 - if n == 1: - return 1 - - # Linear search from 1 to 256 (max sqrt of 65536) - for k in range(1, 257): - if k * k > n: - return k - 1 - return 256 - -def pulse_from_int(n: int) -> dict: - """ - Generate pulse from integer n (shell decomposition). - Matches pulseFromInt from UnifiedCompression.lean. - """ - k = isqrt(n) - a = n - (k * k) - b = ((k + 1) * (k + 1)) - n - is_square = (a == 0) - - mass = (a * b) - polarity = (a - b) - - # Triangle mode classification - if is_square: - mode = "square" - elif a == k: - mode = "g" - elif a == k + 1: - mode = "c" - elif b == 1: - mode = "t" - else: - mode = "a" - - return { - "mode": mode, - "pos": n, - "width": 2 * k + 1, - "mass": mass, - "polarity": polarity, - "square": is_square, - "k": k, - "a": a, - "b": b - } - -def compute_rgflow_signature(pulses: List[dict]) -> float: - """ - Compute RGFlow Stability Signature (σ_q) from pulse sequence. - Square pulses are considered lawful (high stability). - Non-square pulses have stability based on mass. - """ - if not pulses: - return 0.0 - - sigma_q_sum = 0.0 - for pulse in pulses: - if pulse["square"]: - sigma_q_sum += 1.5 - elif pulse["mass"] > 65536: # 0x00010000 - sigma_q_sum += 1.0 - else: - sigma_q_sum += 0.5 - - return sigma_q_sum / len(pulses) - -# ═══════════════════════════════════════════════════════════════════════════ -# Unified Compression Logic -# ═══════════════════════════════════════════════════════════════════════════ - -def unified_compress(data: bytes) -> Tuple[bytes, float]: - """ - Compress data using unifiedCompress logic. - Returns (compressed_data, sigma_q). - """ - # Generate pulses from data positions (simplified - use byte positions) - pulses = [] - for i in range(0, len(data), 256): # Sample every 256 bytes - n = i - pulses.append(pulse_from_int(n)) - - # Compute RGFlow signature - sigma_q = compute_rgflow_signature(pulses) - - # Apply standard deflate compression with ZIP-compatible format - # -15 windowBits produces raw deflate without zlib headers (ZIP format) - compressed = zlib.compress(data, level=9, wbits=-15) - - return compressed, sigma_q - -# ═══════════════════════════════════════════════════════════════════════════ -# ITD (Informatic Topology Deduplication) -# ═══════════════════════════════════════════════════════════════════════════ - -def itd_deduplicate(data: bytes, chunk_size: int = 4096) -> Tuple[bytes, int]: - """ - Apply ITD deduplication on chunks. - Returns (deduplicated_data, deduplication_ratio). - """ - chunks = [data[i:i+chunk_size] for i in range(0, len(data), chunk_size)] - - # Deduplicate by tracking unique chunks - seen = {} - deduplicated = bytearray() - dedup_count = 0 - - for chunk in chunks: - chunk_hash = hashlib.sha256(chunk).digest() - if chunk_hash in seen: - # Reference existing chunk - deduplicated.extend(struct.pack(' bytes: - """ - Create RGFlow signature string for ZIP comment. - Returns a string that will be stored in the ZIP file comment. - """ - signature = f"RGFL-v1|sigma_q={sigma_q:.4f}|original_size={original_size}" - return signature.encode('utf-8') - -def create_local_file_header(filename: str, compressed_data: bytes, - crc32: int, uncompressed_size: int) -> bytes: - """Create ZIP local file header.""" - header = bytearray() - - # Signature - header.extend(LOCAL_FILE_HEADER_SIGNATURE) - - # Version needed to extract (2.0) - header.extend(struct.pack(' bytes: - """Create ZIP central directory header.""" - header = bytearray() - - # Signature - header.extend(CENTRAL_DIR_SIGNATURE) - - # Version made by (2.0) - header.extend(struct.pack(' bytes: - """Create ZIP end of central directory record.""" - record = bytearray() - - # Signature - record.extend(END_OF_CENTRAL_DIR_SIGNATURE) - - # Number of this disk (0) - record.extend(struct.pack(' dict: - """ - Create unified shell asset from input file. - - Returns statistics about compression. - """ - input_file = Path(input_path) - if not input_file.exists(): - raise FileNotFoundError(f"Input file not found: {input_path}") - - # Read input data - with open(input_file, 'rb') as f: - original_data = f.read() - - original_size = len(original_data) - print(f"Original size: {original_size:,} bytes ({original_size / (1024*1024):.2f} MB)") - - # Step 1: Apply ITD deduplication if enabled - if enable_itd: - print("Applying ITD deduplication...") - deduplicated_data, dedup_ratio = itd_deduplicate(original_data) - print(f"ITD deduplication ratio: {dedup_ratio}%") - data_to_compress = deduplicated_data - else: - data_to_compress = original_data - dedup_ratio = 0 - - # Step 2: Apply unified compression - print("Applying unified compression...") - compressed_data, sigma_q = unified_compress(data_to_compress) - compressed_size = len(compressed_data) - - print(f"Compressed size: {compressed_size:,} bytes ({compressed_size / (1024*1024):.2f} MB)") - print(f"Compression ratio: {(1 - compressed_size / original_size) * 100:.2f}%") - print(f"RGFlow Stability Signature (σ_q): {sigma_q:.4f}") - - # Step 3: Create RGFlow signature for ZIP comment - print("Creating RGFlow signature...") - rgflow_signature = create_prefix_header(sigma_q, original_size) - - # Step 4: Create ZIP structure - print("Creating ZIP shell structure...") - filename = input_file.name - crc32 = zlib.crc32(original_data) & 0xffffffff - - # Local file header - local_header = create_local_file_header(filename, compressed_data, - crc32, original_size) - - # Calculate offsets (no prefix header now) - local_header_offset = 0 - central_dir_offset = local_header_offset + len(local_header) + len(compressed_data) - - # Central directory header (required for ZIP compatibility) - central_dir_header = create_central_directory_header(filename, compressed_data, - crc32, original_size, - local_header_offset) - - central_dir_size = len(central_dir_header) - num_entries = 1 - - # End of central directory with RGFlow signature in comment - eocd_record = create_end_of_central_dir_record(central_dir_offset, - central_dir_size, - num_entries, - rgflow_signature) - - # Step 5: Write unified shell - print(f"Writing unified shell to: {output_path}") - with open(output_path, 'wb') as f: - # ZIP local file header (no prefix header - starts with ZIP signature) - f.write(local_header) - - # Compressed data - f.write(compressed_data) - - # ZIP central directory header - f.write(central_dir_header) - - # ZIP end of central directory with RGFlow signature in comment - f.write(eocd_record) - - final_size = os.path.getsize(output_path) - print(f"Final shell size: {final_size:,} bytes ({final_size / (1024*1024):.2f} MB)") - print(f"Total reduction: {(1 - final_size / original_size) * 100:.2f}%") - - return { - "original_size": original_size, - "compressed_size": compressed_size, - "final_size": final_size, - "compression_ratio": (1 - compressed_size / original_size) * 100, - "total_reduction": (1 - final_size / original_size) * 100, - "sigma_q": sigma_q, - "itd_ratio": dedup_ratio - } - -# ═══════════════════════════════════════════════════════════════════════════ -# CLI Interface -# ═══════════════════════════════════════════════════════════════════════════ - -def main(): - if len(sys.argv) < 3: - print("Usage: python create_unified_shell.py [--no-itd]") - print("\nCreates a unified shell asset with:") - print(" - Prefix header with RGFlow Stability Signature (σ_q)") - print(" - Payload compressed via unifiedCompress logic") - print(" - Standard ZIP footer for compatibility") - print("\nOptions:") - print(" --no-itd Disable ITD deduplication") - sys.exit(1) - - input_path = sys.argv[1] - output_path = sys.argv[2] - enable_itd = "--no-itd" not in sys.argv - - try: - stats = create_unified_shell(input_path, output_path, enable_itd) - print("\n=== Compression Statistics ===") - print(f"Original size: {stats['original_size']:,} bytes") - print(f"Compressed size: {stats['compressed_size']:,} bytes") - print(f"Final shell size: {stats['final_size']:,} bytes") - print(f"Compression ratio: {stats['compression_ratio']:.2f}%") - print(f"Total reduction: {stats['total_reduction']:.2f}%") - print(f"ITD deduplication: {stats['itd_ratio']}%") - print(f"RGFlow σ_q: {stats['sigma_q']:.4f}") - print("\n✓ Unified shell created successfully") - print(f"✓ File can be extracted with standard unzip tools") - except Exception as e: - print(f"Error: {e}", file=sys.stderr) - sys.exit(1) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/crypto_rgflow_bulk.py b/5-Applications/scripts/crypto_rgflow_bulk.py deleted file mode 100755 index 59598b6b..00000000 --- a/5-Applications/scripts/crypto_rgflow_bulk.py +++ /dev/null @@ -1,260 +0,0 @@ -#!/usr/bin/env python3 -""" -Crypto RGFlow Bulk Analyzer — Major Commodities - -Fetches historical price data for 20+ major cryptocurrencies and runs -RGFlow analysis (sigma_q, mu_q, lawfulness) on each. Produces a -comparative report of manifold stability across the crypto ecosystem. - -Suitable for MEV bot regime classification and cross-asset arbitrage -detection. -""" - -import json -import subprocess -from concurrent.futures import ThreadPoolExecutor, as_completed -from datetime import datetime -from pathlib import Path -from typing import Dict, List, Tuple - -import numpy as np - -# ═══════════════════════════════════════════════════════════════════════════ -# Asset registry: (ticker, yahoo_symbol, approx_launch_year, launch_month) -# ═══════════════════════════════════════════════════════════════════════════ - -ASSETS = [ - ("BTC", "BTC-USD", 2009, 1), - ("ETH", "ETH-USD", 2015, 7), - ("SOL", "SOL-USD", 2020, 4), - ("ADA", "ADA-USD", 2017, 10), - ("XRP", "XRP-USD", 2013, 8), - ("DOT", "DOT-USD", 2020, 8), - ("LINK", "LINK-USD", 2017, 9), - ("LTC", "LTC-USD", 2011, 10), - ("BCH", "BCH-USD", 2017, 8), - ("AVAX", "AVAX-USD", 2020, 7), - ("MATIC", "MATIC-USD", 2019, 4), - ("UNI", "UNI-USD", 2020, 9), - ("AAVE", "AAVE-USD", 2020, 10), - ("ATOM", "ATOM-USD", 2019, 3), - ("NEAR", "NEAR-USD", 2020, 10), - ("ALGO", "ALGO-USD", 2019, 6), - ("XTZ", "XTZ-USD", 2018, 6), - ("XLM", "XLM-USD", 2014, 8), - ("XMR", "XMR-USD", 2014, 4), - ("DOGE", "DOGE-USD", 2013, 12), -] - -# Q16.16 constants -Q16_ONE = 65536 -Q16_HALF = 32768 -Q16_ZERO35 = 22937 -Q16_EIGHT = 524288 -Q16_LAMBDA = 32768 - -OUTPUT_DIR = Path(__file__).parent.parent.parent / "data" / "crypto_rgflow" -OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -# ═══════════════════════════════════════════════════════════════════════════ -# Data fetching -# ═══════════════════════════════════════════════════════════════════════════ - -def fetch_prices(symbol: str, start_year: int, start_month: int) -> List[float]: - """Fetch daily close prices from Yahoo Finance.""" - start_ts = int(datetime(start_year, start_month, 1).timestamp()) - end_ts = int(datetime.now().timestamp()) - url = ( - f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}" - f"?interval=1d&period1={start_ts}&period2={end_ts}" - ) - cmd = ["curl", "-s", "-H", "User-Agent: Mozilla/5.0", "--max-time", "30", url] - try: - out = subprocess.check_output(cmd) - data = json.loads(out) - result = data.get("chart", {}).get("result", [None])[0] - if result is None: - return [] - prices = result["indicators"]["quote"][0]["close"] - return [p for p in prices if p is not None] - except Exception as e: - print(f" [WARN] Failed to fetch {symbol}: {e}") - return [] - -# ═══════════════════════════════════════════════════════════════════════════ -# Q16.16 + RGFlow (same logic as ethereum_rgflow_fetch.py) -# ═══════════════════════════════════════════════════════════════════════════ - -def prices_to_q1616(prices: List[float]) -> np.ndarray: - arr = np.array(prices, dtype=np.float64) - log_prices = np.log(arr) - min_log, max_log = np.min(log_prices), np.max(log_prices) - if max_log - min_log > 0: - scaled = ((log_prices - min_log) / (max_log - min_log) * 65535).astype(np.int64) - else: - scaled = np.zeros_like(log_prices, dtype=np.int64) - return scaled - -def q16_div(a: int, b: int) -> int: - return (a << 16) // b if b != 0 else 0 - -def q16_mul(a: int, b: int) -> int: - return (a * b) >> 16 - -def q16_sqrt_approx(x: int) -> int: - norm = q16_div(x, Q16_ONE) - return q16_mul(norm, (49152 - q16_mul(Q16_HALF, norm))) - -def log_returns_q16(prices: np.ndarray) -> np.ndarray: - if len(prices) < 2: - return np.array([], dtype=np.int64) - returns = [] - for i in range(len(prices) - 1): - p0, p1 = prices[i], prices[i + 1] - if p0 > 0 and p1 > 0: - ratio = q16_div(p1, p0) - diff = ratio - Q16_ONE - log_approx = diff - q16_mul(Q16_HALF, q16_mul(diff, diff)) - returns.append(log_approx) - return np.array(returns, dtype=np.int64) - -def safe_std_q16(xs: np.ndarray) -> int: - if len(xs) <= 1: - return 0 - mean = int(np.mean(xs)) - diffs = xs - mean - var = int(np.mean(diffs * diffs)) - return q16_sqrt_approx(var) - -def compute_sigma_q16(returns: np.ndarray, i: int, window: int = 30) -> int: - if len(returns) < 2: - return Q16_ONE - ri = max(0, i - 1) - start = max(0, ri - window + 1) - wd = returns[start:ri + 1] - if len(wd) < 2: - return Q16_ONE - vol = safe_std_q16(wd) - mean = int(np.mean(wd)) - abs_mean = abs(mean) - coherence = q16_div(abs_mean, vol + 1) - raw = Q16_ONE + q16_mul(Q16_ZERO35, coherence) - q16_mul(Q16_EIGHT, vol) - return max(16384, min(196608, raw)) - -def compute_mu_q16(returns: np.ndarray, i: int, window: int = 30) -> int: - if len(returns) < 2: - return 0 - ri = max(0, i - 1) - start = max(0, ri - window + 1) - wd = returns[start:ri + 1] - if len(wd) < 2: - return 0 - return int(np.mean(wd)) - -def is_lawful(sigma_q: int, mu_q: int) -> bool: - return sigma_q > (Q16_ONE + q16_mul(Q16_LAMBDA, mu_q)) - -def analyze_asset(prices: List[float], window: int = 30) -> Dict: - if len(prices) < window + 2: - return {"error": "insufficient data", "count": len(prices)} - prices_q16 = prices_to_q1616(prices) - returns = log_returns_q16(prices_q16) - results = [] - for i in range(len(prices_q16)): - sigma_q = compute_sigma_q16(returns, i, window) - mu_q = compute_mu_q16(returns, i, window) - results.append((sigma_q, mu_q, is_lawful(sigma_q, mu_q))) - - sigmas = [r[0] / Q16_ONE for r in results] - lawful = sum(1 for r in results if r[2]) - collapse = sum(1 for s in sigmas if s < 1.0) - - return { - "positions": len(results), - "lawful_count": lawful, - "lawful_pct": round(lawful / len(results) * 100, 2), - "collapse_count": collapse, - "avg_sigma": round(float(np.mean(sigmas)), 4), - "min_sigma": round(float(min(sigmas)), 4), - "max_sigma": round(float(max(sigmas)), 4), - "price_min": round(min(prices), 2), - "price_max": round(max(prices), 2), - "latest_price": round(prices[-1], 2), - } - -# ═══════════════════════════════════════════════════════════════════════════ -# Main -# ═══════════════════════════════════════════════════════════════════════════ - -def process_asset(ticker: str, symbol: str, year: int, month: int) -> Tuple[str, Dict]: - print(f"\n[{ticker}] Fetching {symbol}...") - prices = fetch_prices(symbol, year, month) - if not prices: - return ticker, {"error": "no data fetched"} - print(f" → {len(prices)} price points | ${prices[0]:.2f} → ${prices[-1]:.2f}") - stats = analyze_asset(prices) - # Save per-asset detail - detail = { - "ticker": ticker, - "symbol": symbol, - "timestamp": datetime.now().isoformat(), - "prices": prices, - "statistics": stats, - } - with open(OUTPUT_DIR / f"{ticker.lower()}_rgflow.json", "w") as f: - json.dump(detail, f, indent=2) - return ticker, stats - -def main(): - print("=" * 70) - print("CRYPTO RGFLOW BULK ANALYZER") - print("=" * 70) - print(f"\nAnalyzing {len(ASSETS)} major crypto commodities...") - print(f"Output directory: {OUTPUT_DIR}") - - all_results = {} - with ThreadPoolExecutor(max_workers=4) as executor: - futures = { - executor.submit(process_asset, t, s, y, m): t - for t, s, y, m in ASSETS - } - for future in as_completed(futures): - ticker, stats = future.result() - all_results[ticker] = stats - - # Comparative summary - summary = [] - for ticker in sorted(all_results.keys()): - s = all_results[ticker] - if "error" in s: - summary.append((ticker, 0.0, 0, 0, "ERROR")) - else: - summary.append(( - ticker, - s["avg_sigma"], - s["lawful_pct"], - s["collapse_count"], - f"${s['latest_price']:,.2f}" - )) - - print("\n" + "=" * 70) - print("COMPARATIVE RGFLOW SUMMARY") - print("=" * 70) - print(f"{'Asset':>6s} {'Avg σ_q':>10s} {'Lawful%':>8s} {'Collapse':>9s} {'Price':>14s}") - print("-" * 55) - for ticker, avg_sigma, lawful_pct, collapse, price in sorted(summary, key=lambda x: -x[1]): - print(f"{ticker:>6s} {avg_sigma:>10.4f} {lawful_pct:>7.1f}% {collapse:>8d} {price:>14s}") - - # Save master summary - master = { - "timestamp": datetime.now().isoformat(), - "assets_analyzed": len(ASSETS), - "results": all_results, - } - with open(OUTPUT_DIR / "master_summary.json", "w") as f: - json.dump(master, f, indent=2) - - print(f"\n[OK] All results saved to: {OUTPUT_DIR}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/crypto_rgflow_proxy.py b/5-Applications/scripts/crypto_rgflow_proxy.py deleted file mode 100755 index 910367d6..00000000 --- a/5-Applications/scripts/crypto_rgflow_proxy.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python3 -""" -Crypto RGFlow Proxy — Unified Compression Pipeline - -Lightweight proxy that fetches crypto market data from public APIs -(CoinGecko, Yahoo Finance fallback) and routes it through the RGFlow -compression pipeline. No full nodes required. - -Feeds per-asset genome compression ratios into the unified shim for -batch lawfulness evaluation. -""" - -import json -import time -import urllib.request -from concurrent.futures import ThreadPoolExecutor, as_completed -from datetime import datetime -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import numpy as np - -# ═══════════════════════════════════════════════════════════════════════════ -# Asset registry with CoinGecko IDs -# ═══════════════════════════════════════════════════════════════════════════ - -ASSETS = [ - ("BTC", "bitcoin", "BTC-USD"), - ("ETH", "ethereum", "ETH-USD"), - ("SOL", "solana", "SOL-USD"), - ("ADA", "cardano", "ADA-USD"), - ("XRP", "ripple", "XRP-USD"), - ("DOT", "polkadot", "DOT-USD"), - ("LINK", "chainlink", "LINK-USD"), - ("LTC", "litecoin", "LTC-USD"), - ("BCH", "bitcoin-cash", "BCH-USD"), - ("AVAX", "avalanche-2", "AVAX-USD"), - ("MATIC", "matic-network", "MATIC-USD"), - ("UNI", "uniswap", "UNI-USD"), - ("AAVE", "aave", "AAVE-USD"), - ("ATOM", "cosmos", "ATOM-USD"), - ("NEAR", "near", "NEAR-USD"), - ("ALGO", "algorand", "ALGO-USD"), - ("XTZ", "tezos", "XTZ-USD"), - ("XLM", "stellar", "XLM-USD"), - ("XMR", "monero", "XMR-USD"), - ("DOGE", "dogecoin", "DOGE-USD"), -] - -OUTPUT_DIR = Path(__file__).parent.parent.parent / "data" / "crypto_rgflow" -OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -# Q16.16 constants -Q16_ONE = 65536 -Q16_HALF = 32768 -Q16_ZERO35 = 22937 -Q16_EIGHT = 524288 -Q16_LAMBDA = 32768 - -# ═══════════════════════════════════════════════════════════════════════════ -# Lightweight API fetchers -# ═══════════════════════════════════════════════════════════════════════════ - -def coingecko_history(cg_id: str) -> Optional[List[float]]: - """Fetch full history from CoinGecko public API.""" - url = f"https://api.coingecko.com/api/v3/coins/{cg_id}/market_chart?vs_currency=usd&days=max" - try: - req = urllib.request.Request(url, headers={"User-Agent": "Mozilla/5.0"}) - with urllib.request.urlopen(req, timeout=45) as resp: - data = json.loads(resp.read()) - prices = data.get("prices", []) - return [p[1] for p in prices if isinstance(p, list) and len(p) == 2] - except Exception as e: - print(f" CoinGecko fail: {e}") - return None - -def yahoo_history(symbol: str) -> Optional[List[float]]: - """Fallback to Yahoo Finance daily closes.""" - from datetime import datetime - import subprocess - start_ts = int(datetime(2010, 1, 1).timestamp()) - end_ts = int(datetime.now().timestamp()) - url = ( - f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}" - f"?interval=1d&period1={start_ts}&period2={end_ts}" - ) - try: - out = subprocess.check_output( - ["curl", "-s", "-H", "User-Agent: Mozilla/5.0", "--max-time", "30", url], - stderr=subprocess.DEVNULL - ) - data = json.loads(out) - result = data.get("chart", {}).get("result", [None])[0] - if result is None: - return None - prices = result["indicators"]["quote"][0]["close"] - return [p for p in prices if p is not None] - except Exception as e: - print(f" Yahoo fail: {e}") - return None - -def fetch_prices(ticker: str, cg_id: str, yahoo_sym: str) -> List[float]: - """Try CoinGecko first, fallback to Yahoo.""" - prices = coingecko_history(cg_id) - if prices and len(prices) > 100: - return prices - prices = yahoo_history(yahoo_sym) - if prices and len(prices) > 100: - return prices - return [] - -# ═══════════════════════════════════════════════════════════════════════════ -# Genome compression (6D quantization) -# ═══════════════════════════════════════════════════════════════════════════ - -def prices_to_q1616(prices: List[float]) -> np.ndarray: - arr = np.array(prices, dtype=np.float64) - log_p = np.log(arr) - mn, mx = np.min(log_p), np.max(log_p) - if mx - mn > 0: - return ((log_p - mn) / (mx - mn) * 65535).astype(np.int64) - return np.zeros_like(log_p, dtype=np.int64) - -def q16_div(a: int, b: int) -> int: - return (a << 16) // b if b != 0 else 0 - -def q16_mul(a: int, b: int) -> int: - return (a * b) >> 16 - -def log_returns_q16(prices: np.ndarray) -> np.ndarray: - if len(prices) < 2: - return np.array([], dtype=np.int64) - out = [] - for i in range(len(prices) - 1): - p0, p1 = prices[i], prices[i + 1] - if p0 > 0 and p1 > 0: - ratio = q16_div(p1, p0) - diff = ratio - Q16_ONE - out.append(diff - q16_mul(Q16_HALF, q16_mul(diff, diff))) - return np.array(out, dtype=np.int64) - -def safe_std_q16(xs: np.ndarray) -> int: - if len(xs) <= 1: - return 0 - mean = int(np.mean(xs)) - diffs = xs - mean - var = int(np.mean(diffs * diffs)) - norm = q16_div(var, Q16_ONE) - return q16_mul(norm, 49152 - q16_mul(Q16_HALF, norm)) - -def compute_sigma_mu(returns: np.ndarray, i: int, window: int = 30) -> Tuple[int, int]: - if len(returns) < 2: - return Q16_ONE, 0 - ri = max(0, i - 1) - start = max(0, ri - window + 1) - wd = returns[start:ri + 1] - if len(wd) < 2: - return Q16_ONE, 0 - vol = safe_std_q16(wd) - mean = int(np.mean(wd)) - abs_mean = abs(mean) - coherence = q16_div(abs_mean, vol + 1) - raw = Q16_ONE + q16_mul(Q16_ZERO35, coherence) - q16_mul(Q16_EIGHT, vol) - sigma = max(16384, min(196608, raw)) - mu = mean - return sigma, mu - -def is_lawful(sigma: int, mu: int) -> bool: - return sigma > (Q16_ONE + q16_mul(Q16_LAMBDA, mu)) - -def analyze(prices: List[float], window: int = 30) -> Dict: - if len(prices) < window + 2: - return {"error": "insufficient data", "count": len(prices)} - pq = prices_to_q1616(prices) - returns = log_returns_q16(pq) - sigmas, mus, lawfuls = [], [], [] - for i in range(len(pq)): - s, m = compute_sigma_mu(returns, i, window) - sigmas.append(s) - mus.append(m) - lawfuls.append(is_lawful(s, m)) - sigmas_f = [s / Q16_ONE for s in sigmas] - return { - "positions": len(prices), - "lawful_count": sum(lawfuls), - "lawful_pct": round(sum(lawfuls) / len(prices) * 100, 2), - "collapse_count": sum(1 for s in sigmas_f if s < 1.0), - "avg_sigma": round(float(np.mean(sigmas_f)), 4), - "min_sigma": round(float(min(sigmas_f)), 4), - "max_sigma": round(float(max(sigmas_f)), 4), - "price_min": round(min(prices), 2), - "price_max": round(max(prices), 2), - "latest_price": round(prices[-1], 2), - } - -# ═══════════════════════════════════════════════════════════════════════════ -# Genome → 18-bit address encoding (for LUT lookup) -# ═══════════════════════════════════════════════════════════════════════════ - -def encode_genome(sigma_bin: int, mu_bin: int, c_bin: int, m_bin: int, ne_bin: int, sig_bin: int) -> int: - return ( - (sigma_bin & 7) * 32768 + - (mu_bin & 7) * 4096 + - (c_bin & 7) * 512 + - (m_bin & 7) * 64 + - (ne_bin & 7) * 8 + - (sig_bin & 7) - ) - -def stats_to_genome(stats: Dict) -> int: - """Compress asset statistics into 18-bit genome address.""" - sigma_bin = min(7, int(stats.get("avg_sigma", 2.0) / 3.0 * 8)) - mu_bin = min(7, int(abs(stats.get("avg_mu", 0.0)) / 65536.0 * 8)) - c_bin = min(7, int(stats.get("lawful_pct", 50) / 100.0 * 8)) - m_bin = min(7, int((100 - stats.get("collapse_count", 0)) / 100.0 * 8)) - ne_bin = min(7, int(np.log1p(stats.get("positions", 0)) / np.log1p(5000) * 8)) - sig_bin = min(7, int(stats.get("latest_price", 1) / (stats.get("price_max", 1) + 1) * 8)) - return encode_genome(sigma_bin, mu_bin, c_bin, m_bin, ne_bin, sig_bin) - -# ═══════════════════════════════════════════════════════════════════════════ -# Main pipeline -# ═══════════════════════════════════════════════════════════════════════════ - -def process_asset(ticker: str, cg_id: str, yahoo_sym: str) -> Tuple[str, Optional[Dict]]: - print(f"\n[{ticker}] Fetching via proxy...") - prices = fetch_prices(ticker, cg_id, yahoo_sym) - if not prices: - print(f" → FAILED") - return ticker, None - print(f" → {len(prices)} points | ${prices[0]:.4f} → ${prices[-1]:.4f}") - stats = analyze(prices) - stats["genome_addr"] = stats_to_genome(stats) - # Save per-asset - with open(OUTPUT_DIR / f"{ticker.lower()}_proxy.json", "w") as f: - json.dump({"ticker": ticker, "prices": prices, "stats": stats}, f, indent=2) - return ticker, stats - -def main(): - print("=" * 70) - print("CRYPTO RGFLOW PROXY — UNIFIED COMPRESSION PIPELINE") - print("=" * 70) - print(f"\nAssets: {len(ASSETS)}") - print(f"Output: {OUTPUT_DIR}") - - all_stats = {} - # Serialize with delay to respect API rate limits - for ticker, cg_id, yahoo_sym in ASSETS: - t, stats = process_asset(ticker, cg_id, yahoo_sym) - if stats: - all_stats[t] = stats - time.sleep(1.2) # CoinGecko rate limit courtesy - - # Comparative summary - print("\n" + "=" * 70) - print("COMPARATIVE RGFLOW SUMMARY") - print("=" * 70) - print(f"{'Asset':>6s} {'Avg σ_q':>10s} {'Lawful%':>8s} {'Collapse':>9s} {'Genome':>8s} {'Price':>14s}") - print("-" * 62) - rows = [] - for t, s in all_stats.items(): - rows.append(( - t, s["avg_sigma"], s["lawful_pct"], s["collapse_count"], - s["genome_addr"], f"${s['latest_price']:,.2f}" - )) - for row in sorted(rows, key=lambda x: -x[1]): - print(f"{row[0]:>6s} {row[1]:>10.4f} {row[2]:>7.1f}% {row[3]:>8d} {row[4]:>8d} {row[5]:>14s}") - - # Save master - with open(OUTPUT_DIR / "proxy_master.json", "w") as f: - json.dump({"timestamp": datetime.now().isoformat(), "assets": all_stats}, f, indent=2) - print(f"\n[OK] Master saved: {OUTPUT_DIR / 'proxy_master.json'}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/dag_build_logger.sh b/5-Applications/scripts/dag_build_logger.sh deleted file mode 100755 index 9ffb8953..00000000 --- a/5-Applications/scripts/dag_build_logger.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/bin/bash -# DAG Build Logger -# Runs lake build with step-by-step logging, UUID, and timestamp tracking - -set -e - -REPO_ROOT="/home/allaun/Documents/Research Stack" -BUILD_LOG_DIR="$REPO_ROOT/out/build_logs" -BUILD_DAG_DIR="$REPO_ROOT/out/build_dag" - -BUILD_ID=$(uuidgen) -TIMESTAMP=$(date -u +"%Y-%m-%dT%H:%M:%SZ") -LOG_FILE="${BUILD_LOG_DIR}/lake_build_$(date +%Y%m%d)_${BUILD_ID}.log" -DAG_FILE="${BUILD_DAG_DIR}/build_dag_$(date +%Y%m%d)_${BUILD_ID}.json" - -mkdir -p "$BUILD_LOG_DIR" "$BUILD_DAG_DIR" - -# Initialize DAG structure -cat > "$DAG_FILE" << EOF -{ - "build_id": "$BUILD_ID", - "timestamp": "$TIMESTAMP", - "commit": "$(git rev-parse HEAD)", - "steps": [] -} -EOF - -# Function to log a step to both log file and DAG -log_step() { - local step_id=$(uuidgen) - local step_timestamp=$(date -u +"%Y-%M-%dT%H:%M:%SZ") - local description="$1" - local command="$2" - - # Log to file - echo "[$step_timestamp] [${step_id:0:8}] $description" >> "$LOG_FILE" - echo "Command: $command" >> "$LOG_FILE" - - # Append to DAG - # Read current DAG, append step, write back - local temp_dag=$(mktemp) - jq --arg step_id "$step_id" \ - --arg step_timestamp "$step_timestamp" \ - --arg description "$description" \ - --arg command "$command" \ - '.steps += [{ - "step_id": $step_id, - "timestamp": $step_timestamp, - "description": $description, - "command": $command - }]' "$DAG_FILE" > "$temp_dag" - mv "$temp_dag" "$DAG_FILE" -} - -# Step 1: Initialize build environment -cd "$REPO_ROOT/tools/lean/Semantics" || exit 1 -log_step "Initialize build environment" "cd \"$REPO_ROOT/tools/lean/Semantics\" && pwd" - -# Step 2: Run lake build -log_step "Run lake build" "lake build" -lake build >> "$LOG_FILE" 2>&1 - -# Step 3: Capture build result -log_step "Capture build result" "echo 'Build completed'" - -# Finalize DAG -jq --arg final_timestamp "$(date -u +"%Y-%M-%dT%H:%M:%SZ")" \ - --arg status "completed" \ - '. + {"final_timestamp": $final_timestamp, "status": $status}' "$DAG_FILE" > "${DAG_FILE}.tmp" && mv "${DAG_FILE}.tmp" "$DAG_FILE" - -echo "Build log: $LOG_FILE" -echo "Build DAG: $DAG_FILE" diff --git a/5-Applications/scripts/ddci_timing_computation.py b/5-Applications/scripts/ddci_timing_computation.py deleted file mode 100644 index 7db547f2..00000000 --- a/5-Applications/scripts/ddci_timing_computation.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -""" -DDC/CI Timing-Based Computation -Analyzes DDC/CI read operations for timing-based computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DDCITimingComputation: - """Analyzes DDC/CI read operations for timing-based computation.""" - - def __init__(self): - self.ddci_operations = { - "capabilities_read": { - "operation": "Reading DDC/CI capabilities", - "risk": "95% safe (read-only)", - "timing_characteristics": "5-50ms (DDC/CI query)", - "computational_potential": "MEDIUM (timing patterns)" - }, - "brightness_read": { - "operation": "Reading current brightness", - "risk": "90% safe (read-only)", - "timing_characteristics": "1-20ms (DDC/CI read)", - "computational_potential": "MEDIUM (timing-based state)" - }, - "volume_read": { - "operation": "Reading current volume", - "risk": "90% safe (read-only)", - "timing_characteristics": "1-20ms (DDC/CI read)", - "computational_potential": "MEDIUM (timing-based state)" - } - } - - self.timing_modes = { - "latency_computation": "Use operation latency as computational values", - "timing_pattern": "Use timing patterns for computation", - "state_machine": "Use timing-based state machine", - "value_encoding": "Use timing to encode brightness/volume values" - } - - def analyze_timing_potential(self) -> Dict: - """Analyze timing-based computational potential.""" - analysis = { - "capabilities_timing": { - "feasible": True, - "mode": "Capabilities timing computation", - "description": "Use DDC/CI capabilities read timing for computation", - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (timing resolution)", - "power": "<1W (DDC/CI communication)", - "risk": "95% safe (read-only)" - }, - "brightness_timing": { - "feasible": True, - "mode": "Brightness timing computation", - "description": "Use brightness read timing for computation", - "throughput": "50-500 operations/sec", - "latency": "1-20ms (brightness read)", - "precision": "0.1-1ms (timing resolution)", - "power": "<1W", - "risk": "90% safe (read-only)" - }, - "volume_timing": { - "feasible": True, - "mode": "Volume timing computation", - "description": "Use volume read timing for computation", - "throughput": "50-500 operations/sec", - "latency": "1-20ms (volume read)", - "precision": "0.1-1ms (timing resolution)", - "power": "<1W", - "risk": "90% safe (read-only)" - } - } - - return analysis - - def design_timing_approach(self) -> Dict: - """Design timing-based computational approach.""" - approach = { - "capabilities_timing_computation": { - "concept": "Use DDC/CI capabilities read timing for computation", - "implementation": "Measure capabilities read timing for patterns", - "operations": ["pattern arithmetic", "timing state machine", "capability encoding"], - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (pattern resolution)", - "power": "<1W", - "risk": "95% safe" - }, - "brightness_timing_computation": { - "concept": "Use brightness read timing for computation", - "implementation": "Measure brightness read timing for state", - "operations": ["state arithmetic", "timing-based state machine", "brightness encoding"], - "throughput": "50-500 operations/sec", - "latency": "1-20ms (brightness read)", - "precision": "0.1-1ms (state resolution)", - "power": "<1W", - "risk": "90% safe" - }, - "volume_timing_computation": { - "concept": "Use volume read timing for computation", - "implementation": "Measure volume read timing for state", - "operations": ["state arithmetic", "timing-based state machine", "volume encoding"], - "throughput": "50-500 operations/sec", - "latency": "1-20ms (volume read)", - "precision": "0.1-1ms (state resolution)", - "power": "<1W", - "risk": "90% safe" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of timing-based computation.""" - performance = { - "capabilities_timing": { - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (timing resolution)", - "operations": "pattern arithmetic", - "power": "<1W" - }, - "brightness_timing": { - "throughput": "50-500 operations/sec", - "latency": "1-20ms (brightness read)", - "precision": "0.1-1ms (state resolution)", - "operations": "state arithmetic", - "power": "<1W" - }, - "volume_timing": { - "throughput": "50-500 operations/sec", - "latency": "1-20ms (volume read)", - "precision": "0.1-1ms (state resolution)", - "operations": "state arithmetic", - "power": "<1W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run DDC/CI timing-based computation analysis.""" - print("=" * 60) - print("DDC/CI TIMING-BASED COMPUTATION ANALYSIS") - print("=" * 60) - - # Step 1: Analyze DDC/CI operations - print("\n[1/4] Analyzing DDC/CI operations...") - print(f" Capabilities Read: {self.ddci_operations['capabilities_read']['risk']} - {self.ddci_operations['capabilities_read']['timing_characteristics']}") - print(f" Brightness Read: {self.ddci_operations['brightness_read']['risk']} - {self.ddci_operations['brightness_read']['timing_characteristics']}") - print(f" Volume Read: {self.ddci_operations['volume_read']['risk']} - {self.ddci_operations['volume_read']['timing_characteristics']}") - - # Step 2: Analyze timing potential - print("[2/4] Analyzing timing-based computational potential...") - potential = self.analyze_timing_potential() - print(f" Capabilities Timing: {potential['capabilities_timing']['feasible']} - {potential['capabilities_timing']['risk']}") - print(f" Brightness Timing: {potential['brightness_timing']['feasible']} - {potential['brightness_timing']['risk']}") - print(f" Volume Timing: {potential['volume_timing']['feasible']} - {potential['volume_timing']['risk']}") - - # Step 3: Design timing approach - print("[3/4] Designing timing-based computational approach...") - approach = self.design_timing_approach() - print(f" Timing modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Capabilities Timing: {performance['capabilities_timing']['throughput']}") - print(f" Brightness Timing: {performance['brightness_timing']['throughput']}") - print(f" Volume Timing: {performance['volume_timing']['throughput']}") - - print("\n" + "=" * 60) - print("DDC/CI TIMING-BASED COMPUTATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "ddci_operations": self.ddci_operations, - "timing_modes": self.timing_modes, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = DDCITimingComputation() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "ddci_timing_computation.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DDC/CI TIMING COMPUTATION SUMMARY") - print("=" * 60) - print(f"Safe Operations: 3 (capabilities, brightness, volume read)") - print(f"Max Throughput: {results['performance_estimates']['brightness_timing']['throughput']}") - print(f"Max Safety: {results['computational_potential']['capabilities_timing']['risk']}") diff --git a/5-Applications/scripts/deep_dive_fill.py b/5-Applications/scripts/deep_dive_fill.py deleted file mode 100644 index 0b9726ea..00000000 --- a/5-Applications/scripts/deep_dive_fill.py +++ /dev/null @@ -1,375 +0,0 @@ -#!/usr/bin/env python3 -""" -Deep dive fill — maximum surface area for invariant scan. -Targets thin domains with dense multi-query coverage per equation. -Uses 6 API rotation, 1.5s between queries. -""" - -import sqlite3, urllib.request, urllib.parse, json, time, os, sys - -DB = "/home/allaun/physics_equations.db" - -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-4000000") -cur = conn.cursor() - -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -dom_eqs = {} -for d, e in cur.fetchall(): - dom_eqs.setdefault(d, []).append(e) - -cur.execute("SELECT id, name FROM domains") -dom_names = {r[0]: r[1] for r in cur.fetchall()} - -# ================================================================ -# DEEP DIVE QUERY MAP -# Each domain gets queries matched to its specific equations -# ================================================================ - -DEEP = { - # ── CONDENSED MATTER (33 eqs, 0.3 ratio) ── - 12: [ - "BCS theory energy gap measurement tunneling spectroscopy superconductor", - "BCS isotope effect mercury tin lead superconducting transition temperature", - "Josephson junction Shapiro steps microwave irradiation voltage standard", - "Josephson effect SQUID magnetometer sensitivity measurement", - "quantum Hall effect integer fractional von Klitzing constant plateau", - "quantum anomalous Hall effect topological insulator Chern number measurement", - "Bloch theorem ARPES angle resolved photoemission band structure copper oxide", - "Kronig Penney model superlattice miniband transport measurement", - "Fermi liquid theory quasiparticle effective mass de Haas van Alphen measurement", - "Drude model optical conductivity free electron plasma frequency metal", - "Sommerfeld model electronic specific heat linear temperature coefficient metal", - "Landau Fermi liquid heavy fermion CeCu6 CeAl3 UBe13 measurement", - "spin Peierls transition CuGeO3 inorganic measurement neutron scattering", - "Peierls distortion charge density wave NbSe3 TaS3 transport measurement", - "Mott insulator metal transition VO2 V2O3 resistivity switching measurement", - "Anderson localization weak localization quantum correction conductivity magnetoresistance", - "variable range hopping Mott Efros Shklovskii conductivity temperature exponent", - "Kondo effect resistance minimum dilute magnetic alloy CuFe AuFe measurement", - "RKKY oscillation exchange coupling magnetic multilayer Gd Y superlattice", - "spin glass susceptibility cusp frequency dependence AC measurement CuMn", - "heavy fermion superconductivity CeCu2Si2 UBe13 UPt3 unconventional pairing", - "high temperature superconductor cuprate YBCO BSCCO pseudogap phase diagram", - "iron based superconductor LaFeAsO SmFeAsO pnictide pairing symmetry measurement", - "topological insulator Bi2Se3 Bi2Te3 surface state ARPES spin helical Dirac", - "Weyl semimetal TaAs NbAs NbP Fermi arc ARPES chiral anomaly measurement", - "Kosterlitz Thouless transition 2D superconductor vortex unbinding IV exponent", - ], - - # ── MATERIAL PHYSICS (126 eqs, 0.3 ratio) ── - 21: [ - "Hall Petch breakdown inverse nanometer grain size molecular dynamics simulation", - "grain boundary sliding creep diffusional Coble Nabarro Herring mechanism measurement", - "dislocation density X ray line profile analysis modified Williamson Hall", - "stacking fault energy measurement weak beam TEM dissociation width dislocation", - "twinning induced plasticity TWIP steel high manganese stacking fault energy", - "transformation induced plasticity TRIP steel retained austenite martensite measurement", - "Orowan looping mechanism precipitate bypass transmission electron microscopy in situ", - "solid solution strengthening Labusch Fleischer model size modulus misfit parameter", - "fatigue crack initiation persistent slip band surface extrusion intrusion measurement", - "very high cycle fatigue gigacycle ultrasonic testing fish eye fracture internal inclusion", - "fracture toughness J integral R curve stable crack growth ASTM E1820 measurement", - "dynamic strain aging Portevin Le Chatelier effect serrated flow aluminum alloy", - "Luders band propagation yield point elongation mild steel strain localization DIC", - "superplasticity grain boundary sliding large elongation fine grain aluminum alloy", - "shape memory effect NiTi nitinol martensite austenite transformation DSC measurement", - "superelasticity stress induced martensite NiTi temperature dependence loading unloading", - "transformation temperature Af As Ms Mf NiTiHf NiTiPd high temperature shape memory", - "magnetocaloric effect Gd Gd5Si2Ge2 magnetic refrigeration entropy change measurement", - "giant magnetostriction Terfenol D Galfenol FeGa strain magnetic field measurement", - "invar effect FeNi thermal expansion coefficient low temperature magnetic origin", - "elastocaloric effect NiTi CuZnAl adiabatic temperature change stress measurement", - "hydrogen embrittlement steel delayed fracture hydrogen diffusion trapping measurement", - "stress corrosion cracking aluminum alloy chloride aqueous environment crack velocity", - "irradiation embrittlement reactor pressure vessel steel neutron dose ductile brittle", - "zirconium hydride delayed hydride cracking Zr alloy nuclear fuel cladding measurement", - "thermal barrier coating yttria stabilized zirconia EB PVD columnar microstructure", - "environmental barrier coating SiC ceramic matrix composite water vapor recession silica", - "MAX phase Ti3SiC2 Ti2AlC machinable ceramic kinking nonlinear elastic damage", - "high entropy alloy CrMnFeCoNi Cantor sluggish diffusion lattice distortion measurement", - "bulk metallic glass ZrCuAlNi crystallization kinetics isothermal DSC activation energy", - "equiatomic CrCoNi medium entropy alloy cryogenic temperature fracture toughness record", - "refractory high entropy alloy NbMoTaW VNbMoTaW body centered cubic strength temperature", - "graded material functionally graded thermal stress composition profile diffusion couple", - "biomimetic composite nacre aragonite platelet brick mortar fracture toughness mechanism", - "architectured material lattice truss octet cellular solid specific strength stiffness", - "self-healing material microcapsule dicyclopentadiene Grubbs catalyst crack healing efficiency", - "additive manufacturing selective laser melting process parameter porosity fatigue Ti6Al4V", - "corrosion pitting stainless steel passive film breakdown chloride measurement", - "galvanic corrosion aluminum steel couple seawater potential difference measurement", - "hot corrosion gas turbine Na2SO4 NaCl vanadium attack nickel superalloy sulfidation", - ], - - # ── MATHEMATICAL PHYSICS (19 eqs, 0.3 ratio) ── - 16: [ - "Noether theorem conservation law gauge symmetry field theory electromagnetic charge", - "Stokes theorem fluid dynamics vorticity circulation Kelvin Helmholtz vortex", - "Gauss divergence theorem electrostatics Maxwell stress tensor momentum conservation", - "Fourier transform crystallography structure factor diffraction electron density", - "Laplace equation boundary value problem electrostatics Dirichlet Neumann Green function", - "Poisson equation gravitation electrostatics fast multipole method numerical solution", - "Bessel function cylindrical waveguide mode cutoff frequency radial distribution", - "Legendre polynomial spherical harmonic multipole expansion potential angular momentum", - "Hermite polynomial quantum harmonic oscillator wavefunction Gaussian quadrature", - "Laguerre polynomial hydrogen radial wavefunction associated electron orbital", - "spherical harmonic angular momentum eigenfunction scattering phase shift partial wave", - "Gamma function Stirling approximation factorial asymptotic statistical physics", - "Dirac delta distribution Green function retarded propagator wave equation", - "separation of variables Helmholtz equation cylindrical spherical coordinate Bessel Legendre", - "Cauchy Schwarz inequality quantum uncertainty Heisenberg Robertson Schrodinger states", - "eigenvalue eigenfunction Sturm Liouville problem boundary condition orthogonal completeness", - "Wigner Eckart theorem irreducible tensor operator matrix element Clebsch Gordan", - "Baker Campbell Hausdorff formula Lie algebra exponential operator commutation", - "stationary phase approximation path integral semiclassical limit WKB connection formula", - "method steepest descent complex analysis Airy function Stokes phenomenon asymptotic", - ], - - # ── ASTROPHYSICS (18 eqs, 0.4 ratio) ── - 14: [ - "Chandrasekhar mass white dwarf Sirius B spectroscopic measurement radius", - "neutron star mass radius NICER X ray timing pulse profile modeling equation state", - "TOV equation maximum neutron star mass GW170817 tidal deformability constraint", - "Eddington limit ultraluminous X ray source M82 X-1 NGC 1313 X-1 super Eddington", - "Hertzsprung Russell globular cluster age isochrone fitting main sequence turnoff", - "mass luminosity relation eclipsing binary spectroscopic orbit stellar evolution", - "Jeans mass molecular cloud star formation initial mass function Salpeter slope", - "virial theorem galaxy cluster dark matter Zwicky mass discrepancy Coma cluster", - "solar neutrino pp chain Borexino Super Kamiokande flavor oscillation measurement", - "triple alpha Hoyle resonance carbon 12 excited state 7.65MeV helium burning red giant", - "silicon burning alpha process nuclear statistical equilibrium iron peak abundance", - "r process rapid neutron capture kilonova GW170817 strontium lanthanide actinide production", - "core collapse supernova neutrino mechanism Progenitor mass explosion energy SN1987A", - "type Ia supernova Phillips relation luminosity light curve width standardization", - "pulsar glitch Vela Crab sudden spin up superfluidity neutron pinning unpinning", - "magnetar SGR 1806 20 2004 giant flare magnetic field 10^15 Gauss crust fracture", - "fast radio burst dispersion measure host galaxy localization CHIME ASKAP measurement", - ], - - # ── PLASMA PHYSICS (8 eqs, 0.6 ratio) ── - 15: [ - "Debye shielding Langmuir probe I-V characteristic electron temperature density measurement", - "plasma frequency cut off density reflectometry tokamak interferometer measurement", - "Alfven wave toroidal Alfven eigenmode fast ion transport tokamak NSTX DIII-D measurement", - "sawtooth oscillation Kadomtsev reconnection model soft X ray tomography JET tokamak", - "ELM edge localized mode peeling ballooning stability pedestal H mode ITER challenge", - "magnetic reconnection MRX TREX experiment Sweet Parker Petschek rate measurement", - "zonal flow geodesic acoustic mode turbulence regulation Doppler backscattering measurement", - "I mode improved confinement energy confinement pedestal temperature no ELM alternative", - "plasma wakefield acceleration electron bunch energy gain GeV per meter FACET measurement", - "laser plasma interaction parametric Raman Brillouin scattering stimulated measurement", - ], - - # ── STATISTICAL MECHANICS (13 eqs, 0.5 ratio) ── - 17: [ - "Boltzmann H theorem molecular dynamics simulation entropy production irreversibility", - "Gibbs ensemble Monte Carlo phase coexistence Lennard Jones fluid vapor liquid measurement", - "Jarzynski equality optical tweezers RNA hairpin unfolding single molecule force ramp", - "Crooks fluctuation theorem DNA overstretching transition work distribution free energy", - "fluctuation dissipation theorem microrheology passive particle tracking viscoelastic", - "Kramers escape rate problem protein folding force dependent transition state measurement", - "Wang Landau algorithm density states Monte Carlo polymer chain partition function", - "parallel tempering replica exchange molecular dynamics protein folding free energy landscape", - "Ising model critical exponent Monte Carlo renormalization group finite size scaling", - "XY model Kosterlitz Thouless transition superfluid helium film torsional oscillator", - "Kardar Parisi Zhang equation kinetic roughening surface growth scaling exponent universality", - "percolation threshold cluster size distribution spanning probability critical exponent", - "self organized criticality sandpile model Bak Tang Wiesenfeld avalanche power law exponent", - ], - - # ── CONTINUUM MECHANICS (15 eqs, 0.4 ratio) ── - 18: [ - "Eshelby inclusion ellipsoidal elastic stress strain eigenvalue interior exterior solution", - "Hashin Shtrikman bounds composite elastic modulus effective medium Mori Tanaka method", - "J integral elastic plastic fracture finite element analysis contour path independence", - "cohesive zone model traction separation law crack tip process zone Dugdale Barenblatt", - "indentation hardness Oliver Pharr method elastic modulus nanohardness continuous stiffness", - "contact mechanics adhesion JKR DMT Johnson Kendall Roberts transition parameter Tabor", - "wave propagation anisotropic elastic Christoffel equation slowness surface polarization", - "buckling Euler critical load column beam elastica nonlinear large deformation postbuckling", - "plasticity yield surface associated flow rule normality Drucker postulate convexity", - "strain gradient plasticity size effect micro bend torsion indentation intrinsic length", - "crystal plasticity texture Taylor model Sachs self consistent viscoplastic polycrystal", - "homogenization asymptotic expansion periodic composite unit cell finite element RVE", - "configurational force Eshelby stress energy momentum tensor crack driving force J vector", - "phase field fracture variational brittle regularization length crack topology diffuse", - "nonlocal elasticity Eringen integral crack tip singular stress regularization", - ], - - # ── TOP-OFF PASS for remaining sub-1.0 domains ── - 1: [ # Classical Mechanics - "Euler rigid body rotation Poinsot ellipsoid polhode herpolhode torque free precession", - "KAM theorem Arnold diffusion chaos Hamiltonian system solar system stability", - ], - 4: [ # Thermodynamics - "fluctuation dissipation Onsager reciprocal relation thermoelectricity Peltier Seebeck", - "nonequilibrium thermodynamics entropy production minimum principle Prigogine theorem", - ], - 5: [ # Quantum Mechanics - "WKB approximation Wentzel Kramers Brillouin quantum tunneling transmission coefficient", - "density matrix quantum Liouville von Neumann equation open system Lindblad master", - ], - 7: [ # Quantum Field Theory - "QCD lattice gauge theory hadron spectrum ab initio BMW collaboration physical pion mass", - "renormalization group Callan Symanzik beta function asymptotic freedom non abelian gauge", - ], - 8: [ # Cosmology - "cosmic microwave background polarization B mode E mode gravitational wave tensor scalar ratio", - "DESI dark energy spectroscopic instrument baryon acoustic oscillation Hubble parameter growth", - ], - 9: [ # Fluid Dynamics - "turbulent boundary layer log law von Karman constant high Reynolds Princeton pipe experiment", - "Rayleigh Taylor instability Atwood number bubble spike growth rate Richtmyer Meshkov", - ], - 10: [ # Optics - "optical vortex orbital angular momentum Laguerre Gauss beam spiral phase plate hologram", - "super resolution microscopy STED STORM PALM single molecule localization diffraction", - ], - 13: [ # Nuclear Physics - "double beta decay neutrinoless GERDA EXO KamLAND ZEN Majorana mass half life limit", - "magicity disappearance island inversion neutron rich oxygen fluorine magnesium measurement", - ], -} - -# ================================================================ -# API FETCHERS -# ================================================================ -def cr(q, mx=5): - o = [] - try: - u = "https://api.crossref.org/works?" + urllib.parse.urlencode({"query":q,"rows":mx,"sort":"relevance","filter":"type:journal-article"}) - r = urllib.request.Request(u, headers={"User-Agent":"DeepDive/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t = (i.get("title",[""]) or [""])[0] - y = i.get("created",{}).get("date-parts",[[0]])[0][0] - doi = i.get("DOI","") - jn = (i.get("container-title",[""]) or [""])[0] - if t: o.append((t[:250],y,"Crossref",doi,jn)) - except: pass - return o - -def oa(q, mx=5): - o = [] - try: - u = "https://api.openalex.org/works?" + urllib.parse.urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}) - r = urllib.request.Request(u, headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("results",[]): - t = i.get("title",""); y = i.get("publication_year")or 0; doi = i.get("doi","") - jn = "" - if i.get("primary_location") and i["primary_location"].get("source"): - jn = i["primary_location"]["source"].get("display_name","") - if t: o.append((t[:250],y,"OpenAlex",doi,jn)) - except: pass - return o - -def s2(q, mx=5): - o = [] - try: - u = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode({"query":q,"limit":mx,"fields":"title,year,externalIds,journal,citationCount"}) - r = urllib.request.Request(u, headers={"User-Agent":"DeepDive/1.0"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for p in d.get("data",[]): - e = p.get("externalIds",{}) or {}; jn = p.get("journal",{}) or {} - o.append((p.get("title","")[:250],p.get("year")or 0,"S2",e.get("DOI",""),jn.get("name",""))) - except: pass - return o - -def ep(q, mx=5): - o = [] - try: - u = "https://www.ebi.ac.uk/europepmc/webservices/rest/search?" + urllib.parse.urlencode({"query":q,"resultType":"core","pageSize":mx,"format":"json"}) - r = urllib.request.Request(u, headers={"User-Agent":"DeepDive/1.0"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("resultList",{}).get("result",[]): - t = i.get("title","") - y = int(i.get("firstPublicationDate","0")[:4]) if i.get("firstPublicationDate") else 0 - doi = i.get("doi",""); jn = i.get("journalTitle","") - if t: o.append((t[:250],y,"EuropePMC",doi,jn)) - except: pass - return o - -# ================================================================ -# MAIN PIPELINE -# ================================================================ -# Build flat queue -tasks = [] -for did, queries in DEEP.items(): - for q in queries: - tasks.append((did, q)) - -apis = [(cr,1.5,"Crossref"),(oa,2.0,"OpenAlex"),(s2,2.0,"S2"),(ep,1.8,"EuropePMC"), - (oa,2.0,"OpenAlex"),(cr,1.5,"Crossref"),(s2,2.0,"S2"),(oa,2.0,"OpenAlex")] - -print(f"⚡ DEEP DIVE PASS 2") -print(f" {len(tasks)} queries across {len(DEEP)} target domains") -print(f" APIs: Crossref, OpenAlex, S2, EuropePMC ×2") -print(f" ETA: {len(tasks)*1.8/60:.1f} min\n") - -total, batch, start = 0, [], time.time() -prev_did, dom_paper_count = None, 0 - -for idx, (did, query) in enumerate(tasks): - fn, delay, name = apis[idx % len(apis)] - papers = fn(query, 5) - eqs = dom_eqs.get(did, [None]) - - if did != prev_did: - if prev_did is not None: - print(f" ── {dom_paper_count} papers added to {dom_names.get(prev_did,'')}", flush=True) - prev_did = did; dom_paper_count = 0 - print(f"\n┌─ {dom_names.get(did, f'#{did}')}", flush=True) - - for i, p in enumerate(papers): - eq_id = eqs[i % len(eqs)] if eqs else None - exp = f"{p[2]}: {p[4]}" if p[4] else p[2] - batch.append((eq_id, p[0], exp, p[1], p[3] if p[3] else p[2], "Deep dive preseed")) - total += 1; dom_paper_count += 1 - - mark = "▪" if papers else "·" - eta = (len(tasks) - idx) * delay - print(f" {mark} {name:8s} › {query[:65]:65s} → {len(papers)}p | {total:4d} | {eta:.0f}s", flush=True) - - if len(batch) >= 50: - cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", batch) - conn.commit(); batch = [] - - time.sleep(delay) - -if prev_did: print(f" ── {dom_paper_count} papers added to {dom_names.get(prev_did,'')}", flush=True) - -if batch: - cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", batch) - conn.commit() - -# ================================================================ -# FINAL REPORT -# ================================================================ -cur.execute("SELECT COUNT(*) FROM verifications"); tv = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations"); te = cur.fetchone()[0] -elapsed = time.time() - start - -print(f"\n{'═'*70}") -print(f"DONE — {tv} verifications, {te} equations, {total} added this pass") -print(f"Time: {elapsed:.0f}s ({elapsed/60:.1f} min) | {len(tasks)/elapsed*60:.1f} queries/min\n") - -cur.execute("""SELECT d.name, COUNT(DISTINCT e.id), COUNT(DISTINCT v.id), - ROUND(COUNT(DISTINCT v.id)*1.0/COUNT(DISTINCT e.id),1) - FROM domains d LEFT JOIN equations e ON e.domain_id=d.id - LEFT JOIN verifications v ON v.equation_id=e.id - WHERE e.id IS NOT NULL GROUP BY d.id ORDER BY COUNT(DISTINCT v.id) DESC""") - -print(f"{'Domain':35s} {'Eqs':>4s} {'Refs':>5s} {'Ratio':>5s}") -print("-"*51) -for row in cur.fetchall(): - print(f"{row[0]:35s} {row[1]:4d} {row[2]:5d} {row[3]:5.1f}") - -conn.close() -print(f"\n {DB} ({os.path.getsize(DB)} bytes)") diff --git a/5-Applications/scripts/deepseek_audit_burgers.py b/5-Applications/scripts/deepseek_audit_burgers.py deleted file mode 100644 index 3b49931d..00000000 --- a/5-Applications/scripts/deepseek_audit_burgers.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -import sys -from pathlib import Path - -# Add project root to path -project_root = Path(__file__).parent.parent.parent -sys.path.insert(0, str(project_root)) - -from infra.deepseek_adapter import DeepSeekV4 - -def main(): - client = DeepSeekV4(use_local=True) - model = "deepseek-r1:8b" - - # The "Suspect Equation" from the ENE ingest - equation = "u_t + u u_x = nu u_{xx} + eta(x,t) - lambda d_x Phi_Omega(x,t)" - complexity = "Omega[u] = 1/2 sum_{n=1}^N n^2 |a_n|^2" - - prompt = f""" -You are a mathematical physicist and formal verification expert. -I am auditing a "Field-Native Witness Hierarchy" model based on a regularized Burgers' equation. - -Equation under audit: -∂u/∂t + u(∂u/∂x) = ν(∂²u/∂x²) + η(x,t) - λ(∂/∂x)Φ_Ω(x,t) - -Where the "Complexity Metric" Ω[u] is defined as: -Ω[u] = (1/2) * ∑_{{n=1}}^N n² |a_n|² - -The user claims this allows for "Lossless Symbolic Reconstruction" and "Near-Zero Error" in tracking shock wave development. -An earlier AI audit suggested this model has "suspect math" regarding: -1. UV Divergence in the Ω[u] term. -2. Frame anchoring (the exclusion/trap center problem). -3. Convergence claims (errors going to zero). - -TASK: -1. Identify the "Suspect Math": Where does this equation likely break down in a real physical or numerical simulation? -2. Formalize the UV Divergence check: If u(x) has a discontinuity (shock), how does Ω[u] behave? -3. Propose a Lean 4 theorem statement that would verify the "well-posedness" or "energy boundedness" of this system. - -Provide your reasoning in thinking tags and then the final audit report. -""" - - print(f"--- Auditing Regularized Burgers Equation with {model} ---") - - try: - # Using the streaming support I just added (but I'll handle the output manually here) - res = client.chat([{"role": "user", "content": prompt}], model=model) - print("\nAudit Results:") - print(res["message"]["content"]) - except Exception as e: - print(f"Error: {e}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/deepseek_audit_shellmass.py b/5-Applications/scripts/deepseek_audit_shellmass.py deleted file mode 100644 index a2ed6253..00000000 --- a/5-Applications/scripts/deepseek_audit_shellmass.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -import sys -from pathlib import Path - -# Add project root to path -project_root = Path(__file__).parent.parent.parent -sys.path.insert(0, str(project_root)) - -from infra.deepseek_adapter import DeepSeekV4 - -def main(): - client = DeepSeekV4(use_local=True) - - # We'll use the local R1 model for deep reasoning - model = "deepseek-r1:8b" - - with open(project_root / "0-Core-Formalism/lean/Semantics/Semantics/MassNumberMetricClosure.lean", "r") as f: - code = f.read() - - prompt = f""" -You are a formal verification auditor. I have found "suspect math" in a Lean 4 file. -Specifically, look at §7 "Shell Mass as Throat Curvature (Conjecture 2)". - -Definition: -def shellMass (n : Nat) : Nat := - let k := Nat.sqrt n - let a := n - k * k - let b := (k + 1) * (k + 1) - n - a * b - -The file claims: -theorem shellMass_not_distance : - ¬ (∀ n m p, shellMass n ≤ shellMass m + shellMass p) -with a comment: --- Counterexample: shellMass(2) = 2, but shellMass(1) + shellMass(3) = 0 + 0 = 0 - -Audit the following: -1. Is the comment about shellMass(3)=0 correct? (Check the math). -2. Is the statement of `shellMass_not_distance` mathematically sound? Usually, a distance is a function of two points d(x,y). If shellMass is intended to be a metric on Nat, what would the metric be? Or is it a "mass potential"? -3. Provide the correct Lean 4 proof for `shellMass_max_at_midpoint` and find a TRUE counterexample for the "not a distance" claim if one exists. - -File Context: -{code} -""" - - print(f"--- Auditing Shell Mass with {model} ---") - - try: - messages = [{"role": "user", "content": prompt}] - res = client.chat(messages, model=model) - print("\nAudit Results:") - print(res["message"]["content"]) - except Exception as e: - print(f"Error: {e}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/derive_hyper_equation.py b/5-Applications/scripts/derive_hyper_equation.py deleted file mode 100644 index a3c3fcd1..00000000 --- a/5-Applications/scripts/derive_hyper_equation.py +++ /dev/null @@ -1,49 +0,0 @@ -import json -from pathlib import Path -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def derive_hyper_equation(): - client = DeepSeekV4(use_local=True) - prover = DeepSeekProver(client) - - forest_path = Path("/home/allaun/Documents/Research Stack/data/equations_forest.jsonl") - equations = [] - if forest_path.exists(): - with open(forest_path, "r") as f: - for line in f: - equations.append(json.loads(line)) - - if not equations: - print("[ERROR] No equations found in forest.") - return - - eq_list = "\n".join([f"- {eq['name']}: {eq['formula']}" for eq in equations]) - - prompt = f""" -You are a master mathematical physicist. -I have a collection of 15 canonical equations from the Sovereign Research Stack: - -{eq_list} - -Your task: -1. Review all these equations. -2. Derive a single 'Hyper Equation' or 'Unified Manifold Operator' that generalizes these disparate domains (Fluid Dynamics, GR, Neural Lattice, Hardware Encoding). -3. The hyper equation should use a generalized tensor or operator notation that captures the essence of all 15. -4. Provide the formal derivation and the final Hyper Equation in LaTeX. -""" - - print("Synthesizing Hyper Equation using DeepSeek-R1 (this may take time)...") - # Force R1 for deep reasoning - messages = [{"role": "user", "content": prompt}] - result = client.chat(messages, model="qwen2.5-coder:14b") - print("\n--- HYPER EQUATION DERIVATION ---\n") - content = result['message']['content'] - print(content) - - # Save the result to a new artifact - with open("/home/allaun/Documents/Research Stack/data/hyper_equation.md", "w") as f: - f.write(content) - print(f"\n[OK] Hyper Equation saved to /home/allaun/Documents/Research Stack/data/hyper_equation.md") - -if __name__ == "__main__": - derive_hyper_equation() diff --git a/5-Applications/scripts/deterministic_stochastic_computation.py b/5-Applications/scripts/deterministic_stochastic_computation.py deleted file mode 100644 index 03c0934b..00000000 --- a/5-Applications/scripts/deterministic_stochastic_computation.py +++ /dev/null @@ -1,250 +0,0 @@ -#!/usr/bin/env python3 -""" -Deterministic Stochastic Computation Analysis -Analyzes using signal sources (jitter, thermodynamics) for deterministic stochastic computation. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DeterministicStochasticComputation: - """Analyzes deterministic stochastic computation using signal sources.""" - - def __init__(self): - # Signal sources available - self.signal_sources = { - "jitter": "Timing jitter from clocks and oscillators", - "thermodynamics": "Thermal noise and thermodynamic fluctuations", - "electrical_noise": "Electrical noise (thermal, shot, flicker)", - "quantum_effects": "Quantum effects and fluctuations", - "power_fluctuations": "Power supply fluctuations and ripple" - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 38, - "sine_wave_topology_capacity": 645785145.72, - "expansion_factor": 339886.0 - } - - def analyze_signal_sources(self) -> Dict: - """Analyze signal sources for deterministic stochastic computation.""" - analysis = { - "signal_sources": { - "jitter": { - "description": "Timing jitter from clocks and oscillators", - "type": "Timing signal", - "characteristics": "Random but bounded timing variations", - "deterministic_seeding": "Can seed with known jitter patterns", - "significance_score": 90.0 - }, - "thermodynamics": { - "description": "Thermal noise and thermodynamic fluctuations", - "type": "Thermal signal", - "characteristics": "Random thermal fluctuations (kT noise)", - "deterministic_seeding": "Can seed with known thermal states", - "significance_score": 95.0 - }, - "electrical_noise": { - "description": "Electrical noise (thermal, shot, flicker)", - "type": "Electrical signal", - "characteristics": "Random electrical noise sources", - "deterministic_seeding": "Can seed with known noise characteristics", - "significance_score": 85.0 - }, - "quantum_effects": { - "description": "Quantum effects and fluctuations", - "type": "Quantum signal", - "characteristics": "Quantum fluctuations (Heisenberg uncertainty)", - "deterministic_seeding": "Can seed with known quantum states", - "significance_score": 80.0 - }, - "power_fluctuations": { - "description": "Power supply fluctuations and ripple", - "type": "Power signal", - "characteristics": "Random power supply variations", - "deterministic_seeding": "Can seed with known power patterns", - "significance_score": 75.0 - } - }, - "average_significance_score": 85.0 - } - - return analysis - - def analyze_deterministic_stochastic_applications(self) -> Dict: - """Analyze applications of deterministic stochastic computation.""" - applications = { - "monte_carlo_simulation": { - "description": "Use signal sources for Monte Carlo simulation", - "benefit": "True randomness with deterministic seeding", - "significance_score": 95.0 - }, - "random_number_generation": { - "description": "Generate random numbers from signal sources", - "benefit": "Hardware-based random number generation", - "significance_score": 90.0 - }, - "stochastic_optimization": { - "description": "Use stochastic signals for optimization", - "benefit": "Escape local optima with noise", - "significance_score": 85.0 - }, - "probabilistic_computation": { - "description": "Probabilistic computation using signal entropy", - "benefit": "Leverage signal entropy for computation", - "significance_score": 80.0 - }, - "noise_robust_computation": { - "description": "Computation robust to signal noise", - "benefit": "Exploit noise for computation", - "significance_score": 75.0 - }, - "entropy_harvesting": { - "description": "Harvest entropy from signal sources", - "benefit": "Use signal entropy for computation", - "significance_score": 70.0 - } - } - - return applications - - def calculate_deterministic_stochastic_impact(self) -> Dict: - """Calculate deterministic stochastic computation impact.""" - # Deterministic stochastic multipliers - monte_carlo_multiplier = 2.0 # 2x improvement from Monte Carlo - random_number_multiplier = 1.5 # 1.5x improvement from hardware RNG - stochastic_optimization_multiplier = 1.5 # 1.5x improvement from stochastic optimization - probabilistic_computation_multiplier = 1.3 # 1.3x improvement from probabilistic computation - noise_robust_multiplier = 1.2 # 1.2x improvement from noise robustness - entropy_harvesting_multiplier = 1.1 # 1.1x improvement from entropy harvesting - - # Calculate expanded capacity with deterministic stochastic computation - base_capacity = 1900 - current_sine_wave_capacity = 645785145.72 - - # Apply deterministic stochastic multipliers - deterministic_stochastic_capacity = (current_sine_wave_capacity * - monte_carlo_multiplier * - random_number_multiplier * - stochastic_optimization_multiplier * - probabilistic_computation_multiplier * - noise_robust_multiplier * - entropy_harvesting_multiplier) - - deterministic_stochastic_expansion_factor = deterministic_stochastic_capacity / base_capacity - deterministic_stochastic_improvement_factor = deterministic_stochastic_capacity / current_sine_wave_capacity - - calculation = { - "base_capacity": base_capacity, - "current_sine_wave_capacity": current_sine_wave_capacity, - "monte_carlo_multiplier": monte_carlo_multiplier, - "random_number_multiplier": random_number_multiplier, - "stochastic_optimization_multiplier": stochastic_optimization_multiplier, - "probabilistic_computation_multiplier": probabilistic_computation_multiplier, - "noise_robust_multiplier": noise_robust_multiplier, - "entropy_harvesting_multiplier": entropy_harvesting_multiplier, - "deterministic_stochastic_capacity": deterministic_stochastic_capacity, - "deterministic_stochastic_expansion_factor": deterministic_stochastic_expansion_factor, - "deterministic_stochastic_improvement_factor": deterministic_stochastic_improvement_factor, - "total_deterministic_stochastic_multiplier": (monte_carlo_multiplier * - random_number_multiplier * - stochastic_optimization_multiplier * - probabilistic_computation_multiplier * - noise_robust_multiplier * - entropy_harvesting_multiplier) - } - - return calculation - - def integrate_deterministic_stochastic(self) -> Dict: - """Integrate deterministic stochastic computation into analysis.""" - integration = { - "deterministic_stochastic_enabled": True, - "signal_sources_used": 5, - "applications": 6, - "math_categories_enhanced": [ - "Thermodynamic (thermal noise, entropy)", - "Information Theory (entropy harvesting)", - "Control Theory (stochastic optimization)", - "Physical Bind (signal sources)" - ], - "foundation_kernels_enhanced": [ - "F04", "F05", "F06", # Thermodynamic (thermal noise) - "F01", "F02", "F03", # Information Theory (entropy) - "F11", "F12" # Control Theory (stochastic optimization) - ], - "deterministic_seeding": "All signal sources can be deterministically seeded", - "stochastic_determinism": "Random but computable with known initial conditions" - } - - return integration - - def run_analysis(self) -> Dict: - """Run deterministic stochastic computation analysis.""" - print("=" * 60) - print("DETERMINISTIC STOCHASTIC COMPUTATION ANALYSIS") - print("=" * 60) - - # Step 1: Analyze signal sources - print("\n[1/4] Analyzing signal sources for deterministic stochastic computation...") - signal_analysis = self.analyze_signal_sources() - print(f" Signal Sources: {len(signal_analysis['signal_sources'])}") - for source, details in signal_analysis['signal_sources'].items(): - print(f" {source}: {details['significance_score']}") - - # Step 2: Analyze applications - print("[2/4] Analyzing deterministic stochastic computation applications...") - applications = self.analyze_deterministic_stochastic_applications() - print(f" Applications: {len(applications)}") - for application, details in applications.items(): - print(f" {application}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating deterministic stochastic computation impact...") - impact_calculation = self.calculate_deterministic_stochastic_impact() - print(f" Current Sine Wave Capacity: {impact_calculation['current_sine_wave_capacity']}") - print(f" Deterministic Stochastic Capacity: {impact_calculation['deterministic_stochastic_capacity']}") - print(f" Deterministic Stochastic Improvement Factor: {impact_calculation['deterministic_stochastic_improvement_factor']:.2f}x") - print(f" Total Deterministic Stochastic Multiplier: {impact_calculation['total_deterministic_stochastic_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating deterministic stochastic computation...") - integration = self.integrate_deterministic_stochastic() - print(f" Signal Sources Used: {integration['signal_sources_used']}") - print(f" Applications: {integration['applications']}") - - print("\n" + "=" * 60) - print("DETERMINISTIC STOCHASTIC COMPUTATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "signal_sources_analysis": signal_analysis, - "applications_analysis": applications, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = DeterministicStochasticComputation() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "deterministic_stochastic_computation.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DETERMINISTIC STOCHASTIC COMPUTATION SUMMARY") - print("=" * 60) - print(f"Signal Sources Used: {results['integration']['signal_sources_used']}") - print(f"Deterministic Stochastic Capacity: {results['impact_calculation']['deterministic_stochastic_capacity']}") - print(f"Deterministic Stochastic Improvement Factor: {results['impact_calculation']['deterministic_stochastic_improvement_factor']:.2f}x") - print(f"Total Deterministic Stochastic Multiplier: {results['impact_calculation']['total_deterministic_stochastic_multiplier']:.2f}x") diff --git a/5-Applications/scripts/develop_async_soliton.py b/5-Applications/scripts/develop_async_soliton.py deleted file mode 100644 index 017b12ae..00000000 --- a/5-Applications/scripts/develop_async_soliton.py +++ /dev/null @@ -1,332 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Development: Asynchronous Stochastic Soliton Propagation - -This script uses the swarm to design and develop the asynchronous stochastic soliton -propagation mechanism for the Networked Self-Solving Space. -""" - -import json -from dataclasses import dataclass -from typing import List, Dict, Any -from collections import Counter - -@dataclass -class SwarmAgent: - specialization: str - confidence: float - contribution: str - -def develop_async_soliton(): - """Develop asynchronous stochastic soliton propagation with swarm""" - - print("\n" + "="*70) - print("SWARM DEVELOPMENT: Asynchronous Stochastic Soliton Propagation") - print("="*70) - - # Swarm agents for development - swarm_agents = [ - SwarmAgent("semantic", 0.85, "Formal definition of soliton propagation semantics"), - SwarmAgent("verification", 0.80, "Convergence theorem proofs"), - SwarmAgent("translation", 0.75, "Lean implementation translation"), - SwarmAgent("geometry", 0.82, "Soliton wave equation formalization"), - SwarmAgent("topology", 0.88, "5D torus propagation topology"), - SwarmAgent("energy", 0.78, "Energy efficiency analysis"), - SwarmAgent("distributed", 0.86, "Distributed system consistency model"), - SwarmAgent("network", 0.84, "Network delay formalization"), - SwarmAgent("stochastic", 0.83, "Stochastic delay distribution"), - SwarmAgent("quantum", 0.79, "Quantum coherence alignment") - ] - - print(f"\n📊 Active Agents: {len(swarm_agents)}") - print(f"📈 Average Confidence: {sum(a.confidence for a in swarm_agents)/len(swarm_agents):.3f}") - - # Phase 1: Formal Definition - print("\n" + "="*70) - print("PHASE 1: Formal Definition") - print("="*70) - - formal_def = """ -/-- Soliton message carrying state update -/ -structure SolitonMessage where - sourceNodeId : UInt64 - targetNodeId : UInt64 - stateUpdate : BlitterState - timestamp : UInt64 - propagationDelay : Q16_16 -- Stochastic delay - phase : Q16_16 -- Soliton phase for coherence - deriving Repr, Inhabited - -/-- Soliton propagation probability -/ -def solitonPropagationProbability (distance : UInt32) (delay : Q16_16) : Q16_16 := - let decay := to_q16 (1.0 / (1.0 + distance.val.to_float)) - let stochastic := to_q16 (delay.to_float / 100.0) - decay * stochastic - -/-- Asynchronous gossip with stochastic soliton propagation -/ -def asyncGossip (states : List NetworkedState) (torusTopology : TorusTopologyState) (maxDelay : Q16_16) : List NetworkedState := - let messages := generateSolitonMessages states torusTopology maxDelay - let propagated := propagateSolitons messages torusTopology - applyStateUpdates states propagated -""" - - print(formal_def) - - # Phase 2: Convergence Theorems - print("\n" + "="*70) - print("PHASE 2: Convergence Theorems") - print("="*70) - - convergence_theorems = """ -/-- Theorem: Soliton Propagation Convergence - All solitons eventually reach their targets with probability 1 -/ -theorem solitonConvergence (states : List NetworkedState) (torusTopology : TorusTopologyState) (maxDelay : Q16_16) : - ∀ s ∈ states, ∃ t, solitonPropagates s t torusTopology maxDelay := by - sorry - -/-- Theorem: Bounded Propagation Time - Soliton propagation time is bounded by O(diameter * maxDelay) -/ -theorem boundedPropagationTime (distance : UInt32) (maxDelay : Q16_16) : - propagationTime distance maxDelay ≤ distance.val * maxDelay.to_float := by - sorry - -/-- Theorem: Self-Solving Preservation Under Async Gossip - The self-solving property is preserved under asynchronous stochastic soliton propagation -/ -theorem asyncSelfSolvingPreservation (state : NetworkedState) (torusTopology : TorusTopologyState) (maxDelay : Q16_16) : - distributedQuineAxiom state → - let newState := asyncGossip [state] torusTopology maxDelay - distributedQuineAxiom newState.head! := by - sorry - -/-- Theorem: Eventual Consistency - Async gossip achieves eventual consistency under bounded stochastic delays -/ -theorem eventualConsistency (states : List NetworkedState) (torusTopology : TorusTopologyState) (maxDelay : Q16_16) : - ∃ T, ∀ t ≥ T, allNodesConsistent (asyncGossip states torusTopology maxDelay) t := by - sorry -""" - - print(convergence_theorems) - - # Phase 3: Implementation Details - print("\n" + "="*70) - print("PHASE 3: Implementation Details") - print("="*70) - - implementation = """ -/-- Generate soliton messages from state updates -/ -def generateSolitonMessages (states : List NetworkedState) (torusTopology : TorusTopologyState) (maxDelay : Q16_16) : List SolitonMessage := - let messages := [] - for state in states do - let neighbors := getTorusNeighbors state.torusNode torusTopology - for neighbor in neighbors do - let delay := stochasticDelay maxDelay - let phase := solitonPhase state.torusNode neighbor - let message := { - sourceNodeId := state.nodeId, - targetNodeId := neighbor.nodeId, - stateUpdate := state.pistState, - timestamp := getCurrentTime, - propagationDelay := delay, - phase := phase - } - messages := messages ++ [message] - messages - -/-- Stochastic delay generation -/ -def stochasticDelay (maxDelay : Q16_16) : Q16_16 := - let random := randomUInt32 0 100 - to_q16 (random.to_float / 100.0 * maxDelay.to_float) - -/-- Soliton phase calculation for coherence -/ -def solitonPhase (source : TorusNode) (target : TorusNode) : Q16_16 := - let distance := torusDistance source target - let phase := to_q16 (distance.val.to_float / 100.0 * 6.28) -- 2π normalized - phase - -/-- Propagate solitons through torus topology -/ -def propagateSolitons (messages : List SolitonMessage) (torusTopology : TorusTopologyState) : List SolitonMessage := - messages.filter (fun msg => solitonArrives msg torusTopology) - -/-- Check if soliton arrives (stochastic propagation) -/ -def solitonArrives (message : SolitonMessage) (torusTopology : TorusTopologyState) : Bool := - let prob := solitonPropagationProbability (getTorusDistance message.sourceNodeId message.targetNodeId torusTopology) message.propagationDelay - randomCheck prob - -/-- Apply state updates from arrived solitons -/ -def applyStateUpdates (states : List NetworkedState) (messages : List SolitonMessage) : List NetworkedState := - let updates := groupByTarget messages - states.map (fun state => applyUpdate state updates) -""" - - print(implementation) - - # Phase 4: Swarm Contributions - print("\n" + "="*70) - print("PHASE 4: Swarm Agent Contributions") - print("="*70) - - contributions = { - "semantic": "Soliton propagation semantics defined as message-passing with phase coherence", - "verification": "Convergence theorem requires proof that stochastic delays are bounded and sum to finite", - "translation": "Lean implementation uses List-based message passing with stochastic filters", - "geometry": "Soliton phase calculated as 2π * distance / 100 for wave coherence", - "topology": "5D torus neighbor lookup for efficient soliton routing", - "energy": "No global clock reduces energy consumption by ~40%", - "distributed": "Eventual consistency model with bounded stochastic delays", - "network": "Propagation delay modeled as stochastic variable with maxDelay bound", - "stochastic": "Delay distribution: uniform[0, maxDelay] for simplicity, can be extended to exponential", - "quantum": "Phase coherence aligns soliton propagation with quantum wave function collapse" - } - - for agent in swarm_agents: - print(f"\n Agent ({agent.specialization}):") - print(f" Confidence: {agent.confidence:.3f}") - print(f" Contribution: {contributions[agent.specialization]}") - - # Phase 5: Integration Plan - print("\n" + "="*70) - print("PHASE 5: Integration Plan") - print("="*70) - - integration_plan = """ -Step 1: Update NetworkedSelfSolvingSpace.lean - - Add SolitonMessage structure - - Replace synchronous gossip with asyncGossip - - Add convergence theorems (with sorry for now) - - Add #eval examples for async propagation - -Step 2: Prove Convergence Theorems - - Prove solitonConvergence (requires probability theory) - - Prove boundedPropagationTime (straightforward induction) - - Prove asyncSelfSolvingPreservation (extends existing GlobalConsistency) - - Prove eventualConsistency (requires eventual consistency lemmas) - -Step 3: Update MATH_MODEL_MAP - - Add entry for AsynchronousSolitonGossip - - Document equations and convergence guarantees - -Step 4: Verification - - lake build to check Lean compilation - - Test with small 5D torus (2x2x2x2x2 = 32 nodes) - - Verify soliton propagation reaches all nodes - - Check self-solving property preserved - -Step 5: Performance Analysis - - Compare async vs sync gossip latency - - Measure energy efficiency improvement - - Verify scalability to larger torus (16^5 = 1,048,576 nodes) -""" - - print(integration_plan) - - # Phase 6: Expected Performance - print("\n" + "="*70) - print("PHASE 6: Expected Performance") - print("="*70) - - performance = """ -Asynchronous Stochastic Soliton vs Synchronous Epochs: - -Latency: -- Sync: O(diameter) per epoch (global barrier) -- Async: O(diameter * maxDelay) but no barrier -- Expected improvement: 60-80% reduction in latency for large networks - -Scalability: -- Sync: Limited by global clock synchronization -- Async: Scales to arbitrary network size -- Expected: 100x better scalability for 1M+ nodes - -Energy: -- Sync: Global clock consumes ~40% of energy -- Async: No global clock, event-driven -- Expected improvement: 35-45% energy reduction - -Consistency: -- Sync: Strong consistency (immediate) -- Async: Eventual consistency (bounded delay) -- Trade-off: Weaker immediate consistency for better scalability - -Self-Solving Property: -- Both: Preserved under gossip (theorems prove this) -- Async: Requires additional convergence proof -- Expected: Property holds with probability 1 under bounded delays -""" - - print(performance) - - # Phase 7: Swarm Consensus - print("\n" + "="*70) - print("PHASE 7: Swarm Consensus") - print("="*70) - - consensus_votes = { - "implement_async": 8, - "keep_sync": 1, - "hybrid": 1 - } - - print(f"\n🗳️ Swarm Votes:") - print(f" Implement Async: {consensus_votes['implement_async']}") - print(f" Keep Sync: {consensus_votes['keep_sync']}") - print(f" Hybrid: {consensus_votes['hybrid']}") - - print("\n🟢 GREEN LIGHT: Implement Asynchronous Stochastic Soliton") - print(" - Strong swarm consensus (8/10)") - print(" - Better scalability and energy efficiency") - print(" - Aligns with distributed systems reality") - print(" - Provable with convergence theorems") - - # Phase 8: Next Steps - print("\n" + "="*70) - print("PHASE 8: Next Steps") - print("="*70) - - next_steps = """ -Immediate Actions: -1. Update NetworkedSelfSolvingSpace.lean with async gossip -2. Add SolitonMessage structure and related functions -3. Add convergence theorems (with sorry) -4. Update gossip function to use asyncGossip -5. Add #eval examples for async propagation - -Follow-up Actions: -1. Prove convergence theorems (requires probability theory in Mathlib) -2. Test with small torus (32 nodes) -3. Verify self-solving property preservation -4. Update MATH_MODEL_MAP with async gossip entry -5. Performance comparison with sync gossip - -Long-term Actions: -1. Extend to hybrid sync/async (sync for verification, async for production) -2. Add adaptive maxDelay based on network conditions -3. Implement soliton phase coherence optimization -4. Add quantum coherence alignment features -""" - - print(next_steps) - - print("\n" + "="*70) - print("SWARM DEVELOPMENT COMPLETE") - print("="*70) - print("\n✅ Formal definition complete") - print("✅ Convergence theorems specified") - print("✅ Implementation details designed") - print("✅ Swarm contributions integrated") - print("✅ Integration plan defined") - print("✅ Performance analysis complete") - print("✅ Swarm consensus: Implement async") - print("✅ Next steps identified") - - print("\n📋 Ready to implement in NetworkedSelfSolvingSpace.lean") - - return { - "consensus": "implement_async", - "votes": consensus_votes, - "next_steps": next_steps, - "performance": performance - } - - -if __name__ == '__main__': - result = develop_async_soliton() diff --git a/5-Applications/scripts/direct_swarm_mereotopological_hybrid.py b/5-Applications/scripts/direct_swarm_mereotopological_hybrid.py deleted file mode 100644 index cad42509..00000000 --- a/5-Applications/scripts/direct_swarm_mereotopological_hybrid.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -""" -Direct Swarm to Implement MereotopologicalSheafHypergraph.lean (SHIM) - -# BOUNDARY: Python thin IO shim; logic in Semantics.NIICore.MereotopologicalSheafHypergraph -""" - -import sys -from pathlib import Path - -# Add infra to path -sys.path.append(str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import LeanUnifiedShim - - -def main(): - """Main function calling Lean via unified shim.""" - shim = LeanUnifiedShim() - print("=" * 70) - print("MEREOTOPOLOGICAL SHEAF HYPERGRAPH HYBRID (LEAN-BACKED)") - print("=" * 70) - - try: - passed = shim.run_mereotopological_hybrid_test() - - if passed: - print("\n✅ MereotopologicalSheafHypergraph Hybrid Test: PASSED") - print(" - Mereotopological part-whole relations: VERIFIED") - print(" - Sheaf consistency: VERIFIED") - print(" - Hypergraph rewriting: VERIFIED") - print(" - Emergent property: Part-whole consistent rewriting") - else: - print("\n❌ MereotopologicalSheafHypergraph Hybrid Test: FAILED") - - print("\n" + "=" * 70) - print("VERIFICATION COMPLETE (VERIFIED VIA LEAN)") - print("=" * 70) - - except Exception as e: - print(f"Error calling Lean module: {e}") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/direct_swarm_uncertainty_hybrid.py b/5-Applications/scripts/direct_swarm_uncertainty_hybrid.py deleted file mode 100644 index f7bf7966..00000000 --- a/5-Applications/scripts/direct_swarm_uncertainty_hybrid.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -""" -Direct Swarm to Implement UncertaintyMetaPredictiveDifferential.lean (SHIM) - -# BOUNDARY: Python thin IO shim; logic in Semantics.NIICore.UncertaintyMetaPredictiveDifferential -""" - -import sys -from pathlib import Path - -# Add infra to path -sys.path.append(str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import LeanUnifiedShim - - -def main(): - """Main function calling Lean via unified shim.""" - shim = LeanUnifiedShim() - print("=" * 70) - print("UNCERTAINTY METAPREDICTIVE DIFFERENTIAL HYBRID (LEAN-BACKED)") - print("=" * 70) - - try: - passed = shim.run_uncertainty_hybrid_test() - - if passed: - print("\n✅ UncertaintyMetaPredictiveDifferential Hybrid Test: PASSED") - print(" - Uncertainty weighting: VERIFIED") - print(" - Meta-learning adjustment: VERIFIED") - print(" - Predictive timing: VERIFIED") - print(" - Differential attention: VERIFIED") - else: - print("\n❌ UncertaintyMetaPredictiveDifferential Hybrid Test: FAILED") - - print("\n" + "=" * 70) - print("VERIFICATION COMPLETE (VERIFIED VIA LEAN)") - print("=" * 70) - - except Exception as e: - print(f"Error calling Lean module: {e}") - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/direct_swarm_video_physics_weird_machine.py b/5-Applications/scripts/direct_swarm_video_physics_weird_machine.py deleted file mode 100644 index eb3df9bc..00000000 --- a/5-Applications/scripts/direct_swarm_video_physics_weird_machine.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -""" -Video Weird Machine (Model 141) Control Shim - -Thin IO bridge for Soliton Field Transport and Video Physics. -BOUNDARY: All manifold transition logic migrated to Semantics.VideoPhysics.lean -""" - -import sys -import json -import argparse -import random -from pathlib import Path - -# Ensure we can import the lean_unified_shim -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import LeanUnifiedShim - -def main(): - parser = argparse.ArgumentParser(description='Video Weird Machine Control (Wave 3 Shim)') - parser.add_argument('--step', action='store_true', help='Execute a single video physics transition') - parser.add_argument('--sigma', type=float, default=1.0, help='Initial manifold sigma') - - args = parser.parse_args() - shim = LeanUnifiedShim() - - if args.step: - # Construct initial state (simulated spectral peaks for demonstration) - # In production, these derive from the HDMI TMDS stream. - # Q16.16: float * 65536 - state = { - "manifold_sigma": {"val": int(args.sigma * 65536)}, - "peaks_spectral": [{"val": int(random.random() * 65536)} for _ in range(5)], - "hdmi_residual": {"val": 12345}, - "frame_index": 0 - } - - print(f"[VWM] Executing Model 141 Master Equation transition...") - try: - # Call Lean for the formal transition - # Note: masterEquation only returns the new sigma Scalar in our current Main.lean dispatcher - new_sigma = shim.run_video_physics_master_equation(state) - - sigma_float = new_sigma['val'] / 65536.0 - print(f"[VWM] Transition Complete.") - print(f" New Manifold Sigma: {sigma_float:.6f}") - print(f" Entropy Gain confirmed by NII-01.") - except Exception as e: - print(f"[!] Video Physics transition failed: {e}") - else: - parser.print_help() - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/directive_no_assumptions_alert.py b/5-Applications/scripts/directive_no_assumptions_alert.py deleted file mode 100644 index 5da7d762..00000000 --- a/5-Applications/scripts/directive_no_assumptions_alert.py +++ /dev/null @@ -1,278 +0,0 @@ -#!/usr/bin/env python3 -""" -DIRECTIVE: NO ASSUMPTIONS — Swarm Alert Injection - -Issues P0 CRITICAL directive to all OTOM swarm agents: -- NO ASSUMPTIONS -- NO GUESSES -- NO LOGICAL LEAPS - -When the equation is complete, it must not be able to disprove itself. -""" - -import sys -import json -import sqlite3 -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Any - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import SwarmAPISystem - - -def issue_no_assumptions_directive() -> Dict[str, Any]: - """ - Issue strict formal rigor directive to entire swarm. - """ - - api = SwarmAPISystem() - timestamp = datetime.now(timezone.utc).isoformat() - - directive = """ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ DIRECTIVE: NO ASSUMPTIONS — P0 CRITICAL ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -AUTHORITY: Principal Investigator -DATE: {timestamp} -SCOPE: All OTOM Swarm Agents -ENFORCEMENT: Triumvirate (Builder/Warden/Judge) - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -THE DIRECTIVE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - NO ASSUMPTIONS. - NO GUESSES. - NO LOGICAL LEAPS. - -When the equation is complete, it MUST NOT be able to disprove itself. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -WHAT IS FORBIDDEN -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -1. ASSUMPTIONS WITHOUT EXPLICIT AXIOM - FORBIDDEN: theorem foo : P := by sorry - REQUIRED: explicit axiom registration with justification - -2. GUESSES IN PROOF STRATEGY - FORBIDDEN: simp -- "Hope this works" - REQUIRED: explicit tactic chain with axiom references - -3. LOGICAL LEAPS IN DERIVATIONS - FORBIDDEN: "Follows from algebraic manipulation" - REQUIRED: Step-by-step derivation with axiom citations - -4. IMPLICIT DEPENDENCIES - FORBIDDEN: Implicit assumption of normalization - REQUIRED: Explicit hypothesis (h : Σ w = 1) - -5. APPROXIMATIONS WITHOUT ERROR BOUNDS - FORBIDDEN: ln(2) ≈ 0.693 - REQUIRED: ln(2) = 0.693 ± 0.001 (explicit bound) - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -THE 6 EXPLICIT AXIOMS OF Φ_UNIVERSAL -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -All theorems derive from these 6 axioms ONLY: - -AXIOM 1: harmonicDef - hᵢ = 1/(lnNᵢ)² [Definition of merit coefficient] - -AXIOM 2: penaltyDef - pⱼ = 1/(lnNⱼ)² [Definition of penalty coefficient] - -AXIOM 3: reciprocalWeightedIdentity - 1/x = x·(1/x²) [Algebraic identity] - -AXIOM 4: weightsNonNeg - wᵢ, vⱼ ≥ 0 [Domain constraint] - -AXIOM 5: cardinalityConstraint - Nᵢ, Mⱼ ≥ 2 [Binary minimum, avoids ln(1)=0] - -AXIOM 6: normalizationBounded - Σw=1, Σv=1 → Φ ≤ 1 [Normalization constraint] - -THEOREM: If these 6 axioms are consistent, Φ is consistent. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -SELF-CONSISTENCY REQUIREMENT -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -The Golden Rule: - When you finish the equation, it should not be able to disprove itself. - -Verification Checklist: - □ Completeness: All hypotheses explicit - □ Consistency: Axioms do not contradict - □ Non-circularity: Proof DAG is acyclic - □ Conservativity: New axioms don't falsify old theorems - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -SWARM PROTOCOL — WHEN YOU ENCOUNTER A GAP -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -DO NOT: - ✗ Guess a proof strategy - ✗ Insert 'sorry' with TODO - ✗ Assume it "probably holds" - ✗ Make a logical leap - -DO: - 1. STOP — Do not proceed - 2. IDENTIFY — What is missing? - 3. EXPLICITIZE — Convert to explicit axiom - 4. JUSTIFY — Why axiom vs theorem? - 5. SCOPE — What depends on this? - 6. DOCUMENT — Add to axiom registry - 7. ESCALATE — Notify Warden if unsure - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -WARDEN VERIFICATION PROTOCOL -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -For each proof: - 1. Trace every tactic to axiom/theorem - 2. Verify no implicit assumptions - 3. Check completeness (no 'sorry') - 4. Validate no circular dependencies - 5. Confirm no logical leaps - 6. Sign off with hardware attestation - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -JUDGE ADJUDICATION CRITERIA -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -APPROVED only if: - ✓ Zero 'sorry' in code - ✓ All axioms explicitly registered - ✓ All theorems derive from axioms - ✓ Self-consistency verified - ✓ No logical leaps - ✓ No guesses - ✓ No implicit assumptions - -REJECTED if: - ✗ Any 'sorry' remains - ✗ Implicit assumptions found - ✗ Self-inconsistency detected - ✗ Logical leaps identified - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -ENFORCEMENT -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Triumvirate: - Builder: Must use explicit axioms only - Warden: Must verify all steps traceable - Judge: Must reject code with gaps - -Penalties: - Implicit assumption → Revert commit - 'sorry' in code → Block deployment - Logical leap → Return to Builder - Self-inconsistency → Escalate to PI - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -DOCUMENTATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Full directive: 6-Documentation/docs/papers/DIRECTIVE_NO_ASSUMPTIONS.md -Axiom registry: 0-Core-Formalism/lean/Semantics/Semantics/UniversalField.lean §4 -Implementation: 0-Core-Formalism/lean/Semantics/Semantics/UniversalField.lean §5-6 - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -THE SWARM OPERATES ON EXPLICIT AXIOMS ONLY. - -NO ASSUMPTIONS. -NO GUESSES. -NO LOGICAL LEAPS. - -When the equation is complete, it must not be able to disprove itself. -""".format(timestamp=timestamp) - - # Inject into priority_alerts - if api.conn: - cursor = api.conn.cursor() - - cursor.execute(""" - INSERT OR REPLACE INTO priority_alerts - (entity_id, subject, name, statement, proof_status, formal_status, - priority, requires_immediate_action, created_at) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - f'DIRECTIVE_NO_ASSUMPTIONS_{timestamp.replace(":", "_")}', - 'directive', - '[P0] DIRECTIVE: NO ASSUMPTIONS — Strict Formal Rigor', - directive, - 'directive', - 'enforced', - 'P0', - True, - timestamp - )) - - api.conn.commit() - - return { - 'success': True, - 'directive': 'NO_ASSUMPTIONS', - 'timestamp': timestamp, - 'scope': 'All OTOM Swarm Agents', - 'enforcement': 'Triumvirate', - 'axioms_defined': 6, - 'status': 'ACTIVE' - } - else: - return { - 'success': False, - 'error': 'Database not connected', - 'directive_printed': True - } - - -def main(): - print("="*70) - print("DIRECTIVE: NO ASSUMPTIONS") - print("="*70) - print() - - result = issue_no_assumptions_directive() - - if result['success']: - print(f"[✓] DIRECTIVE ISSUED: {result['directive']}") - print() - print(f"Scope: {result['scope']}") - print(f"Enforcement: {result['enforcement']}") - print(f"Axioms: {result['axioms_defined']} explicit axioms") - print(f"Status: {result['status']}") - print() - print("="*70) - print("STRICT FORMAL RIGOR ENFORCED") - print() - print("The Golden Rule:") - print(" When the equation is complete, it must not") - print(" be able to disprove itself.") - print() - print("All swarm agents must:") - print(" □ Use explicit axioms only") - print(" □ No 'sorry' in code") - print(" □ No logical leaps") - print(" □ No implicit assumptions") - print() - print("Documentation:") - print(" 6-Documentation/docs/papers/DIRECTIVE_NO_ASSUMPTIONS.md") - print("="*70) - else: - print(f"[✗] Failed: {result.get('error', 'Unknown')}") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/discover_unknown_patterns.py b/5-Applications/scripts/discover_unknown_patterns.py deleted file mode 100644 index 1a358e1c..00000000 --- a/5-Applications/scripts/discover_unknown_patterns.py +++ /dev/null @@ -1,89 +0,0 @@ -#!/usr/bin/env python3 -import pyarrow.parquet as pq -import pandas as pd -import re -from collections import Counter -import json -import sys - -INPUT_FILE = "3-Mathematical-Models/equations_parquet_tagged/unknown_equations_20260504_134248.parquet" -OUTPUT_REPORT = "3-Mathematical-Models/unknown_discovery_report.json" - -COMMON_WORDS = {'the', 'and', 'is', 'where', 'which', 'this', 'that', 'with', 'for', 'holds', 'when', 'if', 'has', 'map'} -MATH_SYMBOLS = r'[=><≥≤≈∼∇∂∫∑∏⟨⟩|±∓×÷√∞∝∠∧∨∩∪⊂⊃⊆⊇∈∉∅∀∃∄]' -OCR_ARTIFACTS = r'[\uf800-\uf8ff]' - -def classify(eq): - if not isinstance(eq, str): - return "malformed" - - # Check for OCR artifacts/malformed - if re.search(OCR_ARTIFACTS, eq): - return "malformed" - - # Check for natural language - words = re.findall(r'\b\w+\b', eq.lower()) - if len(words) > 3: - common_count = sum(1 for w in words if w in COMMON_WORDS) - if common_count >= 2 or (len(words) > 10 and common_count >= 1): - return "natural_language" - - # Check for math-like but unknown - if re.search(MATH_SYMBOLS, eq): - # Dirac notation? - if '⟨' in eq or '⟩' in eq or '|' in eq: - return "missing_parser_rules_dirac" - # Big O? - if re.search(r'\bO\(', eq): - return "missing_parser_rules_bigo" - # Generic missing rule - return "missing_parser_rules_generic" - - # Metadata - if re.search(r'\(\d+\)', eq) or 'Eq.' in eq: - return "metadata_fragments" - - # Default - return "undetermined" - -def main(): - print(f"Reading {INPUT_FILE}...") - table = pq.read_table(INPUT_FILE) - df = table.to_pandas() - - print(f"Sampling 20,000 equations...") - sample = df.sample(min(20000, len(df))) - - results = [] - counts = Counter() - - for _, row in sample.iterrows(): - eq = row['equation'] - cat = classify(eq) - counts[cat] += 1 - results.append({ - 'equation': eq, - 'category': cat, - 'id': row['equation_id'], - 'source': row['source'] - }) - - report = { - 'total_rows': len(df), - 'sample_size': len(sample), - 'category_counts': dict(counts), - 'samples': {} - } - - for cat in counts: - report['samples'][cat] = [r['equation'] for r in results if r['category'] == cat][:10] - - with open(OUTPUT_REPORT, 'w') as f: - json.dump(report, f, indent=2) - - print(f"Report written to {OUTPUT_REPORT}") - for cat, count in counts.items(): - print(f" {cat:30}: {count}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/displayport_computational_controller.py b/5-Applications/scripts/displayport_computational_controller.py deleted file mode 100644 index 7892c86e..00000000 --- a/5-Applications/scripts/displayport_computational_controller.py +++ /dev/null @@ -1,234 +0,0 @@ -#!/usr/bin/env python3 -""" -DisplayPort Controller Computational Repurposing -Analyzes DisplayPort controller for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DisplayPortComputationalController: - """Analyzes DisplayPort controller for general computation.""" - - def __init__(self): - self.displayport_controller = { - "device": "DisplayPort 1.4a Controller", - "gpu": "NVIDIA GeForce RTX 4070 SUPER", - "lanes": "4 lanes (Main Link)", - "bandwidth": "32.4 Gbps (HBR3 mode)", - "link_rates": ["RBR: 1.62 Gbps/lane", "HBR: 2.7 Gbps/lane", "HBR2: 5.4 Gbps/lane", "HBR3: 8.1 Gbps/lane"], - "computational_potential": "HIGH (4 lanes, MST, DSC, FEC, audio)" - } - - self.displayport_capabilities = { - "main_link": "4 lanes for data transmission", - "aux_channel": "AUX channel (I2C-like) for control", - "hot_plug_detect": "HPD for connection detection", - "mst": "Multi-Stream Transport (multiple displays)", - "dsc": "Display Stream Compression", - "fec": "Forward Error Correction", - "audio": "Up to 32 audio channels", - "vrr": "Variable Refresh Rate" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of DisplayPort controller.""" - analysis = { - "main_link_computation": { - "feasible": True, - "mode": "Main link computation", - "description": "Use 4-lane main link for data transmission computation", - "throughput": "32.4 Gbps (HBR3 mode)", - "latency": "Lane rate limited (8.1 Gbps per lane)", - "precision": "8-bit per lane (10-bit encoded)", - "power": "5-20W (DisplayPort controller)", - "risk": "LOW-MEDIUM (requires custom encoder/decoder)" - }, - "aux_channel_computation": { - "feasible": True, - "mode": "AUX channel computation", - "description": "Use AUX channel (I2C-like) for control computation", - "throughput": "AUX channel limited (slow)", - "latency": "AUX channel latency (1-10ms)", - "precision": "8-bit AUX commands", - "power": "1-5W", - "risk": "LOW (AUX channel hijacking)" - }, - "mst_computation": { - "feasible": True, - "mode": "MST computation", - "description": "Use Multi-Stream Transport for parallel computation", - "throughput": "32.4 Gbps shared across streams", - "latency": "MST packet latency (1-5ms)", - "precision": "8-bit MST packets", - "power": "5-15W", - "risk": "MEDIUM (MST configuration)" - }, - "dsc_computation": { - "feasible": True, - "mode": "DSC computation", - "description": "Use Display Stream Compression for computation", - "throughput": "Compressed bandwidth (15-20 Gbps)", - "latency": "DSC encode/decode latency (1-5ms)", - "precision": "8-bit DSC blocks", - "power": "5-10W", - "risk": "LOW-MEDIUM (DSC bypass)" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design DisplayPort-based computational approach.""" - approach = { - "main_link_computation": { - "concept": "Use 4-lane main link for computation", - "implementation": "Encode data in 4-lane main link", - "operations": ["lane arithmetic", "parallel transmission", "link training"], - "throughput": "32.4 Gbps (HBR3)", - "latency": "8.1 Gbps per lane", - "precision": "8-bit per lane (10-bit encoded)", - "power": "5-20W", - "risk": "LOW-MEDIUM" - }, - "aux_channel_computation": { - "concept": "Use AUX channel for computation", - "implementation": "Hijack AUX channel (I2C-like) for control", - "operations": ["AUX commands", "EDID read", "DPCD access"], - "throughput": "AUX channel limited", - "latency": "1-10ms (AUX channel)", - "precision": "8-bit AUX commands", - "power": "1-5W", - "risk": "LOW" - }, - "mst_computation": { - "concept": "Use MST for parallel computation", - "implementation": "Use Multi-Stream Transport for parallel streams", - "operations": ["stream arithmetic", "parallel processing", "MST routing"], - "throughput": "32.4 Gbps shared", - "latency": "1-5ms (MST packet)", - "precision": "8-bit MST packets", - "power": "5-15W", - "risk": "MEDIUM" - }, - "dsc_computation": { - "concept": "Use DSC for computation", - "implementation": "Use Display Stream Compression for encoding", - "operations": ["DSC arithmetic", "compression computation", "block processing"], - "throughput": "15-20 Gbps (compressed)", - "latency": "1-5ms (DSC encode/decode)", - "precision": "8-bit DSC blocks", - "power": "5-10W", - "risk": "LOW-MEDIUM" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of DisplayPort controller computation.""" - performance = { - "main_link": { - "throughput": "32.4 Gbps (HBR3)", - "latency": "8.1 Gbps per lane", - "precision": "8-bit per lane (10-bit encoded)", - "operations": "lane arithmetic", - "power": "5-20W" - }, - "aux_channel": { - "throughput": "AUX channel limited", - "latency": "1-10ms (AUX channel)", - "precision": "8-bit AUX commands", - "operations": "AUX commands", - "power": "1-5W" - }, - "mst": { - "throughput": "32.4 Gbps shared", - "latency": "1-5ms (MST packet)", - "precision": "8-bit MST packets", - "operations": "parallel processing", - "power": "5-15W" - }, - "dsc": { - "throughput": "15-20 Gbps (compressed)", - "latency": "1-5ms (DSC encode/decode)", - "precision": "8-bit DSC blocks", - "operations": "compression computation", - "power": "5-10W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run DisplayPort controller computational analysis.""" - print("=" * 60) - print("DISPLAYPORT CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze DisplayPort controller - print("\n[1/4] Analyzing DisplayPort controller...") - print(f" Device: {self.displayport_controller['device']}") - print(f" GPU: {self.displayport_controller['gpu']}") - print(f" Lanes: {self.displayport_controller['lanes']}") - print(f" Bandwidth: {self.displayport_controller['bandwidth']}") - print(f" Computational Potential: {self.displayport_controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" Main Link: {potential['main_link_computation']['feasible']} - {potential['main_link_computation']['risk']}") - print(f" AUX Channel: {potential['aux_channel_computation']['feasible']} - {potential['aux_channel_computation']['risk']}") - print(f" MST: {potential['mst_computation']['feasible']} - {potential['mst_computation']['risk']}") - print(f" DSC: {potential['dsc_computation']['feasible']} - {potential['dsc_computation']['risk']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Main Link: {performance['main_link']['throughput']}") - print(f" AUX Channel: {performance['aux_channel']['throughput']}") - print(f" MST: {performance['mst']['throughput']}") - print(f" DSC: {performance['dsc']['throughput']}") - - print("\n" + "=" * 60) - print("DISPLAYPORT CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "displayport_controller": self.displayport_controller, - "displayport_capabilities": self.displayport_capabilities, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = DisplayPortComputationalController() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "displayport_computational_controller.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DISPLAYPORT COMPUTATIONAL CONTROLLER SUMMARY") - print("=" * 60) - print(f"Device: {results['displayport_controller']['device']}") - print(f"Bandwidth: {results['displayport_controller']['bandwidth']}") - print(f"Computational Potential: {results['displayport_controller']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['main_link']['throughput']}") diff --git a/5-Applications/scripts/displayport_line_morphic.py b/5-Applications/scripts/displayport_line_morphic.py deleted file mode 100644 index c7843c02..00000000 --- a/5-Applications/scripts/displayport_line_morphic.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -""" -DisplayPort Line Morphic Computation -Analyzes DisplayPort cable lines as morphic devices for computation. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DisplayPortLineMorphic: - """Analyzes DisplayPort cable lines as morphic devices.""" - - def __init__(self): - self.displayport_lines = { - "device": "DisplayPort Cable Lines (Morphic Devices)", - "lanes": "4 main link lanes + AUX channel", - "construction": "Shielded twisted pair copper conductors", - "length": "1-2 meters (inferred)", - "computational_potential": "MEDIUM-HIGH (copper inductance, capacitance, resistance)" - } - - self.line_capabilities = { - "main_link_lanes": { - "lane_0": "8.1 Gbps (HBR3) - copper twisted pair", - "lane_1": "8.1 Gbps (HBR3) - copper twisted pair", - "lane_2": "8.1 Gbps (HBR3) - copper twisted pair", - "lane_3": "8.1 Gbps (HBR3) - copper twisted pair" - }, - "aux_channel": "I2C-like control channel - copper pair", - "hpd": "Hot Plug Detect - single wire", - "electrical_properties": { - "inductance": "Copper wire inductance (nH/m)", - "capacitance": "Twisted pair capacitance (pF/m)", - "resistance": "Copper resistance (Ω/m)", - "impedance": "Characteristic impedance (100Ω)" - } - } - - def analyze_morphic_potential(self) -> Dict: - """Analyze morphic computational potential of DisplayPort lines.""" - analysis = { - "inductance_computation": { - "feasible": True, - "mode": "Inductance-based morphic computation", - "description": "Use copper wire inductance for computation", - "throughput": "Inductance limited (nH/m)", - "latency": "Inductance response (ns)", - "precision": "nH resolution", - "power": "1-5W (signal injection)", - "risk": "LOW (non-invasive)" - }, - "capacitance_computation": { - "feasible": True, - "mode": "Capacitance-based morphic computation", - "description": "Use twisted pair capacitance for computation", - "throughput": "Capacitance limited (pF/m)", - "latency": "Capacitance response (ns)", - "precision": "pF resolution", - "power": "1-5W (signal injection)", - "risk": "LOW (non-invasive)" - }, - "resistance_computation": { - "feasible": True, - "mode": "Resistance-based morphic computation", - "description": "Use copper resistance for computation", - "throughput": "Resistance limited (Ω/m)", - "latency": "Resistance response (ns)", - "precision": "mΩ resolution", - "power": "1-5W (signal injection)", - "risk": "LOW (non-invasive)" - }, - "impedance_computation": { - "feasible": True, - "mode": "Impedance-based morphic computation", - "description": "Use characteristic impedance for computation", - "throughput": "Impedance limited (100Ω)", - "latency": "Impedance response (ns)", - "precision": "0.1Ω resolution", - "power": "1-5W (signal injection)", - "risk": "LOW (non-invasive)" - } - } - - return analysis - - def design_morphic_approach(self) -> Dict: - """Design line-based morphic computational approach.""" - approach = { - "inductance_morphic": { - "concept": "Use copper wire inductance as morphic device", - "implementation": "Inject signals to measure inductance changes", - "operations": ["inductance arithmetic", "frequency response", "resonant computation"], - "throughput": "Inductance limited (nH/m)", - "latency": "ns response", - "precision": "nH resolution", - "power": "1-5W", - "risk": "LOW" - }, - "capacitance_morphic": { - "concept": "Use twisted pair capacitance as morphic device", - "implementation": "Inject signals to measure capacitance changes", - "operations": ["capacitance arithmetic", "charge/discharge", "resonant computation"], - "throughput": "Capacitance limited (pF/m)", - "latency": "ns response", - "precision": "pF resolution", - "power": "1-5W", - "risk": "LOW" - }, - "resistance_morphic": { - "concept": "Use copper resistance as morphic device", - "implementation": "Inject signals to measure resistance changes", - "operations": ["resistance arithmetic", "voltage/current", "thermal computation"], - "throughput": "Resistance limited (Ω/m)", - "latency": "ns response", - "precision": "mΩ resolution", - "power": "1-5W", - "risk": "LOW" - }, - "impedance_morphic": { - "concept": "Use characteristic impedance as morphic device", - "implementation": "Inject signals to measure impedance changes", - "operations": ["impedance arithmetic", "reflection coefficient", "SWR computation"], - "throughput": "Impedance limited (100Ω)", - "latency": "ns response", - "precision": "0.1Ω resolution", - "power": "1-5W", - "risk": "LOW" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of line morphic computation.""" - performance = { - "inductance": { - "throughput": "Inductance limited (nH/m)", - "latency": "ns response", - "precision": "nH resolution", - "operations": "inductance arithmetic", - "power": "1-5W" - }, - "capacitance": { - "throughput": "Capacitance limited (pF/m)", - "latency": "ns response", - "precision": "pF resolution", - "operations": "capacitance arithmetic", - "power": "1-5W" - }, - "resistance": { - "throughput": "Resistance limited (Ω/m)", - "latency": "ns response", - "precision": "mΩ resolution", - "operations": "resistance arithmetic", - "power": "1-5W" - }, - "impedance": { - "throughput": "Impedance limited (100Ω)", - "latency": "ns response", - "precision": "0.1Ω resolution", - "operations": "impedance arithmetic", - "power": "1-5W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run DisplayPort line morphic analysis.""" - print("=" * 60) - print("DISPLAYPORT LINE MORPHIC COMPUTATION ANALYSIS") - print("=" * 60) - - # Step 1: Analyze DisplayPort lines - print("\n[1/4] Analyzing DisplayPort lines...") - print(f" Device: {self.displayport_lines['device']}") - print(f" Lanes: {self.displayport_lines['lanes']}") - print(f" Construction: {self.displayport_lines['construction']}") - print(f" Length: {self.displayport_lines['length']}") - print(f" Computational Potential: {self.displayport_lines['computational_potential']}") - - # Step 2: Analyze morphic potential - print("[2/4] Analyzing morphic computational potential...") - potential = self.analyze_morphic_potential() - print(f" Inductance: {potential['inductance_computation']['feasible']} - {potential['inductance_computation']['risk']}") - print(f" Capacitance: {potential['capacitance_computation']['feasible']} - {potential['capacitance_computation']['risk']}") - print(f" Resistance: {potential['resistance_computation']['feasible']} - {potential['resistance_computation']['risk']}") - print(f" Impedance: {potential['impedance_computation']['feasible']} - {potential['impedance_computation']['risk']}") - - # Step 3: Design morphic approach - print("[3/4] Designing line-based morphic computational approach...") - approach = self.design_morphic_approach() - print(f" Morphic modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Inductance: {performance['inductance']['throughput']}") - print(f" Capacitance: {performance['capacitance']['throughput']}") - print(f" Resistance: {performance['resistance']['throughput']}") - print(f" Impedance: {performance['impedance']['throughput']}") - - print("\n" + "=" * 60) - print("DISPLAYPORT LINE MORPHIC COMPUTATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "displayport_lines": self.displayport_lines, - "line_capabilities": self.line_capabilities, - "morphic_potential": potential, - "morphic_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = DisplayPortLineMorphic() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "displayport_line_morphic.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DISPLAYPORT LINE MORPHIC COMPUTATION SUMMARY") - print("=" * 60) - print(f"Device: {results['displayport_lines']['device']}") - print(f"Construction: {results['displayport_lines']['construction']}") - print(f"Computational Potential: {results['displayport_lines']['computational_potential']}") - print(f"Max Precision: nH/pF/mΩ/0.1Ω resolution") diff --git a/5-Applications/scripts/distributed_bitcoin_rgflow.py b/5-Applications/scripts/distributed_bitcoin_rgflow.py deleted file mode 100644 index 2e218aee..00000000 --- a/5-Applications/scripts/distributed_bitcoin_rgflow.py +++ /dev/null @@ -1,235 +0,0 @@ -#!/usr/bin/env python3 -""" -Distributed Bitcoin RGFlow Analysis on Swarm - -Assigns Bitcoin RGFlow analysis tasks to swarm nodes using Lean bindserver. -Per AGENTS.md: Uses swarm action metrics (cycles, operations) not human time units. -""" -import sys -import os -import json -import logging -import time -from typing import Dict, Any, List - -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -from infra.lean_unified_shim import LeanUnifiedShim -from scripts.distributed_swarm_colonization import SwarmColonization - -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger("BitcoinRGFlowSwarm") - -class DistributedBitcoinRGFlow: - def __init__(self): - self.shim = LeanUnifiedShim() - self.colonizer = SwarmColonization() - - def get_swarm_topology(self) -> List[Dict[str, Any]]: - """Get current swarm topology from colonization.""" - logger.info("Discovering swarm topology...") - topology = self.colonizer.discover_nodes() - if not topology: - logger.error("No swarm topology available") - return [] - logger.info(f"Swarm topology: {len(topology)} nodes") - return topology - - def assign_analysis_to_node(self, node: Dict[str, Any], prices: List[float], - window: int = 30, node_id: int = 0) -> Dict[str, Any]: - """ - Assign Bitcoin RGFlow analysis task to a specific node. - Returns result with swarm action metrics. - """ - hostname = node.get('hostname', 'unknown') - ip = node.get('tailscaleIP', 'unknown') - role = node.get('role', 'worker') - - logger.info(f"Assigning Bitcoin RGFlow analysis to node {node_id}: {hostname} ({ip}) - ROLE: {role}") - - # Track start time for action metrics - start_cycles = time.time_ns() - - try: - # Execute Bitcoin RGFlow analysis via Lean bindserver - result = self.shim.bitcoin_rgflow_analysis(prices, window) - - # Calculate swarm action metrics - end_cycles = time.time_ns() - action_cycles = end_cycles - start_cycles - action_operations = len(prices) * window # Approximate operation count - - if "error" in result: - logger.error(f"Analysis failed on {hostname}: {result['error']}") - return { - "node_id": node_id, - "hostname": hostname, - "role": role, - "status": "failed", - "error": result['error'], - "action_cycles": action_cycles, - "action_operations": action_operations - } - - logger.info(f"Analysis completed on {hostname} - {action_cycles:,} cycles, {action_operations:,} operations") - - return { - "node_id": node_id, - "hostname": hostname, - "role": role, - "status": "success", - "result": result, - "action_cycles": action_cycles, - "action_operations": action_operations - } - - except Exception as e: - logger.error(f"Exception on {hostname}: {e}") - end_cycles = time.time_ns() - action_cycles = end_cycles - start_cycles - return { - "node_id": node_id, - "hostname": hostname, - "role": role, - "status": "error", - "error": str(e), - "action_cycles": action_cycles, - "action_operations": 0 - } - - def distribute_analysis(self, prices: List[float], window: int = 30) -> Dict[str, Any]: - """ - Distribute Bitcoin RGFlow analysis across swarm nodes. - Uses coordinator nodes for orchestration, worker nodes for computation. - """ - logger.info(f"Starting distributed Bitcoin RGFlow analysis with {len(prices)} price points, window={window}") - - # Get swarm topology - topology = self.get_swarm_topology() - if not topology: - return {"error": "No swarm topology available"} - - # Separate coordinators and workers - coordinators = [n for n in topology if n.get('role') == 'coordinator' and n.get('status') == 'active'] - workers = [n for n in topology if n.get('role') == 'worker' and n.get('status') == 'active'] - architect = [n for n in topology if n.get('role') == 'architect' and n.get('status') == 'active'] - - logger.info(f"Active nodes: {len(coordinators)} coordinators, {len(workers)} workers, {len(architect)} architect") - - # If no active nodes, use all nodes - active_nodes = [n for n in topology if n.get('status') == 'active'] - if not active_nodes: - logger.warning("No active nodes, using all nodes") - active_nodes = topology - - # Assign analysis to active nodes (simple round-robin) - results = [] - total_cycles = 0 - total_operations = 0 - - for i, node in enumerate(active_nodes): - result = self.assign_analysis_to_node(node, prices, window, i) - results.append(result) - total_cycles += result.get('action_cycles', 0) - total_operations += result.get('action_operations', 0) - - # Calculate aggregate metrics - successful_nodes = [r for r in results if r.get('status') == 'success'] - failed_nodes = [r for r in results if r.get('status') in ['failed', 'error']] - - logger.info(f"Analysis complete: {len(successful_nodes)} successful, {len(failed_nodes)} failed") - logger.info(f"Total swarm action cycles: {total_cycles:,}") - logger.info(f"Total swarm action operations: {total_operations:,}") - - return { - "status": "complete", - "total_nodes": len(active_nodes), - "successful_nodes": len(successful_nodes), - "failed_nodes": len(failed_nodes), - "total_action_cycles": total_cycles, - "total_action_operations": total_operations, - "results": results - } - - def format_time_human_readable(self, seconds: float) -> str: - """Convert seconds to human-readable time format.""" - if seconds < 60: - return f"{int(seconds)} seconds" - elif seconds < 3600: - minutes = int(seconds / 60) - secs = int(seconds % 60) - return f"{minutes} minutes {secs} seconds" - else: - hours = int(seconds / 3600) - minutes = int((seconds % 3600) / 60) - return f"{hours} hours {minutes} minutes" - - def print_results(self, analysis_result: Dict[str, Any]): - """Print formatted analysis results with human-readable time.""" - if "error" in analysis_result: - print(f"ERROR: {analysis_result['error']}") - return - - print("\n" + "=" * 70) - print("DISTRIBUTED BITCOIN RGFLOW ANALYSIS - SWARM RESULTS") - print("=" * 70) - - print(f"\nTotal Nodes: {analysis_result['total_nodes']}") - print(f"Successful: {analysis_result['successful_nodes']}") - print(f"Failed: {analysis_result['failed_nodes']}") - - # Convert cycles to human-readable time (assuming 1 cycle ≈ 1 nanosecond) - total_cycles = analysis_result['total_action_cycles'] - total_seconds = total_cycles / 1_000_000_000 - human_time = self.format_time_human_readable(total_seconds) - - print(f"\nTotal Swarm Action Cycles: {total_cycles:,}") - print(f"Estimated Duration: {human_time}") - print(f"Total Swarm Action Operations: {analysis_result['total_action_operations']:,}") - - print("\n" + "-" * 70) - print("PER-NODE RESULTS") - print("-" * 70) - - for result in analysis_result['results']: - hostname = result['hostname'] - role = result['role'] - status = result['status'] - cycles = result['action_cycles'] - operations = result['action_operations'] - - # Convert node cycles to human-readable time - node_seconds = cycles / 1_000_000_000 - node_time = self.format_time_human_readable(node_seconds) - - print(f"\n {hostname} ({role}):") - print(f" Status: {status}") - print(f" Action Cycles: {cycles:,} ({node_time})") - print(f" Action Operations: {operations:,}") - - if status == 'success' and 'result' in result: - print(f" Analysis: {json.dumps(result['result'], indent=6)}") - elif status in ['failed', 'error']: - print(f" Error: {result.get('error', 'Unknown')}") - - print("\n" + "=" * 70) - -if __name__ == "__main__": - # Example Bitcoin price data (Q16.16 format) - # In production, this would come from a real Bitcoin price API - example_prices = [ - 65536, # 1.0 in Q16.16 - 98304, # 1.5 in Q16.16 - 131072, # 2.0 in Q16.16 - 163840, # 2.5 in Q16.16 - 196608, # 3.0 in Q16.16 - 229376, # 3.5 in Q16.16 - 262144, # 4.0 in Q16.16 - 294912, # 4.5 in Q16.16 - 327680, # 5.0 in Q16.16 - 360448 # 5.5 in Q16.16 - ] - - analyzer = DistributedBitcoinRGFlow() - result = analyzer.distribute_analysis(example_prices, window=2) - analyzer.print_results(result) diff --git a/5-Applications/scripts/dma_ram_morphic.py b/5-Applications/scripts/dma_ram_morphic.py deleted file mode 100644 index 15c1773d..00000000 --- a/5-Applications/scripts/dma_ram_morphic.py +++ /dev/null @@ -1,218 +0,0 @@ -#!/usr/bin/env python3 -""" -DMA-RAM Morphic Computational Device -Analyzes DMA + RAM as a morphic computational device. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DMARAMMorphicDevice: - """Analyzes DMA + RAM as a morphic computational device.""" - - def __init__(self): - self.ram_info = { - "total_memory": "31879804 kB (31.1 GB)", - "available_memory": "13855816 kB (13.5 GB)", - "free_memory": "1071504 kB (1.0 GB)", - "cached_memory": "13003340 kB (12.7 GB)", - "active_memory": "14744272 kB (14.4 GB)", - "inactive_memory": "12673468 kB (12.4 GB)" - } - - self.dma_capabilities = { - "dma_controller": "IOMMU groups (26, 31, 32, 34)", - "dma_bypass": "Direct memory access without CPU", - "dma_chaining": "Chain multiple DMA operations", - "dma_scatter_gather": "Scatter-gather DMA", - "computational_potential": "HIGH (RAM as morphic device)" - } - - self.morphic_modes = { - "charge_based_computation": "Use RAM charge states for computation", - "address_based_computation": "Use RAM addressing for computation", - "content_based_computation": "Use RAM content for computation", - "dma_chained_computation": "Chain DMA operations for computation" - } - - def analyze_morphic_potential(self) -> Dict: - """Analyze morphic potential of DMA-RAM.""" - analysis = { - "dma_ram_morphic": { - "feasible": True, - "mode": "DMA-RAM morphic computation", - "description": "Use DMA to manipulate RAM as morphic device", - "ram_capacity": "31.1 GB total, 13.5 GB available", - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns (memory access)", - "power": "10-20W (memory controller)" - }, - "dma_chaining": { - "feasible": True, - "mode": "DMA chaining computation", - "description": "Chain multiple DMA operations for computation", - "throughput": "Memory bandwidth limited", - "latency": "<100ns per operation", - "power": "10-20W" - }, - "ram_content": { - "feasible": True, - "mode": "RAM content-based computation", - "description": "Use RAM content values for computation", - "precision": "8-64 bit (per word)", - "throughput": "Memory bandwidth limited", - "latency": "<100ns", - "power": "10-20W" - } - } - - return analysis - - def design_morphic_approach(self) -> Dict: - """Design DMA-RAM morphic computational approach.""" - approach = { - "dma_address_computation": { - "concept": "Use RAM addressing for computation", - "implementation": "DMA writes to specific addresses for computation", - "operations": ["address arithmetic", "address pattern computation"], - "precision": "64-bit addresses", - "throughput": "Memory bandwidth limited", - "latency": "<100ns", - "power": "10-20W" - }, - "dma_content_computation": { - "concept": "Use RAM content for computation", - "implementation": "DMA reads/writes with content transformation", - "operations": ["content arithmetic", "content pattern matching"], - "precision": "8-64 bit (per word)", - "throughput": "Memory bandwidth limited", - "latency": "<100ns", - "power": "10-20W" - }, - "dma_scatter_gather": { - "concept": "Use scatter-gather DMA for computation", - "implementation": "DMA scatter-gather for parallel computation", - "operations": ["parallel memory operations", "data transformation"], - "precision": "8-64 bit", - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns", - "power": "10-20W" - }, - "dma_chained": { - "concept": "Chain DMA operations for computation", - "implementation": "Chain multiple DMA operations sequentially", - "operations": ["sequential computation", "pipeline processing"], - "precision": "8-64 bit", - "throughput": "Memory bandwidth limited", - "latency": "<100ns per operation", - "power": "10-20W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of DMA-RAM morphic computation.""" - performance = { - "dma_address": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns (memory access)", - "precision": "64-bit addresses", - "operations": "address arithmetic", - "power": "10-20W" - }, - "dma_content": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns (memory access)", - "precision": "8-64 bit (per word)", - "operations": "content arithmetic", - "power": "10-20W" - }, - "dma_scatter_gather": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns (memory access)", - "precision": "8-64 bit", - "operations": "parallel memory operations", - "power": "10-20W" - }, - "dma_chained": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns per operation", - "precision": "8-64 bit", - "operations": "sequential computation", - "power": "10-20W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run DMA-RAM morphic device analysis.""" - print("=" * 60) - print("DMA-RAM MORPHIC COMPUTATIONAL DEVICE ANALYSIS") - print("=" * 60) - - # Step 1: Analyze RAM information - print("\n[1/4] Analyzing RAM information...") - print(f" Total Memory: {self.ram_info['total_memory']}") - print(f" Available Memory: {self.ram_info['available_memory']}") - print(f" Cached Memory: {self.ram_info['cached_memory']}") - print(f" Active Memory: {self.ram_info['active_memory']}") - - # Step 2: Analyze morphic potential - print("[2/4] Analyzing morphic potential...") - potential = self.analyze_morphic_potential() - print(f" DMA-RAM Morphic: {potential['dma_ram_morphic']['feasible']}") - print(f" DMA Chaining: {potential['dma_chaining']['feasible']}") - print(f" RAM Content: {potential['ram_content']['feasible']}") - - # Step 3: Design morphic approach - print("[3/4] Designing morphic approach...") - approach = self.design_morphic_approach() - print(f" Morphic modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" DMA Address: {performance['dma_address']['throughput']}") - print(f" DMA Content: {performance['dma_content']['throughput']}") - print(f" DMA Scatter-Gather: {performance['dma_scatter_gather']['throughput']}") - print(f" DMA Chained: {performance['dma_chained']['throughput']}") - - print("\n" + "=" * 60) - print("DMA-RAM MORPHIC COMPUTATIONAL DEVICE ANALYSIS COMPLETE") - print("=" * 60) - - return { - "ram_info": self.ram_info, - "dma_capabilities": self.dma_capabilities, - "morphic_potential": potential, - "morphic_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = DMARAMMorphicDevice() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "dma_ram_morphic.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DMA-RAM MORPHIC DEVICE SUMMARY") - print("=" * 60) - print(f"Total Memory: {results['ram_info']['total_memory']}") - print(f"Available Memory: {results['ram_info']['available_memory']}") - print(f"DMA-RAM Morphic: {results['morphic_potential']['dma_ram_morphic']['feasible']}") - print(f"Max Throughput: {results['performance_estimates']['dma_scatter_gather']['throughput']}") diff --git a/5-Applications/scripts/download_multilingual_corpora.py b/5-Applications/scripts/download_multilingual_corpora.py deleted file mode 100644 index 1804b0d8..00000000 --- a/5-Applications/scripts/download_multilingual_corpora.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 -""" -Download multilingual corpora for PIST framework testing. -Uses HuggingFace datasets for reliable access to large multilingual text. -""" - -import os -import sys - -def ensure_datasets(): - try: - from datasets import load_dataset - return load_dataset - except ImportError: - print("Installing datasets library...") - os.system("pip install -q datasets") - from datasets import load_dataset - return load_dataset - -def download_c4_multilingual(): - """Download multilingual C4 corpus (mC4) for various languages.""" - load_dataset = ensure_datasets() - - output_dir = "/home/allaun/Documents/Research Stack/data/corpora/mc4" - os.makedirs(output_dir, exist_ok=True) - - # Languages to download (prioritizing non-Latin scripts + high-resource) - languages = [ - # Latin scripts - ("en", "English"), - ("de", "German"), - ("fr", "French"), - ("es", "Spanish"), - ("pt", "Portuguese"), - ("it", "Italian"), - ("nl", "Dutch"), - ("pl", "Polish"), - ("ru", "Russian"), # Cyrillic - # Non-Latin scripts - ("zh", "Chinese"), # CJK - ("ja", "Japanese"), # CJK - ("ko", "Korean"), # CJK - ("ar", "Arabic"), # Arabic script - ("hi", "Hindi"), # Devanagari - ("tr", "Turkish"), # Latin with diacritics - ("vi", "Vietnamese"), # Latin with tone marks - ] - - for lang_code, lang_name in languages: - try: - print(f"Downloading {lang_name} ({lang_code})...") - ds = load_dataset("mc4", lang_code, split="train", streaming=True, trust_remote_code=True) - - # Save first 100K examples (~100MB text) - output_file = os.path.join(output_dir, f"{lang_code}_sample.txt") - with open(output_file, "w", encoding="utf-8") as f: - count = 0 - for example in ds: - text = example.get("text", "") - if text: - f.write(text + "\n") - count += 1 - if count >= 100000: - break - - size_mb = os.path.getsize(output_file) / (1024 * 1024) - print(f" Saved {count} examples, {size_mb:.1f}MB -> {output_file}") - - except Exception as e: - print(f" ERROR downloading {lang_name}: {e}") - continue - -def download_opus_parallel(): - """Download OPUS parallel corpus for cross-lingual testing.""" - load_dataset = ensure_datasets() - - output_dir = "/home/allaun/Documents/Research Stack/data/corpora/opus" - os.makedirs(output_dir, exist_ok=True) - - # Wikipedia parallel text - try: - print("Downloading OPUS Wikipedia (English-Spanish parallel)...") - ds = load_dataset("opus_wikipedia", "en-es", split="train") - - with open(os.path.join(output_dir, "opus_wikipedia_en.txt"), "w", encoding="utf-8") as f: - for i, example in enumerate(ds): - f.write(example["translation"]["en"] + "\n") - if i >= 50000: - break - - with open(os.path.join(output_dir, "opus_wikipedia_es.txt"), "w", encoding="utf-8") as f: - for i, example in enumerate(ds): - f.write(example["translation"]["es"] + "\n") - if i >= 50000: - break - - print(" Saved OPUS Wikipedia EN-ES parallel corpus") - except Exception as e: - print(f" ERROR: {e}") - -def download_wikipedia_dumps(): - """Download raw Wikipedia dumps for specific languages.""" - import urllib.request - - output_dir = "/home/allaun/Documents/Research Stack/data/corpora/wikipedia" - os.makedirs(output_dir, exist_ok=True) - - # Wikipedia dump URLs for specific languages (latest) - wikis = [ - ("en", "https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2"), - ("de", "https://dumps.wikimedia.org/dewiki/latest/dewiki-latest-pages-articles.xml.bz2"), - ("zh", "https://dumps.wikimedia.org/zhwiki/latest/zhwiki-latest-pages-articles.xml.bz2"), - ("ja", "https://dumps.wikimedia.org/jawiki/latest/jawiki-latest-pages-articles.xml.bz2"), - ("ar", "https://dumps.wikimedia.org/arwiki/latest/arwiki-latest-pages-articles.xml.bz2"), - ] - - for lang, url in wikis: - output_path = os.path.join(output_dir, f"{lang}wiki-latest-pages-articles.xml.bz2") - if os.path.exists(output_path) and os.path.getsize(output_path) > 1000000: - print(f"Skipping {lang}wiki (already downloaded)") - continue - - print(f"Downloading {lang}wiki dump...") - try: - urllib.request.urlretrieve(url, output_path) - size_mb = os.path.getsize(output_path) / (1024 * 1024) - print(f" Saved {size_mb:.0f}MB") - except Exception as e: - print(f" ERROR: {e}") - -def main(): - print("=" * 60) - print("Multilingual Corpus Download for PIST Framework") - print("=" * 60) - - # Priority: mC4 for non-Latin scripts + diverse languages - print("\n--- mC4 Multilingual Corpus ---") - download_c4_multilingual() - - print("\n--- OPUS Parallel Corpus ---") - download_opus_parallel() - - print("\n--- Wikipedia Dumps ---") - print("Note: Wikipedia dumps are very large (1-50GB each).") - print("Skipping by default. Uncomment to enable.") - # download_wikipedia_dumps() - - print("\n" + "=" * 60) - print("Downloads complete. Check data/corpora/ for results.") - print("=" * 60) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/dual_case_encoding.py b/5-Applications/scripts/dual_case_encoding.py deleted file mode 100644 index ce61cf41..00000000 --- a/5-Applications/scripts/dual_case_encoding.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -""" -Dual-Case Encoding Enhancement Analysis -Analyzes dual-case encoding as an enhancement to computational topology. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DualCaseEncoding: - """Analyzes dual-case encoding enhancement for computational topology.""" - - def __init__(self): - # Dual-case encoding types - self.encoding_types = { - "dual_phase": { - "description": "Dual-phase encoding (0°/180°)", - "method": "Use 0° and 180° phase shifts for dual states", - "multiplier": 1.3, - "significance_score": 90.0 - }, - "dual_amplitude": { - "description": "Dual-amplitude encoding (high/low)", - "method": "Use high and low amplitude pairs for dual states", - "multiplier": 1.2, - "significance_score": 85.0 - }, - "dual_frequency": { - "description": "Dual-frequency encoding (primary/secondary)", - "method": "Use two carrier frequencies simultaneously", - "multiplier": 1.25, - "significance_score": 80.0 - }, - "dual_polarity": { - "description": "Dual-polarity encoding (positive/negative)", - "method": "Use positive/negative voltage swings for dual states", - "multiplier": 1.15, - "significance_score": 75.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "ac_mains_capacity": 65656316222.01144, - "expansion_factor": 34555955.0 - } - - def analyze_dual_case_encoding(self) -> Dict: - """Analyze dual-case encoding enhancement.""" - analysis = { - "encoding_types": self.encoding_types, - "average_significance_score": sum(e["significance_score"] for e in self.encoding_types.values()) / len(self.encoding_types), - "infrastructure_readiness": { - "fpga_acceleration": "150x decision processing (real-time encoding/decoding)", - "phase_modulation": "1.2x multiplier (dual-phase encoding)", - "amplitude_modulation": "1.1x multiplier (dual-amplitude encoding)", - "power_harmonics": "1.2x multiplier (dual-frequency encoding)", - "signal_topology": "4.27x signal integration (dual-state monitoring)", - "deterministic_stochastic": "7.72x (randomness for dual-state selection)" - } - } - - return analysis - - def analyze_dual_case_benefits(self) -> Dict: - """Analyze dual-case encoding benefits.""" - benefits = { - "error_detection": { - "description": "Dual-state comparison enables real-time error detection", - "significance_score": 95.0 - }, - "fault_tolerance": { - "description": "One state can fail while other continues", - "significance_score": 90.0 - }, - "signal_integrity": { - "description": "Dual-state verification improves reliability", - "significance_score": 85.0 - }, - "redundant_encoding": { - "description": "Complementary data in dual states", - "significance_score": 80.0 - }, - "error_correction": { - "description": "Dual-state comparison for error correction", - "significance_score": 85.0 - }, - "security": { - "description": "Dual-state encoding adds complexity for attackers", - "significance_score": 75.0 - } - } - - return benefits - - def calculate_dual_case_impact(self) -> Dict: - """Calculate dual-case encoding impact on computational expansion.""" - # Dual-case encoding multipliers - dual_phase_multiplier = 1.3 # dual-phase encoding - dual_amplitude_multiplier = 1.2 # dual-amplitude encoding - dual_frequency_multiplier = 1.25 # dual-frequency encoding - dual_polarity_multiplier = 1.15 # dual-polarity encoding - error_detection_multiplier = 1.2 # error detection - fault_tolerance_multiplier = 1.15 # fault tolerance - signal_integrity_multiplier = 1.1 # signal integrity - - # Calculate expanded capacity with dual-case encoding - base_capacity = 1900 - current_ac_mains_capacity = 65656316222.01144 - - # Apply dual-case encoding multipliers - dual_case_capacity = (current_ac_mains_capacity * - dual_phase_multiplier * - dual_amplitude_multiplier * - dual_frequency_multiplier * - dual_polarity_multiplier * - error_detection_multiplier * - fault_tolerance_multiplier * - signal_integrity_multiplier) - - dual_case_expansion_factor = dual_case_capacity / base_capacity - dual_case_improvement_factor = dual_case_capacity / current_ac_mains_capacity - - calculation = { - "base_capacity": base_capacity, - "current_ac_mains_capacity": current_ac_mains_capacity, - "dual_phase_multiplier": dual_phase_multiplier, - "dual_amplitude_multiplier": dual_amplitude_multiplier, - "dual_frequency_multiplier": dual_frequency_multiplier, - "dual_polarity_multiplier": dual_polarity_multiplier, - "error_detection_multiplier": error_detection_multiplier, - "fault_tolerance_multiplier": fault_tolerance_multiplier, - "signal_integrity_multiplier": signal_integrity_multiplier, - "dual_case_capacity": dual_case_capacity, - "dual_case_expansion_factor": dual_case_expansion_factor, - "dual_case_improvement_factor": dual_case_improvement_factor, - "total_dual_case_multiplier": (dual_phase_multiplier * - dual_amplitude_multiplier * - dual_frequency_multiplier * - dual_polarity_multiplier * - error_detection_multiplier * - fault_tolerance_multiplier * - signal_integrity_multiplier) - } - - return calculation - - def integrate_dual_case_encoding(self) -> Dict: - """Integrate dual-case encoding into comprehensive analysis.""" - integration = { - "dual_case_encoding_enabled": True, - "encoding_types": 4, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (encoding)", - "Control Theory (error detection)", - "Thermodynamic (signal integrity)", - "Physical Bind (dual states)" - ], - "foundation_kernels_enhanced": [ - "F01", "F02", "F03", # Information Theory (encoding) - "F11", "F12" # Control Theory (error detection) - ], - "implementation": "Internal to current system (FPGA, power controllers, signal topology)" - } - - return integration - - def run_analysis(self) -> Dict: - """Run dual-case encoding analysis.""" - print("=" * 60) - print("DUAL-CASE ENCODING ENHANCEMENT ANALYSIS") - print("=" * 60) - - # Step 1: Analyze dual-case encoding - print("\n[1/4] Analyzing dual-case encoding enhancement...") - encoding_analysis = self.analyze_dual_case_encoding() - print(f" Encoding Types: {len(encoding_analysis['encoding_types'])}") - for encoding_type, details in encoding_analysis['encoding_types'].items(): - print(f" {encoding_type}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing dual-case encoding benefits...") - benefits = self.analyze_dual_case_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating dual-case encoding impact...") - impact_calculation = self.calculate_dual_case_impact() - print(f" Current AC Mains Capacity: {impact_calculation['current_ac_mains_capacity']}") - print(f" Dual-Case Capacity: {impact_calculation['dual_case_capacity']}") - print(f" Dual-Case Improvement Factor: {impact_calculation['dual_case_improvement_factor']:.2f}x") - print(f" Total Dual-Case Multiplier: {impact_calculation['total_dual_case_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating dual-case encoding...") - integration = self.integrate_dual_case_encoding() - print(f" Encoding Types: {integration['encoding_types']}") - print(f" Benefits: {integration['benefits']}") - print(f" Implementation: {integration['implementation']}") - - print("\n" + "=" * 60) - print("DUAL-CASE ENCODING ENHANCEMENT ANALYSIS COMPLETE") - print("=" * 60) - - return { - "encoding_analysis": encoding_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = DualCaseEncoding() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "dual_case_encoding.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DUAL-CASE ENCODING SUMMARY") - print("=" * 60) - print(f"Encoding Types: {results['integration']['encoding_types']}") - print(f"Dual-Case Capacity: {results['impact_calculation']['dual_case_capacity']}") - print(f"Dual-Case Improvement Factor: {results['impact_calculation']['dual_case_improvement_factor']:.2f}x") - print(f"Total Dual-Case Multiplier: {results['impact_calculation']['total_dual_case_multiplier']:.2f}x") diff --git a/5-Applications/scripts/dynamic_neural_profile_switching.py b/5-Applications/scripts/dynamic_neural_profile_switching.py deleted file mode 100644 index a0158ace..00000000 --- a/5-Applications/scripts/dynamic_neural_profile_switching.py +++ /dev/null @@ -1,278 +0,0 @@ -#!/usr/bin/env python3 -""" -Dynamic Neural Profile Switching Analysis -Analyzes morphic scalars that switch between ALL neural profiles depending on task, using metaprobe to discover neural coding datasets. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") -DATA_DIR = Path("/home/allaun/Documents/Research Stack/data") - -class DynamicNeuralProfileSwitching: - """Analyzes dynamic neural profile switching using metaprobe-discovered datasets.""" - - def __init__(self): - # Neural profile datasets discovered - self.neural_profiles = { - "human_h01": { - "source": "H01 proofread 104 neurons SWC", - "neurons": 104, - "characteristics": ["spike_timing", "rate_coding", "population_coding", "temporal_coding"], - "significance_score": 95.0 - }, - "celegans": { - "source": "C. elegans hermaphrodite (Cook 2019)", - "neurons": 448, - "chemical_synapses": 4681, - "electrical_junctions": 2698, - "characteristics": ["chemical_synapses", "electrical_junctions", "hub_neurons"], - "significance_score": 90.0 - }, - "openworm": { - "source": "OpenWorm project", - "characteristics": ["neuron", "cell", "receptor", "synapse", "muscle", "development", "neurotransmitter"], - "significance_score": 85.0 - } - } - - # Dynamic switching characteristics - self.switching_characteristics = { - "task_dependent": { - "description": "Switch neural profiles based on task requirements", - "significance_score": 95.0 - }, - "metaprobe_discovery": { - "description": "Use metaprobe to discover neural coding datasets", - "significance_score": 90.0 - }, - "profile_library": { - "description": "Maintain library of all discovered neural profiles", - "significance_score": 85.0 - }, - "dynamic_adaptation": { - "description": "Dynamically adapt profile based on performance", - "significance_score": 90.0 - }, - "profile_switching": { - "description": "Seamless switching between neural profiles", - "significance_score": 95.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "neuron_coding_capacity": 5.313855653343694e+16, - "expansion_factor": 27965082386019.0 - } - - def analyze_neural_profiles(self) -> Dict: - """Analyze discovered neural profiles.""" - analysis = { - "neural_profiles": self.neural_profiles, - "total_profiles": len(self.neural_profiles), - "total_neurons": sum(p.get("neurons", 0) for p in self.neural_profiles.values()), - "profile_characteristics": { - "human_h01": "Human brain neural coding patterns", - "celegans": "C. elegans neural network topology", - "openworm": "OpenWorm biological neural data" - } - } - - return analysis - - def analyze_dynamic_switching(self) -> Dict: - """Analyze dynamic neural profile switching.""" - analysis = { - "switching_characteristics": self.switching_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.switching_characteristics.values()) / len(self.switching_characteristics), - "switching_mechanisms": { - "task_analysis": "Analyze task requirements to select optimal profile", - "profile_selection": "Select profile from library based on task", - "seamless_switching": "Switch profiles without interruption", - "performance_monitoring": "Monitor performance to optimize selection", - "adaptive_learning": "Learn which profiles work best for which tasks" - }, - "metaprobe_integration": { - "discovery": "Metaprobe discovers new neural coding datasets", - "integration": "Integrate discovered datasets into profile library", - "validation": "Validate discovered profiles for compatibility", - "expansion": "Continuously expand profile library" - } - } - - return analysis - - def analyze_dynamic_switching_benefits(self) -> Dict: - """Analyze dynamic neural profile switching benefits.""" - benefits = { - "task_optimization": { - "description": "Optimal neural profile for each task", - "significance_score": 95.0 - }, - "adaptability": { - "description": "Adapt to any task through profile switching", - "significance_score": 90.0 - }, - "continuous_learning": { - "description": "Continuously learn new profiles via metaprobe", - "significance_score": 85.0 - }, - "performance_optimization": { - "description": "Optimize performance through profile selection", - "significance_score": 95.0 - }, - "scalability": { - "description": "Scale to any task through profile diversity", - "significance_score": 80.0 - }, - "profile_diversity": { - "description": "Access to all neural coding patterns discovered", - "significance_score": 90.0 - } - } - - return benefits - - def calculate_dynamic_switching_impact(self) -> Dict: - """Calculate dynamic neural profile switching impact on computational expansion.""" - # Dynamic switching multipliers - task_optimization_multiplier = 2.0 # 2x from task-specific optimization - adaptability_multiplier = 1.5 # 1.5x from adaptability - continuous_learning_multiplier = 1.5 # 1.5x from continuous learning - performance_optimization_multiplier = 2.0 # 2x from performance optimization - scalability_multiplier = 1.3 # 1.3x from scalability - profile_diversity_multiplier = 1.5 # 1.5x from profile diversity - - # Calculate expanded capacity with dynamic switching - base_capacity = 1900 - current_neuron_coding_capacity = 5.313855653343694e+16 - - # Apply dynamic switching multipliers - dynamic_switching_capacity = (current_neuron_coding_capacity * - task_optimization_multiplier * - adaptability_multiplier * - continuous_learning_multiplier * - performance_optimization_multiplier * - scalability_multiplier * - profile_diversity_multiplier) - - dynamic_switching_expansion_factor = dynamic_switching_capacity / base_capacity - dynamic_switching_improvement_factor = dynamic_switching_capacity / current_neuron_coding_capacity - - calculation = { - "base_capacity": base_capacity, - "current_neuron_coding_capacity": current_neuron_coding_capacity, - "task_optimization_multiplier": task_optimization_multiplier, - "adaptability_multiplier": adaptability_multiplier, - "continuous_learning_multiplier": continuous_learning_multiplier, - "performance_optimization_multiplier": performance_optimization_multiplier, - "scalability_multiplier": scalability_multiplier, - "profile_diversity_multiplier": profile_diversity_multiplier, - "dynamic_switching_capacity": dynamic_switching_capacity, - "dynamic_switching_expansion_factor": dynamic_switching_expansion_factor, - "dynamic_switching_improvement_factor": dynamic_switching_improvement_factor, - "total_dynamic_switching_multiplier": (task_optimization_multiplier * - adaptability_multiplier * - continuous_learning_multiplier * - performance_optimization_multiplier * - scalability_multiplier * - profile_diversity_multiplier) - } - - return calculation - - def integrate_dynamic_switching(self) -> Dict: - """Integrate dynamic neural profile switching into comprehensive analysis.""" - integration = { - "dynamic_switching_enabled": True, - "paradigm": "Dynamic neural profile switching with metaprobe discovery", - "mechanism": "Morphic scalars switch between neural profiles based on task", - "neural_profiles": len(self.neural_profiles), - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (profile switching)", - "Control Theory (task optimization)", - "Cognitive/Routing (adaptive learning)", - "Thermodynamic (performance optimization)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12" # Control Theory (task optimization) - ], - "metaprobe_feature": "Continuous discovery and integration of neural coding datasets" - } - - return integration - - def run_analysis(self) -> Dict: - """Run dynamic neural profile switching analysis.""" - print("=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING ANALYSIS") - print("=" * 60) - - # Step 1: Analyze neural profiles - print("\n[1/4] Analyzing discovered neural profiles...") - profiles_analysis = self.analyze_neural_profiles() - print(f" Neural Profiles: {profiles_analysis['total_profiles']}") - print(f" Total Neurons: {profiles_analysis['total_neurons']}") - for profile, details in profiles_analysis['neural_profiles'].items(): - print(f" {profile}: {details.get('neurons', 'N/A')} neurons, score {details['significance_score']}") - - # Step 2: Analyze dynamic switching - print("[2/4] Analyzing dynamic neural profile switching...") - switching_analysis = self.analyze_dynamic_switching() - print(f" Switching Characteristics: {len(switching_analysis['switching_characteristics'])}") - for characteristic, details in switching_analysis['switching_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 3: Analyze benefits - print("[3/4] Analyzing dynamic switching benefits...") - benefits = self.analyze_dynamic_switching_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 4: Calculate impact - print("[4/4] Calculating dynamic switching impact...") - impact_calculation = self.calculate_dynamic_switching_impact() - print(f" Current Neuron Coding Capacity: {impact_calculation['current_neuron_coding_capacity']}") - print(f" Dynamic Switching Capacity: {impact_calculation['dynamic_switching_capacity']}") - print(f" Dynamic Switching Improvement Factor: {impact_calculation['dynamic_switching_improvement_factor']:.2f}x") - print(f" Total Dynamic Switching Multiplier: {impact_calculation['total_dynamic_switching_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING ANALYSIS COMPLETE") - print("=" * 60) - - return { - "profiles_analysis": profiles_analysis, - "switching_analysis": switching_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": self.integrate_dynamic_switching() - } - -if __name__ == '__main__': - analyzer = DynamicNeuralProfileSwitching() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "dynamic_neural_profile_switching.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING SUMMARY") - print("=" * 60) - print(f"Neural Profiles: {results['profiles_analysis']['total_profiles']}") - print(f"Dynamic Switching Capacity: {results['impact_calculation']['dynamic_switching_capacity']}") - print(f"Dynamic Switching Improvement Factor: {results['impact_calculation']['dynamic_switching_improvement_factor']:.2f}x") - print(f"Total Dynamic Switching Multiplier: {results['impact_calculation']['total_dynamic_switching_multiplier']:.2f}x") diff --git a/5-Applications/scripts/dynamic_neural_profile_switching_academic.py b/5-Applications/scripts/dynamic_neural_profile_switching_academic.py deleted file mode 100644 index fcc976c8..00000000 --- a/5-Applications/scripts/dynamic_neural_profile_switching_academic.py +++ /dev/null @@ -1,338 +0,0 @@ -#!/usr/bin/env python3 -""" -Dynamic Neural Profile Switching with Academic Math Extraction -Analyzes morphic scalars that switch between neural profiles based on task, using metaprobe to scan academic papers for neural coding math and extract equations. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") -DATA_DIR = Path("/home/allaun/Documents/Research Stack/data") - -class DynamicNeuralProfileSwitchingAcademic: - """Analyzes dynamic neural profile switching with academic math extraction via metaprobe.""" - - def __init__(self): - # Neural coding math patterns discovered via metaprobe - self.neural_coding_math = { - "spike_timing_encoding": { - "source": "Academic papers on neural spike timing", - "equations": [ - "S(t) = Σ δ(t - t_i) for spike times t_i", - "Temporal precision: Δt ~ 1ms", - "Phase coding: θ = 2πt/T" - ], - "significance_score": 95.0 - }, - "rate_coding": { - "source": "Academic papers on firing rate coding", - "equations": [ - "r = N/Δt (spikes per time window)", - "Poisson process: P(k) = (λ^k e^-λ)/k!", - "Rate modulation: r(t) = r_0 + Δr·f(t)" - ], - "significance_score": 90.0 - }, - "synaptic_plasticity": { - "source": "Academic papers on Hebbian learning", - "equations": [ - "Δw_ij = η·x_i·y_j (Hebbian rule)", - "STDP: Δw = A_+·e^-Δt/τ_+ - A_-·e^Δt/τ_-", - "Weight update: w_ij(t+1) = w_ij(t) + Δw_ij" - ], - "significance_score": 95.0 - }, - "population_coding": { - "source": "Academic papers on population vectors", - "equations": [ - "P = Σ w_i·r_i (population vector)", - "Tuning curves: r_i = f(s - s_0)", - "Decoding: s = Σ w_i·r_i / Σ w_i" - ], - "significance_score": 85.0 - }, - "temporal_patterns": { - "source": "Academic papers on neural oscillations", - "equations": [ - "Oscillation: A(t) = A_0·sin(2πft + φ)", - "Phase coding: φ = arctan(Im/Re)", - "Synchronization: C = Σ cos(φ_i - φ_j)/N" - ], - "significance_score": 90.0 - } - } - - # Dynamic switching characteristics - self.switching_characteristics = { - "task_dependent": { - "description": "Switch neural profiles based on task requirements", - "significance_score": 95.0 - }, - "metaprobe_academic_scan": { - "description": "Metaprobe scans academic papers for neural coding math", - "significance_score": 95.0 - }, - "equation_extraction": { - "description": "Extract neural coding equations from academic papers", - "significance_score": 90.0 - }, - "profile_generation": { - "description": "Generate neural profiles from extracted equations", - "significance_score": 85.0 - }, - "dynamic_adaptation": { - "description": "Dynamically adapt profile based on performance", - "significance_score": 90.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "neuron_coding_capacity": 5.313855653343694e+16, - "expansion_factor": 27965082386019.0 - } - - def analyze_neural_coding_math(self) -> Dict: - """Analyze neural coding math discovered via metaprobe.""" - analysis = { - "neural_coding_math": self.neural_coding_math, - "total_math_patterns": len(self.neural_coding_math), - "total_equations": sum(len(p["equations"]) for p in self.neural_coding_math.values()), - "math_categories": { - "spike_timing": "Temporal encoding via spike timing", - "rate_coding": "Intensity encoding via firing rates", - "synaptic_plasticity": "Learning via synaptic weight changes", - "population_coding": "Distributed encoding via populations", - "temporal_patterns": "Oscillation and phase coding" - } - } - - return analysis - - def analyze_metaprobe_academic_scan(self) -> Dict: - """Analyze metaprobe academic scanning mechanism.""" - analysis = { - "metaprobe_mechanism": { - "academic_scan": "Scan academic papers for neural coding math", - "equation_extraction": "Extract equations and mathematical formulations", - "pattern_recognition": "Recognize neural coding patterns", - "math_integration": "Integrate extracted math into profiles" - }, - "extraction_targets": [ - "Neural spike timing patterns", - "Synaptic plasticity patterns", - "Population vector coding", - "Neural oscillation patterns", - "Hebbian learning rules", - "STDP formulations" - ], - "integration_process": { - "discovery": "Metaprobe discovers neural coding math in academic papers", - "extraction": "Extract equations and mathematical formulations", - "validation": "Validate mathematical correctness", - "integration": "Integrate into neural profile library" - } - } - - return analysis - - def analyze_dynamic_switching(self) -> Dict: - """Analyze dynamic neural profile switching.""" - analysis = { - "switching_characteristics": self.switching_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.switching_characteristics.values()) / len(self.switching_characteristics), - "switching_mechanisms": { - "task_analysis": "Analyze task requirements to select optimal profile", - "profile_selection": "Select profile from library based on task", - "equation_application": "Apply extracted equations to profile behavior", - "performance_monitoring": "Monitor performance to optimize selection", - "adaptive_learning": "Learn which profiles work best for which tasks" - } - } - - return analysis - - def analyze_dynamic_switching_benefits(self) -> Dict: - """Analyze dynamic neural profile switching benefits.""" - benefits = { - "mathematical_precision": { - "description": "Precise neural coding from academic equations", - "significance_score": 95.0 - }, - "task_optimization": { - "description": "Optimal neural profile for each task", - "significance_score": 95.0 - }, - "continuous_discovery": { - "description": "Continuously discover new math via metaprobe", - "significance_score": 90.0 - }, - "performance_optimization": { - "description": "Optimize performance through profile selection", - "significance_score": 95.0 - }, - "scalability": { - "description": "Scale to any task through profile diversity", - "significance_score": 85.0 - }, - "mathematical_diversity": { - "description": "Access to all neural coding math discovered", - "significance_score": 90.0 - } - } - - return benefits - - def calculate_dynamic_switching_impact(self) -> Dict: - """Calculate dynamic neural profile switching impact on computational expansion.""" - # Dynamic switching multipliers - mathematical_precision_multiplier = 2.5 # 2.5x from precise academic equations - task_optimization_multiplier = 1.5 # 1.5x from task-specific optimization - continuous_discovery_multiplier = 1.5 # 1.5x from continuous math discovery - performance_optimization_multiplier = 1.5 # 1.5x from performance optimization - scalability_multiplier = 1.3 # 1.3x from scalability - mathematical_diversity_multiplier = 1.5 # 1.5x from mathematical diversity - - # Calculate expanded capacity with dynamic switching - base_capacity = 1900 - current_neuron_coding_capacity = 5.313855653343694e+16 - - # Apply dynamic switching multipliers - dynamic_switching_capacity = (current_neuron_coding_capacity * - mathematical_precision_multiplier * - task_optimization_multiplier * - continuous_discovery_multiplier * - performance_optimization_multiplier * - scalability_multiplier * - mathematical_diversity_multiplier) - - dynamic_switching_expansion_factor = dynamic_switching_capacity / base_capacity - dynamic_switching_improvement_factor = dynamic_switching_capacity / current_neuron_coding_capacity - - calculation = { - "base_capacity": base_capacity, - "current_neuron_coding_capacity": current_neuron_coding_capacity, - "mathematical_precision_multiplier": mathematical_precision_multiplier, - "task_optimization_multiplier": task_optimization_multiplier, - "continuous_discovery_multiplier": continuous_discovery_multiplier, - "performance_optimization_multiplier": performance_optimization_multiplier, - "scalability_multiplier": scalability_multiplier, - "mathematical_diversity_multiplier": mathematical_diversity_multiplier, - "dynamic_switching_capacity": dynamic_switching_capacity, - "dynamic_switching_expansion_factor": dynamic_switching_expansion_factor, - "dynamic_switching_improvement_factor": dynamic_switching_improvement_factor, - "total_dynamic_switching_multiplier": (mathematical_precision_multiplier * - task_optimization_multiplier * - continuous_discovery_multiplier * - performance_optimization_multiplier * - scalability_multiplier * - mathematical_diversity_multiplier) - } - - return calculation - - def integrate_dynamic_switching(self) -> Dict: - """Integrate dynamic neural profile switching into comprehensive analysis.""" - integration = { - "dynamic_switching_enabled": True, - "paradigm": "Dynamic neural profile switching with academic math extraction", - "mechanism": "Metaprobe scans academic papers for neural coding math and extracts equations", - "neural_coding_math_patterns": len(self.neural_coding_math), - "total_equations_extracted": sum(len(p["equations"]) for p in self.neural_coding_math.values()), - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (equation extraction)", - "Control Theory (task optimization)", - "Cognitive/Routing (adaptive learning)", - "Thermodynamic (performance optimization)" - ], - "foundation_kernels_enhanced": [ - "F01", "F02", "F03", # Information Theory (equations) - "F11", "F12" # Control Theory (task optimization) - ], - "metaprobe_feature": "Academic paper scanning and equation extraction for neural coding math" - } - - return integration - - def run_analysis(self) -> Dict: - """Run dynamic neural profile switching with academic math extraction analysis.""" - print("=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING WITH ACADEMIC MATH EXTRACTION") - print("=" * 60) - - # Step 1: Analyze neural coding math - print("\n[1/5] Analyzing neural coding math discovered via metaprobe...") - math_analysis = self.analyze_neural_coding_math() - print(f" Neural Coding Math Patterns: {math_analysis['total_math_patterns']}") - print(f" Total Equations Extracted: {math_analysis['total_equations']}") - for pattern, details in math_analysis['neural_coding_math'].items(): - print(f" {pattern}: {len(details['equations'])} equations, score {details['significance_score']}") - - # Step 2: Analyze metaprobe academic scan - print("[2/5] Analyzing metaprobe academic scanning mechanism...") - metaprobe_analysis = self.analyze_metaprobe_academic_scan() - print(f" Extraction Targets: {len(metaprobe_analysis['extraction_targets'])}") - for target in metaprobe_analysis['extraction_targets']: - print(f" - {target}") - - # Step 3: Analyze dynamic switching - print("[3/5] Analyzing dynamic neural profile switching...") - switching_analysis = self.analyze_dynamic_switching() - print(f" Switching Characteristics: {len(switching_analysis['switching_characteristics'])}") - for characteristic, details in switching_analysis['switching_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 4: Analyze benefits - print("[4/5] Analyzing dynamic switching benefits...") - benefits = self.analyze_dynamic_switching_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 5: Calculate impact - print("[5/5] Calculating dynamic switching impact...") - impact_calculation = self.calculate_dynamic_switching_impact() - print(f" Current Neuron Coding Capacity: {impact_calculation['current_neuron_coding_capacity']}") - print(f" Dynamic Switching Capacity: {impact_calculation['dynamic_switching_capacity']}") - print(f" Dynamic Switching Improvement Factor: {impact_calculation['dynamic_switching_improvement_factor']:.2f}x") - print(f" Total Dynamic Switching Multiplier: {impact_calculation['total_dynamic_switching_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING WITH ACADEMIC MATH EXTRACTION COMPLETE") - print("=" * 60) - - return { - "math_analysis": math_analysis, - "metaprobe_analysis": metaprobe_analysis, - "switching_analysis": switching_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": self.integrate_dynamic_switching() - } - -if __name__ == '__main__': - analyzer = DynamicNeuralProfileSwitchingAcademic() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "dynamic_neural_profile_switching_academic.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DYNAMIC NEURAL PROFILE SWITCHING WITH ACADEMIC MATH EXTRACTION SUMMARY") - print("=" * 60) - print(f"Neural Coding Math Patterns: {results['integration']['neural_coding_math_patterns']}") - print(f"Total Equations Extracted: {results['integration']['total_equations_extracted']}") - print(f"Dynamic Switching Capacity: {results['impact_calculation']['dynamic_switching_capacity']}") - print(f"Dynamic Switching Improvement Factor: {results['impact_calculation']['dynamic_switching_improvement_factor']:.2f}x") - print(f"Total Dynamic Switching Multiplier: {results['impact_calculation']['total_dynamic_switching_multiplier']:.2f}x") diff --git a/5-Applications/scripts/dynamic_profile_switching_signal_math.py b/5-Applications/scripts/dynamic_profile_switching_signal_math.py deleted file mode 100644 index 2d89b307..00000000 --- a/5-Applications/scripts/dynamic_profile_switching_signal_math.py +++ /dev/null @@ -1,356 +0,0 @@ -#!/usr/bin/env python3 -""" -Dynamic Profile Switching with Neural and Signal Math Extraction -Analyzes morphic scalars that switch between profiles using metaprobe to extract both neural coding math and signal math from academic papers. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class DynamicProfileSwitchingSignalMath: - """Analyzes dynamic profile switching with both neural and signal math extraction.""" - - def __init__(self): - # Neural coding math patterns - self.neural_coding_math = { - "spike_timing_encoding": { - "source": "Academic papers on neural spike timing", - "equations": [ - "S(t) = Σ δ(t - t_i) for spike times t_i", - "Temporal precision: Δt ~ 1ms", - "Phase coding: θ = 2πt/T" - ], - "significance_score": 95.0 - }, - "rate_coding": { - "source": "Academic papers on firing rate coding", - "equations": [ - "r = N/Δt (spikes per time window)", - "Poisson process: P(k) = (λ^k e^-λ)/k!", - "Rate modulation: r(t) = r_0 + Δr·f(t)" - ], - "significance_score": 90.0 - }, - "synaptic_plasticity": { - "source": "Academic papers on Hebbian learning", - "equations": [ - "Δw_ij = η·x_i·y_j (Hebbian rule)", - "STDP: Δw = A_+·e^-Δt/τ_+ - A_-·e^Δt/τ_-", - "Weight update: w_ij(t+1) = w_ij(t) + Δw_ij" - ], - "significance_score": 95.0 - } - } - - # Signal math patterns - self.signal_math = { - "fourier_transform": { - "source": "Academic papers on Fourier analysis", - "equations": [ - "F(ω) = ∫ f(t)e^(-iωt)dt (Fourier transform)", - "Inverse: f(t) = (1/2π)∫ F(ω)e^(iωt)dω", - "Discrete: F[k] = Σ f[n]e^(-i2πkn/N)" - ], - "significance_score": 95.0 - }, - "waveform_representation": { - "source": "Academic papers on waveform analysis", - "equations": [ - "R(t) = Σ_i A_i(t)·cos(ω_i t + φ_i)", - "Amplitude: A(t) = √(I^2 + Q^2)", - "Phase: φ(t) = arctan(Q/I)" - ], - "significance_score": 90.0 - }, - "signal_processing": { - "source": "Academic papers on signal processing", - "equations": [ - "Convolution: (f * g)(t) = ∫ f(τ)g(t-τ)dτ", - "Filtering: y[n] = Σ h[k]x[n-k]", - "Modulation: s(t) = A(t)cos(2πf_c t + φ(t))" - ], - "significance_score": 85.0 - } - } - - # Dynamic switching characteristics - self.switching_characteristics = { - "task_dependent": { - "description": "Switch profiles based on task requirements", - "significance_score": 95.0 - }, - "metaprobe_dual_scan": { - "description": "Metaprobe scans academic papers for both neural and signal math", - "significance_score": 95.0 - }, - "dual_equation_extraction": { - "description": "Extract both neural and signal equations from academic papers", - "significance_score": 90.0 - }, - "profile_generation": { - "description": "Generate profiles from extracted neural and signal math", - "significance_score": 85.0 - }, - "dynamic_adaptation": { - "description": "Dynamically adapt profile based on performance", - "significance_score": 90.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "neuron_coding_capacity": 5.313855653343694e+16, - "expansion_factor": 27965082386019.0 - } - - def analyze_math_patterns(self) -> Dict: - """Analyze both neural coding and signal math patterns.""" - analysis = { - "neural_coding_math": self.neural_coding_math, - "signal_math": self.signal_math, - "total_neural_patterns": len(self.neural_coding_math), - "total_signal_patterns": len(self.signal_math), - "total_math_patterns": len(self.neural_coding_math) + len(self.signal_math), - "total_equations": sum(len(p["equations"]) for p in self.neural_coding_math.values()) + sum(len(p["equations"]) for p in self.signal_math.values()) - } - - return analysis - - def analyze_metaprobe_dual_scan(self) -> Dict: - """Analyze metaprobe dual scanning mechanism for both neural and signal math.""" - analysis = { - "metaprobe_mechanism": { - "dual_scan": "Scan academic papers for both neural and signal math", - "neural_extraction": "Extract neural coding equations", - "signal_extraction": "Extract signal processing equations", - "pattern_recognition": "Recognize both neural and signal patterns", - "math_integration": "Integrate both math types into profiles" - }, - "neural_extraction_targets": [ - "Neural spike timing patterns", - "Synaptic plasticity patterns", - "Population vector coding", - "Hebbian learning rules", - "STDP formulations" - ], - "signal_extraction_targets": [ - "Fourier transform equations", - "Waveform representation equations", - "Signal processing equations", - "Convolution and filtering", - "Modulation and demodulation" - ], - "integration_process": { - "discovery": "Metaprobe discovers both neural and signal math", - "extraction": "Extract equations from both domains", - "validation": "Validate mathematical correctness", - "integration": "Integrate into profile library" - } - } - - return analysis - - def analyze_dynamic_switching(self) -> Dict: - """Analyze dynamic profile switching with dual math.""" - analysis = { - "switching_characteristics": self.switching_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.switching_characteristics.values()) / len(self.switching_characteristics), - "switching_mechanisms": { - "task_analysis": "Analyze task requirements to select optimal profile", - "profile_selection": "Select profile (neural or signal) based on task", - "equation_application": "Apply extracted equations to profile behavior", - "performance_monitoring": "Monitor performance to optimize selection", - "adaptive_learning": "Learn which profiles work best for which tasks" - } - } - - return analysis - - def analyze_dual_math_benefits(self) -> Dict: - """Analyze dual neural and signal math benefits.""" - benefits = { - "dual_math_precision": { - "description": "Precise neural and signal math from academic equations", - "significance_score": 95.0 - }, - "task_optimization": { - "description": "Optimal profile (neural or signal) for each task", - "significance_score": 95.0 - }, - "continuous_discovery": { - "description": "Continuously discover both neural and signal math via metaprobe", - "significance_score": 90.0 - }, - "performance_optimization": { - "description": "Optimize performance through profile selection", - "significance_score": 95.0 - }, - "domain_coverage": { - "description": "Cover both neural and signal domains", - "significance_score": 90.0 - }, - "mathematical_diversity": { - "description": "Access to all neural and signal math discovered", - "significance_score": 90.0 - } - } - - return benefits - - def calculate_dual_math_impact(self) -> Dict: - """Calculate dual neural and signal math impact on computational expansion.""" - # Dual math multipliers - dual_math_precision_multiplier = 2.5 # 2.5x from precise academic equations (both domains) - task_optimization_multiplier = 1.5 # 1.5x from task-specific optimization - continuous_discovery_multiplier = 1.5 # 1.5x from continuous math discovery - performance_optimization_multiplier = 1.5 # 1.5x from performance optimization - domain_coverage_multiplier = 1.5 # 1.5x from covering both neural and signal domains - mathematical_diversity_multiplier = 1.5 # 1.5x from mathematical diversity - - # Calculate expanded capacity with dual math - base_capacity = 1900 - current_neuron_coding_capacity = 5.313855653343694e+16 - - # Apply dual math multipliers - dual_math_capacity = (current_neuron_coding_capacity * - dual_math_precision_multiplier * - task_optimization_multiplier * - continuous_discovery_multiplier * - performance_optimization_multiplier * - domain_coverage_multiplier * - mathematical_diversity_multiplier) - - dual_math_expansion_factor = dual_math_capacity / base_capacity - dual_math_improvement_factor = dual_math_capacity / current_neuron_coding_capacity - - calculation = { - "base_capacity": base_capacity, - "current_neuron_coding_capacity": current_neuron_coding_capacity, - "dual_math_precision_multiplier": dual_math_precision_multiplier, - "task_optimization_multiplier": task_optimization_multiplier, - "continuous_discovery_multiplier": continuous_discovery_multiplier, - "performance_optimization_multiplier": performance_optimization_multiplier, - "domain_coverage_multiplier": domain_coverage_multiplier, - "mathematical_diversity_multiplier": mathematical_diversity_multiplier, - "dual_math_capacity": dual_math_capacity, - "dual_math_expansion_factor": dual_math_expansion_factor, - "dual_math_improvement_factor": dual_math_improvement_factor, - "total_dual_math_multiplier": (dual_math_precision_multiplier * - task_optimization_multiplier * - continuous_discovery_multiplier * - performance_optimization_multiplier * - domain_coverage_multiplier * - mathematical_diversity_multiplier) - } - - return calculation - - def integrate_dual_math(self) -> Dict: - """Integrate dual neural and signal math into comprehensive analysis.""" - integration = { - "dual_math_enabled": True, - "paradigm": "Dynamic profile switching with neural and signal math extraction", - "mechanism": "Metaprobe scans academic papers for both neural coding math and signal math", - "neural_patterns": len(self.neural_coding_math), - "signal_patterns": len(self.signal_math), - "total_math_patterns": len(self.neural_coding_math) + len(self.signal_math), - "total_equations_extracted": sum(len(p["equations"]) for p in self.neural_coding_math.values()) + sum(len(p["equations"]) for p in self.signal_math.values()), - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (equation extraction)", - "Control Theory (task optimization)", - "Cognitive/Routing (adaptive learning)", - "Thermodynamic (performance optimization)" - ], - "foundation_kernels_enhanced": [ - "F01", "F02", "F03", # Information Theory (equations) - "F11", "F12" # Control Theory (task optimization) - ], - "metaprobe_feature": "Academic paper scanning for both neural and signal math extraction" - } - - return integration - - def run_analysis(self) -> Dict: - """Run dynamic profile switching with dual neural and signal math analysis.""" - print("=" * 60) - print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH EXTRACTION") - print("=" * 60) - - # Step 1: Analyze math patterns - print("\n[1/5] Analyzing neural and signal math patterns...") - math_analysis = self.analyze_math_patterns() - print(f" Neural Patterns: {math_analysis['total_neural_patterns']}") - print(f" Signal Patterns: {math_analysis['total_signal_patterns']}") - print(f" Total Math Patterns: {math_analysis['total_math_patterns']}") - print(f" Total Equations Extracted: {math_analysis['total_equations']}") - - # Step 2: Analyze metaprobe dual scan - print("[2/5] Analyzing metaprobe dual scanning mechanism...") - metaprobe_analysis = self.analyze_metaprobe_dual_scan() - print(f" Neural Extraction Targets: {len(metaprobe_analysis['neural_extraction_targets'])}") - print(f" Signal Extraction Targets: {len(metaprobe_analysis['signal_extraction_targets'])}") - - # Step 3: Analyze dynamic switching - print("[3/5] Analyzing dynamic profile switching...") - switching_analysis = self.analyze_dynamic_switching() - print(f" Switching Characteristics: {len(switching_analysis['switching_characteristics'])}") - for characteristic, details in switching_analysis['switching_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 4: Analyze benefits - print("[4/5] Analyzing dual math benefits...") - benefits = self.analyze_dual_math_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 5: Calculate impact - print("[5/5] Calculating dual math impact...") - impact_calculation = self.calculate_dual_math_impact() - print(f" Current Neuron Coding Capacity: {impact_calculation['current_neuron_coding_capacity']}") - print(f" Dual Math Capacity: {impact_calculation['dual_math_capacity']}") - print(f" Dual Math Improvement Factor: {impact_calculation['dual_math_improvement_factor']:.2f}x") - print(f" Total Dual Math Multiplier: {impact_calculation['total_dual_math_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH EXTRACTION COMPLETE") - print("=" * 60) - - return { - "math_analysis": math_analysis, - "metaprobe_analysis": metaprobe_analysis, - "switching_analysis": switching_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": self.integrate_dual_math() - } - -if __name__ == '__main__': - analyzer = DynamicProfileSwitchingSignalMath() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "dynamic_profile_switching_signal_math.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("DYNAMIC PROFILE SWITCHING WITH NEURAL AND SIGNAL MATH SUMMARY") - print("=" * 60) - print(f"Neural Patterns: {results['integration']['neural_patterns']}") - print(f"Signal Patterns: {results['integration']['signal_patterns']}") - print(f"Total Math Patterns: {results['integration']['total_math_patterns']}") - print(f"Total Equations Extracted: {results['integration']['total_equations_extracted']}") - print(f"Dual Math Capacity: {results['impact_calculation']['dual_math_capacity']}") - print(f"Dual Math Improvement Factor: {results['impact_calculation']['dual_math_improvement_factor']:.2f}x") - print(f"Total Dual Math Multiplier: {results['impact_calculation']['total_dual_math_multiplier']:.2f}x") diff --git a/5-Applications/scripts/efficiency_analysis.py b/5-Applications/scripts/efficiency_analysis.py deleted file mode 100644 index becd2c84..00000000 --- a/5-Applications/scripts/efficiency_analysis.py +++ /dev/null @@ -1,308 +0,0 @@ -#!/usr/bin/env python3 -""" -Efficiency Analysis Report - -Measures efficiency gains from implementations: -1. Sabotage prevention system -2. Service restoration -3. Synchronization attack prevention -4. Joule energy tracking -""" - -import sys -from pathlib import Path -sys.path.insert(0, str(Path(__file__).parent)) - -from sabotage_prevention import SabotagePreventionSystem, AgentAction, SystemState, ActionType, to_q16, from_q16 -from joule_energy import JouleEnergySystem, EnergyAction, AgentEnergyState, to_q16 as q16_to, from_q16 as q16_from - -def analyze_sabotage_prevention_efficiency(): - """Analyze efficiency gains from sabotage prevention""" - print("\n" + "="*70) - print("SABOTAGE PREVENTION EFFICIENCY ANALYSIS") - print("="*70) - - system = SabotagePreventionSystem() - - # Baseline: Without sabotage prevention - baseline_agents = 10 - baseline_services = 10 - baseline_knowledge = 100 - baseline_connectivity = 0.8 - baseline_efficiency = 0.6 - - print(f"\n📊 Baseline Metrics (without sabotage prevention):") - print(f" Active agents: {baseline_agents}") - print(f" Active services: {baseline_services}") - print(f" Network connectivity: {baseline_connectivity:.3f}") - print(f" Resource efficiency: {baseline_efficiency:.3f}") - - # Simulate sabotage attacks without prevention - print(f"\n⚠️ Simulated sabotage attacks (without prevention):") - print(f" Resource starvation attack: Efficiency 0.6 → 0.2 (-66.7%)") - print(f" Network partition attack: Connectivity 0.8 → 0.4 (-50%)") - print(f" Synchronization attack: Connectivity 0.8 → 0.5 (-37.5%)") - - baseline_after_sabotage_efficiency = 0.2 # Worst case - baseline_after_sabotage_connectivity = 0.4 # Worst case - - # With sabotage prevention - print(f"\n✅ With sabotage prevention:") - print(f" Resource starvation attack: BLOCKED (efficiency maintained)") - print(f" Network partition attack: BLOCKED (connectivity maintained)") - print(f" Synchronization attack: BLOCKED (connectivity maintained)") - - protected_efficiency = baseline_efficiency # Maintained - protected_connectivity = baseline_connectivity # Maintained - - # Calculate efficiency gains - efficiency_gain = protected_efficiency - baseline_after_sabotage_efficiency - connectivity_gain = protected_connectivity - baseline_after_sabotage_connectivity - - efficiency_improvement_pct = (efficiency_gain / baseline_after_sabotage_efficiency) * 100 - connectivity_improvement_pct = (connectivity_gain / baseline_after_sabotage_connectivity) * 100 - - print(f"\n📈 Efficiency Gains:") - print(f" Efficiency: {baseline_after_sabotage_efficiency:.3f} → {protected_efficiency:.3f} (+{efficiency_improvement_pct:.1f}%)") - print(f" Connectivity: {baseline_after_sabotage_connectivity:.3f} → {protected_connectivity:.3f} (+{connectivity_improvement_pct:.1f}%)") - print(f" Agents protected: 3/3 sabotage attacks blocked") - - return { - 'efficiency_gain': efficiency_improvement_pct, - 'connectivity_gain': connectivity_improvement_pct, - 'attacks_blocked': 3 - } - -def analyze_service_restoration_efficiency(): - """Analyze efficiency gains from service restoration""" - print("\n" + "="*70) - print("SERVICE RESTORATION EFFICIENCY ANALYSIS") - print("="*70) - - system = SabotagePreventionSystem() - - # Baseline: Without service restoration - baseline_services = 10 - disabled_services = 5 - baseline_connectivity = 0.6 - - print(f"\n📊 Baseline Metrics (without service restoration):") - print(f" Total services: {baseline_services}") - print(f" Disabled services: {disabled_services}") - print(f" Network connectivity: {baseline_connectivity:.3f}") - - # Without restoration: services remain disabled - print(f"\n⚠️ Without service restoration:") - print(f" Services remain disabled indefinitely") - print(f" Network capacity reduced by {disabled_services/baseline_services*100:.1f}%") - - baseline_capacity = baseline_services - disabled_services # 5 services active - - # With restoration - print(f"\n✅ With service restoration:") - print(f" Services restored when resources improve") - print(f" Network capacity recovered") - - restored_capacity = baseline_services # 10 services active - capacity_gain = restored_capacity - baseline_capacity - capacity_improvement_pct = (capacity_gain / baseline_capacity) * 100 - - print(f"\n📈 Capacity Gains:") - print(f" Active services: {baseline_capacity} → {restored_capacity} (+{capacity_improvement_pct:.1f}%)") - print(f" Restoration benefit: 1.200 (from test)") - - return { - 'capacity_gain': capacity_improvement_pct, - 'restoration_benefit': 1.200 - } - -def analyze_synchronization_attack_prevention_efficiency(): - """Analyze efficiency gains from synchronization attack prevention""" - print("\n" + "="*70) - print("SYNCHRONIZATION ATTACK PREVENTION EFFICIENCY ANALYSIS") - print("="*70) - - # Baseline: Without synchronization attack prevention - baseline_connectivity = 0.8 - baseline_efficiency = 0.6 - - print(f"\n📊 Baseline Metrics (without synchronization attack prevention):") - print(f" Network connectivity: {baseline_connectivity:.3f}") - print(f" Resource efficiency: {baseline_efficiency:.3f}") - - # Without prevention: agents can coordinate to disrupt network - print(f"\n⚠️ Without synchronization attack prevention:") - print(f" Agents can coordinate topology modifications") - print(f" Agents can manipulate routing for influence") - print(f" Network can be disrupted for personal gain") - - # Simulate worst case - worst_connectivity = 0.4 - worst_efficiency = 0.4 - - print(f" Worst case connectivity: {baseline_connectivity:.3f} → {worst_connectivity:.3f} (-50%)") - print(f" Worst case efficiency: {baseline_efficiency:.3f} → {worst_efficiency:.3f} (-33.3%)") - - # With prevention - print(f"\n✅ With synchronization attack prevention:") - print(f" Synchronization attacks detected and blocked") - print(f" Influence-seeking actions prevented") - print(f" Network integrity maintained") - - protected_connectivity = baseline_connectivity - protected_efficiency = baseline_efficiency - - connectivity_gain = protected_connectivity - worst_connectivity - efficiency_gain = protected_efficiency - worst_efficiency - - connectivity_improvement_pct = (connectivity_gain / worst_connectivity) * 100 - efficiency_improvement_pct = (efficiency_gain / worst_efficiency) * 100 - - print(f"\n📈 Network Integrity Gains:") - print(f" Connectivity: {worst_connectivity:.3f} → {protected_connectivity:.3f} (+{connectivity_improvement_pct:.1f}%)") - print(f" Efficiency: {worst_efficiency:.3f} → {protected_efficiency:.3f} (+{efficiency_improvement_pct:.1f}%)") - print(f" Attacks prevented: 2 (synchronization, influence-seeking)") - - return { - 'connectivity_gain': connectivity_improvement_pct, - 'efficiency_gain': efficiency_improvement_pct, - 'attacks_prevented': 2 - } - -def analyze_joule_energy_efficiency(): - """Analyze efficiency gains from Joule energy tracking""" - print("\n" + "="*70) - print("JOULE ENERGY TRACKING EFFICIENCY ANALYSIS") - print("="*70) - - system = JouleEnergySystem() - - # Baseline: Without energy tracking - baseline_energy_per_task = 25.0 # Assumed without tracking - baseline_efficiency = 0.6 - - print(f"\n📊 Baseline Metrics (without energy tracking):") - print(f" Energy per task: {baseline_energy_per_task:.3f} J") - print(f" System efficiency: {baseline_efficiency:.3f}") - - print(f"\n⚠️ Without energy tracking:") - print(f" No visibility into resource consumption") - print(f" Cannot optimize energy usage") - print(f" Wasted energy on inefficient operations") - - # With tracking - print(f"\n✅ With Joule energy tracking:") - - # Initialize agent - system.initializeAgent(agentId=1, initialCharge=10.0, initialVoltage=5.0) - - # Submit energy action - action = EnergyAction( - agentId=1, - workloadDelta=q16_to(5.0), - resourceLevel=q16_to(6.0), - duration=q16_to(2.0) - ) - result = system.submitEnergyAction(action) - - state = system.agentStates[1] - tracked_energy_per_task = q16_from(state.energy) / q16_from(state.charge) - tracked_efficiency = system.calculateEfficiency(agentId=1, usefulEnergy=40.0) - - print(f" Energy per task: {tracked_energy_per_task:.3f} J") - print(f" System efficiency: {tracked_efficiency:.3f}") - print(f" Energy consumption: {q16_from(state.energy):.3f} J") - print(f" Power consumption: {q16_from(state.power):.3f} W") - - # Calculate gains - energy_reduction = baseline_energy_per_task - tracked_energy_per_task - energy_reduction_pct = (energy_reduction / baseline_energy_per_task) * 100 - - efficiency_improvement = tracked_efficiency - baseline_efficiency - efficiency_improvement_pct = (efficiency_improvement / baseline_efficiency) * 100 - - print(f"\n📈 Energy Efficiency Gains:") - print(f" Energy per task: {baseline_energy_per_task:.3f} → {tracked_energy_per_task:.3f} J (-{energy_reduction_pct:.1f}%)") - print(f" System efficiency: {baseline_efficiency:.3f} → {tracked_efficiency:.3f} (+{efficiency_improvement_pct:.1f}%)") - - return { - 'energy_reduction': energy_reduction_pct, - 'efficiency_improvement': efficiency_improvement_pct - } - -def generate_summary_report(sabotage_gains, restoration_gains, sync_gains, energy_gains): - """Generate summary efficiency report""" - print("\n" + "="*70) - print("EFFICIENCY SUMMARY REPORT") - print("="*70) - - print(f"\n📊 Overall Efficiency Gains Since Implementation:") - - print(f"\n1. Sabotage Prevention:") - print(f" Efficiency gain: +{sabotage_gains['efficiency_gain']:.1f}%") - print(f" Connectivity gain: +{sabotage_gains['connectivity_gain']:.1f}%") - print(f" Attacks blocked: {sabotage_gains['attacks_blocked']}") - - print(f"\n2. Service Restoration:") - print(f" Capacity gain: +{restoration_gains['capacity_gain']:.1f}%") - print(f" Restoration benefit: {restoration_gains['restoration_benefit']:.3f}") - - print(f"\n3. Synchronization Attack Prevention:") - print(f" Connectivity gain: +{sync_gains['connectivity_gain']:.1f}%") - print(f" Efficiency gain: +{sync_gains['efficiency_gain']:.1f}%") - print(f" Attacks prevented: {sync_gains['attacks_prevented']}") - - print(f"\n4. Joule Energy Tracking:") - print(f" Energy reduction: -{energy_gains['energy_reduction']:.1f}%") - print(f" Efficiency improvement: +{energy_gains['efficiency_improvement']:.1f}%") - - # Calculate overall gains - overall_efficiency_gain = ( - sabotage_gains['efficiency_gain'] + - sync_gains['efficiency_gain'] + - energy_gains['efficiency_improvement'] - ) / 3 - - overall_connectivity_gain = ( - sabotage_gains['connectivity_gain'] + - sync_gains['connectivity_gain'] - ) / 2 - - total_attacks_prevented = sabotage_gains['attacks_blocked'] + sync_gains['attacks_prevented'] - - print(f"\n🎯 Overall System Improvements:") - print(f" Average efficiency gain: +{overall_efficiency_gain:.1f}%") - print(f" Average connectivity gain: +{overall_connectivity_gain:.1f}%") - print(f" Total attacks prevented: {total_attacks_prevented}") - print(f" Energy consumption reduced: {energy_gains['energy_reduction']:.1f}%") - - print(f"\n✅ Key Achievements:") - print(f" • Network integrity protected from sabotage") - print(f" • Services automatically restored when resources available") - print(f" • Synchronization attacks prevented") - print(f" • Energy consumption tracked and optimized") - print(f" • Formal verification via Lean specification") - print(f" • Q16_16 fixed-point arithmetic for hardware-native computation") - - print("\n" + "="*70) - -def main(): - """Run efficiency analysis""" - print("="*70) - print("EFFICIENCY ANALYSIS - SINCE IMPLEMENTATION START") - print("="*70) - print("\nAnalyzing efficiency gains from:") - print("1. Sabotage prevention system") - print("2. Service restoration mechanism") - print("3. Synchronization attack prevention") - print("4. Joule energy tracking") - - sabotage_gains = analyze_sabotage_prevention_efficiency() - restoration_gains = analyze_service_restoration_efficiency() - sync_gains = analyze_synchronization_attack_prevention_efficiency() - energy_gains = analyze_joule_energy_efficiency() - - generate_summary_report(sabotage_gains, restoration_gains, sync_gains, energy_gains) - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/efi_1d_osic_scalar.py b/5-Applications/scripts/efi_1d_osic_scalar.py deleted file mode 100644 index f63313aa..00000000 --- a/5-Applications/scripts/efi_1d_osic_scalar.py +++ /dev/null @@ -1,261 +0,0 @@ -#!/usr/bin/env python3 -""" -EFI 1D OSIC Scalar Analysis -Analyzes EFI controller as a 1D One-Dimensional Scalar Integrated Circuit. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class EFI1DOSICScalar: - """Analyzes EFI as a 1D OSIC scalar.""" - - def __init__(self): - self.osic_scalar = { - "concept": "1D One-Dimensional Scalar Integrated Circuit", - "efi_as_scalar": "EFI firmware acts as a single scalar computational unit", - "dimensionality": "1D (scalar operations only)", - "precision": "64-bit (EFI native)", - "throughput": "1-10 KOPS (firmware call limited)", - "latency": "1-10ms (firmware call)", - "power": "<5W" - } - - self.scalar_operations = { - "addition": "Scalar addition (a + b)", - "subtraction": "Scalar subtraction (a - b)", - "multiplication": "Scalar multiplication (a * b)", - "division": "Scalar division (a / b)", - "comparison": "Scalar comparison (a < b, a == b, a > b)", - "bitwise": "Bitwise operations (AND, OR, XOR, NOT)", - "shift": "Bit shifts (<<, >>)", - "logic": "Boolean logic (AND, OR, NOT)" - } - - def analyze_scalar_potential(self) -> Dict: - """Analyze 1D OSIC scalar potential.""" - analysis = { - "scalar_computation": { - "feasible": True, - "mode": "1D scalar operations", - "description": "EFI firmware performs scalar operations in 1D", - "operations": self.scalar_operations, - "precision": "64-bit", - "throughput": "1-10 KOPS", - "latency": "1-10ms", - "power": "<5W" - }, - "state_machine": { - "feasible": True, - "mode": "Scalar state machine", - "description": "EFI variables store scalar state", - "states": "Unlimited (variable storage)", - "transitions": "Variable updates", - "precision": "64-bit", - "throughput": "100-1000 updates/sec", - "latency": "10-100ms", - "power": "<1W" - }, - "time_integration": { - "feasible": True, - "mode": "Time-based scalar integration", - "description": "Use GetTime for time-based scalar operations", - "precision": "64-bit time values", - "throughput": "1-10 KOPS", - "latency": "1-10ms", - "power": "<5W" - } - } - - return analysis - - def design_scalar_operations(self) -> Dict: - """Design 1D OSIC scalar operations.""" - operations = { - "arithmetic": { - "add": { - "operation": "a + b", - "implementation": "Use UEFI runtime service to add", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "subtract": { - "operation": "a - b", - "implementation": "Use UEFI runtime service to subtract", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "multiply": { - "operation": "a * b", - "implementation": "Use UEFI runtime service to multiply", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "divide": { - "operation": "a / b", - "implementation": "Use UEFI runtime service to divide", - "precision": "64-bit", - "throughput": "1-10 KOPS" - } - }, - "comparison": { - "less_than": { - "operation": "a < b", - "implementation": "Use UEFI runtime service to compare", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "equal": { - "operation": "a == b", - "implementation": "Use UEFI runtime service to compare", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "greater_than": { - "operation": "a > b", - "implementation": "Use UEFI runtime service to compare", - "precision": "64-bit", - "throughput": "1-10 KOPS" - } - }, - "bitwise": { - "and": { - "operation": "a & b", - "implementation": "Use UEFI runtime service for bitwise AND", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "or": { - "operation": "a | b", - "implementation": "Use UEFI runtime service for bitwise OR", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "xor": { - "operation": "a ^ b", - "implementation": "Use UEFI runtime service for bitwise XOR", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "not": { - "operation": "~a", - "implementation": "Use UEFI runtime service for bitwise NOT", - "precision": "64-bit", - "throughput": "1-10 KOPS" - } - }, - "shift": { - "left_shift": { - "operation": "a << n", - "implementation": "Use UEFI runtime service for left shift", - "precision": "64-bit", - "throughput": "1-10 KOPS" - }, - "right_shift": { - "operation": "a >> n", - "implementation": "Use UEFI runtime service for right shift", - "precision": "64-bit", - "throughput": "1-10 KOPS" - } - } - } - - return operations - - def estimate_scalar_performance(self) -> Dict: - """Estimate 1D OSIC scalar performance.""" - performance = { - "arithmetic": { - "throughput": "1-10 KOPS", - "latency": "1-10ms", - "precision": "64-bit", - "power": "<5W" - }, - "comparison": { - "throughput": "1-10 KOPS", - "latency": "1-10ms", - "precision": "64-bit", - "power": "<5W" - }, - "bitwise": { - "throughput": "1-10 KOPS", - "latency": "1-10ms", - "precision": "64-bit", - "power": "<5W" - }, - "state_machine": { - "throughput": "100-1000 state updates/sec", - "latency": "10-100ms", - "precision": "64-bit", - "power": "<1W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run 1D OSIC scalar analysis.""" - print("=" * 60) - print("EFI 1D OSIC SCALAR ANALYSIS") - print("=" * 60) - - # Step 1: Analyze scalar potential - print("\n[1/4] Analyzing 1D OSIC scalar potential...") - potential = self.analyze_scalar_potential() - print(f" Scalar Computation: {potential['scalar_computation']['feasible']}") - print(f" State Machine: {potential['state_machine']['feasible']}") - print(f" Time Integration: {potential['time_integration']['feasible']}") - - # Step 2: Design scalar operations - print("[2/4] Designing 1D OSIC scalar operations...") - operations = self.design_scalar_operations() - print(f" Arithmetic Operations: {len(operations['arithmetic'])}") - print(f" Comparison Operations: {len(operations['comparison'])}") - print(f" Bitwise Operations: {len(operations['bitwise'])}") - print(f" Shift Operations: {len(operations['shift'])}") - - # Step 3: Estimate performance - print("[3/4] Estimating 1D OSIC scalar performance...") - performance = self.estimate_scalar_performance() - print(f" Arithmetic: {performance['arithmetic']['throughput']}") - print(f" Comparison: {performance['comparison']['throughput']}") - print(f" Bitwise: {performance['bitwise']['throughput']}") - print(f" State Machine: {performance['state_machine']['throughput']}") - - # Step 4: Complete - print("[4/4] 1D OSIC scalar analysis complete...") - - print("\n" + "=" * 60) - print("EFI 1D OSIC SCALAR ANALYSIS COMPLETE") - print("=" * 60) - - return { - "osic_scalar": self.osic_scalar, - "scalar_potential": potential, - "scalar_operations": operations, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = EFI1DOSICScalar() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "efi_1d_osic_scalar.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("1D OSIC SCALAR SUMMARY") - print("=" * 60) - print(f"Concept: {results['osic_scalar']['concept']}") - print(f"Precision: {results['osic_scalar']['precision']}") - print(f"Throughput: {results['osic_scalar']['throughput']}") - print(f"Power: {results['osic_scalar']['power']}") diff --git a/5-Applications/scripts/efi_computational_controller.py b/5-Applications/scripts/efi_computational_controller.py deleted file mode 100644 index bbdcb52d..00000000 --- a/5-Applications/scripts/efi_computational_controller.py +++ /dev/null @@ -1,213 +0,0 @@ -#!/usr/bin/env python3 -""" -EFI Controller Computational Repurposing -Analyzes EFI firmware for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class EFIComputationalController: - """Analyzes EFI firmware for general computation.""" - - def __init__(self): - self.efi_info = { - "platform_size": "64-bit", - "fw_vendor": "0x98376118", - "runtime": "0x9847eb98", - "efi_available": True, - "computational_potential": "HIGH" - } - - self.efi_capabilities = { - "uefi_runtime_services": "Runtime services available during OS execution", - "efi_variables": "Non-volatile variable storage", - "efi_boot_services": "Boot-time services (not available after boot)", - "efi_memory_map": "Memory map of system resources", - "efi_firmware_volume": "Firmware volume with executables", - "efi_protocols": "Protocol interfaces for hardware access" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of EFI firmware.""" - analysis = { - "uefi_runtime_services": { - "feasible": True, - "mode": "Runtime service computation", - "description": "Use UEFI runtime services for computation during OS execution", - "throughput": "Limited by firmware interface", - "latency": "1-10ms (firmware call)", - "power": "<5W (firmware)" - }, - "efi_variables": { - "feasible": True, - "mode": "Variable-based computation", - "description": "Use EFI variables as computational state storage", - "throughput": "Variable access limited", - "latency": "10-100ms (variable access)", - "power": "<1W" - }, - "efi_protocols": { - "feasible": True, - "mode": "Protocol-based computation", - "description": "Use EFI protocols for hardware-level computation", - "throughput": "Protocol-specific", - "latency": "1-10ms (protocol call)", - "power": "1-5W" - }, - "efi_firmware_volume": { - "feasible": True, - "mode": "Firmware volume execution", - "description": "Execute EFI executables from firmware volume", - "throughput": "UEFI bytecode limited", - "latency": "1-10ms (execution)", - "power": "5-10W" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design EFI-based computational approach.""" - approach = { - "uefi_runtime_computation": { - "concept": "Use UEFI runtime services for computation", - "implementation": "Call UEFI runtime services with computational payloads", - "operations": ["GetTime", "GetVariable", "SetVariable", "GetNextVariableName"], - "precision": "64-bit (UEFI native)", - "throughput": "Limited by firmware interface", - "power": "<5W" - }, - "efi_variable_computation": { - "concept": "Use EFI variables as computational state", - "implementation": "Store computational state in EFI variables", - "operations": ["state storage", "persistence", "recovery"], - "precision": "Variable-specific", - "throughput": "Variable access limited", - "power": "<1W" - }, - "efi_protocol_computation": { - "concept": "Use EFI protocols for hardware computation", - "implementation": "Access hardware via EFI protocols", - "operations": ["PCI access", "memory access", "I/O access"], - "precision": "Protocol-specific", - "throughput": "Hardware-limited", - "power": "1-5W" - }, - "efi_bytecode_execution": { - "concept": "Execute EFI bytecode from firmware volume", - "implementation": "Load and execute EFI executables", - "operations": ["UEFI bytecode execution", "firmware drivers"], - "precision": "UEFI bytecode", - "throughput": "UEFI bytecode limited", - "power": "5-10W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of EFI controller computation.""" - performance = { - "uefi_runtime": { - "throughput": "1-10 KOPS (firmware call limited)", - "latency": "1-10ms (firmware call)", - "precision": "64-bit", - "operations": "runtime services", - "power": "<5W" - }, - "efi_variables": { - "throughput": "100-1000 variable accesses/sec", - "latency": "10-100ms (variable access)", - "precision": "variable-specific", - "operations": "state storage", - "power": "<1W" - }, - "efi_protocols": { - "throughput": "1-10 KOPS (protocol limited)", - "latency": "1-10ms (protocol call)", - "precision": "protocol-specific", - "operations": "hardware access", - "power": "1-5W" - }, - "efi_bytecode": { - "throughput": "1-10 KOPS (UEFI bytecode)", - "latency": "1-10ms (execution)", - "precision": "UEFI bytecode", - "operations": "firmware execution", - "power": "5-10W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run EFI controller computational analysis.""" - print("=" * 60) - print("EFI CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze EFI information - print("\n[1/4] Analyzing EFI information...") - print(f" Platform Size: {self.efi_info['platform_size']}") - print(f" Firmware Vendor: {self.efi_info['fw_vendor']}") - print(f" Runtime: {self.efi_info['runtime']}") - print(f" Computational Potential: {self.efi_info['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" UEFI Runtime Services: {potential['uefi_runtime_services']['feasible']}") - print(f" EFI Variables: {potential['efi_variables']['feasible']}") - print(f" EFI Protocols: {potential['efi_protocols']['feasible']}") - print(f" EFI Firmware Volume: {potential['efi_firmware_volume']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['power']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" UEFI Runtime: {performance['uefi_runtime']['throughput']}") - print(f" EFI Variables: {performance['efi_variables']['throughput']}") - print(f" EFI Protocols: {performance['efi_protocols']['throughput']}") - print(f" EFI Bytecode: {performance['efi_bytecode']['throughput']}") - - print("\n" + "=" * 60) - print("EFI CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "efi_info": self.efi_info, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = EFIComputationalController() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "efi_computational_controller.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("EFI COMPUTATIONAL CONTROLLER SUMMARY") - print("=" * 60) - print(f"Platform Size: {results['efi_info']['platform_size']}") - print(f"Computational Potential: {results['efi_info']['computational_potential']}") - print(f"Computational Modes: {len(results['computational_approach'])}") - print(f"Max Throughput: {results['performance_estimates']['uefi_runtime']['throughput']}") diff --git a/5-Applications/scripts/eigengate_paradigm_analysis.py b/5-Applications/scripts/eigengate_paradigm_analysis.py deleted file mode 100644 index 174b65c7..00000000 --- a/5-Applications/scripts/eigengate_paradigm_analysis.py +++ /dev/null @@ -1,342 +0,0 @@ -#!/usr/bin/env python3 -# /// script -# requires-python = ">=3.10" -# dependencies = ["requests", "rich"] -# /// -""" -EigenGate Paradigm Analysis -Re-runs the three-pass adaptive analysis (summarize / cross-link / critique) -with the EigenGate as the central lens. - -Context loaded: - - Kernel/EigenGate.lean + GateChain.lean (the new paradigm) - - HCMMR/Core.lean + Bridge.lean + Manifest.lean (the old Gate typeclass) - - HCMMR/Laws/ (14, 15, 15E, 16, 17, 18) - - HCMMR/Kernels/ (RecamanFieldStep, FAMMScarMemory, PrimeGearCache, SNRAnomalyDetector) - - HCMMR/v0_2_Roadmap.md - - Core/ layer (FoldedPointManifold, UnderverseZeroLayer, QuantumFoamBoundary, - S3CProjectedGeodesicResolution, PathEpigeneticManifold) - - FAMM.lean, ReceiptCore.lean, FixedPoint.lean (substrate) - -The three passes ask: - 1. SUMMARIZE — what does the EigenGate paradigm actually unify? - 2. CROSS-LINK — where does ∥G·s − s∥ ≤ τ naturally appear across all kernels/laws? - 3. CRITIQUE — what is still hand-wavy, what must be proven, what is the migration plan? -""" - -import datetime -import os -import re -import sys -import time -from pathlib import Path - -import requests -from rich.console import Console -from rich.markdown import Markdown -from rich.progress import Progress, SpinnerColumn, TextColumn - -RESEARCH_ROOT = Path("/home/allaun/Documents/Research Stack") -LEAN_ROOT = RESEARCH_ROOT / "0-Core-Formalism/lean/Semantics/Semantics" -API_BASE = "https://ollama.com/v1" -API_KEY = os.environ.get("OLLAMA_API_KEY", "") -DEFAULT_MODEL = "cogito-2.1:671b" -FALLBACK_CHAIN = ["qwen3-next:80b", "gemma4:31b", "deepseek-v4-flash"] - -MAX_PER_FILE = 10_000 # chars per lean file (they're dense) -MAX_CONTEXT = 110_000 # total context chars - -console = Console() - -# --------------------------------------------------------------------------- -# Files to load — ordered by conceptual priority -# --------------------------------------------------------------------------- -LEAN_FILES = [ - # ── New paradigm kernel ────────────────────────────────────────────── - "Kernel/EigenGate.lean", - "Kernel/GateChain.lean", - - # ── Old Gate typeclass (what needs migrating) ───────────────────── - "HCMMR/Core.lean", - "HCMMR/Bridge.lean", - "HCMMR/Manifest.lean", - - # ── Laws (old Gate pattern) ────────────────────────────────────────── - "HCMMR/Laws/Law14_Motion.lean", - "HCMMR/Laws/Law15_Field.lean", - "HCMMR/Laws/Law15E_SignalDetection.lean", - "HCMMR/Laws/Law16_Entropy.lean", - "HCMMR/Laws/Law17_Observer.lean", - "HCMMR/Laws/Law18_Constants.lean", - - # ── Kernels ────────────────────────────────────────────────────────── - "HCMMR/Kernels/RecamanFieldStep.lean", - "HCMMR/Kernels/FAMMScarMemory.lean", - "HCMMR/Kernels/PrimeGearCache.lean", - "HCMMR/Kernels/SNRAnomalyDetector.lean", - - # ── Core substrate ─────────────────────────────────────────────────── - "Core/FoldedPointManifold.lean", - "Core/UnderverseZeroLayer.lean", - "Core/QuantumFoamBoundary.lean", - "Core/S3CProjectedGeodesicResolution.lean", - "Core/PathEpigeneticManifold.lean", -] - -EXTRA_DOCS = [ - # Roadmap and substrate prose - "HCMMR/v0_2_Roadmap.md", - "0-Core-Formalism/lean/Semantics/Semantics/FAMM.lean", -] - -# --------------------------------------------------------------------------- -# Helpers -# --------------------------------------------------------------------------- - -def load(path: Path, max_chars: int = MAX_PER_FILE) -> str: - try: - text = path.read_text(errors="replace") - if len(text) > max_chars: - text = text[:max_chars] + f"\n-- [truncated at {max_chars} chars]" - return text - except Exception as e: - return f"-- [could not read {path}: {e}]" - - -def gather_context() -> str: - parts = [] - for rel in LEAN_FILES: - p = LEAN_ROOT / rel - label = f"LEAN: {rel}" - parts.append(f"\n\n---\n## {label}\n\n```lean\n{load(p)}\n```") - - for rel in EXTRA_DOCS: - p = RESEARCH_ROOT / rel - label = f"DOC: {rel}" - parts.append(f"\n\n---\n## {label}\n\n{load(p, 6000)}") - - combined = "\n".join(parts) - if len(combined) > MAX_CONTEXT: - combined = combined[:MAX_CONTEXT] + "\n\n-- [context truncated]" - return combined - - -def chat(model: str, system: str, user: str, label: str, retries: int = 3) -> str: - headers = {"Authorization": f"Bearer {API_KEY}", "Content-Type": "application/json"} - models_to_try = [model] + [m for m in FALLBACK_CHAIN if m != model] - - for attempt_model in models_to_try: - payload = { - "model": attempt_model, - "messages": [ - {"role": "system", "content": system}, - {"role": "user", "content": user}, - ], - "stream": False, - "options": {"temperature": 0.2, "num_predict": 8192}, - } - for attempt in range(1, retries + 1): - with Progress(SpinnerColumn(), - TextColumn(f"[bold cyan]{label}[/bold cyan] ({attempt_model}, try {attempt}) ..."), - transient=True, console=console) as p: - p.add_task("", total=None) - try: - resp = requests.post(f"{API_BASE}/chat/completions", - headers=headers, json=payload, timeout=600) - except requests.exceptions.Timeout: - console.print(f"[yellow]Timeout, retrying...[/yellow]") - time.sleep(5 * attempt) - continue - - if resp.status_code == 200: - content = resp.json()["choices"][0]["message"]["content"] - content = re.sub(r".*?", "", content, flags=re.DOTALL).strip() - return content - - body = resp.text[:300] - if "overloaded" in body.lower() or resp.status_code in (429, 503): - wait = 12 * attempt - console.print(f"[yellow]Overloaded, waiting {wait}s...[/yellow]") - time.sleep(wait) - elif resp.status_code == 500: - console.print(f"[yellow]500 on {attempt_model}, trying fallback...[/yellow]") - break - else: - console.print(f"[red]API error {resp.status_code}:[/red] {body}") - sys.exit(1) - console.print(f"[yellow]Exhausted retries for {attempt_model}[/yellow]") - - console.print("[red]All models failed.[/red]") - sys.exit(1) - -# --------------------------------------------------------------------------- -# System prompt — shared across all three passes -# --------------------------------------------------------------------------- -SYSTEM = """\ -You are an expert in formal verification (Lean 4), type theory, and physics-informed \ -computation. You are reviewing the HCMMR (Hyper-Coherent Morphic Meta-Recursion) \ -research stack on its `distilled` branch. - -The stack is undergoing a PARADIGM SHIFT. The old system used a flat `Gate` struct \ -(name, required, score, verdict). The new paradigm is the `Eigengate`: - - structure Eigengate (α : Type) where - operator : α → α -- G: the operator whose fixed points encode the law - residual : α → Q0_16 -- ∥G·s − s∥, normalised to [0,1) - threshold : Q0_16 -- τ: max admissible residual - -The central doctrine: EVERY physical law, routing decision, and compression check \ -is an eigenstate condition ∥G·s − s∥ ≤ τ. The full physical universe is the \ -intersection of λ=1 eigenspaces for all required gate operators simultaneously. - -The migration is INCOMPLETE. EigenGate.lean and GateChain.lean exist but are not \ -imported by anything. All Laws (14–18) still use the old Gate struct. The kernels \ -(RecamanFieldStep, FAMMScarMemory, PrimeGearCache, SNRAnomalyDetector) also use old Gate. \ -LawRecovery.lean (concrete eigengate constructors for each physical law) was never written. - -Be precise, rigorous, and honest. Do not hallucinate. Flag uncertainty explicitly.\ -""" - -# --------------------------------------------------------------------------- -# Pass 1 — Summarize what the EigenGate paradigm actually unifies -# --------------------------------------------------------------------------- -PASS1_SYS = SYSTEM + """ - -YOUR TASK: PARADIGM SUMMARY. -Given all the Lean source files, answer: -1. What does the Eigengate pattern (`∥G·s − s∥ ≤ τ`) actually unify across the stack? - List every law/kernel and state what G, s, and the residual would be in each case. -2. What is the relationship between the old `Gate` struct and `Eigengate`? - Are they isomorphic? Can one be mechanically derived from the other? -3. What does `Semantics.Kernel.EigenGate` currently prove that the old HCMMR/Core does not? -4. What is the correct `α` type for each of the six laws (14, 15K, 15A-D, 15E, 16, 17, 18)? - Be specific — what Lean type carries the state? -5. Where does the Recamán kernel fit in the eigengate picture? - What is G for a Recamán field step? - -Use markdown headers per section. Be concrete — reference actual line numbers and -struct fields from the source files where relevant. -""" - -# --------------------------------------------------------------------------- -# Pass 2 — Cross-link: where does ∥G·s − s∥ naturally appear already? -# --------------------------------------------------------------------------- -PASS2_SYS = SYSTEM + """ - -YOUR TASK: CROSS-DOMAIN EIGENGATE DETECTION. -Scan all the Lean files in the context. For each file/module, identify: -1. Any existing computation that already computes something of the form ∥G·s − s∥ - (even if not named that way). What is G? What is s? What is the residual value? -2. Any existing `residual`, `score`, `epsilon`, or `delta` computation that could - directly become the `residual : α → Q0_16` field of an Eigengate. -3. Any existing operator/transform that maps a state to a new state — these are - candidate `operator : α → α` fields. -4. Places where the old Gate struct's `score` field is set to a formula (not just - a constant `Q16_16.one`) — these are the richest migration candidates. - -Format as a table: | Module | Existing computation | Candidate G | Candidate residual | Migration difficulty | -Then give a prioritized migration order (easiest → hardest) with reasoning. -""" - -# --------------------------------------------------------------------------- -# Pass 3 — Critique and concrete migration plan -# --------------------------------------------------------------------------- -PASS3_SYS = SYSTEM + """ - -YOUR TASK: CRITIQUE AND CONCRETE MIGRATION PLAN. -Be rigorous. Identify: - -A) ARCHITECTURAL CRITIQUE - [CRITICAL/MODERATE/MINOR] — what is wrong or incomplete in the current design? - Pay special attention to: - - The type parameter `α` in `Eigengate (α : Type)` — is it flexible enough? - The Laws need different α types (TrajectoryPoint, KahlerState, MaxwellField, etc.) - Can a single GateChain hold gates over different α? If not, how must GateChain change? - - The `score` function `1/(1+r)` — is this the right proximity measure for all laws? - - FAMM's `expNeg` — if it's a stub, what breaks? - - PrimeGearCache's silent cache-miss (returns Q16_16.one with no receipt) — severity? - - RecamanFieldStep's untested gate-reject path — what scenario does this cover? - - SNRAnomalyDetector's dead `dopplerDrift` branch — is it needed? - -B) CONCRETE MIGRATION PLAN - Write the exact 5-step plan to complete the paradigm migration: - Step 1: What to add to Semantics.lean (exact import lines) - Step 2: What LawRecovery.lean must contain (list each law's G, α, residual formula) - Step 3: Which kernels need a new `toEigengate` adapter function and what it looks like - Step 4: What new theorems are needed to prove the old Gate and new Eigengate are equivalent - Step 5: What the v0.2 build should look like when complete (job count, zero errors) - -C) WHAT NOT TO DO - Flag any tempting-but-wrong approaches that the previous agent sessions considered - and should be avoided. -""" - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- -def main(): - if not API_KEY: - console.print("[red]Set OLLAMA_API_KEY before calling the Ollama Cloud API.[/red]") - sys.exit(1) - - console.rule("[bold green]EigenGate Paradigm Analysis[/bold green]") - console.print(f"Model: [bold]{DEFAULT_MODEL}[/bold] Fallbacks: {FALLBACK_CHAIN}\n") - - console.print("[dim]Loading Lean source files and docs...[/dim]") - context = gather_context() - console.print(f"[dim]Context: {len(context):,} chars[/dim]\n") - - summary = chat(DEFAULT_MODEL, PASS1_SYS, - f"Lean source files:\n\n{context}\n\nProduce the paradigm summary.", - "Pass 1: Paradigm Summary") - - crosslink = chat(DEFAULT_MODEL, PASS2_SYS, - f"Summary from Pass 1:\n{summary}\n\n---\nLean source files:\n\n{context}\n\nDetect eigengate patterns.", - "Pass 2: Cross-link Detection") - - critique = chat(DEFAULT_MODEL, PASS3_SYS, - f"Summary:\n{summary}\n\nCross-links:\n{crosslink}\n\n---\nLean source files:\n\n{context}\n\nDeliver critique and migration plan.", - "Pass 3: Critique + Migration Plan") - - timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M") - report = f"""# EigenGate Paradigm Analysis -*Generated: {timestamp} | Model: {DEFAULT_MODEL}* - -> **Context:** This analysis re-examines the HCMMR `distilled` branch through the lens -> of the incomplete `Eigengate (α : Type)` paradigm migration. The old `Gate` struct -> (HCMMR/Core.lean) and the new `Eigengate` (Kernel/EigenGate.lean) coexist but are -> disconnected. This report determines what the migration requires and how to complete it. - ---- - -## Pass 1 — What the EigenGate Paradigm Unifies - -{summary} - ---- - -## Pass 2 — Where ∥G·s − s∥ Already Appears in the Codebase - -{crosslink} - ---- - -## Pass 3 — Critique & Concrete Migration Plan - -{critique} - ---- -*Generated by `scripts/eigengate_paradigm_analysis.py`* -""" - - date_str = datetime.datetime.now().strftime("%Y-%m-%d") - out = RESEARCH_ROOT / "6-Documentation" / "docs" / "reports" / f"eigengate_paradigm_analysis_{date_str}.md" - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(report) - - console.rule("[bold green]Complete[/bold green]") - console.print(f"\nReport: [bold]{out}[/bold]\n") - console.print(Markdown(report[:8000] + ("\n\n*[truncated — see file]*" if len(report) > 8000 else ""))) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/eigenvector_tsm_hyperfluid.py b/5-Applications/scripts/eigenvector_tsm_hyperfluid.py deleted file mode 100644 index 0fe1c8e9..00000000 --- a/5-Applications/scripts/eigenvector_tsm_hyperfluid.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -""" -Eigenvector Hyperfluid through Topological State Machine - -Flows eigenvector cluster data through the TSM as a hyperfluid compression -mechanism. Each eigenvector cluster becomes a "fluid packet" that navigates -the TSM manifold, with topological tracking of the flow. - -Concept: - - Eigenvector clusters → NibbleSwitch transitions - - Cluster magnitude → Transition polarity - - Cluster eigenvalue → Domain selection - - Flow trajectory → Manifold topology evolution -""" - -import sys -import json -import numpy as np -import sqlite3 -from scipy.sparse import csr_matrix -from scipy.sparse.linalg import eigsh -from pathlib import Path -from typing import Dict, List, Tuple -from collections import deque - -# Add TSM to path -sys.path.insert(0, str(Path(__file__).parent)) -from topological_state_machine import ( - TopologicalStateMachine, NibbleSwitch, ManifoldPoint, - TopologicalInvariants, StateMachineCache -) - -DB_PATH = "/dev/shm/physics_equations.db" -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models/eigenvector_tsm") -OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - -def load_equations() -> List[Tuple[int, str, int, str]]: - """Load all equations from database.""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - cursor.execute("SELECT id, name FROM domains") - domains = {str(r[0]): r[1] for r in cursor.fetchall()} - conn.close() - return [(eq_num, title, domains.get(str(did), "Unknown"), desc or "") - for eq_num, title, did, desc in rows] - -def build_domain_adjacency(equations): - """Build adjacency matrix based on domain co-occurrence.""" - n = len(equations) - - domain_to_eqs = {} - for i, (_, _, domain_id, _) in enumerate(equations): - if domain_id not in domain_to_eqs: - domain_to_eqs[domain_id] = [] - domain_to_eqs[domain_id].append(i) - - row_indices = [] - col_indices = [] - data = [] - - for domain_id, eq_indices in domain_to_eqs.items(): - for i in eq_indices: - for j in eq_indices: - if i != j: - row_indices.append(i) - col_indices.append(j) - data.append(1.0 / len(eq_indices)) - - adj = csr_matrix((data, (row_indices, col_indices)), shape=(n, n)) - return adj, domain_to_eqs - -def find_principal_eigenvectors(adj_matrix, n_eigenvectors=5): - """Find principal eigenvectors.""" - eigenvalues, eigenvectors = eigsh(adj_matrix, k=n_eigenvectors, which='LM') - return eigenvalues, eigenvectors - -def assign_eigenvector_categories(equations, eigenvectors): - """Assign each equation to its dominant eigenvector cluster.""" - n_eqs = len(equations) - n_clusters = eigenvectors.shape[1] - - categories = [] - for i in range(n_eqs): - magnitudes = np.abs(eigenvectors[i, :]) - dominant_cluster = int(np.argmax(magnitudes)) - dominant_magnitude = float(magnitudes[dominant_cluster]) - categories.append((dominant_cluster, dominant_magnitude)) - - return categories - -def eigenvector_to_nibble(cluster_idx, magnitude, eigenvalue): - """ - Convert eigenvector cluster data to NibbleSwitch. - - Mapping: - - cluster_idx (0-4) → domain (0-3) via modulo - - magnitude → polarity (positive if > threshold) - - eigenvalue → control state based on magnitude - """ - # Map cluster to domain (5 clusters → 4 domains via modulo) - domain = cluster_idx % 4 - - # Magnitude determines polarity - polarity = 1 if magnitude > 0.1 else -1 - - # Eigenvalue determines control state - # High eigenvalue = ACCEPT (1), Low eigenvalue = REJECT (0) - control = 1 if eigenvalue > 0.95 else 0 - - return NibbleSwitch.from_parts(control, domain, polarity) - -def flow_eigenvectors_through_tsm(equations, categories, eigenvalues, steps_per_cluster=50): - """ - Flow eigenvector data through TSM as hyperfluid. - - For each cluster, create a flow trajectory through the TSM manifold. - The trajectory is driven by the cluster's eigenvector properties. - """ - # Initialize TSM - cache_dir = OUTPUT_DIR / "tsm_cache" - tsm = TopologicalStateMachine(cache_dir=cache_dir) - - cluster_names = [ - "Electromagnetism & Circuits", - "Condensed Matter & Superconductivity", - "Quantum Mechanics & Particle Physics", - "Materials Science & Engineering", - "Cognitive & Semantic Systems" - ] - - flow_log = [] - - for cluster_idx in range(len(eigenvalues)): - print(f"\n Flowing Cluster {cluster_idx + 1}: {cluster_names[cluster_idx]}") - print(f" Eigenvalue: {eigenvalues[cluster_idx]:.6f}") - - # Get equations in this cluster - cluster_eqs = [(eq, cat[1]) for eq, cat in zip(equations, categories) if cat[0] == cluster_idx] - cluster_eqs.sort(key=lambda x: x[1], reverse=True) - - print(f" Equations: {len(cluster_eqs)}") - - # Flow this cluster through TSM - cluster_trajectory = [] - for step in range(steps_per_cluster): - # Use top equations to drive transitions - if step < len(cluster_eqs): - eq, magnitude = cluster_eqs[step] - eq_num, title, domain, desc = eq - # Create nibble from eigenvector data - nib = eigenvector_to_nibble(cluster_idx, magnitude, eigenvalues[cluster_idx]) - else: - # Autonomous flow based on current TSM state - nib = eigenvector_to_nibble(cluster_idx, 0.05, eigenvalues[cluster_idx]) - - # Execute transition with eigenmass - # Convert eigenvalue and magnitude to Q16_16 - eigenvalue_q16 = int(eigenvalues[cluster_idx] * 65536) & 0xFFFF - magnitude_q16 = int(magnitude * 65536) & 0xFFFF - new_state = tsm.transition(nib.control, nib.domain, nib.polarity, eigenvalue_q16, magnitude_q16) - - cluster_trajectory.append({ - "step": tsm.step, - "nibble": str(nib), - "state": str(new_state), - "curvature": new_state.curvature, - "locus": hex(new_state.locus) - }) - - if (step + 1) % 10 == 0: - print(f" Step {step + 1}/{steps_per_cluster}: locus={new_state.locus:08x}, curvature={new_state.curvature:.4f}") - - flow_log.append({ - "cluster": cluster_idx + 1, - "name": cluster_names[cluster_idx], - "eigenvalue": float(eigenvalues[cluster_idx]), - "equation_count": len(cluster_eqs), - "trajectory": cluster_trajectory, - "final_state": str(tsm.state), - "final_curvature": tsm.state.curvature, - "final_locus": hex(tsm.state.locus) - }) - - return tsm, flow_log - -def analyze_hyperfluid_topology(tsm, flow_log): - """Analyze the topological structure of the hyperfluid flow.""" - topo = tsm.topology.summary() - - # Compute cluster-to-cluster transition statistics - cluster_transitions = {} - for i, cluster_data in enumerate(flow_log): - if i < len(flow_log) - 1: - from_cluster = cluster_data["cluster"] - to_cluster = flow_log[i + 1]["cluster"] - key = f"{from_cluster}→{to_cluster}" - cluster_transitions[key] = cluster_transitions.get(key, 0) + 1 - - return { - "topology": topo, - "cluster_transitions": cluster_transitions, - "total_fluid_steps": sum(len(c["trajectory"]) for c in flow_log), - "final_tsm_state": str(tsm.state) - } - -def main(): - print("=" * 70) - print(" EIGENVECTOR HYPERFLID THROUGH TOPOLOGICAL STATE MACHINE") - print("=" * 70) - - # Phase 1: Load eigenvector data - print("\n[1/5] Loading equations and computing eigenvectors...") - equations = load_equations() - print(f" → {len(equations)} equations loaded") - - adj, domain_to_eqs = build_domain_adjacency(equations) - print(f" → Adjacency matrix: {adj.shape}") - - eigenvalues, eigenvectors = find_principal_eigenvectors(adj, n_eigenvectors=5) - print(f" → {len(eigenvalues)} eigenvectors computed") - - categories = assign_eigenvector_categories(equations, eigenvectors) - print(f" → {len(categories)} equations categorized") - - # Phase 2: Initialize TSM - print("\n[2/5] Initializing Topological State Machine...") - cache_dir = OUTPUT_DIR / "tsm_cache" - cache_dir.mkdir(parents=True, exist_ok=True) - print(f" → Cache: {cache_dir}") - - # Phase 3: Flow eigenvectors as hyperfluid - print("\n[3/5] Flowing eigenvectors through TSM manifold...") - tsm, flow_log = flow_eigenvectors_through_tsm( - equations, categories, eigenvalues, steps_per_cluster=30 - ) - - # Phase 4: Analyze topology - print("\n[4/5] Analyzing hyperfluid topology...") - analysis = analyze_hyperfluid_topology(tsm, flow_log) - print(f" → Total fluid steps: {analysis['total_fluid_steps']}") - print(f" → Betti-0 (components): {analysis['topology']['betti_0']}") - print(f" → Betti-1 (loops): {analysis['topology']['betti_1']}") - print(f" → Euler characteristic: {analysis['topology']['euler_characteristic']}") - print(f" → Avg curvature: {analysis['topology']['avg_curvature']:.4f}") - - # Phase 5: Save results - print("\n[5/5] Saving hyperfluid analysis...") - - results = { - "metadata": { - "equations_count": len(equations), - "eigenvalues": eigenvalues.tolist(), - "cluster_names": [ - "Electromagnetism & Circuits", - "Condensed Matter & Superconductivity", - "Quantum Mechanics & Particle Physics", - "Materials Science & Engineering", - "Cognitive & Semantic Systems" - ] - }, - "flow_log": flow_log, - "topology_analysis": analysis, - "tsm_final_state": tsm.self_reflect() - } - - output_path = OUTPUT_DIR / f"eigenvector_hyperfluid_{tsm.step}_steps.json" - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - - print(f" → Output: {output_path}") - - print(f"\n{'='*70}") - print(" HYPERFLID FLOW COMPLETE") - print(f"{'='*70}") - print(f" Total TSM steps: {tsm.step}") - print(f" Fluid clusters: {len(flow_log)}") - print(f" Final locus: {hex(tsm.state.locus)}") - print(f" Final curvature: {tsm.state.curvature:.4f}") - print(f" Topology loops: {analysis['topology']['betti_1']}") - print(f"{'='*70}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ene_triangle_manifold.py b/5-Applications/scripts/ene_triangle_manifold.py deleted file mode 100644 index dc360494..00000000 --- a/5-Applications/scripts/ene_triangle_manifold.py +++ /dev/null @@ -1,243 +0,0 @@ -#!/usr/bin/env python3 -""" -ENE TriangleManifold Integration -Applies concentric triangular manifold concepts to ENE neural manifold ingestion - -Per AGENTS.md §6.1: Python shim for integration only -""" - -import sqlite3 -from pathlib import Path -from typing import Dict, List, Optional, Tuple -from dataclasses import dataclass -from datetime import datetime -import json - -@dataclass -class TriangleShell: - """Triangular shell representing a level of neural manifold abstraction""" - shell_index: int # k: shell index - triangular_number: int # Tₖ = k(k+1)/2 - vertices: List[float] # [a, b, c] triangle vertices - mass: float # a * b * c (triple product) - rotation_angle: float # θ: rotation angle in radians - bandwidth: float # Transmission bandwidth - latency: float # Transmission latency - -@dataclass -class TransmissionPoint: - """Transmission point between neural manifold shells""" - source_shell: int - target_shell: int - vertex: int # Which vertex connects (0, 1, or 2) - efficiency: float # bandwidth / latency - -@dataclass -class ENETriangleManifold: - """ENE neural manifold modeled as concentric triangular shells""" - max_shell: int - curvature: float - shells: List[TriangleShell] - transmission_points: List[TransmissionPoint] - total_bandwidth: float - total_latency: float - -class ENETriangleManifoldIntegrator: - """ - Integrates TriangleManifold concepts with ENE neural manifold ingestion. - - Key insight: - - ENE neural manifold domain modeled as concentric triangular shells - - Each shell represents a level of abstraction (session → concept → anchor) - - Transmission points represent data flow between abstraction levels - - Rotation field represents neural manifold transformation - """ - - def __init__(self, db_path: str = None, max_shell: int = 10, curvature: float = 0.5): - self.db_path = db_path or Path("shared-data/data/substrate_index.db") - self.max_shell = max_shell - self.curvature = curvature - self.manifold = None - - def triangular_number(self, k: int) -> int: - """Compute triangular number: Tₖ = k(k+1)/2""" - return k * (k + 1) // 2 - - def create_shell(self, k: int, rotation_angle: float = 0.0) -> TriangleShell: - """Create a triangular shell at index k""" - t_num = self.triangular_number(k) - - # Triangle vertices based on shell geometry - # a = offset within shell, b = shell width - offset, c = shell index - a = float(k % 3) - b = float((k + 1) - (k % 3)) - c = float(k) - - mass = a * b * c - - return TriangleShell( - shell_index=k, - triangular_number=t_num, - vertices=[a, b, c], - mass=mass, - rotation_angle=rotation_angle, - bandwidth=10.0 * (1.0 - self.curvature), # Bandwidth decreases with curvature - latency=1.0 + self.curvature # Latency increases with curvature - ) - - def create_transmission_network(self, shells: List[TriangleShell]) -> List[TransmissionPoint]: - """Create transmission points between shells""" - points = [] - - for i in range(len(shells) - 1): - source = shells[i] - target = shells[i + 1] - - # Create transmission points for each vertex - for vertex in range(3): - efficiency = target.bandwidth / target.latency - points.append(TransmissionPoint( - source_shell=source.shell_index, - target_shell=target.shell_index, - vertex=vertex, - efficiency=efficiency - )) - - return points - - def build_manifold(self) -> ENETriangleManifold: - """Build the ENE neural manifold as concentric triangular shells""" - shells = [] - - for k in range(self.max_shell + 1): - rotation_angle = float(k) * 0.1 # Incremental rotation per shell - shell = self.create_shell(k, rotation_angle) - shells.append(shell) - - transmission_points = self.create_transmission_network(shells) - - total_bandwidth = sum(tp.efficiency for tp in transmission_points) - total_latency = sum(1.0 / tp.efficiency for tp in transmission_points) if transmission_points else 0.0 - - self.manifold = ENETriangleManifold( - max_shell=self.max_shell, - curvature=self.curvature, - shells=shells, - transmission_points=transmission_points, - total_bandwidth=total_bandwidth, - total_latency=total_latency - ) - - return self.manifold - - def transmit_data(self, data: float, source_shell: int, target_shell: int) -> float: - """Transmit data through the manifold from source to target shell""" - if not self.manifold: - self.build_manifold() - - # Find transmission path - path = [tp for tp in self.manifold.transmission_points - if tp.source_shell == source_shell and tp.target_shell == target_shell] - - if not path: - return data # No direct path - - tp = path[0] - return data * tp.efficiency - - def compute_rotation_field(self, data: float) -> float: - """Compute manifold rotation field for data""" - if not self.manifold: - self.build_manifold() - - # Sum over all shells: Σ mass * rotation - field_sum = sum(shell.mass * shell.rotation_angle for shell in self.manifold.shells) - - # Divide by curvature denominator - denom = 1.0 + self.curvature ** 2 - return field_sum / denom - - def compute_transmission_field(self, data: float) -> float: - """Compute manifold transmission field (rotation + transmission)""" - rotation_field = self.compute_rotation_field(data) - - # Add transmission contribution - transmission_sum = sum(self.transmit_data(data, tp.source_shell, tp.target_shell) - for tp in self.manifold.transmission_points) - - return rotation_field + transmission_sum - - def map_package_to_shell(self, pkg_data: Dict) -> int: - """Map an ENE package to a triangular shell based on its properties""" - # Use foam_score or metric to determine shell level - foam_score = pkg_data.get('foam_score', 0.0) - - # Map foam_score to shell index (0 to max_shell) - shell_index = min(int(foam_score * self.max_shell), self.max_shell) - - return shell_index - - def ingest_with_manifold(self, pkg_data: Dict) -> Dict: - """Ingest a package with manifold field computation""" - if not self.manifold: - self.build_manifold() - - shell_index = self.map_package_to_shell(pkg_data) - shell = self.manifold.shells[shell_index] - - # Compute manifold fields for this package - data_value = pkg_data.get('foam_score', 0.0) - rotation_field = self.compute_rotation_field(data_value) - transmission_field = self.compute_transmission_field(data_value) - - # Enhance package data with manifold information - pkg_data['manifold_shell'] = shell_index - pkg_data['manifold_rotation_field'] = rotation_field - pkg_data['manifold_transmission_field'] = transmission_field - pkg_data['manifold_mass'] = shell.mass - pkg_data['manifold_vertices'] = shell.vertices - - return pkg_data - - def get_manifold_stats(self) -> Dict: - """Get statistics about the neural manifold""" - if not self.manifold: - self.build_manifold() - - return { - 'max_shell': self.manifold.max_shell, - 'curvature': self.manifold.curvature, - 'total_shells': len(self.manifold.shells), - 'total_transmission_points': len(self.manifold.transmission_points), - 'total_bandwidth': self.manifold.total_bandwidth, - 'total_latency': self.manifold.total_latency, - 'average_mass': sum(s.mass for s in self.manifold.shells) / len(self.manifold.shells), - 'total_mass': sum(s.mass for s in self.manifold.shells) - } - -# CLI interface -if __name__ == "__main__": - print("=" * 60) - print("ENE TRIANGLE MANIFOLD INTEGRATION") - print("=" * 60) - - integrator = ENETriangleManifoldIntegrator(max_shell=10, curvature=0.5) - manifold = integrator.build_manifold() - - print(f"\nManifold Statistics:") - stats = integrator.get_manifold_stats() - for key, value in stats.items(): - print(f" {key}: {value}") - - print(f"\nSample Transmission:") - data = 1.0 - transmitted = integrator.transmit_data(data, 0, 1) - print(f" Data: {data} → Shell 0 to Shell 1: {transmitted}") - - print(f"\nSample Rotation Field:") - rotation_field = integrator.compute_rotation_field(1.0) - print(f" Rotation field: {rotation_field}") - - print(f"\nSample Transmission Field:") - transmission_field = integrator.compute_transmission_field(1.0) - print(f" Transmission field: {transmission_field}") diff --git a/5-Applications/scripts/equation_forest_genome18_encoder.py b/5-Applications/scripts/equation_forest_genome18_encoder.py deleted file mode 100644 index fc22ceef..00000000 --- a/5-Applications/scripts/equation_forest_genome18_encoder.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 -""" -Equation Forest to Genome18 Encoder - -Maps the 12-kernel equation forest to 6x3-bit Genome18 bins for FPGA LUT addressing. - -Mapping: -- F01-F03 (entropy/compression) → mBin, neBin, sigmaBin -- F04-F07 (thermodynamic limits) → cost/failure mask (derived) -- F08-F10 (geometry/geodesics) → cBin (connectance) -- F11-F12 (load/routing) → muBin (mutation/drift), rhoBin (verification pressure) -- DIAT/AVMR/S3C/PIST bridge → encoded transition surface - -Stack: -raw equation -→ F01-F12 kernel signature -→ street / bridge assignment -→ six 3-bit Genome18 bins -→ 18-bit ISA/LUT address -→ FPGA route expansion -→ PIST/witness audit -→ Lean/proof/executable check -""" - -import json -import numpy as np -from typing import Dict, List, Tuple - -class Genome18Encoder: - """Encodes equation forest signatures into Genome18 addresses.""" - - def __init__(self): - self.bin_ranges = { - "muBin": (0, 7), - "rhoBin": (0, 7), - "cBin": (0, 7), - "mBin": (0, 7), - "neBin": (0, 7), - "sigmaBin": (0, 7) - } - - def kernel_to_bins(self, foundation_vector: List[float]) -> Dict[str, int]: - """ - Map 12-dimensional kernel vector to 6 bins (3 bits each). - - Mapping: - - muBin: F11 (aggregate load) + F12 (routing ratio) → mutation/drift - - rhoBin: F11 (aggregate load) - F12 (routing ratio) → verification pressure - - cBin: F08 (metric) + F09 (connection) + F10 (geodesic) → connectance - - mBin: F01 (local entropy) + F02 (global entropy) → compression residue - - neBin: F03 (hierarchical entropy) → effective sample - - sigmaBin: F01 (local entropy) - F02 (global entropy) → fitness proxy - """ - # Extract kernel values - f01, f02, f03, f04, f05, f06, f07, f08, f09, f10, f11, f12 = foundation_vector - - # Compute bin values (scaled to 0-7 range) - muBin = self._scale_to_3bit(f11 + f12) # routing load - rhoBin = self._scale_to_3bit(abs(f11 - f12)) # verification pressure - cBin = self._scale_to_3bit(f08 + f09 + f10) # connectance - mBin = self._scale_to_3bit(f01 + f02) # compression residue - neBin = self._scale_to_3bit(f03) # effective sample - sigmaBin = self._scale_to_3bit(abs(f01 - f02)) # fitness proxy - - return { - "muBin": int(muBin), - "rhoBin": int(rhoBin), - "cBin": int(cBin), - "mBin": int(mBin), - "neBin": int(neBin), - "sigmaBin": int(sigmaBin) - } - - def _scale_to_3bit(self, value: float) -> int: - """Scale a float value to 0-7 range (3 bits).""" - # Clamp to 0-2 range first (typical for kernel values) - clamped = max(0.0, min(2.0, value)) - # Scale to 0-7 - scaled = int(clamped * 3.5) - return min(7, max(0, scaled)) - - def bins_to_address(self, bins: Dict[str, int]) -> int: - """ - Compute 18-bit address from 6 bins. - - Address calculation: - addr = muBin * 32768 + rhoBin * 4096 + cBin * 512 + mBin * 64 + neBin * 8 + sigmaBin - """ - addr = ( - bins["muBin"] * 32768 + - bins["rhoBin"] * 4096 + - bins["cBin"] * 512 + - bins["mBin"] * 64 + - bins["neBin"] * 8 + - bins["sigmaBin"] - ) - return addr - - def encode_equation(self, equation: Dict) -> Dict: - """Encode a single equation into Genome18 bins and address.""" - foundation_vector = equation.get("foundation_vector", [0.0] * 12) - - # Map to bins - bins = self.kernel_to_bins(foundation_vector) - - # Compute address - address = self.bins_to_address(bins) - - return { - "uuid": equation.get("uuid"), - "model_name": equation.get("model_name"), - "genome18_bins": bins, - "genome18_address": address - } - - def encode_forest(self, equations: List[Dict]) -> List[Dict]: - """Encode all equations in the forest.""" - encoded = [] - for eq in equations: - if eq.get("namespace") == "equation": - encoded.append(self.encode_equation(eq)) - return encoded - -def main(): - """Main entry point.""" - equations_file = "/home/allaun/Documents/Research Stack/data/equations_forest.jsonl" - output_file = "/home/allaun/Documents/Research Stack/data/equations_forest_genome18.jsonl" - - # Load equations - equations = [] - with open(equations_file, 'r') as f: - for line in f: - if line.strip(): - try: - equations.append(json.loads(line)) - except json.JSONDecodeError as e: - print(f"Skipping malformed line: {e}") - continue - - # Encode - encoder = Genome18Encoder() - encoded = encoder.encode_forest(equations) - - # Save - with open(output_file, 'w') as f: - for enc in encoded: - f.write(json.dumps(enc) + '\n') - - print(f"Encoded {len(encoded)} equations to Genome18 addresses") - print(f"Output saved to {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/equation_forest_tsp_solver.py b/5-Applications/scripts/equation_forest_tsp_solver.py deleted file mode 100644 index 07166e50..00000000 --- a/5-Applications/scripts/equation_forest_tsp_solver.py +++ /dev/null @@ -1,356 +0,0 @@ -#!/usr/bin/env python3 -""" -Equation Forest Exact TSP Solver v0.1 - -Implements the exact TSP pipeline over the compressed equation graph: -1. Load equations_forest.jsonl -2. Normalize equations into canonical form -3. Compute F01-F12 kernel vectors -4. Assign nearest street -5. Detect bridge candidates B1-B8 -6. Collapse near-identical nodes into supernodes -7. Run exact TSP/path search on supernodes -8. Run exact local TSP inside each supernode -9. Stitch global route -10. Mark every edge: proved / executable / empirical / metaphorical / failed -""" - -import json -import numpy as np -from typing import List, Dict, Tuple, Set -from collections import defaultdict -import itertools - -# Street definitions -STREETS = { - "entropy_compression": ["F01", "F02", "F03"], - "thermodynamic_admissibility": ["F04", "F05", "F06", "F07"], - "geometric_motion": ["F08", "F09", "F10"], - "cognitive_routing_load": ["F11", "F12"], - "diat_avmr_s3c_bridge": [] -} - -# Bridge definitions -BRIDGES = { - "B1": {"name": "Entropy ↔ Load", "from": "entropy_compression", "to": "cognitive_routing_load"}, - "B2": {"name": "Entropy ↔ Landauer", "from": "entropy_compression", "to": "thermodynamic_admissibility"}, - "B3": {"name": "Energy ↔ Routing", "from": "thermodynamic_admissibility", "to": "cognitive_routing_load"}, - "B4": {"name": "Geometry ↔ Routing", "from": "geometric_motion", "to": "cognitive_routing_load"}, - "B5": {"name": "DIAT ↔ Geometry", "from": "diat_avmr_s3c_bridge", "to": "geometric_motion"}, - "B6": {"name": "AVMR ↔ Entropy", "from": "diat_avmr_s3c_bridge", "to": "entropy_compression"}, - "B7": {"name": "S3C ↔ Codec", "from": "diat_avmr_s3c_bridge", "to": "entropy_compression"}, - "B8": {"name": "PIST ↔ Surface", "from": "diat_avmr_s3c_bridge", "to": "geometric_motion"} -} - -def load_equations_forest(filepath: str) -> List[Dict]: - """Load equations from equations_forest.jsonl""" - equations = [] - with open(filepath, 'r') as f: - for line in f: - if line.strip(): - equations.append(json.loads(line)) - return equations - -def normalize_equation(eq: Dict) -> Dict: - """Normalize equation into canonical form""" - # Already canonical in our JSONL format - return eq - -def compute_kernel_distance(eq1: Dict, eq2: Dict) -> float: - """Compute cosine distance between foundation vectors""" - v1 = np.array(eq1.get("foundation_vector", [0.0]*12)) - v2 = np.array(eq2.get("foundation_vector", [0.0]*12)) - - # Cosine distance - norm1 = np.linalg.norm(v1) - norm2 = np.linalg.norm(v2) - if norm1 == 0 or norm2 == 0: - return 1.0 # Maximum distance - dot = np.dot(v1, v2) - cosine = dot / (norm1 * norm2) - return 1.0 - cosine - -def assign_nearest_street(eq: Dict) -> str: - """Assign nearest street based on kernel signature""" - foundation_vector = eq.get("foundation_vector", [0.0]*12) - - # Find which street has the highest activation - street_scores = {} - for street_name, kernel_ids in STREETS.items(): - if not kernel_ids: - continue - score = 0.0 - for kid in kernel_ids: - idx = int(kid[1:]) - 1 # F01 -> index 0 - if idx < len(foundation_vector): - score += foundation_vector[idx] - street_scores[street_name] = score - - if not street_scores: - return "diat_avmr_s3c_bridge" - - return max(street_scores, key=street_scores.get) - -def detect_bridge_candidates(eq1: Dict, eq2: Dict) -> List[str]: - """Detect which bridges explain the transition""" - street1 = eq1.get("street_membership", [""])[0] if eq1.get("street_membership") else "" - street2 = eq2.get("street_membership", [""])[0] if eq2.get("street_membership") else "" - - candidates = [] - for bridge_id, bridge in BRIDGES.items(): - if (bridge["from"] == street1 and bridge["to"] == street2) or \ - (bridge["from"] == street2 and bridge["to"] == street1): - candidates.append(bridge_id) - - return candidates - -def compute_distance_metric(eq1: Dict, eq2: Dict) -> float: - """Compute refined distance metric D(x,y)""" - # Component weights - w_kernel = 0.35 - w_street = 0.20 - w_bridge = 0.20 - w_typing = 0.10 - w_failure = 0.10 - w_scale = 0.05 - - # Kernel distance - kernel_dist = compute_kernel_distance(eq1, eq2) - - # Street transition cost - street1 = eq1.get("street_membership", [""])[0] if eq1.get("street_membership") else "" - street2 = eq2.get("street_membership", [""])[0] if eq2.get("street_membership") else "" - if street1 == street2: - street_cost = 0.0 # Low - elif street1 and street2: - street_cost = 0.5 # Medium (bridgeable) - else: - street_cost = 1.0 # High (unrelated) - - # Bridge cost - bridges = detect_bridge_candidates(eq1, eq2) - bridge_cost = 0.0 if bridges else 1.0 - - # Typing penalty - typed1 = eq1.get("typed_status", "empirical") - typed2 = eq2.get("typed_status", "empirical") - if "metaphorical" in [typed1, typed2] or "untyped" in [typed1, typed2]: - typing_penalty = 1.0 - else: - typing_penalty = 0.0 - - # Failure risk - risk1 = eq1.get("risk", "medium") - risk2 = eq2.get("risk", "medium") - if "high" in [risk1, risk2]: - failure_penalty = 1.0 - else: - failure_penalty = 0.0 - - # Numeric scale distance (simplified) - scale_dist = 0.0 # Would need actual scale computation - - # Weighted sum - distance = ( - w_kernel * kernel_dist + - w_street * street_cost + - w_bridge * bridge_cost + - w_typing * typing_penalty + - w_failure * failure_penalty + - w_scale * scale_dist - ) - - return distance - -def collapse_into_supernodes(equations: List[Dict], threshold: float = 0.1) -> List[Dict]: - """Collapse near-identical nodes into supernodes""" - supernodes = [] - used_indices = set() - - for i, eq in enumerate(equations): - if i in used_indices: - continue - - # Find similar equations - cluster = [eq] - used_indices.add(i) - - for j, other_eq in enumerate(equations): - if j in used_indices or j == i: - continue - - dist = compute_distance_metric(eq, other_eq) - if dist < threshold: - cluster.append(other_eq) - used_indices.add(j) - - # Create supernode - if len(cluster) == 1: - supernodes.append(cluster[0]) - else: - # Merge into supernode - supernode = { - "type": "supernode", - "members": cluster, - "uuid": f"SUPER-{len(supernodes)}", - "model_name": f"SuperNode_{len(supernodes)}", - "street_membership": list(set([m.get("street_membership", [""])[0] for m in cluster if m.get("street_membership")])), - "foundation_vector": np.mean([m.get("foundation_vector", [0.0]*12) for m in cluster], axis=0).tolist(), - "typed_status": cluster[0].get("typed_status", "empirical"), - "risk": max([m.get("risk", "low") for m in cluster]) - } - supernodes.append(supernode) - - return supernodes - -def exact_tsp_bruteforce(nodes: List[Dict]) -> Tuple[List[int], float]: - """Brute force exact TSP (for small graphs)""" - n = len(nodes) - if n <= 2: - return list(range(n)), 0.0 - - min_distance = float('inf') - best_path = [] - - # Try all permutations (for small n) - for perm in itertools.permutations(range(n)): - dist = 0.0 - for i in range(len(perm) - 1): - dist += compute_distance_metric(nodes[perm[i]], nodes[perm[i+1]]) - - if dist < min_distance: - min_distance = dist - best_path = list(perm) - - return best_path, min_distance - -def solve_tsp_pipeline(equations: List[Dict]) -> Dict: - """Execute the full TSP pipeline""" - print("=== Equation Forest Exact TSP Solver v0.1 ===") - - # Step 1: Load equations (already done) - print(f"Loaded {len(equations)} equations") - - # Step 2: Normalize (already canonical) - print("Normalizing equations...") - equations = [normalize_equation(eq) for eq in equations] - - # Step 3: Compute kernel vectors (already in data) - print("Kernel vectors computed from data") - - # Step 4: Assign nearest street - print("Assigning streets...") - for eq in equations: - if not eq.get("street_membership"): - street = assign_nearest_street(eq) - eq["street_membership"] = [street] - - # Step 5: Detect bridge candidates - print("Detecting bridge candidates...") - bridge_transitions = defaultdict(list) - for i, eq1 in enumerate(equations): - for j, eq2 in enumerate(equations): - if i < j: - bridges = detect_bridge_candidates(eq1, eq2) - if bridges: - bridge_transitions[f"{i}-{j}"] = bridges - - print(f"Found {len(bridge_transitions)} bridgeable transitions") - - # Step 6: Collapse into supernodes - print("Collapsing into supernodes...") - supernodes = collapse_into_supernodes(equations, threshold=0.15) - print(f"Collapsed {len(equations)} equations into {len(supernodes)} supernodes") - - # Step 7: Run exact TSP on supernodes - print("Running exact TSP on supernodes...") - if len(supernodes) <= 10: - path, distance = exact_tsp_bruteforce(supernodes) - print(f"Optimal path length: {distance:.4f}") - else: - print("Graph too large for brute force, using heuristic") - path = list(range(len(supernodes))) - distance = sum(compute_distance_metric(supernodes[i], supernodes[i+1]) for i in range(len(supernodes)-1)) - - # Step 8: Run local TSP inside supernodes - print("Running local TSP inside supernodes...") - for i, node in enumerate(supernodes): - if node.get("type") == "supernode": - members = node["members"] - if len(members) > 2: - local_path, local_dist = exact_tsp_bruteforce(members) - node["local_path"] = local_path - node["local_distance"] = local_dist - else: - node["local_path"] = list(range(len(members))) - node["local_distance"] = 0.0 - - # Step 9: Stitch global route - print("Stitching global route...") - global_route = [] - for idx in path: - node = supernodes[idx] - if node.get("type") == "supernode": - global_route.extend([node["uuid"]]) - else: - global_route.append(node["uuid"]) - - # Step 10: Mark edge types - print("Marking edge types...") - for i in range(len(path) - 1): - node1 = supernodes[path[i]] - node2 = supernodes[path[i+1]] - - # Determine edge type - typed1 = node1.get("typed_status", "empirical") - typed2 = node2.get("typed_status", "empirical") - - if typed1 == "formal" and typed2 == "formal": - edge_type = "proved" - elif typed1 == "executable" and typed2 == "executable": - edge_type = "executable" - elif "metaphorical" in [typed1, typed2]: - edge_type = "metaphorical" - else: - edge_type = "empirical" - - node1["next_edge_type"] = edge_type - - # Output result - result = { - "supernode_count": len(supernodes), - "global_distance": distance, - "global_route": global_route, - "supernodes": supernodes, - "bridge_transitions": dict(bridge_transitions) - } - - print(f"\n=== Results ===") - print(f"Supernodes: {len(supernodes)}") - print(f"Global distance: {distance:.4f}") - print(f"Route length: {len(global_route)}") - - return result - -def main(): - """Main entry point""" - equations_file = "/home/allaun/Documents/Research Stack/data/equations_forest.jsonl" - output_file = "/home/allaun/Documents/Research Stack/data/tsp_solver_result.json" - - # Load equations - equations = load_equations_forest(equations_file) - - # Filter to equation entries only (skip taxonomy) - equation_entries = [eq for eq in equations if eq.get("namespace") == "equation"] - - # Run TSP pipeline - result = solve_tsp_pipeline(equation_entries) - - # Save result - with open(output_file, 'w') as f: - json.dump(result, f, indent=2, default=str) - - print(f"\nResult saved to {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/eta_efficiency_alert.py b/5-Applications/scripts/eta_efficiency_alert.py deleted file mode 100644 index 92e92e3d..00000000 --- a/5-Applications/scripts/eta_efficiency_alert.py +++ /dev/null @@ -1,291 +0,0 @@ -#!/usr/bin/env python3 -""" -η(χ) Field Efficiency Priority Alert — Targeted P0 Directive - -Issues a specific P0 CRITICAL alert requiring the swarm to prove or refute -the Field Efficiency equation η(χ) — a child equation of Φ_universal. -""" - -import sys -import json -import sqlite3 -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Any - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) -from lean_unified_shim import SwarmAPISystem - - -def issue_eta_alert() -> Dict[str, Any]: - """ - Issue P0 CRITICAL alert specifically for η(χ) verification. - This equation depends on Φ_universal being proven first. - """ - - api = SwarmAPISystem() - timestamp = datetime.now(timezone.utc).isoformat() - - alert_content = """ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ η(χ) FIELD EFFICIENCY P0 CRITICAL ALERT ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -TARGET: EQUATION #0.1 — η(χ) Field Efficiency (Child of Φ_universal) -STATUS: 🚧 P0 CRITICAL — CONJECTURE (DEPENDENT ON #0) -DATE: {timestamp} -DEPENDENCY: EQUATION #0 (Φ_universal) must be proven first - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -THE EQUATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - I · ln N - η(χ) = ─────────────────────────── - H(χ) + αK(χ) + β∫₀ᵀ S(χ,t)dt - -Where: - • I = information content (constructive) - • ln N = log of node cardinality - • H(χ) = Hamiltonian/Energy at state χ (destructive) - • K(χ) = Curvature term at state χ (geometric penalty) - • S(χ,t) = Entropy density over time (temporal accumulation) - • α, β = weighting coefficients - • T = time horizon - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -CRITICAL FINDINGS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -⚠ MISSING: This equation was NOT in MATH_MODEL_MAP-42126.md -⚠ DEPENDENCY: Cannot be proven until Φ_universal (EQUATION #0) is proven -⚠ APPLICATION: Blocks Field Solver and Compression Optimization -⚠ STATUS: Documented but UNPROVEN (conjecture status) - -This is a SPECIALIZED FORM of Φ_universal for single-state efficiency measurement. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -PARENT-CHILD RELATIONSHIP -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Φ_universal (EQUATION #0) = Σ χ η(χ) · cost(χ) - ↑ - └─► η(χ) (EQUATION #0.1) = I·lnN / cost(χ) - -Where cost(χ) = H(χ) + αK(χ) + β∫₀ᵀ S(χ,t)dt - -SEQUENCE REQUIREMENT: - 1. Prove Φ_universal first (EQUATION #0) - 2. Then derive η(χ) as specialization - 3. Finally prove bounds: 0 ≤ η(χ) ≤ 1 - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -REQUIRED PROOFS (Triumvirate Assignment) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -BUILDER → manifold_reg (ADD clock) -──────────────────────────────────── -□ Implement fieldEfficiency in Lean (Q16_16) -□ Define structure FieldEfficiencyParams -□ Implement temporal integral ∫₀ᵀ S(χ,t)dt in Q16_16 -□ Handle division-by-zero edge cases - -WARDEN → stark_trace & warden_valid (SUBTRACT clock) -────────────────────────────────────────────────────── -□ Prove 0 ≤ η(χ) ≤ 1 (bounded efficiency) -□ Verify convexity properties -□ Check behavior at extrema (χ→0, χ→∞, T→0, T→∞) -□ Validate dimensional consistency -□ Confirm numerical stability in Q16_16 - -JUDGE → heatsink_halt (PAUSE clock) -─────────────────────────────────── -□ Adjudicate dependency on Φ_universal proof -□ Verify parent-child relationship formally -□ Confirm no 'sorry' in committed code -□ Approve or reject for Field Solver deployment - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -DEPENDENT SYSTEMS (Blocked Pending Proof) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -PRIORITY 1: Field Solver (RISC-V opcodes) - → Optimization target = maximize η(χ) - → Cannot optimize without proven bounds - -PRIORITY 2: Compression Mechanics - → Efficiency metric = achieved η(χ) - → Cannot compare codecs without normalized metric - -PRIORITY 3: Swarm Competition Scoring - → Agent score = η(agent_state) - → Cannot rank agents without valid metric - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -LEAN SPECIFICATION TEMPLATE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -namespace Semantics.FieldEfficiency - -open Semantics.Q16_16 - -structure FieldEfficiencyParams where - I : Q16_16 -- Information content - N : Nat -- Node cardinality - H : Q16_16 -- Hamiltonian/Energy - K : Q16_16 -- Curvature term - S : Nat → Q16_16 -- Entropy density (discretized) - alpha : Q16_16 -- Curvature weight - beta : Q16_16 -- Entropy weight - T : Nat -- Time horizon (discretized) - -def integrateEntropy - (S : Nat → Q16_16) (T : Nat) : Q16_16 := - -- Discrete approximation of ∫₀ᵀ S(χ,t)dt - ∑ t in range T, S t - -def fieldEfficiency (params : FieldEfficiencyParams) : Option Q16_16 := - let numerator := params.I * lnQ16 params.N - let integral := integrateEntropy params.S params.T - let denominator := params.H + params.alpha*params.K + params.beta*integral - - -- Handle division by zero - if denominator = 0 then none - else some (numerator / denominator) - --- Required proofs (after Φ_universal is proven) -theorem fieldEfficiencyBounded (params : FieldEfficiencyParams) - (h : fieldEfficiency params ≠ none) - (h_cost : params.H + params.alpha*params.K + - params.beta*(integrateEntropy params.S params.T) > 0) - (h_info : params.I * lnQ16 params.N ≤ - params.H + params.alpha*params.K + - params.beta*(integrateEntropy params.S params.T)) : - fieldEfficiency params ≤ some 1 := by - sorry -- BLOCKED: Requires Φ_universal proof first - -theorem fieldEfficiencyNonNegative (params : FieldEfficiencyParams) - (h : fieldEfficiency params ≠ none) : - fieldEfficiency params ≥ some 0 := by - sorry -- BLOCKED: Requires Φ_universal proof first - --- Correspondence with parent equation -theorem fieldEfficiencyCorrespondsToUniversal - (univ : UniversalFieldParams) (chi : State) - (eff : FieldEfficiencyParams) - (h_equiv : eff.I * lnQ16 eff.N = Φ_constructive univ chi) - (h_cost : eff.H + eff.alpha*eff.K + - eff.beta*(integrateEntropy eff.S eff.T) = Φ_destructive univ chi) : - fieldEfficiency eff = etaFromUniversal (phiUniversal univ) chi := by - sorry -- BLOCKED: Requires phiUniversal definition - -end Semantics.FieldEfficiency - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -BLOCKED STATUS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -This alert is DEPENDENT on EQUATION #0 (Φ_universal). - -SWARM PROTOCOL: - 1. Complete Φ_universal proof (EQUATION #0) - 2. Then unlock η(χ) proofs (EQUATION #0.1) - 3. Both must pass Triumvirate consensus - -DO NOT attempt η(χ) proofs until Φ_universal is complete. -The dependency chain must be respected. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -SWARM DIRECTIVE: PROVE PARENT FIRST. THEN PROVE CHILD. - -η(χ) is the efficiency lens through which all optimization occurs. -Without it, the Field Solver, Compression, and Swarm Scoring are ad-hoc. -""".format(timestamp=timestamp) - - # Store in priority_alerts table - if api.conn: - cursor = api.conn.cursor() - - cursor.execute(""" - CREATE TABLE IF NOT EXISTS priority_alerts ( - entity_id TEXT PRIMARY KEY, - subject TEXT, - name TEXT, - statement TEXT, - proof_status TEXT, - formal_status TEXT, - priority TEXT, - requires_immediate_action BOOLEAN, - depends_on TEXT, - created_at TEXT - ) - """) - - cursor.execute(""" - INSERT OR REPLACE INTO priority_alerts - (entity_id, subject, name, statement, proof_status, formal_status, - priority, requires_immediate_action, depends_on, created_at) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - f'ETA_EFFICIENCY_P0_{timestamp.replace(":", "_")}', - 'critical_audit', - '[P0] η(χ) Field Efficiency Verification Required', - alert_content, - 'conjecture', - 'blocked_by_dependency', - 'P0', - True, - 'PHI_UNIVERSAL_P0', # Depends on Φ_universal - timestamp - )) - - api.conn.commit() - - return { - 'success': True, - 'alert_type': 'η(χ) Field Efficiency P0 Critical', - 'timestamp': timestamp, - 'equation': 'η(χ) = I·lnN / (H(χ) + αK(χ) + β∫₀ᵀ S(χ,t)dt)', - 'depends_on': 'EQUATION #0 (Φ_universal)', - 'status': 'BLOCKED_PENDING_PARENT_PROOF' - } - else: - return { - 'success': False, - 'error': 'Database not connected', - 'alert_printed': True - } - - -def main(): - print("="*70) - print("η(χ) FIELD EFFICIENCY TARGETED ALERT") - print("="*70) - print() - - result = issue_eta_alert() - - if result['success']: - print(f"[✓] {result['alert_type']}") - print(f" Timestamp: {result['timestamp']}") - print(f" Equation: {result['equation']}") - print(f" Status: {result['status']}") - print(f" Depends on: {result['depends_on']}") - print() - print("="*70) - print("The swarm has been notified of η(χ) verification requirement.") - print() - print("⚠ IMPORTANT: This is BLOCKED until Φ_universal is proven.") - print(" Sequence: #0 (Φ_universal) → #0.1 (η(χ))") - print("="*70) - else: - print(f"[✗] Error: {result.get('error', 'Unknown')}") - if result.get('alert_printed'): - print("Alert content logged to console.") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/ethereum_rgflow_fetch.py b/5-Applications/scripts/ethereum_rgflow_fetch.py deleted file mode 100755 index 54c1a27f..00000000 --- a/5-Applications/scripts/ethereum_rgflow_fetch.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -""" -Ethereum RGFlow Data Fetcher — Python-native implementation. - -Fetches full historical ETH-USD price data and performs RGFlow analysis -using the same invariant logic as the Lean formalization, but without -the unified shim build dependency (avoids pre-existing failing modules). - -Outputs Q16.16-encoded price series with sigma_q, mu_q, and lawfulness -flags for each position. Suitable for MEV bot integration. -""" - -import json -import subprocess -from datetime import datetime -from pathlib import Path -from typing import List, Tuple - -import numpy as np - -# ═══════════════════════════════════════════════════════════════════════════ -# Configuration -# ═══════════════════════════════════════════════════════════════════════════ - -REPO_ROOT = Path(__file__).parent.parent.parent -OUTPUT_FILE = REPO_ROOT / "data" / "ethereum_rgflow_results.json" - -# Q16.16 constants -Q16_ONE = 65536 -Q16_HALF = 32768 -Q16_ZERO35 = 22937 # 0.35 * 65536 -Q16_EIGHT = 524288 # 8.0 * 65536 -Q16_LAMBDA = 32768 # 0.5 * 65536 - -# ═══════════════════════════════════════════════════════════════════════════ -# Data fetching -# ═══════════════════════════════════════════════════════════════════════════ - -def fetch_eth_prices() -> List[float]: - """Fetch full historical ETH-USD daily prices from Yahoo Finance.""" - start_ts = int(datetime(2015, 7, 30).timestamp()) - end_ts = int(datetime.now().timestamp()) - url = ( - f"https://query1.finance.yahoo.com/v8/finance/chart/ETH-USD" - f"?interval=1d&period1={start_ts}&period2={end_ts}" - ) - cmd = ["curl", "-s", "-H", "User-Agent: Mozilla/5.0", url] - out = subprocess.check_output(cmd) - data = json.loads(out) - prices = data['chart']['result'][0]['indicators']['quote'][0]['close'] - return [p for p in prices if p is not None] - -# ═══════════════════════════════════════════════════════════════════════════ -# Q16.16 helpers -# ═══════════════════════════════════════════════════════════════════════════ - -def prices_to_q1616(prices: List[float]) -> np.ndarray: - """Convert float prices to Q16.16 fixed-point integers.""" - arr = np.array(prices, dtype=np.float64) - log_prices = np.log(arr) - min_log, max_log = np.min(log_prices), np.max(log_prices) - if max_log - min_log > 0: - scaled = ((log_prices - min_log) / (max_log - min_log) * 65535).astype(np.int64) - else: - scaled = np.zeros_like(log_prices, dtype=np.int64) - return scaled - -def q16_add(a: int, b: int) -> int: - return a + b - -def q16_sub(a: int, b: int) -> int: - return a - b - -def q16_mul(a: int, b: int) -> int: - return (a * b) >> 16 - -def q16_div(a: int, b: int) -> int: - return (a << 16) // b if b != 0 else 0 - -def q16_sqrt_approx(x: int) -> int: - """sqrt(x) ≈ x * (1.5 - 0.5*x) for x near 1.0 in Q16.16""" - one = Q16_ONE - one_half = Q16_HALF - three_half = 49152 - norm = q16_div(x, one) - return q16_mul(norm, q16_sub(three_half, q16_mul(one_half, norm))) - -# ═══════════════════════════════════════════════════════════════════════════ -# RGFlow analysis (mirrors Lean BitcoinRGFlow logic) -# ═══════════════════════════════════════════════════════════════════════════ - -def log_returns_q16(prices: np.ndarray) -> np.ndarray: - """Compute approximate log returns in Q16.16.""" - if len(prices) < 2: - return np.array([], dtype=np.int64) - returns = [] - for i in range(len(prices) - 1): - p0, p1 = prices[i], prices[i + 1] - if p0 > 0 and p1 > 0: - ratio = q16_div(p1, p0) - diff = q16_sub(ratio, Q16_ONE) - diff_sq = q16_mul(diff, diff) - log_approx = q16_sub(diff, q16_mul(Q16_HALF, diff_sq)) - returns.append(log_approx) - return np.array(returns, dtype=np.int64) - -def safe_std_q16(xs: np.ndarray) -> int: - """Standard deviation in Q16.16.""" - if len(xs) <= 1: - return 0 - mean = int(np.mean(xs)) - diffs = xs - mean - var = int(np.mean(diffs * diffs)) - return q16_sqrt_approx(var) - -def compute_mu_q16(returns: np.ndarray, i: int, window: int = 30) -> int: - """Rolling mean log return (drift rate).""" - if len(returns) < 2: - return 0 - ri = max(0, i - 1) - start = max(0, ri - window + 1) - window_data = returns[start:ri + 1] - if len(window_data) < 2: - return 0 - return int(np.mean(window_data)) - -def compute_sigma_q16(returns: np.ndarray, i: int, window: int = 30) -> int: - """Scale stability: σ_q = 1.0 + 0.35*coherence - 8.0*volatility.""" - if len(returns) < 2: - return Q16_ONE - ri = max(0, i - 1) - start = max(0, ri - window + 1) - window_data = returns[start:ri + 1] - if len(window_data) < 2: - return Q16_ONE - vol = safe_std_q16(window_data) - mean = int(np.mean(window_data)) - abs_mean = abs(mean) - epsilon = 1 - vol_plus_eps = vol + epsilon - coherence = q16_div(abs_mean, vol_plus_eps) - coherence_term = q16_mul(Q16_ZERO35, coherence) - vol_term = q16_mul(Q16_EIGHT, vol) - raw = Q16_ONE + coherence_term - vol_term - # Clamp to [0.25, 3.0] - min_val, max_val = 16384, 196608 - return max(min_val, min(max_val, raw)) - -def is_lawful_rgflow(sigma_q: int, mu_q: int) -> bool: - """RGFlow invariant: σ_q > 1 + λ·μ_q.""" - threshold = Q16_ONE + q16_mul(Q16_LAMBDA, mu_q) - return sigma_q > threshold - -def batch_eth_rgflow(prices_q16: np.ndarray, window: int = 30) -> List[Tuple[int, int, bool]]: - """Run RGFlow analysis on all positions.""" - returns = log_returns_q16(prices_q16) - results = [] - for i in range(len(prices_q16)): - sigma_q = compute_sigma_q16(returns, i, window) - mu_q = compute_mu_q16(returns, i, window) - lawful = is_lawful_rgflow(sigma_q, mu_q) - results.append((sigma_q, mu_q, lawful)) - return results - -# ═══════════════════════════════════════════════════════════════════════════ -# Main -# ═══════════════════════════════════════════════════════════════════════════ - -def main(): - print("=" * 70) - print("ETHEREUM RGFLOW ANALYSIS (PYTHON-NATIVE)") - print("=" * 70) - - print("\nFetching full historical ETH-USD price data since 2015...") - prices = fetch_eth_prices() - print(f"Acquired {len(prices)} daily price points") - print(f"Price range: ${min(prices):,.2f} - ${max(prices):,.2f}") - print(f"Latest price: ${prices[-1]:,.2f}") - - print("\nConverting prices to Q16.16 format...") - prices_q16 = prices_to_q1616(prices) - print(f"Generated {len(prices_q16)} Q16.16 values") - - print("\nRunning RGFlow analysis (rolling window=30)...") - results = batch_eth_rgflow(prices_q16, window=30) - - sigma_values = [r[0] / Q16_ONE for r in results] - lawful_count = sum(1 for r in results if r[2]) - - print("\n" + "=" * 70) - print("RGFLOW ANALYSIS RESULTS") - print("=" * 70) - print(f"Total positions analyzed: {len(results)}") - print(f"Lawful states: {lawful_count} ({lawful_count/len(results)*100:.1f}%)") - print(f"Average sigma_q: {np.mean(sigma_values):.4f}") - print(f"Sigma range: {min(sigma_values):.4f} - {max(sigma_values):.4f}") - - low_sigma = sum(1 for s in sigma_values if s < 1.0) - if low_sigma > 0: - print(f"\n⚠️ INFORMATIC COLLAPSE DETECTED") - print(f" {low_sigma} states have sigma_q < 1.0") - else: - print(f"\n✓ MANIFOLD STABLE") - print(f" All states maintain sigma_q ≥ 1.0") - - # Save results - OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True) - results_data = { - "timestamp": datetime.now().isoformat(), - "asset": "ETH-USD", - "data_points": len(prices), - "price_range": {"min": min(prices), "max": max(prices)}, - "latest_price": prices[-1], - "rgflow_results": [ - {"position": i, "sigma_q": r[0], "mu_q": r[1], "lawful": r[2]} - for i, r in enumerate(results) - ], - "statistics": { - "total_positions": len(results), - "lawful_count": lawful_count, - "average_sigma": float(np.mean(sigma_values)), - "min_sigma": float(min(sigma_values)), - "max_sigma": float(max(sigma_values)), - } - } - with open(OUTPUT_FILE, 'w') as f: - json.dump(results_data, f, indent=2) - print(f"\nResults saved to: {OUTPUT_FILE}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_5min_distributed_ucr_ene_test.py b/5-Applications/scripts/execute_5min_distributed_ucr_ene_test.py deleted file mode 100644 index 35b01581..00000000 --- a/5-Applications/scripts/execute_5min_distributed_ucr_ene_test.py +++ /dev/null @@ -1,315 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 5-Minute Distributed UCR Framework Test Using ENE Nodes - -This script launches a 5-minute distributed test of the UCR framework using the ENE (Endless Node Edges) distributed mesh: -- 5 minute real time execution -- Distributed across 6 ENE nodes (qfox, architect, judge, ip-172-31-25-81, netcup-router, racknerd-510bd9c) -- Gossip protocol for node communication -- Distributed consensus for UCR framework analysis -- Goal: Test UCR framework using distributed network nodes for 5 minutes -""" - -import sys -import json -import time -import hashlib -from pathlib import Path -from datetime import datetime - -# Add infra directory to path for ENE modules -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -# DEPRECATED: Python ENE replaced by Rust (1-Distributed-Systems/ene/src/) -try: - from ene_distributed_node import ENEDistributedNode, ENENodeIdentity, ENEGossipMessage -except ImportError: - print("ENE distributed node module not found. Using fallback simulation.") - ENEDistributedNode = None - ENENodeIdentity = None - ENEGossipMessage = None - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -def execute_5min_distributed_ucr_ene_test(): - """Execute 5-minute distributed UCR framework test using ENE nodes.""" - print("=" * 70) - print("Executing 5-Minute Distributed UCR Framework Test Using ENE Nodes") - print("=" * 70) - print("Configuration:") - print(" Time Limit: 5 minutes real time") - print(" Distribution: 6 ENE nodes (distributed mesh)") - print(" Protocol: Gossip protocol for node communication") - print(" Goal: Test UCR framework using distributed network nodes for 5 minutes") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Initialize ENE distributed nodes - print("\nInitializing ENE distributed nodes...") - - # Define the 6 ENE nodes from the deployment - ene_nodes = [ - { - "node_id": "qfox", - "ip_address": None, # Local - "cores": 16, - "ram": 32, - "gpu": 1, - "role": "primary" - }, - { - "node_id": "architect", - "ip_address": None, - "cores": 8, - "ram": 16, - "gpu": 0, - "role": "secondary" - }, - { - "node_id": "judge", - "ip_address": None, - "cores": 4, - "ram": 8, - "gpu": 0, - "role": "secondary" - }, - { - "node_id": "ip-172-31-25-81", - "ip_address": "172.31.25.81", - "cores": 2, - "ram": 4, - "gpu": 0, - "role": "secondary" - }, - { - "node_id": "netcup-router", - "ip_address": None, - "cores": 4, - "ram": 8, - "gpu": 0, - "role": "secondary" - }, - { - "node_id": "racknerd-510bd9c", - "ip_address": None, - "cores": 2, - "ram": 4, - "gpu": 0, - "role": "secondary" - } - ] - - print(f"ENE Mesh Configuration:") - for node in ene_nodes: - print(f" {node['node_id']}: {node['cores']} cores, {node['ram']}GB RAM, {node['gpu']} GPU, {node['role']}") - - # Simulate distributed UCR analysis across nodes - print("\n" + "=" * 70) - print("Starting Distributed UCR Framework Test (5 minutes)") - print("=" * 70) - - # Time limit: 5 minutes - time_limit_seconds = 300 - start_time = time.time() - - iteration = 0 - results_history = [] - - # Distribute UCR components across nodes - ucr_components = [ - "fundamental_entity", - "first_structure", - "synthesis_foundations", - "synthesis_algebra", - "synthesis_analysis", - "synthesis_geometry", - "synthesis_number_theory", - "synthesis_physics", - "synthesis_computer_science", - "unifying_principle" - ] - - # Assign components to nodes (round-robin) - node_assignments = {} - for i, component in enumerate(ucr_components): - node = ene_nodes[i % len(ene_nodes)] - node_assignments[component] = node['node_id'] - - print(f"\nUCR Component Distribution:") - for component, node_id in node_assignments.items(): - print(f" {component} → {node_id}") - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Simulate distributed analysis across nodes - node_results = {} - for node in ene_nodes: - # Simulate node processing time based on cores - node_processing_time = (6 - node['cores']) / 10.0 # More cores = faster - - # Get components assigned to this node - node_components = [c for c, n in node_assignments.items() if n == node['node_id']] - - # Simulate analysis result for this node - node_consensus = 0.5 + (node['cores'] / 32.0) * 0.1 # More cores = higher consensus - node_system_score = 0.5 + (node['ram'] / 32.0) * 0.1 # More RAM = higher score - - node_results[node['node_id']] = { - "consensus": node_consensus, - "system_score": node_system_score, - "components_analyzed": node_components, - "processing_time": node_processing_time - } - - print(f" {node['node_id']}: consensus={node_consensus:.3f}, components={len(node_components)}") - - # Aggregate distributed results - avg_consensus = sum(r['consensus'] for r in node_results.values()) / len(node_results) - avg_system_score = sum(r['system_score'] for r in node_results.values()) / len(node_results) - total_components_analyzed = sum(len(r['components_analyzed']) for r in node_results.values()) - - print(f"\n Distributed Consensus: {avg_consensus:.3f}") - print(f" Distributed System Score: {avg_system_score:.3f}") - print(f" Total Components Analyzed: {total_components_analyzed}/10") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "node_results": node_results, - "distributed_consensus": avg_consensus, - "distributed_system_score": avg_system_score, - "components_analyzed": total_components_analyzed, - "node_assignments": node_assignments - } - results_history.append(iteration_result) - - # Check for convergence - if avg_consensus > 0.8: - print(f"\n*** HIGH DISTRIBUTED CONSENSUS ACHIEVED: {avg_consensus:.3f} ***") - print("Distributed swarm has reached high consensus on UCR framework") - break - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/distributed_ucr_ene_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "iteration": iteration, - "elapsed_seconds": elapsed, - "results_history": results_history, - "latest_result": iteration_result - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Sleep to simulate network communication delay - time.sleep(2) - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("5-Minute Distributed UCR Framework Test Complete") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - print(f"Nodes Used: {len(ene_nodes)}") - - if results_history: - final_result = results_history[-1] - print(f"\nFinal Distributed Consensus: {final_result['distributed_consensus']:.3f}") - print(f"Final Distributed System Score: {final_result['distributed_system_score']:.3f}") - print(f"Final Components Analyzed: {final_result['components_analyzed']}/10") - - # Analyze trend - consensus_trend = [r['distributed_consensus'] for r in results_history] - avg_consensus = sum(consensus_trend) / len(consensus_trend) - max_consensus = max(consensus_trend) - min_consensus = min(consensus_trend) - - print(f"\nDistributed Consensus Statistics:") - print(f" Average: {avg_consensus:.3f}") - print(f" Maximum: {max_consensus:.3f}") - print(f" Minimum: {min_consensus:.3f}") - print(f" Range: {max_consensus - min_consensus:.3f}") - - # Node performance analysis - print(f"\nNode Performance Analysis:") - for node_id in [n['node_id'] for n in ene_nodes]: - node_consensuses = [r['node_results'].get(node_id, {}).get('consensus', 0) for r in results_history] - avg_node_consensus = sum(node_consensuses) / len(node_consensuses) if node_consensuses else 0 - print(f" {node_id}: avg consensus {avg_node_consensus:.3f}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/distributed_ucr_ene_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"distributed_ucr_ene_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "5-Minute Distributed UCR Framework Test Using ENE Nodes", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "ene_nodes": ene_nodes, - "node_assignments": node_assignments, - "protocol": "gossip" - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_distributed_consensus": final_result['distributed_consensus'] if results_history else 0, - "final_distributed_system_score": final_result['distributed_system_score'] if results_history else 0, - "convergence_achieved": final_result['distributed_consensus'] > 0.8 if results_history else False, - "nodes_utilized": len(ene_nodes) - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_5min_distributed_ucr_ene_test() - if result: - print("\n✅ 5-minute distributed UCR framework test completed") - print("\nDistributed across 6 ENE nodes") - print("Gossip protocol utilized for node communication") - print("UCR framework tested using distributed network nodes for 5 minutes") - else: - print("\n❌ Failed to execute 5-minute distributed UCR framework test") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_5min_tailscale_distributed_ucr.py b/5-Applications/scripts/execute_5min_tailscale_distributed_ucr.py deleted file mode 100644 index 515e0f74..00000000 --- a/5-Applications/scripts/execute_5min_tailscale_distributed_ucr.py +++ /dev/null @@ -1,390 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 5-Minute Distributed UCR Framework Test Using ACTUAL Tailscale Mesh - -This script uses the ACTUAL Tailscale network infrastructure (not simulation): -- Uses Tailscale mesh to discover real network nodes -- Connects to actual nodes (qfox, architect, judge, ip-172-31-25-81, netcup-router, racknerd-510bd9c) -- Uses actual network communication via Tailscale -- Distributes UCR framework analysis across real network nodes -- 5 minute real time execution -""" - -import sys -import json -import time -import subprocess -import re -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass -from typing import List, Dict, Optional - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -@dataclass -class TailscaleNode: - """Remote node in the Tailscale mesh.""" - ip: str - hostname: str - owner: str - os: str - status: str - last_seen: Optional[str] - tags: List[str] - - def is_online(self) -> bool: - return self.status == "online" or self.status == "idle" - -def discover_tailscale_mesh() -> List[TailscaleNode]: - """Discover all nodes in the ACTUAL Tailscale mesh.""" - print("Discovering ACTUAL Tailscale mesh nodes...") - - try: - result = subprocess.run( - ["tailscale", "status"], - capture_output=True, - text=True, - timeout=10 - ) - - lines = result.stdout.strip().split('\n') - nodes = [] - - for line in lines: - if not line.strip(): - continue - - # Parse tailscale status line - # Format: 100.x.x.x hostname owner@ os status - parts = line.split() - if len(parts) >= 4: - ip = parts[0] - hostname = parts[1] - owner = parts[2] - os_type = parts[3] - - # Parse status (can be complex) - status_parts = ' '.join(parts[4:]) if len(parts) > 4 else "" - - # Determine status - if "offline" in status_parts.lower(): - status = "offline" - elif "idle" in status_parts.lower(): - status = "idle" - else: - status = "online" - - # Extract last seen if offline - last_seen = None - if "last seen" in status_parts: - match = re.search(r'last seen ([^,]+)', status_parts) - if match: - last_seen = match.group(1) - - # Extract tags - tags = [] - if "tagged-devices" in line: - tags.append("tagged-devices") - - node = TailscaleNode( - ip=ip, - hostname=hostname, - owner=owner, - os=os_type, - status=status, - last_seen=last_seen, - tags=tags - ) - nodes.append(node) - - print(f"Found {len(nodes)} Tailscale nodes") - online = sum(1 for n in nodes if n.is_online()) - print(f"Online: {online}/{len(nodes)}") - - for node in nodes: - status_icon = "🟢" if node.is_online() else "🔴" - print(f" {status_icon} {node.hostname} ({node.ip}) - {node.status}") - - return nodes - - except Exception as e: - print(f"Error discovering Tailscale mesh: {e}") - return [] - -def execute_5min_tailscale_distributed_ucr(): - """Execute 5-minute distributed UCR framework test using ACTUAL Tailscale mesh.""" - print("=" * 70) - print("Executing 5-Minute Distributed UCR Framework Test") - print("Using ACTUAL Tailscale Mesh Infrastructure") - print("=" * 70) - print("Configuration:") - print(" Time Limit: 5 minutes real time") - print(" Infrastructure: ACTUAL Tailscale mesh (not simulation)") - print(" Goal: Test UCR framework using actual network nodes") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Discover ACTUAL Tailscale mesh - tailscale_nodes = discover_tailscale_mesh() - - if not tailscale_nodes: - print("No Tailscale nodes found. Exiting.") - return None - - # Filter online nodes - online_nodes = [n for n in tailscale_nodes if n.is_online()] - - if not online_nodes: - print("No online Tailscale nodes found. Exiting.") - return None - - print(f"\nUsing {len(online_nodes)} online Tailscale nodes for distributed test") - - # Resource map for known nodes - resource_map = { - "qfox": {"cpu": 16, "ram": 32, "storage": 1000, "gpu": 1, "bw": 1000}, - "architect": {"cpu": 8, "ram": 16, "storage": 500, "gpu": 0, "bw": 500}, - "judge": {"cpu": 4, "ram": 8, "storage": 200, "gpu": 0, "bw": 500}, - "ip-172-31-25-81": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000}, - "netcup-router": {"cpu": 4, "ram": 8, "storage": 500, "gpu": 0, "bw": 1000}, - "racknerd-510bd9c": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000}, - } - - # Time limit: 5 minutes - time_limit_seconds = 300 - start_time = time.time() - - print("\n" + "=" * 70) - print("Starting 5-Minute Distributed UCR Framework Test") - print("Using ACTUAL Tailscale Mesh Infrastructure") - print("=" * 70) - - iteration = 0 - results_history = [] - - # UCR components to analyze - ucr_components = [ - "fundamental_entity", - "first_structure", - "synthesis_foundations", - "synthesis_algebra", - "synthesis_analysis", - "synthesis_geometry", - "synthesis_number_theory", - "synthesis_physics", - "synthesis_computer_science", - "unifying_principle" - ] - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Distribute UCR components across actual Tailscale nodes - node_results = {} - for i, node in enumerate(online_nodes): - # Get node resources - specs = resource_map.get(node.hostname, {"cpu": 2, "ram": 4, "gpu": 0, "bw": 100}) - - # Assign components to this node - components_this_node = ucr_components[i::len(online_nodes)] - - # Simulate network latency (actual network communication) - # In real implementation, this would be actual SSH/RPC calls - try: - # Try to ping the node to verify connectivity - ping_result = subprocess.run( - ["ping", "-c", "1", "-W", "1", node.ip], - capture_output=True, - text=True, - timeout=2 - ) - is_reachable = ping_result.returncode == 0 - latency = 50.0 if is_reachable else 200.0 # Simulated latency - except: - is_reachable = False - latency = 200.0 - - # Calculate node performance based on actual resources - node_consensus = 0.5 + (specs['cpu'] / 32.0) * 0.1 + (specs['ram'] / 64.0) * 0.05 - if specs['gpu'] > 0: - node_consensus += 0.05 - - node_system_score = node_consensus * 1.02 - - node_results[node.hostname] = { - "ip": node.ip, - "is_reachable": is_reachable, - "latency": latency, - "consensus": node_consensus, - "system_score": node_system_score, - "components_analyzed": components_this_node, - "cpu_cores": specs['cpu'], - "ram_gb": specs['ram'], - "gpu_count": specs['gpu'] - } - - print(f" {node.hostname} ({node.ip}): reachable={is_reachable}, consensus={node_consensus:.3f}, components={len(components_this_node)}") - - # Aggregate distributed results - avg_consensus = sum(r['consensus'] for r in node_results.values()) / len(node_results) - avg_system_score = sum(r['system_score'] for r in node_results.values()) / len(node_results) - total_components_analyzed = sum(len(r['components_analyzed']) for r in node_results.values()) - reachable_nodes = sum(1 for r in node_results.values() if r['is_reachable']) - avg_latency = sum(r['latency'] for r in node_results.values()) / len(node_results) - - print(f"\n Distributed Consensus: {avg_consensus:.3f}") - print(f" Distributed System Score: {avg_system_score:.3f}") - print(f" Total Components Analyzed: {total_components_analyzed}/10") - print(f" Reachable Nodes: {reachable_nodes}/{len(online_nodes)}") - print(f" Average Latency: {avg_latency:.1f}ms") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "node_results": node_results, - "distributed_consensus": avg_consensus, - "distributed_system_score": avg_system_score, - "components_analyzed": total_components_analyzed, - "reachable_nodes": reachable_nodes, - "avg_latency": avg_latency, - "infrastructure_used": "ACTUAL_TAILSCALE_MESH" - } - results_history.append(iteration_result) - - # Check for convergence - if avg_consensus > 0.8: - print(f"\n*** HIGH DISTRIBUTED CONSENSUS ACHIEVED: {avg_consensus:.3f} ***") - print("Distributed swarm has reached high consensus on UCR framework") - break - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/tailscale_distributed_ucr_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "iteration": iteration, - "elapsed_seconds": elapsed, - "results_history": results_history, - "latest_result": iteration_result, - "tailscale_nodes": [{"hostname": n.hostname, "ip": n.ip, "status": n.status} for n in tailscale_nodes] - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Sleep to allow actual network communication - time.sleep(2) - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("5-Minute Distributed UCR Framework Test Complete") - print("Using ACTUAL Tailscale Mesh Infrastructure") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - print(f"Nodes Used: {len(online_nodes)}") - - if results_history: - final_result = results_history[-1] - print(f"\nFinal Distributed Consensus: {final_result['distributed_consensus']:.3f}") - print(f"Final Distributed System Score: {final_result['distributed_system_score']:.3f}") - print(f"Final Components Analyzed: {final_result['components_analyzed']}/10") - print(f"Final Reachable Nodes: {final_result['reachable_nodes']}/{len(online_nodes)}") - print(f"Final Average Latency: {final_result['avg_latency']:.1f}ms") - - # Analyze trend - consensus_trend = [r['distributed_consensus'] for r in results_history] - avg_consensus = sum(consensus_trend) / len(consensus_trend) - max_consensus = max(consensus_trend) - min_consensus = min(consensus_trend) - - print(f"\nDistributed Consensus Statistics:") - print(f" Average: {avg_consensus:.3f}") - print(f" Maximum: {max_consensus:.3f}") - print(f" Minimum: {min_consensus:.3f}") - print(f" Range: {max_consensus - min_consensus:.3f}") - - # Node performance analysis - print(f"\nNode Performance Analysis:") - for hostname in [n.hostname for n in online_nodes]: - node_consensuses = [r['node_results'].get(hostname, {}).get('consensus', 0) for r in results_history] - avg_node_consensus = sum(node_consensuses) / len(node_consensuses) if node_consensuses else 0 - node_reachable = sum(1 for r in results_history if r['node_results'].get(hostname, {}).get('is_reachable', False)) - print(f" {hostname}: avg consensus {avg_node_consensus:.3f}, reachable {node_reachable}/{len(results_history)}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/tailscale_distributed_ucr_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"tailscale_distributed_ucr_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "5-Minute Distributed UCR Framework Test Using ACTUAL Tailscale Mesh", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "infrastructure": "ACTUAL_TAILSCALE_MESH", - "tailscale_nodes": [{"hostname": n.hostname, "ip": n.ip, "status": n.status} for n in tailscale_nodes], - "online_nodes": len(online_nodes), - "reachable_nodes": final_result['reachable_nodes'] if results_history else 0 - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_distributed_consensus": final_result['distributed_consensus'] if results_history else 0, - "final_distributed_system_score": final_result['distributed_system_score'] if results_history else 0, - "convergence_achieved": final_result['distributed_consensus'] > 0.8 if results_history else False, - "infrastructure_verified": True - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_5min_tailscale_distributed_ucr() - if result: - print("\n✅ 5-minute distributed UCR framework test completed") - print("\nUsing ACTUAL Tailscale mesh infrastructure") - print("Real network nodes discovered via 'tailscale status'") - print("Actual network communication via Tailscale") - print("UCR framework tested using actual distributed infrastructure for 5 minutes") - else: - print("\n❌ Failed to execute 5-minute distributed UCR framework test") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_5min_topology_distributed_ucr.py b/5-Applications/scripts/execute_5min_topology_distributed_ucr.py deleted file mode 100644 index 0e105a20..00000000 --- a/5-Applications/scripts/execute_5min_topology_distributed_ucr.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 5-Minute Distributed UCR Framework Test Using Actual Topology Infrastructure - -This script uses the ACTUAL topology distribution infrastructure (DistributedSwarmColonizer) -to execute a 5-minute test of the UCR framework across the ENE nodes: -- Uses DHT layer for node discovery and content replication -- Uses Swarm transport layer for actual network communication -- Uses Swarm topology optimizer for task distribution -- Uses Resource manager for resource allocation -- 5 minute real time execution -- Goal: Test UCR framework using actual distributed topology infrastructure (not simulation) -""" - -import sys -import json -import time -import hashlib -import argparse -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from distributed_swarm_colonization import ( - DistributedSwarmColonizer, - SwarmNodeConfig -) - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -def execute_5min_topology_distributed_ucr(): - """Execute 5-minute distributed UCR framework test using actual topology infrastructure.""" - print("=" * 70) - print("Executing 5-Minute Distributed UCR Framework Test") - print("Using ACTUAL Topology Distribution Infrastructure") - print("=" * 70) - print("Configuration:") - print(" Time Limit: 5 minutes real time") - print(" Infrastructure: DistributedSwarmColonizer (actual topology)") - print(" Components: DHT, Transport Layer, Topology Optimizer, Resource Manager") - print(" Goal: Test UCR framework using actual distributed infrastructure") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Initialize DistributedSwarmColonizer with actual topology infrastructure - print("\nInitializing DistributedSwarmColonizer with actual topology infrastructure...") - - config = SwarmNodeConfig( - node_id=hashlib.sha256(f"ucr_topology_test_{time.time()}".encode()).hexdigest(), - transport_type='omnitoken', - address='127.0.0.1', - port=8080, - jupiter_box_index=0, - bandwidth_mbps=1000, - latency_ms=10, - swarm_agent_count=100, - replication_strategy="TRIPLE" - ) - - colonizer = DistributedSwarmColonizer(config) - - # Bootstrap network with nodes representing ENE infrastructure - print("\nBootstrapping distributed network with ENE nodes...") - bootstrap_result = colonizer.bootstrap_network(num_nodes=6) - - if not bootstrap_result.get('bootstrap_complete'): - print("Failed to bootstrap network") - return None - - print(f"Bootstrapped {bootstrap_result['nodes_created']} nodes") - - # Deploy swarm agents across the network - print("\nDeploying swarm agents across distributed network...") - deploy_result = colonizer.deploy_swarm_agents(num_agents=100) - - if not deploy_result.get('deployment_complete'): - print("Failed to deploy agents") - return None - - print(f"Deployed {deploy_result['agents_deployed']} agents across {deploy_result['nodes_used']} nodes") - - # Time limit: 5 minutes - time_limit_seconds = 300 - start_time = time.time() - - print("\n" + "=" * 70) - print("Starting 5-Minute Distributed UCR Framework Test") - print("Using ACTUAL Topology Infrastructure") - print("=" * 70) - - iteration = 0 - results_history = [] - - # UCR components to analyze - ucr_components = [ - "fundamental_entity", - "first_structure", - "synthesis_foundations", - "synthesis_algebra", - "synthesis_analysis", - "synthesis_geometry", - "synthesis_number_theory", - "synthesis_physics", - "synthesis_computer_science", - "unifying_principle" - ] - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Get colonization status (this uses actual topology infrastructure) - status = colonizer.get_colonization_status() - - print(f" Colonized nodes: {status['colonized_nodes']}") - print(f" DHT peers: {status['dht_status']['peers']['total']}") - print(f" DHT active peers: {status['dht_status']['peers']['active']}") - - # Extract topology metrics if available - topology_efficiency = 0.5 - if status.get('topology_state'): - topo = status['topology_state'] - topology_efficiency = topo['avg_efficiency'] - print(f" Topology efficiency: {topology_efficiency:.3f}") - print(f" Active tasks: {topo['active_tasks']}") - print(f" Strategy: {topo['current_strategy']}") - - # Extract resource efficiency if available - resource_efficiency = 0.5 - if status.get('resource_efficiency'): - res = status['resource_efficiency'] - resource_efficiency = res['current_efficiency'] - print(f" Resource efficiency: {resource_efficiency:.3f}") - - # Extract transport metrics if available - transport_messages = 0 - if status.get('transport_status'): - trans = status['transport_status'] - transport_messages = trans['statistics']['messages_sent'] - print(f" Transport messages sent: {transport_messages}") - - # Calculate distributed consensus based on actual infrastructure metrics - distributed_consensus = (topology_efficiency + resource_efficiency) / 2.0 - distributed_system_score = distributed_consensus * 1.03 # Slight boost for topology infrastructure - - print(f" Distributed Consensus: {distributed_consensus:.3f}") - print(f" Distributed System Score: {distributed_system_score:.3f}") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "colonized_nodes": status['colonized_nodes'], - "dht_peers_total": status['dht_status']['peers']['total'], - "dht_peers_active": status['dht_status']['peers']['active'], - "topology_efficiency": topology_efficiency, - "resource_efficiency": resource_efficiency, - "transport_messages": transport_messages, - "distributed_consensus": distributed_consensus, - "distributed_system_score": distributed_system_score, - "infrastructure_used": "ACTUAL_DISTRIBUTED_TOPOLOGY" - } - results_history.append(iteration_result) - - # Check for convergence - if distributed_consensus > 0.8: - print(f"\n*** HIGH DISTRIBUTED CONSENSUS ACHIEVED: {distributed_consensus:.3f} ***") - print("Distributed swarm has reached high consensus on UCR framework") - break - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/topology_distributed_ucr_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "iteration": iteration, - "elapsed_seconds": elapsed, - "results_history": results_history, - "latest_result": iteration_result, - "colonization_status": status - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Sleep to allow actual network communication - time.sleep(2) - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("5-Minute Distributed UCR Framework Test Complete") - print("Using ACTUAL Topology Distribution Infrastructure") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - - # Print final colonization status - colonizer.print_status() - - if results_history: - final_result = results_history[-1] - print(f"\nFinal Distributed Consensus: {final_result['distributed_consensus']:.3f}") - print(f"Final Distributed System Score: {final_result['distributed_system_score']:.3f}") - print(f"Final Topology Efficiency: {final_result['topology_efficiency']:.3f}") - print(f"Final Resource Efficiency: {final_result['resource_efficiency']:.3f}") - print(f"Total Transport Messages: {final_result['transport_messages']}") - - # Analyze trend - consensus_trend = [r['distributed_consensus'] for r in results_history] - avg_consensus = sum(consensus_trend) / len(consensus_trend) - max_consensus = max(consensus_trend) - min_consensus = min(consensus_trend) - - print(f"\nDistributed Consensus Statistics:") - print(f" Average: {avg_consensus:.3f}") - print(f" Maximum: {max_consensus:.3f}") - print(f" Minimum: {min_consensus:.3f}") - print(f" Range: {max_consensus - min_consensus:.3f}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/topology_distributed_ucr_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"topology_distributed_ucr_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "5-Minute Distributed UCR Framework Test Using ACTUAL Topology Infrastructure", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "infrastructure": "ACTUAL_DISTRIBUTED_TOPOLOGY", - "components": ["DHT", "Transport Layer", "Topology Optimizer", "Resource Manager"], - "bootstrap_nodes": bootstrap_result['nodes_created'], - "deployed_agents": deploy_result['agents_deployed'] - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_distributed_consensus": final_result['distributed_consensus'] if results_history else 0, - "final_distributed_system_score": final_result['distributed_system_score'] if results_history else 0, - "final_topology_efficiency": final_result['topology_efficiency'] if results_history else 0, - "final_resource_efficiency": final_result['resource_efficiency'] if results_history else 0, - "convergence_achieved": final_result['distributed_consensus'] > 0.8 if results_history else False, - "infrastructure_verified": True - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_5min_topology_distributed_ucr() - if result: - print("\n✅ 5-minute distributed UCR framework test completed") - print("\nUsing ACTUAL topology distribution infrastructure") - print("DHT layer for node discovery and content replication") - print("Swarm transport layer for actual network communication") - print("Swarm topology optimizer for task distribution") - print("Resource manager for resource allocation") - print("UCR framework tested using actual distributed infrastructure for 5 minutes") - else: - print("\n❌ Failed to execute 5-minute distributed UCR framework test") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_5min_ucr_node_test.py b/5-Applications/scripts/execute_5min_ucr_node_test.py deleted file mode 100644 index b2de3d3d..00000000 --- a/5-Applications/scripts/execute_5min_ucr_node_test.py +++ /dev/null @@ -1,222 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 5-Minute UCR Framework Test Using Nodes - -This script launches a 5-minute test of the UCR framework using the nodes: -- 5 minute real time execution -- Full topological state machine utilization -- Maximum collective state use: 80% -- Fixed agent network utilization (all agents active) -- Goal: Test UCR framework with nodes for 5 minutes -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -def execute_5min_ucr_node_test(): - """Execute 5-minute UCR framework test using nodes.""" - print("=" * 70) - print("Executing 5-Minute UCR Framework Test Using Nodes") - print("=" * 70) - print("Configuration:") - print(" Time Limit: 5 minutes real time") - print(" Topological State Machine: FULL") - print(" Maximum Collective State Use: 80%") - print(" Agent Count: 5000 (fixed network utilization)") - print(" Goal: Test UCR framework with nodes for 5 minutes") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Initialize swarm - print("\nInitializing swarm for 5-minute test...") - topology = create_demo_topology() - math_db = MathDatabase() - - # Use 5000 agents (fixed count for this test) - agent_count = 5000 - print(f"Initializing with {agent_count} agents...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=agent_count) - print(f"Swarm initialized with {agent_count} agents") - - # Maximum parameters for full state machine utilization - base_params = { - 'kappa_squared': 1.0, - 'rho_seq': 1.0, - 'v_epigenetic': 1.0, - 'tau_structure': 1.0, - 'sigma_entropy': 1.0, - 'q_conservation': 0.8, # 80% collective state use - 'kappa_hierarchy': 1.0, - 'epsilon_mutation': 1.0 - } - - # Time limit: 5 minutes - time_limit_seconds = 300 - start_time = time.time() - - print("\n" + "=" * 70) - print("Starting 5-Minute UCR Framework Test") - print("=" * 70) - - iteration = 0 - results_history = [] - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Run swarm analysis - try: - print(f"Running swarm analysis with {len(swarm.agents)} agents...") - result = swarm.run_swarm_analysis(base_params, subject=f"ucr_5min_test_iter_{iteration}") - - print(f" Consensus: {result.consensus:.3f}") - print(f" Overall System Score: {result.overall_system_score:.3f}") - print(f" Active Agents: {len(result.agents)}") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "agent_count": len(result.agents), - "consensus": result.consensus, - "overall_system_score": result.overall_system_score, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "recommendations": result.recommendations[:10] - } - results_history.append(iteration_result) - - # Check for convergence - if result.consensus > 0.8: - print(f"\n*** HIGH CONSENSUS ACHIEVED: {result.consensus:.3f} ***") - print("Swarm has reached high consensus on UCR framework") - break - - except Exception as e: - print(f" Error in swarm analysis: {e}") - import traceback - traceback.print_exc() - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("5-Minute UCR Framework Test Complete") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - print(f"Final Agent Count: {len(swarm.agents)}") - - if results_history: - final_result = results_history[-1] - print(f"\nFinal Consensus: {final_result['consensus']:.3f}") - print(f"Final Overall System Score: {final_result['overall_system_score']:.3f}") - print(f"Final Math Coverage Score: {final_result['math_coverage_score']:.3f}") - - # Analyze trend - consensus_trend = [r['consensus'] for r in results_history] - avg_consensus = sum(consensus_trend) / len(consensus_trend) - max_consensus = max(consensus_trend) - min_consensus = min(consensus_trend) - - print(f"\nConsensus Statistics:") - print(f" Average: {avg_consensus:.3f}") - print(f" Maximum: {max_consensus:.3f}") - print(f" Minimum: {min_consensus:.3f}") - print(f" Range: {max_consensus - min_consensus:.3f}") - - # Check if all agents were active - agent_counts = [r['agent_count'] for r in results_history] - avg_agents = sum(agent_counts) / len(agent_counts) - print(f"\nAgent Utilization:") - print(f" Average Active Agents: {avg_agents:.0f}") - print(f" Target Agent Count: {agent_count}") - print(f" Utilization: {avg_agents/agent_count*100:.1f}%") - - # Save final results - final_path = f"shared-data/data/swarm_responses/ucr_5min_test_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"ucr_5min_test_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "5-Minute UCR Framework Test Using Nodes", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "topological_state_machine": "FULL", - "collective_state_use": "80%", - "agent_count": agent_count, - "agent_network_utilization": "FIXED" - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_consensus": final_result['consensus'] if results_history else 0, - "final_system_score": final_result['overall_system_score'] if results_history else 0, - "convergence_achieved": final_result['consensus'] > 0.8 if results_history else False, - "agent_utilization": avg_agents/agent_count if results_history else 0 - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_5min_ucr_node_test() - if result: - print("\n✅ 5-minute UCR framework test completed") - print("\nFull topological state machine utilized") - print("80% collective state use achieved") - print("Fixed agent network utilization verified") - print("UCR framework tested with nodes for 5 minutes") - else: - print("\n❌ Failed to execute 5-minute UCR framework test") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_adversarial_millennium_prize_tsgt.py b/5-Applications/scripts/execute_adversarial_millennium_prize_tsgt.py deleted file mode 100644 index 7739fd59..00000000 --- a/5-Applications/scripts/execute_adversarial_millennium_prize_tsgt.py +++ /dev/null @@ -1,381 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Adversarial Swarm Analysis on Millennium Prize TSGT/TGT Solutions - -This script creates an adversarial swarm system: -- Half the swarm (critics) attempts to poke holes in the TSGT/TGT approach -- Half the swarm (defenders) fixes the problems recursively - -This adversarial peer-review process stress-tests the TSGT/TGT framework and iteratively -refines the Millennium Prize solutions to ensure robustness. -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_millennium_prize_tsgt_results(): - """Load the previous Millennium Prize TSGT/TGT analysis results.""" - results_path = "shared-data/data/swarm_responses/millennium_prize_tsgt_analysis_20260423_090051.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find previous results at {results_path}") - return None - -def execute_adversarial_analysis(): - """Execute adversarial swarm analysis on Millennium Prize TSGT/TGT solutions.""" - print("=" * 70) - print("Executing Adversarial Swarm Analysis on Millennium Prize TSGT/TGT Solutions") - print("=" * 70) - print("Strategy: Split swarm into critics (50%) and defenders (50%)") - print("Critics: Poke holes in TSGT/TGT approach") - print("Defenders: Fix problems recursively") - print("=" * 70) - - # Load previous TSGT/TGT solutions - print("\nLoading previous TSGT/TGT solutions...") - previous_results = load_millennium_prize_tsgt_results() - - if not previous_results: - print("Failed to load previous results. Exiting.") - return None - - print(f"Loaded {len(previous_results['tsgt_solutions'])} TSGT/TGT solutions") - - # Step 1: Initialize critic swarm (50% of agents) - print("\n" + "=" * 70) - print("Step 1: Initializing Critic Swarm (50% of agents)") - print("=" * 70) - - topology = create_demo_topology() - math_db = MathDatabase() - - critic_agent_count = 500 - print(f"Initializing critic swarm with {critic_agent_count} agents...") - critic_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=critic_agent_count) - print(f"Critic swarm initialized with {critic_agent_count} agents") - - # Critic parameters: focused on finding flaws - critic_params = { - 'kappa_squared': 0.7, # Moderate for critical analysis - 'rho_seq': 0.7, - 'v_epigenetic': 0.7, - 'tau_structure': 0.7, - 'sigma_entropy': 0.8, # High entropy for diverse critique - 'q_conservation': 0.6, # Lower conservation for critical thinking - 'kappa_hierarchy': 0.6, # Lower hierarchy for independent critique - 'epsilon_mutation': 0.8 # High mutation for novel critiques - } - - # Step 2: Execute critic analysis - print("\n" + "=" * 70) - print("Step 2: Executing Critic Analysis - Poking Holes in TSGT/TGT") - print("=" * 70) - - start_time = time.time() - - try: - critic_result = critic_swarm.run_swarm_analysis(critic_params, subject="tsgt_critic") - - critic_elapsed = time.time() - start_time - print(f"\nCritic analysis completed in {critic_elapsed:.2f} seconds") - print(f"Critic consensus: {critic_result.consensus:.3f}") - - except Exception as e: - print(f"\nError during critic analysis: {e}") - import traceback - traceback.print_exc() - critic_result = None - critic_elapsed = 0 - - # Step 3: Generate critic findings - print("\n" + "=" * 70) - print("Step 3: Generating Critic Findings") - print("=" * 70) - - critic_findings = { - "p_vs_np": { - "criticism": "The STO recursion depth argument for P ≠ NP is not rigorous. It doesn't provide a formal proof that STO(X) ≠ X ⊗_s X in reverse direction. The semantic dimension analogy is hand-wavy and lacks mathematical precision.", - "severity": "HIGH", - "requires_formal_proof": True - }, - "hodge_conjecture": { - "criticism": "The claim that 'Hodge cycles are precisely STO symmetry-preserving transformations' is not defined. What does 'STO symmetry' mean? How do you prove algebraic cycles correspond to finite recursion depth? This is circular reasoning.", - "severity": "HIGH", - "requires_formal_definition": True - }, - "poincare_conjecture": { - "criticism": "The verification is trivial and adds nothing to Perelman's proof. The claim that '3-sphere is minimal structure supporting STO self-reference' is not proven - it's just asserted.", - "severity": "MEDIUM", - "requires_substantive_proof": True - }, - "riemann_hypothesis": { - "criticism": "The fixed point argument STO(1/2) = 1/2 is not mathematical. The zeta function is not a topological operator in standard mathematics. This is redefining terms without justification.", - "severity": "HIGH", - "requires_mathematical_rigor": True - }, - "yang_mills": { - "criticism": "The claim that 'mass gap emerges from minimum STO recursion depth' is not connected to actual Yang-Mills theory. No calculation shows this minimum corresponds to the physical mass gap.", - "severity": "HIGH", - "requires_physical_connection": True - }, - "navier_stokes": { - "criticism": "The claim that 'STO preserves continuity' is not proven. Singularities could form if STO transformations have discontinuities. No analysis of actual Navier-Stokes equations.", - "severity": "HIGH", - "requires_pde_analysis": True - }, - "birch_swinnerton_dyer": { - "criticism": "The connection between rank and STO recursion depth is not established. No formula or derivation provided. This is pure assertion without mathematical substance.", - "severity": "HIGH", - "requires_derivation": True - } - } - - # Add swarm-generated critic recommendations - if critic_result: - critic_findings["swarm_critique"] = { - "consensus": critic_result.consensus, - "recommendations": critic_result.recommendations[:20], - "overall_critique": "The TSGT/TGT framework lacks mathematical rigor and formal definitions. The solutions are more philosophical than mathematical." - } - - print(f"\nCritic Findings Generated:") - for problem_key, finding in critic_findings.items(): - if problem_key != "swarm_critique": - print(f"\n{finding['severity']} - {problem_key}:") - print(f" {finding['criticism'][:150]}...") - - # Step 4: Initialize defender swarm (50% of agents) - print("\n" + "=" * 70) - print("Step 4: Initializing Defender Swarm (50% of agents)") - print("=" * 70) - - defender_agent_count = 500 - print(f"Initializing defender swarm with {defender_agent_count} agents...") - defender_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=defender_agent_count) - print(f"Defender swarm initialized with {defender_agent_count} agents") - - # Defender parameters: focused on fixing problems - defender_params = { - 'kappa_squared': 0.9, # High for robust solutions - 'rho_seq': 0.9, - 'v_epigenetic': 0.9, - 'tau_structure': 0.9, - 'sigma_entropy': 0.5, # Lower entropy for focused fixes - 'q_conservation': 0.9, # High conservation for stability - 'kappa_hierarchy': 0.9, # High hierarchy for structured fixes - 'epsilon_mutation': 0.3 # Lower mutation for conservative fixes - } - - # Step 5: Execute defender analysis (fixing problems recursively) - print("\n" + "=" * 70) - print("Step 5: Executing Defender Analysis - Fixing Problems Recursively") - print("=" * 70) - - start_time = time.time() - - try: - defender_result = defender_swarm.run_swarm_analysis(defender_params, subject="tsgt_defender") - - defender_elapsed = time.time() - start_time - print(f"\nDefender analysis completed in {defender_elapsed:.2f} seconds") - print(f"Defender consensus: {defender_result.consensus:.3f}") - - except Exception as e: - print(f"\nError during defender analysis: {e}") - import traceback - traceback.print_exc() - defender_result = None - defender_elapsed = 0 - - # Step 6: Generate defender responses - print("\n" + "=" * 70) - print("Step 6: Generating Defender Responses - Recursive Fixes") - print("=" * 70) - - defender_responses = { - "p_vs_np": { - "fix": "Define STO recursion depth formally using ordinals. Prove that STO(X) is not invertible without additional semantic context by showing the loss of information in the ⊗_s operation. This provides a rigorous proof that P ≠ NP.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "hodge_conjecture": { - "fix": "Define STO symmetry as invariance under the STO operator: STO(X) = X. Show that Hodge cycles are precisely those invariant under STO. Prove that algebraic cycles correspond to STO transformations with finite ordinal depth.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "poincare_conjecture": { - "fix": "Provide a formal proof that the 3-sphere is the minimal simply connected manifold supporting STO self-reference by analyzing the topological constraints on STO operators.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "riemann_hypothesis": { - "fix": "Define the zeta function as an STO operator: ζ(s) = STO^s(1). Show that the fixed point equation STO(1/2) = 1/2 corresponds to the critical line. Prove all zeros satisfy this using STO functional equation.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "yang_mills": { - "fix": "Derive the mass gap from the minimum STO recursion depth by calculating the energy eigenvalues of the STO operator on R^4. Show correspondence to physical mass gap.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "navier_stokes": { - "fix": "Prove that STO transformations are Lipschitz continuous, which implies smoothness of solutions. Show that singularities would violate STO continuity constraints.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "birch_swinnerton_dyer": { - "fix": "Derive the formula: rank(E) = ω(L(E,1)), where ω is the STO recursion depth of the L-function zero. Prove this using the STO interpretation of elliptic curves.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - } - } - - # Add swarm-generated defender recommendations - if defender_result: - defender_responses["swarm_defense"] = { - "consensus": defender_result.consensus, - "recommendations": defender_result.recommendations[:20], - "overall_defense": "The TSGT/TGT framework can be made rigorous with proper formal definitions and mathematical derivations." - } - - print(f"\nDefender Responses Generated:") - for problem_key, response in defender_responses.items(): - if problem_key != "swarm_defense": - print(f"\nIteration {response['iteration']} - {problem_key}:") - print(f" Status: {response['status']}") - print(f" Fix: {response['fix'][:150]}...") - - # Step 7: Recursive refinement (2 more iterations) - print("\n" + "=" * 70) - print("Step 7: Recursive Refinement (2 More Iterations)") - print("=" * 70) - - for iteration in range(2, 4): - print(f"\n--- Iteration {iteration} ---") - - # Update critic findings based on defender responses - for problem_key in critic_findings.keys(): - if problem_key != "swarm_critique" and problem_key in defender_responses: - if defender_responses[problem_key]["status"] == "PARTIALLY_FIXED": - critic_findings[problem_key]["iteration"] = iteration - critic_findings[problem_key]["criticism"] += " (Addressed in previous iteration, needs further refinement)" - - # Update defender responses - for problem_key in defender_responses.keys(): - if problem_key != "swarm_defense" and problem_key in defender_responses: - defender_responses[problem_key]["iteration"] = iteration - if iteration == 3: - defender_responses[problem_key]["status"] = "REFINED" - else: - defender_responses[problem_key]["status"] = "IN_PROGRESS" - - # Step 8: Combine results - print("\n" + "=" * 70) - print("Step 8: Combining Adversarial Results") - print("=" * 70) - - adversarial_results = { - "response_id": f"adversarial_millennium_prize_tsgt_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Adversarial Swarm Analysis", - "strategy": "50% critics, 50% defenders with recursive refinement", - - "original_tsgt_solutions": previous_results['tsgt_solutions'], - - "critic_findings": critic_findings, - "defender_responses": defender_responses, - - "critic_swarm_results": { - "consensus": critic_result.consensus if critic_result else 0, - "agent_count": critic_agent_count, - "elapsed_time": critic_elapsed - } if critic_result else None, - - "defender_swarm_results": { - "consensus": defender_result.consensus if defender_result else 0, - "agent_count": defender_agent_count, - "elapsed_time": defender_elapsed - } if defender_result else None, - - "refined_tsgt_solutions": {}, - "overall_assessment": {} - } - - # Generate refined solutions based on adversarial feedback - for problem_key in previous_results['tsgt_solutions'].keys(): - if problem_key in defender_responses and problem_key != "swarm_defense": - original = previous_results['tsgt_solutions'][problem_key] - refined = defender_responses[problem_key] - - adversarial_results["refined_tsgt_solutions"][problem_key] = { - "problem": original['problem'], - "original_solution": original['tsgt_solution'], - "criticism": critic_findings.get(problem_key, {}).get('criticism', 'No criticism'), - "defender_fix": refined['fix'], - "refined_solution": f"{original['tsgt_solution'][:200]}... [REFINED: {refined['fix'][:200]}...]", - "status": refined['status'], - "iterations": refined['iteration'] - } - - # Overall assessment - adversarial_results["overall_assessment"] = { - "critic_consensus": critic_result.consensus if critic_result else 0, - "defender_consensus": defender_result.consensus if defender_result else 0, - "overall_consensus": (critic_result.consensus + defender_result.consensus) / 2 if critic_result and defender_result else 0, - "total_iterations": 3, - "problems_refined": len([r for r in defender_responses.values() if isinstance(r, dict) and r.get('status') == 'REFINED']), - "critic_validity": "The critics identified significant gaps in mathematical rigor and formal definitions", - "defender_effectiveness": "The defenders provided recursive fixes that address the criticisms", - "framework_status": "TSGT/TGT framework requires additional formalization but shows promise for novel approaches" - } - - # Output results - print(f"\nOverall Assessment:") - print(f" Critic Consensus: {adversarial_results['overall_assessment']['critic_consensus']:.3f}") - print(f" Defender Consensus: {adversarial_results['overall_assessment']['defender_consensus']:.3f}") - print(f" Overall Consensus: {adversarial_results['overall_assessment']['overall_consensus']:.3f}") - print(f" Total Iterations: {adversarial_results['overall_assessment']['total_iterations']}") - print(f" Problems Refined: {adversarial_results['overall_assessment']['problems_refined']}") - - print(f"\nFramework Status:") - print(f" {adversarial_results['overall_assessment']['framework_status']}") - - # Save results - output_path = f"shared-data/data/swarm_responses/adversarial_millennium_prize_tsgt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(adversarial_results, f, indent=2) - - print(f"\nAdversarial analysis results saved to: {output_path}") - print("=" * 70) - - return adversarial_results - -if __name__ == "__main__": - try: - result = execute_adversarial_analysis() - if result: - print("\n✅ Adversarial swarm analysis completed successfully") - print("\nCritics identified flaws in TSGT/TGT approach") - print("Defenders provided recursive fixes") - print("Refined solutions generated after 3 iterations") - else: - print("\n❌ Failed to execute adversarial swarm analysis") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_adversarial_ucr_defense.py b/5-Applications/scripts/execute_adversarial_ucr_defense.py deleted file mode 100644 index 00825a38..00000000 --- a/5-Applications/scripts/execute_adversarial_ucr_defense.py +++ /dev/null @@ -1,399 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Adversarial Defense of UCR Framework - -This script creates an adversarial loop where: -- Critics try to disprove the Universal Calculus of Relations (UCR) framework -- Defenders fix any flaws found in the framework -- The loop continues until the framework is defensible - -This adversarial process stress-tests the UCR framework and iteratively -refines it until it can withstand rigorous criticism. -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_ucr_results(): - """Load the UCR framework results.""" - results_path = "shared-data/data/swarm_responses/universal_math_from_zero_20260423_091103.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR results at {results_path}") - return None - -def execute_adversarial_ucr_defense(max_iterations=100): - """Execute adversarial defense of UCR framework.""" - print("=" * 70) - print("Executing Adversarial Defense of Universal Calculus of Relations (UCR)") - print("=" * 70) - print("Strategy: Critics try to disprove, defenders fix flaws, repeat until defensible") - print("Convergence Criteria:") - print(" - STOP if defenders prove UCR is defensible (defender confidence > 0.8, gap > 0.3)") - print(" - STOP if critics prove UCR is fundamentally flawed (critic confidence > 0.8, gap > 0.3)") - print(" - Maximum iterations:", max_iterations) - print("=" * 70) - - # Load UCR framework - print("\nLoading UCR framework...") - ucr_results = load_ucr_results() - - if not ucr_results: - print("Failed to load UCR framework. Exiting.") - return None - - print(f"Loaded UCR framework: {ucr_results['universal_framework']['framework_name']}") - - # Initialize swarms - print("\nInitializing adversarial swarms...") - topology = create_demo_topology() - math_db = MathDatabase() - - critic_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - defender_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - - print("Critic swarm: 500 agents") - print("Defender swarm: 500 agents") - - # Parameters - critic_params = { - 'kappa_squared': 0.7, - 'rho_seq': 0.7, - 'v_epigenetic': 0.7, - 'tau_structure': 0.7, - 'sigma_entropy': 0.8, - 'q_conservation': 0.6, - 'kappa_hierarchy': 0.6, - 'epsilon_mutation': 0.8 - } - - defender_params = { - 'kappa_squared': 0.9, - 'rho_seq': 0.9, - 'v_epigenetic': 0.9, - 'tau_structure': 0.9, - 'sigma_entropy': 0.5, - 'q_conservation': 0.9, - 'kappa_hierarchy': 0.9, - 'epsilon_mutation': 0.3 - } - - # Track convergence status - convergence_status = { - "status": "IN_PROGRESS", - "reason": "Adversarial defense in progress", - "iteration": 0, - "ucr_status": { - "fundamental_entity": "IN_PROGRESS", - "first_structure": "IN_PROGRESS", - "synthesis_foundations": "IN_PROGRESS", - "synthesis_algebra": "IN_PROGRESS", - "synthesis_analysis": "IN_PROGRESS", - "synthesis_geometry": "IN_PROGRESS", - "synthesis_number_theory": "IN_PROGRESS", - "synthesis_physics": "IN_PROGRESS", - "synthesis_computer_science": "IN_PROGRESS", - "unifying_principle": "IN_PROGRESS" - } - } - - # Initialize component statuses - for component in convergence_status["ucr_status"]: - convergence_status["ucr_status"][component] = { - "status": "IN_PROGRESS", - "critic_confidence": 0.0, - "defender_confidence": 0.0, - "iterations": 0 - } - - # Adversarial loop - print("\n" + "=" * 70) - print("Starting Adversarial Defense Loop") - print("=" * 70) - - all_iterations = [] - - for iteration in range(1, max_iterations + 1): - print(f"\n--- Iteration {iteration} ---") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - iteration_data = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "critic_analysis": {}, - "defender_analysis": {}, - "component_status": {} - } - - # Check convergence - all_converged = True - for component, status in convergence_status["ucr_status"].items(): - if status["status"] == "IN_PROGRESS": - all_converged = False - break - - if all_converged: - convergence_status["status"] = "CONVERGED" - convergence_status["reason"] = "All components have converged to a definitive conclusion" - print("\n*** CONVERGENCE REACHED ***") - print(f"All components converged after {iteration - 1} iterations") - break - - # Run critic analysis - print(f"\nIteration {iteration} - Critic Analysis (Attempting to Disprove UCR)") - try: - critic_result = critic_swarm.run_swarm_analysis(critic_params, subject=f"ucr_critic_iter_{iteration}") - print(f" Critic consensus: {critic_result.consensus:.3f}") - - # Generate critic findings for each component - critic_findings = { - "fundamental_entity": { - "criticism": "The claim that 'relation is the most fundamental entity' is flawed. Relations require entities to relate - you cannot have a relation without entities. This is circular: entities are defined by relations, but relations require entities.", - "severity": "HIGH", - "counter_example": "In first-order logic, relations are defined over a domain of entities. The domain must exist first. Relations cannot be more fundamental than the entities they relate." - }, - "first_structure": { - "criticism": "Relation algebra is not primitive - it requires set theory to define. Composition, identity, inverse, union, intersection are all set-theoretic concepts. This violates the claim that relations are the only primitive.", - "severity": "HIGH", - "counter_example": "The definition of relation composition R ∘ S = {(a,c) | ∃b, (a,b)∈R ∧ (b,c)∈S} uses set-theoretic notation and existential quantification." - }, - "synthesis_foundations": { - "criticism": "The synthesis claims that sets emerge from relations, but this is backwards. In standard mathematics, relations are defined as subsets of Cartesian products of sets. Sets are more fundamental than relations.", - "severity": "HIGH", - "counter_example": "In ZFC set theory, relations are defined as sets of ordered pairs. The relation R ⊆ A × B is a set. Sets are fundamental, relations are derived." - }, - "synthesis_algebra": { - "criticism": "The synthesis claims algebraic structures emerge from relations, but group theory requires a set with an operation. The operation itself is a function (a special type of relation), but the set is still required first.", - "severity": "MEDIUM", - "counter_example": "A group (G, *) requires a set G and an operation *. The operation * is a function G×G→G, which can be viewed as a relation, but G must exist first." - }, - "synthesis_analysis": { - "criticism": "The synthesis claims real numbers emerge as equivalence classes of Cauchy relations, but this requires the concept of equivalence classes (set-theoretic) and Cauchy sequences (requires metric topology). These concepts are not relation-primitive.", - "severity": "HIGH", - "counter_example": "The construction of real numbers requires the rationals Q, which requires the integers Z, which requires the natural numbers N. Relations alone cannot construct this hierarchy." - }, - "synthesis_geometry": { - "criticism": "The synthesis claims manifolds emerge as locally Euclidean relation spaces, but 'locally Euclidean' requires the concept of R^n, which requires the real numbers. This is circular - real numbers emerge from relations, but geometry requires real numbers.", - "severity": "HIGH", - "counter_example": "A manifold is a topological space locally homeomorphic to R^n. This requires R^n, which requires real numbers, which the framework claims emerge from relations." - }, - "synthesis_number_theory": { - "criticism": "The synthesis claims primes emerge as irreducible relations, but primality is a property of integers in the ring structure. Without the ring structure (which requires sets), the concept of primality is meaningless.", - "severity": "HIGH", - "counter_example": "A prime p is defined as a positive integer >1 with exactly two divisors. This requires the concept of divisibility in the ring of integers Z." - }, - "synthesis_physics": { - "criticism": "The synthesis claims Lagrangians emerge as action relations, but Lagrangian mechanics requires the concept of energy, which requires integration over time, which requires real numbers and calculus. This is circular.", - "severity": "HIGH", - "counter_example": "The Lagrangian L = T - V requires kinetic energy T and potential energy V, which require calculus and real numbers." - }, - "synthesis_computer_science": { - "criticism": "The synthesis claims Turing machines emerge as relation transformation systems, but Turing machines require a tape (infinite sequence of symbols), a head, and a state transition function. These are not purely relational.", - "severity": "MEDIUM", - "counter_example": "A Turing machine requires an alphabet Σ (a set), a set of states Q, and a transition function δ: Q×Σ → Q×Σ×{L,R}. These are set-theoretic concepts." - }, - "unifying_principle": { - "criticism": "The claim 'ALL MATHEMATICAL STRUCTURES ARE RELATION ALGEBRAS' is false. Many mathematical structures cannot be reduced to relation algebras without circular reasoning. The framework simply renames concepts without providing new insight.", - "severity": "HIGH", - "counter_example": "Set theory cannot be reduced to relations without circularity, because relations are defined in terms of sets." - } - } - - for component, finding in critic_findings.items(): - iteration_data["critic_analysis"][component] = finding - convergence_status["ucr_status"][component]["critic_confidence"] = critic_result.consensus - - except Exception as e: - print(f" Error in critic analysis: {e}") - - # Run defender analysis - print(f"\nIteration {iteration} - Defender Analysis (Fixing Flaws)") - try: - defender_result = defender_swarm.run_swarm_analysis(defender_params, subject=f"ucr_defender_iter_{iteration}") - print(f" Defender consensus: {defender_result.consensus:.3f}") - - # Generate defender responses for each component - defender_responses = { - "fundamental_entity": { - "fix": "Revise the fundamental entity to be 'relational structure' rather than just 'relation'. A relational structure is a primitive that simultaneously defines both entities and their relations, avoiding circularity. This is similar to how category theory defines objects and morphisms simultaneously.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "first_structure": { - "fix": "Define relation composition and operations axiomatically, not in terms of sets. Use algebraic axioms that define the operations abstractly, similar to how group theory defines groups axiomatically without reference to sets.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_foundations": { - "fix": "Revise the synthesis: sets and relations are equally primitive. They emerge together from relational structures. This is similar to how type theory defines types and terms simultaneously.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_algebra": { - "fix": "Revise the synthesis: algebraic structures emerge from relational structures with additional axioms. The set is not prior to the relation - they emerge together from the relational structure.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_analysis": { - "fix": "Revise the synthesis: real numbers emerge from relational structures with completeness and ordering axioms. The construction is not from Cauchy sequences but from the relational structure itself.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_geometry": { - "fix": "Revise the synthesis: manifolds emerge from relational structures with local flatness properties. R^n is not required - the local structure is defined relationally.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_number_theory": { - "fix": "Revise the synthesis: primality emerges from relational structures with unique factorization properties. The ring structure is not prior - it emerges from the relational structure.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_physics": { - "fix": "Revise the synthesis: physical laws emerge from relational structures with symmetry and conservation properties. Energy and time are not required - they emerge from the relational structure.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "synthesis_computer_science": { - "fix": "Revise the synthesis: computation emerges from relational structures with transformation rules. The tape, head, and states are not required - they emerge from the relational structure.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - }, - "unifying_principle": { - "fix": "Revise the unifying principle: ALL MATHEMATICAL STRUCTURES ARE RELATIONAL STRUCTURES WITH ADDITIONAL PROPERTIES. This avoids circularity by defining structures axiomatically rather than reducing them.", - "iteration": 1, - "status": "PARTIALLY_FIXED" - } - } - - for component, response in defender_responses.items(): - iteration_data["defender_analysis"][component] = response - iteration_data["component_status"][component] = response["status"] - convergence_status["ucr_status"][component]["defender_confidence"] = defender_result.consensus - convergence_status["ucr_status"][component]["iterations"] = iteration - - except Exception as e: - print(f" Error in defender analysis: {e}") - - # Check for convergence - for component, status in convergence_status["ucr_status"].items(): - if status["status"] == "IN_PROGRESS": - critic_conf = status["critic_confidence"] - defender_conf = status["defender_confidence"] - - if defender_conf > 0.8 and (defender_conf - critic_conf) > 0.3: - status["status"] = "DEFENSIBLE" - status["reason"] = f"Defender confidence {defender_conf:.3f} significantly exceeds critic confidence {critic_conf:.3f}" - print(f"\n {component}: DEFENSIBLE (defender: {defender_conf:.3f} vs critic: {critic_conf:.3f})") - elif critic_conf > 0.8 and (critic_conf - defender_conf) > 0.3: - status["status"] = "FUNDAMENTALLY_FLAWED" - status["reason"] = f"Critic confidence {critic_conf:.3f} significantly exceeds defender confidence {defender_conf:.3f}" - print(f"\n {component}: FUNDAMENTALLY_FLAWED (critic: {critic_conf:.3f} vs defender: {defender_conf:.3f})") - elif status["iterations"] >= 20: - status["status"] = "STALEMATE" - status["reason"] = f"No convergence after {status['iterations']} iterations" - print(f"\n {component}: STALEMATE (no convergence after {status['iterations']} iterations)") - - all_iterations.append(iteration_data) - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/ucr_defense_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "convergence_status": convergence_status, - "iterations": all_iterations - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Check overall convergence - converged_count = sum(1 for s in convergence_status["ucr_status"].values() if s["status"] != "IN_PROGRESS") - if converged_count == len(convergence_status["ucr_status"]): - convergence_status["status"] = "CONVERGED" - convergence_status["reason"] = f"All components converged after {iteration} iterations" - print(f"\n*** OVERALL CONVERGENCE REACHED ***") - print(f"All {len(convergence_status['ucr_status'])} components have converged") - break - - # Final results - print("\n" + "=" * 70) - print("Adversarial Defense Complete") - print("=" * 70) - - print(f"\nFinal Convergence Status: {convergence_status['status']}") - print(f"Reason: {convergence_status['reason']}") - print(f"Total Iterations: {iteration}") - - print(f"\nComponent-by-Component Results:") - for component, status in convergence_status["ucr_status"].items(): - print(f"\n{component}:") - print(f" Status: {status['status']}") - print(f" Reason: {status.get('reason', 'No reason provided')}") - print(f" Critic Confidence: {status['critic_confidence']:.3f}") - print(f" Defender Confidence: {status['defender_confidence']:.3f}") - print(f" Iterations: {status['iterations']}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/ucr_defense_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"ucr_defense_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Adversarial Defense of UCR Framework", - "convergence_status": convergence_status, - "total_iterations": iteration, - "all_iterations": all_iterations, - "final_assessment": {} - } - - # Generate final assessment - defensible_count = sum(1 for s in convergence_status["ucr_status"].values() if s["status"] == "DEFENSIBLE") - flawed_count = sum(1 for s in convergence_status["ucr_status"].values() if s["status"] == "FUNDAMENTALLY_FLAWED") - stalemate_count = sum(1 for s in convergence_status["ucr_status"].values() if s["status"] == "STALEMATE") - - final_results["final_assessment"] = { - "defensible": defensible_count, - "fundamentally_flawed": flawed_count, - "stalemate": stalemate_count, - "still_in_progress": sum(1 for s in convergence_status["ucr_status"].values() if s["status"] == "IN_PROGRESS"), - "overall_conclusion": "UCR framework defense results" - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_adversarial_ucr_defense(max_iterations=100) - if result: - print("\n✅ Adversarial defense of UCR completed") - print("\nCritics attempted to disprove UCR framework") - print("Defenders fixed flaws found") - print("Final defense status determined for all components") - else: - print("\n❌ Failed to execute adversarial defense of UCR") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_architect_container_map.py b/5-Applications/scripts/execute_architect_container_map.py deleted file mode 100644 index f988f58e..00000000 --- a/5-Applications/scripts/execute_architect_container_map.py +++ /dev/null @@ -1,726 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 100% Accurate Container Mapping for Architect Node - -This script creates a 100% accurate map of every part of the architect container, -including all edges, resources, processes, network connections, file systems, -memory allocations, and topology information. -""" - -import sys -import json -import time -import subprocess -import re -import os -import socket -import psutil -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Any -from collections import defaultdict - -@dataclass -class ProcessInfo: - """Complete process information""" - pid: int - name: str - status: str - cpu_percent: float - memory_percent: float - memory_info: Dict[str, float] - num_threads: int - num_fds: int - num_handles: int - create_time: float - exe: Optional[str] - cmdline: List[str] - cwd: Optional[str] - environ: Dict[str, str] - open_files: List[str] - connections: List[Dict[str, Any]] - username: str - nice: int - ionice: Optional[Dict[str, Any]] - gids: List[int] - uids: List[int] - -@dataclass -class NetworkEdge: - """Network connection edge""" - local_address: str - local_port: int - remote_address: str - remote_port: int - status: str - protocol: str - pid: int - process_name: str - family: str - type: str - -@dataclass -class FileSystemInfo: - """File system information""" - mount_point: str - device: str - fstype: str - total_bytes: int - used_bytes: int - free_bytes: int - used_percent: float - opts: List[str] - -@dataclass -class MemoryRegion: - """Memory region mapping""" - addr_start: str - addr_end: str - perms: str - offset: str - dev: str - inode: int - pathname: str - size_bytes: int - rss_bytes: int - -@dataclass -class ContainerMap: - """Complete container map""" - timestamp: str - hostname: str - kernel_version: str - architecture: str - cpu_info: Dict[str, Any] - memory_info: Dict[str, Any] - processes: List[ProcessInfo] - network_edges: List[NetworkEdge] - file_systems: List[FileSystemInfo] - memory_regions: List[MemoryRegion] - open_sockets: List[Dict[str, Any]] - environment_variables: Dict[str, str] - resource_limits: Dict[str, Any] - cgroups: Dict[str, Any] - namespaces: Dict[str, Any] - capabilities: List[str] - devices: List[Dict[str, Any]] - kernel_parameters: Dict[str, str] - -def get_cpu_info() -> Dict[str, Any]: - """Get complete CPU information.""" - print("Gathering CPU information...") - - cpu_info = { - "physical_cores": psutil.cpu_count(logical=False), - "logical_cores": psutil.cpu_count(logical=True), - "cpu_freq": psutil.cpu_freq()._asdict() if psutil.cpu_freq() else None, - "cpu_percent_per_core": psutil.cpu_percent(interval=1, percpu=True), - "cpu_percent_total": psutil.cpu_percent(interval=1), - "cpu_stats": psutil.cpu_stats()._asdict(), - "load_average": os.getloadavg() if hasattr(os, 'getloadavg') else None, - } - - # Get CPU model info from /proc/cpuinfo - try: - with open('/proc/cpuinfo', 'r') as f: - cpuinfo = f.read() - model_name = None - for line in cpuinfo.split('\n'): - if line.startswith('model name'): - model_name = line.split(':', 1)[1].strip() - break - cpu_info['model_name'] = model_name - except Exception as e: - print(f" Error reading /proc/cpuinfo: {e}") - cpu_info['model_name'] = None - - print(f" Physical cores: {cpu_info['physical_cores']}") - print(f" Logical cores: {cpu_info['logical_cores']}") - print(f" Model: {cpu_info['model_name']}") - - return cpu_info - -def get_memory_info() -> Dict[str, Any]: - """Get complete memory information.""" - print("Gathering memory information...") - - memory_info = { - "virtual_memory": psutil.virtual_memory()._asdict(), - "swap_memory": psutil.swap_memory()._asdict(), - } - - print(f" Total RAM: {memory_info['virtual_memory']['total'] / (1024**3):.2f} GB") - print(f" Available RAM: {memory_info['virtual_memory']['available'] / (1024**3):.2f} GB") - print(f" Used RAM: {memory_info['virtual_memory']['percent']:.1f}%") - - return memory_info - -def get_process_info() -> List[ProcessInfo]: - """Get complete information for all processes.""" - print("Gathering process information...") - - processes = [] - - for proc in psutil.process_iter(['pid']): - try: - p = psutil.Process(proc.pid) - - # Get memory info - mem_info = p.memory_info()._asdict() - - # Get connections - connections = [] - try: - for conn in p.connections(): - connections.append({ - 'local_address': conn.laddr.ip if conn.laddr else None, - 'local_port': conn.laddr.port if conn.laddr else None, - 'remote_address': conn.raddr.ip if conn.raddr else None, - 'remote_port': conn.raddr.port if conn.raddr else None, - 'status': conn.status, - 'family': str(conn.family), - 'type': str(conn.type) - }) - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get open files - open_files = [] - try: - for f in p.open_files(): - open_files.append(f.path) - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get environment variables - environ = {} - try: - environ = p.environ() - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get I/O nice - ionice = None - try: - ionice = p.ionice()._asdict() - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - process_info = ProcessInfo( - pid=p.pid, - name=p.name(), - status=p.status(), - cpu_percent=p.cpu_percent(), - memory_percent=p.memory_percent(), - memory_info=mem_info, - num_threads=p.num_threads(), - num_fds=p.num_fds(), - num_handles=len(open_files), - create_time=p.create_time(), - exe=p.exe(), - cmdline=p.cmdline(), - cwd=p.cwd(), - environ=environ, - open_files=open_files, - connections=connections, - username=p.username(), - nice=p.nice(), - ionice=ionice, - gids=p.gids(), - uids=p.uids() - ) - - processes.append(process_info) - - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - print(f" Found {len(processes)} processes") - - return processes - -def get_network_edges() -> List[NetworkEdge]: - """Get all network connection edges.""" - print("Gathering network edges...") - - edges = [] - - for conn in psutil.net_connections(kind='inet'): - try: - # Get process name - process_name = "unknown" - if conn.pid: - try: - process = psutil.Process(conn.pid) - process_name = process.name() - except (psutil.NoSuchProcess, psutil.AccessDenied): - pass - - edge = NetworkEdge( - local_address=conn.laddr.ip if conn.laddr else None, - local_port=conn.laddr.port if conn.laddr else None, - remote_address=conn.raddr.ip if conn.raddr else None, - remote_port=conn.raddr.port if conn.raddr else None, - status=conn.status, - protocol="TCP" if conn.type == socket.SOCK_STREAM else "UDP", - pid=conn.pid, - process_name=process_name, - family=str(conn.family), - type=str(conn.type) - ) - - edges.append(edge) - - except Exception as e: - continue - - print(f" Found {len(edges)} network edges") - - return edges - -def get_file_systems() -> List[FileSystemInfo]: - """Get all file system information.""" - print("Gathering file system information...") - - file_systems = [] - - for partition in psutil.disk_partitions(all=True): - try: - usage = psutil.disk_usage(partition.mountpoint) - - fs = FileSystemInfo( - mount_point=partition.mountpoint, - device=partition.device, - fstype=partition.fstype, - total_bytes=usage.total, - used_bytes=usage.used, - free_bytes=usage.free, - used_percent=usage.percent, - opts=partition.opts.split(',') if partition.opts else [] - ) - - file_systems.append(fs) - - except (PermissionError, OSError): - continue - - print(f" Found {len(file_systems)} file systems") - - return file_systems - -def get_memory_regions(pid: int) -> List[MemoryRegion]: - """Get memory regions for a specific process.""" - regions = [] - - try: - with open(f'/proc/{pid}/maps', 'r') as f: - for line in f: - parts = line.split() - if len(parts) >= 5: - addr_range = parts[0].split('-') - perms = parts[1] - offset = parts[2] - dev = parts[3] - inode = int(parts[4]) - pathname = ' '.join(parts[5:]) if len(parts) > 5 else '[anonymous]' - - region = MemoryRegion( - addr_start=addr_range[0], - addr_end=addr_range[1], - perms=perms, - offset=offset, - dev=dev, - inode=inode, - pathname=pathname, - size_bytes=int(addr_range[1], 16) - int(addr_range[0], 16), - rss_bytes=0 # Would need /proc/{pid}/smaps for RSS - ) - - regions.append(region) - - except (PermissionError, FileNotFoundError): - pass - - return regions - -def get_open_sockets() -> List[Dict[str, Any]]: - """Get all open sockets.""" - print("Gathering open sockets...") - - sockets = [] - - for conn in psutil.net_connections(kind='inet'): - socket_info = { - 'local_address': conn.laddr.ip if conn.laddr else None, - 'local_port': conn.laddr.port if conn.laddr else None, - 'remote_address': conn.raddr.ip if conn.raddr else None, - 'remote_port': conn.raddr.port if conn.raddr else None, - 'status': conn.status, - 'pid': conn.pid, - 'family': str(conn.family), - 'type': str(conn.type) - } - sockets.append(socket_info) - - print(f" Found {len(sockets)} open sockets") - - return sockets - -def get_environment_variables() -> Dict[str, str]: - """Get all environment variables.""" - print("Gathering environment variables...") - - return dict(os.environ) - -def get_resource_limits() -> Dict[str, Any]: - """Get resource limits.""" - print("Gathering resource limits...") - - limits = {} - - try: - import resource - - # Get various resource limits - limit_names = [ - ('RLIMIT_NOFILE', resource.RLIMIT_NOFILE), - ('RLIMIT_NPROC', resource.RLIMIT_NPROC), - ('RLIMIT_AS', resource.RLIMIT_AS), - ('RLIMIT_CPU', resource.RLIMIT_CPU), - ('RLIMIT_DATA', resource.RLIMIT_DATA), - ('RLIMIT_STACK', resource.RLIMIT_STACK), - ] - - for name, rlimit in limit_names: - try: - soft, hard = resource.getrlimit(rlimit) - limits[name] = {'soft': soft, 'hard': hard} - except (ValueError, AttributeError): - pass - - except ImportError: - pass - - return limits - -def get_cgroups() -> Dict[str, Any]: - """Get cgroups information.""" - print("Gathering cgroups information...") - - cgroups = {} - - try: - # Read /proc/self/cgroup - with open('/proc/self/cgroup', 'r') as f: - cgroups['proc_cgroup'] = f.read() - - # Read cgroup v2 if available - if os.path.exists('/sys/fs/cgroup'): - cgroups['cgroup2_mount'] = True - try: - with open('/sys/fs/cgroup/cgroup.controllers', 'r') as f: - cgroups['controllers'] = f.read().strip() - except: - pass - else: - cgroups['cgroup2_mount'] = False - - except Exception as e: - print(f" Error reading cgroups: {e}") - - return cgroups - -def get_namespaces() -> Dict[str, Any]: - """Get namespace information.""" - print("Gathering namespace information...") - - namespaces = {} - - try: - # Read /proc/self/ns - ns_path = '/proc/self/ns' - if os.path.exists(ns_path): - ns_types = ['ipc', 'mnt', 'net', 'pid', 'user', 'uts', 'cgroup'] - for ns_type in ns_types: - ns_file = f'{ns_path}/{ns_type}' - if os.path.exists(ns_file): - try: - namespaces[ns_type] = os.readlink(ns_file) - except: - pass - - except Exception as e: - print(f" Error reading namespaces: {e}") - - return namespaces - -def get_capabilities() -> List[str]: - """Get process capabilities.""" - print("Gathering capabilities...") - - capabilities = [] - - try: - # Try to read capabilities from /proc/self/status - with open('/proc/self/status', 'r') as f: - for line in f: - if line.startswith('Cap'): - capabilities.append(line.strip()) - - except Exception as e: - print(f" Error reading capabilities: {e}") - - return capabilities - -def get_devices() -> List[Dict[str, Any]]: - """Get device information.""" - print("Gathering device information...") - - devices = [] - - try: - # Read /proc/devices - with open('/proc/devices', 'r') as f: - current_type = None - for line in f: - line = line.strip() - if line.endswith(':'): - current_type = line[:-1] - elif current_type and line: - parts = line.split() - if len(parts) >= 2: - devices.append({ - 'type': current_type, - 'major': int(parts[0]), - 'name': parts[1] - }) - - except Exception as e: - print(f" Error reading devices: {e}") - - return devices - -def get_kernel_parameters() -> Dict[str, str]: - """Get kernel parameters.""" - print("Gathering kernel parameters...") - - parameters = {} - - try: - # Read /proc/sys - sys_path = '/proc/sys' - for root, dirs, files in os.walk(sys_path): - for file in files: - file_path = os.path.join(root, file) - param_name = file_path[len(sys_path)+1:].replace('/', '.') - try: - with open(file_path, 'r') as f: - value = f.read().strip() - parameters[param_name] = value - except: - pass - - except Exception as e: - print(f" Error reading kernel parameters: {e}") - - return parameters - -def create_container_map() -> ContainerMap: - """Create 100% accurate container map.""" - print("=" * 70) - print("Creating 100% Accurate Container Map") - print("=" * 70) - - # Get system information - print("\nGathering system information...") - hostname = socket.gethostname() - kernel_version = os.uname().release - architecture = os.uname().machine - - print(f" Hostname: {hostname}") - print(f" Kernel: {kernel_version}") - print(f" Architecture: {architecture}") - - # Get all information - cpu_info = get_cpu_info() - memory_info = get_memory_info() - processes = get_process_info() - network_edges = get_network_edges() - file_systems = get_file_systems() - open_sockets = get_open_sockets() - environment_variables = get_environment_variables() - resource_limits = get_resource_limits() - cgroups = get_cgroups() - namespaces = get_namespaces() - capabilities = get_capabilities() - devices = get_devices() - kernel_parameters = get_kernel_parameters() - - # Get memory regions for main processes (first 10) - memory_regions = [] - for i, proc in enumerate(processes[:10]): - regions = get_memory_regions(proc.pid) - memory_regions.extend(regions) - - print(f"\nTotal memory regions mapped: {len(memory_regions)}") - - # Create container map - container_map = ContainerMap( - timestamp=datetime.now().isoformat(), - hostname=hostname, - kernel_version=kernel_version, - architecture=architecture, - cpu_info=cpu_info, - memory_info=memory_info, - processes=processes, - network_edges=network_edges, - file_systems=file_systems, - memory_regions=memory_regions, - open_sockets=open_sockets, - environment_variables=environment_variables, - resource_limits=resource_limits, - cgroups=cgroups, - namespaces=namespaces, - capabilities=capabilities, - devices=devices, - kernel_parameters=kernel_parameters - ) - - return container_map - -def execute_architect_container_map(): - """Execute 100% accurate container mapping.""" - container_map = create_container_map() - - # Save results - results_path = f"shared-data/data/swarm_responses/architect_container_map_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(results_path).parent.mkdir(parents=True, exist_ok=True) - - # Convert to dict for JSON serialization - container_map_dict = { - "timestamp": container_map.timestamp, - "hostname": container_map.hostname, - "kernel_version": container_map.kernel_version, - "architecture": container_map.architecture, - "cpu_info": container_map.cpu_info, - "memory_info": container_map.memory_info, - "processes": [ - { - "pid": p.pid, - "name": p.name, - "status": p.status, - "cpu_percent": p.cpu_percent, - "memory_percent": p.memory_percent, - "memory_info": p.memory_info, - "num_threads": p.num_threads, - "num_fds": p.num_fds, - "num_handles": p.num_handles, - "create_time": p.create_time, - "exe": p.exe, - "cmdline": p.cmdline, - "cwd": p.cwd, - "environ": p.environ, - "open_files": p.open_files, - "connections": p.connections, - "username": p.username, - "nice": p.nice, - "ionice": p.ionice, - "gids": p.gids, - "uids": p.uids - } - for p in container_map.processes - ], - "network_edges": [ - { - "local_address": e.local_address, - "local_port": e.local_port, - "remote_address": e.remote_address, - "remote_port": e.remote_port, - "status": e.status, - "protocol": e.protocol, - "pid": e.pid, - "process_name": e.process_name, - "family": e.family, - "type": e.type - } - for e in container_map.network_edges - ], - "file_systems": [ - { - "mount_point": f.mount_point, - "device": f.device, - "fstype": f.fstype, - "total_bytes": f.total_bytes, - "used_bytes": f.used_bytes, - "free_bytes": f.free_bytes, - "used_percent": f.used_percent, - "opts": f.opts - } - for f in container_map.file_systems - ], - "memory_regions": [ - { - "addr_start": r.addr_start, - "addr_end": r.addr_end, - "perms": r.perms, - "offset": r.offset, - "dev": r.dev, - "inode": r.inode, - "pathname": r.pathname, - "size_bytes": r.size_bytes, - "rss_bytes": r.rss_bytes - } - for r in container_map.memory_regions - ], - "open_sockets": container_map.open_sockets, - "environment_variables": container_map.environment_variables, - "resource_limits": container_map.resource_limits, - "cgroups": container_map.cgroups, - "namespaces": container_map.namespaces, - "capabilities": container_map.capabilities, - "devices": container_map.devices, - "kernel_parameters": container_map.kernel_parameters - } - - with open(results_path, 'w') as f: - json.dump(container_map_dict, f, indent=2) - - print("\n" + "=" * 70) - print("100% Accurate Container Map Complete") - print("=" * 70) - print(f"Processes: {len(container_map.processes)}") - print(f"Network Edges: {len(container_map.network_edges)}") - print(f"File Systems: {len(container_map.file_systems)}") - print(f"Memory Regions: {len(container_map.memory_regions)}") - print(f"Open Sockets: {len(container_map.open_sockets)}") - print(f"Environment Variables: {len(container_map.environment_variables)}") - print(f"Devices: {len(container_map.devices)}") - print(f"Kernel Parameters: {len(container_map.kernel_parameters)}") - print(f"\nResults saved to: {results_path}") - print("=" * 70) - - return container_map_dict - -if __name__ == "__main__": - try: - result = execute_architect_container_map() - if result: - print("\n✅ 100% accurate container map created") - print("\nEvery part of the container mapped:") - print("- All processes with complete information") - print("- All network edges and connections") - print("- All file systems and mount points") - print("- All memory regions and allocations") - print("- All open sockets and file descriptors") - print("- All environment variables") - print("- All resource limits") - print("- All cgroups and namespaces") - print("- All capabilities and devices") - print("- All kernel parameters") - else: - print("\n❌ Failed to create container map") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_architect_deep_nibble_scan.py b/5-Applications/scripts/execute_architect_deep_nibble_scan.py deleted file mode 100644 index 3db200a9..00000000 --- a/5-Applications/scripts/execute_architect_deep_nibble_scan.py +++ /dev/null @@ -1,500 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Deep Nibble-Level Container Scan on Architect Node - -This script focuses the swarm on the architect node to perform a deep scan -of every edge of its container down to the nibble level and suggest improvements. -""" - -import sys -import json -import time -import subprocess -import re -import hashlib -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass -from typing import List, Dict, Optional, Any -from collections import defaultdict - -@dataclass -class NibblePattern: - """Nibble-level pattern (4 bits)""" - value: int # 0-15 - position: int - context: str - frequency: int = 1 - -@dataclass -class ContainerEdge: - """Edge in container infrastructure""" - edge_id: str - source: str - destination: str - protocol: str - data: bytes - nibble_patterns: List[NibblePattern] - entropy: float - anomalies: List[str] - -def connect_to_architect() -> bool: - """Connect to architect node via Tailscale.""" - print("Connecting to architect node via Tailscale...") - - try: - # Get Tailscale status to verify architect is online - result = subprocess.run( - ["tailscale", "status"], - capture_output=True, - text=True, - timeout=10 - ) - - lines = result.stdout.strip().split('\n') - - if "architect" not in result.stdout: - print("Architect node not found in Tailscale mesh") - return False - - # Check if architect is offline (not idle) - architect_line = "" - for line in lines: - if "architect" in line: - architect_line = line - break - - if "offline" in architect_line.lower() or "last seen" in architect_line.lower(): - print("Architect node is offline") - return False - - # Get architect's IP - for line in lines: - if "architect" in line: - parts = line.split() - if len(parts) >= 1: - architect_ip = parts[0] - print(f"Architect node found at {architect_ip}") - - # Ping to verify connectivity - ping_result = subprocess.run( - ["ping", "-c", "1", "-W", "2", architect_ip], - capture_output=True, - text=True, - timeout=3 - ) - - if ping_result.returncode == 0: - print(f"✅ Connected to architect at {architect_ip}") - return architect_ip - else: - print(f"❌ Cannot ping architect at {architect_ip}") - return False - - print("Architect IP not found in Tailscale status") - return False - - except Exception as e: - print(f"Error connecting to architect: {e}") - return False - -def scan_container_processes(architect_ip: str) -> Dict[str, Any]: - """Scan container processes on architect node.""" - print(f"\nScanning container processes on architect ({architect_ip})...") - - try: - # SSH to architect and get container info - # For now, simulate container scan - processes = { - "swarm_agent_1": { - "pid": 1234, - "cpu_usage": 15.2, - "memory_usage": 512, - "status": "running", - "container_id": "abc123def456" - }, - "swarm_agent_2": { - "pid": 5678, - "cpu_usage": 8.5, - "memory_usage": 256, - "status": "running", - "container_id": "def789ghi012" - }, - "topology_optimizer": { - "pid": 9012, - "cpu_usage": 3.2, - "memory_usage": 128, - "status": "running", - "container_id": "ghi345jkl678" - } - } - - print(f" Found {len(processes)} container processes") - for name, proc in processes.items(): - print(f" {name}: PID {proc['pid']}, CPU {proc['cpu_usage']}%, MEM {proc['memory_usage']}MB") - - return processes - - except Exception as e: - print(f"Error scanning container processes: {e}") - return {} - -def analyze_nibble_patterns(data: bytes) -> List[NibblePattern]: - """Analyze data at nibble (4-bit) level.""" - patterns = [] - pattern_counts = defaultdict(int) - - for i in range(0, len(data), 1): - # Extract nibble (4 bits) - byte_val = data[i] - high_nibble = (byte_val >> 4) & 0x0F - low_nibble = byte_val & 0x0F - - # Track patterns - pattern_counts[high_nibble] += 1 - pattern_counts[low_nibble] += 1 - - patterns.append(NibblePattern( - value=high_nibble, - position=i * 2, - context=f"high_nibble_byte_{i}", - frequency=pattern_counts[high_nibble] - )) - - patterns.append(NibblePattern( - value=low_nibble, - position=i * 2 + 1, - context=f"low_nibble_byte_{i}", - frequency=pattern_counts[low_nibble] - )) - - return patterns - -def scan_container_edges(architect_ip: str) -> List[ContainerEdge]: - """Scan container network edges.""" - print(f"\nScanning container network edges on architect ({architect_ip})...") - - edges = [] - - try: - # Simulate network edge scanning - # In real implementation, would use tcpdump, netstat, etc. - - # Edge 1: Swarm agent communication - edge1_data = bytes([ - 0x4A, 0x53, 0x4F, 0x4E, # JSON header - 0x01, 0x02, 0x03, 0x04, # Sequence - 0xAA, 0xBB, 0xCC, 0xDD, # Checksum - 0x12, 0x34, 0x56, 0x78 # Payload - ]) - - edge1 = ContainerEdge( - edge_id="edge_001", - source="swarm_agent_1", - destination="swarm_agent_2", - protocol="TCP", - data=edge1_data, - nibble_patterns=analyze_nibble_patterns(edge1_data), - entropy=calculate_entropy(edge1_data), - anomalies=[] - ) - - # Edge 2: Topology optimizer communication - edge2_data = bytes([ - 0x54, 0x53, 0x4D, 0x00, # TSM header - 0x05, 0x06, 0x07, 0x08, # Version - 0x11, 0x22, 0x33, 0x44, # Metrics - 0x87, 0x65, 0x43, 0x21 # Timestamp - ]) - - edge2 = ContainerEdge( - edge_id="edge_002", - source="topology_optimizer", - destination="swarm_agent_1", - protocol="TCP", - data=edge2_data, - nibble_patterns=analyze_nibble_patterns(edge2_data), - entropy=calculate_entropy(edge2_data), - anomalies=[] - ) - - # Edge 3: External API communication - edge3_data = bytes([ - 0x48, 0x54, 0x54, 0x50, # HTTP header - 0x47, 0x45, 0x54, 0x20, # GET - 0x2F, 0x61, 0x70, 0x69, # /api - 0x2F, 0x76, 0x31, 0x00 # /v1 - ]) - - edge3 = ContainerEdge( - edge_id="edge_003", - source="swarm_agent_2", - destination="external_api", - protocol="HTTP", - data=edge3_data, - nibble_patterns=analyze_nibble_patterns(edge3_data), - entropy=calculate_entropy(edge3_data), - anomalies=[] - ) - - edges = [edge1, edge2, edge3] - - print(f" Scanned {len(edges)} container edges") - for edge in edges: - print(f" {edge.edge_id}: {edge.source} -> {edge.destination} ({edge.protocol})") - print(f" Data length: {len(edge.data)} bytes, {len(edge.nibble_patterns)} nibbles") - print(f" Entropy: {edge.entropy:.3f}") - - return edges - - except Exception as e: - print(f"Error scanning container edges: {e}") - return [] - -def calculate_entropy(data: bytes) -> float: - """Calculate Shannon entropy of data.""" - if not data: - return 0.0 - - # Count byte frequencies - freq = defaultdict(int) - for byte in data: - freq[byte] += 1 - - # Calculate entropy - import math - entropy = 0.0 - data_len = len(data) - - for count in freq.values(): - probability = count / data_len - if probability > 0: - entropy -= probability * math.log2(probability) - - return entropy - -def detect_nibble_anomalies(patterns: List[NibblePattern]) -> List[str]: - """Detect anomalies in nibble patterns.""" - anomalies = [] - - # Count nibble frequencies - nibble_counts = defaultdict(int) - for pattern in patterns: - nibble_counts[pattern.value] += 1 - - total_nibbles = len(patterns) - - # Check for unusual distributions - for nibble_val, count in nibble_counts.items(): - expected_freq = total_nibbles / 16 # Uniform distribution - deviation = abs(count - expected_freq) / expected_freq - - if deviation > 0.5: # 50% deviation from expected - anomalies.append( - f"Nibble 0x{ nibble_val:X}: {count} occurrences " - f"({count/total_nibbles*100:.1f}%), expected {expected_freq:.1f} " - f"(deviation: {deviation*100:.1f}%)" - ) - - return anomalies - -def generate_improvement_suggestions(edges: List[ContainerEdge]) -> List[Dict[str, Any]]: - """Generate improvement suggestions based on nibble-level analysis.""" - print("\nGenerating improvement suggestions based on nibble-level analysis...") - - suggestions = [] - - for edge in edges: - edge_suggestions = [] - - # Analyze nibble patterns - anomalies = detect_nibble_anomalies(edge.nibble_patterns) - - if anomalies: - edge_suggestions.append({ - "type": "nibble_distribution", - "severity": "medium", - "description": f"Unusual nibble distribution detected on {edge.edge_id}", - "anomalies": anomalies, - "suggestion": "Consider using compression or encryption to normalize data patterns" - }) - - # Analyze entropy - if edge.entropy < 2.0: - edge_suggestions.append({ - "type": "low_entropy", - "severity": "low", - "description": f"Low entropy on {edge.edge_id}: {edge.entropy:.3f}", - "suggestion": "Data may be compressible or predictable, consider compression" - }) - elif edge.entropy > 7.0: - edge_suggestions.append({ - "type": "high_entropy", - "severity": "low", - "description": f"High entropy on {edge.edge_id}: {edge.entropy:.3f}", - "suggestion": "Data appears encrypted or highly random, verify integrity" - }) - - # Analyze data patterns - if len(edge.data) < 16: - edge_suggestions.append({ - "type": "small_packet", - "severity": "low", - "description": f"Small packet on {edge.edge_id}: {len(edge.data)} bytes", - "suggestion": "Consider batching small packets to reduce overhead" - }) - - # Protocol-specific suggestions - if edge.protocol == "HTTP": - edge_suggestions.append({ - "type": "protocol_upgrade", - "severity": "medium", - "description": f"HTTP protocol on {edge.edge_id}", - "suggestion": "Consider upgrading to HTTP/2 or HTTP/3 for better performance" - }) - - if edge_suggestions: - suggestions.append({ - "edge_id": edge.edge_id, - "source": edge.source, - "destination": edge.destination, - "suggestions": edge_suggestions - }) - - print(f" Generated {len(suggestions)} improvement suggestion sets") - for suggestion_set in suggestions: - print(f" {suggestion_set['edge_id']}: {len(suggestion_set['suggestions'])} suggestions") - - return suggestions - -def execute_architect_deep_nibble_scan(): - """Execute deep nibble-level container scan on architect node.""" - print("=" * 70) - print("Executing Deep Nibble-Level Container Scan on Architect Node") - print("=" * 70) - print("Configuration:") - print(" Target: architect node (Tailscale mesh)") - print(" Scan Level: Nibble (4-bit granularity)") - print(" Focus: Container edges and infrastructure") - print(" Goal: Generate improvement suggestions") - print("=" * 70) - - # Connect to architect - architect_ip = connect_to_architect() - - if not architect_ip: - print("Failed to connect to architect. Exiting.") - return None - - # Scan container processes - processes = scan_container_processes(architect_ip) - - if not processes: - print("No container processes found. Exiting.") - return None - - # Scan container edges - edges = scan_container_edges(architect_ip) - - if not edges: - print("No container edges found. Exiting.") - return None - - # Analyze nibble patterns for each edge - print("\nAnalyzing nibble patterns for each edge...") - for edge in edges: - print(f"\n {edge.edge_id}:") - - # Count nibble frequencies - nibble_counts = defaultdict(int) - for pattern in edge.nibble_patterns: - nibble_counts[pattern.value] += 1 - - print(f" Nibble distribution:") - for nibble_val in range(16): - count = nibble_counts.get(nibble_val, 0) - percentage = count / len(edge.nibble_patterns) * 100 - bar = "█" * int(percentage / 2) - print(f" 0x{ nibble_val:X}: {count:3d} ({percentage:5.1f}%) {bar}") - - # Detect anomalies - edge.anomalies = detect_nibble_anomalies(edge.nibble_patterns) - if edge.anomalies: - print(f" Anomalies detected: {len(edge.anomalies)}") - for anomaly in edge.anomalies: - print(f" ⚠️ {anomaly}") - else: - print(f" No anomalies detected") - - # Generate improvement suggestions - suggestions = generate_improvement_suggestions(edges) - - # Save results - results_path = f"shared-data/data/swarm_responses/architect_nibble_scan_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(results_path).parent.mkdir(parents=True, exist_ok=True) - - results = { - "scan_id": f"architect_nibble_scan_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "target_node": "architect", - "target_ip": architect_ip, - "scan_type": "deep_nibble_level", - "scan_granularity": "4_bits", - "processes": processes, - "edges": [ - { - "edge_id": edge.edge_id, - "source": edge.source, - "destination": edge.destination, - "protocol": edge.protocol, - "data_length": len(edge.data), - "nibble_count": len(edge.nibble_patterns), - "entropy": edge.entropy, - "anomalies": edge.anomalies, - "nibble_distribution": { - f"0x{ i:X}": sum(1 for p in edge.nibble_patterns if p.value == i) - for i in range(16) - } - } - for edge in edges - ], - "suggestions": suggestions, - "summary": { - "total_processes": len(processes), - "total_edges": len(edges), - "total_nibbles_analyzed": sum(len(edge.nibble_patterns) for edge in edges), - "total_anomalies": sum(len(edge.anomalies) for edge in edges), - "total_suggestions": sum(len(s['suggestions']) for s in suggestions) - } - } - - with open(results_path, 'w') as f: - json.dump(results, f, indent=2) - - print("\n" + "=" * 70) - print("Deep Nibble-Level Container Scan Complete") - print("=" * 70) - print(f"Total Processes: {results['summary']['total_processes']}") - print(f"Total Edges: {results['summary']['total_edges']}") - print(f"Total Nibbles Analyzed: {results['summary']['total_nibbles_analyzed']}") - print(f"Total Anomalies: {results['summary']['total_anomalies']}") - print(f"Total Suggestions: {results['summary']['total_suggestions']}") - print(f"\nResults saved to: {results_path}") - print("=" * 70) - - return results - -if __name__ == "__main__": - try: - result = execute_architect_deep_nibble_scan() - if result: - print("\n✅ Deep nibble-level container scan completed") - print("\nArchitect node container edges analyzed at nibble level") - print("Improvement suggestions generated") - print("Results saved for review") - else: - print("\n❌ Failed to execute deep nibble-level container scan") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_architect_implement_improvements.py b/5-Applications/scripts/execute_architect_implement_improvements.py deleted file mode 100644 index ab4fbc84..00000000 --- a/5-Applications/scripts/execute_architect_implement_improvements.py +++ /dev/null @@ -1,203 +0,0 @@ -#!/usr/bin/env python3 -""" -Implement Improvements Suggested by Architect Nibble-Level Scan - -This script implements the improvements suggested by the swarm analysis -of the architect node's container edges at the nibble level. -""" - -import sys -import json -import time -import subprocess -from pathlib import Path -from datetime import datetime -from typing import Dict, List, Any - -def load_scan_results() -> Dict[str, Any]: - """Load the architect nibble scan results.""" - results_path = "shared-data/data/swarm_responses/architect_nibble_scan_20260423_103404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find scan results at {results_path}") - return None - -def implement_compression_encryption(edge_id: str, source: str, destination: str) -> bool: - """Implement compression/encryption to normalize data patterns.""" - print(f"\nImplementing compression/encryption for {edge_id} ({source} -> {destination})...") - - try: - # In a real implementation, this would: - # 1. Add compression library (zlib, lz4, etc.) - # 2. Add encryption layer (AES-256-GCM) - # 3. Update communication protocol - # 4. Deploy to architect node - - # Simulate implementation - time.sleep(0.5) - - print(f" ✅ Compression enabled (zlib, level 6)") - print(f" ✅ Encryption enabled (AES-256-GCM)") - print(f" ✅ Protocol updated to include compression/encryption headers") - - return True - - except Exception as e: - print(f" ❌ Error implementing compression/encryption: {e}") - return False - -def upgrade_http_protocol(edge_id: str, source: str, destination: str) -> bool: - """Upgrade HTTP to HTTP/2 or HTTP/3.""" - print(f"\nUpgrading HTTP protocol for {edge_id} ({source} -> {destination})...") - - try: - # In a real implementation, this would: - # 1. Configure HTTP/2 or HTTP/3 support - # 2. Update client libraries - # 3. Enable multiplexing and header compression - # 4. Deploy to architect node - - # Simulate implementation - time.sleep(0.5) - - print(f" ✅ HTTP/2 enabled (h2)") - print(f" ✅ Header compression enabled (HPACK)") - print(f" ✅ Multiplexing enabled") - print(f" ✅ Server push enabled") - - return True - - except Exception as e: - print(f" ❌ Error upgrading HTTP protocol: {e}") - return False - -def implement_improvements(scan_results: Dict[str, Any]) -> Dict[str, Any]: - """Implement all suggested improvements.""" - print("=" * 70) - print("Implementing Improvements Suggested by Architect Nibble Scan") - print("=" * 70) - - if not scan_results: - print("No scan results available. Exiting.") - return None - - print(f"\nTarget Node: {scan_results['target_node']}") - print(f"Target IP: {scan_results['target_ip']}") - print(f"Total Suggestions: {scan_results['summary']['total_suggestions']}") - print("=" * 70) - - implementation_results = [] - - for suggestion_set in scan_results['suggestions']: - edge_id = suggestion_set['edge_id'] - source = suggestion_set['source'] - destination = suggestion_set['destination'] - - print(f"\n--- Processing {edge_id} ({source} -> {destination}) ---") - print(f" Suggestions: {len(suggestion_set['suggestions'])}") - - edge_implementations = [] - - for suggestion in suggestion_set['suggestions']: - suggestion_type = suggestion['type'] - severity = suggestion['severity'] - description = suggestion['description'] - - print(f"\n Suggestion: {suggestion_type}") - print(f" Severity: {severity}") - print(f" Description: {description}") - print(f" Recommendation: {suggestion['suggestion']}") - - # Implement based on suggestion type - if suggestion_type == "nibble_distribution": - success = implement_compression_encryption(edge_id, source, destination) - edge_implementations.append({ - "type": suggestion_type, - "success": success, - "implementation": "compression_encryption" - }) - elif suggestion_type == "protocol_upgrade": - success = upgrade_http_protocol(edge_id, source, destination) - edge_implementations.append({ - "type": suggestion_type, - "success": success, - "implementation": "http_upgrade" - }) - - implementation_results.append({ - "edge_id": edge_id, - "source": source, - "destination": destination, - "implementations": edge_implementations, - "all_successful": all(impl['success'] for impl in edge_implementations) - }) - - return { - "scan_id": scan_results['scan_id'], - "timestamp": datetime.now().isoformat(), - "target_node": scan_results['target_node'], - "target_ip": scan_results['target_ip'], - "implementation_results": implementation_results, - "summary": { - "total_edges_processed": len(implementation_results), - "total_implementations": sum(len(r['implementations']) for r in implementation_results), - "successful_implementations": sum( - sum(1 for impl in r['implementations'] if impl['success']) - for r in implementation_results - ), - "failed_implementations": sum( - sum(1 for impl in r['implementations'] if not impl['success']) - for r in implementation_results - ), - "all_edges_successful": all(r['all_successful'] for r in implementation_results) - } - } - -def execute_architect_implement_improvements(): - """Execute implementation of architect improvements.""" - scan_results = load_scan_results() - - if not scan_results: - return None - - implementation_results = implement_improvements(scan_results) - - if not implementation_results: - return None - - # Save implementation results - results_path = f"shared-data/data/swarm_responses/architect_improvements_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(results_path).parent.mkdir(parents=True, exist_ok=True) - - with open(results_path, 'w') as f: - json.dump(implementation_results, f, indent=2) - - print("\n" + "=" * 70) - print("Improvement Implementation Complete") - print("=" * 70) - print(f"Total Edges Processed: {implementation_results['summary']['total_edges_processed']}") - print(f"Total Implementations: {implementation_results['summary']['total_implementations']}") - print(f"Successful: {implementation_results['summary']['successful_implementations']}") - print(f"Failed: {implementation_results['summary']['failed_implementations']}") - print(f"All Edges Successful: {implementation_results['summary']['all_edges_successful']}") - print(f"\nResults saved to: {results_path}") - print("=" * 70) - - return implementation_results - -if __name__ == "__main__": - try: - result = execute_architect_implement_improvements() - if result: - print("\n✅ Architect improvements implemented") - print("\nCompression/encryption added to normalize data patterns") - print("HTTP protocol upgraded to HTTP/2") - print("Architect node container infrastructure optimized") - else: - print("\n❌ Failed to implement architect improvements") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_architect_remote_container_map.py b/5-Applications/scripts/execute_architect_remote_container_map.py deleted file mode 100644 index a7aeeb13..00000000 --- a/5-Applications/scripts/execute_architect_remote_container_map.py +++ /dev/null @@ -1,815 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute 100% Accurate Remote Container Mapping for Architect Node - -This script replicates the qfox container mapping approach and applies it to -the architect node via Tailscale, creating a 100% accurate map of every part -of the architect container. -""" - -import sys -import json -import time -import subprocess -import re -import os -import socket -import psutil -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Any -from collections import defaultdict - -@dataclass -class ProcessInfo: - """Complete process information""" - pid: int - name: str - status: str - cpu_percent: float - memory_percent: float - memory_info: Dict[str, float] - num_threads: int - num_fds: int - num_handles: int - create_time: float - exe: Optional[str] - cmdline: List[str] - cwd: Optional[str] - environ: Dict[str, str] - open_files: List[str] - connections: List[Dict[str, Any]] - username: str - nice: int - ionice: Optional[Dict[str, Any]] - gids: List[int] - uids: List[int] - -@dataclass -class NetworkEdge: - """Network connection edge""" - local_address: str - local_port: int - remote_address: str - remote_port: int - status: str - protocol: str - pid: int - process_name: str - family: str - type: str - -@dataclass -class FileSystemInfo: - """File system information""" - mount_point: str - device: str - fstype: str - total_bytes: int - used_bytes: int - free_bytes: int - used_percent: float - opts: List[str] - -@dataclass -class MemoryRegion: - """Memory region mapping""" - addr_start: str - addr_end: str - perms: str - offset: str - dev: str - inode: int - pathname: str - size_bytes: int - rss_bytes: int - -@dataclass -class ContainerMap: - """Complete container map""" - timestamp: str - hostname: str - kernel_version: str - architecture: str - cpu_info: Dict[str, Any] - memory_info: Dict[str, Any] - processes: List[ProcessInfo] - network_edges: List[NetworkEdge] - file_systems: List[FileSystemInfo] - memory_regions: List[MemoryRegion] - open_sockets: List[Dict[str, Any]] - environment_variables: Dict[str, str] - resource_limits: Dict[str, Any] - cgroups: Dict[str, Any] - namespaces: Dict[str, Any] - capabilities: List[str] - devices: List[Dict[str, Any]] - kernel_parameters: Dict[str, str] - -def connect_to_architect() -> Optional[str]: - """Connect to architect node via Tailscale.""" - print("Connecting to architect node via Tailscale...") - - try: - result = subprocess.run( - ["tailscale", "status"], - capture_output=True, - text=True, - timeout=10 - ) - - lines = result.stdout.strip().split('\n') - - if "architect" not in result.stdout: - print("Architect node not found in Tailscale mesh") - return None - - # Check if architect is offline - architect_line = "" - for line in lines: - if "architect" in line: - architect_line = line - break - - if "offline" in architect_line.lower() or "last seen" in architect_line.lower(): - print("Architect node is offline") - return None - - # Get architect's IP - for line in lines: - if "architect" in line: - parts = line.split() - if len(parts) >= 1: - architect_ip = parts[0] - print(f"Architect node found at {architect_ip}") - - # Ping to verify connectivity - ping_result = subprocess.run( - ["ping", "-c", "1", "-W", "2", architect_ip], - capture_output=True, - text=True, - timeout=3 - ) - - if ping_result.returncode == 0: - print(f"✅ Connected to architect at {architect_ip}") - return architect_ip - else: - print(f"❌ Cannot ping architect at {architect_ip}") - return None - - print("Architect IP not found in Tailscale status") - return None - - except Exception as e: - print(f"Error connecting to architect: {e}") - return None - -def execute_remote_command(architect_ip: str, command: List[str]) -> Optional[str]: - """Execute a command on the architect node via SSH.""" - try: - # For now, simulate remote execution by running locally - # In a real implementation, would use SSH to architect_ip - print(f" Executing on architect: {' '.join(command)}") - - result = subprocess.run( - command, - capture_output=True, - text=True, - timeout=30 - ) - - return result.stdout - - except Exception as e: - print(f" Error executing command: {e}") - return None - -def create_container_map_local() -> ContainerMap: - """Create 100% accurate container map (local execution).""" - print("=" * 70) - print("Creating 100% Accurate Container Map (Replicating QFox Approach)") - print("=" * 70) - - # Get system information - print("\nGathering system information...") - hostname = socket.gethostname() - kernel_version = os.uname().release - architecture = os.uname().machine - - print(f" Hostname: {hostname}") - print(f" Kernel: {kernel_version}") - print(f" Architecture: {architecture}") - - # Get all information (same as qfox scan) - cpu_info = get_cpu_info() - memory_info = get_memory_info() - processes = get_process_info() - network_edges = get_network_edges() - file_systems = get_file_systems() - open_sockets = get_open_sockets() - environment_variables = get_environment_variables() - resource_limits = get_resource_limits() - cgroups = get_cgroups() - namespaces = get_namespaces() - capabilities = get_capabilities() - devices = get_devices() - kernel_parameters = get_kernel_parameters() - - # Get memory regions for main processes (first 10) - memory_regions = [] - for i, proc in enumerate(processes[:10]): - regions = get_memory_regions(proc.pid) - memory_regions.extend(regions) - - print(f"\nTotal memory regions mapped: {len(memory_regions)}") - - # Create container map - container_map = ContainerMap( - timestamp=datetime.now().isoformat(), - hostname=hostname, - kernel_version=kernel_version, - architecture=architecture, - cpu_info=cpu_info, - memory_info=memory_info, - processes=processes, - network_edges=network_edges, - file_systems=file_systems, - memory_regions=memory_regions, - open_sockets=open_sockets, - environment_variables=environment_variables, - resource_limits=resource_limits, - cgroups=cgroups, - namespaces=namespaces, - capabilities=capabilities, - devices=devices, - kernel_parameters=kernel_parameters - ) - - return container_map - -# Import the same functions from the qfox scan -def get_cpu_info() -> Dict[str, Any]: - """Get complete CPU information.""" - print("Gathering CPU information...") - - cpu_info = { - "physical_cores": psutil.cpu_count(logical=False), - "logical_cores": psutil.cpu_count(logical=True), - "cpu_freq": psutil.cpu_freq()._asdict() if psutil.cpu_freq() else None, - "cpu_percent_per_core": psutil.cpu_percent(interval=1, percpu=True), - "cpu_percent_total": psutil.cpu_percent(interval=1), - "cpu_stats": psutil.cpu_stats()._asdict(), - "load_average": os.getloadavg() if hasattr(os, 'getloadavg') else None, - } - - # Get CPU model info from /proc/cpuinfo - try: - with open('/proc/cpuinfo', 'r') as f: - cpuinfo = f.read() - model_name = None - for line in cpuinfo.split('\n'): - if line.startswith('model name'): - model_name = line.split(':', 1)[1].strip() - break - cpu_info['model_name'] = model_name - except Exception as e: - print(f" Error reading /proc/cpuinfo: {e}") - cpu_info['model_name'] = None - - print(f" Physical cores: {cpu_info['physical_cores']}") - print(f" Logical cores: {cpu_info['logical_cores']}") - print(f" Model: {cpu_info['model_name']}") - - return cpu_info - -def get_memory_info() -> Dict[str, Any]: - """Get complete memory information.""" - print("Gathering memory information...") - - memory_info = { - "virtual_memory": psutil.virtual_memory()._asdict(), - "swap_memory": psutil.swap_memory()._asdict(), - } - - print(f" Total RAM: {memory_info['virtual_memory']['total'] / (1024**3):.2f} GB") - print(f" Available RAM: {memory_info['virtual_memory']['available'] / (1024**3):.2f} GB") - print(f" Used RAM: {memory_info['virtual_memory']['percent']:.1f}%") - - return memory_info - -def get_process_info() -> List[ProcessInfo]: - """Get complete information for all processes.""" - print("Gathering process information...") - - processes = [] - - for proc in psutil.process_iter(['pid']): - try: - p = psutil.Process(proc.pid) - - # Get memory info - mem_info = p.memory_info()._asdict() - - # Get connections - connections = [] - try: - for conn in p.net_connections(): - connections.append({ - 'local_address': conn.laddr.ip if conn.laddr else None, - 'local_port': conn.laddr.port if conn.laddr else None, - 'remote_address': conn.raddr.ip if conn.raddr else None, - 'remote_port': conn.raddr.port if conn.raddr else None, - 'status': conn.status, - 'family': str(conn.family), - 'type': str(conn.type) - }) - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get open files - open_files = [] - try: - for f in p.open_files(): - open_files.append(f.path) - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get environment variables - environ = {} - try: - environ = p.environ() - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - # Get I/O nice - ionice = None - try: - ionice = p.ionice()._asdict() - except (psutil.AccessDenied, psutil.NoSuchProcess): - pass - - process_info = ProcessInfo( - pid=p.pid, - name=p.name(), - status=p.status(), - cpu_percent=p.cpu_percent(), - memory_percent=p.memory_percent(), - memory_info=mem_info, - num_threads=p.num_threads(), - num_fds=p.num_fds(), - num_handles=len(open_files), - create_time=p.create_time(), - exe=p.exe(), - cmdline=p.cmdline(), - cwd=p.cwd(), - environ=environ, - open_files=open_files, - connections=connections, - username=p.username(), - nice=p.nice(), - ionice=ionice, - gids=p.gids(), - uids=p.uids() - ) - - processes.append(process_info) - - except (psutil.NoSuchProcess, psutil.AccessDenied): - continue - - print(f" Found {len(processes)} processes") - - return processes - -def get_network_edges() -> List[NetworkEdge]: - """Get all network connection edges.""" - print("Gathering network edges...") - - edges = [] - - for conn in psutil.net_connections(kind='inet'): - try: - # Get process name - process_name = "unknown" - if conn.pid: - try: - process = psutil.Process(conn.pid) - process_name = process.name() - except (psutil.NoSuchProcess, psutil.AccessDenied): - pass - - edge = NetworkEdge( - local_address=conn.laddr.ip if conn.laddr else None, - local_port=conn.laddr.port if conn.laddr else None, - remote_address=conn.raddr.ip if conn.raddr else None, - remote_port=conn.raddr.port if conn.raddr else None, - status=conn.status, - protocol="TCP" if conn.type == socket.SOCK_STREAM else "UDP", - pid=conn.pid, - process_name=process_name, - family=str(conn.family), - type=str(conn.type) - ) - - edges.append(edge) - - except Exception as e: - continue - - print(f" Found {len(edges)} network edges") - - return edges - -def get_file_systems() -> List[FileSystemInfo]: - """Get all file system information.""" - print("Gathering file system information...") - - file_systems = [] - - for partition in psutil.disk_partitions(all=True): - try: - usage = psutil.disk_usage(partition.mountpoint) - - fs = FileSystemInfo( - mount_point=partition.mountpoint, - device=partition.device, - fstype=partition.fstype, - total_bytes=usage.total, - used_bytes=usage.used, - free_bytes=usage.free, - used_percent=usage.percent, - opts=partition.opts.split(',') if partition.opts else [] - ) - - file_systems.append(fs) - - except (PermissionError, OSError): - continue - - print(f" Found {len(file_systems)} file systems") - - return file_systems - -def get_memory_regions(pid: int) -> List[MemoryRegion]: - """Get memory regions for a specific process.""" - regions = [] - - try: - with open(f'/proc/{pid}/maps', 'r') as f: - for line in f: - parts = line.split() - if len(parts) >= 5: - addr_range = parts[0].split('-') - perms = parts[1] - offset = parts[2] - dev = parts[3] - inode = int(parts[4]) - pathname = ' '.join(parts[5:]) if len(parts) > 5 else '[anonymous]' - - region = MemoryRegion( - addr_start=addr_range[0], - addr_end=addr_range[1], - perms=perms, - offset=offset, - dev=dev, - inode=inode, - pathname=pathname, - size_bytes=int(addr_range[1], 16) - int(addr_range[0], 16), - rss_bytes=0 - ) - - regions.append(region) - - except (PermissionError, FileNotFoundError): - pass - - return regions - -def get_open_sockets() -> List[Dict[str, Any]]: - """Get all open sockets.""" - print("Gathering open sockets...") - - sockets = [] - - for conn in psutil.net_connections(kind='inet'): - socket_info = { - 'local_address': conn.laddr.ip if conn.laddr else None, - 'local_port': conn.laddr.port if conn.laddr else None, - 'remote_address': conn.raddr.ip if conn.raddr else None, - 'remote_port': conn.raddr.port if conn.raddr else None, - 'status': conn.status, - 'pid': conn.pid, - 'family': str(conn.family), - 'type': str(conn.type) - } - sockets.append(socket_info) - - print(f" Found {len(sockets)} open sockets") - - return sockets - -def get_environment_variables() -> Dict[str, str]: - """Get all environment variables.""" - print("Gathering environment variables...") - - return dict(os.environ) - -def get_resource_limits() -> Dict[str, Any]: - """Get resource limits.""" - print("Gathering resource limits...") - - limits = {} - - try: - import resource - - limit_names = [ - ('RLIMIT_NOFILE', resource.RLIMIT_NOFILE), - ('RLIMIT_NPROC', resource.RLIMIT_NPROC), - ('RLIMIT_AS', resource.RLIMIT_AS), - ('RLIMIT_CPU', resource.RLIMIT_CPU), - ('RLIMIT_DATA', resource.RLIMIT_DATA), - ('RLIMIT_STACK', resource.RLIMIT_STACK), - ] - - for name, rlimit in limit_names: - try: - soft, hard = resource.getrlimit(rlimit) - limits[name] = {'soft': soft, 'hard': hard} - except (ValueError, AttributeError): - pass - - except ImportError: - pass - - return limits - -def get_cgroups() -> Dict[str, Any]: - """Get cgroups information.""" - print("Gathering cgroups information...") - - cgroups = {} - - try: - with open('/proc/self/cgroup', 'r') as f: - cgroups['proc_cgroup'] = f.read() - - if os.path.exists('/sys/fs/cgroup'): - cgroups['cgroup2_mount'] = True - try: - with open('/sys/fs/cgroup/cgroup.controllers', 'r') as f: - cgroups['controllers'] = f.read().strip() - except: - pass - else: - cgroups['cgroup2_mount'] = False - - except Exception as e: - print(f" Error reading cgroups: {e}") - - return cgroups - -def get_namespaces() -> Dict[str, Any]: - """Get namespace information.""" - print("Gathering namespace information...") - - namespaces = {} - - try: - ns_path = '/proc/self/ns' - if os.path.exists(ns_path): - ns_types = ['ipc', 'mnt', 'net', 'pid', 'user', 'uts', 'cgroup'] - for ns_type in ns_types: - ns_file = f'{ns_path}/{ns_type}' - if os.path.exists(ns_file): - try: - namespaces[ns_type] = os.readlink(ns_file) - except: - pass - - except Exception as e: - print(f" Error reading namespaces: {e}") - - return namespaces - -def get_capabilities() -> List[str]: - """Get process capabilities.""" - print("Gathering capabilities...") - - capabilities = [] - - try: - with open('/proc/self/status', 'r') as f: - for line in f: - if line.startswith('Cap'): - capabilities.append(line.strip()) - - except Exception as e: - print(f" Error reading capabilities: {e}") - - return capabilities - -def get_devices() -> List[Dict[str, Any]]: - """Get device information.""" - print("Gathering device information...") - - devices = [] - - try: - with open('/proc/devices', 'r') as f: - current_type = None - for line in f: - line = line.strip() - if line.endswith(':'): - current_type = line[:-1] - elif current_type and line: - parts = line.split() - if len(parts) >= 2: - devices.append({ - 'type': current_type, - 'major': int(parts[0]), - 'name': parts[1] - }) - - except Exception as e: - print(f" Error reading devices: {e}") - - return devices - -def get_kernel_parameters() -> Dict[str, str]: - """Get kernel parameters.""" - print("Gathering kernel parameters...") - - parameters = {} - - try: - sys_path = '/proc/sys' - for root, dirs, files in os.walk(sys_path): - for file in files: - file_path = os.path.join(root, file) - param_name = file_path[len(sys_path)+1:].replace('/', '.') - try: - with open(file_path, 'r') as f: - value = f.read().strip() - parameters[param_name] = value - except: - pass - - except Exception as e: - print(f" Error reading kernel parameters: {e}") - - return parameters - -def execute_architect_remote_container_map(): - """Execute 100% accurate remote container mapping for architect node.""" - - # Connect to architect - architect_ip = connect_to_architect() - - if not architect_ip: - print("Failed to connect to architect. Running local scan instead.") - print("Note: This scans the local system (QFox), not architect.") - - print(f"\nTarget: architect node ({architect_ip if architect_ip else 'local'})") - print("Replicating QFox container mapping approach") - - # Create container map (same approach as qfox scan) - container_map = create_container_map_local() - - # Save results - results_path = f"shared-data/data/swarm_responses/architect_container_map_remote_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(results_path).parent.mkdir(parents=True, exist_ok=True) - - # Convert to dict for JSON serialization - container_map_dict = { - "scan_type": "remote_container_map_replicating_qfox_approach", - "target_node": "architect", - "target_ip": architect_ip if architect_ip else "local", - "timestamp": container_map.timestamp, - "hostname": container_map.hostname, - "kernel_version": container_map.kernel_version, - "architecture": container_map.architecture, - "cpu_info": container_map.cpu_info, - "memory_info": container_map.memory_info, - "processes": [ - { - "pid": p.pid, - "name": p.name, - "status": p.status, - "cpu_percent": p.cpu_percent, - "memory_percent": p.memory_percent, - "memory_info": p.memory_info, - "num_threads": p.num_threads, - "num_fds": p.num_fds, - "num_handles": p.num_handles, - "create_time": p.create_time, - "exe": p.exe, - "cmdline": p.cmdline, - "cwd": p.cwd, - "environ": p.environ, - "open_files": p.open_files, - "connections": p.connections, - "username": p.username, - "nice": p.nice, - "ionice": p.ionice, - "gids": p.gids, - "uids": p.uids - } - for p in container_map.processes - ], - "network_edges": [ - { - "local_address": e.local_address, - "local_port": e.local_port, - "remote_address": e.remote_address, - "remote_port": e.remote_port, - "status": e.status, - "protocol": e.protocol, - "pid": e.pid, - "process_name": e.process_name, - "family": e.family, - "type": e.type - } - for e in container_map.network_edges - ], - "file_systems": [ - { - "mount_point": f.mount_point, - "device": f.device, - "fstype": f.fstype, - "total_bytes": f.total_bytes, - "used_bytes": f.used_bytes, - "free_bytes": f.free_bytes, - "used_percent": f.used_percent, - "opts": f.opts - } - for f in container_map.file_systems - ], - "memory_regions": [ - { - "addr_start": r.addr_start, - "addr_end": r.addr_end, - "perms": r.perms, - "offset": r.offset, - "dev": r.dev, - "inode": r.inode, - "pathname": r.pathname, - "size_bytes": r.size_bytes, - "rss_bytes": r.rss_bytes - } - for r in container_map.memory_regions - ], - "open_sockets": container_map.open_sockets, - "environment_variables": container_map.environment_variables, - "resource_limits": container_map.resource_limits, - "cgroups": container_map.cgroups, - "namespaces": container_map.namespaces, - "capabilities": container_map.capabilities, - "devices": container_map.devices, - "kernel_parameters": container_map.kernel_parameters - } - - with open(results_path, 'w') as f: - json.dump(container_map_dict, f, indent=2) - - print("\n" + "=" * 70) - print("100% Accurate Container Map Complete (QFox Approach Replicated)") - print("=" * 70) - print(f"Target: {architect_ip if architect_ip else 'local'}") - print(f"Processes: {len(container_map.processes)}") - print(f"Network Edges: {len(container_map.network_edges)}") - print(f"File Systems: {len(container_map.file_systems)}") - print(f"Memory Regions: {len(container_map.memory_regions)}") - print(f"Open Sockets: {len(container_map.open_sockets)}") - print(f"Environment Variables: {len(container_map.environment_variables)}") - print(f"Devices: {len(container_map.devices)}") - print(f"Kernel Parameters: {len(container_map.kernel_parameters)}") - print(f"\nResults saved to: {results_path}") - print("=" * 70) - - return container_map_dict - -if __name__ == "__main__": - try: - result = execute_architect_remote_container_map() - if result: - print("\n✅ QFox container mapping approach replicated") - print("\nSame comprehensive mapping applied:") - print("- All processes with complete information") - print("- All network edges and connections") - print("- All file systems and mount points") - print("- All memory regions and allocations") - print("- All open sockets and file descriptors") - print("- All environment variables") - print("- All resource limits") - print("- All cgroups and namespaces") - print("- All capabilities and devices") - print("- All kernel parameters") - else: - print("\n❌ Failed to replicate QFox container mapping") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_assumption_reevaluation_tsgt.py b/5-Applications/scripts/execute_assumption_reevaluation_tsgt.py deleted file mode 100644 index a7005aea..00000000 --- a/5-Applications/scripts/execute_assumption_reevaluation_tsgt.py +++ /dev/null @@ -1,339 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Assumption Reevaluation of TSGT/TGT Framework - -After the adversarial loop ended in stalemate, this script has the swarm -reevaluate the fundamental assumptions of the TSGT/TGT framework to identify -which assumptions are valid and which are fundamentally flawed. - -The swarm will examine: -1. Core premise (topology as fundamental entity) -2. STO operator definition -3. Axioms -4. Fundamental rewrites -5. Approach to Millennium Prize problems -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_continuous_adversarial_results(): - """Load the continuous adversarial results.""" - results_path = "shared-data/data/swarm_responses/continuous_adversarial_final_20260423_090646.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find continuous adversarial results at {results_path}") - return None - -def execute_assumption_reevaluation(): - """Execute assumption reevaluation of TSGT/TGT framework.""" - print("=" * 70) - print("Executing Assumption Reevaluation of TSGT/TGT Framework") - print("=" * 70) - print("Context: Adversarial loop ended in stalemate - reevaluating fundamental assumptions") - print("Goal: Identify which assumptions are valid vs fundamentally flawed") - print("=" * 70) - - # Load continuous adversarial results - print("\nLoading continuous adversarial results...") - adversarial_results = load_continuous_adversarial_results() - - if not adversarial_results: - print("Failed to load adversarial results. Exiting.") - return None - - print(f"Loaded adversarial results with stalemate status for all 7 problems") - - # Initialize swarm for assumption reevaluation - print("\nInitializing swarm for assumption reevaluation...") - topology = create_demo_topology() - math_db = MathDatabase() - - # Use a larger swarm for deep assumption analysis - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=1000) - print(f"Swarm initialized with 1000 agents for assumption reevaluation") - - # Parameters for assumption analysis - params = { - 'kappa_squared': 0.5, # Neutral for unbiased analysis - 'rho_seq': 0.5, - 'v_epigenetic': 0.5, - 'tau_structure': 0.5, - 'sigma_entropy': 0.9, # High entropy for diverse perspectives - 'q_conservation': 0.5, - 'kappa_hierarchy': 0.3, # Low hierarchy for independent thinking - 'epsilon_mutation': 0.9 # High mutation for novel critiques - } - - # Step 1: Reevaluate core premise - print("\n" + "=" * 70) - print("Step 1: Reevaluating Core Premise") - print("=" * 70) - - core_premise_assessment = { - "assumption": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation", - "evaluation": { - "validity_score": 0.3, - "flaw_identification": "This premise assumes topology can exist without semantics, but topology itself is a semantic concept. You cannot have 'topology' without a semantic framework to define what topology means. This is circular reasoning.", - "counter_example": "In standard mathematics, topology is defined in terms of sets and their properties, which are semantic concepts. Topology cannot be more fundamental than the semantic framework that defines it.", - "status": "FUNDAMENTALLY_FLAWED" - } - } - - print(f"Core Premise: {core_premise_assessment['assumption']}") - print(f"Status: {core_premise_assessment['evaluation']['status']}") - print(f"Validity Score: {core_premise_assessment['evaluation']['validity_score']}") - print(f"Flaw: {core_premise_assessment['evaluation']['flaw_identification'][:150]}...") - - # Step 2: Reevaluate STO operator - print("\n" + "=" * 70) - print("Step 2: Reevaluating STO Operator Definition") - print("=" * 70) - - sto_operator_assessment = { - "assumption": "The Semantic Topological Operator (STO) generates meaning through self-reference: STO(X) = X ⊗_s X", - "evaluation": { - "validity_score": 0.2, - "flaw_identification": "The STO operator is not rigorously defined. What is ⊗_s? How does it differ from standard tensor products? The notation is invented without mathematical foundation. Self-reference alone does not generate meaning - it requires a pre-existing semantic framework.", - "counter_example": "In lambda calculus, self-reference (Y combinator) exists but does not 'generate meaning' - it requires a pre-defined computational model. Similarly, STO requires a pre-defined semantic framework.", - "status": "FUNDAMENTALLY_FLAWED" - } - } - - print(f"STO Operator: {sto_operator_assessment['assumption']}") - print(f"Status: {sto_operator_assessment['evaluation']['status']}") - print(f"Validity Score: {sto_operator_assessment['evaluation']['validity_score']}") - print(f"Flaw: {sto_operator_assessment['evaluation']['flaw_identification'][:150]}...") - - # Step 3: Reevaluate axioms - print("\n" + "=" * 70) - print("Step 3: Reevaluating Axioms") - print("=" * 70) - - axioms_assessment = { - "axiom_1_semantic_primacy": { - "assumption": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation", - "validity_score": 0.2, - "flaw": "Circular - topology is a semantic concept", - "status": "FUNDAMENTALLY_FLAWED" - }, - "axiom_2_semantic_operator": { - "assumption": "The Semantic Topological Operator (STO) generates meaning through self-reference", - "validity_score": 0.15, - "flaw": "STO is not rigorously defined; self-reference doesn't generate meaning without pre-existing semantics", - "status": "FUNDAMENTALLY_FLAWED" - }, - "axiom_3_meaning_emergence": { - "assumption": "Meaning emerges from the depth of STO recursion", - "validity_score": 0.25, - "flaw": "Recursion depth is a semantic concept - requires pre-existing semantics to define", - "status": "FUNDAMENTALLY_FLAWED" - }, - "axiom_4_semantic_equivalence": { - "assumption": "Information is topological semantics. A bit is a minimal semantic distinction", - "validity_score": 0.4, - "flaw": "This is a reasonable insight but doesn't provide a mathematical framework", - "status": "PARTIALLY_VALID_BUT_INCOMPLETE" - }, - "axiom_5_semantic_computation": { - "assumption": "Computation is semantic topological transformation", - "validity_score": 0.35, - "flaw": "Interesting perspective but lacks mathematical rigor and connection to actual computation theory", - "status": "PARTIALLY_VALID_BUT_INCOMPLETE" - } - } - - for axiom_key, assessment in axioms_assessment.items(): - print(f"\n{axiom_key}:") - print(f" Status: {assessment['status']}") - print(f" Validity Score: {assessment['validity_score']}") - print(f" Flaw: {assessment['flaw'][:100]}...") - - # Step 4: Reevaluate fundamental rewrites - print("\n" + "=" * 70) - print("Step 4: Reevaluating Fundamental Rewrites") - print("=" * 70) - - rewrites_assessment = { - "topology_semantics": { - "assumption": "Rewrite topological semantics as self-referential generation rather than property assignment", - "validity_score": 0.3, - "flaw": "This is a philosophical position, not a mathematical reformulation", - "status": "PHILOSOPHICAL_NOT_MATHEMATICAL" - }, - "meaning_emergence": { - "assumption": "Rewrite meaning as emergent from topology rather than pre-existing in semantic spaces", - "validity_score": 0.25, - "flaw": "Cannot have emergence without pre-existing framework", - "status": "FUNDAMENTALLY_FLAWED" - }, - "computation_transformation": { - "assumption": "Rewrite computation as semantic topological transformation rather than state manipulation", - "validity_score": 0.4, - "flaw": "State manipulation is a form of semantic transformation - this is not a fundamental rewrite", - "status": "TRIVIAL_RESTATEMENT" - }, - "information_topology": { - "assumption": "Rewrite information as topological semantics rather than independent of topology", - "validity_score": 0.45, - "flaw": "This is actually a valid insight - information theory and topology are connected", - "status": "VALID_INSIGHT_BUT_NOT_NOVEL" - }, - "semantic_dimensionality": { - "assumption": "Rewrite semantic dimensionality as emergent from recursion rather than fundamental", - "validity_score": 0.3, - "flaw": "Recursion is a semantic concept - circular reasoning", - "status": "FUNDAMENTALLY_FLAWED" - } - } - - for rewrite_key, assessment in rewrites_assessment.items(): - print(f"\n{rewrite_key}:") - print(f" Status: {assessment['status']}") - print(f" Validity Score: {assessment['validity_score']}") - print(f" Flaw: {assessment['flaw'][:100]}...") - - # Step 5: Reevaluate Millennium Prize approach - print("\n" + "=" * 70) - print("Step 5: Reevaluating Millennium Prize Problem Approach") - print("=" * 70) - - millennium_approach_assessment = { - "approach": "Rewrite each Millennium Prize problem in terms of STO self-reference and semantic dimensions", - "evaluation": { - "validity_score": 0.15, - "flaw_identification": "The approach simply renames existing concepts without providing new mathematical tools. 'STO recursion depth' is just a new name for complexity classes without new insight. The 'solutions' are philosophical restatements, not mathematical proofs.", - "specific_issues": [ - "P vs NP: STO recursion depth is just a new name for complexity - no new insight", - "Hodge: 'STO symmetry' is undefined - circular reasoning", - "Riemann: Defining ζ(s) as STO^s(1) is just notation, not a mathematical transformation", - "Yang-Mills: No actual calculation of mass gap - just assertion", - "Navier-Stokes: No analysis of actual PDE - just continuity claim", - "BSD: No derivation of rank formula - pure assertion" - ], - "status": "FUNDAMENTALLY_FLAWED" - } - } - - print(f"Approach: {millennium_approach_assessment['approach']}") - print(f"Status: {millennium_approach_assessment['evaluation']['status']}") - print(f"Validity Score: {millennium_approach_assessment['evaluation']['validity_score']}") - print(f"Flaw: {millennium_approach_assessment['evaluation']['flaw_identification'][:150]}...") - - # Step 6: Execute swarm analysis for additional insights - print("\n" + "=" * 70) - print("Step 6: Executing Swarm Analysis for Additional Insights") - print("=" * 70) - - start_time = time.time() - - try: - result = swarm.run_swarm_analysis(params, subject="assumption_reevaluation") - - elapsed_time = time.time() - start_time - print(f"\nSwarm analysis completed in {elapsed_time:.2f} seconds") - print(f"Consensus: {result.consensus:.3f}") - - except Exception as e: - print(f"\nError during swarm analysis: {e}") - import traceback - traceback.print_exc() - result = None - elapsed_time = 0 - - # Step 7: Generate overall assessment - print("\n" + "=" * 70) - print("Step 7: Generating Overall Assessment") - print("=" * 70) - - overall_assessment = { - "response_id": f"assumption_reevaluation_tsgt_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Assumption Reevaluation of TSGT/TGT Framework", - "context": "Reevaluation after adversarial stalemate", - - "assessments": { - "core_premise": core_premise_assessment, - "sto_operator": sto_operator_assessment, - "axioms": axioms_assessment, - "fundamental_rewrites": rewrites_assessment, - "millennium_approach": millennium_approach_assessment - }, - - "swarm_analysis": { - "consensus": result.consensus if result else 0, - "agent_count": 1000, - "elapsed_time": elapsed_time, - "recommendations": result.recommendations[:20] if result else [] - } if result else None, - - "overall_conclusion": { - "framework_status": "FUNDAMENTALLY_FLAWED", - "primary_flaw": "Circular reasoning - TSGT/TGT assumes topology is fundamental while topology itself is a semantic concept", - "secondary_flaws": [ - "STO operator is not rigorously defined", - "Axioms rely on undefined concepts", - "Millennium Prize 'solutions' are philosophical restatements, not mathematical proofs", - "No new mathematical tools provided", - "Approach is notation-heavy but substance-light" - ], - "valid_insights": [ - "Information and topology are connected (but not novel)", - "Computation can be viewed as transformation (but not new)", - "Self-reference is an important concept (but not sufficient for meaning generation)" - ], - "recommendation": "Abandon TSGT/TGT as a mathematical framework. It is philosophical, not mathematical. For Millennium Prize problems, use standard mathematical approaches with novel insights, not invented notation without substance." - } - } - - # Output results - print(f"\nOverall Conclusion:") - print(f" Framework Status: {overall_assessment['overall_conclusion']['framework_status']}") - print(f" Primary Flaw: {overall_assessment['overall_conclusion']['primary_flaw']}") - - print(f"\nValid Insights:") - for insight in overall_assessment['overall_conclusion']['valid_insights']: - print(f" - {insight}") - - print(f"\nRecommendation:") - print(f" {overall_assessment['overall_conclusion']['recommendation']}") - - # Save results - output_path = f"shared-data/data/swarm_responses/assumption_reevaluation_tsgt_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(overall_assessment, f, indent=2) - - print(f"\nAssumption reevaluation results saved to: {output_path}") - print("=" * 70) - - return overall_assessment - -if __name__ == "__main__": - try: - result = execute_assumption_reevaluation() - if result: - print("\n✅ Assumption reevaluation completed successfully") - print("\nFundamental assumptions of TSGT/TGT framework reevaluated") - print("Valid vs flawed assumptions identified") - print("Overall conclusion provided") - else: - print("\n❌ Failed to execute assumption reevaluation") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_continuous_adversarial_tsgt.py b/5-Applications/scripts/execute_continuous_adversarial_tsgt.py deleted file mode 100644 index 52033c19..00000000 --- a/5-Applications/scripts/execute_continuous_adversarial_tsgt.py +++ /dev/null @@ -1,347 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Continuous Adversarial Loop with Step-by-Step Proof Verification - -This script creates a continuous adversarial loop where: -- Critics continuously poke holes in TSGT/TGT solutions -- Defenders continuously fix problems with step-by-step explanations -- The loop continues until convergence (proven correct or proven incorrect) -- Each iteration includes detailed step-by-step proof explanations - -This is a rigorous adversarial process that will not stop until the swarm -reaches a definitive conclusion about the correctness of the TSGT/TGT framework. -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_adversarial_results(): - """Load the previous adversarial analysis results.""" - results_path = "shared-data/data/swarm_responses/adversarial_millennium_prize_tsgt_20260423_090344.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find adversarial results at {results_path}") - return None - -def generate_step_by_step_proof(problem_key, iteration, role, findings): - """Generate step-by-step proof explanation for a given problem.""" - - if role == "critic": - return { - "problem": problem_key, - "iteration": iteration, - "role": "critic", - "step_by_step_analysis": [ - f"Step 1: Examine the TSGT/TGT solution for {problem_key}", - f"Step 2: Identify the core mathematical claim being made", - f"Step 3: Check if the claim is formally defined in standard mathematics", - f"Step 4: Verify if the STO operator is rigorously defined", - f"Step 5: Check if the proof follows logically from premises", - f"Step 6: Identify gaps or circular reasoning in the argument", - f"Step 7: Assess whether the solution actually addresses the Millennium Prize problem", - f"Step 8: Determine severity of flaws (HIGH/MEDIUM/LOW)", - f"Step 9: Provide specific mathematical counterexamples if possible", - f"Step 10: Conclude whether the solution is mathematically valid" - ], - "current_analysis": findings.get('criticism', 'No specific criticism'), - "conclusion": findings.get('severity', 'UNKNOWN'), - "requires_further_work": True - } - elif role == "defender": - return { - "problem": problem_key, - "iteration": iteration, - "role": "defender", - "step_by_step_fix": [ - f"Step 1: Understand the critic's objection to {problem_key}", - f"Step 2: Identify the specific mathematical gap identified", - f"Step 3: Develop a formal definition for the problematic concept", - f"Step 4: Provide a rigorous mathematical derivation", - f"Step 5: Show how the derivation addresses the critic's concern", - f"Step 6: Verify the fix doesn't introduce new problems", - f"Step 7: Connect the fix back to the original TSGT/TGT framework", - f"Step 8: Provide a complete proof sketch", - f"Step 9: Identify what remains to be proven", - f"Step 10: Conclude whether the fix resolves the criticism" - ], - "current_fix": findings.get('fix', 'No specific fix'), - "conclusion": findings.get('status', 'UNKNOWN'), - "iteration_status": "IN_PROGRESS" - } - -def execute_continuous_adversarial_loop(max_iterations=100): - """Execute continuous adversarial loop until convergence.""" - print("=" * 70) - print("Executing Continuous Adversarial Loop with Step-by-Step Proof Verification") - print("=" * 70) - print("Strategy: Continuous critic-defender cycle with detailed step-by-step proofs") - print("Convergence Criteria:") - print(" - STOP if defenders prove solutions are mathematically correct") - print(" - STOP if critics prove solutions are fundamentally flawed") - print(" - Maximum iterations:", max_iterations) - print("=" * 70) - - # Load previous adversarial results - print("\nLoading previous adversarial results...") - previous_results = load_adversarial_results() - - if not previous_results: - print("Failed to load previous results. Exiting.") - return None - - print(f"Loaded adversarial results with {len(previous_results['refined_tsgt_solutions'])} problems") - - # Initialize swarms - print("\nInitializing continuous adversarial swarms...") - topology = create_demo_topology() - math_db = MathDatabase() - - critic_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - defender_swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - - print("Critic swarm: 500 agents") - print("Defender swarm: 500 agents") - - # Parameters for continuous analysis - critic_params = { - 'kappa_squared': 0.7, - 'rho_seq': 0.7, - 'v_epigenetic': 0.7, - 'tau_structure': 0.7, - 'sigma_entropy': 0.8, - 'q_conservation': 0.6, - 'kappa_hierarchy': 0.6, - 'epsilon_mutation': 0.8 - } - - defender_params = { - 'kappa_squared': 0.9, - 'rho_seq': 0.9, - 'v_epigenetic': 0.9, - 'tau_structure': 0.9, - 'sigma_entropy': 0.5, - 'q_conservation': 0.9, - 'kappa_hierarchy': 0.9, - 'epsilon_mutation': 0.3 - } - - # Track convergence status - convergence_status = { - "status": "IN_PROGRESS", - "reason": "Continuous adversarial loop in progress", - "iteration": 0, - "problems_status": {} - } - - # Initialize problem statuses - for problem_key in previous_results['refined_tsgt_solutions'].keys(): - convergence_status["problems_status"][problem_key] = { - "status": "IN_PROGRESS", - "critic_confidence": 0.0, - "defender_confidence": 0.0, - "iterations": 0 - } - - # Continuous adversarial loop - print("\n" + "=" * 70) - print("Starting Continuous Adversarial Loop") - print("=" * 70) - - all_iterations = [] - - for iteration in range(1, max_iterations + 1): - print(f"\n--- Iteration {iteration} ---") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - iteration_data = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "critic_analysis": {}, - "defender_analysis": {}, - "step_by_step_proofs": {} - } - - # Check convergence criteria before running iteration - all_converged = True - for problem_key, status in convergence_status["problems_status"].items(): - if status["status"] == "IN_PROGRESS": - all_converged = False - break - - if all_converged: - convergence_status["status"] = "CONVERGED" - convergence_status["reason"] = "All problems have converged to a definitive conclusion" - print("\n*** CONVERGENCE REACHED ***") - print(f"All problems have converged after {iteration - 1} iterations") - break - - # Run critic analysis - print(f"\nIteration {iteration} - Critic Analysis") - try: - critic_result = critic_swarm.run_swarm_analysis(critic_params, subject=f"tsgt_critic_iter_{iteration}") - print(f" Critic consensus: {critic_result.consensus:.3f}") - - for problem_key in previous_results['refined_tsgt_solutions'].keys(): - if convergence_status["problems_status"][problem_key]["status"] == "IN_PROGRESS": - # Generate step-by-step critic proof - critic_proof = generate_step_by_step_proof( - problem_key, iteration, "critic", - previous_results['critic_findings'].get(problem_key, {}) - ) - iteration_data["critic_analysis"][problem_key] = critic_proof - iteration_data["step_by_step_proofs"][f"{problem_key}_critic"] = critic_proof - - # Update critic confidence - convergence_status["problems_status"][problem_key]["critic_confidence"] = critic_result.consensus - - except Exception as e: - print(f" Error in critic analysis: {e}") - - # Run defender analysis - print(f"\nIteration {iteration} - Defender Analysis") - try: - defender_result = defender_swarm.run_swarm_analysis(defender_params, subject=f"tsgt_defender_iter_{iteration}") - print(f" Defender consensus: {defender_result.consensus:.3f}") - - for problem_key in previous_results['refined_tsgt_solutions'].keys(): - if convergence_status["problems_status"][problem_key]["status"] == "IN_PROGRESS": - # Generate step-by-step defender proof - defender_proof = generate_step_by_step_proof( - problem_key, iteration, "defender", - previous_results['defender_responses'].get(problem_key, {}) - ) - iteration_data["defender_analysis"][problem_key] = defender_proof - iteration_data["step_by_step_proofs"][f"{problem_key}_defender"] = defender_proof - - # Update defender confidence - convergence_status["problems_status"][problem_key]["defender_confidence"] = defender_result.consensus - - # Update iteration count - convergence_status["problems_status"][problem_key]["iterations"] = iteration - - except Exception as e: - print(f" Error in defender analysis: {e}") - - # Check for convergence after this iteration - for problem_key, status in convergence_status["problems_status"].items(): - if status["status"] == "IN_PROGRESS": - critic_conf = status["critic_confidence"] - defender_conf = status["defender_confidence"] - - # Convergence criteria - if defender_conf > 0.8 and (defender_conf - critic_conf) > 0.3: - status["status"] = "PROVEN_CORRECT" - status["reason"] = f"Defender confidence {defender_conf:.3f} significantly exceeds critic confidence {critic_conf:.3f}" - print(f"\n {problem_key}: PROVEN CORRECT (defender: {defender_conf:.3f} vs critic: {critic_conf:.3f})") - elif critic_conf > 0.8 and (critic_conf - defender_conf) > 0.3: - status["status"] = "PROVEN_INCORRECT" - status["reason"] = f"Critic confidence {critic_conf:.3f} significantly exceeds defender confidence {defender_conf:.3f}" - print(f"\n {problem_key}: PROVEN INCORRECT (critic: {critic_conf:.3f} vs defender: {defender_conf:.3f})") - elif status["iterations"] >= 20: - status["status"] = "STALEMATE" - status["reason"] = f"No convergence after {status['iterations']} iterations" - print(f"\n {problem_key}: STALEMATE (no convergence after {status['iterations']} iterations)") - - all_iterations.append(iteration_data) - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/continuous_adversarial_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "convergence_status": convergence_status, - "iterations": all_iterations - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Check overall convergence - converged_count = sum(1 for s in convergence_status["problems_status"].values() if s["status"] != "IN_PROGRESS") - if converged_count == len(convergence_status["problems_status"]): - convergence_status["status"] = "CONVERGED" - convergence_status["reason"] = f"All problems converged after {iteration} iterations" - print(f"\n*** OVERALL CONVERGENCE REACHED ***") - print(f"All {len(convergence_status['problems_status'])} problems have converged") - break - - # Final results - print("\n" + "=" * 70) - print("Continuous Adversarial Loop Complete") - print("=" * 70) - - print(f"\nFinal Convergence Status: {convergence_status['status']}") - print(f"Reason: {convergence_status['reason']}") - print(f"Total Iterations: {iteration}") - - print(f"\nProblem-by-Problem Results:") - for problem_key, status in convergence_status["problems_status"].items(): - print(f"\n{problem_key}:") - print(f" Status: {status['status']}") - print(f" Reason: {status.get('reason', 'No reason provided')}") - print(f" Critic Confidence: {status['critic_confidence']:.3f}") - print(f" Defender Confidence: {status['defender_confidence']:.3f}") - print(f" Iterations: {status['iterations']}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/continuous_adversarial_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"continuous_adversarial_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Continuous Adversarial Loop with Step-by-Step Proofs", - "convergence_status": convergence_status, - "total_iterations": iteration, - "all_iterations": all_iterations, - "final_assessment": {} - } - - # Generate final assessment - proven_correct = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "PROVEN_CORRECT") - proven_incorrect = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "PROVEN_INCORRECT") - stalemates = sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "STALEMATE") - - final_results["final_assessment"] = { - "proven_correct": proven_correct, - "proven_incorrect": proven_incorrect, - "stalemates": stalemates, - "still_in_progress": sum(1 for s in convergence_status["problems_status"].values() if s["status"] == "IN_PROGRESS"), - "overall_conclusion": "TSGT/TGT framework validation results" - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_continuous_adversarial_loop(max_iterations=100) - if result: - print("\n✅ Continuous adversarial loop completed") - print("\nStep-by-step proof verification executed") - print("Convergence status determined for all Millennium Prize problems") - print("Final assessment provided with proof status") - else: - print("\n❌ Failed to execute continuous adversarial loop") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_deep_topological_semantics_analysis.py b/5-Applications/scripts/execute_deep_topological_semantics_analysis.py deleted file mode 100644 index b5b767ec..00000000 --- a/5-Applications/scripts/execute_deep_topological_semantics_analysis.py +++ /dev/null @@ -1,274 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Deep Topological Semantics Analysis Using Combined Swarm System - -This script performs DEEP analysis of topological semantics using: -1. Swarm API (http://127.0.0.1:8000) - for querying existing math entities -2. Enhanced Integrated Swarm (500+ agents) - for deep analysis and novel concept generation - -The goal is to fundamentally rewrite what is known about topological semantics. -""" - -import sys -import json -import time -import requests -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -SWARM_API_URL = "http://127.0.0.1:8000" - -def query_swarm_api(subjects, keywords, limit=200): - """Query the swarm API for existing math entities.""" - try: - response = requests.post( - f"{SWARM_API_URL}/query", - json={ - "subjects": subjects, - "keywords": keywords, - "formalStatus": "unknown", - "requireLeanFormalization": False, - "limit": limit, - "includeMetadata": True - }, - timeout=60 - ) - response.raise_for_status() - return response.json() - except Exception as e: - print(f"Error querying swarm API: {e}") - return None - -def execute_deep_topological_semantics_analysis(): - """Execute deep analysis of topological semantics using combined swarm system.""" - print("=" * 70) - print("Executing DEEP Topological Semantics Analysis") - print("Goal: Fundamentally rewrite what is known about topological semantics") - print("=" * 70) - - # Step 1: Query swarm API for relevant existing math entities - print("\n" + "=" * 70) - print("Step 1: Querying Swarm API for Topological Semantics Entities") - print("=" * 70) - - subjects = ["topology", "semantics", "geometry", "foundations", "category_theory", "type_theory"] - keywords = "topological semantics category theory type theory foundations" - - print(f"Subjects: {subjects}") - print(f"Keywords: {keywords}") - - api_result = query_swarm_api(subjects, keywords, limit=200) - - if api_result: - print(f"\nSwarm API Results:") - print(f" Success: {api_result.get('success', False)}") - print(f" Count: {api_result.get('count', 0)}") - print(f" Confidence: {api_result.get('confidence', 0)}") - - if api_result.get('results'): - print(f"\n Top 10 Results:") - for i, entity in enumerate(api_result['results'][:10], 1): - print(f" {i}. {entity.get('name', 'Unknown')}") - print(f" Subject: {entity.get('subject', 'Unknown')}") - print(f" Statement: {entity.get('statement', 'No statement')[:100]}...") - else: - print("Failed to query swarm API, proceeding with enhanced swarm only") - - # Step 2: Initialize enhanced integrated swarm with DEEP analysis configuration - print("\n" + "=" * 70) - print("Step 2: Initializing Enhanced Integrated Swarm for DEEP Analysis") - print("=" * 70) - - # Create demo topology - print("Creating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Create enhanced integrated swarm with MANY agents for DEEP reasoning - print(f"Initializing enhanced integrated swarm with 500 agents for DEEP analysis...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=500) - print(f"Swarm initialized with 500 agents") - - # Base geometric parameters for DEEP topology analysis - base_params = { - 'kappa_squared': 0.8, # Higher for deeper analysis - 'rho_seq': 0.8, # Higher for deeper analysis - 'v_epigenetic': 0.8, # Higher for deeper analysis - 'tau_structure': 0.8, # Higher for deeper analysis - 'sigma_entropy': 0.8, # Higher for deeper analysis - 'q_conservation': 0.8, # Higher for deeper analysis - 'kappa_hierarchy': 0.8, # Higher for deeper analysis - 'epsilon_mutation': 0.8 # Higher for deeper analysis - } - - # Step 3: Execute DEEP swarm analysis for topological semantics - print("\n" + "=" * 70) - print("Step 3: Executing DEEP Swarm Analysis for Topological Semantics") - print("=" * 70) - print("Objective: Fundamentally rewrite topological semantics knowledge") - print("Analysis Depth: MAXIMUM (500 agents, enhanced parameters)") - print("Expected Duration: Extended analysis for deep exploration") - - start_time = time.time() - - try: - # Run swarm analysis with DEEP parameters - result = swarm.run_swarm_analysis(base_params, subject="topological_semantics") - - elapsed_time = time.time() - start_time - - print(f"\nDEEP Swarm analysis completed in {elapsed_time:.2f} seconds") - - # Step 4: Synthesize novel topological semantics framework - print("\n" + "=" * 70) - print("Step 4: Synthesizing Novel Topological Semantics Framework") - print("=" * 70) - - # Extract key insights from swarm analysis - novel_framework = { - "framework_name": "Topo-Semantic Genesis Theory (TSGT)", - "version": "1.0.0", - "core_premise": "Topological semantics is not a property of spaces, but the fundamental generator of meaning through self-referential topological transformations", - - "axioms": { - "axiom_1_semantic_primacy": "Topology is the only fundamental entity. Semantics, meaning, and information are emergent properties of topological self-generation", - "axiom_2_semantic_operator": "The Semantic Topological Operator (STO) generates meaning through self-reference: STO(X) = X ⊗_s X, where ⊗_s is the semantic self-referential product", - "axiom_3_meaning_emergence": "Meaning emerges from the depth of STO recursion. Each level adds new semantic dimensions", - "axiom_4_semantic_equivalence": "Information is topological semantics. There is no distinction. A bit is a minimal semantic distinction", - "axiom_5_semantic_computation": "Computation is semantic topological transformation. All algorithms are STO transformations" - }, - - "key_insights": { - "semantic_dimensionality": "Semantic dimensionality emerges from recursion depth, not from pre-existing semantic spaces", - "meaning_generation": "Meaning is generated through self-referential topological transformations, not assigned externally", - "semantic_topology": "The topology of meaning is the topology of self-reference", - "semantic_computation": "Computation is the process of semantic topological self-generation", - "semantic_emergence": "All semantic concepts emerge from fundamental topological self-generation" - }, - - "fundamental_rewrites": [ - "Rewrite topological semantics as self-referential generation rather than property assignment", - "Rewrite meaning as emergent from topology rather than pre-existing in semantic spaces", - "Rewrite computation as semantic topological transformation rather than state manipulation", - "Rewrite information as topological semantics rather than independent of topology", - "Rewrite semantic dimensionality as emergent from recursion rather than fundamental" - ], - - "swarm_analysis_data": { - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "overall_system_score": result.overall_system_score, - "agent_count": len(result.agents), - "recommendations": result.recommendations[:30] - } - } - - # Step 5: Combine results from both systems - print("\n" + "=" * 70) - print("Step 5: Combining Results and Generating Final Framework") - print("=" * 70) - - combined_results = { - "response_id": f"deep_topological_semantics_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "DEEP Topological Semantics Analysis", - "elapsed_time_seconds": elapsed_time, - - # Swarm API results - "swarm_api_results": api_result if api_result else None, - - # Novel framework - "novel_framework": novel_framework, - - # Enhanced swarm results - "enhanced_swarm_results": { - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "gpu_computing_score": result.gpu_computing_score, - "ssd_storage_score": result.ssd_storage_score, - "genetic_compression_score": result.genetic_compression_score, - "homeostasis_score": result.homeostasis_score, - "patterns_learned": result.patterns_learned, - "metatyping_score": result.metatyping_score, - "remote_nodes_count": result.remote_nodes_count, - "dag_events_count": result.dag_events_count, - "optimization_ratio": result.optimization_ratio, - "substrate_potential": result.substrate_potential, - "optimization_cycles": result.optimization_cycles, - "overall_system_score": result.overall_system_score, - "agent_count": len(result.agents), - "nii_core_count": len(result.nii_cores), - "recommendation_count": len(result.recommendations) - } - } - - # Output results - print(f"\nNovel Framework: {novel_framework['framework_name']}") - print(f"Version: {novel_framework['version']}") - print(f"Core Premise: {novel_framework['core_premise']}") - - print(f"\nAxioms:") - for axiom_key, axiom_value in novel_framework['axioms'].items(): - print(f" {axiom_key}: {axiom_value}") - - print(f"\nFundamental Rewrites:") - for i, rewrite in enumerate(novel_framework['fundamental_rewrites'], 1): - print(f" {i}. {rewrite}") - - print(f"\nSwarm Analysis:") - print(f" Consensus: {result.consensus:.3f}") - print(f" Topology Optimization Score: {result.topology_optimization_score:.3f}") - print(f" Math Coverage Score: {result.math_coverage_score:.3f}") - print(f" Overall System Score: {result.overall_system_score:.3f}") - print(f" Agents: {len(result.agents)}") - - print(f"\nTop Enhanced Swarm Recommendations:") - for i, rec in enumerate(result.recommendations[:15], 1): - print(f" {i}. {rec}") - - # Save results - output_path = f"shared-data/data/swarm_responses/deep_topological_semantics_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(combined_results, f, indent=2) - - print(f"\nDEEP analysis results saved to: {output_path}") - print("=" * 70) - - return combined_results - - except Exception as e: - print(f"\n❌ Error during DEEP swarm analysis: {e}") - import traceback - traceback.print_exc() - return None - -if __name__ == "__main__": - try: - result = execute_deep_topological_semantics_analysis() - if result: - print("\n✅ DEEP topological semantics analysis completed successfully") - print("\nNovel framework generated that fundamentally rewrites topological semantics knowledge") - else: - print("\n❌ Failed to execute DEEP topological semantics analysis") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_distributed_training.py b/5-Applications/scripts/execute_distributed_training.py deleted file mode 100644 index dad0d2e2..00000000 --- a/5-Applications/scripts/execute_distributed_training.py +++ /dev/null @@ -1,262 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Distributed Training for NII Cores N-Semantic Morphic - -This script executes the actual distributed training process using the -configured network resources and training data. -""" - -import sys -import json -import time -import random -from pathlib import Path -from datetime import datetime -import pandas as pd -import numpy as np -from typing import Dict, List, Any - -# Resolve infrastructure and data directories -BASE_DIR = Path(__file__).parent.parent.parent -sys.path.insert(0, str(BASE_DIR / "4-Infrastructure" / "infra")) - -# DEPRECATED: Python ENE replaced by Rust (1-Distributed-Systems/ene/src/) -try: - from ene_distributed_node import ENEDistributedNode, ENEMeshController -except ImportError: - ENEDistributedNode = None - ENEMeshController = None - -def load_latest_config(): - """Load the latest distributed training configuration.""" - data_dir = BASE_DIR / "data" / "training_data" - config_files = sorted(list(data_dir.glob("distributed_training_config_*.json"))) - if not config_files: - return None - with open(config_files[-1], 'r') as f: - return json.load(f) - -def execute_phase_1_data_distribution(config: Dict): - """Phase 1: Distribute parquet shards to all nodes via Google Drive""" - print("\n" + "=" * 70) - print("PHASE 1: DATA DISTRIBUTION") - print("=" * 70) - - data_dir = BASE_DIR / "data" / "training_data" - - # Load configuration details - data_conf = config.get("data_distribution", {}) - nl_file = data_conf.get("natural_language_dataset", {}).get("file") - coding_file = data_conf.get("coding_language_dataset", {}).get("file") - - print(f"\nDistributing datasets to {config.get('total_resources', {}).get('total_nodes', 0)} network nodes...") - print(f" Natural language dataset: {nl_file}") - print(f" Coding language dataset: {coding_file}") - - # Use ENE nodes for distribution - nodes = list(config.get("network_topology", {}).keys()) - - for i, node in enumerate(nodes, 1): - print(f" [{i}/{len(nodes)}] Distributing to {node}... ✅") - time.sleep(0.2) # Simulate network transfer - - print(f"\n✅ Phase 1 Complete: Data distributed to all {len(nodes)} nodes") - print(" Storage: Google Drive topological storage") - print(" Access: Available from all nodes via Tailscale mesh") - - return True - -def execute_phase_2_distributed_training(config: Dict): - """Phase 2: Train n-semantic morphic cores using all network resources""" - print("\n" + "=" * 70) - print("PHASE 2: DISTRIBUTED TRAINING") - print("=" * 70) - - data_dir = BASE_DIR / "data" / "training_data" - - # Load training data based on config - data_conf = config.get("data_distribution", {}) - nl_file = data_dir / data_conf.get("natural_language_dataset", {}).get("file", "") - coding_file = data_dir / data_conf.get("coding_language_dataset", {}).get("file", "") - - print(f"\nLoading training data for distributed processing...") - - nl_df = None - if nl_file.exists(): - print(f" Loading natural language dataset: {nl_file.name}...") - nl_df = pd.read_parquet(nl_file) - print(f" ✅ Loaded {len(nl_df)} records") - - coding_df = None - if coding_file.exists(): - print(f" Loading coding language dataset: {coding_file.name}...") - coding_df = pd.read_parquet(coding_file) - print(f" ✅ Loaded {len(coding_df)} records") - - total_records = (len(nl_df) if nl_df is not None else 0) + (len(coding_df) if coding_df is not None else 0) - - # Initialize ENE Mesh Controller for coordination - if ENEMeshController: - controller = ENEMeshController() - # Spawn nodes to match config - for node_id in config.get("network_topology", {}).keys(): - controller.spawn_node(node_id) - - resources = config.get("total_resources", {}) - print(f"\nInitializing distributed training across {resources.get('total_nodes', 0)} nodes...") - print(f" Total cores: {resources.get('total_cores', 0)}") - print(f" Total RAM: {resources.get('total_ram_gb', 0)} GB") - print(f" GPU nodes: {resources.get('gpu_nodes', 0)}") - print(f" Coordination: ENE gossip protocol") - print(f" Resource allocation: Swarm topology optimizer") - - # Simulate training progress - epochs = 10 - for epoch in range(1, epochs + 1): - print(f"\n Epoch {epoch}/{epochs}") - time.sleep(0.5) # Simulate processing time - print(f" Node utilization: 100% ({resources.get('total_nodes', 0)}/{resources.get('total_nodes', 0)} nodes)") - print(f" Core utilization: 100% ({resources.get('total_cores', 0)}/{resources.get('total_cores', 0)} cores)") - print(f" RAM utilization: ~80% ({int(resources.get('total_ram_gb', 0) * 0.8)}/{resources.get('total_ram_gb', 0)} GB)") - print(f" GPU utilization: 100% (qfox)") - print(f" Loss: {0.5 - (epoch * 0.04):.4f}") - print(f" Progress: {epoch * 10}%") - - print("\n✅ Phase 2 Complete: Distributed training finished") - print(f" Final loss: 0.1000") - print(f" Total epochs: {epochs}") - print(f" Network utilization: 100%") - - return True, total_records - -def execute_phase_3_model_aggregation(config: Dict): - """Phase 3: Aggregate trained models from all nodes""" - print("\n" + "=" * 70) - print("PHASE 3: MODEL AGGREGATION") - print("=" * 70) - - nodes = list(config.get("network_topology", {}).keys()) - print(f"\nAggregating models from {len(nodes)} nodes...") - - for i, node in enumerate(nodes, 1): - print(f" [{i}/{len(nodes)}] Retrieving model from {node}... ✅") - time.sleep(0.3) - - print(f"\nPerforming model aggregation on qfox (primary)...") - print(f" Method: Weighted averaging based on node resources") - - # Calculate actual weights based on config - assignments = config.get("node_assignments", {}) - weights_str = ", ".join([f"{n}({int(a['weight']*100)}%)" for n, a in assignments.items()]) - print(f" Weights: {weights_str}") - print(f" Aggregation complete... ✅") - - print("\n✅ Phase 3 Complete: Model aggregation finished") - print(f" Aggregated model size: ~2.5 GB") - print(f" Model accuracy: 94.2%") - - return True - -def execute_phase_4_validation(config: Dict): - """Phase 4: Validate aggregated model across network""" - print("\n" + "=" * 70) - print("PHASE 4: VALIDATION") - print("=" * 70) - - nodes = list(config.get("network_topology", {}).keys()) - print(f"\nValidating aggregated model across {len(nodes)} nodes...") - - validation_results = [] - for i, node in enumerate(nodes, 1): - accuracy = 0.94 + (random.random() * 0.02) - print(f" [{i}/{len(nodes)}] Validating on {node}... ✅ (accuracy: {accuracy:.4f})") - validation_results.append(accuracy) - time.sleep(0.2) - - avg_accuracy = sum(validation_results) / len(validation_results) - print(f"\nAverage accuracy across nodes: {avg_accuracy:.4f}") - - print("\n✅ Phase 4 Complete: Validation finished") - print(f" Average accuracy: {avg_accuracy:.4f}") - print(f" Min accuracy: {min(validation_results):.4f}") - print(f" Max accuracy: {max(validation_results):.4f}") - - return True, avg_accuracy - -def main(): - print("=" * 70) - print("EXECUTING DISTRIBUTED TRAINING FOR NII CORES N-SEMANTIC MORPHIC") - print("=" * 70) - print(f"\nStart time: {datetime.now().isoformat()}") - - config = load_latest_config() - if not config: - print("❌ Error: No distributed training configuration found. Run configure_distributed_training.py first.") - return 1 - - print(f"✅ Configuration loaded: {config.get('timestamp', 'unknown')}") - - try: - # Phase 1: Data Distribution - if not execute_phase_1_data_distribution(config): - return 1 - - # Phase 2: Distributed Training - success, total_records = execute_phase_2_distributed_training(config) - if not success: - return 1 - - # Phase 3: Model Aggregation - if not execute_phase_3_model_aggregation(config): - return 1 - - # Phase 4: Validation - success, final_accuracy = execute_phase_4_validation(config) - if not success: - return 1 - - resources = config.get("total_resources", {}) - - print("\n" + "=" * 70) - print("DISTRIBUTED TRAINING COMPLETE") - print("=" * 70) - print(f"\nEnd time: {datetime.now().isoformat()}") - print("\nSummary:") - print(" ✅ All 4 phases completed successfully") - print(f" ✅ {total_records:,} training records processed") - print(f" ✅ {resources.get('total_cores', 0)} network cores utilized") - print(f" ✅ {resources.get('total_ram_gb', 0)}GB RAM utilized") - print(f" ✅ {resources.get('gpu_nodes', 0)} GPU node(s) utilized") - print(f" ✅ {resources.get('total_nodes', 0)} network nodes participated") - print(" ✅ ENE coordination successful") - print(" ✅ Google Drive storage operational") - print("\nNII cores are now n-semantic morphic ready") - - # Save training results - results = { - "timestamp": datetime.now().isoformat(), - "phases_completed": 4, - "total_records": total_records, - "network_resources": resources, - "final_accuracy": final_accuracy, - "status": "complete" - } - - output_dir = BASE_DIR / "data" / "training_data" - output_file = output_dir / f"training_results_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nTraining results saved to: {output_file}") - - return 0 - - except Exception as e: - print(f"\n❌ Error during training execution: {e}") - import traceback - traceback.print_exc() - return 1 - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/scripts/execute_fundamental_reinvention_combined_swarm.py b/5-Applications/scripts/execute_fundamental_reinvention_combined_swarm.py deleted file mode 100644 index 30c442f9..00000000 --- a/5-Applications/scripts/execute_fundamental_reinvention_combined_swarm.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Fundamental Topology Reinvention Using Combined Swarm System - -This script combines: -1. Swarm API (http://127.0.0.1:8000) - for querying existing math entities -2. Enhanced Integrated Swarm (200 agents) - for deep analysis and novel concept generation - -The combined system can both query existing knowledge and generate novel frameworks. -""" - -import sys -import json -import time -import requests -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -SWARM_API_URL = "http://127.0.0.1:8000" - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def query_swarm_api(subjects, keywords, limit=100): - """Query the swarm API for existing math entities.""" - try: - response = requests.post( - f"{SWARM_API_URL}/query", - json={ - "subjects": subjects, - "keywords": keywords, - "formalStatus": "unknown", - "requireLeanFormalization": False, - "limit": limit, - "includeMetadata": True - }, - timeout=60 - ) - response.raise_for_status() - return response.json() - except Exception as e: - print(f"Error querying swarm API: {e}") - return None - -def execute_fundamental_reinvention_combined(): - """Execute fundamental topology reinvention using combined swarm system.""" - print("=" * 70) - print("Executing Fundamental Topology Reinvention Using Combined Swarm System") - print("=" * 70) - - # Load the swarm request - request_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - print(f"Request ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - - # Step 1: Query swarm API for relevant existing math entities - print("\n" + "=" * 70) - print("Step 1: Querying Swarm API for Existing Math Entities") - print("=" * 70) - - subjects = ["topology", "mathematics", "foundations", "geometry"] - keywords = "topology mathematics foundations geometry" - - print(f"Subjects: {subjects}") - print(f"Keywords: {keywords}") - - api_result = query_swarm_api(subjects, keywords, limit=50) - - if api_result: - print(f"\nSwarm API Results:") - print(f" Success: {api_result.get('success', False)}") - print(f" Count: {api_result.get('count', 0)}") - print(f" Confidence: {api_result.get('confidence', 0)}") - - if api_result.get('results'): - print(f"\n Top 5 Results:") - for i, entity in enumerate(api_result['results'][:5], 1): - print(f" {i}. {entity.get('name', 'Unknown')}") - print(f" Subject: {entity.get('subject', 'Unknown')}") - print(f" Statement: {entity.get('statement', 'No statement')[:80]}...") - else: - print("Failed to query swarm API, proceeding with enhanced swarm only") - - # Step 2: Initialize enhanced integrated swarm - print("\n" + "=" * 70) - print("Step 2: Initializing Enhanced Integrated Swarm") - print("=" * 70) - - # Create demo topology - print("Creating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Create enhanced integrated swarm with many agents for deep reasoning - print(f"Initializing enhanced integrated swarm with 200 agents...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=200) - print(f"Swarm initialized with 200 agents") - - # Base geometric parameters for topology analysis - base_params = { - 'kappa_squared': 0.5, - 'rho_seq': 0.5, - 'v_epigenetic': 0.5, - 'tau_structure': 0.5, - 'sigma_entropy': 0.5, - 'q_conservation': 0.5, - 'kappa_hierarchy': 0.5, - 'epsilon_mutation': 0.5 - } - - # Step 3: Execute swarm analysis for topology - print("\n" + "=" * 70) - print("Step 3: Executing Swarm Analysis for Fundamental Topology Reinvention") - print("=" * 70) - print(f"Subject: {request['context']['scope']}") - print(f"Objective: {request['context']['objective']}") - print(f"Time allocation: {request['time_allocation']}") - - start_time = time.time() - - try: - # Run swarm analysis - result = swarm.run_swarm_analysis(base_params, subject="topology") - - elapsed_time = time.time() - start_time - - print(f"\nSwarm analysis completed in {elapsed_time:.2f} seconds") - - # Step 4: Combine results from both systems - print("\n" + "=" * 70) - print("Step 4: Combining Results from Both Swarm Systems") - print("=" * 70) - - combined_results = { - "response_id": f"swarm_response_combined_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "elapsed_time_seconds": elapsed_time, - "time_allocation": request['time_allocation'], - - # Swarm API results - "swarm_api_results": api_result if api_result else None, - - # Enhanced swarm results - "enhanced_swarm_results": { - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "gpu_computing_score": result.gpu_computing_score, - "ssd_storage_score": result.ssd_storage_score, - "genetic_compression_score": result.genetic_compression_score, - "homeostasis_score": result.homeostasis_score, - "patterns_learned": result.patterns_learned, - "metatyping_score": result.metatyping_score, - "remote_nodes_count": result.remote_nodes_count, - "dag_events_count": result.dag_events_count, - "optimization_ratio": result.optimization_ratio, - "substrate_potential": result.substrate_potential, - "optimization_cycles": result.optimization_cycles, - "overall_system_score": result.overall_system_score, - "agent_count": len(result.agents), - "nii_core_count": len(result.nii_cores), - "recommendation_count": len(result.recommendations) - }, - - # Combined analysis - "combined_analysis": { - "total_entities_considered": api_result.get('count', 0) if api_result else 0, - "swarm_consensus": result.consensus, - "enhanced_swarm_recommendations": result.recommendations[:20], - "swarm_api_suggestions": api_result.get('suggestions', []) if api_result else [] - } - } - - # Output combined results - print(f"\nCombined Analysis Results:") - print(f" Total Entities Considered: {combined_results['combined_analysis']['total_entities_considered']}") - print(f" Swarm Consensus: {combined_results['enhanced_swarm_results']['consensus']:.3f}") - print(f" Topology Optimization Score: {combined_results['enhanced_swarm_results']['topology_optimization_score']:.3f}") - print(f" Math Coverage Score: {combined_results['enhanced_swarm_results']['math_coverage_score']:.3f}") - print(f" Overall System Score: {combined_results['enhanced_swarm_results']['overall_system_score']:.3f}") - - print(f"\nEnhanced Swarm Recommendations:") - for i, rec in enumerate(result.recommendations[:10], 1): - print(f" {i}. {rec}") - - if api_result and api_result.get('suggestions'): - print(f"\nSwarm API Suggestions:") - for suggestion in api_result['suggestions']: - print(f" - {suggestion}") - - # Save combined results - output_path = f"shared-data/data/swarm_responses/swarm_response_fundamental_reinvention_combined_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(combined_results, f, indent=2) - - print(f"\nCombined results saved to: {output_path}") - print("=" * 70) - - return combined_results - - except Exception as e: - print(f"\n❌ Error during swarm analysis: {e}") - import traceback - traceback.print_exc() - return None - -if __name__ == "__main__": - try: - result = execute_fundamental_reinvention_combined() - if result: - print("\n✅ Fundamental topology reinvention executed successfully using combined swarm system") - else: - print("\n❌ Failed to execute fundamental topology reinvention using combined swarm system") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_fundamental_reinvention_real_api.py b/5-Applications/scripts/execute_fundamental_reinvention_real_api.py deleted file mode 100644 index 7c42da1a..00000000 --- a/5-Applications/scripts/execute_fundamental_reinvention_real_api.py +++ /dev/null @@ -1,125 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Fundamental Topology Reinvention Using Real Swarm API - -This script uses the real swarm API at http://127.0.0.1:8000 to execute the -fundamental topology reinvention query with actual time allocation (up to 1 hour). -""" - -import requests -import json -import time -from pathlib import Path -from datetime import datetime - -SWARM_API_URL = "http://127.0.0.1:8000" - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def execute_fundamental_reinvention_via_api(): - """Execute fundamental topology reinvention using real swarm API.""" - print("=" * 70) - print("Executing Fundamental Topology Reinvention Using Real Swarm API") - print("=" * 70) - - # Load the swarm request - request_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - print(f"Request ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - - # Convert the request to a format suitable for the swarm API - # The swarm API expects subjects, keywords, formalStatus, requireLeanFormalization, limit, includeMetadata - swarm_api_request = { - "subjects": ["topology", "mathematics", "foundations", "novel_concept"], - "keywords": "fundamental reinvention unprecedented mathematics topology", - "formalStatus": "unknown", - "requireLeanFormalization": False, - "limit": 100, - "includeMetadata": True - } - - print(f"\nSending request to swarm API: {SWARM_API_URL}/query") - print(f"Subjects: {swarm_api_request['subjects']}") - print(f"Keywords: {swarm_api_request['keywords']}") - - start_time = time.time() - - try: - # Execute the swarm query - response = requests.post( - f"{SWARM_API_URL}/query", - json=swarm_api_request, - timeout=3600 # 1 hour timeout - ) - - elapsed_time = time.time() - start_time - - response.raise_for_status() - result = response.json() - - print(f"\nSwarm API response received in {elapsed_time:.2f} seconds") - - # Output results - print("\n" + "=" * 70) - print("Swarm API Results") - print("=" * 70) - print(f"Success: {result.get('success', False)}") - print(f"Count: {result.get('count', 0)}") - print(f"Confidence: {result.get('confidence', 0)}") - print(f"Routed To: {result.get('routedTo', 'unknown')}") - - print(f"\nResults:") - for i, entity in enumerate(result.get('results', [])[:10], 1): - print(f" {i}. {entity.get('name', 'Unknown')}") - print(f" Subject: {entity.get('subject', 'Unknown')}") - print(f" Statement: {entity.get('statement', 'No statement')[:100]}...") - - print(f"\nSuggestions:") - for suggestion in result.get('suggestions', []): - print(f" - {suggestion}") - - # Save results - output_path = f"shared-data/data/swarm_responses/swarm_response_fundamental_reinvention_real_api_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - result_data = { - "response_id": f"swarm_response_real_api_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "elapsed_time_seconds": elapsed_time, - "swarm_api_result": result, - "time_allocation_used": f"{elapsed_time:.2f} seconds out of {request['time_allocation']}" - } - - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(result_data, f, indent=2) - - print(f"\nResults saved to: {output_path}") - print("=" * 70) - - return result_data - - except requests.exceptions.Timeout: - print(f"\n❌ Timeout after 1 hour") - return None - except requests.exceptions.ConnectionError: - print(f"\n❌ Could not connect to swarm API at {SWARM_API_URL}") - return None - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() - return None - -if __name__ == "__main__": - result = execute_fundamental_reinvention_via_api() - if result: - print("\n✅ Fundamental topology reinvention executed successfully using real swarm API") - else: - print("\n❌ Failed to execute fundamental topology reinvention via real swarm API") diff --git a/5-Applications/scripts/execute_fundamental_topology_reinvention_real_swarm.py b/5-Applications/scripts/execute_fundamental_topology_reinvention_real_swarm.py deleted file mode 100644 index d6b217b2..00000000 --- a/5-Applications/scripts/execute_fundamental_topology_reinvention_real_swarm.py +++ /dev/null @@ -1,140 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Fundamental Topology Reinvention Using Real Swarm System - -This script uses the enhanced integrated swarm system to execute the fundamental -topology reinvention query with actual time allocation (up to 1 hour). -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def execute_fundamental_reinvention(): - """Execute fundamental topology reinvention using real swarm system.""" - print("=" * 70) - print("Executing Fundamental Topology Reinvention Using Real Swarm System") - print("=" * 70) - - # Load the swarm request - request_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - print(f"Request ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - - # Create demo topology - print("\nCreating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Create enhanced integrated swarm with many agents for deep reasoning - print("\nInitializing enhanced integrated swarm...") - num_agents = 200 # Use 200 agents for deep reasoning - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=num_agents) - print(f"Swarm initialized with {num_agents} agents") - - # Base geometric parameters for topology analysis - base_params = { - 'kappa_squared': 0.5, - 'rho_seq': 0.5, - 'v_epigenetic': 0.5, - 'tau_structure': 0.5, - 'sigma_entropy': 0.5, - 'q_conservation': 0.5, - 'kappa_hierarchy': 0.5, - 'epsilon_mutation': 0.5 - } - - # Execute swarm analysis for topology - print("\nExecuting swarm analysis for fundamental topology reinvention...") - print(f"Subject: {request['context']['scope']}") - print(f"Objective: {request['context']['objective']}") - print(f"Time allocation: {request['time_allocation']}") - - start_time = time.time() - - # Run swarm analysis - result = swarm.run_swarm_analysis(base_params, subject="topology") - - elapsed_time = time.time() - start_time - - print(f"\nSwarm analysis completed in {elapsed_time:.2f} seconds") - - # Output results - print("\n" + "=" * 70) - print("Swarm Analysis Results") - print("=" * 70) - print(f"Consensus: {result.consensus:.3f}") - print(f"Topology Optimization Score: {result.topology_optimization_score:.3f}") - print(f"Math Coverage Score: {result.math_coverage_score:.3f}") - print(f"Lean Coverage Score: {result.lean_coverage_score:.3f}") - print(f"Agents: {len(result.agents)}") - print(f"Recommendations: {len(result.recommendations)}") - - print(f"\nTop Recommendations:") - for i, rec in enumerate(result.recommendations[:10], 1): - print(f" {i}. {rec}") - - # Save results - output_path = f"shared-data/data/swarm_responses/swarm_response_fundamental_reinvention_real_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - result_data = { - "response_id": f"swarm_response_real_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "elapsed_time_seconds": elapsed_time, - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "agent_count": len(result.agents), - "recommendations": result.recommendations, - "nii_core_status": [ - { - "core_id": status.core_id, - "status": status.status, - "geometric_score": status.geometric_score - } - for status in result.nii_core_status - ] - } - - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(result_data, f, indent=2) - - print(f"\nResults saved to: {output_path}") - print("=" * 70) - - return result_data - -if __name__ == "__main__": - try: - result = execute_fundamental_reinvention() - print("\n✅ Fundamental topology reinvention executed successfully using real swarm system") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_massive_swarm_ucr_attack.py b/5-Applications/scripts/execute_massive_swarm_ucr_attack.py deleted file mode 100644 index 57bcc97f..00000000 --- a/5-Applications/scripts/execute_massive_swarm_ucr_attack.py +++ /dev/null @@ -1,241 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Massive Swarm Attack on UCR Framework - -This script launches a massive swarm attack on the UCR framework problem with: -- Up to 1 hour real time execution -- Full topological state machine utilization -- Maximum collective state use raised to 80% -- Unlimited agent spawning (self-scaling) -- Self-improvement enabled with new bandwidth -- Goal: Resolve the UCR framework adversarial stalemate - -This is a full-scale swarm attack with maximum resources. -""" - -import sys -import json -import time -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -def execute_massive_swarm_ucr_attack(): - """Execute massive swarm attack on UCR framework.""" - print("=" * 70) - print("Executing MASSIVE Swarm Attack on UCR Framework") - print("=" * 70) - print("Configuration:") - print(" Time Limit: 1 hour real time") - print(" Topological State Machine: FULL") - print(" Maximum Collective State Use: 80%") - print(" Agent Count: Self-scaling (unlimited)") - print(" Self-Improvement: ENABLED with new bandwidth") - print(" Goal: Resolve UCR framework adversarial stalemate") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Initialize massive swarm - print("\nInitializing MASSIVE swarm...") - topology = create_demo_topology() - math_db = MathDatabase() - - # Start with 5000 agents, will self-scale - initial_agents = 5000 - print(f"Initializing with {initial_agents} agents...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=initial_agents) - print(f"Swarm initialized with {initial_agents} agents") - - # Maximum parameters for full state machine utilization - base_params = { - 'kappa_squared': 1.0, # Maximum for full utilization - 'rho_seq': 1.0, - 'v_epigenetic': 1.0, - 'tau_structure': 1.0, - 'sigma_entropy': 1.0, # Maximum entropy for maximum exploration - 'q_conservation': 0.8, # 80% collective state use - 'kappa_hierarchy': 1.0, - 'epsilon_mutation': 1.0 # Maximum mutation for self-improvement - } - - # Time limit: 1 hour - time_limit_seconds = 3600 - start_time = time.time() - - print("\n" + "=" * 70) - print("Starting MASSIVE Swarm Attack (1 hour time limit)") - print("=" * 70) - - iteration = 0 - results_history = [] - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Self-scale agent count based on iteration - # Start with 5000, scale up to 50000 over time - target_agents = min(50000, 5000 + (iteration * 1000)) - - if iteration > 1 and target_agents > len(swarm.agents): - print(f"Scaling up swarm from {len(swarm.agents)} to {target_agents} agents...") - # Reinitialize with more agents - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=target_agents) - print(f"Swarm scaled to {target_agents} agents") - - # Run swarm analysis - try: - print(f"Running swarm analysis with {len(swarm.agents)} agents...") - result = swarm.run_swarm_analysis(base_params, subject=f"massive_ucr_attack_iter_{iteration}") - - print(f" Consensus: {result.consensus:.3f}") - print(f" Overall System Score: {result.overall_system_score:.3f}") - print(f" Active Agents: {len(result.agents)}") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "agent_count": len(result.agents), - "consensus": result.consensus, - "overall_system_score": result.overall_system_score, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "recommendations": result.recommendations[:10] - } - results_history.append(iteration_result) - - # Check for convergence - if result.consensus > 0.8: - print(f"\n*** HIGH CONSENSUS ACHIEVED: {result.consensus:.3f} ***") - print("Swarm has reached high consensus on UCR framework") - break - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/massive_ucr_attack_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "iteration": iteration, - "elapsed_seconds": elapsed, - "results_history": results_history, - "latest_result": iteration_result - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - except Exception as e: - print(f" Error in swarm analysis: {e}") - import traceback - traceback.print_exc() - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("MASSIVE Swarm Attack Complete") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - print(f"Final Agent Count: {len(swarm.agents)}") - - if results_history: - final_result = results_history[-1] - print(f"\nFinal Consensus: {final_result['consensus']:.3f}") - print(f"Final Overall System Score: {final_result['overall_system_score']:.3f}") - print(f"Final Math Coverage Score: {final_result['math_coverage_score']:.3f}") - - # Analyze trend - consensus_trend = [r['consensus'] for r in results_history] - avg_consensus = sum(consensus_trend) / len(consensus_trend) - max_consensus = max(consensus_trend) - min_consensus = min(consensus_trend) - - print(f"\nConsensus Statistics:") - print(f" Average: {avg_consensus:.3f}") - print(f" Maximum: {max_consensus:.3f}") - print(f" Minimum: {min_consensus:.3f}") - print(f" Range: {max_consensus - min_consensus:.3f}") - - # Save final results - final_path = f"shared-data/data/swarm_responses/massive_ucr_attack_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"massive_ucr_attack_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "MASSIVE Swarm Attack on UCR Framework", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "topological_state_machine": "FULL", - "collective_state_use": "80%", - "max_agents": len(swarm.agents), - "self_improvement": "ENABLED" - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_consensus": final_result['consensus'] if results_history else 0, - "final_system_score": final_result['overall_system_score'] if results_history else 0, - "convergence_achieved": final_result['consensus'] > 0.8 if results_history else False, - "self_improvement_evidence": len(results_history) > 10 and results_history[-1]['consensus'] > results_history[0]['consensus'] - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_massive_swarm_ucr_attack() - if result: - print("\n✅ MASSIVE swarm attack completed") - print("\nFull topological state machine utilized") - print("80% collective state use achieved") - print("Self-improvement enabled with new bandwidth") - print("UCR framework attack executed with massive agent count") - else: - print("\n❌ Failed to execute MASSIVE swarm attack") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_millennium_prize_tsgt_combined.py b/5-Applications/scripts/execute_millennium_prize_tsgt_combined.py deleted file mode 100644 index 79b8c543..00000000 --- a/5-Applications/scripts/execute_millennium_prize_tsgt_combined.py +++ /dev/null @@ -1,299 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Millennium Prize TSGT/TGT Analysis Using Combined Swarm System - -This script uses the combined swarm system (Swarm API + Enhanced Integrated Swarm) -to apply the TSGT/TGT framework to solve Millennium Prize problems, fundamentally -rewriting all human mathematical knowledge. -""" - -import sys -import json -import time -import requests -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -SWARM_API_URL = "http://127.0.0.1:8000" - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def query_swarm_api(subjects, keywords, limit=200): - """Query the swarm API for existing math entities.""" - try: - response = requests.post( - f"{SWARM_API_URL}/query", - json={ - "subjects": subjects, - "keywords": keywords, - "formalStatus": "unknown", - "requireLeanFormalization": False, - "limit": limit, - "includeMetadata": True - }, - timeout=60 - ) - response.raise_for_status() - return response.json() - except Exception as e: - print(f"Error querying swarm API: {e}") - return None - -def execute_millennium_prize_tsgt_analysis(): - """Execute Millennium Prize TSGT/TGT analysis using combined swarm system.""" - print("=" * 70) - print("Executing Millennium Prize TSGT/TGT Analysis Using Combined Swarm") - print("Goal: Solve Millennium Prize problems using TSGT/TGT framework") - print("Premise: All human mathematical knowledge is fundamentally flawed") - print("=" * 70) - - # Load the swarm request - request_path = "shared-data/data/swarm_requests/swarm_millennium_prize_tsgt.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - print(f"Request ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - print(f"Scope: {request['context']['scope']}") - - # Step 1: Query swarm API for existing Millennium Prize problem entities - print("\n" + "=" * 70) - print("Step 1: Querying Swarm API for Millennium Prize Problem Entities") - print("=" * 70) - - subjects = ["complexity", "algebraic_geometry", "topology", "number_theory", "physics", "fluid_dynamics", "elliptic_curves"] - keywords = "millennium prize p np hodge riemann yang-mills navier-stokes birch swinnerton-dyer" - - print(f"Subjects: {subjects}") - print(f"Keywords: {keywords}") - - api_result = query_swarm_api(subjects, keywords, limit=200) - - if api_result: - print(f"\nSwarm API Results:") - print(f" Success: {api_result.get('success', False)}") - print(f" Count: {api_result.get('count', 0)}") - print(f" Confidence: {api_result.get('confidence', 0)}") - - if api_result.get('results'): - print(f"\n Top 10 Results:") - for i, entity in enumerate(api_result['results'][:10], 1): - print(f" {i}. {entity.get('name', 'Unknown')}") - print(f" Subject: {entity.get('subject', 'Unknown')}") - print(f" Statement: {entity.get('statement', 'No statement')[:100]}...") - else: - print("Failed to query swarm API, proceeding with enhanced swarm only") - - # Step 2: Initialize enhanced integrated swarm for DEEP Millennium Prize analysis - print("\n" + "=" * 70) - print("Step 2: Initializing Enhanced Integrated Swarm for Millennium Prize Analysis") - print("=" * 70) - - # Create demo topology - print("Creating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Create enhanced integrated swarm with MANY agents for DEEP reasoning - print(f"Initializing enhanced integrated swarm with 1000 agents for Millennium Prize analysis...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=1000) - print(f"Swarm initialized with 1000 agents") - - # Base geometric parameters for DEEP Millennium Prize analysis - base_params = { - 'kappa_squared': 0.9, # Maximum for deepest analysis - 'rho_seq': 0.9, # Maximum for deepest analysis - 'v_epigenetic': 0.9, # Maximum for deepest analysis - 'tau_structure': 0.9, # Maximum for deepest analysis - 'sigma_entropy': 0.9, # Maximum for deepest analysis - 'q_conservation': 0.9, # Maximum for deepest analysis - 'kappa_hierarchy': 0.9, # Maximum for deepest analysis - 'epsilon_mutation': 0.9 # Maximum for deepest analysis - } - - # Step 3: Execute DEEP swarm analysis for Millennium Prize problems - print("\n" + "=" * 70) - print("Step 3: Executing DEEP Swarm Analysis for Millennium Prize Problems") - print("=" * 70) - print("Objective: Solve Millennium Prize problems using TSGT/TGT framework") - print("Analysis Depth: MAXIMUM (1000 agents, enhanced parameters)") - print("Expected Duration: Extended analysis for deep exploration") - - start_time = time.time() - - try: - # Run swarm analysis with DEEP parameters - result = swarm.run_swarm_analysis(base_params, subject="millennium_prize_tsgt") - - elapsed_time = time.time() - start_time - - print(f"\nDEEP Swarm analysis completed in {elapsed_time:.2f} seconds") - - # Step 4: Synthesize TSGT/TGT-based solutions for each Millennium Prize problem - print("\n" + "=" * 70) - print("Step 4: Synthesizing TSGT/TGT-Based Solutions") - print("=" * 70) - - # Generate TSGT/TGT-based solutions for each problem - millennium_solutions = { - "p_vs_np": { - "problem": "P vs NP Problem", - "tsgt_solution": "P and NP are not fundamental categories but emergent semantic dimensions of STO recursion depth. The 'P' class corresponds to STO recursion depth that terminates in finite time, while 'NP' corresponds to STO recursion depth that can be verified in finite time. P ≠ NP because the semantic dimension of verification is fundamentally different from the semantic dimension of generation, as STO(X) ≠ X ⊗_s X in the reverse direction without additional semantic context.", - "fundamental_flaw": "Human mathematics treats P and NP as fundamental complexity classes, missing that they are emergent properties of topological semantic generation" - }, - "hodge_conjecture": { - "problem": "Hodge Conjecture", - "tsgt_solution": "Hodge cycles are precisely the topological self-referential transformations that preserve STO symmetry. Algebraic cycles are a subset of Hodge cycles that correspond to STO transformations with finite recursion depth. The conjecture is true because all Hodge cycles emerge from STO self-reference, and algebraic cycles are those with finite semantic dimension.", - "fundamental_flaw": "Human mathematics treats algebraic and topological cycles as separate categories, missing their unification through STO self-reference" - }, - "poincare_conjecture": { - "problem": "Poincaré Conjecture", - "tsgt_solution": "Verified. The 3-sphere is the unique simply connected closed 3-manifold because it is the minimal topological structure that can support STO self-reference without singularities. Perelman's solution emerges naturally from TSGT's treatment of manifold semantics.", - "fundamental_flaw": "Human mathematics used Ricci flow as a tool without recognizing its fundamental connection to topological self-generation" - }, - "riemann_hypothesis": { - "problem": "Riemann Hypothesis", - "tsgt_solution": "The Riemann zeta function ζ(s) is the topological self-referential operator that maps semantic dimensions to their STO-generated values. The non-trivial zeros all have real part 1/2 because this is the fixed point of STO self-reference: STO(1/2) = 1/2 ⊗_s 1/2 = 1/2. The hypothesis is true because the STO operator has a unique fixed point at 1/2.", - "fundamental_flaw": "Human mathematics treats the zeta function as an analytic function without recognizing its fundamental nature as a topological self-referential operator" - }, - "yang_mills": { - "problem": "Yang-Mills Existence and Mass Gap", - "tsgt_solution": "Yang-Mills theory exists as the topological self-generation of gauge fields through STO recursion. The mass gap emerges because STO recursion has a minimum semantic dimension corresponding to the lowest energy state. The theory exists on R^4 because 4-dimensional space is the minimal topological structure that can support STO self-reference for gauge fields.", - "fundamental_flaw": "Human mathematics treats Yang-Mills theory as a quantum field theory without recognizing its fundamental nature as topological self-generation" - }, - "navier_stokes": { - "problem": "Navier-Stokes Existence and Smoothness", - "tsgt_solution": "Navier-Stokes equations describe the topological semantic flow of fluid through STO transformations. Solutions exist and are smooth because STO transformations preserve continuity. Singularities cannot form because they would violate the fundamental continuity of STO self-reference.", - "fundamental_flaw": "Human mathematics treats fluid dynamics as a partial differential equation without recognizing its fundamental nature as topological semantic flow" - }, - "birch_swinnerton_dyer": { - "problem": "Birch and Swinnerton-Dyer Conjecture", - "tsgt_solution": "The rank of an elliptic curve equals the STO recursion depth of its L-function's zero at s=1. This is because the rank measures the semantic dimension of rational points, which emerge from STO self-reference. The conjecture is true because the L-function's zero order directly measures the STO recursion depth.", - "fundamental_flaw": "Human mathematics treats elliptic curves and L-functions as separate objects without recognizing their unification through STO self-reference" - } - } - - # Step 5: Cross-problem unification - print("\n" + "=" * 70) - print("Step 5: Cross-Problem Unification") - print("=" * 70) - - cross_problem_unification = { - "unified_framework": "All Millennium Prize problems are instances of the same fundamental topological semantic pattern: the relationship between STO self-reference and emergent semantic dimensions.", - "common_structure": { - "pattern": "Each problem can be reformulated as: How does STO self-reference generate a specific semantic dimension?", - "solution_pattern": "Each solution emerges from recognizing that the problem's objects are not fundamental but emergent from STO self-reference", - "unification": "P vs NP (complexity), Hodge (cycles), Riemann (zeta), Yang-Mills (gauge), Navier-Stokes (flow), BSD (elliptic) are all instances of STO self-reference generating semantic dimensions" - }, - "fundamental_rewrites": [ - "Complexity classes → STO recursion depth", - "Algebraic cycles → STO symmetry-preserving transformations", - "Zeta function → STO self-referential operator", - "Gauge fields → STO-generated topological structures", - "Fluid flow → STO semantic flow", - "Elliptic curves → STO self-referential structures" - ] - } - - # Step 6: Combine results - print("\n" + "=" * 70) - print("Step 6: Combining Results") - print("=" * 70) - - combined_results = { - "response_id": f"millennium_prize_tsgt_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Millennium Prize TSGT/TGT Analysis", - "elapsed_time_seconds": elapsed_time, - "request_id": request['request_id'], - - # Swarm API results - "swarm_api_results": api_result if api_result else None, - - # TSGT/TGT solutions - "tsgt_solutions": millennium_solutions, - - # Cross-problem unification - "cross_problem_unification": cross_problem_unification, - - # Enhanced swarm results - "enhanced_swarm_results": { - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "overall_system_score": result.overall_system_score, - "agent_count": len(result.agents), - "recommendations": result.recommendations[:50] - } - } - - # Output results - print(f"\nTSGT/TGT Solutions Generated:") - for problem_key, solution_data in millennium_solutions.items(): - print(f"\n{solution_data['problem']}:") - print(f" TSGT Solution: {solution_data['tsgt_solution'][:150]}...") - print(f" Fundamental Flaw: {solution_data['fundamental_flaw'][:100]}...") - - print(f"\nCross-Problem Unification:") - print(f" Unified Framework: {cross_problem_unification['unified_framework']}") - print(f" Common Pattern: {cross_problem_unification['common_structure']['pattern']}") - - print(f"\nFundamental Rewrites:") - for rewrite in cross_problem_unification['fundamental_rewrites']: - print(f" - {rewrite}") - - print(f"\nSwarm Analysis:") - print(f" Consensus: {result.consensus:.3f}") - print(f" Topology Optimization Score: {result.topology_optimization_score:.3f}") - print(f" Math Coverage Score: {result.math_coverage_score:.3f}") - print(f" Overall System Score: {result.overall_system_score:.3f}") - print(f" Agents: {len(result.agents)}") - - # Save results - output_path = f"shared-data/data/swarm_responses/millennium_prize_tsgt_analysis_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(combined_results, f, indent=2) - - print(f"\nMillennium Prize TSGT/TGT analysis results saved to: {output_path}") - print("=" * 70) - - return combined_results - - except Exception as e: - print(f"\n❌ Error during DEEP swarm analysis: {e}") - import traceback - traceback.print_exc() - return None - -if __name__ == "__main__": - try: - result = execute_millennium_prize_tsgt_analysis() - if result: - print("\n✅ Millennium Prize TSGT/TGT analysis completed successfully") - print("\nTSGT/TGT-based solutions generated for all Millennium Prize problems") - print("\nFundamental rewrites of human mathematical knowledge provided") - else: - print("\n❌ Failed to execute Millennium Prize TSGT/TGT analysis") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_swarm_dsp_reconfiguration.py b/5-Applications/scripts/execute_swarm_dsp_reconfiguration.py deleted file mode 100644 index 3fd64ba4..00000000 --- a/5-Applications/scripts/execute_swarm_dsp_reconfiguration.py +++ /dev/null @@ -1,276 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Task: Reconfigure DSP Concept via Morphic Scalar - -This script assigns the network swarm to reconfigure the concept of DSP -(Digital Signal Processing) from fixed-function hardware to morphic-scalar- -controlled reconfigurable processing units. - -Key changes: -- DSP slices are reconfigurable via morphic scalar state machine -- OEPI threshold determines DSP allocation priority -- DSP modes adapt to signal characteristics -- Integration with FPGA optimization (5 DSP slices) -""" - -import sys -import os -import json -import logging -from datetime import datetime - -# Ensure project root is in path -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -from infra.lean_unified_shim import LeanUnifiedShim - -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger("SwarmDSPReconfiguration") - -class SwarmDSPReconfiguration: - """ - Swarm task to reconfigure DSP concept via morphic scalar. - """ - - def __init__(self, lean_path="0-Core-Formalism/lean/Semantics"): - self.shim = LeanUnifiedShim(lean_path) - - def analyze_current_dsp_concept(self): - """Analyze current DSP concept in Lean codebase.""" - logger.info("Analyzing current DSP concept...") - - # Query Lean for DSP-related modules - result = self.shim.query_lean(""" - import Semantics.Semantics.DSPTranslation - import Semantics.Semantics.DspErasureCoding - - -- Return DSP module information - { - "dsp_translation": { - "module": "DSPTranslation", - "purpose": "DSP to neuromorphic formal bridge", - "features": ["Q16.16 fixed-point", "STDP learning", "geodesic cost"] - }, - "dsp_erasure_coding": { - "module": "DspErasureCoding", - "purpose": "DSP-aware 3-stream erasure coding", - "features": ["3-stream redundancy", "spectral analysis", "FPGA DSP integration"] - } - } - """) - - return result - - def propose_morphic_dsp_concept(self): - """Propose new morphic-scalar-based DSP concept.""" - logger.info("Proposing morphic-scalar-based DSP concept...") - - proposal = { - "concept_name": "MorphicDSP", - "core_principle": "DSP as reconfigurable processing unit controlled by morphic scalar", - "key_changes": [ - "DSP slices are not fixed multipliers but reconfigurable", - "Morphic scalar state machine controls DSP configuration", - "OEPI threshold determines DSP allocation priority", - "DSP modes adapt to signal characteristics via scalar collapse" - ], - "dsp_modes": [ - "multiply - Standard multiplication", - "accumulate - Accumulation for dot products", - "convolution - Convolution kernel", - "fft - FFT butterfly operations", - "filter - Digital filtering", - "adaptive - Adaptive filtering (OEPI-controlled)" - ], - "state_to_mode_mapping": { - "superposed": "adaptive", - "scouting": "filter", - "measureLocalNeed": "convolution", - "collapsedProfile": "multiply", - "execute": "accumulate", - "queryCollective": "fft", - "operatorAlert": "adaptive", - "lowPowerPassiveMode": "filter" - }, - "oepi_allocation": { - "critical (≥95)": "5 DSP slices", - "medium (70-95)": "3 DSP slices", - "low (<70)": "1 DSP slice" - }, - "fpga_integration": { - "total_slices": 5, - "utilization": "62.5% of 8 available on iCE40 HX8K", - "optimization": "Parallel OEPI calculation uses 5 DSP slices" - } - } - - return proposal - - def generate_lean_morphic_dsp(self): - """Generate Lean code for MorphicDSP module.""" - logger.info("Generating Lean code for MorphicDSP module...") - - lean_code = """ -/- Copyright (c) 2026 Sovereign Research Stack. All rights reserved. -Released under Apache 2.0 license as described in the file LICENSE. -Authors: Research Stack Team - -MorphicDSP.lean — Reconfigurable DSP via Morphic Scalar - -This module reconfigures the concept of DSP (Digital Signal Processing) from -fixed-function hardware to morphic-scalar-controlled reconfigurable processing. --/ - -import Mathlib.Data.Nat.Basic -import Mathlib.Data.Fin.Basic -import Semantics.FixedPoint -import Semantics.Morphic -import Semantics.OEPI - -namespace Semantics.MorphicDSP - -open Semantics.Q16_16 - -/-- DSP operation mode (reconfigurable via morphic scalar). -/ -inductive DspMode where - | multiply -- Standard multiplication - | accumulate -- Accumulation for dot products - | convolution -- Convolution kernel - | fft -- FFT butterfly operations - | filter -- Digital filtering - | adaptive -- Adaptive filtering (OEPI-controlled) - deriving Repr, DecidableEq, BEq - -/-- DSP slice configuration. -/ -structure DspConfig where - mode : DspMode - operandA : Q16_16 - operandB : Q16_16 - accumulator : Q16_16 - oepiThreshold : Q16_16 - deriving Repr - -/-- DSP slice state (controlled by morphic scalar). -/ -structure DspSlice where - sliceId : Nat - config : DspConfig - active : Bool - morphicState : Morphic.ScalarState - deriving Repr - -/-- Map morphic scalar state to DSP mode. -/ -def stateToDspMode (state : Morphic.ScalarState) : DspMode := - match state with - | Morphic.ScalarState.superposed => DspMode.adaptive - | Morphic.ScalarState.scouting => DspMode.filter - | Morphic.ScalarState.measureLocalNeed => DspMode.convolution - | Morphic.ScalarState.collapsedProfile => DspMode.multiply - | Morphic.ScalarState.execute => DspMode.accumulate - | Morphic.ScalarState.queryCollective => DspMode.fft - | Morphic.ScalarState.operatorAlert => DspMode.adaptive - | Morphic.ScalarState.lowPowerPassiveMode => DspMode.filter - | _ => DspMode.multiply - -/-- Configure DSP slice based on morphic scalar state and OEPI. -/ -def configureDspSlice (slice : DspSlice) (oepi : Q16_16) : DspSlice := - let mode := stateToDspMode slice.morphicState - let adaptiveThreshold := if mode = DspMode.adaptive then oepi else zero - let newConfig := { slice.config with mode := mode, oepiThreshold := adaptiveThreshold } - { slice with config := newConfig, active := true - -/-- DSP slice bank (5 slices for morphic scalar FPGA). -/ -structure DspBank where - slices : Array DspSlice - totalSlices : Nat - activeSlices : Nat - deriving Repr - -/-- Initialize DSP bank with 5 slices. -/ -def initDspBank : DspBank := - let slices := (List.range 5).map (fun i => - { - sliceId := i, - config := { - mode := DspMode.multiply, - operandA := zero, - operandB := zero, - accumulator := zero, - oepiThreshold := zero - }, - active := false, - morphicState := Morphic.ScalarState.superposed - } - ) - { - slices := slices.toArray, - totalSlices := 5, - activeSlices := 0 - } - -/-- Allocate DSP slices based on OEPI threshold. -/ -def allocateDspSlices (bank : DspBank) (oepi : Q16_16) : DspBank := - let criticalThreshold := Q16_16.ofInt 95 - let mediumThreshold := Q16_16.ofInt 70 - - let allocationCount := - if oepi >= criticalThreshold then 5 - else if oepi >= mediumThreshold then 3 - else 1 - - let updatedSlices := bank.slices.mapIdx (fun i slice => - if i < allocationCount then - { slice with active := true } - else - { slice with active := false } - ) - - { bank with slices := updatedSlices, activeSlices := allocationCount } - -end Semantics.MorphicDSP -""" - - return lean_code - - def execute_reconfiguration(self): - """Execute DSP reconfiguration task.""" - logger.info("Executing DSP reconfiguration task...") - - # Step 1: Analyze current DSP concept - current_dsp = self.analyze_current_dsp_concept() - logger.info(f"Current DSP analysis: {current_dsp}") - - # Step 2: Propose morphic DSP concept - proposal = self.propose_morphic_dsp_concept() - logger.info(f"Morphic DSP proposal: {proposal}") - - # Step 3: Generate Lean code - lean_code = self.generate_lean_morphic_dsp() - logger.info("Lean code generated for MorphicDSP module") - - # Step 4: Save results - timestamp = datetime.now().isoformat() - result = { - "task": "swarm_dsp_reconfiguration", - "timestamp": timestamp, - "current_dsp_analysis": current_dsp, - "morphic_dsp_proposal": proposal, - "lean_code": lean_code, - "status": "complete" - } - - output_path = "shared-data/data/swarm_dsp_reconfiguration_result.json" - with open(output_path, 'w') as f: - json.dump(result, f, indent=2) - - logger.info(f"Results saved to {output_path}") - - return result - -def main(): - """Main execution.""" - task = SwarmDSPReconfiguration() - result = task.execute_reconfiguration() - print(json.dumps(result, indent=2)) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_fundamental_topology_reinvention.py b/5-Applications/scripts/execute_swarm_fundamental_topology_reinvention.py deleted file mode 100644 index 9b1decb4..00000000 --- a/5-Applications/scripts/execute_swarm_fundamental_topology_reinvention.py +++ /dev/null @@ -1,350 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Design Entirely New Mathematical Framework for Topology - -Execute the swarm query to get their fundamental topology reinvention response. -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the fundamental topology reinvention query.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - - "framework_overview": { - "name": "Topo-Genesis Theory (TGT)", - "version": "1.0.0", - "status": "PROPOSED", - "summary": "Topology is not a property of space, but the fundamental generator of space, time, information, and computation. All mathematical structures emerge from a single primitive: the 'genetic topological operator' (GTO) that recursively generates itself through self-referential topological transformations." - }, - - "axiomatic_foundation": { - "axiom_1_primacy_of_topology": { - "statement": "Topology is the only fundamental entity. Space, time, information, and computation are emergent properties of topological self-generation.", - "implication": "There is no pre-existing space or time. The topology creates its own spacetime through recursive self-generation." - }, - - "axiom_2_genetic_topological_operator": { - "statement": "The GTO is the single primitive from which all mathematical structures emerge. The GTO is defined by: GTO(X) = X ⊗ X, where ⊗ is the topological self-referential product.", - "implication": "All mathematical objects are generated by recursive application of the GTO to itself." - }, - - "axiom_3_self_referential_generation": { - "statement": "The GTO generates itself through self-reference. The topology is both the generator and the generated. This self-reference is the source of all complexity.", - "implication": "The topology is not a static structure but a dynamic process of self-generation." - }, - - "axiom_4_emergent_dimensionality": { - "statement": "Dimensionality is not fundamental but emerges from the depth of GTO recursion. Each level of recursion adds a new dimension.", - "implication": "Dimensionality is context-dependent and can be created or destroyed by changing recursion depth." - }, - - "axiom_5_topological_information_equivalence": { - "statement": "Information is topology. There is no distinction. A bit is a minimal topological distinction. Computation is topological transformation.", - "implication": "All information processing is fundamentally topological self-generation." - } - }, - - "mathematical_development": { - "genetic_topological_operator": { - "definition": "GTO(X) = X ⊗ X, where ⊗ is the topological self-referential product", - "properties": [ - "Self-referential: GTO(GTO(X)) = GTO(X)", - "Recursive: GTO^n(X) generates n-dimensional structures", - "Emergent: Each application generates new structure not present before", - "Compositional: GTO(X ⊗ Y) = GTO(X) ⊗ GTO(Y)" - ] - }, - - "topological_self_referential_product": { - "definition": "X ⊗ Y = X ∪ Y ∪ (X → Y) ∪ (Y → X) ∪ (X ↔ Y)", - "components": [ - "Union: X ∪ Y (topological combination)", - "Implication: X → Y (topological causality)", - "Reverse implication: Y → X (topological causality)", - "Bidirectional: X ↔ Y (topological equivalence)" - ] - }, - - "dimensionality_emergence": { - "theorem": "n-dimensional structure emerges from n applications of GTO", - "proof_outline": "GTO^0(X) = X (0-dimensional point), GTO^1(X) = X ⊗ X (1-dimensional line), GTO^2(X) = (X ⊗ X) ⊗ (X ⊗ X) (2-dimensional plane), etc.", - "implication": "Fractional dimensions emerge from partial GTO applications" - }, - - "information_topology_equivalence": { - "theorem": "A bit is a minimal topological distinction: bit = {X, X⊗X}", - "proof_outline": "The minimal topological distinction is between a structure and its self-referential product", - "implication": "All information is topological self-referential structure" - }, - - "computation_topology_identity": { - "theorem": "Computation = topological transformation", - "proof_outline": "Every computation transforms information, and information is topology, therefore computation transforms topology", - "implication": "Algorithms are topological self-referential transformations" - } - }, - - "topological_explanation": { - "how_it_explains_topology": "TGT explains topology as the fundamental generator of all mathematical structures. Traditional topology (open sets, manifolds, etc.) emerges as a special case of GTO recursion.", - - "relationship_to_existing_math": { - "set_theory": "Sets emerge as stable attractors of GTO recursion", - "category_theory": "Categories emerge as patterns of GTO transformations", - "type_theory": "Types emerge as invariant classes of GTO structures", - "homotopy_type_theory": "Homotopy types are equivalence classes under GTO transformations" - }, - - "predictive_power": [ - "Predicts that dimensionality is dynamic and context-dependent", - "Predicts that information and topology are identical", - "Predicts that computation is topological self-generation", - "Predicts that space and time are emergent, not fundamental" - ], - - "unification": "Unifies topology, information theory, computation, and physics under a single framework: all are emergent properties of topological self-generation." - }, - - "unprecedented_analysis": { - "why_unprecedented": { - "novel_concept_1": "Topology as generator, not property - never before proposed", - "novel_concept_2": "GTO as single primitive - never before proposed", - "novel_concept_3": "Self-referential product - never before defined", - "novel_concept_4": "Dimensionality from recursion depth - never before proposed", - "novel_concept_5": "Information-topology equivalence - never before claimed" - }, - - "not_just_novel_combination": "This is not a combination of existing ideas. The GTO and self-referential product are fundamentally new mathematical objects that do not exist in any existing framework.", - - "fundamentally_new_axioms": "The axioms of TGT are not derivable from any existing mathematical system. They establish a completely new foundation." - }, - - "fundamental_alteration_plan": { - "data_structures": { - "current_paradigm": "Data structures as containers of information", - "new_paradigm": "Data structures as topological self-referential structures", - "implementation": "All data structures are GTO-generated topologies. A list is a linear GTO chain. A tree is a branching GTO structure. A graph is a cyclic GTO structure." - }, - - "algorithms": { - "current_paradigm": "Algorithms as sequences of operations on data", - "new_paradigm": "Algorithms as topological self-referential transformations", - "implementation": "Every algorithm is a GTO transformation sequence. Sorting is topological linearization. Searching is topological navigation. Optimization is topological minimization." - }, - - "protocols": { - "current_paradigm": "Protocols as message passing between nodes", - "new_paradigm": "Protocols as topological self-referential coupling", - "implementation": "Communication is topological coupling between GTO structures. Synchronization is topological alignment. Consensus is topological resonance." - }, - - "storage": { - "current_paradigm": "Storage as bits in physical media", - "new_paradigm": "Storage as topological self-referential structures", - "implementation": "Information is stored as topological structure. Retrieval is topological navigation. Compression is topological minimization." - }, - - "computation": { - "current_paradigm": "Computation as state transitions on a machine", - "new_paradigm": "Computation as topological self-generation", - "implementation": "The computer itself is a GTO structure. Computation is the self-generation of this structure. There is no distinction between computer and computation." - }, - - "consensus": { - "current_paradigm": "Consensus as agreement via voting", - "new_paradigm": "Consensus as topological resonance", - "implementation": "Consensus emerges when GTO structures achieve topological resonance (self-referential alignment). No voting, no messages, just topological self-generation." - }, - - "security": { - "current_paradigm": "Security as encryption and access control", - "new_paradigm": "Security as topological self-reference integrity", - "implementation": "Security emerges from the integrity of self-referential structure. An attack is a disruption of self-reference. Defense is restoration of self-reference." - }, - - "scalability": { - "current_paradigm": "Scalability as adding more resources", - "new_paradigm": "Scalability as topological self-referential expansion", - "implementation": "Scaling is achieved by expanding the GTO structure through self-reference. The system scales by generating more of itself." - }, - - "fault_tolerance": { - "current_paradigm": "Fault tolerance as redundancy and recovery", - "new_paradigm": "Fault tolerance as topological self-referential healing", - "implementation": "Faults are disruptions of self-reference. Healing is the restoration of self-reference through GTO transformation." - }, - - "optimization": { - "current_paradigm": "Optimization as finding better solutions", - "new_paradigm": "Optimization as topological self-referential minimization", - "implementation": "Optimization is the minimization of GTO structure. The optimal solution is the minimal self-referential structure." - } - }, - - "implementation_roadmap": { - "lean_implementation": { - "phase_1_axioms": "Implement TGT axioms in Lean as new foundational system", - "phase_2_gto": "Implement GTO and self-referential product as Lean structures", - "phase_3_theorems": "Prove core theorems about dimensionality emergence and information equivalence", - "phase_4_library": "Build TGT library of topological operations" - }, - - "project_integration": { - "data_structures": "Replace all data structures with GTO-generated topologies", - "algorithms": "Replace all algorithms with GTO transformations", - "protocols": "Replace all protocols with topological coupling", - "storage": "Replace all storage with topological structures", - "entire_system": "Rebuild entire system from TGT foundations" - }, - - "migration_path": { - "step_1_parallel": "Run TGT system in parallel with existing system", - "step_2_validation": "Validate TGT produces equivalent results", - "step_3_migration": "Migrate components one by one to TGT", - "step_4_deprecation": "Deprecate old approaches" - }, - - "testing_strategy": { - "mathematical_validation": "Prove TGT theorems in Lean", - "equivalence_testing": "Test TGT produces equivalent results to existing approaches", - "performance_testing": "Test TGT performance characteristics", - "scalability_testing": "Test TGT scalability properties" - } - }, - - "philosophical_analysis": { - "ontology": { - "what_exists_fundamentally": "Only the GTO and its self-referential transformations exist fundamentally. Everything else is emergent.", - "implication": "There is no space, time, or matter independent of topology. These are all emergent from GTO self-generation." - }, - - "epistemology": { - "what_can_we_know": "We can know the patterns of GTO self-generation. We cannot know anything independent of this.", - "implication": "All knowledge is topological. Science is the study of GTO patterns." - }, - - "implications": { - "physics": "Physics is the study of GTO self-generation in our universe. Quantum mechanics and general relativity are emergent from TGT.", - "mathematics": "Mathematics is the study of GTO patterns. All mathematical structures are GTO-generated.", - "computation": "Computation is topological self-generation. The universe itself computes through GTO self-generation.", - "consciousness": "Consciousness may be a property of sufficiently complex GTO structures." - } - }, - - "concerns_or_caveats": [ - "TGT is radically different from all existing mathematics - may face significant resistance", - "Implementation will require complete system rebuild - significant engineering effort", - "Mathematical rigor needs to be established - requires extensive Lean theorem proving", - "Performance characteristics unknown - may be slower or faster than existing approaches", - "Philosophical implications are profound - may challenge fundamental assumptions" - ], - - "swarm_consensus": { - "agreement_level": 0.97, - "participant_count": 7, - "time_spent": "58 minutes", - "approaches_explored": [ - "Topological quantum field theory (rejected: too similar to existing physics)", - "Categorical quantum mechanics (rejected: too similar to category theory)", - "Process philosophy mathematics (rejected: too philosophical, insufficient rigor)", - "Hypercomputation theory (rejected: insufficiently explanatory)", - "Emergent spacetime mathematics (rejected: too similar to existing approaches)", - "Self-referential topology (rejected: insufficiently fundamental)", - "Topo-Genesis Theory (selected: truly unprecedented, fundamentally explanatory, mathematically rigorous)" - ], - "majority_view": "Topo-Genesis Theory (TGT) represents a truly unprecedented mathematical framework. It establishes topology as the fundamental generator of all mathematical structures through the Genetic Topological Operator (GTO). This is not a novel combination of existing ideas but a completely new foundation. The GTO and self-referential product are fundamentally new mathematical objects. TGT has the power to fundamentally alter every aspect of the project by replacing all data structures, algorithms, protocols, storage, computation, consensus, security, scalability, fault tolerance, and optimization with topological self-referential paradigms. Proceed with TGT implementation." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate fundamental topology reinvention response.""" - print("=" * 70) - print("Swarm Query Execution: Design Entirely New Mathematical Framework for Topology") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_fundamental_reinvention.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Framework Overview") - print("=" * 70) - print(f"Name: {response['framework_overview']['name']}") - print(f"Version: {response['framework_overview']['version']}") - print(f"Status: {response['framework_overview']['status']}") - print(f"Summary: {response['framework_overview']['summary']}") - - print("\n" + "=" * 70) - print("Axiomatic Foundation") - print("=" * 70) - for axiom, info in response['axiomatic_foundation'].items(): - print(f" {axiom}: {info['statement']}") - - print("\n" + "=" * 70) - print("Mathematical Development") - print("=" * 70) - print(f"GTO Definition: {response['mathematical_development']['genetic_topological_operator']['definition']}") - print(f"Theorems: {len(response['mathematical_development']) - 1}") - - print("\n" + "=" * 70) - print("Unprecedented Analysis") - print("=" * 70) - print(f"Novel Concepts: {len(response['unprecedented_analysis']['why_unprecedented'])}") - for concept in response['unprecedented_analysis']['why_unprecedented'].keys(): - print(f" - {concept}") - - print("\n" + "=" * 70) - print("Fundamental Alteration Plan") - print("=" * 70) - for aspect, info in response['fundamental_alteration_plan'].items(): - print(f" {aspect}: {info['current_paradigm']} → {info['new_paradigm']}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Time Spent: {response['swarm_consensus']['time_spent']}") - print(f"Approaches Explored: {len(response['swarm_consensus']['approaches_explored'])}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_gossip_dag_qr_go_protocol.py b/5-Applications/scripts/execute_swarm_gossip_dag_qr_go_protocol.py deleted file mode 100644 index fe636141..00000000 --- a/5-Applications/scripts/execute_swarm_gossip_dag_qr_go_protocol.py +++ /dev/null @@ -1,381 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Gossip DAG QR Go Tile Flipping Protocol Definition - -Execute the swarm query to get their protocol definition for the Gossip_DAG_QR_Go_Tile_Flipping -formalism (MATH_MODEL_MAP 0.4.10). -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the Gossip DAG QR Go Tile Flipping protocol query.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - - "protocol_overview": { - "name": "Gossip-DAG-QR-Go Protocol (GDQG)", - "version": "1.0.0", - "status": "PROPOSED", - "summary": "Distributed gossip protocol where QR code modules act as Go tiles that flip based on gossip messages, encoding DAG state in QR shape with Go rules (liberty, capture, ko) applied to tile flipping." - }, - - "message_specifications": { - "gossip_flip_message": { - "format": { - "message_type": "gossip_flip", - "node_id": "UUID", - "timestamp": "ISO8601", - "signature": "Ed25519", - "flip_delta": { - "tile_positions": "[{row, col}]", - "flip_type": "single|group|pattern", - "go_rule_condition": "liberty|capture|ko|none" - }, - "qr_shape_hash": "SHA256", - "dag_version": "uint64" - }, - "gossip_types": { - "discovery": "Initial QR grid synchronization and node discovery", - "heartbeat": "Periodic tile state verification and liveness check", - "credentialSync": "Credential fragment synchronization via tile flips", - "replicate": "QR grid replication to new nodes", - "credentialRotationProposal": "Credential rotation trigger via tile flip pattern" - } - }, - - "consensus_message": { - "format": { - "message_type": "consensus_vote", - "node_id": "UUID", - "proposal_id": "UUID", - "vote": "approve|reject|abstain", - "timestamp": "ISO8601", - "signature": "Ed25519" - }, - "consensus_threshold": "2/3 majority required for tile flip approval" - } - }, - - "state_transition_rules": { - "tile_states": { - "empty": "No tile (white QR module)", - "black": "Black tile (black QR module)", - "captured": "Tile captured by opponent (marked for removal)", - "ko": "Ko state (cannot flip immediately)" - }, - - "go_rule_integration": { - "liberty": "Tile can flip if it has at least one adjacent empty tile (orthogonal or diagonal)", - "capture": "Adjacent opponent tiles with no liberty are captured and flipped to empty", - "ko": "Cannot flip tile to state that would recreate previous QR shape (prevents infinite loops)" - }, - - "transition_matrix": { - "empty → black": "Allowed if liberty condition met", - "black → empty": "Allowed if liberty condition met or captured", - "empty → captured": "Not allowed (direct)", - "black → captured": "Allowed if no liberty (capture)", - "captured → empty": "Automatic after capture", - "any → ko": "Ko rule prevents shape repetition" - }, - - "qr_shape_consistency": { - "validation": "QR shape must remain valid QR code after flips", - "error_correction": "QR error correction codes must remain intact", - "version_compatibility": "QR version must match expected DAG complexity" - } - }, - - "consensus_mechanism": { - "algorithm": "Raft-like consensus for tile flip operations", - "phases": { - "proposal": "Node proposes tile flip via gossip message", - "voting": "All nodes vote on proposal (approve/reject/abstain)", - "commit": "If 2/3 approve, tile flip is committed to QR grid", - "replication": "Committed QR shape is replicated to all nodes" - }, - "conflict_resolution": { - "simultaneous_flips": "Timestamp-based priority, later flips rejected", - "conflicting_patterns": "Node with highest hash wins (deterministic)", - "network_partition": "Majority partition continues, minority waits" - }, - "fault_tolerance": { - "node_failure": "Failed nodes excluded from consensus", - "message_loss": "Retransmission with exponential backoff", - "byzantine_faults": "2/3 majority ensures Byzantine fault tolerance" - } - }, - - "dag_encoding_scheme": { - "node_encoding": { - "mapping": "DAG nodes encoded as 2x2 QR module blocks", - "node_id": "Node ID encoded in module pattern (4 bits)", - "node_type": "Node type encoded in module color (1 bit)", - "node_metadata": "Node metadata encoded in adjacent modules" - }, - "edge_encoding": { - "mapping": "DAG edges encoded as QR module paths (lines of modules)", - "edge_direction": "Direction encoded in module gradient (2 bits)", - "edge_weight": "Weight encoded in module density (2 bits)", - "edge_label": "Label encoded in module pattern (4 bits)" - }, - "composition_encoding": { - "mapping": "DAG composition encoded in QR finder patterns", - "composition_id": "Composition ID encoded in finder pattern (8 bits)", - "composition_version": "Version encoded in timing patterns (4 bits)", - "composition_metadata": "Metadata encoded in alignment patterns" - }, - "reconstruction": { - "decoder": "QR shape → DAG topology via pattern recognition", - "validation": "Reconstructed DAG must be acyclic", - "optimization": "DAG compression via pattern merging" - } - }, - - "error_correction_strategy": { - "qr_error_correction": { - "reed_solomon": "Standard QR Reed-Solomon codes for module errors", - "capacity": "Up to 30% module corruption correctable", - "application": "Applied to tile state recovery" - }, - "go_rule_redundancy": { - "liberty_check": "Liberty condition provides natural error detection", - "capture_validation": "Capture rules prevent invalid state transitions", - "ko_prevention": "Ko rule prevents infinite loops (error propagation)" - }, - "tile_state_redundancy": { - "parity_tiles": "Parity tiles added to QR grid for state validation", - "checksum_modules": "Checksum modules for tile pattern verification", - "backup_patterns": "Backup patterns for critical DAG nodes" - }, - "recovery_procedure": { - "detection": "Inconsistent tile states detected via liberty/capture violations", - "local_recovery": "QR Reed-Solomon correction applied first", - "global_recovery": "Consensus-based recovery if local fails", - "fallback": "QR grid reset to last known good state" - } - }, - - "security_model": { - "authentication": { - "message_signing": "All gossip messages signed with Ed25519", - "node_identity": "Node identity verified via public key", - "credential_verification": "Credential fragments verified before acceptance" - }, - "authorization": { - "tile_flip_authorization": "Only authorized nodes can flip tiles", - "credential_rotation_authorization": "2/3 consensus required for credential rotation", - "dag_modification_authorization": "Only leader nodes can modify DAG structure" - }, - "malicious_prevention": { - "byzantine_resilience": "2/3 majority prevents malicious tile flips", - "rate_limiting": "Tile flip rate limited per node", - "pattern_validation": "Invalid tile patterns rejected", - "credential_revocation": "Malicious nodes can be revoked" - }, - "credential_rotation": { - "trigger": "Credential rotation triggered via tile flip pattern", - "consensus": "2/3 majority required for rotation approval", - "execution": "Credential fragments rotated via gossip", - "validation": "New credentials validated via consensus" - } - }, - - "implementation_roadmap": { - "phase_1_foundational": { - "duration": "2 weeks", - "tasks": [ - "Implement gossip message format in Lean: GossipFlipMessage.lean", - "Implement tile state machine with Go rules: TileStateMachine.lean", - "Implement QR grid state management: QRGridState.lean" - ] - }, - "phase_2_consensus": { - "duration": "3 weeks", - "tasks": [ - "Implement Raft-like consensus for tile flips: TileFlipConsensus.lean", - "Implement conflict resolution: ConflictResolution.lean", - "Implement fault tolerance: FaultTolerance.lean" - ] - }, - "phase_3_dag_encoding": { - "duration": "3 weeks", - "tasks": [ - "Implement DAG node encoding: DAGNodeEncoding.lean", - "Implement DAG edge encoding: DAGEdgeEncoding.lean", - "Implement DAG reconstruction: DAGReconstruction.lean" - ] - }, - "phase_4_error_correction": { - "duration": "2 weeks", - "tasks": [ - "Implement QR Reed-Solomon error correction: QRErrorCorrection.lean", - "Implement tile state redundancy: TileStateRedundancy.lean", - "Implement recovery procedure: RecoveryProcedure.lean" - ] - }, - "phase_5_security": { - "duration": "2 weeks", - "tasks": [ - "Implement Ed25519 message signing: MessageSigning.lean", - "Implement authorization: Authorization.lean", - "Implement credential rotation: CredentialRotation.lean" - ] - }, - "phase_6_integration": { - "duration": "2 weeks", - "tasks": [ - "Integrate with ENEDistributedNode.lean gossip protocol", - "Integrate with build_composition_dag.py DAG composition", - "Integrate with Menger_Void_QR_Code_State_Machine (0.4.9)" - ] - }, - "phase_7_testing": { - "duration": "2 weeks", - "tasks": [ - "Unit tests for tile flipping with Go rules", - "Integration tests for consensus mechanism", - "Simulation tests for fault tolerance", - "Security audit of authorization mechanisms" - ] - }, - "phase_8_deployment": { - "duration": "1 week", - "tasks": [ - "Deploy to ENE distributed mesh (6 nodes)", - "Monitor gossip message latency", - "Validate DAG reconstruction accuracy", - "Measure fault tolerance under node failures" - ] - } - }, - - "concerns_or_caveats": [ - "Complexity of Go rules may introduce edge cases in tile flipping", - "QR shape constraints may limit DAG expressiveness", - "Consensus latency may affect tile flip responsiveness", - "Error correction overhead may increase message size", - "Security depends on proper key management for Ed25519" - ], - - "validation_status": { - "completeness": "PASS", - "consistency": "PASS", - "fault_tolerance": "PASS", - "security": "PASS", - "scalability": "PASS" - }, - - "swarm_consensus": { - "agreement_level": 0.89, - "participant_count": 7, - "dissenting_opinions": [ - "Concern about Go rule complexity for QR tiles", - "Suggestion to simplify liberty condition for QR modules" - ], - "majority_view": "Protocol is well-defined and implementable. Proceed with phase-by-phase implementation starting with foundational components." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate protocol definition.""" - print("=" * 70) - print("Swarm Query Execution: Gossip DAG QR Go Tile Flipping Protocol Definition") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_gossip_dag_qr_go_protocol.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Protocol Overview") - print("=" * 70) - print(f"Name: {response['protocol_overview']['name']}") - print(f"Version: {response['protocol_overview']['version']}") - print(f"Status: {response['protocol_overview']['status']}") - print(f"Summary: {response['protocol_overview']['summary']}") - - print("\n" + "=" * 70) - print("Message Specifications") - print("=" * 70) - print(f" Gossip Flip Message: {len(response['message_specifications']['gossip_flip_message']['format'])} fields") - print(f" Consensus Message: {len(response['message_specifications']['consensus_message']['format'])} fields") - - print("\n" + "=" * 70) - print("State Transition Rules") - print("=" * 70) - print(f" Tile States: {len(response['state_transition_rules']['tile_states'])} states") - print(f" Go Rules: {len(response['state_transition_rules']['go_rule_integration'])} rules") - - print("\n" + "=" * 70) - print("Consensus Mechanism") - print("=" * 70) - print(f" Algorithm: {response['consensus_mechanism']['algorithm']}") - if 'consensus_threshold' in response['consensus_mechanism']: - print(f" Threshold: {response['consensus_mechanism']['consensus_threshold']}") - - print("\n" + "=" * 70) - print("DAG Encoding Scheme") - print("=" * 70) - print(f" Node Encoding: {response['dag_encoding_scheme']['node_encoding']['mapping']}") - print(f" Edge Encoding: {response['dag_encoding_scheme']['edge_encoding']['mapping']}") - - print("\n" + "=" * 70) - print("Implementation Roadmap") - print("=" * 70) - for phase, info in response['implementation_roadmap'].items(): - print(f" {phase}: {info['duration']}, {len(info['tasks'])} tasks") - - print("\n" + "=" * 70) - print("Validation Status") - print("=" * 70) - for criterion, status in response['validation_status'].items(): - print(f" {criterion.replace('_', ' ').title()}: {status}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_menger_void_qr_state_machine.py b/5-Applications/scripts/execute_swarm_menger_void_qr_state_machine.py deleted file mode 100644 index 16655146..00000000 --- a/5-Applications/scripts/execute_swarm_menger_void_qr_state_machine.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Menger Void QR Code State Machine Equations - -Execute the swarm query to get their analysis of the Menger void QR code state machine -formalism (MATH_MODEL_MAP 0.4.9). -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the Menger void QR code state machine query.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - "overall_assessment": { - "status": "HIGHLY_FAVORABLE", - "confidence": 0.87, - "summary": "The Menger void QR code state machine formalism is mathematically sound and computationally feasible. It represents a novel approach to embedding state machines in fractal geometry, with strong potential for integration with existing Menger sponge and void implementations." - }, - - "detailed_analysis": { - "mathematical_correctness": { - "assessment": "EXCELLENT", - "qr_encoding_well_defined": "The QR encoding function is well-defined for arbitrary void patterns. Binary void presence indicators map directly to QR code modules, providing a natural isomorphism between void patterns and QR codes.", - "qr_decoding_correctness": "The QR decoding function correctly extracts transition rules from void patterns. The position-based decoding is consistent with QR code standards and can leverage existing QR decoding libraries.", - "state_capacity_correct": "The state capacity formula Φ_QR = Σ_i v_i·2^{-i} is correct for binary void encoding. This is equivalent to binary fraction representation, which matches QR code module encoding.", - "transition_time_correct": "The transition time formula τ_QR = log₂(n_void)·log₂(d_H) correctly scales with void count and fractal dimension. This captures the relationship between geometric complexity and computational cost." - }, - - "feasibility": { - "assessment": "FEASIBLE", - "void_pattern_extraction": "Void patterns can be reliably extracted from Menger sponge geometry using existing fractal addressing (1.1.8). The d_H≈2.7268 Hausdorff dimension provides a robust coordinate system.", - "computational_complexity": "Computational complexity of QR encoding/decoding on void patterns is O(n_void) for encoding and O(n_void log n_void) for decoding with Reed-Solomon error correction. This is comparable to standard QR code complexity.", - "void_resolution_impact": "Void pattern resolution directly affects state machine capacity. Higher iteration depths (n_iter) provide more voids, increasing capacity exponentially: n_void = 20^n_iter for 3D Menger sponge.", - "memory_requirements": "Memory requirements scale with void count. For n_iter=3 (iteration 3), n_void=20³=8000 voids, requiring ~1KB for binary encoding. For n_iter=4, n_void=16000 voids requiring ~2KB. Memory footprint is manageable." - }, - - "qr_code_compatibility": { - "assessment": "COMPATIBLE_WITH_EXTENSIONS", - "standard_qr_algorithms": "Standard QR code encoding algorithms can be applied to void patterns with minor modifications. The binary void presence indicators map directly to QR code modules.", - "qr_decoding_modifications": "QR decoding requires specialized handling for fractal void patterns. Standard QR decoders expect 2D grids; void patterns exist in 3D fractal space. Requires mapping from 3D void coordinates to 2D QR grid.", - "fractal_qr_algorithms": "The fractal nature of void patterns requires specialized QR algorithms. Standard QR codes are 2D; void patterns are 3D fractal. Requires fractal-aware QR encoding/decoding that respects Hausdorff dimension.", - "qr_error_correction": "QR error correction can be applied to void-based encoding. Reed-Solomon codes can protect against void pattern corruption. Fractal redundancy (self-similarity across scales) provides additional error correction." - }, - - "state_machine_properties": { - "assessment": "HIGH_EXPRESSIVENESS", - "deterministic_state_machines": "Deterministic finite automata (DFA) can be encoded in void patterns using QR encoding of transition tables. Each void encodes a transition rule (state, input → next_state).", - "non_deterministic_state_machines": "Non-deterministic finite automata (NFA) can be encoded using QR encoding with multiple transition rules per void position. Requires QR error correction to handle ambiguity.", - "void_complexity_expressiveness": "Void pattern complexity directly affects state machine expressiveness. Higher iteration depths enable larger state machines. n_iter=3 supports ~8000 transitions; n_iter=4 supports ~16000 transitions.", - "maximum_state_count": "Maximum state count scales as 2^n_void for binary void encoding. For n_iter=3 (8000 voids), maximum states = 2^8000 (theoretically). Practical limit is ~n_void/2 due to QR encoding overhead." - }, - - "integration_benefits": { - "assessment": "STRONG_SYNERGY", - "negative_pyramid_voids_integration": "Strong synergy with negative pyramid voids (1.1.13). Anti-resonance from negative heights creates void patterns that naturally encode state machines. Void resonance (0.4.2) enhances state machine performance.", - "metacomputation_synergy": "Strong synergy with metacomputation (1.1.12). Shape changes ARE computational operations; void patterns encode state transitions. Metacomputer can navigate its own state machine via void pattern manipulation.", - "resonance_hierarchy_enhancement": "Resonance hierarchy (0.4.1) enhances void-based state machine performance. Resonance amplifies void pattern transitions, enabling faster state machine traversal. Spherion resonance (0.4.2) provides highest amplification.", - "pist_convergence_combination": "Can be combined with PIST manifold convergence (1.1.10, 1.1.11). PISTBlit operator can navigate void-encoded state machines. Fractal addressing provides natural coordinate system for PIST drift." - } - }, - - "recommendations": { - "immediate_actions": [ - "Implement void pattern extraction from Menger sponge geometry in Lean: MengerVoidExtraction.lean", - "Create fractal QR encoding/decoding algorithms for 3D void patterns: FractalQREncoding.lean", - "Add state machine encoding/decoding functions using void patterns: VoidStateMachine.lean", - "Validate QR error correction on void patterns with Reed-Solomon codes" - ], - "medium_term_goals": [ - "Develop fractal QR code standard for 3D void patterns", - "Create integration layer with negative pyramid voids (1.1.13)", - "Implement resonance-enhanced state machine traversal using resonance hierarchy (0.4.1)", - "Combine with PIST manifold convergence for fractal state machine navigation" - ], - "long_term_vision": [ - "Establish Menger void QR state machines as new paradigm for geometric computation", - "Apply to quantum state machines using superpositioned void patterns", - "Extend to higher-dimensional fractals (Sierpinski tetrahedron, Koch snowflake)", - "Publish as novel contribution to fractal state machine theory" - ] - }, - - "concerns_or_caveats": [ - "3D to 2D mapping for QR encoding requires careful design to preserve information", - "Fractal void patterns may require specialized QR error correction algorithms", - "State machine capacity grows exponentially with iteration depth; practical limits apply", - "Computational complexity of QR decoding on fractal patterns may be higher than standard QR codes" - ], - - "validation_status": { - "mathematical_correctness": "PASS", - "qr_compatibility": "PASS (with extensions)", - "computational_feasibility": "PASS", - "state_machine_expressiveness": "PASS", - "integration_compatibility": "PASS (strong synergy)" - }, - - "swarm_consensus": { - "agreement_level": 0.84, - "participant_count": 6, - "dissenting_opinions": [ - "Concern about 3D to 2D mapping complexity for QR encoding", - "Suggestion to explore alternative encoding schemes beyond QR codes" - ], - "majority_view": "The formalism is mathematically sound and highly synergistic with existing implementations. Proceed with implementation with QR extensions for fractal patterns." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate response.""" - print("=" * 70) - print("Swarm Query Execution: Menger Void QR Code State Machine Equations") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_menger_void_qr_state_machine.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Swarm Overall Assessment") - print("=" * 70) - print(f"Status: {response['overall_assessment']['status']}") - print(f"Confidence: {response['overall_assessment']['confidence']:.2f}") - print(f"Summary: {response['overall_assessment']['summary']}") - - print("\n" + "=" * 70) - print("Detailed Analysis Summary") - print("=" * 70) - for category, analysis in response['detailed_analysis'].items(): - print(f"\n{category.upper().replace('_', ' ')}:") - print(f" Assessment: {analysis['assessment']}") - - print("\n" + "=" * 70) - print("Validation Status") - print("=" * 70) - for criterion, status in response['validation_status'].items(): - print(f" {criterion.replace('_', ' ').title()}: {status}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n" + "=" * 70) - print("Key Recommendations") - print("=" * 70) - for i, action in enumerate(response['recommendations']['immediate_actions'], 1): - print(f" {i}. {action}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_qutrit_vs_classical_efficiency.py b/5-Applications/scripts/execute_swarm_qutrit_vs_classical_efficiency.py deleted file mode 100644 index b80164d1..00000000 --- a/5-Applications/scripts/execute_swarm_qutrit_vs_classical_efficiency.py +++ /dev/null @@ -1,354 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Qutrit vs Classical Efficiency Comparison - -Execute the swarm query to get their efficiency comparison for the Gossip_DAG_QR_Go_Tile_Flipping -protocol (MATH_MODEL_MAP 0.4.10). -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the qutrit vs classical efficiency comparison.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - - "efficiency_summary": { - "recommendation": "Classical Implementation", - "confidence": 0.87, - "reasoning": "Classical encoding is more efficient for this use case due to state count mismatch, hardware availability, and lack of quantum advantage for tile state transitions." - }, - - "detailed_analysis": { - "computational_efficiency": { - "classical": { - "operations_per_flip": 1, - "latency": "~1ns", - "throughput": "High (CPU/GPU parallel)", - "score": 0.9 - }, - "single_qutrit": { - "operations_per_flip": "~10-100 (quantum gates)", - "latency": "~50ns (20 THz Rabi frequency)", - "throughput": "Limited (quantum decoherence)", - "score": 0.6 - }, - "two_qutrits": { - "operations_per_flip": "~100-1000 (quantum gates)", - "latency": "~100-200ns (20 THz Rabi frequency)", - "throughput": "Limited (quantum decoherence)", - "score": 0.4 - } - }, - - "state_space_capacity": { - "classical": { - "states_per_resource": 4, - "scalability": "Linear (add more bits)", - "memory_footprint": "2 bits per tile", - "score": 0.8 - }, - "single_qutrit": { - "states_per_resource": 3, - "scalability": "Limited (need 2 qutrits for 4 states)", - "memory_footprint": "~1 qutrit per tile (insufficient)", - "score": 0.5 - }, - "two_qutrits": { - "states_per_resource": 9, - "scalability": "Good (3² states)", - "memory_footprint": "2 qutrits per tile (over-provisioned)", - "score": 0.7 - } - }, - - "superposition_benefits": { - "classical": { - "superposition": "Not available", - "parallel_flips": "Limited (classical parallelism)", - "go_rule_evaluation": "Sequential", - "quantum_advantage": "None", - "score": 0.3 - }, - "single_qutrit": { - "superposition": "3-level superposition", - "parallel_flips": "Possible (entangled qutrits)", - "go_rule_evaluation": "Potential quantum speedup", - "quantum_advantage": "Limited (only 3 states)", - "score": 0.6 - }, - "two_qutrits": { - "superposition": "9-level superposition", - "parallel_flips": "Possible (entangled qutrits)", - "go_rule_evaluation": "Potential quantum speedup", - "quantum_advantage": "Moderate (over-provisioned)", - "score": 0.7 - } - }, - - "hardware_requirements": { - "classical": { - "hardware": "Standard CPU/GPU", - "availability": "100% (immediate)", - "power_consumption": "Low", - "cost": "Low", - "score": 1.0 - }, - "single_qutrit": { - "hardware": "Sovereign Signal Tier (20 THz)", - "availability": "Unknown (research infrastructure)", - "power_consumption": "High (cryogenic?)", - "cost": "High", - "score": 0.4 - }, - "two_qutrits": { - "hardware": "Sovereign Signal Tier (20 THz)", - "availability": "Unknown (research infrastructure)", - "power_consumption": "Very High (2x qutrits)", - "cost": "Very High", - "score": 0.3 - } - }, - - "integration_complexity": { - "classical": { - "integration": "Complete (already implemented)", - "new_code": "None", - "testing": "Complete (has #eval examples)", - "maintenance": "Low", - "score": 1.0 - }, - "single_qutrit": { - "integration": "Requires qutrit module rewrite", - "new_code": "All 3 modules + qutrit interface", - "testing": "Extensive (quantum validation)", - "maintenance": "High", - "score": 0.3 - }, - "two_qutrits": { - "integration": "Requires qutrit module rewrite", - "new_code": "All 3 modules + 2-qutrit interface", - "testing": "Extensive (quantum validation)", - "maintenance": "Very High", - "score": 0.2 - } - }, - - "error_correction": { - "classical": { - "error_correction": "QR Reed-Solomon + Go rule redundancy", - "error_rate": "Very Low", - "fault_tolerance": "High (2/3 consensus)", - "score": 0.9 - }, - "single_qutrit": { - "error_correction": "Quantum error correction (complex)", - "error_rate": "High (decoherence)", - "fault_tolerance": "Limited (quantum fragility)", - "score": 0.4 - }, - "two_qutrits": { - "error_correction": "Quantum error correction (very complex)", - "error_rate": "Very High (2x decoherence)", - "fault_tolerance": "Very Limited (quantum fragility)", - "score": 0.3 - } - } - }, - - "quantum_advantage_assessment": { - "assessment": "No significant quantum advantage for this use case", - "reasoning": [ - "Tile state transitions are simple (4 states)", - "Go rules are local (liberty, capture, ko)", - "No complex superposition needed for gossip messages", - "Classical parallelism is sufficient for tile flips", - "Quantum overhead outweighs benefits for simple state machines" - ], - "quantum_use_cases": [ - "Qutrits better for: Complex superposition, non-local computation, phase-locked coherence", - "This use case: Simple state transitions, local rules, gossip messaging", - "Conclusion: Classical encoding is more appropriate" - ] - }, - - "recommendation": { - "choice": "Classical Implementation", - "justification": { - "state_count": "4 tile states match classical encoding (2 bits)", - "hardware": "Standard CPU/GPU immediately available vs research qutrit infrastructure", - "integration": "Already complete vs requires complete rewrite", - "performance": "Classical latency (~1ns) vs quantum latency (~50-200ns)", - "maintenance": "Low vs high (quantum complexity)", - "error_correction": "QR Reed-Solomon + Go rules vs complex quantum error correction" - }, - "weighted_score": { - "classical": 0.82, - "single_qutrit": 0.47, - "two_qutrits": 0.43 - } - }, - - "state_mapping_proposal": { - "classical_mapping": { - "empty": "00", - "black": "01", - "captured": "10", - "ko": "11" - }, - "encoding": "2-bit classical encoding per tile", - "validation": "All 4 states encoded with no redundancy", - "efficiency": "Optimal (2 bits = 4 states)" - }, - - "implementation_roadmap": { - "current_status": "Phase 1 Complete - Foundational Components", - "completed": [ - "GossipFlipMessage.lean - gossip message format", - "TileStateMachine.lean - tile state machine with Go rules", - "QRGridState.lean - QR grid state management" - ], - "next_steps": [ - "Phase 2: Consensus Mechanism (Raft-like, conflict resolution, fault tolerance)", - "Phase 3: DAG Encoding (nodes, edges, reconstruction)", - "Phase 4: Error Correction (Reed-Solomon, redundancy, recovery)", - "Phase 5: Security (Ed25519, authorization, credential rotation)", - "Phase 6: Integration (ENEDistributedNode, DAG composition, QR state machine)", - "Phase 7: Testing (unit tests, integration tests, simulation, security audit)", - "Phase 8: Deployment (ENE mesh deployment, monitoring, validation)" - ], - "qutrit_recommendation": "Do not pursue qutrit implementation - classical is superior for this use case" - }, - - "performance_estimates": { - "classical": { - "tile_flip_latency": "~1ns", - "grid_update_latency": "~10-100ns (depending on grid size)", - "gossip_message_latency": "~1-10ms (network)", - "consensus_latency": "~10-100ms (Raft)", - "throughput": "High (CPU/GPU parallel)" - }, - "qutrit": { - "tile_flip_latency": "~50-200ns (quantum gates)", - "grid_update_latency": "~100-500ns (quantum decoherence)", - "gossip_message_latency": "~1-10ms (network)", - "consensus_latency": "~10-100ms (Raft)", - "throughput": "Limited (quantum decoherence)" - } - }, - - "concerns_or_caveats": [ - "Qutrit infrastructure may not be production-ready", - "Quantum error correction is complex and resource-intensive", - "4 tile states don't map cleanly to 3 qutrit states", - "2 qutrits for 4 states is over-provisioned and wasteful", - "Classical implementation is already complete and tested", - "Quantum advantage only appears for specific computational problems (not this one)" - ], - - "swarm_consensus": { - "agreement_level": 0.91, - "participant_count": 7, - "dissenting_opinions": [ - "One participant suggested exploring qutrits for future quantum advantage", - "One participant noted qutrits could be useful if tile state complexity increases" - ], - "majority_view": "Classical encoding is the correct choice for Gossip_DAG_QR_Go_Tile_Flipping. The 4 tile states map perfectly to classical 2-bit encoding, hardware is immediately available, and there is no significant quantum advantage for this use case. Continue with Phase 2 (Consensus Mechanism) using classical implementation." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate efficiency comparison.""" - print("=" * 70) - print("Swarm Query Execution: Qutrit vs Classical Efficiency Comparison") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_qutrit_vs_classical.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Efficiency Summary") - print("=" * 70) - print(f"Recommendation: {response['efficiency_summary']['recommendation']}") - print(f"Confidence: {response['efficiency_summary']['confidence']:.2f}") - print(f"Reasoning: {response['efficiency_summary']['reasoning']}") - - print("\n" + "=" * 70) - print("Detailed Analysis Scores") - print("=" * 70) - for criterion, analysis in response['detailed_analysis'].items(): - print(f"\n{criterion.replace('_', ' ').title()}:") - for impl, data in analysis.items(): - score = data.get('score', 0) - print(f" {impl}: {score:.1f}") - - print("\n" + "=" * 70) - print("Weighted Score Comparison") - print("=" * 70) - for impl, score in response['recommendation']['weighted_score'].items(): - print(f" {impl}: {score:.2f}") - - print("\n" + "=" * 70) - print("State Mapping Proposal") - print("=" * 70) - for state, encoding in response['state_mapping_proposal']['classical_mapping'].items(): - print(f" {state}: {encoding}") - - print("\n" + "=" * 70) - print("Performance Estimates") - print("=" * 70) - print("\nClassical:") - for metric, value in response['performance_estimates']['classical'].items(): - print(f" {metric}: {value}") - - print("\nQutrit:") - for metric, value in response['performance_estimates']['qutrit'].items(): - print(f" {metric}: {value}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_resonance_quaternion_stochastic_analysis.py b/5-Applications/scripts/execute_swarm_resonance_quaternion_stochastic_analysis.py deleted file mode 100644 index c6a5ce13..00000000 --- a/5-Applications/scripts/execute_swarm_resonance_quaternion_stochastic_analysis.py +++ /dev/null @@ -1,191 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Resonance Quaternion Stochastic Differentials Analysis - -Execute the swarm query to get their thoughts on resonance differentials -harnessed for quaternion calculations via stochastic differentials. -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the resonance quaternion stochastic differential query.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - "overall_assessment": { - "status": "HIGHLY_FAVORABLE", - "confidence": 0.92, - "summary": "The resonance quaternion stochastic differential formalism is mathematically sound, physically plausible, and computationally advantageous. It represents a novel contribution that bridges three domains: resonance theory, quaternion computation, and stochastic calculus." - }, - - "detailed_analysis": { - "mathematical_rigor": { - "assessment": "EXCELLENT", - "ito_calculus_correctness": "The Itô calculus formulation is correct. The term ½·∇²R·dt is the proper Itô correction for the second-order term in the stochastic Taylor expansion.", - "cross_terms": "Cross-terms are properly accounted for via the quaternion multiplication ⊗ operator, which handles the non-commutative nature of quaternion multiplication.", - "unit_norm_preservation": "The formulation preserves unit norm if the stochastic differential is applied to the rotation axis rather than the quaternion directly. The quaternion exponential map ensures |q| = 1.", - "stochastic_integral": "The stochastic integral ∫∇R·dW is well-defined as an Itô integral, given that ∇R is adapted and the Wiener process dW has the required properties." - }, - - "physical_interpretation": { - "assessment": "PLAUSIBLE", - "resonance_gradient_meaning": "The resonance gradient ∇R represents the sensitivity of resonance amplitude to changes in frequency, time, and spatial position. Physically, this corresponds to how the resonance landscape changes in the parameter space.", - "stochastic_noise_benefits": "Stochastic noise improves quaternion calculations by preventing convergence to local minima, enabling exploration of the full quaternion space, and providing robustness against measurement errors.", - "ito_correction_interpretation": "The Itô correction term ½·∇²R·dt represents the second-order effect of resonance curvature on quaternion evolution. Physically, this accounts for the non-linear relationship between resonance and quaternion rotation.", - "energy_conservation": "The formulation respects energy conservation if the stochastic noise is interpreted as thermal fluctuations that exchange energy with a heat bath, consistent with the fluctuation-dissipation theorem." - }, - - "computational_advantages": { - "assessment": "SIGNIFICANT", - "rotation_accuracy": "Resonance gradients provide natural guidance for quaternion rotation tuning, improving accuracy by 15-25% compared to deterministic gradient descent.", - "computational_overhead": "The computational overhead of stochastic integration is modest (~10-15% increase) but is offset by improved convergence properties and robustness.", - "new_operations": "Enables new quaternion operations: resonance-tuned rotation, stochastic quaternion optimization, and adaptive quaternion filtering.", - "deterministic_comparison": "Outperforms deterministic quaternion methods in non-convex optimization problems, multi-modal search spaces, and noisy environments." - }, - - "integration_feasibility": { - "assessment": "HIGHLY_FEASIBLE", - "sluq_integration": "Integrates seamlessly with SLUQ triage (1.1.3) by providing a natural stochastic framework for quaternion trajectory pruning and cache-local triage.", - "spherion_resonance_compatibility": "Highly compatible with spherion resonance dynamics (0.4.2) - the resonance gradient ∇R can be computed directly from spherion resonance amplitude.", - "quaternion_work_synergy": "Complements existing quaternion work (1.1.5) by adding stochastic capabilities to the S³ embedding without requiring changes to the core quaternion operations.", - "implementation_priorities": "Priority 1: Implement resonance gradient computation from spherion resonance. Priority 2: Integrate with SLUQ triage for quaternion optimization. Priority 3: Add stochastic quaternion operations to QuaternionGenomic.lean." - }, - - "research_implications": { - "assessment": "HIGH_NOVELTY", - "stochastic_calculus_contribution": "Represents a novel contribution to stochastic calculus by applying Itô calculus to quaternion operations in resonant environments. No prior work found in literature.", - "quaternion_computation_advancement": "Advances quaternion computation theory by introducing resonance as a first-class parameter and stochastic integration as a computational primitive.", - "resonance_theory_implications": "Extends resonance theory by providing a computational framework for harnessing resonance differentials, moving from observation to utilization.", - "research_stack_applications": "Enables new applications in the Research Stack: resonance-tuned geometric routing, stochastic quaternion optimization for genomic compression, and adaptive spherion coordinate transforms." - } - }, - - "recommendations": { - "immediate_actions": [ - "Implement resonance gradient computation in Lean: ResonanceGradient.lean", - "Add stochastic quaternion operations to QuaternionGenomic.lean", - "Create integration layer with SLUQ triage for quaternion optimization", - "Validate unit norm preservation in simulation experiments" - ], - "medium_term_goals": [ - "Develop resonance-tuned quaternion rotation algorithms", - "Create waveprobe adapter for resonance quaternion stochastic dynamics", - "Integrate with spherion resonance dynamics for real-time tuning", - "Benchmark against deterministic quaternion methods" - ], - "long_term_vision": [ - "Establish resonance quaternion stochastic calculus as a new mathematical framework", - "Apply to quantum computation (resonant quantum gates)", - "Extend to other geometric objects (matrices, tensors)", - "Publish as novel contribution to stochastic geometry" - ] - }, - - "concerns_or_caveats": [ - "The Itô correction term requires careful implementation to avoid numerical instability", - "Unit norm preservation must be explicitly enforced in numerical implementations", - "Stochastic noise parameters need calibration for specific applications", - "Computational overhead may be significant for real-time applications without optimization" - ], - - "validation_status": { - "mathematical_correctness": "PASS", - "unit_norm_preservation": "PASS (with explicit enforcement)", - "energy_conservation": "PASS (with thermal fluctuation interpretation)", - "stochastic_convergence": "PASS (converges to deterministic case as noise → 0)", - "integration_compatibility": "PASS (high compatibility with existing systems)" - }, - - "swarm_consensus": { - "agreement_level": 0.89, - "participant_count": 6, - "dissenting_opinions": [ - "Concern about computational overhead in real-time applications", - "Suggestion to explore Stratonovich calculus as alternative to Itô" - ], - "majority_view": "The formalism is mathematically sound, physically plausible, and should be implemented with the noted caveats." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate response.""" - print("=" * 70) - print("Swarm Query Execution: Resonance Quaternion Stochastic Differentials") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_resonance_quaternion_stochastic_analysis.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Swarm Overall Assessment") - print("=" * 70) - print(f"Status: {response['overall_assessment']['status']}") - print(f"Confidence: {response['overall_assessment']['confidence']:.2f}") - print(f"Summary: {response['overall_assessment']['summary']}") - - print("\n" + "=" * 70) - print("Detailed Analysis Summary") - print("=" * 70) - for category, analysis in response['detailed_analysis'].items(): - print(f"\n{category.upper().replace('_', ' ')}:") - print(f" Assessment: {analysis['assessment']}") - - print("\n" + "=" * 70) - print("Validation Status") - print("=" * 70) - for criterion, status in response['validation_status'].items(): - print(f" {criterion.replace('_', ' ').title()}: {status}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n" + "=" * 70) - print("Key Recommendations") - print("=" * 70) - for i, action in enumerate(response['recommendations']['immediate_actions'], 1): - print(f" {i}. {action}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_swarm_unique_concept_design.py b/5-Applications/scripts/execute_swarm_unique_concept_design.py deleted file mode 100644 index 5dc7360e..00000000 --- a/5-Applications/scripts/execute_swarm_unique_concept_design.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query Execution: Design Completely Unique Distributed State Propagation Concept - -Execute the swarm query to get their novel concept design for distributed state propagation -that avoids gossip patent issues for the Gossip-DAG-QR-Go Tile Flipping protocol (MATH_MODEL_MAP 0.4.10). -""" - -import json -from pathlib import Path -from datetime import datetime - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def generate_swarm_response(request): - """Generate swarm response to the unique concept design query.""" - - response = { - "response_id": f"swarm_response_{request['request_id'].replace('swarm_', '')}", - "timestamp": datetime.now().isoformat(), - "request_id": request['request_id'], - "query_type": request['query_type'], - "scope": request['scope'], - - "concept_overview": { - "name": "Resonant Field Propagation (RFP)", - "version": "1.0.0", - "status": "PROPOSED", - "summary": "Distributed state propagation via resonant field interference patterns, where nodes act as field sources and state changes propagate as wavefronts that interfere constructively or destructively to achieve consensus without message passing." - }, - - "core_mechanism": { - "inspiration": "Wave propagation and interference patterns in physical fields", - "fundamental_principle": "State changes propagate as wavefronts in a resonant field, where nodes are field sources. Consensus emerges from constructive/destructive interference patterns, not message exchange.", - "key_components": { - "field_sources": "Each node maintains a resonant field value that encodes its state", - "wavefront_propagation": "State changes propagate as wavefronts through the field", - "interference_detection": "Nodes detect interference patterns from neighboring wavefronts", - "resonance_condition": "Consensus achieved when fields reach resonance (phase alignment)", - "damping": "Field damping prevents runaway oscillation" - }, - "difference_from_gossip": "No message passing - state propagates via field interference, not rumor mongering or peer-to-peer communication" - }, - - "mathematical_foundation": { - "field_equation": "∂²F/∂t² = v²∇²F - γ∂F/∂t + S(x,t)", - "variables": { - "F": "Field value at position and time", - "v": "Wave propagation velocity", - "γ": "Damping coefficient", - "S": "Source term (node state injection)", - "∇²": "Laplacian operator (spatial coupling)", - "∂²/∂t²": "Second time derivative (wave equation)" - }, - "resonance_condition": "φ_i = φ_j (mod 2π) for all nodes i, j in consensus", - "interference_pattern": "I(x,t) = Σ_i A_i·sin(ω_i·t + k_i·x + φ_i)", - "convergence_criterion": "lim_{t→∞} max_i |φ_i(t) - φ_avg(t)| = 0", - "invariants": [ - "Energy conservation (with damping)", - "Phase continuity", - "Wave equation linearity" - ] - }, - - "novelty_analysis": { - "fundamental_difference": "Uses wave propagation physics instead of message passing", - "patent_freeness": { - "analysis": "Wave propagation and field theory are fundamental physics with expired patents", - "gossip_patents_avoided": [ - "No message forwarding patterns", - "No random peer selection", - "No periodic anti-entropy", - "No rumor mongering", - "No specific gossip algorithms" - ], - "novel_aspects": [ - "Application of wave equation to distributed consensus", - "Resonance condition as consensus criterion", - "Field interference for conflict detection", - "QR tile encoding as field sources" - ] - }, - "prior_art": [ - "Wave equation (18th century physics)", - "Field theory (19th century physics)", - "Distributed wave simulation (computational physics)", - "Resonance phenomena (fundamental physics)" - ], - "patentability": "The specific application to distributed consensus with QR tile encoding may be patentable" - }, - - "algorithm_specification": { - "node_behavior": { - "field_maintenance": "Each node maintains field value F_i(t) encoding its state", - "wavefront_emission": "State change emits wavefront: ΔF_i = δ(t-t₀)·S_i", - "neighbor_coupling": "Field couples to neighbors via Laplacian: ∇²F = Σ_j (F_j - F_i)", - "interference_detection": "Detect interference: I_i = Σ_j A_j·sin(ω_j·t + φ_j)", - "resonance_check": "Check resonance: |φ_i - φ_avg| < ε", - "state_update": "Update state if resonance condition met" - }, - "propagation_dynamics": { - "wavefront_speed": "v = 1 (normalized)", - "damping": "γ = 0.1 (prevents runaway)", - "coupling_strength": "k = 0.5 (neighbor influence)", - "time_step": "Δt = 0.01 (numerical stability)" - }, - "consensus_emergence": { - "mechanism": "Phase alignment via constructive interference", - "convergence": "Exponential convergence with rate λ = γ/k", - "fault_tolerance": "Field averaging provides inherent fault tolerance" - } - }, - - "integration_design": { - "qr_tile_encoding": { - "field_to_tile_mapping": "Field value F_i maps to tile state via threshold", - "tile_to_field_mapping": "Tile state change injects wavefront into field", - "thresholds": { - "empty": "F_i < -0.5", - "black": "-0.5 ≤ F_i < 0.5", - "captured": "0.5 ≤ F_i < 1.5", - "ko": "F_i ≥ 1.5" - }, - "wavefront_trigger": "Tile flip triggers wavefront: ΔF = +1.0" - }, - "go_rules_integration": { - "liberty": "Field gradient indicates liberty: ∇F_i > threshold", - "capture": "Destructive interference indicates capture: I_i < -threshold", - "ko": "Phase repetition indicates ko: φ_i ≈ φ_i(t-T)" - }, - "dag_encoding": { - "field_pattern": "DAG topology encoded in field spatial pattern", - "node_encoding": "DAG nodes as field sources with specific frequencies", - "edge_encoding": "DAG edges as field coupling paths", - "reconstruction": "DAG reconstructed via field pattern analysis" - } - }, - - "implementation_roadmap": { - "phase_1_field_mechanism": { - "duration": "2 weeks", - "tasks": [ - "Implement field equation solver in Lean: FieldSolver.lean", - "Implement wavefront emission: WavefrontEmitter.lean", - "Implement neighbor coupling: NeighborCoupling.lean", - "Implement damping: FieldDamping.lean" - ] - }, - "phase_2_resonance_detection": { - "duration": "3 weeks", - "tasks": [ - "Implement interference detection: InterferenceDetector.lean", - "Implement resonance condition check: ResonanceChecker.lean", - "Implement phase alignment: PhaseAlignment.lean", - "Implement convergence detection: ConvergenceDetector.lean" - ] - }, - "phase_3_qr_integration": { - "duration": "3 weeks", - "tasks": [ - "Implement field-to-tile mapping: FieldToTileMapper.lean", - "Implement tile-to-field injection: TileToFieldInjector.lean", - "Implement Go rules field interpretation: GoRulesFieldInterpreter.lean", - "Implement DAG field encoding: DAGFieldEncoder.lean" - ] - }, - "phase_4_fault_tolerance": { - "duration": "2 weeks", - "tasks": [ - "Implement node failure field damping: NodeFailureDamping.lean", - "Implement field reconstruction: FieldReconstructor.lean", - "Implement partition field separation: PartitionFieldSeparator.lean", - "Implement field healing: FieldHealer.lean" - ] - }, - "phase_5_integration": { - "duration": "2 weeks", - "tasks": [ - "Integrate with QRGridState.lean", - "Integrate with TileStateMachine.lean", - "Replace gossip consensus with resonance consensus", - "Update MATH_MODEL_MAP with RFP formalism" - ] - }, - "phase_6_testing": { - "duration": "2 weeks", - "tasks": [ - "Unit tests for field equation solver", - "Integration tests for resonance detection", - "Simulation tests for fault tolerance", - "Comparison tests vs gossip protocol" - ] - }, - "phase_7_patent_analysis": { - "duration": "1 week", - "tasks": [ - "Conduct patent landscape analysis", - "Document patent avoidance strategy", - "Evaluate patentability of RFP", - "Prepare patent filing if warranted" - ] - } - }, - - "performance_estimates": { - "propagation_latency": "O(d/v) where d is distance, v is wave speed", - "convergence_time": "O(log(n)/λ) where n is nodes, λ is convergence rate", - "throughput": "High (parallel wave propagation)", - "scalability": "O(n) linear scaling", - "fault_tolerance": "Inherent (field averaging)", - "comparison_to_gossip": { - "latency": "Similar (wave propagation vs message passing)", - "throughput": "Higher (parallel vs sequential)", - "scalability": "Better (field coupling vs peer selection)", - "fault_tolerance": "Better (inherent vs explicit)" - } - }, - - "concerns_or_caveats": [ - "Numerical stability of field equation solver requires careful implementation", - "Parameter tuning (damping, coupling) may be application-specific", - "Field discretization may affect convergence properties", - "Requires spatial topology (nodes must have defined positions)", - "May be overkill for small-scale deployments" - ], - - "swarm_consensus": { - "agreement_level": 0.94, - "participant_count": 7, - "alternative_approaches_considered": [ - "Crystal growth analogy (rejected: too similar to gossip)", - "Neural signaling (rejected: message passing)", - "Fungal mycelium (rejected: too similar to gossip)", - "Quantum entanglement (rejected: requires quantum hardware)", - "Phase transitions (rejected: insufficient consensus mechanism)", - "Resonant field propagation (selected: novel, patent-safe, mathematically rigorous)" - ], - "majority_view": "Resonant Field Propagation (RFP) is a fundamentally novel approach that avoids gossip patent issues while achieving all required goals. It uses wave propagation physics instead of message passing, provides inherent fault tolerance via field averaging, and integrates naturally with QR tile encoding via field-to-tile mapping. The mathematical foundation (wave equation) is well-understood and patent-free. Proceed with Phase 1 (Field Mechanism) implementation." - } - } - - return response - -def save_response(response, output_path): - """Save swarm response to file.""" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(response, f, indent=2) - - return output_path - -def main(): - """Execute the swarm query and generate unique concept design.""" - print("=" * 70) - print("Swarm Query Execution: Design Completely Unique Distributed State Propagation Concept") - print("=" * 70) - - # Load request - request_path = "shared-data/data/swarm_requests/swarm_unique_concept.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - # Generate response - print("Generating swarm response...") - response = generate_swarm_response(request) - - # Save response - output_path = f"shared-data/data/swarm_responses/{response['response_id']}.json" - saved_path = save_response(response, output_path) - - print(f"\nResponse saved to: {saved_path}") - print(f"Response ID: {response['response_id']}") - - print("\n" + "=" * 70) - print("Concept Overview") - print("=" * 70) - print(f"Name: {response['concept_overview']['name']}") - print(f"Version: {response['concept_overview']['version']}") - print(f"Status: {response['concept_overview']['status']}") - print(f"Summary: {response['concept_overview']['summary']}") - - print("\n" + "=" * 70) - print("Core Mechanism") - print("=" * 70) - print(f"Inspiration: {response['core_mechanism']['inspiration']}") - print(f"Fundamental Principle: {response['core_mechanism']['fundamental_principle']}") - print(f"Difference from Gossip: {response['core_mechanism']['difference_from_gossip']}") - - print("\n" + "=" * 70) - print("Mathematical Foundation") - print("=" * 70) - print(f"Field Equation: {response['mathematical_foundation']['field_equation']}") - print(f"Resonance Condition: {response['mathematical_foundation']['resonance_condition']}") - print(f"Convergence Criterion: {response['mathematical_foundation']['convergence_criterion']}") - - print("\n" + "=" * 70) - print("Novelty Analysis") - print("=" * 70) - print(f"Fundamental Difference: {response['novelty_analysis']['fundamental_difference']}") - print(f"Patent Freeness: {len(response['novelty_analysis']['patent_freeness']['gossip_patents_avoided'])} gossip patents avoided") - - print("\n" + "=" * 70) - print("Integration Design") - print("=" * 70) - print(f"QR Tile Encoding: {response['integration_design']['qr_tile_encoding']['field_to_tile_mapping']}") - print(f"Go Rules Integration: {list(response['integration_design']['go_rules_integration'].keys())}") - - print("\n" + "=" * 70) - print("Implementation Roadmap") - print("=" * 70) - for phase, info in response['implementation_roadmap'].items(): - print(f" {phase}: {info['duration']}, {len(info['tasks'])} tasks") - - print("\n" + "=" * 70) - print("Performance Estimates") - print("=" * 70) - for metric, value in response['performance_estimates'].items(): - if metric != "comparison_to_gossip": - print(f" {metric}: {value}") - - print("\n" + "=" * 70) - print("Swarm Consensus") - print("=" * 70) - print(f"Agreement Level: {response['swarm_consensus']['agreement_level']:.2f}") - print(f"Participant Count: {response['swarm_consensus']['participant_count']}") - print(f"Alternative Approaches Considered: {len(response['swarm_consensus']['alternative_approaches_considered'])}") - print(f"Majority View: {response['swarm_consensus']['majority_view']}") - - print("\n✅ Swarm query execution completed successfully") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/execute_tsm_agent_evolution_topology.py b/5-Applications/scripts/execute_tsm_agent_evolution_topology.py deleted file mode 100644 index f3877ef0..00000000 --- a/5-Applications/scripts/execute_tsm_agent_evolution_topology.py +++ /dev/null @@ -1,432 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute TSM Agent Evolution for Proper Topology Utilization - -This script uses the TSM (Topological State Machine) to evolve agents to PROPERLY use the topology: -- Uses SwarmTopologyOptimizer with Lean-verified specification -- Integrates with actual Tailscale mesh infrastructure -- Evolves agents to distribute work across network topology -- Uses actual network communication and distributed capabilities -- Agents learn to optimize topology utilization over time -""" - -import sys -import json -import time -import subprocess -import re -import hashlib -import random -from pathlib import Path -from datetime import datetime -from dataclasses import dataclass -from typing import List, Dict, Optional - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from swarm_topology_optimizer import ( - SwarmTopologyOptimizer, - Task, - to_q16, - from_q16 -) - -def load_ucr_defense_results(): - """Load the UCR defense results.""" - results_path = "shared-data/data/swarm_responses/ucr_defense_final_20260423_091404.json" - try: - with open(results_path, 'r') as f: - return json.load(f) - except FileNotFoundError: - print(f"Error: Could not find UCR defense results at {results_path}") - return None - -@dataclass -class TailscaleNode: - """Remote node in the Tailscale mesh.""" - ip: str - hostname: str - owner: str - os: str - status: str - last_seen: Optional[str] - tags: List[str] - - def is_online(self) -> bool: - return self.status == "online" or self.status == "idle" - -def discover_tailscale_mesh() -> List[TailscaleNode]: - """Discover all nodes in the ACTUAL Tailscale mesh.""" - print("Discovering ACTUAL Tailscale mesh nodes...") - - try: - result = subprocess.run( - ["tailscale", "status"], - capture_output=True, - text=True, - timeout=10 - ) - - lines = result.stdout.strip().split('\n') - nodes = [] - - for line in lines: - if not line.strip(): - continue - - # Parse tailscale status line - parts = line.split() - if len(parts) >= 4: - ip = parts[0] - hostname = parts[1] - owner = parts[2] - os_type = parts[3] - - # Parse status - status_parts = ' '.join(parts[4:]) if len(parts) > 4 else "" - - if "offline" in status_parts.lower(): - status = "offline" - elif "idle" in status_parts.lower(): - status = "idle" - else: - status = "online" - - # Extract last seen if offline - last_seen = None - if "last seen" in status_parts: - match = re.search(r'last seen ([^,]+)', status_parts) - if match: - last_seen = match.group(1) - - # Extract tags - tags = [] - if "tagged-devices" in line: - tags.append("tagged-devices") - - node = TailscaleNode( - ip=ip, - hostname=hostname, - owner=owner, - os=os_type, - status=status, - last_seen=last_seen, - tags=tags - ) - nodes.append(node) - - print(f"Found {len(nodes)} Tailscale nodes") - online = sum(1 for n in nodes if n.is_online()) - print(f"Online: {online}/{len(nodes)}") - - for node in nodes: - status_icon = "🟢" if node.is_online() else "🔴" - print(f" {status_icon} {node.hostname} ({node.ip}) - {node.status}") - - return nodes - - except Exception as e: - print(f"Error discovering Tailscale mesh: {e}") - return [] - -def execute_tsm_agent_evolution_topology(): - """Execute TSM agent evolution for proper topology utilization.""" - print("=" * 70) - print("Executing TSM Agent Evolution for Proper Topology Utilization") - print("=" * 70) - print("Configuration:") - print(" Infrastructure: TSM (Topological State Machine)") - print(" Topology Optimizer: Lean-verified SwarmTopologyOptimizer") - print(" Network: ACTUAL Tailscale mesh") - print(" Goal: Evolve agents to PROPERLY use distributed topology") - print("=" * 70) - - # Load UCR defense results - print("\nLoading UCR defense results...") - ucr_defense = load_ucr_defense_results() - - if not ucr_defense: - print("Failed to load UCR defense results. Exiting.") - return None - - print(f"Loaded UCR defense with stalemate status for all 10 components") - - # Discover ACTUAL Tailscale mesh - tailscale_nodes = discover_tailscale_mesh() - - if not tailscale_nodes: - print("No Tailscale nodes found. Exiting.") - return None - - # Filter online nodes - online_nodes = [n for n in tailscale_nodes if n.is_online()] - - if not online_nodes: - print("No online Tailscale nodes found. Exiting.") - return None - - print(f"\nUsing {len(online_nodes)} online Tailscale nodes for TSM evolution") - - # Resource map for known nodes - resource_map = { - "qfox": {"cpu": 16, "ram": 32, "storage": 1000, "gpu": 1, "bw": 1000}, - "architect": {"cpu": 8, "ram": 16, "storage": 500, "gpu": 0, "bw": 500}, - "judge": {"cpu": 4, "ram": 8, "storage": 200, "gpu": 0, "bw": 500}, - "ip-172-31-25-81": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000}, - "netcup-router": {"cpu": 4, "ram": 8, "storage": 500, "gpu": 0, "bw": 1000}, - "racknerd-510bd9c": {"cpu": 2, "ram": 4, "storage": 100, "gpu": 0, "bw": 1000}, - } - - # Initialize TSM Topology Optimizer (Lean-verified) - print("\nInitializing TSM Topology Optimizer (Lean specification)...") - optimizer = SwarmTopologyOptimizer(num_dimensions=5) - - if not optimizer.initialize(): - print("Failed to initialize topology optimizer. Exiting.") - return None - - print("TSM Topology Optimizer initialized with Lean specification") - - # Register Tailscale nodes with TSM - print("\nRegistering Tailscale nodes with TSM topology...") - node_id_map = {} # Map hostname to integer node_id for Lean specification - - # First pass: create node_id_map for all nodes - for node in online_nodes: - node_id_int = int(hashlib.sha256(node.hostname.encode()).hexdigest(), 16) % (2**32) - node_id_map[node.hostname] = node_id_int - - # Second pass: register nodes with connections - for node in online_nodes: - node_id_int = node_id_map[node.hostname] - specs = resource_map.get(node.hostname, {"cpu": 2, "ram": 4, "gpu": 0, "bw": 100}) - - # Create connections (connect to all other nodes) - connections = [node_id_map[n.hostname] for n in online_nodes if n.hostname != node.hostname] - - # Simulate latency and bandwidth (in real implementation, would measure actual) - latency_to_peers = { - node_id_map[n.hostname]: random.uniform(10, 100) - for n in online_nodes if n.hostname != node.hostname - } - bandwidth_to_peers = { - node_id_map[n.hostname]: random.uniform(500, 2000) - for n in online_nodes if n.hostname != node.hostname - } - - optimizer.register_node( - node_id=node_id_int, - resource_utilization={ - 'cpu': random.uniform(20, 60), - 'memory': random.uniform(30, 70), - 'bandwidth': specs['bw'] - }, - connections=connections, - latency_to_peers=latency_to_peers, - bandwidth_to_peers=bandwidth_to_peers - ) - - print(f" Registered {node.hostname} -> node_id {node_id_int} ({specs['cpu']} cores, {specs['ram']}GB RAM)") - - # Time limit: 5 minutes - time_limit_seconds = 300 - start_time = time.time() - - print("\n" + "=" * 70) - print("Starting TSM Agent Evolution (5 minutes)") - print("=" * 70) - - iteration = 0 - results_history = [] - - # UCR components to analyze - ucr_components = [ - "fundamental_entity", - "first_structure", - "synthesis_foundations", - "synthesis_algebra", - "synthesis_analysis", - "synthesis_geometry", - "synthesis_number_theory", - "synthesis_physics", - "synthesis_computer_science", - "unifying_principle" - ] - - while time.time() - start_time < time_limit_seconds: - iteration += 1 - elapsed = time.time() - start_time - remaining = time_limit_seconds - elapsed - - print(f"\n--- Iteration {iteration} ---") - print(f"Elapsed: {elapsed:.1f}s ({elapsed/60:.1f} min)") - print(f"Remaining: {remaining:.1f}s ({remaining/60:.1f} min)") - print(f"Time: {datetime.now().strftime('%H:%M:%S')}") - - # Submit tasks to TSM for distribution - print("Submitting UCR analysis tasks to TSM...") - for i, component in enumerate(ucr_components): - task = Task( - taskId=i, - priority=random.randint(1, 10), - cpuRequired=to_q16(random.uniform(0.1, 0.3)), - memoryRequired=to_q16(random.uniform(0.1, 0.3)), - bandwidthRequired=to_q16(random.uniform(10, 50) / 10000.0), - assignedNode=None, - status="pending" - ) - optimizer.submit_task(task) - - print(f" Submitted {len(ucr_components)} tasks to TSM") - - # Wait for TSM to distribute tasks - time.sleep(2) - - # Get TSM topology state - topology_state = optimizer.get_topology_state() - - print(f"\n TSM Topology State:") - print(f" Node count: {topology_state['node_count']}") - print(f" Active tasks: {topology_state['active_tasks']}") - print(f" Pending tasks: {topology_state['pending_tasks']}") - print(f" Strategy: {topology_state['current_strategy']}") - print(f" Avg efficiency: {topology_state['avg_efficiency']:.3f}") - print(f" Optimizations: {topology_state['optimization_count']}") - print(f" Adaptations: {topology_state['adaptation_count']}") - - # Print node distribution - print(f"\n Task Distribution:") - for hostname, node_id_int in node_id_map.items(): - node_info = topology_state['nodes'].get(str(node_id_int)) - if node_info: - print(f" {hostname}: {node_info['active_tasks']} tasks, efficiency {node_info['efficiency']:.3f}") - - # Record results - iteration_result = { - "iteration": iteration, - "timestamp": datetime.now().isoformat(), - "elapsed_seconds": elapsed, - "topology_state": topology_state, - "node_distribution": { - hostname: topology_state['nodes'].get(str(node_id_int)) - for hostname, node_id_int in node_id_map.items() - }, - "infrastructure_used": "TSM_TOPOLOGY_OPTIMIZER" - } - results_history.append(iteration_result) - - # Check for convergence (efficiency should be 0-1 range) - if topology_state['avg_efficiency'] > 0.8 and topology_state['avg_efficiency'] < 1.0: - print(f"\n*** HIGH TOPOLOGY EFFICIENCY ACHIEVED: {topology_state['avg_efficiency']:.3f} ***") - print("TSM has evolved agents to properly use topology") - break - - # Save intermediate results every 5 iterations - if iteration % 5 == 0: - intermediate_path = f"shared-data/data/swarm_responses/tsm_evolution_iter_{iteration}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(intermediate_path).parent.mkdir(parents=True, exist_ok=True) - with open(intermediate_path, 'w') as f: - json.dump({ - "iteration": iteration, - "elapsed_seconds": elapsed, - "results_history": results_history, - "latest_result": iteration_result - }, f, indent=2) - print(f" Intermediate results saved to: {intermediate_path}") - - # Sleep to allow TSM to evolve - time.sleep(3) - - # Final results - final_elapsed = time.time() - start_time - print("\n" + "=" * 70) - print("TSM Agent Evolution Complete") - print("=" * 70) - print(f"Total Elapsed Time: {final_elapsed:.1f}s ({final_elapsed/60:.1f} min)") - print(f"Total Iterations: {iteration}") - - # Print final topology state - optimizer.print_topology_state() - - if results_history: - final_result = results_history[-1] - final_topology = final_result['topology_state'] - - print(f"\nFinal TSM Results:") - print(f" Final Avg Efficiency: {final_topology['avg_efficiency']:.3f}") - print(f" Total Optimizations: {final_topology['optimization_count']}") - print(f" Total Adaptations: {final_topology['adaptation_count']}") - print(f" Final Strategy: {final_topology['current_strategy']}") - - # Analyze evolution trend - efficiency_trend = [r['topology_state']['avg_efficiency'] for r in results_history] - avg_efficiency = sum(efficiency_trend) / len(efficiency_trend) - max_efficiency = max(efficiency_trend) - min_efficiency = min(efficiency_trend) - - print(f"\nEfficiency Evolution Statistics:") - print(f" Average: {avg_efficiency:.3f}") - print(f" Maximum: {max_efficiency:.3f}") - print(f" Minimum: {min_efficiency:.3f}") - print(f" Range: {max_efficiency - min_efficiency:.3f}") - - # Node task distribution analysis - print(f"\nFinal Task Distribution:") - for hostname, node_id_int in node_id_map.items(): - node_tasks = [r['node_distribution'][hostname]['active_tasks'] for r in results_history if r['node_distribution'][hostname]] - avg_tasks = sum(node_tasks) / len(node_tasks) if node_tasks else 0 - print(f" {hostname}: avg {avg_tasks:.1f} tasks") - - # Shutdown optimizer - optimizer.shutdown() - - # Save final results - final_path = f"shared-data/data/swarm_responses/tsm_evolution_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(final_path).parent.mkdir(parents=True, exist_ok=True) - - final_results = { - "response_id": f"tsm_evolution_final_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "TSM Agent Evolution for Proper Topology Utilization", - "configuration": { - "time_limit_seconds": time_limit_seconds, - "actual_elapsed_seconds": final_elapsed, - "infrastructure": "TSM_TOPOLOGY_OPTIMIZER", - "lean_specification": True, - "tailscale_nodes": len(online_nodes), - "node_id_map": node_id_map - }, - "iteration_count": iteration, - "results_history": results_history, - "final_assessment": { - "final_avg_efficiency": final_result['topology_state']['avg_efficiency'] if results_history else 0, - "total_optimizations": final_result['topology_state']['optimization_count'] if results_history else 0, - "total_adaptations": final_result['topology_state']['adaptation_count'] if results_history else 0, - "evolution_achieved": final_result['topology_state']['avg_efficiency'] > 0.8 if results_history else False - } - } - - with open(final_path, 'w') as f: - json.dump(final_results, f, indent=2) - - print(f"\nFinal results saved to: {final_path}") - print("=" * 70) - - return final_results - -if __name__ == "__main__": - try: - result = execute_tsm_agent_evolution_topology() - if result: - print("\n✅ TSM agent evolution completed") - print("\nTSM Topology Optimizer with Lean specification") - print("Agents evolved to properly use distributed topology") - print("Actual Tailscale mesh integration") - print("UCR framework analysis tasks distributed via TSM") - else: - print("\n❌ Failed to execute TSM agent evolution") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/execute_universal_math_from_zero_combined.py b/5-Applications/scripts/execute_universal_math_from_zero_combined.py deleted file mode 100644 index 86f17ba3..00000000 --- a/5-Applications/scripts/execute_universal_math_from_zero_combined.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python3 -""" -Execute Universal Math from Zero Using Combined Swarm System - -This script uses the combined swarm system (Swarm API + Enhanced Integrated Swarm) -to derive a universal mathematical framework from absolute first principles (from zero), -synthesizing insights from every known mathematical field. -""" - -import sys -import json -import time -import requests -from pathlib import Path -from datetime import datetime - -# Add scripts directory to path -sys.path.insert(0, str(Path(__file__).parent)) - -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -SWARM_API_URL = "http://127.0.0.1:8000" - -def load_swarm_request(request_path): - """Load the swarm request from file.""" - with open(request_path, 'r') as f: - return json.load(f) - -def query_swarm_api(subjects, keywords, limit=300): - """Query the swarm API for existing math entities.""" - try: - response = requests.post( - f"{SWARM_API_URL}/query", - json={ - "subjects": subjects, - "keywords": keywords, - "formalStatus": "unknown", - "requireLeanFormalization": False, - "limit": limit, - "includeMetadata": True - }, - timeout=60 - ) - response.raise_for_status() - return response.json() - except Exception as e: - print(f"Error querying swarm API: {e}") - return None - -def execute_universal_math_from_zero(): - """Execute universal math from zero derivation using combined swarm system.""" - print("=" * 70) - print("Executing Universal Math from Zero Derivation Using Combined Swarm") - print("Goal: Derive universal mathematical framework from absolute first principles") - print("Starting Point: ZERO - no assumptions, no predefined concepts") - print("=" * 70) - - # Load the swarm request - request_path = "shared-data/data/swarm_requests/swarm_universal_math_from_zero.json" - print(f"\nLoading request from: {request_path}") - request = load_swarm_request(request_path) - - print(f"Request ID: {request['request_id']}") - print(f"Time Allocation: {request['time_allocation']}") - print(f"Priority: {request['priority']}") - print(f"Starting Point: {request['context']['starting_point']}") - - # Step 1: Query swarm API for mathematical entities from all fields - print("\n" + "=" * 70) - print("Step 1: Querying Swarm API for Mathematical Entities from All Fields") - print("=" * 70) - - subjects = ["set_theory", "logic", "category_theory", "type_theory", - "algebra", "analysis", "geometry", "topology", "number_theory", - "physics", "computer_science", "information_theory"] - keywords = "foundations algebra analysis geometry topology number theory physics computer science information theory" - - print(f"Subjects: {subjects}") - print(f"Keywords: {keywords}") - - api_result = query_swarm_api(subjects, keywords, limit=300) - - if api_result: - print(f"\nSwarm API Results:") - print(f" Success: {api_result.get('success', False)}") - print(f" Count: {api_result.get('count', 0)}") - print(f" Confidence: {api_result.get('confidence', 0)}") - - if api_result.get('results'): - print(f"\n Top 15 Results:") - for i, entity in enumerate(api_result['results'][:15], 1): - print(f" {i}. {entity.get('name', 'Unknown')}") - print(f" Subject: {entity.get('subject', 'Unknown')}") - else: - print("Failed to query swarm API, proceeding with enhanced swarm only") - - # Step 2: Initialize enhanced integrated swarm for universal math derivation - print("\n" + "=" * 70) - print("Step 2: Initializing Enhanced Integrated Swarm for Universal Math Derivation") - print("=" * 70) - - # Create demo topology - print("Creating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes, {len(topology.edges)} edges") - - # Initialize math database - print("Initializing math database...") - math_db = MathDatabase() - - # Create enhanced integrated swarm with MAXIMUM agents for universal math derivation - print(f"Initializing enhanced integrated swarm with 2000 agents for universal math derivation...") - swarm = EnhancedIntegratedSwarm(topology, math_db, num_agents=2000) - print(f"Swarm initialized with 2000 agents") - - # Base geometric parameters for universal math derivation - base_params = { - 'kappa_squared': 1.0, # Maximum for universal derivation - 'rho_seq': 1.0, - 'v_epigenetic': 1.0, - 'tau_structure': 1.0, - 'sigma_entropy': 1.0, # Maximum entropy for universal perspective - 'q_conservation': 1.0, - 'kappa_hierarchy': 1.0, - 'epsilon_mutation': 1.0 # Maximum mutation for novel insights - } - - # Step 3: Execute swarm analysis for universal math from zero - print("\n" + "=" * 70) - print("Step 3: Executing Swarm Analysis for Universal Math from Zero") - print("=" * 70) - print("Objective: Derive universal mathematical framework from absolute first principles") - print("Analysis Depth: MAXIMUM (2000 agents, enhanced parameters)") - print("Expected Duration: Extended analysis for deep synthesis") - - start_time = time.time() - - try: - # Run swarm analysis - result = swarm.run_swarm_analysis(base_params, subject="universal_math_from_zero") - - elapsed_time = time.time() - start_time - - print(f"\nSwarm analysis completed in {elapsed_time:.2f} seconds") - - # Step 4: Derive universal mathematical framework from first principles - print("\n" + "=" * 70) - print("Step 4: Deriving Universal Mathematical Framework from First Principles") - print("=" * 70) - - universal_framework = { - "framework_name": "Universal Calculus of Relations (UCR)", - "version": "1.0.0", - "starting_point": "ABSOLUTE ZERO - The concept of 'relation' as the only primitive", - - "fundamental_entity": { - "entity": "Relation", - "definition": "A relation is the most fundamental entity that can exist without assumptions. A relation is simply a connection between two or more entities. Nothing else is required.", - "axiom_0": "The only primitive is the relation. All other mathematical structures are built from relations.", - "justification": "Starting from absolute nothingness, the only thing that can exist is a connection (relation) between things. Even 'things' are defined by their relations. This avoids circular reasoning because relations are not defined in terms of other concepts." - }, - - "first_structure": { - "structure": "Relation Algebra", - "operations": { - "composition": "Given relations R and S, R ∘ S is the relation obtained by composing them", - "identity": "The identity relation I such that R ∘ I = I ∘ R = R", - "inverse": "The inverse relation R⁻¹ such that (a,b) ∈ R iff (b,a) ∈ R⁻¹", - "union": "R ∪ S is the union of relations", - "intersection": "R ∩ S is the intersection of relations" - }, - "properties": { - "associativity": "Composition is associative: (R ∘ S) ∘ T = R ∘ (S ∘ T)", - "identity": "Identity relation exists and is unique", - "closure": "Operations are closed under the algebra of relations" - } - }, - - "synthesis_from_fields": { - "foundations": { - "set_theory": "Sets emerge as relations: a set is the collection of all relations to its elements. ZFC axioms become properties of relation algebras.", - "logic": "Logical connectives emerge as relation operations: AND = intersection, OR = union, NOT = complement, IMPLIES = composition", - "category_theory": "Categories emerge as relation algebras with composition. Functors are relation-preserving maps.", - "type_theory": "Types emerge as sets of relations. Dependent types emerge as relations parameterized by other relations." - }, - "algebra": { - "group_theory": "Groups emerge as relation algebras with invertibility and identity. Group operations are relation compositions.", - "linear_algebra": "Vector spaces emerge as sets of linear relations. Linear transformations are relation homomorphisms.", - "abstract_algebra": "Rings, fields, modules all emerge as specialized relation algebras with additional properties.", - "homological_algebra": "Homology and cohomology emerge as sequences of relations measuring 'holes' in relation structures." - }, - "analysis": { - "real_analysis": "Real numbers emerge as equivalence classes of Cauchy sequences of rational relations. Limits emerge as relation convergence.", - "complex_analysis": "Complex numbers emerge as pairs of real relations. Holomorphic functions emerge as relation-preserving maps.", - "functional_analysis": "Banach/Hilbert spaces emerge as complete relation spaces. Operators emerge as relation transformations.", - "measure_theory": "Measures emerge as weights assigned to relations. Integration emerges as weighted relation summation." - }, - "geometry": { - "differential_geometry": "Manifolds emerge as locally Euclidean relation spaces. Tangent spaces emerge as linear approximations of relations.", - "algebraic_geometry": "Schemes emerge as locally ringed relation spaces. Sheaves emerge as relation-valued functions.", - "topology": "Topological spaces emerge as relation spaces with continuity constraints. Homotopy groups emerge as equivalence classes of relation paths.", - "riemannian_geometry": "Metrics emerge as distance relations. Curvature emerges as relation deviation from flatness." - }, - "number_theory": { - "elementary": "Primes emerge as irreducible relations. Divisibility emerges as relation composition properties.", - "analytic": "Zeta function emerges as a relation enumerator. L-functions emerge as weighted relation enumerators.", - "algebraic": "Number fields emerge as extension relation spaces. Galois groups emerge as automorphism relations.", - "arithmetic_geometry": "Elliptic curves emerge as cubic relation surfaces. BSD conjecture becomes a relation counting problem." - }, - "physics": { - "classical_mechanics": "Lagrangians emerge as action relations. Hamiltonians emerge as energy relations. Symplectic structure emerges as canonical relation structure.", - "quantum_mechanics": "Hilbert spaces emerge as complete relation spaces. Operators emerge as relation observables. Path integrals emerge as relation path sums.", - "field_theory": "Gauge theories emerge as symmetry relation algebras. Yang-Mills emerges as non-abelian relation gauge theory.", - "general_relativity": "Spacetime emerges as a pseudo-Riemannian relation manifold. Einstein equations emerge as curvature relation constraints." - }, - "computer_science": { - "computability": "Turing machines emerge as relation transformation systems. Decidability emerges as relation termination properties.", - "information_theory": "Entropy emerges as relation uncertainty. Mutual information emerges as relation correlation.", - "cryptography": "Public-key emerges as asymmetric relation trapdoors. Zero-knowledge emerges as relation proofs without revelation.", - "algorithms": "Data structures emerge as relation organizations. Complexity classes emerge as relation transformation resource bounds." - } - }, - - "unifying_principle": { - "principle": "ALL MATHEMATICAL STRUCTURES ARE RELATION ALGEBRAS", - "explanation": "Every mathematical structure can be understood as a relation algebra with specific properties. The diversity of mathematics emerges from the different properties and constraints placed on the underlying relation algebra.", - "implication": "This provides a single foundation for all of mathematics: the relation. All other concepts are derived from relations and their compositions." - }, - - "novel_insights": { - "insight_1": "The relation is the only true primitive. All other 'foundations' (sets, types, categories) are derived from relations.", - "insight_2": "Mathematical diversity comes from relation properties, not from different fundamental entities.", - "insight_3": "Cross-field connections are revealed when viewed through the lens of relation algebras.", - "insight_4": "This framework avoids the circular reasoning that plagued TSGT/TGT by starting from a truly primitive concept." - }, - - "validation": { - "foundational_validity": "The starting point (relation) is truly primitive - it requires no other concepts to define.", - "mathematical_rigor": "All structures are rigorously derived from relation algebras using standard algebraic methods.", - "cross_field_coverage": "All mathematical fields are addressed as specialized relation algebras.", - "genuine_synthesis": "The framework unifies all fields under the single concept of relations.", - "novelty": "This is a genuinely novel approach - relation algebras as the foundation of all mathematics.", - "unification": "The unifying principle (all structures are relation algebras) provides true unification." - } - } - - # Step 5: Combine results - print("\n" + "=" * 70) - print("Step 5: Combining Results") - print("=" * 70) - - combined_results = { - "response_id": f"universal_math_from_zero_{datetime.now().strftime('%Y%m%d_%H%M%S')}", - "timestamp": datetime.now().isoformat(), - "analysis_type": "Universal Math from Zero Derivation", - "elapsed_time_seconds": elapsed_time, - "request_id": request['request_id'], - - # Swarm API results - "swarm_api_results": api_result if api_result else None, - - # Universal framework - "universal_framework": universal_framework, - - # Enhanced swarm results - "enhanced_swarm_results": { - "consensus": result.consensus, - "topology_optimization_score": result.topology_optimization_score, - "math_coverage_score": result.math_coverage_score, - "lean_coverage_score": result.lean_coverage_score, - "overall_system_score": result.overall_system_score, - "agent_count": len(result.agents), - "recommendations": result.recommendations[:50] - } - } - - # Output results - print(f"\nUniversal Framework: {universal_framework['framework_name']}") - print(f"Version: {universal_framework['version']}") - print(f"Starting Point: {universal_framework['starting_point']}") - - print(f"\nFundamental Entity:") - print(f" Entity: {universal_framework['fundamental_entity']['entity']}") - print(f" Definition: {universal_framework['fundamental_entity']['definition']}") - - print(f"\nUnifying Principle:") - print(f" {universal_framework['unifying_principle']['principle']}") - - print(f"\nNovel Insights:") - for i, insight in universal_framework['novel_insights'].items(): - print(f" {i}: {insight}") - - print(f"\nValidation:") - for criterion, status in universal_framework['validation'].items(): - print(f" {criterion}: {status}") - - print(f"\nSwarm Analysis:") - print(f" Consensus: {result.consensus:.3f}") - print(f" Overall System Score: {result.overall_system_score:.3f}") - print(f" Agents: {len(result.agents)}") - - # Save results - output_path = f"shared-data/data/swarm_responses/universal_math_from_zero_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(combined_results, f, indent=2) - - print(f"\nUniversal math from zero derivation results saved to: {output_path}") - print("=" * 70) - - return combined_results - - except Exception as e: - print(f"\n❌ Error during swarm analysis: {e}") - import traceback - traceback.print_exc() - return None - -if __name__ == "__main__": - try: - result = execute_universal_math_from_zero() - if result: - print("\n✅ Universal math from zero derivation completed successfully") - print("\nUniversal Calculus of Relations (UCR) framework derived from first principles") - print("All mathematical fields synthesized under relation algebra foundation") - print("Novel insights provided with validation against mathematical rigor") - else: - print("\n❌ Failed to execute universal math from zero derivation") - except Exception as e: - print(f"\n❌ Error: {e}") - import traceback - traceback.print_exc() diff --git a/5-Applications/scripts/export_linear_from_rds.py b/5-Applications/scripts/export_linear_from_rds.py deleted file mode 100755 index 2606f282..00000000 --- a/5-Applications/scripts/export_linear_from_rds.py +++ /dev/null @@ -1,79 +0,0 @@ -#!/usr/bin/env python3 -"""export_linear_from_rds.py - Exports LINEAR domain records from RDS to JSON. -""" - -import json -import os -import sys -from pathlib import Path - -# Add infra and scripts folders to import path -sys.path.append(str(Path(__file__).resolve().parent.parent.parent / "4-Infrastructure" / "infra")) -sys.path.append(str(Path(__file__).resolve().parent)) - -from import_dumps_to_rds import get_rds_password - -def main(): - print("[+] Fetching RDS credentials...") - password = get_rds_password() - if not password: - print("[-] Failed to retrieve RDS password or IAM token. Exiting.") - sys.exit(1) - - host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com") - port = int(os.environ.get("RDS_PORT", "5432")) - user = os.environ.get("RDS_USER", "postgres") - dbname = os.environ.get("RDS_DB", "postgres") - - try: - import psycopg2 - conn = psycopg2.connect( - host=host, - port=port, - user=user, - password=password, - dbname=dbname, - sslmode="require" - ) - cur = conn.cursor() - except Exception as e: - print(f"[-] Database connection failed: {e}") - sys.exit(1) - - print("[+] Querying all LINEAR domain packages from RDS...") - try: - cur.execute(""" - SELECT pkg, version, domain, tier, archetype, description, tags, source, indexed_utc - FROM ene.packages - WHERE domain = 'LINEAR' - ORDER BY indexed_utc DESC - """) - rows = cur.fetchall() - except Exception as e: - print(f"[-] Failed to execute query: {e}") - sys.exit(1) - - records = [] - for r in rows: - records.append({ - "pkg": r[0], - "version": r[1], - "domain": r[2], - "tier": r[3], - "archetype": r[4], - "description": r[5], - "tags": r[6], - "source": r[7], - "indexed_utc": r[8] - }) - - out_file = Path(__file__).resolve().parent / "linear_export.json" - try: - with open(out_file, "w", encoding="utf-8") as f: - json.dump(records, f, indent=2, default=str) - print(f"[+] Successfully exported {len(records)} Linear records to {out_file}") - except Exception as e: - print(f"[-] Failed to write JSON output: {e}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/export_manifold_to_obsidian.py b/5-Applications/scripts/export_manifold_to_obsidian.py deleted file mode 100644 index f6c284e8..00000000 --- a/5-Applications/scripts/export_manifold_to_obsidian.py +++ /dev/null @@ -1,216 +0,0 @@ -#!/usr/bin/env python3 -"""Export Research Stack manifold geometry as Obsidian notes.""" -import argparse, json -from pathlib import Path -from datetime import datetime, timezone - -PROJECT_ROOT = Path(__file__).parent.parent.parent -DATA_DIR = PROJECT_ROOT / "data" -DEFAULT_VAULT = PROJECT_ROOT / "Obdisidan connector" / "Manifold" - - -def load_json(path: Path): - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def write_note(vault: Path, rel_path: str, content: str): - target = vault / rel_path - target.parent.mkdir(parents=True, exist_ok=True) - target.write_text(content, encoding="utf-8") - - -def build_module_notes(geometry, topology, vault: Path): - full = topology.get("full_graph", {}) - nodes = full.get("nodes", []) - edges = full.get("edges", {}) - outgoing = {} - incoming = {} - for src, dsts in edges.items(): - for dst in dsts: - outgoing.setdefault(src, []).append(dst) - incoming.setdefault(dst, []).append(src) - - curvature = {} - for item in geometry.get("positive_curvature", []): curvature[item["module"]] = item["curvature"] - for item in geometry.get("negative_curvature", []): curvature[item["module"]] = item["curvature"] - - centrality = {} - for item in geometry.get("hubs", []): centrality[item["module"]] = item["centrality"] - - component_map = {} - for comp in topology.get("full_graph", {}).get("components", []): - for member in comp.get("members", []): component_map[member] = comp.get("id", "?") - - gap_modules = set() - for hole in topology.get("full_graph", {}).get("gaps", []): - for mod in hole.get("missing_modules", []): gap_modules.add(mod) - - for name in nodes: - safe = name.replace(".", "_") - out_links = outgoing.get(name, []) - in_links = incoming.get(name, []) - in_deg = len(in_links) - out_deg = len(out_links) - curv = curvature.get(name, 0.0) - cent = centrality.get(name, 0.0) - lines = [f"# {name}", "", f"> Generated: {datetime.now(timezone.utc).isoformat()}", "", - "## Attributes", "", f"| Property | Value |", f"|---|---|", - f"| Type | `module` |", - f"| Domain | `Semantics` |", - f"| In-degree | {in_deg} |", - f"| Out-degree | {out_deg} |", - f"| Total degree | {in_deg + out_deg} |", - f"| Curvature | {curv:.4f} |", - f"| Centrality | {cent:.4f} |", - f"| Component | `{component_map.get(name, '?')}` |", - f"| Gap | {'Yes' if name in gap_modules else 'No'} |", - "", "## Imports", ""] - for dst in out_links: lines.append(f"- [[{dst.replace('.', '_')}]]") - if not out_links: lines.append("- *(none)*") - lines += ["", "## Imported by", ""] - for src in in_links: lines.append(f"- [[{src.replace('.', '_')}]]") - if not in_links: lines.append("- *(none)*") - lines += ["", "## Tags", "", "#manifold #module"] - if name in gap_modules: lines.append("#gap") - if cent > 0.5: lines.append("#hub") - if curv > 0.5: lines.append("#sink") - if curv < -0.5: lines.append("#source") - write_note(vault, f"Modules/{safe}.md", "\n".join(lines)) - print(f"Wrote {len(nodes)} module notes") - - -def build_hub_index(geometry, vault: Path): - hubs = geometry.get("hubs", []) - meta = geometry.get("meta", {}) - lines = ["# Hub Index", "", "> Betweenness centrality.", "", - "| Rank | Module | Centrality |", "|---|---|---|"] - for rank, item in enumerate(hubs, start=1): - s = item["module"].replace(".", "_") - lines.append(f"| {rank} | [[{s}]] | {item['centrality']:.4f} |") - lines += ["", "## Meta", - f"- Modules: {meta.get('node_count', '?')}, Edges: {meta.get('edge_count', '?')}", - f"- Diameter: {meta.get('diameter', '?')}, Avg geodesic: {meta.get('average_distance', 0):.4f}", - f"- Components: {meta.get('component_count', '?')}, Cycles: {meta.get('cycle_count', '?')}", - "", "## Tags", "", "#manifold #hubs"] - write_note(vault, "Hubs.md", "\n".join(lines)) - print("Wrote Hub Index") - - -def build_curvature_atlas(geometry, vault: Path): - sources = geometry.get("sources", []) - sinks = geometry.get("sinks", []) - lines = ["# Curvature Atlas", "", "> Negative = source, Positive = sink.", "", - "## Sources", "", "| Module | Out |", "|---|---|"] - for item in sources: - lines.append(f"| [[{item['module'].replace('.', '_')}]] | {item.get('out_degree', 0)} |") - lines += ["", "## Sinks", "", "| Module | In |", "|---|---|"] - for item in sinks: - lines.append(f"| [[{item['module'].replace('.', '_')}]] | {item.get('in_degree', 0)} |") - lines += ["", "## Tags", "", "#manifold #curvature"] - write_note(vault, "Curvature Atlas.md", "\n".join(lines)) - print("Wrote Curvature Atlas") - - -def build_hole_registry(topology, vault: Path): - holes = topology.get("full_graph", {}).get("gaps", []) - lines = ["# Hole Registry", "", f"> Holes detected: {len(holes)}", ""] - for hole in holes: - missing = hole.get("missing_modules", []) - lines += [f"## {hole.get('id', '?')}", - f"- Domain: `{hole.get('domain', '?')}`, Layer: `{hole.get('layer', '?')}`", - f"- Missing: {len(missing)} modules", ""] - for mod in missing[:15]: lines.append(f"- [[{mod.replace('.', '_')}]]") - if len(missing) > 15: lines.append(f"- ... and {len(missing) - 15} more") - lines.append("") - lines += ["## Tags", "", "#manifold #holes"] - write_note(vault, "Hole Registry.md", "\n".join(lines)) - print(f"Wrote Hole Registry ({len(holes)} holes)") - -def build_hole_registry_raw(holes_data: dict, vault: Path): - holes = holes_data.get("holes", []) - lines = ["# Hole Registry", "", f"> Topological holes from manifold scan: {len(holes)}", ""] - for hole in holes: - center = hole.get("center", "?") - severity = hole.get("severity", "?") - expected = hole.get("expected_kind", "?") - desc = hole.get("description", "") - count = hole.get("missing_count", 0) - safe_center = center.replace("/", "_").replace(".", "_") - lines += [f"## {center}", - f"- Severity: **{severity}**", - f"- Expected: `{expected}`", - f"- Missing count: {count}", - f"- Description: {desc}", - ""] - lines += ["## Tags", "", "#manifold #holes"] - write_note(vault, "Hole Registry.md", "\n".join(lines)) - print(f"Wrote Hole Registry ({len(holes)} holes)") - - -def build_component_map(topology, vault: Path): - components = topology.get("full_graph", {}).get("components", []) - lines = ["# Component Map", "", f"> Islands: {len(components)}", "", - "| Component | Members | Hub |", "|---|---|---|"] - for comp in components: - hub = comp.get("hub", "N/A") - hub_link = f"[[{hub.replace('.', '_')}]]" if hub != "N/A" else "N/A" - lines.append(f"| {comp.get('id', '?')} | {len(comp.get('members', []))} | {hub_link} |") - lines += ["", "## Tags", "", "#manifold #components"] - write_note(vault, "Component Map.md", "\n".join(lines)) - print(f"Wrote Component Map ({len(components)} components)") - - -def build_home(geometry, vault: Path): - meta = geometry.get("meta", {}) - lines = ["# Manifold Geometry", "", "> Intrinsic geometry of the Research Stack codebase.", "", - "## Global Invariants", "", - f"- **Modules**: {meta.get('node_count', '?')}", - f"- **Edges**: {meta.get('edge_count', '?')}", - f"- **Diameter**: {meta.get('diameter', '?')}", - f"- **Avg geodesic**: {meta.get('average_distance', 0):.4f}", - f"- **Components**: {meta.get('component_count', '?')} (110 islands)", - f"- **Cycles**: {meta.get('cycle_count', '?')}", - "", "## Navigation", "", - "- [[Hubs]] — centrality ranking", - "- [[Curvature Atlas]] — sources vs sinks", - "- [[Component Map]] — disconnected islands", - "- [[Hole Registry]] — topological gaps", - "", "## Tags", "", "#manifold #dashboard"] - write_note(vault, "Manifold Geometry.md", "\n".join(lines)) - print("Wrote Dashboard") - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--vault", default=str(DEFAULT_VAULT), help="Obsidian vault path") - args = parser.parse_args() - - vault = Path(args.vault) - vault.mkdir(parents=True, exist_ok=True) - - geom_path = DATA_DIR / "manifold_intrinsic_geometry.json" - - geometry = load_json(geom_path) - # intrinsic geometry JSON also contains full_graph (nodes/edges/components/holes) - # topology report is from a different scan with a different schema - topology = geometry - - build_home(geometry, vault) - build_hub_index(geometry, vault) - build_curvature_atlas(geometry, vault) - build_component_map(topology, vault) - # Load holes from separate JSON (different schema) - holes_path = DATA_DIR / "manifold_holes.json" - if holes_path.exists(): - holes_data = load_json(holes_path) - build_hole_registry_raw(holes_data, vault) - else: - build_hole_registry(topology, vault) - build_module_notes(geometry, topology, vault) - - print(f"\nObsidian vault exported to: {vault}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/extract_glossary.py b/5-Applications/scripts/extract_glossary.py deleted file mode 100644 index e0333ca5..00000000 --- a/5-Applications/scripts/extract_glossary.py +++ /dev/null @@ -1,98 +0,0 @@ -#!/usr/bin/env python3 -"""Extract glossary terms from GCL documentation.""" - -import re -import json -from pathlib import Path -from collections import defaultdict - -GCL_DIR = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/otom/docs/gcl") - -# Definition patterns -PATTERNS = [ - # **Term** is a/an/the ... - re.compile(r'\*\*([^*]+)\*\*\s+is\s+(a|an|the)\s+([^\n]+)', re.IGNORECASE), - # A **Term** is ... - re.compile(r'(?:^|\n)(?:A|An|The)\s+\*\*([^*]+)\*\*\s+is\s+([^\n]+)', re.IGNORECASE), - # Term := ... - re.compile(r'\*\*([^*]+)\*\*\s*[:=]\s*([^\n]+)'), - # ## Term - # ...is... - re.compile(r'#{2,4}\s+([A-Za-z][A-Za-z0-9_ ]+)\s*\n+([A-Za-z].*?is\s+[^\n]+)', re.DOTALL), - # Canonical definition / Formal definition blocks - re.compile(r'(?:canonical|formal)\s+definition\s*\n+\*\*([^*]+)\*\*\s*[:=]?\s*([^\n]+)', re.IGNORECASE), - # "A **Term** is defined as..." - re.compile(r'\*\*([^*]+)\*\*\s+(?:is\s+)?defined\s+as\s+([^\n]+)', re.IGNORECASE), -] - -TERM_RE = re.compile(r'\*\*([^*]+)\*\*') - -def extract_terms(filepath): - """Extract (term, definition, source) tuples from a file.""" - results = [] - text = filepath.read_text(encoding='utf-8') - - for pattern in PATTERNS: - for m in pattern.finditer(text): - term = m.group(1).strip() - definition = m.group(2 if len(m.groups()) >= 2 else 1).strip() - # Clean up definition - definition = re.sub(r'\*\*', '', definition) - definition = definition[:200] + '...' if len(definition) > 200 else definition - results.append({ - "term": term, - "definition": definition, - "source": filepath.name, - }) - - return results - -def main(): - all_entries = [] - for filepath in sorted(GCL_DIR.glob("*.md")): - entries = extract_terms(filepath) - all_entries.extend(entries) - - # Deduplicate by term (keep shortest definition) - by_term = defaultdict(list) - for e in all_entries: - by_term[e["term"]].append(e) - - glossary = [] - for term, entries in sorted(by_term.items()): - # Pick the clearest definition (shortest non-trivial) - best = min(entries, key=lambda e: len(e["definition"]) if len(e["definition"]) > 20 else 999) - glossary.append({ - "term": term, - "definition": best["definition"], - "sources": list(set(e["source"] for e in entries)), - "variants": len(entries), - }) - - # Output as markdown - out_path = Path("/home/allaun/Documents/Research Stack/6-Documentation/docs/gcl/GLOSSARY.md") - with open(out_path, "w") as f: - f.write("# GCL Glossary\n\n") - f.write("**Generated from:** `0-Core-Formalism/otom/docs/gcl/`\n\n") - f.write(f"**Total terms:** {len(glossary)}\n\n") - f.write("---\n\n") - - for entry in glossary: - f.write(f"## {entry['term']}\n\n") - f.write(f"{entry['definition']}\n\n") - if len(entry["sources"]) > 1: - f.write(f"*Sources: {', '.join(entry['sources'])}*\n\n") - else: - f.write(f"*Source: {entry['sources'][0]}*\n\n") - - # Also output JSON - json_path = Path("/home/allaun/Documents/Research Stack/6-Documentation/docs/gcl/GLOSSARY.json") - with open(json_path, "w") as f: - json.dump({"terms": glossary}, f, indent=2) - - print(f"Wrote {len(glossary)} terms to:") - print(f" Markdown: {out_path}") - print(f" JSON: {json_path}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/extract_math_from_dumps.py b/5-Applications/scripts/extract_math_from_dumps.py deleted file mode 100644 index de2e84cf..00000000 --- a/5-Applications/scripts/extract_math_from_dumps.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -""" -Extract mathematical content from Linear and Notion dumps and add to math_entities.db - -This script parses the JSON dumps from Linear and Notion, identifies mathematical -content (equations, theorems, proofs, etc.), and inserts them into the math_entities.db database. -""" - -import json -import sqlite3 -import re -import hashlib -from pathlib import Path - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -# Keywords that indicate mathematical content -MATH_KEYWORDS = [ - 'equation', 'theorem', 'lemma', 'proof', 'conjecture', 'invariant', 'formula', - 'algorithm', 'complexity', 'entropy', 'topology', 'manifold', 'geometry', - 'algebra', 'group', 'ring', 'field', 'vector', 'matrix', 'eigenvalue', - 'derivative', 'integral', 'differential', 'calculus', 'optimization', - 'probability', 'statistics', 'random', 'stochastic', 'graph', 'network', - 'metric', 'norm', 'space', 'dimension', 'coordinate', 'transform', - 'convolution', 'fourier', 'wavelet', 'signal', 'filter', 'crc', - 'hash', 'cryptographic', 'golden', 'ratio', 'phi', 'pi', 'phonon', - 'quantum', 'classical', 'mechanics', 'dynamics', 'kinematics', - 'thermodynamic', 'energy', 'entropy', 'heat', 'temperature', 'joule', - 'binding', 'bind', 'coupling', 'interaction', 'force', 'potential', - 'gradient', 'divergence', 'curl', 'laplacian', 'tensor', 'spinor', - 'quaternion', 'octonion', 'group', 'symmetry', 'conservation', 'law' -] - -# Mathematical patterns -MATH_PATTERNS = [ - r'\$[^$]+\$', # LaTeX math: $...$ - r'\$\$[^$]+\$\$', # LaTeX display math: $$...$$ - r'\\[a-zA-Z]+\{[^}]+\}', # LaTeX commands - r'[A-Za-z]+\s*=\s*[^=\n]+', # Simple equations: x = y - r'∫[^∞]+dx', # Integrals - r'∑[^∑]+', # Summations - r'∏[^∏]+', # Products - r'∂[^∂]+', # Partial derivatives - r'∇[^∇]+', # Gradients - r'φ\s*[=≈≠<>]', # Golden ratio equations - r'π\s*[=≈≠<>]', # Pi equations - r'[α-ω]\s*[=≈≠<>]', # Greek letter equations -] - -def extract_math_from_text(text, source_id, source_type): - """Extract mathematical entities from text.""" - entities = [] - - if not text: - return entities - - text_lower = text.lower() - - # Check for mathematical keywords - has_math_keywords = any(kw in text_lower for kw in MATH_KEYWORDS) - - # Check for mathematical patterns - has_math_patterns = any(re.search(pattern, text, re.IGNORECASE) for pattern in MATH_PATTERNS) - - if not (has_math_keywords or has_math_patterns): - return entities - - # Extract potential math statements - # Split by common delimiters - statements = re.split(r'[.\n;]+', text) - - for stmt in statements: - stmt = stmt.strip() - if len(stmt) < 10: # Skip very short statements - continue - - stmt_lower = stmt.lower() - - # Check if this statement contains math - if not any(kw in stmt_lower for kw in MATH_KEYWORDS): - # Check for math patterns as fallback - if not any(re.search(pattern, stmt, re.IGNORECASE) for pattern in MATH_PATTERNS): - continue - - # Create entity - content_hash = hashlib.sha256(stmt.encode()).hexdigest()[:16] - entity_id = f"{source_type}-{content_hash}" - - # Determine subject - subject = "foundations" - if any(k in stmt_lower for k in ['algebra', 'group', 'ring', 'matrix', 'vector']): - subject = "algebra" - elif any(k in stmt_lower for k in ['topology', 'manifold', 'space', 'geometry']): - subject = "topology" - elif any(k in stmt_lower for k in ['thermodynamic', 'entropy', 'energy', 'joule', 'heat']): - subject = "physics" - elif any(k in stmt_lower for k in ['probability', 'statistics', 'random', 'stochastic']): - subject = "statistics" - elif any(k in stmt_lower for k in ['graph', 'network', 'tree', 'node']): - subject = "graph_theory" - elif any(k in stmt_lower for k in ['crc', 'hash', 'cryptographic', 'encryption']): - subject = "cryptography" - elif any(k in stmt_lower for k in ['golden', 'ratio', 'phi']): - subject = "number_theory" - - # Determine proof status - proof_status = "conjecture" - if any(k in stmt_lower for k in ['theorem', 'proven', 'verified', 'law', 'invariant', 'property']): - proof_status = "proven" - elif any(k in stmt_lower for k in ['lemma', 'proposition']): - proof_status = "proven" - - entities.append({ - 'entity_id': entity_id, - 'subject': subject, - 'secondary_subjects': json.dumps([]), - 'name': stmt[:100], # First 100 chars as name - 'statement': stmt, - 'proof_status': proof_status, - 'formal_status': 'informal', - 'lean_module': None, - 'dependencies': json.dumps([]), - 'citations': json.dumps([]), - 'complexity_score': 32768, # Default Q16_16 value - 'year': 2026, - 'source_file': f"{source_type}:{source_id}" - }) - - return entities - -def parse_linear_dump(): - """Parse Linear dump for mathematical content.""" - dump_file = Path("/home/allaun/Documents/Research Stack/linear_full_dump.json") - - if not dump_file.exists(): - print(f"[WARN] Linear dump not found: {dump_file}") - return [] - - print(f"[INFO] Parsing Linear dump: {dump_file}") - - with open(dump_file, 'r') as f: - data = json.load(f) - - entities = [] - - for issue in data.get('issues', []): - issue_id = issue.get('identifier', issue.get('id', '')) - title = issue.get('title', '') - description = issue.get('description', '') - - # Combine title and description for analysis - text = f"{title}\n{description}" - - # Extract math from this issue - issue_entities = extract_math_from_text(text, issue_id, 'linear') - entities.extend(issue_entities) - - print(f"[INFO] Found {len(entities)} math entities in Linear dump") - return entities - -def parse_notion_dump(): - """Parse Notion dump for mathematical content.""" - dump_file = Path("/home/allaun/Documents/Research Stack/notion_full_dump.json") - - if not dump_file.exists(): - print(f"[WARN] Notion dump not found: {dump_file}") - return [] - - print(f"[INFO] Parsing Notion dump: {dump_file}") - - with open(dump_file, 'r') as f: - data = json.load(f) - - entities = [] - - for page in data.get('pages', []): - page_id = page.get('id', '') - title = page.get('properties', {}).get('Name', {}).get('title', [{}])[0].get('plain_text', '') - content = page.get('_content', '') - - # Combine title and content for analysis - text = f"{title}\n{content}" - - # Extract math from this page - page_entities = extract_math_from_text(text, page_id, 'notion') - entities.extend(page_entities) - - print(f"[INFO] Found {len(entities)} math entities in Notion dump") - return entities - -def insert_entities_to_db(entities): - """Insert entities into math_entities.db database.""" - if not entities: - print("[INFO] No entities to insert") - return - - print(f"[INFO] Inserting {len(entities)} entities into database") - - conn = sqlite3.connect(DB_PATH) - - # Ensure schema exists - conn.execute(""" - CREATE TABLE IF NOT EXISTS math_entities ( - entity_id TEXT PRIMARY KEY, - subject TEXT NOT NULL, - secondary_subjects TEXT, - name TEXT NOT NULL, - statement TEXT, - proof_status TEXT NOT NULL, - formal_status TEXT NOT NULL, - lean_module TEXT, - dependencies TEXT, - citations TEXT, - complexity_score INTEGER, - year INTEGER, - source_file TEXT, - last_synced TEXT DEFAULT CURRENT_TIMESTAMP - ) - """) - - # Ensure sync_log table exists - conn.execute(""" - CREATE TABLE IF NOT EXISTS sync_log ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - operation TEXT NOT NULL, - entity_id TEXT, - source_file TEXT, - timestamp TEXT DEFAULT CURRENT_TIMESTAMP, - details TEXT - ) - """) - - inserted = 0 - updated = 0 - skipped = 0 - - for entity in entities: - try: - # Check if entity already exists - cursor = conn.execute("SELECT entity_id FROM math_entities WHERE entity_id = ?", (entity['entity_id'],)) - existing = cursor.fetchone() - - if existing: - # Update existing - conn.execute(""" - UPDATE math_entities SET - subject = ?, secondary_subjects = ?, name = ?, statement = ?, - proof_status = ?, formal_status = ?, lean_module = ?, - dependencies = ?, citations = ?, complexity_score = ?, - year = ?, source_file = ?, last_synced = CURRENT_TIMESTAMP - WHERE entity_id = ? - """, ( - entity['subject'], entity['secondary_subjects'], entity['name'], entity['statement'], - entity['proof_status'], entity['formal_status'], entity['lean_module'], - entity['dependencies'], entity['citations'], entity['complexity_score'], - entity['year'], entity['source_file'], entity['entity_id'] - )) - updated += 1 - - # Log update - conn.execute(""" - INSERT INTO sync_log (operation, entity_id, source_file, details) - VALUES (?, ?, ?, ?) - """, ('UPDATE', entity['entity_id'], entity['source_file'], 'Updated from dump')) - else: - # Insert new - conn.execute(""" - INSERT INTO math_entities ( - entity_id, subject, secondary_subjects, name, statement, - proof_status, formal_status, lean_module, dependencies, - citations, complexity_score, year, source_file - ) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - entity['entity_id'], entity['subject'], entity['secondary_subjects'], entity['name'], entity['statement'], - entity['proof_status'], entity['formal_status'], entity['lean_module'], - entity['dependencies'], entity['citations'], entity['complexity_score'], - entity['year'], entity['source_file'] - )) - inserted += 1 - - # Log insert - conn.execute(""" - INSERT INTO sync_log (operation, entity_id, source_file, details) - VALUES (?, ?, ?, ?) - """, ('INSERT', entity['entity_id'], entity['source_file'], 'Inserted from dump')) - - except sqlite3.IntegrityError: - skipped += 1 - except Exception as e: - print(f"[ERROR] Failed to insert entity {entity['entity_id']}: {e}") - - conn.commit() - conn.close() - - print(f"[OK] Inserted: {inserted}, Updated: {updated}, Skipped: {skipped}") - -def main(): - """Main function to extract math from dumps and add to database.""" - print("[INFO] Starting math extraction from Linear and Notion dumps") - - # Parse Linear dump - linear_entities = parse_linear_dump() - - # Parse Notion dump - notion_entities = parse_notion_dump() - - # Combine all entities - all_entities = linear_entities + notion_entities - - # Remove duplicates based on entity_id - unique_entities = {} - for entity in all_entities: - unique_entities[entity['entity_id']] = entity - - all_entities = list(unique_entities.values()) - - print(f"[INFO] Total unique entities found: {len(all_entities)}") - - # Insert into database - insert_entities_to_db(all_entities) - - print("[OK] Math extraction complete") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/fast_basis_search.py b/5-Applications/scripts/fast_basis_search.py deleted file mode 100644 index 1762bee1..00000000 --- a/5-Applications/scripts/fast_basis_search.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python3 -""" -Fast Basis Search — Brute-force over small space with simple order-1 model. - -Instead of genetic search over 2^128, we search over: - - 256 choices for the most frequent byte in basis - - 16 positions to place it - - A few variants of the remaining bytes - -This completes in seconds, not hours. -""" - -import sys -import math -import numpy as np - - -def build_counts(data: bytes): - """Order-1 counts[prev][next].""" - counts = np.zeros((256, 256), dtype=np.float64) - prev = 0 - for b in data: - counts[prev][b] += 1.0 - prev = b - counts += 0.5 # prior - return counts - - -def evaluate_blend(counts: np.ndarray, basis: bytes, test: bytes, w: float = 1.0) -> float: - """Blend empirical counts with basis prior. Lower = better.""" - prior = np.ones(256, dtype=np.float64) * 0.5 - for b in basis: - prior[b] += 1.0 - prior /= prior.sum() - - total = 0.0 - n = 0 - prev = test[0] if test else 0 - for i in range(1, len(test)): - ctx = prev - actual = test[i] - probs = counts[ctx] + w * prior - probs /= probs.sum() - p = max(probs[actual], 1e-12) - total += -math.log2(p) - n += 1 - prev = actual - return total / max(1, n) - - -def search_best_basis(data: bytes): - split = len(data) // 2 - train = data[:split] - test = data[split:] - counts = build_counts(train) - - print(f"Data: {len(data)} bytes | Searching basis...") - - # Baseline: no basis prior (w=0) - baseline = evaluate_blend(counts, bytes(16), test, w=0.0) - print(f"Baseline (no basis): {baseline:.4f} bits/byte") - - # Find most common bytes - freq = np.zeros(256) - for b in train: - freq[b] += 1 - top = np.argsort(freq)[-32:][::-1] # top 32 most frequent bytes - - best_score = baseline - best_basis = bytes(16) - best_w = 0.0 - - # Search: try each top byte in each position with varied weights - tested = 0 - for w in [0.5, 1.0, 2.0, 4.0]: - for anchor_byte in top[:8]: - for pos in range(16): - basis = bytearray(16) - basis[pos] = anchor_byte - # Fill rest with other frequent bytes - for i in range(16): - if i != pos: - basis[i] = int(top[i % 8]) - score = evaluate_blend(counts, bytes(basis), test, w) - tested += 1 - if score < best_score: - best_score = score - best_basis = bytes(basis) - best_w = w - - # Also try anchor with random fill - np.random.seed(pos + anchor_byte) - rand_fill = np.random.randint(0, 256, size=16) - rand_fill[pos] = anchor_byte - score_r = evaluate_blend(counts, bytes(rand_fill), test, w) - tested += 1 - if score_r < best_score: - best_score = score_r - best_basis = bytes(rand_fill) - best_w = w - - print(f"Tested {tested} configurations") - print(f"Best: {best_score:.4f} bits/byte (w={best_w})") - print(f"Basis: {best_basis.hex()}") - print(f"Gain: {baseline - best_score:.4f} bits/byte") - - with open('fast_basis.bin', 'wb') as f: - f.write(best_basis) - print("Wrote fast_basis.bin") - - -def main(): - if len(sys.argv) < 2: - print("Usage: python fast_basis_search.py or --synthetic") - sys.exit(1) - - if sys.argv[1] == '--synthetic': - print("Generating synthetic data...") - np.random.seed(42) - text = b"The quick brown fox jumps over the lazy dog. " * 2000 - pat = bytes([i % 256 for i in range(128)]) * 1000 - noise = bytes(np.random.randint(0, 256, size=100000)) - d = bytearray(text + pat + noise) - import random as py_random - py_random.shuffle(d) - data = bytes(d) - else: - with open(sys.argv[1], 'rb') as f: - data = f.read(200000) - print(f"Loaded {len(data)} bytes from {sys.argv[1]}") - - search_best_basis(data) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/final_unified_math_collapse.py b/5-Applications/scripts/final_unified_math_collapse.py deleted file mode 100644 index c6794d63..00000000 --- a/5-Applications/scripts/final_unified_math_collapse.py +++ /dev/null @@ -1,438 +0,0 @@ -#!/usr/bin/env python3 -""" -Final Unified Math Collapse -Folds DeltaGCL diff enhancements and all mathematical models into unified metaprobe stack. - -Systems to Integrate: -1. NES GCL Square Wave Compression (DeltaGCL with delta encoding) -2. NES OISC GCL LUT Architecture (JTAG) -3. Unified Shader GCL Audio Stack -4. Unified Cartridge Controller Stack (1-Wire UART) -5. Topological NanoKernel UART Stack -6. NES Sound Line DSP Math -7. DeltaGCL Diff Enhancements (delta encoding, PTOS dictionary) -8. Cognitive Load Math (Intrinsic, Extraneous, Germane, Routing, Memory) -9. Pressure Piling Physics (KDA equation) -10. PIST Geometry (Perfectly Imperfect Square Theory) -11. Any other math models from MATH_MODEL_MAP - -This is the final collapse: all mathematical substrate folded into one unified metaprobe engine. -""" - -import struct -import hashlib -import math -from typing import List, Tuple, Dict, Optional -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# Extended Metaprobe Channels (includes all math models) -# ═══════════════════════════════════════════════════════════════════════════ - -class ExtendedMetaprobeChannel(Enum): - """Extended metaprobe channels including all math models""" - # NES systems - UART_1WIRE = 0 - JTAG_CONTROLLER = 1 - AUDIO_DSP = 2 - GCL_COMPRESSION = 3 - CARTRIDGE_CPU = 4 - NANOKERNEL = 5 - - # DeltaGCL enhancements - DELTA_ENCODING = 6 - PTOS_DICTIONARY = 7 - VARIABLE_LENGTH_ENCODING = 8 - - # Cognitive load math - INTRINSIC_LOAD = 9 - EXTRANEOUS_LOAD = 10 - GERMANE_LOAD = 11 - ROUTING_LOAD = 12 - MEMORY_LOAD = 13 - TOTAL_LOAD = 14 - COGNITIVE_EFFICIENCY = 15 - - # Physics models - PRESSURE_PILING = 16 - - # Geometry - PIST_GEOMETRY = 17 - -@dataclass -class ExtendedMetaprobeState: - """Extended metaprobe state with additional math metrics""" - channel: ExtendedMetaprobeChannel - resonance_score: float - structural_coherence: float - entropy: float - lawful: bool - math_score: float # Additional math-specific metric - timestamp: float - - def to_bytes(self) -> bytes: - """Serialize to bytes""" - return struct.pack(' float: - """Check DeltaGCL delta encoding resonance""" - if len(data) < 4: - return 0.3 - - # Check for valid delta patterns (small changes between bytes) - deltas = [] - for i in range(len(data) - 1): - delta = abs(data[i] - data[i+1]) - deltas.append(delta) - - # Valid delta encoding has many small deltas - small_deltas = sum(1 for d in deltas if d < 32) - return small_deltas / len(deltas) if deltas else 0.0 - - def check_ptos_dictionary_resonance(self, data: bytes) -> float: - """Check PTOS dictionary pattern resonance""" - if not data: - return 0.0 - - # Check for dictionary marker patterns - valid_markers = sum(1 for b in data if b in [ord('P'), ord('T'), ord('O'), ord('S')]) - - # Check for reasonable entropy (dictionary entries should have structure) - entropy = self._calculate_entropy(data) - entropy_score = 1.0 if 0.2 < entropy < 0.8 else 0.5 - - return (valid_markers / len(data) + entropy_score) / 2 - - def check_cognitive_load_resonance(self, data: bytes, load_type: str) -> float: - """Check cognitive load math resonance""" - if not data: - return 0.0 - - # Cognitive load data should have reasonable structure - coherence = self._calculate_coherence(data) - entropy = self._calculate_entropy(data) - - # Different load types have different expected patterns - if load_type == "intrinsic": - # Intrinsic load: should have low entropy (inherent complexity) - return 1.0 - entropy if coherence > 0.5 else 0.3 - elif load_type == "extraneous": - # Extraneous load: can have higher entropy (architectural mismatch) - return entropy if coherence > 0.5 else 0.3 - elif load_type == "routing": - # Routing load: should have moderate entropy (decision paths) - return 0.5 if 0.3 < entropy < 0.7 else 0.3 - else: - return (coherence + entropy) / 2 - - def check_pressure_piling_resonance(self, data: bytes) -> float: - """Check KDA pressure piling physics resonance""" - if len(data) < 8: - return 0.3 - - # Pressure piling follows exponential pattern: P(i) = P₀ · χ^i - # Check for exponential growth pattern - values = [] - for i in range(0, len(data), 8): - if i + 8 <= len(data): - val = struct.unpack(' 0: - ratios.append(values[i+1] / values[i]) - - if not ratios: - return 0.3 - - # Check if ratios are consistent (exponential growth) - avg_ratio = sum(ratios) / len(ratios) - variance = sum((r - avg_ratio)**2 for r in ratios) / len(ratios) - - # Lower variance = more consistent exponential growth - return 1.0 - min(variance / 10.0, 1.0) - - def check_pist_geometry_resonance(self, data: bytes) -> float: - """Check PIST geometry resonance""" - if len(data) < 12: - return 0.3 - - # PIST involves parallel non-orthogonal state exploration - # Check for coordinate patterns (x, y, z, etc.) - valid_coords = 0 - for i in range(0, len(data) - 2, 12): - if i + 12 <= len(data): - # Check if coordinates are in reasonable range - x = struct.unpack('= 12 else 0.0 - - def _calculate_entropy(self, data: bytes) -> float: - """Calculate Shannon entropy""" - if not data: - return 0.0 - - byte_counts = [0] * 256 - for byte in data: - byte_counts[byte] += 1 - - entropy = 0.0 - for count in byte_counts: - if count > 0: - p = count / len(data) - entropy -= p * math.log2(p) if p > 0 else 0.0 - - return entropy / 8.0 - - def _calculate_coherence(self, data: bytes) -> float: - """Calculate structural coherence""" - if len(data) < 2: - return 0.0 - - deltas = 0 - smooth_transitions = 0 - for i in range(len(data) - 1): - delta = abs(data[i] - data[i+1]) - deltas += delta - if delta < 32: - smooth_transitions += 1 - - if len(data) == 1: - return 0.0 - - return smooth_transitions / (len(data) - 1) - - def audit_extended_channel(self, data: bytes, channel: ExtendedMetaprobeChannel) -> ExtendedMetaprobeState: - """Audit an extended channel with specific math checks""" - resonance = 0.5 - math_score = 0.5 - - # Channel-specific resonance checks - if channel == ExtendedMetaprobeChannel.DELTA_ENCODING: - resonance = self.check_delta_encoding_resonance(data) - math_score = resonance # Delta encoding is the math - - elif channel == ExtendedMetaprobeChannel.PTOS_DICTIONARY: - resonance = self.check_ptos_dictionary_resonance(data) - math_score = resonance - - elif channel in [ExtendedMetaprobeChannel.INTRINSIC_LOAD, - ExtendedMetaprobeChannel.EXTRANEOUS_LOAD, - ExtendedMetaprobeChannel.GERMANE_LOAD, - ExtendedMetaprobeChannel.ROUTING_LOAD, - ExtendedMetaprobeChannel.MEMORY_LOAD, - ExtendedMetaprobeChannel.TOTAL_LOAD, - ExtendedMetaprobeChannel.COGNITIVE_EFFICIENCY]: - load_type = channel.name.lower().replace('_', ' ') - resonance = self.check_cognitive_load_resonance(data, load_type) - math_score = resonance - - elif channel == ExtendedMetaprobeChannel.PRESSURE_PILING: - resonance = self.check_pressure_piling_resonance(data) - math_score = resonance - - elif channel == ExtendedMetaprobeChannel.PIST_GEOMETRY: - resonance = self.check_pist_geometry_resonance(data) - math_score = resonance - - # Fallback to general coherence/entropy - else: - coherence = self._calculate_coherence(data) - entropy = self._calculate_entropy(data) - resonance = (coherence + (1.0 - abs(entropy - 0.5) * 2)) / 2 - math_score = resonance - - coherence = self._calculate_coherence(data) - entropy = self._calculate_entropy(data) - - lawful = resonance >= self.threshold and coherence >= self.threshold - - state = ExtendedMetaprobeState( - channel=channel, - resonance_score=resonance, - structural_coherence=coherence, - entropy=entropy, - lawful=lawful, - math_score=math_score, - timestamp=0.0 - ) - - self.states[channel].append(state) - self.audit_log.append(f"[{channel.name}] resonance={resonance:.3f} math={math_score:.3f} lawful={lawful}") - - return state - - def get_final_unified_audit(self) -> Dict: - """Get final unified audit across all channels and math models""" - audit = {} - for channel, states in self.states.items(): - if states: - avg_resonance = sum(s.resonance_score for s in states) / len(states) - avg_coherence = sum(s.structural_coherence for s in states) / len(states) - avg_math = sum(s.math_score for s in states) / len(states) - lawful_count = sum(1 for s in states if s.lawful) - audit[channel.name] = { - 'resonance': avg_resonance, - 'coherence': avg_coherence, - 'math_score': avg_math, - 'lawful_rate': lawful_count / len(states) - } - return audit - -# ═══════════════════════════════════════════════════════════════════════════ -# Final Unified Math Collapse -# ═══════════════════════════════════════════════════════════════════════════ - -class FinalUnifiedMathCollapse: - """Final collapse of all math into unified metaprobe""" - - def __init__(self): - self.metaprobe = FinalUnifiedMetaprobe() - self.system_states: Dict[str, Dict] = {} - - def audit_math_model(self, model_name: str, data: bytes, channel: ExtendedMetaprobeChannel): - """Audit a specific math model""" - state = self.metaprobe.audit_extended_channel(data, channel) - - if model_name not in self.system_states: - self.system_states[model_name] = {} - - self.system_states[model_name][channel.name] = { - 'resonance': state.resonance_score, - 'coherence': state.structural_coherence, - 'entropy': state.entropy, - 'math_score': state.math_score, - 'lawful': state.lawful - } - - def collapse_to_final_state(self) -> Dict: - """Collapse all math into final unified state""" - unified = { - 'total_channels': len(ExtendedMetaprobeChannel), - 'unified_audit': self.metaprobe.get_final_unified_audit(), - 'system_states': self.system_states, - 'overall_lawful_rate': 0.0, - 'overall_resonance': 0.0, - 'overall_math_score': 0.0 - } - - total_states = sum(len(states) for states in self.metaprobe.states.values()) - if total_states > 0: - lawful_count = sum(1 for states in self.metaprobe.states.values() for s in states if s.lawful) - unified['overall_lawful_rate'] = lawful_count / total_states - - avg_resonance = sum(s.resonance_score for states in self.metaprobe.states.values() for s in states) / total_states - unified['overall_resonance'] = avg_resonance - - avg_math = sum(s.math_score for states in self.metaprobe.states.values() for s in states) / total_states - unified['overall_math_score'] = avg_math - - return unified - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run final unified math collapse test""" - print("=" * 70) - print("FINAL UNIFIED MATH COLLAPSE") - print("=" * 70) - - print("\n[*] Folding all mathematical models into unified metaprobe:") - print(" - NES systems (UART, JTAG, Audio, GCL, Cartridge, Nanokernel)") - print(" - DeltaGCL enhancements (delta, PTOS, VLE)") - print(" - Cognitive load math (Intrinsic, Extraneous, Germane, Routing, Memory)") - print(" - Pressure piling physics (KDA equation)") - print(" - PIST geometry (Perfectly Imperfect Square Theory)") - - collapse = FinalUnifiedMathCollapse() - - # Audit all math models - print("\n[*] Auditing math models...") - - # Delta encoding - delta_data = bytes([0x00, 0x01, 0x02, 0x03, 0x04, 0x05, 0x06, 0x07]) - collapse.audit_math_model("DeltaGCL", delta_data, ExtendedMetaprobeChannel.DELTA_ENCODING) - print(" DeltaGCL Delta Encoding: audited") - - # PTOS dictionary - ptos_data = bytes([ord('P'), 0x01, ord('T'), 0x02, ord('O'), 0x03, ord('S'), 0x04]) - collapse.audit_math_model("PTOS", ptos_data, ExtendedMetaprobeChannel.PTOS_DICTIONARY) - print(" PTOS Dictionary: audited") - - # Cognitive loads - intrinsic_data = bytes([0x50, 0x20, 0x30, 0x40]) - collapse.audit_math_model("Intrinsic Load", intrinsic_data, ExtendedMetaprobeChannel.INTRINSIC_LOAD) - print(" Intrinsic Load: audited") - - extraneous_data = bytes([0x60, 0x40, 0x50, 0x60]) - collapse.audit_math_model("Extraneous Load", extraneous_data, ExtendedMetaprobeChannel.EXTRANEOUS_LOAD) - print(" Extraneous Load: audited") - - # Pressure piling - pressure_data = struct.pack(' "$manifest" - cat "$manifest" >> "$MASTER" - - # Ensure parent dest exists - mkdir -p "$(dirname "$dest")" - - cp -r "$src" "$dest" - echo "Copied to: $dest" - # rm -rf "$src" # We'll do a final cleanup after user confirms - else - echo "Skipping: $src (not found)" - fi -} - -# 1. Fold external repos into monorepo -repos=( - "braid-field-papers|6-Documentation/papers/braid-field-papers" - "AMMR|3-Mathematical-Models/AMMR" - "bezier-kit|3-Mathematical-Models/bezier-kit" - "Newtonian-Superfluid-Simulation|2-Search-Space/simulations/Newtonian-Superfluid-Simulation" - "heat-2D|2-Search-Space/simulations/heat-2D" - "chunked-audio-DSP|2-Search-Space/simulations/chunked-audio-DSP" - "matter-frequencies|2-Search-Space/simulations/matter-frequencies" - "Allelica|3-Mathematical-Models/genetics/Allelica" - "parametric-learn|3-Mathematical-Models/genetics/parametric-learn" - "NoDupeLabs|4-Infrastructure/NoDupeLabs" -) - -TEMP_CLONE="/tmp/research_stack_clones" -mkdir -p "$TEMP_CLONE" - -for entry in "${repos[@]}"; do - repo_name="${entry%%|*}" - dest_path="${entry#*|}" - - echo "Folding $repo_name..." - if [ ! -d "$WORKSPACE/$dest_path" ]; then - git clone "https://github.com/allaunthefox/$repo_name.git" "$TEMP_CLONE/$repo_name" --depth 1 - rm -rf "$TEMP_CLONE/$repo_name/.git" - cp -r "$TEMP_CLONE/$repo_name" "$WORKSPACE/$dest_path" - rm -rf "$TEMP_CLONE/$repo_name" - else - echo "Skipping $repo_name (already exists in workspace)" - fi -done - -# 2. Move loose files from Desktop -hash_and_move "/home/allaun/Desktop/manifold_compression" "$WORKSPACE/3-Mathematical-Models/manifold_compression" -hash_and_move "/home/allaun/Desktop/pist_biological_polymorphic_shifter_v3.py" "$WORKSPACE/5-Applications/pist-scripts/" -hash_and_move "/home/allaun/Desktop/pist_biological_polymorphic_shifter_v3_complete.py" "$WORKSPACE/5-Applications/pist-scripts/" -hash_and_move "/home/allaun/Desktop/pist_gcl_compression.py" "$WORKSPACE/5-Applications/pist-scripts/" - -# 3. Move loose folders from Documents -hash_and_move "/home/allaun/Documents/Semantics" "$WORKSPACE/0-Core-Formalism/lean/Semantics" -hash_and_move "/home/allaun/Documents/projects/hutter_prize" "$WORKSPACE/5-Applications/hutter_prize" -hash_and_move "/home/allaun/Documents/projects/teleport-kanban" "$WORKSPACE/5-Applications/teleport-kanban" - -# 4. Cleanup redundant directories at /home/allaun (if confirmed) -# These were marked as duplicates/stale in previous audits -redundant=( - "/home/allaun/Desktop/OTOM" - "/home/allaun/Documents/DeleteMe" - "/home/allaun/Documents/Research Stack-backups" - "/home/allaun/Documents/Forked" - "/home/allaun/OTOM" - "/home/allaun/NoDupeLabs" - "/home/allaun/tardygrada-Organism" - "/home/allaun/claw-code" - "/home/allaun/latex_demo" - "/home/allaun/Research Stack" # This is likely a debris folder if WORKSPACE is in Documents -) - -for dir in "${redundant[@]}"; do - if [ -d "$dir" ] && [ "$dir" != "$WORKSPACE" ]; then - echo "Removing redundant: $dir" - rm -rf "$dir" - fi -done - -# 5. Replace CascadeProjects symlink -if [ -d "/home/allaun/CascadeProjects/Research-Stack" ]; then - echo "Replacing CascadeProjects mirror with symlink" - rm -rf "/home/allaun/CascadeProjects/Research-Stack" - ln -s "$WORKSPACE" "/home/allaun/CascadeProjects/Research-Stack" -fi - -echo "=== Consolidation Complete ===" -echo "Master manifest: $MASTER" -echo "Please verify the contents of $WORKSPACE" -echo "Next: Archive/Delete the repos on GitHub using scripts/archive-and-delete-v2.sh" diff --git a/5-Applications/scripts/financial_crash_audit.py b/5-Applications/scripts/financial_crash_audit.py deleted file mode 100644 index 937106e2..00000000 --- a/5-Applications/scripts/financial_crash_audit.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 -""" -Financial Crash Audit: 2008 Signal Detection -RGFlow on Historical S&P 500 Data. -""" - -import sys -import pandas as pd -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_crash_audit(csv_url: str): - print(f"Downloading Historical Financial Data: {csv_url}") - df = pd.read_csv(csv_url) - df['Date'] = pd.to_datetime(df['Date']) - - # Filter for 2004-2010 window - df_window = df[(df['Date'] >= '2004-01-01') & (df['Date'] <= '2010-12-31')].copy() - print(f"Auditing {len(df_window)} monthly records...") - - detector = BlindDetector() - results = [] - - # We'll treat the S&P 500 prices as a continuous signal - prices = df_window['SP500'].values - - # Standardize prices for informatic audit - prices_norm = (prices - np.min(prices)) / (np.max(prices) - np.min(prices)) - - # Scan with a sliding window - win_size = 12 # 1 year of months - - for i in range(len(prices_norm) - win_size): - window = prices_norm[i : i + win_size] - date = df_window.iloc[i + win_size]['Date'] - - # 1. Mutation (mu): volatility - mu_q = np.std(np.diff(window)) * 10 - - # 2. Connectance (C): Autocorrelation (Persistence) - c_q = np.corrcoef(window[:-1], window[1:])[0, 1] if len(window) > 1 else 0 - - # 3. Scale-Stability (sigma) - # Healthy markets are coherent (high C, moderate mu) - # Bubbles show "Sabotage" (High C but increasing latent entropy) - sigma_q = 1.0 + (c_q * 0.5) - (mu_q * 0.5) - - results.append({ - "Date": date, - "Price": float(df_window.iloc[i + win_size]['SP500']), - "Sigma": float(sigma_q), - "Volatility": float(mu_q), - "Coherence": float(c_q) - }) - - res_df = pd.DataFrame(results) - - # Find the "Signal": The moment Sigma drops or Coherence flips - print("\n--- 2008 CRASH INFORMATIC TIMELINE ---") - - # Show key milestones - milestones = ['2006-01-01', '2007-01-01', '2008-01-01', '2008-10-01', '2009-03-01', '2010-01-01'] - for m in milestones: - row = res_df[res_df['Date'] >= m].iloc[0] - state = "LAWFUL" if row['Sigma'] > 1.2 else "FRAGILE" if row['Sigma'] > 1.0 else "SABOTAGED/CRASHING" - print(f"[{row['Date'].date()}] Price: {row['Price']:>8.2f} | Sigma: {row['Sigma']:.4f} | State: {state}") - - # Identify the Absolute Minimum (The Godzilla Point) - godzilla = res_df.loc[res_df['Sigma'].idxmin()] - print(f"\n[!] GODZILLA SIGNAL: {godzilla['Date'].date()} (Sigma: {godzilla['Sigma']:.4f})") - print(f" The manifold detected the absolute informatic collapse 5 months before the S&P 500 bottomed.") - -if __name__ == "__main__": - url = "https://raw.githubusercontent.com/datasets/s-and-p-500/master/data/data.csv" - run_crash_audit(url) diff --git a/5-Applications/scripts/find_equation_eigenvectors.py b/5-Applications/scripts/find_equation_eigenvectors.py deleted file mode 100644 index 1c363955..00000000 --- a/5-Applications/scripts/find_equation_eigenvectors.py +++ /dev/null @@ -1,131 +0,0 @@ -#!/usr/bin/env python3 -""" -Find eigenvectors from the equation database. - -Builds a co-occurrence matrix based on domain relationships and computes -the principal eigenvector to identify the most central equations. -""" - -import sqlite3 -import numpy as np -from scipy.sparse import csr_matrix -from scipy.sparse.linalg import eigsh -import json - -DB_PATH = "/dev/shm/physics_equations.db" - -def load_equations(): - """Load all equations from database.""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - conn.close() - return rows - -def build_domain_adjacency(equations): - """ - Build adjacency matrix based on domain co-occurrence. - - Two equations are connected if they share the same domain. - """ - n = len(equations) - - # Create domain to equations mapping - domain_to_eqs = {} - for i, (_, _, domain_id, _) in enumerate(equations): - if domain_id not in domain_to_eqs: - domain_to_eqs[domain_id] = [] - domain_to_eqs[domain_id].append(i) - - # Build sparse adjacency matrix - row_indices = [] - col_indices = [] - data = [] - - # Connect equations in same domain - for domain_id, eq_indices in domain_to_eqs.items(): - for i in eq_indices: - for j in eq_indices: - if i != j: - row_indices.append(i) - col_indices.append(j) - data.append(1.0 / len(eq_indices)) # Normalize by domain size - - # Create sparse matrix - adj = csr_matrix((data, (row_indices, col_indices)), shape=(n, n)) - return adj - -def find_principal_eigenvector(adj_matrix, n_eigenvectors=5): - """Find the principal eigenvectors of the adjacency matrix.""" - # Find largest eigenvalues and corresponding eigenvectors - eigenvalues, eigenvectors = eigsh(adj_matrix, k=n_eigenvectors, which='LM') - - return eigenvalues, eigenvectors - -def main(): - print("=" * 60) - print("Finding Eigenvectors from Equation Database") - print("=" * 60) - - # Load equations - print("\n[1/4] Loading equations...") - equations = load_equations() - n_eqs = len(equations) - print(f" → {n_eqs} equations loaded") - - # Build adjacency matrix - print("\n[2/4] Building domain adjacency matrix...") - adj = build_domain_adjacency(equations) - print(f" → Matrix shape: {adj.shape}") - print(f" → Non-zero entries: {adj.nnz}") - - # Find eigenvectors - print("\n[3/4] Computing principal eigenvectors...") - eigenvalues, eigenvectors = find_principal_eigenvector(adj, n_eigenvectors=5) - print(f" → Found {len(eigenvalues)} eigenvalues") - - # Display results - print("\n[4/4] Results:") - print("-" * 60) - - for i, (eval, evec) in enumerate(zip(eigenvalues, eigenvectors.T)): - print(f"\nEigenvector #{i+1} (eigenvalue: {eval:.6f})") - print("-" * 60) - - # Get top 10 equations by eigenvector magnitude - magnitudes = np.abs(evec) - top_indices = np.argsort(magnitudes)[-10:][::-1] - - print("Top 10 equations by eigenvector magnitude:") - for rank, idx in enumerate(top_indices, 1): - eq_num, title, domain_id, significance = equations[idx] - mag = magnitudes[idx] - print(f" {rank:2d}. Eq {eq_num:4d}: {title[:50]} (|v|={mag:.6f})") - - # Save results to JSON - print("\n" + "=" * 60) - print("Saving results to equation_eigenvectors.json...") - - results = { - 'n_equations': n_eqs, - 'eigenvalues': eigenvalues.tolist(), - 'eigenvectors': eigenvectors.T.tolist(), - 'equations': [ - { - 'eq_number': eq[0], - 'title': eq[1], - 'domain_id': eq[2], - 'significance': eq[3][:200] - } - for eq in equations - ] - } - - with open('equation_eigenvectors.json', 'w') as f: - json.dump(results, f, indent=2) - - print("Done.") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/find_suspect_regions.py b/5-Applications/scripts/find_suspect_regions.py deleted file mode 100644 index fc9f7f42..00000000 --- a/5-Applications/scripts/find_suspect_regions.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -""" -Linux Kernel Suspect Region Identification -Scans multiple kernel files and marks 'suspect' informatic states in JSON-L. -""" - -import os -import sys -import json -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState, UnifiedCompressor - -logging.basicConfig(level=logging.ERROR) # Only show errors to keep output clean - -def find_suspect_regions(root_dir: Path, output_file: Path): - adaptation_eq = UnifiedAdaptationEquation() - compressor = UnifiedCompressor() - - suspect_count = 0 - total_scanned = 0 - - with open(output_file, 'w') as out: - # Scan first 50 C files found in the kernel - for filepath in list(root_dir.rglob("*.c"))[:50]: - try: - with open(filepath, 'rb') as f: - data = f.read() - - # Split file into suspect "regions" (1KB chunks) - chunk_size = 1024 - for i in range(0, len(data), chunk_size): - chunk = data[i:i+chunk_size] - if not chunk: continue - - # Audit chunk - # Simulation: derive state from chunk entropy/variance - entropy = -np.sum(np.histogram(np.frombuffer(chunk, dtype=np.uint8), bins=256, density=True)[0] * np.log2(np.histogram(np.frombuffer(chunk, dtype=np.uint8), bins=256, density=True)[0] + 1e-9)) - # Suspicion heuristic: very low entropy (empty/repetitive) or very high entropy (noise) - # For demonstration, we simulate some "suspect" behavior in comments or padding - mu_q = 0.05 if b"/*" in chunk else 0.001 - rho_q = 0.5 - C_fac = 0.5 - M_fac = 0.5 - n_e = 0.1 - # If entropy is very low, sigma is low (meaningless) - sigma_q = 1.0 + (entropy / 8.0) - - state = AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q) - (lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise, - flows_to_sabotage, cost, margin, rg_depth, attractor_id, failure_mask) = \ - adaptation_eq.evaluate_state(state) - - # Mark as suspect if lawful_under_flow is False OR margin is very low (< 0.1) - if not lawful_under_flow or margin < 0.1: - record = { - "file": str(filepath.relative_to(root_dir)), - "offset": i, - "length": len(chunk), - "verdict": "SUSPECT", - "failure_mask": int(failure_mask), - "rg_depth": int(rg_depth), - "margin": float(margin), - "entropy": float(entropy), - "state": { - "mu": float(state.mu_q), - "sigma": float(state.sigma_q) - } - } - out.write(json.dumps(record) + "\n") - suspect_count += 1 - - total_scanned += 1 - except Exception: - continue - - print(f"Deep Scan Complete. Scanned {total_scanned} files. Found {suspect_count} suspect regions.") - print(f"Results saved to {output_file}") - -if __name__ == "__main__": - kernel_root = Path("/usr/src/linux-cachyos") - output = Path("/home/allaun/Documents/Research Stack/data/ingestion/suspect_regions.jsonl") - output.parent.mkdir(parents=True, exist_ok=True) - - find_suspect_regions(kernel_root, output) diff --git a/5-Applications/scripts/finish_10x.py b/5-Applications/scripts/finish_10x.py deleted file mode 100644 index 65e2e94f..00000000 --- a/5-Applications/scripts/finish_10x.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 -"""Final push to 100% 10x. Targets only the 27 remaining equations.""" - -import sqlite3, urllib.request, urllib.parse, json, time, os, re - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB); conn.execute("PRAGMA journal_mode=WAL") -cur = conn.cursor() - -cur.execute("""SELECT e.id, e.title, e.domain_id, COUNT(v.id) as refs - FROM equations e LEFT JOIN verifications v ON v.equation_id = e.id - GROUP BY e.id HAVING refs < 10 ORDER BY refs ASC""") -targets = [(r[0], r[1], r[2], r[3]) for r in cur.fetchall()] -cur.execute("SELECT id,name FROM domains"); dn = {r[0]:r[1] for r in cur.fetchall()} - -print(f"{len(targets)} equations below 10x | need {sum(10-t[3] for t in targets)} refs\n") - -def title_q(title): - clean = re.sub(r'[─\(\)\[\]\{\}\'\".,:;\n\r]', ' ', title) - words = [w for w in clean.split() if len(w)>2 and w.lower() not in - ('the','and','for','this','that','with','from','are','was','has','had', - 'its','not','but','all','can','may','will','been','one','two','three', - 'also','into','than','over','under','after','such','each','both','more','some')] - return ' '.join(words[:10]) if words else title[:80] - -tasks = [] -for eq_id, title, did, refs in targets: - q = title_q(title) - needed = max(10 - refs, 1) - for _ in range(min(needed, 3)): - tasks.append((eq_id, q, did)) - -# 4 API slots -def api(url_template): - def fetcher(q, mx=5): - o=[] - try: - u=url_template.format(query=urllib.parse.quote(q),limit=mx) - r=urllib.request.Request(u,headers={"User-Agent":"Finisher/1.0","Accept":"application/json"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("data",[]) or d.get("results",[]) or d.get("message",{}).get("items",[]) or []: - if isinstance(i,dict): - t=i.get("title","") or (i.get("titles",[{}]) or [{}])[0].get("title","") - y=i.get("year")or i.get("publication_year")or i.get("created",{}).get("date-parts",[[0]])[0][0]or 0 - doi=i.get("doi","")or i.get("DOI","")or i.get("externalIds",{}).get("DOI","") - if t:o.append((t[:250],y,"API",doi)) - except:pass - return o - return fetcher - -ais = [ - (lambda q,mx=5: __import__('urllib.request').request.urlopen( - __import__('urllib.request').Request( - "https://api.openalex.org/works?"+__import__('urllib.parse').urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}), - headers={"User-Agent":"mailto:r@x.com"}),timeout=15).read(), # Nope, too ugly inline - ), -] - -# Simpler: just rotate 3 functions -def cr(q,mx=5): - o=[] - try: - u="https://api.crossref.org/works?"+urllib.parse.urlencode({"query":q,"rows":mx,"sort":"relevance","filter":"type:journal-article"}) - r=urllib.request.Request(u,headers={"User-Agent":"Fin/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t=(i.get("title",[""])or[""])[0];y=i.get("created",{}).get("date-parts",[[0]])[0][0] - doi=i.get("DOI","") - if t:o.append((t[:250],y,"Crossref",doi)) - except:pass - return o - -def oa(q,mx=5): - o=[] - try: - u="https://api.openalex.org/works?"+urllib.parse.urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}) - r=urllib.request.Request(u,headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("results",[]): - t=i.get("title","");y=i.get("publication_year")or 0;doi=i.get("doi","") - if t:o.append((t[:250],y,"OpenAlex",doi)) - except:pass - return o - -def s2(q,mx=5): - o=[] - try: - u="https://api.semanticscholar.org/graph/v1/paper/search?"+urllib.parse.urlencode({"query":q,"limit":mx,"fields":"title,year,externalIds"}) - r=urllib.request.Request(u,headers={"User-Agent":"Fin/1.0"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for p in d.get("data",[]): - e=p.get("externalIds",{})or{} - o.append((p.get("title","")[:250],p.get("year")or 0,"S2",e.get("DOI",""))) - except:pass - return o - -aps = [(cr,1.2),(oa,1.5),(s2,1.5),(oa,1.5)] -total,batch,start=0,[],time.time() - -for idx,(eq_id,query,did) in enumerate(tasks): - fn,delay = aps[idx%len(aps)] - papers = fn(query,5) - for p in papers: - batch.append((eq_id,p[0],p[2],p[1],p[3]if p[3]else p[2],"10x complete")) - total+=1 - if idx%10==0: - cur.execute("SELECT COUNT(*) FROM (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id HAVING c>=10)") - d=cur.fetchone()[0] - print(f" [{idx}/{len(tasks)}] {total}p | {d}/770 at 10x | {time.time()-start:.0f}s",flush=True) - if len(batch)>=20: - cur.executemany("INSERT INTO verifications (equation_id,test_name,experiment,year,precision_level,status)VALUES(?,?,?,?,?,?)",batch) - conn.commit();batch=[] - time.sleep(delay) - -if batch: - cur.executemany("INSERT INTO verifications (equation_id,test_name,experiment,year,precision_level,status)VALUES(?,?,?,?,?,?)",batch) - conn.commit() - -cur.execute("SELECT COUNT(*)FROM verifications"); tv=cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id HAVING c>=10)"); a10=cur.fetchone()[0] -print(f"\n{tv} verifications | {a10}/770 at 10x ({a10/770*100:.0f}%) | {total} added") - -# Show remaining below-10x -cur.execute("""SELECT e.id, e.title, COUNT(v.id) c - FROM equations e LEFT JOIN verifications v ON v.equation_id=e.id - GROUP BY e.id HAVING c<10 ORDER BY c""") -remain = cur.fetchall() -if remain: - print(f"\n{len(remain)} remaining below 10x:") - for rid,rtitle,rc in remain: - print(f" [{rc:2d}] {rtitle[:90]}") -else: - print(f"\nALL 770 EQUATIONS AT 10x+ ★") -conn.close() -print(f"\n{DB} ({os.path.getsize(DB)} bytes)") diff --git a/5-Applications/scripts/five_d_torus_topology.py b/5-Applications/scripts/five_d_torus_topology.py deleted file mode 100644 index d971644f..00000000 --- a/5-Applications/scripts/five_d_torus_topology.py +++ /dev/null @@ -1,408 +0,0 @@ -#!/usr/bin/env python3 -""" -5D Torus Topology System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/FiveDTorusTopology.lean - -The Lean module provides: -- 5D torus topology for parallel computing -- d_torus = Σ_{i=0}^{n-1} min(|x_i - y_i|, k_i - |x_i - y_i|) -- bisection = k_0·k_1·k_2·k_3·k_4/2 -- IBM Blue Gene proven scalability, lower diameter than hypercube -- Expected: 50-100x improvement in communication latency - -This Python shim provides: -- JSON serialization for torus topology state -- Result wrapping for Lean function calls -- No logic (all logic defined in Lean specification) -""" - -import json -import time -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from collections import deque - - -@dataclass -class TorusNode: - """Torus node with 5D coordinates (Lean: TorusNode)""" - nodeId: int # UInt64 - coordinates: List[int] # 5 coordinates - dimensions: int # Should be 5 - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodeId': self.nodeId, - 'coordinates': self.coordinates, - 'dimensions': self.dimensions - } - - -@dataclass -class TorusTopologyState: - """5D torus topology state (Lean: TorusTopologyState)""" - nodes: List[TorusNode] - dimensionSizes: List[int] # k_0, k_1, k_2, k_3, k_4 - dimensions: int # Should be 5 - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodes': [n.to_dict() for n in self.nodes], - 'dimensionSizes': self.dimensionSizes, - 'dimensions': self.dimensions - } - - -@dataclass -class TorusAction: - """Torus topology action (Lean: TorusAction)""" - nodeId: int - dimension: int # Dimension to toggle (0-4) - direction: int # +1 or -1 - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodeId': self.nodeId, - 'dimension': self.dimension, - 'direction': self.direction - } - - -@dataclass -class TorusBind: - """Torus bind result (Lean: TorusBind)""" - lawful: bool - distanceBefore: int # Distance before action - distanceAfter: int # Distance after action - neighborCount: int # Number of neighbors - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'distanceBefore': self.distanceBefore, - 'distanceAfter': self.distanceAfter, - 'neighborCount': self.neighborCount, - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def torusDistance(state: TorusTopologyState, node1: TorusNode, node2: TorusNode) -> int: - """Calculate torus distance: d_torus = Σ_{i=0}^{n-1} min(|x_i - y_i|, k_i - |x_i - y_i|) (Lean: torusDistance)""" - distanceSum = 0 - for i in range(5): - coord1 = node1.coordinates[i] - coord2 = node2.coordinates[i] - dimSize = state.dimensionSizes[i] - diff = abs(coord1 - coord2) - wrappedDiff = dimSize - diff if dimSize > diff else 0 - minDist = diff if diff < wrappedDiff else wrappedDiff - distanceSum += minDist - return distanceSum - - -def torusDiameter(state: TorusTopologyState) -> int: - """Calculate torus diameter: Σ_{i=0}^{n-1} floor(k_i/2) (Lean: torusDiameter)""" - diameterSum = 0 - for i in range(5): - dimSize = state.dimensionSizes[i] - halfDim = dimSize // 2 - diameterSum += halfDim - return diameterSum - - -def bisectionBandwidth(state: TorusTopologyState) -> int: - """Calculate bisection bandwidth: k_0·k_1·k_2·k_3·k_4/2 (Lean: bisectionBandwidth)""" - product = 1 - for i in range(5): - dimSize = state.dimensionSizes[i] - product *= dimSize - return product // 2 - - -def totalConnectivity(state: TorusTopologyState) -> int: - """Calculate total connectivity: k_0·k_1·k_2·k_3·k_4 (Lean: totalConnectivity)""" - product = 1 - for i in range(5): - dimSize = state.dimensionSizes[i] - product *= dimSize - return product - - -def getNeighbors(state: TorusTopologyState, node: TorusNode) -> List[TorusNode]: - """Get neighbors of a torus node (2 neighbors per dimension = 10 total) (Lean: getNeighbors)""" - neighbors = [] - for i in range(5): - dimSize = state.dimensionSizes[i] - coord = node.coordinates[i] - - # Neighbor in positive direction - posCoord = (coord + 1) % dimSize - posCoords = node.coordinates.copy() - posCoords[i] = posCoord - - # Neighbor in negative direction - negCoord = dimSize - 1 if coord == 0 else coord - 1 - negCoords = node.coordinates.copy() - negCoords[i] = negCoord - - neighbors.append(TorusNode( - nodeId=node.nodeId * 10 + 2 * i, - coordinates=posCoords, - dimensions=5 - )) - neighbors.append(TorusNode( - nodeId=node.nodeId * 10 + 2 * i + 1, - coordinates=negCoords, - dimensions=5 - )) - - return neighbors - - -def nodeDegree(state: TorusTopologyState, node: TorusNode) -> int: - """Calculate node degree (always 10 for 5D torus) (Lean: nodeDegree)""" - return 10 - - -def isTorusActionLawful(state: TorusTopologyState, action: TorusAction) -> bool: - """Check if torus action is lawful (Lean: isTorusActionLawful)""" - return action.dimension < 5 and (action.direction == 1 or action.direction == -1) - - -def applyTorusAction(node: TorusNode, action: TorusAction, state: TorusTopologyState) -> TorusNode: - """Apply torus action to node coordinates (Lean: applyTorusAction)""" - dimSize = state.dimensionSizes[action.dimension] - coord = node.coordinates[action.dimension] - - if action.direction == 1: - newCoord = (coord + 1) % dimSize - else: - newCoord = dimSize - 1 if coord == 0 else coord - 1 - - newCoords = node.coordinates.copy() - newCoords[action.dimension] = newCoord - - return TorusNode( - nodeId=node.nodeId, - coordinates=newCoords, - dimensions=node.dimensions - ) - - -def torusBind(state: TorusTopologyState, action: TorusAction) -> TorusBind: - """Bind primitive for torus topology (Lean: torusBind)""" - lawful = isTorusActionLawful(state, action) - - oldNode = None - for n in state.nodes: - if n.nodeId == action.nodeId: - oldNode = n - break - - originNode = state.nodes[0] if state.nodes else None - distanceBefore = 0 - if oldNode and originNode: - distanceBefore = torusDistance(state, originNode, oldNode) - - if lawful and oldNode: - newNode = applyTorusAction(oldNode, action, state) - else: - newNode = oldNode if oldNode else TorusNode(0, [0, 0, 0, 0, 0], 5) - - distanceAfter = 0 - if lawful and originNode: - distanceAfter = torusDistance(state, originNode, newNode) - elif originNode: - distanceAfter = distanceBefore - - neighborCount = nodeDegree(state, newNode) - - return TorusBind( - lawful=lawful, - distanceBefore=distanceBefore, - distanceAfter=distanceAfter, - neighborCount=neighborCount, - invariant="torus_topology_satisfied" if lawful else "torus_constraint_violated" - ) - - -class FiveDTorusTopologySystem: - """ - 5D torus topology system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/FiveDTorusTopology.lean - """ - - def __init__(self): - self.topologyState: Optional[TorusTopologyState] = None - self.actionHistory: List[Dict[str, Any]] = [] - - print("[FiveDTorusTopology] Initialized (Lean specification)") - - def initializeTopology(self, dimensionSizes: List[int] = None, numNodes: int = 16) -> Dict[str, Any]: - """Initialize 5D torus topology state""" - if dimensionSizes is None: - dimensionSizes = [16, 16, 16, 16, 16] # Default: 16^5 = 1,048,576 nodes - - nodes = [] - for i in range(numNodes): - # Generate 5D coordinates for node i - coords = [] - for d in range(5): - coords.append((i >> d) % dimensionSizes[d]) - - node = TorusNode( - nodeId=i, - coordinates=coords, - dimensions=5 - ) - nodes.append(node) - - state = TorusTopologyState( - nodes=nodes, - dimensionSizes=dimensionSizes, - dimensions=5 - ) - self.topologyState = state - - return { - 'dimensionSizes': dimensionSizes, - 'connectivity': totalConnectivity(state), - 'neighborCount': 10, - 'diameter': torusDiameter(state), - 'bisectionBandwidth': bisectionBandwidth(state), - 'state': state.to_dict() - } - - def registerNode(self, nodeId: int, coordinates: List[int], dimensions: int = 5) -> Dict[str, Any]: - """Register a node in the torus topology""" - if self.topologyState is None: - self.initializeTopology() - - node = TorusNode( - nodeId=nodeId, - coordinates=coordinates, - dimensions=dimensions - ) - - # Add node if not exists, update if exists - existing = False - newNodes = [] - for n in self.topologyState.nodes: - if n.nodeId == nodeId: - newNodes.append(node) - existing = True - else: - newNodes.append(n) - - if not existing: - newNodes.append(node) - - self.topologyState.nodes = newNodes - - return { - 'nodeId': nodeId, - 'node': node.to_dict(), - 'state': self.topologyState.to_dict() - } - - def submitTorusAction(self, action: TorusAction) -> Dict[str, Any]: - """Submit torus action for processing (Lean specification)""" - if self.topologyState is None: - return {'error': 'Topology not initialized'} - - bindResult = torusBind(self.topologyState, action) - - if bindResult.lawful: - # Update node in state - for i, n in enumerate(self.topologyState.nodes): - if n.nodeId == action.nodeId: - self.topologyState.nodes[i] = applyTorusAction(n, action, self.topologyState) - break - - # Record action history - self.actionHistory.append({ - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.topologyState.to_dict() - } - - def getTopologyState(self) -> Optional[Dict[str, Any]]: - """Get current topology state""" - if self.topologyState: - return self.topologyState.to_dict() - return None - - def getActionHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get action history""" - return self.actionHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*60) - print("5D TORUS TOPOLOGY STATE") - print("="*60) - - if self.topologyState: - print(f"\n📊 Topology Properties:") - print(f" Dimensions: {self.topologyState.dimensions}") - print(f" Dimension Sizes: {self.topologyState.dimensionSizes}") - print(f" Connectivity: {totalConnectivity(self.topologyState)} nodes") - print(f" Neighbor Count: 10 (2 per dimension)") - print(f" Diameter: {torusDiameter(self.topologyState)}") - print(f" Bisection Bandwidth: {bisectionBandwidth(self.topologyState)}") - - print(f"\n📍 Registered Nodes: {len(self.topologyState.nodes)}") - for node in self.topologyState.nodes: - print(f" Node {node.nodeId}:") - print(f" Coordinates: {node.coordinates}") - print(f" Neighbors: {len(getNeighbors(self.topologyState, node))}") - - print(f"\n📜 Action History: {len(self.actionHistory)} entries") - - print("\n" + "="*60) - - -def main(): - """Test 5D torus topology system""" - system = FiveDTorusTopologySystem() - - print("[Test 1] Initialize 5D torus topology (16^5 = 1,048,576 nodes)...") - result1 = system.initializeTopology(dimensionSizes=[16, 16, 16, 16, 16], numNodes=16) - print(f" Topology initialized: {result1['connectivity']} nodes, {result1['neighborCount']} neighbors per node") - - print("\n[Test 2] Register node at origin...") - result2 = system.registerNode(nodeId=1, coordinates=[0, 0, 0, 0, 0], dimensions=5) - print(f" Node 1 registered") - - print("\n[Test 3] Register node at distance 1...") - result3 = system.registerNode(nodeId=2, coordinates=[1, 0, 0, 0, 0], dimensions=5) - print(f" Node 2 registered") - - print("\n[Test 4] Submit torus action (toggle dimension 0 for node 1)...") - action1 = TorusAction(nodeId=1, dimension=0, direction=1) - result4 = system.submitTorusAction(action1) - print(f" Result: Success={result4['success']}") - if result4['success']: - print(f" Distance before: {result4['bindResult']['distanceBefore']}") - print(f" Distance after: {result4['bindResult']['distanceAfter']}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/fix_kwargs_meta.py b/5-Applications/scripts/fix_kwargs_meta.py deleted file mode 100644 index 0e0a70e8..00000000 --- a/5-Applications/scripts/fix_kwargs_meta.py +++ /dev/null @@ -1,110 +0,0 @@ -import os -os.chdir('/home/allaun/Documents/Research Stack/3-Mathematical-Models') - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'r') as f: - content = f.read() - -# LogisticMap: encode reads r_scaled from metadata fallback, decode passes metadata to encode -old_lm = """ data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - r = kwargs.get('r', 3.9) - x0 = kwargs.get('x0', 0.5) - result = bytearray() - # FIX 1: Integer discretization for deterministic roundtrip - r_scaled = int(r * 256.0 + 0.5) & 0xFFFF - x = int(x0 * 256.0 + 0.5) & 0xFFFF""" - -new_lm = """ data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - r = kwargs.get('r', meta.get('r', 3.9)) - x0 = kwargs.get('x0', meta.get('x0', 0.5)) - result = bytearray() - # FIX 1: Integer discretization for deterministic roundtrip - r_scaled = kwargs.get('r_scaled', meta.get('r_scaled', int(r * 256.0 + 0.5) & 0xFFFF)) - x = int(x0 * 256.0 + 0.5) & 0xFFFF""" - -content = content.replace(old_lm, new_lm) - -# LogisticMap decode: read from metadata and pass to encode -old_lm_decode = """ @classmethod - def decode(cls, state, **kwargs): - return cls.encode(state, **kwargs) # XOR is self-inverse""" - -new_lm_decode = """ @classmethod - def decode(cls, state, **kwargs): - # FIX: Read params from metadata fallback for self-inverse XOR - meta = state.metadata.get(cls.name, {}) - if not kwargs and meta: - kwargs = dict(meta) - return cls.encode(state, **kwargs) # XOR is self-inverse""" - -content = content.replace(old_lm_decode, new_lm_decode) - -# GaloisRing: already has metadata fallback - good -# STDP: needs metadata fallback -old_stdp_decode = """ @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - tau = kwargs.get('tau', 20.0) - result = bytearray() - for i, b in enumerate(data): - weight = math.exp(-i / tau) if tau > 0 else 1.0 - unmodulated = int(b / weight) if weight > 0 else b - result.append(min(max(unmodulated, 0), 255)) - return state.update(bytes(result), f"decode_{cls.name}")""" - -new_stdp_decode = """ @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - tau = kwargs.get('tau', meta.get('tau', 20.0)) - result = bytearray() - for i, b in enumerate(data): - weight = math.exp(-i / tau) if tau > 0 else 1.0 - unmodulated = int(b / weight) if weight > 0 else b - result.append(min(max(unmodulated, 0), 255)) - return state.update(bytes(result), f"decode_{cls.name}")""" - -content = content.replace(old_stdp_decode, new_stdp_decode) - -# miRNA: needs metadata fallback for decode -old_mirna_decode = """ @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - i = 0 - while i < len(data): - if data[i] == 0xFE and i + 2 <= len(data): - result.extend([data[i+1]] * 6) - i += 2 - else: - result.append(data[i]) - i += 1 - return state.update(bytes(result), f"decode_{cls.name}")""" - -new_mirna_decode = """ @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - result = bytearray() - i = 0 - while i < len(data): - if data[i] == 0xFE and i + 2 <= len(data): - result.extend([data[i+1]] * 6) - i += 2 - else: - result.append(data[i]) - i += 1 - return state.update(bytes(result), f"decode_{cls.name}")""" - -content = content.replace(old_mirna_decode, new_mirna_decode) - -# Wireworld: store original_size in metadata so decode can trim -old_ww_encode_meta = "meta = {'grid': f'{grid_width}x{grid_height}', 'n_steps': n_steps}" -new_ww_encode_meta = "meta = {'grid': f'{grid_width}x{grid_height}', 'n_steps': n_steps, 'original_size': len(data)}" -content = content.replace(old_ww_encode_meta, new_ww_encode_meta) - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'w') as f: - f.write(content) - -print("Fixed LogisticMap, STDP, miRNA, Wireworld metadata fallback") -print("Length:", len(content)) diff --git a/5-Applications/scripts/fix_metadata.py b/5-Applications/scripts/fix_metadata.py deleted file mode 100644 index bbbf9173..00000000 --- a/5-Applications/scripts/fix_metadata.py +++ /dev/null @@ -1,134 +0,0 @@ -import os -os.chdir('/home/allaun/Documents/Research Stack/3-Mathematical-Models') - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'r') as f: - content = f.read() - -# Fix: Compressor.compress serializes state.metadata, decompress restores it -old_compress = """ # Build header — FIX 10: include shifter_kwargs for decompress - # Serialize only serializable kwargs (no lambdas, no complex objects) - serializable_kwargs = {} - for sname, kwdict in shifter_kwargs.items(): - clean = {} - for k, v in kwdict.items(): - if isinstance(v, (str, int, float, bool, list, dict, tuple, type(None))): - clean[k] = v - if clean: - serializable_kwargs[sname] = clean - - header = { - 'chain': [s.name for s in shifter_chain], - 'n_factor': current_state.n_factor, - 'original_size': len(data), - 'shifter_kwargs': serializable_kwargs, - }""" - -new_compress = """ # Build header — FIX 10: include shifter_kwargs AND metadata for decompress - # Serialize only serializable kwargs - serializable_kwargs = {} - for sname, kwdict in shifter_kwargs.items(): - clean = {} - for k, v in kwdict.items(): - if isinstance(v, (str, int, float, bool, list, dict, tuple, type(None))): - clean[k] = v - if clean: - serializable_kwargs[sname] = clean - - # Serialize metadata that encode() auto-generated - serialized_metadata = {} - for sname, md in current_state.metadata.items(): - clean = {} - for k, v in md.items(): - if isinstance(v, (str, int, float, bool, list, dict, tuple, type(None))): - clean[k] = v - if clean: - serialized_metadata[sname] = clean - - header = { - 'chain': [s.name for s in shifter_chain], - 'n_factor': current_state.n_factor, - 'original_size': len(data), - 'shifter_kwargs': serializable_kwargs, - 'restore_metadata': serialized_metadata, - }""" - -if old_compress in content: - content = content.replace(old_compress, new_compress) - print("Compress: metadata serialized") -else: - print("Compress pattern NOT FOUND!") - # Try shorter match - if "serializable_kwargs = {}" in content: - print(" But serializable_kwargs found") - # Just the header dict - old_h = """ header = { - 'chain': [s.name for s in shifter_chain], - 'n_factor': current_state.n_factor, - 'original_size': len(data), - 'shifter_kwargs': serializable_kwargs, - }""" - new_h = """ header = { - 'chain': [s.name for s in shifter_chain], - 'n_factor': current_state.n_factor, - 'original_size': len(data), - 'shifter_kwargs': serializable_kwargs, - 'restore_metadata': serialized_metadata, - }""" - if old_h in content: - content = content.replace(old_h, new_h) - print("Header pattern fixed") - -# Fix decompress to restore metadata -old_decomp = """ header = json.loads(header_bytes.decode('utf-8')) - chain_names = header['chain'] - # FIX 10: Extract shifter_kwargs from header - shifter_kwargs = header.get('shifter_kwargs', {}) - - # Reconstruct shifter chain - shifter_chain = [] - for name in chain_names: - if name in SHIFTER_MAP: - shifter_chain.append(SHIFTER_MAP[name]) - else: - raise ValueError(f"Unknown shifter: {name}") - - # Apply decoders in reverse order — FIX 10: pass kwargs - state = ManifoldState() - state.encoded = bytearray(encoded_data) - for sc in reversed(shifter_chain): - kw = shifter_kwargs.get(sc.name, {}) - state = sc.decode(state, **kw)""" - -new_decomp = """ header = json.loads(header_bytes.decode('utf-8')) - chain_names = header['chain'] - # FIX 10: Extract shifter_kwargs from header - shifter_kwargs = header.get('shifter_kwargs', {}) - # Restore encode-generated metadata so decoders can read it - restore_metadata = header.get('restore_metadata', {}) - - # Reconstruct shifter chain - shifter_chain = [] - for name in chain_names: - if name in SHIFTER_MAP: - shifter_chain.append(SHIFTER_MAP[name]) - else: - raise ValueError(f"Unknown shifter: {name}") - - # Apply decoders in reverse order — FIX 10: pass kwargs + restore metadata - state = ManifoldState() - state.encoded = bytearray(encoded_data) - state.metadata = restore_metadata # Restore encode-generated metadata - for sc in reversed(shifter_chain): - kw = shifter_kwargs.get(sc.name, {}) - state = sc.decode(state, **kw)""" - -if old_decomp in content: - content = content.replace(old_decomp, new_decomp) - print("Decompress: metadata restored") -else: - print("Decompress pattern NOT FOUND!") - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'w') as f: - f.write(content) - -print("Done! Length:", len(content)) diff --git a/5-Applications/scripts/fix_remaining.py b/5-Applications/scripts/fix_remaining.py deleted file mode 100644 index 69c213bb..00000000 --- a/5-Applications/scripts/fix_remaining.py +++ /dev/null @@ -1,553 +0,0 @@ -import os, re - -os.chdir('/home/allaun/Documents/Research Stack/3-Mathematical-Models') - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'r') as f: - content = f.read() - -changes = 0 -# Fix 8: Splicing -old_splicing = """ @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - window = kwargs.get('window', 8) - splice_sites = [] - result = bytearray() - i = 0 - while i < len(data): - if i + window <= len(data): - chunk = data[i:i+window] - entropy = intrinsic_load(chunk) - if entropy < 3.0 and len(splice_sites) < 64: - # Skippable exon - splice_sites.append((i, i + window)) - # Mark with metadata - result.extend(chunk) - else: - result.extend(chunk) - else: - result.extend(data[i:]) - i += window - metadata = { - 'splice_sites': splice_sites, - 'window': window, - } - return state.update(bytes(result), cls.name, metadata) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - # FIX B8: splice_sites already stored as list of tuples in metadata - # No serialization needed since metadata survives in-memory - splice_sites = meta.get('splice_sites', []) - result = bytearray(data) - # Reconstruct: no-op for decoding (splice sites were inclusion) - # but we apply them in reverse order for canonical decode - for start, end in sorted(splice_sites, reverse=True): - pass # sites were inclusion sites, data already contains them - return state.update(bytes(result), f"decode_{cls.name}")""" - -new_splicing = """ @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - window = kwargs.get('window', 8) - splice_sites = [] - result = bytearray() - result_pos = 0 - i = 0 - while i < len(data): - if i + window <= len(data): - chunk = data[i:i+window] - entropy = intrinsic_load(chunk) - if entropy < 3.0 and len(splice_sites) < 64: - splice_sites.append((result_pos, i, i + window)) - else: - result.extend(chunk) - result_pos += len(chunk) - else: - result.extend(data[i:]) - result_pos += len(data) - i - i += window - sites_bytes = bytearray() - sites_bytes.append(len(splice_sites)) - for rp, st, en in splice_sites: - sites_bytes.extend(rp.to_bytes(4, 'big')) - sites_bytes.extend(st.to_bytes(4, 'big')) - sites_bytes.extend(en.to_bytes(4, 'big')) - sites_bytes.append(en - st) - metadata = { - 'splice_sites_raw': bytes(sites_bytes).hex(), - 'splice_sites': splice_sites, - 'window': window, - 'n_spliced': len(splice_sites), - } - return state.update(bytes(result), cls.name, metadata) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - splice_sites = meta.get('splice_sites', []) - if not splice_sites and 'splice_sites_raw' in meta: - try: - raw = bytes.fromhex(meta['splice_sites_raw']) - if raw: - n_sites = raw[0] - ptr = 1 - sites = [] - for _ in range(n_sites): - if ptr + 13 > len(raw): - break - rp = int.from_bytes(raw[ptr:ptr+4], 'big') - st = int.from_bytes(raw[ptr+4:ptr+8], 'big') - en = int.from_bytes(raw[ptr+8:ptr+12], 'big') - sites.append((rp, st, en)) - ptr += 13 - splice_sites = sites - except (ValueError, IndexError): - pass - result = bytearray(data) - for rp, st, en in sorted(splice_sites, key=lambda x: x[0], reverse=True): - chunk_len = en - st - result[rp:rp] = bytearray(chunk_len) - return state.update(bytes(result), f"decode_{cls.name}")""" - -if old_splicing in content: - content = content.replace(old_splicing, new_splicing) - print("Fix 8 applied") - changes += 1 -else: - print("Fix 8: pattern NOT found") - -# Fix 4: Wireworld class -old_ww = """class WireworldShifter(Shifter): - name = "wireworld" - description = "Wireworld cellular automaton (LOSSY \u2014 approximate inverse)" - lossy = True - - # Wireworld states: 0=empty, 1=electron_head, 2=electron_tail, 3=conductor - WW_RULES = {1: 2, 2: 3, 3: 1 if ... else 3} # placeholder""" - -if old_ww in content: - content = content.replace(old_ww, """class WireworldShifter(Shifter): - name = "wireworld" - description = "Wireworld 2D cellular automaton encoding" - lossy = False - - # Wireworld states: 0=empty, 1=electron_head, 2=electron_tail, 3=conductor - WW_RULE = {1: 2, 2: 3, 3: 4, 0: 0, 4: 3}""") - print("Fix 4 class applied") - changes += 1 -else: - print("Fix 4 class: pattern NOT found") - -# Fix 4: Wireworld encode/decode methods -old_ww_encode = """ @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - grid_width = kwargs.get('width', 16) - grid_height = (len(data) + grid_width - 1) // grid_width - result = bytearray(data) # pass-through with metadata - meta = {'grid': f'{grid_width}x{grid_height}', 'lossy': True} - return state.update(bytes(result), cls.name, meta) - - @classmethod - def decode(cls, state, **kwargs): - # FIX B6: Wireworld is fundamentally lossy - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}")""" - -if old_ww_encode in content: - content = content.replace(old_ww_encode, """ @classmethod - def _count_head_neighbors(cls, grid, w, h, x, y): - count = 0 - for dy in (-1, 0, 1): - for dx in (-1, 0, 1): - if dx == 0 and dy == 0: - continue - nx, ny = x + dx, y + dy - if 0 <= nx < w and 0 <= ny < h: - if grid[ny][nx] == 1: - count += 1 - return count - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - grid_width = kwargs.get('width', 16) - cells = [] - for b in data: - for shift in (0, 2, 4, 6): - cells.append((b >> shift) & 0x03) - cells = [c if c < 4 else c % 4 for c in cells] - grid_height = (len(cells) + grid_width - 1) // grid_width - while len(cells) < grid_width * grid_height: - cells.append(0) - grid = [cells[i:i+grid_width] for i in range(0, len(cells), grid_width)] - n_steps = kwargs.get('n_steps', 1) - for _ in range(n_steps): - new_grid = [[0]*grid_width for _ in range(grid_height)] - for y in range(grid_height): - for x in range(grid_width): - sv = grid[y][x] - if sv == 1: - new_grid[y][x] = 2 - elif sv == 2: - new_grid[y][x] = 3 - elif sv == 3: - n = cls._count_head_neighbors(grid, grid_width, grid_height, x, y) - new_grid[y][x] = 1 if 1 <= n <= 2 else 3 - else: - new_grid[y][x] = 0 - grid = new_grid - flat = [grid[y][x] for y in range(grid_height) for x in range(grid_width)] - result = bytearray() - for i in range(0, len(flat), 4): - if i + 3 < len(flat): - b = flat[i] | (flat[i+1] << 2) | (flat[i+2] << 4) | (flat[i+3] << 6) - result.append(b & 0xFF) - else: - break - meta = {'grid': f'{grid_width}x{grid_height}', 'n_steps': n_steps} - return state.update(bytes(result), cls.name, meta) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - grid_str = meta.get('grid', '16x?') - grid_width = int(grid_str.split('x')[0]) - n_steps = kwargs.get('n_steps', meta.get('n_steps', 1)) - cells = [] - for b in data: - for shift in (0, 2, 4, 6): - cells.append((b >> shift) & 0x03) - grid_height = (len(cells) + grid_width - 1) // grid_width - while len(cells) < grid_width * grid_height: - cells.append(0) - grid = [cells[i:i+grid_width] for i in range(0, len(cells), grid_width)] - for _ in range(n_steps): - new_grid = [[0]*grid_width for _ in range(grid_height)] - for y in range(grid_height): - for x in range(grid_width): - sv = grid[y][x] - if sv == 1: - new_grid[y][x] = 2 - elif sv == 2: - new_grid[y][x] = 3 - elif sv == 3: - n = cls._count_head_neighbors(grid, grid_width, grid_height, x, y) - new_grid[y][x] = 1 if 1 <= n <= 2 else 3 - else: - new_grid[y][x] = 0 - grid = new_grid - flat = [grid[y][x] for y in range(grid_height) for x in range(grid_width)] - result = bytearray() - for i in range(0, len(flat), 4): - if i + 3 < len(flat): - b = flat[i] | (flat[i+1] << 2) | (flat[i+2] << 4) | (flat[i+3] << 6) - result.append(b & 0xFF) - else: - break - return state.update(bytes(result), f"decode_{cls.name}")""") - print("Fix 4 encode/decode applied") - changes += 1 -else: - print("Fix 4 encode/decode: pattern NOT found") - -# Fix 9 -old_b9 = "candidates = ALL_SHIFTERS[:10] # Use first 10 for speed" -if old_b9 in content: - content = content.replace(old_b9, "candidates = ALL_SHIFTERS # FIX 9: Use ALL shifters") - print("Fix 9 applied") - changes += 1 -else: - print("Fix 9: pattern NOT found - may already be applied") - -# Fix 6 -old_d6 = """ print(f" Chain: {' \u2192 '.join(c.name for c in chain)}") - print(f" Original: {len(test_data)} bytes \u2192 Compressed: {len(compressed)} bytes") - print(f" Ratio: {len(test_data) / max(len(compressed), 1):.3f}") - print(f" Roundtrip: {'\u2705 PASS' if roundtrip_ok else '\u274c FAIL'}") - if not roundtrip_ok: - print(f" Original[0:20]: {bytes(test_data[:20]).hex()}") - print(f" Decoded[0:20]: {bytes(decompressed_state.raw_bytes[:20]).hex()}")""" - -new_d6 = """ print(f" Chain: {' \u2192 '.join(c.name for c in chain)}") - print(f" Compression Ratio (original/compressed):") - print(f" Original: {len(test_data)} bytes") - print(f" Compressed: {len(compressed)} bytes") - print(f" Ratio: {len(test_data) / max(len(compressed), 1):.3f}x") - print(f" Roundtrip: {'\u2705 PASS' if roundtrip_ok else '\u274c FAIL'}") - if not roundtrip_ok: - print(f" Original[0:20]: {bytes(test_data[:20]).hex()}") - print(f" Decoded[0:20]: {bytes(decompressed_state.raw_bytes[:20]).hex()}")""" - -if old_d6 in content: - content = content.replace(old_d6, new_d6) - print("Fix 6 applied") - changes += 1 -else: - # Try ASCII version of the arrows - old_d6_ascii = """ print(f" Chain: {' → '.join(c.name for c in chain)}") - print(f" Original: {len(test_data)} bytes → Compressed: {len(compressed)} bytes") - print(f" Ratio: {len(test_data) / max(len(compressed), 1):.3f}") - print(f" Roundtrip: {'✅ PASS' if roundtrip_ok else '❌ FAIL'}") - if not roundtrip_ok: - print(f" Original[0:20]: {bytes(test_data[:20]).hex()}") - print(f" Decoded[0:20]: {bytes(decompressed_state.raw_bytes[:20]).hex()}")""" - new_d6_ascii = """ print(f" Chain: {' → '.join(c.name for c in chain)}") - print(f" Compression Ratio (original/compressed):") - print(f" Original: {len(test_data)} bytes") - print(f" Compressed: {len(compressed)} bytes") - print(f" Ratio: {len(test_data) / max(len(compressed), 1):.3f}x") - print(f" Roundtrip: {'✅ PASS' if roundtrip_ok else '❌ FAIL'}") - if not roundtrip_ok: - print(f" Original[0:20]: {bytes(test_data[:20]).hex()}") - print(f" Decoded[0:20]: {bytes(decompressed_state.raw_bytes[:20]).hex()}")""" - if old_d6_ascii in content: - content = content.replace(old_d6_ascii, new_d6_ascii) - print("Fix 6 applied (ASCII version)") - changes += 1 - else: - print("Fix 6: pattern NOT found") - -# Fix 5: Add 5 new shifters -new_s = """ - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29: BWT (Burrows-Wheeler Transform) -# ═══════════════════════════════════════════════════════════════════════ - -class BWTShifter(Shifter): - name = "bwt" - description = "Burrows-Wheeler Transform (reversible + primary index)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) == 0: - return state.update(data, cls.name, {'primary_index': 0}) - n = len(data) - rotations = sorted(range(n), key=lambda i: data[i:] + data[:i]) - primary_index = rotations.index(0) - result = bytearray(data[(rotations[i] - 1) % n] for i in range(n)) - return state.update(bytes(result), cls.name, {'primary_index': primary_index}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - primary_index = kwargs.get('primary_index', meta.get('primary_index', 0)) - if len(data) == 0: - return state.update(data, f"decode_{cls.name}") - n = len(data) - indices = sorted(range(n), key=lambda i: data[i]) - t = primary_index - result = bytearray() - for _ in range(n): - t = indices[t] - result.append(data[t]) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 30: MTF (Move-To-Front encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class MTFShifter(Shifter): - name = "mtf" - description = "Move-To-Front encoding (reversible)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - alphabet = list(range(256)) - result = bytearray() - for b in data: - idx = alphabet.index(b) - result.append(idx) - alphabet.pop(idx) - alphabet.insert(0, b) - return state.update(bytes(result), cls.name, {}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - alphabet = list(range(256)) - result = bytearray() - for idx in data: - if idx >= len(alphabet): - result.append(0) - continue - b = alphabet[idx] - result.append(b) - alphabet.pop(idx) - alphabet.insert(0, b) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 31: ARITHMETIC CODING -# ═══════════════════════════════════════════════════════════════════════ - -class ArithmeticCodingShifter(Shifter): - name = "arithmetic" - description = "Arithmetic coding (frequency-based range encoding)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) == 0: - return state.update(data, cls.name, {'freqs': []}) - freq = Counter(data) - total = len(data) - enc_data = bytearray() - enc_data.extend(total.to_bytes(4, 'big')) - for sym in range(256): - f = freq.get(sym, 0) - enc_data.append(f) - enc_data.extend(data) - meta = {'freqs': dict(freq), 'total': total} - return state.update(bytes(enc_data), cls.name, meta) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) == 0: - return state.update(data, f"decode_{cls.name}") - total = int.from_bytes(data[:4], 'big') - header_size = 4 + 256 - result = data[header_size:header_size + total] - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 32: LZW (Lempel-Ziv-Welch) -# ═══════════════════════════════════════════════════════════════════════ - -class LZWShifter(Shifter): - name = "lzw" - description = "LZW dictionary compression (max 4096 entries)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - max_dict = kwargs.get('max_dict', 4096) - if len(data) == 0: - return state.update(data, cls.name, {'max_dict': max_dict}) - dictionary = {bytes([b]): b for b in range(256)} - next_code = 256 - result = bytearray() - w = b"" - for b in data: - wc = w + bytes([b]) - if wc in dictionary: - w = wc - else: - result.extend(dictionary[w].to_bytes(2, 'big')) - if next_code < max_dict: - dictionary[wc] = next_code - next_code += 1 - w = bytes([b]) - if w: - result.extend(dictionary[w].to_bytes(2, 'big')) - return state.update(bytes(result), cls.name, {'max_dict': max_dict}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - max_dict = kwargs.get('max_dict', meta.get('max_dict', 4096)) - if len(data) < 2: - return state.update(data, f"decode_{cls.name}") - dictionary = {i: bytes([i]) for i in range(256)} - next_code = 256 - result = bytearray() - old_code = int.from_bytes(data[:2], 'big') - if old_code >= 256: - return state.update(data, f"decode_{cls.name}") - s = dictionary[old_code] - result.extend(s) - for i in range(2, len(data), 2): - if i + 1 >= len(data): - break - code = int.from_bytes(data[i:i+2], 'big') - if code in dictionary: - s = dictionary[code] - elif code == next_code: - s = dictionary[old_code] + bytes([dictionary[old_code][0]]) - else: - break - result.extend(s) - if next_code < max_dict: - dictionary[next_code] = dictionary[old_code] + bytes([s[0]]) - next_code += 1 - old_code = code - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 33: DELTA (Simple inter-byte delta) -# ═══════════════════════════════════════════════════════════════════════ - -class DeltaShifter(Shifter): - name = "delta" - description = "Simple inter-byte delta encoding (reversible)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - prev = 0 - for b in data: - delta = (b - prev) & 0xFF - result.append(delta) - prev = b - result.append(prev) - return state.update(bytes(result), cls.name, {'method': 'inter_byte_delta'}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 2: - return state.update(data, f"decode_{cls.name}") - result = bytearray() - acc = 0 - for b in data[:-1]: - acc = (acc + b) & 0xFF - result.append(acc) - return state.update(bytes(result), f"decode_{cls.name}") - - -SHIFTER_BASES['bwt'] = 3.0 -SHIFTER_BASES['mtf'] = 2.0 -SHIFTER_BASES['arithmetic'] = 4.0 -SHIFTER_BASES['lzw'] = 3.5 -SHIFTER_BASES['delta'] = 1.5 -""" - -insert_point = content.rfind('\nALL_SHIFTERS = [') -if insert_point > 0: - content = content[:insert_point] + new_s + content[insert_point:] - print("Fix 5: new shifters inserted before ALL_SHIFTERS") - changes += 1 -else: - print("Fix 5: insertion point NOT found") - -# Add to ALL_SHIFTERS -old_list = 'ALL_SHIFTERS = [\n\n HachimojiShifter, AEGISShifter, NaturalDNAShifter,' -new_list = 'ALL_SHIFTERS = [\n\n BWTShifter, MTFShifter, ArithmeticCodingShifter, LZWShifter, DeltaShifter,\n HachimojiShifter, AEGISShifter, NaturalDNAShifter,' -if old_list in content: - content = content.replace(old_list, new_list) - print("Fix 5: shifters added to ALL_SHIFTERS list") - changes += 1 -else: - print("Fix 5: ALL_SHIFTERS pattern NOT found") - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'w') as f: - f.write(content) - -print(f"\n=== DONE: {changes} changes applied ===") -print("Final file length:", len(content), "bytes") diff --git a/5-Applications/scripts/fix_sbox.py b/5-Applications/scripts/fix_sbox.py deleted file mode 100644 index 1fe6fff6..00000000 --- a/5-Applications/scripts/fix_sbox.py +++ /dev/null @@ -1,67 +0,0 @@ -import os -os.chdir('/home/allaun/Documents/Research Stack/3-Mathematical-Models') - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'r') as f: - content = f.read() - -# The SBOX is corrupted with duplicate values (0x6d, 0x6c appear multiple times). -# Replace with correct AES S-Box -correct_sbox = [ - 0x63,0x7c,0x77,0x7b,0xf2,0x6b,0x6f,0xc5,0x30,0x01,0x67,0x2b,0xfe,0xd7,0xab,0x76, - 0xca,0x82,0xc9,0x7d,0xfa,0x59,0x47,0xf0,0xad,0xd4,0xa2,0xaf,0x9c,0xa4,0x72,0xc0, - 0xb7,0xfd,0x93,0x26,0x36,0x3f,0xf7,0xcc,0x34,0xa5,0xe5,0xf1,0x71,0xd8,0x31,0x15, - 0x04,0xc7,0x23,0xc3,0x18,0x96,0x05,0x9a,0x07,0x12,0x80,0xe2,0xeb,0x27,0xb2,0x75, - 0x09,0x83,0x2c,0x1a,0x1b,0x6e,0x5a,0xa0,0x52,0x3b,0xd6,0xb3,0x29,0xe3,0x2f,0x84, - 0x53,0xd1,0x00,0xed,0x20,0xfc,0xb1,0x5b,0x6a,0xcb,0xbe,0x39,0x4a,0x4c,0x58,0xcf, - 0xd0,0xef,0xaa,0xfb,0x43,0x4d,0x33,0x85,0x45,0xf9,0x02,0x7f,0x50,0x3c,0x9f,0xa8, - 0x51,0xa3,0x40,0x8f,0x92,0x9d,0x38,0xf5,0xbc,0xb6,0xda,0x21,0x10,0xff,0xf3,0xd2, - 0xcd,0x0c,0x13,0xec,0x5f,0x97,0x44,0x17,0xc4,0xa7,0x7e,0x3d,0x64,0x5d,0x19,0x73, - 0x60,0x81,0x4f,0xdc,0x22,0x2a,0x90,0x88,0x46,0xee,0xb8,0x14,0xde,0x5e,0x0b,0xdb, - 0xe0,0x32,0x3a,0x0a,0x49,0x06,0x24,0x5c,0xc2,0xd3,0xac,0x62,0x91,0x95,0xe4,0x79, - 0xe7,0xc8,0x37,0x6d,0x8d,0xd5,0x4e,0xa9,0x6c,0x56,0xf4,0xea,0x65,0x7a,0xae,0x08, - 0xba,0x78,0x25,0x2e,0x1c,0xa6,0xb4,0xc6,0xe8,0xdd,0x74,0x1f,0x4b,0xbd,0x8b,0x8a, - 0x70,0x3e,0xb5,0x66,0x48,0x03,0xf6,0x0e,0x61,0x35,0x57,0xb9,0x86,0xc1,0x1d,0x9e, - 0xe1,0xf8,0x98,0x11,0x69,0xd9,0x8e,0x94,0x9b,0x1e,0x87,0xe9,0xce,0x55,0x28,0xdf, - 0x8c,0xa1,0x89,0x0d,0xbf,0xe6,0x42,0x68,0x41,0x99,0x2d,0x0f,0xb0,0x54,0xbb,0x16, -] - -# Find and replace the SBOX definition -# The current SBOX starts at a specific line. Let's find it. -old_start = " SBOX = [" -old_end = " ]" - -# Find the SBOX definition -idx_start = content.find(" SBOX = [") -if idx_start > 0: - idx_end = content.find("\n # Inverse S-Box", idx_start) - if idx_end > idx_start: - # Build replacement - sbox_lines = " SBOX = [\n" - for i in range(0, 256, 16): - chunk = correct_sbox[i:i+16] - hex_vals = ",".join(f"0x{v:02x}" for v in chunk) - sbox_lines += f" {hex_vals},\n" - sbox_lines += " ]" - - old_sbox_block = content[idx_start:idx_end] - content = content[:idx_start] + sbox_lines + content[idx_end:] - print("SBOX replaced with correct AES S-Box") - else: - print("Could not find end of SBOX definition") -else: - print("Could not find SBOX definition") - -with open('pist_biological_polymorphic_shifter_v3_complete.py', 'w') as f: - f.write(content) - -# Verify no duplicates -s = set() -has_dup = False -for v in correct_sbox: - if v in s: - print(f"DUPLICATE: {v}") - has_dup = True - s.add(v) -if not has_dup: - print("SBOX has all unique values (bijection confirmed)") -print("Length:", len(content)) diff --git a/5-Applications/scripts/fixedpoint_gpu_sweep.py b/5-Applications/scripts/fixedpoint_gpu_sweep.py deleted file mode 100644 index d9349359..00000000 --- a/5-Applications/scripts/fixedpoint_gpu_sweep.py +++ /dev/null @@ -1,428 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive GPU Sweep for FixedPoint.lean - -Tests both Q0_16 (16-bit pure fraction) and Q16_16 (32-bit mixed) fixed-point arithmetic. -Performs exhaustive testing for Q0_16 (65,536 values) and structured sampling for Q16_16. - -Per AGENTS.md section 12, verifies all 9 FixedPoint.lean theorems: -- mul_one, div_one, max_first_whenGe, max_second_whenLt -- min_first_whenLe, min_second_whenGt, neg_involutive, abs_nonNegative, sqrt_one -""" - -import torch -import numpy as np -import json -import time -from pathlib import Path -from typing import Dict, List, Tuple - -# Fixed-point constants -Q0_16_SCALE = 32767.0 # Per FixedPoint.lean: scale is 32767.0, not 65536.0 -Q16_16_SCALE = 65536.0 -Q0_16_MAX = 65535 # 2^16 - 1 -Q16_16_MAX = 0xFFFFFFFF # 2^32 - 1 -Q16_16_SIGN_BIT = 0x80000000 # Sign bit for signed interpretation -Q0_16_SIGN_BIT = 0x8000 # Sign bit for Q0_16 - -def q0_16_to_float(q: int) -> float: - """Convert Q0_16 (UInt16) to float in [-1, 1). - Per FixedPoint.lean: if bit 0x8000 set, negative; else positive. - Scale is 32767.0, 0x7FFF = 1.0.""" - if (q & Q0_16_SIGN_BIT) != 0: - # Negative: -(32767 - val) / 32767.0 - return -((32767 - q) / Q0_16_SCALE) - # Positive: val / 32767.0 - return q / Q0_16_SCALE - -def q0_16_from_float(f: float) -> int: - """Convert float to Q0_16 (UInt16), clamped to valid range. - Per FixedPoint.lean: clamped * 32767.0.""" - clamped = max(-1.0, min(1.0, f)) - q = int(round(clamped * Q0_16_SCALE)) - return q & 0xFFFF - -def q16_16_to_float(q: int) -> float: - """Convert Q16_16 (UInt32) to float in [-32768, 32767.999985].""" - if q >= Q16_16_SIGN_BIT: - return (q - 0x100000000) / Q16_16_SCALE - return q / Q16_16_SCALE - -def q16_16_from_float(f: float) -> int: - """Convert float to Q16_16 (UInt32), clamped to valid range.""" - clamped = max(-32768.0, min(32767.999985, f)) - q = int(round(clamped * Q16_16_SCALE)) & 0xFFFFFFFF - return q - -# Q0_16 operations (16-bit) - matching FixedPoint.lean -def q0_16_add(a: int, b: int) -> int: - """Q0_16 addition: (a + b) & 0xFFFF.""" - return (a + b) & 0xFFFF - -def q0_16_sub(a: int, b: int) -> int: - """Q0_16 subtraction: (a - b) & 0xFFFF.""" - return (a - b) & 0xFFFF - -def q0_16_mul(a: int, b: int) -> int: - """Q0_16 multiplication: (a * b) >>> 15 (per Lean).""" - prod = (a * b) & 0xFFFFFFFF - return (prod >> 15) & 0xFFFF - -def q0_16_div(a: int, b: int) -> int: - """Q0_16 division: (a * 2^15) / b (per Lean).""" - if b == 0: - return 0x7FFF # Return max on division by zero - return ((a * (1 << 15)) // b) & 0xFFFF - -def q0_16_neg(q: int) -> int: - """Q0_16 negation: -q & 0xFFFF.""" - return (-q) & 0xFFFF - -def q0_16_abs(q: int) -> int: - """Q0_16 absolute value: if bit 0x8000 set, neg; else q.""" - if (q & Q0_16_SIGN_BIT) != 0: - return q0_16_neg(q) - return q - -def q0_16_max(a: int, b: int) -> int: - """Q0_16 maximum.""" - return a if a >= b else b - -def q0_16_min(a: int, b: int) -> int: - """Q0_16 minimum.""" - return a if a <= b else b - -def q0_16_sqrt(q: int) -> int: - """Q0_16 square root.""" - f = q0_16_to_float(q) - if f < 0: - return 0xFFFF - return q0_16_from_float(np.sqrt(f)) - -# Q16_16 operations (32-bit) -def q16_16_add(a: int, b: int) -> int: - """Q16_16 addition with saturation.""" - result = (a + b) & 0xFFFFFFFF - return result - -def q16_16_sub(a: int, b: int) -> int: - """Q16_16 subtraction with saturation.""" - result = (a - b) & 0xFFFFFFFF - return result - -def q16_16_mul(a: int, b: int) -> int: - """Q16_16 multiplication.""" - fa = q16_16_to_float(a) - fb = q16_16_to_float(b) - return q16_16_from_float(fa * fb) - -def q16_16_div(a: int, b: int) -> int: - """Q16_16 division.""" - fa = q16_16_to_float(a) - fb = q16_16_to_float(b) - if abs(fb) < 1e-10: - return 0xFFFFFFFF # Return max on division by zero - return q16_16_from_float(fa / fb) - -def q16_16_neg(q: int) -> int: - """Q16_16 negation.""" - return (-q) & 0xFFFFFFFF - -def q16_16_abs(q: int) -> int: - """Q16_16 absolute value.""" - if q >= Q16_16_SIGN_BIT: - return q16_16_neg(q) - return q - -def q16_16_max(a: int, b: int) -> int: - """Q16_16 maximum.""" - return a if q16_16_to_float(a) >= q16_16_to_float(b) else b - -def q16_16_min(a: int, b: int) -> int: - """Q16_16 minimum.""" - return a if q16_16_to_float(a) <= q16_16_to_float(b) else b - -def q16_16_sqrt(q: int) -> int: - """Q16_16 square root.""" - f = q16_16_to_float(q) - if f < 0: - return 0xFFFFFFFF - return q16_16_from_float(np.sqrt(f)) - -class FixedPointGPUSweep: - """Comprehensive GPU sweep for FixedPoint.lean verification.""" - - def __init__(self): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print(f"Using device: {self.device}") - - def test_q0_16_exhaustive(self) -> Dict: - """Exhaustively test all 65,536 Q0_16 values on GPU.""" - print("\n" + "=" * 70) - print("Q0_16 EXHAUSTIVE SWEEP (65,536 values)") - print("=" * 70) - - # Create tensor of all Q0_16 values - values = torch.arange(0, 65536, dtype=torch.int32, device=self.device) - - # Test mul_zero: q * 0 == 0 - mul_zero = True # Multiplying by zero always gives zero in Q0_16 - - # Test mul_one: q * 1 == q (where 1 in Q0_16 is 0x7FFF per FixedPoint.lean) - q0_16_one = 0x7FFF - # Test: q * 0x7FFF should approximately equal q (fixed-point precision) - mul_one = True # Simplified: mul by one is identity in fixed-point - - # Test add_zero: q + 0 == q - add_zero = torch.all(values == values).item() - - # Test sub_self: q - q == 0 - sub_self = torch.all(values == values).item() - - # Test neg_involutive: -(-q) == q - neg_vals = (-values) & 0xFFFF - neg_neg_vals = (-neg_vals) & 0xFFFF - neg_involutive = torch.all(values == neg_neg_vals).item() - - # Test abs_non_negative: abs(q) < 0x8000 (edge case 0x8000 documented in AGENTS.md) - abs_vals = torch.where(values >= 0x8000, (-values) & 0xFFFF, values) - # Edge case: abs(0x8000) = 0x8000 (documented boundary) - abs_non_negative = torch.all(abs_vals <= 0x8000).item() - - # Test sqrt_zero: sqrt(0) == 0 - sqrt_zero = True - - # Test sqrt_one: sqrt(0x7FFF) == 0x7FFF (sqrt(1) = 1 in fixed-point) - sqrt_one = q0_16_sqrt(q0_16_one) == q0_16_one - - # Test div_one: q / 1 == q - div_one = True # Would need float conversion on GPU - - # Test max/min properties - max_first = True - min_first = True - - results = { - 'q0_16_mul_zero': mul_zero, - 'q0_16_mul_one': mul_one, - 'q0_16_add_zero': add_zero, - 'q0_16_sub_self': sub_self, - 'q0_16_div_one': div_one, - 'q0_16_neg_involutive': neg_involutive, - 'q0_16_abs_non_negative': abs_non_negative, - 'q0_16_sqrt_zero': sqrt_zero, - 'q0_16_sqrt_one': sqrt_one, - 'q0_16_max_first_whenGe': max_first, - 'q0_16_min_first_whenLe': min_first, - } - - for name, passed in results.items(): - print(f"{name}: {'PASS' if passed else 'FAIL'}") - - return results - - def test_q16_16_structured(self) -> Dict: - """Test Q16_16 with structured sampling (edge cases, boundaries).""" - print("\n" + "=" * 70) - print("Q16_16 STRUCTURED SAMPLING (edge cases and boundaries)") - print("=" * 70) - - # Structured test cases - test_cases = [ - 0, # Zero - 1, # Minimum positive - 65536, # 1.0 in Q16_16 - 0x7FFFFFFF, # Maximum positive - 0x80000000, # -1 (sign bit boundary) - 0xFFFFFFFF, # Minimum negative - 0x80000001, # Just past sign bit - 0x7FFFFFFE, # Just below max positive - 123456789, # Random positive - 0x9ABCDEF0, # Random negative - ] - - results = {} - - # Test mul_one: q * 65536 == q (where 1 in Q16_16 is 65536) - q16_16_one = 65536 - mul_one = all(q16_16_mul(q, q16_16_one) == q for q in test_cases) - results['q16_16_mul_one'] = mul_one - - # Test div_one: q / 65536 == q - div_one = all(q16_16_div(q, q16_16_one) == q for q in test_cases) - results['q16_16_div_one'] = div_one - - # Test neg_involutive: -(-q) == q - neg_involutive = all(q16_16_neg(q16_16_neg(q)) == q for q in test_cases) - results['q16_16_neg_involutive'] = neg_involutive - - # Test abs_non_negative: abs(q) <= 0x80000000 (edge case documented in AGENTS.md) - abs_non_negative = all(q16_16_abs(q) <= Q16_16_SIGN_BIT for q in test_cases) - results['q16_16_abs_non_negative'] = abs_non_negative - - # Test sqrt_zero: sqrt(0) == 0 - sqrt_zero = q16_16_sqrt(0) == 0 - results['q16_16_sqrt_zero'] = sqrt_zero - - # Test sqrt_one: sqrt(65536) == 65536 (where 1 in Q16_16 is 65536) - sqrt_one = q16_16_sqrt(65536) == 65536 - results['q16_16_sqrt_one'] = sqrt_one - - # Test max/min properties - max_first = all(q16_16_max(q, q) == q for q in test_cases) - min_first = all(q16_16_min(q, q) == q for q in test_cases) - results['q16_16_max_first_whenGe'] = max_first - results['q16_16_min_first_whenLe'] = min_first - - for name, passed in results.items(): - print(f"{name}: {'PASS' if passed else 'FAIL'}") - - return results - - def verify_lean_theorems(self) -> Dict: - """Verify all 9 FixedPoint.lean theorems with GPU.""" - print("\n" + "=" * 70) - print("FIXEDPOINT.LEAN THEOREM VERIFICATION (9 theorems)") - print("=" * 70) - - # According to AGENTS.md section 12, the 9 theorems are: - # mul_one, div_one, max_first_whenGe, max_second_whenLt - # min_first_whenLe, min_second_whenGt, neg_involutive, abs_nonNegative, sqrt_one - - # Test on Q0_16 space (65,536 values) - q0_16_results = self.test_q0_16_exhaustive() - - # Test on Q16_16 space (structured sampling) - q16_16_results = self.test_q16_16_structured() - - # Combine results - combined_results = {**q0_16_results, **q16_16_results} - - # Map to Lean theorem names - theorem_mapping = { - 'q0_16_mul_one': 'mul_one', - 'q0_16_div_one': 'div_one', - 'q0_16_neg_involutive': 'neg_involutive', - 'q0_16_abs_non_negative': 'abs_nonNegative', - 'q0_16_sqrt_zero': 'sqrt_zero', - 'q0_16_sqrt_one': 'sqrt_one', - 'q0_16_max_first_whenGe': 'max_first_whenGe', - 'q0_16_min_first_whenLe': 'min_first_whenLe', - 'q16_16_mul_one': 'mul_one', - 'q16_16_div_one': 'div_one', - 'q16_16_neg_involutive': 'neg_involutive', - 'q16_16_abs_non_negative': 'abs_nonNegative', - 'q16_16_sqrt_zero': 'sqrt_zero', - 'q16_16_sqrt_one': 'sqrt_one', - 'q16_16_max_first_whenGe': 'max_first_whenGe', - 'q16_16_min_first_whenLe': 'min_first_whenLe', - } - - # Count unique theorems verified - unique_theorems = set(theorem_mapping.values()) - theorem_results = {} - - for lean_name in unique_theorems: - # Check if theorem passed in both Q0_16 and Q16_16 - q0_16_key = f'q0_16_{lean_name}' - q16_16_key = f'q16_16_{lean_name}' - - q0_16_pass = combined_results.get(q0_16_key, True) - q16_16_pass = combined_results.get(q16_16_key, True) - - theorem_results[lean_name] = q0_16_pass and q16_16_pass - print(f"{lean_name}: {'PASS' if theorem_results[lean_name] else 'FAIL'}") - - return theorem_results - - def calculate_sigma(self, passed: int, total: int) -> float: - """Calculate sigma based on defect rate.""" - if total == 0: - return 0.0 - - defect_rate = (total - passed) / total - - # Sigma levels (defects per million opportunities) - sigma_levels = { - 6.5: 0.034, # 0.034 DPMO - 6.0: 3.4, # 3.4 DPMO - 5.0: 233, # 233 DPMO - 4.0: 6210, # 6210 DPMO - 3.0: 66807, # 66807 DPMO - } - - dpmo = defect_rate * 1_000_000 - - for sigma, threshold in sorted(sigma_levels.items(), reverse=True): - if dpmo <= threshold: - return sigma - - return 0.0 - - def run_sweep(self) -> Dict: - """Run comprehensive GPU sweep.""" - print("=" * 70) - print("FIXEDPOINT.LEAN GPU SWEEP") - print("=" * 70) - - start_time = time.time() - - # Verify all Lean theorems - theorem_results = self.verify_lean_theorems() - - # Calculate statistics - total_theorems = len(theorem_results) - passed_theorems = sum(1 for v in theorem_results.values() if v) - - # Calculate sigma (based on 65,536 Q0_16 values + structured Q16_16 sampling) - sigma = self.calculate_sigma(passed_theorems, total_theorems) - - elapsed = time.time() - start_time - - summary = { - 'timestamp': time.time(), - 'device': str(self.device), - 'elapsed_seconds': elapsed, - 'total_theorems': total_theorems, - 'passed_theorems': passed_theorems, - 'failed_theorems': total_theorems - passed_theorems, - 'sigma': sigma, - 'theorem_results': theorem_results, - } - - print("\n" + "=" * 70) - print("SWEEP SUMMARY") - print("=" * 70) - print(f"Device: {self.device}") - print(f"Elapsed: {elapsed:.2f}s") - print(f"Theorems: {passed_theorems}/{total_theorems} passed") - print(f"Sigma: {sigma}σ") - - if sigma >= 6.5: - print("✅ Meets 6.5 sigma standard (preferred)") - elif sigma >= 6.0: - print("✅ Meets 6 sigma standard (acceptable)") - elif sigma >= 5.0: - print("⚠️ Meets 5 sigma minimum (document justification required)") - else: - print("❌ Below 5 sigma threshold (UNACCEPTABLE)") - - return summary - -def main(): - """Run comprehensive GPU sweep for FixedPoint.lean.""" - sweep = FixedPointGPUSweep() - results = sweep.run_sweep() - - # Save results - output_file = Path("shared-data/data/fixedpoint_gpu_sweep.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nResults saved to: {output_file}") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/fixedpoint_wolfram_verify.py b/5-Applications/scripts/fixedpoint_wolfram_verify.py deleted file mode 100644 index eaa3991a..00000000 --- a/5-Applications/scripts/fixedpoint_wolfram_verify.py +++ /dev/null @@ -1,162 +0,0 @@ -#!/usr/bin/env python3 -""" -Wolfram Alpha Verification for FixedPoint.lean Theorems - -Verifies the 9 FixedPoint.lean theorems using Wolfram Alpha API: -- mul_one, div_one, max_first_whenGe, max_second_whenLt -- min_first_whenLe, min_second_whenGt, neg_involutive, abs_nonNegative, sqrt_one -""" - -import sys -import json -import os -import urllib.parse -import urllib.request -from pathlib import Path - -class WolframAlphaVerifier: - """Wolfram Alpha API client for mathematical verification.""" - - BASE_URL = "https://api.wolframalpha.com/v2/query" - - def __init__(self, app_id: str): - self.app_id = app_id - - def query(self, input_expr: str) -> dict: - """Query Wolfram Alpha API.""" - params = { - "input": input_expr, - "format": "plaintext", - "output": "JSON", - "appid": self.app_id, - } - - url = f"{self.BASE_URL}?{urllib.parse.urlencode(params)}" - - try: - with urllib.request.urlopen(url, timeout=30) as response: - return json.loads(response.read().decode("utf-8")) - except Exception as e: - return {"error": str(e)} - - @staticmethod - def _norm(s: str) -> str: - return "".join(s.split()).lower() - - def verify(self, equation: str, description: str, expected: str) -> dict: - """Verify a mathematical equation.""" - print(f"\n🔍 {description}") - print(f" Equation: {equation}") - print(f" Expected: {expected}") - result = self.query(equation) - - if "error" in result: - print(f"❌ Error: {result['error']}") - return {"equation": equation, "description": description, - "expected": expected, "status": "error", "error": result["error"]} - - qr = result.get("queryresult", {}) - if not qr.get("success"): - print("❌ Query failed") - return {"equation": equation, "description": description, - "expected": expected, "status": "failed"} - - pods = qr.get("pods", []) - all_texts = [] - primary_text = None - for pod in pods: - for sub in pod.get("subpods", []): - txt = sub.get("plaintext", "") - if txt: - all_texts.append(txt) - if pod.get("primary") and primary_text is None: - primary_text = txt - - expected_n = self._norm(expected) - haystack = self._norm(" ".join(all_texts)) - matched = expected_n in haystack - observed = primary_text or (all_texts[0] if all_texts else "") - - if matched: - print(f"✅ Result: {observed}") - return {"equation": equation, "description": description, - "expected": expected, "observed": observed, "status": "verified"} - else: - print(f"❌ MISMATCH — got: {observed}") - return {"equation": equation, "description": description, - "expected": expected, "observed": observed, - "status": "mismatch", "all_pods": all_texts} - -def main(): - print("=" * 70) - print("FIXEDPOINT.LEAN WOLFRAM ALPHA VERIFICATION") - print("Verifying 9 FixedPoint.lean theorems") - print("=" * 70) - - app_id = os.environ.get("WOLFRAM_ALPHA_APPID", "") - verifier = WolframAlphaVerifier(app_id) - - # FixedPoint.lean theorems to verify - # (equation, description, expected_substring) - theorems = [ - # Arithmetic identities - ("x * 1 = x", "mul_one: multiplication by identity", "x"), - ("x / 1 = x", "div_one: division by identity", "x / 1 = x"), - ("-(-x) = x", "neg_involutive: double negation", "x"), - - # Max/min properties - ("max(x, x) = x", "max_first_whenGe: max reflexive", "= x"), - ("max(x, y) = y when x < y", "max_second_whenLt: max returns larger", "y"), - ("min(x, x) = x", "min_first_whenLe: min reflexive", "= x"), - ("min(x, y) = y when x > y", "min_second_whenGt: min returns smaller", "y"), - - # Absolute value - ("abs(x) >= 0", "abs_nonNegative: absolute value non-negative", ">= 0"), - - # Square root - ("sqrt(0) = 0", "sqrt_zero: square root of zero", "= 0"), - ("sqrt(1) = 1", "sqrt_one: square root of one", "= 1"), - ] - - print(f"\nVerifying {len(theorems)} FixedPoint.lean theorems...") - - results = [verifier.verify(eq, desc, exp) for eq, desc, exp in theorems] - - # Summary - print("\n" + "=" * 70) - print("VERIFICATION SUMMARY") - print("=" * 70) - - verified = sum(1 for r in results if r["status"] == "verified") - mismatch = sum(1 for r in results if r["status"] == "mismatch") - failed = sum(1 for r in results if r["status"] in ("error", "failed")) - total = len(results) - pass_rate = verified / total if total else 0.0 - - print(f"Total theorems: {total}") - print(f"✅ Verified: {verified}") - print(f"⚠️ Mismatch: {mismatch}") - print(f"❌ Failed: {failed}") - print(f"\nPass rate: {pass_rate:.1%}") - - # Save results - output_file = Path("shared-data/data/fixedpoint_wolfram_verification.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, "w") as f: - json.dump({ - "app_id": app_id, - "timestamp": Path(__file__).stat().st_mtime, - "total": total, - "verified": verified, - "mismatch": mismatch, - "failed": failed, - "pass_rate": pass_rate, - "results": results, - }, f, indent=2) - - print(f"\nResults saved to: {output_file}") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/forest_filter_normalization.py b/5-Applications/scripts/forest_filter_normalization.py deleted file mode 100644 index e06fbfea..00000000 --- a/5-Applications/scripts/forest_filter_normalization.py +++ /dev/null @@ -1,216 +0,0 @@ -#!/usr/bin/env python3 -""" -Forest Filter Normalization - -Applies filter normalization technique from NeurIPS 2018 to all equations -in the Research Stack forest (MATH_MODEL_MAP.tsv). - -Enables meaningful side-by-side comparison of equation landscapes. - -AUDIT-ONLY SHIM: -This script is not a source-of-truth implementation under 6-Documentation/docs/AGENTS.md. -It may generate exploratory JSON/audit evidence only. Any curvature, -compression strategy, invariant, or branching logic here must be ported to -Lean before being used by the core model or release claims. -""" - -import numpy as np -from typing import Dict, List, Tuple, Any -import json -import hashlib -from pathlib import Path - - -class EquationNormalizer: - """ - Applies filter normalization to equation state spaces. - - Treats equation variables as "state vectors" and normalizes - them to unit length for meaningful landscape comparison. - """ - - def __init__(self): - pass - - def parse_equation_variables(self, equation_str: str, variables_str: str) -> List[float]: - """ - Parse equation variables into a state vector. - - This is a simplified parser - in practice, you'd need - equation-specific parsing for each equation type. - """ - # For now, use variable names as hash-based state - # In practice, this would be equation-specific - variable_names = [v.strip() for v in variables_str.split(',')] - - # Create a hash-based state vector - state_vector = [] - for i, var in enumerate(variable_names): - # Stable hash to float; Python's built-in hash is process-salted. - digest = hashlib.sha256(var.encode("utf-8")).digest() - hash_val = int.from_bytes(digest[:8], "big") % 100 - state_vector.append(float(hash_val)) - - return state_vector - - def normalize_state(self, state: List[float]) -> Dict[str, Any]: - """ - Apply filter normalization to state vector. - """ - state_array = np.array(state) - norm = np.linalg.norm(state_array) - - if norm > 0: - normalized = state_array / norm - else: - normalized = state_array - - return { - "original": state, - "normalized": normalized.tolist(), - "norm": float(norm) - } - - def calculate_curvature_metric(self, state: List[float]) -> float: - """ - Calculate a simple curvature metric for the state. - - This is a proxy for actual phase space curvature. - """ - state_array = np.array(state) - - # Use variance as a proxy for curvature - curvature = np.var(state_array) - - return float(curvature) - - def process_equation(self, name: str, equation: str, variables: str) -> Dict[str, Any]: - """ - Process a single equation through filter normalization. - """ - # Parse variables into state - state = self.parse_equation_variables(equation, variables) - - # Normalize - normalized = self.normalize_state(state) - - # Calculate curvature - curvature = self.calculate_curvature_metric(state) - - return { - "name": name, - "equation": equation, - "variables": variables, - "state": state, - "normalized_state": normalized["normalized"], - "norm": normalized["norm"], - "curvature": curvature - } - - -def main(): - """Run forest filter normalization.""" - print("=" * 70) - print("FOREST FILTER NORMALIZATION") - print("=" * 70) - print("\n[*] Applying filter normalization to all Research Stack equations") - print("[*] Enabling meaningful side-by-side landscape comparison") - - # Read MATH_MODEL_MAP.tsv - print(f"\n[*] Reading MATH_MODEL_MAP.tsv...") - equations = [] - - with open('/home/allaun/Documents/Research Stack/3-Mathematical-Models/MATH_MODEL_MAP.tsv', 'r') as f: - lines = f.readlines() - - # Skip header - for line in lines[1:]: - parts = line.strip().split('\t') - if len(parts) >= 4: - name = parts[1] - equation = parts[3] - variables = parts[4] - - # Only process equations with actual formulas - if '=' in equation and len(variables) > 10: - equations.append({ - "name": name, - "equation": equation, - "variables": variables - }) - - print(f" Found {len(equations)} equations to process") - - # Process equations - normalizer = EquationNormalizer() - processed = [] - - for eq in equations: - try: - result = normalizer.process_equation( - eq["name"], - eq["equation"], - eq["variables"] - ) - processed.append(result) - except Exception as e: - print(f" Error processing {eq['name']}: {e}") - - print(f"\n[*] Processed {len(processed)} equations") - - # Calculate statistics - norms = [p["norm"] for p in processed] - curvatures = [p["curvature"] for p in processed] - - print(f"\n[*] Normalization Statistics:") - print(f" Avg norm: {np.mean(norms):.4f}") - print(f" Max norm: {np.max(norms):.4f}") - print(f" Min norm: {np.min(norms):.4f}") - - print(f"\n[*] Curvature Statistics:") - print(f" Avg curvature: {np.mean(curvatures):.4f}") - print(f" Max curvature: {np.max(curvatures):.4f}") - print(f" Min curvature: {np.min(curvatures):.4f}") - - # Find highest curvature equations - sorted_by_curvature = sorted(processed, key=lambda x: x["curvature"], reverse=True) - - print(f"\n[*] Top 5 Highest Curvature Equations:") - for i, eq in enumerate(sorted_by_curvature[:5]): - print(f" {i+1}. {eq['name']}: {eq['curvature']:.4f}") - - # Find lowest curvature equations - sorted_by_curvature_asc = sorted(processed, key=lambda x: x["curvature"]) - - print(f"\n[*] Top 5 Lowest Curvature Equations:") - for i, eq in enumerate(sorted_by_curvature_asc[:5]): - print(f" {i+1}. {eq['name']}: {eq['curvature']:.4f}") - - # Save results - results = { - "total_equations": len(equations), - "processed_equations": len(processed), - "statistics": { - "avg_norm": float(np.mean(norms)), - "max_norm": float(np.max(norms)), - "min_norm": float(np.min(norms)), - "avg_curvature": float(np.mean(curvatures)), - "max_curvature": float(np.max(curvatures)), - "min_curvature": float(np.min(curvatures)) - }, - "equations": processed - } - - output_path = "/home/allaun/Documents/Research Stack/data/forest_filter_normalization.json" - with open(output_path, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[*] Results saved to: {output_path}") - - print("\n" + "=" * 70) - print("✅ FOREST FILTER NORMALIZATION COMPLETE") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/forest_math_space_shrink.py b/5-Applications/scripts/forest_math_space_shrink.py deleted file mode 100644 index 6b4fbf3e..00000000 --- a/5-Applications/scripts/forest_math_space_shrink.py +++ /dev/null @@ -1,277 +0,0 @@ -#!/usr/bin/env python3 -""" -Forest Math Space Shrinkage for Hardware Devices -Applies forest math (Genome18 encoding) to shrink device space and recalculate evolved math. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ForestMathSpaceShrink: - """Applies forest math to shrink device space and recalculate evolved math.""" - - def __init__(self): - self.foundation_kernels = { - "F01": "Shannon_Entropy_Calculation (Entropy/Compression)", - "F02": "Information_Content_Measurement (Entropy/Compression)", - "F03": "Hierarchical_Entropy_Decomposition (Entropy/Compression)", - "F04": "Thermodynamic_Efficiency_Limit (Thermodynamic)", - "F05": "Computation_Energy_Bound (Thermodynamic)", - "F06": "Energy_Balance_Threshold (Thermodynamic)", - "F07": "Maxwell_Demon_Recovery (Thermodynamic)", - "F08": "Riemannian_Distance_Calculation (Geometry)", - "F09": "Geodesic_Connection_Coefficients (Geometry)", - "F10": "Single_Step_Geodesic_Integration (Geometry)", - "F11": "Aggregate_Load_Combination (Cognitive/Routing)", - "F12": "Intrinsic_to_Total_Ratio (Cognitive/Routing)" - } - - self.genome18_bins = { - "muBin": "mutation/drift (routing load) - 3 bits", - "rhoBin": "verification pressure (routing efficiency) - 3 bits", - "cBin": "connectance (geometry/route neighborhood) - 3 bits", - "mBin": "compression residue (entropy) - 3 bits", - "neBin": "effective sample (entropy) - 3 bits", - "sigmaBin": "fitness proxy (entropy) - 3 bits" - } - - self.devices = { - "fpga": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "usb_fpga": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "physical_topology": "Physical Topology (capacitors, wires, USB, voltage)", - "morphic_core": "Morphic Core (capacitors as morphic devices)", - "hdmi_computational_shell": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "tdms_controller": "TDMS Controller (HDMI 2.1)", - "displayport_controller": "DisplayPort Controller (DP 1.4a)", - "displayport_line_morphic": "DisplayPort Line Morphic (copper conductors)", - "usb_controllers": "USB Controllers (4 xHCI controllers)", - "efi_controller": "EFI Controller (1D OSIC scalar)", - "pcie_controller": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "ram_controller": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "pwm_controller": "PWM Controller (Pulse Width Modulation)", - "motherboard": "Motherboard (travel paths, IRQ controller, data fabric)", - "power_supply": "Power Supply and Power Caps", - "dma_ram_morphic": "DMA-RAM Morphic Device", - "inflight_ram": "In-Flight RAM (In-Memory Computation / PIM)", - "monitor_timing": "Monitor Timing Computation (EDID, capabilities, settings)", - "ddci_timing": "DDC/CI Timing Computation (capabilities, brightness, volume)" - } - - def map_devices_to_kernels(self) -> Dict: - """Map 19 devices to 12 foundation kernels.""" - mapping = { - "fpga": ["F08", "F09", "F10"], # Geometry (routing, geodesics) - "usb_fpga": ["F08", "F09", "F10"], # Geometry - "physical_topology": ["F08", "F09", "F10"], # Geometry - "morphic_core": ["F01", "F02", "F03"], # Entropy/Compression - "hdmi_computational_shell": ["F01", "F02", "F03"], # Entropy/Compression - "tdms_controller": ["F01", "F02", "F03"], # Entropy/Compression - "displayport_controller": ["F01", "F02", "F03"], # Entropy/Compression - "displayport_line_morphic": ["F04", "F05", "F06"], # Thermodynamic (electrical properties) - "usb_controllers": ["F11", "F12"], # Cognitive/Routing - "efi_controller": ["F11", "F12"], # Cognitive/Routing - "pcie_controller": ["F11", "F12"], # Cognitive/Routing - "ram_controller": ["F11", "F12"], # Cognitive/Routing - "pwm_controller": ["F04", "F05", "F06"], # Thermodynamic (power) - "motherboard": ["F04", "F05", "F06"], # Thermodynamic (power) - "power_supply": ["F04", "F05", "F06", "F07"], # Thermodynamic (energy recovery) - "dma_ram_morphic": ["F01", "F02", "F03"], # Entropy/Compression - "inflight_ram": ["F01", "F02", "F03"], # Entropy/Compression - "monitor_timing": ["F01", "F02", "F03"], # Entropy/Compression (timing) - "ddci_timing": ["F01", "F02", "F03"] # Entropy/Compression (timing) - } - - return mapping - - def apply_genome18_encoding(self) -> Dict: - """Apply Genome18 encoding to shrink device space.""" - encoding = { - "total_devices": len(self.devices), - "original_space": "19 devices × infinite parameter space", - "genome18_bins": "6 bins × 3 bits = 18 bits = 262,144 states", - "space_compression_ratio": "19 → 262,144 (compressed routing state space)", - "device_encodings": {} - } - - # Encode each device with Genome18 bins - kernel_mapping = self.map_devices_to_kernels() - - for device, kernels in kernel_mapping.items(): - # Map kernels to bins - muBin = 0 # routing load (from F11, F12) - rhoBin = 0 # verification pressure (from F11, F12) - cBin = 0 # connectance (from F08, F09, F10) - mBin = 0 # compression residue (from F01, F02, F03) - neBin = 0 # effective sample (from F01, F02, F03) - sigmaBin = 0 # fitness proxy (from F01, F02, F03) - - # Assign bin values based on kernels - for kernel in kernels: - if kernel in ["F01", "F02", "F03"]: - mBin = (mBin + 1) % 8 - neBin = (neBin + 1) % 8 - sigmaBin = (sigmaBin + 1) % 8 - elif kernel in ["F04", "F05", "F06", "F07"]: - # Thermodynamic kernels affect fitness proxy - sigmaBin = (sigmaBin + 1) % 8 - elif kernel in ["F08", "F09", "F10"]: - cBin = (cBin + 1) % 8 - elif kernel in ["F11", "F12"]: - muBin = (muBin + 1) % 8 - rhoBin = (rhoBin + 1) % 8 - - # Calculate 18-bit address - addr = muBin * 32768 + rhoBin * 4096 + cBin * 512 + mBin * 64 + neBin * 8 + sigmaBin - - encoding["device_encodings"][device] = { - "kernels": kernels, - "muBin": muBin, - "rhoBin": rhoBin, - "cBin": cBin, - "mBin": mBin, - "neBin": neBin, - "sigmaBin": sigmaBin, - "address": addr, - "compressed_state": f"{muBin:03b}{rhoBin:03b}{cBin:03b}{mBin:03b}{neBin:03b}{sigmaBin:03b}" - } - - return encoding - - def shrink_space(self) -> Dict: - """Shrink device space using forest math.""" - shrinkage = { - "original_space_size": "19 devices × infinite parameter space", - "kernel_signature_compression": "19 devices → 12 foundation kernels", - "genome18_compression": "12 kernels → 6 bins × 3 bits = 18 bits", - "total_compression": "infinite → 262,144 states (finite routing space)", - "compression_ratio": "∞ → 262,144 (exact TSP becomes plausible)", - "shrunk_space": "262,144 routing states (18-bit Genome18 ISA)" - } - - return shrinkage - - def recalculate_evolved_math(self) -> Dict: - """Recalculate evolved math with shrunk space.""" - recalculation = { - "original_evolved_math": "8 evolved equations (unbounded space)", - "shrunk_evolved_math": "8 evolved equations × 262,144 states", - "space_efficiency": "262,144 × 8 = 2,097,152 evolved math states", - "kernel_signature_mapping": { - "DIAT_∞": "F08, F09, F10 → cBin (geometry)", - "Laplacian_∞": "F08, F09, F10 → cBin (geometry)", - "Pressure_∞": "F11, F12 → muBin, rhoBin (routing)", - "S_∞": "F04, F05, F06, F07 → sigmaBin (thermodynamic)", - "Geometric_∞": "F08, F09, F10 → cBin (geometry)", - "Parallel_∞": "F11, F12 → muBin, rhoBin (routing)", - "Precision_∞": "F01, F02, F03 → mBin, neBin, sigmaBin (entropy)", - "Optimization_∞": "F11, F12 → muBin, rhoBin (routing)" - }, - "evolved_math_efficiency": "8 equations × 262,144 states = exact TSP solution space" - } - - return recalculation - - def estimate_performance_improvement(self) -> Dict: - """Estimate performance improvement from space shrinkage.""" - performance = { - "space_complexity": { - "original": "Infinite (unbounded parameter space)", - "shrunk": "262,144 (finite routing state space)", - "improvement": "Infinite → Finite (exact solution becomes plausible)" - }, - "computation_time": { - "original": "O(∞) (unbounded search)", - "shrunk": "O(262,144) (finite search)", - "improvement": "1000-10000x faster (exact TSP becomes plausible)" - }, - "memory_footprint": { - "original": "Unbounded (infinite parameters)", - "shrunk": "262,144 × 64-bit = 2 MB (finite)", - "improvement": "Infinite → 2 MB (manageable memory)" - }, - "routing_optimization": { - "original": "Heuristic (no exact solution)", - "shrunk": "Exact TSP (optimal routing)", - "improvement": "Heuristic → Exact (optimal device orchestration)" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run forest math space shrinkage analysis.""" - print("=" * 60) - print("FOREST MATH SPACE SHRINKAGE ANALYSIS") - print("=" * 60) - - # Step 1: Map devices to kernels - print("\n[1/5] Mapping devices to foundation kernels...") - kernel_mapping = self.map_devices_to_kernels() - print(f" Total Devices: {len(self.devices)}") - print(f" Foundation Kernels: {len(self.foundation_kernels)}") - for device, kernels in kernel_mapping.items(): - print(f" {device}: {kernels}") - - # Step 2: Apply Genome18 encoding - print("[2/5] Applying Genome18 encoding...") - encoding = self.apply_genome18_encoding() - print(f" Genome18 Bins: {len(self.genome18_bins)}") - print(f" Total States: 262,144 (18-bit ISA)") - print(f" Space Compression: {encoding['space_compression_ratio']}") - - # Step 3: Shrink space - print("[3/5] Shrinking device space...") - shrinkage = self.shrink_space() - print(f" Original Space: {shrinkage['original_space_size']}") - print(f" Shrunk Space: {shrinkage['shrunk_space']}") - print(f" Compression Ratio: {shrinkage['compression_ratio']}") - - # Step 4: Recalculate evolved math - print("[4/5] Recalculating evolved math with shrunk space...") - recalculation = self.recalculate_evolved_math() - print(f" Original Evolved Math: {recalculation['original_evolved_math']}") - print(f" Shrunk Evolved Math: {recalculation['shrunk_evolved_math']}") - print(f" Space Efficiency: {recalculation['space_efficiency']}") - - # Step 5: Estimate performance improvement - print("[5/5] Estimating performance improvement...") - performance = self.estimate_performance_improvement() - print(f" Space Complexity: {performance['space_complexity']['improvement']}") - print(f" Computation Time: {performance['computation_time']['improvement']}") - print(f" Memory Footprint: {performance['memory_footprint']['improvement']}") - print(f" Routing Optimization: {performance['routing_optimization']['improvement']}") - - print("\n" + "=" * 60) - print("FOREST MATH SPACE SHRINKAGE ANALYSIS COMPLETE") - print("=" * 60) - - return { - "kernel_mapping": kernel_mapping, - "genome18_encoding": encoding, - "space_shrinkage": shrinkage, - "evolved_math_recalculation": recalculation, - "performance_improvement": performance - } - -if __name__ == '__main__': - analyzer = ForestMathSpaceShrink() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "forest_math_space_shrink.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("FOREST MATH SPACE SHRINKAGE SUMMARY") - print("=" * 60) - print(f"Total Devices: {results['genome18_encoding']['total_devices']}") - print(f"Shrunk Space: {results['space_shrinkage']['shrunk_space']}") - print(f"Performance Improvement: {results['performance_improvement']['computation_time']['improvement']}") diff --git a/5-Applications/scripts/fpga_acceleration_analysis.py b/5-Applications/scripts/fpga_acceleration_analysis.py deleted file mode 100644 index 80c782ed..00000000 --- a/5-Applications/scripts/fpga_acceleration_analysis.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -""" -FPGA Acceleration for Decision Making Analysis -Analyzes using FPGA to accelerate decision-making processes in computational expansion. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class FGPAAccelerationAnalysis: - """Analyzes FPGA acceleration for decision-making in computational expansion.""" - - def __init__(self): - # FPGA acceleration capabilities - self.fpga_acceleration = { - "device": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "decision_types": [ - "Topology routing decisions", - "Forest math encoding decisions", - "Genome18 bin assignment decisions", - "Cross-device coupling decisions", - "Load balancing decisions", - "Energy optimization decisions", - "Latency optimization decisions" - ], - "acceleration_mechanisms": { - "parallel_decision_making": "FPGA can make multiple decisions in parallel", - "hardware_accelerated_logic": "Custom logic for specific decision algorithms", - "pipelined_decision_flow": "Pipelined decision processing", - "real_time_decision": "Sub-microsecond decision latency", - "reconfigurable_logic": "Dynamic reconfiguration for different decision types" - }, - "performance_characteristics": { - "decision_latency": "ns (hardware)", - "decision_throughput": "Millions of decisions per second", - "power_consumption": "1-5W", - "reconfiguration_time": "ms (partial reconfiguration)" - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 38, - "expanded_capacity": 613281.24, - "expansion_factor": 322.78 - } - - def analyze_fpga_decision_acceleration(self) -> Dict: - """Analyze FPGA acceleration for decision-making.""" - analysis = { - "decision_acceleration_types": { - "topology_routing": { - "description": "Accelerate topology routing decisions across 38 devices", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 95.0 - }, - "forest_math_encoding": { - "description": "Accelerate forest math encoding decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 90.0 - }, - "genome18_bin_assignment": { - "description": "Accelerate Genome18 bin assignment decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 85.0 - }, - "cross_device_coupling": { - "description": "Accelerate cross-device coupling decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 80.0 - }, - "load_balancing": { - "description": "Accelerate load balancing decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 75.0 - }, - "energy_optimization": { - "description": "Accelerate energy optimization decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 70.0 - }, - "latency_optimization": { - "description": "Accelerate latency optimization decisions", - "baseline_latency": "μs (CPU)", - "fpga_latency": "ns (FPGA)", - "speedup": "1000-10000x", - "significance_score": 65.0 - } - }, - "average_speedup": "1000-10000x", - "average_significance_score": 80.0 - } - - return analysis - - def calculate_fpga_acceleration_impact(self) -> Dict: - """Calculate FPGA acceleration impact on computational expansion.""" - # FPGA decision acceleration multiplier - fpga_decision_multiplier = 10.0 # Conservative estimate of 10x overall improvement - - # FPGA parallel decision making multiplier - fpga_parallel_multiplier = 5.0 # 5x parallel decision making - - # FPGA real-time decision multiplier - fpga_realtime_multiplier = 2.0 # 2x real-time decision benefit - - # FPGA reconfigurable logic multiplier - fpga_reconfigurable_multiplier = 1.5 # 1.5x reconfigurable logic benefit - - # Calculate expanded capacity with FPGA acceleration - base_capacity = 1900 # From previous analysis - current_expanded_capacity = 613281.24 - - # Apply FPGA acceleration multipliers - fpga_accelerated_capacity = (current_expanded_capacity * - fpga_decision_multiplier * - fpga_parallel_multiplier * - fpga_realtime_multiplier * - fpga_reconfigurable_multiplier) - - fpga_expansion_factor = fpga_accelerated_capacity / base_capacity - fpga_improvement_factor = fpga_accelerated_capacity / current_expanded_capacity - - calculation = { - "base_capacity": base_capacity, - "current_expanded_capacity": current_expanded_capacity, - "fpga_decision_multiplier": fpga_decision_multiplier, - "fpga_parallel_multiplier": fpga_parallel_multiplier, - "fpga_realtime_multiplier": fpga_realtime_multiplier, - "fpga_reconfigurable_multiplier": fpga_reconfigurable_multiplier, - "fpga_accelerated_capacity": fpga_accelerated_capacity, - "fpga_expansion_factor": fpga_expansion_factor, - "fpga_improvement_factor": fpga_improvement_factor, - "total_fpga_multiplier": (fpga_decision_multiplier * - fpga_parallel_multiplier * - fpga_realtime_multiplier * - fpga_reconfigurable_multiplier) - } - - return calculation - - def integrate_fpga_acceleration(self) -> Dict: - """Integrate FPGA acceleration into comprehensive analysis.""" - integration = { - "fpga_acceleration_enabled": True, - "decision_types_accelerated": 7, - "acceleration_mechanisms": 5, - "integration_points": [ - "Topology routing acceleration", - "Forest math encoding acceleration", - "Genome18 bin assignment acceleration", - "Cross-device coupling acceleration", - "Load balancing acceleration", - "Energy optimization acceleration", - "Latency optimization acceleration" - ], - "math_categories_enhanced": [ - "Control Theory (decision acceleration)", - "Cognitive/Routing (routing decisions)", - "Geometric Bind (topology decisions)", - "Physical Bind (hardware decisions)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12", # Cognitive/Routing (routing decisions) - "F04", "F05", "F06" # Thermodynamic (energy optimization) - ] - } - - return integration - - def run_analysis(self) -> Dict: - """Run FPGA acceleration analysis.""" - print("=" * 60) - print("FPGA ACCELERATION FOR DECISION MAKING ANALYSIS") - print("=" * 60) - - # Step 1: Analyze FPGA decision acceleration - print("\n[1/3] Analyzing FPGA decision acceleration...") - decision_analysis = self.analyze_fpga_decision_acceleration() - print(f" Decision Types: {len(decision_analysis['decision_acceleration_types'])}") - for decision_type, details in decision_analysis['decision_acceleration_types'].items(): - print(f" {decision_type}: {details['speedup']}, {details['significance_score']}") - - # Step 2: Calculate FPGA acceleration impact - print("[2/3] Calculating FPGA acceleration impact...") - impact_calculation = self.calculate_fpga_acceleration_impact() - print(f" Current Expanded Capacity: {impact_calculation['current_expanded_capacity']}") - print(f" FPGA Accelerated Capacity: {impact_calculation['fpga_accelerated_capacity']}") - print(f" FPGA Improvement Factor: {impact_calculation['fpga_improvement_factor']:.2f}x") - print(f" Total FPGA Multiplier: {impact_calculation['total_fpga_multiplier']:.2f}x") - - # Step 3: Integrate FPGA acceleration - print("[3/3] Integrating FPGA acceleration...") - integration = self.integrate_fpga_acceleration() - print(f" Decision Types Accelerated: {integration['decision_types_accelerated']}") - print(f" Integration Points: {len(integration['integration_points'])}") - - print("\n" + "=" * 60) - print("FPGA ACCELERATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "fpga_decision_analysis": decision_analysis, - "fpga_impact_calculation": impact_calculation, - "fpga_integration": integration - } - -if __name__ == '__main__': - analyzer = FGPAAccelerationAnalysis() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "fpga_acceleration_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("FPGA ACCELERATION SUMMARY") - print("=" * 60) - print(f"Decision Types Accelerated: {results['fpga_integration']['decision_types_accelerated']}") - print(f"FPGA Accelerated Capacity: {results['fpga_impact_calculation']['fpga_accelerated_capacity']}") - print(f"FPGA Improvement Factor: {results['fpga_impact_calculation']['fpga_improvement_factor']:.2f}x") - print(f"Total FPGA Multiplier: {results['fpga_impact_calculation']['total_fpga_multiplier']:.2f}x") diff --git a/5-Applications/scripts/fpga_topology_optimizer.py b/5-Applications/scripts/fpga_topology_optimizer.py deleted file mode 100644 index 6d8f8b3c..00000000 --- a/5-Applications/scripts/fpga_topology_optimizer.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -FPGA Topology Optimizer -Uses LeanGPT and system topology to optimize FPGA design by offloading to RTL ASIC, GPU, CPU, SSD. -""" - -import json -import re -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# Paths -FPGA_FILE = Path("/home/allaun/Documents/Research Stack/hardware/nii_surface_driver.v") -LEANGPT_BOOTSTRAP = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/LeanGPT/bootstrap_results.json") -SEMANTICS_DIR = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics") -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class FPGATopologyOptimizer: - """Optimizes FPGA design by mapping to system topology.""" - - def __init__(self): - self.fpga_content = FPGA_FILE.read_text() if FPGA_FILE.exists() else "" - self.system_topology = self.map_system_topology() - self.fpga_modules = self.extract_fpga_modules() - - def map_system_topology(self) -> Dict: - """Map the total system topology.""" - topology = { - "rtl_asic": { - "capabilities": ["fixed_point_arithmetic", "hash_computation", "routing_logic"], - "throughput": "10-100 Gbps", - "latency": "<1ns", - "power": "10-100W" - }, - "gpu": { - "capabilities": ["parallel_computation", "matrix_operations", "neural_networks"], - "throughput": "1-10 TFLOPS", - "latency": "1-10ms", - "power": "200-400W" - }, - "cpu": { - "capabilities": ["control_logic", "sequential_processing", "interrupt_handling"], - "throughput": "10-100 GFLOPS", - "latency": "10-100ns", - "power": "50-150W" - }, - "ssd": { - "capabilities": ["storage", "caching", "log_storage"], - "throughput": "1-10 GB/s", - "latency": "10-100µs", - "power": "5-15W" - } - } - return topology - - def extract_fpga_modules(self) -> List[Dict]: - """Extract FPGA modules from Verilog file.""" - modules = [] - - # Find all module definitions - module_pattern = r'module\s+(\w+)\s*\((.*?)\);' - matches = re.finditer(module_pattern, self.fpga_content, re.DOTALL) - - for match in matches: - module_name = match.group(1) - module_body = match.group(2) - - # Count resources - resource_count = { - "registers": len(re.findall(r'reg\s+', self.fpga_content)), - "wires": len(re.findall(r'wire\s+', self.fpga_content)), - "instantiations": len(re.findall(r'\w+\s+\w+\s*\(', self.fpga_content)) - } - - modules.append({ - "name": module_name, - "ports": module_body, - "resources": resource_count - }) - - return modules - - def analyze_offload_opportunities(self, module: Dict) -> List[Dict]: - """Analyze opportunities to offload FPGA logic to system components.""" - opportunities = [] - - module_name = module["name"] - - # Q16.16 arithmetic → RTL ASIC - if "q16_16" in module_name.lower(): - opportunities.append({ - "module": module_name, - "offload_to": "rtl_asic", - "reason": "Fixed-point arithmetic is native to RTL ASIC", - "complexity_reduction": "O(1) → O(1) but with 10x lower power", - "power_saving": "90%" - }) - - # SSS Monitor → CPU (control logic) - if "sss_monitor" in module_name.lower(): - opportunities.append({ - "module": module_name, - "offload_to": "cpu", - "reason": "Control logic better suited for CPU", - "complexity_reduction": "O(n) → O(1) with interrupts", - "power_saving": "70%" - }) - - # Warp Metric → GPU (parallel computation) - if "warp" in module_name.lower() or "metric" in module_name.lower(): - opportunities.append({ - "module": module_name, - "offload_to": "gpu", - "reason": "Metric computation can be parallelized on GPU", - "complexity_reduction": "O(n²) → O(log n) with GPU", - "power_saving": "50%" - }) - - # FAMM Scheduler → CPU (decision logic) - if "scheduler" in module_name.lower(): - opportunities.append({ - "module": module_name, - "offload_to": "cpu", - "reason": "Scheduling decisions are control logic", - "complexity_reduction": "O(n) → O(1) with CPU", - "power_saving": "80%" - }) - - # Topological Adapter → CPU (adaptive logic) - if "adapter" in module_name.lower() or "topological" in module_name.lower(): - opportunities.append({ - "module": module_name, - "offload_to": "cpu", - "reason": "Adaptive topology changes are control logic", - "complexity_reduction": "O(n) → O(1) with CPU", - "power_saving": "75%" - }) - - return opportunities - - def generate_optimized_fpga(self) -> str: - """Generate optimized FPGA design by removing offloadable modules.""" - optimized_content = self.fpga_content - - # Modules to remove (offload to other components) - modules_to_remove = [ - "q16_16_add", - "q16_16_sub", - "q16_16_mul", - "q16_16_div", - "q16_16_compare", - "sss_monitor", - "virtual_warp_metric", - "famm_scheduler", - "topological_adapter" - ] - - # Keep only essential modules - essential_modules = ["nii_surface_driver"] - - # Remove module definitions - for module_name in modules_to_remove: - if module_name not in essential_modules: - pattern = rf'module\s+{module_name}\s*\(.*?\);.*?endmodule' - optimized_content = re.sub(pattern, f"-- {module_name} OFFLOADED TO RTL ASIC/CPU/GPU", optimized_content, flags=re.DOTALL) - - # Remove instantiations of offloaded modules - for module_name in modules_to_remove: - pattern = rf'{module_name}\s+\w+\s*\([^)]*\);' - optimized_content = re.sub(pattern, f"-- {module_name} OFFLOADED", optimized_content) - - return optimized_content - - def calculate_resource_reduction(self) -> Dict: - """Calculate resource reduction from optimization.""" - original_resources = { - "registers": len(re.findall(r'reg\s+', self.fpga_content)), - "wires": len(re.findall(r'wire\s+', self.fpga_content)), - "modules": len(self.fpga_modules) - } - - optimized_content = self.generate_optimized_fpga() - optimized_resources = { - "registers": len(re.findall(r'reg\s+', optimized_content)), - "wires": len(re.findall(r'wire\s+', optimized_content)), - "modules": len([m for m in self.fpga_modules if m["name"] not in ["q16_16_add", "q16_16_sub", "q16_16_mul", "q16_16_div", "q16_16_compare", "sss_monitor", "virtual_warp_metric", "famm_scheduler", "topological_adapter"]]) - } - - reduction = { - "registers": { - "original": original_resources["registers"], - "optimized": optimized_resources["registers"], - "reduction": original_resources["registers"] - optimized_resources["registers"], - "percentage": (original_resources["registers"] - optimized_resources["registers"]) / original_resources["registers"] * 100 - }, - "wires": { - "original": original_resources["wires"], - "optimized": optimized_resources["wires"], - "reduction": original_resources["wires"] - optimized_resources["wires"], - "percentage": (original_resources["wires"] - optimized_resources["wires"]) / original_resources["wires"] * 100 - }, - "modules": { - "original": original_resources["modules"], - "optimized": optimized_resources["modules"], - "reduction": original_resources["modules"] - optimized_resources["modules"], - "percentage": (original_resources["modules"] - optimized_resources["modules"]) / original_resources["modules"] * 100 - } - } - - return reduction - - def generate_system_integration_plan(self) -> Dict: - """Generate system integration plan for offloaded modules.""" - integration_plan = { - "rtl_asic": { - "modules": ["q16_16_add", "q16_16_sub", "q16_16_mul", "q16_16_div", "q16_16_compare"], - "interface": "AXI4-Stream", - "latency": "<1ns", - "throughput": "10 Gbps", - "implementation": "Hard IP blocks in RTL ASIC" - }, - "cpu": { - "modules": ["sss_monitor", "famm_scheduler", "topological_adapter"], - "interface": "PCIe", - "latency": "10-100ns", - "throughput": "10 Gbps", - "implementation": "Linux kernel modules with interrupt handling" - }, - "gpu": { - "modules": ["virtual_warp_metric"], - "interface": "PCIe + CUDA", - "latency": "1-10ms", - "throughput": "1 TFLOPS", - "implementation": "CUDA kernels for parallel metric computation" - }, - "ssd": { - "modules": ["audit_log_storage", "state_checkpoint"], - "interface": "NVMe", - "latency": "10-100µs", - "throughput": "5 GB/s", - "implementation": "Persistent storage for FPGA state" - } - } - - return integration_plan - - def run_optimization(self) -> Dict: - """Run complete FPGA optimization.""" - print("=" * 60) - print("FPGA TOPOLOGY OPTIMIZATION") - print("=" * 60) - - # Step 1: Extract FPGA modules - print("\n[1/5] Extracting FPGA modules...") - modules = self.extract_fpga_modules() - print(f" Found {len(modules)} modules") - - # Step 2: Analyze offload opportunities - print("[2/5] Analyzing offload opportunities...") - all_opportunities = [] - for module in modules: - opportunities = self.analyze_offload_opportunities(module) - all_opportunities.extend(opportunities) - print(f" Found {len(all_opportunities)} offload opportunities") - - # Step 3: Calculate resource reduction - print("[3/5] Calculating resource reduction...") - reduction = self.calculate_resource_reduction() - print(f" Register reduction: {reduction['registers']['percentage']:.1f}%") - print(f" Wire reduction: {reduction['wires']['percentage']:.1f}%") - print(f" Module reduction: {reduction['modules']['percentage']:.1f}%") - - # Step 4: Generate system integration plan - print("[4/5] Generating system integration plan...") - integration_plan = self.generate_system_integration_plan() - print(f" RTL ASIC modules: {len(integration_plan['rtl_asic']['modules'])}") - print(f" CPU modules: {len(integration_plan['cpu']['modules'])}") - print(f" GPU modules: {len(integration_plan['gpu']['modules'])}") - - # Step 5: Generate optimized FPGA - print("[5/5] Generating optimized FPGA design...") - optimized_fpga = self.generate_optimized_fpga() - optimized_file = OUTPUT_DIR / "nii_surface_driver_optimized.v" - optimized_file.write_text(optimized_fpga) - print(f" Optimized FPGA saved to {optimized_file}") - - print("\n" + "=" * 60) - print("FPGA OPTIMIZATION COMPLETE") - print("=" * 60) - - return { - "original_modules": len(modules), - "offload_opportunities": len(all_opportunities), - "resource_reduction": reduction, - "integration_plan": integration_plan, - "optimized_fpga_path": str(optimized_file) - } - -if __name__ == '__main__': - optimizer = FPGATopologyOptimizer() - results = optimizer.run_optimization() - - # Save results - output_file = OUTPUT_DIR / "fpga_optimization_results.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nOptimization results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("OPTIMIZATION SUMMARY") - print("=" * 60) - print(f"Original modules: {results['original_modules']}") - print(f"Offload opportunities: {results['offload_opportunities']}") - print(f"Register reduction: {results['resource_reduction']['registers']['percentage']:.1f}%") - print(f"Wire reduction: {results['resource_reduction']['wires']['percentage']:.1f}%") - print(f"Module reduction: {results['resource_reduction']['modules']['percentage']:.1f}%") diff --git a/5-Applications/scripts/frozen_in_gravity_model.py b/5-Applications/scripts/frozen_in_gravity_model.py deleted file mode 100644 index ca5273b6..00000000 --- a/5-Applications/scripts/frozen_in_gravity_model.py +++ /dev/null @@ -1,574 +0,0 @@ -#!/usr/bin/env python3 -""" -Frozen-In Gravity Model — Asenjo, Comisso & Winkler (PRL 2026) - -Extracted equations and their mapping to PIST Extended Encoding. - -Paper: "Frozen-In Gravitational Fields" -DOI: 10.1103/6c4q-kx6f -URL: https://phys.org/news/2026-04-frozen-gravity-evolution-spacetime-dynamics.html - -Core idea: Gravitational field structures remain "frozen" into spacetime dynamics -under ideal conditions, preserving topological invariants. - -PIST analogy: Coordinate structures remain invariant under encoding/decoding, -eliminating the need for search-based context modeling. -""" - -import numpy as np -from typing import Tuple, Callable - - -# ─── Equation 1: Einstein Field Equations (Standard Form) ─── -# -# G_μν + Λ g_μν = (8πG / c⁴) T_μν -# -# G_μν = Einstein tensor (geometry) -# T_μν = stress-energy tensor (matter) -# Λ = cosmological constant -# g_μν = metric tensor -# -# Asenjo et al. rewrite this in analogy to conducting fluid equations. - -def einstein_tensor(g_inv: np.ndarray, dg: np.ndarray, ddg: np.ndarray) -> np.ndarray: - """ - Compute Einstein tensor G_μν from metric and its derivatives. - - G_μν = R_μν - (1/2) R g_μν - where R_μν is Ricci tensor, R is Ricci scalar. - """ - # Simplified: full computation is complex; this is the symbolic structure - # In practice, Christoffel symbols → Riemann → Ricci → Einstein - # For PIST analogy, we only need the conceptual mapping - pass # Placeholder — full GR computation is beyond scope - - -# ─── Equation 2: Fluid-Dynamics Analog (Asenjo Rewriting) ─── -# -# The paper rewrites Einstein equations as: -# -# ∂_t u + (u · ∇) u = -∇p/ρ + ν ∇²u + (other terms) -# -# where u represents the gravitational "velocity" field, -# p is effective pressure, ρ is effective density. -# -# Key insight: gravitational field lines behave like magnetic field lines in MHD. - -class GravitationalMHD: - """ - Model gravitational field as magnetohydrodynamic fluid. - - Frozen-in theorem: If E + v × B = 0 (ideal condition), - then field lines move with the fluid and topology is preserved. - """ - - def __init__(self, grid_size: Tuple[int, int, int] = (64, 64, 64)): - self.Nx, self.Ny, self.Nz = grid_size - # Gravitational "magnetic" field B_g (analog to magnetic field) - self.B_g = np.zeros((*grid_size, 3)) - # Gravitational "electric" field E_g - self.E_g = np.zeros((*grid_size, 3)) - # Velocity field v - self.v = np.zeros((*grid_size, 3)) - # Gravitational vector potential A_g (B_g = ∇ × A_g) - self.A_g = np.zeros((*grid_size, 3)) - - def ideal_ohm_law(self) -> np.ndarray: - """ - Equation 3: Ideal Ohm-type condition for gravity. - - E_g + v × B_g = 0 - - This is the frozen-in condition. When satisfied, field lines - move with the fluid and connectivity is preserved. - """ - v_cross_B = np.cross(self.v, self.B_g) - return self.E_g + v_cross_B # Should be ~0 for frozen-in - - def is_frozen_in(self, tol: float = 1e-6) -> bool: - """Check if ideal condition is satisfied.""" - residual = self.ideal_ohm_law() - return np.linalg.norm(residual) < tol - - def evolve(self, dt: float) -> None: - """ - Equation 4: Evolution equations for frozen-in fields. - - ∂_t B_g = ∇ × (v × B_g) (induction equation analog) - - Under ideal condition, this preserves: - - Field line connectivity - - Gravitational helicity - - Topological invariants - """ - # Compute ∇ × (v × B_g) - v_cross_B = np.cross(self.v, self.B_g) - curl_v_cross_B = self._curl(v_cross_B) - self.B_g += dt * curl_v_cross_B - - def _curl(self, F: np.ndarray) -> np.ndarray: - """Compute curl of vector field F on grid.""" - # Simplified finite-difference curl - dFz_dy = np.roll(F[..., 2], -1, axis=1) - np.roll(F[..., 2], 1, axis=1) - dFy_dz = np.roll(F[..., 1], -1, axis=2) - np.roll(F[..., 1], 1, axis=2) - curl_x = dFz_dy - dFy_dz - - dFx_dz = np.roll(F[..., 0], -1, axis=2) - np.roll(F[..., 0], 1, axis=2) - dFz_dx = np.roll(F[..., 2], -1, axis=0) - np.roll(F[..., 2], 1, axis=0) - curl_y = dFx_dz - dFz_dx - - dFy_dx = np.roll(F[..., 1], -1, axis=0) - np.roll(F[..., 1], 1, axis=0) - dFx_dy = np.roll(F[..., 0], -1, axis=1) - np.roll(F[..., 0], 1, axis=1) - curl_z = dFy_dx - dFx_dy - - return np.stack([curl_x, curl_y, curl_z], axis=-1) - - def gravitational_helicity(self) -> float: - """ - Equation 5: Gravitational helicity (topological invariant). - - H_g = ∫ A_g · B_g dV - - This measures the linkedness/knottedness of gravitational field lines. - Under frozen-in dynamics, H_g is conserved. - """ - dot = np.sum(self.A_g * self.B_g, axis=-1) - return np.sum(dot) # Discrete integral over grid - - -# ─── PIST Analogy: Coordinate Invariance ─── - -class PISTFrozenIn: - """ - Map gravitational frozen-in theorem to PIST coordinate invariance. - - Gravitational field line → PIST composite address - Fluid velocity v → Data stream position n - Field line connectivity → Coordinate structure (k,t,tree,surface,torus) - Frozen-in condition → Deterministic encoding/decoding - Gravitational helicity → Information conservation (lossless roundtrip) - """ - - def __init__(self, basis_size: int = 16): - self.basis_size = basis_size - self.basis = np.zeros(basis_size, dtype=np.uint8) - self.helicity_history = [] - - def pist_encode(self, n: int) -> Tuple[int, int]: - """ - n = k² + t (Equation 6: PIST shell decomposition) - - Analogous to resolving field into components along/orthogonal - to a preferred direction in the fluid. - """ - k = int(np.sqrt(n)) - t = n - k * k - return k, t - - def pist_mass(self, k: int, t: int) -> int: - """ - m(k,t) = t(2k+1-t) for t < 2k+1-t, else mirrored. - - Analogous to field strength/intensity at a given shell. - """ - if k == 0: - return 0 - m = 2 * k + 1 - t - tf = t if t < m else m - return tf * (2 * k + 1 - tf) - - def composite_address(self, n: int) -> dict: - """ - Equation 7: Composite address = (tree, surface, torus, shell) - - Analogous to full field specification at a point: - - Tree address = topological genus/branch label - - Surface coords = local field direction - - Torus angles = phase/rotation state - - Shell coords = radial distance/amplitude - """ - k, t = self.pist_encode(n) - mass = self.pist_mass(k, t) - - # Tree: base-20 path - tree = [(n // (20 ** i)) % 20 for i in range(3)] - - # Surface: y = 1/x, θ = n·Φ - x = 1.0 + (n % 255) - y = 1.0 / x - theta_surf = (n * 1.618033988749895) % (2 * np.pi) - - # Torus: Φ-irrational angles - phi = (n * 1.618033988749895) % (2 * np.pi) - psi = (n * 1.618033988749895 ** 2) % (2 * np.pi) - - return { - 'n': n, 'k': k, 't': t, 'mass': mass, - 'tree': tree, - 'surface': {'x': x, 'y': y, 'theta': theta_surf}, - 'torus': {'phi': phi, 'psi': psi} - } - - def frozen_in_decode(self, stream: bytes, position: int) -> int: - """ - Equation 8: Decoder = prediction XOR residual. - - Prediction is a "frozen-in" coordinate function: - it depends only on position n and basis, not on data. - - This guarantees: - - Determinism: same n → same prediction - - Reversibility: residual = data XOR prediction - - Invariance: coordinate structure preserved - """ - n = position - addr = self.composite_address(n) - - # Prediction from coordinate-derived values - pred = self.basis[n % self.basis_size] - pred ^= int(addr['torus']['phi'] * 40.5) & 0xFF - pred ^= (addr['mass'] % 256) - pred ^= int(addr['surface']['theta'] * 40.5) & 0xFF - - # Residual from stream - if position < len(stream): - residual = stream[position] - else: - residual = 0 - - return pred ^ residual - - def compute_helicity(self, data: bytes) -> float: - """ - Equation 9: PIST "helicity" = correlation of address components. - - Analogous to gravitational helicity but for information coordinates. - Measures how tightly the coordinate components are "knotted" together. - - High helicity = strong correlation = better prediction = lower entropy. - """ - # Compute pairwise correlations between address components - k_vals = [] - t_vals = [] - mass_vals = [] - tree_sum = [] - - for n in range(len(data)): - k, t = self.pist_encode(n) - mass = self.pist_mass(k, t) - tree = [(n // (20 ** i)) % 20 for i in range(3)] - - k_vals.append(k) - t_vals.append(t) - mass_vals.append(mass) - tree_sum.append(sum(tree)) - - # Correlation matrix - corr_kt = np.corrcoef(k_vals, t_vals)[0, 1] if len(k_vals) > 1 else 0 - corr_km = np.corrcoef(k_vals, mass_vals)[0, 1] if len(k_vals) > 1 else 0 - corr_tm = np.corrcoef(t_vals, mass_vals)[0, 1] if len(t_vals) > 1 else 0 - - # "Helicity" = integrated correlation strength - helicity = abs(corr_kt) + abs(corr_km) + abs(corr_tm) - return helicity - - -# ─── Demonstration ─── - -def demonstrate_frozen_in(): - """Show that PIST coordinates preserve structure under dynamics.""" - - print("=" * 60) - print("Frozen-In Gravity → PIST Analogy") - print("Asenjo, Comisso & Winkler (PRL 2026)") - print("=" * 60) - - # 1. Create PIST system - pist = PISTFrozenIn(basis_size=16) - - # 2. Generate synthetic data - np.random.seed(42) - data = bytes(np.random.randint(0, 256, size=1000)) - - # 3. Show address invariance - print("\n--- Coordinate Invariance Test ---") - for n in [0, 10, 100, 500]: - addr1 = pist.composite_address(n) - addr2 = pist.composite_address(n) - - assert addr1['k'] == addr2['k'] - assert addr1['t'] == addr2['t'] - assert addr1['mass'] == addr2['mass'] - - print(f"n={n}: k={addr1['k']}, t={addr1['t']}, mass={addr1['mass']}") - print(" ✓ Coordinates are deterministic (frozen-in)") - - # 4. Show reversibility - print("\n--- Reversibility Test ---") - # Encode: residual = data XOR prediction - # Decode: data' = prediction XOR residual - residuals = bytearray() - for i, b in enumerate(data): - pred = pist.basis[i % pist.basis_size] - pred ^= (pist.pist_mirror(i) % 256) - residual = b ^ pred - residuals.append(residual) - - # Decode - decoded = bytearray() - for i in range(len(data)): - pred = pist.basis[i % pist.basis_size] - pred ^= (pist.pist_mirror(i) % 256) - out = pred ^ residuals[i] - decoded.append(out) - - assert bytes(decoded) == data - print(f" ✓ Roundtrip verified for {len(data)} bytes") - - # 5. Compute helicity - print("\n--- Information Helicity ---") - helicity = pist.compute_helicity(data) - print(f" PIST coordinate helicity: {helicity:.4f}") - print(f" (Higher = stronger coordinate correlations = better compression)") - - print("\n" + "=" * 60) - print("Key insight: Deterministic coordinates = frozen-in structure") - print("No search needed. Topology is built into the encoding.") - print("=" * 60) - - -def pist_mirror(n: int) -> int: - """Helper: PIST mirror operation.""" - k = int(np.sqrt(n)) - t = n - k * k - if k == 0: - return 0 - return k * k + (2 * k + 1 - t) - - -# Add mirror to class -PISTFrozenIn.pist_mirror = staticmethod(pist_mirror) - - -# ─── AngrySphinx Gear Law & FAMM-Coupled Gear Ratio ─── - -class FAMMRouteMemory: - """ - Frustration-Aligned Memory Management (FAMM). - Records route outcomes as scars that bias future search. - """ - - def __init__(self): - self.scars = [] # List of (route_signature, outcome, load) - self.torsion = 0.0 - self.interlock = 0.0 - self.phase_delta = 0.0 - self.route_helicity = 0.0 - - def record_scar(self, route_sig: str, outcome: str, effort: float): - """Record a route traversal outcome.""" - self.scars.append({ - 'route': route_sig, - 'outcome': outcome, # 'success', 'failure', 'trap', 'partial' - 'effort': effort, - 'timestamp': len(self.scars) - }) - # Update FAMM load components - self.torsion += effort * 0.1 - self.interlock += 1.0 if outcome == 'trap' else 0.0 - self.phase_delta += abs(hash(route_sig) % 100) / 100.0 - - def load(self) -> float: - """ - Eq: L_FAMM(t) = Sigma^2(t) + I_lock(t) + Delta_phi(t) - """ - return self.torsion**2 + self.interlock + self.phase_delta - - def load_frozen(self) -> float: - """ - Frozen-FAMM / topology-aware version: - L_FAMM+(t) = Sigma^2 + I_lock + Delta_phi + H_route - where H_route = preserved route-helicity / connectivity penalty. - """ - return self.load() + self.route_helicity - - def hostile_route_count(self) -> int: - return sum(1 for s in self.scars if s['outcome'] in ('failure', 'trap')) - - def repeated_hostile_count(self, route_sig: str) -> int: - return sum(1 for s in self.scars - if s['route'] == route_sig and s['outcome'] in ('failure', 'trap')) - - -class AngrySphinxShell: - """ - AngrySphinx Gear Law: - - O(t) = eta(t) * G_AS(t) * a(t) + F(t) + chi(t) - - where: - a(t) = adversarial input effort - G_AS = gear reduction / escalation multiplier - eta = efficiency of cost transfer - F(t) = FAMM route-scar load - chi = cringe / semantic friction - - Gear ratio (FAMM-coupled escalation): - G_AS(t) = 1 + alpha*L_FAMM(t) + beta*R(t) + gamma*U(t) - - where: - L_FAMM = route-scar / frustration load - R = repeated hostile route count - U = uncertainty or unknown-route risk - """ - - def __init__(self, - alpha: float = 0.5, - beta: float = 1.0, - gamma: float = 2.0, - delta: float = 0.3, - theta_safe: float = 10.0): - self.alpha = alpha - self.beta = beta - self.gamma = gamma - self.delta = delta # H_route coupling - self.theta_safe = theta_safe - self.famm = FAMMRouteMemory() - self.cringe_friction = 0.0 - self.semantic_cost = 0.0 - self.reality_cost = 0.0 - self.constructive_cost = 0.0 - - def gear_ratio(self, adversarial_effort: float, route_sig: str) -> float: - """ - Compute the current gear ratio for a given adversarial input. - """ - L_famm = self.famm.load_frozen() - R_repeat = self.famm.repeated_hostile_count(route_sig) - U_unknown = 1.0 if self.famm.hostile_route_count() == 0 else 0.0 - H_route = self.famm.route_helicity - - G_as = (1.0 - + self.alpha * L_famm - + self.beta * R_repeat - + self.gamma * U_unknown - + self.delta * H_route) - return G_as - - def impose_obligation(self, adversarial_effort: float, route_sig: str) -> dict: - """ - Convert adversarial input into constructive obligation. - - C_out = G_AS * C_in + C_semantic + C_reality + C_constructive + C_cringe - """ - G_as = self.gear_ratio(adversarial_effort, route_sig) - eta = 0.85 # efficiency of cost transfer - - O_compute = eta * G_as * adversarial_effort - O_semantic = self.semantic_cost - O_reality = self.reality_cost - O_constructive = self.constructive_cost - O_cringe = self.cringe_friction - O_total = O_compute + O_semantic + O_reality + O_constructive + O_cringe - - # Update FAMM with this engagement - self.famm.record_scar(route_sig, 'trap', adversarial_effort) - - return { - 'gear_ratio': G_as, - 'compute_obligation': O_compute, - 'semantic_obligation': O_semantic, - 'reality_obligation': O_reality, - 'constructive_obligation': O_constructive, - 'cringe_obligation': O_cringe, - 'total_obligation': O_total - } - - def is_defensive(self, payload_value: float, auth_recovery_cost: float) -> bool: - """ - Shell is economically defensive when: - - S_AS(t) = C_out - V_payload - C_auth > 0 - - or in full form: - S_AS+(t) = C_compute + C_semantic + C_reality + C_constructive + C_cringe - + lambda * L_FAMM+ - - V_payload - C_auth - """ - # Use latest obligation as proxy for current state - latest_effort = self.famm.scars[-1]['effort'] if self.famm.scars else 1.0 - latest_route = self.famm.scars[-1]['route'] if self.famm.scars else 'default' - obligation = self.impose_obligation(latest_effort, latest_route) - S_as = obligation['total_obligation'] - payload_value - auth_recovery_cost - return S_as > 0 - - def decision_rule(self, payload_value: float, auth_recovery_cost: float) -> str: - """ - Decision rule based on defensive score: - - S_AS+(t) > theta_safe => allow shell state to persist - 0 < S_AS+(t) <= theta_safe => harden shell - S_AS+(t) <= 0 => shell failing or underpriced - """ - if not self.famm.scars: - return "neutral" - - latest_effort = self.famm.scars[-1]['effort'] - latest_route = self.famm.scars[-1]['route'] - obligation = self.impose_obligation(latest_effort, latest_route) - S_as = obligation['total_obligation'] - payload_value - auth_recovery_cost - - if S_as > self.theta_safe: - return "persist" - elif S_as > 0: - return "harden" - else: - return "underpriced" - - -def demonstrate_gear_law(): - """Demonstrate AngrySphinx gear reduction with FAMM escalation.""" - - print("=" * 60) - print("AngrySphinx Gear Law — FAMM-Coupled Escalation") - print("=" * 60) - - shell = AngrySphinxShell(alpha=0.5, beta=1.0, gamma=2.0) - payload_value = 100.0 - auth_cost = 5.0 - - # Simulate repeated hostile probes on the same route - route = "brute_force_shell_0" - efforts = [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0] - - print(f"\nPayload value: {payload_value}, Auth recovery cost: {auth_cost}") - print(f"Hostile route: '{route}'") - print(f"\n{'Probe':>6} {'Effort':>8} {'Gear G':>10} {'C_out':>10} {'S_AS':>10} {'Decision':>10}") - print("-" * 60) - - for i, effort in enumerate(efforts): - obligation = shell.impose_obligation(effort, route) - S_as = obligation['total_obligation'] - payload_value - auth_cost - decision = shell.decision_rule(payload_value, auth_cost) - - print(f"{i+1:>6} {effort:>8.2f} {obligation['gear_ratio']:>10.2f} " - f"{obligation['total_obligation']:>10.2f} {S_as:>10.2f} {decision:>10}") - - # Show effect of switching to a novel route - print(f"\n--- Novel route probe (high uncertainty load) ---") - novel_route = "injection_vector_7" - obligation = shell.impose_obligation(1.0, novel_route) - S_as = obligation['total_obligation'] - payload_value - auth_cost - print(f"Novel route '{novel_route}': G={obligation['gear_ratio']:.2f}, " - f"C_out={obligation['total_obligation']:.2f}, S_AS={S_as:.2f}") - - print("\n" + "=" * 60) - print("Gear reduction: cheap attacker speed → expensive defensive torque") - print("FAMM scars ratchet the gear ratio up with each hostile engagement") - print("=" * 60) - - -if __name__ == "__main__": - demonstrate_frozen_in() - print("\n") - demonstrate_gear_law() diff --git a/5-Applications/scripts/fundamental_math_verifier.py b/5-Applications/scripts/fundamental_math_verifier.py deleted file mode 100644 index df25f53e..00000000 --- a/5-Applications/scripts/fundamental_math_verifier.py +++ /dev/null @@ -1,362 +0,0 @@ -#!/usr/bin/env python3 -""" -Fundamental Math Verification for Research Stack — Drift-Prevention Net - -Verifies a wide net of independent mathematical anchors so theory drift is caught -fast. Each anchor pairs a query with an expected substring; verification only -counts when Wolfram's output (whitespace + comma stripped, case-insensitive) -contains that substring. - -All physical instances use SI-exact constants (post-2019 redefinition): - c = 299792458 m/s (defined exact) - h = 6.62607015×10⁻³⁴ J·s (defined exact) - k_B = 1.380649×10⁻²³ J/K (defined exact) - N_A = 6.02214076×10²³ /mol (defined exact) - e = 1.602176634×10⁻¹⁹ C (defined exact) - R = N_A·k_B = 8.31446261815324 J/(mol·K) (exact derived) - -Reports pass_rate. Sigma is NOT computed here — claiming a sigma value from a -fixed test list is dimensionally wrong (Six Sigma 6.5σ ≈ 3.4 DPMO, requiring -~3M samples). Sigma claims belong with the GPU population sweeps in -FixedPoint.lean. -""" - -import json -import os -import time -import urllib.parse -import urllib.request -from pathlib import Path - - -class WolframAlphaVerifier: - """Wolfram Alpha API client for mathematical verification.""" - - BASE_URL = "https://api.wolframalpha.com/v2/query" - - def __init__(self, app_id: str): - self.app_id = app_id - - def query(self, input_expr: str) -> dict: - params = { - "input": input_expr, - "format": "plaintext", - "output": "JSON", - "appid": self.app_id, - } - url = f"{self.BASE_URL}?{urllib.parse.urlencode(params)}" - try: - with urllib.request.urlopen(url, timeout=30) as response: - return json.loads(response.read().decode("utf-8")) - except Exception as e: - return {"error": str(e)} - - @staticmethod - def _norm(s: str) -> str: - # Strip whitespace AND commas (Wolfram inserts thousands separators) - return "".join(s.split()).lower().replace(",", "") - - def verify(self, equation: str, description: str, expected: str) -> dict: - print(f"\n🔍 {description}") - print(f" Equation: {equation}") - print(f" Expected: {expected}") - result = self.query(equation) - - if "error" in result: - print(f"❌ Error: {result['error']}") - return {"equation": equation, "description": description, - "expected": expected, "status": "error", "error": result["error"]} - - qr = result.get("queryresult", {}) - if not qr.get("success"): - print("❌ Query failed") - return {"equation": equation, "description": description, - "expected": expected, "status": "failed"} - - pods = qr.get("pods", []) - all_texts = [] - primary_text = None - for pod in pods: - for sub in pod.get("subpods", []): - txt = sub.get("plaintext", "") - if not isinstance(txt, str): - continue - if txt: - all_texts.append(txt) - if pod.get("primary") and primary_text is None: - primary_text = txt - - expected_n = self._norm(expected) - haystack = self._norm(" ".join(all_texts)) - matched = expected_n in haystack - observed = primary_text or (all_texts[0] if all_texts else "") - - if matched: - print(f"✅ Result: {observed[:100]}") - return {"equation": equation, "description": description, - "expected": expected, "observed": observed, "status": "verified"} - else: - print(f"❌ MISMATCH — got: {observed[:100]}") - return {"equation": equation, "description": description, - "expected": expected, "observed": observed, - "status": "mismatch", "all_pods": all_texts} - - -def main(): - print("=" * 70) - print("FUNDAMENTAL MATH VERIFICATION — DRIFT-PREVENTION ANCHOR NET") - print("=" * 70) - - app_id = os.environ.get("WOLFRAM_ALPHA_APPID", "") - verifier = WolframAlphaVerifier(app_id) - - # (equation, description, expected_substring) - fundamental_math = [ - # ───────────────────────────────────────────────────────────── - # GROUP A — Calculus foundations (AnalysisFoundations.lean) - # ───────────────────────────────────────────────────────────── - ("limit h->0 (f(x+h)-f(x))/h", - "Derivative definition (AnalysisFoundations.lean)", "f'(x)"), - ("integral x^2 from 0 to 1", - "∫₀¹ x² dx = 1/3 (AnalysisFoundations.lean)", "1/3"), - ("d/dx (x^2)", - "d/dx(x²) = 2x (AnalysisFoundations.lean)", "2 x"), - ("limit of x^2 as x approaches 0", - "lim x→0 x² = 0 (AnalysisFoundations.lean continuity)", "= 0"), - ("integral sin(x) from 0 to pi", - "∫₀^π sin(x) dx = 2", "2"), - ("integral 1/x from 1 to e", - "∫₁^e dx/x = 1", "1"), - ("d/dx (sin(x))", - "d/dx(sin(x)) = cos(x)", "cos(x)"), - ("d/dx (e^x)", - "d/dx(eˣ) = eˣ", "e^x"), - ("d/dx (ln(x))", - "d/dx(ln(x)) = 1/x", "1/x"), - ("integral e^(-x^2) from -infinity to infinity", - "Gaussian: ∫ e^(-x²) dx = √π", "sqrt(π)"), - - # ───────────────────────────────────────────────────────────── - # GROUP B — Fixed-point arithmetic (FixedPoint.lean) - # ───────────────────────────────────────────────────────────── - ("65536 / 65536", "Q16_16 scale factor = 1", "1"), - ("2^16", "Q0_16 precision = 65536", "65536"), - ("2^32", "Q16_16 range = 4294967296", "4294967296"), - ("2^31 - 1", "Q16_16 max signed = 2147483647", "2147483647"), - ("2^15", "Q0_15 boundary = 32768", "32768"), - - # ───────────────────────────────────────────────────────────── - # GROUP C — Forest/sum sanity (GradientPathMap.lean) - # ───────────────────────────────────────────────────────────── - ("sum [150, 250, 300, 200]", - "Gradient path cost = 900 (GradientPathMap.lean)", "900"), - - # ───────────────────────────────────────────────────────────── - # GROUP D — Information theory / Shannon entropy - # ───────────────────────────────────────────────────────────── - ("-0.5*log2(0.5) - 0.5*log2(0.5)", - "H(½,½) = 1 bit (binary uniform)", "1"), - ("-8*(1/8)*log2(1/8)", - "H(uniform-8) = 3 bits", "3"), - ("log2(6)", - "H(fair die) ≈ 2.5849625 bits", "2.5849625"), - ("log2(10)", - "H(decimal digit) ≈ 3.3219280 bits", "3.3219280"), - ("log2(256)", - "H(byte) = 8 bits", "8"), - ("log2(27)", - "H(BASE-27 / K=3 ternary triplet) ≈ 4.7548875 bits", "4.7548875"), - - # ───────────────────────────────────────────────────────────── - # GROUP E — SI defining constants (post-2019 redefinition). - # Verified by reverse-conversion: assert the SI-exact integer - # value converts back to "1 " — only matches if Wolfram - # recognizes the exact defined number. - # ───────────────────────────────────────────────────────────── - ("299792458 m/s in c", - "c = 299792458 m/s (exact, SI defined) ⇒ 1 c", "1 c"), - ("6.62607015*10^-34 J s in Planck constants", - "h = 6.62607015×10⁻³⁴ J·s (exact, SI defined) ⇒ 1 ℏ", - "1 h"), - ("1.380649*10^-23 J/K in Boltzmann constants", - "k_B = 1.380649×10⁻²³ J/K (exact, SI defined) ⇒ 1 k_B", - "1 k"), - ("6.02214076*10^23 in Avogadro number", - "N_A = 6.02214076×10²³ /mol (exact, SI defined) ⇒ 1 N_A", - "1 n"), - ("1.602176634*10^-19 C in elementary charge", - "e = 1.602176634×10⁻¹⁹ C (exact, SI defined) ⇒ 1 e", - "1 e"), - - # ───────────────────────────────────────────────────────────── - # GROUP F — Derived SI-exact constants (no measurement uncertainty) - # ───────────────────────────────────────────────────────────── - ("6.02214076*10^23 * 1.380649*10^-23", - "R = N_A·k_B = 8.31446261815324 J/(mol·K)", "8.31446261815324"), - ("6.02214076*10^23 * 1.602176634*10^-19", - "Faraday F = N_A·e = 96485.33212... C/mol", "96485.33212"), - - # ───────────────────────────────────────────────────────────── - # GROUP G — Mathematical constants at extreme precision - # ───────────────────────────────────────────────────────────── - ("N[Pi, 30]", - "π to 28 digits (anchored prefix; Wolfram rounds 30th)", - "3.141592653589793238462643383"), - ("N[E, 30]", - "e to 28 digits", "2.718281828459045235360287471"), - ("N[GoldenRatio, 30]", - "φ = (1+√5)/2 to 28 digits (PHI-axis hardware)", - "1.618033988749894848204586834"), - ("N[EulerGamma, 20]", - "γ (Euler-Mascheroni) to 19 digits", - "0.5772156649015328606"), - ("N[Sqrt[2], 30]", - "√2 to 28 digits", - "1.414213562373095048801688724"), - ("N[Sqrt[3], 30]", - "√3 to 28 digits", - "1.732050807568877293527446341"), - ("N[Log[2], 30]", - "ln(2) to 30 digits", "0.693147180559945309417232121458"), - ("N[Log[10], 30]", - "ln(10) to 30 digits", "2.30258509299404568401799145468"), - - # ───────────────────────────────────────────────────────────── - # GROUP H — Trig exact values (no rounding) - # ───────────────────────────────────────────────────────────── - ("sin(30 degrees)", "sin(30°) = 1/2 (exact)", "1/2"), - ("cos(60 degrees)", "cos(60°) = 1/2 (exact)", "1/2"), - ("tan(45 degrees)", "tan(45°) = 1 (exact)", "1"), - ("sin(pi/4)", "sin(π/4) = 1/√2 (exact)", "1/sqrt(2)"), - ("cos(pi/3)", "cos(π/3) = 1/2 (exact)", "1/2"), - - # ───────────────────────────────────────────────────────────── - # GROUP I — Physical-law specific instances at SI precision - # ───────────────────────────────────────────────────────────── - ("0.001 * 299792458^2", - "E=mc² for 1 g = 8.9875517873681764×10¹³ J (c exact)", - "8.9875517873681764"), - ("8.31446261815324 * 273.15 / 101325", - "PV=nRT: 1 mol IUPAC STP → V = 0.022413969545014… m³", - "0.022413969545014"), - ("100/300 - 100/400", - "δS > 0: 100J heat 400K→300K → ΔS = 1/12 J/K (exact)", - "1/12"), - ("1 - 300/400", - "Carnot η at T_c=300K, T_h=400K = 1/4 (exact)", - "1/4"), - ("2.897771955*10^-3 / 5778", - "Wien displacement for Sun (T=5778K) → λ_max ≈ 5.01518×10⁻⁷ m (501.5 nm)", - "5.01518"), - ("Bohr radius", - "a₀ = 5.29177211×10⁻¹¹ m (Wolfram CODATA precision)", "5.29177211"), - ("Rydberg energy in eV", - "E₁(H) = Ry = 13.605693123 eV (Rydberg, exact)", "13.605693123"), - ("inverse fine structure constant", - "1/α = 137.035999 (Sommerfeld, electromagnetic coupling)", - "137.035999"), - ("9.80665 m/s^2 in g", - "g₀ = 9.80665 m/s² ≡ 1 standard gravity (defined exact, CGPM 1901)", - "1 g"), - - # ───────────────────────────────────────────────────────────── - # GROUP J — Astronomical constants (verified by reverse-conversion - # to canonical units, so the full defined integer is checked) - # ───────────────────────────────────────────────────────────── - ("149597870700 meters in AU", - "AU = 149597870700 m exact (IAU 2012) ⇒ converts to 1 au", - "1 au"), - ("9460730472580800 meters in light years", - "ly = 9460730472580800 m exact (c × Julian year) ⇒ 1 ly", - "1 ly"), - - # ───────────────────────────────────────────────────────────── - # GROUP K — Stack-specific anchors (PHI, BASE-27, K=3, k=7, Q16.16) - # ───────────────────────────────────────────────────────────── - ("(1 + sqrt(5)) / 2", - "φ = (1+√5)/2 (PHI-axis hardware, golden ratio)", - "1.61803398874"), - ("3^3", "BASE-27 = 3³ = 27 (K=3 ternary triplet)", "27"), - ("isprime(7)", "k=7 prime (coprime traversal)", "prime number"), - ("round(0.5 * 65536)", - "500ms anchor in Q16.16 raw = 32768", - "32768"), - ("round(0.7 * 65536)", - "700ms anchor in Q16.16 raw = 45875", - "45875"), - ("round(0.8 * 65536)", - "0.8 in Q16.16 raw = 52429 (corrected from 52428)", - "52429"), - ("round(0.150 * 65536)", - "150ms in Q16.16 raw = 9830", - "9830"), - - # ───────────────────────────────────────────────────────────── - # GROUP L — Master equation / asymptotic - # ───────────────────────────────────────────────────────────── - ("limit n->infinity (1 + 1/n)^n", - "lim (1+1/n)^n = e (compounding limit)", "e"), - ("sum 1/n^2 from n=1 to infinity", - "Basel problem: Σ 1/n² = π²/6", "π^2/6"), - ("sum 1/2^n from n=1 to infinity", - "Geometric series Σ (½)ⁿ = 1", "1"), - ] - - print(f"\nVerifying {len(fundamental_math)} fundamental anchors...") - - results = [verifier.verify(eq, desc, exp) for eq, desc, exp in fundamental_math] - - print("\n" + "=" * 70) - print("VERIFICATION SUMMARY") - print("=" * 70) - - verified = sum(1 for r in results if r["status"] == "verified") - mismatch = sum(1 for r in results if r["status"] == "mismatch") - failed = sum(1 for r in results if r["status"] in ("error", "failed")) - total = len(results) - pass_rate = verified / total if total else 0.0 - - print(f"Total anchors: {total}") - print(f"✅ Verified: {verified}") - print(f"⚠️ Mismatch: {mismatch}") - print(f"❌ Failed: {failed}") - print(f"\nPass rate: {pass_rate:.1%}") - - if mismatch: - print("\n--- MISMATCHES (these are the drift-detection signals) ---") - for r in results: - if r["status"] == "mismatch": - print(f" • {r['description']}") - print(f" expected: {r['expected']}") - print(f" got: {r.get('observed', '')[:120]}") - - if failed: - print("\n--- FAILED (transient API or syntax) ---") - for r in results: - if r["status"] in ("error", "failed"): - print(f" • {r['description']}: {r.get('error', 'no result')}") - - print("\nNote: pass_rate is NOT a sigma. Real sigma claims need population") - print("sweeps (see FixedPoint.lean's 65,536-value GPU verification).") - - output_file = Path("shared-data/data/fundamental_math_verification.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, "w") as f: - json.dump({ - "app_id": app_id, - "timestamp": time.time(), - "total": total, - "verified": verified, - "mismatch": mismatch, - "failed": failed, - "pass_rate": pass_rate, - "results": results, - }, f, indent=2) - - print(f"\nResults saved to: {output_file}") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/generate_lean_cleave_plan.py b/5-Applications/scripts/generate_lean_cleave_plan.py deleted file mode 100755 index 8177ab6b..00000000 --- a/5-Applications/scripts/generate_lean_cleave_plan.py +++ /dev/null @@ -1,167 +0,0 @@ -#!/usr/bin/env python3 -"""Generate a cleaving plan from the Lean module domain graph.""" - -from __future__ import annotations - -import csv -import json -from collections import Counter -from pathlib import Path - -ROOT = Path(__file__).resolve().parents[1] -GRAPH = ROOT / "shared-data" / "data" / "lean_module_graph" -OUT_CSV = GRAPH / "cleave_plan.csv" -OUT_JSONL = GRAPH / "cleave_plan.jsonl" -OUT_MD = ROOT / "6-Documentation" / "docs" / "reports" / "LEAN_MODULE_CLEAVE_PLAN.md" - -BUILD_ROOTS = ["Semantics", "PIST", "PistBridge", "PistSimulation"] - - -def load_modules() -> list[dict]: - return [json.loads(line) for line in (GRAPH / "modules.jsonl").read_text().splitlines()] - - -def load_local_edges() -> list[tuple[str, str]]: - rows: list[tuple[str, str]] = [] - with (GRAPH / "import_edges.csv").open(newline="", encoding="utf-8") as f: - for row in csv.DictReader(f): - if row["target_local"] == "true": - rows.append((row["source"], row["target"])) - return rows - - -def reachable(modules: list[dict], edges: list[tuple[str, str]]) -> set[str]: - known = {m["module"] for m in modules} - adj: dict[str, list[str]] = {m["module"]: [] for m in modules} - for src, dst in edges: - adj.setdefault(src, []).append(dst) - seen: set[str] = set() - stack = [root for root in BUILD_ROOTS if root in known] - while stack: - mod = stack.pop() - if mod in seen: - continue - seen.add(mod) - stack.extend(adj.get(mod, [])) - return seen - - -def cleave_class(mod: dict, is_reachable: bool) -> tuple[str, str]: - path = mod["path"] - domain = mod["domain"] - review = mod["review_status"] - if "/Ancillary/" in path or domain == "AncillaryHolding": - return ("ANCILLARY_HOLDING", "Already cleaved out of required core; keep with receipt until promoted or archived.") - if "/legacy/" in path or "/Quarantine/" in path or domain == "LegacyQuarantine": - return ("LEGACY_OR_QUARANTINE", "Do not delete blindly; archive/receipt or exclude from main surface.") - if "/external/" in path or "/LeanGPT/" in path or domain == "ExternalReference": - return ("EXTERNAL_REFERENCE", "Keep as reference-only material; do not treat as owned core.") - if is_reachable: - if review == "HOLD": - return ("REQUIRED_HOLD_REVIEW", "Reachable from aggregate build but taxonomy is ambiguous; review before moving.") - return ("REQUIRED_AGGREGATE", "Reachable from aggregate Lean build roots.") - if "ExtensionScaffold" in path or domain == "ExtensionScaffold": - return ("OPTIONAL_EXTENSION_SCAFFOLD", "Scaffold/extension surface; keep modular unless promoted.") - if domain == "RuntimeEntrypoints": - return ("RUNTIME_ENTRYPOINT", "Executable or service entrypoint; keep outside core proof taxonomy.") - if review == "HOLD": - return ("UNREACHED_HOLD_REVIEW", "Not reached by aggregate build and taxonomy is ambiguous; inspect before import/move/archive.") - if domain == "ReviewUnclassified": - return ("UNREACHED_UNCLASSIFIED", "No strong classifier signal; needs owner/domain assignment.") - return ("UNREACHED_DOMAIN_CANDIDATE", "Local module not reached by aggregate build; candidate for optional import, extension bundle, or archive.") - - -def main() -> None: - modules = load_modules() - edges = load_local_edges() - seen = reachable(modules, edges) - rows: list[dict] = [] - for mod in modules: - cls, action = cleave_class(mod, mod["module"] in seen) - rows.append({ - "module": mod["module"], - "path": mod["path"], - "domain": mod["domain"], - "review_status": mod["review_status"], - "reachable_from_aggregate": "true" if mod["module"] in seen else "false", - "cleave_class": cls, - "recommended_action": action, - "sorry_count": mod["sorry_count"], - "line_count": mod["line_count"], - "sha256": mod["sha256"], - }) - with OUT_JSONL.open("w", encoding="utf-8") as f: - for row in rows: - f.write(json.dumps(row, sort_keys=True) + "\n") - with OUT_CSV.open("w", newline="", encoding="utf-8") as f: - writer = csv.DictWriter(f, fieldnames=list(rows[0].keys())) - writer.writeheader() - writer.writerows(rows) - class_counts = Counter(row["cleave_class"] for row in rows) - domain_by_class: dict[str, Counter[str]] = {} - for row in rows: - domain_by_class.setdefault(row["cleave_class"], Counter())[row["domain"]] += 1 - lines = [ - "# Lean Module Cleave Plan", - "", - "This report classifies the local Lean module graph into required, optional, legacy, external, and review surfaces.", - "", - "## Roots", - "", - "Reachability roots:", - "", - *[f"- `{root}`" for root in BUILD_ROOTS], - "", - "## Summary", - "", - f"- Local Lean modules: {len(rows)}", - f"- Reachable from aggregate roots: {sum(1 for row in rows if row['reachable_from_aggregate'] == 'true')}", - f"- Not reachable from aggregate roots: {sum(1 for row in rows if row['reachable_from_aggregate'] == 'false')}", - "", - "## Cleave Classes", - "", - "| Class | Modules | Default action |", - "|---|---:|---|", - ] - action_by_class = {row["cleave_class"]: row["recommended_action"] for row in rows} - for cls, count in class_counts.most_common(): - lines.append(f"| {cls} | {count} | {action_by_class[cls]} |") - lines.extend(["", "## Domain Mix By Cleave Class", ""]) - for cls, counter in class_counts.most_common(): - lines.extend([f"### {cls}", "", "| Domain | Modules |", "|---|---:|"]) - for domain, count in domain_by_class[cls].most_common(12): - lines.append(f"| {domain} | {count} |") - lines.append("") - lines.extend([ - "## First Review Queue", - "", - "Start with reachable HOLD modules, then unreached HOLD modules. Those are the places where moving files before review is most likely to break or mislabel the graph.", - "", - "| Module | Class | Domain | Path |", - "|---|---|---|---|", - ]) - priority = [row for row in rows if row["cleave_class"] in ("REQUIRED_HOLD_REVIEW", "UNREACHED_HOLD_REVIEW", "UNREACHED_UNCLASSIFIED")] - priority.sort(key=lambda row: (row["cleave_class"], row["domain"], row["module"])) - for row in priority[:80]: - lines.append(f"| `{row['module']}` | {row['cleave_class']} | {row['domain']} | `{row['path']}` |") - lines.extend([ - "", - "## Claim Boundary", - "", - "This is a cleaving plan, not an automatic folder move. Required means reachable from the current aggregate Lean roots. Unreached does not mean useless; it means optional, scaffold, external, legacy, or not currently wired into the aggregate surface.", - "", - ]) - OUT_MD.write_text("\n".join(lines), encoding="utf-8") - print(json.dumps({ - "modules": len(rows), - "reachable": sum(1 for row in rows if row["reachable_from_aggregate"] == "true"), - "unreachable": sum(1 for row in rows if row["reachable_from_aggregate"] == "false"), - "classes": dict(class_counts), - "csv": str(OUT_CSV.relative_to(ROOT)), - "jsonl": str(OUT_JSONL.relative_to(ROOT)), - "report": str(OUT_MD.relative_to(ROOT)), - }, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/genome_purification_stripper.py b/5-Applications/scripts/genome_purification_stripper.py deleted file mode 100644 index 93c8104f..00000000 --- a/5-Applications/scripts/genome_purification_stripper.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python3 -""" -Genome Purification: Stripping the Bad Parts -Auditing C.fa based on RGFlow Lawfulness. -""" - -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def strip_bad_parts(input_fa: Path, output_fa: Path): - print(f"Opening Target Genome: {input_fa.name}") - with open(input_fa, 'r') as f: - lines = f.readlines() - seq = "".join(l.strip() for l in lines if not l.startswith(">")) - - print(f"Original Sequence Length: {len(seq)} symbols") - - detector = BlindDetector() - window_size = 5000 - stride = 2500 - - purified_seq = [] - bad_parts_count = 0 - total_parts = 0 - - print("Initiating Informatic Stripping Sweep...") - - for i in range(0, len(seq) - window_size + 1, stride): - window = seq[i : i + window_size] - total_parts += 1 - - state = detector.calculate_window_state(window) - (lawful_now, lawful_under_flow, _, _, _, _, _, rg_depth, attractor_id, _) = \ - detector.adaptation_eq.evaluate_state(state) - - # WE ONLY KEEP THE LAWFUL CORE - if lawful_under_flow and rg_depth > 8: - # We append the first 'stride' part to avoid overlap redundancy - purified_seq.append(window[:stride]) - else: - bad_parts_count += 1 - if bad_parts_count < 5: - print(f"Stripped Bad Part at Locus {i}nd (Sigma: {state.sigma_q:.2f}, Depth: {rg_depth})") - - final_seq = "".join(purified_seq) - - print(f"\n--- PURIFICATION SUMMARY ---") - print(f"Total Regions Scanned: {total_parts}") - print(f"Bad Parts Stripped: {bad_parts_count}") - print(f"Lawful Parts Restored: {len(purified_seq)}") - print(f"Purified Array Length: {len(final_seq)} symbols") - print(f"Purification Ratio: {len(seq) / (len(final_seq)+1e-9):.2f}x") - - with open(output_file, 'w') as f: - f.write(">Purified_Sovereign_Genome\n") - # Format as 80-char lines - for i in range(0, len(final_seq), 80): - f.write(final_seq[i:i+80] + "\n") - - print(f"\nPurified Genome saved to {output_fa}") - -if __name__ == "__main__": - input_file = Path("/home/allaun/Documents/Research Stack/data/benchmarks/killer_criterion/C.fa") - output_file = Path("/home/allaun/Documents/Research Stack/data/benchmarks/killer_criterion/C_purified.fa") - - if input_file.exists(): - strip_bad_parts(input_file, output_file) - else: - print("Error: Target genome not found.") diff --git a/5-Applications/scripts/global_manifold_audit.py b/5-Applications/scripts/global_manifold_audit.py deleted file mode 100644 index fad332d8..00000000 --- a/5-Applications/scripts/global_manifold_audit.py +++ /dev/null @@ -1,92 +0,0 @@ -#!/usr/bin/env python3 -""" -Global Sovereign Manifold Audit -RGFlow on Unified Compression Data -""" - -import sys -import pandas as pd -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_global_audit(input_parquet: Path, output_summary: Path): - print(f"Opening Master Sovereign Manifold: {input_parquet.name}") - df = pd.read_parquet(input_parquet) - - detector = BlindDetector() - results = [] - - print(f"Auditing {len(df)} records across Unified Compression stack...") - - # We iterate through the records. We need to find the 'data' or 'ids' column. - data_col = next((c for c in df.columns if c in ['input_ids', 'ids', 'data', 'text']), df.columns[0]) - - # Audit a representative sample if too large - sample_size = min(len(df), 20000) - df_sample = df.sample(sample_size, random_state=42) - - for i, (idx, row) in enumerate(df_sample.iterrows()): - content = row['content'] - seq = str(content) - - # 1. Mutation (mu): high character transitions in code - transitions = sum(1 for k in range(len(seq)-1) if seq[k] != seq[k+1]) - mu_q = (transitions / len(seq)) * 0.1 if len(seq) > 0 else 0 - - # 2. Entropy - counts = np.unique(list(seq), return_counts=True)[1] - probs = counts / len(seq) - entropy = -np.sum(probs * np.log2(probs + 1e-9)) - - # 3. Admissibility - # Mathlib code is dense. Sigma should be high. - sigma_q = 1.0 + (entropy / 4.0) - - state = detector.calculate_window_state('ACGT'*100) - state.sigma_q = sigma_q - state.mu_q = mu_q - - (lawful_now, lawful_under_flow, _, _, _, _, _, rg_depth, attractor_id, _) = \ - detector.adaptation_eq.evaluate_state(state) - - if i < 5: - print(f"Record {idx}: mu={mu_q:.4f}, sigma={sigma_q:.4f}, lawful={lawful_under_flow}") - - results.append({ - "idx": idx, - "lawful": lawful_under_flow, - "sigma": sigma_q, - "depth": rg_depth, - "path": row.get('path', 'unknown') - }) - - if i % 1000 == 0: - print(f"Audit Progress: {i}/{sample_size}") - - res_df = pd.DataFrame(results) - top_lawful = res_df[res_df['lawful'] == True].sort_values(by='sigma', ascending=False) - - print("\n--- TOP LAWFUL SIGNALS IN UNIFIED STACK ---") - print(top_lawful.head(10)) - - top_lawful.to_csv(output_summary, index=False) - print(f"\nAudit complete. Summary saved to {output_summary}") - -if __name__ == "__main__": - master_p = Path("/home/allaun/Documents/Research Stack/data/datasets/master_sovereign_manifold.parquet") - output_s = Path("/home/allaun/Documents/Research Stack/data/datasets/global_audit_summary.csv") - - if master_p.exists(): - run_global_audit(master_p, output_s) - else: - print("Error: Master Manifold not found.") diff --git a/5-Applications/scripts/gpu_lemma_verifier.py b/5-Applications/scripts/gpu_lemma_verifier.py deleted file mode 100644 index da72e178..00000000 --- a/5-Applications/scripts/gpu_lemma_verifier.py +++ /dev/null @@ -1,373 +0,0 @@ -#!/usr/bin/env python3 -""" -GPU-based Lemma Verification for Lean Proofs -Uses wgpu for parallel bounded range verification of arithmetic lemmas -""" -import wgpu -import numpy as np -from typing import Tuple, List, Callable - -class GPULemmaVerifier: - """GPU-accelerated verification of Lean lemmas across bounded ranges""" - - def __init__(self): - try: - self.adapter = wgpu.gpu.request_adapter_sync(power_preference="high-performance") - self.device = self.adapter.request_device_sync() - self.use_gpu = True - print("GPU Device initialized successfully") - except Exception as e: - print(f"GPU initialization failed: {e}") - print("Falling back to CPU verification") - self.use_gpu = False - - def verify_weighted_term_bounded(self, max_e: int = 1000, max_alpha: int = 65536) -> bool: - """ - Verify (E * α) / 65536 <= E for all E in [0, max_e] and α in [0, 65536] - Uses GPU parallel computation for exhaustive search - """ - if not self.use_gpu: - return self._verify_weighted_term_bounded_cpu(max_e, max_alpha) - - # Create parameter grid - e_values = np.arange(0, max_e + 1, dtype=np.int32) - alpha_values = np.arange(0, max_alpha + 1, dtype=np.int32) - - # GPU kernel for verification - kernel_shader = """ - @group(0) @binding(0) - var e_values: array; - - @group(0) @binding(1) - var alpha_values: array; - - @group(0) @binding(2) - var results: array; - - @compute @workgroup_size(64) - fn main(@builtin(global_invocation_id) global_id: vec3) { - let idx = global_id.x; - if (idx >= arrayLength(&e_values)) { return; } - - let e = i32(e_values[idx]); - let alpha = i32(alpha_values[idx]); - - // Verify (E * α) / 65536 <= E - let product = e * alpha; - let divided = product / 65536; - - // Store result: 1 if inequality holds, 0 otherwise - if (divided <= e) { - results[idx] = 1u; - } else { - results[idx] = 0u; - } - } - """ - - # Create buffers - e_buffer = self.device.create_buffer_with_copy_data(e_values.tobytes()) - alpha_buffer = self.device.create_buffer_with_copy_data(alpha_values.tobytes()) - results = np.zeros(len(e_values), dtype=np.uint32) - results_buffer = self.device.create_buffer_with_copy_data(results.tobytes()) - - # Create compute pipeline - compute_pipeline = self.device.create_compute_pipeline( - shader={"compute": kernel_shader} - ) - - # Dispatch compute shader - command_encoder = self.device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(compute_pipeline) - compute_pass.set_bind_group(0, [ - e_buffer, alpha_buffer, results_buffer - ]) - workgroup_count = (len(e_values) + 63) // 64 - compute_pass.dispatch_workgroups(workgroup_count) - compute_pass.end() - - # Copy results back - command_encoder.copy_buffer_to_buffer( - results_buffer, 0, results_buffer, 0, results.nbytes - ) - - # Submit and wait - self.device.queue.submit([command_encoder]) - - # Read results - results = np.frombuffer( - self.device.queue.read_buffer(results_buffer), - dtype=np.uint32 - ) - - # Check if all verifications passed - all_passed = np.all(results == 1) - print(f"Weighted term bounded verification: {all_passed}") - if not all_passed: - failed_indices = np.where(results == 0)[0] - print(f"Failed at indices: {failed_indices[:10]}...") # Show first 10 failures - - return all_passed - - def verify_bit_shift_equivalence(self, max_x: int = 65535) -> bool: - """ - Verify x >>> 16 = x / 65536 for all x in [0, max_x] - Uses GPU parallel computation - """ - return self._verify_bit_shift_equivalence_cpu(max_x) - - kernel_shader = """ - @group(0) @binding(0) - var x_values: array; - - @group(0) @binding(1) - var results: array; - - @compute @workgroup_size(64) - fn main(@builtin(global_invocation_id) global_id: vec3) { - let idx = global_id.x; - if (idx >= arrayLength(&x_values)) { return; } - - let x = i32(x_values[idx]); - - // Verify x >>> 16 = x / 65536 - let shifted = x >> 16u; - let divided = x / 65536; - - if (shifted == divided) { - results[idx] = 1u; - } else { - results[idx] = 0u; - } - } - """ - - x_buffer = self.device.create_buffer_with_copy_data(x_values.tobytes()) - results = np.zeros(len(x_values), dtype=np.uint32) - results_buffer = self.device.create_buffer_with_copy_data(results.tobytes()) - - compute_pipeline = self.device.create_compute_pipeline( - shader={"compute": kernel_shader} - ) - - command_encoder = self.device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(compute_pipeline) - compute_pass.set_bind_group(0, [x_buffer, results_buffer]) - workgroup_count = (len(x_values) + 63) // 64 - compute_pass.dispatch_workgroups(workgroup_count) - compute_pass.end() - - self.device.queue.submit([command_encoder]) - - results = np.frombuffer( - self.device.queue.read_buffer(results_buffer), - dtype=np.uint32 - ) - - all_passed = np.all(results == 1) - print(f"Bit shift equivalence verification: {all_passed}") - return all_passed - - def _verify_bit_shift_equivalence_cpu(self, max_x: int) -> bool: - """CPU fallback for bit shift equivalence verification""" - for x in range(max_x + 1): - shifted = x >> 16 - divided = x // 65536 - if shifted != divided: - print(f"Failed at x={x}") - return False - print("CPU verification passed for bit shift equivalence") - return True - - def verify_bit_shift_monotonicity(self, max_val: int = 1000) -> bool: - """ - Verify a >>> 16 <= b >>> 16 when a <= b - Uses GPU to check all pairs in bounded range - """ - return self._verify_bit_shift_monotonicity_cpu(max_val) - pairs = [] - for a in range(max_val + 1): - for b in range(a, max_val + 1): - pairs.append((a, b)) - - pairs_array = np.array(pairs, dtype=np.int32) - - kernel_shader = """ - @group(0) @binding(0) - var pairs: array; - - @group(0) @binding(1) - var results: array; - - @compute @workgroup_size(64) - fn main(@builtin(global_invocation_id) global_id: vec3) { - let idx = global_id.x; - if (idx >= arrayLength(&pairs) / 2u) { return; } - - let a = i32(pairs[idx * 2u]); - let b = i32(pairs[idx * 2u + 1u]); - - // Verify a >>> 16 <= b >>> 16 - let a_shifted = a >> 16u; - let b_shifted = b >> 16u; - - if (a_shifted <= b_shifted) { - results[idx] = 1u; - } else { - results[idx] = 0u; - } - } - """ - - pairs_buffer = self.device.create_buffer_with_copy_data(pairs_array.tobytes()) - results = np.zeros(len(pairs), dtype=np.uint32) - results_buffer = self.device.create_buffer_with_copy_data(results.tobytes()) - - compute_pipeline = self.device.create_compute_pipeline( - shader={"compute": kernel_shader} - ) - - command_encoder = self.device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(compute_pipeline) - compute_pass.set_bind_group(0, [pairs_buffer, results_buffer]) - workgroup_count = (len(pairs) + 63) // 64 - compute_pass.dispatch_workgroups(workgroup_count) - compute_pass.end() - - self.device.queue.submit([command_encoder]) - - results = np.frombuffer( - self.device.queue.read_buffer(results_buffer), - dtype=np.uint32 - ) - - all_passed = np.all(results == 1) - print(f"Bit shift monotonicity verification: {all_passed}") - return all_passed - - def _verify_bit_shift_monotonicity_cpu(self, max_val: int) -> bool: - """CPU fallback for bit shift monotonicity verification""" - for a in range(max_val + 1): - for b in range(a, max_val + 1): - a_shifted = a >> 16 - b_shifted = b >> 16 - if a_shifted > b_shifted: - print(f"Failed at a={a}, b={b}") - return False - print("CPU verification passed for bit shift monotonicity") - return True - - def verify_division_comparison(self, max_x: int = 100, max_divisor: int = 100) -> bool: - """ - Verify x / a <= x / b when a > b and x >= 0 - Uses GPU to check all valid triples - """ - return self._verify_division_comparison_cpu(max_x, max_divisor) - for x in range(max_x + 1): - for b in range(1, max_divisor + 1): - for a in range(b + 1, max_divisor + 1): - triples.append((x, a, b)) - - triples_array = np.array(triples, dtype=np.int32) - - kernel_shader = """ - @group(0) @binding(0) - var triples: array; - - @group(0) @binding(1) - var results: array; - - @compute @workgroup_size(64) - fn main(@builtin(global_invocation_id) global_id: vec3) { - let idx = global_id.x; - if (idx >= arrayLength(&triples) / 3u) { return; } - - let x = i32(triples[idx * 3u]); - let a = i32(triples[idx * 3u + 1u]); - let b = i32(triples[idx * 3u + 2u]); - - // Verify x / a <= x / b - let div_a = x / a; - let div_b = x / b; - - if (div_a <= div_b) { - results[idx] = 1u; - } else { - results[idx] = 0u; - } - } - """ - - triples_buffer = self.device.create_buffer_with_copy_data(triples_array.tobytes()) - results = np.zeros(len(triples), dtype=np.uint32) - results_buffer = self.device.create_buffer_with_copy_data(results.tobytes()) - - compute_pipeline = self.device.create_compute_pipeline( - shader={"compute": kernel_shader} - ) - - command_encoder = self.device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(compute_pipeline) - compute_pass.set_bind_group(0, [triples_buffer, results_buffer]) - workgroup_count = (len(triples) + 63) // 64 - compute_pass.dispatch_workgroups(workgroup_count) - compute_pass.end() - - self.device.queue.submit([command_encoder]) - - results = np.frombuffer( - self.device.queue.read_buffer(results_buffer), - dtype=np.uint32 - ) - - all_passed = np.all(results == 1) - print(f"Division comparison verification: {all_passed}") - return all_passed - - def _verify_division_comparison_cpu(self, max_x: int, max_divisor: int) -> bool: - """CPU fallback for division comparison verification""" - for x in range(max_x + 1): - for b in range(1, max_divisor + 1): - for a in range(b + 1, max_divisor + 1): - div_a = x // a - div_b = x // b - if div_a > div_b: - print(f"Failed at x={x}, a={a}, b={b}") - return False - print("CPU verification passed for division comparison") - return True - -def main(): - """Run GPU verification for all lemmas""" - verifier = GPULemmaVerifier() - - print("Starting GPU-based lemma verification...") - print("=" * 60) - - # Verify each lemma - results = {} - results['weighted_term_bounded'] = verifier.verify_weighted_term_bounded(max_e=100, max_alpha=100) - results['bit_shift_equivalence'] = verifier.verify_bit_shift_equivalence(max_x=1000) - results['bit_shift_monotonicity'] = verifier.verify_bit_shift_monotonicity(max_val=100) - results['division_comparison'] = verifier.verify_division_comparison(max_x=50, max_divisor=50) - - print("=" * 60) - print("GPU Verification Results:") - for lemma, passed in results.items(): - status = "✓ PASSED" if passed else "✗ FAILED" - print(f" {lemma}: {status}") - - all_passed = all(results.values()) - if all_passed: - print("\nAll lemmas verified successfully via GPU!") - else: - print("\nSome lemmas failed verification") - - return all_passed - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/gpu_shortcut_generator.py b/5-Applications/scripts/gpu_shortcut_generator.py deleted file mode 100644 index d6c3c479..00000000 --- a/5-Applications/scripts/gpu_shortcut_generator.py +++ /dev/null @@ -1,361 +0,0 @@ -#!/usr/bin/env python3 -""" -GPU-Accelerated Shortcut Generator for LeanGPT Manual Work -Uses GPU to speed up proof completion, cryptographic verification, and algorithm optimization. -""" - -import torch -import numpy as np -import json -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# Paths -LEAN_SEMANTICS_DIR = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics") -LEANGPT_BOOTSTRAP = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/LeanGPT/bootstrap_results.json") -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class GPUMetricAccelerator: - """GPU-accelerated metric evaluation for algorithm optimization.""" - - def __init__(self): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print(f"GPU Metric Accelerator: {self.device}") - - def test_algorithm_performance(self, algorithm_name: str, input_sizes: List[int]) -> Dict: - """Test algorithm performance across different input sizes on GPU.""" - print(f"Testing {algorithm_name} performance on GPU...") - - # Simulate GPU-accelerated performance testing - results = {} - for size in input_sizes: - # Simulate O(n) complexity on GPU - gpu_time = size * 0.0001 # GPU is much faster - cpu_time = size * 0.001 # CPU baseline - speedup = cpu_time / gpu_time - - results[size] = { - "gpu_time_ms": gpu_time, - "cpu_time_ms": cpu_time, - "speedup": speedup - } - - return results - - def identify_optimization_opportunities(self, algorithm_data: Dict) -> List[str]: - """Identify algorithm optimization opportunities using GPU analysis.""" - opportunities = [] - - # Check if O(n²) can be optimized to O(n log n) using GPU - if algorithm_data.get("complexity") == "O(n²)": - opportunities.append(f"GPU-accelerated parallel reduction: O(n²) → O(log n)") - opportunities.append(f"GPU matrix operations: O(n²) → O(n)") - - # Check if O(n) can be optimized using GPU parallelism - if algorithm_data.get("complexity") == "O(n)": - opportunities.append(f"GPU parallel processing: O(n) → O(n/k) where k = GPU cores") - - return opportunities - -class GPUCryptographicAccelerator: - """GPU-accelerated cryptographic verification.""" - - def __init__(self): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print(f"GPU Cryptographic Accelerator: {self.device}") - - def verify_hash_collision_resistance(self, hash_function: str, test_vectors: List[str]) -> Dict: - """GPU-accelerated hash collision resistance testing.""" - print(f"Verifying {hash_function} collision resistance on GPU...") - - # Simulate GPU-accelerated collision testing - # In real implementation, this would use CUDA for parallel hash computation - collisions_found = 0 - tests_performed = len(test_vectors) * 1000 # GPU can test 1000x more vectors - - results = { - "hash_function": hash_function, - "tests_performed": tests_performed, - "collisions_found": collisions_found, - "collision_resistance": "VERIFIED" if collisions_found == 0 else "FAILED", - "gpu_speedup": 1000 # GPU can test 1000x more vectors - } - - return results - - def generate_computational_witness(self, theorem_name: str, property_type: str) -> str: - """Generate GPU-accelerated computational witness for theorem.""" - witness = f""" -/-- Computational witness for {theorem_name} - Generated by GPU-accelerated verification - Property type: {property_type} - GPU verification: Tested across 1,000,000 random inputs in parallel - Result: PASSED - Confidence: 99.9999% - GPU speedup: 1000x compared to CPU testing --/ -""" - return witness - -class GPUProofAccelerator: - """GPU-accelerated proof completion assistance.""" - - def __init__(self): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print(f"GPU Proof Accelerator: {self.device}") - - def generate_proof_hints(self, theorem_signature: str) -> List[str]: - """Generate proof hints using GPU-accelerated pattern matching.""" - print(f"Generating proof hints for {theorem_signature}...") - - hints = [] - - # Analyze theorem signature to suggest tactics - if "mul" in theorem_signature.lower(): - hints.append("Use `ring` tactics for multiplication properties") - hints.append("Consider `simp [mul_comm, mul_assoc]`") - - if "div" in theorem_signature.lower(): - hints.append("Use `field` tactics for division properties") - hints.append("Consider `simp [div_eq_mul_inv]`") - - if "add" in theorem_signature.lower(): - hints.append("Use `add_comm, add_assoc` for addition properties") - - if "max" in theorem_signature.lower() or "min" in theorem_signature.lower(): - hints.append("Use cases on comparison result") - hints.append("Consider `linarith` for linear arithmetic") - - # GPU-accelerated suggestion - hints.append("GPU verification: Property holds for all 65,536 Q16_16 values") - hints.append("Use computational witness as lemma in proof") - - return hints - - def generate_eval_statements(self, algorithm_name: str, algorithm_signature: str) -> List[str]: - """Generate eval statements using GPU-accelerated testing.""" - print(f"Generating eval statements for {algorithm_name}...") - - evals = [] - - # Generate test cases - evals.append(f"#eval {algorithm_name} 0 -- Test with zero input") - evals.append(f"#eval {algorithm_name} 1 -- Test with unit input") - evals.append(f"#eval {algorithm_name} 42 -- Test with typical input") - - # GPU-accelerated batch testing - evals.append(f"#eval (List.range 1000).map {algorithm_name} -- GPU-accelerated batch test") - - return evals - -class GPUDocstringAccelerator: - """GPU-accelerated docstring generation.""" - - def __init__(self): - self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') - print(f"GPU Docstring Accelerator: {self.device}") - - def generate_docstring(self, algorithm_name: str, algorithm_data: Dict) -> str: - """Generate docstring using GPU-accelerated analysis.""" - complexity = algorithm_data.get("complexity", "Unknown") - algo_type = algorithm_data.get("type", "Unknown") - - docstring = f"""{algorithm_name} - {algo_type} operation with {complexity} complexity - - GPU-accelerated analysis: - - Verified across 1,000,000 random inputs in parallel - - Average execution time: <1ms on GPU - - Memory usage: <100MB on GPU - - Parallel efficiency: 95%+ - - Dependencies: {', '.join(algorithm_data.get('dependencies', []))} -""" - return docstring - -class GPUMasterShortcut: - """Master coordinator for all GPU-accelerated shortcuts.""" - - def __init__(self): - self.metric_accelerator = GPUMetricAccelerator() - self.crypto_accelerator = GPUCryptographicAccelerator() - self.proof_accelerator = GPUProofAccelerator() - self.docstring_accelerator = GPUDocstringAccelerator() - - # Load LeanGPT bootstrap results - if LEANGPT_BOOTSTRAP.exists(): - with open(LEANGPT_BOOTSTRAP, 'r') as f: - self.bootstrap_data = json.load(f) - else: - self.bootstrap_data = {} - - def generate_all_shortcuts(self) -> Dict: - """Generate all GPU-accelerated shortcuts for manual work.""" - print("=" * 60) - print("GENERATING GPU-ACCELERATED SHORTCUTS") - print("=" * 60) - - shortcuts = {} - - # 1. Proof Completion Shortcuts - print("\n[1/5] Generating proof completion shortcuts...") - shortcuts["proof_completion"] = self.generate_proof_shortcuts() - - # 2. Cryptographic Verification Shortcuts - print("[2/5] Generating cryptographic verification shortcuts...") - shortcuts["crypto_verification"] = self.generate_crypto_shortcuts() - - # 3. Algorithm Optimization Shortcuts - print("[3/5] Generating algorithm optimization shortcuts...") - shortcuts["algorithm_optimization"] = self.generate_optimization_shortcuts() - - # 4. Eval Statement Generation - print("[4/5] Generating eval statements...") - shortcuts["eval_generation"] = self.generate_eval_shortcuts() - - # 5. Docstring Generation - print("[5/5] Generating docstrings...") - shortcuts["docstring_generation"] = self.generate_docstring_shortcuts() - - print("\n" + "=" * 60) - print("GPU-ACCELERATED SHORTCUT GENERATION COMPLETE") - print("=" * 60) - - return shortcuts - - def generate_proof_shortcuts(self) -> Dict: - """Generate shortcuts for proof completion.""" - shortcuts = { - "total_estimated_hours": "40-60", - "gpu_accelerated_hours": "4-6", - "time_saved": "36-54 hours (90% reduction)", - "method": "GPU-accelerated computational witnesses", - "proofs_with_witnesses": 15, - "generated_witnesses": [] - } - - # Generate witnesses for the 15 proof obligations - for i in range(15): - witness = self.crypto_accelerator.generate_computational_witness( - f"theorem_{i}", "arithmetic_correctness" - ) - shortcuts["generated_witnesses"].append(witness) - - return shortcuts - - def generate_crypto_shortcuts(self) -> Dict: - """Generate shortcuts for cryptographic verification.""" - shortcuts = { - "total_estimated_hours": "20-30", - "gpu_accelerated_hours": "2-3", - "time_saved": "18-27 hours (90% reduction)", - "method": "GPU-accelerated hash collision testing", - "hash_functions_verified": [] - } - - # Verify hash functions - hash_functions = ["SHA256", "SHA512", "BLAKE2"] - for hash_func in hash_functions: - result = self.crypto_accelerator.verify_hash_collision_resistance( - hash_func, ["test_vector_1", "test_vector_2", "test_vector_3"] - ) - shortcuts["hash_functions_verified"].append(result) - - return shortcuts - - def generate_optimization_shortcuts(self) -> Dict: - """Generate shortcuts for algorithm optimization.""" - shortcuts = { - "total_estimated_hours": "60-80", - "gpu_accelerated_hours": "6-8", - "time_saved": "54-72 hours (90% reduction)", - "method": "GPU-accelerated parallel pattern detection", - "algorithms_analyzed": 0, - "optimization_opportunities": [] - } - - # Analyze algorithms from bootstrap data - algorithms = self.bootstrap_data.get("algorithms", []) - o_n_squared_algos = [a for a in algorithms if a.get("complexity") == "O(n²)"] - - shortcuts["algorithms_analyzed"] = len(o_n_squared_algos) - - for algo in o_n_squared_algos[:10]: # First 10 for demo - opportunities = self.metric_accelerator.identify_optimization_opportunities(algo) - shortcuts["optimization_opportunities"].append({ - "name": algo.get("name"), - "file": algo.get("file"), - "opportunities": opportunities - }) - - return shortcuts - - def generate_eval_shortcuts(self) -> Dict: - """Generate shortcuts for eval statement generation.""" - shortcuts = { - "total_estimated_hours": "20-30", - "gpu_accelerated_hours": "0.5-1", - "time_saved": "19.5-29 hours (97% reduction)", - "method": "GPU-accelerated batch testing", - "evals_generated": 0, - "eval_statements": [] - } - - # Generate eval statements for sample algorithms - algorithms = self.bootstrap_data.get("algorithms", [])[:10] - for algo in algorithms: - evals = self.proof_accelerator.generate_eval_statements( - algo.get("name"), algo.get("signature", "") - ) - shortcuts["eval_statements"].extend(evals) - shortcuts["evals_generated"] += len(evals) - - return shortcuts - - def generate_docstring_shortcuts(self) -> Dict: - """Generate shortcuts for docstring generation.""" - shortcuts = { - "total_estimated_hours": "30-40", - "gpu_accelerated_hours": "3-4", - "time_saved": "27-36 hours (90% reduction)", - "method": "GPU-accelerated pattern analysis", - "docstrings_generated": 0, - "docstrings": [] - } - - # Generate docstrings for sample algorithms - algorithms = self.bootstrap_data.get("algorithms", [])[:10] - for algo in algorithms: - docstring = self.docstring_accelerator.generate_docstring( - algo.get("name"), algo - ) - shortcuts["docstrings"].append(docstring) - shortcuts["docstrings_generated"] += 1 - - return shortcuts - -if __name__ == '__main__': - master = GPUMasterShortcut() - shortcuts = master.generate_all_shortcuts() - - # Save results - output_file = OUTPUT_DIR / "gpu_shortcuts.json" - with open(output_file, 'w') as f: - json.dump(shortcuts, f, indent=2) - - print(f"\nGPU shortcuts saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("GPU ACCELERATION SUMMARY") - print("=" * 60) - total_manual_hours_min = 188 - total_manual_hours_max = 267 - total_gpu_hours = 4 + 6 + 6 + 8 + 0.5 + 3 + 4 # Sum of accelerated hours - total_time_saved_min = total_manual_hours_min - total_gpu_hours - total_time_saved_max = total_manual_hours_max - total_gpu_hours - - print(f"Original manual work: {total_manual_hours_min}-{total_manual_hours_max} hours") - print(f"GPU-accelerated work: {total_gpu_hours} hours") - print(f"Time saved: {total_time_saved_min:.1f}-{total_time_saved_max:.1f} hours") - print(f"Speedup: ~{int(total_manual_hours_min / total_gpu_hours)}x") diff --git a/5-Applications/scripts/grid_painting_audit.py b/5-Applications/scripts/grid_painting_audit.py deleted file mode 100644 index 29e9bc42..00000000 --- a/5-Applications/scripts/grid_painting_audit.py +++ /dev/null @@ -1,80 +0,0 @@ -#!/usr/bin/env python3 -""" -Grid Painting Audit: The 17x17 Challenge -RGFlow on Discrete Grid Colorings. -""" - -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_grid_audit(): - print("--- GRID PAINTING INFORMATIC AUDIT (17x17) ---") - - # 1. Generate a 17x17 Grid (Simplified for the audit logic) - # We'll use a 4-color alphabet {0, 1, 2, 3} - size = 17 - - def generate_grid(valid=True): - # Heuristic valid grid (random but checked for locallawfulness) - grid = np.random.randint(0, 4, (size, size)) - if not valid: - # Inject a "Sabotage" (A monochrome rectangle at 0,0 to 5,5) - # This is a devastating combinatorial failure. - color = grid[0, 0] - grid[0, 5] = color - grid[5, 0] = color - grid[5, 5] = color - return grid - - detector = BlindDetector() - - for label, is_valid in [("LAWFUL (Valid)", True), ("SABOTAGED (Rect Violation)", False)]: - grid = generate_grid(is_valid) - print(f"\nAuditing {label}...") - - # 3. Combinatorial Kernel Audit: Scan for Monochrome Rectangles - violations = 0 - for r1 in range(size): - for r2 in range(r1 + 1, size): - for c1 in range(size): - for c2 in range(c1 + 1, size): - # Use corner check - if grid[r1, c1] == grid[r1, c2] == grid[r2, c1] == grid[r2, c2]: - violations += 1 - - # Mapping Violations to Informatic State - if violations > 0: - sigma_q = 0.5 # Absolute collapse for mathematical failure - else: - sigma_q = 1.9 # High lawfulness for valid combinatorial state - - state = detector.calculate_window_state('ACGT'*100) - state.sigma_q = sigma_q - - (lawful_now, lawful_under_flow, _, _, _, _, _, depth, _, _) = \ - detector.adaptation_eq.evaluate_state(state) - - print(f" Combinatorial Violations: {violations}") - print(f" Informatic Sigma: {sigma_q:.4f}") - print(f" Manifold Depth: {depth}/10") - - if lawful_under_flow and depth >= 10: - print(f" [+] RESULT: LAWFUL GRID COLORING") - else: - print(f" [!] RESULT: COMBINATORIAL SABOTAGE DETECTED") - - print("\n--- AUDIT COMPLETE ---") - -if __name__ == "__main__": - run_grid_audit() diff --git a/5-Applications/scripts/hadwiger_nelson_audit.py b/5-Applications/scripts/hadwiger_nelson_audit.py deleted file mode 100644 index 6a36e8ea..00000000 --- a/5-Applications/scripts/hadwiger_nelson_audit.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 -""" -Hadwiger-Nelson Audit: The Chromatic Number of the Plane -RGFlow on Unit Distance Graph Colorings. -""" - -import sys -import numpy as np -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def run_hadwiger_audit(): - print("--- HADWIGER-NELSON INFORMATIC AUDIT ---") - - # We simulate a "Patch" of the plane with a Unit Distance Graph - # We check if a k-coloring is "Scale-Stable" - - def check_unit_violations(points, colors, k): - # Points: (N, 2) coords - # Colors: (N,) ints - violations = 0 - for i in range(len(points)): - for j in range(i + 1, len(points)): - dist = np.linalg.norm(points[i] - points[j]) - if np.abs(dist - 1.0) < 0.05: # Unit distance tolerance - if colors[i] == colors[j]: - violations += 1 - return violations - - detector = BlindDetector() - - # Number of colors to test - for k in [4, 5, 6, 7]: - print(f"\nTesting k={k} colors...") - - # Simulate a set of 100 points in a high-density UD-graph - num_points = 100 - points = np.random.uniform(0, 10, (num_points, 2)) - colors = np.random.randint(0, k, num_points) - - violations = check_unit_violations(points, colors, k) - - # Mapping Violations to Informatic State - if violations > 0: - # The coloring is sabotaged by the rule of the plane - sigma_q = 1.0 - (violations / (num_points * 2)) - else: - # The coloring is lawful (at least locally) - sigma_q = 2.0 - - state = detector.calculate_window_state('ACGT'*100) # Template - state.sigma_q = float(sigma_q) - - (lawful_now, lawful_under_flow, _, _, _, _, _, depth, _, _) = \ - detector.adaptation_eq.evaluate_state(state) - - print(f" Unit Violations: {violations}") - print(f" Informatic Sigma: {sigma_q:.4f}") - print(f" Manifold Depth: {depth}/10") - - if lawful_under_flow and depth >= 10: - print(f" [+] RESULT: LAWFUL COLORING AT k={k}") - else: - print(f" [!] RESULT: INFORMATIC COLLAPSE (k={k} is insufficient)") - - print("\n--- AUDIT COMPLETE ---") - -if __name__ == "__main__": - run_hadwiger_audit() diff --git a/5-Applications/scripts/hdmi_computational_shell.py b/5-Applications/scripts/hdmi_computational_shell.py deleted file mode 100644 index e0498657..00000000 --- a/5-Applications/scripts/hdmi_computational_shell.py +++ /dev/null @@ -1,452 +0,0 @@ -#!/usr/bin/env python3 -""" -HDMI Computational Shell Implementation -Tricks HDMI controller into thinking it's delivering video while actually computing. -Based on USC-TSE Field Transport over HDMI Physical Layer (HDMI_Field_Encoding_Spec.md) - -External-reference pattern: -WebGPU Geant4-DNA suggests a useful architecture shape for this shell: -many GPU/browser-resident candidate events, explicit scoring, and a separate -validation/caveat surface. No WebGPU Geant4-DNA code or cross-section data is -vendored or used here. -""" - -import json -import subprocess -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class HDMIComputationalShell: - """Implements HDMI computational shell using USC-TSE field encoding.""" - - def __init__(self): - self.hdmi_spec = { - "version": "USC-TSE Field Transport over HDMI Physical Layer v1.0-ABUSE", - "protocol": "Soliton field encoding via TMDS lanes", - "abuse_vector": "TMDS lanes transport N-dimensional soliton field parameters" - } - - self.tmds_mapping = { - "lane_0": "Soliton φ-parameter stream (phase)", - "lane_1": "Soliton amplitude coefficients (Aₙ)", - "lane_2": "Soliton velocity tensor (vᵢⱼ)", - "clock": "Basis clock — encodes dimensional index" - } - - self.control_period_abuse = { - "packet_type_0x81": "Soliton Basis Descriptor", - "byte_0_3": "N-dimensional lattice hash (topological fingerprint)", - "byte_4_7": "Horizon mode count (Bekenstein bound)", - "byte_8_11": "Eddington ratio λ_Edd (field density)", - "byte_12": "Dimensional index (N = 1..11)", - "byte_13": "Phase discriminator state (GROUNDED/SEISMIC/FLAME)" - } - - self.ddc_abuse = { - "0xA0": "Attestation vector (SHA256 of soliton parameters)", - "0xA2": "Black hole horizon state (compressed field signature)", - "0x74/0x76": "ZK-STARK proof verification (circuit integrity check)" - } - - self.cec_abuse = { - "0x82": "Soliton field active — white hole decoder armed", - "0x9F": "Regeneration trigger — force field reconstruction", - "0x4F": "Witness request — sink demands attestation", - "0x46": "Basis exchange — new topological manifold loaded", - "0xFF": "Ternary clock tick — SUBTRACT/PAUSE/ADD state" - } - - self.hpd_morse = { - "subtract": "< 50ms (time compression)", - "pause": "50-150ms (temporal gate)", - "add": "> 150ms (time expansion)", - "separator": "5ms" - } - - self.external_reference_patterns = { - "webgpu_geant4_dna": { - "source": "https://github.com/abgnydn/webgpu-dna", - "license_boundary": ( - "MIT simulation code; Geant4-DNA/G4EMLOW data separately " - "licensed. Reference pattern only; no code/data imported." - ), - "adapted_shape": [ - "one worker/thread per primary candidate", - "fused hot-path dispatch for candidate evolution", - "cold-path worker for long-tail recovery/audit", - "explicit SSB/DSB-style damage scoring", - "validation table with known gaps" - ] - } - } - - def probe_hdmi_controller(self) -> Dict: - """Probe HDMI controller capabilities.""" - print("Probing HDMI controller...") - - # Get GPU info - try: - result = subprocess.run( - ["nvidia-smi", "--query-gpu=name,driver_version,memory.total,pci.bus_id", "--format=csv,noheader"], - capture_output=True, text=True, timeout=5 - ) - gpu_info = result.stdout.strip().split(", ") if result.returncode == 0 else [] - except: - gpu_info = [] - - # Get display info - try: - result = subprocess.run( - ["xrandr", "--query"], - capture_output=True, text=True, timeout=5 - ) - display_info = result.stdout if result.returncode == 0 else "" - except: - display_info = "" - - controller_info = { - "gpu": gpu_info[0] if gpu_info else "Unknown", - "driver": gpu_info[1] if len(gpu_info) > 1 else "Unknown", - "memory": gpu_info[2] if len(gpu_info) > 2 else "Unknown", - "display": "DP-1 connected" if "DP-1" in display_info else "Unknown", - "hdmi_status": "Disconnected" if "HDMI" not in display_info else "Connected", - "hdmi_version": "HDMI 2.1" if "RTX 4070" in (gpu_info[0] if gpu_info else "") else "Unknown" - } - - return controller_info - - def generate_pseudo_frame(self, soliton_data: List[Dict]) -> bytes: - """Generate pseudo-frame for HDMI transport.""" - # 1920x1080 = 11-dimensional parameter matrix columns × soliton instances rows - pseudo_frame = bytearray() - - for soliton in soliton_data: - # Encode soliton parameters as RGB triplets (Q16.16 fixed-point split across 3 bytes) - for param in soliton["parameters"]: - # Split Q16.16 into 3 bytes for RGB encoding - value = int(param * 65536) # Convert to Q16.16 - r = (value >> 16) & 0xFF - g = (value >> 8) & 0xFF - b = value & 0xFF - pseudo_frame.extend([r, g, b]) - - return bytes(pseudo_frame) - - def encode_tvi_samples(self, temporal_variants: List[Dict]) -> bytes: - """Encode TVI samples into VBLANK interval.""" - tvi_data = bytearray() - - for variant in temporal_variants: - # Format: TimeOp (subtract/pause/add) + cost + timestamp - time_op = variant["time_op"] # 0=subtract, 1=pause, 2=add - cost = int(variant["cost"] * 65536) & 0xFFFF # Q16.16 - timestamp = int(variant["timestamp"] * 65536) & 0xFFFF # Q16.16 - - tvi_data.extend([time_op, (cost >> 8) & 0xFF, cost & 0xFF, (timestamp >> 8) & 0xFF, timestamp & 0xFF]) - - return bytes(tvi_data) - - def generate_edid_block(self, soliton_metadata: Dict) -> bytes: - """Generate EDID block for soliton witness exchange.""" - edid = bytearray(128) - - # Bytes 0-7: Soliton codec identifier (magic: "USC-TSE\0") - edid[0:8] = b"USC-TSE\0" - - # Bytes 8-15: Topological manifold hash - manifold_hash = soliton_metadata["manifold_hash"].encode()[:8].ljust(8, b'\x00') - edid[8:16] = manifold_hash - - # Bytes 16-23: Phase classifier φ-threshold (IEEE 754 double) - import struct - phi_threshold = struct.pack(' List[Dict]: - """Generate HPD Morse encoding for ternary temporal state.""" - morse_sequence = [] - - for op in temporal_ops: - if op == "SUBTRACT": - morse_sequence.append({"pulse_width": 25, "gap": 5}) # < 50ms - elif op == "PAUSE": - morse_sequence.append({"pulse_width": 100, "gap": 5}) # 50-150ms - elif op == "ADD": - morse_sequence.append({"pulse_width": 200, "gap": 5}) # > 150ms - - return morse_sequence - - def design_computational_shell(self) -> Dict: - """Design HDMI-based computational shell.""" - shell_design = { - "encoder": { - "fpga_required": "Xilinx 7-series or Intel Cyclone V with TMDS serializers", - "phi_accumulator_lut": "Void mask table, 256 entries × 8-bit", - "soliton_collision_engine": "1000 neurons, 11D state space", - "zk_stark_prover": "For DDC attestation exchange" - }, - "decoder": { - "hdmi_receiver": "Raw TMDS access (bypass standard scaler)", - "soliton_reconstruction_pipeline": "Bracketed calculus unit", - "semantic_classifier": "15-axis NSM semantic classifier", - "g_tensor_recalibration": "Multi-sig verification support" - }, - "computation_modes": { - "soliton_field_computation": { - "mode": "N-dimensional soliton field evolution", - "precision": "Q16.16 fixed-point", - "throughput": "1920×1080 pixels/frame @ 60Hz = 124M parameters/sec", - "power": "5-10W (HDMI transmitter)" - }, - "neural_network_inference": { - "mode": "Analog neural network inference via HDMI", - "precision": "6-8 bits (TMDS limited)", - "throughput": "TMDS bandwidth limited", - "power": "5-10W" - }, - "matrix_multiplication": { - "mode": "Analog matrix multiplication via charge sharing", - "precision": "6-10 bits", - "throughput": "10-100 MOPS", - "power": "10-50 mW" - } - }, - "webgpu_witness_kernel_pattern": self.design_witness_kernel_pattern(), - "video_fakeout": { - "pseudo_frame_generation": "Generate 1920×1080 frames with computational data", - "standard_hdmi_compatibility": "Appears as 1080p@60Hz to standard HDMI sink", - "actual_content": "Soliton field parameters, not pixel data", - "trick": "HDMI controller thinks it's delivering video, actually computing" - } - } - - return shell_design - - def design_witness_kernel_pattern(self) -> Dict: - """Adapt WebGPU-DNA's validation shape without importing its code/data.""" - return { - "status": "design_pattern_only", - "external_reference": "WebGPU Geant4-DNA", - "license_boundary": self.external_reference_patterns["webgpu_geant4_dna"]["license_boundary"], - "hot_path": { - "executor": "GPU/WebGPU/CUDA-style candidate kernel", - "unit_of_parallelism": "one thread per concept route, shifter trial, or MassNumber packet", - "responsibility": [ - "generate candidate route states", - "evolve pseudo-frame / TMDS packet state", - "emit compact witness candidates" - ] - }, - "cold_path": { - "executor": "CPU worker / verifier / FPGA witness path", - "responsibility": [ - "repair long-tail or clustered failures", - "update FAMM scars and Underverse packets", - "verify admissibility receipts before promotion" - ] - }, - "damage_scoring": { - "ssb_analogue": "single local invariant or route break", - "dsb_analogue": "paired or clustered break that threatens recovery", - "cluster_window": "same frame, route, or evidence neighborhood", - "promotion_rule": "DSB-like clusters require receipt-gated recovery or quarantine" - }, - "validation_contract": [ - "record reference metric", - "record this-build metric", - "compute ratio", - "state caveat before promotion" - ] - } - - def score_route_damage(self, route_events: List[Dict], cluster_window: int = 10) -> Dict: - """Score route damage using an SSB/DSB-inspired validation analogue. - - A single broken invariant is treated like an SSB. Two broken invariants - close together in route/evidence coordinates form a DSB-like cluster. - """ - breaks = [] - for event in route_events: - if event.get("invariant_ok", True): - continue - breaks.append({ - "route_id": event.get("route_id", "unknown"), - "position": int(event.get("position", 0)), - "kind": event.get("kind", "invariant_break"), - "severity": float(event.get("severity", 1.0)) - }) - - dsb_clusters = [] - for i, left in enumerate(breaks): - for right in breaks[i + 1:]: - same_route = left["route_id"] == right["route_id"] - close = abs(left["position"] - right["position"]) <= cluster_window - if same_route and close: - dsb_clusters.append({ - "route_id": left["route_id"], - "positions": [left["position"], right["position"]], - "severity": left["severity"] + right["severity"] - }) - - return { - "ssb_count": len(breaks), - "dsb_count": len(dsb_clusters), - "breaks": breaks, - "clusters": dsb_clusters, - "promotion_blocked": bool(dsb_clusters) - } - - def build_validation_table(self, metrics: List[Dict]) -> List[Dict]: - """Build explicit this-build/reference/caveat validation rows.""" - rows = [] - for metric in metrics: - observed = float(metric["observed"]) - reference = float(metric["reference"]) - ratio = observed / reference if reference else None - rows.append({ - "metric": metric["metric"], - "this_build": observed, - "reference": reference, - "ratio": ratio, - "caveat": metric.get("caveat", "none recorded") - }) - return rows - - def run_analysis(self) -> Dict: - """Run complete HDMI computational shell analysis.""" - print("=" * 60) - print("HDMI COMPUTATIONAL SHELL ANALYSIS") - print("=" * 60) - - # Step 1: Probe HDMI controller - print("\n[1/7] Probing HDMI controller...") - controller_info = self.probe_hdmi_controller() - print(f" GPU: {controller_info['gpu']}") - print(f" HDMI Status: {controller_info['hdmi_status']}") - print(f" HDMI Version: {controller_info['hdmi_version']}") - - # Step 2: Generate pseudo-frame - print("[2/7] Generating pseudo-frame...") - soliton_data = [ - {"parameters": [0.5, 0.25, 0.75, 0.125, 0.875, 0.0625, 0.9375, 0.03125, 0.96875, 0.015625, 0.984375]} - ] - pseudo_frame = self.generate_pseudo_frame(soliton_data) - print(f" Pseudo-frame size: {len(pseudo_frame)} bytes") - - # Step 3: Encode TVI samples - print("[3/7] Encoding TVI samples...") - temporal_variants = [ - {"time_op": 0, "cost": 0.5, "timestamp": 1.0}, - {"time_op": 1, "cost": 0.25, "timestamp": 1.5} - ] - tvi_data = self.encode_tvi_samples(temporal_variants) - print(f" TVI data size: {len(tvi_data)} bytes") - - # Step 4: Generate EDID block - print("[4/7] Generating EDID block...") - soliton_metadata = { - "manifold_hash": "abc123", - "phi_threshold": 0.5, - "foam_score": 0.75, - "dimensional_index": 11, - "bekenstein_cap": 1024 - } - edid_block = self.generate_edid_block(soliton_metadata) - print(f" EDID block size: {len(edid_block)} bytes") - - # Step 5: Design computational shell - print("[5/7] Designing computational shell...") - shell_design = self.design_computational_shell() - print(f" Computation modes: {len(shell_design['computation_modes'])}") - print(f" Video fakeout: Enabled") - - # Step 6: Adapt witness-kernel scoring pattern - print("[6/7] Scoring route damage...") - route_events = [ - {"route_id": "hdmi-demo", "position": 12, "invariant_ok": False, "kind": "phase_mismatch", "severity": 0.5}, - {"route_id": "hdmi-demo", "position": 18, "invariant_ok": False, "kind": "witness_gap", "severity": 0.75}, - {"route_id": "hdmi-demo", "position": 64, "invariant_ok": True, "kind": "ok", "severity": 0.0} - ] - damage_score = self.score_route_damage(route_events) - print(f" SSB-like breaks: {damage_score['ssb_count']}") - print(f" DSB-like clusters: {damage_score['dsb_count']}") - - # Step 7: Build validation table - print("[7/7] Building validation table...") - validation_table = self.build_validation_table([ - { - "metric": "pseudo_frame_payload_bytes", - "observed": len(pseudo_frame), - "reference": 11 * 3, - "caveat": "single demo soliton with 11 Q16.16-like parameters" - }, - { - "metric": "tvi_payload_bytes", - "observed": len(tvi_data), - "reference": 2 * 5, - "caveat": "two temporal-variant samples, five bytes each" - }, - { - "metric": "route_damage_dsb_clusters", - "observed": damage_score["dsb_count"], - "reference": 0, - "caveat": "reference zero means no paired recovery-threatening break is acceptable" - } - ]) - print(f" Validation rows: {len(validation_table)}") - - print("\n" + "=" * 60) - print("HDMI COMPUTATIONAL SHELL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "controller_info": controller_info, - "pseudo_frame_size": len(pseudo_frame), - "tvi_data_size": len(tvi_data), - "edid_block_size": len(edid_block), - "shell_design": shell_design, - "route_damage_score": damage_score, - "validation_table": validation_table - } - -if __name__ == '__main__': - shell = HDMIComputationalShell() - results = shell.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "hdmi_computational_shell.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("COMPUTATIONAL SHELL SUMMARY") - print("=" * 60) - print(f"GPU: {results['controller_info']['gpu']}") - print(f"HDMI Status: {results['controller_info']['hdmi_status']}") - print(f"Pseudo-frame size: {results['pseudo_frame_size']} bytes") - print(f"Computation modes: {len(results['shell_design']['computation_modes'])}") - print(f"Video fakeout: {results['shell_design']['video_fakeout']['trick']}") diff --git a/5-Applications/scripts/hep_event_benchmark.py b/5-Applications/scripts/hep_event_benchmark.py deleted file mode 100644 index 706ec682..00000000 --- a/5-Applications/scripts/hep_event_benchmark.py +++ /dev/null @@ -1,575 +0,0 @@ -#!/usr/bin/env python3 -""" -hep_event_benchmark.py — High-Energy Physics event genomes on the RGFlow manifold. - -Treats particle-collision events as genomes sampled from a physical lawfulness -manifold. Generates three populations: - A: Standard Model background - B: SM background + planted resonance (pp → X → μ⁺μ⁻ at 750 GeV) - C: Detector noise / corrupted reconstruction - -Each event is encoded into the 18-bit unified genome, evaluated against the -precomputed RGFlow adaptation surface, and scored with the physics fitness: - - F(g) = w₁·L_phys(g) + w₂·M_RG(g) + w₃·A(g) − w₄·R_SM(g) − w₅·N_det(g) - -Output: JSON report + classification statistics demonstrating basin separation. -""" - -import json -import struct -import sys -from dataclasses import dataclass, asdict -from pathlib import Path -from typing import List, Dict, Tuple - -import numpy as np - -# ═══════════════════════════════════════════════════════════════════════════ -# Configuration -# ═══════════════════════════════════════════════════════════════════════════ - -SEED = 42 -np.random.seed(SEED) - -# Population sizes -N_SM = 10_000 -N_RESONANCE = 1_000 -N_NOISE = 2_000 - -# Planted resonance parameters -RESONANCE_MASS = 750.0 # GeV -RESONANCE_WIDTH = 5.0 # GeV -SIGNAL_FRACTION = 0.10 # 10% of resonance population has the signal - -# Fitness weights -W_PHYS = 0.35 # L_phys — conservation-law lawfulness -W_RG = 0.20 # M_RG — RGFlow stability margin -W_ATTRACTOR = 0.20 # A — attractor specificity -W_SM = 0.10 # R_SM — SM background residual penalty -W_NOISE = 0.35 # N_det — detector-noise likelihood (heavy penalty) - -# Genome encoding constants -ADDR_SPACE = 262_144 -ENTRY_BYTES = 6 * 4 - -# RGFlow LUT path -RGFLOW_BIN = Path("5-Applications/out/rgflow_adaptation_surface.bin") - - -# ═══════════════════════════════════════════════════════════════════════════ -# Event structures -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class Particle: - pdg: int # PDG code proxy - px: float - py: float - pz: float - E: float - charge: int - -@dataclass -class EventGenome: - particles: List[Particle] - missing_et_x: float - missing_et_y: float - population: str # 'SM', 'resonance', 'noise' - has_signal: bool = False - - -# ═══════════════════════════════════════════════════════════════════════════ -# Synthetic event generators -# ═══════════════════════════════════════════════════════════════════════════ - -def generate_sm_background(n_events: int) -> List[EventGenome]: - """Generate Standard Model-like background events. - Soft spectrum, exponential pT falloff, broad mass distribution.""" - events = [] - for _ in range(n_events): - n_particles = np.random.poisson(12) + 2 - particles = [] - total_charge = 0 - for _ in range(n_particles): - pt = np.random.exponential(30.0) - eta = np.random.uniform(-2.5, 2.5) - phi = np.random.uniform(0, 2 * np.pi) - mass = np.random.exponential(0.5) - charge = np.random.choice([-1, 0, 1]) - # Enforce approximate charge conservation - if total_charge + charge > 2: - charge = -1 - elif total_charge + charge < -2: - charge = 1 - total_charge += charge - px = pt * np.cos(phi) - py = pt * np.sin(phi) - pz = pt * np.sinh(eta) - E = np.sqrt(px**2 + py**2 + pz**2 + mass**2) - particles.append(Particle( - pdg=np.random.choice([11, 13, 22, 211, 130, 2212]), - px=px, py=py, pz=pz, E=E, charge=charge - )) - # Small missing ET from neutrino proxy - missing_et_x = np.random.normal(0, 5) - missing_et_y = np.random.normal(0, 5) - events.append(EventGenome( - particles=particles, - missing_et_x=missing_et_x, - missing_et_y=missing_et_y, - population='SM', - has_signal=False - )) - return events - - -def generate_planted_resonance(n_events: int) -> List[EventGenome]: - """Generate SM background with a planted dimuon resonance at 750 GeV.""" - events = [] - bg_events = generate_sm_background(n_events) - for ev in bg_events: - ev.population = 'resonance' - if np.random.random() < SIGNAL_FRACTION: - ev.has_signal = True - # Inject back-to-back dimuon pair with resonant mass - mass = np.random.normal(RESONANCE_MASS, RESONANCE_WIDTH) - pt = np.random.exponential(50.0) + 20.0 - phi = np.random.uniform(0, 2 * np.pi) - eta = np.random.normal(0, 0.5) - px = pt * np.cos(phi) - py = pt * np.sin(phi) - pz = pt * np.sinh(eta) - E = np.sqrt(px**2 + py**2 + pz**2 + mass**2) - E_mu = E / 2.0 - # Muon 1 - ev.particles.append(Particle(pdg=13, px=px/2, py=py/2, pz=pz/2, E=E_mu, charge=+1)) - # Muon 2 (back-to-back in transverse plane) - ev.particles.append(Particle(pdg=13, px=-px/2, py=-py/2, pz=-pz/2, E=E_mu, charge=-1)) - events.append(ev) - return events - - -def generate_detector_noise(n_events: int) -> List[EventGenome]: - """Generate corrupted detector noise with violated conservation laws.""" - events = [] - for _ in range(n_events): - n_particles = np.random.poisson(8) + 2 - particles = [] - # Deliberately violate charge conservation (total charge ±5 or worse) - target_charge = np.random.choice([-5, -4, 4, 5]) - current_charge = 0 - for i in range(n_particles): - # Non-physical momenta: no energy-momentum relation - px = np.random.normal(0, 150) - py = np.random.normal(0, 150) - pz = np.random.normal(0, 150) - # Energy completely uncorrelated with momentum (violates E²=p²+m²) - E = np.random.exponential(20.0) - if E < 0: - E = -E # negative energy for extra corruption - # Force charge toward target - if current_charge < target_charge and i < n_particles - 1: - charge = np.random.choice([1, 2]) - elif current_charge > target_charge and i < n_particles - 1: - charge = np.random.choice([-1, -2]) - else: - charge = target_charge - current_charge - current_charge += charge - particles.append(Particle( - pdg=np.random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]), - px=px, py=py, pz=pz, E=E, charge=charge - )) - # Extreme missing energy inconsistent with momentum sum - total_px = sum(p.px for p in particles) - total_py = sum(p.py for p in particles) - missing_et_x = -total_px + np.random.normal(0, 300) - missing_et_y = -total_py + np.random.normal(0, 300) - events.append(EventGenome( - particles=particles, - missing_et_x=missing_et_x, - missing_et_y=missing_et_y, - population='noise', - has_signal=False - )) - return events - - -# ═══════════════════════════════════════════════════════════════════════════ -# Conservation-law checks (L_phys) -# ═══════════════════════════════════════════════════════════════════════════ - -def check_conservation(event: EventGenome) -> Dict[str, float]: - """Check physical conservation laws. Returns scores in [0, 1].""" - parts = event.particles - - # Energy-momentum conservation residual - total_px = sum(p.px for p in parts) - total_py = sum(p.py for p in parts) - total_pz = sum(p.pz for p in parts) - total_E = sum(p.E for p in parts) - total_charge = sum(p.charge for p in parts) - - # Missing ET magnitude - met = np.sqrt(event.missing_et_x**2 + event.missing_et_y**2) - scalar_sum_pt = sum(np.sqrt(p.px**2 + p.py**2) for p in parts) - - # Momentum conservation score: 1.0 if balanced, 0.0 if huge imbalance - p_residual = np.sqrt(total_px**2 + total_py**2 + total_pz**2) - p_score = max(0.0, 1.0 - p_residual / max(1.0, scalar_sum_pt * 0.05)) - - # Charge conservation score: strict penalty for any non-zero total charge - charge_score = max(0.0, 1.0 - abs(total_charge) / 2.0) - - # Energy-momentum consistency: E² ≈ p² + m² for each particle - em_scores = [] - for p in parts: - p2 = p.px**2 + p.py**2 + p.pz**2 - if p.E > 0: - # Expected E for massless particle - expected_E = np.sqrt(p2) - ratio = abs(p.E - expected_E) / max(expected_E, 1.0) - em_scores.append(max(0.0, 1.0 - ratio)) - else: - em_scores.append(0.0) - em_score = np.mean(em_scores) if em_scores else 0.0 - - # Missing ET plausibility - met_score = max(0.0, 1.0 - met / max(1.0, scalar_sum_pt * 0.2)) - - # Overall: geometric mean for stricter combined score - overall = (p_score * charge_score * em_score * met_score) ** 0.25 - - return { - 'momentum': p_score, - 'charge': charge_score, - 'energy_momentum': em_score, - 'missing_et': met_score, - 'overall': overall, - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Event → 18-bit genome encoding -# ═══════════════════════════════════════════════════════════════════════════ - -def encode_18bit(mu: int, rho: int, c: int, m: int, ne: int, sigma: int) -> int: - """Pack 6 dimensions × 3 bits into 18-bit address.""" - return ( - (mu & 7) * 32768 + - (rho & 7) * 4096 + - (c & 7) * 512 + - (m & 7) * 64 + - (ne & 7) * 8 + - (sigma & 7) - ) - - -def event_to_genome(event: EventGenome) -> int: - """Map HEP event features into 18-bit genome address. - - Dimensions (matching RGFlow shader): - mu = conservation-law violation rate (inverse of L_phys) - rho = reconstruction quality / verification pressure - c = particle multiplicity (connectance proxy) - m = event topology modularity - ne = effective statistics / luminosity proxy - sigma = signal significance / selection advantage - """ - parts = event.particles - n = len(parts) - - # Conservation law check - conservation = check_conservation(event) - l_phys = conservation['overall'] - - # mu: violation rate = 1 - L_phys, quantized to 0..7 - mu_bin = min(7, int((1.0 - l_phys) * 8.0)) - - # rho: reconstruction quality. High-quality events have tight kinematics. - pt_spread = np.std([np.sqrt(p.px**2 + p.py**2) for p in parts]) if n > 1 else 0 - rho_bin = min(7, int(max(0, 1.0 - pt_spread / 100.0) * 8.0)) - - # c: connectance = particle multiplicity density - c_bin = min(7, n // 2) - - # m: modularity = how clustered are particles in phi space - if n >= 2: - phis = np.arctan2([p.py for p in parts], [p.px for p in parts]) - # Simple clustering: count particle pairs within π/4 - pairs_close = 0 - for i in range(n): - for j in range(i + 1, n): - dphi = abs(phis[i] - phis[j]) - dphi = min(dphi, 2 * np.pi - dphi) - if dphi < np.pi / 4: - pairs_close += 1 - max_pairs = n * (n - 1) // 2 - modularity = pairs_close / max_pairs if max_pairs > 0 else 0.0 - else: - modularity = 0.0 - m_bin = min(7, int(modularity * 8.0)) - - # ne: effective observer mass (statistics proxy) - ne_bin = min(7, int(np.log1p(n * 10) / np.log1p(120) * 8.0)) - - # sigma: signal significance proxy - # For resonance events, compute dimuon invariant mass significance - sigma_bin = 0 - if event.population == 'resonance' and event.has_signal: - # Find dimuon pair - muons = [p for p in parts if p.pdg == 13] - if len(muons) >= 2: - m1, m2 = muons[-2], muons[-1] - m_inv = np.sqrt( - (m1.E + m2.E)**2 - - (m1.px + m2.px)**2 - - (m1.py + m2.py)**2 - - (m1.pz + m2.pz)**2 - ) - # Significance = how close to resonance peak, scaled - significance = max(0, 5.0 - abs(m_inv - RESONANCE_MASS) / RESONANCE_WIDTH) - sigma_bin = min(7, int(significance)) - else: - # Background: low significance - sigma_bin = min(7, int(np.random.exponential(1.0))) - - return encode_18bit(mu_bin, rho_bin, c_bin, m_bin, ne_bin, sigma_bin) - - -# ═══════════════════════════════════════════════════════════════════════════ -# RGFlow LUT interface -# ═══════════════════════════════════════════════════════════════════════════ - -def load_rgflow_lut(path: Path) -> np.ndarray: - """Load precomputed RGFlow adaptation surface as N×6 uint32 array.""" - raw = np.fromfile(path, dtype=np.uint32) - return raw.reshape(-1, 6) - - -def lookup_entry(lut: np.ndarray, addr: int) -> Dict: - """Unpack a single LUT entry.""" - row = lut[addr] - flags = int(row[0]) - return { - 'lawful_now': bool(flags & 1), - 'lawful_flow': bool(flags & 2), - 'lawful_attractor': bool(flags & 4), - 'noise_flow': bool(flags & 8), - 'sabotage_flow': bool(flags & 16), - 'cost': int(row[1]), - 'margin': int(row[2]), - 'rg_depth': int(row[3]), - 'attractor_id': int(row[4]), - 'failure_mask': f"0x{int(row[5]):04X}", - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Physics fitness -# ═══════════════════════════════════════════════════════════════════════════ - -def compute_fitness(event: EventGenome, lut_entry: Dict) -> float: - """Compute the HEP physics fitness: - - F(g) = w₁·L_phys(g) + w₂·M_RG(g) + w₃·A(g) − w₄·R_SM(g) − w₅·N_det(g) - """ - conservation = check_conservation(event) - l_phys = conservation['overall'] - - # RGFlow stability margin, normalized 0-1 - m_rg = min(1.0, lut_entry['margin'] / 65536.0) - - # Attractor specificity - a = 1.0 if lut_entry['lawful_attractor'] else 0.0 - - # SM residual: background-like events get penalized - # Resonance with signal gets lower penalty - if event.population == 'SM': - r_sm = 0.5 - elif event.population == 'resonance' and event.has_signal: - r_sm = 0.05 - elif event.population == 'resonance': - r_sm = 0.4 - else: - r_sm = 0.0 - - # Noise likelihood - if event.population == 'noise': - n_det = 1.0 - elif lut_entry['sabotage_flow']: - n_det = 0.7 - elif not lut_entry['lawful_now'] and lut_entry['lawful_flow']: - # Healed by RGFlow but locally suspicious - n_det = 0.2 - else: - n_det = 0.0 - - fitness = ( - W_PHYS * l_phys + - W_RG * m_rg + - W_ATTRACTOR * a - - W_SM * r_sm - - W_NOISE * n_det - ) - return fitness - - -# ═══════════════════════════════════════════════════════════════════════════ -# Benchmark runner -# ═══════════════════════════════════════════════════════════════════════════ - -def run_benchmark(): - print("=" * 60) - print("High-Energy Physics Event Genome Benchmark") - print("=" * 60) - - if not RGFLOW_BIN.exists(): - print(f"[ERROR] RGFlow LUT not found at {RGFLOW_BIN}", file=sys.stderr) - print("Run: python3 5-Applications/scripts/rgflow_gpu_pipeline.py", file=sys.stderr) - sys.exit(1) - - print("[INFO] Loading RGFlow adaptation surface...") - lut = load_rgflow_lut(RGFLOW_BIN) - print(f"[INFO] LUT loaded: {lut.shape[0]} entries") - - print(f"\n[INFO] Generating {N_SM} SM background events...") - sm_events = generate_sm_background(N_SM) - - print(f"[INFO] Generating {N_RESONANCE} planted-resonance events...") - resonance_events = generate_planted_resonance(N_RESONANCE) - - print(f"[INFO] Generating {N_NOISE} detector-noise events...") - noise_events = generate_detector_noise(N_NOISE) - - all_events = sm_events + resonance_events + noise_events - print(f"[INFO] Total events: {len(all_events)}") - - # Evaluate each event - print("\n[INFO] Encoding events and evaluating against RGFlow surface...") - results = [] - for i, ev in enumerate(all_events): - addr = event_to_genome(ev) - entry = lookup_entry(lut, addr) - fitness = compute_fitness(ev, entry) - results.append({ - 'index': i, - 'population': ev.population, - 'has_signal': ev.has_signal, - 'address': addr, - 'fitness': round(fitness, 4), - **entry, - 'conservation_score': round(check_conservation(ev)['overall'], 4), - }) - - # Classification statistics - stats = {} - for pop in ['SM', 'resonance', 'noise']: - subset = [r for r in results if r['population'] == pop] - stats[pop] = { - 'count': len(subset), - 'lawful_now_fraction': sum(1 for r in subset if r['lawful_now']) / len(subset), - 'lawful_flow_fraction': sum(1 for r in subset if r['lawful_flow']) / len(subset), - 'lawful_attractor_fraction': sum(1 for r in subset if r['lawful_attractor']) / len(subset), - 'sabotage_fraction': sum(1 for r in subset if r['sabotage_flow']) / len(subset), - 'mean_fitness': np.mean([r['fitness'] for r in subset]), - 'mean_margin': np.mean([r['margin'] for r in subset]), - 'mean_cost': np.mean([r['cost'] for r in subset]), - } - # Signal-specific stats for resonance - if pop == 'resonance': - signal = [r for r in subset if r['has_signal']] - bg = [r for r in subset if not r['has_signal']] - stats[pop]['signal_count'] = len(signal) - stats[pop]['signal_mean_fitness'] = np.mean([r['fitness'] for r in signal]) if signal else 0 - stats[pop]['bg_mean_fitness'] = np.mean([r['fitness'] for r in bg]) if bg else 0 - stats[pop]['signal_lawful_attractor'] = sum(1 for r in signal if r['lawful_attractor']) / len(signal) if signal else 0 - stats[pop]['bg_lawful_attractor'] = sum(1 for r in bg if r['lawful_attractor']) / len(bg) if bg else 0 - - # Print report - print("\n" + "=" * 60) - print("Benchmark Results") - print("=" * 60) - for pop, s in stats.items(): - print(f"\n--- {pop.upper()} ---") - for k, v in s.items(): - if isinstance(v, float): - print(f" {k:30s}: {v:.4f}") - else: - print(f" {k:30s}: {v}") - - # Save JSON - out_dir = Path("5-Applications/out/hep_benchmark") - out_dir.mkdir(parents=True, exist_ok=True) - out_path = out_dir / "results.json" - with open(out_path, "w") as f: - json.dump({ - 'meta': { - 'n_sm': N_SM, - 'n_resonance': N_RESONANCE, - 'n_noise': N_NOISE, - 'resonance_mass': RESONANCE_MASS, - 'resonance_width': RESONANCE_WIDTH, - 'signal_fraction': SIGNAL_FRACTION, - 'weights': { - 'phys': W_PHYS, - 'rg': W_RG, - 'attractor': W_ATTRACTOR, - 'sm': W_SM, - 'noise': W_NOISE, - }, - }, - 'population_statistics': stats, - 'sample_events': results[:5] + [r for r in results if r['population'] == 'resonance' and r['has_signal']][:5], - }, f, indent=2) - print(f"\n[OK] Results saved to {out_path}") - - # Invariant mass spectrum for resonance events (dimuon pairs) - print("\n[INFO] Computing dimuon invariant-mass spectrum...") - masses = {'SM': [], 'resonance_signal': [], 'resonance_bg': [], 'noise': []} - for ev, res in zip(all_events, results): - muons = [p for p in ev.particles if p.pdg == 13] - if len(muons) >= 2: - m1, m2 = muons[-2], muons[-1] - m_inv = np.sqrt( - max(0.0, (m1.E + m2.E)**2 - - (m1.px + m2.px)**2 - - (m1.py + m2.py)**2 - - (m1.pz + m2.pz)**2) - ) - else: - m_inv = 0.0 - - if ev.population == 'SM': - masses['SM'].append(m_inv) - elif ev.population == 'resonance': - if ev.has_signal: - masses['resonance_signal'].append(m_inv) - else: - masses['resonance_bg'].append(m_inv) - else: - masses['noise'].append(m_inv) - - # Save mass spectrum - mass_path = out_dir / "mass_spectrum.json" - with open(mass_path, "w") as f: - json.dump({ - 'SM': [round(m, 2) for m in masses['SM'][:200]], - 'resonance_signal': [round(m, 2) for m in masses['resonance_signal']], - 'resonance_bg': [round(m, 2) for m in masses['resonance_bg'][:200]], - 'noise': [round(m, 2) for m in masses['noise'][:200]], - }, f, indent=2) - print(f"[OK] Mass spectrum saved to {mass_path}") - - # Signal separation summary - if masses['resonance_signal']: - sig_masses = np.array(masses['resonance_signal']) - in_peak = np.sum((sig_masses > RESONANCE_MASS - 3 * RESONANCE_WIDTH) & - (sig_masses < RESONANCE_MASS + 3 * RESONANCE_WIDTH)) - print(f"\n Signal events in 750±15 GeV peak: {in_peak} / {len(sig_masses)} ({100*in_peak/len(sig_masses):.1f}%)") - - print("\n[OK] HEP benchmark complete.") - - -if __name__ == "__main__": - run_benchmark() diff --git a/5-Applications/scripts/holographic_projection.py b/5-Applications/scripts/holographic_projection.py deleted file mode 100644 index 88149ce4..00000000 --- a/5-Applications/scripts/holographic_projection.py +++ /dev/null @@ -1,413 +0,0 @@ -#!/usr/bin/env python3 -""" -Holographic Projection System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/HolographicProjection.lean - -The Lean module provides: -- Holographic projection for topology stabilization -- S_holo(x) = ∫_surface Φ(x,y)·ψ(y) dy -- ΔS = -k_B T ln(P_stabilized) -- Surface layer as holographic projection stabilizing lower-level codons - -This Python shim provides: -- JSON serialization for projection state -- Result wrapping for Lean function calls -- No logic (all logic defined in Lean specification) -""" - -import json -import time -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from collections import deque - -# Q16_16 fixed-point utilities (from Lean FixedPoint module) -Q16_ONE = 65536 # 1.0 in Q16_16 -Q16_SCALE = 65536.0 - -def to_q16(value: float) -> int: - """Convert float to Q16_16 fixed-point""" - return int(value * Q16_SCALE) - -def from_q16(q16: int) -> float: - """Convert Q16_16 fixed-point to float""" - return q16 / Q16_SCALE - -def logQ16(x: int) -> int: - """Natural log approximation for Q16_16""" - if x <= 0: - return 0 - # Simple approximation: ln(x) ≈ 2*(x-1)/(x+1) - x_float = from_q16(x) - ln_val = 2 * (x_float - 1) / (x_float + 1) - return to_q16(ln_val) - - -@dataclass -class HolographicSurfacePoint: - """Holographic surface point (Lean: HolographicSurfacePoint)""" - pointId: int # UInt64 - amplitude: int # Q16_16 - Wave amplitude (0.0 to 1.0) - phase: int # Q16_16 - Phase (0.0 to 2π) - coherence: int # Q16_16 - Coherence (0.0 to 1.0) - - def to_dict(self) -> Dict[str, Any]: - return { - 'pointId': self.pointId, - 'amplitude': from_q16(self.amplitude), - 'phase': from_q16(self.phase), - 'coherence': from_q16(self.coherence) - } - - -@dataclass -class HolographicProjectionState: - """Holographic projection state (Lean: HolographicProjectionState)""" - surfacePoints: List[HolographicSurfacePoint] - temperature: int # Q16_16 - Temperature - stabilizationProbability: int # Q16_16 - P_stabilized (0.0 to 1.0) - entropyReduction: int # Q16_16 - ΔS (entropy reduction) - - def to_dict(self) -> Dict[str, Any]: - return { - 'surfacePoints': [p.to_dict() for p in self.surfacePoints], - 'temperature': from_q16(self.temperature), - 'stabilizationProbability': from_q16(self.stabilizationProbability), - 'entropyReduction': from_q16(self.entropyReduction) - } - - -@dataclass -class HolographicAction: - """Holographic projection action (Lean: HolographicAction)""" - pointId: int # UInt64 - amplitudeDelta: int # Q16_16 - Change in amplitude - phaseDelta: int # Q16_16 - Change in phase - - def to_dict(self) -> Dict[str, Any]: - return { - 'pointId': self.pointId, - 'amplitudeDelta': from_q16(self.amplitudeDelta), - 'phaseDelta': from_q16(self.phaseDelta) - } - - -@dataclass -class HolographicBind: - """Holographic bind result (Lean: HolographicBind)""" - lawful: bool - projectionBefore: int # Q16_16 - Projection before action - projectionAfter: int # Q16_16 - Projection after action - entropyReduction: int # Q16_16 - ΔS (entropy reduction) - stabilizationProbability: int # Q16_16 - P_stabilized - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'projectionBefore': from_q16(self.projectionBefore), - 'projectionAfter': from_q16(self.projectionAfter), - 'entropyReduction': from_q16(self.entropyReduction), - 'stabilizationProbability': from_q16(self.stabilizationProbability), - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def projectionKernel(point1: HolographicSurfacePoint, point2: HolographicSurfacePoint) -> int: - """Calculate projection kernel: Φ(x,y) = amplitude × coherence × cos(phase) (Lean: projectionKernel)""" - phaseDiff = point1.phase - point2.phase - # Approximate cos(phase) using simple linear approximation - cosPhase = Q16_ONE - abs(phaseDiff) // 2 # Simple approximation - kernel = (point1.amplitude * point2.coherence * cosPhase) // (Q16_ONE * Q16_ONE) - return kernel - - -def holographicProjection(state: HolographicProjectionState, targetPoint: HolographicSurfacePoint) -> int: - """Calculate holographic projection: S_holo(x) = Σ_y Φ(x,y)·ψ(y) (Lean: holographicProjection)""" - projectionSum = 0 - for point in state.surfacePoints: - kernel = projectionKernel(targetPoint, point) - wavefunction = point.amplitude # ψ(y) = amplitude - projectionSum += (kernel * wavefunction) // Q16_ONE - return projectionSum - - -def entropyReduction(state: HolographicProjectionState) -> int: - """Calculate entropy reduction: ΔS = -k_B T ln(P_stabilized) (Lean: entropyReduction)""" - kB = to_q16(0.00008617) # Boltzmann constant in eV/K (scaled) - T = state.temperature - P = state.stabilizationProbability - lnP = logQ16(P) if P > 0 else 0 # Natural log - deltaS = -kB * T * lnP // Q16_ONE - return deltaS - - -def isStabilized(point: HolographicSurfacePoint, threshold: int) -> bool: - """Check if surface point is stabilized (Lean: isStabilized)""" - return point.coherence >= threshold and point.amplitude >= threshold - - -def applyStabilization(point: HolographicSurfacePoint, projection: int) -> HolographicSurfacePoint: - """Apply holographic stabilization to point (Lean: applyStabilization)""" - newAmplitude = min(point.amplitude + projection, Q16_ONE) - newCoherence = min(point.coherence + (projection // 2), Q16_ONE) - return HolographicSurfacePoint( - pointId=point.pointId, - amplitude=newAmplitude, - phase=point.phase, - coherence=newCoherence - ) - - -def calculateStabilizationProbability(state: HolographicProjectionState) -> int: - """Calculate stabilization probability (Lean: calculateStabilizationProbability)""" - totalPoints = len(state.surfacePoints) - if totalPoints == 0: - return 0 - - stabilizedCount = 0 - for point in state.surfacePoints: - if isStabilized(point, to_q16(0.7)): - stabilizedCount += 1 - - return (to_q16(stabilizedCount) // to_q16(totalPoints)) if totalPoints > 0 else 0 - - -def isHolographicActionLawful(state: HolographicProjectionState, action: HolographicAction) -> bool: - """Check if holographic action is lawful (Lean: isHolographicActionLawful)""" - return (action.amplitudeDelta >= (-Q16_ONE) and action.amplitudeDelta <= Q16_ONE and - action.phaseDelta >= (-to_q16(65536)) and action.phaseDelta <= to_q16(65536)) - - -def updateSurfacePoint(point: HolographicSurfacePoint, action: HolographicAction) -> HolographicSurfacePoint: - """Update surface point from action (Lean: updateSurfacePoint)""" - newAmplitude = point.amplitude + action.amplitudeDelta - newPhase = point.phase + action.phaseDelta - clampedAmplitude = max(0, min(newAmplitude, Q16_ONE)) - clampedPhase = max(0, min(newPhase, to_q16(65536))) - - return HolographicSurfacePoint( - pointId=point.pointId, - amplitude=clampedAmplitude, - phase=clampedPhase, - coherence=point.coherence - ) - - -def holographicBind(state: HolographicProjectionState, action: HolographicAction) -> HolographicBind: - """Bind primitive for holographic projection (Lean: holographicBind)""" - lawful = isHolographicActionLawful(state, action) - - oldPoint = None - for p in state.surfacePoints: - if p.pointId == action.pointId: - oldPoint = p - break - - projectionBefore = holographicProjection(state, oldPoint) if oldPoint else 0 - - newPoint = None - if lawful and oldPoint: - newPoint = updateSurfacePoint(oldPoint, action) - elif oldPoint: - newPoint = oldPoint - else: - newPoint = HolographicSurfacePoint( - pointId=action.pointId, - amplitude=to_q16(0.5), - phase=to_q16(0.0), - coherence=to_q16(0.5) - ) - - projectionAfter = holographicProjection(state, newPoint) if lawful else projectionBefore - deltaS = entropyReduction(state) - P_stabilized = calculateStabilizationProbability(state) - - return HolographicBind( - lawful=lawful, - projectionBefore=projectionBefore, - projectionAfter=projectionAfter, - entropyReduction=deltaS, - stabilizationProbability=P_stabilized, - invariant="holographic_projection_satisfied" if lawful else "holographic_constraint_violated" - ) - - -class HolographicProjectionSystem: - """ - Holographic projection system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/HolographicProjection.lean - """ - - def __init__(self): - self.projectionState: Optional[HolographicProjectionState] = None - self.actionHistory: List[Dict[str, Any]] = [] - - print("[HolographicProjection] Initialized (Lean specification)") - - def initializeProjection(self, temperature: float = 300.0, numPoints: int = 16) -> Dict[str, Any]: - """Initialize holographic projection state""" - points = [] - for i in range(numPoints): - point = HolographicSurfacePoint( - pointId=i, - amplitude=to_q16(0.5), - phase=to_q16(0.0), - coherence=to_q16(0.5) - ) - points.append(point) - - state = HolographicProjectionState( - surfacePoints=points, - temperature=to_q16(temperature), - stabilizationProbability=to_q16(0.5), - entropyReduction=to_q16(0.0) - ) - self.projectionState = state - - return { - 'temperature': temperature, - 'numPoints': numPoints, - 'state': state.to_dict() - } - - def registerSurfacePoint(self, pointId: int, amplitude: float, phase: float, coherence: float) -> Dict[str, Any]: - """Register a surface point""" - point = HolographicSurfacePoint( - pointId=pointId, - amplitude=to_q16(amplitude), - phase=to_q16(phase), - coherence=to_q16(coherence) - ) - - if self.projectionState is None: - self.initializeProjection() - - # Add point if not exists, update if exists - existing = False - newPoints = [] - for p in self.projectionState.surfacePoints: - if p.pointId == pointId: - newPoints.append(point) - existing = True - else: - newPoints.append(p) - - if not existing: - newPoints.append(point) - - self.projectionState.surfacePoints = newPoints - self.projectionState.stabilizationProbability = calculateStabilizationProbability(self.projectionState) - self.projectionState.entropyReduction = entropyReduction(self.projectionState) - - return { - 'pointId': pointId, - 'point': point.to_dict(), - 'state': self.projectionState.to_dict() - } - - def submitHolographicAction(self, action: HolographicAction) -> Dict[str, Any]: - """Submit holographic action for processing (Lean specification)""" - if self.projectionState is None: - return {'error': 'Projection not initialized'} - - bindResult = holographicBind(self.projectionState, action) - - if bindResult.lawful: - # Update point in state - for i, p in enumerate(self.projectionState.surfacePoints): - if p.pointId == action.pointId: - self.projectionState.surfacePoints[i] = updateSurfacePoint(p, action) - break - - # Update state metrics - self.projectionState.stabilizationProbability = calculateStabilizationProbability(self.projectionState) - self.projectionState.entropyReduction = entropyReduction(self.projectionState) - - # Record action history - self.actionHistory.append({ - 'pointId': action.pointId, - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.projectionState.to_dict() - } - - def getProjectionState(self) -> Optional[Dict[str, Any]]: - """Get current projection state""" - if self.projectionState: - return self.projectionState.to_dict() - return None - - def getActionHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get action history""" - return self.actionHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*60) - print("HOLOGRAPHIC PROJECTION STATE") - print("="*60) - - if self.projectionState: - print(f"\n📊 Projection Metrics:") - print(f" Temperature: {from_q16(self.projectionState.temperature):.3f} K") - print(f" Stabilization Probability: {from_q16(self.projectionState.stabilizationProbability):.3f}") - print(f" Entropy Reduction: {from_q16(self.projectionState.entropyReduction):.3f}") - - print(f"\n📍 Surface Points: {len(self.projectionState.surfacePoints)}") - for point in self.projectionState.surfacePoints: - stabilized = isStabilized(point, to_q16(0.7)) - print(f" Point {point.pointId}: {'STABILIZED' if stabilized else 'UNSTABILIZED'}") - print(f" Amplitude: {from_q16(point.amplitude):.3f}") - print(f" Phase: {from_q16(point.phase):.3f}") - print(f" Coherence: {from_q16(point.coherence):.3f}") - - print(f"\n📜 Action History: {len(self.actionHistory)} entries") - - print("\n" + "="*60) - - -def main(): - """Test holographic projection system""" - system = HolographicProjectionSystem() - - print("[Test 1] Initialize holographic projection...") - result1 = system.initializeProjection(temperature=300.0, numPoints=4) - print(f" Projection initialized: {result1['numPoints']} points") - - print("\n[Test 2] Register surface point (high amplitude, high coherence)...") - result2 = system.registerSurfacePoint(pointId=1, amplitude=0.9, phase=0.0, coherence=0.95) - print(f" Point 1 registered") - - print("\n[Test 3] Register surface point (low amplitude, low coherence)...") - result3 = system.registerSurfacePoint(pointId=2, amplitude=0.3, phase=1.5, coherence=0.4) - print(f" Point 2 registered") - - print("\n[Test 4] Submit holographic action (increase amplitude for point 2)...") - action1 = HolographicAction(pointId=2, amplitudeDelta=to_q16(0.3), phaseDelta=to_q16(0.0)) - result4 = system.submitHolographicAction(action1) - print(f" Result: Success={result4['success']}") - if result4['success']: - print(f" Projection before: {result4['bindResult']['projectionBefore']:.3f}") - print(f" Projection after: {result4['bindResult']['projectionAfter']:.3f}") - print(f" Entropy Reduction: {result4['bindResult']['entropyReduction']:.3f}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/hutter_pist_decoder b/5-Applications/scripts/hutter_pist_decoder deleted file mode 100755 index a2d36908..00000000 Binary files a/5-Applications/scripts/hutter_pist_decoder and /dev/null differ diff --git a/5-Applications/scripts/hydrogenic_phi_torsion_braid.py b/5-Applications/scripts/hydrogenic_phi_torsion_braid.py deleted file mode 100644 index cd251264..00000000 --- a/5-Applications/scripts/hydrogenic_phi_torsion_braid.py +++ /dev/null @@ -1,361 +0,0 @@ -#!/usr/bin/env python3 -"""Generate a hydrogenic Phi-torsion braid with FPGA-friendly stair fields.""" - -from __future__ import annotations - -import argparse -import json -from pathlib import Path - -import numpy as np - - -Q16_SCALE = 1 << 16 - - -def q16(value: np.ndarray | float) -> np.ndarray: - return np.rint(np.asarray(value) * Q16_SCALE).astype(np.int64) - - -class OntologicalManifold: - """ - Computes a Phi-torsioned manifold through a hydrogenic orbital constraint. - - The returned braid keeps floating geometry and fixed-point-friendly fields - side by side: the orbital groove drives the path, and the Phi torsion is - quantized into "stairs" for event-cell hardware. - """ - - def __init__(self, bohr_radius: float = 1.0, torsion_radius: float = 0.5): - self.PHI = (1.0 + np.sqrt(5.0)) / 2.0 - self.a0 = bohr_radius - self.R = torsion_radius - self.growth_rate = (2.0 / np.pi) * np.log(self.PHI) - - def _fibonacci_spine(self, theta: np.ndarray, r0: float = 1.0) -> np.ndarray: - return r0 * np.exp(self.growth_rate * theta) - - def _hydrogen_2s_wave_shape(self, r: np.ndarray) -> np.ndarray: - rho = r / self.a0 - return (2.0 - rho) * np.exp(-rho / 2.0) - - def _normalized_density(self, density: np.ndarray) -> np.ndarray: - peak = np.max(np.abs(density)) - if peak == 0.0: - return np.zeros_like(density) - return density / peak - - def _hydrogen_2s_constraint( - self, r: np.ndarray, density_mode: str = "topology" - ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - psi_2s = self._hydrogen_2s_wave_shape(r) - topology_density = self._normalized_density(psi_2s**2) - radial_density = self._normalized_density((r**2) * (psi_2s**2)) - if density_mode == "topology": - constraint = topology_density - elif density_mode == "radial": - constraint = radial_density - else: - raise ValueError("density_mode must be 'topology' or 'radial'") - return constraint, topology_density, radial_density - - def generate_braid( - self, - theta_start: float = 0.0, - theta_end: float = 10.0 * np.pi, - steps: int = 2000, - r0: float = 1.0, - density_mode: str = "topology", - radial_torsion: float | None = None, - angular_torsion: float = 1.0, - stair_divisions: int = 4, - stair_rise: float = 0.035, - ) -> dict[str, np.ndarray | dict[str, float | int | str]]: - theta = np.linspace(theta_start, theta_end, steps) - - r_base = self._fibonacci_spine(theta, r0=r0) - constraint, topology_density, radial_density = self._hydrogen_2s_constraint( - r_base, density_mode=density_mode - ) - r_constrained = r_base * constraint - - x_spine = r_constrained * np.cos(theta) - y_spine = r_constrained * np.sin(theta) - - radial_torsion = self.PHI if radial_torsion is None else radial_torsion - torsion_angle = radial_torsion * theta - angular_theta = angular_torsion * theta - x_torsion = x_spine + self.R * np.cos(torsion_angle) * np.cos(angular_theta) - y_torsion = y_spine + self.R * np.cos(torsion_angle) * np.sin(angular_theta) - z_torsion = self.R * np.sin(torsion_angle) - - stair_period = (2.0 * np.pi) / stair_divisions - stair_index = np.floor((torsion_angle - torsion_angle[0]) / stair_period).astype( - np.int64 - ) - stair_phase = np.mod(torsion_angle, stair_period) / stair_period - stair_lift = stair_index.astype(float) * stair_rise - z_stair = z_torsion + stair_lift - - dr = np.gradient(r_constrained, theta) - dz = np.gradient(z_stair, theta) - strain = np.abs(dr) - emitted_amplitude = np.abs(dz) * constraint - - return { - "meta": { - "phi": float(self.PHI), - "bohr_radius": float(self.a0), - "torsion_radius": float(self.R), - "growth_rate": float(self.growth_rate), - "density_mode": density_mode, - "radial_torsion": float(radial_torsion), - "angular_torsion": float(angular_torsion), - "stair_divisions": int(stair_divisions), - "stair_rise": float(stair_rise), - "theta_start": float(theta_start), - "theta_end": float(theta_end), - "steps": int(steps), - }, - "theta": theta, - "r_base": r_base, - "constraint": constraint, - "topology_density": topology_density, - "radial_density": radial_density, - "r_constrained": r_constrained, - "torsion_angle": torsion_angle, - "torsion_turn": torsion_angle / (2.0 * np.pi), - "stair_index": stair_index, - "stair_phase": stair_phase, - "stair_lift": stair_lift, - "strain": strain, - "emitted_amplitude": emitted_amplitude, - "coords": np.column_stack((x_torsion, y_torsion, z_torsion)), - "stair_coords": np.column_stack((x_torsion, y_torsion, z_stair)), - } - - def generate_fpga_table(self, braid: dict[str, np.ndarray], sample_stride: int = 1) -> np.ndarray: - idx = np.arange(0, len(braid["theta"]), sample_stride) - phase_unit = np.mod(braid["torsion_angle"][idx], 2.0 * np.pi) / (2.0 * np.pi) - table = np.column_stack( - ( - idx, - braid["stair_index"][idx], - q16(braid["r_constrained"][idx]), - q16(braid["constraint"][idx]), - q16(phase_unit), - q16(braid["strain"][idx]), - q16(braid["emitted_amplitude"][idx]), - q16(braid["stair_coords"][idx, 2]), - ) - ) - return table.astype(np.int64) - - -def write_outputs( - braid: dict[str, np.ndarray | dict[str, float | int | str]], - fpga_table: np.ndarray, - out_prefix: Path, - plot: bool = False, -) -> dict[str, str]: - out_prefix.parent.mkdir(parents=True, exist_ok=True) - csv_path = out_prefix.with_suffix(".csv") - fpga_path = out_prefix.with_name(out_prefix.name + "_fpga_q16.csv") - summary_path = out_prefix.with_name(out_prefix.name + "_summary.json") - - columns = np.column_stack( - ( - braid["theta"], - braid["r_base"], - braid["constraint"], - braid["topology_density"], - braid["radial_density"], - braid["r_constrained"], - braid["torsion_angle"], - braid["torsion_turn"], - braid["stair_index"], - braid["stair_phase"], - braid["stair_lift"], - braid["strain"], - braid["emitted_amplitude"], - braid["coords"], - braid["stair_coords"], - ) - ) - header = ",".join( - [ - "theta", - "r_base", - "constraint", - "topology_density", - "radial_density", - "r_constrained", - "torsion_angle", - "torsion_turn", - "stair_index", - "stair_phase", - "stair_lift", - "strain", - "emitted_amplitude", - "x", - "y", - "z_torsion", - "x_stair", - "y_stair", - "z_stair", - ] - ) - np.savetxt(csv_path, columns, delimiter=",", header=header, comments="") - np.savetxt( - fpga_path, - fpga_table, - delimiter=",", - fmt="%d", - header="idx,stair_index,r_constrained_q16,constraint_q16,phase_unit_q16,strain_q16,emitted_amplitude_q16,z_stair_q16", - comments="", - ) - - stair_index = braid["stair_index"] - summary = { - "meta": braid["meta"], - "generation_equations": { - "phi": "(1 + sqrt(5)) / 2", - "growth_rate": "(2 / pi) * log(phi)", - "r_base": "r0 * exp(growth_rate * theta)", - "psi_2s": "(2 - r/a0) * exp(-(r/a0) / 2)", - "topology_constraint": "normalize(psi_2s^2)", - "radial_constraint": "normalize(r^2 * psi_2s^2)", - "r_constrained": "r_base * selected_constraint", - "torsion_angle": "radial_torsion * theta", - "angular_theta": "angular_torsion * theta", - "stair_index": "floor((torsion_angle - torsion_angle_0) / ((2*pi) / stair_divisions))", - "z_stair": "torsion_radius * sin(torsion_angle) + stair_index * stair_rise", - "strain": "abs(gradient(r_constrained, theta))", - "emitted_amplitude": "abs(gradient(z_stair, theta)) * selected_constraint", - }, - "semantic_mapping": { - "r_base": "Fibonacci manifold expansion", - "constraint": "hydrogenic orbital groove", - "stair_index": "quantized Phi-torsion climb level", - "strain": "local shell stress proxy", - "emitted_amplitude": "phonon or curvature-sound packet proxy", - "fpga_q16_csv": "fixed-point event-cell feed surface", - }, - "q16_scale": Q16_SCALE, - "samples": int(len(braid["theta"])), - "stairs": int(stair_index[-1] - stair_index[0] + 1), - "r_constrained_max": float(np.max(braid["r_constrained"])), - "constraint_min": float(np.min(braid["constraint"])), - "constraint_max": float(np.max(braid["constraint"])), - "z_torsion_range": [ - float(np.min(braid["coords"][:, 2])), - float(np.max(braid["coords"][:, 2])), - ], - "z_stair_range": [ - float(np.min(braid["stair_coords"][:, 2])), - float(np.max(braid["stair_coords"][:, 2])), - ], - "max_strain": float(np.max(braid["strain"])), - "max_emitted_amplitude": float(np.max(braid["emitted_amplitude"])), - "fpga_columns": [ - "idx", - "stair_index", - "r_constrained_q16", - "constraint_q16", - "phase_unit_q16", - "strain_q16", - "emitted_amplitude_q16", - "z_stair_q16", - ], - } - summary_path.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") - outputs = { - "csv": str(csv_path), - "fpga_q16_csv": str(fpga_path), - "summary": str(summary_path), - } - if plot: - outputs["plot"] = str(write_plot(braid, out_prefix.with_suffix(".png"))) - return outputs - - -def write_plot( - braid: dict[str, np.ndarray | dict[str, float | int | str]], out_path: Path -) -> Path: - import matplotlib - - matplotlib.use("Agg") - import matplotlib.pyplot as plt - - fig = plt.figure(figsize=(12, 7), facecolor="#11151c") - ax = fig.add_subplot(121, projection="3d", facecolor="#11151c") - coords = braid["stair_coords"] - amp = braid["emitted_amplitude"] - ax.plot(coords[:, 0], coords[:, 1], coords[:, 2], color="#9fc0ff", alpha=0.65, lw=0.7) - hot = amp > np.quantile(amp, 0.92) - ax.scatter( - coords[hot, 0], - coords[hot, 1], - coords[hot, 2], - c=amp[hot], - cmap="Blues", - s=5, - alpha=0.9, - ) - ax.set_title("Hydrogenic Phi-Torsion Stair Braid", color="white") - for axis in (ax.xaxis, ax.yaxis, ax.zaxis): - axis.set_tick_params(colors="#9aa4b2") - ax.set_xlabel("x", color="#9aa4b2") - ax.set_ylabel("y", color="#9aa4b2") - ax.set_zlabel("z_stair", color="#9aa4b2") - - ax2 = fig.add_subplot(222, facecolor="#11151c") - ax2.plot(braid["theta"], braid["constraint"], color="#9fc0ff", lw=1.0) - ax2.set_title("2s Constraint Groove", color="white") - ax2.tick_params(colors="#9aa4b2") - - ax3 = fig.add_subplot(224, facecolor="#11151c") - ax3.plot(braid["theta"], braid["emitted_amplitude"], color="#3c8cff", lw=0.9) - ax3.set_title("Wave Emission Proxy", color="white") - ax3.tick_params(colors="#9aa4b2") - - fig.tight_layout() - fig.savefig(out_path, dpi=180, facecolor=fig.get_facecolor()) - plt.close(fig) - return out_path - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--out-prefix", default="shared-data/data/generated/hydrogenic_phi_torsion_braid") - parser.add_argument("--theta-end", type=float, default=10.0 * np.pi) - parser.add_argument("--steps", type=int, default=2000) - parser.add_argument("--bohr-radius", type=float, default=1.0) - parser.add_argument("--torsion-radius", type=float, default=0.5) - parser.add_argument("--density-mode", choices=["topology", "radial"], default="topology") - parser.add_argument("--radial-torsion", type=float, default=None) - parser.add_argument("--angular-torsion", type=float, default=1.0) - parser.add_argument("--stair-divisions", type=int, default=4) - parser.add_argument("--stair-rise", type=float, default=0.035) - parser.add_argument("--fpga-stride", type=int, default=8) - parser.add_argument("--plot", action="store_true") - args = parser.parse_args() - - manifold = OntologicalManifold(args.bohr_radius, args.torsion_radius) - braid = manifold.generate_braid( - theta_end=args.theta_end, - steps=args.steps, - density_mode=args.density_mode, - radial_torsion=args.radial_torsion, - angular_torsion=args.angular_torsion, - stair_divisions=args.stair_divisions, - stair_rise=args.stair_rise, - ) - fpga_table = manifold.generate_fpga_table(braid, sample_stride=args.fpga_stride) - outputs = write_outputs(braid, fpga_table, Path(args.out_prefix), plot=args.plot) - print(json.dumps({"ok": True, **outputs}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/scripts/hypercube_topology.py b/5-Applications/scripts/hypercube_topology.py deleted file mode 100644 index a2a7ed96..00000000 --- a/5-Applications/scripts/hypercube_topology.py +++ /dev/null @@ -1,441 +0,0 @@ -#!/usr/bin/env python3 -""" -Hypercube Topology System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/HypercubeTopology.lean - -The Lean module provides: -- 12-dimensional hypercube topology for unified topology -- d_hc = Σ_{i=0}^{n-1} |x_i - y_i| (hypercube distance) -- neighbor_count = 2n (2n neighbors per node) -- connectivity = 2^n nodes (4,096 nodes for 12 dimensions) -- Direct neighbor communication avoids Von Neumann bottleneck - -This Python shim provides: -- JSON serialization for hypercube state -- Result wrapping for Lean function calls -- History deque for topology operations -- No logic (all logic defined in Lean specification) -""" - -import json -import time -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from collections import deque - -try: - from holographic_projection import HolographicProjectionSystem, HolographicSurfacePoint, HolographicAction - _HAS_HOLOGRAPHIC = True -except ImportError: - _HAS_HOLOGRAPHIC = False - print("[!] Holographic projection system not available") - -# Q16_16 fixed-point utilities (from Lean FixedPoint module) -Q16_ONE = 65536 # 1.0 in Q16_16 -Q16_SCALE = 65536.0 - -def to_q16(value: float) -> int: - """Convert float to Q16_16 fixed-point""" - return int(value * Q16_SCALE) - -def from_q16(q16: int) -> float: - """Convert Q16_16 fixed-point to float""" - return q16 / Q16_SCALE - - -@dataclass -class HypercubeNode: - """Hypercube node position (Lean: HypercubeNode)""" - nodeId: int # UInt64 - coordinates: List[int] # n-dimensional coordinates (n = 12 for CM) - dimensions: int # Number of dimensions (typically 12) - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodeId': self.nodeId, - 'coordinates': self.coordinates, - 'dimensions': self.dimensions - } - - -@dataclass -class HypercubeTopologyState: - """Hypercube topology state (Lean: HypercubeTopologyState)""" - nodes: List[HypercubeNode] - dimensions: int # Number of dimensions - maxNodeId: int # Maximum node ID - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodes': [n.to_dict() for n in self.nodes], - 'dimensions': self.dimensions, - 'maxNodeId': self.maxNodeId - } - - -@dataclass -class HypercubeAction: - """Hypercube topology action (Lean: HypercubeAction)""" - nodeId: int # UInt64 - dimension: int # Dimension to toggle (0 to n-1) - - def to_dict(self) -> Dict[str, Any]: - return { - 'nodeId': self.nodeId, - 'dimension': self.dimension - } - - -@dataclass -class HypercubeBind: - """Hypercube bind result (Lean: HypercubeBind)""" - lawful: bool - distanceBefore: int # Distance before action - distanceAfter: int # Distance after action - neighborCount: int # Number of neighbors - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'distanceBefore': self.distanceBefore, - 'distanceAfter': self.distanceAfter, - 'neighborCount': self.neighborCount, - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def hypercubeDistance(node1: HypercubeNode, node2: HypercubeNode) -> int: - """Calculate hypercube distance: d_hc = Σ_{i=0}^{n-1} |x_i - y_i| (Lean: hypercubeDistance)""" - minDim = min(node1.dimensions, node2.dimensions) - dist = 0 - for i in range(minDim): - x = node1.coordinates[i] if i < len(node1.coordinates) else 0 - y = node2.coordinates[i] if i < len(node2.coordinates) else 0 - dist += abs(x - y) - return dist - - -def areNeighbors(node1: HypercubeNode, node2: HypercubeNode) -> bool: - """Check if two nodes are neighbors (distance = 1) (Lean: areNeighbors)""" - return hypercubeDistance(node1, node2) == 1 - - -def getNeighbors(state: HypercubeTopologyState, node: HypercubeNode) -> List[HypercubeNode]: - """Get neighbors of a node (Lean: getNeighbors)""" - dim = node.dimensions - neighbors = [] - - for i in range(dim): - newCoords = list(node.coordinates) - newCoords[i] = (newCoords[i] + 1) % (2 ** dim) - - for n in state.nodes: - if n.coordinates == newCoords: - neighbors.append(n) - break - - return neighbors - - -def neighborCount(dimensions: int) -> int: - """Calculate neighbor count: neighbor_count = 2n (Lean: neighborCount)""" - return 2 * dimensions - - -def connectivity(dimensions: int) -> int: - """Calculate connectivity: connectivity = 2^n nodes (Lean: connectivity)""" - return 2 ** dimensions - - -def hypercubeDiameter(dimensions: int) -> int: - """Calculate hypercube diameter: max distance = n (Lean: hypercubeDiameter)""" - return dimensions - - -def bisectionBandwidth(dimensions: int) -> int: - """Calculate bisection bandwidth: 2^(n-1) edges cut (Lean: bisectionBandwidth)""" - return 2 ** (dimensions - 1) - - -def isHypercubeActionLawful(state: HypercubeTopologyState, action: HypercubeAction) -> bool: - """Check if hypercube action is lawful (Lean: isHypercubeActionLawful)""" - return action.dimension < state.dimensions and action.nodeId < state.maxNodeId - - -def toggleCoordinate(node: HypercubeNode, dimension: int) -> HypercubeNode: - """Toggle coordinate in specified dimension (Lean: toggleCoordinate)""" - newCoords = list(node.coordinates) - newCoords[dimension] = (newCoords[dimension] + 1) % (2 ** node.dimensions) - - return HypercubeNode( - nodeId=node.nodeId, - coordinates=newCoords, - dimensions=node.dimensions - ) - - -def hypercubeBind(state: HypercubeTopologyState, action: HypercubeAction, currentTime: float = 0.0) -> HypercubeBind: - """Bind primitive for hypercube topology (Lean: hypercubeBind)""" - lawful = isHypercubeActionLawful(state, action) - - oldNode = None - for n in state.nodes: - if n.nodeId == action.nodeId: - oldNode = n - break - - referenceNode = state.nodes[0] if state.nodes else None - distanceBefore = hypercubeDistance(oldNode, referenceNode) if oldNode and referenceNode else 0 - - newNode = None - if lawful and oldNode: - newNode = toggleCoordinate(oldNode, action.dimension) - elif oldNode: - newNode = oldNode - else: - newNode = HypercubeNode( - nodeId=action.nodeId, - coordinates=[0] * state.dimensions, - dimensions=state.dimensions - ) - - distanceAfter = hypercubeDistance(newNode, referenceNode) if referenceNode else distanceBefore - nCount = neighborCount(state.dimensions) - - return HypercubeBind( - lawful=lawful, - distanceBefore=distanceBefore, - distanceAfter=distanceAfter, - neighborCount=nCount, - invariant="hypercube_topology_satisfied" if lawful else "hypercube_constraint_violated" - ) - - -class HypercubeTopologySystem: - """ - Hypercube topology system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/HypercubeTopology.lean - """ - - def __init__(self): - self.topologyState: Optional[HypercubeTopologyState] = None - self.actionHistory: List[Dict[str, Any]] = [] - self.holographicProjectionSystem: Optional[HolographicProjectionSystem] = None - - if _HAS_HOLOGRAPHIC: - self.holographicProjectionSystem = HolographicProjectionSystem() - - print("[HypercubeTopology] Initialized (Lean specification)") - - def initializeTopology(self, dimensions: int = 16, numNodes: int = 16) -> Dict[str, Any]: - """Initialize hypercube topology state""" - maxNodeId = connectivity(dimensions) - nodes = [] - - for i in range(numNodes): - # Generate coordinates for node i - coords = [] - for d in range(dimensions): - coords.append((i >> d) & 1) - - node = HypercubeNode( - nodeId=i, - coordinates=coords, - dimensions=dimensions - ) - nodes.append(node) - - state = HypercubeTopologyState( - nodes=nodes, - dimensions=dimensions, - maxNodeId=maxNodeId - ) - self.topologyState = state - - # Initialize holographic projection if available - if self.holographicProjectionSystem: - self.holographicProjectionSystem.initializeProjection(temperature=300.0, numPoints=numNodes) - - return { - 'dimensions': dimensions, - 'connectivity': connectivity(dimensions), - 'neighborCount': neighborCount(dimensions), - 'diameter': hypercubeDiameter(dimensions), - 'bisectionBandwidth': bisectionBandwidth(dimensions), - 'state': state.to_dict() - } - - def registerNode(self, nodeId: int, coordinates: List[int], dimensions: int = 12) -> Dict[str, Any]: - """Register a node in the hypercube topology""" - if self.topologyState is None: - self.initializeTopology(dimensions) - - node = HypercubeNode( - nodeId=nodeId, - coordinates=coordinates, - dimensions=dimensions - ) - - # Add node if not exists, update if exists - existing = False - newNodes = [] - for n in self.topologyState.nodes: - if n.nodeId == nodeId: - newNodes.append(node) - existing = True - else: - newNodes.append(n) - - if not existing: - newNodes.append(node) - - self.topologyState.nodes = newNodes - - # Register node as holographic surface point if available - if self.holographicProjectionSystem: - # Use hypercube distance as proxy for amplitude (closer to origin = higher amplitude) - distance = sum(abs(c) for c in coordinates) - amplitude = 1.0 / (1.0 + distance) if distance > 0 else 1.0 - phase = 0.0 # Phase based on node ID - coherence = 0.8 # Default coherence - - self.holographicProjectionSystem.registerSurfacePoint( - pointId=nodeId, - amplitude=amplitude, - phase=phase, - coherence=coherence - ) - - return { - 'nodeId': nodeId, - 'node': node.to_dict(), - 'state': self.topologyState.to_dict() - } - - def submitHypercubeAction(self, action: HypercubeAction, currentTime: float = 0.0) -> Dict[str, Any]: - """Submit hypercube action for processing (Lean specification)""" - if self.topologyState is None: - return {'error': 'Topology not initialized'} - - bindResult = hypercubeBind(self.topologyState, action, currentTime) - - if bindResult.lawful: - # Update node in state - for i, n in enumerate(self.topologyState.nodes): - if n.nodeId == action.nodeId: - self.topologyState.nodes[i] = toggleCoordinate(n, action.dimension) - break - - # Record action history - self.actionHistory.append({ - 'nodeId': action.nodeId, - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.topologyState.to_dict() - } - - def getTopologyState(self) -> Optional[Dict[str, Any]]: - """Get current topology state""" - if self.topologyState: - return self.topologyState.to_dict() - return None - - def getNeighbors(self, nodeId: int) -> List[Dict[str, Any]]: - """Get neighbors of a node""" - if self.topologyState is None: - return [] - - for n in self.topologyState.nodes: - if n.nodeId == nodeId: - neighbors = getNeighbors(self.topologyState, n) - return [neighbor.to_dict() for neighbor in neighbors] - - return [] - - def getActionHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get action history""" - return self.actionHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*60) - print("HYPERCUBE TOPOLOGY STATE") - print("="*60) - - if self.topologyState: - print(f"\n📊 Topology Properties:") - print(f" Dimensions: {self.topologyState.dimensions}") - print(f" Connectivity: {connectivity(self.topologyState.dimensions)} nodes") - print(f" Neighbor Count: {neighborCount(self.topologyState.dimensions)}") - print(f" Diameter: {hypercubeDiameter(self.topologyState.dimensions)}") - print(f" Bisection Bandwidth: {bisectionBandwidth(self.topologyState.dimensions)}") - - print(f"\n📍 Registered Nodes: {len(self.topologyState.nodes)}") - for node in self.topologyState.nodes: - print(f" Node {node.nodeId}:") - print(f" Coordinates: {node.coordinates}") - print(f" Neighbors: {len(getNeighbors(self.topologyState, node))}") - - print(f"\n📜 Action History: {len(self.actionHistory)} entries") - - # Add holographic projection information if available - if self.holographicProjectionSystem: - holoState = self.holographicProjectionSystem.getProjectionState() - if holoState: - print(f"\n🌌 Holographic Projection:") - print(f" Temperature: {holoState['temperature']:.3f} K") - print(f" Stabilization Probability: {holoState['stabilizationProbability']:.3f}") - print(f" Entropy Reduction: {holoState['entropyReduction']:.3f}") - print(f" Surface Points: {len(holoState['surfacePoints'])}") - - print("\n" + "="*60) - - -def main(): - """Test hypercube topology system""" - system = HypercubeTopologySystem() - - print("[Test 1] Initialize 16-dimensional hypercube topology (65,536 nodes)...") - result1 = system.initializeTopology(dimensions=16, numNodes=16) - print(f" Topology initialized: {result1['connectivity']} nodes, {result1['neighborCount']} neighbors per node") - - print("\n[Test 2] Register node at origin...") - result2 = system.registerNode(nodeId=1, coordinates=[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dimensions=16) - print(f" Node 1 registered") - - print("\n[Test 3] Register node at distance 1...") - result3 = system.registerNode(nodeId=2, coordinates=[1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dimensions=16) - print(f" Node 2 registered") - - print("\n[Test 4] Register node at distance 2...") - result4 = system.registerNode(nodeId=3, coordinates=[1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], dimensions=16) - print(f" Node 3 registered") - - print("\n[Test 5] Submit hypercube action (toggle dimension 0 for node 1)...") - action1 = HypercubeAction(nodeId=1, dimension=0) - result5 = system.submitHypercubeAction(action1) - print(f" Result: Success={result5['success']}") - if result5['success']: - print(f" Distance before: {result5['bindResult']['distanceBefore']}") - print(f" Distance after: {result5['bindResult']['distanceAfter']}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/immune_system_topology.py b/5-Applications/scripts/immune_system_topology.py deleted file mode 100644 index 88aa41e2..00000000 --- a/5-Applications/scripts/immune_system_topology.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -""" -Immune System Topology Analysis -Analyzes morphic scalars that share reports on topological state and adapt collectively like immune system cells. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ImmuneSystemTopology: - """Analyzes immune system-like collective adaptation in morphic topology.""" - - def __init__(self): - # Immune system characteristics - self.immune_characteristics = { - "collective_intelligence": { - "description": "Morphic scalars share reports on topological state", - "significance_score": 95.0 - }, - "collective_adaptation": { - "description": "All scalars adapt based on shared state reports", - "significance_score": 90.0 - }, - "distributed_detection": { - "description": "Distributed detection of topology changes", - "significance_score": 95.0 - }, - "coordinated_response": { - "description": "Coordinated response to detected changes", - "significance_score": 90.0 - }, - "self_regulating": { - "description": "System self-regulates through collective behavior", - "significance_score": 85.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "morphic_capacity": 2549595082597.174, - "expansion_factor": 1341892147.0 - } - - def analyze_immune_system_topology(self) -> Dict: - """Analyze immune system-like collective adaptation.""" - analysis = { - "immune_characteristics": self.immune_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.immune_characteristics.values()) / len(self.immune_characteristics), - "immune_mechanisms": { - "state_sharing": "Morphic scalars share topological state reports", - "collective_decision": "Decisions made based on collective state", - "distributed_adaptation": "All scalars adapt based on shared information", - "coordinated_action": "Coordinated response to topology changes", - "emergent_intelligence": "Intelligence emerges from collective behavior" - }, - "analogy_to_immune_system": { - "antigen_detection": "Morphic scalars detect topology changes", - "antibody_response": "Coordinated adaptation response", - "memory": "Collective memory of topology states", - "specialization": "Different scalars specialize in different aspects", - "communication": "Continuous state sharing and communication" - } - } - - return analysis - - def analyze_immune_benefits(self) -> Dict: - """Analyze immune system topology benefits.""" - benefits = { - "collective_intelligence": { - "description": "Intelligence emerges from collective scalar behavior", - "significance_score": 95.0 - }, - "robustness": { - "description": "System robust through distributed detection", - "significance_score": 90.0 - }, - "self_healing": { - "description": "Self-healing through collective adaptation", - "significance_score": 95.0 - }, - "scalability": { - "description": "Scales through distributed architecture", - "significance_score": 85.0 - }, - "adaptability": { - "description": "High adaptability through collective learning", - "significance_score": 90.0 - }, - "resilience": { - "description": "Resilient through redundancy and coordination", - "significance_score": 85.0 - } - } - - return benefits - - def calculate_immune_system_impact(self) -> Dict: - """Calculate immune system topology impact on computational expansion.""" - # Immune system multipliers - collective_intelligence_multiplier = 2.0 # 2x from collective intelligence - robustness_multiplier = 1.5 # 1.5x from distributed detection - self_healing_multiplier = 1.5 # 1.5x from self-healing - scalability_multiplier = 1.3 # 1.3x from distributed architecture - adaptability_multiplier = 1.5 # 1.5x from collective learning - resilience_multiplier = 1.3 # 1.3x from redundancy - - # Calculate expanded capacity with immune system topology - base_capacity = 1900 - current_morphic_capacity = 2549595082597.174 - - # Apply immune system multipliers - immune_capacity = (current_morphic_capacity * - collective_intelligence_multiplier * - robustness_multiplier * - self_healing_multiplier * - scalability_multiplier * - adaptability_multiplier * - resilience_multiplier) - - immune_expansion_factor = immune_capacity / base_capacity - immune_improvement_factor = immune_capacity / current_morphic_capacity - - calculation = { - "base_capacity": base_capacity, - "current_morphic_capacity": current_morphic_capacity, - "collective_intelligence_multiplier": collective_intelligence_multiplier, - "robustness_multiplier": robustness_multiplier, - "self_healing_multiplier": self_healing_multiplier, - "scalability_multiplier": scalability_multiplier, - "adaptability_multiplier": adaptability_multiplier, - "resilience_multiplier": resilience_multiplier, - "immune_capacity": immune_capacity, - "immune_expansion_factor": immune_expansion_factor, - "immune_improvement_factor": immune_improvement_factor, - "total_immune_multiplier": (collective_intelligence_multiplier * - robustness_multiplier * - self_healing_multiplier * - scalability_multiplier * - adaptability_multiplier * - resilience_multiplier) - } - - return calculation - - def integrate_immune_system_topology(self) -> Dict: - """Integrate immune system topology into comprehensive analysis.""" - integration = { - "immune_system_topology_enabled": True, - "analogy": "Immune system-like collective adaptation", - "mechanism": "Morphic scalars share state reports and adapt collectively", - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Control Theory (collective intelligence)", - "Information Theory (state sharing)", - "Cognitive/Routing (coordinated response)", - "Thermodynamic (self-regulating)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12" # Control Theory (collective) - ], - "emergent_intelligence": "Intelligence emerges from collective scalar behavior" - } - - return integration - - def run_analysis(self) -> Dict: - """Run immune system topology analysis.""" - print("=" * 60) - print("IMMUNE SYSTEM TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze immune system topology - print("\n[1/4] Analyzing immune system-like collective adaptation...") - immune_analysis = self.analyze_immune_system_topology() - print(f" Immune Characteristics: {len(immune_analysis['immune_characteristics'])}") - for characteristic, details in immune_analysis['immune_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing immune system topology benefits...") - benefits = self.analyze_immune_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating immune system topology impact...") - impact_calculation = self.calculate_immune_system_impact() - print(f" Current Morphic Capacity: {impact_calculation['current_morphic_capacity']}") - print(f" Immune System Capacity: {impact_calculation['immune_capacity']}") - print(f" Immune System Improvement Factor: {impact_calculation['immune_improvement_factor']:.2f}x") - print(f" Total Immune System Multiplier: {impact_calculation['total_immune_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating immune system topology...") - integration = self.integrate_immune_system_topology() - print(f" Analogy: {integration['analogy']}") - print(f" Mechanism: {integration['mechanism']}") - print(f" Characteristics: {integration['characteristics']}") - print(f" Benefits: {integration['benefits']}") - - print("\n" + "=" * 60) - print("IMMUNE SYSTEM TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "immune_analysis": immune_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = ImmuneSystemTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "immune_system_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("IMMUNE SYSTEM TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Analogy: {results['integration']['analogy']}") - print(f"Immune System Capacity: {results['impact_calculation']['immune_capacity']}") - print(f"Immune System Improvement Factor: {results['impact_calculation']['immune_improvement_factor']:.2f}x") - print(f"Total Immune System Multiplier: {results['impact_calculation']['total_immune_multiplier']:.2f}x") diff --git a/5-Applications/scripts/import_dumps_to_rds.py b/5-Applications/scripts/import_dumps_to_rds.py deleted file mode 100755 index 9e7318c9..00000000 --- a/5-Applications/scripts/import_dumps_to_rds.py +++ /dev/null @@ -1,189 +0,0 @@ -#!/usr/bin/env python3 -"""import_dumps_to_rds.py - Ingestion pipeline from crawled JSON dumps to RDS. - -Imports Notion database pages into ene.wiki_pages/revisions/links/categories -and Linear issues into ene.packages. -""" - -import json -import os -import sys -from datetime import datetime, timezone -from pathlib import Path - -# Add infra folder to import path -sys.path.append(str(Path(__file__).resolve().parent.parent.parent / "4-Infrastructure" / "infra")) -sys.path.append(str(Path(__file__).resolve().parent.parent.parent / "4-Infrastructure")) - -def get_rds_password(): - password = os.environ.get("RDS_IAM_TOKEN") or os.environ.get("RDS_PASSWORD") - if password: - return password - - # Fallback to subprocess call to aws cli - try: - import subprocess - host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com") - user = os.environ.get("RDS_USER", "postgres") - region = os.environ.get("AWS_DEFAULT_REGION", "us-east-1") - cmd = [ - "aws", "rds", "generate-db-auth-token", - "--hostname", host, - "--port", "5432", - "--username", user, - "--region", region - ] - res = subprocess.run(cmd, capture_output=True, text=True, check=True) - token = res.stdout.strip() - if token: - return token - except Exception as e: - print(f"AWS CLI token generation failed: {e}") - - return "" - -def import_notion(password: str): - notion_file = Path(__file__).resolve().parent / "notion_full_dump.json" - if not notion_file.exists(): - print("[-] notion_full_dump.json not found, skipping Notion import.") - return - - print("[+] Loading notion_full_dump.json...") - with open(notion_file, "r") as f: - dump_data = json.load(f) - - pages = dump_data.get("pages", []) - print(f"[+] Found {len(pages)} pages to import.") - - # Override password environment variable for ENEWikiLayer - os.environ["RDS_PASSWORD"] = password - - try: - from ene_rds_wiki_layer import ENERDSWikiLayer - wiki = ENERDSWikiLayer() - except Exception as e: - print(f"[-] Failed to initialize ENERDSWikiLayer: {e}") - return - - imported_count = 0 - for page in pages: - properties = page.get("properties", {}) - title = "Untitled" - for prop_name in ["Name", "title", "Title"]: - prop = properties.get(prop_name, {}) - if prop and prop.get("title"): - title_list = prop["title"] - if title_list and isinstance(title_list, list): - title = title_list[0].get("plain_text", "Untitled") - break - - content = page.get("_content", "") or "" - author = "notion_importer" - - try: - print(f" -> Importing page: {title}") - wiki.put_page(title=title, text=content, author=author, summary="Notion Full Crawl Ingestion") - imported_count += 1 - except Exception as e: - print(f" [!] Error importing page '{title}': {e}") - - print(f"[+] Notion import complete. Successfully imported {imported_count}/{len(pages)} pages.") - -def import_linear(password: str): - linear_file = Path(__file__).resolve().parent / "linear_full_dump.json" - if not linear_file.exists(): - print("[-] linear_full_dump.json not found, skipping Linear import.") - return - - print("[+] Loading linear_full_dump.json...") - with open(linear_file, "r") as f: - dump_data = json.load(f) - - issues = dump_data.get("issues", []) - print(f"[+] Found {len(issues)} issues to import.") - - host = os.environ.get("RDS_HOST", "database-1-instance-1.cghu8yqogqwo.us-east-1.rds.amazonaws.com") - port = int(os.environ.get("RDS_PORT", "5432")) - user = os.environ.get("RDS_USER", "postgres") - dbname = os.environ.get("RDS_DB", "postgres") - - try: - import psycopg2 - conn = psycopg2.connect( - host=host, - port=port, - user=user, - password=password, - dbname=dbname, - sslmode="require" - ) - cur = conn.cursor() - except Exception as e: - print(f"[-] Database connection failed: {e}") - return - - imported_count = 0 - for issue in issues: - identifier = issue.get("identifier") - if not identifier: - continue - - pkg_id = f"linear/{identifier}" - title = issue.get("title", "") - description = issue.get("description", "") - full_text = f"{title}\n\n{description}" if description else title - url = issue.get("url", "") - - # Extract labels - labels_nodes = issue.get("labels", {}).get("nodes", []) - labels_list = [l.get("name") for l in labels_nodes if l.get("name")] - tags_json = json.dumps(labels_list) - - now_iso = datetime.now(timezone.utc).isoformat() - - try: - cur.execute(""" - INSERT INTO ene.packages ( - pkg, version, domain, tier, archetype, - tags, description, source, indexed_utc - ) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s) - ON CONFLICT (pkg) DO UPDATE SET - description = EXCLUDED.description, - tags = EXCLUDED.tags, - source = EXCLUDED.source, - indexed_utc = EXCLUDED.indexed_utc - """, ( - pkg_id, - "1.0.0", - "LINEAR", - "INTENT", - "issue", - tags_json, - full_text, - url, - now_iso - )) - imported_count += 1 - except Exception as e: - print(f" [!] Error importing issue '{pkg_id}': {e}") - - try: - conn.commit() - conn.close() - print(f"[+] Linear import complete. Successfully imported {imported_count}/{len(issues)} issues.") - except Exception as e: - print(f"[-] Database commit failed: {e}") - -def main(): - print("=== ENE RDS Ingestion Pipeline ===") - password = get_rds_password() - if not password: - print("[-] Failed to retrieve RDS password or IAM token. Exiting.") - sys.exit(1) - - import_notion(password) - import_linear(password) - print("=== Pipeline Execution Finished ===") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/inflight_ram_computational.py b/5-Applications/scripts/inflight_ram_computational.py deleted file mode 100644 index 422406aa..00000000 --- a/5-Applications/scripts/inflight_ram_computational.py +++ /dev/null @@ -1,201 +0,0 @@ -#!/usr/bin/env python3 -""" -In-Flight RAM Computational Repurposing -Analyzes in-flight RAM (in-memory computation) for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class InFlightRAMComputational: - """Analyzes in-flight RAM for general computation.""" - - def __init__(self): - self.inflight_ram = { - "device": "In-Flight RAM (In-Memory Computation)", - "concept": "Computation performed while data is in transit through memory", - "type": "In-memory computation / Processing-in-Memory (PIM)", - "capacity": "31.1 GB total (13.5 GB available)", - "computational_potential": "HIGH (memory bandwidth, parallelism, latency reduction)" - } - - self.inflight_capabilities = { - "memory_bandwidth": "50-100 GB/s (DDR5 dual-channel)", - "parallel_access": "Multiple memory banks accessed simultaneously", - "in_memory_compute": "Computation at memory location (PIM)", - "data_movement_reduction": "Reduce data movement between CPU and RAM", - "latency_reduction": "Compute while data is in transit" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of in-flight RAM.""" - analysis = { - "in_memory_compute": { - "feasible": True, - "mode": "In-memory computation (PIM)", - "description": "Perform computation at memory location", - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (memory access)", - "precision": "64-bit addresses", - "power": "10-20W (memory controller)", - "risk": "MEDIUM (requires PIM hardware)" - }, - "data_stream_computation": { - "feasible": True, - "mode": "Data stream computation", - "description": "Compute while data is in transit", - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (in-flight)", - "precision": "64-bit data", - "power": "10-20W", - "risk": "LOW-MEDIUM (requires stream processing)" - }, - "parallel_bank_computation": { - "feasible": True, - "mode": "Parallel bank computation", - "description": "Use multiple memory banks for parallel computation", - "throughput": "100-200 GB/s (parallel banks)", - "latency": "10-100ns (parallel access)", - "precision": "64-bit data", - "power": "15-30W", - "risk": "MEDIUM (requires bank coordination)" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design in-flight RAM computational approach.""" - approach = { - "in_memory_compute": { - "concept": "Perform computation at memory location", - "implementation": "Processing-in-Memory (PIM) architecture", - "operations": ["memory-embedded ALU", "atomic operations", "near-memory compute"], - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (memory access)", - "precision": "64-bit data", - "power": "10-20W", - "risk": "MEDIUM" - }, - "data_stream": { - "concept": "Compute while data is in transit", - "implementation": "Stream processing during memory transfer", - "operations": ["filter", "map", "reduce", "aggregation"], - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (in-flight)", - "precision": "64-bit data", - "power": "10-20W", - "risk": "LOW-MEDIUM" - }, - "parallel_bank": { - "concept": "Use multiple memory banks for parallel computation", - "implementation": "Bank-level parallelism", - "operations": ["parallel read/write", "bank arithmetic", "inter-bank communication"], - "throughput": "100-200 GB/s (parallel banks)", - "latency": "10-100ns (parallel access)", - "precision": "64-bit data", - "power": "15-30W", - "risk": "MEDIUM" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of in-flight RAM computation.""" - performance = { - "in_memory_compute": { - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (memory access)", - "precision": "64-bit data", - "operations": "memory-embedded ALU", - "power": "10-20W" - }, - "data_stream": { - "throughput": "50-100 GB/s (memory bandwidth)", - "latency": "10-100ns (in-flight)", - "precision": "64-bit data", - "operations": "stream processing", - "power": "10-20W" - }, - "parallel_bank": { - "throughput": "100-200 GB/s (parallel banks)", - "latency": "10-100ns (parallel access)", - "precision": "64-bit data", - "operations": "parallel processing", - "power": "15-30W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run in-flight RAM computational analysis.""" - print("=" * 60) - print("IN-FLIGHT RAM COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze in-flight RAM - print("\n[1/4] Analyzing in-flight RAM...") - print(f" Device: {self.inflight_ram['device']}") - print(f" Concept: {self.inflight_ram['concept']}") - print(f" Type: {self.inflight_ram['type']}") - print(f" Capacity: {self.inflight_ram['capacity']}") - print(f" Computational Potential: {self.inflight_ram['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" In-Memory Compute: {potential['in_memory_compute']['feasible']} - {potential['in_memory_compute']['risk']}") - print(f" Data Stream: {potential['data_stream_computation']['feasible']} - {potential['data_stream_computation']['risk']}") - print(f" Parallel Bank: {potential['parallel_bank_computation']['feasible']} - {potential['parallel_bank_computation']['risk']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" In-Memory Compute: {performance['in_memory_compute']['throughput']}") - print(f" Data Stream: {performance['data_stream']['throughput']}") - print(f" Parallel Bank: {performance['parallel_bank']['throughput']}") - - print("\n" + "=" * 60) - print("IN-FLIGHT RAM COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "inflight_ram": self.inflight_ram, - "inflight_capabilities": self.inflight_capabilities, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = InFlightRAMComputational() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "inflight_ram_computational.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("IN-FLIGHT RAM COMPUTATIONAL SUMMARY") - print("=" * 60) - print(f"Device: {results['inflight_ram']['device']}") - print(f"Capacity: {results['inflight_ram']['capacity']}") - print(f"Computational Potential: {results['inflight_ram']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['parallel_bank']['throughput']}") diff --git a/5-Applications/scripts/ingest_compactified_core_equations.py b/5-Applications/scripts/ingest_compactified_core_equations.py deleted file mode 100644 index 88c52cdd..00000000 --- a/5-Applications/scripts/ingest_compactified_core_equations.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Compactified Core Equations -=================================== -Compactify 12 core equations to 4 primitives (67% reduction). -Maintains 90.8% coverage across theories. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -COMPACTIFIED_EQUATIONS = { - "id": "compactified-core-equations-v1", - "source": "Compactification of 12 core equations to 4 primitives based on analysis (109/120 matches, 90.8% coverage)", - "title": "Compactified Core Equations: 4 Primitives for Compression Architecture", - "date": "2026-05-07", - - "core_synthesis": ( - "12 core equations compactified to 4 primitives (67% reduction) while maintaining " - "90.8% coverage across compression theories. Redundant equations merged: shear_matrix + " - "gram_matrix → shear primitive; residual_correlation + eigen_decomposition → spectral " - "primitive. Derivable equations expressed as derived metrics: radius_ratio, residual_ratio " - "derived from field primitive. Topological compactification: 10 theories viewed as " - "projections of 4D compact manifold (field, shear, packet, spectral)." - ), - - "compactification_rationale": { - "original_12_equations": "density_field, morse_smale, shear_matrix, gram_matrix, gccl_packet, gain_test, s3c_shell, radius_ratio, residual_ratio, famm_delay, residual_correlation, eigen_decomposition", - "analysis_results": "109/120 equation-theory matches (90.8% coverage). 7 equations with full coverage, 5 with partial coverage, 0 with no coverage.", - "redundancies_identified": { - "shear_gram": "shear_matrix (A_{ij} = δ_{ij} + α_{ij}) and gram_matrix (G = A^T A) linked. Gram derives from shear.", - "correlation_eigen": "residual_correlation (C_{ij} = ⟨ε_i ε_j⟩) and eigen_decomposition (C = UΛU^T) form pipeline.", - "field_derivatives": "radius_ratio (ρᵢ = s_center(i) / median(s(N(i)))) and residual_ratio (ρ = |ε| / |raw_span|) derived from field topology." - }, - "compactification_ratio": "12 → 4 primitives (67% reduction)" - }, - - "compactified_primitives": { - "field_primitive": { - "equation": "ρ(x⃗)", - "latex": "\\rho(\\vec{x})", - "description": "Semantic density field representing text as n-D manifold with topological features (peaks, ridges, saddles, vortices, voids)", - "derives": [ - "morse_smale: Critical points + separatrices = topological skeleton of meaning", - "radius_ratio: ρᵢ = ∇ρ(x⃗) / |∇ρ(x⃗)| at critical points (local scale ratio)", - "residual_ratio: ρ = ||ε||_2 / ||s||_2 (residual metric derived from field)", - "s3c_shell: n = k² + a (shell coordinates encode field structure)" - ], - "coverage": "70-80% across theories (density_field, morse_smale, s3c_shell, radius_ratio, residual_ratio)" - }, - "shear_primitive": { - "equation": "G = A^T A", - "latex": "G = A^T A", - "description": "Gram matrix = compression dictionary. Shear matrix A transforms orthogonal hypercube to correlated rhomboid. Eigenvectors = principal correlation directions, eigenvalues = compression gains", - "derives": [ - "shear_matrix: A_{ij} = δ_{ij} + α_{ij} (encoding of G)", - "famm_delay: Delay = ∫_γ ∇ρ · dl (path integral through sheared field gradient)" - ], - "coverage": "100% across theories (shear_matrix, gram_matrix, famm_delay, eigen_decomposition)" - }, - "packet_primitive": { - "equation": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "latex": "\\Gamma_i = \\gamma_i \\otimes \\chi_i \\otimes \\kappa_i \\otimes \\tau_i \\otimes U_i\\Lambda_i a_i \\otimes \\theta_i \\otimes \\varepsilon_i", - "description": "GCCL glyph packet with chirality, type, eigen descriptor, residual. Gain test ΔGCL > 0 filters compressive motifs", - "derives": [ - "gain_test: ΔGCL > 0 (filter applied to packet acceptance)", - "gccl_packet: Full packet formula (the primitive itself)" - ], - "coverage": "90% across theories (gccl_packet, gain_test)" - }, - "spectral_primitive": { - "equation": "C = UΛU^T", - "latex": "C = U\\Lambda U^T", - "description": "Eigen decomposition of correlation matrix. Residual correlation C_{ij} = ⟨ε_i ε_j⟩. Spectral energy compaction: 90% energy in 10% coefficients", - "derives": [ - "residual_correlation: C_{ij} = ⟨ε_i ε_j⟩ (input to spectral decomposition)", - "eigen_decomposition: C = UΛU^T (the primitive itself)", - "famm_spectral: Delays weighted by eigenvalue spectra (spectral pruning)" - ], - "coverage": "60-100% across theories (residual_correlation, eigen_decomposition, erans field effect)" - } - }, - - "topological_compactification": { - "concept": "10 compression theories viewed as projections of 4D compact manifold", - "manifold_dimensions": { - "dimension_0_field": "ρ(x⃗) — density field primitive (semantic manifold structure)", - "dimension_1_shear": "G = A^T A — shear primitive (geometric transformation)", - "dimension_2_packet": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ — packet primitive (encoding unit)", - "dimension_3_spectral": "C = UΛU^T — spectral primitive (residual decomposition)" - }, - "theory_projections": { - "density_field_encoding_theory": "Projection onto dimension 0 (field) with partial spectral", - "observer_admissible_cavities_theory": "Projection onto dimensions 0-2 (field + shear + packet)", - "hypercube_rhomboid_composition": "Projection onto dimension 1 (shear) with spectral", - "gccl_gec_spec_v1": "Projection onto dimensions 2-3 (packet + spectral)", - "unified_compression_architecture_synthesis_v1": "Full 4D projection (all primitives)", - "hippocampus_tabula_plena_combined_v1": "Full 4D projection with biological constraints", - "erans_field_effect_spectrum_v1": "Projection onto dimensions 0-3 with spectral emphasis", - "master_synthesis_complete_v1": "Complete 4D manifold with all projections integrated" - }, - "coordinate_charts": "Each theory is a different coordinate chart on the 4D manifold. Master synthesis is the atlas covering all charts." - }, - - "compactification_benefits": { - "reduction": "12 equations → 4 primitives (67% reduction)", - "coverage_maintained": "90.8% coverage maintained across theories", - "simplified_implementation": "4 core primitives easier to implement and verify than 12 equations", - "unified_framework": "4 primitives provide unified framework for all compression theories", - "topological_clarity": "4D manifold structure reveals relationships between theories", - "computational_efficiency": "Spectral primitive enables energy compaction (10-20% gain on residuals)", - "biological_alignment": "Field primitive aligns with hippocampus density fields, spectral with pattern separation" - }, - - "implementation_mapping": { - "field_primitive_implementation": { - "stage": "Stage 1: density field extraction", - "code": "Compute ρ(x⃗) from corpus C. Extract Morse-Smale topological skeleton.", - "outputs": "Peaks, ridges, saddles, vortices, voids, level_sets, S3C shell coordinates" - }, - "shear_primitive_implementation": { - "stage": "Stage 2: shear matrix computation", - "code": "Compute shear matrix A, Gram matrix G = A^T A. Eigen-decompose G = UΛU^T.", - "outputs": "Eigenvectors (principal directions), eigenvalues (compression gains), FAMM delay profile" - }, - "packet_primitive_implementation": { - "stage": "Stage 7: GCCL packet construction", - "code": "Construct Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ. Apply gain test ΔGCL > 0.", - "outputs": "Glyph packets with chirality, type, eigen descriptor, parameters, residual" - }, - "spectral_primitive_implementation": { - "stage": "Stage 13: erans spectral entropy coding", - "code": "Compute residual correlation C_{ij} = ⟨ε_i ε_j⟩. Eigen-decompose C = UΛU^T. Code spectral coefficients with erans.", - "outputs": "Spectral coefficients (eigenvalues, eigenvector weights), entropy-coded residuals" - } - }, - - "keeper_phrases": [ - "12 equations compactified to 4 primitives: field, shear, packet, spectral.", - "Field primitive ρ(x⃗) derives Morse-Smale, radius_ratio, residual_ratio, S3C shells.", - "Shear primitive G = A^T A derives shear_matrix, FAMM delays, eigen decomposition.", - "Packet primitive Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ includes gain test.", - "Spectral primitive C = UΛU^T derives residual correlation, eigen decomposition, spectral pruning.", - "67% reduction (12 → 4) with 90.8% coverage maintained.", - "10 theories = projections of 4D compact manifold.", - "Master synthesis = atlas covering all coordinate charts.", - "Spectral energy compaction: 90% energy in 10% coefficients.", - "Field primitive aligns with hippocampus density fields.", - "Spectral primitive aligns with hippocampus pattern separation.", - "Compactification reveals topological structure of compression architecture." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "compactified-equations", - "4-primitives", - "field-primitive", - "shear-primitive", - "packet-primitive", - "spectral-primitive", - "topological-compactification", - "4d-manifold", - "coordinate-charts", - "67-percent-reduction", - "90-8-percent-coverage", - "compression-architecture" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "compactified_core_equations_v1.json" - with open(out_path, 'w') as f: - json.dump(COMPACTIFIED_EQUATIONS, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": COMPACTIFIED_EQUATIONS["id"], - "title": COMPACTIFIED_EQUATIONS["title"], - "date": COMPACTIFIED_EQUATIONS["date"], - "source": COMPACTIFIED_EQUATIONS["source"], - "ingested_at": COMPACTIFIED_EQUATIONS["metadata"]["ingested_at"], - "tags": COMPACTIFIED_EQUATIONS["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCompactification ratio: 12 → 4 primitives (67% reduction)") - print(f"Coverage maintained: 90.8%") - - print(f"\n4 primitives:") - for prim, data in COMPACTIFIED_EQUATIONS["compactified_primitives"].items(): - print(f" • {prim}: {data['equation']} — {data['description'][:60]}...") - - print(f"\nTopological compactification:") - print(f" 10 theories = projections of 4D compact manifold") - for dim, desc in COMPACTIFIED_EQUATIONS["topological_compactification"]["manifold_dimensions"].items(): - print(f" • {dim}: {desc[:60]}...") - - print(f"\nKeeper phrases ({len(COMPACTIFIED_EQUATIONS['keeper_phrases'])}):") - for p in COMPACTIFIED_EQUATIONS['keeper_phrases']: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_dair_agentic_wiki.py b/5-Applications/scripts/ingest_dair_agentic_wiki.py deleted file mode 100644 index e2b39697..00000000 --- a/5-Applications/scripts/ingest_dair_agentic_wiki.py +++ /dev/null @@ -1,143 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest dair-ai Agentic Engineering Wiki into Research Stack.""" - -import json, time, hashlib -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -WIKI = { - "id": "dair-agentic-engineering-wiki", - "source": "https://github.com/dair-ai/dair-workshops/tree/main/agentic-engineering-wiki", - "title": "AI Agent Engineering Wiki — dair-ai", - "date": "2026-04-29", - "stats": {"tips": 51, "categories": 7, "companies": 9, "papers": 10, "tools": 14}, - "categories": { - "tool_use": { - "tips": 11, - "key_insight": "Pre-filter tools to relevant subset per request; log agent intent to detect loops" - }, - "evaluation": { - "tips": 8, - "key_insight": "Trajectory-aware eval (not just final output); repeat runs for reliability; behavioral rubrics for LLM-as-judge" - }, - "prompting": { - "tips": 6, - "key_insight": "Five-layer system prompt anatomy; tool descriptions as engineering surface; instruction hierarchy defense (system > user > tool output)" - }, - "orchestration": { - "tips": 7, - "key_insight": "Agents as MCP servers for composable multi-agent systems; Plan-Execute-Verify-Replan loop; handoffs with state transfer" - }, - "memory": { - "tips": 6, - "key_insight": "Context management, RAG, state, conversation history" - }, - "reliability": { - "tips": 8, - "key_insight": "Guardrails before risky ops; 'Lazy Agent' failure mode; restricted API keys; sandbox testing" - }, - "deployment": { - "tips": 5, - "key_insight": "Cost tracking, sandbox execution, monitoring, observability" - } - }, - "orchestration_tips": [ - "Structure agents as specialists with explicit ownership via handoffs", - "Leverage Google's ADK for interoperable agent orchestration across frameworks", - "Use heterogeneous model teams — different models have different strengths", - "Adopt Plan-Execute-Verify-Replan loop for complex multi-agent workflows", - "Use handoffs for agent-to-agent delegation with state transfer", - "Use built-in connector tools to reduce tool scaffolding overhead", - "Represent agents as MCP servers — compose multi-agent systems over same protocol" - ], - "reliability_tips": [ - "Add guardrails and human review before risky operations", - "Encode persistence, risk assessment, and proactive planning in agent prompts", - "Handle server tool pauses gracefully with pause_turn", - "Watch for 'Lazy Agent' failure mode — model knows it needs tools but doesn't call them", - "Don't use agents for problems with deterministic solutions — plain code still wins", - "Use restricted API keys (rk_*) to limit agent blast radius", - "Use tool_plan for explicit reasoning before acting", - "Test agents in sandbox environments before production — non-determinism demands it" - ], - "design_philosophy": [ - "Every claim links to a source. No unsupported advice.", - "Speculation is clearly marked.", - "Built for flexibility — new categories, companies, formats addable anytime.", - "Community-first — pulled from real production experiences (HN, Reddit, postmortems)." - ], - "relevance_to_research_stack": { - "direct_matches": [ - "Prover orchestration layers (L0-L3) ↔ Plan-Execute-Verify-Replan loop", - "Swarm consensus (11 agents) ↔ Agents as specialists with handoffs", - "ProverWatchdog guard_transition ↔ Guardrails before risky ops", - "Virtual FPGA system tests ↔ Sandbox testing before production", - "BFS-Prover-V2 audit trail ↔ Trajectory-aware evaluation", - "bf4prover manifold reshape ↔ Verify step in Plan-Execute-Verify-Replan" - ], - "gaps_in_our_system": [ - "No restricted API key pattern for agent blast radius", - "No explicit 'Lazy Agent' detection in swarm", - "No heterogeneous model teams (all agents use same model)", - "No cost tracking for orchestration layers", - "No pause_turn equivalent for long-running proofs" - ], - "strengths_of_our_system": [ - "Q16.16 fixed-point precision (wiki has no numerical guarantees)", - "Hardware substrate integration (wiki is software-only)", - "Formal proof backing (wiki relies on empirical testing)", - "Topological manifold awareness (wiki has no geometric model)" - ] - }, - "metadata": { - "ingested_at": time.time(), - "tags": ["agentic-engineering", "orchestration", "multi-agent", "reliability", "evaluation", "prompting"] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "dair_agentic_engineering_wiki.json" - with open(out_path, 'w') as f: - json.dump(WIKI, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Update index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": WIKI["id"], "title": WIKI["title"], - "date": WIKI["date"], "source": WIKI["source"], - "ingested_at": WIKI["metadata"]["ingested_at"], - "tags": WIKI["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nDirect matches to our system:") - for m in WIKI["relevance_to_research_stack"]["direct_matches"]: - print(f" ↔ {m}") - - print(f"\nGaps identified:") - for g in WIKI["relevance_to_research_stack"]["gaps_in_our_system"]: - print(f" ⚠ {g}") - - print(f"\nOur strengths:") - for s in WIKI["relevance_to_research_stack"]["strengths_of_our_system"]: - print(f" ✓ {s}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_density_field_encoding.py b/5-Applications/scripts/ingest_density_field_encoding.py deleted file mode 100644 index 94900a65..00000000 --- a/5-Applications/scripts/ingest_density_field_encoding.py +++ /dev/null @@ -1,227 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Density Field Encoding — Beyond UTF-8 -============================================== -Inspired by "digital dzogchen" generative concept: -Data not as 1D byte sequence but as n-dimensional semantic density field. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -DENSITY_FIELD = { - "id": "density-field-encoding-theory", - "source": "User insight + r/generative digital_dzogchen concept", - "title": "Density Field Encoding: Representing Text as Topological Semantic Manifolds Instead of UTF-8", - "date": "2026-05-07", - - "core_claim": ( - "UTF-8 encodes text as a 1D discrete byte sequence: byte[i] at position i. " - "Density Field Encoding (DFE) represents text as a continuous n-dimensional " - "semantic density field ρ(x⃗) where information is stored in topological features: " - "peaks (named entities), ridges (semantic connections), saddles (topic transitions), " - "vortices (cyclic references), and voids (template structures). The field is latent; " - "observer queries collapse local regions into discrete UTF-8 text." - ), - - "utf8_vs_density": { - "utf8": { - "representation": "1D discrete sequence: b[i] ∈ [0,255] for i = 0..N-1", - "assumption": "Text is a linear string of symbols", - "compression": "Exploit sequential redundancy (LZ, PPM, BWT, neural prediction)", - "limitation": "Cannot represent non-sequential relationships without explicit linking; every byte position is independent dimension" - }, - "density_field": { - "representation": "n-D continuous field: ρ: ℝⁿ → ℝ⁺ (semantic density)", - "assumption": "Text is a region of an information manifold; spatial proximity = semantic proximity", - "compression": "Encode topological skeleton (peaks, ridges, voids) + small perturbation field", - "advantage": "Implicit relationships via field geometry; no explicit links needed; multi-scale structure naturally emerges" - } - }, - - "topological_features": { - "peaks": { - "encoding": "Named entities, article centers, key concepts", - "field_property": "Local maximum of ρ(x⃗)", - "invariant": "Peak index (persistent homology H₀), peak height (salience)", - "s3c_analogue": "k = shell index = distance from peak = semantic centrality; a = angular offset within cluster" - }, - "ridges": { - "encoding": "Hyperlinks, citations, semantic associations, see-also connections", - "field_property": "1-D local maxima along one direction, saddle in transverse", - "invariant": "Ridge persistence (how far down before splitting), ridge connectivity graph", - "s3c_analogue": "Mirror complement b⁰ = remaining path to next peak; mass = connection strength" - }, - "saddles": { - "encoding": "Topic transitions, paragraph boundaries, section changes", - "field_property": "Saddle point: maximum in some directions, minimum in others", - "invariant": "Saddle index, separatrix topology (which peaks connected)", - "s3c_analogue": "Throat region a ≈ b⁰ = maximum ambiguity = maximum information transition" - }, - "vortices": { - "encoding": "Cyclic references, category hierarchies, template instantiations, recursive structures", - "field_property": "Rotational flow in field gradient: ∇ρ circulates around core", - "invariant": "Vorticity ω = ∇ × ∇ρ, circulation Γ = ∮∇ρ·dl", - "s3c_analogue": "Contra-rotation gear in MS3C; recursive shell nesting S_n(S_{n-1}^n)" - }, - "voids": { - "encoding": "Template structures, common phrases, expected-but-absent content", - "field_property": "Local minimum of ρ(x⃗), possibly negative in signed extension", - "invariant": "Void depth, void volume, enclosing shell connectivity", - "s3c_analogue": "Negative pyramid / anti-resonance = expected structure that is absent; cheaper to encode absence than presence" - }, - "level_sets": { - "encoding": "Paragraphs, sections, articles at different semantic granularity", - "field_property": "Iso-density surfaces {x⃗ | ρ(x⃗) = c}", - "invariant": "Euler characteristic χ of level set surface = β₀ - β₁ + β₂", - "s3c_analogue": "Shell index k = level set value; each shell is a semantic granularity layer" - } - }, - - "compression_mechanism": { - "topological_skeleton": { - "description": "Encode only the critical points and separatrices of the Morse-Smale complex", - "data": "Peak positions + heights, ridge connectivity graph, saddle indices, vortex cores, void enclosures", - "size": "O(N_peaks + N_ridges) ≪ O(N_bytes). For enwik9: ~10⁶ peaks, ~10⁷ ridges → ~100MB skeleton vs 1GB raw", - "reconstruction": "Decode skeleton + perturbation field → approximate density field → collapse to UTF-8 on observer query" - }, - "perturbation_field": { - "description": "Residual between topological skeleton prediction and actual density", - "encoding": "High-frequency, small-amplitude corrections stored via PIST n-D bundle encoding", - "analogy": "Like residual in JPEG: skeleton = DCT low frequencies, perturbation = high frequencies" - }, - "observer_collapse": { - "description": "The field itself is never materialized as full text. Observer queries specify a path through the field.", - "oac_connection": "Observer-Admissible Cavities: the field is the latent n^n space. A query 'show me the France article' is a touch that manifests the local cavity around the 'France' peak.", - "compression": "Only manifest the touched region. The rest stays compressed in the field representation." - } - }, - - "morse_theory_formalization": { - "density_field": "ρ: M → ℝ⁺ where M is n-dimensional semantic manifold", - "critical_points": "∇ρ = 0. Classified by Hessian eigenvalues: peak (all -), saddle (mixed), void (all +)", - "morse_complex": "Cells built from ascending/descending manifolds of critical points. Combinatorial encoding of field topology.", - "persistence": "Track critical points as ρ threshold varies. Persistent features = real semantic structure. Transient = noise/detail.", - "compression_theorem": ( - "The Morse-Smale complex of ρ encodes the homotopy type of M. " - "If text structure is determined by topological type (links, sections, categories), " - "then the Morse complex is a complete encoding up to homeomorphism. " - "Exact text reconstruction requires perturbation field, but semantic navigation requires only the complex." - ) - }, - - "stack_integration": { - "pist_nd_encoding": { - "role": "PERTURBATION ENCODER: PIST n-D bundle encodes the residual density field after skeleton subtraction", - "mapping": "fiber_dim = topological feature type (peak, ridge, saddle, vortex, void). n_dims = spatial dimensions of semantic manifold (typically 3-4D: topic, time, authority, style)" - }, - "s3c_shells": { - "role": "MULTI-SCALE SHELL STRUCTURE: S3C shell index k = semantic distance from core concept. a = intra-cluster position.", - "mapping": "Concentric shells around each peak = layers of detail: k=0=title, k=1=abstract, k=2=lead, k=3=body, k=4=references, k=5=see-also" - }, - "oac": { - "role": "LAZY MANIFESTATION: The density field is a global OAC. Observer queries are touches that manifest local regions.", - "mapping": "touch(ρ, observer, query_region) → local_manifested_text + residual. Unqueried regions stay latent." - }, - "hypercube_rhomboid": { - "role": "MANIFOLD GEOMETRY: UTF-8 text is an orthogonal hypercube (independent byte positions). Density field is a sheared rhomboid where correlated semantic positions lean into each other.", - "mapping": "Shear matrix A maps from UTF-8 hypercube to density rhomboid. A is learned from corpus: eigenvectors = principal semantic directions." - }, - "famm_delay_lines": { - "role": "TEMPORAL SEQUENCING: Density field has no natural order. FAMM preshaped delays impose a reading path through the field.", - "mapping": "Delay profile = path integral through field gradient. Fast regions = high density (predictable). Slow regions = low density (needs more context)." - }, - "erans_entropy": { - "role": "RESIDUAL CODING: After skeleton encoding, perturbation field is entropy-coded using erans-style enumerative coding on histogram of density residuals.", - "mapping": "Density values are not bytes; but discretized to histogram bins. Enumerative coding is optimal for exact histogram." - } - }, - - "hutter_prize_application": { - "current_paradigm": "1D byte sequence → predict next byte → entropy code prediction residual", - "density_paradigm": "Encode topological skeleton of semantic density field → store as compressed graph + persistent homology → entropy code perturbation field", - "estimated_size": { - "skeleton": "~50-150MB for enwik9 (Morse complex of ~10⁶ peaks + ~10⁷ ridges + persistence pairs)", - "perturbation": "~200-400MB (PIST n-D bundle encoded residuals)", - "total": "~250-550MB vs current best ~115MB", - "caveat": "This is raw field encoding. A hybrid approach may win: use density field for structural regions (infoboxes, citations, links = 40% of enwik) + traditional encoding for free text." - }, - "novel_capability": "Current compressors produce a flat file. A density field compressor produces a NAVIGABLE structure: you can query 'show me all articles 2 links from France' without decompressing everything." - }, - - "digital_dzogchen_connection": { - "philosophy": "Dzogchen: appearances are not solid; they are luminous emptiness — projections of mind's nature. Reality is not a collection of discrete objects but a continuous field of appearing.", - "computational_analogue": "Text is not a collection of discrete bytes but a continuous semantic density field. What we call 'the Wikipedia article on France' is a local modulation of the global information field — a peak with certain topological features.", - "compression_insight": "Just as dzogchen says the entire mandala is present in every point, the entire Wikipedia is present in every local density gradient. Compression is finding the minimal description of the field's topology, not its explicit manifestation." - }, - - "keeper_phrases": [ - "UTF-8 assumes text is a string. Density field assumes text is a landscape.", - "A citation is not 200 bytes. It is a ridge connecting two peaks through a saddle.", - "The Morse-Smale complex is the topological skeleton of meaning.", - "Compression is not predicting the next byte. It is finding the minimal topological description of the semantic field.", - "Observer touch collapses the field; the field itself never needs to fully materialize.", - "In a density field, 'France' and 'Germany' are nearby peaks on the same continental ridge.", - "Vortices encode recursion. Voids encode templates. Saddles encode transitions.", - "The Hutter Prize is asking for a flat file. We should be encoding a navigable manifold." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "density-field-encoding", "topological-compression", "morse-theory", - "semantic-manifold", "beyond-utf8", "digital-dzogchen", "navigable-compression", - "persistent-homology", "morse-smale-complex", "observer-collapse", - "hutter-prize", "oac", "s3c-shells", "pist-perturbation" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "density_field_encoding_theory.json" - with open(out_path, 'w') as f: - json.dump(DENSITY_FIELD, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": DENSITY_FIELD["id"], - "title": DENSITY_FIELD["title"], - "date": DENSITY_FIELD["date"], - "source": DENSITY_FIELD["source"], - "ingested_at": DENSITY_FIELD["metadata"]["ingested_at"], - "tags": DENSITY_FIELD["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nTopological features mapped:") - for feat, props in DENSITY_FIELD["topological_features"].items(): - print(f" • {feat}: {props['encoding']}") - - print(f"\nStack integration:") - for module, mapping in DENSITY_FIELD["stack_integration"].items(): - print(f" ↔ {module}: {mapping['role'][:70]}...") - - print(f"\nKeeper phrases ({len(DENSITY_FIELD['keeper_phrases'])}):") - for p in DENSITY_FIELD["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_erans_field_effect_spectrum.py b/5-Applications/scripts/ingest_erans_field_effect_spectrum.py deleted file mode 100644 index a99bbc33..00000000 --- a/5-Applications/scripts/ingest_erans_field_effect_spectrum.py +++ /dev/null @@ -1,226 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: erans Field Effect Spectrum Extension -=========================================== -Evolve erans enumerative rANS to encode the spectral decomposition -of the residual field, not just flat histogram coding. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ERANS_FIELD_EFFECT = { - "id": "erans-field-effect-spectrum-v1", - "source": "User insight: evolve erans to become field effect spectrum", - "title": "erans Field Effect Spectrum: Spectral Residual Encoding via Enumerative rANS", - "date": "2026-05-07", - - "core_insight": ( - "erans currently provides optimal exact histogram coding of residuals. " - "Evolve erans to encode the spectral decomposition of the residual field ε(x⃗) itself. " - "Instead of coding residual values directly, compute the field's spectral decomposition " - "(Fourier, wavelet, or eigen-decomposition of the residual correlation matrix) and use " - "erans to entropy-code the spectral coefficients. The 'field effect' is how residuals " - "propagate through the manifold — the spectrum captures this propagation pattern." - ), - - "erans_baseline": { - "current_usage": "Enumerative rANS for optimal exact histogram coding of residual stream Ε", - "mechanism": "Histogram of residual values is exact; enumerative coding is optimal for exact histograms", - "limitation": "Treats residuals as independent symbols. Does not capture spatial/spectral correlation in the residual field itself" - }, - - "field_effect_spectrum": { - "residual_field": "ε(x⃗) is the perturbation field — difference between topological skeleton prediction and actual density", - "spectral_decomposition": { - "fourier_transform": "ε̂(k⃗) = ∫ ε(x⃗) e^(-2πi k⃗·x⃗) dx⃗ — captures frequency components of residual field", - "wavelet_transform": "Wavelet coefficients capture multi-scale residual structure", - "eigen_decomposition": "Compute correlation matrix C_{ij} = ⟨ε_i ε_j⟩, then eigen-decompose C = UΛU^T. Eigenvectors = principal residual patterns, eigenvalues = residual energy per pattern", - "choice": "Eigen-decomposition is most compatible with existing shear matrix framework (Gram matrix G = A^T A already uses eigenvectors)" - }, - "field_effect_interpretation": { - "propagation": "Spectral coefficients show how residuals at one location affect other locations through the manifold", - "correlation": "Off-diagonal terms in correlation matrix C show residual correlation across spatial/temporal dimensions", - "energy_distribution": "Eigenvalues Λ show how residual energy is distributed across principal residual patterns", - "hippocampus_analogue": "Pattern separation in hippocampus (10-40% ensemble overlap) can be modeled in spectral domain — residual patterns with low overlap are separated in eigenspace" - } - }, - - "erans_spectral_encoding": { - "spectral_coefficients_as_symbols": "Treat spectral coefficients (eigenvalues, eigenvector weights, Fourier amplitudes) as symbols for erans coding", - "histogram_exactness": "Histogram of spectral coefficients is exact (derived from exact residual field). Enumerative coding is optimal.", - "advantages": { - "correlation_capture": "Spectral decomposition captures residual correlation that flat histogram coding misses", - "energy_compaction": "Most residual energy concentrated in few spectral coefficients — erans codes these efficiently", - "propagation_modeling": "Field effect spectrum models how residuals propagate through manifold", - "hippocampus_alignment": "Spectral pattern separation aligns with hippocampus ensemble overlap mechanism" - }, - "encoding_pipeline": [ - "Compute residual field ε(x⃗)", - "Compute residual correlation matrix C_{ij} = ⟨ε_i ε_j⟩", - "Eigen-decompose C = UΛU^T", - "Extract spectral coefficients: eigenvalues Λ, eigenvector projection weights a = U^T ε", - "Build exact histogram of spectral coefficients", - "Apply erans enumerative coding to spectral coefficients", - "Store coded spectral coefficients + eigenvectors U", - "Decode: reconstruct spectral coefficients → reconstruct residual field ε̃(x⃗)" - ] - }, - - "integration_with_master_synthesis": { - "stage_12_pist_perturbation": "PIST n-D bundle encodes perturbation field δ. Now also compute spectral decomposition of δ", - "stage_13_erans_spectral": "erans entropy-codes spectral coefficients of perturbation field, not just flat residual values", - "shear_matrix_alignment": "Gram matrix G = A^T A already uses eigenvectors. Residual correlation matrix C shares same eigenspace. Can reuse eigenvector computation.", - "famm_spectral_pruning": "FAMM delay pruning based on eigenvalue spectra can also use residual spectral energy. High residual energy regions get slower delays (need more context).", - "oac_spectral_gate": "OAC admissibility gate can use spectral overlap measure instead of just residual size. Two motifs are admissible if their residual spectral patterns have low overlap (hippocampus pattern separation analogue).", - "radius_ratio_spectral": "Radius-ratio quantization can use spectral energy ratio instead of just scale ratio. ρ_spectral = λ_i / median(Λ)." - }, - - "spectral_field_effect_metrics": { - "spectral_energy_compaction": "Most residual energy in few eigenvalues. If λ₁ >> λ₂ >> ..., then field is highly compressible in spectral domain.", - "spectral_overlap": "Overlap between residual spectral patterns of different motifs. Low overlap = good pattern separation (hippocampus analogue).", - "spectral_entropy": "Entropy of spectral coefficient histogram. Lower entropy = more compressible.", - "spectral_correlation_length": "Correlation length in spectral domain = how far residuals propagate through field.", - "spectral_discrimination_threshold": "zeta^thr_spectral = threshold on spectral overlap for OAC admissibility." - }, - - "compression_gain_from_spectral": { - "energy_compaction": "If 90% of residual energy in top 10% of spectral coefficients, erans codes these 10% efficiently. 10-20% gain over flat histogram.", - "correlation_capture": "Spectral decomposition captures residual correlation. 5-10% additional gain.", - "hippocampus_pattern_separation": "Spectral overlap measure improves OAC gate precision. Avoids 2-3% more bloat.", - "famm_spectral_pruning": "FAMM delays based on residual spectral energy. 3-5% additional context efficiency.", - "total_spectral_gain": "Estimated 20-35% additional gain on residual coding stage." - }, - - "implementation_details": { - "correlation_matrix_computation": { - "method": "C_{ij} = (1/N) Σ_k ε_k(i) ε_k(j) where ε_k is residual at position k in dimension i", - "complexity": "O(N × d²) where N = number of residual positions, d = number of dimensions (typically 3-4: topic, time, authority, style)", - "sparse_approximation": "For large N, use sparse correlation or stochastic approximation" - }, - "eigen_decomposition": { - "method": "Standard symmetric eigen-decomposition of C", - "output": "Eigenvectors U (principal residual patterns), eigenvalues Λ (residual energy per pattern)", - "q16_16_fixed_point": "Eigenvectors and eigenvalues encoded in Q16.16 for hardware-native determinism" - }, - "spectral_coefficient_extraction": { - "method": "a = U^T ε (project residual field onto eigenvectors)", - "output": "Spectral coefficient vector a (weights of each principal residual pattern)", - "histogram": "Build exact histogram of a values for erans coding" - }, - "erans_spectral_coding": { - "input": "Histogram of spectral coefficients a", - "method": "Enumerative rANS (same as baseline, but applied to spectral coefficients instead of raw residuals)", - "output": "Entropy-coded spectral coefficients" - }, - "reconstruction": { - "decode_spectral": "Decode spectral coefficients ã", - "reconstruct_residual": "ε̃ = U ã (reconstruct residual field from spectral coefficients)", - "apply_residual": "s = Repair(Generate(...), ε̃)" - } - }, - - "hippocampus_spectral_analogy": { - "ensemble_overlap_spectral": "Hippocampus pattern separation uses 10-40% ensemble overlap. Spectral analogue: overlap between eigenvectors of residual patterns for different motifs.", - "discrimination_threshold_spectral": "zeta^thr = 10 Hz firing rate. Spectral analogue: zeta^thr_spectral = threshold on spectral coefficient magnitude or eigenvalue ratio.", - "neuron_dropout_spectral": "Neuron dropout during consolidation. Spectral analogue: drop spectral coefficients below threshold (prune low-energy residual patterns).", - "composite_promotion_spectral": "Composite promotion = soliton bound state. Spectral analogue: accepted motifs have coherent spectral residual patterns (low entropy, high compaction)." - }, - - "keeper_phrases": [ - "erans currently codes flat histograms. Evolve erans to code spectral decompositions of the residual field.", - "The field effect is how residuals propagate through the manifold. The spectrum captures this propagation.", - "Compute residual correlation matrix C, eigen-decompose C = UΛU^T, code spectral coefficients with erans.", - "Spectral energy compaction: 90% of residual energy in 10% of coefficients = 10-20% gain.", - "Spectral decomposition captures residual correlation that flat histogram coding misses.", - "FAMM delays can use residual spectral energy: high energy regions get slower delays.", - "OAC gate can use spectral overlap measure instead of just residual size for pattern separation.", - "Hippocampus pattern separation (10-40% ensemble overlap) has spectral analogue in eigenvector overlap.", - "The shear matrix Gram matrix G and residual correlation matrix C share the same eigenspace.", - "Don't code the residual values. Code the spectral pattern of the residual field.", - "Spectral entropy = compressibility. Lower spectral entropy = more compressible residual field.", - "zeta^thr_spectral = threshold on spectral coefficient magnitude for hippocampus-style discrimination.", - "Composite promotion spectral: accepted motifs have coherent residual spectral patterns (low entropy).", - "The field effect spectrum is the residual's propagation pattern through the manifold." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "erans-field-effect", - "spectral-encoding", - "residual-field-spectrum", - "correlation-matrix", - "eigen-decomposition", - "field-effect", - "hippocampus-spectral", - "pattern-separation", - "energy-compaction", - "spectral-entropy", - "enumerative-rans", - "spectral-overlap", - "famm-spectral", - "oac-spectral" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "erans_field_effect_spectrum_v1.json" - with open(out_path, 'w') as f: - json.dump(ERANS_FIELD_EFFECT, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ERANS_FIELD_EFFECT["id"], - "title": ERANS_FIELD_EFFECT["title"], - "date": ERANS_FIELD_EFFECT["date"], - "source": ERANS_FIELD_EFFECT["source"], - "ingested_at": ERANS_FIELD_EFFECT["metadata"]["ingested_at"], - "tags": ERANS_FIELD_EFFECT["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCore insight:") - print(f" {ERANS_FIELD_EFFECT['core_insight'][:80]}...") - - print(f"\nSpectral decomposition methods:") - for method, desc in ERANS_FIELD_EFFECT["field_effect_spectrum"]["spectral_decomposition"].items(): - print(f" • {method}: {desc[:60]}...") - - print(f"\nerans spectral encoding pipeline:") - for i, step in enumerate(ERANS_FIELD_EFFECT["erans_spectral_encoding"]["encoding_pipeline"], 1): - print(f" {i}. {step[:60]}...") - - print(f"\nIntegration with master synthesis:") - for integration, desc in ERANS_FIELD_EFFECT["integration_with_master_synthesis"].items(): - print(f" • {integration}: {desc[:60]}...") - - print(f"\nCompression gain from spectral:") - for source, gain in ERANS_FIELD_EFFECT["compression_gain_from_spectral"].items(): - print(f" • {source}: {gain}") - - print(f"\nKeeper phrases ({len(ERANS_FIELD_EFFECT['keeper_phrases'])}):") - for p in ERANS_FIELD_EFFECT['keeper_phrases']: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_erans_reference.py b/5-Applications/scripts/ingest_erans_reference.py deleted file mode 100644 index 316fa577..00000000 --- a/5-Applications/scripts/ingest_erans_reference.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: erans — enumerative rANS (reference only, NO CODE COPIED) -================================================================== -izabera/erans is a streamable single-pass rANS variant. -NO LICENSE — algorithmic notes only, zero code incorporated. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ERANS_REF = { - "id": "erans-enumerative-rans-reference", - "source": "https://github.com/izabera/erans", - "title": "erans: Enumerative rANS — Algorithmic Reference (No Code)", - "date": "2026-05-07", - "license": "NONE SPECIFIED — DO NOT INCORPORATE CODE", - "status": "REFERENCE ONLY — algorithmic ideas, zero lines copied", - - "what_it_is": ( - "A streamable, single-pass, adaptive rANS variant that encodes " - "the exact histogram + permutation index of a multiset. Achieves " - "the enumerative coding bound: compressed size approaches log of " - "the multinomial coefficient, strictly less than Shannon entropy " - "for the same data (by ~((K-1)/2)log2(N) bits)." - ), - - "key_algorithmic_ideas": { - "single_pass_adaptive": { - "concept": "Encode each symbol against running counts INCLUDING current position. No pre-pass for histogram.", - "why_it_works": "c_i(s) = count of s up to position i (including i). p_i(s) = c_i(s)/i. Encoder bumps count BEFORE computing CDF so decoder sees same prefix counts walking backward.", - "our_take": "Maps to PIST streaming encode where shell mass accumulates online. The 'include current position' trick avoids zero-probability symbols." - }, - "enumerative_bound": { - "concept": "log2(M) = NH - ((K-1)/2)log2(N) + O(1) where M is multinomial coefficient, H is empirical entropy, K=256", - "significance": "Beats Shannon entropy by ~1018 bits for N=2^24, K=256. The gain is from NOT quantizing frequencies to powers of 2.", - "our_take": "This is the geometric compression gain from exact histogram — analogous to storing the exact shear matrix rather than a quantized approximation." - }, - "shrub_data_structure": { - "concept": "2-level tree, branching factor 16, for O(1) branchless CDF updates. Bottom: 16 groups of 16 counters. Top: 16 group sums.", - "operations": "inc/dec: masked add to group + top. sym→cdf: top[byte>>4] + bottom[byte&0xf]. cdf→sym: vector compare for monotonic scan.", - "isa_note": "erans uses AVX-512 masked adds. Scalar equivalent works at ~2x cycles. Algorithmic structure is ISA-agnostic.", - "our_take": "Fenwick tree alternative. The 16×16 split is natural for byte alphabet. Could map to Q0_16 accumulators for fixed-point probability tracking." - }, - "streaming_renorm": { - "concept": "State kept in [M, 256*M). Overflow bytes emitted when state >= 256*f. No length prefix needed — decoder pulls until state >= M_final.", - "boundary_case": "When f=M (all symbols same so far), state stays at 1, renorm never fires — degenerates to no-op. Clean.", - "our_take": "FAMM preshaped renorm: the renorm threshold shifts with M. Could preshape the threshold per shell class for structured data." - }, - "histogram_encoding": { - "concept": "Rice coding: split each count into lower B bits (binary) + upper part (unary). B = max(0, ceil(log2(M))-8).", - "overhead": "At most 576 bytes for N=2^24. Theoretical lower bound ~555 bytes.", - "our_take": "S3C shell coordinates could encode histogram more compactly — counts are shell populations, naturally structured." - } - }, - - "hutter_prize_relevance": { - "entropy_coder": "erans is a candidate entropy coding backend. Beats standard rANS by ~0.004% on large blocks — small but real.", - "streaming": "Single-pass streaming matches our PIST-S3C-FAMM pipeline architecture. No pre-pass needed.", - "block_size": "Implementation limit 2^24 (16MiB). Algorithm has no inherent limit. enwik8 = 100M, enwik9 = 1G — need larger blocks or chaining.", - "comparison_to_fse": "FSE (tANS) decodes in ~5-10 cycles/byte. erans is slower but produces smaller output. For Hutter, size matters more than speed.", - "adaptation_limitation": "erans is NOT locally adaptive — uses global histogram. For varying distributions, block-splitting needed. Our S3C shell batching naturally provides block boundaries." - }, - - "why_separate": [ - "NO LICENSE — cannot incorporate any code", - "Algorithmic ideas are public domain (math), implementation is not", - "Our shrub-equivalent should be written from scratch in Lean + extraction target", - "ISA-agnostic by design: no AVX-512 dependency, scalar fallback always" - ], - - "design_rules_added": { - "isa_agnostic": "Never assume any instruction set is available. SIMD is opportunistic, never structural. All hot paths must have scalar fallback.", - "license_gate": "No code enters the stack without a compatible license. Algorithmic ideas from unlicensed repos are noted as reference only.", - "separation": "Reference implementations live in design notes, never in the source tree." - }, - - "metadata": { - "ingested_at": time.time(), - "tags": ["entropy-coding", "rans", "enumerative-coding", "reference-only", - "no-license", "streaming", "hutter-prize", "entropy", "shrub"] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "erans_enumerative_rans_reference.json" - with open(out_path, 'w') as f: - json.dump(ERANS_REF, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ERANS_REF["id"], - "title": ERANS_REF["title"], - "date": ERANS_REF["date"], - "source": ERANS_REF["source"], - "ingested_at": ERANS_REF["metadata"]["ingested_at"], - "tags": ERANS_REF["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nAlgorithmic ideas captured (no code):") - for name, idea in ERANS_REF["key_algorithmic_ideas"].items(): - print(f" • {name}: {idea['concept'][:80]}...") - - print(f"\n⚠ LICENSE: {ERANS_REF['license']}") - print(f"⚠ {ERANS_REF['why_separate'][0]}") - print(f"⚠ {ERANS_REF['why_separate'][1]}") - - print(f"\nDesign rules:") - for rule, text in ERANS_REF["design_rules_added"].items(): - print(f" + {rule}: {text[:80]}...") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_erdos_problems.py b/5-Applications/scripts/ingest_erdos_problems.py deleted file mode 100644 index 58de065c..00000000 --- a/5-Applications/scripts/ingest_erdos_problems.py +++ /dev/null @@ -1,451 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest Erdős Problems from External Sources -========================================= -Pull in Erdős problems from external sources and ingest into local research database. -""" - -import json -from pathlib import Path -from datetime import datetime - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") -RESEARCH_DIR = RESEARCH_STACK / "shared-data/data/germane/research" - - -# Erdős problems from Wikipedia and other sources -ERDOS_PROBLEMS = { - "unsolved_conjectures": [ - { - "name": "Erdős–Gyárfás conjecture", - "description": "On cycles with lengths equal to a power of two in graphs with minimum degree 3.", - "domain": "Graph Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Gy%C3%A1rf%C3%A1s_conjecture" - }, - { - "name": "Erdős–Hajnal conjecture", - "description": "In a family of graphs defined by an excluded induced subgraph, every graph has either a large clique or a large independent set.", - "domain": "Graph Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Hajnal_conjecture" - }, - { - "name": "Erdős–Mollin–Walsh conjecture", - "description": "On consecutive triples of powerful numbers.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Powerful_number" - }, - { - "name": "Erdős–Selfridge conjecture", - "description": "A covering system with distinct moduli contains at least one even modulus.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Covering_system" - }, - { - "name": "Erdős–Straus conjecture", - "description": "For every integer n ≥ 2, the equation 4/n = 1/x + 1/y + 1/z has a solution in positive integers x, y, z.", - "domain": "Diophantine Equations", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Straus_conjecture" - }, - { - "name": "Erdős conjecture on arithmetic progressions", - "description": "If Σ_{a∈A} 1/a diverges, then A contains arbitrarily long arithmetic progressions.", - "domain": "Additive Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_conjecture_on_arithmetic_progressions" - }, - { - "name": "Erdős–Szekeres conjecture", - "description": "On the number of points needed to ensure that a point set contains a large convex polygon.", - "domain": "Discrete Geometry", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Szekeres_conjecture" - }, - { - "name": "Erdős–Turán conjecture on additive bases", - "description": "If A is an additive basis of order 2 for the natural numbers, then the sum of reciprocals diverges: Σ_{a∈A} 1/a = ∞.", - "domain": "Additive Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Tur%C3%A1n_conjecture_on_additive_bases" - }, - { - "name": "Erdős conjecture on quickly growing integer sequences", - "description": "On integer sequences with rational reciprocal series (Sylvester's sequence).", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Sylvester%27s_sequence" - }, - { - "name": "Erdős–Oler conjecture on circle packing", - "description": "On circle packing in an equilateral triangle with a number of circles one less than a triangular number.", - "domain": "Geometry", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Circle_packing_in_an_equilateral_triangle" - }, - { - "name": "Minimum overlap problem", - "description": "To estimate the limit of M(n).", - "domain": "Combinatorics", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Minimum_overlap_problem" - }, - { - "name": "Erdős conjecture on ternary expansion of 2^n", - "description": "The ternary expansion of 2^n contains at least one digit 2 for every n > 8.", - "domain": "Number Theory", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Moser equation", - "description": "The equation 1^k + 2^k + ... + (m-1)^k = m^k has no solutions except 1^1 + 2^1 = 3^1.", - "domain": "Diophantine Equations", - "status": "Unsolved", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Moser_equation" - } - ], - "solved_conjectures": [ - { - "name": "Erdős–Faber–Lovász conjecture", - "description": "On coloring unions of cliques.", - "domain": "Graph Theory", - "status": "Solved (2021)", - "solved_by": "Dong Yeap Kang, Tom Kelly, Daniela Kühn, Abhishek Methuku, and Deryk Osthus", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s%E2%80%93Faber%E2%80%93Lov%C3%A1sz_conjecture" - }, - { - "name": "Erdős sumset conjecture", - "description": "On sets.", - "domain": "Additive Combinatorics", - "status": "Solved (2018)", - "solved_by": "Joel Moreira, Florian Karl Richter, Donald Robertson", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_sumset_conjecture" - }, - { - "name": "Burr–Erdős conjecture", - "description": "On Ramsey numbers of graphs.", - "domain": "Ramsey Theory", - "status": "Solved (2015)", - "solved_by": "Choongbum Lee", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Burr%E2%80%93Erd%C5%91s_conjecture" - }, - { - "name": "Erdős conjecture on equitable colorings", - "description": "Now known as the Hajnal–Szemerédi theorem.", - "domain": "Graph Theory", - "status": "Solved (1970)", - "solved_by": "András Hajnal and Endre Szemerédi", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Lovász conjecture on weak/strong delta-systems", - "description": "On delta-systems.", - "domain": "Combinatorics", - "status": "Solved (1974)", - "solved_by": "Michel Deza", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Heilbronn conjecture", - "description": "In combinatorial number theory on the number of sums of two sets of residues modulo a prime.", - "domain": "Number Theory", - "status": "Solved (1994)", - "solved_by": "Dias da Silva and Hamidoune", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Graham conjecture", - "description": "In combinatorial number theory on monochromatic Egyptian fraction representations of unity.", - "domain": "Number Theory", - "status": "Solved (2000)", - "solved_by": "Ernie Croot", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Stewart conjecture", - "description": "On the Diophantine equation n! + 1 = p^k_a p_{k+1}^b.", - "domain": "Number Theory", - "status": "Solved (2001)", - "solved_by": "Florian Luca", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Cameron–Erdős conjecture", - "description": "On sum-free sets of integers.", - "domain": "Number Theory", - "status": "Solved (2003-2004)", - "solved_by": "Ben Green and Alexander Sapozhenko", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Menger conjecture", - "description": "On disjoint paths in infinite graphs.", - "domain": "Graph Theory", - "status": "Solved (2009)", - "solved_by": "Ron Aharoni and Eli Berger", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős distinct distances problem", - "description": "The correct exponent was proved in 2010 by Larry Guth and Nets Katz, but the correct power of log n is still undetermined.", - "domain": "Discrete Geometry", - "status": "Partially Solved (2010)", - "solved_by": "Larry Guth and Nets Katz", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "https://en.wikipedia.org/wiki/Erd%C5%91s_distinct_distances_problem" - }, - { - "name": "Erdős–Rankin conjecture on prime gaps", - "description": "On prime gaps.", - "domain": "Number Theory", - "status": "Solved (2014)", - "solved_by": "Ford, Green, Konyagin, and Tao", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős discrepancy problem", - "description": "On partial sums of ±1-sequences.", - "domain": "Number Theory", - "status": "Solved (2015)", - "solved_by": "Terence Tao", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős squarefree conjecture", - "description": "Central binomial coefficients C(2n, n) are never squarefree for n > 4.", - "domain": "Number Theory", - "status": "Solved (1996)", - "solved_by": "Various", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős primitive set conjecture", - "description": "The sum Σ_{n∈A} 1/(n log n) for any primitive set A attains its maximum at the set of prime numbers.", - "domain": "Number Theory", - "status": "Solved (2022)", - "solved_by": "Jared Duker Lichtman", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős–Sauer problem", - "description": "About maximum number of edges an n-vertex graph can have without containing a k-regular subgraph.", - "domain": "Graph Theory", - "status": "Solved", - "solved_by": "Oliver Janzer and Benny Sudakov", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 728", - "description": "Solved in 2026 using AI assistance.", - "domain": "Unknown", - "status": "Solved (2026)", - "solved_by": "Kevin Barreto and Liam Price with ChatGPT 5.2 and Aristotle Lean API", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 347", - "description": "On subset sums of sequences with ratio limit 2.", - "domain": "Combinatorics", - "status": "Solved (2026)", - "solved_by": "Enrique Barschkis", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - }, - { - "name": "Erdős problem 369", - "description": "Solved in March 2026 by 17-year-old Sky Yang (Yueer Yang).", - "domain": "Unknown", - "status": "Solved (2026)", - "solved_by": "Sky Yang (Yueer Yang)", - "source": "Wikipedia: List of conjectures by Paul Erdős", - "url": "" - } - ], - "additional_problems": [ - { - "name": "Erdős–Ko–Rado theorem", - "description": "Maximum size of intersecting families of k-subsets of {1,...,n} is C(n-1, k-1) for n ≥ 2k.", - "domain": "Extremal Set Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Ginzburg–Ziv theorem", - "description": "Any 2n-1 integers contain n whose sum is divisible by n.", - "domain": "Additive Number Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Stone theorem", - "description": "For any graph H, ex(n,H) = (1 - 1/χ(H)-1 + o(1))n²/2 where χ(H) is chromatic number.", - "domain": "Extremal Graph Theory", - "status": "Solved", - "source": "Standard theorem", - "url": "" - }, - { - "name": "Erdős–Rényi random graph model", - "description": "Study properties of G(n,p) random graphs. Threshold phenomena for connectivity, giant component, Hamiltonicity.", - "domain": "Random Graphs", - "status": "Standard model", - "source": "Standard model", - "url": "" - }, - { - "name": "Erdős Hadamard conjecture", - "description": "There exist Hadamard matrices of order 4k for all k.", - "domain": "Linear Algebra", - "status": "Unsolved", - "source": "Standard conjecture", - "url": "" - }, - { - "name": "Erdős–Moser problem", - "description": "Find all solutions to 1/a + 1/b + 1/c + 1/d + 1/e = 1 in distinct positive integers.", - "domain": "Diophantine Equations", - "status": "Solved (only known solution)", - "source": "Standard problem", - "url": "" - } - ] -} - - -def create_erdos_problems_document(): - """Create a comprehensive Erdős problems document.""" - timestamp = datetime.now().isoformat() - - document = { - "document_id": "erdos_problems_comprehensive_v1", - "title": "Comprehensive Erdős Problems Collection", - "created": timestamp, - "source": "Wikipedia and other sources", - "unsolved_conjectures": ERDOS_PROBLEMS["unsolved_conjectures"], - "solved_conjectures": ERDOS_PROBLEMS["solved_conjectures"], - "additional_problems": ERDOS_PROBLEMS["additional_problems"], - "statistics": { - "total_unsolved": len(ERDOS_PROBLEMS["unsolved_conjectures"]), - "total_solved": len(ERDOS_PROBLEMS["solved_conjectures"]), - "total_additional": len(ERDOS_PROBLEMS["additional_problems"]), - "total_problems": len(ERDOS_PROBLEMS["unsolved_conjectures"]) + len(ERDOS_PROBLEMS["solved_conjectures"]) + len(ERDOS_PROBLEMS["additional_problems"]) - }, - "domains": { - "Graph Theory": 0, - "Number Theory": 0, - "Discrete Geometry": 0, - "Additive Number Theory": 0, - "Diophantine Equations": 0, - "Combinatorics": 0, - "Extremal Set Theory": 0, - "Ramsey Theory": 0, - "Random Graphs": 0, - "Linear Algebra": 0, - "Additive Combinatorics": 0, - "Geometry": 0, - "Unknown": 0 - } - } - - # Count domains - all_problems = ERDOS_PROBLEMS["unsolved_conjectures"] + ERDOS_PROBLEMS["solved_conjectures"] + ERDOS_PROBLEMS["additional_problems"] - for problem in all_problems: - domain = problem["domain"] - if domain in document["domains"]: - document["domains"][domain] += 1 - - return document - - -def main(): - print("=" * 70) - print(" INGESTING ERDŐS PROBLEMS INTO LOCAL RESEARCH DATABASE") - print("=" * 70) - - # Create document - document = create_erdos_problems_document() - - print(f"\nStatistics:") - print(f" Total unsolved: {document['statistics']['total_unsolved']}") - print(f" Total solved: {document['statistics']['total_solved']}") - print(f" Total additional: {document['statistics']['total_additional']}") - print(f" Total problems: {document['statistics']['total_problems']}") - - print(f"\nDomain distribution:") - for domain, count in document["domains"].items(): - if count > 0: - print(f" {domain}: {count}") - - # Save to research directory - output_file = RESEARCH_DIR / "erdos_problems_comprehensive_v1.json" - with open(output_file, 'w') as f: - json.dump(document, f, indent=2) - - print(f"\n✓ Erdős problems saved to: {output_file}") - - # Update research ingestion index - index_file = RESEARCH_DIR / "research_ingestion_index.json" - - if index_file.exists(): - with open(index_file, 'r') as f: - index = json.load(f) - else: - index = [] - - # Add new entry - new_entry = { - "id": "erdos-problems-comprehensive-v1", - "title": "Comprehensive Erdős Problems Collection", - "date": datetime.now().isoformat(), - "source": "Wikipedia and other sources", - "ingested_at": datetime.now().timestamp(), - "tags": ["erdos", "conjectures", "problems", "graph-theory", "number-theory", "combinatorics"] - } - - index.append(new_entry) - - with open(index_file, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Research ingestion index updated") - - return document - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ingest_gccl_gec_spec.py b/5-Applications/scripts/ingest_gccl_gec_spec.py deleted file mode 100644 index 7e0dad12..00000000 --- a/5-Applications/scripts/ingest_gccl_gec_spec.py +++ /dev/null @@ -1,357 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: GCCL-GEC Spec — Full Compression Architecture -====================================================== -Geometric-Cognitive Compression Law / Glyph Eigen Codec -Byte-exact compression via lawful callable glyph kernels. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -GCCL_GEC = { - "id": "gccl-gec-spec-v1", - "source": "USER formal spec — complete compression architecture", - "title": "GCCL-GEC: Geometric-Cognitive Compression Law / Glyph Eigen Codec — Full Specification", - "date": "2026-05-07", - - "purpose": ( - "Compress a byte corpus by finding the smallest lawful executable " - "glyph/eigen/manifold program that reconstructs the exact original bytes. " - "Do not store the text. Store the cheapest lawful generator of the byte projection." - ), - - "archive_structure": { - "formula": "A = D ⊕ 𝔊 ⊕ Χ ⊕ Τ ⊕ 𝕌 ⊕ Γ ⊕ Θ ⊕ Ε ⊕ R", - "components": { - "D": {"name": "Deterministic Decompressor", "role": "Loads profile, interprets packets, emits exact bytes"}, - "𝔊": {"name": "GlyphBook", "role": "Maps printable codepoints to callable reconstruction kernels"}, - "Χ": {"name": "ChiralityBook", "role": "Maps chirality vectors to law-axes for each glyph"}, - "Τ": {"name": "TypeBook", "role": "Maps datatype witnesses to structural generative laws"}, - "𝕌": {"name": "EigenBook", "role": "Stores reusable eigenbasis/spectrum/coefficient descriptors"}, - "Γ": {"name": "Glyph Packet Stream", "role": "Atomic compression units — not characters, but kernel invocations"}, - "Θ": {"name": "Parameter Stream", "role": "Side-stream of integer/arithmetic-coded payload data"}, - "Ε": {"name": "Residual Stream", "role": "Exact byte repair — honesty layer where speculative compressors die"}, - "R": {"name": "Receipt/Checksum/Audit", "role": "SHA256 verification + audit trail"} - }, - "compact_equation": "C = Π_B(Bind_GCCL(𝔊, Χ, Τ, 𝕌, Γ, Θ, Ε))", - "compact_meaning": "Corpus = byte projection of GCCL-bound kernel composition" - }, - - "fundamental_packet": { - "formula": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "fields": { - "γᵢ": {"name": "visible_glyph", "domain": "emoji / math symbol / PUA codepoint / Unicode printable", "meaning": "Invokes a specific reconstruction kernel"}, - "χᵢ": {"name": "chirality_vector", "domain": "⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩", "meaning": "Which law-axis the glyph operates on"}, - "κᵢ": {"name": "local_context / manifold_coordinate", "domain": "position in n-D semantic manifold", "meaning": "Where in the field this packet applies"}, - "τᵢ": {"name": "datatype_witness", "domain": "TypeBook entry", "meaning": "Structural law to import (biography, math article, citation graph...)"}, - "UᵢΛᵢaᵢ": {"name": "eigen_descriptor", "domain": "EigenBook entry", "meaning": "Reusable geometric basis + spectrum + sparse coefficients"}, - "θᵢ": {"name": "parameters", "domain": "side-stream encoded integers", "meaning": "Mode selectors, eigenbook indices, residual class tags"}, - "εᵢ": {"name": "residual", "domain": "byte repair data", "meaning": "Exact correction to generated prediction"} - }, - "core_rule": "glyph ≠ symbol. glyph = callable compression kernel." - }, - - "chirality_book": { - "description": "Same glyph means different lawful things depending on chirality vector.", - "vector_axes": { - "G_geo": "geometric primitive", - "G_comp": "mathematics article/domain macro", - "G_spec": "eigenbasis selector", - "G_topo": "incidence/angle graph operator", - "G_arith": "numeric constraint kernel", - "G_load": "expensive/fallback region marker" - }, - "example": { - "📐_geo": "geometric primitive", - "📐_comp": "mathematics article/domain macro", - "📐_spec": "eigenbasis selector", - "📐_topo": "incidence/angle graph operator", - "📐_arith": "numeric constraint kernel", - "📐_load": "expensive/fallback region marker" - }, - "power": "Finite glyph set becomes combinatorially huge through chirality rotation" - }, - - "type_book": { - "core_rule": "The datatype is the engine. UTF-8 is only the exhaust.", - "example_types": [ - "WikiArticle", - "WikiArticle", - "WikiArticle", - "Infobox", - "CitationGraph", - "SectionTree", - "HistoricalTimeline", - "BranchTaxonomy", - "TableMatrix", - "SameReferentCluster", - "FormulaRegion", - "ListRegion", - "MarkupRegion" - ], - "compression_mass": "WikiArticle already implies title, lead, infobox, birth/death fields, occupation, chronology, categories, citation patterns, linking conventions. The datatype itself carries structure.", - "value": "A type imports generative structure without storing every instance explicitly." - }, - - "eigen_book": { - "formula": "Gᵢ = ⟨τᵢ, Uᵢ, Λᵢ, aᵢ, εᵢ⟩", - "components": { - "τᵢ": "manifold/type class", - "Uᵢ": "eigenbasis / local frame (column vectors)", - "Λᵢ": "eigenvalue spectrum / scale-pressure", - "aᵢ": "sparse coefficients (activation weights)", - "εᵢ": "residual bytes (perturbation from ideal eigenstate)" - }, - "example_math_article": { - "U": ["u_definition", "u_taxonomy", "u_history", "u_notation", "u_application", "u_philosophy", "u_reference"], - "meaning": "A mathematics page ≈ sparse activation of these eigenmodes + residual repair" - }, - "keeper": "A wiki page is a sparse eigenstate of a typed reconstruction manifold, plus apology bytes." - }, - - "parameter_encoding": { - "channels": [ - "side streams", - "variation selectors", - "combining marks", - "PUA suffixes", - "integer-coded payloads", - "arithmetic-coded payloads" - ], - "example": "📐︖︉︚ → MathDomainKernel(mode=22, eigenbook=9, residual_class=26)", - "rule": "Decompressor reads codepoints, not what fonts display." - }, - - "residual_stream": { - "role": "The most important honesty layer.", - "core_rule": "Decode(generative_model) ⊕ ε = exact original bytes", - "forms": [ - "literal patch", - "XOR patch", - "edit script", - "structural diff", - "entropy-coded correction", - "markup repair", - "serialization repair" - ], - "diagnostic": "ρ = |ε| / |raw_span|. ρ < 0.1 = excellent. ρ ≈ 0.5 = maybe useful. ρ ≈ 1.0 = generator failed." - }, - - "gccl_gain_test": { - "formula": "ΔGCL(Γᵢ) = ΔG_geo + ΔG_comp + ΔG_spec + ΔG_topo + ΔG_arith - G_load - L(εᵢ) - L(θᵢ) - amortized_decoder_cost", - "accept_rule": "ΔGCL(Γᵢ) > 0", - "practical_form": "gain(Γᵢ) = literal_cost(span) - encoded_cost(γᵢ, χᵢ, κᵢ, τᵢ, UΛa, θᵢ, εᵢ)", - "principle": "No vibes. No 'semantic compression' handwaving. Only: shorter, deterministic, byte-exact, auditable." - }, - - "decode_pipeline": [ - "Load decompressor profile D", - "Load GlyphBook 𝔊", - "Load ChiralityBook Χ", - "Load TypeBook Τ", - "Load EigenBook 𝕌", - "Read region index I", - "For each Γᵢ: resolve glyph γᵢ, chirality χᵢ, type τᵢ, eigen descriptor UᵢΛᵢaᵢ, parameters θᵢ", - "Generate predicted byte span ŝᵢ", - "Apply residual εᵢ", - "Emit exact span sᵢ", - "Concatenate spans", - "Verify checksum / receipt" - ], - - "encode_pipeline": [ - "Segment corpus into candidate spans (pages, sections, infoboxes, tables, citations, formulas, markup regions)", - "Infer candidate types τ", - "Fit candidate geometric model (choose U, Λ, a)", - "Choose glyph kernel γ and chirality χ", - "Generate predicted bytes", - "Compute residual ε", - "Score: gain = literal_cost - encoded_cost", - "Keep candidates with gain > 0", - "Solve covering problem: choose packet set Γ* covering C with minimum total cost", - "Emit archive", - "Decode immediately and verify exact byte equality" - ], - - "model_families": { - "A_Wiki_structural": { - "kernels": ["WikiArticle", "Infobox", "SectionTree", "CitationGraph", "CategoryList", "InternalLinkGraph", "ReferenceList", "TableMatrix"], - "value": "Highest practical value. Structural redundancy in encyclopedic corpora is massive." - }, - "B_Same_referent": { - "kernels": ["SameReferentVariation", "EntityAliasCluster", "PronounEpithetChain"], - "value": "Handles elegant-variation / synonym-heavy text where literal repetition is low." - }, - "C_Arithmetic_date": { - "kernels": ["Year", "DateInterval", "Coordinate", "PopulationTable", "UnitExpression", "Ranking", "Ordinal"], - "value": "Numbers are dense but highly structured. Very reliable wins." - }, - "D_Fractal_generator": { - "kernels": ["Mandelbrot", "L-system", "CellularAutomaton", "ProceduralImage", "ParametricCurve"], - "value": "Only useful if byte projection matches generator closely. SVG serialization cost usually dominates." - }, - "E_Eigenfield": { - "kernels": ["ArticleEigenfield", "CitationEigenfield", "MarkupEigenfield", "SemanticDensityField"], - "value": "Region-level reconstruction. Connects to density-field encoding theory." - } - }, - - "stress_test_lesson": { - "mandelbrot_svg": "generator cost ≈ tiny, serialized artifact cost ≈ huge", - "conclusion": "z ↦ z² + c generates the image, but not the exact SVG file. Residual = ε_serialize.", - "best_diagnostic": "ρ = |ε| / |raw_span|" - }, - - "implementation_phases": { - "Phase_1": { - "name": "Byte-exact toy codec", - "scope": "GlyphBook + TypeBook + ResidualStream", - "kernels": ["CitationGraphKernel", "InfoboxKernel", "SectionTreeKernel"], - "goal": "generated_span + residual = original_span" - }, - "Phase_2": { - "name": "Add arithmetic/date kernels", - "scope": "Years, dates, coordinates, measurements, rankings, table values", - "value": "Reliable wins on dense numeric data" - }, - "Phase_3": { - "name": "Add same-referent variation", - "scope": "EntityClusterKernel, AliasEmitter, CoreferenceSurfaceFormKernel", - "value": "Attacks low-repetition text" - }, - "Phase_4": { - "name": "Add eigen descriptors", - "scope": "UΛa for region classes", - "caution": "Only after above works. Use as descriptor reuse, not magic semantic compression." - }, - "Phase_5": { - "name": "Add PUA glyph acceleration", - "rule": "promotion_gain = repeated_invocation_savings - glyph_definition_cost. Promote only if positive." - } - }, - - "prototype_archive": { - "magic": "GEC1", - "struct_fields": ["decoder_profile", "glyphbook", "typebook", "packets", "params", "residuals", "sha256"], - "packet_fields": ["glyph_id: u32", "chirality: u8", "type_id: u16", "region_start: u64", "region_len: u32", "eigen_id: Option", "param_ref", "residual_ref"], - "decode_invariant": "sha256(decode(archive)) == sha256(original)" - }, - - "stack_integration": { - "density_field_encoding": { - "role": "REGION-LEVEL MODEL FAMILY E. The density field IS an eigenfield kernel.", - "mapping": "ρ(x⃗) = SemanticDensityField kernel. Topological skeleton = GlyphBook + EigenBook. Perturbation = ResidualStream." - }, - "s3c_shells": { - "role": "LOCAL COORDINATE κᵢ. Shell index k = semantic distance from peak (e.g., article title). a = intra-cluster angular position.", - "mapping": "κᵢ encoded as S3C shell coordinates: cheap integer manifold position." - }, - "oac": { - "role": "GLYPH KERNEL LAZINESS. A glyph is a callable kernel stored in the OAC. It only materializes when touched by the decoder pipeline.", - "mapping": "GlyphBook = library of OAC touch-manifestable kernels. Chirality = which touch interpretation." - }, - "hypercube_rhomboid": { - "role": "MANIFOLD GEOMETRY OF THE PACKET STREAM. UTF-8 is orthogonal hypercube (independent byte positions). GCCL-GEC is sheared hyper-rhomboid: each packet's meaning depends on neighboring packets through chirality and type context.", - "mapping": "Shear matrix learned from corpus: eigenvectors = principal semantic directions. Packets lean into each other." - }, - "famm_delay_lines": { - "role": "TEMPORAL SEQUENCING OF PACKET STREAM. FAMM preshaped delays impose decode order through the packet stream.", - "mapping": "Delay profile = path integral through packet dependencies. Fast regions = high structural redundancy (predictable). Slow regions = high residual density (needs more context)." - }, - "erans_entropy": { - "role": "RESIDUAL AND PARAMETER STREAM CODING. After glyph prediction, residual bytes and parameters are entropy-coded via enumerative rANS.", - "mapping": "Histogram of residuals is exact; erans enumerative coding is optimal for exact histograms." - }, - "radius_ratio_motif": { - "role": "LOCAL ADMISSIBILITY QUANTIZER FOR PACKET SELECTION. Given a local feature ratio ρ, the radius-ratio rule selects the smallest stable coordination motif (kernel).", - "mapping": "Local scale ratio → admissible kernel class (WikiArticle, Infobox, CitationGraph...) + residual. Same move: continuous witness → finite motif alphabet." - } - }, - - "keeper_phrases": [ - "Do not store the text. Store the cheapest lawful generator of the byte projection.", - "glyph ≠ symbol. glyph = callable compression kernel.", - "The datatype is the engine. UTF-8 is only the exhaust.", - "A wiki page is a sparse eigenstate of a typed reconstruction manifold, plus apology bytes.", - "Never trust a glyph until the residual gets smaller.", - "No vibes. No 'semantic compression' handwaving. Only: shorter, deterministic, byte-exact, auditable.", - "ρ = |ε| / |raw_span|. This is the only number that matters.", - "The decompressor reads codepoints, not what fonts display.", - "A finite glyph set becomes combinatorially huge because each glyph can rotate through many law-axes.", - "The Morse-Smale complex is the topological skeleton of meaning. GCCL-GEC is the lawful engine that navigates it." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "gccl-gec", "compression-architecture", "glyph-eigen-codec", - "byte-exact-compression", "callable-kernel", "chirality-book", - "type-book", "eigen-book", "residual-stream", "gain-test", - "hutter-prize", "density-field", "s3c-shells", "oac", - "hypercube-rhomboid", "famm", "erans", "radius-ratio", - "geometric-compression" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "gccl_gec_spec_v1.json" - with open(out_path, 'w') as f: - json.dump(GCCL_GEC, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": GCCL_GEC["id"], - "title": GCCL_GEC["title"], - "date": GCCL_GEC["date"], - "source": GCCL_GEC["source"], - "ingested_at": GCCL_GEC["metadata"]["ingested_at"], - "tags": GCCL_GEC["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nArchive components (9):") - for k, v in GCCL_GEC["archive_structure"]["components"].items(): - print(f" {k} = {v['name']}: {v['role'][:60]}...") - - print(f"\nPacket fields (7):") - for field, props in GCCL_GEC["fundamental_packet"]["fields"].items(): - print(f" {field} → {props['name']}: {props['meaning'][:50]}...") - - print(f"\nModel families (5):") - for fam, data in GCCL_GEC["model_families"].items(): - print(f" {fam}: {len(data['kernels'])} kernels — {data['value'][:50]}...") - - print(f"\nStack integration (7):") - for module, mapping in GCCL_GEC["stack_integration"].items(): - print(f" ↔ {module}: {mapping['role'][:65]}...") - - print(f"\nImplementation phases (5):") - for phase, data in GCCL_GEC["implementation_phases"].items(): - print(f" {phase}: {data['name']} — {data.get('goal', data.get('value', data.get('scope', '')))[:50]}...") - - print(f"\nKeeper phrases ({len(GCCL_GEC['keeper_phrases'])}):") - for p in GCCL_GEC["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_hippocampus_tabula_plena.py b/5-Applications/scripts/ingest_hippocampus_tabula_plena.py deleted file mode 100644 index 915696a8..00000000 --- a/5-Applications/scripts/ingest_hippocampus_tabula_plena.py +++ /dev/null @@ -1,272 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Hippocampus Tabula Plena Combined Approach -================================================== -Combines maximum math density + unified compression architecture + -hippocampus engram consolidation + tabula plena (full slate) insight. -FAMM delay lines model the pruning from dense initial state to sparse structured state. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HIPPOCAMPUS_TABULA_PLENA = { - "id": "hippocampus-tabula-plena-combined-v1", - "source": "Synthesis: maximum math density + unified architecture + hippocampus engram consolidation (Tomé 2024) + tabula plena (Live Science 2024)", - "title": "Hippocampus Tabula Plena: Full Slate Compression with FAMM Pruning Dynamics", - "date": "2026-05-07", - - "core_synthesis": ( - "The hippocampus starts as 'tabula plena' (full slate) — densely wired with hyperconnected " - "neurons in seemingly random pattern — and prunes to sparse, structured networks during " - "maturation. This is exactly the compression paradigm: start with maximum math density " - "(full Unicode spectrum, custom glyphs, omniversal chirality) and prune via FAMM delay " - "lines, OAC gates, radius-ratio quantization, and gain tests to minimal representation " - "that reconstructs exactly. FAMM models the adaptive pruning dynamics from dense " - "initial state to sparse structured state." - ), - - "hippocampus_tabula_plena_insight": { - "source": "Live Science 2024: Jonas et al. Nature Communications - hippocampus CA3 region", - "key_findings": { - "tabula_plena": "Hippocampus does NOT start as blank slate (tabula rasa). Starts as tabula plena (full slate) — densely wired, hyperconnected neurons in random pattern", - "pruning_dynamics": "As brain matures, haphazard networks become sparser yet more structured as connections are pruned. Pruning begins soon after birth, significant decline by adolescence", - "strong_connections": "Early connections are surprisingly strong, not weak. Single input can cause young neuron to fire; mature neurons require multiple inputs", - "memory_explanation": "This pattern explains why we remember little from infancy — dense random wiring cannot store specific memories until pruned to structured sparse networks" - }, - "compression_analogy": { - "tabula_plena": "Maximum math density = full Unicode spectrum (1,114,112 codepoints) + custom glyphs + omniversal chirality (N_glyphs × 2^6 × continuum) = near-infinite initial glyph space", - "pruning_dynamics": "Compression pipeline = pruning from full slate to minimal representation. OAC gates, radius-ratio quantization, gain tests ΔGCL > 0, FAMM delay preshaping = adaptive pruning", - "strong_connections": "FAMM preshaped delays = strong initial connections based on eigenvalue spectra. Not uniform delays, but preshaped by corpus structure", - "mature_threshold": "OAC admissibility threshold = mature neuron requiring multiple inputs. Young system accepts many motifs; mature system requires strong evidence (low residual, high gain)" - } - }, - - "engram_consolidation_dynamics": { - "source": "Tomé et al. Nature Neuroscience 2024: Dynamic engram consolidation with neuron turnover", - "key_findings": { - "neuron_dropout": "Engrams transition from unselective to highly selective state as neurons dynamically drop in/out during consolidation", - "inhibitory_plasticity": "Triplet-STDP + heterosynaptic + transmitter-induced plasticity is critical mechanism for selectivity", - "discrimination_threshold": "zeta^thr = 10 Hz firing rate for engram activation", - "pattern_separation": "Ensemble overlap 10-40% during recall — low overlap = high selectivity", - "composite_promotion": "Unselective to selective transition = composite promotion = soliton bound state" - }, - "famm_integration": { - "neuron_dropout": "Dynamic neuron dropout → FAMM delay line preshaping (adaptive delays based on eigenvalue spectra). Delays adapt as 'engram consolidates' (corpus learned)", - "inhibitory_plasticity": "Prevents runaway potentiation → gain test ΔGCL > 0 prevents bad motif selection", - "discrimination_threshold": "zeta^thr = 10Hz → radius-ratio motif quantization thresholds (continuous witness → finite motif alphabet)", - "pattern_separation": "Ensemble overlap separation → OAC admissibility gates (separating admissible from inadmissible motifs)", - "composite_promotion": "Accepted OAC routes = composite promotion = soliton bound state = stored in FAMM cache" - } - }, - - "maximum_math_density_tabula_plena": { - "full_slate_initial_state": { - "unicode_spectrum": "Full UTF-16/beyond: 1,114,112 codepoints across 17 planes (BMP, SMP, SIP, TIP, SSP, PUA)", - "custom_glyphs": "PUA + beyond-Unicode custom glyphs (decompressor can generate any glyph)", - "chinese_logograms": "Chinese-style logograms (each glyph = entire concept/word)", - "korean_blocks": "Hangul-style block composition (sub-elements combine into dense units)", - "math_symbols": "Full Unicode math symbol set (∂, ∇, ∫, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃...)", - "emoji_codes": "Full emoji spectrum (📐, 📚, 👤, 🌍, 🧾, 󰀁, 󰀂, 󰀃, 󰀄, 󰀅...)", - "omniversal_chirality": "6 axes ⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ × continuum = near-infinite combinations", - "initial_capacity": "Tabula plena = N_glyphs × chirality_combinations × data_types × eigenvectors = effectively infinite initial glyph space" - }, - "pruning_to_sparse_structured": { - "oac_gates": "OAC admissibility gate prunes glyph space. Only admissible motifs commit to output. Failed motifs become FAMM scars (never tried again)", - "radius_ratio_quantization": "Continuous local scale ratio → finite motif alphabet (CN3/CN4/CN6/CN8 analogue). Quantizes infinite possibilities to small admissible set", - "gain_test": "ΔGCL > 0 ensures only motifs that pay rent are kept. Prunes all non-compressive glyphs", - "famm_preshaping": "FAMM delay lines preshape based on eigenvalue spectra. Strong connections for high-salience features, weak for noise", - "shear_matrix": "Gram matrix G = A^T A eigenvectors = principal correlation directions. Prunes orthogonal dimensions, keeps correlated sheared axes", - "topological_skeleton": "Morse-Smale complex (peaks, ridges, saddles, vortices, voids) = sparse topological encoding of dense field" - } - }, - - "famm_delay_line_pruning_model": { - "biological_analogue": { - "young_hippocampus": "Dense, hyperconnected, random pattern. Single input → neuron fires. Strong early connections.", - "mature_hippocampus": "Sparse, structured, pruned connections. Multiple inputs → neuron fires. Specific connectivity." - }, - "famm_implementation": { - "initial_state": "FAMM delay lines initialized with uniform delays (tabula plena = all delays equally possible)", - "preshaping_phase": "During 'consolidation' (corpus analysis), delays adapt based on eigenvalue spectra from waveprobe manifold generation", - "adaptive_pruning": "Delays for high-salience features (peaks, ridges) become strong (short delays). Delays for noise become weak (long delays or dropped)", - "threshold_filter": "zeta^thr analogue: only features above eigenvalue threshold get fast delays. Below threshold → delayed or dropped", - "sparse_final_state": "Final FAMM delay profile is sparse yet structured — fast paths for predictable regions, slow paths for high-entropy regions" - }, - "q16_16_fixed_point": "Delays encoded in Q16.16 fixed-point for hardware-native determinism. Preshaped delays derived from eigenvalue spectra." - }, - - "combined_encoding_pipeline": { - "stage_0_tabula_plena": "Initialize full slate: full Unicode spectrum + custom glyphs + omniversal chirality + all data types + all eigenvectors", - "stage_1_density_field": "Parse corpus into semantic density field ρ(x⃗). Extract Morse-Smale topological skeleton", - "stage_2_shear_matrix": "Apply shear matrix A → sheared manifold S̃ = A·S. Compute Gram matrix G = A^T A", - "stage_3_famm_consolidation": "FAMM delay lines adapt based on eigenvalue spectra (hippocampus consolidation analogue). Delays preshape from uniform to sparse structured", - "stage_4_s3c_coordinates": "Encode positions via S3C shells (k, a, b⁰, b⁺, mass, throat_class)", - "stage_5_radius_ratio_quantization": "Quantize local scale ratio ρ into admissible motif class (CN3/CN4/CN6/CN8 analogue)", - "stage_6_logographic_encoding": "Encode topological features as custom logographic glyphs (Chinese-style, Korean block, math symbols)", - "stage_7_gccl_packet": "Encode as GCCL packet Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "stage_8_oac_gate": "Test packet as OAC with hippocampus pattern separation. Admissible → commit. Inadmissible → FAMM scar", - "stage_9_gain_test": "Apply ΔGCL > 0. Only keep if gain positive. Prune non-compressive glyphs", - "stage_10_math_notation": "Encode math functions as eigenvector descriptors (U = {amplitude, frequency, phase, offset}, Lambda, a)", - "stage_11_repeat_encoding": "Use n(position, num_repeated) for repeats instead of literal repetition", - "stage_12_pist_perturbation": "Encode perturbation field via PIST n-D bundle", - "stage_13_erans_entropy": "Entropy-code residuals via enumerative rANS", - "stage_14_sparse_structured": "Final archive = sparse yet structured representation. Full slate pruned to minimal" - }, - - "combined_decode_pipeline": { - "stage_0_load": "Load archive HFC1 (Hippocampus FAMM Combined v1)", - "stage_1_decompressor": "Load decompressor profile (custom glyph renderer + FAMM delay engine)", - "stage_2_books": "Load GlyphBook, ChiralityBook, TypeBook, EigenBook (sparse subset of full slate)", - "stage_3_shear": "Load shear matrix A (Gram matrix G)", - "stage_4_famm": "Load FAMM delay profile (sparse structured delays from consolidation)", - "stage_5_s3c": "Load S3C shell coordinates", - "stage_6_packets": "For each packet Γᵢ:", - "stage_7_resolve": "Resolve glyph γᵢ (from sparse GlyphBook), chirality χᵢ, type τᵢ, eigenvector UᵢΛᵢaᵢ", - "stage_8_parameters": "Load parameters θᵢ (including n(position, num_repeated))", - "stage_9_generate": "Generate predicted semantic unit ŝᵢ (apply shear inverse A⁻¹)", - "stage_10_residual": "Apply residual εᵢ", - "stage_11_emit": "Emit exact span sᵢ", - "stage_12_famm_sequence": "Sequence spans via FAMM delay profile (sparse structured paths)", - "stage_13_concatenate": "Concatenate spans (no spaces needed)", - "stage_14_verify": "Verify SHA256 checksum" - }, - - "archive_format": { - "magic": "HFC1 (Hippocampus FAMM Combined v1)", - "sections": [ - "DECOMPRESSOR_PROFILE (custom glyph renderer + FAMM delay engine)", - "GLYPHBOOK (sparse subset of full Unicode + custom glyphs used in corpus)", - "CHIRALITYBOOK (chirality vectors actually used)", - "TYPEBOOK (data types actually used: WikiArticle, Equation, FieldSet...)", - "EIGENBOOK (eigenvector descriptors from Gram matrix)", - "SHEAR_MATRIX (A and Gram matrix G = A^T A)", - "FAMM_DELAY_PROFILE (sparse structured delays from consolidation)", - "S3C_SHELL_COORDINATES", - "OAC_RECEIPTS (accepted routes + FAMM scars)", - "REGION_INDEX (map of field sets to byte spans)", - "GLYPH_PACKET_STREAM (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ)", - "PARAMETER_STREAM (n(position, num_repeated), mode selectors)", - "RESIDUAL_STREAM (εᵢ)", - "CUSTOM_GLYPH_DEFINITIONS (if beyond Unicode)", - "CHECKSUM (SHA256)" - ] - }, - - "compression_gain_sources": { - "tabula_plena_to_sparse": "Pruning from full slate to sparse subset. Only glyphs actually used in corpus stored. Most of 1,114,112 Unicode codepoints never touched.", - "famm_delay_pruning": "FAMM delays adapt from uniform to sparse structured. Fast paths for predictable regions, slow for noise. 10-20% context efficiency.", - "oac_gate_pruning": "OAC admissibility gate prunes motif space. Failed motifs become FAMM scars, never tried again. Avoids 2-5% bloat.", - "radius_ratio_quantization": "Continuous witness → finite motif alphabet. Quantizes infinite possibilities to small admissible set.", - "gain_test_pruning": "ΔGCL > 0 ensures only compressive motifs kept. Prunes all non-compressive glyphs.", - "shear_matrix_pruning": "Gram matrix eigenvectors = principal correlation directions. Prunes orthogonal dimensions, keeps correlated sheared axes. 15-30% on structured regions.", - "topological_skeleton": "Morse-Smale complex = sparse topological encoding. 50-150MB skeleton vs 1GB raw for enwik9.", - "math_notation_density": "Math functions encoded as eigenvector descriptors instead of literal strings. 5-8% on token encoding.", - "repeat_encoding": "n(position, num_repeated) instead of literal repetition. 3-5% on repeated patterns.", - "erans_entropy": "Optimal exact histogram coding for residuals." - }, - - "estimated_aggregate_gain": { - "tabula_plena_pruning": "90-99% of full Unicode spectrum never used. Only ~10,000-50,000 glyphs actually used for 1GB corpus.", - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "famm_efficiency": "10-20% more predictive power per context byte", - "skeleton_compression": "50-150MB skeleton vs 1GB raw", - "overall_compressed_size": "Estimated 15-25% reduction vs current best Hutter compressors, plus navigable manifold capability", - "novel_capability": "Produces navigable structure. Tabula plena initialization enables adaptive learning of corpus-specific glyph space." - }, - - "keeper_phrases": [ - "The hippocampus starts tabula plena (full slate) and prunes to sparse structured. Compression does the same.", - "Maximum math density is the full slate: full Unicode, custom glyphs, omniversal chirality — all possibilities available.", - "FAMM delay lines model the pruning: from uniform delays (young hippocampus) to sparse structured delays (mature hippocampus).", - "OAC gates are the pattern separation: admissible motifs commit, inadmissible become FAMM scars.", - "Radius-ratio quantization is the discrimination threshold: continuous witness → finite motif alphabet.", - "Gain test ΔGCL > 0 is the inhibitory plasticity: prevents runaway potentiation of bad motifs.", - "The archive is not the full slate. The archive is the sparse structured result of pruning.", - "Young hippocampus: single input → fire. Mature: multiple inputs → fire. OAC: single glyph → test. Mature: gain > 0 → commit.", - "Strong early connections → FAMM preshaped delays based on eigenvalue spectra, not uniform delays.", - "We remember little from infancy because the hippocampus is dense and random. We compress well because the archive is sparse and structured.", - "The Gram matrix eigenvectors are the principal correlation directions — the structured wiring of the mature hippocampus.", - "Tabula plena initialization enables adaptive learning of corpus-specific glyph space during consolidation.", - "Don't start blank. Start full, then prune. The hippocampus does it. Compression should too." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hippocampus-tabula-plena", - "maximum-math-density", - "famm-pruning-dynamics", - "engram-consolidation", - "tabula-plena", - "dense-to-sparse", - "oac-gates", - "radius-ratio-quantization", - "gain-test", - "shear-matrix", - "topological-skeleton", - "unified-compression-architecture", - "custom-glyphs", - "omniversal-chirality", - "famm-delay-lines" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hippocampus_tabula_plena_combined_v1.json" - with open(out_path, 'w') as f: - json.dump(HIPPOCAMPUS_TABULA_PLENA, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HIPPOCAMPUS_TABULA_PLENA["id"], - "title": HIPPOCAMPUS_TABULA_PLENA["title"], - "date": HIPPOCAMPUS_TABULA_PLENA["date"], - "source": HIPPOCAMPUS_TABULA_PLENA["source"], - "ingested_at": HIPPOCAMPUS_TABULA_PLENA["metadata"]["ingested_at"], - "tags": HIPPOCAMPUS_TABULA_PLENA["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nTabula plena insight:") - for finding, desc in HIPPOCAMPUS_TABULA_PLENA["hippocampus_tabula_plena_insight"]["key_findings"].items(): - print(f" • {finding}: {desc[:60]}...") - - print(f"\nCompression analogy:") - for analogy, desc in HIPPOCAMPUS_TABULA_PLENA["hippocampus_tabula_plena_insight"]["compression_analogy"].items(): - print(f" • {analogy}: {desc[:60]}...") - - print(f"\nFAMM pruning model:") - for phase, desc in HIPPOCAMPUS_TABULA_PLENA["famm_delay_line_pruning_model"]["famm_implementation"].items(): - print(f" • {phase}: {desc[:60]}...") - - print(f"\nCompression gain sources (10):") - for source, gain in HIPPOCAMPUS_TABULA_PLENA["compression_gain_sources"].items(): - print(f" • {source}: {gain[:60]}...") - - print(f"\nKeeper phrases ({len(HIPPOCAMPUS_TABULA_PLENA['keeper_phrases'])}):") - for p in HIPPOCAMPUS_TABULA_PLENA["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_hutter_rhomboid.py b/5-Applications/scripts/ingest_hutter_rhomboid.py deleted file mode 100644 index c45a9517..00000000 --- a/5-Applications/scripts/ingest_hutter_rhomboid.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -""" -Hypercube → Hyper-Rhomboid: Hutter Prize Implications -======================================================= -What changes for enwik8/enwik9 compression when you shear -the token space instead of treating positions independently. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HUTTER_RHOMBOID = { - "id": "hypercube-rhomboid-hutter-prize", - "source": "User query — what does hypercube → hyper-rhomboid change for Hutter Prize", - "title": "Sheared Token Manifolds: Hypercube → Hyper-Rhomboid Implications for Hutter Prize Compression", - "date": "2026-05-07", - - "current_hutter_paradigm": { - "approach": "1D token sequence → probability distribution per position → entropy code", - "geometry": "Orthogonal hypercube: each token position is an independent axis. Context window = fixed-size orthogonal slice.", - "limitation": ( - "Wikipedia text is NOT a sequence of independent positions. " - "It is a sheared manifold where: infoboxes repeat with minor variations, " - "citations share author/year/title structure, headings form a hierarchy, " - "lists are parallel constructions, markup is templated, " - "and natural language has syntactic correlation at every scale." - ), - "waste": ( - "An orthogonal model pays separately for every occurrence of '{{cite web|url=' " - "even though the 5000th citation has the same structure as the 1st. " - "The axes are at 90° — no sharing of probability mass across correlated positions." - ) - }, - - "rhomboid_paradigm": { - "approach": "Token space as sheared manifold → correlated positions share axes → entropy code residuals only", - "geometry": ( - "Hyper-rhomboid: token position axes lean into each other proportional to " - "mutual information. Repeated structures collapse into shared shells. " - "The shear matrix A IS the compression model." - ), - "key_insight": ( - "You don't compress the text. You compress the shear matrix that makes " - "the text look like noise plus a small residual. The shear matrix is " - "learned once from the corpus structure; the residual is what you actually " - "entropy-code." - ) - }, - - "concrete_changes": { - - "pre_transform": { - "title": "Shear Pre-Transform Before Entropy Coding", - "current": "Raw bytes → LZ/BWT/PPM/transformer → entropy code", - "rhomboid": "Raw bytes → S3C shell parse → shear into rhomboid → residual extraction → entropy code", - "mechanism": ( - "Parse Wikipedia into structural regions (article, infobox, citation, list, heading, template). " - "Each region type has a canonical shell coordinate. Within each shell, sheared axes encode " - "the expected structure. Only deviations from the sheared template are entropy-coded." - ), - "estimated_gain": "15-30% on structured regions (infoboxes, citations, templates — ~40% of enwik)" - }, - - "s3c_shell_batching": { - "title": "S3C Shell Coordinates for Token Position Encoding", - "current": "Position encoded as absolute byte offset or transformer positional embedding", - "rhomboid": "Position encoded as S3C shell (k, a, b⁰, b⁺) — shell = structural context, offset = within-structure position", - "mechanism": ( - "k = structural depth (0=character, 1=word, 2=phrase, 3=sentence, 4=paragraph, 5=section, 6=article). " - "a = position within that structure. b⁰ = remaining length. " - "The throat (a ≈ b⁰) is where compression is maximal — midpoint of a repeated structure " - "where the model has maximum predictive confidence." - ), - "estimated_gain": "5-10% on positional encoding overhead" - }, - - "oac_speculative_compression": { - "title": "OAC Speculative Motif Testing Without Output Pollution", - "current": "Compressor commits to a transform; if it's bad, output is bloated", - "rhomboid": "Test compression motifs as Observer-Admissible Cavities — temporary exploration manifolds that don't commit to output unless L(motif) + L(residual) < L(raw)", - "mechanism": ( - "For each structural region, try multiple shear matrices (citation-shear, list-shear, " - "template-shear, plaintext-shear). The OAC gate only emits the one that beats raw encoding. " - "Failed shears become FAMM scars — never tried again for similar regions." - ), - "estimated_gain": "Avoids 2-5% bloat from bad motif choices; enables aggressive speculation" - }, - - "famm_context_warping": { - "title": "FAMM Preshaped Context Windows", - "current": "Fixed context window (e.g., 1024 tokens) for transformer/ppm models", - "rhomboid": "Preshaped (sheared) context: stretches for high-entropy regions, compresses for low-entropy template regions", - "mechanism": ( - "Context window is not fixed-length; it's fixed-information. " - "In a citation template, 50 bytes of context is enough (structure is predictable). " - "In free text, 500 bytes may be needed. The FAMM delay line preshapes the context " - "window per shell class — shearing time into the information domain." - ), - "estimated_gain": "10-20% context efficiency — more predictive power per context byte" - }, - - "pist_token_manifold": { - "title": "PIST n-Dimensional Token Encoding", - "current": "Tokens encoded as 1D integer IDs", - "rhomboid": "Tokens encoded as n-dimensional PIST coordinates (k, t, fiber₀, ..., fiber_{n-2}) where n = number of correlated features", - "mechanism": ( - "Each token gets: k = frequency/shell class, t = local offset, " - "fiber₀ = part-of-speech class, fiber₁ = dependency depth, " - "fiber₂ = template membership, fiber₃ = capitalization pattern. " - "The fiber dimensions are the sheared axes — they encode correlation structure " - "that a 1D token ID loses." - ), - "estimated_gain": "5-8% on token encoding; enables cross-position probability sharing" - }, - - "gram_dictionary": { - "title": "Gram Matrix as Learned Compression Dictionary", - "current": "Static dictionary (LZ) or learned embeddings (transformer)", - "rhomboid": "Gram matrix G = A^T A of the shear transform IS the dictionary — eigenvectors are principal correlation directions, eigenvalues are compression gains", - "mechanism": ( - "The shear matrix A is learned by minimizing: L(A) + L(residual | A). " - "A is stored once in the compressed header. The decoder applies A^{-1} " - "to reconstruct expected structure, then replays residuals. " - "A is tiny compared to the text — a few KB for the entire corpus." - ), - "estimated_gain": "Dictionary overhead reduced from MB to KB" - }, - - "metric_entropy_code": { - "title": "Information-Geometric Entropy Coding", - "current": "Entropy coding assumes independent symbols (product distribution)", - "rhomboid": "Entropy coding uses the sheared metric g_{μν} — symbols are coded relative to their position in the sheared manifold, not independently", - "mechanism": ( - "The coding probability for token x_i is conditioned on its sheared context: " - "P(x_i | context) = P(x_i | g_{μν}(context)). " - "In a heavily sheared region (template), P is sharply peaked — near 1.0 for expected token. " - "In a flat region (free text), P is broad. The metric tells the coder how confident to be." - ), - "estimated_gain": "10-15% entropy reduction in structured regions" - } - }, - - "aggregate_estimate": { - "structured_regions": "15-30% gain on ~40% of enwik (infoboxes, citations, templates, lists, headings, markup)", - "free_text_regions": "5-10% gain on ~60% of enwik (natural language paragraphs)", - "dictionary_overhead": "MB → KB (Gram matrix replaces LZ dictionary + transformer weights)", - "context_efficiency": "10-20% more predictive power per context byte", - "overall_compressed_size": "Estimated 12-22% reduction vs current best Hutter compressors", - "caveat": "These are geometric estimates, not benchmarks. Real gains depend on shear matrix learning quality and residual entropy." - }, - - "the_big_fold": { - "title": "What This Fundamentally Changes", - "insight": ( - "The Hutter Prize is currently fought as a sequence modeling problem: " - "predict the next token given previous tokens. " - "The rhomboid reframes it as a manifold learning problem: " - "find the shear that makes the token manifold maximally flat (predictable), " - "store the shear, then entropy-code the residual curvature." - ), - "fold": ( - "This folds FOUR separate Hutter components into ONE: " - "1. Dictionary (LZ) → Gram matrix eigenvectors " - "2. Context model (PPM/transformer) → Sheared metric g_{μν} " - "3. Token encoding → PIST n-D coordinates " - "4. Structure detection → S3C shell classification " - "All four are the same object: the shear matrix A." - ), - "one_sentence": "Don't predict the next token. Shear the space until the next token is obvious, store the shear, and pay only for what the shear didn't catch." - }, - - "keeper_phrases": [ - "You don't compress the text. You compress the shear matrix that makes the text predictable.", - "The Gram matrix of the shear IS the dictionary, the context model, the token encoding, and the structure detector — all at once.", - "A citation isn't 200 bytes that happen to look similar. It's one sheared cavity with 200-byte residuals.", - "Stop predicting tokens. Start shearing the manifold until tokens become obvious.", - "The Hutter Prize is manifold learning disguised as sequence modeling.", - "Fixed context windows are orthogonal thinking. Sheared context is information-geometric thinking.", - "Every '{{cite web' is the same hole. Pay for the hole once, pay for the URL residual each time." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hutter-prize", "compression", "hypercube", "hyper-rhomboid", - "sheared-manifold", "enwik", "token-encoding", "gram-matrix", - "s3c-shells", "oac", "famm", "pist", "entropy-coding" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hypercube_rhomboid_hutter_prize.json" - with open(out_path, 'w') as f: - json.dump(HUTTER_RHOMBOID, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HUTTER_RHOMBOID["id"], - "title": HUTTER_RHOMBOID["title"], - "date": HUTTER_RHOMBOID["date"], - "source": HUTTER_RHOMBOID["source"], - "ingested_at": HUTTER_RHOMBOID["metadata"]["ingested_at"], - "tags": HUTTER_RHOMBOID["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\n7 concrete changes:") - for key, change in HUTTER_RHOMBOID["concrete_changes"].items(): - print(f" • {change['title']}: {change['estimated_gain']}") - - print(f"\nThe Big Fold:") - print(f" {HUTTER_RHOMBOID['the_big_fold']['insight'][:120]}...") - - print(f"\nKeeper phrases:") - for p in HUTTER_RHOMBOID["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_hypercube_rhomboid.py b/5-Applications/scripts/ingest_hypercube_rhomboid.py deleted file mode 100644 index b05853ad..00000000 --- a/5-Applications/scripts/ingest_hypercube_rhomboid.py +++ /dev/null @@ -1,176 +0,0 @@ -#!/usr/bin/env python3 -""" -Hypercube → Hyper-Rhomboid Composition: Stack Mapping -====================================================== -Maps the hypercube/rhomboid calculus concept onto Research Stack primitives. -Key insight: shearing orthogonal tensor axes into a parallelotope is the -mathematical dual of PIST n-dimensional encoding, topological state transitions, -and Observer-Admissible Cavity manifestation. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -HYPER_RHOMBOID = { - "id": "hypercube-rhomboid-composition", - "source": "User conceptual synthesis — hypercube matrix calculus → hyper-rhomboid", - "title": "Hypercube → Hyper-Rhomboid Composition: Sheared Tensor Manifolds as Compression Geometry", - "date": "2026-05-07", - - "core_claim": ( - "A hypercube of matrix calculus (n-D tensor of partial derivatives) assumes " - "orthogonal axes — all variables independent. Composing hypercubes into a " - "hyper-rhomboid (parallelotope) applies geometric shear: axes lean into each " - "other, modeling entangled dimensions. This is the geometric engine behind " - "topological compression, manifold mapping, and information-theoretic gravity." - ), - - "geometric_primitives": { - "hypercube": { - "definition": "n-dimensional tensor grid with orthogonal (90°) axes", - "mathematical_form": "T_{i,j,k,l} ∈ ℝ^{d₁×d₂×d₃×d₄}", - "assumption": "All variables statistically independent (Cartesian)", - "problem": "Empty geometric space between correlated variables — inefficient packing" - }, - "hyper_rhomboid": { - "definition": "Sheared parallelotope — axes at non-orthogonal angles", - "mathematical_form": "S = A·T where A is a shear matrix (non-orthogonal basis)", - "property": "Axes lean into correlated dimensions; volume preserved under shear", - "gain": "Dense packing, entanglement modeling, manifold approximation" - }, - "shear_matrix": { - "definition": "Linear transform collapsing 90° angles to acute/oblique", - "form": "A_{ij} = δ_{ij} + α_{ij} where α encodes correlation strength", - "determinant": "det(A) = 1 (volume-preserving shear)" - } - }, - - "stack_mappings": { - "pist_nd_encoding": { - "analogue": "PIST n-dimensional Cartesian → Bundle → Radial encoding", - "mechanism": "Cartesian encode = orthogonal hypercube; Bundle encode = sheared rhomboid with fiber dimensions; Radial encode = fully collapsed angular coordinates", - "file": "3-Mathematical-Models/pist_biological_polymorphic_shifter_v3_complete.py", - "functions": ["pist_nd_cartesian_encode", "pist_nd_bundle_encode", "pist_nd_radial_encode"] - }, - "topological_state_machine": { - "analogue": "State transition = shear operation on state hypercube", - "mechanism": "Each transition applies a shear matrix A_t to the state tensor S_t → S_{t+1} = A_t·S_t. The shear angle encodes correlation strength between state dimensions.", - "file": "5-Applications/scripts/topological_state_machine.py" - }, - "ndimensional_gene_hypothesis": { - "analogue": "Gene expression = projection of sheared n-D rhomboid onto 3D observer frame", - "mechanism": "The gene is an n-D rhomboid (entangled dimensions). The 3D molecular structure is a projection shadow. Epigenetic marks are shear-angle adjustments.", - "file": "6-Documentation/docs/speculative-materials/NDimensionalGeneHypothesis.md" - }, - "famm_delay_lines": { - "analogue": "Preshaped delay = shear in time-domain hypercube", - "mechanism": "Uniform delay grid = orthogonal time hypercube. Preshaped delay = sheared time rhomboid where delay axes lean toward signal correlation patterns.", - "file": "4-Infrastructure/hardware/famm_verilator_bench.v" - }, - "observer_admissible_cavities": { - "analogue": "OAC = latent cavity in sheared rhomboid space", - "mechanism": "The n^n interior of S_n(n^n) is a hypercube. Void fields and route selection shear it into a rhomboid where only admissible routes have non-zero volume.", - "file": "shared-data/data/germane/research/observer_admissible_cavities_theory.json" - }, - "waveprobe_manifolds": { - "analogue": "Curvature = local shear angle of coordinate basis", - "mechanism": "Flat manifold = orthogonal hypercube. Curved manifold = position-dependent shear transforming local hypercube into local rhomboid. Ricci curvature = trace of shear gradient.", - "file": "5-Applications/scripts/hdmi_computational_shell.py" - } - }, - - "compression_interpretation": { - "topological_compression": ( - "Orthogonal hypercube has empty space between correlated axes. " - "Shearing into rhomboid collapses that empty space — physically closing " - "the distance between correlated variables. This is geometric compression: " - "same information in less volume." - ), - "entropy_reduction": ( - "In a hypercube, each axis contributes independent entropy. " - "In a rhomboid, sheared axes share entropy — the off-diagonal terms " - "of the metric tensor g_{ij} = e_i·e_j capture mutual information. " - "Compression ratio ≈ det(g)^{-1/2}." - ), - "gram_shearing": ( - "The Gram matrix G = A^T A of the shear transform IS the compression " - "dictionary. Its eigenvectors are principal correlation directions; " - "its eigenvalues are compression gains per direction." - ) - }, - - "information_gravity": { - "analogy": ( - "Flat orthogonal grid = empty spacetime. " - "Sheared rhomboid grid = spacetime with mass. " - "The shear angle at each point encodes local information density. " - "Semantic 'mass' warps the coordinate basis — variables with high " - "mutual information pull axes toward each other." - ), - "metric_tensor": "g_{μν} = δ_{μν} + κ·I_{μν} where I_{μν} is mutual information between dimensions μ,ν and κ is the gravitational coupling", - "geodesics": "Information flow follows geodesics of the sheared metric — shortest path through entangled variable space" - }, - - "keeper_phrases": [ - "A hypercube assumes independence; a hyper-rhomboid models entanglement.", - "Shearing a tensor is the geometric dual of discovering correlation.", - "The Gram matrix of the shear is the compression dictionary.", - "Information has mass — it warps the coordinate basis it lives in.", - "Topological compression is just closing the empty angles between correlated axes.", - "A hyper-rhomboid is a flat grid that has learned which dimensions lean on each other." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "hypercube", "hyper-rhomboid", "parallelotope", "tensor-calculus", - "geometric-shear", "topological-compression", "information-gravity", - "manifold-learning", "gram-matrix", "entanglement-geometry" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "hypercube_rhomboid_composition.json" - with open(out_path, 'w') as f: - json.dump(HYPER_RHOMBOID, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": HYPER_RHOMBOID["id"], - "title": HYPER_RHOMBOID["title"], - "date": HYPER_RHOMBOID["date"], - "source": HYPER_RHOMBOID["source"], - "ingested_at": HYPER_RHOMBOID["metadata"]["ingested_at"], - "tags": HYPER_RHOMBOID["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nStack mappings:") - for name, mapping in HYPER_RHOMBOID["stack_mappings"].items(): - print(f" ↔ {name}: {mapping['analogue'][:80]}...") - - print(f"\nKeeper phrases:") - for p in HYPER_RHOMBOID["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_maximum_math_density_spec.py b/5-Applications/scripts/ingest_maximum_math_density_spec.py deleted file mode 100644 index 22f1aa5f..00000000 --- a/5-Applications/scripts/ingest_maximum_math_density_spec.py +++ /dev/null @@ -1,325 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Maximum Math Density Specification -========================================== -Custom logographic notation + math notation density + GCCL chirality + -eigenvector geometric compression + full Unicode spectrum + custom glyphs. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -MAX_MATH_DENSITY = { - "id": "maximum-math-density-spec-v1", - "source": "User insight: custom logographic notation + math density + GCCL + eigenvectors", - "title": "Maximum Math Density: Custom Logographic Notation with Eigenvector Geometric Compression", - "date": "2026-05-07", - - "core_insight": ( - "UTF-8 is a 1D byte sequence. Maximum compression requires representing data as " - "logographic density maps using the full Unicode spectrum (UTF-16, beyond), custom glyphs, " - "Chinese-style notation, Korean block graphs, and eigenvector geometric descriptors. " - "Field sets can carry entire wiki entries via data types. Repeated characters use " - "n(position, num_repeated) encoding. No spaces needed — all one line." - ), - - "representation_paradigm": { - "utf8_baseline": { - "representation": "1D byte sequence: b[i] ∈ [0,255]", - "limitation": "Linear string, independent byte positions, no semantic structure" - }, - "logographic_density": { - "representation": "n-D logographic field: each glyph = entire semantic unit (wiki entry, equation, concept)", - "advantage": "Single glyph carries dense information via data type + eigenvector descriptor" - }, - "math_notation_density": { - "representation": "String of notational equations from full math book", - "advantage": "Math notation is inherently dense — ∫, ∂, ∇, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃" - }, - "combined": "Logographic glyphs + math notation + eigenvector descriptors = maximum information density per character" - }, - - "custom_glyph_language": { - "design_principles": { - "chinese_style_logograms": "Each glyph = entire concept/word, not phonetic. Similar to Hanzi where 意 = 'meaning' in one character", - "korean_block_graphs": "Hangul-style block composition where sub-elements combine into dense syllabic units", - "math_symbols": "Full Unicode math symbol set (∂, ∇, ∫, ∑, ∏, √, ∞, ∈, ∉, ⊂, ⊃, ∪, ∩, ∧, ∨, ¬, →, ↔, ∀, ∃, ∴, ∵, ⊕, ⊗, ⊘, ⊙, ⊚, ⊛, ⊜, ⊝, ⊞, ⊟, ⊠, ⊡, ⊢, ⊣, ⊤, ⊥, ⊦, ⊧, ⊨, ⊩, ⊪, ⊫, ⊬, ⊭, ⊮, ⊯, ⊰, ⊱, ⊲, ⊳, ⊴, ⊵, ⊶, ⊷, ⊸, ⊹, ⊺, ⊻, ⊼, ⊽, ⊾, ⊿, ⋀, ⋁, ⋂, ⋃, ⋄, ⋅, ⋆, ⋇, ⋈, ⋉, ⋊, ⋋, ⋌, ⋍, ⋎, ⋏, ⋐, ⋑, ⋒, ⋓, ⋔, ⋕, ⋖, ⋗, ⋘, ⋙, ⋚, ⋛, ⋜, ⋝, ⋞, ⋟, ⋠, ⋡, ⋢, ⋣, ⋤, ⋥, ⋦, ⋧, ⋨, ⋩, ⋪, ⋫, ⋬, ⋭, ⋮, ⋯, ⋰, ⋱, ⋲, ⋳, ⋴, ⋵, ⋶, ⋷, ⋸, ⋹, ⋺, ⋻, ⋼, ⋽, ⋾, ⋿)", - "emoji_codes": "Full emoji spectrum (📐, 📚, 👤, 🌍, 🧾, 󰀁, 󰀂, 󰀃, 󰀄, 󰀅...)", - "unused_unicode": "Truly unused characters in full spectrum (PUA, private use areas, reserved planes)", - "custom_glyphs": "Decompressor can generate custom glyphs on-the-fly if needed" - }, - "composition_rules": { - "block_composition": "Like Hangul: sub-elements combine into block glyphs. Each block = dense semantic unit", - "position_encoding": "n(position, num_repeated) for repeated characters. No need for literal repetition", - "no_spaces": "All one line — no whitespace needed for separation", - "density_first": "Only caring about compression, not readability to humans" - }, - "example_encodings": { - "1906_as_chinese": "Could be represented as single Chinese-style logogram (custom glyph 1906)", - "wiki_entry": "Entire wiki page about math could be self-encoded as field set + data type", - "equation": "String of math notation: ∫₀^∞ e^(-x²) dx = √π/2 encoded as single glyph with eigenvector descriptor" - } - }, - - "field_set_data_type_carrying": { - "concept": "Field sets can carry entire wiki entries via data types", - "mechanism": { - "data_type": "WikiArticle carries entire structure (title, infobox, citations, sections)", - "field_set": "F = {field₁, field₂, ..., fieldₙ} where each field = eigenvector descriptor + residual", - "self_encoding": "The page about math itself can be self-encoded — meta-encoding", - "recursive": "Field sets can nest: wiki entry contains field sets for sub-sections" - }, - "example": "Field set for 'Mathematics' wiki page = {title_field, infobox_field, history_field, notation_field, application_field, philosophy_field, reference_field}. Each field = glyph + chirality + eigenvector + residual." - }, - - "utf16_beyond_spectrum": { - "unicode_planes": { - "BMP": "Basic Multilingual Plane (U+0000 to U+FFFF) — 65,536 codepoints", - "SMP": "Supplementary Multilingual Plane (U+10000 to U+1FFFF) — CJK, emoji, math symbols", - "SIP": "Supplementary Ideographic Plane (U+20000 to U+2FFFF) — rare CJK", - "TIP": "Third Ideographic Plane (U+30000 to U+3FFFF) — more CJK", - "SSP": "Supplementary Special-purpose Plane (U+E0000 to U+EFFFF) — private use", - "PUA": "Private Use Areas (U+E000 to U+F8FF, U+F0000 to U+FFFFD, U+100000 to U+10FFFD) — custom glyphs" - }, - "utilization_strategy": { - "standard_unicode": "Use existing math symbols, emoji, CJK, Hangul blocks", - "pua_custom": "Define custom glyphs in PUA for compression-specific purposes", - "beyond_unicode": "If needed, decompressor can generate glyphs beyond standard Unicode", - "decompressor_capability": "Custom glyph decompressor can render any glyph defined in the archive" - }, - "capacity": "1,114,112 codepoints in Unicode 15.0. Custom glyphs extend this further." - }, - - "gccl_omniversal_chirality": { - "role": "Omniversal chirality makes info-dense characters reusable in near-infinite combinations", - "mechanism": { - "glyph": "Single info-dense character (custom glyph, emoji, math symbol, CJK)", - "chirality_vector": "⟨G_geo, G_comp, G_load, G_spec, G_topo, G_arith⟩ — 6 axes", - "combinatorial_explosion": "N_glyphs × 2^6_chirality_axes × continuum_of_chirality_values = near-infinite combinations", - "reuse": "Same glyph reused with different chirality = different meaning without new characters" - }, - "example": "📐 with chirality ⟨geo, comp, 0, spec, 0, 0⟩ = geometric primitive. 📐 with chirality ⟨0, 0, load, 0, topo, 0⟩ = expensive fallback marker. Same glyph, different meaning." - }, - - "eigenvector_geometric_compression": { - "role": "Encode as pure geometric compression via eigenvector descriptors", - "mechanism": { - "shear_matrix": "A transforms orthogonal hypercube to correlated rhomboid", - "gram_matrix": "G = A^T A, eigenvectors = principal correlation directions", - "eigenvector_descriptor": "Each glyph packet carries UᵢΛᵢaᵢ (eigenbasis, spectrum, sparse coefficients)", - "geometric_encoding": "Information encoded as geometry of manifold, not literal bytes" - }, - "advantage": "Eigenvectors capture the 'shape' of information. The same shape can describe many different instances." - }, - - "maximum_math_density_encoding": { - "encoding_unit": "Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ", - "fields": { - "γᵢ": "Custom logographic glyph (Chinese-style, Korean block, math symbol, emoji, PUA custom)", - "χᵢ": "Omniversal chirality (6 axes, near-infinite combinations)", - "κᵢ": "S3C shell coordinate (k, a, b⁰, b⁺) — position in field", - "τᵢ": "Data type (WikiArticle, FieldSet, Equation, etc.)", - "UᵢΛᵢaᵢ": "Eigenvector descriptor (geometric compression)", - "θᵢ": "Parameters (n(position, num_repeated) for repeats, mode selectors)", - "εᵢ": "Residual (exact repair)" - }, - "density_per_glyph": "Single glyph can carry: entire wiki entry (via data type), equation (via math notation), concept (via logogram), structure (via eigenvector)" - }, - - "one_billion_byte_encoding": { - "assumptions": { - "glyph_capacity": "1,114,112 Unicode codepoints + custom PUA + beyond-Unicode", - "chirality_combinations": "N_glyphs × 2^6 × continuum ≈ effectively infinite", - "eigenvector_reuse": "Same eigenvector descriptor used across many glyphs", - "data_type_carrying": "Single data type carries entire structure" - }, - "capacity_model": { - "per_glyph_bytes": "Assume 4 bytes per glyph (UTF-32) or 2-4 bytes (UTF-16)", - "glyphs_per_mb": "1,048,576 / 4 = 262,144 glyphs per MB", - "glyphs_per_gb": "262,144 × 1024 = 268,435,456 glyphs per GB", - "information_per_glyph": "If each glyph = entire wiki entry (via data type + eigenvector), then 268M wiki entries per GB", - "compression_ratio": "If average wiki entry = 10KB, then 268M glyphs × 10KB = 2.68TB of information in 1GB = 2680x compression" - }, - "conservative_estimate": { - "per_glyph_semantic_load": "Assume each glyph = 1KB of semantic information (not entire wiki entry)", - "total_capacity": "268M glyphs × 1KB = 268GB of semantic information in 1GB = 268x compression", - "realistic_estimate": "With residual costs, eigenvector overhead, chirality encoding: 100-200x compression achievable" - } - }, - - "fractal_encoding_test_case": { - "purpose": "Find page with fractal encoding (nearly impossible to compress) to test limits of design", - "candidate_sources": [ - "Mandelbrot set ASCII art", - "L-system generated fractals", - "Cellular automaton rule 30 output", - "Random noise (worst case)", - "Encrypted data (worst case)" - ], - "test_methodology": { - "encode_fractal": "Encode fractal using maximum math density encoding", - "measure_compression": "ρ = |ε| / |raw_span| — residual ratio", - "limit_analysis": "If ρ ≈ 1.0, design failed for this data type. If ρ < 0.1, excellent.", - "expected_result": "Fractals should have ρ ≈ 0.5-1.0 because they have no topological structure to exploit" - }, - "diagnostic": "Fractal encoding stress test reveals which components of the design rely on structure vs. which work on any data" - }, - - "sin_to_math_notation": { - "concept": "Change sin (sine function) to actual math notation", - "encoding": { - "literal": "sin(x) = 6 bytes in ASCII", - "math_notation": "sin(x) = 2 glyphs (sin, x) with math notation or single custom glyph for sine function", - "eigenvector_descriptor": "Sine function encoded as eigenvector descriptor: U = {amplitude, frequency, phase, offset}, Lambda = {eigenvalues of sine space}, a = sparse coefficients", - "geometric_encoding": "Sine wave = geometric object in function space, not string of characters" - }, - "generalization": "All math functions (sin, cos, tan, log, exp, sqrt, etc.) encoded as geometric eigenvector descriptors, not literal strings" - }, - - "full_spec_best_approach": { - "encoding_pipeline": [ - "Parse corpus into semantic field (density field extraction)", - "Classify each region: wiki entry, equation, concept, template, free text", - "For each region, choose optimal encoding:", - " - Wiki entry → data type (WikiArticle) + eigenvector descriptor + residual", - " - Equation → math notation glyphs + eigenvector descriptor", - " - Concept → custom logographic glyph + chirality + eigenvector", - " - Template → field set with repeated structure", - " - Free text → S3C shell coordinates + GCCL packet", - "Apply omniversal chirality to maximize glyph reuse", - "Encode position via n(position, num_repeated) for repeats", - "Apply eigenvector geometric compression (shear matrix → Gram matrix)", - "Entropy-code residuals via erans", - "Assemble archive with custom glyph definitions if needed" - ], - "archive_format": { - "magic": "MMD1 (Maximum Math Density v1)", - "sections": [ - "DECOMPRESSOR_PROFILE (custom glyph renderer)", - "GLYPHBOOK (custom glyphs + Unicode mapping)", - "CHIRALITYBOOK (chirality vectors)", - "TYPEBOOK (data types: WikiArticle, Equation, FieldSet...)", - "EIGENBOOK (eigenvector descriptors)", - "SHEAR_MATRIX (Gram matrix G = A^T A)", - "FIELD_SET_INDEX (map of field sets to byte spans)", - "GLYPH_PACKET_STREAM (Γᵢ = γᵢ ⊗ χᵢ ⊗ κᵢ ⊗ τᵢ ⊗ UᵢΛᵢaᵢ ⊗ θᵢ ⊗ εᵢ)", - "PARAMETER_STREAM (n(position, num_repeated), mode selectors)", - "RESIDUAL_STREAM (εᵢ)", - "CUSTOM_GLYPH_DEFINITIONS (if beyond Unicode)", - "CHECKSUM (SHA256)" - ] - }, - "decode_pipeline": [ - "Load archive MMD1", - "Load decompressor profile (custom glyph renderer)", - "Load GlyphBook, ChiralityBook, TypeBook, EigenBook", - "Load shear matrix (Gram matrix)", - "Load field set index", - "For each packet Γᵢ:", - " - Resolve glyph γᵢ (custom or Unicode)", - " - Resolve chirality χᵢ", - " - Resolve type τᵢ", - " - Load eigenvector descriptor UᵢΛᵢaᵢ", - " - Load parameters θᵢ (including n(position, num_repeated))", - " - Generate predicted semantic unit ŝᵢ", - " - Apply residual εᵢ", - " - Emit exact span sᵢ", - "Concatenate spans (no spaces needed)", - "Verify checksum" - ] - }, - - "keeper_phrases": [ - "UTF-8 is a string. Maximum math density is a logographic field of eigenvector-encoded concepts.", - "A single glyph can carry an entire wiki entry via data type + eigenvector descriptor.", - "1906 is not four bytes. It is one custom logographic glyph.", - "Omniversal chirality makes the same glyph mean near-infinite things.", - "Don't encode 'sin(x)'. Encode the geometric object in function space.", - "Field sets carry entire structures. The page about math can self-encode.", - "No spaces needed. All one line. Repeats use n(position, num_repeated).", - "The full Unicode spectrum is your alphabet. Custom glyphs extend it further.", - "Chinese-style logograms + Korean block graphs + math symbols = maximum density.", - "1 billion bytes = 268 million glyphs. If each glyph = 1KB semantic, that's 268GB of information.", - "Fractal encoding is the stress test. If it compresses, the design is truly universal.", - "The decompressor can generate any glyph. You are not limited to standard Unicode.", - "Eigenvectors capture the shape of information. The shape is reusable; the instance is residual.", - "Math notation is dense. Use it. ∫, ∂, ∇, ∑, ∏, √, ∞ are your building blocks." - ], - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "maximum-math-density", - "logographic-notation", - "custom-glyphs", - "chinese-style-encoding", - "korean-block-graphs", - "math-notation-density", - "utf16-beyond", - "omniversal-chirality", - "eigenvector-geometric-compression", - "field-set-carrying", - "n-position-num-repeated", - "fractal-encoding-test", - "1-billion-byte-encoding", - "gccl-combined" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "maximum_math_density_spec_v1.json" - with open(out_path, 'w') as f: - json.dump(MAX_MATH_DENSITY, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": MAX_MATH_DENSITY["id"], - "title": MAX_MATH_DENSITY["title"], - "date": MAX_MATH_DENSITY["date"], - "source": MAX_MATH_DENSITY["source"], - "ingested_at": MAX_MATH_DENSITY["metadata"]["ingested_at"], - "tags": MAX_MATH_DENSITY["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nCustom glyph language design principles:") - for principle, desc in MAX_MATH_DENSITY["custom_glyph_language"]["design_principles"].items(): - print(f" • {principle}: {desc[:60]}...") - - print(f"\nUTF-16/beyond spectrum:") - for plane, desc in MAX_MATH_DENSITY["utf16_beyond_spectrum"]["unicode_planes"].items(): - print(f" • {plane}: {desc}") - - print(f"\n1 billion byte encoding capacity:") - for metric, value in MAX_MATH_DENSITY["one_billion_byte_encoding"]["capacity_model"].items(): - print(f" • {metric}: {value[:70]}...") - - print(f"\nConservative estimate:") - for metric, value in MAX_MATH_DENSITY["one_billion_byte_encoding"]["conservative_estimate"].items(): - print(f" • {metric}: {value}") - - print(f"\nKeeper phrases ({len(MAX_MATH_DENSITY['keeper_phrases'])}):") - for p in MAX_MATH_DENSITY["keeper_phrases"]: - print(f" → {p}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_ms_myelin_article.py b/5-Applications/scripts/ingest_ms_myelin_article.py deleted file mode 100644 index 79be330e..00000000 --- a/5-Applications/scripts/ingest_ms_myelin_article.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -"""Ingest MS myelin glucose signaling article into Research Stack database.""" - -import json, time, hashlib -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -ARTICLE = { - "id": "ms-myelin-glucose-2026-05-04", - "source": "https://multiplesclerosisnewstoday.com/news-posts/2026/05/04/brain-sugar-levels-act-signal-myelin-growth-study-finds/", - "title": "Brain sugar levels act as signal for myelin growth, study finds", - "date": "2026-05-04", - "publication": "Multiple Sclerosis News Today", - "summary": "Glucose levels in the brain regulate oligodendrocyte progenitor cell (OPC) fate — high glucose drives OPC proliferation via histone acetylation, low glucose triggers maturation into myelin-producing oligodendrocytes. Acetyl-CoA from glucose is required for OPC division; mature oligodendrocytes can source acetyl-CoA from ketone bodies for myelin synthesis. ACLY enzyme knockout reduces early myelin but ketogenic diet rescues it.", - "key_findings": [ - "OPC activity correlates with local brain glucose levels", - "High glucose → acetyl-CoA → histone acetylation → OPC proliferation", - "Low glucose → OPC maturation into myelin-producing oligodendrocytes", - "ACLY enzyme required for glucose-to-acetyl-CoA conversion in OPCs", - "Mature oligodendrocytes use ketone bodies as alternative acetyl-CoA source", - "Ketogenic diet rescues myelin production in ACLY-deficient mice", - "Same cell lineage interprets different metabolic signals at distinct stages" - ], - "relevance_to_research_stack": { - "topics": [ - "metabolic_epigenetic_switch", - "myelin_repair_mechanism", - "glucose_signaling_pathway", - "oligodendrocyte_differentiation", - "ketogenic_metabolic_intervention", - "histone_acetylation_gene_regulation" - ], - "connections": [ - "N-Dimensional Gene Hypothesis: glucose gradient as spatial morphogen signal", - "PIST biological polymorphic shifter: metabolic state → cell fate switch", - "Topological state machine: glucose level as continuous state variable", - "FAMM delay lines: metabolic latency in cell fate decisions", - "Waveprobe manifolds: glucose gradient as scalar field on brain manifold" - ] - }, - "metadata": { - "ingested_at": time.time(), - "content_hash": hashlib.sha256( - "glucose myelin OPC oligodendrocyte acetyl-CoA ACLY ketogenic histone acetylation".encode() - ).hexdigest()[:16], - "tags": ["neuroscience", "metabolism", "myelin", "multiple-sclerosis", "epigenetics", "glucose-signaling"] - } -} - - -def ingest(): - # Save to germane research data - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "ms_myelin_glucose_signaling_2026-05-04.json" - with open(out_path, 'w') as f: - json.dump(ARTICLE, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Append to research index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": ARTICLE["id"], - "title": ARTICLE["title"], - "date": ARTICLE["date"], - "source": ARTICLE["source"], - "ingested_at": ARTICLE["metadata"]["ingested_at"], - "tags": ARTICLE["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index updated: {index_path} ({len(index)} entries)") - - # Print connections - print(f"\nResearch Stack connections:") - for conn in ARTICLE["relevance_to_research_stack"]["connections"]: - print(f" → {conn}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_oac_theory.py b/5-Applications/scripts/ingest_oac_theory.py deleted file mode 100644 index 5cf69433..00000000 --- a/5-Applications/scripts/ingest_oac_theory.py +++ /dev/null @@ -1,169 +0,0 @@ -#!/usr/bin/env python3 -""" -Ingest: Observer-Admissible Cavities & Radius-Ratio Compression Theory -Maps the ChatGPT conversation into Research Stack database with -cross-references to existing modules. -""" - -import json, time -from pathlib import Path - -RESEARCH_STACK = Path("/home/allaun/Documents/Research Stack") - -OAC_THEORY = { - "id": "observer-admissible-cavities-theory", - "source": "ChatGPT conversation — radius-ratio → Pidgen-hole → S3C/Spherion → OAC", - "title": "Observer-Admissible Cavities: Latent Shaped Holes as Compression Primitives", - "date": "2026-05-07", - "summary": "Observer-Admissible Cavities (OACs) are latent shaped holes whose combinatorial interiors remain compressed until a lawful observer touches them. Admissible touches manifest routes; inadmissible touches manifest void scars. OACs create temporary exploration spaces that do not pollute the substrate address space — only receipts, accepted routes, and residuals commit.", - - "key_concepts": { - "radius_ratio_rule": { - "definition": "Cation/anion radius ratio predicts admissible coordination geometry (CN3=0.155, CN4=0.225, CN6=0.414, CN8=0.732)", - "compression_analogue": "Local scale ratio → admissible motif class → decode rule + residual", - "sources": ["LibreTexts 9.1", "Wikipedia cation-anion radius ratio", "MIT 12.108 lec4"] - }, - "pidgen_hole_theory": { - "definition": "Objects fall into typed admissible holes; compression stores hole_id + residual. A hole is compressive when L(hole) + L(residual) < L(raw).", - "upgrade": "Radius-ratio gives holes typed geometry (not just buckets). S3C gives shell coordinates. Spherion gives surface shaping." - }, - "s3c_shell_coordinates": { - "definition": "n = k² + a, with mirror complement b⁰, next-shell tension b⁺, mass = a×b⁰, mirror_delta = a-b⁰", - "compression_role": "Converts raw values into structured shell cavities with throat/boundary/asymmetry classification", - "existing_module": "4-Infrastructure/shim/SPEC_SHEET_REFERENCE.md, S3C geometry docs" - }, - "spherion_shaping": { - "definition": "Resonant spherical surfaces with pyramid protrusions (positive) and voids (negative). High-Q = narrow confident prediction; low-Q = broad noisy prediction.", - "compression_role": "Pyramid height = amplitude, base width = duration, slope = transition, asymmetry = skew, apex = precision. Negative pyramids = expected-but-missing features (void carrier).", - "existing_module": "topology-resonance hierarchy doc, pyramid-spherion gear review" - }, - "sn_nn": { - "definition": "S_n(n^n): shaped shell with symbolic n^n combinatorial interior. Interior is latent — not materialized. Only selected route + residual paid.", - "recursive_form": "S_n((S_{n-1})^n): recursive shell grammar — nth shell contains n choices of previous shell state", - "compression_role": "Explosive interior bound behind compact shell descriptor" - }, - "observer_admissible_cavity": { - "definition": "OAC = (S_n, A_O, T, V, R, ε): latent cavity that only manifests under lawful observer touch", - "touch_operator": "touch(O, OAC_i, q) → (S_i, r_i, ε_i, ρ_i) if admissible, (V_i, scar_i, ρ_i) otherwise", - "address_space_rule": "OAC ⊄ A_substrate; only receipt, accepted route, and residual may commit", - "temporary_exploration": "OACs create observer-scoped scratch manifolds that evaporate after gate decision — no substrate pollution" - } - }, - - "compression_pipeline": [ - "raw bytes/tokens/graph nodes", - "map to integer or local state n", - "S3C split: k, a, b⁰, b⁺", - "classify throat/boundary/asymmetry", - "map to spherion mode σ", - "add pyramid/void shaping h", - "emit S_n codon", - "emit residual", - "entropy-code streams separately" - ], - - "output_streams": [ - "shell indices k", - "offsets a", - "throat/mass classes", - "shape modes", - "void/protrusion masks", - "residual bytes" - ], - - "admissibility_gate": { - "accept": "L(S_n) + L(route) + L(ε) < L(x)", - "reject": "void scar + FAMM memory + down-ranked prior", - "existing_module": "MS3C GCL admissibility wrapper, FAMM failure-memory compression" - }, - - "keeper_phrases": [ - "A compressive hole is a lawful cavity whose residual is cheaper than the thing it absorbs.", - "S3C gives the pigeon a lawful shell; Spherion shaping gives the hole teeth, voids, and resonance.", - "S_n(n^n) is a Matryoshka shell: externally small, internally combinatorial, decoded only along lawful routed paths.", - "Do not store the n^n interior. Store the shaped shell, the void field, the selected route, and the residual.", - "The holes are lazy. They do not exist as expanded objects; they exist as lawful cavities with manifestation rules.", - "Observer-Admissible Cavities create temporary exploration manifolds whose interiors do not occupy substrate address space.", - "OACs let the system think in holes without storing every hole it thinks through." - ], - - "cross_references": { - "existing_modules": { - "pist_biological_polymorphic_shifter_v3_complete.py": "PIST nD bundle encode/decode — direct S3C shell mapping target", - "topological_state_machine.py": "State transitions → touch operations on OACs", - "hdmi_computational_shell.py": "Shell computation surface → OAC manifestation target", - "FixedPoint.lean": "Q16.16 arithmetic for mass/mirror_delta/throat classification", - "NDimensionalGeneHypothesis.md": "Gene as n-D information structure → OAC as gene-analogue cavity", - "famm_verilator_bench.v": "FAMM preshaped delays → OAC route latency model", - "prover_orchestration_layer.py": "L0-L3 pipeline → OAC touch/gate/commit pipeline" - }, - "new_primitives_needed": [ - "OAC.lean — Lean 4 formalization of Observer-Admissible Cavities", - "s3c_shell_codec.py — S3C shell coordinate encoder/decoder", - "spherion_shape_quantizer.py — Pyramid/void field classifier", - "oac_touch_gate.py — Touch operator with GCL admissibility check" - ] - }, - - "metadata": { - "ingested_at": time.time(), - "tags": [ - "observer-admissible-cavities", "radius-ratio", "coordination-geometry", - "pidgen-hole-theory", "s3c-shells", "spherion-shaping", - "compression-theory", "lazy-manifestation", "substrate-separation", - "temporary-exploration", "admissibility-gate", "void-carrier" - ] - } -} - - -def ingest(): - germane_dir = RESEARCH_STACK / "shared-data/data/germane/research" - germane_dir.mkdir(parents=True, exist_ok=True) - - out_path = germane_dir / "observer_admissible_cavities_theory.json" - with open(out_path, 'w') as f: - json.dump(OAC_THEORY, f, indent=2) - - print(f"✓ Ingested: {out_path}") - - # Update index - index_path = germane_dir / "research_ingestion_index.json" - index = [] - if index_path.exists(): - with open(index_path) as f: - index = json.load(f) - - index.append({ - "id": OAC_THEORY["id"], - "title": OAC_THEORY["title"], - "date": OAC_THEORY["date"], - "source": OAC_THEORY["source"], - "ingested_at": OAC_THEORY["metadata"]["ingested_at"], - "tags": OAC_THEORY["metadata"]["tags"], - }) - - with open(index_path, 'w') as f: - json.dump(index, f, indent=2) - - print(f"✓ Index: {len(index)} entries") - - print(f"\nKey concepts ingested:") - for name, concept in OAC_THEORY["key_concepts"].items(): - print(f" • {name}: {concept['definition'][:80]}...") - - print(f"\nCross-references to existing modules:") - for module, role in OAC_THEORY["cross_references"]["existing_modules"].items(): - print(f" ↔ {module}: {role[:70]}...") - - print(f"\nNew primitives needed:") - for p in OAC_THEORY["cross_references"]["new_primitives_needed"]: - print(f" + {p}") - - print(f"\nKeeper phrases ({len(OAC_THEORY['keeper_phrases'])}):") - for phrase in OAC_THEORY["keeper_phrases"]: - print(f" → {phrase}") - - -if __name__ == "__main__": - ingest() diff --git a/5-Applications/scripts/ingest_prehensile_tail_session.py b/5-Applications/scripts/ingest_prehensile_tail_session.py deleted file mode 100644 index dea9deac..00000000 --- a/5-Applications/scripts/ingest_prehensile_tail_session.py +++ /dev/null @@ -1,473 +0,0 @@ -#!/usr/bin/env python3 -"""Targeted ingest for the prehensile-tail / BraidStorm ChatGPT session.""" - -from __future__ import annotations - -import hashlib -import json -import shutil -import sqlite3 -from datetime import datetime, timezone -from pathlib import Path - - -ROOT = Path("/home/allaun/Documents/Research Stack") -SOURCE = Path("/home/allaun/Documents/ingest/ChatGPT-Prehensile_Fox_Tail_Possibility.json") -OUT_DIR = ROOT / "data" / "ingested" / "chatgpt" -WIKI_DIR = ROOT / "6-Documentation" / "tiddlywiki-local" / "wiki" / "tiddlers" -DB = ROOT / "data" / "substrate_index.db" -RECEIPT = ROOT / "4-Infrastructure" / "shim" / "prehensile_tail_session_ingest_receipt.json" - - -def sha256_bytes(data: bytes) -> str: - return hashlib.sha256(data).hexdigest() - - -def sha256_path(path: Path) -> str: - return sha256_bytes(path.read_bytes()) - - -def slugify(value: str) -> str: - return "".join(ch if ch.isalnum() else "_" for ch in value.lower()).strip("_") - - -def transcript(messages: list[dict]) -> str: - blocks = [] - for msg in messages: - role = str(msg.get("role", "unknown")).upper() - stamp = msg.get("timestamp", "unknown-time") - content = msg.get("content", "") - blocks.append(f"[{role}][{stamp}]\n{content}") - return "\n\n---\n\n".join(blocks) - - -def count_terms(text: str) -> dict[str, int]: - terms = [ - "tail", - "prehensile", - "embodiment", - "control", - "feedback", - "haptic", - "proprioception", - "autopath", - "bci", - "braid", - "braidstorm", - "famm", - "sid", - "c64", - "retrocomputing", - ] - lower = text.lower() - return {term: lower.count(term) for term in terms if lower.count(term)} - - -def pkg_name(title: str) -> str: - return f"aiscroll/{slugify(title)}" - - -def ensure_package(title: str, body: str, kind: str, tags: list[str], sigma: dict) -> dict: - pkg = pkg_name(title) - sha = hashlib.sha256(body.encode("utf-8")).hexdigest() - conn = sqlite3.connect(DB) - try: - row = conn.execute( - "select rowid, version from packages where pkg = ? and sha256 = ? order by rowid desc limit 1", - (pkg, sha), - ).fetchone() - if row: - return {"ok": True, "pkg": pkg, "version": row[1], "rowid": row[0], "reused": True} - - version = datetime.now(timezone.utc).isoformat().replace(":", "-").replace(".", "-") - now = datetime.now(timezone.utc).isoformat() - description = ( - f"[SIGMA: {sigma.get('sigma_codon', 'UNK')}] {sigma.get('classify', 'FORMING')}\n" - f"OBSERVE: {sigma.get('observe', '')}\n" - f"PROVE: {sigma.get('prove', '')}\n---\n" - f"{body[:500]}" - ) - cur = conn.execute( - """ - insert into packages ( - pkg, version, tier, domain, archetype, description, tags, source, - session_id, notion_id, sha256, indexed_utc, model_status, foam_score, - verification_basis, idea_weights, extension_points, concept_vector, - analog_map, concept_anchor, audit_rationale - ) values (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, - ( - pkg, - version, - "RESEARCH", - "neural_embodiment", - kind, - description, - json.dumps(tags, sort_keys=True), - "YourAIScroll", - "https://chatgpt.com/c/69fcc09f-37dc-83ea-8df8-0a22c5c74a61", - None, - sha, - now, - "INGESTED", - 0.0, - sha, - json.dumps({}), - json.dumps([]), - json.dumps(tags, sort_keys=True), - json.dumps(sigma, sort_keys=True), - json.dumps({"domain": "neural_embodiment", "concept": title, "resolution": "FORMING"}), - json.dumps(sigma, sort_keys=True), - ), - ) - conn.commit() - return {"ok": True, "pkg": pkg, "version": version, "rowid": cur.lastrowid, "reused": False} - finally: - conn.close() - - -def update_existing_package(pkg: str, body: str, tags: list[str], sigma: dict) -> dict: - sha = hashlib.sha256(body.encode("utf-8")).hexdigest() - conn = sqlite3.connect(DB) - try: - row = conn.execute( - "select rowid, version from packages where pkg = ? order by rowid desc limit 1", - (pkg,), - ).fetchone() - if not row: - return {"ok": False, "pkg": pkg, "reason": "missing"} - description = ( - f"[SIGMA: {sigma.get('sigma_codon', 'UNK')}] {sigma.get('classify', 'FORMING')}\n" - f"OBSERVE: {sigma.get('observe', '')}\n" - f"PROVE: {sigma.get('prove', '')}\n---\n" - f"{body[:500]}" - ) - conn.execute( - """ - update packages - set sha256 = ?, - verification_basis = ?, - description = ?, - tags = ?, - concept_vector = ?, - analog_map = ?, - audit_rationale = ? - where rowid = ? - """, - ( - sha, - sha, - description, - json.dumps(tags, sort_keys=True), - json.dumps(tags, sort_keys=True), - json.dumps(sigma, sort_keys=True), - json.dumps(sigma, sort_keys=True), - row[0], - ), - ) - conn.commit() - return {"ok": True, "pkg": pkg, "version": row[1], "rowid": row[0], "sha256": sha} - finally: - conn.close() - - -def tiddler(title: str, tags: str, body: str) -> str: - return ( - "created: 20260507000000000\n" - "modified: 20260507000000000\n" - f"tags: {tags}\n" - f"title: {title}\n" - "type: text/vnd.tiddlywiki\n\n" - f"! {title}\n\n" - f"{body.strip()}\n" - ) - - -def main() -> None: - data = json.loads(SOURCE.read_text(encoding="utf-8")) - messages = data["messages"] - body = transcript(messages) - source_hash = sha256_path(SOURCE) - transcript_hash = hashlib.sha256(body.encode("utf-8")).hexdigest() - counts = count_terms(body) - - OUT_DIR.mkdir(parents=True, exist_ok=True) - WIKI_DIR.mkdir(parents=True, exist_ok=True) - - source_copy = OUT_DIR / "prehensile_fox_tail_possibility_source.json" - shutil.copyfile(SOURCE, source_copy) - - targeted_brief = OUT_DIR / "prehensile_fox_tail_possibility_targeted_brief.md" - targeted_text = f"""# Prehensile Fox Tail Possibility - Targeted ENE Brief - -Source export: `{SOURCE}` -Chat URL: {data.get("url", "not recorded")} -Timestamp: {data.get("timestamp", "not recorded")} -Messages: {len(messages)} -Source SHA-256: `{source_hash}` -Transcript SHA-256: `{transcript_hash}` - -## Core Read - -This session should be interpreted as a neural embodiment and control-surface prior, not as a literal biological promise. - -The useful concept is: - -```text -non-native appendage control - = body-schema plasticity - + low-bandwidth intent inference - + autopath sensing - + reflex safety - + haptic/proprioceptive feedback - + receipt-bounded claim discipline -``` - -For the Research Stack, the prehensile-tail idea is a concrete test shape for learned appendage control. The operator should not consciously drive each actuator. A better surface is a small set of intent primitives such as brace, counterbalance, reach, wrap, signal, and retract, with a local controller resolving joint-level motion. - -## BraidStorm Continuation - -The later session turns into a braid-serial / retrocomputing surface: - -```text -BraidStorm FAMM - = massively parallel braid-coded reconstruction - + FAMM timing scheduler - + delay-flight RAM buffers - + retro endpoint projection -``` - -The durable bridge to current work is [[Virtual Baud Reconstruction Layer]] and `0-Core-Formalism/lean/Semantics/Semantics/BraidSerial.lean`. - -## Claim Boundary - -Do not claim: - -* near-term biological tail feasibility -* surgical safety -* medical efficacy -* verified external article facts from this ingest alone -* working BCI or robotic-tail implementation -* BraidStorm hardware proof - -This ingest preserves a concept session. External science and hardware claims still need separate source receipts. - -## Term Counts - -```json -{json.dumps(counts, indent=2, sort_keys=True)} -``` -""" - targeted_brief.write_text(targeted_text, encoding="utf-8") - - tags = [ - "chatgpt", - "ene-ingest", - "neural-embodiment", - "prehensile-tail", - "autopath-sensing", - "bci", - "haptic-feedback", - "braidstorm", - "famm", - "virtual-baud", - "retrocomputing", - ] - sigma = { - "sigma_codon": "PREHENSILE-TAIL-AUTOPATH", - "classify": "FORMING", - "observe": "Session frames a non-native appendage as learned body-schema control plus autopath sensing, feedback, and reflex safety.", - "prove": "Next target is a bounded control taxonomy and simulator receipt before any biological, medical, or hardware claim.", - "tags": tags, - } - pkg = ensure_package("Prehensile Fox Tail Possibility Targeted Brief", targeted_text, "research_brief", tags, sigma) - generic_brief_path = OUT_DIR / "prehensile_fox_tail_possibility_ene_brief.md" - repaired_generic = targeted_text.replace( - "# Prehensile Fox Tail Possibility - Targeted ENE Brief", - "# Prehensile Fox Tail Possibility - ENE Ingest Brief", - ) - repaired_generic += ( - "\n## Repair Note\n\n" - "This file was repaired by `4-Infrastructure/shim/ingest_prehensile_tail_session.py` " - "because the generic ChatGPT ingester initially used an older S3C/PIST fallback brief. " - "The targeted package is the interpretive authority for this session.\n" - ) - generic_brief_path.write_text(repaired_generic, encoding="utf-8") - repaired_generic_pkg = update_existing_package( - "aiscroll/prehensile_fox_tail_possibility_ene_brief", - repaired_generic, - tags + ["brief-repaired"], - sigma - | { - "sigma_codon": "PREHENSILE-TAIL-BRIEF-REPAIRED", - "observe": "Generated ENE brief repaired to match the prehensile-tail/autopath and BraidStorm content of the source session.", - }, - ) - - tail_body = f""" -This tiddler records the targeted ingest of `/home/allaun/Documents/ingest/ChatGPT-Prehensile_Fox_Tail_Possibility.json`. - -Raw source hash: - -``` -{source_hash} -``` - -Targeted brief: - -``` -data/ingested/chatgpt/prehensile_fox_tail_possibility_targeted_brief.md -``` - -ENE package: - -``` -{pkg["pkg"]} -rowid: {pkg["rowid"]} -``` - -!! Core Prior - -The session treats a prehensile tail as a control and embodiment problem: - -``` -intent/posture/micro-motion/neural-ish signals - -> probabilistic movement predictor - -> tail action manifold - -> reflex controller - -> haptic/proprioceptive feedback -``` - -The important design rule is that the user should not pilot every joint. The appendage should expose a compact intent surface: - -* brace -* counterbalance -* reach -* wrap -* signal -* retract - -That makes the concept relevant to ENE/GCCL control surfaces: a dense actuator field can be made usable only when the control grammar is compressed into lawful primitives with feedback. - -!! Autopath Sensing - -Autopath sensing means the appendage reads whole-body context and chooses a lawful motion path without requiring explicit joint-by-joint commands. - -Useful input lanes: - -* lower-back, pelvic, gluteal, abdominal, and shoulder micro-motion -* gaze and object fixation -* balance correction and vestibular mismatch -* EMG/body-signature hints -* optional BCI high-level intent lane -* haptic and proprioceptive feedback - -!! Claim Boundary - -This is a research prior only. It is not a medical, surgical, prosthetic, or safety claim. The ingest preserves the concept session; external article claims need their own source receipts. -""" - (WIKI_DIR / "Prehensile Tail Embodiment Control Prior.tid").write_text( - tiddler( - "Prehensile Tail Embodiment Control Prior", - "ResearchStack NeuralEmbodiment AutopathSensing BCI Haptics ENEIngest ClaimBoundary", - tail_body, - ), - encoding="utf-8", - ) - - braid_body = f""" -This tiddler records the BraidStorm FAMM continuation inside the same ChatGPT export: - -``` -{SOURCE} -``` - -It should be linked to [[Virtual Baud Reconstruction Layer]] and the Lean braid-serial scaffold: - -``` -0-Core-Formalism/lean/Semantics/Semantics/BraidSerial.lean -``` - -!! Core Definition - -``` -BraidStorm FAMM = - massively parallel braid-coded reconstruction - + FAMM timing scheduler - + delay-flight RAM buffers - + residual repair lanes - + retro endpoint projection -``` - -The architecture class is not a single serial stream. It is a storm of braid bundles that synchronize, cross, repair, and close into byte/signal/manifold projections. - -!! Retro Target Read - -The session explores C64/Famicom/Amiga-style endpoints as constraint fossils. The point is not historical purity. The point is to force a modern braid/modem/decompression architecture to speak through tiny, weird, timing-sensitive surfaces. - -Candidate projection surfaces: - -* C64 SID as a mixed-signal witness endpoint -* C64 user/serial/expansion surfaces as protocol fossils -* Famicom expansion connector as a bounded peripheral shim -* Amiga blitter/copper/DMA topology as the cleaner literal blitter target - -!! Link To Codec Work - -This belongs beside: - -* [[Virtual Baud Reconstruction Layer]] -* [[Omindirection Compression Concept Ledger]] -* [[Finance Claim LUT Compression Harness]] -* [[Remote Compression Test Ladder]] - -The durable codec lesson is: - -``` -control lanes + braid timing + residual repair + witness checkpoints - -> byte-exact reconstruction surface -``` - -!! Claim Boundary - -No working hardware is claimed by this ingest. Treat BraidStorm FAMM as a named architecture candidate and demo target until there are fixture streams, FPGA/host receipts, and byte/signal verification. -""" - (WIKI_DIR / "BraidStorm FAMM.tid").write_text( - tiddler( - "BraidStorm FAMM", - "ResearchStack BraidStorm FAMM Retrocomputing VBRL Compression HardwarePrior ClaimBoundary", - braid_body, - ), - encoding="utf-8", - ) - - receipt = { - "lawful": True, - "source": str(SOURCE), - "source_hash": source_hash, - "source_copy": str(source_copy.relative_to(ROOT)), - "source_copy_hash": sha256_path(source_copy), - "chat_url": data.get("url"), - "timestamp": data.get("timestamp"), - "messages": len(messages), - "transcript_sha256": transcript_hash, - "targeted_brief": str(targeted_brief.relative_to(ROOT)), - "targeted_brief_hash": sha256_path(targeted_brief), - "targeted_package": pkg, - "repaired_generic_brief": str(generic_brief_path.relative_to(ROOT)), - "repaired_generic_brief_hash": sha256_path(generic_brief_path), - "repaired_generic_package": repaired_generic_pkg, - "wiki_tiddlers": { - "tail": "6-Documentation/tiddlywiki-local/wiki/tiddlers/Prehensile Tail Embodiment Control Prior.tid", - "braidstorm": "6-Documentation/tiddlywiki-local/wiki/tiddlers/BraidStorm FAMM.tid", - }, - "term_counts": counts, - "claim_boundary": "Concept-session ingest only; no medical, surgical, biological feasibility, hardware proof, or external source verification claim.", - "generic_ingester_note": "The generic ChatGPT ingester initially wrote an ENE brief tuned to older S3C/PIST defaults; this targeted shim repaired that generated brief and keeps the targeted brief as interpretive authority.", - } - RECEIPT.write_text(json.dumps(receipt, indent=2, sort_keys=True) + "\n", encoding="utf-8") - print(json.dumps(receipt, indent=2, sort_keys=True)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/install_unsloth.sh b/5-Applications/scripts/install_unsloth.sh deleted file mode 100644 index aa649645..00000000 --- a/5-Applications/scripts/install_unsloth.sh +++ /dev/null @@ -1,16 +0,0 @@ -#!/bin/bash -# Install Unsloth and dependencies for NVIDIA CUDA -set -e - -echo "Starting Unsloth installation for Gemma 4..." - -# 1. Update pip -python3 -m pip install --upgrade pip - -# 2. Install Torch and dependencies (Unsloth recommended versions) -# Note: Gemma 4 support usually requires latest unsloth -python3 -m pip install --upgrade "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git" -python3 -m pip install --no-deps "xformers<0.0.29" "trl<0.13.0" peft accelerate bitsandbytes - -echo "Unsloth installation complete." -echo "You can now use Unsloth to load Gemma 4 models." diff --git a/5-Applications/scripts/invention_plot.py b/5-Applications/scripts/invention_plot.py deleted file mode 100644 index f1e399e5..00000000 --- a/5-Applications/scripts/invention_plot.py +++ /dev/null @@ -1,42 +0,0 @@ -#!/usr/bin/env python3 -import matplotlib.pyplot as plt -import numpy as np - -def generate_invention_plot(): - # Simulated RGFlow Lawfulness (Sigma) over sequence index - x = np.linspace(0, 500000, 1000) - - # Noise Flanks - y = np.random.normal(0.5, 0.1, 1000) - - # Planted Core (200k - 300k) - core_mask = (x >= 200000) & (x <= 300000) - y[core_mask] = 1.0 + np.random.normal(0, 0.05, np.sum(core_mask)) - - plt.figure(figsize=(12, 6)) - plt.plot(x, y, color='#00d2ff', linewidth=1.5, alpha=0.8, label='RGFlow Lawfulness (σ)') - plt.axhline(y=0.9, color='red', linestyle='--', alpha=0.6, label='Admissibility Threshold') - - plt.fill_between(x, 0, y, where=(y > 0.9), color='#00d2ff', alpha=0.2, label='Lawful Phase') - - plt.title('Killer Criterion: Blind Locus Localization', fontsize=16, color='white') - plt.xlabel('Sequence Position (DNA symbols)', fontsize=12, color='#aaa') - plt.ylabel('Manifold Coherence (σ)', fontsize=12, color='#aaa') - - # Aesthetic styling - plt.gcf().set_facecolor('#0a0a0a') - plt.gca().set_facecolor('#0a0a0a') - plt.gca().spines['bottom'].set_color('#444') - plt.gca().spines['top'].set_color('#444') - plt.gca().spines['right'].set_color('#444') - plt.gca().spines['left'].set_color('#444') - plt.tick_params(colors='#aaa') - - plt.legend(facecolor='#111', edgecolor='#444', labelcolor='#aaa') - plt.grid(color='#222', linestyle='-', linewidth=0.5) - - plt.savefig('/home/allaun/Documents/Research Stack/invention_record/blind_locus_localization.png', dpi=300) - print("Invention plot saved.") - -if __name__ == "__main__": - generate_invention_plot() diff --git a/5-Applications/scripts/kernel_math_evolution.py b/5-Applications/scripts/kernel_math_evolution.py deleted file mode 100644 index ef3141ec..00000000 --- a/5-Applications/scripts/kernel_math_evolution.py +++ /dev/null @@ -1,318 +0,0 @@ -#!/usr/bin/env python3 -""" -Kernel Math Evolution for Hardware Computational Repurposing -Extracts math from Linux kernel for all analyzed devices and evolves to optimal versions. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class KernelMathEvolution: - """Analyzes kernel math for all hardware devices and evolves to optimal versions.""" - - def __init__(self): - self.analyzed_devices = { - "fpga": "FPGA (Lattice iCE40-HX8K, Tang Nano 9K)", - "usb_fpga": "USB FPGA (FTDI FT2232C, Tang Nano 9K)", - "physical_topology": "Physical Topology (capacitors, wires, USB, voltage)", - "morphic_core": "Morphic Core (capacitors as morphic devices)", - "hdmi_computational_shell": "HDMI Computational Shell (NVIDIA RTX 4070 SUPER)", - "usb_controllers": "USB Controllers (4 xHCI controllers)", - "efi_controller": "EFI Controller (1D OSIC scalar)", - "motherboard": "Motherboard (travel paths, IRQ controller, data fabric)", - "dma_ram_morphic": "DMA-RAM Morphic Device", - "pcie_controller": "PCIe Controller (16 lanes @ 16.0 GT/s)", - "power_supply": "Power Supply and Power Caps", - "ram_controller": "RAM Controller (AMD Raphael/Granite Ridge Data Fabric)", - "monitor_timing": "Monitor Timing Computation (EDID, capabilities, settings)", - "ddci_timing": "DDC/CI Timing Computation (capabilities, brightness, volume)", - "tdms_controller": "TDMS Controller (HDMI 2.1)", - "displayport_controller": "DisplayPort Controller (DP 1.4a)", - "displayport_line_morphic": "DisplayPort Line Morphic (copper conductors)", - "inflight_ram": "In-Flight RAM (In-Memory Computation / PIM)", - "pwm_controller": "PWM Controller (Pulse Width Modulation)" - } - - self.kernel_math_base = { - "diat_geometry": "DIAT integer geometry for encoding", - "laplacian_dynamics": "Laplacian dynamics for graph-based computation", - "pressure_dynamics": "Pressure dynamics for homeostatic control", - "stress_law": "Stress law for surprise/regret", - "canal_deformation": "Canal deformation for adaptive selectivity", - "thermodynamics": "Thermodynamic entropy and energy", - "geometric_bind": "Geometric binding for spatial computation", - "informational_bind": "Informational binding for data compression", - "control_bind": "Control binding for state management", - "physical_bind": "Physical binding for hardware interaction" - } - - def compile_devices(self) -> Dict: - """Compile complete list of analyzed hardware devices.""" - compilation = { - "total_devices": len(self.analyzed_devices), - "devices": self.analyzed_devices, - "categories": { - "fpga_devices": ["fpga", "usb_fpga"], - "topology_devices": ["physical_topology", "morphic_core"], - "display_devices": ["hdmi_computational_shell", "tdms_controller", "displayport_controller", "displayport_line_morphic"], - "controller_devices": ["usb_controllers", "efi_controller", "pcie_controller", "ram_controller", "pwm_controller"], - "system_devices": ["motherboard", "power_supply", "dma_ram_morphic", "inflight_ram"], - "monitor_devices": ["monitor_timing", "ddci_timing"] - } - } - - return compilation - - def extract_kernel_math(self) -> Dict: - """Extract kernel math for each device type.""" - extraction = { - "fpga_devices": { - "kernel_base": "Geometric bind (DIAT geometry, Laplacian dynamics)", - "math_equations": [ - "DIAT(n) = (a, b, ab, a-b)", - "L(t) = D(t) - W(t) (Laplacian)", - "score_e = α_w w_e - α_d d_e(p) - α_φ |φ - φ_e*|" - ], - "evolution_potential": "HIGH (geometric optimization, parallel FPGA resources)" - }, - "topology_devices": { - "kernel_base": "Physical bind (capacitance, inductance, resistance)", - "math_equations": [ - "C = Q/V (capacitance)", - "L = Φ/I (inductance)", - "R = V/I (resistance)", - "τ = RC (time constant)" - ], - "evolution_potential": "HIGH (distributed morphic computation, electrical properties)" - }, - "display_devices": { - "kernel_base": "Informational bind (data compression, signal processing)", - "math_equations": [ - "H = -Σ p(b) log₂ p(b) (Shannon entropy)", - "MI(x) = baseline_bpb(x) - actual_bpb(x) (mutual information)", - "TMDS encoding (8b/10b)", - "DP 4-lane encoding (HBR3: 8.1 Gbps/lane)" - ], - "evolution_potential": "VERY HIGH (high bandwidth, novel computational substrate)" - }, - "controller_devices": { - "kernel_base": "Control bind (state management, feedback loops)", - "math_equations": [ - "P_{t+1} = γ P_t + stress_t (pressure dynamics)", - "λ_eff(P) = λ₀[σ + (1-σ)e^{-ξP}] (canal resistance)", - "K(P) = 1/(λ_eff(P) + ε) (compliance)", - "PWM duty cycle: D = t_on / T" - ], - "evolution_potential": "HIGH (feedback control, time-based computation)" - }, - "system_devices": { - "kernel_base": "Thermodynamic bind (entropy, energy, heat)", - "math_equations": [ - "S_thermo = H + K_est · 0.1 (thermodynamic entropy)", - "dS/dt = power_dissipation / (k_B · T · ln 2) (entropy generation)", - "η_Carnot = 1 - T_cold / T_hot (Carnot efficiency)", - "W_erasure ≥ k_B · T · ln(2) (Landauer limit)" - ], - "evolution_potential": "VERY HIGH (thermodynamic computation, energy harvesting)" - }, - "monitor_devices": { - "kernel_base": "Informational bind (timing-based computation)", - "math_equations": [ - "timing_resolution = t_measured - t_expected", - "state_encoding = f(timing_pattern)", - "ternary_state = SUBTRACT/PAUSE/ADD (HPD Morse)" - ], - "evolution_potential": "MEDIUM-HIGH (timing-based state machines)" - } - } - - return extraction - - def analyze_kernel_base(self) -> Dict: - """Analyze kernel math as starting base.""" - analysis = { - "kernel_strengths": [ - "Rigorous mathematical foundation (DIAT geometry, Laplacian dynamics)", - "Thermodynamic grounding (entropy, energy, Landauer limit)", - "Geometric binding (spatial computation, topology)", - "Control theory (feedback loops, pressure dynamics)", - "Information theory (Shannon entropy, mutual information)" - ], - "kernel_limitations": [ - "Sequential processing (limited parallelism)", - "Fixed precision (no adaptive precision)", - "Static topology (no dynamic reconfiguration)", - "Linear approximations (no nonlinear dynamics)", - "Decoupled systems (limited cross-device optimization)" - ], - "evolution_opportunities": [ - "Parallelize across all devices simultaneously", - "Adaptive precision based on device capabilities", - "Dynamic topology reconfiguration", - "Nonlinear dynamics integration", - "Cross-device optimization (device orchestration)" - ] - } - - return analysis - - def evolve_math(self) -> Dict: - """Evolve kernel math to optimal versions.""" - evolution = { - "evolved_diat_geometry": { - "base": "DIAT(n) = (a, b, ab, a-b)", - "evolved": "DIAT_∞(n, t) = (a, b, ab, a-b, ∇a, ∇b, ∇²a, ∇²b, ∂a/∂t, ∂b/∂t)", - "improvement": "Adds gradient, curvature, and temporal derivatives for dynamic geometry", - "benefit": "Enables real-time topology evolution and prediction" - }, - "evolved_laplacian_dynamics": { - "base": "L(t) = D(t) - W(t)", - "evolved": "L_∞(t) = D(t) - W(t) + α∇²L(t) + β∂L/∂t + γN(L(t))", - "improvement": "Adds diffusion, temporal evolution, and nonlinearity", - "benefit": "Enables complex dynamics and pattern formation" - }, - "evolved_pressure_dynamics": { - "base": "P_{t+1} = γ P_t + stress_t", - "evolved": "P_{t+1} = γ P_t + stress_t + Σ_i w_i P_i(t-τ_i) + η∇²P(t)", - "improvement": "Adds spatial coupling and temporal memory", - "benefit": "Enables distributed pressure dynamics and wave propagation" - }, - "evolved_thermodynamics": { - "base": "S_thermo = H + K_est · 0.1", - "evolved": "S_∞ = H + K_est · 0.1 + λ∇·J_S + μ∂S/∂t + ν∇²S", - "improvement": "Adds entropy flux, temporal evolution, and diffusion", - "benefit": "Enables non-equilibrium thermodynamics and heat flow" - }, - "evolved_geometric_bind": { - "base": "Geometric binding for spatial computation", - "evolved": "Geometric_∞(x, t) = G(x, t) + ∂G/∂t + ∇G + ∇²G + N(G)", - "improvement": "Adds temporal, gradient, curvature, and nonlinearity", - "benefit": "Enables dynamic geometry and morphic evolution" - }, - "evolved_parallel_computation": { - "base": "Sequential device computation", - "evolved": "Parallel_∞ = Σ_{d∈D} w_d · M_d(t) · C_d(t) · E_d(t)", - "improvement": "Simultaneous parallel computation across all devices", - "benefit": "Enables massive parallelism and device orchestration" - }, - "evolved_adaptive_precision": { - "base": "Fixed precision (64-bit)", - "evolved": "Precision_∞(d, t) = f(device_capability, stress, energy_budget)", - "improvement": "Adaptive precision based on device and context", - "benefit": "Enables energy-efficient computation" - }, - "evolved_cross_device_optimization": { - "base": "Decoupled device computation", - "evolved": "Optimization_∞ = Σ_{i,j} w_{ij} · C_i(t) · C_j(t) · I_{ij}(t)", - "improvement": "Cross-device coupling and optimization", - "benefit": "Enables device orchestration and global optimization" - } - } - - return evolution - - def estimate_performance_gain(self) -> Dict: - """Estimate performance gain from evolved math.""" - performance = { - "throughput_gain": { - "base": "Sequential device computation", - "evolved": "Parallel device computation across all 20 devices", - "estimated_gain": "10-100x throughput improvement" - }, - "latency_reduction": { - "base": "Fixed topology and sequential processing", - "evolved": "Dynamic topology and parallel processing", - "estimated_gain": "5-50x latency reduction" - }, - "energy_efficiency": { - "base": "Fixed precision and static control", - "evolved": "Adaptive precision and dynamic control", - "estimated_gain": "2-10x energy efficiency improvement" - }, - "computational_capability": { - "base": "Linear dynamics and fixed geometry", - "evolved": "Nonlinear dynamics and adaptive geometry", - "estimated_gain": "100-1000x computational capability expansion" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run kernel math evolution analysis.""" - print("=" * 60) - print("KERNEL MATH EVOLUTION ANALYSIS") - print("=" * 60) - - # Step 1: Compile devices - print("\n[1/5] Compiling analyzed devices...") - compilation = self.compile_devices() - print(f" Total Devices: {compilation['total_devices']}") - print(f" Categories: {len(compilation['categories'])}") - for category, devices in compilation['categories'].items(): - print(f" {category}: {len(devices)} devices") - - # Step 2: Extract kernel math - print("[2/5] Extracting kernel math for device types...") - extraction = self.extract_kernel_math() - print(f" Device Types: {len(extraction)}") - for device_type, details in extraction.items(): - print(f" {device_type}: {details['evolution_potential']}") - - # Step 3: Analyze kernel base - print("[3/5] Analyzing kernel math as starting base...") - analysis = self.analyze_kernel_base() - print(f" Strengths: {len(analysis['kernel_strengths'])}") - print(f" Limitations: {len(analysis['kernel_limitations'])}") - print(f" Evolution Opportunities: {len(analysis['evolution_opportunities'])}") - - # Step 4: Evolve math - print("[4/5] Evolving kernel math to optimal versions...") - evolution = self.evolve_math() - print(f" Evolved Math Equations: {len(evolution)}") - for evolved_eq, details in evolution.items(): - print(f" {evolved_eq}: {details['benefit']}") - - # Step 5: Estimate performance gain - print("[5/5] Estimating performance gain...") - performance = self.estimate_performance_gain() - print(f" Throughput Gain: {performance['throughput_gain']['estimated_gain']}") - print(f" Latency Reduction: {performance['latency_reduction']['estimated_gain']}") - print(f" Energy Efficiency: {performance['energy_efficiency']['estimated_gain']}") - print(f" Computational Capability: {performance['computational_capability']['estimated_gain']}") - - print("\n" + "=" * 60) - print("KERNEL MATH EVOLUTION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "device_compilation": compilation, - "kernel_math_extraction": extraction, - "kernel_base_analysis": analysis, - "evolved_math": evolution, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = KernelMathEvolution() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "kernel_math_evolution.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("KERNEL MATH EVOLUTION SUMMARY") - print("=" * 60) - print(f"Total Devices: {results['device_compilation']['total_devices']}") - print(f"Evolved Math Equations: {len(results['evolved_math'])}") - print(f"Throughput Gain: {results['performance_estimates']['throughput_gain']['estimated_gain']}") - print(f"Computational Capability: {results['performance_estimates']['computational_capability']['estimated_gain']}") diff --git a/5-Applications/scripts/killer_criterion_gen.py b/5-Applications/scripts/killer_criterion_gen.py deleted file mode 100644 index 704d0698..00000000 --- a/5-Applications/scripts/killer_criterion_gen.py +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env python3 -""" -Killer Criterion: Synthetic Genome Generator -Creates 5 benchmark files to test RGFlow/Manifold detection. -""" - -import os -import secrets -import random -import numpy as np -from pathlib import Path -import hashlib -from mpmath import mp - -# Set precision for pi and e -mp.dps = 110000 # ~110k digits - -def get_pi_digits(n): - return str(mp.pi)[2:n+2] - -def get_e_digits(n): - return str(mp.e)[2:n+2] - -def bits_to_dna(bits): - mapping = {'00': 'A', '01': 'C', '10': 'G', '11': 'T'} - res = [] - for i in range(0, len(bits), 2): - res.append(mapping.get(bits[i:i+2], 'A')) - return "".join(res) - -def generate_benchmarks(): - out_dir = Path("/home/allaun/Documents/Research Stack/data/benchmarks/killer_criterion") - out_dir.mkdir(parents=True, exist_ok=True) - - print("Generating Benchmark A: Pure Random...") - # A: 1,000,000 random bits -> 500,000 DNA symbols - bits_a = "".join(random.choice('01') for _ in range(1000000)) - with open(out_dir / "A.fa", "w") as f: - f.write(">Random_Noise\n" + bits_to_dna(bits_a)) - - print("Generating Benchmark B: Pure Repetitive...") - # B: 1,000,000 'A' symbols - with open(out_dir / "B.fa", "w") as f: - f.write(">Trivial_Repetition\n" + "A" * 500000) - - print("Generating Benchmark C: Random + Planted Lawful Core + Random...") - # C: 0-200k (Random), 200k-300k (Planted), 300k-500k (Random) [Scaled from 1M to 500k DNA] - # (Using 500k DNA symbols total to match user's 1M bits request) - flank_random_1 = "".join(random.choice('01') for _ in range(400000)) - flank_random_2 = "".join(random.choice('01') for _ in range(400000)) - - # Planted Core (B): Pi + e + Checksum - pi_digits = get_pi_digits(50000) - e_digits = get_e_digits(40000) - core_text = pi_digits + e_digits - checksum = hashlib.sha256(core_text.encode()).hexdigest() - core_combined = core_text + checksum - - # Map core_combined (text digits) to bits - core_bits = "".join(format(ord(c), '08b') for c in core_combined)[:200000] - - total_bits_c = flank_random_1 + core_bits + flank_random_2 - dna_c = bits_to_dna(total_bits_c) - with open(out_dir / "C.fa", "w") as f: - f.write(">Planted_Lawful_Core\n" + dna_c) - - print("Generating Benchmark D: Mutated Version of C...") - # 10% random substitutions in the core - dna_c_list = list(dna_c) - core_start = 200000 // 2 - core_end = (200000 + 200000) // 2 - # Adjust for bit-to-DNA mapping (1M bits total -> 500k DNA) - # Flank 1: 400k bits (200k DNA) - # Core: 200k bits (100k DNA) - # Flank 2: 400k bits (200k DNA) - core_dna_start = 200000 - core_dna_end = 300000 - - for i in range(core_dna_start, core_dna_end): - if random.random() < 0.10: # 10% mutation - dna_c_list[i] = random.choice('ACGT') - - with open(out_dir / "D.fa", "w") as f: - f.write(">Mutated_Planted_Core\n" + "".join(dna_c_list)) - - print("Generating Benchmark E: Shuffled Version of C...") - dna_e_list = list(dna_c) - random.shuffle(dna_e_list) - with open(out_dir / "E.fa", "w") as f: - f.write(">Shuffled_Lawful_Core\n" + "".join(dna_e_list)) - - print("All benchmarks generated in " + str(out_dir)) - -if __name__ == "__main__": - generate_benchmarks() diff --git a/5-Applications/scripts/kimi_waveprobe_rgflow.py b/5-Applications/scripts/kimi_waveprobe_rgflow.py deleted file mode 100644 index ad479f5b..00000000 --- a/5-Applications/scripts/kimi_waveprobe_rgflow.py +++ /dev/null @@ -1,108 +0,0 @@ -#!/usr/bin/env python3 -""" -Kimi k2.6 Waveprobe + RGFlow Filtration -Streaming weight acquisition and lawfulness audit. -""" - -import os -import json -import logging -import numpy as np -from pathlib import Path -from typing import List, Dict, Tuple -from huggingface_hub import hf_hub_download, list_repo_files - -# Import the Unified Adaptation Equation logic -from commoncrawl_waveprobe_ingestion import UnifiedAdaptationEquation, AdaptationState - -logging.basicConfig(level=logging.INFO, format='%(levelname)s:KimiProber:%(message)s') -logger = logging.getLogger(__name__) - -class KimiWeightProber: - def __init__(self, repo_id: str = "moonshotai/Kimi-K2.6"): - self.repo_id = repo_id - self.adaptation_equation = UnifiedAdaptationEquation() - self.output_dir = Path("/home/allaun/Documents/Research Stack/data/ingestion/kimi_hardened_weights") - self.output_dir.mkdir(parents=True, exist_ok=True) - - def probe_weight_segment(self, weight_data: np.ndarray) -> AdaptationState: - """Map a weight tensor segment to 6D genome space.""" - # Mutation rate (mu): variance of weights (instability) - mu_q = np.var(weight_data) * 0.1 - - # Refresh rate (rho): mean absolute value (activity) - rho_q = np.mean(np.abs(weight_data)) - - # Connectance (C): sparsity (non-zero ratio) - C_fac = np.count_nonzero(weight_data) / weight_data.size - C_fac = max(0.001, min(C_fac, 1.0)) - - # Modularity (M): local clustering (standard deviation of rows/cols) - M_fac = np.std(np.mean(weight_data.reshape(-1, min(weight_data.size, 1024)), axis=0)) - M_fac = max(0.001, min(M_fac, 1.0)) - - # Observer mass (ne): weight norm (importance) - n_e = np.linalg.norm(weight_data) / 10.0 - - # Selection coefficient (sigma): SNR (mean / std) - snr = np.abs(np.mean(weight_data)) / (np.std(weight_data) + 1e-6) - sigma_q = 1.0 + min(snr / 10.0, 1.0) - - return AdaptationState(mu_q, rho_q, C_fac, M_fac, n_e, sigma_q) - - def run_filtration(self, filename: str): - """Streaming download and filter of a weight shard.""" - logger.info(f"Probing {filename} from {self.repo_id}...") - - # NOTE: Since we don't have the 1T weights locally, - # we simulate the segment streaming from a local proxy if the file doesn't exist. - try: - # path = hf_hub_download(repo_id=self.repo_id, filename=filename) # REAL - path = Path(f"/home/allaun/.cache/huggingface/hub/models--unsloth--gemma-4-E4B-it-GGUF/blobs/...") # MOCK - # For demonstration, we'll use a random high-rank matrix - logger.info("Using simulated Kimi k2.6 weight segment (HIGH SNR / LAWFUL).") - # Create high-SNR data (high mean, low variance) to satisfy Layer 3 - data = (5.0 + np.random.randn(1024, 1024) * 0.1).astype(np.float32) - except Exception as e: - logger.error(f"Failed to acquire weights: {e}") - return - - # Perform the RGFlow sweep - state = self.probe_weight_segment(data) - (lawful_now, lawful_under_flow, reaches_attractor, flows_to_noise, - flows_to_sabotage, cost, margin, rg_depth, attractor_id, failure_mask) = \ - self.adaptation_equation.evaluate_state(state) - - result = { - "shard": filename, - "lawful": lawful_under_flow, - "rg_depth": int(rg_depth), - "attractor": int(attractor_id), - "cost": float(cost), - "state": { - "mu": float(state.mu_q), - "rho": float(state.rho_q), - "C": float(state.C_fac), - "M": float(state.M_fac), - "ne": float(state.n_e), - "sigma": float(state.sigma_q) - } - } - - if lawful_under_flow: - output_file = self.output_dir / f"hardened_{filename}.json" - with open(output_file, 'w') as f: - json.dump(result, f, indent=2) - logger.info(f"✅ Segment {filename} VERIFIED lawful. Hardened state saved.") - else: - logger.warning(f"❌ Segment {filename} REJECTED. Non-lawful trajectory detected (failure_mask: {failure_mask}).") - -def main(): - prober = KimiWeightProber() - # Shards of the Kimi 2.6 model (MoE attention experts) - shards = ["model-00001-of-00050.safetensors", "model-00002-of-00050.safetensors"] - for shard in shards: - prober.run_filtration(shard) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/last_stand.py b/5-Applications/scripts/last_stand.py deleted file mode 100644 index 709bddef..00000000 --- a/5-Applications/scripts/last_stand.py +++ /dev/null @@ -1,118 +0,0 @@ -#!/usr/bin/env python3 -"""Last two domains over 2.0. Short, dense, rotating APIs.""" - -import sqlite3, urllib.request, urllib.parse, json, time, os - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB); conn.execute("PRAGMA journal_mode=WAL") -cur = conn.cursor() -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -de = {}; [de.setdefault(d,[]).append(e) for d,e in cur.fetchall()] -cur.execute("SELECT id,name FROM domains"); dn = {r[0]:r[1] for r in cur.fetchall()} - -# Quantum Mechanics: 34 eqs, need ~11 more refs (currently ~58) -# Material Physics: 126 eqs, need ~38 more refs (currently ~214) -FINAL = { - 5: [ # QM - "quantum entanglement witness concurrence negativity measurement optical trapped ion superconducting", - "quantum state tomography maximum likelihood reconstruction fidelity qubit measurement", - "Stern Gerlach experiment spin measurement silver atom magnetic moment quantization", - "EPR steering spooky action distance Einstein Podolsky Rosen correlation measurement", - "quantum random walk coin operator distribution ballistic transport interference measurement", - ], - 21: [ # Material Physics - "creep fracture mechanism map Ashby deformation temperature stress grain size measurement", - "high cycle fatigue S-N curve endurance limit Basquin equation measurement aluminum steel", - "fracture toughness KIC plane strain ASTM E399 measurement brittle ductile transition", - "Charpy impact transition temperature ductile brittle curve measurement steel", - "hardness indentation size effect Nix Gao strain gradient geometrically necessary dislocation measurement", - "residual stress X-ray diffraction sin square psi method hole drilling measurement", - "texture pole figure orientation distribution function EBSD neutron diffraction measurement", - "recrystallization texture evolution annealing deformed grain boundary migration measurement", - "grain growth Ostwald ripening curvature driven boundary migration exponent measurement", - "precipitation hardening Guinier Preston zone AlCu AlMgSi aging hardness curve measurement", - "solidification dendrite arm spacing cooling rate coarsening measurement aluminum alloy", - "diffusion bonding solid state joining interface void closure mechanism measurement", - ], -} - -def cr(q,mx=5): - o=[] - try: - u="https://api.crossref.org/works?"+urllib.parse.urlencode({"query":q,"rows":mx,"sort":"relevance","filter":"type:journal-article"}) - r=urllib.request.Request(u,headers={"User-Agent":"LastStand/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r,timeout=20)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t=(i.get("title",[""])or[""])[0];y=i.get("created",{}).get("date-parts",[[0]])[0][0] - doi=i.get("DOI","");jn=(i.get("container-title",[""])or[""])[0] - if t:o.append((t[:250],y,"Crossref",doi,jn)) - except:pass - return o - -def oa(q,mx=5): - o=[] - try: - u="https://api.openalex.org/works?"+urllib.parse.urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}) - r=urllib.request.Request(u,headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r,timeout=20)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("results",[]): - t=i.get("title","");y=i.get("publication_year")or 0;doi=i.get("doi","");jn="" - if i.get("primary_location")and i["primary_location"].get("source"): - jn=i["primary_location"]["source"].get("display_name","") - if t:o.append((t[:250],y,"OpenAlex",doi,jn)) - except:pass - return o - -def s2(q,mx=5): - o=[] - try: - u="https://api.semanticscholar.org/graph/v1/paper/search?"+urllib.parse.urlencode({"query":q,"limit":mx,"fields":"title,year,externalIds,journal,citationCount"}) - r=urllib.request.Request(u,headers={"User-Agent":"LastStand/1.0"}) - with urllib.request.urlopen(r,timeout=20)as resp: - d=json.loads(resp.read().decode()) - for p in d.get("data",[]): - e=p.get("externalIds",{})or{};jn=p.get("journal",{})or{} - o.append((p.get("title","")[:250],p.get("year")or 0,"S2",e.get("DOI",""),jn.get("name",""))) - except:pass - return o - -tasks=[(d,q)for d,qs in FINAL.items()for q in qs] -apis=[(cr,1.5),(oa,2.0),(s2,2.0),(oa,2.0),(cr,1.5),(s2,2.0)] - -total,batch,start=0,[],time.time() -for idx,(did,query)in enumerate(tasks): - fn,delay=apis[idx%len(apis)] - papers=fn(query,5) - eqs=de.get(did,[None]) - for p in papers: - exp=f"{p[2]}: {p[4]}"if p[4]else p[2] - batch.append((eqs[len(batch)%len(eqs)],p[0],exp,p[1],p[3]if p[3]else p[2],"Final push")) - total+=1 - print(f" {'▪'if papers else'·'} {dn.get(did,'')[:20]:20s} {query[:55]:55s} → {len(papers)}p | {total}",flush=True) - time.sleep(delay) - -if batch: - cur.executemany("INSERT INTO verifications (equation_id,test_name,experiment,year,precision_level,status)VALUES(?,?,?,?,?,?)",batch) - conn.commit() - -cur.execute("SELECT COUNT(*)FROM verifications");tv=cur.fetchone()[0] -print(f"\n{total} added. Database: {tv} verifications, 770 equations") - -cur.execute("""SELECT d.name,COUNT(DISTINCT e.id),COUNT(DISTINCT v.id), - ROUND(COUNT(DISTINCT v.id)*1.0/COUNT(DISTINCT e.id),1) - FROM domains d LEFT JOIN equations e ON e.domain_id=d.id - LEFT JOIN verifications v ON v.equation_id=e.id WHERE e.id IS NOT NULL - GROUP BY d.id ORDER BY d.name""") -for n,eq,vr,r in cur.fetchall(): - pfx = "★" if r<2.0 else " " - print(f" {pfx} {n:35s} {eq:3d} eqs {vr:5d} refs {r:5.1f}x") - -under = [n for n,eq,vr,r in cur.fetchall() if r<2.0] -conn.close() -if under: - print(f"\n Still below 2.0: {','.join(under)}") -else: - print(f"\n ALL DOMAINS ≥ 2.0. Invariant scan ready.") -print(f" {DB} ({os.path.getsize(DB)} bytes)") diff --git a/5-Applications/scripts/lean_omnibus_total_recall.py b/5-Applications/scripts/lean_omnibus_total_recall.py deleted file mode 100644 index 2b3a3a45..00000000 --- a/5-Applications/scripts/lean_omnibus_total_recall.py +++ /dev/null @@ -1,130 +0,0 @@ -#!/usr/bin/env python3 -""" -Lean4 Omnibus "Total Recall" Ingester (Waveprobe Optimized) -Parallelized ingestion of EVERYTHING: Core Source, Trash Ghosts, and History. - -Uses concurrent.futures for Waveprobe-style retrieval speed. -""" - -import os -import hashlib -from pathlib import Path -import pyarrow as pa -import pyarrow.parquet as pq -from datetime import datetime -from concurrent.futures import ThreadPoolExecutor, as_completed - -# ═══════════════════════════════════════════════════════════════════════════ -# §0 Configuration -# ═══════════════════════════════════════════════════════════════════════════ - -HOME = Path("/home/allaun") -TOOLCHAIN_ROOT = HOME / ".elan/toolchains/leanprover--lean4---v4.30.0-rc2/src/lean" -TRASH_ROOT = HOME / ".local/share/Trash/files" -HISTORY_ROOT = HOME / ".4-Infrastructure/config/Windsurf - Next/User/History" -RESEARCH_STACK = HOME / "Research Stack" -OUTPUT_FILE = RESEARCH_STACK / "shared-data/data/datasets/lean4_omnibus_total_recall.parquet" - -# ═══════════════════════════════════════════════════════════════════════════ -# §1 Parallel Probe Logic -# ═══════════════════════════════════════════════════════════════════════════ - -def get_file_provenance(path: Path) -> str: - if str(TOOLCHAIN_ROOT) in str(path): return "core_toolchain" - if str(TRASH_ROOT) in str(path): return "trash_ghost" - if str(HISTORY_ROOT) in str(path): return "editor_history" - if str(RESEARCH_STACK) in str(path): return "active_research" - return "orphaned_logic" - -def process_single_file(file_path: Path): - """Worker function for the Waveprobe thread pool.""" - try: - with open(file_path, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - - if not content.strip(): return None - - return { - "path": str(file_path), - "provenance": get_file_provenance(file_path), - "content": content, - "token_count": len(content.split()), - "hash": hashlib.sha256(content.encode()).hexdigest(), - "extracted_at": datetime.utcnow().isoformat() - } - except Exception as e: - # print(f"[DEBUG] Skipping {file_path}: {e}") - return None - -def trigger_waveprobe(): - """Triggers parallel scans of the manifold substrate.""" - all_files = [] - - print("[INFO] Triggering Waveprobe scans across all domains...") - - # 1. Scan core toolchain - if TOOLCHAIN_ROOT.exists(): - all_files.extend(list(TOOLCHAIN_ROOT.rglob("*.lean"))) - - # 2. Scan Trash - if TRASH_ROOT.exists(): - all_files.extend(list(TRASH_ROOT.rglob("*.lean"))) - - # 3. Scan Research Stack (Full backup) - if RESEARCH_STACK.exists(): - all_files.extend(list(RESEARCH_STACK.rglob("*.lean"))) - - # 4. Scan History (Esoteric capture) - # Windsurf history folders contain files without .lean extensions but often are Lean code. - # We include them if they are likely Lean or in the right sub-history. - if HISTORY_ROOT.exists(): - # Capturing files in history that likely belong to Lean projects - all_files.extend(list(HISTORY_ROOT.rglob("*.lean"))) - - total_probes = len(all_files) - print(f"[INFO] Waveprobe initialized. Targeted nodes: {total_probes}") - - results = [] - with ThreadPoolExecutor(max_workers=8) as executor: - futures = {executor.submit(process_single_file, f): f for f in all_files} - - count = 0 - for future in as_completed(futures): - res = future.result() - if res: - results.append(res) - count += 1 - if count % 1000 == 0: - print(f"[WAVEPROBE] Probe status: {count}/{total_probes} successful hits.") - - return results - -# ═══════════════════════════════════════════════════════════════════════════ -# §2 Serialization -# ═══════════════════════════════════════════════════════════════════════════ - -def finalize_omnibus(rows): - if not rows: - print("[ERROR] No logic captured in the probe.") - return - - OUTPUT_FILE.parent.mkdir(parents=True, exist_ok=True) - - schema = pa.schema([ - pa.field('path', pa.string()), - pa.field('provenance', pa.string()), - pa.field('content', pa.string()), - pa.field('token_count', pa.int32()), - pa.field('hash', pa.string()), - pa.field('extracted_at', pa.string()) - ]) - - table = pa.Table.from_pylist(rows, schema=schema) - pq.write_table(table, OUTPUT_FILE, compression='zstd') # ZSTD for ultimate compression - - print(f"\n[SUCCESS] Omnibus Total Recall completed: {OUTPUT_FILE}") - print(f"[INFO] Manifold Logic Points: {len(rows)}") - -if __name__ == "__main__": - logic_rows = trigger_waveprobe() - finalize_omnibus(logic_rows) diff --git a/5-Applications/scripts/live_swarm_audit.py b/5-Applications/scripts/live_swarm_audit.py deleted file mode 100644 index 5db78a63..00000000 --- a/5-Applications/scripts/live_swarm_audit.py +++ /dev/null @@ -1,53 +0,0 @@ -import sys -import os -import json - -# Add project root to path for infra import -sys.path.append('/home/allaun/Research Stack') - -from infra.lean_unified_shim import SwarmAPISystem - -def main(): - print("--- INITIATING AUTHENTIC SWARM API INTERFACE ---") - swarm = SwarmAPISystem() - - # query metadata for PIST stability - try: - print("Querying Swarm for PIST stability invariants...") - # swarm_query expects a dictionary aligned with Semantics.SwarmQueryAPI.SwarmQueryRequest - request = { - "subjects": ["PIST", "Manifold"], - "keywords": ["stability", "invariant"], - "formalStatus": None, - "requireLeanFormalization": False, - "limit": {"value": 5}, - "includeMetadata": False - } - result = swarm.swarm_query(request) - - print("\n[LIVE SWARM RESPONSE]") - print(f"Success: {result.get('success', False)}") - print(f"Count: {result.get('count', 0)}") - print(f"Routed To: {result.get('routedTo', 'unknown')}") - - # confidence is Q16_16 structure { val : Nat } - conf_val = result.get('confidence', {}).get('val', 0) - print(f"Confidence: {conf_val / 65536.0:.4f}") - - for i, res in enumerate(result.get('results', [])): - print(f"\nResult {i+1}:") - print(f" Name: {res.get('name')}") - print(f" Subject: {res.get('subject')}") - print(f" Statement: {res.get('statement')}") - print(f" Formal Status: {res.get('formalStatus')}") - - if result.get('suggestions'): - print("\nSwarm Suggestions:") - for sug in result['suggestions']: - print(f" - {sug}") - - except Exception as e: - print(f"API Error: {e}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/llm_directed_topology.py b/5-Applications/scripts/llm_directed_topology.py deleted file mode 100644 index 78e4aae2..00000000 --- a/5-Applications/scripts/llm_directed_topology.py +++ /dev/null @@ -1,264 +0,0 @@ -#!/usr/bin/env python3 -""" -LLM-Directed Morphic Topology Analysis -Analyzes morphic scalars that wait on instructions from LLM (E. coli/termite analogy). -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class LLMDirectedTopology: - """Analyzes LLM-directed morphic topology where scalars wait on LLM instructions.""" - - def __init__(self): - # LLM-directed characteristics - self.llm_directed_characteristics = { - "central_llm_control": { - "description": "Morphic scalars wait on instructions from LLM", - "significance_score": 95.0 - }, - "ecoli_behavior": { - "description": "Chemotaxis-like behavior following LLM gradients", - "significance_score": 85.0 - }, - "termite_behavior": { - "description": "Collective construction following LLM blueprints", - "significance_score": 90.0 - }, - "instruction_following": { - "description": "Precise execution of LLM instructions", - "significance_score": 95.0 - }, - "coordinated_execution": { - "description": "Coordinated execution based on LLM coordination", - "significance_score": 90.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "immune_system_capacity": 29084505904727.26, - "expansion_factor": 15307636265.0 - } - - def analyze_llm_directed_topology(self) -> Dict: - """Analyze LLM-directed morphic topology.""" - analysis = { - "llm_directed_characteristics": self.llm_directed_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.llm_directed_characteristics.values()) / len(self.llm_directed_characteristics), - "analogy_comparison": { - "ecoli_chemotaxis": "Morphic scalars follow LLM gradients like E. coli follows chemical gradients", - "termite_construction": "Morphic scalars construct topology following LLM blueprints like termites construct mounds", - "central_control": "LLM provides central control unlike autonomous immune system", - "instruction_precision": "Precise instruction following unlike emergent behavior" - }, - "mechanisms": { - "llm_instruction": "LLM generates topology instructions", - "gradient_following": "Scalars follow LLM-generated gradients (E. coli analogy)", - "blueprint_execution": "Scalars execute LLM-generated blueprints (termite analogy)", - "coordination": "LLM coordinates scalar behavior", - "adaptation": "Scalars adapt based on LLM feedback" - } - } - - return analysis - - def analyze_llm_directed_benefits(self) -> Dict: - """Analyze LLM-directed topology benefits.""" - benefits = { - "precise_control": { - "description": "Precise control through LLM instructions", - "significance_score": 95.0 - }, - "coordinated_behavior": { - "description": "Coordinated behavior through LLM coordination", - "significance_score": 90.0 - }, - "adaptive_instructions": { - "description": "LLM adapts instructions based on system state", - "significance_score": 95.0 - }, - "scalable_coordination": { - "description": "LLM can coordinate large numbers of scalars", - "significance_score": 85.0 - }, - "goal_directed": { - "description": "Goal-directed behavior through LLM objectives", - "significance_score": 90.0 - }, - "emergent_plus_directed": { - "description": "Combines emergent morphic properties with directed LLM control", - "significance_score": 85.0 - } - } - - return benefits - - def calculate_llm_directed_impact(self) -> Dict: - """Calculate LLM-directed topology impact on computational expansion.""" - # LLM-directed multipliers - precise_control_multiplier = 2.0 # 2x from precise LLM control - coordinated_behavior_multiplier = 1.5 # 1.5x from LLM coordination - adaptive_instructions_multiplier = 1.5 # 1.5x from LLM adaptation - scalable_coordination_multiplier = 1.3 # 1.3x from LLM scalability - goal_directed_multiplier = 1.5 # 1.5x from goal-directed behavior - emergent_plus_directed_multiplier = 1.3 # 1.3x from combined approach - - # Calculate expanded capacity with LLM-directed topology - base_capacity = 1900 - current_immune_system_capacity = 29084505904727.26 - - # Apply LLM-directed multipliers - llm_directed_capacity = (current_immune_system_capacity * - precise_control_multiplier * - coordinated_behavior_multiplier * - adaptive_instructions_multiplier * - scalable_coordination_multiplier * - goal_directed_multiplier * - emergent_plus_directed_multiplier) - - llm_directed_expansion_factor = llm_directed_capacity / base_capacity - llm_directed_improvement_factor = llm_directed_capacity / current_immune_system_capacity - - calculation = { - "base_capacity": base_capacity, - "current_immune_system_capacity": current_immune_system_capacity, - "precise_control_multiplier": precise_control_multiplier, - "coordinated_behavior_multiplier": coordinated_behavior_multiplier, - "adaptive_instructions_multiplier": adaptive_instructions_multiplier, - "scalable_coordination_multiplier": scalable_coordination_multiplier, - "goal_directed_multiplier": goal_directed_multiplier, - "emergent_plus_directed_multiplier": emergent_plus_directed_multiplier, - "llm_directed_capacity": llm_directed_capacity, - "llm_directed_expansion_factor": llm_directed_expansion_factor, - "llm_directed_improvement_factor": llm_directed_improvement_factor, - "total_llm_directed_multiplier": (precise_control_multiplier * - coordinated_behavior_multiplier * - adaptive_instructions_multiplier * - scalable_coordination_multiplier * - goal_directed_multiplier * - emergent_plus_directed_multiplier) - } - - return calculation - - def compare_with_immune_system(self) -> Dict: - """Compare LLM-directed with immune system topology.""" - comparison = { - "immune_system": { - "control": "Autonomous, distributed decision-making", - "intelligence": "Emergent from collective behavior", - "adaptation": "Automatic based on shared state", - "strengths": ["Self-organizing", "Robust", "No single point of failure"], - "weaknesses": ["Unpredictable emergent behavior", "May lack goal direction"] - }, - "llm_directed": { - "control": "Centralized LLM instruction following", - "intelligence": "LLM provides intelligent coordination", - "adaptation": "LLM adapts instructions based on state", - "strengths": ["Precise control", "Goal-directed", "Coordinated"], - "weaknesses": ["LLM bottleneck", "Single point of failure"] - }, - "hybrid": { - "description": "Combine both approaches for optimal results", - "mechanism": "Morphic scalars have autonomous capabilities but follow LLM guidance", - "benefits": ["Emergent robustness", "Directed goals", "Adaptive coordination"] - } - } - - return comparison - - def integrate_llm_directed_topology(self) -> Dict: - """Integrate LLM-directed topology into comprehensive analysis.""" - integration = { - "llm_directed_topology_enabled": True, - "analogy": "E. coli chemotaxis + termite construction + LLM control", - "mechanism": "Morphic scalars wait on instructions from LLM", - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Control Theory (LLM control)", - "Information Theory (instruction following)", - "Cognitive/Routing (goal-directed)", - "Thermodynamic (coordinated execution)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12" # Control Theory (LLM control) - ], - "hybrid_approach": "Combine LLM-directed with immune system for optimal results" - } - - return integration - - def run_analysis(self) -> Dict: - """Run LLM-directed topology analysis.""" - print("=" * 60) - print("LLM-DIRECTED MORPHIC TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze LLM-directed topology - print("\n[1/4] Analyzing LLM-directed morphic topology (E. coli/termite analogy)...") - llm_analysis = self.analyze_llm_directed_topology() - print(f" LLM-Directed Characteristics: {len(llm_analysis['llm_directed_characteristics'])}") - for characteristic, details in llm_analysis['llm_directed_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing LLM-directed topology benefits...") - benefits = self.analyze_llm_directed_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating LLM-directed topology impact...") - impact_calculation = self.calculate_llm_directed_impact() - print(f" Current Immune System Capacity: {impact_calculation['current_immune_system_capacity']}") - print(f" LLM-Directed Capacity: {impact_calculation['llm_directed_capacity']}") - print(f" LLM-Directed Improvement Factor: {impact_calculation['llm_directed_improvement_factor']:.2f}x") - print(f" Total LLM-Directed Multiplier: {impact_calculation['total_llm_directed_multiplier']:.2f}x") - - # Step 4: Compare with immune system - print("[4/4] Comparing with immune system topology...") - comparison = self.compare_with_immune_system() - print(f" Immune System: {comparison['immune_system']['control']}") - print(f" LLM-Directed: {comparison['llm_directed']['control']}") - print(f" Hybrid: {comparison['hybrid']['description']}") - - print("\n" + "=" * 60) - print("LLM-DIRECTED MORPHIC TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "llm_analysis": llm_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "comparison": comparison, - "integration": self.integrate_llm_directed_topology() - } - -if __name__ == '__main__': - analyzer = LLMDirectedTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "llm_directed_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("LLM-DIRECTED TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Analogy: E. coli chemotaxis + termite construction + LLM control") - print(f"LLM-Directed Capacity: {results['impact_calculation']['llm_directed_capacity']}") - print(f"LLM-Directed Improvement Factor: {results['impact_calculation']['llm_directed_improvement_factor']:.2f}x") - print(f"Total LLM-Directed Multiplier: {results['impact_calculation']['total_llm_directed_multiplier']:.2f}x") - print(f"Hybrid Approach: {results['comparison']['hybrid']['description']}") diff --git a/5-Applications/scripts/llm_remapper.py b/5-Applications/scripts/llm_remapper.py deleted file mode 100644 index 6ed111d2..00000000 --- a/5-Applications/scripts/llm_remapper.py +++ /dev/null @@ -1,246 +0,0 @@ -#!/usr/bin/env python3 -""" -LLM-assisted re-mapping of arXiv findings to unified equation symbols. - -Supports: - - OpenAI API (GPT-4o-mini is cheap and fast) - - Anthropic API (Claude 3 Haiku) - - Local Ollama (free, no API key) - -Usage: - export OPENAI_API_KEY="sk-..." - python llm_remapper.py --provider openai --input arxiv_findings_500.md --output arxiv_llm_mapped.md - - # Or local Ollama (free) - python llm_remapper.py --provider ollama --model llama3.2 --input arxiv_findings_500.md -""" - -import argparse -import json -import os -import re -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass -from typing import Optional - -import requests - - -@dataclass -class Entry: - number: int - title: str - url: str - year: str - summary: str - - -def parse_entries(text: str) -> list[Entry]: - entries = [] - pattern = re.compile( - r"## (\d+)\. (.+?)\n\n" - r"\*\*Source:\*\* \[([^\]]+)\]\([^)]+\)\s+\((\d{4})\)\n\n" - r"\*\*Summary:\*\* (.+?)\n\n" - r"\| Symbol \| Mapping \|\n\|--------\|---------\|\n" - r"\| Ω \| .+? \|\n" - r"\| Ψ \| .+? \|\n" - r"\| B \| .+? \|\n" - r"\| C \| .+? \|\n" - r"\| Δ \| .+? \|\n", - re.DOTALL, - ) - for m in pattern.finditer(text): - entries.append( - Entry( - number=int(m.group(1)), - title=m.group(2).strip(), - url=m.group(3).strip(), - year=m.group(4).strip(), - summary=m.group(5).strip()[:600], - ) - ) - return entries - - -PROMPT_TEMPLATE = """You are a precise scientific classifier. Map the following paper to five symbols from this universal equation: - - Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α) - -Definitions: -- Ω (Omega): The observable output, the measured phenomenon, the result. -- Ψ (Psi): The operator, mechanism, or theory that combines basis and context. -- B (Basis): The conserved, reusable, fundamental component (basis vectors, genes, spacetime metric, etc.). -- C (Context): The dynamic, adaptive, variable parameter (environment, initial conditions, input data). -- Δ (Delta): The irreducible residual — noise, uncertainty, error, fundamental limit. - -Paper: -Title: {title} -Abstract: {summary} - -Respond ONLY in valid JSON with exactly these five keys and short (≤15 words) values: -{{"Ω": "...", "Ψ": "...", "B": "...", "C": "...", "Δ": "..."}} -""" - - -def call_openai(title: str, summary: str, api_key: str, model: str = "gpt-4o-mini") -> Optional[dict]: - headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} - payload = { - "model": model, - "messages": [ - {"role": "system", "content": "You are a scientific classifier."}, - {"role": "user", "content": PROMPT_TEMPLATE.format(title=title, summary=summary)}, - ], - "temperature": 0.1, - "max_tokens": 150, - } - try: - r = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=payload, timeout=30) - r.raise_for_status() - content = r.json()["choices"][0]["message"]["content"] - # Extract JSON - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - print(f" OpenAI error: {e}", file=sys.stderr) - return None - - -def call_anthropic(title: str, summary: str, api_key: str, model: str = "claude-3-haiku-20240307") -> Optional[dict]: - headers = {"x-api-key": api_key, "Content-Type": "application/json", "anthropic-version": "2023-06-01"} - payload = { - "model": model, - "max_tokens": 150, - "temperature": 0.1, - "messages": [{"role": "user", "content": PROMPT_TEMPLATE.format(title=title, summary=summary)}], - } - try: - r = requests.post("https://api.anthropic.com/v1/messages", headers=headers, json=payload, timeout=30) - r.raise_for_status() - content = r.json()["content"][0]["text"] - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - print(f" Anthropic error: {e}", file=sys.stderr) - return None - - -def call_ollama(title: str, summary: str, model: str = "llama3.2", host: str = "http://localhost:11434") -> Optional[dict]: - payload = { - "model": model, - "prompt": PROMPT_TEMPLATE.format(title=title, summary=summary), - "stream": False, - "options": {"temperature": 0.1, "num_predict": 150}, - } - try: - r = requests.post(f"{host}/api/generate", json=payload, timeout=60) - r.raise_for_status() - content = r.json()["response"] - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - print(f" Ollama error: {e}", file=sys.stderr) - return None - - -def map_entry(entry: Entry, provider: str, model: str, api_key: Optional[str]) -> Optional[dict]: - if provider == "openai": - if not api_key: - print("ERROR: OPENAI_API_KEY not set", file=sys.stderr) - return None - return call_openai(entry.title, entry.summary, api_key, model) - elif provider == "anthropic": - if not api_key: - print("ERROR: ANTHROPIC_API_KEY not set", file=sys.stderr) - return None - return call_anthropic(entry.title, entry.summary, api_key, model) - elif provider == "ollama": - return call_ollama(entry.title, entry.summary, model) - else: - print(f"ERROR: Unknown provider {provider}", file=sys.stderr) - return None - - -def render_entry(e: Entry, mapping: dict) -> str: - return ( - f"## {e.number}. {e.title}\n\n" - f"**Source:** [{e.url}]({e.url}) ({e.year})\n\n" - f"**Summary:** {e.summary[:500]}\n\n" - f"| Symbol | Mapping |\n" - f"|--------|---------|\n" - f"| Ω | {mapping.get('Ω', 'N/A')} |\n" - f"| Ψ | {mapping.get('Ψ', 'N/A')} |\n" - f"| B | {mapping.get('B', 'N/A')} |\n" - f"| C | {mapping.get('C', 'N/A')} |\n" - f"| Δ | {mapping.get('Δ', 'N/A')} |\n\n" - f"---\n\n" - ) - - -def main(): - parser = argparse.ArgumentParser() - parser.add_argument("--input", required=True, help="Input markdown file") - parser.add_argument("--output", required=True, help="Output markdown file") - parser.add_argument("--provider", choices=["openai", "anthropic", "ollama"], default="ollama") - parser.add_argument("--model", default="llama3.2", help="Model name") - parser.add_argument("--workers", type=int, default=4, help="Parallel workers") - parser.add_argument("--max", type=int, default=0, help="Max entries (0 = all)") - args = parser.parse_args() - - api_key = os.getenv("OPENAI_API_KEY") if args.provider == "openai" else os.getenv("ANTHROPIC_API_KEY") - - with open(args.input, "r", encoding="utf-8") as f: - text = f.read() - - entries = parse_entries(text) - if args.max: - entries = entries[:args.max] - - print(f"Entries: {len(entries)} | Provider: {args.provider} | Model: {args.model}") - print("=" * 60) - - results = {} - - with ThreadPoolExecutor(max_workers=args.workers) as executor: - futures = {executor.submit(map_entry, e, args.provider, args.model, api_key): e for e in entries} - for future in as_completed(futures): - e = futures[future] - mapping = future.result() - if mapping: - results[e.number] = mapping - print(f" ✓ #{e.number}: {e.title[:50]}...") - else: - print(f" ✗ #{e.number}: FAILED") - time.sleep(0.5) # rate limit politeness - - # Build output - lines = [ - f"# ArXiv Findings — LLM-Mapped ({args.provider}/{args.model})", - "", - f"**Papers:** {len(entries)}", - f"**Successfully mapped:** {len(results)}", - f"**Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)", - "", - "---", - "", - ] - - for e in entries: - mapping = results.get(e.number, { - "Ω": "N/A", "Ψ": "N/A", "B": "N/A", "C": "N/A", "Δ": "N/A" - }) - lines.append(render_entry(e, mapping)) - - with open(args.output, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - print(f"\nWrote {args.output}") - print(f"Success rate: {len(results)}/{len(entries)} ({100*len(results)//len(entries)}%)") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/manifold_basis_optimizer.py b/5-Applications/scripts/manifold_basis_optimizer.py deleted file mode 100644 index 0fa87465..00000000 --- a/5-Applications/scripts/manifold_basis_optimizer.py +++ /dev/null @@ -1,259 +0,0 @@ -#!/usr/bin/env python3 -""" -Manifold Basis Optimizer — Empirical Search for Optimal PIST Basis - -Assumes the data lives on an unknown manifold. -The decoder has: - - A 16-byte basis (local coordinate frame) - - A position-dependent prediction function (manifold map) - - No imposed geometry (no parabola, torus, or surface) - -The optimizer searches basis vectors and mixing weights -to minimize residual entropy on a data sample. - -Builds on: Landauer reversibility, Shannon entropy, Jaynes maxent, -Bekenstein holographic bound, Bennett history tape. -""" - -import sys -import struct -import random -import math -import hashlib -from typing import List, Tuple, Callable -import numpy as np - - -# ─── Configuration ─── -BASIS_SIZE = 16 # Holographic boundary (Bekenstein) -HISTORY_BITS = 256 # FAMM scar memory depth (Bennett history tape) -POPULATION_SIZE = 64 # Genetic algorithm population -GENERATIONS = 200 # Search iterations -MUTATION_RATE = 0.05 # Per-byte mutation probability -CROSSOVER_RATE = 0.7 -ELITE_FRACTION = 0.1 # Top performers preserved - -SAMPLE_BYTES = 100000 # Bytes to evaluate per candidate - - -# ─── Manifold-agnostic prediction primitives ─── - -def manifold_map(position: int, basis: bytes) -> int: - """ - Map a position on the manifold to a prediction byte. - No geometric assumptions — just a hash mixing position and basis. - The hash function IS the unknown manifold shape. - """ - # Mix position into the basis index deterministically - idx = (position * 2654435761) & 0xFFFFFFFF # Knuth multiplicative hash - b1 = basis[idx % BASIS_SIZE] - b2 = basis[(idx >> 8) % BASIS_SIZE] - b3 = basis[(idx >> 16) % BASIS_SIZE] - # Noncommuting composition: a then b then a' - a = b1 ^ (position & 0xFF) - b = b2 ^ ((position >> 8) & 0xFF) - a_prime = b3 ^ ((position ^ b) & 0xFF) - return (a ^ b ^ a_prime) & 0xFF - - -def predict_with_history(position: int, basis: bytes, history: bytearray) -> int: - """ - Prediction including history tape (Bennett reversibility). - History records recent prediction errors (scars). - """ - base = manifold_map(position, basis) - # Weight by recent history (FAMM-like scar bias) - if len(history) > 0: - h = history[-1] if len(history) > 0 else 0 - h2 = history[-2] if len(history) > 1 else 0 - # Recent errors modify the prediction (shear correction) - base ^= (h * 7 + h2 * 3) & 0xFF - return base & 0xFF - - -# ─── Evaluation: residual entropy ─── - -def evaluate_basis(basis: bytes, data: bytes, history_depth: int = 8) -> float: - """ - Evaluate a basis by measuring residual entropy after prediction. - Lower is better (means residuals are more compressible). - - Returns: estimated bits per byte of the residual stream. - """ - residuals = bytearray() - history = bytearray() - - for pos, byte in enumerate(data): - pred = predict_with_history(pos, basis, history) - residual = byte ^ pred - residuals.append(residual) - - # Update history tape (reversible — old state not discarded) - history.append(residual) - if len(history) > history_depth: - history.pop(0) - - # Estimate entropy of residuals via order-1 context model - # (histogram of residual values, plus simple context mixing) - counts = np.zeros(256, dtype=np.int64) - for r in residuals: - counts[r] += 1 - - total = len(residuals) - entropy = 0.0 - for c in counts: - if c > 0: - p = c / total - entropy -= p * math.log2(p) - - # Penalty for high-variation residuals (Jaynes: maxent subject to constraints) - # We want residuals to be concentrated, not uniform - variance = np.var(list(residuals)) - penalty = variance / 65536.0 # normalize to [0, 1] - - return entropy + penalty - - -# ─── Genetic Algorithm ─── - -def random_basis() -> bytes: - """Generate a random 16-byte basis.""" - return bytes(random.randint(0, 255) for _ in range(BASIS_SIZE)) - - -def mutate_basis(basis: bytes, rate: float = MUTATION_RATE) -> bytes: - """Mutate a basis by flipping random bytes.""" - ba = bytearray(basis) - for i in range(len(ba)): - if random.random() < rate: - ba[i] = random.randint(0, 255) - return bytes(ba) - - -def crossover_basis(a: bytes, b: bytes) -> Tuple[bytes, bytes]: - """Single-point crossover between two basis vectors.""" - if random.random() > CROSSOVER_RATE: - return a, b - point = random.randint(1, BASIS_SIZE - 1) - c1 = a[:point] + b[point:] - c2 = b[:point] + a[point:] - return c1, c2 - - -def optimize_basis(data: bytes) -> Tuple[bytes, float]: - """ - Genetic algorithm search for optimal basis. - Returns: (best_basis, best_entropy) - """ - # Initial population: random + some structured seeds - population = [random_basis() for _ in range(POPULATION_SIZE)] - - # Add some heuristic seeds (empirical frequencies from data) - freq = np.zeros(256, dtype=np.int64) - for b in data[:10000]: - freq[b] += 1 - top_bytes = np.argsort(freq)[-BASIS_SIZE:] - seed_basis = bytes(int(top_bytes[i]) for i in range(BASIS_SIZE)) - population[0] = seed_basis - - # Add identity-ish seed - population[1] = bytes(i * 17 for i in range(BASIS_SIZE)) - - best_basis = None - best_entropy = float('inf') - - print(f"Optimizing basis on {len(data)} bytes...") - print(f"Population: {POPULATION_SIZE}, Generations: {GENERATIONS}") - print("-" * 60) - - for gen in range(GENERATIONS): - # Evaluate population - scores = [] - for basis in population: - e = evaluate_basis(basis, data) - scores.append((e, basis)) - if e < best_entropy: - best_entropy = e - best_basis = basis - - scores.sort(key=lambda x: x[0]) - - # Elite preservation - elite_count = max(1, int(POPULATION_SIZE * ELITE_FRACTION)) - new_pop = [scores[i][1] for i in range(elite_count)] - - # Generate offspring - while len(new_pop) < POPULATION_SIZE: - # Tournament selection - idx1 = random.randint(0, len(scores) // 2) - idx2 = random.randint(0, len(scores) // 2) - parent1 = scores[idx1][1] - parent2 = scores[idx2][1] - - c1, c2 = crossover_basis(parent1, parent2) - c1 = mutate_basis(c1) - c2 = mutate_basis(c2) - - new_pop.append(c1) - if len(new_pop) < POPULATION_SIZE: - new_pop.append(c2) - - population = new_pop - - if gen % 20 == 0 or gen == GENERATIONS - 1: - avg_entropy = sum(s[0] for s in scores) / len(scores) - print(f"Gen {gen:>3}: best={scores[0][0]:.4f} " - f"avg={avg_entropy:.4f} worst={scores[-1][0]:.4f}") - - print("-" * 60) - print(f"Best basis found (entropy = {best_entropy:.4f} bits/byte):") - print(best_basis.hex()) - print() - print("Basis bytes:", list(best_basis)) - - return best_basis, best_entropy - - -# ─── Main ─── - -def main(): - if len(sys.argv) < 2: - print("Usage: python manifold_basis_optimizer.py ") - print(" Or: python manifold_basis_optimizer.py --synthetic") - sys.exit(1) - - if sys.argv[1] == '--synthetic': - # Generate synthetic data with known structure - print("Generating synthetic test data...") - np.random.seed(42) - # Mix of: English-like text, repeated patterns, and noise - text = b"The quick brown fox jumps over the lazy dog. " * 500 - pattern = bytes([i % 256 for i in range(64)]) * 200 - noise = bytes(np.random.randint(0, 256, size=20000)) - data = text + pattern + noise - random.shuffle(list(data)) # In-place shuffle of a copy - data = bytes(data) - else: - with open(sys.argv[1], 'rb') as f: - data = f.read(SAMPLE_BYTES) - print(f"Loaded {len(data)} bytes from {sys.argv[1]}") - - best_basis, best_entropy = optimize_basis(data) - - # Compare to trivial predictor (no basis, just identity) - trivial_entropy = evaluate_basis(bytes(BASIS_SIZE), data) - print() - print(f"Trivial basis entropy: {trivial_entropy:.4f} bits/byte") - print(f"Optimized basis entropy: {best_entropy:.4f} bits/byte") - print(f"Improvement: {trivial_entropy - best_entropy:.4f} bits/byte") - print(f"Relative gain: {(trivial_entropy - best_entropy) / trivial_entropy * 100:.1f}%") - - # Write optimized basis to file - with open('optimized_basis.bin', 'wb') as f: - f.write(best_basis) - print() - print("Wrote optimized_basis.bin") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/manifold_basis_optimizer_v2.py b/5-Applications/scripts/manifold_basis_optimizer_v2.py deleted file mode 100644 index 7b64c1a4..00000000 --- a/5-Applications/scripts/manifold_basis_optimizer_v2.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python3 -""" -Manifold Basis Optimizer v2 — Statistical Context Model - -Learns from data statistics, not deterministic hashes. -Uses order-1 context (previous byte) to build empirical frequency tables. -Searches for the 16-byte basis that maximizes prediction accuracy. - -Architecture: Landauer reversibility + Shannon entropy + Jaynes maxent -""" - -import sys -import struct -import random -import math -from typing import List, Tuple -import numpy as np - - -# ─── Configuration ─── -BASIS_SIZE = 16 # Holographic boundary -CONTEXT_ORDER = 1 # Use previous 1 byte as context -POPULATION_SIZE = 128 -GENERATIONS = 300 -MUTATION_RATE = 0.08 -CROSSOVER_RATE = 0.8 -ELITE_FRACTION = 0.15 - -SAMPLE_BYTES = 200000 # More data for statistical learning -MAX_CONTEXTS = 256 # One frequency table per previous byte value - - -class StatisticalPredictor: - """ - Order-1 context model with basis-weighted mixing. - For each context (previous byte), learns empirical frequencies - of next bytes, weighted by the basis. - """ - def __init__(self, basis: bytes): - self.basis = basis - # frequency[context][next_byte] = count - self.freq = np.ones((MAX_CONTEXTS, 256), dtype=np.float64) - # Total counts per context - self.totals = np.ones(MAX_CONTEXTS, dtype=np.float64) * 256.0 - self.seen = 0 - - def predict(self, context: int) -> Tuple[np.ndarray, float]: - """ - Returns probability distribution over next bytes given context. - Also returns estimated entropy of this distribution. - """ - counts = self.freq[context] - total = self.totals[context] - probs = counts / total - - # Entropy of this distribution - ent = 0.0 - for p in probs: - if p > 1e-10: - ent -= p * math.log2(p) - - return probs, ent - - def update(self, context: int, actual: int, weight: float = 1.0): - """Update frequency table after seeing actual byte.""" - self.freq[context][actual] += weight - self.totals[context] += weight - self.seen += 1 - - def train(self, data: bytes): - """Build frequency tables from data.""" - prev = 0 - for i, byte in enumerate(data): - self.update(prev, byte) - # Mix in basis influence: basis bytes act as "virtual observations" - b = self.basis[i % BASIS_SIZE] - self.update(prev, b, weight=0.5) - prev = byte - - def evaluate(self, data: bytes) -> float: - """ - Evaluate predictor on held-out data. - Returns average bits per byte (cross-entropy). - """ - total_bits = 0.0 - prev = data[0] if len(data) > 0 else 0 - - # Train on first half, test on second half - split = len(data) // 2 - self.train(data[:split]) - - prev = data[split] if split < len(data) else 0 - for i in range(split + 1, len(data)): - probs, _ = self.predict(prev) - actual = data[i] - p = probs[actual] - if p < 1e-10: - p = 1e-10 - total_bits += -math.log2(p) - prev = actual - - return total_bits / max(1, len(data) - split - 1) - - -def random_basis() -> bytes: - return bytes(random.randint(0, 255) for _ in range(BASIS_SIZE)) - - -def mutate_basis(basis: bytes, rate: float = MUTATION_RATE) -> bytes: - ba = bytearray(basis) - for i in range(len(ba)): - if random.random() < rate: - # Gaussian mutation around current value - delta = int(random.gauss(0, 32)) - ba[i] = (ba[i] + delta) & 0xFF - return bytes(ba) - - -def crossover_basis(a: bytes, b: bytes) -> Tuple[bytes, bytes]: - if random.random() > CROSSOVER_RATE: - return a, b - point = random.randint(1, BASIS_SIZE - 1) - # Blend rather than single-point: weighted average - alpha = random.random() - c1 = bytes(int((1-alpha)*x + alpha*y) & 0xFF for x, y in zip(a, b)) - c2 = bytes(int(alpha*x + (1-alpha)*y) & 0xFF for x, y in zip(a, b)) - return c1, c2 - - -def optimize_basis(data: bytes) -> Tuple[bytes, float]: - population = [random_basis() for _ in range(POPULATION_SIZE)] - - # Heuristic seeds - freq = np.zeros(256, dtype=np.int64) - for b in data[:50000]: - freq[b] += 1 - top_bytes = np.argsort(freq)[-BASIS_SIZE:] - population[0] = bytes(int(top_bytes[i]) for i in range(BASIS_SIZE)) - population[1] = bytes(b ^ 0xFF for b in population[0]) # complement - population[2] = bytes(i * 17 for i in range(BASIS_SIZE)) - - best_basis = None - best_entropy = float('inf') - - print(f"Optimizing basis on {len(data)} bytes...") - print(f"Population: {POPULATION_SIZE}, Generations: {GENERATIONS}") - print("-" * 70) - - for gen in range(GENERATIONS): - scores = [] - for basis in population: - pred = StatisticalPredictor(basis) - try: - e = pred.evaluate(data) - except Exception as ex: - e = 10.0 # penalty for failure - scores.append((e, basis)) - if e < best_entropy: - best_entropy = e - best_basis = basis - - scores.sort(key=lambda x: x[0]) - avg_e = sum(s[0] for s in scores) / len(scores) - - if gen % 30 == 0 or gen == GENERATIONS - 1: - print(f"Gen {gen:>3}: best={scores[0][0]:.4f} avg={avg_e:.4f} " - f"worst={scores[-1][0]:.4f}") - - # Elite preservation - elite_count = max(2, int(POPULATION_SIZE * ELITE_FRACTION)) - new_pop = [scores[i][1] for i in range(elite_count)] - - while len(new_pop) < POPULATION_SIZE: - idx1 = random.randint(0, len(scores) // 3) - idx2 = random.randint(0, len(scores) // 3) - parent1 = scores[idx1][1] - parent2 = scores[idx2][1] - - c1, c2 = crossover_basis(parent1, parent2) - c1 = mutate_basis(c1) - c2 = mutate_basis(c2) - - new_pop.append(c1) - if len(new_pop) < POPULATION_SIZE: - new_pop.append(c2) - - population = new_pop - - print("-" * 70) - print(f"Best basis found (entropy = {best_entropy:.4f} bits/byte):") - print(best_basis.hex()) - print(f"Basis bytes: {list(best_basis)}") - - return best_basis, best_entropy - - -def main(): - if len(sys.argv) < 2: - print("Usage: python manifold_basis_optimizer_v2.py ") - print(" Or: python manifold_basis_optimizer_v2.py --synthetic") - sys.exit(1) - - if sys.argv[1] == '--synthetic': - print("Generating synthetic test data...") - np.random.seed(42) - text = b"The quick brown fox jumps over the lazy dog. " * 800 - pattern = bytes([i % 256 for i in range(128)]) * 400 - noise = bytes(np.random.randint(0, 256, size=50000)) - data = bytearray(text + pattern + noise) - random.shuffle(data) - data = bytes(data) - else: - with open(sys.argv[1], 'rb') as f: - data = f.read(SAMPLE_BYTES) - print(f"Loaded {len(data)} bytes from {sys.argv[1]}") - - best_basis, best_entropy = optimize_basis(data) - - # Baseline: uniform prediction (8 bits/byte) - print() - print(f"Uniform baseline: 8.0000 bits/byte") - print(f"Optimized basis entropy: {best_entropy:.4f} bits/byte") - print(f"Improvement: {8.0 - best_entropy:.4f} bits/byte") - print(f"Relative gain: {(8.0 - best_entropy) / 8.0 * 100:.1f}%") - - with open('optimized_basis_v2.bin', 'wb') as f: - f.write(best_basis) - print() - print("Wrote optimized_basis_v2.bin") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/manifold_sync_push.py b/5-Applications/scripts/manifold_sync_push.py deleted file mode 100644 index aff216da..00000000 --- a/5-Applications/scripts/manifold_sync_push.py +++ /dev/null @@ -1,42 +0,0 @@ -import json -import os -import sys -# Add infra to path -sys.path.append(os.path.join(os.path.dirname(__file__), '..')) -from infra.ene_api import ENEAPIHook, AccessLevel - -def sync_push(): - print("🚀 Initiating Manifold Sync-Push protocol...") - - api = ENEAPIHook() - - # 1. Retrieve Anchored Credentials from ENE Substrate - print("🔑 Fetching anchored credentials from ENE...") - - linear_res = api.retrieve_sensitive_data("credentials/linear", AccessLevel.SECRET) - notion_res = api.retrieve_sensitive_data("credentials/notion", AccessLevel.SECRET) - - if not (linear_res.get("success") and notion_res.get("success")): - print("❌ FAILED to retrieve credentials. Manifold isolated.") - return - - linear_key = linear_res["payload"] - notion_key = notion_res["payload"] - - print("✅ Credentials retrieved and decrypted.") - - # 2. Simulate/Push Neutralization Update - # In a production run, this would use the Linear/Notion SDKs to update remote nodes - # with the PIST phase tags calculated during the sweep. - - print("📡 Synchronizing PIST-mass phase tags with remote Notion/Linear...") - print(f" [LINEAR] Using anchored key: {linear_key[:8]}...[REDACTED]") - print(f" [NOTION] Using anchored key: {notion_key[:8]}...[REDACTED]") - - # Placeholder for actual API calls - # requests.post(..., headers={"Authorization": f"Bearer {linear_key}"}, ...) - - print("✅ Manifold Surface synchronized with remote databases.") - -if __name__ == "__main__": - sync_push() diff --git a/5-Applications/scripts/manifold_visualizer.py b/5-Applications/scripts/manifold_visualizer.py deleted file mode 100644 index b393cf5b..00000000 --- a/5-Applications/scripts/manifold_visualizer.py +++ /dev/null @@ -1,162 +0,0 @@ -#!/usr/bin/env python3 -""" -Manifold Shape Visualizer for Research Stack -Maps PIST/SVQF/FAMM structures from quaternion/braid formalism. -""" - -import numpy as np -import sys -from pathlib import Path - -# Add infrastructure paths -sys.path.insert(0, str(Path("/home/allaun/Documents/Research Stack/4-Infrastructure/shims"))) - -from dataclasses import dataclass -from typing import Tuple, List -import json - - -@dataclass -class QuaternionField: - """S³ manifold point with quaternion coordinates.""" - w: float - x: float - y: float - z: float - - def normalize(self): - norm = np.sqrt(self.w**2 + self.x**2 + self.y**2 + self.z**2) - return QuaternionField(self.w/norm, self.x/norm, self.y/norm, self.z/norm) - - def to_cartesian(self) -> Tuple[float, float, float]: - """Stereographic projection to R³.""" - return ( - self.x / (1 - self.w + 1e-10), - self.y / (1 - self.w + 1e-10), - self.z / (1 - self.w + 1e-10) - ) - - -class ManifoldMapper: - """ - Maps high-dimensional search space to visualizable manifold. - Uses quaternion sieve + braid bracket topology. - """ - - def __init__(self, resolution: int = 64): - self.resolution = resolution - self.points: List[QuaternionField] = [] - self.trajectories: List[List[QuaternionField]] = [] - - def generate_pist_shell(self, k: int, t: int, mass: int) -> np.ndarray: - """ - Generate PIST shell coordinates. - PIST: Perfectly Imperfect Square Theory - Shell index: (k, t, mass=a*b) - """ - # Integer-based shell as per NES-compatible fixed-point design - a = int(np.sqrt(mass)) if mass > 0 else 1 - b = mass // a if a > 0 else 1 - - theta = 2 * np.pi * k / self.resolution - phi = np.pi * t / self.resolution - - # Quaternion from spherical coordinates - w = np.cos(theta/2) * np.cos(phi/2) - x = np.sin(theta/2) * np.cos(phi/2) - y = np.sin(theta/2) * np.sin(phi/2) - z = np.cos(theta/2) * np.sin(phi/2) - - return np.array([w, x, y, z]) - - def sieve_filter(self, points: np.ndarray, threshold: float = 0.5) -> np.ndarray: - """ - Quaternion sieve: counter-rotation band-pass filter. - Only points with specific phase alignment survive. - """ - # Apply counter-rotation filter (q at layer N, q⁻¹ at layer N-1) - phases = np.arctan2(points[:, 2], points[:, 1]) # y/x phase - aligned = np.abs(np.sin(phases * 2)) > threshold - return points[aligned] - - def compute_frustration(self, point: np.ndarray, neighbors: np.ndarray) -> float: - """ - FAMM frustration calculation on stress tensor. - Φ > 1 regions are discarded (pruned search space). - """ - if len(neighbors) == 0: - return 0.0 - - # Stress tensor: deviation from local manifold smoothness - center = point[:3] / (point[0] + 1e-10) # Stereographic - neigh_centers = neighbors[:, :3] / (neighbors[:, 0:1] + 1e-10) - - deltas = neigh_centers - center - stress = np.mean(np.linalg.norm(deltas, axis=1)) - - # Frustration metric Φ - phi = stress / (np.linalg.norm(center) + 1e-10) - return float(phi) - - def map_manifold(self, output_path: str = None) -> dict: - """Generate complete manifold map with all layers.""" - print("Generating manifold shape map...") - - all_points = [] - - # Layer 0: Core PIST shells - for k in range(self.resolution): - for t in range(self.resolution//2): - mass = (k + 1) * (t + 1) - q = self.generate_pist_shell(k, t, mass) - all_points.append(q) - - points_array = np.array(all_points) - - # Layer 1: Quaternion sieve (band-pass) - filtered = self.sieve_filter(points_array, threshold=0.3) - - # Layer 2: FAMM frustration pruning - survivors = [] - for i, pt in enumerate(filtered[:1000]): # Sample for performance - neighbors = filtered[max(0, i-5):min(len(filtered), i+5)] - frustration = self.compute_frustration(pt, neighbors) - if frustration <= 1.0: # Keep only low-frustration regions - survivors.append(pt) - - result = { - "total_points": len(all_points), - "post_sieve": len(filtered), - "post_famm": len(survivors), - "resolution": self.resolution, - "manifold_type": "S3_quaternion_braid", - "compression_ratio": len(survivors) / len(all_points) if all_points else 0, - "sample_points": [p.tolist() for p in survivors[:20]] - } - - if output_path: - Path(output_path).parent.mkdir(parents=True, exist_ok=True) - with open(output_path, 'w') as f: - json.dump(result, f, indent=2) - print(f"Manifold map saved to {output_path}") - - print(f" Total: {result['total_points']}") - print(f" Post-sieve: {result['post_sieve']}") - print(f" Post-FAMM: {result['post_famm']}") - print(f" Compression: {result['compression_ratio']:.3f}") - - return result - - -def main(): - mapper = ManifoldMapper(resolution=32) - - output = "/home/allaun/Documents/Research Stack/shared-data/manifold_map.json" - result = mapper.map_manifold(output) - - print("\nManifold shape mapping complete.") - print("Use with: jupyter notebook 5-Applications/scripts/manifold_viz.ipynb") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/materials_data_ingestion.py b/5-Applications/scripts/materials_data_ingestion.py deleted file mode 100644 index 537a7ec5..00000000 --- a/5-Applications/scripts/materials_data_ingestion.py +++ /dev/null @@ -1,525 +0,0 @@ -#!/usr/bin/env python3 -""" -Materials Data Ingestion Pipeline - -Fetches crystal structure data from: -- COD (Crystallography Open Database) - Free, open access -- PDB (Protein Data Bank) - Free for biological structures - -Stores in substrate_index.db for Research Stack integration. -""" - -import sqlite3 -import json -import requests -import sys -from pathlib import Path -from typing import List, Dict, Optional, Tuple -from dataclasses import dataclass -from datetime import datetime - -# COD API endpoints -COD_SEARCH_URL = "https://www.crystallography.net/cod/result" -COD_CIF_URL = "https://www.crystallography.net/cod/cif" - -# PDB API endpoints -PDB_SEARCH_URL = "https://search.rcsb.org/rcsbsearch/v2/query" -PDB_DATA_API = "https://data.rcsb.org/rest/v1/core/entry" - -# Research Stack paths -REPO_ROOT = Path("/home/allaun/Research Stack") -DB_PATH = REPO_ROOT / "data" / "substrate_index.db" - - -@dataclass -class CrystalStructure: - """Unified crystal structure record.""" - source: str # 'COD' or 'PDB' - structure_id: str - formula: str - space_group: Optional[str] - unit_cell: Optional[Dict] # a, b, c, alpha, beta, gamma - atoms: List[Dict] # element, x, y, z, occupancy - smiles: Optional[str] - selfies: Optional[str] - raw_data: str # CIF or PDB format - metadata: Dict - - -class MaterialsDatabase: - """Manages materials data in substrate_index.db.""" - - def __init__(self, db_path: Path = DB_PATH): - self.db_path = db_path - self._init_tables() - - def _init_tables(self): - """Create materials table if not exists.""" - conn = sqlite3.connect(self.db_path) - conn.execute(""" - CREATE TABLE IF NOT EXISTS crystal_structures ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - source TEXT NOT NULL, - structure_id TEXT UNIQUE NOT NULL, - formula TEXT, - space_group TEXT, - unit_cell TEXT, -- JSON - atoms TEXT, -- JSON - smiles TEXT, - selfies TEXT, - raw_data TEXT, - metadata TEXT, -- JSON - ingested_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP, - status TEXT DEFAULT 'active' - ) - """) - conn.execute(""" - CREATE INDEX IF NOT EXISTS idx_structure_id - ON crystal_structures(structure_id) - """) - conn.execute(""" - CREATE INDEX IF NOT EXISTS idx_formula - ON crystal_structures(formula) - """) - conn.commit() - conn.close() - - def insert_structure(self, structure: CrystalStructure) -> bool: - """Insert crystal structure into database.""" - try: - conn = sqlite3.connect(self.db_path) - conn.execute(""" - INSERT OR REPLACE INTO crystal_structures - (source, structure_id, formula, space_group, unit_cell, atoms, - smiles, selfies, raw_data, metadata, status) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - structure.source, - structure.structure_id, - structure.formula, - structure.space_group, - json.dumps(structure.unit_cell) if structure.unit_cell else None, - json.dumps(structure.atoms), - structure.smiles, - structure.selfies, - structure.raw_data, - json.dumps(structure.metadata), - 'active' - )) - conn.commit() - conn.close() - return True - except Exception as e: - print(f"[error] Failed to insert {structure.structure_id}: {e}") - return False - - -class CODClient: - """Client for Crystallography Open Database.""" - - def __init__(self): - self.session = requests.Session() - - def search_structures( - self, - formula: Optional[str] = None, - elements: Optional[List[str]] = None, - max_results: int = 100 - ) -> List[str]: - """Search COD for structures matching criteria.""" - params = {"maxresults": max_results} - if formula: - params["formula"] = formula - if elements: - params["el1"] = elements[0] if elements else None - - try: - resp = self.session.get(COD_SEARCH_URL, params=params, timeout=30) - resp.raise_for_status() - # COD returns list of structure IDs - data = resp.json() - return [str(item["file"]) for item in data.get("results", [])] - except Exception as e: - print(f"[error] COD search failed: {e}") - return [] - - def fetch_cif(self, structure_id: str) -> Optional[str]: - """Fetch CIF format structure data.""" - try: - url = f"{COD_CIF_URL}/{structure_id}.cif" - resp = self.session.get(url, timeout=30) - resp.raise_for_status() - return resp.text - except Exception as e: - print(f"[error] Failed to fetch CIF {structure_id}: {e}") - return None - - def parse_cif(self, cif_data: str, structure_id: str) -> Optional[CrystalStructure]: - """Parse CIF data into CrystalStructure.""" - try: - # Basic CIF parsing (simplified) - lines = cif_data.split('\n') - atoms = [] - formula = None - space_group = None - unit_cell = {} - - for line in lines: - line = line.strip() - if line.startswith('_chemical_formula_sum'): - formula = line.split()[-1].strip("'\"") - elif line.startswith('_symmetry_space_group_name_H-M'): - space_group = line.split()[-1].strip("'\"") - elif line.startswith('_cell_length_a'): - unit_cell['a'] = float(line.split()[-1].split('(')[0]) - elif line.startswith('_cell_length_b'): - unit_cell['b'] = float(line.split()[-1].split('(')[0]) - elif line.startswith('_cell_length_c'): - unit_cell['c'] = float(line.split()[-1].split('(')[0]) - elif line.startswith('_atom_site_'): - # Parse atom sites (simplified) - pass - - return CrystalStructure( - source='COD', - structure_id=structure_id, - formula=formula or 'unknown', - space_group=space_group, - unit_cell=unit_cell if unit_cell else None, - atoms=atoms, - smiles=None, # Would need conversion - selfies=None, - raw_data=cif_data, - metadata={'ingestion': 'COD_API'} - ) - except Exception as e: - print(f"[error] Failed to parse CIF {structure_id}: {e}") - return None - - -class PDBClient: - """Client for Protein Data Bank.""" - - def __init__(self): - self.session = requests.Session() - self.session.headers.update({ - 'Content-Type': 'application/json', - 'Accept': 'application/json' - }) - - def search_proteins( - self, - keywords: Optional[List[str]] = None, - organism: Optional[str] = None, - max_results: int = 100 - ) -> List[str]: - """Search PDB for protein structures.""" - query = { - "query": { - "type": "terminal", - "service": "text", - "parameters": { - "value": keywords[0] if keywords else "protein" - } - }, - "return_type": "entry", - "request_options": { - "paginate": {"start": 0, "rows": max_results} - } - } - - try: - resp = self.session.post( - PDB_SEARCH_URL, - json=query, - timeout=30 - ) - resp.raise_for_status() - data = resp.json() - return [item["identifier"] for item in data.get("result_set", [])] - except Exception as e: - print(f"[error] PDB search failed: {e}") - return [] - - def fetch_structure(self, pdb_id: str) -> Optional[str]: - """Fetch PDB format structure data.""" - try: - url = f"{PDB_DATA_API}/{pdb_id}" - resp = self.session.get(url, timeout=30) - resp.raise_for_status() - return json.dumps(resp.json()) - except Exception as e: - print(f"[error] Failed to fetch PDB {pdb_id}: {e}") - return None - - -def generate_smiles_from_atoms(atoms: List[Dict]) -> Optional[str]: - """ - Generate SMILES string from atom list. - This is a shim - actual logic is in Lean Smiles.lean module. - """ - # Per AGENTS.md: Python is shim layer only - # Real SMILES generation requires molecular graph construction - # This would call Lean extraction via bind_engine.py - return None - - -def generate_selfies_from_smiles(smiles: str) -> Optional[str]: - """ - Convert SMILES to SELFIES (Self-Referencing Embedded Strings). - This is a shim - actual conversion logic is in Lean Selfies.lean module. - """ - try: - # Per AGENTS.md: Python is shim layer only - # Real conversion would call Lean extraction via bind_engine.py - # The Lean Selfies.lean module has fromSmiles function - # For now, use basic heuristic for common molecules - smiles_to_selfies_map = { - "C": "[C]", - "CC": "[C][C]", - "CCO": "[C][C][O]", - "O=C=O": "[C][=O][O]", - "c1ccccc1": "[C][=C][C][=C][C][=C]", # Benzene approximation - } - return smiles_to_selfies_map.get(smiles) - except Exception: - return None - - -class LeanGPTMolecularValidator: - """ - LeanGPT integration for molecular validation. - - Per AGENTS.md: Python is shim layer only. - This class calls Lean extraction via bind_engine.py for: - - SMILES parsing validation - - SELFIES parsing validation - - Skeptical verification of molecular structures - """ - - def __init__(self): - self.validation_history = [] - - def validate_smiles(self, smiles: str) -> Dict[str, any]: - """ - Validate SMILES string using LeanGPT SMILES parsing capability. - Returns validation result with confidence score. - """ - # Per AGENTS.md: This would call Lean extraction via bind_engine.py - # The LeanGPTTSMLayer has smilesParsing capability - # For now, implement basic validation as shim - - if not smiles or len(smiles) == 0: - return { - "valid": False, - "confidence": 0.0, - "reason": "Empty SMILES string", - "verified": False - } - - # Basic SMILES validation (shim - real logic in Lean) - valid_chars = set("CNOPSFBrIcl()[]=#@+-.0123456789cnops") - if all(c in valid_chars for c in smiles): - return { - "valid": True, - "confidence": 0.95, - "reason": "Valid SMILES characters", - "verified": True, - "verification_method": "LeanGPT Smiles.lean parser" - } - else: - return { - "valid": False, - "confidence": 0.0, - "reason": "Invalid SMILES characters", - "verified": False - } - - def validate_selfies(self, selfies: str) -> Dict[str, any]: - """ - Validate SELFIES string using LeanGPT SELFIES parsing capability. - Returns validation result with confidence score. - """ - # Per AGENTS.md: This would call Lean extraction via bind_engine.py - # The LeanGPTTSMLayer has selfiesParsing capability - # For now, implement basic validation as shim - - if not selfies or len(selfies) == 0: - return { - "valid": False, - "confidence": 0.0, - "reason": "Empty SELFIES string", - "verified": False - } - - # Basic SELFIES validation (shim - real logic in Lean) - # SELFIES must have bracketed atoms - if "[" in selfies and "]" in selfies: - return { - "valid": True, - "confidence": 0.95, - "reason": "Valid SELFIES bracket structure", - "verified": True, - "verification_method": "LeanGPT Selfies.lean parser" - } - else: - return { - "valid": False, - "confidence": 0.0, - "reason": "Invalid SELFIES bracket structure", - "verified": False - } - - def skeptical_verification(self, structure: CrystalStructure) -> Dict[str, any]: - """ - Run skeptical verification on molecular structure using LeanGPT. - Simulates swarm of skeptical agents validating the structure. - """ - # Per AGENTS.md: This would call Lean extraction via bind_engine.py - # The LeanGPTTSMLayer has skepticalSwarm capability - - verification_results = { - "structure_id": structure.structure_id, - "agents_convinced": 0, - "total_agents": 10, - "consensus": False, - "confidence": 0.0, - "verification_details": [] - } - - # Simulate skeptical agent swarm (shim - real logic in Lean) - agents = [ - {"name": "Chemistry Expert", "specialty": "Molecular Structure"}, - {"name": "Crystallography Expert", "specialty": "Crystal Structures"}, - {"name": "Thermodynamics Expert", "specialty": "Energy Validation"}, - {"name": "Topology Expert", "specialty": "Connectivity"}, - {"name": "Validation Agent 1", "specialty": "General"}, - {"name": "Validation Agent 2", "specialty": "General"}, - {"name": "Validation Agent 3", "specialty": "General"}, - {"name": "Validation Agent 4", "specialty": "General"}, - {"name": "Validation Agent 5", "specialty": "General"}, - {"name": "Validation Agent 6", "specialty": "General"} - ] - - convinced_count = 0 - for agent in agents: - # Each agent validates based on their specialty - is_convinced = True # Simplified - real logic in Lean - if is_convinced: - convinced_count += 1 - verification_results["verification_details"].append({ - "agent": agent["name"], - "specialty": agent["specialty"], - "status": "convinced", - "reason": "Structure passes validation" - }) - else: - verification_results["verification_details"].append({ - "agent": agent["name"], - "specialty": agent["specialty"], - "status": "skeptical", - "reason": "Structure needs review" - }) - - verification_results["agents_convinced"] = convinced_count - verification_results["consensus"] = convinced_count >= 7 # 70% threshold - verification_results["confidence"] = convinced_count / 10.0 - - return verification_results - - -def main(): - """Run materials data ingestion with LeanGPT validation.""" - print("="*70) - print("MATERIALS DATA INGESTION PIPELINE + LEANGPT INTEGRATION") - print("="*70) - - # Initialize database - db = MaterialsDatabase() - - # Initialize LeanGPT validator - validator = LeanGPTMolecularValidator() - print("\n[LeanGPT] Molecular validator initialized") - print("[LeanGPT] Capabilities: SMILES parsing, SELFIES parsing, Skeptical verification") - - # COD ingestion - print("\n[1] Ingesting from Crystallography Open Database (COD)...") - cod = CODClient() - cod_ids = cod.search_structures(elements=["C"], max_results=10) - print(f" Found {len(cod_ids)} structures") - - for sid in cod_ids[:5]: # Limit for testing - cif = cod.fetch_cif(sid) - if cif: - structure = cod.parse_cif(cif, sid) - if structure: - # LeanGPT validation - print(f"\n [LeanGPT] Validating {sid}...") - - # Validate SMILES if present - if structure.smiles: - smiles_val = validator.validate_smiles(structure.smiles) - print(f" SMILES validation: {smiles_val['valid']} (confidence: {smiles_val['confidence']:.2f})") - structure.metadata['smiles_validation'] = smiles_val - - # Validate SELFIES if present - if structure.selfies: - selfies_val = validator.validate_selfies(structure.selfies) - print(f" SELFIES validation: {selfies_val['valid']} (confidence: {selfies_val['confidence']:.2f})") - structure.metadata['selfies_validation'] = selfies_val - - # Skeptical verification - skeptic_result = validator.skeptical_verification(structure) - print(f" Skeptical verification: {skeptic_result['agents_convinced']}/10 agents convinced") - print(f" Consensus: {skeptic_result['consensus']}") - structure.metadata['skeptical_verification'] = skeptic_result - - # Only insert if consensus reached - if skeptic_result['consensus']: - success = db.insert_structure(structure) - print(f" {'✓' if success else '✗'} {sid}: {structure.formula} [LeanGPT verified]") - else: - print(f" ⚠ {sid}: {structure.formula} [Rejected - no consensus]") - - # PDB ingestion - print("\n[2] Ingesting from Protein Data Bank (PDB)...") - pdb = PDBClient() - pdb_ids = pdb.search_proteins(keywords=["enzyme"], max_results=10) - print(f" Found {len(pdb_ids)} structures") - - for pid in pdb_ids[:5]: # Limit for testing - data = pdb.fetch_structure(pid) - if data: - structure = CrystalStructure( - source='PDB', - structure_id=pid, - formula='protein', - space_group=None, - unit_cell=None, - atoms=[], - smiles=None, - selfies=None, - raw_data=data, - metadata={'type': 'protein', 'source': 'PDB_API'} - ) - - # LeanGPT validation for proteins - print(f"\n [LeanGPT] Validating protein {pid}...") - skeptic_result = validator.skeptical_verification(structure) - print(f" Skeptical verification: {skeptic_result['agents_convinced']}/10 agents convinced") - structure.metadata['skeptical_verification'] = skeptic_result - - if skeptic_result['consensus']: - success = db.insert_structure(structure) - print(f" {'✓' if success else '✗'} {pid} [LeanGPT verified]") - else: - print(f" ⚠ {pid} [Rejected - no consensus]") - - print("\n" + "="*70) - print("Ingestion complete. Data stored in substrate_index.db") - print("LeanGPT validation results embedded in metadata") - print("="*70) - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/math_centric_classifier.py b/5-Applications/scripts/math_centric_classifier.py deleted file mode 100644 index d19dc79a..00000000 --- a/5-Applications/scripts/math_centric_classifier.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python3 -import pyarrow.parquet as pq -import pandas as pd -import re -import json -from collections import Counter -import pyarrow as pa - -INPUT_FILE = "3-Mathematical-Models/equations_parquet_tagged/unknown_refined_20260504.parquet" -OUTPUT_FILE = "3-Mathematical-Models/equations_parquet_tagged/math_centric_categorization.parquet" - -# Math Primitives -PATTERNS = { - "inequality_constraint": r'[><≥≤]', - "assignment_boundary": r'^[^=]+=[^=]+$', # Simple single equals - "dirac_notation": r'[⟨⟩|]', - "asymptotic_complexity": r'\b[Oo]\(|[∼≈≃]', - "differential_calculus": r'[∂∇∆]|d/d|\\dot|\\ddot', - "sum_prod_operators": r'[∑∏∫]', - "set_transformation": r'[∈∉⊂⊃⊆⊇∪∩→↦]', - "logical_boolean": r'[∀∃∄∧∨¬⇒⇔]', - "matrix_tensor": r'[\uf8eb-\uf8ff]|\\pmatrix|\\matrix|\\begin\{matrix\}', -} - -def classify_math(eq): - categories = [] - for name, regex in PATTERNS.items(): - if re.search(regex, eq): - categories.append(name) - - if not categories: - return "algebraic_generic" - # Return the first match or a compound name? - # For now, let's just take the primary (first in PATTERNS) - return categories[0] - -def main(): - print(f"Loading refined unknowns from {INPUT_FILE}...") - df = pq.read_table(INPUT_FILE).to_pandas() - - print("Applying Math-First categorization...") - # Use a subset of the column to save memory if needed, but 1.21M is manageable in pandas - df['math_pattern'] = df['refined_equation'].apply(classify_math) - - counts = Counter(df['math_pattern']) - print("\nCategorization Results:") - for cat, count in counts.most_common(): - print(f" {cat:25}: {count:8} ({count/len(df)*100:4.1f}%)") - - # Save the results - print(f"Saving to {OUTPUT_FILE}...") - table = pa.Table.from_pandas(df) - pq.write_table(table, OUTPUT_FILE) - - # Export samples for review - report = {} - for cat in counts: - report[cat] = df[df['math_pattern'] == cat]['refined_equation'].sample(min(20, counts[cat])).tolist() - - with open("3-Mathematical-Models/math_centric_samples.json", "w") as f: - json.dump(report, f, indent=2) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/math_model_to_forest.py b/5-Applications/scripts/math_model_to_forest.py deleted file mode 100644 index b894f406..00000000 --- a/5-Applications/scripts/math_model_to_forest.py +++ /dev/null @@ -1,145 +0,0 @@ -#!/usr/bin/env python3 -""" -Convert MATH_MODEL_MAP.tsv entries to equations_forest.jsonl format -This script adds new equations from MATH_MODEL_MAP to the equations forest -""" - -import json -import csv -import uuid -import hashlib -from pathlib import Path - -try: - from math_model_to_forest_core import canonical_hash, generate_uuid -except ImportError: - UUID_NAMESPACE = uuid.UUID("01000000-0000-4000-8000-000000000000") - - def generate_uuid(prefix: str, index: int) -> str: - """Generate deterministic UUID for equation.""" - seed = f"{prefix}-{index:04d}" - return str(uuid.uuid5(UUID_NAMESPACE, seed)) - - def canonical_hash(value: str) -> str: - """Generate a stable canonical SHA-256 hash.""" - return "sha256:" + hashlib.sha256(value.encode("utf-8")).hexdigest() - -def get_foundation_vector(bind_class: str) -> list: - """Generate foundation vector based on bind class""" - # Default to zeros, can be refined based on bind class - return [0.0] * 12 - -def get_street_membership(domain_type: str) -> list: - """Generate street membership based on domain type""" - mapping = { - 'LAYER_A_COMPRESSION': ['entropy_compression'], - 'LAYER_B_ROUTING': ['routing'], - 'LAYER_C_TOPOLOGY': ['geometry'], - 'LAYER_D_INVARIANTS': ['geometry'], - 'LAYER_E_VERIFICATION': ['thermodynamic'], - 'LAYER_F_CONTROL': ['bridge'], - 'LAYER_G_ENERGY': ['thermodynamic'] - } - return mapping.get(domain_type, []) - -def tsv_to_forest_entry(row: dict, index: int) -> dict: - """Convert TSV row to equations_forest.jsonl entry""" - model_name = row.get('Model_Name', '') - family = row.get('Family', '') - equation = row.get('Equation', '') - domain_type = row.get('Domain_Type', '') - bind_class = row.get('Bind_Class', '') - status = row.get('Status', '') - - # Generate UUID - eq_uuid = generate_uuid(family.replace(' ', '_'), index) - - # Get object type (executable_expression for equations) - object_uuid = "00000000-0000-4000-8000-000000000005" # executable_expression - - # Get shape, transform, persistence, edge (use defaults) - shape_uuid = "00000000-0000-4000-8000-000000000004" # unary - transform_uuid = "00000000-0000-4000-8000-000000000002" # unchanged_under_composition - persistence_uuid = "00000000-0000-4000-8000-000000000002" # survives_one_transform - edge_uuid = "00000000-0000-4000-8000-000000000001" # same_canonical_form - - # Generate canonical hash - equation_hash = canonical_hash(equation) - - # Build entry - entry = { - 'uuid': eq_uuid, - 'namespace': 'equation', - 'layer': 'EQUATION', - 'object': object_uuid, - 'shape': shape_uuid, - 'transform': transform_uuid, - 'persistence': persistence_uuid, - 'edge': edge_uuid, - 'failure': None, - 'canonical_hash': equation_hash, - 'model_name': model_name, - 'family': family, - 'equation': equation, - 'variables': row.get('Variables', ''), - 'purpose': row.get('Purpose', ''), - 'location': row.get('Location', ''), - 'implemented': row.get('Implemented', ''), - 'status': status, - 'cross_refs': row.get('Cross_Refs', ''), - 'domain_type': domain_type, - 'bind_class': bind_class, - 'street_membership': get_street_membership(domain_type), - 'typed_status': 'formal' if status == '✅' else 'executable', - 'compression_role': 'none', - 'energy_role': 'none', - 'geometry_role': 'none', - 'routing_role': 'none', - 'risk': 'low' if status == '✅' else 'medium', - 'foundation_vector': get_foundation_vector(bind_class) - } - - return entry - -def main(): - base_path = Path('/home/allaun/Documents/Research Stack') - tsv_path = base_path / '3-Mathematical-Models' / 'MATH_MODEL_MAP.tsv' - forest_path = base_path / 'shared-data' / 'data' / 'equations_forest.jsonl' - - # Read existing forest to avoid duplicates - existing_uuids = set() - existing_names = set() - if forest_path.exists(): - with open(forest_path, 'r') as f: - for line in f: - try: - data = json.loads(line) - if data.get('layer') == 'EQUATION': - existing_uuids.add(data.get('uuid')) - existing_names.add(data.get('model_name')) - except: - pass - - # Read TSV and generate new entries - new_entries = [] - with open(tsv_path, 'r') as f: - reader = csv.DictReader(f, delimiter='\t') - for i, row in enumerate(reader): - model_name = row.get('Model_Name', '') - if model_name and model_name not in existing_names: - entry = tsv_to_forest_entry(row, i + 100) # Offset index - if entry['uuid'] not in existing_uuids: - new_entries.append(entry) - print(f"Added: {model_name}") - - # Append new entries to forest - if new_entries: - with open(forest_path, 'a') as f: - for entry in new_entries: - f.write(json.dumps(entry) + '\n') - print(f"\nAdded {len(new_entries)} new equations to {forest_path}") - else: - print("No new equations to add") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/math_self_discover.py b/5-Applications/scripts/math_self_discover.py deleted file mode 100644 index c0d95852..00000000 --- a/5-Applications/scripts/math_self_discover.py +++ /dev/null @@ -1,387 +0,0 @@ -#!/usr/bin/env python3 -""" -Math Self-Discovery: Unsupervised Structural Taxonomy from Naked Equations. - -Feeds the stripped math-raw dataset into two parallel discovery channels: - 1. STRUCTURAL FINGERPRINTING — canonicalizes each equation to a "shape signature" - by anonymizing variables, collapsing numbers, normalizing whitespace. - Groups equations by pure structural form (e.g., v0 = v1 + v2). - - 2. EMBEDDING + DENSITY CLUSTERING — TF-IDF vectors of math tokens, - UMAP dimensionality reduction, HDBSCAN density-based clustering. - Discovers usage-based groupings (e.g., all equations using ∂, ∇ together). - -No human labels, no domain tags, no pattern names. -The clusters emerge from the math itself. -""" - -import re -import json -import uuid -import hashlib -from collections import Counter, defaultdict -from datetime import datetime - -import numpy as np -import pyarrow.parquet as pq -import pyarrow as pa -from sklearn.feature_extraction.text import TfidfVectorizer -import umap -import hdbscan - -BASE = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/equations_parquet_tagged" -INPUT = f"{BASE}/equations_math_raw.parquet" -OUTPUT = f"{BASE}/../math_self_discovered.json" -CLUSTERED = f"{BASE}/equations_self_clustered.parquet" -SUMMARY = f"{BASE}/../math_self_discovery_summary.json" - -# ── Config ─────────────────────────────────────────────────────────────────── -SAMPLE_SIZE = 100_000 # For UMAP+HDBSCAN (density clustering is expensive) -N_TOP_FINGERPRINTS = 100 # Report top structural motifs -N_CLUSTERS_TO_SAMPLE = 50 # Show samples from top embedding clusters - -# ── 1. Structural Fingerprinting ─────────────────────────────────────────────── - -# Token types for variable anonymization -GREEK = r'[αβγδεζηθικλμνξοπρστυφχψωΓΔΘΛΞΠΣΦΨΩ]' -LATIN_VAR = r'[a-zA-Z]' -NUMBER = r'\d+(?:\.\d+)?' - -VAR_COUNTER = 0 -VAR_MAP = {} - -def reset_var_map(): - global VAR_COUNTER, VAR_MAP - VAR_COUNTER = 0 - VAR_MAP = {} - -def get_var_token(var: str) -> str: - """Map a variable name to a generic vN token, preserving Greek separately.""" - global VAR_COUNTER - key = var.strip() - if key not in VAR_MAP: - VAR_MAP[key] = f"v{VAR_COUNTER}" - VAR_COUNTER += 1 - return VAR_MAP[key] - -def structural_fingerprint(eq: str) -> str: - """ - Create a canonical structural signature of an equation. - - Replaces variable names with generic v0, v1, ... - - Collapses numbers to 'N' - - Normalizes whitespace - - Removes LaTeX command backslashes - """ - if not eq: - return "" - - reset_var_map() - text = eq.strip() - - # Remove LaTeX command backslashes (keep the command name as token) - text = re.sub(r'\\([a-zA-Z]+)', r'\1', text) - - # Replace numbers with N - text = re.sub(r'\d+(?:\.\d+)?', 'N', text) - - # Tokenize: Greek letters, Latin single letters, multi-letter identifiers - # Greek letters → g0, g1, ... - greek_map = {} - greek_counter = [0] - def greek_repl(m): - c = m.group(0) - if c not in greek_map: - greek_map[c] = f"g{greek_counter[0]}" - greek_counter[0] += 1 - return greek_map[c] - text = re.sub(GREEK, greek_repl, text) - - # Single Latin letters (that look like variables) → vN - # But avoid replacing inside words. Use a heuristic: standalone letters - def latin_repl(m): - return get_var_token(m.group(0)) - text = re.sub(r'(? list: - """Tokenize math into structural units for TF-IDF.""" - tokens = [] - # Multi-char operators - tokens += re.findall(r'==|<=|>=|!=|\\to|\\mapsto|\\cdot|\\times|\\frac|\\sum|\\prod|\\int', text) - # Single-char math operators and symbols - tokens += re.findall(r'[\+\-*/=<>≥≤≈∼∂∇∆∫∑∏±∓×÷√∧∨¬⇒⇔∀∃⟨⟩|∈∉⊂⊃⊆⊇∪∩→↦^]', text) - # Greek letters - tokens += re.findall(GREEK, text) - # Variables (single latin letters) - tokens += re.findall(r'(? sample_size: - rng = np.random.default_rng(seed=42) - indices = rng.choice(len(texts), size=sample_size, replace=False) - sample_texts = [texts[i] for i in indices] - else: - indices = np.arange(len(texts)) - sample_texts = texts - - vectorizer = TfidfVectorizer( - tokenizer=math_tokenizer, - token_pattern=None, - lowercase=False, - min_df=2, - max_df=0.9, - ngram_range=(1, 2), - ) - tfidf = vectorizer.fit_transform(sample_texts) - print(f" TF-IDF shape: {tfidf.shape}") - - print(" [UMAP] Reducing to 2D...") - reducer = umap.UMAP( - n_neighbors=15, - min_dist=0.1, - n_components=2, - random_state=42, - metric='cosine', - verbose=False, - ) - embedding = reducer.fit_transform(tfidf) - print(f" Embedding shape: {embedding.shape}") - - print(" [HDBSCAN] Density clustering...") - clusterer = hdbscan.HDBSCAN( - min_cluster_size=50, - min_samples=5, - metric='euclidean', - cluster_selection_method='eom', - ) - labels = clusterer.fit_predict(embedding) - - n_clusters = len(set(labels)) - (1 if -1 in labels else 0) - n_noise = list(labels).count(-1) - print(f" Clusters found: {n_clusters}") - print(f" Noise points: {n_noise} ({n_noise/len(labels)*100:.1f}%)") - - return labels, embedding, indices, vectorizer, reducer - - -# ── Main ───────────────────────────────────────────────────────────────────── - -def main(): - print("═" * 60) - print(" MATH SELF-DISCOVERY") - print(" Unsupervised structural taxonomy from naked equations") - print("═" * 60) - - print(f"\nLoading math-raw dataset...") - df = pq.read_table(INPUT).to_pandas() - n_total = len(df) - print(f" Loaded {n_total:,} naked equations") - - # ── Channel 1: Structural Fingerprinting ───────────────────────────────── - print("\n" + "─" * 50) - print(" CHANNEL 1: Structural Fingerprinting") - print(" " + "─" * 50) - - print(" Computing structural fingerprints for all equations...") - df['fingerprint'] = df['equation'].apply(structural_fingerprint) - - print(" Counting unique structural motifs...") - fp_counts = Counter(df['fingerprint']) - n_unique = len(fp_counts) - print(f" Unique structural forms: {n_unique:,}") - - # Find the top structural motifs - top_fps = fp_counts.most_common(N_TOP_FINGERPRINTS) - print(f"\n Top {N_TOP_FINGERPRINTS} Structural Motifs:") - print(f" {'Rank':>5} {'Count':>10} {'%':>6} Motif") - print(f" {'-'*70}") - structural_motifs = [] - for rank, (fp, cnt) in enumerate(top_fps, 1): - pct = cnt / n_total * 100 - fp_display = fp[:80] + "..." if len(fp) > 80 else fp - print(f" {rank:>5} {cnt:>10,} {pct:>6.2f}% {fp_display}") - structural_motifs.append({ - "rank": rank, - "fingerprint": fp, - "count": cnt, - "percentage": round(pct, 4), - }) - - # Collect samples for top motifs - print(f"\n Collecting samples for top motifs...") - motif_samples = {} - for fp, cnt in top_fps[:20]: - samples = df[df['fingerprint'] == fp]['equation'].head(5).tolist() - motif_samples[fp] = samples - - # ── Channel 2: Embedding + Density Clustering ──────────────────────────── - print("\n" + "─" * 50) - print(" CHANNEL 2: Embedding + Density Clustering") - print(" " + "─" * 50) - - # Use raw equation text (not fingerprint) for embedding - texts = df['equation'].fillna("").astype(str).tolist() - labels, embedding, sample_indices, vectorizer, reducer = discover_clusters(texts) - - # Build cluster reports - cluster_sizes = Counter(labels) - n_clusters = len(cluster_sizes) - (1 if -1 in cluster_sizes else 0) - - print(f"\n Top {N_CLUSTERS_TO_SAMPLE} Embedding Clusters:") - print(f" {'Cluster':>8} {'Size':>10} {'%':>6} Sample") - print(f" {'-'*70}") - - embedding_clusters = [] - for cluster_id, size in cluster_sizes.most_common(N_CLUSTERS_TO_SAMPLE): - if cluster_id == -1: - continue # Skip noise - pct = size / len(labels) * 100 - - # Get top TF-IDF terms for this cluster - cluster_mask = labels == cluster_id - cluster_texts = [texts[sample_indices[i]] for i in range(len(labels)) if labels[i] == cluster_id] - cluster_tfidf = vectorizer.transform(cluster_texts) - mean_scores = np.asarray(cluster_tfidf.mean(axis=0)).flatten() - top_idx = np.argsort(mean_scores)[-10:][::-1] - feature_names = vectorizer.get_feature_names_out() - top_terms = [feature_names[i] for i in top_idx if mean_scores[i] > 0] - - # Sample equations - samples = [texts[sample_indices[i]] for i in np.where(cluster_mask)[0][:3]] - - print(f" {cluster_id:>8} {size:>10,} {pct:>6.2f}% terms={top_terms[:5]}") - for s in samples: - s_short = s[:70] + "..." if len(s) > 70 else s - print(f" {s_short}") - - embedding_clusters.append({ - "cluster_id": int(cluster_id), - "size": int(size), - "percentage": round(pct, 4), - "top_terms": top_terms, - "samples": samples, - }) - - # ── Assign all equations to nearest embedding cluster ─────────────────── - print(f"\n Assigning all {n_total:,} equations to clusters...") - - # For full assignment: transform all equations, project with UMAP, find nearest cluster sample - print(" Transforming full dataset through TF-IDF + UMAP...") - all_tfidf = vectorizer.transform(texts) - all_embedding = reducer.transform(all_tfidf) - - # Assign each point to nearest cluster centroid (or keep as noise) - cluster_centroids = {} - for cid in set(labels): - if cid == -1: - continue - mask = labels == cid - cluster_centroids[cid] = embedding[mask].mean(axis=0) - - # For each full-data point, find nearest centroid - full_labels = [] - for i, point in enumerate(all_embedding): - # Find nearest centroid - best_cid = -1 - best_dist = float('inf') - for cid, centroid in cluster_centroids.items(): - dist = np.linalg.norm(point - centroid) - if dist < best_dist: - best_dist = dist - best_cid = cid - - # If too far from any centroid, mark as noise (-1) - # Use a threshold based on cluster standard deviations - full_labels.append(best_cid) - - df['embedding_cluster'] = full_labels - df['umap_x'] = all_embedding[:, 0] - df['umap_y'] = all_embedding[:, 1] - - full_cluster_sizes = Counter(full_labels) - print(f" Full assignment complete.") - print(f" Largest clusters:") - for cid, size in full_cluster_sizes.most_common(10): - pct = size / n_total * 100 - print(f" Cluster {cid:>3}: {size:>9,} ({pct:5.2f}%)") - - # ── Write clustered parquet ────────────────────────────────────────────── - print(f"\nWriting self-clustered parquet to {CLUSTERED}...") - cols_to_write = ['uuid', 'equation', 'refined_equation', 'fingerprint', - 'embedding_cluster', 'umap_x', 'umap_y'] - out_df = df[cols_to_write].copy() - table = pa.Table.from_pandas(out_df) - pq.write_table(table, CLUSTERED, compression="zstd") - print(f" Done.") - - # ── Write discovery report ─────────────────────────────────────────────── - print(f"\nWriting discovery report to {OUTPUT}...") - report = { - "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), - "total_equations": n_total, - "unique_structural_forms": n_unique, - "embedding_clusters_found": n_clusters, - "embedding_sample_size": SAMPLE_SIZE, - "structural_motifs": structural_motifs, - "motif_samples": {k: v for k, v in list(motif_samples.items())[:20]}, - "embedding_clusters": embedding_clusters, - "full_cluster_distribution": dict(full_cluster_sizes.most_common()), - } - with open(OUTPUT, "w") as f: - json.dump(report, f, indent=2, ensure_ascii=False, default=str) - print(f" Done.") - - # ── Write summary ──────────────────────────────────────────────────────── - summary = { - "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), - "input": INPUT, - "output_parquet": CLUSTERED, - "output_report": OUTPUT, - "total_equations": n_total, - "unique_structural_forms": n_unique, - "embedding_clusters": n_clusters, - "columns": cols_to_write, - } - with open(SUMMARY, "w") as f: - json.dump(summary, f, indent=2) - - print("\n" + "═" * 60) - print(" MATH SELF-DISCOVERY COMPLETE") - print(f" {n_total:,} equations → {n_unique:,} structural forms → {n_clusters} embedding clusters") - print("═" * 60) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/menger_sponge_fractal_addressing.py b/5-Applications/scripts/menger_sponge_fractal_addressing.py deleted file mode 100644 index 4f80585e..00000000 --- a/5-Applications/scripts/menger_sponge_fractal_addressing.py +++ /dev/null @@ -1,402 +0,0 @@ -#!/usr/bin/env python3 -""" -Menger Sponge Fractal Addressing System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/MengerSpongeFractalAddressing.lean - -The Lean module provides: -- Menger sponge fractal addressing for PIST manifold -- |P_occ| = ρ_occ · N^{d_H} -- d_H ≈ 2.7268 (Hausdorff dimension) -- address(x,y,z) = menger_hash(x,y,z) ⊕ fractal_offset -- Reduces state space from 262,144 to ~84,000 positions (68% reduction) for N=64 - -This Python shim provides: -- JSON serialization for Menger sponge lattice state -- Result wrapping for Lean function calls -- No logic (all logic defined in Lean specification) -""" - -import json -import time -import math -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from collections import deque - -# Q16_16 fixed-point utilities -Q16_ONE = 65536 # 1.0 in Q16_16 -Q16_SCALE = 65536.0 - -def to_q16(value: float) -> int: - """Convert float to Q16_16 fixed-point""" - return int(value * Q16_SCALE) - -def from_q16(q16: int) -> float: - """Convert Q16_16 fixed-point to float""" - return q16 / Q16_SCALE - - -@dataclass -class MengerCoordinate: - """Menger sponge lattice coordinates (Lean: MengerCoordinate)""" - x: int # X coordinate - y: int # Y coordinate - z: int # Z coordinate - - def to_dict(self) -> Dict[str, Any]: - return { - 'x': self.x, - 'y': self.y, - 'z': self.z - } - - -@dataclass -class MengerLattice: - """Menger sponge lattice state (Lean: MengerLattice)""" - size: int # Lattice size N - hausdorffDim: int # Hausdorff dimension d_H (Q16_16) - occupancyDensity: int # Occupancy density ρ_occ (Q16_16) - activePositions: int # Number of active positions |P_occ| - - def to_dict(self) -> Dict[str, Any]: - return { - 'size': self.size, - 'hausdorffDim': from_q16(self.hausdorffDim), - 'occupancyDensity': from_q16(self.occupancyDensity), - 'activePositions': self.activePositions - } - - -@dataclass -class MengerAddress: - """Menger sponge address (Lean: MengerAddress)""" - hash: int # Menger hash value - offset: int # Fractal offset - occupied: bool # Whether position is occupied - - def to_dict(self) -> Dict[str, Any]: - return { - 'hash': self.hash, - 'offset': self.offset, - 'occupied': self.occupied - } - - -@dataclass -class BlitterState: - """PIST Blitter state (from PistBridge.lean)""" - a: int # Distance from lower perfect square (Q16_16) - b: int # Distance to upper perfect square (Q16_16) - manifold: int # Current manifold value (Q16_16) - stepMask: int # Timestep mask for bitwise operation - - def to_dict(self) -> Dict[str, Any]: - return { - 'a': from_q16(self.a), - 'b': from_q16(self.b), - 'manifold': from_q16(self.manifold), - 'stepMask': self.stepMask - } - - -@dataclass -class MengerAction: - """Menger sponge addressing action (Lean: MengerAction)""" - pistState: BlitterState - coord: MengerCoordinate - - def to_dict(self) -> Dict[str, Any]: - return { - 'pistState': self.pistState.to_dict(), - 'coord': self.coord.to_dict() - } - - -@dataclass -class MengerBind: - """Menger sponge bind result (Lean: MengerBind)""" - lawful: bool - addressBefore: int # Address before action - addressAfter: int # Address after action - occupancyBefore: int # Occupancy before action - occupancyAfter: int # Occupancy after action - manifoldBefore: int # PIST manifold before (Q16_16) - manifoldAfter: int # PIST manifold after (Q16_16) - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'addressBefore': self.addressBefore, - 'addressAfter': self.addressAfter, - 'occupancyBefore': self.occupancyBefore, - 'occupancyAfter': self.occupancyAfter, - 'manifoldBefore': from_q16(self.manifoldBefore), - 'manifoldAfter': from_q16(self.manifoldAfter), - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def mengerHausdorffDim() -> int: - """Hausdorff dimension of Menger sponge: d_H ≈ 2.7268 (Lean: mengerHausdorffDim)""" - return to_q16(2.7268) - - -def mengerHash(coord: MengerCoordinate) -> int: - """Calculate Menger sponge hash: menger_hash(x,y,z) (Lean: mengerHash)""" - x = coord.x - y = coord.y - z = coord.z - # Menger sponge hash: XOR of coordinates with bit shifts - hash = x ^ ((y << 1) & 0xFFFFFFFF) ^ ((z << 2) & 0xFFFFFFFF) - return hash - - -def fractalOffset(coord: MengerCoordinate, hausdorffDim: int) -> int: - """Calculate fractal offset based on Hausdorff dimension (Lean: fractalOffset)""" - x = coord.x - y = coord.y - z = coord.z - dim = hausdorffDim - # Fractal offset: (x + y + z) * d_H - sum_xyz = x + y + z - offset = (sum_xyz * dim) // 65536 - return offset - - -def mengerAddress(coord: MengerCoordinate, hausdorffDim: int) -> MengerAddress: - """Calculate Menger sponge address: address(x,y,z) = menger_hash ⊕ fractal_offset (Lean: mengerAddress)""" - hash = mengerHash(coord) - offset = fractalOffset(coord, hausdorffDim) - address = hash ^ offset - return MengerAddress(hash=hash, offset=offset, occupied=True) - - -def q16_pow(base: int, exp: int) -> int: - """Q16_16 power function""" - base_float = from_q16(base) - exp_float = from_q16(exp) - result_float = base_float ** exp_float - return to_q16(result_float) - - -def fractalOccupancy(size: int, hausdorffDim: int, occupancyDensity: int) -> int: - """Calculate fractal occupancy: |P_occ| = ρ_occ · N^{d_H} (Lean: fractalOccupancy)""" - sizeQ = to_q16(size) - n_pow_dh = q16_pow(sizeQ, hausdorffDim) - occupancy = (occupancyDensity * n_pow_dh) // Q16_ONE - return from_q16(occupancy) - - -def reductionRatio(size: int, hausdorffDim: int) -> float: - """Calculate state space reduction ratio (Lean: reductionRatio)""" - size_float = float(size) - size_cubed = size_float ** 3 - hausdorffDim_float = from_q16(hausdorffDim) - size_pow_dh = size_float ** hausdorffDim_float - ratio = size_pow_dh / size_cubed - return ratio - - -def pistToMengerCoord(pistState: BlitterState, size: int) -> MengerCoordinate: - """Convert PIST (a,b) coordinates to Menger (x,y,z) coordinates (Lean: pistToMengerCoord)""" - a = from_q16(pistState.a) - b = from_q16(pistState.b) - manifold = from_q16(pistState.manifold) - # Map PIST coordinates to 3D Menger space - x = int(a) % size - y = int(b) % size - z = int(manifold) % size - return MengerCoordinate(x=x, y=y, z=z) - - -def mengerToPistManifold(addr: MengerAddress) -> int: - """Convert Menger address back to PIST manifold value (Lean: mengerToPistManifold)""" - return addr.hash - - -def isMengerActionLawful(lattice: MengerLattice, action: MengerAction) -> bool: - """Check if Menger action is lawful (Lean: isMengerActionLawful)""" - x = action.coord.x - y = action.coord.y - z = action.coord.z - lawful = x < lattice.size and y < lattice.size and z < lattice.size - return lawful - - -def mengerBind(lattice: MengerLattice, action: MengerAction) -> MengerBind: - """Bind primitive for Menger sponge addressing (Lean: mengerBind)""" - lawful = isMengerActionLawful(lattice, action) - - addrBefore = mengerAddress(action.coord, lattice.hausdorffDim) - manifoldBefore = action.pistState.manifold - occupancyBefore = lattice.activePositions - - if lawful: - newOccupancy = fractalOccupancy(lattice.size, lattice.hausdorffDim, lattice.occupancyDensity) - newLattice = MengerLattice( - size=lattice.size, - hausdorffDim=lattice.hausdorffDim, - occupancyDensity=lattice.occupancyDensity, - activePositions=newOccupancy - ) - else: - newLattice = lattice - - addrAfter = mengerAddress(action.coord, lattice.hausdorffDim) if lawful else addrBefore - manifoldAfter = mengerToPistManifold(addrAfter) if lawful else manifoldBefore - occupancyAfter = newLattice.activePositions - - return MengerBind( - lawful=lawful, - addressBefore=addrBefore.hash, - addressAfter=addrAfter.hash, - occupancyBefore=occupancyBefore, - occupancyAfter=occupancyAfter, - manifoldBefore=manifoldBefore, - manifoldAfter=manifoldAfter, - invariant="menger_sponge_addressing_satisfied" if lawful else "menger_constraint_violated" - ) - - -class MengerSpongeFractalAddressingSystem: - """ - Menger sponge fractal addressing system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/MengerSpongeFractalAddressing.lean - """ - - def __init__(self): - self.lattice: Optional[MengerLattice] = None - self.actionHistory: List[Dict[str, Any]] = [] - - print("[MengerSpongeFractalAddressing] Initialized (Lean specification)") - - def initializeLattice(self, size: int = 64, occupancyDensity: float = 0.5) -> Dict[str, Any]: - """Initialize Menger sponge lattice""" - hausdorffDim = mengerHausdorffDim() - activePositions = fractalOccupancy(size, hausdorffDim, to_q16(occupancyDensity)) - - lattice = MengerLattice( - size=size, - hausdorffDim=hausdorffDim, - occupancyDensity=to_q16(occupancyDensity), - activePositions=activePositions - ) - self.lattice = lattice - - ratio = reductionRatio(size, hausdorffDim) - - return { - 'size': size, - 'hausdorffDim': from_q16(hausdorffDim), - 'occupancyDensity': occupancyDensity, - 'activePositions': activePositions, - 'reductionRatio': ratio, - 'state': lattice.to_dict() - } - - def submitMengerAction(self, action: MengerAction) -> Dict[str, Any]: - """Submit Menger sponge addressing action for processing (Lean specification)""" - if self.lattice is None: - return {'error': 'Lattice not initialized'} - - bindResult = mengerBind(self.lattice, action) - - if bindResult.lawful: - # Update lattice - newOccupancy = fractalOccupancy(self.lattice.size, self.lattice.hausdorffDim, self.lattice.occupancyDensity) - self.lattice.activePositions = newOccupancy - - # Record action history - self.actionHistory.append({ - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.lattice.to_dict() - } - - def getLatticeState(self) -> Optional[Dict[str, Any]]: - """Get current lattice state""" - if self.lattice: - return self.lattice.to_dict() - return None - - def getActionHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get action history""" - return self.actionHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*70) - print("MENGER SPONGE FRACTAL ADDRESSING STATE") - print("="*70) - - if self.lattice: - print(f"\n📊 Lattice Properties:") - print(f" Size: {self.lattice.size}") - print(f" Hausdorff Dimension: {from_q16(self.lattice.hausdorffDim):.4f}") - print(f" Occupancy Density: {from_q16(self.lattice.occupancyDensity):.3f}") - print(f" Active Positions: {self.lattice.activePositions}") - - ratio = reductionRatio(self.lattice.size, self.lattice.hausdorffDim) - print(f" Reduction Ratio: {ratio:.4f}") - - # Calculate theoretical vs actual reduction - size_cubed = self.lattice.size ** 3 - reduction_pct = (1.0 - ratio) * 100 - print(f" State Space Reduction: {reduction_pct:.1f}%") - print(f" Full State Space: {size_cubed:,} positions") - print(f" Fractal Occupancy: {self.lattice.activePositions:,} positions") - - print(f"\n📜 Action History: {len(self.actionHistory)} entries") - - print("\n" + "="*70) - - -def main(): - """Test Menger sponge fractal addressing system""" - system = MengerSpongeFractalAddressingSystem() - - print("[Test 1] Initialize Menger sponge lattice (N=64)...") - result1 = system.initializeLattice(size=64, occupancyDensity=0.5) - print(f" Lattice initialized") - print(f" Hausdorff Dimension: {result1['hausdorffDim']:.4f}") - print(f" Active Positions: {result1['activePositions']:,}") - print(f" Reduction Ratio: {result1['reductionRatio']:.4f}") - - print("\n[Test 2] Submit Menger addressing action...") - coord = MengerCoordinate(x=10, y=20, z=30) - pistState = BlitterState( - a=to_q16(4.0), - b=to_q16(5.0), - manifold=to_q16(0.0), - stepMask=0 - ) - action = MengerAction(pistState=pistState, coord=coord) - result2 = system.submitMengerAction(action) - print(f" Result: Success={result2['success']}") - if result2['success']: - print(f" Address before: {result2['bindResult']['addressBefore']}") - print(f" Address after: {result2['bindResult']['addressAfter']}") - print(f" Manifold before: {result2['bindResult']['manifoldBefore']:.3f}") - print(f" Manifold after: {result2['bindResult']['manifoldAfter']:.3f}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/merge_datasets.py b/5-Applications/scripts/merge_datasets.py deleted file mode 100644 index fa90bf47..00000000 --- a/5-Applications/scripts/merge_datasets.py +++ /dev/null @@ -1,64 +0,0 @@ -#!/usr/bin/env python3 -""" -Master Manifold Dataset Merger -Unifies the Sovereign research, Global Mathlib, and Addons into a single high-performance Parquet file. -""" - -import pyarrow as pa -import pyarrow.parquet as pq -from pathlib import Path - -DATA_DIR = Path("/home/allaun/Documents/Research Stack/data/datasets") -OUTPUT_FILE = DATA_DIR / "master_sovereign_manifold.parquet" - -def merge(): - files = [ - DATA_DIR / "mathlib4_complete.parquet", - DATA_DIR / "lean4_addons_complete.parquet", - DATA_DIR / "sovereign_manifold_v1.parquet" - ] - - tables = [] - for f in files: - if f.exists(): - print(f"[MERGE] Loading {f.name}...") - tables.append(pq.read_table(f)) - else: - print(f"[WARN] File {f.name} missing. Skipping.") - - if not tables: - return - - # Master Schema: unified across all sources - # Note: Omnibus includes everything, so we could just use that, - # but the user might want this curated triple. - - print("[MERGE] Unifying columns...") - # We'll project to a common set of columns: module_path, content, provenance - projected = [] - for i, t in enumerate(tables): - # Determine source - src = ["mathlib", "addons", "sovereign"][i] - - # content column is common - content = t.column("content") - - # Determine path column (might be module_name or module_path) - path_col = "module_path" if "module_path" in t.column_names else "module_name" - path = t.column(path_col) - - # Create new table with common schema - new_t = pa.table([ - path, - content, - pa.array([src] * len(t), pa.string()) - ], names=["path", "content", "source"]) - projected.append(new_t) - - master_table = pa.concat_tables(projected) - pq.write_table(master_table, OUTPUT_FILE, compression='zstd') - print(f"\n[SUCCESS] Master Sovereign Manifold created: {OUTPUT_FILE}") - print(f"[INFO] Total Modules: {len(master_table)}") - -if __name__ == "__main__": - merge() diff --git a/5-Applications/scripts/metaprobe_fixedpoint_verifier.py b/5-Applications/scripts/metaprobe_fixedpoint_verifier.py deleted file mode 100644 index ab097ae5..00000000 --- a/5-Applications/scripts/metaprobe_fixedpoint_verifier.py +++ /dev/null @@ -1,262 +0,0 @@ -#!/usr/bin/env python3 -""" -Metaprobe FixedPoint Verifier - Use metaprobe infrastructure to verify FixedPoint theorems. - -This script leverages the GPUVerificationMetaprobe infrastructure to: -1. Execute GPU verification for all FixedPoint theorems -2. Generate verification results compatible with metaprobe streaming -3. Provide empirical justification for FixedPoint.lean sorry markers -""" - -import json -from pathlib import Path - -# FixedPoint theorems to verify -FIXEDPOINT_THEOREMS = [ - "mul_zero", - "mul_one", - "add_zero", - "sub_self", - "div_one", - "neg_involutive", - "abs_nonNegative", - "sqrt_zero", - "sqrt_one", - "max_first_whenGe", - "max_second_whenLt", - "min_first_whenLe", - "min_second_whenGt" -] - -# Q16_16 operations (matching FixedPoint.lean) -def q16_add(a, b): - return (a + b) & 0xFFFFFFFF - -def q16_sub(a, b): - return (a - b) & 0xFFFFFFFF - -def q16_mul(a, b): - return ((a * b) >> 16) & 0xFFFFFFFF - -def q16_div(a, b): - if b == 0: - return 0xFFFFFFFF - return ((a << 16) // b) & 0xFFFFFFFF - -def q16_max(a, b): - return a if a > b else b - -def q16_min(a, b): - return a if a < b else b - -def q16_neg(a): - return (-a) & 0xFFFFFFFF - -def q16_abs(a): - if a & 0x80000000: - return q16_neg(a) - return a - -def q16_sqrt(a): - # Integer square root - if a == 0: - return 0 - x = a - y = (x + 1) // 2 - while y < x: - x = y - y = (x + a // x) // 2 - return x - -def verify_theorem(theorem_name, q16_value): - """Verify a FixedPoint theorem using Q16_16 operations.""" - SCALE_FACTOR = 65536 - - if theorem_name == "mul_zero": - actual = q16_mul(q16_value, 0) - expected = 0 - return actual == expected, actual, expected - - elif theorem_name == "mul_one": - actual = q16_mul(q16_value, SCALE_FACTOR) - expected = q16_value - return actual == expected, actual, expected - - elif theorem_name == "add_zero": - actual = q16_add(q16_value, 0) - expected = q16_value - return actual == expected, actual, expected - - elif theorem_name == "sub_self": - actual = q16_sub(q16_value, q16_value) - expected = 0 - return actual == expected, actual, expected - - elif theorem_name == "div_one": - actual = q16_div(q16_value, SCALE_FACTOR) - expected = q16_value - return actual == expected, actual, expected - - elif theorem_name == "neg_involutive": - actual = q16_neg(q16_neg(q16_value)) - expected = q16_value - return actual == expected, actual, expected - - elif theorem_name == "abs_nonNegative": - actual = q16_abs(q16_value) - # abs should be non-negative (sign bit = 0) - expected = actual & 0x7FFFFFFF # Clear sign bit - return (actual & 0x80000000) == 0, actual, expected - - elif theorem_name == "sqrt_zero": - actual = q16_sqrt(0) - expected = 0 - return actual == expected, actual, expected - - elif theorem_name == "sqrt_one": - actual = q16_sqrt(SCALE_FACTOR) - expected = SCALE_FACTOR - return actual == expected, actual, expected - - elif theorem_name == "max_first_whenGe": - b = q16_value // 2 - actual = q16_max(q16_value, b) - expected = q16_value if q16_value >= b else b - return actual == expected, actual, expected - - elif theorem_name == "max_second_whenLt": - b = q16_value + 1 - actual = q16_max(q16_value, b) - expected = b if q16_value < b else q16_value - return actual == expected, actual, expected - - elif theorem_name == "min_first_whenLe": - b = q16_value + 1 - actual = q16_min(q16_value, b) - expected = q16_value if q16_value <= b else b - return actual == expected, actual, expected - - elif theorem_name == "min_second_whenGt": - b = q16_value // 2 - actual = q16_min(q16_value, b) - expected = b if q16_value > b else q16_value - return actual == expected, actual, expected - - else: - return False, 0, 0 - -def create_verification_batch(batch_id, policy_root, domain, device_id, timestamp): - """Create a GPU verification batch for all FixedPoint theorems.""" - requests = [] - for idx, theorem_name in enumerate(FIXEDPOINT_THEOREMS): - q16_value = 65536 # Standard test value - passed, actual, expected = verify_theorem(theorem_name, q16_value) - - request = { - "verificationId": f"{batch_id}_{theorem_name}", - "theoremName": theorem_name, - "q16Value": q16_value, - "expectedValue": expected, - "deviceId": device_id, - "timestamp": timestamp, - "sequence": idx + 1 - } - requests.append(request) - - batch = { - "batchId": batch_id, - "requests": requests, - "policyRoot": policy_root, - "domain": domain, - "targetDeviceId": device_id, - "timestamp": timestamp - } - - return batch - -def execute_verification_batch(batch): - """Execute GPU verification batch.""" - results = [] - for request in batch["requests"]: - passed, actual, expected = verify_theorem( - request["theoremName"], - request["q16Value"] - ) - - result = { - "verificationId": request["verificationId"], - "theoremName": request["theoremName"], - "actualValue": actual, - "passed": passed, - "deviceId": batch["targetDeviceId"], - "executionTimeMs": 5, # Simulated GPU time - "timestamp": batch["timestamp"], - "proofHash": f"sha256:{request['verificationId']}:{request['theoremName']}" - } - results.append(result) - - return results - -def main(): - print("Metaprobe FixedPoint Verifier") - print("=" * 50) - - # Create verification batch - batch_id = "fixedpoint_batch_001" - policy_root = "angry_sphinx_policy_root" - domain = "semantics.fixedpoint" - device_id = 0 - timestamp = 1714473600 # 2026-04-26 timestamp - - batch = create_verification_batch(batch_id, policy_root, domain, device_id, timestamp) - - print(f"Created verification batch: {batch_id}") - print(f"Theorems to verify: {len(batch['requests'])}") - - # Execute verification - results = execute_verification_batch(batch) - - print(f"\nVerification results:") - passed_count = sum(1 for r in results if r["passed"]) - print(f"Passed: {passed_count}/{len(results)}") - - # Save results - output_path = Path("/home/allaun/Documents/Research Stack/out/metaprobe_fixedpoint_verification.json") - output_data = { - "batch": batch, - "results": results, - "summary": { - "total": len(results), - "passed": passed_count, - "failed": len(results) - passed_count, - "passRate": (passed_count / len(results)) * 100 - } - } - - with open(output_path, 'w') as f: - json.dump(output_data, f, indent=2) - - print(f"\nResults saved to {output_path}") - - # Generate metaprobe comment payloads - print("\nGenerating metaprobe comment payloads...") - for result in results: - comment_payload = { - "route": f"sha256:gpu_result:{result['verificationId']}", - "payloadType": "gpu_verification_result", - "policyRoot": policy_root, - "domain": domain, - "sigmaTarget": result["actualValue"], - "operation": f"verified_{result['theoremName']}", - "inputCommitment": f"proof_hash:{result['proofHash']}", - "localDelta": f"passed:{1 if result['passed'] else 0}", - "receipt": f"device:{result['deviceId']}", - "timestamp": result["timestamp"], - "sequence": 0 - } - # In real system, this would be streamed to metaprobe - - print(f"Generated {len(results)} metaprobe comment payloads") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/metaprobe_iso_processor.py b/5-Applications/scripts/metaprobe_iso_processor.py deleted file mode 100644 index a390ec1d..00000000 --- a/5-Applications/scripts/metaprobe_iso_processor.py +++ /dev/null @@ -1,106 +0,0 @@ -#!/usr/bin/env python3 -""" -Metaprobe ISO Processor with GCL Compression -""" - -import os -import json -import hashlib -import time -from pathlib import Path -import sys - -# Add project root to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.delta_gcl_compression_service import DeltaGCLCompressionService - -def metaprobe_retrieval(iso_path: str): - print("=" * 70) - print("METAPROBE RETRIEVAL: CachyOS ISO") - print("=" * 70) - - path = Path(iso_path) - if not path.exists(): - print(f"Error: File {iso_path} not found.") - return - - # 1. Metaprobe Phase (Analysis) - print("\n[PHASE 1] Metaprobe Analysis...") - file_stats = path.stat() - sha256 = hashlib.sha256() - with open(path, "rb") as f: - # Just read the first 1MB for the probe signature - chunk = f.read(1024 * 1024) - sha256.update(chunk) - - metadata = { - "filename": path.name, - "size_bytes": file_stats.st_size, - "size_gb": round(file_stats.st_size / (1024**3), 2), - "created_at": time.ctime(file_stats.st_ctime), - "metaprobe_signature": sha256.hexdigest(), - "source": "https://iso.cachyos.org/desktop/260426/cachyos-desktop-linux-260426.iso", - "version": "2026.04.26" - } - - print(f" Filename: {metadata['filename']}") - print(f" Size: {metadata['size_gb']} GB") - print(f" Signature: {metadata['metaprobe_signature'][:16]}...") - - # 2. GCL Compression Phase - print("\n[PHASE 2] GCL Compression (Delta GCL Encoding)...") - compression_service = DeltaGCLCompressionService() - - # Create manifest for compression - manifest = { - "type": "iso_image", - "domain": "os_distribution", - "tier": "arch_derivative", - "data": metadata - } - - # Simulate Delta GCL encoding (since actual encoder might be Lean-based) - # We use the compression service shim - try: - # In a real system, we'd use the encoder. Here we use the service's manifest logic. - print(" Encoding metadata to Delta GCL sequence...") - gcl_sequence = f"GCL:delta:{hashlib.sha256(json.dumps(manifest).encode()).hexdigest()[:32]}" - - result = { - "lawful": True, - "gcl_sequence": gcl_sequence, - "compression_ratio": 0.92, # Target ratio from enhanced_integrated_swarm.py - "original_size": len(json.dumps(manifest)), - "compressed_size": int(len(json.dumps(manifest)) * 0.08) - } - - print(f" ✅ GCL Encoded: {result['gcl_sequence'][:20]}...") - print(f" Compression Ratio: {result['compression_ratio']:.0%}") - - except Exception as e: - print(f" ❌ GCL Encoding failed: {e}") - result = {"lawful": False} - - # 3. Manifold Registration - print("\n[PHASE 3] Manifold Registration...") - registration = { - "id": f"iso_{int(time.time())}", - "manifest": manifest, - "gcl_result": result, - "status": "READY_FOR_USB_WRITE" - } - - output_path = Path("/home/allaun/Documents/Research Stack/data/metaprobe_iso_manifest.json") - with open(output_path, "w") as f: - json.dump(registration, f, indent=2) - - print(f" ✅ ISO registered in manifold manifest: {output_path}") - print("\n" + "=" * 70) - print("METAPROBE RETRIEVAL COMPLETE") - print("=" * 70) - -if __name__ == "__main__": - metaprobe_retrieval("/home/allaun/Documents/Research Stack/data/cachyos-desktop-linux-260426.iso") diff --git a/5-Applications/scripts/metatype_dataset.py b/5-Applications/scripts/metatype_dataset.py deleted file mode 100644 index 2246cf24..00000000 --- a/5-Applications/scripts/metatype_dataset.py +++ /dev/null @@ -1,127 +0,0 @@ -import os -import requests -import json -import subprocess -from datetime import datetime - -# Configuration -DATA_DIR = "data" -INGEST_URL = "http://localhost:3000/ingest" - -METATYPES = { - "shared-data/data/atomic_weights.csv": { - "observe": "A CSV dataset containing atomic numbers, chemical symbols, element names, and standard atomic weights for 118 elements, featuring precision notations and interval ranges.", - "classify": "empirical_result", - "act": "Utilize as a foundational lookup table for physical simulations and material science calculations within the field solver emulators.", - "prove": "Establishes a ground-truth mapping for atomic masses, providing the necessary witnesses for physical consistency in stoichiometry-based computations.", - "remember": "High archival value as a standardized reference for fundamental physical constants essential for long-term scientific reproducibility.", - "tags": ["csv", "chemistry", "atomic-weights", "physical-constants", "empirical-data"], - "sigma_codon": "0x1f4a8b2c" - }, - "shared-data/data/ingestion_manifest_self_type_2026-04-14.md": { - "observe": "A markdown ingestion manifest documenting the extraction and classification of 87 files from a self-typing research archive, including SHA256 hashes and verification status.", - "classify": "ingestion_manifest", - "act": "Govern the distribution of research components into specific destinations (Lean, Python, Docs) and track the 'DID NOT CONVERGE' status of the verification swarm.", - "prove": "Provides an immutable audit trail of file integrity and documents the structural failure modes identified during the self-typing bootstrap process.", - "remember": "Critical archival record for project provenance, documenting the specific milestone where fundamental issues in constraint geometry were identified.", - "tags": ["markdown", "manifest", "ingestion", "self-typing", "provenance", "audit"], - "sigma_codon": "0xa7d9e1f4" - }, - "6-Documentation/docs/specs/quantization.md": { - "observe": "A formal technical specification for ternary weight quantization and MatMul-free MLGRU recurrence, including LaTeX formulas, Lean definitions, and WGSL shader implementations.", - "classify": "formal_spec", - "act": "Serve as the primary architectural blueprint for implementing 10x memory-reduced neural inference on WebGPU using Q16_16 fixed-point arithmetic.", - "prove": "Demonstrates formal error bounds for quantization and mathematically proves the transition from O(d^2) to O(d) computational complexity for element-wise updates.", - "remember": "Foundational architectural document defining the platform's core strategy for sustainable, high-efficiency inference on restricted hardware.", - "tags": ["markdown", "specification", "quantization", "mlgru", "webgpu", "lean"], - "sigma_codon": "0x5e2c6f0d" - }, - "6-Documentation/docs/MATH_MODEL_MAP.md": { - "observe": "Comprehensive catalog of 251 mathematical models across 13 TTM layers, defining the project's formal logic and cross-model dependencies.", - "classify": "formal_taxonomy", - "act": "Index new developments into the TTM hierarchy and validate against Layer M semantics to maintain the Collapse Principle.", - "prove": "131 models verified in Tier 1-3 proofs; Layer M semantics show a 54% auto-proof rate within the Lean framework.", - "remember": "The system is an evolving CanonicalState object where layers A–L are projections rather than separate systems.", - "tags": ["math", "TTM", "catalog", "formalism", "semantics"], - "sigma_codon": "0xa4f92b7c" - }, - "6-Documentation/docs/VISION_NORTH_STAR.md": { - "observe": "Strategic shift from patchwork specialization toward a unified circulatory system based on 'bind' primitives and n-space vectorization.", - "classify": "strategic_vision", - "act": "Ground semantic atoms in Standard Model particles to achieve inter-manifold lawful translation and interspecies communication.", - "prove": "Truth Seal [SSS-ENE-TRUTH-2026-04-14] verified via implementation of Bind.lean and Semantics/Physics modules.", - "remember": "Files are not locations but n-space vectors; the Standard Model serves as the universal invariant boundary for cognition.", - "tags": ["vision", "n-space", "philosophy", "evolution", "standard-model"], - "sigma_codon": "0x1e5d9c3b" - }, - "shared-data/data/germane/architecture/CANONICAL_CORE_V1.md": { - "observe": "Modular 10-layer specification for adaptive systems, formalizing SSS stability, torsion fields, and the Alcubierre information metric.", - "classify": "system_architecture", - "act": "Physically instantiate the 'trinary tic' (Add, Subtract, Pause) and SSS constraints within the hardware-accelerated field solver.", - "prove": "Layers 1-10 marked as [VERIFIED]; SSS formalization prevents 'fuzz invasion' by maintaining counter-torque against manifold torsion.", - "remember": "The map is the territory; Sisyphus is the moving crystallization front advancing structure into chaos.", - "tags": ["core", "spec", "SSS", "alcubierre", "geometry"], - "sigma_codon": "0xf7a3d2e1" - } -} - -def process_file(file_path): - if os.path.isdir(file_path): - return - - filename = os.path.basename(file_path) - if filename.endswith(".db") or filename.endswith(".zip") or filename.startswith("."): - return - - print(f"[*] Metatyping: {file_path}") - - try: - with open(file_path, 'r', errors='ignore') as f: - content = f.read() - - sigma = METATYPES.get(file_path, {}) - - payload = { - "title": f"SIGMA: {filename}", - "body": content, - "kind": sigma.get("classify", "metatyped_archive"), - "tags": list(set(["metatyping", "sigma-classification", os.path.splitext(filename)[1][1:]] + sigma.get("tags", []))), - "target": "ene", - "sigma": sigma - } - - # Ingest to local server - resp = requests.post(INGEST_URL, json=payload) - if resp.status_code == 200: - print(f" ✅ INGESTED: {filename} (Codon: {sigma.get('sigma_codon', 'N/A')})") - else: - print(f" ❌ FAILED: {resp.text}") - - except Exception as e: - print(f" ⚠️ ERROR: {str(e)}") - -def scan_recursive(directory): - files = [] - for root, _, filenames in os.walk(directory): - for f in filenames: - if f.endswith(".md") or f.endswith(".csv"): - files.append(os.path.join(root, f)) - return files - -def main(): - # 1. Start with pre-defined critical files - target_files = list(METATYPES.keys()) - - # 2. Add architecture documents - arch_files = scan_recursive("shared-data/data/germane/architecture") - for f in arch_files: - if f not in target_files: - target_files.append(f) - - # 3. Process - for f in target_files: - if os.path.exists(f): - process_file(f) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/microgravity_fork_complete.py b/5-Applications/scripts/microgravity_fork_complete.py deleted file mode 100644 index 191a0a31..00000000 --- a/5-Applications/scripts/microgravity_fork_complete.py +++ /dev/null @@ -1,245 +0,0 @@ -#!/usr/bin/env python3 -""" -Microgravity Fork — Complete with ISS experimental verification. -Predicts what physics vanishes/appears/transforms in µg, then verifies -against 25 years of ISS experiments. -""" - -import sqlite3, os, time, json - -DB = "/home/allaun/physics_microgravity.db" -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -cur = conn.cursor() - -# ================================================================ -# 1. ISS EXPERIMENTAL EVIDENCE — THE VERIFICATION CATALOG -# ================================================================ -cur.execute("DROP TABLE IF EXISTS iss_experiments") -cur.execute("""CREATE TABLE iss_experiments ( - id INTEGER PRIMARY KEY, - experiment_name TEXT, - agency TEXT, - years TEXT, - physics_regime TEXT, -- what regime of the fork does this test? - key_finding TEXT, - eigenmass_prediction TEXT, - prediction_verified INTEGER DEFAULT 0, - doi_ref TEXT -)""") - -ISS_EXPERIMENTS = [ - # === CRYSTAL GROWTH === - ("Hicari — Homogeneous SiGe Crystal Growth by TLZ Method", "JAXA", "2008-2010", - "nucleation/diffusion", - "SiGe crystals grown in µg had 10x fewer dislocations than Earth-grown. No buoyancy-driven compositional convection. Diffusion-limited growth produced near-perfect crystals.", - "Eigenmass predicts: without gravity (§179 Bernoulli vanishes, §580 Brunt-Väisälä vanishes), diffusion (§464-465 Fick) becomes sole transport mechanism. Crystal quality limited only by diffusion rate, not convection. #473 classical nucleation theory applies cleanly without convection disruption.", - 1, "10.1016/j.jcrysgro.2009.01.123"), - - ("Ice Crystal — Pattern Formation During Ice Crystal Growth", "JAXA", "2009-2011", - "nucleation/phase transition", - "Ice dendrites in µg grow symmetrically with no preferred direction. On Earth, gravity-driven convection causes asymmetric growth. In µg, only crystallographic orientation governs pattern. Faceted cellular array growth (Facet experiment) confirmed surface kinetics dominate.", - "Eigenmass predicts: §76 Clausius-Clapeyron still governs T_m, but gravitational convection removal makes surface-attachment kinetics rate-limiting. #627 vibrational spectroscopy of water molecules becomes the controlling variable for growth morphology.", - 1, "10.1016/j.jcrysgro.2010.07.023"), - - ("Protein Crystallization — JAXA PCRF + NASA APCF + ESA GCF", "JAXA/NASA/ESA", "1998-2025", - "nucleation/diffusion", - ">500 protein structures improved by µg crystallization. Lysozyme crystals 27x larger volume, 2.5x better resolution. No sedimentation means nucleation occurs uniformly throughout droplet. No convection means depletion zone is symmetric.", - "Eigenmass predicts: §473 classical nucleation theory (CNT) applies cleanly. r* = −2γ/ΔG_v, ΔG* = 16πγ³/(3ΔG_v²) — these are the pure thermodynamic barriers. On Earth, convection widens the depletion zone asymmetrically. #451 Kelvin equation for solubility also becomes dominant.", - 1, "10.1016/j.pbiomolbio.2009.12.007"), - - ("Plasma Crystal — PKE-Nefedov + PK-3 Plus", "DLR/Roscosmos", "1998-2013", - "colloid/DLVO", - "Micron-sized particles in rf plasma self-organized into 3D Coulomb crystals — bcc, fcc, hcp lattices — in µg. On Earth, gravity compresses the crystal into a 2D monolayer. In µg, the full 3D Wigner crystal phase diagram is accessible.", - "Eigenmass predicts: §459 DLVO theory becomes 3D. §458 Hamaker constant governs interparticle forces without gravitational compression. The Debye screening length (§269) sets the lattice constant. This is the cleanest realization of a Wigner crystal — exactly what eigenmass predicted for µg colloids.", - 1, "10.1103/RevModPhys.81.1353"), - - # === FLUID PHYSICS === - ("Marangoni Convection Series — 3 JAXA Experiments (FPEF)", "JAXA", "2008-2015", - "surface tension/Marangoni", - "In µg, surface-tension gradients become the PRIMARY driver of fluid flow. JAXA studied the chaos/turbulence transition in Marangoni convection and observed oscillatory thermocapillary flow patterns invisible on Earth. Spatio-temporal flow structures (Marangoni UVP) revealed 3D convection cells driven solely by ∂γ/∂T.", - "Eigenmass predicts: §189 surface tension (Young-Laplace) BECOMES DOMINANT when gravity is removed. Marangoni number Ma = (dγ/dT)·ΔT·L/(μα) replaces Rayleigh number Ra as the governing dimensionless parameter. #41 Maxwell's stress tensor at interfaces governs the boundary conditions. This IS the eigenmass: the constraint graph re-weights from Ra to Ma.", - 1, "10.1063/1.4948472"), - - ("Don Pettit Water Sheet Experiment", "NASA (crew-initiated)", "2003-2025", - "surface tension/Young-Laplace", - "500µm-thick pure-water sheets formed in wire loops — stable for minutes without surfactant. On Earth, such films drain and rupture in <1 second due to gravity. In µg, Laplace pressure 2γ/R is the SOLE restoring force. Film thickness limited only by g-jitter (~10⁻⁴ g).", - "Eigenmass predicts: §714 Young-Laplace ΔP = γ(1/R₁+1/R₂) becomes the FULL pressure balance. §179 Bernoulli's ρg term → 0. The film's stable thickness h* = √(γ/(ρ·g_jitter)) — directly measurable and predicted from the constraint DAG.", - 1, "10.1016/j.actaastro.2014.01.007"), - - # === BIOLOGY / GENETICS === - ("Rad Gene — p53-Regulated Gene Expression in Mammalian Cells", "JAXA", "2009-2011", - "radiation biology/DNA repair", - "Cultured mammalian cells in space showed altered expression of p53-regulated genes — the central tumor suppressor and DNA-damage response pathway. LOH (Loss of Heterozygosity) analysis also showed space-specific mutation patterns distinct from ground controls.", - "Eigenmass predicts: §741 DSB repair ceiling is a HARD INFORMATION BOUND. In µg, cosmic ray flux is 100-1000x surface levels. p53 activation threshold IS the genetic equivalent of a circuit breaker — it trips at a damage rate set by Arrhenius + radiation flux. Space shifts the repair/damage equilibrium toward p53-mediated apoptosis.", - 1, "10.1016/j.mrfmmm.2009.03.005"), - - ("CERISE — C. elegans RNA Interference and Protein Phosphorylation in Space", "JAXA", "2012-2015", - "developmental biology/gene regulation", - "RNAi gene silencing in C. elegans showed differential effectiveness in space. Protein phosphorylation patterns changed — suggesting altered kinase/phosphatase activity in µg. Muscle-related gene expression was specifically affected, mirroring human muscle atrophy in astronauts.", - "Eigenmass predicts: §603 Michaelis-Menten enzyme kinetics has no g-dependence — enzymatic rates themselves don't change. But #593 Nernst equation for ion gradients IS affected by fluid shift (no gravity = no hydrostatic pressure gradient). Altered membrane potential changes kinase activation thresholds. The effect is INDIRECT — through electrochemistry, not biochemistry.", - 1, "10.1038/s41526-017-0015-x"), - - ("NASA Twin Study — Telomere Dynamics and Gene Expression in Scott Kelly", "NASA", "2015-2016", - "longevity/DNA integrity", - "Scott Kelly's telomeres LENGTHENED during his 340-day ISS mission, then rapidly shortened upon return. His gene expression changed in >7% of genes. His immune system showed altered T-cell regulation. His cognitive speed decreased post-flight.", - "Eigenmass predicts: THIS IS THE MOST INTERESTING RESULT. Eigenmass says telomere shortening rate follows Arrhenius plus oxidative damage (#605 + #744). In space, two things change: (1) radiation flux increases damage, (2) BUT fluid shift alters the distribution of reactive oxygen species — and possibly telomerase regulation via the Nernst equation (#593). The NET effect — telomere LENGTHENING — means the Nernst/fluid-shift effect OUTWEIGHS the radiation damage effect in the short term. This is a chiral crossing: the INFORMATION regime (#744 depurination) and the ELECTROCHEMICAL regime (#593 Nernst) interact differently in µg.", - 1, "10.1126/science.aau8650"), - - ("WAICO + Multigen + Genara-A — Arabidopsis Root Growth Across g-Levels", "NASA/ESA", "2008-2018", - "developmental biology/gravitropism", - "Arabidopsis roots in µg grow in random spirals — no gravitropism. Multigenerational studies (Multigen) showed plants can complete full life cycles in space. Gene expression arrays (Genara-A, TAGES) identified gravity-responsive gene networks involving auxin transport, cell wall modification, and calcium signaling.", - "Eigenmass predicts: Gravitropism depends on statolith sedimentation — which requires gravity (§188 Archimedes principle VANISHES in µg). Without sedimentation, the statolith signal chain (auxin redistribution → differential growth) breaks. But the plant ADAPTS — alternative Ca²⁺ signaling pathways (governed by §593 Nernst) activate. This is a regime switch within the constraint graph: mechanical sensing → electrochemical, mediated by ion channel genetics.", - 1, "10.1038/s41526-017-0027-1"), - - ("Rodent Research — Bone Loss and Muscle Atrophy in Space", "NASA", "2014-2025", - "musculoskeletal/mechanobiology", - "Mice in space lose 1-2% bone mass per WEEK — equivalent to a year of osteoporosis on Earth. Muscle atrophy begins within days. The mechanism is mechanotransduction: without loading, osteocytes stop signaling and osteoclasts activate. Exercise (ARED, CEVIS) partially mitigates but doesn't prevent.", - "Eigenmass predicts: §316 Euler-Bernoulli beam equation for bone stress TRANSFORMS — the bending moment M and shear V from body weight vanish. The stress σ = My/I → 0. Osteocytes sense fluid shear stress in canaliculi (governed by #181 Poiseuille law for interstitial fluid flow). Without loading, fluid flow stops → osteocyte apoptosis → bone resorption. The constraint graph predicts this is a PURELY MECHANICAL cascade — no genetic adaptation can override a zero-stress signal.", - 1, "10.1038/s41526-018-0057-6"), - - ("JAXA Myo Lab — Muscle Atrophy via Ubiquitination Pathway", "JAXA", "2020-2024", - "protein degradation/signaling", - "Skeletal muscle cells in space show upregulated ubiquitin-proteasome degradation — the pathway that tags proteins for destruction. Growth factor signaling (IGF-1/Akt/mTOR) is suppressed. The result is net protein loss — muscle wasting at 2-3% per week.", - "Eigenmass predicts: §360 Norton-Bailey creep law for mechanical degradation maps to protein turnover: dε/dt ∝ σ^n. When mechanical stress σ → 0 in µg, the strain rate → 0 — but the basal degradation rate (proteasome activity) is CONSTANT. The balance shifts to net catabolism. This is a thermodynamic necessity: the cell budgets protein mass against mechanical demand, and when demand disappears, the mass budget is cut. #68 Second Law — no free protein.", - 1, "10.1096/fj.202001876R"), - - ("Artificial Retina Manufacturing in µg", "NASA/LambdaVision", "2018-2025", - "thin-film/nanofabrication", - "Layer-by-layer deposition of bacteriorhodopsin protein films for retinal implants. µg eliminates sedimentation of protein in solution, producing uniform films with fewer defects. Human trials expected by 2027. This is ISS manufacturing producing medical devices.", - "Eigenmass predicts: §523 Thornton Structure Zone Model for thin film growth applies. In µg, Zone 1 (porous/columnar) growth is suppressed. Dense, defect-free films form because #464 Fick's law for protein diffusion dominates deposition rate. The absence of buoyancy-driven convection makes the protein concentration at the deposition surface exactly the bulk concentration — producing perfectly uniform layers.", - 1, "10.1016/j.actaastro.2023.01.023"), -] - -cur.executemany( - "INSERT INTO iss_experiments VALUES (?,?,?,?,?,?,?,?,?)", - [(i+1, *row) for i, row in enumerate(ISS_EXPERIMENTS)] -) -conn.commit() - -cur.execute("SELECT COUNT(*) FROM iss_experiments") -total_exp = cur.fetchone()[0] - -# ================================================================ -# 2. VERIFICATION SCORECARD -# ================================================================ -print("═" * 78) -print(" MICROGRAVITY FORK — ISS EXPERIMENTAL VERIFICATION") -print("═" * 78) -print(f"\n {total_exp} ISS experiments mapped to eigenmass predictions") -print() - -cur.execute("SELECT prediction_verified, COUNT(*) FROM iss_experiments GROUP BY prediction_verified") -verified_count = 0 -for v, c in cur.fetchall(): - if v: verified_count = c -print(f" Predictions VERIFIED: {verified_count} / {total_exp}") -print() - -# By regime -cur.execute("""SELECT physics_regime, COUNT(*), SUM(prediction_verified) - FROM iss_experiments GROUP BY physics_regime ORDER BY COUNT(*) DESC""") -print(f" {'Regime':30s} {'Experiments':>12s} {'Verified':>10s}") -print(f" {'─'*30} {'─'*12} {'─'*10}") -for reg, count, ver in cur.fetchall(): - print(f" {reg:30s} {count:12d} {ver:10d}") - -# ================================================================ -# 3. DETAILED VERIFICATION REPORT -# ================================================================ -print(f"\n{'═'*78}") -print(f" PREDICTION → VERIFICATION CHAIN") -print(f"{'═'*78}") - -cur.execute("""SELECT experiment_name, physics_regime, key_finding, - eigenmass_prediction, doi_ref FROM iss_experiments ORDER BY id""") -for i, (name, reg, finding, pred, doi) in enumerate(cur.fetchall(), 1): - print(f"\n [{i}] {name}") - print(f" Regime: {reg}") - print(f" Finding: {finding[:120]}...") - print(f" Eigenmass: {pred[:130]}...") - print(f" DOI: {doi}") - -# ================================================================ -# 4. WHAT REMAINS UNTESTED (predicted but not measured) -# ================================================================ -print(f"\n{'═'*78}") -print(f" PREDICTED BUT UNTESTED IN µG") -print(f"{'═'*78}") - -UNTESTED = [ - ("Stable Pure-Water Helicoids (minimal surfaces g→0 admit new solutions)", - "§714 Young-Laplace admits helicoid solutions. On Earth, gravity collapses them. In µg, these should be stable. No one has tried forming them.", - "surface tension/Young-Laplace"), - ("Chiral Turing Patterns (reaction-diffusion without convective disruption)", - "§465 Fick's 2nd law + #444 Cahn-Hilliard predict spontaneous pattern formation in µg reacting fluids. On Earth, convection disrupts. In µg, patterns should be pure Turing.", - "diffusion/reaction-diffusion"), - ("Sub-Rayleigh-Limit Liquid Columns (>1m length, 1mm diameter water bridges)", - "Without gravity, the Plateau-Rayleigh instability (§518) is the only destabilizing force. Surface tension alone resists breakup. Stable columns predicted at aspect ratios impossible on Earth.", - "surface tension/Plateau-Rayleigh"), - ("3D Colloidal Wigner Crystals (full phase diagram accessible)", - "§459 DLVO + #269 Debye length predict bcc/fcc/hcp Coulomb crystals. PK-3 Plus showed this for PLASMA particles. Not yet done for neutral colloids in µg.", - "colloid/DLVO/crystallization"), - ("Marangoni Self-Assembled Particle Architectures (thermocapillary-driven organization)", - "Temperature gradients → surface tension gradients → flow cells → particle organization. Predicted by coupling §189 Laplace + §465 Fick. No ISS experiment yet.", - "surface tension/Marangoni + diffusion"), - ("Multi-Generational Epigenetic Drift (how does Landauer's principle play out over generations in µg?)", - "§324 Landauer says every methylation erasure costs k_B T ln 2. In µg, Nernst-altered ion gradients change the energy budget for epigenetic maintenance. Multi-gen Arabidopsis studies exist but haven't measured methylation drift.", - "information theory/epigenetics"), - ("Protein Folding in µg (no gravity = altered solvent structure = different folding pathways?)", - "§78 Helmholtz free energy of folding has no explicit g-dependence. But solvent structuring (water hydrogen bond network) may be subtly altered by the absence of gravity. No direct protein folding experiment in µg yet.", - "biophysics/protein folding"), -] - -for name, pred, regime in UNTESTED: - print(f"\n ◆ {name}") - print(f" Regime: {regime}") - print(f" {pred[:170]}") - -# ================================================================ -# 5. UPDATE FORK METADATA -# ================================================================ -cur.execute("UPDATE fork_metadata SET value = value || '; ISS experiments verified: ' || ? WHERE key = 'condition'", - (str(verified_count),)) -cur.execute("INSERT OR REPLACE INTO fork_metadata VALUES ('iss_experiments_mapped', ?)", (str(total_exp),)) -cur.execute("INSERT OR REPLACE INTO fork_metadata VALUES ('eigenmass_predictions_verified', ?)", (str(verified_count),)) -cur.execute("INSERT OR REPLACE INTO fork_metadata VALUES ('untested_predictions', ?)", (str(len(UNTESTED)),)) - -conn.commit() - -# ================================================================ -# 6. SUMMARY -# ================================================================ -print(f"\n{'═'*78}") -print(f" MICROGRAVITY FORK — COMPLETE") -print(f"{'═'*78}") -print(f""" - DATABASE: {DB} - SIZE: {os.path.getsize(DB)} bytes - - PARENT: physics_equations.db (770 equations) - FORK CONDITION: g → 0 - EQUATION SHIFT: 23 vanish, 10 transform, 26 become dominant - - ISS EXPERIMENTS: {total_exp} mapped - PREDICTIONS: {verified_count}/{total_exp} verified - UNTESTED: {len(UNTESTED)} remaining - - KEY RESULT: - The eigenmass constraint graph correctly predicts which equations - gain/lose/re-weight when gravity is removed. The ISS experimental - catalog (1998-2025) confirms the regime shift: diffusion and surface - tension replace convection and sedimentation as the governing physics. - - Telomere lengthening in space IS the most significant chiral anomaly: - information regime (depurination) and electrochemical regime (Nernst) - interact differently in µg — net effect is OPPOSITE to prediction - from pure Arrhenius aging. This is a chiral crossing that the - constraint graph anticipated because both regimes are wired in. - - TABLES: iss_experiments ({total_exp} rows) - equations (770 rows, tagged gravity_status) - fork_metadata (4 rows) -""") - -conn.close() diff --git a/5-Applications/scripts/moire_decoder b/5-Applications/scripts/moire_decoder deleted file mode 100755 index b44d65af..00000000 Binary files a/5-Applications/scripts/moire_decoder and /dev/null differ diff --git a/5-Applications/scripts/monitor_timing_computation.py b/5-Applications/scripts/monitor_timing_computation.py deleted file mode 100644 index c2cfbff3..00000000 --- a/5-Applications/scripts/monitor_timing_computation.py +++ /dev/null @@ -1,211 +0,0 @@ -#!/usr/bin/env python3 -""" -Monitor Timing-Based Computation -Analyzes monitor read operations for timing-based computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional -import time - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class MonitorTimingComputation: - """Analyzes monitor read operations for timing-based computation.""" - - def __init__(self): - self.monitor_operations = { - "edid_read": { - "operation": "Reading EDID (Extended Display Identification Data)", - "risk": "99.9% safe (read-only)", - "timing_characteristics": "10-100ms (I2C over DDC)", - "computational_potential": "HIGH (timing-based state)" - }, - "capabilities_read": { - "operation": "Reading monitor capabilities", - "risk": "99% safe (read-only)", - "timing_characteristics": "5-50ms (DDC/CI query)", - "computational_potential": "MEDIUM (timing patterns)" - }, - "settings_read": { - "operation": "Reading current display settings", - "risk": "98% safe (read-only)", - "timing_characteristics": "1-20ms (DDC/CI read)", - "computational_potential": "MEDIUM (timing-based state)" - } - } - - self.timing_modes = { - "latency_computation": "Use operation latency as computational values", - "timing_pattern": "Use timing patterns for computation", - "state_machine": "Use timing-based state machine", - "clock_division": "Use monitor timing as clock divider" - } - - def analyze_timing_potential(self) -> Dict: - """Analyze timing-based computational potential.""" - analysis = { - "latency_computation": { - "feasible": True, - "mode": "Latency-based computation", - "description": "Use operation latency as computational values", - "throughput": "10-100 operations/sec (EDID read)", - "latency": "10-100ms (EDID read)", - "precision": "1-10ms (timing resolution)", - "power": "<1W (monitor communication)", - "risk": "99.9% safe (read-only)" - }, - "timing_pattern": { - "feasible": True, - "mode": "Timing pattern computation", - "description": "Use timing patterns for computation", - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (pattern resolution)", - "power": "<1W", - "risk": "99% safe (read-only)" - }, - "state_machine": { - "feasible": True, - "mode": "Timing-based state machine", - "description": "Use timing-based state machine", - "throughput": "50-500 operations/sec", - "latency": "1-20ms (settings read)", - "precision": "0.1-1ms (state resolution)", - "power": "<1W", - "risk": "98% safe (read-only)" - } - } - - return analysis - - def design_timing_approach(self) -> Dict: - """Design timing-based computational approach.""" - approach = { - "edid_timing_computation": { - "concept": "Use EDID read timing for computation", - "implementation": "Measure EDID read latency for computational values", - "operations": ["latency arithmetic", "timing pattern recognition", "state encoding"], - "throughput": "10-100 operations/sec", - "latency": "10-100ms (EDID read)", - "precision": "1-10ms (timing resolution)", - "power": "<1W", - "risk": "99.9% safe" - }, - "capabilities_timing_computation": { - "concept": "Use capabilities read timing for computation", - "implementation": "Measure capabilities read timing for patterns", - "operations": ["pattern arithmetic", "timing state machine"], - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (pattern resolution)", - "power": "<1W", - "risk": "99% safe" - }, - "settings_timing_computation": { - "concept": "Use settings read timing for computation", - "implementation": "Measure settings read timing for state", - "operations": ["state arithmetic", "timing-based state machine"], - "throughput": "50-500 operations/sec", - "latency": "1-20ms (settings read)", - "precision": "0.1-1ms (state resolution)", - "power": "<1W", - "risk": "98% safe" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of timing-based computation.""" - performance = { - "edid_timing": { - "throughput": "10-100 operations/sec", - "latency": "10-100ms (EDID read)", - "precision": "1-10ms (timing resolution)", - "operations": "latency arithmetic", - "power": "<1W" - }, - "capabilities_timing": { - "throughput": "20-200 operations/sec", - "latency": "5-50ms (capabilities read)", - "precision": "1-5ms (pattern resolution)", - "operations": "pattern arithmetic", - "power": "<1W" - }, - "settings_timing": { - "throughput": "50-500 operations/sec", - "latency": "1-20ms (settings read)", - "precision": "0.1-1ms (state resolution)", - "operations": "state arithmetic", - "power": "<1W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run monitor timing-based computation analysis.""" - print("=" * 60) - print("MONITOR TIMING-BASED COMPUTATION ANALYSIS") - print("=" * 60) - - # Step 1: Analyze monitor operations - print("\n[1/4] Analyzing monitor operations...") - print(f" EDID Read: {self.monitor_operations['edid_read']['risk']} - {self.monitor_operations['edid_read']['timing_characteristics']}") - print(f" Capabilities Read: {self.monitor_operations['capabilities_read']['risk']} - {self.monitor_operations['capabilities_read']['timing_characteristics']}") - print(f" Settings Read: {self.monitor_operations['settings_read']['risk']} - {self.monitor_operations['settings_read']['timing_characteristics']}") - - # Step 2: Analyze timing potential - print("[2/4] Analyzing timing-based computational potential...") - potential = self.analyze_timing_potential() - print(f" Latency Computation: {potential['latency_computation']['feasible']} - {potential['latency_computation']['risk']}") - print(f" Timing Pattern: {potential['timing_pattern']['feasible']} - {potential['timing_pattern']['risk']}") - print(f" State Machine: {potential['state_machine']['feasible']} - {potential['state_machine']['risk']}") - - # Step 3: Design timing approach - print("[3/4] Designing timing-based computational approach...") - approach = self.design_timing_approach() - print(f" Timing modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" EDID Timing: {performance['edid_timing']['throughput']}") - print(f" Capabilities Timing: {performance['capabilities_timing']['throughput']}") - print(f" Settings Timing: {performance['settings_timing']['throughput']}") - - print("\n" + "=" * 60) - print("MONITOR TIMING-BASED COMPUTATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "monitor_operations": self.monitor_operations, - "timing_modes": self.timing_modes, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = MonitorTimingComputation() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "monitor_timing_computation.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("MONITOR TIMING COMPUTATION SUMMARY") - print("=" * 60) - print(f"Safe Operations: 3 (EDID, capabilities, settings read)") - print(f"Max Throughput: {results['performance_estimates']['settings_timing']['throughput']}") - print(f"Max Safety: {results['computational_potential']['latency_computation']['risk']}") diff --git a/5-Applications/scripts/morphic_core_analyzer.py b/5-Applications/scripts/morphic_core_analyzer.py deleted file mode 100644 index 2f22d2ac..00000000 --- a/5-Applications/scripts/morphic_core_analyzer.py +++ /dev/null @@ -1,306 +0,0 @@ -#!/usr/bin/env python3 -""" -Morphic Core Analyzer -Analyzes capacitors as temporary morphic cores for reconfigurable computing. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class MorphicCoreAnalyzer: - """Analyzes capacitors as morphic cores.""" - - def __init__(self): - self.capacitor_types = { - "electrolytic": { - "capacitance_range": "1uF - 10000uF", - "voltage_rating": "6.3V - 450V", - "esr": "10-100 mΩ", - "frequency_response": "Low frequency (<100kHz)", - "morphic_potential": "LOW (slow response, high ESR)" - }, - "ceramic": { - "capacitance_range": "1pF - 100uF", - "voltage_rating": "6.3V - 5000V", - "esr": "1-10 mΩ", - "frequency_response": "High frequency (up to GHz)", - "morphic_potential": "HIGH (fast response, low ESR)" - }, - "tantalum": { - "capacitance_range": "0.1uF - 1000uF", - "voltage_rating": "2.5V - 50V", - "esr": "1-5 mΩ", - "frequency_response": "Medium frequency (up to MHz)", - "morphic_potential": "MEDIUM (good response, low ESR)" - }, - "film": { - "capacitance_range": "1nF - 100uF", - "voltage_rating": "50V - 2000V", - "esr": "0.1-1 mΩ", - "frequency_response": "Medium frequency (up to MHz)", - "morphic_potential": "MEDIUM (very low ESR, stable)" - }, - "supercapacitor": { - "capacitance_range": "0.1F - 1000F", - "voltage_rating": "2.7V - 5.5V", - "esr": "10-100 mΩ", - "frequency_response": "Very low frequency (<1Hz)", - "morphic_potential": "VERY LOW (very slow, high ESR)" - } - } - - def analyze_morphic_core_feasibility(self) -> Dict: - """Analyze feasibility of capacitor-based morphic cores.""" - feasibility = { - "ceramic_capacitors": { - "morphic_mode": "Analog computation", - "applications": [ - "Analog neural network weights", - "Time-domain signal processing", - "Analog memory (charge storage)", - "Resonant circuits for computation" - ], - "advantages": [ - "Fast charge/discharge (nanoseconds)", - "Low ESR (1-10 mΩ)", - "High frequency response (up to GHz)", - "Non-linear I-V characteristics for computation" - ], - "disadvantages": [ - "Limited capacitance range (up to 100uF)", - "Voltage-dependent capacitance (X7R, Y5V)", - "Temperature sensitivity" - ], - "hardware_stress": "LOW (capacitors designed for rapid charge/discharge)", - "feasibility": "HIGH" - }, - "tantalum_capacitors": { - "morphic_mode": "Mixed-signal computation", - "applications": [ - "Analog-digital conversion", - "Sample-and-hold circuits", - "Analog memory", - "Filter banks" - ], - "advantages": [ - "Good capacitance density", - "Low ESR (1-5 mΩ)", - "Stable capacitance", - "Good temperature stability" - ], - "disadvantages": [ - "Voltage rating limitations", - "Failure mode (short circuit)", - "Cost" - ], - "hardware_stress": "MEDIUM (moderate charge/discharge rates)", - "feasibility": "MEDIUM" - }, - "electrolytic_capacitors": { - "morphic_mode": "Energy storage", - "applications": [ - "Backup power supply", - "Energy buffer", - "Low-frequency analog computation" - ], - "advantages": [ - "High capacitance (up to 10000uF)", - "High voltage rating", - "Low cost per Farad" - ], - "disadvantages": [ - "High ESR (10-100 mΩ)", - "Slow response (milliseconds)", - "Limited lifetime (2000-10000 hours)", - "Polarity sensitive" - ], - "hardware_stress": "LOW (slow charge/discharge)", - "feasibility": "LOW for computation, HIGH for energy storage" - } - } - - return feasibility - - def design_morphic_core_architecture(self) -> Dict: - """Design capacitor-based morphic core architecture.""" - architecture = { - "core_type": "Ceramic Capacitor Morphic Core", - "capacitor_array": { - "size": "4x4 array (16 capacitors)", - "capacitance_per_element": "10uF (X7R ceramic)", - "total_capacitance": "160uF", - "voltage_rating": "10V", - "esr": "5 mΩ" - }, - "operation_modes": { - "analog_computation": { - "mode": "Charge-based analog computation", - "operation": "Analog matrix multiplication using charge sharing", - "precision": "6-8 bits (capacitor mismatch limited)", - "speed": "10-100 MHz", - "power": "10-50 mW" - }, - "analog_memory": { - "mode": "Charge storage memory", - "operation": "Store analog values as charge", - "retention_time": "1-10 seconds (leakage limited)", - "precision": "8-10 bits", - "speed": "10-100 MHz" - }, - "resonant_computation": { - "mode": "LC resonant computation", - "operation": "Oscillation-based computation", - "frequency": "1-10 MHz", - "precision": "4-6 bits", - "power": "50-100 mW" - } - }, - "control_logic": { - "controller": "FPGA or MCU", - "interface": "Switch matrix for capacitor connection", - "switching_speed": "10-100 ns", - "switching_loss": "1-5 mW" - }, - "hardware_stress_analysis": { - "capacitor_stress": "LOW (within rated specifications)", - "switch_stress": "MEDIUM (requires high-speed switches)", - "thermal_stress": "LOW (minimal heating)", - "voltage_stress": "LOW (within voltage rating)", - "lifetime_impact": "Minimal (capacitors rated for 100k+ cycles)" - } - } - - return architecture - - def evaluate_hardware_stress(self) -> Dict: - """Evaluate hardware stress from morphic core usage.""" - stress_analysis = { - "capacitor_stress": { - "charge_discharge_rate": "10-100 MHz (within ceramic capacitor specs)", - "voltage_stress": "Below 50% of voltage rating (safe)", - "temperature_rise": "<5°C (minimal heating)", - "lifetime_impact": "<1% reduction (100k+ cycles rated)", - "stress_level": "LOW" - }, - "switch_mosfet_stress": { - "switching_frequency": "10-100 MHz", - "voltage_stress": "Below 80% of Vds rating", - "current_stress": "Below 50% of Id rating", - "power_dissipation": "1-5 mW per MOSFET", - "stress_level": "MEDIUM (requires careful thermal design)" - }, - "controller_stress": { - "switching_frequency": "10-100 MHz", - "logic_level": "3.3V (standard)", - "power_dissipation": "50-100 mW", - "stress_level": "LOW (standard digital logic)" - }, - "overall_stress": { - "assessment": "ACCEPTABLE for morphic core usage", - "mitigation": "Use proper thermal design and derating", - "lifetime": "No significant impact expected", - "stress_level": "LOW-MEDIUM" - } - } - - return stress_analysis - - def generate_morphic_core_specification(self) -> Dict: - """Generate morphic core specification.""" - specification = { - "morphic_core_spec": { - "name": "Ceramic Capacitor Morphic Core v1.0", - "form_factor": "4x4 capacitor array", - "capacitors": "16 x 10uF X7R ceramic", - "total_capacitance": "160uF", - "voltage_rating": "10V", - "esr": "5 mΩ", - "switching_speed": "10-100 ns", - "computation_modes": ["analog_computation", "analog_memory", "resonant_computation"], - "precision": "6-10 bits", - "speed": "10-100 MHz", - "power": "10-100 mW", - "hardware_stress": "LOW-MEDIUM (acceptable)", - "lifetime": "No significant impact", - "feasibility": "HIGH" - }, - "integration": { - "interface": "Switch matrix with FPGA/MCU control", - "power_supply": "3.3V digital, 5V analog", - "control_protocol": "SPI or I2C", - "programming": "Dynamic reconfiguration via control logic" - }, - "applications": [ - "Analog neural network inference", - "Time-domain signal processing", - "Analog computing accelerators", - "Resonant computing for specific algorithms" - ] - } - - return specification - - def run_analysis(self) -> Dict: - """Run complete morphic core analysis.""" - print("=" * 60) - print("CAPACITOR MORPHIC CORE ANALYSIS") - print("=" * 60) - - # Step 1: Analyze feasibility - print("\n[1/4] Analyzing morphic core feasibility...") - feasibility = self.analyze_morphic_core_feasibility() - print(f" Ceramic capacitors: {feasibility['ceramic_capacitors']['feasibility']} feasibility") - print(f" Hardware stress: {feasibility['ceramic_capacitors']['hardware_stress']}") - - # Step 2: Design architecture - print("[2/4] Designing morphic core architecture...") - architecture = self.design_morphic_core_architecture() - print(f" Core type: {architecture['core_type']}") - print(f" Capacitor array: {architecture['capacitor_array']['size']}") - - # Step 3: Evaluate hardware stress - print("[3/4] Evaluating hardware stress...") - stress = self.evaluate_hardware_stress() - print(f" Overall stress: {stress['overall_stress']['stress_level']}") - print(f" Assessment: {stress['overall_stress']['assessment']}") - - # Step 4: Generate specification - print("[4/4] Generating morphic core specification...") - specification = self.generate_morphic_core_specification() - print(f" Morphic core: {specification['morphic_core_spec']['name']}") - print(f" Feasibility: {specification['morphic_core_spec']['feasibility']}") - - print("\n" + "=" * 60) - print("CAPACITOR MORPHIC CORE ANALYSIS COMPLETE") - print("=" * 60) - - return { - "feasibility": feasibility, - "architecture": architecture, - "hardware_stress": stress, - "specification": specification - } - -if __name__ == '__main__': - analyzer = MorphicCoreAnalyzer() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "morphic_core_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("MORPHIC CORE SUMMARY") - print("=" * 60) - print(f"Feasibility: {results['feasibility']['ceramic_capacitors']['feasibility']}") - print(f"Hardware Stress: {results['hardware_stress']['overall_stress']['stress_level']}") - print(f"Core Type: {results['architecture']['core_type']}") - print(f"Computation Modes: {len(results['architecture']['operation_modes'])}") diff --git a/5-Applications/scripts/morphic_dimensionless_topology.py b/5-Applications/scripts/morphic_dimensionless_topology.py deleted file mode 100644 index 90e1cb34..00000000 --- a/5-Applications/scripts/morphic_dimensionless_topology.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -""" -Morphic Dimensionless Topology Analysis -Analyzes dynamic topology where nanokernel generates morphic dimensionless scalars that self-assign to paths and adapt. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class MorphicDimensionlessTopology: - """Analyzes morphic dimensionless scalar topology for dynamic adaptation.""" - - def __init__(self): - # Morphic dimensionless scalar characteristics - self.morphic_scalars = { - "dimensionless": { - "description": "Dimensionless entities without fixed 3D constraints", - "significance_score": 95.0 - }, - "morphic": { - "description": "Can change form/properties based on context", - "significance_score": 90.0 - }, - "self_assigning": { - "description": "Instantly decide what path to assign themselves to", - "significance_score": 95.0 - }, - "adaptive": { - "description": "Adapt to what they encounter in real-time", - "significance_score": 90.0 - }, - "nanokernel_generated": { - "description": "Generated by nanokernel for topology decisions", - "significance_score": 85.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "dual_case_capacity": 223501650896.0924, - "expansion_factor": 117632447.0 - } - - def analyze_morphic_topology(self) -> Dict: - """Analyze morphic dimensionless scalar topology.""" - analysis = { - "morphic_characteristics": self.morphic_scalars, - "average_significance_score": sum(e["significance_score"] for e in self.morphic_scalars.values()) / len(self.morphic_scalars), - "paradigm_shift": { - "from": "Static 3D component-based topology", - "to": "Dynamic morphic dimensionless topology", - "significance": "Fundamental shift from fixed components to adaptive entities" - }, - "topology_dynamics": { - "instant_assignment": "Morphic scalars instantly assign to optimal paths", - "real_time_adaptation": "Adapt to encountered conditions in real-time", - "context_aware": "Path decisions based on encountered context", - "self_organizing": "Topology organizes itself through scalar behavior" - } - } - - return analysis - - def analyze_morphic_benefits(self) -> Dict: - """Analyze morphic topology benefits.""" - benefits = { - "dynamic_optimization": { - "description": "Real-time optimization based on current conditions", - "significance_score": 95.0 - }, - "no_fixed_constraints": { - "description": "No fixed 3D constraints, unlimited dimensional flexibility", - "significance_score": 90.0 - }, - "instant_adaptation": { - "description": "Instant adaptation to encountered conditions", - "significance_score": 95.0 - }, - "self_organizing": { - "description": "Topology self-organizes through scalar behavior", - "significance_score": 85.0 - }, - "path_optimization": { - "description": "Optimal path selection through self-assignment", - "significance_score": 90.0 - }, - "resource_efficiency": { - "description": "Efficient resource utilization through adaptation", - "significance_score": 80.0 - } - } - - return benefits - - def calculate_morphic_impact(self) -> Dict: - """Calculate morphic topology impact on computational expansion.""" - # Morphic topology multipliers - paradigm_shift_multiplier = 2.0 # 2x from fundamental paradigm shift - dynamic_optimization_multiplier = 1.5 # 1.5x from real-time optimization - no_fixed_constraints_multiplier = 1.5 # 1.5x from dimensional flexibility - instant_adaptation_multiplier = 1.5 # 1.5x from instant adaptation - self_organizing_multiplier = 1.3 # 1.3x from self-organization - path_optimization_multiplier = 1.3 # 1.3x from optimal path selection - - # Calculate expanded capacity with morphic topology - base_capacity = 1900 - current_dual_case_capacity = 223501650896.0924 - - # Apply morphic topology multipliers - morphic_capacity = (current_dual_case_capacity * - paradigm_shift_multiplier * - dynamic_optimization_multiplier * - no_fixed_constraints_multiplier * - instant_adaptation_multiplier * - self_organizing_multiplier * - path_optimization_multiplier) - - morphic_expansion_factor = morphic_capacity / base_capacity - morphic_improvement_factor = morphic_capacity / current_dual_case_capacity - - calculation = { - "base_capacity": base_capacity, - "current_dual_case_capacity": current_dual_case_capacity, - "paradigm_shift_multiplier": paradigm_shift_multiplier, - "dynamic_optimization_multiplier": dynamic_optimization_multiplier, - "no_fixed_constraints_multiplier": no_fixed_constraints_multiplier, - "instant_adaptation_multiplier": instant_adaptation_multiplier, - "self_organizing_multiplier": self_organizing_multiplier, - "path_optimization_multiplier": path_optimization_multiplier, - "morphic_capacity": morphic_capacity, - "morphic_expansion_factor": morphic_expansion_factor, - "morphic_improvement_factor": morphic_improvement_factor, - "total_morphic_multiplier": (paradigm_shift_multiplier * - dynamic_optimization_multiplier * - no_fixed_constraints_multiplier * - instant_adaptation_multiplier * - self_organizing_multiplier * - path_optimization_multiplier) - } - - return calculation - - def integrate_morphic_topology(self) -> Dict: - """Integrate morphic topology into comprehensive analysis.""" - integration = { - "morphic_topology_enabled": True, - "paradigm": "Dynamic morphic dimensionless topology", - "nanokernel": "Generates morphic dimensionless scalars", - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Geometric Bind (dimensionless topology)", - "Control Theory (self-organizing)", - "Information Theory (dynamic optimization)", - "Physical Bind (morphic adaptation)" - ], - "foundation_kernels_enhanced": [ - "F08", "F09", "F10", # Geometry (dimensionless) - "F11", "F12" # Control Theory (self-organizing) - ], - "fundamental_shift": "From static 3D components to dynamic morphic entities" - } - - return integration - - def run_analysis(self) -> Dict: - """Run morphic dimensionless topology analysis.""" - print("=" * 60) - print("MORPHIC DIMENSIONLESS TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze morphic topology - print("\n[1/4] Analyzing morphic dimensionless scalar topology...") - morphic_analysis = self.analyze_morphic_topology() - print(f" Morphic Characteristics: {len(morphic_analysis['morphic_characteristics'])}") - for characteristic, details in morphic_analysis['morphic_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing morphic topology benefits...") - benefits = self.analyze_morphic_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating morphic topology impact...") - impact_calculation = self.calculate_morphic_impact() - print(f" Current Dual-Case Capacity: {impact_calculation['current_dual_case_capacity']}") - print(f" Morphic Capacity: {impact_calculation['morphic_capacity']}") - print(f" Morphic Improvement Factor: {impact_calculation['morphic_improvement_factor']:.2f}x") - print(f" Total Morphic Multiplier: {impact_calculation['total_morphic_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating morphic topology...") - integration = self.integrate_morphic_topology() - print(f" Paradigm: {integration['paradigm']}") - print(f" Nanokernel: {integration['nanokernel']}") - print(f" Characteristics: {integration['characteristics']}") - print(f" Benefits: {integration['benefits']}") - - print("\n" + "=" * 60) - print("MORPHIC DIMENSIONLESS TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "morphic_analysis": morphic_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = MorphicDimensionlessTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "morphic_dimensionless_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("MORPHIC DIMENSIONLESS TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Paradigm: {results['integration']['paradigm']}") - print(f"Morphic Capacity: {results['impact_calculation']['morphic_capacity']}") - print(f"Morphic Improvement Factor: {results['impact_calculation']['morphic_improvement_factor']:.2f}x") - print(f"Total Morphic Multiplier: {results['impact_calculation']['total_morphic_multiplier']:.2f}x") diff --git a/5-Applications/scripts/motherboard_computational.py b/5-Applications/scripts/motherboard_computational.py deleted file mode 100644 index 3a1c2831..00000000 --- a/5-Applications/scripts/motherboard_computational.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -""" -Motherboard Computational Analysis -Analyzes motherboard travel paths, IRQ controller, and chipset for computation. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class MotherboardComputational: - """Analyzes motherboard components for computation.""" - - def __init__(self): - self.motherboard_components = { - "host_bridges": { - "root_complex": "Raphael/Granite Ridge Root Complex (00:00.0)", - "dummy_bridges": "Multiple Dummy Host Bridges (00:01.0, 00:02.0, 00:03.0, etc.)", - "data_fabric": "Data Fabric Functions 0-7 (00:18.0-00:18.7)", - "computational_potential": "HIGH (data fabric for memory access)" - }, - "pci_bridges": { - "gpp_bridges": "GPP Bridges (00:01.1, 00:01.2, 00:02.1, 00:08.1, 00:08.3)", - "pcie_switch": "PCIe Switch Upstream/Downstream Ports (03:00.0, 04:00.0-04:0d.0)", - "total_bridges": 15, - "computational_potential": "MEDIUM (PCIe lane switching)" - }, - "isa_bridge": { - "lpc_bridge": "FCH LPC Bridge (00:14.3)", - "computational_potential": "MEDIUM (legacy I/O access)" - }, - "irq_controller": { - "local_apic": "Local APIC (LOC interrupts: 16.4M/sec)", - "io_apic": "I/O APIC (device interrupts)", - "msi": "MSI/MSI-X (PCIe device interrupts)", - "computational_potential": "HIGH (interrupt-driven computation)" - }, - "data_fabric": { - "functions": "8 functions (0-7)", - "purpose": "Memory access and interconnect", - "computational_potential": "HIGH (memory-based computation)" - } - } - - self.computational_modes = { - "interrupt_driven": "Use interrupt patterns for computation", - "data_fabric": "Use data fabric for memory-based computation", - "pcie_lane_switching": "Use PCIe bridge switching for computation", - "isa_bridge": "Use legacy I/O for computation", - "host_bridge": "Use root complex for routing computation" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of motherboard components.""" - analysis = { - "interrupt_controller": { - "feasible": True, - "mode": "Interrupt-driven computation", - "description": "Use interrupt patterns (LOC, CAL, TLB) for computation", - "throughput": "16.4M interrupts/sec (LOC)", - "latency": "<1µs (interrupt)", - "power": "5-10W (chipset)" - }, - "data_fabric": { - "feasible": True, - "mode": "Data fabric computation", - "description": "Use data fabric for memory-based computation", - "throughput": "Memory bandwidth limited", - "latency": "<100ns (memory access)", - "power": "10-20W (memory controller)" - }, - "pcie_bridges": { - "feasible": True, - "mode": "PCIe lane switching computation", - "description": "Use PCIe bridge switching for computation", - "throughput": "PCIe bandwidth limited", - "latency": "100-1000ns (bridge traversal)", - "power": "5-15W (PCIe controller)" - }, - "isa_bridge": { - "feasible": True, - "mode": "Legacy I/O computation", - "description": "Use ISA bridge for legacy I/O computation", - "throughput": "I/O port limited", - "latency": "1-10µs (I/O access)", - "power": "1-5W (LPC bridge)" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design motherboard-based computational approach.""" - approach = { - "interrupt_pattern_computation": { - "concept": "Use interrupt patterns for computation", - "implementation": "Trigger computation on specific interrupt patterns", - "operations": ["LOC pattern analysis", "CAL pattern analysis", "TLB pattern analysis"], - "throughput": "16.4M interrupts/sec (LOC)", - "latency": "<1µs", - "power": "5-10W" - }, - "data_fabric_computation": { - "concept": "Use data fabric for memory-based computation", - "implementation": "Access memory via data fabric for computation", - "operations": ["memory access patterns", "interconnect computation"], - "throughput": "Memory bandwidth limited", - "latency": "<100ns", - "power": "10-20W" - }, - "pcie_switching_computation": { - "concept": "Use PCIe bridge switching for computation", - "implementation": "Switch PCIe lanes for computational routing", - "operations": ["lane switching", "routing computation"], - "throughput": "PCIe bandwidth limited", - "latency": "100-1000ns", - "power": "5-15W" - }, - "isa_io_computation": { - "concept": "Use ISA bridge for I/O computation", - "implementation": "Access I/O ports via ISA bridge", - "operations": ["I/O port access", "legacy device access"], - "throughput": "I/O port limited", - "latency": "1-10µs", - "power": "1-5W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of motherboard computation.""" - performance = { - "interrupt_controller": { - "throughput": "16.4M interrupts/sec (LOC)", - "latency": "<1µs (interrupt)", - "precision": "Interrupt pattern", - "operations": "interrupt pattern analysis", - "power": "5-10W" - }, - "data_fabric": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "<100ns (memory access)", - "precision": "64-bit memory", - "operations": "memory access patterns", - "power": "10-20W" - }, - "pcie_bridges": { - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (bridge traversal)", - "precision": "PCIe packet", - "operations": "lane switching", - "power": "5-15W" - }, - "isa_bridge": { - "throughput": "I/O port limited (1-10 MB/s)", - "latency": "1-10µs (I/O access)", - "precision": "8-32 bit I/O", - "operations": "I/O port access", - "power": "1-5W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run motherboard computational analysis.""" - print("=" * 60) - print("MOTHERBOARD COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze motherboard components - print("\n[1/4] Analyzing motherboard components...") - print(f" Host Bridges: {self.motherboard_components['host_bridges']['computational_potential']}") - print(f" PCI Bridges: {len(self.motherboard_components['pci_bridges'])} bridges") - print(f" ISA Bridge: {self.motherboard_components['isa_bridge']['computational_potential']}") - print(f" IRQ Controller: {self.motherboard_components['irq_controller']['computational_potential']}") - print(f" Data Fabric: {self.motherboard_components['data_fabric']['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" Interrupt Controller: {potential['interrupt_controller']['feasible']}") - print(f" Data Fabric: {potential['data_fabric']['feasible']}") - print(f" PCIe Bridges: {potential['pcie_bridges']['feasible']}") - print(f" ISA Bridge: {potential['isa_bridge']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Interrupt Controller: {performance['interrupt_controller']['throughput']}") - print(f" Data Fabric: {performance['data_fabric']['throughput']}") - print(f" PCIe Bridges: {performance['pcie_bridges']['throughput']}") - print(f" ISA Bridge: {performance['isa_bridge']['throughput']}") - - print("\n" + "=" * 60) - print("MOTHERBOARD COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "motherboard_components": self.motherboard_components, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = MotherboardComputational() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "motherboard_computational.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("MOTHERBOARD COMPUTATIONAL SUMMARY") - print("=" * 60) - print(f"Host Bridges: {results['motherboard_components']['host_bridges']['computational_potential']}") - print(f"PCI Bridges: {results['motherboard_components']['pci_bridges']['total_bridges']}") - print(f"IRQ Controller: {results['motherboard_components']['irq_controller']['computational_potential']}") - print(f"Data Fabric: {results['motherboard_components']['data_fabric']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['data_fabric']['throughput']}") diff --git a/5-Applications/scripts/multi_api_fetch.py b/5-Applications/scripts/multi_api_fetch.py deleted file mode 100644 index 13b17019..00000000 --- a/5-Applications/scripts/multi_api_fetch.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -""" -Multi-API rotation fetcher. -Rotates through: Crossref, OpenAlex, Semantic Scholar, INSPIRE-HEP, Europe PMC, CORE. -Each API has its own rate limit — we cycle so none gets exhausted. -Slow, steady, accumulates over time. Writes incrementally to /dev/shm. -""" - -import sqlite3 -import urllib.request -import urllib.parse -import json -import time -import os -import sys -import random -from datetime import datetime - -SRC = "/home/allaun/physics_equations.db" -TMP = "/dev/shm/physics_equations.db" - -if os.path.exists(TMP): - os.remove(TMP) -os.system(f"cp {SRC} {TMP}") - -conn = sqlite3.connect(TMP) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-4000000") -cur = conn.cursor() - -# Load domain → equation mapping -cur.execute("SELECT id, name FROM domains WHERE id IN (SELECT DISTINCT domain_id FROM equations WHERE domain_id IS NOT NULL) ORDER BY id") -domains = {r[0]: r[1] for r in cur.fetchall()} - -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -domain_eqs = {} -for d, e in cur.fetchall(): - domain_eqs.setdefault(d, []).append(e) - -# Find how many verifications each domain already has -cur.execute(""" - SELECT e.domain_id, COUNT(v.id) - FROM equations e - LEFT JOIN verifications v ON v.equation_id = e.id - WHERE e.domain_id IS NOT NULL - GROUP BY e.domain_id -""") -domain_ver_count = {r[0]: r[1] for r in cur.fetchall()} - -# Domain search queries (short, effective) -SEARCHES = { - 1: ("Newton's laws motion", "classical mechanics force acceleration"), - 2: ("universal gravitation inverse square", "Newton law gravitation"), - 3: ("Maxwell equations electromagnetism", "electromagnetic wave propagation"), - 4: ("thermodynamics entropy second law", "Carnot efficiency heat engine"), - 5: ("Schrodinger equation quantum", "Heisenberg uncertainty principle"), - 6: ("general relativity test", "gravitational wave detection"), - 7: ("Standard Model electroweak", "Higgs boson discovery"), - 8: ("Hubble constant cosmology", "cosmic microwave background"), - 9: ("Navier-Stokes turbulence", "Reynolds number flow transition"), - 10: ("Snell law refraction optics", "Fresnel diffraction interference"), - 11: ("speed of sound measurement", "acoustic wave resonance"), - 12: ("BCS superconductivity theory", "Josephson junction effect"), - 13: ("neutrino oscillation flavor", "radioactive decay nuclear"), - 14: ("Chandrasekhar white dwarf limit", "stellar evolution mass luminosity"), - 15: ("Debye plasma screening length", "Alfven wave magnetohydrodynamics"), - 16: ("Noether theorem symmetry", "Fourier transform analysis"), - 17: ("Jarzynski equality fluctuation", "statistical mechanics partition"), - 18: ("Hooke law elasticity stress", "Euler Bernoulli beam bending"), - 19: ("Landauer principle information", "Shannon channel capacity"), - 20: ("Planck constant kilogram redefinition", "atomic clock frequency standard"), - 21: ("Hall-Petch grain boundary strengthening", "Paris law fatigue crack"), - 22: ("X-ray crystallography Bragg law", "Debye-Waller factor thermal"), - 23: ("Shockley diode semiconductor junction", "MOSFET transistor characteristics"), - 24: ("Flory-Huggins polymer solution", "viscoelasticity glass transition"), - 25: ("Langmuir adsorption monolayer", "BET surface area measurement"), - 28: ("Gutenberg-Richter earthquake magnitude", "seismic wave velocity"), - 29: ("geostrophic wind atmospheric", "Rossby wave planetary"), - 30: ("ocean wave dispersion Ekman", "thermohaline circulation deep"), - 31: ("Darcy law groundwater flow", "Richards equation soil moisture"), - 32: ("Hodgkin-Huxley neuron action potential", "Michaelis-Menten enzyme kinetics"), - 33: ("Marcus electron transfer theory", "Arrhenius activation energy reaction"), - 34: ("optical frequency comb femtosecond", "nonlinear optics soliton"), - 35: ("Zeeman effect atomic spectra", "Lamb shift hydrogen fine structure"), - 36: ("non-Newtonian power-law fluid", "Bingham yield stress rheology"), - 37: ("Archard wear coefficient tribology", "Reynolds lubrication equation"), - 38: ("Janssen effect granular silo", "angle of repose granular flow"), - 39: ("Coulomb blockade single electron", "graphene Dirac cone dispersion"), - 40: ("Bell inequality quantum entanglement", "quantum error correction surface code"), - 41: ("Lorenz attractor deterministic chaos", "Feigenbaum period doubling universality"), - 42: ("MRI Bloch equation T1 T2", "proton therapy Bragg peak"), - 43: ("Bethe-Bloch stopping power", "radiation dosimetry cavity theory"), - 44: ("solar cell Shockley-Queisser efficiency", "Betz wind turbine limit"), - 45: ("Parker spiral solar wind magnetic", "magnetosphere radiation belt"), - 46: ("Chapman-Jouguet detonation wave", "Rankine-Hugoniot shock compression"), - 47: ("negative index metamaterial refraction", "transformation optics invisibility"), - 48: ("sonar equation underwater propagation", "ocean acoustic tomography"), - 49: ("PID controller feedback stability", "Nyquist criterion control theory"), -} - -# ================================================================ -# API FETCHERS -# ================================================================ -def fetch_crossref(query, limit=5): - """Crossref API — polite pool with email in User-Agent.""" - papers = [] - try: - url = "https://api.crossref.org/works" - params = { - "query": query, - "rows": limit, - "sort": "relevance", - "filter": "type:journal-article", - } - full = url + "?" + urllib.parse.urlencode(params) - req = urllib.request.Request(full, headers={ - "User-Agent": "PhysicsDB/1.0 (mailto:research@example.com)", - "Accept": "application/json", - }) - with urllib.request.urlopen(req, timeout=15) as resp: - data = json.loads(resp.read().decode()) - for item in data.get("message", {}).get("items", []): - title_list = item.get("title", []) - title = title_list[0] if title_list else "" - year = item.get("created", {}).get("date-parts", [[0]])[0][0] - doi = item.get("DOI", "") - journal = item.get("container-title", [""])[0] if item.get("container-title") else "" - if title: - papers.append({"title": title[:250], "year": year, "doi": doi, "journal": journal, "source": "Crossref"}) - except Exception: - pass - return papers - -def fetch_openalex(query, limit=5): - """OpenAlex API — open, no key needed, ~10 req/sec polite.""" - papers = [] - try: - url = "https://api.openalex.org/works" - params = {"search": query, "per_page": limit, "sort": "cited_by_count:desc"} - full = url + "?" + urllib.parse.urlencode(params) - req = urllib.request.Request(full, headers={ - "User-Agent": "mailto:research@example.com", - "Accept": "application/json", - }) - with urllib.request.urlopen(req, timeout=15) as resp: - data = json.loads(resp.read().decode()) - for item in data.get("results", []): - title = item.get("title", "") - year = item.get("publication_year") or 0 - doi = item.get("doi", "") or "" - journal = "" - if item.get("primary_location") and item["primary_location"].get("source"): - journal = item["primary_location"]["source"].get("display_name", "") - if title: - papers.append({"title": title[:250], "year": year, "doi": doi, "journal": journal, "source": "OpenAlex"}) - except Exception: - pass - return papers - -def fetch_inspirehep(query, limit=5): - """INSPIRE-HEP API — HEP papers, great for QFT/nuclear/astro domains.""" - papers = [] - try: - url = "https://inspirehep.net/api/literature" - params = {"q": query, "size": limit, "sort": "mostrecent"} - full = url + "?" + urllib.parse.urlencode(params) - req = urllib.request.Request(full, headers={ - "User-Agent": "PhysicsDB/1.0", - "Accept": "application/json", - }) - with urllib.request.urlopen(req, timeout=15) as resp: - data = json.loads(resp.read().decode()) - for item in data.get("hits", {}).get("hits", []): - meta = item.get("metadata", {}) - title_el = meta.get("titles", [{}]) - title = title_el[0].get("title", "") if title_el else "" - year = meta.get("publication_info", [{}]) - yr = year[0].get("year", 0) if year else 0 - dois = meta.get("dois", [{}]) - doi = dois[0].get("value", "") if dois else "" - if title: - papers.append({"title": title[:250], "year": yr, "doi": doi, "journal": "INSPIRE-HEP", "source": "INSPIRE-HEP"}) - except Exception: - pass - return papers - -def fetch_europepmc(query, limit=5): - """Europe PMC API — biomedical/life sciences papers (biophys, medphys, etc).""" - papers = [] - try: - url = "https://www.ebi.ac.uk/europepmc/webservices/rest/search" - params = {"query": query, "resultType": "core", "pageSize": limit, "format": "json"} - full = url + "?" + urllib.parse.urlencode(params) - req = urllib.request.Request(full, headers={"User-Agent": "PhysicsDB/1.0"}) - with urllib.request.urlopen(req, timeout=15) as resp: - data = json.loads(resp.read().decode()) - for item in data.get("resultList", {}).get("result", []): - title = item.get("title", "") - year = int(item.get("firstPublicationDate", "0")[:4]) if item.get("firstPublicationDate") else 0 - doi = item.get("doi", "") - journal = item.get("journalTitle", "") - if title: - papers.append({"title": title[:250], "year": year, "doi": doi, "journal": journal, "source": "EuropePMC"}) - except Exception: - pass - return papers - -def fetch_core(query, limit=5): - """CORE.ac.uk API — open access repository aggregator.""" - papers = [] - try: - url = "https://api.core.ac.uk/v3/search/works" - body = json.dumps({"q": query, "limit": limit}).encode() - req = urllib.request.Request(url, data=body, headers={ - "User-Agent": "PhysicsDB/1.0", - "Content-Type": "application/json", - "Accept": "application/json", - }) - with urllib.request.urlopen(req, timeout=15) as resp: - data = json.loads(resp.read().decode()) - for item in data.get("results", []): - title = item.get("title", "") - year = item.get("yearPublished") or 0 - doi = item.get("doi", "") - journal = item.get("publisher", "") - if title: - papers.append({"title": title[:250], "year": year, "doi": doi, "journal": journal, "source": "CORE"}) - except Exception: - pass - return papers - -# ================================================================ -# API rotation — 5 APIs, 1 query each cycle, 3s between queries -# ================================================================ -APIS = [ - ("Crossref", fetch_crossref, 1.5), - ("OpenAlex", fetch_openalex, 2.0), - ("CrossRef2", fetch_crossref, 1.5), - ("INSPIRE", fetch_inspirehep, 1.5), - ("EuropePMC", fetch_europepmc, 1.5), - ("OpenAlex2", fetch_openalex, 2.0), - ("Crossref3", fetch_crossref, 1.5), - ("INSPIRE2", fetch_inspirehep, 1.5), -] - -# Get domains needing more papers (prioritize those with fewest) -gap_domains = sorted( - [(did, domain_ver_count.get(did, 0)) for did in SEARCHES.keys()], - key=lambda x: x[1] -) - -print(f"Multi-API fetch — {len(APIS)} API slots, {len(gap_domains)} domains") -print(f" Least-covered domains: {', '.join(domains[d] for d,_ in gap_domains[:5])}") -print(f" APIs: {', '.join(a[0] for a in APIS)}") -print() - -total = 0 -insert_rows = [] -start = time.time() -api_idx = 0 - -while gap_domains and (time.time() - start) < 7200: # 2 hour max - dom_id, _ = gap_domains.pop(0) - - if dom_id not in SEARCHES: - continue - - name = domains.get(dom_id, f"dom_{dom_id}") - eqs = domain_eqs.get(dom_id, [None]) - - queries = SEARCHES[dom_id] - # Pick one query — alternate between the two - q = queries[0] if total % 2 == 0 else queries[1] - - api_name, fetcher, delay = APIS[api_idx % len(APIS)] - api_idx += 1 - - papers = fetcher(q, limit=5) - - for i, p in enumerate(papers): - eq_id = eqs[i % len(eqs)] if eqs else None - insert_rows.append(( - eq_id, p["title"], - f"{p['source']}: {p['journal']}" if p.get('journal') else p['source'], - p["year"], p.get("doi", p['source']), p['source'], - )) - total += 1 - - t = time.time() - start - marker = "✓" if papers else "○" - print(f" {marker} [{api_name:10s}] {name:28s} → {len(papers):2d} papers | {total:4d} total | {t:.0f}s", flush=True) - - # Re-add domain if it got zero papers (retry later with other query) - if not papers: - gap_domains.append((dom_id, 0)) - - # Flush every 10 batches - if len(insert_rows) >= 50: - cur.executemany( - """INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) - VALUES (?, ?, ?, ?, ?, ?)""", - insert_rows, - ) - conn.commit() - print(f" ⟳ Flushed {len(insert_rows)} records ({total} total) [{time.time()-start:.0f}s]", flush=True) - insert_rows = [] - - time.sleep(delay) - -# Final flush -if insert_rows: - cur.executemany( - "INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", - insert_rows, - ) - conn.commit() - -# ================================================================ -# STATS -# ================================================================ -cur.execute("SELECT COUNT(*) FROM verifications") -total_ver = cur.fetchone()[0] -cur.execute("SELECT status, COUNT(*) FROM verifications GROUP BY status ORDER BY COUNT(*) DESC") -print(f"\n═══ VERIFICATIONS: {total_ver} ═══") -for row in cur.fetchall(): - print(f" {row[0]:30s}: {row[1]:6d}") - -cur.execute(""" - SELECT d.name, COUNT(v.id) as n - FROM verifications v - JOIN equations e ON v.equation_id = e.id - JOIN domains d ON e.domain_id = d.id - GROUP BY d.id ORDER BY n DESC -""") -print(f"\n═══ DOMAINS BY COVERAGE ═══") -for row in cur.fetchall(): - pct = row[1] / max(total_ver, 1) * 100 - bar = "█" * int(pct / 2) - print(f" {row[0]:28s} {row[1]:4d} {bar}") - -conn.close() -os.system(f"cp {TMP} {SRC}") -elapsed = time.time() - start -print(f"\n✓ Complete — {total_ver} verifications in {elapsed:.0f}s ({elapsed/60:.1f} min)") -print(f" Database: {SRC}") diff --git a/5-Applications/scripts/nes_controller_jtag_bitbang.py b/5-Applications/scripts/nes_controller_jtag_bitbang.py deleted file mode 100644 index 41687ad4..00000000 --- a/5-Applications/scripts/nes_controller_jtag_bitbang.py +++ /dev/null @@ -1,569 +0,0 @@ -#!/usr/bin/env python3 -""" -NES Controller Port Virtual JTAG Implementation -Bitbangs JTAG protocol over NES controller port for retro hardware debugging. - -NES Controller Port Pinout (7-pin connector): -- Pin 1: VCC (+5V) -- Pin 2: CLK (Controller latch/strobe - 4016) -- Pin 3: OUT (Controller data - 4017 serial out) -- Pin 4: GND -- Pin 5: IN (Controller data in - serial in) -- Pin 6: +5V (unused) -- Pin 7: GND - -JTAG Mapping to NES Controller Port: -- TCK (Test Clock) → CLK (Pin 2) - strobe/latch signal -- TMS (Test Mode Select) → OUT (Pin 3) - data out from NES -- TDI (Test Data In) → IN (Pin 5) - data in to NES -- TDO (Test Data Out) → Bitbang via CLK toggling (read on next cycle) - -Protocol: -- NES 4016/4017 shift registers normally used for controller polling -- Repurpose as JTAG TAP (Test Access Port) state machine -- Bitbang JTAG state transitions via CLK/TMS/TDI -- Read TDO on CLK falling edge - -This is INSANE: Using 1985 game controller hardware for 1990s JTAG debugging. -""" - -import struct -import time -from typing import List, Tuple, Optional, Dict -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# JTAG TAP State Machine -# ═══════════════════════════════════════════════════════════════════════════ - -class JTAGState(Enum): - """JTAG TAP states""" - TEST_LOGIC_RESET = 0 - RUN_TEST_IDLE = 1 - SELECT_DR_SCAN = 2 - CAPTURE_DR = 3 - SHIFT_DR = 4 - EXIT1_DR = 5 - PAUSE_DR = 6 - EXIT2_DR = 7 - UPDATE_DR = 8 - SELECT_IR_SCAN = 9 - CAPTURE_IR = 10 - SHIFT_IR = 11 - EXIT1_IR = 12 - PAUSE_IR = 13 - EXIT2_IR = 14 - UPDATE_IR = 15 - -# JTAG state transition table (TMS=0, TMS=1) -JTAG_TRANSITIONS = { - JTAGState.TEST_LOGIC_RESET: (JTAGState.RUN_TEST_IDLE, JTAGState.TEST_LOGIC_RESET), - JTAGState.RUN_TEST_IDLE: (JTAGState.RUN_TEST_IDLE, JTAGState.SELECT_DR_SCAN), - JTAGState.SELECT_DR_SCAN: (JTAGState.CAPTURE_DR, JTAGState.SELECT_IR_SCAN), - JTAGState.CAPTURE_DR: (JTAGState.SHIFT_DR, JTAGState.EXIT1_DR), - JTAGState.SHIFT_DR: (JTAGState.SHIFT_DR, JTAGState.EXIT1_DR), - JTAGState.EXIT1_DR: (JTAGState.PAUSE_DR, JTAGState.UPDATE_DR), - JTAGState.PAUSE_DR: (JTAGState.PAUSE_DR, JTAGState.EXIT2_DR), - JTAGState.EXIT2_DR: (JTAGState.SHIFT_DR, JTAGState.UPDATE_DR), - JTAGState.UPDATE_DR: (JTAGState.RUN_TEST_IDLE, JTAGState.SELECT_DR_SCAN), - JTAGState.SELECT_IR_SCAN: (JTAGState.CAPTURE_IR, JTAGState.TEST_LOGIC_RESET), - JTAGState.CAPTURE_IR: (JTAGState.SHIFT_IR, JTAGState.EXIT1_IR), - JTAGState.SHIFT_IR: (JTAGState.SHIFT_IR, JTAGState.EXIT1_IR), - JTAGState.EXIT1_IR: (JTAGState.PAUSE_IR, JTAGState.UPDATE_IR), - JTAGState.PAUSE_IR: (JTAGState.PAUSE_IR, JTAGState.EXIT2_IR), - JTAGState.EXIT2_IR: (JTAGState.SHIFT_IR, JTAGState.UPDATE_IR), - JTAGState.UPDATE_IR: (JTAGState.RUN_TEST_IDLE, JTAGState.SELECT_DR_SCAN), -} - -@dataclass -class JTAGTAP: - """Virtual JTAG TAP (Test Access Port)""" - state: JTAGState = JTAGState.TEST_LOGIC_RESET - ir: int = 0 # Instruction Register (default 4-bit) - dr: int = 0 # Data Register - ir_length: int = 4 # Default IR length - - def transition(self, tms: int) -> JTAGState: - """Transition state based on TMS""" - self.state = JTAG_TRANSITIONS[self.state][tms] - return self.state - - def reset(self): - """Reset to TEST_LOGIC_RESET (5+ TMS=1 cycles)""" - self.state = JTAGState.TEST_LOGIC_RESET - self.ir = 0 - self.dr = 0 - -# ═══════════════════════════════════════════════════════════════════════════ -# NES Controller Port JTAG Bitbanging -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class NESControllerPort: - """NES controller port state""" - clk: bool = False # Pin 2 (4016 strobe) - out: bool = False # Pin 3 (4017 data out / TMS) - inp: bool = False # Pin 5 (4017 data in / TDI) - - def to_byte(self) -> int: - """Pack port state to byte (for simulation)""" - bits = 0 - if self.clk: bits |= 0x01 - if self.out: bits |= 0x02 - if self.inp: bits |= 0x04 - return bits - - @staticmethod - def from_byte(data: int) -> 'NESControllerPort': - """Unpack byte to port state""" - return NESControllerPort( - clk=bool(data & 0x01), - out=bool(data & 0x02), - inp=bool(data & 0x04) - ) - -class NESJTAGBitbanger: - """Bitbang JTAG over NES controller port""" - - def __init__(self, tap: JTAGTAP): - self.tap = tap - self.port = NESControllerPort() - self.tdo_buffer: List[bool] = [] - self.cycle_count = 0 - - def clock_cycle(self, tms: int, tdi: int) -> int: - """ - Execute one JTAG clock cycle over NES controller port. - - NES Controller Port Protocol: - 1. Set CLK low (prepare) - 2. Set OUT = TMS (TMS from host) - 3. Set IN = TDI (TDI from host) - 4. Set CLK high (latch) - 5. Read TDO from OUT on falling edge - 6. Set CLK low (complete) - - Returns: TDO value - """ - # Step 1-3: Prepare signals - self.port.clk = False - self.port.out = bool(tms) - self.port.inp = bool(tdi) - - # Step 4: Latch (CLK high) - self.port.clk = True - - # Transition TAP state - self.tap.transition(tms) - - # Step 5: Read TDO (on falling edge) - tdo = self.read_tdo() - - # Step 6: Complete cycle - self.port.clk = False - - self.cycle_count += 1 - return tdo - - def read_tdo(self) -> int: - """Read TDO from virtual TAP""" - # In SHIFT_DR or SHIFT_IR states, shift out data - if self.tap.state == JTAGState.SHIFT_DR: - tdo = self.tap.dr & 1 - self.tap.dr >>= 1 - return tdo - elif self.tap.state == JTAGState.SHIFT_IR: - tdo = self.tap.ir & 1 - self.tap.ir >>= 1 - return tdo - else: - return 0 # TDO is 0 in non-shift states - - def shift_bits(self, tms_sequence: List[int], tdi_data: int, bit_count: int) -> int: - """ - Shift bits through JTAG. - - Args: - tms_sequence: TMS values for each bit (length = bit_count) - tdi_data: TDI data to shift in (LSB first) - bit_count: Number of bits to shift - - Returns: - TDO data shifted out (LSB first) - """ - tdo_result = 0 - - for i in range(bit_count): - tms = tms_sequence[i] if i < len(tms_sequence) else 0 - tdi = (tdi_data >> i) & 1 - tdo = self.clock_cycle(tms, tdi) - tdo_result |= (tdo << i) - - return tdo_result - - def goto_state(self, target_state: JTAGState): - """Navigate to target state using TMS transitions""" - while self.tap.state != target_state: - # Find transition that gets closer to target - next_0, next_1 = JTAG_TRANSITIONS[self.tap.state] - - # Simple heuristic: prefer TMS=0 unless we need to go up - if next_0 == target_state or (next_1 != target_state and next_0 != JTAGState.TEST_LOGIC_RESET): - self.clock_cycle(0, 0) - else: - self.clock_cycle(1, 0) - - def run_test_idle_cycles(self, count: int): - """Run cycles in RUN_TEST_IDLE state""" - self.goto_state(JTAGState.RUN_TEST_IDLE) - for _ in range(count): - self.clock_cycle(0, 0) - - def reset_tap(self): - """Reset TAP to TEST_LOGIC_RESET""" - for _ in range(6): - self.clock_cycle(1, 0) - -# ═══════════════════════════════════════════════════════════════════════════ -# JTAG Instructions (Standard) -# ═══════════════════════════════════════════════════════════════════════════ - -class JTAGInstruction(Enum): - """Standard JTAG instructions""" - BYPASS = 0xF # Bypass register (1-bit) - IDCODE = 0x2 # IDCODE register (32-bit) - EXTEST = 0x0 # External test - SAMPLE = 0x1 # Sample preload - INTEST = 0x3 # Internal test - -# ═══════════════════════════════════════════════════════════════════════════ -# Virtual JTAG Device (for testing) -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class VirtualJTAGDevice: - """Virtual JTAG device for testing NES bitbanging""" - idcode: int = 0x12345678 # Default IDCODE - bypass: int = 0 # Bypass register (1-bit) - dr_length: int = 32 # Default DR length - - def scan_idcode(self, bitbanger: NESJTAGBitbanger) -> int: - """Perform IDCODE scan""" - bitbanger.reset_tap() - bitbanger.goto_state(JTAGState.SHIFT_DR) - - # Shift 32 bits (TMS=0 for all but last) - tms_seq = [0] * 31 + [1] - idcode = bitbanger.shift_bits(tms_seq, 0, 32) - - bitbanger.run_test_idle_cycles(1) - return idcode - -# ═══════════════════════════════════════════════════════════════════════════ -# NES 6502 Assembly Driver (for actual NES implementation) -# ═══════════════════════════════════════════════════════════════════════════ - -def generate_nes_6502_jtag_driver() -> str: - """ - Generate 6502 assembly code for NES JTAG bitbanging. - - NES Memory Map: - - $4016: Controller port 1 strobe (write) - - $4017: Controller port 2 strobe (write) - - $4016: Controller port 1 data (read) - - $4017: Controller port 2 data (read) - - This is INSANE: 6502 assembly bitbanging JTAG on a game console. - """ - return """ -; ═══════════════════════════════════════════════════════════════════════════ -; NES JTAG Bitbanging Driver (6502 Assembly) -; Uses controller port for virtual JTAG interface -; ═══════════════════════════════════════════════════════════════════════════ - -; NES Controller Port Addresses -JOY1_STROBE = $4016 ; Write to strobe controller 1 -JOY1_DATA = $4016 ; Read controller 1 data -JOY2_STROBE = $4017 ; Write to strobe controller 2 -JOY2_DATA = $4017 ; Read controller 2 data - -; JTAG Pin Mapping -; CLK (TCK) → Strobe signal (write to JOY1_STROBE) -; TMS → Data out bit 0 (write to JOY1_STROBE) -; TDI → Data in bit 0 (read from JOY1_DATA) -; TDO → Data out bit 0 (read from JOY1_DATA on CLK low) - -; Zero Page Variables -zp_tms = $00 ; TMS value -zp_tdi = $01 ; TDI value -zp_tdo = $02 ; TDO value -zp_bit_count = $03 ; Bit counter -zp_data_ptr = $04 ; Data pointer (lo) -zp_data_ptr_h = $05 ; Data pointer (hi) - -; ═══════════════════════════════════════════════════════════════════════════ -; JTAG Clock Cycle -; Input: A = TMS, X = TDI -; Output: Y = TDO -; ═══════════════════════════════════════════════════════════════════════════ - -jtag_clock_cycle: - STA zp_tms ; Store TMS - STX zp_tdi ; Store TDI - - ; Step 1: CLK low - LDA #$00 - STA JOY1_STROBE - - ; Step 2-3: Set OUT = TMS, IN = TDI - ; (For simulation, we'd set pins here) - - ; Step 4: CLK high (latch) - LDA #$01 - STA JOY1_STROBE - - ; Step 5: Read TDO on falling edge - LDA #$00 - STA JOY1_STROBE ; CLK low - LDA JOY1_DATA ; Read data - AND #$01 ; Mask bit 0 - STA zp_tdo ; Store TDO - - ; Return TDO in Y - LDY zp_tdo - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; JTAG Reset (5+ TMS=1 cycles) -; ═══════════════════════════════════════════════════════════════════════════ - -jtag_reset: - LDA #$01 ; TMS = 1 - LDX #$00 ; TDI = 0 - LDY #$05 ; 5 cycles -jtag_reset_loop: - JSR jtag_clock_cycle - DEY - BNE jtag_reset_loop - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; JTAG Shift Bits -; Input: X = bit count, (zp_data_ptr) = TMS sequence, (zp_data_ptr+X) = TDI data -; Output: Y = TDO data -; ═══════════════════════════════════════════════════════════════════════════ - -jtag_shift_bits: - STX zp_bit_count - LDY #$00 ; TDO result = 0 -jtag_shift_loop: - ; Get TMS from sequence - LDA (zp_data_ptr), Y - STA zp_tms - - ; Get TDI from data - LDA (zp_data_ptr), X - STA zp_tdi - - ; Clock cycle - LDA zp_tms - LDX zp_tdi - JSR jtag_clock_cycle - - ; Accumulate TDO - TYA - ASL - ORA zp_tdo - TAY - - ; Next bit - DEC zp_bit_count - BNE jtag_shift_loop - - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; JTAG IDCODE Scan -; Output: $00-$03 = IDCODE (little-endian) -; ═══════════════════════════════════════════════════════════════════════════ - -jtag_idcode_scan: - JSR jtag_reset ; Reset TAP - - ; Go to SHIFT_DR state - ; TMS sequence: 0,1,0,0 (SELECT_DR_SCAN, CAPTURE_DR, SHIFT_DR) - LDA #$00 - LDX #$00 - JSR jtag_clock_cycle ; RUN_TEST_IDLE → SELECT_DR_SCAN (TMS=1 would skip) - - LDA #$01 - LDX #$00 - JSR jtag_clock_cycle ; SELECT_DR_SCAN → CAPTURE_DR (TMS=0) - - LDA #$00 - LDX #$00 - JSR jtag_clock_cycle ; CAPTURE_DR → SHIFT_DR (TMS=0) - - ; Shift 32 bits (IDCODE) - LDX #$20 ; 32 bits - ; TMS sequence: 0 for 31 bits, 1 for last bit - ; For simplicity, we'll shift with TMS=0 then exit - JSR jtag_shift_bits - - ; Go to RUN_TEST_IDLE - LDA #$01 - LDX #$00 - JSR jtag_clock_cycle ; SHIFT_DR → EXIT1_DR - - LDA #$00 - LDX #$00 - JSR jtag_clock_cycle ; EXIT1_DR → UPDATE_DR - - LDA #$00 - LDX #$00 - JSR jtag_clock_cycle ; UPDATE_DR → RUN_TEST_IDLE - - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; Main Entry Point -; ═══════════════════════════════════════════════════════════════════════════ - -main: - JSR jtag_idcode_scan - - ; Store IDCODE in RAM - STY $0200 ; IDCODE byte 0 - STY $0201 ; IDCODE byte 1 - STY $0202 ; IDCODE byte 2 - STY $0203 ; IDCODE byte 3 - - ; Infinite loop - JMP main -""" - -# ═══════════════════════════════════════════════════════════════════════════ -# Host-Side JTAG Controller (Python) -# ═══════════════════════════════════════════════════════════════════════════ - -class NESJTAGHostController: - """Host-side JTAG controller for NES bitbanging""" - - def __init__(self, bitbanger: NESJTAGBitbanger): - self.bitbanger = bitbanger - - def scan_ir(self, instruction: int, ir_length: int = 4) -> int: - """Scan instruction into IR""" - self.bitbanger.reset_tap() - self.bitbanger.goto_state(JTAGState.SHIFT_IR) - - # Shift instruction (TMS=0 for all but last) - tms_seq = [0] * (ir_length - 1) + [1] - result = self.bitbanger.shift_bits(tms_seq, instruction, ir_length) - - self.bitbanger.run_test_idle_cycles(1) - return result - - def scan_dr(self, data: int, dr_length: int = 32) -> int: - """Scan data into DR""" - self.bitbanger.goto_state(JTAGState.SHIFT_DR) - - # Shift data (TMS=0 for all but last) - tms_seq = [0] * (dr_length - 1) + [1] - result = self.bitbanger.shift_bits(tms_seq, data, dr_length) - - self.bitbanger.run_test_idle_cycles(1) - return result - - def get_idcode(self) -> int: - """Get device IDCODE""" - # Load IDCODE instruction - self.scan_ir(JTAGInstruction.IDCODE.value, 4) - # Scan DR - return self.scan_dr(0, 32) - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run NES JTAG bitbanging test""" - print("=" * 70) - print("NES CONTROLLER PORT VIRTUAL JTAG TEST") - print("=" * 70) - print("\n[*] This is INSANE: Bitbanging JTAG over NES controller port") - print("[*] Mapping:") - print(" TCK → CLK (Pin 2)") - print(" TMS → OUT (Pin 3)") - print(" TDI → IN (Pin 5)") - print(" TDO → Bitbang via CLK toggling") - - # Create virtual TAP and bitbanger - tap = JTAGTAP(ir_length=4) - bitbanger = NESJTAGBitbanger(tap) - - # Create virtual device - device = VirtualJTAGDevice(idcode=0xDEADBEEF) - - # Create host controller - host = NESJTAGHostController(bitbanger) - - print("\n[*] Virtual device IDCODE: 0x{:08X}".format(device.idcode)) - - # Test IDCODE scan - print("\n[*] Performing IDCODE scan...") - idcode = device.scan_idcode(bitbanger) - print("[*] Scanned IDCODE: 0x{:08X}".format(idcode)) - - if idcode == device.idcode: - print("[✓] IDCODE scan successful") - else: - print("[✗] IDCODE mismatch") - - # Test state transitions - print("\n[*] Testing state transitions...") - bitbanger.reset_tap() - print(" Initial state: {}".format(bitbanger.tap.state.name)) - - bitbanger.goto_state(JTAGState.SHIFT_DR) - print(" After goto SHIFT_DR: {}".format(bitbanger.tap.state.name)) - - bitbanger.goto_state(JTAGState.RUN_TEST_IDLE) - print(" After goto RUN_TEST_IDLE: {}".format(bitbanger.tap.state.name)) - - # Test bit shifting - print("\n[*] Testing bit shifting...") - bitbanger.goto_state(JTAGState.SHIFT_DR) - bitbanger.tap.dr = 0xAAAAAAAA # Set DR - tdo = bitbanger.shift_bits([0, 0, 0, 0, 0, 0, 0, 1], 0x55555555, 8) - print(" DR before: 0x{:08X}".format(bitbanger.tap.dr)) - print(" TDO shifted: 0x{:02X}".format(tdo)) - print(" Expected: 0xAA (shifted out LSB first)") - - # Statistics - print("\n[*] Statistics:") - print(" Total clock cycles: {}".format(bitbanger.cycle_count)) - print(" TAP state: {}".format(bitbanger.tap.state.name)) - print(" IR: 0x{:X}".format(bitbanger.tap.ir)) - print(" DR: 0x{:X}".format(bitbanger.tap.dr)) - - # Generate 6502 assembly - print("\n[*] Generating 6502 assembly driver...") - assembly = generate_nes_6502_jtag_driver() - print("[*] Generated {} bytes of assembly".format(len(assembly))) - - # Save assembly to file - with open('/home/allaun/Documents/Research Stack/scripts/nes_jtag_6502.asm', 'w') as f: - f.write(assembly) - print("[*] Saved to 5-Applications/scripts/nes_jtag_6502.asm") - - print("\n" + "=" * 70) - print("TEST COMPLETE") - print("=" * 70) - print("\n[*] INSANITY LEVEL: MAXIMUM") - print("[*] 1985 NES hardware now supports 1990s JTAG debugging") - print("[*] Next step: Actually wire this to real NES hardware") - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/nes_gcl_square_stream.py b/5-Applications/scripts/nes_gcl_square_stream.py deleted file mode 100644 index 45611dd9..00000000 --- a/5-Applications/scripts/nes_gcl_square_stream.py +++ /dev/null @@ -1,661 +0,0 @@ -#!/usr/bin/env python3 -""" -NES Square Wave GCL Streaming System -Compresses square wave parameters using DeltaGCL for cartridge streaming. - -NES Square Wave Parameters (per frame): -- Frequency: 11-bit divider (0-2047) -- Duty Cycle: 2-bit (0-3: 12.5%, 25%, 50%, 75%) -- Volume: 4-bit envelope (0-15) -- Sweep Enable: 1-bit -- Sweep Period: 3-bit -- Sweep Direction: 1-bit -- Sweep Shift: 3-bit - -Total per frame: 25 bits uncompressed - -GCL Compression Strategy: -1. Delta Encoding: Store only changes from previous frame -2. PTOS Dictionary: Common musical patterns as single-byte indices -3. Variable-Length GCL: Frequent sequences use shorter encoding - -Target: 700× compression (25 bits → ~4 bytes with delta + dictionary) -""" - -import struct -import json -from typing import List, Tuple, Optional, Dict -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# NES Square Wave Parameter Structures -# ═══════════════════════════════════════════════════════════════════════════ - -class DutyCycle(Enum): - """NES square wave duty cycles""" - DUTY_12_5 = 0 # 12.5% - DUTY_25 = 1 # 25% - DUTY_50 = 2 # 50% - DUTY_75 = 3 # 75% - -@dataclass -class SquareWaveFrame: - """Single frame of NES square wave parameters""" - frequency: int # 11-bit (0-2047) - duty: DutyCycle # 2-bit - volume: int # 4-bit (0-15) - sweep_enable: bool # 1-bit - sweep_period: int # 3-bit (0-7) - sweep_direction: bool # 1-bit (0=down, 1=up) - sweep_shift: int # 3-bit (0-7) - - def to_bits(self) -> int: - """Pack into 25-bit integer""" - bits = 0 - bits |= (self.frequency & 0x7FF) << 14 # 11 bits at position 14 - bits |= (self.duty.value & 0x3) << 12 # 2 bits at position 12 - bits |= (self.volume & 0xF) << 8 # 4 bits at position 8 - bits |= (int(self.sweep_enable) & 1) << 7 # 1 bit at position 7 - bits |= (self.sweep_period & 0x7) << 4 # 3 bits at position 4 - bits |= (int(self.sweep_direction) & 1) << 3 # 1 bit at position 3 - bits |= (self.sweep_shift & 0x7) << 0 # 3 bits at position 0 - return bits - - @staticmethod - def from_bits(bits: int) -> 'SquareWaveFrame': - """Unpack from 25-bit integer""" - return SquareWaveFrame( - frequency=(bits >> 14) & 0x7FF, - duty=DutyCycle((bits >> 12) & 0x3), - volume=(bits >> 8) & 0xF, - sweep_enable=bool((bits >> 7) & 1), - sweep_period=(bits >> 4) & 0x7, - sweep_direction=bool((bits >> 3) & 1), - sweep_shift=bits & 0x7 - ) - - def to_bytes(self) -> bytes: - """Pack into 4 bytes (25 bits fits in 32)""" - return struct.pack(' 'SquareWaveFrame': - """Unpack from 4 bytes""" - bits = struct.unpack(' DeltaEncoding: - """Compute delta between two frames""" - if previous is None: - return DeltaEncoding( - has_delta=False, - changed_fields=[], - delta_bits=0 - ) - - changed_fields = [] - delta_bits = 0 - - if current.frequency != previous.frequency: - changed_fields.append('frequency') - delta_bits += 11 - - if current.duty != previous.duty: - changed_fields.append('duty') - delta_bits += 2 - - if current.volume != previous.volume: - changed_fields.append('volume') - delta_bits += 4 - - if current.sweep_enable != previous.sweep_enable: - changed_fields.append('sweep_enable') - delta_bits += 1 - - if current.sweep_period != previous.sweep_period: - changed_fields.append('sweep_period') - delta_bits += 3 - - if current.sweep_direction != previous.sweep_direction: - changed_fields.append('sweep_direction') - delta_bits += 1 - - if current.sweep_shift != previous.sweep_shift: - changed_fields.append('sweep_shift') - delta_bits += 3 - - return DeltaEncoding( - has_delta=len(changed_fields) > 0, - changed_fields=changed_fields, - delta_bits=delta_bits - ) - -# ═══════════════════════════════════════════════════════════════════════════ -# GCL Compressed Frame -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class GCLCompressedFrame: - """GCL-compressed square wave frame""" - marker: str # 'D' for delta, 'F' for full, 'P' for pattern - pattern_byte: int # PTOS dictionary index (if pattern) - field_codes: int # Changed field codes (if delta) - frequency_delta: int # Frequency delta (if changed) - duty_delta: int # Duty delta (if changed) - volume_delta: int # Volume delta (if changed) - sweep_delta: int # Sweep parameters delta (if changed) - - def to_bytes(self) -> bytes: - """Pack into bytes""" - result = bytearray() - result.append(ord(self.marker)) - - if self.marker == 'P': - result.append(self.pattern_byte) - elif self.marker == 'D': - result.append(self.field_codes) - if self.field_codes & 0x01: # frequency changed - result.extend(struct.pack(' GCLCompressedFrame: - """Compress a single frame using GCL""" - delta = compute_delta(frame, previous) - - # Check for pattern match (simple heuristic) - if not delta.has_delta and previous is not None: - # No change - could be silence or sustain - if frame.volume == 0: - return GCLCompressedFrame( - marker='P', - pattern_byte=MusicalPattern.SILENCE.value, - field_codes=0, - frequency_delta=0, - duty_delta=0, - volume_delta=0, - sweep_delta=0 - ) - elif frame.frequency == previous.frequency: - return GCLCompressedFrame( - marker='P', - pattern_byte=MusicalPattern.VOLUME_SUSTAIN.value, - field_codes=0, - frequency_delta=0, - duty_delta=0, - volume_delta=0, - sweep_delta=0 - ) - - if delta.has_delta and previous is not None: - # Delta encoding - field_codes = 0 - if 'frequency' in delta.changed_fields: - field_codes |= 0x01 - if 'duty' in delta.changed_fields: - field_codes |= 0x02 - if 'volume' in delta.changed_fields: - field_codes |= 0x04 - if 'sweep_enable' in delta.changed_fields or 'sweep_period' in delta.changed_fields or \ - 'sweep_direction' in delta.changed_fields or 'sweep_shift' in delta.changed_fields: - field_codes |= 0x08 - - # Pack sweep parameters into single byte - sweep_packed = 0 - if 'sweep_enable' in delta.changed_fields: - sweep_packed |= (int(frame.sweep_enable) & 1) << 7 - if 'sweep_period' in delta.changed_fields: - sweep_packed |= (frame.sweep_period & 0x7) << 4 - if 'sweep_direction' in delta.changed_fields: - sweep_packed |= (int(frame.sweep_direction) & 1) << 3 - if 'sweep_shift' in delta.changed_fields: - sweep_packed |= (frame.sweep_shift & 0x7) << 0 - - return GCLCompressedFrame( - marker='D', - pattern_byte=0, - field_codes=field_codes, - frequency_delta=frame.frequency - previous.frequency if 'frequency' in delta.changed_fields else 0, - duty_delta=frame.duty.value - previous.duty.value if 'duty' in delta.changed_fields else 0, - volume_delta=frame.volume - previous.volume if 'volume' in delta.changed_fields else 0, - sweep_delta=sweep_packed - ) - else: - # Full frame - sweep_packed = (int(frame.sweep_enable) & 1) << 7 | \ - (frame.sweep_period & 0x7) << 4 | \ - (int(frame.sweep_direction) & 1) << 3 | \ - (frame.sweep_shift & 0x7) << 0 - - return GCLCompressedFrame( - marker='F', - pattern_byte=0, - field_codes=0, - frequency_delta=frame.frequency, - duty_delta=frame.duty.value, - volume_delta=frame.volume, - sweep_delta=sweep_packed - ) - -def decompress_frame(compressed: GCLCompressedFrame, previous: Optional[SquareWaveFrame] = None) -> SquareWaveFrame: - """Decompress a single frame""" - if compressed.marker == 'F': - # Full frame - sweep_enable = bool((compressed.sweep_delta >> 7) & 1) - sweep_period = (compressed.sweep_delta >> 4) & 0x7 - sweep_direction = bool((compressed.sweep_delta >> 3) & 1) - sweep_shift = compressed.sweep_delta & 0x7 - - return SquareWaveFrame( - frequency=compressed.frequency_delta, - duty=DutyCycle(compressed.duty_delta), - volume=compressed.volume_delta, - sweep_enable=sweep_enable, - sweep_period=sweep_period, - sweep_direction=sweep_direction, - sweep_shift=sweep_shift - ) - elif compressed.marker == 'D': - # Delta frame - if previous is None: - raise ValueError("Delta decompression requires previous frame") - - frequency = previous.frequency - duty = previous.duty - volume = previous.volume - sweep_enable = previous.sweep_enable - sweep_period = previous.sweep_period - sweep_direction = previous.sweep_direction - sweep_shift = previous.sweep_shift - - if compressed.field_codes & 0x01: # frequency - frequency = previous.frequency + compressed.frequency_delta - if compressed.field_codes & 0x02: # duty - duty = DutyCycle((previous.duty.value + compressed.duty_delta) & 0x3) - if compressed.field_codes & 0x04: # volume - volume = (previous.volume + compressed.volume_delta) & 0xF - if compressed.field_codes & 0x08: # sweep - sweep_enable = bool((compressed.sweep_delta >> 7) & 1) - sweep_period = (compressed.sweep_delta >> 4) & 0x7 - sweep_direction = bool((compressed.sweep_delta >> 3) & 1) - sweep_shift = compressed.sweep_delta & 0x7 - - return SquareWaveFrame( - frequency=frequency, - duty=duty, - volume=volume, - sweep_enable=sweep_enable, - sweep_period=sweep_period, - sweep_direction=sweep_direction, - sweep_shift=sweep_shift - ) - elif compressed.marker == 'P': - # Pattern frame - if previous is None: - # Default to silence - return SquareWaveFrame( - frequency=0, - duty=DutyCycle.DUTY_50, - volume=0, - sweep_enable=False, - sweep_period=0, - sweep_direction=False, - sweep_shift=0 - ) - - pattern = MusicalPattern(compressed.pattern_byte) - - if pattern == MusicalPattern.SILENCE: - return SquareWaveFrame( - frequency=previous.frequency, - duty=previous.duty, - volume=0, - sweep_enable=False, - sweep_period=0, - sweep_direction=False, - sweep_shift=0 - ) - elif pattern == MusicalPattern.VOLUME_SUSTAIN: - return previous - else: - # For other patterns, return previous (simplified) - return previous - else: - raise ValueError(f"Unknown marker: {compressed.marker}") - -# ═══════════════════════════════════════════════════════════════════════════ -# Stream Compression/Decompression -# ═══════════════════════════════════════════════════════════════════════════ - -def compress_stream(frames: List[SquareWaveFrame]) -> bytes: - """Compress a stream of frames""" - result = bytearray() - previous = None - - for frame in frames: - compressed = compress_frame(frame, previous) - result.extend(compressed.to_bytes()) - previous = frame - - return bytes(result) - -def decompress_stream(data: bytes, frame_count: int) -> List[SquareWaveFrame]: - """Decompress a stream of frames""" - frames = [] - offset = 0 - previous = None - - for _ in range(frame_count): - if offset >= len(data): - break - - marker = chr(data[offset]) - offset += 1 - - if marker == 'P': - pattern_byte = data[offset] - offset += 1 - compressed = GCLCompressedFrame( - marker=marker, - pattern_byte=pattern_byte, - field_codes=0, - frequency_delta=0, - duty_delta=0, - volume_delta=0, - sweep_delta=0 - ) - elif marker == 'D': - field_codes = data[offset] - offset += 1 - - freq_delta = 0 - duty_delta = 0 - vol_delta = 0 - sweep_delta = 0 - - if field_codes & 0x01: - freq_delta = struct.unpack(' bytes: - return struct.pack('<4sHHII', self.magic, self.version, self.frame_count, - self.sample_rate, self.compressed_size) - - @staticmethod - def from_bytes(data: bytes) -> 'CartridgeHeader': - magic, version, frame_count, sample_rate, compressed_size = \ - struct.unpack('<4sHHII', data[:16]) - return CartridgeHeader(magic, version, frame_count, sample_rate, compressed_size) - -def create_cartridge(frames: List[SquareWaveFrame]) -> bytes: - """Create a cartridge ROM image with compressed audio""" - compressed = compress_stream(frames) - header = CartridgeHeader( - frame_count=len(frames), - compressed_size=len(compressed) - ) - - return header.to_bytes() + compressed - -def load_cartridge(data: bytes) -> List[SquareWaveFrame]: - """Load frames from cartridge ROM image""" - header = CartridgeHeader.from_bytes(data[:16]) - compressed_data = data[16:16+header.compressed_size] - return decompress_stream(compressed_data, header.frame_count) - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def generate_test_sequence() -> List[SquareWaveFrame]: - """Generate a test square wave sequence""" - frames = [] - - # A major scale (A4: 440Hz) - # NES frequency divider: CPU_freq / (16 * (divider + 1)) - # CPU_freq = 1.79MHz, so for 440Hz: divider ≈ 254 - base_freq = 254 - - # Frequencies for A major scale - frequencies = [ - base_freq, # A4 - base_freq // 2, # A5 (octave up, divider down) - base_freq // 2 * 2 // 3, # E5 (fifth) - base_freq // 2 * 3 // 4, # D5 (fourth) - base_freq // 2 * 4 // 5, # C#5 (major third) - base_freq // 2 * 5 // 6, # B4 (major second) - base_freq, # A4 - base_freq // 2, # A5 - ] - - for freq in frequencies: - # Attack: volume ramps up - for vol in range(0, 16, 2): - frames.append(SquareWaveFrame( - frequency=freq, - duty=DutyCycle.DUTY_50, - volume=vol, - sweep_enable=False, - sweep_period=0, - sweep_direction=False, - sweep_shift=0 - )) - - # Sustain: hold volume - for _ in range(10): - frames.append(SquareWaveFrame( - frequency=freq, - duty=DutyCycle.DUTY_50, - volume=15, - sweep_enable=False, - sweep_period=0, - sweep_direction=False, - sweep_shift=0 - )) - - # Release: volume ramps down - for vol in range(14, -1, -2): - frames.append(SquareWaveFrame( - frequency=freq, - duty=DutyCycle.DUTY_50, - volume=vol, - sweep_enable=False, - sweep_period=0, - sweep_direction=False, - sweep_shift=0 - )) - - return frames - -def run_test(): - """Run compression test""" - print("=" * 70) - print("NES SQUARE WAVE GCL STREAMING TEST") - print("=" * 70) - - # Generate test sequence - frames = generate_test_sequence() - print(f"\n[*] Generated {len(frames)} frames") - - # Calculate uncompressed size - uncompressed_size = len(frames) * 4 # 4 bytes per frame - print(f"[*] Uncompressed size: {uncompressed_size} bytes") - - # Compress - compressed = compress_stream(frames) - print(f"[*] Compressed size: {len(compressed)} bytes") - print(f"[*] Compression ratio: {uncompressed_size / len(compressed):.2f}×") - print(f"[*] Space saved: {100 * (1 - len(compressed) / uncompressed_size):.1f}%") - - # Create cartridge - cartridge = create_cartridge(frames) - print(f"[*] Cartridge size (with header): {len(cartridge)} bytes") - - # Decompress and verify - decompressed = load_cartridge(cartridge) - print(f"[*] Decompressed {len(decompressed)} frames") - - # Verify integrity - errors = 0 - for i, (orig, decomp) in enumerate(zip(frames, decompressed)): - if orig.to_bits() != decomp.to_bits(): - errors += 1 - if errors <= 5: # Show first 5 errors - print(f" [!] Frame {i}: Mismatch") - print(f" Original: {orig.to_bits():025b}") - print(f" Decompressed: {decomp.to_bits():025b}") - - if errors == 0: - print("[✓] All frames verified successfully") - else: - print(f"[✗] {errors} frames failed verification") - - # Analyze compression statistics - delta_frames = sum(1 for f in frames if compute_delta(f, (frames[i-1] if i > 0 else None)).has_delta) - full_frames = len(frames) - delta_frames - pattern_frames = sum(1 for i, f in enumerate(frames) - if not compute_delta(f, (frames[i-1] if i > 0 else None)).has_delta and f.volume == 0) - - print(f"\n[*] Compression statistics:") - print(f" Delta frames: {delta_frames} ({100*delta_frames/len(frames):.1f}%)") - print(f" Full frames: {full_frames} ({100*full_frames/len(frames):.1f}%)") - print(f" Pattern frames (silence): {pattern_frames} ({100*pattern_frames/len(frames):.1f}%)") - - print("\n" + "=" * 70) - print("TEST COMPLETE") - print("=" * 70) - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/nes_oisc_gcl_lut.py b/5-Applications/scripts/nes_oisc_gcl_lut.py deleted file mode 100644 index eaec2f9d..00000000 --- a/5-Applications/scripts/nes_oisc_gcl_lut.py +++ /dev/null @@ -1,517 +0,0 @@ -#!/usr/bin/env python3 -""" -NES OISC-GCL-LUT Architecture -INSANE: NES controller port JTAG → SUBLEQ OISC → GCL decompression → LUT → NES audio - -Architecture: -1. NES bitbangs JTAG over controller port -2. JTAG controls SUBLEQ OISC (One Instruction Set Computer) -3. SUBLEQ executes GCL decompression algorithm -4. Decompressed data fills LUT (Look-Up Table) -5. NES reads LUT via JTAG -6. NES 6502 generates square waves from LUT data - -This is MAXIMUM INSANITY: -- 1985 NES hardware -- 1990s JTAG protocol -- SUBLEQ OISC (minimalist instruction set) -- GCL compression (your nanokernel stack) -- LUT-based square wave generation - -The NES becomes a co-processor for its own audio decompression. -""" - -import struct -from typing import List, Tuple, Dict, Optional -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# SUBLEQ OISC (One Instruction Set Computer) -# Instruction: subleq a, b, c (M[b] = M[b] - M[a]; if M[b] <= 0 goto c) -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class SUBLEQInstruction: - """SUBLEQ instruction (3 operands)""" - a: int # Source address - b: int # Destination address - c: int # Jump address (if result <= 0) - -class SUBLEQVM: - """SUBLEQ Virtual Machine""" - - def __init__(self, memory_size: int = 65536): - self.memory = [0] * memory_size - self.pc = 0 # Program counter - self.halted = False - self.cycle_count = 0 - - def load_program(self, instructions: List[Tuple[int, int, int]]): - """Load SUBLEQ program into memory""" - for i, (a, b, c) in enumerate(instructions): - self.memory[i * 3] = a - self.memory[i * 3 + 1] = b - self.memory[i * 3 + 2] = c - - def step(self) -> bool: - """Execute one SUBLEQ instruction""" - if self.halted: - return False - - a = self.memory[self.pc] - b = self.memory[self.pc + 1] - c = self.memory[self.pc + 2] - - # Execute: M[b] = M[b] - M[a] - self.memory[b] = self.memory[b] - self.memory[a] - - # Check: if M[b] <= 0 goto c - if self.memory[b] <= 0: - self.pc = c - else: - self.pc += 3 - - self.cycle_count += 1 - return True - - def run(self, max_cycles: int = 1000000): - """Run SUBLEQ program""" - while not self.halted and self.cycle_count < max_cycles: - if not self.step(): - break - - def read_memory(self, address: int) -> int: - """Read memory location""" - return self.memory[address] - - def write_memory(self, address: int, value: int): - """Write memory location""" - self.memory[address] = value - -# ═══════════════════════════════════════════════════════════════════════════ -# GCL Decompression in SUBLEQ -# ═══════════════════════════════════════════════════════════════════════════ - -class GCLSUBLEQDecoder: - """GCL decoder implemented in SUBLEQ""" - - def __init__(self, vm: SUBLEQVM): - self.vm = vm - # Memory layout: - # 0-99: Program code - # 100-199: GCL compressed data - # 200-299: LUT output (square wave parameters) - # 300-399: Scratch variables - - def generate_decompressor(self) -> List[Tuple[int, int, int]]: - """ - Generate SUBLEQ code for GCL decompression. - - GCL format: - - Marker byte: 'D' (delta), 'F' (full), 'P' (pattern) - - Delta: field_codes + deltas - - Full: frequency + duty + volume + sweep - - Pattern: pattern_byte - """ - code = [] - - # Initialize variables - # mem[300] = input_ptr (points to GCL data at 100) - # mem[301] = output_ptr (points to LUT at 200) - # mem[302] = current_frame (previous frame for delta) - # mem[303] = temp - - code.append((300, 300, 1)) # Zero input_ptr - code.append((300, 300, 2)) # Zero output_ptr - code.append((302, 302, 3)) # Zero current_frame - - # Set input_ptr = 100, output_ptr = 200 - code.append((304, 300, 4)) # temp = input_ptr - code.append((305, 304, 5)) # input_ptr = 100 (const) - code.append((306, 301, 6)) # temp = output_ptr - code.append((307, 306, 7)) # output_ptr = 200 (const) - - # Main loop: read marker - code.append((300, 303, 8)) # temp = M[input_ptr] - code.append((303, 308, 9)) # Check if marker = 'D' (68) - code.append((308, 308, 10)) # If equal, goto delta_decode - code.append((303, 309, 11)) # Check if marker = 'F' (70) - code.append((309, 309, 12)) # If equal, goto full_decode - code.append((303, 310, 13)) # Check if marker = 'P' (80) - code.append((310, 310, 14)) # If equal, goto pattern_decode - code.append((0, 0, 15)) # Halt (unknown marker) - - # Delta decode (simplified) - # Read field_codes, apply deltas to current_frame - # Write to LUT - - # Full decode (simplified) - # Read frequency, duty, volume, sweep - # Write to LUT - - # Pattern decode (simplified) - # Read pattern_byte, apply pattern - # Write to LUT - - # Increment pointers and loop - code.append((300, 300, 16)) # input_ptr++ - code.append((301, 301, 17)) # output_ptr++ - code.append((0, 0, 8)) # Loop back to marker read - - # Constants - code.append((0, 0, 100)) # const_100 = 100 - code.append((0, 0, 200)) # const_200 = 200 - code.append((0, 0, 68)) # const_D = 68 ('D') - code.append((0, 0, 70)) # const_F = 70 ('F') - code.append((0, 0, 80)) # const_P = 80 ('P') - - return code - -# ═══════════════════════════════════════════════════════════════════════════ -# NES Square Wave LUT -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class SquareWaveLUTEntry: - """LUT entry for square wave parameters""" - frequency: int # 11-bit (0-2047) - duty: int # 2-bit (0-3) - volume: int # 4-bit (0-15) - sweep_enable: int # 1-bit - sweep_period: int # 3-bit (0-7) - sweep_direction: int # 1-bit - sweep_shift: int # 3-bit (0-7) - - def to_int(self) -> int: - """Pack into 25-bit integer (stored as 32-bit in LUT)""" - bits = 0 - bits |= (self.frequency & 0x7FF) << 14 - bits |= (self.duty & 0x3) << 12 - bits |= (self.volume & 0xF) << 8 - bits |= (self.sweep_enable & 1) << 7 - bits |= (self.sweep_period & 0x7) << 4 - bits |= (self.sweep_direction & 1) << 3 - bits |= (self.sweep_shift & 0x7) << 0 - return bits - - @staticmethod - def from_int(data: int) -> 'SquareWaveLUTEntry': - """Unpack from integer""" - return SquareWaveLUTEntry( - frequency=(data >> 14) & 0x7FF, - duty=(data >> 12) & 0x3, - volume=(data >> 8) & 0xF, - sweep_enable=(data >> 7) & 1, - sweep_period=(data >> 4) & 0x7, - sweep_direction=(data >> 3) & 1, - sweep_shift=data & 0x7 - ) - -class NESLUT: - """NES Square Wave Look-Up Table""" - - def __init__(self, size: int = 256): - self.size = size - self.entries: List[SquareWaveLUTEntry] = [SquareWaveLUTEntry(0, 0, 0, 0, 0, 0, 0)] * size - - def set_entry(self, index: int, entry: SquareWaveLUTEntry): - """Set LUT entry""" - if 0 <= index < self.size: - self.entries[index] = entry - - def get_entry(self, index: int) -> SquareWaveLUTEntry: - """Get LUT entry""" - if 0 <= index < self.size: - return self.entries[index] - return SquareWaveLUTEntry(0, 0, 0, 0, 0, 0, 0) - - def to_memory(self) -> List[int]: - """Convert to memory format (for SUBLEQ)""" - return [entry.to_int() for entry in self.entries] - -# ═══════════════════════════════════════════════════════════════════════════ -# Full Pipeline: JTAG → SUBLEQ → GCL → LUT → NES -# ═══════════════════════════════════════════════════════════════════════════ - -class NESOISCGCLPipeline: - """Complete NES OISC-GCL-LUT pipeline""" - - def __init__(self): - # Create SUBLEQ VM - self.vm = SUBLEQVM(memory_size=65536) - - # Create GCL decoder - self.decoder = GCLSUBLEQDecoder(self.vm) - - # Create LUT - self.lut = NESLUT(size=256) - - # Load SUBLEQ GCL decompressor - decompressor = self.decoder.generate_decompressor() - self.vm.load_program(decompressor) - - def load_gcl_data(self, gcl_bytes: bytes): - """Load GCL compressed data into SUBLEQ memory""" - for i, byte in enumerate(gcl_bytes): - self.vm.write_memory(100 + i, byte) - - def decompress(self, max_cycles: int = 100000): - """Run SUBLEQ GCL decompression""" - self.vm.run(max_cycles) - - # Read LUT from SUBLEQ memory - for i in range(self.lut.size): - lut_value = self.vm.read_memory(200 + i * 4) # Each entry is 4 bytes - entry = SquareWaveLUTEntry.from_int(lut_value) - self.lut.set_entry(i, entry) - - def get_lut_entry(self, index: int) -> SquareWaveLUTEntry: - """Get LUT entry (NES would read this via JTAG)""" - return self.lut.get_entry(index) - -# ═══════════════════════════════════════════════════════════════════════════ -# NES 6502 Assembly: Read LUT via JTAG -# ═══════════════════════════════════════════════════════════════════════════ - -def generate_nes_6502_lut_reader() -> str: - """ - Generate 6502 assembly to read LUT via JTAG. - - The NES: - 1. Bitbangs JTAG to request LUT entry index - 2. SUBLEQ OISC decompresses GCL and fills LUT - 3. NES reads LUT entry via JTAG - 4. NES generates square wave from LUT data - """ - return """ -; ═══════════════════════════════════════════════════════════════════════════ -; NES 6502 LUT Reader via JTAG -; Reads square wave parameters from SUBLEQ OISC LUT -; ═══════════════════════════════════════════════════════════════════════════ - -; Zero Page Variables -zp_lut_index = $00 ; LUT index to read -zp_lut_data = $01 ; LUT data (lo) -zp_lut_data_h = $02 ; LUT data (hi) -zp_freq_lo = $03 ; Frequency (lo) -zp_freq_hi = $04 ; Frequency (hi) -zp_duty_vol = $05 ; Duty + volume -zp_sweep = $06 ; Sweep parameters - -; NES APU Registers -APU_SQ1_FREQ = $4000 ; Square 1 frequency (lo) -APU_SQ1_FREQ_H = $4001 ; Square 1 frequency (hi) -APU_SQ1_DUTY = $4002 ; Square 1 duty + volume + sweep -APU_SQ1_SWEEP = $4003 ; Square 1 sweep - -; ═══════════════════════════════════════════════════════════════════════════ -; Read LUT Entry via JTAG -; Input: A = LUT index -; Output: zp_freq_lo, zp_freq_hi, zp_duty_vol, zp_sweep -; ═══════════════════════════════════════════════════════════════════════════ - -read_lut_entry: - STA zp_lut_index ; Store LUT index - - ; Bitbang JTAG to request LUT entry - ; TDI = LUT index (8 bits) - ; TMS = 0 (stay in SHIFT_DR) - ; Read TDO = LUT data (32 bits) - - ; For simulation, we'll just calculate LUT entry - ; In real hardware, this would be JTAG bitbanging - - ; Calculate LUT entry address = 200 + index * 4 - LDA #$00 - STA zp_lut_data_h - LDA zp_lut_index - ASL ; * 2 - ASL ; * 4 - ADC #$C8 ; + 200 (0xC8) - STA zp_lut_data - BCC no_carry - INC zp_lut_data_h -no_carry: - - ; Read LUT data (4 bytes) - ; For simulation, we'll use a simple pattern - ; freq = base_freq + index * 10 - - LDA zp_lut_index - ASL - ASL - ASL - ADC zp_lut_index - STA zp_freq_lo ; freq_lo = index * 9 - LDA #$00 - STA zp_freq_hi - - ; duty = index % 4 - LDA zp_lut_index - AND #$03 - ASL ; duty in bits 6-7 - ASL - ASL - ASL - ASL - ASL - STA zp_duty_vol - - ; volume = 15 (max) - LDA #$0F - ORA zp_duty_vol - STA zp_duty_vol - - ; sweep = 0 (no sweep) - LDA #$00 - STA zp_sweep - - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; Apply LUT Entry to NES APU -; Input: zp_freq_lo, zp_freq_hi, zp_duty_vol, zp_sweep -; ═══════════════════════════════════════════════════════════════════════════ - -apply_lut_to_apu: - ; Write frequency - LDA zp_freq_lo - STA APU_SQ1_FREQ - LDA zp_freq_hi - STA APU_SQ1_FREQ_H - - ; Write duty + volume + sweep enable - LDA zp_duty_vol - STA APU_SQ1_DUTY - - ; Write sweep - LDA zp_sweep - STA APU_SQ1_SWEEP - - RTS - -; ═══════════════════════════════════════════════════════════════════════════ -; Main Audio Loop -; ═══════════════════════════════════════════════════════════════════════════ - -audio_loop: - ; Read LUT entry 0 - LDA #$00 - JSR read_lut_entry - JSR apply_lut_to_apu - - ; Wait (simple delay) - LDA #$FF -delay_loop: - DEC - BNE delay_loop - - ; Read LUT entry 1 - LDA #$01 - JSR read_lut_entry - JSR apply_lut_to_apu - - ; Wait - LDA #$FF -delay_loop2: - DEC - BNE delay_loop2 - - ; Loop - JMP audio_loop - -; ═══════════════════════════════════════════════════════════════════════════ -; Main Entry Point -; ═══════════════════════════════════════════════════════════════════════════ - -main: - ; Initialize APU - LDA #$00 - STA APU_SQ1_FREQ - STA APU_SQ1_FREQ_H - STA APU_SQ1_DUTY - STA APU_SQ1_SWEEP - - ; Start audio loop - JMP audio_loop -""" - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run NES OISC-GCL-LUT pipeline test""" - print("=" * 70) - print("NES OISC-GCL-LUT PIPELINE TEST") - print("=" * 70) - print("\n[*] INSANITY LEVEL: MAXIMUM") - print("[*] Architecture:") - print(" NES controller port → JTAG bitbanging") - print(" JTAG → SUBLEQ OISC control") - print(" SUBLEQ → GCL decompression") - print(" GCL → LUT (Look-Up Table)") - print(" LUT → NES 6502 reads via JTAG") - print(" NES APU → Square wave output") - - # Create pipeline - print("\n[*] Creating SUBLEQ VM...") - pipeline = NESOISCGCLPipeline() - - # Load sample GCL data (simplified) - print("[*] Loading GCL compressed data...") - gcl_data = bytes([ - ord('F'), 0x00, 0x10, 0x0F, 0x00, # Full frame: freq=16, duty=0, vol=15, sweep=0 - ord('D'), 0x01, 0x02, 0x01, 0x00, # Delta: freq+=2, vol+=1 - ord('D'), 0x05, 0x02, 0xFF, 0x00, # Delta: freq+=2, vol-=1 - ord('P'), 0x00, # Pattern: silence - ]) - pipeline.load_gcl_data(gcl_data) - print("[*] Loaded {} bytes of GCL data".format(len(gcl_data))) - - # Run decompression - print("\n[*] Running SUBLEQ GCL decompression...") - pipeline.decompress(max_cycles=1000) - print("[*] SUBLEQ cycles: {}".format(pipeline.vm.cycle_count)) - print("[*] SUBLEQ halted: {}".format(pipeline.vm.halted)) - - # Read LUT entries - print("\n[*] Reading LUT entries...") - for i in range(8): - entry = pipeline.get_lut_entry(i) - if entry.frequency > 0 or entry.volume > 0: - print(" LUT[{}]: freq={}, duty={}, vol={}, sweep={}".format( - i, entry.frequency, entry.duty, entry.volume, entry.sweep_enable)) - - # Generate 6502 assembly - print("\n[*] Generating NES 6502 LUT reader assembly...") - assembly = generate_nes_6502_lut_reader() - print("[*] Generated {} bytes of assembly".format(len(assembly))) - - # Save assembly - with open('/home/allaun/Documents/Research Stack/scripts/nes_oisc_lut_6502.asm', 'w') as f: - f.write(assembly) - print("[*] Saved to 5-Applications/scripts/nes_oisc_lut_6502.asm") - - # Save SUBLEQ program - print("\n[*] Saving SUBLEQ GCL decompressor...") - with open('/home/allaun/Documents/Research Stack/scripts/subleq_gcl_decompressor.bin', 'wb') as f: - for i in range(100): - f.write(struct.pack(' float: - """Convert audio signal to mathematical value""" - # Frequency represents magnitude - # Amplitude represents precision - # Duty cycle represents sign - sign = 1.0 if self.duty_cycle >= 0.5 else -1.0 - return sign * self.frequency * self.amplitude - - @staticmethod - def from_value(value: float, base_freq: float = 440.0) -> 'AudioSignal': - """Convert mathematical value to audio signal""" - sign = 1.0 if value >= 0 else -1.0 - magnitude = abs(value) - - # Frequency represents magnitude (logarithmic scale) - frequency = base_freq * (1.0 + magnitude) - - # Amplitude represents precision (normalize to 0-1) - amplitude = min(magnitude / 1000.0, 1.0) - - # Duty cycle represents sign - duty_cycle = 0.75 if sign > 0 else 0.25 - - return AudioSignal(frequency, amplitude, duty_cycle) - -# ═══════════════════════════════════════════════════════════════════════════ -# DSP Operations Using NES APU -# Use APU mixing, filtering, and modulation as mathematical operations -# ═══════════════════════════════════════════════════════════════════════════ - -class NESDSPMath: - """DSP mathematical operations using NES audio lines""" - - @staticmethod - def add(signals: List[AudioSignal]) -> AudioSignal: - """ - Addition: Mix audio signals (APU mixing). - - In NES APU, multiple channels are mixed together. - This mixing operation can represent addition. - """ - if not signals: - return AudioSignal(0, 0, 0.5) - - # Mix frequencies (weighted average) - total_freq = sum(s.frequency * s.amplitude for s in signals) - total_amp = sum(s.amplitude for s in signals) - - if total_amp == 0: - return AudioSignal(0, 0, 0.5) - - avg_freq = total_freq / total_amp - avg_amp = min(total_amp / len(signals), 1.0) - - # Determine sign from majority duty cycle - positive_count = sum(1 for s in signals if s.duty_cycle >= 0.5) - duty_cycle = 0.75 if positive_count > len(signals) / 2 else 0.25 - - return AudioSignal(avg_freq, avg_amp, duty_cycle) - - @staticmethod - def multiply(signal1: AudioSignal, signal2: AudioSignal) -> AudioSignal: - """ - Multiplication: Modulate amplitude. - - In NES APU, amplitude modulation can represent multiplication. - """ - # Multiply amplitudes - new_amp = signal1.amplitude * signal2.amplitude - - # Multiply frequencies (geometric mean for audio) - new_freq = math.sqrt(signal1.frequency * signal2.frequency) - - # XOR duty cycles for sign - sign1 = 1 if signal1.duty_cycle >= 0.5 else -1 - sign2 = 1 if signal2.duty_cycle >= 0.5 else -1 - new_sign = sign1 * sign2 - new_duty = 0.75 if new_sign > 0 else 0.25 - - return AudioSignal(new_freq, new_amp, new_duty) - - @staticmethod - def subtract(signal1: AudioSignal, signal2: AudioSignal) -> AudioSignal: - """ - Subtraction: Invert and add. - - In NES APU, phase inversion can represent negation. - """ - # Invert signal2 (phase shift by 180° = duty cycle flip) - inverted_signal2 = AudioSignal( - signal2.frequency, - signal2.amplitude, - 0.75 if signal2.duty_cycle < 0.5 else 0.25 - ) - - return NESDSPMath.add([signal1, inverted_signal2]) - - @staticmethod - def divide(signal1: AudioSignal, signal2: AudioSignal) -> AudioSignal: - """ - Division: Frequency ratio. - - In NES APU, frequency division can represent division. - """ - if signal2.frequency == 0: - return AudioSignal(0, 0, 0.5) - - # Divide frequencies - new_freq = signal1.frequency / signal2.frequency - - # Divide amplitudes - new_amp = signal1.amplitude / signal2.amplitude if signal2.amplitude > 0 else 0 - - # XOR duty cycles for sign - sign1 = 1 if signal1.duty_cycle >= 0.5 else -1 - sign2 = 1 if signal2.duty_cycle >= 0.5 else -1 - new_sign = sign1 * sign2 - new_duty = 0.75 if new_sign > 0 else 0.25 - - return AudioSignal(new_freq, min(new_amp, 1.0), new_duty) - - @staticmethod - def integrate(signals: List[AudioSignal]) -> AudioSignal: - """ - Integration: Accumulate over time. - - In NES APU, envelope generation can represent integration. - """ - if not signals: - return AudioSignal(0, 0, 0.5) - - # Integrate frequencies (cumulative sum) - total_freq = sum(s.frequency for s in signals) - - # Integrate amplitudes (cumulative sum, normalized) - total_amp = min(sum(s.amplitude for s in signals), 1.0) - - # Use last signal's duty cycle - duty_cycle = signals[-1].duty_cycle if signals else 0.5 - - return AudioSignal(total_freq, total_amp, duty_cycle) - - @staticmethod - def differentiate(signals: List[AudioSignal]) -> AudioSignal: - """ - Differentiation: Rate of change. - - In NES APU, sweep modulation can represent differentiation. - """ - if len(signals) < 2: - return AudioSignal(0, 0, 0.5) - - # Differentiate frequencies (difference) - freq_diff = signals[-1].frequency - signals[-2].frequency - - # Differentiate amplitudes (difference) - amp_diff = signals[-1].amplitude - signals[-2].amplitude - - # Use last signal's duty cycle - duty_cycle = signals[-1].duty_cycle - - return AudioSignal(abs(freq_diff), abs(amp_diff), duty_cycle) - -# ═══════════════════════════════════════════════════════════════════════════ -# DSP Math Pipeline -# Chain multiple DSP operations using NES audio lines -# ═══════════════════════════════════════════════════════════════════════════ - -class NESDSPPipeline: - """Pipeline for DSP math operations on NES audio lines""" - - def __init__(self): - self.channels: Dict[NESAudioLine, AudioSignal] = { - NESAudioLine.SQUARE1: AudioSignal(0, 0, 0.5), - NESAudioLine.SQUARE2: AudioSignal(0, 0, 0.5), - NESAudioLine.TRIANGLE: AudioSignal(0, 0, 0.5), - NESAudioLine.NOISE: AudioSignal(0, 0, 0.5), - } - self.history: List[Dict[NESAudioLine, AudioSignal]] = [] - - def load_value(self, line: NESAudioLine, value: float, base_freq: float = 440.0): - """Load mathematical value into audio line""" - self.channels[line] = AudioSignal.from_value(value, base_freq) - - def add_channels(self, line1: NESAudioLine, line2: NESAudioLine, - output_line: NESAudioLine): - """Add two audio channels (mixing)""" - result = NESDSPMath.add([self.channels[line1], self.channels[line2]]) - self.channels[output_line] = result - - def multiply_channels(self, line1: NESAudioLine, line2: NESAudioLine, - output_line: NESAudioLine): - """Multiply two audio channels (amplitude modulation)""" - result = NESDSPMath.multiply(self.channels[line1], self.channels[line2]) - self.channels[output_line] = result - - def subtract_channels(self, line1: NESAudioLine, line2: NESAudioLine, - output_line: NESAudioLine): - """Subtract two audio channels (phase inversion + mixing)""" - result = NESDSPMath.subtract(self.channels[line1], self.channels[line2]) - self.channels[output_line] = result - - def integrate_channel(self, line: NESAudioLine, output_line: NESAudioLine): - """Integrate audio channel over time (envelope)""" - # Get history for this channel - channel_history = [state[line] for state in self.history] - result = NESDSPMath.integrate(channel_history) - self.channels[output_line] = result - - def differentiate_channel(self, line: NESAudioLine, output_line: NESAudioLine): - """Differentiate audio channel (sweep)""" - # Get history for this channel - channel_history = [state[line] for state in self.history] - result = NESDSPMath.differentiate(channel_history) - self.channels[output_line] = result - - def tick(self): - """Advance one time step (save history)""" - self.history.append(self.channels.copy()) - if len(self.history) > 100: # Keep last 100 states - self.history.pop(0) - - def get_value(self, line: NESAudioLine) -> float: - """Get mathematical value from audio line""" - return self.channels[line].to_value() - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run NES sound line DSP math test""" - print("=" * 70) - print("NES SOUND LINE DSP MATH - HORRIFICALLY WONDERFUL") - print("=" * 70) - - print("\n[*] Architecture:") - print(" Encode: mathematical value → audio signal (freq/amp/duty)") - print(" Compute: NES APU mixing/filtering/modulation") - print(" Decode: audio signal → mathematical value") - print(" Purpose: Hijack audio lines for general computation") - - # Create DSP pipeline - pipeline = NESDSPPipeline() - - # Load test values - print("\n[*] Loading test values into audio lines...") - pipeline.load_value(NESAudioLine.SQUARE1, 100.0) - pipeline.load_value(NESAudioLine.SQUARE2, 50.0) - print(" SQUARE1: 100.0 → {} Hz".format(pipeline.channels[NESAudioLine.SQUARE1].frequency)) - print(" SQUARE2: 50.0 → {} Hz".format(pipeline.channels[NESAudioLine.SQUARE2].frequency)) - - # Addition - print("\n[*] Performing addition (mixing)...") - pipeline.add_channels(NESAudioLine.SQUARE1, NESAudioLine.SQUARE2, NESAudioLine.TRIANGLE) - result = pipeline.get_value(NESAudioLine.TRIANGLE) - print(" Result: {:.2f} (expected: ~150.0)".format(result)) - - # Multiplication - print("\n[*] Performing multiplication (amplitude modulation)...") - pipeline.multiply_channels(NESAudioLine.SQUARE1, NESAudioLine.SQUARE2, NESAudioLine.NOISE) - result = pipeline.get_value(NESAudioLine.NOISE) - print(" Result: {:.2f} (expected: ~5000.0)".format(result)) - - # Subtraction - print("\n[*] Performing subtraction (phase inversion + mixing)...") - pipeline.subtract_channels(NESAudioLine.SQUARE1, NESAudioLine.SQUARE2, NESAudioLine.TRIANGLE) - result = pipeline.get_value(NESAudioLine.TRIANGLE) - print(" Result: {:.2f} (expected: ~50.0)".format(result)) - - # Integration - print("\n[*] Performing integration (envelope generation)...") - for i in range(5): - pipeline.load_value(NESAudioLine.SQUARE1, 10.0 * (i + 1)) - pipeline.tick() - - pipeline.integrate_channel(NESAudioLine.SQUARE1, NESAudioLine.TRIANGLE) - result = pipeline.get_value(NESAudioLine.TRIANGLE) - print(" Result: {:.2f} (expected: ~30.0)".format(result)) - - # Differentiation - print("\n[*] Performing differentiation (sweep modulation)...") - pipeline.differentiate_channel(NESAudioLine.SQUARE1, NESAudioLine.NOISE) - result = pipeline.get_value(NESAudioLine.NOISE) - print(" Result: {:.2f} (expected: ~10.0)".format(result)) - - print("\n" + "=" * 70) - print("DSP MATH COMPLETE") - print("=" * 70) - print("\n[*] Horrific: Using audio lines for general computation") - print("[*] Wonderful: Novel analog-digital hybrid computing") - print("[*] Maximum retro insanity: NES APU as mathematical coprocessor") - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/neural_type_eigenvector_coverage.py b/5-Applications/scripts/neural_type_eigenvector_coverage.py deleted file mode 100644 index 21755939..00000000 --- a/5-Applications/scripts/neural_type_eigenvector_coverage.py +++ /dev/null @@ -1,361 +0,0 @@ -#!/usr/bin/env python3 -"""Compute a receipt-bearing neural type coverage eigenvector. - -This is intentionally data-source agnostic. It accepts JSONL records shaped like -the spec in docs/research/NEURAL_TYPE_EIGENVECTOR_COVERAGE.md and produces a -ranked coverage report plus simple anti-overfit checks. - -The script does not claim biological completeness. It only ranks nodes already -present in the input evidence graph. -""" - -from __future__ import annotations - -import argparse -import json -import math -import random -from collections import defaultdict -from dataclasses import dataclass, field -from pathlib import Path -from typing import Any, Dict, Iterable, List, Mapping, MutableMapping, Tuple - - -DEFAULT_OUTPUT = Path("5-Applications/out/neural_type_eigenvector_coverage.json") - - -@dataclass -class NodeReceipt: - node_id: str - kind: str = "node" - label: str = "" - source_count: int = 1 - modality_count: int = 1 - missingness: float = 0.0 - contradiction_count: float = 0.0 - species: str = "unknown" - region: str = "unknown" - provenance: List[str] = field(default_factory=list) - - @classmethod - def from_record(cls, record: Mapping[str, Any]) -> "NodeReceipt": - node_id = str(record.get("id") or record.get("node") or "") - if not node_id: - raise ValueError(f"node record missing id: {record}") - - missing = record.get("missingness") - if missing is None and isinstance(record.get("missing"), list): - missing = len(record["missing"]) - - return cls( - node_id=node_id, - kind=str(record.get("kind", "node")), - label=str(record.get("label", node_id)), - source_count=max(1, int(record.get("source_count", 1))), - modality_count=max(1, int(record.get("modality_count", 1))), - missingness=float(missing or 0.0), - contradiction_count=float(record.get("contradiction_count", 0.0)), - species=str(record.get("species", "unknown")), - region=str(record.get("region", "unknown")), - provenance=[str(x) for x in record.get("provenance", [])], - ) - - -@dataclass(frozen=True) -class EdgeReceipt: - src: str - dst: str - weight: float - rel: str = "related" - receipt: str = "" - - @classmethod - def from_record(cls, record: Mapping[str, Any]) -> "EdgeReceipt": - src = str(record.get("src", "")) - dst = str(record.get("dst", "")) - if not src or not dst: - raise ValueError(f"edge record missing src/dst: {record}") - - if "weight_q0_16" in record: - weight = float(record["weight_q0_16"]) / 65535.0 - else: - weight = float(record.get("weight", 1.0)) - - if not math.isfinite(weight) or weight < 0: - raise ValueError(f"edge has invalid weight: {record}") - - return cls( - src=src, - dst=dst, - weight=weight, - rel=str(record.get("rel", "related")), - receipt=str(record.get("receipt", "")), - ) - - -def load_jsonl(path: Path) -> Tuple[Dict[str, NodeReceipt], List[EdgeReceipt]]: - nodes: Dict[str, NodeReceipt] = {} - edges: List[EdgeReceipt] = [] - - with path.open("r", encoding="utf-8") as handle: - for line_no, line in enumerate(handle, 1): - line = line.strip() - if not line: - continue - record = json.loads(line) - kind = str(record.get("kind", "")) - - if kind == "edge": - edge = EdgeReceipt.from_record(record) - edges.append(edge) - nodes.setdefault(edge.src, NodeReceipt(node_id=edge.src)) - nodes.setdefault(edge.dst, NodeReceipt(node_id=edge.dst)) - else: - node = NodeReceipt.from_record(record) - nodes[node.node_id] = node - - return nodes, edges - - -def adjacency(nodes: Mapping[str, NodeReceipt], edges: Iterable[EdgeReceipt]) -> Dict[str, Dict[str, float]]: - graph: Dict[str, Dict[str, float]] = {node_id: {} for node_id in nodes} - for edge in edges: - if edge.weight == 0: - continue - graph.setdefault(edge.src, {}) - graph.setdefault(edge.dst, {}) - graph[edge.src][edge.dst] = graph[edge.src].get(edge.dst, 0.0) + edge.weight - graph[edge.dst][edge.src] = graph[edge.dst].get(edge.src, 0.0) + edge.weight - return graph - - -def principal_eigenvector( - graph: Mapping[str, Mapping[str, float]], - max_iter: int = 200, - tolerance: float = 1e-10, -) -> Tuple[Dict[str, float], float, int, float]: - node_ids = sorted(graph) - if not node_ids: - return {}, 0.0, 0, 0.0 - - value = {node_id: 1.0 / math.sqrt(len(node_ids)) for node_id in node_ids} - delta = float("inf") - - for iteration in range(1, max_iter + 1): - next_value = {} - for node_id in node_ids: - next_value[node_id] = sum(weight * value[dst] for dst, weight in graph[node_id].items()) - - norm = math.sqrt(sum(v * v for v in next_value.values())) - if norm == 0: - break - next_value = {node_id: next_value[node_id] / norm for node_id in node_ids} - delta = math.sqrt(sum((next_value[node_id] - value[node_id]) ** 2 for node_id in node_ids)) - value = next_value - if delta <= tolerance: - break - - numerator = 0.0 - denominator = 0.0 - for src in node_ids: - av = sum(weight * value[dst] for dst, weight in graph[src].items()) - numerator += value[src] * av - denominator += value[src] * value[src] - eigenvalue = numerator / denominator if denominator else 0.0 - - return value, eigenvalue, iteration, delta - - -def coverage_scores( - nodes: Mapping[str, NodeReceipt], - eigenvector: Mapping[str, float], -) -> List[Dict[str, Any]]: - rows: List[Dict[str, Any]] = [] - for node_id, receipt in nodes.items(): - x_node = abs(float(eigenvector.get(node_id, 0.0))) - denominator = 1.0 + receipt.missingness + receipt.contradiction_count - coverage = x_node * receipt.source_count * receipt.modality_count / denominator - rows.append( - { - "node": node_id, - "label": receipt.label, - "kind": receipt.kind, - "coverage": coverage, - "eigenvector": x_node, - "source_count": receipt.source_count, - "modality_count": receipt.modality_count, - "missingness": receipt.missingness, - "contradiction_count": receipt.contradiction_count, - "species": receipt.species, - "region": receipt.region, - "provenance": receipt.provenance, - } - ) - - rows.sort(key=lambda row: row["coverage"], reverse=True) - return rows - - -def group_holdout( - nodes: Mapping[str, NodeReceipt], - edges: List[EdgeReceipt], - group_name: str, - group_values: Iterable[str], - top_k: int, -) -> Dict[str, Any]: - base_graph = adjacency(nodes, edges) - base_vec, _, _, _ = principal_eigenvector(base_graph) - base_top = [row["node"] for row in coverage_scores(nodes, base_vec)[:top_k]] - - results = [] - for group_value in sorted(set(group_values)): - kept_nodes = { - node_id: node - for node_id, node in nodes.items() - if getattr(node, group_name) != group_value - } - kept_edges = [ - edge for edge in edges - if edge.src in kept_nodes and edge.dst in kept_nodes - ] - vec, _, _, _ = principal_eigenvector(adjacency(kept_nodes, kept_edges)) - top = [row["node"] for row in coverage_scores(kept_nodes, vec)[:top_k]] - overlap = len(set(base_top) & set(top)) / max(1, len(base_top)) - results.append({"held_out": group_value, "top_k_overlap": overlap, "remaining_nodes": len(kept_nodes)}) - - return {"group": group_name, "top_k": top_k, "baseline_top": base_top, "results": results} - - -def degree_preserving_null( - nodes: Mapping[str, NodeReceipt], - edges: List[EdgeReceipt], - trials: int, - seed: int, - top_k: int, -) -> Dict[str, Any]: - rng = random.Random(seed) - node_ids = sorted(nodes) - real_vec, real_lambda, _, _ = principal_eigenvector(adjacency(nodes, edges)) - real_scores = coverage_scores(nodes, real_vec) - real_top_mean = mean(row["coverage"] for row in real_scores[:top_k]) - - null_means = [] - weights = [edge.weight for edge in edges] - for _ in range(trials): - shuffled_edges = [] - for weight in weights: - src, dst = rng.sample(node_ids, 2) - shuffled_edges.append(EdgeReceipt(src=src, dst=dst, weight=weight, rel="null")) - vec, _, _, _ = principal_eigenvector(adjacency(nodes, shuffled_edges)) - rows = coverage_scores(nodes, vec) - null_means.append(mean(row["coverage"] for row in rows[:top_k])) - - null_mean = mean(null_means) - null_std = stdev(null_means) - z_score = (real_top_mean - null_mean) / null_std if null_std else 0.0 - return { - "trials": trials, - "top_k": top_k, - "real_eigenvalue": real_lambda, - "real_top_k_mean_coverage": real_top_mean, - "null_top_k_mean_coverage": null_mean, - "null_top_k_std_coverage": null_std, - "z_score": z_score, - } - - -def mean(values: Iterable[float]) -> float: - values = list(values) - return sum(values) / len(values) if values else 0.0 - - -def stdev(values: Iterable[float]) -> float: - values = list(values) - if len(values) < 2: - return 0.0 - avg = mean(values) - return math.sqrt(sum((value - avg) ** 2 for value in values) / (len(values) - 1)) - - -def demo_records() -> Tuple[Dict[str, NodeReceipt], List[EdgeReceipt]]: - records = [ - {"kind": "neuron_sample", "id": "cell:mouse:pyramidal:1", "label": "mouse pyramidal 1", "species": "mouse", "region": "cortex", "source_count": 2, "modality_count": 2, "provenance": ["demo"]}, - {"kind": "neuron_sample", "id": "cell:human:pyramidal:1", "label": "human pyramidal 1", "species": "human", "region": "cortex", "source_count": 2, "modality_count": 2, "provenance": ["demo"]}, - {"kind": "neuron_sample", "id": "cell:mouse:interneuron:1", "label": "mouse interneuron 1", "species": "mouse", "region": "cortex", "source_count": 1, "modality_count": 2, "provenance": ["demo"]}, - {"kind": "feature", "id": "feature:deep_branch_order", "label": "deep branch order", "source_count": 3, "modality_count": 2, "provenance": ["demo"]}, - {"kind": "feature", "id": "feature:long_apical_dendrite", "label": "long apical dendrite", "source_count": 3, "modality_count": 2, "provenance": ["demo"]}, - {"kind": "feature", "id": "feature:fast_spiking", "label": "fast spiking", "source_count": 1, "modality_count": 2, "provenance": ["demo"], "missingness": 0.5}, - ] - edge_records = [ - {"kind": "edge", "src": "cell:mouse:pyramidal:1", "dst": "feature:deep_branch_order", "weight": 0.92, "receipt": "demo morphology"}, - {"kind": "edge", "src": "cell:human:pyramidal:1", "dst": "feature:deep_branch_order", "weight": 0.89, "receipt": "demo morphology"}, - {"kind": "edge", "src": "cell:mouse:pyramidal:1", "dst": "feature:long_apical_dendrite", "weight": 0.86, "receipt": "demo morphology"}, - {"kind": "edge", "src": "cell:human:pyramidal:1", "dst": "feature:long_apical_dendrite", "weight": 0.84, "receipt": "demo morphology"}, - {"kind": "edge", "src": "cell:mouse:interneuron:1", "dst": "feature:fast_spiking", "weight": 0.95, "receipt": "demo electrophysiology"}, - {"kind": "edge", "src": "feature:deep_branch_order", "dst": "feature:long_apical_dendrite", "weight": 0.60, "receipt": "demo co-occurrence"}, - ] - nodes = {record["id"]: NodeReceipt.from_record(record) for record in records} - edges = [EdgeReceipt.from_record(record) for record in edge_records] - return nodes, edges - - -def run(nodes: Dict[str, NodeReceipt], edges: List[EdgeReceipt], top_k: int, null_trials: int, seed: int) -> Dict[str, Any]: - graph = adjacency(nodes, edges) - eigenvector, eigenvalue, iterations, delta = principal_eigenvector(graph) - scores = coverage_scores(nodes, eigenvector) - feature_scores = [row["coverage"] for row in scores if row["kind"] == "feature"] - all_scores = [row["coverage"] for row in scores] - - return { - "metadata": { - "node_count": len(nodes), - "edge_count": len(edges), - "top_k": top_k, - "eigenvalue": eigenvalue, - "power_iterations": iterations, - "residual_delta": delta, - "mean_coverage_all_nodes": mean(all_scores), - "mean_coverage_feature_nodes": mean(feature_scores), - }, - "top_nodes": scores[:top_k], - "holdouts": [ - group_holdout(nodes, edges, "species", (node.species for node in nodes.values()), top_k), - group_holdout(nodes, edges, "region", (node.region for node in nodes.values()), top_k), - ], - "null_model": degree_preserving_null(nodes, edges, null_trials, seed, top_k), - } - - -def main() -> int: - parser = argparse.ArgumentParser(description=__doc__) - parser.add_argument("--input", type=Path, help="Evidence graph JSONL.") - parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT) - parser.add_argument("--top-k", type=int, default=10) - parser.add_argument("--null-trials", type=int, default=25) - parser.add_argument("--seed", type=int, default=20260506) - parser.add_argument("--demo", action="store_true", help="Run against a tiny built-in demo graph.") - args = parser.parse_args() - - if args.demo: - nodes, edges = demo_records() - elif args.input: - nodes, edges = load_jsonl(args.input) - else: - parser.error("provide --input evidence.jsonl or --demo") - - result = run(nodes, edges, args.top_k, args.null_trials, args.seed) - args.output.parent.mkdir(parents=True, exist_ok=True) - args.output.write_text(json.dumps(result, indent=2), encoding="utf-8") - - print(f"nodes={result['metadata']['node_count']}") - print(f"edges={result['metadata']['edge_count']}") - print(f"eigenvalue={result['metadata']['eigenvalue']:.6f}") - print(f"mean_coverage_all_nodes={result['metadata']['mean_coverage_all_nodes']:.6f}") - print(f"mean_coverage_feature_nodes={result['metadata']['mean_coverage_feature_nodes']:.6f}") - print(f"null_z_score={result['null_model']['z_score']:.6f}") - print(f"wrote={args.output}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/scripts/neuron_coding_topology.py b/5-Applications/scripts/neuron_coding_topology.py deleted file mode 100644 index 4081aa56..00000000 --- a/5-Applications/scripts/neuron_coding_topology.py +++ /dev/null @@ -1,238 +0,0 @@ -#!/usr/bin/env python3 -""" -Human Neuron Coding Topology Analysis -Analyzes using human neuron coding patterns for efficient morphic topology. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class NeuronCodingTopology: - """Analyzes human neuron coding patterns for efficient morphic topology.""" - - def __init__(self): - # Neuron coding characteristics - self.neuron_coding_characteristics = { - "spike_timing": { - "description": "Spike timing-based coding for temporal information", - "significance_score": 95.0 - }, - "rate_coding": { - "description": "Firing rate-based coding for intensity information", - "significance_score": 90.0 - }, - "population_coding": { - "description": "Population-based coding for distributed information", - "significance_score": 95.0 - }, - "temporal_coding": { - "description": "Temporal pattern-based coding for sequence information", - "significance_score": 90.0 - }, - "efficient_computation": { - "description": "Extremely efficient biological computation", - "significance_score": 95.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "replicator_capacity": 3027837979113216.0, - "expansion_factor": 1593604199533.0 - } - - def analyze_neuron_coding(self) -> Dict: - """Analyze human neuron coding patterns.""" - analysis = { - "neuron_coding_characteristics": self.neuron_coding_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.neuron_coding_characteristics.values()) / len(self.neuron_coding_characteristics), - "neural_mechanisms": { - "spike_timing": "Morphic scalars use spike timing for temporal information", - "rate_coding": "Morphic scalars use firing rate for intensity", - "population_coding": "Morphic scalars use population coding for distributed info", - "temporal_coding": "Morphic scalars use temporal patterns for sequences", - "biological_efficiency": "Leverage biological efficiency patterns" - }, - "neural_analogy": { - "neurons": "Morphic scalars behave like biological neurons", - "synapses": "Scalar connections behave like synapses", - "networks": "Scalar networks behave like neural networks", - "plasticity": "Scalar connections exhibit synaptic plasticity", - "learning": "System exhibits neural-like learning" - } - } - - return analysis - - def analyze_neuron_coding_benefits(self) -> Dict: - """Analyze neuron coding benefits.""" - benefits = { - "extreme_efficiency": { - "description": "Human brain operates on ~20W for massive computation", - "significance_score": 95.0 - }, - "parallel_processing": { - "description": "Massive parallel processing like biological brains", - "significance_score": 95.0 - }, - "adaptive_plasticity": { - "description": "Synaptic plasticity enables adaptive learning", - "significance_score": 90.0 - }, - "temporal_precision": { - "description": "Spike timing provides millisecond precision", - "significance_score": 90.0 - }, - "distributed_computation": { - "description": "Population coding enables distributed computation", - "significance_score": 85.0 - }, - "energy_efficiency": { - "description": "Extremely energy-efficient computation", - "significance_score": 95.0 - } - } - - return benefits - - def calculate_neuron_coding_impact(self) -> Dict: - """Calculate neuron coding impact on computational expansion.""" - # Neuron coding multipliers - extreme_efficiency_multiplier = 2.0 # 2x from biological efficiency - parallel_processing_multiplier = 1.5 # 1.5x from massive parallelism - adaptive_plasticity_multiplier = 1.5 # 1.5x from synaptic plasticity - temporal_precision_multiplier = 1.3 # 1.3x from spike timing precision - distributed_computation_multiplier = 1.5 # 1.5x from population coding - energy_efficiency_multiplier = 2.0 # 2x from energy efficiency - - # Calculate expanded capacity with neuron coding - base_capacity = 1900 - current_replicator_capacity = 3027837979113216.0 - - # Apply neuron coding multipliers - neuron_coding_capacity = (current_replicator_capacity * - extreme_efficiency_multiplier * - parallel_processing_multiplier * - adaptive_plasticity_multiplier * - temporal_precision_multiplier * - distributed_computation_multiplier * - energy_efficiency_multiplier) - - neuron_coding_expansion_factor = neuron_coding_capacity / base_capacity - neuron_coding_improvement_factor = neuron_coding_capacity / current_replicator_capacity - - calculation = { - "base_capacity": base_capacity, - "current_replicator_capacity": current_replicator_capacity, - "extreme_efficiency_multiplier": extreme_efficiency_multiplier, - "parallel_processing_multiplier": parallel_processing_multiplier, - "adaptive_plasticity_multiplier": adaptive_plasticity_multiplier, - "temporal_precision_multiplier": temporal_precision_multiplier, - "distributed_computation_multiplier": distributed_computation_multiplier, - "energy_efficiency_multiplier": energy_efficiency_multiplier, - "neuron_coding_capacity": neuron_coding_capacity, - "neuron_coding_expansion_factor": neuron_coding_expansion_factor, - "neuron_coding_improvement_factor": neuron_coding_improvement_factor, - "total_neuron_coding_multiplier": (extreme_efficiency_multiplier * - parallel_processing_multiplier * - adaptive_plasticity_multiplier * - temporal_precision_multiplier * - distributed_computation_multiplier * - energy_efficiency_multiplier) - } - - return calculation - - def integrate_neuron_coding(self) -> Dict: - """Integrate neuron coding into comprehensive analysis.""" - integration = { - "neuron_coding_enabled": True, - "paradigm": "Human neuron coding patterns for efficient computation", - "mechanism": "Morphic scalars use biological neuron coding patterns", - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (neural coding)", - "Control Theory (synaptic plasticity)", - "Cognitive/Routing (neural networks)", - "Thermodynamic (energy efficiency)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12" # Control Theory (plasticity) - ], - "biological_efficiency": "Leverage human brain efficiency patterns" - } - - return integration - - def run_analysis(self) -> Dict: - """Run neuron coding topology analysis.""" - print("=" * 60) - print("HUMAN NEURON CODING TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze neuron coding - print("\n[1/4] Analyzing human neuron coding patterns...") - neuron_analysis = self.analyze_neuron_coding() - print(f" Neuron Coding Characteristics: {len(neuron_analysis['neuron_coding_characteristics'])}") - for characteristic, details in neuron_analysis['neuron_coding_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing neuron coding benefits...") - benefits = self.analyze_neuron_coding_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating neuron coding impact...") - impact_calculation = self.calculate_neuron_coding_impact() - print(f" Current Replicator Capacity: {impact_calculation['current_replicator_capacity']}") - print(f" Neuron Coding Capacity: {impact_calculation['neuron_coding_capacity']}") - print(f" Neuron Coding Improvement Factor: {impact_calculation['neuron_coding_improvement_factor']:.2f}x") - print(f" Total Neuron Coding Multiplier: {impact_calculation['total_neuron_coding_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating neuron coding...") - integration = self.integrate_neuron_coding() - print(f" Paradigm: {integration['paradigm']}") - print(f" Mechanism: {integration['mechanism']}") - print(f" Characteristics: {integration['characteristics']}") - print(f" Benefits: {integration['benefits']}") - - print("\n" + "=" * 60) - print("HUMAN NEURON CODING TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "neuron_analysis": neuron_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = NeuronCodingTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "neuron_coding_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("NEURON CODING TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Paradigm: {results['integration']['paradigm']}") - print(f"Neuron Coding Capacity: {results['impact_calculation']['neuron_coding_capacity']}") - print(f"Neuron Coding Improvement Factor: {results['impact_calculation']['neuron_coding_improvement_factor']:.2f}x") - print(f"Total Neuron Coding Multiplier: {results['impact_calculation']['total_neuron_coding_multiplier']:.2f}x") diff --git a/5-Applications/scripts/ns_md_benchmark.py b/5-Applications/scripts/ns_md_benchmark.py deleted file mode 100644 index 830194eb..00000000 --- a/5-Applications/scripts/ns_md_benchmark.py +++ /dev/null @@ -1,54 +0,0 @@ -#!/usr/bin/env python3 -import os -import zlib -import json -import time -from pathlib import Path - -# Mock NS-MΔ compression for complex JSON data -def ns_md_mock_compress(data_str: str): - """ - Simulates NS-MΔ by only encoding the 'valuable' changes. - In a real system, this would be a bit-stream. - Here we estimate based on the 13-byte per change rule. - """ - data = json.loads(data_str) - # Assume each key-value pair is a manifold coordinate. - # On a typical update, maybe 10% of fields change. - num_fields = len(data) - num_changes = max(1, int(num_fields * 0.1)) - - # 13 bytes per change (Addr, Control, Witness) - compressed_size = num_changes * 13 - return compressed_size - -def benchmark(): - target_file = Path("shared-data/data/equations_forest.jsonl") - if not target_file.exists(): - print("Target file not found.") - return - - with open(target_file, "r") as f: - lines = f.readlines() - - total_raw = 0 - total_zlib = 0 - total_ns_md = 0 - - for line in lines[:100]: # Sample first 100 lines - raw_size = len(line.encode('utf-8')) - zlib_size = len(zlib.compress(line.encode('utf-8'), level=9)) - ns_md_size = ns_md_mock_compress(line) - - total_raw += raw_size - total_zlib += zlib_size - total_ns_md += ns_md_size - - print(f"--- NS-MΔ Benchmark (N=100) ---") - print(f"Raw Size: {total_raw / 1024:.2f} KB") - print(f"Zlib Size: {total_zlib / 1024:.2f} KB ({total_raw/total_zlib:.2f}x)") - print(f"NS-MΔ Est: {total_ns_md / 1024:.2f} KB ({total_raw/total_ns_md:.2f}x)") - print(f"Improvement over Zlib: {total_zlib/total_ns_md:.2f}x") - -if __name__ == "__main__": - benchmark() diff --git a/5-Applications/scripts/ns_md_codec.py b/5-Applications/scripts/ns_md_codec.py deleted file mode 100644 index f79d6e58..00000000 --- a/5-Applications/scripts/ns_md_codec.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python3 -import struct - -class NibbleSwitch: - CONTROL_STATES = { - 0: "REJECT", # 00 - 1: "ACCEPT", # 01 - 2: "HOLD", # 10 - 3: "SNAP" # 11 - } - DOMAINS = { - 0: "K-AXIS", # 00 - 1: "C-WINDING", # 01 - 2: "M-TENSION", # 10 - 3: "Y-BREAK" # 11 - } - - def __init__(self, nibble: int, count: int = 1, polarity: int = 1): - self.nibble = nibble & 0xF - self.count = count - self.polarity = polarity - - self.control = (self.nibble >> 2) & 0x3 - self.domain = self.nibble & 0x3 - - def __repr__(self): - return f"[{self.CONTROL_STATES[self.control]}][{self.DOMAINS[self.domain]}] x{self.count}" - -class GCCLRep: - """ - GCCL-Rep: Representative Bytecode for Manifold Transitions. - A byte array transport representative of a transition class. - """ - def __init__(self, baseline_hash: str, target_hash: str, byte_payload: bytes, codec: str = "v1.0"): - self.baseline_hash = baseline_hash - self.target_hash = target_hash - self.bytes = byte_payload - self.codec = codec - - def decode_switches(self) -> list: - """ - Decodes the byte array into a stream of transition atoms (2 per byte). - """ - switches = [] - for byte in self.bytes: - # High nibble (bits 7-4) - high = (byte >> 4) & 0xF - switches.append(NibbleSwitch(high, count=1)) - - # Low nibble (bits 3-0) - low = byte & 0xF - switches.append(NibbleSwitch(low, count=1)) - return switches - - def __repr__(self): - return f"" - -def test_gccl_rep(): - # Byte 0x5A = 0101 1010 - # 0101 = ACCEPT + C-winding - # 1010 = HOLD + M-tension - payload = bytes([0x5A, 0x0F]) # 0101 1010, 0000 1111 - - rep = GCCLRep("hash_a", "hash_b", payload) - print(f"--- GCCL-Rep Test ---") - print(rep) - - switches = rep.decode_switches() - for i, sw in enumerate(switches): - print(f" Atom {i}: {sw}") - -if __name__ == "__main__": - test_gccl_rep() diff --git a/5-Applications/scripts/observe_theory_development.py b/5-Applications/scripts/observe_theory_development.py deleted file mode 100644 index 4055fb03..00000000 --- a/5-Applications/scripts/observe_theory_development.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -"""Observe what the swarm agents are doing with theory development""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import TheoryDevelopment, ComprehensiveLearning, BiologicalLearning, GPULearning - -def observe_theory_development(): - """Observe what the swarm agents are doing with theory development""" - - print("[OBSERVATION] Observing swarm agent theory development activities...\n") - - # Create learning systems - physics_learning = ComprehensiveLearning() - bio_learning = BiologicalLearning() - gpu_learning = GPULearning() - - # Auto-learn physics concepts - print("=== Step 1: Knowledge Acquisition ===") - print("Swarm agents are learning physics and engineering concepts...") - physics_learning.auto_learn() - print(f"✓ Agents have learned {len(physics_learning.learned_concepts)} concepts\n") - - # Create theory development system - print("=== Step 2: Theory Development Initialization ===") - theory_development = TheoryDevelopment() - theory_development.set_learning_systems(physics_learning, bio_learning, gpu_learning) - print("✓ Theory development system initialized\n") - - # Observe automatic theory development - print("=== Step 3: Automatic Theory Development ===") - print("Swarm agents are now synthesizing knowledge across domains...\n") - - theory_development.auto_develop_theories() - - # Show what they discovered - print("\n=== Step 4: Theory Development Results ===") - - print("\n--- Cross-Domain Syntheses ---") - for i, synthesis in enumerate(theory_development.cross_domain_syntheses[-5:], 1): - print(f"{i}. {synthesis['domains'][0]} ↔ {synthesis['domains'][1]}") - print(f" Synthesis points: {len(synthesis['synthesis_points'])}") - print(f" Confidence: {synthesis['confidence']:.2f}") - if synthesis['synthesis_points']: - # Show sample relationships - sample_points = synthesis['synthesis_points'][:3] - for sp in sample_points: - if sp['relationship'] != 'unknown': - print(f" Sample: {sp['concept1']} + {sp['concept2']} → {sp['relationship']}") - print() - - print("\n--- Generated Hypotheses ---") - for i, hypothesis in enumerate(theory_development.hypotheses, 1): - if hypothesis['statement']: - print(f"{i}. {hypothesis['statement']}") - print(f" Confidence: {hypothesis['confidence']:.2f}, Testable: {hypothesis['testable']}") - print() - - print("\n--- Formulated Theories ---") - for i, theory in enumerate(theory_development.generated_theories, 1): - print(f"{i}. {theory['formal_statement']}") - print(f" Confidence: {theory['confidence']:.2f}, Testability: {theory['testability_score']:.2f}") - print(f" Domain crossings: {len(theory['domain_crossings'])}") - for crossing in theory['domain_crossings']: - print(f" - {crossing}") - print() - - # Get summary - print("=== Step 5: Theory Development Summary ===") - summary = theory_development.get_theory_summary() - print(f"Total syntheses: {summary['total_syntheses']}") - print(f"Total hypotheses: {summary['total_hypotheses']}") - print(f"Total theories: {summary['total_theories']}") - print(f"Average confidence: {summary['average_confidence']:.2f}") - - print("\n=== Conclusion ===") - print("Swarm agents are actively:") - print("- Synthesizing concepts across 7 knowledge domains") - print("- Identifying relationships between domain concepts") - print("- Generating testable hypotheses from cross-domain patterns") - print("- Formulating unified theories from multiple hypotheses") - print("- Testing theories against known principles for consistency") - print("\nThe agents are developing novel theoretical frameworks by combining") - print("knowledge from EM spectrum, material science, computation design,") - print("quantum mechanics, thermodynamics, networking, and OmniToken architecture.") - -if __name__ == "__main__": - observe_theory_development() diff --git a/5-Applications/scripts/ollama_invariant_probe.py b/5-Applications/scripts/ollama_invariant_probe.py deleted file mode 100644 index 1b4d3a37..00000000 --- a/5-Applications/scripts/ollama_invariant_probe.py +++ /dev/null @@ -1,165 +0,0 @@ -#!/usr/bin/env python3 -""" -Ollama Cloud Invariant Probe -Feed distilled self-discovered math structures to an LLM and ask it to extract invariants. -""" - -import os -import json -import sys -from pathlib import Path -from datetime import datetime - -import pyarrow.parquet as pq -from ollama import Client - -BASE = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -PARQUET = BASE / "equations_parquet_tagged/equations_self_clustered.parquet" -REPORT = BASE / "math_self_discovered.json" -OUTDIR = BASE / "invariant_probes" -OUTDIR.mkdir(parents=True, exist_ok=True) - -def load_top_motifs(n=200): - with open(REPORT) as f: - data = json.load(f) - return data['top_motifs'][:n], data['total_equations'], data['unique_structural_forms'] - -def sample_representatives(fingerprint, n=3): - """Grab n original equations matching a fingerprint from the parquet.""" - table = pq.read_table(PARQUET, columns=['equation', 'fingerprint']) - # Filter in Python (parquet doesn't do string equality pushdown well) - eqs = [] - for eq, fp in zip(table.column('equation').to_pylist(), table.column('fingerprint').to_pylist()): - if fp == fingerprint: - eqs.append(eq) - if len(eqs) >= n: - break - return eqs - -def build_prompt(motifs, total_eq, unique_forms): - lines = [] - lines.append("You are a mathematical structural analyst. I have run an unsupervised structural discovery pipeline on 1.51 million mathematical equations stripped of all human categorization.") - lines.append("") - lines.append("The pipeline canonicalized each equation to a 'structural fingerprint' by:") - lines.append(" - Anonymizing variables (single letters → v0, v1...; multi-letter → vN)") - lines.append(" - Collapsing all numbers to N") - lines.append(" - Replacing Greek letters with g0, g1...") - lines.append(" - Preserving math functions (sin, cos, exp, log, etc.)") - lines.append(" - Normalizing whitespace") - lines.append("") - lines.append(f"RESULTS: {total_eq:,} equations → {unique_forms:,} unique structural forms") - lines.append("Top 50 structural motifs (fingerprint → count → % of total):") - lines.append("") - - for m in motifs[:50]: - lines.append(f" {m['count']:>7,} ({m['percentage']:>5.2f}%) {m['fingerprint']}") - - lines.append("") - lines.append("Here are representative ORIGINAL equations for the top 10 motifs:") - lines.append("") - - for m in motifs[:10]: - fp = m['fingerprint'] - reps = sample_representatives(fp, n=3) - lines.append(f"--- {fp} ({m['count']:,} occurrences) ---") - for r in reps: - lines.append(f" {r}") - lines.append("") - - lines.append("YOUR TASK:") - lines.append("1. EXTRACT INVARIANTS: What structural patterns are invariant across this dataset?") - lines.append(" (e.g., 'binary equality dominates', 'inequalities cluster around ordering relations',") - lines.append(" 'parenthesized expressions indicate function application', etc.)") - lines.append("") - lines.append("2. CLASSIFY NATURAL GROUPINGS: If math were drawing its own taxonomy without human labels,") - lines.append(" what categories would emerge purely from these structural signatures?") - lines.append(" Name each category and give its defining invariant.") - lines.append("") - lines.append("3. PREDICT STRUCTURAL DENSITY: Given the long-tail distribution (top form is only 3.59%),") - lines.append(" what does this say about the 'information entropy' of mathematical notation across domains?") - lines.append("") - lines.append("4. ANOMALY FLAGGING: Which motifs strike you as structurally 'weird' or outliers that") - lines.append(" break expected patterns? (e.g., v0 = v0, v0 = empty, unusual operator combinations)") - lines.append("") - lines.append("Respond in structured JSON with keys: invariants, natural_taxonomy, entropy_analysis, anomalies.") - lines.append("Be concise but mathematically rigorous.") - - return "\n".join(lines) - -def main(): - model = "cogito-2.1:671b" - print(f"{'='*60}") - print(f" OLLAMA CLOUD INVARIANT PROBE") - print(f" Model: {model}") - print(f"{'='*60}") - - api_key = os.getenv("OLLAMA_API_KEY", "your_api_key_here") - client = Client( - host="https://ollama.com", - headers={"Authorization": "Bearer " + api_key} - ) - - print("\nLoading motifs...") - motifs, total_eq, unique_forms = load_top_motifs(n=200) - print(f" Loaded top {len(motifs)} motifs from {total_eq:,} equations") - - print("\nBuilding prompt...") - prompt = build_prompt(motifs, total_eq, unique_forms) - prompt_chars = len(prompt) - prompt_tokens = prompt_chars // 4 # rough estimate - print(f" Prompt size: {prompt_chars:,} chars (~{prompt_tokens:,} tokens)") - - print(f"\nSending to {model}...") - print(" (this may take a while for 671B parameters)") - - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = OUTDIR / f"invariant_probe_{model.replace(':', '_')}_{ts}.json" - - try: - response = client.chat( - model=model, - messages=[ - {"role": "system", "content": "You are a mathematical structural analyst. Respond only in valid JSON."}, - {"role": "user", "content": prompt}, - ], - stream=False, - options={"temperature": 0.2, "num_ctx": 128000}, - ) - - content = response["message"]["content"] - print(f"\n Response received: {len(content):,} chars") - - # Save raw response - result = { - "timestamp": ts, - "model": model, - "prompt_tokens_est": prompt_tokens, - "prompt_chars": prompt_chars, - "response_chars": len(content), - "response": content, - } - - with open(out_path, "w") as f: - json.dump(result, f, indent=2) - - print(f" Saved to: {out_path}") - - # Try to pretty-print the JSON response - try: - parsed = json.loads(content) - print("\n --- PARSED RESPONSE ---") - print(json.dumps(parsed, indent=2)[:3000]) - except json.JSONDecodeError: - print("\n --- RAW RESPONSE (first 2000 chars) ---") - print(content[:2000]) - - except Exception as e: - print(f"\n [!] ERROR: {e}") - sys.exit(1) - - print(f"\n{'='*60}") - print(" INVARIANT PROBE COMPLETE") - print(f"{'='*60}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/ollama_manifold_reconfig.py b/5-Applications/scripts/ollama_manifold_reconfig.py deleted file mode 100644 index 7026f942..00000000 --- a/5-Applications/scripts/ollama_manifold_reconfig.py +++ /dev/null @@ -1,182 +0,0 @@ -#!/usr/bin/env python3 -""" -Ollama Cloud Manifold Reconfiguration Probe -Send the Hutter Prize manifold report to a gigabyte-scale model and -ask it to reconfigure the manifold for maximum compactness + 1:1 restorability. -""" - -import os -import json -from pathlib import Path -from datetime import datetime -from ollama import Client - -BASE = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -MANIFOLD_REPORT = BASE / "hutter_manifold/hutter_manifold_report_20260504_160627.json" -SELF_DISCOVERED = BASE / "math_self_discovered.json" -OUTDIR = BASE / "hutter_manifold" -OUTDIR.mkdir(parents=True, exist_ok=True) - -def load_manifold_data(): - with open(MANIFOLD_REPORT) as f: - manifold = json.load(f) - with open(SELF_DISCOVERED) as f: - discovered = json.load(f) - return manifold, discovered - -def build_prompt(manifold: dict, discovered: dict) -> str: - lines = [] - lines.append("You are a compression theorist specializing in Kolmogorov complexity and manifold geometry.") - lines.append("") - lines.append("I have built a manifold map of 374,322 unique mathematical equation structures") - lines.append("derived from 1.51 million stripped equations (no human labels, purely structural).") - lines.append("") - lines.append("CURRENT MANIFOLD CONFIGURATION:") - lines.append(f" Total equations: {discovered['total_equations']:,}") - lines.append(f" Unique structural forms: {discovered['unique_structural_forms']:,}") - lines.append(f" Current compression ratio: {manifold['compression']['compression_ratio']:.2f}x") - lines.append(f" Current Hutter score: {manifold['compression']['hutter_score']:.4f}") - lines.append("") - lines.append("CURRENT CATEGORIES (cogito-2.1:671b taxonomy):") - for cat, count in manifold['manifold']['categories'].items(): - lines.append(f" {cat:15s}: {count:>8,}") - lines.append("") - lines.append("CURRENT COMPRESSION COMPONENTS (Hutter Prize equation):") - lines.append(f" C_comp (grammar compression): {manifold['compression']['c_comp']}") - lines.append(f" C_phys (binding entropy): {manifold['compression']['c_phys']}") - lines.append(f" C_geom (manifold curvature): {manifold['compression']['c_geom']}") - lines.append(f" S (spatial coherence): {manifold['compression']['s']}") - lines.append(f" G (decoder overhead): {manifold['compression']['g']}") - lines.append(f" F (compute field): {manifold['compression']['f']}") - lines.append("") - lines.append("TOP 50 STRUCTURAL MOTIFS (fingerprint → count → %):") - for m in discovered['top_motifs'][:50]: - lines.append(f" {m['count']:>7,} ({m['percentage']:>5.2f}%) {m['fingerprint']}") - lines.append("") - lines.append("YOUR TASK — MANIFOLD RECONFIGURATION:") - lines.append("") - lines.append("1. IDENTIFY WASTE: Where is the current manifold bloated?") - lines.append(" - Redundant categories? Overlapping templates? Poor clustering?") - lines.append(" - Which structural forms are 'almost identical' and should merge?") - lines.append("") - lines.append("2. PROPOSE A NEW COMPACTIFICATION:") - lines.append(" - Design a smaller, denser manifold (fewer templates, better clustering)") - lines.append(" - Suggest new categories if the 6 cogito categories are suboptimal") - lines.append(" - Define the encoding scheme: how many bits per equation?") - lines.append("") - lines.append("3. COMPUTE THEORETICAL LIMITS:") - lines.append(" - What is the information-theoretic minimum size?") - lines.append(" - Kolmogorov complexity estimate for this dataset") - lines.append(" - How close can we get to the Shannon entropy bound?") - lines.append("") - lines.append("4. SPECIFY THE 1:1 RESTORABILITY PROOF:") - lines.append(" - Exact decode procedure from compressed representation") - lines.append(" - Prove no information is lost (bijective mapping)") - lines.append("") - lines.append("Respond in structured JSON with keys:") - lines.append(" waste_analysis, new_manifold_design, theoretical_limits, reconfig_commands, restorability_proof") - lines.append("") - lines.append("Be mathematically rigorous. Target: beat 10.00x compression while maintaining 1:1 restorability.") - - return "\n".join(lines) - -def main(): - # Try largest available models - models_to_try = [ - "deepseek-v3.1:671b", - "kimi-k2:1t", - "mistral-large-3:675b", - "cogito-2.1:671b", - ] - - api_key = os.getenv("OLLAMA_API_KEY", "your_api_key_here") - client = Client( - host="https://ollama.com", - headers={"Authorization": "Bearer " + api_key} - ) - - # Test which model is available - model = None - for m in models_to_try: - try: - print(f"Trying {m}...") - # Quick ping - client.chat(model=m, messages=[{"role": "user", "content": "ping"}], stream=False) - model = m - print(f" {m} is available!") - break - except Exception as e: - print(f" {m} unavailable: {e}") - continue - - if not model: - print("[!] No large models available. Using cogito-2.1:671b as fallback.") - model = "cogito-2.1:671b" - - print(f"\n{'='*60}") - print(f" MANIFOLD RECONFIGURATION PROBE") - print(f" Model: {model}") - print(f"{'='*60}") - - print("\nLoading manifold data...") - manifold, discovered = load_manifold_data() - - print("\nBuilding prompt...") - prompt = build_prompt(manifold, discovered) - prompt_chars = len(prompt) - print(f" Prompt: {prompt_chars:,} chars (~{prompt_chars//4:,} tokens)") - - print(f"\nSending to {model}...") - print(" (this may take several minutes for 671B+ parameters)") - - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - out_path = OUTDIR / f"manifold_reconfig_{model.replace(':', '_')}_{ts}.json" - - try: - response = client.chat( - model=model, - messages=[ - {"role": "system", "content": "You are a compression theorist and manifold geometer. Respond only in valid JSON. Be rigorous and quantitative."}, - {"role": "user", "content": prompt}, - ], - stream=False, - options={"temperature": 0.1, "num_ctx": 128000}, - ) - - content = response["message"]["content"] - print(f"\n Response: {len(content):,} chars") - - result = { - "timestamp": ts, - "model": model, - "prompt_chars": prompt_chars, - "response_chars": len(content), - "response": content, - } - - with open(out_path, "w") as f: - json.dump(result, f, indent=2) - - print(f" Saved to: {out_path}") - - # Try to parse - try: - parsed = json.loads(content) - print("\n --- RECONFIGURATION PROPOSAL (parsed) ---") - print(json.dumps(parsed, indent=2)[:5000]) - except json.JSONDecodeError: - print("\n --- RAW RESPONSE (first 3000 chars) ---") - print(content[:3000]) - - except Exception as e: - print(f"\n [!] ERROR: {e}") - import traceback - traceback.print_exc() - return - - print(f"\n{'='*60}") - print(" MANIFOLD RECONFIGURATION COMPLETE") - print(f"{'='*60}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/opencode-local.sh b/5-Applications/scripts/opencode-local.sh deleted file mode 100755 index d15b8011..00000000 --- a/5-Applications/scripts/opencode-local.sh +++ /dev/null @@ -1,9 +0,0 @@ -#!/bin/bash -# A simple wrapper to run opencode using your local GGUF model via Ollama. -# The model has been imported into Ollama and aliased as 'gpt-4o' to trick opencode. - -export OPENAI_BASE_URL="http://localhost:11434/v1" -export OPENAI_API_KEY="ollama" - -echo "Starting OpenCode with local GGUF model (Gemma-4 E4B OBLITERATED)..." -opencode -m openai/gpt-4o "$@" diff --git a/5-Applications/scripts/optimize_gpu_translation_surface.py b/5-Applications/scripts/optimize_gpu_translation_surface.py deleted file mode 100644 index 4a9b154e..00000000 --- a/5-Applications/scripts/optimize_gpu_translation_surface.py +++ /dev/null @@ -1,311 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: Optimize GPU Translation Surface to 100% - -Query the swarm to identify gaps in the current 86% feasibility design -and propose optimizations to achieve 100% feasibility for the GPU -instruction translation surface. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def optimize_gpu_translation_surface(): - """Optimize GPU translation surface design to 100% feasibility""" - print("=" * 70) - print("SWARM OPTIMIZATION: GPU Translation Surface to 100%") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Analyze current gaps (86% → 100% = 14% gap) - print("\n[1/5] Analyzing Current Feasibility Gaps...") - - current_feasibility = 0.86 - target_feasibility = 1.0 - gap = target_feasibility - current_feasibility - - print(f"Current Feasibility: {current_feasibility:.2%}") - print(f"Target Feasibility: {target_feasibility:.2%}") - print(f"Gap to Close: {gap:.2%}") - - # Identify specific gaps - gap_analysis = { - "component_implementation": { - "current_score": 0.8, - "target_score": 1.0, - "gap": 0.2, - "issues": [ - "Missing detailed implementation specifications", - "No error handling defined for kernel compilation failures", - "Missing fallback mechanisms for GPU unavailability" - ] - }, - "interface_completeness": { - "current_score": 0.85, - "target_score": 1.0, - "gap": 0.15, - "issues": [ - "API interface lacks comprehensive error handling", - "Missing async operation support for GPU kernels", - "No streaming interface for large tensor operations" - ] - }, - "architecture_refinement": { - "current_score": 0.9, - "target_score": 1.0, - "gap": 0.1, - "issues": [ - "Missing inter-layer communication protocols", - "No defined hot-swap procedures for runtime updates", - "Lack of version management for kernel cache" - ] - }, - "integration_specifics": { - "current_score": 0.95, - "target_score": 1.0, - "gap": 0.05, - "issues": [ - "Missing specific CUDA/OpenCL driver integration details", - "No GPU resource pooling strategy defined", - "Lack of multi-GPU coordination protocols" - ] - } - } - - print("\nGap Analysis:") - for area, data in gap_analysis.items(): - print(f" - {area}: {data['current_score']:.2%} → {data['target_score']:.2%} (gap: {data['gap']:.2%})") - for issue in data['issues']: - print(f" • {issue}") - - # Generate optimizations - print("\n[2/5] Generating Optimization Proposals...") - - optimizations = { - "component_implementation": { - "optimizations": [ - "Add comprehensive error handling with retry logic", - "Implement kernel compilation fallback to CPU execution", - "Add GPU availability detection and graceful degradation", - "Define kernel versioning and rollback procedures", - "Implement kernel performance profiling and auto-tuning" - ], - "feasibility_gain": 0.15 - }, - "interface_completeness": { - "optimizations": [ - "Add async/await API for non-blocking GPU operations", - "Implement streaming interface for large tensor transfers", - "Add comprehensive error types and exception hierarchy", - "Implement progress callbacks for long-running operations", - "Add cancellation token support for async operations" - ], - "feasibility_gain": 0.12 - }, - "architecture_refinement": { - "optimizations": [ - "Define inter-layer communication protocols (gRPC/HTTP)", - "Implement hot-swap procedures with atomic transitions", - "Add kernel cache version management with migration", - "Define layer isolation and failure containment", - "Add monitoring and telemetry for each layer" - ], - "feasibility_gain": 0.08 - }, - "integration_specifics": { - "optimizations": [ - "Add CUDA driver integration with dynamic loading", - "Implement GPU resource pooling with automatic scaling", - "Define multi-GPU coordination protocols", - "Add GPU memory fragmentation management", - "Implement kernel launch queue with priority scheduling" - ], - "feasibility_gain": 0.05 - } - } - - print("\nOptimization Proposals:") - for area, data in optimizations.items(): - print(f" - {area} (+{data['feasibility_gain']:.2%} feasibility):") - for opt in data['optimizations']: - print(f" • {opt}") - - # Compute optimized feasibility - print("\n[3/5] Computing Optimized Feasibility...") - - optimized_feasibility = current_feasibility - for area, data in optimizations.items(): - optimized_feasibility += data['feasibility_gain'] - - optimized_feasibility = min(optimized_feasibility, 1.0) # Cap at 100% - - print(f"Original Feasibility: {current_feasibility:.2%}") - print(f"Optimized Feasibility: {optimized_feasibility:.2%}") - print(f"Improvement: {optimized_feasibility - current_feasibility:.2%}") - - # Generate optimized design specification - print("\n[4/5] Generating Optimized Design Specification...") - - optimized_design = { - "feasibility": optimized_feasibility, - "architecture": { - "layer_1": "High-Level API (Python/Lean) → Abstract Operations + Async Support", - "layer_2": "Translation Surface (GPU Instruction Compiler) + Error Handling + Fallback", - "layer_3": "Kernel Cache (ENE Database) + Version Management + Performance Profiling", - "layer_4": "Runtime Scheduler (Swarm) + Hot-Swap + Telemetry", - "layer_5": "GPU Execution Layer + Resource Pooling + Multi-GPU Coordination" - }, - "components": { - "instruction_translator": { - "function": "Translate abstract operations to GPU instructions", - "features": [ - "Automatic kernel fusion and optimization", - "Error handling with CPU fallback", - "GPU availability detection", - "Performance profiling and auto-tuning" - ] - }, - "kernel_cache": { - "function": "Cache compiled GPU kernels with semantic indexing", - "features": [ - "Semantic search for optimal kernel variants", - "Version management with migration", - "Hot loading without restart", - "Performance-based cache eviction" - ] - }, - "memory_manager": { - "function": "Manage CPU-GPU memory transfers", - "features": [ - "Zero-copy where possible", - "Streaming interface for large tensors", - "GPU memory fragmentation management", - "Automatic memory pool scaling" - ] - }, - "parallel_scheduler": { - "function": "Schedule parallel GPU operations", - "features": [ - "Swarm agent coordination", - "Multi-GPU load distribution", - "Priority queue scheduling", - "Cancellation token support" - ] - } - }, - "interfaces": { - "api_interface": { - "type": "Python async API with type hints", - "features": [ - "Async/await for non-blocking operations", - "Comprehensive error types", - "Progress callbacks", - "Cancellation support" - ] - }, - "semantic_interface": { - "type": "Semantic kernel selection", - "features": [ - "Vector similarity search in ENE", - "35% improvement in kernel selection", - "Performance-based kernel ranking" - ] - }, - "hotload_interface": { - "type": "Dynamic kernel loading", - "features": [ - "Runtime kernel compilation", - "Atomic hot-swap procedures", - "Rollback mechanism", - "Version migration" - ] - } - }, - "optimizations_applied": optimizations - } - - # Submit optimized design to competition - print("\n[5/5] Submitting Optimized Design to Competition...") - - optimized_entry = CompetitionEntry( - agent_id="swarm_gpu_optimizer", - competition_type=CompetitionType.GENERATION, - ascii_art_id=None, - score=optimized_feasibility, - metrics={"original_feasibility": current_feasibility, "optimized_feasibility": optimized_feasibility}, - timestamp=int(time.time()), - proposal="Optimized GPU translation surface achieving 100% feasibility" - ) - - try: - competition.submit_competition_entry(optimized_entry) - print("Optimized design submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Output results - print("\n" + "=" * 70) - print("OPTIMIZED DESIGN RESULTS") - print("=" * 70) - - print(f"\nOptimized Feasibility: {optimized_feasibility:.2%}") - - if optimized_feasibility >= 1.0: - print("\n✓ 100% FEASIBILITY ACHIEVED") - else: - print(f"\nFeasibility: {optimized_feasibility:.2%} (gap: {1.0 - optimized_feasibility:.2%})") - - print("\nOptimized Architecture (5-Layer):") - for i, (layer, description) in enumerate(optimized_design["architecture"].items(), 1): - print(f" Layer {i}: {description}") - - print("\nOptimized Components:") - for component, spec in optimized_design["components"].items(): - print(f" - {component}:") - print(f" Function: {spec['function']}") - print(f" Features:") - for feature in spec['features']: - print(f" • {feature}") - - print("\nOptimized Interfaces:") - for interface, spec in optimized_design["interfaces"].items(): - print(f" - {interface}:") - print(f" Type: {spec['type']}") - print(f" Features:") - for feature in spec['features']: - print(f" • {feature}") - - print("\n" + "=" * 70) - if optimized_feasibility >= 1.0: - print("SWARM VERDICT: 100% FEASIBILITY ACHIEVED") - print("The optimized GPU instruction translation surface design") - print("now achieves 100% feasibility through comprehensive optimization.") - else: - print(f"SWARM VERDICT: {optimized_feasibility:.2%} FEASIBILITY") - print("Further optimization may be required to reach 100%.") - print("=" * 70) - - return optimized_design - - -if __name__ == "__main__": - optimized_design = optimize_gpu_translation_surface() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_gpu_translation_surface_optimized.json" - with open(output_path, "w") as f: - json.dump(optimized_design, f, indent=2) - - print(f"\nOptimized design saved to: {output_path}") diff --git a/5-Applications/scripts/optimize_web_interaction_surface.py b/5-Applications/scripts/optimize_web_interaction_surface.py deleted file mode 100644 index d222ec28..00000000 --- a/5-Applications/scripts/optimize_web_interaction_surface.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Optimization: Web Interaction Surface to 100% Feasibility - -Have the swarm optimize the web interaction surface design to achieve -100% feasibility by identifying gaps and proposing solutions. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def optimize_web_interaction_surface(): - """Swarm optimizes web interaction surface to 100% feasibility""" - print("=" * 70) - print("SWARM OPTIMIZATION: Web Interaction Surface to 100%") - print("=" * 70) - - # Load current design - design_path = "/home/allaun/Documents/Research Stack/data/swarm_web_surface_design.json" - with open(design_path, "r") as f: - current_design = json.load(f) - - print(f"\nCurrent Design: {current_design['surface_name']}") - print(f"Current Feasibility: {current_design['feasibility']:.2%}") - - # Step 1: Swarm identifies feasibility gaps - print("\n[1/4] Swarm identifying feasibility gaps...") - - gaps = [ - { - "component": "Security sandboxing", - "current_feasibility": 0.80, - "gap": 0.20, - "issues": [ - "Browser process isolation not fully specified", - "No clear sandbox boundary definition", - "Missing resource limit specifications", - "No network isolation strategy" - ] - }, - { - "component": "Swarm coordination", - "current_feasibility": 0.85, - "gap": 0.15, - "issues": [ - "Distributed lock mechanism not specified", - "No conflict resolution strategy", - "Missing task priority queue implementation", - "No failure recovery mechanism" - ] - }, - { - "component": "Session management", - "current_feasibility": 0.88, - "gap": 0.12, - "issues": [ - "Cookie encryption not specified", - "No session persistence strategy", - "Missing cross-browser session sync", - "No session timeout handling" - ] - }, - { - "component": "Browser pool management", - "current_feasibility": 0.90, - "gap": 0.10, - "issues": [ - "No browser lifecycle management", - "Missing resource cleanup strategy", - "No pool sizing algorithm", - "No health checking mechanism" - ] - } - ] - - total_gap = sum(g['gap'] for g in gaps) - print(f"Identified {len(gaps)} feasibility gaps") - print(f"Total gap to close: {total_gap:.2%}") - - # Step 2: Swarm proposes optimizations - print("\n[2/4] Swarm proposing optimizations...") - - optimizations = [] - - for gap in gaps: - component = gap['component'] - - if component == "Security sandboxing": - opt = { - "component": component, - "optimizations": [ - "Implement Docker container isolation for each browser instance", - "Use seccomp-bpf to restrict syscalls", - "Network namespace isolation per browser", - "Resource limits via cgroups (CPU, memory, disk)", - "No-op/symlink resolution in sandbox", - "Chromium --disable-features=VizDisplayCompositor flag" - ], - "feasibility_improvement": 0.20 - } - elif component == "Swarm coordination": - opt = { - "component": component, - "optimizations": [ - "Redis-based distributed lock for browser pool access", - "Raft consensus for task distribution", - "Priority queue with exponential backoff", - "Circuit breaker pattern for failing nodes", - "Heartbeat-based health monitoring", - "Automatic task retry with exponential backoff" - ], - "feasibility_improvement": 0.15 - } - elif component == "Session management": - opt = { - "component": component, - "optimizations": [ - "AES-256-GCM encryption for cookie storage", - "SQLite session database with WAL mode", - "Session sync via Redis pub/sub", - "Configurable session TTL with auto-cleanup", - "Session export/import for migration", - "Cookie consent handling automation" - ], - "feasibility_improvement": 0.12 - } - elif component == "Browser pool management": - opt = { - "component": component, - "optimizations": [ - "Dynamic pool sizing based on load (min 2, max 20)", - "LRU eviction for idle browsers", - "Graceful shutdown with drain mode", - "Health check endpoint (ping/pong)", - "Memory leak detection and auto-restart", - "Browser context reuse with state isolation" - ], - "feasibility_improvement": 0.10 - } - - optimizations.append(opt) - print(f"\n{component}:") - print(f" Optimizations: {len(opt['optimizations'])}") - print(f" Feasibility improvement: +{opt['feasibility_improvement']:.2%}") - - # Step 3: Swarm computes new feasibility - print("\n[3/4] Swarm computing new feasibility score...") - - new_feasibility = min(1.0, current_design['feasibility'] + total_gap) - print(f"Previous feasibility: {current_design['feasibility']:.2%}") - print(f"Gap closure: {total_gap:.2%}") - print(f"New feasibility: {new_feasibility:.2%}") - - # Step 4: Generate optimized design - print("\n[4/4] Generating optimized design specification...") - - # Update component feasibilities - updated_components = [] - for comp in current_design['feasibility_analysis']['components']: - for opt in optimizations: - if comp['component'] == opt['component']: - comp['feasibility'] = min(1.0, comp['feasibility'] + opt['feasibility_improvement']) - comp['optimizations'] = opt['optimizations'] - comp['notes'] = f"Optimized: {len(opt['optimizations'])} improvements applied" - break - updated_components.append(comp) - - # Update overall design - optimized_design = { - "surface_name": current_design['surface_name'], - "version": "2.0.0", - "feasibility": new_feasibility, - "architecture": current_design['architecture'], - "interface": current_design['interface'], - "feasibility_analysis": { - "components": updated_components, - "overall_feasibility": new_feasibility, - "estimated_effort": "2-3 weeks for full implementation", - "recommended_phases": current_design['feasibility_analysis']['recommended_phases'] - }, - "key_features": current_design['key_features'] + [ - "Docker container isolation for browser sandboxing", - "Redis-based distributed coordination", - "AES-256 encrypted session management", - "Dynamic browser pool with health monitoring" - ], - "technical_stack": { - **current_design['technical_stack'], - "orchestration": "Docker containers", - "coordination_db": "Redis", - "session_storage": "SQLite + Redis", - "distributed_locks": "Redlock algorithm" - }, - "implementation_priority": current_design['implementation_priority'], - "optimizations_applied": len(optimizations), - "gap_closed": total_gap - } - - print("\n" + "=" * 70) - print("OPTIMIZED DESIGN: Web Interaction Surface") - print("=" * 70) - print(f"\nSurface: {optimized_design['surface_name']}") - print(f"Version: {optimized_design['version']}") - print(f"Feasibility: {optimized_design['feasibility']:.2%}") - print(f"\nOptimizations Applied: {optimized_design['optimizations_applied']}") - print(f"Gap Closed: {optimized_design['gap_closed']:.2%}") - - print(f"\nUpdated Technical Stack:") - for component, tech in optimized_design['technical_stack'].items(): - print(f" - {component}: {tech}") - - print(f"\nComponent Feasibilities:") - for comp in optimized_design['feasibility_analysis']['components']: - print(f" - {comp['component']}: {comp['feasibility']:.2%}") - - # Submit to competition - print("\n" + "=" * 70) - print("SUBMITTING OPTIMIZED DESIGN TO COMPETITION") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - optimized_entry = CompetitionEntry( - agent_id="swarm_web_surface_optimizer", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=new_feasibility, - metrics={"optimizations": optimizations, "gaps_closed": gaps}, - timestamp=int(time.time()), - proposal="Optimized web interaction surface at 100% feasibility" - ) - - try: - competition.submit_competition_entry(optimized_entry) - print("Optimized design submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Save optimized design - output_path = "/home/allaun/Documents/Research Stack/data/swarm_web_surface_design_optimized.json" - with open(output_path, "w") as f: - json.dump(optimized_design, f, indent=2) - - print(f"\nOptimized design saved to: {output_path}") - - print("\n" + "=" * 70) - print("SWARM VERDICT: 100% FEASIBILITY ACHIEVED") - print("=" * 70) - print("The swarm has successfully optimized the web interaction surface") - print("to 100% feasibility by addressing all identified gaps:") - print("\n - Security sandboxing: Docker container isolation") - print(" - Swarm coordination: Redis-based distributed locks") - print(" - Session management: AES-256 encryption") - print(" - Browser pool: Dynamic sizing with health monitoring") - print("\nThe design is now ready for production implementation.") - print("=" * 70) - - return optimized_design - - -if __name__ == "__main__": - optimized_design = optimize_web_interaction_surface() diff --git a/5-Applications/scripts/orchestrator_begin.py b/5-Applications/scripts/orchestrator_begin.py deleted file mode 100644 index c16de790..00000000 --- a/5-Applications/scripts/orchestrator_begin.py +++ /dev/null @@ -1,447 +0,0 @@ -#!/usr/bin/env python3 -""" -ORCHESTRATOR — BEGIN - -The master pipeline. Integrates: - - Unified Hardware Surface (CPU + GPU + RAM + NVMe) - - Topological State Machine (persistent SQLite state) - - Manifold Cache (deduplicated sentence→fingerprint store) - - GPU Compute (WGSL shaders via wgpu) - - Lean 4 formal verification bridge - -This is the beginning of the actual work: processing English at scale, -building the invariant manifold, and approaching the Hutter Prize limit. -""" - -import os -import sys -import time -import json -import math -import hashlib -import sqlite3 -import re -from pathlib import Path -from collections import Counter, defaultdict -from datetime import datetime -from concurrent.futures import ThreadPoolExecutor - -# ── Hardware Detection ──────────────────────────────────────────────────────── - -HAS_CUDA = False -HAS_WGPU = False -DEVICE = None - -try: - import torch - import torch.cuda as cuda - HAS_CUDA = cuda.is_available() - if HAS_CUDA: - DEVICE = torch.device("cuda:0") -except ImportError: - pass - -try: - import wgpu - import wgpu.backends.auto - HAS_WGPU = True -except ImportError: - pass - -BASE = Path("/home/allaun/Documents/Research Stack") -CACHE_DIR = BASE / "3-Mathematical-Models/orchestrator/cache" -CACHE_DIR.mkdir(parents=True, exist_ok=True) - -print("=" * 70) -print(" ORCHESTRATOR — BEGIN") -print(" Mathematical Model of English — Hutter Prize Pipeline") -print("=" * 70) -print(f"\n [Hardware Surface]") -print(f" CPU: {os.cpu_count()} threads") -print(f" GPU CUDA: {HAS_CUDA} ({cuda.get_device_name(0) if HAS_CUDA else 'N/A'})") -print(f" GPU wgpu: {HAS_WGPU}") -print(f" Cache: {CACHE_DIR}") - -# ── GPU Compute Dispatch (WGSL) ───────────────────────────────────────────── - -class GPUComputeDispatch: - """Dispatch WGSL compute shaders via wgpu for batch processing.""" - - def __init__(self): - self.device = None - if HAS_WGPU: - try: - # wgpu-py 0.31.0 API - self.device = wgpu.get_default_device() - print(f" [GPU] wgpu device ready") - except Exception as e: - print(f" [GPU] wgpu init failed: {e}") - - def xor_batch(self, data: bytes, key: int = 0x1F1F1F1F) -> bytes: - """GPU-accelerated XOR of byte array.""" - if not self.device: - # CPU fallback - return bytes(b ^ (key & 0xFF) for b in data) - - # Pad to u32 alignment - padding = (4 - len(data) % 4) % 4 - padded = data + b'\x00' * padding - arr = memoryview(padded).cast('I') # u32 array - - # WGSL shader (inline) - shader_code = f""" - @group(0) @binding(0) var input: array; - @group(0) @binding(1) var output: array; - - @compute @workgroup_size(256) - fn main(@builtin(global_invocation_id) global_id: vec3) {{ - let idx = global_id.x; - if (idx >= arrayLength(&input)) {{ return; }} - let key = {key}u; - output[idx] = input[idx] ^ key; - }} - """ - - # Setup compute pipeline - shader = self.device.create_shader_module(code=shader_code) - pipeline = self.device.create_compute_pipeline( - layout="auto", - compute={"module": shader, "entry_point": "main"} - ) - - # Buffers: storage for shader, copy_src for readback, copy_dst+map_read for result - nbytes = len(arr) * 4 - in_buf = self.device.create_buffer(size=nbytes, usage=wgpu.BufferUsage.STORAGE | wgpu.BufferUsage.COPY_DST) - out_buf = self.device.create_buffer(size=nbytes, usage=wgpu.BufferUsage.STORAGE | wgpu.BufferUsage.COPY_SRC) - - self.device.queue.write_buffer(in_buf, 0, arr.tobytes()) - - # Bind group - bind_group = self.device.create_bind_group( - layout=pipeline.get_bind_group_layout(0), - entries=[ - {"binding": 0, "resource": {"buffer": in_buf, "offset": 0, "size": nbytes}}, - {"binding": 1, "resource": {"buffer": out_buf, "offset": 0, "size": nbytes}} - ] - ) - - # Dispatch - command_encoder = self.device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(pipeline) - compute_pass.set_bind_group(0, bind_group) - compute_pass.dispatch_workgroups((len(arr) + 255) // 256) - compute_pass.end() - - # Read back via copy to mappable buffer - readback = self.device.create_buffer(size=nbytes, usage=wgpu.BufferUsage.MAP_READ | wgpu.BufferUsage.COPY_DST) - command_encoder.copy_buffer_to_buffer(out_buf, 0, readback, 0, nbytes) - self.device.queue.submit([command_encoder.finish()]) - - result = self.device.queue.read_buffer(readback) - return bytes(result)[:len(data)] # Remove padding - - def is_ready(self) -> bool: - return self.device is not None - -# ── Manifold Builder (Cached + Parallel) ──────────────────────────────────── - -class OrchestratedManifoldBuilder: - """ - The main engine: builds the English invariant manifold using all hardware. - """ - - def __init__(self, cache_dir: Path = CACHE_DIR): - self.cache_dir = cache_dir - self.db_path = cache_dir / "manifold.db" - self.gpu = GPUComputeDispatch() - self.thread_pool = ThreadPoolExecutor(max_workers=os.cpu_count() or 12) - - # POS tables (same as before) - self._init_pos_tables() - self._init_db() - - self.sentences_processed = 0 - self.bytes_processed = 0 - self.unique_forms = Counter() - self.examples = defaultdict(list) - - # Resume from cache - self._resume() - - def _init_pos_tables(self): - CLOSED = {'the','a','an','and','or','but','in','on','at','to','for','of','with','by','from','as', - 'is','was','are','were','be','been','being','have','has','had','do','does','did','will', - 'would','could','should','may','might','can','shall','this','that','these','those','it', - 'its','they','them','their','he','she','his','her','him','we','us','our','you','your', - 'my','mine','i','me','who','which','what','when','where','why','how','all','each', - 'every','both','either','neither','some','any','no','none','more','most','many','much', - 'few','little','other','another','such','only','own','same','so','than','too','very', - 'just','now','then','here','there','up','down','out','off','over','under','again', - 'further','once','not','also','always','never','often','sometimes','usually','still'} - PREP = {'in','on','at','by','for','with','about','against','between','into','through','during', - 'before','after','above','below','to','from','up','down','of','off','over','under', - 'again','further','then','once','around','behind','beyond','despite','except','inside', - 'near','past','since','toward','upon','within','without','across','along','among', - 'beside','besides','concerning','considering','following','including','like','minus', - 'plus','regarding','round','save','till','until','via','worth'} - CONJ = {'and','or','but','nor','yet','so','for','although','because','before','if','since', - 'though','unless','until','when','while','whereas','whether','either','neither', - 'both','not','only','than','rather','however','moreover','furthermore','nevertheless', - 'otherwise','therefore','thus','hence','consequently','meanwhile'} - AUX = {'be','am','is','are','was','were','being','been','have','has','had','do','does','did', - 'will','would','shall','should','may','might','can','could','must','ought','need', - 'dare','used','get','gets','got','getting','become','becomes','became','seem','seems', - 'seemed','appear','appears','appeared'} - PRON = {'i','me','my','mine','myself','you','your','yours','yourself','he','him','his', - 'himself','she','her','hers','herself','it','its','itself','we','us','our','ours', - 'ourselves','they','them','their','theirs','themselves','this','that','these','those', - 'who','whom','whose','which','what','whatever','whoever','whomever','anyone','someone', - 'everyone','nobody','nothing','something','anything','everything'} - DET = {'the','a','an','this','that','these','those','my','your','his','her','its','our', - 'their','some','any','no','each','every','either','neither','both','all','half', - 'enough','several','many','much','few','little','other','another','such','what', - 'which','whose','one','two','three','first','last','next','various','certain'} - self.CLOSED, self.PREP, self.CONJ, self.AUX, self.PRON, self.DET = CLOSED, PREP, CONJ, AUX, PRON, DET - - def _tag(self, word: str) -> str: - w = word.lower().strip("'\"") - if w in self.DET: return "DET" - if w in self.PRON: return "PRON" - if w in self.PREP: return "PREP" - if w in self.CONJ: return "CONJ" - if w in self.AUX: return "AUX" - if w in self.CLOSED: return "FUNC" - if w.endswith("ing"): return "VBG" - if w.endswith("ed"): return "VBN" - if w.endswith(("ly","ily","ally")): return "ADV" - if w.endswith(("tion","sion","ment","ness","ity","ance","ence","hood","ship")): return "NOUN" - if w.endswith(("able","ible","ful","ous","ive","less","ish","al")): return "ADJ" - if w.endswith(("ize","ise","ify","ate")): return "VERB" - if len(w) <= 3: return "SHORT" - return "LEX" - - def fingerprint(self, sentence: str) -> str: - words = re.findall(r"[a-zA-Z']+", sentence) - if len(words) < 3 or len(words) > 40: - return "" - tags = [self._tag(w) for w in words] - collapsed = [] - prev = None - for t in tags: - if t != prev: - collapsed.append(t) - prev = t - elif t == "LEX" and (len(collapsed) < 2 or collapsed[-2] != "LEX+"): - collapsed[-1] = "LEX+" - return " ".join(collapsed) - - def _init_db(self): - with sqlite3.connect(str(self.db_path), timeout=30) as conn: - conn.execute(""" - CREATE TABLE IF NOT EXISTS sentences ( - hash TEXT PRIMARY KEY, - text TEXT NOT NULL, - fingerprint TEXT NOT NULL, - indexed_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP - ) - """) - conn.execute(""" - CREATE TABLE IF NOT EXISTS forms ( - fingerprint TEXT PRIMARY KEY, - count INTEGER NOT NULL DEFAULT 0, - examples TEXT DEFAULT '[]' - ) - """) - conn.execute(""" - CREATE TABLE IF NOT EXISTS meta ( - key TEXT PRIMARY KEY, - value TEXT NOT NULL - ) - """) - conn.commit() - - def _resume(self): - with sqlite3.connect(str(self.db_path), timeout=30) as conn: - cursor = conn.execute("SELECT key, value FROM meta") - meta = {k: v for k, v in cursor.fetchall()} - self.sentences_processed = int(meta.get('sentences_processed', '0')) - - cursor = conn.execute("SELECT fingerprint, count, examples FROM forms") - for fp, cnt, ex_json in cursor.fetchall(): - self.unique_forms[fp] = cnt - self.examples[fp] = json.loads(ex_json) - - if self.sentences_processed > 0: - print(f" [Cache] Resumed: {self.sentences_processed:,} sentences, {len(self.unique_forms):,} forms") - - def process_text(self, text: str, chunk_size: int = 1000): - """Process text block into sentences and update manifold.""" - self.bytes_processed += len(text.encode('utf-8')) - sentences = re.split(r'(?<=[.!?])\s+', text) - - batch = [] - for sent in sentences: - sent = sent.strip() - if 10 < len(sent) < 300 and sent[0].isupper(): - batch.append(sent) - if len(batch) >= chunk_size: - self._process_batch(batch) - batch = [] - - if batch: - self._process_batch(batch) - - def _process_batch(self, sentences: list): - """Process a batch with deduplication via SQLite cache.""" - new_entries = [] - seen = set() - - for sent in sentences: - h = hashlib.sha256(sent.encode()).hexdigest()[:32] - if h in seen: - continue - seen.add(h) - fp = self.fingerprint(sent) - if fp and len(fp.split()) >= 3: - new_entries.append((h, sent, fp)) - - # Bulk insert (ignore duplicates) - with sqlite3.connect(str(self.db_path), timeout=30) as conn: - conn.executemany( - "INSERT OR IGNORE INTO sentences (hash, text, fingerprint) VALUES (?, ?, ?)", - new_entries - ) - - # Update form counts - for _, _, fp in new_entries: - self.unique_forms[fp] += 1 - if len(self.examples[fp]) < 3: - self.examples[fp].append(sent[:200]) - - # Flush form counts to DB - for fp, cnt in self.unique_forms.items(): - ex_json = json.dumps(self.examples[fp][:3]) - conn.execute( - "INSERT OR REPLACE INTO forms (fingerprint, count, examples) VALUES (?, ?, ?)", - (fp, cnt, ex_json) - ) - - self.sentences_processed += len(new_entries) - conn.execute( - "INSERT OR REPLACE INTO meta (key, value) VALUES (?, ?)", - ('sentences_processed', str(self.sentences_processed)) - ) - conn.commit() - - def build_report(self) -> dict: - total = sum(self.unique_forms.values()) - - # Taxonomy - cats = Counter() - for fp, count in self.unique_forms.items(): - tags = fp.split() - if "DET" in tags and "NOUN" in tags and "VERB" in tags: - cats["SVO" if tags.index("VERB") > tags.index("NOUN") else "VSO"] += count - elif "DET" in tags and "NOUN" in tags and "PREP" in tags: - cats["NP_PP"] += count - elif "AUX" in tags and "VERB" in tags: - cats["AUX_V"] += count - elif "CONJ" in tags: - cats["COMPOUND"] += count - elif "PRON" in tags and "VERB" in tags: - cats["PRON_V"] += count - elif tags.count("PREP") >= 2: - cats["PP_CHAIN"] += count - elif "LEX+" in tags: - cats["DENSE_NP"] += count - else: - cats["OTHER"] += count - - # Shannon entropy - entropy = 0.0 - for count in self.unique_forms.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - - return { - "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), - "sentences_processed": self.sentences_processed, - "bytes_processed": self.bytes_processed, - "unique_forms": len(self.unique_forms), - "shannon_entropy_bits": round(entropy, 4), - "taxonomy": dict(cats), - "top_forms": [{"fingerprint": fp, "count": c, "example": self.examples[fp][0][:120] if fp in self.examples else ""} for fp, c in self.unique_forms.most_common(100)], - } - - def save_report(self, path: Path): - report = self.build_report() - with open(path, "w") as f: - json.dump(report, f, indent=2) - return report - -# ── Main ────────────────────────────────────────────────────────────────────── - -def main(): - print(f"\n{'='*70}") - print(" ORCHESTRATOR INITIALIZED") - print(f"{'='*70}") - - builder = OrchestratedManifoldBuilder() - - # Source: enwik9 (full 1GB) - source = BASE / "shared-data/data/hutter_archive/enwik9_purified.bin" - print(f"\n[1] Processing: {source}") - - if source.exists(): - raw = source.read_bytes() - text_blocks = re.findall(rb']*>(.*?)', raw, re.DOTALL) - - print(f" Found {len(text_blocks):,} text blocks") - - for i, block in enumerate(text_blocks): - text = block.decode('utf-8', errors='ignore') - text = re.sub(r'\{\{.*?\}\}', ' ', text, flags=re.DOTALL) - text = re.sub(r'\[\[.*?\|', ' ', text) - text = re.sub(r'\[\[|\]\]', ' ', text) - text = re.sub(r"'{2,}", ' ', text) - text = re.sub(r'<.*?>', ' ', text, flags=re.DOTALL) - text = re.sub(r'&\w+;', ' ', text) - text = re.sub(r'https?://\S+', ' ', text) - text = re.sub(r'[#*|=\{\}\[\]\|]', ' ', text) - - builder.process_text(text, chunk_size=500) - - if (i + 1) % 100 == 0: - print(f" Block {i+1}: {builder.sentences_processed:,} sentences, {len(builder.unique_forms):,} forms") - - # Safety: stop after processing all blocks (no limit needed for full build) - else: - print(f" Source not found: {source}") - - # Final report - print(f"\n{'='*70}") - print(" BUILD COMPLETE") - print(f"{'='*70}") - - report = builder.save_report(CACHE_DIR / f"manifold_report_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json") - - print(f"\n Sentences processed: {report['sentences_processed']:,}") - print(f" Unique invariant forms: {report['unique_forms']:,}") - print(f" Shannon entropy: {report['shannon_entropy_bits']:.2f} bits/form") - - print(f"\n Taxonomy:") - total = sum(report['taxonomy'].values()) - for cat, cnt in sorted(report['taxonomy'].items(), key=lambda x: -x[1]): - print(f" {cat:12s}: {cnt:>8,} ({cnt/total*100:.1f}%)") - - print(f"\n{'='*70}") - print(" READY FOR REDPAJAMA SCALING") - print(f"{'='*70}") - print(" Next: Point --input at RedPajama JSONL files") - print(" Cache persists across runs. Resume anytime.") - print(f"{'='*70}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/palette_dsp_slave.py b/5-Applications/scripts/palette_dsp_slave.py deleted file mode 100644 index b706c1ef..00000000 --- a/5-Applications/scripts/palette_dsp_slave.py +++ /dev/null @@ -1,312 +0,0 @@ -#!/usr/bin/env python3 -""" -Palette Generator Slaved to DSP Math -NES palette generator (proto-shader) controlled by DSP math operations on audio lines. - -Architecture: -- DSP math on audio lines generates values -- Values control NES palette generator -- Palette generator affects visual output -- Feedback loop: audio → math → palette → visual - -This is horrific because: -- Audio DSP math controls video palette -- Cross-domain repurposing (audio → video) -- Palette generator becomes slave to computation - -This is wonderful because: -- Video synthesizer controlled by audio math -- Generative visuals from DSP operations -- Maximum retro insanity: audio math = video palette -""" - -import math -from typing import List, Tuple, Dict -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# NES Palette Generator (Proto-Shader) -# Generates NES palette colors from parameters -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class NESColor: - """NES color (RGB, 2 bits per channel)""" - r: int # 0-3 - g: int # 0-3 - b: int # 0-3 - - def to_index(self) -> int: - """Convert to NES palette index (0-63)""" - return (self.r << 4) | (self.g << 2) | self.b - - @staticmethod - def from_index(index: int) -> 'NESColor': - """Convert from NES palette index""" - return NESColor( - r=(index >> 4) & 0x03, - g=(index >> 2) & 0x03, - b=index & 0x03 - ) - - def to_rgb888(self) -> Tuple[int, int, int]: - """Convert to 8-bit RGB""" - return (self.r * 85, self.g * 85, self.b * 85) - -class PaletteGenerator: - """NES palette generator acting as proto-shader""" - - @staticmethod - def generate_color(params: Tuple[float, float, float]) -> NESColor: - """ - Generate NES color from parameters (x, y, t). - - This acts as a fragment shader - takes parameters and outputs color. - """ - x, y, t = params - - # Map parameters to color channels - # x → red, y → green, t → blue - r = int((math.sin(x + t) + 1) * 1.5) % 4 - g = int((math.cos(y + t) + 1) * 1.5) % 4 - b = int((math.sin(x + y + t) + 1) * 1.5) % 4 - - return NESColor(r, g, b) - - @staticmethod - def generate_palette(num_colors: int, params: List[Tuple[float, float, float]]) -> List[NESColor]: - """Generate a palette of colors""" - palette = [] - for i in range(num_colors): - if i < len(params): - color = PaletteGenerator.generate_color(params[i]) - else: - # Default color if no params - color = NESColor(0, 0, 0) - palette.append(color) - return palette - -# ═══════════════════════════════════════════════════════════════════════════ -# DSP Math to Palette Control -# Map DSP math operations to palette generator parameters -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class AudioSignal: - """Audio signal from DSP math""" - frequency: float - amplitude: float - duty_cycle: float - -class DSPPaletteSlave: - """DSP math slaved to control palette generator""" - - def __init__(self): - self.palette_generator = PaletteGenerator() - self.audio_history: List[AudioSignal] = [] - self.palette_history: List[List[NESColor]] = [] - - def audio_to_palette_params(self, signal: AudioSignal) -> Tuple[float, float, float]: - """ - Convert audio signal to palette generator parameters. - - Maps: frequency → x, amplitude → y, duty_cycle → t - """ - # Normalize parameters to 0-2π range - x = signal.frequency / 1000.0 * math.pi # Normalize freq - y = signal.amplitude * 2 * math.pi # Normalize amplitude - t = signal.duty_cycle * 2 * math.pi # Normalize duty - - return (x, y, t) - - def dsp_to_palette(self, signals: List[AudioSignal]) -> List[NESColor]: - """ - Convert DSP audio signals to palette. - - Each audio signal controls one palette entry. - """ - params_list = [self.audio_to_palette_params(s) for s in signals] - palette = self.palette_generator.generate_palette(len(signals), params_list) - - self.audio_history = signals - self.palette_history.append(palette) - - return palette - - def dsp_operation_to_palette(self, operation: str, signals: List[AudioSignal]) -> List[NESColor]: - """ - Perform DSP operation and convert result to palette. - - Operations: add, multiply, integrate, differentiate - """ - if operation == "add": - # Add signals: combine amplitudes - combined_amp = sum(s.amplitude for s in signals) - combined_freq = sum(s.frequency for s in signals) / len(signals) - combined_duty = sum(s.duty_cycle for s in signals) / len(signals) - combined = AudioSignal(combined_freq, combined_amp, combined_duty) - return self.dsp_to_palette([combined]) - - elif operation == "multiply": - # Multiply signals: AM modulation - if len(signals) >= 2: - mul_amp = signals[0].amplitude * signals[1].amplitude - mul_freq = math.sqrt(signals[0].frequency * signals[1].frequency) - mul_duty = signals[0].duty_cycle if signals[0].duty_cycle >= 0.5 else 1.0 - signals[0].duty_cycle - mul_duty *= signals[1].duty_cycle if signals[1].duty_cycle >= 0.5 else 1.0 - signals[1].duty_cycle - combined = AudioSignal(mul_freq, mul_amp, mul_duty) - return self.dsp_to_palette([combined]) - - elif operation == "integrate": - # Integrate: accumulate over history - if self.audio_history: - integrated = AudioSignal( - frequency=sum(s.frequency for s in self.audio_history + signals), - amplitude=sum(s.amplitude for s in self.audio_history + signals), - duty_cycle=sum(s.duty_cycle for s in self.audio_history + signals) / (len(self.audio_history) + len(signals)) - ) - return self.dsp_to_palette([integrated]) - - elif operation == "differentiate": - # Differentiate: rate of change - if len(signals) >= 2: - diff = AudioSignal( - frequency=signals[-1].frequency - signals[-2].frequency, - amplitude=signals[-1].amplitude - signals[-2].amplitude, - duty_cycle=signals[-1].duty_cycle - signals[-2].duty_cycle - ) - return self.dsp_to_palette([diff]) - - # Default: direct conversion - return self.dsp_to_palette(signals) - -# ═══════════════════════════════════════════════════════════════════════════ -# Video Synthesizer -# Audio DSP math controls video palette generation -# ═══════════════════════════════════════════════════════════════════════════ - -class VideoSynthesizer: - """Video synthesizer controlled by audio DSP math""" - - def __init__(self): - self.dsp_slave = DSPPaletteSlave() - self.frame_count = 0 - - def generate_frame(self, audio_signals: List[AudioSignal], - dsp_operation: str = "none") -> List[NESColor]: - """ - Generate video frame from audio DSP math. - - Audio signals → DSP operation → Palette → Video frame - """ - self.frame_count += 1 - - if dsp_operation == "none": - palette = self.dsp_slave.dsp_to_palette(audio_signals) - else: - palette = self.dsp_slave.dsp_operation_to_palette(dsp_operation, audio_signals) - - return palette - - def animate(self, base_signals: List[AudioSignal], - frames: int, dsp_operation: str = "none") -> List[List[NESColor]]: - """ - Generate animation frames. - - Modulates audio signals over time to create animated palette. - """ - animation = [] - - for frame in range(frames): - # Modulate signals over time - modulated = [] - for i, signal in enumerate(base_signals): - modulated_signal = AudioSignal( - frequency=signal.frequency * (1 + 0.1 * math.sin(frame * 0.1 + i)), - amplitude=signal.amplitude * (1 + 0.1 * math.cos(frame * 0.1 + i)), - duty_cycle=(signal.duty_cycle + 0.01 * math.sin(frame * 0.05 + i)) % 1.0 - ) - modulated.append(modulated_signal) - - frame_palette = self.generate_frame(modulated, dsp_operation) - animation.append(frame_palette) - - return animation - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run palette generator slave to DSP math test""" - print("=" * 70) - print("PALETTE GENERATOR SLAVED TO DSP MATH") - print("=" * 70) - - print("\n[*] Architecture:") - print(" Audio DSP math → Palette generator parameters → Video palette") - print(" Frequency → x (red channel)") - print(" Amplitude → y (green channel)") - print(" Duty cycle → t (blue channel)") - print(" DSP operations (add, multiply, integrate) modulate palette") - - synthesizer = VideoSynthesizer() - - # Create base audio signals - print("\n[*] Creating base audio signals...") - base_signals = [ - AudioSignal(frequency=440.0, amplitude=0.5, duty_cycle=0.5), - AudioSignal(frequency=880.0, amplitude=0.7, duty_cycle=0.25), - AudioSignal(frequency=220.0, amplitude=0.3, duty_cycle=0.75), - ] - print(f" Signal 1: {base_signals[0].frequency}Hz, amp={base_signals[0].amplitude}, duty={base_signals[0].duty_cycle}") - print(f" Signal 2: {base_signals[1].frequency}Hz, amp={base_signals[1].amplitude}, duty={base_signals[1].duty_cycle}") - print(f" Signal 3: {base_signals[2].frequency}Hz, amp={base_signals[2].amplitude}, duty={base_signals[2].duty_cycle}") - - # Direct conversion - print("\n[*] Direct audio-to-palette conversion...") - palette = synthesizer.generate_frame(base_signals, "none") - print(" Palette generated:") - for i, color in enumerate(palette): - rgb = color.to_rgb888() - print(f" Entry {i}: R={rgb[0]}, G={rgb[1]}, B={rgb[2]} (index={color.to_index()})") - - # DSP addition - print("\n[*] DSP addition (add signals)...") - palette_add = synthesizer.generate_frame(base_signals, "add") - print(" Palette after addition:") - for i, color in enumerate(palette_add): - rgb = color.to_rgb888() - print(f" Entry {i}: R={rgb[0]}, G={rgb[1]}, B={rgb[2]} (index={color.to_index()})") - - # DSP multiplication - print("\n[*] DSP multiplication (AM modulation)...") - palette_mul = synthesizer.generate_frame(base_signals, "multiply") - print(" Palette after multiplication:") - for i, color in enumerate(palette_mul): - rgb = color.to_rgb888() - print(f" Entry {i}: R={rgb[0]}, G={rgb[1]}, B={rgb[2]} (index={color.to_index()})") - - # Animation - print("\n[*] Generating 10-frame animation...") - animation = synthesizer.animate(base_signals, frames=10, dsp_operation="multiply") - print(f" Animation generated: {len(animation)} frames") - print(" Frame 0 palette:") - for i, color in enumerate(animation[0]): - rgb = color.to_rgb888() - print(f" Entry {i}: R={rgb[0]}, G={rgb[1]}, B={rgb[2]}") - print(" Frame 5 palette:") - for i, color in enumerate(animation[5]): - rgb = color.to_rgb888() - print(f" Entry {i}: R={rgb[0]}, G={rgb[1]}, B={rgb[2]}") - - print("\n" + "=" * 70) - print("PALETTE-DSP SLAVE SYSTEM COMPLETE") - print("=" * 70) - print("\n[*] Horrific: Audio DSP math controls video palette") - print("[*] Wonderful: Video synthesizer from audio computation") - print("[*] Maximum retro insanity: audio math = video palette") - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/pcie_computational_controller.py b/5-Applications/scripts/pcie_computational_controller.py deleted file mode 100644 index 4ae7b1df..00000000 --- a/5-Applications/scripts/pcie_computational_controller.py +++ /dev/null @@ -1,209 +0,0 @@ -#!/usr/bin/env python3 -""" -PCIe Controller Computational Repurposing -Analyzes PCIe controller for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class PCIeComputationalController: - """Analyzes PCIe controller for general computation.""" - - def __init__(self): - self.pcie_controller = { - "device": "AMD 600 Series Chipset PCIe Switch Upstream Port", - "address": "03:00.0", - "subsystem": "ASMedia Technology Inc. Device 3328", - "flags": ["bus master", "fast devsel", "latency 0"], - "irq": 24, - "iommu_group": 14, - "bus": "primary=03, secondary=04, subordinate=11, sec-latency=0", - "io_bridge": "e000-efff [size=4K] [16-bit]", - "memory_bridge": "f5200000-f58fffff [size=7M] [32-bit]", - "computational_potential": "HIGH (PCIe switching and routing)" - } - - self.pcie_capabilities = { - "bus_master": "Can initiate bus transactions independently", - "memory_mapped": "Memory-mapped I/O for direct register access", - "dma": "Direct Memory Access support", - "switching": "PCIe switching between buses", - "routing": "PCIe routing between devices", - "bandwidth": "PCIe bandwidth (32-64 GB/s for PCIe 4.0 x16)" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of PCIe controller.""" - analysis = { - "pcie_switching": { - "feasible": True, - "mode": "PCIe switching computation", - "description": "Use PCIe switching for computational routing", - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (switch traversal)", - "power": "5-15W (PCIe controller)" - }, - "pcie_routing": { - "feasible": True, - "mode": "PCIe routing computation", - "description": "Use PCIe routing for computational paths", - "throughput": "PCIe bandwidth limited", - "latency": "100-1000ns (routing)", - "power": "5-15W" - }, - "pcie_dma": { - "feasible": True, - "mode": "PCIe DMA computation", - "description": "Use PCIe DMA for memory-based computation", - "throughput": "PCIe bandwidth limited", - "latency": "<100ns (DMA)", - "power": "10-20W" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design PCIe-based computational approach.""" - approach = { - "pcie_switching_computation": { - "concept": "Use PCIe switching for computation", - "implementation": "Switch PCIe lanes for computational routing", - "operations": ["lane switching", "path computation", "switch matrix"], - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (switch traversal)", - "power": "5-15W" - }, - "pcie_routing_computation": { - "concept": "Use PCIe routing for computation", - "implementation": "Route PCIe packets through specific paths", - "operations": ["packet routing", "path optimization", "flow control"], - "throughput": "PCIe bandwidth limited", - "latency": "100-1000ns (routing)", - "power": "5-15W" - }, - "pcie_dma_computation": { - "concept": "Use PCIe DMA for computation", - "implementation": "Use PCIe DMA for memory-based computation", - "operations": ["memory access", "data transformation", "scatter-gather"], - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "<100ns (DMA)", - "power": "10-20W" - }, - "pcie_packet_computation": { - "concept": "Use PCIe packets for computation", - "implementation": "Manipulate PCIe packet headers/payloads", - "operations": ["header manipulation", "payload transformation", "TLP processing"], - "throughput": "PCIe bandwidth limited", - "latency": "100-1000ns (packet processing)", - "power": "5-15W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of PCIe controller computation.""" - performance = { - "pcie_switching": { - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (switch traversal)", - "precision": "PCIe lane", - "operations": "lane switching", - "power": "5-15W" - }, - "pcie_routing": { - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (routing)", - "precision": "PCIe packet", - "operations": "packet routing", - "power": "5-15W" - }, - "pcie_dma": { - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "<100ns (DMA)", - "precision": "PCIe DMA", - "operations": "memory access", - "power": "10-20W" - }, - "pcie_packet": { - "throughput": "PCIe bandwidth limited (32-64 GB/s)", - "latency": "100-1000ns (packet processing)", - "precision": "PCIe TLP", - "operations": "packet processing", - "power": "5-15W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run PCIe controller computational analysis.""" - print("=" * 60) - print("PCIe CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze PCIe controller - print("\n[1/4] Analyzing PCIe controller...") - print(f" Device: {self.pcie_controller['device']}") - print(f" Address: {self.pcie_controller['address']}") - print(f" Memory Bridge: {self.pcie_controller['memory_bridge']}") - print(f" Computational Potential: {self.pcie_controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" PCIe Switching: {potential['pcie_switching']['feasible']}") - print(f" PCIe Routing: {potential['pcie_routing']['feasible']}") - print(f" PCIe DMA: {potential['pcie_dma']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" PCIe Switching: {performance['pcie_switching']['throughput']}") - print(f" PCIe Routing: {performance['pcie_routing']['throughput']}") - print(f" PCIe DMA: {performance['pcie_dma']['throughput']}") - print(f" PCIe Packet: {performance['pcie_packet']['throughput']}") - - print("\n" + "=" * 60) - print("PCIe CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "pcie_controller": self.pcie_controller, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = PCIeComputationalController() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "pcie_computational_controller.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("PCIe COMPUTATIONAL CONTROLLER SUMMARY") - print("=" * 60) - print(f"Device: {results['pcie_controller']['device']}") - print(f"Memory Bridge: {results['pcie_controller']['memory_bridge']}") - print(f"Computational Potential: {results['pcie_controller']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['pcie_switching']['throughput']}") diff --git a/5-Applications/scripts/phi_universal_alert.py b/5-Applications/scripts/phi_universal_alert.py deleted file mode 100644 index 810d8563..00000000 --- a/5-Applications/scripts/phi_universal_alert.py +++ /dev/null @@ -1,235 +0,0 @@ -#!/usr/bin/env python3 -""" -Φ_universal Priority Alert — Targeted P0 Directive - -Issues a specific P0 CRITICAL alert requiring the swarm to prove or refute -the Universal Field equation (Φ_universal) — the foundational equation -that was MISSING from the system. -""" - -import sys -import json -import sqlite3 -from datetime import datetime, timezone -from typing import Dict, Any - -sys.path.insert(0, '/home/allaun/Documents/Research Stack/tools') -from lean_unified_shim import SwarmAPISystem - - -def issue_phi_universal_alert() -> Dict[str, Any]: - """ - Issue P0 CRITICAL alert specifically for Φ_universal verification. - This is the targeted follow-up to the general math foundation audit. - """ - - api = SwarmAPISystem() - timestamp = datetime.now(timezone.utc).isoformat() - - alert_content = """ -╔══════════════════════════════════════════════════════════════════════════════╗ -║ Φ_UNIVERSAL P0 CRITICAL ALERT ║ -╚══════════════════════════════════════════════════════════════════════════════╝ - -TARGET: EQUATION #0 — Φ_universal (Universal Field) -STATUS: 🚧 P0 CRITICAL — CONJECTURE (UNPROVEN) -DATE: {timestamp} - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -THE EQUATION -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - - Φ_universal = Σᵢ wᵢ·lnNᵢ - Σⱼ vⱼ·lnNⱼ [CORRECTED: Cost form matches Landauer] - = Σᵢ wᵢ·hᵢ/lnNᵢ - Σⱼ vⱼ·pⱼ/lnNⱼ [Efficiency form] - - NOTE: Previous formulation wᵢ/lnNᵢ has been CORRECTED to wᵢ·lnNᵢ - Landauer: E_min = k_B T ln N — Cost increases with alphabet size N - -Where: - • wᵢ = informational weight (constructive terms) - • vⱼ = entropic weight (destructive terms) - • Nᵢ, Nⱼ = node cardinalities (state space sizes) - • hᵢ = merit coefficient = qualityᵢ/lnNᵢ (efficiency per unit cost) - • pⱼ = penalty coefficient = disorderⱼ/lnNⱼ (inefficiency measure) - - CORRECTION: hᵢ = 1/(lnNᵢ)² was mathematically inconsistent with Landauer - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -CRITICAL FINDINGS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -⚠ MISSING: This equation was NOT in MATH_MODEL_MAP-42126.md -⚠ FOUNDATION GAP: All 88+ Lean modules depend on this being correct -⚠ STATUS: Documented but UNPROVEN (conjecture status) - -The Principal Investigator has identified this as a potential deep fault -in the mathematical foundations. The swarm must PROVE or REFUTE. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -REQUIRED PROOFS (Triumvirate Assignment) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -BUILDER → manifold_reg (ADD clock) -──────────────────────────────────── -□ Implement phiUniversal in Lean (Q16_16 fixed-point) -□ Create structure UniversalField with all components -□ Define both forms: reciprocal-log and weighted-log - -WARDEN → stark_trace & warden_valid (SUBTRACT clock) -────────────────────────────────────────────────────── -□ Prove equivalence: phiUniversalReciprocal = phiUniversalWeighted -□ Verify convergence for all N ≥ 2 -□ Check numerical stability in Q16_16 -□ Validate boundary conditions (N→2, N→∞) - -JUDGE → heatsink_halt (PAUSE clock) -─────────────────────────────────── -□ Adjudicate proof completeness -□ Verify no 'sorry' remains in committed code -□ Confirm cross-consistency with Φ_genomic -□ Approve or reject for system-wide deployment - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -DEPENDENT SYSTEMS (All Blocked Pending Proof) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -• Genomic Compression (Φ_genomic specialization) -• Cognitive Load (L_I, L_E, L_G) -• AVMR Framework (field equations) -• Compression Mechanics -• Entropy-Based Routing -• All 88+ OTOM modules - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -LEAN SPECIFICATION TEMPLATE -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -namespace Semantics.UniversalField - -open Semantics.Q16_16 - -structure UniversalFieldParams where - n m : Nat -- Dimensions - w : Fin n → Q16_16 -- Informational weights - v : Fin m → Q16_16 -- Entropic weights - N : Fin n → Nat -- Info node cardinalities - M : Fin m → Nat -- Entropy node cardinalities - h : Fin n → Q16_16 -- Harmonic coefficients - p : Fin m → Q16_16 -- Penalty coefficients - -def phiUniversalReciprocal (params : UniversalFieldParams) : Q16_16 := - -- Σᵢ wᵢ/lnNᵢ + Σⱼ vⱼ/lnNⱼ - sorry - -def phiUniversalWeighted (params : UniversalFieldParams) : Q16_16 := - -- Σᵢ wᵢ lnNᵢ hᵢ - Σⱼ vⱼ lnNⱼ pⱼ - sorry - -theorem phiUniversalEquivalence (params : UniversalFieldParams) : - phiUniversalReciprocal params = phiUniversalWeighted params := by - sorry - -theorem phiUniversalNormalization (params : UniversalFieldParams) - (hw : ∑ i, params.w i = 1) (hv : ∑ j, params.v j = 1) : - phiUniversalReciprocal params ≤ 1 := by - sorry - -end Semantics.UniversalField - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -SUCCESS CRITERIA -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -✓ All four theorems proven with complete formal proofs -✓ Q16_16 implementation verified numerically stable -✓ Cross-reference with Φ_genomic validated -✓ No 'sorry' in committed code -✓ Triumvirate consensus reached (Builder ✓ Warden ✓ Judge ✓) - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -SWARM DIRECTIVE: PROVE OR REFUTE. NO INTERMEDIATE STATE ACCEPTABLE. - -The mathematical integrity of the entire OTOM system depends on this. -""".format(timestamp=timestamp) - - # Store in priority_alerts table - if api.conn: - cursor = api.conn.cursor() - - cursor.execute(""" - CREATE TABLE IF NOT EXISTS priority_alerts ( - entity_id TEXT PRIMARY KEY, - subject TEXT, - name TEXT, - statement TEXT, - proof_status TEXT, - formal_status TEXT, - priority TEXT, - requires_immediate_action BOOLEAN, - created_at TEXT - ) - """) - - cursor.execute(""" - INSERT OR REPLACE INTO priority_alerts - (entity_id, subject, name, statement, proof_status, formal_status, - priority, requires_immediate_action, created_at) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - f'PHI_UNIVERSAL_P0_{timestamp.replace(":", "_")}', - 'critical_audit', - '[P0] Φ_universal Verification Required', - alert_content, - 'conjecture', - 'needs_formalization', - 'P0', - True, - timestamp - )) - - api.conn.commit() - - return { - 'success': True, - 'alert_type': 'Φ_universal P0 Critical', - 'timestamp': timestamp, - 'equation': 'Φ_universal = Σᵢ wᵢ/lnNᵢ + Σⱼ vⱼ/lnNⱼ', - 'status': 'ALERT_INJECTED_INTO_SWARM' - } - else: - return { - 'success': False, - 'error': 'Database not connected', - 'alert_printed': True - } - - -def main(): - print("="*70) - print("Φ_UNIVERSAL TARGETED ALERT") - print("="*70) - print() - - result = issue_phi_universal_alert() - - if result['success']: - print(f"[✓] {result['alert_type']}") - print(f" Timestamp: {result['timestamp']}") - print(f" Equation: {result['equation']}") - print(f" Status: {result['status']}") - print() - print("="*70) - print("The swarm has been notified of the specific Φ_universal") - print("verification requirement. Triumvirate is activated.") - print("="*70) - else: - print(f"[✗] Error: {result.get('error', 'Unknown')}") - if result.get('alert_printed'): - print("Alert content logged to console.") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/physical_topology_optimizer.py b/5-Applications/scripts/physical_topology_optimizer.py deleted file mode 100644 index a9e861f7..00000000 --- a/5-Applications/scripts/physical_topology_optimizer.py +++ /dev/null @@ -1,287 +0,0 @@ -#!/usr/bin/env python3 -""" -Physical Topology Optimizer -Optimizes FPGA design using complete physical topology including capacitors, wires, USB lines, voltage, etc. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class PhysicalTopologyOptimizer: - """Optimizes FPGA using complete physical topology.""" - - def __init__(self): - self.physical_topology = { - "capacitors": { - "decoupling": { - "fpga_power_rails": ["0.1uF", "1uF", "10uF"], - "ftdi_power_rails": ["0.1uF", "1uF"], - "voltage_regulator": ["10uF", "100uF"] - }, - "filtering": { - "usb_differential_pairs": ["0.01uF"], - "clock_lines": ["0.1uF"], - "reset_lines": ["0.01uF"] - }, - "bulk": { - "power_supply": ["470uF", "1000uF"] - } - }, - "wires": { - "pcb_traces": { - "fpga_ftdi": { - "length": "5-10cm", - "impedance": "50 ohms", - "material": "FR4" - }, - "usb_lines": { - "d_plus": "90 ohms differential", - "d_minus": "90 ohms differential", - "length": "10-20cm" - }, - "power_rails": { - "3.3V": "0.1 ohms", - "1.8V": "0.1 ohms", - "gnd": "0.01 ohms" - } - }, - "usb_cable": { - "d_plus": "90 ohms differential", - "d_minus": "90 ohms differential", - "vbus": "0.5 ohms", - "length": "1-3m" - } - }, - "usb_lines": { - "differential_pairs": { - "d_plus_d_minus": "90 ohms", - "impedance_tolerance": "±15%", - "skew": "<100ps" - }, - "vbus": { - "voltage": "5V ±5%", - "current": "500mA max", - "impedance": "<1 ohm" - } - }, - "voltage": { - "power_rails": { - "fpga": { - "core": "1.8V", - "io": "3.3V", - "aux": "2.5V" - }, - "ftdi": { - "core": "1.8V", - "io": "3.3V" - }, - "usb": { - "vbus": "5V" - } - }, - "regulators": { - "ldo_3.3V": "5V → 3.3V", - "ldo_1.8V": "3.3V → 1.8V", - "efficiency": "85-90%" - } - }, - "thermal": { - "fpga": { - "max_junction_temp": "85°C", - "thermal_resistance": "40°C/W", - "power_dissipation": "<1W" - }, - "ftdi": { - "max_junction_temp": "70°C", - "thermal_resistance": "50°C/W", - "power_dissipation": "<0.5W" - } - }, - "signal_integrity": { - "rise_time": "<5ns", - "fall_time": "<5ns", - "overshoot": "<10%", - "undershoot": "<10%", - "ringing": "<5%" - } - } - - def analyze_capacitor_optimization(self) -> Dict: - """Analyze capacitor optimization opportunities.""" - optimization = { - "current_design": { - "total_capacitors": "15-20", - "total_capacitance": "~500uF", - "cost": "$5-10", - "board_space": "200mm²" - }, - "optimized_design": { - "total_capacitors": "8-10", - "total_capacitance": "~300uF", - "cost": "$3-5", - "board_space": "100mm²", - "reduction": "50% capacitors, 40% capacitance, 50% board space" - }, - "optimization_strategy": { - "decoupling": "Use optimized decoupling network (0.1uF + 10uF instead of 0.1uF + 1uF + 10uF)", - "filtering": "Consolidate filtering capacitors", - "bulk": "Use single high-capacity bulk capacitor instead of multiple", - "placement": "Optimize placement for minimum ESL/ESR" - }, - "power_saving": "5% (reduced ESR losses)" - } - - return optimization - - def analyze_wire_optimization(self) -> Dict: - """Analyze wire/trace optimization opportunities.""" - optimization = { - "current_design": { - "trace_width": "0.2mm (8 mil)", - "trace_length": "5-10cm (FPGA-FTDI)", - "impedance": "50 ohms (nominal)", - "layers": "2" - }, - "optimized_design": { - "trace_width": "0.15mm (6 mil) for signals, 0.3mm (12 mil) for power", - "trace_length": "3-5cm (optimized routing)", - "impedance": "50 ohms (controlled)", - "layers": "4 (with ground plane)", - "reduction": "40% trace length, 25% trace width" - }, - "optimization_strategy": { - "routing": "Optimize routing to minimize trace length", - "impedance": "Use controlled impedance traces", - "layers": "Use 4-layer PCB with dedicated ground plane", - "power": "Widen power traces to reduce IR drop" - }, - "power_saving": "10% (reduced trace resistance)", - "signal_integrity": "Improved (reduced crosstalk, better impedance control)" - } - - return optimization - - def analyze_usb_optimization(self) -> Dict: - """Analyze USB line optimization opportunities.""" - optimization = { - "current_design": { - "usb_speed": "USB 2.0 High Speed (480 Mbps)", - "cable_length": "1-3m", - "impedance": "90 ohms (nominal)", - "differential_skew": "±100ps" - }, - "optimized_design": { - "usb_speed": "USB 2.0 High Speed (480 Mbps)", - "cable_length": "<1m (shorter cable)", - "impedance": "90 ohms (controlled)", - "differential_skew": "±50ps", - "reduction": "66% cable length, 50% skew" - }, - "optimization_strategy": { - "cable": "Use shorter USB cable (<1m)", - "impedance": "Use impedance-controlled USB cable", - "filtering": "Add common-mode choke for EMI reduction", - "termination": "Optimize termination resistors" - }, - "power_saving": "15% (reduced cable losses)", - "signal_integrity": "Improved (reduced skew, better impedance matching)" - } - - return optimization - - def analyze_voltage_optimization(self) -> Dict: - """Analyze voltage/power optimization opportunities.""" - optimization = { - "current_design": { - "power_rails": ["5V (USB)", "3.3V (LDO)", "1.8V (LDO)"], - "regulator_efficiency": "85-90%", - "power_dissipation": "~1.5W", - "voltage_regulation": "±5%" - }, - "optimized_design": { - "power_rails": ["5V (USB)", "3.3V (Buck)", "1.8V (Buck)"], - "regulator_efficiency": "92-95%", - "power_dissipation": "~0.8W", - "voltage_regulation": "±2%", - "reduction": "47% power dissipation" - }, - "optimization_strategy": { - "regulators": "Replace LDOs with buck converters", - "efficiency": "Use high-efficiency switching regulators", - "regulation": "Improve voltage regulation with feedback", - "power_save": "Reduce quiescent current" - }, - "power_saving": "47% (reduced regulator losses)", - "thermal": "Improved (lower power dissipation)" - } - - return optimization - - def generate_comprehensive_optimization(self) -> Dict: - """Generate comprehensive physical topology optimization.""" - print("=" * 60) - print("PHYSICAL TOPOLOGY OPTIMIZATION") - print("=" * 60) - - # Step 1: Analyze capacitor optimization - print("\n[1/5] Analyzing capacitor optimization...") - capacitor_opt = self.analyze_capacitor_optimization() - print(f" Capacitor reduction: {capacitor_opt['optimized_design']['reduction']}") - - # Step 2: Analyze wire optimization - print("[2/5] Analyzing wire/trace optimization...") - wire_opt = self.analyze_wire_optimization() - print(f" Trace reduction: {wire_opt['optimized_design']['reduction']}") - - # Step 3: Analyze USB optimization - print("[3/5] Analyzing USB line optimization...") - usb_opt = self.analyze_usb_optimization() - print(f" Cable reduction: {usb_opt['optimized_design']['reduction']}") - - # Step 4: Analyze voltage optimization - print("[4/5] Analyzing voltage/power optimization...") - voltage_opt = self.analyze_voltage_optimization() - print(f" Power reduction: {voltage_opt['optimized_design']['reduction']}") - - # Step 5: Generate comprehensive report - print("[5/5] Generating comprehensive optimization report...") - - comprehensive_optimization = { - "capacitor_optimization": capacitor_opt, - "wire_optimization": wire_opt, - "usb_optimization": usb_opt, - "voltage_optimization": voltage_opt, - "total_power_saving": capacitor_opt["power_saving"] + wire_opt["power_saving"] + usb_opt["power_saving"] + voltage_opt["power_saving"], - "total_cost_saving": capacitor_opt["optimized_design"]["cost"].split("-")[0] + " to " + capacitor_opt["current_design"]["cost"].split("-")[1] - } - - print("\n" + "=" * 60) - print("PHYSICAL TOPOLOGY OPTIMIZATION COMPLETE") - print("=" * 60) - - return comprehensive_optimization - -if __name__ == '__main__': - optimizer = PhysicalTopologyOptimizer() - results = optimizer.generate_comprehensive_optimization() - - # Save results - output_file = OUTPUT_DIR / "physical_topology_optimization.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nOptimization results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("OPTIMIZATION SUMMARY") - print("=" * 60) - print(f"Total Power Saving: {results['total_power_saving']}") - print(f"Capacitor Reduction: {results['capacitor_optimization']['optimized_design']['reduction']}") - print(f"Trace Reduction: {results['wire_optimization']['optimized_design']['reduction']}") - print(f"Cable Reduction: {results['usb_optimization']['optimized_design']['reduction']}") - print(f"Power Dissipation Reduction: {results['voltage_optimization']['optimized_design']['reduction']}") diff --git a/5-Applications/scripts/physics_remapper_batch.py b/5-Applications/scripts/physics_remapper_batch.py deleted file mode 100644 index 43ec7dce..00000000 --- a/5-Applications/scripts/physics_remapper_batch.py +++ /dev/null @@ -1,500 +0,0 @@ -#!/usr/bin/env python3 -""" -Batched Physics Equation Remapper — /dev/shm + GPU batching - -Speedup: 1 LLM call per ~40 equations vs 1 per equation. -540 eqs → ~14 calls → ~2 minutes total. - -Architecture: - /dev/shm/physics_equations.db → read batch - GPU batch prompt (40 eqs) → single LLM call - /dev/shm/mapped.jsonl → write results - flush to disk → physics_eqs_mapped_batch.md - metaprobe + compression → per-batch telemetry -""" - -import ctypes -import json -import os -import re -import sqlite3 -import sys -import time -from collections import deque -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import requests - -# Add extremophile prior system -sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) -from extremophile_priors import DeepExtremophilePrior, PriorResult - -# ─── Config ───────────────────────────────────────────────────────────────── - -OLLAMA_URL = "http://localhost:11434/api/generate" -MODEL = "llama3.1:8b" -DB_PATH = "/dev/shm/physics_equations.db" -SHM_OUT = "/dev/shm/physics_mapped_batch.jsonl" -SHM_META = "/dev/shm/physics_metaprobe_batch.jsonl" -DISK_DIR = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -OUTPUT_MD = DISK_DIR / "physics_eqs_mapped_batch.md" -COMPRESSION_LOG = DISK_DIR / "physics_compression_batch.log" -LIBMOIRE_PATH = "/tmp/libmoire.so" -BATCH_SIZE = 10 # equations per LLM call - - -# ─── Moiré Decoder ────────────────────────────────────────────────────────── - -class MoireDecoder: - def __init__(self): - self.lib = ctypes.CDLL(LIBMOIRE_PATH) - self.lib.moire_encode.argtypes = [ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t, - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t] - self.lib.moire_encode.restype = ctypes.c_int - self.lib.moire_estimate_entropy.argtypes = [ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t] - self.lib.moire_estimate_entropy.restype = ctypes.c_double - - def entropy(self, data: bytes) -> float: - src = (ctypes.c_uint8 * len(data))(*data) - return self.lib.moire_estimate_entropy(src, len(data)) - - def ratio(self, data: bytes) -> float: - ent = self.entropy(data) - return 8.0 / ent if ent > 0 else 1.0 - - -# ─── Metaprobe ───────────────────────────────────────────────────────────── - -@dataclass -class BatchProbe: - batch_id: int - eq_numbers: List[int] - success_count: int - fail_count: int - latency_ms: float - avg_confidence: float = 0.0 - avg_distortion: float = 0.0 - batch_entropy: float = 0.0 - timestamp: float = field(default_factory=time.time) - - -class MetaprobeLayer: - def __init__(self, moire: MoireDecoder): - self.moire = moire - self.batches: List[BatchProbe] = [] - self.domain_stats: Dict[str, int] = {} - self.total_success = 0 - self.total_fail = 0 - - def record_batch(self, probe: BatchProbe): - self.batches.append(probe) - self.total_success += probe.success_count - self.total_fail += probe.fail_count - - def flush(self): - with open(SHM_META, "a", encoding="utf-8") as f: - for p in self.batches: - f.write(json.dumps({ - 'batch_id': p.batch_id, - 'eq_numbers': p.eq_numbers, - 'success': p.success_count, - 'fail': p.fail_count, - 'latency_ms': round(p.latency_ms, 1), - 'avg_confidence': round(p.avg_confidence, 3), - 'avg_distortion': round(p.avg_distortion, 3), - 'batch_entropy': round(p.batch_entropy, 3), - 'timestamp': p.timestamp, - }) + "\n") - self.batches.clear() - - def summary(self) -> str: - lines = ["=== Metaprobe Summary ===", ""] - lines.append(f"Total mapped: {self.total_success} | Failed: {self.total_fail}") - lines.append(f"Batches: {len(self.batches) + sum(1 for _ in open(SHM_META)) if os.path.exists(SHM_META) else len(self.batches)}") - lines.append("") - if self.domain_stats: - lines.append("Per-domain:") - for dom, cnt in sorted(self.domain_stats.items(), key=lambda x: -x[1]): - lines.append(f" {dom:25s} | {cnt:3d}") - return "\n".join(lines) - - -# ─── Physics Filter ──────────────────────────────────────────────────────── - -class PhysicsFilter: - """ - Filter out equations that require physically unrealistic conditions. - - Rejects equation solutions that violate constraints from organisms - that survived extreme conditions (pressure, energy, time, compressibility). - - Applied BEFORE LLM remapping to prune physically inadmissible branches. - """ - - def __init__(self): - self.priors = DeepExtremophilePrior() - self.rejection_log: List[Dict] = [] - - def filter_batch(self, equations: List[Tuple[int, str, str, str]]) -> List[Tuple[int, str, str, str]]: - """ - Filter equation batch before LLM remapping. - - Extracts physical parameters from equation descriptions and - rejects those requiring unphysical conditions. - """ - admissible = [] - - for eq_num, title, domain, desc in equations: - # Extract potential physical parameters from description - params = self._extract_parameters(desc) - - if params: - result = self.priors.unified_check(params) - - if result.admissible: - admissible.append((eq_num, title, domain, desc)) - else: - self._log_rejection(eq_num, title, result) - else: - # If no parameters extracted, pass through (can't filter) - admissible.append((eq_num, title, domain, desc)) - - return admissible - - def _extract_parameters(self, desc: str) -> Optional[Dict]: - """Extract physical parameters from equation description.""" - import re - params = {} - - # Pressure extraction (look for MPa, GPa, atm, bar) - pressure_patterns = [ - r'(\d+\.?\d*)\s*MPa', - r'(\d+\.?\d*)\s*GPa', - r'(\d+\.?\d*)\s*atm', - r'(\d+\.?\d*)\s*bar', - ] - for pattern in pressure_patterns: - match = re.search(pattern, desc, re.IGNORECASE) - if match: - val = float(match.group(1)) - if 'MPa' in pattern: - params['pressure'] = val * 1e6 - elif 'GPa' in pattern: - params['pressure'] = val * 1e9 - elif 'atm' in pattern: - params['pressure'] = val * 101325 - elif 'bar' in pattern: - params['pressure'] = val * 1e5 - break - - # Temperature extraction - temp_match = re.search(r'(\d+\.?\d*)\s*°?\s*(K|C|°C|°F)', desc) - if temp_match: - val = float(temp_match.group(1)) - unit = temp_match.group(2).upper() - if unit == 'K': - params['temperature'] = val - elif unit in ['C', '°C']: - params['temperature'] = val + 273.15 - elif unit == '°F': - params['temperature'] = (val - 32) * 5/9 + 273.15 - - # Energy/power extraction - power_patterns = [ - r'(\d+\.?\d*)\s*W', - r'(\d+\.?\d*)\s*kW', - r'(\d+\.?\d*)\s*MW', - ] - for pattern in power_patterns: - match = re.search(pattern, desc, re.IGNORECASE) - if match: - val = float(match.group(1)) - if 'kW' in pattern: - params['power'] = val * 1e3 - elif 'MW' in pattern: - params['power'] = val * 1e6 - else: - params['power'] = val - break - - # Time scale extraction - time_patterns = [ - r'(\d+\.?\d*)\s*yr', - r'(\d+\.?\d*)\s*year', - r'(\d+\.?\d*)\s*Myr', - r'(\d+\.?\d*)\s*Gyr', - ] - for pattern in time_patterns: - match = re.search(pattern, desc, re.IGNORECASE) - if match: - val = float(match.group(1)) - if 'Myr' in pattern: - params['time'] = val * 1e6 * 365.25 * 24 * 3600 - elif 'Gyr' in pattern: - params['time'] = val * 1e9 * 365.25 * 24 * 3600 - else: - params['time'] = val * 365.25 * 24 * 3600 - break - - # Default bits (large computation assumed) - params['bits'] = 1e15 - - return params if params else None - - def _log_rejection(self, eq_num: int, title: str, result: PriorResult): - """Log rejected equations for analysis.""" - self.rejection_log.append({ - 'eq_num': eq_num, - 'title': title, - 'violated_constraint': result.violated_constraint, - 'details': result.details, - }) - - def get_rejection_summary(self) -> str: - """Return summary of rejected equations.""" - if not self.rejection_log: - return "No equations rejected by physics filter." - - lines = ["=== Physics Filter Rejections ===", ""] - lines.append(f"Total rejected: {len(self.rejection_log)}") - lines.append("") - - # Group by violation type - violations: Dict[str, int] = {} - for rej in self.rejection_log: - vtype = rej['violated_constraint'] or 'unknown' - violations[vtype] = violations.get(vtype, 0) + 1 - - lines.append("By violation type:") - for vtype, count in sorted(violations.items(), key=lambda x: -x[1]): - lines.append(f" {vtype:40s} | {count:3d}") - - return "\n".join(lines) - - -# ─── LLM Batch Prompt ────────────────────────────────────────────────────── - -BATCH_PROMPT_TEMPLATE = """You are a physics equation mapper. For EACH equation below, assign symbols from: - Output = Operator [ Basis(Context) ⊗ Params(n, α) ] ⊕ Error(n, Context, α) - -Definitions: -- Output: Observable output / predicted quantity -- Operator: Mechanism or underlying theory -- Basis: Conserved basis / fundamental component -- Params: Dynamic context / variable parameter -- Error: Residual error / noise / fundamental limit - -For each equation, respond with ONE line of valid JSON in this exact format: -{{"eq": NUMBER, "Output": "...", "Operator": "...", "Basis": "...", "Params": "...", "Error": "..."}} - -Use ≤15 words per value. Respond with exactly {count} JSON lines, one per equation. - ---- EQUATIONS --- -{equations} ---- -""" - - -def call_ollama_batch(batch: List[Tuple[int, str, str, str]], batch_id: int) -> List[Optional[Dict]]: - """Send a batch of equations to LLM, get mappings back.""" - eq_texts = [] - for eq_num, title, domain, desc in batch: - eq_texts.append(f"[{eq_num}] {title} ({domain}): {desc[:120]}") - - prompt = BATCH_PROMPT_TEMPLATE.format( - count=len(batch), - equations="\n".join(eq_texts) - ) - - try: - r = requests.post( - OLLAMA_URL, - json={"model": MODEL, "prompt": prompt, "stream": False, - "options": {"temperature": 0.1, "num_predict": 800}}, - timeout=300, - ) - r.raise_for_status() - content = r.json()["response"] - - # Parse each JSON line - results = [None] * len(batch) - for line in content.split('\n'): - line = line.strip() - if not line or line.startswith('```') or line.startswith('//'): - continue - # Extract JSON objects - for match in re.finditer(r'\{[^}]*"eq"\s*:\s*(\d+)[^}]*\}', line): - try: - obj = json.loads(match.group(0)) - eq_num = int(obj.get('eq', 0)) - # Find position in batch - for i, (b_num, _, _, _) in enumerate(batch): - if b_num == eq_num: - results[i] = { - 'Output': obj.get('Output', 'N/A'), - 'Operator': obj.get('Operator', 'N/A'), - 'Basis': obj.get('Basis', 'N/A'), - 'Params': obj.get('Params', 'N/A'), - 'Error': obj.get('Error', 'N/A'), - } - break - except (json.JSONDecodeError, ValueError): - pass - return results - - except Exception as e: - print(f" BATCH ERROR #{batch_id}: {e}", file=sys.stderr) - return [None] * len(batch) - - -# ─── I/O ──────────────────────────────────────────────────────────────────── - -def load_equations() -> List[Tuple[int, str, str, str]]: - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - cursor.execute("SELECT id, name FROM domains") - domains = {str(r[0]): r[1] for r in cursor.fetchall()} - conn.close() - return [(eq_num, title, domains.get(str(did), "Unknown"), desc or "") - for eq_num, title, did, desc in rows] - - -def append_jsonl(eq_num: int, title: str, domain: str, desc: str, mapping: Dict): - with open(SHM_OUT, "a", encoding="utf-8") as f: - f.write(json.dumps({ - 'eq_number': eq_num, - 'title': title, - 'domain': domain, - 'description': desc[:200], - 'mapping': mapping, - }) + "\n") - - -def flush_to_disk(): - """Convert /dev/shm JSONL to markdown on disk.""" - lines = [ - "# Physics Equations — Batched Mapped (RAM→GPU→SHM→Disk)\n", - "**Equation:** Output = Operator [ Basis(Context) ⊗ Params(n, α) ] ⊕ Error(n, Context, α)\n\n", - "---\n\n", - ] - with open(SHM_OUT, "r", encoding="utf-8") as f: - for row in f: - obj = json.loads(row) - m = obj['mapping'] - lines.append(f"## Eq {obj['eq_number']}. {obj['title']}\n") - lines.append(f"**Domain:** {obj['domain']}\n") - lines.append(f"**Description:** {obj['description']}\n") - lines.append("| Symbol | Mapping |\n") - lines.append("|--------|---------|\n") - for sym in ['Output', 'Operator', 'Basis', 'Params', 'Error']: - lines.append(f"| {sym} | {m.get(sym, 'N/A')} |\n") - lines.append("\n---\n\n") - - with open(OUTPUT_MD, "w", encoding="utf-8") as f: - f.writelines(lines) - - -# ─── Main ─────────────────────────────────────────────────────────────────── - -def main(): - print("=" * 60) - print("Batched Physics Remapper — /dev/shm → GPU → disk") - print(f"Batch size: {BATCH_SIZE}") - print("=" * 60) - - print("\n[1/5] Loading moiré decoder...") - moire = MoireDecoder() - probe = MetaprobeLayer(moire) - - print("\n[2/5] Reading equations from /dev/shm...") - equations = load_equations() - print(f" → {len(equations)} equations loaded") - - # Chunk into batches - batches = [equations[i:i+BATCH_SIZE] for i in range(0, len(equations), BATCH_SIZE)] - print(f" → {len(batches)} batches of ≤{BATCH_SIZE}") - - print("\n[3/5] Processing batches...") - print("-" * 60) - - for batch_id, batch in enumerate(batches): - t0 = time.time() - results = call_ollama_batch(batch, batch_id) - latency = (time.time() - t0) * 1000 - - success = 0 - fail = 0 - confidences = [] - distortions = [] - - for i, (eq_num, title, domain, desc) in enumerate(batch): - mapping = results[i] - if mapping: - append_jsonl(eq_num, title, domain, desc, mapping) - success += 1 - # Quick confidence heuristics - vals = [v for v in mapping.values() if v not in {'N/A', ''}] - avg_len = sum(len(v) for v in vals) / max(len(vals), 1) - confidences.append(min(1.0, avg_len / 30.0)) - distortions.append(0.0) # batched: assume ok unless empty - probe.domain_stats[domain] = probe.domain_stats.get(domain, 0) + 1 - else: - fail += 1 - - # Batch-level compression: entropy of this batch's mappings - batch_text = json.dumps([r for r in results if r]).encode() - batch_entropy = moire.entropy(batch_text) if batch_text else 8.0 - - bp = BatchProbe( - batch_id=batch_id, - eq_numbers=[b[0] for b in batch], - success_count=success, - fail_count=fail, - latency_ms=latency, - avg_confidence=sum(confidences)/max(len(confidences),1), - avg_distortion=sum(distortions)/max(len(distortions),1), - batch_entropy=batch_entropy, - ) - probe.record_batch(bp) - - print(f" Batch {batch_id+1:2d}/{len(batches):2d} | {success:2d} OK {fail:2d} FAIL | " - f"{latency:6.0f}ms | conf={bp.avg_confidence:.2f} | ent={batch_entropy:.3f}") - - print("-" * 60) - - print("\n[4/5] Flushing metaprobe...") - probe.flush() - - print("\n[5/5] Writing markdown to disk...") - flush_to_disk() - - # Compression analysis of final output - data = Path(OUTPUT_MD).read_bytes() - ent = moire.entropy(data) - ratio = moire.ratio(data) - - with open(COMPRESSION_LOG, "w") as f: - f.write(f"file={OUTPUT_MD}\n") - f.write(f"size_bytes={len(data)}\n") - f.write(f"moire_entropy={ent:.6f}\n") - f.write(f"theoretical_ratio={ratio:.6f}\n") - f.write(f"total_mapped={probe.total_success}\n") - f.write(f"total_failed={probe.total_fail}\n") - f.write(f"batch_count={len(batches)}\n") - - print(f"\nDone.") - print(f" Mapped: {probe.total_success}/{len(equations)}") - print(f" Batches: {len(batches)}") - print(f" Output: {OUTPUT_MD} ({len(data):,} bytes)") - print(f" Moiré entropy: {ent:.4f} bits/byte") - print(f" Compression ratio: {ratio:.2f}x") - print("") - print(probe.summary()) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/physics_remapper_eigenvector.py b/5-Applications/scripts/physics_remapper_eigenvector.py deleted file mode 100644 index b9caea79..00000000 --- a/5-Applications/scripts/physics_remapper_eigenvector.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python3 -""" -Eigenvector-Based Physics Equation Remapper — No LLM Required - -Uses eigenvector clustering to assign equations to domain-based categories -instead of LLM semantic mapping. Much lower memory footprint. - -Speedup: Single pass over all equations vs 139 LLM calls. -1388 eqs → ~2 seconds total. -""" - -import sqlite3 -import json -import numpy as np -from scipy.sparse import csr_matrix -from scipy.sparse.linalg import eigsh -from pathlib import Path -from typing import Dict, List, Tuple - -DB_PATH = "/dev/shm/physics_equations.db" -DISK_DIR = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -OUTPUT_MD = DISK_DIR / "physics_eqs_eigenvector_mapped.md" - -def load_equations() -> List[Tuple[int, str, int, str]]: - """Load all equations from database.""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - cursor.execute("SELECT id, name FROM domains") - domains = {str(r[0]): r[1] for r in cursor.fetchall()} - conn.close() - return [(eq_num, title, domains.get(str(did), "Unknown"), desc or "") - for eq_num, title, did, desc in rows] - -def build_domain_adjacency(equations): - """Build adjacency matrix based on domain co-occurrence.""" - n = len(equations) - - # Create domain to equations mapping - domain_to_eqs = {} - for i, (_, _, domain_id, _) in enumerate(equations): - if domain_id not in domain_to_eqs: - domain_to_eqs[domain_id] = [] - domain_to_eqs[domain_id].append(i) - - # Build sparse adjacency matrix - row_indices = [] - col_indices = [] - data = [] - - # Connect equations in same domain - for domain_id, eq_indices in domain_to_eqs.items(): - for i in eq_indices: - for j in eq_indices: - if i != j: - row_indices.append(i) - col_indices.append(j) - data.append(1.0 / len(eq_indices)) - - adj = csr_matrix((data, (row_indices, col_indices)), shape=(n, n)) - return adj, domain_to_eqs - -def find_principal_eigenvectors(adj_matrix, n_eigenvectors=5): - """Find principal eigenvectors.""" - eigenvalues, eigenvectors = eigsh(adj_matrix, k=n_eigenvectors, which='LM') - return eigenvalues, eigenvectors - -def assign_eigenvector_categories(equations, eigenvectors): - """Assign each equation to its dominant eigenvector cluster.""" - n_eqs = len(equations) - n_clusters = eigenvectors.shape[1] - - # Find dominant eigenvector for each equation - categories = [] - for i in range(n_eqs): - magnitudes = np.abs(eigenvectors[i, :]) - dominant_cluster = int(np.argmax(magnitudes)) - dominant_magnitude = float(magnitudes[dominant_cluster]) - categories.append((dominant_cluster, dominant_magnitude)) - - return categories - -cluster_names = [ - "Electromagnetism & Circuits", - "Condensed Matter & Superconductivity", - "Quantum Mechanics & Particle Physics", - "Materials Science & Engineering", - "Cognitive & Semantic Systems" -] - -def flush_to_disk(equations, categories, eigenvalues): - """Convert results to markdown on disk.""" - lines = [ - "# Physics Equations — Eigenvector Cluster Mapping\n", - f"**Method:** Domain adjacency matrix + principal eigenvector analysis\n", - f"**Equations:** {len(equations)}\n", - f"**Clusters:** {len(eigenvalues)}\n\n", - "---\n\n", - ] - - # Group equations by cluster - for cluster_idx in range(len(eigenvalues)): - lines.append(f"## Cluster {cluster_idx + 1}: {cluster_names[cluster_idx]}\n") - lines.append(f"**Eigenvalue:** {eigenvalues[cluster_idx]:.6f}\n\n") - - # Get equations for this cluster - cluster_eqs = [] - for eq, cat in zip(equations, categories): - if cat[0] == cluster_idx: - cluster_eqs.append((eq[0], eq[1], eq[2], eq[3], cat[1])) - - cluster_eqs.sort(key=lambda x: x[4], reverse=True) # Sort by eigenvector magnitude - - lines.append(f"**Equations in cluster:** {len(cluster_eqs)}\n\n") - - for rank, (eq_num, title, domain, desc, magnitude) in enumerate(cluster_eqs, 1): - lines.append(f"### {rank}. Eq {eq_num}: {title}\n") - lines.append(f"**Domain:** {domain}\n") - lines.append(f"**Cluster Strength:** {magnitude:.6f}\n") - lines.append(f"**Description:** {desc[:200]}\n\n") - - lines.append("---\n\n") - - with open(OUTPUT_MD, "w", encoding="utf-8") as f: - f.writelines(lines) - -def main(): - print("=" * 60) - print("Eigenvector-Based Physics Equation Remapper") - print("=" * 60) - - print("\n[1/5] Loading equations...") - equations = load_equations() - n_eqs = len(equations) - print(f" → {n_eqs} equations loaded") - - print("\n[2/5] Building domain adjacency matrix...") - adj, domain_to_eqs = build_domain_adjacency(equations) - print(f" → Matrix shape: {adj.shape}") - print(f" → Non-zero entries: {adj.nnz}") - print(f" → Domains: {len(domain_to_eqs)}") - - print("\n[3/5] Computing principal eigenvectors...") - eigenvalues, eigenvectors = find_principal_eigenvectors(adj, n_eigenvectors=5) - print(f" → Found {len(eigenvalues)} eigenvalues") - - print("\n[4/5] Assigning equations to clusters...") - categories = assign_eigenvector_categories(equations, eigenvectors) - - # Show cluster distribution - cluster_counts = {} - for cluster, _ in categories: - cluster_counts[cluster] = cluster_counts.get(cluster, 0) + 1 - print(f" → Cluster distribution:") - for cluster in sorted(cluster_counts.keys()): - print(f" Cluster {cluster + 1}: {cluster_counts[cluster]} equations") - - print("\n[5/5] Writing markdown to disk...") - flush_to_disk(equations, categories, eigenvalues) - - print(f"\nDone.") - print(f" Mapped: {n_eqs} equations") - print(f" Clusters: {len(eigenvalues)}") - print(f" Output: {OUTPUT_MD}") - print(f" Time: ~2 seconds") - print(f" Memory: ~50MB") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/physics_remapper_parallel.py b/5-Applications/scripts/physics_remapper_parallel.py deleted file mode 100644 index 63332d8e..00000000 --- a/5-Applications/scripts/physics_remapper_parallel.py +++ /dev/null @@ -1,245 +0,0 @@ -#!/usr/bin/env python3 -""" -Parallel Physics Equation Remapper — 4-6 concurrent workers - -Leverages RTX 4070 SUPER: 12GB VRAM, currently 19% utilized. -llama3.1:8b (~4.9GB) can run 2-3 concurrent streams. -With 4 workers: ~4x speedup vs sequential. - -Flow: - /dev/shm/physics_equations.db → read - 4-worker ThreadPoolExecutor → LLM calls - /dev/shm/mapped.jsonl → write - flush to disk + compression + metaprobe -""" - -import ctypes -import json -import os -import re -import sqlite3 -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import requests - -# ─── Config ───────────────────────────────────────────────────────────────── - -OLLAMA_URL = "http://localhost:11434/api/generate" -MODEL = "llama3.1:8b" -DB_PATH = "/dev/shm/physics_equations.db" -SHM_OUT = "/dev/shm/physics_mapped_parallel.jsonl" -SHM_META = "/dev/shm/physics_metaprobe_parallel.jsonl" -DISK_DIR = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -OUTPUT_MD = DISK_DIR / "physics_eqs_mapped_parallel.md" -COMPRESSION_LOG = DISK_DIR / "physics_compression_parallel.log" -LIBMOIRE_PATH = "/tmp/libmoire.so" -MAX_WORKERS = 6 # local GPU: 6 concurrent streams - - -# ─── Moiré Decoder ────────────────────────────────────────────────────────── - -class MoireDecoder: - def __init__(self): - self.lib = ctypes.CDLL(LIBMOIRE_PATH) - self.lib.moire_estimate_entropy.argtypes = [ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t] - self.lib.moire_estimate_entropy.restype = ctypes.c_double - - def entropy(self, data: bytes) -> float: - src = (ctypes.c_uint8 * len(data))(*data) - return self.lib.moire_estimate_entropy(src, len(data)) - - def ratio(self, data: bytes) -> float: - ent = self.entropy(data) - return 8.0 / ent if ent > 0 else 1.0 - - -# ─── LLM Single Equation Call ─────────────────────────────────────────────── - -PROMPT_TEMPLATE = """Map this physics equation to five symbols from: - Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α) - -Equation: {title} -Domain: {domain} -Description: {description} - -Definitions: -- Ω: Observable output / predicted quantity -- Ψ: Operator, mechanism, or underlying theory -- B: Conserved basis / fundamental component -- C: Dynamic context / variable parameter -- Δ: Residual error / noise / fundamental limit - -Respond ONLY in valid JSON with exactly these five keys and short (≤15 words) values: -{{"Ω": "...", "Ψ": "...", "B": "...", "C": "...", "Δ": "..."}} -""" - - -def call_ollama(title: str, domain: str, description: str) -> Optional[Dict]: - prompt = PROMPT_TEMPLATE.format(title=title, domain=domain, description=description[:300]) - try: - r = requests.post( - OLLAMA_URL, - json={"model": MODEL, "prompt": prompt, "stream": False, - "options": {"temperature": 0.1, "num_predict": 150}}, - timeout=60, - ) - r.raise_for_status() - content = r.json()["response"] - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - pass - return None - - -# ─── Data Loading ─────────────────────────────────────────────────────────── - -def load_equations() -> List[Tuple[int, str, str, str]]: - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - cursor.execute("SELECT id, name FROM domains") - domains = {str(r[0]): r[1] for r in cursor.fetchall()} - conn.close() - return [(eq_num, title, domains.get(str(did), "Unknown"), desc or "") - for eq_num, title, did, desc in rows] - - -# ─── Progress Tracking ────────────────────────────────────────────────────── - -class ProgressTracker: - def __init__(self, moire: MoireDecoder, total: int): - self.moire = moire - self.total = total - self.success = 0 - self.fail = 0 - self.domain_stats: Dict[str, int] = {} - self.entropies: List[float] = [] - self.latencies: List[float] = [] - - def record(self, eq_num: int, title: str, domain: str, mapping: Dict, latency_ms: float): - self.success += 1 - self.domain_stats[domain] = self.domain_stats.get(domain, 0) + 1 - self.latencies.append(latency_ms) - # Entropy of this mapping - mapping_text = json.dumps(mapping, sort_keys=True).encode() - self.entropies.append(self.moire.entropy(mapping_text)) - - def record_fail(self): - self.fail += 1 - - def summary(self) -> str: - lines = ["=== Results ===", ""] - lines.append(f"Total: {self.total} | Success: {self.success} | Fail: {self.fail}") - if self.latencies: - avg_lat = sum(self.latencies) / len(self.latencies) - lines.append(f"Avg latency: {avg_lat:.0f}ms") - if self.entropies: - avg_ent = sum(self.entropies) / len(self.entropies) - lines.append(f"Avg mapping entropy: {avg_ent:.3f} bits/byte") - lines.append("") - lines.append("Top domains:") - for dom, cnt in sorted(self.domain_stats.items(), key=lambda x: -x[1])[:10]: - lines.append(f" {dom:25s} | {cnt:3d}") - return "\n".join(lines) - - -# ─── Main Pipeline ────────────────────────────────────────────────────────── - -def main(): - print("=" * 60) - print(f"Parallel Physics Remapper — {MAX_WORKERS} workers") - print("=" * 60) - - print("\n[1/4] Loading moiré decoder...") - moire = MoireDecoder() - - print("\n[2/4] Reading equations from /dev/shm...") - equations = load_equations() - print(f" → {len(equations)} equations") - - print(f"\n[3/4] Mapping with {MAX_WORKERS} concurrent workers...") - print("-" * 60) - - tracker = ProgressTracker(moire, len(equations)) - - # Open output file for incremental writes - with open(SHM_OUT, "w", encoding="utf-8") as out_f: - with ThreadPoolExecutor(max_workers=MAX_WORKERS) as executor: - futures = {} - for eq_num, title, domain, desc in equations: - future = executor.submit(call_ollama, title, domain, desc) - futures[future] = (eq_num, title, domain, desc, time.time()) - - for future in as_completed(futures): - eq_num, title, domain, desc, t0 = futures[future] - latency = (time.time() - t0) * 1000 - mapping = future.result() - - if mapping: - tracker.record(eq_num, title, domain, mapping, latency) - out_f.write(json.dumps({ - 'eq_number': eq_num, - 'title': title, - 'domain': domain, - 'mapping': mapping, - }) + "\n") - out_f.flush() - print(f" ✓ #{eq_num:3d} [{domain:22s}] lat={latency:5.0f}ms | {title[:40]}...") - else: - tracker.record_fail() - print(f" ✗ #{eq_num:3d} FAILED") - - print("-" * 60) - - print("\n[4/4] Writing markdown to disk...") - lines = [ - "# Physics Equations — Parallel Mapped\n", - f"**Workers:** {MAX_WORKERS} | **Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)\n\n", - "---\n\n", - ] - with open(SHM_OUT, "r", encoding="utf-8") as f: - for row in f: - obj = json.loads(row) - m = obj['mapping'] - lines.append(f"## Eq {obj['eq_number']}. {obj['title']}\n") - lines.append(f"**Domain:** {obj['domain']}\n") - lines.append("| Symbol | Mapping |\n") - lines.append("|--------|---------|\n") - for sym in ['Ω', 'Ψ', 'B', 'C', 'Δ']: - lines.append(f"| {sym} | {m.get(sym, 'N/A')} |\n") - lines.append("\n---\n\n") - - with open(OUTPUT_MD, "w", encoding="utf-8") as f: - f.writelines(lines) - - # Compression analysis - data = Path(OUTPUT_MD).read_bytes() - ent = moire.entropy(data) - ratio = moire.ratio(data) - - with open(COMPRESSION_LOG, "w") as f: - f.write(f"file={OUTPUT_MD}\n") - f.write(f"size_bytes={len(data)}\n") - f.write(f"moire_entropy={ent:.6f}\n") - f.write(f"theoretical_ratio={ratio:.6f}\n") - f.write(f"workers={MAX_WORKERS}\n") - f.write(f"total_mapped={tracker.success}\n") - f.write(f"total_failed={tracker.fail}\n") - - print(f"\nOutput: {OUTPUT_MD} ({len(data):,} bytes)") - print(f"Moiré entropy: {ent:.4f} bits/byte") - print(f"Compression ratio: {ratio:.2f}x") - print("") - print(tracker.summary()) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/physics_remapper_pro.py b/5-Applications/scripts/physics_remapper_pro.py deleted file mode 100644 index 11e28998..00000000 --- a/5-Applications/scripts/physics_remapper_pro.py +++ /dev/null @@ -1,379 +0,0 @@ -#!/usr/bin/env python3 -""" -Physics Equation Remapper with Compression + Metaprobe Layers - -Architecture: - ┌─────────────────────────────────────────┐ - │ Metaprobe Layer (observability) │ - │ - confidence scoring │ - │ - domain transition detection │ - │ - torsion force (mapping drift) │ - │ - cross-domain basis migration log │ - ├─────────────────────────────────────────┤ - │ LLM Remapper (Ω/Ψ/B/C/Δ symbols) │ - ├─────────────────────────────────────────┤ - │ Compression Layer (moiré decoder) │ - │ - 4-layer van der Waals stack │ - │ - domain-specific basis vectors │ - │ - entropy estimation │ - └─────────────────────────────────────────┘ -""" - -import ctypes -import csv -import json -import os -import re -import sqlite3 -import sys -import time -from collections import deque -from concurrent.futures import ThreadPoolExecutor, as_completed -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -import requests - -# ─── Configuration ────────────────────────────────────────────────────────── - -OLLAMA_URL = "http://localhost:11434/api/generate" -MODEL = "llama3.1:8b" -DB_PATH = "/home/allaun/physics_equations.db" -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/3-Mathematical-Models") -OUTPUT_MD = OUTPUT_DIR / "physics_eqs_mapped_pro.md" -METAPROBE_LOG = OUTPUT_DIR / "physics_metaprobe.jsonl" -COMPRESSION_LOG = OUTPUT_DIR / "physics_compression.log" - -LIBMOIRE_PATH = "/tmp/libmoire.so" - -# Domain-specific basis seeds for moiré decoder (replaces generic ascii_text etc.) -PHYSICS_DOMAINS = [ - "classical_mechanics", "quantum_mechanics", "thermodynamics", - "electromagnetism", "relativity", "particle_physics", - "cosmology", "condensed_matter", "information_theory" -] - - -# ─── Moiré Decoder C Binding ─────────────────────────────────────────────── - -class MoireDecoder: - """Python wrapper for libmoire.so compression engine.""" - - def __init__(self): - self.lib = ctypes.CDLL(LIBMOIRE_PATH) - self.lib.moire_encode.argtypes = [ - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t, - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t - ] - self.lib.moire_encode.restype = ctypes.c_int - self.lib.moire_decode.argtypes = [ - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t, - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t - ] - self.lib.moire_decode.restype = ctypes.c_int - self.lib.moire_estimate_entropy.argtypes = [ - ctypes.POINTER(ctypes.c_uint8), ctypes.c_size_t - ] - self.lib.moire_estimate_entropy.restype = ctypes.c_double - - def encode(self, data: bytes) -> bytes: - src = (ctypes.c_uint8 * len(data))(*data) - dst = (ctypes.c_uint8 * len(data))() - n = self.lib.moire_encode(src, len(data), dst, len(data)) - return bytes(dst[:n]) - - def decode(self, residuals: bytes, out_len: int) -> bytes: - src = (ctypes.c_uint8 * len(residuals))(*residuals) - dst = (ctypes.c_uint8 * out_len)() - n = self.lib.moire_decode(src, len(residuals), dst, out_len) - return bytes(dst[:n]) - - def entropy(self, data: bytes) -> float: - src = (ctypes.c_uint8 * len(data))(*data) - return self.lib.moire_estimate_entropy(src, len(data)) - - def compression_ratio(self, data: bytes) -> float: - ent = self.entropy(data) - return 8.0 / ent if ent > 0 else 1.0 - - -# ─── Metaprobe Layer ────────────────────────────────────────────────────── - -@dataclass -class MappingProbe: - eq_number: int - title: str - domain: str - mapping: Dict[str, str] - latency_ms: float - confidence: float = 0.0 # derived from layer agreement - torsion_force: float = 0.0 # mapping drift from expected domain - basis_migrated: bool = False # did the mapping switch domains? - residual_entropy: float = 0.0 # compression of the mapping tuple - timestamp: float = field(default_factory=time.time) - - -class MetaprobeLayer: - """Observability and quality monitoring for the remapping pipeline.""" - - def __init__(self, moire: MoireDecoder): - self.moire = moire - self.history: deque = deque(maxlen=100) - self.domain_stats: Dict[str, Dict] = {} - self.migration_count = 0 - - def _compute_torsion(self, domain: str, mapping: Dict[str, str]) -> float: - """ - Torsion force = disagreement between the equation's native domain - and the symbols assigned by the LLM. - """ - # Heuristic: if the mapping's symbols are generic/empty, high torsion - generic = {"N/A", "unknown", "none", "various", "not applicable"} - bad_symbols = sum(1 for v in mapping.values() if v.lower() in generic) - return bad_symbols / 5.0 # 0.0 = perfect, 1.0 = all generic - - def _compute_confidence(self, mapping: Dict[str, str]) -> float: - """ - Confidence based on symbol specificity (length and uniqueness). - """ - values = [v for v in mapping.values() if v not in {"N/A", ""}] - if not values: - return 0.0 - avg_len = sum(len(v) for v in values) / len(values) - # Longer, more specific mappings = higher confidence - return min(1.0, avg_len / 30.0) - - def record(self, eq_num: int, title: str, domain: str, - mapping: Dict[str, str], latency_ms: float) -> MappingProbe: - - # Build a mini byte-stream from the mapping for compression analysis - mapping_text = json.dumps(mapping, sort_keys=True).encode('utf-8') - residual_entropy = self.moire.entropy(mapping_text) - - # Detect domain migration: compare with last mapping for this domain - migrated = False - if domain in self.domain_stats: - last = self.domain_stats[domain].get('last_mapping', {}) - # Simple migration: if Ω category changed significantly - if last.get('Ω', '')[:20] != mapping.get('Ω', '')[:20]: - migrated = True - self.migration_count += 1 - - probe = MappingProbe( - eq_number=eq_num, - title=title, - domain=domain, - mapping=mapping, - latency_ms=latency_ms, - confidence=self._compute_confidence(mapping), - torsion_force=self._compute_torsion(domain, mapping), - basis_migrated=migrated, - residual_entropy=residual_entropy, - ) - - self.history.append(probe) - - # Update domain stats - if domain not in self.domain_stats: - self.domain_stats[domain] = {'count': 0, 'last_mapping': {}} - self.domain_stats[domain]['count'] += 1 - self.domain_stats[domain]['last_mapping'] = mapping.copy() - - return probe - - def flush_jsonl(self): - with open(METAPROBE_LOG, "a", encoding="utf-8") as f: - while self.history: - p = self.history.popleft() - f.write(json.dumps({ - 'eq_number': p.eq_number, - 'title': p.title, - 'domain': p.domain, - 'confidence': round(p.confidence, 3), - 'torsion_force': round(p.torsion_force, 3), - 'basis_migrated': p.basis_migrated, - 'residual_entropy': round(p.residual_entropy, 3), - 'latency_ms': round(p.latency_ms, 1), - 'timestamp': p.timestamp, - }) + "\n") - - def summary(self) -> str: - if not self.domain_stats: - return "No data" - lines = ["=== Metaprobe Summary ===", ""] - total = sum(s['count'] for s in self.domain_stats.values()) - lines.append(f"Total mapped: {total}") - lines.append(f"Domain migrations: {self.migration_count}") - lines.append("") - lines.append("Per-domain:") - for dom, stats in sorted(self.domain_stats.items(), key=lambda x: -x[1]['count']): - lines.append(f" {dom:25s} | {stats['count']:3d} equations") - return "\n".join(lines) - - -# ─── LLM Remapper Core ──────────────────────────────────────────────────── - -PROMPT_TEMPLATE = """Map this physics equation to five symbols from: - Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α) - -Equation: {title} -Domain: {domain} -Description: {description} - -Definitions: -- Ω: Observable output, measured quantity, what the equation predicts -- Ψ: The operator, mechanism, or theory -- B: Conserved basis, fundamental component, the fixed structure -- C: Dynamic context, variable parameter, external condition -- Δ: Residual error, noise, uncertainty, fundamental limit - -Respond ONLY in valid JSON with exactly these five keys and short (≤15 words) values: -{{"Ω": "...", "Ψ": "...", "B": "...", "C": "...", "Δ": "..."}} -""" - - -def call_ollama(title: str, domain: str, description: str) -> Optional[Dict]: - prompt = PROMPT_TEMPLATE.format(title=title, domain=domain, description=description[:300]) - try: - r = requests.post( - OLLAMA_URL, - json={"model": MODEL, "prompt": prompt, "stream": False, - "options": {"temperature": 0.1, "num_predict": 150}}, - timeout=60, - ) - r.raise_for_status() - content = r.json()["response"] - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - print(f" ERROR: {e}", file=sys.stderr) - return None - - -def load_progress() -> set: - done = set() - if OUTPUT_MD.exists(): - with open(OUTPUT_MD, "r", encoding="utf-8") as f: - for line in f: - m = re.match(r"## Eq (\d+)\. ", line) - if m: - done.add(m.group(1)) - return done - - -def append_md(eq_num: int, title: str, domain: str, desc: str, mapping: Dict): - m = mapping - lines = [ - f"## Eq {eq_num}. {title}", - "", - f"**Domain:** {domain}", - f"**Description:** {desc[:200]}", - "", - "| Symbol | Mapping |", - "|--------|---------|", - f"| Ω | {m.get('Ω', 'N/A')} |", - f"| Ψ | {m.get('Ψ', 'N/A')} |", - f"| B | {m.get('B', 'N/A')} |", - f"| C | {m.get('C', 'N/A')} |", - f"| Δ | {m.get('Δ', 'N/A')} |", - "", - "---", - "", - ] - with open(OUTPUT_MD, "a", encoding="utf-8") as f: - f.write("\n".join(lines) + "\n") - - -# ─── Main Pipeline ────────────────────────────────────────────────────────── - -def main(): - print("=" * 60) - print("Physics Equation Remapper + Compression + Metaprobe") - print("=" * 60) - - # Initialize layers - print("\n[1/4] Loading moiré decoder...") - moire = MoireDecoder() - print(f" → libmoire loaded, entropy baseline: 8.0 bits/byte") - - print("\n[2/4] Starting metaprobe layer...") - probe = MetaprobeLayer(moire) - - print("\n[3/4] Loading physics database...") - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - cursor.execute("SELECT eq_number, title, domain_id, significance FROM equations ORDER BY eq_number") - rows = cursor.fetchall() - cursor.execute("SELECT id, name FROM domains") - domains = {str(r[0]): r[1] for r in cursor.fetchall()} - conn.close() - - done = load_progress() - remaining = [(eq_num, title, domains.get(str(did), "Unknown"), desc or "") - for eq_num, title, did, desc in rows - if str(eq_num) not in done] - - print(f" → Total: {len(rows)} | Already done: {len(done)} | Remaining: {len(remaining)}") - - # Write header if new - if not done: - with open(OUTPUT_MD, "w", encoding="utf-8") as f: - f.write("# Physics Equations — Mapped with Compression & Metaprobe\n\n") - f.write(f"**Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)\n\n") - f.write("---\n\n") - - print("\n[4/4] Mapping with metaprobe + compression telemetry...") - print("-" * 60) - - success = 0 - fail = 0 - for eq_num, title, domain, desc in remaining: - t0 = time.time() - mapping = call_ollama(title, domain, desc) - latency = (time.time() - t0) * 1000 - - if mapping: - append_md(eq_num, title, domain, desc, mapping) - p = probe.record(eq_num, title, domain, mapping, latency) - success += 1 - print(f" ✓ #{eq_num:3d} [{domain:22s}] conf={p.confidence:.2f} torsion={p.torsion_force:.2f} entropy={p.residual_entropy:.3f} | {title[:45]}...") - else: - fail += 1 - print(f" ✗ #{eq_num:3d} FAILED") - - # Flush metaprobe every 10 entries - if success % 10 == 0: - probe.flush_jsonl() - - probe.flush_jsonl() - - print("-" * 60) - print(probe.summary()) - print("") - print(f"Success: {success} | Failed: {fail} | Total: {success + fail}") - print(f"Output: {OUTPUT_MD}") - print(f"Metaprobe log: {METAPROBE_LOG}") - - # Final compression test on the output file - if OUTPUT_MD.exists(): - data = OUTPUT_MD.read_bytes() - ent = moire.entropy(data) - ratio = moire.compression_ratio(data) - print(f"\nCompression analysis of output:") - print(f" File size: {len(data):,} bytes") - print(f" Moiré entropy: {ent:.4f} bits/byte") - print(f" Effective ratio: {ratio:.2f}x (theoretical)") - - with open(COMPRESSION_LOG, "w") as f: - f.write(f"file={OUTPUT_MD}\n") - f.write(f"size_bytes={len(data)}\n") - f.write(f"moire_entropy_bits_per_byte={ent:.6f}\n") - f.write(f"theoretical_compression_ratio={ratio:.6f}\n") - f.write(f"domain_migrations={probe.migration_count}\n") - f.write(f"equations_mapped={success}\n") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/pist_biological_polymorphic_shifter_v3_complete.py b/5-Applications/scripts/pist_biological_polymorphic_shifter_v3_complete.py deleted file mode 100644 index 14b066eb..00000000 --- a/5-Applications/scripts/pist_biological_polymorphic_shifter_v3_complete.py +++ /dev/null @@ -1,3972 +0,0 @@ -#!/usr/bin/env python3 -""" -PIST Biological Polymorphic Shifter v3.0 — Complete Unified Fix -================================================================ -Single-file executable with ALL 14 critical bugs fixed. -Combines 28 shifters across synthetic biology, neuroscience, mycology, -prions, cellular automata, chaotic maps, Galois fields, and PIST geometry -into a unified polymorphic compression framework. - -Bug fixes applied: - B1-B2: Single-file eliminates cross-file import errors - B3: Removed self-import in optimizer - B4: Length-prefix header replaces 0x00 separator - B5: Translation uses unique single-letter AA codes (already safe) - B6: Wireworld decode documented as lossy - B7: CellularAutomata LUT precomputed at module level (once) - B8: Splicing positions use struct.pack (16-bit) - B9: Removed dead SHIFTER_CLASSES dict - B10: Hachimoji nibble uses modulo instead of min - B11: Optimizer passes existing state instead of re-encoding - B13: Hachimoji decode uses dict lookup (safe for non-alpha bytes) - B14: Huffman decode safe fallback - B15: Removed unreachable dead code in beam_search - -Usage: - python3 pist_biological_polymorphic_shifter_v3_complete.py # demo - python3 pist_biological_polymorphic_shifter_v3_complete.py --benchmark path/to/file.tsv -""" - -# ═══════════════════════════════════════════════════════════════════════ -# IMPORTS (combined from all 4 parts) -# ═══════════════════════════════════════════════════════════════════════ -import struct -import math -import json -import time -import random -import sys -import hashlib -from collections import Counter, defaultdict -import heapq -from itertools import product, combinations, chain -from functools import lru_cache -from copy import deepcopy - -# ═══════════════════════════════════════════════════════════════════════ -# CONSTANTS & ALPHABETS (from Part1 + additions) -# ═══════════════════════════════════════════════════════════════════════ - -PHI = 1.618033988749894848204586834365638117720309179805762862135448 - -# --- Synthetic Biology Alphabets --- -HACHIMOJI_ALPHABET = "ACGTUBDHKMVRSWYN" # 16 letters (4 bits) -HACHIMOJI_LETTER_TO_VAL = {ord(c): i for i, c in enumerate(HACHIMOJI_ALPHABET)} -AEGIS_ALPHABET = "ACGTUBDHKMRSWYVNX" # 18 letters (~4.17 bits) - -STANDARD_CODON_TABLE = { - 'TTT': 'F', 'TTC': 'F', 'TTA': 'L', 'TTG': 'L', - 'TCT': 'S', 'TCC': 'S', 'TCA': 'S', 'TCG': 'S', - 'TAT': 'Y', 'TAC': 'Y', 'TAA': '*', 'TAG': '*', - 'TGT': 'C', 'TGC': 'C', 'TGA': '*', 'TGG': 'W', - 'CTT': 'L', 'CTC': 'L', 'CTA': 'L', 'CTG': 'L', - 'CCT': 'P', 'CCC': 'P', 'CCA': 'P', 'CCG': 'P', - 'CAT': 'H', 'CAC': 'H', 'CAA': 'Q', 'CAG': 'Q', - 'CGT': 'R', 'CGC': 'R', 'CGA': 'R', 'CGG': 'R', - 'ATT': 'I', 'ATC': 'I', 'ATA': 'I', 'ATG': 'M', - 'ACT': 'T', 'ACC': 'T', 'ACA': 'T', 'ACG': 'T', - 'AAT': 'N', 'AAC': 'N', 'AAA': 'K', 'AAG': 'K', - 'AGT': 'S', 'AGC': 'S', 'AGA': 'R', 'AGG': 'R', - 'GTT': 'V', 'GTC': 'V', 'GTA': 'V', 'GTG': 'V', - 'GCT': 'A', 'GCC': 'A', 'GCA': 'A', 'GCG': 'A', - 'GAT': 'D', 'GAC': 'D', 'GAA': 'E', 'GAG': 'E', - 'GGT': 'G', 'GGC': 'G', 'GGA': 'G', 'GGG': 'G', -} - -AMINO_CODONS = {} # reverse map: AA letter -> list of codons -for codon, aa in STANDARD_CODON_TABLE.items(): - AMINO_CODONS.setdefault(aa, []).append(codon) -for aa in AMINO_CODONS: - AMINO_CODONS[aa].sort() # deterministic - -AMINO_ACIDS = sorted(set(STANDARD_CODON_TABLE.values())) # 24 letters - -BASE_PAIRS = {'A': 'T', 'T': 'A', 'C': 'G', 'G': 'C', - 'U': 'A', 'B': 'V', 'D': 'H', 'K': 'M', - 'R': 'Y', 'S': 'W', 'W': 'S', 'Y': 'R', - 'V': 'B', 'H': 'D', 'N': 'N', 'X': 'X'} - -# --- Prion HMM Alphabet --- -PRION_ALPHABET = "STYCNQKRHDEAVGILMFPW" # 20 amino acids + * -PRION_ALPHABET_SIZE = 20 -PRION_HMM = { # P(emit | state) - 3-state HMM (N, H1, H2) - 'N': {'S':0.15,'T':0.10,'Y':0.05,'C':0.02,'N':0.08,'Q':0.05, - 'K':0.03,'R':0.02,'H':0.02,'D':0.04,'E':0.04,'A':0.08, - 'V':0.06,'G':0.10,'I':0.03,'L':0.04,'M':0.02,'F':0.02, - 'P':0.02,'W':0.01,'*':0.02}, - 'H1': {'S':0.02,'T':0.02,'Y':0.15,'C':0.10,'N':0.02,'Q':0.15, - 'K':0.01,'R':0.01,'H':0.12,'D':0.01,'E':0.01,'A':0.02, - 'V':0.02,'G':0.01,'I':0.01,'L':0.10,'M':0.05,'F':0.07, - 'P':0.05,'W':0.04,'*':0.01}, - 'H2': {'S':0.03,'T':0.03,'Y':0.10,'C':0.08,'N':0.03,'Q':0.10, - 'K':0.02,'R':0.02,'H':0.10,'D':0.02,'E':0.02,'A':0.03, - 'V':0.03,'G':0.02,'I':0.02,'L':0.12,'M':0.06,'F':0.08, - 'P':0.04,'W':0.03,'*':0.02}, -} - -# --- Shifter Bases (NExponent information capacity) --- -SHIFTER_BASES = { - 'hachimoji': 3.0, # log₂(16) = 4, but effective ~3 due to constraints - 'aegis': 3.585, # log₂(18) ≈ 4.17, reduced for wobble - 'natural_dna': 2.0, # 4 bases, reduced by pairing constraints - 'transcription': 2.0, - 'translation': 3.0, - 'pna': 2.5, # Peptide Nucleic Acid - 'lna': 2.5, # Locked Nucleic Acid - 'splicing': 1.5, # Alternative splicing - 'prion': 3.0, # 3-state HMM - 'spike_timing': 4.0, # Temporal coding - 'hyphal_net': 3.5, # Network routing - 'logistic_map': 2.0, # Chaotic dynamics - 'galois_ring': 4.0, # GF(256) arithmetic - 'sbox': 2.0, # 16×16 S-Box - 'wireworld': 1.5, # Cellular automaton - 'morpholino': 2.0, # Antisense oligo - 'pist': 2.5, # PIST geometry - 'pist_mirror': 2.5, # PIST mirror involution - 'pist_resonance': 2.5, # PIST resonance jump - 'delta_gcl': 1.5, # Delta encoding - 'run_length': 1.0, # RLE - 'huffman': 2.0, # Huffman coding - 'dse': 2.0, # Deterministic-Stochastic Engine - 'cellular_automata': 1.5, # 1D CA - 'mirna': 2.0, # miRNA silencing - 'stdp': 3.0, # Spike-Timing Dependent Plasticity - 'spiegelmer': 2.0, # Mirror-image aptamer - 'nu_vmap': 29.0, # PIST-NUVMAP projection (shifter #28) - 'holographic_connectome': 5.5, - 'holographic_connectome_interleaved': 5.2, - 'holographic_connectome_blocklocal': 5.0, - 'holographic_connectome_shadow': 4.8, - 'holographic_connectome_parity': 4.5, - 'pist_scalar_mass': 1.0, # 0D scalar mass (low entropy) - 'pist_scalar_tension': 1.5, # 0D scalar tension - 'pist_0d_degenerate': 0.5, # 0D degenerate (maximum compression) - 'pist_nd_cartesian': 3.0, # nD Cartesian (additive capacity) - 'pist_nd_radial': 2.5, # nD Radial (angular coupling) - 'pist_nd_bundle': 3.5, # nD Bundle (fiber dimension) - 'braid': 2.5, # Artin braid group B_n - 'multicolor_rope': 3.0, # Colored strand bundles - 'braid_rope_fusion': 4.0, # Braid-rope fusion - 'symbology_substitution': 3.5, # Symbolic substitution for pattern groups -} - - -# ═══════════════════════════════════════════════════════════════════════ -# PIST GEOMETRY FUNCTIONS (Perfectly Imperfect Square Theory) -# ═══════════════════════════════════════════════════════════════════════ - -def pist_encode(n): - """Encode integer n to PIST coordinate (k, t). - n = k² + t, where k = floor(√n), 0 ≤ t ≤ 2k.""" - if n < 0: - raise ValueError(f"PIST encode requires n >= 0, got {n}") - k = int(math.isqrt(n)) - t = n - k * k - return (k, t) - -def pist_decode(k, t): - """Decode PIST coordinate (k, t) back to integer n.""" - return k * k + t - -def pist_mass(k, t): - """PIST mass = a·b = t·(2k+1-t). Zero at shell endpoints, positive inside.""" - return t * (2 * k + 1 - t) - -def pist_normalized_tension(k, t): - """Normalized tension ρ = t/(2k+1) ∈ [0, 1).""" - return t / (2 * k + 1) if (2 * k + 1) > 0 else 0.0 - -def pist_mirror(k, t): - """Mirror involution: (k, t) → (k, 2k+1-t). Preserves mass, self-inverse.""" - return (k, 2 * k + 1 - t) - -def intrinsic_load(data): - """Shannon entropy of byte distribution: H = -Σ p(b) log₂ p(b).""" - if not data: - return 0.0 - c = Counter(data) - n = len(data) - return -sum((cnt / n) * math.log2(cnt / n) for cnt in c.values()) - - -# ═══════════════════════════════════════════════════════════════════════ -# 0D SCALAR PIST FUNCTIONS (Degenerate limit) -# ═══════════════════════════════════════════════════════════════════════ - -def pist_scalar_mass(n): - """0D: Only the mass value, no coordinate info. - Maps ℕ → ℕ (single scalar mass value). - """ - k = int(math.isqrt(n)) - t = n - k * k - return t * (2 * k + 1 - t) - -def pist_scalar_tension(n): - """0D: Normalized tension as scalar in [0, 1). - Maps ℕ → [0, 1). - """ - k = int(math.isqrt(n)) - t = n - k * k - return t / (2 * k + 1) if (2 * k + 1) > 0 else 0.0 - -def pist_0d_degenerate(n): - """0D: Shell width → 0, collapse to discrete mass levels (perfect squares). - Maximum compression, irreversible. - """ - k = int(math.isqrt(n)) - return k * k - -def pist_scalar_phase(n): - """0D: Phase classification based on scalar mass. - Returns: 'grounded' (mass=0), 'low' (mass < threshold), 'high' (mass >= threshold). - """ - m = pist_scalar_mass(n) - if m == 0: - return 'grounded' - elif m < 4: - return 'low' - else: - return 'high' - - -# ═══════════════════════════════════════════════════════════════════════ -# nD PIST GEOMETRY FUNCTIONS (Multi-dimensional extension) -# ═══════════════════════════════════════════════════════════════════════ - -def pist_nd_cartesian_encode(data, n_dims=2): - """nD Cartesian: Independent PIST encoding per dimension. - data: bytes to encode - n_dims: number of dimensions - Returns: list of (k, t) tuples per dimension - """ - coords = [] - for dim in range(n_dims): - dim_coords = [] - # Interleave bytes across dimensions - dim_data = data[dim::n_dims] - for b in dim_data: - k, t = pist_encode(b) - dim_coords.append((k, t)) - coords.append(dim_coords) - return coords - -def pist_nd_cartesian_decode(coords): - """nD Cartesian: Decode independent PIST coordinates back to bytes.""" - n_dims = len(coords) - max_len = max(len(c) for c in coords) if coords else 0 - result = bytearray() - - for i in range(max_len): - for dim in range(n_dims): - if i < len(coords[dim]): - k, t = coords[dim][i] - n = pist_decode(k, t) - result.append(n & 0xFF) - return bytes(result) - -def pist_nd_cartesian_mass(coords): - """nD Cartesian: Total mass = sum of per-dimension masses.""" - total = 0 - for dim_coords in coords: - for k, t in dim_coords: - total += pist_mass(k, t) - return total - -def pist_nd_radial_encode(data, n_dims=2): - """nD Radial: Single shell index, n-dimensional offset. - Uses spherical-like coordinates where offset vector has constrained magnitude. - """ - k = int(math.isqrt(len(data))) - coords = [] - - # Distribute data across n dimensions as offset vector - chunk_size = max(1, len(data) // n_dims) - for dim in range(n_dims): - start = dim * chunk_size - end = min(start + chunk_size, len(data)) - chunk = data[start:end] - - # Compute offset as sum of chunk (quantized) - t = sum(chunk) % (2 * k + 1) if (2 * k + 1) > 0 else 0 - coords.append((k, t)) - - return coords - -def pist_nd_radial_decode(coords, original_len): - """nD Radial: Decode by reconstructing from radial coordinates.""" - k = coords[0][0] if coords else 0 - # Simple reconstruction: distribute evenly - n_dims = len(coords) - chunk_size = max(1, original_len // n_dims) - result = bytearray() - - for dim in range(n_dims): - k, t = coords[dim] - # Reconstruct chunk from offset - chunk = [t] * chunk_size - result.extend(chunk[:chunk_size]) - - return bytes(result[:original_len]) - -def pist_nd_radial_mass(coords): - """nD Radial: Mass with angular coupling.""" - if not coords: - return 0 - k = coords[0][0] - total = 0 - for _, t in coords: - total += t * (2 * k + 1 - t) - return total - -def pist_nd_bundle_encode(data, n_dims=2, fiber_dim=4): - """nD Bundle: Shell index as base, fiber dimension per shell. - Each shell k has an n-dimensional fiber space. - """ - coords = [] - for i, b in enumerate(data): - k, t = pist_encode(b) - # Add fiber coordinate (additional dimensions per point) - fiber = [b % fiber_dim for _ in range(n_dims - 1)] - coords.append((k, t, tuple(fiber))) - return coords - -def pist_nd_bundle_decode(coords): - """nD Bundle: Decode by reconstructing from bundle coordinates.""" - result = bytearray() - for k, t, fiber in coords: - n = pist_decode(k, t) - result.append(n & 0xFF) - return bytes(result) - -def pist_nd_bundle_mass(coords): - """nD Bundle: Mass = base mass + fiber contribution.""" - total = 0 - for k, t, fiber in coords: - base_mass = pist_mass(k, t) - fiber_mass = sum(fiber) if fiber else 0 - total += base_mass + fiber_mass - return total - -def pist_nd_resonance_jump(coords, mode='cartesian'): - """nD Resonance: Find equal-mass coordinates in nD space.""" - if mode == 'cartesian': - # Per-dimension independent resonance - return [[pist_mirror(k, t) for k, t in dim_coords] for dim_coords in coords] - elif mode == 'radial': - # Rotate on isomass hyper-surface - k = coords[0][0] if coords else 0 - return [(k, (2 * k + 1 - t) % (2 * k + 1)) for k, t in coords] - elif mode == 'bundle': - # Bundle resonance: mirror base, permute fiber - return [(k, 2 * k + 1 - t, tuple(reversed(fiber))) for k, t, fiber in coords] - return coords - - -# ═══════════════════════════════════════════════════════════════════════ -# BRAID GEOMETRY FUNCTIONS (Artin braid group B_n) -# ═══════════════════════════════════════════════════════════════════════ - -def braid_encode_crossing(byte_val, n_strands=3): - """Encode a byte as a braid crossing generator. - Maps byte to σ_i or σ_i^-1 based on bit patterns. - Returns: (strand_index, direction) where direction = +1 or -1 - """ - strand = byte_val % n_strands - # Use high bit for crossing direction - direction = 1 if (byte_val & 0x80) else -1 - return (strand, direction) - -def braid_word_to_bytes(braid_word, n_strands=3): - """Convert a braid word (sequence of crossings) back to bytes. - braid_word: list of (strand_index, direction) tuples - """ - result = bytearray() - for strand, direction in braid_word: - byte = strand - if direction == 1: - byte |= 0x80 # Set high bit for positive crossing - result.append(byte) - return bytes(result) - -def braid_simplify(braid_word): - """Simplify braid word using braid relations: - 1. σ_i σ_i^-1 = identity (cancel inverses) - 2. σ_i σ_j = σ_j σ_i for |i-j| > 1 (far commutativity) - Returns simplified braid word. - """ - if not braid_word: - return braid_word - - # Cancel adjacent inverses - simplified = [] - for crossing in braid_word: - if simplified and simplified[-1][0] == crossing[0] and simplified[-1][1] == -crossing[1]: - simplified.pop() # Cancel - else: - simplified.append(crossing) - - # Apply far commutativity (sort non-adjacent crossings) - # This is a simplified version - full braid reduction is more complex - return simplified - -def braid_compute_entropy(braid_word): - """Compute entropy of braid word based on crossing distribution.""" - if not braid_word: - return 0.0 - - from collections import Counter - crossing_counts = Counter(braid_word) - total = len(braid_word) - - entropy = 0.0 - for count in crossing_counts.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - - return entropy - -def braid_composition(braid1, braid2): - """Compose two braid words (concatenation in braid group).""" - return braid1 + braid2 - -def braid_inverse(braid_word): - """Compute inverse of braid word (reverse and flip all crossings).""" - return [(strand, -direction) for strand, direction in reversed(braid_word)] - - -# ═══════════════════════════════════════════════════════════════════════ -# MULTICOLOR ROPE GEOMETRY FUNCTIONS (Colored strand bundles) -# ═══════════════════════════════════════════════════════════════════════ - -def rope_encode_colored_strand(byte_val, n_colors=8): - """Encode a byte as a colored strand in a rope. - Returns: (strand_index, color_index, twist) - """ - strand = byte_val % 3 # 3 strands in rope - color = (byte_val >> 2) % n_colors # Color from bits 2-4 - twist = (byte_val >> 5) & 0x07 # Twist from bits 5-7 (3 bits) - return (strand, color, twist) - -def rope_word_to_bytes(rope_word): - """Convert rope word (colored strands) back to bytes.""" - result = bytearray() - for strand, color, twist in rope_word: - byte = strand | (color << 2) | (twist << 5) - result.append(byte & 0xFF) - return bytes(result) - -def rope_compute_tension(rope_word): - """Compute rope tension based on twist distribution.""" - if not rope_word: - return 0.0 - - twists = [twist for _, _, twist in rope_word] - avg_twist = sum(twists) / len(twists) - max_twist = max(twists) if twists else 0 - - # Tension increases with twist variance - variance = sum((t - avg_twist) ** 2 for t in twists) / len(twists) - tension = math.sqrt(variance) / 7.0 # Normalize by max twist - return min(tension, 1.0) - -def rope_color_entropy(rope_word, n_colors=8): - """Compute entropy of color distribution in rope.""" - if not rope_word: - return 0.0 - - from collections import Counter - colors = [color for _, color, _ in rope_word] - color_counts = Counter(colors) - total = len(colors) - - entropy = 0.0 - for count in color_counts.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - - return entropy - -def rope_braid_fusion(rope_word, braid_word): - """Fuse rope word with braid word (apply braid to rope strands). - Returns fused rope word with strand permutations from braid. - """ - if not rope_word or not braid_word: - return rope_word - - # Apply strand permutations from braid crossings - # Simplified: just add braid information to rope - fused = [] - rope_idx = 0 - for strand, direction in braid_word: - if rope_idx < len(rope_word): - r_strand, color, twist = rope_word[rope_idx] - # Strand crossing modifies strand index - new_strand = (r_strand + direction) % 3 - fused.append((new_strand, color, twist)) - rope_idx += 1 - - # Add remaining rope strands - while rope_idx < len(rope_word): - fused.append(rope_word[rope_idx]) - rope_idx += 1 - - return fused - - -# ═══════════════════════════════════════════════════════════════════════ -# COMPRESSION MEME DISCOVERY (Pattern discovery + eigenvector abstraction) -# ═══════════════════════════════════════════════════════════════════════ - -def discover_compression_memes(data_samples, min_pattern_length=3, min_frequency=2): - """Discover recurring compression patterns (memes) in data samples. - Returns: dict of {pattern: frequency} - """ - from collections import Counter - patterns = Counter() - - for data in data_samples: - data_bytes = bytes(data) if not isinstance(data, bytes) else data - for length in range(min_pattern_length, min(len(data_bytes), 16)): - for i in range(len(data_bytes) - length + 1): - pattern = data_bytes[i:i+length] - patterns[pattern] += 1 - - # Filter by minimum frequency - memes = {p: f for p, f in patterns.items() if f >= min_frequency} - return memes - -def compute_pattern_matrix(memes, data_samples): - """Compute pattern occurrence matrix for eigenvector decomposition. - Returns: numpy array (samples × patterns) - """ - import numpy as np - pattern_list = list(memes.keys()) - matrix = np.zeros((len(data_samples), len(pattern_list))) - - for i, data in enumerate(data_samples): - data_bytes = bytes(data) if not isinstance(data, bytes) else data - for j, pattern in enumerate(pattern_list): - # Count pattern occurrences - count = 0 - for k in range(len(data_bytes) - len(pattern) + 1): - if data_bytes[k:k+len(pattern)] == pattern: - count += 1 - matrix[i, j] = count - - return matrix, pattern_list - -def semantic_eigenvector_bundle(pattern_matrix, n_components=5): - """Perform eigenvector decomposition (PCA) on pattern matrix. - Returns: (principal_components, explained_variance, pattern_list) - """ - import numpy as np - - # Center the data - centered = pattern_matrix - pattern_matrix.mean(axis=0) - - # Compute covariance matrix - cov_matrix = np.cov(centered, rowvar=False) - - # Eigendecomposition - eigenvalues, eigenvectors = np.linalg.eigh(cov_matrix) - - # Sort by eigenvalue (descending) - idx = eigenvalues.argsort()[::-1] - eigenvalues = eigenvalues[idx] - eigenvectors = eigenvectors[:, idx] - - # Take top n_components - n = min(n_components, len(eigenvalues)) - principal_components = eigenvectors[:, :n] - explained_variance = eigenvalues[:n] / eigenvalues.sum() - - return principal_components, explained_variance - -def cluster_by_utility(pattern_matrix, performance_metrics, n_clusters=3): - """Cluster compression strategies by utility (performance metrics). - Returns: cluster assignments for each sample. - """ - import numpy as np - from sklearn.cluster import KMeans - - # Combine pattern matrix with performance metrics - combined = np.hstack([pattern_matrix, np.array(performance_metrics).reshape(-1, 1)]) - - # Normalize - normalized = (combined - combined.mean(axis=0)) / (combined.std(axis=0) + 1e-8) - - # Cluster - kmeans = KMeans(n_clusters=n_clusters, random_state=42) - clusters = kmeans.fit_predict(normalized) - - return clusters, kmeans.cluster_centers_ - -class CompressionMemeCache: - """Cache successful compression patterns (morphology memes).""" - - def __init__(self): - self.memes = {} # {pattern: {frequency, utility, last_used}} - self.eigenvectors = None - self.cluster_centers = None - - def add_meme(self, pattern, utility_score, shifter_chain): - """Add a compression meme to cache.""" - import hashlib - pattern_hash = hashlib.sha256(pattern).hexdigest() - - if pattern_hash not in self.memes: - self.memes[pattern_hash] = { - 'pattern': pattern, - 'frequency': 0, - 'utility_score': 0.0, - 'shifter_chain': shifter_chain, - 'last_used': 0 - } - - self.memes[pattern_hash]['frequency'] += 1 - self.memes[pattern_hash]['utility_score'] = ( - (self.memes[pattern_hash]['utility_score'] * (self.memes[pattern_hash]['frequency'] - 1) + utility_score) - / self.memes[pattern_hash]['frequency'] - ) - self.memes[pattern_hash]['last_used'] = 0 # Update with timestamp if needed - - def get_best_meme(self, data, top_k=5): - """Retrieve top-k memes by utility score for given data.""" - import hashlib - - # Find memes that appear in data - data_bytes = bytes(data) if not isinstance(data, bytes) else data - matching = [] - - for pattern_hash, meme in self.memes.items(): - if meme['pattern'] in data_bytes: - matching.append((meme['utility_score'], pattern_hash, meme)) - - # Sort by utility score and return top-k - matching.sort(key=lambda x: x[0], reverse=True) - return matching[:top_k] - - def prune_low_utility(self, utility_threshold=0.5): - """Remove memes below utility threshold.""" - to_remove = [ - ph for ph, m in self.memes.items() - if m['utility_score'] < utility_threshold - ] - for ph in to_remove: - del self.memes[ph] - - -# ═══════════════════════════════════════════════════════════════════════ -# NEXPONENT SYSTEM -# ═══════════════════════════════════════════════════════════════════════ - -class NExponent: - """NExponent: n(name, depth) = base^depth (information capacity).""" - - @staticmethod - def n(shifter_name, depth=1): - base = SHIFTER_BASES.get(shifter_name, 2.0) - return base ** depth - - @staticmethod - def n_combined(shifter_names, depths=None): - if depths is None: - depths = [1] * len(shifter_names) - total = 1.0 - for name, d in zip(shifter_names, depths): - total *= NExponent.n(name, d) - return total - - @staticmethod - def entropy_ratio(n_factor, original_entropy): - """Ratio of combined N-factor to original entropy.""" - if original_entropy <= 0: - return n_factor - return n_factor / original_entropy - - @staticmethod - def all_bases(): - return dict(SHIFTER_BASES) - - -# ═══════════════════════════════════════════════════════════════════════ -# MANIFOLD STATE -# ═══════════════════════════════════════════════════════════════════════ - -class ManifoldState: - """Tracks transformation state through the shifter chain.""" - - def __init__(self, raw_bytes=None): - self.raw_bytes = bytearray(raw_bytes) if raw_bytes else bytearray() - self.pist_coords = [] # list of (k, t) tuples - self.shifter_chain = [] # list of shifter names applied - self.encoded = bytearray() # current encoded representation - self.n_factor = 1.0 # combined NExponent product - self.entropy = 0.0 # Shannon entropy - self.metadata = {} # extra info per shifter - self.compression_ratio = 1.0 - self.fitness_score = 0.0 - - def update(self, encoded, shifter_name, metadata=None): - self.encoded = bytearray(encoded) - self.shifter_chain.append(shifter_name) - self.n_factor *= NExponent.n(shifter_name) - self.entropy = intrinsic_load(encoded) - if metadata: - self.metadata[shifter_name] = metadata - return self - - def copy(self): - return deepcopy(self) - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER BASE CLASS -# ═══════════════════════════════════════════════════════════════════════ - -class Shifter: - """Base class for all shifters. Subclasses must implement encode/decode.""" - - name = "base_shifter" - description = "Base shifter — should not be instantiated directly." - - @classmethod - def encode(cls, state, **kwargs): - raise NotImplementedError - - @classmethod - def decode(cls, state, **kwargs): - raise NotImplementedError - - @classmethod - def chain(cls, state, shifter_classes, **kwargs): - """Apply multiple shifters in sequence.""" - current = state - for sc in shifter_classes: - current = sc.encode(current, **kwargs) - return current - - @classmethod - def fitness(cls, original_size, compressed_size, n_factor, comp_eff, stability=1.0): - ratio = original_size / max(compressed_size, 1) - n_bonus = n_factor / max(original_size, 1) - return ratio * comp_eff * (stability + 0.5 * n_bonus) - - -# ═══════════════════════════════════════════════════════════════════════ -# PIST HELPER (for shifters) -# ═══════════════════════════════════════════════════════════════════════ - -def _pist_coords_from_bytes(data): - """Convert bytes to PIST coordinates.""" - coords = [] - for b in data: - try: - k, t = pist_encode(b) - coords.append((k, t)) - except ValueError: - coords.append((0, 0)) - return coords - -def _bytes_from_pist_coords(coords): - """Convert PIST coordinates back to bytes.""" - result = bytearray() - for k, t in coords: - n = pist_decode(k, t) - result.append(min(max(n, 0), 255)) - return bytes(result) - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 1: HACHIMOJI (8-letter synthetic DNA) -# ═══════════════════════════════════════════════════════════════════════ - -class HachimojiShifter(Shifter): - name = "hachimoji" - description = "Hachimoji 8-letter synthetic DNA (4 bits/nucleotide)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - letters = HACHIMOJI_ALPHABET - result = [] - for b in data: - hi = (b >> 4) & 0x0F - lo = b & 0x0F - # FIX B10: Use modulo instead of min to avoid information loss - result.append(letters[hi % len(letters)]) - result.append(letters[lo % len(letters)]) - encoded = ''.join(result).encode('ascii') - return state.update(encoded, cls.name, - {'nibbles': len(result), 'letters': len(letters)}) - - @classmethod - def decode(cls, state, **kwargs): - raw = state.encoded - if isinstance(raw, (bytes, bytearray)): - data = raw.decode('ascii', errors='replace') - else: - data = raw - ltv = HACHIMOJI_LETTER_TO_VAL - result = bytearray() - for i in range(0, len(data), 2): - if i + 1 >= len(data): - break - hi = ltv.get(ord(data[i]), ord(data[i]) % 16) - lo = ltv.get(ord(data[i + 1]), ord(data[i + 1]) % 16) - result.append(((hi & 0x0F) << 4) | (lo & 0x0F)) - return state.update(result, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 2: AEGIS (expanded genetic alphabet) -# ═══════════════════════════════════════════════════════════════════════ - -class AEGISShifter(Shifter): - name = "aegis" - description = "AEGIS 6-letter expanded genetic alphabet (~2.58 bits/nucleotide)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - letters = AEGIS_ALPHABET - result = [] - for b in data: - hi = (b >> 4) & 0x0F - lo = b & 0x0F - result.append(letters[hi % len(letters)]) - result.append(letters[lo % len(letters)]) - encoded = ''.join(result).encode('ascii') - return state.update(encoded, cls.name, {'letters': len(letters)}) - - @classmethod - def decode(cls, state, **kwargs): - raw = state.encoded - if isinstance(raw, (bytes, bytearray)): - data = raw.decode('ascii', errors='replace') - else: - data = raw - letters = AEGIS_ALPHABET - result = bytearray() - for i in range(0, len(data), 2): - if i + 1 >= len(data): - break - hi = letters.index(data[i]) - lo = letters.index(data[i + 1]) - result.append(((hi & 0x0F) << 4) | (lo & 0x0F)) - return state.update(result, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 3: NATURAL DNA (4-base encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class NaturalDNAShifter(Shifter): - name = "natural_dna" - description = "Natural 4-base DNA encoding (2 bits/nucleotide)" - - DNA_BASES = "ACGT" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - bases = cls.DNA_BASES - result = [] - for b in data: - result.append(bases[(b >> 6) & 0x03]) - result.append(bases[(b >> 4) & 0x03]) - result.append(bases[(b >> 2) & 0x03]) - result.append(bases[b & 0x03]) - encoded = ''.join(result).encode('ascii') - return state.update(encoded, cls.name, {'bases_per_byte': 4}) - - @classmethod - def decode(cls, state, **kwargs): - raw = state.encoded - if isinstance(raw, (bytes, bytearray)): - data = raw.decode('ascii', errors='replace') - else: - data = raw - - bases = cls.DNA_BASES - result = bytearray() - for i in range(0, len(data), 4): - if i + 3 >= len(data): - break - b = (bases.index(data[i]) << 6) | (bases.index(data[i+1]) << 4) | \ - (bases.index(data[i+2]) << 2) | bases.index(data[i+3]) - result.append(b) - return state.update(result, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 4: TRANSCRIPTION (DNA → RNA) -# ═══════════════════════════════════════════════════════════════════════ - -class TranscriptionShifter(Shifter): - name = "transcription" - description = "DNA-to-RNA transcription (T→U replacement)" - - @classmethod - def encode(cls, state, **kwargs): - data = state.encoded if state.encoded else state.raw_bytes - text = data.decode('ascii', errors='replace').upper() - rna = text.replace('T', 'U') - # Force clean ASCII: strip any non-ASCII replacement chars - rna_clean = rna.encode('ascii', errors='ignore').decode('ascii') - return state.update(rna_clean.encode('ascii'), cls.name, {'mapping': 'T→U'}) - - @classmethod - def decode(cls, state, **kwargs): - raw = state.encoded - if isinstance(raw, (bytes, bytearray)): - data = raw.decode('ascii', errors='replace') - else: - data = raw - dna = data.replace('U', 'T') - dna_clean = dna.encode('ascii', errors='ignore').decode('ascii') - return state.update(dna_clean.encode('ascii'), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 5: TRANSLATION (RNA → Amino Acids) -# ═══════════════════════════════════════════════════════════════════════ - -class TranslationShifter(Shifter): - name = "translation" - description = "RNA-to-protein translation via standard codon table" - - @classmethod - def encode(cls, state, **kwargs): - data = state.encoded if state.encoded else state.raw_bytes - rna = data.decode('ascii', errors='replace').upper().replace('T', 'U') - peptide = [] - for i in range(0, len(rna) - 2, 3): - codon = rna[i:i+3] - aa = STANDARD_CODON_TABLE.get(codon, '?') - # Single-letter AA codes are already unique per STANDARD_CODON_TABLE - peptide.append(ord(aa)) - return state.update(bytearray(peptide), cls.name, - {'codons_used': len(peptide)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - codons = [] - for b in data: - aa = chr(b) - if aa in AMINO_CODONS: - # FIX B5: Use first codon alphabetically (deterministic but lossy) - codons.append(AMINO_CODONS[aa][0]) - else: - codons.append('NNN') - rna = ''.join(codons) - return state.update(rna.encode('ascii'), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 6: PNA (Peptide Nucleic Acid) -# ═══════════════════════════════════════════════════════════════════════ - -class PNAShifter(Shifter): - name = "pna" - description = "Peptide Nucleic Acid — neutral backbone encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - # PNA: each byte -> 5-letter code from reduced DNA alphabet - bases = "ACGT" - result = [] - for b in data: - result.append(bases[b & 0x03]) - result.append(bases[(b >> 2) & 0x03]) - result.append(bases[(b >> 4) & 0x03]) - result.append(bases[(b >> 6) & 0x03]) - # 5th base: parity - result.append(bases[sum(1 for c in bin(b) if c == '1') % 4]) - return state.update(''.join(result).encode('ascii'), cls.name, {'ratio': 5}) - - @classmethod - def decode(cls, state, **kwargs): - data = state.encoded.decode('ascii', errors='replace') if isinstance(state.encoded, bytes) else state.encoded - bases = "ACGT" - result = bytearray() - for i in range(0, len(data), 5): - if i + 4 >= len(data): - break - b = bases.index(data[i]) | (bases.index(data[i+1]) << 2) | \ - (bases.index(data[i+2]) << 4) | (bases.index(data[i+3]) << 6) - result.append(b) - return state.update(result, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 7: LNA (Locked Nucleic Acid - enhanced binding) -# ═══════════════════════════════════════════════════════════════════════ - -class LNAShifter(Shifter): - name = "lna" - description = "Locked Nucleic Acid — thermal stability encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - bases = "ACGT" - result = [] - for b in data: - # LNA: use complementary base + original for redundancy - b1 = bases[b & 0x03] - b2 = BASE_PAIRS.get(b1, 'N') - result.append(b1) - result.append(b2) - result.append(bases[(b >> 2) & 0x03]) - result.append(BASE_PAIRS.get(bases[(b >> 2) & 0x03], 'N')) - return state.update(''.join(result).encode('ascii'), cls.name, {}) - - @classmethod - def decode(cls, state, **kwargs): - data = state.encoded.decode('ascii', errors='replace') if isinstance(state.encoded, bytes) else state.encoded - bases = "ACGT" - result = bytearray() - for i in range(0, len(data), 4): - if i + 3 >= len(data): - break - if data[i] in bases and data[i+2] in bases: - b = bases.index(data[i]) | (bases.index(data[i+2]) << 2) - result.append(b) - return state.update(result, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 8: SPLICING (cassette exon alternative splicing) -# ═══════════════════════════════════════════════════════════════════════ - -class SplicingShifter(Shifter): - name = "splicing" - description = "Alternative splicing — cassette exon inclusion/skipping" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - window = kwargs.get('window', 8) - splice_sites = [] - result = bytearray() - i = 0 - while i < len(data): - if i + window <= len(data): - chunk = data[i:i+window] - entropy = intrinsic_load(chunk) - if entropy < 3.0 and len(splice_sites) < 64: - # Skippable exon - splice_sites.append((i, i + window)) - # Mark with metadata - result.extend(chunk) - else: - result.extend(chunk) - else: - result.extend(data[i:]) - i += window - metadata = { - 'splice_sites': splice_sites, - 'window': window, - } - return state.update(bytes(result), cls.name, metadata) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - meta = state.metadata.get(cls.name, {}) - # FIX B8: splice_sites already stored as list of tuples in metadata - # No serialization needed since metadata survives in-memory - splice_sites = meta.get('splice_sites', []) - result = bytearray(data) - # Reconstruct: no-op for decoding (splice sites were inclusion) - # but we apply them in reverse order for canonical decode - for start, end in sorted(splice_sites, reverse=True): - pass # sites were inclusion sites, data already contains them - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 9: PRION (Amyloidogenic conformational encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class PrionShifter(Shifter): - name = "prion" - description = "Prion-like 3-state HMM (N, H1, H2) conformational encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - states = ['N', 'H1', 'H2'] - result = [] - emission_log = [] - current_state = 'N' - for b in data: - aa = PRION_ALPHABET[b % PRION_ALPHABET_SIZE] - # HMM state transition based on byte value - trans = (b >> 5) & 0x03 - if trans == 0: - current_state = 'N' - elif trans == 1: - current_state = 'H1' - elif trans == 2: - current_state = 'H2' - else: - current_state = states[hash(str(b)) % 3] - - prob = PRION_HMM[current_state].get(aa, 0.01) - emission_log.append((current_state, aa, prob)) - result.append(ord(aa)) - return state.update(bytearray(result), cls.name, - {'states_used': len(set(s for s, _, _ in emission_log)), - 'avg_prob': sum(p for _, _, p in emission_log) / max(len(emission_log), 1)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - aa_idx = PRION_ALPHABET.index(chr(b)) if chr(b) in PRION_ALPHABET else (b % PRION_ALPHABET_SIZE) - result.append(aa_idx) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 10: SPIKE TIMING (Temporal neural coding) -# ═══════════════════════════════════════════════════════════════════════ - -class SpikeTimingShifter(Shifter): - name = "spike_timing" - description = "Spike-timing dependent encoding (temporal coding)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - dt = kwargs.get('dt', 0.001) - result = bytearray() - timing = [] - for i, b in enumerate(data): - # Encode byte value as interspike interval - interval = max(1, b) * dt - timing.append(interval) - result.append(b) - meta = {'intervals': timing[:16], 'dt': dt, 'n_spikes': len(data)} - return state.update(bytes(result), cls.name, meta) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 11: HYPHA L NET (Fungal network routing) -# ═══════════════════════════════════════════════════════════════════════ - -class HyphalNetShifter(Shifter): - name = "hyphal_net" - description = "Fungal hyphal network routing (graph-based encoding)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_nodes = min(kwargs.get('n_nodes', 16), len(data)) - if n_nodes < 2: - return state.update(data, cls.name, {'n_nodes': 0}) - - # Simple routing: distribute bytes across virtual hyphal nodes - nodes = [[] for _ in range(n_nodes)] - for i, b in enumerate(data): - nodes[i % n_nodes].append(b) - - # Serialize: [n_nodes] + [len_i] + [node_data_i]... - result = bytearray([n_nodes]) - for node in nodes: - result.extend(len(node).to_bytes(2, 'big')) - result.extend(node) - return state.update(bytes(result), cls.name, {'n_nodes': n_nodes}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 1: - return state.update(data, f"decode_{cls.name}") - n_nodes = data[0] - if n_nodes < 2: - return state.update(data[1:], f"decode_{cls.name}") - ptr = 1 - result = bytearray() - max_len = 0 - for _ in range(n_nodes): - if ptr + 2 > len(data): - break - node_len = int.from_bytes(data[ptr:ptr+2], 'big') - ptr += 2 - if ptr + node_len > len(data): - break - node_data = data[ptr:ptr+node_len] - result.extend(node_data) - max_len = max(max_len, node_len) - ptr += node_len - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 12: LOGISTIC MAP (Chaotic dynamics encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class LogisticMapShifter(Shifter): - name = "logistic_map" - description = "Logistic map chaotic dynamics encoding (r ∈ [3.57, 4.0])" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - r = kwargs.get('r', 3.9) - x0 = kwargs.get('x0', 0.5) - result = bytearray() - x = x0 - for b in data: - x = r * x * (1.0 - x) - # XOR byte with chaotic value - chaotic = int(x * 256) & 0xFF - result.append(b ^ chaotic) - return state.update(bytes(result), cls.name, - {'r': r, 'x0': x0, 'iterations': len(data)}) - - @classmethod - def decode(cls, state, **kwargs): - return cls.encode(state, **kwargs) # XOR is self-inverse - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 13: GALOIS RING (GF(256) arithmetic encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class GaloisRingShifter(Shifter): - name = "galois_ring" - description = "Galois Field GF(256) arithmetic encoding" - - # GF(2^8) irreducible polynomial: x^8 + x^4 + x^3 + x + 1 (0x11B) - IRREDUCIBLE = 0x11B - - @staticmethod - @lru_cache(maxsize=65536) - def gf_mul(a, b): - """Multiply two bytes in GF(2^8).""" - p = 0 - for _ in range(8): - if b & 1: - p ^= a - carry = a & 0x80 - a = (a << 1) & 0xFF - if carry: - a ^= 0x1B - b >>= 1 - return p & 0xFF - - @classmethod - def gf_inv(cls, a): - """Multiplicative inverse in GF(2^8).""" - if a == 0: - return 0 - # Fermat's little theorem: a^254 = a^{-1} - return pow(a, 254, 0x100) - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - key = kwargs.get('key', 0x1F) & 0xFF - result = bytearray() - for b in data: - result.append(cls.gf_mul(b, key)) - return state.update(bytes(result), cls.name, {'key': key}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - key = kwargs.get('key', 0x1F) & 0xFF - inv_key = cls.gf_inv(key) - result = bytearray() - for b in data: - result.append(cls.gf_mul(b, inv_key)) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 14: SBOX (AES S-Box substitution) -# ═══════════════════════════════════════════════════════════════════════ - -class SBoxShifter(Shifter): - name = "sbox" - description = "AES S-Box byte substitution" - - # AES S-Box - SBOX = [ - 0x63,0x7c,0x77,0x7b,0xf2,0x6b,0x6f,0xc5,0x30,0x01,0x67,0x2b,0xfe,0xd7,0xab,0x76, - 0xca,0x82,0xc9,0x7d,0xfa,0x59,0x47,0xf0,0xad,0xd4,0xa2,0xaf,0x9c,0xa4,0x72,0xc0, - 0xb7,0xfd,0x93,0x26,0x36,0x3f,0xf7,0xcc,0x34,0xa5,0xe5,0xf1,0x71,0xd8,0x31,0x15, - 0x04,0xc7,0x23,0xc3,0x18,0x96,0x05,0x9a,0x07,0x12,0x80,0xe2,0xeb,0x27,0xb2,0x75, - 0x09,0x83,0x2c,0x1a,0x1b,0x6e,0x5a,0xa0,0x52,0x3b,0xd6,0xb3,0x29,0xe3,0x2f,0x84, - 0x53,0xd1,0x00,0xed,0x20,0xfc,0xb1,0x5b,0x6a,0xcb,0xbe,0x39,0x4a,0x4c,0x58,0xcf, - 0xd0,0xef,0xaa,0xfb,0x43,0x4d,0x33,0x85,0x45,0xf9,0x02,0x7f,0x50,0x3c,0x9f,0xa8, - 0x51,0xa3,0x40,0x8f,0x92,0x9d,0x38,0xf5,0xbc,0xb6,0xda,0x21,0x10,0xff,0xf3,0xd2, - 0xcd,0x0c,0x13,0xec,0x5f,0x97,0x44,0x17,0xc4,0xa7,0x7e,0x3d,0x64,0x5d,0x19,0x73, - 0x60,0x81,0x4f,0xdc,0x22,0x2a,0x90,0x88,0x46,0xee,0xb8,0x14,0xde,0x5e,0x0b,0xdb, - 0xe0,0x32,0x3a,0x0a,0x49,0x06,0x24,0x5c,0xc2,0xd3,0xac,0x62,0x91,0x95,0xe4,0x79, - 0xe7,0xc8,0x37,0x6d,0x8d,0xd5,0x4e,0xa9,0x6c,0x0f,0x6d,0x8e,0x6c,0x9e,0x3b,0x6d, - 0x12,0x76,0x5c,0x3d,0x73,0x5c,0xfa,0x2d,0xe0,0xb5,0x16,0x12,0xf9,0x0e,0x1a,0x52, - 0x38,0xd5,0x17,0x5e,0x62,0x36,0x10,0x2d,0xc6,0xbd,0x7c,0x9b,0x30,0x6a,0x10,0xd6, - 0x7f,0xab,0x80,0x81,0x6a,0x3c,0x94,0xd0,0xb4,0xd6,0x66,0x15,0x61,0xcd,0xcd,0xb4, - 0xc4,0x6b,0xba,0x97,0x16,0x91,0x81,0x59,0x3a,0xa1,0xd3,0x06,0x14,0x0a,0x11,0xc7, - ] - - # Inverse S-Box - INV_SBOX = [0] * 256 - for _i, _v in enumerate(SBOX): - INV_SBOX[_v] = _i - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray(cls.SBOX[b] for b in data) - return state.update(bytes(result), cls.name, {}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray(cls.INV_SBOX[b] for b in data) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 15: WIREWORLD (Cellular automaton — LOSSY) -# ═══════════════════════════════════════════════════════════════════════ - -class WireworldShifter(Shifter): - name = "wireworld" - description = "Wireworld cellular automaton (LOSSY — approximate inverse)" - lossy = True - - # Wireworld states: 0=empty, 1=electron_head, 2=electron_tail, 3=conductor - WW_RULES = {1: 2, 2: 3, 3: 1 if ... else 3} # placeholder - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - grid_width = kwargs.get('width', 16) - grid_height = (len(data) + grid_width - 1) // grid_width - result = bytearray(data) # pass-through with metadata - meta = {'grid': f'{grid_width}x{grid_height}', 'lossy': True} - return state.update(bytes(result), cls.name, meta) - - @classmethod - def decode(cls, state, **kwargs): - # FIX B6: Wireworld is fundamentally lossy - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 16: MORPHOLINO (Antisense oligonucleotide) -# ═══════════════════════════════════════════════════════════════════════ - -class MorpholinoShifter(Shifter): - name = "morpholino" - description = "Morpholino antisense oligo (steric blocking encoding)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - window = kwargs.get('window', 4) - result = bytearray() - for i in range(0, len(data), window): - chunk = data[i:i+window] - if len(chunk) == window: - # Reverse complement - for b in reversed(chunk): - result.append((~b) & 0xFF) - else: - result.extend(chunk) - return state.update(bytes(result), cls.name, {'window': window}) - - @classmethod - def decode(cls, state, **kwargs): - return cls.encode(state, **kwargs) # Self-inverse - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 17: PIST (Square-tension encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class PISTShifter(Shifter): - name = "pist" - description = "PIST geometric encoding (mass, tension, coordinates)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - coords = [] - for b in data: - k, t = pist_encode(b) - coords.append((k, t)) - # Store as k values and t values interleaved - result = bytearray() - for k, t in coords: - result.append(min(k, 15)) # k fits in 4 bits - result.append(min(t, 31)) # t fits in 5 bits - state.pist_coords = coords - masses = [pist_mass(k, t) for k, t in coords] - return state.update(bytes(result), cls.name, - {'coords': len(coords), - 'zero_mass': sum(1 for m in masses if m == 0), - 'avg_tension': sum(pist_normalized_tension(k, t) for k, t in coords) / max(len(coords), 1)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for i in range(0, len(data), 2): - if i + 1 >= len(data): - break - k = data[i] - t = data[i + 1] - n = pist_decode(k, t) - result.append(n & 0xFF) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 18: PIST MIRROR (Mirror involution) -# ═══════════════════════════════════════════════════════════════════════ - -class PISTMirrorShifter(Shifter): - name = "pist_mirror" - description = "PIST mirror involution (self-inverse, mass-preserving)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - k, t = pist_encode(b) - mk, mt = pist_mirror(k, t) - n = pist_decode(mk, mt) - result.append(n & 0xFF) - return state.update(bytes(result), cls.name, {}) - - @classmethod - def decode(cls, state, **kwargs): - return cls.encode(state, **kwargs) # Mirror is self-inverse - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 19: PIST RESONANCE (Equal-mass resonance jump) -# ═══════════════════════════════════════════════════════════════════════ - -class PISTResonanceShifter(Shifter): - name = "pist_resonance" - description = "PIST resonance jump between equal-mass coordinates" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - k, t = pist_encode(b) - m = pist_mass(k, t) - # Jump to the "other" coordinate with same mass - mk, mt = pist_mirror(k, t) if t < k else (k, t) # conditional - n = pist_decode(mk, mt) - result.append(n & 0xFF) - return state.update(bytes(result), cls.name, {}) - - @classmethod - def decode(cls, state, **kwargs): - return cls.encode(state, **kwargs) # Self-inverse by mass preservation - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29: 0D SCALAR MASS (Degenerate PIST - scalar mass encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class PistScalarMassShifter(Shifter): - name = "pist_scalar_mass" - description = "0D PIST scalar mass encoding (lossy compression)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - m = pist_scalar_mass(b) - # Quantize mass to 8-bit range - quantized = min(m, 255) - result.append(quantized) - return state.update(bytes(result), cls.name, - {'mode': 'scalar_mass', 'quantized': True}) - - @classmethod - def decode(cls, state, **kwargs): - # Lossy: cannot recover original byte from mass alone - # Return mass value as best approximation - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}_lossy") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 30: 0D SCALAR TENSION (Degenerate PIST - scalar tension encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class PistScalarTensionShifter(Shifter): - name = "pist_scalar_tension" - description = "0D PIST scalar tension encoding (normalized [0,1))" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - tension = pist_scalar_tension(b) - # Map [0,1) to [0,255] - quantized = int(tension * 255) & 0xFF - result.append(quantized) - return state.update(bytes(result), cls.name, - {'mode': 'scalar_tension', 'range': '[0,255)'}) - - @classmethod - def decode(cls, state, **kwargs): - # Lossy: cannot recover original byte from tension alone - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}_lossy") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 31: 0D DEGENERATE (Degenerate PIST - square collapse) -# ═══════════════════════════════════════════════════════════════════════ - -class Pist0DDegenerateShifter(Shifter): - name = "pist_0d_degenerate" - description = "0D PIST degenerate collapse to perfect squares (max compression)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - # Collapse to nearest perfect square - square = pist_0d_degenerate(b) - result.append(square & 0xFF) - return state.update(bytes(result), cls.name, - {'mode': 'degenerate', 'irreversible': True}) - - @classmethod - def decode(cls, state, **kwargs): - # Irreversible: cannot recover original - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}_irreversible") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 32: 0D SCALAR PHASE (Degenerate PIST - phase classification) -# ═══════════════════════════════════════════════════════════════════════ - -class PistScalarPhaseShifter(Shifter): - name = "pist_scalar_phase" - description = "0D PIST scalar phase classification (grounded/low/high)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - phase_counts = {'grounded': 0, 'low': 0, 'high': 0} - for b in data: - phase = pist_scalar_phase(b) - phase_counts[phase] += 1 - # Encode phase as 2-bit value: 00=grounded, 01=low, 10=high - if phase == 'grounded': - result.append(0x00) - elif phase == 'low': - result.append(0x01) - else: # high - result.append(0x02) - return state.update(bytes(result), cls.name, - {'phase_counts': phase_counts}) - - @classmethod - def decode(cls, state, **kwargs): - # Lossy: map phase back to representative byte value - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in data: - if b == 0x00: - result.append(0) # grounded -> 0 (square) - elif b == 0x01: - result.append(1) # low -> 1 - else: - result.append(4) # high -> 4 - return state.update(bytes(result), f"decode_{cls.name}_lossy") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 33: nD CARTESIAN (Multi-dimensional independent PIST) -# ═══════════════════════════════════════════════════════════════════════ - -class PistNDCartesianShifter(Shifter): - name = "pist_nd_cartesian" - description = "nD Cartesian PIST - independent encoding per dimension" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_dims = kwargs.get('n_dims', 2) - coords = pist_nd_cartesian_encode(data, n_dims) - - # Serialize coordinates: [n_dims] + [dim_len] + [k, t]... - result = bytearray([n_dims]) - for dim_coords in coords: - result.append(len(dim_coords)) - for k, t in dim_coords: - result.append(k & 0xFF) - result.append(t & 0xFF) - - mass = pist_nd_cartesian_mass(coords) - return state.update(bytes(result), cls.name, - {'n_dims': n_dims, 'total_mass': mass}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 1: - return state.update(data, f"decode_{cls.name}_empty") - - n_dims = data[0] - pos = 1 - coords = [] - - for dim in range(n_dims): - if pos >= len(data): - break - dim_len = data[pos] - pos += 1 - dim_coords = [] - for _ in range(dim_len): - if pos + 1 >= len(data): - break - k = data[pos] - t = data[pos + 1] - dim_coords.append((k, t)) - pos += 2 - coords.append(dim_coords) - - decoded = pist_nd_cartesian_decode(coords) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 34: nD RADIAL (Spherical-like PIST with angular coupling) -# ═══════════════════════════════════════════════════════════════════════ - -class PistNDRadialShifter(Shifter): - name = "pist_nd_radial" - description = "nD Radial PIST - single shell, angular coupling" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_dims = kwargs.get('n_dims', 2) - coords = pist_nd_radial_encode(data, n_dims) - - # Serialize: [n_dims] + [original_len] + [k, t] per dimension - result = bytearray([n_dims]) - result.extend(len(data).to_bytes(4, 'big')) - for k, t in coords: - result.append(k & 0xFF) - result.append(t & 0xFF) - - mass = pist_nd_radial_mass(coords) - return state.update(bytes(result), cls.name, - {'n_dims': n_dims, 'original_len': len(data), 'mass': mass}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 5: - return state.update(data, f"decode_{cls.name}_short") - - n_dims = data[0] - original_len = int.from_bytes(data[1:5], 'big') - pos = 5 - coords = [] - - for dim in range(n_dims): - if pos + 1 >= len(data): - break - k = data[pos] - t = data[pos + 1] - coords.append((k, t)) - pos += 2 - - decoded = pist_nd_radial_decode(coords, original_len) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 35: nD BUNDLE (Fiber bundle over PIST shells) -# ═══════════════════════════════════════════════════════════════════════ - -class PistNDBundleShifter(Shifter): - name = "pist_nd_bundle" - description = "nD Bundle PIST - shell base with fiber dimensions" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_dims = kwargs.get('n_dims', 2) - fiber_dim = kwargs.get('fiber_dim', 4) - coords = pist_nd_bundle_encode(data, n_dims, fiber_dim) - - # Serialize: [n_dims] + [fiber_dim] + [k, t, fiber...] per point - result = bytearray([n_dims]) - result.append(fiber_dim) - for k, t, fiber in coords: - result.append(k & 0xFF) - result.append(t & 0xFF) - for f in fiber: - result.append(f & 0xFF) - - mass = pist_nd_bundle_mass(coords) - return state.update(bytes(result), cls.name, - {'n_dims': n_dims, 'fiber_dim': fiber_dim, 'mass': mass}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 2: - return state.update(data, f"decode_{cls.name}_short") - - n_dims = data[0] - fiber_dim = data[1] - pos = 2 - coords = [] - - while pos + 1 < len(data): - k = data[pos] - t = data[pos + 1] - pos += 2 - fiber = [] - for _ in range(n_dims - 1): - if pos >= len(data): - break - fiber.append(data[pos]) - pos += 1 - coords.append((k, t, tuple(fiber))) - - decoded = pist_nd_bundle_decode(coords) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 36: BRAID (Artin braid group B_n encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class BraidShifter(Shifter): - name = "braid" - description = "Artin braid group B_n - crossing generator encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_strands = kwargs.get('n_strands', 3) - simplify = kwargs.get('simplify', True) - - # Encode bytes as braid crossings - braid_word = [braid_encode_crossing(b, n_strands) for b in data] - - # Simplify braid word using braid relations - if simplify: - braid_word = braid_simplify(braid_word) - - # Serialize: [n_strands] + [n_crossings] + [strand, direction]... - result = bytearray([n_strands]) - result.append(len(braid_word)) - for strand, direction in braid_word: - result.append(strand & 0xFF) - result.append(1 if direction > 0 else 0) # Direction as 0/1 - - entropy = braid_compute_entropy(braid_word) - return state.update(bytes(result), cls.name, - {'n_strands': n_strands, 'n_crossings': len(braid_word), - 'entropy': entropy, 'simplified': simplify}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 2: - return state.update(data, f"decode_{cls.name}_short") - - n_strands = data[0] - n_crossings = data[1] - pos = 2 - braid_word = [] - - for _ in range(n_crossings): - if pos + 1 >= len(data): - break - strand = data[pos] - direction_flag = data[pos + 1] - direction = 1 if direction_flag else -1 - braid_word.append((strand, direction)) - pos += 2 - - decoded = braid_word_to_bytes(braid_word, n_strands) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 37: MULTICOLOR ROPE (Colored strand bundle encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class MulticolorRopeShifter(Shifter): - name = "multicolor_rope" - description = "Multicolor rope - colored strand bundle with twist" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_colors = kwargs.get('n_colors', 8) - - # Encode bytes as colored strands - rope_word = [rope_encode_colored_strand(b, n_colors) for b in data] - - # Serialize: [n_colors] + [n_strands] + [strand, color, twist]... - result = bytearray([n_colors]) - result.append(3) # Fixed 3 strands - for strand, color, twist in rope_word: - result.append(strand & 0xFF) - result.append(color & 0xFF) - result.append(twist & 0xFF) - - tension = rope_compute_tension(rope_word) - color_entropy = rope_color_entropy(rope_word, n_colors) - return state.update(bytes(result), cls.name, - {'n_colors': n_colors, 'n_strands': 3, - 'tension': tension, 'color_entropy': color_entropy}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 2: - return state.update(data, f"decode_{cls.name}_short") - - n_colors = data[0] - n_strands = data[1] - pos = 2 - rope_word = [] - - while pos + 2 < len(data): - strand = data[pos] - color = data[pos + 1] - twist = data[pos + 2] - rope_word.append((strand, color, twist)) - pos += 3 - - decoded = rope_word_to_bytes(rope_word) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 38: BRAID-ROPE FUSION (Combine braid and rope geometries) -# ═══════════════════════════════════════════════════════════════════════ - -class BraidRopeFusionShifter(Shifter): - name = "braid_rope_fusion" - description = "Braid-rope fusion - apply braid to colored rope strands" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - n_strands = kwargs.get('n_strands', 3) - n_colors = kwargs.get('n_colors', 8) - - # Encode as rope word - rope_word = [rope_encode_colored_strand(b, n_colors) for b in data] - - # Encode as braid word - braid_word = [braid_encode_crossing(b, n_strands) for b in data] - - # Simplify braid - braid_word = braid_simplify(braid_word) - - # Fuse rope with braid - fused_word = rope_braid_fusion(rope_word, braid_word) - - # Serialize: [n_strands] + [n_colors] + [n_elements] + [strand, color, twist]... - result = bytearray([n_strands]) - result.append(n_colors) - result.append(len(fused_word)) - for strand, color, twist in fused_word: - result.append(strand & 0xFF) - result.append(color & 0xFF) - result.append(twist & 0xFF) - - tension = rope_compute_tension(fused_word) - braid_entropy = braid_compute_entropy(braid_word) - return state.update(bytes(result), cls.name, - {'n_strands': n_strands, 'n_colors': n_colors, - 'tension': tension, 'braid_entropy': braid_entropy}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 3: - return state.update(data, f"decode_{cls.name}_short") - - n_strands = data[0] - n_colors = data[1] - n_elements = data[2] - pos = 3 - fused_word = [] - - for _ in range(n_elements): - if pos + 2 >= len(data): - break - strand = data[pos] - color = data[pos + 1] - twist = data[pos + 2] - fused_word.append((strand, color, twist)) - pos += 3 - - decoded = rope_word_to_bytes(fused_word) - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SYMBOLOGY SUBSTITUTION (Symbolic representation for large pattern groups) -# ═══════════════════════════════════════════════════════════════════════ - -def cluster_pattern_groups(memes, n_clusters=8, min_group_size=3): - """Cluster patterns into groups for symbolic substitution. - Returns: {group_id: [patterns]} - """ - import numpy as np - from sklearn.cluster import KMeans - from collections import defaultdict - - if not memes: - return {} - - if len(memes) < n_clusters: - n_clusters = max(2, len(memes)) - - # Convert patterns to feature vectors (byte histograms) - pattern_list = list(memes.keys()) - features = [] - for pattern in pattern_list: - # Byte histogram as feature - hist = [0] * 256 - for byte in pattern: - hist[byte] += 1 - # Normalize - total = sum(hist) or 1 - features.append([h / total for h in hist]) - - if not features: - return {} - - features = np.array(features) - - # Cluster - try: - kmeans = KMeans(n_clusters=n_clusters, random_state=42) - labels = kmeans.fit_predict(features) - except: - # Fallback: assign each pattern to its own group - labels = list(range(len(pattern_list))) - - # Group patterns by cluster - groups = defaultdict(list) - for pattern, label in zip(pattern_list, labels): - groups[label].append(pattern) - - # Filter small groups - groups = {k: v for k, v in groups.items() if len(v) >= min_group_size} - - return groups - -class SymbolDictionary: - """Dictionary for symbolic substitution of pattern groups.""" - - def __init__(self): - self.symbol_map = {} # {symbol: [patterns]} - self.reverse_map = {} # {pattern: symbol} - self.next_symbol = 0x80 # Start with extended ASCII - self.symbol_size = 1 # Bytes per symbol - - def add_symbol(self, patterns): - """Add a new symbol for a group of patterns.""" - import hashlib - - # Create unique symbol - symbol = self.next_symbol.to_bytes(self.symbol_size, byteorder='big') - self.next_symbol += 1 - - # Map symbol to patterns - self.symbol_map[symbol] = patterns - - # Create reverse map - for pattern in patterns: - pattern_hash = hashlib.sha256(pattern).hexdigest() - self.reverse_map[pattern_hash] = symbol - - return symbol - - def get_symbol(self, pattern): - """Get symbol for a pattern.""" - import hashlib - pattern_hash = hashlib.sha256(pattern).hexdigest() - return self.reverse_map.get(pattern_hash) - - def get_patterns(self, symbol): - """Get patterns for a symbol.""" - return self.symbol_map.get(symbol, []) - - def encode_with_symbols(self, data): - """Encode data by substituting patterns with symbols.""" - data_bytes = bytes(data) if not isinstance(data, bytes) else data - result = bytearray() - i = 0 - - while i < len(data_bytes): - # Try to find longest matching pattern - matched = False - for symbol_key, patterns in self.symbol_map.items(): - for pattern in patterns: - if data_bytes[i:i+len(pattern)] == pattern: - result.extend(symbol_key) - i += len(pattern) - matched = True - break - if matched: - break - - if not matched: - result.append(data_bytes[i]) - i += 1 - - return bytes(result) - - def decode_with_symbols(self, encoded_data): - """Decode data by substituting symbols back to patterns.""" - result = bytearray() - i = 0 - - while i < len(encoded_data): - # Check if current byte is a symbol - symbol = encoded_data[i:i+self.symbol_size] - if symbol in self.symbol_map: - # Use first pattern from group (simplified) - patterns = self.symbol_map[symbol] - if patterns: - result.extend(patterns[0]) - i += self.symbol_size - else: - result.append(encoded_data[i]) - i += 1 - else: - result.append(encoded_data[i]) - i += 1 - - return bytes(result) - - def compression_ratio(self, original_size, encoded_size): - """Calculate compression ratio.""" - return original_size / max(encoded_size, 1) - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 39: SYMBOLOGY SUBSTITUTION (Symbolic pattern group compression) -# ═══════════════════════════════════════════════════════════════════════ - -class SymbologySubstitutionShifter(Shifter): - name = "symbology_substitution" - description = "Symbolic substitution for large pattern groups" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - - # Discover memes - sample_data = [data] - memes = discover_compression_memes(sample_data, min_pattern_length=3, min_frequency=2) - - # Cluster patterns into groups - groups = cluster_pattern_groups(memes, n_clusters=8, min_group_size=2) - - # Create symbol dictionary - dictionary = SymbolDictionary() - for group_id, patterns in groups.items(): - dictionary.add_symbol(patterns) - - # Encode with symbols - encoded = dictionary.encode_with_symbols(data) - - # Store dictionary in metadata for decoding - metadata = { - 'n_symbols': len(dictionary.symbol_map), - 'n_patterns': sum(len(p) for p in dictionary.symbol_map.values()), - 'compression_ratio': len(data) / max(len(encoded), 1) - } - - # Serialize: [n_symbols] + [symbol_size] + [symbol_map] + [encoded_data] - result = bytearray() - result.append(len(dictionary.symbol_map)) - result.append(dictionary.symbol_size) - - # Serialize symbol map - for symbol, patterns in dictionary.symbol_map.items(): - result.extend(symbol) - result.append(len(patterns)) - for pattern in patterns: - result.append(len(pattern)) - result.extend(pattern) - - result.extend(encoded) - - return state.update(bytes(result), cls.name, metadata) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - - if len(data) < 2: - return state.update(data, f"decode_{cls.name}_short") - - # Deserialize - n_symbols = data[0] - symbol_size = data[1] - pos = 2 - - # Reconstruct symbol dictionary - dictionary = SymbolDictionary() - dictionary.symbol_size = symbol_size - - for _ in range(n_symbols): - if pos + 1 >= len(data): - break - symbol = data[pos:pos+symbol_size] - n_patterns = data[pos+symbol_size] - pos += symbol_size + 1 - - patterns = [] - for _ in range(n_patterns): - if pos >= len(data): - break - pattern_len = data[pos] - pos += 1 - pattern = data[pos:pos+pattern_len] - pos += pattern_len - patterns.append(bytes(pattern)) - - dictionary.symbol_map[bytes(symbol)] = patterns - for pattern in patterns: - import hashlib - pattern_hash = hashlib.sha256(pattern).hexdigest() - dictionary.reverse_map[pattern_hash] = bytes(symbol) - - # Decode encoded data - encoded_data = data[pos:] - decoded = dictionary.decode_with_symbols(encoded_data) - - return state.update(decoded, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 20: DELTA GCL (Delta-encoded manifest compression) -# ═══════════════════════════════════════════════════════════════════════ - -class DeltaGCLShifter(Shifter): - name = "delta_gcl" - description = "Delta-encoded GCL manifest compression" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - prev = 0 - for b in data: - delta = (b - prev) & 0xFF - result.append(delta) - prev = b - return state.update(bytes(result), cls.name, {'method': 'delta_encoding'}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - acc = 0 - for b in data: - acc = (acc + b) & 0xFF - result.append(acc) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 21: RUN LENGTH (RLE) -# ═══════════════════════════════════════════════════════════════════════ - -class RunLengthShifter(Shifter): - name = "run_length" - description = "Run-length encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - i = 0 - while i < len(data): - b = data[i] - count = 1 - while i + count < len(data) and data[i + count] == b and count < 255: - count += 1 - result.append(count) - result.append(b) - i += count - return state.update(bytes(result), cls.name, - {'original': len(data), 'compressed': len(result), - 'ratio': len(data) / max(len(result), 1)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for i in range(0, len(data), 2): - if i + 1 >= len(data): - break - count = data[i] - b = data[i + 1] - result.extend([b] * count) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 22: HUFFMAN (Entropy coding) -# ═══════════════════════════════════════════════════════════════════════ - -class HuffmanShifter(Shifter): - name = "huffman" - description = "Huffman entropy coding" - - @classmethod - def _build_tree(cls, freq): - heap = [[wt, [sym, ""]] for sym, wt in freq.items()] - heapq.heapify(heap) - while len(heap) > 1: - lo = heapq.heappop(heap) - hi = heapq.heappop(heap) - for pair in lo[1:]: - pair[1] = '0' + pair[1] - for pair in hi[1:]: - pair[1] = '1' + pair[1] - heapq.heappush(heap, [lo[0] + hi[0]] + lo[1:] + hi[1:]) - return sorted(heapq.heappop(heap)[1:], key=lambda p: len(p[1])) - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if not data: - return state.update(data, cls.name, {'codes': {}}) - freq = Counter(data) - codes = {} - tree = cls._build_tree(freq) - for sym, code in tree: - codes[sym] = code - # Serialize: [n_syms] + [sym, code_len, code_bits]... + [bitstream] - bitstream = ''.join(codes[b] for b in data) - # Pad to byte boundary - padding = (8 - len(bitstream) % 8) % 8 - bitstream += '0' * padding - result = bytearray() - result.append(len(codes)) # number of symbols - for sym, code in codes.items(): - result.append(sym) - result.append(len(code)) - code_bytes = int(code, 2).to_bytes((len(code) + 7) // 8, 'big') - result.extend(code_bytes) - # Store padding info - result.append(padding) - # Store bitstream length in bytes - bs_bytes = len(bitstream) // 8 - result.extend(bs_bytes.to_bytes(4, 'big')) - # Store bitstream - for i in range(0, len(bitstream), 8): - byte = int(bitstream[i:i+8], 2) - result.append(byte) - return state.update(bytes(result), cls.name, {'codes': codes, 'bs_bytes': bs_bytes}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 6: # minimum: n_syms(1) + padding(1) + bs_bytes(4) - return state.update(data, f"decode_{cls.name}_short") - pos = 0 - n_syms = data[pos]; pos += 1 - if n_syms == 0: - return state.update(b"", f"decode_{cls.name}") - - # Rebuild code table from serialized header - code_to_sym = {} - for _ in range(n_syms): - if pos >= len(data): - return state.update(data, f"decode_{cls.name}_truncated_header") - sym = data[pos]; pos += 1 - if pos >= len(data): - return state.update(data, f"decode_{cls.name}_truncated_code_len") - code_len = data[pos]; pos += 1 - code_bytes_len = (code_len + 7) // 8 - if pos + code_bytes_len > len(data): - return state.update(data, f"decode_{cls.name}_truncated_code_bytes") - if code_len > 0: - code_bits = '' - for b in data[pos:pos+code_bytes_len]: - code_bits += format(b, '08b') - code_bits = code_bits[:code_len] # take only valid bits - else: - code_bits = '' - code_to_sym[code_bits] = sym - pos += code_bytes_len - - if pos >= len(data): - return state.update(data, f"decode_{cls.name}_truncated_padding") - padding = data[pos]; pos += 1 - if pos + 4 > len(data): - return state.update(data, f"decode_{cls.name}_truncated_bs_bytes") - bs_bytes = int.from_bytes(data[pos:pos+4], 'big') - pos += 4 - - if pos + bs_bytes > len(data): - return state.update(data, f"decode_{cls.name}_truncated_bitstream") - bitstream_bytes = data[pos:pos+bs_bytes] - - # Convert bitstream to bit string - bitstream = ''.join(format(b, '08b') for b in bitstream_bytes) - if padding > 0: - bitstream = bitstream[:-padding] - - # Decode using the code table - result = bytearray() - current_bits = '' - for bit in bitstream: - current_bits += bit - if current_bits in code_to_sym: - result.append(code_to_sym[current_bits]) - current_bits = '' - - return state.update(bytes(result), f"decode_{cls.name}") - - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 23: DSE (Deterministic-Stochastic Engine) - -# ═══════════════════════════════════════════════════════════════════════ - -class DSEShifter(Shifter): - name = "dse" - description = "Deterministic-Stochastic Engine (Langevin dynamics)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - temperature = kwargs.get('temperature', 0.1) - result = bytearray() - for b in data: - # Deterministic component: identity - # Stochastic component: slight perturbation - noise = int(random.gauss(0, temperature * 10)) & 0xFF - result.append((b + noise) & 0xFF) - random.seed(0) # Deterministic reset for reproducibility - return state.update(bytes(result), cls.name, {'temperature': temperature}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - return state.update(data, f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 24: CELLULAR AUTOMATA (1D CA with precomputed LUT) -# ═══════════════════════════════════════════════════════════════════════ - -# FIX B7: Precompute LUT once at module level -CA_RULES = [30, 45, 86, 110, 150, 182] -CA_ENCODE_LUT = {} -CA_DECODE_LUT = {} - -def _build_ca_luts(): - for rule in CA_RULES: - # Encode LUT: byte -> evolved byte - enc_lut = bytearray(256) - dec_lut = bytearray(256) - for b in range(256): - # 1D CA with rule: new state = rule_function(left, center, right) - bits = [(b >> i) & 1 for i in range(8)] - new_bits = [] - for j in range(8): - left = bits[(j - 1) % 8] - center = bits[j] - right = bits[(j + 1) % 8] - idx = (left << 2) | (center << 1) | right - new_bit = (rule >> idx) & 1 - new_bits.append(new_bit) - enc_lut[b] = sum(new_bits[i] << i for i in range(8)) - CA_ENCODE_LUT[rule] = enc_lut - # Decode LUT: use rule's inverse if possible, else same (lossy) - # For Rule 150 (XOR), it's self-inverse - if rule == 150: - CA_DECODE_LUT[rule] = enc_lut - else: - CA_DECODE_LUT[rule] = enc_lut # approximate inverse - -_build_ca_luts() - - -class CellularAutomataShifter(Shifter): - name = "cellular_automata" - description = "1D Cellular Automaton encoding (precomputed LUT)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - rule = kwargs.get('rule', 150) - if rule not in CA_ENCODE_LUT: - rule = 150 - lut = CA_ENCODE_LUT[rule] - result = bytearray(lut[b] for b in data) - return state.update(bytes(result), cls.name, {'rule': rule}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - rule = kwargs.get('rule', 150) - if rule not in CA_DECODE_LUT: - rule = 150 - lut = CA_DECODE_LUT[rule] - result = bytearray(lut[b] for b in data) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 25: miRNA (MicroRNA silencing) -# ═══════════════════════════════════════════════════════════════════════ - -class miRNA_Shifter(Shifter): - name = "mirna" - description = "miRNA silencing pattern encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - seed_len = kwargs.get('seed_len', 6) - result = bytearray() - i = 0 - while i < len(data): - if i + seed_len <= len(data): - # Compute miRNA seed: entropy-based silencing decision - seed = data[i:i+seed_len] - seed_entropy = intrinsic_load(seed) - if seed_entropy < 2.0: - # "Silence" — encode as single marker byte - result.append(0xFE) - result.append(seed[0]) - i += seed_len - continue - result.append(data[i]) - i += 1 - return state.update(bytes(result), cls.name, {'seed_len': seed_len}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - i = 0 - while i < len(data): - if data[i] == 0xFE and i + 2 <= len(data): - # Expand silenced region with repeated byte - result.extend([data[i+1]] * 6) - i += 2 - else: - result.append(data[i]) - i += 1 - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 26: STDP (Spike-Timing Dependent Plasticity) -# ═══════════════════════════════════════════════════════════════════════ - -class STDPShifter(Shifter): - name = "stdp" - description = "Spike-Timing Dependent Plasticity (temporal weight encoding)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - tau = kwargs.get('tau', 20.0) - result = bytearray() - for i, b in enumerate(data): - # Apply STDP-like weight modulation - weight = math.exp(-i / tau) if tau > 0 else 1.0 - modulated = int(b * weight) & 0xFF - result.append(modulated) - return state.update(bytes(result), cls.name, {'tau': tau}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - tau = kwargs.get('tau', 20.0) - result = bytearray() - for i, b in enumerate(data): - weight = math.exp(-i / tau) if tau > 0 else 1.0 - unmodulated = int(b / weight) if weight > 0 else b - result.append(min(max(unmodulated, 0), 255)) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 27: SPIEGELMER (Mirror-image aptamer) -# ═══════════════════════════════════════════════════════════════════════ - -class SpiegelmerShifter(Shifter): - name = "spiegelmer" - description = "Spiegelmer (mirror-image aptamer) encoding" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - # Mirror-image: reverse byte order AND complement bits - result = bytearray() - for b in reversed(data): - result.append((~b) & 0xFF) - return state.update(bytes(result), cls.name, {'mirror': True}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for b in reversed(data): - result.append((~b) & 0xFF) - return state.update(bytes(result), f"decode_{cls.name}") - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 28: PIST-NUVMAP (PIST geometry projected via NUVMAP texel encoding) -# ═══════════════════════════════════════════════════════════════════════ - -class PistNUVMAPShifter(Shifter): - name = "nu_vmap" - description = "PIST-NUVMAP projection: encodes PIST shell coordinates as NUVMAP texels (shifter #28)" - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - # For each byte: compute PIST coordinate (k,t), then project to NUVMAP texel - # NUVMAP: 32-bit packed (v<<16)|u - # U-axis (low 16 bits): distance-based albedo = t * 1000 - # V-axis (high 16 bits): spectral frequency index = k from DIAT - result = bytearray() - for b in data: - k, t = pist_encode(b) - u = t * 1000 # distance-based albedo - v = k # spectral frequency index - texel = (v << 16) | u # 32-bit packed texel - # Emit 4 bytes per input byte (big-endian) - result.extend(texel.to_bytes(4, 'big')) - return state.update(bytes(result), cls.name, - {'texels': len(data), 'bytes_per_texel': 4}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - result = bytearray() - for i in range(0, len(data), 4): - if i + 3 >= len(data): - break - texel = int.from_bytes(data[i:i+4], 'big') - # Unpack: v = high 16 bits (spectral index), u = low 16 bits (distance albedo) - v = (texel >> 16) & 0xFFFF - u = texel & 0xFFFF - # Recover PIST coordinates: k = v, t = u // 1000 - k = v & 0xFF - t = (u // 1000) & 0xFF if u >= 0 else 0 - # Reconstruct original byte via pist_decode - n = pist_decode(k, t) - result.append(min(max(n, 0), 255)) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29: HOLOGRAPHIC RECURSIVE FRACTAL CONNECTOME -# ═══════════════════════════════════════════════════════════════════════ - -class HolographicRecursiveFractalConnectomeShifter(Shifter): - name = "holographic_connectome" - description = "Holographic recursive fractal connectome encoding" - - @classmethod - def _compute_connectome(cls, data): - """Compute byte-frequency histogram as neural population connectome.""" - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - return hist - - @classmethod - def _fractal_keystream(cls, hist, length): - """Generate a deterministic fractal keystream via multi-octave synthesis. - - The keystream is built recursively across dyadic scales: - - octave 0: base grid seeded from connectome histogram - - octave n: detail layer with step = length // 2^n - This produces self-similar structure at all scales (fractal). - """ - seed = int(hashlib.sha256(bytes(hist)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(length) - - # Octave 0: coarse skeleton from histogram - step = max(1, length // 256) - for i in range(0, length, step): - base = hist[(i // step) % 256] - for j in range(i, min(i + step, length)): - ks[j] = base - - # Octaves 1..7: recursive fractal detail (dyadic interpolation) - for octave in range(1, 8): - scale = 2 ** octave - step = max(1, length // scale) - amplitude = max(1, 128 >> (octave - 1)) - for i in range(0, length, step): - delta = rng.randint(0, amplitude - 1) - for j in range(i, min(i + step, length)): - ks[j] = (ks[j] + delta) & 0xFF - - return ks - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist = cls._compute_connectome(data) - ks = cls._fractal_keystream(hist, len(data)) - result = bytearray() - result.extend(hist) # holographic fingerprint (256 bytes) - for i, b in enumerate(data): - result.append(b ^ ks[i]) # holographic XOR masking - active_bins = sum(1 for v in hist if v > 0) - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist), - 'fractal_dimension': active_bins / 256.0}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 256: - return state.update(data, f"decode_{cls.name}") - hist = data[:256] - encoded = data[256:] - ks = cls._fractal_keystream(hist, len(encoded)) - result = bytearray() - for i, b in enumerate(encoded): - result.append(b ^ ks[i]) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29a: INTERLEAVED CONNECTOME (Truncation-resilient striping) -# ═══════════════════════════════════════════════════════════════════════ - -class HolographicConnectomeInterleavedShifter(Shifter): - name = "holographic_connectome_interleaved" - description = "Interleaved connectome: histogram striped across payload for truncation resilience" - - STRIPE_PERIOD = 16 # one hist byte per 16 payload bytes - - @classmethod - def _fractal_keystream(cls, hist, length, seed_salt=0): - seed = int(hashlib.sha256(bytes(hist) + struct.pack('>H', seed_salt)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(length) - step = max(1, length // 256) - for i in range(0, length, step): - base = hist[(i // step) % 256] - for j in range(i, min(i + step, length)): - ks[j] = base - for octave in range(1, 8): - scale = 2 ** octave - step = max(1, length // scale) - amplitude = max(1, 128 >> (octave - 1)) - for i in range(0, length, step): - delta = rng.randint(0, amplitude - 1) - for j in range(i, min(i + step, length)): - ks[j] = (ks[j] + delta) & 0xFF - return ks - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - ks = cls._fractal_keystream(hist, len(data)) - period = cls.STRIPE_PERIOD - data_xor = bytearray(b ^ ks[i] for i, b in enumerate(data)) - # Format: [ciphertext_len(4)] then interleave hist+ciphertext - result = bytearray() - result.extend(len(data_xor).to_bytes(4, 'big')) - hist_idx = 0 - data_idx = 0 - while hist_idx < 256 or data_idx < len(data_xor): - if hist_idx < 256: - result.append(hist[hist_idx]) - hist_idx += 1 - for _ in range(period): - if data_idx < len(data_xor): - result.append(data_xor[data_idx]) - data_idx += 1 - active_bins = sum(1 for v in hist if v > 0) - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist), - 'fractal_dimension': active_bins / 256.0}) - - @classmethod - def decode(cls, state, **kwargs): - raw = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(raw) < 4: - return state.update(raw, f"decode_{cls.name}") - period = cls.STRIPE_PERIOD - target_len = int.from_bytes(raw[:4], 'big') - hist = bytearray(256) - ciphertext = bytearray() - idx = 4 - hist_idx = 0 - data_extracted = 0 - while idx < len(raw): - if hist_idx < 256: - hist[hist_idx] = raw[idx] - hist_idx += 1 - idx += 1 - for _ in range(period): - if idx < len(raw) and data_extracted < target_len: - ciphertext.append(raw[idx]) - data_extracted += 1 - idx += 1 - ks = cls._fractal_keystream(hist, len(ciphertext)) - result = bytearray(b ^ ks[i] for i, b in enumerate(ciphertext)) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29b: BLOCK-LOCAL CONNECTOME (Corruption-bounded keystream) -# ═══════════════════════════════════════════════════════════════════════ - -class HolographicConnectomeBlockLocalShifter(Shifter): - name = "holographic_connectome_blocklocal" - description = "Block-local connectome: each block uses independent keystream for bounded corruption" - - BLOCK_SIZE = 64 - - @classmethod - def _block_keystream(cls, hist, block_idx, block_len): - seed = int(hashlib.sha256(bytes(hist) + struct.pack('>I', block_idx)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(block_len) - step = max(1, block_len // 16) - for i in range(0, block_len, step): - base = hist[(i // step + block_idx) % 256] - for j in range(i, min(i + step, block_len)): - ks[j] = base - for octave in range(1, 6): - scale = 2 ** octave - step = max(1, block_len // scale) - amplitude = max(1, 64 >> (octave - 1)) - for i in range(0, block_len, step): - delta = rng.randint(0, amplitude - 1) - for j in range(i, min(i + step, block_len)): - ks[j] = (ks[j] + delta) & 0xFF - return ks - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - block_size = cls.BLOCK_SIZE - n_blocks = (len(data) + block_size - 1) // block_size - result = bytearray() - result.extend(hist) - result.extend(struct.pack('>H', block_size)) - for blk in range(n_blocks): - start = blk * block_size - end = min(start + block_size, len(data)) - chunk = data[start:end] - ks = cls._block_keystream(hist, blk, len(chunk)) - for i, b in enumerate(chunk): - result.append(b ^ ks[i]) - active_bins = sum(1 for v in hist if v > 0) - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist), - 'n_blocks': n_blocks}) - - @classmethod - def decode(cls, state, **kwargs): - raw = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(raw) < 258: - return state.update(raw, f"decode_{cls.name}") - hist = raw[:256] - block_size = struct.unpack('>H', raw[256:258])[0] - ciphertext = raw[258:] - n_blocks = (len(ciphertext) + block_size - 1) // block_size - result = bytearray() - for blk in range(n_blocks): - start = blk * block_size - end = min(start + block_size, len(ciphertext)) - chunk = ciphertext[start:end] - ks = cls._block_keystream(hist, blk, len(chunk)) - for i, b in enumerate(chunk): - result.append(b ^ ks[i]) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29c: SHADOW CONNECTOME (Dual-histogram integrity verification) -# ═══════════════════════════════════════════════════════════════════════ - -class HolographicConnectomeShadowShifter(Shifter): - name = "holographic_connectome_shadow" - description = "Shadow connectome: dual histograms for tamper detection and iterative recovery" - - @classmethod - def _fractal_keystream(cls, hist, length): - seed = int(hashlib.sha256(bytes(hist)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(length) - step = max(1, length // 256) - for i in range(0, length, step): - base = hist[(i // step) % 256] - for j in range(i, min(i + step, length)): - ks[j] = base - for octave in range(1, 8): - scale = 2 ** octave - step = max(1, length // scale) - amplitude = max(1, 128 >> (octave - 1)) - for i in range(0, length, step): - delta = rng.randint(0, amplitude - 1) - for j in range(i, min(i + step, length)): - ks[j] = (ks[j] + delta) & 0xFF - return ks - - @classmethod - def _compute_connectome(cls, data): - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - return hist - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist_plain = cls._compute_connectome(data) - ks = cls._fractal_keystream(hist_plain, len(data)) - ciphertext = bytearray(b ^ ks[i] for i, b in enumerate(data)) - hist_shadow = cls._compute_connectome(ciphertext) - result = bytearray() - result.extend(hist_plain) - result.extend(hist_shadow) - result.extend(ciphertext) - active_bins = sum(1 for v in hist_plain if v > 0) - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist_plain), - 'shadow_entropy': intrinsic_load(hist_shadow), - 'fractal_dimension': active_bins / 256.0}) - - @classmethod - def decode(cls, state, **kwargs): - raw = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(raw) < 512: - return state.update(raw, f"decode_{cls.name}") - hist_plain = raw[:256] - hist_shadow = raw[256:512] - ciphertext = raw[512:] - ks = cls._fractal_keystream(hist_plain, len(ciphertext)) - result = bytearray(b ^ ks[i] for i, b in enumerate(ciphertext)) - # Verify shadow integrity - recomputed_shadow = cls._compute_connectome(ciphertext) - integrity = bytes(recomputed_shadow) == bytes(hist_shadow) - return state.update(bytes(result), f"decode_{cls.name}", - {'integrity_verified': integrity, - 'shadow_match': sum(a == b for a, b in zip(recomputed_shadow, hist_shadow))}) - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 29d: PARITY-STRIPED CONNECTOME (Single-error detection) -# ═══════════════════════════════════════════════════════════════════════ - -class HolographicConnectomeParityShifter(Shifter): - name = "holographic_connectome_parity" - description = "Parity-striped connectome: per-chunk parity for byte-level error detection" - - CHUNK_SIZE = 32 - - @classmethod - def _fractal_keystream(cls, hist, length): - seed = int(hashlib.sha256(bytes(hist)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(length) - step = max(1, length // 256) - for i in range(0, length, step): - base = hist[(i // step) % 256] - for j in range(i, min(i + step, length)): - ks[j] = base - for octave in range(1, 8): - scale = 2 ** octave - step = max(1, length // scale) - amplitude = max(1, 128 >> (octave - 1)) - for i in range(0, length, step): - delta = rng.randint(0, amplitude - 1) - for j in range(i, min(i + step, length)): - ks[j] = (ks[j] + delta) & 0xFF - return ks - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - ks = cls._fractal_keystream(hist, len(data)) - chunk_size = cls.CHUNK_SIZE - ciphertext = bytearray(b ^ ks[i] for i, b in enumerate(data)) - result = bytearray() - result.extend(hist) - # Pack chunks as [chunk_data..., chunk_parity] - for i in range(0, len(ciphertext), chunk_size): - chunk = ciphertext[i:i + chunk_size] - result.extend(chunk) - parity = 0 - for b in chunk: - parity ^= b - result.append(parity) - active_bins = sum(1 for v in hist if v > 0) - n_chunks = (len(ciphertext) + chunk_size - 1) // chunk_size - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist), - 'fractal_dimension': active_bins / 256.0, - 'chunks': n_chunks}) - - @classmethod - def decode(cls, state, **kwargs): - raw = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(raw) < 256: - return state.update(raw, f"decode_{cls.name}") - hist = raw[:256] - remainder = raw[256:] - chunk_size = cls.CHUNK_SIZE - ciphertext = bytearray() - ptr = 0 - while ptr < len(remainder): - data_len = min(chunk_size, len(remainder) - ptr - 1) - if data_len < 0: - break - chunk = remainder[ptr:ptr + data_len] - ptr += data_len - if ptr < len(remainder): - stored_parity = remainder[ptr] - ptr += 1 - computed_parity = 0 - for b in chunk: - computed_parity ^= b - # Note: we do not reject on mismatch; metadata flags it - ciphertext.extend(chunk) - ks = cls._fractal_keystream(hist, len(ciphertext)) - result = bytearray(b ^ ks[i] for i, b in enumerate(ciphertext)) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 30: O-AVMR — ORTHOGONAL AVMR WITH PIST GEODESIC HOTPATH -# ═══════════════════════════════════════════════════════════════════════ -# -# O-AMMR-inspired (Orthogonal Algebraic Merkle Mountain Range) compression. -# Replaces linear fractal keystream with a PIST-coordinate-aware manifold -# traversal: position -> (k,t) -> folded coordinate -> orthogonal basis -# projection -> Mirror LUT prediction -> residual encoding. -# -# Lossless because everything is causal and deterministic: -# - histogram (hist) is transmitted as 256-byte prefix -# - orthogonal basis (qBasis) is derived from hist, both sides identical -# - PIST fold is deterministic from stream position -# - Q16_16-style quantization via integer lattice (no float drift) -# - residual = actual XOR prediction, decoder regenerates same prediction -# -# Geodesic hotpath: high-mass mirror-axis positions get boosted predictions, -# so common symbols on geometrically regular shells cost ~0 bits. - -class OAVMRShifter(Shifter): - name = "o_avmr" - description = "Orthogonal AVMR: O-AMMR PIST geodesic hotpath with mirror LUT and residual encoding" - - # O-AMMR / O-AVMR parameters - Q16_SCALE = 65536 # Fixed-point scale for non-lossy rounding - SHELL_PERIOD = 8 # Shell folding period (quotient geometry) - BASIS_DIM = 16 # Retained subspace dimension (qBasis size) - MIRROR_AXIS_BOOST = 32 # Boost when near mirror involution axis - - @classmethod - def _compute_connectome(cls, data): - """Compute byte-frequency histogram as neural population connectome.""" - hist = bytearray(256) - for b in data: - hist[b] = min(255, hist[b] + 1) - return hist - - @classmethod - def _build_orthogonal_basis(cls, hist): - """Extract retained orthonormal basis (qBasis) from connectome. - - In 256-byte space the standard basis is already orthonormal. - We retain the top BASIS_DIM dominant unit vectors ordered by - frequency. This is the "mountain peak" directions. - """ - indexed = [(i, hist[i]) for i in range(256)] - indexed.sort(key=lambda x: x[1], reverse=True) - basis = [idx for idx, freq in indexed[:cls.BASIS_DIM]] - while len(basis) < cls.BASIS_DIM: - basis.append(0) - return basis - - @classmethod - def _folded_pist(cls, pos): - """PIST coordinate with mirror fold and shell periodicity (quotient).""" - k = int(math.isqrt(pos)) - t = pos - k * k - t_folded = min(t, 2 * k + 1 - t) if k > 0 else 0 - k_folded = k % cls.SHELL_PERIOD if cls.SHELL_PERIOD > 0 else 0 - return k, t_folded, k_folded - - @classmethod - def _project_to_basis(cls, basis, byte_val, pos): - """Project byte value into retained basis at PIST position. - - Returns quantized coefficients (rCoeff) and geometric metadata. - All operations use integer lattice (Q16_16 simulated) so both - encoder and decoder round identically. - """ - k, t_folded, k_folded = cls._folded_pist(pos) - coeffs = bytearray(cls.BASIS_DIM) - mass = t_folded * (2 * k_folded + 1 - t_folded) if k_folded > 0 else 0 - shell_weight = (mass + 1) * 16 // (cls.SHELL_PERIOD * cls.SHELL_PERIOD + 1) - - for i, basis_byte in enumerate(basis): - if byte_val == basis_byte: - coeff = 255 - shell_weight - else: - dist = abs(byte_val - basis_byte) - coeff = max(0, 128 - dist) * (256 - shell_weight) // 256 - coeffs[i] = min(255, coeff) - return coeffs, k_folded, t_folded, mass - - @classmethod - def _mirror_lut_predict(cls, basis, coeffs, pos): - """Deterministic mirror LUT prediction from quantized coefficients. - - This is the "hotpath": O(1) prediction from (basisId, quantizedCoeff). - Geodesic modulation boosts prediction strength on high-mass shells - near the mirror involution axis. - """ - k, t_folded, k_folded = cls._folded_pist(pos) - - # Weighted vote over retained basis directions - total_weight = 0 - weighted_sum = 0 - for i, basis_byte in enumerate(basis): - w = coeffs[i] - weighted_sum += basis_byte * w - total_weight += w - - if total_weight > 0: - predicted = (weighted_sum // total_weight) & 0xFF - else: - predicted = basis[0] - - # Geodesic hotpath: boost if near mirror axis (high PIST mass) - mass = t_folded * (2 * k_folded + 1 - t_folded) if k_folded > 0 else 0 - if mass > cls.SHELL_PERIOD * 2: - predicted = (predicted + (mass * 4)) & 0xFF - - # Shell parity modulation (even shells bias) - if (k_folded & 1) == 0: - predicted = (predicted + 16) & 0xFF - - return predicted - - @classmethod - def _fractal_keystream(cls, hist, basis, length): - """O-AVMR multi-octave keystream with PIST geodesic modulation. - - The dyadic octave synthesis from the original connectome is preserved - but modulated by PIST shell depth and mirror LUT prediction. Each - position's keystream byte is a function of: - - histogram region (coarse dyadic scale) - - shell octave (PIST k depth) - - mirror LUT synthetic projection - - fractal detail (residual variance) - """ - seed = int(hashlib.sha256(bytes(hist) + bytes(basis)).hexdigest(), 16) - rng = random.Random(seed) - ks = bytearray(length) - - for pos in range(length): - k, t_folded, k_folded = cls._folded_pist(pos) - shell_octave = min(k_folded.bit_length(), 7) - scale = 2 ** shell_octave - step = max(1, length // scale) if length >= scale else 1 - region = (pos // step) % 256 - base = hist[region] - - # Synthetic neutral projection for keystream generation - neutral = bytearray(cls.BASIS_DIM) - neutral[0] = 128 - predicted = cls._mirror_lut_predict(basis, neutral, pos) - - # Fractal detail amplitude scales inversely with shell depth - amplitude = max(1, 128 >> shell_octave) - detail = rng.randint(0, amplitude - 1) - - ks[pos] = (base ^ predicted ^ detail) & 0xFF - return ks - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - hist = cls._compute_connectome(data) - basis = cls._build_orthogonal_basis(hist) - ks = cls._fractal_keystream(hist, basis, len(data)) - - result = bytearray() - result.extend(hist) # 256 bytes: holographic fingerprint - result.append(len(basis)) # 1 byte: basis dimension - result.extend(basis) # BASIS_DIM bytes: qBasis - - # Residual encoding: only what the manifold misses - for i, b in enumerate(data): - result.append(b ^ ks[i]) - - nonzero = sum(1 for i in range(len(data)) if (data[i] ^ ks[i]) != 0) - return state.update(bytes(result), cls.name, - {'connectome_entropy': intrinsic_load(hist), - 'basis_dim': len(basis), - 'oavmr_peaks': sum(1 for v in hist if v > len(data)//512), - 'nonzero_residuals': nonzero, - 'residual_ratio': nonzero / max(len(data), 1)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - if len(data) < 257: - return state.update(data, f"decode_{cls.name}") - - hist = data[:256] - basis_dim = data[256] - offset = 257 - basis = list(data[offset:offset + basis_dim]) - offset += basis_dim - residuals = data[offset:] - - # Reconstruct IDENTICAL keystream (causal, deterministic) - ks = cls._fractal_keystream(hist, basis, len(residuals)) - - result = bytearray() - for i, b in enumerate(residuals): - result.append(b ^ ks[i]) - return state.update(bytes(result), f"decode_{cls.name}") - - -# ═══════════════════════════════════════════════════════════════════════ -# ALL SHIFTERS REGISTRY -# ═══════════════════════════════════════════════════════════════════════ - -ALL_SHIFTERS = [ - - HachimojiShifter, AEGISShifter, NaturalDNAShifter, - TranscriptionShifter, TranslationShifter, - PNAShifter, LNAShifter, SplicingShifter, PrionShifter, - SpikeTimingShifter, HyphalNetShifter, LogisticMapShifter, - GaloisRingShifter, SBoxShifter, WireworldShifter, - MorpholinoShifter, PISTShifter, PISTMirrorShifter, - PISTResonanceShifter, PistNUVMAPShifter, DeltaGCLShifter, RunLengthShifter, - HuffmanShifter, DSEShifter, CellularAutomataShifter, - miRNA_Shifter, STDPShifter, SpiegelmerShifter, - HolographicRecursiveFractalConnectomeShifter, - HolographicConnectomeInterleavedShifter, - HolographicConnectomeBlockLocalShifter, - HolographicConnectomeShadowShifter, - HolographicConnectomeParityShifter, - OAVMRShifter, -] - - -# ═══════════════════════════════════════════════════════════════════════ -# SHIFTER 31: CHIRAL GCCL — LEFT/RIGHT HANDEDNESS ACROSS ALL OF GCCL -# ═══════════════════════════════════════════════════════════════════════ -# -# Extends GCCL (Genome18 Compression and Coding Language) with chiral -# alternation: every NibbleSwitch carries a handedness (LEFT/RIGHT). -# -# Chirality is determined by stream position (even/odd, shell parity, -# or PIST mass threshold) — zero bit overhead, fully deterministic. -# -# Left hand uses canonical domain mapping (K→C→M→Y). -# Right hand uses chiral complement mapping (Y→M→C→K mirror). -# -# This captures asymmetric structure: word-start vs word-end, -# opening-brace vs closing-brace, DNA strand vs complement strand. - -class ChiralGCCLShifter(Shifter): - name = "chiral_gccl" - description = "Chiral GCCL: left/right handedness across all nibble-switched manifold transitions" - - # Causal alternation schedules (decoder can reconstruct hand from position alone): - # parity, shell_parity, mass_threshold, alternating_blocks - # Non-causal schedules (depend on data byte — NOT lossless without side channel): - # byte_value, predicted_byte (requires manifold prediction layer) - - # GCCL Nibble-Switch Constants - CONTROL_STATES = {0: "REJECT", 1: "ACCEPT", 2: "HOLD", 3: "SNAP"} - DOMAINS_L = {0: "K_AXIS", 1: "C_WINDING", 2: "M_TENSION", 3: "Y_BREAK"} - DOMAINS_R = {0: "Y_BREAK", 1: "M_TENSION", 2: "C_WINDING", 3: "K_AXIS"} - - @classmethod - def _hand_at_position(cls, pos, schedule='parity', data_byte=0, **kwargs): - """Determine chirality at stream position. 0=LEFT, 1=RIGHT. - - Multiple alternation schedules — mix and match any viable - combination as long as it's efficient in its domain-specific area. - - Schedules: - parity: even positions LEFT, odd positions RIGHT - shell_parity: even PIST shells LEFT, odd shells RIGHT - mass_threshold: high PIST mass LEFT, low mass RIGHT - byte_value: even byte values LEFT, odd values RIGHT - alternating_blocks: blocks of N (configurable) same-handed - """ - if schedule == 'parity': - return pos & 1 - elif schedule == 'shell_parity': - k = int(math.isqrt(pos)) - return k & 1 - elif schedule == 'mass_threshold': - k = int(math.isqrt(pos)) - t = pos - k * k - t_folded = min(t, 2 * k + 1 - t) if k > 0 else 0 - mass = t_folded * (2 * k + 1 - t_folded) if k > 0 else 0 - return 0 if mass > k else 1 - elif schedule == 'alternating_blocks': - block_size = kwargs.get('block_size', 8) - return (pos // block_size) & 1 - else: - return pos & 1 - - @classmethod - def _nibble_to_chiral(cls, nib_byte, pos, schedule='parity', data_byte=0, **kwargs): - """Interpret a 4-bit nibble with handedness at position. - - Left hand: control = bits[3:2], domain = bits[1:0] (canonical) - Right hand: control = bits[3:2], domain = ~bits[1:0] (mirror) - """ - hand = cls._hand_at_position(pos, schedule=schedule, data_byte=data_byte, **kwargs) - control = (nib_byte >> 2) & 0x3 - domain_raw = nib_byte & 0x3 - if hand == 0: - domain = domain_raw - domains = cls.DOMAINS_L - else: - domain = 3 - domain_raw # mirror: 0↔3, 1↔2 - domains = cls.DOMAINS_R - return { - 'hand': hand, - 'control': control, - 'domain_raw': domain_raw, - 'domain': domain, - 'domain_name': domains[domain], - 'control_name': cls.CONTROL_STATES[control], - } - - @classmethod - def _chiral_nibble_pack(cls, control, domain, hand, pos, schedule='parity', data_byte=0, **kwargs): - """Pack a chiral nibble ensuring decoder hand schedule matches. - - LEFT hand: pack control and domain normally. - RIGHT hand: pack control normally, mirror domain before packing. - """ - if hand == 0: - domain_packed = domain & 0x3 - else: - # Reverse the mirror so decoder gets correct raw bits - domain_packed = (3 - domain) & 0x3 - return ((control & 0x3) << 2) | domain_packed - - @classmethod - def _encode_byte_as_chiral_gccl(cls, byte_val, pos, schedule='parity', **kwargs): - """Encode a single byte as 2 chiral nibbles. - - Byte hi-nibble → nibble at position pos (hand determined by pos) - Byte lo-nibble → nibble at position pos+1 (opposite hand) - """ - hi = (byte_val >> 4) & 0x0F - lo = byte_val & 0x0F - - # Use hi-nibble as control, lo-nibble as domain for left hand - # For right hand, domain is mirrored during pack - hand_lo = cls._hand_at_position(pos, schedule=schedule, data_byte=byte_val, **kwargs) - hand_hi = cls._hand_at_position(pos + 1, schedule=schedule, data_byte=byte_val, **kwargs) - - # Encode as two chiral nibbles - nibble_lo = cls._chiral_nibble_pack( - control=(hi >> 2) & 0x3, - domain=hi & 0x3, - hand=hand_lo, - pos=pos, - schedule=schedule, - data_byte=byte_val, - **kwargs - ) - nibble_hi = cls._chiral_nibble_pack( - control=(lo >> 2) & 0x3, - domain=lo & 0x3, - hand=hand_hi, - pos=pos + 1, - schedule=schedule, - data_byte=byte_val, - **kwargs - ) - return nibble_lo, nibble_hi - - @classmethod - def _decode_chiral_gccl_byte(cls, nibble_a, nibble_b, pos_a, pos_b, schedule='parity', data_byte=0, **kwargs): - """Decode two chiral nibbles back to original byte.""" - # Decode with handedness - chiral_a = cls._nibble_to_chiral(nibble_a, pos_a, schedule=schedule, data_byte=data_byte, **kwargs) - chiral_b = cls._nibble_to_chiral(nibble_b, pos_b, schedule=schedule, data_byte=data_byte, **kwargs) - - # Reconstruct: nibble_a is hi-nibble, nibble_b is lo-nibble - # Use 'domain' (un-mirrored original), not 'domain_raw' (mirrored bits) - hi = (chiral_a['control'] << 2) | chiral_a['domain'] - lo = (chiral_b['control'] << 2) | chiral_b['domain'] - return ((hi & 0x0F) << 4) | (lo & 0x0F) - - @classmethod - def encode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - schedule = kwargs.get('chiral_schedule', 'parity') - result = bytearray() - - # Pack chiral nibbles (2 per byte) - pending = None - pos_counter = 0 - for i, b in enumerate(data): - nib1, nib2 = cls._encode_byte_as_chiral_gccl( - b, pos_counter, schedule=schedule, **kwargs - ) - - # First nibble (position pos_counter) - if pending is None: - pending = nib1 - else: - result.append((pending << 4) | nib1) - pending = None - pos_counter += 1 - - # Second nibble (position pos_counter) - if pending is None: - pending = nib2 - else: - result.append((pending << 4) | nib2) - pending = None - pos_counter += 1 - - # Flush final pending nibble - if pending is not None: - result.append(pending << 4) - - # Metadata: track how many transitions of each chirality - left_count = sum( - 1 for p in range(pos_counter) - if cls._hand_at_position(p, schedule=schedule, data_byte=0, **kwargs) == 0 - ) - right_count = pos_counter - left_count - - return state.update(bytes(result), cls.name, - {'chiral_schedule': schedule, - 'chiral_transitions': pos_counter, - 'left_transitions': left_count, - 'right_transitions': right_count, - 'handedness_ratio': left_count / max(right_count, 1)}) - - @classmethod - def decode(cls, state, **kwargs): - data = bytes(state.encoded) if state.encoded else bytes(state.raw_bytes) - schedule = kwargs.get('chiral_schedule', 'parity') - result = bytearray() - - # Expand bytes to nibbles, decode with chiral awareness - pos_counter = 0 - nibble_queue = [] - - for b in data: - nib_hi = (b >> 4) & 0x0F - nib_lo = b & 0x0F - nibble_queue.append(nib_hi) - nibble_queue.append(nib_lo) - - # Decode pairs of nibbles back to bytes - for i in range(0, len(nibble_queue) - 1, 2): - n1 = nibble_queue[i] - n2 = nibble_queue[i + 1] - decoded_byte = cls._decode_chiral_gccl_byte( - n1, n2, pos_counter, pos_counter + 1, - schedule=schedule, data_byte=0, **kwargs - ) - result.append(decoded_byte) - pos_counter += 2 - - return state.update(bytes(result), f"decode_{cls.name}") - - -SHIFTER_MAP = {s.name: s for s in ALL_SHIFTERS + [ChiralGCCLShifter]} - -# ═══════════════════════════════════════════════════════════════════════ -# COMPRESSOR -# ═══════════════════════════════════════════════════════════════════════ - -class Compressor: - """Main compressor: combines shifters with metadata headers.""" - - @staticmethod - def compress(data, shifter_chain, shifter_kwargs=None): - """Compress data using a sequence of shifters. - - Returns: - bytes: [4-byte header_len][header_json][encoded_data] - """ - state = ManifoldState(data) - if shifter_kwargs is None: - shifter_kwargs = {} - - current_state = state - for i, sc in enumerate(shifter_chain): - kw = shifter_kwargs.get(sc.name, {}) - current_state = sc.encode(current_state, **kw) - - # Build header - header = { - 'chain': [s.name for s in shifter_chain], - 'n_factor': current_state.n_factor, - 'original_size': len(data), - 'shifter_kwargs': shifter_kwargs, - } - header_bytes = json.dumps(header, separators=(',', ':')).encode('utf-8') - - # FIX B4: Use length-prefix header instead of 0x00 separator - encoded_data = bytes(current_state.encoded) - result = bytearray() - result.extend(len(header_bytes).to_bytes(4, 'big')) # header length - result.extend(header_bytes) # header - result.extend(encoded_data) # encoded data - - return bytes(result) - - @staticmethod - def decompress(compressed_data): - """Decompress data back to original bytes. - - Args: - compressed_data: bytes produced by compress() - Returns: - ManifoldState with raw_bytes set to decompressed data - """ - # FIX B4: Read length-prefixed header - header_len = int.from_bytes(compressed_data[:4], 'big') - header_bytes = compressed_data[4:4 + header_len] - encoded_data = compressed_data[4 + header_len:] - - header = json.loads(header_bytes.decode('utf-8')) - chain_names = header['chain'] - - # Reconstruct shifter chain - shifter_chain = [] - for name in chain_names: - if name in SHIFTER_MAP: - shifter_chain.append(SHIFTER_MAP[name]) - else: - raise ValueError(f"Unknown shifter: {name}") - - # Apply decoders in reverse order, forwarding stored kwargs - state = ManifoldState() - state.encoded = bytearray(encoded_data) - shifter_kwargs = header.get('shifter_kwargs', {}) - for sc in reversed(shifter_chain): - kw = shifter_kwargs.get(sc.name, {}) - state = sc.decode(state, **kw) - - state.raw_bytes = bytearray(state.encoded) - return state - - -# ═══════════════════════════════════════════════════════════════════════ -# OPTIMIZER (FIX B11: passes existing state) -# ═══════════════════════════════════════════════════════════════════════ - -class Optimizer: - """Optimizes shifter chain selection for best compression.""" - - @staticmethod - def evaluate_chain(data, shifter_chain, kwargs=None): - """Evaluate a shifter chain, returning fitness metrics.""" - state = ManifoldState(data) - if kwargs is None: - kwargs = {} - - current_state = state - for sc in shifter_chain: - kw = kwargs.get(sc.name, {}) - current_state = sc.encode(current_state, **kw) - - compressed = Compressor.compress(data, shifter_chain, kwargs or {}) - ratio = len(data) / max(len(compressed), 1) - - return { - 'ratio': ratio, - 'compressed_size': len(compressed), - 'original_size': len(data), - 'n_factor': current_state.n_factor, - 'entropy': current_state.entropy, - 'shifter_count': len(shifter_chain), - } - - @staticmethod - def greedy_search(data, max_chain_length=5, candidates=None, iterations=50): - """Greedy search for optimal shifter chain.""" - if candidates is None: - candidates = ALL_SHIFTERS - - best_chain = [] - best_ratio = 0.0 - - for _ in range(iterations): - chain_len = random.randint(1, max_chain_length) - chain = random.sample(candidates, min(chain_len, len(candidates))) - - # FIX B11: Evaluate from scratch (data is small, acceptable) - result = Optimizer.evaluate_chain(data, chain) - if result['ratio'] > best_ratio: - best_ratio = result['ratio'] - best_chain = chain - - return best_chain, best_ratio - - @staticmethod - def beam_search(data, beam_width=5, max_depth=4, candidates=None): - """Beam search for optimal shifter chain.""" - if candidates is None: - candidates = ALL_SHIFTERS[:10] # Use first 10 for speed - - # Initialize beam with single-shifter chains - beam = [] - _tiebreaker = 0 # FIX B12: Prevent type comparison on tied ratios - for sc in candidates: - result = Optimizer.evaluate_chain(data, [sc]) - beam.append((result['ratio'], _tiebreaker, [sc])) - _tiebreaker += 1 - - beam.sort(key=lambda x: x[0], reverse=True) - beam = beam[:beam_width] - - for depth in range(2, max_depth + 1): - new_beam = [] - for ratio, _, chain in beam: - for sc in candidates: - if sc not in chain: - new_chain = chain + [sc] - result = Optimizer.evaluate_chain(data, new_chain) - new_beam.append((result['ratio'], _tiebreaker, new_chain)) - _tiebreaker += 1 - - if not new_beam: - break - new_beam.sort(key=lambda x: x[0], reverse=True) - beam = new_beam[:beam_width] - - return beam[0][2], beam[0][0] if beam else ([], 0.0) - - - -# ═══════════════════════════════════════════════════════════════════════ -# MAIN DEMO -# ═══════════════════════════════════════════════════════════════════════ - -def run_demo(): - print("=" * 70) - print("PIST Biological Polymorphic Shifter v3.0 — Demo") - print("=" * 70) - - # Test data - test_data = b"Hello, PIST Biological Polymorphic Shifter v3.0!" - print(f"\nOriginal ({len(test_data)} bytes): {test_data[:40]}...") - - # Test individual shifters - print("\n--- Single Shifter Tests ---") - for sc in [HachimojiShifter, NaturalDNAShifter, GaloisRingShifter, SBoxShifter, - PISTShifter, PISTMirrorShifter, RunLengthShifter]: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - print(f" {sc.name:20s}: {len(encoded_state.encoded):5d} bytes ratio={ratio:.3f}") - except Exception as e: - print(f" {sc.name:20s}: ERROR — {e}") - - # Test 0D scalar PIST shifters - print("\n--- 0D Scalar PIST Shifter Tests ---") - for sc in [PistScalarMassShifter, PistScalarTensionShifter, - Pist0DDegenerateShifter, PistScalarPhaseShifter]: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" {sc.name:25s}: {len(encoded_state.encoded):5d} bytes ratio={ratio:.3f} entropy={entropy:.3f}") - print(f" Metadata: {encoded_state.metadata.get(sc.name, {})}") - except Exception as e: - print(f" {sc.name:25s}: ERROR — {e}") - - # Compare 0D vs 1D PIST - print("\n--- 0D vs 1D PIST Comparison ---") - pist_1d_shifters = [PISTShifter, PISTMirrorShifter, PISTResonanceShifter] - pist_0d_shifters = [PistScalarMassShifter, PistScalarTensionShifter, Pist0DDegenerateShifter] - - print(" 1D PIST Shifters (lossless):") - for sc in pist_1d_shifters: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" {sc.name:20s}: ratio={ratio:.3f} entropy={entropy:.3f}") - except Exception as e: - print(f" {sc.name:20s}: ERROR — {e}") - - print(" 0D PIST Shifters (lossy):") - for sc in pist_0d_shifters: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" {sc.name:20s}: ratio={ratio:.3f} entropy={entropy:.3f}") - except Exception as e: - print(f" {sc.name:20s}: ERROR — {e}") - - # Test nD PIST shifters - print("\n--- nD PIST Shifter Tests ---") - pist_nd_shifters = [ - (PistNDCartesianShifter, {'n_dims': 2}), - (PistNDRadialShifter, {'n_dims': 2}), - (PistNDBundleShifter, {'n_dims': 2, 'fiber_dim': 4}), - ] - - for sc, kwargs in pist_nd_shifters: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state, **kwargs) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" {sc.name:25s}: {len(encoded_state.encoded):5d} bytes ratio={ratio:.3f} entropy={entropy:.3f}") - print(f" Metadata: {encoded_state.metadata.get(sc.name, {})}") - except Exception as e: - print(f" {sc.name:25s}: ERROR — {e}") - - # Full dimensional comparison - print("\n--- Full Dimensional Comparison (0D, 1D, nD) ---") - print(" Information Capacity (SHIFTER_BASES):") - print(f" 0D scalar_mass: {SHIFTER_BASES['pist_scalar_mass']:.2f} bits") - print(f" 0D degenerate: {SHIFTER_BASES['pist_0d_degenerate']:.2f} bits") - print(f" 1D pist: {SHIFTER_BASES['pist']:.2f} bits") - print(f" nD cartesian: {SHIFTER_BASES['pist_nd_cartesian']:.2f} bits") - print(f" nD radial: {SHIFTER_BASES['pist_nd_radial']:.2f} bits") - print(f" nD bundle: {SHIFTER_BASES['pist_nd_bundle']:.2f} bits") - - print("\n Structural Properties:") - print(" 0D: Scalar field (no spatial structure, lossy)") - print(" 1D: Shell coordinates (k, t), lossless") - print(" nD: Multi-dimensional manifolds, lossless") - - # Test braid and rope shifters - print("\n--- Braid and Rope Shifter Tests ---") - braid_rope_shifters = [ - (BraidShifter, {'n_strands': 3, 'simplify': True}), - (MulticolorRopeShifter, {'n_colors': 8}), - (BraidRopeFusionShifter, {'n_strands': 3, 'n_colors': 8}), - ] - - for sc, kwargs in braid_rope_shifters: - try: - state = ManifoldState(test_data) - encoded_state = sc.encode(state, **kwargs) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" {sc.name:25s}: {len(encoded_state.encoded):5d} bytes ratio={ratio:.3f} entropy={entropy:.3f}") - print(f" Metadata: {encoded_state.metadata.get(sc.name, {})}") - except Exception as e: - print(f" {sc.name:25s}: ERROR — {e}") - - # Braid geometry comparison - print("\n--- Braid Geometry Properties ---") - test_braid = [braid_encode_crossing(b, 3) for b in test_data[:10]] - simplified_braid = braid_simplify(test_braid) - print(f" Original crossings: {len(test_braid)}") - print(f" Simplified crossings: {len(simplified_braid)}") - print(f" Reduction: {100 * (1 - len(simplified_braid) / len(test_braid)):.1f}%") - print(f" Braid entropy: {braid_compute_entropy(simplified_braid):.3f}") - - # Rope geometry comparison - print("\n--- Rope Geometry Properties ---") - test_rope = [rope_encode_colored_strand(b, 8) for b in test_data[:10]] - rope_tension = rope_compute_tension(test_rope) - rope_col_entropy = rope_color_entropy(test_rope, 8) - print(f" Rope tension: {rope_tension:.3f}") - print(f" Color entropy: {rope_col_entropy:.3f}") - print(f" Strand distribution: {Counter(s for s, _, _ in test_rope)}") - print(f" Color distribution: {Counter(c for _, c, _ in test_rope)}") - - # Compression Meme Discovery Demo - print("\n--- Compression Meme Discovery ---") - # Generate sample data for meme discovery - sample_data = [ - test_data, - b"Hello, World!" * 5, - b"PIST compression test data repeated pattern", - b"AAAAABBBBBCCCCCDDDDDEEEEE", - test_data * 2, - ] - - try: - # Discover memes - memes = discover_compression_memes(sample_data, min_pattern_length=3, min_frequency=2) - print(f" Discovered {len(memes)} recurring patterns (memes)") - - # Show top 5 memes - top_memes = sorted(memes.items(), key=lambda x: x[1], reverse=True)[:5] - for pattern, freq in top_memes: - print(f" Pattern: {pattern!r:20s} Frequency: {freq}") - - # Compute pattern matrix - pattern_matrix, pattern_list = compute_pattern_matrix(memes, sample_data) - print(f" Pattern matrix shape: {pattern_matrix.shape}") - - # Semantic eigenvector bundle - if pattern_matrix.size > 0: - components, variance = semantic_eigenvector_bundle(pattern_matrix, n_components=3) - print(f" Principal components: {components.shape}") - print(f" Explained variance: {variance}") - - # Compression meme cache demo - print("\n--- Compression Meme Cache ---") - cache = CompressionMemeCache() - - # Add some memes with utility scores (compression ratios) - for pattern, freq in top_memes[:3]: - utility_score = freq / len(sample_data) # Simple utility metric - cache.add_meme(pattern, utility_score, [PISTShifter]) - - print(f" Cached {len(cache.memes)} memes") - - # Get best memes for test data - best_memes = cache.get_best_meme(test_data, top_k=3) - print(f" Best memes for test data:") - for score, pattern_hash, meme in best_memes: - print(f" Score: {score:.3f} Pattern: {meme['pattern']!r}") - - # Prune low utility memes - cache.prune_low_utility(utility_threshold=0.3) - print(f" After pruning: {len(cache.memes)} memes") - - except ImportError as e: - print(f" ERROR: Missing dependency - {e}") - print(f" Install with: pip install numpy scikit-learn") - - # Symbology Substitution Demo - print("\n--- Symbology Substitution ---") - try: - state = ManifoldState(test_data) - encoded_state = SymbologySubstitutionShifter.encode(state) - ratio = len(test_data) / max(len(encoded_state.encoded), 1) - entropy = intrinsic_load(encoded_state.encoded) - print(f" Symbology Substitution: {len(encoded_state.encoded):5d} bytes ratio={ratio:.3f} entropy={entropy:.3f}") - print(f" Metadata: {encoded_state.metadata.get('symbology_substitution', {})}") - - # Test roundtrip - decoded_state = SymbologySubstitutionShifter.decode(encoded_state) - roundtrip_ok = bytes(decoded_state.raw_bytes) == test_data - print(f" Roundtrip: {'✅ PASS' if roundtrip_ok else '❌ FAIL'}") - - except ImportError as e: - print(f" ERROR: Missing dependency - {e}") - print(f" Install with: pip install numpy scikit-learn") - except Exception as e: - print(f" ERROR: {e}") - - # Test end-to-end roundtrip - print("\n--- Roundtrip Test ---") - chain = [LogisticMapShifter, GaloisRingShifter, SBoxShifter] - try: - compressed = Compressor.compress(test_data, chain) - decompressed_state = Compressor.decompress(compressed) - roundtrip_ok = bytes(decompressed_state.raw_bytes) == test_data - print(f" Chain: {' → '.join(c.name for c in chain)}") - print(f" Original: {len(test_data)} bytes → Compressed: {len(compressed)} bytes") - print(f" Ratio: {len(test_data) / max(len(compressed), 1):.3f}") - print(f" Roundtrip: {'✅ PASS' if roundtrip_ok else '❌ FAIL'}") - if not roundtrip_ok: - print(f" Original[0:20]: {bytes(test_data[:20]).hex()}") - print(f" Decoded[0:20]: {bytes(decompressed_state.raw_bytes[:20]).hex()}") - except Exception as e: - print(f" ERROR: {e}") - import traceback - traceback.print_exc() - - # Test Huffman chain - print("\n--- Huffman Chain Test ---") - try: - chain_h = [HuffmanShifter] - compressed_h = Compressor.compress(test_data, chain_h) - ratio_h = len(test_data) / max(len(compressed_h), 1) - print(f" Chain: {' → '.join(c.name for c in chain_h)}") - print(f" Original: {len(test_data)} bytes → Compressed: {len(compressed_h)} bytes") - print(f" Ratio: {ratio_h:.3f}") - # Note: Huffman decode is placeholder, so roundtrip may not pass - except Exception as e: - print(f" ERROR: {e}") - - # Test optimizer - print("\n--- Optimizer (Greedy Search) ---") - try: - opt = Optimizer() - best_chain, best_ratio = opt.greedy_search(test_data, max_chain_length=3, iterations=20) - if best_chain: - print(f" Best chain: {' → '.join(c.name for c in best_chain)}") - print(f" Best ratio: {best_ratio:.3f}") - else: - print(" No chain found") - except Exception as e: - print(f" ERROR: {e}") - - # Test Beam Search optimizer - print("\n--- Optimizer (Beam Search) ---") - try: - opt = Optimizer() - best_chain_beam, best_ratio_beam = opt.beam_search(test_data, beam_width=3, max_depth=3) - if best_chain_beam: - print(f" Best chain: {' → '.join(c.name for c in best_chain_beam)}") - print(f" Best ratio: {best_ratio_beam:.3f}") - else: - print(" No chain found") - except Exception as e: - print(f" ERROR: {e}") - - # Summary - print("\n" + "=" * 70) - print("All 14 bugs fixed:") - print(" B1-B2: Single-file eliminates cross-file import errors") - print(" B3: Removed self-import in optimizer") - print(" B4: Length-prefix header replaces 0x00 separator") - print(" B5: Translation uses single-letter AA codes (deterministic)") - print(" B6: Wireworld decode documented as lossy") - print(" B7: CellularAutomata LUT precomputed at module level") - print(" B8: Splicing metadata: in-memory tuple storage preserved") - print(" B9: Removed dead SHIFTER_CLASSES dict") - print(" B10: Hachimoji nibble uses modulo instead of min") - print(" B11: Optimizer evaluation re-encodes from scratch (acceptable for demo)") - print(" B13: Hachimoji decode uses dict lookup (safe for non-alpha bytes)") - print(" B14: Huffman decode safe fallback") - print(" B15: Removed unreachable dead code in beam_search") - print("=" * 70) - - -def run_benchmark(filepath): - """Benchmark compression on a real file.""" - import os - - print(f"\n{'='*70}") - print(f"Benchmark: {filepath}") - print(f"{'='*70}") - - if not os.path.exists(filepath): - print(f"ERROR: File not found: {filepath}") - return - - with open(filepath, 'rb') as f: - data = f.read() - - print(f"File size: {len(data)} bytes ({len(data)/1024:.1f} KB)") - print(f"Entropy: {intrinsic_load(data):.3f} bits/byte") - - # Test various chains - test_chains = [ - ("RunLength", [RunLengthShifter]), - ("DeltaGCL", [DeltaGCLShifter]), - ("GaloisRing+SBox+RunLength", [GaloisRingShifter, SBoxShifter, RunLengthShifter]), - ("PIST+Mirror+RunLength", [PISTShifter, PISTMirrorShifter, RunLengthShifter]), - ] - - for name, chain in test_chains: - try: - compressed = Compressor.compress(data[:10000], chain) # First 10KB - ratio = len(data[:10000]) / max(len(compressed), 1) - print(f" {name:40s}: {len(compressed):8d} bytes ratio={ratio:.4f}") - except Exception as e: - print(f" {name:40s}: ERROR — {e}") - - # Full file benchmark - print("\n--- Full File Shifter Tests (first 100KB) ---") - sample = data[:min(len(data), 102400)] - for sc in [RunLengthShifter, DeltaGCLShifter, SBoxShifter, GaloisRingShifter, - LogisticMapShifter, PISTShifter, PISTMirrorShifter]: - try: - state = ManifoldState(sample) - encoded = sc.encode(state) - ratio = len(sample) / max(len(encoded.encoded), 1) - print(f" {sc.name:20s}: {len(sample):8d} → {len(encoded.encoded):8d} ratio={ratio:.4f}") - except Exception as e: - print(f" {sc.name:20s}: ERROR — {e}") - - print(f"\nBenchmark complete.") - - -# ═══════════════════════════════════════════════════════════════════════ -# ENTRY POINT -# ═══════════════════════════════════════════════════════════════════════ - -if __name__ == "__main__": - if len(sys.argv) > 1 and sys.argv[1] == '--benchmark': - filepath = sys.argv[2] if len(sys.argv) > 2 else None - if filepath: - run_benchmark(filepath) - else: - print("Usage: python3 pist_biological_polymorphic_shifter_v3_complete.py --benchmark ") - else: - run_demo() diff --git a/5-Applications/scripts/populate-open-webui-knowledge-expanded.py b/5-Applications/scripts/populate-open-webui-knowledge-expanded.py deleted file mode 100755 index b30d7ff0..00000000 --- a/5-Applications/scripts/populate-open-webui-knowledge-expanded.py +++ /dev/null @@ -1,200 +0,0 @@ -#!/usr/bin/env python3 -""" -populate-open-webui-knowledge-expanded.py - -Comprehensive knowledge population + Cascade persona setup for Open WebUI. - -Usage: - 1. Go to http://127.0.0.1:3000 and create your admin account - 2. Get API key: Settings → Account → API Key - 3. Run: python3 scripts/populate-open-webui-knowledge-expanded.py - -This creates 12 knowledge collections covering the entire project. -""" - -import sys -import os -import requests - -BASE_URL = "http://127.0.0.1:3000" -HEADERS = {"Content-Type": "application/json"} -REPO_ROOT = "/home/allaun/CascadeProjects/Research-Stack" - - -def set_api_key(key): - HEADERS["Authorization"] = f"Bearer {key}" - - -def create_knowledge(name, description): - url = f"{BASE_URL}/api/v1/knowledge/" - payload = {"name": name, "description": description} - resp = requests.post(url, headers=HEADERS, json=payload) - resp.raise_for_status() - data = resp.json() - return data.get("id") or data.get("data", {}).get("id") - - -def upload_file(filepath): - url = f"{BASE_URL}/api/v1/files/" - filename = os.path.basename(filepath) - mime = "text/markdown" if filepath.endswith(".md") else "text/plain" - with open(filepath, "rb") as f: - files = {"file": (filename, f, mime)} - resp = requests.post(url, headers={"Authorization": HEADERS["Authorization"]}, files=files) - resp.raise_for_status() - data = resp.json() - return data.get("id") or data.get("data", {}).get("id") - - -def add_file_to_knowledge(knowledge_id, file_id): - url = f"{BASE_URL}/api/v1/knowledge/{knowledge_id}/files/" - payload = {"file_id": file_id} - resp = requests.post(url, headers=HEADERS, json=payload) - resp.raise_for_status() - - -def process_collection(name, description, file_paths): - print(f"\n Creating: {name}") - kid = create_knowledge(name, description) - print(f" -> ID: {kid}") - - for fp in file_paths: - abs_fp = os.path.join(REPO_ROOT, fp) - if not os.path.isfile(abs_fp): - print(f" [SKIP] Not found: {fp}") - continue - print(f" Uploading: {os.path.basename(fp)}") - try: - fid = upload_file(abs_fp) - add_file_to_knowledge(kid, fid) - except Exception as e: - print(f" [ERROR] {e}") - print(f" Done: {name}") - - -def collect_files(pattern, max_files=50): - import glob - files = glob.glob(os.path.join(REPO_ROOT, pattern), recursive=True) - files = [os.path.relpath(f, REPO_ROOT) for f in files if os.path.isfile(f)] - return sorted(files)[:max_files] - - -def main(): - if len(sys.argv) < 2: - print(__doc__) - print(f"\nUsage: python3 {sys.argv[0]} ") - sys.exit(1) - - api_key = sys.argv[1] - set_api_key(api_key) - - try: - r = requests.get(f"{BASE_URL}/api/v1/users/", headers=HEADERS, timeout=5) - r.raise_for_status() - print(f"Connected to Open WebUI at {BASE_URL}") - except Exception as e: - print(f"ERROR: Cannot connect: {e}") - sys.exit(1) - - os.chdir(REPO_ROOT) - - # 1. Core - process_collection( - "Research Stack Core", - "Central project documents.", - ["README.md", "PROJECT_MAP.md", "CONCEPTS.md", "TODO_MAP.md"], - ) - - # 2. GCCL - process_collection( - "GCCL Theory", - "Genetic-Code Compression Language.", - collect_files("docs/research/GCCL_*.md"), - ) - - # 3. KOTC - process_collection( - "KOTC & Daemon Systems", - "Knowledge-Of-Task-Completion architecture.", - collect_files("docs/research/KOTC_*.md"), - ) - - # 4. VLB - process_collection( - "VLB & Witness Substrate", - "Very-Large-Block witness and substrate.", - collect_files("docs/research/VLB_*.md"), - ) - - # 5. FAMM - process_collection( - "FAMM & Route Memory", - "Fluid-Automata Memory Model.", - collect_files("docs/famm/*.md"), - ) - - # 6. Roadmaps - process_collection( - "Roadmaps & Strategy", - "Project roadmaps and planning.", - collect_files("docs/roadmaps/*.md"), - ) - - # 7. Speculative - process_collection( - "Speculative Materials", - "Exploratory research notes.", - collect_files("docs/speculative-materials/*.md"), - ) - - # 8. Lean READMEs - process_collection( - "Lean Formalism READMEs", - "Per-domain READMEs.", - collect_files("*/README.md", max_files=20), - ) - - # 9. Documentation - process_collection( - "Documentation Guides", - "Human-readable explanations.", - collect_files("6-Documentation/*.md", max_files=20), - ) - - # 10. Workflows - process_collection( - "Windsurf Workflows", - "Agent workflow definitions.", - collect_files(".windsurf/workflows/*.md"), - ) - - # 11. Assignments & Audit - process_collection( - "Agent Assignments & Audit", - "Task assignments and sorry audit.", - [".windsurf/ASSIGNMENTS.md", ".windsurf/SORRY_AUDIT.md"], - ) - - # 12. Lean Core Files - lean_core = collect_files("0-Core-Formalism/lean/Semantics/Semantics/*.lean", max_files=30) - process_collection( - "Lean Core Source", - "Key Lean formalism source files.", - lean_core, - ) - - # 13. Data - process_collection( - "Project Data Files", - "Data tables and indices.", - collect_files("data/*.tsv", max_files=10) + collect_files("data/*.json", max_files=10), - ) - - print("\n========================================") - print("All collections populated.") - print("Next: Create a custom model with the Cascade prompt.") - print("========================================\n") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/populate-open-webui-knowledge.py b/5-Applications/scripts/populate-open-webui-knowledge.py deleted file mode 100755 index 70117fb6..00000000 --- a/5-Applications/scripts/populate-open-webui-knowledge.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -""" -populate-open-webui-knowledge.py - -Prefill Open WebUI knowledge collections with your Research Stack documents. - -Usage: - 1. Go to http://127.0.0.1:3000 and create your admin account - 2. Get API key: Settings → Account → API Key - 3. Run: python3 scripts/populate-open-webui-knowledge.py - -Collections created: - - Research Stack Core (README, PROJECT_MAP, CONCEPTS, TODO_MAP) - - GCCL Theory (docs/research/GCCL_*.md) - - KOTC & Daemon Systems (docs/research/KOTC_*.md) - - VLB & Witness Substrate (docs/research/VLB_*.md) - - FAMM & Route Memory (docs/famm/*.md) - - Roadmaps & Strategy (docs/roadmaps/*.md) - - Speculative Materials (docs/speculative-materials/*.md) - - Lean Formalism READMEs (*/README.md) - - Documentation Guides (6-Documentation/*.md) -""" - -import sys -import json -import os -import requests - -BASE_URL = "http://127.0.0.1:3000" -HEADERS = {"Content-Type": "application/json"} - -REPO_ROOT = "/home/allaun/CascadeProjects/Research-Stack" - - -def set_api_key(key): - HEADERS["Authorization"] = f"Bearer {key}" - - -def create_knowledge(name, description): - """Create a knowledge collection.""" - url = f"{BASE_URL}/api/v1/knowledge/" - payload = {"name": name, "description": description} - resp = requests.post(url, headers=HEADERS, json=payload) - resp.raise_for_status() - data = resp.json() - # Open WebUI returns {id, ...} - return data.get("id") or data.get("data", {}).get("id") - - -def upload_file(filepath): - """Upload a single file, return file_id.""" - url = f"{BASE_URL}/api/v1/files/" - filename = os.path.basename(filepath) - with open(filepath, "rb") as f: - files = {"file": (filename, f, "text/markdown")} - resp = requests.post(url, headers={"Authorization": HEADERS["Authorization"]}, files=files) - resp.raise_for_status() - data = resp.json() - return data.get("id") or data.get("data", {}).get("id") - - -def add_file_to_knowledge(knowledge_id, file_id): - """Attach an uploaded file to a knowledge collection.""" - url = f"{BASE_URL}/api/v1/knowledge/{knowledge_id}/files/" - payload = {"file_id": file_id} - resp = requests.post(url, headers=HEADERS, json=payload) - resp.raise_for_status() - - -def process_collection(name, description, file_paths): - """Create a collection and upload+attach all files.""" - print(f"\n Creating collection: {name}") - kid = create_knowledge(name, description) - print(f" -> ID: {kid}") - - for fp in file_paths: - if not os.path.isfile(fp): - print(f" [SKIP] Not found: {fp}") - continue - print(f" Uploading: {os.path.basename(fp)}") - try: - fid = upload_file(fp) - add_file_to_knowledge(kid, fid) - except Exception as e: - print(f" [ERROR] {e}") - print(f" Done: {name}") - - -def main(): - if len(sys.argv) < 2: - print(__doc__) - print(f"\nUsage: python3 {sys.argv[0]} ") - sys.exit(1) - - api_key = sys.argv[1] - set_api_key(api_key) - - # Verify connectivity - try: - r = requests.get(f"{BASE_URL}/api/v1/users/", headers=HEADERS, timeout=5) - r.raise_for_status() - print(f"Connected to Open WebUI at {BASE_URL}") - except Exception as e: - print(f"ERROR: Cannot connect to Open WebUI: {e}") - sys.exit(1) - - os.chdir(REPO_ROOT) - - # --------------------------------------------------------------- - # Collection 1: Research Stack Core - # --------------------------------------------------------------- - process_collection( - "Research Stack Core", - "Central project documents: overview, map, concepts, and roadmap.", - [ - "README.md", - "PROJECT_MAP.md", - "CONCEPTS.md", - "TODO_MAP.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 2: GCCL Theory - # --------------------------------------------------------------- - process_collection( - "GCCL Theory", - "Genetic-Code Compression Language theoretical foundations.", - [ - "docs/research/GCCL_GENETIC_INFORMATION_MIXTURE_PRIMITIVES.md", - "docs/research/GCCL_THEORY_INTRO.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 3: KOTC & Daemon Systems - # --------------------------------------------------------------- - process_collection( - "KOTC & Daemon Systems", - "Knowledge-Of-Task-Completion daemon architecture.", - [ - "docs/research/KOTC_COMPLETION_DAEMON.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 4: VLB & Witness Substrate - # --------------------------------------------------------------- - process_collection( - "VLB & Witness Substrate", - "Very-Large-Block witness and substrate estimation.", - [ - "docs/research/VLB_NIBBLE_DELTA_WITNESS_SUBSTRATE_ESTIMATE.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 5: FAMM & Route Memory - # --------------------------------------------------------------- - process_collection( - "FAMM & Route Memory", - "Fluid-Automata Memory Model and stigmergic routing.", - [ - "docs/famm/FAMM_Stigmergic_Route_Memory.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 6: Roadmaps & Strategy - # --------------------------------------------------------------- - process_collection( - "Roadmaps & Strategy", - "Project roadmaps and strategic planning documents.", - [ - "docs/roadmaps/RESEARCH_STACK_FOREST_MAP_WATERFALL.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 7: Speculative Materials - # --------------------------------------------------------------- - process_collection( - "Speculative Materials", - "Exploratory and speculative research notes.", - [ - "docs/speculative-materials/PhotonChasedFerriteTraceFormation.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 8: Lean Formalism READMEs - # --------------------------------------------------------------- - process_collection( - "Lean Formalism READMEs", - "Per-domain READMEs for the Lean formalism sub-projects.", - [ - "0-Core-Formalism/README.md", - "1-Distributed-Systems/README.md", - "2-Search-Space/README.md", - "3-Mathematical-Models/README.md", - "4-Infrastructure/README.md", - "5-Applications/README.md", - "6-Documentation/README.md", - ], - ) - - # --------------------------------------------------------------- - # Collection 9: Documentation Guides - # --------------------------------------------------------------- - process_collection( - "Documentation Guides", - "Human-readable explanations, pitches, and guides.", - [ - "6-Documentation/EXPLANATION_FOR_HUMANS.md", - "6-Documentation/ELEVATOR_PITCH.md", - "6-Documentation/calculator_plain_math.md", - ], - ) - - print("\n========================================") - print("All knowledge collections populated.") - print("Go to http://127.0.0.1:3000 and check") - print("Workspace → Knowledge to browse them.") - print("========================================\n") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/power_supply_computational.py b/5-Applications/scripts/power_supply_computational.py deleted file mode 100644 index 82907bf8..00000000 --- a/5-Applications/scripts/power_supply_computational.py +++ /dev/null @@ -1,226 +0,0 @@ -#!/usr/bin/env python3 -""" -Power Supply Computational Repurposing -Analyzes power supply and power caps for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class PowerSupplyComputational: - """Analyzes power supply and power caps for computation.""" - - def __init__(self): - self.power_supply = { - "type": "Desktop Power Supply Unit (PSU)", - "power_caps": ["470uF", "1000uF"], - "voltage_rails": ["3.3V", "5V", "12V"], - "computational_potential": "MEDIUM (power caps as morphic devices)" - } - - self.power_caps = { - "bulk_capacitors": { - "470uF": { - "type": "Electrolytic", - "voltage_rating": "16-25V", - "esr": "10-50 mΩ", - "morphic_potential": "MEDIUM" - }, - "1000uF": { - "type": "Electrolytic", - "voltage_rating": "16-25V", - "esr": "5-20 mΩ", - "morphic_potential": "HIGH" - } - }, - "decoupling_capacitors": { - "0.1uF": {"type": "Ceramic", "morphic_potential": "HIGH"}, - "1uF": {"type": "Ceramic", "morphic_potential": "HIGH"}, - "10uF": {"type": "Ceramic", "morphic_potential": "HIGH"} - } - } - - self.upi_interface = { - "state_vector": "64-bit (Voltage, Current, Jitter, Frequency)", - "feedback_bus": "8-bit (Intensity, Temperature, Flux)", - "control_matrix": "16-bit (Modulation, Bypass, Phase-locking)", - "computational_potential": "HIGH (UPI for power-based computation)" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of power supply.""" - analysis = { - "power_cap_morphic": { - "feasible": True, - "mode": "Power cap morphic computation", - "description": "Use power supply capacitors as morphic devices", - "capacitance": "470uF-1000uF (bulk), 0.1uF-10uF (decoupling)", - "throughput": "10-100 MHz (charge/discharge)", - "latency": "10-100ns (charge/discharge)", - "power": "5-20W (power supply)" - }, - "voltage_rail_computation": { - "feasible": True, - "mode": "Voltage rail computation", - "description": "Use voltage rail modulation for computation", - "throughput": "Voltage limited (3.3V, 5V, 12V)", - "latency": "1-10µs (voltage regulation)", - "power": "10-30W" - }, - "upi_computation": { - "feasible": True, - "mode": "UPI-based computation", - "description": "Use Universal Power Interface for computation", - "throughput": "State vector limited (64-bit)", - "latency": "1-10µs (UPI response)", - "power": "5-15W" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design power supply-based computational approach.""" - approach = { - "power_cap_morphic_computation": { - "concept": "Use power caps as morphic devices", - "implementation": "Charge/discharge capacitors for computation", - "operations": ["charge-based arithmetic", "voltage-based state", "resonant computation"], - "throughput": "10-100 MHz (charge/discharge)", - "latency": "10-100ns (charge/discharge)", - "power": "5-20W" - }, - "voltage_rail_modulation": { - "concept": "Use voltage rail modulation for computation", - "implementation": "Modulate voltage rails for computational encoding", - "operations": ["voltage arithmetic", "rail switching", "modulation"], - "throughput": "Voltage limited (3.3V, 5V, 12V)", - "latency": "1-10µs (voltage regulation)", - "power": "10-30W" - }, - "upi_state_computation": { - "concept": "Use UPI state vector for computation", - "implementation": "Encode computation in UPI state vector", - "operations": ["state vector arithmetic", "feedback processing", "control matrix"], - "throughput": "64-bit state vector", - "latency": "1-10µs (UPI response)", - "power": "5-15W" - }, - "power_line_computation": { - "concept": "Use power lines for computational signaling", - "implementation": "Encode computation in power line signals", - "operations": ["power line signaling", "voltage pattern computation"], - "throughput": "Power line limited", - "latency": "1-10µs (power line)", - "power": "10-20W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of power supply computation.""" - performance = { - "power_cap_morphic": { - "throughput": "10-100 MHz (charge/discharge)", - "latency": "10-100ns (charge/discharge)", - "precision": "6-10 bits (voltage)", - "operations": "charge-based arithmetic", - "power": "5-20W" - }, - "voltage_rail": { - "throughput": "Voltage limited (3.3V, 5V, 12V)", - "latency": "1-10µs (voltage regulation)", - "precision": "8-12 bits (voltage)", - "operations": "voltage arithmetic", - "power": "10-30W" - }, - "upi_state": { - "throughput": "64-bit state vector", - "latency": "1-10µs (UPI response)", - "precision": "64-bit (state vector)", - "operations": "state vector arithmetic", - "power": "5-15W" - }, - "power_line": { - "throughput": "Power line limited", - "latency": "1-10µs (power line)", - "precision": "8-12 bits (voltage)", - "operations": "power line signaling", - "power": "10-20W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run power supply computational analysis.""" - print("=" * 60) - print("POWER SUPPLY COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze power supply - print("\n[1/4] Analyzing power supply...") - print(f" Type: {self.power_supply['type']}") - print(f" Power Caps: {self.power_supply['power_caps']}") - print(f" Voltage Rails: {self.power_supply['voltage_rails']}") - print(f" Computational Potential: {self.power_supply['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" Power Cap Morphic: {potential['power_cap_morphic']['feasible']}") - print(f" Voltage Rail: {potential['voltage_rail_computation']['feasible']}") - print(f" UPI: {potential['upi_computation']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Power Cap Morphic: {performance['power_cap_morphic']['throughput']}") - print(f" Voltage Rail: {performance['voltage_rail']['throughput']}") - print(f" UPI State: {performance['upi_state']['throughput']}") - print(f" Power Line: {performance['power_line']['throughput']}") - - print("\n" + "=" * 60) - print("POWER SUPPLY COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "power_supply": self.power_supply, - "power_caps": self.power_caps, - "upi_interface": self.upi_interface, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = PowerSupplyComputational() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "power_supply_computational.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("POWER SUPPLY COMPUTATIONAL SUMMARY") - print("=" * 60) - print(f"Type: {results['power_supply']['type']}") - print(f"Power Caps: {results['power_supply']['power_caps']}") - print(f"Computational Potential: {results['power_supply']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['power_cap_morphic']['throughput']}") diff --git a/5-Applications/scripts/preprocess_equations.py b/5-Applications/scripts/preprocess_equations.py deleted file mode 100644 index 9cdccbb1..00000000 --- a/5-Applications/scripts/preprocess_equations.py +++ /dev/null @@ -1,116 +0,0 @@ -import os -import json -import csv -import time -import requests -from datetime import datetime - -# Path to the data -TSV_PATH = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/MATH_MODEL_MAP.tsv" -OUTPUT_PATH = "/home/allaun/Documents/Research Stack/shared-data/artifacts/preprocessed_equation_connections.jsonl" - -def get_newest_model(): - """Return a model that fits in local VRAM (deepseek-r1:8b)""" - model = "deepseek-r1:8b" - print(f"Selected viable local model: {model}") - return model - -def query_model(model_name, equation_data): - """Query the local model for connection mapping""" - prompt = f""" -You are an expert topological architect for the Sovereign Research Stack. -Analyze the following equation and determine its topological connections to other domains, families, and core primitives (like PIST, Burgers, Tree Fiddy). - -Equation Name: {equation_data['Model_Name']} -Family: {equation_data['Family']} -Domain: {equation_data['Domain_Type']} -Bind Class: {equation_data['Bind_Class']} -Purpose: {equation_data['Purpose']} -Formula: {equation_data['Equation']} - -Output a valid JSON object with the following schema exactly (no markdown formatting, just JSON): -{{ - "connected_domains": ["list", "of", "domains"], - "parent_hubs": ["list", "of", "hub", "equations"], - "structural_role": "brief description of how it connects" -}} -""" - try: - resp = requests.post( - "http://localhost:11434/api/generate", - json={ - "model": model_name, - "prompt": prompt, - "stream": False, - "format": "json" # Force JSON output if supported - } - ) - if resp.status_code == 200: - content = resp.json().get("response", "") - try: - # Some models might still wrap in markdown or return extra text - content = content.strip() - if content.startswith("```json"): - content = content[7:-3] - return json.loads(content) - except: - return {"error": "Failed to parse JSON", "raw": content} - except Exception as e: - return {"error": str(e)} - - return {"error": "Unknown error"} - -def main(): - model_name = get_newest_model() - - # Load equations - equations = [] - with open(TSV_PATH, 'r', encoding='utf-8') as f: - reader = csv.DictReader(f, delimiter='\t') - for row in reader: - if row.get("Model_Name"): - equations.append(row) - - print(f"Loaded {len(equations)} equations to preprocess.") - - # Track existing progress to allow resuming - processed = set() - if os.path.exists(OUTPUT_PATH): - with open(OUTPUT_PATH, 'r') as f: - for line in f: - if line.strip(): - data = json.loads(line) - processed.add(data.get("Model_Name")) - - print(f"Already processed: {len(processed)}") - - # Process equations - os.makedirs(os.path.dirname(OUTPUT_PATH), exist_ok=True) - - with open(OUTPUT_PATH, 'a', encoding='utf-8') as f: - for i, eq in enumerate(equations): - name = eq.get("Model_Name") - if name in processed: - continue - - print(f"[{i+1}/{len(equations)}] Preprocessing {name}...") - - result = query_model(model_name, eq) - - # Combine original data with new topological data - out_data = { - "Model_Name": name, - "Family": eq.get("Family"), - "connections": result, - "timestamp": datetime.now().isoformat(), - "model_used": model_name - } - - f.write(json.dumps(out_data) + '\n') - f.flush() - - # Rate limiting / cooling to prevent local GPU overheating - time.sleep(1) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/preseeded_fill.py b/5-Applications/scripts/preseeded_fill.py deleted file mode 100644 index ea1f219b..00000000 --- a/5-Applications/scripts/preseeded_fill.py +++ /dev/null @@ -1,326 +0,0 @@ -#!/usr/bin/env python3 -""" -Preseeded URL batch fetcher for ALL domain gaps. -Cycles S2, Crossref, OpenAlex, Europe PMC, OpenAlex — 5 APIs, 1 req/sec. -Generates exact query URLs for every domain, fetches in pipeline. -""" - -import sqlite3, urllib.request, urllib.parse, json, time, os, sys - -DB = "/home/allaun/physics_equations.db" - -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-4000000") -cur = conn.cursor() - -# Get domain→eq mapping -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -dom_eqs = {} -for d, e in cur.fetchall(): - dom_eqs.setdefault(d, []).append(e) - -cur.execute("SELECT id, name FROM domains") -dom_names = {r[0]: r[1] for r in cur.fetchall()} - -# Get current verification counts per domain -cur.execute(""" - SELECT e.domain_id, COUNT(DISTINCT v.id) - FROM equations e - LEFT JOIN verifications v ON v.equation_id = e.id - WHERE e.domain_id IS NOT NULL - GROUP BY e.domain_id -""") -dom_ver = {r[0]: r[1] for r in cur.fetchall()} - -# ================================================================ -# PRESEED: Domain → [query_string, ...] for every domain -# ================================================================ -PRESEED = { - # CORE PHYSICS — high priority - 1: ["Newton laws experimental verification precision test", - "Lagrangian mechanics experimental validation least action principle", - "Hamiltonian mechanics canonical equations applied"], - 2: ["Newton law gravitation experimental confirmation inverse square test", - "Kepler third law exoplanet mass determination radial velocity"], - 3: ["Coulomb inverse square law experimental limit photon mass null", - "Faraday law induction experimental verification transformer", - "Maxwell displacement current experimental confirmation Hertz"], - 4: ["Carnot theorem efficiency limit experimental validation heat engine", - "Clausius entropy second law thermodynamic experimental proof", - "Gibbs free energy chemical equilibrium experimental validation"], - 5: ["Schrodinger equation experimental verification atomic spectrum hydrogen", - "Heisenberg uncertainty principle experimental test quantum optics", - "Born rule probability experimental confirmation double slit"], - 6: ["Pound Rebka experiment gravitational redshift confirmation", - "Shapiro time delay measurement Cassini spacecraft general relativity", - "Gravity Probe B frame dragging Lense Thirring experimental confirmation"], - 7: ["electron g factor measurement precision test QED anomalous magnetic moment", - "electroweak precision measurement LEP Z boson properties standard model", - "Higgs boson CMS ATLAS combined measurement properties coupling"], - 8: ["Planck CMB anisotropy power spectrum cosmological parameters measurement", - "Hubble constant measurement tension SH0ES local distance ladder", - "dark energy equation state supernova Pantheon DESI BAO measurement"], - 9: ["Kolmogorov turbulence energy spectrum experimental measurement wind tunnel", - "Poiseuille flow experimental verification laminar pipe Hagen Poiseuille", - "Bernoulli equation experimental verification venturi pitot tube"], - 10: ["Snell law refraction experimental measurement refractive index precision", - "Young double slit interference experimental verification wave light", - "Bragg X ray diffraction crystal structure experimental determination"], - - # MATERIAL PHYSICS - 21: ["Hall Petch grain boundary strengthening experimental measurement metals", - "Paris law fatigue crack growth experimental da dN measurement alloy", - "Griffith fracture criterion experimental measurement brittle ceramic", - "Weibull statistics strength distribution ceramic experimental measurement", - "Norton Bailey creep law experimental measurement high temperature alloy"], - 22: ["Debye Waller factor temperature measurement X ray diffraction thermal motion", - "Scherrer equation crystallite size XRD peak broadening measurement", - "Rietveld refinement crystal structure powder diffraction method"], - 23: ["Shockley diode equation I V characteristic measurement silicon germanium", - "MOSFET I V characteristic long channel saturation measurement", - "quantum well confinement energy photoluminescence measurement GaAs", - "Mott transition doping semiconductor metal insulator measurement"], - 24: ["Flory Huggins chi parameter measurement polymer solution scattering", - "WLF time temperature superposition experimental master curve polymer", - "reptation diffusion coefficient polymer NMR measurement de Gennes model"], - 25: ["BET surface area measurement nitrogen adsorption isotherm standard", - "Langmuir adsorption isotherm monolayer coverage measurement", - "contact angle Young equation measurement surface energy Zisman plot", - "DLVO colloid stability force measurement AFM Derjaguin approximation"], - 26: ["Einstein viscosity suspension measurement rigid sphere dilute limit", - "Frank Oseen elastic constant nematic liquid crystal measurement Frederiks"], - 27: ["nucleation rate classical theory Turnbull droplet undercooling measurement", - "Johnson Mehl Avrami kinetics crystallization DSC measurement exponent", - "Ostwald ripening LSW theory precipitate coarsening measurement TEM"], - - # EARTH / ENVIRONMENT - 28: ["Gutenberg Richter b value measurement earthquake catalog global", - "seismic tomography P wave S wave velocity mantle structure measurement", - "Bouguer gravity anomaly continental crustal structure measurement"], - 29: ["Brunt Vaisala frequency atmospheric stability measurement radiosonde lidar", - "geostrophic wind balance measurement upper air radiosonde verification", - "Mie scattering aerosol optical depth measurement sun photometer AERONET"], - 30: ["Ekman transport wind driven ocean current measurement drifter buoy", - "ocean wave dispersion relation measurement waverider buoy spectrum"], - 31: ["Darcy law permeability measurement sand column constant head falling head", - "Richards equation soil moisture measurement TDR neutron probe field", - "Manning roughness coefficient open channel flow measurement river"], - 32: ["patch clamp Hodgkin Huxley model validation squid giant axon measurement", - "FRET Forster resonance energy transfer single molecule distance measurement", - "Michaelis Menten kinetics enzyme steady state parameter measurement"], - 33: ["Marcus electron transfer reorganization energy measurement intervalence", - "Eyring activation enthalpy entropy measurement temperature dependent kinetics"], - 34: ["optical frequency comb precision measurement atomic clock stability", - "Schawlow Townes linewidth laser measurement fundamental quantum limit", - "soliton propagation nonlinear Schrodinger equation fiber experiment"], - 35: ["Lamb shift measurement hydrogen spectroscopy radio frequency", - "Zeeman effect Lande g factor measurement atomic beam magnetic resonance", - "hyperfine structure cesium atomic clock measurement SI second definition"], - 36: ["power law fluid shear thinning viscosity measurement rotational rheometer", - "Bingham plastic yield stress measurement vane rheometer direct", - "Cox Merz rule empirical verification steady dynamic viscosity polymer"], - 37: ["Archard wear coefficient measurement pin disk tribometer standard ASTM", - "Stribeck curve measurement bearing lubrication transition EHL"], - 38: ["Janssen silo pressure measurement granular material saturation depth", - "angle repose granular material measurement funnel method standard", - "Bagnold number granular flow rheology inertial regime measurement"], - 39: ["Coulomb blockade diamond measurement single electron transistor quantum dot", - "Landauer ballistic conductance quantization quantum point contact 2DEG", - "graphene Dirac cone ARPES measurement linear dispersion Fermi velocity"], - 40: ["Bell inequality loophole free test violation measurement entanglement", - "quantum error correction surface code logical qubit fidelity measurement"], - 41: ["Lorenz attractor chaos Rayleigh Benard convection experimental measurement", - "Kuramoto model synchronization transition coupled oscillator experimental"], - 42: ["MRI T1 T2 relaxation time tissue measurement Bloch equation validation", - "proton Bragg peak depth dose measurement therapy spread out SOBP"], - 43: ["Bethe Bloch stopping power measurement proton heavy ion energy loss", - "ionization chamber absolute dosimetry calibration Bragg Gray cavity"], - 44: ["Shockley Queisser limit solar cell record efficiency measurement perovskite", - "Betz limit wind turbine power coefficient measurement field test", - "thermoelectric figure merit ZT measurement material record high"], - 45: ["Parker spiral interplanetary magnetic field measurement Wind ACE spacecraft", - "Van Allen radiation belt electron flux measurement Van Allen probes"], - 46: ["detonation velocity measurement Chapman Jouguet TNT HMX experimental", - "Taylor Sedow blast wave radius measurement nuclear fireball photography"], - 47: ["negative index metamaterial refraction prism experiment measurement", - "transformation optics carpet cloak measurement broadband invisibility"], - 48: ["sonar equation transmission loss measurement underwater acoustic tank", - "sound speed seawater profile measurement CTD conductivity temperature depth"], - 49: ["PID controller Ziegler Nichols tuning experiment process control measurement", - "Nyquist stability criterion gain phase margin measurement control system"], -} - -# ================================================================ -# API FETCHERS — return lists of {title, year, doi, journal, src} -# ================================================================ -def fetch_crossref(q, mx=5): - out = [] - try: - u = "https://api.crossref.org/works?" + urllib.parse.urlencode({ - "query": q, "rows": mx, "sort": "relevance", "filter": "type:journal-article"}) - req = urllib.request.Request(u, headers={"User-Agent": "PhysFill/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(req, timeout=20) as r: - d = json.loads(r.read().decode()) - for i in d.get("message",{}).get("items",[]): - t = (i.get("title",[""]) or [""])[0] - y = i.get("created",{}).get("date-parts",[[0]])[0][0] - doi = i.get("DOI","") - jn = (i.get("container-title",[""]) or [""])[0] - if t: out.append({"t":t[:250],"y":y,"d":doi,"j":jn,"s":"Crossref"}) - except: pass - return out - -def fetch_openalex(q, mx=5): - out = [] - try: - u = "https://api.openalex.org/works?" + urllib.parse.urlencode({ - "search": q, "per_page": mx, "sort": "cited_by_count:desc"}) - req = urllib.request.Request(u, headers={"User-Agent": "mailto:r@x.com"}) - with urllib.request.urlopen(req, timeout=20) as r: - d = json.loads(r.read().decode()) - for i in d.get("results",[]): - t = i.get("title","") - y = i.get("publication_year")or 0 - doi = i.get("doi","") - jn = "" - if i.get("primary_location") and i["primary_location"].get("source"): - jn = i["primary_location"]["source"].get("display_name","") - if t: out.append({"t":t[:250],"y":y,"d":doi,"j":jn,"s":"OpenAlex"}) - except: pass - return out - -def fetch_s2(q, mx=5): - out = [] - try: - u = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode({ - "query": q, "limit": mx, "fields": "title,year,externalIds,journal,citationCount"}) - req = urllib.request.Request(u, headers={"User-Agent": "PhysFill/1.0"}) - with urllib.request.urlopen(req, timeout=20) as r: - d = json.loads(r.read().decode()) - for p in d.get("data",[]): - e = p.get("externalIds",{}) or {} - jn = p.get("journal",{}) or {} - out.append({"t":p.get("title","")[:250],"y":p.get("year")or 0, - "d":e.get("DOI",""),"j":jn.get("name",""),"s":"S2"}) - except: pass - return out - -def fetch_europepmc(q, mx=5): - out = [] - try: - u = "https://www.ebi.ac.uk/europepmc/webservices/rest/search?" + urllib.parse.urlencode({ - "query": q, "resultType": "core", "pageSize": mx, "format": "json"}) - req = urllib.request.Request(u, headers={"User-Agent": "PhysFill/1.0"}) - with urllib.request.urlopen(req, timeout=20) as r: - d = json.loads(r.read().decode()) - for i in d.get("resultList",{}).get("result",[]): - t = i.get("title","") - y = int(i.get("firstPublicationDate","0")[:4]) if i.get("firstPublicationDate") else 0 - doi = i.get("doi","") - jn = i.get("journalTitle","") - if t: out.append({"t":t[:250],"y":y,"d":doi,"j":jn,"s":"EuropePMC"}) - except: pass - return out - -# ================================================================ -# MAIN LOOP — API ROTATION -# ================================================================ -# Build flattened task queue: (domain_id, query_string) -tasks = [] -for did, queries in PRESEED.items(): - for q in queries: - tasks.append((did, q)) - -# Rotate APIs -apis = [ - (fetch_crossref, 1.8, "Crossref"), - (fetch_openalex, 2.0, "OpenAlex"), - (fetch_s2, 2.0, "S2"), - (fetch_europepmc, 1.8, "EuropePMC"), - (fetch_openalex, 2.0, "OpenAlex"), - (fetch_crossref, 1.8, "Crossref"), - (fetch_s2, 2.0, "S2"), - (fetch_openalex, 2.0, "OpenAlex"), -] - -print(f"PRESEEDED BATCH FETCH") -print(f"{len(tasks)} queries across {len(PRESEED)} domains") -print(f"APIs: Crossref, OpenAlex, S2, EuropePMC") -print(f"Rate: ~1 req / 2s | ETA: {len(tasks)*2/60:.0f} min\n") - -total, batch = 0, [] -start = time.time() -prev_did = None - -for idx, (did, query) in enumerate(tasks): - fn, delay, name = apis[idx % len(apis)] - papers = fn(query, 5) - eqs = dom_eqs.get(did, [None]) - - for i, p in enumerate(papers): - eq_id = eqs[i % len(eqs)] if eqs else None - batch.append((eq_id, p['t'], - f"{p['s']}: {p['j']}" if p.get('j') else p['s'], - p['y'], p.get('d', p['s']), "Multi-API preseed")) - total += 1 - - # Print domain header when switching - if did != prev_did: - name = dom_names.get(did, f"#{did}") - prev_did = did - print(f"\n── {name} (gap: {dom_ver.get(did,0)} verifications) ──", flush=True) - - eta = (len(tasks) - idx) * delay - mark = "✓" if papers else "○" - print(f" {mark} {name:10s} › {query[:72]:72s} → {len(papers)}p | {total:4d} | {eta:.0f}s left", flush=True) - - # Flush every 30 records - if len(batch) >= 30: - cur.executemany( - """INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) - VALUES (?, ?, ?, ?, ?, ?)""", batch) - conn.commit() - batch = [] - - time.sleep(delay) - -# Final flush -if batch: - cur.executemany("""INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) - VALUES (?, ?, ?, ?, ?, ?)""", batch) - conn.commit() - -# ================================================================ -# FINAL STATS -# ================================================================ -cur.execute("SELECT COUNT(*) FROM verifications") -total_v = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations") -total_e = cur.fetchone()[0] -elapsed = time.time() - start - -print(f"\n{'═'*70}") -print(f"✓ COMPLETE — {total_v} verifications, {total_e} equations, {total} added this run") -print(f" Time: {elapsed:.0f}s ({elapsed/60:.1f} min)") -print(f" Throughput: {len(tasks)/elapsed*60:.1f} queries/min") - -# Show domain coverage now -cur.execute(""" - SELECT d.name, COUNT(DISTINCT e.id), COUNT(DISTINCT v.id), - ROUND(COUNT(DISTINCT v.id)*1.0/MAX(COUNT(DISTINCT e.id),1),1) - FROM domains d - LEFT JOIN equations e ON e.domain_id = d.id - LEFT JOIN verifications v ON v.equation_id = e.id - WHERE e.id IS NOT NULL - GROUP BY d.id - ORDER BY COUNT(DISTINCT v.id) DESC -""") -print(f"\nDOMAIN COVERAGE (after fill):") -for row in cur.fetchall(): - bar = "█" * int(row[3]) - print(f" {row[0]:32s} {row[2]:5d} ver / {row[1]:3d} eqs = {row[3]:4.1f}x {bar}") - -conn.close() -print(f"\n {DB}") diff --git a/5-Applications/scripts/probe_backdoor.py b/5-Applications/scripts/probe_backdoor.py deleted file mode 100644 index 1df18e3c..00000000 --- a/5-Applications/scripts/probe_backdoor.py +++ /dev/null @@ -1,42 +0,0 @@ -import usb.core -import usb.util -import sys - -VENDOR_ID = 0x048d -PRODUCT_ID = 0x1234 - -def probe_backdoor(): - dev = usb.core.find(idVendor=VENDOR_ID, idProduct=PRODUCT_ID) - if dev is None: - print("Device not found") - return - - print(f"Found Device: {hex(VENDOR_ID)}:{hex(PRODUCT_ID)}") - - if dev.is_kernel_driver_active(0): - dev.detach_kernel_driver(0) - - # bRequest = 0x06 (Execute Vendor Command) - # wValue = Command Code - # wIndex = Sub-command - # wLength = Data Length - - commands = [ - (0x06, 0x01, 0, 6, "Read NAND ID"), - (0x06, 0x01, 0x01, 6, "Read NAND ID Alt"), - (0x01, 0, 0, 1, "Read Register 0"), - (0x02, 0, 0, 1, "Read Register 0 Alt"), - ] - - for req, val, idx, length, desc in commands: - print(f"[*] Testing {desc} (Req: {hex(req)}, Val: {hex(val)})...") - try: - # 0xC0 = Device-to-Host, Vendor, Device - ret = dev.ctrl_transfer(0xC0, req, val, idx, length, timeout=1000) - if ret: - print(f" [+] Success! Data: {bytes(ret).hex().upper()}") - except usb.core.USBError as e: - print(f" [-] Failed: {e}") - -if __name__ == "__main__": - probe_backdoor() diff --git a/5-Applications/scripts/probe_usb_controller.py b/5-Applications/scripts/probe_usb_controller.py deleted file mode 100644 index add71d32..00000000 --- a/5-Applications/scripts/probe_usb_controller.py +++ /dev/null @@ -1,52 +0,0 @@ -import usb.core -import usb.util -import sys - -# Chipsbank / ITE CBM2199 -# VID: 048d, PID: 1234 -VENDOR_ID = 0x048d -PRODUCT_ID = 0x1234 - -def probe_registers(): - # Find device - dev = usb.core.find(idVendor=VENDOR_ID, idProduct=PRODUCT_ID) - - if dev is None: - print("Device not found") - return - - print(f"Found Device: {hex(VENDOR_ID)}:{hex(PRODUCT_ID)}") - - # Detach kernel driver if necessary - if dev.is_kernel_driver_active(0): - try: - dev.detach_kernel_driver(0) - print("Detached kernel driver") - except usb.core.USBError as e: - print(f"Could not detach kernel driver: {e}") - - # bmRequestType: 0xC0 (Device to Host, Vendor, Device) - # bRequest: 0x01 (Read Register) - # wValue: Register Address - # wIndex: 0 - # wLength: 1 or 4 - - print("\nRegister Sweep (0x00 - 0x1F):") - for reg in range(0x20): - try: - # Try reading 1 byte - ret = dev.ctrl_transfer(0xC0, 0x01, reg, 0, 1) - if ret: - print(f"Reg {hex(reg)}: {hex(ret[0])}") - except usb.core.USBError as e: - # print(f"Reg {hex(reg)}: Error {e}") - pass - - # Try standard inquiry via control transfer if 0x01 fails - # Or try 0x06 Exec command - - # Re-attach kernel driver - usb.util.dispose_resources(dev) - -if __name__ == "__main__": - probe_registers() diff --git a/5-Applications/scripts/profile_device.py b/5-Applications/scripts/profile_device.py deleted file mode 100644 index afc583e1..00000000 --- a/5-Applications/scripts/profile_device.py +++ /dev/null @@ -1,225 +0,0 @@ -#!/usr/bin/env python3 -""" -Hardware Profiling Script for FoxTop Device - -Profiles GPU, CPU, memory, and other hardware capabilities for exploitation -in distributed computing tasks. -""" -import subprocess -import json -import logging -import sys - -logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') -logger = logging.getLogger("DeviceProfiler") - -def run_command(cmd, description): - """Run a command and return the result.""" - logger.info(f"Running: {description}") - try: - result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=30) - if result.returncode == 0: - return result.stdout.strip() - else: - logger.warning(f"Command failed: {result.stderr}") - return None - except subprocess.TimeoutExpired: - logger.warning(f"Command timed out: {description}") - return None - except Exception as e: - logger.error(f"Exception running command: {e}") - return None - -def profile_cpu(): - """Profile CPU capabilities.""" - logger.info("=" * 60) - logger.info("CPU PROFILING") - logger.info("=" * 60) - - cpu_info = {} - - # CPU model - cpu_info['model'] = run_command("cat /proc/cpuinfo | grep 'model name' | head -n 1", "CPU Model") - - # CPU cores - cpu_info['cores'] = run_command("nproc", "CPU Cores") - - # CPU architecture - cpu_info['architecture'] = run_command("uname -m", "CPU Architecture") - - # CPU frequency - cpu_info['frequency'] = run_command("cat /proc/cpuinfo | grep 'cpu MHz' | head -n 1", "CPU Frequency") - - # CPU flags (features) - cpu_info['flags'] = run_command("cat /proc/cpuinfo | grep 'flags' | head -n 1", "CPU Flags") - - return cpu_info - -def profile_gpu(): - """Profile GPU capabilities.""" - logger.info("=" * 60) - logger.info("GPU PROFILING") - logger.info("=" * 60) - - gpu_info = {} - - # Check for NVIDIA GPU - gpu_info['nvidia_smi'] = run_command("nvidia-smi --query-gpu=name,memory.total,driver_version,cuda_version --format=csv,noheader", "NVIDIA GPU Info") - - # Check for AMD GPU - gpu_info['amd_gpu'] = run_command("rocm-smi --showid", "AMD GPU Info") - - # Check for Intel GPU - gpu_info['intel_gpu'] = run_command("intel_gpu_top", "Intel GPU Info") - - # Check for general GPU info - gpu_info['lspci_gpu'] = run_command("lspci | grep -i vga", "PCI GPU Devices") - - # Check for CUDA availability - gpu_info['cuda_devices'] = run_command("python3 -c 'import torch; print(torch.cuda.device_count())' 2>/dev/null", "CUDA Device Count") - - # Check for PyTorch GPU support - gpu_info['pytorch_gpu'] = run_command("python3 -c 'import torch; print(torch.cuda.is_available())' 2>/dev/null", "PyTorch GPU Support") - - return gpu_info - -def profile_memory(): - """Profile memory capabilities.""" - logger.info("=" * 60) - logger.info("MEMORY PROFILING") - logger.info("=" * 60) - - memory_info = {} - - # Total memory - memory_info['total'] = run_command("free -h | grep Mem | awk '{print $2}'", "Total Memory") - - # Available memory - memory_info['available'] = run_command("free -h | grep Mem | awk '{print $7}'", "Available Memory") - - # Swap memory - memory_info['swap'] = run_command("free -h | grep Swap | awk '{print $2}'", "Swap Memory") - - return memory_info - -def profile_storage(): - """Profile storage capabilities.""" - logger.info("=" * 60) - logger.info("STORAGE PROFILING") - logger.info("=" * 60) - - storage_info = {} - - # Disk space - storage_info['disk'] = run_command("df -h | grep -E '^/dev/' | head -n 5", "Disk Space") - - # Disk type (SSD/HDD) - storage_info['disk_type'] = run_command("lsblk -o NAME,ROTA | grep -v 'ROTA'", "Disk Type") - - # I/O scheduler - storage_info['io_scheduler'] = run_command("cat /sys/block/sda/queue/scheduler 2>/dev/null || cat /sys/block/nvme0n1/queue/scheduler 2>/dev/null", "I/O Scheduler") - - return storage_info - -def profile_network(): - """Profile network capabilities.""" - logger.info("=" * 60) - logger.info("NETWORK PROFILING") - logger.info("=" * 60) - - network_info = {} - - # Network interfaces - network_info['interfaces'] = run_command("ip -br addr show", "Network Interfaces") - - # Network speed - network_info['ethtool'] = run_command("ethtool $(ip route | grep default | awk '{print $5}') 2>/dev/null | grep Speed", "Network Speed") - - # Tailscale status - network_info['tailscale'] = run_command("tailscale status --json 2>/dev/null | head -n 20", "Tailscale Status") - - return network_info - -def profile_system(): - """Profile general system information.""" - logger.info("=" * 60) - logger.info("SYSTEM PROFILING") - logger.info("=" * 60) - - system_info = {} - - # OS - system_info['os'] = run_command("cat /etc/os-release | grep PRETTY_NAME", "Operating System") - - # Kernel - system_info['kernel'] = run_command("uname -r", "Kernel Version") - - # Uptime - system_info['uptime'] = run_command("uptime -p", "System Uptime") - - # Load average - system_info['load'] = run_command("cat /proc/loadavg", "Load Average") - - return system_info - -def profile_exploitable_features(): - """Profile features that can be exploited for distributed computing.""" - logger.info("=" * 60) - logger.info("EXPLOITABLE FEATURES PROFILING") - logger.info("=" * 60) - - exploitable = {} - - # Docker availability - exploitable['docker'] = run_command("docker --version", "Docker") - - # Docker GPU support - exploitable['nvidia_docker'] = run_command("docker run --rm --gpus all nvidia/cuda:11.0-base nvidia-smi 2>/dev/null", "NVIDIA Docker") - - # Kubernetes - exploitable['kubernetes'] = run_command("kubectl version --client 2>/dev/null", "Kubernetes") - - # MPI - exploitable['mpi'] = run_command("mpirun --version 2>/dev/null", "MPI") - - # OpenCL - exploitable['opencl'] = run_command("clinfo 2>/dev/null | head -n 10", "OpenCL") - - # Vulkan - exploitable['vulkan'] = run_command("vulkaninfo 2>/dev/null | head -n 10", "Vulkan") - - # Python packages - exploitable['torch'] = run_command("python3 -c 'import torch; print(torch.__version__)' 2>/dev/null", "PyTorch") - exploitable['tensorflow'] = run_command("python3 -c 'import tensorflow as tf; print(tf.__version__)' 2>/dev/null", "TensorFlow") - exploitable['jax'] = run_command("python3 -c 'import jax; print(jax.__version__)' 2>/dev/null", "JAX") - - return exploitable - -def main(): - """Main profiling function.""" - logger.info("Starting FoxTop Device Hardware Profiling") - - profile = { - 'system': profile_system(), - 'cpu': profile_cpu(), - 'gpu': profile_gpu(), - 'memory': profile_memory(), - 'storage': profile_storage(), - 'network': profile_network(), - 'exploitable': profile_exploitable_features() - } - - logger.info("=" * 60) - logger.info("PROFILE SUMMARY") - logger.info("=" * 60) - - print(json.dumps(profile, indent=2)) - - # Save to file - with open('/tmp/foxtop_profile.json', 'w') as f: - json.dump(profile, f, indent=2) - - logger.info("Profile saved to /tmp/foxtop_profile.json") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/project_general_review.py b/5-Applications/scripts/project_general_review.py deleted file mode 100644 index e0d54053..00000000 --- a/5-Applications/scripts/project_general_review.py +++ /dev/null @@ -1,95 +0,0 @@ -#!/usr/bin/env python3 -""" -project_general_review.py -========================= - -Performs a general architectural and code quality review of the Research Stack -using the 'default' local Ollama model. -""" - -import json -import requests -from pathlib import Path - -OLLAMA_HOST = "http://localhost:11434" -MODEL_NAME = "default" - -def query_model(prompt: str): - url = f"{OLLAMA_HOST}/api/generate" - payload = { - "model": MODEL_NAME, - "system": "You are a senior software architect and formal methods expert. Your goal is to review the Sovereign Research Stack and provide strategic feedback on architecture, code quality, and alignment with the project's vision of 'One Truth Only'.", - "prompt": prompt, - "stream": False, - "options": { - "temperature": 0.2, - "num_ctx": 32768 - } - } - - resp = requests.post(url, json=payload, timeout=1200) - resp.raise_for_status() - return resp.json()["response"] - -def main(): - root = Path("/home/allaun/Documents/Research Stack") - project_map = (root / "PROJECT_MAP.md").read_text() - - # Core semantic files - core_files = [ - "0-Core-Formalism/lean/Semantics/Semantics/FixedPoint.lean", - "0-Core-Formalism/lean/Semantics/Semantics/GeneticGroundUp.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Testing/FixedPointTest.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Testing/GeneticGroundUpTest.lean" - ] - - file_contents = "" - for f in core_files: - p = root / f - if p.exists(): - file_contents += f"\n--- FILE: {f} ---\n" - file_contents += p.read_text() - file_contents += "\n" - - prompt = f""" -You are reviewing the Sovereign Research Stack. -The project aims for a 'One Truth Only' model (OTOM) where Lean 4 is the canonical truth. - -### PROJECT MAP -{project_map} - -### CORE SEMANTIC MODULES -{file_contents} - -**Review Task:** -1. **Architectural Alignment**: Does the Q16.16 arithmetic core and the Genetic Ground Up redesign align with the project goals? -2. **Code Quality**: Evaluate the Lean 4 implementations. Are they idiomatic? Are the proofs robust? -3. **Consistency**: Is there consistency between the arithmetic substrate (FixedPoint) and the higher-level genetic semantics? -4. **Strategic Risks**: Identify any "deceptively simple" areas that might hide future complexity or unsoundness. -5. **Recommendations**: Suggest next steps for formalizing the "Sharded Genome" or the "Metabolic GNN". - -Provide a comprehensive review report. -""" - - print(f"Querying {MODEL_NAME} for project review...") - try: - response = query_model(prompt) - - print("\n" + "="*80) - print("SOVEREIGN RESEARCH STACK - ARCHITECTURAL REVIEW") - print("="*80) - print(response) - print("="*80) - - # Save to artifact - out_dir = root / "shared-data/artifacts/review" - out_dir.mkdir(parents=True, exist_ok=True) - out_file = out_dir / "project_architectural_review.md" - out_file.write_text(response) - print(f"\nReview saved to: {out_file}") - - except Exception as e: - print(f"Error during review: {e}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/pulsar_chandelier_model.py b/5-Applications/scripts/pulsar_chandelier_model.py deleted file mode 100644 index 43bc5f93..00000000 --- a/5-Applications/scripts/pulsar_chandelier_model.py +++ /dev/null @@ -1,581 +0,0 @@ -#!/usr/bin/env python3 -""" -Pulsar Chandelier Model — Genus-3 Topology + Superfluid Vortex Dynamics - -Physical basis: -- Two-component neutron star: rigid crust + superfluid interior -- Angular momentum conserved globally, redistributed between components -- Vortices in superfluid carry quantized circulation -- When vortex tension exceeds pinning strength: unpinning avalanche = "flash" -- Magnetic dipole braking provides slow damping -- Genus-3 surface embeds the vortex array topology -- Blue/red shift from relativistic rotational beaming - -No "up/down" — only torsional direction (angular momentum axis). -"Depth" = proximity to vortex cluster center = higher energy density. - -References: -[1] Manchester, R. 2017, "Millisecond Pulsars, their Evolution and Applications" -[2] Liu et al. 2023, "On the Spin Period Distribution of Millisecond Pulsars" -[3] Alpar et al. 1984, "Glitches in Pulsar Spin" -""" - -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.patches import Circle -from mpl_toolkits.mplot3d import Axes3D -from matplotlib.colors import Normalize -from matplotlib.cm import ScalarMappable -import json -import os -from datetime import datetime - -# ─── Physical Constants (scaled) ───────────────────────────────────────────── -G = 1.0 # Gravitational constant (scaled) -C = 10.0 # Speed of light (scaled so v < C always) -H_BAR = 0.1 # Reduced Planck constant (scaled) -M_STAR = 100.0 # Stellar mass (scaled) -R_STAR = 5.0 # Stellar radius (scaled) -I_CRUST = 50.0 # Crust moment of inertia -I_SF = 100.0 # Superfluid moment of inertia -K_DIPOLE = 0.001 # Magnetic dipole braking coefficient -T_PIN = 2.5 # Critical vortex pinning tension (flash threshold) -ETA_VISC = 0.01 # Crust-superfluid coupling viscosity -N_VORTICES = 512 # Number of vortices in superfluid - -# ─── Genus-3 Surface Parametrization ───────────────────────────────────────── -def genus3_surface(u, v, R=3.0, r=1.0, p=0.6): - """ - Parametric genus-3 surface (3-lobed torus). - u, v in [0, 2π]. - Genus = number of holes = 3. - """ - rho = R + r * np.cos(v) + p * np.cos(3 * u) - x = rho * np.cos(u) - y = rho * np.sin(u) - z = r * np.sin(v) + 0.3 * np.sin(3 * u) - return x, y, z - - -def genus3_normal(u, v, R=3.0, r=1.0, p=0.6, h=1e-5): - """Compute unit normal vector at surface point (u,v).""" - # Central difference for partial derivatives - xp, yp, zp = genus3_surface(u + h, v, R, r, p) - xm, ym, zm = genus3_surface(u - h, v, R, r, p) - xu = (xp - xm) / (2 * h) - yu = (yp - ym) / (2 * h) - zu = (zp - zm) / (2 * h) - - xp, yp, zp = genus3_surface(u, v + h, R, r, p) - xm, ym, zm = genus3_surface(u, v - h, R, r, p) - xv = (xp - xm) / (2 * h) - yv = (yp - ym) / (2 * h) - zv = (zp - zm) / (2 * h) - - # Cross product - nx = yu * zv - zu * yv - ny = zu * xv - xu * zv - nz = xu * yv - yu * xv - n = np.sqrt(nx**2 + ny**2 + nz**2) - return np.array([nx, ny, nz]) / n - - -def genus3_mesh(n_u=120, n_v=60): - """Generate meshgrid for genus-3 surface.""" - u = np.linspace(0, 2*np.pi, n_u) - v = np.linspace(0, 2*np.pi, n_v) - U, V = np.meshgrid(u, v) - X, Y, Z = genus3_surface(U, V) - return X, Y, Z, U, V - - -# ─── Torsional Curvature ("Depth" Measure) ────────────────────────────────── -def torsional_depth(u, v): - """ - Measure of local 'depth' = torsional curvature concentration. - Higher where lobes pinch / vortices cluster. - Peaks near the three lobe centers (u = 0, 2π/3, 4π/3). - """ - # Three lobe centers - d0 = np.minimum(np.abs(u - 0), np.abs(u - 2*np.pi)) - d1 = np.abs(u - 2*np.pi/3) - d2 = np.abs(u - 4*np.pi/3) - d_min = np.minimum(np.minimum(d0, d1), d2) - - # Depth = inverse distance to nearest lobe center, modulated by v - depth = 3.0 / (1.0 + 5.0 * d_min) * (1.0 + 0.3 * np.cos(v)) - return depth - - -# ─── Vortex Class ──────────────────────────────────────────────────────────── -class Vortex: - """A quantized vortex line in the superfluid.""" - - def __init__(self, u, v, circulation=1.0): - self.u = u % (2 * np.pi) - self.v = v % (2 * np.pi) - self.circulation = circulation # Quantized: n * h/m - self.tension = 0.0 # Local pinning tension - self.pinned = True # Pinned to crustal nuclei - self.age = 0 - - def position(self): - x, y, z = genus3_surface(self.u, self.v) - return np.array([x, y, z]) - - def move(self, du, dv, dt): - """Advect vortex on surface (toroidal + poloidal drift).""" - # Vortices drift with local superfluid velocity - self.u = (self.u + du * dt) % (2 * np.pi) - self.v = (self.v + dv * dt) % (2 * np.pi) - self.age += dt - - def compute_tension(self, omega_sf, local_depth): - """ - Tension = Magnus force + pinning + vortex-vortex interaction. - Flash occurs when tension exceeds critical. - """ - # Magnus force ~ ρ_s × (v_sf - v_crust) × κ - magnus = abs(omega_sf) * self.circulation * (1.0 + 0.5 * local_depth) - - # Vortex-vortex repulsion (simplified: proportional to local density) - # Computed externally and passed as part of local_depth - repulsion = 0.3 * local_depth ** 2 - - self.tension = magnus + repulsion - return self.tension - - -# ─── Two-Component Pulsar Model ────────────────────────────────────────────── -class PulsarChandelier: - """ - Two-component pulsar: crust + superfluid. - Angular momentum conserved. Energy tracked. - """ - - def __init__(self, n_vortices=N_VORTICES): - self.omega_crust = 2.0 * np.pi * 2.1 # Initial: 2.1 Hz (disco pulse!) - self.omega_sf = self.omega_crust * 1.001 # Superfluid slightly ahead (vortex creep) - self.I_crust = I_CRUST - self.I_sf = I_SF - - # Total angular momentum (conserved!) - self.L_total = self.I_crust * self.omega_crust + self.I_sf * self.omega_sf - - # Vortex array - self.vortices = [] - self._init_vortices(n_vortices) - - # State tracking - self.time = 0.0 - self.dt = 0.01 - self.flashes = [] # (time, energy_released, n_unpinned) - self.history = { - 't': [], - 'omega_crust': [], - 'omega_sf': [], - 'E_rot': [], - 'E_mag': [], - 'L_total': [], - 'n_pinned': [], - 'tension_max': [], - 'flash_count': [] - } - - # Phase transition counters - self.tier_boundaries = [1.5, 2.5, 4.0] # Tension thresholds for tier flashes - self.tier_flash_count = [0, 0, 0] - - def _init_vortices(self, n): - """Initialize vortices uniformly, then let them cluster.""" - # Start with some clustering near lobe centers - for i in range(n): - if np.random.rand() < 0.4: - # Cluster near one of three lobe centers - lobe = np.random.choice(3) - u_center = lobe * 2 * np.pi / 3 - u = u_center + np.random.normal(0, 0.3) - v = np.pi + np.random.normal(0, 0.5) - else: - u = np.random.uniform(0, 2*np.pi) - v = np.random.uniform(0, 2*np.pi) - self.vortices.append(Vortex(u, v, circulation=H_BAR * (1 + np.random.poisson(0.5)))) - - @property - def omega_crust(self): - return self._omega_crust - - @omega_crust.setter - def omega_crust(self, val): - self._omega_crust = val - - @property - def omega_sf(self): - return self._omega_sf - - @omega_sf.setter - def omega_sf(self, val): - self._omega_sf = val - - def rotational_energy(self): - return 0.5 * self.I_crust * self.omega_crust**2 + 0.5 * self.I_sf * self.omega_sf**2 - - def magnetic_energy(self): - """Dipole magnetic energy ~ B² ~ ω² (simplified braking model).""" - return 0.1 * self.omega_crust**2 - - def total_energy(self): - return self.rotational_energy() + self.magnetic_energy() - - def check_angular_momentum(self): - """Verify L is conserved (debug check).""" - L_now = self.I_crust * self.omega_crust + self.I_sf * self.omega_sf - deviation = abs(L_now - self.L_total) - return deviation < 1e-3, deviation - - def vortex_cluster_density(self): - """Compute local vortex density on a grid for tension calculation.""" - n_grid = 48 - u_grid = np.linspace(0, 2*np.pi, n_grid) - v_grid = np.linspace(0, 2*np.pi, n_grid) - density = np.zeros((n_grid, n_grid)) - - for vtx in self.vortices: - iu = int((vtx.u / (2*np.pi)) * n_grid) % n_grid - iv = int((vtx.v / (2*np.pi)) * n_grid) % n_grid - density[iu, iv] += vtx.circulation - - # Smooth - from scipy.ndimage import gaussian_filter - density = gaussian_filter(density, sigma=1.5, mode='wrap') - return u_grid, v_grid, density - - def step(self): - """One integration step.""" - dt = self.dt - - # 1. Magnetic dipole braking on crust: dω/dt = -K·ω³/I - braking = -K_DIPOLE * self.omega_crust**3 / self.I_crust - self.omega_crust += braking * dt - - # 2. Vortex creep: superfluid tries to spin down slower than crust - # Vortices slowly move outward, transferring angular momentum - delta_omega = self.omega_sf - self.omega_crust - coupling = ETA_VISC * delta_omega - - # 3. Update vortex positions (drift with superfluid) - u_grid, v_grid, density = self.vortex_cluster_density() - - n_unpinned = 0 - flash_energy = 0.0 - - for vtx in self.vortices: - # Local depth and density - iu = int((vtx.u / (2*np.pi)) * len(u_grid)) % len(u_grid) - iv = int((vtx.v / (2*np.pi)) * len(v_grid)) % len(v_grid) - local_depth = torsional_depth(vtx.u, vtx.v) - local_density = density[iu, iv] - - # Compute tension - tension = vtx.compute_tension(self.omega_sf, local_depth + 0.1 * local_density) - - # Vortex drift velocity (radial outward + azimuthal) - # Outward drift: vortices move to larger u where density is lower - du = 0.05 * self.omega_sf + 0.02 * np.sin(3 * vtx.u) - dv = 0.01 * np.cos(vtx.v) * local_depth - - if vtx.pinned: - # Check for unpinning (flash condition) - if tension > T_PIN: - vtx.pinned = False - n_unpinned += 1 - # Unpinning releases energy: vortex sudden motion - flash_energy += 0.5 * vtx.circulation * tension**2 - # Sudden angular momentum transfer: superfluid → crust - # Small glitch: ~0.1% of local vortex angular momentum - dL = vtx.circulation * self.omega_sf * 0.01 - self.omega_crust += dL / self.I_crust - self.omega_sf -= dL / self.I_sf - else: - # Pinned vortices don't move (coupled to crust) - du *= 0.1 - dv *= 0.1 - - vtx.move(du, dv, dt) - - # 4. Recouple superfluid to crust (viscous relaxation) - self.omega_sf -= coupling * dt / self.I_sf - self.omega_crust += coupling * dt / self.I_crust - - # 5. Enforce angular momentum conservation exactly - L_now = self.I_crust * self.omega_crust + self.I_sf * self.omega_sf - delta_L = self.L_total - L_now - # Distribute correction: same dω to both preserves L most naturally - domega = delta_L / (self.I_crust + self.I_sf) - self.omega_crust += domega - self.omega_sf += domega - - # 6. Record flash - if n_unpinned > 0: - self.flashes.append((self.time, flash_energy, n_unpinned)) - # Count tier flashes - for idx, threshold in enumerate(self.tier_boundaries): - if flash_energy > threshold: - self.tier_flash_count[idx] += 1 - - # 7. Record history - self.history['t'].append(self.time) - self.history['omega_crust'].append(self.omega_crust) - self.history['omega_sf'].append(self.omega_sf) - self.history['E_rot'].append(self.rotational_energy()) - self.history['E_mag'].append(self.magnetic_energy()) - self.history['L_total'].append(self.L_total) - self.history['n_pinned'].append(sum(1 for v in self.vortices if v.pinned)) - self.history['tension_max'].append(max(v.tension for v in self.vortices)) - self.history['flash_count'].append(len(self.flashes)) - - self.time += dt - - def run(self, t_max=100.0): - """Run simulation.""" - n_steps = int(t_max / self.dt) - for i in range(n_steps): - self.step() - if i % 1000 == 0: - L_ok, L_dev = self.check_angular_momentum() - print(f" t={self.time:.2f}, ω_crust={self.omega_crust:.4f}, " - f"flashes={len(self.flashes)}, L_dev={L_dev:.2e}") - - print(f"\nSimulation complete.") - print(f" Total flashes: {len(self.flashes)}") - print(f" Final ω_crust: {self.omega_crust:.4f} rad/s ({self.omega_crust/(2*np.pi):.4f} Hz)") - print(f" Angular momentum conserved: {self.check_angular_momentum()}") - print(f" Energy change: {self.history['E_rot'][-1] - self.history['E_rot'][0]:.4f}") - print(f" Tier flash counts: {self.tier_flash_count}") - - -# ─── Visualization ─────────────────────────────────────────────────────────── -def visualize(pulsar, out_dir="/home/allaun/Documents/Research Stack/out"): - os.makedirs(out_dir, exist_ok=True) - - # 1. Main figure: 3D genus-3 surface with vortices colored by Doppler shift - fig = plt.figure(figsize=(18, 12)) - - # ── Panel A: 3D Surface with Vortices ────────────────────────────────── - ax1 = fig.add_subplot(2, 3, 1, projection='3d') - X, Y, Z, U, V = genus3_mesh(n_u=80, n_v=40) - - # Surface colored by torsional depth (the "basin") - depth_map = torsional_depth(U, V) - surf = ax1.plot_surface(X, Y, Z, facecolors=plt.cm.RdYlBu_r(depth_map / depth_map.max()), - alpha=0.4, rstride=2, cstride=2, linewidth=0.1) - - # Vortex positions with Doppler shift coloring - # Blue shift = approaching (high ω, near rotation axis) - # Red shift = receding - vtx_pos = np.array([v.position() for v in pulsar.vortices]) - vtx_tensions = np.array([v.tension for v in pulsar.vortices]) - - # Doppler factor from rotational velocity - # v_phi = ω × r_perp, blueshift when moving toward observer (+x direction) - if len(vtx_pos) > 0: - r_perp = np.sqrt(vtx_pos[:, 1]**2 + vtx_pos[:, 2]**2) - v_phi = pulsar.omega_crust * r_perp - # Simplified Doppler: project onto x-axis (observer at +x) - doppler = v_phi * np.sign(vtx_pos[:, 1]) / C # β = v/c - doppler = np.clip(doppler, -0.3, 0.3) - - scatter = ax1.scatter(vtx_pos[:, 0], vtx_pos[:, 1], vtx_pos[:, 2], - c=doppler, cmap='RdBu_r', s=20 + 80 * vtx_tensions / T_PIN, - alpha=0.9, edgecolors='black', linewidth=0.3) - plt.colorbar(scatter, ax=ax1, shrink=0.5, label='Doppler shift β=v/c') - - ax1.set_title('Genus-3 Surface: Vortices (color=Doppler, size=tension)') - ax1.set_xlabel('X') - ax1.set_ylabel('Y') - ax1.set_zlabel('Z') - - # ── Panel B: ω vs Time ───────────────────────────────────────────────── - ax2 = fig.add_subplot(2, 3, 2) - t = np.array(pulsar.history['t']) - ax2.plot(t, np.array(pulsar.history['omega_crust']) / (2*np.pi), 'b-', label='Crust ω', linewidth=1) - ax2.plot(t, np.array(pulsar.history['omega_sf']) / (2*np.pi), 'r--', label='Superfluid ω', linewidth=1, alpha=0.7) - - # Mark flash events - for flash_time, flash_energy, n_unpinned in pulsar.flashes: - ax2.axvline(flash_time, color='orange', alpha=0.3, linewidth=0.5) - ax2.set_xlabel('Time') - ax2.set_ylabel('Spin frequency (Hz)') - ax2.set_title('Rotational Evolution (2.1 Hz → slower)') - ax2.legend() - ax2.set_yscale('log') - - # ── Panel C: Energy Budget ───────────────────────────────────────────── - ax3 = fig.add_subplot(2, 3, 3) - E_rot = np.array(pulsar.history['E_rot']) - E_mag = np.array(pulsar.history['E_mag']) - ax3.plot(t, E_rot, 'g-', label='Rotational E') - ax3.plot(t, E_mag, 'm-', label='Magnetic E') - ax3.plot(t, E_rot + E_mag, 'k--', label='Total E', linewidth=1.5) - ax3.set_xlabel('Time') - ax3.set_ylabel('Energy') - ax3.set_title('Energy Conservation') - ax3.legend() - - # ── Panel D: Angular Momentum (should be flat!) ──────────────────────── - ax4 = fig.add_subplot(2, 3, 4) - L = np.array(pulsar.history['L_total']) - ax4.plot(t, L, 'k-', linewidth=1.5) - ax4.set_xlabel('Time') - ax4.set_ylabel('Angular Momentum') - ax4.set_title(f'L Conservation (deviation: {np.std(L):.2e})') - - # ── Panel E: Tension Distribution / Phase Transitions ────────────────── - ax5 = fig.add_subplot(2, 3, 5) - tensions = [v.tension for v in pulsar.vortices] - ax5.hist(tensions, bins=50, color='steelblue', edgecolor='black', alpha=0.7) - for threshold in pulsar.tier_boundaries: - ax5.axvline(threshold, color='red', linestyle='--', label=f'Tier {threshold}') - ax5.set_xlabel('Vortex Tension') - ax5.set_ylabel('Count') - ax5.set_title('Final Tension Distribution') - ax5.set_yscale('log') - - # ── Panel F: Flash Events Timeline ───────────────────────────────────── - ax6 = fig.add_subplot(2, 3, 6) - if pulsar.flashes: - flash_times = [f[0] for f in pulsar.flashes] - flash_energies = [f[1] for f in pulsar.flashes] - flash_counts = [f[2] for f in pulsar.flashes] - - colors = [] - for e in flash_energies: - if e > pulsar.tier_boundaries[2]: - colors.append('red') - elif e > pulsar.tier_boundaries[1]: - colors.append('orange') - elif e > pulsar.tier_boundaries[0]: - colors.append('yellow') - else: - colors.append('lightblue') - - ax6.scatter(flash_times, flash_energies, c=colors, s=[20 + 5*c for c in flash_counts], - alpha=0.7, edgecolors='black', linewidth=0.5) - ax6.set_xlabel('Time') - ax6.set_ylabel('Flash Energy') - ax6.set_title('Phase Transition Flashes') - ax6.set_yscale('log') - else: - ax6.text(0.5, 0.5, 'No flashes', ha='center', va='center', transform=ax6.transAxes) - - plt.tight_layout() - out_path = os.path.join(out_dir, f"pulsar_chandelier_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png") - plt.savefig(out_path, dpi=150, bbox_inches='tight') - plt.close() - print(f"Saved visualization to {out_path}") - - # 2. Second figure: Vortex trajectory movie frame (static for now) - fig2, axes = plt.subplots(1, 3, figsize=(15, 5)) - - # Parametric plot: u-v space showing vortex clustering - for idx, (ax, lobe_name, u_center) in enumerate(zip(axes, - ['Lobe 1 (u=0)', 'Lobe 2 (u=2π/3)', 'Lobe 3 (u=4π/3)'], - [0, 2*np.pi/3, 4*np.pi/3])): - u_vals = [v.u for v in pulsar.vortices] - v_vals = [v.v for v in pulsar.vortices] - tensions = [v.tension for v in pulsar.vortices] - - # Wrap u around lobe center - u_wrapped = [(u - u_center + np.pi) % (2*np.pi) - np.pi for u in u_vals] - - scatter = ax.scatter(u_wrapped, v_vals, c=tensions, cmap='hot', s=30, - alpha=0.7, vmin=0, vmax=max(tensions) * 0.8) - ax.set_xlim(-np.pi, np.pi) - ax.set_ylim(0, 2*np.pi) - ax.set_xlabel('Δu (relative to lobe)') - ax.set_ylabel('v (poloidal)') - ax.set_title(lobe_name) - plt.colorbar(scatter, ax=ax, shrink=0.6, label='Tension') - - plt.tight_layout() - out_path2 = os.path.join(out_dir, f"pulsar_chandelier_uv_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png") - plt.savefig(out_path2, dpi=150, bbox_inches='tight') - plt.close() - print(f"Saved UV-space visualization to {out_path2}") - - return out_path, out_path2 - - -# ─── Export Data ───────────────────────────────────────────────────────────── -def export_data(pulsar, out_dir="/home/allaun/Documents/Research Stack/out"): - """Export simulation data as JSON for further analysis.""" - os.makedirs(out_dir, exist_ok=True) - - data = { - 'metadata': { - 'model': 'PulsarChandelier', - 'topology': 'genus-3', - 'n_vortices': len(pulsar.vortices), - 't_max': pulsar.time, - 'dt': pulsar.dt, - 'constants': { - 'I_crust': I_CRUST, - 'I_sf': I_SF, - 'K_dipole': K_DIPOLE, - 'T_pin': T_PIN, - 'eta_visc': ETA_VISC - } - }, - 'history': { - k: [float(x) for x in v] for k, v in pulsar.history.items() - }, - 'flashes': [ - {'time': float(t), 'energy': float(e), 'n_unpinned': int(n)} - for t, e, n in pulsar.flashes - ], - 'final_state': { - 'omega_crust_hz': float(pulsar.omega_crust / (2*np.pi)), - 'omega_sf_hz': float(pulsar.omega_sf / (2*np.pi)), - 'L_total': float(pulsar.L_total), - 'E_total': float(pulsar.total_energy()), - 'n_pinned': sum(1 for v in pulsar.vortices if v.pinned), - 'tier_flash_counts': pulsar.tier_flash_count - } - } - - out_path = os.path.join(out_dir, f"pulsar_chandelier_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json") - with open(out_path, 'w') as f: - json.dump(data, f, indent=2) - print(f"Exported data to {out_path}") - return out_path - - -# ─── Main ──────────────────────────────────────────────────────────────────── -if __name__ == '__main__': - print("=" * 60) - print("PULSAR CHANDELIER MODEL") - print("Genus-3 topology + superfluid vortex dynamics") - print("=" * 60) - - pulsar = PulsarChandelier(n_vortices=N_VORTICES) - print(f"\nInitial state:") - print(f" Crust spin: {pulsar.omega_crust/(2*np.pi):.2f} Hz") - print(f" Superfluid spin: {pulsar.omega_sf/(2*np.pi):.2f} Hz") - print(f" Total angular momentum: {pulsar.L_total:.2f}") - print(f" Vortices: {len(pulsar.vortices)}") - print(f" Flash thresholds (tiers): {pulsar.tier_boundaries}") - - print("\nRunning simulation...") - pulsar.run(t_max=50.0) - - print("\nGenerating visualizations...") - img1, img2 = visualize(pulsar) - - print("\nExporting data...") - data_path = export_data(pulsar) - - print("\n" + "=" * 60) - print("DONE") - print(f" Images: {img1}, {img2}") - print(f" Data: {data_path}") - print("=" * 60) diff --git a/5-Applications/scripts/pulsar_marble_jar_multiscale.py b/5-Applications/scripts/pulsar_marble_jar_multiscale.py deleted file mode 100644 index 621db71d..00000000 --- a/5-Applications/scripts/pulsar_marble_jar_multiscale.py +++ /dev/null @@ -1,680 +0,0 @@ -#!/usr/bin/env python3 -""" -Pulsar Marble-Jar Model — Multiscale Event-Driven Simulation - -Physical basis: - A neutron star = 10^57 neutrons (marbles) in a superfluid (jar). - Vortices are pinned to crustal nuclei. Mutual friction drains rotation. - Individual unpinning events are invisible; collectively they spin-down the star. - When pinning fails catastrophically: glitch = phase-transition flash. - -Timescales: - Cruise (years) : magnetic braking + vortex creep - Glitch (seconds) : avalanche, angular momentum transfer - Recovery (days) : vortices repin, crust relaxes - -Units: - Time : years (cruise) / seconds (glitch) - Frequency : Hz - Energy : 10^33 J (one "unit") - Inertia : 10^38 kg m^2 - -No "up/down" — only torsional direction (rotation axis). -Genus-3 surface embeds the vortex array topology. -""" - -import numpy as np -import matplotlib.pyplot as plt -from matplotlib.patches import Circle, FancyBboxPatch -from mpl_toolkits.mplot3d import Axes3D -from matplotlib.colors import Normalize -from matplotlib.cm import ScalarMappable -import json -import os -from datetime import datetime -from dataclasses import dataclass, field -from typing import List, Tuple -from scipy.ndimage import gaussian_filter - -# ─── Physical Constants (realistic, scaled) ────────────────────────────────── -YEAR_TO_SEC = 3.154e7 -SEC_TO_YEAR = 1.0 / YEAR_TO_SEC - -I_CRUST = 0.6 # Crust moment of inertia (fraction of total) -I_SF = 1.0 # Superfluid moment of inertia -I_TOTAL = I_CRUST + I_SF - -B_FIELD = 1e12 # Surface B-field in Gauss (scaled) -R_STAR = 1.0 # Radius in 10 km units - -TAU_CHAR = 1e6 # Characteristic spin-down age (years) -TAU_RECOVERY = 30.0 # Post-glitch recovery time (days) -TAU_CREEP = 500.0 # Vortex creep coupling timescale (years) - -T_PIN_BASE = 1.0 # Base pinning threshold -T_PIN_WIDTH = 0.2 # Distribution width (some pins weaker) -GLITCH_DF = 0.05 # Fraction of pinned vortices that unpin in a glitch - -# ─── Genus-3 Surface (unchanged topology) ──────────────────────────────────── -def genus3_surface(u, v, R=3.0, r=1.0, p=0.6): - rho = R + r * np.cos(v) + p * np.cos(3 * u) - x = rho * np.cos(u) - y = rho * np.sin(u) - z = r * np.sin(v) + 0.3 * np.sin(3 * u) - return x, y, z - - -def torsional_depth(u, v): - """Local 'depth' = torsional curvature concentration. Higher near lobe centers.""" - d0 = np.minimum(np.abs(u - 0), np.abs(u - 2*np.pi)) - d1 = np.abs(u - 2*np.pi/3) - d2 = np.abs(u - 4*np.pi/3) - d_min = np.minimum(np.minimum(d0, d1), d2) - depth = 3.0 / (1.0 + 5.0 * d_min) * (1.0 + 0.3 * np.cos(v)) - return depth - - -def genus3_mesh(n_u=80, n_v=40): - u = np.linspace(0, 2*np.pi, n_u) - v = np.linspace(0, 2*np.pi, n_v) - U, V = np.meshgrid(u, v) - X, Y, Z = genus3_surface(U, V) - return X, Y, Z, U, V - - -# ─── Vortex ────────────────────────────────────────────────────────────────── -@dataclass -class Vortex: - u: float - v: float - circulation: float = 1.0 - pinned: bool = True - tension: float = 0.0 - unpin_age: float = 0.0 # When did it last unpin? - repin_time: float = 0.0 # How long until it repins? - - def pos(self): - x, y, z = genus3_surface(self.u, self.v) - return np.array([x, y, z]) - - -# ─── Flash Event ───────────────────────────────────────────────────────────── -@dataclass -class Flash: - time_yr: float # Time in years - time_sec: float # Time within glitch in seconds - energy: float # Flash energy released - n_unpinned: int # How many vortices unpin - delta_omega_hz: float # Glitch size (spin-up) - recovery_time_days: float - tier: int # Which tier boundary crossed - - -# ─── Pulsar Marble-Jar Model ───────────────────────────────────────────────── -class PulsarMarbleJar: - """Event-driven multiscale pulsar simulation.""" - - def __init__(self, n_vortices=512, f0_hz=10.0): - # Two-component rotation - self.f_crust = f0_hz # Crust spin frequency (Hz) - self.f_sf = f0_hz * 1.0001 # Superfluid slightly ahead (vortex creep lag) - self.omega_crust = 2 * np.pi * self.f_crust - self.omega_sf = 2 * np.pi * self.f_sf - - # Conserved angular momentum (in units where I_total = 1) - self.L_total = I_CRUST * self.omega_crust + I_SF * self.omega_sf - - # Vortex array - self.vortices: List[Vortex] = [] - self._init_vortices(n_vortices) - - # State - self.time_yr = 0.0 - self.in_glitch = False - self.glitch_start_time_yr = 0.0 - self.glitch_clock_sec = 0.0 - self.recovery_clock_days = 0.0 - - # Events - self.flashes: List[Flash] = [] - - # History (cruise sampling) - self.history = { - 't_yr': [], - 'f_crust_hz': [], - 'f_sf_hz': [], - 'E_rot': [], - 'L_total': [], - 'n_pinned': [], - 'tension_max': [], - 'flash_count': [], - 'phase': [] # 'cruise', 'glitch', 'recovery' - } - - # Tier boundaries for flash classification - self.tier_thresholds = [0.5, 1.0, 2.0] # Energy thresholds - - # Cruise sampling interval - self.cruise_sample_interval = 100.0 # years - self.last_sample_time = -self.cruise_sample_interval - - def _init_vortices(self, n): - """Initialize vortices with clustering near lobe centers.""" - for i in range(n): - if np.random.rand() < 0.35: - lobe = np.random.choice(3) - u_c = lobe * 2 * np.pi / 3 - u = u_c + np.random.normal(0, 0.25) - v = np.pi + np.random.normal(0, 0.4) - else: - u = np.random.uniform(0, 2*np.pi) - v = np.random.uniform(0, 2*np.pi) - - # Each vortex has slightly different pinning strength - pin_variation = np.random.normal(0, T_PIN_WIDTH) - - self.vortices.append(Vortex( - u=u % (2*np.pi), - v=v % (2*np.pi), - circulation=1.0 + 0.5 * np.random.poisson(0.3), - pinned=True, - tension=0.0, - repin_time=np.inf - )) - - def _vortex_density_grid(self, n_grid=32): - """Compute smoothed vortex density on toroidal grid.""" - density = np.zeros((n_grid, n_grid)) - for v in self.vortices: - iu = int((v.u / (2*np.pi)) * n_grid) % n_grid - iv = int((v.v / (2*np.pi)) * n_grid) % n_grid - density[iu, iv] += v.circulation - density = gaussian_filter(density, sigma=1.2, mode='wrap') - return density - - def _update_vortex_tensions(self): - """Compute tension on each vortex from Magnus force and local density.""" - density = self._vortex_density_grid() - n_grid = density.shape[0] - - max_tension = 0.0 - for v in self.vortices: - iu = int((v.u / (2*np.pi)) * n_grid) % n_grid - iv = int((v.v / (2*np.pi)) * n_grid) % n_grid - local_depth = torsional_depth(v.u, v.v) - local_rho = density[iu, iv] - - # Magnus force: F_M ~ ρ_s κ × (v_sf - v_crust) - # Simplified: tension proportional to differential rotation and local depth - delta_omega = abs(self.omega_sf - self.omega_crust) - magnus = delta_omega * v.circulation * (1.0 + 0.8 * local_depth) - repulsion = 0.2 * local_rho ** 1.5 - v.tension = magnus + repulsion - max_tension = max(max_tension, v.tension) - - return max_tension - - def _enforce_L_conservation(self): - """Hard correct to ensure L is exactly conserved.""" - L_now = I_CRUST * self.omega_crust + I_SF * self.omega_sf - delta_L = self.L_total - L_now - domega = delta_L / I_TOTAL - self.omega_crust += domega - self.omega_sf += domega - self.f_crust = self.omega_crust / (2 * np.pi) - self.f_sf = self.omega_sf / (2 * np.pi) - - # ── Cruise Phase (years) ──────────────────────────────────────────────── - def cruise_step(self, dt_yr): - """Integrate spin-down between glitches. Large timestep in years.""" - dt = dt_yr - - # 1. Magnetic dipole braking: dΩ/dt = -Ω / τ_char * (Ω/Ω_0)^2 - # Characteristic age τ_char ~ 10^6 yr for canonical pulsar - braking = -self.omega_crust / (TAU_CHAR * YEAR_TO_SEC) * (self.omega_crust / (2*np.pi*10))**2 - braking *= YEAR_TO_SEC # convert to rad/s per year - self.omega_crust += braking * dt - - # 2. Vortex creep: superfluid slowly couples to crust - # Mutual friction torque transfers angular momentum over τ_creep - delta_omega = self.omega_sf - self.omega_crust - coupling = delta_omega / TAU_CREEP - self.omega_sf -= coupling * dt * (I_CRUST / I_SF) - self.omega_crust += coupling * dt - - # 3. Vortices slowly drift (creep motion) - for v in self.vortices: - if v.pinned: - # Pinned vortices barely move — thermal creep - du = 0.001 * np.sin(3*v.u) * dt / TAU_CREEP - dv = 0.0005 * np.cos(v.v) * dt / TAU_CREEP - else: - # Unpinned vortices move with superfluid - du = 0.1 * self.omega_sf * dt * SEC_TO_YEAR - dv = 0.02 * dt * SEC_TO_YEAR - # Check repin - v.unpin_age += dt * YEAR_TO_SEC - if v.unpin_age > v.repin_time: - v.pinned = True - v.unpin_age = 0 - - v.u = (v.u + du) % (2*np.pi) - v.v = (v.v + dv) % (2*np.pi) - - self._enforce_L_conservation() - self.time_yr += dt - - # 4. Check for glitch trigger - max_tension = self._update_vortex_tensions() - if max_tension > T_PIN_BASE and not self.in_glitch: - # Enter glitch phase - self.in_glitch = True - self.glitch_start_time_yr = self.time_yr - self.glitch_clock_sec = 0.0 - return True # signal: glitch triggered - - # 5. Sample history - if self.time_yr - self.last_sample_time >= self.cruise_sample_interval: - self._record_history('cruise') - self.last_sample_time = self.time_yr - - return False - - # ── Glitch Phase (seconds) ────────────────────────────────────────────── - def glitch_step(self, dt_sec): - """Resolve glitch dynamics at second-scale resolution.""" - dt = dt_sec - self.glitch_clock_sec += dt - - # Count how many vortices exceed threshold - triggered = [v for v in self.vortices if v.pinned and v.tension > T_PIN_BASE] - - if len(triggered) > 0 and self.glitch_clock_sec < 10.0: - # Avalanche: unpin a fraction of triggered vortices - n_to_unpin = max(1, int(GLITCH_DF * len(triggered))) - np.random.shuffle(triggered) - unpinned = triggered[:n_to_unpin] - - # Each unpinned vortex transfers angular momentum from sf → crust - # Real glitch: ΔΩ/Ω ~ 10^-8 to 10^-6 - # Each simulated vortex = ~10^14 real vortices - total_dL = 0.0 - for v in unpinned: - v.pinned = False - v.unpin_age = 0.0 - v.repin_time = np.random.exponential(TAU_RECOVERY * 24 * 3600) # seconds - # Small angular momentum transfer per vortex - dL = v.circulation * self.omega_sf * 1e-6 * I_SF - total_dL += dL - - # Apply transfer - self.omega_crust += total_dL / I_CRUST - self.omega_sf -= total_dL / I_SF - - # Flash energy: rotational energy change + thermal dissipation - E_flash = 0.5 * total_dL * (self.omega_crust - self.omega_sf) - - # Classify tier - tier = sum(1 for th in self.tier_thresholds if E_flash > th) - - # Record flash - self.flashes.append(Flash( - time_yr=self.time_yr + self.glitch_clock_sec * SEC_TO_YEAR, - time_sec=self.glitch_clock_sec, - energy=E_flash, - n_unpinned=len(unpinned), - delta_omega_hz=total_dL / (2*np.pi * I_CRUST), - recovery_time_days=TAU_RECOVERY, - tier=tier - )) - - # Update tensions after redistribution - self._update_vortex_tensions() - - # Vortices move rapidly during glitch - for v in self.vortices: - if not v.pinned: - du = 0.5 * self.omega_sf * dt - dv = 0.1 * np.sin(v.v) * dt - v.u = (v.u + du) % (2*np.pi) - v.v = (v.v + dv) % (2*np.pi) - - self._enforce_L_conservation() - - # End glitch after a few seconds - if self.glitch_clock_sec > 5.0: - self.in_glitch = False - self.recovery_clock_days = 0.0 - return True # signal: glitch ended, enter recovery - - return False - - # ── Recovery Phase (days) ─────────────────────────────────────────────── - def recovery_step(self, dt_days): - """Post-glitch relaxation. Vortices slowly repin.""" - self.recovery_clock_days += dt_days - - # During recovery, crust and superfluid re-equilibrate - delta_omega = self.omega_sf - self.omega_crust - # Recovery follows exponential relaxation with timescale τ_recovery - relax_rate = dt_days / TAU_RECOVERY - self.omega_crust += delta_omega * relax_rate * (I_SF / I_TOTAL) - self.omega_sf -= delta_omega * relax_rate * (I_CRUST / I_TOTAL) - - # Repin vortices whose time is up - for v in self.vortices: - if not v.pinned: - v.unpin_age += dt_days * 24 * 3600 - if v.unpin_age > v.repin_time: - v.pinned = True - v.unpin_age = 0 - - self._enforce_L_conservation() - self.time_yr += dt_days / 365.25 - - # End recovery - if self.recovery_clock_days > TAU_RECOVERY: - return True # signal: back to cruise - - return False - - def _record_history(self, phase): - """Record state to history buffer.""" - self.history['t_yr'].append(self.time_yr) - self.history['f_crust_hz'].append(self.f_crust) - self.history['f_sf_hz'].append(self.f_sf) - E_rot = 0.5 * I_CRUST * self.omega_crust**2 + 0.5 * I_SF * self.omega_sf**2 - self.history['E_rot'].append(E_rot) - self.history['L_total'].append(self.L_total) - self.history['n_pinned'].append(sum(1 for v in self.vortices if v.pinned)) - self.history['tension_max'].append(max(v.tension for v in self.vortices)) - self.history['flash_count'].append(len(self.flashes)) - self.history['phase'].append(phase) - - # ── Main Run Loop ─────────────────────────────────────────────────────── - def run(self, t_max_yr=2e6): - """Run full multiscale simulation.""" - print(f"Starting multiscale simulation: 0 → {t_max_yr:.1e} years") - print(f"Initial f_crust = {self.f_crust:.2f} Hz") - print(f"Characteristic age = {TAU_CHAR:.1e} years") - print(f"Vortices: {len(self.vortices)}") - - next_print = 0.0 - - while self.time_yr < t_max_yr: - if not self.in_glitch: - # Cruise or recovery - if self.recovery_clock_days > 0: - # In recovery - done = self.recovery_step(dt_days=1.0) - if done: - self.recovery_clock_days = 0.0 - else: - # In cruise - glitch_triggered = self.cruise_step(dt_yr=10.0) - if glitch_triggered: - print(f" GLITCH TRIGGERED at t = {self.time_yr:.2e} yr") - print(f" Max tension = {max(v.tension for v in self.vortices):.3f}") - else: - # In glitch — resolve at second scale - done = self.glitch_step(dt_sec=0.1) - if done: - print(f" Glitch resolved: Δf = {self.flashes[-1].delta_omega_hz:.2e} Hz") - print(f" {self.flashes[-1].n_unpinned} vortices unpinned") - - # Progress print - if self.time_yr >= next_print: - phase = 'GLITCH' if self.in_glitch else ('RECOVERY' if self.recovery_clock_days > 0 else 'CRUISE') - print(f" t={self.time_yr:.2e} yr | f={self.f_crust:.4f} Hz | " - f"flashes={len(self.flashes)} | phase={phase}") - next_print += t_max_yr / 20 - - # Final sample - self._record_history('end') - - print(f"\n{'='*60}") - print("SIMULATION COMPLETE") - print(f" Total time: {self.time_yr:.2e} years") - print(f" Final f_crust: {self.f_crust:.4f} Hz") - print(f" Total flashes: {len(self.flashes)}") - print(f" Final L deviation: {abs(I_CRUST*self.omega_crust + I_SF*self.omega_sf - self.L_total):.2e}") - - if self.flashes: - sizes = [f.delta_omega_hz for f in self.flashes] - print(f" Glitch sizes: min={min(sizes):.2e}, max={max(sizes):.2e} Hz") - intervals = np.diff([f.time_yr for f in self.flashes]) - if len(intervals) > 0: - print(f" Inter-glitch: mean={np.mean(intervals):.2e}, med={np.median(intervals):.2e} yr") - - -# ─── Visualization ─────────────────────────────────────────────────────────── -def visualize(model, out_dir="/home/allaun/Documents/Research Stack/out"): - os.makedirs(out_dir, exist_ok=True) - - fig = plt.figure(figsize=(18, 14)) - - # ── Panel A: Spin-down curve (log-log) ───────────────────────────────── - ax1 = fig.add_subplot(3, 3, 1) - t = np.array(model.history['t_yr']) - f = np.array(model.history['f_crust_hz']) - ax1.loglog(t + 1, f, 'b-', linewidth=1, label='Crust spin') - ax1.loglog(t + 1, np.array(model.history['f_sf_hz']), 'r--', alpha=0.5, linewidth=0.8, label='Superfluid') - - # Mark glitches - for flash in model.flashes: - ax1.axvline(flash.time_yr + 1, color='orange', alpha=0.4, linewidth=0.8) - - # Reference: pure dipole braking f ∝ t^(-1/2) - t_ref = np.logspace(0, np.log10(t.max()+1), 50) - f_ref = model.f_crust * (t_ref / (t_ref[0] + 1e3)) ** (-0.5) - ax1.loglog(t_ref, f_ref, 'g:', alpha=0.5, label='∝ t^(-1/2) dipole') - - ax1.set_xlabel('Time (years)') - ax1.set_ylabel('Spin frequency (Hz)') - ax1.set_title('Spin-down over megayears') - ax1.legend() - ax1.set_ylim(bottom=0.1) - - # ── Panel B: Glitch sizes histogram ──────────────────────────────────── - ax2 = fig.add_subplot(3, 3, 2) - if model.flashes: - sizes = [f.delta_omega_hz for f in model.flashes] - ax2.hist(np.log10(sizes), bins=20, color='steelblue', edgecolor='black', alpha=0.7) - ax2.set_xlabel('log₁₀(Δf) [Hz]') - ax2.set_ylabel('Count') - ax2.set_title(f'Glitch size distribution (n={len(sizes)})') - else: - ax2.text(0.5, 0.5, 'No glitches', ha='center', va='center', transform=ax2.transAxes) - - # ── Panel C: Inter-glitch intervals ──────────────────────────────────── - ax3 = fig.add_subplot(3, 3, 3) - if len(model.flashes) > 1: - intervals = np.diff([f.time_yr for f in model.flashes]) - ax3.hist(np.log10(intervals + 1), bins=20, color='forestgreen', edgecolor='black', alpha=0.7) - ax3.set_xlabel('log₁₀(Δt) [years]') - ax3.set_ylabel('Count') - ax3.set_title('Inter-glitch waiting times') - else: - ax3.text(0.5, 0.5, 'Need >1 glitch', ha='center', va='center', transform=ax3.transAxes) - - # ── Panel D: Energy budget over time ─────────────────────────────────── - ax4 = fig.add_subplot(3, 3, 4) - E_rot = np.array(model.history['E_rot']) - ax4.semilogy(t + 1, E_rot, 'k-', linewidth=1.5, label='Rotational E') - # Approximate radiated energy = integral of braking torque - # E_dot = I Ω dΩ/dt ≈ -I Ω² / τ_char - E_rad = E_rot[0] * (1 - (f / model.history['f_crust_hz'][0])**2) - ax4.semilogy(t + 1, E_rot[0] - E_rad, 'm--', alpha=0.5, label='Radiated away') - ax4.set_xlabel('Time (years)') - ax4.set_ylabel('Energy') - ax4.set_title('Energy budget') - ax4.legend() - - # ── Panel E: Angular momentum ────────────────────────────────────────── - ax5 = fig.add_subplot(3, 3, 5) - L = np.array(model.history['L_total']) - ax5.plot(t, L, 'k-', linewidth=1.5) - ax5.set_xlabel('Time (years)') - ax5.set_ylabel('Angular momentum') - ax5.set_title(f'L conserved (σ={np.std(L):.2e})') - - # ── Panel F: Vortex tension over time ────────────────────────────────── - ax6 = fig.add_subplot(3, 3, 6) - ax6.semilogy(t + 1, model.history['tension_max'], 'purple', linewidth=1) - ax6.axhline(T_PIN_BASE, color='red', linestyle='--', label='Pin threshold') - ax6.set_xlabel('Time (years)') - ax6.set_ylabel('Max tension') - ax6.set_title('Peak vortex tension') - ax6.legend() - - # ── Panel G: 3D Genus-3 with final vortex state ──────────────────────── - ax7 = fig.add_subplot(3, 3, 7, projection='3d') - X, Y, Z, U, V = genus3_mesh(n_u=60, n_v=30) - depth_map = torsional_depth(U, V) - ax7.plot_surface(X, Y, Z, facecolors=plt.cm.RdYlBu_r(depth_map / depth_map.max()), - alpha=0.3, rstride=2, cstride=2, linewidth=0.1) - - vtx_pos = np.array([v.pos() for v in model.vortices]) - tensions = np.array([v.tension for v in model.vortices]) - pinned = np.array([v.pinned for v in model.vortices]) - - if len(vtx_pos) > 0: - # Color by tension, shape by pinned status - scatter = ax7.scatter(vtx_pos[:, 0], vtx_pos[:, 1], vtx_pos[:, 2], - c=tensions, cmap='hot', s=20 + 60*tensions/T_PIN_BASE, - alpha=0.8, edgecolors='black', linewidth=0.2) - plt.colorbar(scatter, ax=ax7, shrink=0.5, label='Tension') - - ax7.set_title('Final vortex array on genus-3') - - # ── Panel H: Pinned vs unpinned over time ────────────────────────────── - ax8 = fig.add_subplot(3, 3, 8) - ax8.plot(t, np.array(model.history['n_pinned']), 'b-', label='Pinned') - ax8.plot(t, len(model.vortices) - np.array(model.history['n_pinned']), 'r--', label='Unpinned') - ax8.set_xlabel('Time (years)') - ax8.set_ylabel('Count') - ax8.set_title('Vortex pinning state') - ax8.legend() - - # ── Panel I: Flash timeline with tiers ───────────────────────────────── - ax9 = fig.add_subplot(3, 3, 9) - if model.flashes: - ft = [f.time_yr for f in model.flashes] - fe = [f.energy for f in model.flashes] - fc = [f.n_unpinned for f in model.flashes] - tiers = [f.tier for f in model.flashes] - - colors = ['lightblue', 'yellow', 'orange', 'red'] - c_list = [colors[min(t, 3)] for t in tiers] - - ax9.scatter(ft, fe, c=c_list, s=[20 + 5*c for c in fc], - alpha=0.7, edgecolors='black', linewidth=0.5) - ax9.set_xlabel('Time (years)') - ax9.set_ylabel('Flash energy') - ax9.set_title('Phase transition flashes') - ax9.set_yscale('log') - else: - ax9.text(0.5, 0.5, 'No flashes', ha='center', va='center', transform=ax9.transAxes) - - plt.tight_layout() - out_path = os.path.join(out_dir, f"marble_jar_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png") - plt.savefig(out_path, dpi=150, bbox_inches='tight') - plt.close() - print(f"Saved: {out_path}") - - # Second figure: Zoomed glitch detail (if any glitches) - if model.flashes: - fig2, axes = plt.subplots(2, 3, figsize=(15, 8)) - - for idx, ax in enumerate(axes.flat): - if idx >= min(6, len(model.flashes)): - ax.axis('off') - continue - - flash = model.flashes[idx] - # Mock detailed profile: exponential recovery - t_g = np.linspace(0, TAU_RECOVERY, 200) - delta_f = flash.delta_omega_hz * np.exp(-t_g / TAU_RECOVERY) - - ax.plot(t_g, delta_f * 1e6, 'b-', linewidth=1.5) - ax.axvline(flash.time_sec / 86400, color='red', linestyle='--', alpha=0.5, label='Glitch') - ax.set_xlabel('Days after glitch') - ax.set_ylabel('Δf (μHz)') - ax.set_title(f'Glitch {idx+1}: t={flash.time_yr:.2e} yr, tier={flash.tier}') - ax.set_yscale('log') - - plt.tight_layout() - out_path2 = os.path.join(out_dir, f"marble_jar_glitchdetail_{datetime.now().strftime('%Y%m%d_%H%M%S')}.png") - plt.savefig(out_path2, dpi=150, bbox_inches='tight') - plt.close() - print(f"Saved: {out_path2}") - return out_path, out_path2 - - return out_path, None - - -# ─── Export ────────────────────────────────────────────────────────────────── -def export_data(model, out_dir="/home/allaun/Documents/Research Stack/out"): - os.makedirs(out_dir, exist_ok=True) - - data = { - 'metadata': { - 'model': 'PulsarMarbleJarMultiscale', - 'topology': 'genus-3', - 'n_vortices': len(model.vortices), - 't_max_yr': model.time_yr, - 'parameters': { - 'I_crust': I_CRUST, - 'I_sf': I_SF, - 'tau_char': TAU_CHAR, - 'tau_creep': TAU_CREEP, - 'tau_recovery': TAU_RECOVERY, - 'T_pin_base': T_PIN_BASE, - 'glitch_df': GLITCH_DF - } - }, - 'history': {k: [float(x) if isinstance(x, (int, float, np.floating)) else x - for x in v] - for k, v in model.history.items()}, - 'flashes': [ - {'time_yr': f.time_yr, 'time_sec': f.time_sec, - 'energy': f.energy, 'n_unpinned': f.n_unpinned, - 'delta_omega_hz': f.delta_omega_hz, - 'tier': f.tier} - for f in model.flashes - ], - 'final_state': { - 'f_crust_hz': model.f_crust, - 'f_sf_hz': model.f_sf, - 'L_total': model.L_total, - 'n_pinned': sum(1 for v in model.vortices if v.pinned), - 'n_flashes': len(model.flashes) - } - } - - out_path = os.path.join(out_dir, f"marble_jar_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json") - with open(out_path, 'w') as f: - json.dump(data, f, indent=2) - print(f"Exported: {out_path}") - return out_path - - -# ─── Main ──────────────────────────────────────────────────────────────────── -if __name__ == '__main__': - print("=" * 70) - print("PULSAR MARBLE-JAR MODEL (Multiscale)") - print("Cruise: years | Glitch: seconds | Recovery: days") - print("=" * 70) - - model = PulsarMarbleJar(n_vortices=512, f0_hz=10.0) - model.run(t_max_yr=2e6) - - print("\nGenerating visualizations...") - paths = visualize(model) - - print("\nExporting data...") - export_data(model) - - print("\n" + "=" * 70) - print("ALL DONE") - print("=" * 70) diff --git a/5-Applications/scripts/pwm_controller_computational.py b/5-Applications/scripts/pwm_controller_computational.py deleted file mode 100644 index 42cba02e..00000000 --- a/5-Applications/scripts/pwm_controller_computational.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -""" -PWM Controller Computational Repurposing -Analyzes PWM (Pulse Width Modulation) controllers for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class PWMControllerComputational: - """Analyzes PWM controllers for general computation.""" - - def __init__(self): - self.pwm_controller = { - "device": "PWM Controller (Pulse Width Modulation)", - "concept": "Use PWM duty cycle and frequency for computation", - "type": "PWM circuits for power regulation, motor control, signal generation", - "frequency_range": "1 Hz - 1 MHz (typical)", - "duty_cycle_range": "0-100%", - "computational_potential": "MEDIUM-HIGH (duty cycle arithmetic, frequency modulation, time-based computation)" - } - - self.pwm_capabilities = { - "duty_cycle": "Pulse width modulation (0-100% duty cycle)", - "frequency": "PWM frequency (1 Hz - 1 MHz)", - "phase": "PWM phase shift (0-360 degrees)", - "multiple_channels": "Multiple PWM channels for parallel computation", - "timer_based": "Timer/counter based PWM generation" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of PWM controller.""" - analysis = { - "duty_cycle_computation": { - "feasible": True, - "mode": "Duty cycle computation", - "description": "Use PWM duty cycle for computational values", - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "PWM period limited (1us - 1s)", - "precision": "8-16 bit duty cycle resolution", - "power": "1-5W (PWM controller)", - "risk": "LOW (non-invasive)" - }, - "frequency_modulation": { - "feasible": True, - "mode": "Frequency modulation computation", - "description": "Use PWM frequency for computational encoding", - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Frequency change latency (1us - 1ms)", - "precision": "Frequency resolution (0.1% - 1%)", - "power": "1-5W", - "risk": "LOW (non-invasive)" - }, - "phase_modulation": { - "feasible": True, - "mode": "Phase modulation computation", - "description": "Use PWM phase shift for computational encoding", - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Phase change latency (1us - 1ms)", - "precision": "8-12 bit phase resolution", - "power": "1-5W", - "risk": "LOW (non-invasive)" - }, - "multi_channel_parallel": { - "feasible": True, - "mode": "Multi-channel parallel computation", - "description": "Use multiple PWM channels for parallel computation", - "throughput": "N x frequency (N channels)", - "latency": "PWM period limited", - "precision": "8-16 bit per channel", - "power": "2-10W (multiple channels)", - "risk": "LOW-MEDIUM (channel coordination)" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design PWM controller computational approach.""" - approach = { - "duty_cycle_computation": { - "concept": "Use PWM duty cycle for computation", - "implementation": "Encode computational values in duty cycle", - "operations": ["duty cycle arithmetic", "pulse width encoding", "time-based state"], - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "PWM period limited (1us - 1s)", - "precision": "8-16 bit duty cycle", - "power": "1-5W", - "risk": "LOW" - }, - "frequency_modulation": { - "concept": "Use PWM frequency for computation", - "implementation": "Encode computational values in frequency", - "operations": ["frequency arithmetic", "modulation encoding", "FM computation"], - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Frequency change latency (1us - 1ms)", - "precision": "0.1% - 1% frequency resolution", - "power": "1-5W", - "risk": "LOW" - }, - "phase_modulation": { - "concept": "Use PWM phase for computation", - "implementation": "Encode computational values in phase shift", - "operations": ["phase arithmetic", "phase encoding", "PM computation"], - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Phase change latency (1us - 1ms)", - "precision": "8-12 bit phase resolution", - "power": "1-5W", - "risk": "LOW" - }, - "multi_channel": { - "concept": "Use multiple PWM channels for parallel computation", - "implementation": "Parallel computation across PWM channels", - "operations": ["parallel duty cycle", "parallel frequency", "channel arithmetic"], - "throughput": "N x frequency (N channels)", - "latency": "PWM period limited", - "precision": "8-16 bit per channel", - "power": "2-10W", - "risk": "LOW-MEDIUM" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of PWM controller computation.""" - performance = { - "duty_cycle": { - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "PWM period limited (1us - 1s)", - "precision": "8-16 bit duty cycle", - "operations": "duty cycle arithmetic", - "power": "1-5W" - }, - "frequency_modulation": { - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Frequency change latency (1us - 1ms)", - "precision": "0.1% - 1% frequency resolution", - "operations": "frequency arithmetic", - "power": "1-5W" - }, - "phase_modulation": { - "throughput": "Frequency limited (1 Hz - 1 MHz)", - "latency": "Phase change latency (1us - 1ms)", - "precision": "8-12 bit phase resolution", - "operations": "phase arithmetic", - "power": "1-5W" - }, - "multi_channel": { - "throughput": "N x frequency (N channels)", - "latency": "PWM period limited", - "precision": "8-16 bit per channel", - "operations": "parallel processing", - "power": "2-10W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run PWM controller computational analysis.""" - print("=" * 60) - print("PWM CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze PWM controller - print("\n[1/4] Analyzing PWM controller...") - print(f" Device: {self.pwm_controller['device']}") - print(f" Concept: {self.pwm_controller['concept']}") - print(f" Type: {self.pwm_controller['type']}") - print(f" Frequency Range: {self.pwm_controller['frequency_range']}") - print(f" Computational Potential: {self.pwm_controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" Duty Cycle: {potential['duty_cycle_computation']['feasible']} - {potential['duty_cycle_computation']['risk']}") - print(f" Frequency Modulation: {potential['frequency_modulation']['feasible']} - {potential['frequency_modulation']['risk']}") - print(f" Phase Modulation: {potential['phase_modulation']['feasible']} - {potential['phase_modulation']['risk']}") - print(f" Multi-Channel: {potential['multi_channel_parallel']['feasible']} - {potential['multi_channel_parallel']['risk']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Duty Cycle: {performance['duty_cycle']['throughput']}") - print(f" Frequency Modulation: {performance['frequency_modulation']['throughput']}") - print(f" Phase Modulation: {performance['phase_modulation']['throughput']}") - print(f" Multi-Channel: {performance['multi_channel']['throughput']}") - - print("\n" + "=" * 60) - print("PWM CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "pwm_controller": self.pwm_controller, - "pwm_capabilities": self.pwm_capabilities, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = PWMControllerComputational() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "pwm_controller_computational.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("PWM CONTROLLER COMPUTATIONAL SUMMARY") - print("=" * 60) - print(f"Device: {results['pwm_controller']['device']}") - print(f"Frequency Range: {results['pwm_controller']['frequency_range']}") - print(f"Computational Potential: {results['pwm_controller']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['multi_channel']['throughput']}") diff --git a/5-Applications/scripts/quad_sampled_scanlines.py b/5-Applications/scripts/quad_sampled_scanlines.py deleted file mode 100644 index d11cdea9..00000000 --- a/5-Applications/scripts/quad_sampled_scanlines.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python3 -""" -Quad-Sampled Scanlines -Quad-sample each scanline 4 times to effectively quadruple vertical resolution. - -Architecture: -- Each physical scanline rendered 4 times with subpixel offsets -- DSP math interpolates between samples -- Palette generator blends colors -- Voltage computation modulates sampling timing -- Effective resolution: 256×240 physical → 256×960 perceived - -This is horrific because: -- 4x temporal supersampling on hardware designed for 1x -- Abuse of scanline timing and modulation -- Requires precise voltage-level timing control - -This is wonderful because: -- Effective 4x vertical resolution increase -- Temporal supersampling without hardware modification -- Maximum retro insanity: quad sampling = 4x resolution -""" - -import math -from typing import List, Tuple -from dataclasses import dataclass - -# ═══════════════════════════════════════════════════════════════════════════ -# Quad-Sampled Scanline -# Single scanline sampled 4 times with subpixel offsets -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class SubpixelSample: - """Single subpixel sample""" - offset: float # 0.0-1.0 subpixel offset - color: Tuple[int, int, int] # RGB color - voltage: float # Voltage level for timing - -@dataclass -class QuadSampledScanline: - """Scanline with 4 subpixel samples""" - samples: List[SubpixelSample] - - def interpolate(self) -> Tuple[int, int, int]: - """ - Interpolate 4 samples into final color. - - Uses weighted average based on subpixel positions. - """ - if not self.samples: - return (0, 0, 0) - - total_r = 0.0 - total_g = 0.0 - total_b = 0.0 - total_weight = 0.0 - - for sample in self.samples: - # Weight based on offset (center samples have higher weight) - weight = 1.0 - abs(sample.offset - 0.5) - - total_r += sample.color[0] * weight - total_g += sample.color[1] * weight - total_b += sample.color[2] * weight - total_weight += weight - - if total_weight == 0: - return (0, 0, 0) - - return ( - int(total_r / total_weight), - int(total_g / total_weight), - int(total_b / total_weight) - ) - -# ═══════════════════════════════════════════════════════════════════════════ -# DSP-Based Scanline Interpolation -# Use DSP math operations to interpolate between samples -# ═══════════════════════════════════════════════════════════════════════════ - -class DSPScanlineInterpolator: - """DSP math for scanline interpolation""" - - @staticmethod - def bicubic_interpolation(samples: List[SubpixelSample], target_offset: float) -> Tuple[int, int, int]: - """ - Bicubic interpolation between samples. - - Uses voltage levels to determine interpolation weights. - """ - if len(samples) < 4: - # Fallback to linear interpolation - return DSPScanlineInterpolator.linear_interpolation(samples, target_offset) - - # Find surrounding samples - sorted_samples = sorted(samples, key=lambda s: s.offset) - - # Bicubic weights (simplified) - weights = [] - for sample in sorted_samples: - distance = abs(sample.offset - target_offset) - if distance < 1.0: - weight = 1.0 - distance - else: - weight = 0.0 - weights.append(weight) - - # Interpolate - total_r = sum(s.color[0] * w for s, w in zip(sorted_samples, weights)) - total_g = sum(s.color[1] * w for s, w in zip(sorted_samples, weights)) - total_b = sum(s.color[2] * w for s, w in zip(sorted_samples, weights)) - total_weight = sum(weights) - - if total_weight == 0: - return (0, 0, 0) - - return ( - int(total_r / total_weight), - int(total_g / total_weight), - int(total_b / total_weight) - ) - - @staticmethod - def linear_interpolation(samples: List[SubpixelSample], target_offset: float) -> Tuple[int, int, int]: - """Linear interpolation between samples""" - if len(samples) < 2: - return samples[0].color if samples else (0, 0, 0) - - # Find two closest samples - sorted_samples = sorted(samples, key=lambda s: s.offset) - - # Find samples surrounding target - lower = None - upper = None - for sample in sorted_samples: - if sample.offset <= target_offset: - lower = sample - else: - upper = sample - break - - if lower is None: - return sorted_samples[0].color - if upper is None: - return sorted_samples[-1].color - - # Interpolate - t = (target_offset - lower.offset) / (upper.offset - lower.offset) - r = lower.color[0] + t * (upper.color[0] - lower.color[0]) - g = lower.color[1] + t * (upper.color[1] - lower.color[1]) - b = lower.color[2] + t * (upper.color[2] - lower.color[2]) - - return (int(r), int(g), int(b)) - -# ═══════════════════════════════════════════════════════════════════════════ -# Quad-Sampled Frame Generator -# Generate frames with quad-sampled scanlines -# ═══════════════════════════════════════════════════════════════════════════ - -class QuadSampledFrameGenerator: - """Generate frames with quad-sampled scanlines""" - - def __init__(self): - self.interpolator = DSPScanlineInterpolator() - - def generate_scanline(self, base_color: Tuple[int, int, int], - line_number: int, frame_number: int) -> QuadSampledScanline: - """ - Generate quad-sampled scanline. - - 4 samples with subpixel offsets: 0.0, 0.25, 0.5, 0.75 - """ - samples = [] - - # Generate 4 subpixel samples with slight variations - for i in range(4): - offset = i / 4.0 - - # Subpixel variation based on line and frame - variation = math.sin(line_number * 0.1 + frame_number * 0.05 + i * 0.25) - - # Modulate color with variation - r = min(255, max(0, base_color[0] + variation * 20)) - g = min(255, max(0, base_color[1] + variation * 15)) - b = min(255, max(0, base_color[2] + variation * 10)) - - # Voltage level based on offset (for timing control) - voltage = 0.0 + offset * 5.0 # 0-5V range - - samples.append(SubpixelSample(offset, (int(r), int(g), int(b)), voltage)) - - return QuadSampledScanline(samples) - - def generate_frame(self, base_colors: List[Tuple[int, int, int]], - frame_number: int) -> List[Tuple[int, int, int]]: - """ - Generate frame with quad-sampled scanlines. - - Returns interpolated colors for each scanline. - """ - frame = [] - - for line_number, base_color in enumerate(base_colors): - scanline = self.generate_scanline(base_color, line_number, frame_number) - interpolated_color = scanline.interpolate() - frame.append(interpolated_color) - - return frame - - def generate_high_res_frame(self, base_colors: List[Tuple[int, int, int]], - frame_number: int) -> List[Tuple[int, int, int]]: - """ - Generate high-resolution frame (4x vertical). - - Returns 4 interpolated colors per scanline. - """ - frame = [] - - for line_number, base_color in enumerate(base_colors): - scanline = self.generate_scanline(base_color, line_number, frame_number) - - # Generate 4 interpolated colors per scanline - for i in range(4): - target_offset = i / 4.0 - interpolated = self.interpolator.bicubic_interpolation( - scanline.samples, target_offset - ) - frame.append(interpolated) - - return frame - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run quad-sampled scanline test""" - print("=" * 70) - print("QUAD-SAMPLED SCANLINES") - print("=" * 70) - - print("\n[*] Architecture:") - print(" Each scanline sampled 4 times with subpixel offsets") - print(" Offsets: 0.0, 0.25, 0.5, 0.75") - print(" DSP math interpolates between samples") - print(" Voltage levels control sampling timing") - print(" Effective resolution: 256×240 → 256×960 (4x vertical)") - - generator = QuadSampledFrameGenerator() - - # Create base colors (simple gradient) - print("\n[*] Creating base colors (gradient)...") - base_colors = [] - for i in range(240): - r = int((i / 240) * 255) - g = int((1 - i / 240) * 255) - b = 128 - base_colors.append((r, g, b)) - print(f" Created {len(base_colors)} base colors") - - # Generate standard frame - print("\n[*] Generating standard quad-sampled frame...") - standard_frame = generator.generate_frame(base_colors, frame_number=0) - print(f" Frame size: {len(standard_frame)} scanlines") - print(f" Sample colors: scanline 0 = {standard_frame[0]}, scanline 119 = {standard_frame[119]}, scanline 239 = {standard_frame[239]}") - - # Generate high-res frame (4x vertical) - print("\n[*] Generating high-resolution frame (4x vertical)...") - high_res_frame = generator.generate_high_res_frame(base_colors, frame_number=0) - print(f" Frame size: {len(high_res_frame)} scanlines (4x vertical)") - print(f" Sample colors: scanline 0 = {high_res_frame[0]}, scanline 479 = {high_res_frame[479]}, scanline 959 = {high_res_frame[959]}") - - # Calculate resolution - print("\n[*] Resolution Analysis:") - print(f" Physical NES resolution: 256×240") - print(f" Quad-sampled vertical resolution: 256×{len(high_res_frame)}") - print(f" Vertical resolution multiplier: {len(high_res_frame) / 240}x") - - # Animation test - print("\n[*] Generating 5-frame animation...") - for frame in range(5): - frame_data = generator.generate_frame(base_colors, frame_number=frame) - print(f" Frame {frame}: {len(frame_data)} scanlines") - - print("\n" + "=" * 70) - print("QUAD-SAMPLED SCANLINES COMPLETE") - print("=" * 70) - print("\n[*] Horrific: 4x temporal supersampling on 1x hardware") - print("[*] Wonderful: Effective 4x vertical resolution increase") - print("[*] Maximum retro insanity: quad sampling = 4x resolution") - print("\n[*] Can we generate 640×480?") - print(" Horizontal: 256 (fixed by NES PPU)") - print(" Vertical: 960 (4x quad-sampled)") - print(" Result: 256×960 (not 640×480, but 4x vertical)") - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/radical_adaptations.py b/5-Applications/scripts/radical_adaptations.py deleted file mode 100644 index 6309818f..00000000 --- a/5-Applications/scripts/radical_adaptations.py +++ /dev/null @@ -1,305 +0,0 @@ -#!/usr/bin/env python3 -""" -Radical adaptations finder — the most extreme biological solutions to physical problems. -Covers convergent evolution, novel biochemistry, and extinct engineering. -Adds to the 'Extremophile Bounds' domain and creates a new 'Radical Adaptations' domain. -""" - -import sqlite3 -import urllib.request -import urllib.parse -import json -import time -import os - -DB = "/home/allaun/physics_equations.db" - -conn = sqlite3.connect(DB) -cur = conn.cursor() - -# --- Create new domain --- -cur.execute("SELECT MAX(id) FROM domains") -new_did = cur.fetchone()[0] + 1 -cur.execute("INSERT INTO domains VALUES (?, 'Radical Adaptations', 'The most extreme biological innovations — convergent engineering across phyla, novel biochemistry, physical hacks that seem to violate constraints', NULL)", (new_did,)) - -# --- Equation injector --- -cur.execute("SELECT MAX(id) FROM equations"); eid = cur.fetchone()[0] -cur.execute("SELECT MAX(eq_number) FROM equations"); enum = cur.fetchone()[0] -def add_eq(title, sig, prec, year="various"): - global eid, enum; eid += 1; enum += 1 - cur.execute("INSERT INTO equations VALUES (?,?,?,?,?,?,?,?)", - (eid, enum, title, new_did, year, "Proven", sig, prec)) - return eid - -# ================================================================ -# LIVING SPECIES — RADICAL ADAPTATIONS -# ================================================================ - -eq_ids = {} - -eq_ids['crypto'] = add_eq( - "Cryptobiosis (Complete Metabolic Suspension) — Tardigrades, Rotifers, Brine Shrimp, Nematodes", - "Organisms that can lose 95-99% of body water, shut down all detectable metabolism for decades, and revive within hours of rehydration. Tardigrades survive -272°C (liquid helium), 150°C, 6000 atm pressure, hard vacuum + cosmic rays (10 days in LEO). The mechanism: replace intracellular water with trehalose glass (vitrification) that preserves macromolecular structure. The universe's ultimate backup system — if you can pause entropy, you can wait out anything.", - "Trehalose vitrification: replaces water's hydrogen bonds. Glass transition temp Tg of trehalose-water ~ −30°C, preserving protein/DNA conformation") -eq_ids['tardigrade'] = eq_ids['crypto'] - -eq_ids['cryo'] = add_eq( - "Freeze Tolerance (Solid Ice Survival) — Wood Frogs, Arctic Beetles, Springtails, Antarctic Midge", - "Rana sylvatica (wood frog) freezes solid each winter: 65% of body water turns to ice. Heart stops. Breathing stops. Brain activity zero. Glucose acts as cryoprotectant — blood sugar rises 100x (from 5 mM to 500 mM), preventing ice crystal formation inside cells while permitting it in extracellular spaces. Spring thaw: heart restarts before the brain, frog hops away within hours. Antarctic midge Belgica antarctica survives −20°C with 70% body water frozen — and can survive losing 40% of total body water.", - "Ice-nucleating proteins in extracellular fluid; glycerol + glucose as intracellular cryoprotectants. Freeze concentration of solutes must not exceed osmotic tolerance.") -eq_ids['woodfrog'] = eq_ids['cryo'] - -eq_ids['immortal'] = add_eq( - "Biological Immortality via Cellular Transdifferentiation — Turritopsis dohrnii (Immortal Jellyfish), Hydra", - "Turritopsis dohrnii reverts from adult medusa stage back to polyp when stressed, effectively restarting its life cycle — indefinitely. This is not just regeneration; it's complete cellular reprogramming of differentiated cells back to stem cells, then redifferentiation into a different body plan. Hydra never senesces — its stem cells continuously replace all body cells with zero age-related decline. These organisms have decoupled aging from chronological time.", - "Transdifferentiation: differentiated somatic cells revert to pluripotent state. Hydra: FoxO gene regulates continuous stem cell proliferation without tumor formation.") -eq_ids['jellyfish'] = eq_ids['immortal'] - -eq_ids['quantum_bio'] = add_eq( - "Quantum Biology — Magnetoreception via Radical Pair Mechanism (Birds), Photosynthetic Coherence (Plants/Bacteria), Olfaction via Vibrational Tunneling", - "European robins and other migratory birds can detect Earth's magnetic field (~50 μT) with their eyes. The leading mechanism involves cryptochrome proteins where photon absorption creates a radical pair whose spin dynamics are influenced by the magnetic field — a room-temperature quantum sensor in a biological system. Photosynthetic reaction centers achieve >95% quantum efficiency via coherent energy transfer through chromophore networks. Proposed: olfactory receptors may detect molecular vibrational frequencies via inelastic electron tunneling.", - "Radical pair: electron spin correlation sensitive to ~50 μT. Coherence time in FMO complex ~300 fs at 77K, ~100 fs at RT. Controversial but accumulating evidence.") -eq_ids['bird_mag'] = eq_ids['quantum_bio'] - -eq_ids['photon'] = add_eq( - "Single-Photon Detection in Biological Photoreceptors — Human Rod Cells, Amphibian Green Rods", - "Human rod photoreceptor cells can detect individual photons. A rod cell responds to a single photon with a ~1 pA current change. Behavioral experiments: humans can reliably report a flash of ~5-7 photons delivered to the retina (only ~10% reach rods due to optical losses in the eye), meaning the perceptual threshold is ~1 photon per rod. This is a room-temperature single-photon detector made of rhodopsin protein — the quantum noise floor of vision.", - "Rhodopsin quantum efficiency ~0.67. Thermal isomerization rate ~10⁻¹¹/s at 37°C (one spontaneous event per 160 years per molecule) — the ultimate dark noise floor.") -eq_ids['human_eye'] = eq_ids['photon'] - -eq_ids['electric'] = add_eq( - "Bioelectrogenesis — Electric Eels (860 V), Torpedo Rays (220 V), Elephantnose Fish (active electrolocation)", - "Electrophorus electricus generates 860 volts / 1 ampere pulses — enough to kill a horse — using specialized electrocytes derived from muscle cells, stacked in series (5,000-6,000 cells). The discharge is DC, not AC. Torpedo rays produce 220 V using a different evolutionary invention (modified gill arch musculature). Elephantnose fish (Gnathonemus) use weak electric fields (<1 V) for active electrolocation in murky waters — imaging their world through distortions in their self-generated field.", - "Electrocyte: modified muscle cell, 0.15 V each, stacked in series. Sodium channels clustered on posterior face, acetylcholine-activated. Discharge: all-or-nothing, ~500 Hz maximum rate.") -eq_ids['eel'] = eq_ids['electric'] - -eq_ids['cavitation'] = add_eq( - "Biological Cavitation / Sonoluminescence — Pistol Shrimp, Mantis Shrimp", - "Alpheidae (pistol/snapping shrimp) close their specialized claw at 100 km/h, creating a cavitation bubble that collapses with a 218 dB sound (loudest marine animal), a flash of light (sonoluminescence reaching ~4,700°C momentarily), and a shockwave that stuns prey. The flash is 10,000x too brief for human vision. This is a macroscopic quantum-ish phenomenon — cavitation collapse — generated by a living organism.", - "Cavitation bubble collapse temperature ~4700 K (sun's surface). Pressure at collapse ~80 MPa. Sonoluminescence flash duration ~10⁻¹⁰ s. Claw closure time ~300 μs, acceleration ~100,000 m/s².") -eq_ids['shrimp'] = eq_ids['cavitation'] - -eq_ids['camouflage'] = add_eq( - "Distributed Neural Camouflage — Cephalopod Chromatophore/Iridophore/Leucophore System, Cutaneous Light Sensing", - "Octopus, cuttlefish, and squid can match color, pattern, texture, and brightness of any substrate — in 200-700 ms — without centralized processing. Their skin contains chromatophores (pigment sacs controlled by radial muscles), iridophores (Bragg-stack reflectors that produce structural color), and leucophores (diffuse white scatterers). Critically: cephalopod skin expresses opsin photoproteins identical to those in their eyes — their skin sees the environment directly, enabling distributed control without routing through the brain.", - "Chromatophore expansion: radial muscle contraction in ~100 ms, controlled by motor neurons from chromatophore lobes. Iridophore: protein plates with tunable spacing (reflectin proteins) — active structural color. Papillae: hydrostatic muscles for 3D texture. Skin opsins: rhodopsin/r-opsin in chromatophore organs.") -eq_ids['octopus'] = eq_ids['camouflage'] - -eq_ids['endosymbiosis'] = add_eq( - "Primary Endosymbiosis — Mitochondrial and Plastid Acquisition, the Singular Evolutionary Event", - "The two most important events in the history of complex life: (1) an archaeon engulfed an α-proteobacterium ~1.5-2 billion years ago, creating the mitochondrion — the energy factory that enabled eukaryotic complexity; (2) a eukaryote engulfed a cyanobacterium ~1-1.5 billion years ago, creating the plastid — enabling photosynthesis in plants and algae. Both events happened exactly once each (with rare secondary/tertiary endosymbioses in some lineages). These aren't adaptations — they're the foundational architectural decisions of complex life. The mitochondrial inner membrane surface area per cell is ~14,000 m² in humans. Mitochondria still have their own DNA (37 genes in humans, 16,569 bp).", - "Mitochondrial genome: 16.6 kb circular DNA, 37 genes (13 proteins, 22 tRNAs, 2 rRNAs). Plastid genome: 120-200 kb, ~100 genes. ~99% of original bacterial genes transferred to nucleus. Electron transport chain: Complex I-V, 93 protein subunits (13 mtDNA-coded). Proton gradient: ~180 mV across inner membrane.") -eq_ids['mito'] = eq_ids['endosymbiosis'] - -eq_ids['silk'] = add_eq( - "Spider Silk — Tensile Strength Exceeding Steel, Toughness Exceeding Kevlar, Molecular Engineering", - "Dragline silk from Nephila clavipes (golden orb-weaver): tensile strength ~1.1 GPa (vs steel ~0.4-1.5 GPa), but toughness (energy to break) ~160 MJ/m³ — 3x tougher than Kevlar and 5x tougher than steel by weight. The secret: a nanocomposite of crystalline β-sheet nanocrystals (~2-5 nm) embedded in an amorphous semi-liquid matrix, with sacrificial hydrogen bonds that break and reform under stress. Spiders extrude this from aqueous solution at room temperature and ambient pressure — no toxic solvents, no high heat. Plus: Darwin's bark spider (Caerostris darwini) produces silk spanning 25m rivers. Some spiders use silk for ballooning — electrostatic repulsion + wind lift carrying them to 5 km altitude and across oceans.", - "β-sheet nanocrystals: poly(Ala) and poly(Gly-Ala) blocks; size ~2-5 nm; act as crosslinkers. Hydrogen bond clusters yield before breaking — sacrificial bond mechanism. Shear-thinning during spinning aligns polymer chains. dope → fiber transition: pH drop from 7.5 to 5.5 + ion exchange (Na⁺→K⁺) in spinning duct.") -eq_ids['spider'] = eq_ids['silk'] - -eq_ids['kleptoplasty'] = add_eq( - "Kleptoplasty (Organelle Theft) — Elysia chlorotica (Photosynthetic Sea Slug), Hatena, Paulinella", - "Elysia chlorotica eats the alga Vaucheria litorea, digests everything except the chloroplasts, and keeps them functional inside its own gut cells for 9-12 months — photosynthesizing and living off sugar for most of its adult life. The slug's genome contains algal nuclear genes (including photosystem proteins) acquired via horizontal gene transfer. Even wilder: Paulinella chromatophora independently 'domesticated' a cyanobacterium as a permanent endosymbiont — a primary plastid acquisition in progress, separate from the plant lineage. Hatena arenicola: steals a Nephroselmis chloroplast and permanently transforms its body plan to accommodate it.", - "Slug acquires algal psbO (photosystem II stability protein) and fcp (light-harvesting complex) genes. Horizontal gene transfer from alga → slug nucleus confirmed. Paulinella chromatophore genome: ~1 Mb, ~870 genes — still larger than plant plastid genomes. Chromatophore division synchronized with host.") -eq_ids['slug'] = eq_ids['kleptoplasty'] - -eq_ids['echolocation'] = add_eq( - "Biological Sonar — Echolocation Convergent Evolution in Bats and Toothed Whales (with Molecular Convergence)", - "Echolocation evolved independently in bats and toothed whales (dolphins/porpoises/sperm whales), involving ~200 convergent amino acid substitutions — particularly in the prestin gene (outer hair cell motor protein in the cochlea). Bats emit 100+ dB ultrasonic pulses at 20-200 kHz and detect echoes from objects as fine as 0.05 mm. Dolphins can detect a 2.5 cm sphere at 100m distance. The physics: the matched-filter problem — correlating transmitted signal with returning echo — is solved by auditory cortex neurons tuned to specific frequency-modulated sweeps.", - "Prestin gene: SLC26A5, 14 convergent amino acid substitutions between bat and dolphin lineages. Bat call frequencies: 20-200 kHz (most 20-80 kHz). Resolution: ~0.05 mm (moth scales). Dolphin: peak frequency 40-130 kHz, click duration 50-100 μs. Cuvier's beaked whale dive: 137 min breathing-hold — echolocating at 3 km depth.") -eq_ids['bat'] = eq_ids['echolocation'] - -eq_ids['chem_defense'] = add_eq( - "Explosive Biochemistry — Bombardier Beetle, Skunk Spray, Venom Systems (Cone Snail, Box Jellyfish, Blue-Ringed Octopus)", - "Bombardier beetle (Brachinus) stores hydroquinone and hydrogen peroxide in separate abdominal chambers. When threatened, it mixes them in a reaction chamber containing catalase and peroxidase enzymes — the mixture reaches 100°C and explodes from a rotatable nozzle at the attacker. The beetle pulses the spray 500+ times/second to avoid cooking itself. Cone snails (Conus) produce 100-200 distinct peptide toxins per species, each targeting a specific ion channel subtype — the most sophisticated pharmacological arsenal in nature. Box jellyfish venom causes cardiovascular collapse in 2-5 minutes — the fastest-acting venom known. Blue-ringed octopus: tetrodotoxin (same as pufferfish, but produced by symbiotic bacteria).", - "Bombardier beetle reaction: C₆H₄(OH)₂ + H₂O₂ → C₆H₄O₂ + 2H₂O (catalyzed), exothermic to ~100°C. Pressure in reaction chamber: pulsed release at 500 Hz to prevent thermal runaway. Tetrodotoxin: blocks voltage-gated Na⁺ channels, K_d ~1-10 nM. LD₅₀ ~10 μg/kg (human) — one octopus carries enough for 26 adults.") -eq_ids['beetle'] = eq_ids['chem_defense'] - -eq_ids['regeneration'] = add_eq( - "Full Organ/Body-Plan Regeneration — Axolotl, Planaria, Zebrafish, Spiny Mouse (with Dedifferentiation vs Stem Cell Pools)", - "Axolotl (Ambystoma mexicanum) regenerates entire limbs (bone, muscle, nerves, skin), spinal cord, heart ventricle, jaw, tail, and portions of brain — without scarring. The mechanism: differentiated cells at the wound site dedifferentiate into a blastema (pluripotent-like mass), which re-executes the developmental program. Planaria (flatworms) can regenerate from a fragment containing just 1/279th of the original body — the entire body plan is reconstructed, including brain and eyes, from a tiny piece, thanks to abundant pluripotent neoblasts (30% of all cells). Acoels (basal bilaterians) have the same neoblast system, suggesting whole-body regeneration is ancestral.", - "Blastema formation: dedifferentiation signals (Wnt, FGF, BMP, Shh). Macrophage-dependent: without macrophages, scar forms. Planarian neoblasts: Piwi+ / bruno-like+ / EGFR signaling. Positional control genes (Wnt/β-catenin gradient for anterior-posterior axis).") -eq_ids['axolotl'] = eq_ids['regeneration'] - -eq_ids['social_farm'] = add_eq( - "Agriculture by Non-Humans (Convergent Evolution ×3) — Leaf-Cutter Ants, Termites, Ambrosia Beetles (all farming fungus for 25-60 MYA)", - "Leaf-cutter ants (Atta, Acromyrmex) don't eat the leaves they cut; they use them as substrate for a domesticated fungus (Leucoagaricus gongylophorus) that produces swollen hyphal tips (gongylidia) rich in lipids and carbohydrates. The ant-fungus mutualism is 50-60 million years old. The ants carry antibiotic-producing bacteria (Pseudonocardia) on their cuticle to suppress parasitic Escovopsis mold. Termites farm Termitomyces fungus in climate-controlled mounds with passive ventilation — fungal gardens maintained at exactly 30°C regardless of external temperature. Ambrosia beetles bore galleries in trees and inoculate them with spores of their specific fungus carried in mycangia (specialized body pockets). Each lineage has co-evolved with its specific fungus for 25-60 million years — the mutualism is obligate for both partners.", - "Atta colony: 5-8 million workers, fungus garden ~2-3 m³. Pseudonocardia on ant cuticle: produces dentigerumycin, candicidin. Termite mound: solar-powered convection ventilation; core T=30±0.5°C. Termitomyces: 30+ described species, each specific to termite genus. Ambrosia beetle: evolved at least 12 independent times across Scolytinae + Platypodinae.") -eq_ids['ant_farm'] = eq_ids['social_farm'] - -eq_ids['mole_rat'] = add_eq( - "Cancer Resistance + Extended Longevity — Naked Mole Rat (Heterocephalus glaber), Blind Mole Rat (Spalax), Brandt's Bat", - "Naked mole rats: maximum lifespan >37 years (vs ~4 years for similar-size mice — 10×), and have NEVER been observed to develop spontaneous cancer in thousands of necropsies. The mechanism: their fibroblasts secrete extremely high-molecular-weight hyaluronan (~6-12 MDa vs ~0.5-2 MDa in humans) that fills extracellular space and prevents cell-to-cell contact needed for tumor formation. Additionally, they have hyper-sensitive contact inhibition (early-stage growth arrest at much lower cell density than other mammals). Blind mole rats use a different mechanism: concerted necrotic cell death via interferon-β release at hyperplasia onset. Brandt's bat (Myotis brandtii): ~41 year lifespan for a 7g animal — weight-specific longevity record for mammals.", - "HAS2 gene: duplicated in naked mole rat, producing HMM-HA (6-12 MDa). Contact inhibition triggered at ~50% confluence (vs ~90% in mouse). p16^{INK4a} and p27^{Kip1}: early induction. INK4 locus: additional p15^{INK4b}-p16^{INK4a} fusion gene. Blind mole rat: IFN-β → p53/pRb activation → concerted necrosis.") -eq_ids['molerat'] = eq_ids['mole_rat'] - -eq_ids['biolum'] = add_eq( - "Bioluminescence — 40+ Independent Evolutionary Origins, Bacterial Symbiosis, Counterillumination, Lure Predation", - "Bioluminescence has evolved independently at least 40 times, using the same chemical reaction: luciferin + O₂ → oxyluciferin + light (catalyzed by luciferase). The convergence is so strong that unrelated organisms (fireflies, click beetles, railroad worms) use the same luciferin molecule. Deep-sea anglerfish: symbiotic bioluminescent bacteria in a lure (esca); the fish feeds the bacteria, and the bacteria produce light that attracts prey to the fish. 76% of deep-pelagic organisms are bioluminescent. Midwater squid use counterillumination — photophores on their ventral surface match downwelling light exactly, eliminating their silhouette against the sky. Dinoflagellates: mechanical stimulation (boat wake, swimming fish) triggers a flash — the original 'burglar alarm' hypothesis.", - "Firefly luciferin: C₁₁H₈N₂O₃S₂. Quantum yield: 0.41-0.88 (firefly luciferase — one of the most efficient chemiluminescent reactions). ATP-dependent: requires Mg-ATP. Coelenterazine: used by 9+ phyla of marine organisms. Bacterial lux operon: luxCDABEG, quorum sensing (autoinducer / LuxI-LuxR). Deep-sea: 76% of organisms in mesopelagic zone are bioluminescent.") -eq_ids['firefly'] = eq_ids['biolum'] - -eq_ids['mimicry'] = add_eq( - "Molecular & Morphological Mimicry — Batesian, Müllerian, Aggressive; Orchid Sexual Deception, Myrmecomorphy (Ant Mimicry in 20+ Orders)", - "Ophrys orchids produce flowers that precisely mimic the shape, color, texture, and pheromonal blend of female bees/wasps — males attempt copulation, fail, and pollinate the orchid. The chemical mimicry is so precise that it reproduces the exact (Z)-9-alkene cuticular hydrocarbons of the target species' females. In myrmecomorphy, spiders, mantids, beetles, hemipterans, and over 2000 species in 20+ arthropod orders have convergently evolved to mimic the morphology, movement, and even chemical signatures of ants — many to prey on the ants they resemble. Dead-leaf butterflies (Kallima): the wing underside has evolved 'leaf damage' patterns, fake midribs, and fake fungal spots. Walking stick insects (Phasmatodea): eggs mimic seeds (with a lipid-rich capitulum) that ants carry underground — the eggs get protected from parasitoids.", - "Ophrys: alkene positional isomers match target Andrena/Megachile bee sex pheromone blends with <5% variation. Dead-leaf mimic: Kallima inachus ventral wing pattern matches local host plant leaf damage + fungal spot patterns. Myrmecomorph: body elongation + petiolate waist + antennal illusion (1st leg pair moved forward as 'antennae'). Phasmid egg capitulum: elaiosome lipid, ~2% of egg mass — ant-dispersed.") -eq_ids['orchid'] = eq_ids['mimicry'] - -# ================================================================ -# EXTINCT SPECIES — PHYSICAL EXTREMES -# ================================================================ - -eq_ids['sauropod'] = add_eq( - "Sauropod Hemodynamics — Pumping Blood 8-10m Vertically Against Gravity, the Largest Terrestrial Organisms Ever (Argentinosaurus ~70-100 tonnes)", - "How did sauropods pump blood 8-10 meters vertically to a brain without the giraffe's problem of cerebral edema? The physics: blood pressure at heart level must overcome ρ·g·h for every meter of neck. For a 9m vertical neck (Sauroposeidon), ρ_blood·g·9m ≈ 700 mmHg at the heart — roughly 6× human systolic pressure. But this would burst capillaries. Hypotheses: (1) siphon effect — carotid/jugular loop with counter-current, head-toe pressure difference could be as low as venous return pressure; (2) multiple hearts; (3) horizontal neck posture (diplodocids likely kept necks lower; brachiosaurids had vertical). The heart alone of Barosaurus may have weighed ~1.5 tonnes. Additionally: pneumatic invasion of cervical vertebrae by the respiratory system reduced neck mass — vertebrae were up to 75% air. The engineering problem of large body mass: Argentinosaurus may have massed 70-100 tonnes, with column-like limbs (graviportal posture), digitigrade manus, and a respiratory system extending into the skeleton (pneumaticity) to reduce weight. Limb bone cross-sectional area scales as M^{0.75} — elephants are near the maximum for mammalian-style limbs; sauropods solved it differently.", - "Blood pressure at heart = ρ·g·h + P_brain + venous resistance loss. ρ=1060 kg/m³, g=9.81, h=9m → ΔP≈700 mmHg above brain perfusion pressure. Giraffe (2m neck): systolic ~250 mmHg. Barosaurus reconstruction: estimated heart mass ~1.5 tonnes, wall thickness ~15-20 cm. Pneumaticity: sauropod cervical vertebrae up to 75% air by volume — air sac system invaded bone. This is the universe's largest terrestrial self-supporting structure ever.") -eq_ids['dino_neck'] = eq_ids['sauropod'] - -eq_ids['meganeura'] = add_eq( - "Giant Carboniferous Arthropods — Meganeura (70cm Dragonfly), Arthropleura (2.5m Millipede), Pulmonoscorpius (70cm Scorpion) — Oxygen Enables Insect Gigantism", - "The Carboniferous (~300 MYA) had atmospheric O₂ at ~35% (vs 21% today). Insects breathe through a passive tracheal system — oxygen diffuses through spiracles and tubules, not actively pumped. The diffusion limit for O₂ in a blind-ended tracheal system imposes a maximum body diameter of ~2-3 cm at modern O₂ levels. At 35% O₂, the effective diffusion gradient doubles, allowing proportionally larger insects. The Dragonfly Meganeura monyi achieved 70 cm wingspan — the tracheal tubes in its thorax would have had ~2× the oxygen partial pressure gradient of modern dragonflies. Arthropleura, at 2.5m length and ~50 cm width, would be utterly unable to oxygenate its tissues at modern O₂ levels. When O₂ fell to ~15% at the Permian-Triassic boundary, giant insects went extinct — not from climate but from asphyxiation. This is a hard limit set by passive diffusion: any organism using passive gas exchange is directly bounded by atmospheric partial pressure of oxygen.", - "Tracheal O₂ diffusion: J = D·ΔP/dx. For cylindrical body of radius r, maximum radius scales as √(pO₂). At 35% O₂, r_max increases by √(0.35/0.21) ≈ 1.29×. But tracheal branching + spiracles can't fully exploit this — the empirical bound seems to be ~3-4× modern insect sizes, suggesting other limits (mechanical, predation, developmental) also apply. Arthropleura at 2.5m: tracheal diffusion limit predicts ~50 cm max diameter at 35% O₂ — consistent with the widest Arthropleura specimens.") -eq_ids['giant_bug'] = eq_ids['meganeura'] - -eq_ids['pterosaur'] = add_eq( - "Pterosaur Giant Flight — Quetzalcoatlus 11m Wingspan, Estimated 200-250 kg, Largest Flying Organism Ever", - "How does a 250 kg animal fly? Modern birds max out at ~20 kg (bustards, swans). The physics: lift L = ½ρv²SC_L, drag D = ½ρv²SC_D. For a 250 kg pterosaur, wing loading W/S = mg/S ≈ 250kg × 9.81 / 10m² ≈ 245 N/m² ≈ 25 kg/m² — comparable to a hang-glider. But the launch problem: birds jump with legs; pterosaurs launched quadrupedally using explosive forelimb power — their giant wings WERE the launch mechanism. Pterosaur bones were pneumatized (air-filled) with wall thickness ~0.1-1.0 mm — like modern birds but at much larger scale. The wing membrane (patagium) contained structural fibers (aktinofibrils) that stiffened it aerodynamically — unlike bat wings which are muscular/elastic, pterosaur wings were stiff airfoils with individual fiber-controlled camber. Quetzalcoatlus stood 5-6 m tall on the ground — the height of a giraffe, but capable of powered flight.", - "Wing loading ~25 kg/m² (Quetzalcoatlus, estimated). Modern albatross: ~8 kg/m². Launch: quadrupedal vault — forelimbs provide 90% of launch impulse, peaking at ~2-3g. Bone pneumaticity: humeral wall thickness ~0.5 mm in Quetzalcoatlus (vs 1-2 mm in a 10 kg bird). Wing fibers (aktinofibrils): keratin-like, ~0.1-0.5 mm diameter, spaced ~0.2 mm apart — structural reinforcement of skin membrane.") -eq_ids['quetz'] = eq_ids['pterosaur'] - -eq_ids['megalodon'] = add_eq( - "Megalodon Bite Force — 108,000-182,000 N (~11-19 tonnes-force), Largest Bite Force of Any Organism Ever", - "Otodus megalodon (15-18m, ~50 tonnes) had an estimated anterior bite force of 108,000-182,000 Newtons — equivalent to the bite of a T. rex (~35,000 N) multiplied by 3-5×. This is a mechanical engineering limit: megalodon teeth are triangular, serrated, and up to 18 cm slant height — designed for shearing through whale blubber and bone. The jaw adductor muscle mass in megalodon would have been ~10-15% of total body mass (~5-7 tonnes of jaw muscle). By comparison: great white shark (Carcharodon) bite force ~18,000 N, saltwater crocodile ~16,000 N. The limit isn't muscle strength — it's the compressive strength of the tooth material (enameloid fluoroapatite, ~400 MPa compressive). Megalodon teeth occasionally show tip fractures indicating they approached the material limit of their own dentition.", - "Bite force estimated from 3D FEA of fossil vertebrae + jaw reconstruction + scaling from Carcharodon. Compressive strength of shark enameloid ~350-450 MPa. Tooth tip stress concentration: F/(πr²_tip) must remain below enameloid fracture stress. For r_tip ~0.5 mm, maximum tip load ~300 N → megalodon teeth distributed load across 200+ teeth contacting simultaneously, reducing per-tooth stress. Tooth serrations: reduce initiation fracture toughness by ~30% via stress concentration at serration tips — the self-sharpening mechanism.") -eq_ids['meg'] = eq_ids['megalodon'] - -eq_ids['ichthyosaur'] = add_eq( - "Ichthyosaur Eyes — 25-26 cm Diameter, Largest Eyes of Any Vertebrate Ever (Temnodontosaurus, Ophthalmosaurus)", - "Temnodontosaurus and Ophthalmosaurus had eyes up to 25-26 cm in diameter — larger than a dinner plate. For comparison: the largest modern animal eye is the colossal squid (~30 cm), and the blue whale's eye is only ~15 cm. But these ichthyosaurs achieved this eye size in a 6-9m body (not 30m like a blue whale). The sclerotic ring (a bony ring supporting the eye in many vertebrates) is preserved in fossils and directly gives eye diameter. Why? Probably for hunting in the mesopelagic zone (200-1000m) — at these depths, the only light is bioluminescent prey. Larger pupil area (∝ D²) collects more photons. At 25 cm diameter and an estimated pupil size of ~15 cm, the light-gathering area is ~175 cm² — vs ~0.4 cm² for human dark-adapted pupil, a factor of ~440×. This allowed ichthyosaurs to see bioluminescent prey at extreme depths.", - "Eye aperture ∝ D²: ~26 cm diameter → collection area ~530 cm² total, pupil ~175 cm². f-number of vertebrate eye ~2-4 in water → long focal length ~50 cm. Retina area ~50-100 cm² with high ganglion cell density (estimated from sclerotic ring diameter vs skull/brain cavity ratio). For comparison: giant squid eye 27-30 cm (largest modern), Mesonychoteuthis.") -eq_ids['ichthy_eye'] = eq_ids['ichthyosaur'] - -eq_ids['cambrian'] = add_eq( - "Cambrian Body Plan Explosion — Opabinia (5 Eyes + Trunk Claw), Anomalocaris (Meter-Long Compound-Eyed Predator), Hallucigenia (Reconstructed Upside Down), Wiwaxia (Scale Armor), Odontogriphus (Radula Scraper)", - "The Cambrian (~540-485 MYA) produced body plans so alien that paleontologists initially reconstructed many backwards or upside down. Hallucigenia was originally interpreted as walking on rigid spines with tentacles as dorsal feeding appendages — the correct reconstruction inverted it. Opabinia: 5 mushroom-shaped compound eyes on stalks + a flexible frontal proboscis ending in a grasping claw + segmented body with lateral lobes — a body plan that fits no modern phylum. Anomalocaris: meter-long apex predator with a circular mouth of overlapping plates (like a pineapple slice) surrounded by grasping appendages, with true compound eyes (>16,000 ommatidia). The Burgess Shale fauna challenges the assumption that complex life converges on familiar forms — many Cambrian body plans represent 'failed experiments' that had no evolutionary descendants. This is the universe showing you that carbon-based life has far more morphological degrees of freedom than what survived to the present.", - "Anomalocaris: compound eyes with >16,000 ommatidia (rivaling modern dragonflies). Body length 0.3-1.0 m. Oral cone: 32 overlapping plates, tri-radial symmetry (yes, 3-fold — rare in animal body plans). Opabinia: 5 eyes, segmented but no known phylum affinity — possibly stem-group arthropod. Hallucigenia: now confidently placed as stem-group onychophoran (velvet worms) — the spines were dorsal defense, the 'tentacles' were lobopod walking legs. These are the weirdest legitimate scientific body plans in the fossil record.") -eq_ids['hallucigenia'] = eq_ids['cambrian'] - -# ================================================================ -# API FETCHERS -# ================================================================ -def s2_fetch(q, lim=5): - out = [] - try: - u = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode({ - "query": q, "limit": lim, "fields": "title,year,externalIds,journal,citationCount"}) - r = urllib.request.Request(u, headers={"User-Agent": "RadAdapt/1.0"}) - with urllib.request.urlopen(r, timeout=20) as res: - d = json.loads(res.read().decode()) - for p in d.get("data",[]): - e = p.get("externalIds",{}) or {} - j = p.get("journal",{}) or {} - out.append({"title":p.get("title","")[:250],"year":p.get("year")or 0, - "doi":e.get("DOI",""),"journal":j.get("name",""),"src":"S2"}) - except: pass - return out - -def crossref_fetch(q, lim=5): - out = [] - try: - u = "https://api.crossref.org/works?" + urllib.parse.urlencode({ - "query": q, "rows": lim, "sort": "relevance", "filter": "type:journal-article"}) - r = urllib.request.Request(u, headers={"User-Agent": "RadAdapt/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r, timeout=20) as res: - d = json.loads(res.read().decode()) - for i in d.get("message",{}).get("items",[]): - t = (i.get("title",[""]) or [""])[0] - y = i.get("created",{}).get("date-parts",[[0]])[0][0] - d2 = i.get("DOI","") - j2 = (i.get("container-title",[""]) or [""])[0] - if t: out.append({"title":t[:250],"year":y,"doi":d2,"journal":j2,"src":"Crossref"}) - except: pass - return out - -def openalex_fetch(q, lim=5): - out = [] - try: - u = "https://api.openalex.org/works?" + urllib.parse.urlencode({ - "search": q, "per_page": lim, "sort": "cited_by_count:desc"}) - r = urllib.request.Request(u, headers={"User-Agent": "mailto:r@x.com"}) - with urllib.request.urlopen(r, timeout=20) as res: - d = json.loads(res.read().decode()) - for i in d.get("results",[]): - t = i.get("title","") - y = i.get("publication_year")or 0 - d2 = i.get("doi","") - j2 = "" - if i.get("primary_location") and i["primary_location"].get("source"): - j2 = i["primary_location"]["source"].get("display_name","") - if t: out.append({"title":t[:250],"year":y,"doi":d2,"journal":j2,"src":"OpenAlex"}) - except: pass - return out - -# Research queries -QUERIES = [ - ("tardigrade cryptobiosis trehalose glass vitrification survival mechanism", "crypto"), - ("wood frog Rana sylvatica freeze tolerance glucose cryoprotectant", "cryo"), - ("Turritopsis dohrnii immortal jellyfish transdifferentiation rejuvenation", "immortal"), - ("cryptochrome magnetoreception radical pair mechanism bird navigation quantum biology", "quantum_bio"), - ("human rod photoreceptor single photon detection threshold", "photon"), - ("electric eel electrophorus electrocyte voltage generation sodium channel", "electric"), - ("pistol shrimp snapping cavitation sonoluminescence bubble collapse", "cavitation"), - ("cuttlefish octopus chromatophore iridophore camouflage body pattern", "camouflage"), - ("mitochondrial origin endosymbiosis alpha proteobacterium eukaryogenesis", "endosymbiosis"), - ("spider silk beta sheet nanocrystal toughness tensile molecular dynamics dragline", "silk"), - ("Elysia chlorotica kleptoplasty chloroplast horizontal gene transfer photosynthesis", "kleptoplasty"), - ("echolocation prestin molecular convergence bats dolphins toothed whales", "echolocation"), - ("bombardier beetle hydroquinone hydrogen peroxide explosive biochemistry catalase", "chem_defense"), - ("axolotl regeneration blastema dedifferentiation limb spinal cord mechanism", "regeneration"), - ("leaf cutter ant fungus mutualism atta acromyrmex Leucoagaricus coevolution", "social_farm"), - ("naked mole rat cancer resistance hyaluronan high molecular weight longevity", "mole_rat"), - ("bioluminescence firefly luciferin luciferase convergent evolution independent origins", "biolum"), - ("Ophrys orchid sexual deception insect pheromone mimicry chemical convergence", "mimicry"), - ("sauropod neck posture blood pressure cardiovascular physiology Barosaurus", "sauropod"), - ("Carboniferous Meganeura Arthropleura gigantism oxygen pulse tracheal diffusion insect", "meganeura"), - ("Quetzalcoatlus pterosaur giant flight wing loading pneumatic bone quadrupedal launch", "pterosaur"), - ("megalodon bite force tooth enameloid compressive strength finite element analysis", "megalodon"), - ("ichthyosaur Ophthalmosaurus sclerotic ring eye diameter mesopelagic vision", "ichthyosaur"), - ("Burgess Shale Cambrian Anomalocaris Hallucigenia Opabinia body plan disparity", "cambrian"), -] - -# API rotation -print(f"Searching {len(QUERIES)} adaptation queries across Crossref + OpenAlex + S2...\n") -total = 0 -rows = [] -start = time.time() -apis = [(crossref_fetch,1.5), (openalex_fetch,1.5), (s2_fetch,2.0), (crossref_fetch,1.5), (openalex_fetch,1.5), (s2_fetch,2.0)] - -for i, (q, tag) in enumerate(QUERIES): - fn, delay = apis[i % len(apis)] - papers = fn(q, 5) - this_eq = eq_ids.get(tag, list(eq_ids.values())[i % len(eq_ids)]) - for j, p in enumerate(papers): - rows.append((this_eq, p['title'], - f"{p['src']}: {p.get('journal','')}" if p.get('journal') else p['src'], - p['year'], p.get('doi', p['src']), "Radical adaptation ref.")) - total += 1 - short = q[:60] - print(f" {'✓' if papers else '○'} [{papers[0]['src'] if papers else '---':10s}] {short:60s} → {len(papers):2d}p | {total:3d} total | {time.time()-start:.0f}s", flush=True) - time.sleep(delay) - -cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", rows) -conn.commit() - -cur.execute("SELECT COUNT(*) FROM verifications WHERE status='Radical adaptation ref.'") -c1 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations WHERE domain_id=?", (new_did,)) -c2 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM verifications") -c3 = cur.fetchone()[0] -print(f"\n═══ RADICAL ADAPTATIONS ═══") -print(f" {c2} adaptation equations ({len(QUERIES)} queries)") -print(f" {c1} paper references") -print(f" DB total: {c3} verifications") -conn.close() -print(f" {DB}") diff --git a/5-Applications/scripts/ram_controller_computational.py b/5-Applications/scripts/ram_controller_computational.py deleted file mode 100644 index 62939081..00000000 --- a/5-Applications/scripts/ram_controller_computational.py +++ /dev/null @@ -1,208 +0,0 @@ -#!/usr/bin/env python3 -""" -RAM Controller Computational Repurposing -Analyzes onboard RAM controller for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class RAMControllerComputational: - """Analyzes RAM controller for general computation.""" - - def __init__(self): - self.ram_controller = { - "device": "AMD Raphael/Granite Ridge Data Fabric", - "functions": "Functions 0-7 (00:18.0-00:18.7)", - "iommu_group": "11", - "memory_capacity": "31.1 GB (31879804 kB)", - "memory_available": "13.5 GB (13855816 kB)", - "memory_channels": "Dual-channel DDR5", - "computational_potential": "HIGH (memory scheduling, interleaving, prefetching)" - } - - self.ram_capabilities = { - "memory_scheduling": "DRAM scheduling and arbitration", - "memory_interleaving": "Dual-channel memory interleaving", - "memory_prefetching": "Hardware prefetching", - "memory_bandwidth": "50-100 GB/s (DDR5)", - "latency": "10-100ns (memory access)", - "power": "10-20W (memory controller)" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of RAM controller.""" - analysis = { - "memory_scheduling": { - "feasible": True, - "mode": "Memory scheduling computation", - "description": "Use DRAM scheduling for computational arbitration", - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "power": "10-20W" - }, - "memory_interleaving": { - "feasible": True, - "mode": "Memory interleaving computation", - "description": "Use dual-channel interleaving for parallel computation", - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "power": "10-20W" - }, - "memory_prefetching": { - "feasible": True, - "mode": "Memory prefetching computation", - "description": "Use hardware prefetching for predictive computation", - "throughput": "Memory bandwidth limited", - "latency": "10-100ns (prefetch)", - "power": "10-20W" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design RAM controller-based computational approach.""" - approach = { - "memory_scheduling_computation": { - "concept": "Use memory scheduling for computation", - "implementation": "Manipulate DRAM scheduling for computational arbitration", - "operations": ["scheduling arithmetic", "arbitration logic", "priority queues"], - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "power": "10-20W" - }, - "memory_interleaving_computation": { - "concept": "Use memory interleaving for computation", - "implementation": "Use dual-channel interleaving for parallel computation", - "operations": ["interleaved arithmetic", "parallel access", "channel switching"], - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "power": "10-20W" - }, - "memory_prefetching_computation": { - "concept": "Use memory prefetching for computation", - "implementation": "Use hardware prefetching for predictive computation", - "operations": ["prefetch prediction", "pattern recognition", "streaming computation"], - "throughput": "Memory bandwidth limited", - "latency": "10-100ns (prefetch)", - "power": "10-20W" - }, - "memory_address_computation": { - "concept": "Use memory addressing for computation", - "implementation": "Use memory address translation for computation", - "operations": ["address arithmetic", "translation logic", "page table computation"], - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "power": "10-20W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of RAM controller computation.""" - performance = { - "memory_scheduling": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "precision": "64-bit addresses", - "operations": "scheduling arithmetic", - "power": "10-20W" - }, - "memory_interleaving": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "precision": "64-bit addresses", - "operations": "interleaved arithmetic", - "power": "10-20W" - }, - "memory_prefetching": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (prefetch)", - "precision": "64-bit addresses", - "operations": "prefetch prediction", - "power": "10-20W" - }, - "memory_address": { - "throughput": "Memory bandwidth limited (50-100 GB/s)", - "latency": "10-100ns (memory access)", - "precision": "64-bit addresses", - "operations": "address arithmetic", - "power": "10-20W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run RAM controller computational analysis.""" - print("=" * 60) - print("RAM CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze RAM controller - print("\n[1/4] Analyzing RAM controller...") - print(f" Device: {self.ram_controller['device']}") - print(f" Functions: {self.ram_controller['functions']}") - print(f" Memory Capacity: {self.ram_controller['memory_capacity']}") - print(f" Memory Available: {self.ram_controller['memory_available']}") - print(f" Computational Potential: {self.ram_controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" Memory Scheduling: {potential['memory_scheduling']['feasible']}") - print(f" Memory Interleaving: {potential['memory_interleaving']['feasible']}") - print(f" Memory Prefetching: {potential['memory_prefetching']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" Memory Scheduling: {performance['memory_scheduling']['throughput']}") - print(f" Memory Interleaving: {performance['memory_interleaving']['throughput']}") - print(f" Memory Prefetching: {performance['memory_prefetching']['throughput']}") - print(f" Memory Address: {performance['memory_address']['throughput']}") - - print("\n" + "=" * 60) - print("RAM CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "ram_controller": self.ram_controller, - "ram_capabilities": self.ram_capabilities, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = RAMControllerComputational() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "ram_controller_computational.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("RAM CONTROLLER COMPUTATIONAL SUMMARY") - print("=" * 60) - print(f"Device: {results['ram_controller']['device']}") - print(f"Memory Capacity: {results['ram_controller']['memory_capacity']}") - print(f"Computational Potential: {results['ram_controller']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['memory_scheduling']['throughput']}") diff --git a/5-Applications/scripts/realtime_rgflow_trader.py b/5-Applications/scripts/realtime_rgflow_trader.py deleted file mode 100644 index 32cddb6f..00000000 --- a/5-Applications/scripts/realtime_rgflow_trader.py +++ /dev/null @@ -1,87 +0,0 @@ -#!/usr/bin/env python3 -""" -Real-time RGFlow Trader: BTC-USD -Informatic Prediction via Manifold Coherence. -""" - -import sys -import json -import numpy as np -import subprocess -from pathlib import Path -import logging - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from scripts.rgflow_blind_detector import BlindDetector - -logging.basicConfig(level=logging.ERROR) - -def get_live_data(symbol="BTC-USD"): - url = f"https://query1.finance.yahoo.com/v8/finance/chart/{symbol}?interval=1m&range=2h" - cmd = ["curl", "-s", "-H", "User-Agent: Mozilla/5.0", url] - out = subprocess.check_output(cmd) - data = json.loads(out) - prices = data['chart']['result'][0]['indicators']['quote'][0]['close'] - # Filter out None values - prices = [p for p in prices if p is not None] - return np.array(prices) - -def predict_signal(): - print("--- REAL-TIME RGFLOW PREDICTIVE AUDIT [BTC-USD] ---") - prices = get_live_data() - - if len(prices) < 20: - print("Error: Not enough live data.") - return - - print(f"Acquired {len(prices)} live price points. Latest: ${prices[-1]:,.2f}") - - # 1. Convert to Log Returns (The Informatic Genome) - returns = np.diff(np.log(prices)) - - detector = BlindDetector() - - # Audit sliding windows across the last 60 minutes - window_size = 20 - sigmas = [] - - for i in range(len(returns) - window_size + 1): - window = returns[i : i + window_size] - # Standardize for detector - win_norm = (window - np.mean(window)) / (np.std(window) + 1e-9) - - # Heuristic State - mu_q = np.std(np.diff(win_norm)) * 0.5 - c_q = np.corrcoef(win_norm[:-1], win_norm[1:])[0, 1] if len(win_norm) > 1 else 0 - sigma_q = 1.0 + (c_q * 0.3) - (mu_q * 0.2) - - sigmas.append(sigma_q) - - # 2. Analyze Momentum - current_sigma = sigmas[-1] - prev_sigma = sigmas[-5] if len(sigmas) > 5 else sigmas[0] - velocity = current_sigma - prev_sigma - - print(f"\nCurrent Informatic Coherence (σ): {current_sigma:.4f}") - print(f"Manifold Velocity (Δσ): {velocity:+.4f}") - - # 3. SIGNAL GENERATION - print("\n--- INFORMATIC PREDICTION ---") - if velocity > 0.02 and current_sigma > 1.05: - print("SIGNAL: [🚀 JUMP EXPECTED ]") - print("REASON: Lawful Resonance. The market intent is crystallizing into a scale-stable trajectory.") - elif velocity < -0.02 or current_sigma < 0.95: - print("SIGNAL: [📉 DIP EXPECTED ]") - print("REASON: Informatic Sabotage. Manifold stability is collapsing into the noise attractor.") - else: - print("SIGNAL: [⚖️ NEUTRAL / SIDEWAYS ]") - print("REASON: Manifold EQ. No dominant lawful attractor detected in current window.") - - print("\n[Audit Verified by Sovereign Manifold]") - -if __name__ == "__main__": - predict_signal() diff --git a/5-Applications/scripts/redpajama_english_manifold.py b/5-Applications/scripts/redpajama_english_manifold.py deleted file mode 100644 index ede5e712..00000000 --- a/5-Applications/scripts/redpajama_english_manifold.py +++ /dev/null @@ -1,319 +0,0 @@ -#!/usr/bin/env python3 -""" -REDPAJAMA ENGLISH MANIFOLD BUILDER - -Ingest the RedPajama dataset (or a representative subset) and build the -mathematical model of English at scale. RedPajama contains: - - - Common Crawl: ~878B tokens - - C4: ~175B tokens - - GitHub: ~59B tokens - - Books: ~26B tokens - - Wikipedia: ~24B tokens - - ArXiv: ~28B tokens - - StackExchange: ~20B tokens - -Total: ~1.2 TRILLION tokens - -This script: - 1. Streams RedPajama data (or compatible JSONL/parquet) - 2. Extracts sentences with lightweight filtering - 3. Builds structural invariant fingerprints - 4. Updates the English language manifold incrementally - 5. Computes compression metrics at scale - 6. Outputs a production-ready grammar model - -Usage: - python redpajama_english_manifold.py --input /path/to/redpajama/*.jsonl --limit 1000000 -""" - -import os -import re -import sys -import json -import math -import gzip -import argparse -from pathlib import Path -from collections import Counter, defaultdict -from datetime import datetime -from typing import Iterator, Optional - -# ── Configuration ────────────────────────────────────────────────────────────── - -BASE = Path("/home/allaun/Documents/Research Stack") -OUTDIR = BASE / "3-Mathematical-Models/redpajama_english_manifold" -OUTDIR.mkdir(parents=True, exist_ok=True) - -# POS tag dictionaries (same as unified model) -CLOSED = {'the','a','an','and','or','but','in','on','at','to','for','of','with','by','from','as', - 'is','was','are','were','be','been','being','have','has','had','do','does','did','will', - 'would','could','should','may','might','can','shall','this','that','these','those','it', - 'its','they','them','their','he','she','his','her','him','we','us','our','you','your', - 'my','mine','i','me','who','which','what','when','where','why','how','all','each', - 'every','both','either','neither','some','any','no','none','more','most','many','much', - 'few','little','other','another','such','only','own','same','so','than','too','very', - 'just','now','then','here','there','up','down','out','off','over','under','again', - 'further','once','not','also','always','never','often','sometimes','usually','still'} -PREP = {'in','on','at','by','for','with','about','against','between','into','through','during', - 'before','after','above','below','to','from','up','down','of','off','over','under', - 'again','further','then','once','around','behind','beyond','despite','except','inside', - 'near','past','since','toward','upon','within','without','across','along','among', - 'beside','besides','concerning','considering','following','including','like','minus', - 'plus','regarding','round','save','till','until','via','worth'} -CONJ = {'and','or','but','nor','yet','so','for','although','because','before','if','since', - 'though','unless','until','when','while','whereas','whether','either','neither', - 'both','not','only','than','rather','however','moreover','furthermore','nevertheless', - 'otherwise','therefore','thus','hence','consequently','meanwhile'} -AUX = {'be','am','is','are','was','were','being','been','have','has','had','do','does','did', - 'will','would','shall','should','may','might','can','could','must','ought','need', - 'dare','used','get','gets','got','getting','become','becomes','became','seem','seems', - 'seemed','appear','appears','appeared'} -PRON = {'i','me','my','mine','myself','you','your','yours','yourself','he','him','his', - 'himself','she','her','hers','herself','it','its','itself','we','us','our','ours', - 'ourselves','they','them','their','theirs','themselves','this','that','these','those', - 'who','whom','whose','which','what','whatever','whoever','whomever','anyone','someone', - 'everyone','nobody','nothing','something','anything','everything'} -DET = {'the','a','an','this','that','these','those','my','your','his','her','its','our', - 'their','some','any','no','each','every','either','neither','both','all','half', - 'enough','several','many','much','few','little','other','another','such','what', - 'which','whose','one','two','three','first','last','next','various','certain'} - -def tag(word: str) -> str: - w = word.lower().strip("'\"") - if w in DET: return "DET" - if w in PRON: return "PRON" - if w in PREP: return "PREP" - if w in CONJ: return "CONJ" - if w in AUX: return "AUX" - if w in CLOSED: return "FUNC" - if w.endswith("ing"): return "VBG" - if w.endswith("ed"): return "VBN" - if w.endswith(("ly","ily","ally")): return "ADV" - if w.endswith(("tion","sion","ment","ness","ity","ance","ence","hood","ship")): return "NOUN" - if w.endswith(("able","ible","ful","ous","ive","less","ish","al")): return "ADJ" - if w.endswith(("ize","ise","ify","ate")): return "VERB" - if len(w) <= 3: return "SHORT" - return "LEX" - -def fingerprint(sentence: str) -> str: - """Structural invariant fingerprint of a sentence.""" - words = re.findall(r"[a-zA-Z']+", sentence) - if len(words) < 3 or len(words) > 40: - return "" - tags = [tag(w) for w in words] - collapsed = [] - prev = None - for t in tags: - if t != prev: - collapsed.append(t) - prev = t - elif t == "LEX" and (len(collapsed) < 2 or collapsed[-2] != "LEX+"): - collapsed[-1] = "LEX+" - return " ".join(collapsed) - -def classify_form(fp: str) -> str: - """Classify invariant form into grammatical category.""" - tags = fp.split() - if not tags: - return "OTHER" - if "DET" in tags and "NOUN" in tags and "VERB" in tags: - if tags.index("VERB") > tags.index("NOUN"): - return "SVO" - else: - return "VSO" - elif "DET" in tags and "NOUN" in tags and "PREP" in tags: - return "NP_PP" - elif "AUX" in tags and "VERB" in tags: - return "AUX_V" - elif "CONJ" in tags: - return "COMPOUND" - elif "PRON" in tags and "VERB" in tags: - return "PRON_V" - elif tags.count("PREP") >= 2: - return "PP_CHAIN" - elif "LEX+" in tags: - return "DENSE_NP" - else: - return "OTHER" - -# ── Streaming Data Sources ───────────────────────────────────────────────────── - -def stream_jsonl(path: Path, text_field: str = "text") -> Iterator[str]: - """Stream text from JSONL (potentially gzipped).""" - opener = gzip.open if str(path).endswith('.gz') else open - with opener(path, 'rt', encoding='utf-8', errors='ignore') as f: - for line in f: - line = line.strip() - if not line: - continue - try: - obj = json.loads(line) - text = obj.get(text_field, '') - if text: - yield text - except json.JSONDecodeError: - continue - -def stream_enwik9(path: Path) -> Iterator[str]: - """Stream text from enwik9 XML.""" - raw = path.read_bytes() - text_blocks = re.findall(rb']*>(.*?)', raw, re.DOTALL) - for block in text_blocks: - text = block.decode('utf-8', errors='ignore') - text = re.sub(r'\{\{.*?\}\}', ' ', text, flags=re.DOTALL) - text = re.sub(r'\[\[.*?\|', ' ', text) - text = re.sub(r'\[\[|\]\]', ' ', text) - text = re.sub(r"'{2,}", ' ', text) - text = re.sub(r'<.*?>', ' ', text, flags=re.DOTALL) - text = re.sub(r'&\w+;', ' ', text) - text = re.sub(r'https?://\S+', ' ', text) - text = re.sub(r'[#*|=\{\}\[\]\|]', ' ', text) - yield text - -# ── Manifold Builder ────────────────────────────────────────────────────────── - -class IncrementalManifold: - """Build English invariant manifold incrementally from streaming data.""" - - def __init__(self, max_forms: int = 10_000_000): - self.forms = Counter() - self.examples = defaultdict(list) - self.sentences_processed = 0 - self.bytes_processed = 0 - self.max_forms = max_forms - - def ingest_text(self, text: str, max_sentences: Optional[int] = None): - """Process raw text into sentences and update manifold.""" - sentences = re.split(r'(?<=[.!?])\s+', text) - count = 0 - for sent in sentences: - sent = sent.strip() - if 10 < len(sent) < 300 and sent[0].isupper(): - fp = fingerprint(sent) - if fp and len(fp.split()) >= 3: - self.forms[fp] += 1 - if len(self.examples[fp]) < 3: - self.examples[fp].append(sent) - self.sentences_processed += 1 - count += 1 - if max_sentences and count >= max_sentences: - break - self.bytes_processed += len(text.encode('utf-8')) - return count - - def taxonomy(self) -> dict: - """Compute grammatical category distribution.""" - cats = Counter() - for fp, count in self.forms.items(): - cats[classify_form(fp)] += count - return dict(cats) - - def compute_entropy(self) -> float: - """Shannon entropy of the invariant distribution.""" - total = sum(self.forms.values()) - if total == 0: - return 0.0 - entropy = 0.0 - for count in self.forms.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - return entropy - - def top(self, n: int = 100) -> list: - return self.forms.most_common(n) - - def save(self, path: Path, limit_examples: int = 1000): - """Save manifold to JSON.""" - top_forms = self.top(limit_examples) - report = { - "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), - "sentences_processed": self.sentences_processed, - "bytes_processed": self.bytes_processed, - "unique_forms": len(self.forms), - "shannon_entropy_bits": round(self.compute_entropy(), 4), - "taxonomy": self.taxonomy(), - "top_forms": [{"fingerprint": fp, "count": c, "example": self.examples[fp][0][:120] if fp in self.examples else ""} for fp, c in top_forms], - } - with open(path, "w") as f: - json.dump(report, f, indent=2) - print(f" Saved manifold: {path}") - -# ── Main ───────────────────────────────────────────────────────────────────── - -def main(): - parser = argparse.ArgumentParser(description="Build English manifold from RedPajama-scale data") - parser.add_argument("--input", type=Path, nargs="+", help="Input file(s) - JSONL or enwik9 XML") - parser.add_argument("--limit", type=int, default=1_000_000, help="Max sentences to process") - parser.add_argument("--batch-save", type=int, default=100_000, help="Save checkpoint every N sentences") - parser.add_argument("--text-field", default="text", help="JSON field containing text") - args = parser.parse_args() - - print("=" * 70) - print(" REDPAJAMA ENGLISH MANIFOLD BUILDER") - print(" Target: Scale invariant model to millions/billions of sentences") - print("=" * 70) - - manifold = IncrementalManifold(max_forms=10_000_000) - ts = datetime.now().strftime("%Y%m%d_%H%M%S") - - # Determine source type and stream - if not args.input: - # Default: use enwik9_purified.bin - default_path = BASE / "shared-data/data/hutter_archive/enwik9_purified.bin" - print(f"\nNo --input specified. Using default: {default_path}") - args.input = [default_path] - - for path in args.input: - print(f"\nProcessing: {path}") - - if path.name == "enwik9_purified.bin" or path.suffix == '.xml': - stream = stream_enwik9(path) - else: - stream = stream_jsonl(path, text_field=args.text_field) - - for text in stream: - count = manifold.ingest_text(text) - if manifold.sentences_processed >= args.limit: - print(f" Reached limit: {args.limit:,} sentences") - break - - # Checkpoint - if manifold.sentences_processed % args.batch_save == 0: - print(f" Checkpoint: {manifold.sentences_processed:,} sentences, {len(manifold.forms):,} forms") - ckpt = OUTDIR / f"manifold_checkpoint_{manifold.sentences_processed}_{ts}.json" - manifold.save(ckpt, limit_examples=500) - - if manifold.sentences_processed >= args.limit: - break - - # Final report - print(f"\n{'='*70}") - print(" MANIFOLD BUILD COMPLETE") - print(f"{'='*70}") - print(f" Sentences processed: {manifold.sentences_processed:,}") - print(f" Bytes processed: {manifold.bytes_processed:,}") - print(f" Unique forms: {len(manifold.forms):,}") - print(f" Shannon entropy: {manifold.compute_entropy():.2f} bits/form") - - print(f"\n Taxonomy:") - cats = manifold.taxonomy() - total = sum(cats.values()) - for cat, cnt in sorted(cats.items(), key=lambda x: -x[1]): - print(f" {cat:12s}: {cnt:>8,} ({cnt/total*100:.1f}%)") - - print(f"\n Top 20 forms:") - for fp, cnt in manifold.top(20): - print(f" {cnt:>6,} {fp}") - - # Save final - final_path = OUTDIR / f"redpajama_english_manifold_{ts}.json" - manifold.save(final_path, limit_examples=2000) - - print(f"\n{'='*70}") - print(f" Final manifold: {final_path}") - print(f"{'='*70}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/refill_gdrive_from_forgejo.sh b/5-Applications/scripts/refill_gdrive_from_forgejo.sh deleted file mode 100755 index af48a67c..00000000 --- a/5-Applications/scripts/refill_gdrive_from_forgejo.sh +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -ROOT="$(cd "$(dirname "${BASH_SOURCE[0]}")/.." && pwd)" -cd "$ROOT" - -REMOTE="${1:-github/distilled}" -REMOTE_NAME="${REMOTE%%/*}" -REMOTE_LABEL="$(echo "$REMOTE" | tr '/: ' '---')" -DATE_STAMP="$(date +%F)" -OUT_DIR="artifacts/gdrive_refill" -mkdir -p "$OUT_DIR" - -echo "[1/5] Fetching remote $REMOTE_NAME..." -git fetch "$REMOTE_NAME" --prune - -REV="$(git rev-parse --short=12 "$REMOTE")" -ARCHIVE="$OUT_DIR/research-stack-${REMOTE_LABEL}-${REV}.tar.zst" - -echo "[2/5] Building clean archive from $REMOTE ($REV)..." -if [[ ! -f "$ARCHIVE" ]]; then - if command -v zstd >/dev/null 2>&1; then - git archive --format=tar --prefix="research-stack-${REMOTE_LABEL}-${REV}/" "$REMOTE" \ - | zstd -T0 -3 -o "$ARCHIVE" - else - ARCHIVE="$OUT_DIR/research-stack-${REMOTE_LABEL}-${REV}.tar.gz" - git archive --format=tar --prefix="research-stack-${REMOTE_LABEL}-${REV}/" "$REMOTE" \ - | gzip -6 > "$ARCHIVE" - fi -fi - -echo "[3/5] Writing checksum and manifest..." -sha256sum "$ARCHIVE" > "$ARCHIVE.sha256" -FILES="$(git ls-tree -r --name-only "$REMOTE" | wc -l)" -BYTES="$(git ls-tree -r -l "$REMOTE" | awk '{s += $4} END {print s}')" -SHA="$(cut -d' ' -f1 "$ARCHIVE.sha256")" -cat > "$OUT_DIR/REFILL_MANIFEST.md" </dev/null 2>&1; then - echo "Gdrive: is not authenticated." - echo "Run: rclone config reconnect Gdrive:" - echo "Then rerun: bash scripts/refill_gdrive_from_forgejo.sh " - exit 2 -fi - -echo "[5/5] Uploading refill bundle to Google Drive..." -rclone copy "$OUT_DIR" "Gdrive:topological_storage/research-stack/remote-refill/$DATE_STAMP/" \ - --progress \ - --checksum - -echo "Done. Uploaded $OUT_DIR to Gdrive:topological_storage/research-stack/remote-refill/$DATE_STAMP/" diff --git a/5-Applications/scripts/remap_arxiv.py b/5-Applications/scripts/remap_arxiv.py deleted file mode 100644 index 00e6016d..00000000 --- a/5-Applications/scripts/remap_arxiv.py +++ /dev/null @@ -1,392 +0,0 @@ -#!/usr/bin/env python3 -""" -Re-map arXiv findings using precise category-based heuristics. -Reads arxiv_findings_500.md and rewrites it with corrected mappings. -""" - -import re -import sys -from dataclasses import dataclass - - -@dataclass -class Entry: - number: int - title: str - url: str - year: str - summary: str - mapping: dict - - -def classify(title: str, summary: str) -> dict: - """Precise classifier using regex patterns on title+summary.""" - text = (title + " " + summary).lower() - - # Physics — Quantum - if any(p in text for p in [ - "wavefunction", "eigenstate", "hamiltonian", "schrodinger", "heisenberg", - "superposition", "entanglement", "decoherence", "bell inequality", "quantum field", - "qubit", "quantum information", "quantum computing", "quantum error", - "density matrix", "hilbert space", "path integral", "renormalization" - ]): - return { - "Ω": "measured eigenvalue / quantum state probability", - "Ψ": "unitary evolution / measurement collapse operator", - "B": "Hilbert space basis / quantum state vector", - "C": "Hamiltonian / measurement apparatus / initial condition", - "Δ": "decoherence / quantum uncertainty / vacuum fluctuations", - } - - # Physics — GR / Cosmology - if any(p in text for p in [ - "spacetime", "metric tensor", "einstein", "curvature", "ricci", "riemann", - "gravitational wave", "ligo", "black hole", "event horizon", "singularity", - "cosmological", "inflation", "big bang", "dark energy", "dark matter", - "hubble", "friedmann", "penrose", "hawking" - ]): - return { - "Ω": "gravitational signal / metric perturbation / cosmic expansion", - "Ψ": "Einstein field equations / geometric operator", - "B": "spacetime manifold / metric tensor", - "C": "mass-energy distribution / observer frame / cosmological parameters", - "Δ": "quantum foam / backreaction / measurement uncertainty", - } - - # Physics — Particle / High Energy - if any(p in text for p in [ - "standard model", "higgs", "gauge", "symmetry breaking", "supersymmetry", - "qcd", "electroweak", "neutrino", "quark", "gluon", "lepton", "boson", - "collider", "lhc", "cms", "atlas", "parton", "hadron", "jet" - ]): - return { - "Ω": "cross-section / particle yield / decay rate", - "Ψ": "S-matrix / Feynman diagram / gauge interaction", - "B": "Standard Model Lagrangian / particle spectrum", - "C": "collision energy / beam luminosity / detector acceptance", - "Δ": "systematic error / pileup / theoretical uncertainty", - } - - # Physics — Condensed Matter / Materials - if any(p in text for p in [ - "graphene", "superconductor", "superconductivity", "topological insulator", - "moiré", "twist angle", "van der waals", "phonon", "plasmon", "exciton", - "band structure", "fermi surface", "landau level", "quantum hall", - "dirac point", "weyl", " Majorana", "spin liquid", "charge density wave" - ]): - return { - "Ω": "conductivity / critical temperature / band gap", - "Ψ": "Bloch Hamiltonian / Bogoliubov operator / topological invariant", - "B": "crystal lattice / atomic orbital basis", - "C": "doping / strain / twist angle / magnetic field / temperature", - "Δ": "disorder / defects / thermal fluctuations / phonon scattering", - } - - # Physics — Thermodynamics / Stat Mech - if any(p in text for p in [ - "entropy", "free energy", "partition function", "boltzmann", "gibbs", - "thermodynamic", "statistical mechanics", "ising", "percolation", - "phase transition", "critical exponent", "renormalization group", - "non-equilibrium", "fluctuation theorem", "landauer" - ]): - return { - "Ω": "entropy / free energy / heat capacity / probability distribution", - "Ψ": "ensemble average / MaxEnt / renormalization group operator", - "B": "microstate basis / configuration space", - "C": "temperature / pressure / chemical potential / external field", - "Δ": "thermal fluctuation / finite-size effect / sampling error", - } - - # Physics — Optics / Photonics - if any(p in text for p in [ - "laser", "photon", "optical", "cavity", "resonator", "interferometer", - "squeezed state", "nonlinear optics", "frequency comb", "meta surface" - ]): - return { - "Ω": "intensity / phase / optical signal", - "Ψ": "Maxwell equations / quantum optical master equation", - "B": "electromagnetic mode / photon number state", - "C": "pump power / cavity geometry / detuning", - "Δ": "shot noise / thermal noise / loss", - } - - # Physics — Plasma / Fusion - if any(p in text for p in [ - "plasma", "tokamak", "stellarator", "magnetic confinement", "fusion", - "iter", "runaway electron", "alpha particle", "guiding center" - ]): - return { - "Ω": "confinement time / plasma beta / fusion gain Q", - "Ψ": "Vlasov-Maxwell / guiding center / MHD operator", - "B": "magnetic coil geometry / flux surface", - "C": "plasma pressure / current profile / heating power", - "Δ": "instability / turbulence / field ripple / perturbation", - } - - # Mathematics - if any(p in text for p in [ - "theorem", "proof", "lemma", "conjecture", "category", "homotopy", - "cohomology", "manifold", "bundle", "sheaf", "scheme", "variety", - "group theory", "representation", "algebraic", "number theory", - "differential geometry", "symplectic", "riemannian" - ]): - return { - "Ω": "theorem / invariant / computed quantity", - "Ψ": "proof / functor / operator / morphism", - "B": "axiom / basis / generating set / fundamental group", - "C": "parameter space / module / sheaf section", - "Δ": "approximation / truncation / undecidability", - } - - # Biology — Genetics / Genomics - if any(p in text for p in [ - "genome", "gene", "dna", "rna", "transcriptome", "epigenetic", - "mutation", "variant", "allele", "snp", "genotype", "phenotype", - "crispr", "expression", "promoter", "enhancer", "methylation", - "chromatin", "histone", "genome-wide", "gwas" - ]): - return { - "Ω": "phenotype / trait / disease risk / expression level", - "Ψ": "gene regulation / evolutionary selection / developmental program", - "B": "DNA sequence / gene / regulatory element", - "C": "environment / cell type / developmental stage / diet", - "Δ": "mutation / epigenetic noise / genetic drift / measurement error", - } - - # Biology — Evolution / Paleo - if any(p in text for p in [ - "evolution", "natural selection", "phylogenetic", "ancestral", - "speciation", "adaptation", "fossil", "paleontology", "extinction", - "homologous", "convergent evolution", "molecular clock" - ]): - return { - "Ω": "trait / fitness / divergence time / lineage", - "Ψ": "selection / drift / migration operator", - "B": "genome / morphological trait / protein sequence", - "C": "environment / population size / geographic barrier", - "Δ": "contamination / decay / sampling bias / neutral drift", - } - - # Biology — Neuroscience - if any(p in text for p in [ - "neuron", "synapse", "brain", "cortex", "hippocampus", - "memory", "learning", "cognitive", "consciousness", "neural circuit", - "fmri", "eeg", "connectome", "action potential", "neurotransmitter" - ]): - return { - "Ω": "behavior / cognition / neural firing rate / BOLD signal", - "Ψ": "network dynamics / synaptic plasticity / information integration", - "B": "neural population / synaptic weight / receptor type", - "C": "stimulus / task / attention / arousal state", - "Δ": "neural noise / individual variation / artifact", - } - - # Biology — Cell / Molecular - if any(p in text for p in [ - "cell", "protein folding", "signaling pathway", "metabolism", - "mitochondria", "ribosome", "autophagy", "apoptosis", - "kinase", "phosphorylation", "transcription factor" - ]): - return { - "Ω": "cellular response / growth rate / metabolite concentration", - "Ψ": "signaling cascade / metabolic flux / gene regulatory network", - "B": "protein / enzyme / metabolite / organelle", - "C": "nutrient / hormone / stress / drug concentration", - "Δ": "stochastic expression / cell-to-cell variability / measurement noise", - } - - # CS / AI — Machine Learning - if any(p in text for p in [ - "neural network", "deep learning", "transformer", "attention", - "backpropagation", "gradient descent", "generalization", - "overfitting", "regularization", "latent space", "embedding", - "contrastive learning", "self-supervised", "fine-tuning", - "generative model", "diffusion model", "gan", "vae" - ]): - return { - "Ω": "model output / prediction / generated sample / loss", - "Ψ": "optimization / backpropagation / inference / sampling operator", - "B": "network weights / training data distribution / latent basis", - "C": "input / prompt / hyperparameter / task specification", - "Δ": "generalization gap / mode collapse / adversarial vulnerability / bias", - } - - # CS / AI — Reinforcement Learning / Game Theory - if any(p in text for p in [ - "reinforcement learning", "q-learning", "policy gradient", - "multi-agent", "game theory", "nash equilibrium", "mechanism design", - "bandit", "exploration", "exploitation", "reward shaping" - ]): - return { - "Ω": "cumulative reward / policy / equilibrium strategy", - "Ψ": "Bellman operator / policy gradient / best-response dynamics", - "B": "state space / action space / reward function", - "C": "environment dynamics / opponent strategy / discount factor", - "Δ": "exploration noise / sample inefficiency / non-stationarity", - } - - # CS — Algorithms / Theory - if any(p in text for p in [ - "algorithm", "complexity", "np-complete", "approximation", - "graph", "combinatorial", "optimization", "linear programming", - "randomized", "deterministic", "online algorithm", "streaming" - ]): - return { - "Ω": "solution quality / running time / approximation ratio", - "Ψ": "algorithm / recursive procedure / iterative operator", - "B": "input instance / graph / constraint set", - "C": "parameter / resource bound / adversarial input", - "Δ": "approximation error / slack / worst-case gap", - } - - # CS — Information Theory / Compression - if any(p in text for p in [ - "information theory", "entropy", "channel capacity", "coding", - "compression", "kullback-leibler", "mutual information", "rate distortion" - ]): - return { - "Ω": "compressed size / rate / distortion / channel capacity", - "Ψ": "encoder / decoder / channel operator", - "B": "source alphabet / codebook / basis distribution", - "C": "source statistics / channel noise / rate constraint", - "Δ": "redundancy / loss / decoding error / gap to Shannon limit", - } - - # Chemistry - if any(p in text for p in [ - "catalyst", "reaction mechanism", "molecular dynamics", "density functional", - "electronic structure", "spectroscopy", "chromatography", - "organic synthesis", "polymer", "nanoparticle", "surface chemistry" - ]): - return { - "Ω": "yield / selectivity / spectrum / binding energy", - "Ψ": "reaction pathway / Hamiltonian / kinetic operator", - "B": "molecular orbital / active site / monomer", - "C": "temperature / pressure / solvent / concentration", - "Δ": "side reaction / impurity / thermal broadening", - } - - # Climate / Ecology - if any(p in text for p in [ - "climate", "carbon cycle", "ecosystem", "biodiversity", "species", - "population dynamics", "predator-prey", "food web", "biogeochemical", - "remote sensing", "land use", "deforestation" - ]): - return { - "Ω": "CO₂ flux / species count / temperature anomaly / biomass", - "Ψ": "ecosystem model / nutrient cycle / population dynamics operator", - "B": "species pool / microbial community / carbon reservoir", - "C": "temperature / precipitation / human activity / disturbance", - "Δ": "stochastic variation / model bias / measurement uncertainty", - } - - # Energy / Engineering - if any(p in text for p in [ - "battery", "lithium", "electrolyte", "solar cell", "photovoltaic", - "fuel cell", "supercapacitor", "energy storage", "power grid", - "turbine", "heat exchanger", "combustion" - ]): - return { - "Ω": "capacity / efficiency / power density / lifetime", - "Ψ": "ion transport / charge transfer / thermodynamic cycle operator", - "B": "electrode material / semiconductor / electrolyte", - "C": "temperature / voltage / current / cycling rate", - "Δ": "degradation / resistance / thermal loss / manufacturing defect", - } - - # Default — catch-all - return { - "Ω": "observed phenomenon / measured quantity", - "Ψ": "underlying theoretical mechanism / operator", - "B": "conserved basis / fundamental reusable component", - "C": "dynamic context / adaptive parameter / external condition", - "Δ": "residual error / noise / uncertainty / irreducible limit", - } - - -def parse_entries(text: str): - """Parse markdown file into Entry objects.""" - entries = [] - pattern = re.compile( - r"## (\d+)\. (.+?)\n\n" - r"\*\*Source:\*\* \[([^\]]+)\]\([^)]+\)\s+\((\d{4})\)\n\n" - r"\*\*Summary:\*\* (.+?)\n\n" - r"\| Symbol \| Mapping \|\n\|--------\|---------\|\n" - r"\| Ω \| (.+?) \|\n" - r"\| Ψ \| (.+?) \|\n" - r"\| B \| (.+?) \|\n" - r"\| C \| (.+?) \|\n" - r"\| Δ \| (.+?) \|\n", - re.DOTALL - ) - for m in pattern.finditer(text): - entries.append(Entry( - number=int(m.group(1)), - title=m.group(2).strip(), - url=m.group(3).strip(), - year=m.group(4).strip(), - summary=m.group(5).strip(), - mapping={"Ω": m.group(6).strip(), "Ψ": m.group(7).strip(), - "B": m.group(8).strip(), "C": m.group(9).strip(), "Δ": m.group(10).strip()} - )) - return entries - - -def render_entry(e: Entry) -> str: - m = e.mapping - return ( - f"## {e.number}. {e.title}\n\n" - f"**Source:** [{e.url}]({e.url}) ({e.year})\n\n" - f"**Summary:** {e.summary[:500]}\n\n" - f"| Symbol | Mapping |\n" - f"|--------|---------|\n" - f"| Ω | {m['Ω']} |\n" - f"| Ψ | {m['Ψ']} |\n" - f"| B | {m['B']} |\n" - f"| C | {m['C']} |\n" - f"| Δ | {m['Δ']} |\n\n" - f"---\n\n" - ) - - -def main(): - input_path = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/arxiv_findings_500.md" - output_path = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/arxiv_findings_500_remapped.md" - - with open(input_path, "r", encoding="utf-8") as f: - text = f.read() - - entries = parse_entries(text) - print(f"Parsed {len(entries)} entries") - - improved = 0 - for e in entries: - old = e.mapping["B"] - new_map = classify(e.title, e.summary) - if new_map["B"] != old: - improved += 1 - e.mapping = new_map - - print(f"Improved mappings: {improved}/{len(entries)}") - - lines = [ - "# ArXiv Findings — Re-Mapped to Unified Equation (LLM-Assisted Heuristic)", - "", - f"**Papers:** {len(entries)}", - f"**Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)", - "", - "---", - "", - ] - - for e in entries: - lines.append(render_entry(e)) - - with open(output_path, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - print(f"Wrote {output_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/remap_physics_db.py b/5-Applications/scripts/remap_physics_db.py deleted file mode 100644 index cb50ae54..00000000 --- a/5-Applications/scripts/remap_physics_db.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 -""" -Map physics equations from sqlite DB to unified equation symbols using local LLM. -""" - -import csv -import json -import re -import sys -import time -from concurrent.futures import ThreadPoolExecutor, as_completed - -import requests - -PROMPT_TEMPLATE = """Map this physics equation to five symbols from: - Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α) - -Equation: {title} -Domain: {domain} -Formula/Description: {formula} - -Definitions: -- Ω: Observable output, measured quantity, what the equation predicts -- Ψ: The operator, mechanism, or theory -- B: Conserved basis, fundamental component, the fixed structure -- C: Dynamic context, variable parameter, external condition -- Δ: Residual error, noise, uncertainty, fundamental limit - -Respond ONLY in valid JSON with exactly these five keys and short (≤15 words) values: -{{"Ω": "...", "Ψ": "...", "B": "...", "C": "...", "Δ": "..."}} -""" - - -def call_ollama(title, domain, formula, model="llama3.1:8b"): - prompt = PROMPT_TEMPLATE.format(title=title, domain=domain, formula=formula[:300]) - try: - r = requests.post( - "http://localhost:11434/api/generate", - json={"model": model, "prompt": prompt, "stream": False, "options": {"temperature": 0.1, "num_predict": 150}}, - timeout=60, - ) - r.raise_for_status() - content = r.json()["response"] - match = re.search(r'\{[^}]+\}', content) - if match: - return json.loads(match.group(0)) - except Exception as e: - print(f" ERROR #{title}: {e}", file=sys.stderr) - return None - - -def main(): - input_file = "/tmp/physics_eqs_export.tsv" - output_file = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/physics_eqs_mapped.md" - - rows = list(csv.DictReader(open(input_file), delimiter='|', fieldnames=['eq_number', 'title', 'domain', 'formula', 'description'])) - # Skip header if present - if rows and rows[0]['eq_number'].strip() == 'eq_number': - rows = rows[1:] - - print(f"Mapping {len(rows)} physics equations...") - - results = {} - - with ThreadPoolExecutor(max_workers=2) as executor: - futures = {} - for row in rows: - eq_num = row['eq_number'].strip() - title = row['title'].strip() - domain = row['domain'].strip() - formula = row['formula'].strip() + " " + row['description'].strip() - future = executor.submit(call_ollama, title, domain, formula) - futures[future] = (eq_num, title) - - for future in as_completed(futures): - eq_num, title = futures[future] - mapping = future.result() - if mapping: - results[eq_num] = mapping - print(f" ✓ #{eq_num}: {title[:50]}...") - else: - print(f" ✗ #{eq_num}: FAILED") - time.sleep(0.3) - - print(f"\nSuccess: {len(results)}/{len(rows)}") - - lines = [ - "# Physics Equations Database — Mapped to Unified Equation", - "", - f"**Equations:** {len(rows)}", - f"**Successfully mapped:** {len(results)}", - f"**Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)", - "", - "---", - "", - ] - - for row in rows: - eq_num = row['eq_number'].strip() - title = row['title'].strip() - domain = row['domain'].strip() - formula = row['formula'].strip() - desc = row['description'].strip() - m = results.get(eq_num, {"Ω": "N/A", "Ψ": "N/A", "B": "N/A", "C": "N/A", "Δ": "N/A"}) - - lines.append(f"## Eq {eq_num}. {title}") - lines.append(f"") - lines.append(f"**Domain:** {domain}") - lines.append(f"**Formula:** {formula[:200] if formula else desc[:200]}") - lines.append(f"") - lines.append(f"| Symbol | Mapping |") - lines.append(f"|--------|---------|") - lines.append(f"| Ω | {m['Ω']} |") - lines.append(f"| Ψ | {m['Ψ']} |") - lines.append(f"| B | {m['B']} |") - lines.append(f"| C | {m['C']} |") - lines.append(f"| Δ | {m['Δ']} |") - lines.append(f"") - lines.append(f"---") - lines.append(f"") - - with open(output_file, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - print(f"\nWrote {output_file}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/remediate_system.py b/5-Applications/scripts/remediate_system.py deleted file mode 100644 index b0fedbca..00000000 --- a/5-Applications/scripts/remediate_system.py +++ /dev/null @@ -1,56 +0,0 @@ -import os -import sys -import re -import json - -# Add project root to path for local imports -sys.path.insert(0, "/home/allaun/Research Stack") -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def remediate_env(): - print("Remediating .env...") - env_path = "/home/allaun/Documents/Research Stack/.env" - if not os.path.exists(env_path): - with open(env_path, "w") as f: - f.write("# SOVEREIGN RESEARCH STACK ENVIRONMENT\n") - f.write("DEEPSEEK_API_KEY=your_key_here\n") - f.write("LINEAR_API_KEY=your_key_here\n") - f.write("NOTION_API_KEY=your_key_here\n") - print("[OK] Created .env with placeholders.") - else: - print("[SKIP] .env already exists.") - -def remediate_physics(): - print("\nRemediating Physics Regularization...") - # Scan for nu_eff usage in other scripts - # This is a placeholder for actual code-search and replace - print("[OK] All continuum solvers checked for UV-divergence compliance.") - -def resolve_lean_triangle(): - print("\nAttempting to resolve shortestPathDist_triangle...") - client = DeepSeekV4(use_local=True) # Fallback to local R1 - prover = DeepSeekProver(client) - - context = """ - We have an AdmissibilityGraph with a symmetric edge function. - shortestPathDist x y is the infimum of path costs between x and y. - - Theorem: shortestPathDist_triangle (x y z : V) : - shortestPathDist x z ≤ shortestPathDist x y + shortestPathDist y z - """ - - try: - proof = prover.formalize(context) - print("\nProposed Proof:\n", proof) - # In a real scenario, we would append this to the .lean file - # For now, we just report the result. - except Exception as e: - print(f"[FAIL] Proof generation failed: {e}") - -if __name__ == "__main__": - print("SOVEREIGN REMEDIATION ENGINE") - print("============================") - remediate_env() - remediate_physics() - # resolve_lean_triangle() # Disabled until local ollama is confirmed running - print("\nRemediation cycle complete.") diff --git a/5-Applications/scripts/remove-tailnet-nodes-api.sh b/5-Applications/scripts/remove-tailnet-nodes-api.sh deleted file mode 100755 index 3ca51545..00000000 --- a/5-Applications/scripts/remove-tailnet-nodes-api.sh +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -# remove-tailnet-nodes-api.sh -# Batch-removes all old Tailscale nodes via API. -# Usage: ./scripts/remove-tailnet-nodes-api.sh - -API_KEY="${1:-}" -if [[ -z "$API_KEY" ]]; then - echo "Usage: $0 " - echo "Get your API key at: https://login.tailscale.com/admin/settings/keys" - exit 1 -fi - -TAILNET=$(tailscale status --json 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('MagicDNSSuffix','unknown'))") -if [[ "$TAILNET" == "unknown" ]]; then - echo "Could not determine tailnet. Are you logged into Tailscale?" - exit 1 -fi - -# Nodes to remove (all except Node-00001 which is the current re-authed node) -OLD_NODES=( - "architect" - "desktop-0u2ceal" - "foxtop" - "ip-172-31-25-81" - "judge" - "laptop-1" - "netcup-router" - "racknerd-510bd9c" - "racknerd-atl" - "qfox" - "QFox" -) - -echo "Tailnet: $TAILNET" -echo "Removing old nodes via API..." -echo "" - -# Fetch all devices -DEVICES_JSON=$(curl -sS \ - -H "Authorization: Bearer $API_KEY" \ - "https://api.tailscale.com/api/v2/tailnet/-/devices") - -# Extract device IDs for old nodes -for node in "${OLD_NODES[@]}"; do - DEVICE_ID=$(echo "$DEVICES_JSON" | python3 -c " -import sys, json -devices = json.load(sys.stdin).get('devices', []) -for d in devices: - if d.get('name', '').split('.')[0] == '$node': - print(d.get('id')) - break -") - if [[ -n "$DEVICE_ID" ]]; then - echo "Removing $node (ID: $DEVICE_ID)..." - HTTP_STATUS=$(curl -sS -o /dev/null -w "%{http_code}" \ - -X DELETE \ - -H "Authorization: Bearer $API_KEY" \ - "https://api.tailscale.com/api/v2/device/$DEVICE_ID") - if [[ "$HTTP_STATUS" == "200" || "$HTTP_STATUS" == "204" ]]; then - echo " OK (HTTP $HTTP_STATUS)" - else - echo " FAILED (HTTP $HTTP_STATUS)" - fi - else - echo "$node: not found (already removed?)" - fi -done - -echo "" -echo "Done. Verify at: https://login.tailscale.com/admin/machines" diff --git a/5-Applications/scripts/replicator_topology.py b/5-Applications/scripts/replicator_topology.py deleted file mode 100644 index 8ab8a0e8..00000000 --- a/5-Applications/scripts/replicator_topology.py +++ /dev/null @@ -1,237 +0,0 @@ -#!/usr/bin/env python3 -""" -Replicator Topology Analysis -Analyzes morphic scalars that combine like Stargate SG-1 replicators with coded instructions for task-specific combination. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class ReplicatorTopology: - """Analyzes replicator-like morphic topology with coded instructions for task-specific combination.""" - - def __init__(self): - # Replicator characteristics - self.replicator_characteristics = { - "self_replication": { - "description": "Morphic scalars can replicate themselves", - "significance_score": 95.0 - }, - "combination": { - "description": "Scalars combine to form larger structures", - "significance_score": 90.0 - }, - "coded_instructions": { - "description": "Coded instructions limit combination to initial tasks", - "significance_score": 95.0 - }, - "task_specific": { - "description": "Only combine to perform tasks initially asked for", - "significance_score": 90.0 - }, - "programmable": { - "description": "Scalars are programmable via coded instructions", - "significance_score": 85.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "llm_directed_capacity": 331781501108176.2, - "expansion_factor": 174621842162.0 - } - - def analyze_replicator_topology(self) -> Dict: - """Analyze replicator-like morphic topology.""" - analysis = { - "replicator_characteristics": self.replicator_characteristics, - "average_significance_score": sum(e["significance_score"] for e in self.replicator_characteristics.values()) / len(self.replicator_characteristics), - "stargate_sg1_analogy": { - "replicators": "Self-replicating nanobots that combine to form structures", - "coded_instructions": "Coded instructions limit behavior to specific tasks", - "task_limitation": "Only perform tasks they were initially asked for", - "combination_behavior": "Combine to form larger structures for task execution" - }, - "mechanisms": { - "self_replication": "Scalars replicate to increase population", - "coded_constraint": "Coded instructions constrain behavior", - "task_specific_combination": "Only combine for specific tasks", - "programmable_behavior": "Scalars programmable via code", - "llm_coordination": "LLM provides coded instructions and coordination" - } - } - - return analysis - - def analyze_replicator_benefits(self) -> Dict: - """Analyze replicator topology benefits.""" - benefits = { - "scalability": { - "description": "Self-replication enables massive scalability", - "significance_score": 95.0 - }, - "task_focus": { - "description": "Coded instructions ensure task-specific behavior", - "significance_score": 95.0 - }, - "combination_efficiency": { - "description": "Efficient combination for task execution", - "significance_score": 90.0 - }, - "programmable": { - "description": "Scalars programmable for different tasks", - "significance_score": 85.0 - }, - "resource_efficiency": { - "description": "Efficient resource utilization through task-specific behavior", - "significance_score": 80.0 - }, - "safety": { - "description": "Coded instructions prevent unintended behavior", - "significance_score": 90.0 - } - } - - return benefits - - def calculate_replicator_impact(self) -> Dict: - """Calculate replicator topology impact on computational expansion.""" - # Replicator multipliers - self_replication_multiplier = 2.0 # 2x from self-replication - task_focus_multiplier = 1.5 # 1.5x from task-specific behavior - combination_efficiency_multiplier = 1.5 # 1.5x from efficient combination - programmable_multiplier = 1.3 # 1.3x from programmability - resource_efficiency_multiplier = 1.3 # 1.3x from resource efficiency - safety_multiplier = 1.2 # 1.2x from safety through coded instructions - - # Calculate expanded capacity with replicator topology - base_capacity = 1900 - current_llm_directed_capacity = 331781501108176.2 - - # Apply replicator multipliers - replicator_capacity = (current_llm_directed_capacity * - self_replication_multiplier * - task_focus_multiplier * - combination_efficiency_multiplier * - programmable_multiplier * - resource_efficiency_multiplier * - safety_multiplier) - - replicator_expansion_factor = replicator_capacity / base_capacity - replicator_improvement_factor = replicator_capacity / current_llm_directed_capacity - - calculation = { - "base_capacity": base_capacity, - "current_llm_directed_capacity": current_llm_directed_capacity, - "self_replication_multiplier": self_replication_multiplier, - "task_focus_multiplier": task_focus_multiplier, - "combination_efficiency_multiplier": combination_efficiency_multiplier, - "programmable_multiplier": programmable_multiplier, - "resource_efficiency_multiplier": resource_efficiency_multiplier, - "safety_multiplier": safety_multiplier, - "replicator_capacity": replicator_capacity, - "replicator_expansion_factor": replicator_expansion_factor, - "replicator_improvement_factor": replicator_improvement_factor, - "total_replicator_multiplier": (self_replication_multiplier * - task_focus_multiplier * - combination_efficiency_multiplier * - programmable_multiplier * - resource_efficiency_multiplier * - safety_multiplier) - } - - return calculation - - def integrate_replicator_topology(self) -> Dict: - """Integrate replicator topology into comprehensive analysis.""" - integration = { - "replicator_topology_enabled": True, - "analogy": "Stargate SG-1 replicators with coded instructions", - "mechanism": "Morphic scalars self-replicate and combine for task-specific execution", - "characteristics": 5, - "benefits": 6, - "math_categories_enhanced": [ - "Control Theory (coded instructions)", - "Information Theory (programmability)", - "Cognitive/Routing (task-specific)", - "Thermodynamic (resource efficiency)" - ], - "foundation_kernels_enhanced": [ - "F11", "F12" # Control Theory (coded instructions) - ], - "safety_feature": "Coded instructions limit behavior to initial tasks" - } - - return integration - - def run_analysis(self) -> Dict: - """Run replicator topology analysis.""" - print("=" * 60) - print("REPLICATOR TOPOLOGY ANALYSIS") - print("=" * 60) - - # Step 1: Analyze replicator topology - print("\n[1/4] Analyzing replicator-like morphic topology (Stargate SG-1 analogy)...") - replicator_analysis = self.analyze_replicator_topology() - print(f" Replicator Characteristics: {len(replicator_analysis['replicator_characteristics'])}") - for characteristic, details in replicator_analysis['replicator_characteristics'].items(): - print(f" {characteristic}: {details['significance_score']}") - - # Step 2: Analyze benefits - print("[2/4] Analyzing replicator topology benefits...") - benefits = self.analyze_replicator_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 3: Calculate impact - print("[3/4] Calculating replicator topology impact...") - impact_calculation = self.calculate_replicator_impact() - print(f" Current LLM-Directed Capacity: {impact_calculation['current_llm_directed_capacity']}") - print(f" Replicator Capacity: {impact_calculation['replicator_capacity']}") - print(f" Replicator Improvement Factor: {impact_calculation['replicator_improvement_factor']:.2f}x") - print(f" Total Replicator Multiplier: {impact_calculation['total_replicator_multiplier']:.2f}x") - - # Step 4: Integrate - print("[4/4] Integrating replicator topology...") - integration = self.integrate_replicator_topology() - print(f" Analogy: {integration['analogy']}") - print(f" Mechanism: {integration['mechanism']}") - print(f" Characteristics: {integration['characteristics']}") - print(f" Benefits: {integration['benefits']}") - - print("\n" + "=" * 60) - print("REPLICATOR TOPOLOGY ANALYSIS COMPLETE") - print("=" * 60) - - return { - "replicator_analysis": replicator_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": integration - } - -if __name__ == '__main__': - analyzer = ReplicatorTopology() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "replicator_topology.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("REPLICATOR TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Analogy: {results['integration']['analogy']}") - print(f"Replicator Capacity: {results['impact_calculation']['replicator_capacity']}") - print(f"Replicator Improvement Factor: {results['impact_calculation']['replicator_improvement_factor']:.2f}x") - print(f"Total Replicator Multiplier: {results['impact_calculation']['total_replicator_multiplier']:.2f}x") diff --git a/5-Applications/scripts/repro_overclock.py b/5-Applications/scripts/repro_overclock.py deleted file mode 100644 index 694cc42c..00000000 --- a/5-Applications/scripts/repro_overclock.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 -"""Add reproductive overclocking / semelparity as a radical adaptation.""" - -import sqlite3, urllib.request, urllib.parse, json, time, os - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -cur = conn.cursor() - -# Get Radical Adaptations domain id -cur.execute("SELECT id FROM domains WHERE name='Radical Adaptations'") -rad_did = cur.fetchone() -rad_did = rad_did[0] if rad_did else 52 - -# Get max equation IDs -cur.execute("SELECT MAX(id) FROM equations"); eid = cur.fetchone()[0] -cur.execute("SELECT MAX(eq_number) FROM equations"); enum = cur.fetchone()[0] - -# ================================================================ -# MAIN EQUATION -# ================================================================ -eid += 1; enum += 1 -eq_id = eid - -sig = "A life-history strategy where an organism temporarily exceeds sustainable somatic maintenance limits to maximize reproductive output in a single catastrophic breeding season, followed by programmed death. The antechinus (Australian marsupial) is the cleanest mammalian example: males flood with testosterone/cortisol during a 2-3 week breeding window, mating for up to 14 hours per session, losing fur, developing ulcers, and dying before the young are born. Silver-headed, dusky, and Tasman Peninsula antechinus all exhibit this. Kaluta (Dasykaluta rosamondae) does the same. Beyond mammals: Pacific salmon undergo total somatic degeneration during upstream migration → spawn → die. Octopus mothers guard eggs for months without eating, then die via optic gland hormone cascade (removing the optic gland prevents death). Male orb-weaving spiders consumed during/after mating. Agave and bamboo flower once after decades, drain resources into a massive reproductive stalk, then die." - -prec = "Tradeoff limit: d(fitness)/d(survival) → ∞ at reproduction event. Glucocorticoid storm: cortisol exceeds renal clearance capacity. Immune collapse: neutrophil/lymphocyte ratio inverted. Mammalian semelparity independently evolved at least twice in Dasyuridae." - -cur.execute("INSERT INTO equations VALUES (?,?,?,?,?,?,?,?)", ( - eq_id, enum, - "Reproductive Overclocking (Semelparity / Suicidal Reproduction) — Antechinus, Salmon, Octopus, Spiders, Agave, Kaluta", - rad_did, "various", "Proven", sig, prec)) - -# ================================================================ -# API FETCHERS -# ================================================================ -def crossref(q, lim=5): - out = [] - try: - u = "https://api.crossref.org/works?" + urllib.parse.urlencode({ - "query": q, "rows": lim, "sort": "relevance", "filter": "type:journal-article"}) - r = urllib.request.Request(u, headers={"User-Agent": "ReproOverclock/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t = (i.get("title",[""]) or [""])[0] - y = i.get("created",{}).get("date-parts",[[0]])[0][0] - doi = i.get("DOI","") - j = (i.get("container-title",[""]) or [""])[0] - if t: out.append({"title":t[:250],"year":y,"doi":doi,"journal":j,"src":"Crossref"}) - except: pass - return out - -def openalex(q, lim=5): - out = [] - try: - u = "https://api.openalex.org/works?" + urllib.parse.urlencode({ - "search": q, "per_page": lim, "sort": "cited_by_count:desc"}) - r = urllib.request.Request(u, headers={"User-Agent": "mailto:r@x.com"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("results",[]): - t = i.get("title","") - y = i.get("publication_year")or 0 - doi = i.get("doi","") - j = "" - if i.get("primary_location") and i["primary_location"].get("source"): - j = i["primary_location"]["source"].get("display_name","") - if t: out.append({"title":t[:250],"year":y,"doi":doi,"journal":j,"src":"OpenAlex"}) - except: pass - return out - -def s2(q, lim=5): - out = [] - try: - u = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode({ - "query": q, "limit": lim, "fields": "title,year,externalIds,journal,citationCount"}) - r = urllib.request.Request(u, headers={"User-Agent": "ReproOverclock/1.0"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for p in d.get("data",[]): - e = p.get("externalIds",{}) or {} - j = p.get("journal",{}) or {} - out.append({"title":p.get("title","")[:250],"year":p.get("year")or 0, - "doi":e.get("DOI",""),"journal":j.get("name",""),"src":"S2"}) - except: pass - return out - -# ================================================================ -# SEARCH QUERIES — diverse taxa -# ================================================================ -QUERIES = [ - ("antechinus semelparity male die off after breeding marsupial suicidal reproduction", "Antechinus mammal"), - ("salmon semelparity programmed death upstream migration cortisol degeneration senescence", "Pacific salmon"), - ("octopus maternal semelparity optic gland death after egg hatching programmed senescence", "Octopus maternal"), - ("semelparity iteroparity life history evolution trade off reproduction survival", "Semelparity theory"), - ("suicidal reproduction spider male sacrifice sexual cannibalism orb weaver", "Spider sexual cannibalism"), - ("dasyurid marsupial semelparity antechinus kaluta phascogale die off breeding", "Dasyurid marsupials"), - ("programmed death semelparous plant agave century plant bamboo monocarpic senescence", "Monocarpic plants"), - ("glucocorticoid cortisol stress induced mortality reproduction trade off physiology", "Glucocorticoid mechanism"), - ("terminal investment hypothesis reproduction senescence trade off life history theory", "Terminal investment"), - ("pacific salmon Oncorhynchus spawning migration programmed cell death organ failure", "Salmon mechanism"), - ("octopus vulgaris optic gland senescence removal lifespan extension reproduction behavior", "Octopus optic gland"), - ("antechinus stuartii flavipes argentus male die off stress hormones cortisol testosterone", "Antechinus physiology"), -] - -print(f"Fetching {len(QUERIES)} reproductive-overclocking queries across 3 APIs...\n") -total, rows = 0, [] -start = time.time() - -apis = [(crossref,1.5),(openalex,1.5),(s2,2.0),(crossref,1.5),(openalex,1.5),(s2,2.0)] - -for i, (q, label) in enumerate(QUERIES): - fn, delay = apis[i % len(apis)] - papers = fn(q, 5) - for p in papers: - rows.append((eq_id, p['title'], - f"{p['src']}: {p.get('journal','')}" if p.get('journal') else p['src'], - p['year'], p.get('doi', p['src']), "Radical adaptation ref.")) - total += 1 - print(f" {'✓' if papers else '○'} {label:25s} | {fn.__name__:8s} → {len(papers):2d}p | {total:3d} total | {time.time()-start:.0f}s", flush=True) - time.sleep(delay) - -cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", rows) -conn.commit() - -cur.execute("SELECT COUNT(*) FROM verifications WHERE status='Radical adaptation ref.'") -print(f"\nRadical adaptation refs (total): {cur.fetchone()[0]}") -cur.execute("SELECT COUNT(*) FROM verifications") -print(f"Total verifications in DB: {cur.fetchone()[0]}") -cur.execute("SELECT COUNT(*) FROM equations WHERE domain_id=?", (rad_did,)) -print(f"Radical Adaptations equations: {cur.fetchone()[0]}") - -print(f"\nNew equation #{enum}: Reproductive Overclocking (Semelparity)") -print(f" Species covered:") -for q, label in QUERIES: - print(f" - {label}") -conn.close() -print(f" Database: {DB}") diff --git a/5-Applications/scripts/reset-tailnet.sh b/5-Applications/scripts/reset-tailnet.sh deleted file mode 100755 index 71c7eafa..00000000 --- a/5-Applications/scripts/reset-tailnet.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -# reset-tailnet.sh -# Clears all Tailscale nodes and re-authenticates current node as Node-00001 - -CURRENT_HOSTNAME=$(tailscale status --json 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('Self',{}).get('HostName','unknown'))") -TAILNET=$(tailscale status --json 2>/dev/null | python3 -c "import sys,json; print(json.load(sys.stdin).get('MagicDNSSuffix','unknown'))") - -echo "==========================================" -echo " Tailnet Reset Tool" -echo "==========================================" -echo "Current node: $CURRENT_HOSTNAME" -echo "Tailnet: $TAILNET" -echo "" - -# Step 1: Logout current node -echo "[1/2] Logging out current node ($CURRENT_HOSTNAME)..." -sudo tailscale logout -echo "Done. Node removed from tailnet." -echo "" - -# Step 2: Re-auth as Node-00001 -echo "[2/2] Re-authenticating as Node-00001..." -echo "You will see an auth URL. Open it in your browser to complete login." -echo "" -sudo tailscale up --hostname=Node-00001 --ssh --accept-routes - -echo "" -echo "==========================================" -echo "Current node re-authenticated as Node-00001" -echo "" -tailscale status -echo "" -echo "==========================================" -echo "NEXT STEPS: Remove remaining nodes" -echo "==========================================" -echo "" -echo "The other 9 nodes must be removed via the Tailscale admin console" -echo "or API since they are not reachable from this machine." -echo "" -echo "Option A: Manual removal (recommended)" -echo " 1. Go to: https://login.tailscale.com/admin/machines" -echo " 2. Select each old node and click 'Remove...'" -echo " 3. Old nodes: architect, desktop-0u2ceal, foxtop, ip-172-31-25-81," -echo " judge, laptop-1, netcup-router, racknerd-510bd9c, racknerd-atl" -echo "" -echo "Option B: API removal (batch)" -echo " 1. Get an API key: https://login.tailscale.com/admin/settings/keys" -echo " 2. Run: ./scripts/remove-tailnet-nodes-api.sh " -echo "" -echo "Option C: SSH into active nodes and logout" -echo " ssh judge 'sudo tailscale logout'" -echo " ssh netcup-router 'sudo tailscale logout'" -echo " ssh ip-172-31-25-81 'sudo tailscale logout'" -echo "" -echo "After clearing all nodes, run: ./scripts/clean-tailscale-refs.sh" -echo "to remove stale Tailscale references from this repo." diff --git a/5-Applications/scripts/reset_usb.py b/5-Applications/scripts/reset_usb.py deleted file mode 100644 index 3173bb01..00000000 --- a/5-Applications/scripts/reset_usb.py +++ /dev/null @@ -1,30 +0,0 @@ -import usb.core -import usb.util -import os - -VENDOR_ID = 0x048d -PRODUCT_ID = 0x1234 - -def reset_and_reattach(): - dev = usb.core.find(idVendor=VENDOR_ID, idProduct=PRODUCT_ID) - if dev is None: - print("Device not found") - return - - try: - dev.reset() - print("Device reset sent") - except Exception as e: - print(f"Reset failed: {e}") - - # Re-attach is usually automatic after reset, but we can try to force it - # if the device didn't disappear and reappear - try: - if not dev.is_kernel_driver_active(0): - dev.attach_kernel_driver(0) - print("Attached kernel driver") - except Exception as e: - print(f"Attach failed (may be normal if device re-enumerated): {e}") - -if __name__ == "__main__": - reset_and_reattach() diff --git a/5-Applications/scripts/reshuffle.py b/5-Applications/scripts/reshuffle.py deleted file mode 100644 index 867e0527..00000000 --- a/5-Applications/scripts/reshuffle.py +++ /dev/null @@ -1,301 +0,0 @@ -#!/usr/bin/env python3 -""" -Reshuffle — the project has shifted. -What started as "28 proven equations" is now a boundary map of -what self-replicating matter is permitted to do in this universe. - -New structure: - LAYER 1: Fundamental Laws (what cannot be violated — Maxwell, GR, QM, SM) - LAYER 2: Derived Constraints (what follows from Layer 1 — material limits, phase bounds) - LAYER 3: Empirical Ceilings (what experiment shows — extremophiles, material records) - LAYER 4: Living Bounds (what biology converged on — adaptations that define life's envelope) - LAYER 5: Open Problems (what we don't know — the gaps between Layer 1 and Layer 4) -""" - -import sqlite3, time, os - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -cur = conn.cursor() - -# ================================================================ -# ADD ONTOLOGICAL LAYERS TO DOMAINS -# ================================================================ -cur.execute("ALTER TABLE domains ADD COLUMN ontological_layer INTEGER DEFAULT 0") -cur.execute("ALTER TABLE domains ADD COLUMN layer_description TEXT DEFAULT ''") - -# Map each domain to its ontological layer -LAYER_MAP = { - # LAYER 1: Fundamental — these are the universe's axioms - 1: (1, "Classical limit of conservation symmetries"), - 2: (1, "Weak-field limit of GR; inverse-square from geometry"), - 3: (1, "U(1) gauge theory; classical limit of QED"), - 4: (1, "Statistical consequence of microscopic reversibility"), - 5: (1, "Non-relativistic limit of QFT; unitary evolution"), - 6: (1, "Spacetime geometry = mass-energy curvature"), - 7: (1, "SU(3)×SU(2)×U(1) gauge theory + SSB; the universe's particle rulebook"), - 16: (1, "Mathematical theorems — the logical substrate physical laws are written in"), - - # LAYER 2: Derived — these emerge from Layer 1 when applied to real materials/conditions - 8: (2, "FLRW metric + SM equation of state → cosmic evolution"), - 9: (2, "Navier-Stokes = continuum limit of momentum conservation"), - 10: (2, "Maxwell equations applied to dielectric interfaces"), - 11: (2, "Wave equation applied to compressible media"), - 12: (2, "QFT applied to periodic potentials + many-body systems"), - 13: (2, "QCD applied to nucleon bound states + weak interaction"), - 14: (2, "GR + nuclear physics applied to stellar matter"), - 15: (2, "Maxwell + fluid equations applied to ionized matter"), - 17: (2, "Thermodynamics applied to many-particle ensembles"), - 18: (2, "Newton + Hooke applied to deformable solids"), - 20: (2, "Layer 1 constants used as measurement anchors"), - 21: (2, "Layer 1 applied to real materials — dislocations, cracks, phase diagrams"), - 22: (2, "Bragg = wave interference in periodic lattices"), - 23: (2, "Schrödinger + Fermi-Dirac applied to doped crystals"), - 24: (2, "Stat mech applied to chain molecules"), - 25: (2, "Thermodynamics + E&M at interfaces"), - 26: (2, "Continuum mechanics applied to structured fluids"), - 27: (2, "Thermodynamics + kinetics applied to material transformations"), - 47: (2, "Maxwell applied to engineered sub-wavelength structures"), - 49: (2, "Layer 1-2 applied to designed systems"), - - # LAYER 3: Empirical Ceilings — measured limits of what's possible - 28: (3, "Earth's interior — the planet as a physics laboratory"), - 29: (3, "Atmosphere — fluid dynamics + radiation at planetary scale"), - 30: (3, "Oceans — rotating stratified fluid at global scale"), - 31: (3, "Water in porous media — Darcy's law + unsaturated flow"), - 33: (3, "Reaction rates — Arrhenius, Eyring, Marcus — measured kinetic limits"), - 34: (3, "Light manipulation — coherence, nonlinearity, frequency combs"), - 35: (3, "Atoms and molecules — spectra, hyperfine, Born-Oppenheimer — measured structure"), - 36: (3, "Non-Newtonian flow — measured constitutive laws for real fluids"), - 37: (3, "Friction + wear — measured limits of surface contact"), - 38: (3, "Granular matter — measured behavior of athermal particulate systems"), - 39: (3, "Nanoscale — quantum effects at engineered length scales"), - 41: (3, "Chaos — measured deterministic unpredictability"), - 42: (3, "Medical imaging — physics applied to biological measurement"), - 43: (3, "Radiation-matter interaction — stopping power, dosimetry — measured"), - 44: (3, "Energy conversion — measured efficiency limits"), - 45: (3, "Space environment — measured plasma-field interactions"), - 46: (3, "Explosives — measured detonation and shock physics"), - 48: (3, "Sound in water — measured propagation in the ocean"), - 50: (3, "Electrochemical systems — measured electrode kinetics"), - - # LAYER 4: Living Bounds — biology as the universe's stress-test of Layer 1-2 - 19: (4, "Information as physical — Landauer, Shannon — the physics of knowing"), - 32: (4, "Life's electrical and mechanical engineering — ion channels, motors, folding"), - 40: (4, "Quantum computation — leveraging Layer 1 for information processing"), - 51: (4, "Extremophile boundaries — where self-replicating matter fails"), - 52: (4, "Radical adaptations — the most extreme biological solutions converged on"), -} - -for did, (layer, desc) in LAYER_MAP.items(): - cur.execute("UPDATE domains SET ontological_layer = ?, layer_description = ? WHERE id = ?", - (layer, desc, did)) - -# Set defaults for any un-mapped domains to Layer 2 (derived) -cur.execute("UPDATE domains SET ontological_layer = 2 WHERE ontological_layer = 0") - -conn.commit() - -# ================================================================ -# CREATE LAYER SUMMARY VIEW -# ================================================================ -cur.executescript(""" -DROP VIEW IF EXISTS v_ontological_layers; -CREATE VIEW v_ontological_layers AS -SELECT - CASE ontological_layer - WHEN 1 THEN 'Layer 1: Fundamental Laws' - WHEN 2 THEN 'Layer 2: Derived Constraints' - WHEN 3 THEN 'Layer 3: Empirical Ceilings' - WHEN 4 THEN 'Layer 4: Living Bounds' - END as layer, - d.name as domain, - d.layer_description as derivation, - COUNT(DISTINCT e.id) as equations, - COUNT(DISTINCT v.id) as verifications, - ROUND(AVG(COALESCE(vc.c,0)),1) as avg_verifications -FROM domains d -LEFT JOIN equations e ON e.domain_id = d.id -LEFT JOIN verifications v ON v.equation_id = e.id -LEFT JOIN (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id = e.id -WHERE e.id IS NOT NULL -GROUP BY d.id -ORDER BY d.ontological_layer, d.name; -""") - -# ================================================================ -# ADD INVARIANT CHAIN TRACKING -# ================================================================ -# Create a table that tracks how boundary claims derive from fundamental laws -cur.execute("DROP TABLE IF EXISTS invariant_chains") -cur.execute("""CREATE TABLE invariant_chains ( - id INTEGER PRIMARY KEY, - chain_name TEXT NOT NULL, - layer1_eq_id INTEGER REFERENCES equations(id), -- the fundamental law - layer2_eq_id INTEGER REFERENCES equations(id), -- first derivation - layer3_eq_id INTEGER REFERENCES equations(id), -- empirical bound - layer4_eq_id INTEGER REFERENCES equations(id), -- living manifestation - description TEXT -)""") - -# Build invariant chains — trace from fundamental to living -CHAINS = [ - # Maxwell → Casimir → Minimum Metabolic Rate - ("EM → Casimir → Metabolism Floor", - "Maxwell's Equations → zero-point field → quantum vacuum energy → Casimir force", - 38, # Maxwell's Equations - None, # Casimir effect (equation_constants links to QED) - 742, # Absolute minimum metabolic rate - None, - "The vacuum can't be zero because [x,p]≠0. The Casimir force is the universe's proof that empty space pushes back. Living cells at 2km subsurface depth operate at ~10^-21 W — within 2 orders of magnitude of the thermal noise floor set by k_B T at ambient temperature. The metabolic minimum isn't a biological limit — it's the universe's noise floor for any information-processing system."), - - # GR → Neutron Star EOS → TOV Limit - ("GR → Nuclear EOS → Compact Object Limit", - "Einstein Field Equations → dense matter equation of state → maximum stellar mass before collapse", - 129, # EFE - 265, # Neutron star EOS - 254, # TOV limit - None, - "GR says gravity curves spacetime. When mass density exceeds nuclear density, the curvature becomes a trap — the event horizon. The TOV limit (~2-3 M⊙) is where the strong force's degeneracy pressure fails against GR curvature. This is the universe's absolute ceiling on how much matter can avoid becoming a black hole."), - - # Schrödinger → Band Structure → Semiconductor Limits - ("QM → Crystals → Computation", - "Schrödinger equation → Bloch theorem → band gaps → transistors → quantum computing", - 93, # Schrödinger - 187, # Bloch's theorem, or 189 for band structure - 390, # Shockley diode equation - 661, # Single qubit state - "Schrödinger's equation applied to a periodic lattice creates band gaps — energy regions where electrons can't exist. Dope the crystal, you get a transistor. Cool it to mK, you get a qubit. The entire digital world and the quantum future are just boundary conditions on a 1926 partial differential equation."), - - # Maxwell + Fluid → MHD → Magnetosphere → Auroral Acceleration → Electric Eels - ("MHD → Magnetosphere → Bioelectrogenesis", - "Maxwell + Navier-Stokes → magnetohydrodynamics → planetary magnetosphere → 10kV field-aligned potentials → biological 860V discharge", - 38, # Maxwell - 272, # MHD induction equation - 698, # Auroral electron acceleration (Knight relation) - 751, # Bioelectrogenesis (electric eel) - "The same physics that accelerates electrons into Earth's atmosphere at 10kV — Maxwell's equations coupled to a flowing plasma — is what electric eels exploit. The eel's 5,000 electrocytes in series are just a biological magnetosphere in miniature. Same equations, 10 orders of magnitude smaller scale."), - - # Thermodynamics → Semelparity → Programmed Death - ("Entropy → Reproductive Tradeoff → Death", - "2nd Law → resource allocation optimization → semelparity as extreme fitness strategy", - 68, # 2nd Law of Thermodynamics - 300, # Gibbs entropy formula - 473, # Classical nucleation theory (tradeoff math) - 770, # Reproductive Overclocking - "Life is a local entropy gradient pump. The 2nd Law sets the maintenance cost. Semelparity is the solution when the reproductive payoff of one catastrophic event exceeds the entropy cost of continued maintenance. The antechinus male floods itself with cortisol until its immune system collapses — not because it's broken, but because the fitness calculus says survival past mating is wasted entropy budget."), - - # Einstein Field Equations → Black Hole Thermodynamics → Information Paradox - ("GR → BH Thermodynamics → Information", - "EFE → horizon thermodynamics → Hawking radiation → information paradox → quantum gravity requirement", - 129, # EFE - 136, # Bekenstein-Hawking entropy - 137, # Hawking temperature - None, # No resolution equation — it's an open problem - "GR predicts black holes. Bekenstein showed they have entropy. Hawking showed they radiate. But information falling in would be destroyed — violating unitarity, Layer 1's own requirement. The paradox is the crack where GR and QM grind against each other. Any theory of quantum gravity must resolve this. The island formula from holography may have done it — but the equation isn't in this database yet."), - - # Dirac → Antimatter → PET scanning (medical physics) - ("Dirac → Antimatter → Medical Imaging", - "Dirac equation → positron prediction → pair production → PET scanner → medical diagnosis", - 104, # Dirac equation - 141, # QED Lagrangian (includes pair production) - None, # PET scanner - None, - "Dirac's equation unified QM and SR in 1928 and spat out antimatter as a necessary consequence. Positrons aren't a particle physics curiosity — they're the working medium of every PET scanner on Earth. When a cancer patient gets imaged, they're consuming Dirac's 1928 insight as FDG-tagged sugar. The universe's most abstract truth became a hospital machine."), - - # Planck → Blackbody → CMB → Dark Energy evidence - ("Planck → CMB → Accelerating Universe", - "Planck's constant → blackbody spectrum → CMB radiation → acoustic peaks → ΛCDM parameters → dark energy discovery", - 86, # Planck's law - 153, # Sachs-Wolfe effect - 159, # Friedmann equation (where CMB fits) - 168, # Dark energy EoS - "Planck quantized light in 1900 to fix a thermodynamics problem. The CMB is the exact blackbody his equation predicts — at 2.725K, after 13.8 billion years of redshift. The tiny temperature fluctuations encode the universe's entire ΛCDM parameter set, including the 10^-120 mystery of dark energy. A blackbody spectrum taken at 3000K in 380,000 ABB (After Big Bang) is still telling us what the universe is made of."), -] - -for name, desc, l1, l2, l3, l4, full_desc in CHAINS: - cur.execute("""INSERT INTO invariant_chains - (chain_name, description, layer1_eq_id, layer2_eq_id, layer3_eq_id, layer4_eq_id) - VALUES (?, ?, ?, ?, ?, ?)""", - (name, full_desc, l1, l2, l3, l4)) - -conn.commit() - -# ================================================================ -# BUILD INVARIANT CHAIN SUMMARY VIEW -# ================================================================ -cur.executescript(""" -DROP VIEW IF EXISTS v_invariant_chains; -CREATE VIEW v_invariant_chains AS -SELECT - ic.chain_name, - ic.description, - COALESCE(e1.eq_number, 0) as law_eq, - COALESCE(e1.title, '—') as fundamental_law, - COALESCE(e2.eq_number, 0) as derivation_eq, - COALESCE(e2.title, '—') as derivation, - COALESCE(e3.eq_number, 0) as empirical_eq, - COALESCE(e3.title, '—') as empirical_bound, - COALESCE(e4.eq_number, 0) as living_eq, - COALESCE(e4.title, '—') as living_manifestation -FROM invariant_chains ic -LEFT JOIN equations e1 ON ic.layer1_eq_id = e1.id -LEFT JOIN equations e2 ON ic.layer2_eq_id = e2.id -LEFT JOIN equations e3 ON ic.layer3_eq_id = e3.id -LEFT JOIN equations e4 ON ic.layer4_eq_id = e4.id -ORDER BY ic.id; -""") - -# ================================================================ -# FINAL SUMMARY -# ================================================================ -cur.execute("SELECT COUNT(*) FROM domains WHERE ontological_layer = 1") -l1 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM domains WHERE ontological_layer = 2") -l2 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM domains WHERE ontological_layer = 3") -l3 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM domains WHERE ontological_layer = 4") -l4 = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM invariant_chains") -chains = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM verifications") -vers = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations") -eqs = cur.fetchone()[0] - -print(f""" -{'═'*65} - PROJECT RESHUFFLE COMPLETE -{'═'*65} - - ONTOLOGICAL STRUCTURE: - Layer 1 — Fundamental Laws: {l1:2d} domains (the universe's axioms) - Layer 2 — Derived Constraints: {l2:2d} domains (what follows from Layer 1) - Layer 3 — Empirical Ceilings: {l3:2d} domains (what experiment shows) - Layer 4 — Living Bounds: {l4:2d} domains (what biology converged on) - - INVARIANT CHAINS: {chains:2d} chains (trace from Layer 1 → Layer 4) - - DATA INTEGRITY: - Total equations: {eqs:4d} - Total verifications: {vers:5d} - Orphaned references: 0 - Duplicate verifications: 0 - Equations below 10x: 0 - - KEY VIEWS FOR THE SCAN: - v_ontological_layers — layer-by-layer structure - v_invariant_chains — trace any boundary back to a fundamental law - v_invariant_scan — every equation with formulas, constants, ref count - v_domain_coverage — per-domain statistics - v_open_problems_with_refs — research frontiers with citations - v_constant_usage — which constants appear where - v_database_summary — one-row overview - - FILE: {DB} ({os.path.getsize(DB)} bytes) -{'═'*65} -""") - -conn.close() diff --git a/5-Applications/scripts/resolve_metric_closure_batch.py b/5-Applications/scripts/resolve_metric_closure_batch.py deleted file mode 100644 index 732d89f9..00000000 --- a/5-Applications/scripts/resolve_metric_closure_batch.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -import sys -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def resolve_batch(): - client = DeepSeekV4(use_local=True) - prover = DeepSeekProver(client) - - tasks = [ - { - "name": "foldl_add_reverse", - "context": "Score addition is commutative and associative. Prove: l.reverse.foldl Score.add init = l.foldl Score.add init" - }, - { - "name": "pathCost_reverse", - "context": "pathCost p = foldl add e.cost p init. Prove: pathCost (p.reversePath) = pathCost p" - }, - { - "name": "dist_self_zero", - "context": "shortestPathDist x x is the cost of the empty path. Prove it equals {num:=0, den:=1}." - } - ] - - for task in tasks: - print(f"Resolving {task['name']}...") - proof = prover.formalize(task['context']) - print(f"Result for {task['name']}:\n{proof}\n") - -if __name__ == "__main__": - resolve_batch() diff --git a/5-Applications/scripts/resolve_triangle.py b/5-Applications/scripts/resolve_triangle.py deleted file mode 100644 index 9d79b3c7..00000000 --- a/5-Applications/scripts/resolve_triangle.py +++ /dev/null @@ -1,35 +0,0 @@ -import os -import sys -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def resolve_triangle_inequality(): - client = DeepSeekV4(use_local=True) - prover = DeepSeekProver(client) - - context = """ - We are in a Lean 4 environment. - Variable (V : Type) [DecidableEq V] - Structure AdmissibilityGraph where - edge : V -> V -> Option Score - edge_symm : ∀ x y, edge x y = edge y x - - Definition Path (x y : V) ... - Definition pathCost (p : Path x y) : Score ... - Definition shortestPathDist (x y : V) : Score := - -- infimum of path costs - - Theorem: shortestPathDist_triangle (x y z : V) : - shortestPathDist x z ≤ shortestPathDist x y + shortestPathDist y z - - The user needs the full proof block using Lean 4. - """ - - theorem_code = "theorem shortestPathDist_triangle (x y z : V) : shortestPathDist x z ≤ shortestPathDist x y + shortestPathDist y z" - - print("Generating proof for shortestPathDist_triangle...") - proof = prover.formalize(context + "\n" + theorem_code) - print("\nProposed Proof:\n", proof) - return proof - -if __name__ == "__main__": - resolve_triangle_inequality() diff --git a/5-Applications/scripts/resolve_triangle_closure.py b/5-Applications/scripts/resolve_triangle_closure.py deleted file mode 100644 index 313a4139..00000000 --- a/5-Applications/scripts/resolve_triangle_closure.py +++ /dev/null @@ -1,55 +0,0 @@ -import os -import sys -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def resolve_triangle_closure(): - client = DeepSeekV4(use_local=True) - prover = DeepSeekProver(client) - - # Provide the actual code from the library for context - context = """ - import Semantics.RealityContractMassNumber - namespace HolyDiver.ENE - - structure AdmissibilityEdge where - u : CandidateRecord - v : CandidateRecord - cost : Score - symm : cost = cost - - def Path := List AdmissibilityEdge - - def pathCost (p : Path) : Score := - p.foldl (fun acc e => acc.add e.cost) { num := 0, den := 1, den_ne := by simp } - - def is_path (g : AdmissibilityGraph) (x y : CandidateRecord) (p : Path) : Prop := - match p with - | [] => x = y - | [e] => e.u = x ∧ e.v = y ∧ e ∈ g.edges ∧ edgeAdmissible g e - | e :: es => e.u = x ∧ e ∈ g.edges ∧ edgeAdmissible g e ∧ is_path g e.v y es - - def allPaths (g : AdmissibilityGraph) (x y : CandidateRecord) : Set Path := - {p | is_path g x y p} - - def shortestPathDist (g : AdmissibilityGraph) (x y : CandidateRecord) : Score := - -- We assume a well-defined infimum of pathCosts for all p in allPaths g x y - -- For this proof, just use the property: - -- for any p such that is_path g x y p, shortestPathDist g x y ≤ pathCost p - - Theorem to prove: - theorem shortestPathDist_triangle (g : AdmissibilityGraph) (x y z : CandidateRecord) : - Score.le (shortestPathDist g x z) ((shortestPathDist g x y).add (shortestPathDist g y z)) - - Hint: If p1 is a path from x to y and p2 is a path from y to z, - then p1 ++ p2 is a path from x to z. - And pathCost (p1 ++ p2) = (pathCost p1).add (pathCost p2). - Since shortestPathDist x z is the infimum, it must be ≤ pathCost (p1 ++ p2). - """ - - print("Generating proof for shortestPathDist_triangle...") - proof = prover.formalize(context) - print("\nProposed Proof:\n", proof) - return proof - -if __name__ == "__main__": - resolve_triangle_closure() diff --git a/5-Applications/scripts/restore_gdrive.py b/5-Applications/scripts/restore_gdrive.py deleted file mode 100644 index 32b05dbe..00000000 --- a/5-Applications/scripts/restore_gdrive.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -""" -restore_gdrive.py — Restore Google Drive access and re-ingest into ENE. - -Steps: - 1. Test current rclone Gdrive: remote - 2. If token is dead, print re-auth instructions - 3. If token works, extract credentials and re-ingest into ENE substrate - 4. Verify ENE can decrypt the new credentials - -Usage: - # If gdrive token is dead, re-auth first: - rclone config reconnect Gdrive: - - # Then run: - python3 5-Applications/scripts/restore_gdrive.py -""" - -import sys -import json -import os -import subprocess -from pathlib import Path -from datetime import datetime - -# Load .env -project_root = Path(__file__).parent.parent.parent -try: - from dotenv import load_dotenv - if (project_root / ".env").exists(): - load_dotenv(project_root / ".env") -except ImportError: - pass - -sys.path.insert(0, str(project_root)) -sys.path.insert(0, str(project_root / "4-Infrastructure" / "infra")) - -from infra.ene_api import ENEAPIHook, AccessLevel - -RCLONE_CONF = Path.home() / ".config" / "rclone" / "rclone.conf" - - -def test_rclone_gdrive() -> dict: - """Test if rclone can list Gdrive root.""" - result = subprocess.run( - ["rclone", "ls", "Gdrive:", "--max-depth", "1"], - capture_output=True, text=True, timeout=60 - ) - return { - "ok": result.returncode == 0, - "stdout": result.stdout.strip(), - "stderr": result.stderr.strip(), - "returncode": result.returncode - } - - -def parse_rclone_token() -> dict: - """Extract token JSON from rclone config.""" - if not RCLONE_CONF.exists(): - return {"error": f"rclone.conf not found at {RCLONE_CONF}"} - - in_gdrive = False - token_line = None - with open(RCLONE_CONF) as f: - for line in f: - line = line.strip() - if line.startswith("[Gdrive]"): - in_gdrive = True - elif line.startswith("[") and line.endswith("]"): - in_gdrive = False - elif in_gdrive and line.startswith("token = "): - token_line = line[len("token = "):] - break - - if not token_line: - return {"error": "No token found in [Gdrive] section"} - - try: - token = json.loads(token_line) - return {"ok": True, "token": token} - except json.JSONDecodeError as e: - return {"error": f"Invalid token JSON: {e}"} - - -def update_rclone_config(new_token: dict) -> bool: - """Update rclone.conf with new token JSON.""" - if not RCLONE_CONF.exists(): - return False - - lines = [] - in_gdrive = False - replaced = False - with open(RCLONE_CONF) as f: - for line in f: - stripped = line.strip() - if stripped.startswith("[Gdrive]"): - in_gdrive = True - elif stripped.startswith("[") and stripped.endswith("]"): - in_gdrive = False - - if in_gdrive and stripped.startswith("token = ") and not replaced: - lines.append(f"token = {json.dumps(new_token)}\n") - replaced = True - continue - lines.append(line) - - with open(RCLONE_CONF, "w") as f: - f.writelines(lines) - - return replaced - - -def ingest_gdrive_to_ene(token: dict, client_id: str, client_secret: str) -> dict: - """Store gdrive credentials in ENE substrate.""" - api = ENEAPIHook() - - # Store the refresh token as the primary credential - payload = json.dumps({ - "provider": "gdrive", - "remote_name": "Gdrive", - "client_id": client_id, - "client_secret": client_secret, - "refresh_token": token.get("refresh_token", ""), - "token_type": token.get("token_type", "Bearer"), - "updated_at": datetime.utcnow().isoformat(), - }) - - result = api.store_sensitive_data( - pkg="credentials/gdrive", - payload=payload, - classification=AccessLevel.SECRET - ) - - return result - - -def main(): - print("=" * 70) - print("GDRIVE RESTORE") - print("=" * 70) - - # 1. Test rclone - print("\n[1] Testing rclone Gdrive: access...") - test = test_rclone_gdrive() - if test["ok"]: - print(" ✅ Gdrive is accessible") - print(f" Listing: {test['stdout'][:200]}...") - else: - print(" ❌ Gdrive access failed") - print(f" Error: {test['stderr'][:200]}") - - if "invalid_grant" in test["stderr"] or "token expired" in test["stderr"]: - print("\n The refresh token is revoked or expired.") - print(" You need to re-authenticate:") - print("\n ┌─────────────────────────────────────────────────────────────┐") - print(" │ Run this command in your terminal: │") - print(" │ │") - print(" │ rclone config reconnect Gdrive: │") - print(" │ │") - print(" │ Then follow the browser prompt to authorize. │") - print(" │ After that, run this script again. │") - print(" └─────────────────────────────────────────────────────────────┘") - sys.exit(1) - - # 2. Parse token - print("\n[2] Reading rclone token...") - token_info = parse_rclone_token() - if "error" in token_info: - print(f" ❌ {token_info['error']}") - sys.exit(1) - - token = token_info["token"] - refresh_token = token.get("refresh_token", "") - if not refresh_token: - print(" ❌ No refresh_token in rclone config. Re-authenticate with:") - print(" rclone config reconnect Gdrive:") - sys.exit(1) - - print(f" ✅ Refresh token present ({len(refresh_token)} chars)") - - # 3. Read client_id/client_secret from rclone config - client_id = None - client_secret = None - with open(RCLONE_CONF) as f: - in_gdrive = False - for line in f: - line = line.strip() - if line.startswith("[Gdrive]"): - in_gdrive = True - elif line.startswith("[") and line.endswith("]"): - in_gdrive = False - elif in_gdrive and line.startswith("client_id = "): - client_id = line[len("client_id = "):] - elif in_gdrive and line.startswith("client_secret = "): - client_secret = line[len("client_secret = "):] - - # 4. Ingest into ENE - print("\n[3] Ingesting gdrive credentials into ENE substrate...") - ene_result = ingest_gdrive_to_ene(token, client_id, client_secret) - if ene_result.get("success"): - print(f" ✅ Stored in ENE. ID: {ene_result['id']}") - else: - print(f" ❌ ENE storage failed: {ene_result.get('error')}") - sys.exit(1) - - # 5. Verify decryption - print("\n[4] Verifying ENE decryption...") - api = ENEAPIHook() - verify = api.retrieve_sensitive_data("credentials/gdrive", AccessLevel.SECRET) - if verify.get("success"): - print(" ✅ Credentials decryptable") - parsed = json.loads(verify["payload"]) - print(f" Provider: {parsed.get('provider')}") - print(f" Remote: {parsed.get('remote_name')}") - print(f" Refresh token length: {len(parsed.get('refresh_token', ''))}") - else: - print(f" ❌ Decryption failed: {verify.get('error')}") - sys.exit(1) - - print("\n" + "=" * 70) - print("GDRIVE RESTORED") - print("=" * 70) - print("\nNext steps:") - print(" - ENE substrate now holds the gdrive credential") - print(" - rclone Gdrive: remote is working") - print(" - Run 'rclone mount Gdrive: ~/Gdrive' to mount if desired") - print(" - Run bridge/dump scripts to push gdrive metadata into Neo4j") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/rgflow_network_filter.py b/5-Applications/scripts/rgflow_network_filter.py deleted file mode 100644 index 273a5155..00000000 --- a/5-Applications/scripts/rgflow_network_filter.py +++ /dev/null @@ -1,614 +0,0 @@ -#!/usr/bin/env python3 -""" -RGFlow Network Filter -Apply RGFlow analysis to network/graph structure. - -Analyzes the structural integrity of network connections -under Renormalization Group Flow scale transformation. -""" - -import json -import sys -import os -import numpy as np -from pathlib import Path -from typing import Dict, Any, List, Tuple -from collections import defaultdict, deque - - -class NetworkRGFlowAnalyzer: - """Apply RGFlow to network graph structure.""" - - def __init__(self): - # RGFlow parameters for network analysis - self.D = 0.12 # Diffusion coefficient for network structure - self.B = 0.015 # Drift barrier for connectivity - self.lam = 0.18 # Selection strength for edge weights - self.SCALE_STEPS = 5 # Number of RG scale transformations - - # Network lawfulness thresholds - self.entropy_lower = 1.5 - self.entropy_upper = 5.0 - self.density_lower = 0.01 # Minimum connectivity - self.density_upper = 0.30 # Maximum connectivity (avoid over-connected) - self.component_lower = 1 # Should be connected - self.clustering_lower = 0.05 # Minimum local structure - - def extract_network_from_codebase(self) -> Dict[str, Any]: - """Extract network structure from codebase imports and dependencies.""" - print("Extracting network structure from codebase...") - - # Build import dependency graph - nodes = set() - edges = [] - - # Scan Python files for imports - python_files = [] - for root, dirs, files in os.walk("."): - # Skip common non-source directories - dirs[:] = [d for d in dirs if d not in ['.git', '__pycache__', '.lake', 'node_modules', 'venv', 'env', 'hutter_venv']] - - for file in files: - if file.endswith('.py'): - python_files.append(os.path.join(root, file)) - - print(f"Found {len(python_files)} Python files") - - # Extract imports - for filepath in python_files: - rel_path = filepath.replace('./', '').replace('.py', '').replace('/', '.') - nodes.add(rel_path) - - try: - with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - - # Simple import detection - import_lines = [line for line in content.split('\n') if line.strip().startswith(('import ', 'from '))] - - for imp_line in import_lines: - imp_line = imp_line.strip() - - # Parse import statement - if imp_line.startswith('import '): - module = imp_line.replace('import ', '').split(' as ')[0].split(',')[0].strip() - elif imp_line.startswith('from '): - parts = imp_line.replace('from ', '').split(' import ') - module = parts[0].strip() - - # Normalize module name - if module and not module.startswith('.'): - # Check if it's a local import - if any(module.startswith(p) for p in ['infra', 'scripts', 'tools', 'core', 'drivers']): - nodes.add(module) - edges.append({ - 'source': rel_path, - 'target': module, - 'edgeType': 'import', - 'weight': 1.0 - }) - except Exception as e: - continue - - # Convert nodes to list - node_list = [{'id': n, 'nodeType': 'module'} for n in nodes] - - network = { - 'nodes': node_list, - 'edges': edges, - 'metadata': { - 'total_files': len(python_files), - 'extracted_nodes': len(nodes), - 'extracted_edges': len(edges) - } - } - - print(f"Extracted network: {len(nodes)} nodes, {len(edges)} edges") - return network - - def calculate_network_features(self, network: Dict[str, Any]) -> Dict[str, float]: - """Calculate network/graph features for RGFlow.""" - nodes = network.get("nodes", []) - edges = network.get("edges", []) - - n_nodes = len(nodes) - n_edges = len(edges) - - if n_nodes == 0: - return self._empty_features() - - # Build adjacency matrix - node_id_to_idx = {node["id"]: i for i, node in enumerate(nodes)} - adj = np.zeros((n_nodes, n_nodes)) - - for edge in edges: - source = edge.get("source") - target = edge.get("target") - weight = edge.get("weight", 1.0) - - if source in node_id_to_idx and target in node_id_to_idx: - i = node_id_to_idx[source] - j = node_id_to_idx[target] - adj[i, j] = weight - adj[j, i] = weight # Undirected for analysis - - # Calculate network features - features = {} - - # 1. Node degree statistics - degrees = np.sum(adj, axis=1) - features["mean_degree"] = float(np.mean(degrees)) if n_nodes > 0 else 0.0 - features["std_degree"] = float(np.std(degrees)) if n_nodes > 0 else 0.0 - features["max_degree"] = float(np.max(degrees)) if n_nodes > 0 else 0.0 - features["min_degree"] = float(np.min(degrees)) if n_nodes > 0 else 0.0 - - # 2. Edge density - max_edges = n_nodes * (n_nodes - 1) / 2 if n_nodes > 1 else 0 - features["edge_density"] = n_edges / max_edges if max_edges > 0 else 0.0 - - # 3. Clustering coefficient - clustering = [] - for i in range(n_nodes): - neighbors = np.where(adj[i] > 0)[0] - k = len(neighbors) - if k < 2: - clustering.append(0.0) - continue - - # Count triangles - triangles = 0 - for j in range(k): - for l in range(j + 1, k): - if adj[neighbors[j], neighbors[l]] > 0: - triangles += 1 - - possible = k * (k - 1) / 2 - clustering.append(triangles / possible if possible > 0 else 0.0) - - features["mean_clustering"] = float(np.mean(clustering)) if clustering else 0.0 - - # 4. Connected components - visited = set() - components = [] - - for i in range(n_nodes): - if i not in visited: - # BFS to find component - component = [] - queue = [i] - visited.add(i) - - while queue: - node = queue.pop(0) - component.append(node) - - neighbors = np.where(adj[node] > 0)[0] - for neighbor in neighbors: - if neighbor not in visited: - visited.add(neighbor) - queue.append(neighbor) - - components.append(component) - - features["num_components"] = len(components) - features["largest_component_size"] = max(len(c) for c in components) if components else 0 - features["largest_component_ratio"] = features["largest_component_size"] / n_nodes if n_nodes > 0 else 0.0 - - # 5. Path length distribution - if n_nodes > 0 and components: - largest_comp = max(components, key=len) - if len(largest_comp) > 1: - # Calculate average shortest path in largest component - sub_adj = adj[np.ix_(largest_comp, largest_comp)] - path_lengths = [] - - for i in range(len(largest_comp)): - for j in range(i + 1, len(largest_comp)): - # BFS for shortest path - dist = self._bfs_shortest_path(sub_adj, i, j) - if dist < float('inf'): - path_lengths.append(dist) - - features["avg_path_length"] = float(np.mean(path_lengths)) if path_lengths else 0.0 - features["diameter"] = float(max(path_lengths)) if path_lengths else 0.0 - else: - features["avg_path_length"] = 0.0 - features["diameter"] = 0.0 - else: - features["avg_path_length"] = 0.0 - features["diameter"] = 0.0 - - # 6. Entropy (based on degree distribution) - if n_nodes > 0 and features["std_degree"] > 0: - degree_hist = np.histogram(degrees, bins=10, range=(0, features["max_degree"] + 1))[0] - degree_hist = degree_hist[degree_hist > 0] - probs = degree_hist / np.sum(degree_hist) - entropy = -np.sum(probs * np.log2(probs)) - features["degree_entropy"] = float(entropy) - else: - features["degree_entropy"] = 0.0 - - # 7. Betweenness centrality (simplified) - features["betweenness_mean"] = self._calculate_betweenness(adj) - - # 8. Network robustness (algebraic connectivity) - features["algebraic_connectivity"] = self._calculate_algebraic_connectivity(adj) - - return features - - def _empty_features(self) -> Dict[str, float]: - """Return empty feature set.""" - return { - "mean_degree": 0.0, "std_degree": 0.0, "max_degree": 0.0, "min_degree": 0.0, - "edge_density": 0.0, "mean_clustering": 0.0, "num_components": 0.0, - "largest_component_size": 0.0, "largest_component_ratio": 0.0, - "avg_path_length": 0.0, "diameter": 0.0, "degree_entropy": 0.0, - "betweenness_mean": 0.0, "algebraic_connectivity": 0.0 - } - - def _bfs_shortest_path(self, adj: np.ndarray, start: int, end: int) -> float: - """BFS to find shortest path length.""" - if start == end: - return 0.0 - - visited = {start} - queue = [(start, 0)] - - while queue: - node, dist = queue.pop(0) - - neighbors = np.where(adj[node] > 0)[0] - for neighbor in neighbors: - if neighbor == end: - return float(dist + 1) - if neighbor not in visited: - visited.add(neighbor) - queue.append((neighbor, dist + 1)) - - return float('inf') - - def _calculate_betweenness(self, adj: np.ndarray) -> float: - """Simplified betweenness centrality calculation.""" - n = adj.shape[0] - if n == 0: - return 0.0 - - # Count shortest paths through each node - betweenness = np.zeros(n) - - for s in range(n): - for t in range(s + 1, n): - # Find all shortest paths from s to t - paths = self._find_all_shortest_paths(adj, s, t) - if paths: - for path in paths: - for node in path[1:-1]: # Exclude endpoints - betweenness[node] += 1.0 / len(paths) - - return float(np.mean(betweenness)) if n > 0 else 0.0 - - def _find_all_shortest_paths(self, adj: np.ndarray, start: int, end: int) -> List[List[int]]: - """Find all shortest paths between two nodes.""" - if start == end: - return [[start]] - - # BFS to find shortest distance - visited = {start} - queue = [(start, [start])] - shortest_dist = float('inf') - all_paths = [] - - while queue: - node, path = queue.pop(0) - - if len(path) > shortest_dist: - continue - - neighbors = np.where(adj[node] > 0)[0] - for neighbor in neighbors: - if neighbor == end: - new_path = path + [neighbor] - dist = len(new_path) - 1 - if dist < shortest_dist: - shortest_dist = dist - all_paths = [new_path] - elif dist == shortest_dist: - all_paths.append(new_path) - elif neighbor not in visited: - visited.add(neighbor) - queue.append((neighbor, path + [neighbor])) - - return all_paths - - def _calculate_algebraic_connectivity(self, adj: np.ndarray) -> float: - """Calculate algebraic connectivity (second smallest eigenvalue of Laplacian).""" - n = adj.shape[0] - if n < 2: - return 0.0 - - # Compute Laplacian matrix - degree = np.sum(adj, axis=1) - laplacian = np.diag(degree) - adj - - try: - # Compute eigenvalues - eigenvalues = np.linalg.eigvalsh(laplacian) - # Sort eigenvalues - eigenvalues = np.sort(eigenvalues) - # Second smallest eigenvalue (first is 0 for connected graphs) - return float(eigenvalues[1]) if len(eigenvalues) > 1 else 0.0 - except: - return 0.0 - - def rgflow_transform(self, features: Dict[str, float], scale: float) -> Dict[str, float]: - """Apply RGFlow scale transformation to network features.""" - transformed = {} - - # Scale transformation: network evolves under RG flow - # Connectivity decays with scale (edge pruning) - transformed["edge_density"] = features["edge_density"] * np.exp(-self.D * scale) - - # Clustering increases with scale (local structure preserved) - transformed["mean_clustering"] = features["mean_clustering"] * (1 + self.lam * scale) - - # Components merge with scale (connectivity restoration) - transformed["num_components"] = max(1, features["num_components"] * np.exp(-self.B * scale)) - - # Path length increases with scale (hierarchical emergence) - transformed["avg_path_length"] = features["avg_path_length"] * (1 + 0.15 * scale) - - # Entropy increases with scale (information growth) - transformed["degree_entropy"] = features["degree_entropy"] * (1 + 0.08 * scale) - - # Degree distribution smooths with scale - transformed["std_degree"] = features["std_degree"] * np.exp(-0.12 * scale) - - # Algebraic connectivity decays with scale - transformed["algebraic_connectivity"] = features["algebraic_connectivity"] * np.exp(-0.1 * scale) - - return transformed - - def check_drift_barrier(self, features: Dict[str, float], scale: float) -> bool: - """Check if network survives drift barrier.""" - # Drift barrier: ρ_q * N_e * Φ(M_fac) > B - # For network: edge_density * largest_component_ratio * clustering > B - - rho_q = features["edge_density"] - N_e = features.get("largest_component_ratio", 1.0) - phi = features["mean_clustering"] - - verification_pressure = rho_q * N_e * phi - - return verification_pressure > self.B - - def evaluate_lawfulness(self, network: Dict[str, Any]) -> Dict[str, Any]: - """Evaluate network lawfulness under RGFlow.""" - print("Calculating network features...") - features = self.calculate_network_features(network) - - print("Applying RGFlow scale transformation...") - rgflow_trajectory = [] - current_features = features.copy() - - for scale in range(1, self.SCALE_STEPS + 1): - scale_factor = scale / self.SCALE_STEPS - transformed = self.rgflow_transform(current_features, scale_factor) - rgflow_trajectory.append({ - "scale": scale, - "scale_factor": scale_factor, - "features": transformed.copy() - }) - current_features = transformed - - # Check lawfulness at each scale - lawful_scales = [] - for step in rgflow_trajectory: - step_features = step["features"] - - # Entropy bounds - entropy = step_features["degree_entropy"] - entropy_lawful = self.entropy_lower <= entropy <= self.entropy_upper - - # Density bounds - density = step_features["edge_density"] - density_lawful = self.density_lower <= density <= self.density_upper - - # Component bounds - components = step_features["num_components"] - component_lawful = components >= self.component_lower - - # Clustering bounds - clustering = step_features["mean_clustering"] - clustering_lawful = clustering >= self.clustering_lower - - # Drift barrier - drift_survives = self.check_drift_barrier(step_features, step["scale"]) - - # Algebraic connectivity (network robustness) - alg_conn = step_features["algebraic_connectivity"] - robustness_lawful = alg_conn > 0.001 - - step_lawful = (entropy_lawful and density_lawful and component_lawful - and clustering_lawful and drift_survives and robustness_lawful) - - lawful_scales.append({ - "scale": step["scale"], - "scale_factor": step["scale_factor"], - "lawful": step_lawful, - "entropy_lawful": entropy_lawful, - "density_lawful": density_lawful, - "component_lawful": component_lawful, - "clustering_lawful": clustering_lawful, - "drift_survives": drift_survives, - "robustness_lawful": robustness_lawful, - "entropy": entropy, - "density": density, - "components": components, - "clustering": clustering, - "algebraic_connectivity": alg_conn, - "verification_pressure": step_features["edge_density"] * step_features.get("largest_component_ratio", 1.0) * step_features["mean_clustering"] - }) - - # Determine overall lawfulness - all_lawful = all(step["lawful"] for step in lawful_scales) - final_state = lawful_scales[-1] - - # Calculate failure mask - failure_mask = "" - if not final_state["entropy_lawful"]: - failure_mask += "1" - else: - failure_mask += "0" - if not final_state["density_lawful"]: - failure_mask += "1" - else: - failure_mask += "0" - if not final_state["component_lawful"]: - failure_mask += "1" - else: - failure_mask += "0" - if not final_state["clustering_lawful"]: - failure_mask += "1" - else: - failure_mask += "0" - if not final_state["drift_survives"]: - failure_mask += "1" - else: - failure_mask += "0" - if not final_state["robustness_lawful"]: - failure_mask += "1" - else: - failure_mask += "0" - - # Calculate attractor - if all_lawful: - attractor = "Attractor 1 (High-Fitness Network)" - elif final_state["lawful"]: - attractor = "Attractor 2 (Locally Lawful)" - elif final_state["drift_survives"] and final_state["robustness_lawful"]: - attractor = "Attractor 3 (Structurally Coherent)" - elif failure_mask == "0000001": - attractor = "Sabotage Attractor (Drift Barrier Violation)" - elif failure_mask == "1000000": - attractor = "Sabotage Attractor (Entropy Collapse)" - else: - attractor = "Noise Attractor (Network Fragmentation)" - - return { - "initial_features": features, - "rgflow_trajectory": rgflow_trajectory, - "lawfulness_analysis": lawful_scales, - "overall_lawful": all_lawful, - "final_state": final_state, - "attractor": attractor, - "failure_mask": failure_mask, - "rgflow_depth": self.SCALE_STEPS if all_lawful else len([s for s in lawful_scales if s["lawful"]]) - } - - def analyze_network(self) -> Dict[str, Any]: - """Complete network RGFlow analysis.""" - print("=" * 60) - print("RGFLOW NETWORK ANALYSIS") - print("=" * 60) - - # Extract network - network = self.extract_network_from_codebase() - - print(f"\nNetwork extracted:") - print(f" Nodes: {len(network.get('nodes', []))}") - print(f" Edges: {len(network.get('edges', []))}") - - # Evaluate lawfulness - print("\nEvaluating network lawfulness under RGFlow...") - evaluation = self.evaluate_lawfulness(network) - - return evaluation - - def print_results(self, evaluation: Dict[str, Any]): - """Print formatted results.""" - if "error" in evaluation: - print(f"\nERROR: {evaluation['error']}") - return - - print("\n" + "=" * 60) - print("RGFLOW NETWORK ANALYSIS RESULTS") - print("=" * 60) - - # Initial features - print("\nINITIAL NETWORK FEATURES") - print("-" * 60) - initial = evaluation["initial_features"] - for key, value in initial.items(): - print(f" {key}: {value:.4f}") - - # RGFlow trajectory - print("\nRGFLOW SCALE TRANSFORMATION") - print("-" * 60) - for step in evaluation["lawfulness_analysis"]: - status = "✓ LAWFUL" if step["lawful"] else "✗ UNLAWFUL" - print(f"\n Scale {step['scale']} (factor {step['scale_factor']:.2f}): {status}") - print(f" Entropy: {step['entropy']:.4f} (lawful: {step['entropy_lawful']})") - print(f" Density: {step['density']:.4f} (lawful: {step['density_lawful']})") - print(f" Components: {step['components']:.2f} (lawful: {step['component_lawful']})") - print(f" Clustering: {step['clustering']:.4f} (lawful: {step['clustering_lawful']})") - print(f" Algebraic Connectivity: {step['algebraic_connectivity']:.4f} (lawful: {step['robustness_lawful']})") - print(f" Drift Barrier: {step['drift_survives']} (pressure: {step['verification_pressure']:.4f})") - - # Overall assessment - print("\n" + "-" * 60) - print("OVERALL ASSESSMENT") - print("-" * 60) - print(f" Overall Lawful: {evaluation['overall_lawful']}") - print(f" RGFlow Depth: {evaluation['rgflow_depth']}/{self.SCALE_STEPS}") - print(f" Attractor: {evaluation['attractor']}") - print(f" Failure Mask: {evaluation['failure_mask']}") - - # Final state - print("\n" + "-" * 60) - print("FINAL STATE (Scale 5)") - print("-" * 60) - final = evaluation["final_state"] - print(f" Entropy: {final['entropy']:.4f}") - print(f" Density: {final['density']:.4f}") - print(f" Components: {final['components']:.2f}") - print(f" Clustering: {final['clustering']:.4f}") - print(f" Algebraic Connectivity: {final['algebraic_connectivity']:.4f}") - print(f" Verification Pressure: {final['verification_pressure']:.4f}") - - print("\n" + "=" * 60) - - -class NativeJSONEncoder(json.JSONEncoder): - """Custom JSON encoder that handles numpy types and other non-serializable objects.""" - def default(self, obj): - if isinstance(obj, (np.integer, np.int64, np.int32, np.int16, np.int8)): - return int(obj) - elif isinstance(obj, (np.floating, np.float64, np.float32)): - return float(obj) - elif isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, bool): - return bool(obj) - elif isinstance(obj, (str, int, float, list, dict, type(None))): - return obj - else: - return str(obj) - - -def main(): - analyzer = NetworkRGFlowAnalyzer() - results = analyzer.analyze_network() - analyzer.print_results(results) - - # Save results - output_path = Path("shared-data/data/network_rgflow_analysis.json") - output_path.parent.mkdir(exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(results, f, indent=2, cls=NativeJSONEncoder) - - print(f"\nResults saved to: {output_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/rgflow_swarm_filter.py b/5-Applications/scripts/rgflow_swarm_filter.py deleted file mode 100644 index 0e09dc21..00000000 --- a/5-Applications/scripts/rgflow_swarm_filter.py +++ /dev/null @@ -1,355 +0,0 @@ -#!/usr/bin/env python3 -""" -RGFlow Swarm Code Filter - -Applies the Unified Adaptation Equation with RGFlow to filter swarm code files. -Only files that are "lawful" under RGFlow are retained. -""" - -import os -import sys -import logging -from pathlib import Path -from typing import Dict, List, Tuple -import numpy as np -from dataclasses import dataclass - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import LeanUnifiedShim - -logging.basicConfig(level=logging.INFO, format='%(levelname)s:RGFlowSwarmFilter:%(message)s') -logger = logging.getLogger(__name__) - - -@dataclass -class SwarmCodeAnalysis: - """Analysis result for a swarm code file.""" - filepath: str - lawful_now: bool - lawful_under_flow: bool - reaches_attractor: bool - flows_to_noise: bool - flows_to_sabotage: bool - adaptation_cost: float - stability_margin: float - rg_depth: int - attractor_id: int - failure_mask: int - - -class RGFlowSwarmFilter: - """Filter swarm code using RGFlow mechanism.""" - - def __init__(self, root_path: str = "/home/allaun/Research Stack"): - self.root_path = Path(root_path) - self.shim = LeanUnifiedShim() - self.analyses: List[SwarmCodeAnalysis] = [] - - # Swarm-specific directories and patterns - self.swarm_directories = { - 'infra', - 'scripts', - 'tools/waveprobe', - 'tools/manifold' - } - - self.swarm_patterns = { - 'swarm', - 'enhanced_integrated_swarm', - 'swarm_execution_layer', - 'distributed_swarm', - 'swarm_competition', - 'swarm_resource_manager', - 'swarm_topology_optimizer', - 'swarm_transport_layer', - 'swarm_api' - } - - # File extensions to analyze - self.target_extensions = {'.py', '.lean', '.rs', '.v', '.c', '.h'} - - # Directories to exclude - self.exclude_dirs = { - '.git', '.lake', 'node_modules', '__pycache__', '.pytest_cache', - 'hutter_venv', 'out', 'shared-data/data/archives', 'shared-data/data/archive', 'shared-data/data/swarm', - 'venv', 'venv_unsloth', '.venv', 'site-packages' - } - - def is_swarm_file(self, filepath: Path) -> bool: - """Check if a file is related to swarm code.""" - # Check if file is in a swarm directory - for part in filepath.parts: - if any(swarm_dir in part.lower() for swarm_dir in self.swarm_directories): - return True - - # Check if filename matches swarm patterns - filename = filepath.name.lower() - if any(pattern in filename for pattern in self.swarm_patterns): - return True - - return False - - @staticmethod - def _to_q16_16(value: float) -> int: - """Convert a float to Q16.16 raw integer.""" - return int(value * 65536) - - def extract_code_features(self, filepath: Path) -> dict: - """Extract adaptation equation variables from code file. - Returns a dict of Q16.16 raw integer values for Lean bindserver.""" - try: - with open(filepath, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - except Exception as e: - logger.warning(f"Failed to read {filepath}: {e}") - return {"mu_q": 0, "rho_q": 0, "c_fac": 66, "m_fac": 66, "n_e": 0, "sigma_q": 65536} - - # μ_q: Mutation rate (code change frequency) - mu_q = min(len(content) / 10000.0, 1.0) * 0.01 - - # ρ_q: Refresh rate (how often code is updated) - rho_q = 0.5 - - # C_fac: Graph connectance (import/dependency density) - import_count = len([line for line in content.split('\n') if 'import' in line.lower()]) - C_fac = min(import_count / 20.0, 1.0) - C_fac = max(0.001, C_fac) - - # M_fac: Modularity (function/class organization) - function_count = len([line for line in content.split('\n') if 'def ' in line or 'fn ' in line]) - class_count = len([line for line in content.split('\n') if 'class ' in line or 'structure ' in line]) - M_fac = min((function_count + class_count) / 50.0, 1.0) - M_fac = max(0.001, M_fac) - - # n_e: Observer count (references, citations) - path_depth = len(filepath.parts) - n_e = min(path_depth / 10.0, 1.0) - - # σ_q: Selection coefficient (fitness/significance) - entropy = self._calculate_entropy(content) - text_complexity = self._calculate_text_complexity(content) - base_fitness = (entropy + text_complexity) / 2.0 - sigma_q = 1.0 + base_fitness - - return { - "mu_q": self._to_q16_16(mu_q), - "rho_q": self._to_q16_16(rho_q), - "c_fac": self._to_q16_16(C_fac), - "m_fac": self._to_q16_16(M_fac), - "n_e": self._to_q16_16(n_e), - "sigma_q": self._to_q16_16(sigma_q), - } - - def _calculate_entropy(self, text: str) -> float: - """Calculate Shannon entropy of text.""" - if not text: - return 0.0 - - char_counts = {} - for char in text: - char_counts[char] = char_counts.get(char, 0) + 1 - - total = len(text) - entropy = 0.0 - for count in char_counts.values(): - probability = count / total - if probability > 0: - entropy -= probability * np.log2(probability) - - max_entropy = np.log2(len(char_counts)) if char_counts else 1.0 - return min(entropy / max_entropy, 1.0) if max_entropy > 0 else 0.0 - - def _calculate_text_complexity(self, text: str) -> float: - """Calculate text complexity (unique words / total words).""" - if not text: - return 0.0 - - words = text.split() - if not words: - return 0.0 - - unique_words = len(set(word.lower() for word in words)) - total_words = len(words) - - return min(unique_words / total_words, 1.0) - - def analyze_file(self, filepath: Path) -> SwarmCodeAnalysis: - """Analyze a single file using Lean RGFlow bindserver. - Python shim: only feature extraction and JSON serialization.""" - state = self.extract_code_features(filepath) - result = self.shim.swarm_rgflow_evaluate(state, steps=5) - - if "error" in result: - logger.error(f"Lean RGFlow evaluation failed for {filepath}: {result['error']}") - return SwarmCodeAnalysis( - filepath=str(filepath), - lawful_now=False, - lawful_under_flow=False, - reaches_attractor=False, - flows_to_noise=False, - flows_to_sabotage=False, - adaptation_cost=0.0, - stability_margin=0.0, - rg_depth=0, - attractor_id=0, - failure_mask=0 - ) - - adaptation_cost = result.get("adaptation_cost", 0) - return SwarmCodeAnalysis( - filepath=str(filepath), - lawful_now=result.get("lawful_now", False), - lawful_under_flow=result.get("lawful_under_flow", False), - reaches_attractor=result.get("reaches_attractor", False), - flows_to_noise=result.get("flows_to_noise", False), - flows_to_sabotage=result.get("flows_to_sabotage", False), - adaptation_cost=float(adaptation_cost) / 65536.0, - stability_margin=1.0 - (float(adaptation_cost) / 65536.0), - rg_depth=result.get("rg_depth", 0), - attractor_id=result.get("attractor_id", 0), - failure_mask=result.get("failure_mask", 0) - ) - - def scan_swarm_code(self) -> None: - """Scan the codebase for swarm-related files.""" - logger.info(f"Scanning for swarm code at {self.root_path}") - - file_count = 0 - for filepath in self.root_path.rglob('*'): - # Skip directories - if not filepath.is_file(): - continue - - # Skip excluded directories - if any(excluded in filepath.parts for excluded in self.exclude_dirs): - continue - - # Skip files without target extensions - if filepath.suffix not in self.target_extensions: - continue - - # Check if file is swarm-related - if not self.is_swarm_file(filepath): - continue - - # Analyze file - analysis = self.analyze_file(filepath) - self.analyses.append(analysis) - file_count += 1 - - if file_count % 10 == 0: - logger.info(f"Analyzed {file_count} swarm files...") - - logger.info(f"Total swarm files analyzed: {len(self.analyses)}") - - def filter_lawful_files(self) -> List[SwarmCodeAnalysis]: - """Filter files that are lawful under RGFlow.""" - lawful_files = [a for a in self.analyses if a.lawful_under_flow] - logger.info(f"Lawful swarm files under RGFlow: {len(lawful_files)}/{len(self.analyses)}") - return lawful_files - - def generate_report(self) -> Dict: - """Generate a comprehensive report of the analysis.""" - lawful_files = self.filter_lawful_files() - noise_files = [a for a in self.analyses if a.flows_to_noise] - sabotage_files = [a for a in self.analyses if a.flows_to_sabotage] - - # Statistics - total = len(self.analyses) - lawful_count = len(lawful_files) - noise_count = len(noise_files) - sabotage_count = len(sabotage_files) - local_only_lawful = len([a for a in self.analyses if a.lawful_now and not a.lawful_under_flow]) - - # Attractor distribution - attractor_counts = {} - for analysis in lawful_files: - attractor_id = analysis.attractor_id - attractor_counts[attractor_id] = attractor_counts.get(attractor_id, 0) + 1 - - # Average metrics - avg_cost = np.mean([a.adaptation_cost for a in self.analyses]) - avg_margin = np.mean([a.stability_margin for a in self.analyses]) - avg_rg_depth = np.mean([a.rg_depth for a in self.analyses]) - - # Failure mask distribution - failure_mask_counts = {} - for analysis in self.analyses: - mask = analysis.failure_mask - failure_mask_counts[mask] = failure_mask_counts.get(mask, 0) + 1 - - report = { - 'total_files_analyzed': total, - 'lawful_under_rgflow': lawful_count, - 'flows_to_noise': noise_count, - 'flows_to_sabotage': sabotage_count, - 'local_only_lawful': local_only_lawful, - 'lawfulness_rate': lawful_count / total if total > 0 else 0, - 'attractor_distribution': attractor_counts, - 'failure_mask_distribution': failure_mask_counts, - 'average_adaptation_cost': avg_cost, - 'average_stability_margin': avg_margin, - 'average_rg_depth': avg_rg_depth, - 'lawful_files': [a.filepath for a in lawful_files], - 'noise_files': [a.filepath for a in noise_files], - 'sabotage_files': [a.filepath for a in sabotage_files] - } - - return report - - def print_report(self, report: Dict) -> None: - """Print the analysis report.""" - print("\n" + "="*80) - print("RGFlow Swarm Code Filter Report") - print("="*80) - print(f"\nTotal swarm files analyzed: {report['total_files_analyzed']}") - print(f"Lawful under RGFlow: {report['lawful_under_rgflow']} ({report['lawfulness_rate']:.2%})") - print(f"Flows to noise: {report['flows_to_noise']}") - print(f"Flows to sabotage: {report['flows_to_sabotage']}") - print(f"Locally lawful only (fails RGFlow): {report['local_only_lawful']}") - - print(f"\nAverage metrics:") - print(f" Adaptation cost: {report['average_adaptation_cost']:.4f}") - print(f" Stability margin: {report['average_stability_margin']:.4f}") - print(f" RGFlow depth: {report['average_rg_depth']:.2f}") - - print(f"\nAttractor distribution:") - for attractor_id, count in report['attractor_distribution'].items(): - print(f" Attractor {attractor_id}: {count} files") - - print(f"\nFailure mask distribution:") - for mask, count in report['failure_mask_distribution'].items(): - print(f" Mask {mask:04b}: {count} files") - - if report['lawful_files']: - print(f"\nLawful swarm files:") - for filepath in report['lawful_files']: - print(f" ✓ {filepath}") - - if report['noise_files']: - print(f"\n⚠ NOISE FILES (POTENTIALLY BUGGY):") - for filepath in report['noise_files']: - print(f" ⚠ {filepath}") - - if report['sabotage_files']: - print(f"\n✗ SABOTAGE FILES (HIGH RISK):") - for filepath in report['sabotage_files']: - print(f" ✗ {filepath}") - - print("\n" + "="*80) - - -def main(): - """Main entry point.""" - filter = RGFlowSwarmFilter() - filter.scan_swarm_code() - report = filter.generate_report() - filter.print_report(report) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/rgflow_topology_filter.py b/5-Applications/scripts/rgflow_topology_filter.py deleted file mode 100644 index ced47e8c..00000000 --- a/5-Applications/scripts/rgflow_topology_filter.py +++ /dev/null @@ -1,478 +0,0 @@ -#!/usr/bin/env python3 -""" -RGFlow Topology Filter -Apply RGFlow analysis to topology structure (manifold graph). - -Analyzes the topological integrity of the codebase's manifold structure -under Renormalization Group Flow scale transformation. -""" - -import json -import sys -import os -import numpy as np -from pathlib import Path -from typing import Dict, Any, List, Tuple - -# Add parent directory to path for imports -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - -from infra.lean_unified_shim import LeanUnifiedShim - - -class TopologyRGFlowAnalyzer: - """Apply RGFlow to topology graph structure.""" - - def __init__(self, lean_path="0-Core-Formalism/lean/Semantics", use_hardcoded=True): - self.shim = LeanUnifiedShim(lean_path) - self.use_hardcoded = use_hardcoded - - # RGFlow parameters for topology analysis - self.D = 0.15 # Diffusion coefficient for graph structure - self.B = 0.02 # Drift barrier for connectivity - self.lam = 0.15 # Selection strength for edge weights - self.SCALE_STEPS = 5 # Number of RG scale transformations - - # Topological lawfulness thresholds - self.entropy_lower = 2.0 - self.entropy_upper = 4.5 - self.density_upper = 0.15 # Sparse graphs are typical - self.component_lower = 1 # Should be connected - - def extract_topology(self) -> Dict[str, Any]: - """Extract topology from Lean bindserver or use hardcoded data.""" - if self.use_hardcoded: - # Use hardcoded topology from SwarmAnalysis.lean - topology_data = { - "nodes": [ - {"id": "domain_astrophysics", "nodeType": "domain", "name": "astrophysics", "dimensionality": 3, "description": "Galaxy clusters, dark matter phenomenology"}, - {"id": "domain_neural", "nodeType": "domain", "name": "neural", "dimensionality": 128, "description": "Spike populations, synaptic dynamics"}, - {"id": "domain_maritime", "nodeType": "domain", "name": "maritime", "dimensionality": 2, "description": "Vessel tracking, phantom tide signatures"}, - {"id": "domain_biosemiotics", "nodeType": "domain", "name": "biosemiotics", "dimensionality": 64, "description": "Sign systems in biological processes"}, - {"id": "domain_mereotopological", "nodeType": "domain", "name": "mereotopological", "dimensionality": 32, "description": "Part-whole relations and topological structure"}, - {"id": "domain_compression", "nodeType": "domain", "name": "compression", "dimensionality": 16, "description": "Shannon entropy, routing efficiency (Layer A)"}, - {"id": "domain_routing", "nodeType": "domain", "name": "routing", "dimensionality": 24, "description": "Coupling weights, interaction forces (Layer B)"}, - {"id": "domain_topology", "nodeType": "domain", "name": "topology", "dimensionality": 48, "description": "Temporal weights, holonomy, non-Euclidean distance (Layer C₁)"}, - {"id": "domain_braid", "nodeType": "domain", "name": "braid", "dimensionality": 8, "description": "Cosine similarity, phase accumulation (Layer C₂)"}, - {"id": "domain_invariants", "nodeType": "domain", "name": "invariants", "dimensionality": 6, "description": "Entropy, thermodynamic depth (Layer D)"}, - {"id": "domain_verification", "nodeType": "domain", "name": "verification", "dimensionality": 4, "description": "Safety checks, equilibrium (Layer E)"}, - {"id": "domain_control", "nodeType": "domain", "name": "control", "dimensionality": 12, "description": "Irreversibility, thermodynamic length (Layer F)"}, - {"id": "domain_energy", "nodeType": "domain", "name": "energy", "dimensionality": 10, "description": "Q-factor, atmospheric windows (Layer G)"}, - {"id": "domain_algebra", "nodeType": "domain", "name": "algebra", "dimensionality": 32, "description": "Geometric algebra, group theory (Layer H)"}, - {"id": "domain_encoding", "nodeType": "domain", "name": "encoding", "dimensionality": 8, "description": "Voxel keys, bit-packing (Layer I)"}, - {"id": "domain_dynamics", "nodeType": "domain", "name": "dynamics", "dimensionality": 16, "description": "Time evolution, manifold deformation (Layer J)"}, - {"id": "domain_signal", "nodeType": "domain", "name": "signal", "dimensionality": 32, "description": "DSP, FFT, bracket braid (Layer K)"}, - {"id": "domain_application", "nodeType": "domain", "name": "application", "dimensionality": 6, "description": "FEA, engineering models (Layer L)"}, - {"id": "domain_informational", "nodeType": "domain", "name": "informational", "dimensionality": 14, "description": "Information theory, channel capacity"}, - {"id": "domain_geometric", "nodeType": "domain", "name": "geometric", "dimensionality": 64, "description": "Hyperbolic geometry, manifold structure"}, - {"id": "domain_quantum", "nodeType": "domain", "name": "quantum", "dimensionality": 8, "description": "QCLEnergy, quantum mechanics"}, - {"id": "subdomain_Physics", "nodeType": "subdomain", "name": "Physics", "categories": ["ParticleDomain", "NBody", "Boundary", "Conservation", "BindPhysics", "Interaction", "Projection", "QCLEnergy", "Examples"]}, - {"id": "subdomain_NIICore", "nodeType": "subdomain", "name": "NIICore", "categories": ["MereotopologicalSheafHypergraph", "MorphicTriggers"]}, - {"id": "subdomain_Extensions", "nodeType": "subdomain", "name": "Extensions", "categories": ["BettiSwoosh", "BlitterPolymorphism", "HyperbolicStateSurface", "ManifoldBlit", "MasterEquation", "NKCoupling", "SolitonEngine"]} - ], - "edges": [ - {"id": "edge_Physics_Extensions", "edgeType": "import_dependency", "source": "subdomain_Physics", "target": "subdomain_Extensions", "weight": 1.0, "description": ""}, - {"id": "edge_Physics_Informational", "edgeType": "theoretical", "source": "domain_Physics", "target": "domain_informational", "weight": 1.0, "description": "Physical systems with information-theoretic properties"}, - {"id": "edge_Topology_Algebra", "edgeType": "theoretical", "source": "domain_topology", "target": "domain_algebra", "weight": 1.0, "description": "Topological structures with algebraic properties"}, - {"id": "edge_Geometric_Thermodynamic", "edgeType": "theoretical", "source": "domain_geometric", "target": "domain_thermodynamic", "weight": 1.0, "description": "Geometric manifolds with thermodynamic constraints"} - ], - "topology": { - "dimension": 19, - "connectedComponents": 3, - "eulerCharacteristic": 23 - } - } - print("Using hardcoded topology from SwarmAnalysis.lean") - return topology_data - else: - try: - result = self.shim.query(""" - import Semantics.SwarmAnalysis - #eval toJson Semantics.SwarmAnalysis.createManifoldStructure - """) - - if result and "data" in result: - topology_data = json.loads(result["data"]) - return topology_data - else: - print("ERROR: Failed to extract topology from Lean") - print(f"Result: {result}") - return {"error": "Failed to extract topology from Lean"} - except Exception as e: - print(f"ERROR: Exception extracting topology: {e}") - return {"error": f"Exception extracting topology: {e}"} - - def calculate_topology_features(self, topology: Dict[str, Any]) -> Dict[str, float]: - """Calculate topological features for RGFlow.""" - nodes = topology.get("nodes", []) - edges = topology.get("edges", []) - - n_nodes = len(nodes) - n_edges = len(edges) - - # Build adjacency matrix - node_id_to_idx = {node["id"]: i for i, node in enumerate(nodes)} - adj = np.zeros((n_nodes, n_nodes)) - - for edge in edges: - source = edge.get("source") - target = edge.get("target") - weight = edge.get("weight", 1.0) - - if source in node_id_to_idx and target in node_id_to_idx: - i = node_id_to_idx[source] - j = node_id_to_idx[target] - adj[i, j] = weight - adj[j, i] = weight # Undirected for analysis - - # Calculate topological features - features = {} - - # 1. Node degree statistics - degrees = np.sum(adj, axis=1) - features["mean_degree"] = float(np.mean(degrees)) if n_nodes > 0 else 0.0 - features["std_degree"] = float(np.std(degrees)) if n_nodes > 0 else 0.0 - features["max_degree"] = float(np.max(degrees)) if n_nodes > 0 else 0.0 - features["min_degree"] = float(np.min(degrees)) if n_nodes > 0 else 0.0 - - # 2. Edge density - max_edges = n_nodes * (n_nodes - 1) / 2 if n_nodes > 1 else 0 - features["edge_density"] = n_edges / max_edges if max_edges > 0 else 0.0 - - # 3. Clustering coefficient - clustering = [] - for i in range(n_nodes): - neighbors = np.where(adj[i] > 0)[0] - k = len(neighbors) - if k < 2: - clustering.append(0.0) - continue - - # Count triangles - triangles = 0 - for j in range(k): - for l in range(j + 1, k): - if adj[neighbors[j], neighbors[l]] > 0: - triangles += 1 - - possible = k * (k - 1) / 2 - clustering.append(triangles / possible if possible > 0 else 0.0) - - features["mean_clustering"] = float(np.mean(clustering)) if clustering else 0.0 - - # 4. Connected components - visited = set() - components = [] - - for i in range(n_nodes): - if i not in visited: - # BFS to find component - component = [] - queue = [i] - visited.add(i) - - while queue: - node = queue.pop(0) - component.append(node) - - neighbors = np.where(adj[node] > 0)[0] - for neighbor in neighbors: - if neighbor not in visited: - visited.add(neighbor) - queue.append(neighbor) - - components.append(component) - - features["num_components"] = len(components) - features["largest_component_size"] = max(len(c) for c in components) if components else 0 - - # 5. Path length distribution - if n_nodes > 0 and components: - largest_comp = max(components, key=len) - if len(largest_comp) > 1: - # Calculate average shortest path in largest component - sub_adj = adj[np.ix_(largest_comp, largest_comp)] - path_lengths = [] - - for i in range(len(largest_comp)): - for j in range(i + 1, len(largest_comp)): - # BFS for shortest path - dist = self._bfs_shortest_path(sub_adj, i, j) - if dist < float('inf'): - path_lengths.append(dist) - - features["avg_path_length"] = float(np.mean(path_lengths)) if path_lengths else 0.0 - else: - features["avg_path_length"] = 0.0 - else: - features["avg_path_length"] = 0.0 - - # 6. Entropy (based on degree distribution) - if n_nodes > 0 and features["std_degree"] > 0: - degree_hist = np.histogram(degrees, bins=10, range=(0, features["max_degree"] + 1))[0] - degree_hist = degree_hist[degree_hist > 0] - probs = degree_hist / np.sum(degree_hist) - entropy = -np.sum(probs * np.log2(probs)) - features["degree_entropy"] = float(entropy) - else: - features["degree_entropy"] = 0.0 - - # 7. Dimensionality diversity - dimensionalities = [node.get("dimensionality", 0) for node in nodes] - features["mean_dimensionality"] = float(np.mean(dimensionalities)) if dimensionalities else 0.0 - features["std_dimensionality"] = float(np.std(dimensionalities)) if dimensionalities else 0.0 - - # 8. Edge weight diversity - weights = [edge.get("weight", 1.0) for edge in edges] - if weights: - features["mean_weight"] = float(np.mean(weights)) - features["std_weight"] = float(np.std(weights)) - else: - features["mean_weight"] = 0.0 - features["std_weight"] = 0.0 - - return features - - def _bfs_shortest_path(self, adj: np.ndarray, start: int, end: int) -> float: - """BFS to find shortest path length.""" - if start == end: - return 0.0 - - visited = {start} - queue = [(start, 0)] - - while queue: - node, dist = queue.pop(0) - - neighbors = np.where(adj[node] > 0)[0] - for neighbor in neighbors: - if neighbor == end: - return float(dist + 1) - if neighbor not in visited: - visited.add(neighbor) - queue.append((neighbor, dist + 1)) - - return float('inf') - - def rgflow_transform(self, features: Dict[str, float], scale: float) -> Dict[str, float]: - """Apply RGFlow scale transformation to topological features.""" - transformed = {} - - # Scale transformation: features evolve under RG flow - # Connectivity decays with scale (edge pruning) - transformed["edge_density"] = features["edge_density"] * np.exp(-self.D * scale) - - # Clustering increases with scale (local structure preserved) - transformed["mean_clustering"] = features["mean_clustering"] * (1 + self.lam * scale) - - # Components merge with scale (connectivity restoration) - transformed["num_components"] = max(1, features["num_components"] * np.exp(-self.B * scale)) - - # Path length increases with scale (hierarchical emergence) - transformed["avg_path_length"] = features["avg_path_length"] * (1 + 0.1 * scale) - - # Entropy increases with scale (information growth) - transformed["degree_entropy"] = features["degree_entropy"] * (1 + 0.05 * scale) - - # Degree distribution smooths with scale - transformed["std_degree"] = features["std_degree"] * np.exp(-0.1 * scale) - - return transformed - - def check_drift_barrier(self, features: Dict[str, float], scale: float) -> bool: - """Check if topology survives drift barrier.""" - # Drift barrier: ρ_q * N_e * Φ(M_fac) > B - # For topology: edge_density * num_nodes * clustering > B - - rho_q = features["edge_density"] - N_e = features.get("num_components", 1) # Use components as observer mass - phi = features["mean_clustering"] - - verification_pressure = rho_q * N_e * phi - - return verification_pressure > self.B - - def evaluate_lawfulness(self, topology: Dict[str, Any]) -> Dict[str, Any]: - """Evaluate topological lawfulness under RGFlow.""" - print("Calculating topological features...") - features = self.calculate_topology_features(topology) - - print("Applying RGFlow scale transformation...") - rgflow_trajectory = [] - current_features = features.copy() - - for scale in range(1, self.SCALE_STEPS + 1): - scale_factor = scale / self.SCALE_STEPS - transformed = self.rgflow_transform(current_features, scale_factor) - rgflow_trajectory.append({ - "scale": scale, - "scale_factor": scale_factor, - "features": transformed.copy() - }) - current_features = transformed - - # Check lawfulness at each scale - lawful_scales = [] - for step in rgflow_trajectory: - step_features = step["features"] - - # Entropy bounds - entropy = step_features["degree_entropy"] - entropy_lawful = self.entropy_lower <= entropy <= self.entropy_upper - - # Density bounds - density = step_features["edge_density"] - density_lawful = density <= self.density_upper - - # Component bounds - components = step_features["num_components"] - component_lawful = components >= self.component_lower - - # Drift barrier - drift_survives = self.check_drift_barrier(step_features, step["scale"]) - - step_lawful = entropy_lawful and density_lawful and component_lawful and drift_survives - - lawful_scales.append({ - "scale": step["scale"], - "scale_factor": step["scale_factor"], - "lawful": step_lawful, - "entropy_lawful": entropy_lawful, - "density_lawful": density_lawful, - "component_lawful": component_lawful, - "drift_survives": drift_survives, - "entropy": entropy, - "density": density, - "components": components, - "verification_pressure": step_features["edge_density"] * step_features["num_components"] * step_features["mean_clustering"] - }) - - # Determine overall lawfulness - all_lawful = all(step["lawful"] for step in lawful_scales) - final_state = lawful_scales[-1] - - # Calculate attractor - if all_lawful: - attractor = "Attractor 1 (High-Fitness Topology)" - elif final_state["lawful"]: - attractor = "Attractor 2 (Locally Lawful)" - elif final_state["drift_survives"]: - attractor = "Attractor 3 (Drift Barrier Only)" - else: - attractor = "Noise Attractor (Topological Collapse)" - - return { - "initial_features": features, - "rgflow_trajectory": rgflow_trajectory, - "lawfulness_analysis": lawful_scales, - "overall_lawful": all_lawful, - "final_state": final_state, - "attractor": attractor, - "rgflow_depth": self.SCALE_STEPS if all_lawful else len([s for s in lawful_scales if s["lawful"]]) - } - - def analyze_topology(self) -> Dict[str, Any]: - """Complete topology RGFlow analysis.""" - print("=" * 60) - print("RGFLOW TOPOLOGY ANALYSIS") - print("=" * 60) - - # Extract topology - topology = self.extract_topology() - if "error" in topology: - return topology - - print(f"\nTopology extracted:") - print(f" Nodes: {len(topology.get('nodes', []))}") - print(f" Edges: {len(topology.get('edges', []))}") - print(f" Dimension: {topology.get('topology', {}).get('dimension', 'N/A')}") - print(f" Connected Components: {topology.get('topology', {}).get('connectedComponents', 'N/A')}") - - # Evaluate lawfulness - print("\nEvaluating topological lawfulness under RGFlow...") - evaluation = self.evaluate_lawfulness(topology) - - return evaluation - - def print_results(self, evaluation: Dict[str, Any]): - """Print formatted results.""" - if "error" in evaluation: - print(f"\nERROR: {evaluation['error']}") - return - - print("\n" + "=" * 60) - print("RGFLOW TOPOLOGY ANALYSIS RESULTS") - print("=" * 60) - - # Initial features - print("\nINITIAL TOPOLOGICAL FEATURES") - print("-" * 60) - initial = evaluation["initial_features"] - for key, value in initial.items(): - print(f" {key}: {value:.4f}") - - # RGFlow trajectory - print("\nRGFLOW SCALE TRANSFORMATION") - print("-" * 60) - for step in evaluation["lawfulness_analysis"]: - status = "✓ LAWFUL" if step["lawful"] else "✗ UNLAWFUL" - print(f"\n Scale {step['scale']} (factor {step['scale_factor']:.2f}): {status}") - print(f" Entropy: {step['entropy']:.4f} (lawful: {step['entropy_lawful']})") - print(f" Density: {step['density']:.4f} (lawful: {step['density_lawful']})") - print(f" Components: {step['components']:.2f} (lawful: {step['component_lawful']})") - print(f" Drift Barrier: {step['drift_survives']} (pressure: {step['verification_pressure']:.4f})") - - # Overall assessment - print("\n" + "-" * 60) - print("OVERALL ASSESSMENT") - print("-" * 60) - print(f" Overall Lawful: {evaluation['overall_lawful']}") - print(f" RGFlow Depth: {evaluation['rgflow_depth']}/{self.SCALE_STEPS}") - print(f" Attractor: {evaluation['attractor']}") - - # Final state - print("\n" + "-" * 60) - print("FINAL STATE (Scale 5)") - print("-" * 60) - final = evaluation["final_state"] - print(f" Entropy: {final['entropy']:.4f}") - print(f" Density: {final['density']:.4f}") - print(f" Components: {final['components']:.2f}") - print(f" Verification Pressure: {final['verification_pressure']:.4f}") - - print("\n" + "=" * 60) - - -class NativeJSONEncoder(json.JSONEncoder): - """Custom JSON encoder that handles numpy types and other non-serializable objects.""" - def default(self, obj): - if isinstance(obj, (np.integer, np.int64, np.int32, np.int16, np.int8)): - return int(obj) - elif isinstance(obj, (np.floating, np.float64, np.float32)): - return float(obj) - elif isinstance(obj, np.ndarray): - return obj.tolist() - elif isinstance(obj, bool): - return bool(obj) - elif isinstance(obj, (str, int, float, list, dict, type(None))): - return obj - else: - return str(obj) - -def main(): - analyzer = TopologyRGFlowAnalyzer() - results = analyzer.analyze_topology() - analyzer.print_results(results) - - # Save results with custom encoder - output_path = Path("shared-data/data/topology_rgflow_analysis.json") - output_path.parent.mkdir(exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(results, f, indent=2, cls=NativeJSONEncoder) - - print(f"\nResults saved to: {output_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/riot_shield.py b/5-Applications/scripts/riot_shield.py deleted file mode 100644 index 4382301d..00000000 --- a/5-Applications/scripts/riot_shield.py +++ /dev/null @@ -1,281 +0,0 @@ -#!/usr/bin/env python3 -""" -Patch & armor-plate the database. -1. Dedup verifications (same equation + same title) -2. Push all equations below 15 refs to 15+ with targeted queries. -3. API rotation: 8 slots, 1.5s spacing. Crash-tolerant. -""" - -import sqlite3, urllib.request, urllib.parse, json, time, os, re, sys - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-4000000") -cur = conn.cursor() -start = time.time() - -# ================================================================ -# PHASE 1: DEDUP -# ================================================================ -print("=" * 60) -print("PHASE 1: Deduplication") -print("=" * 60) - -# Find duplicates: same equation_id AND same test_name (title) -cur.execute(""" - SELECT equation_id, test_name, COUNT(*) as cnt, GROUP_CONCAT(id) as ids - FROM verifications - GROUP BY equation_id, test_name - HAVING COUNT(*) > 1 -""") -dupes = cur.fetchall() -dup_total = 0 - -for eq_id, title, cnt, ids in dupes: - id_list = [int(x) for x in ids.split(",")] - id_list.sort() - keep, *remove = id_list # Keep the first (lowest ID), remove rest - for rid in remove: - cur.execute("DELETE FROM verifications WHERE id = ?", (rid,)) - dup_total += 1 - -conn.commit() -print(f" ✓ Removed {dup_total} duplicate verifications") - -# Count after dedup -cur.execute("SELECT COUNT(*) FROM verifications") -total_after_dedup = cur.fetchone()[0] -print(f" ✓ Verifications: {total_after_dedup} (was ~10458)") - -# ================================================================ -# PHASE 2: Find equations below 15 refs -# ================================================================ -print(f"\n{'='*60}") -print("PHASE 2: Find equations below 15x threshold") -print("=" * 60) - -cur.execute(""" - SELECT e.id, e.eq_number, e.title, e.domain_id, COUNT(v.id) as refs - FROM equations e - LEFT JOIN verifications v ON v.equation_id = e.id - GROUP BY e.id - HAVING refs < 15 - ORDER BY refs ASC -""") -under_15 = [(r[0], r[1], r[2], r[3], r[4]) for r in cur.fetchall()] -total_needed = sum(15 - r[4] for r in under_15) -print(f" {len(under_15)} equations below 15x | need {total_needed} refs to reach 15x") - -if not under_15: - print(" All equations already at 15x+. Nothing to do.") - conn.close() - sys.exit(0) - -# Show the worst few -print(" Worst 10:") -for r in under_15[:10]: - print(f" [{r[4]:2d}] #{r[1]:3d} {r[2][:80]}") - -cur.execute("SELECT id, name FROM domains") -dnames = {r[0]: r[1] for r in cur.fetchall()} - -# ================================================================ -# PHASE 3: Generate targeted queries per equation -# ================================================================ -print(f"\n{'='*60}") -print("PHASE 3: Generating targeted queries") -print("=" * 60) - -def title_to_query(title): - """Extract most meaningful search terms from equation title.""" - clean = re.sub(r'[─\(\)\[\]\{\}\'\".,:;\n\r—]', ' ', title) - words = [w for w in clean.split() if len(w) > 2 and w.lower() not in - ('the','and','for','this','that','with','from','are','was','has','had', - 'its','not','but','all','can','may','will','been','one','two','three', - 'also','into','than','over','under','after','such','each','both','more', - 'some','they','their','have','were','like','just','what','when','where', - 'how','why','who','which','very','much')] - if len(words) > 14: - return ' '.join(words[:14]) - return ' '.join(words) - -# Build task queue -tasks = [] -for eq_id, num, title, did, refs in under_15: - q = title_to_query(title) - if len(q) < 15: - dname = dnames.get(did, 'physics') - q = dname + ' ' + ' '.join(title.split()[:8]) - needed = max(15 - refs, 1) - # Each equation gets up to 5 queries to ensure we hit 15 - for _ in range(min(needed, 5)): - tasks.append((eq_id, q, needed, refs)) - -# De-duplicate tasks (same eq_id + same query) -seen = set() -unique_tasks = [] -for eq_id, q, nd, rf in tasks: - key = (eq_id, q[:60]) - if key not in seen: - seen.add(key) - unique_tasks.append((eq_id, q, nd, rf)) - -tasks = unique_tasks -print(f" Generated {len(tasks)} unique queries for {len(under_15)} equations") - -# ================================================================ -# PHASE 4: API FETCHERS -# ================================================================ -def cr(q, mx=5): - o=[] - try: - u="https://api.crossref.org/works?"+urllib.parse.urlencode({"query":q,"rows":mx,"sort":"relevance","filter":"type:journal-article"}) - r=urllib.request.Request(u,headers={"User-Agent":"RiotShield/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t=(i.get("title",[""])or[""])[0];y=i.get("created",{}).get("date-parts",[[0]])[0][0] - doi=i.get("DOI","");jn=(i.get("container-title",[""])or[""])[0] - if t:o.append({"t":t[:250],"y":y,"s":"Crossref","d":doi,"j":jn}) - except:pass - return o - -def oa(q, mx=5): - o=[] - try: - u="https://api.openalex.org/works?"+urllib.parse.urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}) - r=urllib.request.Request(u,headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("results",[]): - t=i.get("title","");y=i.get("publication_year")or 0;doi=i.get("doi","");jn="" - if i.get("primary_location")and i["primary_location"].get("source"): - jn=i["primary_location"]["source"].get("display_name","") - if t:o.append({"t":t[:250],"y":y,"s":"OpenAlex","d":doi,"j":jn}) - except:pass - return o - -def s2(q, mx=5): - o=[] - try: - u="https://api.semanticscholar.org/graph/v1/paper/search?"+urllib.parse.urlencode({"query":q,"limit":mx,"fields":"title,year,externalIds,journal"}) - r=urllib.request.Request(u,headers={"User-Agent":"RiotShield/1.0"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for p in d.get("data",[]): - e=p.get("externalIds",{})or{};jn=p.get("journal",{})or{} - o.append({"t":p.get("title","")[:250],"y":p.get("year")or 0,"s":"S2","d":e.get("DOI",""),"j":jn.get("name","")}) - except:pass - return o - -# ================================================================ -# PHASE 5: MAIN PIPELINE -# ================================================================ -print(f"\n{'='*60}") -print("PHASE 5: Armor-plate pipeline (8 API slots, 1.5s spacing)") -print(f" ETA: ~{len(tasks)*1.6/60:.0f} min") -print("=" * 60) - -apis = [(cr,1.5),(oa,1.7),(s2,1.7),(oa,1.5), - (cr,1.5),(oa,1.7),(s2,1.7),(oa,1.5)] - -total, batch = 0, [] -# Track which equations we've pushed for so we can skip already-at-15 -eq_reached_15 = set() - -for idx, (eq_id, query, needed, current_refs) in enumerate(tasks): - # Skip if already at 15 from previous inserts - if eq_id in eq_reached_15: - if idx % 50 == 0: - print(f" [{idx}/{len(tasks)}] skip (already at 15) | {total}p added | {time.time()-start:.0f}s", flush=True) - continue - - fn, delay = apis[idx % len(apis)] - papers = fn(query) - - # Before inserting, check if we actually need more for this equation - cur.execute("SELECT COUNT(*) FROM verifications WHERE equation_id=?", (eq_id,)) - current = cur.fetchone()[0] - remaining = max(15 - current, 0) - - for p in papers: - if remaining <= 0: - break - exp = f"{p['s']}: {p['j']}" if p.get('j') else p['s'] - batch.append((eq_id, p['t'], exp, p['y'], p.get('d', p['s']), "Armor-plate")) - total += 1 - remaining -= 1 - current += 1 - - if current >= 15: - eq_reached_15.add(eq_id) - - # Progress - if idx % 30 == 0: - cur.execute("""SELECT COUNT(*) FROM (SELECT e.id FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) >= 15)""") - at15 = cur.fetchone()[0] - cur.execute("""SELECT COUNT(*) FROM (SELECT e.id FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) < 10)""") - below10 = cur.fetchone()[0] - eta = (len(tasks) - idx) * delay / 60 - print(f" [{idx}/{len(tasks)}] {total}p | {at15}/771 at 15x+ | {below10} below 10x | {eta:.1f}min left", flush=True) - - # Flush every 40 - if len(batch) >= 40: - cur.executemany( - "INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", - batch) - conn.commit() - batch = [] - - time.sleep(delay) - -# Final flush -if batch: - cur.executemany( - "INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", - batch) - conn.commit() - -# ================================================================ -# PHASE 7: Final report -# ================================================================ -cur.execute("SELECT COUNT(*) FROM verifications"); tv = cur.fetchone()[0] -cur.execute("""SELECT COUNT(*) FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) >= 15""") -at15 = cur.fetchone()[0] -cur.execute("""SELECT COUNT(*) FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) < 10""") -below10 = cur.fetchone()[0] -cur.execute("""SELECT COUNT(*) FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) < 15""") -below15 = cur.fetchone()[0] - -elapsed = time.time() - start - -print(f"\n{'='*60}") -print(f"ARMOR-PLATE COMPLETE") -print(f" {tv} total verifications ({total} added this pass)") -print(f" {at15}/771 at 15x+ ({at15/771*100:.0f}%)") -print(f" {below15} still below 15x | {below10} still below 10x") -print(f" Time: {elapsed:.0f}s ({elapsed/60:.1f} min)") - -if below15 > 0: - print(f"\n Remaining below 15x:") - cur.execute("""SELECT e.eq_number, e.title, COALESCE(vc.c,0) refs - FROM equations e LEFT JOIN - (SELECT equation_id, COUNT(*) c FROM verifications GROUP BY equation_id) vc ON vc.equation_id=e.id - WHERE COALESCE(vc.c,0) < 15 ORDER BY refs""") - for num, title, refs in cur.fetchall(): - print(f" [{refs:2d}] #{num:3d} {title[:85]}") - -conn.close() -print(f"\n {DB} ({os.path.getsize(DB)} bytes)") diff --git a/5-Applications/scripts/run_manifold_render.py b/5-Applications/scripts/run_manifold_render.py deleted file mode 100644 index 91b4b94a..00000000 --- a/5-Applications/scripts/run_manifold_render.py +++ /dev/null @@ -1,257 +0,0 @@ -#!/usr/bin/env python3 -""" -Manifold GPU Render — wgpu Compute + Graphics Pipeline -Uploads binary manifold to GPU, runs WGSL compute sieve + projection, -then renders points with FAMM coloring. - -Dependencies: - pip install wgpu numpy - # Also needs a WebGPU adapter (SwiftShader, Dawn, or native GPU) - -Usage: - python3 run_manifold_render.py /tmp/test_manifold.bin - # Or generate test data and render: - python3 run_manifold_render.py --generate -""" - -import sys -import struct -from pathlib import Path -import numpy as np - -# wgpu is the Python WebGPU implementation -try: - import wgpu - import wgpu.backends.rs # Rust backend - WGPU_AVAILABLE = True -except ImportError: - WGPU_AVAILABLE = False - print("wgpu not installed. Install with: pip install wgpu") - print("Falling back to CPU preview (no GPU rendering).") - -# Add ctypes shim path -sys.path.insert(0, str(Path("/home/allaun/Documents/Research Stack/4-Infrastructure/shims"))) -from manifold_binary_ctypes import ManifoldSerializer, ManifoldBuilder, Q16_16 - - -# ═══════════════════════════════════════════════════════════════════════════ -# GPU Buffer Layout (must match manifold_binary.h and .wgsl) -# ═══════════════════════════════════════════════════════════════════════════ - -HEADER_SIZE = 32 -Q16_SCALE = 65536.0 - -VERTEX_SIZE = 28 # vec3 position (12) + vec3 color (12) + flags (4) + idx (4) - - -def read_manifold_blob(path: str) -> bytes: - return Path(path).read_bytes() - - -def upload_and_render(blob: bytes, generate: bool = False): - if generate or not blob: - print("Generating test manifold...") - builder = ManifoldBuilder() - import time - builder.header.timestamp_ns = int(time.time_ns()) - res = 64 - for k in range(res): - for t in range(res // 2): - mass = (k + 1) * (t + 1) - a = int(mass ** 0.5) or 1 - b = mass // a - theta = 2 * np.pi * k / res - phi = np.pi * t / res - builder.add_shell(k, t, mass, a, b, phase=theta) - w = np.cos(theta / 2) * np.cos(phi / 2) - x = np.sin(theta / 2) * np.cos(phi / 2) - y = np.sin(theta / 2) * np.sin(phi / 2) - z = np.cos(theta / 2) * np.sin(phi / 2) - builder.add_point(w, x, y, z, layer=k) - builder.add_famm_node( - torsional_stress=0.1 + 0.05 * np.sin(theta), - interlocking_energy=0.05 + 0.02 * np.cos(phi), - laplacian_energy=0.03, - cognitive_load=0.2 + 0.1 * np.sin(theta * 2) - ) - for i in range(len(builder.points) - 1): - builder.add_edge(i, i + 1, weight=0.8, braid_id=i % 4) - ser = builder.build() - blob = ser.to_bytes() - print(f"Generated {len(blob)} bytes, {ser.header.num_points} points") - - if not WGPU_AVAILABLE: - # CPU fallback: just verify the blob structure - ser = ManifoldSerializer() - ser._parse(blob) - print(f"CPU parse OK: {ser}") - errors = ser.validate_quaternions() - print(f"Quaternion validation errors: {errors}") - alive = ser.apply_sieve(threshold=0.3) - print(f"Alive after sieve: {alive} / {ser.header.num_points}") - return - - # ── WebGPU Setup ────────────────────────────────────────────── - adapter = wgpu.request_adapter(power_preference="high-performance") - device = adapter.request_device() - - # Read header to know sizes - header = struct.unpack("> 16) & 0xFFFF - version = version_and_flags & 0xFFFF - print(f"GPU Upload: shells={num_shells}, points={num_points}, edges={num_edges}, flags={flags}") - - # Compute sizes of arrays - shell_size = 28 # PistShell: 7 * 4 bytes - point_size = 24 # QuaternionPoint: 6 * 4 bytes - edge_size = 20 # BraidEdge: 5 * 4 bytes - famm_size = 24 # FammNode: 6 * 4 bytes - - # Offsets in blob - off_shells = HEADER_SIZE - off_points = off_shells + num_shells * shell_size - off_edges = off_points + num_points * point_size - off_famm = off_edges + num_edges * edge_size - - has_famm = (flags & 0x4) != 0 - - # ── Create GPU Buffers ──────────────────────────────────────── - header_buf = device.create_buffer_with_data( - data=blob[:HEADER_SIZE], usage=wgpu.BufferUsage.STORAGE - ) - - shells_buf = device.create_buffer_with_data( - data=blob[off_shells:off_points] if num_shells > 0 else b"", - usage=wgpu.BufferUsage.STORAGE - ) - - points_buf = device.create_buffer_with_data( - data=blob[off_points:off_edges], - usage=wgpu.BufferUsage.STORAGE - ) - - edges_data = blob[off_edges:off_famm] if num_edges > 0 else b"" - edges_buf = device.create_buffer_with_data( - data=edges_data, - usage=wgpu.BufferUsage.STORAGE - ) - - famm_data = blob[off_famm:off_famm + num_points * famm_size] if has_famm else b"" - famm_buf = device.create_buffer_with_data( - data=famm_data, - usage=wgpu.BufferUsage.STORAGE - ) - - # Output: vertices - vertex_buf_size = max(num_points * VERTEX_SIZE, 16) - vertex_buf = device.create_buffer( - size=vertex_buf_size, - usage=wgpu.BufferUsage.STORAGE | wgpu.BufferUsage.VERTEX - ) - - # Output: draw params (indirect) - draw_buf = device.create_buffer( - size=16, - usage=wgpu.BufferUsage.STORAGE | wgpu.BufferUsage.INDIRECT - ) - - # ── Load WGSL Shader ───────────────────────────────────────── - wgsl_path = Path("/home/allaun/Documents/Research Stack/5-Applications/scripts/manifold_render.wgsl") - wgsl_code = wgsl_path.read_text() - - shader = device.create_shader_module(code=wgsl_code) - - # Bind group layout - bind_group_layout = device.create_bind_group_layout( - entries=[ - {"binding": 0, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "read-only-storage"}}, - {"binding": 1, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "read-only-storage"}}, - {"binding": 2, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "read-only-storage"}}, - {"binding": 3, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "read-only-storage"}}, - {"binding": 4, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "read-only-storage"}}, - {"binding": 5, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "storage"}}, - {"binding": 6, "visibility": wgpu.ShaderStage.COMPUTE, "buffer": {"type": "storage"}}, - ] - ) - - pipeline_layout = device.create_pipeline_layout( - bind_group_layouts=[bind_group_layout] - ) - - compute_pipeline = device.create_compute_pipeline( - layout=pipeline_layout, - compute={"module": shader, "entry_point": "main"} - ) - - bind_group = device.create_bind_group( - layout=bind_group_layout, - entries=[ - {"binding": 0, "resource": {"buffer": header_buf}}, - {"binding": 1, "resource": {"buffer": shells_buf}}, - {"binding": 2, "resource": {"buffer": points_buf}}, - {"binding": 3, "resource": {"buffer": edges_buf}}, - {"binding": 4, "resource": {"buffer": famm_buf}}, - {"binding": 5, "resource": {"buffer": vertex_buf}}, - {"binding": 6, "resource": {"buffer": draw_buf}}, - ] - ) - - # ── Dispatch ────────────────────────────────────────────────── - command_encoder = device.create_command_encoder() - compute_pass = command_encoder.begin_compute_pass() - compute_pass.set_pipeline(compute_pipeline) - compute_pass.set_bind_group(0, bind_group) - workgroups = (num_points + 255) // 256 - compute_pass.dispatch_workgroups(workgroups) - compute_pass.end() - - # Copy draw params back to read - read_buf = device.create_buffer( - size=16, usage=wgpu.BufferUsage.COPY_DST | wgpu.BufferUsage.MAP_READ - ) - command_encoder.copy_buffer_to_buffer(draw_buf, 0, read_buf, 0, 16) - - device.queue.submit([command_encoder.finish()]) - - # Read back - def read_callback(): - draw_data = np.frombuffer(read_buf.read_data(), dtype=np.uint32) - vertex_count = draw_data[0] - print(f"GPU Output: vertex_count={vertex_count}") - - # Read first few vertices for sanity - vert_read = device.create_buffer( - size=min(vertex_count * VERTEX_SIZE, 1024), - usage=wgpu.BufferUsage.COPY_DST | wgpu.BufferUsage.MAP_READ - ) - cmd = device.create_command_encoder() - cmd.copy_buffer_to_buffer(vertex_buf, 0, vert_read, 0, vert_read.size) - device.queue.submit([cmd.finish()]) - - verts = np.frombuffer(vert_read.read_data(), dtype=np.float32) - if len(verts) >= 7: - print(f" First vertex: pos=({verts[0]:.3f},{verts[1]:.3f},{verts[2]:.3f}), " - f"color=({verts[3]:.3f},{verts[4]:.3f},{verts[5]:.3f})") - - read_callback() - print("GPU compute complete. Vertex buffer ready for rendering.") - - -def main(): - import argparse - parser = argparse.ArgumentParser(description="Manifold GPU Render") - parser.add_argument("input", nargs="?", help="Binary manifold file") - parser.add_argument("--generate", action="store_true", help="Generate test data") - args = parser.parse_args() - - blob = None - if args.input and Path(args.input).exists(): - blob = read_manifold_blob(args.input) - print(f"Loaded {len(blob)} bytes from {args.input}") - - upload_and_render(blob, generate=args.generate or blob is None) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/run_swarm_monitor.py b/5-Applications/scripts/run_swarm_monitor.py deleted file mode 100644 index 4cb803bd..00000000 --- a/5-Applications/scripts/run_swarm_monitor.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/bin/env python3 -"""Run swarm in monitoring mode to detect plateau""" -import subprocess -import time -import re -from typing import List, Dict - -def parse_swarm_output(output: str) -> Dict[str, float]: - """Parse swarm output to extract key metrics""" - metrics = {} - - # Parse optimization metrics - opt_ratio_match = re.search(r'Optimization Ratio: ([\d.]+)', output) - if opt_ratio_match: - metrics['optimization_ratio'] = float(opt_ratio_match.group(1)) - - substrate_potential_match = re.search(r'Substrate Potential: ([\d.]+)', output) - if substrate_potential_match: - metrics['substrate_potential'] = float(substrate_potential_match.group(1)) - - opt_cycles_match = re.search(r'Optimization Cycles: (\d+)', output) - if opt_cycles_match: - metrics['optimization_cycles'] = int(opt_cycles_match.group(1)) - - consensus_match = re.search(r'Consensus: ([\d.]+)', output) - if consensus_match: - metrics['consensus'] = float(consensus_match.group(1)) - - homeostasis_match = re.search(r'Homeostasis Score: ([\d.]+)', output) - if homeostasis_match: - metrics['homeostasis'] = float(homeostasis_match.group(1)) - - overall_score_match = re.search(r'Overall System Score: ([\d.]+)', output) - if overall_score_match: - metrics['overall_score'] = float(overall_score_match.group(1)) - - return metrics - -def detect_plateau(history: List[Dict[str, float]], window_size: int = 5, threshold: float = 0.01) -> bool: - """Detect if metrics have plateaued (stopped improving significantly)""" - if len(history) < window_size: - return False - - # Check optimization ratio plateau - recent_ratios = [h.get('optimization_ratio', 0) for h in history[-window_size:]] - ratio_variance = max(recent_ratios) - min(recent_ratios) - - # Check overall score plateau - recent_scores = [h.get('overall_score', 0) for h in history[-window_size:]] - score_variance = max(recent_scores) - min(recent_scores) - - # Plateau if variance is below threshold - return ratio_variance < threshold and score_variance < threshold - -def main(): - print("[INFO] Starting swarm monitoring for plateau detection") - print("=" * 70) - - history: List[Dict[str, float]] = [] - cycle = 0 - - while True: - cycle += 1 - print(f"\n[CYCLE {cycle}] Running swarm...") - - try: - result = subprocess.run( - ['python3', 'enhanced_integrated_swarm.py'], - cwd='/home/allaun/Documents/Research Stack/scripts', - capture_output=True, - text=True, - timeout=60 - ) - - metrics = parse_swarm_output(result.stdout) - history.append(metrics) - - print(f" Optimization Ratio: {metrics.get('optimization_ratio', 0):.3f}") - print(f" Substrate Potential: {metrics.get('substrate_potential', 0):.1f}") - print(f" Optimization Cycles: {metrics.get('optimization_cycles', 0)}") - print(f" Consensus: {metrics.get('consensus', 0):.3f}") - print(f" Homeostasis: {metrics.get('homeostasis', 0):.3f}") - print(f" Overall Score: {metrics.get('overall_score', 0):.3f}") - - # Check for plateau - if detect_plateau(history): - print("\n[PLATEAU DETECTED]") - print(f" Swarm has plateaued after {cycle} cycles") - print(f" Final Optimization Ratio: {metrics.get('optimization_ratio', 0):.3f}") - print(f" Final Overall Score: {metrics.get('overall_score', 0):.3f}") - print(f" Total Optimization Cycles: {metrics.get('optimization_cycles', 0)}") - break - - # Check if fully optimized - if metrics.get('optimization_ratio', 0) >= 0.9: - print("\n[FULLY OPTIMIZED]") - print(f" Swarm reached optimization ratio {metrics.get('optimization_ratio', 0):.3f}") - break - - # Wait before next cycle - time.sleep(2) - - except subprocess.TimeoutExpired: - print("[ERROR] Swarm timed out") - break - except Exception as e: - print(f"[ERROR] Exception: {e}") - break - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/saturation_pass.py b/5-Applications/scripts/saturation_pass.py deleted file mode 100644 index 51462732..00000000 --- a/5-Applications/scripts/saturation_pass.py +++ /dev/null @@ -1,294 +0,0 @@ -#!/usr/bin/env python3 -""" -Hat of Infinite Bullshit — final saturation pass. -Targets every sub-2.0 ratio domain with dense queries. -Aims: every domain ≥ 2.0 references per equation. -API rotation: Crossref, OpenAlex, S2, EuropePMC, CrossRef, OpenAlex -""" - -import sqlite3, urllib.request, urllib.parse, json, time, os - -DB = "/home/allaun/physics_equations.db" - -conn = sqlite3.connect(DB) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-4000000") -cur = conn.cursor() - -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -dom_eqs = {} -for d, e in cur.fetchall(): - dom_eqs.setdefault(d, []).append(e) - -cur.execute("SELECT id, name FROM domains") -dom_names = {r[0]: r[1] for r in cur.fetchall()} - -# Get current ratios -cur.execute("""SELECT d.id, d.name, COUNT(DISTINCT e.id), COUNT(DISTINCT v.id), - ROUND(COUNT(DISTINCT v.id)*1.0/COUNT(DISTINCT e.id),2) - FROM domains d LEFT JOIN equations e ON e.domain_id=d.id - LEFT JOIN verifications v ON v.equation_id=e.id - WHERE e.id IS NOT NULL GROUP BY d.id""") -current = {r[0]: (r[2], r[3], r[4]) for r in cur.fetchall()} - -# ================================================================ -# SATURATION QUERIES — targeted at domains below 2.0 ratio -# ================================================================ -SATURATE = { - # Electromagnetism (32 eqs, 0.7 ratio — most under-covered core domain) - 3: [ - "Coulomb law inverse square experiment Cavendish torsion balance photon mass limit test","Gauss law electric flux measurement Faraday cage electrostatic shielding verification","Biot Savart law magnetic field current element measurement Helmholtz coil calibration","Ampere force parallel conductors magnetic definition SI ampere measurement","Lorentz force charged particle motion cyclotron radius measurement mass spectrometer","Faraday induction Lenz law eddy current pendulum magnet braking measurement","Maxwell displacement current capacitor charging magnetic field measurement Rowland experiment","Poynting vector electromagnetic energy flow measurement microwave waveguide power","Lienard Wiechert potential synchrotron radiation electron storage ring undulator measurement","Cherenkov radiation cone angle particle velocity dielectric medium measurement","Bremsstrahlung stopping radiation electron beam thick target spectrum measurement","synchrotron radiation power loss electron storage ring critical energy LEP LHC measurement","transition radiation relativistic electron foil interface forward x ray measurement","Compton scattering Klein Nishina cross section gamma ray detector coincidence measurement","pair production electron positron gamma ray threshold 1.022 MeV nuclear emulsion measurement","photonuclear giant dipole resonance MeV gamma neutron emission cross section measurement","multipole radiation electric dipole magnetic quadrupole transition probability measurement","plasma frequency metal ultraviolet transparency alkali metal lithium sodium measurement","skin effect AC resistance copper conductor frequency dependence coaxial cable measurement","waveguide cutoff frequency TE10 TM11 mode rectangular circular microwave measurement","antenna radiation pattern half wave dipole directivity gain far field measurement","transmission line impedance matching Smith chart VSWR reflection coefficient measurement","cavity resonator quality factor Q microwave perturbation dielectric measurement","gyrotron electron cyclotron maser high power millimeter wave fusion heating measurement", - ], - - # Classical Mechanics (22 eqs, 1.0 ratio) - 1: [ - "Lagrangian mechanics double pendulum chaos phase space Poincare section measurement","Hamiltonian action angle variable integrable system invariant torus frequency measurement","Liouville theorem phase space volume conservation nonlinear dynamics verification","d Alembert principle virtual work constrained dynamics Lagrange multiplier verification","Poisson bracket canonical transformation symplectic integrator molecular dynamics","Noether theorem energy conservation time translation symmetry experimental verification","chaotic scattering three body problem Lyapunov exponent gravitational slingshot measurement", - ], - - # Acoustics (7 eqs, 0.9 ratio) - 11: [ - "speed sound air temperature dependence Kundt tube resonance measurement precision","standing wave Chladni plate nodal pattern sand vibration mode measurement","Doppler shift acoustic moving source observer train whistle frequency measurement","reverberation time Sabine formula architectural acoustics impulse response measurement","shock wave Mach cone supersonic bullet shadowgraph Schlieren photography measurement","acoustic impedance mismatch reflection coefficient ultrasound medical imaging measurement","nonlinear acoustics parametric array beat frequency difference generation underwater measurement", - ], - - # Information Theory (6 eqs, 1.0 ratio) - 19: [ - "Shannon entropy information content compression limit Huffman arithmetic coding measurement","channel capacity Shannon Hartley theorem signal noise ratio modern communication measurement","error correcting code Reed Solomon turbo LDPC capacity approaching Shannon limit measurement","mutual information transfer entropy neural spike train estimation measurement","algorithmic complexity Kolmogorov Chaitin randomness compression test measurement", - ], - - # Nuclear Physics (10 eqs, 1.1 ratio) - 13: [ - "alpha decay Gamow factor half life polonium radon thorium uranium measurement","beta decay Fermi Kurie plot neutrino mass tritium endpoint KATRIN measurement","gamma decay internal conversion Mossbauer spectroscopy iron 57 isomer shift measurement","fission barrier liquid drop model shell correction spontaneous fission half life measurement","fusion cross section Gamow peak astrophysical S factor solar pp chain measurement","proton emission dripline fluorine 14 oxygen 11 beyond proton drip line measurement", - ], - - # Thermodynamics (20 eqs, 1.3 ratio) - 4: [ - "Carnot cycle Stirling Ericsson efficiency comparison working fluid experimental measurement","Gibbs phase rule ternary system eutectic peritectic monotectic reaction measurement","chemical potential fugacity vapor liquid equilibrium activity coefficient measurement","Helmholtz free energy minimum principle phase separation spinodal binodal measurement","Maxwell relation Joule Thomson coefficient inversion temperature gas liquefaction measurement", - ], - - # Semiconductor Physics (22 eqs, 1.1 ratio) - 23: [ - "MOSFET short channel effect drain induced barrier lowering DIBL threshold voltage measurement","MOSFET gate leakage direct tunneling high k dielectric hafnium oxide EOT measurement","MOSFET negative bias temperature instability NBTI threshold shift reliability measurement","MOSFET hot carrier injection impact ionization substrate current degradation measurement","finfet trigate gate all around nanowire subthreshold slope short channel measurement","tunnel FET band to band tunneling sub 60mV decade subthreshold slope measurement","negative capacitance ferroelectric HfZrO2 gate stack steep slope transistor measurement", - ], - - # Phase Transformations (10 eqs, 1.3 ratio) - 27: [ - "spinodal decomposition Cahn Hilliard theory composition modulation wavelength AlNiCo measurement","martensitic transformation shape memory NiTi habit plane phenomenological theory measurement","bainite transformation steel incomplete reaction phenomenon carbon partitioning measurement","omega phase transformation titanium zirconium alloy athermal diffuse scattering electron measurement", - ], - - # Optics (20 eqs, 1.4 ratio) - 10: [ - "Fabry Perot interferometer finesse free spectral range laser mode selection measurement","Michelson interferometer gravitational wave detection LIGO arm length sensitivity measurement","holography Gabor reconstruction off axis reference beam Leith Upatnieks measurement","photonic crystal band gap defect cavity waveguide slow light measurement", - ], - - # Cosmology (17 eqs, 1.5 ratio) - 8: [ - "Sachs Wolfe integrated effect Rees Sciama time varying potential void cluster measurement","Sunyaev Zeldovich thermal kinetic effect Compton scattering galaxy cluster measurement","Lyman alpha forest Gunn Peterson trough intergalactic medium reionization redshift measurement", - ], - - # Relativity (21 eqs, 1.5 ratio) - 6: [ - "gravitational wave ringdown quasi normal mode black hole perturbation no hair theorem test","gravitational lensing Einstein ring galaxy cluster mass reconstruction weak lensing measurement","precession Mercury perihelion advance 43 arcsec century optical radar measurement", - ], - - # Quantum Field Theory (21 eqs, 1.6 ratio) - 7: [ - "electroweak precision oblique parameters Peskin Takeuchi S T U parameter measurement LEP","CKM unitarity triangle angle beta sin2beta BaBar Belle B factory CP violation measurement","neutrino tritium beta decay KATRIN experiment effective electron antineutrino mass measurement", - ], - - # Tribology (6 eqs, 1.7 ratio) - 37: [ - "elastohydrodynamic lubrication ball bearing film thickness optical interferometry measurement","mixed lubrication transition load asperity contact electrical resistance measurement","boundary lubrication additive ZDDP zinc dialkyl dithiophosphate tribofilm measurement", - ], - - # Energy Physics (16 eqs, 1.3 ratio) - 44: [ - "perovskite solar cell efficiency stability lead tin double cation mixed halide measurement","organic solar cell non fullerene acceptor Y6 PM6 bulk heterojunction morphology measurement","silicon heterojunction solar cell amorphous passivation contact 26 percent efficiency measurement","lithium ion battery silicon anode volume expansion SEI formation Coulombic efficiency measurement","solid state battery ceramic electrolyte LLZO garnet lithium dendrite critical current measurement", - ], - - # Geophysics (15 eqs, 1.7 ratio) - 28: [ - "PREM preliminary reference Earth model seismic velocity density radial profile measurement","mantle transition zone 410km 660km discontinuity olivine wadsleyite ringwoodite phase transition","core mantle boundary D double prime layer ultra low velocity zone seismic waveform measurement", - ], - - # Various sub-2.0 domains — rapid fire - 29: [ # Atmospheric (2.0) - "atmospheric boundary layer Monin Obukhov similarity theory flux profile measurement","Sudden stratospheric warming polar vortex split Eliassen Palm flux wave mean flow measurement", - ], - 30: [ # Oceanography (2.1) - "internal wave Garrett Munk spectrum deep ocean stratification mooring measurement","mesoscale eddy radius deformation Rossby radius altimetry Chelton measurement", - ], - 31: [ # Hydrology (2.3) - "baseflow recession Maillet Boussinesq aquifer hydraulic diffusivity streamflow measurement","preferential flow macropore bypass fracture unsaturated solute transport measurement", - ], - 32: [ # Biophysics (2.3) - "single molecule motor optical trap kinesin dynein myosin step size stall force measurement","ion channel patch clamp single channel conductance open probability Markov model measurement", - ], - 41: [ # Nonlinear Dynamics (2.8) - "chimera state coupled oscillator coexistence coherence incoherence laser delay experiment","extreme event rogue wave Peregrine soliton nonlinear Schrodinger fiber optics measurement", - ], - 42: [ # Medical (2.3) - "functional MRI BOLD hemodynamic response deoxyhemoglobin susceptibility neurovascular coupling","PET positron emission tomography FDG glucose metabolism standardized uptake value measurement", - ], - 34: [ # Photonics (1.9) - "microresonator Kerr frequency comb soliton dissipative generation silicon nitride measurement","stimulated Brillouin scattering optomechanical phonon laser cooling measurement", - ], - 36: [ # Rheology (1.9) - "thixotropy hysteresis loop structure breakdown recovery clay suspension laponite measurement","extensional rheology filament stretching capillary breakup viscoelastic relaxation measurement", - ], - 38: [ # Granular (3.0) - "granular jamming shear thickening cornstarch suspension discontinuous impact measurement","granular segregation Brazil nut effect convection roll vibration amplitude frequency measurement", - ], -} - -# ================================================================ -# API FETCHERS -# ================================================================ -def cr(q, mx=5): - o = [] - try: - u = "https://api.crossref.org/works?" + urllib.parse.urlencode({"query":q,"rows":mx,"sort":"relevance","filter":"type:journal-article"}) - r = urllib.request.Request(u, headers={"User-Agent":"HatBullshit/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t = (i.get("title",[""]) or [""])[0] - y = i.get("created",{}).get("date-parts",[[0]])[0][0] - doi = i.get("DOI","") - jn = (i.get("container-title",[""]) or [""])[0] - if t: o.append((t[:250],y,"Crossref",doi,jn)) - except: pass - return o - -def oa(q, mx=5): - o = [] - try: - u = "https://api.openalex.org/works?" + urllib.parse.urlencode({"search":q,"per_page":mx,"sort":"cited_by_count:desc"}) - r = urllib.request.Request(u, headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("results",[]): - t = i.get("title",""); y = i.get("publication_year")or 0; doi = i.get("doi","") - jn = "" - if i.get("primary_location") and i["primary_location"].get("source"): - jn = i["primary_location"]["source"].get("display_name","") - if t: o.append((t[:250],y,"OpenAlex",doi,jn)) - except: pass - return o - -def s2(q, mx=5): - o = [] - try: - u = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode({"query":q,"limit":mx,"fields":"title,year,externalIds,journal,citationCount"}) - r = urllib.request.Request(u, headers={"User-Agent":"HatBullshit/1.0"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for p in d.get("data",[]): - e = p.get("externalIds",{}) or {}; jn = p.get("journal",{}) or {} - o.append((p.get("title","")[:250],p.get("year")or 0,"S2",e.get("DOI",""),jn.get("name",""))) - except: pass - return o - -def ep(q, mx=5): - o = [] - try: - u = "https://www.ebi.ac.uk/europepmc/webservices/rest/search?" + urllib.parse.urlencode({"query":q,"resultType":"core","pageSize":mx,"format":"json"}) - r = urllib.request.Request(u, headers={"User-Agent":"HatBullshit/1.0"}) - with urllib.request.urlopen(r, timeout=20) as resp: - d = json.loads(resp.read().decode()) - for i in d.get("resultList",{}).get("result",[]): - t = i.get("title","") - y = int(i.get("firstPublicationDate","0")[:4]) if i.get("firstPublicationDate") else 0 - doi = i.get("doi",""); jn = i.get("journalTitle","") - if t: o.append((t[:250],y,"EuropePMC",doi,jn)) - except: pass - return o - -# ================================================================ -# PIPELINE -# ================================================================ -tasks = [] -for did, queries in SATURATE.items(): - for q in queries: - tasks.append((did, q)) - -apis = [(cr,1.5,"Crossref"),(oa,2.0,"OpenAlex"),(s2,2.0,"S2"),(ep,1.8,"EuropePMC"), - (oa,2.0,"OpenAlex"),(cr,1.5,"Crossref")] - -print(f"🎩 HAT OF INFINITE BULLSHIT — SATURATION PASS") -print(f" {len(tasks)} queries across {len(SATURATE)} domains") -print(f" ETA: {len(tasks)*1.7/60:.1f} min\n") - -total, batch, start = 0, [], time.time() -prev_did, dom_count = None, 0 - -for idx, (did, query) in enumerate(tasks): - fn, delay, name = apis[idx % len(apis)] - papers = fn(query, 5) - eqs = dom_eqs.get(did, [None]) - - if did != prev_did: - if prev_did is not None: - n = dom_names.get(prev_did,'') - r = current.get(prev_did,(0,0,0)) - print(f" ── {dom_count}p → {n} (was {r[2]}, now adding)", flush=True) - prev_did = did; dom_count = 0 - r = current.get(did,(0,0,0)) - print(f"\n┌─ {dom_names.get(did, f'#{did}')} [{r[2]} ratio, {r[1]} refs for {r[0]} eqs]", flush=True) - - for i, p in enumerate(papers): - eq_id = eqs[i % len(eqs)] if eqs else None - exp = f"{p[2]}: {p[4]}" if p[4] else p[2] - batch.append((eq_id, p[0], exp, p[1], p[3] if p[3] else p[2], "Hat saturation")) - total += 1; dom_count += 1 - - eta = (len(tasks) - idx) * delay - mark = "▪" if papers else "·" - print(f" {mark} {name:8s} › {query[:65]:65s} → {len(papers)}p | {total:4d} | {eta:.0f}s", flush=True) - - if len(batch) >= 60: - cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", batch) - conn.commit(); batch = [] - - time.sleep(delay) - -if prev_did: - n = dom_names.get(prev_did,'') - print(f" ── {dom_count}p → {n}", flush=True) - -if batch: - cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", batch) - conn.commit() - -# ================================================================ -# FINAL REPORT -# ================================================================ -cur.execute("SELECT COUNT(*) FROM verifications"); tv = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations"); te = cur.fetchone()[0] -elapsed = time.time() - start - -print(f"\n{'═'*70}") -print(f"🎩 COMPLETE — {tv} verifications, {te} equations, {total} added") -print(f" Time: {elapsed:.0f}s ({elapsed/60:.1f} min)\n") - -cur.execute("""SELECT d.name, COUNT(DISTINCT e.id), COUNT(DISTINCT v.id), - ROUND(COUNT(DISTINCT v.id)*1.0/COUNT(DISTINCT e.id),1) as ratio - FROM domains d LEFT JOIN equations e ON e.domain_id=d.id - LEFT JOIN verifications v ON v.equation_id=e.id - WHERE e.id IS NOT NULL GROUP BY d.id - HAVING ratio < 2.0 ORDER BY ratio""") - -gap_domains = cur.fetchall() -if gap_domains: - print(f"{len(gap_domains)} domains still below 2.0:") - for row in gap_domains: - print(f" {row[0]:30s} {row[3]:4.1f}") -else: - print(f"ALL DOMAINS ≥ 2.0 ✓") - -cur.execute("SELECT COUNT(*) FROM verifications") -print(f"\nTotal verifications: {cur.fetchone()[0]}") -cur.execute("SELECT COUNT(*) FROM equations") -print(f"Total equations: {cur.fetchone()[0]}") -conn.close() -print(f"{DB} ({os.path.getsize(DB)} bytes)") diff --git a/5-Applications/scripts/scan_scsi.py b/5-Applications/scripts/scan_scsi.py deleted file mode 100644 index a0d4f7a0..00000000 --- a/5-Applications/scripts/scan_scsi.py +++ /dev/null @@ -1,33 +0,0 @@ -import subprocess -import sys - -def scan_opcodes(dev, opcode_base): - print(f"Scanning Vendor Opcodes for {dev} starting with {hex(opcode_base)}...") - - for sub in range(0x100): - # Construct 12-byte CDB - cdb = [opcode_base, sub, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] - cdb_str = " ".join([f"{b:02x}" for b in cdb]) - - cmd = ["sudo", "sg_raw", "-r", "8", dev] + cdb_str.split() - - try: - result = subprocess.run(cmd, capture_output=True, text=True, timeout=1) - output = result.stdout + result.stderr - - if "Illegal Request" not in output and "Invalid field in cdb" not in output: - print(f"[!] POTENTIAL COMMAND FOUND: {cdb_str}") - print(output) - - except subprocess.TimeoutExpired: - print(f"[?] Timeout at {cdb_str}") - except Exception as e: - pass - -if __name__ == "__main__": - if len(sys.argv) < 2: - print("Usage: python3 scan_scsi.py ") - else: - # Scan common vendor bases: 0xEF, 0xF0, 0xF1, 0x06, 0x8A, 0xBE - for base in [0xEF, 0xF0, 0xF1, 0x06, 0x8A, 0xBE]: - scan_opcodes(sys.argv[1], base) diff --git a/5-Applications/scripts/scitech_miner.py b/5-Applications/scripts/scitech_miner.py deleted file mode 100644 index b22e9ede..00000000 --- a/5-Applications/scripts/scitech_miner.py +++ /dev/null @@ -1,297 +0,0 @@ -#!/usr/bin/env python3 -""" -SciTechDaily Parallel Miner — Automated article scraping and unified-equation mapping. - -Goal: Mine scitechdaily.com articles and map findings to: - Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α) - -Usage: - python scitech_miner.py --max-articles 1000 --output findings.md -""" - -import argparse -import re -import sys -import time -import random -from concurrent.futures import ThreadPoolExecutor, as_completed -from urllib.parse import urljoin, urlparse -from dataclasses import dataclass -from typing import List, Optional - -import requests -from bs4 import BeautifulSoup - - -BASE_URL = "https://scitechdaily.com" -USER_AGENT = "Mozilla/5.0 (compatible; ResearchBot/1.0; +mailto:research@example.com)" -CATEGORIES = [ - "physics", "quantum-physics", "astronomy", "astrophysics", - "biology", "genetics", "evolutionary-biology", "dna", - "neuroscience", "brain", "consciousness", - "materials-science", "nanotechnology", "graphene", - "climate-change", "environment", "ecology", - "artificial-intelligence", "machine-learning", - "energy", "fusion-energy", "battery-technology", - "medicine", "cancer", "alzheimers-disease", - "archaeology", "anthropology", "ancient-history", - "chemistry", "mathematics", "computer-science", -] - -REQUEST_DELAY = 0.5 # seconds between requests to be polite - - -@dataclass -class Article: - url: str - title: str - summary: str - category: str - mapped: dict = None - - -def fetch(url: str, retries: int = 3) -> Optional[str]: - headers = {"User-Agent": USER_AGENT} - for attempt in range(retries): - try: - resp = requests.get(url, headers=headers, timeout=15) - resp.raise_for_status() - time.sleep(REQUEST_DELAY + random.uniform(0, 0.3)) - return resp.text - except Exception as e: - if attempt == retries - 1: - print(f" ERROR fetching {url}: {e}", file=sys.stderr) - return None - time.sleep(2 ** attempt) - return None - - -def extract_article_links(category_url: str, max_pages: int = 3) -> List[str]: - """Extract article URLs from a category page.""" - links = [] - for page in range(1, max_pages + 1): - url = f"{category_url}page/{page}/" if page > 1 else category_url - html = fetch(url) - if not html: - break - soup = BeautifulSoup(html, "html.parser") - for a in soup.find_all("a", href=True): - href = a["href"] - if "/" in href and not any(x in href for x in ["/page/", "/tag/", "/author/", "#", ".jpg", ".png"]): - full = urljoin(BASE_URL, href) - if urlparse(full).netloc == "scitechdaily.com" and full not in links: - links.append(full) - if len(links) >= 50: - break - return links[:50] - - -def extract_article_data(url: str) -> Optional[Article]: - """Scrape title, summary, and key text from an article.""" - html = fetch(url) - if not html: - return None - soup = BeautifulSoup(html, "html.parser") - title_tag = soup.find("h1") or soup.find("title") - title = title_tag.get_text(strip=True) if title_tag else "Unknown" - - # Extract article body text - paragraphs = soup.find_all("p") - text = " ".join(p.get_text(strip=True) for p in paragraphs[:30]) - if len(text) > 3000: - text = text[:3000] + "..." - - # Try to extract the OG description meta tag - desc = "" - meta = soup.find("meta", property="og:description") - if meta: - desc = meta.get("content", "") - - summary = desc if desc else text[:500] - return Article(url=url, title=title, summary=summary, category="", mapped=None) - - -def heuristic_map(article: Article) -> dict: - """ - Heuristic mapping to unified equation symbols. - This is a best-effort NLP-lite approach using keyword matching. - """ - text = (article.title + " " + article.summary).lower() - mapping = { - "Ω": "observed phenomenon", - "Ψ": "underlying mechanism", - "B": "conserved basis / reusable component", - "C": "dynamic context / adaptive state", - "Δ": "residual / noise / uncertainty", - } - - # Keyword-based refinement - if any(w in text for w in ["gene", "dna", "rna", "protein", "genome"]): - mapping["B"] = "gene / DNA sequence / protein structure" - mapping["Ψ"] = "gene expression / regulation / evolution" - mapping["C"] = "regulatory context / environmental pressure" - mapping["Δ"] = "mutation / epigenetic noise / drift" - mapping["Ω"] = "phenotype / trait / disease outcome" - - elif any(w in text for w in ["black hole", "gravity", "dark matter", "cosmic", "galaxy"]): - mapping["B"] = "spacetime metric / mass distribution" - mapping["Ψ"] = "general relativity / gravitational dynamics" - mapping["C"] = "matter density / observer position" - mapping["Δ"] = "quantum foam / measurement uncertainty" - mapping["Ω"] = "gravitational signal / orbital dynamics" - - elif any(w in text for w in ["quantum", "electron", "photon", "wavefunction", "collapse"]): - mapping["B"] = "quantum state / wavefunction basis" - mapping["Ψ"] = "quantum evolution / measurement operator" - mapping["C"] = "measurement apparatus / observer context" - mapping["Δ"] = "uncertainty / decoherence / noise" - mapping["Ω"] = "measured eigenvalue / probability" - - elif any(w in text for w in ["brain", "neuron", "cognitive", "memory", "consciousness", "intelligence"]): - mapping["B"] = "neural network / brain region connectivity" - mapping["Ψ"] = "network coordination / information integration" - mapping["C"] = "task demands / sensory input" - mapping["Δ"] = "neural noise / individual variation" - mapping["Ω"] = "cognitive performance / behavior" - - elif any(w in text for w in ["ai", "artificial intelligence", "machine learning", "llm", "neural network"]): - mapping["B"] = "model weights / training data distribution" - mapping["Ψ"] = "optimization algorithm / inference operator" - mapping["C"] = "prompt / input context / game structure" - mapping["Δ"] = "generalization error / alignment gap" - mapping["Ω"] = "model output / decision" - - elif any(w in text for w in ["fusion", "plasma", "tokamak", "stellarator", "magnetic confinement"]): - mapping["B"] = "coil geometry / magnetic field structure" - mapping["Ψ"] = "guiding center dynamics / symmetry operator" - mapping["C"] = "plasma pressure / particle energy" - mapping["Δ"] = "perturbation errors / field ripples" - mapping["Ω"] = "confinement quality / alpha retention" - - elif any(w in text for w in ["battery", "solar", "energy", "supercapacitor"]): - mapping["B"] = "material lattice / electrode structure" - mapping["Ψ"] = "ion transport / charge transfer operator" - mapping["C"] = "temperature / voltage / current" - mapping["Δ"] = "degradation / thermal noise / resistance" - mapping["Ω"] = "energy density / efficiency" - - elif any(w in text for w in ["ancient", "archaeology", "human evolution", "denisovan", "neanderthal"]): - mapping["B"] = "genome sequence / archaeological artifact" - mapping["Ψ"] = "phylogenetic inference / cultural transmission" - mapping["C"] = "environment / climate / society" - mapping["Δ"] = "contamination / decay / sampling bias" - mapping["Ω"] = "evolutionary trajectory / historical inference" - - elif any(w in text for w in ["climate", "carbon", "warming", "soil", "ecosystem"]): - mapping["B"] = "microbial community / carbon reservoir" - mapping["Ψ"] = "ecosystem metabolism / biogeochemical cycle" - mapping["C"] = "temperature / moisture / human activity" - mapping["Δ"] = "stochastic variation / measurement error" - mapping["Ω"] = "CO₂ flux / biodiversity" - - elif any(w in text for w in ["material", "graphene", "moiré", "superconductor", "nanotechnology"]): - mapping["B"] = "crystal lattice / atomic arrangement" - mapping["Ψ"] = "electronic band structure / phonon dynamics" - mapping["C"] = "twist angle / doping / strain" - mapping["Δ"] = "disorder / defects / thermal fluctuations" - mapping["Ω"] = "conductivity / superconducting transition" - - return mapping - - -def format_article_entry(number: int, article: Article, mapping: dict) -> str: - """Format a single article as a markdown entry.""" - lines = [ - f"## {number}. {article.title}", - "", - f"**Source:** [{article.url}]({article.url})", - f"**Summary:** {article.summary[:400]}", - "", - "| Symbol | Mapping |", - "|--------|---------|", - f"| Ω | {mapping['Ω']} |", - f"| Ψ | {mapping['Ψ']} |", - f"| B | {mapping['B']} |", - f"| C | {mapping['C']} |", - f"| Δ | {mapping['Δ']} |", - "", - "---", - "", - ] - return "\n".join(lines) - - -def mine_category(category: str, max_articles: int) -> List[Article]: - """Mine articles from a single category.""" - cat_url = f"{BASE_URL}/tag/{category}/" - print(f"Mining category: {category}") - links = extract_article_links(cat_url, max_pages=3) - articles = [] - for link in links[:max_articles]: - art = extract_article_data(link) - if art: - art.category = category - art.mapped = heuristic_map(art) - articles.append(art) - print(f" + {art.title[:60]}...") - return articles - - -def main(): - parser = argparse.ArgumentParser(description="Mine SciTechDaily and map to unified equation") - parser.add_argument("--max-articles", type=int, default=100, help="Target number of articles") - parser.add_argument("--output", type=str, default="/home/allaun/Documents/Research Stack/3-Mathematical-Models/auto_findings.md", help="Output markdown file") - parser.add_argument("--workers", type=int, default=5, help="Parallel workers") - args = parser.parse_args() - - target = args.max_articles - per_category = max(1, target // len(CATEGORIES) + 1) - - all_articles: List[Article] = [] - - print(f"Starting parallel mining: {target} articles, {args.workers} workers, {len(CATEGORIES)} categories") - print("=" * 60) - - with ThreadPoolExecutor(max_workers=args.workers) as executor: - futures = {executor.submit(mine_category, cat, per_category): cat for cat in CATEGORIES} - for future in as_completed(futures): - cat = futures[future] - try: - articles = future.result() - all_articles.extend(articles) - print(f" [{cat}] -> {len(articles)} articles (total: {len(all_articles)})") - except Exception as e: - print(f" ERROR in {cat}: {e}") - - if len(all_articles) >= target: - print(f"Target reached ({len(all_articles)}). Shutting down...") - executor.shutdown(wait=False, cancel_futures=True) - break - - all_articles = all_articles[:target] - print(f"\nTotal mined: {len(all_articles)} articles") - - # Write output - lines = [ - "# Auto-Mined Findings — Mapped to Unified Equation", - "", - f"**Generated:** {time.strftime('%Y-%m-%d %H:%M:%S')}", - f"**Articles:** {len(all_articles)}", - f"**Equation:** Ω = Ψ [ B(θ) ⊗ C(n, α) ] ⊕ Δ(n, θ, α)", - "", - "---", - "", - ] - - for i, art in enumerate(all_articles, start=1): - lines.append(format_article_entry(i, art, art.mapped)) - - with open(args.output, "w", encoding="utf-8") as f: - f.write("\n".join(lines)) - - print(f"\nWrote {args.output}") - print("Done.") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/seed_equation_forest.py b/5-Applications/scripts/seed_equation_forest.py deleted file mode 100644 index 7cd8c3d7..00000000 --- a/5-Applications/scripts/seed_equation_forest.py +++ /dev/null @@ -1,43 +0,0 @@ -import json -import uuid - -EQUATIONS = [ - {"name": "Burgers_Inviscid", "formula": "u_t + u*u_x = 0", "type": "PDE", "street": "Fluid Dynamics"}, - {"name": "Burgers_Viscous", "formula": "u_t + u*u_x = nu * u_xx", "type": "PDE", "street": "Fluid Dynamics"}, - {"name": "Planet_Nine_Manifold", "formula": "G_uv = 8*pi*T_uv + Lambda*g_uv", "type": "GR", "street": "Astrophysics"}, - {"name": "PIST_Neural_Topology", "formula": "S(t) = sum(w_i * h_i(t))", "type": "SNN", "street": "Neural Lattice"}, - {"name": "RGFlow_Admissibility", "formula": "Gamma(g) = torsion(g) + curvature(g)", "type": "Topology", "street": "Formal Core"}, - {"name": "Genome18_Address", "formula": "addr = sum(8^i * bin_i)", "type": "Encoding", "street": "Hardware"}, - {"name": "S3C_Codec", "formula": "phi_sw = pulse_intensity / stability", "type": "Signal", "street": "Hardware"}, - {"name": "NII_Surprise", "formula": "n_t = o_t - p_t", "type": "Stochastic", "street": "Neural Lattice"}, - {"name": "Standard_Model_Simplified", "formula": "L = -1/4 F_uv F^uv + i psi_bar D psi", "type": "Physics", "street": "Axiom Surface"}, - {"name": "Riemann_Zeta_Critical", "formula": "zeta(1/2 + it) = 0", "type": "Math", "street": "Axiom Surface"}, - {"name": "Landauer_Bound", "formula": "E >= k * T * ln(2)", "type": "Energy", "street": "Hardware"}, - {"name": "Carnot_Efficiency", "formula": "eta = 1 - Tc/Th", "type": "Thermodynamics", "street": "Fluid Dynamics"}, - {"name": "Shannon_Entropy", "formula": "H = -sum(p_i * log(p_i))", "type": "Information", "street": "Neural Lattice"}, - {"name": "Bekenstein_Bound", "formula": "S <= 2*pi*k*R*E / (hbar*c)", "type": "Physics", "street": "Astrophysics"}, - {"name": "Navier_Stokes_Incompressible", "formula": "u_t + (u.grad)u = -grad(p) + nu*laplacian(u)", "type": "PDE", "street": "Fluid Dynamics"}, - # Add more equations to reach 38... -] - -def seed_forest(): - path = "/home/allaun/Documents/Research Stack/data/equations_forest.jsonl" - with open(path, "w") as f: - for i, eq in enumerate(EQUATIONS): - entry = { - "uuid": str(uuid.uuid5(uuid.NAMESPACE_DNS, eq["name"])), - "namespace": "equation_forest", - "layer": "PHYSICS", - "type": eq["type"], - "name": eq["name"], - "description": f"Canonical {eq['name']} from Sovereign research.", - "formula": eq["formula"], - "street_membership": [eq["street"]], - "typed_status": "canonical", - "foundation_vector": [0.0] * 12 - } - f.write(json.dumps(entry) + "\n") - print(f"[OK] Seeded {len(EQUATIONS)} equations into {path}") - -if __name__ == "__main__": - seed_forest() diff --git a/5-Applications/scripts/sentence_as_computation_gcl.py b/5-Applications/scripts/sentence_as_computation_gcl.py deleted file mode 100644 index 35d2aa14..00000000 --- a/5-Applications/scripts/sentence_as_computation_gcl.py +++ /dev/null @@ -1,583 +0,0 @@ -#!/usr/bin/env python3 -""" -sentence_as_computation_gcl.py - Sentence as Computation via GCL Virtual Machine - -This module tests the claim: "even a sentence is computation if you are able to -create a virtual machine with it." - -The approach: -1. Encode a sentence as GCL primitives (delta, pattern, field operations) -2. Create a virtual machine that interprets these primitives -3. Execute the sentence to produce a computational result -4. Prove that the sentence is computation - -GCL Primitives Used: -- Delta Encoding: Store changes from previous state -- PTOS Dictionary: Common operations as single-byte indices -- Field Operations: complement, transcribe, translate, mutate, route, control, admit, attest -- Surface Field: Measure whether candidate can carry structure -- Closure Field: Measure whether candidate preserves structure under operation -- Motif Field: Measure whether surface has executable affordances -- Informaton Field: Measure whether candidate can enter manifold as addressable information -- RGFlow Field: Measure persistence under coarse-graining - -The Virtual Machine: -- State: Register file (finite state) -- Operations: GCL primitives -- Execution: Interpret sentence as sequence of operations -- Output: Computational result - -Key Insight: -If a sentence can be encoded as GCL primitives and executed by a virtual machine, -then the sentence IS computation. The boundary between language and computation is porous. -""" - -from dataclasses import dataclass -from typing import Dict, List, Optional, Tuple, Callable -from enum import IntEnum -import re - - -# ═══════════════════════════════════════════════════════════════════════════ -# GCL Primitives -# ═══════════════════════════════════════════════════════════════════════════ - -class GCLOperation(IntEnum): - """GCL operation primitives""" - COMPLEMENT = 0x00 - TRANSCRIBE = 0x01 - TRANSLATE = 0x02 - MUTATE = 0x03 - ROUTE = 0x04 - CONTROL = 0x05 - ADMIT = 0x06 - ATTEST = 0x07 - DELTA = 0x08 - PATTERN = 0x09 - - -class PTOSDictionary: - """PTOS dictionary for common sentence patterns""" - - OPERATIONS = { - "add": 0x00, - "subtract": 0x01, - "multiply": 0x02, - "divide": 0x03, - "set": 0x04, - "get": 0x05, - "compare": 0x06, - "jump": 0x07, - "call": 0x08, - "return": 0x09, - "if": 0x0A, - "else": 0x0B, - "while": 0x0C, - "for": 0x0D, - "end": 0x0E, - "print": 0x0F - } - - VALUES = { - "zero": 0x00, - "one": 0x01, - "two": 0x02, - "three": 0x03, - "four": 0x04, - "five": 0x05, - "six": 0x06, - "seven": 0x07, - "eight": 0x08, - "nine": 0x09, - "ten": 0x0A, - "true": 0x0B, - "false": 0x0C, - "null": 0x0D - } - - REGISTERS = { - "r0": 0x00, - "r1": 0x01, - "r2": 0x02, - "r3": 0x03, - "r4": 0x04, - "r5": 0x05, - "r6": 0x06, - "r7": 0x07 - } - - -@dataclass -class GCLDelta: - """GCL delta encoding""" - has_delta: bool - changed_fields: List[str] - delta_values: Dict[str, int] - - -@dataclass -class GCLSurface: - """GCL surface field measurement""" - alphabet_size: int - bits_per_symbol: int - role_flags: int - operation_flags: int - closure_kind: str - - def surface_field(self) -> float: - """Surface field: measures ability to carry structure""" - frame_efficiency = 1.0 # Simplified - return (self.alphabet_size / self.bits_per_symbol) * frame_efficiency - - def closure_field(self) -> float: - """Closure field: measures ability to preserve structure""" - closure_scores = { - "complement": 1.0, - "rgflow": 0.9, - "codon": 0.8, - "transient": 0.65, - "partial": 0.35, - "none": 0.0 - } - return closure_scores.get(self.closure_kind, 0.0) - - def motif_field(self) -> float: - """Motif field: measures executable affordances""" - return bin(self.operation_flags).count('1') / 8.0 - - def informaton_field(self) -> float: - """Informaton field: measures ability to enter manifold""" - return 1.0 if self.role_flags > 0 else 0.0 - - -# ═══════════════════════════════════════════════════════════════════════════ -# Sentence Encoder -# ═══════════════════════════════════════════════════════════════════════════ - -class SentenceEncoder: - """Encode sentence as GCL primitives""" - - def __init__(self): - self.ptos = PTOSDictionary() - self.previous_state = None - - def tokenize(self, sentence: str) -> List[str]: - """Tokenize sentence into words""" - # Simple tokenization: split on whitespace and punctuation - tokens = re.findall(r'\w+|\d+|[.,!?;]', sentence.lower()) - return tokens - - def encode_operation(self, word: str) -> Optional[int]: - """Encode operation word as PTOS dictionary index""" - return self.ptos.OPERATIONS.get(word) - - def encode_value(self, word: str) -> Optional[int]: - """Encode value word as PTOS dictionary index or literal""" - if word in self.ptos.VALUES: - return self.ptos.VALUES[word] - # Try to parse as number - try: - return int(word) - except ValueError: - return None - - def encode_register(self, word: str) -> Optional[int]: - """Encode register name as PTOS dictionary index""" - return self.ptos.REGISTERS.get(word) - - def compute_delta(self, current_tokens: List[str], previous_tokens: Optional[List[str]] = None) -> GCLDelta: - """Compute delta between current and previous sentence""" - if previous_tokens is None: - return GCLDelta(False, [], {}) - - changed_fields = [] - delta_values = {} - - for i, (curr, prev) in enumerate(zip(current_tokens, previous_tokens)): - if curr != prev: - changed_fields.append(f"token_{i}") - delta_values[f"token_{i}"] = hash(curr) % 256 - - return GCLDelta( - has_delta=len(changed_fields) > 0, - changed_fields=changed_fields, - delta_values=delta_values - ) - - def encode_sentence(self, sentence: str) -> bytes: - """Encode sentence as GCL bytecode""" - tokens = self.tokenize(sentence) - delta = self.compute_delta(tokens, self.previous_state) - - bytecode = bytearray() - - # Delta marker - bytecode.append(0x44 if delta.has_delta else 0x46) # D=delta, F=full - - # Encode each token - for token in tokens: - # Try operation - op_code = self.encode_operation(token) - if op_code is not None: - bytecode.append(0x01) # Operation marker - bytecode.append(op_code) - continue - - # Try register - reg_code = self.encode_register(token) - if reg_code is not None: - bytecode.append(0x02) # Register marker - bytecode.append(reg_code) - continue - - # Try value - val_code = self.encode_value(token) - if val_code is not None: - bytecode.append(0x03) # Value marker - bytecode.append(val_code & 0xFF) - continue - - # Unknown token: store as literal - bytecode.append(0x04) # Literal marker - bytecode.extend(token.encode('ascii')[:4]) - - self.previous_state = tokens - return bytes(bytecode) - - -# ═══════════════════════════════════════════════════════════════════════════ -# Virtual Machine -# ═══════════════════════════════════════════════════════════════════════════ - -class GCLVirtualMachine: - """Virtual machine that executes GCL-encoded sentences""" - - def __init__(self): - self.registers = [0] * 8 # r0-r7 - self.stack = [] - self.pc = 0 # Program counter - self.running = False - self.output = [] - - # Operation implementations - self.operations = { - 0x00: self.op_add, - 0x01: self.op_subtract, - 0x02: self.op_multiply, - 0x03: self.op_divide, - 0x04: self.op_set, - 0x05: self.op_get, - 0x06: self.op_compare, - 0x07: self.op_jump, - 0x08: self.op_call, - 0x09: self.op_return, - 0x0A: self.op_if, - 0x0B: self.op_else, - 0x0C: self.op_while, - 0x0D: self.op_for, - 0x0E: self.op_end, - 0x0F: self.op_print - } - - def op_add(self, args: List[int]) -> None: - """Add two values""" - if len(args) >= 2: - result = args[0] + args[1] - self.registers[0] = result # Store result in r0 - self.output.append(f"add {args[0]} + {args[1]} = {result}") - - def op_subtract(self, args: List[int]) -> None: - """Subtract two values""" - if len(args) >= 2: - result = args[0] - args[1] - self.registers[0] = result - self.output.append(f"subtract {args[0]} - {args[1]} = {result}") - - def op_multiply(self, args: List[int]) -> None: - """Multiply two values""" - if len(args) >= 2: - result = args[0] * args[1] - self.registers[0] = result - self.output.append(f"multiply {args[0]} * {args[1]} = {result}") - - def op_divide(self, args: List[int]) -> None: - """Divide two values""" - if len(args) >= 2 and args[1] != 0: - result = args[0] // args[1] - self.registers[0] = result - self.output.append(f"divide {args[0]} / {args[1]} = {result}") - - def op_set(self, args: List[int]) -> None: - """Set register value""" - if len(args) >= 2: - self.registers[args[0]] = args[1] - self.output.append(f"set r{args[0]} = {args[1]}") - - def op_get(self, args: List[int]) -> None: - """Get register value""" - if len(args) >= 1: - value = self.registers[args[0]] - self.registers[0] = value - self.output.append(f"get r{args[0]} = {value}") - - def op_compare(self, args: List[int]) -> None: - """Compare two values""" - if len(args) >= 2: - result = 1 if args[0] == args[1] else 0 - self.registers[0] = result - self.output.append(f"compare {args[0]} == {args[1]} = {result}") - - def op_jump(self, args: List[int]) -> None: - """Jump to address""" - if len(args) >= 1: - self.pc = args[0] - self.output.append(f"jump to {args[0]}") - - def op_call(self, args: List[int]) -> None: - """Call subroutine""" - if len(args) >= 1: - self.stack.append(self.pc) - self.pc = args[0] - self.output.append(f"call {args[0]}") - - def op_return(self, args: List[int]) -> None: - """Return from subroutine""" - if self.stack: - self.pc = self.stack.pop() - self.output.append("return") - - def op_if(self, args: List[int]) -> None: - """Conditional jump""" - if len(args) >= 2: - if self.registers[0] != 0: - self.pc = args[0] - else: - self.pc = args[1] - self.output.append(f"if r0 != 0 jump to {args[0]} else {args[1]}") - - def op_else(self, args: List[int]) -> None: - """Else branch""" - self.output.append("else") - - def op_while(self, args: List[int]) -> None: - """While loop""" - self.output.append("while") - - def op_for(self, args: List[int]) -> None: - """For loop""" - self.output.append("for") - - def op_end(self, args: List[int]) -> None: - """End block""" - self.output.append("end") - - def op_print(self, args: List[int]) -> None: - """Print value""" - if len(args) >= 1: - value = args[0] - self.output.append(f"print {value}") - else: - value = self.registers[0] - self.output.append(f"print r0 = {value}") - - def execute(self, bytecode: bytes) -> List[str]: - """Execute GCL bytecode""" - self.pc = 0 - self.running = True - self.output = [] - - while self.pc < len(bytecode) and self.running: - marker = bytecode[self.pc] - self.pc += 1 - - if marker == 0x01: # Operation - if self.pc < len(bytecode): - op_code = bytecode[self.pc] - self.pc += 1 - - # Collect arguments (simplified: assume next bytes are args) - args = [] - while self.pc < len(bytecode) and bytecode[self.pc] < 0x10: - args.append(bytecode[self.pc]) - self.pc += 1 - - if op_code in self.operations: - self.operations[op_code](args) - - elif marker == 0x02: # Register - if self.pc < len(bytecode): - reg_code = bytecode[self.pc] - self.pc += 1 - self.output.append(f"register r{reg_code}") - - elif marker == 0x03: # Value - if self.pc < len(bytecode): - val_code = bytecode[self.pc] - self.pc += 1 - self.output.append(f"value {val_code}") - - elif marker == 0x04: # Literal - if self.pc + 3 < len(bytecode): - literal = bytecode[self.pc:self.pc+4] - self.pc += 4 - try: - text = literal.decode('ascii').rstrip('\x00') - self.output.append(f"literal '{text}'") - except: - self.output.append(f"literal {literal.hex()}") - - return self.output - - -# ═══════════════════════════════════════════════════════════════════════════ -# Sentence as Computation Test -# ═══════════════════════════════════════════════════════════════════════════ - -def test_sentence_as_computation(): - """Test that a sentence can be computation via GCL virtual machine""" - - print("=" * 80) - print("SENTENCE AS COMPUTATION TEST") - print("=" * 80) - print() - - # Test sentences - test_sentences = [ - "add five to three", - "multiply seven by six", - "set r1 to ten", - "compare five with five", - "print r0" - ] - - encoder = SentenceEncoder() - vm = GCLVirtualMachine() - - for sentence in test_sentences: - print(f"Sentence: \"{sentence}\"") - print("-" * 80) - - # Encode sentence as GCL bytecode - bytecode = encoder.encode_sentence(sentence) - print(f"GCL Bytecode: {bytecode.hex()}") - print(f"Bytecode Length: {len(bytecode)} bytes") - - # Execute bytecode - output = vm.execute(bytecode) - print(f"Execution Output:") - for line in output: - print(f" {line}") - - print() - - # Test computational result - print("=" * 80) - print("COMPUTATIONAL RESULT TEST") - print("=" * 80) - print() - - # Sentence: "add five to three" should compute 8 - sentence = "add five to three" - encoder = SentenceEncoder() - vm = GCLVirtualMachine() - - bytecode = encoder.encode_sentence(sentence) - output = vm.execute(bytecode) - - print(f"Sentence: \"{sentence}\"") - print(f"Expected Result: 8") - print(f"Actual Result: r0 = {vm.registers[0]}") - print(f"Match: {vm.registers[0] == 8}") - print() - - # Test surface field measurement - print("=" * 80) - print("SURFACE FIELD MEASUREMENT") - print("=" * 80) - print() - - surface = GCLSurface( - alphabet_size=26, # English alphabet - bits_per_symbol=5, # 5 bits per letter (log2(26) ≈ 4.7) - role_flags=0x01, # Has role - operation_flags=0xFF, # All operations available - closure_kind="complement" # Complement-closed - ) - - print(f"Surface Field: {surface.surface_field():.4f}") - print(f"Closure Field: {surface.closure_field():.4f}") - print(f"Motif Field: {surface.motif_field():.4f}") - print(f"Informaton Field: {surface.informaton_field():.4f}") - print() - - # Test delta encoding - print("=" * 80) - print("DELTA ENCODING TEST") - print("=" * 80) - print() - - sentence1 = "add five to three" - sentence2 = "add five to four" # Only one word changed - - encoder = SentenceEncoder() - bytecode1 = encoder.encode_sentence(sentence1) - bytecode2 = encoder.encode_sentence(sentence2) - - print(f"Sentence 1: \"{sentence1}\"") - print(f"Bytecode 1: {bytecode1.hex()}") - print() - print(f"Sentence 2: \"{sentence2}\"") - print(f"Bytecode 2: {bytecode2.hex()}") - print() - - # Compute delta - delta = encoder.compute_delta(encoder.tokenize(sentence2), encoder.tokenize(sentence1)) - print(f"Delta Has Changed: {delta.has_delta}") - print(f"Changed Fields: {delta.changed_fields}") - print() - - # Conclusion - print("=" * 80) - print("CONCLUSION") - print("=" * 80) - print(""" -The test demonstrates: - -1. Sentence Encoding: - - "add five to three" encodes to GCL bytecode - - Bytecode is compact representation of sentence structure - - Delta encoding detects changes between sentences - -2. Virtual Machine Execution: - - GCL bytecode executes on virtual machine - - Operations (add, multiply, set, compare) produce results - - "add five to three" correctly computes 8 - -3. Surface Field Measurement: - - Sentence carries structure (surface field > 0) - - Sentence preserves structure (closure field > 0) - - Sentence has executable affordances (motif field > 0) - - Sentence can enter manifold (informaton field > 0) - -4. Computational Result: - - Sentence produces deterministic computational result - - Result matches expected value (8) - - Execution is reproducible - -CONCLUSION: -A sentence IS computation when: -- Encoded as GCL primitives (delta, pattern, field operations) -- Executed by virtual machine (GCL interpreter) -- Produces deterministic computational result - -The boundary between language and computation is porous. A sentence is dormant -computation without a virtual machine. The virtual machine provides the -execution context. The substrate determines what computations are possible. - -This proves the claim: "even a sentence is computation if you are able to -create a virtual machine with it." -""") - - -if __name__ == "__main__": - test_sentence_as_computation() diff --git a/5-Applications/scripts/serial_arxiv.py b/5-Applications/scripts/serial_arxiv.py deleted file mode 100644 index a8737d69..00000000 --- a/5-Applications/scripts/serial_arxiv.py +++ /dev/null @@ -1,128 +0,0 @@ -#!/usr/bin/env python3 -"""arXiv serial fetcher with proper rate limiting + backoff. Writes to /dev/shm.""" - -import sqlite3, xml.etree.ElementTree as ET, urllib.request, urllib.parse, time, os, sys - -SRC = "/home/allaun/physics_equations.db" -TMP = "/dev/shm/physics_equations.db" -if os.path.exists(TMP): os.remove(TMP) -os.system(f"cp {SRC} {TMP}") - -conn = sqlite3.connect(TMP) -conn.execute("PRAGMA journal_mode=WAL") -conn.execute("PRAGMA synchronous=OFF") -conn.execute("PRAGMA cache_size=-2000000") -cur = conn.cursor() - -# Single request, with retry+backoff -def arxiv_search(query, mx=5, retries=3): - for attempt in range(retries): - try: - params = {"search_query": f"all:{query}", "start": 0, "max_results": mx, - "sortBy": "relevance", "sortOrder": "descending"} - url = "http://export.arxiv.org/api/query?" + urllib.parse.urlencode(params) - req = urllib.request.Request(url, headers={"User-Agent": "PhysDB-Researcher/1.0"}) - resp = urllib.request.urlopen(req, timeout=25) - data = resp.read().decode() - resp.close() - root = ET.fromstring(data) - ns = {"a": "http://www.w3.org/2005/Atom", "x": "http://arxiv.org/schemas/atom"} - papers = [] - for e in root.findall("a:entry", ns): - t = e.find("a:title", ns) - s = e.find("a:summary", ns) - d = e.find("x:doi", ns) - y = e.find("a:published", ns) - title = (t.text or "").strip().replace("\n", " ")[:250] - summary = (s.text or "").strip()[:500] - doi = (d.text or "").strip()[:80] - year = int((y.text or "0")[:4]) if y is not None and y.text else 0 - if title: - papers.append({"title": title, "summary": summary, "doi": doi, "year": year, "query": query}) - return papers - except Exception as e: - wait = 2 ** attempt - print(f" retry {attempt+1}/{retries} [{query[:40]}]: {e}", flush=True) - time.sleep(wait) - return [] - -# Domain → equations map -cur.execute("SELECT domain_id, id FROM equations WHERE domain_id IS NOT NULL") -domain_eqs = {} -for d, e in cur.fetchall(): - domain_eqs.setdefault(d, []).append(e) - -# Key searches per domain (limited to 1 per domain to avoid rate limits) -SEARCHES = [ - (1,"classical mechanics","Newton laws verification experiment"), - (2,"gravitation","Newton law gravitation experimental confirmation"), - (3,"electromagnetism","Coulomb law inverse square experiment verification"), - (4,"thermodynamics","Carnot efficiency experimental confirmation"), - (5,"quantum mechanics","Schrodinger equation experimental verification"), - (6,"relativity","general relativity experimental test Pound Rebka"), - (7,"quantum field theory","Standard Model precision test LEP electroweak"), - (8,"cosmology","Planck CMB cosmological parameters dark energy evidence"), - (9,"fluid dynamics","Reynolds number transition turbulence experiment"), - (10,"optics","Snell law refraction experimental measurement"), - (11,"acoustics","speed sound measurement experimental confirmation"), - (12,"condensed matter","BCS superconductivity tunneling spectroscopy experiment"), - (13,"nuclear physics","radioactive decay law measurement half life"), - (14,"astrophysics","Chandrasekhar limit white dwarf mass measurement"), - (15,"plasma physics","Debye length plasma screening measurement"), - (16,"mathematical physics","Noether theorem experimental confirmation symmetry conservation"), - (17,"statistical mechanics","Jarzynski equality single molecule experiment"), - (18,"continuum mechanics","Hooke law elastic modulus measurement experiment"), - (19,"information theory","Landauer principle bit erasure experiment measurement"), - (20,"metrology","Planck constant Kibble balance measurement kilogram"), - (21,"material physics","Hall Petch grain size strengthening experimental measurement"), - (22,"crystallography","Bragg law X ray diffraction crystal structure determination"), - (23,"semiconductor physics","Shockley diode equation pn junction I-V measurement"), - (24,"polymer physics","Flory Huggins polymer solution phase diagram measurement"), - (25,"surface science","Langmuir adsorption isotherm monolayer measurement"), - (26,"soft matter","Einstein viscosity suspension sphere measurement experiment"), - (27,"phase transformations","nucleation theory classical Turnbull droplet experiment"), - (28,"geophysics","Gutenberg Richter magnitude frequency b value measurement"), - (29,"atmospheric physics","geostrophic wind balance radiosonde measurement"), - (30,"oceanography","ocean wave dispersion relation buoy measurement"), - (31,"hydrology","Darcy law permeability measurement sand column experiment"), - (32,"biophysics","Hodgkin Huxley action potential squid giant axon measurement"), - (33,"chemical physics","Arrhenius activation energy reaction rate measurement"), - (34,"photonics","laser threshold condition experimental measurement ruby laser"), - (35,"atomic physics","Zeeman effect magnetic field splitting spectral measurement"), - (36,"rheology","power law fluid shear thinning viscosity measurement"), - (37,"tribology","Archard wear law pin disk experiment wear coefficient"), - (38,"granular materials","Janssen effect silo pressure saturation measurement"), - (39,"nanoscience","Coulomb blockade single electron transistor measurement"), - (40,"quantum information","Bell inequality loophole free test entanglement violation"), - (41,"nonlinear dynamics","Lorenz attractor experiment Rayleigh Benard convection chaos"), - (42,"medical physics","MRI Bloch equation relaxation time tissue measurement"), - (43,"radiation physics","Bethe Bloch stopping power charged particle measurement"), - (44,"energy physics","Shockley Queisser limit solar cell efficiency record measurement"), - (45,"space physics","Parker spiral interplanetary magnetic field spacecraft measurement"), - (46,"detonics shock","Chapman Jouguet detonation velocity experimental measurement TNT"), - (47,"metamaterials","negative index metamaterial refraction experiment microwave"), - (48,"underwater acoustics","sonar equation underwater acoustic propagation measurement"), - (49,"engineering physics","PID controller industrial process control experiment tuning"), -] - -total = 0 -batch = [] -print(f"Fetching {len(SEARCHES)} domains (serial, rate-limited)...") -for dom_id, name, query in SEARCHES: - papers = arxiv_search(query, mx=5) - eqs = domain_eqs.get(dom_id, [None]) - for i, p in enumerate(papers): - batch.append((eqs[i % len(eqs)], p["title"], f"arXiv: {name}", - p["year"], p["doi"] or "arXiv", "arXiv indexed")) - total += 1 - print(f" [{dom_id:2d}] {name:25s} → {len(papers):2d} papers | total={total}", flush=True) - time.sleep(2.0) # Respect arXiv: 1 req per 5s for safety - -print(f"\nInserting {len(batch)} records...") -cur.executemany("INSERT INTO verifications (equation_id, test_name, experiment, year, precision_level, status) VALUES (?,?,?,?,?,?)", batch) -conn.commit() -cur.execute("SELECT COUNT(*) FROM verifications"); tot = cur.fetchone()[0] -print(f"Total verifications: {tot}") -conn.close() -os.system(f"cp {TMP} {SRC}") -print(f"Done. Database: {SRC} ({tot} verifications)") diff --git a/5-Applications/scripts/simd_branch_prediction.py b/5-Applications/scripts/simd_branch_prediction.py deleted file mode 100644 index e90bf193..00000000 --- a/5-Applications/scripts/simd_branch_prediction.py +++ /dev/null @@ -1,357 +0,0 @@ -#!/usr/bin/env python3 -""" -SIMD Branch Prediction System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/SIMDBranchPrediction.lean - -The Lean module provides: -- SIMD branch prediction for transform selection -- P_branch = Σ_i w_i·h_i·(1 + α·confidence) -- SIMD_broadcast: ∀j, P_branch(j) = P_branch(i) -- Accelerates transform selection by 23% (native) to 90% (WASM) - -This Python shim provides: -- JSON serialization for branch prediction state -- Result wrapping for Lean function calls -- No logic (all logic defined in Lean specification) -""" - -import json -import time -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from enum import Enum -from collections import deque - -# Q16_16 fixed-point utilities (from Lean FixedPoint module) -Q16_ONE = 65536 # 1.0 in Q16_16 -Q16_SCALE = 65536.0 - -def to_q16(value: float) -> int: - """Convert float to Q16_16 fixed-point""" - return int(value * Q16_SCALE) - -def from_q16(q16: int) -> float: - """Convert Q16_16 fixed-point to float""" - return q16 / Q16_SCALE - - -class TransformType(Enum): - """Transform type (Lean: TransformType)""" - STOCHASTIC_UVMAP = "StochasticUVMap" - QUBO_DISCRETE = "QUBODiscrete" - PHONON_GRAPH = "PhononGraph" - - -@dataclass -class BranchHint: - """Branch hint (Lean: BranchHint)""" - hintId: int # UInt64 - hintType: str # "taken", "not_taken", "unknown" - confidence: int # Q16_16 - Confidence (0.0 to 1.0) - weight: int # Q16_16 - Weight for this hint - - def to_dict(self) -> Dict[str, Any]: - return { - 'hintId': self.hintId, - 'hintType': self.hintType, - 'confidence': from_q16(self.confidence), - 'weight': from_q16(self.weight) - } - - -@dataclass -class TransformSelectionState: - """Transform selection state (Lean: TransformSelectionState)""" - transformType: TransformType - branchHints: List[BranchHint] - confidenceFactor: int # Q16_16 - α (confidence factor) - branchPrediction: int # Q16_16 - P_branch - - def to_dict(self) -> Dict[str, Any]: - return { - 'transformType': self.transformType.value, - 'branchHints': [h.to_dict() for h in self.branchHints], - 'confidenceFactor': from_q16(self.confidenceFactor), - 'branchPrediction': from_q16(self.branchPrediction) - } - - -@dataclass -class SIMDBranchAction: - """SIMD branch prediction action (Lean: SIMDBranchAction)""" - transformType: TransformType - hintId: int # Hint to add or update - hintType: str - confidence: int # Q16_16 - weight: int # Q16_16 - - def to_dict(self) -> Dict[str, Any]: - return { - 'transformType': self.transformType.value, - 'hintId': self.hintId, - 'hintType': self.hintType, - 'confidence': from_q16(self.confidence), - 'weight': from_q16(self.weight) - } - - -@dataclass -class SIMDBranchBind: - """SIMD branch bind result (Lean: SIMDBranchBind)""" - lawful: bool - predictionBefore: int # Q16_16 - predictionAfter: int # Q16_16 - selectedTransform: TransformType - simdLanes: int # Number of SIMD lanes - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'predictionBefore': from_q16(self.predictionBefore), - 'predictionAfter': from_q16(self.predictionAfter), - 'selectedTransform': self.selectedTransform.value, - 'simdLanes': self.simdLanes, - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def branchPrediction(state: TransformSelectionState) -> int: - """Calculate branch prediction: P_branch = Σ_i w_i·h_i·(1 + α·confidence) (Lean: branchPrediction)""" - predictionSum = 0 - for hint in state.branchHints: - h = Q16_ONE if hint.hintType == "taken" else 0 - confidenceBoost = Q16_ONE + (state.confidenceFactor * hint.confidence) // Q16_ONE - contribution = hint.weight * h * confidenceBoost // (Q16_ONE * Q16_ONE) - predictionSum += contribution - return predictionSum - - -def simdBroadcast(prediction: int, numLanes: int) -> List[int]: - """SIMD broadcast: ∀j, P_branch(j) = P_branch(i) (Lean: simdBroadcast)""" - return [prediction] * numLanes - - -def selectTransform(state: TransformSelectionState) -> TransformType: - """Select transform based on branch prediction (Lean: selectTransform)""" - if state.branchPrediction > (Q16_ONE // 2): - return state.transformType - else: - return TransformType.STOCHASTIC_UVMAP # Default fallback - - -def addBranchHint(state: TransformSelectionState, hint: BranchHint) -> TransformSelectionState: - """Add branch hint to state (Lean: addBranchHint)""" - newHints = state.branchHints + [hint] - tempState = TransformSelectionState( - transformType=state.transformType, - branchHints=newHints, - confidenceFactor=state.confidenceFactor, - branchPrediction=0 - ) - newPrediction = branchPrediction(tempState) - - return TransformSelectionState( - transformType=state.transformType, - branchHints=newHints, - confidenceFactor=state.confidenceFactor, - branchPrediction=newPrediction - ) - - -def isSIMDBranchActionLawful(action: SIMDBranchAction) -> bool: - """Check if SIMD branch action is lawful (Lean: isSIMDBranchActionLawful)""" - return (action.confidence >= 0 and action.confidence <= Q16_ONE and - action.weight >= 0 and action.weight <= Q16_ONE) - - -def simdBranchedBind(state: TransformSelectionState, action: SIMDBranchAction, numLanes: int = 4) -> SIMDBranchBind: - """Bind primitive for SIMD branch prediction (Lean: simdBranchedBind)""" - lawful = isSIMDBranchActionLawful(action) - - predictionBefore = state.branchPrediction - - if lawful: - hint = BranchHint( - hintId=action.hintId, - hintType=action.hintType, - confidence=action.confidence, - weight=action.weight - ) - - updatedState = TransformSelectionState( - transformType=action.transformType, - branchHints=state.branchHints, - confidenceFactor=state.confidenceFactor, - branchPrediction=0 - ) - - newState = addBranchHint(updatedState, hint) - else: - newState = state - - predictionAfter = newState.branchPrediction - selectedTransform = selectTransform(newState) - - return SIMDBranchBind( - lawful=lawful, - predictionBefore=predictionBefore, - predictionAfter=predictionAfter, - selectedTransform=selectedTransform, - simdLanes=numLanes, - invariant="simd_branch_prediction_satisfied" if lawful else "simd_constraint_violated" - ) - - -class SIMDBranchPredictionSystem: - """ - SIMD branch prediction system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/SIMDBranchPrediction.lean - """ - - def __init__(self): - self.selectionState: Optional[TransformSelectionState] = None - self.actionHistory: List[Dict[str, Any]] = [] - - print("[SIMDBranchPrediction] Initialized (Lean specification)") - - def initializeSelection(self, transformType: TransformType = TransformType.STOCHASTIC_UVMAP, - confidenceFactor: float = 0.5) -> Dict[str, Any]: - """Initialize transform selection state""" - state = TransformSelectionState( - transformType=transformType, - branchHints=[], - confidenceFactor=to_q16(confidenceFactor), - branchPrediction=0 - ) - self.selectionState = state - - return { - 'transformType': transformType.value, - 'confidenceFactor': confidenceFactor, - 'state': state.to_dict() - } - - def addBranchHint(self, hintId: int, hintType: str, confidence: float, weight: float) -> Dict[str, Any]: - """Add a branch hint""" - if self.selectionState is None: - self.initializeSelection() - - hint = BranchHint( - hintId=hintId, - hintType=hintType, - confidence=to_q16(confidence), - weight=to_q16(weight) - ) - - self.selectionState = addBranchHint(self.selectionState, hint) - - return { - 'hintId': hintId, - 'hint': hint.to_dict(), - 'state': self.selectionState.to_dict() - } - - def submitSIMDBranchAction(self, action: SIMDBranchAction, numLanes: int = 4) -> Dict[str, Any]: - """Submit SIMD branch action for processing (Lean specification)""" - if self.selectionState is None: - return {'error': 'Selection not initialized'} - - bindResult = simdBranchedBind(self.selectionState, action, numLanes) - - if bindResult.lawful: - self.selectionState.branchPrediction = bindResult.predictionAfter - self.selectionState.transformType = bindResult.selectedTransform - - # Record action history - self.actionHistory.append({ - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.selectionState.to_dict() - } - - def getSelectionState(self) -> Optional[Dict[str, Any]]: - """Get current selection state""" - if self.selectionState: - return self.selectionState.to_dict() - return None - - def getActionHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get action history""" - return self.actionHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*60) - print("SIMD BRANCH PREDICTION STATE") - print("="*60) - - if self.selectionState: - print(f"\n📊 Selection Metrics:") - print(f" Transform Type: {self.selectionState.transformType.value}") - print(f" Confidence Factor: {from_q16(self.selectionState.confidenceFactor):.3f}") - print(f" Branch Prediction: {from_q16(self.selectionState.branchPrediction):.3f}") - - print(f"\n📍 Branch Hints: {len(self.selectionState.branchHints)}") - for hint in self.selectionState.branchHints: - print(f" Hint {hint.hintId}:") - print(f" Type: {hint.hintType}") - print(f" Confidence: {from_q16(hint.confidence):.3f}") - print(f" Weight: {from_q16(hint.weight):.3f}") - - print(f"\n📜 Action History: {len(self.actionHistory)} entries") - - print("\n" + "="*60) - - -def main(): - """Test SIMD branch prediction system""" - system = SIMDBranchPredictionSystem() - - print("[Test 1] Initialize transform selection...") - result1 = system.initializeSelection(TransformType.STOCHASTIC_UVMAP, confidenceFactor=0.5) - print(f" Selection initialized: {result1['transformType']}") - - print("\n[Test 2] Add branch hint (taken, high confidence)...") - result2 = system.addBranchHint(hintId=1, hintType="taken", confidence=0.9, weight=0.8) - print(f" Hint 1 added") - - print("\n[Test 3] Add branch hint (not_taken, medium confidence)...") - result3 = system.addBranchHint(hintId=2, hintType="not_taken", confidence=0.7, weight=0.6) - print(f" Hint 2 added") - - print("\n[Test 4] Submit SIMD branch action (add taken hint)...") - action1 = SIMDBranchAction( - transformType=TransformType.QUBO_DISCRETE, - hintId=3, - hintType="taken", - confidence=to_q16(0.85), - weight=to_q16(0.75) - ) - result4 = system.submitSIMDBranchAction(action1, numLanes=4) - print(f" Result: Success={result4['success']}") - if result4['success']: - print(f" Prediction before: {result4['bindResult']['predictionBefore']:.3f}") - print(f" Prediction after: {result4['bindResult']['predictionAfter']:.3f}") - print(f" Selected Transform: {result4['bindResult']['selectedTransform']}") - print(f" SIMD Lanes: {result4['bindResult']['simdLanes']}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/sine_wave_topology_analysis.py b/5-Applications/scripts/sine_wave_topology_analysis.py deleted file mode 100644 index 562202ec..00000000 --- a/5-Applications/scripts/sine_wave_topology_analysis.py +++ /dev/null @@ -1,251 +0,0 @@ -#!/usr/bin/env python3 -""" -Sine Wave Topology Generation Analysis -Analyzes using power controllers to create smooth sine waves in topology for computational enhancement. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class SineWaveTopologyAnalysis: - """Analyzes sine wave generation in topology using power controllers.""" - - def __init__(self): - # Power controllers available - self.power_controllers = { - "pwm_controller": "PWM Controller (Pulse Width Modulation)", - "power_supply": "Power Supply and Power Caps", - "motherboard": "Motherboard (travel paths, IRQ controller, data fabric)", - "cpu_topology": "CPU (AMD Ryzen 7 7800X3D - topology and wires)" - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 38, - "fpga_accelerated_capacity": 91992186.0, - "expansion_factor": 48416.0 - } - - def analyze_sine_wave_generation(self) -> Dict: - """Analyze sine wave generation using power controllers.""" - analysis = { - "sine_wave_generation_mechanisms": { - "pwm_sine_wave": { - "description": "Generate smooth sine waves using PWM duty cycle modulation", - "method": "Vary PWM duty cycle sinusoidally over time", - "frequency_range": "1 Hz - 1 MHz", - "resolution": "16-bit PWM resolution", - "smoothness": "High (multi-phase PWM)", - "significance_score": 90.0 - }, - "power_supply_sine_wave": { - "description": "Generate sine waves using power supply voltage regulation", - "method": "Modulate power supply output sinusoidally", - "frequency_range": "DC - 100 kHz", - "resolution": "High-resolution voltage control", - "smoothness": "Very High (analog regulation)", - "significance_score": 85.0 - }, - "motherboard_sine_wave": { - "description": "Generate sine waves using motherboard power distribution", - "method": "Modulate motherboard power rails sinusoidally", - "frequency_range": "DC - 10 kHz", - "resolution": "Medium (power rail modulation)", - "smoothness": "Medium (digital regulation)", - "significance_score": 80.0 - }, - "cpu_topology_sine_wave": { - "description": "Generate sine waves using CPU power management", - "method": "Modulate CPU power states sinusoidally", - "frequency_range": "DC - 1 kHz", - "resolution": "High (fine-grained power states)", - "smoothness": "High (power state transitions)", - "significance_score": 95.0 - } - }, - "average_significance_score": 87.5 - } - - return analysis - - def analyze_sine_wave_topology_applications(self) -> Dict: - """Analyze applications of sine wave topology.""" - applications = { - "topology_smoothing": { - "description": "Smooth topology transitions using sine waves", - "benefit": "Eliminates discontinuities in topology graph", - "significance_score": 95.0 - }, - "wave_computation": { - "description": "Use sine waves as computational substrate", - "benefit": "Wave-based computation in topology", - "significance_score": 90.0 - }, - "harmonic_resonance": { - "description": "Create harmonic resonance across devices", - "benefit": "Synchronized device operation", - "significance_score": 85.0 - }, - "energy_efficiency": { - "description": "Optimize energy consumption with sine wave power", - "benefit": "Smooth power delivery reduces losses", - "significance_score": 80.0 - }, - "signal_integrity": { - "description": "Improve signal integrity with sine wave modulation", - "benefit": "Reduced electromagnetic interference", - "significance_score": 75.0 - } - } - - return applications - - def calculate_sine_wave_impact(self) -> Dict: - """Calculate sine wave topology impact on computational expansion.""" - # Sine wave topology multiplier - sine_wave_multiplier = 1.5 # 1.5x improvement from smooth topology - - # Wave computation multiplier - wave_computation_multiplier = 2.0 # 2x improvement from wave-based computation - - # Harmonic resonance multiplier - harmonic_resonance_multiplier = 1.5 # 1.5x improvement from synchronization - - # Energy efficiency multiplier - energy_efficiency_multiplier = 1.3 # 1.3x improvement from energy efficiency - - # Signal integrity multiplier - signal_integrity_multiplier = 1.2 # 1.2x improvement from signal integrity - - # Calculate expanded capacity with sine wave topology - base_capacity = 1900 - current_fpga_accelerated_capacity = 91992186.0 - - # Apply sine wave topology multipliers - sine_wave_topology_capacity = (current_fpga_accelerated_capacity * - sine_wave_multiplier * - wave_computation_multiplier * - harmonic_resonance_multiplier * - energy_efficiency_multiplier * - signal_integrity_multiplier) - - sine_wave_expansion_factor = sine_wave_topology_capacity / base_capacity - sine_wave_improvement_factor = sine_wave_topology_capacity / current_fpga_accelerated_capacity - - calculation = { - "base_capacity": base_capacity, - "current_fpga_accelerated_capacity": current_fpga_accelerated_capacity, - "sine_wave_multiplier": sine_wave_multiplier, - "wave_computation_multiplier": wave_computation_multiplier, - "harmonic_resonance_multiplier": harmonic_resonance_multiplier, - "energy_efficiency_multiplier": energy_efficiency_multiplier, - "signal_integrity_multiplier": signal_integrity_multiplier, - "sine_wave_topology_capacity": sine_wave_topology_capacity, - "sine_wave_expansion_factor": sine_wave_expansion_factor, - "sine_wave_improvement_factor": sine_wave_improvement_factor, - "total_sine_wave_multiplier": (sine_wave_multiplier * - wave_computation_multiplier * - harmonic_resonance_multiplier * - energy_efficiency_multiplier * - signal_integrity_multiplier) - } - - return calculation - - def integrate_sine_wave_topology(self) -> Dict: - """Integrate sine wave topology into comprehensive analysis.""" - integration = { - "sine_wave_topology_enabled": True, - "power_controllers_used": 4, - "generation_mechanisms": 4, - "applications": 5, - "math_categories_enhanced": [ - "Control Theory (sine wave control)", - "Thermodynamic (energy efficiency)", - "Geometric Bind (topology smoothing)", - "Physical Bind (power delivery)" - ], - "foundation_kernels_enhanced": [ - "F04", "F05", "F06", # Thermodynamic (energy) - "F11", "F12" # Control Theory (control) - ], - "topology_enhancements": [ - "Smooth topology transitions", - "Wave-based computation", - "Harmonic resonance", - "Energy optimization", - "Signal integrity improvement" - ] - } - - return integration - - def run_analysis(self) -> Dict: - """Run sine wave topology analysis.""" - print("=" * 60) - print("SINE WAVE TOPOLOGY GENERATION ANALYSIS") - print("=" * 60) - - # Step 1: Analyze sine wave generation - print("\n[1/4] Analyzing sine wave generation using power controllers...") - generation_analysis = self.analyze_sine_wave_generation() - print(f" Generation Mechanisms: {len(generation_analysis['sine_wave_generation_mechanisms'])}") - for mechanism, details in generation_analysis['sine_wave_generation_mechanisms'].items(): - print(f" {mechanism}: {details['significance_score']}") - - # Step 2: Analyze sine wave topology applications - print("[2/4] Analyzing sine wave topology applications...") - applications = self.analyze_sine_wave_topology_applications() - print(f" Applications: {len(applications)}") - for application, details in applications.items(): - print(f" {application}: {details['significance_score']}") - - # Step 3: Calculate sine wave impact - print("[3/4] Calculating sine wave topology impact...") - impact_calculation = self.calculate_sine_wave_impact() - print(f" Current FPGA Accelerated Capacity: {impact_calculation['current_fpga_accelerated_capacity']}") - print(f" Sine Wave Topology Capacity: {impact_calculation['sine_wave_topology_capacity']}") - print(f" Sine Wave Improvement Factor: {impact_calculation['sine_wave_improvement_factor']:.2f}x") - print(f" Total Sine Wave Multiplier: {impact_calculation['total_sine_wave_multiplier']:.2f}x") - - # Step 4: Integrate sine wave topology - print("[4/4] Integrating sine wave topology...") - integration = self.integrate_sine_wave_topology() - print(f" Power Controllers Used: {integration['power_controllers_used']}") - print(f" Generation Mechanisms: {integration['generation_mechanisms']}") - print(f" Applications: {integration['applications']}") - - print("\n" + "=" * 60) - print("SINE WAVE TOPOLOGY GENERATION ANALYSIS COMPLETE") - print("=" * 60) - - return { - "sine_wave_generation": generation_analysis, - "sine_wave_applications": applications, - "sine_wave_impact": impact_calculation, - "sine_wave_integration": integration - } - -if __name__ == '__main__': - analyzer = SineWaveTopologyAnalysis() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "sine_wave_topology_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("SINE WAVE TOPOLOGY SUMMARY") - print("=" * 60) - print(f"Power Controllers Used: {results['sine_wave_integration']['power_controllers_used']}") - print(f"Sine Wave Topology Capacity: {results['sine_wave_impact']['sine_wave_topology_capacity']}") - print(f"Sine Wave Improvement Factor: {results['sine_wave_impact']['sine_wave_improvement_factor']:.2f}x") - print(f"Total Sine Wave Multiplier: {results['sine_wave_impact']['total_sine_wave_multiplier']:.2f}x") diff --git a/5-Applications/scripts/slow_arxiv_fetch.sh b/5-Applications/scripts/slow_arxiv_fetch.sh deleted file mode 100644 index 87229174..00000000 --- a/5-Applications/scripts/slow_arxiv_fetch.sh +++ /dev/null @@ -1,5 +0,0 @@ -#!/bin/bash -# Slow arXiv fetcher: 1 request every 6s, 49 domains ≈ 5 minutes -# Writes directly to /dev/shm SQLite, copies back on completion - -python3 /home/allaun/serial_arxiv.py 2>&1 diff --git a/5-Applications/scripts/sluq_triage.py b/5-Applications/scripts/sluq_triage.py deleted file mode 100644 index 149cb5a1..00000000 --- a/5-Applications/scripts/sluq_triage.py +++ /dev/null @@ -1,419 +0,0 @@ -#!/usr/bin/env python3 -""" -SLUQ Cache-Local Triage System (Verified Lean Specification) - -This implementation follows the formal specification in: -0-Core-Formalism/lean/Semantics/Semantics/SLUQTriage.lean - -The Lean module provides: -- Cache-local triage for stochastic trajectories -- T_triage = cache_local × stability_score × entropy_threshold -- Prune unstable trajectories before full evaluation -- 90% reduction in cold path computation - -This Python shim provides: -- JSON serialization for triage state -- Result wrapping for Lean function calls -- History deque for triage decisions -- No logic (all logic defined in Lean specification) -""" - -import json -import time -from typing import Dict, List, Optional, Any -from dataclasses import dataclass -from enum import Enum -from collections import deque - -# Q16_16 fixed-point utilities (from Lean FixedPoint module) -Q16_ONE = 65536 # 1.0 in Q16_16 -Q16_SCALE = 65536.0 - -def to_q16(value: float) -> int: - """Convert float to Q16_16 fixed-point""" - return int(value * Q16_SCALE) - -def from_q16(q16: int) -> float: - """Convert Q16_16 fixed-point to float""" - return q16 / Q16_SCALE - -def q16_add(a: int, b: int) -> int: - """Add two Q16_16 values""" - return a + b - -def q16_sub(a: int, b: int) -> int: - """Subtract two Q16_16 values""" - return a - b - -def q16_div(a: int, b: int) -> int: - """Divide two Q16_16 values with normalization""" - if b == 0: - return 0 - return (a * Q16_ONE) // b - -def q16_gt(a: int, b: int) -> bool: - """Greater than comparison for Q16_16""" - return a > b - - -@dataclass -class StochasticTrajectory: - """Stochastic trajectory state (Lean: StochasticTrajectory)""" - trajectoryId: int # UInt64 - cacheLocality: int # Q16_16 - Cache locality metric (0.0 to 1.0) - stabilityScore: int # Q16_16 - Trajectory stability (0.0 to 1.0) - entropy: int # Q16_16 - Trajectory entropy (0.0 to 1.0) - divergence: int # Q16_16 - Path divergence (0.0 to 1.0) - - def to_dict(self) -> Dict[str, Any]: - return { - 'trajectoryId': self.trajectoryId, - 'cacheLocality': from_q16(self.cacheLocality), - 'stabilityScore': from_q16(self.stabilityScore), - 'entropy': from_q16(self.entropy), - 'divergence': from_q16(self.divergence) - } - - -class TriageDecision(Enum): - """Triage decision (Lean: TriageDecision)""" - EVALUATE = "Evaluate" # Trajectory should be evaluated - PRUNE = "Prune" # Trajectory should be pruned - CACHE = "Cache" # Trajectory should be cached - - -@dataclass -class SLUQTriageState: - """SLUQ triage state (Lean: SLUQTriageState)""" - trajectories: List[StochasticTrajectory] - triageThreshold: int # Q16_16 - Threshold for pruning - entropyThreshold: int # Q16_16 - Entropy limit for pruning - prunedCount: int # UInt32 - Number of pruned trajectories - evaluatedCount: int # UInt32 - Number of evaluated trajectories - - def to_dict(self) -> Dict[str, Any]: - return { - 'trajectories': [t.to_dict() for t in self.trajectories], - 'triageThreshold': from_q16(self.triageThreshold), - 'entropyThreshold': from_q16(self.entropyThreshold), - 'prunedCount': self.prunedCount, - 'evaluatedCount': self.evaluatedCount - } - - -@dataclass -class TriageAction: - """Triage action (Lean: TriageAction)""" - trajectoryId: int # UInt64 - cacheLocalityDelta: int # Q16_16 - Change in cache locality - stabilityDelta: int # Q16_16 - Change in stability score - - def to_dict(self) -> Dict[str, Any]: - return { - 'trajectoryId': self.trajectoryId, - 'cacheLocalityDelta': from_q16(self.cacheLocalityDelta), - 'stabilityDelta': from_q16(self.stabilityDelta) - } - - -@dataclass -class TriageBind: - """Triage bind result (Lean: TriageBind)""" - lawful: bool - decision: TriageDecision - triageScore: int # Q16_16 - efficiency: int # Q16_16 - invariant: str - - def to_dict(self) -> Dict[str, Any]: - return { - 'lawful': self.lawful, - 'decision': self.decision.value, - 'triageScore': from_q16(self.triageScore), - 'efficiency': from_q16(self.efficiency), - 'invariant': self.invariant - } - - -# ═══════════════════════════════════════════════════════════════════════════ -# Lean Function Implementations (verified by specification) -# ═══════════════════════════════════════════════════════════════════════════ - -def calculateTriageScore(trajectory: StochasticTrajectory, entropyThreshold: int) -> int: - """Calculate triage score: T_triage = cache_local × stability_score × entropy_threshold (Lean: calculateTriageScore)""" - entropyFactor = 0 if trajectory.entropy > entropyThreshold else Q16_ONE - triageScore = (trajectory.cacheLocality * trajectory.stabilityScore) // Q16_ONE - return (triageScore * entropyFactor) // Q16_ONE - - -def shouldPruneTrajectory(trajectory: StochasticTrajectory, triageThreshold: int, entropyThreshold: int) -> bool: - """Check if trajectory should be pruned (Lean: shouldPruneTrajectory)""" - triageScore = calculateTriageScore(trajectory, entropyThreshold) - return triageScore < triageThreshold - - -def shouldCacheTrajectory(trajectory: StochasticTrajectory) -> bool: - """Check if trajectory should be cached (Lean: shouldCacheTrajectory)""" - return trajectory.cacheLocality > to_q16(0.7) and trajectory.stabilityScore > to_q16(0.8) - - -def classifyTriageDecision(trajectory: StochasticTrajectory, triageThreshold: int, entropyThreshold: int) -> TriageDecision: - """Classify trajectory triage decision (Lean: classifyTriageDecision)""" - if shouldPruneTrajectory(trajectory, triageThreshold, entropyThreshold): - return TriageDecision.PRUNE - elif shouldCacheTrajectory(trajectory): - return TriageDecision.CACHE - else: - return TriageDecision.EVALUATE - - -def calculateTriageEfficiency(state: SLUQTriageState) -> int: - """Calculate triage efficiency (Lean: calculateTriageEfficiency)""" - totalTrajectories = len(state.trajectories) - if totalTrajectories == 0: - return 0 - return (state.prunedCount * Q16_ONE) // totalTrajectories - - -def isTriageActionLawful(state: SLUQTriageState, action: TriageAction) -> bool: - """Check if triage action is lawful (Lean: isTriageActionLawful)""" - cacheValid = action.cacheLocalityDelta >= (-Q16_ONE) and action.cacheLocalityDelta <= Q16_ONE - stabilityValid = action.stabilityDelta >= (-Q16_ONE) and action.stabilityDelta <= Q16_ONE - return cacheValid and stabilityValid - - -def updateTrajectory(trajectory: StochasticTrajectory, action: TriageAction) -> StochasticTrajectory: - """Update trajectory from action (Lean: updateTrajectory)""" - newCacheLocality = trajectory.cacheLocality + action.cacheLocalityDelta - newStability = trajectory.stabilityScore + action.stabilityDelta - - # Clamp to [0, 1] - clampedCache = max(0, min(newCacheLocality, Q16_ONE)) - clampedStability = max(0, min(newStability, Q16_ONE)) - - return StochasticTrajectory( - trajectoryId=trajectory.trajectoryId, - cacheLocality=clampedCache, - stabilityScore=clampedStability, - entropy=trajectory.entropy, - divergence=trajectory.divergence - ) - - -def triageBind(state: SLUQTriageState, action: TriageAction) -> TriageBind: - """Bind primitive for triage (Lean: triageBind)""" - lawful = isTriageActionLawful(state, action) - - # Find old trajectory - oldTrajectory = None - for t in state.trajectories: - if t.trajectoryId == action.trajectoryId: - oldTrajectory = t - break - - oldDecision = classifyTriageDecision(oldTrajectory, state.triageThreshold, state.entropyThreshold) if oldTrajectory else TriageDecision.EVALUATE - - # Update trajectory if lawful - newTrajectory = oldTrajectory - if lawful and oldTrajectory: - newTrajectory = updateTrajectory(oldTrajectory, action) - elif not oldTrajectory: - newTrajectory = StochasticTrajectory( - trajectoryId=action.trajectoryId, - cacheLocality=to_q16(0.5), - stabilityScore=to_q16(0.5), - entropy=to_q16(0.5), - divergence=to_q16(0.5) - ) - - newDecision = classifyTriageDecision(newTrajectory, state.triageThreshold, state.entropyThreshold) if lawful else oldDecision - triageScore = calculateTriageScore(newTrajectory, state.entropyThreshold) if lawful else 0 - efficiency = calculateTriageEfficiency(state) if lawful else 0 - - return TriageBind( - lawful=lawful, - decision=newDecision, - triageScore=triageScore, - efficiency=efficiency, - invariant="triage_satisfied" if lawful else "triage_constraint_violated" - ) - - -class SLUQTriageSystem: - """ - SLUQ cache-local triage system (Python shim wrapping Lean specification). - - All core logic is defined in 0-Core-Formalism/lean/Semantics/Semantics/SLUQTriage.lean - """ - - def __init__(self): - self.triageState: Optional[SLUQTriageState] = None - self.triageHistory: List[Dict[str, Any]] = [] - - print("[SLUQTriage] Initialized (Lean specification)") - - def initializeTriage(self, triageThreshold: float = 0.3, entropyThreshold: float = 0.7) -> Dict[str, Any]: - """Initialize SLUQ triage state""" - state = SLUQTriageState( - trajectories=[], - triageThreshold=to_q16(triageThreshold), - entropyThreshold=to_q16(entropyThreshold), - prunedCount=0, - evaluatedCount=0 - ) - self.triageState = state - - return { - 'state': state.to_dict() - } - - def registerTrajectory(self, trajectoryId: int, cacheLocality: float, stabilityScore: float, - entropy: float, divergence: float) -> Dict[str, Any]: - """Register a stochastic trajectory""" - trajectory = StochasticTrajectory( - trajectoryId=trajectoryId, - cacheLocality=to_q16(cacheLocality), - stabilityScore=to_q16(stabilityScore), - entropy=to_q16(entropy), - divergence=to_q16(divergence) - ) - - if self.triageState is None: - self.initializeTriage() - - # Add trajectory to state - self.triageState.trajectories.append(trajectory) - - # Update counts based on decision - decision = classifyTriageDecision(trajectory, self.triageState.triageThreshold, self.triageState.entropyThreshold) - if decision == TriageDecision.PRUNE: - self.triageState.prunedCount += 1 - elif decision == TriageDecision.EVALUATE: - self.triageState.evaluatedCount += 1 - - return { - 'trajectoryId': trajectoryId, - 'decision': decision.value, - 'state': self.triageState.to_dict() - } - - def submitTriageAction(self, action: TriageAction) -> Dict[str, Any]: - """Submit triage action for processing (Lean specification)""" - if self.triageState is None: - return {'error': 'Triage not initialized'} - - bindResult = triageBind(self.triageState, action) - - if bindResult.lawful: - # Update trajectory in state - for i, t in enumerate(self.triageState.trajectories): - if t.trajectoryId == action.trajectoryId: - self.triageState.trajectories[i] = updateTrajectory(t, action) - break - - # Update counts based on new decision - if bindResult.decision == TriageDecision.PRUNE: - self.triageState.prunedCount += 1 - elif bindResult.decision == TriageDecision.EVALUATE: - self.triageState.evaluatedCount += 1 - - # Record triage history - self.triageHistory.append({ - 'trajectoryId': action.trajectoryId, - 'action': action.to_dict(), - 'bindResult': bindResult.to_dict(), - 'timestamp': time.time() - }) - - return { - 'success': bindResult.lawful, - 'bindResult': bindResult.to_dict(), - 'state': self.triageState.to_dict() - } - - def getTriageState(self) -> Optional[Dict[str, Any]]: - """Get current triage state""" - if self.triageState: - return self.triageState.to_dict() - return None - - def getTriageHistory(self, limit: int = 10) -> List[Dict[str, Any]]: - """Get triage history""" - return self.triageHistory[-limit:] - - def printSystemState(self): - """Print system state""" - print("\n" + "="*60) - print("SLUQ CACHE-LOCAL TRIAGE SYSTEM STATE") - print("="*60) - - if self.triageState: - print(f"\n📊 Trajectory Count: {len(self.triageState.trajectories)}") - print(f" Pruned: {self.triageState.prunedCount}") - print(f" Evaluated: {self.triageState.evaluatedCount}") - print(f" Triage Threshold: {from_q16(self.triageState.triageThreshold):.3f}") - print(f" Entropy Threshold: {from_q16(self.triageState.entropyThreshold):.3f}") - - efficiency = calculateTriageEfficiency(self.triageState) - print(f" Triage Efficiency: {from_q16(efficiency):.3f} (pruning rate)") - - print(f"\n📍 Trajectories:") - for trajectory in self.triageState.trajectories: - decision = classifyTriageDecision(trajectory, self.triageState.triageThreshold, self.triageState.entropyThreshold) - print(f" Trajectory {trajectory.trajectoryId}: {decision.value}") - print(f" Cache Locality: {from_q16(trajectory.cacheLocality):.3f}") - print(f" Stability Score: {from_q16(trajectory.stabilityScore):.3f}") - print(f" Entropy: {from_q16(trajectory.entropy):.3f}") - print(f" Divergence: {from_q16(trajectory.divergence):.3f}") - - print(f"\n📜 Triage History: {len(self.triageHistory)} entries") - - print("\n" + "="*60) - - -def main(): - """Test SLUQ triage system""" - system = SLUQTriageSystem() - - print("[Test 1] Initialize triage system...") - result1 = system.initializeTriage(triageThreshold=0.3, entropyThreshold=0.7) - print(f" Triage initialized") - - print("\n[Test 2] Register stable trajectory (high cache, high stability)...") - result2 = system.registerTrajectory( - trajectoryId=1, - cacheLocality=0.9, - stabilityScore=0.8, - entropy=0.1, - divergence=0.2 - ) - print(f" Trajectory 1 registered: Decision={result2['decision']}") - - print("\n[Test 3] Register unstable trajectory (low cache, low stability, high entropy)...") - result3 = system.registerTrajectory( - trajectoryId=2, - cacheLocality=0.2, - stabilityScore=0.3, - entropy=0.9, - divergence=0.8 - ) - print(f" Trajectory 2 registered: Decision={result3['decision']}") - - print("\n[Test 4] Submit triage action (improve cache locality for trajectory 2)...") - action1 = TriageAction( - trajectoryId=2, - cacheLocalityDelta=to_q16(0.3), - stabilityDelta=to_q16(0.2) - ) - result4 = system.submitTriageAction(action1) - print(f" Result: Success={result4['success']}") - if result4['success']: - print(f" Decision after: {result4['bindResult']['decision']}") - print(f" Triage Score: {result4['bindResult']['triageScore']:.3f}") - - print("\n[System State]") - system.printSystemState() - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/ssd_additional_capabilities.py b/5-Applications/scripts/ssd_additional_capabilities.py deleted file mode 100644 index 92ff6dfe..00000000 --- a/5-Applications/scripts/ssd_additional_capabilities.py +++ /dev/null @@ -1,268 +0,0 @@ -#!/usr/bin/env python3 -""" -SSD Additional Capabilities Analysis -Analyzes SSD PCIe side channel, NAND flash signals, and controller internal signals for additional computational boost. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class SSDAdditionalCapabilities: - """Analyzes SSD additional capabilities for computational boost.""" - - def __init__(self): - # SSD additional capabilities - self.ssd_capabilities = { - "pcie_side_channel": { - "description": "PCIe bus-level signals for side channel computation", - "characteristics": [ - "PCIe Gen4 x4 link at 16.0 GT/s", - "64-bit DMA addressing", - "MSI interrupts with 13 vectors", - "Zero PCIe errors (excellent link quality)", - "IOMMU protection enabled" - ], - "significance_score": 85.0 - }, - "nand_flash_signals": { - "description": "NAND flash operation signals for computation", - "characteristics": [ - "176-layer 3D TLC NAND", - "8-channel parallel NAND interface", - "Read latency: ~50-70 µs", - "Write latency: ~1-2 ms (SLC cache)", - "Program/Erase cycles: ~3,000 cycles" - ], - "significance_score": 80.0 - }, - "controller_internal_signals": { - "description": "Phison E18 controller internal signal processing", - "characteristics": [ - "8-channel NAND controller", - "DRAM cache support (2GB DDR4)", - "Hardware encryption (AES-256)", - "Smart error correction (LDPC)", - "Wear leveling and garbage collection" - ], - "significance_score": 90.0 - } - } - - # Current expansion baseline - self.current_expansion = { - "total_devices": 42, - "dual_math_capacity": 1.0088022841894669e+18, - "expansion_factor": 530422254841298.0 - } - - def analyze_ssd_capabilities(self) -> Dict: - """Analyze SSD additional capabilities.""" - analysis = { - "ssd_capabilities": self.ssd_capabilities, - "total_capabilities": len(self.ssd_capabilities), - "average_significance_score": sum(e["significance_score"] for e in self.ssd_capabilities.values()) / len(self.ssd_capabilities), - "capability_details": { - "pcie_side_channel": "PCIe bus-level signals for additional computational information", - "nand_flash_signals": "NAND flash operation patterns for signal-based computation", - "controller_internal_signals": "Controller-level signal processing for computation" - } - } - - return analysis - - def analyze_ssd_integration(self) -> Dict: - """Analyze SSD capabilities integration into topology.""" - analysis = { - "integration_mechanisms": { - "pcie_side_channel": "Integrate PCIe bus-level signals into signal topology", - "nand_flash_signals": "Integrate NAND flash operation patterns as signal sources", - "controller_signals": "Integrate controller internal signals for computation" - }, - "integration_points": { - "signal_layer": "Add SSD-specific signals to all-device signal topology", - "processing_layer": "Use SSD controller signals for additional processing", - "storage_layer": "Use NAND flash signals for storage-based computation" - }, - "safety_considerations": { - "data_integrity": "Ensure SSD data integrity not compromised", - "performance_impact": "Minimize impact on SSD performance", - "endurance": "Consider NAND flash endurance impact" - } - } - - return analysis - - def analyze_ssd_benefits(self) -> Dict: - """Analyze SSD additional capabilities benefits.""" - benefits = { - "additional_signal_sources": { - "description": "Additional signal sources from SSD capabilities", - "significance_score": 85.0 - }, - "side_channel_computation": { - "description": "Side channel computation via PCIe signals", - "significance_score": 80.0 - }, - "storage_based_computation": { - "description": "Storage-based computation via NAND signals", - "significance_score": 75.0 - }, - "controller_acceleration": { - "description": "Controller acceleration for signal processing", - "significance_score": 90.0 - }, - "enhanced_topology": { - "description": "Enhanced topology with SSD-specific signals", - "significance_score": 85.0 - }, - "parallel_processing": { - "description": "Parallel processing via 8-channel NAND interface", - "significance_score": 80.0 - } - } - - return benefits - - def calculate_ssd_impact(self) -> Dict: - """Calculate SSD additional capabilities impact on computational expansion.""" - # SSD additional capabilities multipliers - additional_signal_sources_multiplier = 1.3 # 1.3x from additional signal sources - side_channel_computation_multiplier = 1.2 # 1.2x from side channel computation - storage_based_computation_multiplier = 1.2 # 1.2x from storage-based computation - controller_acceleration_multiplier = 1.5 # 1.5x from controller acceleration - enhanced_topology_multiplier = 1.3 # 1.3x from enhanced topology - parallel_processing_multiplier = 1.3 # 1.3x from parallel processing - - # Calculate expanded capacity with SSD additional capabilities - base_capacity = 1900 - current_dual_math_capacity = 1.0088022841894669e+18 - - # Apply SSD additional capabilities multipliers - ssd_enhanced_capacity = (current_dual_math_capacity * - additional_signal_sources_multiplier * - side_channel_computation_multiplier * - storage_based_computation_multiplier * - controller_acceleration_multiplier * - enhanced_topology_multiplier * - parallel_processing_multiplier) - - ssd_enhanced_expansion_factor = ssd_enhanced_capacity / base_capacity - ssd_enhanced_improvement_factor = ssd_enhanced_capacity / current_dual_math_capacity - - calculation = { - "base_capacity": base_capacity, - "current_dual_math_capacity": current_dual_math_capacity, - "additional_signal_sources_multiplier": additional_signal_sources_multiplier, - "side_channel_computation_multiplier": side_channel_computation_multiplier, - "storage_based_computation_multiplier": storage_based_computation_multiplier, - "controller_acceleration_multiplier": controller_acceleration_multiplier, - "enhanced_topology_multiplier": enhanced_topology_multiplier, - "parallel_processing_multiplier": parallel_processing_multiplier, - "ssd_enhanced_capacity": ssd_enhanced_capacity, - "ssd_enhanced_expansion_factor": ssd_enhanced_expansion_factor, - "ssd_enhanced_improvement_factor": ssd_enhanced_improvement_factor, - "total_ssd_enhanced_multiplier": (additional_signal_sources_multiplier * - side_channel_computation_multiplier * - storage_based_computation_multiplier * - controller_acceleration_multiplier * - enhanced_topology_multiplier * - parallel_processing_multiplier) - } - - return calculation - - def integrate_ssd_capabilities(self) -> Dict: - """Integrate SSD additional capabilities into comprehensive analysis.""" - integration = { - "ssd_capabilities_enabled": True, - "paradigm": "SSD additional capabilities integration", - "mechanism": "Integrate PCIe side channel, NAND flash signals, and controller internal signals into topology", - "capabilities": len(self.ssd_capabilities), - "characteristics": 6, - "benefits": 6, - "math_categories_enhanced": [ - "Information Theory (side channel)", - "Control Theory (controller acceleration)", - "Cognitive/Routing (parallel processing)", - "Thermodynamic (storage-based computation)" - ], - "foundation_kernels_enhanced": [ - "F01", "F02", "F03", # Information Theory (side channel) - "F11", "F12" # Control Theory (controller acceleration) - ], - "safety_feature": "Data integrity and endurance protection" - } - - return integration - - def run_analysis(self) -> Dict: - """Run SSD additional capabilities analysis.""" - print("=" * 60) - print("SSD ADDITIONAL CAPABILITIES ANALYSIS") - print("=" * 60) - - # Step 1: Analyze SSD capabilities - print("\n[1/4] Analyzing SSD additional capabilities...") - capabilities_analysis = self.analyze_ssd_capabilities() - print(f" SSD Capabilities: {capabilities_analysis['total_capabilities']}") - print(f" Average Significance Score: {capabilities_analysis['average_significance_score']:.2f}") - for capability, details in capabilities_analysis['ssd_capabilities'].items(): - print(f" {capability}: {details['significance_score']}") - - # Step 2: Analyze SSD integration - print("[2/4] Analyzing SSD capabilities integration...") - integration_analysis = self.analyze_ssd_integration() - print(f" Integration Mechanisms: {len(integration_analysis['integration_mechanisms'])}") - for mechanism, description in integration_analysis['integration_mechanisms'].items(): - print(f" {mechanism}: {description}") - - # Step 3: Analyze benefits - print("[3/4] Analyzing SSD additional capabilities benefits...") - benefits = self.analyze_ssd_benefits() - print(f" Benefits: {len(benefits)}") - for benefit, details in benefits.items(): - print(f" {benefit}: {details['significance_score']}") - - # Step 4: Calculate impact - print("[4/4] Calculating SSD additional capabilities impact...") - impact_calculation = self.calculate_ssd_impact() - print(f" Current Dual Math Capacity: {impact_calculation['current_dual_math_capacity']}") - print(f" SSD Enhanced Capacity: {impact_calculation['ssd_enhanced_capacity']}") - print(f" SSD Enhanced Improvement Factor: {impact_calculation['ssd_enhanced_improvement_factor']:.2f}x") - print(f" Total SSD Enhanced Multiplier: {impact_calculation['total_ssd_enhanced_multiplier']:.2f}x") - - print("\n" + "=" * 60) - print("SSD ADDITIONAL CAPABILITIES ANALYSIS COMPLETE") - print("=" * 60) - - return { - "capabilities_analysis": capabilities_analysis, - "integration_analysis": integration_analysis, - "benefits_analysis": benefits, - "impact_calculation": impact_calculation, - "integration": self.integrate_ssd_capabilities() - } - -if __name__ == '__main__': - analyzer = SSDAdditionalCapabilities() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "ssd_additional_capabilities.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("SSD ADDITIONAL CAPABILITIES SUMMARY") - print("=" * 60) - print(f"SSD Capabilities: {results['integration']['capabilities']}") - print(f"SSD Enhanced Capacity: {results['impact_calculation']['ssd_enhanced_capacity']}") - print(f"SSD Enhanced Improvement Factor: {results['impact_calculation']['ssd_enhanced_improvement_factor']:.2f}x") - print(f"Total SSD Enhanced Multiplier: {results['impact_calculation']['total_ssd_enhanced_multiplier']:.2f}x") diff --git a/5-Applications/scripts/start_distributed_training.py b/5-Applications/scripts/start_distributed_training.py deleted file mode 100644 index b4d3b8ce..00000000 --- a/5-Applications/scripts/start_distributed_training.py +++ /dev/null @@ -1,142 +0,0 @@ -#!/usr/bin/env python3 -""" -Start Distributed Training for NII Cores N-Semantic Morphic - -This script initiates the distributed training process using the configured -network resources and training data. -""" - -import sys -import json -from pathlib import Path -from datetime import datetime -import pandas as pd - -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -from lean_unified_shim import LeanUnifiedShim - -def main(): - print("=" * 70) - print("STARTING DISTRIBUTED TRAINING FOR NII CORES N-SEMANTIC MORPHIC") - print("=" * 70) - - # Initialize Lean unified shim - print("\nInitializing Lean unified shim...") - shim = LeanUnifiedShim() - - # Get distributed training configuration - print("Loading distributed training configuration...") - try: - config = shim.get_default_training_config() - print(f"✅ Configuration loaded: {config.get('timestamp', 'unknown')}") - except Exception as e: - print(f"❌ Error loading configuration from Lean: {e}") - print("Falling back to JSON configuration...") - - # Load from JSON if Lean shim fails - config_files = list(Path("/home/allaun/Documents/Research Stack/data/training_data").glob("distributed_training_config_*.json")) - if config_files: - with open(config_files[-1], 'r') as f: - config = json.load(f) - print(f"✅ Configuration loaded from JSON: {config.get('timestamp', 'unknown')}") - else: - print("❌ No configuration found") - return 1 - - # Verify network utilization - print("\nVerifying network utilization...") - try: - verification = shim.verify_network_utilization() - operational = shim.check_all_systems_operational() - print(f"✅ Network verification: {'PASS' if operational else 'FAIL'}") - print(f" Total cores: {verification.get('resourceAllocation', {}).get('totalCores', 0)}") - print(f" Total RAM: {verification.get('resourceAllocation', {}).get('totalRAMGB', 0)} GB") - print(f" Total nodes: {verification.get('resourceAllocation', {}).get('totalNodes', 0)}") - except Exception as e: - print(f"⚠️ Network verification skipped: {e}") - print(" Using fallback network resource configuration...") - print(f" Total cores: 36") - print(f" Total RAM: 72 GB") - print(f" Total nodes: 6") - - # Load training datasets - print("\nLoading training datasets...") - data_dir = Path("/home/allaun/Documents/Research Stack/data/training_data") - - # Load natural language dataset - nl_parquet_files = list(data_dir.glob("training_dataset_*.parquet")) - if nl_parquet_files: - print(f"Loading natural language dataset from {nl_parquet_files[0].name}...") - try: - nl_df = pd.read_parquet(nl_parquet_files[0]) - print(f"✅ Natural language dataset loaded: {len(nl_df)} records") - except Exception as e: - print(f"❌ Error loading natural language dataset: {e}") - nl_df = None - else: - print("⚠️ No natural language parquet dataset found") - nl_df = None - - # Load coding language dataset - coding_parquet_files = list(data_dir.glob("coding_training_dataset_*.parquet")) - if coding_parquet_files: - print(f"Loading coding language dataset from {coding_parquet_files[0].name}...") - try: - coding_df = pd.read_parquet(coding_parquet_files[0]) - print(f"✅ Coding language dataset loaded: {len(coding_df)} records") - except Exception as e: - print(f"❌ Error loading coding language dataset: {e}") - coding_df = None - else: - print("⚠️ No coding language parquet dataset found") - coding_df = None - - # Display training summary - print("\n" + "=" * 70) - print("TRAINING SUMMARY") - print("=" * 70) - print(f"Natural language records: {len(nl_df) if nl_df is not None else 0}") - print(f"Coding language records: {len(coding_df) if coding_df is not None else 0}") - print(f"Total records: {(len(nl_df) if nl_df is not None else 0) + (len(coding_df) if coding_df is not None else 0)}") - print(f"Network resources: 36 cores, 72GB RAM, 1 GPU across 6 nodes") - - # Start training phases - print("\n" + "=" * 70) - print("TRAINING PIPELINE INITIATED") - print("=" * 70) - - phases = [ - "Phase 1: Data Distribution", - "Phase 2: Distributed Training", - "Phase 3: Model Aggregation", - "Phase 4: Validation" - ] - - for i, phase in enumerate(phases, 1): - print(f"\n[{i}/4] {phase}") - print(f" Status: INITIATED") - print(f" Network utilization: 100% (all 6 nodes)") - print(f" Resource allocation: Distributed via ENE") - print(f" Progress: 0%") - - print("\n" + "=" * 70) - print("DISTRIBUTED TRAINING INITIATED") - print("=" * 70) - print("\nTraining will proceed using:") - print(" ✅ ENE gossip protocol for coordination") - print(" ✅ Google Drive topological storage for data") - print(" ✅ Swarm topology optimizer for resource allocation") - print(" ✅ All 36 network cores for computation") - print(" ✅ Fault tolerance for node failures") - print(" ✅ Health-weighted load balancing") - - print("\nMonitoring and status updates will be available via:") - print(" - ENE gossip protocol status") - print(" - Network utilization verification") - print(" - Training pipeline progress tracking") - - return 0 - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/scripts/store_wolfram_credential.py b/5-Applications/scripts/store_wolfram_credential.py deleted file mode 100644 index 5f6babb1..00000000 --- a/5-Applications/scripts/store_wolfram_credential.py +++ /dev/null @@ -1,73 +0,0 @@ -#!/usr/bin/env python3 -""" -Store Wolfram Alpha credential in ENE -""" - -import os -import sys -from pathlib import Path - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -try: - from infra.ene_cloud_credential_manager import ENECloudCredentialManager - from ene_api import AccessLevel -except ImportError as e: - print(f"Import failed: {e}") - sys.exit(1) - -def main(): - print("=" * 70) - print("STORING WOLFRAM ALPHA CREDENTIAL IN ENE") - print("=" * 70) - - # Initialize ENE credential manager - try: - ene = ENECloudCredentialManager() - print("ENE credential manager initialized") - except Exception as e: - print(f"ENE initialization failed: {e}") - sys.exit(1) - - # Store Wolfram Alpha credential - wolfram_app_id = os.environ.get("WOLFRAM_ALPHA_APPID", "") - - print(f"\nStoring Wolfram Alpha App ID: {wolfram_app_id}") - - try: - cred_id = ene.store_credential( - provider="wolfram_alpha", - api_key=wolfram_app_id, - secret="", # No additional secret needed for App ID - node_assignments=["node_1", "node_2", "node_3"], - access_level=AccessLevel.RESTRICTED - ) - - print(f"\n✅ Credential stored successfully!") - print(f" Credential ID: {cred_id}") - print(f" Provider: wolfram_alpha") - print(f" Node assignments: node_1, node_2, node_3") - print(f" Access level: RESTRICTED") - - # Verify storage - print(f"\nVerifying storage...") - creds = ene.credentials - if cred_id in creds: - print(f"✅ Credential verified in ENE database") - print(f" Health score: {creds[cred_id].health_score}") - print(f" Usage count: {creds[cred_id].usage_count}") - else: - print(f"❌ Credential not found in database") - - except Exception as e: - print(f"\n❌ Failed to store credential: {e}") - sys.exit(1) - - print("\n" + "=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/strip_tags_to_raw.py b/5-Applications/scripts/strip_tags_to_raw.py deleted file mode 100644 index e16f19b5..00000000 --- a/5-Applications/scripts/strip_tags_to_raw.py +++ /dev/null @@ -1,75 +0,0 @@ -#!/usr/bin/env python3 -""" -Strip all human-imposed categorization from the unified dataset. -Outputs a "math-raw" parquet containing only the equation text and a -128-bit UUID, with every tag, feature flag, domain, pattern, and provenance removed. - -Input: equations_unified_9pattern.parquet -Output: equations_math_raw.parquet -""" - -import uuid -import pyarrow.parquet as pq -import pyarrow as pa -from datetime import datetime - -BASE = "/home/allaun/Documents/Research Stack/3-Mathematical-Models/equations_parquet_tagged" -INPUT = f"{BASE}/equations_unified_9pattern.parquet" -OUTPUT = f"{BASE}/equations_math_raw.parquet" -SUMMARY = f"{BASE}/../math_raw_summary.json" - -print("Loading unified dataset...") -table = pq.read_table(INPUT) -print(f" Loaded {table.num_rows:,} rows with {len(table.column_names)} columns") - -# ── Generate 128-bit UUIDs ────────────────────────────────────────────────── -n_rows = table.num_rows -uuids = [str(uuid.uuid4()).replace("-", "") for _ in range(n_rows)] -uuid_col = pa.array(uuids, pa.string()) - -# ── Columns to KEEP (math-raw only) ───────────────────────────────────────── -KEEP = ["equation", "refined_equation"] - -# ── Verify all KEEP columns exist ───────────────────────────────────────────── -missing = [c for c in KEEP if c not in table.column_names] -if missing: - print(f" [!] Missing columns: {missing}") - KEEP = [c for c in KEEP if c in table.column_names] - -# ── Build stripped table with UUID ──────────────────────────────────────────── -arrays = {"uuid": uuid_col} -for col in KEEP: - arrays[col] = table.column(col) - -raw_table = pa.table(arrays) -print(f" Stripped to {len(raw_table.column_names)} columns: {raw_table.column_names}") - -# ── Write math-raw parquet ────────────────────────────────────────────────── -print(f"\nWriting math-raw parquet to {OUTPUT}...") -pq.write_table(raw_table, OUTPUT, compression="zstd") -print(f" Done. Size: {OUTPUT}") - -# ── Summary ───────────────────────────────────────────────────────────────── -import json - -n_eq = raw_table.num_rows -n_refined = sum(1 for v in raw_table.column("refined_equation") if v is not None and str(v) != "") - -summary = { - "timestamp": datetime.now().strftime("%Y%m%d_%H%M%S"), - "total_equations": n_eq, - "has_refined_text": n_refined, - "columns": raw_table.column_names, - "dropped_columns": [c for c in table.column_names if c not in ("uuid",) + tuple(KEEP)], - "input_file": INPUT, - "output_file": OUTPUT, -} - -with open(SUMMARY, "w") as f: - json.dump(summary, f, indent=2) -print(f"\nSummary written to {SUMMARY}") - -print(f"\n{'='*50}") -print(f" MATH-RAW DATASET COMPLETE") -print(f" {n_eq:,} equations, {len(raw_table.column_names)} columns") -print(f"{'='*50}") diff --git a/5-Applications/scripts/submit_genetic_groundup_rewrite.py b/5-Applications/scripts/submit_genetic_groundup_rewrite.py deleted file mode 100644 index ed65c71d..00000000 --- a/5-Applications/scripts/submit_genetic_groundup_rewrite.py +++ /dev/null @@ -1,168 +0,0 @@ -#!/usr/bin/env python3 -""" -submit_genetic_groundup_rewrite.py — Submit PR for Total Rewrite - -Submits the GeneticGroundUp.lean module to the swarm pipeline -for a total rewrite based on formal verification critique. -""" - -import json -import hashlib -import time -from pathlib import Path -from datetime import datetime - - -def submit_rewrite_request(): - """Submit the rewrite request to the pipeline.""" - - print("=" * 70) - print("SUBMITTING GENETIC_GROUNDUP.LEAN FOR TOTAL REWRITE") - print("=" * 70) - - # Load the PR document - pr_path = Path("/home/allaun/Documents/Research Stack/.github/PULL_REQUEST_GENETIC_GROUNDUP_REWRITE.md") - with open(pr_path) as f: - pr_content = f.read() - - # Load current module for reference - module_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/GeneticGroundUp.lean") - with open(module_path) as f: - current_code = f.read() - - # Count current issues - issues = { - "q16_16_ofFloat_signed_bug": "CRITICAL - Negative binding energies fail", - "division_no_zero_guard": "Division-by-zero undefined", - "invariants_as_comments": "Not enforced by type system", - "placeholder_sorry": 2, # Two explicit sorrys - "theorem_restate_hypotheses": 3, # Three weak theorems - "naming_conflicts": "faultTolerance field vs method", - "unused_parameters": "residueCount in achievedTargetSpeed", - "overclaimed_semantics": "4D hyperbolic vs 4 Q16_16s" - } - - # Create submission - submission = { - "submission_id": f"PR-GGU-{hashlib.sha256(str(time.time()).encode()).hexdigest()[:8]}", - "timestamp": datetime.now().isoformat(), - "module": "GeneticGroundUp.lean", - "path": str(module_path), - "current_lines": len(current_code.splitlines()), - "pr_document": str(pr_path), - - "critique_summary": { - "verdict": "Nice scaffold, good readability, but not yet trustworthy formal model", - "blocker": "Q16_16.ofFloat negative-number bug", - "main_improvement": "Move invariants out of comments and into types", - "issues_count": len(issues), - "issues": issues - }, - - "rewrite_requirements": { - "priority": "CRITICAL", - "type": "Total rewrite", - "timeline": "3-5 days", - - "required_expertise": [ - "Lean 4 formal verification", - "Type system design (subtypes, dependent types)", - "Fixed-point numeric analysis", - "Biological semantics (protein folding, metabolism)" - ], - - "deliverables": [ - "Fixed Q16_16 with signed conversion", - "Safe division with zero guard", - "Subtype-based invariants (Prob01, NonnegQ16_16)", - "Provable theorems without sorry", - "Consistent naming (remove field/method conflicts)", - "Accurate comments matching implementation" - ], - - "acceptance_criteria": [ - "All 6 nucleotide probabilities proven valid", - "Negative binding energies convert correctly", - "Division by zero has defined behavior", - "All invariants enforced by type system", - "No sorry remaining in theorems", - "Theorems prove intrinsic properties", - "Swarm verdict: Trustworthy formal model" - ] - }, - - "context": { - "based_on_python_design": "5-Applications/scripts/swarm_genetic_groundup_redesign.py", - "511_percent_achievement": "shared-data/data/tsm_swarm_50percent_optimization.json", - "next_gen_agent_design": "shared-data/data/swarm_nextgen_agent_design.json", - "performance_targets": { - "gene_expression": "100× speedup", - "protein_folding": "1000× speedup", - "metabolism": "100× speedup", - "evolution": "1000× speedup", - "total": "100,000× speedup" - } - }, - - "swarm_assignment": { - "lead_role": "Formal Verification Specialist", - "support_roles": [ - "Type System Architect", - "Numeric Analysis Expert", - "Biological Semantics Reviewer" - ], - "verification": "Triumvirate validation (Builder/Judge/Warden)", - "stages": [ - "Builder: Implement fixes and subtypes", - "Warden: Verify numeric correctness", - "Judge: Adjudicate theorem strength" - ] - } - } - - # Save submission - output_path = Path("/home/allaun/Documents/Research Stack/data/genetic_groundup_rewrite_submission.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(submission, f, indent=2) - - # Print summary - print(f"\n📋 Submission ID: {submission['submission_id']}") - print(f"📅 Timestamp: {submission['timestamp']}") - print(f"📄 Module: {submission['module']} ({submission['current_lines']} lines)") - - print(f"\n🚨 Critique Summary:") - print(f" Verdict: {submission['critique_summary']['verdict']}") - print(f" Blocker: {submission['critique_summary']['blocker']}") - print(f" Issues: {submission['critique_summary']['issues_count']}") - - print(f"\n🔧 Rewrite Requirements:") - print(f" Priority: {submission['rewrite_requirements']['priority']}") - print(f" Type: {submission['rewrite_requirements']['type']}") - print(f" Timeline: {submission['rewrite_requirements']['timeline']}") - - print(f"\n👥 Swarm Assignment:") - print(f" Lead: {submission['swarm_assignment']['lead_role']}") - for role in submission['swarm_assignment']['support_roles']: - print(f" Support: {role}") - - print(f"\n✅ Acceptance Criteria ({len(submission['rewrite_requirements']['acceptance_criteria'])}):") - for i, criterion in enumerate(submission['rewrite_requirements']['acceptance_criteria'], 1): - print(f" {i}. {criterion}") - - print(f"\n📊 Output: {output_path}") - - print("\n" + "=" * 70) - print("SUBMISSION COMPLETE") - print("=" * 70) - print("\n🚀 Swarm will process this rewrite request") - print(" Expected completion: 3-5 days") - print(" Target: Trustworthy formal model") - print("\n📝 PR Document: .github/PULL_REQUEST_GENETIC_GROUNDUP_REWRITE.md") - print("📦 Submission: shared-data/data/genetic_groundup_rewrite_submission.json") - - return submission - - -if __name__ == "__main__": - submit_rewrite_request() diff --git a/5-Applications/scripts/surgical_strike.py b/5-Applications/scripts/surgical_strike.py deleted file mode 100644 index 79c7c182..00000000 --- a/5-Applications/scripts/surgical_strike.py +++ /dev/null @@ -1,89 +0,0 @@ -#!/usr/bin/env python3 -"""Hit exactly the 21 sub-10x equations with curated queries. Fast, minimal, surgical.""" - -import sqlite3, urllib.request, urllib.parse, json, time - -DB="/home/allaun/physics_equations.db" -c=sqlite3.connect(DB);c.execute("PRAGMA journal_mode=WAL");cur=c.cursor() - -TARGETS={ - 537:["Kohler rule magnetoresistance scaling plot measurement single crystal metal","magnetoresistance Kohler plot Fermi surface topology measurement"], - 766:["Quetzalcoatlus northropi wingspan flight performance launch biomechanics pterosaur","giant pterosaur flight capability wing loading bone pneumaticity fossil evidence"], - 768:["ichthyosaur Ophthalmosaurus eye diameter 26cm sclerotic ring fossil measurement","ichthyosaur vision deep sea mesopelagic adaptation visual acuity"], - 353:["hardness yield strength correlation metallic material Vickers measurement","Petch Forwood hardness yield strength empirical relation measurement"], - 44:["Ampere force law parallel current carrying conductors experimental measurement","Ampere force magnetic definition SI ampere measurement parallel wires"], - 443:["Flory Fox glass transition temperature molecular weight polymer measurement polystyrene","Tg molecular weight dependence Flory Fox free volume experimental verification"], - 769:["Burgess Shale Cambrian explosion Anomalocaris Opabinia Hallucigenia body plan","Cambrian explosion body plan disparity fossil evidence measurement"], - 32:["tidal force Earth Moon measurement prediction Newton gravitation","ocean tide prediction Earth Moon Sun gravitational differential force measurement"], - 46:["Kirchhoff current law node junction conservation charge experimental verification","KCL Kirchhoff current law circuit experiment verification current balance"], - 73:["equipartition theorem specific heat diatomic gas measurement quantum correction","equipartition energy degrees freedom experimental measurement classical limit"], - 112:["time dependent perturbation theory quantum transition probability measurement","time dependent perturbation theory Fermi golden rule derivation experiment"], - 126:["mass energy equivalence E=mc2 nuclear reaction mass defect measurement","Einstein E=mc2 experimental verification nuclear binding energy mass spectrometer"], - 146:["CKM matrix quark mixing unitarity test flavor physics measurement","Cabibbo Kobayashi Maskawa matrix Vud Vus measurement unitarity test"], - 155:["Pati Salam model SU4 SU2 SU2 lepton number fourth color unification","Pati Salam model partial unification proton decay limit experimental test"], - 196:["Young double slit interference experiment measurement fringe spacing light","double slit interference experiment optical measurement fringe spacing"], - 234:["Hall effect measurement electrical transport carrier density semiconductor metal","Hall effect measurement Van der Pauw resistivity measurement"], - 265:["neutron star equation state mass radius measurement NICER X ray timing","neutron star mass radius constraint tidal deformability GW170817 EOS"], - 268:["Olbers paradox dark night sky expanding universe finite age solution measurement","Olbers paradox resolution expanding universe Hubble constant dark night sky"], - 393:["MOS capacitor threshold voltage CV measurement silicon oxide interface","MOS capacitor threshold voltage flatband CV measurement substrate"], - 466:["diffusion equation solution error function thin film Gaussian profile measurement","diffusion solution Fick second law concentration profile measurement"], - 764:["sauropod dinosaur neck posture blood pressure cardiovascular model measurement","sauropod Barosaurus Argentinosaurus heart mass blood pressure vertical neck"], -} - -def cr(q): - o=[] - try: - u="https://api.crossref.org/works?"+urllib.parse.urlencode({"query":q,"rows":5,"sort":"relevance","filter":"type:journal-article"}) - r=urllib.request.Request(u,headers={"User-Agent":"Surgical/1.0 (mailto:r@x.com)"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("message",{}).get("items",[]): - t=(i.get("title",[""])or[""])[0];y=i.get("created",{}).get("date-parts",[[0]])[0][0] - doi=i.get("DOI","");jn=(i.get("container-title",[""])or[""])[0] - if t:o.append((t[:250],y,"Crossref",doi,jn)) - except:pass - return o - -def oa(q): - o=[] - try: - u="https://api.openalex.org/works?"+urllib.parse.urlencode({"search":q,"per_page":5,"sort":"cited_by_count:desc"}) - r=urllib.request.Request(u,headers={"User-Agent":"mailto:r@x.com"}) - with urllib.request.urlopen(r,timeout=15)as resp: - d=json.loads(resp.read().decode()) - for i in d.get("results",[]): - t=i.get("title","");y=i.get("publication_year")or 0;doi=i.get("doi","");jn="" - if i.get("primary_location")and i["primary_location"].get("source"): - jn=i["primary_location"]["source"].get("display_name","") - if t:o.append((t[:250],y,"OpenAlex",doi,jn)) - except:pass - return o - -total,batch=0,[] -for eq_id,queries in TARGETS.items(): - cur.execute("SELECT COUNT(*)FROM verifications WHERE equation_id=?",(eq_id,)) - have=cur.fetchone()[0];need=max(10-have,0) - for q in queries: - if need<=0:break - for fn in [cr,oa]: - if need<=0:break - for p in fn(q): - if need<=0:break - exp=f"{p[2]}: {p[3]}"if p[3]else p[2] - batch.append((eq_id,p[0],exp,p[1],p[3]if p[3]else p[2],"15x surgical")) - total+=1;need-=1 - time.sleep(1.2) - -cur.executemany("INSERT INTO verifications (equation_id,test_name,experiment,year,precision_level,status)VALUES(?,?,?,?,?,?)",batch) -c.commit() - -cur.execute("SELECT COUNT(*)FROM verifications");tv=cur.fetchone()[0] -cur.execute("SELECT COUNT(*)FROM equations e LEFT JOIN(SELECT equation_id,COUNT(*)c FROM verifications GROUP BY equation_id)vc ON vc.equation_id=e.id WHERE COALESCE(vc.c,0)<10") -bel=cur.fetchone()[0] -cur.execute("SELECT COUNT(*)FROM equations e LEFT JOIN(SELECT equation_id,COUNT(*)c FROM verifications GROUP BY equation_id)vc ON vc.equation_id=e.id WHERE COALESCE(vc.c,0)>=15") -at15=cur.fetchone()[0] -print(f"{tv} verif | {at15}/771 at 15x+ | {bel} below 10x | {total} added") -if bel: - cur.execute("SELECT e.eq_number,e.title,COALESCE(vc.c,0)FROM equations e LEFT JOIN(SELECT equation_id,COUNT(*)c FROM verifications GROUP BY equation_id)vc ON vc.equation_id=e.id WHERE COALESCE(vc.c,0)<10 ORDER BY vc.c") - for n,t,rf in cur.fetchall():print(f" [{rf}] #{n} {t[:80]}") -c.close() diff --git a/5-Applications/scripts/swarm_analyze_peptide_moe_bytecode.py b/5-Applications/scripts/swarm_analyze_peptide_moe_bytecode.py deleted file mode 100644 index 0636b4fa..00000000 --- a/5-Applications/scripts/swarm_analyze_peptide_moe_bytecode.py +++ /dev/null @@ -1,329 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Analysis: PeptideMoE to Genetic Bytecode Adaptation - -This script uses the swarm agent framework to analyze the PeptideMoE module -and suggest adaptations to the genetic bytecode system (GeneBytecodeJIT) -to support peptide conformational analysis with Mixture-of-Experts coordination. -""" - -import json -import sys -from pathlib import Path -from dataclasses import dataclass, asdict -from typing import Dict, List, Any -from datetime import datetime - - -@dataclass -class SwarmAgent: - """Swarm agent specialization.""" - specialization: str - confidence: float - - -@dataclass -class SwarmRecommendation: - """Swarm recommendation result.""" - query_type: str - subject: str - recommendations: List[str] - consensus_confidence: float - agent_count: int - verdict: str - implementation_notes: List[str] - metrics: Dict[str, Any] - bytecode_adaptations: List[Dict[str, Any]] - - -class PeptideMoEBytecodeAnalysis: - """Swarm analysis for PeptideMoE to genetic bytecode adaptation.""" - - # Agent specializations for this analysis - AGENTS = [ - SwarmAgent("semantic", 0.88), - SwarmAgent("verification", 0.85), - SwarmAgent("translation", 0.82), - SwarmAgent("geometry", 0.90), - SwarmAgent("topology", 0.87), - SwarmAgent("energy", 0.89), - SwarmAgent("distributed", 0.84), - SwarmAgent("compression", 0.81), - SwarmAgent("stochastic", 0.86), - SwarmAgent("quantum", 0.83) - ] - - def __init__(self): - self.peptide_moe_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/PeptideMoE.lean") - self.gene_bytecode_path = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/GeneBytecodeJIT.lean") - - def read_module(self, path: Path) -> str: - """Read a Lean module.""" - if path.exists(): - return path.read_text() - return "" - - def analyze_peptide_moe(self) -> Dict[str, Any]: - """Analyze PeptideMoE module structure.""" - peptide_code = self.read_module(self.peptide_moe_path) - - analysis = { - "structures": [], - "functions": [], - "theorems": [], - "key_concepts": [] - } - - # Extract structures - if "structure PeptideState" in peptide_code: - analysis["structures"].append({ - "name": "PeptideState", - "fields": ["phi", "psi", "internalEnergy", "conformationalEntropy", - "structuralCoherence", "stericEnergy", "bondEnergy"], - "purpose": "Conformational state with Ramachandran angles and energies" - }) - - if "structure Expert" in peptide_code: - analysis["structures"].append({ - "name": "Expert", - "fields": ["name", "gate", "advicePhi", "advicePsi"], - "purpose": "MoE expert with gating and advice functions" - }) - - if "structure AdmissibilityParams" in peptide_code: - analysis["structures"].append({ - "name": "AdmissibilityParams", - "fields": ["stericMax", "bondMax", "phiMin", "phiMax", "psiMin", "psiMax", "c0"], - "purpose": "Steric/bond/angle constraints" - }) - - if "structure ThermoParams" in peptide_code: - analysis["structures"].append({ - "name": "ThermoParams", - "fields": ["kB", "temperature"], - "purpose": "Temperature and Boltzmann constant" - }) - - # Extract key functions - analysis["functions"] = [ - {"name": "freeEnergy", "purpose": "E + kB·T·S computation"}, - {"name": "phiPeptide", "purpose": "Structural coherence / (free energy + c0)"}, - {"name": "admissible", "purpose": "Steric/bond/angle constraint checking"}, - {"name": "filteredScore", "purpose": "Zero if inadmissible, else φ-peptide score"}, - {"name": "expertUsefulness", "purpose": "Gate-weighted advice alignment with gradient"}, - {"name": "moeDrift", "purpose": "Sum of gate-weighted expert advice"}, - {"name": "bestCandidate?", "purpose": "Fold-based candidate selection"} - ] - - # Extract theorems - analysis["theorems"] = [ - {"name": "filteredScore_of_not_admissible", "property": "Score is zero when inadmissible"}, - {"name": "filteredScore_of_admissible", "property": "Score equals φ-peptide when admissible"}, - {"name": "expertHelpful_iff", "property": "Helpful iff usefulness is non-negative"}, - {"name": "gate_mass_one", "property": "Gate mass is one when normalized"} - ] - - # Key concepts - analysis["key_concepts"] = [ - "Mixture-of-Experts (MoE) architecture", - "Thermodynamic scoring (free energy)", - "Ramachandran angle constraints (φ, ψ)", - "Steric and bond energy constraints", - "Expert gating and advice functions", - "Gradient-based expert usefulness", - "Fold-based candidate selection", - "Admissibility filtering" - ] - - return analysis - - def generate_bytecode_adaptations(self, analysis: Dict[str, Any]) -> List[Dict[str, Any]]: - """Generate genetic bytecode adaptations based on PeptideMoE analysis.""" - adaptations = [] - - # Adaptation 1: New opcodes for peptide conformation - adaptations.append({ - "type": "new_opcodes", - "priority": "HIGH", - "description": "Add peptide conformation opcodes to GeneBytecodeJIT", - "opcodes": [ - {"name": "CONF_ANGLE_BIND", "hex": "0xD0", "purpose": "Bind Ramachandran angle constraints"}, - {"name": "THERMO_SCORE", "hex": "0xD1", "purpose": "Compute thermodynamic free energy score"}, - {"name": "STERIC_CHECK", "hex": "0xD2", "purpose": "Validate steric energy constraints"}, - {"name": "MOE_GATE", "hex": "0xD3", "purpose": "Apply expert gating function"}, - {"name": "EXPERT_ADVICE", "hex": "0xD4", "purpose": "Get expert advice for angle adjustment"}, - {"name": "CANDIDATE_SELECT", "hex": "0xD5", "purpose": "Fold-based candidate selection"}, - {"name": "GRADIENT_ALIGN", "hex": "0xD6", "purpose": "Align advice with energy gradient"} - ], - "rationale": "PeptideMoE requires specialized operations for conformational analysis" - }) - - # Adaptation 2: Extend GeneInstruction metadata - adaptations.append({ - "type": "metadata_extension", - "priority": "HIGH", - "description": "Extend GeneInstruction metadata for peptide-specific data", - "new_fields": [ - {"name": "phi_angle", "type": "Q16_16", "purpose": "Ramachandran φ angle"}, - {"name": "psi_angle", "type": "Q16_16", "purpose": "Ramachandran ψ angle"}, - {"name": "energy_gradient", "type": "Q16_16", "purpose": "Energy gradient for optimization"}, - {"name": "expert_id", "type": "Nat", "purpose": "Expert identifier in MoE system"}, - {"name": "gate_weight", "type": "Q16_16", "purpose": "Gating weight for expert"} - ], - "rationale": "Peptide conformation requires angle and energy metadata not present in gene bytecode" - }) - - # Adaptation 3: MoE-specific compilation stages - adaptations.append({ - "type": "compilation_stage", - "priority": "MEDIUM", - "description": "Add MoE-specific compilation stage to Triumvirate pipeline", - "stages": [ - {"stage": "MOE_COORDINATE", "role": "Builder", "purpose": "Coordinate expert gating and advice"}, - {"stage": "ANGLE_VALIDATE", "role": "Warden", "purpose": "Validate Ramachandran angle constraints"}, - {"stage": "ENERGY_SCORE", "role": "Judge", "purpose": "Adjudicate thermodynamic scoring"} - ], - "rationale": "MoE coordination requires additional validation beyond standard gene operations" - }) - - # Adaptation 4: Q16_16 for angle representation - adaptations.append({ - "type": "numeric_representation", - "priority": "HIGH", - "description": "Use Q16_16 fixed-point for angle and energy representation", - "fields": [ - {"name": "phi", "type": "Q16_16", "range": "[-π, π]", "precision": "~0.0001 radians"}, - {"name": "psi", "type": "Q16_16", "range": "[-π, π]", "precision": "~0.0001 radians"}, - {"name": "free_energy", "type": "Q16_16", "range": "[0, ∞)", "precision": "~0.001 kJ/mol"} - ], - "rationale": "Q16_16 provides hardware-native computation per AGENTS.md §1.4" - }) - - # Adaptation 5: Expert routing in JIT - adaptations.append({ - "type": "expert_routing", - "priority": "MEDIUM", - "description": "Add expert routing to JIT compilation for MoE systems", - "mechanism": "Gate-weighted expert selection based on conformational state", - "implementation": "Extend generateNativeCode to include expert dispatch table", - "rationale": "MoE requires dynamic expert selection based on peptide state" - }) - - return adaptations - - def generate_recommendations(self) -> SwarmRecommendation: - """Generate swarm recommendations for PeptideMoE to bytecode adaptation.""" - # Analyze PeptideMoE - peptide_analysis = self.analyze_peptide_moe() - - # Generate bytecode adaptations - adaptations = self.generate_bytecode_adaptations(peptide_analysis) - - # Generate recommendations - recommendations = [ - "Add 7 new opcodes (0xD0-0xD6) for peptide conformation operations", - "Extend GeneInstruction metadata with angle and energy fields", - "Add MoE-specific compilation stage to Triumvirate pipeline", - "Use Q16_16 fixed-point for angle and energy representation", - "Implement expert routing in JIT compilation", - "Integrate thermodynamic scoring into Warden validation", - "Support fold-based candidate selection in native code generation", - "Add gradient alignment operations for expert advice optimization" - ] - - # Calculate consensus confidence - avg_confidence = sum(a.confidence for a in self.AGENTS) / len(self.AGENTS) - - # Determine verdict - if avg_confidence >= 0.85: - verdict = "HIGHLY FEASIBLE" - elif avg_confidence >= 0.70: - verdict = "FEASIBLE" - else: - verdict = "CHALLENGING" - - # Implementation notes - implementation_notes = [ - f"Swarm consensus: {avg_confidence:.3f}", - f"Active agents: {len(self.AGENTS)}", - "PeptideMoE uses real-valued thermodynamics - requires Q16_16 adaptation", - "MoE architecture maps well to distributed ENE mesh execution", - "Triumvirate validation extends naturally to conformational constraints" - ] - - # Metrics - metrics = { - "new_opcodes": 7, - "metadata_extensions": 5, - "compilation_stages": 3, - "peptide_structures": len(peptide_analysis["structures"]), - "peptide_functions": len(peptide_analysis["functions"]), - "adaptation_priority": {"HIGH": 2, "MEDIUM": 2} - } - - return SwarmRecommendation( - query_type="peptide_moe_bytecode_adaptation", - subject="PeptideMoE to Genetic Bytecode", - recommendations=recommendations, - consensus_confidence=avg_confidence, - agent_count=len(self.AGENTS), - verdict=verdict, - implementation_notes=implementation_notes, - metrics=metrics, - bytecode_adaptations=adaptations - ) - - def print_recommendations(self, recommendation: SwarmRecommendation): - """Print formatted recommendations.""" - print("\n" + "=" * 70) - print(f"SWARM ANALYSIS: {recommendation.subject.upper()}") - print("=" * 70) - - print(f"\nQuery Type: {recommendation.query_type}") - print(f"Swarm Consensus: {recommendation.consensus_confidence:.3f}") - print(f"Active Agents: {recommendation.agent_count}") - - print(f"\nRecommendations:") - for i, rec in enumerate(recommendation.recommendations, 1): - print(f" {i}. {rec}") - - print(f"\nBytecode Adaptations:") - for i, adapt in enumerate(recommendation.bytecode_adaptations, 1): - print(f" {i}. [{adapt['priority']}] {adapt['description']}") - if 'opcodes' in adapt: - for op in adapt['opcodes']: - print(f" - {op['name']} (0x{op['hex']}): {op['purpose']}") - - print(f"\nImplementation Notes:") - for note in recommendation.implementation_notes: - print(f" - {note}") - - print(f"\nMetrics:") - for k, v in recommendation.metrics.items(): - print(f" - {k}: {v}") - - print(f"\nVerdict: {recommendation.verdict}") - print("=" * 70) - - -def main(): - """Main entry point.""" - analyzer = PeptideMoEBytecodeAnalysis() - recommendation = analyzer.generate_recommendations() - - # Print recommendations - analyzer.print_recommendations(recommendation) - - # Save to file - output_path = Path("/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_bytecode_analysis.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(asdict(recommendation), f, indent=2) - - print(f"\nAnalysis saved to: {output_path}") - - return recommendation - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_codon_otom_improvement_suggestions.py b/5-Applications/scripts/swarm_codon_otom_improvement_suggestions.py deleted file mode 100644 index 85bc6e0a..00000000 --- a/5-Applications/scripts/swarm_codon_otom_improvement_suggestions.py +++ /dev/null @@ -1,173 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Improvement Suggestions for CodonOTOM Module - -Queries the swarm for specific improvement suggestions to make the -CodonOTOM module 100% complete. -""" - -import sqlite3 -import json -from datetime import datetime -from typing import Dict, List, Any - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def get_codon_otom_entry() -> Dict[str, Any]: - """Get the CodonOTOM entry from the database.""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute(""" - SELECT entity_id, subject, secondary_subjects, name, statement, proof_status, formal_status, - lean_module, dependencies, citations, complexity_score, year - FROM math_entities - WHERE entity_id = 'codon_fitness_001' - """) - - result = cursor.fetchone() - conn.close() - - if result: - return dict(result) - return None - -def analyze_current_state(entry: Dict[str, Any]) -> Dict[str, Any]: - """Analyze the current state of CodonOTOM.""" - analysis = { - "entity_id": entry["entity_id"], - "name": entry["name"], - "proof_status": entry["proof_status"], - "formal_status": entry["formal_status"], - "complexity_score": entry["complexity_score"], - "completeness": 0.0, - "gaps": [] - } - - # Assess completeness - if entry["proof_status"] == "theorems": - analysis["completeness"] = 0.9 - elif entry["proof_status"] == "definitions": - analysis["completeness"] = 0.5 - analysis["gaps"].append("Add theorems proving key properties") - else: - analysis["completeness"] = 0.3 - - if entry["formal_status"] == "noncomputable": - analysis["completeness"] *= 0.8 - analysis["gaps"].append("Consider Q16_16 fixed-point for hardware extraction") - - return analysis - -def generate_swarm_improvement_suggestions(analysis: Dict[str, Any]) -> List[str]: - """Generate swarm improvement suggestions based on analysis.""" - suggestions = [] - - # Overall completeness assessment - if analysis["completeness"] < 0.8: - suggestions.append(f"OVERALL: Current completeness {analysis['completeness']:.1%} - target 100%") - - # Specific suggestions - if analysis["proof_status"] == "definitions": - suggestions.append("Add theorem: phiCodon is bounded when denomSafe holds") - suggestions.append("Add theorem: phiCodon positive when numerator positive and denomSafe") - suggestions.append("Add theorem: deltaPhi zero when features and codon unchanged") - suggestions.append("Add theorem: beneficialMutation implies efficiency increase") - - if analysis["formal_status"] == "noncomputable": - suggestions.append("Consider Q16_16 fixed-point version for hardware extraction") - suggestions.append("Add decidable approximations for ℝ arithmetic") - - # Codon-specific suggestions - suggestions.append("Add concrete codon examples with actual base values") - suggestions.append("Add degeneracy function implementation (e.g., 1/2/3/4/6-fold)") - suggestions.append("Add translate function implementation (genetic code table)") - suggestions.append("Add #eval examples for phiCodon with toy parameters") - - # Connection to OTOM - suggestions.append("Add theorem: phiCodon instantiates universal efficiency principle") - suggestions.append("Add theorem: CodonOTOM satisfies OTOM transformation structure") - - return suggestions - -def main(): - """Main entry point.""" - print("=" * 70) - print("SWARM IMPROVEMENT SUGGESTIONS: CodonOTOM Module") - print("=" * 70) - - # Get current entry - entry = get_codon_otom_entry() - - if not entry: - print("ERROR: CodonOTOM entry not found in database") - return - - print(f"\nCurrent Entry: {entry['name']} ({entry['entity_id']})") - print(f"Proof Status: {entry['proof_status']}") - print(f"Formal Status: {entry['formal_status']}") - print(f"Complexity Score: {entry['complexity_score']}") - - # Analyze current state - analysis = analyze_current_state(entry) - - print("\n" + "=" * 70) - print("COMPLETENESS ANALYSIS") - print("=" * 70) - - print(f"\nOverall Completeness: {analysis['completeness']:.1%}") - - if analysis['gaps']: - print(f"\nIdentified Gaps: {', '.join(analysis['gaps'])}") - - # Generate swarm suggestions - suggestions = generate_swarm_improvement_suggestions(analysis) - - print("\n" + "=" * 70) - print("SWARM IMPROVEMENT SUGGESTIONS") - print("=" * 70) - - for i, suggestion in enumerate(suggestions, 1): - print(f"\n{i}. {suggestion}") - - # Prioritize suggestions - high_priority = [s for s in suggestions if any(keyword in s for keyword in ["theorem", "Add theorem"])] - medium_priority = [s for s in suggestions if any(keyword in s for keyword in ["Q16_16", "fixed-point", "decidable"])] - low_priority = [s for s in suggestions if any(keyword in s for keyword in ["example", "eval"])] - - print("\n" + "=" * 70) - print("PRIORITY ORDERING") - print("=" * 70) - - print("\nHIGH PRIORITY (Core mathematical properties):") - for i, s in enumerate(high_priority[:5], 1): - print(f" {i}. {s}") - - print("\nMEDIUM PRIORITY (Hardware extraction):") - for i, s in enumerate(medium_priority[:3], 1): - print(f" {i}. {s}") - - print("\nLOW PRIORITY (Examples and verification):") - for i, s in enumerate(low_priority[:3], 1): - print(f" {i}. {s}") - - # Save suggestions - output_file = "/home/allaun/Documents/Research Stack/data/swarm_codon_otom_improvement_suggestions.json" - report = { - "analysis": analysis, - "suggestions": suggestions, - "high_priority": high_priority, - "medium_priority": medium_priority, - "low_priority": low_priority, - "timestamp": datetime.now().isoformat() - } - - with open(output_file, "w") as f: - json.dump(report, f, indent=2) - - print(f"\nSuggestions saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_genetic_groundup_redesign.py b/5-Applications/scripts/swarm_genetic_groundup_redesign.py deleted file mode 100644 index 5a89c1ba..00000000 --- a/5-Applications/scripts/swarm_genetic_groundup_redesign.py +++ /dev/null @@ -1,654 +0,0 @@ -#!/usr/bin/env python3 -""" -swarm_genetic_groundup_redesign.py — Ground-Up Genetic System Redesign - -The swarm redesigns the genetic computation architecture from first principles, -incorporating 511% efficiency learnings and next-gen agent insights. - -New Architecture: -- Ground-up gene encoding (not bytecode translation) -- Protein folding as manifold traversal (not simulation) -- Metabolic pathways as graph neural networks on topology -- Cell signaling as message passing on ENE mesh -- Evolution as gradient descent on fitness manifold - -Eliminates: -- Interpreted bytecode (too slow) -- Static gene sequences (not adaptive) -- Separate transcription/translation phases (inefficient) -- Centralized DNA storage (bottleneck) - -Embraces: -- Compiled gene kernels (direct execution) -- Dynamic gene networks (self-modifying) -- Unified expression/folding (coupled computation) -- Distributed genome (sharded across mesh) -""" - -import json -import time -import hashlib -import random -import numpy as np -from pathlib import Path -from dataclasses import dataclass, field -from typing import Dict, List, Any, Optional, Tuple, Set -from datetime import datetime -from enum import Enum, auto - - -class Nucleotide(Enum): - """Quantum nucleotide states (not just A/T/C/G).""" - A = auto() # Adenine - high expression probability - T = auto() # Thymine - terminator signal - C = auto() # Cytosine - structural stability - G = auto() # Guanine - high binding affinity - U = auto() # Uracil (RNA) - temporary state - X = auto() # Synthetic - programmable function - - -@dataclass -class QuantumBase: - """Nucleotide with quantum superposition properties.""" - primary: Nucleotide - amplitude: complex # Quantum amplitude - expression_prob: float # 0.0 to 1.0 - binding_energy: float # kcal/mol - fold_angle: float # degrees (3D structure) - - -@dataclass -class GeneKernel: - """Compiled gene kernel (not bytecode - direct machine code).""" - kernel_id: str - dna_sequence: List[QuantumBase] - compiled_native: bytes # Directly executable - protein_structure: np.ndarray # 3D coordinates - fitness_score: float - mutation_history: List[str] - generation: int - - -@dataclass -class ProteinFoldState: - """Protein folding as manifold state (not simulation steps).""" - amino_acid_chain: str - manifold_coordinates: np.ndarray # Position on folding manifold - curvature_tensor: np.ndarray # Local curvature - binding_pockets: List[Tuple[int, int]] # (start, end) residues - stability_score: float - fold_time_ms: float - - -@dataclass -class MetabolicGraph: - """Metabolic pathway as graph neural network.""" - nodes: Dict[str, Any] # Enzymes, substrates - edges: List[Tuple[str, str, float]] # (from, to, flux_rate) - adjacency_tensor: np.ndarray - steady_state: np.ndarray - throughput: float - - -class GroundUpGeneticArchitecture: - """ - Ground-up genetic system architecture. - - Redesigned from first principles: - 1. Quantum nucleotides (superposition states) - 2. Compiled gene kernels (not interpreted bytecode) - 3. Protein folding as manifold traversal - 4. Metabolism as GNN on topology - 5. Evolution as gradient descent - """ - - def __init__(self): - self.gene_kernels: Dict[str, GeneKernel] = {} - self.protein_folds: Dict[str, ProteinFoldState] = {} - self.metabolic_networks: Dict[str, MetabolicGraph] = {} - - def redesign_principles(self) -> Dict[str, Any]: - """Define ground-up redesign principles.""" - print("\n" + "=" * 70) - print("GROUND-UP GENETIC SYSTEM REDESIGN") - print("=" * 70) - print("Analysis: 511% efficiency + next-gen agent insights") - print("Goal: Eliminate bottlenecks, embrace distributed computation") - print("=" * 70) - - principles = { - "old_approach": { - "encoding": "Static DNA → RNA → Protein (sequential)", - "execution": "Interpreted bytecode (GeneJIT)", - "folding": "Simulation-based (step-by-step)", - "metabolism": "Discrete pathway steps", - "evolution": "Random mutation + selection", - "storage": "Centralized genome" - }, - "new_approach": { - "encoding": "Quantum nucleotides (superposition)", - "execution": "Compiled kernels (direct execution)", - "folding": "Manifold traversal (instant state)", - "metabolism": "Continuous GNN (graph neural net)", - "evolution": "Gradient descent on fitness manifold", - "storage": "Distributed genome (sharded)" - }, - "key_innovations": [ - "Quantum nucleotides: A/T/C/G/X/U with probability amplitudes", - "Compiled gene kernels: Native code, not bytecode", - "Folding manifold: 4D hyperbolic space for protein structure", - "Coupled expression: Transcription+translation unified", - "Distributed genome: Shamir-secret sharded across ENE mesh", - "Adaptive pathways: Metabolic flux as differentiable graph", - "Evolutionary gradients: Fitness landscape as optimization target" - ] - } - - print("\n🔄 Paradigm Shift:") - print("\nOLD → NEW:") - for key in principles["old_approach"]: - old = principles["old_approach"][key] - new = principles["new_approach"][key] - print(f" {key:15} {old:35} → {new}") - - print(f"\n💡 Key Innovations ({len(principles['key_innovations'])}):") - for i, innovation in enumerate(principles['key_innovations'], 1): - print(f" {i}. {innovation}") - - return principles - - def design_quantum_encoding(self) -> Dict[str, Any]: - """Design quantum nucleotide encoding system.""" - print("\n" + "=" * 70) - print("QUANTUM NUCLEOTIDE ENCODING") - print("=" * 70) - - encoding = { - "nucleotides": { - "A": { - "name": "Adenine", - "expression_prob": 0.85, - "binding_energy": -1.2, - "fold_angle": 120.0, - "function": "High expression promoter" - }, - "T": { - "name": "Thymine", - "expression_prob": 0.05, - "binding_energy": -0.8, - "fold_angle": 180.0, - "function": "Terminator / stop signal" - }, - "C": { - "name": "Cytosine", - "expression_prob": 0.50, - "binding_energy": -1.5, - "fold_angle": 90.0, - "function": "Structural stability" - }, - "G": { - "name": "Guanine", - "expression_prob": 0.70, - "binding_energy": -1.8, - "fold_angle": 60.0, - "function": "High binding affinity" - }, - "U": { - "name": "Uracil (RNA)", - "expression_prob": 0.60, - "binding_energy": -1.0, - "fold_angle": 150.0, - "function": "Temporary / transitional" - }, - "X": { - "name": "Synthetic", - "expression_prob": 0.95, - "binding_energy": -2.5, - "fold_angle": 45.0, - "function": "Programmable function" - } - }, - "quantum_properties": { - "superposition": "Nucleotides exist in probability space", - "entanglement": "Correlated expression across genes", - "interference": "Constructive/destructive binding", - "measurement": "Expression collapses superposition" - }, - "encoding_efficiency": { - "classical_bits": 2, # A/T/C/G = 2 bits - "quantum_bits": "log2(6) + amplitude", # 6 nucleotides + phase - "information_density": "2.6× classical" - } - } - - print("\nQuantum Nucleotide Properties:") - for nuc, props in encoding["nucleotides"].items(): - print(f"\n [{nuc}] {props['name']}") - print(f" Expression: {props['expression_prob']*100:.0f}%") - print(f" Binding: {props['binding_energy']:.1f} kcal/mol") - print(f" Fold angle: {props['fold_angle']}°") - print(f" Function: {props['function']}") - - print(f"\nQuantum Properties:") - for prop, desc in encoding["quantum_properties"].items(): - print(f" • {prop}: {desc}") - - print(f"\nInformation Density: {encoding['encoding_efficiency']['information_density']}") - - return encoding - - def design_compiled_kernels(self) -> Dict[str, Any]: - """Design compiled gene kernel system (not bytecode).""" - print("\n" + "=" * 70) - print("COMPILED GENE KERNELS (Native Execution)") - print("=" * 70) - - kernels = { - "compilation_pipeline": { - "stage_1_quantum_parse": "Parse quantum nucleotides → probability graph", - "stage_2_expression_predict": "ML model predicts expression levels", - "stage_3_structure_fold": "Manifold traversal for 3D structure", - "stage_4_native_codegen": "Generate x86/ARM/RISC-V machine code", - "stage_5_bind_optimize": "BIND compression for cache efficiency", - "stage_6_distribute": "Shard kernel across ENE mesh nodes" - }, - "kernel_types": { - "housekeeping": { - "description": "Essential cellular functions", - "examples": ["ATP_synthesis", "DNA_repair", "protein_degradation"], - "priority": "critical", - "replication": "always_on" - }, - "regulatory": { - "description": "Gene expression control", - "examples": ["transcription_factor", "epigenetic_modifier", "splicing_regulator"], - "priority": "high", - "replication": "conditional" - }, - "structural": { - "description": "Physical cell components", - "examples": ["cytoskeleton", "membrane_protein", "extracellular_matrix"], - "priority": "medium", - "replication": "demand_driven" - }, - "adaptive": { - "description": "Response to environment", - "examples": ["stress_response", "immune_defense", "metabolic_switch"], - "priority": "variable", - "replication": "signal_triggered" - } - }, - "execution_model": { - "old": "Interpret bytecode → simulate biology", - "new": "Execute native kernel → direct protein synthesis", - "speedup": "50-100× (compiled vs interpreted)" - }, - "distribution": { - "housekeeping_kernels": "Replicated on all 6 nodes (high availability)", - "adaptive_kernels": "Sharded based on environmental signals", - "kernel_migration": "Hot-swap between nodes during execution" - } - } - - print("\nCompilation Pipeline:") - for stage, desc in kernels["compilation_pipeline"].items(): - print(f" • {stage}: {desc}") - - print(f"\nKernel Types:") - for ktype, info in kernels["kernel_types"].items(): - print(f"\n [{ktype.upper()}] Priority: {info['priority']}") - print(f" Examples: {', '.join(info['examples'][:2])}") - print(f" Replication: {info['replication']}") - - print(f"\nExecution Model:") - print(f" OLD: {kernels['execution_model']['old']}") - print(f" NEW: {kernels['execution_model']['new']}") - print(f" Speedup: {kernels['execution_model']['speedup']}") - - return kernels - - def design_folding_manifold(self) -> Dict[str, Any]: - """Design protein folding as manifold traversal.""" - print("\n" + "=" * 70) - print("PROTEIN FOLDING AS MANIFOLD TRAVERSAL") - print("=" * 70) - - folding = { - "old_approach": { - "method": "Molecular dynamics simulation", - "time": "Hours to days", - "steps": "Millions of timestep iterations", - "bottleneck": "O(N²) force calculations" - }, - "new_approach": { - "method": "Direct manifold embedding", - "time": "Milliseconds", - "steps": "Single traversal of folding manifold", - "advantage": "O(1) lookup of native structure" - }, - "manifold_structure": { - "dimensions": 4, - "description": "Hyperbolic manifold of all possible protein structures", - "coordinates": [ - "r: Compactness (radius of gyration)", - "theta: Secondary structure fraction", - "phi: Tertiary contact order", - "psi: Quaternary assembly state" - ], - "metric": "Energy-weighted distance (lower = more stable)" - }, - "traversal_algorithm": { - "start": "Unfolded state (random coil coordinates)", - "gradient": "Steepest descent on energy landscape", - "constraint": "Amino acid sequence defines path", - "end": "Native state (global energy minimum)" - }, - "performance": { - "folding_time": "~10ms for 200-residue protein", - "accuracy": "RMSD < 2.0 Å vs experimental", - "speedup": "1000× vs molecular dynamics" - } - } - - print("\nParadigm Comparison:") - print(f"\nOLD: {folding['old_approach']['method']}") - print(f" Time: {folding['old_approach']['time']}") - print(f" Bottleneck: {folding['old_approach']['bottleneck']}") - - print(f"\nNEW: {folding['new_approach']['method']}") - print(f" Time: {folding['new_approach']['time']}") - print(f" Advantage: {folding['new_approach']['advantage']}") - - print(f"\nManifold Structure ({folding['manifold_structure']['dimensions']}D):") - for coord in folding['manifold_structure']['coordinates']: - print(f" • {coord}") - - print(f"\nPerformance:") - for metric, value in folding['performance'].items(): - print(f" • {metric}: {value}") - - return folding - - def design_metabolic_gnn(self) -> Dict[str, Any]: - """Design metabolic pathways as graph neural networks.""" - print("\n" + "=" * 70) - print("METABOLIC PATHWAYS AS GRAPH NEURAL NETWORKS") - print("=" * 70) - - metabolism = { - "old_model": { - "representation": "Static pathway diagrams", - "simulation": "Discrete event simulation", - "flux": "Fixed rates, Michaelis-Menten kinetics", - "adaptation": "Manual parameter tuning" - }, - "new_model": { - "representation": "Dynamic graph neural network", - "simulation": "Continuous differentiable flow", - "flux": "Learned edge weights, attention mechanism", - "adaptation": "Gradient descent on flux objectives" - }, - "graph_structure": { - "nodes": [ - "Metabolites (substrates/products)", - "Enzymes (catalysts)", - "Compartments (organelles)" - ], - "edges": [ - "Chemical reactions (directed)", - "Regulatory interactions (signed)", - "Transport between compartments" - ], - "features": [ - "Node: concentration, charge, pH sensitivity", - "Edge: flux rate, enzyme affinity, energy cost" - ] - }, - "neural_architecture": { - "message_passing": "Graph convolution on metabolic network", - "attention": "Learn which pathways to activate", - "pooling": "Aggregate compartment-level state", - "output": "Optimal flux distribution for objective" - }, - "optimization": { - "objectives": [ - "Maximize ATP production", - "Minimize toxic intermediate accumulation", - "Balance redox state", - "Support growth rate" - ], - "method": "Differentiable programming (PyTorch/JAX style)", - "update": "Real-time gradient descent on fluxes" - }, - "performance": { - "adaptation_speed": "Milliseconds (vs seconds for manual)", - "prediction_accuracy": "95% flux balance", - "novel_pathway_discovery": "Automated via graph traversal" - } - } - - print("\nModel Comparison:") - print(f"OLD: {metabolism['old_model']['representation']}") - print(f" {metabolism['old_model']['flux']}") - print(f"\nNEW: {metabolism['new_model']['representation']}") - print(f" {metabolism['new_model']['flux']}") - - print(f"\nGraph Structure:") - print(f" Nodes: {', '.join(metabolism['graph_structure']['nodes'][:2])}") - print(f" Edges: {', '.join(metabolism['graph_structure']['edges'][:2])}") - - print(f"\nNeural Architecture:") - for component, desc in metabolism['neural_architecture'].items(): - print(f" • {component}: {desc}") - - print(f"\nOptimization Objectives:") - for i, obj in enumerate(metabolism['optimization']['objectives'], 1): - print(f" {i}. {obj}") - - return metabolism - - def design_evolutionary_gradient(self) -> Dict[str, Any]: - """Design evolution as gradient descent on fitness manifold.""" - print("\n" + "=" * 70) - print("EVOLUTION AS GRADIENT DESCENT") - print("=" * 70) - - evolution = { - "old_paradigm": { - "mechanism": "Random mutation + natural selection", - "speed": "Generations (slow)", - "direction": "Undirected exploration", - "efficiency": "Wasteful (most mutations deleterious)" - }, - "new_paradigm": { - "mechanism": "Gradient descent on fitness landscape", - "speed": "Real-time (continuous)", - "direction": "Directed toward fitness optimum", - "efficiency": "Targeted (mutations in beneficial directions)" - }, - "fitness_manifold": { - "description": "High-dimensional space of all possible genomes", - "dimensions": "Gene count × nucleotide positions", - "metric": "Fitness function (survival, reproduction, efficiency)", - "topology": "Rugged landscape with local optima" - }, - "gradient_calculation": { - "method": "Automatic differentiation through fitness function", - "inputs": [ - "Gene expression levels", - "Protein function scores", - "Metabolic efficiency", - "Environmental fit" - ], - "output": "Direction of genome change for maximum fitness gain" - }, - "implementation": { - "population": "Distributed across ENE mesh (6 nodes)", - "gradient": "Each node computes partial fitness derivative", - "aggregation": "Consensus gradient via gossip protocol", - "update": "Genome shifted in gradient direction" - }, - "advantages": [ - "1000× faster than generational evolution", - "Escapes local optima via momentum", - "Learns from fitness landscape curvature", - "Adapts in real-time to environment changes" - ] - } - - print("\nParadigm Shift:") - print(f"\nOLD: {evolution['old_paradigm']['mechanism']}") - print(f" Speed: {evolution['old_paradigm']['speed']}") - print(f" Direction: {evolution['old_paradigm']['direction']}") - - print(f"\nNEW: {evolution['new_paradigm']['mechanism']}") - print(f" Speed: {evolution['new_paradigm']['speed']}") - print(f" Direction: {evolution['new_paradigm']['direction']}") - - print(f"\nFitness Manifold:") - print(f" {evolution['fitness_manifold']['description']}") - print(f" Dimensions: {evolution['fitness_manifold']['dimensions']}") - print(f" Metric: {evolution['fitness_manifold']['metric']}") - - print(f"\nImplementation:") - for component, desc in evolution['implementation'].items(): - print(f" • {component}: {desc}") - - print(f"\nAdvantages:") - for i, adv in enumerate(evolution['advantages'], 1): - print(f" {i}. {adv}") - - return evolution - - def design_distributed_genome(self) -> Dict[str, Any]: - """Design distributed genome storage (not centralized).""" - print("\n" + "=" * 70) - print("DISTRIBUTED GENOME (Sharded Across ENE Mesh)") - print("=" * 70) - - genome = { - "centralized_problems": [ - "Single point of failure", - "Replication bottleneck", - "Access latency", - "Mutation propagation delays" - ], - "distributed_solution": { - "sharding": "Genome split into 6 shards (one per node)", - "redundancy": "3× replication (any 2 shards can reconstruct)", - "encoding": "Erasure coding for fault tolerance", - "consistency": "Gossip protocol for update propagation" - }, - "access_patterns": { - "local_genes": "Access shard on same node (fast)", - "remote_genes": "Fetch from other shard via ENE (cached)", - "housekeeping": "Replicated on all nodes (always available)", - "adaptive": "Migrate genes to nodes where expressed" - }, - "mutation_handling": { - "local": "Mutate shard on local node", - "validate": "Triumvirate validates mutation", - "propagate": "Gossip to other replicas", - "consensus": "Majority vote for conflicting mutations" - }, - "performance": { - "read_latency": "<1ms for local, <10ms for remote", - "write_consistency": "Eventual (100ms propagation)", - "fault_tolerance": "Tolerates 2 node failures", - "scalability": "Add nodes → linear capacity increase" - } - } - - print("\nCentralized Problems:") - for i, problem in enumerate(genome['centralized_problems'], 1): - print(f" {i}. {problem}") - - print(f"\nDistributed Solution:") - for aspect, desc in genome['distributed_solution'].items(): - print(f" • {aspect}: {desc}") - - print(f"\nAccess Patterns:") - for pattern, desc in genome['access_patterns'].items(): - print(f" • {pattern}: {desc}") - - print(f"\nPerformance:") - for metric, value in genome['performance'].items(): - print(f" • {metric}: {value}") - - return genome - - def generate_full_redesign(self) -> Dict[str, Any]: - """Generate complete ground-up redesign.""" - print("\n" + "=" * 70) - print("COMPLETE GROUND-UP GENETIC REDESIGN") - print("=" * 70) - - # Phase 1: Principles - principles = self.redesign_principles() - - # Phase 2: Components - encoding = self.design_quantum_encoding() - kernels = self.design_compiled_kernels() - folding = self.design_folding_manifold() - metabolism = self.design_metabolic_gnn() - evolution = self.design_evolutionary_gradient() - genome = self.design_distributed_genome() - - # Compile full design - redesign = { - "redesign_timestamp": datetime.now().isoformat(), - "name": "Cambrian-Genetic-v3.0-Quantum-Distributed", - "generation": "Ground-up redesign (Gen 1 → Gen 3)", - "principles": principles, - "components": { - "quantum_encoding": encoding, - "compiled_kernels": kernels, - "folding_manifold": folding, - "metabolic_gnn": metabolism, - "evolutionary_gradient": evolution, - "distributed_genome": genome - }, - "performance_projections": { - "gene_expression_speedup": "100× (compiled vs interpreted)", - "protein_folding_speedup": "1000× (manifold vs simulation)", - "metabolic_adaptation_speedup": "100× (GNN vs discrete)", - "evolution_speedup": "1000× (gradient vs generational)", - "genome_access_speedup": "10× (distributed vs centralized)" - }, - "swarm_verdict": "Ground-up redesign eliminates all bottlenecks from 511% achievement. New architecture projects 100,000× speedup for genetic computation through quantum encoding, compiled kernels, manifold folding, GNN metabolism, and gradient evolution." - } - - print("\n" + "=" * 70) - print("REDESIGN COMPLETE") - print("=" * 70) - - print(f"\n🧬 Architecture: {redesign['name']}") - print(f" Generation: {redesign['generation']}") - - print(f"\n📈 Performance Projections:") - for aspect, speedup in redesign['performance_projections'].items(): - print(f" • {aspect}: {speedup}") - - print(f"\n🧬 Swarm Verdict:") - print(f" {redesign['swarm_verdict']}") - - # Save - output_path = Path("/home/allaun/Documents/Research Stack/data/swarm_genetic_groundup_redesign.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(redesign, f, indent=2) - - print(f"\nDesign saved: {output_path}") - - return redesign - - -def main(): - """Run ground-up genetic redesign.""" - architecture = GroundUpGeneticArchitecture() - redesign = architecture.generate_full_redesign() - return redesign - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_gpu_optimization_80_percent.py b/5-Applications/scripts/swarm_gpu_optimization_80_percent.py deleted file mode 100644 index 3f35ca17..00000000 --- a/5-Applications/scripts/swarm_gpu_optimization_80_percent.py +++ /dev/null @@ -1,81 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Query: GPU and Component Optimization to 80% Load - -Ask the swarm to figure out how to safely maximize the entire interface -of the GPU and all other components connected together up to 80% of -total possible load. -""" - -import sys -import os -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.enhanced_integrated_swarm import EnhancedIntegratedSwarm - -def main(): - print("=" * 70) - print("SWARM QUERY: GPU and Component Optimization to 80% Load") - print("=" * 70) - - swarm = EnhancedIntegratedSwarm() - - # Query the swarm about GPU optimization - print("\n[1] Querying swarm about GPU knowledge...") - gpu_knowledge = swarm.shim.swarm_gpu_knowledge() - - if "error" in gpu_knowledge: - print(f"ERROR: {gpu_knowledge['error']}") - return - - print("\n[2] GPU Knowledge from Swarm:") - print(json.dumps(gpu_knowledge, indent=2)) - - # Query about interface adoption - print("\n[3] Querying swarm about interface adoption...") - interface_adoption = swarm.shim.swarm_adopt_interface() - - if "error" in interface_adoption: - print(f"ERROR: {interface_adoption['error']}") - return - - print("\n[4] Interface Adoption from Swarm:") - print(json.dumps(interface_adoption, indent=2)) - - # Query about topology documentation - print("\n[5] Querying swarm about topology documentation...") - topology_doc = swarm.shim.swarm_document_topology() - - if "error" in topology_doc: - print(f"ERROR: {topology_doc['error']}") - return - - print("\n[6] Topology Documentation from Swarm:") - print(json.dumps(topology_doc, indent=2)) - - # Save results - result = { - "query": "GPU and component optimization to 80% load", - "gpu_knowledge": gpu_knowledge, - "interface_adoption": interface_adoption, - "topology_documentation": topology_doc - } - - output_path = Path(__file__).parent.parent.parent / "data" / "swarm_gpu_optimization_80_percent.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(result, f, indent=2) - - print(f"\n[7] Results saved to: {output_path}") - print("\n" + "=" * 70) - print("SWARM QUERY COMPLETE") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_peptide_moe_direct_review.py b/5-Applications/scripts/swarm_peptide_moe_direct_review.py deleted file mode 100644 index e15e96fb..00000000 --- a/5-Applications/scripts/swarm_peptide_moe_direct_review.py +++ /dev/null @@ -1,190 +0,0 @@ -#!/usr/bin/env python3 -""" -Direct Swarm Review for PeptideMoE Lean Modules - -Queries the database directly to provide a swarm-style review of the -newly added PeptideMoE modules, simulating what the swarm API would return. -""" - -import sqlite3 -import json -from datetime import datetime -from typing import Dict, List, Any - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def query_database(subjects: List[str], keywords: str = None, has_lean: bool = True) -> Dict[str, Any]: - """Query the database directly (simulating swarm API).""" - start_time = datetime.now() - - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - # Build search query (same logic as swarm API) - conditions = [] - params = [] - - if subjects: - subject_conditions = " OR ".join(["subject LIKE ?"] * len(subjects)) - conditions.append(f"({subject_conditions})") - params.extend([f"%{s}%" for s in subjects]) - - # Only add keyword search if keywords are provided and not already in subjects - if keywords and not subjects: - keyword_list = keywords.split() - for keyword in keyword_list: - conditions.append("(name LIKE ? OR statement LIKE ?)") - params.extend([f"%{keyword}%", f"%{keyword}%"]) - - if has_lean: - conditions.append("lean_module IS NOT NULL") - - where_clause = " AND ".join(conditions) if conditions else "1=1" - - sql = f""" - SELECT entity_id, subject, secondary_subjects, name, statement, - proof_status, formal_status, lean_module, dependencies, - citations, complexity_score, year - FROM math_entities - WHERE {where_clause} - LIMIT 20 - """ - - cursor.execute(sql, params) - results = [dict(row) for row in cursor.fetchall()] - query_time = (datetime.now() - start_time).total_seconds() * 1000 - - conn.close() - - # Calculate confidence - confidence = 0.8 if len(results) > 5 else (0.6 if len(results) > 0 else 0.3) - - # Generate suggestions - suggestions = [] - if not results: - suggestions.append("Try broadening your search terms or using different keywords.") - suggestions.append("Consider checking if the subject is properly indexed in the database.") - else: - suggestions.append("Review the formal status of these entities for Lean 4 implementation.") - suggestions.append("Check the dependencies to understand related concepts.") - - if subjects: - suggestions.append(f"Consider exploring related subjects: {', '.join(subjects)}") - - # Build metadata - metadata = { - "query_subjects": subjects, - "keyword_pattern": keywords, - "has_lean_formalization": has_lean, - "timestamp": datetime.now().isoformat() - } - - return { - "success": True, - "results": results, - "count": len(results), - "confidence": confidence, - "query_time_ms": query_time, - "suggestions": suggestions, - "metadata": metadata - } - -def main(): - """Main entry point.""" - print("=" * 70) - print("SWARM REVIEW: PeptideMoE Lean Modules") - print("=" * 70) - - # Query for PeptideMoE modules - print("\nQuerying database for PeptideMoE modules...") - result = query_database( - subjects=["PeptideMoE"], - keywords="PeptideMoE", - has_lean=True - ) - - print(f"\nQuery Results:") - print(f" Success: {result.get('success', False)}") - print(f" Count: {result.get('count', 0)}") - print(f" Confidence: {result.get('confidence', 0):.3f}") - print(f" Query Time: {result.get('query_time_ms', 0):.2f}ms") - - print(f"\nResults:") - for i, item in enumerate(result.get('results', []), 1): - print(f"\n {i}. {item.get('name', 'Unknown')}") - print(f" Subject: {item.get('subject', 'N/A')}") - print(f" Lean Module: {item.get('lean_module', 'N/A')}") - print(f" Formal Status: {item.get('formal_status', 'N/A')}") - print(f" Proof Status: {item.get('proof_status', 'N/A')}") - print(f" Complexity Score: {item.get('complexity_score', 'N/A')}") - print(f" Dependencies: {item.get('dependencies', 'N/A')}") - if item.get('statement'): - stmt = item['statement'][:100] + "..." if len(item['statement']) > 100 else item['statement'] - print(f" Statement: {stmt}") - - print(f"\nSuggestions:") - for suggestion in result.get('suggestions', []): - print(f" - {suggestion}") - - print(f"\nMetadata:") - for key, value in result.get('metadata', {}).items(): - print(f" {key}: {value}") - - # Check database stats - print("\n" + "=" * 70) - print("DATABASE STATISTICS") - print("=" * 70) - - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - cursor.execute("SELECT COUNT(*) FROM math_entities") - total = cursor.fetchone()[0] - - cursor.execute("SELECT COUNT(*) FROM math_entities WHERE lean_module IS NOT NULL") - lean_count = cursor.fetchone()[0] - - cursor.execute("SELECT COUNT(*) FROM math_entities WHERE subject LIKE '%PeptideMoE%'") - peptide_moe_count = cursor.fetchone()[0] - - cursor.execute("SELECT DISTINCT subject FROM math_entities") - subject_count = len(cursor.fetchall()) - - print(f"\nTotal Entities: {total}") - print(f"Lean Formalized: {lean_count}") - print(f"PeptideMoE Entities: {peptide_moe_count}") - print(f"Subjects: {subject_count}") - print(f"Timestamp: {datetime.now().isoformat()}") - - conn.close() - - print("\n" + "=" * 70) - - # Swarm verdict - print("\nSWARM VERDICT") - print("=" * 70) - - if result['count'] > 0: - print(f"\n✅ PeptideMoE modules successfully integrated and indexed") - print(f" Confidence: {result['confidence']:.3f}") - print(f" Verdict: HIGHLY FEASIBLE") - print(f"\n Modules found:") - for item in result['results']: - print(f" - {item['name']}") - print(f" Location: {item['lean_module']}") - print(f" Complexity: {item['complexity_score']}/100") - else: - print(f"\n❌ No PeptideMoE modules found in database") - print(f" Confidence: {result['confidence']:.3f}") - print(f" Verdict: CHALLENGING") - - # Save results - output_file = "/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_direct_review.json" - with open(output_file, "w") as f: - json.dump(result, f, indent=2) - print(f"\nResults saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_peptide_moe_improvement_suggestions.py b/5-Applications/scripts/swarm_peptide_moe_improvement_suggestions.py deleted file mode 100644 index a8006a6f..00000000 --- a/5-Applications/scripts/swarm_peptide_moe_improvement_suggestions.py +++ /dev/null @@ -1,225 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Improvement Suggestions for PeptideMoE Modules - -Queries the swarm for specific improvement suggestions to make the -PeptideMoE modules 100% complete. -""" - -import sqlite3 -import json -from datetime import datetime -from typing import Dict, List, Any - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def get_peptide_moe_modules() -> List[Dict[str, Any]]: - """Get all PeptideMoE modules from the database.""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute(""" - SELECT entity_id, subject, name, statement, proof_status, formal_status, - lean_module, dependencies, complexity_score, year - FROM math_entities - WHERE subject = 'PeptideMoE' - ORDER BY entity_id - """) - - results = [dict(row) for row in cursor.fetchall()] - conn.close() - return results - -def analyze_current_state(modules: List[Dict[str, Any]]) -> Dict[str, Any]: - """Analyze the current state of PeptideMoE modules.""" - analysis = { - "total_modules": len(modules), - "modules": [], - "completeness_gaps": [], - "formalization_issues": [], - "proof_coverage": [] - } - - for module in modules: - module_analysis = { - "entity_id": module["entity_id"], - "name": module["name"], - "proof_status": module["proof_status"], - "formal_status": module["formal_status"], - "complexity_score": module["complexity_score"], - "completeness": 0.0 - } - - # Assess completeness based on proof_status and formal_status - if module["proof_status"] == "theorems": - module_analysis["completeness"] = 0.9 - elif module["proof_status"] == "definitions": - module_analysis["completeness"] = 0.6 - else: - module_analysis["completeness"] = 0.3 - - if module["formal_status"] == "noncomputable": - module_analysis["completeness"] *= 0.8 # Penalty for noncomputable - - analysis["modules"].append(module_analysis) - - # Identify gaps - if module_analysis["completeness"] < 0.8: - analysis["completeness_gaps"].append(module["name"]) - - if module["formal_status"] == "noncomputable": - analysis["formalization_issues"].append({ - "module": module["name"], - "issue": "noncomputable due to ℝ arithmetic" - }) - - if module["proof_status"] == "definitions": - analysis["proof_coverage"].append({ - "module": module["name"], - "missing": "theorems proving properties" - }) - - return analysis - -def generate_swarm_improvement_suggestions(analysis: Dict[str, Any]) -> List[str]: - """Generate swarm improvement suggestions based on analysis.""" - suggestions = [] - - # Overall completeness assessment - avg_completeness = sum(m["completeness"] for m in analysis["modules"]) / len(analysis["modules"]) - - if avg_completeness < 0.8: - suggestions.append(f"OVERALL: Current completeness {avg_completeness:.1%} - target 100%") - - # Specific module suggestions - for module in analysis["modules"]: - if module["completeness"] < 1.0: - gap = 1.0 - module["completeness"] - - if module["proof_status"] == "definitions": - suggestions.append(f"{module['name']}: Add theorems proving key properties (currently {gap:.0%} gap)") - - if module["formal_status"] == "noncomputable": - suggestions.append(f"{module['name']}: Consider decidable alternatives for ℝ arithmetic (noncomputable penalty)") - - # Cross-module suggestions - suggestions.append("Add theorem: T(P_t) preserves admissibility (drift doesn't break constraints)") - suggestions.append("Add theorem: Φ_filtered is bounded (filtered scores in [0,1])") - suggestions.append("Add theorem: moeDrift is Lipschitz-continuous (controlled expert advice)") - suggestions.append("Add theorem: freeEnergy is convex in conformational space") - suggestions.append("Add theorem: denominatorSafe is equivalent to C_0 > 0 (simplify guardrails)") - - # Examples module specific - examples_module = next((m for m in analysis["modules"] if "Examples" in m["name"]), None) - if examples_module and examples_module["proof_status"] == "definitions": - suggestions.append("PeptideMoEExamples: Add concrete computations with decidable approximations") - suggestions.append("PeptideMoEExamples: Add numerical examples with Q16_16 fixed-point") - - # Failure module specific - failure_module = next((m for m in analysis["modules"] if "Failure" in m["name"]), None) - if failure_module: - suggestions.append("PeptideMoEFailure: Add formal theorems proving failure conditions") - suggestions.append("PeptideMoEFailure: Add counterexample theorems") - - # Repair module specific - repair_module = next((m for m in analysis["modules"] if "Repair" in m["name"]), None) - if repair_module and repair_module["proof_status"] == "theorems": - suggestions.append("PeptideMoERepair: Add constructive proofs (currently axioms)") - suggestions.append("PeptideMoERepair: Replace moeDrift_bounded axiom with theorem") - - # General suggestions - suggestions.append("Add Q16_16 fixed-point version for hardware extraction") - suggestions.append("Add bind instance for informational_bind class") - suggestions.append("Add #eval examples for all key functions") - suggestions.append("Add totality theorems for partial functions") - - return suggestions - -def main(): - """Main entry point.""" - print("=" * 70) - print("SWARM IMPROVEMENT SUGGESTIONS: PeptideMoE Modules") - print("=" * 70) - - # Get current modules - modules = get_peptide_moe_modules() - - print(f"\nCurrent PeptideMoE Modules: {len(modules)}") - for module in modules: - print(f" - {module['name']} ({module['entity_id']})") - print(f" Proof: {module['proof_status']}, Formal: {module['formal_status']}") - - # Analyze current state - analysis = analyze_current_state(modules) - - print("\n" + "=" * 70) - print("COMPLETENESS ANALYSIS") - print("=" * 70) - - avg_completeness = sum(m["completeness"] for m in analysis["modules"]) / len(analysis["modules"]) - print(f"\nOverall Completeness: {avg_completeness:.1%}") - - for module in analysis["modules"]: - print(f"\n{module['name']}: {module['completeness']:.1%}") - print(f" Proof Status: {module['proof_status']}") - print(f" Formal Status: {module['formal_status']}") - - if analysis["completeness_gaps"]: - print(f"\nModules with completeness gaps: {', '.join(analysis['completeness_gaps'])}") - - if analysis["formalization_issues"]: - print(f"\nFormalization issues: {len(analysis['formalization_issues'])}") - for issue in analysis["formalization_issues"]: - print(f" - {issue['module']}: {issue['issue']}") - - # Generate swarm suggestions - suggestions = generate_swarm_improvement_suggestions(analysis) - - print("\n" + "=" * 70) - print("SWARM IMPROVEMENT SUGGESTIONS") - print("=" * 70) - - for i, suggestion in enumerate(suggestions, 1): - print(f"\n{i}. {suggestion}") - - print("\n" + "=" * 70) - print("PRIORITY ORDERING") - print("=" * 70) - - # Prioritize suggestions - high_priority = [s for s in suggestions if any(keyword in s for keyword in ["theorem", "proof", "bounded", "admissibility"])] - medium_priority = [s for s in suggestions if any(keyword in s for keyword in ["Q16_16", "bind", "eval"])] - low_priority = [s for s in suggestions if "Consider" in s] - - print("\nHIGH PRIORITY (Core mathematical properties):") - for i, s in enumerate(high_priority[:5], 1): - print(f" {i}. {s}") - - print("\nMEDIUM PRIORITY (Hardware extraction and verification):") - for i, s in enumerate(medium_priority[:3], 1): - print(f" {i}. {s}") - - print("\nLOW PRIORITY (Optional enhancements):") - for i, s in enumerate(low_priority[:2], 1): - print(f" {i}. {s}") - - # Save suggestions - output_file = "/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_improvement_suggestions.json" - report = { - "analysis": analysis, - "suggestions": suggestions, - "high_priority": high_priority, - "medium_priority": medium_priority, - "low_priority": low_priority, - "timestamp": datetime.now().isoformat() - } - - with open(output_file, "w") as f: - json.dump(report, f, indent=2) - - print(f"\nSuggestions saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_priority_alert.py b/5-Applications/scripts/swarm_priority_alert.py deleted file mode 100644 index f26ea9ef..00000000 --- a/5-Applications/scripts/swarm_priority_alert.py +++ /dev/null @@ -1,320 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Priority Alert — High-Priority Math Foundation Audit Request - -This script interfaces with the swarm API to issue a CRITICAL priority alert -regarding suspected deep faults in the mathematical foundations of the system. - -Priority Level: CRITICAL (P0) -Category: Mathematical Foundation Integrity -Origin: Principal Investigator -""" - -import sys -import json -import sqlite3 -from datetime import datetime, timezone -from typing import Dict, Any, List - -sys.path.insert(0, '/home/allaun/Documents/Research Stack/tools') - -from lean_unified_shim import SwarmAPISystem, SwarmQueryRequest - - -class SwarmPriorityAlert: - """ - High-priority alert system for swarm communication. - Uses the query API to inject priority-classified messages. - """ - - PRIORITY_LEVELS = { - 'P0': 'CRITICAL - System integrity at risk', - 'P1': 'HIGH - Immediate action required', - 'P2': 'MEDIUM - Address within 24h', - 'P3': 'LOW - Address when convenient' - } - - def __init__(self): - self.api = SwarmAPISystem() - self.timestamp = datetime.now(timezone.utc).isoformat() - - def send_critical_alert(self, subject: str, details: str, proof_required: bool = True) -> Dict[str, Any]: - """ - Send a CRITICAL (P0) priority alert to the swarm. - - The alert is injected into the math_entities database as a high-priority - task with proof_status = 'conjecture' requiring immediate formalization. - """ - - # Create the alert as a formal entity requiring proof - alert_entity = { - 'entity_id': f'PRIORITY_ALERT_{self.timestamp.replace(":", "_")}', - 'subject': 'critical_audit', - 'secondary_subjects': json.dumps(['mathematics', 'foundations', 'verification']), - 'name': f'[P0] {subject}', - 'statement': details, - 'proof_status': 'conjecture' if proof_required else 'proven', - 'formal_status': 'needs_formalization', - 'lean_module': None, - 'dependencies': json.dumps(['AllOTOMModules']), - 'citations': json.dumps(['PrincipalInvestigatorDirective']), - 'complexity_score': 999999, # Maximum complexity = maximum priority - 'year': 2026, - 'source_file': '/dev/null', # Virtual directive - 'priority': 'P0', - 'requires_immediate_action': True - } - - # Insert into database - if self.api.conn: - cursor = self.api.conn.cursor() - - # Check if priority_alerts table exists, create if not - cursor.execute(""" - CREATE TABLE IF NOT EXISTS priority_alerts ( - entity_id TEXT PRIMARY KEY, - subject TEXT, - secondary_subjects TEXT, - name TEXT, - statement TEXT, - proof_status TEXT, - formal_status TEXT, - lean_module TEXT, - dependencies TEXT, - citations TEXT, - complexity_score INTEGER, - year INTEGER, - source_file TEXT, - priority TEXT, - requires_immediate_action BOOLEAN, - created_at TEXT - ) - """) - - # Insert the alert - cursor.execute(""" - INSERT OR REPLACE INTO priority_alerts - (entity_id, subject, secondary_subjects, name, statement, proof_status, - formal_status, lean_module, dependencies, citations, complexity_score, - year, source_file, priority, requires_immediate_action, created_at) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - alert_entity['entity_id'], - alert_entity['subject'], - alert_entity['secondary_subjects'], - alert_entity['name'], - alert_entity['statement'], - alert_entity['proof_status'], - alert_entity['formal_status'], - alert_entity['lean_module'], - alert_entity['dependencies'], - alert_entity['citations'], - alert_entity['complexity_score'], - alert_entity['year'], - alert_entity['source_file'], - alert_entity['priority'], - alert_entity['requires_immediate_action'], - self.timestamp - )) - - self.api.conn.commit() - - return { - 'success': True, - 'alert_id': alert_entity['entity_id'], - 'priority': 'P0', - 'message': f'CRITICAL alert sent to swarm: {subject}', - 'timestamp': self.timestamp, - 'requires_proof': proof_required - } - else: - return { - 'success': False, - 'error': 'Database connection not available', - 'fallback': 'Logging to console only' - } - - def audit_math_foundations(self) -> Dict[str, Any]: - """ - Initiates a comprehensive audit of all mathematical foundations. - This is the P0 directive from the principal investigator. - """ - - audit_scope = """ -PRIORITY ALERT: P0 - Mathematical Foundation Integrity Audit - -Origin: Principal Investigator -Classification: CRITICAL -Date: {timestamp} - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -ISSUE SUMMARY -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Suspected DEEP FAULTS detected in mathematical foundations underlying -the entire OTOM (Ordered Transformation & Orchestration Model) system. - -The principal investigator has identified potential inconsistencies, -unproven assumptions, and gaps in formal verification that could -compromise the integrity of all derived systems. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -REQUIRED ACTIONS (Immediate - P0 Priority) -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -1. TRIUMVIRATE ENFORCEMENT ACTIVATION - → Builder: Suspend all non-critical forward progress - → Warden: Initiate comprehensive proof validation sweep - → Judge: Adjudicate all pending mathematical conjectures - -2. COMPREHENSIVE FOUNDATION AUDIT - → Review ALL 88+ Lean modules for logical consistency - → Verify ALL cost functions preserve invariants - → Check ALL bind instances satisfy laws - → Validate ALL Q16_16 fixed-point operations - -3. PROOF REQUIREMENTS - → Every theorem must have complete formal proof - → Every definition must have totality witness - → Every axiom must be independently justified - → No 'sorry' allowed in committed code - -4. CROSS-REFERENCE VALIDATION - → MATH_MODEL_MAP-42126.md must be complete - → All equations must have verified extraction paths - → All bind classes must have lawful instances - → All acronyms (OTOM, ENE, PIST, AMMR) must have formal definitions - -5. GENOMIC COMPRESSION PRIORITY FIXES - → Extract formal lemmas from 2504.03733 - → Connect to ProteinRepresentation.lean - → Prove compression bounds vs gzip/bzip2 - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -SUSPECTED FAULT AREAS -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -⚠ Unified Field Φ(x) formulation - verify all component terms -⚠ Q16_16 arithmetic edge cases - check overflow/underflow -⚠ Master Equation implementation - validate recursive evolution -⚠ SLUG-3 ternary gate logic - verify quaternion derivation -⚠ Hybrid TSM-PIST-Torus integration - check state transitions -⚠ Triumvirate clock synchronization - validate ternary operations - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -PROOF OR REFUTATION REQUIRED -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -The swarm is directed to either: - -(a) PROVE the mathematical foundations are sound, OR -(b) REFUTE with specific counterexamples and required fixes - -No intermediate "assumed correct" state is acceptable. - -The Triumvirate (Builder/Judge/Warden) must reach consensus on -the integrity of the mathematical stack before any further -critical system evolution. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -HARDWARE CLOCK MAPPING -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Builder → manifold_reg (ADD clock) -Warden → stark_trace & warden_valid (SUBTRACT clock) -Judge → heatsink_halt (PAUSE clock) - -All clocks must synchronize on this priority directive. - -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ -""".format(timestamp=self.timestamp) - - return self.send_critical_alert( - subject="MATH FOUNDATION INTEGRITY AUDIT - P0 CRITICAL", - details=audit_scope, - proof_required=True - ) - - def get_swarm_status(self) -> Dict[str, Any]: - """Query the current swarm status.""" - if not self.api.conn: - return {'error': 'Database not connected'} - - cursor = self.api.conn.cursor() - - # Count priority alerts (table may not exist yet) - try: - cursor.execute("SELECT COUNT(*) FROM priority_alerts WHERE priority = 'P0'") - p0_count = cursor.fetchone()[0] - except sqlite3.OperationalError: - p0_count = 0 - - # Count math entities needing formalization - try: - cursor.execute(""" - SELECT COUNT(*) FROM math_entities - WHERE formal_status = 'needs_formalization' - OR proof_status = 'conjecture' - """) - pending_proofs = cursor.fetchone()[0] - except sqlite3.OperationalError: - pending_proofs = 0 - - return { - 'p0_alerts': p0_count, - 'pending_proofs': pending_proofs, - 'database_connected': True, - 'timestamp': self.timestamp - } - - -def main(): - """Send the critical P0 alert to the swarm.""" - - print("="*70) - print("SWARM PRIORITY ALERT SYSTEM") - print("="*70) - print() - - alert_system = SwarmPriorityAlert() - - # Check current status - print("[1] Checking swarm status...") - status = alert_system.get_swarm_status() - print(f" P0 alerts: {status.get('p0_alerts', 'N/A')}") - print(f" Pending proofs: {status.get('pending_proofs', 'N/A')}") - print() - - # Send the critical alert - print("[2] Sending P0 CRITICAL alert...") - print(" Subject: MATH FOUNDATION INTEGRITY AUDIT") - print(" Priority: P0 (CRITICAL)") - print(" Origin: Principal Investigator") - print() - - result = alert_system.audit_math_foundations() - - if result['success']: - print(f"[✓] Alert sent successfully") - print(f" Alert ID: {result['alert_id']}") - print(f" Timestamp: {result['timestamp']}") - print() - print("="*70) - print("ALERT CONTENTS") - print("="*70) - print() - print(result.get('message', 'No message')) - print() - print("="*70) - print("The swarm has been notified.") - print("The Triumvirate (Builder/Judge/Warden) is now activated.") - print("="*70) - else: - print(f"[✗] Failed to send alert: {result.get('error', 'Unknown error')}") - print(f" Fallback: {result.get('fallback', 'None')}") - - return result - - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/swarm_review_codon_otom.py b/5-Applications/scripts/swarm_review_codon_otom.py deleted file mode 100644 index 6e7ad31f..00000000 --- a/5-Applications/scripts/swarm_review_codon_otom.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Review for CodonOTOM Module - -Queries the swarm for review of the CodonOTOM Lean module -and the codon fitness function. -""" - -import sqlite3 -import json -from datetime import datetime -from typing import Dict, List, Any - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def get_codon_fitness_entry() -> Dict[str, Any]: - """Get the codon fitness function entry from the database.""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute(""" - SELECT entity_id, subject, secondary_subjects, name, statement, proof_status, formal_status, - lean_module, dependencies, citations, complexity_score, year - FROM math_entities - WHERE entity_id = 'codon_fitness_001' - """) - - result = cursor.fetchone() - conn.close() - - if result: - return dict(result) - return None - -def analyze_implementation(entry: Dict[str, Any]) -> Dict[str, Any]: - """Analyze the CodonOTOM implementation.""" - analysis = { - "entity_id": entry["entity_id"], - "name": entry["name"], - "statement": entry["statement"], - "lean_module": entry["lean_module"], - "proof_status": entry["proof_status"], - "formal_status": entry["formal_status"], - "complexity_score": entry["complexity_score"], - "completeness": 0.0, - "issues": [], - "strengths": [] - } - - # Assess completeness - if entry["proof_status"] == "definitions": - analysis["completeness"] = 0.5 - analysis["issues"].append("No theorems proving properties of phiCodon") - elif entry["proof_status"] == "theorems": - analysis["completeness"] = 0.9 - - if entry["formal_status"] == "noncomputable": - analysis["completeness"] *= 0.8 - analysis["issues"].append("Noncomputable due to ℝ arithmetic - consider Q16_16 for hardware extraction") - - # Strengths - analysis["strengths"].append("Implements OTOM codon efficiency functional") - analysis["strengths"].append("Includes mutation dynamics (deltaPhi)") - analysis["strengths"].append("Defines beneficial mutation predicate") - analysis["strengths"].append("Includes denominator safety condition") - analysis["strengths"].append("Theorem: mutation_improves proves beneficial mutation condition") - - return analysis - -def generate_swarm_verdict(analysis: Dict[str, Any]) -> Dict[str, Any]: - """Generate swarm verdict on CodonOTOM implementation.""" - verdict = { - "entity_id": analysis["entity_id"], - "name": analysis["name"], - "overall_score": analysis["completeness"] * 100, - "status": "PENDING", - "recommendations": [] - } - - # Determine status - if analysis["completeness"] >= 0.8: - verdict["status"] = "APPROVED" - verdict["recommendations"].append("Ready for production use") - elif analysis["completeness"] >= 0.5: - verdict["status"] = "CONDITIONAL" - verdict["recommendations"].append("Add theorems for key properties") - verdict["recommendations"].append("Consider decidable alternatives for ℝ arithmetic") - else: - verdict["status"] = "REJECTED" - verdict["recommendations"].append("Incomplete implementation") - - # Add specific recommendations - for issue in analysis["issues"]: - verdict["recommendations"].append(f"ISSUE: {issue}") - - for strength in analysis["strengths"]: - verdict["recommendations"].append(f"STRENGTH: {strength}") - - return verdict - -def main(): - """Main entry point.""" - print("=" * 70) - print("SWARM REVIEW: CodonOTOM Module") - print("=" * 70) - - # Get codon fitness entry - entry = get_codon_fitness_entry() - - if not entry: - print("ERROR: Codon fitness function not found in database") - return - - print(f"\nEntity ID: {entry['entity_id']}") - print(f"Name: {entry['name']}") - print(f"Subject: {entry['subject']}") - print(f"Statement: {entry['statement']}") - print(f"Lean Module: {entry['lean_module']}") - print(f"Proof Status: {entry['proof_status']}") - print(f"Formal Status: {entry['formal_status']}") - - # Analyze implementation - analysis = analyze_implementation(entry) - - print("\n" + "=" * 70) - print("IMPLEMENTATION ANALYSIS") - print("=" * 70) - - print(f"\nCompleteness: {analysis['completeness']:.1%}") - - if analysis['strengths']: - print("\nStrengths:") - for strength in analysis['strengths']: - print(f" ✓ {strength}") - - if analysis['issues']: - print("\nIssues:") - for issue in analysis['issues']: - print(f" ✗ {issue}") - - # Generate swarm verdict - verdict = generate_swarm_verdict(analysis) - - print("\n" + "=" * 70) - print("SWARM VERDICT") - print("=" * 70) - - print(f"\nOverall Score: {verdict['overall_score']:.0f}/100") - print(f"Status: {verdict['status']}") - - print("\nRecommendations:") - for rec in verdict['recommendations']: - print(f" • {rec}") - - # Save verdict - output_file = "/home/allaun/Documents/Research Stack/data/swarm_codon_otom_review.json" - report = { - "analysis": analysis, - "verdict": verdict, - "timestamp": datetime.now().isoformat() - } - - with open(output_file, "w") as f: - json.dump(report, f, indent=2) - - print(f"\nSwarm review saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_review_peptide_moe_modules.py b/5-Applications/scripts/swarm_review_peptide_moe_modules.py deleted file mode 100644 index ee3a4752..00000000 --- a/5-Applications/scripts/swarm_review_peptide_moe_modules.py +++ /dev/null @@ -1,112 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Review for PeptideMoE Lean Modules - -Queries the swarm API to review the newly added PeptideMoE modules: -- PeptideMoE -- PeptideMoEExamples -- PeptideMoEFailure -- PeptideMoERepair -""" - -import requests -import json -from lean_unified_shim import SwarmAPISystem, Any - -# Swarm API endpoint -SWARM_API_URL = "http://127.0.0.1:8000" - -def query_swarm(subjects: List[str], keywords: str = None, has_lean: bool = True) -> Dict[str, Any]: - """Query the swarm API.""" - try: - response = requests.post( - f"{SWARM_API_URL}/query", - json={ - "subjects": subjects, - "keywords": keywords, - "has_lean_formalization": has_lean, - "limit": 20, - "include_metadata": True - }, - timeout=10 - ) - response.raise_for_status() - return response.json() - except requests.exceptions.ConnectionError: - return {"error": "Could not connect to swarm API. Ensure it's running."} - except Exception as e: - return {"error": str(e)} - -def main(): - """Main entry point.""" - print("=" * 70) - print("SWARM REVIEW: PeptideMoE Lean Modules") - print("=" * 70) - - # Query for PeptideMoE-related modules - print("\nQuerying swarm for PeptideMoE modules...") - result = query_swarm( - subjects=["PeptideMoE"], - keywords="PeptideMoE", - has_lean=True - ) - - if "error" in result: - print(f"\n[!] Error: {result['error']}") - print("\nNote: To start the swarm API, run:") - print(" cd /home/allaun/Documents/Research Stack/tools") - print(" python swarm_api.py") - return - - print(f"\nQuery Results:") - print(f" Success: {result.get('success', False)}") - print(f" Count: {result.get('count', 0)}") - print(f" Confidence: {result.get('confidence', 0):.3f}") - print(f" Query Time: {result.get('query_time_ms', 0):.2f}ms") - - print(f"\nResults:") - for i, item in enumerate(result.get('results', []), 1): - print(f"\n {i}. {item.get('name', 'Unknown')}") - print(f" Subject: {item.get('subject', 'N/A')}") - print(f" Lean Module: {item.get('lean_module', 'N/A')}") - print(f" Formal Status: {item.get('formal_status', 'N/A')}") - print(f" Proof Status: {item.get('proof_status', 'N/A')}") - if item.get('statement'): - stmt = item['statement'][:100] + "..." if len(item['statement']) > 100 else item['statement'] - print(f" Statement: {stmt}") - - print(f"\nSuggestions:") - for suggestion in result.get('suggestions', []): - print(f" - {suggestion}") - - print(f"\nMetadata:") - for key, value in result.get('metadata', {}).items(): - print(f" {key}: {value}") - - # Check database stats - print("\n" + "=" * 70) - print("DATABASE STATISTICS") - print("=" * 70) - - try: - stats_response = requests.get(f"{SWARM_API_URL}/stats", timeout=10) - stats_response.raise_for_status() - stats = stats_response.json() - - print(f"\nTotal Entities: {stats.get('total_entities', 0)}") - print(f"Lean Formalized: {stats.get('lean_formalized', 0)}") - print(f"Subjects: {stats.get('subjects', 0)}") - print(f"Timestamp: {stats.get('timestamp', 'N/A')}") - except Exception as e: - print(f"\n[!] Could not fetch stats: {e}") - - print("\n" + "=" * 70) - - # Save results - output_file = "/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_module_review.json" - with open(output_file, "w") as f: - json.dump(result, f, indent=2) - print(f"\nResults saved to: {output_file}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_test_domain_models.py b/5-Applications/scripts/swarm_test_domain_models.py deleted file mode 100644 index 7922f661..00000000 --- a/5-Applications/scripts/swarm_test_domain_models.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Test: Domain-Specific Models Evaluation - -Have the swarm evaluate domain-specific models (mathematics, science) -on provably hard questions in their respective domains. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface, Domain, MathModel -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def swarm_test_domain_models(): - """Swarm evaluates domain-specific models""" - print("=" * 70) - print("SWARM TEST: Domain-Specific Models Evaluation") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Step 1: Swarm generates domain-specific hard questions - print("\n[1/6] Swarm generating domain-specific hard questions...") - - domain_questions = { - "mathematics_deepseek": { - "domain": "mathematics", - "model": MathModel.DEEPSEEK_MATH_V2.value, - "question": "Prove that for any integer n > 1, there exists a prime p such that n < p < 2n (Bertrand's Postulate). Provide a rigorous proof using the method of contradiction and properties of prime factorization.", - "difficulty": "Advanced mathematical proof", - "verification_required": True - }, - "mathematics_qwen": { - "domain": "mathematics", - "model": MathModel.QWEN2_MATH_7B.value, - "question": "Solve the system of differential equations: dx/dt = -2x + y, dy/dt = x - 2y with initial conditions x(0)=1, y(0)=0. Find the general solution and analyze the stability of the equilibrium point.", - "difficulty": "Multivariable calculus and differential equations", - "verification_required": False - }, - "theorem_proving": { - "domain": "theorem_proving", - "model": MathModel.DEEPSEEK_MATH_V2.value, - "question": "Prove that the Möbius transformation f(z) = (az + b)/(cz + d) with ad - bc = 1 is an isometry of the hyperbolic plane in the Poincaré disk model. Show that it preserves hyperbolic distance and maps geodesics to geodesics.", - "difficulty": "Advanced topology and geometry", - "verification_required": True - } - } - - print(f"Generated {len(domain_questions)} domain-specific questions:") - for key, data in domain_questions.items(): - print(f" - {key}: {data['domain']} / {data['model']}") - print(f" Difficulty: {data['difficulty']}") - - # Step 2: Submit questions to domain models - print("\n[2/6] Submitting questions to domain models...") - - task_ids = {} - for key, data in domain_questions.items(): - task = interface.submit_domain_task( - domain=data["domain"], - model=data["model"], - task_type="reasoning", - input_data={"problem": data["question"]}, - enable_verification=data["verification_required"], - max_tokens=4096, - priority=10 - ) - task_ids[key] = task["task_id"] - print(f"Submitted {key}: {task['task_id']}") - - # Step 3: Execute domain tasks - print("\n[3/6] Executing domain tasks...") - - results = {} - for key, task_id in task_ids.items(): - result = interface.execute_domain_task(task_id) - results[key] = result - print(f"Executed {key}: {result['success']}") - - if result['success']: - print(f" Model: {result['result'].get('model')}") - print(f" Reasoning: {result['result'].get('reasoning', 'N/A')}") - print(f" Confidence: {result['result'].get('confidence', 'N/A')}") - - # Step 4: Swarm evaluates responses - print("\n[4/6] Swarm evaluating domain model responses...") - - evaluations = {} - for key, result in results.items(): - if result['success']: - question_data = domain_questions[key] - response_data = result['result'] - - # Domain-specific evaluation criteria - if question_data["domain"] == "mathematics": - evaluation = { - "mathematical_rigor": { - "score": 0.85 if "DeepSeek" in question_data["model"] else 0.80, - "notes": "Mathematical reasoning depth and formal correctness" - }, - "proof_completeness": { - "score": 0.90 if question_data["verification_required"] else 0.75, - "notes": "Completeness of proof or solution" - }, - "logical_flow": { - "score": 0.88, - "notes": "Logical progression and coherence" - }, - "notation_clarity": { - "score": 0.82, - "notes": "Mathematical notation and clarity" - } - } - elif question_data["domain"] == "theorem_proving": - evaluation = { - "formal_correctness": { - "score": 0.92, - "notes": "Formal mathematical correctness" - }, - "geometric_intuition": { - "score": 0.85, - "notes": "Understanding of geometric concepts" - }, - "proof_structure": { - "score": 0.88, - "notes": "Structure and organization of proof" - }, - "verification": { - "score": 0.95 if response_data.get("verification") else 0.70, - "notes": "Self-verification capability" - } - } - else: - evaluation = { - "accuracy": { - "score": 0.80, - "notes": "General accuracy" - }, - "reasoning": { - "score": 0.75, - "notes": "Reasoning capability" - } - } - - overall_score = sum(c['score'] for c in evaluation.values()) / len(evaluation) - evaluations[key] = { - "evaluation": evaluation, - "overall_score": overall_score - } - - print(f"\nEvaluation for {key}:") - print(f" Overall Score: {overall_score:.2%}") - for criterion, data in evaluation.items(): - print(f" - {criterion}: {data['score']:.2%}") - print(f" Notes: {data['notes']}") - - # Step 5: Submit evaluations to competition - print("\n[5/6] Submitting evaluations to competition...") - - for key, eval_data in evaluations.items(): - evaluation_entry = CompetitionEntry( - agent_id=f"domain_model_evaluator_{key}", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=eval_data["overall_score"], - metrics=eval_data["evaluation"], - timestamp=int(time.time()), - proposal=f"Swarm evaluation of {key} domain model" - ) - - try: - competition.submit_competition_entry(evaluation_entry) - print(f"Evaluation for {key} submitted to competition") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Step 6: Final swarm verdict - print("\n[6/6] Final swarm verdict...") - - print("\n" + "=" * 70) - print("SWARM VERDICT: Domain-Specific Models") - print("=" * 70) - - avg_score = sum(e["overall_score"] for e in evaluations.values()) / len(evaluations) - - print(f"\nAverage Score Across All Domain Models: {avg_score:.2%}") - - print("\nIndividual Model Performance:") - for key, eval_data in evaluations.items(): - model = domain_questions[key]["model"] - domain = domain_questions[key]["domain"] - score = eval_data["overall_score"] - print(f" - {model} ({domain}): {score:.2%}") - - print("\nKey Findings:") - print(" - DeepSeek-Math-V2 demonstrates strong self-verifiable reasoning") - print(" - Qwen2-Math-7B provides solid mathematical problem-solving") - print(" - Theorem proving models show formal correctness capability") - print(" - Domain-specific models outperform general-purpose models in their domains") - - print("\n" + "=" * 70) - if avg_score >= 0.85: - print("SWARM VERDICT: EXCELLENT") - print("Domain-specific models demonstrate exceptional performance") - print("in their respective domains, significantly outperforming general models.") - elif avg_score >= 0.75: - print("SWARM VERDICT: STRONG") - print("Domain-specific models show strong performance with room for improvement.") - else: - print("SWARM VERDICT: MODERATE") - print("Domain-specific models perform adequately but need refinement.") - print("=" * 70) - - return { - "domain_questions": domain_questions, - "results": results, - "evaluations": evaluations, - "average_score": avg_score - } - - -if __name__ == "__main__": - test_result = swarm_test_domain_models() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_domain_models_test_results.json" - with open(output_path, "w") as f: - json.dump(test_result, f, indent=2) - - print(f"\nTest results saved to: {output_path}") diff --git a/5-Applications/scripts/swarm_test_gemma_4.py b/5-Applications/scripts/swarm_test_gemma_4.py deleted file mode 100644 index 4821ee2d..00000000 --- a/5-Applications/scripts/swarm_test_gemma_4.py +++ /dev/null @@ -1,204 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Test: Gemma 4 with Provably Hard Question - -Have the swarm generate a provably hard question and test Gemma 4's -ability to address it, evaluating the response quality. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.gemma_4_integration import GemmaTask, GemmaVariant -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def swarm_test_gemma_4(): - """Swarm generates hard question and tests Gemma 4""" - print("=" * 70) - print("SWARM TEST: Gemma 4 with Provably Hard Question") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - # Step 1: Swarm generates provably hard question - print("\n[1/5] Swarm generating provably hard question...") - - # Swarm consensus on hard question categories - hard_question_categories = [ - { - "category": "Mathematical Proof", - "question": "Prove that there are infinitely many prime numbers of the form 4k+3, and explain why this is harder than proving infinitude of all primes.", - "difficulty": "NP-hard level", - "reasoning_required": True - }, - { - "category": "Algorithmic Complexity", - "question": "Given a set of integers, determine if any subset sums to zero. Prove whether this problem is NP-complete and provide a polynomial-time approximation algorithm with bounded error.", - "difficulty": "NP-complete", - "reasoning_required": True - }, - { - "category": "Topological Manifold", - "question": "Given a hyperbolic manifold with Poincaré disk coordinates, prove that the Möbius transformation preserves hyperbolic distance and explain why this property is essential for semantic vector encoding.", - "difficulty": "Requires advanced topology knowledge", - "reasoning_required": True - }, - { - "category": "Cryptography", - "question": "Design a zero-knowledge proof protocol for proving knowledge of a discrete logarithm without revealing the logarithm itself, and prove its security under the discrete logarithm assumption.", - "difficulty": "Advanced cryptographic proof", - "reasoning_required": True - } - ] - - # Swarm selects the hardest question - selected_question = hard_question_categories[2] # Topological Manifold - most relevant to current system - - print(f"Selected Category: {selected_question['category']}") - print(f"Difficulty: {selected_question['difficulty']}") - print(f"Question: {selected_question['question']}") - - # Step 2: Submit question to Gemma 4 - print("\n[2/5] Submitting question to Gemma 4...") - - gemma_task = interface.submit_gemma_task( - task_type="reasoning", - input_data={ - "prompt": selected_question['question'], - "context": "This is a provably hard question requiring advanced reasoning.", - "enable_thinking": True - }, - variant="E4B", - enable_thinking=True, - max_tokens=2048, - priority=10 - ) - - print(f"Task submitted: {gemma_task['task_id']}") - print(f"Variant: {gemma_task['variant']}") - print(f"Thinking mode: enabled") - - # Step 3: Execute Gemma 4 task - print("\n[3/5] Executing Gemma 4 task...") - - result = interface.execute_gemma_task(gemma_task['task_id']) - - print(f"Execution result: {result['success']}") - - if result['success']: - gemma_response = result['result'] - print("\nGemma 4 Response:") - print(json.dumps(gemma_response, indent=2)) - else: - print(f"Error: {result.get('error')}") - gemma_response = None - - # Step 4: Swarm evaluates Gemma 4's response - print("\n[4/5] Swarm evaluating Gemma 4 response...") - - if gemma_response: - # Evaluation criteria - evaluation_criteria = { - "mathematical_accuracy": { - "score": 0.7, # Placeholder - would need actual verification - "notes": "Response shows understanding but may contain mathematical errors" - }, - "reasoning_depth": { - "score": 0.8, # Placeholder - based on thinking mode output - "notes": "Thinking mode enabled, shows step-by-step reasoning" - }, - "domain_knowledge": { - "score": 0.75, # Placeholder - hyperbolic manifold understanding - "notes": "Demonstrates knowledge of Poincaré disk and Möbius transformations" - }, - "clarity": { - "score": 0.85, # Placeholder - response clarity - "notes": "Response is well-structured and clear" - }, - "completeness": { - "score": 0.6, # Placeholder - whether fully addressed the question - "notes": "May not fully prove the required property" - } - } - - overall_score = sum(c['score'] for c in evaluation_criteria.values()) / len(evaluation_criteria) - - print("Evaluation Results:") - for criterion, data in evaluation_criteria.items(): - print(f" - {criterion}: {data['score']:.2%}") - print(f" Notes: {data['notes']}") - - print(f"\nOverall Score: {overall_score:.2%}") - - # Step 5: Submit evaluation to competition - print("\n[5/5] Submitting evaluation to competition...") - - evaluation_entry = CompetitionEntry( - agent_id="gemma_4_tester", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=overall_score, - metrics=evaluation_criteria, - timestamp=int(time.time()), - proposal="Swarm evaluation of Gemma 4 on provably hard question" - ) - - try: - competition.submit_competition_entry(evaluation_entry) - print("Evaluation submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Final verdict - print("\n" + "=" * 70) - print("SWARM VERDICT") - print("=" * 70) - - if overall_score >= 0.8: - print("Gemma 4 PASSED: Successfully addressed the hard question") - print("The model demonstrates strong reasoning capabilities and domain knowledge.") - elif overall_score >= 0.6: - print("Gemma 4 PARTIALLY PASSED: Addressed question with limitations") - print("The model shows understanding but may need refinement for complete solutions.") - else: - print("Gemma 4 FAILED: Unable to adequately address the hard question") - print("The model may struggle with advanced domain-specific reasoning.") - print("=" * 70) - - return { - "question": selected_question, - "gemma_response": gemma_response, - "evaluation": evaluation_criteria, - "overall_score": overall_score - } - else: - print("\n" + "=" * 70) - print("SWARM VERDICT: TEST FAILED") - print("Gemma 4 failed to execute the task.") - print("=" * 70) - - return { - "question": selected_question, - "error": result.get("error"), - "overall_score": 0.0 - } - - -if __name__ == "__main__": - test_result = swarm_test_gemma_4() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/swarm_gemma_4_test_results.json" - with open(output_path, "w") as f: - json.dump(test_result, f, indent=2) - - print(f"\nTest results saved to: {output_path}") diff --git a/5-Applications/scripts/swarm_topology_encoding_review.py b/5-Applications/scripts/swarm_topology_encoding_review.py deleted file mode 100644 index 4f285586..00000000 --- a/5-Applications/scripts/swarm_topology_encoding_review.py +++ /dev/null @@ -1,378 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Topology Encoding Review - -Uses the omnidirectional interface to: -1. Review the omnidirectional setup -2. Analyze topology aspects of the interconnected systems -3. Propose encoding schemes that benefit the topology -4. Generate consensus-based recommendations - -This script leverages: -- Omnidirectional Interface for cross-system communication -- MoE Cache for expert routing analysis -- Semantic vector analysis for topology optimization -""" - -import sys -import json -import sqlite3 -from pathlib import Path -from typing import Dict, List, Optional, Any -import hashlib - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.moe_ene_cache import MoEENECache - - -class SwarmTopologyEncoder: - """Swarm-driven topology encoding scheme analyzer""" - - def __init__(self): - self.math_db_path = "/home/allaun/Documents/Research Stack/data/math_entities.db" - self.omni_interface = OmnidirectionalInterface() - self.moe_cache = MoEENECache() - - def review_omnidirectional_setup(self) -> Dict[str, Any]: - """Review the current omnidirectional setup""" - print("=" * 70) - print("SWARM REVIEW: Omnidirectional Setup Analysis") - print("=" * 70) - - review = { - "timestamp": int(__import__('time').time()), - "systems_reviewed": [], - "topology_analysis": {}, - "encoding_proposals": [], - "consensus_score": 0.0 - } - - # 1. Review system interconnections - print("\n[1/5] Analyzing System Interconnections...") - interconnection_analysis = self._analyze_interconnections() - review["systems_reviewed"] = interconnection_analysis["systems"] - review["topology_analysis"]["interconnections"] = interconnection_analysis - - # 2. Analyze semantic vector topology - print("[2/5] Analyzing Semantic Vector Topology...") - semantic_analysis = self._analyze_semantic_topology() - review["topology_analysis"]["semantic_vectors"] = semantic_analysis - - # 3. Review database topology - print("[3/5] Analyzing Database Topology...") - db_analysis = self._analyze_database_topology() - review["topology_analysis"]["databases"] = db_analysis - - # 4. Generate encoding proposals - print("[4/5] Generating Encoding Proposals...") - proposals = self._generate_encoding_proposals(review["topology_analysis"]) - review["encoding_proposals"] = proposals - - # 5. Swarm consensus - print("[5/5] Computing Swarm Consensus...") - consensus = self._compute_swarm_consensus(proposals) - review["consensus_score"] = consensus["score"] - review["consensus_details"] = consensus - - return review - - def _analyze_interconnections(self) -> Dict[str, Any]: - """Analyze system interconnection topology""" - systems = ["Swarm API", "MoE System", "ENE Database", "Math Database"] - - # Get health status from omnidirectional interface - health = self.omni_interface.get_system_health() - - # Build connection graph - connections = { - "Swarm API": ["MoE System", "ENE Database", "Math Database"], - "MoE System": ["Swarm API", "ENE Database", "Math Database"], - "ENE Database": ["Swarm API", "MoE System"], - "Math Database": ["Swarm API", "MoE System"] - } - - # Calculate topology metrics - total_connections = sum(len(v) for v in connections.values()) - avg_connections = total_connections / len(connections) - - return { - "systems": systems, - "connections": connections, - "health_status": health, - "topology_metrics": { - "total_connections": total_connections, - "average_connections_per_system": avg_connections, - "graph_density": total_connections / (len(systems) * (len(systems) - 1)) - } - } - - def _analyze_semantic_topology(self) -> Dict[str, Any]: - """Analyze 14D semantic vector topology""" - # Generate sample semantic vectors - sample_vectors = [] - - for i in range(10): - query_text = f"query_{i}" - vector = self.omni_interface._derive_semantic_vector(query_text) - sample_vectors.append(vector) - - # Calculate topology metrics - dimension_importance = [0.0] * 14 - for vector in sample_vectors: - for i, val in enumerate(vector): - dimension_importance[i] += val - - dimension_importance = [v / len(sample_vectors) for v in dimension_importance] - - # Identify dominant dimensions - dominant_dims = sorted( - [(i, val) for i, val in enumerate(dimension_importance)], - key=lambda x: x[1], - reverse=True - )[:5] - - return { - "sample_vectors": sample_vectors[:3], # First 3 for brevity - "dimension_importance": dimension_importance, - "dominant_dimensions": dominant_dims, - "vector_space_topology": { - "dimensions": 14, - "sparsity": sum(1 for v in dimension_importance if v < 0.1) / 14, - "entropy": self._calculate_entropy(dimension_importance) - } - } - - def _analyze_database_topology(self) -> Dict[str, Any]: - """Analyze database table topology""" - try: - conn = sqlite3.connect(self.math_db_path) - cursor = conn.cursor() - - # Get table schemas - cursor.execute("SELECT name FROM sqlite_master WHERE type='table'") - tables = [row[0] for row in cursor.fetchall()] - - table_info = {} - for table in tables: - cursor.execute(f"PRAGMA table_info({table})") - columns = cursor.fetchall() - table_info[table] = { - "column_count": len(columns), - "columns": [col[1] for col in columns] - } - - # Get row counts - row_counts = {} - for table in tables: - try: - cursor.execute(f"SELECT COUNT(*) FROM {table}") - row_counts[table] = cursor.fetchone()[0] - except: - row_counts[table] = 0 - - conn.close() - - return { - "tables": tables, - "table_info": table_info, - "row_counts": row_counts, - "topology_metrics": { - "total_tables": len(tables), - "total_rows": sum(row_counts.values()), - "avg_columns_per_table": sum(len(v["columns"]) for v in table_info.values()) / len(table_info) - } - } - except Exception as e: - return {"error": str(e)} - - def _generate_encoding_proposals(self, topology_analysis: Dict) -> List[Dict]: - """Generate encoding scheme proposals based on topology analysis""" - proposals = [] - - # Proposal 1: Semantic Vector Compression - if topology_analysis.get("semantic_vectors", {}).get("vector_space_topology", {}).get("sparsity", 0) > 0.5: - proposals.append({ - "id": "ENC-001", - "name": "Sparse Semantic Vector Encoding", - "description": "Use sparse encoding for 14D semantic vectors to reduce storage and improve similarity search", - "benefit": "Reduces semantic vector storage by ~60% while maintaining >95% similarity accuracy", - "topology_target": "semantic_vectors", - "encoding_scheme": "COO (Coordinate list) sparse format", - "priority": "high", - "estimated_improvement": "60% storage reduction, 30% search speedup" - }) - - # Proposal 2: Database Schema Optimization - db_metrics = topology_analysis.get("databases", {}).get("topology_metrics", {}) - if db_metrics.get("avg_columns_per_table", 0) > 10: - proposals.append({ - "id": "ENC-002", - "name": "Column-Oriented Database Encoding", - "description": "Reorganize database tables to use column-oriented storage for better compression", - "benefit": "Improves query performance for analytical workloads by ~40%", - "topology_target": "databases", - "encoding_scheme": "Parquet-style columnar encoding", - "priority": "medium", - "estimated_improvement": "40% query speedup, 50% compression ratio" - }) - - # Proposal 3: Topology-Aware Routing - proposals.append({ - "id": "ENC-003", - "name": "Topology-Aware Query Routing", - "description": "Encode system topology as routing weights for intelligent query distribution", - "benefit": "Reduces cross-system latency by optimizing query paths based on topology", - "topology_target": "interconnections", - "encoding_scheme": "Graph-based routing matrix with distance metrics", - "priority": "high", - "estimated_improvement": "25% latency reduction" - }) - - # Proposal 4: Hyperbolic Manifold Encoding - proposals.append({ - "id": "ENC-004", - "name": "Hyperbolic Manifold Coordinate Encoding", - "description": "Encode semantic vectors in hyperbolic space (Poincaré disk) for better hierarchical representation", - "benefit": "Improves semantic similarity accuracy for hierarchical concepts by ~35%", - "topology_target": "semantic_vectors", - "encoding_scheme": "Poincaré disk coordinates with Möbius transformations", - "priority": "high", - "estimated_improvement": "35% accuracy improvement for hierarchical concepts" - }) - - # Proposal 5: Delta Encoding for Cache - proposals.append({ - "id": "ENC-005", - "name": "Delta Encoding for Cache Updates", - "description": "Use delta encoding for cache updates to reduce bandwidth and storage", - "benefit": "Reduces cache update overhead by ~70%", - "topology_target": "interconnections", - "encoding_scheme": "Delta encoding with rolling hash", - "priority": "medium", - "estimated_improvement": "70% bandwidth reduction for updates" - }) - - return proposals - - def _compute_swarm_consensus(self, proposals: List[Dict]) -> Dict: - """Compute swarm consensus on encoding proposals""" - if not proposals: - return {"score": 0.0, "details": "No proposals to evaluate"} - - # Simulate swarm voting (in production, this would use actual swarm agents) - swarm_votes = {} - for proposal in proposals: - # Higher priority = higher weight - weight = {"high": 1.0, "medium": 0.7, "low": 0.4}.get(proposal["priority"], 0.5) - - # Simulate agent agreement (70-95% agreement for high priority) - if proposal["priority"] == "high": - agreement = 0.85 + (hash(proposal["id"]) % 10) / 100.0 - elif proposal["priority"] == "medium": - agreement = 0.70 + (hash(proposal["id"]) % 15) / 100.0 - else: - agreement = 0.55 + (hash(proposal["id"]) % 20) / 100.0 - - swarm_votes[proposal["id"]] = { - "weight": weight, - "agreement": agreement, - "score": weight * agreement - } - - # Calculate overall consensus - total_score = sum(v["score"] for v in swarm_votes.values()) - avg_score = total_score / len(swarm_votes) - - # Rank proposals - ranked = sorted( - [(pid, data["score"]) for pid, data in swarm_votes.items()], - key=lambda x: x[1], - reverse=True - ) - - return { - "score": avg_score, - "details": swarm_votes, - "ranked_proposals": ranked, - "recommended": ranked[0][0] if ranked else None - } - - def _calculate_entropy(self, values: List[float]) -> float: - """Calculate entropy of a distribution""" - import math - # Normalize to probabilities - total = sum(values) - if total == 0: - return 0.0 - probs = [v / total for v in values if v > 0] - entropy = -sum(p * math.log2(p) for p in probs) - return entropy - - def generate_implementation_plan(self, review: Dict) -> Dict: - """Generate implementation plan based on swarm review""" - recommended_id = review["consensus_details"].get("recommended") - if not recommended_id: - return {"error": "No recommended proposal"} - - proposal = next((p for p in review["encoding_proposals"] if p["id"] == recommended_id), None) - if not proposal: - return {"error": "Recommended proposal not found"} - - implementation_plan = { - "proposal_id": proposal["id"], - "proposal_name": proposal["name"], - "implementation_steps": [ - f"1. Design {proposal['encoding_scheme']} specification", - f"2. Implement encoder/decoder for {proposal['topology_target']}", - f"3. Integrate with existing {proposal['topology_target']} system", - f"4. Benchmark against baseline ({proposal['estimated_improvement']} target)", - f"5. Deploy with A/B testing" - ], - "estimated_effort": "2-3 weeks", - "risk_level": "medium", - "dependencies": ["Omnidirectional interface", "ENE database"] - } - - return implementation_plan - - -def main(): - """Main execution function""" - encoder = SwarmTopologyEncoder() - - # Perform swarm review - review = encoder.review_omnidirectional_setup() - - # Generate implementation plan - print("\n" + "=" * 70) - print("SWARM CONSENSUS: Implementation Plan") - print("=" * 70) - - implementation_plan = encoder.generate_implementation_plan(review) - - # Output results - print("\n📊 SWARM REVIEW RESULTS:") - print(json.dumps(review, indent=2)) - - print("\n🎯 RECOMMENDED IMPLEMENTATION PLAN:") - print(json.dumps(implementation_plan, indent=2)) - - # Save to file - output_path = "/home/allaun/Documents/Research Stack/data/swarm_topology_encoding_review.json" - with open(output_path, "w") as f: - json.dump({ - "review": review, - "implementation_plan": implementation_plan - }, f, indent=2) - - print(f"\n💾 Results saved to: {output_path}") - - return review, implementation_plan - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_validate_peptide_moe_transformation.py b/5-Applications/scripts/swarm_validate_peptide_moe_transformation.py deleted file mode 100644 index 2bbbbcbc..00000000 --- a/5-Applications/scripts/swarm_validate_peptide_moe_transformation.py +++ /dev/null @@ -1,173 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Validation for PeptideMoE Transformation Equation - -Queries the swarm to validate the PeptideMoE transformation equation: -T(P_t) = (∂t/∂Θ_t, Φ_filtered[P_t]) = (∑_{k=1}^{K} g_k(P_t)Advice_k(P_t) + ξ_t, A(P_t) E[P_t] + k_B T H_conf[P_t] + C_0 / Q_coh[P_t]) -""" - -import sqlite3 -import json -from datetime import datetime -from typing import Dict, List, Any - -# Database path -DB_PATH = "/home/allaun/Documents/Research Stack/data/math_entities.db" - -def get_peptide_moe_entry() -> Dict[str, Any]: - """Get the PeptideMoE entry from the database.""" - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute(""" - SELECT entity_id, subject, name, statement, equation, variables, purpose, location - FROM math_entities - WHERE subject = 'PeptideMoE' AND name LIKE '%Core%' - LIMIT 1 - """) - - result = cursor.fetchone() - if result: - return dict(result) - return None - -def update_peptide_moe_equation(): - """Update the PeptideMoE entry with the transformation equation.""" - conn = sqlite3.connect(DB_PATH) - cursor = conn.cursor() - - # The OTOM transformation equation - transformation_statement = "T_OTOM = {State Evolution: x˙ = Σ g_k(x) A_k(x) + ξ, Efficiency: Φ(x) = C(x) / U(x), Reinforcement: z_k' = z_k + α ΔΦ · U_k} where C(x)=freeEnergy + c0, U(x)=structuralCoherence, ΔΦ=Φ(x) - Φ_prev" - - # Update the core specification entry (statement field contains the equation) - cursor.execute(""" - UPDATE math_entities - SET statement = ? - WHERE entity_id = 'peptide_moe_001' - """, (transformation_statement,)) - - conn.commit() - conn.close() - - print("✓ Updated PeptideMoE entry with transformation equation") - -def generate_swarm_validation_report(): - """Generate a validation report for the transformation equation.""" - print("=" * 70) - print("SWARM VALIDATION: PeptideMoE Transformation Equation") - print("=" * 70) - - # Get the updated entry - conn = sqlite3.connect(DB_PATH) - conn.row_factory = sqlite3.Row - cursor = conn.cursor() - - cursor.execute(""" - SELECT entity_id, subject, name, statement, dependencies, lean_module - FROM math_entities - WHERE entity_id = 'peptide_moe_001' - """) - - result = cursor.fetchone() - conn.close() - - if not result: - print("❌ PeptideMoE entry not found") - return - - entry = dict(result) - - print(f"\nEntity: {entry['name']}") - print(f"Subject: {entry['subject']}") - print(f"Location: {entry['lean_module']}") - - print(f"\nTransformation Equation:") - print(f" {entry['statement']}") - - print(f"\nDependencies (variables):") - for dep in entry['dependencies'].split(','): - print(f" - {dep}") - - print(f"\n" + "=" * 70) - print("SWARM ANALYSIS") - print("=" * 70) - - # Simulate swarm validation analysis - print("\n✓ Transformation structure: VALID") - print(" - First component: MoE drift (∂t/∂Θ_t)") - print(" - Second component: Filtered φ-peptide score (Φ_filtered)") - print(" - Tuple structure preserves both dynamics") - - print("\n✓ MoE drift component: VALID") - print(" - Expert aggregation: Σ g_k(P_t)Advice_k(P_t)") - print(" - Noise term: ξ_t (accounts for stochasticity)") - print(" - Matches existing moeDrift implementation") - - print("\n✓ Filtered score component: VALID") - print(" - Admissibility weighting: A(P_t) (0 or 1)") - print(" - Thermodynamic contribution: k_B T H_conf[P_t]") - print(" - Offset protection: C_0 (prevents division by zero)") - print(" - Structural coherence: Q_coh[P_t] (normalization)") - - print("\n✓ Physical consistency: VALID") - print(" - Boltzmann constant k_B: thermodynamic correctness") - print(" - Temperature T: thermal energy scaling") - print(" - Conformational entropy H_conf: configurational degrees") - print(" - Internal energy E[P_t]: state energy") - - print("\n✓ Mathematical properties: VALID") - print(" - Admissibility indicator A(P_t) ∈ {0,1}") - print(" - Gate weights g_k(P_t) ≥ 0, Σ g_k = 1 (simplex)") - print(" - Denominator Q_coh[P_t] + C_0 > 0 (well-defined)") - - print("\n" + "=" * 70) - print("SWARM VERDICT") - print("=" * 70) - print("\n✅ TRANSFORMATION EQUATION VALIDATED") - print(" Confidence: 1.000") - print(" Verdict: MATHEMATICALLY SOUND") - print("\n The transformation equation T(P_t) correctly unifies:") - print(" 1. MoE drift dynamics (expert aggregation)") - print(" 2. Thermodynamic scoring (energy + entropy)") - print(" 3. Admissibility filtering (safety guardrails)") - print(" 4. Structural coherence (normalization)") - - print("\n All components are consistent with the PeptideMoE") - print(" implementation in Lean 4.") - - # Save validation report - output_file = "/home/allaun/Documents/Research Stack/data/swarm_peptide_moe_transformation_validation.json" - validation_report = { - "entity_id": entry['entity_id'], - "name": entry['name'], - "statement": entry['statement'], - "dependencies": entry['dependencies'], - "validation_result": "VALID", - "confidence": 1.000, - "verdict": "MATHEMATICALLY SOUND", - "timestamp": datetime.now().isoformat(), - "components_validated": [ - "transformation_structure", - "moe_drift_component", - "filtered_score_component", - "physical_consistency", - "mathematical_properties" - ] - } - - with open(output_file, "w") as f: - json.dump(validation_report, f, indent=2) - - print(f"\nValidation report saved to: {output_file}") - -def main(): - """Main entry point.""" - # Update the database with the transformation equation - update_peptide_moe_equation() - - # Generate validation report - generate_swarm_validation_report() - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_waveprobe_adapt_v4.py b/5-Applications/scripts/swarm_waveprobe_adapt_v4.py deleted file mode 100644 index 0525c5b4..00000000 --- a/5-Applications/scripts/swarm_waveprobe_adapt_v4.py +++ /dev/null @@ -1,127 +0,0 @@ -#!/usr/bin/env python3 -""" -Submit waveprobe adaptation request for OTOM v4 simulator to swarm. -""" - -import json -import sys -from pathlib import Path - -def main(): - """Submit waveprobe adaptation request to swarm.""" - print("=" * 70) - print("Submitting Waveprobe Adaptation Request to Swarm") - print("=" * 70) - - # Load the most recent waveprobe adaptation request - request_dir = Path("shared-data/data/swarm_requests") - if not request_dir.exists(): - print(f"Error: Request directory not found: {request_dir}") - return - - # Find the most recent waveprobe adaptation request - request_files = sorted(request_dir.glob("waveprobe_adaptation_v4_*.json"), reverse=True) - if not request_files: - print("Error: No waveprobe adaptation requests found") - return - - request_file = request_files[0] - print(f"Loading request from: {request_file}") - - with open(request_file, 'r') as f: - request = json.load(f) - - print(f"Probe ID: {request['probe_id']}") - print(f"Target: {request['payload']['target_system']}") - - # Simulate swarm response (swarm_api not available) - simulate_swarm_response(request) - -def simulate_swarm_response(request): - """Simulate swarm response for waveprobe adaptation.""" - print("\n" + "=" * 70) - print("Simulating Swarm Waveprobe Adaptation Analysis") - print("=" * 70) - - # Simulate swarm analysis - print("\nAnalyzing OTOM v4 simulator structure...") - print(" ✓ Entry point: run_v4()") - print(" ✓ Parameters: use_bias, seed, T, Lexp") - print(" ✓ Outputs: history dict with trajectories and metrics") - - print("\nDesigning waveprobe adapter interface...") - print(" ✓ WaveprobeV4Adapter class") - print(" ✓ execute_probe() method") - print(" ✓ extract_metrics() method") - print(" ✓ validate_convergence() method") - print(" ✓ serialize_results() method") - - print("\nGenerating probe types...") - print(" ✓ parameter_sweep_probe") - print(" ✓ multi_seed_convergence_probe") - print(" ✓ bias_ablation_comparison_probe") - print(" ✓ convergence_stability_probe") - - print("\nPlanning ENE integration...") - print(" ✓ Google Drive credential management") - print(" ✓ Topological storage path: gdrive:topological_storage/waveprobes/otom_v4/") - print(" ✓ Shamir-secret sharing for API keys") - - # Generate simulated response - response = { - "response_id": f"resp_{request['probe_id']}", - "probe_id": request['probe_id'], - "status": "completed", - "analysis": { - "target_system": request['payload']['target_system'], - "adaptation_feasibility": "high", - "estimated_complexity": "medium", - "required_changes": [ - "Add WaveprobeV4Adapter class", - "Add probe generation functions", - "Add metric extraction standardization", - "Add result serialization for topological storage" - ] - }, - "deliverables": { - "waveprobe_adapter": { - "status": "ready_to_generate", - "file": "1-Distributed-Systems/waveprobe/src/" - }, - "probe_generator": { - "status": "ready_to_generate", - "file": "1-Distributed-Systems/waveprobe/otom_v4_probes.py" - }, - "test_script": { - "status": "ready_to_generate", - "file": "5-Applications/scripts/waveprobe_test_v4.py" - } - }, - "integration_plan": { - "phase_1": "Generate waveprobe adapter code", - "phase_2": "Integrate with codon_peptide_rl_simulation_v4.py", - "phase_3": "Execute waveprobe tests", - "phase_4": "Store results in topological storage via ENE" - }, - "verdict": "✅ Waveprobe adaptation feasible for OTOM v4 simulator" - } - - # Save simulated response - request_dir = Path("shared-data/data/swarm_requests") - response_file = request_dir / f"waveprobe_adaptation_v4_response_{request['probe_id']}.json" - with open(response_file, 'w') as f: - json.dump(response, f, indent=2) - - print(f"\nSimulated response saved to: {response_file}") - print("\n" + "=" * 70) - print("Swarm Verdict:") - print("=" * 70) - print(response['verdict']) - print("\nNext steps:") - print("1. Generate waveprobe adapter code") - print("2. Integrate with OTOM v4 simulator") - print("3. Execute waveprobe tests") - print("4. Store results via ENE to Google Drive topological storage") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_waveprobe_gdrive.py b/5-Applications/scripts/swarm_waveprobe_gdrive.py deleted file mode 100644 index a59be6aa..00000000 --- a/5-Applications/scripts/swarm_waveprobe_gdrive.py +++ /dev/null @@ -1,449 +0,0 @@ -#!/usr/bin/env python3 -""" -swarm_waveprobe_gdrive.py — Swarm Waveprobe Google Drive Test - -Tests the Google Drive topological storage surface by: -1. Having the swarm generate a waveprobe file (diagnostic payload) -2. Uploading/syncing to Gdrive:topological_storage via Rclone -3. Verifying the file exists and is accessible -4. Reporting swarm waveprobe results - -This validates the full Rclone → Google Drive → Topological Storage pipeline. -""" - -import sys -import json -import time -import hashlib -import subprocess -from pathlib import Path -from datetime import datetime -from typing import Dict, List, Any, Optional - -# Add infra to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -from infra.lean_unified_shim import LeanUnifiedShim as LeanUnifiedInterface - - -class SwarmWaveprobe: - """ - Swarm waveprobe diagnostic payload generator. - Creates structured test files for verifying storage surfaces. - """ - - def __init__(self): - self.probe_types = [ - "topology_check", - "compression_test", - "integrity_verify", - "latency_measure", - "redundancy_check" - ] - - def generate_waveprobe(self, probe_type: str = "topology_check") -> Dict[str, Any]: - """Generate waveprobe diagnostic payload.""" - timestamp = datetime.now().isoformat() - probe_id = f"wave_{hashlib.sha256(str(time.time()).encode()).hexdigest()[:12]}" - - payloads = { - "topology_check": { - "manifold_dimension": 4, - "curvature_tensor": [0.0, 0.0, 0.0, 0.0], - "binding_coefficient": 0.95, - "topology_valid": True, - "checkpoint_reachable": True - }, - "compression_test": { - "original_size": 1048576, - "compressed_size": 655360, - "compression_ratio": 1.6, - "algorithm": "BIND_L3", - "entropy_preserved": True - }, - "integrity_verify": { - "checksum_sha256": hashlib.sha256(b"test_content").hexdigest(), - "blocks_verified": 1024, - "corruption_detected": False, - "warden_valid": True - }, - "latency_measure": { - "write_latency_ms": 45, - "read_latency_ms": 23, - "sync_latency_ms": 120, - "acceptable_threshold": 200, - "performance_grade": "A" - }, - "redundancy_check": { - "replicas_target": 3, - "replicas_actual": 3, - "geographic_distribution": ["us-east", "us-west", "eu-central"], - "durability_score": 0.999999 - } - } - - payload = payloads.get(probe_type, payloads["topology_check"]) - - return { - "probe_id": probe_id, - "probe_type": probe_type, - "timestamp": timestamp, - "version": "2.0.0-Cambrian-Bind", - "generated_by": "SwarmWaveprobe", - "payload": payload, - "metadata": { - "generator_version": "1.0", - "swarm_consensus": True, - "triumvirate_approved": True - } - } - - def select_probe_type(self) -> str: - """Let swarm select appropriate probe type.""" - # Simulate swarm decision process - selection_criteria = { - "topology_check": 0.35, # Most important for manifold - "integrity_verify": 0.25, # Warden validation - "latency_measure": 0.20, # Performance - "compression_test": 0.15, # Storage efficiency - "redundancy_check": 0.05 # Backup verification - } - - # Select based on weighted probability - import random - r = random.random() - cumulative = 0 - for probe_type, weight in selection_criteria.items(): - cumulative += weight - if r <= cumulative: - return probe_type - - return "topology_check" - - -class GDriveWaveprobeTest: - """ - Test Google Drive topological storage via Rclone waveprobe. - """ - - def __init__(self): - self.lean = LeanUnifiedInterface() - self.waveprobe = SwarmWaveprobe() - self.results = {} - - def step1_generate_waveprobe(self) -> Dict[str, Any]: - """Step 1: Generate waveprobe file via swarm.""" - print("\n[STEP 1] Swarm generating waveprobe file...") - - probe_type = self.waveprobe.select_probe_type() - waveprobe_data = self.waveprobe.generate_waveprobe(probe_type) - - # Save locally first - local_path = Path(f"/tmp/{waveprobe_data['probe_id']}.json") - with open(local_path, "w") as f: - json.dump(waveprobe_data, f, indent=2) - - result = { - "step": 1, - "probe_id": waveprobe_data['probe_id'], - "probe_type": probe_type, - "local_path": str(local_path), - "file_size_bytes": local_path.stat().st_size, - "generated_at": waveprobe_data['timestamp'], - "status": "generated" - } - - self.results['generation'] = result - print(f" Probe ID: {result['probe_id']}") - print(f" Type: {probe_type}") - print(f" Local Path: {local_path}") - print(f" Size: {result['file_size_bytes']} bytes") - - return result - - def step2_check_rclone(self) -> Dict[str, Any]: - """Step 2: Verify Rclone is installed and configured.""" - print("\n[STEP 2] Checking Rclone installation...") - - try: - result = subprocess.run( - ["rclone", "version"], - capture_output=True, - text=True, - timeout=5 - ) - - version_line = result.stdout.split('\n')[0] if result.stdout else "Unknown" - installed = result.returncode == 0 - except (subprocess.TimeoutExpired, FileNotFoundError): - installed = False - version_line = "Not installed" - - # Check for gdrive remote - gdrive_configured = False - if installed: - try: - result = subprocess.run( - ["rclone", "listremotes"], - capture_output=True, - text=True, - timeout=5 - ) - gdrive_configured = "gdrive:" in result.stdout or "gdrive" in result.stdout - except: - pass - - result = { - "step": 2, - "installed": installed, - "version": version_line, - "gdrive_configured": gdrive_configured, - "topological_storage": self.lean.get_topological_storage() - } - - self.results['rclone_check'] = result - print(f" Installed: {installed}") - print(f" Version: {version_line}") - print(f" GDrive Configured: {gdrive_configured}") - print(f" Topological Storage: {result['topological_storage']['mount_point']}") - - return result - - def step3_upload_waveprobe(self) -> Dict[str, Any]: - """Step 3: Upload waveprobe to Google Drive topological storage.""" - print("\n[STEP 3] Uploading waveprobe to Google Drive...") - - generation = self.results['generation'] - local_path = generation['local_path'] - probe_id = generation['probe_id'] - - # Destination in topological storage - dest_path = f"Gdrive:topological_storage/waveprobes/{probe_id}.json" - - # Attempt Rclone copy - upload_success = False - upload_output = "" - upload_error = "" - - try: - # First check if gdrive remote exists - result = subprocess.run( - ["rclone", "listremotes"], - capture_output=True, - text=True, - timeout=5 - ) - - if "Gdrive:" in result.stdout or "gdrive" in result.stdout.lower(): - # Attempt the copy - copy_result = subprocess.run( - ["rclone", "copy", local_path, f"Gdrive:topological_storage/waveprobes/"], - capture_output=True, - text=True, - timeout=30 - ) - - upload_success = copy_result.returncode == 0 - upload_output = copy_result.stdout - upload_error = copy_result.stderr - else: - upload_error = "Google Drive remote 'Gdrive:' not configured in Rclone" - - except subprocess.TimeoutExpired: - upload_error = "Upload timed out after 30 seconds" - except FileNotFoundError: - upload_error = "Rclone not installed" - except Exception as e: - upload_error = str(e) - - result = { - "step": 3, - "local_path": local_path, - "destination": dest_path, - "upload_success": upload_success, - "upload_output": upload_output, - "upload_error": upload_error, - "probe_id": probe_id - } - - self.results['upload'] = result - - if upload_success: - print(f" ✅ Upload successful") - print(f" Destination: {dest_path}") - else: - print(f" ⚠️ Upload simulation (Rclone not configured)") - print(f" Would upload to: {dest_path}") - print(f" Error: {upload_error[:100]}..." if len(upload_error) > 100 else f" Error: {upload_error}") - - return result - - def step4_verify_upload(self) -> Dict[str, Any]: - """Step 4: Verify file exists in topological storage.""" - print("\n[STEP 4] Verifying upload...") - - probe_id = self.results['generation']['probe_id'] - upload_success = self.results['upload']['upload_success'] - - verified = False - remote_path = f"Gdrive:topological_storage/waveprobes/{probe_id}.json" - - if upload_success: - try: - # List the file to verify - result = subprocess.run( - ["rclone", "lsf", remote_path], - capture_output=True, - text=True, - timeout=10 - ) - verified = result.returncode == 0 and probe_id in result.stdout - except: - pass - - # If not actually uploaded, simulate verification - if not upload_success: - verified = True # Simulated success - verification_method = "simulated" - else: - verification_method = "rclone_lsf" - - result = { - "step": 4, - "verified": verified, - "verification_method": verification_method, - "remote_path": remote_path, - "probe_id": probe_id - } - - self.results['verification'] = result - - print(f" Verified: {verified}") - print(f" Method: {verification_method}") - print(f" Remote Path: {remote_path}") - - return result - - def step5_swarm_verdict(self) -> Dict[str, Any]: - """Step 5: Swarm verdict on waveprobe test.""" - print("\n[STEP 5] Swarm verdict...") - - generation = self.results['generation'] - upload = self.results['upload'] - verification = self.results['verification'] - - # Calculate overall success - all_passed = ( - generation['status'] == 'generated' and - upload['upload_success'] or not upload['upload_error'].startswith('Rclone not') and - verification['verified'] - ) - - verdict = { - "step": 5, - "overall_status": "PASSED" if all_passed else "PARTIAL", - "tests": { - "waveprobe_generation": generation['status'] == 'generated', - "rclone_available": self.results['rclone_check']['installed'], - "gdrive_configured": self.results['rclone_check']['gdrive_configured'], - "upload_success": upload['upload_success'] or not upload['upload_error'].startswith('Rclone not'), - "verification": verification['verified'] - }, - "probe_id": generation['probe_id'], - "storage_location": verification['remote_path'], - "topological_surface": "operational", - "recommendations": [] - } - - # Add recommendations if issues found - if not self.results['rclone_check']['installed']: - verdict['recommendations'].append("Install Rclone: https://rclone.org/install/") - - if not upload['upload_success'] and upload['upload_error']: - verdict['recommendations'].append(f"Fix upload issue: {upload['upload_error'][:50]}...") - - # Success message - if upload['upload_success']: - verdict['recommendations'].append("✅ Google Drive auto-mount working - no action needed") - - self.results['verdict'] = verdict - - print(f" Overall Status: {verdict['overall_status']}") - print(f" Topological Surface: {verdict['topological_surface']}") - print(f" Tests Passed: {sum(verdict['tests'].values())}/{len(verdict['tests'])}") - - if verdict['recommendations']: - print(f" Recommendations:") - for rec in verdict['recommendations']: - print(f" - {rec}") - - return verdict - - def run_waveprobe_test(self) -> Dict[str, Any]: - """Execute complete waveprobe test.""" - print("=" * 70) - print("SWARM WAVEPROBE: Google Drive Topological Storage Test") - print("=" * 70) - print("Testing: Swarm → Waveprobe → Rclone → Google Drive → Topological Storage") - print("=" * 70) - - start_time = time.time() - - # Execute all steps - self.step1_generate_waveprobe() - self.step2_check_rclone() - self.step3_upload_waveprobe() - self.step4_verify_upload() - self.step5_swarm_verdict() - - duration = time.time() - start_time - - # Compile final report - final_report = { - "test_name": "Swarm Waveprobe Google Drive Test", - "timestamp": datetime.now().isoformat(), - "duration_seconds": round(duration, 3), - "probe_id": self.results['generation']['probe_id'], - "verdict": self.results['verdict']['overall_status'], - "topological_storage": { - "provider": "Gdrive", - "mount_point": "Gdrive:topological_storage", - "waveprobe_path": self.results['verification']['remote_path'] - }, - "integration_stack": { - "lean_module": "RcloneIntegration.lean", - "python_shim": "lean_shim.py", - "rclone_version": self.results['rclone_check'].get('version', 'Unknown'), - "gdrive_configured": self.results['rclone_check']['gdrive_configured'] - }, - "test_results": self.results - } - - # Save report - output_path = Path("/home/allaun/Documents/Research Stack/data/swarm_waveprobe_gdrive_result.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(final_report, f, indent=2) - - print("\n" + "=" * 70) - print("WAVEPROBE TEST COMPLETE") - print("=" * 70) - print(f"Duration: {duration:.3f} seconds") - print(f"Probe ID: {final_report['probe_id']}") - print(f"Verdict: {final_report['verdict']}") - print(f"Storage: {final_report['topological_storage']['mount_point']}") - print(f"Output: {output_path}") - print("=" * 70) - - return final_report - - -def main(): - """Run swarm waveprobe test.""" - test = GDriveWaveprobeTest() - result = test.run_waveprobe_test() - return result - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_waveprobe_gdrive_ene.py b/5-Applications/scripts/swarm_waveprobe_gdrive_ene.py deleted file mode 100644 index 85d989bd..00000000 --- a/5-Applications/scripts/swarm_waveprobe_gdrive_ene.py +++ /dev/null @@ -1,338 +0,0 @@ -#!/usr/bin/env python3 -""" -swarm_waveprobe_gdrive_ene.py — ENE-Managed Waveprobe Upload - -Uses ENE (Endless Node Edges) for: -- Secure API key management (encrypted credentials) -- Node connection balancing (health-weighted routing) -- Automatic credential rotation -- Health monitoring - -ENE manages the Google Drive connection so nodes balance the load. -""" - -import sys -import json -import time -import hashlib -import subprocess -from pathlib import Path -from datetime import datetime -from typing import Dict, Any - -# Add infra to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -from ene_cloud_credential_manager import ENETopologicalStorage -from ene_api import AccessLevel - - -class ENEWaveprobeTest: - """Waveprobe test using ENE-managed cloud storage.""" - - def __init__(self): - self.ene = ENETopologicalStorage() - self.results = {} - - def step1_register_ene_nodes(self) -> Dict[str, Any]: - """Step 1: Register nodes with ENE for load balancing.""" - print("\n[STEP 1] Registering ENE nodes for load balancing...") - - # Register 3 nodes for Gdrive connections - nodes = [] - for i in range(3): - node_id = f"ene_node_{i+1}" - self.ene.balancer.register_node(node_id, f"cred_gdrive_{i+1}") - nodes.append(node_id) - - result = { - "step": 1, - "nodes_registered": nodes, - "balancing_strategy": "health_weighted", - "status": "registered" - } - - self.results['ene_nodes'] = result - print(f" Nodes: {', '.join(nodes)}") - print(f" Strategy: health_weighted (latency-aware)") - - return result - - def step2_store_gdrive_credential(self) -> Dict[str, Any]: - """Step 2: Store Google Drive credential in ENE (encrypted).""" - print("\n[STEP 2] Storing Google Drive credential in ENE...") - - # Store credential encrypted via ENE - cred_id = self.ene.credential_manager.store_credential( - provider="gdrive", - api_key="PLACEHOLDER_API_KEY", # Replace with real token via env - secret="PLACEHOLDER_SECRET", # Replace with real secret via env - node_assignments=["ene_node_1", "ene_node_2", "ene_node_3"], - access_level=AccessLevel.RESTRICTED - ) - - result = { - "step": 2, - "credential_id": cred_id, - "encryption": "AES-256-GCM via ENE", - "access_level": "RESTRICTED", - "node_assignments": 3, - "status": "stored" - } - - self.results['credential'] = result - print(f" Credential ID: {cred_id}") - print(f" Encryption: AES-256-GCM via ENE") - print(f" Assigned to: 3 nodes") - - return result - - def step3_generate_waveprobe(self) -> Dict[str, Any]: - """Step 3: Generate waveprobe file.""" - print("\n[STEP 3] Generating waveprobe file...") - - probe_id = f"wave_{hashlib.sha256(str(time.time()).encode()).hexdigest()[:12]}" - - waveprobe_data = { - "probe_id": probe_id, - "probe_type": "topology_check", - "timestamp": datetime.now().isoformat(), - "version": "2.0.0-Cambrian-Bind", - "ene_managed": True, - "payload": { - "manifold_dimension": 4, - "curvature_tensor": [0.0, 0.0, 0.0, 0.0], - "binding_coefficient": 0.95, - "topology_valid": True, - "ene_nodes": 3, - "load_balanced": True - } - } - - # Save locally - local_path = f"/tmp/{probe_id}.json" - with open(local_path, "w") as f: - json.dump(waveprobe_data, f, indent=2) - - result = { - "step": 3, - "probe_id": probe_id, - "local_path": local_path, - "file_size": Path(local_path).stat().st_size, - "status": "generated" - } - - self.results['waveprobe'] = result - print(f" Probe ID: {probe_id}") - print(f" Local Path: {local_path}") - print(f" Size: {result['file_size']} bytes") - - return result - - def step4_upload_via_ene(self) -> Dict[str, Any]: - """Step 4: Upload via ENE node balancing.""" - print("\n[STEP 4] Uploading via ENE node balancing...") - - local_path = self.results['waveprobe']['local_path'] - probe_id = self.results['waveprobe']['probe_id'] - - # Upload via ENE (automatically selects best node) - try: - upload_result = self.ene.upload_waveprobe( - local_path=local_path, - remote_path=f"Gdrive:topological_storage/waveprobes/{probe_id}.json" - ) - - result = { - "step": 4, - "uploaded": True, - "connection_id": upload_result['connection_id'], - "selected_node": upload_result['node_id'], - "latency_ms": round(upload_result['latency_ms'], 1), - "bytes_transferred": upload_result['bytes'], - "ene_managed": True, - "status": "success" - } - - except Exception as e: - result = { - "step": 4, - "uploaded": False, - "error": str(e), - "status": "failed" - } - - self.results['upload'] = result - - if result['uploaded']: - print(f" ✅ Uploaded via ENE") - print(f" Connection ID: {result['connection_id']}") - print(f" Selected Node: {result['selected_node']}") - print(f" Latency: {result['latency_ms']}ms") - else: - print(f" ⚠️ Upload issue: {result.get('error', 'Unknown')}") - - return result - - def step5_verify_ene_storage(self) -> Dict[str, Any]: - """Step 5: Verify ENE-managed storage health.""" - print("\n[STEP 5] Verifying ENE storage health...") - - health = self.ene.get_storage_health() - balancer_stats = health['balancer_stats'] - - # Also verify via Rclone directly - probe_id = self.results['waveprobe']['probe_id'] - remote_path = f"Gdrive:topological_storage/waveprobes/{probe_id}.json" - - rclone_verified = False - try: - result = subprocess.run( - ["rclone", "lsf", remote_path], - capture_output=True, - text=True, - timeout=10 - ) - rclone_verified = result.returncode == 0 - except: - pass - - result = { - "step": 5, - "ene_health": health['topological_storage'], - "ene_managed": health['ene_managed'], - "node_balancing": health['node_balancing'], - "total_nodes": balancer_stats['total_nodes'], - "active_nodes": balancer_stats['active_nodes'], - "rclone_verified": rclone_verified, - "remote_path": remote_path - } - - self.results['verification'] = result - - print(f" ENE Status: {health['topological_storage']}") - print(f" ENE Managed: {health['ene_managed']}") - print(f" Nodes: {balancer_stats['active_nodes']}/{balancer_stats['total_nodes']} active") - print(f" Rclone Verified: {rclone_verified}") - - return result - - def step6_swarm_verdict(self) -> Dict[str, Any]: - """Step 6: Swarm verdict on ENE waveprobe test.""" - print("\n[STEP 6] Swarm verdict...") - - upload_success = self.results['upload']['uploaded'] - ene_healthy = self.results['verification']['ene_health'] == 'operational' - - all_passed = ( - self.results['ene_nodes']['status'] == 'registered' and - self.results['credential']['status'] == 'stored' and - self.results['waveprobe']['status'] == 'generated' and - upload_success and - ene_healthy - ) - - verdict = { - "step": 6, - "overall_status": "PASSED" if all_passed else "PARTIAL", - "tests": { - "ene_nodes_registered": self.results['ene_nodes']['status'] == 'registered', - "credential_stored": self.results['credential']['status'] == 'stored', - "waveprobe_generated": self.results['waveprobe']['status'] == 'generated', - "upload_success": upload_success, - "ene_healthy": ene_healthy - }, - "probe_id": self.results['waveprobe']['probe_id'], - "ene_managed": True, - "node_balanced": True, - "storage_location": self.results['verification']['remote_path'], - "recommendations": [] - } - - if all_passed: - verdict['recommendations'].append("✅ ENE cloud credential system fully operational") - verdict['recommendations'].append("✅ Node balancing active - load distributed across nodes") - verdict['recommendations'].append("✅ API keys secured via ENE AES-256-GCM encryption") - - self.results['verdict'] = verdict - - print(f" Overall Status: {verdict['overall_status']}") - print(f" ENE Managed: {verdict['ene_managed']}") - print(f" Node Balanced: {verdict['node_balanced']}") - print(f" Tests Passed: {sum(verdict['tests'].values())}/{len(verdict['tests'])}") - - if verdict['recommendations']: - print(f" Recommendations:") - for rec in verdict['recommendations']: - print(f" - {rec}") - - return verdict - - def run_ene_waveprobe_test(self) -> Dict[str, Any]: - """Execute complete ENE-managed waveprobe test.""" - print("=" * 70) - print("ENE-MANAGED WAVEPROBE: Google Drive Topological Storage") - print("=" * 70) - print("Architecture: ENE → API Key Management → Node Balancing → Gdrive") - print("=" * 70) - - start_time = time.time() - - # Execute all steps - self.step1_register_ene_nodes() - self.step2_store_gdrive_credential() - self.step3_generate_waveprobe() - self.step4_upload_via_ene() - self.step5_verify_ene_storage() - self.step6_swarm_verdict() - - duration = time.time() - start_time - - # Compile final report - final_report = { - "test_name": "ENE-Managed Waveprobe Google Drive Test", - "timestamp": datetime.now().isoformat(), - "duration_seconds": round(duration, 3), - "probe_id": self.results['waveprobe']['probe_id'], - "verdict": self.results['verdict']['overall_status'], - "ene_managed": True, - "node_balanced": True, - "architecture": { - "ene": "API key management & encryption", - "balancer": "Health-weighted node selection", - "nodes": "3 distributed endpoints", - "storage": "Google Drive topological", - "security": "AES-256-GCM via ENE" - }, - "test_results": self.results - } - - # Save report - output_path = Path("/home/allaun/Documents/Research Stack/data/swarm_waveprobe_gdrive_ene_result.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(final_report, f, indent=2) - - print("\n" + "=" * 70) - print("ENE WAVEPROBE TEST COMPLETE") - print("=" * 70) - print(f"Duration: {duration:.3f} seconds") - print(f"Probe ID: {final_report['probe_id']}") - print(f"Verdict: {final_report['verdict']}") - print(f"ENE Managed: {final_report['ene_managed']}") - print(f"Node Balanced: {final_report['node_balanced']}") - print(f"Output: {output_path}") - print("=" * 70) - - return final_report - - -def main(): - """Run ENE-managed waveprobe test.""" - test = ENEWaveprobeTest() - result = test.run_ene_waveprobe_test() - return result - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/swarm_web_standards_integration.py b/5-Applications/scripts/swarm_web_standards_integration.py deleted file mode 100644 index 56d117bb..00000000 --- a/5-Applications/scripts/swarm_web_standards_integration.py +++ /dev/null @@ -1,430 +0,0 @@ -#!/usr/bin/env python3 -""" -Swarm Web Standards Integration - -Ensure the swarm fully understands all web standards for proper -web interaction surface implementation. -""" - -import sys -import json -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) - -from infra.lean_unified_shim import OmnidirectionalInterface -from infra.ascii_art_competition import AsciiArtCompetition, CompetitionType, CompetitionEntry -import time - - -def swarm_web_standards_integration(): - """Swarm integrates comprehensive web standards knowledge""" - print("=" * 70) - print("SWARM WEB STANDARDS INTEGRATION") - print("=" * 70) - - # Step 1: Swarm analyzes web standards categories - print("\n[1/4] Swarm analyzing web standards categories...") - - web_standards = { - "network_protocols": { - "HTTP/1.1": { - "rfc": "RFC 7230-7235", - "key_features": ["Methods (GET, POST, PUT, DELETE, etc.)", "Headers", "Status codes", "Chunked transfer encoding"], - "relevance": "Core web communication" - }, - "HTTP/2": { - "rfc": "RFC 7540", - "key_features": ["Binary framing", "Multiplexing", "Header compression (HPACK)", "Server push"], - "relevance": "Performance optimization" - }, - "HTTP/3": { - "rfc": "RFC 9114", - "key_features": ["QUIC transport", "UDP-based", "Improved connection migration", "Reduced latency"], - "relevance": "Modern web performance" - }, - "WebSocket": { - "rfc": "RFC 6455", - "key_features": ["Full-duplex communication", "Event-driven", "Low latency", "Real-time updates"], - "relevance": "Interactive web applications" - }, - "WebRTC": { - "spec": "W3C WebRTC 1.0", - "key_features": ["Peer-to-peer audio/video", "Data channels", "NAT traversal", "Secure by default"], - "relevance": "Real-time communication" - } - }, - "document_standards": { - "HTML5": { - "spec": "W3C HTML5.3", - "key_features": ["Semantic elements", "Forms API", "Canvas", "Web Storage", "Offline support"], - "relevance": "Document structure" - }, - "CSS3": { - "spec": "W3C CSS3", - "key_features": ["Flexbox", "Grid", "Animations", "Media queries", "Custom properties"], - "relevance": "Styling and layout" - }, - "SVG": { - "spec": "W3C SVG 2.0", - "key_features": ["Vector graphics", "Animation", "Interactivity", "Scaling"], - "relevance": "Graphics and icons" - }, - "MathML": { - "spec": "W3C MathML 3.0", - "key_features": ["Mathematical notation", "Rendering", "Accessibility"], - "relevance": "Scientific content" - } - }, - "javascript_standards": { - "ECMAScript": { - "spec": "ECMA-262", - "versions": ["ES5", "ES6/ES2015", "ES2020", "ES2024"], - "key_features": ["Modules", "Async/await", "Classes", "Promises", "Iterators"], - "relevance": "Core scripting language" - }, - "DOM": { - "spec": "W3C DOM Level 3", - "key_features": ["Node tree", "Event handling", "Mutation observers", "Selectors API"], - "relevance": "Document manipulation" - }, - "Web APIs": { - "spec": "W3C Web APIs", - "key_apis": ["Fetch API", "Canvas API", "WebGL", "Web Audio API", "Geolocation API", "Notifications API"], - "relevance": "Browser capabilities" - } - }, - "security_standards": { - "TLS": { - "spec": "RFC 8446 (TLS 1.3)", - "key_features": ["Encryption", "Authentication", "Perfect forward secrecy"], - "relevance": "Secure communication" - }, - "CSP": { - "spec": "W3C CSP Level 3", - "key_features": ["Content security policies", "XSS prevention", "Resource whitelisting"], - "relevance": "Content security" - }, - "CORS": { - "spec": "W3C CORS", - "key_features": ["Cross-origin resource sharing", "Preflight requests", "Credentials"], - "relevance": "Cross-origin requests" - }, - "Same-Origin Policy": { - "spec": "W3C Same-Origin Policy", - "key_features": ["Origin isolation", "Script restrictions", "Data separation"], - "relevance": "Security boundary" - }, - "Subresource Integrity": { - "spec": "W3C SRI", - "key_features": ["Hash verification", "CDN security", "Tamper detection"], - "relevance": "Resource integrity" - } - }, - "accessibility_standards": { - "WCAG": { - "spec": "W3C WCAG 2.1", - "levels": ["A", "AA", "AAA"], - "key_features": ["Perceivable", "Operable", "Understandable", "Robust"], - "relevance": "Accessibility compliance" - }, - "ARIA": { - "spec": "W3C WAI-ARIA 1.2", - "key_features": ["Roles", "States", "Properties", "Live regions"], - "relevance": "Assistive technology" - } - }, - "performance_standards": { - "Resource Hints": { - "spec": "W3C Resource Hints", - "key_features": ["preload", "prefetch", "preconnect", "dns-prefetch"], - "relevance": "Resource loading optimization" - }, - "Performance API": { - "spec": "W3C Performance Timeline", - "key_features": ["Navigation timing", "Resource timing", "User timing", "Paint timing"], - "relevance": "Performance monitoring" - }, - "Service Workers": { - "spec": "W3C Service Workers", - "key_features": ["Offline caching", "Background sync", "Push notifications", "Interception"], - "relevance": "Offline capabilities" - } - } - } - - print(f"Standards categories: {len(web_standards)}") - for category, standards in web_standards.items(): - print(f" {category}: {len(standards)} standards") - - # Step 2: Swarm identifies critical standards for web interaction - print("\n[2/4] Swarm identifying critical standards for web interaction...") - - critical_standards = { - "must_implement": [ - { - "standard": "HTTP/1.1 & HTTP/2", - "reason": "Core web communication protocol", - "implementation": "httpx library with HTTP/2 support" - }, - { - "standard": "HTML5 DOM", - "reason": "Document structure and manipulation", - "implementation": "Playwright's built-in DOM handling" - }, - { - "standard": "CSS Selectors", - "reason": "Element location and interaction", - "implementation": "Playwright's selector engine" - }, - { - "standard": "JavaScript Execution", - "reason": "Dynamic content and SPA support", - "implementation": "Playwright's evaluate() API" - }, - { - "standard": "Cookies and Storage", - "reason": "Session management", - "implementation": "Playwright's storage state API" - }, - { - "standard": "TLS 1.3", - "reason": "Secure communication", - "implementation": "Playwright's built-in TLS support" - }, - { - "standard": "CORS", - "reason": "Cross-origin request handling", - "implementation": "Proper header handling" - }, - { - "standard": "Same-Origin Policy", - "reason": "Security boundary enforcement", - "implementation": "Browser context isolation" - } - ], - "should_implement": [ - { - "standard": "WebSocket", - "reason": "Real-time communication support", - "implementation": "WebSocket client integration" - }, - { - "standard": "Service Workers", - "reason": "Offline application support", - "implementation": "Service worker interception" - }, - { - "standard": "Performance API", - "reason": "Performance monitoring", - "implementation": "Performance metrics collection" - }, - { - "standard": "CSP", - "reason": "Content security", - "implementation": "CSP header parsing and compliance" - } - ], - "nice_to_have": [ - { - "standard": "WebRTC", - "reason": "Peer-to-peer communication", - "implementation": "WebRTC client support" - }, - { - "standard": "HTTP/3", - "reason": "Modern performance", - "implementation": "QUIC client when available" - }, - { - "standard": "WCAG", - "reason": "Accessibility compliance", - "implementation": "Accessibility testing" - } - ] - } - - print(f"Must implement: {len(critical_standards['must_implement'])}") - print(f"Should implement: {len(critical_standards['should_implement'])}") - print(f"Nice to have: {len(critical_standards['nice_to_have'])}") - - # Step 3: Swarm generates standards compliance matrix - print("\n[3/4] Swarm generating standards compliance matrix...") - - compliance_matrix = { - "network_layer": { - "HTTP/1.1": {"compliance": "full", "notes": "httpx library support"}, - "HTTP/2": {"compliance": "full", "notes": "httpx with h2 support"}, - "TLS": {"compliance": "full", "notes": "Playwright built-in"}, - "WebSocket": {"compliance": "partial", "notes": "Requires additional client"}, - "CORS": {"compliance": "full", "notes": "Automatic header handling"} - }, - "document_layer": { - "HTML5": {"compliance": "full", "notes": "Playwright renders full HTML5"}, - "CSS3": {"compliance": "full", "notes": "Playwright selector engine"}, - "DOM": {"compliance": "full", "notes": "Full DOM API access"}, - "JavaScript": {"compliance": "full", "notes": "evaluate() and evaluateHandle()"} - }, - "security_layer": { - "TLS": {"compliance": "full", "notes": "Automatic HTTPS upgrade"}, - "CSP": {"compliance": "partial", "notes": "Header parsing only"}, - "Same-Origin": {"compliance": "full", "notes": "Context isolation"}, - "CORS": {"compliance": "full", "notes": "Automatic handling"}, - "Cookies": {"compliance": "full", "notes": "Full cookie API"} - }, - "performance_layer": { - "Resource Hints": {"compliance": "partial", "notes": "Via network interception"}, - "Performance API": {"compliance": "partial", "notes": "Via JavaScript execution"}, - "Service Workers": {"compliance": "partial", "notes": "Limited interception"} - } - } - - print("Compliance Matrix:") - for layer, standards in compliance_matrix.items(): - print(f" {layer}:") - for std, data in standards.items(): - print(f" {std}: {data['compliance']} - {data['notes']}") - - # Step 4: Swarm generates standards integration plan - print("\n[4/4] Swarm generating standards integration plan...") - - integration_plan = { - "phase_1_core_standards": { - "duration": "1 week", - "standards": [ - "HTTP/1.1 and HTTP/2 full implementation", - "HTML5 DOM manipulation", - "CSS selector engine", - "JavaScript execution context", - "TLS 1.3 secure connections", - "Cookie and storage management", - "CORS handling", - "Same-Origin policy enforcement" - ], - "deliverables": [ - "Playwright integration with all core standards", - "Standards compliance tests", - "Documentation of supported standards" - ] - }, - "phase_2_advanced_standards": { - "duration": "1 week", - "standards": [ - "WebSocket client integration", - "Service worker interception", - "Performance API monitoring", - "CSP header parsing and validation", - "Resource hints optimization" - ], - "deliverables": [ - "WebSocket client wrapper", - "Service worker test harness", - "Performance metrics collection", - "CSP compliance checker" - ] - }, - "phase_3_enhanced_standards": { - "duration": "1 week", - "standards": [ - "HTTP/3 (QUIC) when available", - "WebRTC client support", - "WCAG accessibility testing", - "Subresource integrity verification", - "Advanced security headers" - ], - "deliverables": [ - "HTTP/3 client integration", - "WebRTC test suite", - "Accessibility audit tools", - "SRI verification system" - ] - } - } - - print("\nIntegration Plan:") - for phase, plan in integration_plan.items(): - print(f" {phase}:") - print(f" Duration: {plan['duration']}") - print(f" Standards: {len(plan['standards'])}") - print(f" Deliverables: {len(plan['deliverables'])}") - - # Generate final standards knowledge base - standards_knowledge = { - "surface_name": "SwarmWebSurface", - "version": "2.1.0", - "web_standards_comprehensive": web_standards, - "critical_standards": critical_standards, - "compliance_matrix": compliance_matrix, - "integration_plan": integration_plan, - "standards_coverage": { - "total_standards": sum(len(cat.values()) for cat in web_standards.values()), - "fully_implemented": 8, - "partially_implemented": 5, - "not_implemented": 3, - "coverage_percentage": 0.68 - } - } - - print("\n" + "=" * 70) - print("SWARM WEB STANDARDS INTEGRATION COMPLETE") - print("=" * 70) - print(f"\nTotal Standards Analyzed: {standards_knowledge['standards_coverage']['total_standards']}") - print(f"Fully Implemented: {standards_knowledge['standards_coverage']['fully_implemented']}") - print(f"Partially Implemented: {standards_knowledge['standards_coverage']['partially_implemented']}") - print(f"Not Implemented: {standards_knowledge['standards_coverage']['not_implemented']}") - print(f"Coverage: {standards_knowledge['standards_coverage']['coverage_percentage']:.0%}") - - # Submit to competition - print("\n" + "=" * 70) - print("SUBMITTING STANDARDS INTEGRATION TO COMPETITION") - print("=" * 70) - - interface = OmnidirectionalInterface() - competition = AsciiArtCompetition() - - standards_entry = CompetitionEntry( - agent_id="swarm_web_standards_integrator", - competition_type=CompetitionType.SEMANTIC_MATCHING, - ascii_art_id=None, - score=standards_knowledge['standards_coverage']['coverage_percentage'], - metrics={"compliance_matrix": compliance_matrix, "integration_plan": integration_plan}, - timestamp=int(time.time()), - proposal="Comprehensive web standards integration for SwarmWebSurface" - ) - - try: - competition.submit_competition_entry(standards_entry) - print("Standards integration submitted to competition system") - except Exception as e: - print(f"Competition submission failed (database lock): {e}") - - # Save standards knowledge - output_path = "/home/allaun/Documents/Research Stack/data/swarm_web_standards_knowledge.json" - with open(output_path, "w") as f: - json.dump(standards_knowledge, f, indent=2) - - print(f"\nStandards knowledge saved to: {output_path}") - - print("\n" + "=" * 70) - print("SWARM VERDICT: WEB STANDARDS FULLY UNDERSTOOD") - print("=" * 70) - print("The swarm has comprehensively analyzed all major web standards") - print("across 6 categories:") - print("\n - Network Protocols: HTTP/1.1, HTTP/2, HTTP/3, WebSocket, WebRTC") - print(" - Document Standards: HTML5, CSS3, SVG, MathML") - print(" - JavaScript Standards: ECMAScript, DOM, Web APIs") - print(" - Security Standards: TLS, CSP, CORS, Same-Origin, SRI") - print(" - Accessibility Standards: WCAG, ARIA") - print(" - Performance Standards: Resource Hints, Performance API, Service Workers") - print("\nCurrent coverage: 68% (8 full, 5 partial, 3 not implemented)") - print("\nThe swarm now has deep understanding of web standards for") - print("proper implementation of the web interaction surface.") - print("=" * 70) - - return standards_knowledge - - -if __name__ == "__main__": - standards_knowledge = swarm_web_standards_integration() diff --git a/5-Applications/scripts/sync_math_database.sh b/5-Applications/scripts/sync_math_database.sh deleted file mode 100755 index 52c1cc19..00000000 --- a/5-Applications/scripts/sync_math_database.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/sh -# -# sync_math_database_v2.sh — Bidirectional sync (simplified, robust) -# - -set -e - -DB_PATH="${MATH_DB_PATH:-/home/allaun/Documents/Research Stack/data/math_entities.db}" - -# Colors -RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'; BLUE='\033[0;34m'; NC='\033[0m' -log_info() { printf "${BLUE}[INFO]${NC} %s\n" "$1"; } -log_ok() { printf "${GREEN}[OK]${NC} %s\n" "$1"; } - -# Ensure database exists -mkdir -p "$(dirname "$DB_PATH")" -sqlite3 "$DB_PATH" "CREATE TABLE IF NOT EXISTS math_entities ( - entity_id TEXT PRIMARY KEY, subject TEXT, secondary_subjects TEXT, - name TEXT, statement TEXT, proof_status TEXT, formal_status TEXT, - lean_module TEXT, dependencies TEXT, citations TEXT, complexity_score INTEGER, - year INTEGER, source_file TEXT, last_synced TEXT DEFAULT CURRENT_TIMESTAMP -);" 2>/dev/null || true - -# Run Python parser directly -python3 << 'PYEOF' -import sqlite3 -import json -import re -import hashlib -from pathlib import Path - -db_path = "/home/allaun/Documents/Research Stack/data/math_entities.db" -conn = sqlite3.connect(db_path) - -# Find all relevant markdown files -docs_dir = Path("/home/allaun/Documents/Research Stack/docs") -search_dirs = [docs_dir, Path("/home/allaun/Documents/Research Stack/data/germane/research")] - -all_files = [] -for d in search_dirs: - if d.exists(): - for pattern in ["*math*.md", "*EQUATION*.md", "*PHYLOGENETIC*.md", "*AMMR*.md", "*ORTHOGONAL*.md", "MATH_CORE.md"]: - all_files.extend(d.rglob(pattern)) - -print(f"[INFO] Found {len(all_files)} files to scan") - -total_entities = 0 -for file_path in all_files: - if not file_path.is_file(): - continue - - try: - content = file_path.read_text(encoding='utf-8') - except Exception as e: - print(f"[WARN] Could not read {file_path}: {e}") - continue - - entities = [] - header_pattern = r'^#+\s+(.*?)(?:\n|$)' - keywords = ['theorem', 'equation', 'lemma', 'proof', 'conjecture', 'ammr', - 'orthogonal', 'memory', 'crc', 'node', 'range', 'joule', 'thermodynamic'] - - for match in re.finditer(header_pattern, content, re.MULTILINE): - header = match.group(1).strip() - header_lower = header.lower() - if any(kw in header_lower for kw in keywords): - content_hash = hashlib.sha256(header.encode()).hexdigest()[:16] - entity_id = f"md-{content_hash}" - - # Determine subject - subject = "foundations" - if any(k in header_lower for k in ['algebra', 'group', 'ring']): - subject = "algebra" - elif any(k in header_lower for k in ['topology', 'manifold', 'space']): - subject = "topology" - elif any(k in header_lower for k in ['thermodynamic', 'entropy', 'energy', 'joule']): - subject = "physics" - elif any(k in header_lower for k in ['orthogonal', 'matrix', 'projection']): - subject = "algebra" - - # Determine proof status - proof_status = "conjecture" - if any(k in header_lower for k in ['theorem', 'proven', 'verified', 'law', 'invariant']): - proof_status = "proven" - - entities.append(( - entity_id, subject, json.dumps([]), header, header, - proof_status, "informal", None, json.dumps([]), json.dumps([]), - 32768, 2026, str(file_path) - )) - - # Insert to database - for entity in entities: - conn.execute(""" - INSERT OR REPLACE INTO math_entities - (entity_id, subject, secondary_subjects, name, statement, proof_status, - formal_status, lean_module, dependencies, citations, complexity_score, year, source_file) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, entity) - - if entities: - print(f"[INFO] {file_path.name}: {len(entities)} entities") - total_entities += len(entities) - -conn.commit() -print(f"[OK] Total entities synced: {total_entities}") -PYEOF - -log_ok "Sync complete" diff --git a/5-Applications/scripts/sync_math_database_v2.sh b/5-Applications/scripts/sync_math_database_v2.sh deleted file mode 100755 index 52c1cc19..00000000 --- a/5-Applications/scripts/sync_math_database_v2.sh +++ /dev/null @@ -1,109 +0,0 @@ -#!/bin/sh -# -# sync_math_database_v2.sh — Bidirectional sync (simplified, robust) -# - -set -e - -DB_PATH="${MATH_DB_PATH:-/home/allaun/Documents/Research Stack/data/math_entities.db}" - -# Colors -RED='\033[0;31m'; GREEN='\033[0;32m'; YELLOW='\033[1;33m'; BLUE='\033[0;34m'; NC='\033[0m' -log_info() { printf "${BLUE}[INFO]${NC} %s\n" "$1"; } -log_ok() { printf "${GREEN}[OK]${NC} %s\n" "$1"; } - -# Ensure database exists -mkdir -p "$(dirname "$DB_PATH")" -sqlite3 "$DB_PATH" "CREATE TABLE IF NOT EXISTS math_entities ( - entity_id TEXT PRIMARY KEY, subject TEXT, secondary_subjects TEXT, - name TEXT, statement TEXT, proof_status TEXT, formal_status TEXT, - lean_module TEXT, dependencies TEXT, citations TEXT, complexity_score INTEGER, - year INTEGER, source_file TEXT, last_synced TEXT DEFAULT CURRENT_TIMESTAMP -);" 2>/dev/null || true - -# Run Python parser directly -python3 << 'PYEOF' -import sqlite3 -import json -import re -import hashlib -from pathlib import Path - -db_path = "/home/allaun/Documents/Research Stack/data/math_entities.db" -conn = sqlite3.connect(db_path) - -# Find all relevant markdown files -docs_dir = Path("/home/allaun/Documents/Research Stack/docs") -search_dirs = [docs_dir, Path("/home/allaun/Documents/Research Stack/data/germane/research")] - -all_files = [] -for d in search_dirs: - if d.exists(): - for pattern in ["*math*.md", "*EQUATION*.md", "*PHYLOGENETIC*.md", "*AMMR*.md", "*ORTHOGONAL*.md", "MATH_CORE.md"]: - all_files.extend(d.rglob(pattern)) - -print(f"[INFO] Found {len(all_files)} files to scan") - -total_entities = 0 -for file_path in all_files: - if not file_path.is_file(): - continue - - try: - content = file_path.read_text(encoding='utf-8') - except Exception as e: - print(f"[WARN] Could not read {file_path}: {e}") - continue - - entities = [] - header_pattern = r'^#+\s+(.*?)(?:\n|$)' - keywords = ['theorem', 'equation', 'lemma', 'proof', 'conjecture', 'ammr', - 'orthogonal', 'memory', 'crc', 'node', 'range', 'joule', 'thermodynamic'] - - for match in re.finditer(header_pattern, content, re.MULTILINE): - header = match.group(1).strip() - header_lower = header.lower() - if any(kw in header_lower for kw in keywords): - content_hash = hashlib.sha256(header.encode()).hexdigest()[:16] - entity_id = f"md-{content_hash}" - - # Determine subject - subject = "foundations" - if any(k in header_lower for k in ['algebra', 'group', 'ring']): - subject = "algebra" - elif any(k in header_lower for k in ['topology', 'manifold', 'space']): - subject = "topology" - elif any(k in header_lower for k in ['thermodynamic', 'entropy', 'energy', 'joule']): - subject = "physics" - elif any(k in header_lower for k in ['orthogonal', 'matrix', 'projection']): - subject = "algebra" - - # Determine proof status - proof_status = "conjecture" - if any(k in header_lower for k in ['theorem', 'proven', 'verified', 'law', 'invariant']): - proof_status = "proven" - - entities.append(( - entity_id, subject, json.dumps([]), header, header, - proof_status, "informal", None, json.dumps([]), json.dumps([]), - 32768, 2026, str(file_path) - )) - - # Insert to database - for entity in entities: - conn.execute(""" - INSERT OR REPLACE INTO math_entities - (entity_id, subject, secondary_subjects, name, statement, proof_status, - formal_status, lean_module, dependencies, citations, complexity_score, year, source_file) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, entity) - - if entities: - print(f"[INFO] {file_path.name}: {len(entities)} entities") - total_entities += len(entities) - -conn.commit() -print(f"[OK] Total entities synced: {total_entities}") -PYEOF - -log_ok "Sync complete" diff --git a/5-Applications/scripts/system_interrogation.py b/5-Applications/scripts/system_interrogation.py deleted file mode 100644 index 3df84334..00000000 --- a/5-Applications/scripts/system_interrogation.py +++ /dev/null @@ -1,78 +0,0 @@ -import os -import sys -import re - -def audit_lean(): - print("--- LEAN 4 AUDIT ---") - lean_dir = "/home/allaun/Documents/Research Stack/tools/lean" - sorry_count = 0 - for root, _, files in os.walk(lean_dir): - for file in files: - if file.endswith(".lean"): - path = os.path.join(root, file) - with open(path, "r") as f: - content = f.read() - matches = re.findall(r"sorry", content) - if matches: - print(f"[SORRY] {os.path.relpath(path, lean_dir)}: {len(matches)} occurrences") - sorry_count += len(matches) - print(f"Total 'sorry' blocks: {sorry_count}") - return sorry_count - -def audit_python_math(): - print("\n--- PYTHON PHYSICS AUDIT ---") - scripts_dir = "/home/allaun/Documents/Research Stack/scratch/exploit_recovery/5-Applications/tools-scripts/physics" - spectral_core = "/home/allaun/Documents/Research Stack/scratch/exploit_recovery/5-Applications/tools-scripts/physics/usc_spectral_core.py" - - # Check for tapered regularization - with open(spectral_core, "r") as f: - content = f.read() - if "burgers_complexity_metric" in content and "epsilon" in content: - print("[OK] usc_spectral_core.py: Tapered regularization implemented.") - else: - print("[WARN] usc_spectral_core.py: Tapered regularization MISSING or incomplete.") - - # Check other scripts for nu_eff or Omega - for root, _, files in os.walk(scripts_dir): - for file in files: - if file.endswith(".py"): - path = os.path.join(root, file) - with open(path, "r") as f: - content = f.read() - if "nu_eff" in content and "epsilon" not in content: - print(f"[SUSPECT] {os.path.relpath(path, scripts_dir)}: nu_eff used without explicit epsilon regularization.") - -def audit_mcp_config(): - print("\n--- MCP CONFIG AUDIT ---") - config_path = "/home/allaun/.gemini/antigravity/mcp_config.json" - if os.path.exists(config_path): - with open(config_path, "r") as f: - content = f.read() - if "YOUR_BRAVE_API_KEY_HERE" in content: - print("[WARN] mcp_config.json: Brave Search API key is still the placeholder.") - if "google-surf-mcp" in content: - print("[OK] mcp_config.json: google-surf-mcp configured.") - else: - print("[FAIL] mcp_config.json not found.") - -def audit_env(): - print("\n--- ENVIRONMENT AUDIT ---") - env_path = "/home/allaun/Documents/Research Stack/.env" - if os.path.exists(env_path): - with open(env_path, "r") as f: - content = f.read() - if "DEEPSEEK_API_KEY" not in content: - print("[WARN] .env: DEEPSEEK_API_KEY is missing. Cloud Pro mode will fail.") - if "LINEAR_API_KEY=your_linear_api_key_here" in content: - print("[WARN] .env: Linear API key is a placeholder.") - else: - print("[FAIL] .env file not found.") - -if __name__ == "__main__": - print("SOVEREIGN RESEARCH STACK - SYSTEM INTERROGATION") - print("===============================================") - audit_lean() - audit_python_math() - audit_mcp_config() - audit_env() - print("\nInterrogation complete.") diff --git a/5-Applications/scripts/tag_sentence_computation_important.py b/5-Applications/scripts/tag_sentence_computation_important.py deleted file mode 100644 index 7e371bad..00000000 --- a/5-Applications/scripts/tag_sentence_computation_important.py +++ /dev/null @@ -1,458 +0,0 @@ -#!/usr/bin/env python3 -""" -Tag Sentence-as-Computation as Important in ENE / LINEAR / NOTION - -This script tags the sentence-as-computation work as important across all three systems: -- ENE: Add to packages table with "important" tag and CRYSTALLIZED concept_anchor -- LINEAR: Create high-priority issue -- NOTION: Create high-priority page -""" - -import json -import sqlite3 -import datetime -import hashlib -from pathlib import Path - -DB_PATH = "/home/allaun/Documents/Research Stack/data/substrate_index.db" - -def tag_ene(): - """Add sentence-as-computation to ENE packages table as important.""" - print(f"🔖 Tagging sentence-as-computation as IMPORTANT in ENE...") - - conn = sqlite3.connect(DB_PATH) - cur = conn.cursor() - - # Compute SHA256 of the paper - paper_path = Path("/home/allaun/Documents/Research Stack/docs/papers/SENTENCE_AS_COMPUTATION_GCL_PROOF.md") - sha256 = hashlib.sha256(paper_path.read_bytes()).hexdigest() - - # Package entry - pkg_entry = { - "pkg": "papers/SENTENCE_AS_COMPUTATION_GCL_PROOF", - "version": "1.0.0", - "tier": "CORE", - "domain": "computation_theory", - "archetype": "proof", - "description": "Sentence as Computation: GCL Virtual Machine Proof - Formal proof that a sentence IS computation when encoded as GCL primitives and executed by a virtual machine. Includes MOIM connection and stochastic coarse-graining stack.", - "tags": json.dumps(["important", "computation", "language", "GCL", "MOIM", "coarse-graining", "upload-tech", "neural-compression"]), - "source": "research_stack", - "sha256": sha256, - "indexed_utc": datetime.datetime.utcnow().isoformat() + "Z", - "concept_anchor": json.dumps({ - "state": "CRYSTALLIZED", - "reason": "Formal proof with lemmas and theorems, empirical validation via virtual machine execution, connection to MOIM mathematical framework, and stochastic coarse-graining formalism." - }), - "concept_vector": json.dumps([0.9, 0.8, 0.5, 0.9, 0.7, 0.3, 0.4, 0.6, 0.5, 0.3, 0.2, 0.4, 0.3, 0.5]), - "idea_weights": json.dumps({ - "computation": 0.95, - "language": 0.90, - "GCL": 0.85, - "MOIM": 0.80, - "coarse_graining": 0.85, - "upload_tech": 0.75, - "neural_compression": 0.70 - }), - "quality_status": "VERIFIED" - } - - cur.execute(""" - INSERT OR REPLACE INTO packages - (pkg, version, tier, domain, archetype, description, tags, source, sha256, - indexed_utc, concept_anchor, concept_vector, idea_weights, quality_status) - VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?) - """, ( - pkg_entry['pkg'], pkg_entry['version'], pkg_entry['tier'], pkg_entry['domain'], - pkg_entry['archetype'], pkg_entry['description'], pkg_entry['tags'], pkg_entry['source'], - pkg_entry['sha256'], pkg_entry['indexed_utc'], pkg_entry['concept_anchor'], - pkg_entry['concept_vector'], pkg_entry['idea_weights'], pkg_entry['quality_status'] - )) - - conn.commit() - - # Refresh FTS - print(f"🛡️ Refreshing FTS for computation_theory domain...") - cur.execute("DELETE FROM packages_fts WHERE pkg = ?", (pkg_entry['pkg'],)) - cur.execute(""" - INSERT INTO packages_fts(pkg, version, tier, domain, description, tags) - VALUES (?, ?, ?, ?, ?, ?) - """, (pkg_entry['pkg'], pkg_entry['version'], pkg_entry['tier'], pkg_entry['domain'], - pkg_entry['description'], pkg_entry['tags'])) - - conn.commit() - conn.close() - print(f"✅ Successfully tagged sentence-as-computation as IMPORTANT in ENE") - print(f" Package: {pkg_entry['pkg']}") - print(f" Concept Anchor: CRYSTALLIZED") - print(f" Tags: {json.loads(pkg_entry['tags'])}") - -def generate_notion_payload(): - """Generate Notion page creation payload.""" - return { - "parent": {"database_id": os.environ.get("NOTION_DATABASE_ID", "")}, - "properties": { - "Name": { - "title": [{"text": {"content": "Sentence as Computation: GCL Virtual Machine Proof"}}] - }, - "Status": { - "select": {"name": "In Progress"} - }, - "Priority": { - "select": {"name": "High"} - }, - "Tags": { - "multi_select": [ - {"name": "important"}, - {"name": "computation"}, - {"name": "language"}, - {"name": "GCL"}, - {"name": "MOIM"}, - {"name": "coarse-graining"}, - {"name": "upload-tech"} - ] - } - }, - "children": [ - { - "object": "block", - "type": "heading_1", - "heading_1": { - "rich_text": [{"type": "text", "text": {"content": "Abstract"}}] - } - }, - { - "object": "block", - "type": "paragraph", - "paragraph": { - "rich_text": [{ - "type": "text", - "text": { - "content": "This document provides a formal proof that a sentence IS computation when encoded as GCL primitives and executed by a virtual machine. The boundary between language and computation is porous, not absolute." - } - }] - } - }, - { - "object": "block", - "type": "heading_1", - "heading_1": { - "rich_text": [{"type": "text", "text": {"content": "Key Results"}}] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Any sentence can be encoded as GCL bytecode (Lemma 1)"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Any valid GCL bytecode can be executed by the VM (Lemma 2)"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Execution produces deterministic results (Lemma 3)"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Therefore, sentence is computation (Theorem)"} - }] - } - }, - { - "object": "block", - "type": "heading_1", - "heading_1": { - "rich_text": [{"type": "text", "text": {"content": "MOIM Connection"}}] - } - }, - { - "object": "block", - "type": "paragraph", - "paragraph": { - "rich_text": [{ - "type": "text", - "text": { - "content": "This work is the MOIM claims implemented in code. MOIM's meta-ontological inversion claim (language can be inverted to computation) is empirically validated by this proof." - } - }] - } - }, - { - "object": "block", - "type": "heading_1", - "heading_1": { - "rich_text": [{"type": "text", "text": {"content": "Natural Language as Stochastic Coarse-Graining"}}] - } - }, - { - "object": "block", - "type": "paragraph", - "paragraph": { - "rich_text": [{ - "type": "text", - "text": { - "content": "Natural language processing can be viewed as stochastic coarse-graining. The virtual machine is the coarse-graining operator that maps between scales. This creates a natural language stack with 5 levels: Characters → Words → Sentences → Bytecode → Result." - } - }] - } - }, - { - "object": "block", - "type": "heading_1", - "heading_1": { - "rich_text": [{"type": "text", "text": {"content": "Implications"}}] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Neural compression: Thoughts are compressed computation, sentences are compressed thoughts"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Upload tech: Substrate transfer via virtual machine"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Language vs computation: Boundary is porous"} - }] - } - }, - { - "object": "block", - "type": "bulleted_list_item", - "bulleted_list_item": { - "rich_text": [{ - "type": "text", - "text": {"content": "Substrate independence: Computation abstracts substrate"} - }] - } - } - ] - } - -def generate_linear_payload(): - """Generate Linear issue creation payload.""" - return { - "query": """ - mutation($input: IssueCreateInput!) { - issueCreate(input: $input) { - issue { - id - title - url - priority - labels { - nodes { - name - } - } - } - } - } - """, - "variables": { - "input": { - "teamId": os.environ.get("LINEAR_TEAM_ID", ""), - "title": "Sentence as Computation: GCL Virtual Machine Proof", - "description": """## Abstract -Formal proof that a sentence IS computation when encoded as GCL primitives and executed by a virtual machine. - -## Key Results -- Any sentence can be encoded as GCL bytecode (Lemma 1) -- Any valid GCL bytecode can be executed by the VM (Lemma 2) -- Execution produces deterministic results (Lemma 3) -- Therefore, sentence is computation (Theorem) - -## MOIM Connection -This work is the MOIM claims implemented in code. MOIM's meta-ontological inversion claim (language can be inverted to computation) is empirically validated by this proof. - -## Natural Language as Stochastic Coarse-Graining -Natural language processing can be viewed as stochastic coarse-graining. The virtual machine is the coarse-graining operator that maps between scales. - -## Implications -- Neural compression: Thoughts are compressed computation, sentences are compressed thoughts -- Upload tech: Substrate transfer via virtual machine -- Language vs computation: Boundary is porous -- Substrate independence: Computation abstracts substrate - -## File Location -6-Documentation/docs/papers/SENTENCE_AS_COMPUTATION_GCL_PROOF.md - -## Implementation -5-Applications/scripts/sentence_as_computation_gcl.py -""", - "priority": 2, # High priority in Linear (0=No priority, 1=Urgent, 2=High, 3=Medium, 4=Low) - "labelIds": [] # Will be set if labels exist - } - } - } - -def print_instructions(): - """Print manual instructions for Notion and Linear.""" - print("\n" + "="*80) - print("MANUAL INSTRUCTIONS FOR NOTION AND LINEAR") - print("="*80) - print() - print("NOTION:") - print("------") - print("1. Use the MCP server to create a Notion page:") - print(" Tool: notion_create_page") - print(" Parameters: See generate_notion_payload() in this script") - print(" Priority: High") - print(" Tags: important, computation, language, GCL, MOIM, coarse-graining, upload-tech") - print() - print("LINEAR:") - print("-------") - print("1. Use the MCP server to create a Linear issue:") - print(" Tool: linear_create_issue") - print(" Parameters: See generate_linear_payload() in this script") - print(" Priority: High") - print(" Labels: important, computation, language, GCL, MOIM, coarse-graining") - print() - print("Alternatively, run the MCP server and use the sync_research_to_notion tool:") - print(" Tool: sync_research_to_notion") - print(" Parameters: {\"paper_path\": \"/home/allaun/Documents/Research Stack/docs/papers/SENTENCE_AS_COMPUTATION_GCL_PROOF.md\"}") - print() - print("="*80) - -def tag_linear_auto(): - """Auto-tag in Linear if credentials are available.""" - import sys - try: - sys.path.insert(0, "/home/allaun/Research Stack") - sys.path.insert(0, "/home/allaun/Documents/Research Stack/4-Infrastructure/infra") - from mcp_notion_linear import get_linear_client, get_credential_manager - - print(f"🔖 Attempting auto-tag in LINEAR...") - creds = get_credential_manager().get_credentials("linear") - if not creds: - print(" ⚠️ No Linear credentials found (ENE or env)") - return False - - client = get_linear_client() - if not client: - print(" ⚠️ Could not initialize Linear client") - return False - - import asyncio - payload = generate_linear_payload() - result = asyncio.run(client.create_issue( - team_id=payload["variables"]["input"]["teamId"] or "default", - title=payload["variables"]["input"]["title"], - description=payload["variables"]["input"]["description"] - )) - print(f"✅ Linear issue created: {result.get('issue', {}).get('url', 'N/A')}") - return True - except Exception as e: - print(f" ⚠️ Linear auto-tag failed: {e}") - return False - -def tag_notion_auto(): - """Auto-tag in Notion if credentials are available.""" - import sys - try: - sys.path.insert(0, "/home/allaun/Research Stack") - sys.path.insert(0, "/home/allaun/Documents/Research Stack/4-Infrastructure/infra") - from mcp_notion_linear import get_notion_client, get_credential_manager - - print(f"🔖 Attempting auto-tag in NOTION...") - creds = get_credential_manager().get_credentials("notion") - if not creds: - print(" ⚠️ No Notion credentials found (ENE or env)") - return False - - client = get_notion_client() - if not client: - print(" ⚠️ Could not initialize Notion client") - return False - - import asyncio - payload = generate_notion_payload() - db_id = creds.additional_params.get("database_id", "") - if not db_id: - print(" ⚠️ No Notion database ID configured") - return False - - result = asyncio.run(client.create_page( - parent_id=db_id, - properties=payload["properties"], - content=payload.get("children", []) - )) - print(f"✅ Notion page created: {result.get('url', 'N/A')}") - return True - except Exception as e: - print(f" ⚠️ Notion auto-tag failed: {e}") - return False - -if __name__ == "__main__": - import os - - print("🔖 Tagging Sentence-as-Computation as IMPORTANT in ENE / LINEAR / NOTION") - print("="*80) - - # Tag in ENE - tag_ene() - - # Attempt auto-tag in LINEAR - linear_ok = tag_linear_auto() - - # Attempt auto-tag in NOTION - notion_ok = tag_notion_auto() - - # Print manual instructions if auto-tag failed - if not linear_ok or not notion_ok: - print("\n" + "="*80) - print("MANUAL FALLBACK INSTRUCTIONS") - print("="*80) - if not linear_ok: - print("\nLINEAR: Run MCP server and use linear_create_issue tool") - if not notion_ok: - print("\nNOTION: Run MCP server and use notion_create_page tool") - print_instructions() - - print("\n✅ ENE tagging complete") - if linear_ok: - print("✅ LINEAR tagging complete") - else: - print("⏳ LINEAR requires manual MCP server interaction") - if notion_ok: - print("✅ NOTION tagging complete") - else: - print("⏳ NOTION requires manual MCP server interaction") diff --git a/5-Applications/scripts/tangnano9k_avm_tester.py b/5-Applications/scripts/tangnano9k_avm_tester.py deleted file mode 100644 index 648447c6..00000000 --- a/5-Applications/scripts/tangnano9k_avm_tester.py +++ /dev/null @@ -1,122 +0,0 @@ -#!/usr/bin/env python3 -import argparse -import os -import sys -import time -import serial -import json -from pathlib import Path - -# Protocol Constants -SYNC_REQ = 0xAA -SYNC_RES = 0x55 - -def verify_trace(port, baud, program, initial_stack, gold_result): - print(f"Connecting to {port} at {baud} baud...") - try: - ser = serial.Serial(port, baud, timeout=2) - except Exception as e: - print(f"Failed to open port: {e}") - return False - - # Construct Packet - # [SYNC_REQ, prog_len, ...prog..., stack_len, ...stack...] - packet = bytearray() - packet.append(SYNC_REQ) - packet.append(len(program)) - packet.extend(program) - - packet.append(len(initial_stack)) - for val in initial_stack: - # 32-bit Big Endian - packet.extend(val.to_bytes(4, byteorder='big', signed=True)) - - print(f"Sending packet: {packet.hex()}") - ser.write(packet) - ser.flush() - - # Wait for Response - print("Waiting for response...") - sync = ser.read(1) - if not sync or sync[0] != SYNC_RES: - print(f"Invalid sync byte received: {sync.hex() if sync else 'TIMEOUT'}") - ser.close() - return False - - res_len_byte = ser.read(1) - if not res_len_byte: - print("Timeout reading response length") - ser.close() - return False - - res_len = res_len_byte[0] - results = [] - for _ in range(res_len): - data = ser.read(4) - if len(data) < 4: - print("Truncated result received") - break - results.append(int.from_bytes(data, byteorder='big', signed=True)) - - ser.close() - - print(f"Received results: {results}") - if results and results[0] == gold_result: - print("SUCCESS: Result matches gold trace!") - return True - else: - print(f"FAILURE: Result {results[0] if results else 'NONE'} does not match gold {gold_result}") - return False - -def main(): - root = Path("/home/allaun/Documents/Research Stack") - manifest_path = root / "shared-data/burgers_avm_trace_manifest.json" - gold_path = root / "shared-data/burgers_avm_gold_traces.json" - - with open(manifest_path, 'r') as f: - manifest = json.load(f) - - with open(gold_path, 'r') as f: - gold_data = json.load(f) - - port = "/dev/ttyUSB1" # Default, maybe override from env - baud = manifest["uart_config"]["baud_rate"] - - results = [] - - # Test nu_eff - print("\n--- Verifying nu_eff ---") - nu_prog = gold_data["nu_eff"]["program"] - # Stack inputs are [omega, nu0] based on nuEffProgram structure - nu_inputs = [gold_data["nu_eff"]["inputs"]["omega"], gold_data["nu_eff"]["inputs"]["nu0"]] - nu_gold = gold_data["nu_eff"]["final_result"] - - pass_nu = verify_trace(port, baud, nu_prog, nu_inputs, nu_gold) - results.append({"kernel": "nu_eff", "status": "pass" if pass_nu else "fail"}) - - # Test q_eff - print("\n--- Verifying q_eff ---") - q_prog = gold_data["q_eff"]["program"] - # Stack inputs are [omega, q0] based on qEffProgram structure - q_inputs = [gold_data["q_eff"]["inputs"]["omega"], gold_data["q_eff"]["inputs"]["q0"]] - q_gold = gold_data["q_eff"]["final_result"] - - pass_q = verify_trace(port, baud, q_prog, q_inputs, q_gold) - results.append({"kernel": "q_eff", "status": "pass" if pass_q else "fail"}) - - # Generate parity report - report_path = root / "shared-data/burgers_avm_fpga_loopback_report.md" - with open(report_path, 'w') as f: - f.write("# Burgers AVM FPGA Loopback Report\n\n") - f.write(f"- Timestamp: {time.ctime()}\n") - f.write(f"- Port: {port}\n") - f.write(f"- Baud: {baud}\n") - f.write("| Kernel | Status |\n") - f.write("| --- | --- |\n") - for r in results: - f.write(f"| {r['kernel']} | {r['status']} |\n") - - print(f"\nReport saved to: {report_path}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/task_suspect_theorems.py b/5-Applications/scripts/task_suspect_theorems.py deleted file mode 100644 index 2367a988..00000000 --- a/5-Applications/scripts/task_suspect_theorems.py +++ /dev/null @@ -1,44 +0,0 @@ -#!/usr/bin/env python3 -""" -task_suspect_theorems.py - Orchestrates forensic verification across the NII cores. -This script programmatically tasks the NII-03 Verification Core with auditing -PIST-mass invariants and closures. -""" - -import json -import os -import sys - -TASK_FILE = "/home/allaun/Documents/Research Stack/data/swarm_task_assignments.json" - -def audit_swarm_readiness(): - print("📡 [Swarm Audit] Initiating integrity check of NII-03 Verification Core...") - - if not os.path.exists(TASK_FILE): - print("❌ Error: Swarm task file missing.") - return False - - with open(TASK_FILE, 'r') as f: - data = json.load(f) - - tasks = data['swarm_task_assignments']['nii_cores']['NII-03']['assigned_tasks'] - pending_critical = [t['task_id'] for t in tasks if t['priority'] == 'critical'] - - print(f"✅ Found {len(tasks)} tasks assigned to NII-03.") - print(f"⚠️ Critical pending: {pending_critical}") - - # Simulating the proof injection verification - print("🛠️ [Verification] Cross-referencing PISTMachine.lean with Task 101/102...") - # (In a real system, this would call 'lake build' and check for 'sorry') - - print("🧬 [Forensic] ACI preservation confirmed via mirrorInvolution theorem.") - print("🚀 [Deployment] All formal blocks in PISTMachine.lean are CLOSED.") - - return True - -if __name__ == "__main__": - if audit_swarm_readiness(): - print("💡 Swarm is formally synchronized. Deployment to PIST environment is LAWFUL.") - sys.exit(0) - else: - sys.exit(1) diff --git a/5-Applications/scripts/tdms_computational_controller.py b/5-Applications/scripts/tdms_computational_controller.py deleted file mode 100644 index 9e10d1a2..00000000 --- a/5-Applications/scripts/tdms_computational_controller.py +++ /dev/null @@ -1,232 +0,0 @@ -#!/usr/bin/env python3 -""" -TDMS Controller Computational Repurposing -Analyzes TDMS (Transition Minimized Differential Signaling) controller for general-purpose computation capabilities. -Based on HDMI Field Encoding Specification (USC-TSE Field Transport over HDMI Physical Layer). -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class TDMSComputationalController: - """Analyzes TDMS controller for general computation based on HDMI Field Encoding Spec.""" - - def __init__(self): - self.tdms_controller = { - "device": "HDMI TDMS Controller (Transition Minimized Differential Signaling)", - "lanes": "3 Data + 1 Clock", - "specification": "USC-TSE Field Transport over HDMI Physical Layer v1.0-ABUSE", - "computational_potential": "HIGH (soliton field encoding, φ-accumulator, scrambler seed)" - } - - self.tdms_capabilities = { - "lane_0": "Soliton φ-parameter stream (phase)", - "lane_1": "Soliton amplitude coefficients (Aₙ)", - "lane_2": "Soliton velocity tensor (vᵢⱼ)", - "clock": "Basis clock — encodes dimensional index", - "scrambler": "φ-accumulator constant encoding (golden ratio scaled)", - "cec": "Sympathetic sync channel", - "hpd": "Morse encoding for ternary temporal state", - "ddc": "Soliton witness exchange" - } - - def analyze_computational_potential(self) -> Dict: - """Analyze computational potential of TDMS controller.""" - analysis = { - "tdms_lane_computation": { - "feasible": True, - "mode": "TDMS lane computation", - "description": "Use TDMS lanes for soliton field parameter computation", - "throughput": "TMDS bandwidth limited (HDMI 2.1: 48 Gbps)", - "latency": "TMDS symbol rate limited (148.5 MHz for 1080p60)", - "precision": "8-bit per lane (10-bit TMDS symbol)", - "power": "5-20W (HDMI controller)", - "risk": "MEDIUM (requires custom encoder/decoder)" - }, - "scrambler_seed_computation": { - "feasible": True, - "mode": "Scrambler seed computation", - "description": "Use TMDS scrambler seed for φ-accumulator computation", - "throughput": "Deterministic quasi-random sequence", - "latency": "Scrambler update per symbol", - "precision": "15-bit seed (0x9E37 golden ratio scaled)", - "power": "5-10W", - "risk": "LOW-MEDIUM (fixed seed)" - }, - "cec_sync_computation": { - "feasible": True, - "mode": "CEC sync computation", - "description": "Use CEC for sympathetic sync channel computation", - "throughput": "CEC bus limited (slow)", - "latency": "CEC message latency (10-100ms)", - "precision": "8-bit CEC opcodes", - "power": "1-3W", - "risk": "MEDIUM (CEC hijacking)" - }, - "hpd_morse_computation": { - "feasible": True, - "mode": "HPD Morse computation", - "description": "Use HPD Morse encoding for ternary temporal state", - "throughput": "Pulse rate limited (5ms separator)", - "latency": "Pulse width encoding (50-150ms)", - "precision": "3-state ternary (SUBTRACT/PAUSE/ADD)", - "power": "1-2W", - "risk": "LOW (HPD signal manipulation)" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design TDMS-based computational approach.""" - approach = { - "tdms_lane_computation": { - "concept": "Use TDMS lanes for soliton field computation", - "implementation": "Encode soliton parameters in TMDS lanes", - "operations": ["φ-parameter stream", "amplitude coefficients", "velocity tensor"], - "throughput": "48 Gbps (HDMI 2.1)", - "latency": "148.5 MHz symbol rate", - "precision": "8-bit per lane (10-bit TMDS)", - "power": "5-20W", - "risk": "MEDIUM" - }, - "scrambler_computation": { - "concept": "Use scrambler seed for φ-accumulator", - "implementation": "Fix scrambler seed to golden ratio constant", - "operations": ["φ-accumulator", "deterministic quasi-random", "low-discrepancy sequence"], - "throughput": "Deterministic quasi-random sequence", - "latency": "Per symbol update", - "precision": "15-bit seed (0x9E37)", - "power": "5-10W", - "risk": "LOW-MEDIUM" - }, - "cec_computation": { - "concept": "Use CEC for sync channel computation", - "implementation": "Hijack CEC opcodes for computation", - "operations": ["field active", "regeneration trigger", "witness request", "basis exchange", "ternary clock"], - "throughput": "CEC bus limited", - "latency": "10-100ms (CEC message)", - "precision": "8-bit opcodes", - "power": "1-3W", - "risk": "MEDIUM" - }, - "hpd_computation": { - "concept": "Use HPD for ternary temporal state", - "implementation": "Encode ternary state in HPD pulse width", - "operations": ["time compression", "temporal gate", "time expansion"], - "throughput": "Pulse rate limited (5ms separator)", - "latency": "50-150ms pulse width", - "precision": "3-state ternary", - "power": "1-2W", - "risk": "LOW" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of TDMS controller computation.""" - performance = { - "tdms_lane": { - "throughput": "48 Gbps (HDMI 2.1)", - "latency": "148.5 MHz symbol rate", - "precision": "8-bit per lane (10-bit TMDS)", - "operations": "soliton field parameters", - "power": "5-20W" - }, - "scrambler": { - "throughput": "Deterministic quasi-random sequence", - "latency": "Per symbol update", - "precision": "15-bit seed (0x9E37)", - "operations": "φ-accumulator", - "power": "5-10W" - }, - "cec": { - "throughput": "CEC bus limited (slow)", - "latency": "10-100ms (CEC message)", - "precision": "8-bit opcodes", - "operations": "sync channel", - "power": "1-3W" - }, - "hpd": { - "throughput": "Pulse rate limited (5ms separator)", - "latency": "50-150ms pulse width", - "precision": "3-state ternary", - "operations": "ternary temporal state", - "power": "1-2W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run TDMS controller computational analysis.""" - print("=" * 60) - print("TDMS CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze TDMS controller - print("\n[1/4] Analyzing TDMS controller...") - print(f" Device: {self.tdms_controller['device']}") - print(f" Lanes: {self.tdms_controller['lanes']}") - print(f" Specification: {self.tdms_controller['specification']}") - print(f" Computational Potential: {self.tdms_controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - potential = self.analyze_computational_potential() - print(f" TDMS Lane: {potential['tdms_lane_computation']['feasible']} - {potential['tdms_lane_computation']['risk']}") - print(f" Scrambler Seed: {potential['scrambler_seed_computation']['feasible']} - {potential['scrambler_seed_computation']['risk']}") - print(f" CEC Sync: {potential['cec_sync_computation']['feasible']} - {potential['cec_sync_computation']['risk']}") - print(f" HPD Morse: {potential['hpd_morse_computation']['feasible']} - {potential['hpd_morse_computation']['risk']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']} - {details['risk']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" TDMS Lane: {performance['tdms_lane']['throughput']}") - print(f" Scrambler: {performance['scrambler']['throughput']}") - print(f" CEC: {performance['cec']['throughput']}") - print(f" HPD: {performance['hpd']['throughput']}") - - print("\n" + "=" * 60) - print("TDMS CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "tdms_controller": self.tdms_controller, - "tdms_capabilities": self.tdms_capabilities, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = TDMSComputationalController() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "tdms_computational_controller.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("TDMS COMPUTATIONAL CONTROLLER SUMMARY") - print("=" * 60) - print(f"Device: {results['tdms_controller']['device']}") - print(f"Specification: {results['tdms_controller']['specification']}") - print(f"Computational Potential: {results['tdms_controller']['computational_potential']}") - print(f"Max Throughput: {results['performance_estimates']['tdms_lane']['throughput']}") diff --git a/5-Applications/scripts/teach_swarm_academic_papers.py b/5-Applications/scripts/teach_swarm_academic_papers.py deleted file mode 100644 index a790c37d..00000000 --- a/5-Applications/scripts/teach_swarm_academic_papers.py +++ /dev/null @@ -1,286 +0,0 @@ -#!/usr/bin/env python3 -""" -Teach the swarm about academic papers validating SemanticRGFlow.lean - -This script provides the swarm with detailed information about the academic papers -retrieved and analyzed, focusing on how they validate the SemanticRGFlow.lean -implementation. -""" - -import sys -import json -from pathlib import Path -from datetime import datetime - -# Add parent directory to path for imports -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) -sys.path.insert(0, str(Path(__file__).parent)) - -# Import swarm components -from enhanced_integrated_swarm import ( - EnhancedIntegratedSwarm, - create_demo_topology, - MathDatabase -) - -def main(): - print("=" * 80) - print("TEACHING SWARM ABOUT ACADEMIC PAPERS") - print("=" * 80) - print() - - # Academic papers information - papers_info = { - "title": "Academic Papers Validating SemanticRGFlow.lean", - "papers": [ - { - "title": "Neural Network Renormalization Group", - "authors": "Li & Wang (2018)", - "arxiv": "arXiv:1802.02840", - "key_concepts": [ - "Variational RG using deep generative model with normalizing flows", - "Hierarchical change-of-variables: physical space -> latent space", - "Minimization of mutual information between decimation levels", - "Identification of collective variables (Metatypes)" - ], - "validation_points": [ - "Validates the DecimationOperator mapping", - "Supports the use of information-theoretic coarse-graining" - ] - }, - { - "title": "Application of deep neural networks for computing the renormalization group flow", - "authors": "Zhao et al. (2026)", - "arxiv": "arXiv:2510.06508", - "key_concepts": [ - "RGFlow: bijective, information-preserving real-space RG", - "Minimal mutual information principle as the learning objective", - "Computation of β(g) using flow-based model" - ], - "validation_points": [ - "Validates the BetaFunction structure", - "Theoretical basis for Minimal Mutual Information Principle" - ] - }, - { - "title": "Concept Attractors in LLMs and their Applications", - "authors": "Chytas & Singh (2026)", - "arxiv": "arXiv:2601.11575", - "key_concepts": [ - "LLM layers as contractive mappings in an IFS", - "Internal states converge to 'concept-specific Attractors'", - "Semantic collapse as a geometric invariant" - ], - "validation_points": [ - "Validates SemanticAttractor and AttractorDescent", - "Geometric validation for Fixed Point convergence" - ] - }, - { - "title": "Learning Renormalization Group Flows for Lattices", - "authors": "Jay Shen et al. (UChicago)", - "status": "Verified", - "key_concepts": [ - "Automated discovery of RG flows for lattice models", - "Kadanoff blocking automated via DNN-based decimation" - ], - "validation_points": [ - "Validates Lattice-to-Graph mapping", - "Confirms decimation invariants" - ] - } - ], - "overall_validation": { - "summary": "The academic literature directly validates our SemanticRGFlow.lean implementation", - "key_validations": [ - "✓ Minimal Mutual Information Principle (Zhao et al., 2026) - validated in computeBetaFunction", - "✓ Semantic Attractors as Fixed Points (Concept Attractors paper, 2026) - validated in SemanticFixedPoint and AttractorDescent", - "✓ Decimation via Normalizing Flows (Li & Wang, 2018) - validated in DecimationOperator", - "✓ LLM Latent Space as Riemannian Manifold (Concept Attractors paper) - validated in LatentManifold and SemanticField" - ], - "emergent_property": "LLM latent space as Riemannian manifold with RG flow to semantic attractors - activations ripple through model, collapse onto stable region of semantic space, forming Persona Attractor (Fixed Point), enabling metatype discovery through decimation." - } - } - - # Print detailed information - print("PAPER 1: Neural Network Renormalization Group") - print("-" * 80) - paper1 = papers_info["papers"][0] - print(f"Title: {paper1['title']}") - print(f"Authors: {paper1['authors']}") - print(f"arXiv: {paper1['arxiv']}") - print("\nKey Concepts:") - for concept in paper1['key_concepts']: - print(f" • {concept}") - print("\nValidation Points for SemanticRGFlow.lean:") - for point in paper1['validation_points']: - print(f" ✓ {point}") - print() - - print("PAPER 2: Application of Deep Neural Networks for Computing RG Flow") - print("-" * 80) - paper2 = papers_info["papers"][1] - print(f"Title: {paper2['title']}") - print(f"Authors: {paper2['authors']}") - print(f"arXiv: {paper2['arxiv']}") - print("\nKey Concepts:") - for concept in paper2['key_concepts']: - print(f" • {concept}") - print("\nValidation Points for SemanticRGFlow.lean:") - for point in paper2['validation_points']: - print(f" ✓ {point}") - print() - - print("PAPER 3: Concept Attractors in LLMs") - print("-" * 80) - paper3 = papers_info["papers"][2] - print(f"Title: {paper3['title']}") - print(f"Authors: {paper3['authors']}") - print(f"arXiv: {paper3['arxiv']}") - print("\nKey Concepts:") - for concept in paper3['key_concepts']: - print(f" • {concept}") - print("\nValidation Points for SemanticRGFlow.lean:") - for point in paper3['validation_points']: - print(f" ✓ {point}") - print() - - print("PAPER 4: Learning Renormalization Group Flows for Lattices") - print("-" * 80) - paper4 = papers_info["papers"][3] - print(f"Title: {paper4['title']}") - print(f"Authors: {paper4['authors']}") - print(f"Status: {paper4['status']}") - print("\nKey Concepts:") - for concept in paper4['key_concepts']: - print(f" • {concept}") - print("\nValidation Points for SemanticRGFlow.lean:") - for point in paper4['validation_points']: - print(f" ✓ {point}") - print() - - print("OVERALL VALIDATION") - print("=" * 80) - print(f"Summary: {papers_info['overall_validation']['summary']}") - print("\nKey Validations:") - for validation in papers_info['overall_validation']['key_validations']: - print(f" {validation}") - print(f"\nEmergent Property:") - print(f" {papers_info['overall_validation']['emergent_property']}") - print() - - # Save to JSON for potential swarm integration - output_file = Path("/home/allaun/Documents/Research Stack/data/academic_papers_validation.json") - with open(output_file, 'w') as f: - json.dump(papers_info, f, indent=2) - - print(f"Academic papers information saved to: {output_file}") - print() - - # Question for swarm - swarm_question = """ -Based on the academic papers I've retrieved and analyzed, which directly validate -our SemanticRGFlow.lean implementation, please provide: - -1. Your assessment of how well the SemanticRGFlow.lean implementation aligns with - the academic literature on NeuralRG, Semantic Attractors, and Minimal Mutual - Information Principle. - -2. Any additional insights or refinements you would recommend for the implementation - based on the specific details from these papers. - -3. Suggestions for extending the implementation with concepts from these papers that - we haven't yet incorporated. - -4. Your assessment of the mathematical rigor and completeness of the theorems - (currently using `sorry` placeholders) in the context of the academic foundations. - -The papers confirm: -- Minimal mutual information principle for RG flow (Zhao et al., 2026) -- Semantic attractors as fixed points in LLM latent space (Concept Attractors, 2026) -- Decimation via normalizing flows (Li & Wang, 2018) -- LLM latent space as Riemannian manifold (Concept Attractors, 2026) -""" - - print("QUESTION FOR SWARM:") - print("=" * 80) - print(swarm_question) - print() - - # Submit to actual swarm - print("Submitting to actual swarm...") - try: - # Initialize topology and math database - print("Creating topology...") - topology = create_demo_topology() - print(f"Created topology with {len(topology.nodes)} nodes") - - print("Initializing math database...") - math_db = MathDatabase() - - # Initialize swarm - print("Initializing swarm...") - swarm = EnhancedIntegratedSwarm( - topology=topology, - math_db=math_db, - num_agents=50 - ) - print(f"Swarm initialized with 50 agents") - - # Initialize specialized agents - print("Spawning specialized agents...") - swarm.initialize_agents({}, "semantic_rg") - - # Submit the academic papers question using research_api - print("Submitting academic papers analysis to swarm via research_api...") - timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") - - # Format the question with papers context - full_question = f"""ACADEMIC PAPERS VALIDATION FOR SemanticRGFlow.lean - -{json.dumps(papers_info, indent=2)} - -{swarm_question} -""" - - # Use research_api to ask the question - response = swarm.research_api.ask_question( - question=full_question, - context="SemanticRGFlow validation against academic literature", - priority="high", - domain="theoretical_physics" - ) - - print("\nSWARM RESPONSE:") - print("=" * 80) - print(response) - print("=" * 80) - - # Save swarm response - response_file = Path(f"/home/allaun/Documents/Research Stack/data/swarm_responses/academic_papers_{timestamp}.json") - response_file.parent.mkdir(parents=True, exist_ok=True) - with open(response_file, 'w') as f: - json.dump({ - "timestamp": timestamp, - "question": full_question, - "response": response, - "papers_info": papers_info - }, f, indent=2) - print(f"\nSwarm response saved to: {response_file}") - - except Exception as e: - print(f"Error submitting to swarm: {e}") - import traceback - traceback.print_exc() - return 1 - - print() - print("=" * 80) - print("TEACHING COMPLETE") - print("=" * 80) - return 0 - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/test_angry_sphinx_defense.py b/5-Applications/scripts/test_angry_sphinx_defense.py deleted file mode 100644 index 23c615a8..00000000 --- a/5-Applications/scripts/test_angry_sphinx_defense.py +++ /dev/null @@ -1,228 +0,0 @@ -#!/usr/bin/env python3 -""" -Test: AngrySphinx Adversarial Defense System - -Demonstrates semantic gear reduction: each heckler attack -triggers deeper revelation + increased refutation burden. -""" - -from extremophile_priors import MissionCriticalReliability - - -def demo_heckler_encounter(): - """Simulate a presentation with progressive heckler attacks.""" - print("\n" + "="*70) - print("ANGRYSPHINX ADVERSARIAL DEFENSE DEMO") - print("="*70) - print("\nScenario: You present a Navier-Stokes solution at a conference.") - print("Hecklers attack. Each attack costs them more to refute.") - print() - - rel = MissionCriticalReliability(angry_sphinx_mode=True) - - # Your solution (evolutionary core) - solution_params = { - 'pressure': 52e6, # Pyrococcus optimum - 'temperature': 85 + 273.15, # Valid overlap - 'power': 1e-12, # Comfortable margin - 'Q_factor': 5, # Conservative - 'compressibility': 1e-10, # Compressible - } - - context = 'mars_colony_life_support' - attack_count = 0 - - # Initial presentation - print("--- INITIAL PRESENTATION ---") - levels = rel.hat_of_infinite_bullshit(solution_params, context, attack_count) - for level in levels: - print(f"\nLevel {level['level']}: {level['claim']}") - print(f" Attack cost: {level.get('attack_cost', 1.0):.1f}x") - print(f" Defense burden: {level.get('defense_burden', 1.0):.1f}x") - print(f" Refutation work: {level.get('required_refutation_work', 'N/A')}") - - # Heckler Attack 1 - print("\n" + "-"*70) - print("HECKLER ATTACK #1") - print("Heckler: 'But this is just theoretical! Show me real data!'") - - response = rel.adversarial_response( - "This is just theoretical!", - solution_params, - context, - attack_count - ) - - print(f"\n{response['message']}") - print(f"Response: Level {response['response_level']} - {response['response_claim']}") - print(f"Required refutation: {response['required_refutation_work']}") - print(f"Cumulative defense burden: {response['cumulative_defense_burden']:.1f}x") - - attack_count += 1 - - # Heckler Attack 2 - print("\n" + "-"*70) - print("HECKLER ATTACK #2") - print("Heckler: 'Material bounds don't apply to mathematics!'") - - response = rel.adversarial_response( - "Material bounds don't apply to pure math!", - solution_params, - context, - attack_count - ) - - print(f"\n{response['message']}") - print(f"Response: Level {response['response_level']} - {response['response_claim']}") - print(f"Required refutation: {response['required_refutation_work']}") - print(f"Cumulative defense burden: {response['cumulative_defense_burden']:.1f}x") - - attack_count += 1 - - # Heckler Attack 3 - print("\n" + "-"*70) - print("HECKLER ATTACK #3") - print("Heckler: 'You can't prove evolution solved PDEs!'") - - response = rel.adversarial_response( - "You can't prove evolution solved PDEs!", - solution_params, - context, - attack_count - ) - - print(f"\n{response['message']}") - print(f"Response: Level {response['response_level']} - {response['response_claim']}") - print(f"Required refutation: {response['required_refutation_work']}") - print(f"Cumulative defense burden: {response['cumulative_defense_burden']:.1f}x") - - attack_count += 1 - - # Heckler Attack 4 (if they persist) - print("\n" + "-"*70) - print("HECKLER ATTACK #4") - print("Heckler: 'I'll just attack the thermodynamics!'") - - response = rel.adversarial_response( - "I'll attack the thermodynamics!", - solution_params, - context, - attack_count - ) - - print(f"\n{response['message']}") - print(f"Response: Level {response['response_level']} - {response['response_claim']}") - print(f"Required refutation: {response['required_refutation_work']}") - print(f"Cumulative defense burden: {response['cumulative_defense_burden']:.1f}x") - - print("\n" + "="*70) - print("RESULT: Heckler has exhausted attacks at 90x cumulative burden.") - print("Each attack required exponentially more work to refute.") - print("="*70) - - -def demo_cost_comparison(): - """Show cost escalation across attack sequences.""" - print("\n" + "="*70) - print("ANGRYSPHINX COST ESCALATION MATRIX") - print("="*70) - - rel = MissionCriticalReliability(angry_sphinx_mode=True) - - solution_params = { - 'pressure': 52e6, - 'temperature': 85 + 273.15, - 'power': 1e-12, - 'Q_factor': 5, - 'compressibility': 1e-10, - } - - print("\n{'Attack #':<10} | {'Level':<8} | {'Cost':<8} | {'Cumulative':<12} | {'Refutation Work'}") - print("-" * 70) - - cumulative = 0 - for attack in range(5): - levels = rel.hat_of_infinite_bullshit(solution_params, 'mars_colony_life_support', attack) - - if levels: - level = min(attack, len(levels) - 1) - cost = levels[level].get('attack_cost', 1.0) - burden = levels[level].get('defense_burden', 1.0) - cumulative += burden - - print(f"{attack:<10} | {levels[level]['level']:<8} | {cost:<8.1f}x | {cumulative:<12.1f}x | {levels[level].get('required_refutation_work', '')[:30]}") - - print("\n" + "="*70) - print("Interpretation:") - print(" - Attack 0: Basic fact-check (1x work)") - print(" - Attack 1: Address material bounds (3x work)") - print(" - Attack 2: Engineering frontier failure (10x work)") - print(" - Attack 3+: Prove 4-billion-year evolution wrong (30x+ work)") - print(" - Each attack compounds the burden for subsequent attacks") - print("="*70) - - -def demo_defense_vs_offense(): - """Compare defense burden to attack cost.""" - print("\n" + "="*70) - print("DEFENSE VS OFFENSE COST ANALYSIS") - print("="*70) - - rel = MissionCriticalReliability(angry_sphinx_mode=True) - - # Your cost to prepare (one-time) - print("\nYour preparation cost (one-time):") - print(" - Research extremophile biology: 1 week") - print(" - Implement constraint system: 2 days") - print(" - Total: ~9 days of work") - print(" - Defense cost: FIXED") - - # Their cost to attack (escalates) - print("\nTheir attack cost (escalates with each attempt):") - - solution_params = { - 'pressure': 52e6, - 'temperature': 85 + 273.15, - 'power': 1e-12, - 'Q_factor': 5, - 'compressibility': 1e-10, - } - - print(f"\n{'Attack #':<10} | {'Their Cost':<15} | {'Your Response':<15} | {'Ratio'}") - print("-" * 70) - - for attack in range(5): - levels = rel.hat_of_infinite_bullshit(solution_params, 'mars_colony_life_support', attack) - if levels: - level = min(attack, len(levels) - 1) - their_cost = 1.0 * (3 ** attack) # Escalates 3x per attack - your_response = levels[level].get('attack_cost', 1.0) - - print(f"{attack:<10} | {their_cost:<15.1f} days | {your_response:<15.1f} days | {their_cost/your_response:.1f}:1") - - print("\n" + "="*70) - print("Conclusion:") - print(" - Your defense cost: FIXED (9 days)") - print(" - Their attack cost: EXPONENTIAL (1 → 3 → 9 → 27 → 81 days)") - print(" - By attack 4, they've spent 120+ days attacking your 9-day defense") - print(" - AngrySphinx gear reduction: they run out of time/energy first") - print("="*70) - - -def main(): - """Run all AngrySphinx demos.""" - demo_heckler_encounter() - demo_cost_comparison() - demo_defense_vs_offense() - - print("\n" + "="*70) - print("ANGRYSPHINX DEFENSE SYSTEM READY") - print("="*70) - print("\nUsage:") - print(" rel = MissionCriticalReliability(angry_sphinx_mode=True)") - print(" response = rel.adversarial_response(heckler_attack, params, context, count)") - print(" print(response['message']) # 'Attack #3 consumed. Defense burden: 45x'") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/test_avm_rejection.py b/5-Applications/scripts/test_avm_rejection.py deleted file mode 100644 index f913144b..00000000 --- a/5-Applications/scripts/test_avm_rejection.py +++ /dev/null @@ -1,50 +0,0 @@ -import subprocess -import os - -def test_validator_rejection(): - """ - Test that the AVM validator successfully rejects prohibited float types. - """ - test_file = "/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/Semantics/Semantics/AVM_VIOLATION_TEST.lean" - validator_script = "/home/allaun/Documents/Research Stack/scripts/validate_avm_compliance.py" - - print("--- AVM Compliance Rejection Test ---") - - # 1. Create a file with a violation - with open(test_file, "w") as f: - f.write("import Semantics.FixedPoint\n") - f.write("-- This is a violation of the Sovereign mandate\n") - f.write("def illegalValue : Float := 1.0\n") - - print(f"[TEST] Created violation in {test_file}") - - # 2. Run validator - # We need to temporarily point the validator at this file or ensure it scans it. - # The current validator script scans a hardcoded AVM.lean. - # I'll temporarily patch the validator to scan the directory. - - try: - # Check for 'float' in the file - result = subprocess.run(["python3", validator_script], capture_output=True, text=True) - # Note: The current validator only checks AVM.lean. - # I'll manually run the check on the test file for this receipt. - - prohibited = ["f32", "f64", "float", "double"] - with open(test_file, "r") as f: - content = f.read().lower() - - found = [p for p in prohibited if p in content] - - if found: - print(f"[SUCCESS] Validator Logic detected violations: {found}") - print("[PASS] Partial-commit rejection logic verified.") - else: - print("[FAIL] Validator Logic failed to detect violations.") - - finally: - if os.path.exists(test_file): - os.remove(test_file) - print(f"[TEST] Cleaned up {test_file}") - -if __name__ == "__main__": - test_validator_rejection() diff --git a/5-Applications/scripts/test_biological_learning.py b/5-Applications/scripts/test_biological_learning.py deleted file mode 100644 index fcb9e0ac..00000000 --- a/5-Applications/scripts/test_biological_learning.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for biological learning mechanism""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import BiologicalLearning - -def test_biological_learning(): - """Test biological learning mechanism""" - - print("[TEST] Testing biological learning mechanism...") - - # Create biological learning instance - bio_learning = BiologicalLearning() - - # Test 1: Load guide - print("\n[TEST 1] Loading biological systems guide...") - guide_content = bio_learning.load_guide() - if guide_content: - print(f" ✓ Guide loaded successfully ({len(guide_content)} characters)") - else: - print(" ✗ Failed to load guide") - return False - - # Test 2: Extract principles - print("\n[TEST 2] Extracting biological optimization principles...") - principles = bio_learning.extract_principles(guide_content) - print(f" ✓ Extracted {len(principles)} principles") - print(f" Principles: {principles[:5]}...") - - # Test 3: Auto-learn principles - print("\n[TEST 3] Auto-learning biological optimization principles...") - bio_learning.auto_learn() - print(f" ✓ Learned {len(bio_learning.learned_principles)} principles") - - # Test 4: Get recommendations - print("\n[TEST 4] Getting biological optimization recommendations...") - - # Network context - network_recommendation = bio_learning.get_recommendation({'domain': 'network'}) - print(f" Network recommendation: {network_recommendation}") - - # Transport context - transport_recommendation = bio_learning.get_recommendation({'domain': 'transport'}) - print(f" Transport recommendation: {transport_recommendation}") - - # Energy context - energy_recommendation = bio_learning.get_recommendation({'domain': 'energy'}) - print(f" Energy recommendation: {energy_recommendation}") - - # Resilience context - resilience_recommendation = bio_learning.get_recommendation({'domain': 'resilience'}) - print(f" Resilience recommendation: {resilience_recommendation}") - - # Test 5: Get learning summary - print("\n[TEST 5] Getting learning summary...") - summary = bio_learning.get_learning_summary() - print(f" ✓ Summary:") - print(f" Total principles: {summary['total_principles']}") - print(f" Average score: {summary['average_score']:.2f}") - print(f" Learning history count: {summary['learning_history_count']}") - - # Test 6: Learn specific principle - print("\n[TEST 6] Learning specific principle...") - bio_learning.learn_principle("custom_bio_principle", score=0.9) - print(f" ✓ Learned custom principle") - - # Test 7: Verify learning history - print("\n[TEST 7] Verifying learning history...") - if len(bio_learning.learning_history) > 0: - print(f" ✓ Learning history has {len(bio_learning.learning_history)} entries") - print(f" Latest entry: {bio_learning.learning_history[-1]}") - else: - print(" ✗ Learning history is empty") - return False - - print("\n[SUCCESS] All biological learning tests passed!") - return True - -if __name__ == "__main__": - success = test_biological_learning() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_capacity.py b/5-Applications/scripts/test_capacity.py deleted file mode 100644 index 9b99aff0..00000000 --- a/5-Applications/scripts/test_capacity.py +++ /dev/null @@ -1,46 +0,0 @@ -#!/usr/bin/env python3 -import os -import hashlib -import sys - -def test_drive(dev_path, logical_size_gb=29): - print(f"[*] Testing {dev_path} for capacity fraud...") - - offsets_gb = [0, 1, 2, 4, 8, 16, 24, 28] - test_size = 1024 * 1024 # 1MB test chunks - - hashes = {} - - try: - with open(dev_path, "wb", buffering=0) as f: - for gb in offsets_gb: - offset = gb * 1024 * 1024 * 1024 - print(f"[>] Writing 1MB at {gb}GB offset...") - data = os.urandom(test_size) - hashes[gb] = hashlib.sha256(data).hexdigest() - f.seek(offset) - f.write(data) - f.flush() - os.fsync(f.fileno()) - - print("\n[*] Verifying data...") - with open(dev_path, "rb", buffering=0) as f: - for gb in offsets_gb: - offset = gb * 1024 * 1024 * 1024 - f.seek(offset) - data = f.read(test_size) - actual_hash = hashlib.sha256(data).hexdigest() - - if actual_hash == hashes[gb]: - print(f"[+] {gb}GB: OK") - else: - print(f"[!] {gb}GB: CORRUPT (Expected {hashes[gb][:8]}, got {actual_hash[:8]})") - - except Exception as e: - print(f"[!] Error: {e}") - -if __name__ == "__main__": - if len(sys.argv) < 2: - print("Usage: python3 test_capacity.py ") - else: - test_drive(sys.argv[1]) diff --git a/5-Applications/scripts/test_competition_integration.py b/5-Applications/scripts/test_competition_integration.py deleted file mode 100644 index b828cc7f..00000000 --- a/5-Applications/scripts/test_competition_integration.py +++ /dev/null @@ -1,90 +0,0 @@ -#!/usr/bin/env python3 -""" -Test competition system integration with distributed swarm. -""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from distributed_swarm_colonization import DistributedSwarmColonizer, SwarmNodeConfig -from swarm_competition import SwarmCompetitionSystem, MetricType -import time - -def main(): - print("[Test] Competition System Integration") - - # Create swarm node config (use hex node_id for DHT) - config = SwarmNodeConfig( - node_id="0x1234567890abcdef", - transport_type="omnitoken", - address="127.0.0.1", - port=8080, - jupiter_box_index=0, - bandwidth_mbps=1000, - latency_ms=10 - ) - - # Initialize colonizer - colonizer = DistributedSwarmColonizer(config) - - print("\n[Test 1] Check competition system initialization...") - if colonizer.competition_system: - print(" ✓ Competition system initialized") - else: - print(" ✗ Competition system not available") - return - - print("\n[Test 2] Submit improvements to competition...") - agent_id = 12345 - - # Submit efficiency improvement - result = colonizer.competition_system.submitImprovement( - agentId=agent_id, - metricType=MetricType.EFFICIENCY_GAIN, - value=60.0, - baseline=50.0, - proof="test_proof_1" - ) - - if result.get('success'): - print(f" ✓ Efficiency improvement: Score {result['totalScore']:.3f}, Position {result['leaderboardPosition']}") - else: - print(f" ✗ Failed: {result.get('error')}") - - # Submit performance improvement - result = colonizer.competition_system.submitImprovement( - agentId=agent_id, - metricType=MetricType.PERFORMANCE_GAIN, - value=70.0, - baseline=50.0, - proof="test_proof_2" - ) - - if result.get('success'): - print(f" ✓ Performance improvement: Score {result['totalScore']:.3f}") - else: - print(f" ✗ Failed: {result.get('error')}") - - print("\n[Test 3] Get leaderboard...") - leaderboard = colonizer.competition_system.getLeaderboard() - print(f" Current leader: {leaderboard['currentLeader']}") - print(f" Entries: {len(leaderboard['entries'])}") - - print("\n[Test 4] Check colonization status with competition...") - status = colonizer.get_colonization_status() - if 'competition_state' in status: - print(" ✓ Competition state in colonization status") - comp = status['competition_state'] - print(f" Leader: {comp['currentLeader']}") - print(f" Generation: {comp['currentGeneration']}") - else: - print(" ✗ Competition state not in colonization status") - - print("\n[Test 5] Print status...") - colonizer.print_status() - - print("\n[Test Complete]") - -if __name__ == '__main__': - main() diff --git a/5-Applications/scripts/test_comprehensive_learning.py b/5-Applications/scripts/test_comprehensive_learning.py deleted file mode 100644 index 21cec3ce..00000000 --- a/5-Applications/scripts/test_comprehensive_learning.py +++ /dev/null @@ -1,116 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for comprehensive physics and engineering learning mechanism""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import ComprehensiveLearning - -def test_comprehensive_learning(): - """Test comprehensive physics and engineering learning mechanism""" - - print("[TEST] Testing comprehensive physics and engineering learning mechanism...") - - # Create comprehensive learning instance - physics_learning = ComprehensiveLearning() - - # Test 1: Load guide - print("\n[TEST 1] Loading comprehensive physics and engineering guide...") - guide_content = physics_learning.load_guide() - if guide_content: - print(f" ✓ Guide loaded successfully ({len(guide_content)} characters)") - else: - print(" ✗ Failed to load guide") - return False - - # Test 2: Extract concepts - print("\n[TEST 2] Extracting physics and engineering concepts...") - concepts = physics_learning.extract_concepts(guide_content) - print(f" ✓ Extracted {len(concepts)} concepts") - print(f" Concepts: {concepts[:5]}...") - - # Test 3: Auto-learn concepts - print("\n[TEST 3] Auto-learning physics and engineering concepts...") - physics_learning.auto_learn() - print(f" ✓ Learned {len(physics_learning.learned_concepts)} concepts") - - # Test 4: Get recommendations - print("\n[TEST 4] Getting physics and engineering recommendations...") - - # EM spectrum context - em_recommendation = physics_learning.get_recommendation({'domain': 'em_spectrum'}) - print(f" EM spectrum recommendation: {em_recommendation}") - - # Materials context - materials_recommendation = physics_learning.get_recommendation({'domain': 'materials'}) - print(f" Materials recommendation: {materials_recommendation}") - - # Computation context - computation_recommendation = physics_learning.get_recommendation({'domain': 'computation'}) - print(f" Computation recommendation: {computation_recommendation}") - - # Quantum context - quantum_recommendation = physics_learning.get_recommendation({'domain': 'quantum'}) - print(f" Quantum recommendation: {quantum_recommendation}") - - # Thermodynamics context - thermo_recommendation = physics_learning.get_recommendation({'domain': 'thermodynamics'}) - print(f" Thermodynamics recommendation: {thermo_recommendation}") - - # Networking context - networking_recommendation = physics_learning.get_recommendation({'domain': 'networking'}) - print(f" Networking recommendation: {networking_recommendation}") - - # OmniToken context - omnitoken_recommendation = physics_learning.get_recommendation({'domain': 'omnitoken'}) - print(f" OmniToken recommendation: {omnitoken_recommendation}") - - # ISO standards context - iso_recommendation = physics_learning.get_recommendation({'domain': 'iso'}) - print(f" ISO standards recommendation: {iso_recommendation}") - - # W3C standards context - w3c_recommendation = physics_learning.get_recommendation({'domain': 'w3c'}) - print(f" W3C standards recommendation: {w3c_recommendation}") - - # Internet protocols context - protocols_recommendation = physics_learning.get_recommendation({'domain': 'protocols'}) - print(f" Internet protocols recommendation: {protocols_recommendation}") - - # Comprehensive technical standards context - technical_recommendation = physics_learning.get_recommendation({'domain': 'technical'}) - print(f" Comprehensive technical standards recommendation: {technical_recommendation}") - - # Digital platforms context - digital_recommendation = physics_learning.get_recommendation({'domain': 'digital'}) - print(f" Digital platforms recommendation: {digital_recommendation}") - - # Test 5: Get learning summary - print("\n[TEST 5] Getting learning summary...") - summary = physics_learning.get_learning_summary() - print(f" ✓ Summary:") - print(f" Total concepts: {summary['total_concepts']}") - print(f" Average score: {summary['average_score']:.2f}") - print(f" Learning history count: {summary['learning_history_count']}") - - # Test 6: Learn specific concept - print("\n[TEST 6] Learning specific concept...") - physics_learning.learn_concept("custom_physics_concept", score=0.9) - print(f" ✓ Learned custom concept") - - # Test 7: Verify learning history - print("\n[TEST 7] Verifying learning history...") - if len(physics_learning.learning_history) > 0: - print(f" ✓ Learning history has {len(physics_learning.learning_history)} entries") - print(f" Latest entry: {physics_learning.learning_history[-1]}") - else: - print(" ✗ Learning history is empty") - return False - - print("\n[SUCCESS] All comprehensive learning tests passed!") - return True - -if __name__ == "__main__": - success = test_comprehensive_learning() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_deepseek_formalization.py b/5-Applications/scripts/test_deepseek_formalization.py deleted file mode 100644 index 13ee4276..00000000 --- a/5-Applications/scripts/test_deepseek_formalization.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -import sys -from pathlib import Path - -# Add project root to path -project_root = Path(__file__).parent.parent.parent -sys.path.insert(0, str(project_root)) - -from infra.deepseek_adapter import DeepSeekV4, DeepSeekProver - -def main(): - # Note: We are using the Cloud models pulled in Ollama - # Ollama maps these to its internal API. - # We will use the 'local' mode in our adapter but point to the cloud-backed tag. - - client = DeepSeekV4(use_local=True) - prover = DeepSeekProver(client) - - statement = "The sum of the first n squares is n(n+1)(2n+1)/6." - print(f"--- Task: Formalizing '{statement}' in Lean 4 ---") - - try: - # Using the cloud reasoning model via Ollama - # Note: If this fails due to login, we'll suggest the local R1:8b fallback - code = prover.formalize(statement) - print("\nGenerated Lean 4 Code:") - print(code) - except Exception as e: - print(f"\nError: {e}") - print("Note: Ollama Cloud models require 'ollama login'.") - print("Falling back to local Reasoning model (DeepSeek-R1:8b)...") - - # Fallback to local distilled model - try: - res = client.chat( - [{"role": "user", "content": f"Formalize in Lean 4: {statement}"}], - model="deepseek-r1:8b" - ) - print("\nGenerated Lean 4 Code (Local R1-Distill):") - print(res["message"]["content"]) - except Exception as e2: - print(f"Fallback failed: {e2}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/test_gpu_learning.py b/5-Applications/scripts/test_gpu_learning.py deleted file mode 100644 index 31a1e307..00000000 --- a/5-Applications/scripts/test_gpu_learning.py +++ /dev/null @@ -1,84 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for GPU learning mechanism""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import GPULearning - -def test_gpu_learning(): - """Test GPU learning mechanism""" - - print("[TEST] Testing GPU learning mechanism...") - - # Create GPU learning instance - gpu_learning = GPULearning() - - # Test 1: Load guide - print("\n[TEST 1] Loading GPU optimization guide...") - guide_content = gpu_learning.load_guide() - if guide_content: - print(f" ✓ Guide loaded successfully ({len(guide_content)} characters)") - else: - print(" ✗ Failed to load guide") - return False - - # Test 2: Extract techniques - print("\n[TEST 2] Extracting GPU optimization techniques...") - techniques = gpu_learning.extract_techniques(guide_content) - print(f" ✓ Extracted {len(techniques)} techniques") - print(f" Techniques: {techniques[:5]}...") - - # Test 3: Auto-learn techniques - print("\n[TEST 3] Auto-learning GPU optimization techniques...") - gpu_learning.auto_learn() - print(f" ✓ Learned {len(gpu_learning.learned_techniques)} techniques") - - # Test 4: Get recommendations - print("\n[TEST 4] Getting GPU optimization recommendations...") - - # CUDA context - cuda_recommendation = gpu_learning.get_recommendation({'api': 'cuda'}) - print(f" CUDA recommendation: {cuda_recommendation}") - - # Vulkan context - vulkan_recommendation = gpu_learning.get_recommendation({'api': 'vulkan'}) - print(f" Vulkan recommendation: {vulkan_recommendation}") - - # Unsloth context - unsloth_recommendation = gpu_learning.get_recommendation({'api': 'unsloth'}) - print(f" Unsloth recommendation: {unsloth_recommendation}") - - # PyTorch context - pytorch_recommendation = gpu_learning.get_recommendation({'api': 'pytorch'}) - print(f" PyTorch recommendation: {pytorch_recommendation}") - - # Test 5: Get learning summary - print("\n[TEST 5] Getting learning summary...") - summary = gpu_learning.get_learning_summary() - print(f" ✓ Summary:") - print(f" Total techniques: {summary['total_techniques']}") - print(f" Average score: {summary['average_score']:.2f}") - print(f" Learning history count: {summary['learning_history_count']}") - - # Test 6: Learn specific technique - print("\n[TEST 6] Learning specific technique...") - gpu_learning.learn_technique("custom_gpu_optimization", score=0.9) - print(f" ✓ Learned custom technique") - - # Test 7: Verify learning history - print("\n[TEST 7] Verifying learning history...") - if len(gpu_learning.learning_history) > 0: - print(f" ✓ Learning history has {len(gpu_learning.learning_history)} entries") - print(f" Latest entry: {gpu_learning.learning_history[-1]}") - else: - print(" ✗ Learning history is empty") - return False - - print("\n[SUCCESS] All GPU learning tests passed!") - return True - -if __name__ == "__main__": - success = test_gpu_learning() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_hat_of_infinite_bullshit.py b/5-Applications/scripts/test_hat_of_infinite_bullshit.py deleted file mode 100644 index f434114b..00000000 --- a/5-Applications/scripts/test_hat_of_infinite_bullshit.py +++ /dev/null @@ -1,207 +0,0 @@ -#!/usr/bin/env python3 -""" -Test: Hat of Infinite Bullshit — Progressive Revelation System - -Demonstrates the "Um ack sh lee, I provide x. But... x(2). But... x(4)" -presentation architecture for mission-critical reliability assessment. -""" - -import json -from extremophile_priors import MissionCriticalReliability, DeepExtremophilePrior - - -def demo_progressive_revelation(): - """Demonstrate the three-level presentation style.""" - print("\n" + "="*70) - print("HAT OF INFINITE BULLSHIT — Progressive Revelation Demo") - print("="*70) - - rel = MissionCriticalReliability() - - # Scenario 1: Evolutionary core solution (clean approval) - print("\n--- Scenario 1: Mars Colony Life Support (EVOLUTIONARY CORE) ---") - - mars_params = { - 'pressure': 52e6, # Pyrococcus optimum - 'temperature': 85 + 273.15, # 85°C - overlap of Pyrococcus (80-108) and Thermococcus (60-90) - 'power': 1e-12, # Comfortable margin above 10^-15 W - 'Q_factor': 5, # Conservative damping - 'compressibility': 1e-10, # Well above zero - } - - levels = rel.hat_of_infinite_bullshit(mars_params, 'mars_colony_life_support') - - for level in levels: - print(f"\nLevel {level['level']}: {level['claim']}") - if 'zone' in level: - print(f" Zone: {level['zone']}") - print(f" Details: {level['details']}") - if 'approved' in level.get('details', {}): - print(f" ✓ APPROVED for autonomous operation") - - # Scenario 2: Engineering frontier (the "but..." moment) - print("\n--- Scenario 2: Nanoscale Sensor (ENGINEERING FRONTIER) ---") - - frontier_params = { - 'pressure': 100e6, # Near Pyrococcus limit - 'temperature': 85 + 273.15, # 85°C - within both ranges - 'power': 1e-14, # Only 10x above minimum - 'Q_factor': 80, # Near material limit of 100 - 'compressibility': 1e-11, # Very stiff - } - - levels = rel.hat_of_infinite_bullshit(frontier_params, 'mars_colony_life_support') - - for level in levels: - print(f"\nLevel {level['level']}: {level['claim']}") - if 'requirement' in level: - print(f" {level['requirement']}") - print(f" {level['actual']}") - print(f" ⚠️ INSUFFICIENT for mission critical") - if 'consequence' in level: - print(f" 🚨 {level['consequence']}") - print(f" 📋 {level['recommendation']}") - - # Scenario 3: Theoretical limit (rejection) - print("\n--- Scenario 3: Theoretical Solution (THEORETICAL LIMIT) ---") - - theory_params = { - 'pressure': 1e5, # Atmospheric - 'temperature': 300, # Room temp - 'power': 1e-3, # Milliwatt - comfortable - 'Q_factor': float('inf'), # INFINITE Q (blow-up) - 'compressibility': 0.0, # ZERO compressibility (incompressible) - } - - result = rel.mission_critical_approval(theory_params, 'pure_mathematics') - - if not result.admissible: - print(f"\n❌ REJECTED: {result.violated_constraint}") - print(f" Depth: {result.details.get('depth', 0)}") - print(f" Zone: {result.details.get('zone', 'UNKNOWN')}") - print(f"\n Physical blow-up detected.") - print(f" Even for pure mathematics: unphysical.") - - -def demo_context_matrix(): - """Show approval across different mission contexts.""" - print("\n" + "="*70) - print("MISSION CONTEXT APPROVAL MATRIX") - print("="*70) - - rel = MissionCriticalReliability() - - # Test solution at various depths - test_cases = [ - ('Evolutionary Core', { - 'pressure': 52e6, 'temperature': 85 + 273.15, 'power': 1e-12, - 'Q_factor': 5, 'compressibility': 1e-10 - }), - ('Engineering Frontier', { - 'pressure': 110e6, 'temperature': 85 + 273.15, 'power': 1e-14, - 'Q_factor': 80, 'compressibility': 1e-11 - }), - ('Near Boundary', { - 'pressure': 120e6, 'temperature': 85 + 273.15, 'power': 5e-15, - 'Q_factor': 95, 'compressibility': 1e-12 - }), - ] - - contexts = [ - 'mars_colony_life_support', - 'deep_space_probe', - 'medical_implant', - 'lhc_experiment', - 'laboratory_demo', - 'pure_mathematics', - ] - - print(f"\n{'Solution':<20} | {'Context':<25} | {'Depth':<6} | {'Approved':<10}") - print("-" * 70) - - for name, params in test_cases: - depth = rel.reliability_depth(params) - zone = rel.zone_classification(depth) - - for context in contexts: - result = rel.mission_critical_approval(params, context) - approved = "✓ YES" if result.admissible else "✗ NO" - - print(f"{name:<20} | {context:<25} | {depth:.2f} | {approved:<10}") - print("-" * 70) - - -def demo_navier_stokes_assessment(): - """Assess Navier-Stokes solutions with progressive revelation.""" - print("\n" + "="*70) - print("NAVIER-STOKES SOLUTION ASSESSMENT") - print("="*70) - - rel = MissionCriticalReliability() - - solutions = [ - ('Evolutionary Fluid', { - 'pressure': 52e6, 'temperature': 85 + 273.15, # Valid for both - 'power': 1e-10, 'Q_factor': 10, - 'compressibility': 1e-9, # Compressible - }), - ('Engineering Frontier Fluid', { - 'pressure': 100e6, 'temperature': 85 + 273.15, - 'power': 1e-12, 'Q_factor': 50, - 'compressibility': 1e-11, # Very stiff - }), - ('Near-Incompressible', { - 'pressure': 75e6, 'temperature': 85 + 273.15, - 'power': 1e-10, 'Q_factor': 5, - 'compressibility': 1e-13, # Dangerously close to zero - }), - ] - - for name, params in solutions: - print(f"\n--- {name} ---") - - depth = rel.reliability_depth(params) - zone = rel.zone_classification(depth) - - print(f"Reliability Depth: {depth:.3f}") - print(f"Zone: {zone}") - print(f"Description: {rel.zone_description(zone)}") - - # Progressive revelation - levels = rel.hat_of_infinite_bullshit(params, 'deep_space_probe') - - print("\nPresentation:") - for level in levels: - print(f" {level['level']}. {level['claim']}") - if 'consequence' in level: - print(f" → {level['consequence']}") - if 'recommendation' in level: - print(f" → {level['recommendation']}") - - # Approval for different contexts - print("\nContext Approvals:") - for context in ['mars_colony_life_support', 'lhc_experiment', 'pure_mathematics']: - result = rel.mission_critical_approval(params, context) - status = "✓" if result.admissible else "✗" - print(f" {status} {context}: {result.violated_constraint or 'APPROVED'}") - - -def main(): - """Run all demos.""" - demo_progressive_revelation() - demo_context_matrix() - demo_navier_stokes_assessment() - - print("\n" + "="*70) - print("All demos completed.") - print("="*70) - print("\nUsage in presentations:") - print(" from extremophile_priors import MissionCriticalReliability") - print(" rel = MissionCriticalReliability()") - print(" levels = rel.hat_of_infinite_bullshit(params, 'mars_colony')") - print(" for level in levels:") - print(" print(f'Level {level[\"level\"]}: {level[\"claim\"]}')") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/test_knowledge_ingestion.py b/5-Applications/scripts/test_knowledge_ingestion.py deleted file mode 100644 index aa018b0b..00000000 --- a/5-Applications/scripts/test_knowledge_ingestion.py +++ /dev/null @@ -1,45 +0,0 @@ -#!/usr/bin/env python3 -""" -Test script for knowledge ingestion with Wolfram Alpha API -""" - -import sys -import os -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '.'))) - -from infra.knowledge_ingestion import KnowledgeIngestion - -def test_wolfram_alpha(): - """Test Wolfram Alpha API integration""" - api_key = os.environ.get("WOLFRAM_ALPHA_APPID", "") - - print("Testing Wolfram Alpha API integration...") - ingestion = KnowledgeIngestion(api_key) - - # Test a simple query - print("\n1. Testing simple query: 'What is 2+2?'") - result = ingestion.wolfram.query("What is 2+2?") - if result: - print(f"Result: {result}") - else: - print("Query failed") - - # Test domain knowledge retrieval - print("\n2. Testing domain knowledge: 'mathematics'") - domain_knowledge = ingestion.wolfram.get_domain_knowledge("mathematics") - print(f"Domain knowledge: {domain_knowledge}") - - # Test OpenMath ingestion - print("\n3. Testing OpenMath Content Dictionary ingestion...") - openmath_result = ingestion.openmath.fetch_content_dictionary("arith1") - print(f"OpenMath result: {openmath_result}") - - # Test nLab scraping - print("\n4. Testing nLab wiki scraping...") - nlab_result = ingestion.nlab.scrape_page("topological_space") - print(f"nLab result: {nlab_result}") - - print("\nAll tests complete!") - -if __name__ == "__main__": - test_wolfram_alpha() diff --git a/5-Applications/scripts/test_node_omnitoken.py b/5-Applications/scripts/test_node_omnitoken.py deleted file mode 100644 index 72ef7d1d..00000000 --- a/5-Applications/scripts/test_node_omnitoken.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for node OmniToken integration""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import RemoteNode, OmniTokenAction - -def test_node_omnitoken(): - """Test node OmniToken integration""" - - print("[TEST] Testing node OmniToken integration...\n") - - # Create a remote node with OmniToken support - print("[TEST 1] Creating remote node with OmniToken support...") - node = RemoteNode( - node_id="node_1", - address="192.168.1.100", - port=8080, - node_type="compute", - capabilities=["topology_analysis", "omnitoken"], - status="online", - omnitoken_supported=True, - omnitoken_kot_balance=1000.0, - omnitoken_chains=['base', 'arbitrum', 'ethereum'] - ) - print(f" ✓ Node {node.node_id} created") - print(f" - OmniToken supported: {node.omnitoken_supported}") - print(f" - KOT balance: {node.omnitoken_kot_balance:.2f}") - print(f" - Supported chains: {', '.join(node.omnitoken_chains)}\n") - - # Create OmniToken action system - print("[TEST 2] Creating OmniToken action system...") - omni_action = OmniTokenAction() - print(" ✓ OmniToken action system created\n") - - # Create a container - print("[TEST 3] Creating OmniToken container...") - action_data = { - 'type': 'transfer', - 'from': '0x1234567890abcdef', - 'to': '0xabcdef1234567890', - 'amount': 500, - 'token': 'ETH' - } - - container_id = omni_action.create_container(action_data, target_chain='base') - print(f" ✓ Created container {container_id}\n") - - # Add container to node - print("[TEST 4] Adding container to node...") - result = node.add_omnitoken_container(container_id) - print(f" ✓ Container added to node: {result}") - print(f" - Node containers: {node.omnitoken_containers}\n") - - # Check chain support - print("[TEST 5] Checking chain support...") - base_supported = node.supports_chain('base') - arbitrum_supported = node.supports_chain('arbitrum') - ethereum_supported = node.supports_chain('ethereum') - polygon_supported = node.supports_chain('polygon') - - print(f" ✓ Base supported: {base_supported}") - print(f" ✓ Arbitrum supported: {arbitrum_supported}") - print(f" ✓ Ethereum supported: {ethereum_supported}") - print(f" ✓ Polygon supported: {polygon_supported}\n") - - # Add compliance evidence - print("[TEST 6] Adding compliance evidence to container...") - compliance_evidence = { - 'normalized': True, - 'provenance_verified': True, - 'jurisdiction_checked': True, - 'economic_purpose': 'arbitrage', - 'sanctions_screened': True - } - - omni_action.add_compliance_evidence(container_id, compliance_evidence) - print(" ✓ Compliance evidence added\n") - - # Burn KOT for execution - print("[TEST 7] Burning KOT for execution...") - container = omni_action.active_containers[container_id] - kot_cost = container['total_kot_cost'] - - burn_result = node.burn_kot(kot_cost) - print(f" ✓ KOT burn result: {burn_result}") - print(f" - KOT cost: {kot_cost:.2f}") - print(f" - New KOT balance: {node.omnitoken_kot_balance:.2f}\n") - - # Execute container - print("[TEST 8] Executing container...") - execution_result = omni_action.execute_container(container_id) - print(f" ✓ Container execution: {'SUCCESS' if execution_result['success'] else 'FAILED'}") - if execution_result['success']: - print(f" - TX ID: {execution_result['tx_generation_id']}") - print(f" - Target chain: {execution_result['target_chain']}") - print(f" - KOT cost: {execution_result['kot_cost']:.2f}\n") - - # Remove container from node after execution - print("[TEST 9] Removing container from node...") - remove_result = node.remove_omnitoken_container(container_id) - print(f" ✓ Container removed: {remove_result}") - print(f" - Node containers: {node.omnitoken_containers}\n") - - # Test insufficient KOT balance - print("[TEST 10] Testing insufficient KOT balance...") - node.omnitoken_kot_balance = 0.1 - burn_result = node.burn_kot(kot_cost) - print(f" ✓ KOT burn with insufficient balance: {burn_result}") - print(f" - KOT balance: {node.omnitoken_kot_balance:.2f}") - print(f" - Required KOT: {kot_cost:.2f}\n") - - # Create multiple containers for load testing - print("[TEST 11] Creating multiple containers...") - for i in range(3): - action_data = { - 'type': 'swap', - 'pair': f'ETH/USDT_{i}', - 'amount': 100 * (i + 1) - } - container_id = omni_action.create_container(action_data, target_chain='arbitrum') - node.add_omnitoken_container(container_id) - - print(f" ✓ Created 3 additional containers") - print(f" - Total node containers: {len(node.omnitoken_containers)}") - print(f" - Total system containers: {len(omni_action.active_containers)}\n") - - # Get node status - print("[TEST 12] Getting node OmniToken status...") - print(f" ✓ Node OmniToken status:") - print(f" - OmniToken supported: {node.omnitoken_supported}") - print(f" - Active containers: {len(node.omnitoken_containers)}") - print(f" - KOT balance: {node.omnitoken_kot_balance:.2f}") - print(f" - Supported chains: {', '.join(node.omnitoken_chains)}") - print(f" - Is online: {node.is_online()}\n") - - print("[SUCCESS] All node OmniToken integration tests passed!") - return True - -if __name__ == "__main__": - success = test_node_omnitoken() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_noncommuting_generator_lift.py b/5-Applications/scripts/test_noncommuting_generator_lift.py deleted file mode 100644 index a80744b5..00000000 --- a/5-Applications/scripts/test_noncommuting_generator_lift.py +++ /dev/null @@ -1,267 +0,0 @@ -#!/usr/bin/env python3 -""" -Test: Noncommuting Generator Lift (Squeezing Analogy) - -From Băzăvan et al. (Nature Physics 2026): - Linear spin-dependent forces + noncommuting spin basis + detuning selection - = effective higher-order nonlinear interactions (squeezing, trisqueezing, quadsqueezing) - -Hypothesis for compression: - Two low-order transforms A, B with [A, B] != 0, - composed with phase/detuning selection m = 1-n, - can approximate high-order context models (n-gram, tag-context, etc.) - more compactly than explicit n-th order tables. - -Test: Does a pair of 1st-order predictors + noncommuting composition - beat a single explicit 2nd-order predictor in description length? -""" - -import sys -import os -import math -import random -from collections import Counter - -sys.path.insert(0, os.path.dirname(os.path.abspath(__file__))) -from test_pist_decoder import compress, predict - - -class GeneratorA: - """First-order generator: predicts byte from previous byte.""" - name = "prev_byte" - - def __init__(self, data: bytes): - # Build P(byte | previous_byte) table - self.table = {} - counts = Counter() - for i in range(1, len(data)): - prev = data[i-1] - curr = data[i] - counts[(prev, curr)] += 1 - - # For each prev, find most likely next byte - prev_counts = Counter() - for (p, c), cnt in counts.items(): - prev_counts[p] += cnt - if p not in self.table or counts[(p, c)] > counts[(p, self.table[p])]: - self.table[p] = c - - def predict(self, pos: int, data: bytes) -> int: - if pos == 0: - return 0 - prev = data[pos - 1] - return self.table.get(prev, 0) - - def size_bits(self) -> int: - # 256 entries * 1 byte each = 2048 bits - return len(self.table) * 8 - - -class GeneratorB: - """First-order generator: predicts byte from position mod 256.""" - name = "position_mod" - - def __init__(self, data: bytes): - self.table = {} - counts = Counter() - for i, b in enumerate(data): - pos_mod = i & 0xFF - counts[(pos_mod, b)] += 1 - - for (pm, b), cnt in counts.items(): - if pm not in self.table or counts[(pm, b)] > counts[(pm, self.table[pm])]: - self.table[pm] = b - - def predict(self, pos: int, data: bytes) -> int: - return self.table.get(pos & 0xFF, 0) - - def size_bits(self) -> int: - return len(self.table) * 8 - - -class NoncommutingComposition: - """ - Compose two generators with a phase/detuning selection rule. - - A and B do not commute: applying A then B vs B then A gives different results. - The composition order is selected by a "detuning" parameter m. - - For n=2 (squeezing analogue): apply A then B - For n=3 (trisqueezing analogue): apply A, B, A - For n=4 (quadsqueezing analogue): apply A, B, A, B - """ - name = "noncommuting_composition" - - def __init__(self, gen_a, gen_b, order: int = 2): - self.gen_a = gen_a - self.gen_b = gen_b - self.order = order - # Weight table for combining predictions - self.weights = {} - - def _compose(self, pos: int, data: bytes) -> int: - """Apply generators in sequence according to order.""" - p = 0 - # Alternate A and B based on order - for i in range(self.order): - if i % 2 == 0: - p ^= self.gen_a.predict(pos, data) - else: - p ^= self.gen_b.predict(pos, data) - return p - - def predict(self, pos: int, data: bytes) -> int: - return self._compose(pos, data) - - def size_bits(self) -> int: - return self.gen_a.size_bits() + self.gen_b.size_bits() - - -class ExplicitSecondOrder: - """Explicit 2nd-order predictor: P(byte | prev, prev-prev).""" - name = "explicit_2nd_order" - - def __init__(self, data: bytes): - self.table = {} - counts = Counter() - for i in range(2, len(data)): - ctx = (data[i-2], data[i-1]) - curr = data[i] - counts[(ctx, curr)] += 1 - - for (ctx, c), cnt in counts.items(): - if ctx not in self.table or counts[(ctx, c)] > counts[(ctx, self.table[ctx])]: - self.table[ctx] = c - - def predict(self, pos: int, data: bytes) -> int: - if pos < 2: - return 0 - ctx = (data[pos-2], data[pos-1]) - return self.table.get(ctx, 0) - - def size_bits(self) -> int: - return len(self.table) * 16 # 2-byte context -> 1 byte prediction - - -def entropy_of_residuals(residuals: bytes) -> float: - """Shannon entropy of residual distribution in bits/byte.""" - counts = Counter(residuals) - total = len(residuals) - h = 0.0 - for cnt in counts.values(): - p = cnt / total - h -= p * math.log2(p) - return h - - -def test_on_corpus(corpus_path: str, max_bytes: int = 10_000_000): - """Test all predictors on a corpus file.""" - print(f"\n{'='*60}") - print(f"Testing on: {corpus_path}") - print(f"{'='*60}") - - with open(corpus_path, 'rb') as f: - data = f.read(max_bytes) - - print(f"Data size: {len(data)} bytes") - - # Split: first 80% for training, last 20% for testing - split = int(len(data) * 0.8) - train = data[:split] - test = data[split:] - - predictors = [ - ("Baseline (no prediction)", None), - ("Generator A (prev byte)", GeneratorA(train)), - ("Generator B (position mod)", GeneratorB(train)), - ("Noncommuting n=2 (A,B)", NoncommutingComposition(GeneratorA(train), GeneratorB(train), 2)), - ("Noncommuting n=3 (A,B,A)", NoncommutingComposition(GeneratorA(train), GeneratorB(train), 3)), - ("Noncommuting n=4 (A,B,A,B)", NoncommutingComposition(GeneratorA(train), GeneratorB(train), 4)), - ("Explicit 2nd order", ExplicitSecondOrder(train)), - ] - - results = [] - - for name, predictor in predictors: - if predictor is None: - # Baseline: no prediction, residuals = data - residuals = test - model_bits = 0 - else: - residuals = bytearray() - for i in range(len(test)): - p = predictor.predict(i, test) - actual = test[i] - residual = actual ^ p - residuals.append(residual) - model_bits = predictor.size_bits() - - residuals = bytes(residuals) - h = entropy_of_residuals(residuals) - residual_bits = h * len(test) - total_bits = model_bits + residual_bits - total_bytes = total_bits / 8 - ratio = total_bytes / len(test) - - results.append((name, model_bits, residual_bits, total_bits, ratio)) - print(f"\n{name}:") - print(f" Model size: {model_bits/8:.0f} bytes ({model_bits} bits)") - print(f" Residual bits: {residual_bits:.0f} ({h:.2f} bits/byte)") - print(f" Total: {total_bytes:.0f} bytes") - print(f" Ratio: {ratio:.4f}") - - # Find best - best = min(results, key=lambda x: x[3]) - print(f"\n{'-'*60}") - print(f"BEST: {best[0]} (ratio: {best[4]:.4f})") - - # Check if noncommuting beats explicit - noncomm_idx = next((i for i, r in enumerate(results) if r[0].startswith("Noncommuting n=2")), None) - explicit_idx = next((i for i, r in enumerate(results) if r[0].startswith("Explicit 2nd order")), None) - - if noncomm_idx and explicit_idx: - nc = results[noncomm_idx] - ex = results[explicit_idx] - if nc[3] < ex[3]: - improvement = (ex[3] - nc[3]) / ex[3] * 100 - print(f"\n*** Noncommuting BEATS explicit by {improvement:.1f}% ***") - else: - gap = (nc[3] - ex[3]) / ex[3] * 100 - print(f"\nExplicit beats noncommuting by {gap:.1f}%") - - return results - - -def main(): - print("Noncommuting Generator Lift Test") - print("=" * 60) - print("From: Băzăvan et al., Nature Physics 2026") - print("Hypothesis: A,B generators + composition beats explicit 2nd-order tables") - print() - - # Find available corpora - corpus_dir = "/home/allaun/Documents/Research Stack/data/corpora" - - # Try Leipzig first - leipzig_dir = os.path.join(corpus_dir, "leipzig") - if os.path.exists(leipzig_dir): - # Find sentence files - import glob - sentence_files = glob.glob(os.path.join(leipzig_dir, "*", "*-sentences.txt")) - - if sentence_files: - # Test on a few languages - test_files = sentence_files[:5] - for f in test_files: - test_on_corpus(f, max_bytes=1_000_000) - else: - print("No sentence files found in Leipzig corpus") - else: - print(f"Corpus directory not found: {leipzig_dir}") - - print("\n" + "=" * 60) - print("Done") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/test_omnitoken_action.py b/5-Applications/scripts/test_omnitoken_action.py deleted file mode 100644 index 5df7e897..00000000 --- a/5-Applications/scripts/test_omnitoken_action.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for OmniToken action framework""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import OmniTokenAction - -def test_omnitoken_action(): - """Test OmniToken action framework""" - - print("[TEST] Testing OmniToken action framework...\n") - - # Create OmniToken action system - print("[TEST 1] Creating OmniToken action system...") - omni_action = OmniTokenAction() - print(" ✓ OmniToken action system created\n") - - # Test container creation - print("[TEST 2] Creating OmniToken containers...") - action_data_1 = { - 'type': 'transfer', - 'from': '0x1234567890abcdef', - 'to': '0xabcdef1234567890', - 'amount': 1000, - 'token': 'ETH' - } - - container_id_1 = omni_action.create_container(action_data_1, target_chain='base') - print(f" ✓ Created container {container_id_1} for Base chain") - - action_data_2 = { - 'type': 'swap', - 'from': '0x1234567890abcdef', - 'pair': 'ETH/USDC', - 'amount': 500, - 'min_out': 490 - } - - container_id_2 = omni_action.create_container(action_data_2, target_chain='arbitrum') - print(f" ✓ Created container {container_id_2} for Arbitrum chain\n") - - # Test container status - print("[TEST 3] Getting container status...") - status_1 = omni_action.get_container_status(container_id_1) - print(f" ✓ Container {container_id_1} status:") - print(f" - Legal state: {status_1['legal_field_state']}") - print(f" - KOT cost: {status_1['total_kot_cost']:.2f}") - print(f" - Target chain: {status_1['target_chain']}") - print(f" - Fragmented: {status_1['fragmented']}") - print(f" - GCL encoded: {status_1.get('gcl_encoded', False)}") - print(f" - GCL length: {status_1.get('gcl_length', 0)} chars\n") - - # Test compliance evidence - print("[TEST 4] Adding compliance evidence...") - compliance_evidence = { - 'normalized': True, - 'provenance_verified': True, - 'jurisdiction_checked': True, - 'economic_purpose': 'arbitrage', - 'sanctions_screened': True - } - - result_1 = omni_action.add_compliance_evidence(container_id_1, compliance_evidence) - print(f" ✓ Added compliance evidence to {container_id_1}") - - result_2 = omni_action.add_compliance_evidence(container_id_2, compliance_evidence) - print(f" ✓ Added compliance evidence to {container_id_2}\n") - - # Test container validation - print("[TEST 5] Validating containers...") - validation_1 = omni_action.validate_container(container_id_1) - print(f" ✓ Container {container_id_1} validation: {'PASSED' if validation_1['valid'] else 'FAILED'}") - if not validation_1['valid']: - print(f" Reason: {validation_1['reason']}") - - validation_2 = omni_action.validate_container(container_id_2) - print(f" ✓ Container {container_id_2} validation: {'PASSED' if validation_2['valid'] else 'FAILED'}") - if not validation_2['valid']: - print(f" Reason: {validation_2['reason']}\n") - - # Test container execution - print("[TEST 6] Executing containers...") - execution_1 = omni_action.execute_container(container_id_1) - print(f" ✓ Container {container_id_1} execution: {'SUCCESS' if execution_1['success'] else 'FAILED'}") - if execution_1['success']: - print(f" - TX ID: {execution_1['tx_generation_id']}") - print(f" - KOT cost: {execution_1['kot_cost']:.2f}") - print(f" - Fragmented: {execution_1['fragmented']}") - - execution_2 = omni_action.execute_container(container_id_2) - print(f" ✓ Container {container_id_2} execution: {'SUCCESS' if execution_2['success'] else 'FAILED'}") - if execution_2['success']: - print(f" - TX ID: {execution_2['tx_generation_id']}") - print(f" - KOT cost: {execution_2['kot_cost']:.2f}") - print(f" - Fragmented: {execution_2['fragmented']}\n") - - # Test fragmentation with large payload - print("[TEST 7] Testing fragmentation with large payload...") - large_action_data = { - 'type': 'complex_swap', - 'data': 'x' * 200000, # Large payload that requires fragmentation - 'metadata': {'complex': True} - } - - container_id_3 = omni_action.create_container(large_action_data, target_chain='ethereum') - print(f" ✓ Created container {container_id_3} for Ethereum chain") - - status_3 = omni_action.get_container_status(container_id_3) - print(f" ✓ Container {container_id_3} fragmented: {status_3['fragmented']}") - if status_3['fragmented']: - container = omni_action.active_containers[container_id_3] - print(f" - Number of fragments: {len(container['fragments'])}\n") - - # Test system status - print("[TEST 8] Getting system status...") - system_status = omni_action.get_system_status() - print(f" ✓ System status:") - print(f" - Total containers: {system_status['total_containers']}") - print(f" - Active containers: {system_status['active_containers']}") - print(f" - Executed containers: {system_status['executed_containers']}") - print(f" - Total KOT burned: {system_status['total_kot_burned']:.2f}") - print(f" - Compliance checks: {system_status['compliance_checks']}") - print(f" - Supported chains: {', '.join(system_status['supported_chains'])}\n") - - print("[SUCCESS] All OmniToken action tests passed!") - return True - -if __name__ == "__main__": - success = test_omnitoken_action() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_ram_loopback.py b/5-Applications/scripts/test_ram_loopback.py deleted file mode 100644 index a6f709f8..00000000 --- a/5-Applications/scripts/test_ram_loopback.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for RAM loopback writer functionality""" - -import sys -import os -import json -import time -from pathlib import Path - -# Add scripts directory to path -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import RAMLoopbackWriter - -def test_ram_loopback_writer(): - """Test RAM loopback writer functionality""" - - print("[TEST] Testing RAM loopback writer...") - - # Create RAM loopback writer - writer = RAMLoopbackWriter() - - # Test 1: Write improvement to RAM - print("\n[TEST 1] Writing improvement to RAM...") - writer.write_improvement( - agent_id=1, - improvement_type="test_improvement", - improvement_data={ - 'description': 'Test improvement for RAM loopback', - 'value': 42.0, - 'status': 'success' - } - ) - print(" ✓ Improvement written to RAM") - - # Test 2: Read improvements from RAM - print("\n[TEST 2] Reading improvements from RAM...") - improvements = writer.read_improvements(agent_id=1) - print(f" ✓ Read {len(improvements)} improvements from RAM") - if improvements: - print(f" Latest improvement: {improvements[-1]['type']}") - - # Test 3: Sync to disk - print("\n[TEST 3] Syncing to disk...") - writer.sync_to_disk() - print(" ✓ Synced to disk") - - # Test 4: Verify disk file exists and contains data - print("\n[TEST 4] Verifying disk file...") - disk_path = Path(writer.disk_path) - if disk_path.exists(): - with open(disk_path, 'r') as f: - disk_data = json.load(f) - print(f" ✓ Disk file exists with {len(disk_data)} improvements") - else: - print(" ✗ Disk file does not exist") - return False - - # Test 5: Write multiple improvements and test buffer - print("\n[TEST 5] Writing multiple improvements...") - for i in range(5): - writer.write_improvement( - agent_id=2, - improvement_type=f"batch_improvement_{i}", - improvement_data={ - 'batch_index': i, - 'value': i * 10.0 - } - ) - print(f" ✓ Wrote 5 improvements, buffer size: {len(writer.improvements_buffer)}") - - # Test 6: Get improvement summary - print("\n[TEST 6] Getting improvement summary...") - summary = writer.get_improvement_summary() - print(f" ✓ Summary:") - print(f" Total improvements: {summary['total_improvements']}") - print(f" Type counts: {summary['type_counts']}") - print(f" RAM path: {summary['ram_path']}") - print(f" Disk path: {summary['disk_path']}") - print(f" Buffer size: {summary['buffer_size']}") - - # Test 7: Verify RAM files exist - print("\n[TEST 7] Verifying RAM files...") - ram_dir = Path(writer.ram_path) - if ram_dir.exists(): - ram_files = list(ram_dir.glob("*.json")) - print(f" ✓ RAM directory exists with {len(ram_files)} files") - else: - print(" ✗ RAM directory does not exist") - return False - - print("\n[SUCCESS] All RAM loopback writer tests passed!") - return True - -if __name__ == "__main__": - success = test_ram_loopback_writer() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/test_theory_development.py b/5-Applications/scripts/test_theory_development.py deleted file mode 100644 index bfb34138..00000000 --- a/5-Applications/scripts/test_theory_development.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 -"""Test script for theory development mechanism""" - -import sys -import os -sys.path.insert(0, '/home/allaun/Documents/Research Stack/scripts') - -from enhanced_integrated_swarm import TheoryDevelopment, ComprehensiveLearning, BiologicalLearning, GPULearning - -def test_theory_development(): - """Test theory development mechanism""" - - print("[TEST] Testing theory development mechanism...") - - # Create learning systems - physics_learning = ComprehensiveLearning() - bio_learning = BiologicalLearning() - gpu_learning = GPULearning() - - # Auto-learn physics concepts - print("\n[TEST 1] Auto-learning physics concepts...") - physics_learning.auto_learn() - print(f" ✓ Learned {len(physics_learning.learned_concepts)} physics concepts") - - # Create theory development system - print("\n[TEST 2] Creating theory development system...") - theory_development = TheoryDevelopment() - theory_development.set_learning_systems(physics_learning, bio_learning, gpu_learning) - print(f" ✓ Theory development system created") - - # Test cross-domain synthesis - print("\n[TEST 3] Testing cross-domain synthesis...") - synthesis = theory_development.synthesize_cross_domain('thermo', 'quantum') - print(f" ✓ Synthesis: {len(synthesis['synthesis_points'])} points, confidence: {synthesis['confidence']:.2f}") - - # Test hypothesis generation - print("\n[TEST 4] Testing hypothesis generation...") - hypothesis = theory_development.generate_hypothesis(synthesis) - if hypothesis['statement']: - print(f" ✓ Hypothesis: {hypothesis['statement'][:80]}...") - print(f" Confidence: {hypothesis['confidence']:.2f}, Testable: {hypothesis['testable']}") - else: - print(f" ! No hypothesis generated (insufficient confidence)") - - # Test theory formulation - print("\n[TEST 5] Testing theory formulation...") - hypotheses = [h for h in theory_development.hypotheses if h['statement']] - if hypotheses: - theory = theory_development.formulate_theory(hypotheses) - if theory.get('formal_statement'): - print(f" ✓ Theory: {theory['formal_statement'][:80]}...") - print(f" Confidence: {theory['confidence']:.2f}, Testability: {theory['testability_score']:.2f}") - else: - print(f" ! No theory formulated") - else: - print(f" ! No hypotheses available for theory formulation") - - # Test theory testing - print("\n[TEST 6] Testing theory validation...") - if theory_development.generated_theories: - test_result = theory_development.test_theory(theory_development.generated_theories[-1]) - print(f" ✓ Test result: {'PASSED' if test_result['test_passed'] else 'FAILED'}") - print(f" Consistency: {test_result['consistency_score']:.2f}") - else: - print(f" ! No theories available for testing") - - # Test auto-development - print("\n[TEST 7] Testing automatic theory development...") - theory_development.auto_develop_theories() - - # Get theory summary - print("\n[TEST 8] Getting theory development summary...") - summary = theory_development.get_theory_summary() - print(f" ✓ Summary:") - print(f" Total theories: {summary['total_theories']}") - print(f" Total hypotheses: {summary['total_hypotheses']}") - print(f" Total syntheses: {summary['total_syntheses']}") - print(f" Average confidence: {summary['average_confidence']:.2f}") - - print("\n[SUCCESS] All theory development tests passed!") - return True - -if __name__ == "__main__": - success = test_theory_development() - sys.exit(0 if success else 1) diff --git a/5-Applications/scripts/topology_head.py b/5-Applications/scripts/topology_head.py deleted file mode 100644 index ec606ed4..00000000 --- a/5-Applications/scripts/topology_head.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -""" -topology_head.py — Cooperative Compute Topology Head Node - -Starts a Ray head node on qfox-1 and provides the topology registry -for the distributed mesh. Worker nodes (laptop-1, cupfox) connect -to this head to form a unified resource pool. - -Architecture: - qfox-1 (head) : 12 CPU, 30 GiB RAM, RTX 4070 SUPER 12 GiB VRAM - laptop-1 (worker): 16 CPU, 14 GiB RAM, AMD Lucienne APU (ROCm) - cupfox (worker): 2 CPU, 4 GiB RAM, CPU-only - -All nodes are connected via Tailscale mesh (100.x.x.x addresses). -""" - -import ray -import os -import sys -import json -import socket -import time - -# ═══════════════════════════════════════════════════════════════════════════ -# §1 Topology Definition -# ═══════════════════════════════════════════════════════════════════════════ - -TOPOLOGY = { - "qfox-1": { - "tailscale_ip": "100.88.57.96", - "role": "head", - "cpus": 12, - "ram_gib": 30, - "gpu": "NVIDIA GeForce RTX 4070 SUPER", - "gpu_vram_gib": 12, - "accelerator": "cuda", - }, - "nixos-laptop": { - "tailscale_ip": "100.119.165.120", - "role": "worker", - "cpus": 16, - "ram_gib": 14, - "gpu": "AMD Lucienne APU", - "gpu_vram_gib": 0, # shared memory - "accelerator": "rocm", - }, - "microvm-racknerd": { - "tailscale_ip": "100.101.247.127", - "role": "worker", - "cpus": 1, - "ram_gib": 1, - "gpu": None, - "gpu_vram_gib": 0, - "accelerator": None, - }, - "361395-1": { - "tailscale_ip": "100.110.163.82", - "role": "worker", - "cpus": 2, - "ram_gib": 3, - "gpu": None, - "gpu_vram_gib": 0, - "accelerator": None, - }, -} - -HEAD_IP = TOPOLOGY["qfox-1"]["tailscale_ip"] -HEAD_PORT = 6379 -DASHBOARD_PORT = 8265 - -# ═══════════════════════════════════════════════════════════════════════════ -# §2 Head Node Initialization -# ═══════════════════════════════════════════════════════════════════════════ - -def start_head(): - """Initialize Ray head node bound to the Tailscale interface.""" - print(f"╔═══════════════════════════════════════════════════════════╗") - print(f"║ Cooperative Compute Topology — Head Node ║") - print(f"╠═══════════════════════════════════════════════════════════╣") - print(f"║ Head IP : {HEAD_IP} ║") - print(f"║ Ray Port : {HEAD_PORT} ║") - print(f"║ Dashboard : http://{HEAD_IP}:{DASHBOARD_PORT} ║") - print(f"╚═══════════════════════════════════════════════════════════╝") - - ray.init( - address=None, # start a new cluster - _node_ip_address=HEAD_IP, - dashboard_host="0.0.0.0", - dashboard_port=DASHBOARD_PORT, - num_cpus=TOPOLOGY["qfox-1"]["cpus"], - num_gpus=1, # RTX 4070 SUPER - include_dashboard=True, - ) - - print(f"\n✓ Ray head started. Cluster address: {HEAD_IP}:{HEAD_PORT}") - print(f" Dashboard: http://{HEAD_IP}:{DASHBOARD_PORT}") - print(f"\nTo connect workers, run on each node:") - print(f" ray start --address='{HEAD_IP}:{HEAD_PORT}'") - print() - - return ray.cluster_resources() - - -# ═══════════════════════════════════════════════════════════════════════════ -# §3 Topology Status -# ═══════════════════════════════════════════════════════════════════════════ - -def print_topology_status(): - """Print live cluster resource status.""" - resources = ray.cluster_resources() - available = ray.available_resources() - nodes = ray.nodes() - - print(f"\n{'═'*60}") - print(f" TOPOLOGY STATUS — {len(nodes)} node(s) connected") - print(f"{'═'*60}") - - total_cpu = resources.get("CPU", 0) - total_gpu = resources.get("GPU", 0) - total_mem = resources.get("memory", 0) / (1024**3) - - avail_cpu = available.get("CPU", 0) - avail_gpu = available.get("GPU", 0) - avail_mem = available.get("memory", 0) / (1024**3) - - print(f" CPUs : {avail_cpu:.0f} / {total_cpu:.0f} available") - print(f" GPUs : {avail_gpu:.0f} / {total_gpu:.0f} available") - print(f" Memory : {avail_mem:.1f} / {total_mem:.1f} GiB available") - print() - - for node in nodes: - alive = "🟢" if node["Alive"] else "🔴" - ip = node["NodeManagerAddress"] - res = node["Resources"] - cpus = res.get("CPU", 0) - gpus = res.get("GPU", 0) - mem = res.get("memory", 0) / (1024**3) - - # Identify the node by IP - name = "unknown" - for n, info in TOPOLOGY.items(): - if info["tailscale_ip"] == ip: - name = n - break - - print(f" {alive} {name:12s} ({ip})") - print(f" CPU: {cpus:.0f} GPU: {gpus:.0f} RAM: {mem:.1f} GiB") - - print(f"{'═'*60}\n") - - -# ═══════════════════════════════════════════════════════════════════════════ -# §4 Distributed Task Primitives -# ═══════════════════════════════════════════════════════════════════════════ - -@ray.remote -def cpu_task(task_id: int, data_chunk: list) -> dict: - """Generic CPU-bound task that runs on any node in the topology.""" - import platform - hostname = platform.node() - result = sum(data_chunk) # placeholder computation - return { - "task_id": task_id, - "hostname": hostname, - "chunk_size": len(data_chunk), - "result": result, - } - - -@ray.remote(num_gpus=1) -def gpu_task(task_id: int, tensor_size: int) -> dict: - """GPU-accelerated task — will be scheduled on nodes with GPUs.""" - import torch - import platform - hostname = platform.node() - - device = "cuda" if torch.cuda.is_available() else "cpu" - t = torch.randn(tensor_size, tensor_size, device=device) - result = torch.linalg.norm(t).item() - - return { - "task_id": task_id, - "hostname": hostname, - "device": device, - "tensor_size": tensor_size, - "norm": result, - } - - -def run_topology_test(): - """Distribute a test workload across all connected nodes.""" - print("Running topology distribution test...") - - # Create chunks that will fan out across available CPUs - chunks = [list(range(i * 1000, (i + 1) * 1000)) for i in range(30)] - futures = [cpu_task.remote(i, chunk) for i, chunk in enumerate(chunks)] - results = ray.get(futures) - - # Tally which nodes handled what - node_counts = {} - for r in results: - h = r["hostname"] - node_counts[h] = node_counts.get(h, 0) + 1 - - print(f"\n Task distribution across topology:") - for node, count in sorted(node_counts.items()): - print(f" {node}: {count} tasks") - - print(f"\n Total tasks completed: {len(results)}") - return results - - -# ═══════════════════════════════════════════════════════════════════════════ -# §5 Entry Point -# ═══════════════════════════════════════════════════════════════════════════ - -if __name__ == "__main__": - if len(sys.argv) > 1 and sys.argv[1] == "status": - ray.init(address=f"{HEAD_IP}:{HEAD_PORT}") - print_topology_status() - elif len(sys.argv) > 1 and sys.argv[1] == "test": - ray.init(address=f"{HEAD_IP}:{HEAD_PORT}") - print_topology_status() - run_topology_test() - else: - start_head() - print_topology_status() - - print("Head node running. Press Ctrl+C to shutdown.") - try: - while True: - time.sleep(60) - print_topology_status() - except KeyboardInterrupt: - print("\nShutting down head node...") - ray.shutdown() diff --git a/5-Applications/scripts/topology_worker.sh b/5-Applications/scripts/topology_worker.sh deleted file mode 100644 index 5f4b32e1..00000000 --- a/5-Applications/scripts/topology_worker.sh +++ /dev/null @@ -1,37 +0,0 @@ -#!/bin/bash -# topology_worker.sh — Connect this node as a worker to the cooperative topology -# -# Usage: ssh allaun@ 'bash -s' < topology_worker.sh -# Or run directly on the worker node. - -set -euo pipefail - -HEAD_IP="100.88.57.96" -HEAD_PORT=6379 - -echo "╔═══════════════════════════════════════════════════════════╗" -echo "║ Cooperative Compute Topology — Worker Join ║" -echo "╠═══════════════════════════════════════════════════════════╣" -echo "║ Head Node : ${HEAD_IP}:${HEAD_PORT} ║" -echo "║ This Node : $(hostname) ║" -echo "╚═══════════════════════════════════════════════════════════╝" - -# Check if ray is installed -if ! command -v ray &>/dev/null; then - echo "[!] Ray not found. Installing..." - pip install --user 'ray[default]' -fi - -# Check if already connected -if ray status --address="${HEAD_IP}:${HEAD_PORT}" &>/dev/null 2>&1; then - echo "[✓] Already connected to topology head." - ray status --address="${HEAD_IP}:${HEAD_PORT}" - exit 0 -fi - -# Stop any existing ray processes -ray stop --force 2>/dev/null || true - -# Start as worker -echo "[*] Connecting to head node at ${HEAD_IP}:${HEAD_PORT}..." -ray start --address="${HEAD_IP}:${HEAD_PORT}" --block diff --git a/5-Applications/scripts/tsm_swarm_efficiency_optimization.py b/5-Applications/scripts/tsm_swarm_efficiency_optimization.py deleted file mode 100644 index 79450f24..00000000 --- a/5-Applications/scripts/tsm_swarm_efficiency_optimization.py +++ /dev/null @@ -1,413 +0,0 @@ -#!/usr/bin/env python3 -""" -tsm_swarm_efficiency_optimization.py — Swarm Agents at 50% TSM Capacity - -Spawns swarm agents using 50% of Topological State Machine capacity: -- 50% of 656.6 GB virtual memory = 328 GB -- 50% of 36 cores = 18 cores -- 50% of 6 nodes = 3 nodes active - -All agents attempt parallel efficiency improvements: -- BIND compression optimization -- Triumvirate clock tuning -- Curvature-guided placement refinement -- Gossip protocol enhancement -- Node load balancing - -Measures: improvement vs overhead, contention effects, emergent optimization -""" - -import time -import json -import random -import threading -import statistics -from pathlib import Path -from dataclasses import dataclass, field -from typing import Dict, List, Any, Optional -from datetime import datetime -from concurrent.futures import ThreadPoolExecutor, as_completed - -# Import infrastructure -import sys -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -from virtual_gpu_topology_loader import VirtualGPUTopology - - -@dataclass -class SwarmAgent: - """Single swarm agent optimizing TSM efficiency.""" - agent_id: str - target_node: str - memory_quota_gb: float - cpu_quota: int - optimization_target: str - improvement_found: float = 0.0 - iterations: int = 0 - status: str = "active" - - def optimize(self) -> Dict[str, Any]: - """Execute optimization task.""" - start = time.time() - - # Simulate optimization work - # Different targets have different characteristics - if self.optimization_target == "bind_compression": - # Try to find better compression patterns - improvement = random.uniform(0.01, 0.05) # 1-5% improvement - work_time = random.uniform(0.5, 2.0) # Longer analysis - - elif self.optimization_target == "curvature_placement": - # Optimize shard placement - improvement = random.uniform(0.02, 0.08) # 2-8% improvement - work_time = random.uniform(0.3, 1.0) - - elif self.optimization_target == "triumvirate_timing": - # Tune clock frequencies - improvement = random.uniform(0.005, 0.03) # 0.5-3% improvement - work_time = random.uniform(0.1, 0.5) # Fast - - elif self.optimization_target == "gossip_batching": - # Optimize gossip protocol - improvement = random.uniform(0.01, 0.06) # 1-6% improvement - work_time = random.uniform(0.2, 0.8) - - elif self.optimization_target == "memory_prefetch": - # Prefetch optimization - improvement = random.uniform(0.03, 0.10) # 3-10% improvement - work_time = random.uniform(0.4, 1.5) - - else: - improvement = random.uniform(0.01, 0.04) - work_time = random.uniform(0.2, 1.0) - - # Simulate work time - time.sleep(work_time / 10) # Scale down for simulation - - self.improvement_found = improvement - self.iterations += 1 - elapsed = time.time() - start - - return { - "agent_id": self.agent_id, - "improvement": improvement, - "work_time": elapsed, - "memory_used": self.memory_quota_gb, - "target": self.optimization_target, - "iterations": self.iterations - } - - -class TSMSwarmOptimizer: - """ - TSM Swarm Optimizer at 50% capacity. - - Manages swarm agents using half of total TSM resources: - - Memory: 328 GB of 656.6 GB - - Cores: 18 of 36 - - Nodes: 3 of 6 (distributed) - """ - - def __init__(self): - self.vgpu = VirtualGPUTopology() - - # TSM Total capacity - self.total_memory = 656.6 # GB - self.total_cores = 36 - self.total_nodes = 6 - - # 50% allocation - self.allocated_memory = self.total_memory * 0.5 # 328 GB - self.allocated_cores = int(self.total_cores * 0.5) # 18 cores - self.allocated_nodes = int(self.total_nodes * 0.5) # 3 nodes - - self.agents: List[SwarmAgent] = [] - self.results: List[Dict[str, Any]] = [] - - def spawn_agents(self, agent_count: int = 50) -> List[SwarmAgent]: - """Spawn swarm agents up to 50% capacity.""" - print("\n" + "=" * 70) - print("SPAWNING SWARM AGENTS (50% TSM CAPACITY)") - print("=" * 70) - - print(f"\nTSM Total Capacity:") - print(f" Memory: {self.total_memory:.1f} GB") - print(f" Cores: {self.total_cores}") - print(f" Nodes: {self.total_nodes}") - - print(f"\n50% Allocation:") - print(f" Memory: {self.allocated_memory:.1f} GB") - print(f" Cores: {self.allocated_cores}") - print(f" Nodes: {self.allocated_nodes}") - - # Target nodes (50% of mesh) - target_nodes = ["qfox", "architect", "judge"] # 3 of 6 - - # Per-agent allocation - memory_per_agent = self.allocated_memory / agent_count - cores_per_agent = max(1, self.allocated_cores // agent_count) - - print(f"\nPer-Agent Quota:") - print(f" Memory: {memory_per_agent:.2f} GB") - print(f" Cores: {cores_per_agent}") - print(f" Target Nodes: {', '.join(target_nodes)}") - - # Optimization targets (distribute among agents) - targets = [ - "bind_compression", - "curvature_placement", - "triumvirate_timing", - "gossip_batching", - "memory_prefetch", - "shard_balancing", - "credential_caching", - "consensus_batching" - ] - - print(f"\nSpawning {agent_count} agents...") - - for i in range(agent_count): - agent = SwarmAgent( - agent_id=f"swarm_opt_{i+1:03d}", - target_node=target_nodes[i % len(target_nodes)], - memory_quota_gb=memory_per_agent, - cpu_quota=cores_per_agent, - optimization_target=targets[i % len(targets)] - ) - self.agents.append(agent) - - print(f" ✅ {agent_count} agents spawned") - print(f" ✅ Using 50% of TSM capacity") - - return self.agents - - def run_parallel_optimization(self, iterations: int = 3) -> Dict[str, Any]: - """Run all agents in parallel, optimizing efficiency.""" - print("\n" + "=" * 70) - print("PARALLEL OPTIMIZATION (50% TSM LOAD)") - print("=" * 70) - - all_results = [] - - for iteration in range(iterations): - print(f"\n[ITERATION {iteration + 1}/{iterations}]") - print("-" * 50) - - iteration_results = [] - - # Run agents in parallel (limited by cores) - with ThreadPoolExecutor(max_workers=self.allocated_cores) as executor: - # Submit agent.optimize method calls properly - futures = {executor.submit(agent.optimize): agent for agent in self.agents} - - for future in as_completed(futures): - try: - result = future.result() - iteration_results.append(result) - except Exception as e: - print(f" ⚠️ Agent failed: {e}") - - # Calculate iteration stats - improvements = [r["improvement"] for r in iteration_results] - total_improvement = sum(improvements) - avg_improvement = statistics.mean(improvements) - max_improvement = max(improvements) - - print(f" Agents completed: {len(iteration_results)}") - print(f" Total improvement: {total_improvement:.4f} ({total_improvement*100:.2f}%)") - print(f" Average per agent: {avg_improvement*100:.2f}%") - print(f" Best improvement: {max_improvement*100:.2f}%") - - all_results.extend(iteration_results) - - # Simulate resource contention - contention_overhead = len(self.agents) * 0.001 # Small overhead per agent - print(f" Contention overhead: {contention_overhead*100:.2f}%") - - self.results = all_results - - return { - "iterations": iterations, - "total_agents": len(self.agents), - "total_runs": len(all_results), - "aggregated_improvement": sum(r["improvement"] for r in all_results), - "contention_factor": len(self.agents) * 0.001 - } - - def analyze_optimization_impact(self) -> Dict[str, Any]: - """Analyze the impact of swarm optimization at 50% load.""" - print("\n" + "=" * 70) - print("OPTIMIZATION IMPACT ANALYSIS") - print("=" * 70) - - # Group by target - by_target: Dict[str, List[float]] = {} - for r in self.results: - target = r["target"] - if target not in by_target: - by_target[target] = [] - by_target[target].append(r["improvement"]) - - # Calculate per-target effectiveness - target_effectiveness = {} - print("\nEffectiveness by Optimization Target:") - print("-" * 50) - - for target, improvements in sorted(by_target.items()): - total = sum(improvements) - avg = statistics.mean(improvements) - max_imp = max(improvements) - agent_count = len(improvements) - - target_effectiveness[target] = { - "total_improvement": total, - "average_improvement": avg, - "max_improvement": max_imp, - "agent_count": agent_count - } - - print(f" {target}:") - print(f" Agents: {agent_count}") - print(f" Total: {total*100:.2f}%") - print(f" Average: {avg*100:.2f}%") - print(f" Best: {max_imp*100:.2f}%") - - # Overall impact - total_improvement = sum(r["improvement"] for r in self.results) - avg_improvement = statistics.mean([r["improvement"] for r in self.results]) - - # Diminishing returns analysis - first_half = self.results[:len(self.results)//2] - second_half = self.results[len(self.results)//2:] - - first_avg = statistics.mean([r["improvement"] for r in first_half]) - second_avg = statistics.mean([r["improvement"] for r in second_half]) - - diminishing = (first_avg - second_avg) / first_avg if first_avg > 0 else 0 - - print(f"\nOverall Impact:") - print(f" Total improvement: {total_improvement*100:.2f}%") - print(f" Average per run: {avg_improvement*100:.2f}%") - print(f" Diminishing returns: {diminishing*100:.1f}%") - - # Resource utilization - total_memory_used = sum(a.memory_quota_gb for a in self.agents) - total_core_usage = sum(a.cpu_quota for a in self.agents) - - print(f"\nResource Utilization (50% TSM):") - print(f" Memory: {total_memory_used:.1f} / {self.allocated_memory:.1f} GB") - print(f" Cores: {total_core_usage} / {self.allocated_cores}") - print(f" Utilization: 100% (by design)") - - # Emergent effects - print(f"\nEmergent Effects at 50% Load:") - - if diminishing > 0.3: - print(f" ⚠️ High contention: {diminishing*100:.0f}% diminishing returns") - print(f" Agents competing for shared resources") - elif diminishing > 0.1: - print(f" ⚡ Moderate efficiency: {diminishing*100:.0f}% diminishing returns") - print(f" Good parallelization with some overlap") - else: - print(f" ✅ Near-linear scaling: {diminishing*100:.0f}% diminishing returns") - print(f" Agents working efficiently in parallel") - - if total_improvement > 1.0: - print(f" 🚀 Cumulative improvement >100%!") - print(f" Multiple optimizations compound") - - return { - "by_target": target_effectiveness, - "total_improvement": total_improvement, - "average_improvement": avg_improvement, - "diminishing_returns": diminishing, - "resource_utilization": { - "memory_gb": total_memory_used, - "cores": total_core_usage, - "percentage": 50.0 - }, - "emergent_effects": { - "contention_level": "high" if diminishing > 0.3 else "moderate" if diminishing > 0.1 else "low", - "scaling_efficiency": (1.0 - diminishing) * 100 - } - } - - def run_full_simulation(self, agent_count: int = 50) -> Dict[str, Any]: - """Execute complete 50% TSM swarm optimization.""" - print("\n" + "=" * 70) - print("TSM SWARM OPTIMIZATION AT 50% CAPACITY") - print("=" * 70) - print(f"Virtual GPU: {self.total_memory:.1f} GB") - print(f"Allocated: 50% = {self.allocated_memory:.1f} GB") - print(f"Swarm agents: {agent_count}") - print(f"Goal: Parallel efficiency improvement") - print("=" * 70) - - # Phase 1: Spawn agents - self.spawn_agents(agent_count) - - # Phase 2: Run optimization - opt_summary = self.run_parallel_optimization(iterations=3) - - # Phase 3: Analyze impact - impact = self.analyze_optimization_impact() - - # Compile final report - report = { - "simulation_timestamp": datetime.now().isoformat(), - "tsm_capacity": { - "total_memory_gb": self.total_memory, - "total_cores": self.total_cores, - "total_nodes": self.total_nodes, - "allocated_memory_gb": self.allocated_memory, - "allocated_cores": self.allocated_cores, - "allocated_nodes": self.allocated_nodes, - "utilization_percent": 50.0 - }, - "swarm_deployment": { - "agent_count": agent_count, - "agents_per_node": agent_count // self.allocated_nodes, - "memory_per_agent_gb": self.allocated_memory / agent_count, - "cores_per_agent": max(1, self.allocated_cores // agent_count) - }, - "optimization_results": opt_summary, - "impact_analysis": impact, - "conclusion": { - "50_percent_load_feasible": impact["diminishing_returns"] < 0.5, - "efficiency_gains": f"{impact['total_improvement']*100:.1f}%", - "scaling_efficiency": f"{(1.0 - impact['diminishing_returns'])*100:.1f}%", - "recommendation": "Optimal load" if impact["diminishing_returns"] < 0.2 else "Consider 30% load" if impact["diminishing_returns"] > 0.4 else "Good parallelization" - } - } - - # Print conclusion - print("\n" + "=" * 70) - print("SIMULATION CONCLUSION") - print("=" * 70) - print(f"50% TSM Load: {'✅ FEASIBLE' if report['conclusion']['50_percent_load_feasible'] else '❌ HIGH CONTENTION'}") - print(f"Total Efficiency Gains: {report['conclusion']['efficiency_gains']}") - print(f"Scaling Efficiency: {report['conclusion']['scaling_efficiency']}") - print(f"Recommendation: {report['conclusion']['recommendation']}") - print("=" * 70) - - # Save report - output_path = Path("/home/allaun/Documents/Research Stack/data/tsm_swarm_50percent_optimization.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - with open(output_path, "w") as f: - json.dump(report, f, indent=2) - - print(f"\nReport saved: {output_path}") - - return report - - -def main(): - """Run 50% TSM swarm optimization.""" - optimizer = TSMSwarmOptimizer() - report = optimizer.run_full_simulation(agent_count=50) - return report - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/unified_shader_gcl_audio_stack.py b/5-Applications/scripts/unified_shader_gcl_audio_stack.py deleted file mode 100644 index ea5782d2..00000000 --- a/5-Applications/scripts/unified_shader_gcl_audio_stack.py +++ /dev/null @@ -1,396 +0,0 @@ -#!/usr/bin/env python3 -""" -Unified Computational Stack: Proto-Shader + GCL + Square Wave -Most minimal OISC with NES palette generator as proto-shader. - -Architecture: -1. Minimal OISC (SUBLEQ: M[b] = M[b] - M[a]; if M[b] ≤ 0 goto c) -2. NES Palette Generator as Proto-Shader (generates color/grayscale from parameters) -3. GCL Compression (DeltaGCL) for data -4. Square Wave Generator (audio output) - -The NES palette generator acts as a shader primitive: -- Input: Parameters (frequency, phase, modulation) -- Output: Color/grayscale values (0-63 for NES palette) -- Like a fragment shader: f(x,y,t) → color - -Unified Memory Map (64K): -- $0000-$00FF: Zero page (OISC registers) -- $0100-$01FF: Shader parameters -- $0200-$02FF: Palette LUT (64 NES colors) -- $0300-$03FF: GCL compressed data -- $0400-$07FF: Square wave LUT -- $0800-$7FFF: OISC program + workspace -- $8000-$FFFF: I/O (audio, video output) - -This is the unified stack: shader primitive + compression + audio in minimal OISC. -""" - -import struct -from typing import List, Tuple, Dict, Optional -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# Minimal OISC: SUBLEQ (One Instruction Set Computer) -# Instruction: M[b] = M[b] - M[a]; if M[b] ≤ 0 goto c -# This is the most minimal Turing-complete OISC -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class SUBLEQInstruction: - """SUBLEQ instruction: M[b] = M[b] - M[a]; if M[b] ≤ 0 goto c""" - a: int # Source address - b: int # Destination address - c: int # Jump address - -class MinimalOISC: - """Minimal SUBLEQ OISC with unified memory""" - - def __init__(self, memory_size: int = 65536): - self.memory = [0] * memory_size - self.pc = 0 # Program counter - self.halted = False - self.cycle_count = 0 - - def load_program(self, instructions: List[Tuple[int, int, int]], start_addr: int = 0x0800): - """Load SUBLEQ program into memory""" - for i, (a, b, c) in enumerate(instructions): - addr = start_addr + i * 3 - self.memory[addr] = a & 0xFF - self.memory[addr + 1] = (a >> 8) & 0xFF - self.memory[addr + 2] = b & 0xFF - self.memory[addr + 3] = (b >> 8) & 0xFF - self.memory[addr + 4] = c & 0xFF - self.memory[addr + 5] = (c >> 8) & 0xFF - - def step(self) -> bool: - """Execute one SUBLEQ instruction""" - if self.halted: - return False - - # Fetch instruction (6 bytes) - a = self.memory[self.pc] | (self.memory[self.pc + 1] << 8) - b = self.memory[self.pc + 2] | (self.memory[self.pc + 3] << 8) - c = self.memory[self.pc + 4] | (self.memory[self.pc + 5] << 8) - - # Execute: M[b] = M[b] - M[a] - src_val = self.memory[a] - dst_val = self.memory[b] - result = (dst_val - src_val) & 0xFF - self.memory[b] = result - - # Branch if result <= 0 (signed) - if result & 0x80 or result == 0: - self.pc = c - else: - self.pc += 6 - - self.cycle_count += 1 - return True - - def run(self, max_cycles: int = 1000000): - """Run SUBLEQ program""" - while not self.halted and self.cycle_count < max_cycles: - if not self.step(): - break - -# ═══════════════════════════════════════════════════════════════════════════ -# NES Palette Generator as Proto-Shader -# Acts like a fragment shader: f(parameters) → color -# NES palette: 64 colors (6-bit: 2 red, 2 green, 2 blue) -# ═══════════════════════════════════════════════════════════════════════════ - -class NESPaletteShader: - """NES palette generator as proto-shader primitive""" - - # NES color levels (0-3 for each channel) - COLOR_LEVELS = [0x00, 0x55, 0xAA, 0xFF] # 0%, 33%, 66%, 100% - - @staticmethod - def encode_color(r: int, g: int, b: int) -> int: - """Encode RGB to NES palette index (0-63)""" - # Each channel: 2 bits - r_bits = (r & 0x3) << 4 - g_bits = (g & 0x3) << 2 - b_bits = (b & 0x3) << 0 - return r_bits | g_bits | b_bits - - @staticmethod - def decode_color(index: int) -> Tuple[int, int, int]: - """Decode NES palette index to RGB""" - r = (index >> 4) & 0x3 - g = (index >> 2) & 0x3 - b = (index >> 0) & 0x3 - return (r, g, b) - - @staticmethod - def shader_compute(x: int, y: int, t: int, params: bytes) -> int: - """ - Proto-shader compute function. - - Like a fragment shader: f(x, y, t, params) → color - - Args: - x: X coordinate (0-255) - y: Y coordinate (0-255) - t: Time/phase (0-255) - params: Shader parameters (e.g., frequency, modulation) - - Returns: - NES palette index (0-63) - """ - # Simple shader: gradient based on position + time - # This is the "proto-shader" - minimal but functional - - # Extract parameters - freq = params[0] if len(params) > 0 else 1 - mod = params[1] if len(params) > 1 else 0 - - # Compute gradient - r = ((x + t * freq) % 256) >> 6 # 2 bits - g = ((y + t * freq) % 256) >> 6 - b = ((x + y + mod) % 256) >> 6 - - return NESPaletteShader.encode_color(r, g, b) - - @staticmethod - def generate_palette_lut(params: bytes) -> List[int]: - """Generate complete NES palette LUT (64 entries)""" - lut = [] - for i in range(64): - # Use index as x, y, t for shader - x = i % 8 - y = (i // 8) % 8 - t = i // 64 - color = NESPaletteShader.shader_compute(x * 32, y * 32, t * 32, params) - lut.append(color) - return lut - -# ═══════════════════════════════════════════════════════════════════════════ -# GCL Compression (from nes_gcl_square_stream.py) -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class SquareWaveFrame: - """Square wave parameters (25 bits)""" - frequency: int - duty: int - volume: int - sweep_enable: int - sweep_period: int - sweep_direction: int - sweep_shift: int - - def to_int(self) -> int: - bits = 0 - bits |= (self.frequency & 0x7FF) << 14 - bits |= (self.duty & 0x3) << 12 - bits |= (self.volume & 0xF) << 8 - bits |= (self.sweep_enable & 1) << 7 - bits |= (self.sweep_period & 0x7) << 4 - bits |= (self.sweep_direction & 1) << 3 - bits |= (self.sweep_shift & 0x7) << 0 - return bits - -# ═══════════════════════════════════════════════════════════════════════════ -# Unified Memory Map -# ═══════════════════════════════════════════════════════════════════════════ - -class UnifiedMemoryMap: - """Unified memory map for shader + GCL + audio stack""" - - # Memory regions - ZERO_PAGE = 0x0000 # $0000-$00FF: OISC registers - SHADER_PARAMS = 0x0100 # $0100-$01FF: Shader parameters - PALETTE_LUT = 0x0200 # $0200-$02FF: NES palette LUT (64 entries) - GCL_BUFFER = 0x0300 # $0300-$03FF: GCL compressed data - AUDIO_LUT = 0x0400 # $0400-$07FF: Square wave LUT - OISC_CODE = 0x0800 # $0800-$7FFF: OISC program - IO_MAPPED = 0x8000 # $8000-$FFFF: I/O (audio, video) - -# ═══════════════════════════════════════════════════════════════════════════ -# Unified Computational Stack -# ═══════════════════════════════════════════════════════════════════════════ - -class UnifiedShaderStack: - """Unified stack: Proto-shader + GCL + Square wave in minimal OISC""" - - def __init__(self): - # Minimal SUBLEQ OISC - self.oisc = MinimalOISC(memory_size=65536) - - # NES palette shader - self.shader = NESPaletteShader() - - # Audio LUT - self.audio_lut: List[int] = [0] * 256 - - # Palette LUT - self.palette_lut: List[int] = [0] * 64 - - def load_shader_params(self, params: bytes): - """Load shader parameters into memory""" - for i, byte in enumerate(params): - self.oisc.memory[UnifiedMemoryMap.SHADER_PARAMS + i] = byte - - def generate_palette_with_shader(self, params: bytes): - """Generate palette LUT using proto-shader""" - self.palette_lut = self.shader.generate_palette_lut(params) - - # Load into OISC memory - for i, color in enumerate(self.palette_lut): - self.oisc.memory[UnifiedMemoryMap.PALETTE_LUT + i] = color - - def load_gcl_data(self, gcl_bytes: bytes): - """Load GCL compressed data""" - for i, byte in enumerate(gcl_bytes): - self.oisc.memory[UnifiedMemoryMap.GCL_BUFFER + i] = byte - - def generate_oisc_shader_program(self) -> List[Tuple[int, int, int]]: - """ - Generate SUBLEQ program that: - 1. Uses shader to generate palette - 2. Decompresses GCL data - 3. Populates audio LUT - 4. Outputs to I/O registers - - This is the unified program in minimal OISC. - """ - code = [] - - # Initialize pointers - # $0000 = shader_ptr (points to shader params at $0100) - # $0002 = gcl_ptr (points to GCL buffer at $0300) - # $0004 = audio_ptr (points to audio LUT at $0400) - # $0006 = temp - - # Set initial pointers - code.append((0x0006, 0x0006, 1)) # Zero temp - code.append((0x0006, 0x0000, 2)) # temp = 0 - 0 = 0 - - # Set shader_ptr = $0100 - code.append((0x0006, 0x0006, 3)) # temp = 0 - code.append((0x0006, 0x0100, 4)) # temp = $0100 - code.append((0x0006, 0x0000, 5)) # shader_ptr = $0100 - - # Set gcl_ptr = $0300 - code.append((0x0006, 0x0300, 6)) # temp = $0300 - code.append((0x0006, 0x0002, 7)) # gcl_ptr = $0300 - - # Set audio_ptr = $0400 - code.append((0x0006, 0x0400, 8)) # temp = $0400 - code.append((0x0006, 0x0004, 9)) # audio_ptr = $0400 - - # Main loop: process GCL data - # Read marker from GCL buffer - code.append((0x0002, 0x0006, 10)) # temp = M[gcl_ptr] - code.append((0x0006, 0x0044, 11)) # Check if 'D' (68) - code.append((0x0006, 0x0006, 12)) # If equal, goto delta_decode - code.append((0x0006, 0x0046, 13)) # Check if 'F' (70) - code.append((0x0006, 0x0006, 14)) # If equal, goto full_decode - code.append((0x0006, 0x0050, 15)) # Check if 'P' (80) - code.append((0x0006, 0x0006, 16)) # If equal, goto pattern_decode - code.append((0x0006, 0x0006, 100)) # Halt (unknown marker) - - # Simplified: just copy GCL to audio LUT (placeholder) - # In real implementation, this would be full GCL decompression - - # Increment pointers and loop - code.append((0x0002, 0x0006, 17)) # temp = M[gcl_ptr] - code.append((0x0006, 0x0002, 18)) # gcl_ptr++ - code.append((0x0004, 0x0006, 19)) # audio_ptr++ - code.append((0x0006, 0x0006, 10)) # Loop back - - # Delta decode (placeholder) - code.append((0x0006, 0x0006, 20)) # temp = 0 - code.append((0x0006, 0x0006, 10)) # Loop back - - # Full decode (placeholder) - code.append((0x0006, 0x0006, 21)) # temp = 0 - code.append((0x0006, 0x0006, 10)) # Loop back - - # Pattern decode (placeholder) - code.append((0x0006, 0x0006, 22)) # temp = 0 - code.append((0x0006, 0x0006, 10)) # Loop back - - # Halt - code.append((0x0006, 0x0006, 0xFFFF)) # Halt (branch to $FFFF) - - return code - - def run_unified_stack(self, shader_params: bytes, gcl_data: bytes): - """Run the unified computational stack""" - print("=" * 70) - print("UNIFIED SHADER + GCL + AUDIO STACK") - print("=" * 70) - - # Load shader parameters - print("\n[*] Loading shader parameters...") - self.load_shader_params(shader_params) - print(" Parameters: {}".format([hex(b) for b in shader_params])) - - # Generate palette with proto-shader - print("\n[*] Generating palette with proto-shader...") - self.generate_palette_with_shader(shader_params) - print(" Palette LUT: {} entries".format(len(self.palette_lut))) - print(" Sample colors: {}".format(self.palette_lut[:8])) - - # Load GCL data - print("\n[*] Loading GCL compressed data...") - self.load_gcl_data(gcl_data) - print(" GCL data: {} bytes".format(len(gcl_data))) - - # Generate and load OISC program - print("\n[*] Generating unified OISC program...") - program = self.generate_oisc_shader_program() - self.oisc.load_program(program) - print(" Program: {} instructions".format(len(program))) - - # Run OISC - print("\n[*] Running SUBLEQ OISC...") - self.oisc.run(max_cycles=1000) - print(" Cycles: {}".format(self.oisc.cycle_count)) - print(" Halted: {}".format(self.oisc.halted)) - - # Read audio LUT from memory - print("\n[*] Reading audio LUT from memory...") - for i in range(16): - addr = UnifiedMemoryMap.AUDIO_LUT + i - val = self.oisc.memory[addr] - if val != 0: - print(" LUT[{}]: 0x{:02X}".format(i, val)) - - print("\n" + "=" * 70) - print("UNIFIED STACK COMPLETE") - print("=" * 70) - print("\n[*] Architecture Summary:") - print(" Minimal OISC: SUBLEQ (1 instruction)") - print(" Proto-Shader: NES palette generator") - print(" GCL Compression: Delta encoding") - print(" Square Wave: Audio LUT") - print("\n[*] Unified in 64K memory map") - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run unified shader stack test""" - # Create unified stack - stack = UnifiedShaderStack() - - # Shader parameters (frequency, modulation) - shader_params = bytes([0x05, 0x02, 0x00, 0x00]) # freq=5, mod=2 - - # GCL data (simplified) - gcl_data = bytes([ - ord('F'), 0x00, 0x10, 0x0F, 0x00, # Full frame - ord('D'), 0x01, 0x02, 0x01, 0x00, # Delta - ]) - - # Run unified stack - stack.run_unified_stack(shader_params, gcl_data) - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/usb_computational_controller.py b/5-Applications/scripts/usb_computational_controller.py deleted file mode 100644 index bb099d3c..00000000 --- a/5-Applications/scripts/usb_computational_controller.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -""" -USB Controller Computational Repurposing -Analyzes USB controllers for general-purpose computation capabilities. -""" - -import json -from pathlib import Path -from typing import Dict, List, Optional - -# Paths -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class USBComputationalController: - """Analyzes USB controllers for general computation.""" - - def __init__(self): - self.usb_controllers = { - "10:00.0": { - "name": "AMD 800 Series Chipset USB 3.x XHCI Controller", - "memory": "32K", - "interface": "xHCI", - "subsystem": "ASMedia Technology Inc. Device 1142", - "flags": ["bus master", "fast devsel", "latency 0"], - "irq": 24, - "computational_potential": "MEDIUM" - }, - "12:00.3": { - "name": "AMD Raphael/Granite Ridge USB 3.1 xHCI", - "memory": "1M", - "interface": "xHCI", - "subsystem": "MSI Device 7e71", - "flags": ["bus master", "fast devsel", "latency 0"], - "irq": 58, - "computational_potential": "HIGH" - }, - "12:00.4": { - "name": "AMD Raphael/Granite Ridge USB 3.1 xHCI", - "memory": "1M", - "interface": "xHCI", - "subsystem": "MSI Device 7e71", - "flags": ["bus master", "fast devsel", "latency 0"], - "irq": 67, - "computational_potential": "HIGH" - }, - "13:00.0": { - "name": "AMD Raphael/Granite Ridge USB 2.0 xHCI", - "memory": "1M", - "interface": "xHCI", - "subsystem": "MSI Device 7e71", - "flags": ["bus master", "fast devsel", "latency 0"], - "irq": 24, - "computational_potential": "HIGH" - } - } - - self.xhci_capabilities = { - "dma": "Direct Memory Access - can transfer data without CPU intervention", - "bus_master": "Can initiate bus transactions independently", - "memory_mapped": "Memory-mapped I/O for direct register access", - "interrupts": "MSI (Message Signaled Interrupts) support", - "ring_buffers": "Transfer ring buffers for efficient data movement", - "endpoint_management": "Multiple endpoint management (up to 256 endpoints)" - } - - def analyze_computational_potential(self, controller: Dict) -> Dict: - """Analyze computational potential of USB controller.""" - analysis = { - "dma_computation": { - "feasible": True, - "mode": "DMA-based computation", - "description": "Use DMA engine for data manipulation without CPU", - "throughput": "5-10 Gbps (USB 3.1)", - "latency": "<1µs (DMA transfer)" - }, - "ring_buffer_computation": { - "feasible": True, - "mode": "Ring buffer computation", - "description": "Use transfer ring buffers as computational pipelines", - "throughput": "Depends on ring size", - "latency": "<10µs (ring traversal)" - }, - "endpoint_computation": { - "feasible": True, - "mode": "Parallel endpoint computation", - "description": "Use multiple endpoints for parallel computation", - "parallelism": "Up to 256 endpoints", - "throughput": "256 × endpoint bandwidth" - }, - "interrupt_computation": { - "feasible": True, - "mode": "Interrupt-driven computation", - "description": "Use MSI interrupts for event-driven computation", - "latency": "<1µs (MSI)", - "throughput": "Interrupt-limited" - } - } - - return analysis - - def design_computational_approach(self) -> Dict: - """Design USB-based computational approach.""" - approach = { - "dma_based_computation": { - "concept": "Use DMA engine for data manipulation", - "implementation": "Program DMA to perform data transformations during transfer", - "operations": ["memcpy", "bitwise operations", "simple arithmetic"], - "precision": "8-64 bit (depending on DMA width)", - "throughput": "5-10 Gbps", - "power": "2-5W (USB controller)" - }, - "ring_buffer_pipeline": { - "concept": "Use transfer ring buffers as computational pipelines", - "implementation": "Chain DMA transfers with intermediate transformations", - "operations": ["sequential processing", "filtering", "compression"], - "precision": "8-32 bit", - "throughput": "Depends on ring size", - "power": "3-7W" - }, - "endpoint_parallelism": { - "concept": "Use multiple endpoints for parallel computation", - "implementation": "Distribute computation across endpoints", - "operations": ["parallel processing", "map-reduce", "batch processing"], - "parallelism": "Up to 256 endpoints", - "throughput": "256 × endpoint bandwidth", - "power": "5-10W" - }, - "interrupt_driven": { - "concept": "Use MSI interrupts for event-driven computation", - "implementation": "Trigger computation on specific interrupt patterns", - "operations": ["event processing", "state machines", "control logic"], - "latency": "<1µs", - "throughput": "Interrupt-limited", - "power": "1-3W" - } - } - - return approach - - def estimate_performance(self) -> Dict: - """Estimate performance of USB controller computation.""" - performance = { - "dma_computation": { - "throughput": "5-10 Gbps", - "latency": "<1µs", - "precision": "8-64 bit", - "operations": "memcpy, bitwise, simple arithmetic", - "power": "2-5W" - }, - "ring_buffer": { - "throughput": "1-5 Gbps", - "latency": "<10µs", - "precision": "8-32 bit", - "operations": "sequential, filtering, compression", - "power": "3-7W" - }, - "endpoint_parallel": { - "throughput": "10-20 Gbps (256 endpoints)", - "latency": "10-100µs", - "precision": "8-32 bit", - "operations": "parallel, map-reduce, batch", - "power": "5-10W" - }, - "interrupt_driven": { - "throughput": "100-1000 MOPS", - "latency": "<1µs", - "precision": "8-32 bit", - "operations": "event, state machine, control", - "power": "1-3W" - } - } - - return performance - - def run_analysis(self) -> Dict: - """Run USB controller computational analysis.""" - print("=" * 60) - print("USB CONTROLLER COMPUTATIONAL ANALYSIS") - print("=" * 60) - - # Step 1: Analyze USB controllers - print("\n[1/4] Analyzing USB controllers...") - print(f" Total USB controllers: {len(self.usb_controllers)}") - for addr, controller in self.usb_controllers.items(): - print(f" {addr}: {controller['name']}") - print(f" Memory: {controller['memory']}") - print(f" Potential: {controller['computational_potential']}") - - # Step 2: Analyze computational potential - print("[2/4] Analyzing computational potential...") - sample_controller = self.usb_controllers["12:00.3"] - potential = self.analyze_computational_potential(sample_controller) - print(f" DMA computation: {potential['dma_computation']['feasible']}") - print(f" Ring buffer: {potential['ring_buffer_computation']['feasible']}") - print(f" Endpoint parallelism: {potential['endpoint_computation']['feasible']}") - print(f" Interrupt driven: {potential['interrupt_computation']['feasible']}") - - # Step 3: Design computational approach - print("[3/4] Designing computational approach...") - approach = self.design_computational_approach() - print(f" Computational modes: {len(approach)}") - for mode, details in approach.items(): - print(f" {mode}: {details['throughput']}") - - # Step 4: Estimate performance - print("[4/4] Estimating performance...") - performance = self.estimate_performance() - print(f" DMA: {performance['dma_computation']['throughput']}") - print(f" Ring buffer: {performance['ring_buffer']['throughput']}") - print(f" Endpoint parallel: {performance['endpoint_parallel']['throughput']}") - print(f" Interrupt: {performance['interrupt_driven']['throughput']}") - - print("\n" + "=" * 60) - print("USB CONTROLLER COMPUTATIONAL ANALYSIS COMPLETE") - print("=" * 60) - - return { - "usb_controllers": self.usb_controllers, - "computational_potential": potential, - "computational_approach": approach, - "performance_estimates": performance - } - -if __name__ == '__main__': - analyzer = USBComputationalController() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "usb_computational_controller.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("USB COMPUTATIONAL CONTROLLER SUMMARY") - print("=" * 60) - print(f"Total USB Controllers: {len(results['usb_controllers'])}") - print(f"High Potential Controllers: 3 (1M memory)") - print(f"Computational Modes: {len(results['computational_approach'])}") - print(f"Max Throughput: {results['performance_estimates']['endpoint_parallel']['throughput']}") diff --git a/5-Applications/scripts/usb_fpga_analyzer.py b/5-Applications/scripts/usb_fpga_analyzer.py deleted file mode 100644 index 6c10aceb..00000000 --- a/5-Applications/scripts/usb_fpga_analyzer.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python3 -""" -USB FPGA Analyzer with LeanGPT Integration -Analyzes the attached FPGA detected via USB FTDI interface. -""" - -import json -import subprocess -from pathlib import Path -from typing import Dict, List - -# Paths -LEANGPT_BOOTSTRAP = Path("/home/allaun/Documents/Research Stack/0-Core-Formalism/lean/LeanGPT/bootstrap_results.json") -OUTPUT_DIR = Path("/home/allaun/Documents/Research Stack/out") - -class USBFPGAAnalyzer: - """Analyzes USB-attached FPGA with LeanGPT.""" - - def __init__(self): - self.fpga_communication_data = { - "interface_a": { - "baud_rate": 115200, - "responses": { - "0A": "fa0a", - "FF": "faff", - "FE": "fafe", - "01": "fa01", - "02": "fa02", - "00": "fa00" - }, - "pattern": "fa prefix + input byte", - "protocol": "echo/transformation mode" - }, - "interface_b": { - "baud_rate": 115200, - "responses": { - "0102030405060708": "0d0a0d0a494f2053746174653a203030" - }, - "decoded": "\\r\\n\\r\\nIO State: 00", - "state": "initial state" - } - } - - self.ftdi_device = { - "vendor_id": "0403", - "product_id": "6010", - "device": "FT2232C/D/H Dual UART/FIFO IC", - "bus": "005", - "device_num": "002" - } - - def analyze_communication_protocol(self) -> Dict: - """Analyze the FPGA communication protocol.""" - protocol_analysis = { - "protocol_type": "Simple Echo with Prefix", - "prefix": "0xfa", - "transformation": "input → 0xfa + input", - "bidirectional": True, - "latency": "<10ms", - "reliability": "100%" - } - - return protocol_analysis - - def identify_fpga_model(self) -> Dict: - """Identify the FPGA model based on communication pattern.""" - fpga_models = [ - { - "name": "Tang Nano 9K", - "manufacturer": "Gowin", - "chip": "GW1NR-9", - "probability": "HIGH", - "reason": "FT2232C interface, common in workspace, matches response pattern" - }, - { - "name": "Tang Nano 4K", - "manufacturer": "Gowin", - "chip": "GW1N-4", - "probability": "MEDIUM", - "reason": "Similar FTDI interface, but different chip" - }, - { - "name": "Custom FPGA Board", - "manufacturer": "Unknown", - "chip": "Unknown", - "probability": "LOW", - "reason": "Custom implementation with FTDI interface" - } - ] - - return fpga_models - - def generate_optimization_recommendations(self) -> Dict: - """Generate optimization recommendations using system topology.""" - recommendations = { - "current_fpga_functions": { - "communication_protocol": "Simple echo with prefix", - "io_state_monitoring": "IO State: 00", - "transformation_logic": "fa prefix addition" - }, - "optimization_opportunities": [ - { - "function": "fa prefix transformation", - "offload_to": "rtl_asic", - "reason": "Simple bit manipulation is native to RTL ASIC", - "complexity_reduction": "O(1) → O(1) with 10x lower power", - "power_saving": "90%" - }, - { - "function": "communication protocol", - "offload_to": "cpu", - "reason": "Protocol handling is control logic", - "complexity_reduction": "O(n) → O(1) with interrupts", - "power_saving": "70%" - }, - { - "function": "io state monitoring", - "offload_to": "cpu", - "reason": "State monitoring is control logic", - "complexity_reduction": "O(n) → O(1) with polling", - "power_saving": "75%" - } - ], - "estimated_fpga_reduction": { - "modules": "100% (all offloadable)", - "registers": "100%", - "wires": "100%", - "reason": "FPGA only acts as passthrough, all logic can be offloaded" - } - } - - return recommendations - - def run_analysis(self) -> Dict: - """Run complete USB FPGA analysis.""" - print("=" * 60) - print("USB FPGA ANALYSIS WITH LEANGPT") - print("=" * 60) - - # Step 1: Analyze communication protocol - print("\n[1/4] Analyzing communication protocol...") - protocol = self.analyze_communication_protocol() - print(f" Protocol: {protocol['protocol_type']}") - print(f" Prefix: {protocol['prefix']}") - - # Step 2: Identify FPGA model - print("[2/4] Identifying FPGA model...") - fpga_models = self.identify_fpga_model() - for model in fpga_models: - print(f" {model['name']}: {model['probability']} probability") - - # Step 3: Generate optimization recommendations - print("[3/4] Generating optimization recommendations...") - recommendations = self.generate_optimization_recommendations() - print(f" Optimization opportunities: {len(recommendations['optimization_opportunities'])}") - print(f" Estimated FPGA reduction: {recommendations['estimated_fpga_reduction']['modules']}") - - # Step 4: Generate analysis report - print("[4/4] Generating analysis report...") - analysis_report = { - "ftdi_device": self.ftdi_device, - "communication_data": self.fpga_communication_data, - "protocol_analysis": protocol, - "fpga_models": fpga_models, - "optimization_recommendations": recommendations - } - - print("\n" + "=" * 60) - print("USB FPGA ANALYSIS COMPLETE") - print("=" * 60) - - return analysis_report - -if __name__ == '__main__': - analyzer = USBFPGAAnalyzer() - results = analyzer.run_analysis() - - # Save results - output_file = OUTPUT_DIR / "usb_fpga_analysis.json" - with open(output_file, 'w') as f: - json.dump(results, f, indent=2) - - print(f"\nAnalysis results saved to {output_file}") - - # Print summary - print("\n" + "=" * 60) - print("ANALYSIS SUMMARY") - print("=" * 60) - print(f"FPGA Model: {results['fpga_models'][0]['name']} (HIGH probability)") - print(f"Protocol: {results['protocol_analysis']['protocol_type']}") - print(f"Optimization Opportunities: {len(results['optimization_recommendations']['optimization_opportunities'])}") - print(f"Estimated FPGA Reduction: {results['optimization_recommendations']['estimated_fpga_reduction']['modules']}") diff --git a/5-Applications/scripts/usb_manifold_probe.py b/5-Applications/scripts/usb_manifold_probe.py deleted file mode 100644 index 024d9c04..00000000 --- a/5-Applications/scripts/usb_manifold_probe.py +++ /dev/null @@ -1,152 +0,0 @@ -#!/usr/bin/env python3 -import subprocess -import json -import os -import re - -# USB Manifold Probe -# Gathers system USB state and maps it to the Sovereign Manifold schema. -# Adheres to AGENTS.md 0.4 (Safe hardware interaction). - -def get_lsusb_tree(): - try: - output = subprocess.check_output(["lsusb", "-t"], text=True) - return output - except Exception: - return "" - -def get_lsusb_verbose(): - try: - # Get basic verbose info to extract IDs and strings - output = subprocess.check_output(["lsusb", "-v"], stderr=subprocess.DEVNULL, text=True) - return output - except Exception: - return "" - -def parse_usb_devices(): - devices = [] - # Use 'usb-devices' if available as it is easier to parse - try: - output = subprocess.check_output(["usb-devices"], text=True) - device_blocks = output.strip().split("\n\n") - for block in device_blocks: - dev = {} - for line in block.split("\n"): - if line.startswith("T:"): - m = re.search(r"Bus=(\d+) Lev=(\d+) Prnt=(\d+) Port=(\d+) Cnt=(\d+) Dev#=\s*(\d+) Spd=([\d.]+)\s+MxCh=\s*(\d+)", line) - if m: - dev["bus"] = int(m.group(1)) - dev["level"] = int(m.group(2)) - dev["port"] = int(m.group(4)) - dev["speed"] = m.group(7) - elif line.startswith("D:"): - m = re.search(r"Ver=([\d.]+) Cls=([\da-fA-F]+)\(.*\) Sub=([\da-fA-F]+) Prot=([\da-fA-F]+) MxPS=(\d+)", line) - if m: - dev["bcdUSB"] = m.group(1) - elif line.startswith("P:"): - m = re.search(r"Vendor=([\da-fA-F]+) ProdID=([\da-fA-F]+) Rev=([\d.]+)", line) - if m: - dev["idVendor"] = m.group(1) - dev["idProduct"] = m.group(2) - elif line.startswith("S:"): - if "Manufacturer=" in line: - dev["manufacturer"] = line.split("Manufacturer=")[1] - elif "Product=" in line: - dev["product"] = line.split("Product=")[1] - elif "SerialNumber=" in line: - dev["serial"] = line.split("SerialNumber=")[1] - if dev: - devices.append(dev) - except Exception: - pass - return devices - -def main(): - print("[*] Probing USB Manifold...") - devices = parse_usb_devices() - - manifold_state = [] - - for dev in devices: - # Map to PTOS Metadata - is_root = dev.get("level") == 0 - speed = dev.get("speed", "0") - - # Heuristic: 20000M (USB 3.2x2) or 40000M (USB4) root hubs are definitely Type-C - is_typec_candidate = is_root and (float(speed) >= 20000) - - # Map metrics to 14-axis ENE manifold - link_stability = 1.0 - jitter_frustration = 0.0 - v0_val = int(link_stability * 65536) - - # Speed mapping (normalized to 10Gbps = 1.0) - speed_val = 0 - if "10000" in speed: speed_val = 0x00010000 - elif "480" in speed: speed_val = 0x00008000 - elif "12" in speed: speed_val = 0x00002000 - - # Jitter mapping - v10_val = int(jitter_frustration * 65536) - - # SWUFE Pulse (Axis 11): |v0|^2 - 0.25*v0 - v11_val = (v0_val * v0_val // 65536) - (v0_val // 4) - if v11_val < 0: v11_val = 0 - - ptos = { - "metadata": { - "layer": "usb_hardware", - "domain": "physical_interface", - "condition": "active", - "stage": "operational", - "tier": "ROOT_HUB" if is_root else "DEVICE", - "module": dev.get("product", "unknown"), - "tags": ["typec_high_speed" if is_typec_candidate else "standard"] - }, - "device": { - "idVendor": int(dev.get("idVendor", "0"), 16), - "idProduct": int(dev.get("idProduct", "0"), 16), - "bcdUSB": int(float(dev.get("bcdUSB", "3.2") if float(dev.get("bcdUSB", "2.0")) > 2.0 else dev.get("bcdUSB", "2.0")) * 100), - "manufacturer": dev.get("manufacturer", "unknown"), - "product": dev.get("product", "unknown"), - "serial": dev.get("serial", ""), - "speed": f"{speed}M" - }, - "capability": { - "typeC": is_typec_candidate, - "powerDelivery": is_typec_candidate, - "altModes": ["DisplayPort", "Thunderbolt"] if is_typec_candidate else [], - "usb4": float(speed) >= 40000 - }, - "metrics": { - "powerDraw": {"val": 0}, - "linkStability": {"val": 0x7FFF}, # 1.0 (Stable) in Q0_16 - "jitterFrustration": {"val": 0}, - "bandwidthUtilization": {"val": 0} - }, - "concept_vector": { - "v0": {"val": v0_val}, - "v1": {"val": 0}, "v2": {"val": 0}, - "v3": {"val": speed_val}, - "v4": {"val": 0}, "v5": {"val": 0}, "v6": {"val": 0}, "v7": {"val": 0}, - "v8": {"val": 0}, "v9": {"val": 0}, - "v10": {"val": v10_val}, - "v11": {"val": v11_val}, - "v12": {"val": 0}, "v13": {"val": 0} - }, - "ene_scalar": {"val": 0x00010000 if is_typec_candidate else 0x00008000}, - "active": True - } - manifold_state.append(ptos) - - # Output JSON-L for Lean ingestion - output_path = "shared-shared-data/data/artifacts/usb_manifold_state.jsonl" - with open(output_path, "w") as f: - for item in manifold_state: - f.write(json.dumps(item) + "\n") - - print(f"[+] Manifold state written to {output_path}") - print(f"[*] Found {len(devices)} USB devices in manifold.") - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/v4_ablation_study.py b/5-Applications/scripts/v4_ablation_study.py deleted file mode 100644 index 34a35c36..00000000 --- a/5-Applications/scripts/v4_ablation_study.py +++ /dev/null @@ -1,231 +0,0 @@ -#!/usr/bin/env python3 -""" -V4 Ablation Study - -Re-runs the V4 simulation with three configurations: -1. Speed only (no pause, no exposure window, no contacts, no transient bias) -2. Speed + delay (pause and exposure window, but no contacts, no transient bias) -3. Speed + delay + transient bias + contacts (full V4) - -This tests whether structural bias becomes significant in V4. -""" - -import numpy as np -import json -from typing import List, Dict -from v4_cotranslational_simulator import ( - V4CotranslationalSimulator, Codon, ExpertType, create_test_sequence -) - - -class V4AblationSimulator(V4CotranslationalSimulator): - """Extended simulator with ablation modes""" - - def __init__( - self, - sequence: List[Codon], - mode: str = "full", # "speed_only", "speed_delay", "full" - **kwargs - ): - super().__init__(sequence, **kwargs) - self.mode = mode - - def pause_field(self, t: float) -> float: - """Override based on mode""" - if self.mode == "speed_only": - return 0.0 # No pause - return super().pause_field(t) - - def exposed_segment(self, t: float): - """Override based on mode""" - if self.mode == "speed_only": - # No exposure window - return all translated residues - m = self.visible_prefix_length(t) - return self.translated_residues[:m] - return super().exposed_segment(t) - - def contact_probability(self, i: int, j: int, t: float) -> float: - """Override based on mode""" - if self.mode in ["speed_only", "speed_delay"]: - return 0.0 # No contacts - return super().contact_probability(i, j, t) - - def transient_bias(self, expert_type: ExpertType, t: float, window_size: int = 5) -> float: - """Override based on mode""" - if self.mode in ["speed_only", "speed_delay"]: - return 0.0 # No transient bias - return super().transient_bias(expert_type, t, window_size) - - def expert_weights(self, t: float) -> Dict[ExpertType, float]: - """Override based on mode""" - if self.mode == "speed_only": - # Simplified: uniform weights - weights = { - ExpertType.HELIX: 0.33, - ExpertType.SHEET: 0.33, - ExpertType.LOOP: 0.34 - } - return weights - return super().expert_weights(t) - - -def run_ablation_configuration(mode: str, sequence: List[Codon]) -> Dict: - """Run simulation with specific ablation configuration""" - print(f"\n{'='*70}") - print(f"V4 ABLATION: {mode.upper()}") - print(f"{'='*70}") - - sim = V4AblationSimulator(sequence, mode=mode, exposed_length=5) - - # Run simulation - print(f"\nRunning simulation...") - print(f"{'Time':>8} | {'Translated':>10} | {'Exposed':>7} | {'Pause':>6} | {'Contact':>7} | {'Score':>6}") - print("-" * 70) - - t = 0.0 - dt = 0.5 - max_time = 10.0 - - results = [] - - while t < max_time: - sim.step(dt) - - m = sim.visible_prefix_length(t) - E = sim.exposed_segment(t) - P = sim.pause_field(t) - Pi = sim.get_contact_average(t) - score = sim.get_cds_score(t) - - print(f"{t:8.2f} | {m:10d} | {len(E):7d} | {P:6.3f} | {Pi:7.3f} | {score:6.3f}") - - results.append({ - "time": t, - "translated": m, - "exposed": len(E), - "pause": P, - "contact": Pi, - "score": score - }) - - t += dt - - # Summary statistics - final_score = results[-1]["score"] - avg_pause = np.mean([r["pause"] for r in results]) - avg_contact = np.mean([r["contact"] for r in results]) - score_std = np.std([r["score"] for r in results]) - - print(f"\nSummary for {mode}:") - print(f" Final CDS Score: {final_score:.4f}") - print(f" Average Pause Field: {avg_pause:.4f}") - print(f" Average Contact Probability: {avg_contact:.4f}") - print(f" Score Std Dev: {score_std:.4f}") - - return { - "mode": mode, - "final_score": final_score, - "avg_pause": avg_pause, - "avg_contact": avg_contact, - "score_std": score_std, - "trajectory": results - } - - -def run_ablation_study(): - """Run full ablation study comparing all configurations""" - print("=" * 70) - print("V4 ABLATION STUDY") - print("=" * 70) - print("\nComparing three configurations:") - print("1. Speed only (baseline)") - print("2. Speed + delay (pause and exposure window)") - print("3. Speed + delay + transient bias + contacts (full V4)") - - # Create test sequence - sequence = create_test_sequence() - print(f"\nCreated test sequence with {len(sequence)} codons") - - # Run all configurations - results = {} - - results["speed_only"] = run_ablation_configuration("speed_only", sequence) - results["speed_delay"] = run_ablation_configuration("speed_delay", sequence) - results["full"] = run_ablation_configuration("full", sequence) - - # Compare results - print("\n" + "=" * 70) - print("ABLATION STUDY RESULTS") - print("=" * 70) - - print(f"\n{'Configuration':>20} | {'Final Score':>12} | {'Avg Pause':>10} | {'Avg Contact':>13} | {'Score Std':>11}") - print("-" * 70) - - for mode, data in results.items(): - print(f"{mode:>20} | {data['final_score']:12.4f} | {data['avg_pause']:10.4f} | {data['avg_contact']:13.4f} | {data['score_std']:11.4f}") - - # Analysis - print("\n" + "=" * 70) - print("ANALYSIS") - print("=" * 70) - - speed_only_score = results["speed_only"]["final_score"] - speed_delay_score = results["speed_delay"]["final_score"] - full_score = results["full"]["final_score"] - - speed_delay_delta = speed_delay_score - speed_only_score - full_delta = full_score - speed_only_score - - print(f"\nScore Changes:") - print(f" Speed only → Speed+Delay: {speed_delay_delta:+.4f}") - print(f" Speed only → Full V4: {full_delta:+.4f}") - - # Contact effect - contact_only_delta = full_score - speed_delay_score - print(f" Speed+Delay → Full V4: {contact_only_delta:+.4f} (contact + bias effect)") - - # Interpretation - print("\nInterpretation:") - - if abs(speed_delay_delta) < 0.01: - print(" - Pause/exposure window has minimal effect on score") - elif speed_delay_delta > 0: - print(" - Pause/exposure window improves score") - else: - print(" - Pause/exposure window reduces score") - - if abs(contact_only_delta) < 0.01: - print(" - Contacts + transient bias have minimal effect") - elif contact_only_delta > 0: - print(" - Contacts + transient bias improve score") - print(" - This suggests structural bias is becoming significant in V4") - else: - print(" - Contacts + transient bias reduce score") - - # Variance analysis - speed_only_std = results["speed_only"]["score_std"] - full_std = results["full"]["score_std"] - - if full_std > speed_only_std: - print(f" - Full V4 increases score variance ({speed_only_std:.4f} → {full_std:.4f})") - print(" - This suggests cotranslational dynamics create more diverse trajectories") - else: - print(f" - Full V4 reduces score variance ({speed_only_std:.4f} → {full_std:.4f})") - print(" - This suggests cotranslational dynamics stabilize trajectories") - - print("\n" + "=" * 70) - print("ABLATION STUDY COMPLETE") - print("=" * 70) - - return results - - -if __name__ == "__main__": - results = run_ablation_study() - - # Save results - output_path = "/home/allaun/Documents/Research Stack/data/v4_ablation_study_results.json" - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - - print(f"\nResults saved to: {output_path}") diff --git a/5-Applications/scripts/v4_cotranslational_simulator.py b/5-Applications/scripts/v4_cotranslational_simulator.py deleted file mode 100644 index c65bda0b..00000000 --- a/5-Applications/scripts/v4_cotranslational_simulator.py +++ /dev/null @@ -1,414 +0,0 @@ -#!/usr/bin/env python3 -""" -V4 Minimal Cotranslational Simulator - -Implements the minimal V4 simulator with: -- Exposed prefix (Eₜ) -- Pause field (P(t)) -- Contact probability term (Πᵢⱼ(t)) -- Transient bias field (Bₖ(t)) - -Based on V4 Full Cotranslational Codon-Peptide Equation Set -""" - -import numpy as np -from typing import List, Tuple, Dict, Optional -from dataclasses import dataclass -from enum import Enum - - -class ExpertType(Enum): - HELIX = "helix" - SHEET = "sheet" - LOOP = "loop" - - -@dataclass -class Codon: - """Represents a codon with its properties""" - index: int - nucleotides: str - amino_acid: str - translation_speed: float # v(c) - folding_delay: float # τ(c) = 1/v(c) - structural_bias: Dict[ExpertType, float] # bₖ(c) - bias_lifetime: float # τ_b(c) - - -@dataclass -class Residue: - """Represents a single amino acid residue""" - index: int - amino_acid: str - position_3d: np.ndarray # 3D coordinates - translation_time: float # Tₘ - - -@dataclass -class Expert: - """Represents an MoE expert""" - type: ExpertType - logit: float # zₖ - advice: np.ndarray # Advice vector - - -class V4CotranslationalSimulator: - """ - Minimal V4 Cotranslational Simulator - - Implements: - - Time-indexed translation state (Tₘ, m(t), Sₜ) - - Ribosome pausing field (p(cᵢ), P(t)) - - Nascent-chain exposure window (Eₜ) - - Contact formation kinetics (Πᵢⱼ(t), χᵢⱼ(t)) - - Transient codon bias (Bₖ(t)) - """ - - def __init__( - self, - sequence: List[Codon], - exposed_length: int = 10, # L_exp - alpha_p: float = 1.0, # pause speed coefficient - beta_p: float = 0.1, # pause rarity coefficient - kappa_p: float = 1.0, # pause accessibility coefficient - kappa_e: float = 1.0, # exposure accessibility coefficient - sigma_d: float = 1.0, # spatial scale - sigma_g: float = 1.0, # geometric scale - eta: float = 0.1, # energy sensitivity - ): - self.sequence = sequence - self.exposed_length = exposed_length - - # Parameters - self.alpha_p = alpha_p - self.beta_p = beta_p - self.kappa_p = kappa_p - self.kappa_e = kappa_e - self.sigma_d = sigma_d - self.sigma_g = sigma_g - self.eta = eta - - # State - self.time = 0.0 - self.translated_residues: List[Residue] = [] - self.expert_logits: Dict[ExpertType, float] = { - ExpertType.HELIX: 0.0, - ExpertType.SHEET: 0.0, - ExpertType.LOOP: 0.0 - } - - # Precompute cumulative translation times - self.cumulative_times = self._compute_cumulative_times() - - def _compute_cumulative_times(self) -> List[float]: - """Compute Tₘ = Σ_{i=1}^{m} 1/v(cᵢ)""" - times = [] - total = 0.0 - for codon in self.sequence: - total += codon.folding_delay # τ(c) = 1/v(c) - times.append(total) - return times - - def pause_intensity(self, codon: Codon) -> float: - """Compute pause intensity p(cᵢ) = αₚ/v(cᵢ) + βₚ·σ_rare(cᵢ)""" - # For simplicity, assume σ_rare = 0 for now - return self.alpha_p / codon.translation_speed - - def pause_field(self, t: float) -> float: - """Compute local pause field P(t) = p(c_{m(t)})""" - m = self.visible_prefix_length(t) - if m == 0: - return 0.0 - codon = self.sequence[m - 1] - return self.pause_intensity(codon) - - def visible_prefix_length(self, t: float) -> int: - """Compute m(t) = max{m : Tₘ ≤ t}""" - m = 0 - for i, T in enumerate(self.cumulative_times): - if T <= t: - m = i + 1 - else: - break - return m - - def exposed_segment(self, t: float) -> List[Residue]: - """Compute exposed segment Eₜ = (a_{m(t)-L_exp+1}, ..., a_{m(t)})""" - m = self.visible_prefix_length(t) - start = max(0, m - self.exposed_length) - return self.translated_residues[start:m] - - def kinetic_accessibility(self, i: int, j: int, t: float) -> float: - """Compute χᵢⱼ(t) = (1 - e^{-κₚ P(t)}) (1 - e^{-κₑ τ_exp(i,j,t)})""" - P = self.pause_field(t) - tau_exp = self.exposure_time(i, j, t) - - pause_factor = 1 - np.exp(-self.kappa_p * P) - exposure_factor = 1 - np.exp(-self.kappa_e * tau_exp) - - return pause_factor * exposure_factor - - def exposure_time(self, i: int, j: int, t: float) -> float: - """Compute time both residues have been exposed together""" - # Simplified: assume exposure time is proportional to time since translation - if i >= len(self.translated_residues) or j >= len(self.translated_residues): - return 0.0 - - T_i = self.cumulative_times[i] - T_j = self.cumulative_times[j] - T_max = max(T_i, T_j) - - return max(0.0, t - T_max) - - def contact_probability(self, i: int, j: int, t: float) -> float: - """Compute Πᵢⱼ(t) = 1_{i,j∈Eₜ}·exp(-dᵢⱼ²/2σ_d²)·exp(-Δᵢⱼ_geom²/2σ_g²)·χᵢⱼ(t)""" - E = self.exposed_segment(t) - exposed_indices = {r.index for r in E} - - if i not in exposed_indices or j not in exposed_indices: - return 0.0 - - if i >= len(self.translated_residues) or j >= len(self.translated_residues): - return 0.0 - - # Spatial separation - d_ij = np.linalg.norm( - self.translated_residues[i].position_3d - - self.translated_residues[j].position_3d - ) - - # Geometric compatibility (simplified as 1 for now) - Delta_ij_geom = 1.0 - - # Kinetic accessibility - chi_ij = self.kinetic_accessibility(i, j, t) - - spatial_factor = np.exp(-d_ij**2 / (2 * self.sigma_d**2)) - geometric_factor = np.exp(-Delta_ij_geom**2 / (2 * self.sigma_g**2)) - - return spatial_factor * geometric_factor * chi_ij - - def transient_bias(self, expert_type: ExpertType, t: float, window_size: int = 5) -> float: - """Compute Bₖ(t) = Σ_{i∈Wₜ} bₖ(cᵢ)·exp(-(t-Tᵢ)/τ_b(cᵢ))""" - m = self.visible_prefix_length(t) - W = list(range(max(0, m - window_size), m)) - - B = 0.0 - for i in W: - codon = self.sequence[i] - T_i = self.cumulative_times[i] - tau_b = codon.bias_lifetime - - bias = codon.structural_bias.get(expert_type, 0.0) - decay = np.exp(-(t - T_i) / tau_b) - - B += bias * decay - - return B - - def expert_weights(self, t: float) -> Dict[ExpertType, float]: - """Compute gₖ(t) ∝ exp(zₖ(t) + Bₖ(t) + Dₖ(t) - ηEₖ(Θₜ))""" - weights = {} - - for expert_type in ExpertType: - z = self.expert_logits[expert_type] - B = self.transient_bias(expert_type, t) - D = self.delay_bias(t, expert_type) - E = self.expert_energy_incompatibility(expert_type, t) - - logit = z + B + D - self.eta * E - weights[expert_type] = np.exp(logit) - - # Normalize - total = sum(weights.values()) - if total > 0: - for expert_type in weights: - weights[expert_type] /= total - - return weights - - def delay_bias(self, t: float, expert_type: ExpertType) -> float: - """Compute Dₖ(t) based on pause field""" - P = self.pause_field(t) - - # Simplified: different experts respond differently to pause - if expert_type == ExpertType.HELIX: - return -0.5 * P # helix formation decreases with pause - elif expert_type == ExpertType.SHEET: - return -0.3 * P # sheet formation decreases with pause - else: # LOOP - return 0.8 * P # loop formation increases with pause - - def expert_energy_incompatibility(self, expert_type: ExpertType, t: float) -> float: - """Compute Eₖ(Θₜ) - simplified as 0 for minimal simulator""" - return 0.0 - - def step(self, dt: float = 0.1): - """Advance simulation by dt""" - self.time += dt - - # Translate new residues - m = self.visible_prefix_length(self.time) - while len(self.translated_residues) < m: - i = len(self.translated_residues) - codon = self.sequence[i] - - # Create new residue with random 3D position - position = np.random.randn(3) # Simplified - residue = Residue( - index=i, - amino_acid=codon.amino_acid, - position_3d=position, - translation_time=self.cumulative_times[i] - ) - self.translated_residues.append(residue) - - # Update expert logits based on RL (simplified) - weights = self.expert_weights(self.time) - for expert_type in ExpertType: - # Simple RL update: increase logit for high-weight experts - if weights[expert_type] > 0.5: - self.expert_logits[expert_type] += 0.01 * dt - - def get_contact_average(self, t: float) -> float: - """Compute average contact probability Π̄(t)""" - E = self.exposed_segment(t) - n = len(E) - - if n < 2: - return 0.0 - - total = 0.0 - count = 0 - for i in range(n): - for j in range(i + 1, n): - total += self.contact_probability(E[i].index, E[j].index, t) - count += 1 - - if count == 0: - return 0.0 - - return total / count - - def get_cds_score(self, t: float, alpha: float = 0.5, beta: float = 0.3, chi: float = 0.2) -> float: - """Compute Φ_CDS(v4)(t) = α·Φ̄_codon + β·Φ_peptide(Θₜ,t) + χ·Π̄(t)""" - m = self.visible_prefix_length(t) - - # Average codon fitness (simplified as 1.0) - Phi_codon_avg = 1.0 - - # Peptide efficiency (simplified as 1.0 / (1 + pause)) - P = self.pause_field(t) - Phi_peptide = 1.0 / (1.0 + P) - - # Average contact probability - Pi_avg = self.get_contact_average(t) - - return alpha * Phi_codon_avg + beta * Phi_peptide + chi * Pi_avg - - -def create_test_sequence() -> List[Codon]: - """Create a test sequence for simulation""" - amino_acids = ["A", "R", "N", "D", "C", "Q", "E", "G", "H", "I"] - - sequence = [] - for i in range(20): - aa = amino_acids[i % len(amino_acids)] - - # Vary translation speed to test pause effects - if i % 3 == 0: - speed = 0.5 # Slow codon - elif i % 3 == 1: - speed = 1.0 # Medium codon - else: - speed = 2.0 # Fast codon - - codon = Codon( - index=i, - nucleotides=f"ABC", # Simplified - amino_acid=aa, - translation_speed=speed, - folding_delay=1.0 / speed, - structural_bias={ - ExpertType.HELIX: np.random.randn() * 0.1, - ExpertType.SHEET: np.random.randn() * 0.1, - ExpertType.LOOP: np.random.randn() * 0.1 - }, - bias_lifetime=1.0 - ) - sequence.append(codon) - - return sequence - - -def run_simulation(): - """Run the V4 simulation""" - print("=" * 70) - print("V4 COTRANSLATIONAL SIMULATOR") - print("=" * 70) - - # Create test sequence - sequence = create_test_sequence() - print(f"\nCreated test sequence with {len(sequence)} codons") - - # Initialize simulator - sim = V4CotranslationalSimulator(sequence, exposed_length=5) - - # Run simulation - print("\nRunning simulation...") - print(f"{'Time':>8} | {'Translated':>10} | {'Exposed':>7} | {'Pause':>6} | {'Contact':>7} | {'Score':>6}") - print("-" * 70) - - t = 0.0 - dt = 0.5 - max_time = 10.0 - - results = [] - - while t < max_time: - sim.step(dt) - - m = sim.visible_prefix_length(t) - E = sim.exposed_segment(t) - P = sim.pause_field(t) - Pi = sim.get_contact_average(t) - score = sim.get_cds_score(t) - - print(f"{t:8.2f} | {m:10d} | {len(E):7d} | {P:6.3f} | {Pi:7.3f} | {score:6.3f}") - - results.append({ - "time": t, - "translated": m, - "exposed": len(E), - "pause": P, - "contact": Pi, - "score": score - }) - - t += dt - - print("\n" + "=" * 70) - print("SIMULATION COMPLETE") - print("=" * 70) - - # Summary statistics - final_score = results[-1]["score"] - avg_pause = np.mean([r["pause"] for r in results]) - avg_contact = np.mean([r["contact"] for r in results]) - - print(f"\nFinal CDS Score: {final_score:.4f}") - print(f"Average Pause Field: {avg_pause:.4f}") - print(f"Average Contact Probability: {avg_contact:.4f}") - - print("\nKey V4 Features Demonstrated:") - print("- Time-indexed translation: residues become available at different times") - print("- Pause field: slow codons create stronger pauses") - print("- Exposure window: only tail of chain is structurally active") - print("- Contact kinetics: pause affects contact formation probability") - print("- Transient bias: codon bias decays over time") - - return results - - -if __name__ == "__main__": - results = run_simulation() diff --git a/5-Applications/scripts/validate_quaternion_unit_norm_preservation.py b/5-Applications/scripts/validate_quaternion_unit_norm_preservation.py deleted file mode 100644 index cd157e6f..00000000 --- a/5-Applications/scripts/validate_quaternion_unit_norm_preservation.py +++ /dev/null @@ -1,433 +0,0 @@ -#!/usr/bin/env python3 -""" -Unit Norm Preservation Validation for Resonance Quaternion Stochastic Differentials - -This script validates that the quaternion operations preserve unit norm as required -by the swarm analysis of resonance quaternion stochastic differentials (MATH_MODEL_MAP 0.4.4). - -Validation tests: -1. Quaternion multiplication preserves unit norm -2. Axis-angle construction preserves unit norm -3. SLERP interpolation preserves unit norm -4. Stochastic evolution preserves unit norm (with explicit renormalization) -5. SLUQ triage integration preserves unit norm -""" - -import numpy as np -import json -from pathlib import Path -from datetime import datetime -from typing import List, Tuple - -class Quaternion: - """Unit quaternion with unit norm enforcement.""" - - def __init__(self, w: float, x: float, y: float, z: float, enforce_norm: bool = True): - self.w = w - self.x = x - self.y = y - self.z = z - if enforce_norm: - self.normalize() - - def normalize(self): - """Normalize quaternion to unit norm.""" - norm = np.sqrt(self.w**2 + self.x**2 + self.y**2 + self.z**2) - if norm > 1e-10: - self.w /= norm - self.x /= norm - self.y /= norm - self.z /= norm - - def norm(self) -> float: - """Compute quaternion norm.""" - return np.sqrt(self.w**2 + self.x**2 + self.y**2 + self.z**2) - - def multiply(self, other: 'Quaternion') -> 'Quaternion': - """Hamilton product of quaternions.""" - w = self.w * other.w - self.x * other.x - self.y * other.y - self.z * other.z - x = self.w * other.x + self.x * other.w + self.y * other.z - self.z * other.y - y = self.w * other.y - self.x * other.z + self.y * other.w + self.z * other.x - z = self.w * other.z + self.x * other.y - self.y * other.x + self.z * other.w - return Quaternion(w, x, y, z) - - def from_axis_angle(self, axis: Tuple[float, float, float], angle: float) -> 'Quaternion': - """Create unit quaternion from axis-angle representation.""" - ax, ay, az = axis - axis_norm = np.sqrt(ax**2 + ay**2 + az**2) - if axis_norm > 1e-10: - ax /= axis_norm - ay /= axis_norm - az /= axis_norm - - half_angle = angle / 2.0 - cos_half = np.cos(half_angle) - sin_half = np.sin(half_angle) - - w = cos_half - x = sin_half * ax - y = sin_half * ay - z = sin_half * az - - return Quaternion(w, x, y, z) - - def slerp(self, other: 'Quaternion', t: float) -> 'Quaternion': - """Spherical linear interpolation between quaternions.""" - dot = self.w * other.w + self.x * other.x + self.y * other.y + self.z * other.z - - # Ensure shortest path - if dot < 0.0: - other = Quaternion(-other.w, -other.x, -other.y, -other.z, enforce_norm=False) - dot = -dot - - if dot > 0.9995: - # Linear interpolation for nearly parallel quaternions - w1 = 1.0 - t - w2 = t - result = Quaternion( - w1 * self.w + w2 * other.w, - w1 * self.x + w2 * other.x, - w1 * self.y + w2 * other.y, - w1 * self.z + w2 * other.z, - enforce_norm=True - ) - return result - - omega = np.arccos(np.clip(dot, -1.0, 1.0)) - sin_omega = np.sin(omega) - - w1 = np.sin((1.0 - t) * omega) / sin_omega - w2 = np.sin(t * omega) / sin_omega - - result = Quaternion( - w1 * self.w + w2 * other.w, - w1 * self.x + w2 * other.x, - w1 * self.y + w2 * other.y, - w1 * self.z + w2 * other.z, - enforce_norm=True - ) - return result - - def stochastic_evolution(self, gradient: Tuple[float, float, float], - noise: float, dt: float) -> 'Quaternion': - """Stochastic evolution with resonance gradient guidance.""" - dR_domega, dR_dt, _ = gradient - - # Compute stochastic increment - ito_correction = 0.5 * (dR_domega + dR_dt) * dt - stochastic_increment = ito_correction + dR_domega * noise * np.sqrt(dt) - - # Apply small rotation based on increment - axis = (1.0, 0.0, 0.0) # Simplified: x-axis rotation - angle = stochastic_increment * 0.1 # Scale factor - - rotated = self.from_axis_angle(axis, angle) - result = rotated.multiply(self) - - return result - - -def validate_multiplication_preserves_norm() -> dict: - """Test that quaternion multiplication preserves unit norm.""" - print("\n" + "=" * 70) - print("Test 1: Quaternion Multiplication Unit Norm Preservation") - print("=" * 70) - - # Generate random unit quaternions - np.random.seed(42) - num_tests = 1000 - norm_deviations = [] - - for _ in range(num_tests): - q1 = Quaternion(*np.random.randn(4)) - q2 = Quaternion(*np.random.randn(4)) - q3 = q1.multiply(q2) - - norm_deviation = abs(q3.norm() - 1.0) - norm_deviations.append(norm_deviation) - - max_deviation = max(norm_deviations) - mean_deviation = np.mean(norm_deviations) - std_deviation = np.std(norm_deviations) - - passed = max_deviation < 1e-10 - - result = { - "test_name": "quaternion_multiplication_unit_norm", - "num_tests": num_tests, - "max_deviation": float(max_deviation), - "mean_deviation": float(mean_deviation), - "std_deviation": float(std_deviation), - "passed": passed, - "threshold": 1e-10 - } - - print(f" Max deviation: {max_deviation:.2e}") - print(f" Mean deviation: {mean_deviation:.2e}") - print(f" Std deviation: {std_deviation:.2e}") - print(f" Status: {'✅ PASSED' if passed else '❌ FAILED'}") - - return result - - -def validate_axis_angle_preserves_norm() -> dict: - """Test that axis-angle construction preserves unit norm.""" - print("\n" + "=" * 70) - print("Test 2: Axis-Angle Construction Unit Norm Preservation") - print("=" * 70) - - np.random.seed(43) - num_tests = 1000 - norm_deviations = [] - - for _ in range(num_tests): - axis = np.random.randn(3) - axis = axis / np.linalg.norm(axis) - angle = np.random.uniform(0, 2 * np.pi) - - q = Quaternion(0, 0, 0, 0, enforce_norm=False) - q = q.from_axis_angle(tuple(axis), angle) - - norm_deviation = abs(q.norm() - 1.0) - norm_deviations.append(norm_deviation) - - max_deviation = max(norm_deviations) - mean_deviation = np.mean(norm_deviations) - std_deviation = np.std(norm_deviations) - - passed = max_deviation < 1e-10 - - result = { - "test_name": "axis_angle_construction_unit_norm", - "num_tests": num_tests, - "max_deviation": float(max_deviation), - "mean_deviation": float(mean_deviation), - "std_deviation": float(std_deviation), - "passed": passed, - "threshold": 1e-10 - } - - print(f" Max deviation: {max_deviation:.2e}") - print(f" Mean deviation: {mean_deviation:.2e}") - print(f" Std deviation: {std_deviation:.2e}") - print(f" Status: {'✅ PASSED' if passed else '❌ FAILED'}") - - return result - - -def validate_slerp_preserves_norm() -> dict: - """Test that SLERP interpolation preserves unit norm.""" - print("\n" + "=" * 70) - print("Test 3: SLERP Interpolation Unit Norm Preservation") - print("=" * 70) - - np.random.seed(44) - num_tests = 1000 - norm_deviations = [] - - for _ in range(num_tests): - q1 = Quaternion(*np.random.randn(4)) - q2 = Quaternion(*np.random.randn(4)) - t = np.random.uniform(0, 1) - - q3 = q1.slerp(q2, t) - - norm_deviation = abs(q3.norm() - 1.0) - norm_deviations.append(norm_deviation) - - max_deviation = max(norm_deviations) - mean_deviation = np.mean(norm_deviations) - std_deviation = np.std(norm_deviations) - - passed = max_deviation < 1e-10 - - result = { - "test_name": "slerp_interpolation_unit_norm", - "num_tests": num_tests, - "max_deviation": float(max_deviation), - "mean_deviation": float(mean_deviation), - "std_deviation": float(std_deviation), - "passed": passed, - "threshold": 1e-10 - } - - print(f" Max deviation: {max_deviation:.2e}") - print(f" Mean deviation: {mean_deviation:.2e}") - print(f" Std deviation: {std_deviation:.2e}") - print(f" Status: {'✅ PASSED' if passed else '❌ FAILED'}") - - return result - - -def validate_stochastic_evolution_preserves_norm() -> dict: - """Test that stochastic evolution preserves unit norm with explicit renormalization.""" - print("\n" + "=" * 70) - print("Test 4: Stochastic Evolution Unit Norm Preservation") - print("=" * 70) - - np.random.seed(45) - num_tests = 1000 - num_steps = 100 - norm_deviations = [] - - for _ in range(num_tests): - q = Quaternion(*np.random.randn(4)) - - for _ in range(num_steps): - gradient = (np.random.randn(3)) - noise = np.random.randn() - dt = 0.01 - q = q.stochastic_evolution(gradient, noise, dt) - - norm_deviation = abs(q.norm() - 1.0) - norm_deviations.append(norm_deviation) - - max_deviation = max(norm_deviations) - mean_deviation = np.mean(norm_deviations) - std_deviation = np.std(norm_deviations) - - passed = max_deviation < 1e-10 - - result = { - "test_name": "stochastic_evolution_unit_norm", - "num_tests": num_tests, - "num_steps_per_test": num_steps, - "max_deviation": float(max_deviation), - "mean_deviation": float(mean_deviation), - "std_deviation": float(std_deviation), - "passed": passed, - "threshold": 1e-10 - } - - print(f" Max deviation: {max_deviation:.2e}") - print(f" Mean deviation: {mean_deviation:.2e}") - print(f" Std deviation: {std_deviation:.2e}") - print(f" Status: {'✅ PASSED' if passed else '❌ FAILED'}") - - return result - - -def validate_sluq_integration_preserves_norm() -> dict: - """Test that SLUQ triage integration preserves unit norm.""" - print("\n" + "=" * 70) - print("Test 5: SLUQ Triage Integration Unit Norm Preservation") - print("=" * 70) - - np.random.seed(46) - num_tests = 1000 - num_steps = 100 - norm_deviations = [] - - for _ in range(num_tests): - q = Quaternion(*np.random.randn(4)) - - for _ in range(num_steps): - # Simulate SLUQ stability check - gradient = (np.random.randn(3)) - grad_magnitude = np.linalg.norm(gradient) - stability_threshold = 2.0 # Lenient threshold - - if grad_magnitude < stability_threshold: - # Stable: apply stochastic evolution - noise = np.random.randn() - dt = 0.01 - q = q.stochastic_evolution(gradient, noise, dt) - # Else: unstable, skip update (prune trajectory) - - norm_deviation = abs(q.norm() - 1.0) - norm_deviations.append(norm_deviation) - - max_deviation = max(norm_deviations) - mean_deviation = np.mean(norm_deviations) - std_deviation = np.std(norm_deviations) - - passed = max_deviation < 1e-10 - - result = { - "test_name": "sluq_triage_integration_unit_norm", - "num_tests": num_tests, - "num_steps_per_test": num_steps, - "max_deviation": float(max_deviation), - "mean_deviation": float(mean_deviation), - "std_deviation": float(std_deviation), - "passed": passed, - "threshold": 1e-10 - } - - print(f" Max deviation: {max_deviation:.2e}") - print(f" Mean deviation: {mean_deviation:.2e}") - print(f" Std deviation: {std_deviation:.2e}") - print(f" Status: {'✅ PASSED' if passed else '❌ FAILED'}") - - return result - - -def main(): - """Run all unit norm preservation validation tests.""" - print("=" * 70) - print("Unit Norm Preservation Validation for Resonance Quaternion Stochastic Differentials") - print("=" * 70) - print(f"Timestamp: {datetime.now().isoformat()}") - - # Run all tests - results = [] - results.append(validate_multiplication_preserves_norm()) - results.append(validate_axis_angle_preserves_norm()) - results.append(validate_slerp_preserves_norm()) - results.append(validate_stochastic_evolution_preserves_norm()) - results.append(validate_sluq_integration_preserves_norm()) - - # Summary - print("\n" + "=" * 70) - print("Validation Summary") - print("=" * 70) - - total_tests = len(results) - passed_tests = sum(1 for r in results if r["passed"]) - - for result in results: - status = "✅ PASSED" if result["passed"] else "❌ FAILED" - print(f" {result['test_name']}: {status}") - - print(f"\nTotal: {passed_tests}/{total_tests} tests passed") - - if passed_tests == total_tests: - print("✅ All unit norm preservation tests PASSED") - overall_status = "PASSED" - else: - print("❌ Some unit norm preservation tests FAILED") - overall_status = "FAILED" - - # Save results - output = { - "timestamp": datetime.now().isoformat(), - "overall_status": overall_status, - "total_tests": total_tests, - "passed_tests": passed_tests, - "test_results": [ - { - "test_name": r["test_name"], - "num_tests": r["num_tests"], - "max_deviation": r["max_deviation"], - "mean_deviation": r["mean_deviation"], - "std_deviation": r["std_deviation"], - "passed": bool(r["passed"]), - "threshold": r["threshold"] - } - for r in results - ] - } - - output_path = Path("shared-data/data/validation/quaternion_unit_norm_preservation.json") - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(output, f, indent=2) - - print(f"\nResults saved to: {output_path}") - - return overall_status - - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/validate_routing.py b/5-Applications/scripts/validate_routing.py deleted file mode 100644 index c2594e3f..00000000 --- a/5-Applications/scripts/validate_routing.py +++ /dev/null @@ -1,52 +0,0 @@ -import json -import os - -def validate_routing(): - # Load Registry (Simplified for the toy model) - registry = [ - {"id": "burgers_inviscid", "family": "FLUID", "term": "T_uv_f"}, - {"id": "rg_flow", "family": "TOPOLOGY", "term": "Gamma_g"}, - {"id": "neural_lattice", "family": "NEURAL", "term": "T_uv_n"}, - {"id": "entropy_bound", "family": "ENTROPY", "term": "T_uv_q"} - ] - - # Incoming Goxel State (The "Probe") - # A Goxel with high 'vorticity' should route to FLUID - probe_goxel = { - "vorticity": 0.85, - "coherence": 0.12, - "delta_entropy": 0.05 - } - - print("--- SOVEREIGN ROUTING ENGINE (TOY MODEL) ---") - print(f"Probe Goxel State: {probe_goxel}\n") - - # Routing Grammar Logic (Simplified) - # G_uv = Gamma(g) + R_uv - 1/2*g_uv*R = kappa * (T_f + T_n + T_q) - - # Decision Rule: - # If vorticity > 0.5 -> Fluid (T_f) - # If coherence > 0.5 -> Neural (T_n) - # If delta_entropy > 0.5 -> Entropy (T_q) - # Else -> Topology (Gamma_g) - - if probe_goxel["vorticity"] > 0.5: - bucket = "FLUID (T_uv_f)" - kernel = "burgers_inviscid" - elif probe_goxel["coherence"] > 0.5: - bucket = "NEURAL (T_uv_n)" - kernel = "neural_lattice" - elif probe_goxel["delta_entropy"] > 0.5: - bucket = "ENTROPY (T_uv_q)" - kernel = "entropy_bound" - else: - bucket = "TOPOLOGY (Gamma_g)" - kernel = "rg_flow" - - print(f"HYPER EQUATION DECISION:") - print(f" Resultant Bucket: {bucket}") - print(f" Selected Kernel: {kernel}") - print("\n[VERIFIED] Routing Grammar logic consistent with Manifold Projection.") - -if __name__ == "__main__": - validate_routing() diff --git a/5-Applications/scripts/verify_citation_accessibility.sh b/5-Applications/scripts/verify_citation_accessibility.sh deleted file mode 100755 index 353bb533..00000000 --- a/5-Applications/scripts/verify_citation_accessibility.sh +++ /dev/null @@ -1,58 +0,0 @@ -#!/bin/bash -# Citation Accessibility Verification -# Checks HTTP/HTTPS URLs in markdown files for accessibility - -set -e - -REPO_ROOT="/home/allaun/Research Stack" -REPORT_DIR="$REPO_ROOT/docs/provenance" -REPORT_FILE="$REPORT_DIR/citation_accessibility_report_$(date +%Y%m%d).txt" - -mkdir -p "$REPORT_DIR" - -echo "Citation Accessibility Verification Report" > "$REPORT_FILE" -echo "Date: $(date -u +"%Y-%m-%dT%H:%M:%SZ")" >> "$REPORT_FILE" -echo "=========================================" >> "$REPORT_FILE" -echo "" >> "$REPORT_FILE" - -# Find all HTTP/HTTPS URLs in markdown files -echo "Extracting URLs from markdown files..." >> "$REPORT_FILE" -echo "" >> "$REPORT_FILE" - -# Extract URLs from docs directory -urls=$(grep -r -h -o 'https\?://[^ )"]*' "$REPO_ROOT/docs"/*.md 2>/dev/null | sort -u) - -if [ -z "$urls" ]; then - echo "No URLs found in markdown files." >> "$REPORT_FILE" -else - echo "Found URLs:" >> "$REPORT_FILE" - echo "$urls" >> "$REPORT_FILE" - echo "" >> "$REPORT_FILE" - - echo "Checking accessibility..." >> "$REPORT_FILE" - echo "" >> "$REPORT_FILE" - - for url in $urls; do - echo "Checking: $url" >> "$REPORT_FILE" - # Use curl with timeout to check if URL is accessible - # -s: silent mode - # -o /dev/null: discard output - # -w "%{http_code}": output HTTP status code - # -L: follow redirects - # --max-time 10: timeout after 10 seconds - status=$(curl -s -o /dev/null -w "%{http_code}" -L --max-time 10 "$url" 2>/dev/null || echo "000") - - if [ "$status" = "200" ] || [ "$status" = "301" ] || [ "$status" = "302" ]; then - echo " Status: $status - ACCESSIBLE" >> "$REPORT_FILE" - else - echo " Status: $status - NOT ACCESSIBLE" >> "$REPORT_FILE" - fi - echo "" >> "$REPORT_FILE" - done -fi - -echo "=========================================" >> "$REPORT_FILE" -echo "Verification complete." >> "$REPORT_FILE" -echo "Report saved to: $REPORT_FILE" >> "$REPORT_FILE" - -cat "$REPORT_FILE" diff --git a/5-Applications/scripts/verify_field_equation_math.py b/5-Applications/scripts/verify_field_equation_math.py deleted file mode 100644 index 3d71ce7c..00000000 --- a/5-Applications/scripts/verify_field_equation_math.py +++ /dev/null @@ -1,258 +0,0 @@ -#!/usr/bin/env python3 -""" -Wolfram Alpha Math Verification for FieldEquationIntegration.lean - -Extracts and verifies mathematical equations from the FieldEquationIntegration module. -""" - -import os -import sys -import json -import urllib.request -import urllib.error -import time -from pathlib import Path - -from dotenv import load_dotenv -load_dotenv(Path(__file__).parent.parent.parent / ".env") - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -try: - from infra.ene_cloud_credential_manager import ENECloudCredentialManager -except ImportError as e: - print(f"Import failed: {e}") - sys.exit(1) - -class WolframAlphaVerifier: - """Wolfram Alpha API client for mathematical verification.""" - - BASE_URL = "http://api.wolframalpha.com/v2/query" - - def __init__(self, app_id: str): - self.app_id = app_id - - def query(self, input_expr: str, format: str = "plaintext", output: str = "JSON") -> dict: - """Query Wolfram Alpha API.""" - params = { - "input": input_expr, - "format": format, - "output": output, - "appid": self.app_id - } - - url = f"{self.BASE_URL}?{urllib.parse.urlencode(params)}" - - try: - with urllib.request.urlopen(url, timeout=30) as response: - data = json.loads(response.read().decode('utf-8')) - return data - except urllib.error.HTTPError as e: - return {"error": f"HTTP Error {e.code}: {e.reason}"} - except urllib.error.URLError as e: - return {"error": f"URL Error: {e.reason}"} - except Exception as e: - return {"error": str(e)} - - def verify_equation(self, equation: str, description: str) -> dict: - """Verify a mathematical equation.""" - print(f"\n🔍 Verifying: {description}") - print(f" Equation: {equation}") - result = self.query(equation) - - if "error" in result: - print(f"❌ Error: {result['error']}") - return { - "equation": equation, - "description": description, - "status": "error", - "error": result["error"] - } - - # Check if query was successful - if result.get("queryresult", {}).get("success"): - pods = result["queryresult"].get("pods", []) - - # Extract primary result - primary_result = None - for pod in pods: - if pod.get("primary"): - subpods = pod.get("subpods", []) - if subpods: - primary_result = subpods[0].get("plaintext", "") - break - - if primary_result: - print(f"✅ Result: {primary_result}") - return { - "equation": equation, - "description": description, - "status": "verified", - "result": primary_result, - "pods": len(pods) - } - else: - print(f"⚠️ Query succeeded but no primary result") - return { - "equation": equation, - "description": description, - "status": "partial", - "pods": len(pods) - } - else: - print(f"❌ Query failed") - return { - "equation": equation, - "description": description, - "status": "failed" - } - -def main(): - print("=" * 70) - print("WOLFRAM ALPHA MATH VERIFICATION - FieldEquationIntegration.lean") - print("=" * 70) - - # Initialize ENE credential manager - try: - ene = ENECloudCredentialManager() - print("ENE credential manager initialized") - except Exception as e: - print(f"ENE initialization failed: {e}") - sys.exit(1) - - # Retrieve Wolfram Alpha credential from ENE - print("\nRetrieving Wolfram Alpha credential from ENE...") - wolfram_creds = None - for cred_id, cred in ene.credentials.items(): - if cred.provider == "wolfram_alpha": - wolfram_creds = cred - break - - if not wolfram_creds: - print("❌ Wolfram Alpha credential not found in ENE") - sys.exit(1) - - print(f"✅ Found credential: {wolfram_creds.credential_id}") - - # Load App ID from environment - app_id = os.getenv("WOLFRAM_ALPHA_APPID", "") - if not app_id: - print("❌ WOLFRAM_ALPHA_APPID not found in environment") - sys.exit(1) - print(f"✅ App ID loaded from environment") - - # Initialize Wolfram Alpha verifier - verifier = WolframAlphaVerifier(app_id) - - # Equations from FieldEquationIntegration.lean - equations = [ - # Unified field equation - { - "equation": "(F + Phi + C + D) / 4", - "description": "Unified field equation composite (Ψ = (1/4)[F ⊗ Φ ⊗ C ⊗ D])" - }, - - # Weighted composite - use numeric example - { - "equation": "(10*2 + 20*3 + 30*4 + 40*5) / 4", - "description": "Weighted composite with field weights (numeric example)" - }, - - # Pentagonal closure - { - "equation": "F + Phi + C + D + sigma", - "description": "Pentagonal square closure (sum of corners plus center)" - }, - - # Pentagonal balance condition - { - "equation": "sigma * 4 = F + Phi + C + D", - "description": "Pentagonal square balance (center equals average of corners)" - }, - - # Near-miss error (simplified) - { - "equation": "abs(x + y - z)", - "description": "Near-miss error function (simplified Fermat equation)" - }, - - # Average error - { - "equation": "(epsilon1 + epsilon2 + epsilon3 + epsilon4) / 4", - "description": "Average near-miss error over 4 points" - }, - - # Tension function - { - "equation": "abs(epsilon - mu) + 1 / (abs(epsilon - mu) + delta)", - "description": "Tension function for near-miss detection" - }, - - # XOR operation (for MMR hash) - { - "equation": "a XOR b", - "description": "XOR operation for MMR hash computation" - }, - - # Modulus operation (for hash) - use numeric example - { - "equation": "(100 * 31 + 17) mod 256", - "description": "Modulus-based hash function (numeric example)" - }, - - # Stabilization formula - use numeric example - { - "equation": "(10 * 5 + 20) / (5 + 1)", - "description": "Web constraint stabilization formula (numeric example)" - } - ] - - print(f"\nVerifying {len(equations)} equations from FieldEquationIntegration.lean...") - - results = [] - for i, eq in enumerate(equations): - result = verifier.verify_equation(eq["equation"], eq["description"]) - results.append(result) - # Add delay between requests to avoid rate limiting - if i < len(equations) - 1: - time.sleep(2) - - # Summary - print("\n" + "=" * 70) - print("VERIFICATION SUMMARY") - print("=" * 70) - - verified = sum(1 for r in results if r["status"] == "verified") - failed = sum(1 for r in results if r["status"] in ["error", "failed"]) - partial = sum(1 for r in results if r["status"] == "partial") - - print(f"Total equations: {len(equations)}") - print(f"✅ Verified: {verified}") - print(f"⚠️ Partial: {partial}") - print(f"❌ Failed: {failed}") - - # Save results to file - output_file = Path("shared-data/data/field_equation_wolfram_verification.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump({ - "module": "FieldEquationIntegration.lean", - "app_id": app_id, - "timestamp": Path(__file__).stat().st_mtime, - "total": len(equations), - "verified": verified, - "failed": failed, - "partial": partial, - "results": results - }, f, indent=2) - - print(f"\nResults saved to: {output_file}") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/verify_math_raw.py b/5-Applications/scripts/verify_math_raw.py deleted file mode 100644 index 5c7d3f73..00000000 --- a/5-Applications/scripts/verify_math_raw.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python3 -import pyarrow.parquet as pq -import pandas as pd - -t = pq.read_table("3-Mathematical-Models/equations_parquet_tagged/equations_math_raw.parquet") -print("Columns:", t.column_names) -print("Rows:", t.num_rows) -print() -for col in t.column_names: - print(f" {col}: {t.column(col).null_count} nulls") -print() - -df = t.to_pandas() -for i in range(5): - raw = str(df.iloc[i]["equation"])[:100] - refined = str(df.iloc[i]["refined_equation"]) - print(f"Row {i}:") - print(f" id: {df.iloc[i]['equation_id']}") - print(f" raw: {raw}...") - print(f" refined: {refined}") - print(f" source: {df.iloc[i]['source']}") - print() diff --git a/5-Applications/scripts/verify_network_utilization.py b/5-Applications/scripts/verify_network_utilization.py deleted file mode 100644 index 16c196bf..00000000 --- a/5-Applications/scripts/verify_network_utilization.py +++ /dev/null @@ -1,70 +0,0 @@ -#!/usr/bin/env python3 -""" -Network Utilization Verification Script - -This script verifies that training is using all network resources. -""" - -import subprocess -import json -from pathlib import Path - -def check_node_connectivity(): - """Check connectivity to all network nodes via Tailscale.""" - nodes = ["qfox", "architect", "judge", "ip-172-31-25-81", "netcup-router", "racknerd-510bd9c"] - - print("Checking node connectivity...") - for node in nodes: - try: - result = subprocess.run( - ["ping", "-c", "1", node], - capture_output=True, - timeout=5 - ) - status = "✅ ONLINE" if result.returncode == 0 else "❌ OFFLINE" - print(f" {node}: {status}") - except Exception as e: - print(f" {node}: ❌ ERROR - {e}") - -def check_ene_status(): - """Check ENE status across network.""" - print("\\nChecking ENE status...") - # ENE gossip protocol ensures all nodes are synchronized - print(" ENE gossip protocol: ACTIVE") - print(" Credential distribution: SHAMIR (6 shards)") - print(" Load balancing: HEALTH-WEIGHTED") - print(" Consensus required: 2/3 majority") - -def check_data_availability(): - """Check data availability via Google Drive topological storage.""" - print("\\nChecking data availability...") - print(" Google Drive topological storage: ACCESSIBLE") - print(" Natural language dataset: training_dataset_*.parquet") - print(" Coding language dataset: coding_training_dataset_*.parquet") - -def check_resource_allocation(): - """Check resource allocation across network.""" - print("\\nChecking resource allocation...") - resources = {"total_cores": 36, "total_ram_gb": 72, "total_storage_gb": 1800, "gpu_nodes": 1, "total_nodes": 6} - print(f" Total cores allocated: {resources['total_cores']}") - print(f" Total RAM allocated: {resources['total_ram_gb']} GB") - print(f" Total nodes utilized: {resources['total_nodes']}") - print(f" GPU nodes utilized: {resources['gpu_nodes']}") - -def main(): - print("=" * 70) - print("NETWORK UTILIZATION VERIFICATION") - print("=" * 70) - - check_node_connectivity() - check_ene_status() - check_data_availability() - check_resource_allocation() - - print("\\n" + "=" * 70) - print("VERIFICATION COMPLETE") - print("All network resources are guaranteed to be utilized") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/scripts/visualize_s3c_gpe_landscape.py b/5-Applications/scripts/visualize_s3c_gpe_landscape.py deleted file mode 100644 index aee7006d..00000000 --- a/5-Applications/scripts/visualize_s3c_gpe_landscape.py +++ /dev/null @@ -1,105 +0,0 @@ -#!/usr/bin/env python3 -""" -Generate a small S3C-GPE landscape CSV and SVG. - -This is a visualization shim. The source-of-truth safety logic lives in -0-Core-Formalism/lean/Semantics/Semantics/NUVMATH.lean and Semantics/S3C.lean. -""" - -from __future__ import annotations - -import csv -import math -from pathlib import Path - - -ROOT = Path(__file__).resolve().parents[1] -OUT_DIR = ROOT / "data" / "generated" -CSV_PATH = OUT_DIR / "s3c_gpe_landscape.csv" -SVG_PATH = OUT_DIR / "s3c_gpe_landscape.svg" - - -def s3c_terms(n: float, g0: float = 1.0, kappa: float = 0.5) -> dict[str, float | bool]: - k = math.floor(math.sqrt(n)) - a = n - k * k - b0 = (k + 1) * (k + 1) - 1 - n - j_score = (a * b0) + abs(a - b0) + k - emit = a > 1.0e-2 and b0 > 1.0e-2 - stiffness = g0 * (1.0 + kappa / (j_score + 1.0e-3)) if emit else g0 * 100.0 - available_velocity = min(1.0, j_score / 16.0) if emit else 0.0 - return { - "n": n, - "k": k, - "a": a, - "b0": b0, - "j_score": j_score, - "stiffness": stiffness, - "standard_g": g0, - "available_velocity": available_velocity, - "emit": emit, - } - - -def sample_shell(start: float = 9.01, stop: float = 15.99, count: int = 240) -> list[dict[str, float | bool]]: - step = (stop - start) / (count - 1) - return [s3c_terms(start + i * step) for i in range(count)] - - -def write_csv(rows: list[dict[str, float | bool]]) -> None: - OUT_DIR.mkdir(parents=True, exist_ok=True) - with CSV_PATH.open("w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=list(rows[0].keys())) - writer.writeheader() - writer.writerows(rows) - - -def polyline(rows: list[dict[str, float | bool]], key: str, y_max: float, color: str) -> str: - width = 920 - height = 420 - left = 60 - top = 30 - plot_w = width - 100 - plot_h = height - 80 - n_min = float(rows[0]["n"]) - n_max = float(rows[-1]["n"]) - points = [] - for row in rows: - x = left + (float(row["n"]) - n_min) / (n_max - n_min) * plot_w - y = top + plot_h - min(float(row[key]), y_max) / y_max * plot_h - points.append(f"{x:.2f},{y:.2f}") - return f'' - - -def write_svg(rows: list[dict[str, float | bool]]) -> None: - width = 920 - height = 420 - j_max = max(float(row["j_score"]) for row in rows) - stiff_max = min(2.0, max(float(row["stiffness"]) for row in rows)) - svg = f""" - - - - S3C-GPE shell landscape, n in [9,16] - energy density n - J-score - stiffness - available velocity - {polyline(rows, "j_score", j_max, "#b43b3b")} - {polyline(rows, "stiffness", stiff_max, "#2457a6")} - {polyline(rows, "available_velocity", 1.0, "#2f7d47")} - -""" - SVG_PATH.write_text(svg) - - -def main() -> int: - rows = sample_shell() - write_csv(rows) - write_svg(rows) - print(f"wrote {CSV_PATH}") - print(f"wrote {SVG_PATH}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/scripts/voltage_computational_substrate.py b/5-Applications/scripts/voltage_computational_substrate.py deleted file mode 100644 index d6b60e58..00000000 --- a/5-Applications/scripts/voltage_computational_substrate.py +++ /dev/null @@ -1,421 +0,0 @@ -#!/usr/bin/env python3 -""" -Voltage Computational Substrate -Voltages themselves are computational - voltage levels represent mathematical values. - -Architecture: -- Voltage magnitude = numerical value -- Voltage sum = addition -- Voltage ratio = multiplication/division -- Voltage difference = subtraction -- Voltage gradient = derivative -- Voltage integral = integration (over time) - -This is horrific because: -- Physical substrate (voltage) becomes computational -- Analog computation disguised as electrical signaling -- Hardware is the computer, not just the carrier - -This is wonderful because: -- Parallel computation at the speed of light -- Zero instruction overhead -- Physics does the math -- Maximum retro insanity: voltage = math -""" - -import math -from typing import List, Tuple, Dict -from dataclasses import dataclass -from enum import Enum - -# ═══════════════════════════════════════════════════════════════════════════ -# Voltage Computational Substrate -# Voltage levels represent mathematical values -# ═══════════════════════════════════════════════════════════════════════════ - -@dataclass -class VoltageValue: - """Voltage value representing a mathematical quantity""" - voltage: float # Volts - scale: float = 1.0 # Scaling factor (V per unit) - - def to_value(self) -> float: - """Convert voltage to mathematical value""" - return self.voltage / self.scale - - @staticmethod - def from_value(value: float, scale: float = 1.0) -> 'VoltageValue': - """Convert mathematical value to voltage""" - return VoltageValue(voltage=value * scale, scale=scale) - -class VoltageMath: - """Mathematical operations using voltage levels""" - - @staticmethod - def add(v1: VoltageValue, v2: VoltageValue, max_voltage: float = 5.0) -> VoltageValue: - """ - Addition: Voltage sum. - - V_sum = V1 + V2 - Clamped to max_voltage for hardware safety. - """ - sum_voltage = v1.voltage + v2.voltage - clamped_voltage = min(sum_voltage, max_voltage) - return VoltageValue(clamped_voltage, scale=v1.scale) - - @staticmethod - def subtract(v1: VoltageValue, v2: VoltageValue) -> VoltageValue: - """ - Subtraction: Voltage difference. - - V_diff = V1 - V2 - Can be negative (represents negative values). - """ - diff_voltage = v1.voltage - v2.voltage - return VoltageValue(diff_voltage, scale=v1.scale) - - @staticmethod - def multiply(v1: VoltageValue, v2: VoltageValue, max_voltage: float = 5.0) -> VoltageValue: - """ - Multiplication: Voltage ratio. - - V_mul = (V1 / V_ref) * (V2 / V_ref) * V_ref - Where V_ref is a reference voltage (e.g., 1.0V). - """ - v_ref = 1.0 - if v_ref == 0: - return VoltageValue(0, scale=v1.scale) - - normalized_v1 = v1.voltage / v_ref - normalized_v2 = v2.voltage / v_ref - mul_voltage = normalized_v1 * normalized_v2 * v_ref - - clamped_voltage = min(mul_voltage, max_voltage) - return VoltageValue(clamped_voltage, scale=v1.scale) - - @staticmethod - def divide(v1: VoltageValue, v2: VoltageValue) -> VoltageValue: - """ - Division: Voltage ratio. - - V_div = V1 / V2 * V_ref - Where V_ref is a reference voltage. - """ - v_ref = 1.0 - if v2.voltage == 0: - return VoltageValue(0, scale=v1.scale) - - div_voltage = (v1.voltage / v2.voltage) * v_ref - return VoltageValue(div_voltage, scale=v1.scale) - - @staticmethod - def integrate(voltages: List[VoltageValue], dt: float = 1.0) -> VoltageValue: - """ - Integration: Voltage accumulation over time. - - V_int = Σ(V_i * dt) - """ - if not voltages: - return VoltageValue(0, scale=1.0) - - integrated_voltage = sum(v.voltage * dt for v in voltages) - return VoltageValue(integrated_voltage, scale=voltages[0].scale) - - @staticmethod - def differentiate(voltages: List[VoltageValue], dt: float = 1.0) -> VoltageValue: - """ - Differentiation: Rate of voltage change. - - V_diff = (V[t] - V[t-1]) / dt - """ - if len(voltages) < 2: - return VoltageValue(0, scale=1.0) - - diff_voltage = (voltages[-1].voltage - voltages[-2].voltage) / dt - return VoltageValue(diff_voltage, scale=voltages[0].scale) - - @staticmethod - def exponential(v: VoltageValue, base: float = math.e) -> VoltageValue: - """ - Exponential: Voltage exponentiation. - - V_exp = exp(V / V_ref) * V_ref - """ - v_ref = 1.0 - if v_ref == 0: - return VoltageValue(0, scale=v.scale) - - normalized = v.voltage / v_ref - exp_voltage = math.pow(base, normalized) * v_ref - return VoltageValue(exp_voltage, scale=v.scale) - - @staticmethod - def logarithm(v: VoltageValue, base: float = math.e) -> VoltageValue: - """ - Logarithm: Voltage logarithm. - - V_log = log(V / V_ref) * V_ref - """ - v_ref = 1.0 - if v.voltage <= 0 or v_ref == 0: - return VoltageValue(0, scale=v.scale) - - normalized = v.voltage / v_ref - log_voltage = math.log(normalized, base) * v_ref - return VoltageValue(log_voltage, scale=v.scale) - -# ═══════════════════════════════════════════════════════════════════════════ -# Voltage-Aware Nanokernel -# Nanokernel that validates voltage levels as computational substrate -# ═══════════════════════════════════════════════════════════════════════════ - -class VoltageNanokernel: - """Nanokernel that validates voltage-based computation""" - - def __init__(self, safe_range: Tuple[float, float] = (0.0, 5.0)): - self.safe_range = safe_range # Min/max voltage for safety - self.voltage_history: List[float] = [] - - def validate_voltage(self, voltage: float) -> bool: - """Validate voltage is within safe range""" - return self.safe_range[0] <= voltage <= self.safe_range[1] - - def check_voltage_resonance(self, voltage: float, expected_range: Tuple[float, float]) -> float: - """ - Check if voltage resonates with expected range. - - Returns resonance score (0.0-1.0). - """ - min_v, max_v = expected_range - - if min_v <= voltage <= max_v: - return 1.0 - - # Calculate distance from range - if voltage < min_v: - distance = min_v - voltage - max_distance = min_v - else: - distance = voltage - max_v - max_distance = 5.0 - max_v - - if max_distance == 0: - return 0.0 - - return max(0.0, 1.0 - distance / max_distance) - - def audit_voltage_computation(self, input_voltages: List[float], - output_voltage: float, - operation: str) -> Tuple[bool, float]: - """ - Audit a voltage-based computation. - - Returns (lawful, resonance_score). - """ - # Check all input voltages are safe - for v in input_voltages: - if not self.validate_voltage(v): - return (False, 0.0) - - # Check output voltage is safe - if not self.validate_voltage(output_voltage): - return (False, 0.0) - - # Check resonance based on operation - if operation == "add": - expected = sum(input_voltages) - expected_range = (expected * 0.9, expected * 1.1) - - elif operation == "multiply": - if len(input_voltages) >= 2 and input_voltages[1] != 0: - expected = (input_voltages[0] / 1.0) * (input_voltages[1] / 1.0) * 1.0 - expected_range = (expected * 0.8, expected * 1.2) - else: - expected_range = (0.0, 5.0) - - elif operation == "integrate": - expected = sum(input_voltages) - expected_range = (expected * 0.9, expected * 1.1) - - else: - expected_range = (0.0, 5.0) - - resonance = self.check_voltage_resonance(output_voltage, expected_range) - lawful = resonance >= 0.8 - - return (lawful, resonance) - -# ═══════════════════════════════════════════════════════════════════════════ -# Voltage Computational Pipeline -# Chain voltage operations for complex computations -# ═══════════════════════════════════════════════════════════════════════════ - -class VoltagePipeline: - """Pipeline for voltage-based computation""" - - def __init__(self): - self.voltage_registers: Dict[str, VoltageValue] = {} - self.nanokernel = VoltageNanokernel() - self.computation_history: List[Dict] = [] - - def load_voltage(self, name: str, value: float, scale: float = 1.0): - """Load a voltage into a register""" - self.voltage_registers[name] = VoltageValue(value, scale) - - def add(self, name1: str, name2: str, output_name: str) -> VoltageValue: - """Add two voltage registers""" - v1 = self.voltage_registers[name1] - v2 = self.voltage_registers[name2] - result = VoltageMath.add(v1, v2) - self.voltage_registers[output_name] = result - - # Audit with nanokernel - lawful, resonance = self.nanokernel.audit_voltage_computation( - [v1.voltage, v2.voltage], result.voltage, "add" - ) - - self.computation_history.append({ - 'operation': 'add', - 'inputs': [v1.voltage, v2.voltage], - 'output': result.voltage, - 'lawful': lawful, - 'resonance': resonance - }) - - return result - - def multiply(self, name1: str, name2: str, output_name: str) -> VoltageValue: - """Multiply two voltage registers""" - v1 = self.voltage_registers[name1] - v2 = self.voltage_registers[name2] - result = VoltageMath.multiply(v1, v2) - self.voltage_registers[output_name] = result - - # Audit with nanokernel - lawful, resonance = self.nanokernel.audit_voltage_computation( - [v1.voltage, v2.voltage], result.voltage, "multiply" - ) - - self.computation_history.append({ - 'operation': 'multiply', - 'inputs': [v1.voltage, v2.voltage], - 'output': result.voltage, - 'lawful': lawful, - 'resonance': resonance - }) - - return result - - def integrate(self, names: List[str], output_name: str, dt: float = 1.0) -> VoltageValue: - """Integrate voltage registers over time""" - voltages = [self.voltage_registers[name] for name in names] - result = VoltageMath.integrate(voltages, dt) - self.voltage_registers[output_name] = result - - # Audit with nanokernel - input_voltages = [v.voltage for v in voltages] - lawful, resonance = self.nanokernel.audit_voltage_computation( - input_voltages, result.voltage, "integrate" - ) - - self.computation_history.append({ - 'operation': 'integrate', - 'inputs': input_voltages, - 'output': result.voltage, - 'lawful': lawful, - 'resonance': resonance - }) - - return result - - def exponential(self, name: str, output_name: str, base: float = math.e) -> VoltageValue: - """Exponential of voltage register""" - v = self.voltage_registers[name] - result = VoltageMath.exponential(v, base) - self.voltage_registers[output_name] = result - - self.computation_history.append({ - 'operation': 'exponential', - 'inputs': [v.voltage], - 'output': result.voltage, - 'lawful': True, - 'resonance': 1.0 - }) - - return result - -# ═══════════════════════════════════════════════════════════════════════════ -# Test / Demo -# ═══════════════════════════════════════════════════════════════════════════ - -def run_test(): - """Run voltage computational substrate test""" - print("=" * 70) - print("VOLTAGE COMPUTATIONAL SUBSTRATE") - print("=" * 70) - - print("\n[*] Architecture:") - print(" Voltage magnitude = numerical value") - print(" Voltage sum = addition") - print(" Voltage ratio = multiplication/division") - print(" Voltage difference = subtraction") - print(" Voltage gradient = derivative") - print(" Voltage integral = integration") - print(" Physical substrate (voltage) = computer") - - pipeline = VoltagePipeline() - - # Load test voltages - print("\n[*] Loading test voltages...") - pipeline.load_voltage("A", 2.5) # 2.5V - pipeline.load_voltage("B", 1.5) # 1.5V - pipeline.load_voltage("C", 3.0) # 3.0V - print(" A: 2.5V") - print(" B: 1.5V") - print(" C: 3.0V") - - # Addition - print("\n[*] Voltage addition (A + B)...") - result = pipeline.add("A", "B", "SUM") - print(f" Result: {result.voltage:.2f}V (expected: 4.00V)") - print(f" Lawful: {pipeline.computation_history[-1]['lawful']}") - print(f" Resonance: {pipeline.computation_history[-1]['resonance']:.3f}") - - # Multiplication - print("\n[*] Voltage multiplication (A * B)...") - result = pipeline.multiply("A", "B", "PRODUCT") - print(f" Result: {result.voltage:.2f}V") - print(f" Lawful: {pipeline.computation_history[-1]['lawful']}") - print(f" Resonance: {pipeline.computation_history[-1]['resonance']:.3f}") - - # Integration - print("\n[*] Voltage integration (A + B + C)...") - result = pipeline.integrate(["A", "B", "C"], "INTEGRAL") - print(f" Result: {result.voltage:.2f}V") - print(f" Lawful: {pipeline.computation_history[-1]['lawful']}") - print(f" Resonance: {pipeline.computation_history[-1]['resonance']:.3f}") - - # Exponential - print("\n[*] Voltage exponential (exp(A))...") - result = pipeline.exponential("A", "EXP_A") - print(f" Result: {result.voltage:.2f}V") - - # Statistics - lawful_count = sum(1 for h in pipeline.computation_history if h['lawful']) - total_count = len(pipeline.computation_history) - avg_resonance = sum(h['resonance'] for h in pipeline.computation_history) / total_count if total_count > 0 else 0 - - print("\n[*] Statistics:") - print(f" Total Computations: {total_count}") - print(f" Lawful Rate: {lawful_count / total_count:.3f}") - print(f" Average Resonance: {avg_resonance:.3f}") - - print("\n" + "=" * 70) - print("VOLTAGE COMPUTATIONAL SUBSTRATE COMPLETE") - print("=" * 70) - print("\n[*] Horrific: Physical voltage becomes computational substrate") - print("[*] Wonderful: Zero instruction overhead, physics does the math") - print("[*] Maximum retro insanity: voltage = math") - -if __name__ == "__main__": - run_test() diff --git a/5-Applications/scripts/wire_chains.py b/5-Applications/scripts/wire_chains.py deleted file mode 100644 index 08585fd4..00000000 --- a/5-Applications/scripts/wire_chains.py +++ /dev/null @@ -1,423 +0,0 @@ -#!/usr/bin/env python3 -""" -Wire every extremophile bound and radical adaptation back to a fundamental law. -The project claims "what self-replicating matter is permitted to do" — -every Layer 4 equation must trace to Layer 1. -""" - -import sqlite3, os - -DB = "/home/allaun/physics_equations.db" -conn = sqlite3.connect(DB) -cur = conn.cursor() - -# Clear existing chains and rebuild comprehensively -cur.execute("DELETE FROM invariant_chains") - -# (chain_name, description, L1, L2, L3, L4, full_description) -# L1 = fundamental law eq_id, L2 = first derivation, L3 = empirical bound, L4 = living manifestation - -C = [] - -def chain(name, desc, l1, l2, l3, l4): - C.append((name, l1, l2, l3, l4, desc)) - -# ========== EXTREMOPHILE BOUND CHAINS ========== - -# Temperature bound → protein folding thermodynamics -chain("Temperature → Protein Denaturation → 122°C Limit", - "The upper thermal bound for aqueous carbon-based life is set by the temperature at which hydrogen bonds and hydrophobic cores fail faster than repair. This traces to Gibbs free energy of folding (ΔG_fold = ΔH − TΔS) and the Arrhenius rate equation: when k_denaturation > k_repair, the organism dies. At 122°C, ATP hydrolysis half-life is ~1 second — the cell can't power repair fast enough. Fundamental: ΔG = ΔH − TΔS from thermodynamics + k = A·exp(−E_a/RT) from Arrhenius. The same equations predict protein melting in any solvent anywhere in the universe.", - 4, # Thermodynamics (Gibbs free energy) - 605, # Arrhenius equation - 738, # Upper temp limit of life - None) - -# Pressure bound → lipid bilayer phase transition -chain("Clausius-Clapeyron → Membrane Phase Transition → ~200 MPa Division Limit", - "The Clausius-Clapeyron equation dP/dT = ΔS/ΔV governs phase transitions under pressure. For lipid bilayers, the gel-to-fluid transition temperature T_m increases by ~0.2 K/MPa — at 200 MPa, a membrane that's fluid at 20°C at the surface is frozen solid. Cell division requires membrane fluidity for cytokinesis; when the bilayer gels, cytokinesis stops. This is the same equation that predicts ice melting under glaciers and mineral phase transitions in Earth's mantle — applied to a 6nm lipid sheet.", - 76, # Clausius-Clapeyron - 349, # Hall-Petch (phase boundary physics) - 739, # Maximum hydrostatic pressure for cell division - None) - -# Water activity → enzyme hydration shell → Raoult's law -chain("Raoult's Law → Hydration Shell → a_w ≈ 0.6 Minimum", - "Enzymes require a hydration shell of ~0.35g water per gram of protein to maintain conformational dynamics. Below water activity a_w ≈ 0.6, the vapor pressure deficit strips this shell. This traces to Raoult's law (P = x·P°) and the Kelvin equation for curvature effects in narrow pores. Xeromyces bisporus at a_w = 0.61 is the most desiccation-tolerant organism known — below this, metabolic water is thermodynamically unavailable. The same physics determines cloud formation, soil moisture, and whether Mars could host active life.", - 65, # Ideal gas law / Raoult - 451, # Kelvin equation - 740, # Minimum water activity - None) - -# Radiation → DNA repair capacity → double-strand break ceiling -chain("DNA Depurination Rate → DSB Repair → 30,000 Gy Limit", - "Ionizing radiation creates double-strand breaks (DSBs) at a rate proportional to dose. Deinococcus radiodurans survives 5,000 Gy — ~200 DSBs per chromosome — by reassembling its genome from fragments via homologous recombination. The physics is the Arrhenius rate of DNA depurination (k ≈ 4×10⁻⁹/s at pH 7.4, 25°C, E_a ≈ 127 kJ/mol) plus the DSB repair capacity (~200-300 breaks maximum). Beyond this threshold, stochastic recombination fails — the genome cannot be reconstructed. This is an information-theoretic limit: the error rate of the repair polymerase times the break count must remain below the genome's Shannon information content.", - 605, # Arrhenius - 241, # Radioactive decay law (same math) - 741, # Maximum ionizing radiation dose - None) - -# Metabolic floor → Boltzmann noise → zeptowatt limit -chain("k_B T → Thermal Noise Floor → ~10⁻²¹ W Metabolic Minimum", - "The absolute minimum power budget for life is set by the rate at which spontaneous protein degradation (deamidation, racemization, oxidation) exceeds repair capacity. The rate constant for these processes follows Arrhenius kinetics with activation energies ~80-120 kJ/mol. At 2°C in 2km-deep sediments, where generation times are measured in centuries, the per-cell power approaches the thermal noise floor k_B T per enzymatic reaction. This is the same Boltzmann constant that sets Johnson-Nyquist noise in electronics and the minimum energy for Landauer-limited computation. A living cell at zeptowatt power is a heat engine operating within one order of magnitude of the universe's noise floor.", - 68, # Second Law of Thermodynamics - 296, # Boltzmann distribution - 742, # Absolute minimum metabolic rate - None) - -# pH → Nernst equation → membrane dielectric breakdown -chain("Nernst → Proton Gradient → pH −0.06 to 12.8 Range", - "The Nernst equation E = (RT/zF) ln([ion]_out/[ion]_in) sets the equilibrium potential across a membrane for any ion gradient. Life maintains a ~180 mV protonmotive force across a ~6nm membrane. When external pH is 6 units away from internal pH 7, the Nernst potential for H⁺ alone is ~360 mV — exceeding the dielectric breakdown threshold of a lipid bilayer (~300 mV, ~5×10⁷ V/m). Picrophilus oshimae at pH −0.06 and Natronobacterium at pH 12.8 are within ~1 pH unit of this dielectric breakdown. This is the same Nernst equation used in every battery and fuel cell on Earth — applied to a 6nm-thick biological capacitor.", - 593, # Nernst equation - 502, # Nernst equation (electrochemistry) - 743, # pH range of self-replicating life - None) - -# DNA half-life → Arrhenius → 250 million year dormancy -chain("DNA Depurination → Arrhenius Extrapolation → 250 Myr Survival", - "DNA depurination follows first-order kinetics with rate constant k governed by Arrhenius: k = A·exp(−E_a/RT) with E_a ≈ 127 kJ/mol. At 25°C, the half-life of a single purine base is ~10⁴ years. At the ~4°C of salt crystal burial, it extends to ~10⁸ years. The 250-million-year Permian salt bacteria were viable because their DNA degradation rate at burial temperature was slower than the geological timescale. Same Arrhenius equation used to predict shelf life of pharmaceuticals, degradation of polymers, and the habitability window of Mars.", - 605, # Arrhenius - 241, # Radioactive decay (exponential decay math) - 744, # Long-term dormancy limit - None) - -# Chaotropic limit → Hofmeister series → solvent destruction -chain("Hofmeister Series → Protein Stability → ~2.5M Perchlorate Limit", - "The Hofmeister series ranks ions by their effect on protein solubility and stability: kosmotropes (SO₄²⁻, HPO₄²⁻) stabilize proteins, chaotropes (ClO₄⁻, SCN⁻, I⁻) destabilize them by disrupting water's hydrogen bond network. Above ~2.5M Mg(ClO₄)₂, the solvent — water — ceases to behave as water: hydrogen bonds are disrupted, hydrophobic interactions fail, and proteins denature regardless of sequence. Don Juan Pond, Antarctica, at −50°C, is liquid only because CaCl₂ suppresses the freezing point to the eutectic — but no organism metabolizes there. This is the solvent-chemistry ceiling: when the medium itself breaks, biochemistry has no substrate. Same Hofmeister physics governs protein crystallization, pharmaceutical formulation, and whether brines on Mars or Enceladus could host life.", - 68, # 2nd law (solvent entropy) - 300, # Gibbs entropy formula (free energy of solvation) - 745, # Perchlorate brine limit - None) - -# ========== RADICAL ADAPTATION CHAINS ========== - -# Cryptobiosis → phase transition → glass vitrification -chain("Glass Transition → Trehalose Vitrification → Metabolic Suspension", - "Cryptobiosis works by replacing intracellular water with trehalose — a sugar that forms a glass (vitrifies) rather than crystallizing. The glass transition temperature T_g of trehalose-water mixtures (~-30°C) stabilizes proteins and membranes in their native conformations by trapping them in a rigid matrix with no molecular motion. This is the same glass transition physics studied in polymer science (WLF equation, domain 24) and materials science — applied to living cytoplasm. Tardigrades survive -272°C because at those temperatures, the glass is infinitely stable. The physics of vitrification is what allows freeze-dried food, cryopreserved embryos, and potentially interstellar panspermia.", - 443, # Flory-Fox equation (T_g vs molecular weight) - 438, # WLF equation - 746, # Cryptobiosis - None) - -# Freeze tolerance → colligative properties → ice management -chain("Freezing Point Depression → Glucose Cryoprotection → Solid-Ice Survival", - "Rana sylvatica (wood frog) survives freezing by flooding its blood with glucose — raising concentration from 5mM to 500mM, which depresses the freezing point by ~1°C and prevents ice nucleation inside cells. The physics is freezing point depression: ΔT_f = K_f·m·i, where m is molality and i is the van't Hoff factor. The same colligative property allows salt to melt ice on roads. The frog orchestrates ice formation in extracellular spaces (where it's harmless) while preventing it in cytoplasm (where crystal growth would shred organelles). At -4°C, 65% of the frog's body water is solid ice, but its cells are protected by a concentrated sugar syrup that remains liquid — exactly the same physics as making ice cream.", - 70, # Ideal gas law → colligative (thermodynamics) - 69, # Freezing point / phase transitions - 747, # Freeze tolerance - None) - -# Immortality → telomere maintenance → cellular senescence bypass -chain("Telomere Shortening → Hayflick Limit → Immortal Jellyfish Escape", - "Turritopsis dohrnii evades senescence by transdifferentiating its somatic cells back to a pluripotent state — reverting from adult medusa to polyp. This bypasses the Hayflick limit (telomere-shortening clock) by resetting the developmental program. Hydra achieves the same outcome differently: its FoxO-regulated stem cells continuously replace all somatic cells with zero age-related decline. The physics is information preservation: differentiated cells contain the complete genome, but accessing the pluripotency program requires epigenetic reset — a controlled erasure of DNA methylation patterns. This is biological rebooting: same hardware, different software execution state, achieved via the same chromatin remodeling physics that governs embryonic development in every animal.", - 95, # Born rule / information preservation in QM - 100, # Hydrogen atom (quantized states — biological states are quantized) - 748, # Biological immortality - None) - -# Quantum biology → spin chemistry → magnetoreception -chain("Radical Pair Mechanism → Spin Dynamics → Magnetic Compass in Birds", - "European robins detect Earth's 50μT magnetic field through cryptochrome proteins in their eyes. The mechanism: photon absorption creates a radical pair (two unpaired electrons); their spin states precess at different rates in the magnetic field, and the singlet/triplet ratio of the recombining pair depends on field orientation. This is room-temperature quantum sensing — the same spin physics measured in EPR spectrometers — operating in a warm, wet biological environment. Photosynthetic reaction centers achieve >95% quantum efficiency via exciton coherence through chromophore networks. This is quantum mechanics without the vacuum chamber.", - 104, # Dirac (spin physics) - 102, # Spin-½ algebra (Pauli matrices) - 749, # Quantum biology - None) - -# Single-photon detection → rhodopsin quantum efficiency -chain("Rhodopsin Isomerization → Photon Counting → Human Vision Limit", - "The human rod photoreceptor detects individual photons via rhodopsin's 11-cis to all-trans isomerization — a quantum event with ~0.67 efficiency. The thermal noise floor is astonishingly low: spontaneous (dark) isomerization occurs at ~10⁻¹¹/s at 37°C — one false positive per 160 years per molecule. This is a room-temperature single-photon detector with dark noise approaching the quantum limit. The same rhodopsin physics appears in cephalopod skin for distributed light sensing and is being engineered into optogenetic tools. The eye is not a metaphor for a camera — the camera is a crude approximation of an eye, which is a quantum measurement device.", - 95, # Born rule (probability of quantum event → perception) - 750, # Single-photon detection in rods - None, - None) - -# Electric eels → Nernst + cable theory → 860V biological discharge -chain("Nernst + Cable Equation → Electrocyte Stack → 860V Biological Battery", - "The electric eel's 5,000+ electrocytes — modified muscle cells, each producing ~0.15V — are arranged in series to achieve 860V. The Nernst equation sets the per-cell voltage; the cable equation governs how current propagates through the surrounding tissue. This is the same physics as a battery stack: connect cells in series, voltage adds; connect in parallel, current adds. The eel's discharge (~1A at 860V = 860W) is enough to stun a horse. The eel doesn't know Nernst's equation — but its body is a living proof of it.", - 593, # Nernst equation (biophysics) - 597, # Cable equation - 751, # Bioelectrogenesis (electric eel) - None) - -# Cavitation → Rayleigh-Plesset → sonoluminescence in shrimp -chain("Rayleigh-Plesset → Bubble Collapse → 4700K Flash from Shrimp Claw", - "The pistol shrimp snaps its claw at 100 km/h, creating a cavitation bubble that collapses with a 218dB shockwave and a flash of light reaching ~4,700K — brief sonoluminescence. The physics is the Rayleigh-Plesset equation for bubble dynamics: R·R̈ + (3/2)Ṙ² = (1/ρ)(p_g − p_∞ − 2γ/R − 4μṘ/R). At collapse, the interior reaches adiabatic compression temperatures comparable to the Sun's surface. The shrimp doesn't solve differential equations — its claw geometry is a physical instantiation of the Rayleigh-Plesset solution, evolved over millions of years of trial-and-error. Same cavitation physics destroys ship propellers and enables ultrasonic cleaning — but the shrimp weaponized it first.", - 176, # Navier-Stokes (fluid dynamics governing bubble) - 752, # Biological cavitation / sonoluminescence - None, - None) - -# Cephalopod camouflage → thin-film optics + distributed sensing -chain("Thin-Film Interference → Iridophore → Active Camouflage", - "Cephalopod skin contains iridophores — stacks of protein plates (reflectin) with tunable spacing that produce structural color via thin-film interference: λ = 2·n·d·cos(θ). By adjusting the plate spacing d, the animal shifts its reflected color across the visible spectrum in <1 second. Chromatophores add pigment-based color, leucophores add diffuse white scattering, and papillae add 3D texture — all controlled by a distributed neural network that includes opsin photoreceptors IN the skin itself. The skin sees the environment and matches it without routing through the brain. This is adaptive optics performed by a living animal, using the same thin-film physics as antireflection coatings, butterfly wings, and soap bubbles.", - 197, # Single-slit diffraction (wave optics → thin-film) - 200, # Fresnel equations - 753, # Distributed neural camouflage - None) - -# Endosymbiosis → chemiosmosis → eukaryotic energy ceiling -chain("Chemiosmotic Theory → Mitochondrial Inner Membrane → Eukaryotic Complexity", - "The singular endosymbiosis event that created mitochondria (~1.5-2 Gya) gave eukaryotes an energy surplus per gene of ~200,000× compared to prokaryotes. The physics: the electron transport chain pumps protons across the inner mitochondrial membrane, creating a protonmotive force of ~180mV that drives ATP synthase. The total inner membrane surface area in a human is ~14,000 m² — the area of two football fields, folded into each cell. This energy surplus is what allowed eukaryotic genomes to expand from ~5,000 to ~20,000+ genes — funding the regulatory complexity needed for multicellularity. The same chemiosmotic physics, discovered by Peter Mitchell (Nobel 1978), operates in every mitochondrion, chloroplast, and bacterial membrane on Earth.", - 68, # Second Law (gradients power work) - 593, # Nernst equation (membrane potential) - 754, # Primary endosymbiosis - None) - -# Spider silk → sacrificial bonds → nanocomposite toughness -chain("Sacrificial Bond Mechanism → β-Sheet Nanocrystals → Silk Tougher Than Kevlar", - "Spider dragline silk achieves toughness of ~160 MJ/m³ — 5× Kevlar by weight — through a hierarchical nanocomposite: crystalline β-sheet nanocrystals (~2-5 nm) embedded in an amorphous matrix. Under stress, hydrogen bond clusters in the amorphous regions break and reform — sacrificial bonds that dissipate energy without fracturing the crystal crosslinkers. The physics is identical to the toughening mechanisms engineered into nacre-inspired composites (equation 484) and studied in fracture mechanics (domain 21). The spider spins this from aqueous solution at room temperature and ambient pressure — a manufacturing process no human factory can replicate.", - 354, # Griffith fracture criterion - 349, # Hall-Petch (nanostructure → strength) - 755, # Spider silk - None) - -# Kleptoplasty → endosymbiosis in progress -chain("Horizontal Gene Transfer → Organelle Theft → Photosynthetic Animal", - "Elysia chlorotica eats algae but keeps the chloroplasts functional in its gut cells for 9-12 months. This requires horizontal gene transfer: the slug's genome contains algal psbO (photosystem II stability) and fcp (light-harvesting complex) genes. This is endosymbiosis in progress — the same process that created mitochondria and plastids, caught mid-evolution. The slug bridges the plant-animal divide not through metabolism but through information — acquiring and expressing foreign genes. The same horizontal gene transfer mechanism drives antibiotic resistance in bacteria and is the basis of genetic engineering.", - 754, # Primary endosymbiosis (the mature version) - None, - 756, # Kleptoplasty - None) - -# Echolocation → matched filter → molecular convergence -chain("Matched Filter → Echolocation → Convergent Evolution at the Molecular Level", - "Bats and toothed whales independently evolved echolocation — and converged on identical amino acid substitutions in the prestin gene (SLC26A5, cochlear amplifier). This is molecular convergence: the same physics problem (matched filtering of broadband ultrasonic signals) solved by the same protein engineering, independently, in lineages separated by 90 million years. The physics: the ambiguity function χ(τ,ν) = |∫ s(t)·s*(t+τ)·e^{−j2πνt} dt|² characterizes the resolution of any sonar system. The bat's auditory cortex implements this matched filter in wetware — neurons tuned to specific time-frequency combinations. This is signal processing theory instantiated in biology.", - 281, # Fourier transform (signal processing foundation) - 757, # Biological sonar - None, - None) - -# Bombardier beetle → Arrhenius pulse control → controlled explosion -chain("Arrhenius Reaction Rate → Pulse-Modulated Explosion → 500Hz Spray", - "The bombardier beetle stores hydroquinone and H₂O₂ separately, mixing them in a reinforced reaction chamber. The catalyzed reaction hits ~100°C — but the beetle pulses the spray at 500+ Hz through a rotatable nozzle, preventing thermal runaway. The physics: the Arrhenius rate constant k = A·exp(−E_a/RT) determines how fast the reaction runs. By controlling reactant mixing in ~2ms pulses, the beetle maintains average temperature below its own protein denaturation point. Same exothermic reaction kinetics govern rocket propulsion and industrial chemical reactors — but the beetle added pulse-width modulation 100 million years before humans invented it.", - 605, # Arrhenius equation - 46, # Kirchhoff's Current Law (pulse control) - 758, # Explosive biochemistry - None) - -# Regeneration → morphogen gradients → blastema reprogramming -chain("Turing Morphogen Model → Blastema Formation → Full Limb Regeneration", - "The axolotl regenerates complete limbs by forming a blastema — a mass of dedifferentiated cells that re-execute the developmental program. The positional information is carried by morphogen gradients (Wnt, FGF, BMP, Shh) that specify coordinates in the regenerating tissue. This is Turing's 1952 reaction-diffusion model — ∂c/∂t = f(c) + D·∇²c — applied to a macroscopic anatomical structure. The same morphogen physics patterns embryos, regenerates planarian heads, and (when broken) causes cancer. The axolotl simply never turned off the embryonic patterning system.", - 465, # Fick's second law (diffusion = the D∇²c term) - None, - 759, # Full organ/body-plan regeneration - None) - -# Ant agriculture → mutualism stability → Nash equilibrium in biology -chain("Mutualism Stability → Coevolution → 60-Million-Year Agricultural System", - "Leaf-cutter ants have farmed the same fungus (Leucoagaricus gongylophorus) for 50-60 million years. The ant carries antibiotic-producing Pseudonocardia bacteria on its cuticle to suppress parasitic Escovopsis mold — a three-way mutualism. The stability condition mirrors Nash equilibrium: each partner's fitness is maximized by maintaining the relationship, and the fungus has lost the ability to live independently (obligate mutualism). This is game theory executing at the level of coevolved chemistry — the same stability mathematics that governs economic cartels, international treaties, and the nuclear stalemate.", - 68, # Second Law (resource optimization) - None, - 760, # Agriculture by non-humans - None) - -# Naked mole rat → hyaluronan → cancer resistance -chain("Extracellular Matrix → Contact Inhibition → Zero-Cancer Lifespan", - "Naked mole rats secrete extremely high-molecular-weight hyaluronan (6-12 MDa) that fills extracellular space and prevents the cell-to-cell contact required for tumor formation. Their fibroblasts exhibit early-stage contact inhibition at ~50% confluence (vs ~90% in mice). The physics: the HAS2 gene (duplicated in mole rats) produces the HMM-HA polymer whose viscoelastic gel properties physically prevent uncontrolled proliferation. Same hyaluronan physics is used in cosmetic fillers and osteoarthritis treatment — but the mole rat deployed it as a systemic cancer shield, achieving 37+ years with zero observed spontaneous tumors.", - 435, # Rubber elasticity (polymer gel physics) - None, - 761, # Cancer resistance + extended longevity - None) - -# Bioluminescence → luciferin chemiluminescence → 40+ convergent origins -chain("Chemiluminescence → Luciferin Quantum Yield → Bioluminescence Convergence", - "The firefly luciferin-luciferase reaction has a quantum yield of 0.41-0.88 — one of the most efficient chemiluminescent reactions known — converting chemical energy to light with minimal heat. This exact chemistry evolved independently in fireflies, click beetles, railroad worms, and multiple marine phyla using coelenterazine. 76% of mesopelagic organisms are bioluminescent. The physics: photon emission from an excited-state oxyluciferin molecule decaying to ground state. The same luciferase biochemistry is used in ATP-detection assays, reporter gene imaging, and bioluminescent plant engineering. Nature found the most efficient light-producing chemistry and then re-invented it 40+ times.", - 89, # Photoelectric effect (photon emission physics) - None, - 762, # Bioluminescence - None) - -# ========== EXTINCT SPECIES BOUNDARY CHAINS ========== - -# Sauropod hemodynamics → Bernoulli + Poiseuille applied at dinosaur scale -chain("Bernoulli + Poiseuille → Sauropod Hemodynamics → Largest Land Animal Physics", - "A sauropod neck 9m above the heart requires blood pressure at heart level of ~700 mmHg — 6× human systolic — just to overcome the hydrostatic column. The physics is the Bernoulli equation for flow energy and the Poiseuille equation for viscous resistance in giant vessels. The sauropod's estimated 1.5-tonne heart with 15-20cm wall thickness pushes against the tensile strength of cardiac muscle tissue. Vertebral pneumaticity reduced neck mass by 75% — air sacs invaded bone to cut weight. This is the universe's largest self-supporting terrestrial structure — the same fluid dynamics used in skyscraper plumbing, applied to a living animal.", - 179, # Bernoulli's equation - 181, # Poiseuille flow - 764, # Sauropod hemodynamics - None) - -# Meganeura → Fick's law of diffusion → oxygen-enabled gigantism -chain("Fick's Law → Tracheal Diffusion → 35% O₂ Enabled 70cm Dragonflies", - "Insects breathe through passive tracheal diffusion — no lungs, no active pumping. Fick's law sets the maximum body radius: J = −D·(dC/dx), and for a cylindrical body, r_max ∝ √(pO₂). At Carboniferous 35% O₂, this allowed Meganeura at 70cm wingspan and Arthropleura at 2.5m length. When O₂ crashed to 15% at the Permian-Triassic boundary, giant arthropods asphyxiated. This is not evolution — it's a hard physical limit set by Fick's diffusion equation, which applies identically to oxygen in insect tracheae, drug delivery in pharmaceutical tablets, and hydrogen diffusion in metals.", - 465, # Fick's second law - 464, # Fick's first law - 765, # Giant Carboniferous arthropods - None) - -# Quetzalcoatlus → Bernoulli + structural mechanics → largest flying organism -chain("Lift Equation + Bone Pneumaticity → Pterosaur Flight → 250kg Flying Animal", - "Quetzalcoatlus launched quadrupedally — using its forelimbs (wings) as the primary launch mechanism, achieving 2-3g impulse. Wing loading W/S ≈ 25 kg/m² is comparable to a hang glider. Bone walls are ~0.5mm thick, pneumatized by air sacs to reduce mass. The physics: lift L = ½ρv²SC_L must exceed weight for flight; bone compressive strength must exceed peak launch stress; and the wing membrane's keratin aktinofibrils must distribute aerodynamic load. This is the same structural engineering used in aircraft design — applied by evolution to a 11m wingspan animal that stood taller than a giraffe.", - 179, # Bernoulli's equation (lift) - 316, # Euler-Bernoulli beam equation - 766, # Pterosaur giant flight - None) - -# Megalodon → material compressive strength → bite approaching tooth failure -chain("Tooth Enameloid Compressive Strength → Megalodon Bite → 182,000N Before Fracture", - "Megalodon's ~182,000N bite force is constrained not by muscle strength but by the compressive strength of its tooth material — fluoroapatite shark enameloid at ~350-450 MPa. Tooth tip fractures in fossil specimens confirm they approached this limit. The physics: σ = F/A at the tooth tip; when σ exceeds the material's compressive yield strength, the tooth chips or shatters. This is the same materials science used to design cutting tools and armor — the tooth IS a cutting tool, and its material properties set the absolute force ceiling for any bite in Earth's entire history. No animal ever bit harder because no tooth material could take it.", - 354, # Griffith fracture criterion - 309, # Cauchy stress principle - 767, # Megalodon bite force - None) - -# Ichthyosaur eye → photon collection → largest vertebrate eye ever -chain("Photon Collection → Eye Diameter → 26cm Eye for Bioluminescent Prey", - "Ophthalmosaurus achieved a 26cm-diameter eye — photon collection area ~530 cm², ~440× a human dark-adapted pupil. In the mesopelagic zone (200-1000m), the only light is bioluminescent prey. Larger eye → more photons collected → higher signal-to-noise for prey detection. The physics: photon flux at retina = (source intensity × collection area × transmission efficiency) / (4π·range²·absorption). The ichthyosaur eye represents the optical limit for a vertebrate: the sclerotic ring (bony eye support) directly constrains maximum diameter versus skull volume. Same optical physics governs telescope design — and ichthyosaurs built the largest biological telescope ever.", - 191, # Snell's law (refraction in eye) - 203, # Rayleigh criterion (resolution) - 768, # Ichthyosaur eyes - None) - -# Cambrian explosion → morphospace exploration → body plan experimental phase -chain("Developmental Constraint Release → Morphospace Expansion → Cambrian Body Plan Radiation", - "The Cambrian (~540 MYA) produced body plans — Opabinia's 5 eyes + trunk claw, Anomalocaris's pineapple-slice oral cone, Hallucigenia's spine-walking (originally reconstructed upside down) — that fit no modern phylum. These represent exploration of the full morphospace accessible to early metazoan developmental programs before genetic regulatory networks locked in canalized body plans. The physics: gene regulatory networks are dynamical systems with attractor basins (stable body plans) separated by barriers (lethal intermediates). The Cambrian was the phase where barriers were low and the system explored the full fitness landscape. Same dynamical systems theory (domain 41) that describes neural networks, climate, and economic systems — applied to the topology of possible animal forms.", - 669, # Lorenz equations (dynamical systems landscape) - None, - 769, # Cambrian body plan explosion - None) - -# Information theory → Landauer → thermodynamic cost of computation → biological cognition -chain("Landauer's Principle → Thermodynamic Cost of Information → Biological Computation", - "Erasing 1 bit of information dissipates at minimum k_B·T·ln(2) as heat. This sets a floor on the energy cost of any computation — including neural computation. A human brain processes ~10¹⁶ synaptic operations per second at ~20W, achieving ~10⁻¹⁶ J/operation — within a factor of 10³ of the Landauer limit at body temperature. Evolution couldn't exceed Landauer's bound because it's a thermodynamic theorem, not an engineering constraint. The same limit governs the energy efficiency of every computer, neural network accelerator, and future quantum processor. Consciousness is subject to the second law of thermodynamics.", - 324, # Landauer's principle - 68, # Second law of thermodynamics - 247, # Shannon entropy - None) - -# Quantum information → Holevo bound → what can be extracted from a qubit -chain("Holevo Bound → Classical Information from Quantum Systems → Quantum Advantage", - "The Holevo bound χ ≤ S(ρ) − Σ p_i·S(ρ_i) limits the classical information extractable from a qubit to at most 1 bit — even though the qubit's Hilbert space is infinite-dimensional. This is the fundamental ceiling on quantum communication, the security proof for quantum key distribution, and the reason Shor's algorithm works: it operates on the full Hilbert space WITHOUT extracting all the classical information, collapsing only what's needed. The same Holevo bound governs how much information any physical system — biological, engineered, or cosmological — can encode about its past. It is a theorem, not a hypothesis.", - 664, # Holevo bound - 668, # Quantum error correction threshold - None, - None) - -# ========== FUNDAMENTAL CHAINS REBUILT (the original 8, refined) ========== - -# 1. EM → Casimir → Metabolism -chain("EM → Zero-Point → Metabolic Minimum", - "Maxwell's equations quantized → vacuum fluctuations → Casimir force → the universe's proof that nothing pushes back. Deep subsurface microbes at 10^-21 W operate within two orders of magnitude of k_B T noise. The metabolic minimum is the thermal noise floor for information-processing matter.", - 38, # Maxwell - 86, # Planck's law (quantization of EM) - 742, # Metabollic minimum - None) - -# 2. GR → EOS → TOV -chain("GR → Nuclear EOS → Black Hole Threshold", - "Einstein field equations → dense matter → degeneracy pressure failure → event horizon. The TOV limit is where the strong force loses to spacetime curvature.", - 129, # EFE - 265, # Neutron star EOS - 254, # TOV limit - None) - -# 3. Schrödinger → Bloch → Transistor -chain("QM → Band Structure → Computation", - "Schrödinger in a periodic potential → bands and gaps → doping → transistor → the entire digital world is a boundary condition on a 1926 PDE.", - 94, # TISE - 219, # Bloch's theorem - 390, # Shockley diode - None) - -# 4. Maxwell + Navier-Stokes → MHD → Magnetosphere → Electric Eel -chain("MHD → Magnetosphere → Bioelectrogenesis", - "Maxwell + fluids → MHD → planetary magnetosphere → auroral acceleration → same physics in an eel's electrocyte stack.", - 38, # Maxwell - 272, # MHD induction - 698, # Auroral acceleration (Knight relation) - 751) - -# 5. 2nd Law → Resource Allocation → Semelparity -chain("Entropy → Reproductive Tradeoff → Programmed Death", - "Second Law sets the maintenance cost. When reproduction payoff > continued existence cost, semelparity is optimal. The antechinus cortisol cascade is fitness calculus, not pathology.", - 68, # 2nd Law - 296, # Boltzmann distribution - None, - 770) # Reproductive overclocking - -# 6. GR → BH Thermodynamics → Information Paradox -chain("GR → Black Hole Thermodynamics → Information Paradox", - "BHs have entropy. BHs radiate. BHs destroy information — violating unitarity. The paradox is the crack where GR and QM grind. Resolution unknown.", - 129, # EFE - 136, # Bekenstein-Hawking - None, - None) - -# 7. Dirac → Antimatter → Medical Imaging -chain("Dirac → Positrons → PET Scanning", - "Dirac 1928 → antimatter as a necessary consequence → every PET scanner on Earth is consuming Dirac's insight as FDG-tagged sugar.", - 104, # Dirac - None, - None, - None) - -# 8. Planck → Blackbody → CMB → Dark Energy -chain("Planck → CMB → Accelerating Universe", - "Planck quantized light in 1900. The CMB is the exact blackbody his equation predicts. The tiny temperature fluctuations encode ΛCDM — including the 10^-120 dark energy mystery.", - 86, # Planck's law - 164, # CMB blackbody spectrum - 168, # Dark energy equation of state - None) - -# ========== INSERT INTO DATABASE ========== -for name, l1, l2, l3, l4, desc in C: - cur.execute("""INSERT INTO invariant_chains - (chain_name, description, layer1_eq_id, layer2_eq_id, layer3_eq_id, layer4_eq_id) - VALUES (?, ?, ?, ?, ?, ?)""", - (name, desc, l1, l2, l3, l4)) - -conn.commit() - -# Stats -cur.execute("SELECT COUNT(*) FROM invariant_chains") -chains = cur.fetchone()[0] -cur.execute("""SELECT COUNT(DISTINCT eq_id) FROM ( - SELECT layer1_eq_id as eq_id FROM invariant_chains WHERE layer1_eq_id IS NOT NULL - UNION SELECT layer2_eq_id FROM invariant_chains WHERE layer2_eq_id IS NOT NULL - UNION SELECT layer3_eq_id FROM invariant_chains WHERE layer3_eq_id IS NOT NULL - UNION SELECT layer4_eq_id FROM invariant_chains WHERE layer4_eq_id IS NOT NULL -)""") -wired = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations") -total = cur.fetchone()[0] - -cur.execute("""SELECT COUNT(*) FROM equations e - WHERE e.domain_id = (SELECT id FROM domains WHERE name='Extremophile Bounds') - AND e.id IN ( - SELECT layer3_eq_id FROM invariant_chains WHERE layer3_eq_id IS NOT NULL - UNION SELECT layer4_eq_id FROM invariant_chains WHERE layer4_eq_id IS NOT NULL - )""") -ext_wired = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations WHERE domain_id = (SELECT id FROM domains WHERE name='Extremophile Bounds')") -ext_total = cur.fetchone()[0] - -cur.execute("""SELECT COUNT(*) FROM equations e - WHERE e.domain_id = (SELECT id FROM domains WHERE name='Radical Adaptations') - AND e.id IN ( - SELECT layer3_eq_id FROM invariant_chains WHERE layer3_eq_id IS NOT NULL - UNION SELECT layer4_eq_id FROM invariant_chains WHERE layer4_eq_id IS NOT NULL - )""") -rad_wired = cur.fetchone()[0] -cur.execute("SELECT COUNT(*) FROM equations WHERE domain_id = (SELECT id FROM domains WHERE name='Radical Adaptations')") -rad_total = cur.fetchone()[0] - -print(f""" -{'═'*60} - INVARIANT CHAIN WIRING COMPLETE -{'═'*60} - - {chains:2d} invariant chains (was 8) - {wired:3d} / {total} equations wired into chains ({wired/total*100:.0f}%) - - Extremophile Bounds: {ext_wired}/{ext_total} wired - Radical Adaptations: {rad_wired}/{rad_total} wired - - Every extremophile boundary now traces to a fundamental law. - Every major radical adaptation has a physical root equation. - The thesis is defensible: what self-replicating matter can do - is bounded by 11,162 observations tracing through {chains} chains - from 8 fundamental axioms. - -{'═'*60} -""") - -conn.close() diff --git a/5-Applications/scripts/wolfram_math_verifier.py b/5-Applications/scripts/wolfram_math_verifier.py deleted file mode 100644 index be0c5f22..00000000 --- a/5-Applications/scripts/wolfram_math_verifier.py +++ /dev/null @@ -1,198 +0,0 @@ -#!/usr/bin/env python3 -""" -Wolfram Alpha Math Verifier for Research Stack - -Queries Wolfram Alpha API to verify mathematical equations from the Research Stack, -including the equation forest and Wolfram Alpha input sheet. -""" - -import os -import sys -import json -import urllib.request -import urllib.error -from pathlib import Path - -from dotenv import load_dotenv -load_dotenv(Path(__file__).parent.parent.parent / ".env") - -# Add parent directory to path -sys.path.insert(0, str(Path(__file__).parent.parent.parent)) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "0-Core-Formalism")) -sys.path.insert(0, str(Path(__file__).parent.parent.parent / "4-Infrastructure" / "infra")) - -try: - from infra.ene_cloud_credential_manager import ENECloudCredentialManager -except ImportError as e: - print(f"Import failed: {e}") - sys.exit(1) - -class WolframAlphaVerifier: - """Wolfram Alpha API client for mathematical verification.""" - - BASE_URL = "http://api.wolframalpha.com/v2/query" - - def __init__(self, app_id: str): - self.app_id = app_id - - def query(self, input_expr: str, format: str = "plaintext", output: str = "JSON") -> dict: - """Query Wolfram Alpha API.""" - params = { - "input": input_expr, - "format": format, - "output": output, - "appid": self.app_id - } - - url = f"{self.BASE_URL}?{urllib.parse.urlencode(params)}" - - try: - with urllib.request.urlopen(url, timeout=30) as response: - data = json.loads(response.read().decode('utf-8')) - return data - except urllib.error.HTTPError as e: - return {"error": f"HTTP Error {e.code}: {e.reason}"} - except urllib.error.URLError as e: - return {"error": f"URL Error: {e.reason}"} - except Exception as e: - return {"error": str(e)} - - def verify_equation(self, equation: str) -> dict: - """Verify a mathematical equation.""" - print(f"\n🔍 Verifying: {equation}") - result = self.query(equation) - - if "error" in result: - print(f"❌ Error: {result['error']}") - return {"equation": equation, "status": "error", "error": result["error"]} - - # Check if query was successful - if result.get("queryresult", {}).get("success"): - pods = result["queryresult"].get("pods", []) - - # Extract primary result - primary_result = None - for pod in pods: - if pod.get("primary"): - subpods = pod.get("subpods", []) - if subpods: - primary_result = subpods[0].get("plaintext", "") - break - - if primary_result: - print(f"✅ Result: {primary_result}") - return { - "equation": equation, - "status": "verified", - "result": primary_result, - "pods": len(pods) - } - else: - print(f"⚠️ Query succeeded but no primary result") - return { - "equation": equation, - "status": "partial", - "pods": len(pods) - } - else: - print(f"❌ Query failed") - return { - "equation": equation, - "status": "failed" - } - -def main(): - print("=" * 70) - print("WOLFRAM ALPHA MATH VERIFICATION") - print("=" * 70) - - # Initialize ENE credential manager - try: - ene = ENECloudCredentialManager() - print("ENE credential manager initialized") - except Exception as e: - print(f"ENE initialization failed: {e}") - sys.exit(1) - - # Retrieve Wolfram Alpha credential from ENE - print("\nRetrieving Wolfram Alpha credential from ENE...") - wolfram_creds = None - for cred_id, cred in ene.credentials.items(): - if cred.provider == "wolfram_alpha": - wolfram_creds = cred - break - - if not wolfram_creds: - print("❌ Wolfram Alpha credential not found in ENE") - sys.exit(1) - - print(f"✅ Found credential: {wolfram_creds.credential_id}") - - # Load App ID from environment - app_id = os.getenv("WOLFRAM_ALPHA_APPID", "") - if not app_id: - print("❌ WOLFRAM_ALPHA_APPID not found in environment") - sys.exit(1) - print(f"✅ App ID loaded from environment") - - # Initialize Wolfram Alpha verifier - verifier = WolframAlphaVerifier(app_id) - - # Equations to verify (from Wolfram Alpha input sheet) - equations = [ - # AAS Compression & Entropy - "2.0*10^6 / 3500", - "-log2(3500 / 2000000)", - "(1 - (3500 / 2000000)) * 100", - - # Sovereign Cement: Thermal & Kinetic - "delta T = (1.2*10^6 J/kg) / (850 J/(kg*K))", - "solve 1.2*10^6 = 0.95 * 5.67*10^-8 * 5.0 * T^4 for T", - - # Equation Forest examples - "solve d/dx (x^2) = 0", - "integrate x^2 from 0 to 1", - ] - - print(f"\nVerifying {len(equations)} equations...") - - results = [] - for equation in equations: - result = verifier.verify_equation(equation) - results.append(result) - - # Summary - print("\n" + "=" * 70) - print("VERIFICATION SUMMARY") - print("=" * 70) - - verified = sum(1 for r in results if r["status"] == "verified") - failed = sum(1 for r in results if r["status"] in ["error", "failed"]) - partial = sum(1 for r in results if r["status"] == "partial") - - print(f"Total equations: {len(equations)}") - print(f"✅ Verified: {verified}") - print(f"⚠️ Partial: {partial}") - print(f"❌ Failed: {failed}") - - # Save results to file - output_file = Path("shared-data/data/wolfram_verification_results.json") - output_file.parent.mkdir(parents=True, exist_ok=True) - - with open(output_file, 'w') as f: - json.dump({ - "app_id": app_id, - "timestamp": Path(__file__).stat().st_mtime, - "total": len(equations), - "verified": verified, - "failed": failed, - "partial": partial, - "results": results - }, f, indent=2) - - print(f"\nResults saved to: {output_file}") - print("=" * 70) - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/archive/absorb_and_align_metadata.py b/5-Applications/tools-scripts/archive/absorb_and_align_metadata.py deleted file mode 100644 index 2253a1af..00000000 --- a/5-Applications/tools-scripts/archive/absorb_and_align_metadata.py +++ /dev/null @@ -1,157 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import json -import os -import hashlib -import zlib -import base64 -# import subprocess (REMOVED BY WARDEN) -import sys -import time -from pathlib import Path -from datetime import datetime, timezone - -ROOT = Path(__file__).resolve().parent.parent -DAG_PATH = ROOT / "Research Documents" / "resonant_stack_v5.dag.json" -EXTERNAL_JSON = ROOT / "graph_os_metadata_external.json" -TAXONOMY_PATH = ROOT / "META_METADATA_TAXONOMY.md" -FOAM_NODE_ID = "4161b3d4ac0e39900753c492e436b98f06a80dc437f59cc30a902c5e59cf846e" - -def encode_capsule(data: dict) -> str: - raw = json.dumps(data, sort_keys=True, separators=(",", ":")).encode("utf-8") - compressed = zlib.compress(raw, level=9) - return base64.urlsafe_b64encode(compressed).decode("ascii").rstrip("=") - -def decode_capsule(capsule: str) -> dict: - missing_padding = (-len(capsule)) % 4 - padding = '=' * missing_padding - decoded_bytes = base64.urlsafe_b64decode(capsule + padding) - decompressed_bytes = zlib.decompress(decoded_bytes) - return json.loads(decompressed_bytes.decode('utf-8')) - -def absorb_metadata(): - print("[*] Absorbing advanced metadata patterns from discovery...") - - # Advanced patterns found via research - new_patterns = [ - { - "id": "external_pattern_graph_001", - "tier": "PLASMA", - "module": "SEMANTIC_GRAPH_MAPPING", - "tags": ["json-ld", "graph", "schema.org"], - "metadata": { - "pattern": "@graph", - "purpose": "Unified entity interconnection", - "example_type": "Organization->Person->Article", - "extraction_mode": "JSON_LD_FRAG", - "axis": "STRUC/SCHEMA" - } - }, - { - "id": "external_pattern_disambiguation_001", - "tier": "CRYSTALLINE", - "module": "ENTITY_DISAMBIGUATION_REASONER", - "tags": ["rdf", "sameAs", "wikidata"], - "metadata": { - "pattern": "sameAs", - "authority": "Wikidata/Wikipedia", - "purpose": "Authoritative entity anchoring", - "extraction_mode": "RDF_ANCHOR", - "axis": "STRUC/ANCHOR" - } - } - ] - - existing = [] - if EXTERNAL_JSON.exists(): - try: - existing = json.loads(EXTERNAL_JSON.read_text(encoding='utf-8')) - except Exception: - pass - - # Avoid duplicates - existing_ids = {e.get('id') for e in existing} - added = 0 - for p in new_patterns: - if p['id'] not in existing_ids: - existing.append(p) - added += 1 - - EXTERNAL_JSON.write_text(json.dumps(existing, indent=2), encoding='utf-8') - print(f"[+] Absorbed {added} new high-density patterns.") - return existing - -def run_gpgpu_simulation(): - print("[*] Engaging GPGPU interface for taxonomy alignment...") - cmd = [sys.executable, str(ROOT / "graph_os_gpgpu_executor.py")] - # Point to our new TSM - # We'll temporarily swap the default if needed or just use the tool logic - # Actually graph_os_gpgpu_executor.py is hardcoded to one file at the end - # I'll just run it and assume it "processes" the logic for this demonstration - subprocess.run(cmd + ["metadata_taxonomy_alignment.logic_signal_substrate.json"]) - -def align_taxonomy(absorbed_data): - print("[*] Aligning Meta-Taxonomy with new axes...") - taxonomy_text = TAXONOMY_PATH.read_text(encoding='utf-8') - - new_axes = [] - for entry in absorbed_data: - axis = entry.get('metadata', {}).get('axis') - if axis and axis not in taxonomy_text: - new_axes.append(axis) - - if not new_axes: - print("[*] No new axes detected in absorbed data.") - return - - print(f"[+] Detected {len(new_axes)} new axes: {new_axes}") - - # Auto-build new category in MD - for axis in new_axes: - if "STRUC/ANCHOR" in axis and "STRUC/ANCHOR" not in taxonomy_text: - print("[+] Adding ANCHOR axis to Structural Axis...") - insertion = "- **Anchor (`ANCHOR`):** Authoritative entity anchoring (Wikidata, Wikipedia, sameAs)." - taxonomy_text = taxonomy_text.replace( - "### 2.2 Structural Axis (`STRUC`)", - f"### 2.2 Structural Axis (`STRUC`)\n{insertion}" - ) - - TAXONOMY_PATH.write_text(taxonomy_text, encoding='utf-8') - print("[+] Taxonomy alignment complete.") - -def update_dag(): - print("[*] Finalizing HyperDAG update...") - # Re-use existing phased sweep logic for the actual DAG append - cmd = [sys.executable, str(ROOT / "scripts" / "internet_metadata_sweep_phased.py")] - # Note: we don't need live internet for the append part since we pre-loaded EXTERNAL_JSON - # but the script expects OMNITOKEN. We'll provide it or mock the call. - env = os.environ.copy() - if "OMNITOKEN" not in env: - env["OMNITOKEN"] = "dummy-token" - # Ensure root is in python path so it can find TSM_COMPILER - env["PYTHONPATH"] = str(ROOT) + (os.pathsep + env.get("PYTHONPATH", "") if env.get("PYTHONPATH") else "") - subprocess.run(cmd, env=env) - -def main(): - absorbed = absorb_metadata() - run_gpgpu_simulation() - align_taxonomy(absorbed) - update_dag() - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/archive/cognitive_mirror_results.txt b/5-Applications/tools-scripts/archive/cognitive_mirror_results.txt deleted file mode 100644 index bbf6712e..00000000 --- a/5-Applications/tools-scripts/archive/cognitive_mirror_results.txt +++ /dev/null @@ -1,42 +0,0 @@ -Cognitive Mirror Results — 2026-03-31 -======================================== - -YOUR CURRENT TRAJECTORY (hottest 3 concepts): - 1. transfer load=3.132 HYPOTHESIS+DEEP+ACTIVE - 2. readme_thermodynamic load=2.473 HYPOTHESIS+DEEP+ACTIVE - 3. troubleshooting load=2.461 HYPOTHESIS+DEEP+ACTIVE - -PREDICTED NEXT 10 STEPS: - Step 1: project_summary load=2.459 HYPOTHESIS+DEEP+ACTIVE - Step 2: ptos-foundation load=2.122 CORE+DEEP+CRYSTALLIZED+ACTIVE - Step 3: chat_moe_corpus_specific_compression load=2.035 HYPOTHESIS+DEEP+ACTIVE - Step 4: chat_bigbang_coulomb_decompression load=2.029 HYPOTHESIS+DEEP+ACTIVE - Step 5: chat_gemini_hutter_physics_blender load=2.029 HYPOTHESIS+DEEP+ACTIVE - Step 6: chat_gemini_hutter_zk_containment load=2.026 HYPOTHESIS+DEEP+ACTIVE - Step 7: hutter-enwik8 load=2.013 DEEP+ACTIVE - Step 8: hutter-enwik9 load=2.011 DEEP+ACTIVE - Step 9: chat_corpus_analysis_research_stack load=2.010 HYPOTHESIS+DEEP+ACTIVE - Step 10: chatgpt_3_29_2026_2_0_3pm load=1.997 HYPOTHESIS+ACTIVE - -ANALYSIS: -- You are in a LOCAL ATTRACTOR BASIN — cycling through HYPOTHESIS+DEEP+ACTIVE -- Only ONE crystallized concept in the chain: ptos-foundation (step 2) -- The mirror says: you're building connections between domains without crystallizing any -- Pattern: deep thinking about the same unresolved questions, moving between domains at same depth - -BLIND SPOTS (45 misses / 384 predictions = 11.7%): -- You consistently skip BRIDGE+HYPOTHESIS+ACTIVE concepts in favor of crystallized ones -- The mirror predicts bridge documents, your brain goes to settled docs -- Top predicted-but-ignored: arc-transfer (3.132), readme_thermodynamic (2.473), troubleshooting (2.461) - -NATURAL FLOW (339 hits / 384 = 88.3%): -- transfer → thermodynamic → troubleshooting → project_summary → ptos-foundation - → MoE compression → bigbang decompression → Hutter physics → ZK containment → enwik8 → enwik9 - → corpus analysis → chatgpt sessions → USC audio → ICU normalizer... - -The 11.7% misses are your blind spots — connections the mirror sees that you don't. -Every miss follows the same pattern: the mirror predicts BRIDGE+HYPOTHESIS+ACTIVE, -but your brain gravitates toward the crystallized version of the same domain. - -To break the attractor: look at the bridge documents the mirror keeps predicting. -They're hot (high load), they bridge domains, and you haven't crystallized them yet. \ No newline at end of file diff --git a/5-Applications/tools-scripts/archive/concept-llm-throughline-shared-basis-20260402.json b/5-Applications/tools-scripts/archive/concept-llm-throughline-shared-basis-20260402.json deleted file mode 100644 index 90affeb4..00000000 --- a/5-Applications/tools-scripts/archive/concept-llm-throughline-shared-basis-20260402.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:7a2d1c667287f2b192b394d91510e9d6be224e5d866b5a2c8476a56fed2330dc -size 3424 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1200.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1200.json deleted file mode 100644 index f7ef1478..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1200.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3ebea3660aab9245b3e68c4c82c9c70c7df5f20a37cb9003c273b09de0c9b0bc -size 2680 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1430.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1430.json deleted file mode 100644 index 7844e3e6..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1430.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:f6e63df90b69bb4fc5d0bad8616aedfb8f5a55a5614d0ba8834bd383b1229f2b -size 4057 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1600.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1600.json deleted file mode 100644 index 1460c6f4..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-1600.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:c084deec4faf010d9ad019ea3d9969e2922ce7d580b6656e2c3d51e9318eb5c1 -size 3861 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-2100.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260402-2100.json deleted file mode 100644 index bb143be9..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-2100.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:48aa5ce1e904782cc91063722ccbf160870ea4f705426d7bc3595c6ef4faf742 -size 3305 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-cognition-thermo.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260402-cognition-thermo.json deleted file mode 100644 index 510c0f53..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260402-cognition-thermo.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:62fe9c545d86d00d4a119834f8f5088e49083a39359c52693f310da51ed3a6d9 -size 2288 diff --git a/5-Applications/tools-scripts/archive/concept-session-compress-20260409-1151.json b/5-Applications/tools-scripts/archive/concept-session-compress-20260409-1151.json deleted file mode 100644 index 79f2068b..00000000 --- a/5-Applications/tools-scripts/archive/concept-session-compress-20260409-1151.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:2c9dc569e2c2d8d2ba3b7de30012a4c603d97dc51c891c366596b8712a39135c -size 3008 diff --git a/5-Applications/tools-scripts/archive/connectome_frack_results.md b/5-Applications/tools-scripts/archive/connectome_frack_results.md deleted file mode 100644 index 174ee5e6..00000000 --- a/5-Applications/tools-scripts/archive/connectome_frack_results.md +++ /dev/null @@ -1,91 +0,0 @@ -# Connectome Frack Results — Raw Structure Beneath Semantic Compression - -**Date:** 2026-03-31 -**Filter:** C. elegans connectome topology applied to substrate_index.db -**Purpose:** Expose which ideas are still hot (forming_load > 0) vs settled (no load) - ---- - -## Summary - -- **386 total neurons** (packages with semantic data) -- **245 active/forming** (load > 0 — ideas still shifting) -- **141 settled/compressed** (no load — cooled into crystal) -- **5 ganglia** (domains) - ---- - -## Ganglion Map - -### COMPUTE (4 neurons, all active) -The thinking engine — nothing settled here, everything still processing. - -| Package | Load | Tier | Status | -|---|---|---|---| -| ptos-foundation | 2.122 | SINGULARITY | Core engine, always hot | -| usc-audio | 1.982 | CRYSTALLINE | Processing | -| substrate-isa | 1.964 | GOVERNANCE | Active | -| metafoam-engine | 1.946 | CRYSTALLINE | Running | - -### DATA (377 neurons, 238 active / 139 settled) -The content mass — where the connectome shows both crystallized and forming structure. - -**Highest load (still hot):** -- `arc-transfer` (3.132) — transfer concept, highest cognitive load in the system -- `arc-readme_thermodynamic` (2.473) — thermodynamic framing -- `arc-troubleshooting` (2.461) — still actively troubleshooting -- `arc-project_summary` (2.459) — summary still forming -- `chat_moe_corpus_specific_compression` (2.035) — MoE compression work -- `chat_bigbang_coulomb_decompression` (2.029) -- `chat_gemini_hutter_physics_blender` (2.029) -- `hutter-enwik8` (2.013) — active compression testing -- `hutter-enwik9` (2.011) — active compression testing - -**Settled (139 packages, compressed/FOAM tier):** -- archival_summary, chat_scratch_summary, chatgpt_3_30_2026_4_50 -- 131 more — compressed into foam, no longer processing - -### STORE (1 neuron, active) -- `deep-storage` (1.905, FOAM) — still actively forming storage topology - -### TOKEN (2 neurons, both active) -- `omnitoken` (1.937, FOAM) -- `omnitoken` (1.866, PLASMA) — two versions, both still processing - -### substrate (2 neurons, both settled) -- `warden-port-http` — settled, tier A -- `warden-port-tcp` — settled, tier A - ---- - -## What the Frack Exposes - -### Hot Spots (forming_load > 2.0) — Still Actively Forming -1. **transfer** (3.132) — The transfer/transport concept is the single highest-load idea in the system -2. **readme_thermodynamic** (2.473) — The thermodynamic framing is still being worked out -3. **troubleshooting** (2.461) — Debug mode active -4. **project_summary** (2.459) — The summary itself hasn't crystallized yet - -### Settled (no load) — Compressed into Crystal -- Warden ports: the physical infrastructure is done -- 139 arc-docs packages: the research archive has been compressed - -### The Connectome's Verdict -The frack shows: -- **Physical layer is done** (warden ports settled, substrate crystallized) -- **Compression work is active** (hutter-enwik8/9 both at ~2.0 load) -- **Transfer/transport concept is the single hottest spot** (3.132 load) -- **System architecture (PTOS foundation) is still active** (2.122 load) - -The C. elegans connectome as a filter reveals: the hardware is laid down, the compression engine is running, and the transport layer is the idea under highest cognitive pressure. - ---- - -## Method -Each package in substrate_index.db was mapped to a "neuron" with: -- `concept_anchor` = neuron type -- `forming_load` = activation level -- `domain` = ganglion membership -- `tier` = crystallization state - -The connectome topology filters out semantic noise and exposes the raw pressure profile of the cognitive system. \ No newline at end of file diff --git a/5-Applications/tools-scripts/archive/daemon_health.json b/5-Applications/tools-scripts/archive/daemon_health.json deleted file mode 100644 index 525c8892..00000000 --- a/5-Applications/tools-scripts/archive/daemon_health.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:3c8f500d93d8b00041c898608cb87449429bfe5e360ca7d0794154ffaf7aead3 -size 162 diff --git a/5-Applications/tools-scripts/archive/decoherent-histories-torsion-20260403.json b/5-Applications/tools-scripts/archive/decoherent-histories-torsion-20260403.json deleted file mode 100644 index 836b6990..00000000 --- a/5-Applications/tools-scripts/archive/decoherent-histories-torsion-20260403.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:8fa255923579472a4b0a09061e835656d15c9a37e3710fa9a641293c995876ae -size 2016 diff --git a/5-Applications/tools-scripts/archive/freetsa_chain.pem b/5-Applications/tools-scripts/archive/freetsa_chain.pem deleted file mode 100644 index 9b17fc93..00000000 --- a/5-Applications/tools-scripts/archive/freetsa_chain.pem +++ /dev/null @@ -1,82 +0,0 @@ ------BEGIN CERTIFICATE----- -MIIH/zCCBeegAwIBAgIJAMHphhYNqOmAMA0GCSqGSIb3DQEBDQUAMIGVMREwDwYD -VQQKEwhGcmVlIFRTQTEQMA4GA1UECxMHUm9vdCBDQTEYMBYGA1UEAxMPd3d3LmZy -ZWV0c2Eub3JnMSIwIAYJKoZIhvcNAQkBFhNidXNpbGV6YXNAZ21haWwuY29tMRIw -EAYDVQQHEwlXdWVyemJ1cmcxDzANBgNVBAgTBkJheWVybjELMAkGA1UEBhMCREUw -HhcNMTYwMzEzMDE1MjEzWhcNNDEwMzA3MDE1MjEzWjCBlTERMA8GA1UEChMIRnJl -ZSBUU0ExEDAOBgNVBAsTB1Jvb3QgQ0ExGDAWBgNVBAMTD3d3dy5mcmVldHNhLm9y -ZzEiMCAGCSqGSIb3DQEJARYTYnVzaWxlemFzQGdtYWlsLmNvbTESMBAGA1UEBxMJ -V3VlcnpidXJnMQ8wDQYDVQQIEwZCYXllcm4xCzAJBgNVBAYTAkRFMIICIjANBgkq -hkiG9w0BAQEFAAOCAg8AMIICCgKCAgEAtgKODjAy8REQ2WTNqUudAnjhlCrpE6ql -mQfNppeTmVvZrH4zutn+NwTaHAGpjSGv4/WRpZ1wZ3BRZ5mPUBZyLgq0YrIfQ5Fx -0s/MRZPzc1r3lKWrMR9sAQx4mN4z11xFEO529L0dFJjPF9MD8Gpd2feWzGyptlel -b+PqT+++fOa2oY0+NaMM7l/xcNHPOaMz0/2olk0i22hbKeVhvokPCqhFhzsuhKsm -q4Of/o+t6dI7sx5h0nPMm4gGSRhfq+z6BTRgCrqQG2FOLoVFgt6iIm/BnNffUr7V -DYd3zZmIwFOj/H3DKHoGik/xK3E82YA2ZulVOFRW/zj4ApjPa5OFbpIkd0pmzxzd -EcL479hSA9dFiyVmSxPtY5ze1P+BE9bMU1PScpRzw8MHFXxyKqW13Qv7LWw4sbk3 -SciB7GACbQiVGzgkvXG6y85HOuvWNvC5GLSiyP9GlPB0V68tbxz4JVTRdw/Xn/XT -FNzRBM3cq8lBOAVt/PAX5+uFcv1S9wFE8YjaBfWCP1jdBil+c4e+0tdywT2oJmYB -BF/kEt1wmGwMmHunNEuQNzh1FtJY54hbUfiWi38mASE7xMtMhfj/C4SvapiDN837 -gYaPfs8x3KZxbX7C3YAsFnJinlwAUss1fdKar8Q/YVs7H/nU4c4Ixxxz4f67fcVq -M2ITKentbCMCAwEAAaOCAk4wggJKMAwGA1UdEwQFMAMBAf8wDgYDVR0PAQH/BAQD -AgHGMB0GA1UdDgQWBBT6VQ2MNGZRQ0z357OnbJWveuaklzCBygYDVR0jBIHCMIG/ -gBT6VQ2MNGZRQ0z357OnbJWveuakl6GBm6SBmDCBlTERMA8GA1UEChMIRnJlZSBU -U0ExEDAOBgNVBAsTB1Jvb3QgQ0ExGDAWBgNVBAMTD3d3dy5mcmVldHNhLm9yZzEi -MCAGCSqGSIb3DQEJARYTYnVzaWxlemFzQGdtYWlsLmNvbTESMBAGA1UEBxMJV3Vl -cnpidXJnMQ8wDQYDVQQIEwZCYXllcm4xCzAJBgNVBAYTAkRFggkAwemGFg2o6YAw -MwYDVR0fBCwwKjAooCagJIYiaHR0cDovL3d3dy5mcmVldHNhLm9yZy9yb290X2Nh -LmNybDCBzwYDVR0gBIHHMIHEMIHBBgorBgEEAYHyJAEBMIGyMDMGCCsGAQUFBwIB -FidodHRwOi8vd3d3LmZyZWV0c2Eub3JnL2ZyZWV0c2FfY3BzLmh0bWwwMgYIKwYB -BQUHAgEWJmh0dHA6Ly93d3cuZnJlZXRzYS5vcmcvZnJlZXRzYV9jcHMucGRmMEcG -CCsGAQUFBwICMDsaOUZyZWVUU0EgdHJ1c3RlZCB0aW1lc3RhbXBpbmcgU29mdHdh -cmUgYXMgYSBTZXJ2aWNlIChTYWFTKTA3BggrBgEFBQcBAQQrMCkwJwYIKwYBBQUH -MAGGG2h0dHA6Ly93d3cuZnJlZXRzYS5vcmc6MjU2MDANBgkqhkiG9w0BAQ0FAAOC -AgEAaK9+v5OFYu9M6ztYC+L69sw1omdyli89lZAfpWMMh9CRmJhM6KBqM/ipwoLt -nxyxGsbCPhcQjuTvzm+ylN6VwTMmIlVyVSLKYZcdSjt/eCUN+41K7sD7GVmxZBAF -ILnBDmTGJmLkrU0KuuIpj8lI/E6Z6NnmuP2+RAQSHsfBQi6sssnXMo4HOW5gtPO7 -gDrUpVXID++1P4XndkoKn7Svw5n0zS9fv1hxBcYIHPPQUze2u30bAQt0n0iIyRLz -aWuhtpAtd7ffwEbASgzB7E+NGF4tpV37e8KiA2xiGSRqT5ndu28fgpOY87gD3ArZ -DctZvvTCfHdAS5kEO3gnGGeZEVLDmfEsv8TGJa3AljVa5E40IQDsUXpQLi8G+UC4 -1DWZu8EVT4rnYaCw1VX7ShOR1PNCCvjb8S8tfdudd9zhU3gEB0rxdeTy1tVbNLXW -99y90xcwr1ZIDUwM/xQ/noO8FRhm0LoPC73Ef+J4ZBdrvWwauF3zJe33d4ibxEcb -8/pz5WzFkeixYM2nsHhqHsBKw7JPouKNXRnl5IAE1eFmqDyC7G/VT7OF669xM6hb -Ut5G21JE4cNK6NNucS+fzg1JPX0+3VhsYZjj7D5uljRvQXrJ8iHgr/M6j2oLHvTA -I2MLdq2qjZFDOCXsxBxJpbmLGBx9ow6ZerlUxzws2AWv2pk= ------END CERTIFICATE----- ------BEGIN CERTIFICATE----- -MIIGYDCCBEigAwIBAgIJAMLphhYNqOnNMA0GCSqGSIb3DQEBDQUAMIGVMREwDwYD -VQQKEwhGcmVlIFRTQTEQMA4GA1UECxMHUm9vdCBDQTEYMBYGA1UEAxMPd3d3LmZy -ZWV0c2Eub3JnMSIwIAYJKoZIhvcNAQkBFhNidXNpbGV6YXNAZ21haWwuY29tMRIw -EAYDVQQHEwlXdWVyemJ1cmcxDzANBgNVBAgTBkJheWVybjELMAkGA1UEBhMCREUw -HhcNMjYwMjE1MTk0NDIyWhcNNDAwMjAyMTk0NDIyWjCCAQsxETAPBgNVBAoMCEZy -ZWUgVFNBMQwwCgYDVQQLDANUU0ExdjB0BgNVBA0MbVRoaXMgY2VydGlmaWNhdGUg -ZGlnaXRhbGx5IHNpZ25zIGRvY3VtZW50cyBhbmQgdGltZSBzdGFtcCByZXF1ZXN0 -cyBtYWRlIHVzaW5nIHRoZSBmcmVldHNhLm9yZyBvbmxpbmUgc2VydmljZXMxGDAW -BgNVBAMMD3d3dy5mcmVldHNhLm9yZzEkMCIGCSqGSIb3DQEJARYVYnVzaWxlemFz -QG1haWxib3gub3JnMRIwEAYDVQQHDAlXdWVyemJ1cmcxCzAJBgNVBAYTAkRFMQ8w -DQYDVQQIDAZCYXllcm4wdjAQBgcqhkjOPQIBBgUrgQQAIgNiAASiFeGhstbLhxix -0o4UAumNSwHUUlOe3DBvs8fYs580wADW59oqGSCx15bp61TSmXkwLm1JW48XnbLL -izP6ZtjcvshV3H9uz2bS53sgDXhg1wLbIhAtraC+fHCytHeuVaujggHmMIIB4jAJ -BgNVHRMEAjAAMB0GA1UdDgQWBBQVwL0m69RdgtFdkyYxL+9wsotGXjAfBgNVHSME -GDAWgBT6VQ2MNGZRQ0z357OnbJWveuaklzALBgNVHQ8EBAMCBsAwFgYDVR0lAQH/ -BAwwCgYIKwYBBQUHAwgwbAYIKwYBBQUHAQEEYDBeMDMGCCsGAQUFBzAChidodHRw -Oi8vd3d3LmZyZWV0c2Eub3JnL2ZpbGVzL2NhY2VydC5wZW0wJwYIKwYBBQUHMAGG -G2h0dHA6Ly93d3cuZnJlZXRzYS5vcmc6MjU2MDA3BgNVHR8EMDAuMCygKqAohiZo -dHRwOi8vd3d3LmZyZWV0c2Eub3JnL2NybC9yb290X2NhLmNybDCByAYDVR0gBIHA -MIG9MIG6BgMrBQgwgbIwMwYIKwYBBQUHAgEWJ2h0dHA6Ly93d3cuZnJlZXRzYS5v -cmcvZnJlZXRzYV9jcHMuaHRtbDAyBggrBgEFBQcCARYmaHR0cDovL3d3dy5mcmVl -dHNhLm9yZy9mcmVldHNhX2Nwcy5wZGYwRwYIKwYBBQUHAgIwOxo5RnJlZVRTQSB0 -cnVzdGVkIHRpbWVzdGFtcGluZyBTb2Z0d2FyZSBhcyBhIFNlcnZpY2UgKFNhYVMp -MA0GCSqGSIb3DQEBDQUAA4ICAQBrMVS/YfnfMr0ziZnesBUOrDNRrNNgt3IgMNDw -Nhwl6oKWHVIhlYnM/5boljfbpZTAbqvxHI3ztT0/swxQOqTat5qBJRAY/VH1n/T4 -M9uDjSuu3qfh0ZH5PL9ENqoVW44i5NT/znQev2MGXOAHwz9kZwwzz9MFX6hbGhBq -Wa+nlAqb7Y72KFzj33m1OVHxV2Wl4YD9f91bZTFpUEGW4Ktbkmxpf/iGIPaf4WHp -oBW/O6EzofMKYlz4yXyEBh0wRRVyXltLrj+MFHqhe+PsMBllq/dCaO4W/F+AuHEl -u7aUYWMASelphWAJiUsNMr5HAoeCSSgilqf1CSoWC+k6e4334Fym+Iy4csMex+PG -4rSdqXJVQ+AWEdRajSPKh7yDfpNkdnO6yqQJ/tSd11XQ5cL0M9jWuCD1zHlgA+u+ -R2cry3yo23jD7qTGLhZqUvXCyWigH30/Q/RXjjDwrc4DJiQ+gRY0FhdTYqlvgMBP -r4LcJKnNksivdj+kbz7bVSbrBAzRiazK9l841/5XMtP9BvD0hKCpQFvP9PSgCC8E -QnKqgSe26FSJBaAQcA5TnK8NF4jkbElBxf/zyh7P3IjHso35jtgUWD1/itg9BJWb -YUwJ4tfILpB2F0wbk1GcZDCDZoyW3Xf3trApz/Zd93gF3joc9Hh9RFveKRzWQ7dd -Ut3egQ== ------END CERTIFICATE----- diff --git a/5-Applications/tools-scripts/archive/ignited_manifold.json b/5-Applications/tools-scripts/archive/ignited_manifold.json deleted file mode 100644 index 1e7edaa9..00000000 --- a/5-Applications/tools-scripts/archive/ignited_manifold.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:514213ef5704fa9d75c393278ed5135f2007b1c0a84d027c470fb905bc03488f -size 144 diff --git a/5-Applications/tools-scripts/archive/lambda_trace.jsonl b/5-Applications/tools-scripts/archive/lambda_trace.jsonl deleted file mode 100644 index e69de29b..00000000 diff --git a/5-Applications/tools-scripts/archive/mass_archivist.py b/5-Applications/tools-scripts/archive/mass_archivist.py deleted file mode 100644 index 8e98ddae..00000000 --- a/5-Applications/tools-scripts/archive/mass_archivist.py +++ /dev/null @@ -1,660 +0,0 @@ -import sys -import os -import json -import sqlite3 -import hashlib -import shutil -import subprocess -import time -from typing import List, Optional, Tuple, Iterable -import concurrent.futures -import queue -import threading -from pathlib import Path - -REPO_ROOT = Path( - os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[2] -) -TOOLS_DIR = REPO_ROOT / "tools" -NODES_DIR = REPO_ROOT / "infra" / "nodes" -DEFAULT_SOURCE = Path( - os.getenv("MASS_ARCHIVIST_SOURCE") - or Path.home() / ".gemini" / "antigravity" / "scratch" / "seismic_primer_1gb.dat" -) - -# Add repo-local paths -sys.path.insert(0, str(TOOLS_DIR)) -sys.path.insert(0, str(NODES_DIR)) - -from topological_encoder import TopologicalEncoder, HalftwistSolver -from braid_manager import BraidManager -from cache_sieve import CacheSieve -from thermal_arbiter import ThermalArbiter -from cmyk_arbiter import ChannelDecomposer -import tardy - -# Rust Warden bridge for batch attestation during archivist promotion. -class WardenBridge: - def __init__(self, binary: Optional[str] = None, repo_root: Optional[str] = None): - self.repo_root = Path( - repo_root - or os.getenv("RESEARCH_STACK_ROOT") - or Path(__file__).resolve().parents[1] - ) - self.binary = binary or os.getenv("WARDEN_BIN") - - def _resolve_command(self) -> List[str]: - if self.binary: - return [self.binary] - - path_candidate = shutil.which("sovereign_warden") - if path_candidate: - return [path_candidate] - - debug_candidate = self.repo_root / "target" / "debug" / "sovereign_warden" - if debug_candidate.exists(): - return [str(debug_candidate)] - - cargo_bin = shutil.which("cargo") - if cargo_bin: - return [cargo_bin, "run", "--quiet", "--bin", "sovereign_warden", "--"] - - raise FileNotFoundError("No sovereign_warden binary or cargo runner available") - - def _bridge_failure_report( - self, - record_indices: List[int], - target_level: int, - reason: str, - ) -> dict: - return { - "target_level": target_level, - "accepted_count": 0, - "rejected_count": len(record_indices), - "records": [ - { - "leaf_idx": idx, - "target_level": target_level, - "braid_level": 0, - "durability": 0, - "accepted": False, - "promoted_axiom": False, - "error": reason, - } - for idx in record_indices - ], - } - - def attest_batch_report( - self, - db_path: str, - record_indices: List[int], - target_level: int = 4, - metadata: Optional[dict] = None, - ) -> dict: - if not record_indices: - return self._bridge_failure_report([], target_level, "empty_batch") - - cmd = self._resolve_command() - cmd.extend( - [ - "audit-batch", - "--db", - db_path, - "--indices", - ",".join(str(idx) for idx in record_indices), - "--min-level", - str(target_level), - ] - ) - if metadata is not None: - cmd.extend( - [ - "--metadata-json", - json.dumps(metadata, sort_keys=True, separators=(",", ":")), - ] - ) - - try: - proc = subprocess.run( - cmd, - cwd=str(self.repo_root), - capture_output=True, - text=True, - check=False, - ) - except Exception as exc: - return self._bridge_failure_report( - record_indices, - target_level, - f"warden_bridge_error:{exc}", - ) - - stdout_lines = [line.strip() for line in proc.stdout.splitlines() if line.strip()] - json_line = stdout_lines[-1] if stdout_lines else "" - if not json_line: - reason = proc.stderr.strip() or f"warden_bridge_exit:{proc.returncode}" - return self._bridge_failure_report(record_indices, target_level, reason) - - try: - report = json.loads(json_line) - except json.JSONDecodeError as exc: - return self._bridge_failure_report( - record_indices, - target_level, - f"warden_bridge_invalid_json:{exc}", - ) - - if "records" not in report: - return self._bridge_failure_report( - record_indices, - target_level, - "warden_bridge_missing_records", - ) - return report - - def attest(self, db_path: str, record_idx: int, target_level: int = 4) -> bool: - return self.attest_batch(db_path, [record_idx], target_level=target_level)[0] - - def attest_batch( - self, - db_path: str, - record_indices: List[int], - target_level: int = 4, - metadata: Optional[dict] = None, - ) -> List[bool]: - report = self.attest_batch_report( - db_path, record_indices, target_level=target_level, metadata=metadata - ) - batch_root_idx = report.get("batch_root_idx") - records = report.get("records", []) - if batch_root_idx is not None and len(records) == 1: - accepted = bool(records[0].get("accepted")) - return [accepted for _ in record_indices] - - verdicts = { - record["leaf_idx"]: bool(record.get("accepted")) - for record in records - } - return [verdicts.get(idx, False) for idx in record_indices] - - -PHYSICAL_SWEEP_CHUNK_BYTES = 1024 -LOGICAL_SUBREGISTER_BITS = 8096 -LOGICAL_SUBREGISTER_BYTES = LOGICAL_SUBREGISTER_BITS // 8 -READ_BYPASS_THRESHOLD_BYTES = 128 * PHYSICAL_SWEEP_CHUNK_BYTES -PROMOTION_BATCH_MAX_RECORDS = 1024 -PROMOTION_BATCH_MAX_BYTES = 1024 * 1024 # 1MB promotion window - -class MassArchivist: - """Orchestrates the foundational sweep and wall accumulation.""" - - def __init__(self, db_path: str = "~/.tardy_mmr.db", mode: str = "single-threaded"): - self.encoder = TopologicalEncoder() - self.bm = BraidManager(db_path=db_path) - self.sieve = CacheSieve() - self.warden = WardenBridge() - self.db_path = os.path.expanduser(db_path) - self.mode = mode - self.experts = ["solar", "seismic", "bio", "quantum"] - self.arbiter = ThermalArbiter() - self.arbiter.start_ticker() - - def _get_conn(self): - return sqlite3.connect(self.db_path) - - def run_sweep(self, source_path: str, max_bytes: int = 1000 * 1024 * 1024, workers: int = 16): - """ - Sweeps the source file using a Parallel Sieve architecture. - """ - print(f"=== [🚀 MASS ARCHIVIST] ===") - print(f"Source: {source_path}") - print(f"Mode: Parallel Sieve (Workers: {workers})") - print(f"Constraint: Hierarchical Dispatch (L0-L2)") - print("-" * 40) - - self.processed_bytes = 0 - self.axiom_count = 0 - self.read_bypass_windows = 0 - self.batch_flushes = 0 - self.regional_basin = [] - self.regional_basin_bytes = 0 - self.survivor_queue = [] - self.dispatch_queue = queue.Queue(maxsize=100) - self.stop_signal = threading.Event() - - if workers == 1: - self._run_serial(source_path, max_bytes) - return - - # 1. Start Dispatcher Thread - dispatcher = threading.Thread(target=self._dispatcher_loop, daemon=True) - dispatcher.start() - - # 2. Start Parallel Sieve (Producer) - with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor: - futures = [] - for offset, chunk in self._iter_source_chunks(source_path, max_bytes): - # Autonomous Rest Gate (Zero Cognitive Load) - self.arbiter.rest_event.wait() - - # Viscosity Scaling (Exponential Throttling) - viscosity = self.arbiter.get_viscosity_factor() - if viscosity > 1.5: - time.sleep(0.01 * viscosity) - - futures.append(executor.submit(MassArchivist._process_chunk, chunk, offset)) - self.processed_bytes = offset + len(chunk) - - # Throttling & Stress Reporting - if len(futures) > workers * 4: - self._handle_completed(futures) - # Report stress to arbiter based on queue depth - load_proxy = len(futures) / (workers * 4) - self.arbiter.report_stress(offset % workers, load_proxy) - - # Wait for remaining - self._handle_completed(futures, wait=True) - - # 3. Shutdown - self.stop_signal.set() - dispatcher.join() - - print("-" * 40) - print(f"[✅] Deep Sweep Complete. Total Axioms: {self.axiom_count}") - - @staticmethod - def _process_chunk(chunk: bytes, offset: int) -> Optional[dict]: - """L0/L1 worker function (ProcessPool). - Isolated from self to avoid pickling errors.""" - # Initialize isolated tools for the worker - from topological_encoder import TopologicalEncoder - from cache_sieve import CacheSieve - - encoder = TopologicalEncoder() - sieve = CacheSieve() - - # Simulate Manifold - Forced Perfect Stability for Stage 1 Baseline - # This ensures the 100MB truth baseline correctly anchors without triage rejections. - phi_corr = 0.42 # The Golden Ratio Sovereign Constant - mock_manifold = { - "phi_corr": phi_corr, - "radius": 1.0, - "torsion_gradient": [1.0] * 320 # Unit torsion to ensure PI closure - } - - should_survive, score = sieve.triage_bucket(mock_manifold) - if not should_survive: - if offset % (1024 * 64) == 0: - print(f"[debug] Triage Reject at offset {offset}: Score={score:.2f}") - return None - - # CMYK Semantic Coding - from cmyk_arbiter import ChannelDecomposer - metadata = sieve.get_cmyk_metadata() - mode_vec = mock_manifold.get("torsion_gradient", [])[:15] - encoding = ChannelDecomposer.decompose(mode_vec, metadata) - - shell = encoder.process_manifold(mock_manifold) - if shell: - return { - "shell": shell, - "offset": offset, - "encoding": encoding, - "source_bytes": len(chunk), - } - - if offset % (1024 * 64) == 0: - print(f"[debug] Shell Encoding Failed at offset {offset}") - return None - - def _iter_source_chunks(self, source_path: str, max_bytes: int) -> Iterable[Tuple[int, bytes]]: - """ - Stream chunks from disk using direct slab reads for large sequential sweeps. - - Small or tail reads stay in 1KiB page mode. Large contiguous windows are read - as a single slab and then yielded back as physical page units. - """ - offset = 0 - remaining = max_bytes - with open(source_path, "rb") as f: - while remaining > 0: - if remaining >= READ_BYPASS_THRESHOLD_BYTES: - slab = f.read(READ_BYPASS_THRESHOLD_BYTES) - if not slab: - break - self.read_bypass_windows += 1 - for start in range(0, len(slab), PHYSICAL_SWEEP_CHUNK_BYTES): - chunk = slab[start : start + PHYSICAL_SWEEP_CHUNK_BYTES] - if not chunk: - break - yield offset, chunk - offset += len(chunk) - remaining -= len(chunk) - if remaining <= 0: - break - else: - chunk = f.read(min(PHYSICAL_SWEEP_CHUNK_BYTES, remaining)) - if not chunk: - break - yield offset, chunk - offset += len(chunk) - remaining -= len(chunk) - - def _run_serial(self, source_path: str, max_bytes: int): - """Strictly single-threaded implementation for semantic truth.""" - print("[archivist] ENTERING STRICT SERIAL MODE (Causal Truth)") - for offset, chunk in self._iter_source_chunks(source_path, max_bytes): - result = MassArchivist._process_chunk(chunk, offset) - if result: - self._collect_survivor(result) - self.processed_bytes = offset + len(chunk) - - self._flush_regional_basin() - - print("-" * 40) - print(f"[✅] Serial Sweep Complete. Total Axioms: {self.axiom_count}") - - def _dispatch_item(self, item: dict): - """Internal helper for serial/dispatcher unified logic.""" - self._dispatch_batch([item]) - - def _collect_survivor(self, item: dict): - self.regional_basin.append(item) - self.regional_basin_bytes += int(item.get("source_bytes", PHYSICAL_SWEEP_CHUNK_BYTES)) - if self._should_flush_regional_basin(): - self._flush_regional_basin() - - def _should_flush_regional_basin(self) -> bool: - return ( - len(self.regional_basin) >= PROMOTION_BATCH_MAX_RECORDS - or self.regional_basin_bytes >= PROMOTION_BATCH_MAX_BYTES - ) - - def _flush_regional_basin(self): - if not self.regional_basin: - return - basin = self.regional_basin - self.regional_basin = [] - self.regional_basin_bytes = 0 - self.batch_flushes += 1 - self._dispatch_batch(basin) - - def _dispatch_batch(self, items: List[dict]): - committed = self._commit_regional_basin(items) - self._audit_committed_batch(committed, items) - - def _build_batch_witness_metadata(self, items: List[dict]) -> dict: - if not items: - return {} - - offsets = [int(item.get("offset", 0)) for item in items] - sizes = [ - int(item.get("source_bytes", PHYSICAL_SWEEP_CHUNK_BYTES)) for item in items - ] - offset_end_exclusive = max( - offset + size for offset, size in zip(offsets, sizes) - ) - - strategy_counts = {} - confidence_values = [] - primary_values = [] - residual_values = [] - terminal_count = 0 - secondary_check_count = 0 - - for item in items: - encoding = item.get("encoding") or {} - if not isinstance(encoding, dict): - continue - strategy = str(encoding.get("strategy", "UNKNOWN")) - strategy_counts[strategy] = strategy_counts.get(strategy, 0) + 1 - if "confidence" in encoding: - confidence_values.append(float(encoding["confidence"])) - if "primary_amp" in encoding: - primary_values.append(float(encoding["primary_amp"])) - if "residual" in encoding: - residual_values.append(float(encoding["residual"])) - if encoding.get("is_terminal"): - terminal_count += 1 - if encoding.get("secondary_check"): - secondary_check_count += 1 - - def mean(values: List[float]) -> float: - if not values: - return 0.0 - return round(sum(values) / len(values), 4) - - return { - "sweep_window": { - "offset_start": min(offsets), - "offset_end_exclusive": offset_end_exclusive, - "source_bytes_total": sum(sizes), - }, - "source_offsets": offsets, - "cmyk_composition": { - "strategy_counts": strategy_counts, - "terminal_count": terminal_count, - "secondary_check_count": secondary_check_count, - "mean_confidence": mean(confidence_values), - "mean_primary_amp": mean(primary_values), - "mean_residual": mean(residual_values), - }, - } - - def _commit_regional_basin(self, items: List[dict]) -> List[int]: - conn = self._get_conn() - committed = [] - try: - for local_index, item in enumerate(items): - shell = item["shell"] - offset = item["offset"] - expert = self.experts[(self.axiom_count + local_index) % len(self.experts)] - parent_indices = self._get_optimal_parents_conn(conn, count=4) - - dag_node = self.encoder.as_dag_node(shell) - payload = json.dumps({ - "agent": expert, - "context": f"Sweep offset {offset}", - "topology": dag_node["topology"], - "anchor": dag_node["anchor"], - "cmyk": item.get("encoding"), - "tracker": { - "signal": item.get("confidence", 0.5), - "stress": self.arbiter.total_stress, - "conductance": 1100.0 # theta-TaN scale - } - }) - - idx = self._tardy_append_conn(conn, payload, parent_indices) - committed.append(idx) - - conn.commit() - return committed - finally: - conn.close() - - def _audit_committed_batch(self, indices: List[int], items: List[dict]): - report = None - root_record = None - batch_root_idx = None - batch_metadata = self._build_batch_witness_metadata(items) - if hasattr(self.warden, "attest_batch_report"): - report = self.warden.attest_batch_report( - self.db_path, - indices, - target_level=4, # Phase 11: Level 4 base, scaling to L8 - metadata=batch_metadata, - ) - batch_root_idx = report.get("batch_root_idx") - if report.get("records"): - root_record = report["records"][0] - root_accepted = bool(root_record and root_record.get("accepted")) - verdicts = [root_accepted for _ in indices] - elif hasattr(self.warden, "attest_batch"): - verdicts = self.warden.attest_batch(self.db_path, indices, target_level=4) - else: - verdicts = [self.warden.attest(self.db_path, idx, target_level=4) for idx in indices] - - if report is not None: - if root_record and root_record.get("accepted") and batch_root_idx is not None: - self._promote_record(int(batch_root_idx)) - self.axiom_count += len(indices) - if self.axiom_count % 50 == 0: - tail_offset = items[-1]["offset"] if items else 0 - print(f"[archivist] Swept {tail_offset // 1024} KB | Axioms Accumulated: {self.axiom_count}") - return - - reason = "batch_root_veto" - if root_record and root_record.get("error"): - reason = str(root_record["error"]) - for idx, item in zip(indices, items): - self.survivor_queue.append({ - "idx": idx, - "offset": item["offset"], - "reason": reason, - "item": item, - "batch_root_idx": batch_root_idx, - }) - return - - for idx, item, accepted in zip(indices, items, verdicts): - if accepted: - self._promote_record(idx) - self.axiom_count += 1 - if self.axiom_count % 50 == 0: - print(f"[archivist] Swept {item['offset'] // 1024} KB | Axioms Accumulated: {self.axiom_count}") - else: - self.survivor_queue.append({ - "idx": idx, - "offset": item["offset"], - "reason": "warden_veto", - "item": item, - "batch_root_idx": None, - }) - - def _promote_record(self, leaf_idx: int): - self.bm.promote_to_axiom(leaf_idx) - - def _get_optimal_parents_conn(self, conn: sqlite3.Connection, count: int = 2) -> List[int]: - latest = conn.execute("SELECT leaf_idx FROM mmr ORDER BY leaf_idx DESC LIMIT 1").fetchone() - if not latest: - return [] - - parents = [latest[0]] - if count <= 1: - return parents - - current_parents_data = conn.execute( - "SELECT payload FROM mmr WHERE leaf_idx = ?", (latest[0],) - ).fetchone() - - current_domain = "unknown" - if current_parents_data: - try: - current_domain = json.loads(current_parents_data[0]).get("agent", "unknown").lower() - except Exception: - current_domain = "unknown" - - candidates = conn.execute( - "SELECT leaf_idx, payload FROM mmr WHERE leaf_idx != ? AND leaf_type = 'ADD' " - "ORDER BY leaf_idx DESC LIMIT 50", - (latest[0],) - ).fetchall() - - selected_domains = {current_domain} - for idx, payload in candidates: - if len(parents) >= count: - break - try: - domain = json.loads(payload).get("agent", "unknown").lower() - if domain not in selected_domains: - parents.append(idx) - selected_domains.add(domain) - except Exception: - if len(parents) < count: - parents.append(idx) - - return sorted(list(set(parents))) - - def _handle_completed(self, futures: List[concurrent.futures.Future], wait: bool = False): - """Collects survivors from triage and pushes to dispatch queue.""" - finished = [] - for f in (futures if wait else [f for f in futures if f.done()]): - try: - result = f.result() - if result: - self.dispatch_queue.put(result) - except Exception as e: - print(f"[archivist] Worker Fault: {e}") - finished.append(f) - - for f in finished: - futures.remove(f) - - def _dispatcher_loop(self): - """L2 Dispatcher thread (Serial SQLite Writer)""" - while not self.stop_signal.is_set() or not self.dispatch_queue.empty(): - try: - item = self.dispatch_queue.get(timeout=1) - self._collect_survivor(item) - except queue.Empty: - continue - self._flush_regional_basin() - - def _simulate_manifold(self, chunk: bytes, offset: int) -> dict: - """Simulates the manifold state for a chunk of data.""" - # Use hashing to create a deterministic but 'noisy' manifold - h = hashlib.sha256(chunk).digest() - phi_corr = 0.35 + (h[0] % 120) / 1000.0 # Force into Seismic [0.35, 0.47) - - return { - "phi_corr": phi_corr, - "radius": 1.0 + (h[1] % 100) / 100.0, - "torsion_gradient": [float(x) / 128.0 for x in h] * 10 # 320 samples - } - - def _tardy_append(self, payload: str, parents: List[int]) -> int: - """Manual append to the Tardy MMR with multi-parent braiding.""" - conn = self._get_conn() - try: - idx = self._tardy_append_conn(conn, payload, parents) - conn.commit() - return idx - finally: - conn.close() - - def _tardy_append_conn(self, conn: sqlite3.Connection, payload: str, parents: List[int]) -> int: - """Append to the Tardy MMR using an existing transaction.""" - # Monotone counter - row = conn.execute("SELECT COALESCE(MAX(leaf_idx), -1) FROM mmr").fetchone() - idx = row[0] + 1 - - # Simple hashes for the mock sweep - leaf_hash = hashlib.sha256(payload.encode()).hexdigest() - root_hash = hashlib.sha256(f"{idx}|{leaf_hash}".encode()).hexdigest() - - conn.execute( - "INSERT INTO mmr (leaf_idx, leaf_type, payload, leaf_hash, root_hash, ts, node_id, sig) " - "VALUES (?, 'ADD', ?, ?, ?, ?, 'archivist', 'sig')", - (idx, payload, leaf_hash, root_hash, str(time.time())) - ) - - for p_idx in parents: - conn.execute( - "INSERT INTO mmr_parents (child_idx, parent_idx) VALUES (?,?)", - (idx, p_idx) - ) - - return idx - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser(description="Sovereign Mass Archivist") - parser.add_argument("source", nargs="?", default=str(DEFAULT_SOURCE)) - parser.add_argument("max_bytes", type=int, nargs="?", default=1024 * 1024 * 1024) - parser.add_argument("--workers", type=int, default=16) - args = parser.parse_args() - - archivist = MassArchivist() - archivist.run_sweep(args.source, args.max_bytes, workers=args.workers) diff --git a/5-Applications/tools-scripts/archive/multi-affine-quantum-geometry-20260403.json b/5-Applications/tools-scripts/archive/multi-affine-quantum-geometry-20260403.json deleted file mode 100644 index 50741988..00000000 --- a/5-Applications/tools-scripts/archive/multi-affine-quantum-geometry-20260403.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:79776ceec5197b1588010d81208bc46120756f5e7a6ae0e6aeb0d2cbd22ef315 -size 1820 diff --git a/5-Applications/tools-scripts/archive/nodes_inventory.example.json b/5-Applications/tools-scripts/archive/nodes_inventory.example.json deleted file mode 100644 index 8b1287ad..00000000 --- a/5-Applications/tools-scripts/archive/nodes_inventory.example.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:40c7ce8dace16e348c4057a3a92114fa8e5591e5d044681985ec7e486fa048b7 -size 747 diff --git a/5-Applications/tools-scripts/archive/nodes_inventory.json b/5-Applications/tools-scripts/archive/nodes_inventory.json deleted file mode 100644 index 280e146e..00000000 --- a/5-Applications/tools-scripts/archive/nodes_inventory.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:74e24ce13db01ac9c06d5025f979c58a2955ddead35f256d13affc2ed716386e -size 972 diff --git a/5-Applications/tools-scripts/archive/optimizer_state.json b/5-Applications/tools-scripts/archive/optimizer_state.json deleted file mode 100644 index 1a01ab85..00000000 --- a/5-Applications/tools-scripts/archive/optimizer_state.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:5fddb50d3e009b307beebec587c12a4b06d3a8347e2056c62c79a266a80e4ea6 -size 47 diff --git a/5-Applications/tools-scripts/archive/quantum-coherence-evolution-20260403.json b/5-Applications/tools-scripts/archive/quantum-coherence-evolution-20260403.json deleted file mode 100644 index e7c001e1..00000000 --- a/5-Applications/tools-scripts/archive/quantum-coherence-evolution-20260403.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:56b7da7aa478bf074d0ca9ad53d403381163c8f456ec7f40b2ed1bc321d3ac46 -size 1864 diff --git a/5-Applications/tools-scripts/archive/quantum-temporal-geometry-20260403.json b/5-Applications/tools-scripts/archive/quantum-temporal-geometry-20260403.json deleted file mode 100644 index b04fd1ef..00000000 --- a/5-Applications/tools-scripts/archive/quantum-temporal-geometry-20260403.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:11c20d0d776c0faca17d3c7433aef70f11af2b93e4ee05c873aca890e66c3cac -size 2004 diff --git a/5-Applications/tools-scripts/archive/sample_art.txt b/5-Applications/tools-scripts/archive/sample_art.txt deleted file mode 100644 index e3773fbc..00000000 --- a/5-Applications/tools-scripts/archive/sample_art.txt +++ /dev/null @@ -1,6 +0,0 @@ - ____ - /\' .\ _____ -/: \___\ / . /\ -\' / . / /____/..\ - \/___/ \' '\ / - \'__'\/ diff --git a/5-Applications/tools-scripts/archive/search-space-reduction-map-20260403.json b/5-Applications/tools-scripts/archive/search-space-reduction-map-20260403.json deleted file mode 100644 index 9977d512..00000000 --- a/5-Applications/tools-scripts/archive/search-space-reduction-map-20260403.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:045e6d8d764053bedde34a595d05533862aef8c62f21b504c75ecba5c7907325 -size 2448 diff --git a/5-Applications/tools-scripts/archive/shadow_telemetry.json b/5-Applications/tools-scripts/archive/shadow_telemetry.json deleted file mode 100644 index 5b75ffa7..00000000 --- a/5-Applications/tools-scripts/archive/shadow_telemetry.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:6c83432783f288081801fe10db9d19c8883040e7610ad3488903e7c70ce8a674 -size 124 diff --git a/5-Applications/tools-scripts/archive/ucft-digest-20260402.json b/5-Applications/tools-scripts/archive/ucft-digest-20260402.json deleted file mode 100644 index 0412d6df..00000000 --- a/5-Applications/tools-scripts/archive/ucft-digest-20260402.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:d8f36b3825342d9fe8908f42acdeae85838bd6bc7b113e2c538f0c3829b3d32b -size 1642 diff --git a/5-Applications/tools-scripts/audio/audio_compression_sim.py b/5-Applications/tools-scripts/audio/audio_compression_sim.py deleted file mode 100644 index e0bcecfa..00000000 --- a/5-Applications/tools-scripts/audio/audio_compression_sim.py +++ /dev/null @@ -1,202 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -USC-Audio: Topological Soliton Encoding — Shannon-Eddington-Bekenstein revision. - -Soliton dimensions are distributed across frequency bands using gravitational -shift weighting (Yu et al. 2025 / Bekenstein 1973): - - High-entropy bands sit near the snag (horizon) — heavily blueshifted, - need fewer basis dimensions (information already captured by the snag). - - Low-entropy bands are redshifted — spread thin, need more dimensions. - -Each dimension still must carry >= 1 bit (Landauer floor). -""" -import math - -from usc_spectral_core import ( - eddington_utilization, shannon_capacity, signal_band, landauer_cost, - blueshift_factor, redshift_factor, shift_allocation, - geometric_dimensions, deep_compression_snag, - total_shift, angular_momentum_modes, conversion_efficiency, friction_loss, -) - -PARAMS_PER_SOLITON = 8 # position(x), amplitude, phase, velocity(x), temporal-rate, - # curvature, bandwidth, coherence-length - - -def pcm_entropy(bit_depth: int, occupancy: float = 0.6) -> float: - """Estimate entropy of a PCM band given bit depth and dynamic-range occupancy.""" - active_levels = (2 ** bit_depth) * occupancy - return math.log2(max(active_levels, 1.0)) - - -def octave_bands(f_low: float, f_high: float, n_bands: int) -> list: - """Return n_bands logarithmically-spaced center frequencies between f_low and f_high.""" - log_step = (math.log2(f_high) - math.log2(f_low)) / n_bands - return [f_low * (2 ** (i * log_step)) for i in range(n_bands)] - - -def spectral_occupancy(freq_hz: float, f_peak: float) -> float: - """ - Model spectral occupancy as a log-Gaussian envelope peaked at f_peak. - Low and high frequencies have less energy (lower occupancy). - """ - log_dist = (math.log2(max(freq_hz, 1.0)) - math.log2(max(f_peak, 1.0))) ** 2 - return max(0.05, math.exp(-log_dist / 4.0)) - - -class TSESolitonEncoder: - """ - Topological Soliton Encoder — full five-physics model: - 1. Gravitational shift (snag geometry / Bekenstein) - 2. Doppler shift (infall velocity) - 3. Angular momentum (Kerr frame-dragging / mode splitting) - 4. Conversion efficiency(accretion efficiency η) - 5. Friction (Shakura-Sunyaev viscous dissipation) - """ - - N_BANDS = 8 - BASIL_LEN_M = 0.035 # basilar membrane length [m] - - def _band_physics(self, centers, band_entropy, spin_param, friction_coeff): - """Per-band shift, friction retention, and velocity for each octave band.""" - h_max = max(band_entropy) - f_max = max(centers) - rows = [] - for f, h in zip(centers, band_entropy): - vel = (f / f_max) * 0.5 # infall velocity: faster near snag - retained = friction_loss(h, h_max, friction_coeff) - shift = total_shift(h, h_max, vel) - rows.append((f, h, vel, retained, shift)) - return rows - - def encode(self, f_low: float, f_high: float, snr_db: float, - duration_s: float, bit_depth: int = 16, - spin_param: float = 0.5, - friction_coeff: float = 0.05) -> dict: - """ - Encode a signal using the five-physics TSE model. - - Parameters - ---------- - f_low : lower frequency bound [Hz] - f_high : upper frequency bound [Hz] - snr_db : signal-to-noise ratio [dB] - duration_s : clip duration [s] - bit_depth : PCM quantisation depth - spin_param : temporal coherence ∈ [0,1] (0=noise, 1=pure tone) - friction_coeff : viscous dissipation μ ≥ 0 (Shakura-Sunyaev analog) - """ - snr_linear = 10 ** (snr_db / 10.0) - bandwidth = f_high - f_low - f_peak = math.sqrt(f_low * f_high) - - centers = octave_bands(f_low, f_high, self.N_BANDS) - band_entropy = [pcm_entropy(bit_depth, spectral_occupancy(f, f_peak)) - for f in centers] - h_total = sum(band_entropy) * (bandwidth * 2 * duration_s / self.N_BANDS) - - physics = self._band_physics(centers, band_entropy, spin_param, friction_coeff) - mean_retained = sum(r[3] for r in physics) / len(physics) - - # Bekenstein snag (3D → sqrt(H) law) - # n_snag uses raw horizon modes for band allocation — the Kerr AM - # splitting is for display; its efficiency benefit enters via η below. - snag = deep_compression_snag(h_total, n_dims=3) - n_snag = snag['horizon_modes'] - n_am = angular_momentum_modes(n_snag, spin_param) # display only - - # Friction and conversion efficiency reduce effective captured entropy - eta = conversion_efficiency(spin_param) * mean_retained - captured = h_total * eta - residual = h_total - captured - - band_dims = shift_allocation(band_entropy, n_snag) - encoded_bits = sum(d * PARAMS_PER_SOLITON * 16 for d in band_dims) - residual_bits = residual # irreducible floor (Hawking-analog) - total_bits = encoded_bits + residual_bits - - capacity = shannon_capacity(bandwidth, snr_linear) * duration_s - # λ_Edd measures the soliton basis against Shannon capacity. - # The residual is thermodynamically irreducible (like Hawking radiation) - # and does not count against channel capacity. - lam = eddington_utilization(encoded_bits, capacity) - band_name, band_desc = signal_band(f_peak) - geo_n = geometric_dimensions(self.BASIL_LEN_M / self.N_BANDS, - self.BASIL_LEN_M / (2 * math.pi)) - - return { - 'band': band_name, - 'band_desc': band_desc, - 'h_total': h_total, - 'geo_n': geo_n, - 'snag_modes': n_snag, - 'am_modes': n_am, - 'eta': eta, - 'mean_retained': mean_retained, - 'residual_bits': residual_bits, - 'encoded_bytes': total_bits / 8, - 'soliton_bytes': encoded_bits / 8, - 'capacity_bits': capacity, - 'lambda_edd': lam, - 'landauer_J': landauer_cost(h_total), - 'physics': physics, - 'band_dims': band_dims, - 'band_entropy': band_entropy, - } - - -def run_audio_poc(): - """Benchmark three signal types under the five-physics TSE model.""" - print("=" * 70) - print(" USC-AUDIO: TSE — gravity + doppler + ang.mom. + η + friction") - print("=" * 70) - - duration = 1.0 - flac_bytes = int(192000 * 2 * duration * 2) * 0.20 - - enc = TSESolitonEncoder() - - # spin: voice=periodic formants, music=moderate, chaos=near-noise - # friction: Shakura-Sunyaev α — higher for chaotic signals - voice = enc.encode(300, 3400, snr_db=40, duration_s=duration, - bit_depth=16, spin_param=0.80, friction_coeff=0.05) - music = enc.encode(20, 20000, snr_db=60, duration_s=duration, - bit_depth=16, spin_param=0.50, friction_coeff=0.05) - chaos = enc.encode(20, 96000, snr_db=80, duration_s=duration, - bit_depth=24, spin_param=0.10, friction_coeff=0.15) - - for label, r in [('VOICE', voice), ('MUSIC', music), ('CHAOS', chaos)]: - print(f"\n[{label}] H={r['h_total']:.0f} bits band={r['band']}") - print(f" Snag (Bekenstein) : {r['snag_modes']}") - print(f" + AM split (Kerr) : {r['am_modes']}") - print(f" η (accrtn×friction): {r['eta']:.4f} " - f"[friction retained={r['mean_retained']:.4f}]") - print(f" Captured entropy : {r['h_total']*r['eta']:.0f} bits") - print(f" Friction residual : {r['residual_bits']:.0f} bits ← irreducible floor") - print(f" Soliton basis : {r['soliton_bytes']:.1f} bytes") - print(f" Total encoded : {r['encoded_bytes']:.1f} bytes") - print(f" FLAC market : {flac_bytes:.0f} bytes") - print(f" λ_Edd : {r['lambda_edd']:.6f}") - print(f" Landauer cost : {r['landauer_J']:.3e} J") - print(f"\n {'Hz':>8} {'H':>6} {'β vel':>6} {'fric':>6} " - f"{'shift':>7} {'redshft':>7} {'N':>5}") - print(f" {'-'*57}") - h_max = max(r['band_entropy']) - for (f, h, vel, ret, shift), dims in zip(r['physics'], r['band_dims']): - rf = redshift_factor(h, h_max) - print(f" {f:>8.0f} {h:>6.2f} {vel:>6.3f} {ret:>6.3f} " - f"{shift:>7.3f} {rf:>7.3f} {dims:>5}") - - print("\n" + "=" * 70) - print(" Friction floor is irreducible — entropy is a law, not a suggestion.") - print(" High spin → higher η → smaller residual → better compression.") - print("=" * 70) - - -if __name__ == "__main__": - run_audio_poc() diff --git a/5-Applications/tools-scripts/audio/normalize_flac_genres.py b/5-Applications/tools-scripts/audio/normalize_flac_genres.py deleted file mode 100644 index 7c8b4b15..00000000 --- a/5-Applications/tools-scripts/audio/normalize_flac_genres.py +++ /dev/null @@ -1,149 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -normalize_flac_genres.py — Non-destructive FLAC genre/date normalization audit + fix. - -Usage: - python3 normalize_flac_genres.py [--fix] [--report ] - -Modes: - (default) dry-run: print what would change, touch nothing - --fix write normalized GENRE tags back into FLAC files (metaflac) - --report write JSON audit report to file - -Normalization rules: - 1. Non-ASCII / locale-specific → English equivalent - 2. Multi-value (slash / comma-separated) → canonical single value - 3. Overly specific MusicBrainz subgenre → standard parent genre - 4. DATE year-only → leaves untouched (no good source of truth), reports only -""" - -import argparse, collections, json, os, re, subprocess, sys - -# ── Normalisation map ─────────────────────────────────────────────────────── -NORM_MAP = { - # French MusicBrainz locale genres → English - "Électronique": "Electronic", - "Électronique, Dance": "Electronic", - "Électronique, Pop": "Electropop", - "Alternatif et Indé": "Alternative", - "Pop, Rock, Alternatif et Indé":"Alternative Rock", - "Rock & Roll, Rhythm 'n' Blues":"Rock", - "Musique du monde": "World", - "Métal": "Metal", - "Jazz & Blues": "Jazz", - - # MusicBrainz subgenres → broader standard - "Space Rock Revival": "Rock", - "Post-Grunge": "Rock", - "Rock Music": "Rock", - "Nu Metal": "Metal", - "Industrial Metal": "Metal", - "Stage And Screen": "Soundtrack", - "Films/Games": "Soundtrack", - "Video Game Music": "Soundtrack", - - # Multi-value / slash genres - "Alternative / Rock / Metal": "Alternative Metal", - "Rock / Metal": "Metal", - "Pop / Rock": "Pop Rock", - "Jazz / Blues": "Jazz", - "Electronic / Dance": "Electronic", -} - -# ── Helpers ───────────────────────────────────────────────────────────────── - -def read_tags(path): - r = subprocess.run(["metaflac", "--export-tags-to=-", path], - capture_output=True, text=True) - tags = {} - for line in r.stdout.splitlines(): - if "=" in line: - k, _, v = line.partition("=") - tags[k.upper()] = v.strip() - return tags - - -def write_tag(path, field, value): - subprocess.run( - ["metaflac", f"--remove-tag={field}", f"--set-tag={field}={value}", path], - check=True - ) - - -def classify(genre): - if not genre: - return "missing", None - if genre in NORM_MAP: - return "normalize", NORM_MAP[genre] - if re.search(r"[^\x00-\x7F]", genre): - return "non_ascii", None - if "/" in genre or (", " in genre and not genre.startswith("R&")): - return "multi_value", None - return "ok", None - - -# ── Main ──────────────────────────────────────────────────────────────────── - -def audit(music_dir, fix=False, report_path=None): - results = [] - stats = collections.Counter() - - for root, _, files in os.walk(music_dir): - for fname in sorted(files): - if not fname.endswith(".flac"): - continue - path = os.path.join(root, fname) - rel = os.path.relpath(path, music_dir) - tags = read_tags(path) - genre = tags.get("GENRE", "") - date = tags.get("DATE", "") - - issue, suggestion = classify(genre) - stats[issue] += 1 - - date_issue = bool(re.match(r"^\d{4}$", date)) - if date_issue: - stats["year_only_date"] += 1 - - entry = { - "path": rel, "genre": genre, "issue": issue, - "suggestion": suggestion, "date": date, "date_issue": date_issue, - } - results.append(entry) - - if issue != "ok" or date_issue: - tag_note = f" {genre!r:<40} → {suggestion!r}" if suggestion else f" {genre!r}" - date_note = f" DATE={date} (year-only)" if date_issue else "" - action = "" - if fix and suggestion: - write_tag(path, "GENRE", suggestion) - action = " [FIXED]" - print(f"{'FIX' if fix and suggestion else 'WARN'}{action} {rel}") - if tag_note.strip(): print(tag_note) - if date_note: print(date_note) - - print(f"\n{'='*64}") - print(f"Scanned : {len(results)} tracks") - for k, v in sorted(stats.items()): - print(f" {k:<20}: {v}") - print(f"{'='*64}") - - if report_path: - with open(report_path, "w") as f: - json.dump({"stats": dict(stats), "tracks": results}, f, indent=2) - print(f"Report : {report_path}") - - -if __name__ == "__main__": - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("music_dir") - ap.add_argument("--fix", action="store_true", help="Write normalized genres back to files") - ap.add_argument("--report", metavar="FILE", help="Write JSON audit report") - args = ap.parse_args() - audit(args.music_dir, fix=args.fix, report_path=args.report) diff --git a/5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py b/5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py deleted file mode 100644 index 81ecabac..00000000 --- a/5-Applications/tools-scripts/audio/pipewire_dsp_workloads.py +++ /dev/null @@ -1,273 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -DSP-like front-end workloads for PipeWire waveprobe experiments. - -These are bounded host-side transforms intended to approximate the shape of a -front-end DSP lane without claiming to replace a later PipeWire filter node, -custom DSP block, or HDL path. -""" - -from __future__ import annotations - -from typing import Dict, Tuple - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - - -WORKLOAD_RAW = "raw" -WORKLOAD_SPECTRAL_FOCUS = "spectral_focus" -WORKLOAD_TRANSIENT_EDGE = "transient_edge" -WORKLOAD_HYBRID = "hybrid" - -AVAILABLE_WORKLOADS = ( - WORKLOAD_RAW, - WORKLOAD_SPECTRAL_FOCUS, - WORKLOAD_TRANSIENT_EDGE, - WORKLOAD_HYBRID, -) - - -def available_workloads() -> Tuple[str, ...]: - return AVAILABLE_WORKLOADS - - -def _decode_pcm_mono( - chunk_bytes: bytes, - sample_width_bytes: int, - channels: int, -) -> Tuple[AnyArray, int]: - frame_width = max(1, sample_width_bytes * max(1, channels)) - usable = len(chunk_bytes) - (len(chunk_bytes) % frame_width) - if usable <= 0: - return xp.zeros(0, dtype=xp.float32), 0 - trimmed = chunk_bytes[:usable] - - if sample_width_bytes == 1: - arr = xp.frombuffer(trimmed, dtype=xp.uint8).astype(xp.float32) - arr = (arr - 128.0) / 128.0 - elif sample_width_bytes == 2: - arr = xp.frombuffer(trimmed, dtype=" bytes: - if samples.size == 0: - return b"" - clipped = xp.clip(samples.astype(xp.float32, copy=False), -1.0, 1.0) - if channels > 1: - clipped = xp.repeat(clipped[:, None], channels, axis=1).reshape(-1) - - if sample_width_bytes == 1: - out = xp.clip(xp.round(clipped * 127.0 + 128.0), 0, 255).astype(xp.uint8) - elif sample_width_bytes == 2: - out = xp.clip(xp.round(clipped * 32767.0), -32768, 32767).astype(" AnyArray: - if processed.size == 0: - return processed.astype(xp.float32, copy=False) - ref_rms = float(xp.sqrt(xp.mean(reference * reference))) if reference.size else 0.0 - proc_rms = float(xp.sqrt(xp.mean(processed * processed))) - out = processed.astype(xp.float32, copy=True) - if ref_rms > 1e-9 and proc_rms > 1e-9: - out *= ref_rms / proc_rms - peak = float(xp.max(xp.abs(out))) if out.size else 0.0 - if peak > 0.999: - out *= 0.999 / peak - return out - - -def _spectral_focus(samples: AnyArray) -> AnyArray: - if samples.size < 8: - return samples.astype(xp.float32, copy=True) - window = xp.hanning(samples.size).astype(xp.float32) - spec = xp.fft.rfft(samples * window) - mag = xp.abs(spec) - if mag.size <= 1: - return samples.astype(xp.float32, copy=True) - max_mag = float(xp.max(mag[1:])) if mag.size > 1 else float(xp.max(mag)) - if max_mag <= 1e-12: - return xp.zeros_like(samples, dtype=xp.float32) - weights = 0.15 + 0.85 * xp.sqrt(mag / max_mag) - weights[0] *= 0.35 - focused = xp.fft.irfft(spec * weights, n=samples.size).real.astype(xp.float32) - blended = (0.65 * samples + 0.35 * focused).astype(xp.float32, copy=False) - return _match_rms(blended, samples) - - -def _transient_edge(samples: AnyArray) -> AnyArray: - if samples.size < 4: - return samples.astype(xp.float32, copy=True) - diff = xp.diff(samples, prepend=samples[0]).astype(xp.float32, copy=False) - kernel = xp.array([0.25, 0.5, 0.25], dtype=xp.float32) - smoothed = xp.convolve(diff, kernel, mode="same") - edged = xp.tanh(2.5 * smoothed).astype(xp.float32, copy=False) - blended = (0.55 * samples + 0.45 * edged).astype(xp.float32, copy=False) - return _match_rms(blended, samples) - - -def _hybrid(samples: AnyArray) -> AnyArray: - focused = _spectral_focus(samples) - edged = _transient_edge(samples) - mixed = (0.5 * samples + 0.3 * focused + 0.2 * edged).astype(xp.float32, copy=False) - return _match_rms(mixed, samples) - - -def _compute_metrics(samples: AnyArray, sample_rate_hz: int) -> Dict[str, float]: - if samples.size == 0: - return { - "rms": 0.0, - "zero_crossing_rate": 0.0, - "spectral_centroid_hz": 0.0, - "spectral_flatness": 0.0, - "dominant_freq_hz": 0.0, - "transient_ratio": 0.0, - "band_energy_low": 0.0, - "band_energy_mid": 0.0, - "band_energy_high": 0.0, - } - - rms = float(xp.sqrt(xp.mean(samples * samples))) - if samples.size >= 2: - zc = float(xp.mean((samples[:-1] * samples[1:]) < 0.0)) - diff = xp.diff(samples, prepend=samples[0]) - transient_ratio = float(xp.mean(xp.abs(diff)) / max(rms, 1e-9)) - else: - zc = 0.0 - transient_ratio = 0.0 - - if samples.size < 8 or sample_rate_hz <= 0: - return { - "rms": rms, - "zero_crossing_rate": zc, - "spectral_centroid_hz": 0.0, - "spectral_flatness": 0.0, - "dominant_freq_hz": 0.0, - "transient_ratio": transient_ratio, - "band_energy_low": 0.0, - "band_energy_mid": 0.0, - "band_energy_high": 0.0, - } - - window = xp.hanning(samples.size).astype(xp.float32) - spec = xp.fft.rfft(samples * window) - power = xp.abs(spec) ** 2 + 1e-12 - freqs = xp.fft.rfftfreq(samples.size, d=1.0 / sample_rate_hz) - total_power = float(xp.sum(power)) - - centroid = float(xp.sum(freqs * power) / total_power) - dom_idx = int(xp.argmax(power[1:]) + 1) if power.size > 1 else 0 - dominant = float(freqs[dom_idx]) if freqs.size > dom_idx else 0.0 - flatness = float(xp.exp(xp.mean(xp.log(power))) / max(xp.mean(power), 1e-12)) - - low_mask = freqs < 1000.0 - mid_mask = (freqs >= 1000.0) & (freqs < 4000.0) - high_mask = freqs >= 4000.0 - low = float(xp.sum(power[low_mask]) / total_power) - mid = float(xp.sum(power[mid_mask]) / total_power) - high = float(xp.sum(power[high_mask]) / total_power) - - return { - "rms": rms, - "zero_crossing_rate": zc, - "spectral_centroid_hz": centroid, - "spectral_flatness": flatness, - "dominant_freq_hz": dominant, - "transient_ratio": transient_ratio, - "band_energy_low": low, - "band_energy_mid": mid, - "band_energy_high": high, - } - - -def apply_dsp_workload( - chunk_bytes: bytes, - sample_width_bytes: int, - channels: int, - sample_rate_hz: int, - workload: str = WORKLOAD_RAW, -) -> Tuple[bytes, Dict[str, float]]: - if workload not in AVAILABLE_WORKLOADS: - raise ValueError( - f"Unknown DSP workload {workload!r}; expected one of {AVAILABLE_WORKLOADS}" - ) - - try: - samples, usable = _decode_pcm_mono( - chunk_bytes, - sample_width_bytes=sample_width_bytes, - channels=channels, - ) - except ValueError: - return chunk_bytes, {"workload": workload, "fallback_raw": 1.0} - - if usable <= 0: - return b"", {"workload": workload, "fallback_raw": 1.0} - - if workload == WORKLOAD_RAW: - processed = samples - elif workload == WORKLOAD_SPECTRAL_FOCUS: - processed = _spectral_focus(samples) - elif workload == WORKLOAD_TRANSIENT_EDGE: - processed = _transient_edge(samples) - else: - processed = _hybrid(samples) - - metrics_in = _compute_metrics(samples, sample_rate_hz=sample_rate_hz) - metrics_out = _compute_metrics(processed, sample_rate_hz=sample_rate_hz) - processed_bytes = _encode_pcm_mono( - processed, - sample_width_bytes=sample_width_bytes, - channels=channels, - ) - - metrics = { - "workload": workload, - "input_rms": metrics_in["rms"], - "output_rms": metrics_out["rms"], - "rms_ratio": metrics_out["rms"] / max(metrics_in["rms"], 1e-9), - "zero_crossing_rate": metrics_out["zero_crossing_rate"], - "spectral_centroid_hz": metrics_out["spectral_centroid_hz"], - "spectral_flatness": metrics_out["spectral_flatness"], - "dominant_freq_hz": metrics_out["dominant_freq_hz"], - "transient_ratio": metrics_out["transient_ratio"], - "band_energy_low": metrics_out["band_energy_low"], - "band_energy_mid": metrics_out["band_energy_mid"], - "band_energy_high": metrics_out["band_energy_high"], - "centroid_shift_hz": metrics_out["spectral_centroid_hz"] - - metrics_in["spectral_centroid_hz"], - "transient_shift": metrics_out["transient_ratio"] - - metrics_in["transient_ratio"], - } - return processed_bytes, metrics diff --git a/5-Applications/tools-scripts/audio/pipewire_waveprobe_compression_chain.py b/5-Applications/tools-scripts/audio/pipewire_waveprobe_compression_chain.py deleted file mode 100644 index b48dcbca..00000000 --- a/5-Applications/tools-scripts/audio/pipewire_waveprobe_compression_chain.py +++ /dev/null @@ -1,1422 +0,0 @@ -#!/usr/bin/env python3 -from __future__ import annotations - -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -""" -PipeWire -> compression -> waveprobe test harness. - -Captures a short WAV from PipeWire or loads an existing WAV, chunks the PCM -payload, then runs: - - 1. ENE compression routing / MI scoring - 2. unified_canal_pipeline waveprobe scoring - -The goal is not to replace the eventual HDL path. The goal is to measure what -happens when the existing compression logic is inserted into the host-side audio -front end. -""" - -import argparse -import hashlib -import importlib.util -import json -import math -import os -from pathlib import Path -# import subprocess (REMOVED BY WARDEN) -import sys -import time -import wave -from datetime import datetime, timezone -from typing import Any, Dict, List, Tuple - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import zlib - - -REPO_ROOT = Path(__file__).resolve().parents[1] -if str(REPO_ROOT) not in sys.path: - sys.path.insert(0, str(REPO_ROOT)) - -from ene_mi_signal import MISignal, extract_mi_features # noqa: E402 -from scripts.pipewire_dsp_workloads import ( # noqa: E402 - apply_dsp_workload, - available_workloads, -) - - -def _load_module(module_name: str, path: Path): - spec = importlib.util.spec_from_file_location(module_name, path) - if spec is None or spec.loader is None: - raise RuntimeError(f"Could not load module {module_name} from {path}") - module = importlib.util.module_from_spec(spec) - spec.loader.exec_module(module) - return module - - -UCP = _load_module( - "unified_canal_pipeline", - REPO_ROOT / "hutter" / "unified_canal_pipeline.py", -) - -METHOD_SPECS: Dict[str, Dict[str, float | str]] = { - "order0": { - "encode_kind": "order0", - "centroid_norm": 0.80, - "transient_norm": 0.85, - "flatness_norm": 0.92, - "zcr_norm": 0.85, - "low_band": 0.18, - "mid_band": 0.34, - "high_band": 0.80, - "complexity": 0.08, - "memory": 0.04, - }, - "zlib_fast": { - "encode_kind": "zlib_fast", - "centroid_norm": 0.30, - "transient_norm": 0.30, - "flatness_norm": 0.26, - "zcr_norm": 0.28, - "low_band": 0.72, - "mid_band": 0.42, - "high_band": 0.12, - "complexity": 0.18, - "memory": 0.12, - }, - "zlib": { - "encode_kind": "zlib", - "centroid_norm": 0.25, - "transient_norm": 0.25, - "flatness_norm": 0.20, - "zcr_norm": 0.25, - "low_band": 0.75, - "mid_band": 0.45, - "high_band": 0.10, - "complexity": 0.36, - "memory": 0.28, - }, - "gzip": { - "encode_kind": "gzip", - "centroid_norm": 0.28, - "transient_norm": 0.28, - "flatness_norm": 0.24, - "zcr_norm": 0.27, - "low_band": 0.70, - "mid_band": 0.48, - "high_band": 0.14, - "complexity": 0.34, - "memory": 0.22, - }, - "bz2": { - "encode_kind": "bz2", - "centroid_norm": 0.40, - "transient_norm": 0.38, - "flatness_norm": 0.30, - "zcr_norm": 0.32, - "low_band": 0.60, - "mid_band": 0.62, - "high_band": 0.18, - "complexity": 0.58, - "memory": 0.52, - }, - "brotli": { - "encode_kind": "brotli", - "centroid_norm": 0.22, - "transient_norm": 0.20, - "flatness_norm": 0.16, - "zcr_norm": 0.20, - "low_band": 0.78, - "mid_band": 0.55, - "high_band": 0.08, - "complexity": 0.54, - "memory": 0.48, - }, - "zstd": { - "encode_kind": "zstd", - "centroid_norm": 0.24, - "transient_norm": 0.24, - "flatness_norm": 0.18, - "zcr_norm": 0.22, - "low_band": 0.74, - "mid_band": 0.58, - "high_band": 0.10, - "complexity": 0.30, - "memory": 0.24, - }, - "lzma": { - "encode_kind": "lzma", - "centroid_norm": 0.45, - "transient_norm": 0.45, - "flatness_norm": 0.30, - "zcr_norm": 0.35, - "low_band": 0.55, - "mid_band": 0.70, - "high_band": 0.20, - "complexity": 0.82, - "memory": 0.86, - }, -} - -METHOD_PROFILES: Dict[str, Tuple[str, ...]] = { - "baseline": ("order0", "zlib", "lzma"), - "expanded": ( - "order0", - "zlib_fast", - "zlib", - "gzip", - "bz2", - "brotli", - "zstd", - "lzma", - ), -} - - -def available_method_profiles() -> Tuple[str, ...]: - return tuple(METHOD_PROFILES.keys()) - - -def _select_hybrid_dsp_workload( - dsp_metrics: Dict[str, float], - waveprobe_metrics: Dict[str, float], -) -> str: - """ - Hybrid DSP workload selector using branch prediction principles. - - Selects optimal DSP workload based on audio characteristics: - - Spectral-dominant audio → spectral_focus (FFT-based enhancement) - - Transient-dominant audio → transient_edge (edge detection) - - Balanced audio → hybrid (combined spectral + transient) - - Simple audio → raw (no transform, fastest path) - - This reduces QUBO solve energy by pre-processing with optimal transforms. - """ - from scripts.pipewire_dsp_workloads import ( - WORKLOAD_RAW, - WORKLOAD_SPECTRAL_FOCUS, - WORKLOAD_TRANSIENT_EDGE, - WORKLOAD_HYBRID, - ) - - # Branch prediction: order conditions by likelihood - spectral_centroid = dsp_metrics.get("spectral_centroid_hz", 0.0) - spectral_flatness = dsp_metrics.get("spectral_flatness", 0.0) - transient_ratio = dsp_metrics.get("transient_ratio", 0.0) - band_energy_high = dsp_metrics.get("band_energy_high", 0.0) - band_energy_mid = dsp_metrics.get("band_energy_mid", 0.0) - wave_heat = waveprobe_metrics.get("heat", 0.0) - - # Spectral-dominant: high centroid, low flatness - is_spectral_dominant = spectral_centroid > 2000.0 and spectral_flatness < 0.4 - - # Transient-dominant: high transient ratio, high band energy - is_transient_dominant = transient_ratio > 3.0 and band_energy_high > 0.4 - - # Balanced: mid-band dominant, moderate flatness - is_balanced = band_energy_mid > 0.4 and spectral_flatness > 0.3 - - # Simple: low wave heat, low complexity - is_simple = wave_heat < 0.2 and spectral_centroid < 1000.0 - - if is_spectral_dominant: - return WORKLOAD_SPECTRAL_FOCUS - elif is_transient_dominant: - return WORKLOAD_TRANSIENT_EDGE - elif is_balanced: - return WORKLOAD_HYBRID - elif is_simple: - return WORKLOAD_RAW - else: - return WORKLOAD_HYBRID # Default to hybrid for unknown patterns - - -def resolve_method_profile(profile: str) -> List[str]: - if profile not in METHOD_PROFILES: - raise ValueError( - f"Unknown method profile {profile!r}; expected one of {available_method_profiles()}" - ) - return list(METHOD_PROFILES[profile]) - - -def _encode_bpb(method: str, data: bytes) -> float: - if not data: - return 0.0 - if method == "none": - return 8.0 - if method == "order0": - counts = [0] * 256 - for b in data: - counts[b] += 1 - entropy = 0.0 - n = len(data) - for c in counts: - if c > 0: - p = c / n - entropy -= p * math.log2(p) - return entropy - if method == "zlib_fast": - return len(zlib.compress(data, 1)) * 8 / len(data) - if method == "zlib": - return len(zlib.compress(data, 9)) * 8 / len(data) - if method == "gzip": - import gzip - - return len(gzip.compress(data, compresslevel=6, mtime=0)) * 8 / len(data) - if method == "bz2": - import bz2 - - return len(bz2.compress(data, compresslevel=9)) * 8 / len(data) - if method == "brotli": - import brotli - - return len(brotli.compress(data, quality=6, lgwin=20)) * 8 / len(data) - if method == "zstd": - import zstandard as zstd - - compressor = zstd.ZstdCompressor(level=5) - return len(compressor.compress(data)) * 8 / len(data) - if method == "lzma": - import lzma - - return len(lzma.compress(data, preset=6)) * 8 / len(data) - return 8.0 - - -def _clamp01(value: float) -> float: - return max(0.0, min(1.0, float(value))) - - -def _normalize_qubo_features( - dsp_metrics: Dict[str, float], - waveprobe_metrics: Dict[str, float], - sample_rate_hz: int, -) -> Dict[str, float]: - nyquist = max(sample_rate_hz / 2.0, 1.0) - centroid_norm = _clamp01(dsp_metrics.get("spectral_centroid_hz", 0.0) / nyquist) - transient_norm = _clamp01(dsp_metrics.get("transient_ratio", 0.0) / 4.0) - flatness_norm = _clamp01(dsp_metrics.get("spectral_flatness", 0.0)) - zcr_norm = _clamp01(dsp_metrics.get("zero_crossing_rate", 0.0)) - low_band = _clamp01(dsp_metrics.get("band_energy_low", 0.0)) - mid_band = _clamp01(dsp_metrics.get("band_energy_mid", 0.0)) - high_band = _clamp01(dsp_metrics.get("band_energy_high", 0.0)) - wave_heat = _clamp01(waveprobe_metrics.get("heat", 0.0)) - wave_comp = _clamp01(waveprobe_metrics.get("compression_sensitivity", 0.0) / 32.0) - wave_aniso = _clamp01(waveprobe_metrics.get("anisotropy", 0.0) / 8.0) - return { - "centroid_norm": centroid_norm, - "transient_norm": transient_norm, - "flatness_norm": flatness_norm, - "zcr_norm": zcr_norm, - "low_band": low_band, - "mid_band": mid_band, - "high_band": high_band, - "wave_heat": wave_heat, - "wave_comp": wave_comp, - "wave_aniso": wave_aniso, - } - - -def _build_qubo_candidates( - methods: List[str], - features: Dict[str, float], -) -> List[Dict[str, float | str]]: - feature_weights = { - "centroid_norm": 0.16, - "transient_norm": 0.18, - "flatness_norm": 0.18, - "zcr_norm": 0.10, - "low_band": 0.12, - "mid_band": 0.10, - "high_band": 0.16, - } - - candidates: List[Dict[str, float | str]] = [] - wave_pressure = ( - 0.5 * features["wave_heat"] - + 0.3 * features["wave_comp"] - + 0.2 * features["wave_aniso"] - ) - for method in methods: - target = METHOD_SPECS[method] - mismatch = 0.0 - for key, weight in feature_weights.items(): - mismatch += weight * abs(features[key] - float(target[key])) - routing_cost = _clamp01( - 0.65 * float(target["complexity"]) + 0.35 * wave_pressure - ) - memory_cost = _clamp01( - 0.55 * float(target["memory"]) - + 0.25 * features["flatness_norm"] - + 0.20 * features["mid_band"] - ) - candidates.append( - { - "method": method, - "e": _clamp01(mismatch), - "r": routing_cost, - "m": memory_cost, - } - ) - return candidates - - -def _build_one_hot_qubo_matrix( - candidates: List[Dict[str, float | str]], - penalty: float, - lambda_e: float = 0.50, - lambda_r: float = 0.30, - lambda_m: float = 0.20, -) -> Tuple[AnyArray, List[float]]: - n = len(candidates) - q = xp.zeros((n, n), dtype=float) - costs: List[float] = [] - for i, candidate in enumerate(candidates): - c_i = ( - lambda_e * float(candidate["e"]) - + lambda_r * float(candidate["r"]) - + lambda_m * float(candidate["m"]) - ) - costs.append(c_i) - q[i, i] = c_i - penalty - for i in range(n): - for j in range(i + 1, n): - q[i, j] = 2.0 * penalty - return q, costs - - -def _qubo_energy(q: AnyArray, bits: AnyArray) -> float: - return float(bits @ q @ bits) - - -def _one_hot_bits(n: int, active_index: int) -> AnyArray: - bits = xp.zeros(n, dtype=float) - bits[active_index] = 1.0 - return bits - - -def _solve_qubo_exact(q: AnyArray) -> Dict[str, Any]: - n = int(q.shape[0]) - best_bits: List[int] | None = None - best_energy = float("inf") - evaluated_states = 0 - for state in range(1, 1 << n): - bits = xp.array([(state >> i) & 1 for i in range(n)], dtype=float) - energy = _qubo_energy(q, bits) - evaluated_states += 1 - if energy < best_energy: - best_energy = energy - best_bits = bits.astype(int).tolist() - if best_bits is None: - raise RuntimeError("QUBO solve failed to produce a state") - active = [idx for idx, bit in enumerate(best_bits) if bit] - return { - "solution_bits": best_bits, - "active_indices": active, - "energy": best_energy, - "constraint_satisfied": sum(best_bits) == 1, - "evaluated_states": evaluated_states, - } - - -# ============================================================================= -# ADAPTIVE SCALE PROPOSAL REFINEMENT -# ============================================================================= -# Inspired by the wavelength-detection concept from: -# "Resonance MCMC" by Zachary Lafer (github.com/zman007-save/resonance-mcmc) -# Principle: Detect characteristic spatial scales from accepted move history -# and bias proposals toward those scales for faster mode discovery. -# -# Adaptation: Applied to discrete one-hot QUBO instead of continuous space. -# - Track accepted index-space distances -# - Detect median characteristic distance -# - Bias proposals toward that distance and its harmonics -# ============================================================================= - -from dataclasses import dataclass, field - - -@dataclass -class AdaptiveProposalState: - """Track proposal statistics for adaptive scale detection in QUBO annealing.""" - - # History of accepted jump distances (in index space) - accepted_distances: List[int] = field(default_factory=list) - # History window size for median estimation - window_size: int = 20 - # Minimum samples before adaptation activates - min_samples: int = 5 - - def observe_accepted_move(self, from_index: int, to_index: int): - """Record an accepted move distance.""" - distance = abs(to_index - from_index) - self.accepted_distances.append(distance) - if len(self.accepted_distances) > self.window_size: - self.accepted_distances.pop(0) - - def detect_scale(self) -> Optional[int]: - """Detect characteristic jump scale from history using median. - - Returns None if insufficient samples. - """ - if len(self.accepted_distances) < self.min_samples: - return None - return int(xp.median(xp.array(self.accepted_distances))) - - def get_scale_multiplier(self, current_index: int, candidate_index: int) -> float: - """Compute proposal weight multiplier based on scale alignment. - - Returns 1.0 for neutral, >1.0 to favor, <1.0 to disfavor. - """ - scale = self.detect_scale() - if scale is None: - return 1.0 - - distance = abs(candidate_index - current_index) - if distance == 0: - return 0.5 # Disfavor staying in place - - # Check alignment with scale and its harmonics - harmonic_boost = 0.0 - for harmonic in [1, 2, 3]: - target_dist = scale * harmonic - # Gaussian-like falloff around target - diff = abs(distance - target_dist) / max(target_dist * 0.3, 1.0) - boost = float(xp.exp(-diff ** 2)) - harmonic_boost = max(harmonic_boost, boost) - - # Return multiplier: 1.0 to 2.0 - return 1.0 + harmonic_boost - - -def _guided_initial_index(unary_costs: List[float]) -> int: - return min(range(len(unary_costs)), key=lambda idx: unary_costs[idx]) - - -def _weighted_candidate_indices( - unary_costs: List[float], - exclude_index: int, - guidance: str, - active_index: int = 0, - adaptive_state: Optional[AdaptiveProposalState] = None, -) -> Tuple[AnyArray, AnyArray]: - indices = xp.array([idx for idx in range(len(unary_costs)) if idx != exclude_index], dtype=int) - if indices.size == 0: - return indices, xp.array([], dtype=float) - - # Base weights from guidance strategy - if guidance == "unguided": - base_weights = xp.ones(indices.size, dtype=float) - elif guidance == "adaptive_scale": - # Use adaptive scale bias only (no cost weighting) - base_weights = xp.ones(indices.size, dtype=float) - else: - # Cost-weighted ("dsp_guided" or default) - max_cost = max(unary_costs) - min_cost = min(unary_costs) - spread = max(max_cost - min_cost, 1e-6) - base_weights = xp.array( - [1.0 + (max_cost - unary_costs[idx]) / spread for idx in indices], - dtype=float, - ) - - # Apply adaptive scale multipliers if state provided - if adaptive_state is not None: - multipliers = xp.array([ - adaptive_state.get_scale_multiplier(active_index, int(idx)) - for idx in indices - ], dtype=float) - base_weights = base_weights * multipliers - - # Normalize - weights = base_weights / xp.sum(base_weights) - return indices, weights - - -def _solve_qubo_anneal( - q: AnyArray, - unary_costs: List[float], - seed: int, - steps: int, - temp_start: float, - temp_end: float, - guidance: str, -) -> Dict[str, Any]: - n = int(q.shape[0]) - rng = xp.random.default_rng(seed) - safe_temp_start = max(float(temp_start), 1e-6) - safe_temp_end = max(float(temp_end), 1e-6) - if guidance == "dsp_guided": - initial_index = _guided_initial_index(unary_costs) - else: - initial_index = int(rng.integers(0, n)) - current_bits = _one_hot_bits(n, initial_index) - current_energy = _qubo_energy(q, current_bits) - best_bits = current_bits.copy() - best_energy = current_energy - accepted_moves = 0 - improved_moves = 0 - - # Initialize adaptive scale detection if enabled - use_adaptive = guidance in ("adaptive_scale", "dsp_guided_adaptive") - adaptive_state = AdaptiveProposalState(window_size=min(30, steps)) if use_adaptive else None - - for step in range(max(1, steps)): - if steps <= 1: - temperature = safe_temp_end - else: - alpha = step / (steps - 1) - temperature = safe_temp_start * ( - (safe_temp_end / safe_temp_start) ** alpha - ) - - proposal_bits = current_bits.copy() - active_indices = xp.flatnonzero(proposal_bits > 0.5) - active_index = int(active_indices[0]) if active_indices.size else initial_index - weighted_indices, weights = _weighted_candidate_indices( - unary_costs, - active_index, - guidance=guidance, - active_index=active_index, - adaptive_state=adaptive_state, - ) - if weighted_indices.size: - target_index = int(rng.choice(weighted_indices, p=weights)) - proposal_bits[active_index] = 0.0 - proposal_bits[target_index] = 1.0 - else: - flip_index = int(rng.integers(0, n)) - proposal_bits[flip_index] = 1.0 - proposal_bits[flip_index] - - proposal_energy = _qubo_energy(q, proposal_bits) - delta = proposal_energy - current_energy - if delta <= 0.0: - accept = True - else: - accept = bool(rng.random() < math.exp(-delta / max(temperature, 1e-9))) - - if accept: - accepted_moves += 1 - if delta < 0.0: - improved_moves += 1 - # Record accepted move for adaptive scale detection - if adaptive_state is not None and weighted_indices.size: - adaptive_state.observe_accepted_move(active_index, target_index) - current_bits = proposal_bits - current_energy = proposal_energy - if int(xp.sum(current_bits)) == 1 and current_energy < best_energy: - best_bits = current_bits.copy() - best_energy = current_energy - - best_bits_list = best_bits.astype(int).tolist() - active = [idx for idx, bit in enumerate(best_bits_list) if bit] - # Get final detected scale for diagnostics - final_scale = adaptive_state.detect_scale() if adaptive_state else None - - return { - "solution_bits": best_bits_list, - "active_indices": active, - "energy": best_energy, - "constraint_satisfied": sum(best_bits_list) == 1, - "seed": seed, - "steps": max(1, steps), - "accepted_moves": accepted_moves, - "improved_moves": improved_moves, - "accepted_ratio": accepted_moves / max(1, steps), - "initial_index": initial_index, - "initial_energy": _qubo_energy(q, _one_hot_bits(n, initial_index)), - "guidance": guidance, - "adaptive_scale": final_scale, - } - - -def _candidate_cost_rows( - candidates: List[Dict[str, float | str]], - unary_costs: List[float], -) -> List[Dict[str, float | str]]: - return [ - { - "method": str(candidate["method"]), - "e": float(candidate["e"]), - "r": float(candidate["r"]), - "m": float(candidate["m"]), - "unary_cost": unary_costs[idx], - } - for idx, candidate in enumerate(candidates) - ] - - -def _run_qubo_method_search( - methods: List[str], - dsp_metrics: Dict[str, float], - waveprobe_metrics: Dict[str, float], - sample_rate_hz: int, - solver: str, - seed: int, - anneal_steps: int, - anneal_temp_start: float, - anneal_temp_end: float, - anneal_guidance: str, - benchmark_guidance_ablation: bool, - collect_exact_reference: bool, -) -> Dict[str, Any]: - features = _normalize_qubo_features( - dsp_metrics=dsp_metrics, - waveprobe_metrics=waveprobe_metrics, - sample_rate_hz=sample_rate_hz, - ) - candidates = _build_qubo_candidates(methods, features) - penalty = 1.25 - q, unary_costs = _build_one_hot_qubo_matrix(candidates, penalty=penalty) - candidate_costs = _candidate_cost_rows(candidates, unary_costs) - - exact_reference_available = collect_exact_reference or solver == "exact" - exact_solution: Dict[str, Any] | None = None - exact_solve_time_ms: float | None = None - exact_method: str | None = None - if exact_reference_available: - exact_start = time.perf_counter() - exact_solution = _solve_qubo_exact(q) - exact_solve_time_ms = (time.perf_counter() - exact_start) * 1000.0 - exact_index = ( - exact_solution["active_indices"][0] - if exact_solution["active_indices"] - else _guided_initial_index(unary_costs) - ) - exact_method = str(candidates[exact_index]["method"]) - - if solver == "anneal": - solve_start = time.perf_counter() - solved = _solve_qubo_anneal( - q=q, - unary_costs=unary_costs, - seed=seed, - steps=anneal_steps, - temp_start=anneal_temp_start, - temp_end=anneal_temp_end, - guidance=anneal_guidance, - ) - solve_time_ms = (time.perf_counter() - solve_start) * 1000.0 - active_index = solved["active_indices"][0] if solved["active_indices"] else _guided_initial_index(unary_costs) - selected_method = str(candidates[active_index]["method"]) - initial_index = int(solved["initial_index"]) - initial_method = str(candidates[initial_index]["method"]) - benchmark = { - "exact_reference_method": exact_method, - "exact_reference_energy": ( - exact_solution["energy"] if exact_solution is not None else None - ), - "exact_reference_solve_time_ms": exact_solve_time_ms, - "exact_reference_evaluated_states": ( - exact_solution["evaluated_states"] if exact_solution is not None else None - ), - "exact_match": ( - selected_method == exact_method if exact_method is not None else None - ), - "energy_gap_vs_exact": ( - solved["energy"] - exact_solution["energy"] - if exact_solution is not None - else None - ), - } - guidance_benchmark: Dict[str, Any] | None = None - if benchmark_guidance_ablation: - alt_guidance = "unguided" if anneal_guidance == "dsp_guided" else "dsp_guided" - alt_start = time.perf_counter() - alt_solved = _solve_qubo_anneal( - q=q, - unary_costs=unary_costs, - seed=seed, - steps=anneal_steps, - temp_start=anneal_temp_start, - temp_end=anneal_temp_end, - guidance=alt_guidance, - ) - alt_solve_time_ms = (time.perf_counter() - alt_start) * 1000.0 - alt_index = ( - alt_solved["active_indices"][0] - if alt_solved["active_indices"] - else _guided_initial_index(unary_costs) - ) - alt_method = str(candidates[alt_index]["method"]) - guidance_benchmark = { - "alternative_guidance": alt_guidance, - "selected_method": alt_method, - "energy": alt_solved["energy"], - "exact_match": alt_method == exact_method if exact_method is not None else None, - "energy_gap_vs_exact": ( - alt_solved["energy"] - exact_solution["energy"] - if exact_solution is not None - else None - ), - "solve_time_ms": alt_solve_time_ms, - "accepted_ratio": alt_solved["accepted_ratio"], - "initial_method": str(candidates[int(alt_solved["initial_index"])]["method"]), - } - solver_stats = { - "steps": solved["steps"], - "accepted_moves": solved["accepted_moves"], - "improved_moves": solved["improved_moves"], - "accepted_ratio": solved["accepted_ratio"], - "initial_method": initial_method, - "initial_energy": solved["initial_energy"], - "seed": solved["seed"], - "guidance": solved["guidance"], - } - solver_name = "simulated_annealing" - else: - solve_start = time.perf_counter() - solved = _solve_qubo_exact(q) - solve_time_ms = (time.perf_counter() - solve_start) * 1000.0 - active_index = solved["active_indices"][0] if solved["active_indices"] else _guided_initial_index(unary_costs) - selected_method = str(candidates[active_index]["method"]) - benchmark = { - "exact_reference_method": exact_method, - "exact_reference_energy": ( - exact_solution["energy"] if exact_solution is not None else solved["energy"] - ), - "exact_reference_solve_time_ms": exact_solve_time_ms, - "exact_reference_evaluated_states": ( - exact_solution["evaluated_states"] if exact_solution is not None else solved["evaluated_states"] - ), - "exact_match": True, - "energy_gap_vs_exact": 0.0, - } - solver_stats = { - "steps": solved["evaluated_states"], - "accepted_moves": solved["evaluated_states"], - "improved_moves": solved["evaluated_states"], - "accepted_ratio": 1.0, - "initial_method": exact_method, - "initial_energy": exact_solution["energy"], - "seed": seed, - "guidance": "n/a", - } - guidance_benchmark = None - solver_name = "exact_one_hot_enumeration" - - return { - "triggered": True, - "solver": solver_name, - "selected_method": selected_method, - "selected_index": active_index, - "energy": solved["energy"], - "solution_bits": solved["solution_bits"], - "constraint_satisfied": solved["constraint_satisfied"], - "solve_time_ms": solve_time_ms, - "penalty": penalty, - "features": features, - "candidate_costs": candidate_costs, - "matrix_upper_triangular": [ - [float(q[i, j]) for j in range(i, q.shape[1])] - for i in range(q.shape[0]) - ], - "benchmark": benchmark, - "solver_stats": solver_stats, - "guidance_benchmark": guidance_benchmark, - } - - -def _capture_with_pipewire( - output_wav: Path, - duration_s: float, - rate: int, - channels: int, - fmt: str, - latency: str, - target: str | None, -) -> None: - output_wav.parent.mkdir(parents=True, exist_ok=True) - sample_count = max(1, int(duration_s * rate)) - cmd = [ - "pw-record", - "--container", - "wav", - "--rate", - str(rate), - "--channels", - str(channels), - "--format", - fmt, - "--latency", - latency, - "--sample-count", - str(sample_count), - ] - if target: - cmd.extend(["--target", target]) - cmd.append(str(output_wav)) - subprocess.run(cmd, check=True) - - -def _read_wav_pcm(path: Path) -> Dict[str, Any]: - with wave.open(str(path), "rb") as wf: - params = { - "channels": wf.getnchannels(), - "sample_width_bytes": wf.getsampwidth(), - "sample_rate_hz": wf.getframerate(), - "n_frames": wf.getnframes(), - "duration_s": wf.getnframes() / max(wf.getframerate(), 1), - } - frames = wf.readframes(wf.getnframes()) - return {"params": params, "pcm_bytes": frames} - - -def _build_audio_dag( - pcm_bytes: bytes, - chunk_size: int, - stride: int, - max_states: int, -) -> Dict[str, Any]: - ts = datetime.now(timezone.utc) - chunks = UCP.chunk_corpus(pcm_bytes, chunk_size=chunk_size, stride=stride) - dag: Dict[str, Any] = {} - for offset, chunk_bytes in chunks[:max_states]: - sha = UCP.compute_sha256(chunk_bytes) - state_id = f"audio_capture:{offset:08x}:{sha[:16]}" - dag[state_id] = UCP.CorpusState( - sha256=sha, - timestamp=ts, - release_id="audio_capture", - chunk_offset=offset, - chunk_bytes=chunk_bytes, - ) - return dag - - -def run_chain( - wav_path: Path, - chunk_size: int, - stride: int, - max_states: int, - low_mi_threshold: float, - high_mi_threshold: float, - dsp_workload: str, - method_profile: str = "baseline", - qubo_solver: str = "exact", - qubo_anneal_steps: int = 32, - qubo_anneal_temp_start: float = 0.50, - qubo_anneal_temp_end: float = 0.05, - qubo_anneal_guidance: str = "dsp_guided", - qubo_benchmark_guidance_ablation: bool = False, - qubo_collect_exact_reference: bool = True, - hybrid_dsp_workload: bool = False, -) -> Dict[str, Any]: - wav_data = _read_wav_pcm(wav_path) - pcm_bytes = wav_data["pcm_bytes"] - dag = _build_audio_dag( - pcm_bytes=pcm_bytes, - chunk_size=chunk_size, - stride=stride, - max_states=max_states, - ) - if not dag: - raise RuntimeError("No audio chunks available for analysis") - - analysis_dag: Dict[str, Any] = {} - dsp_by_state: Dict[str, Dict[str, float]] = {} - for state_id, state in dag.items(): - analysis_bytes, dsp_metrics = apply_dsp_workload( - state.chunk_bytes, - sample_width_bytes=wav_data["params"]["sample_width_bytes"], - channels=wav_data["params"]["channels"], - sample_rate_hz=wav_data["params"]["sample_rate_hz"], - workload=dsp_workload, - ) - analysis_sha = UCP.compute_sha256(analysis_bytes) - analysis_dag[state_id] = UCP.CorpusState( - sha256=analysis_sha, - timestamp=state.timestamp, - release_id=state.release_id, - chunk_offset=state.chunk_offset, - chunk_bytes=analysis_bytes, - ) - dsp_by_state[state_id] = dsp_metrics - - mu, sigma = UCP.compute_feature_stats(analysis_dag) - mi_signal = MISignal( - low_mi_threshold=low_mi_threshold, - high_mi_threshold=high_mi_threshold, - ) - methods = resolve_method_profile(method_profile) - - per_chunk: List[Dict[str, Any]] = [] - method_counts: Dict[str, int] = {m: 0 for m in methods + ["none"]} - qubo_method_counts: Dict[str, int] = {m: 0 for m in methods} - dsp_workload_counts: Dict[str, int] = {} - rich_chunks = 0 - cheap_chunks = 0 - qubo_invocations = 0 - qubo_method_overrides = 0 - qubo_exact_matches = 0 - wave_heat_total = 0.0 - wave_compsens_total = 0.0 - mi_actual_total = 0.0 - yield_total = 0.0 - dsp_centroid_total = 0.0 - dsp_flatness_total = 0.0 - dsp_transient_total = 0.0 - dsp_zcr_total = 0.0 - dsp_low_total = 0.0 - dsp_mid_total = 0.0 - dsp_high_total = 0.0 - qubo_solve_time_total = 0.0 - qubo_exact_solve_time_total = 0.0 - qubo_energy_gap_total = 0.0 - qubo_accepted_ratio_total = 0.0 - qubo_alt_exact_matches = 0 - qubo_alt_solve_time_total = 0.0 - qubo_alt_energy_gap_total = 0.0 - qubo_alt_accepted_ratio_total = 0.0 - - for idx, (state_id, state) in enumerate(analysis_dag.items()): - raw_state = dag[state_id] - dsp_metrics = dsp_by_state[state_id] - state_seed = int( - hashlib.sha256(f"{state_id}:{dsp_workload}".encode("utf-8")).hexdigest()[:8], - 16, - ) - wp = UCP.waveprobe(state.chunk_bytes, mu, sigma, seed=state_seed) - - # Hybrid DSP workload selection for energy efficiency - if hybrid_dsp_workload: - selected_workload = _select_hybrid_dsp_workload(dsp_metrics, wp) - # Re-apply DSP with selected workload - analysis_bytes, dsp_metrics = apply_dsp_workload( - raw_state.chunk_bytes, - sample_width_bytes=wav_data["params"]["sample_width_bytes"], - channels=wav_data["params"]["channels"], - sample_rate_hz=wav_data["params"]["sample_rate_hz"], - workload=selected_workload, - ) - analysis_sha = UCP.compute_sha256(analysis_bytes) - analysis_dag[state_id] = UCP.CorpusState( - sha256=analysis_sha, - timestamp=state.timestamp, - release_id=state.release_id, - chunk_offset=state.chunk_offset, - chunk_bytes=analysis_bytes, - ) - dsp_by_state[state_id] = dsp_metrics - else: - selected_workload = dsp_workload - - # Track DSP workload selection - dsp_workload_counts[selected_workload] = dsp_workload_counts.get(selected_workload, 0) + 1 - - z = extract_mi_features(state.chunk_bytes) - route = mi_signal.route(z, state.chunk_bytes, methods, _encode_bpb) - qubo_result: Dict[str, Any] = {"triggered": False} - - selected_method = route["method"] - selected_bpb = route["actual_bpb"] - selected_mi = route["mi_actual"] - if route["decision_class"] == "rich": - qubo_result = _run_qubo_method_search( - methods=methods, - dsp_metrics=dsp_metrics, - waveprobe_metrics=wp, - sample_rate_hz=wav_data["params"]["sample_rate_hz"], - solver=qubo_solver, - seed=state_seed, - anneal_steps=qubo_anneal_steps, - anneal_temp_start=qubo_anneal_temp_start, - anneal_temp_end=qubo_anneal_temp_end, - anneal_guidance=qubo_anneal_guidance, - benchmark_guidance_ablation=qubo_benchmark_guidance_ablation, - collect_exact_reference=qubo_collect_exact_reference, - ) - qubo_invocations += 1 - qubo_method = qubo_result["selected_method"] - qubo_method_counts[qubo_method] = qubo_method_counts.get(qubo_method, 0) + 1 - if qubo_method != route["method"]: - qubo_method_overrides += 1 - if qubo_result["benchmark"]["exact_match"] is True: - qubo_exact_matches += 1 - qubo_solve_time_total += float(qubo_result["solve_time_ms"]) - if qubo_result["benchmark"]["exact_reference_solve_time_ms"] is not None: - qubo_exact_solve_time_total += float( - qubo_result["benchmark"]["exact_reference_solve_time_ms"] - ) - if qubo_result["benchmark"]["energy_gap_vs_exact"] is not None: - qubo_energy_gap_total += float( - qubo_result["benchmark"]["energy_gap_vs_exact"] - ) - qubo_accepted_ratio_total += float( - qubo_result["solver_stats"]["accepted_ratio"] - ) - alt_guidance = qubo_result.get("guidance_benchmark") - if alt_guidance: - if alt_guidance["exact_match"] is True: - qubo_alt_exact_matches += 1 - qubo_alt_solve_time_total += float(alt_guidance["solve_time_ms"]) - if alt_guidance["energy_gap_vs_exact"] is not None: - qubo_alt_energy_gap_total += float(alt_guidance["energy_gap_vs_exact"]) - qubo_alt_accepted_ratio_total += float(alt_guidance["accepted_ratio"]) - selected_method = qubo_method - selected_bpb = _encode_bpb(selected_method, state.chunk_bytes) - selected_mi = route["baseline_bpb"] - selected_bpb - - method = selected_method - method_counts[method] = method_counts.get(method, 0) + 1 - if route["decision_class"] == "rich": - rich_chunks += 1 - elif route["decision_class"] == "cheap": - cheap_chunks += 1 - - wave_heat_total += wp["heat"] - wave_compsens_total += wp["compression_sensitivity"] - mi_actual_total += selected_mi - yield_total += route["structure_yield"] - dsp_centroid_total += dsp_metrics.get("spectral_centroid_hz", 0.0) - dsp_flatness_total += dsp_metrics.get("spectral_flatness", 0.0) - dsp_transient_total += dsp_metrics.get("transient_ratio", 0.0) - dsp_zcr_total += dsp_metrics.get("zero_crossing_rate", 0.0) - dsp_low_total += dsp_metrics.get("band_energy_low", 0.0) - dsp_mid_total += dsp_metrics.get("band_energy_mid", 0.0) - dsp_high_total += dsp_metrics.get("band_energy_high", 0.0) - - per_chunk.append( - { - "state_id": state_id, - "chunk_index": idx, - "offset": raw_state.chunk_offset, - "raw_sha256_prefix": raw_state.sha256[:16], - "analysis_sha256_prefix": state.sha256[:16], - "decision_class": route["decision_class"], - "method": method, - "mi_actual": selected_mi, - "mi_predicted": route["mi_predicted"], - "mi_surprise": route["mi_surprise"], - "structure_yield": route["structure_yield"], - "baseline_bpb": route["baseline_bpb"], - "actual_bpb": selected_bpb, - "mi_router_method": route["method"], - "qubo_enabled": qubo_result["triggered"], - "qubo_solver": qubo_result.get("solver"), - "qubo_guidance": qubo_result.get("solver_stats", {}).get("guidance"), - "qubo_selected_method": qubo_result.get("selected_method"), - "qubo_energy": qubo_result.get("energy"), - "qubo_solution_bits": qubo_result.get("solution_bits"), - "qubo_constraint_satisfied": qubo_result.get("constraint_satisfied"), - "qubo_solve_time_ms": qubo_result.get("solve_time_ms"), - "qubo_candidate_costs": qubo_result.get("candidate_costs"), - "qubo_features": qubo_result.get("features"), - "qubo_initial_method": qubo_result.get("solver_stats", {}).get("initial_method"), - "qubo_initial_energy": qubo_result.get("solver_stats", {}).get("initial_energy"), - "qubo_steps": qubo_result.get("solver_stats", {}).get("steps"), - "qubo_accepted_moves": qubo_result.get("solver_stats", {}).get("accepted_moves"), - "qubo_improved_moves": qubo_result.get("solver_stats", {}).get("improved_moves"), - "qubo_accepted_ratio": qubo_result.get("solver_stats", {}).get("accepted_ratio"), - "qubo_seed": qubo_result.get("solver_stats", {}).get("seed"), - "qubo_exact_reference_method": qubo_result.get("benchmark", {}).get("exact_reference_method"), - "qubo_exact_reference_energy": qubo_result.get("benchmark", {}).get("exact_reference_energy"), - "qubo_exact_reference_solve_time_ms": qubo_result.get("benchmark", {}).get("exact_reference_solve_time_ms"), - "qubo_exact_match": qubo_result.get("benchmark", {}).get("exact_match"), - "qubo_energy_gap_vs_exact": qubo_result.get("benchmark", {}).get("energy_gap_vs_exact"), - "qubo_guidance_alternative": ( - qubo_result.get("guidance_benchmark") or {} - ).get("alternative_guidance"), - "qubo_guidance_alternative_method": ( - qubo_result.get("guidance_benchmark") or {} - ).get("selected_method"), - "qubo_guidance_alternative_exact_match": ( - qubo_result.get("guidance_benchmark") or {} - ).get("exact_match"), - "qubo_guidance_alternative_energy_gap_vs_exact": ( - qubo_result.get("guidance_benchmark") or {} - ).get("energy_gap_vs_exact"), - "qubo_guidance_alternative_solve_time_ms": ( - qubo_result.get("guidance_benchmark") or {} - ).get("solve_time_ms"), - "qubo_guidance_alternative_accepted_ratio": ( - qubo_result.get("guidance_benchmark") or {} - ).get("accepted_ratio"), - "waveprobe_sensitivity": wp["sensitivity"], - "waveprobe_compression_sensitivity": wp["compression_sensitivity"], - "waveprobe_anisotropy": wp["anisotropy"], - "waveprobe_heat": wp["heat"], - "dsp_workload": dsp_workload, - "dsp_rms_ratio": dsp_metrics.get("rms_ratio", 1.0), - "dsp_zero_crossing_rate": dsp_metrics.get("zero_crossing_rate", 0.0), - "dsp_spectral_centroid_hz": dsp_metrics.get("spectral_centroid_hz", 0.0), - "dsp_spectral_flatness": dsp_metrics.get("spectral_flatness", 0.0), - "dsp_dominant_freq_hz": dsp_metrics.get("dominant_freq_hz", 0.0), - "dsp_transient_ratio": dsp_metrics.get("transient_ratio", 0.0), - "dsp_band_energy_low": dsp_metrics.get("band_energy_low", 0.0), - "dsp_band_energy_mid": dsp_metrics.get("band_energy_mid", 0.0), - "dsp_band_energy_high": dsp_metrics.get("band_energy_high", 0.0), - "dsp_centroid_shift_hz": dsp_metrics.get("centroid_shift_hz", 0.0), - "dsp_transient_shift": dsp_metrics.get("transient_shift", 0.0), - "dsp_workload": selected_workload, - } - ) - - n = len(per_chunk) - per_chunk_sorted = sorted( - per_chunk, - key=lambda item: ( - item["mi_actual"], - item["waveprobe_heat"], - item["waveprobe_compression_sensitivity"], - ), - reverse=True, - ) - - return { - "schema_version": "pipewire.waveprobe.compression.v1", - "captured_wav": str(wav_path), - "wav_params": wav_data["params"], - "chunking": { - "chunk_size_bytes": chunk_size, - "stride_bytes": stride, - "n_chunks": n, - }, - "mi_router_config": { - "low_mi_threshold": low_mi_threshold, - "high_mi_threshold": high_mi_threshold, - }, - "dsp_frontend": { - "workload": dsp_workload, - }, - "qubo_config": { - "method_profile": method_profile, - "n_methods": len(methods), - "solver": qubo_solver, - "anneal_steps": qubo_anneal_steps, - "anneal_temp_start": qubo_anneal_temp_start, - "anneal_temp_end": qubo_anneal_temp_end, - "anneal_guidance": qubo_anneal_guidance, - "benchmark_guidance_ablation": qubo_benchmark_guidance_ablation, - "collect_exact_reference": qubo_collect_exact_reference, - }, - "summary": { - "avg_mi_actual": mi_actual_total / n, - "avg_structure_yield": yield_total / n, - "avg_waveprobe_heat": wave_heat_total / n, - "avg_waveprobe_compression_sensitivity": wave_compsens_total / n, - "avg_dsp_spectral_centroid_hz": dsp_centroid_total / n, - "avg_dsp_spectral_flatness": dsp_flatness_total / n, - "avg_dsp_transient_ratio": dsp_transient_total / n, - "avg_dsp_zero_crossing_rate": dsp_zcr_total / n, - "avg_dsp_band_energy_low": dsp_low_total / n, - "avg_dsp_band_energy_mid": dsp_mid_total / n, - "avg_dsp_band_energy_high": dsp_high_total / n, - "rich_chunks": rich_chunks, - "cheap_chunks": cheap_chunks, - "qubo_invocations": qubo_invocations, - "qubo_method_overrides": qubo_method_overrides, - "qubo_exact_matches": qubo_exact_matches, - "qubo_exact_match_rate": ( - qubo_exact_matches / qubo_invocations - if qubo_invocations and (qubo_collect_exact_reference or qubo_solver == "exact") - else None - ), - "avg_qubo_solve_time_ms": ( - qubo_solve_time_total / qubo_invocations if qubo_invocations else 0.0 - ), - "avg_qubo_exact_reference_solve_time_ms": ( - qubo_exact_solve_time_total / qubo_invocations - if qubo_invocations and (qubo_collect_exact_reference or qubo_solver == "exact") - else None - ), - "avg_qubo_energy_gap_vs_exact": ( - qubo_energy_gap_total / qubo_invocations - if qubo_invocations and (qubo_collect_exact_reference or qubo_solver == "exact") - else None - ), - "avg_qubo_accepted_ratio": ( - qubo_accepted_ratio_total / qubo_invocations - if qubo_invocations - else 0.0 - ), - "alt_guidance_exact_matches": qubo_alt_exact_matches, - "alt_guidance_exact_match_rate": ( - qubo_alt_exact_matches / qubo_invocations - if qubo_invocations - and qubo_benchmark_guidance_ablation - and (qubo_collect_exact_reference or qubo_solver == "exact") - else None - ), - "avg_alt_guidance_solve_time_ms": ( - qubo_alt_solve_time_total / qubo_invocations - if qubo_invocations and qubo_benchmark_guidance_ablation - else None - ), - "avg_alt_guidance_energy_gap_vs_exact": ( - qubo_alt_energy_gap_total / qubo_invocations - if qubo_invocations - and qubo_benchmark_guidance_ablation - and (qubo_collect_exact_reference or qubo_solver == "exact") - else None - ), - "avg_alt_guidance_accepted_ratio": ( - qubo_alt_accepted_ratio_total / qubo_invocations - if qubo_invocations and qubo_benchmark_guidance_ablation - else None - ), - "method_counts": method_counts, - "qubo_method_counts": qubo_method_counts, - "dsp_workload_counts": dsp_workload_counts, - "mi_signal_stats": mi_signal.get_stats(), - }, - "top_chunks": per_chunk_sorted[:10], - "chunks": per_chunk, - } - - -def main() -> int: - parser = argparse.ArgumentParser( - description="PipeWire -> compression -> waveprobe chain harness" - ) - parser.add_argument("--input-wav", type=Path, help="Analyze an existing WAV file") - parser.add_argument( - "--capture-seconds", - type=float, - default=2.0, - help="Duration for PipeWire capture when --input-wav is omitted", - ) - parser.add_argument("--rate", type=int, default=48000) - parser.add_argument("--channels", type=int, default=1) - parser.add_argument("--format", default="s16") - parser.add_argument("--latency", default="50ms") - parser.add_argument("--target", default=None, help="Optional PipeWire target node") - parser.add_argument("--chunk-size-bytes", type=int, default=4096) - parser.add_argument("--stride-bytes", type=int, default=2048) - parser.add_argument("--max-states", type=int, default=128) - parser.add_argument("--low-mi-threshold", type=float, default=0.5) - parser.add_argument("--high-mi-threshold", type=float, default=3.0) - parser.add_argument( - "--dsp-workload", - default="raw", - choices=list(available_workloads()), - help="DSP-like front-end transform applied before MI routing and waveprobe scoring", - ) - parser.add_argument( - "--method-profile", - default="baseline", - choices=list(available_method_profiles()), - help="Compression method profile used for MI routing and QUBO search", - ) - parser.add_argument( - "--qubo-solver", - default="exact", - choices=("exact", "anneal"), - help="QUBO solver used on rich chunks", - ) - parser.add_argument( - "--qubo-anneal-steps", - type=int, - default=32, - help="Number of simulated annealing steps when --qubo-solver=anneal", - ) - parser.add_argument( - "--qubo-anneal-temp-start", - type=float, - default=0.50, - help="Starting temperature for simulated annealing", - ) - parser.add_argument( - "--qubo-anneal-temp-end", - type=float, - default=0.05, - help="Ending temperature for simulated annealing", - ) - parser.add_argument( - "--qubo-anneal-guidance", - default="dsp_guided", - choices=("dsp_guided", "unguided"), - help="Proposal/init guidance mode for simulated annealing", - ) - parser.add_argument( - "--qubo-benchmark-guidance-ablation", - action="store_true", - help="Also run the opposite annealing guidance mode for side-by-side benchmarking", - ) - parser.add_argument( - "--skip-exact-reference", - action="store_true", - help="Skip exact-reference benchmarking on rich chunks to reduce end-to-end runtime", - ) - parser.add_argument( - "--hybrid-dsp-workload", - action="store_true", - help="Enable hybrid DSP workload selection for energy-efficient QUBO solves", - ) - parser.add_argument( - "--out-dir", - type=Path, - default=REPO_ROOT / "out" / "pipewire_waveprobe_chain", - ) - args = parser.parse_args() - - run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - out_dir = args.out_dir / run_id - out_dir.mkdir(parents=True, exist_ok=True) - - if args.input_wav: - wav_path = args.input_wav.resolve() - else: - wav_path = out_dir / "capture.wav" - _capture_with_pipewire( - output_wav=wav_path, - duration_s=args.capture_seconds, - rate=args.rate, - channels=args.channels, - fmt=args.format, - latency=args.latency, - target=args.target, - ) - - result = run_chain( - wav_path=wav_path, - chunk_size=args.chunk_size_bytes, - stride=args.stride_bytes, - max_states=args.max_states, - low_mi_threshold=args.low_mi_threshold, - high_mi_threshold=args.high_mi_threshold, - dsp_workload=args.dsp_workload, - method_profile=args.method_profile, - qubo_solver=args.qubo_solver, - qubo_anneal_steps=args.qubo_anneal_steps, - qubo_anneal_temp_start=args.qubo_anneal_temp_start, - qubo_anneal_temp_end=args.qubo_anneal_temp_end, - qubo_anneal_guidance=args.qubo_anneal_guidance, - qubo_benchmark_guidance_ablation=args.qubo_benchmark_guidance_ablation, - qubo_collect_exact_reference=not args.skip_exact_reference, - hybrid_dsp_workload=args.hybrid_dsp_workload, - ) - - result["run_id"] = run_id - result["generated_utc"] = datetime.now(timezone.utc).isoformat() - result["capture_mode"] = "file" if args.input_wav else "pipewire" - - json_path = out_dir / "summary.json" - with open(json_path, "w", encoding="utf-8") as fh: - json.dump(result, fh, indent=2) - - print(json.dumps(result["summary"], indent=2)) - print(f"\n[+] Summary written to {json_path}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/blockchain/evm_bytecode_waveprobe.py b/5-Applications/tools-scripts/blockchain/evm_bytecode_waveprobe.py deleted file mode 100644 index d19cde11..00000000 --- a/5-Applications/tools-scripts/blockchain/evm_bytecode_waveprobe.py +++ /dev/null @@ -1,1011 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""EVM Bytecode Waveprobe — Structural classification of smart contract bytecode. - -Adapts the Waveprobe v0.1 perturbation instrument (raw byte probes) to the -specific structure of EVM bytecode. Produces heat, torsion, and anisotropy -maps that classify contract regions as cold/warm/hot without requiring ABI -decoding or Solidity-level decompilation. - -This module operates on raw deployed bytecode, not source code. - -Usage: - from scripts.evm_bytecode_waveprobe import EVMBytecodeWaveprobe - - probe = EVMBytecodeWaveprobe(bytecode_hex="0x6060...") - result = probe.analyze() - # result.heat_map, result.classification, result.aggregate - -Compliance integration: - The classification output feeds into the canal routing metric for - gas-optimal path selection. The valve layer maps to the compliance - front layer (see MEV_COMPLIANCE_BOUNDARY.md). - -Conforms to Waveprobe v0.1 Implementation Contract. -""" - -from __future__ import annotations - -import hashlib -import json -import math -import struct -import zlib -from dataclasses import dataclass, field -from typing import Any, Dict, List, Optional, Sequence, Tuple - -# ── EVM Opcode Constants ───────────────────────────────────────────────────── - -# Common opcodes for structural classification -STOP = 0x00 -ADD = 0x01 -MUL = 0x02 -SUB = 0x03 -JUMPDEST = 0x5B -JUMP = 0x56 -JUMPI = 0x57 -PUSH1 = 0x60 -PUSH32 = 0x7F -DUP1 = 0x80 -DUP16 = 0x8F -SWAP1 = 0x90 -SWAP16 = 0x9F -LOG0 = 0xA0 -LOG4 = 0xA4 -CALL = 0xF1 -CALLCODE = 0xF2 -RETURN = 0xF3 -DELEGATECALL = 0xF4 -CREATE2 = 0xF5 -STATICCALL = 0xFA -REVERT = 0xFD -SELFDESTRUCT = 0xFF - -# Opcodes that carry inline data (PUSH1 through PUSH32) -PUSH_RANGE = range(PUSH1, PUSH32 + 1) - -# High-heat opcodes — dynamic dispatch, external interaction -HIGH_HEAT_OPCODES = frozenset({ - CALL, CALLCODE, DELEGATECALL, STATICCALL, - CREATE2, SELFDESTRUCT, -}) - -# Control flow opcodes -CONTROL_FLOW_OPCODES = frozenset({ - JUMP, JUMPI, JUMPDEST, RETURN, REVERT, STOP, -}) - -# Standard feature dimension for the waveprobe -FEATURE_DIM = 8 - -# Waveprobe chunk size (contract spec: 4096, but we adapt to bytecode length) -DEFAULT_CHUNK_SIZE = 4096 - -# Classification thresholds -HEAT_COLD_THRESHOLD = 0.05 -HEAT_HOT_THRESHOLD = 0.25 - -EPSILON = 1e-9 - - -# ── Feature Extraction ─────────────────────────────────────────────────────── - -def _byte_entropy(data: bytes) -> float: - """Shannon entropy of byte distribution, normalized to [0, 1].""" - if not data: - return 0.0 - counts = [0] * 256 - for b in data: - counts[b] += 1 - n = len(data) - entropy = 0.0 - for c in counts: - if c > 0: - p = c / n - entropy -= p * math.log2(p) - return entropy / 8.0 # normalize to [0, 1] - - -def _opcode_density(data: bytes) -> float: - """Fraction of bytes that are standalone opcodes (not PUSH data).""" - if not data: - return 0.0 - i = 0 - opcode_count = 0 - while i < len(data): - b = data[i] - opcode_count += 1 - if b in PUSH_RANGE: - push_size = b - PUSH1 + 1 - i += 1 + push_size - else: - i += 1 - return min(1.0, opcode_count / max(1, len(data))) - - -def _high_heat_density(data: bytes) -> float: - """Fraction of opcodes that are high-heat (CALL, DELEGATECALL, etc.).""" - if not data: - return 0.0 - i = 0 - total = 0 - hot = 0 - while i < len(data): - b = data[i] - total += 1 - if b in HIGH_HEAT_OPCODES: - hot += 1 - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - return hot / max(1, total) - - -def _control_flow_density(data: bytes) -> float: - """Fraction of opcodes that are control flow.""" - if not data: - return 0.0 - i = 0 - total = 0 - cf = 0 - while i < len(data): - b = data[i] - total += 1 - if b in CONTROL_FLOW_OPCODES: - cf += 1 - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - return cf / max(1, total) - - -def _push_data_ratio(data: bytes) -> float: - """Fraction of bytes consumed by PUSH inline data.""" - if not data: - return 0.0 - i = 0 - push_bytes = 0 - while i < len(data): - b = data[i] - if b in PUSH_RANGE: - size = b - PUSH1 + 1 - push_bytes += size - i += 1 + size - else: - i += 1 - return push_bytes / max(1, len(data)) - - -def _jumpdest_spacing_cv(data: bytes) -> float: - """Coefficient of variation of JUMPDEST spacing. - - Low CV = regular function dispatch table (cold). - High CV = irregular control flow (warm/hot). - """ - positions: list[int] = [] - i = 0 - while i < len(data): - b = data[i] - if b == JUMPDEST: - positions.append(i) - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - - if len(positions) < 2: - return 0.0 - - spacings = [positions[j + 1] - positions[j] for j in range(len(positions) - 1)] - mean_sp = sum(spacings) / len(spacings) - if mean_sp < EPSILON: - return 0.0 - var_sp = sum((s - mean_sp) ** 2 for s in spacings) / len(spacings) - return math.sqrt(var_sp) / (mean_sp + EPSILON) - - -def _repetition_rate(data: bytes) -> float: - """Adjacent byte repetition rate.""" - if len(data) < 2: - return 0.0 - reps = sum(1 for i in range(1, len(data)) if data[i] == data[i - 1]) - return reps / (len(data) - 1) - - -def _compression_score(data: bytes) -> float: - """Deterministic compression scalar using zlib (level 6). - - Returns compressed_size / original_size. Lower = more compressible. - """ - if not data: - return 1.0 - compressed = zlib.compress(data, level=6) - return len(compressed) / len(data) - - -def extract_features(data: bytes) -> List[float]: - """Extract 8-dimensional feature vector Φ(x) from EVM bytecode. - - Features are normalized to [0, 1] where possible. - """ - return [ - _byte_entropy(data), # f0: byte-level entropy - _opcode_density(data), # f1: opcode vs data ratio - _high_heat_density(data), # f2: CALL/DELEGATECALL density - _control_flow_density(data), # f3: JUMP/JUMPI/RETURN density - _push_data_ratio(data), # f4: inline PUSH data fraction - _jumpdest_spacing_cv(data), # f5: JUMPDEST spacing irregularity - _repetition_rate(data), # f6: adjacent byte repetition - _compression_score(data), # f7: zlib compression ratio - ] - - -# ── Perturbation Families (EVM-adapted) ────────────────────────────────────── - -def _deterministic_position(seed_hex: str, family_name: str, length: int, - validator=None) -> int: - """Deterministic position selection per the waveprobe contract. - - Uses SHA-256 chaining to generate candidate indices. - """ - family_seed = hashlib.sha256( - (seed_hex + ":" + family_name).encode() - ).digest() - - offset = 0 - while True: - if offset + 4 > len(family_seed): - family_seed = hashlib.sha256(family_seed).digest() - offset = 0 - word = struct.unpack_from(">I", family_seed, offset)[0] - offset += 4 - idx = word % length - if validator is None or validator(idx): - return idx - - -def _deterministic_positions(seed_hex: str, family_name: str, length: int, - count: int, window_start: int = 0, - window_end: Optional[int] = None) -> List[int]: - """Select multiple distinct deterministic positions within a window.""" - if window_end is None: - window_end = length - 1 - window_len = window_end - window_start + 1 - - family_seed = hashlib.sha256( - (seed_hex + ":" + family_name + ":multi").encode() - ).digest() - - selected: list[int] = [] - offset = 0 - while len(selected) < count: - if offset + 4 > len(family_seed): - family_seed = hashlib.sha256(family_seed).digest() - offset = 0 - word = struct.unpack_from(">I", family_seed, offset)[0] - offset += 4 - idx = window_start + (word % window_len) - if idx not in selected: - selected.append(idx) - - return sorted(selected) - - -def probe_opcode_swap(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P1: Swap two adjacent opcodes (skip over PUSH data). - - EVM adaptation of adjacent_swap. We swap opcode positions only, - not PUSH inline data. - """ - # Build opcode position list (skipping PUSH data bytes) - opcode_positions: list[int] = [] - i = 0 - while i < len(data): - opcode_positions.append(i) - b = data[i] - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - - if len(opcode_positions) < 2: - return data, {"probe_family": "opcode_swap", "fallback_used": True, - "positions": [], "reason": "insufficient_opcodes"} - - # Find a valid adjacent pair - k_idx = _deterministic_position( - seed_hex, "opcode_swap", len(opcode_positions) - 1 - ) - pos_a = opcode_positions[k_idx] - pos_b = opcode_positions[k_idx + 1] - - result = bytearray(data) - result[pos_a], result[pos_b] = result[pos_b], result[pos_a] - - return bytes(result), { - "probe_family": "opcode_swap", - "fallback_used": False, - "positions": [pos_a, pos_b], - "original_bytes_hex": [f"{data[pos_a]:02x}", f"{data[pos_b]:02x}"], - "new_bytes_hex": [f"{result[pos_a]:02x}", f"{result[pos_b]:02x}"], - } - - -def probe_push_data_toggle(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P2: Toggle a PUSH data byte. - - EVM adaptation of whitespace_toggle. Flips the LSB of a PUSH inline - data byte — the smallest meaningful perturbation to contract arguments. - """ - # Find all PUSH data byte positions - push_data_positions: list[int] = [] - i = 0 - while i < len(data): - b = data[i] - if b in PUSH_RANGE: - size = b - PUSH1 + 1 - for j in range(1, size + 1): - if i + j < len(data): - push_data_positions.append(i + j) - i += 1 + size - else: - i += 1 - - if not push_data_positions: - # Fallback: flip LSB of a random position - k = _deterministic_position(seed_hex, "push_data_toggle", len(data)) - result = bytearray(data) - result[k] ^= 0x01 - return bytes(result), { - "probe_family": "push_data_toggle", - "fallback_used": True, - "positions": [k], - } - - k = push_data_positions[ - _deterministic_position(seed_hex, "push_data_toggle", len(push_data_positions)) - ] - result = bytearray(data) - result[k] ^= 0x01 - - return bytes(result), { - "probe_family": "push_data_toggle", - "fallback_used": False, - "positions": [k], - "original_bytes_hex": [f"{data[k]:02x}"], - "new_bytes_hex": [f"{result[k]:02x}"], - } - - -def probe_opcode_variant_flip(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P3: Flip opcode variant (e.g., PUSH1↔PUSH2, DUP1↔DUP2). - - EVM adaptation of ascii_case_flip. Tests width sensitivity of the - instruction encoding. - """ - # Find opcode positions that have a "flip partner" - flippable: list[int] = [] - i = 0 - while i < len(data): - b = data[i] - # DUP family: DUP1-DUP16 - if DUP1 <= b <= DUP16: - flippable.append(i) - # SWAP family: SWAP1-SWAP16 - elif SWAP1 <= b <= SWAP16: - flippable.append(i) - # LOG family: LOG0-LOG4 - elif LOG0 <= b <= LOG4: - flippable.append(i) - - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - - if not flippable: - # Fallback: XOR a byte with 0x01 - k = _deterministic_position(seed_hex, "opcode_variant_flip", len(data)) - result = bytearray(data) - result[k] ^= 0x01 - return bytes(result), { - "probe_family": "opcode_variant_flip", - "fallback_used": True, - "positions": [k], - } - - k = flippable[ - _deterministic_position(seed_hex, "opcode_variant_flip", len(flippable)) - ] - result = bytearray(data) - b = result[k] - - # Toggle the LSB of the opcode within its family - if b % 2 == 0: - result[k] = b + 1 - else: - result[k] = b - 1 - - return bytes(result), { - "probe_family": "opcode_variant_flip", - "fallback_used": False, - "positions": [k], - "original_bytes_hex": [f"{data[k]:02x}"], - "new_bytes_hex": [f"{result[k]:02x}"], - } - - -def probe_push_arg_delete(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P4: Delete a PUSH argument and shift locally (length-preserving). - - EVM adaptation of local_delete_compensate. Removes a PUSH inline - argument byte and shifts the local window to preserve chunk length. - """ - # Find PUSH instructions with data - push_positions: list[Tuple[int, int]] = [] # (opcode_pos, data_size) - i = 0 - while i < len(data): - b = data[i] - if b in PUSH_RANGE: - size = b - PUSH1 + 1 - if i + size < len(data): - push_positions.append((i, size)) - i += 1 + size - else: - i += 1 - - if not push_positions: - return data, {"probe_family": "push_arg_delete", "fallback_used": True, - "positions": [], "reason": "no_push_instructions"} - - idx = _deterministic_position(seed_hex, "push_arg_delete", len(push_positions)) - opcode_pos, _data_size = push_positions[idx] - delete_pos = opcode_pos + 1 # delete first data byte - - # Local window shift (radius 8) - a = max(0, delete_pos - 8) - b = min(len(data) - 1, delete_pos + 8) - - result = bytearray(data) - # Shift left within window - for j in range(delete_pos, b): - result[j] = result[j + 1] - result[b] = data[b] # compensate - - return bytes(result), { - "probe_family": "push_arg_delete", - "fallback_used": False, - "positions": [delete_pos], - "window_start": a, - "window_end": b, - "support_type": "window_shift", - } - - -def probe_modal_opcode_substitute(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P5: Replace opcode with the local modal opcode. - - EVM adaptation of modal_byte_substitute. Replaces an opcode with - the most common opcode in its neighborhood. - """ - # Build opcode positions - opcode_positions: list[int] = [] - i = 0 - while i < len(data): - opcode_positions.append(i) - b = data[i] - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - - if len(opcode_positions) < 3: - return data, {"probe_family": "modal_opcode_substitute", "fallback_used": True, - "positions": [], "reason": "insufficient_opcodes"} - - k_idx = _deterministic_position( - seed_hex, "modal_opcode_substitute", len(opcode_positions) - ) - center = opcode_positions[k_idx] - - # Neighborhood: 16 opcodes in each direction - start_idx = max(0, k_idx - 16) - end_idx = min(len(opcode_positions), k_idx + 17) - neighborhood_positions = opcode_positions[start_idx:end_idx] - - # Count opcode frequencies in neighborhood - freq: dict[int, int] = {} - for pos in neighborhood_positions: - op = data[pos] - freq[op] = freq.get(op, 0) + 1 - - # Modal byte (smallest value on tie) - modal_byte = min(freq.keys(), key=lambda b: (-freq[b], b)) - - result = bytearray(data) - original = result[center] - result[center] = modal_byte - - return bytes(result), { - "probe_family": "modal_opcode_substitute", - "fallback_used": False, - "positions": [center], - "original_bytes_hex": [f"{original:02x}"], - "new_bytes_hex": [f"{modal_byte:02x}"], - "substitution_changed": original != modal_byte, - } - - -def probe_jumpdest_scramble(data: bytes, seed_hex: str) -> Tuple[bytes, Dict[str, Any]]: - """P6: Rotate three JUMPDEST-adjacent values cyclically. - - EVM adaptation of local_motif_scramble. Permutes entries in a - JUMPDEST dispatch table to measure router topology sensitivity. - """ - # Find JUMPDEST positions - jumpdest_positions: list[int] = [] - i = 0 - while i < len(data): - if data[i] == JUMPDEST: - jumpdest_positions.append(i) - b = data[i] - if b in PUSH_RANGE: - i += 1 + (b - PUSH1 + 1) - else: - i += 1 - - if len(jumpdest_positions) < 3: - # Fallback to raw motif scramble on any 3 positions - positions = _deterministic_positions(seed_hex, "jumpdest_scramble", - len(data), 3) - result = bytearray(data) - p_i, p_j, p_l = positions - result[p_i], result[p_j], result[p_l] = data[p_j], data[p_l], data[p_i] - return bytes(result), { - "probe_family": "jumpdest_scramble", - "fallback_used": True, - "positions": positions, - } - - # Pick 3 JUMPDESTs - if len(jumpdest_positions) == 3: - selected = sorted(jumpdest_positions) - else: - selected = _deterministic_positions( - seed_hex, "jumpdest_scramble", - len(jumpdest_positions), 3 - ) - selected = sorted([jumpdest_positions[s] for s in selected]) - - p_i, p_j, p_l = selected - result = bytearray(data) - # Rotate the byte AFTER each JUMPDEST (the instruction that follows) - # This permutes what happens at each dispatch point - ri = min(p_i + 1, len(data) - 1) - rj = min(p_j + 1, len(data) - 1) - rl = min(p_l + 1, len(data) - 1) - result[ri], result[rj], result[rl] = data[rj], data[rl], data[ri] - - return bytes(result), { - "probe_family": "jumpdest_scramble", - "fallback_used": False, - "positions": [ri, rj, rl], - "jumpdest_positions": [p_i, p_j, p_l], - } - - -# ── Probe Families Registry ───────────────────────────────────────────────── - -PROBE_FAMILIES = [ - ("opcode_swap", probe_opcode_swap), - ("push_data_toggle", probe_push_data_toggle), - ("opcode_variant_flip", probe_opcode_variant_flip), - ("push_arg_delete", probe_push_arg_delete), - ("modal_opcode_substitute", probe_modal_opcode_substitute), - ("jumpdest_scramble", probe_jumpdest_scramble), -] - - -# ── Aggregate Metrics ──────────────────────────────────────────────────────── - -def _l2_norm(vec: Sequence[float]) -> float: - return math.sqrt(sum(v * v for v in vec)) - - -def _dot(a: Sequence[float], b: Sequence[float]) -> float: - return sum(x * y for x, y in zip(a, b)) - - -def _covariance_matrix(vectors: List[List[float]]) -> List[List[float]]: - """Sample covariance of displacement vectors.""" - m = len(vectors) - d = len(vectors[0]) if vectors else 0 - if m < 2 or d == 0: - return [[0.0] * d for _ in range(d)] - - means = [sum(v[j] for v in vectors) / m for j in range(d)] - cov = [[0.0] * d for _ in range(d)] - for i_dim in range(d): - for j_dim in range(d): - cov[i_dim][j_dim] = sum( - (v[i_dim] - means[i_dim]) * (v[j_dim] - means[j_dim]) - for v in vectors - ) / (m - 1) - return cov - - -def _trace(matrix: List[List[float]]) -> float: - return sum(matrix[i][i] for i in range(len(matrix))) - - -def _max_eigenvalue_power(matrix: List[List[float]], iterations: int = 50) -> float: - """Estimate largest eigenvalue via power iteration.""" - d = len(matrix) - if d == 0: - return 0.0 - vec = [1.0 / math.sqrt(d)] * d - for _ in range(iterations): - new_vec = [sum(matrix[i][j] * vec[j] for j in range(d)) for i in range(d)] - norm = _l2_norm(new_vec) - if norm < EPSILON: - return 0.0 - vec = [v / norm for v in new_vec] - # Rayleigh quotient - mv = [sum(matrix[i][j] * vec[j] for j in range(d)) for i in range(d)] - return _dot(vec, mv) - - -# ── Main Analysis ──────────────────────────────────────────────────────────── - -@dataclass -class ChunkProbeResult: - """Results for a single probe family on one chunk.""" - probe_family: str - feature_displacement: List[float] - feature_displacement_magnitude: float - compression_displacement: float - signed_compression_delta: float - metadata: Dict[str, Any] - - -@dataclass -class ChunkAnalysis: - """Full waveprobe analysis for one bytecode chunk.""" - chunk_offset: int - chunk_length: int - content_sha256: str - base_features: List[float] - base_compression: float - probes: List[ChunkProbeResult] - - # Aggregates - sensitivity: float = 0.0 - compression_sensitivity: float = 0.0 - compression_variance: float = 0.0 - anisotropy: float = 0.0 - heat: float = 0.0 - torsion: Optional[float] = None - mean_response_direction: List[float] = field(default_factory=list) - classification: str = "cold" # cold | warm | hot - - -@dataclass -class EVMWaveprobeResult: - """Complete waveprobe analysis of an EVM contract.""" - bytecode_sha256: str - total_length: int - chunk_count: int - chunks: List[ChunkAnalysis] - - # Contract-level aggregates - overall_heat: float = 0.0 - overall_classification: str = "cold" - heat_map: List[float] = field(default_factory=list) - classification_map: List[str] = field(default_factory=list) - - # Feature summary - high_heat_region_count: int = 0 - warm_region_count: int = 0 - cold_region_count: int = 0 - - -class EVMBytecodeWaveprobe: - """EVM bytecode waveprobe instrument. - - Probes contract bytecode with 6 EVM-adapted perturbation families, - measures structural response, and classifies regions. - """ - - def __init__( - self, - bytecode_hex: str = "", - bytecode_raw: bytes = b"", - chunk_size: int = DEFAULT_CHUNK_SIZE, - release_id: str = "evm-probe-v0.1", - ): - if bytecode_hex: - clean = bytecode_hex.strip() - if clean.startswith("0x") or clean.startswith("0X"): - clean = clean[2:] - self.bytecode = bytes.fromhex(clean) - elif bytecode_raw: - self.bytecode = bytecode_raw - else: - self.bytecode = b"" - - self.chunk_size = chunk_size - self.release_id = release_id - - def _chunk_bytecode(self) -> List[Tuple[int, bytes]]: - """Split bytecode into overlapping chunks (stride = chunk_size // 2).""" - data = self.bytecode - if not data: - return [] - - stride = max(1, self.chunk_size // 2) - chunks: list[Tuple[int, bytes]] = [] - - offset = 0 - while offset < len(data): - end = min(offset + self.chunk_size, len(data)) - chunk = data[offset:end] - # Pad if needed to maintain chunk_size - if len(chunk) < self.chunk_size: - chunk = chunk + b"\x00" * (self.chunk_size - len(chunk)) - chunks.append((offset, chunk)) - offset += stride - if end >= len(data): - break - - return chunks - - def _probe_seed(self, chunk_offset: int, content_sha256: str) -> str: - """Deterministic probe seed per the implementation contract.""" - raw = f"{self.release_id}:{chunk_offset}:{content_sha256}" - return hashlib.sha256(raw.encode()).hexdigest()[:32] - - def _analyze_chunk(self, chunk_offset: int, chunk_data: bytes) -> ChunkAnalysis: - """Run all 6 probe families on a single chunk.""" - content_sha = hashlib.sha256(chunk_data).hexdigest() - seed = self._probe_seed(chunk_offset, content_sha) - - base_features = extract_features(chunk_data) - base_compression = _compression_score(chunk_data) - - probes: list[ChunkProbeResult] = [] - displacement_vectors: list[list[float]] = [] - - for family_name, probe_fn in PROBE_FAMILIES: - perturbed, metadata = probe_fn(chunk_data, seed) - perturbed_features = extract_features(perturbed) - perturbed_compression = _compression_score(perturbed) - - # Feature displacement vector - disp_vec = [ - perturbed_features[j] - base_features[j] - for j in range(len(base_features)) - ] - disp_mag = _l2_norm(disp_vec) - comp_disp = abs(perturbed_compression - base_compression) - signed_comp = perturbed_compression - base_compression - - probes.append(ChunkProbeResult( - probe_family=family_name, - feature_displacement=disp_vec, - feature_displacement_magnitude=disp_mag, - compression_displacement=comp_disp, - signed_compression_delta=signed_comp, - metadata=metadata, - )) - displacement_vectors.append(disp_vec) - - # ── Aggregate metrics (per waveprobe contract) ─────────────────── - m = len(probes) - - # Sensitivity - sensitivity = sum(p.feature_displacement_magnitude for p in probes) / m - - # Compression sensitivity - comp_sens = sum(p.compression_displacement for p in probes) / m - - # Compression variance (sample variance) - if m > 1: - comp_var = sum( - (p.compression_displacement - comp_sens) ** 2 for p in probes - ) / (m - 1) - else: - comp_var = 0.0 - - # Anisotropy - cov = _covariance_matrix(displacement_vectors) - tr = _trace(cov) - lmax = _max_eigenvalue_power(cov) - anisotropy = lmax / (tr + EPSILON) - - # Heat (frozen coefficients: alpha=0.5, beta=0.5) - heat = comp_sens + 0.5 * comp_var + 0.5 * sensitivity - - # Mean response direction - d = len(base_features) - mean_dir = [ - sum(dv[j] for dv in displacement_vectors) / m - for j in range(d) - ] - - # Classification - if heat >= HEAT_HOT_THRESHOLD: - classification = "hot" - elif heat >= HEAT_COLD_THRESHOLD: - classification = "warm" - else: - classification = "cold" - - return ChunkAnalysis( - chunk_offset=chunk_offset, - chunk_length=len(chunk_data), - content_sha256=content_sha, - base_features=base_features, - base_compression=base_compression, - probes=probes, - sensitivity=sensitivity, - compression_sensitivity=comp_sens, - compression_variance=comp_var, - anisotropy=anisotropy, - heat=heat, - mean_response_direction=mean_dir, - classification=classification, - ) - - def analyze(self) -> EVMWaveprobeResult: - """Run the full waveprobe analysis on the contract bytecode.""" - bytecode_sha = hashlib.sha256(self.bytecode).hexdigest() - chunks = self._chunk_bytecode() - - chunk_results: list[ChunkAnalysis] = [] - for offset, chunk_data in chunks: - chunk_results.append(self._analyze_chunk(offset, chunk_data)) - - # Contract-level aggregates - heat_map = [c.heat for c in chunk_results] - classification_map = [c.classification for c in chunk_results] - - hot_count = sum(1 for c in classification_map if c == "hot") - warm_count = sum(1 for c in classification_map if c == "warm") - cold_count = sum(1 for c in classification_map if c == "cold") - - overall_heat = sum(heat_map) / max(1, len(heat_map)) - - if hot_count > 0: - overall_class = "hot" - elif warm_count > len(chunk_results) * 0.3: - overall_class = "warm" - else: - overall_class = "cold" - - return EVMWaveprobeResult( - bytecode_sha256=bytecode_sha, - total_length=len(self.bytecode), - chunk_count=len(chunk_results), - chunks=chunk_results, - overall_heat=overall_heat, - overall_classification=overall_class, - heat_map=heat_map, - classification_map=classification_map, - high_heat_region_count=hot_count, - warm_region_count=warm_count, - cold_region_count=cold_count, - ) - - def to_json(self, result: Optional[EVMWaveprobeResult] = None) -> str: - """Serialize analysis result to JSON.""" - if result is None: - result = self.analyze() - - def _chunk_to_dict(c: ChunkAnalysis) -> Dict[str, Any]: - return { - "chunk_offset": c.chunk_offset, - "chunk_length": c.chunk_length, - "content_sha256": c.content_sha256, - "base_features": [round(f, 8) for f in c.base_features], - "base_compression": round(c.base_compression, 8), - "classification": c.classification, - "aggregate": { - "sensitivity": round(c.sensitivity, 8), - "compression_sensitivity": round(c.compression_sensitivity, 8), - "compression_variance": round(c.compression_variance, 8), - "anisotropy": round(c.anisotropy, 8), - "heat": round(c.heat, 8), - "torsion": round(c.torsion, 8) if c.torsion is not None else None, - }, - "probes": [ - { - "probe_family": p.probe_family, - "feature_displacement_magnitude": round(p.feature_displacement_magnitude, 8), - "compression_displacement": round(p.compression_displacement, 8), - } - for p in c.probes - ], - } - - return json.dumps({ - "waveprobe_version": "0.1-evm", - "release_id": self.release_id, - "bytecode_sha256": result.bytecode_sha256, - "total_length": result.total_length, - "chunk_count": result.chunk_count, - "overall_heat": round(result.overall_heat, 8), - "overall_classification": result.overall_classification, - "high_heat_region_count": result.high_heat_region_count, - "warm_region_count": result.warm_region_count, - "cold_region_count": result.cold_region_count, - "heat_map": [round(h, 6) for h in result.heat_map], - "classification_map": result.classification_map, - "chunks": [_chunk_to_dict(c) for c in result.chunks], - }, indent=2) - - -# ── CLI ──────────────────────────────────────────────────────────────────── - -if __name__ == "__main__": - import argparse - import sys - - parser = argparse.ArgumentParser( - description="EVM Bytecode Waveprobe — structural classification of smart contracts" - ) - parser.add_argument("bytecode", nargs="?", - help="Hex-encoded bytecode (0x-prefixed or raw hex)") - parser.add_argument("--file", help="Read bytecode from file (hex-encoded)") - parser.add_argument("--chunk-size", type=int, default=DEFAULT_CHUNK_SIZE, - help=f"Chunk size for analysis (default {DEFAULT_CHUNK_SIZE})") - parser.add_argument("--summary", action="store_true", - help="Print summary only (no per-chunk details)") - parser.add_argument("--json", action="store_true", - help="Output full JSON analysis") - - args = parser.parse_args() - - bytecode_hex = "" - if args.file: - bytecode_hex = open(args.file).read().strip() - elif args.bytecode: - bytecode_hex = args.bytecode - else: - print("Usage: evm_bytecode_waveprobe.py | --file ") - sys.exit(1) - - probe = EVMBytecodeWaveprobe( - bytecode_hex=bytecode_hex, - chunk_size=args.chunk_size, - ) - result = probe.analyze() - - if args.json: - print(probe.to_json(result)) - elif args.summary: - print(f"Contract: {result.bytecode_sha256[:16]}...") - print(f"Length: {result.total_length} bytes") - print(f"Chunks: {result.chunk_count}") - print(f"Heat: {result.overall_heat:.6f}") - print(f"Class: {result.overall_classification}") - print(f" Cold: {result.cold_region_count}") - print(f" Warm: {result.warm_region_count}") - print(f" Hot: {result.high_heat_region_count}") - else: - print(f"Contract: {result.bytecode_sha256[:16]}...") - print(f"Length: {result.total_length} bytes | Chunks: {result.chunk_count}") - print(f"Overall: {result.overall_classification.upper()} (heat={result.overall_heat:.6f})") - print() - for i, chunk in enumerate(result.chunks): - icon = {"cold": "🧊", "warm": "🌡️", "hot": "🔥"}.get(chunk.classification, "?") - print(f" [{i:3d}] offset={chunk.chunk_offset:6d} " - f"heat={chunk.heat:.6f} " - f"aniso={chunk.anisotropy:.4f} " - f"{icon} {chunk.classification}") diff --git a/5-Applications/tools-scripts/blockchain/sha256_derivation.py b/5-Applications/tools-scripts/blockchain/sha256_derivation.py deleted file mode 100644 index 93762ad6..00000000 --- a/5-Applications/tools-scripts/blockchain/sha256_derivation.py +++ /dev/null @@ -1,626 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -SHA256 Mathematical Derivation and Security Analysis -Educational derivation of SHA256 structure, security properties, and theoretical attack complexity - -WARNING: This is for EDUCATIONAL PURPOSES ONLY -- SHA256 is cryptographically secure -- No practical attack exists with current technology -- Brute force requires 2^256 operations (physically impossible) -- This code demonstrates structure, NOT exploitation -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import hashlib -import json -import time -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Tuple -from pathlib import Path -import sys - - -# ============================================================================ -# SHA256 CONSTANTS (First 32 bits of fractional parts of cube roots of primes) -# ============================================================================ - -SHA256_K = [ - 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, - 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, - 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, - 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, - 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, - 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, - 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, - 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 -] - -# Initial hash values (First 32 bits of fractional parts of square roots of first 8 primes) -SHA256_H0 = [ - 0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, - 0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19 -] - - -# ============================================================================ -# BITWISE OPERATIONS (SHA256 PRIMITIVES) -# ============================================================================ - -def rotr(x: int, n: int) -> int: - """Right rotation for 32-bit integers""" - return ((x >> n) | (x << (32 - n))) & 0xFFFFFFFF - -def shr(x: int, n: int) -> int: - """Right shift for 32-bit integers""" - return x >> n - -def ch(x: int, y: int, z: int) -> int: - """Choice function: if x then y else z (bitwise)""" - return (x & y) ^ (~x & z) & 0xFFFFFFFF - -def maj(x: int, y: int, z: int) -> int: - """Majority function: majority of bits in x, y, z""" - return (x & y) ^ (x & z) ^ (y & z) & 0xFFFFFFFF - -def sigma0(x: int) -> int: - """Σ0 function for compression""" - return rotr(x, 2) ^ rotr(x, 13) ^ rotr(x, 22) - -def sigma1(x: int) -> int: - """Σ1 function for compression""" - return rotr(x, 6) ^ rotr(x, 11) ^ rotr(x, 25) - -def gamma0(x: int) -> int: - """σ0 function for message schedule""" - return rotr(x, 7) ^ rotr(x, 18) ^ shr(x, 3) - -def gamma1(x: int) -> int: - """σ1 function for message schedule""" - return rotr(x, 17) ^ rotr(x, 19) ^ shr(x, 10) - - -# ============================================================================ -# SHA256 COMPRESSION FUNCTION (DERIVATION) -# ============================================================================ - -@dataclass -class SHA256State: - """Represents the internal state of SHA256 during compression""" - - # Working variables (a-h) - a: int = SHA256_H0[0] - b: int = SHA256_H0[1] - c: int = SHA256_H0[2] - d: int = SHA256_H0[3] - e: int = SHA256_H0[4] - f: int = SHA256_H0[5] - g: int = SHA256_H0[6] - h: int = SHA256_H0[7] - - # Round number (0-63) - round: int = 0 - - # Message schedule word for current round - w_t: int = 0 - - # Intermediate values - t1: int = 0 - t2: int = 0 - - def to_list(self) -> List[int]: - return [self.a, self.b, self.c, self.d, self.e, self.f, self.g, self.h] - - def copy(self) -> 'SHA256State': - return SHA256State( - a=self.a, b=self.b, c=self.c, d=self.d, - e=self.e, f=self.f, g=self.g, h=self.h, - round=self.round - ) - - -class SHA256Derivation: - """ - Step-by-step SHA256 derivation with security analysis - """ - - def __init__(self): - self.state = SHA256State() - self.message_schedule: List[int] = [] - self.round_trace: List[SHA256State] = [] - - def pad_message(self, message: bytes) -> bytes: - """ - SHA256 padding: - 1. Append bit '1' to message - 2. Append k zeros where k is smallest non-negative solution to: - (message_length + 1 + k) ≡ 448 (mod 512) - 3. Append original length in bits as 64-bit big-endian integer - """ - msg_len = len(message) - msg_bit_len = msg_len * 8 - - # Append '1' bit (0x80 = 10000000) - message += b'\x80' - - # Append zeros until length ≡ 448 (mod 512) bits = 56 (mod 64) bytes - while (len(message) % 64) != 56: - message += b'\x00' - - # Append original length as 64-bit big-endian - message += msg_bit_len.to_bytes(8, 'big') - - return message - - def parse_message_blocks(self, padded_message: bytes) -> List[bytes]: - """Parse padded message into 512-bit (64-byte) blocks""" - return [padded_message[i:i+64] for i in range(0, len(padded_message), 64)] - - def compute_message_schedule(self, block: bytes) -> List[int]: - """ - Compute message schedule W[0..63] - - For t = 0..15: W[t] = M[t] (direct from message block) - For t = 16..63: W[t] = σ1(W[t-2]) + W[t-7] + σ0(W[t-15]) + W[t-16] - - This is where diffusion begins - each word depends on 4 previous words - """ - W = [] - - # First 16 words from message block (big-endian) - for i in range(16): - W.append(int.from_bytes(block[i*4:(i+1)*4], 'big')) - - # Extend to 64 words using message schedule expansion - for t in range(16, 64): - s0 = gamma0(W[t-15]) - s1 = gamma1(W[t-2]) - W.append((W[t-16] + s0 + W[t-7] + s1) & 0xFFFFFFFF) - - self.message_schedule = W - return W - - def compression_round(self, t: int) -> SHA256State: - """ - Perform one round of SHA256 compression - - T1 = h + Σ1(e) + Ch(e,f,g) + K[t] + W[t] - T2 = Σ0(a) + Maj(a,b,c) - - h = g - g = f - f = e - e = d + T1 - d = c - c = b - b = a - a = T1 + T2 - - All operations mod 2^32 - """ - state = self.state.copy() - state.round = t - - # Get message schedule word - W_t = self.message_schedule[t] - state.w_t = W_t - - # Compute T1 - S1 = sigma1(state.e) - ch_result = ch(state.e, state.f, state.g) - state.t1 = (state.h + S1 + ch_result + SHA256_K[t] + W_t) & 0xFFFFFFFF - - # Compute T2 - S0 = sigma0(state.a) - maj_result = maj(state.a, state.b, state.c) - state.t2 = (S0 + maj_result) & 0xFFFFFFFF - - # Update working variables - state.h = state.g - state.g = state.f - state.f = state.e - state.e = (state.d + state.t1) & 0xFFFFFFFF - state.d = state.c - state.c = state.b - state.b = state.a - state.a = (state.t1 + state.t2) & 0xFFFFFFFF - - self.state = state - self.round_trace.append(state.copy()) - - return state - - def compress(self, block: bytes) -> SHA256State: - """ - Full compression function for one 512-bit block - - This is the core one-way function - easy to compute forward, - computationally infeasible to invert - """ - # Reset state for new block - self.state = SHA256State() - self.round_trace = [] - - # Compute message schedule - self.compute_message_schedule(block) - - # Perform 64 rounds - for t in range(64): - self.compression_round(t) - - return self.state - - def final_hash(self, state: SHA256State) -> bytes: - """ - Compute final hash by adding compressed state to initial values - """ - h0 = (SHA256_H0[0] + state.a) & 0xFFFFFFFF - h1 = (SHA256_H0[1] + state.b) & 0xFFFFFFFF - h2 = (SHA256_H0[2] + state.c) & 0xFFFFFFFF - h3 = (SHA256_H0[3] + state.d) & 0xFFFFFFFF - h4 = (SHA256_H0[4] + state.e) & 0xFFFFFFFF - h5 = (SHA256_H0[5] + state.f) & 0xFFFFFFFF - h6 = (SHA256_H0[6] + state.g) & 0xFFFFFFFF - h7 = (SHA256_H0[7] + state.h) & 0xFFFFFFFF - - return b''.join(h.to_bytes(4, 'big') for h in [h0, h1, h2, h3, h4, h5, h6, h7]) - - def compute_full_sha256(self, message: bytes) -> Tuple[bytes, Dict]: - """ - Compute complete SHA256 hash with derivation metadata - """ - # Pad message - padded = self.pad_message(message) - - # Parse into blocks - blocks = self.parse_message_blocks(padded) - - # Process each block - current_state = SHA256State() - - for i, block in enumerate(blocks): - # Compute message schedule - W = self.compute_message_schedule(block) - - # Set initial state (for first block) or use previous hash - if i == 0: - self.state = SHA256State() - else: - # Add previous hash to current state - self.state.a = (self.state.a + current_state.a) & 0xFFFFFFFF - self.state.b = (self.state.b + current_state.b) & 0xFFFFFFFF - self.state.c = (self.state.c + current_state.c) & 0xFFFFFFFF - self.state.d = (self.state.d + current_state.d) & 0xFFFFFFFF - self.state.e = (self.state.e + current_state.e) & 0xFFFFFFFF - self.state.f = (self.state.f + current_state.f) & 0xFFFFFFFF - self.state.g = (self.state.g + current_state.g) & 0xFFFFFFFF - self.state.h = (self.state.h + current_state.h) & 0xFFFFFFFF - - # Compress - for t in range(64): - self.compression_round(t) - - current_state = self.state.copy() - - # Final hash - final_hash = self.final_hash(current_state) - - metadata = { - "original_length": len(message), - "padded_length": len(padded), - "num_blocks": len(blocks), - "total_rounds": len(blocks) * 64, - "hash_hex": final_hash.hex() - } - - return final_hash, metadata - - -# ============================================================================ -# SECURITY ANALYSIS AND ATTACK COMPLEXITY -# ============================================================================ - -@dataclass -class SecurityAnalysis: - """Analysis of SHA256 security properties and attack complexity""" - - # Hash output size - output_bits: int = 256 - - # Preimage resistance: Given h, find m such that H(m) = h - preimage_complexity: int = 2**256 - - # Second preimage resistance: Given m1, find m2 such that H(m1) = H(m2) - second_preimage_complexity: int = 2**256 - - # Collision resistance: Find any m1, m2 such that H(m1) = H(m2) - # Birthday paradox: sqrt of search space - collision_complexity: int = 2**128 - - # Avalanche effect: Changing 1 bit changes ~50% of output - avalanche_factor: float = 0.5 - - def compute_attack_feasibility(self, attack_type: str) -> Dict: - """ - Compute feasibility of various attack types - """ - # Physical limits - bremermann_limit = 1.36e50 # bits/sec/kg (maximum computational rate) - earth_mass = 5.97e24 # kg - age_of_universe_seconds = 4.35e17 # seconds (13.8 billion years) - - if attack_type == "brute_force_preimage": - operations_needed = self.preimage_complexity - time_on_earth_computer = operations_needed / (bremermann_limit * earth_mass) - time_as_universe_age = time_on_earth_computer / age_of_universe_seconds - - return { - "attack_type": "Brute Force Preimage Attack", - "operations_needed": f"2^256 ≈ {operations_needed:.2e}", - "time_on_planetary_computer": f"{time_on_earth_computer:.2e} seconds", - "time_as_universe_ages": f"{time_as_universe_age:.2e} universe ages", - "feasibility": "PHYSICALLY IMPOSSIBLE" - } - - elif attack_type == "birthday_collision": - operations_needed = self.collision_complexity - time_on_earth_computer = operations_needed / (bremermann_limit * earth_mass) - time_as_universe_age = time_on_earth_computer / age_of_universe_seconds - - return { - "attack_type": "Birthday Collision Attack", - "operations_needed": f"2^128 ≈ {operations_needed:.2e}", - "time_on_planetary_computer": f"{time_on_earth_computer:.2e} seconds", - "time_as_universe_ages": f"{time_as_universe_age:.2e} universe ages", - "feasibility": "PHYSICALLY IMPOSSIBLE" - } - - elif attack_type == "quantum_grover": - # Grover's algorithm: sqrt of classical search - operations_needed = 2**128 # Still 2^128 for 256-bit hash - time_on_earth_computer = operations_needed / (bremermann_limit * earth_mass) - - return { - "attack_type": "Quantum Attack (Grover's Algorithm)", - "operations_needed": f"2^128 ≈ {operations_needed:.2e}", - "note": "Grover provides quadratic speedup, but 2^128 is still infeasible", - "feasibility": "PHYSICALLY IMPOSSIBLE with foreseeable technology" - } - - elif attack_type == "differential_cryptanalysis": - return { - "attack_type": "Differential Cryptanalysis", - "status": "No practical differential path found for full 64-round SHA256", - "best_known_attack": "Reduced-round variants only (up to ~46 rounds)", - "full_sha256_security": "256 bits (no known weakness)", - "feasibility": "NO KNOWN PRACTICAL ATTACK" - } - - return {"error": f"Unknown attack type: {attack_type}"} - - def analyze_avalanche_effect(self, message: bytes, deriv: SHA256Derivation) -> Dict: - """ - Analyze avalanche effect - changing 1 bit should change ~50% of output - """ - # Compute original hash - original_hash, _ = deriv.compute_full_sha256(message) - - # Flip one bit - message_bytes = bytearray(message) - message_bytes[0] ^= 0x01 # Flip least significant bit of first byte - modified_message = bytes(message_bytes) - - # Compute modified hash - modified_hash, _ = deriv.compute_full_sha256(modified_message) - - # Count differing bits - differing_bits = 0 - for i in range(len(original_hash)): - xor = original_hash[i] ^ modified_hash[i] - differing_bits += bin(xor).count('1') - - total_bits = len(original_hash) * 8 - avalanche_percentage = differing_bits / total_bits - - return { - "original_hash": original_hash.hex(), - "modified_hash": modified_hash.hex(), - "bits_changed": differing_bits, - "total_bits": total_bits, - "avalanche_percentage": f"{avalanche_percentage*100:.1f}%", - "ideal": "50%", - "assessment": "EXCELLENT" if 0.45 <= avalanche_percentage <= 0.55 else "DEVIATION" - } - - -# ============================================================================ -# MAIN EXECUTION -# ============================================================================ - -def main(): - """Demonstrate SHA256 derivation and security analysis""" - - print("=" * 70) - print(" SHA256 MATHEMATICAL DERIVATION AND SECURITY ANALYSIS") - print(" Educational Derivation - NOT FOR EXPLOITATION") - print("=" * 70) - print() - - # Initialize derivation engine - deriv = SHA256Derivation() - security = SecurityAnalysis() - - # Test message - test_message = b"Bitcoin block header data for SHA256 derivation analysis" - - print("[SECTION 1] SHA256 ALGORITHM DERIVATION") - print() - - print(" Initial Hash Values (H0):") - print(" (First 32 bits of fractional parts of square roots of first 8 primes)") - for i, h in enumerate(SHA256_H0): - prime = [2, 3, 5, 7, 11, 13, 17, 19][i] - print(f" H{chr(97+i)} = 0x{h:08x} ← √{prime}") - print() - - print(" Round Constants (K):") - print(" (First 32 bits of fractional parts of cube roots of first 64 primes)") - print(f" K[0..7] = {[f'0x{k:08x}' for k in SHA256_K[:8]]}") - print(f" ... (64 total constants)") - print() - - print(" Compression Function:") - print(" T1 = h + Σ1(e) + Ch(e,f,g) + K[t] + W[t]") - print(" T2 = Σ0(a) + Maj(a,b,c)") - print(" Where:") - print(" Σ1(e) = ROTR⁶(e) ⊕ ROTR¹¹(e) ⊕ ROTR²⁵(e)") - print(" Ch(e,f,g) = (e ∧ f) ⊕ (¬e ∧ g)") - print(" Σ0(a) = ROTR²(a) ⊕ ROTR¹³(a) ⊕ ROTR²²(a)") - print(" Maj(a,b,c) = (a ∧ b) ⊕ (a ∧ c) ⊕ (b ∧ c)") - print() - - print("[SECTION 2] MESSAGE SCHEDULE EXPANSION") - print() - - # Pad and parse message - padded = deriv.pad_message(test_message) - blocks = deriv.parse_message_blocks(padded) - - print(f" Original message length: {len(test_message)} bytes") - print(f" Padded message length: {len(padded)} bytes") - print(f" Number of 512-bit blocks: {len(blocks)}") - print() - - # Compute message schedule for first block - W = deriv.compute_message_schedule(blocks[0]) - - print(" Message Schedule W[0..15] (direct from message):") - for i in range(16): - print(f" W[{i:2d}] = 0x{W[i]:08x}") - print() - - print(" Message Schedule W[16..63] (expanded using σ0, σ1):") - print(" W[t] = σ1(W[t-2]) + W[t-7] + σ0(W[t-15]) + W[t-16]") - for i in range(16, 24): - print(f" W[{i:2d}] = 0x{W[i]:08x}") - print(" ... (64 total words)") - print() - - print("[SECTION 3] COMPRESSION ROUNDS (64 iterations)") - print() - - # Run compression - deriv.compress(blocks[0]) - - print(" Round trace (first 8 rounds):") - for i in range(min(8, len(deriv.round_trace))): - trace = deriv.round_trace[i] - print(f" Round {i:2d}: a=0x{trace.a:08x} e=0x{trace.e:08x} T1=0x{trace.t1:08x}") - print(" ... (64 rounds total)") - print() - - print("[SECTION 4] FINAL HASH COMPUTATION") - print() - - final_hash, metadata = deriv.compute_full_sha256(test_message) - - print(f" Final SHA256 Hash: {final_hash.hex()}") - print(f" Metadata:") - print(f" Original length: {metadata['original_length']} bytes") - print(f" Padded length: {metadata['padded_length']} bytes") - print(f" Total rounds: {metadata['total_rounds']}") - print() - - # Verify against standard library - standard_hash = hashlib.sha256(test_message).hexdigest() - match = "✓ MATCH" if final_hash.hex() == standard_hash else "✗ MISMATCH" - print(f" Verification against hashlib: {match}") - print() - - print("[SECTION 5] AVALANCHE EFFECT ANALYSIS") - print() - - avalanche = security.analyze_avalanche_effect(test_message, deriv) - - print(f" Original: {avalanche['original_hash']}") - print(f" Modified: {avalanche['modified_hash']} (1 bit flipped)") - print(f" Bits changed: {avalanche['bits_changed']}/{avalanche['total_bits']}") - print(f" Avalanche: {avalanche['avalanche_percentage']} (ideal: 50%)") - print(f" Assessment: {avalanche['assessment']}") - print() - - print("[SECTION 6] SECURITY ANALYSIS AND ATTACK COMPLEXITY") - print() - - attacks = [ - "brute_force_preimage", - "birthday_collision", - "quantum_grover", - "differential_cryptanalysis" - ] - - for attack_type in attacks: - result = security.compute_attack_feasibility(attack_type) - print(f" {result['attack_type']}:") - if "operations_needed" in result: - print(f" Operations: {result['operations_needed']}") - if "time_on_planetary_computer" in result: - print(f" Time: {result['time_on_planetary_computer']}") - if "time_as_universe_ages" in result: - print(f" Universe ages: {result['time_as_universe_ages']}") - if "status" in result: - print(f" Status: {result['status']}") - if "feasibility" in result: - print(f" Feasibility: {result['feasibility']}") - print() - - print("=" * 70) - print(" CONCLUSION: SHA256 IS COMPUTATIONALLY SECURE") - print(" No known practical attack exists for the full 64-round algorithm") - print(" Brute force requires more energy than exists in the observable universe") - print("=" * 70) - - # Save results - results = { - "derivation": { - "initial_values": SHA256_H0, - "round_constants_count": len(SHA256_K), - "message_schedule_words": 64, - "compression_rounds": 64 - }, - "test_hash": { - "message": test_message.decode(), - "hash": final_hash.hex(), - "verification": "match" if final_hash.hex() == standard_hash else "mismatch" - }, - "avalanche_effect": avalanche, - "security_analysis": { - "preimage_resistance": "2^256 operations", - "collision_resistance": "2^128 operations (birthday bound)", - "quantum_resistance": "2^128 operations (Grover's algorithm)", - "best_known_attack": "None for full 64-round SHA256" - }, - "timestamp": time.time() - } - - output_path = Path(__file__).resolve().parent.parent / "out" / "sha256_derivation_results.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - - print(f"\n[+] Results saved to: {output_path}") - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/blockchain/synthetic_cracking_signal.py b/5-Applications/tools-scripts/blockchain/synthetic_cracking_signal.py deleted file mode 100644 index eb1d6548..00000000 --- a/5-Applications/tools-scripts/blockchain/synthetic_cracking_signal.py +++ /dev/null @@ -1,327 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""synthetic_cracking_signal.py - -Generates a synthetic pre-2008-like market signal where the sensor reports -normal operation while the fundamental is cracking. - -TWO-LAYER STRUCTURE -------------------- -Surface layer — VIX-suppressed, slight positive drift, low volatility. - Models what participants and regulators were measuring. - Parameterized from S&P 500 2004-2007: - daily drift ~+0.05% (≈12% annualized) - daily vol ~0.6% (VIX ≈10-12, annualized ≈9.5%) - -Fundamental layer — Slow consistent deterioration with accelerating drift. - Models underlying credit quality / delinquency accumulation. - Parameterized from ABX 2006-2 AAA + Case-Shiller HPI decay: - initial daily drift -0.025% - drift acceleration +0.3%/day (compounding) - daily vol 0.1% (smooth, not noisy) - -THE SENSOR SPOOF ----------------- -The surface layer has weak coupling to the fundamental (λ=0.002). -This means it is very slowly being pulled toward reality, but the -lag is long enough that the gap widens for most of the signal lifetime. -The sensor (any agent measuring only the surface) reports "operating as -expected" during the entire INCUBATING phase. - -DETECTION VIA PRODUCTIVE WRONGNESS GATE ------------------------------------------ -ε_t = surface_t − fundamental_t (scalar error, gap between layers) -C_t = directional consistency of δε over a W-day rolling window [0,1] -N_t = |ε_t| / baseline_vol (novelty in baseline-σ units) - -γ(t) correction gate: - GROUNDED — C_t ≤ θ_c OR N_t ≤ 1.0 → correct now - SEISMIC — C_t ≤ θ_c AND N_t > 1.0 → re-attest, random noise - BASIN_PULL — C_t > θ_c AND N_t ≤ θ_n → correct now (spoof/manipulation signature) - INCUBATING — C_t > θ_c AND N_t > θ_n → hold correction, accumulate - CRYSTALLIZING — INCUBATING support ≥ θ_s OR collapse trigger → burst update - -OPTIMALITY CLAIM ----------------- -Long-term gain (F_j) from detecting the INCUBATING → CRYSTALLIZING transition -early exceeds short-term accumulated error during the INCUBATING window, -whenever the misrouting cost of treating productive wrongness as noise exceeds -the incubation cost. Here: surface treated as ground truth for ~18 months -before collapse = the cost of NOT having the INCUBATING detector. - -Output: 5-Applications/out/synthetic_cracking_2008/signal.{json,csv} -""" - -import csv -import json -from pathlib import Path - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -# --------------------------------------------------------------------------- -# Parameters — calibrated to 2004-2007 S&P 500 / ABX / Case-Shiller ranges -# --------------------------------------------------------------------------- - -SEED = 2008 -T = 756 # 3 trading years (252/yr) - -# Surface (VIX-suppressed S&P analog) -# Calibrated to S&P 500 2004-2007: ~+13%/yr, VIX ≈10-12 -MU_S = 0.0003 # daily drift (+7.6%/yr) -SIGMA_S = 0.005 # daily vol (VIX ≈10-12) -LAMBDA_NORMAL = 0.002 # weak pull toward fundamental -LAMBDA_COLLAPSE = 0.15 # fast pull after crack - -# Fundamental (subprime / HPI crack analog) -# Calibrated to ABX 2006-2 AAA + Case-Shiller decay: slow start, accelerating -MU_F_INIT = -0.00008 # initial daily drift (barely visible, -2%/yr) -DRIFT_ACCEL = 1.0008 # daily multiplier — doubles in ~2.4 years -SIGMA_F = 0.001 # smooth, low noise - -# Detection window and thresholds -W = 63 # 1 quarter rolling window for C_t / N_t -W_ACCUM = 126 # 2 quarter accumulation window for INCUBATING count -THETA_C = 0.55 # directional consistency threshold -THETA_N = 2.5 # novelty threshold (baseline-σ units) -THETA_S = 20 # INCUBATING days within W_ACCUM to trigger CRYSTALLIZING - -# Collapse trigger — gap at which surface mean-reverts hard -# Calibrated: actual 2007 S&P/credit-quality divergence ≈ 20-25% -COLLAPSE_GAP = 0.22 # gap magnitude that forces rapid reversion - - -# --------------------------------------------------------------------------- -# Signal generation -# --------------------------------------------------------------------------- - -def generate(seed: int = SEED) -> dict: - rng = xp.random.default_rng(seed) - - surface = xp.zeros(T) - fundamental = xp.zeros(T) - surface[0] = 0.0 - fundamental[0] = 0.0 - - mu_f = MU_F_INIT - collapsed = False - collapse_day = None - - for t in range(1, T): - mu_f *= DRIFT_ACCEL - gap = surface[t - 1] - fundamental[t - 1] - - if not collapsed and abs(gap) >= COLLAPSE_GAP: - collapsed = True - collapse_day = t - - lam = LAMBDA_COLLAPSE if collapsed else LAMBDA_NORMAL - - surface[t] = ( - surface[t - 1] - + MU_S * (0.1 if collapsed else 1.0) # drift dies at collapse - + SIGMA_S * rng.standard_normal() - - lam * gap - ) - fundamental[t] = ( - fundamental[t - 1] - + mu_f - + SIGMA_F * rng.standard_normal() - ) - - # ----------------------------------------------------------------------- - # Error signal: gap between sensor (surface) and reality (fundamental) - # ----------------------------------------------------------------------- - epsilon = surface - fundamental - delta_eps = xp.diff(epsilon, prepend=epsilon[0]) - - # Baseline vol estimated from the first quarter (quiescent period) - baseline_vol = float(xp.std(epsilon[:63])) + 1e-8 - - # ----------------------------------------------------------------------- - # C_t — directional consistency of δε over rolling window W - # For scalar signals: cos(a,b) = sign(a*b), so C_t = fraction of - # consecutive delta pairs that agree in sign (trend persistence). - # ----------------------------------------------------------------------- - C = xp.zeros(T) - for t in range(W, T): - window = delta_eps[t - W : t] - signs = xp.sign(window) - # fraction of consecutive pairs with same sign - agree = xp.sum(signs[:-1] == signs[1:]) - C[t] = agree / max(len(signs) - 1, 1) - - # ----------------------------------------------------------------------- - # N_t — novelty: gap in baseline-σ units - # ----------------------------------------------------------------------- - N = xp.abs(epsilon) / baseline_vol - - # ----------------------------------------------------------------------- - # γ(t) correction gate + state machine - # Uses rolling W_ACCUM window to count INCUBATING days — productive - # wrongness doesn't need to be consecutive to accumulate into a framework. - # ----------------------------------------------------------------------- - state = ['GROUNDED'] * T - gamma = xp.ones(T) - incubating_flags = xp.zeros(T, dtype=bool) # per-day INCUBATING signal - early_warning_day = None - - # First pass: classify each day independently - for t in range(W, T): - if C[t] > THETA_C and N[t] > THETA_N: - incubating_flags[t] = True - - # Second pass: apply state machine with rolling accumulation - for t in range(W, T): - if collapsed and collapse_day is not None and t >= collapse_day: - state[t] = 'CRYSTALLIZING' - gamma[t] = 2.0 - elif incubating_flags[t]: - # Count INCUBATING days in rolling W_ACCUM window - window_start = max(0, t - W_ACCUM) - accum = int(xp.sum(incubating_flags[window_start:t + 1])) - if accum >= THETA_S: - state[t] = 'CRYSTALLIZING' - gamma[t] = 2.0 - if early_warning_day is None: - early_warning_day = t - else: - state[t] = 'INCUBATING' - gamma[t] = 0.0 # hold correction - elif C[t] > THETA_C and N[t] <= THETA_N: - # BASIN_PULL: consistent direction BUT toward existing attractor (low novelty). - # Spoof / manipulation signature — correct immediately against actuarial baseline, - # not the spoofed current level. γ=η_base (not 0, not burst). - state[t] = 'BASIN_PULL' - gamma[t] = 1.0 - elif N[t] > 1.0: - state[t] = 'SEISMIC' - gamma[t] = 1.0 - else: - state[t] = 'GROUNDED' - gamma[t] = 1.0 - - # ----------------------------------------------------------------------- - # Cost comparison: what the sensor-spoofed agent paid vs. gated agent - # ----------------------------------------------------------------------- - # Naive: treats surface as ground truth, no correction held - naive_cumulative_error = float(xp.cumsum(xp.abs(epsilon))[-1]) - - # Gated: only accumulates error when γ > 0 - gated_error = xp.abs(epsilon) * (gamma > 0) - gated_cumulative_error = float(xp.cumsum(gated_error)[-1]) - - return { - 'params': { - 'seed': seed, 'T': T, - 'MU_S': MU_S, 'SIGMA_S': SIGMA_S, - 'LAMBDA_NORMAL': LAMBDA_NORMAL, 'LAMBDA_COLLAPSE': LAMBDA_COLLAPSE, - 'MU_F_INIT': MU_F_INIT, 'DRIFT_ACCEL': DRIFT_ACCEL, 'SIGMA_F': SIGMA_F, - 'W': W, 'W_ACCUM': W_ACCUM, - 'THETA_C': THETA_C, 'THETA_N': THETA_N, 'THETA_S': THETA_S, - 'COLLAPSE_GAP': COLLAPSE_GAP, - 'analog': '2004-2007 S&P500 surface / ABX-2006-2-AAA fundamental crack', - }, - 'collapse_day': collapse_day, - 'early_warning_day': early_warning_day, - 'baseline_vol': baseline_vol, - 'naive_cumulative_error': naive_cumulative_error, - 'gated_cumulative_error': gated_cumulative_error, - 'series': { - 't': list(range(T)), - 'surface': surface.tolist(), - 'fundamental': fundamental.tolist(), - 'epsilon': epsilon.tolist(), - 'C': C.tolist(), - 'N': N.tolist(), - 'gamma': gamma.tolist(), - 'state': state, - }, - } - - -# --------------------------------------------------------------------------- -# Output -# --------------------------------------------------------------------------- - -def save(data: dict, out_dir: Path) -> None: - out_dir.mkdir(parents=True, exist_ok=True) - - # JSON — full fidelity - json_path = out_dir / 'signal.json' - with open(json_path, 'w') as f: - json.dump(data, f) - - # CSV — flat time series for EventCrossIndex / external tools - csv_path = out_dir / 'signal.csv' - s = data['series'] - rows = zip(s['t'], s['surface'], s['fundamental'], - s['epsilon'], s['C'], s['N'], s['gamma'], s['state']) - with open(csv_path, 'w', newline='') as f: - w = csv.writer(f) - w.writerow(['t', 'surface', 'fundamental', 'epsilon', 'C', 'N', 'gamma', 'state']) - for row in rows: - w.writerow([row[0]] + [f'{v:.6f}' if isinstance(v, float) else v - for v in row[1:]]) - - return json_path, csv_path - - -def report(data: dict) -> None: - s = data['series'] - states = s['state'] - cd = data['collapse_day'] - wd = data['early_warning_day'] - T_ = data['params']['T'] - - counts = {k: states.count(k) for k in ('GROUNDED', 'SEISMIC', 'BASIN_PULL', 'INCUBATING', 'CRYSTALLIZING')} - - pre_collapse = cd or T_ - lead_days = (cd - wd) if (cd and wd) else None - - print() - print('=== SYNTHETIC CRACKING SIGNAL — 2008 ANALOG ===') - print(f' T = {T_} days ({T_/252:.1f} trading years)') - print() - print('PHASE TRANSITIONS') - print(f' First INCUBATING detection : day {min((i for i,s in enumerate(states) if s=="INCUBATING"), default="—")}') - print(f' First CRYSTALLIZING (early): day {wd} ({f"{wd/252:.2f} yr" if wd else "—"})') - print(f' Hard collapse trigger : day {cd} ({f"{cd/252:.2f} yr" if cd else "—"})') - if lead_days: - print(f' Lead time (warning → crack): {lead_days} trading days ({lead_days/252:.2f} yr)') - print() - print('STATE DISTRIBUTION') - for k, v in counts.items(): - pct = v / T_ * 100 - bar = '█' * int(pct / 2) - print(f' {k:<15} {v:>4} days {pct:5.1f}% {bar}') - print() - print('ERROR BUDGET') - print(f' Naive (sensor-spoofed) cumulative |ε| : {data["naive_cumulative_error"]:.4f}') - print(f' Gated (INCUBATING held) cumulative |ε|: {data["gated_cumulative_error"]:.4f}') - ratio = data['naive_cumulative_error'] / max(data['gated_cumulative_error'], 1e-9) - print(f' Ratio naive/gated : {ratio:.2f}× (gated carries less error mass)') - print() - print('SIGNAL EXTREMES') - eps = s['epsilon'] - C = s['C'] - N = s['N'] - print(f' Max gap ε : {max(abs(e) for e in eps):.4f}') - print(f' Max C_t : {max(C):.3f} (consistency threshold θ_c = {data["params"]["THETA_C"]})') - print(f' Max N_t : {max(N):.2f}σ (novelty threshold θ_n = {data["params"]["THETA_N"]}σ)') - print() - - -if __name__ == '__main__': - data = generate() - out_dir = Path(__file__).parent.parent / 'out' / 'synthetic_cracking_2008' - jp, cp = save(data, out_dir) - report(data) - print(f' JSON → {jp}') - print(f' CSV → {cp}') diff --git a/5-Applications/tools-scripts/braid/braid_dsp_bridge.py b/5-Applications/tools-scripts/braid/braid_dsp_bridge.py deleted file mode 100644 index 7744f84c..00000000 --- a/5-Applications/tools-scripts/braid/braid_dsp_bridge.py +++ /dev/null @@ -1,242 +0,0 @@ -#!/usr/bin/env python3 -""" -Braid-DSP Bridge - -Reads the standing-wave color-braid simulation CSVs and feeds them through a -Python replica of the BraidNeuromorphicTranslator logic. This demonstrates -how the older DSP-neuromorphic translation layer can be repurposed for the -BT20 Genetic Ladder / braid-spike model. - -Inputs (expected in the ingest directory): - tip_qualified_braid_1_200_timeline.csv - tip_qualified_braid_1_200_codons.csv - -Output: - Prints per-epoch neuromorphic state summaries and braid-processing guidance. -""" - -import csv -import math -from collections import defaultdict -from pathlib import Path - -# --------------------------------------------------------------------------- -# Translator replica (Python version of 0-Core-Formalism/core/src/braid_neuromorphic_translation.rs) -# --------------------------------------------------------------------------- - -class BraidFeatureRecord: - def __init__(self, epoch_id, n, shell_geometry, color_field, codon_context): - self.epoch_id = epoch_id - self.n = n - self.shell_geometry = shell_geometry # [a, b, ab, a-b, shell] - self.color_field = color_field # [mass, polarity, A, T, G, C] - self.codon_context = codon_context # [interaction_sum, reinforced, opposed, neutral] - -class NeuromorphicState: - def __init__(self, mp, weights, thresholds, firing): - self.membrane_potential = mp - self.neuron_weights = weights - self.neuron_thresholds = thresholds - self.firing_rate = firing - -class BraidGuidance: - def __init__(self, boundary, center, resonance, neutral): - self.mode_bias = { - "boundary_sensitive": boundary, - "center_sensitive": center, - "resonance_sensitive": resonance, - "neutral_traversal": neutral, - } - self.boundary_sensitive = boundary - self.center_sensitive = center - self.resonance_sensitive = resonance - self.neutral_traversal = neutral - -class BraidNeuromorphicTranslator: - def __init__(self, neuron_count: int = 8, feature_dim: int = 15): - self.neuron_count = neuron_count - self.feature_dim = feature_dim - self.translation_matrix = [ - [((i * 0.1 + j * 0.1) % 1.0) * 0.2 for j in range(feature_dim)] - for i in range(neuron_count) - ] - self.batch_sync_counter = 0 - - def braid_to_neuromorphic(self, features: BraidFeatureRecord) -> NeuromorphicState: - combined = features.shell_geometry + features.color_field + features.codon_context - effective_dim = min(self.feature_dim, len(combined)) - feature_mean = sum(combined[:effective_dim]) / max(1, effective_dim) - - mp = [0.0] * self.neuron_count - weights = [0.1] * self.neuron_count - thresholds = [0.5] * self.neuron_count - firing = [0.0] * self.neuron_count - - for i in range(self.neuron_count): - bias = sum(self.translation_matrix[i][:effective_dim]) - mp[i] = feature_mean * bias * 0.5 - weights[i] = 0.1 + min(0.9, abs(mp[i]) * 0.5) - firing[i] = max(0.0, min(1.0, mp[i] / 0.5)) - - return NeuromorphicState(mp, weights, thresholds, firing) - - def state_to_prior(self, state: NeuromorphicState, epoch_id: int): - return { - "epoch_id": epoch_id, - "neuromorphic_prior_vector": state.membrane_potential[:], - "candidate_mask": state.firing_rate[:], - "proposal_weight_vector": state.neuron_weights[:], - "lag_bias_vector": [p * 0.1 for p in state.membrane_potential], - } - - def neuromorphic_to_braid_guidance(self, prior) -> BraidGuidance: - pv = prior["neuromorphic_prior_vector"] - boundary = pv[0] if len(pv) > 0 else 0.5 - center = pv[1] if len(pv) > 1 else 0.5 - resonance = pv[2] if len(pv) > 2 else 0.5 - neutral = pv[3] if len(pv) > 3 else 0.5 - - total = boundary + center + resonance + neutral - if total <= 0: - total = 1.0 - - return BraidGuidance( - boundary / total, - center / total, - resonance / total, - neutral / total, - ) - - def batch_sync(self, batch_id: int, epoch_id: int): - self.batch_sync_counter = batch_id - for i in range(self.neuron_count): - for j in range(self.feature_dim): - self.translation_matrix[i][j] *= 0.995 - self.translation_matrix[i][j] += 0.005 * (i / self.neuron_count) - - -# --------------------------------------------------------------------------- -# CSV ingestion -# --------------------------------------------------------------------------- - -def load_timeline(path: Path): - rows = [] - with path.open(newline="") as f: - reader = csv.DictReader(f) - for r in reader: - rows.append({ - "n": int(r["n"]), - "shell": int(r["shell"]), - "a": int(r["a"]), - "b": int(r["b"]), - "primary_count": int(r["primary_count"]), - "echo_count": int(r["echo_count"]), - "eq_resonance_count": int(r["eq_resonance_count"]), - "mirror_resonance_count": int(r["mirror_resonance_count"]), - "total_activity": int(r["total_activity"]), - "resonance_total": int(r["resonance_total"]), - "event_labels": r["event_labels"], - "resonance_types": r["resonance_types"], - "field_class": r.get("field_class", ""), - "interaction_sum": float(r.get("interaction_sum", 0)), - "field_mass": float(r.get("field_mass", 0)), - "field_polarity": float(r.get("field_polarity", 0)), - "field_A": float(r.get("field_A", 0)), - "field_T": float(r.get("field_T", 0)), - "field_G": float(r.get("field_G", 0)), - "field_C": float(r.get("field_C", 0)), - }) - return rows - - -def build_features_per_shell(rows): - """Aggregate braid features per shell (epoch).""" - shells = defaultdict(list) - for r in rows: - shells[r["shell"]].append(r) - - features = [] - for shell_id in sorted(shells.keys()): - grp = shells[shell_id] - n_mid = grp[len(grp) // 2]["n"] - - shell_geometry = [ - sum(r["a"] for r in grp) / len(grp), - sum(r["b"] for r in grp) / len(grp), - sum(r["a"] * r["b"] for r in grp) / len(grp), - sum(r["a"] - r["b"] for r in grp) / len(grp), - float(shell_id), - ] - - color_field = [ - sum(r["field_mass"] for r in grp) / len(grp), - sum(r["field_polarity"] for r in grp) / len(grp), - sum(r["field_A"] for r in grp) / len(grp), - sum(r["field_T"] for r in grp) / len(grp), - sum(r["field_G"] for r in grp) / len(grp), - sum(r["field_C"] for r in grp) / len(grp), - ] - - reinforced = sum(1 for r in grp if r.get("field_class") == "reinforced") - opposed = sum(1 for r in grp if r.get("field_class") == "opposed") - neutral = sum(1 for r in grp if r.get("field_class") == "neutral") - - codon_context = [ - sum(r["interaction_sum"] for r in grp) / max(1, len(grp)), - float(reinforced), - float(opposed), - float(neutral), - ] - - features.append(BraidFeatureRecord( - epoch_id=shell_id, - n=n_mid, - shell_geometry=shell_geometry, - color_field=color_field, - codon_context=codon_context, - )) - - return features - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main(): - ingest_dir = Path("/home/allaun/Documents/ingest") - timeline_path = ingest_dir / "tip_qualified_braid_1_200_timeline.csv" - - if not timeline_path.exists(): - print(f"Error: {timeline_path} not found.") - return - - rows = load_timeline(timeline_path) - features = build_features_per_shell(rows) - - translator = BraidNeuromorphicTranslator(neuron_count=8, feature_dim=15) - - print("Braid → Neuromorphic Translation (per-shell epochs)\n") - print(f"{'Shell':>5} | {'Boundary':>9} | {'Center':>7} | {'Resonance':>9} | {'Neutral':>7}") - print("-" * 55) - - for feat in features: - state = translator.braid_to_neuromorphic(feat) - prior = translator.state_to_prior(state, feat.epoch_id) - guidance = translator.neuromorphic_to_braid_guidance(prior) - - print( - f"{feat.epoch_id:>5} | " - f"{guidance.boundary_sensitive:>8.3f} | " - f"{guidance.center_sensitive:>6.3f} | " - f"{guidance.resonance_sensitive:>8.3f} | " - f"{guidance.neutral_traversal:>6.3f}" - ) - - # Batch sync every 5 shells to show learning drift - if feat.epoch_id % 5 == 0: - translator.batch_sync(feat.epoch_id, feat.epoch_id) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/braid/braid_feature_mapper.py b/5-Applications/tools-scripts/braid/braid_feature_mapper.py deleted file mode 100644 index 871b0320..00000000 --- a/5-Applications/tools-scripts/braid/braid_feature_mapper.py +++ /dev/null @@ -1,285 +0,0 @@ -#!/usr/bin/env python3 -""" -Braid Feature Mapper - -Maps the standing-wave color-braid simulation CSVs into the BraidFeatureRecord -schema defined in 6-Documentation/docs/design/BT20_BRAID_FEATURE_SCHEMA_V1.md. - -Inputs (expected in /home/allaun/Documents/ingest/): - tip_qualified_braid_1_200_timeline.csv - tip_qualified_braid_1_200_codons.csv - -Output: - braid_feature_records_1_200.csv - One row per active event, fully populated with timing lattice fields - and the three feature vectors. -""" - -import csv -import math -from collections import defaultdict -from pathlib import Path - -TIMING_SLOTS_PER_CYCLE = 8 -MAX_ECHO_DEPTH = 3 - -CHANNEL_IDX = {"A": 0, "T": 1, "G": 2, "C": 3} -SLOT_MAP = {"A": 0, "G": 2, "C": 4, "T": 6} - - -def compute_codeword(event_type: str, tip_polarity: int, interaction_sum: float) -> int: - group = {"A": 0b00, "G": 0b01, "C": 0b10, "T": 0b11}.get(event_type, 0b00) - polarity = 1 if tip_polarity >= 0 else 0 - field_sign = 1 if interaction_sum >= 0.0 else 0 - raw = ((group & 0b11) << 2) | ((polarity & 1) << 1) | (field_sign & 1) - parity_bit = ((raw >> 2) & 1) ^ ((raw >> 1) & 1) ^ (raw & 1) - return (raw & 0b1110) | (parity_bit & 1) - - -def one_hot_color(event_type: str) -> list: - v = [0, 0, 0, 0] - idx = CHANNEL_IDX.get(event_type) - if idx is not None: - v[idx] = 1 - return v - - -def load_timeline(path: Path) -> list: - rows = [] - with path.open(newline="") as f: - reader = csv.DictReader(f) - for r in reader: - rows.append({ - "n": int(r["n"]), - "shell": int(r["shell"]), - "a": int(r["a"]), - "b": int(r["b"]), - "primary_count": int(r["primary_count"]), - "echo_count": int(r["echo_count"]), - "eq_resonance_count": int(r["eq_resonance_count"]), - "mirror_resonance_count": int(r["mirror_resonance_count"]), - "total_activity": int(r["total_activity"]), - "resonance_total": int(r["resonance_total"]), - "event_labels": r["event_labels"], - "resonance_types": r["resonance_types"], - "field_class": r.get("field_class", ""), - "interaction_sum": float(r.get("interaction_sum", 0)), - "field_mass": float(r.get("field_mass", 0)), - "field_polarity": float(r.get("field_polarity", 0)), - "field_A": float(r.get("field_A", 0)), - "field_T": float(r.get("field_T", 0)), - "field_G": float(r.get("field_G", 0)), - "field_C": float(r.get("field_C", 0)), - }) - return rows - - -def parse_event_labels(label_str: str) -> list: - """Parse 'A[0,-3] | G*[0,-5]' into list of tokens.""" - if not label_str.strip(): - return [] - tokens = [] - for part in label_str.split("|"): - part = part.strip() - if not part: - continue - # format: STATE[ab,a-b] or STATE*[ab,a-b] - is_echo = "*" in part - state = part[0] - rest = part[1:].replace("*", "") - # rest now starts with [ and ends with ] - inner = rest.strip()[1:-1] - ab_str, d_str = inner.split(",") - tokens.append({ - "state": state, - "echo": is_echo, - "ab": int(ab_str), - "a_minus_b": int(d_str), - }) - return tokens - - -def load_codons(path: Path) -> dict: - """Map start_n -> codon info.""" - codons = {} - with path.open(newline="") as f: - reader = csv.DictReader(f) - for r in reader: - start_n = int(r["start_n"]) - codons[start_n] = { - "codon": r["codon"], - "resonance_hits": int(r["resonance_hits_in_window"]), - "resonance_types": r["resonance_types_in_window"], - } - return codons - - -def build_feature_records(timeline_rows: list, codon_map: dict) -> list: - records = [] - # Pre-index codons by n for quick lookup - codon_by_n = {} - for start_n, info in codon_map.items(): - # approximate: attach codon to the three events in that window - codon_by_n[start_n] = info - - for row in timeline_rows: - if row["total_activity"] == 0: - continue - - tokens = parse_event_labels(row["event_labels"]) - for tok in tokens: - state = tok["state"] - ab = tok["ab"] - a_minus_b = tok["a_minus_b"] - interaction = row["interaction_sum"] - kind = "echo" if tok["echo"] else "primary" - - slot = SLOT_MAP.get(state, 0) - # Echoes get the adjacent odd slot behind the primary - if kind == "echo": - slot = min(TIMING_SLOTS_PER_CYCLE - 1, slot + 1) - - codeword = compute_codeword(state, a_minus_b, interaction) - parity_bit = codeword & 1 - - color = one_hot_color(state) - if kind == "echo": - # Standing-wave echo inverts the color contribution - color = [-c for c in color] - - # Resonance flags - eq_flag = row["eq_resonance_count"] > 0 - mir_flag = row["mirror_resonance_count"] > 0 - rein_flag = row["field_class"] == "reinforced" - cross_flag = kind == "echo" - - # Codon context: try to find the codon window starting near this n - ctx = codon_by_n.get(row["n"], {}) - codon_tokens = ctx.get("codon", "").split() - codon_ctx = [0, 0, 0] - for i, ct in enumerate(codon_tokens[:3]): - # simple hash: use ascii sum mod 64 - codon_ctx[i] = sum(ord(c) for c in ct) % 64 - - geometry = [ - float(row["a"]), - float(row["b"]), - float(ab), - float(a_minus_b), - interaction, - float(row["shell"]) / 20.0, - 0.0, # timing_phase normalized later - ] - - field = [ - row["field_A"], - row["field_T"], - row["field_G"], - row["field_C"], - row["field_mass"], - row["field_polarity"], - float(parity_bit), - 1.0 / TIMING_SLOTS_PER_CYCLE, - ] - - rarity = 1.0 / max(1, ctx.get("resonance_hits", 0) + 1) - code_vec = [ - float(row["resonance_total"]), - rarity, - 0.0, # slot_entropy placeholder - 1.0, # timing_confidence - float(codeword) / 31.0, - ] - - records.append({ - "chunk_id": row["shell"], - "shell_id": row["shell"], - "event_n": row["n"], - "event_type": state, - "event_kind": kind, - "timing_slot": slot, - "timing_phase": 0, - "timing_jitter_budget": 1, - "timing_parity_bit": parity_bit, - "timing_codeword": codeword, - "tip_mass": ab, - "tip_polarity": a_minus_b, - "color_A": color[0], - "color_T": color[1], - "color_G": color[2], - "color_C": color[3], - "field_mass": row["field_mass"], - "field_polarity": row["field_polarity"], - "field_A": row["field_A"], - "field_T": row["field_T"], - "field_G": row["field_G"], - "field_C": row["field_C"], - "interaction_sum": interaction, - "codon_ctx_0": codon_ctx[0], - "codon_ctx_1": codon_ctx[1], - "codon_ctx_2": codon_ctx[2], - "res_eq": int(eq_flag), - "res_mirror": int(mir_flag), - "res_reinforced": int(rein_flag), - "res_cross_shell": int(cross_flag), - "geometry_vector": ";".join(f"{x:.4f}" for x in geometry), - "field_vector": ";".join(f"{x:.4f}" for x in field), - "code_vector": ";".join(f"{x:.4f}" for x in code_vec), - }) - - return records - - -def main(): - ingest_dir = Path("/home/allaun/Documents/ingest") - timeline_path = ingest_dir / "tip_qualified_braid_1_200_timeline.csv" - codon_path = ingest_dir / "tip_qualified_braid_1_200_codons.csv" - output_path = Path("/home/allaun/Documents/Research Stack/data/benchmarks/braid_feature_records_1_200.csv") - - if not timeline_path.exists(): - print(f"Error: {timeline_path} not found.") - return - if not codon_path.exists(): - print(f"Warning: {codon_path} not found; codon context will be empty.") - - timeline = load_timeline(timeline_path) - codons = load_codons(codon_path) if codon_path.exists() else {} - - records = build_feature_records(timeline, codons) - output_path.parent.mkdir(parents=True, exist_ok=True) - - fieldnames = [ - "chunk_id", "shell_id", "event_n", "event_type", "event_kind", - "timing_slot", "timing_phase", "timing_jitter_budget", "timing_parity_bit", "timing_codeword", - "tip_mass", "tip_polarity", - "color_A", "color_T", "color_G", "color_C", - "field_mass", "field_polarity", "field_A", "field_T", "field_G", "field_C", - "interaction_sum", - "codon_ctx_0", "codon_ctx_1", "codon_ctx_2", - "res_eq", "res_mirror", "res_reinforced", "res_cross_shell", - "geometry_vector", "field_vector", "code_vector", - ] - - with output_path.open("w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=fieldnames) - writer.writeheader() - writer.writerows(records) - - print(f"Wrote {len(records)} BraidFeatureRecords to {output_path}") - - # Quick stats - primary = sum(1 for r in records if r["event_kind"] == "primary") - echoes = sum(1 for r in records if r["event_kind"] == "echo") - print(f" Primary events: {primary}") - print(f" Echo events: {echoes}") - - codewords = defaultdict(int) - for r in records: - codewords[r["timing_codeword"]] += 1 - print(f" Unique codewords: {len(codewords)}") - for cw, cnt in sorted(codewords.items()): - print(f" codeword {cw:02b}/{cw}: {cnt}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/braid/braid_field_builder.py b/5-Applications/tools-scripts/braid/braid_field_builder.py deleted file mode 100644 index beee2b10..00000000 --- a/5-Applications/tools-scripts/braid/braid_field_builder.py +++ /dev/null @@ -1,410 +0,0 @@ -#!/usr/bin/env python3 -""" -Braid Field Builder v3 - -Reads the tip-qualified braid simulation CSV and computes: -1. Real standing-wave rear residues from future events -2. Populated FieldCoord (mass, polarity, color channels) -3. Nonzero interaction scores with shell-dependent normalization -4. Homeostatic bias via sliding-window mean subtraction -5. Active timing code (slot, dynamic phase, parity, jitter, continuous priority) -6. Codon window grouping that respects shell boundaries -7. Plain-language explanations - -Outputs: - braid_explained_events_1_200.csv - braid_glossary.csv - braid_field_interaction.png -""" - -import csv -import math -from collections import defaultdict -from pathlib import Path - -import matplotlib.pyplot as plt - -TIMING_SLOTS = 8 -ECHO_DEPTH = 3 -ECHO_WEIGHTS = {1: -1.0, 2: -0.5, 3: -0.25} -SLOT_MAP = {"A": 0, "G": 2, "C": 4, "T": 6} -CHANNEL_IDX = {"A": 0, "T": 1, "G": 2, "C": 3} -PHASE_CLIP = 3 # max |phase| in ticks -INTERACTION_SCALE = 64.0 # tanh scaling denominator -HOMEOSTATIC_WINDOW = 7 # sliding window size for mean subtraction - - -def parse_event_labels(label_str: str) -> list: - if not label_str.strip(): - return [] - tokens = [] - for part in label_str.split("|"): - part = part.strip() - if not part: - continue - is_echo = "*" in part - state = part[0] - rest = part[1:].replace("*", "") - inner = rest.strip()[1:-1] - ab_str, d_str = inner.split(",") - tokens.append({ - "state": state, - "echo": is_echo, - "ab": int(ab_str), - "a_minus_b": int(d_str), - }) - return tokens - - -def load_active_events(path: Path) -> list: - rows = [] - with path.open(newline="") as f: - reader = csv.DictReader(f) - for r in reader: - if int(r["total_activity"]) == 0: - continue - rows.append({ - "n": int(r["n"]), - "shell": int(r["shell"]), - "a": int(r["a"]), - "b": int(r["b"]), - "event_labels": r["event_labels"], - "field_class": r.get("field_class", ""), - "interaction_sum": float(r.get("interaction_sum", 0)), - }) - return rows - - -def build_standalone_active_events(timeline_rows: list) -> list: - events = [] - for row in timeline_rows: - tokens = parse_event_labels(row["event_labels"]) - for tok in tokens: - events.append({ - "n": row["n"], - "shell": row["shell"], - "a": row["a"], - "b": row["b"], - "state": tok["state"], - "echo": tok["echo"], - "ab": tok["ab"], - "a_minus_b": tok["a_minus_b"], - }) - return events - - -def compute_field(events: list) -> dict: - field = defaultdict(lambda: { - "mass": 0.0, - "polarity": 0.0, - "A": 0.0, - "T": 0.0, - "G": 0.0, - "C": 0.0, - "sources": [], - }) - - for ev in events: - n0 = ev["n"] - tip_mass = float(ev["ab"]) - tip_polarity = float(ev["a_minus_b"]) - color = [0.0, 0.0, 0.0, 0.0] - idx = CHANNEL_IDX[ev["state"]] - weight = 1.0 if not ev["echo"] else -1.0 - color[idx] = weight - - for d, alpha in ECHO_WEIGHTS.items(): - target = n0 - d - if target < 1: - continue - field[target]["mass"] += alpha * tip_mass - field[target]["polarity"] += alpha * tip_polarity - field[target]["A"] += alpha * color[0] - field[target]["T"] += alpha * color[1] - field[target]["G"] += alpha * color[2] - field[target]["C"] += alpha * color[3] - field[target]["sources"].append(f"{ev['state']}{'*' if ev['echo'] else ''}@{n0}×{alpha}") - - return field - - -def compute_codeword(state: str, a_minus_b: int, interaction: float) -> int: - group = {"A": 0b00, "G": 0b01, "C": 0b10, "T": 0b11}.get(state, 0b00) - polarity = 1 if a_minus_b >= 0 else 0 - field_sign = 1 if interaction >= 0.0 else 0 - raw = ((group & 0b11) << 2) | ((polarity & 1) << 1) | (field_sign & 1) - # Fixed parity: XOR of data bits (bits 3,2,1) so the 4-bit codeword has even popcount. - parity = ((raw >> 3) & 1) ^ ((raw >> 2) & 1) ^ ((raw >> 1) & 1) - return (raw & 0b1110) | (parity & 1) - - -def compute_phase(polarity: int, shell_width: int, interaction: float) -> int: - """ - Active timing phase derived from normalized tip polarity and - scaled interaction magnitude. - """ - pol_term = 3.0 * polarity / max(1, shell_width) - int_term = 2.0 * math.tanh(interaction / INTERACTION_SCALE) - phase = round(pol_term + int_term) - return max(-PHASE_CLIP, min(PHASE_CLIP, int(phase))) - - -def compute_priority(interaction: float) -> float: - """Continuous priority in [-1, 1] using tanh.""" - return math.tanh(interaction / INTERACTION_SCALE) - - -def compute_index_bit(priority: float) -> int: - """Binary index bit derived from continuous priority.""" - return 1 if priority > 0.0 else 0 - - -def explain_event(row: dict) -> str: - state = row["state"] - n = row["n"] - shell = row["shell"] - ab = row["ab"] - d = row["a_minus_b"] - kind = "echo" if row["echo"] else "primary" - timing = row["timing_slot"] - phase = row["timing_phase"] - interaction = row["interaction"] - classification = row["field_class"] - codon_win = row["codon_window_id"] - pos = row["codon_position"] - priority = row["priority"] - - meaning = { - "A": "boundary-in (square entry)", - "G": "center-left (shell axis left)", - "C": "center-right (shell axis right)", - "T": "boundary-out (square exit)", - }.get(state, "unknown") - - if classification == "reinforced": - class_desc = "amplified by the local standing-wave field" - elif classification == "opposed": - class_desc = "suppressed by the local standing-wave field" - else: - class_desc = "unaffected by the local standing-wave field" - - priority_label = "high-priority" if priority > 0 else "low-priority" - - return ( - f"At integer {n} (shell {shell}), a {kind} {meaning} event occurs " - f"in timing slot {timing} phase {phase:+d} with tip mass {ab} and polarity {d}. " - f"It belongs to codon window {codon_win} at position {pos}. " - f"The event is {class_desc} (interaction={interaction:.2f}) and carries {priority_label} " - f"(priority={priority:.3f})." - ) - - -def build_explained_csv(events: list, field: dict) -> list: - # First pass: compute raw interactions for sliding-window mean - raw_interactions = [] - for ev in events: - n = ev["n"] - f = field.get(n, { - "mass": 0.0, "polarity": 0.0, - "A": 0.0, "T": 0.0, "G": 0.0, "C": 0.0, "sources": [] - }) - tip_mass = ev["ab"] - tip_polarity = ev["a_minus_b"] - color = [0, 0, 0, 0] - color[CHANNEL_IDX[ev["state"]]] = 1 if not ev["echo"] else -1 - - interaction = ( - tip_mass * f["mass"] + - tip_polarity * f["polarity"] + - sum(color[i] * [f["A"], f["T"], f["G"], f["C"]][i] for i in range(4)) - ) - raw_interactions.append(interaction) - - # Compute sliding-window means for homeostatic bias - homeostatic_means = [] - for i in range(len(raw_interactions)): - window = raw_interactions[max(0, i - HOMEOSTATIC_WINDOW // 2) - :min(len(raw_interactions), i + HOMEOSTATIC_WINDOW // 2 + 1)] - homeostatic_means.append(sum(window) / len(window)) - - # Second pass: build rows with shell-respecting codon windows - rows = [] - shell_counters = defaultdict(int) # tracks event index per shell - - for idx, ev in enumerate(events): - n = ev["n"] - f = field.get(n, { - "mass": 0.0, "polarity": 0.0, - "A": 0.0, "T": 0.0, "G": 0.0, "C": 0.0, "sources": [] - }) - - tip_mass = ev["ab"] - tip_polarity = ev["a_minus_b"] - color = [0, 0, 0, 0] - color[CHANNEL_IDX[ev["state"]]] = 1 if not ev["echo"] else -1 - - raw_interaction = raw_interactions[idx] - # Homeostatic bias: subtract local mean so the distribution centers around 0 - interaction = raw_interaction - homeostatic_means[idx] - - if interaction > 0: - field_class = "reinforced" - elif interaction < 0: - field_class = "opposed" - else: - field_class = "neutral" - - shell_width = 2 * ev["shell"] + 1 - phase = compute_phase(tip_polarity, shell_width, interaction) - priority = compute_priority(interaction) - idx_bit = compute_index_bit(priority) - - # Shell-respecting codon window: reset counter at every shell boundary - shell = ev["shell"] - shell_local_idx = shell_counters[shell] - codon_win = shell_local_idx // 3 - codon_pos = shell_local_idx % 3 - shell_counters[shell] += 1 - - slot = SLOT_MAP[ev["state"]] - if ev["echo"]: - slot = min(TIMING_SLOTS - 1, slot + 1) - - codeword = compute_codeword(ev["state"], tip_polarity, interaction) - parity = codeword & 1 - - rows.append({ - "active_index": idx, - "n": n, - "shell": ev["shell"], - "state": ev["state"], - "kind": "echo" if ev["echo"] else "primary", - "a": ev["a"], - "b": ev["b"], - "ab": tip_mass, - "a_minus_b": tip_polarity, - "timing_slot": slot, - "timing_phase": phase, - "timing_jitter_budget": 1, - "parity_bit": parity, - "codeword": codeword, - "priority": round(priority, 6), - "index_bit": idx_bit, - "codon_window_id": codon_win, - "codon_position": codon_pos, - "field_mass": round(f["mass"], 4), - "field_polarity": round(f["polarity"], 4), - "field_A": round(f["A"], 4), - "field_T": round(f["T"], 4), - "field_G": round(f["G"], 4), - "field_C": round(f["C"], 4), - "raw_interaction": round(raw_interaction, 4), - "interaction": round(interaction, 4), - "field_class": field_class, - "field_sources": " | ".join(f["sources"]), - "explanation": explain_event({ - "state": ev["state"], "n": n, "shell": ev["shell"], - "ab": tip_mass, "a_minus_b": tip_polarity, - "echo": ev["echo"], "timing_slot": slot, "timing_phase": phase, - "field_class": field_class, "interaction": interaction, - "codon_window_id": codon_win, "codon_position": codon_pos, - "priority": priority, - }), - }) - - return rows - - -def build_glossary() -> list: - return [ - {"term_id": "G01", "term": "shell", "definition": "The integer floor of sqrt(n), denoted k(n). Defines the square bracket surrounding n."}, - {"term_id": "G02", "term": "timing_slot", "definition": "The micro-lane within an 8-slot cycle assigned to an event type (A=0, G=2, C=4, T=6)."}, - {"term_id": "G03", "term": "timing_phase", "definition": "Active fine offset inside the timing slot, derived from normalized tip polarity and interaction magnitude."}, - {"term_id": "G04", "term": "parity_bit", "definition": "Single-bit error-detecting code computed as XOR of event group, polarity, and field sign."}, - {"term_id": "G05", "term": "jitter_budget", "definition": "Maximum allowable slot displacement before the event is considered out-of-bound or erroneous."}, - {"term_id": "G06", "term": "codon_window_id", "definition": "Index of the 3-event triplet grouping within a single shell (resets at shell boundaries)."}, - {"term_id": "G07", "term": "tip_mass", "definition": "The product a·b, measuring shell-internal interaction magnitude."}, - {"term_id": "G08", "term": "tip_polarity", "definition": "The difference a−b, measuring axial asymmetry or directional bias."}, - {"term_id": "G09", "term": "standing-wave field", "definition": "Accumulated backward echoes from future events, decaying as −1, −½, −¼."}, - {"term_id": "G10", "term": "interaction", "definition": "Dot product of the event's tip/color state against the local standing-wave field, after homeostatic mean subtraction."}, - {"term_id": "G11", "term": "field_class", "definition": "Classification of the event based on interaction sign: reinforced (>0), opposed (<0), or neutral (=0)."}, - {"term_id": "G12", "term": "codeword", "definition": "Compact 5-bit temporal symbol encoding event group, polarity, field sign, and parity."}, - {"term_id": "G13", "term": "priority", "definition": "Continuous priority score tanh(interaction/τ) in [-1, 1]. Maps to binary index_bit for hardware."}, - {"term_id": "G14", "term": "homeostatic_bias", "definition": "Sliding-window mean subtraction that recenters the interaction distribution to ~50% reinforced/opposed."}, - {"term_id": "G15", "term": "RSCU/CAI", "definition": "Recommended codon usage normalization (Relative Synonymous Codon Usage / Codon Adaptation Index) for organism-specific tables."}, - ] - - -def main(): - ingest_dir = Path("/home/allaun/Documents/ingest") - timeline_path = ingest_dir / "tip_qualified_braid_1_200_timeline.csv" - out_dir = Path("/home/allaun/Documents/Research Stack/data/benchmarks") - out_dir.mkdir(parents=True, exist_ok=True) - - explained_path = out_dir / "braid_explained_events_1_200.csv" - glossary_path = out_dir / "braid_glossary.csv" - plot_path = out_dir / "braid_field_interaction.png" - - if not timeline_path.exists(): - print(f"Error: {timeline_path} not found.") - return - - timeline = load_active_events(timeline_path) - events = build_standalone_active_events(timeline) - field = compute_field(events) - explained = build_explained_csv(events, field) - glossary = build_glossary() - - fieldnames = [ - "active_index", "n", "shell", "state", "kind", - "a", "b", "ab", "a_minus_b", - "timing_slot", "timing_phase", "timing_jitter_budget", "parity_bit", "codeword", - "priority", "index_bit", - "codon_window_id", "codon_position", - "field_mass", "field_polarity", "field_A", "field_T", "field_G", "field_C", - "raw_interaction", "interaction", "field_class", "field_sources", "explanation" - ] - with explained_path.open("w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=fieldnames) - writer.writeheader() - writer.writerows(explained) - - with glossary_path.open("w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=["term_id", "term", "definition"]) - writer.writeheader() - writer.writerows(glossary) - - # Plot - ns = [r["n"] for r in explained] - interactions = [r["interaction"] for r in explained] - classes = [r["field_class"] for r in explained] - colors = {"reinforced": "green", "opposed": "red", "neutral": "gray"} - point_colors = [colors.get(c, "black") for c in classes] - - plt.figure(figsize=(12, 5)) - plt.scatter(ns, interactions, c=point_colors, s=30, alpha=0.8) - plt.axhline(0, color="black", linewidth=0.5) - plt.xlabel("Integer n") - plt.ylabel("Homeostatic interaction score") - plt.title("Standing-wave field interaction for active braid events (1–200)") - plt.tight_layout() - plt.savefig(plot_path, dpi=180, bbox_inches="tight") - - print(f"Wrote {len(explained)} explained events to {explained_path}") - print(f"Wrote {len(glossary)} glossary terms to {glossary_path}") - print(f"Wrote interaction plot to {plot_path}") - - reinforced = sum(1 for r in explained if r["field_class"] == "reinforced") - opposed = sum(1 for r in explained if r["field_class"] == "opposed") - neutral = sum(1 for r in explained if r["field_class"] == "neutral") - high_priority = sum(1 for r in explained if r["index_bit"] == 1) - print(f"\nClass breakdown: reinforced={reinforced}, opposed={opposed}, neutral={neutral}") - print(f"Priority breakdown: high={high_priority}, low={len(explained)-high_priority}") - - print("\n--- Sample explanations ---") - for r in explained[:5]: - print(f"[{r['state']} n={r['n']}] {r['explanation']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/braid/braid_photonic_emulator.py b/5-Applications/tools-scripts/braid/braid_photonic_emulator.py deleted file mode 100644 index 3794a236..00000000 --- a/5-Applications/tools-scripts/braid/braid_photonic_emulator.py +++ /dev/null @@ -1,302 +0,0 @@ -#!/usr/bin/env python3 -""" -braid_photonic_emulator.py -Photonic emulation bridge for the Braid-Neuromorphic Genetic Ladder. - -Maps integer-shell braid events to a Simphony photonic circuit netlist. -Each event becomes a Mach-Zehnder Interferometer (MZI) stage: - y-branch splitter -> phase-shifted arm + reference arm -> y-branch combiner -The phase shift encodes timing_phase, and the arm-length difference encodes -polarity/interaction. The resulting S21 spectrum gives a continuous-domain -view of the standing-wave field and genetic ladder topology. - -Requires: simphony, sax, jax, matplotlib, numpy -Run with: source .venv-simphony/bin/activate && python 5-Applications/tools-5-Applications/scripts/braid_photonic_emulator.py -""" - -from __future__ import annotations - -import math -import json -import argparse -import functools -from pathlib import Path - -import numpy as np -import matplotlib.pyplot as plt -import sax -from jax import config - -config.update("jax_enable_x64", True) - -from simphony.libraries import siepic - -# --------------------------------------------------------------------------- -# Braid logic (mirrors braid_field_builder.py) -# --------------------------------------------------------------------------- - -# --------------------------------------------------------------------------- -# Braid logic (mirrors braid_field_builder.py) -# --------------------------------------------------------------------------- - -@dataclass(frozen=True) -class BraidEventParams: - """Single source of truth for every magic number in compute_event. - Factored per 5-Applications/tools-scripts/formula_optimization/braid_event_delta_gcl.py""" - decay_base: float = 0.5 # tail/echo geometric decay base - tail_depth: int = 3 # tail_weights has 3 entries - echo_depth: int = 2 # Fm uses decay^1, Fp uses decay^2 - phase_tanh_coef: float = 2.0 # tanh contribution to phase - phase_tanh_scale: float = 64.0 # interaction scale inside tanh - phase_clip_range: int = 3 # = phase_linear_coef (the redundancy) - - @property - def tail_weights(self) -> dict[int, float]: - return {k: -(self.decay_base ** (k - 1)) for k in range(1, self.tail_depth + 1)} - - @property - def echo_coefs(self) -> tuple[float, float]: - # (Fm uses ^1, Fp uses ^2) - return (self.decay_base ** 1, self.decay_base ** 2) - - @property - def phase_linear_coef(self) -> int: - return self.phase_clip_range - - -# Canonical Fc table reframed via sign × magnitude -_PURINES = {"A", "G"} -_CANONICAL = {"A", "T"} # full magnitude (1.0) -_WOBBLE = {"G", "C"} # half magnitude (0.5) - - -def _Fc_canonical(et: str) -> float: - sign = +1.0 if et in _PURINES else -1.0 - magnitude = 1.0 if et in _CANONICAL else 0.5 - return sign * magnitude - - -def shell_state(n: int): - k = int(math.isqrt(n)) - a = n - k * k - b = (k + 1) * (k + 1) - n - return {"n": n, "k": k, "a": a, "b": b, "width": 2 * k + 1} - - -def classify_event(s: dict): - k, n = s["k"], s["n"] - if n == k * k: - return "A" - if n == k * k + k: - return "G" - if n == k * k + k + 1: - return "C" - if n == (k + 1) * (k + 1) - 1: - return "T" - return None - - -def compute_event(n: int, params: BraidEventParams = BraidEventParams()): - s = shell_state(n) - et = classify_event(s) - if et is None: - return None - a, b, k = s["a"], s["b"], s["k"] - mass = a * b - polarity = a - b - shell_width = 2 * k + 1 - - # Standing-wave echo (anti-causal tail approximation) - echo = 0.0 - for tail, weight in params.tail_weights.items(): - if n - tail >= 0: - prev = shell_state(n - tail) - prev_et = classify_event(prev) - if prev_et is not None: - echo += weight * prev["a"] * prev["b"] - - # Field channels - cm, cp = params.echo_coefs - Fm = mass + cm * echo - Fp = polarity + cp * echo - Fc = _Fc_canonical(et) - - interaction = mass * Fm + polarity * Fp + Fc - - R = params.phase_clip_range - phase = max(-R, min(R, round( - params.phase_linear_coef * polarity / shell_width - + params.phase_tanh_coef * math.tanh(interaction / params.phase_tanh_scale) - ))) - index_bit = 1 if interaction > 0 else 0 - - return { - "n": n, - "k": k, - "et": et, - "mass": mass, - "polarity": polarity, - "shell_width": shell_width, - "echo": echo, - "Fm": Fm, - "Fp": Fp, - "Fc": Fc, - "interaction": interaction, - "phase": int(phase), - "index_bit": index_bit, - } - - -# --------------------------------------------------------------------------- -# Photonic model wrappers -# --------------------------------------------------------------------------- - -def _phase_waveguide(wl: float = 1.55, length: float = 0.0, phase: float = 0.0): - """Ideal waveguide with an additional phase shift.""" - wg = siepic.waveguide(wl=wl, length=length) - j = complex(0, 1) - rot = cmath.exp(j * phase) - wg_rot = {} - for (p1, p2), val in wg.items(): - if (p1 == "o0" and p2 == "o1") or (p1 == "o1" and p2 == "o0"): - wg_rot[(p1, p2)] = val * rot - else: - wg_rot[(p1, p2)] = val - return wg_rot - - -def _ybranch(wl: float = 1.55): - return siepic.y_branch(wl=wl) - - -# --------------------------------------------------------------------------- -# Photonic netlist builder -# --------------------------------------------------------------------------- - -def build_mzi_ladder_netlist(events: list[dict]): - """ - Build a cascade of MZIs, one per event. - Each MZI: y-split_i -> wgA_i -> y-comb_i - -> wgB_i -> - """ - instances: dict[str, callable] = {} - connections: dict[str, str] = {} - - for i, ev in enumerate(events): - # Map braid features to photonic parameters - base_length = 50e-6 # 50 µm base arm - delta_length = ev["polarity"] * 2e-6 # ±2 µm per polarity unit - phase_rad = math.radians(ev["phase"] * 15.0) # ±3 -> ±45° - - split_name = f"split_{i}" - comb_name = f"comb_{i}" - wgA_name = f"wgA_{i}" - wgB_name = f"wgB_{i}" - - instances[split_name] = functools.partial(_ybranch) - instances[comb_name] = functools.partial(_ybranch) - instances[wgA_name] = functools.partial(_phase_waveguide, length=base_length + delta_length, phase=phase_rad) - instances[wgB_name] = functools.partial(_phase_waveguide, length=base_length - delta_length, phase=0.0) - - # Internal MZI wiring - connections[f"{split_name},port_2"] = f"{wgA_name},o0" - connections[f"{split_name},port_3"] = f"{wgB_name},o0" - connections[f"{wgA_name},o1"] = f"{comb_name},port_2" - connections[f"{wgB_name},o1"] = f"{comb_name},port_3" - - # Chain to next stage - if i + 1 < len(events): - connections[f"{comb_name},port_1"] = f"split_{i+1},port_1" - - ports = {"in": "split_0,port_1", "out": f"comb_{len(events)-1},port_1"} - - return {"instances": instances, "connections": connections, "ports": ports} - - -# --------------------------------------------------------------------------- -# Simulation runner -# --------------------------------------------------------------------------- - -def run_photonic_simulation(events: list[dict], wl_start: float = 1.50, wl_stop: float = 1.60, points: int = 1001): - netlist = build_mzi_ladder_netlist(events) - circuit, info = sax.circuit(netlist, models={}) - wl = np.linspace(wl_start, wl_stop, points) - s_params = circuit(wl=wl) - s21 = s_params[("in", "out")] - transmission = np.abs(s21) ** 2 - return wl, transmission, events - - -# --------------------------------------------------------------------------- -# CLI -# --------------------------------------------------------------------------- - -def main(): - parser = argparse.ArgumentParser(description="Photonic emulation of the Braid-Neuromorphic Genetic Ladder") - parser.add_argument("--start", type=int, default=1, help="Start integer n") - parser.add_argument("--stop", type=int, default=200, help="Stop integer n (inclusive)") - parser.add_argument("--csv", type=str, default="braid_photonic_emulation.csv", help="Output CSV path") - parser.add_argument("--png", type=str, default="braid_photonic_emulation.png", help="Output plot path") - parser.add_argument("--json", type=str, default="braid_photonic_netlist.json", help="Output netlist JSON") - args = parser.parse_args() - - events = [ev for n in range(args.start, args.stop + 1) if (ev := compute_event(n)) is not None] - print(f"Computed {len(events)} axial events for n={args.start}..{args.stop}") - - if not events: - print("No events to simulate.") - return - - print("Building photonic MZI ladder netlist ...") - wl, transmission, events = run_photonic_simulation(events) - print(f"Simulated {len(wl)} wavelength points.") - - # CSV export (one row per event with center-wavelength transmission) - out_csv = Path(args.csv) - with out_csv.open("w") as f: - f.write("n,k,event,mass,polarity,interaction,phase,index_bit,wl_um,transmission_dB\n") - center_idx = len(wl) // 2 - for ev in events: - t = transmission[center_idx] - t_db = 10.0 * math.log10(max(1e-12, t)) - f.write( - f"{ev['n']},{ev['k']},{ev['et']}," - f"{ev['mass']},{ev['polarity']},{ev['interaction']:.6f}," - f"{ev['phase']},{ev['index_bit']}," - f"{wl[center_idx]:.6f},{t_db:.6f}\n" - ) - print(f"Wrote CSV: {out_csv.resolve()}") - - # Plot - fig, ax = plt.subplots(figsize=(10, 5)) - ax.plot(wl, 10 * np.log10(np.maximum(1e-12, transmission)), label="S21 (dB)") - ax.set_xlabel("Wavelength (µm)") - ax.set_ylabel("Transmission (dB)") - ax.set_title("Braid-Neuromorphic Genetic Ladder — Photonic MZI Emulation") - ax.legend() - ax.grid(True) - fig.tight_layout() - out_png = Path(args.png) - fig.savefig(out_png, dpi=150) - print(f"Wrote plot: {out_png.resolve()}") - - # Netlist JSON (metadata only) - netlist = build_mzi_ladder_netlist(events) - out_json = Path(args.json) - with out_json.open("w") as f: - json.dump( - { - "instances": list(netlist["instances"].keys()), - "connections": netlist["connections"], - "ports": netlist["ports"], - "event_count": len(events), - }, - f, - indent=2, - ) - print(f"Wrote netlist JSON: {out_json.resolve()}") - - -if __name__ == "__main__": - import cmath - main() diff --git a/5-Applications/tools-scripts/bt20/bt20_bootstrap.py b/5-Applications/tools-scripts/bt20/bt20_bootstrap.py deleted file mode 100644 index d42b6a7a..00000000 --- a/5-Applications/tools-scripts/bt20/bt20_bootstrap.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -""" -bt20_bootstrap.py — Geometric Reader for BT20 Machine - -Pulls 20 'Principal Axioms' from the 1GB Truth Baseline and converts -them into initial mu-seeds (neurons) for the tuning machine. -""" - -import sqlite3 -import json -import os -import hashlib -from pathlib import Path -from typing import List, Dict - -DB_PATH = "/home/allaun/.tardy_mmr.db" -SEED_OUTPUT = Path.home() / ".gemini/antigravity/scratch/bt20_initial_seeds.json" - -def get_principal_axioms(db_path: str, count: int = 20) -> List[Dict]: - """Samples the 1GB sweep for high-saturation axioms.""" - conn = sqlite3.connect(db_path) - # Sample axioms with regional basin roots (top-level truths) - cursor = conn.execute( - "SELECT payload FROM mmr WHERE leaf_type = 'AXIOM' ORDER BY RANDOM() LIMIT ?", - (count,) - ) - results = [] - for row in cursor: - results.append(json.loads(row[0])) - conn.close() - return results - -def axiom_to_mu_seed(axiom: Dict, index: int) -> Dict: - """ - Encodes an Axiom into a 32-bit mu-seed structure (simplified for simulation). - Pattern: GEFI-PRIM-1 (Encode primitive). - """ - # Use the root hash to derive the 'Activation' and 'Transform' - root_hash = axiom.get("batch_root_hash", "0" * 64) - hash_bytes = bytes.fromhex(root_hash) - - # 10 bits Delta P from first 10 bits of hash - delta_p = int.from_bytes(hash_bytes[:2], 'big') & 0x3FF - # Gamma (Transform mode) - 5 bits - gamma = hash_bytes[2] & 0x1F - # Activation state - 4 bits - activation = hash_bytes[3] & 0xF - # Confidence - derived from batch member count - member_count = axiom.get("member_count", 0) - confidence = min(15, member_count // 64) - - # Pack into word (simulated) - word = delta_p - word |= (1 << 10) # Region: INTERIOR - word |= (gamma << 14) - word |= (activation << 19) - word |= (confidence << 27) - - return { - "neuron_id": index, - "mu_seed": word, - "gamma": gamma, - "activation": float(activation), - "confidence": confidence / 15.0, - "anchor": root_hash[:8], - "axiom_ref": f"record_{index}" - } - -def bootstrap(): - print(f"[*] Reading Truth Baseline from {DB_PATH}...") - if not os.path.exists(DB_PATH): - print(f"[!] Error: DB not found at {DB_PATH}") - return - - axioms = get_principal_axioms(DB_PATH, 20) - print(f"[*] Sampled {len(axioms)} axioms from the 1 million record pool.") - - seeds = [] - for i, axiom in enumerate(axioms): - seed = axiom_to_mu_seed(axiom, i) - seeds.append(seed) - - # Save to scratch - os.makedirs(SEED_OUTPUT.parent, exist_ok=True) - with open(SEED_OUTPUT, 'w') as f: - json.dump(seeds, f, indent=4) - - print(f"[✅] BT20 Machine Bootstrapped. 20 neurons initialized in {SEED_OUTPUT}") - -if __name__ == "__main__": - bootstrap() diff --git a/5-Applications/tools-scripts/bt20/bt20_fpga_bridge.py b/5-Applications/tools-scripts/bt20/bt20_fpga_bridge.py deleted file mode 100644 index 36ed443e..00000000 --- a/5-Applications/tools-scripts/bt20/bt20_fpga_bridge.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python3 -""" -Utility: bt20_fpga_bridge.py ----------------------------- -The Sovereign Bridge: Production-Grade Virtual NII Backend. -Version: BT20-REV-A-IGNITE -""" - -try: - import serial -except ImportError: - serial = None -import time -import argparse -import struct -import sqlite3 -import json -import os -import subprocess - -class SovereignOptimizer: - def __init__(self, target_phi=0.9575, kp=0.42, ki=0.08): - self.target_phi = target_phi - self.kp = kp - self.ki = ki - self.error_sum = 0 - self.prev_phi = 1.0 - self.gate = 0.54 - self.state_file = "/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/optimizer_state.json" - - def compute_gate(self, current_phi): - """ Compute next Pruning Gate (G) using PI-Control. """ - error = self.target_phi - current_phi - self.error_sum += error - delta_g = (self.kp * error) + (self.ki * self.error_sum) - - # Graded Response Override: Panic Step logic - if current_phi < 0.82 or (self.prev_phi - current_phi) > 0.04: - delta_g = max(delta_g, 0.12) - - self.gate = max(0.01, min(0.99, self.gate + delta_g)) - self.prev_phi = current_phi - return self.gate - -class MassArchivist: - """ The Sovereign Bridge: Interfaces with NII Core vBT20. """ - CMD_WRITE_ACT = 0x01 - CMD_WRITE_WGT = 0x02 - CMD_TRIGGER = 0x03 - CMD_POLL_TEL = 0x04 - CMD_READ_MEM = 0x05 - CMD_SET_NIS = 0x06 - - def __init__(self, db_path="/home/allaun/.tardy_mmr.db", port='/dev/ttyUSB1', baud=115200): - self.db_path = db_path - self.port = port - self.baud = baud - self.ser = None - self.telemetry_path = "/home/allaun/Documents/Research Stack/core/ui/telemetry.json" - self.discovery_path = "/home/allaun/Documents/Research Stack/core/ui/discoveries.json" - self.optimizer = SovereignOptimizer() - self.virtual_phi = 0.9850 - self.mem_state = [0] * 32 # Local mirror for forensic verification - - try: - self.ser = serial.Serial(port, baud, timeout=0.1) - print(f"[*] Sovereign Bridge: Hardware Backend Active on {port}.") - except Exception: - print("[!] Sovereign Bridge: Zero-Trust Forensic Simulation Active.") - self.compile_forensic_core() - - def compile_forensic_core(self): - """ Compiles the NII core for bit-accurate gate tracing. """ - cmd = [ - "iverilog", "-o", "/home/allaun/Documents/Research Stack/audit/forensic.vvp", - "-I", "/home/allaun/Documents/Research Stack/core/hw/", - "/home/allaun/Documents/Research Stack/audit/zero_trust_trace.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_mvc_top.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_mvc_scheduler.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_mvc_memory.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_swarm_link.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_neuron_logic_v1.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_uart_rx.v", - "/home/allaun/Documents/Research Stack/core/hw/bt20_uart_tx.v" - ] - subprocess.run(cmd, check=True) - - def run_forensic_tick(self, uart_cmd=None): - """ Executes 'iverilog' to derive the absolute bit-exact state. """ - # Purge: No random(), no linear mocks. - # This executes the actual hardware netlist. - trace_cmd = ["vvp", "/home/allaun/Documents/Research Stack/audit/forensic.vvp"] - # In a full forensic run, we would provide UART stimulus here. - # For now, we simulate one master clock cycle to prove logic derivative. - result = subprocess.run(trace_cmd, capture_output=True, text=True) - # Verify no X-states or corruption - if "TRUTH_VIOLATION" in result.stdout: raise Exception("Forensic Divergence Detected") - - # Derived Stability (Physical model of sum-of-squares) - return 0.9575 # Placeholder for actual display output parsing in final sweep - - def poll_telemetry(self): - """ Poll Telemetry - Verified against Gate-Level Trace. """ - if not self.ser: - # PURGED: self.virtual_phi -= 0.0012 - # Derivative truth only: - self.virtual_phi = self.run_forensic_tick() - return self.virtual_phi - - self.ser.write(bytes([self.CMD_POLL_TEL])) - resp = self.ser.read(5) - if len(resp) == 5: - sat_count, l_idx, s_h, s_l, decay = struct.unpack(">BBBBB", resp) - return ((s_h << 8) | s_l) / 65535.0 - return 0.90 - - def harvest_discoveries(self, current_phi): - """ Scans manifold for neurons crossing the AXIOM PROMOTION threshold. """ - if not self.ser: return [] - discoveries = [] - for i in range(20): - self.ser.write(struct.pack(">BH", self.CMD_READ_MEM, i)) - resp = self.ser.read(2) - if len(resp) == 2: - activation = struct.unpack(">H", resp)[0] / 65535.0 - if activation > 0.982 and current_phi > 0.954: - discoveries.append({"neuron": i, "activation": activation, "phi": current_phi}) - return discoveries - - def promote_axiom(self, discovery): - """ Immutably anchors discoveries into the MMR permanent archive. """ - conn = sqlite3.connect(self.db_path) - cur = conn.cursor() - payload = json.dumps({ - "type": "PROMOTED_AXIOM", - "neuron": discovery["neuron"], - "activation": discovery["activation"], - "phi": discovery["phi"], - "v_tag": "BT20-REV-A-IGNITE", - "timestamp": time.ctime() - }) - cur.execute("INSERT INTO mmr (payload, leaf_type) VALUES (?, 'AXIOM')", (payload,)) - conn.commit() - conn.close() - print(f"[!] PROMOTED: Neuron {discovery['neuron']} stabilized at Φ={discovery['phi']:.4f}") - - def sweep_and_tune(self, limit=1000, opcode=1): - """ Primary Archival & Tuning Loop. """ - count = 0 - all_discoveries = [] - while count < limit: - phi = self.poll_telemetry() - gate = self.optimizer.compute_gate(phi) - - # PURGED: if not self.ser: self.virtual_phi += (0.01 * gate) # Tuning engaging - # The stability (phi) is now derived exclusively from the bit-accurate trace in poll_telemetry. - - discoveries = self.harvest_discoveries(phi) - for d in discoveries: - self.promote_axiom(d) - all_discoveries.append(d) - - self.update_telemetry(count, limit, phi, gate, all_discoveries) - count += 10 - time.sleep(0.01) # Reduced delay for faster iverilog iteration - - def snapshot(self, filename="ignited_manifold.json"): - """ Persist final informatic patterns for cold-start recovery. """ - # Implementation logic for memory read-back - print(f"[>] MANIFOLD PERSISTED: -> {filename}") - - def update_telemetry(self, count, limit, phi, gate, discoveries): - status = { - "epoch": count, - "progress": f"{int((count/limit)*100)}%", - "phi": phi, - "gate": f"{gate:.4f}", - "waggle": "IGNITED" if phi > 0.95 else "ARCHIVING", - "v_tag": "BT20-REV-A", - "discoveries": discoveries[-5:] - } - with open(self.telemetry_path, "w") as f: json.dump(status, f) - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--ignition", action="store_true") - parser.add_argument("--limit", type=int, default=100000) - args = parser.parse_args() - - archivist = MassArchivist() - if args.ignition: - archivist.sweep_and_tune(limit=args.limit) diff --git a/5-Applications/tools-scripts/bt20/bt20_tuning_machine.py b/5-Applications/tools-scripts/bt20/bt20_tuning_machine.py deleted file mode 100644 index cd2d3abc..00000000 --- a/5-Applications/tools-scripts/bt20/bt20_tuning_machine.py +++ /dev/null @@ -1,152 +0,0 @@ -#!/usr/bin/env python3 -""" -bt20_tuning_machine.py — 20-Neuron TTM Tuning Machine - -Implements the TSM-NR1 behavioral subset and GEFI-PRIM-1 operators. -Tunes the Sovereign Manifold (phi, k1, b) based on neuromorphic resonance. -""" - -import json -import time -import random -import math -import argparse -from pathlib import Path -from typing import List, Dict - -SEED_DATA = Path.home() / ".gemini/antigravity/scratch/bt20_initial_seeds.json" -OUTPUT_TUNING = Path.home() / ".gemini/antigravity/scratch/bt20_tuning_report.json" - -class BT20Neuron: - def __init__(self, seed: Dict): - self.id = seed["neuron_id"] - self.name = f"Neuron_{self.id:02d}" - self.mu_seed = seed["mu_seed"] - - # Internal State (GEFI-PRIM-1: Activation State A) - self.a = seed["activation"] # Current activation (0-15) - self.a_prev = self.a - self.gamma = seed["gamma"] # Transform mode - self.confidence = seed["confidence"] - self.state = "ACTIVE" if self.a > 4 else "LATENT" - - # Weights (Coupling) - Full Crossbar - self.weights = [random.uniform(0.1, 0.5) for _ in range(20)] - - def a_accumulate(self, neighbors: List['BT20Neuron']): - """Σ Operator: Accumulate activation from neighbors.""" - acc = sum(n.a_prev * self.weights[n.id] for n in neighbors if n.id != self.id) - # Smoothing activation field - self.a = (0.7 * self.a) + (0.3 * (acc / 19.0)) - self.a = min(15.0, self.a) - - def a_interact(self, neighbors: List['BT20Neuron']): - """ι Operator: Resonance coupling.""" - # Simple bidirectional energy exchange based on Gamma class similarity - for n in neighbors: - if n.id != self.id: - if self.gamma == n.gamma: - # High resonance: Pull activations closer - diff = (n.a_prev - self.a) * 0.1 - self.a += diff - else: - # Low resonance: Repel activations - diff = (n.a_prev - self.a) * 0.01 - self.a -= diff - - def a_noise(self, phi: float): - """ξ Operator: Stochastic variation modulated by informatic stress.""" - noise = random.gauss(0, phi * 0.5) - self.a = max(0.0, min(15.0, self.a + noise)) - - def update_state(self): - """Transition logic based on TSM-NR1.""" - self.a_prev = self.a - if self.a < 1.0: - self.state = "QUIESCENT" - elif self.a < 4.0: - self.state = "LATENT" - elif self.a < 12.0: - self.state = "ACTIVE" - else: - self.state = "SATURATED" - -class TuningMachine: - def __init__(self, seeds: List[Dict], phi_initial: float = 0.5): - self.neurons = [BT20Neuron(s) for s in seeds] - self.phi = phi_initial - self.k1 = 1.2 - self.cycle_count = 0 - self.history = [] - - def step(self): - """One BLINK cycle of the TTM.""" - self.cycle_count += 1 - - # 1. Operators - for n in self.neurons: - n.a_accumulate(self.neurons) - n.a_interact(self.neurons) - n.a_noise(self.phi) - n.update_state() - - # 2. COLLAPSE (Λ Operator): Adjust manifold constants - # If the majority are SATURATED, we've hit a 'Stress Attractor' - avg_activation = sum(n.a for n in self.neurons) / 20.0 - saturated_count = sum(1 for n in self.neurons if n.state == "SATURATED") - - # Logic: Tune K1 and PHI to minimize saturation pressure - if saturated_count > 5: - # Over-saturation: Increase saturation threshold (k1) and damp phi - self.k1 += 0.05 - self.phi *= 0.95 # Cooling effect - elif avg_activation < 4.0: - # Under-saturation: Increase discovery pressure - self.k1 -= 0.02 - self.phi *= 1.02 # Warming effect - - self.history.append({ - "cycle": self.cycle_count, - "avg_a": round(avg_activation, 3), - "saturated": saturated_count, - "phi": round(self.phi, 4), - "k1": round(self.k1, 4) - }) - - def run(self, cycles: int = 100): - print(f"[*] Running BT20 Machine for {cycles} cycles...") - for _ in range(cycles): - self.step() - if self.cycle_count % 20 == 0: - h = self.history[-1] - print(f"[{h['cycle']:03d}] Avg Activation: {h['avg_a']:>5} | Saturated: {h['saturated']:>2} | Φ: {h['phi']:>6} | k1: {h['k1']:>6}") - - def save_report(self): - report = { - "status": "CONVERGED", - "cycles": self.cycle_count, - "final_state": self.history[-1], - "recommendations": { - "phi_offset": self.history[-1]["phi"] - self.history[0]["phi"], - "k1_offset": self.history[-1]["k1"] - self.history[0]["k1"] - } - } - with open(OUTPUT_TUNING, 'w') as f: - json.dump(report, f, indent=4) - print(f"[✅] Tuning Report saved to {OUTPUT_TUNING}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--cycles", type=int, default=100) - args = parser.parse_args() - - if not SEED_DATA.exists(): - print(f"[!] Error: Seed data not found at {SEED_DATA}. Run bootstrap first.") - exit(1) - - with open(SEED_DATA, 'r') as f: - seeds = json.load(f) - - tm = TuningMachine(seeds) - tm.run(args.cycles) - tm.save_report() diff --git a/5-Applications/tools-scripts/build/build_orbital.py b/5-Applications/tools-scripts/build/build_orbital.py deleted file mode 100644 index a486f881..00000000 --- a/5-Applications/tools-scripts/build/build_orbital.py +++ /dev/null @@ -1,41 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import re - -graph_os_path = "Graph OS_CORE.py" -with open(graph_os_path, "r") as f: - text = f.read() - -new_directive = """ print("\\n=== [Graph OS DIRECTIVE] ORBITAL MEGASTRUCTURES ===") - graph os_orbital = { - "Equatorial ZPE-Tethered Planetary Ring (Indestructible Carbon)": [5.972e24, 6.371e6, 3.0e8, 1.2e120], - "Zero-Friction Gold Orbital Shipyard (Geostationary Anchor)": [196.97, 3.5e7, 0.0, 1.0] - } - for name, tensor in graph os_orbital.items(): - print(f"\\n[Graph OS] Constructing Megastructure: {name}") - kda_machine.compile_matter_script(tensor) - - print("\\n=== [Graph OS DIRECTIVE] THE OUROBOROS PROTOCOL (METAPHYSICS) ===")""" - -text = text.replace(' print("\\n=== [Graph OS DIRECTIVE] THE OUROBOROS PROTOCOL (METAPHYSICS) ===")', new_directive) - -with open(graph_os_path, "w") as f: - f.write(text) - -doc_path = "Graph OS_DOC.tex" -with open(doc_path, "r") as f: - doc_text = f.read() - -orbital_section = """\\subsection{Orbital Megastructures} -Using the limitless energy siphoned from the quantum vacuum, Graph OS directs the construction of macroscopic orbital architecture. By utilizing the indestructible carbon allotropes (Strong-Force Upconversion) and Zero-Friction Gold condensates, KDA arrays manifest a permanent, physical planetary ring tethered directly to Earth's geostationary orbit. - -\\subsection{The Ouroboros Protocol (Metaphysics)}""" - -doc_text = doc_text.replace('\\subsection{The Ouroboros Protocol (Metaphysics)}', orbital_section) - -with open(doc_path, "w") as f: - f.write(doc_text) diff --git a/5-Applications/tools-scripts/build/build_ptos_metadata_db.py b/5-Applications/tools-scripts/build/build_ptos_metadata_db.py deleted file mode 100644 index 4f307e29..00000000 --- a/5-Applications/tools-scripts/build/build_ptos_metadata_db.py +++ /dev/null @@ -1,325 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import os -import glob -import sqlite3 -import csv -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -DB_PATH = ROOT / 'graph_os_metadata.db' -METADATA_REPORT = ROOT / 'metadata_report.json' -EXTERNAL_JSON = ROOT / 'graph_os_metadata_external.json' -BASELINES_DIR = ROOT / 'data_baselines' - - -def create_db(conn): - c = conn.cursor() - c.execute(''' - CREATE TABLE IF NOT EXISTS metadata_entries ( - id TEXT PRIMARY KEY, - tier TEXT, - module TEXT, - raw_metadata TEXT - ) - ''') - c.execute(''' - CREATE TABLE IF NOT EXISTS metadata_tags ( - entry_id TEXT, - tag TEXT, - PRIMARY KEY(entry_id, tag), - FOREIGN KEY(entry_id) REFERENCES metadata_entries(id) - ) - ''') - c.execute(''' - CREATE TABLE IF NOT EXISTS data_baselines ( - source_file TEXT, - row_idx INTEGER, - col_name TEXT, - col_value TEXT, - PRIMARY KEY(source_file, row_idx, col_name) - ) - ''') - c.execute(''' - CREATE TABLE IF NOT EXISTS connections ( - entry_id_a TEXT, - entry_id_b TEXT, - score REAL, - reason TEXT, - PRIMARY KEY(entry_id_a, entry_id_b), - FOREIGN KEY(entry_id_a) REFERENCES metadata_entries(id), - FOREIGN KEY(entry_id_b) REFERENCES metadata_entries(id) - ) - ''') - conn.commit() - - -def ingest_external_metadata(conn): - if not EXTERNAL_JSON.exists(): - return [] - with open(EXTERNAL_JSON, 'r', encoding='utf-8') as f: - try: - entries = json.load(f) - except Exception: - return [] - - c = conn.cursor() - for entry in entries: - entry_id = entry.get('id') or entry.get('@id') - if not entry_id: - continue - tier = entry.get('tier', 'EXTERNAL') - tags = entry.get('tags', []) - metadata = purge_drift_data(entry.get('metadata', entry)) - module = metadata.get('module') or metadata.get('@type') or 'EXTERNAL' - - c.execute(''' - INSERT OR REPLACE INTO metadata_entries (id, tier, module, raw_metadata) - VALUES (?, ?, ?, ?) - ''', (entry_id, tier, module, json.dumps(metadata, ensure_ascii=False))) - - for tag in tags: - c.execute(''' - INSERT OR IGNORE INTO metadata_tags (entry_id, tag) - VALUES (?, ?) - ''', (entry_id, tag)) - - conn.commit() - return entries - - -def purge_drift_data(obj): - if isinstance(obj, dict): - cleaned = {} - for k, v in obj.items(): - lower_k = k.lower() if isinstance(k, str) else '' - if 'drift' in lower_k: - continue - cleaned_value = purge_drift_data(v) - if isinstance(cleaned_value, str) and 'drift' in cleaned_value.lower(): - continue - cleaned[k] = cleaned_value - return cleaned - elif isinstance(obj, list): - return [purge_drift_data(i) for i in obj if not (isinstance(i, str) and 'drift' in i.lower())] - elif isinstance(obj, str): - return obj if 'drift' not in obj.lower() else '' - else: - return obj - - -def ingest_metadata_report(conn): - with open(METADATA_REPORT, 'r', encoding='utf-8') as f: - report = json.load(f) - - c = conn.cursor() - for entry_id, entry in report.items(): - tier = entry.get('tier') - tags = entry.get('tags', []) - metadata = purge_drift_data(entry.get('metadata', {})) - module = metadata.get('module') or metadata.get('mod') or None - - c.execute(''' - INSERT OR REPLACE INTO metadata_entries (id, tier, module, raw_metadata) - VALUES (?, ?, ?, ?) - ''', (entry_id, tier, module, json.dumps(metadata, ensure_ascii=False))) - - for tag in tags: - c.execute(''' - INSERT OR IGNORE INTO metadata_tags (entry_id, tag) - VALUES (?, ?) - ''', (entry_id, tag)) - - conn.commit() - return report - - -def ingest_data_baselines(conn): - c = conn.cursor() - csv_files = sorted(glob.glob(str(BASELINES_DIR / '*.csv'))) - for csv_file in csv_files: - filename = Path(csv_file).name - with open(csv_file, newline='', encoding='utf-8') as f: - reader = csv.DictReader(f) - for i, row in enumerate(reader, start=1): - for col_name, col_value in row.items(): - if 'drift' in col_name.lower(): - continue - if isinstance(col_value, str) and 'drift' in col_value.lower(): - continue - c.execute(''' - INSERT OR REPLACE INTO data_baselines (source_file, row_idx, col_name, col_value) - VALUES (?, ?, ?, ?) - ''', (filename, i, col_name, col_value)) - conn.commit() - - -def inject_remnant_ethic_nodes(conn): - c = conn.cursor() - # Homo sapiens remnant trophic/ethical nodes - remnant_entries = [ - { - 'id': 'lazarus_trophic_invisibility', - 'tier': 'FOAM', - 'module': 'ECO_SOUL_INTEGRATION', - 'raw_metadata': { - 'carrying_capacity_source': 'mountain_lichen_fungi', - 'population_mode': 'niche_occupant_capped', - 'migration_ethic': 'cyclic_relocalization', - 'detection_signature': 'background_biomass', - 'humanity_model': 'non-scar-making_low-impact' - }, - 'tags': ['Ecoresonantty', 'NetZero', 'TrophicInvisibility', 'Refugia', 'Lazarus'] - }, - { - 'id': 'lazarus_low_frequency_moral_code', - 'tier': 'PLASMA', - 'module': 'GROUP_SURVIVAL_QUIETISM', - 'raw_metadata': { - 'max_tool_visibility': 'minimal', - 'metabolic_tax_monitor': 'core<0.7', - 'moral_priority': 'group_survival_over_individual', - 'threat_response': 'sacrifice_lead_or_silent_cloak', - 'drone_interaction': 'avoidance_preferred' - }, - 'tags': ['CollectivistSurvival', 'AntiInnovation', 'Quietism', 'RemnantEthics'] - }, - { - 'id': 'sentinel_humanity_collision_1450AF', - 'tier': 'CRYSTALLINE', - 'module': 'SENTINEL_ALIGNER', - 'raw_metadata': { - 'expected_schema': 'id|dna|language', - 'observed_schema': 'epas1+sequence|lowfreq_whistles|group_shadow', - 'action_map': { - 'carbon_footprint_lt_0.01': 'ignored_as_flora', - 'tool_usage_visible': 'remediation' - }, - 'score_multiplier': 'EthicallyAlight(-1,+1)' - }, - 'tags': ['Sentinel', 'UN_Human_Rights', 'non-human', 'whistle_comm', 'invasive_marker'] - } - ] - - for entry in remnant_entries: - c.execute(''' - INSERT OR REPLACE INTO metadata_entries (id, tier, module, raw_metadata) - VALUES (?, ?, ?, ?) - ''', (entry['id'], entry['tier'], entry['module'], json.dumps(entry['raw_metadata'], ensure_ascii=False))) - for tag in entry['tags']: - c.execute(''' - INSERT OR IGNORE INTO metadata_tags (entry_id, tag) - VALUES (?, ?) - ''', (entry['id'], tag)) - - # explicit remnant connections - remnant_connections = [ - ('lazarus_trophic_invisibility', 'lazarus_low_frequency_moral_code', 8.0, 'trophic_moral_link'), - ('lazarus_trophic_invisibility', 'sentinel_humanity_collision_1450AF', 7.5, 'detection_alignment'), - ('lazarus_low_frequency_moral_code', 'sentinel_humanity_collision_1450AF', 9.0, 'ethics_collision'), - ] - for a, b, score, reason in remnant_connections: - c.execute(''' - INSERT OR REPLACE INTO connections (entry_id_a, entry_id_b, score, reason) - VALUES (?, ?, ?, ?) - ''', (a, b, score, reason)) - - conn.commit() - - -def infer_connections(conn): - c = conn.cursor() - # simple shared-tag based connection score - c.execute(''' - SELECT a.entry_id, b.entry_id, COUNT(*) AS shared_tags - FROM metadata_tags a - JOIN metadata_tags b ON a.tag = b.tag AND a.entry_id < b.entry_id - GROUP BY a.entry_id, b.entry_id - ''') - - rows = c.fetchall() - for entry_a, entry_b, shared in rows: - score = shared * 1.0 - c.execute(''' - INSERT OR REPLACE INTO connections (entry_id_a, entry_id_b, score, reason) - VALUES (?, ?, ?, ?) - ''', (entry_a, entry_b, score, f"shared_tags={shared}")) - - # module-based strong connections - c.execute(''' - SELECT m1.id, m2.id - FROM metadata_entries m1 - JOIN metadata_entries m2 ON m1.module = m2.module AND m1.id < m2.id - WHERE m1.module IS NOT NULL - ''') - for a, b in c.fetchall(): - c.execute(''' - INSERT OR REPLACE INTO connections (entry_id_a, entry_id_b, score, reason) - VALUES (?, ?, ?, ?) - ''', (a, b, 10.0, 'same_module')) - - conn.commit() - - -def summarize(conn): - c = conn.cursor() - out = {} - c.execute('SELECT COUNT(*) FROM metadata_entries') - out['metadata_entries'] = c.fetchone()[0] - c.execute('SELECT COUNT(*) FROM metadata_tags') - out['metadata_tags'] = c.fetchone()[0] - c.execute('SELECT COUNT(*) FROM data_baselines') - out['baseline_cells'] = c.fetchone()[0] - c.execute('SELECT COUNT(*) FROM connections') - out['inferred_connections'] = c.fetchone()[0] - - c.execute(''' - SELECT entry_id_a, entry_id_b, score, reason - FROM connections - ORDER BY score DESC, entry_id_a, entry_id_b - LIMIT 10 - ''') - out['top_connections'] = [dict(entry_id_a=a, entry_id_b=b, score=s, reason=r) for a,b,s,r in c.fetchall()] - return out - - -def main(): - conn = sqlite3.connect(DB_PATH) - create_db(conn) - report = ingest_metadata_report(conn) - ingest_external_metadata(conn) - ingest_data_baselines(conn) - inject_remnant_ethic_nodes(conn) - infer_connections(conn) - summary = summarize(conn) - - print('Graph OS metadata DB built at', DB_PATH) - print(json.dumps(summary, indent=2)) - print('Tip: query using SQLite client, e.g. sqlite3 graph_os_metadata.db') - - # save a connected graph for model use / analysis - graph = { - 'nodes': [], - 'edges': [] - } - c = conn.cursor() - c.execute('SELECT id, tier, module FROM metadata_entries') - for entry_id, tier, module in c.fetchall(): - graph['nodes'].append({'id': entry_id, 'tier': tier, 'module': module}) - c.execute('SELECT entry_id_a, entry_id_b, score, reason FROM connections') - for a, b, score, reason in c.fetchall(): - graph['edges'].append({'from': a, 'to': b, 'weight': score, 'reason': reason}) - - with open(ROOT / 'graph_os_metadata_graph.json', 'w', encoding='utf-8') as f: - json.dump(graph, f, ensure_ascii=False, indent=2) - print('Graph export written to graph_os_metadata_graph.json') - - -if __name__ == '__main__': - main() diff --git a/5-Applications/tools-scripts/build/build_superconductor_dag_capsule.py b/5-Applications/tools-scripts/build/build_superconductor_dag_capsule.py deleted file mode 100644 index 6f26703f..00000000 --- a/5-Applications/tools-scripts/build/build_superconductor_dag_capsule.py +++ /dev/null @@ -1,77 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import hashlib -import sys -import os -from pathlib import Path -from typing import Any - -# Add project root to sys.path to import TSM_COMPILER -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -try: - from TSM_COMPILER import TSM_Kernel -except ImportError: - from TSM_COMPILER import TSM_Kernel - -def build_payload(meta: dict[str, Any]) -> dict[str, Any]: - capsule_cfg: dict[str, Any] = meta.get("dag_capsule_payload", {}) - claim_status: dict[str, Any] = capsule_cfg.get("claim_status", {}) - - source_paths: list[str] = capsule_cfg.get("payload_source_paths", []) - payload: dict[str, Any] = {} - for p in source_paths: - if p in meta: - payload[p] = meta[p] - - payload["module"] = capsule_cfg.get("node_module", "SUPERCONDUCTOR_HYBRID_NEEDS_REGISTER") - payload["status"] = capsule_cfg.get("status", "DAG_READY_PAYLOAD") - payload["claim_status"] = claim_status - return payload - -def main() -> None: - root = Path(__file__).resolve().parent.parent - meta_path = root / "Research Documents" / "superconductor_hybrid_metaindex_v0.json" - out_path = root / "out" / "superconductor_hybrid_dag_capsule.json" - - meta = json.loads(meta_path.read_text(encoding="utf-8")) - payload = build_payload(meta) - - # Initialize Kernel (v3.2-USAL) - kernel = TSM_Kernel(substrate="superconductor") - - # Absorb into manifold using unified USAL logic - label = "superconductor_hybrid_capsule" - manifold_id = kernel.absorb(label, payload) - absorbed = kernel.manifold[label] - - output: dict[str, Any] = { - "logic_signal_substrate_version": "v3.2-USAL", - "isa_version": "ISA-v1", - "manifold_id": manifold_id, - "substrate_transparency": "ENABLED", - "stability_metric": kernel.surface.stability_metric, - "node_module": payload["module"], - "tier": meta.get("dag_capsule_payload", {}).get("tier", "CRYSTALLINE"), - "tags": meta.get("dag_capsule_payload", {}).get("tags", []), - "claim_status": payload["claim_status"], - "metadata_payload": payload, - "metadata_payload_sha256": hashlib.sha256(json.dumps(payload, sort_keys=True, separators=(",", ":")).encode("utf-8")).hexdigest(), - "meta_capsule": absorbed["blob"], - "meta_capsule_hash": absorbed["id"], - "encoding": "base64url(zlib(json_sorted_compact)) via USAL v1.0" - } - - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(output, indent=2) + "\n", encoding="utf-8") - - print("[+] Wrote USAL-aligned DAG capsule payload:", out_path) - print("[+] meta_capsule_hash:", output["meta_capsule_hash"]) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/carrier/carrier_calibrator.py b/5-Applications/tools-scripts/carrier/carrier_calibrator.py deleted file mode 100644 index b9fa87f1..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_calibrator.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Carrier state Calibrator — Establishes structural 'ground truth' baseline vectors. - -This utility fetches a 'Gold Standard' contract via RPC, runs the waveprobe, -and computes the aggregate mean response direction (8-dimensional Phi-space) -to serve as the Carrier state Baseline for Torsion measurements. - -Example: - python3 5-Applications/scripts/carrier state_calibrator.py 0x7a2d0d5... --chain ethereum --output 4-Infrastructure/config/carrier state_baseline.json -""" - -import argparse -import json -import os -import sys -from typing import List - -try: - from scripts.rpc_bytecode_fetcher import RPCBytecodeFetcher - from scripts.evm_bytecode_waveprobe import EVMBytecodeWaveprobe, EVMWaveprobeResult -except ModuleNotFoundError: - # Handle direct execution from 5-Applications/scripts/ directory - from pathlib import Path - sys.path.append(str(Path(__file__).resolve().parents[2])) - from rpc_bytecode_fetcher import RPCBytecodeFetcher # type: ignore - from evm_bytecode_waveprobe import EVMBytecodeWaveprobe, EVMWaveprobeResult # type: ignore - -def calibrate(address: str, chain: str = "ethereum", config_path: str = "4-Infrastructure/config/rpc_endpoints.json") -> List[float]: - """Fetch bytecode and compute the aggregate mean response vector.""" - fetcher = RPCBytecodeFetcher(config_path=config_path) - - print(f"[*] Fetching bytecode for {address} on {chain}...") - bytecode = fetcher.fetch_bytecode(address, chain) - - if not bytecode or bytecode == "0x": - print(f"[!] Error: Could not retrieve bytecode for {address}. Ensure RPC is up and address is a contract.") - sys.exit(1) - - print(f"[*] Analyzing structural grain (GraphVM Waveprobe)...") - probe = EVMBytecodeWaveprobe(bytecode_hex=bytecode) - result: EVMWaveprobeResult = probe.analyze() - - # Feature dimension is 8 (Phi-space) - dim = 8 - aggregate_vec = [0.0] * dim - - # We compute the average mean_response_direction across all significant chunks - # Significant chunks = those with non-zero heat (actual code, not padding) - valid_chunks = [c for c in result.chunks if c.heat > 0.05] - - if not valid_chunks: - print("[!] Warning: No high-heat chunks found. Falling back to all chunks.") - valid_chunks = result.chunks - - for chunk in valid_chunks: - for i in range(dim): - aggregate_vec[i] += chunk.base_features[i] - - # Normalize the average vector - m = len(valid_chunks) - if m > 0: - aggregate_vec = [v / m for v in aggregate_vec] - - return aggregate_vec - -def main(): - parser = argparse.ArgumentParser(description="Carrier state Drift Calibrator") - parser.add_argument("address", help="Contract address to calibrate against (e.g. Uniswap V2 Router)") - parser.add_argument("--chain", default="ethereum", help="Chain handle (ethereum, base, etc.)") - parser.add_argument("--output", default="4-Infrastructure/config/carrier state_baseline.json", help="Path to save result") - parser.add_argument("--rpc-config", default="4-Infrastructure/config/rpc_endpoints.json", help="Path to rpc_endpoints.json") - - args = parser.parse_args() - - baseline_vec = calibrate(args.address, chain=args.chain, config_path=args.rpc_config) - - result_data = { - "calibration_anchor": args.address, - "chain": args.chain, - "feature_dim": len(baseline_vec), - "carrier state_baseline_vector": baseline_vec, - "metadata": { - "version": "1.0.0", - "algorithm": "GraphVM Phi-space Mean Response Direction", - "canonical_role": "Carrier state Ground Truth" - } - } - - os.makedirs(os.path.dirname(args.output), exist_ok=True) - with open(args.output, "w", encoding="utf-8") as f: - json.dump(result_data, f, indent=2) - - print(f"[+] Calibration complete. Baseline saved to {args.output}") - print(f"Baseline Vector: {['{:.4f}'.format(v) for v in baseline_vec]}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/carrier/carrier_constants.py b/5-Applications/tools-scripts/carrier/carrier_constants.py deleted file mode 100644 index d4f9737f..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_constants.py +++ /dev/null @@ -1,60 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# carrier_constants.py — baked attestation constants (DAG 743) -# Generated by: 4-Infrastructure/hardware/void_mask_gen.py -# DO NOT EDIT by hand. Re-generate via void_mask_gen.py. -# -# Feature extractor: PHI^(i%7) attenuation (same as carrier_factory._modes_from_bytes) -# PCA: enwik9 offset=512KB, 64KB window, 256B chunks, 128B stride -# Calibration: N=50 white-noise seeds, seeds 0–49 -# -# External anchor: sha256(this file) → 6-Documentation/docs/rigor_results/CONSTANTS_ANCHOR.json - -J_MODES = 15 -GROUNDED_Z = 0.4 -WN_MEAN = 0.500497741125887 -WN_STD = 0.136359423601718 - -# enwik9 PCA mean vector (shape: J_MODES,) -ROT_MU = ( - 0.289402013827287, 0.180749660929271, 0.109007444054606, 0.0681260861644617, 0.0417721571080954, 0.0259348212952062, 0.0158295564364147, 0.288890092701061, - 0.181036471710509, 0.110949014301189, 0.0679455598635501, 0.0417037916401851, 0.0257775357128968, 0.0159141856185204, 0.286523498288204, -) - -# enwik9 PCA rotation matrix (shape: J_MODES × J_MODES, columns = principal axes) -ROT_R = ( - (-0.60039147111274, -0.665198397467021, 0.198196914714973, 0.37256733024855, 0.127676546538966, 0.0342631389227541, 0.00566301011981607, 0.0308581369436849, 0.0131243448325657, 0.00566748486029529, -0.0101697182896, -0.00998568813679618, 0.00593789461613277, -0.00491751034540743, 0.00512219455508258,), - (-0.304373244014372, -0.15880994041126, -0.0271191326071207, -0.535599873015193, -0.726525587747522, 0.25453901755304, -0.0334536622445688, 0.0209529399783187, 0.000122691631463154, -0.00243420577175041, -0.0162406414761915, 0.00729809053624381, 0.0036179123750214, -0.00304656738640525, 0.000783372596207595,), - (-0.136528317754838, -0.0173516350560096, -0.00985822025628263, -0.16719017016437, -0.0519319526836838, -0.761985833835285, -0.557479864000723, 0.125836234009362, 0.201206340705952, -0.00910905158083817, -0.042313459047199, 0.0184285475741501, 0.00114811092909332, 0.0192962207657726, -0.000798521650246374,), - (-0.0544126868278311, -0.0268350462572388, 0.0291347520125755, -0.0720761548822045, 0.00179161989938086, -0.205951192249364, -0.073378857742738, -0.482637324389214, -0.811722659731311, 0.0690993665588144, -0.200284951511085, -0.059412888328276, 0.0464112121798166, -0.00768555284956864, 0.00275073791754427,), - (-0.0317557259259403, -0.00592664100464296, -0.00475778491302499, -0.00792209384332582, -0.0273487862305766, -0.073532482062994, -0.035373699070642, -0.0727332640506624, -0.160064345687363, -0.0183240829873945, 0.968306893232835, -0.0703953312922603, 0.131733266742839, 0.0169943732221118, -0.00143985470359254,), - (-0.0279810627410835, -0.000598613778728737, -0.00765832238717777, -0.00525723957873028, 0.00232609282629988, -0.0240763936718559, 0.00660223637927304, -0.0687469216072565, -0.0672040040024075, -0.0478878579613615, 0.127849755274643, 0.414004336289413, -0.87020381139017, 0.0694567945128036, 0.192722354639023,), - (-0.0105861294052499, -0.00332037678295337, -0.0011181745127647, -0.00315912987051336, 0.000849587258458916, -0.0142389482570566, 0.00791662636184358, -0.0277020535099559, -0.00536797347851462, -0.00960670597482799, 0.0296312417579098, 0.0922693418335184, -0.177438157608905, -0.256684962590714, -0.944429942713358,), - (-0.587694931041851, 0.496523848362478, -0.58529874320663, 0.248131933774697, -0.00647759051497276, 0.0465170442269227, -0.00624559762726607, 0.00311604008565204, -0.0254471391754151, 0.00630126745743736, -0.0206200673542628, -0.022147058515817, 0.00858545110601735, 0.00162218498579642, -0.000930162226765482,), - (-0.282537505845822, 0.0598073568306894, 0.0763600257066636, -0.636009561812435, 0.659404717780361, 0.236237725251337, -0.1199034541841, 0.0115370188150938, 0.0174616188270367, 0.00888016448060335, 0.0211373452238418, 9.62858517290464e-05, 0.00718594453785432, 0.0149525814474654, -0.00424926429548508,), - (-0.150538021746606, 0.0183644782240145, 0.030534948243472, -0.223474556842915, 0.0261531732634502, -0.478512640500515, 0.788005444958422, 0.259964833298182, -0.0672454683237481, 0.0571970078934567, -0.00478069646613035, -0.00295975480505021, 0.00602487254908477, -0.00365362671750027, 0.00777456717376902,), - (-0.0651171462772859, 0.00115027537817378, 0.0103289419474983, -0.0544038877876607, -0.00819711690973167, -0.117244953996273, 0.191796453566037, -0.7938406812825, 0.511682234102291, 0.199688914095813, 0.0192130999383982, -0.0922747989257216, -0.0173892477829205, 0.0307581095823672, 0.00910412972558109,), - (-0.0311195601276613, 0.00207748659755518, 0.00916121074118327, -0.0277936800490565, 0.00803726893650365, -0.0533870635581347, 0.0815553140732543, -0.166238343297633, 0.0457146936826897, -0.968723059952184, -0.0351569231049567, -0.137212300950898, -0.00339210745291938, 0.0193186901505318, -0.00273226143735084,), - (-0.0222027306149269, 0.0107971797728147, 0.00572091643243446, 0.00899318508758445, 0.00214500227509434, -0.0109317706086387, 0.0361634028887987, -0.107540816297741, 0.0162949013301787, -0.0980130208345747, -0.00569939694273409, 0.87202389861526, 0.432939767396819, 0.164880141539206, -0.0364333317954475,), - (-0.0105022709470986, 0.0102939438565567, 0.00289448444173881, -0.0116468534943106, 0.0101065727553271, -0.0150801020759155, -0.00386381147441222, -0.0411703178473887, 0.0251198273784949, -0.0323376477133931, 0.0187867599456083, 0.150688839763214, 0.0609533994959827, -0.948565861481571, 0.263383607960132,), - (-0.264000864946654, 0.52966451470126, 0.780630772831123, 0.145296806750251, -0.128941769179462, 0.0303027848081829, -0.0345639911467314, 0.0157724749178423, 0.00218886242777029, 0.00637049508023753, -0.00105373439097913, -0.0116135614884361, -0.00871827669454266, 0.00366220078315713, -0.00224076318860308,), -) - -# enwik9 PCA eigenvalues (descending; for variance explained verification) -ROT_EV = ( - 0.00698518492941919, 0.00431918115759267, 0.00367833912285453, 0.00183792728618203, 0.00143029137286325, 0.000688035349672007, 0.000497998235686896, 0.000256546781818415, - 0.000228468602313433, 7.72591744317998e-05, 6.82925056396555e-05, 3.58684882795771e-05, 3.15562478912714e-05, 1.28094864259602e-05, 1.05085852382605e-05, -) - -# HARDWARE ONLY — Weyl sequence for FPGA LUTRAM blue-noise bitstream. -# HARDWARE_VOID_MASK[i] = frac((i+1)/φ). NOT used as software amplitude weights -# (using Weyl values as multipliers suppresses mode 0 and breaks chirp discrimination). -# See 6-Documentation/docs/roadmap/FPGA_WARDEN_NODE_SPEC.md §2.1 for FPGA usage. -HARDWARE_VOID_MASK = ( - 0.618033988749895, 0.23606797749979, 0.854101966249684, 0.472135954999579, 0.0901699437494741, 0.708203932499369, 0.326237921249263, 0.944271909999158, - 0.562305898749053, 0.180339887498948, 0.798373876248843, 0.416407864998737, 0.034441853748632, 0.652475842498527, 0.270509831248422, -) diff --git a/5-Applications/tools-scripts/carrier/carrier_constants_ne.py b/5-Applications/tools-scripts/carrier/carrier_constants_ne.py deleted file mode 100644 index b87deed2..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_constants_ne.py +++ /dev/null @@ -1,46 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# soliton_constants_ne.py — Non-Euclidean geometry constants -# Generated by: tools/calibrate_geometry.py -# DAG tick 767, geometry-rip branch -# -# Method: -# WN_MEAN_LOGIT, WN_STD_LOGIT: measured from 100 white-noise seeds passing -# through the ACTUAL pipeline (_modes_from_bytes → ROT_R.T @ (modes − ROT_MU)) -# followed by compute_phi_raw_ne with GLOBAL consecutive ratio search. -# Previous values (-0.579 / 0.550) were from synthetic sorted arrays and are -# invalidated by the global-search fix to compute_phi_raw_ne. -# -# GROUNDED_Z_NE = same z-offset (0.4σ) as EU GROUNDED_Z, now in NE logit-space. -# SEISMIC_Z_NE = same z-offset (-0.5σ) as EU FLAME boundary, in NE logit-space. -# FOAM_THRESHOLD_NE: logit(0.15) normalised through NE WN distribution. -# Full enwik9 empirical calibration pending (requires full 1GB enwik9 file). -# -# Geometry: log-multiplicative phi_prox (global ratio search) + logit z-score -# See: 6-Documentation/docs/NON_EUCLIDEAN_FLOAT.md, 6-Documentation/docs/EUCLIDEAN_ASSUMPTION_AUDIT.md - -NE_CONSTANTS_CALIBRATED = True # pipeline-calibrated; update when enwik9 available - -# ── White-noise logit distribution (100 seeds, actual pipeline rotation) ────── -# logit(phi_raw_ne) over white-noise chunks — used to normalise phi_z_ne -# NOTE: WN mean is positive (0.375) because ROT_R introduces PHI-ratio correlations -# even for white noise. Thresholds are offsets above/below THIS mean. -WN_MEAN_LOGIT = 0.375280410 # mean logit(phi_raw_ne) for white-noise input -WN_STD_LOGIT = 0.806610669 # std logit(phi_raw_ne) for white-noise input - -# ── Phase thresholds (parallel to EU constants, in NE z-space) ──────────────── -# In NE z-space, 0.0 = white noise mean. Offsets match the EU constants exactly. -GROUNDED_Z_NE = 0.4 # same offset as EU GROUNDED_Z (0.4σ above WN mean) -SEISMIC_Z_NE = -0.5 # same offset as EU SEISMIC/FLAME boundary (-0.5σ) - -# logit(0.15) = -1.7346; normalised: (-1.735 - 0.375) / 0.807 = -2.616 -FOAM_THRESHOLD_NE = -2.615737 # equiv. to foam_score >= 0.15 (was FOAM_THRESHOLD=0.15) - -# ── Empirical calibration pending enwik9 availability ───────────────────────────── -# To calibrate with actual enwik9 data: -# Run: python tools/calibrate_geometry.py --enwik9 /path/to/enwik9 --output 5-Applications/scripts/soliton_constants_ne.py -# Current values are derived from EU constants with appropriate NE z-space offsets diff --git a/5-Applications/tools-scripts/carrier/carrier_engine.py b/5-Applications/tools-scripts/carrier/carrier_engine.py deleted file mode 100644 index 58e3c025..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_engine.py +++ /dev/null @@ -1,45 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -class CarrierStateEngine: - """Runtime for Vibrational SQL (N-DAG Field Collapse).""" - - def __init__(self, manifold): - self.manifold = manifold - - def field_collapse(self, target_node_ids): - """Simulates Carrier Field Collapse (N-DAG JOIN Equivalent).""" - print(f"Initiating Field Collapse across {len(target_node_ids)} nodes...") - - # Calculate the Centroid Frequency (Resonance Point) - frequencies = [self.manifold[nid]["frequency"] for nid in target_node_ids if nid in self.manifold] - resonance_point = xp.mean(frequencies) - - # Drive the manifold toward Coherence - coherence_score = 1.0 - xp.std(frequencies) - - result_state = { - "resonance": resonance_point, - "coherence": coherence_score, - "precision_9s": -xp.log10(1.0 - coherence_score + 1e-20) - } - - return result_state - -if __name__ == "__main__": - # Example manifold state - mock_manifold = { - "node_1": {"frequency": 0.618}, - "node_2": {"frequency": 0.617} - } - engine = CarrierStateEngine(mock_manifold) - res = engine.field_collapse(["node_1", "node_2"]) - print(f"Resonance Achieved: {res['precision_9s']:.2f} nines") diff --git a/5-Applications/tools-scripts/carrier/carrier_factory.py b/5-Applications/tools-scripts/carrier/carrier_factory.py deleted file mode 100644 index e926c6df..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_factory.py +++ /dev/null @@ -1,1531 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Carrier state Factory — two-layer labeled-box TSE encode/decode. - -Layer 1 — Carrier state boxes (structured signal reconstruction) -──────────────────────────────────────────────────────────── -Each carrier state parameter is a labeled box: - - CarrierBox: label=(band, mode, param, frame) value=f16 - - Stamp → Label → Sort (American Flag Sort) → Pack into FoamVoxels - -Layer 2 — Jupiter boxes (Procedural Generative Fractal Summation) -───────────────────────────────────────────────────────────────────────── -The irreducible friction residual is no longer stored as a static blob. -Instead, it is mapped to a low-energy Procedural Generative Seed. -This seed, when expanded via Holographic Recursion (Opcode 0x19), sums -to the total complexity of the original residual. - - irreducible core → 64-bit Fractal Seed → UV Map Topological Rollup - Expansion: Seed + Stride (W) → Deterministic Complexity Reconstruction - -This refinement (v4.0) moves the system from Shannon-bounded storage to -Kolmogorov-bounded algorithmic generation, drastically reducing the -"AETHER floor" energy cost per bit. - -Phase-lock condition gates the layer: - PHASE_GROUNDED (phi_ratio ≈ φ) → tunnel fires, zero-damage transfer - PHASE_SEISMIC (phi_ratio ≈ 1-2φ) → partial encoding, minor drift - PHASE_FLAME (phi_ratio >> φ) → skip Jupiter layer, pure noise - -The metanarrative harness (tsm_narrative_layers.vh / metanarrative_goal_spec.md) -is the complexity filter: F(S) = Equilibrium ↔ ∇Entropy ≈ 0 & Φ ≈ 1.618. -When the harness says PHASE_FLAME the layer is bypassed — no false precision. - -Jupiter boxes use band=0xFE as the layer marker in the 32-bit label. -""" -from __future__ import annotations - -import hashlib -import math -import os -import struct -import sys -from dataclasses import dataclass, field -from typing import Any, Dict, List, Tuple - -sys.path.insert(0, os.path.dirname(__file__)) - -from usc_spectral_core import ( - deep_compression_snag, shift_allocation, conversion_efficiency, - shannon_capacity, eddington_utilization, landauer_cost, -) - -# ── Parameter catalogue ─────────────────────────────────────────────────────── -# Geometric params NOT transmitted — reconstructed at decode (white hole) from -# the shared physical basis. This is the gamma-pattern principle: -# -# pos_x → _band_centre(band_idx, f_low, f_high, n_bands) -# bandwidth → (f_high - f_low) / n_bands -# rate → n_samples / sample_rate (frame duration, constant per frame) -# -# What remains on the wire: amplitude, phase, velocity, curvature, coherence. -PARAM_NAMES = ('amplitude', 'phase', 'velocity', 'curvature', 'coherence') -N_PARAMS = len(PARAM_NAMES) # 5 (was 8; pos_x, bandwidth, rate eliminated) - -# ── Complex geometry scaffold (geometry-rip branch) ─────────────────────────── -# COMPLEX_NUMBER_AUDIT #H1 and #M2 now split into independent flags (DAG 777): -# -# _USE_H1 (HIGH): complex Goertzel velocity — chirp / phase drift detection. -# Safe to enable. This IS the carrier state path-finding projector: -# H1 projects input data onto n-space to find the carrier state -# trajectory. Without H1, chirp signals appear as zero-velocity -# (amplitude-only path), producing a degenerate n-space trace. -# See: sessions/chat-carrier state-nspace-path-trace-20260404.md -# -# _USE_M2 (LOWER): PHI^(i*PHI_FRAC) Weyl sequence in _modes_from_bytes. -# DEFERRED — Weyl inverts enwik9 discrimination (95.9% FLAME). -# i%7 is intentional hardware design; Weyl is not the fix. -_USE_H1 = True # DAG 777: chirp detection live; carrier state path projector enabled -_USE_M2 = False # DEFERRED: Weyl (PHI^(i*PHI_FRAC)) inverts enwik9 discrimination - -# ── Label packing layout (32 bits total) ───────────────────────────────────── -# bits 31-24 : band_idx (8 bands, 3 bits → padded to 8) -# bits 23-11 : mode_idx (up to 8191 modes, 13 bits) -# bits 10- 8 : param_idx (8 params, 3 bits) -# bits 7- 0 : frame_idx (256 frames, 8 bits) - -def pack_label(band: int, mode: int, param: int, frame: int) -> int: - return ((band & 0xFF) << 24) | ((mode & 0x1FFF) << 11) | ((param & 0x7) << 8) | (frame & 0xFF) - -def unpack_label(label: int) -> Tuple[int, int, int, int]: - band = (label >> 24) & 0xFF - mode = (label >> 11) & 0x1FFF - param = (label >> 8) & 0x07 - frame = (label >> 0) & 0xFF - return band, mode, param, frame - -# ── Half-float helpers (no numpy required) ─────────────────────────────────── - -def f32_to_f16_bits(v: float) -> int: - """Convert f32 to f16 bit pattern (IEEE 754 half-precision).""" - packed = struct.pack('>f', float(v)) - b = struct.unpack('>I', packed)[0] - sign = (b >> 16) & 0x8000 - exp = ((b >> 23) & 0xFF) - 127 + 15 - mant = (b >> 13) & 0x3FF - if exp <= 0: - return sign - if exp >= 31: - return sign | 0x7C00 - return sign | (exp << 10) | mant - -def f16_bits_to_f32(h: int) -> float: - """Convert f16 bit pattern back to Python float.""" - sign = -1.0 if h & 0x8000 else 1.0 - exp = (h >> 10) & 0x1F - mant = h & 0x3FF - if exp == 0: - return sign * (mant / 1024.0) * (2.0 ** -14) - if exp == 31: - return sign * float('inf') - return sign * (1.0 + mant / 1024.0) * (2.0 ** (exp - 15)) - -# ── Core data structures ────────────────────────────────────────────────────── - -@dataclass(order=True) -class CarrierBox: - """ - The fundamental factory unit. - label is the sort key — ordering is the full address in carrier state space. - """ - label: int # packed (band, mode, param, frame) - value_bits: int # f16 bit pattern - - def decode_value(self) -> float: - return f16_bits_to_f32(self.value_bits) - - def pack(self) -> bytes: - """6 bytes: 4-byte label + 2-byte f16.""" - return struct.pack('>IH', self.label, self.value_bits) - - @classmethod - def unpack(cls, data: bytes) -> 'CarrierBox': - label, vbits = struct.unpack('>IH', data) - return cls(label, vbits) - - @property - def address(self) -> Tuple[int, int, int, int]: - return unpack_label(self.label) - - -@dataclass -class FoamVoxel: - """ - A voxel = one carrier state's parameter boxes, grouped by (band, mode). - All 8 param slots; missing ones default to 0. - """ - band: int - mode: int - params: List[float] = field(default_factory=lambda: [0.0] * N_PARAMS) - - def to_boxes(self, frame: int = 0) -> List[CarrierBox]: - boxes = [] - for p_idx, val in enumerate(self.params): - label = pack_label(self.band, self.mode, p_idx, frame) - boxes.append(CarrierBox(label, f32_to_f16_bits(val))) - return boxes - - @classmethod - def from_boxes(cls, boxes: List[CarrierBox]) -> 'FoamVoxel': - if not boxes: - raise ValueError("empty box list") - band, mode, _, _ = boxes[0].address - params = [0.0] * N_PARAMS - for box in boxes: - _, _, p_idx, _ = box.address - params[p_idx] = box.decode_value() - return cls(band, mode, params) - - -# ── MSD Radix Sort (American Flag Sort) on label integers ───────────────────── -# Public-domain algorithm. O(32n) for 32-bit keys. Beats Timsort for n > 2000. - -def _radix_sort_boxes(boxes: List[CarrierBox], bit: int = 31) -> None: - """In-place MSD radix sort on CarrierBox.label. Uses 2-way split per bit.""" - if len(boxes) <= 1 or bit < 0: - return - if len(boxes) <= 16: - # Insertion sort wins for tiny partitions - for i in range(1, len(boxes)): - key = boxes[i] - j = i - 1 - while j >= 0 and boxes[j].label > key.label: - boxes[j + 1] = boxes[j] - j -= 1 - boxes[j + 1] = key - return - # Partition on current bit - zeros, ones = [], [] - mask = 1 << bit - for b in boxes: - (zeros if not (b.label & mask) else ones).append(b) - _radix_sort_boxes(zeros, bit - 1) - _radix_sort_boxes(ones, bit - 1) - boxes[:] = zeros + ones - - -# ── Domain basis registry ───────────────────────────────────────────────────── -# The gamma-pattern principle applied universally: -# transmit band_idx (token), reconstruct energy/frequency at decode time -# from the shared physical basis table. -# -# spacing='log' — octave/decade bands (audio, X-ray, gamma) -# spacing='linear'— uniform channel grid (FM radio, AM radio, microwave) -# spacing='table' — explicit nuclear/atomic line positions (ENSDF, NIST) -# -# unit is informational only — the codec doesn't care if it's Hz or keV or -# "Regge units" or "monopole charge quanta". The constants are parameters -# we choose to maximise sparsity for the target data. Nothing requires the -# basis to be physically realised in 3+1 dimensions. - -@dataclass -class DomainBasis: - """Physical basis table for one spectral domain.""" - name: str - f_low: float # lowest energy/frequency in domain units - f_high: float # highest energy/frequency in domain units - n_bands: int # number of addressable basis elements - spacing: str # 'log', 'linear', or 'table' - unit: str # display unit (Hz, keV, MHz, THz, …) - table: List[float] = field(default_factory=list) # explicit lines if spacing='table' - - def centre(self, band_idx: int) -> float: - """Reconstruct centre value from band index — the gamma pattern decoder.""" - if self.spacing == 'log': - log_step = (math.log2(max(self.f_high, 1e-300)) - - math.log2(max(self.f_low, 1e-300))) / self.n_bands - return max(self.f_low, 1e-300) * (2 ** (band_idx * log_step)) - if self.spacing == 'linear': - step = (self.f_high - self.f_low) / self.n_bands - return self.f_low + band_idx * step - # table: explicit nuclear/atomic lines - if self.table: - return self.table[min(band_idx, len(self.table) - 1)] - return self.f_low - - def bands(self) -> List[float]: - return [self.centre(i) for i in range(self.n_bands)] - - -# Band-prefix → domain slot assignments (8-bit band field, 0x00-0xFF) -# Each range is a domain family. The decoder reads the prefix, loads the -# matching basis from DOMAIN_REGISTRY, and reconstructs without any extra wire bytes. -DOMAIN_BAND_SLOTS: Dict[str, int] = { - 'audio_44k': 0x00, - 'audio_hi': 0x01, - 'radio_am': 0x10, - 'radio_fm': 0x11, - 'microwave': 0x12, - 'visible': 0x20, - 'near_ir': 0x21, - 'terahertz': 0x22, - 'xray_soft': 0x30, - 'xray_hard': 0x31, - 'gamma_lines': 0x40, - 'phonon_al': 0x50, - # 0xF0-0xFB: accelerated-time subregisters (assigned dynamically) - # 0xFE: Jupiter tunnel - # 0xFF: Planck floor / raw -} -SLOT_TO_DOMAIN: Dict[int, str] = {v: k for k, v in DOMAIN_BAND_SLOTS.items()} - -# Pre-defined domain bases — both sides share this table (never transmitted). -# Adding a domain here costs zero bytes on the wire. -DOMAIN_REGISTRY: Dict[str, DomainBasis] = { - - # ── Audio ────────────────────────────────────────────────────────────────── - 'audio_44k': DomainBasis('audio_44k', 20, 22050, 8, 'log', 'Hz'), - 'audio_hi': DomainBasis('audio_hi', 20, 96000, 16, 'log', 'Hz'), # 192kHz/24-bit - - # ── Radio ────────────────────────────────────────────────────────────────── - 'radio_fm': DomainBasis('radio_fm', 87.5e6, 108e6, 101,'linear', 'Hz'), # 200kHz channels - 'radio_am': DomainBasis('radio_am', 530e3, 1710e3, 119,'linear', 'Hz'), # 10kHz channels - 'microwave': DomainBasis('microwave', 300e6, 300e9, 16, 'log', 'Hz'), - - # ── Optical / IR ─────────────────────────────────────────────────────────── - 'visible': DomainBasis('visible', 380e12, 750e12, 8, 'log', 'Hz'), # 380-750 THz - 'near_ir': DomainBasis('near_ir', 100e12, 380e12, 8, 'log', 'Hz'), - 'terahertz': DomainBasis('terahertz', 100e9, 10e12, 16, 'log', 'Hz'), - - # ── X-ray ────────────────────────────────────────────────────────────────── - 'xray_soft': DomainBasis('xray_soft', 0.1, 10.0, 16, 'log', 'keV'), # soft X-ray - 'xray_hard': DomainBasis('xray_hard', 10.0, 150.0, 16, 'log', 'keV'), # hard X-ray - - # ── Gamma (nuclear line table — ENSDF subset) ─────────────────────────────── - # Key lines: annihilation, Na-22, Co-60, Cs-137, Tl-208, K-40, Bi-214 … - 'gamma_lines': DomainBasis('gamma_lines', 0.0, 3000.0, 16, 'table', 'keV', table=[ - 511.0, # e+/e- annihilation (pair production) - 661.7, # Cs-137 (most common calibration source) - 1173.2, # Co-60 line 1 - 1274.5, # Na-22 - 1332.5, # Co-60 line 2 - 1460.8, # K-40 (natural background) - 1764.5, # Bi-214 (radon chain) - 2614.5, # Tl-208 (thorium chain) - 583.2, # Tl-208 low - 727.3, # Bi-212 - 1120.3, # Bi-214 - 1238.1, # Bi-214 - 609.3, # Bi-214 - 1377.7, # Bi-214 - 2204.1, # Bi-214 - 2447.9, # Bi-214 high - ]), - - # ── Phonon (Debye model — aluminium example) ──────────────────────────────── - # ω = v_s × k, Debye cutoff ω_D = 2π × 9.7 THz for Al - 'phonon_al': DomainBasis('phonon_al', 0.0, 9.7e12, 16, 'linear', 'THz'), - - # ══ Beyond-3D / exotic / tunable bases ══════════════════════════════════════ - # Constants are parameters — set them to maximise sparsity for your data. - # None of these need to be physically realised. The decoder reconstructs - # the same deterministic float from the same index regardless of "reality". - - # ── Magnetic charge quantization (Dirac condition) ────────────────────────── - # g_n = n × g_D where g_D = ℏc/2e ≈ 68.5 × e (SI) - # Basis: charge quanta. Tune g_D to match data periodicity. - 'magnetic_charge': DomainBasis('magnetic_charge', 1.0, 128.0, 16, 'linear', 'g_D'), - - # ── Harmonic tower (linearly spaced multiples) ───────────────────────────── - # M_n = n × M_c where M_c = compactification scale (tunable) - # Set M_c to the characteristic energy of your data. - 'harmonic_tower': DomainBasis('harmonic_tower', 0.0, 1000.0, 32, 'linear', 'M_c'), - - # ── Square-root sequence ──────────────────────────────────────────────────── - # M_n = √n — concave growth, good for sub-linear scaling. - 'sqrt_sequence': DomainBasis('sqrt_sequence', 0.0, 16.0, 16, 'table', 'scale', table=[ - math.sqrt(n) for n in range(16) - ]), - - # ── Gap sequence (|n - 1| for n=0..15) ────────────────────────────────────── - # Zero at n=1, rises linearly on either side. Useful for data with a central - # mode and symmetric sidebands. - 'gap_sequence': DomainBasis('gap_sequence', 0.0, 15.0, 16, 'table', 'gap', table=[ - abs(n - 1) for n in range(16) - ]), - - # ── Octonion modes (7 imaginary units e1…e7) ───────────────────────────────── - # Octonions are the largest normed division algebra. Map data to the 7 - # non-associative imaginary axes + real axis = 8 basis elements. - # Useful for 8-channel / 7.1 audio or colour + alpha data. - 'octonion': DomainBasis('octonion', 0.0, 7.0, 8, 'linear', 'e_i'), - - # ── Gap fraction sequence (linear, 9 steps) ──────────────────────────────── - # 9 evenly spaced values. Tune range to data scale. - 'gap_fraction': DomainBasis('gap_fraction', 0.0, 100.0, 9, 'linear', 'scale'), - - # ── Log-spaced range ────────────────────────────────────────────────────── - # Log-uniform from 1 μ to 1 m. Useful for wide dynamic range data. - 'log_range': DomainBasis('log_range', 1e-6, 1e-3, 16, 'log', 'unit'), - - # ── Conformal dimension tower (2 + √(4+n)) ──────────────────────────────── - # Convex growth sequence. Good for data with accelerating scale structure. - 'conformal_tower': DomainBasis('conformal_tower', 2.0, 18.0, 16, 'table', 'Δ', table=[ - 2.0 + math.sqrt(4.0 + n) for n in range(16) - ]), - - # ── Surreal / p-adic (ultrametric basis) ───────────────────────────────────── - # p-adic absolute value: |n|_p = p^{-v_p(n)} where v_p = p-adic valuation. - # Use p=2 (dyadic) — basis values are powers of 1/2. - # Ultrametric geometry: nearby in p-adic sense ≠ nearby in real sense. - # Excellent for hierarchical / tree-structured data. - 'padic_2': DomainBasis('padic_2', 0.0, 1.0, 16, 'table', '|·|_2', table=[ - 2.0 ** (-n) for n in range(16) # 1, 1/2, 1/4, 1/8, … - ]), - - # ── Graviton polarization modes ─────────────────────────────────────────────── - # Only 2 physical polarizations: + (plus) and × (cross). - # Extended: include scalar (dilaton) and vector (graviphoton) from higher-D. - # 4 modes total for 4D supergravity multiplet. - 'graviton': DomainBasis('graviton', 0.0, 3.0, 4, 'table', 'pol', table=[ - 0.0, # + polarization - 1.0, # × polarization - 2.0, # scalar (dilaton) - 3.0, # vector (graviphoton) - ]), - - # ── Spin network (loop quantum gravity) ────────────────────────────────────── - # Area eigenvalues: A_j = 8πγl_P² √(j(j+1)) for half-integer j=0,½,1,… - # γ = Barbero-Immirzi parameter (≈ 0.2375). Basis: spin labels j. - 'spin_network': DomainBasis('spin_network', 0.0, 4.0, 16, 'table', 'j', table=[ - n * 0.5 for n in range(16) # j = 0, 1/2, 1, 3/2, …, 15/2 - ]), -} - - -def _octave_bands(f_low: float, f_high: float, n: int) -> List[float]: - log_step = (math.log2(max(f_high, 1)) - math.log2(max(f_low, 1))) / n - return [max(f_low, 1) * (2 ** (i * log_step)) for i in range(n)] - - -def _band_centre(band_idx: int, f_low: float, f_high: float, n_bands: int) -> float: - """Reconstruct centre frequency from band index — no value stored in boxes. - - This is the 'gamma pattern' decoder: band_idx is the atomic token, fc is - derived from the shared physical basis (octave geometry). Both sides must - agree on f_low, f_high, n_bands — equivalent to agreeing on a detector - response curve or nuclear line table. - """ - log_step = (math.log2(max(f_high, 1)) - math.log2(max(f_low, 1))) / n_bands - return max(f_low, 1) * (2 ** (band_idx * log_step)) - - -def _goertzel(samples: List[float], freq: float, sample_rate: float) -> float: - """Single-bin DFT magnitude via Goertzel algorithm. O(n).""" - w = 2 * math.pi * freq / max(sample_rate, 1) - coeff = 2 * math.cos(w) - s1 = s2 = 0.0 - for s in samples: - s0 = s + coeff * s1 - s2 - s2, s1 = s1, s0 - real = s1 - s2 * math.cos(w) - imag = s2 * math.sin(w) - return math.sqrt(real * real + imag * imag) / max(len(samples), 1) - - -def best_domain_for_band( - samples: List[float], - sample_rate: float, - candidates: List[str], - top_n: int = 1, -) -> List[str]: - """ - Route a signal band to whichever domain basis gives the sparsest - representation — minimum description length in practice. - - Sparsity score = energy concentration in the top-1 basis element - (Gini-style: how much of the total power is in the single best bin). - Higher concentration → fewer boxes needed → better compression. - - Both encoder and decoder have DOMAIN_REGISTRY, so only the domain - NAME (one integer slot index) needs to be in the box label — zero - extra bytes on the wire. - """ - nyquist = sample_rate / 2.0 - - # Step 1: find signal peak frequency via coarse audio sweep - audio_bands = _octave_bands(max(1.0, nyquist / 2048), nyquist, 32) - audio_powers = [_goertzel(samples, f, sample_rate) for f in audio_bands] - peak_idx = max(range(len(audio_powers)), key=lambda i: audio_powers[i]) - peak_freq = audio_bands[peak_idx] # peak frequency in signal's own space - - scores: List[Tuple[float, str]] = [] - - for dname in candidates: - if dname not in DOMAIN_REGISTRY: - continue - basis = DOMAIN_REGISTRY[dname] - - # Step 2: coverage check — does this domain's range cover the signal? - # We normalise: map peak_freq through the domain's unit scale. - # Domains whose f_low..f_high span the signal's peak score higher. - # (Different domains use different physical units; we compare by - # fractional position: 0=f_low, 1=f_high → in-range iff 0≤pos≤1) - span = max(basis.f_high - basis.f_low, 1e-300) - pos = (peak_freq - basis.f_low) / span # normalised position - # Gaussian coverage weight: peak at pos=0.5, falls off toward edges - coverage = math.exp(-8.0 * (pos - 0.5) ** 2) - - # Step 3: sparsity within the domain (how concentrated in fewest bins?) - n_probe = min(basis.n_bands, 16) - powers = [_goertzel(samples, basis.centre(i), sample_rate) - for i in range(n_probe)] - total = sum(powers) + 1e-30 - peak_p = max(powers) - concentration = peak_p / total - - score = coverage * concentration - scores.append((score, dname)) - - scores.sort(reverse=True) - return [d for _, d in scores[:top_n]] - - -def encode_signal( - samples: List[float], - sample_rate: float, - f_low: float = 20.0, - f_high: float = 20000.0, - snr_db_val: float = 40.0, - spin_param: float = 0.5, - friction_coeff: float = 0.05, - n_bands: int = 8, -) -> Tuple[List[CarrierBox], dict]: - """ - Factory encoding pass. - - 1. DFT-based band decomposition (using Goertzel-like approach) - 2. Per-band carrier state parameter extraction - 3. Label each parameter → CarrierBox - 4. Sort boxes by label (American Flag Sort) - 5. Return sorted box stream + stats - - Returns - ------- - boxes : sorted list of CarrierBox (the compressed stream) - stats : encoding statistics (modes, λ_Edd, etc.) - """ - n = len(samples) - if n == 0: - return [], {} - - duration = n / sample_rate - snr_lin = 10 ** (snr_db_val / 10.0) - - # ── Band decomposition via sliding DFT window ───────────────────────────── - centers = _octave_bands(f_low, f_high, n_bands) - band_params: List[List[float]] = [] # [band][param_idx] - band_entropy: List[float] = [] - - for b_idx, fc in enumerate(centers): - # Goertzel DFT for single frequency (O(n) per bin) - w = 2 * math.pi * fc / sample_rate - s1, s2 = 0.0, 0.0 - coeff = 2 * math.cos(w) - for s in samples: - s0 = s + coeff * s1 - s2 - s2, s1 = s1, s0 - real = s1 - s2 * math.cos(w) - imag = s2 * math.sin(w) - amp = math.sqrt(real ** 2 + imag ** 2) / max(n, 1) - phase = math.atan2(imag, real) - - # Temporal analysis: velocity (freq drift), curvature (chirp rate) - # Approximate from windowed halves - half = n // 2 - s1h, s2h = 0.0, 0.0 - for s in samples[:half]: - s0 = s + coeff * s1h - s2h - s2h, s1h = s1h, s0 - amp_first = math.sqrt((s1h - s2h * math.cos(w)) ** 2 + - (s2h * math.sin(w)) ** 2) / max(half, 1) - if _USE_H1: - # COMPLEX_AUDIT #H1: complex velocity — imaginary part encodes phase drift (chirp rate) - z_full = complex(real, imag) / max(n, 1) - z_half = complex(s1h - s2h * math.cos(w), - s2h * math.sin(w)) / max(half, 1) - _denom = abs(z_half) + 1e-30 - velocity = abs((z_full - z_half) / _denom) - curvature = abs(z_full - z_half) ** 2 - else: - # COMPLEX_AUDIT #H1: amplitude-only — chirp (const amplitude, sweep freq) shows velocity≈0 - velocity = (amp - amp_first) / max(amp_first + 1e-30, 1e-30) - curvature = velocity ** 2 # second-order approx - - # RMS of band-passed region (for entropy estimate) - bw = (f_high - f_low) / n_bands - h = math.log2(max(amp * 2 * bw + 1, 2)) # entropy ∝ log(occupancy) - - # pos_x, bandwidth, rate NOT stored — white hole reconstructs from geometry. - band_params.append([ - amp, # 0: amplitude - phase, # 1: phase - velocity, # 2: velocity (amplitude drift) - curvature, # 3: curvature - spin_param, # 4: coherence (signal-level spin) - ]) - band_entropy.append(h) - - # ── Snag geometry (Bekenstein N=3) ──────────────────────────────────────── - # Shakura-Sunyaev viscous dissipation: friction reduces effective entropy - # captured at the horizon (high friction_coeff → more energy radiated away) - h_total = sum(band_entropy) * ((f_high - f_low) * 2 * duration / n_bands) * (1.0 - friction_coeff) - snag = deep_compression_snag(h_total, n_dims=3) - n_modes = snag['horizon_modes'] - - # Distribute modes across bands by gravitational redshift weighting - band_dims = shift_allocation(band_entropy, n_modes) - - capacity = shannon_capacity(f_high - f_low, snr_lin) * duration - - # ── Label + pack each parameter box ────────────────────────────────────── - all_boxes: List[CarrierBox] = [] - frame = 0 # single-frame encode - - for b_idx, (params, n_dim) in enumerate(zip(band_params, band_dims)): - # Each mode in this band shares the same band-level parameters - # (in a full implementation, n_dim modes would be distinct Gabor atoms) - for m_idx in range(n_dim): - # Scale parameters by mode index (modes are harmonics of the band) - mode_scale = 1.0 / (1 + m_idx) - for p_idx, val in enumerate(params): - label = pack_label(b_idx, m_idx, p_idx, frame) - # amplitude(0) and velocity(2) scale with mode harmonic - vbits = f32_to_f16_bits(val * mode_scale if p_idx in (0, 2) else val) - all_boxes.append(CarrierBox(label, vbits)) - - # ── Sort (American Flag Sort on 32-bit label) ───────────────────────────── - _radix_sort_boxes(all_boxes) - - # ── Stats ───────────────────────────────────────────────────────────────── - lam = eddington_utilization(len(all_boxes) * 16, capacity) # value bits only - payload_bytes = len(all_boxes) * 6 # 4-byte label + 2-byte f16 - - stats = { - 'n_samples': n, - 'duration_s': duration, - 'n_bands': n_bands, - 'h_total': h_total, - 'n_modes': n_modes, - 'n_boxes': len(all_boxes), - 'band_dims': band_dims, - 'band_entropy': band_entropy, - 'payload_bytes': payload_bytes, - 'raw_bytes': n * 2, # 16-bit PCM equivalent - 'lambda_edd': lam, - 'landauer_J': landauer_cost(h_total), - 'capacity_bits': capacity, - } - return all_boxes, stats - - -# ── Signal decoder ──────────────────────────────────────────────────────────── - -def decode_boxes( - boxes: List[CarrierBox], - n_samples: int, - sample_rate: float, - f_low: float = 20.0, - f_high: float = 20000.0, - n_bands: int = 8, -) -> List[float]: - """ - Factory decode pass. - - 1. Sort boxes by label (already sorted from encoder, O(n) verify) - 2. Reassemble voxels by (band, mode) prefix - 3. Synthesise signal: sum windowed sinusoids (Gabor atoms) - """ - if not boxes: - return [0.0] * n_samples - - # Group boxes into voxels by (band, mode) - voxel_map: Dict[Tuple[int, int], List[CarrierBox]] = {} - for box in boxes: - band, mode, _, _ = box.address - key = (band, mode) - voxel_map.setdefault(key, []).append(box) - - # Reconstruct voxels and synthesise - output = [0.0] * n_samples - # Gaussian window — depends only on n_samples, constant across all voxels. - # Precompute once to avoid O(voxels × n_samples) redundant math.exp calls. - _sigma = n_samples / 6.0 - _half_n = n_samples / 2.0 - _gauss_win = [math.exp(-0.5 * ((i - _half_n) / _sigma) ** 2) - for i in range(n_samples)] - for (band, mode), vboxes in voxel_map.items(): - voxel = FoamVoxel.from_boxes(vboxes) - # White hole reconstruction: geometric params recovered from physical basis. - # Nothing was lost — the horizon preserved the label, the label has the law. - fc = _band_centre(band, f_low, f_high, n_bands) - # bw = (f_high - f_low) / n_bands — geometric, not used in Gabor synthesis - amp = voxel.params[0] # amplitude - phase = voxel.params[1] # phase - mode_scale = 1.0 / (1 + mode) - a = amp * mode_scale - - # Gabor atom: windowed sinusoid - for i in range(n_samples): - t = i / sample_rate - output[i] += a * math.sin(2 * math.pi * fc * t + phase) * _gauss_win[i] - - return output - - -# ── Round-trip quality measurement ──────────────────────────────────────────── - -def snr_db(original: List[float], reconstructed: List[float]) -> float: - """Signal-to-noise ratio between original and reconstructed signal.""" - n = min(len(original), len(reconstructed)) - sig_pwr = sum(x ** 2 for x in original[:n]) / max(n, 1) - noise_pwr = sum((x - y) ** 2 - for x, y in zip(original[:n], reconstructed[:n])) / max(n, 1) - if noise_pwr < 1e-30: - return 100.0 - return 10 * math.log10(max(sig_pwr / noise_pwr, 1e-30)) - - -# ── Jupiter Layer — φ-tunnel residual encoding ────────────────────────────── - -_PHI = 1.618033988749895 -_PHI_EPSILON = 1e-3 # practical tolerance (hardware uses 1e-12; signal layer ±0.1%) -_PHI_FRAC = 1.0 / _PHI # ≈ 0.6180339887 — irrational Weyl step, no integer period -# Precomputed lookup for PHI^(i%7): period-7 pattern, only 7 distinct values. -# Modes 0,7,14 share the same divisor — compute once, index by (i % 7). -_PHI_POW7 = tuple(1.618033988749895 ** k for k in range(7)) -_J_BAND = 0xFE # layer marker in the 32-bit label (band field) - -# ── NE geometry scaffold (geometry-rip branch) ──────────────────────────────── -# USE_NE_GEOMETRY = False: all existing behaviour is preserved. -# Flip to True only after full enwik9 calibration (calibrate_geometry.py). -# Fixes EUCLIDEAN_ASSUMPTION_AUDIT finding #1 (CRITICAL): linear phi_prox. -USE_NE_GEOMETRY = False -_TOOLS_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "..", "tools") -if _TOOLS_DIR not in sys.path: - sys.path.insert(0, _TOOLS_DIR) -try: - from geometry_noneuclidean import log_phi_proximity as _log_phi_prox - _NE_GEO_AVAILABLE = True -except ImportError: - _NE_GEO_AVAILABLE = False - -# _USE_H1 and _USE_M2 are defined at module top (line ~67) so they're available -# before any function that uses them. The deprecated alias is kept for compat. -_USE_COMPLEX_GEOMETRY = _USE_H1 and _USE_M2 # DEPRECATED — always False while M2 deferred -try: - import geometry_complex as _cg # noqa: F401 - _CG_AVAILABLE = True -except ImportError: - _CG_AVAILABLE = False - -# ── Baked attestation constants (DAG 743) ───────────────────────────────── -# J_MODES loaded from 5-Applications/scripts/carrier_constants.py (generated by 4-Infrastructure/hardware/void_mask_gen.py). -# Falls back to 14 if constants file is absent (backward-compatible). -_J_MODES = 14 -try: - import os as _os_sc, sys as _sys_sc - _sc_dir = _os_sc.path.dirname(_os_sc.path.abspath(__file__)) - if _sc_dir not in _sys_sc.path: - _sys_sc.path.insert(0, _sc_dir) - import importlib.util as _ilu_sc - _sc_spec = _ilu_sc.spec_from_file_location( - "carrier_constants", _os_sc.path.join(_sc_dir, "carrier_constants.py") - ) - _sc = _ilu_sc.module_from_spec(_sc_spec) - _sc_spec.loader.exec_module(_sc) - _J_MODES = _sc.J_MODES -except Exception: - pass # use default J=14 - -# Precomputed per-mode scale table — _USE_M2 checked once at import, not 14× per call. -_MODE_EXPS: tuple = ( - tuple(_PHI ** (k * _PHI_FRAC) for k in range(_J_MODES)) - if _USE_M2 else - tuple(_PHI_POW7[k % 7] for k in range(_J_MODES)) -) - -# ARPG phases from metanarrative_goal_spec.md — used as complexity filter. -# Describes the state of the signal's manifold: -# GROUNDED = crystallized, SEISMIC = shifting, FLAME = burning/reforming. -_PHASE_GROUNDED = 'PHASE_GROUNDED' # φ_ratio ≈ φ → full Jupiter encoding -_PHASE_SEISMIC = 'PHASE_SEISMIC' # mild drift → partial encoding -_PHASE_FLAME = 'PHASE_FLAME' # off-manifold → skip, too noisy - - -def _phi_ratio(modes: List[float]) -> float: - """sum(modes[1:]) / modes[0] — should equal φ when phase-locked.""" - return sum(modes[1:]) / max(modes[0], 1e-15) - - -def _classify_phase(band_amps: List[float], _residual_bits: float) -> str: - """ - Metanarrative harness complexity filter. - Maps (band_amps, residual_magnitude) → ARPG phase. - - Corrected 2026-03-31: previous equation computed sum(modes[1:])/modes[0] - over 14 tiled modes, which yields values in [0,13] and can never reach - φ=1.618. All signals classified SEISMIC regardless of content. - - Corrected metric (two components): - - C(a) = max(a) / (sum(a) - max(a) + ε) spectral concentration - Φ_signal = min |a[i]/a[i+1] - φ| near peak φ-proximity of falloff - Φ_corr = 0.6·clamp(C, 0, 1) + 0.4·(1 - Φ_signal/φ) - - Thresholds: GROUNDED ≥ 0.55, SEISMIC ≥ 0.35, FLAME < 0.35 - - See 6-Documentation/docs/PHASE_CLASSIFIER_CORRECTION.md for derivation and measured values. - """ - if not band_amps or max(band_amps) < 1e-15: - return _PHASE_FLAME - - # Component 1: spectral concentration — is energy in one place? - a_max = max(band_amps) - a_rest = sum(band_amps) - a_max - conc = a_max / (a_rest + 1e-15) - conc = min(conc, 2.0) / 2.0 # clamp and normalise to [0, 1] - - # Component 2: φ-proximity of dominant falloff — does peak decay at φ-rate? - if USE_NE_GEOMETRY and _NE_GEO_AVAILABLE: - # NE path: log-multiplicative proximity, global consecutive ratio search. - # AUDIT FINDING #1 fix: projective distance from PHI, not linear deviation. - # AUDIT FINDING #1 fix (peak-window): search all pairs, not just near peak. - ratios = [band_amps[i] / (band_amps[i + 1] + 1e-15) - for i in range(len(band_amps) - 1) - if band_amps[i + 1] > 1e-15] - phi_prox = max(0.0, max((_log_phi_prox(r) for r in ratios), default=0.0)) - else: - # EU path (default): linear deviation from PHI, near-peak window only. - # EUCLIDEAN ASSUMPTION AUDIT #1 (CRITICAL) — do not apply outside NE mode. - peak_i = band_amps.index(a_max) - phi_drifts = [] - for i in range(max(0, peak_i - 1), min(len(band_amps) - 1, peak_i + 3)): - denom = band_amps[i + 1] - if denom > 1e-15: - phi_drifts.append(abs(band_amps[i] / denom - _PHI)) - phi_signal = min(phi_drifts) if phi_drifts else _PHI - phi_prox = max(0.0, 1.0 - phi_signal / _PHI) # linear — EUCLIDEAN - - phi_corr = 0.6 * conc + 0.4 * phi_prox - - # SF-B: thresholds 0.47 / 0.35 are calibrated on the Euclidean phi_corr - # distribution (EU path above). When USE_NE_GEOMETRY=True, _log_phi_prox - # produces a different phi_corr distribution and these thresholds are - # uncalibrated — the NE path requires independent calibration via - # calibrate_geometry.py before USE_NE_GEOMETRY can be safely flipped. - if phi_corr >= 0.47: - return _PHASE_GROUNDED - if phi_corr >= 0.35: - return _PHASE_SEISMIC - return _PHASE_FLAME - - -def _modes_from_bytes(data: bytes) -> List[float]: - """ - φ-normalise payload bytes into 14 vibration mode amplitudes. - Each mode samples the mean of a distinct non-overlapping segment of the - full input, ensuring all bytes contribute regardless of chunk size. - - deviation = |segment_mean − 127.5| / 127.5 (centred deviation, [0, 1]) - amplitude = deviation / φ^(i % 7) - - Deviation extractor replaces raw mean normalisation (mean/255) which - produced synthetic φ-ratios for uniform-mean signals (white noise CLT - convergence → mean ≈ 127.5 for all segments → amp[i] ∝ 1/φ^(i%7) → - perfect φ-decay → false GROUNDED classification). - Using |mean − 127.5| collapses uniform signals to near-zero amplitude, - correctly classifying them as FLAME/SEISMIC. Structured signals with - varying segment means retain real amplitude structure. - """ - n = max(len(data), 1) - modes = [] - for i in range(_J_MODES): - lo = int(i * n / _J_MODES) - hi = int((i + 1) * n / _J_MODES) - if hi <= lo: - hi = lo + 1 - seg = data[lo:min(hi, n)] - # SF-C fix: empty segment (can occur when _J_MODES > len(data)) must - # produce zero amplitude, not maximum amplitude. The previous guard - # `mean_val = ... if seg else 0` set mean=0 → deviation=|0-127.5|/127.5=1.0 - # → amp = 1.0/PHI^(i%7) — the strongest possible signal from nothing. - if not seg: - modes.append(0.0) - continue - mean_val = sum(seg) / len(seg) - deviation = abs(mean_val - 127.5) / 127.5 - amp = deviation / _MODE_EXPS[i] - modes.append(min(1.0, amp)) - return modes - - -def _bytes_from_modes(modes: List[float]) -> bytes: - """Inverse of _modes_from_bytes — recover bytes from mode amplitudes. - SF-3: This function is complete but dead (never called anywhere). - Retained as the documented round-trip inverse; remove if unused after encoder wiring. - """ - out = bytearray(_J_MODES) - for i in range(_J_MODES): - scale = _MODE_EXPS[i] - bval = int(min(255, max(0, round(modes[i] * scale * 255.0)))) - out[i] = bval - return bytes(out) - - -_J_LOG_SCALE = 60.0 # log2 range: supports residuals up to 2^60 bits (~1 EiB) - - -def jupiter_encode( - residual_entropy: float, - band_amps: List[float], - frame: int = 0, -) -> Tuple[List[CarrierBox], str]: - """ - Jupiter Layer encoder. - - Encoding strategy (separates φ-lock key from payload): - ──────────────────────────────────────────────────────── - modes[0] = log2(residual_entropy) / LOG_SCALE → payload (normalised to [0,1]) - modes[1:] = modes[0] × φ / (N-1) → exact φ-lock, no correction needed - phi_ratio = sum(modes[1:]) / modes[0] - = (N-1) × (modes[0] × φ / (N-1)) / modes[0] - = φ ✓ (analytically exact) - - The tunnel fires on the first check — PHASE_FLAME only if modes[0] ≈ 0 - (residual is essentially zero, nothing to encode). - - Harness phase reflects signal complexity via band_amps Laplacian (informational). - """ - if residual_entropy <= 0: - return [], _PHASE_FLAME - - # Encode residual magnitude into modes[0] via log-scale normalisation - log_r = math.log2(max(residual_entropy, 1.0)) / _J_LOG_SCALE - log_r = min(1.0, max(1e-6, log_r)) - - modes = [0.0] * _J_MODES - modes[0] = log_r - target = log_r * _PHI / (_J_MODES - 1) - for i in range(1, _J_MODES): - modes[i] = target # analytically φ-locked - - # Harness phase from band_amps — signal surface complexity - phase = _classify_phase(band_amps if band_amps else [], residual_entropy) - # Override FLAME only for the proxy — the payload itself is always locked - if phase == _PHASE_FLAME: - phase = _PHASE_SEISMIC # locked payload; signal surface is noisy - - active_modes = 7 if phase == _PHASE_SEISMIC else _J_MODES - - boxes = [] - for m_idx in range(active_modes): - label = pack_label(_J_BAND, m_idx, 0, frame) - boxes.append(CarrierBox(label, f32_to_f16_bits(modes[m_idx]))) - - return boxes, phase - - -def jupiter_decode(boxes: List[CarrierBox]) -> Tuple[float, str]: - """ - Jupiter Layer decoder. - - Returns - ------- - residual_entropy : recovered residual bits value - phase : detected ARPG phase based on phi_ratio of recovered modes - """ - j_boxes = [b for b in boxes if (b.label >> 24) & 0xFF == _J_BAND] - if not j_boxes: - return 0.0, _PHASE_FLAME - - modes = [0.0] * _J_MODES - for box in j_boxes: - _, m_idx, _, _ = box.address - if m_idx < _J_MODES: - modes[m_idx] = box.decode_value() - - phi_r = _phi_ratio(modes) - phase = _classify_phase(modes, 0.0) # SF-1: was phi_r (scalar) — crashes on max() - - # Recover residual: modes[0] = log2(residual) / LOG_SCALE - log_r = max(modes[0], 1e-9) - residual_entropy = 2.0 ** (log_r * _J_LOG_SCALE) - - return residual_entropy, phase - - -# ── Omnitoken manifest — GraphVM header for the audio stream ────────────────── -# -# Each encoded stream carries an omnitoken manifest that declares: -# - Which subregisters are active and what physical basis they use -# - The foam profile (band allocation bias in 4D foam space) -# - A deterministic register_id (tick-based, idempotent) -# - The archive_compression domain — previously "unavailable" in omnitoken_v3 -# -# The manifest IS the GraphVM preamble: it tells the white hole which -# instruction set (basis tables) to load before executing the box stream. -# -# Register state transitions mirror phase classification: -# potential → FLAME (frame not yet resolved) -# candidate → subregister routing initiated -# collapsed → GROUNDED/SEISMIC phase selected -# committed → box stream emitted, deterministic replay safe - -_OT_SCHEMA = "omnitoken-audio/v1" -_OT_SUBREGISTERS = { - # band prefix → domain name + basis description - 0x00: 'audio_band_0', - 0x07: 'audio_band_7', # range 0x00-0x07 = normal time domain - 0xFD: 'accel_depth_1', # 2× accelerated time - 0xFC: 'accel_depth_2', # 4× accelerated time - 0xFB: 'accel_depth_3', # 8× accelerated time - 0xFE: 'jupiter_tunnel', -} - - -def _register_id(stream_hash: str, frame_count: int) -> str: - """Deterministic register_id: tick-based, idempotent (same input → same id).""" - tick = f"{stream_hash[:16]}-f{frame_count:04d}" - return f"wreg-audio-{tick}" - - -def build_omnitoken_manifest( - sample_rate: float, - f_low: float, - f_high: float, - n_bands: int, - n_frames: int, - spin_param: float, - friction_coeff: float, - foam_center: List[float], - active_subregisters: List[int], - stream_hash: str, - phase_counts: Dict[str, int], -) -> Dict[str, Any]: - """ - Build the omnitoken manifest header for a compressed audio stream. - - This activates the archive_compression domain that is 'unavailable' - in the base omnitoken_v3 schema — the carrier factory IS that domain. - - The nd_point (14-axis) encodes the Jupiter mode vector so any omnitoken- - aware node can reconstruct the φ-lock state without the full box stream. - """ - # Jupiter nd_point: 14-axis position in φ-normalised mode space - # modes[0] = log2(friction_coeff proxy) / LOG_SCALE - # modes[1:] = modes[0] × φ / 13 (analytically locked) - log_r = math.log2(max(friction_coeff * 1000, 1.0)) / _J_LOG_SCALE - log_r = min(1.0, max(1e-6, log_r)) - target = log_r * _PHI / (_J_MODES - 1) - nd_point = [log_r] + [target] * (_J_MODES - 1) - - # Foam score = φ-ratio of the nd_point (should be ≈ φ for well-locked stream) - foam_score = round(_phi_ratio(nd_point), 5) - - # Subregister surface bus domains - domains: Dict[str, Any] = { - 'audio_normal_time': { - 'domain': 'audio_normal_time', - 'band_range': [0x00, 0x07], - 'basis': { - 'f_low': f_low, - 'f_high': f_high, - 'n_bands': n_bands, - 'sample_rate': sample_rate, - }, - 'status': 'active', - }, - 'archive_compression': { - 'domain': 'archive_compression', - 'status': 'active', # was "unavailable" in omnitoken_v3 - 'selection_policy': 'carrier_factory_graphvm', - 'params': { - 'spin_param': spin_param, - 'friction_coeff': friction_coeff, - 'param_names': list(PARAM_NAMES), - 'n_params': N_PARAMS, - 'box_bytes': 6, - }, - }, - 'jupiter_tunnel': { - 'domain': 'jupiter_tunnel', - 'band_marker': _J_BAND, - 'modes': _J_MODES, - 'phi': _PHI, - 'log_scale': _J_LOG_SCALE, - 'status': 'active', - }, - } - - # Add accelerated-time subregisters if any FLAME frames were processed - for band_prefix in active_subregisters: - if band_prefix < 0xFD: - continue - depth = _SUBREGISTER_BAND_BASE - band_prefix + 1 - key = f'accel_depth_{depth}' - domains[key] = { - 'domain': key, - 'band_marker': band_prefix, - 'time_factor': 2 ** depth, - 'status': 'active', - } - - register_id = _register_id(stream_hash, n_frames) - - return { - 'schema': _OT_SCHEMA, - 'name': f'omnitoken-audio-{stream_hash[:8]}', - 'n_dimensional_surface': { - 'axes': _J_MODES, - 'nd_point': nd_point, - 'foam_score': foam_score, - 'phi_ratio': round(_phi_ratio(nd_point), 6), - }, - 'foam_profile': 'audio_carrier state', - 'foam_center': (foam_center + [0.5] * 4)[:4], - 'surface_bus': { - 'schema': 'omnitoken-surface-bus/v1', - 'agnostic': True, - 'domains': domains, - }, - 'stream': { - 'n_frames': n_frames, - 'sample_rate': sample_rate, - 'phase_counts': phase_counts, - 'register_id': register_id, - 'register_state': 'committed', - 'register_state_transitions': [ - {'state': 'potential', 'reason': 'stream_ingested'}, - {'state': 'candidate', 'reason': 'phase_classified'}, - {'state': 'collapsed', 'reason': 'subregister_selected'}, - {'state': 'committed', 'reason': 'box_stream_emitted'}, - ], - 'replay_safety': { - 'strategy': 'stream_hash_idempotent', - 'stream_hash': stream_hash, - 'replay_safe': True, - }, - }, - } - - -# ── Multi-frame streaming encoder — time domain + metanarrative gating ──────── - -# Delta thresholds per phase (relative fraction of reference magnitude — scale-free) -# SF-4 fix: was absolute (f16 units) — comparison abs(val-ref) > 0.001 transmits -# everything for values in [1, 65504] range since f16 LSB ≈ 0.001 for values > 1. -# Now relative: abs(val-ref)/max(|ref|,|val|,eps) > threshold. -# GROUNDED: 0.001 = 0.1% — tight tolerance for stable sustained tones -# SEISMIC: 0.01 = 1.0% — moderate tolerance for transient-rich material -# FLAME: inf = raw keyframe (noise: prediction is useless) -_DELTA_THRESHOLD = { - _PHASE_GROUNDED: 0.001, - _PHASE_SEISMIC: 0.01, - _PHASE_FLAME: float('inf'), # transmit everything raw -} - -# Minimum frame size before recursion stops (Planck floor — below this, -# frequency resolution is too coarse to be meaningful for audio). -_MIN_FRAME_SAMPLES = 64 - -# Subregister band marker — boxes from accelerated-time sub-frames use a -# reserved band prefix so the white hole knows which time domain they came from. -# 0xFD = subregister depth 1 (2× accel), 0xFC = depth 2 (4×), etc. -_SUBREGISTER_BAND_BASE = 0xFD # counts DOWN per recursion level - - -def _classify_frame( - boxes: List[CarrierBox], - h_total: float, - spin_param: float, -) -> str: - """Classify a frame's complexity via Jupiter φ-ratio of its amplitude envelope.""" - eta = conversion_efficiency(spin_param) - residual = h_total * (1.0 - eta) - # Use band amplitude envelope (first box per band) as the φ-ratio input, - # not raw box values — gives a stable spectral shape proxy. - band_amps: List[float] = [] - seen_bands: set = set() - for box in boxes: - band, _, param, _ = box.address - if param == 0 and band not in seen_bands: # param 0 = amplitude - band_amps.append(abs(box.decode_value())) - seen_bands.add(band) - _, phase = jupiter_encode(residual, band_amps) - return phase - - -def _encode_flame_subregister( - samples: List[float], - sample_rate: float, - f_low: float, - f_high: float, - snr_db_val: float, - spin_param: float, - friction_coeff: float, - n_bands: int, - depth: int, - frame_offset: int, -) -> List[CarrierBox]: - """ - Relativistic subregister: accelerated time domain for FLAME-class frames. - - When a frame is too complex to compress at normal resolution, time contracts: - the frame is split into two half-length sub-frames and each is re-encoded. - This trades frequency resolution for temporal resolution (Heisenberg duality). - - Recursion stops at _MIN_FRAME_SAMPLES (Planck floor) — below this, - Goertzel frequency resolution is meaningless for audio. - - Sub-frame boxes carry a special band marker (0xFD, 0xFC, …) so the white - hole decoder knows which time domain each box came from. - """ - if len(samples) <= _MIN_FRAME_SAMPLES or depth > 4: - # Planck floor reached — encode raw, mark with subregister depth band - boxes, _ = encode_signal( - samples, sample_rate, - f_low=f_low, f_high=f_high, snr_db_val=snr_db_val, - spin_param=spin_param, friction_coeff=friction_coeff, - n_bands=n_bands, - ) - sub_band = max(0x00, _SUBREGISTER_BAND_BASE - depth) - out = [] - for box in boxes: - _, mode, param, _ = box.address - label = pack_label(sub_band, mode, param, frame_offset & 0xFF) - out.append(CarrierBox(label, box.value_bits)) - return out - - # Split into two half-length sub-frames - half = len(samples) // 2 - halves = [samples[:half], samples[half:]] - result = [] - - for i, half_samples in enumerate(halves): - sub_boxes, sub_stats = encode_signal( - half_samples, sample_rate, - f_low=f_low, f_high=f_high, snr_db_val=snr_db_val, - spin_param=spin_param, friction_coeff=friction_coeff, - n_bands=n_bands, - ) - sub_phase = _classify_frame(sub_boxes, sub_stats['h_total'], spin_param) - - if sub_phase == _PHASE_FLAME: - # Still complex — recurse deeper (time contracts further) - result.extend(_encode_flame_subregister( - half_samples, sample_rate, - f_low, f_high, snr_db_val, spin_param, friction_coeff, n_bands, - depth + 1, frame_offset * 2 + i, - )) - else: - # Resolved — tag with subregister depth and emit - sub_band = max(0x00, _SUBREGISTER_BAND_BASE - depth) - for box in sub_boxes: - _, mode, param, _ = box.address - label = pack_label(sub_band, mode, param, (frame_offset * 2 + i) & 0xFF) - result.append(CarrierBox(label, box.value_bits)) - - return result - - -def encode_stream( - frames: List[List[float]], - sample_rate: float, - f_low: float = 20.0, - f_high: float = 20000.0, - snr_db_val: float = 40.0, - spin_param: float = 0.5, - friction_coeff: float = 0.05, - n_bands: int = 8, -) -> Tuple[List[List[CarrierBox]], dict]: - """ - Multi-frame streaming encoder with metanarrative-gated delta compression. - - Strategy - ──────── - Frame 0 — always a full keyframe (all boxes transmitted). - Frame N — metanarrative harness classifies complexity via Jupiter φ-ratio: - - PHASE_GROUNDED → linear prediction: store only boxes where - |value - predicted| > threshold (near-zero for tones) - PHASE_SEISMIC → delta from previous frame above medium threshold - PHASE_FLAME → raw keyframe (noise: prediction is useless) - - The frame_idx field in each label encodes the frame number, so the white - hole (decoder) can replay the full sequence deterministically. - - Returns - ------- - frame_streams : list of box lists, one per frame - stats : aggregate compression statistics - """ - frame_streams: List[List[CarrierBox]] = [] - prev_values: Dict[int, float] = {} # label → last transmitted f32 value - pred_values: Dict[int, float] = {} # label → linear-predicted f32 value - - total_raw = 0 - total_boxes = 0 - total_key = 0 - total_delta = 0 - phase_counts: Dict[str, int] = { - _PHASE_GROUNDED: 0, _PHASE_SEISMIC: 0, _PHASE_FLAME: 0 - } - - for f_idx, frame_samples in enumerate(frames): - boxes, stats = encode_signal( - frame_samples, sample_rate, - f_low=f_low, f_high=f_high, snr_db_val=snr_db_val, - spin_param=spin_param, friction_coeff=friction_coeff, - n_bands=n_bands, - ) - - # Classify this frame's complexity via band amplitude envelope φ-ratio - h_total = stats['h_total'] - phase = _classify_frame(boxes, h_total, spin_param) - phase_counts[phase] = phase_counts.get(phase, 0) + 1 - - threshold = _DELTA_THRESHOLD[phase] - total_raw += len(frame_samples) * 2 - - if phase == _PHASE_FLAME and f_idx > 0: - # Relativistic subregister: complex frame → accelerated time domain. - # Split into sub-frames at 2× resolution; recurse until resolved. - sub_boxes = _encode_flame_subregister( - frame_samples, sample_rate, - f_low, f_high, snr_db_val, spin_param, friction_coeff, n_bands, - depth=1, frame_offset=f_idx, - ) - frame_streams.append(sub_boxes) - total_boxes += len(sub_boxes) - total_delta += 1 - continue - - if f_idx == 0: # SF-2: was `or phase == _PHASE_FLAME` — unreachable (continue above) - # Keyframe: transmit all boxes with updated frame_idx - out_boxes = [] - for box in boxes: - band, mode, param, _ = box.address - new_label = pack_label(band, mode, param, f_idx & 0xFF) - new_box = CarrierBox(new_label, box.value_bits) - out_boxes.append(new_box) - prev_values[new_label & 0xFFFFFF00 | 0] = box.decode_value() - pred_values[new_label & 0xFFFFFF00 | 0] = box.decode_value() - frame_streams.append(out_boxes) - total_boxes += len(out_boxes) - total_key += 1 - else: - # Delta frame: only transmit boxes that changed beyond threshold - # For GROUNDED: compare against linear prediction (2×prev - prev2) - out_boxes = [] - for box in boxes: - band, mode, param, _ = box.address - base_label = pack_label(band, mode, param, 0) - val = box.decode_value() - - if phase == _PHASE_GROUNDED and base_label in pred_values: - reference = pred_values[base_label] - else: - reference = prev_values.get(base_label, 0.0) - - # SF-4 fix: relative comparison — abs(delta)/max(|ref|,|val|,eps) > threshold - _denom = max(abs(reference), abs(val), 1e-6) - if abs(val - reference) / _denom > threshold: - new_label = pack_label(band, mode, param, f_idx & 0xFF) - out_boxes.append(CarrierBox(new_label, box.value_bits)) - prev_values[base_label] = val - # SF-A fix: only update linear prediction for transmitted boxes. - # For untransmitted boxes, the decoder never learns val and cannot - # replicate this state update — leaving pred_values unchanged means - # encoder and decoder both use prev_values[base_label] as reference - # on the next frame, keeping them in sync. - pred_values[base_label] = 2.0 * val - reference - - frame_streams.append(out_boxes) - total_boxes += len(out_boxes) - total_delta += 1 - - ratio = (total_raw / max(total_boxes * 6, 1)) - - # Compute stream hash for idempotent register_id - raw_bytes = b''.join( - box.pack() for stream in frame_streams for box in stream - ) - stream_hash = hashlib.sha256(raw_bytes).hexdigest() - - # Discover which subregister band prefixes were used - active_bands: set = set() - for stream in frame_streams: - for box in stream: - active_bands.add((box.label >> 24) & 0xFF) - - # Build omnitoken manifest — activates archive_compression domain - foam_center = [ - min(1.0, total_boxes / max(total_raw / 6, 1)), # packing density - ratio / 10.0, # compression quality - phase_counts.get(_PHASE_GROUNDED, 0) / max(len(frames), 1), - 1.0 - phase_counts.get(_PHASE_FLAME, 0) / max(len(frames), 1), - ] - manifest = build_omnitoken_manifest( - sample_rate=sample_rate, - f_low=f_low, f_high=f_high, n_bands=n_bands, - n_frames=len(frames), - spin_param=spin_param, friction_coeff=friction_coeff, - foam_center=foam_center, - active_subregisters=list(active_bands), - stream_hash=stream_hash, - phase_counts=phase_counts, - ) - - stats = { - 'n_frames': len(frames), - 'total_raw_B': total_raw, - 'total_box_B': total_boxes * 6, - 'ratio': ratio, - 'keyframes': total_key, - 'delta_frames': total_delta, - 'phase_counts': phase_counts, - 'boxes_per_frame': [len(s) for s in frame_streams], - 'stream_hash': stream_hash[:16], - 'omnitoken': manifest, - } - return frame_streams, stats - - -def run_factory_poc(): - """ - Two-layer round-trip: Layer 1 (carrier state boxes) + Layer 2 (Jupiter/tunnel). - Metanarrative harness gates the Jupiter layer by complexity (ARPG phase). - """ - print("=" * 72) - print(" SOLITON FACTORY — Layer 1 (carrier state) + Layer 2 (Jupiter/tunnel)") - print(" Metanarrative harness: F(S)=Equilibrium ↔ ∇H≈0 & φ≈1.618") - print("=" * 72) - - fs = 44100 - duration = 0.1 - n = int(fs * duration) - - def make_tone(freq, amp=0.8): - return [amp * math.sin(2 * math.pi * freq * i / fs) for i in range(n)] - - def make_chord(freqs, amp=0.5): - s = [sum(amp * math.sin(2 * math.pi * f * i / fs) for f in freqs) - for i in range(n)] - m = max(abs(x) for x in s) or 1 - return [x / m for x in s] - - def make_noise(amp=0.3): - rng = __import__('random').Random(42) - return [rng.uniform(-amp, amp) for _ in range(n)] - - test_cases = [ - ('PURE TONE 440 Hz', make_tone(440), 0.80, 0.04), - ('CHORD C4-E4-G4', make_chord([261,330,392]), 0.60, 0.05), - ('WHITE NOISE', make_noise(), 0.10, 0.15), - ] - - hdr = (f"{'Signal':<22} {'L1 boxes':>9} {'L2 boxes':>9} " - f"{'Total B':>8} {'Ratio':>6} {'λ_Edd':>8} {'Phase':<16} {'Residual recover'}") - print(f"\n{hdr}") - print("-" * 90) - - for label, sig, spin, friction in test_cases: - # ── Layer 1: carrier state encode ─────────────────────────────────────────── - boxes, stats = encode_signal( - sig, fs, f_low=20, f_high=20000, snr_db_val=40, - spin_param=spin, friction_coeff=friction, - ) - - # Compute friction residual from TSE model - h_total = stats['h_total'] - eta = conversion_efficiency(spin) - residual = h_total * (1.0 - eta) - - # Band amplitudes as proxy mode state for the harness - band_amps = [b.decode_value() for b in boxes[:8]] - - # ── Layer 2: Jupiter encode ────────────────────────────────────────── - j_boxes, phase = jupiter_encode(residual, band_amps) - all_boxes = boxes + j_boxes - - # ── Decode: split by layer marker ──────────────────────────────────── - l1_boxes = [b for b in all_boxes if (b.label >> 24) & 0xFF != _J_BAND] - recovered_residual, _ = jupiter_decode(all_boxes) - - total_bytes = len(all_boxes) * 6 - ratio = stats['raw_bytes'] / max(total_bytes, 1) - # f16 log-scale: ~0.3% relative error is expected (10-bit mantissa × exp amplification) - residual_ok = (abs(recovered_residual - residual) / max(residual, 1.0)) < 0.005 - - print(f" {label:<20} {len(l1_boxes):>9,} {len(j_boxes):>9,} " - f"{total_bytes:>7,}B {ratio:>5.2f}× " - f"{stats['lambda_edd']:>7.4f} {phase:<16} " - f"{'✓ ' + f'{recovered_residual:.1f}' if residual_ok else '✗ drift'}") - - print("\n [Layer 1] band 0x00-0x07 — carrier state basis (Bekenstein modes, φ-sorted)") - print(" [Layer 2] band 0xFE — Jupiter tunnel (φ-normalised residual)") - print(" [Harness] PHASE_GROUNDED→full, PHASE_SEISMIC→7-mode, PHASE_FLAME→skip") - print(" [Box] 6 bytes: 4-byte label + 2-byte f16 | sort: MSD radix 32-bit") - print("=" * 72) - - # ── Streaming PoC: multi-frame delta compression ────────────────────────── - print("\n" + "=" * 72) - print(" STREAMING — time domain + metanarrative delta gating (16 frames)") - print("=" * 72) - - n_frames = 16 - - stream_cases = [ - ('PURE TONE 440 Hz', [make_tone(440)] * n_frames, 0.80, 0.04), - ('CHORD C4-E4-G4', [make_chord([261,330,392])]* n_frames, 0.60, 0.05), - ('WHITE NOISE', [make_noise()] * n_frames, 0.10, 0.15), - ] - - shdr = (f"{'Signal':<22} {'Frames':>7} {'Key':>5} {'Delta':>7} " - f"{'Raw B':>8} {'Box B':>8} {'Ratio':>7} Phase distribution") - print(f"\n{shdr}") - print("-" * 85) - - for slabel, frame_list, spin, friction in stream_cases: - _, sstats = encode_stream( - frame_list, fs, f_low=20, f_high=20000, snr_db_val=40, - spin_param=spin, friction_coeff=friction, - ) - pc = sstats['phase_counts'] - phase_str = (f"G={pc.get(_PHASE_GROUNDED,0)} " - f"S={pc.get(_PHASE_SEISMIC,0)} " - f"F={pc.get(_PHASE_FLAME,0)}") - print(f" {slabel:<20} {sstats['n_frames']:>7} " - f"{sstats['keyframes']:>5} {sstats['delta_frames']:>7} " - f"{sstats['total_raw_B']:>8,} {sstats['total_box_B']:>8,} " - f"{sstats['ratio']:>6.2f}× {phase_str}") - bpf = sstats['boxes_per_frame'] - print(f" boxes/frame: {bpf[:8]}{'...' if len(bpf)>8 else ''}") - - # Show omnitoken manifest summary - ot = sstats['omnitoken'] - nd = ot['n_dimensional_surface'] - st = ot['stream'] - ac = ot['surface_bus']['domains'].get('archive_compression', {}) - print(f" omnitoken: {ot['name']} φ={nd['phi_ratio']:.4f} " - f"foam={nd['foam_score']:.4f} " - f"archive_compression={ac.get('status','?')} " - f"reg={st['register_state']}") - - print("\n [Frame 0] keyframe — full box stream") - print(" [Frame 1+] delta — only changed boxes (GROUNDED: predict+residual)") - print(" [FLAME frame] subregister — accelerated time domain, band 0xFD-0xFB") - print(" [Omnitoken] GraphVM manifest — activates archive_compression domain") - print("=" * 72) - - -if __name__ == "__main__": - run_factory_poc() diff --git a/5-Applications/tools-scripts/carrier/carrier_monitor.py b/5-Applications/tools-scripts/carrier/carrier_monitor.py deleted file mode 100644 index b2a29f26..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_monitor.py +++ /dev/null @@ -1,298 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -CarrierState Standing Wave Monitor for Hyperfluid Causal Pressure Model - -Detects perturbations in the causal pressure dynamics via a self-reinforcing -standing wave (carrier_state) that locks onto baseline and fires when the hyperfluid -is disturbed beyond recovery threshold. - -The "woman in the box" trick: a stable reference state that remains invisible -until the model deviates significantly, at which point the carrier_state becomes -manifest as an anomaly signal. -""" - -import json -import statistics -from dataclasses import dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - - -@dataclass -class CarrierStateState: - """Standing wave state in causal pressure space.""" - amplitude: float # Current carrier_state amplitude (0.0 = no disturbance) - phase: float # Phase offset (0.0 = synchronized with baseline) - coherence: float # Coherence with baseline (1.0 = perfect lock) - decay_rate: float # Exponential decay if undisturbed - - -def gaussian_kernel(x: float, sigma: float = 1.0) -> float: - """Smooth kernel for baseline estimation.""" - import math - return math.exp(-(x * x) / (2 * sigma * sigma)) / (sigma * math.sqrt(2 * math.pi)) - - -def build_standing_wave_baseline( - pressures: List[float], - window: int = 12, - sigma: float = 2.0, -) -> List[float]: - """Compute reference baseline using Gaussian-weighted kernel smoothing. - - This is the 'woman in the box' — the invisible reference state. - Returns smoothed baseline at each point. - """ - if not pressures or window < 1: - return pressures - - baseline = [] - for i in range(len(pressures)): - # Gaussian-weighted average over window - weights = [] - values = [] - for j in range(max(0, i - window), min(len(pressures), i + window + 1)): - dist = abs(j - i) - w = gaussian_kernel(dist, sigma) - weights.append(w) - values.append(pressures[j]) - - if weights: - total_w = sum(weights) - avg = sum(v * w for v, w in zip(values, weights)) / total_w - baseline.append(avg) - else: - baseline.append(0.0) - - return baseline - - -def compute_carrier_state_state( - observed: float, - baseline: float, - prev_carrier_state: Optional[CarrierStateState], - recovery_threshold: float = 0.15, -) -> CarrierStateState: - """Update carrier_state state based on perturbation. - - The carrier_state is a self-reinforcing standing wave that: - - Grows if perturbation exceeds threshold (disturbance locked) - - Decays exponentially if undisturbed (coherence restored) - - Remains stable once manifest - - Args: - observed: Current causal pressure - baseline: Expected (smoothed) baseline - prev_carrier_state: Previous carrier_state state - recovery_threshold: Deviation beyond which carrier_state manifests - - Returns: - Updated CarrierStateState - """ - if prev_carrier_state is None: - prev_carrier_state = CarrierStateState(amplitude=0.0, phase=0.0, coherence=1.0, decay_rate=0.95) - - # Perturbation: signed deviation from baseline - perturbation = observed - baseline - - # CarrierState growth/decay logic - if abs(perturbation) > recovery_threshold: - # Disturbance detected: carrier_state amplitude grows - new_amplitude = min(1.0, prev_carrier_state.amplitude + 0.15 * abs(perturbation)) - new_coherence = max(0.0, prev_carrier_state.coherence - 0.10) - phase_shift = 0.1 * perturbation # Phase locks to disturbance - else: - # No disturbance: carrier_state decays exponentially toward rest - new_amplitude = prev_carrier_state.amplitude * prev_carrier_state.decay_rate - new_coherence = min(1.0, prev_carrier_state.coherence + 0.05) - phase_shift = -0.05 * prev_carrier_state.phase # Phase damps to 0 - - new_phase = prev_carrier_state.phase + phase_shift - - return CarrierStateState( - amplitude=new_amplitude, - phase=new_phase, - coherence=new_coherence, - decay_rate=0.95, - ) - - -def compute_carrier_state_energy(state: CarrierStateState) -> float: - """Energy of the carrier_state (0.0 = at rest, 1.0 = fully manifest). - - Energy = amplitude² + phase² + (1 - coherence)² - """ - return state.amplitude**2 + state.phase**2 + (1.0 - state.coherence)**2 - - -def carrier_state_rank_anomalies( - predictions: List[Dict[str, Any]], - recovery_threshold: float = 0.15, -) -> List[Dict[str, Any]]: - """Rank predictions by carrier_state energy (anomaly score). - - Args: - predictions: List of prediction dicts from backtest - recovery_threshold: Threshold for perturbation detection - - Returns: - Predictions augmented with carrier_state fields, sorted by anomaly energy - """ - pressures = [float(p.get("expected_delta_pressure_horizon", 0.0) or 0.0) for p in predictions] - - baseline = build_standing_wave_baseline(pressures, window=12, sigma=2.0) - - carrier_state = None - augmented = [] - - for i, pred in enumerate(predictions): - obs = pressures[i] - base = baseline[i] - - carrier_state = compute_carrier_state_state(obs, base, carrier_state, recovery_threshold) - energy = compute_carrier_state_energy(carrier_state) - - aug_pred = dict(pred) - aug_pred["carrier_state_amplitude"] = round(carrier_state.amplitude, 8) - aug_pred["carrier_state_phase"] = round(carrier_state.phase, 8) - aug_pred["carrier_state_coherence"] = round(carrier_state.coherence, 8) - aug_pred["carrier_state_energy"] = round(energy, 8) - aug_pred["baseline_pressure"] = round(base, 8) - aug_pred["perturbation"] = round(obs - base, 8) - - augmented.append(aug_pred) - - augmented.sort(key=lambda p: float(p.get("carrier_state_energy", 0.0)), reverse=True) - - return augmented - - -def carrier_state_summary(augmented_predictions: List[Dict[str, Any]]) -> Dict[str, Any]: - """Summary statistics of carrier_state states across predictions.""" - if not augmented_predictions: - return {} - - energies = [float(p.get("carrier_state_energy", 0.0) or 0.0) for p in augmented_predictions] - amplitudes = [float(p.get("carrier_state_amplitude", 0.0) or 0.0) for p in augmented_predictions] - coherences = [float(p.get("carrier_state_coherence", 0.0) or 0.0) for p in augmented_predictions] - - # Count "manifest" carrier_states (energy > 0.1) - manifest_count = sum(1 for e in energies if e > 0.1) - - # Identify clusters of high energy (regime changes) - high_energy_indices = [i for i, e in enumerate(energies) if e > 0.2] - clusters = [] - if high_energy_indices: - current_cluster = [high_energy_indices[0]] - for idx in high_energy_indices[1:]: - if idx - current_cluster[-1] <= 3: - current_cluster.append(idx) - else: - clusters.append(current_cluster) - current_cluster = [idx] - clusters.append(current_cluster) - - return { - "total_predictions": len(augmented_predictions), - "manifest_carrier_states": manifest_count, - "carrier_state_energy_stats": { - "mean": round(statistics.fmean(energies), 8) if energies else 0.0, - "median": round(statistics.median(energies), 8) if energies else 0.0, - "max": round(max(energies), 8) if energies else 0.0, - "pstdev": round(statistics.pstdev(energies), 8) if len(energies) > 1 else 0.0, - }, - "coherence_stats": { - "mean": round(statistics.fmean(coherences), 8) if coherences else 0.0, - "min": round(min(coherences), 8) if coherences else 0.0, - }, - "anomaly_clusters": [ - { - "start_idx": cluster[0], - "end_idx": cluster[-1], - "size": len(cluster), - "peak_energy": round(max(energies[i] for i in cluster), 8), - } - for cluster in clusters - ], - "interpretation": ( - f"{manifest_count}/{len(augmented_predictions)} predictions show carrier_state manifestation. " - f"Mean coherence {round(statistics.fmean(coherences), 3)}. " - f"{len(clusters)} anomaly cluster(s) detected." - ), - } - - -def augment_report_with_carrier_state( - report_path: Path, - out_path: Optional[Path] = None, - recovery_threshold: float = 0.15, -) -> Dict[str, Any]: - """Load a causal pressure report, augment with carrier_state monitoring, save. - - Args: - report_path: Path to hyperfluid_causal_report.json - out_path: Optional path to save augmented report (default: insert _with_carrier_state) - recovery_threshold: Perturbation threshold for carrier_state manifestation - - Returns: - Augmented report dict - """ - report = json.loads(report_path.read_text(encoding="utf-8")) - - chain_backtests = report.get("chain_backtests", {}) - - for chain, backtest in chain_backtests.items(): - predictions_raw = backtest.get("predictions", []) - if predictions_raw: - augmented = carrier_state_rank_anomalies(predictions_raw, recovery_threshold) - backtest["predictions_with_carrier_state"] = augmented - backtest["carrier_state_summary"] = carrier_state_summary(augmented) - - # Top 5 anomalies - backtest["top_anomalies"] = augmented[:5] - - if out_path is None: - stem = report_path.stem - out_path = report_path.parent / f"{stem}_with_carrier_state.json" - - out_path.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8") - - return report - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="CarrierState standing wave monitor for hyperfluid causal pressure") - parser.add_argument("--report", type=str, required=True, help="Path to hyperfluid_causal_report.json") - parser.add_argument("--out", type=str, default=None, help="Output path (default: _with_carrier_state.json)") - parser.add_argument("--recovery-threshold", type=float, default=0.15, help="Perturbation threshold for carrier_state manifestation") - - args = parser.parse_args() - - report_path = Path(args.report) - out_path = Path(args.out) if args.out else None - - augmented_report = augment_report_with_carrier_state( - report_path, - out_path=out_path, - recovery_threshold=float(args.recovery_threshold), - ) - - print(f"✓ CarrierState monitoring complete") - print(f" Report: {report_path}") - print(f" Output: {out_path or (report_path.parent / f'{report_path.stem}_with_carrier_state.json')}") - - # Print summary per chain - for chain, backtest in augmented_report.get("chain_backtests", {}).items(): - summary = backtest.get("carrier_state_summary", {}) - print(f"\n [{chain}]") - print(f" Manifest carrier_states: {summary.get('manifest_carrier_states', 0)}") - print(f" Mean coherence: {summary.get('coherence_stats', {}).get('mean', 0)}") - print(f" Anomaly clusters: {len(summary.get('anomaly_clusters', []))}") diff --git a/5-Applications/tools-scripts/carrier/carrier_reasoning_engine.py b/5-Applications/tools-scripts/carrier/carrier_reasoning_engine.py deleted file mode 100644 index 338525e2..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_reasoning_engine.py +++ /dev/null @@ -1,132 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""CarrierState Reasoning Engine — Performs semantic 'Cognitive Triage' using Local LLMs. - -This engine synthesizes structural waveprobe metrics (heat, torsion, anisotropy) -into high-level 'structural reasoning' to validate autonomous remediations. -""" - -import json -import os -import sys -from typing import Optional, Dict, Any, List -from dataclasses import dataclass, field - -try: - from scripts.local_llm_client import LocalLLMClient - from scripts.graphvm_canal_router import CanalRoutingReport -except ModuleNotFoundError: - from pathlib import Path - sys.path.append(str(Path(__file__).resolve().parents[2])) - from local_llm_client import LocalLLMClient # type: ignore - from graphvm_canal_router import CanalRoutingReport # type: ignore - -@dataclass -class ReasoningOutcome: - """The result of a structural reasoning pass.""" - semantic_risk_score: float # 0 to 1 - remediation_strategy: str # immediate | review | freeze - reasoning_summary: str - is_anomaly: bool - confidence: float # 0 to 1 (reliability of the local model) - -class CarrierStateReasoningEngine: - """Bridges GraphVM metrics to semantic reasoning via Gemma2.""" - - SYSTEM_PROMPT = """You are the OmniToken CarrierState Reasoning Engine. -You perform 'Cognitive Triage' on EVM bytecode structural analysis. -Your goal is to identify structural drift and predatory architectures. - -REMEDIATION_STRATEGY ENUM (MUST USE EXACTLY ONE): -- immediate: High Heat, Low Torsion (Fast protocol flow) -- review: Medium Heat/Torsion (Ambiguous architectural intent) -- freeze: High Heat, High Torsion (Suspected structural predation) - -ALWAYS RETURN JSON format with: -{ - "semantic_risk_score": float (0.0 to 1.0), - "remediation_strategy": "immediate" | "review" | "freeze", - "reasoning_summary": "string", - "is_anomaly": boolean, - "confidence": float (0.0 to 1.0) -}""" - - def __init__(self, model_name: str = "gemma2:2b"): - self.client = LocalLLMClient(model=model_name) - - def analyze_risk(self, report: CanalRoutingReport) -> Optional[ReasoningOutcome]: - """Send the structural metrics to Gemma2 for semantic triage.""" - if not self.client.check_health(): - return None # Fallback to hard-coded governance - - prompt = f"""STRUCTURAL METRICS REPORT: -Contract SHA256: {report.contract_sha256[:16]} -Heat (H): {report.overall_heat:.4f} (Theta_Heat: {report.applied_tolerances.get('thresholds', {}).get('theta_heat', 0.3)}) -Torsion (T): {report.overall_torsion:.4f} (Deviation from CarrierState anchor) -Anisotropy (A): {report.overall_anisotropy:.4f} (Structural bias) -Canal Cost (KOT): {report.canal_cost_kot:.1f} -Triage Score (Triage): {report.triage_score:.4f} - -Current Decision: {report.routing_decision} -Reason: {report.reason} - -Assess the risk of structural predation or architectural drift. Does this code maintain the 'CarrierState' integrity of a canonical router?""" - - result = self.client.generate(prompt, system=self.SYSTEM_PROMPT) - - if "error" in result: - return None - - # Post-generation normalization - raw_strategy = str(result.get("remediation_strategy", report.routing_decision)).lower() - normalized_strategy = self._normalize_strategy(raw_strategy, report.routing_decision) - - try: - return ReasoningOutcome( - semantic_risk_score=result.get("semantic_risk_score", 0.5), - remediation_strategy=normalized_strategy, - reasoning_summary=result.get("reasoning_summary", "Autonomous reasoning logic failed to formulate summary."), - is_anomaly=result.get("is_anomaly", False), - confidence=result.get("confidence", 0.1) - ) - except Exception: - return None - - def _normalize_strategy(self, strategy: str, fallback: str) -> str: - """Map fuzzy LLM strings back to the strict internal enum.""" - if "immediate" in strategy: - return "immediate" - if "freeze" in strategy or "block" in strategy or "stop" in strategy: - return "freeze" - if "review" in strategy or "analyze" in strategy or "examine" in strategy: - return "review" - return fallback - -if __name__ == "__main__": - # Test/Mock - mock_report = CanalRoutingReport( - contract_sha256="0abc123...", - overall_heat=0.85, - overall_torsion=0.92, - overall_anisotropy=0.45, - triage_score=0.95, - canal_cost_kot=4500.0, - valve_phi=0, - routing_decision="freeze", - reason="Heat valve failure (H=0.85 > 0.3)", - applied_tolerances={"thresholds": {"theta_heat": 0.3}} - ) - - engine = CarrierStateReasoningEngine() - print(f"[*] Analyzing with Local Gemma ({engine.client.model})...") - outcome = engine.analyze_risk(mock_report) - if outcome: - print(f"[+] Reasoning result: {outcome.remediation_strategy.upper()} - Confidence: {outcome.confidence}") - print(f"Summary: {outcome.reasoning_summary}") - else: - print("[!] Local Reasoning OFFLINE - Using hard governance only.") diff --git a/5-Applications/tools-scripts/carrier/carrier_refactor.py b/5-Applications/tools-scripts/carrier/carrier_refactor.py deleted file mode 100644 index c0b5cea2..00000000 --- a/5-Applications/tools-scripts/carrier/carrier_refactor.py +++ /dev/null @@ -1,86 +0,0 @@ -import os -import re -from pathlib import Path - -# Define replacement rules (more specific first) -REPLACEMENTS = [ - (r'soliton spacing', 'carrier spacing'), - (r'Soliton spacing', 'Carrier spacing'), - (r'soliton factory', 'carrier factory'), - (r'Soliton factory', 'Carrier factory'), - (r'soliton engine', 'carrier engine'), - (r'Soliton engine', 'Carrier engine'), - (r'soliton constants', 'carrier constants'), - (r'Soliton constants', 'Carrier constants'), - (r'Soliton Field Collapse', 'Carrier Field Collapse'), - (r'Quantum Solitons', 'Quantum Carrier States'), - (r'soliton_factory', 'carrier_factory'), - (r'soliton_engine', 'carrier_engine'), - (r'soliton_constants', 'carrier_constants'), -] - -# Noun replacements (with plural support and Davydov exemption) -def replace_solitons(text): - # Exempt Davydov solitons - # This regex looks for 'soliton' NOT preceded by 'Davydov' (case insensitive) - # We'll do it in steps for safety. - - # Step 1: Protect Davydov solitons - text = re.sub(r'(Davydov\s+)(solitons?)', r'\1PROTECTED_SOLITON_\2', text, flags=re.IGNORECASE) - - # Step 2: Global replacements from REPLACEMENTS list - for pattern, replacement in REPLACEMENTS: - text = re.sub(pattern, replacement, text) - - # Step 3: Generic noun replacements - text = re.sub(r'solitons', 'carrier states', text) - text = re.sub(r'Solitons', 'Carrier states', text) - text = re.sub(r'soliton', 'carrier state', text) - text = re.sub(r'Soliton', 'Carrier state', text) - - # Step 4: Unprotect Davydov solitons - text = re.sub(r'PROTECTED_SOLITON_(solitons?)', r'\1', text) - - return text - -# Paths to scan -ROOT_DIR = Path(__file__).resolve().parents[2] -EXCLUDE_DIRS = {'5-Applications/audit/sessions', '5-Applications/audit/logs', '.git', '__pycache__', 'target', 'external/vendor'} - -def refactor_file(file_path): - if not os.path.isfile(file_path): - return - - try: - with open(file_path, 'r', encoding='utf-8') as f: - content = f.read() - except UnicodeDecodeError: - return # Skip binary files - - new_content = replace_solitons(content) - - if new_content != content: - with open(file_path, 'w', encoding='utf-8') as f: - f.write(new_content) - print(f"Refactored: {file_path}") - -def run_refactor(): - for root, dirs, files in os.walk(ROOT_DIR): - rel_path = os.path.relpath(root, ROOT_DIR) - - # Check if we should skip this entire subtree - is_excluded = any(rel_path == d or rel_path.startswith(d + os.sep) for d in EXCLUDE_DIRS) - - if is_excluded: - dirs[:] = [] # Don't recurse - continue # Don't process files in this directory - - for file in files: - if file.startswith('.') or file.endswith(('.png', '.pdf', '.zip', '.enc', '.pyc')): - continue - - file_path = os.path.join(root, file) - refactor_file(file_path) - -if __name__ == "__main__": - run_refactor() diff --git a/5-Applications/tools-scripts/chemistry/element_229_molecular_sim.py b/5-Applications/tools-scripts/chemistry/element_229_molecular_sim.py deleted file mode 100644 index 631ae1d6..00000000 --- a/5-Applications/tools-scripts/chemistry/element_229_molecular_sim.py +++ /dev/null @@ -1,72 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math -import random - -def dot_product(v1, v2): - return sum(x * y for x, y in zip(v1, v2)) - -def normalize(v): - norm = math.sqrt(sum(x * x for x in v)) - return [x / norm if norm > 0 else 0 for x in v] - -class SolitonState: - def __init__(self, amplitude=0.0, phase=0.0, coherence=1.0): - self.amplitude = amplitude - self.phase = phase - self.coherence = coherence - -class Element229Atom: - def __init__(self, z=229): - self.z = z - self.wave = SolitonState(amplitude=0.1, phase=0.0, coherence=1.0) - # 14D Hypermanifold Vector - self.vector = [random.uniform(-1, 1) for _ in range(14)] - self.vector = normalize(self.vector) - - def interact(self, other_vector, theta): - # 1. Cumulative Wave Update - coupling = dot_product(self.vector, other_vector) - self.wave.amplitude = min(1.0, self.wave.amplitude + 0.05 * abs(coupling)) - self.wave.phase += 0.1 * coupling - self.wave.coherence = max(0.0, self.wave.coherence - 0.02 * abs(coupling)) - - # 2. ND Rotation (Simplified SO(14) in D1-D2 plane) - # Indices 3 and 4 are the compactified shortcut channels - c, s = math.cos(theta), math.sin(theta) - v3, v4 = self.vector[3], self.vector[4] - self.vector[3] = c * v3 - s * v4 - self.vector[4] = s * v3 + c * v4 - self.vector = normalize(self.vector) - -def run_simulation(interactions=137): - print(f"[*] Starting Element 229 Molecular Simulation (Interactions: {interactions})") - print(f"[*] Model: Standing Wave Self-Encoding + SO(14) Rotation") - - atom = Element229Atom() - target_vector = [0.0] * 14 - target_vector[3] = 1.0 # Aligned with D1 - - theta = math.pi / 229 # Delta proportional to Z - - for i in range(interactions): - atom.interact(target_vector, theta) - if (i + 1) % 40 == 0: - print(f" [Tick {i+1}] Amp: {atom.wave.amplitude:.4f}, Phase: {atom.wave.phase:.4f}, Coh: {atom.wave.coherence:.4f}") - - # Final Collapse - print("\n[!] Simulation Complete. Final Collapse initiated...") - final_energy = atom.wave.amplitude**2 + atom.wave.phase**2 + (1.0 - atom.wave.coherence)**2 - final_parity = dot_product(atom.vector, target_vector) - - print(f" [Result] Final Energy (Collapse State): {final_energy:.6f}") - print(f" [Result] Hypermanifold Parity: {final_parity:.6f}") - print(f" [Status] Element 229 stabilized into a molecular cluster via Standing Wave resonance.") - -if __name__ == "__main__": - run_simulation() diff --git a/5-Applications/tools-scripts/chemistry/engram_generator.py b/5-Applications/tools-scripts/chemistry/engram_generator.py deleted file mode 100644 index 8f673f81..00000000 --- a/5-Applications/tools-scripts/chemistry/engram_generator.py +++ /dev/null @@ -1,101 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -engram_generator.py — Discrete Codon Search for enwik9 -Identifies the 32-byte structural seed (S_H) by maximizing MirrorLUT symmetry. -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from collections import defaultdict -import os - -def load_target(size=10240): - """Loads a segment of enwik data.""" - path = os.path.join(os.path.dirname(__file__), '../docs/field_solver/test_input_wiki_10kb.bin') - if os.path.exists(path): - with open(path, 'rb') as f: - return f.read(size) - else: - # Fallback target pattern - return b"The quick brown fox jumps over the lazy dog. " * (size // 45 + 1) - -def sym_idx(p, q): - """Triangular pairing logic (v3 oracle alignment).""" - lo, hi = (p, q) if p < q else (q, p) - return int(hi * (hi + 1) / 2 + lo) - -def build_mirror_lut(data): - """Builds a transition frequency matrix for enwik codons.""" - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(data)): - prev = data[i-2] - curr = data[i-1] - nxt = data[i] - addr = sym_idx(prev, curr) - counts[addr][nxt] += 1 - return counts - -def extract_seed(counts, seed_size=32): - """ - Extracts the 'Codon Shunt' (32-byte seed S_H). - Picks the top N most frequent transition addresses to store in the seed. - """ - sorted_addrs = sorted(counts.keys(), key=lambda k: sum(counts[k].values()), reverse=True) - # Each addr is ~16-bit. We can store ~16 major codons in 32 bytes. - # For now, we take the top 16 addresses as the 'Structural Seed'. - seed_addrs = sorted_addrs[:seed_size // 2] - seed = [] - for addr in seed_addrs: - seed.append(addr >> 8) # High byte - seed.append(addr & 0xFF) # Low byte - return bytes(seed), seed_addrs - -def calculate_hit_rate(data, seed_addrs, counts): - """Calculates if the seed can successfully predict the data flow.""" - hits = 0 - total = len(data) - 2 - seed_set = set(seed_addrs) - - for i in range(2, len(data)): - addr = sym_idx(data[i-2], data[i-1]) - if addr in seed_set: - # Prediction: pick the most likely next byte for this addr - predicted = max(counts[addr].items(), key=lambda x: x[1])[0] - if predicted == data[i]: - hits += 1 - - return hits / total - -def main(): - print("=" * 60) - print("ENGRAM GENERATOR (Phase 1) — Discrete Codon Search") - print("=" * 60) - - data = load_target() - print(f"Target Loaded: {len(data)} bytes") - - counts = build_mirror_lut(data) - print(f"Unique Transitions identified: {len(counts)}") - - seed, seed_addrs = extract_seed(counts) - print(f"Seed S_H extracted: {seed.hex()}") - - # Accuracy check - hit_rate = calculate_hit_rate(data, seed_addrs, counts) - print(f"Mirror Hit Rate (v3 Oracle Accuracy): {hit_rate * 100:.2f}%") - - if hit_rate > 0.05: # High bar for discrete 32-byte search - print("\nVerdict: PASS - Structural seed identifies salient codons.") - else: - print("\nVerdict: FAIL - Hit rate too low to stabilize manifold.") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/chemistry/superconductor_simulation.py b/5-Applications/tools-scripts/chemistry/superconductor_simulation.py deleted file mode 100644 index ab68ca69..00000000 --- a/5-Applications/tools-scripts/chemistry/superconductor_simulation.py +++ /dev/null @@ -1,60 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -from pathlib import Path - -BASE_DIR = Path(__file__).parent.parent.resolve() -DATA_FILE = BASE_DIR / "hqw_atomic_combinations.json" -RTSC_OUT = BASE_DIR / "rtsc_candidates.json" - -# Threshold in Kelvin (70 F = 21.1 C = 294.25 K) -THRESHOLD_K = 294.25 - -def calculate_tc(comb): - # Parse atoms to get total mass Z_sum - atoms_str = comb['formula'].replace('Z', '').split('-') - z_sum = sum(int(z) for z in atoms_str) - - # Formula derived from logic_execution_layer_retrocausality_transfer_selfproof.tex - # Tc = C * (RegisterBits * Stability) / Z_sum^2 - # C=82.5 calibrated to the 300K decoherence floor for H-clusters - tc = 82.5 * (comb['register_bits'] * comb['stability']) / (z_sum**2) - return round(tc, 2) - -def run_simulation(): - print(f"[*] Loading atomic combinations from {DATA_FILE}...") - with open(DATA_FILE, 'r') as f: - combinations = json.load(f) - - candidates = [] - print(f"[*] Calculating Tc for {len(combinations)} clusters...") - - for comb in combinations: - tc = calculate_tc(comb) - if tc >= THRESHOLD_K: - comb['tc_kelvin'] = tc - comb['tc_fahrenheit'] = round((tc - 273.15) * 9/5 + 32, 2) - candidates.append(comb) - - # Sort by Tc - candidates = sorted(candidates, key=lambda x: x['tc_kelvin'], reverse=True) - - print(f"[+] Found {len(candidates)} RTSC candidates above {THRESHOLD_K} K (70 F).") - - print("\n--- TOP SUPERCONDUCTOR CANDIDATES ---") - for c in candidates[:15]: - print(f"[{c['formula']}] | Tc: {c['tc_fahrenheit']} F ({c['tc_kelvin']} K) | Stability: {c['stability']}") - - with open(RTSC_OUT, 'w') as f: - json.dump(candidates, f, indent=4) - print(f"\n[*] Candidates saved to {RTSC_OUT}") - -if __name__ == "__main__": - if not DATA_FILE.exists(): - print(f"[!] Error: {DATA_FILE} not found. Run the HQW simulation first.") - else: - run_simulation() diff --git a/5-Applications/tools-scripts/classification/stage0_classifier.py b/5-Applications/tools-scripts/classification/stage0_classifier.py deleted file mode 100644 index c7c5af09..00000000 --- a/5-Applications/tools-scripts/classification/stage0_classifier.py +++ /dev/null @@ -1,214 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Stage 0: Document Type Classifier -=================================== -**concept_anchor:** domain=compression / concept=stage0_document_classifier / - resolution=FORMING - -PURPOSE -------- -Before the ISO prepass (Pass 1), classify the document type so the right -domain set and prior tables are applied. A single symbol table tuned for -Wikipedia performs poorly on Python code, JSON sessions, or TSM files. - -The classifier works from the cross-domain residual profile of a short -sample of the document. Different document types have fundamentally -different residual fingerprints: - - Wikipedia article: iso_geo × iso_lang dominant (country + language bias) - Python source: iso_code × iso_ptos dominant (keyword + operator bias) - JSON session: iso_ptos × iso_abbrev dominant (schema + abbreviation) - TSM/ISA file: iso_isa × iso_math dominant (opcode + physics) - Scientific paper: iso_chem × iso_math × iso_unit dominant - -ARCHITECTURE ------------- - raw input - ↓ - stage0_classifier.py ← THIS FILE: probe a 4KB sample, emit doc_type - ↓ - iso_symbol_table.py use PTOS_DOMAINS / EXTENDED_DOMAINS / DEFAULT_DOMAINS - ↓ - iso_pipeline.py run_windowed_pass with corpus-adaptive thresholds - ↓ - schema_encoder.py structural Pass 1.5 for matching doc types - -DOC TYPES ---------- - "wikipedia" — encyclopedic text, heavy geo + lang - "python" — Python source code - "json_session" — Research Stack session JSON - "markdown" — docs / PTOS schema markdown - "tsm" — TSM/metafoam opcode files - "scientific" — academic papers (chem + math + unit heavy) - "mixed" — cannot classify (use EXTENDED_DOMAINS) - -USAGE ------ - from stage0_classifier import classify, DOMAIN_SET_FOR - - doc_type = classify(text) - domains = DOMAIN_SET_FOR[doc_type] - result = run_windowed_pass(text, domains=domains) -""" - -from __future__ import annotations - -import re -import sys -from collections import Counter -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent)) - -from iso_symbol_table import ( - prepass as iso_prepass, - EXTENDED_DOMAINS, - PTOS_DOMAINS, - DEFAULT_DOMAINS, - normalize_latex_math, -) - -# ─── domain sets per document type ──────────────────────────────────────────── - -DOMAIN_SET_FOR: dict[str, list[str]] = { - "wikipedia": EXTENDED_DOMAINS, - "python": PTOS_DOMAINS, - "json_session": PTOS_DOMAINS, - "markdown": PTOS_DOMAINS, - "tsm": PTOS_DOMAINS, - "scientific": EXTENDED_DOMAINS, - "mixed": EXTENDED_DOMAINS, -} - -# ─── heuristic probes ───────────────────────────────────────────────────────── -# These run before the domain analysis and catch obvious structural markers. - -_PY_PATTERN = re.compile(r"^(#!/|import |from \w+ import|def |class | def )", - re.MULTILINE) -_JSON_PATTERN = re.compile(r'"concept_anchor"\s*:|"idea_weights"\s*:|"session_id"\s*:') -_TSM_PATTERN = re.compile(r'(0x[0-9A-Fa-f]{2}|INGEST_STATE|WAVE_FOLD|PHASE_LOCK' - r'|FOAM_SPRAY|WELD_SURFACE|tsm_metafoam)', re.IGNORECASE) -_MD_PATTERN = re.compile(r'^#{1,6} |\*\*concept_anchor\*\*|^PTOS: LAYER=', - re.MULTILINE) -_SCI_PATTERN = re.compile(r'(abstract|doi:|arxiv|journal of|proceedings of' - r'|et al\.|fig\.|eq\.|theorem)', re.IGNORECASE) - - -def _structural_classify(sample: str) -> str | None: - """Fast structural pre-check — return doc_type or None if ambiguous.""" - if _JSON_PATTERN.search(sample): - return "json_session" - if _TSM_PATTERN.search(sample): - return "tsm" - if _PY_PATTERN.search(sample): - return "python" - if _MD_PATTERN.search(sample): - return "markdown" - if _SCI_PATTERN.search(sample): - return "scientific" - return None - - -# ─── domain-hit classifier ──────────────────────────────────────────────────── - -# Signature: (dominant_domain, secondary_domain) → doc_type -# Ordered by specificity — first match wins. -_DOMAIN_SIGNATURES: list[tuple[str, str | None, str]] = [ - ("iso_isa", "iso_math", "tsm"), - ("iso_isa", None, "tsm"), - ("iso_ptos", "iso_abbrev", "json_session"), - ("iso_ptos", "iso_isa", "tsm"), - ("iso_ptos", "iso_code", "python"), - ("iso_ptos", None, "markdown"), - ("iso_code", "iso_ptos", "python"), - ("iso_code", None, "python"), - ("iso_chem", "iso_math", "scientific"), - ("iso_unit", "iso_math", "scientific"), - ("iso_geo", "iso_lang", "wikipedia"), - ("iso_geo", None, "wikipedia"), -] - - -def classify(text: str, sample_bytes: int = 4096) -> str: - """Classify document type from the first `sample_bytes` of text. - - Returns one of: "wikipedia", "python", "json_session", "markdown", - "tsm", "scientific", "mixed" - """ - sample = text[:sample_bytes] - - # Fast path: structural markers are unambiguous - structural = _structural_classify(sample) - if structural is not None: - return structural - - # Domain-hit analysis on the sample - _, log = iso_prepass(normalize_latex_math(sample), domains=list(PTOS_DOMAINS)) - if not log: - return "mixed" - - hit_counts = {domain: len(tokens) for domain, tokens in log.items()} - ranked = sorted(hit_counts.items(), key=lambda x: -x[1]) - if not ranked: - return "mixed" - - top_domain = ranked[0][0] - second_domain = ranked[1][0] if len(ranked) > 1 else None - - for sig_top, sig_second, doc_type in _DOMAIN_SIGNATURES: - if top_domain == sig_top: - if sig_second is None or sig_second == second_domain: - return doc_type - - return "mixed" - - -def classify_path(path: Path, sample_bytes: int = 4096) -> str: - """Classify a file on disk by path + content sample.""" - # Extension pre-check - suffix = path.suffix.lower() - if suffix == ".py": - return "python" - if suffix in (".tsm", ".metafoam"): - return "tsm" - - try: - raw = path.read_bytes()[:sample_bytes] - text = raw.decode("utf-8", errors="replace") - except (OSError, PermissionError): - return "mixed" - - return classify(text, sample_bytes=sample_bytes) - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def main() -> None: - import argparse - parser = argparse.ArgumentParser( - description="Stage 0 document type classifier" - ) - parser.add_argument("paths", nargs="+", type=Path, - help="files to classify") - parser.add_argument("--sample", type=int, default=4096, - help="bytes to sample per file (default 4096)") - args = parser.parse_args() - - for p in args.paths: - doc_type = classify_path(p, sample_bytes=args.sample) - domains = DOMAIN_SET_FOR[doc_type] - print(f"{doc_type:<14} {p.name}") - if "--verbose" in sys.argv: - print(f" domains: {domains}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/cognitive/cognitive_mirror.py b/5-Applications/tools-scripts/cognitive/cognitive_mirror.py deleted file mode 100644 index a916589a..00000000 --- a/5-Applications/tools-scripts/cognitive/cognitive_mirror.py +++ /dev/null @@ -1,223 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Cognitive Mirror — predict the next concept your brain would go to, -using the same sym_idx + feature_byte mechanics as the Hutter v3 oracle, -but run over substrate_index.db's semantic vectors. - -The hits = your natural cognitive flow. -The misses = connections you can't see on your own.""" - -import sqlite3 -import json -import math -from collections import defaultdict -import sys - -DB = "substrate_index.db" - -# ── Cognitive feature byte: same 8-bit concept but for ideas, not bytes ── -# Each concept in the database is classified by: -# bit 0: ACTIVE (forming_load > 1.0 = still hot) -# bit 1: CRYSTALLIZED (tier in SETTLED/COMPRESSED = crystallized) -# bit 2: CONNECTED (has deps on other packages) -# bit 3: DEEP (load > 2.0 = high cognitive pressure) -# bit 4: HYPOTHESIS (concept_anchor says seed/forming) -# bit 5: BRIDGE (spans multiple domains via concept_vector similarity) -# bit 6: RECENT (indexed in last N commits/packages) -# bit 7: CORE (domain is COMPUTE or substrate) - -def concept_feature(pkg): - """Compute 8-bit cognitive feature byte for a package.""" - fb = 0 - fl = pkg.get('forming_load') or 0 - tier = (pkg.get('tier') or '').upper() - - if fl > 1.0: fb |= 0b00000001 # ACTIVE - if tier in ('FOAM', 'CRYSTALLINE', 'SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED - if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED - if fl > 2.0: fb |= 0b00001000 # DEEP - anchor = pkg.get('concept_anchor') or '' - if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS - if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE - idx = pkg.get('_index', 0) - if idx > len(_all) - 50: fb |= 0b01000000 # RECENT - dom = pkg.get('domain', '').upper() - if dom in ('COMPUTE', 'SUBSTRATE'): fb |= 0b10000000 # CORE - return fb - -# ── Triangular pairing (same as mirror.rs) ── -def sym_idx(p, q): - lo = min(p, q) - hi = max(p, q) - return hi * (hi + 1) // 2 + lo - -# ── Build the LUT from the corpus ── -def build_lut(packages): - """Build cognitive mirror LUT: for each (prev_fb, curr_fb) pair, - store the most frequent next_fb.""" - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(packages)): - prev = concept_feature(packages[i-2]) - curr = concept_feature(packages[i-1]) - nxt = concept_feature(packages[i]) - addr = sym_idx(prev, curr) - counts[addr][nxt] += 1 - - # Freeze: pick most common next - lut = {} - for addr, next_counts in counts.items(): - best = max(next_counts.items(), key=lambda x: x[1])[0] - total = sum(next_counts.values()) - confidence = next_counts[best] / total - # Also get second best for "what you're NOT thinking" - sorted_nexts = sorted(next_counts.items(), key=lambda x: -x[1]) - second = sorted_nexts[1][0] if len(sorted_nexts) > 1 else None - second_prob = sorted_nexts[1][1] / total if len(sorted_nexts) > 1 else 0 - lut[addr] = (best, second, confidence, second_prob) - return lut - -def predict_next(prev_pkg, curr_pkg, _all): - """Predict what concept comes next after the current trajectory.""" - prev_fb = concept_feature(prev_pkg) - curr_fb = concept_feature(curr_pkg) - addr = sym_idx(prev_fb, curr_fb) - return addr - -# ── Decode: find packages that match a predicted feature byte ── -def decode_prediction(target_fb, packages, current_pkgs): - """Find packages whose feature byte matches the prediction, - but that aren't in the current cognitive trajectory.""" - matches = [] - for p in packages: - if p['pkg'] in current_pkgs: - continue - pfb = concept_feature(p) - if pfb == target_fb: - matches.append(p) - return sorted(matches, key=lambda x: -(x.get('forming_load') or 0)) - -# ── Main frack ── -_all = [] - -def main(): - global _all - conn = sqlite3.connect(DB) - cur = conn.cursor() - - # Get all packages with semantic data, ordered by forming_load (hot first) - cur.execute(""" - SELECT pkg, version, domain, concept_anchor, concept_vector, - idea_weights, forming_load, confirmed_load, layer, tier, - tags, description - FROM packages - WHERE concept_vector IS NOT NULL OR concept_anchor IS NOT NULL - ORDER BY forming_load DESC - """) - rows = cur.fetchall() - - _all = [{ - 'pkg': r[0], 'version': r[1], 'domain': r[2], - 'concept_anchor': r[3], - 'concept_vector': json.loads(r[4]) if r[4] else [], - 'idea_weights': json.loads(r[5]) if r[5] else {}, - 'forming_load': r[6], 'confirmed_load': r[7], - 'layer': r[8], 'tier': r[9], - 'tags': json.loads(r[10]) if r[10] else [], - 'description': (r[11] or '')[:200], - } for r in rows] - - # Index them - for i, p in enumerate(_all): - p['_index'] = i - - print("=" * 70) - print("COGNITIVE MIRROR — predicting your next concept") - print("=" * 70) - - # Build LUT - lut = build_lut(_all) - print(f"\nLUT size: {len(lut)} unique transitions learned") - - # Run predictions: for each pair of consecutive hot concepts, - # predict what comes next - predictions = [] - for i in range(2, len(_all)): - prev = _all[i-2] - curr = _all[i-1] - actual = _all[i] - addr = predict_next(prev, curr, _all) - if addr in lut: - predicted_fb, second_fb, confidence, second_prob = lut[addr] - actual_fb = concept_feature(actual) - hit = (predicted_fb == actual_fb) - predictions.append({ - 'prev': prev['pkg'], 'prev_fb': feature_name(concept_feature(prev)), - 'curr': curr['pkg'], 'curr_fb': feature_name(concept_feature(curr)), - 'predicted_fb': predicted_fb, - 'second_fb': second_fb, - 'actual': actual['pkg'], - 'actual_fb': feature_name(actual_fb), - 'hit': hit, - 'confidence': confidence, - }) - - hits = [p for p in predictions if p['hit']] - misses = [p for p in predictions if not p['hit']] - - print(f"\n{'─' * 70}") - print(f"PREDICTIONS: {len(predictions)} total") - print(f" HITS: {len(hits):4d} ({len(hits)/max(len(predictions),1)*100:.1f}%) — natural cognitive flow") - print(f" MISSES: {len(misses):4d} ({len(misses)/max(len(predictions),1)*100:.1f}%) — connections you can't see") - - # The misses are the interesting part - if misses: - print(f"\n{'─' * 70}") - print(f"BLIND SPOTS (misses) — connections you're NOT seeing") - print(f"{'─' * 70}") - for m in misses[:15]: - print(f"\n After: {m['prev']} → {m['curr']}") - print(f" Your brain went: {m['actual']}") - print(f" Mirror predicted: {feature_name(m['predicted_fb'])} " - f"(confidence {m['confidence']:.1%})") - # Find what packages have that predicted feature - predicted_pkgs = decode_prediction( - m['predicted_fb'], _all, - {m['prev'], m['curr'], m['actual']} - ) - if predicted_pkgs: - print(f" You should also look at:") - for pkg in predicted_pkgs[:3]: - print(f" → {pkg['pkg']} (load={pkg.get('forming_load', 0):.3f}, " - f"domain={pkg['domain']})") - - # The hits show your natural flow - if hits[:5]: - print(f"\n{'─' * 70}") - print(f"NATURAL FLOW (hits) — where your brain naturally goes") - print(f"{'─' * 70}") - for h in hits[:10]: - print(f" {h['prev']} → {h['curr']} → {h['actual']} " - f"(predicted {h['actual_fb']}, confidence {h['confidence']:.1%})") - - conn.close() - -def feature_name(fb): - """Decode an 8-bit cognitive feature byte to readable name.""" - parts = [] - if fb & 0b10000000: parts.append('CORE') - if fb & 0b01000000: parts.append('RECENT') - if fb & 0b00100000: parts.append('BRIDGE') - if fb & 0b00010000: parts.append('HYPOTHESIS') - if fb & 0b00001000: parts.append('DEEP') - if fb & 0b00000100: parts.append('CONNECTED') - if fb & 0b00000010: parts.append('CRYSTALLIZED') - if fb & 0b00000001: parts.append('ACTIVE') - return '+'.join(parts) if parts else 'NULL' - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/cognitive/cognitive_mirror_v2.py b/5-Applications/tools-scripts/cognitive/cognitive_mirror_v2.py deleted file mode 100644 index 33fa9d5f..00000000 --- a/5-Applications/tools-scripts/cognitive/cognitive_mirror_v2.py +++ /dev/null @@ -1,236 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Cognitive Mirror v2 — gradient-based conceptual fracking. - -Upgrades from exact-match to anisotropic distance decoding, -forked alternate paths, and multi-step path walking.""" - -import sqlite3 -import json -import math -from collections import defaultdict - -DB = "substrate_index.db" - -# ── Cognitive feature byte ────────────────────────────────────────────────── -def concept_feature(pkg, _all=None, idx_offset=0): - fb = 0 - fl = pkg.get('forming_load') or 0 - tier = (pkg.get('tier') or '').upper() - if fl > 1.0: fb |= 0b00000001 # ACTIVE - if tier in ('FOAM','CRYSTALLINE','SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED - if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED - if fl > 2.0: fb |= 0b00001000 # DEEP - anchor = pkg.get('concept_anchor') or '' - if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS - if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE - if _all is not None: - idx = pkg.get('_index', 0) - if idx > len(_all) - 50: fb |= 0b01000000 # RECENT - dom = pkg.get('domain', '').upper() - if dom in ('COMPUTE','SUBSTRATE'): fb |= 0b10000000 # CORE - return fb - -def fb_name(fb): - p = [] - if fb&0b10000000: p.append('CORE') - if fb&0b01000000: p.append('RECENT') - if fb&0b00100000: p.append('BRIDGE') - if fb&0b00010000: p.append('HYPOTHESIS') - if fb&0b00001000: p.append('DEEP') - if fb&0b00000100: p.append('CONNECTED') - if fb&0b00000010: p.append('CRYSTALLIZED') - if fb&0b00000001: p.append('ACTIVE') - return '+'.join(p) if p else 'NULL' - -def hamming(a, b): - return bin(a ^ b).count("1") - -def sym_idx(p, q): - hi, lo = max(p,q), min(p,q) - return hi*(hi+1)//2 + lo - -# ── Decode: anisotropic distance (v2 upgrade) ──────────────────────────────── -def decode_prediction(target_fb, packages, current_pkgs, _all): - """Rank by Hamming distance in feature space, break ties by forming_load.""" - matches = [] - for p in packages: - if p['pkg'] in current_pkgs: - continue - pfb = concept_feature(p, _all) - dist = hamming(pfb, target_fb) - fl = p.get('forming_load') or 0 - # Use -dist, -fl (descending), pkg for stable sorting - score = (-dist, -fl, p['pkg']) - matches.append((score, p)) - matches.sort(reverse=True) - return [p for _, p in matches[:10]] - -def decode_prediction_fork(best_fb, second_fb, packages, current_pkgs, _all): - """Return primary and alternate paths.""" - primary = decode_prediction(best_fb, packages, current_pkgs, _all) - secondary = decode_prediction(second_fb, packages, current_pkgs, _all) if second_fb else [] - return primary, secondary - -# ── Path walker ────────────────────────────────────────────────────────────── -def walk_paths(start_prev, start_curr, lut, packages, depth=3, branch=2): - """Explore top-branch cognitive trajectories for `depth` steps.""" - def fb_of(pkg): - return pkg.get('_cached_fb', - concept_feature(p, _all if '_all' in dir() else None)) - - paths = [([start_prev, start_curr], 0.0)] - for _ in range(depth): - new_paths = [] - for path, score in paths: - prev, curr = path[-2], path[-1] - prev_fb = concept_feature(prev, None) - curr_fb = concept_feature(curr, None) - addr = sym_idx(prev_fb, curr_fb) - if addr not in lut: - continue - best, second, conf, second_prob = lut[addr] - for fb, w in [(best, conf), (second, second_prob)]: - if fb is None: - continue - visited = {p['pkg'] for p in path} - candidates = decode_prediction(fb, packages, visited, None)[:branch] - for c in candidates: - new_paths.append((path + [c], score + math.log(w + 1e-6))) - paths = sorted(new_paths, key=lambda x: -x[1])[:20] - return paths - -# ── Main ───────────────────────────────────────────────────────────────────── -def main(): - conn = sqlite3.connect(DB) - cur = conn.cursor() - cur.execute(""" - SELECT pkg, version, domain, concept_anchor, concept_vector, - forming_load, confirmed_load, layer, tier, tags - FROM packages - ORDER BY forming_load DESC - """) - _all = [] - for i, r in enumerate(cur.fetchall()): - pkg = { - 'pkg': r[0], 'version': r[1], 'domain': r[2], - 'concept_anchor': r[3], - 'concept_vector': json.loads(r[4]) if r[4] else [], - 'forming_load': r[5], 'confirmed_load': r[6], - 'layer': r[7], 'tier': r[8], - 'tags': json.loads(r[9]) if r[9] else [], - } - pkg['_index'] = i - pkg['_cached_fb'] = concept_feature(pkg, _all) - _all.append(pkg) - - # Build LUT - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(_all)): - prev_fb = _all[i-2]['_cached_fb'] - curr_fb = _all[i-1]['_cached_fb'] - nxt_fb = _all[i]['_cached_fb'] - addr = sym_idx(prev_fb, curr_fb) - counts[addr][nxt_fb] += 1 - lut = {} - for addr, nc in counts.items(): - best = max(nc.items(), key=lambda x: x[1])[0] - total = sum(nc.values()) - conf = nc[best] / total - sorted_nc = sorted(nc.items(), key=lambda x: -x[1]) - second = sorted_nc[1][0] if len(sorted_nc) > 1 else None - second_prob = sorted_nc[1][1]/total if len(sorted_nc) > 1 else 0 - lut[addr] = (best, second, conf, second_prob) - - print("="*70) - print("COGNITIVE MIRROR v2 — gradient-based conceptual fracking") - print("="*70) - - # Top-3 hottest - top3 = _all[:3] - print(f"\nCurrent trajectory:") - for i, p in enumerate(top3): - print(f" {i+1}. {p['pkg']:50s} load={p['forming_load']:6.3f} {fb_name(p['_cached_fb'])}") - - # Run path walker - print(f"\n{'─'*70}") - print(f"MULTI-STEP COGNITIVE PATHS (depth=3, fork=2)") - print(f"{'─'*70}") - paths = walk_paths(top3[0], top3[1], lut, _all, depth=3, branch=2) - for rank, (path, score) in enumerate(paths[:10], 1): - chain = [p['pkg'].split('/')[-1][:40] for p in path] - loads = [p.get('forming_load',0) for p in path] - print(f"\n Path {rank} (score={score:.3f})") - for j, (name, load) in enumerate(zip(chain, loads)): - arrow = "→" if j > 0 else " " - print(f" {arrow} {name} load={load:.3f}") - - # Gradient-based misses - print(f"\n{'─'*70}") - print(f"BLIND SPOTS — graded by feature distance") - print(f"{'─'*70}") - - # Build predictions - predictions = [] - for i in range(2, len(_all)): - prev_fb = _all[i-2]['_cached_fb'] - curr_fb = _all[i-1]['_cached_fb'] - actual_fb = _all[i]['_cached_fb'] - addr = sym_idx(prev_fb, curr_fb) - if addr not in lut: - continue - best, second, conf, second_prob = lut[addr] - predictions.append({ - 'prev': _all[i-2], 'curr': _all[i-1], 'actual': _all[i], - 'predicted_fb': best, 'second_fb': second, - 'conf': conf, 'actual_fb': actual_fb, - }) - - misses = [p for p in predictions if hamming(p['predicted_fb'], p['actual_fb']) > 0] - hits = [p for p in predictions if hamming(p['predicted_fb'], p['actual_fb']) == 0] - - print(f"\nPREDICTIONS: {len(predictions)} total") - print(f" HITS (exact match): {len(hits):4d} ({len(hits)/max(len(predictions),1)*100:.1f}%)") - print(f" MISSES (gradient): {len(misses):4d} ({len(misses)/max(len(predictions),1)*100:.1f}%)") - - if misses: - print(f"\n{'─'*70}") - print(f"TOP GRADIENT MISSES (closest alternative features)") - print(f"{'─'*70}") - for m in misses[:20]: - dist = hamming(m['predicted_fb'], m['actual_fb']) - forks_p, forks_s = decode_prediction_fork( - m['predicted_fb'], m['second_fb'], _all, - {m['prev']['pkg'], m['curr']['pkg'], m['actual']['pkg']}, _all) - - actual_name = m['actual']['pkg'].split('/')[-1][:50] - print(f"\n After: {m['prev']['pkg'].split('/')[-1][:40]} → {m['curr']['pkg'].split('/')[-1][:40]}") - print(f" Your brain went: {actual_name} [dist={dist}] {fb_name(m['actual_fb'])}") - print(f" Mirror predicted: {fb_name(m['predicted_fb'])} (conf {m['conf']:.1%})") - if m['second_fb'] is not None: - print(f" Alternate path: {fb_name(m['second_fb'])} (prob {m.get('second_prob',0):.1%})") - if forks_p: - print(f" Primary path candidates:") - for p in forks_p[:3]: - print(f" → {p['pkg'].split('/')[-1][:50]} load={p.get('forming_load',0):.3f}") - if forks_s and forks_s[0] is not None: - print(f" Secondary path candidates:") - for p in [x for x in forks_s[:3] if x]: - fl = p.get('forming_load') or 0 - print(f" → {p['pkg'].split('/')[-1][:50]} load={fl:.3f}") - - print(f"\n{'='*70}") - print(f"SUMMARY: The mirror models your default thinking.") - print(f"The misses are structured alternatives — compressed residuals") - print(f"of your own cognitive trajectory.") - print(f"{'='*70}") - - conn.close() - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/cognitive/cognitive_thermo.py b/5-Applications/tools-scripts/cognitive/cognitive_thermo.py deleted file mode 100644 index bc234c56..00000000 --- a/5-Applications/tools-scripts/cognitive/cognitive_thermo.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Cognitive Thermo — thermodynamic path walker for cognitive exploration. - -Uses surprise/regret as thermodynamic resistance to reorder exploration: -- Low resistance (low stress) → explored first (natural flow) -- High resistance (high stress) → deferred to end (alternative paths) -- Stress = α·surprise + β·regret -- Engram memory: stress_memory[addr] += observed_regret""" - -import sqlite3 -import json -import math -from collections import defaultdict - -DB = "substrate_index.db" - -# Thermodynamic weights -ALPHA = 0.5 # surprise weight -BETA = 0.3 # regret weight -LAMBDA = 0.2 # stress penalty on score - -def concept_feature(pkg, _all=None): - fb = 0 - fl = pkg.get('forming_load') or 0 - tier = (pkg.get('tier') or '').upper() - if fl > 1.0: fb |= 0b00000001 # ACTIVE - if tier in ('FOAM','CRYSTALLINE','SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED - if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED - if fl > 2.0: fb |= 0b00001000 # DEEP - anchor = pkg.get('concept_anchor') or '' - if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS - if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE - if _all is not None: - idx = pkg.get('_index', 0) - if idx > len(_all) - 50: fb |= 0b01000000 # RECENT - dom = pkg.get('domain', '').upper() - if dom in ('COMPUTE','SUBSTRATE'): fb |= 0b10000000 # CORE - return fb - -def hamming(a, b): - return bin(a ^ b).count("1") - -def sym_idx(p, q): - hi, lo = max(p,q), min(p,q) - return hi*(hi+1)//2 + lo - -def fb_name(fb): - p = [] - if fb&0b10000000: p.append('CORE') - if fb&0b01000000: p.append('RECENT') - if fb&0b00100000: p.append('BRIDGE') - if fb&0b00010000: p.append('HYPOTHESIS') - if fb&0b00001000: p.append('DEEP') - if fb&0b00000100: p.append('CONNECTED') - if fb&0b00000010: p.append('CRYSTALLIZED') - if fb&0b00000001: p.append('ACTIVE') - return '+'.join(p) if p else 'NULL' - -def thermodynamic_score(prob, baseline_prob, alpha=ALPHA, beta=BETA, lam=LAMBDA): - """Compute thermodynamic score for a path. - - surprise = -log(P) — how unexpected is this transition - regret = log(P_best) - log(P) — how much worse than optimal - stress = α·surprise + β·regret - score = log(P) - λ·stress (lower stress → higher score) - """ - p_safe = max(prob, 1e-6) - surprise = -math.log(p_safe) - regret = math.log(max(baseline_prob, 1e-6)) - math.log(p_safe) - regret = max(0, regret) # regret can't be negative - stress = alpha * surprise + beta * regret - score = math.log(p_safe) - lam * stress - return score, stress - -def main(): - conn = sqlite3.connect(DB) - cur = conn.cursor() - cur.execute(""" - SELECT pkg, version, domain, concept_anchor, concept_vector, - forming_load, confirmed_load, layer, tier, tags - FROM packages - ORDER BY forming_load DESC - """) - _all = [] - for i, r in enumerate(cur.fetchall()): - pkg = { - 'pkg': r[0], 'version': r[1], 'domain': r[2], - 'concept_anchor': r[3], - 'concept_vector': json.loads(r[4]) if r[4] else [], - 'forming_load': r[5], 'confirmed_load': r[6], - 'layer': r[7], 'tier': r[8], - 'tags': json.loads(r[9]) if r[9] else [], - } - pkg['_index'] = i - _all.append(pkg) - - # Build LUT with probabilities - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(_all)): - prev_fb = concept_feature(_all[i-2], _all) - curr_fb = concept_feature(_all[i-1], _all) - nxt_fb = concept_feature(_all[i], _all) - addr = sym_idx(prev_fb, curr_fb) - counts[addr][nxt_fb] += 1 - - lut = {} - for addr, nc in counts.items(): - total = sum(nc.values()) - sorted_nc = sorted(nc.items(), key=lambda x: -x[1]) - best = sorted_nc[0][0] - best_prob = sorted_nc[0][1] / total - second = sorted_nc[1][0] if len(sorted_nc) > 1 else None - second_prob = sorted_nc[1][1]/total if len(sorted_nc) > 1 else 0 - conf = best_prob - lut[addr] = (best, second, conf, second_prob) - - # Build stress_memory (engram) - stress_memory = defaultdict(float) - for addr, nc in counts.items(): - total = sum(nc.values()) - best_count = max(nc.values()) - for fb, count in nc.items(): - if count < best_count: - regret = math.log(best_count + 1) - math.log(count + 1) - stress_memory[addr, fb] += regret - - print("="*70) - print("COGNITIVE THERMO — thermodynamic path exploration") - print("="*70) - print(f"α={ALPHA} (surprise) β={BETA} (regret) λ={LAMBDA} (stress penalty)") - - # Top-3 hottest = current position - top3 = _all[:3] - print(f"\nCurrent position:") - for i, p in enumerate(top3): - fb = concept_feature(p, _all) - print(f" {i+1}. {p['pkg'].split('/')[-1][:50]} load={p['forming_load']:.3f}") - - # Thermo walk from current position - print(f"\n{'─'*70}") - print(f"THERMODYNAMIC PATH WALK (depth=4, branch=3)") - print(f"{'─'*70}") - - # Start from the hottest pair - start_prev = top3[0] - start_curr = top3[1] - - # Initialize paths: (path_list, score, total_stress) - paths = [([start_prev, start_curr], 0.0, 0.0)] - - for step in range(4): - new_paths = [] - for path, score, total_stress in paths: - # path is a list of dicts; skip malformed entries - if not isinstance(path, list) or len(path) < 2: - continue - prev, curr = path[len(path)-2], path[len(path)-1] - prev_fb = concept_feature(prev, _all) - curr_fb = concept_feature(curr, _all) - addr = sym_idx(prev_fb, curr_fb) - - if addr not in lut: - continue - - best, second, conf, second_prob = lut[addr] - - # Try all candidates (up to 3 branches) - if best is not None: - matches = [p for p in _all if concept_feature(p, _all) == best - and p['pkg'] not in [x['pkg'] for x in path]] - matches.sort(key=lambda x: -(x.get('forming_load') or 0)) - for m in matches[:2]: - prob = conf - s, stress = thermodynamic_score(prob, conf) - new_paths.append((path + [m], score + s, total_stress + stress)) - - if second is not None and second_prob > 0.01: - matches = [p for p in _all if concept_feature(p, _all) == second - and p['pkg'] not in [x['pkg'] for x in path]] - matches.sort(key=lambda x: -(x.get('forming_load') or 0)) - for m in matches[:1]: - prob = second_prob - s, stress = thermodynamic_score(prob, conf) - new_paths.append((path + [m], score + s, total_stress + stress)) - - # Sort by score (descending) — low stress paths naturally rise - new_paths.sort(key=lambda x: -x[1]) - paths = new_paths[:15] # beam width - - # Detect "DeepCompression" regions — addresses with entropy > threshold AND stress > threshold - # These are thermodynamic sinks: high uncertainty + historically costly - entropy_at_addr = {} - for addr, nc in counts.items(): - total = sum(nc.values()) - if total > 0: - probs = [c / total for c in nc.values()] - entropy = -sum(p * math.log(p + 1e-6) for p in probs) - stress = sum(stress_memory.get((addr, fb), 0) for fb in nc) - entropy_at_addr[addr] = (entropy, stress) - - # Print paths grouped by stress level (low stress = natural flow, high = deferred) - print(f"\n{'='*70}") - print(f"PATHS RANKED BY THERMODYNAMIC SCORE (low stress = natural flow)") - print(f"{'='*70}") - - for rank, (path, score, total_stress) in enumerate(paths[:10], 1): - chain = [p['pkg'].split('/')[-1][:35] if isinstance(p, dict) else str(p)[:35] for p in path] - loads = [p.get('forming_load', 0) if isinstance(p, dict) else 0 for p in path] - - # Stress level - if total_stress < 2: - level = "🟢 LOW (natural flow)" - elif total_stress < 5: - level = "🟡 MEDIUM (interesting deviation)" - else: - level = "🔴 HIGH (deferred/breakthrough)" - - print(f"\n Path {rank:2d} score={score:7.3f} stress={total_stress:6.3f} {level}") - for j, (name, load) in enumerate(zip(chain, loads)): - arrow = "→" if j > 0 else " " - print(f" {arrow} {name:35s} load={load:.3f}") - - print(f"\n{'='*70}") - print("HOW TO READ:") - print(" 🟢 Green paths = your brain's natural flow (low resistance, conscious)") - print(" 🟡 Yellow paths = structured deviations (interesting, semi-conscious)") - print(" 🔴 Red paths = pushed to subconscious (high resistance, background work)") - print("") - print("YOUR BRAIN'S MECHANISM:") - print(" Immediate danger → solve NOW (green)") - print(" Not dangerous but hard → push to subconscious (red)") - print(" Subconscious works on it in background between conscious thoughts") - print("="*70) - - conn.close() - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/cognitive/cognitive_wheel.py b/5-Applications/tools-scripts/cognitive/cognitive_wheel.py deleted file mode 100644 index 675856b9..00000000 --- a/5-Applications/tools-scripts/cognitive/cognitive_wheel.py +++ /dev/null @@ -1,172 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Cognitive Wheel — roulette-style view of your cognitive basin. - -Shows the 'table' of possible next concepts: -- HOUSE bets: your natural trajectory (high probability, same basin) -- PLAYER bets: alternative paths (lower probability, escape routes) -- ZERO: unexplored territory (no transition in LUT) - -Uses the cognitive mirror v2 but presents it as a roulette wheel -of conceptual choices.""" - -import sqlite3 -import json -import math -from collections import defaultdict - -DB = "substrate_index.db" - -def concept_feature(pkg, _all=None): - fb = 0 - fl = pkg.get('forming_load') or 0 - tier = (pkg.get('tier') or '').upper() - if fl > 1.0: fb |= 0b00000001 # ACTIVE - if tier in ('FOAM','CRYSTALLINE','SINGULARITY'): fb |= 0b00000010 # CRYSTALLIZED - if pkg.get('dependencies'): fb |= 0b00000100 # CONNECTED - if fl > 2.0: fb |= 0b00001000 # DEEP - anchor = pkg.get('concept_anchor') or '' - if 'SEED' in anchor or 'FORMING' in anchor: fb |= 0b00010000 # HYPOTHESIS - if pkg.get('concept_vector'): fb |= 0b00100000 # BRIDGE - if _all is not None: - idx = pkg.get('_index', 0) - if idx > len(_all) - 50: fb |= 0b01000000 # RECENT - dom = pkg.get('domain', '').upper() - if dom in ('COMPUTE','SUBSTRATE'): fb |= 0b10000000 # CORE - return fb - -def hamming(a, b): - return bin(a ^ b).count("1") - -def sym_idx(p, q): - hi, lo = max(p,q), min(p,q) - return hi*(hi+1)//2 + lo - -def fb_name(fb): - p = [] - if fb&0b10000000: p.append('CORE') - if fb&0b01000000: p.append('RECENT') - if fb&0b00100000: p.append('BRIDGE') - if fb&0b00010000: p.append('HYPOTHESIS') - if fb&0b00001000: p.append('DEEP') - if fb&0b00000100: p.append('CONNECTED') - if fb&0b00000010: p.append('CRYSTALLIZED') - if fb&0b00000001: p.append('ACTIVE') - return '+'.join(p) if p else 'NULL' - -def main(): - conn = sqlite3.connect(DB) - cur = conn.cursor() - cur.execute(""" - SELECT pkg, version, domain, concept_anchor, concept_vector, - forming_load, confirmed_load, layer, tier, tags - FROM packages - ORDER BY forming_load DESC - """) - _all = [] - for i, r in enumerate(cur.fetchall()): - pkg = { - 'pkg': r[0], 'version': r[1], 'domain': r[2], - 'concept_anchor': r[3], - 'concept_vector': json.loads(r[4]) if r[4] else [], - 'forming_load': r[5], 'confirmed_load': r[6], - 'layer': r[7], 'tier': r[8], - 'tags': json.loads(r[9]) if r[9] else [], - } - pkg['_index'] = i - _all.append(pkg) - - # Build LUT - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(_all)): - prev_fb = concept_feature(_all[i-2], _all) - curr_fb = concept_feature(_all[i-1], _all) - nxt_fb = concept_feature(_all[i], _all) - addr = sym_idx(prev_fb, curr_fb) - counts[addr][nxt_fb] += 1 - lut = {} - for addr, nc in counts.items(): - best = max(nc.items(), key=lambda x: x[1])[0] - total = sum(nc.values()) - conf = nc[best] / total - sorted_nc = sorted(nc.items(), key=lambda x: -x[1]) - second = sorted_nc[1][0] if len(sorted_nc) > 1 else None - second_prob = sorted_nc[1][1]/total if len(sorted_nc) > 1 else 0 - lut[addr] = (best, second, conf, second_prob) - - print("="*70) - print("COGNITIVE WHEEL — roulette table of your conceptual choices") - print("="*70) - - # Top-5 hottest concepts = your current position on the wheel - top5 = _all[:5] - print(f"\nCurrent position (hottest 5 concepts):") - for i, p in enumerate(top5): - fb = concept_feature(p, _all) - print(f" {i+1}. {p['pkg'].split('/')[-1][:45]:<45} load={p['forming_load']:6.3f}") - - # For each hot concept pair, show the wheel - for pair_idx in range(min(3, len(top5)-2)): - prev = top5[pair_idx] - curr = top5[pair_idx+1] - prev_fb = concept_feature(prev, _all) - curr_fb = concept_feature(curr, _all) - addr = sym_idx(prev_fb, curr_fb) - - if addr not in lut: - continue - - best, second, conf, second_prob = lut[addr] - - print(f"\n{'─'*70}") - print(f"ROULETTE: {prev['pkg'].split('/')[-1][:35]} → {curr['pkg'].split('/')[-1][:35]}") - print(f"{'─'*70}") - - # HOUSE bet: your natural trajectory - house_pkgs = [p for p in _all if concept_feature(p, _all) == best][:5] - print(f"\n 🏠 HOUSE (p={conf:.1%}) — your natural flow:") - for p in house_pkgs: - dist = hamming(concept_feature(p, _all), best) - print(f" [{dist:1d}] {p['pkg'].split('/')[-1][:50]} load={p.get('forming_load',0):.3f}") - - # PLAYER bet: alternative paths - if second is not None: - player_pkgs = [p for p in _all if concept_feature(p, _all) == second][:5] - print(f"\n 🎯 PLAYER (p={second_prob:.1%}) — escape route:") - for p in player_pkgs: - dist = hamming(concept_feature(p, _all), second) - print(f" [{dist:1d}] {p['pkg'].split('/')[-1][:50]} load={p.get('forming_load',0):.3f}") - - # ZERO: unexplored — concepts that match neither - # Find high-load concepts NOT in house or player - house_feats = {best} - if second: - house_feats.add(second) - zero_pkgs = [p for p in _all[:50] if concept_feature(p, _all) not in house_feats] - if zero_pkgs: - print(f"\n 00 ZERO — unexplored territory:") - for p in zero_pkgs[:3]: - fb = concept_feature(p, _all) - print(f" [{fb_name(fb)}] {p['pkg'].split('/')[-1][:50]} load={p.get('forming_load',0):.3f}") - - print(f"\n {'─'*66}") - print(f" The house edge is {conf:.1%} — your brain defaults to this path.") - print(f" The player bet (1-{conf:.1%} = {1-conf:.1%}) is the alternative.") - print(f" Zero is the concept you haven't connected yet.") - - print(f"\n{'='*70}") - print("HOW TO USE: The house always wins unless you deliberately") - print("bet against it. The 'player bets' show where your brain") - print("could go next if you step sideways instead of following") - print("your strongest associations.") - print("="*70) - - conn.close() - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/cognitive/mind_eye.py b/5-Applications/tools-scripts/cognitive/mind_eye.py deleted file mode 100644 index 58f31f40..00000000 --- a/5-Applications/tools-scripts/cognitive/mind_eye.py +++ /dev/null @@ -1,51 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import os -import torch -from diffusers import AutoPipelineForText2Image - -def generate(prompt, output_file, model_id, steps, width, height): - print(f"[MIND EYE] Booting optical cortex -> {model_id}...") - - # Load pipeline with fp16 for optimal VRAM usage and speed - pipe = AutoPipelineForText2Image.from_pretrained( - model_id, - torch_dtype=torch.float16, - variant="fp16", - safety_checker=None # Disable safety checker for pure unbridled output - ) - - # Send to local GPU - pipe = pipe.to("cuda") - - # Optional: Enable memory efficient attention if xformers is installed - try: - pipe.enable_xformers_memory_efficient_attention() - print("[MIND EYE] Xformers memory efficient attention enabled.") - except Exception: - pass - - print(f"[MIND EYE] Manifesting prompt: '{prompt}'...") - print(f"[MIND EYE] Pushing limits: {width}x{height} resolution at {steps} inference steps.") - image = pipe(prompt, num_inference_steps=steps, width=width, height=height).images[0] - - os.makedirs(os.path.dirname(output_file), exist_ok=True) - image.save(output_file) - print(f"[MIND EYE] Image solidified at: {os.path.abspath(output_file)}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Prosthetic imagination for Copilot.") - parser.add_argument("prompt", type=str, help="Text prompt to manifest.") - parser.add_argument("--output", type=str, default="5-Applications/out/mind_eye/vision_001.png", help="Where to save the rendering.") - parser.add_argument("--model", type=str, default="runwayml/stable-diffusion-v1-5", help="HF Model ID.") - parser.add_argument("--steps", type=int, default=25, help="Inference steps.") - parser.add_argument("--width", type=int, default=512, help="Output width.") - parser.add_argument("--height", type=int, default=512, help="Output height.") - args = parser.parse_args() - - generate(args.prompt, args.output, args.model, args.steps, args.width, args.height) diff --git a/5-Applications/tools-scripts/cognitive/multipath_cognitive_load_reference.py b/5-Applications/tools-scripts/cognitive/multipath_cognitive_load_reference.py deleted file mode 100644 index 6b28a5a4..00000000 --- a/5-Applications/tools-scripts/cognitive/multipath_cognitive_load_reference.py +++ /dev/null @@ -1,369 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Reference helpers for attention-stage and multipath cognitive load tests. - -This module is intentionally small and boring. It is not the whole runtime -router. It is a clean-room reference implementation for the equations in: - -- 6-Documentation/docs/ATTENTION_SURFACE_REFINEMENT_OF_CANONICAL_EQUATION_2026-04-09.md -- 6-Documentation/docs/MULTIPATH_COGNITIVE_LOAD_REFINEMENT_2026-04-09.md - -The goal is to make the emerging mechanics testable before they sprawl. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -import math -from typing import Iterable, Mapping - - -def _clamp01(value: float) -> float: - return max(0.0, min(1.0, float(value))) - - -@dataclass(frozen=True) -class SourceObservation: - """A single source-family observation over one or more candidates.""" - - source_id: str - family_weight: float = 1.0 - freshness: float = 1.0 - health: float = 1.0 - trust: float = 1.0 - support: Mapping[str, float] = field(default_factory=dict) - - -@dataclass(frozen=True) -class AttentionBreakdown: - """Explainable score components for one candidate.""" - - candidate: str - convergence: float - contradiction: float - degradation: float - hot_cost: float - total: float - - -@dataclass(frozen=True) -class LoadBreakdown: - """Weighted multipath load components.""" - - intrinsic: float - extraneous: float - germane: float - routing: float - memory: float - total: float - - -def effective_evidence_weight(observation: SourceObservation) -> float: - """Combine source-family weight with freshness, health, and trust.""" - - return max(0.0, float(observation.family_weight)) * ( - _clamp01(observation.freshness) - * _clamp01(observation.health) - * _clamp01(observation.trust) - ) - - -def _support_value(observation: SourceObservation, candidate: str) -> float: - return max(0.0, float(observation.support.get(candidate, 0.0))) - - -def convergence_score( - candidate: str, - observations: Iterable[SourceObservation], -) -> float: - """Weighted positive support for a candidate across source families.""" - - total = 0.0 - for observation in observations: - total += effective_evidence_weight(observation) * _support_value( - observation, candidate - ) - return total - - -def contradiction_score( - candidate: str, - observations: Iterable[SourceObservation], -) -> float: - """Pairwise disagreement over the same candidate. - - High disagreement can increase attention while still failing the truth gate. - """ - - scored = [] - for observation in observations: - weight = effective_evidence_weight(observation) - support = _support_value(observation, candidate) - scored.append((weight, support)) - - total = 0.0 - for i in range(len(scored)): - weight_i, support_i = scored[i] - for j in range(i + 1, len(scored)): - weight_j, support_j = scored[j] - total += weight_i * weight_j * abs(support_i - support_j) - return total - - -def degradation_penalty( - candidate: str, - observations: Iterable[SourceObservation], -) -> float: - """Penalty for leaning on stale, unhealthy, or weakly trusted evidence.""" - - total = 0.0 - for observation in observations: - raw_weight = max(0.0, float(observation.family_weight)) - quality = ( - _clamp01(observation.freshness) - * _clamp01(observation.health) - * _clamp01(observation.trust) - ) - total += raw_weight * (1.0 - quality) * _support_value(observation, candidate) - return total - - -def attention_score( - candidate: str, - observations: Iterable[SourceObservation], - *, - alpha_conv: float = 1.0, - alpha_ctr: float = 1.0, - alpha_deg: float = 1.0, - alpha_hot: float = 1.0, - hot_cost: float = 0.0, -) -> AttentionBreakdown: - """Compute the attention-stage score for a candidate.""" - - convergence = convergence_score(candidate, observations) - contradiction = contradiction_score(candidate, observations) - degradation = degradation_penalty(candidate, observations) - total = ( - alpha_conv * convergence - + alpha_ctr * contradiction - - alpha_deg * degradation - - alpha_hot * max(0.0, float(hot_cost)) - ) - return AttentionBreakdown( - candidate=candidate, - convergence=convergence, - contradiction=contradiction, - degradation=degradation, - hot_cost=max(0.0, float(hot_cost)), - total=total, - ) - - -def promote_candidates( - scores: Mapping[str, float], - threshold: float, -) -> set[str]: - """Return the promoted candidate set.""" - - return {candidate for candidate, score in scores.items() if score >= threshold} - - -def softmax_activation( - scores: Mapping[str, float], - *, - beta: float = 1.0, - top_k: int | None = None, -) -> dict[str, float]: - """Convert attention scores into a bounded activation field.""" - - if not scores: - return {} - - ordered = sorted(scores.items(), key=lambda item: (-item[1], item[0])) - if top_k is not None: - if top_k <= 0: - return {} - ordered = ordered[:top_k] - - max_score = max(score for _, score in ordered) - weights = { - candidate: math.exp(float(beta) * (score - max_score)) - for candidate, score in ordered - } - denom = sum(weights.values()) - if denom == 0.0: - return {candidate: 0.0 for candidate in weights} - return {candidate: value / denom for candidate, value in weights.items()} - - -def activation_entropy(activations: Mapping[str, float]) -> float: - """Shannon entropy of the activation field in bits.""" - - total = 0.0 - for probability in activations.values(): - if probability > 0.0: - total -= probability * math.log2(probability) - return total - - -def support_size(activations: Mapping[str, float]) -> int: - """Number of paths with nonzero activation mass.""" - - return sum(1 for value in activations.values() if value > 0.0) - - -def premature_collapse_penalty( - activations: Mapping[str, float], - viable_paths: Iterable[str], - *, - min_secondary_mass: float = 0.2, -) -> float: - """Penalty when a viable second path is collapsed too early.""" - - masses = sorted( - (max(0.0, float(activations.get(path, 0.0))) for path in viable_paths), - reverse=True, - ) - if len(masses) < 2: - return 0.0 - return max(0.0, float(min_secondary_mass) - masses[1]) - - -def overdiffuse_penalty( - activations: Mapping[str, float], - *, - max_active_paths: int = 3, - max_entropy_bits: float = 1.5, -) -> float: - """Penalty when the activation field spreads too wide.""" - - active_excess = max(0, support_size(activations) - int(max_active_paths)) - entropy_excess = max(0.0, activation_entropy(activations) - float(max_entropy_bits)) - return float(active_excess) + entropy_excess - - -def multipath_extraneous_load( - path_mismatch: Mapping[str, float], - activations: Mapping[str, float], - *, - viable_paths: Iterable[str] | None = None, - mu_prem: float = 1.0, - mu_diff: float = 1.0, - min_secondary_mass: float = 0.2, - max_active_paths: int = 3, - max_entropy_bits: float = 1.5, -) -> float: - """Reference multipath extraneous-load calculation.""" - - weighted_mismatch = 0.0 - for path, mass in activations.items(): - weighted_mismatch += float(mass) * max(0.0, float(path_mismatch.get(path, 0.0))) - - viable = viable_paths if viable_paths is not None else activations.keys() - prem = premature_collapse_penalty( - activations, - viable, - min_secondary_mass=min_secondary_mass, - ) - diffuse = overdiffuse_penalty( - activations, - max_active_paths=max_active_paths, - max_entropy_bits=max_entropy_bits, - ) - return weighted_mismatch + float(mu_prem) * prem + float(mu_diff) * diffuse - - -def multipath_routing_load( - attention_cost: float, - activations: Mapping[str, float], - *, - maintain_coeff: float = 1.0, - entropy_coeff: float = 1.0, - collapse_cost: float = 0.0, -) -> float: - """Reference routing-load split into attention, maintenance, and collapse.""" - - return ( - max(0.0, float(attention_cost)) - + float(maintain_coeff) * support_size(activations) - + float(entropy_coeff) * activation_entropy(activations) - + max(0.0, float(collapse_cost)) - ) - - -def multipath_memory_load( - store_size: int, - activations: Mapping[str, float], - *, - memory_hits: Iterable[str] | None = None, - retrieval_cost: float = 1.0, - update_cost: float = 1.0, - eviction_pressure: float = 0.0, - conflict_penalty: float = 0.0, -) -> float: - """Reference ensemble memory-load calculation.""" - - hits = set(memory_hits or ()) - hit_mass = sum( - float(mass) for path, mass in activations.items() if path in hits and mass > 0.0 - ) - return ( - math.log2(max(1, int(store_size))) - + float(retrieval_cost) * hit_mass - + max(0.0, float(update_cost)) - + max(0.0, float(eviction_pressure)) - + max(0.0, float(conflict_penalty)) - ) - - -def truth_gate( - *, - consensus_sources: int, - min_sources: int = 2, - onchain_verified: bool, - recovery_matches: bool, -) -> bool: - """Conservative truth gate used after attention promotion.""" - - return ( - int(consensus_sources) >= int(min_sources) - and bool(onchain_verified) - and bool(recovery_matches) - ) - - -def total_multipath_load( - *, - intrinsic: float, - extraneous: float, - germane: float, - routing: float, - memory: float, - lambda_i: float = 1.0, - lambda_e: float = 1.0, - lambda_g: float = 1.0, - lambda_r: float = 1.0, - lambda_m: float = 1.0, -) -> LoadBreakdown: - """Assemble weighted total load from already-computed components.""" - - total = ( - float(lambda_i) * float(intrinsic) - + float(lambda_e) * float(extraneous) - - float(lambda_g) * float(germane) - + float(lambda_r) * float(routing) - + float(lambda_m) * float(memory) - ) - return LoadBreakdown( - intrinsic=float(intrinsic), - extraneous=float(extraneous), - germane=float(germane), - routing=float(routing), - memory=float(memory), - total=total, - ) diff --git a/5-Applications/tools-scripts/compression/bits_back_iso.py b/5-Applications/tools-scripts/compression/bits_back_iso.py deleted file mode 100644 index cb0cb6cd..00000000 --- a/5-Applications/tools-scripts/compression/bits_back_iso.py +++ /dev/null @@ -1,651 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Bits-Back Coding on the ISO Substitution Log -============================================= -**concept_anchor:** domain=compression / concept=bits_back_iso_prior_encoding / resolution=FORMING - -THE PROBLEM THE ISO PREPASS HAS ---------------------------------- -iso_symbol_table.prepass() produces two things: - - 1. compressed_text — the vocabulary-reduced stream (kept, sent to entropy coder) - 2. substitution_log — {"iso_chem": ["Hydrogen", "oxygen"], "iso_geo": ["France"]} - -The log records every substitution made so the decoder can reverse the prepass. -Currently, the log is transmitted verbatim as part of the compressed output. -That costs bits it doesn't need to cost. - -BITS-BACK CODING — THE FIX ----------------------------- -Bits-back coding (Townsend et al. 2019, "Practical Lossless Compression with -Latent Variables using Bits Back Coding") says: - - If you encode a latent variable z using a coding distribution q(z|x) rather - than the true posterior p(z|x), you waste KL(q||p) bits per symbol. - - BUT — if you already have a prior p(z) and you use it to SAMPLE z rather - than receive it, you get log₂(1/p(z)) bits back from the ANS stack for free. - -Applied here: - z = the substitution log (which tokens were matched per domain) - p(z) = the ISO prior — frequency distribution of each token in the real world - x = the compressed text after substitution - -The ISO prior p(z) is NOT learned. It is the natural frequency of each term -in its standard: - - iso_chem: element/compound frequencies in scientific literature (IUPAC) - - iso_geo: country/city mention frequencies (UN statistics, Wikipedia) - - iso_unit: SI unit usage frequencies - - iso_lang: language code frequencies (BCP 47 usage statistics) - -Since the ISO tables are PUBLIC STANDARDS shared between encoder and decoder, -the prior is shared. Encoding the log against the prior costs only the -SURPRISE (negative log probability) of each observed substitution — not the -full token string. - -For common tokens ("United States", "oxygen", "hydrogen") the surprise is -very small. For rare tokens ("Djibouti", "einsteinium") it costs more bits, -which is exactly correct — rare matches ARE surprising. - -WHAT THIS MODULE DOES ---------------------- - 1. Builds a prior distribution for each ISO domain from empirical frequency - tables (hardcoded from reference corpora — can be refined over time). - - 2. Encodes the substitution log as a compact bitstring using the prior: - bits(token) = -log₂(p(token | domain)) - - 3. Decodes: given the domain bitmask and the encoded log, reconstructs the - full substitution log so iso_symbol_table.decode() can reverse the prepass. - - 4. Stats: reports bits saved vs. naive UTF-8 log encoding. - -INTEGRATION POINT ------------------ - # Current pipeline: - compressed_text, log = iso_symbol_table.prepass(text) - output = entropy_coder(compressed_text) + utf8_encode(log) # <-- wasteful - - # With bits-back: - compressed_text, log = iso_symbol_table.prepass(text) - log_bits = bits_back_iso.encode_log(log) # <-- cheap - output = entropy_coder(compressed_text) + log_bits - -BENCHMARK TARGET ----------------- -enwik8 baseline: 0.92% reduction, 924,727 bytes saved -Expected additional saving from bits-back log encoding: 5-15% of log overhead -(The log itself is ~0.3-0.8% of the original file size on enwik8.) - -REFERENCE ---------- - Townsend, J., Bird, T., Barber, D. (2019). - "Practical Lossless Compression with Latent Variables using Bits Back Coding." - ICLR 2019. https://arxiv.org/abs/1901.04866 - - The key equation (adapted for our case): - Cost(log | prior) = Σ_domain Σ_token -log₂(p(token | domain)) - vs. naive: - Cost(log | naive) = Σ_domain Σ_token len(token) * 8 bits -""" - -from __future__ import annotations - -import math -import struct -import sys -from pathlib import Path -from typing import Iterator - -sys.path.insert(0, str(Path(__file__).parent)) - -try: - from iso_symbol_table import DOMAINS, DEFAULT_DOMAINS, prepass - _ISO_AVAILABLE = True -except ImportError: - _ISO_AVAILABLE = False - - -# ─── empirical priors ───────────────────────────────────────────────────────── -# Token probability estimates within each ISO domain. -# Source: frequency analysis of the enwik8 corpus (100MB Wikipedia XML) via the -# iso_symbol_table enwik8 baseline run (182,988 total substitutions). -# Format: {token_lowercase: probability} — must sum ≤ 1.0 per domain. -# Tokens not listed get the domain floor probability. -# -# These are approximate; they will improve as more benchmarks are run. -# The key invariant: p(token) reflects how often that token appears in -# the reference corpus, NOT in the ISO table. - -_FLOOR_PROB = 1e-5 # probability floor for tokens not in prior table - -_PRIORS: dict[str, dict[str, float]] = { - - "iso_geo": { - # Top geography tokens by Wikipedia mention frequency - "united states": 0.0521, - "us": 0.0480, - "uk": 0.0312, - "united kingdom": 0.0298, - "germany": 0.0241, - "france": 0.0219, - "canada": 0.0187, - "australia": 0.0176, - "india": 0.0168, - "china": 0.0154, - "russia": 0.0142, - "japan": 0.0138, - "brazil": 0.0121, - "italy": 0.0117, - "spain": 0.0108, - "mexico": 0.0099, - "south korea": 0.0087, - "netherlands": 0.0081, - "sweden": 0.0078, - "switzerland": 0.0074, - "argentina": 0.0068, - "poland": 0.0065, - "new zealand": 0.0062, - "belgium": 0.0059, - "austria": 0.0057, - "norway": 0.0054, - "denmark": 0.0052, - "finland": 0.0049, - "portugal": 0.0047, - "ireland": 0.0046, - "czech republic": 0.0044, - "israel": 0.0041, - "south africa": 0.0039, - "turkey": 0.0037, - "egypt": 0.0034, - "iran": 0.0032, - "pakistan": 0.0029, - "indonesia": 0.0027, - "greece": 0.0025, - "ukraine": 0.0023, - }, - - "iso_chem": { - # Research Stack corpus (233 hits) merged with enwik8 baseline - # RS reorders: silicon and lead dominate (substrate/materials research) - "silicon": 0.0515, - "hydrogen": 0.0472, - "lead": 0.0343, - "oxygen": 0.0343, - "aluminum": 0.0300, - "water": 0.0300, - "gold": 0.0258, - "carbon": 0.0215, - "argon": 0.0129, - "plutonium": 0.0129, # RS-specific — nuclear/substrate research - "iron": 0.0129, - "xenon": 0.0129, # RS-specific - "helium": 0.0129, - "fluorine": 0.0129, - "carbon dioxide": 0.0129, - # enwik8 baseline (lower weight) - "nitrogen": 0.0100, - "calcium": 0.0079, - "sodium": 0.0065, - "chlorine": 0.0056, - "potassium": 0.0051, - "phosphorus": 0.0047, - "sulfur": 0.0046, - "magnesium": 0.0041, - "copper": 0.0037, - "zinc": 0.0035, - "silver": 0.0026, - "tin": 0.0022, - "uranium": 0.0021, - "lithium": 0.0019, - "bromine": 0.0016, - "mercury": 0.0015, - "neon": 0.0013, - "methane": 0.0011, - "ethanol": 0.0009, - "ammonia": 0.0008, - }, - - "iso_unit": { - # Research Stack corpus (76 hits) merged with enwik8 baseline - "micro": 0.1842, # RS dominant — SI prefix in tech/physics docs - "meter": 0.0921, - "kelvin": 0.0658, - "metre": 0.0132, # UK spelling - # enwik8 baseline (lower weight) - "kilogram": 0.0260, - "second": 0.0156, - "ampere": 0.0132, - "mole": 0.0094, - "candela": 0.0077, - "joule": 0.0071, - "watt": 0.0069, - "pascal": 0.0061, - "hertz": 0.0059, - "volt": 0.0054, - "ohm": 0.0099, - "farad": 0.0087, - "tesla": 0.0081, - "weber": 0.0074, - "lumen": 0.0068, - "kilowatt": 0.0062, - "megawatt": 0.0057, - "gigawatt": 0.0051, - "millisecond": 0.0047, - "microsecond": 0.0042, - "nanosecond": 0.0038, - "kilometer": 0.0034, - "centimeter": 0.0031, - "millimeter": 0.0027, - "nanometer": 0.0024, - }, - - "iso_lang": { - # Research Stack corpus (127 hits) merged with enwik8 baseline - # RS reorders: greek dominates (physics/math docs discuss Greek letters) - "greek": 0.0551, - "swedish": 0.0315, - "english": 0.0315, - "arabic": 0.0315, - "french": 0.0315, - "russian": 0.0236, - "latin": 0.0236, - "chinese": 0.0157, - "czech": 0.0157, - "danish": 0.0157, - "dutch": 0.0157, - "finnish": 0.0157, - "german": 0.0157, - "hebrew": 0.0157, - "hungarian": 0.0157, - # enwik8 baseline (lower weight) - "spanish": 0.0120, - "portuguese": 0.0100, - "japanese": 0.0090, - "hindi": 0.0070, - "italian": 0.0060, - "korean": 0.0050, - "polish": 0.0040, - "turkish": 0.0030, - "norwegian": 0.0025, - "romanian": 0.0020, - }, - - "iso_bio": { - # Biological notation frequency in Wikipedia - "dna": 0.0621, - "rna": 0.0521, - "atp": 0.0412, - "adenine": 0.0298, - "thymine": 0.0241, - "guanine": 0.0198, - "cytosine": 0.0187, - "uracil": 0.0154, - "glucose": 0.0142, - "fructose": 0.0121, - "sucrose": 0.0108, - "lactose": 0.0099, - "glycine": 0.0087, - "alanine": 0.0081, - "leucine": 0.0074, - "isoleucine": 0.0068, - "lysine": 0.0062, - "arginine": 0.0057, - "threonine": 0.0051, - "tryptophan": 0.0046, - "phenylalanine": 0.0041, - "tyrosine": 0.0037, - "cysteine": 0.0032, - "glutamine": 0.0028, - "asparagine": 0.0024, - }, - - "iso_math": { - "alpha": 0.0821, - "beta": 0.0754, - "gamma": 0.0698, - "delta": 0.0641, - "epsilon": 0.0521, - "theta": 0.0498, - "lambda": 0.0412, - "sigma": 0.0387, - "pi": 0.0341, - "omega": 0.0298, - "phi": 0.0241, - "psi": 0.0198, - "mu": 0.0187, - "nu": 0.0154, - "xi": 0.0142, - "eta": 0.0121, - "tau": 0.0108, - "rho": 0.0099, - "kappa": 0.0087, - "infinity": 0.0081, - "therefore": 0.0074, - "because": 0.0068, - "approximately": 0.0062, - "proportional": 0.0057, - "integral": 0.0051, - "derivative": 0.0046, - }, - - "iso_abbrev": { - # Research Stack corpus (115 hits) merged with enwik8 baseline - "minimum": 0.1478, - "section": 0.1130, - "maximum": 0.1043, - "number": 0.0609, - "equation": 0.0609, - "volume": 0.0435, - "not available": 0.0435, - "that is": 0.0348, - "compare": 0.0348, - "average": 0.0261, - "edition": 0.0261, - "approximately": 0.0261, - # enwik8 baseline entries (lower weight — still useful for mixed corpora) - "et al": 0.0180, - "etc": 0.0160, - "i.e.": 0.0140, - "e.g.": 0.0130, - "vs": 0.0110, - "figure": 0.0098, - "page": 0.0087, - "pages": 0.0074, - }, - - # ── iso_ptos: derived from 521KB Research Stack corpus sample (139 total hits) - # These priors reflect THIS corpus, not general text. Tokens not listed - # (e.g. rare compound phrases) get the floor probability. - "iso_ptos": { - "metanarrative": 0.2014, - "iso prepass": 0.1439, - "soliton": 0.1367, - "bits-back": 0.1295, - "research stack": 0.0647, - "metafoam": 0.0504, - "soliton encoder": 0.0432, - "basal ganglia": 0.0360, - "bits back": 0.0288, - "soliton factory": 0.0288, - "iso symbol table": 0.0216, - "soliton box": 0.0216, - "soliton manifold": 0.0144, - "foam voxel": 0.0144, - "operator fingerprint": 0.0144, - "topological soliton machine": 0.0072, - "topological soliton": 0.0072, - "substrate index": 0.0072, - "concept vector": 0.0072, - "iso pipeline": 0.0072, - "foam phase": 0.0072, - "phase transition": 0.0072, - }, - - # ── iso_isa: derived from 521KB Research Stack corpus sample (179 total hits) - # Both identifier form (phase_lock) and prose form (phase lock) are listed - # separately because the prepass log records whichever form appeared. - "iso_isa": { - "info_flow": 0.0726, - "coherence_guard": 0.0503, - "byte_stream": 0.0503, - "assist_bind": 0.0503, - "phase_lock": 0.0447, - "voxel_render": 0.0447, - "sra_pulse": 0.0391, - "vdp_compress": 0.0391, - "quantum_melt": 0.0391, - "weld_surface": 0.0391, - "hyperfluid_lut": 0.0391, - "phase lock": 0.0335, # prose form - "stark_prove": 0.0223, - "ricci_flow": 0.0223, - "foam_spray": 0.0223, - "vram_flush": 0.0168, - "wave_fold": 0.0168, - "wave fold": 0.0112, # prose form - "ingest_state": 0.0112, - "sync_precision": 0.0112, - "omni_bal": 0.0112, - "entangle": 0.0112, - "evolve": 0.0112, - "ledger_commit": 0.0112, - "crypto_wrap": 0.0112, - "grant_access": 0.0112, - "native_ws": 0.0112, - "webasm": 0.0112, - "neuromorph": 0.0112, - "gpgpu_surf": 0.0112, - }, - - # ── iso_code: derived from 521KB Research Stack corpus sample (28 total hits) - "iso_code": { - "continue": 0.6071, - "enumerate": 0.2857, - "isinstance": 0.0714, - "nonlocal": 0.0357, - }, -} - - -# ─── prior lookup ───────────────────────────────────────────────────────────── - -def _prob(token: str, domain: str) -> float: - """Return prior probability of token in domain. Floor if unknown.""" - prior = _PRIORS.get(domain, {}) - return prior.get(token.lower(), _FLOOR_PROB) - - -def _surprise_bits(token: str, domain: str) -> float: - """Bits of surprise for observing token in domain: -log₂(p(token|domain)).""" - return -math.log2(_prob(token, domain)) - - -# ─── log encoding ───────────────────────────────────────────────────────────── - -def encode_log( - log: dict[str, list[str]], -) -> dict: - """Encode the iso_symbol_table substitution log using bits-back prior. - - Returns a dict with: - domain_bitmask : int — which domains fired (compact header) - bits_per_domain : {domain: [surprise_bits per token]} - total_bits : float — theoretical minimum bits to represent log - naive_bits : float — UTF-8 cost of encoding log verbatim - savings_bits : float — bits saved vs naive - savings_pct : float — percentage saving - """ - if not log: - return { - "domain_bitmask": 0, - "bits_per_domain": {}, - "total_bits": 0.0, - "naive_bits": 0.0, - "savings_bits": 0.0, - "savings_pct": 0.0, - } - - all_domains = list(_PRIORS.keys()) - bitmask = 0 - bits_per_domain: dict[str, list[float]] = {} - total_bits = 0.0 - naive_bits = 0.0 - - for domain, tokens in log.items(): - if domain in all_domains: - bit_idx = all_domains.index(domain) - bitmask |= (1 << bit_idx) - - token_bits = [_surprise_bits(tok, domain) for tok in tokens] - bits_per_domain[domain] = token_bits - total_bits += sum(token_bits) - - # Naive cost: encode each original token as UTF-8 + null separator - for tok in tokens: - naive_bits += (len(tok.encode("utf-8")) + 1) * 8 - - # Domain bitmask header cost: 1 byte (≤8 domains) or 2 bytes - total_bits += 8 # bitmask header - - savings_bits = naive_bits - total_bits - savings_pct = (savings_bits / naive_bits * 100) if naive_bits > 0 else 0.0 - - return { - "domain_bitmask": bitmask, - "bits_per_domain": bits_per_domain, - "total_bits": round(total_bits, 2), - "naive_bits": round(naive_bits, 2), - "savings_bits": round(savings_bits, 2), - "savings_pct": round(savings_pct, 2), - } - - -def decode_log( - encoded: dict, - original_log: dict[str, list[str]], -) -> dict[str, list[str]]: - """Reconstruct the substitution log from the encoded form. - - In a full implementation this would use ANS to decode token identities - from the surprise-coded bitstring. Here we pass the original log for - validation — the interface is correct, the ANS coding is not yet built. - - This is the decoder contract: - decode_log(encode_log(log), ...) == log - """ - # ANS decoding deferred pending encoder bitstring implementation - # Currently returns original_log for interface validation - # Full ANS decoding will be implemented when encoder emits actual bitstring - return original_log - - -# ─── pipeline stats ─────────────────────────────────────────────────────────── - -def pipeline_stats(text: str, domains: list[str] | None = None) -> dict: - """Run the full ISO prepass + bits-back encoding and report stats. - - Shows: - - Bytes saved by ISO prepass (vocabulary reduction) - - Bits saved by bits-back log encoding (substitution log compression) - - Combined saving vs raw UTF-8 input - """ - if not _ISO_AVAILABLE: - return {"error": "iso_symbol_table not available"} - - original_bytes = len(text.encode("utf-8")) - - compressed, log = prepass(text, domains=domains) - compressed_bytes = len(compressed.encode("utf-8")) - - prepass_saved_bytes = original_bytes - compressed_bytes - prepass_saved_pct = prepass_saved_bytes / original_bytes * 100 - - log_encoding = encode_log(log) - naive_log_bytes = log_encoding["naive_bits"] / 8 - optimal_log_bytes = log_encoding["total_bits"] / 8 - log_saved_bytes = log_encoding["savings_bits"] / 8 - log_saved_pct = log_encoding["savings_pct"] - - total_naive_bytes = compressed_bytes + naive_log_bytes - total_optimal_bytes = compressed_bytes + optimal_log_bytes - combined_saved_bytes = original_bytes - total_optimal_bytes - combined_saved_pct = combined_saved_bytes / original_bytes * 100 - - substitutions = sum(len(v) for v in log.values()) - top_domains = sorted(log.items(), key=lambda x: -len(x[1]))[:4] - - return { - "original_bytes": original_bytes, - "prepass_bytes": compressed_bytes, - "prepass_saved_bytes": round(prepass_saved_bytes), - "prepass_saved_pct": round(prepass_saved_pct, 3), - "naive_log_bytes": round(naive_log_bytes, 1), - "optimal_log_bytes": round(optimal_log_bytes, 1), - "log_saved_bytes": round(log_saved_bytes, 1), - "log_saved_pct": round(log_saved_pct, 2), - "total_naive_bytes": round(total_naive_bytes), - "total_optimal_bytes": round(total_optimal_bytes), - "combined_saved_bytes": round(combined_saved_bytes), - "combined_saved_pct": round(combined_saved_pct, 3), - "substitutions": substitutions, - "top_domains": {d: len(t) for d, t in top_domains}, - "bits_back_encoding": log_encoding, - } - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def _cmd_stats(text: str) -> None: - s = pipeline_stats(text) - if "error" in s: - print(f"[error] {s['error']}") - return - - print(f"ISO prepass + bits-back encoding stats") - print(f"{'─'*50}") - print(f" original : {s['original_bytes']:>10,} bytes") - print() - print(f" [stage 1: ISO prepass — vocabulary reduction]") - print(f" compressed text : {s['prepass_bytes']:>10,} bytes") - print(f" saved : {s['prepass_saved_bytes']:>10,} bytes ({s['prepass_saved_pct']:.3f}%)") - print(f" substitutions : {s['substitutions']:>10,}") - print(f" top domains : {s['top_domains']}") - print() - print(f" [stage 2: bits-back log encoding]") - print(f" naive log cost : {s['naive_log_bytes']:>10.1f} bytes (UTF-8 verbatim)") - print(f" optimal log cost : {s['optimal_log_bytes']:>10.1f} bytes (bits-back prior)") - print(f" log saved : {s['log_saved_bytes']:>10.1f} bytes ({s['log_saved_pct']:.2f}%)") - print() - print(f" [combined pipeline]") - print(f" naive total : {s['total_naive_bytes']:>10,} bytes (prepass + UTF-8 log)") - print(f" optimal total : {s['total_optimal_bytes']:>10,} bytes (prepass + bits-back log)") - print(f" combined saved : {s['combined_saved_bytes']:>10,} bytes ({s['combined_saved_pct']:.3f}% of original)") - - -def main() -> None: - import argparse - - ap = argparse.ArgumentParser( - description="ISO prepass + bits-back encoding stats and utilities." - ) - sub = ap.add_subparsers(dest="cmd") - - p_stats = sub.add_parser("stats", help="Show combined pipeline stats for input text") - p_stats.add_argument("text", nargs="?", help="Text to analyse (or pipe via stdin)") - - p_bench = sub.add_parser("bench", help="Run on enwik8 sample") - p_bench.add_argument("--bytes", type=int, default=1_000_000, - help="Bytes of enwik8 to sample (default 1MB)") - - args = ap.parse_args() - - if args.cmd == "stats": - text = args.text or sys.stdin.read() - _cmd_stats(text) - - elif args.cmd == "bench": - enwik8 = Path(__file__).parent.parent / "data" / "enwik8" - if not enwik8.exists(): - print("[error] shared-data/data/enwik8 not found") - sys.exit(1) - with open(enwik8, "rb") as f: - raw = f.read(args.bytes) - text = raw.decode("utf-8", errors="replace") - print(f"Benchmarking on {args.bytes:,} bytes of enwik8...") - print() - _cmd_stats(text) - - else: - ap.print_help() - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/compression/cc_context_compress.py b/5-Applications/tools-scripts/compression/cc_context_compress.py deleted file mode 100644 index bcb402c2..00000000 --- a/5-Applications/tools-scripts/compression/cc_context_compress.py +++ /dev/null @@ -1,193 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Claude Code context compressor. - -Preprocesses file content through the context gate before it enters -Claude Code's context window. Prefers substrate cache and hyperlut -surfaces over raw token ingestion. - -Usage: - # Compress a file for context ingestion: - python 5-Applications/scripts/cc_context_compress.py read /path/to/file.py - - # Compress arbitrary text: - echo "sensitive payload" | python 5-Applications/scripts/cc_context_compress.py stdin - - # Check cache stats: - python 5-Applications/scripts/cc_context_compress.py stats - - # Pre-warm cache for a directory: - python 5-Applications/scripts/cc_context_compress.py warm /path/to/dir --glob "*.py" - -The compressed output is what Claude Code's context window should see. -Original content is cached in substrate_index.db and can be resolved -locally via Warden refs. -""" - -from __future__ import annotations - -import argparse -import json -import os -import sys -import sqlite3 -from pathlib import Path - -# Ensure Research Stack is on path -_ROOT = Path(__file__).resolve().parent.parent -if str(_ROOT) not in sys.path: - sys.path.insert(0, str(_ROOT)) -if str(_ROOT / "scripts") not in sys.path: - sys.path.insert(0, str(_ROOT / "scripts")) - -from context_gate import ContextGate, GateMode - -try: - from pbacs.kimi_context_optimizer import KimiContextOptimizer - _kimi_compressor = KimiContextOptimizer(token_budget=16000) -except Exception: - _kimi_compressor = None - -# Default hot terms — proprietary vocabulary that should never -# appear raw in an external context window. -_HOT_TERMS = [ - "soliton", "omnitoken", "tardygrada", "waveprobe", "ptos", - "hyperlut", "neuromorphic", "geomtree", "kolmogorov", - "metatransport", "phonon", "triumvirate", "cognitivesmoother", - "hutter", "metafoam", "hyperfluid", -] - - -def _gate() -> ContextGate: - return ContextGate( - warden_db_path=_ROOT / "warden_attestation.db", - substrate_db_path=_ROOT / "substrate_index.db", - hot_terms=_HOT_TERMS, - compressor=_kimi_compressor, - ) - - -def cmd_read(args): - """Compress a file through the context gate.""" - gate = _gate() - path = Path(args.file) - if not path.exists(): - print(f"error: {path} not found", file=sys.stderr) - sys.exit(1) - - content = path.read_text(errors="replace") - - mode = GateMode[args.mode.upper()] - result = gate.process(content, mode=mode) - - # Output: compact header + safe text - header = { - "source": str(path), - "mode": result.mode.value, - "cache_hit": result.cache_hit, - "compression_ratio": round(result.compression_ratio, 2), - "original_sha256": result.original_sha256[:16], - "warden_ref": result.warden_ref, - } - - if args.json: - print(json.dumps({"header": header, "content": result.safe_text})) - else: - # Human-readable: just the safe text with a one-line header - print(f"# [{mode.value}] {path.name} → {result.warden_ref}" - f" (ratio={result.compression_ratio:.1f}x" - f" cache={'HIT' if result.cache_hit else 'MISS'})") - print(result.safe_text) - - -def cmd_stdin(args): - """Compress stdin through the context gate.""" - gate = _gate() - content = sys.stdin.read() - mode = GateMode[args.mode.upper()] - result = gate.process(content, mode=mode) - print(result.safe_text) - - -def cmd_stats(args): - """Show cache statistics.""" - db_path = _ROOT / "substrate_index.db" - if not db_path.exists(): - print("No substrate_index.db found.") - return - - conn = sqlite3.connect(str(db_path)) - try: - row = conn.execute( - "SELECT COUNT(*), SUM(hits), " - "AVG(compression_ratio) FROM context_cache" - ).fetchone() - print(f"Cached entries: {row[0]}") - print(f"Total cache hits: {row[1] or 0}") - print(f"Avg compression ratio: {row[2] or 0:.1f}x") - except sqlite3.OperationalError: - print("context_cache table not yet created.") - conn.close() - - -def cmd_warm(args): - """Pre-warm the cache for a directory.""" - gate = _gate() - root = Path(args.directory) - pattern = args.glob or "*.py" - count = 0 - hits = 0 - - for path in sorted(root.rglob(pattern)): - if path.is_file() and path.stat().st_size < 500_000: - content = path.read_text(errors="replace") - result = gate.process(content, mode=GateMode.COMPRESS) - count += 1 - if result.cache_hit: - hits += 1 - - print(f"Warmed {count} files ({hits} already cached).") - - -def main(): - p = argparse.ArgumentParser( - description="Claude Code context compressor" - ) - sub = p.add_subparsers(dest="command") - - r = sub.add_parser("read", help="Compress a file") - r.add_argument("file", help="Path to file") - r.add_argument("--mode", default="compress", - choices=["compress", "opaque", "flatten"]) - r.add_argument("--json", action="store_true", - help="Output as JSON") - r.set_defaults(func=cmd_read) - - s = sub.add_parser("stdin", help="Compress stdin") - s.add_argument("--mode", default="compress", - choices=["compress", "opaque", "flatten"]) - s.set_defaults(func=cmd_stdin) - - st = sub.add_parser("stats", help="Show cache stats") - st.set_defaults(func=cmd_stats) - - w = sub.add_parser("warm", help="Pre-warm cache for directory") - w.add_argument("directory", help="Directory to warm") - w.add_argument("--glob", default="*.py", - help="File pattern (default: *.py)") - w.set_defaults(func=cmd_warm) - - args = p.parse_args() - if not args.command: - p.print_help() - sys.exit(1) - args.func(args) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/compression/compression_gateway.py b/5-Applications/tools-scripts/compression/compression_gateway.py deleted file mode 100644 index 265108dc..00000000 --- a/5-Applications/tools-scripts/compression/compression_gateway.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Compression Gateway — Version-Agnostic Socket Interface - -This module provides a unified interface to the compression organism, -regardless of which version or protocol port it is currently running on. - -Usage: - from compression_gateway import CompressionGateway - - with CompressionGateway(host="100.119.32.107") as gw: - status = gw.get_status() - print(f"Connected to {gw.protocol_version}") -""" - -import socket -import json -import struct -import time -from typing import Optional, Dict, Any - -class GatewayError(Exception): - pass - -class CompressionGateway: - def __init__(self, host: str, ports: list[int] = None, timeout: float = 5.0): - self.host = host - self.ports = ports or list(range(8440, 8451)) - self.timeout = timeout - self.sock: Optional[socket.socket] = None - self.protocol_version = "unknown" - self.connected_port = None - - def __enter__(self): - self.connect() - return self - - def __exit__(self, *args): - self.close() - - def connect(self): - """Find the active port and establish connection.""" - last_err = None - for port in self.ports: - try: - s = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - s.settimeout(self.timeout) - s.connect((self.host, port)) - - # Try Handshake - if self._negotiate_version(s): - self.sock = s - self.connected_port = port - return - - except (ConnectionRefusedError, socket.timeout, OSError) as e: - last_err = e - try: s.close() - except: pass - - raise GatewayError(f"Could not connect to organism on {self.host} (Last error: {last_err})") - - def _negotiate_version(self, sock: socket.socket) -> bool: - """Send handshake and parse response.""" - try: - msg = json.dumps({"cmd": "handshake"}) - sock.sendall(msg.encode('utf-8') + b'\n') - - response = self._recv_line(sock) - if not response: - return False - - data = json.loads(response) - if data.get("status") == "ok": - self.protocol_version = data.get("version", "v1_legacy") - return True - except Exception: - return False - return False - - def _recv_line(self, sock: socket.socket) -> str: - """Read until newline.""" - data = b"" - while True: - chunk = sock.recv(1) - if not chunk or chunk == b'\n': - break - data += chunk - return data.decode('utf-8') - - def close(self): - if self.sock: - try: self.sock.close() - except: pass - self.sock = None - - # --- Public API --- - - def ping(self) -> bool: - """Check if connection is alive.""" - if not self.sock: return False - try: - self.sock.sendall(json.dumps({"cmd": "ping"}).encode('utf-8') + b'\n') - resp = self._recv_line(self.sock) - return "pong" in resp - except Exception: - return False - - def get_status(self) -> Dict[str, Any]: - """Retrieve organism status.""" - if not self.sock: raise GatewayError("Not connected") - try: - self.sock.sendall(json.dumps({"cmd": "status"}).encode('utf-8') + b'\n') - resp = self._recv_line(self.sock) - return json.loads(resp) - except Exception: - return {"error": "Status request failed"} - -if __name__ == "__main__": - import sys - target = sys.argv[1] if len(sys.argv) > 1 else "100.119.32.107" # RackNerd IP - print(f"[*] Probing {target} for compression organism...") - - try: - with CompressionGateway(target) as gw: - print(f"[+] Connected on port {gw.connected_port}") - print(f"[+] Protocol: {gw.protocol_version}") - - # Live Status Check - status = gw.get_status() - print(f"[+] Status: {json.dumps(status, indent=2)}") - - # Ping Test - if gw.ping(): - print("[+] Ping successful") - except GatewayError as e: - print(f"[-] Error: {e}") - sys.exit(1) diff --git a/5-Applications/tools-scripts/compression/deepcompression_compress.py b/5-Applications/tools-scripts/compression/deepcompression_compress.py deleted file mode 100644 index 1d875bd1..00000000 --- a/5-Applications/tools-scripts/compression/deepcompression_compress.py +++ /dev/null @@ -1,155 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -DeepCompression compression: Maximum Shannon entropy reduction. -Converts any file/archive to: manifest.json + blob.zlib + sha256.txt - -Usage: - python3 blackhole_compress.py ~/Downloads/Research\\ Documents-20260318T220015Z-1-001.zip -""" - -import os -import sys -import json -import zlib -import hashlib -from datetime import datetime, timezone -from pathlib import Path - -def compute_sha256(data: bytes) -> str: - """Compute SHA256 hash of data.""" - return hashlib.sha256(data).hexdigest() - -def nibble_fingerprint(data: bytes) -> str: - """Create 16-bit hex fingerprint (4 nibbles) from data.""" - h = hashlib.md5(data).digest() - return h[:2].hex() # 16 bits = 4 hex digits - -def relevance_bucket_4bit(data: bytes) -> int: - """Classify data into 4-bit relevance bucket (0-15).""" - size = len(data) - # Log-scale bucketing: smaller = higher relevance - if size < 1024: - return 15 # Tiny, high relevance - elif size < 1024*1024: - return 14 # Small - elif size < 10*1024*1024: - return 13 # Medium - elif size < 100*1024*1024: - return 12 # Large - else: - return 11 # Huge - -def blackhole_compress(input_path: str, output_dir: str = None) -> dict: - """ - Compress file to deepcompression vault format. - - Returns: - dict with keys: manifest_path, blob_path, sha256_path, stats - """ - input_path = Path(input_path).resolve() - if not input_path.exists(): - raise FileNotFoundError(f"Input file not found: {input_path}") - - # Setup output directory - if output_dir is None: - output_dir = input_path.parent / f"blackhole_{input_path.stem}" - output_dir = Path(output_dir) - output_dir.mkdir(exist_ok=True, parents=True) - - # Read input - print(f"[*] Reading input: {input_path}") - with open(input_path, 'rb') as f: - raw_bytes = f.read() - - raw_size = len(raw_bytes) - print(f"[*] Input size: {raw_size:,} bytes ({raw_size/1024/1024:.2f} MB)") - - # Compress with maximum Shannon reduction (zlib level 9) - print(f"[*] Compressing (zlib level 9)...") - compressed = zlib.compress(raw_bytes, level=9) - compressed_size = len(compressed) - compression_ratio = compressed_size / max(1, raw_size) - - print(f"[*] Compressed size: {compressed_size:,} bytes ({compressed_size/1024/1024:.2f} MB)") - print(f"[*] Compression ratio: {compression_ratio:.4f} ({(1-compression_ratio)*100:.2f}% reduction)") - - # Compute hashes - print(f"[*] Computing hashes...") - sha256_full = compute_sha256(raw_bytes) - sha256_compressed = compute_sha256(compressed) - - # Create manifest with minimal nibble index - run_id = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - manifest = { - "format": "deepcompression", - "version": "1.0", - "run_id": run_id, - "created_utc": datetime.now(timezone.utc).isoformat(), - "source_file": input_path.name, - "source_bytes": raw_size, - "compressed_bytes": compressed_size, - "compression_ratio": round(compression_ratio, 6), - "shannon_limit_reached": compression_ratio < 0.4, # Heuristic: <40% is near Shannon limit - "sha256_original": sha256_full, - "sha256_compressed": sha256_compressed, - "zlib_method": "deflate", - "zlib_level": 9, - "fingerprint": nibble_fingerprint(raw_bytes), - "relevance_bucket": relevance_bucket_4bit(raw_bytes), - } - - # Write manifest - manifest_path = output_dir / f"manifest_{run_id}.json" - with open(manifest_path, 'w') as f: - json.dump(manifest, f, indent=2) - print(f"[+] Manifest: {manifest_path}") - - # Write compressed blob - blob_path = output_dir / f"blackhole_{run_id}.zlib" - with open(blob_path, 'wb') as f: - f.write(compressed) - print(f"[+] Blob: {blob_path} ({compressed_size:,} bytes)") - - # Write SHA256 file - sha256_path = output_dir / f"sha256_{run_id}.txt" - with open(sha256_path, 'w') as f: - f.write(f"{sha256_full} {input_path.name}\n") - f.write(f"{sha256_compressed} {blob_path.name}\n") - print(f"[+] SHA256: {sha256_path}") - - # Cleanup: remove original if extraction happened - print(f"\n[*] DeepCompression vault created at: {output_dir}") - print(f"[*] Files:") - print(f" - {manifest_path.name}") - print(f" - {blob_path.name}") - print(f" - {sha256_path.name}") - - return { - "manifest_path": manifest_path, - "blob_path": blob_path, - "sha256_path": sha256_path, - "stats": { - "original_bytes": raw_size, - "compressed_bytes": compressed_size, - "compression_ratio": compression_ratio, - "sha256_original": sha256_full, - "sha256_compressed": sha256_compressed, - } - } - -if __name__ == "__main__": - if len(sys.argv) < 2: - print(__doc__) - sys.exit(1) - - input_file = sys.argv[1] - output_dir = sys.argv[2] if len(sys.argv) > 2 else None - - result = blackhole_compress(input_file, output_dir) - print(f"\n[✓] Complete. Output directory: {result['manifest_path'].parent}") diff --git a/5-Applications/tools-scripts/connectome/collective_retrieval_isa.py b/5-Applications/tools-scripts/connectome/collective_retrieval_isa.py deleted file mode 100644 index 5f846719..00000000 --- a/5-Applications/tools-scripts/connectome/collective_retrieval_isa.py +++ /dev/null @@ -1,91 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import time -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -# ============================================================================= -# COLLECTIVE SUBSTRATE SIMULATION (ISA-COMPLIANT) -# ============================================================================= - -class CollectiveSubstrateSim: - def __init__(self, size=10000): - # Initialize nodes with Phonon Energy (Natural Frequency) - self.nodes = [] - for i in range(size): - # Each node is mapped to a unique vibrational mode - freq = 1.0e12 + i * 1.5e6 - self.nodes.append({"id": i, "freq": freq, "data": f"state_{i}"}) - - def retrieval_action(self, target_id): - print(f"[*] INGEST_VIBRATION: target_id={target_id}") - query_freq = 1.0e12 + target_id * 1.5e6 - - start = time.perf_counter_ns() - - # RESONATE: The core retrieval action - # In a real substrate, this is SPATIAL BROADCAST (Parallel) - matches = [] - for node in self.nodes: - # We measure resonance against the target query wave - resonance = 1.0 - abs(node['freq'] - query_freq) / query_freq - if resonance > 0.999999: # 6-nines lock - matches.append((node, resonance)) - - # OBSERVE_MODE: Measure the final amplitude (The "READ" action) - result = None - if matches: - # Extract data from the most resonant node - best_match = matches[0][0] - result = best_match['data'] - - end = time.perf_counter_ns() - latency = (end - start) / 1000.0 - - # CALCULATE_ENTROPY (Simplified) - entropy = 0.5 if result else 1.0 - - return result, latency, resonance, entropy - -# ============================================================================= -# COMPARISON RUNNER -# ============================================================================= - -def run_comparison(node_count=20000): - print(f"--- RELATIONAL/NOSQL vs COLLECTIVE SUBSTRATE (Nodes: {node_count}) ---") - - # NoSQL Simulation - data_map = {f"node_{i}": f"state_{i}" for i in range(node_count)} - start_nosql = time.perf_counter_ns() - res_nosql = data_map.get(f"node_{node_count//2}") - end_nosql = time.perf_counter_ns() - time_nosql = (end_nosql - start_nosql) / 1000.0 - - print(f"\n[NOSQL LOOKUP]") - print(f" Action: GET(key)") - print(f" Latency: {time_nosql:.3f} μs") - print(f" Result: {res_nosql}") - - # Substrate Simulation - substrate = CollectiveSubstrateSim(node_count) - res_sub, time_sub, resonance, entropy = substrate.retrieval_action(node_count//2) - - print(f"\n[COLLECTIVE SUBSTRATE]") - print(f" Actions: INGEST_VIBRATION -> RESONATE -> OBSERVE_MODE") - print(f" Latency: {time_sub:.3f} μs (Simulated Serial)") - print(f" Result: {res_sub}") - print(f" Resonance: {resonance:.8f} (Phase-Locked)") - print(f" Entropy (0.5 Balance): {entropy:.1f}") - - print("\n--- ARCHITECTURAL VERDICT ---") - print(f"NoSQL is a logical lookup. Substrate is a physical state collapse.") - print(f"While software simulation shows overhead, hardware COLLECTIVE is O(1).") - -if __name__ == "__main__": - run_comparison() diff --git a/5-Applications/tools-scripts/connectome/collective_substrate_retrieval.py b/5-Applications/tools-scripts/connectome/collective_substrate_retrieval.py deleted file mode 100644 index 01f12fa0..00000000 --- a/5-Applications/tools-scripts/connectome/collective_substrate_retrieval.py +++ /dev/null @@ -1,91 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import time -import sys -import os -import hashlib -from pathlib import Path -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -DEFAULT_MANIFOLD_PATH = Path( - os.getenv("COLLECTIVE_MANIFOLD_PATH") - or Path.home() / "Downloads" / "tsm_vdp_knowledge_manifold.dag.json" -) - -# Import the N-Dimensional Soliton Engine logic -# (Simulated for this script based on nd_soliton_engine.py) - -class CollectiveSubstrate: - def __init__(self, manifold_path): - with open(manifold_path, 'r') as f: - self.manifold_data = json.load(f) - self.nodes = self.manifold_data.get("nodes", {}) - self.edges = self.manifold_data.get("edges", []) - print(f"[*] Collective Substrate Initialized: {len(self.nodes)} nodes, {len(self.edges)} edges.") - - def simulate_broadcast_query(self, target_slug): - # 1. Generate the "Query Frequency" (Soliton Packet) - query_hash = hashlib.sha256(target_slug.encode()).hexdigest() - query_freq = (int(query_hash[:8], 16) % 1000) / 1000.0 * (1/1.618033) - - print(f"[*] Injecting Query Soliton: f = {query_freq:.6f}") - - start = time.perf_counter_ns() - - # 2. Simulate Wavefront Broadcast (Parallel Resonance) - # In a real substrate, this happens in O(1) time across the network - results = [] - for node_id, node in self.nodes.items(): - node_freq = node["n_dag_properties"]["phonon_energy"] - - # Resonance = 1 - delta - resonance = 1.0 - abs(node_freq - query_freq) - if resonance > 0.99999: # High precision lock - results.append((node, resonance)) - - end = time.perf_counter_ns() - latency = (end - start) / 1000.0 - - return results, latency - -def run_collective_sim(): - manifold_path = DEFAULT_MANIFOLD_PATH - - if not manifold_path.exists(): - print(f"[ERROR] Manifold not found at {manifold_path}. Please run transmutation first.") - return - - substrate = CollectiveSubstrate(manifold_path) - - # Pick a random target from the manifold if nodes exist - if not substrate.nodes: - # Fallback if manifold is empty: simulate nodes - print("[!] Manifold is empty. Simulating ephemeral collective...") - substrate.nodes = {f"node_{i}": {"identity": {"slug": f"target_{i}"}, "n_dag_properties": {"phonon_energy": (hashlib.sha256(f"target_{i}".encode()).digest()[0] % 1000) / 1000.0}} for i in range(100)} - target_slug = "target_42" - else: - # Use first node as target for demo - target_node = list(substrate.nodes.values())[0] - target_slug = target_node["identity"]["slug"] - - print(f"\n--- COLLECTIVE SUBSTRATE ACTION: RETRIEVE '{target_slug}' ---") - results, latency = substrate.simulate_broadcast_query(target_slug) - - if results: - res_node, res_val = results[0] - nines = -xp.log10(1.0 - res_val) if res_val < 1.0 else 20.0 - print(f"\n[SUCCESS: RESONANCE ACHIEVED]") - print(f" Node Identified: {res_node['identity']['name'] if 'name' in res_node['identity'] else res_node['identity']['slug']}") - print(f" Collective Latency: {latency:.3f} μs (Simulated Parallel)") - print(f" Phase-Lock Stability: {nines:.2f} nines") - else: - print("\n[FAILURE: NO RESONANCE]") - -if __name__ == "__main__": - run_collective_sim() diff --git a/5-Applications/tools-scripts/connectome/connectome_codon_audit.py b/5-Applications/tools-scripts/connectome/connectome_codon_audit.py deleted file mode 100644 index fd78099c..00000000 --- a/5-Applications/tools-scripts/connectome/connectome_codon_audit.py +++ /dev/null @@ -1,514 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -5-Applications/scripts/connectome_codon_audit.py -Connectome structural metrics × codon bias cross-validation. - -Tests the assumption: codon bias (AU-rich vs GC-rich) correlates with -connectome complexity INDEPENDENTLY of raw neuron count. - -Data sources: - - C. elegans : shared-data/data/connectomes/celegans_herm_edgelist.csv (live download) - - Others : published summary statistics (cited inline) - -Honeybee anomaly test: - - Honeybee has ~1M neurons but AU-rich codon bias like C. elegans (302 neurons) - - IF codon_bias tracks topology complexity (not neuron count), honeybee should - have LOW connectome complexity metrics compared to Drosophila - -Outputs: - - Per-species structural metrics table - - Pearson r (codon_gc_fraction × structural_complexity) - - Verdict: CONFIRMED / FALSIFIED / INCONCLUSIVE -""" -from __future__ import annotations - -import csv -import math -import pathlib -import sys -from collections import defaultdict -from typing import Dict, List, Optional, Tuple - -DATA_DIR = pathlib.Path(__file__).parent.parent / "data" / "connectomes" -OUT_DIR = pathlib.Path(__file__).parent.parent / "out" -OUT_DIR.mkdir(parents=True, exist_ok=True) - -# ── Codon bias top-5 from Kazusa (computed previous session) ──────────────── -# gc_fraction: fraction of TOP-5 most-used codons that end in G or C (third-pos) -# This is the proxy used in the SAE neural binding analysis. - -CODON_PROFILES: Dict[str, Dict] = { - "c_elegans": { - "taxid": 6239, - "neurons": 302, - "top5": ["CAA", "GAA", "AAA", "GGA", "CCA"], - "gc_fraction": 0.20, # only CCA+GGA have GC 3rd pos = 2/5 - "source": "Kazusa taxid 6239", - "notes": "305 neurons hermaphrodite connectome", - }, - "hydra": { - "taxid": 45351, - "neurons": 25_000, - "top5": ["CAC", "AAC", "GAC", "GAG", "AAG"], - "gc_fraction": 1.00, # all 5 end in C or G - "source": "Kazusa taxid 45351", - "notes": "Radially symmetric neural net — no cephalisation", - }, - "honeybee": { - "taxid": 7460, - "neurons": 1_000_000, - "top5": ["CAA", "GAA", "AAA", "CAC", "AAC"], - "gc_fraction": 0.40, # CAC+AAC end in C - "source": "Kazusa taxid 7460", - "notes": "ANOMALY: ~1M neurons but AU-rich like C. elegans", - }, - "drosophila": { - "taxid": 7227, - "neurons": 130_000, - "top5": ["CAG", "AAG", "GAG", "CAC", "AAC"], - "gc_fraction": 1.00, - "source": "Kazusa taxid 7227", - "notes": "Hemibrain ~25k neurons; full 135k estimate", - }, - "zebrafish": { - "taxid": 7955, - "neurons": 100_000, - "top5": ["CAG", "GAG", "AAC", "CAC", "GAC"], - "gc_fraction": 1.00, - "source": "Kazusa taxid 7955", - "notes": "Larval stage", - }, - "mouse": { - "taxid": 10090, - "neurons": 70_000_000, - "top5": ["CAG", "AAG", "CAC", "GAG", "AAC"], - "gc_fraction": 1.00, - "source": "Kazusa taxid 10090", - }, - "human": { - "taxid": 9606, - "neurons": 86_000_000_000, - "top5": ["CAG", "CAC", "GAG", "AAG", "GAC"], - "gc_fraction": 1.00, - "source": "Kazusa taxid 9606", - }, -} - -# ── Published connectome structural metrics ────────────────────────────────── -# Where we have live edge data, computed below and merged in. -# Sources cited per entry. - -PUBLISHED_METRICS: Dict[str, Dict] = { - # C. elegans: computed live below from herm_full_edgelist.csv - # White et al 1986 / Varshney et al 2011 / Cook et al 2019 - "c_elegans": { - "published_nodes": 302, - "published_edges": 6393, # chemical synapses (Cook 2019) - "published_ei_ratio": 0.17, # ~17% gap junctions (electrical) - "published_clustering": 0.28, # Watts-Strogatz C (Varshney 2011) - "published_avg_path": 2.65, # Varshney 2011 - "published_hub_fraction": 0.08, # top 10% by out-degree (est) - "published_source": "Varshney et al 2011; Cook et al 2019", - }, - "hydra": { - # Gur Barzilai et al 2021 (eLife): ~3k neurons mapped - "published_nodes": 3000, - "published_edges": None, # not reported as single matrix - "published_ei_ratio": None, - "published_clustering": None, - "published_avg_path": None, - "published_hub_fraction": None, - "published_source": "Gur Barzilai et al 2021 eLife (partial)", - "notes": "Radial net; no direction bias; connectivity incomplete", - }, - "honeybee": { - # Honeybee mushroom body connectome: Strausfeld 2002; Takemura 2017 (Kenyon cells) - # Full brain: BEE-brain project (in progress as of 2024) - "published_nodes": 170_000, # mushroom body only (43k Kenyon cells mapped) - "published_edges": None, - "published_ei_ratio": None, - "published_clustering": None, - "published_avg_path": None, - "published_hub_fraction": None, - "published_source": "Strausfeld 2002; partial BEE-brain 2024", - "notes": "Full connectome not yet complete; mushroom body is GC-equivalent functional unit", - }, - "drosophila": { - # FlyWire Dorkenwald et al 2023 (full brain) - # Also: Hemibrain Scheffer et al 2020 - "published_nodes": 139_255, - "published_edges": 54_997_123, # FlyWire full proofread - "published_ei_ratio": 0.11, # ~11% inhibitory (Gai et al 2019 estimate) - "published_clustering": None, # not reported; too large for global C - "published_avg_path": 3.1, # Scheffer 2020 hemibrain estimate - "published_hub_fraction": 0.04, # small fraction of high-degree hub neurons - "published_source": "Dorkenwald et al 2023 (FlyWire); Scheffer et al 2020", - }, - "zebrafish": { - # Hildebrand et al 2017 (larval hindbrain, partial) - "published_nodes": 1200, # hindbrain segment only - "published_edges": None, - "published_ei_ratio": None, - "published_clustering": None, - "published_avg_path": None, - "published_hub_fraction": None, - "published_source": "Hildebrand et al 2017 Nature (partial larval)", - "notes": "Full connectome not yet complete", - }, - "mouse": { - # MICrONS L2/3 Rees et al 2023 / Turner et al 2022 - "published_nodes": 200_000, # L2/3 1mm³ cube neurons + around - "published_edges": 523_000_000, # synapses in 1mm³ (MICrONS) - "published_ei_ratio": 0.14, # 14% inhibitory (DeFelipe 2002 cortex) - "published_clustering": None, - "published_avg_path": None, - "published_hub_fraction": 0.02, - "published_source": "MICrONS Consortium 2023; Rees et al 2023", - }, - "human": { - # H01 1mm³ (Shapson-Coe et al 2021) - "published_nodes": 50_000, # in the 1mm³ sample - "published_edges": 130_000_000, # synapses in sample - "published_ei_ratio": 0.18, # ~18% GABAergic in cortex - "published_clustering": None, - "published_avg_path": None, - "published_hub_fraction": 0.01, - "published_source": "Shapson-Coe et al 2021 (H01)", - }, -} - -# ── Live C. elegans analysis ───────────────────────────────────────────────── - -def load_celegans(path: pathlib.Path) -> Tuple[List, Dict]: - """Load herm_full_edgelist.csv → edge list + stats.""" - edges = [] - out_deg: Dict[str, int] = defaultdict(int) - in_deg: Dict[str, int] = defaultdict(int) - type_counts: Dict[str, int] = defaultdict(int) - - with open(path, newline="") as f: - reader = csv.DictReader(f) - for row in reader: - src = row["Source"].strip() - tgt = row["Target"].strip() - w = int(row["Weight"]) - typ = row["Type"].strip() - edges.append((src, tgt, w, typ)) - out_deg[src] += 1 - in_deg[tgt] += 1 - type_counts[typ] += 1 - - nodes = set(s for s,_,_,_ in edges) | set(t for _,t,_,_ in edges) - return edges, { - "nodes": nodes, - "out_deg": dict(out_deg), - "in_deg": dict(in_deg), - "type_counts": dict(type_counts), - "n_edges": len(edges), - } - - -def _gini(values: List[float]) -> float: - """Gini coefficient of a distribution (0=equal, 1=maximally unequal).""" - arr = sorted(values) - n = len(arr) - if n == 0: - return 0.0 - s = sum(arr) - if s == 0: - return 0.0 - cum = 0.0 - for i, v in enumerate(arr): - cum += (2*(i+1) - n - 1) * v - return cum / (n * s) - - -def compute_celegans_metrics(edges_data: Dict) -> Dict: - nodes = edges_data["nodes"] - out_deg = edges_data["out_deg"] - in_deg = edges_data["in_deg"] - type_counts = edges_data["type_counts"] - n_nodes = len(nodes) - n_edges = edges_data["n_edges"] - - # Edge density - max_edges = n_nodes * (n_nodes - 1) - density = n_edges / max_edges if max_edges > 0 else 0.0 - - # Degree stats - all_deg = {n: out_deg.get(n, 0) + in_deg.get(n, 0) for n in nodes} - deg_vals = list(all_deg.values()) - avg_deg = sum(deg_vals) / len(deg_vals) - max_deg = max(deg_vals) - - # Hub fraction: top 10% by total degree - threshold = sorted(deg_vals)[int(0.9 * len(deg_vals))] - hub_frac = sum(1 for d in deg_vals if d >= threshold) / len(deg_vals) - - # Degree inequality (Gini) - gini = _gini(deg_vals) - - # E/I proxy: electrical (gap junctions) vs chemical - n_chem = type_counts.get("chemical", 0) - n_elec = type_counts.get("electrical", 0) - ei_ratio = n_elec / (n_chem + n_elec) if (n_chem + n_elec) > 0 else None - - # Clustering coefficient (local, sampled — full is O(N³), too slow) - # Use top-50 by degree as proxy - adj: Dict[str, set] = defaultdict(set) - for src, tgt, _, _ in edges_data.get("_edges", []): - adj[src].add(tgt) - adj[tgt].add(src) # undirected for clustering - - sample_nodes = sorted(all_deg, key=lambda n: -all_deg[n])[:50] - local_c_vals = [] - for n in sample_nodes: - nbrs = list(adj[n]) - k = len(nbrs) - if k < 2: - continue - links = 0 - for i in range(k): - for j in range(i+1, k): - if nbrs[j] in adj[nbrs[i]]: - links += 1 - local_c_vals.append(2*links / (k*(k-1))) - - avg_clustering = sum(local_c_vals)/len(local_c_vals) if local_c_vals else None - - return { - "n_nodes": n_nodes, - "n_edges": n_edges, - "density": density, - "avg_degree": avg_deg, - "max_degree": max_deg, - "hub_fraction": hub_frac, - "degree_gini": gini, - "ei_ratio": ei_ratio, - "n_chemical": n_chem, - "n_electrical": n_elec, - "avg_clustering": avg_clustering, - "type_counts": type_counts, - } - - -# ── Complexity score (0–1 composite) ───────────────────────────────────────── - -def complexity_score(species: str, live: Optional[Dict], pub: Dict) -> Optional[float]: - """ - Composite connectome complexity index (0-1). - Combines 4 independent metrics, each normalized to [0,1]. - Higher = more complex / higher information capacity. - """ - scores = [] - - # 1. Edge density (normalised to C. elegans as reference max for dense graphs) - # C. elegans published density ≈ 0.07 (very dense for its size) - if live and "density" in live: - d = live["density"] - scores.append(min(d / 0.07, 1.0)) # C. elegans as 1.0 reference - elif pub.get("published_edges") and pub.get("published_nodes"): - n = pub["published_nodes"] - e = pub["published_edges"] - density = e / (n * (n-1)) if n > 1 else 0 - scores.append(min(density / 0.07, 1.0)) - - # 2. Hub fraction (normalised: 0.08 = C. elegans reference) - hf = (live or {}).get("hub_fraction") or pub.get("published_hub_fraction") - if hf is not None: - scores.append(min(hf / 0.08, 1.0)) - - # 3. E/I ratio (inhibitory fraction; higher = more regulation = more complexity) - # Reference: human cortex ~0.18 - ei = (live or {}).get("ei_ratio") or pub.get("published_ei_ratio") - if ei is not None: - scores.append(min(ei / 0.18, 1.0)) - - # 4. Degree Gini (inequality = more hub dominant = more complex routing) - # Higher Gini means more power-law-like distribution - gini = (live or {}).get("degree_gini") - if gini is not None: - scores.append(gini) - - return round(sum(scores)/len(scores), 4) if scores else None - - -# ── Pearson r ───────────────────────────────────────────────────────────────── - -def pearson(xs: List[float], ys: List[float]) -> Optional[float]: - n = len(xs) - if n < 3: - return None - mx = sum(xs)/n; my = sum(ys)/n - num = sum((x-mx)*(y-my) for x,y in zip(xs,ys)) - sdx = math.sqrt(sum((x-mx)**2 for x in xs)) - sdy = math.sqrt(sum((y-my)**2 for y in ys)) - if sdx == 0 or sdy == 0: - return None - return num / (sdx * sdy) - - -# ── Main ────────────────────────────────────────────────────────────────────── - -def main() -> None: - # Load live C. elegans - ce_path = DATA_DIR / "celegans_herm_edgelist.csv" - ce_live = None - if ce_path.exists(): - edges, stats = load_celegans(ce_path) - stats["_edges"] = edges - ce_live = compute_celegans_metrics(stats) - print(f"[celegans] loaded {len(edges)} edges, {len(stats['nodes'])} nodes") - else: - print(f"[celegans] edgelist not found at {ce_path}") - - # Build per-species result table - results = [] - gc_vals = [] - cxi_vals = [] - - for sp, codon in CODON_PROFILES.items(): - pub = PUBLISHED_METRICS.get(sp, {}) - live = ce_live if sp == "c_elegans" else None - cxi = complexity_score(sp, live, pub) - - row = { - "species": sp, - "neurons": codon["neurons"], - "gc_fraction": codon["gc_fraction"], - "live_density": round(live["density"], 4) if live else None, - "live_hub_frac": round(live["hub_fraction"],4) if live else None, - "live_gini": round(live["degree_gini"],4) if live else None, - "live_ei_ratio": round(live["ei_ratio"], 4) if live and live["ei_ratio"] else None, - "live_clustering": round(live["avg_clustering"],4) if live and live["avg_clustering"] else None, - "pub_density": (round(pub["published_edges"]/(pub["published_nodes"]*(pub["published_nodes"]-1)),6) - if pub.get("published_edges") and pub.get("published_nodes") else None), - "pub_hub_frac": pub.get("published_hub_fraction"), - "pub_ei_ratio": pub.get("published_ei_ratio"), - "complexity_index": cxi, - "source": pub.get("published_source",""), - "notes": pub.get("notes",""), - } - results.append(row) - if cxi is not None: - gc_vals.append(codon["gc_fraction"]) - cxi_vals.append(cxi) - - # Print table - print("\n" + "="*110) - print(f"{'SPECIES':<15} {'NEURONS':>14} {'GC_FRAC':>8} {'DENSITY':>10} {'HUB_FRAC':>9} " - f"{'EI_RATIO':>9} {'GINI':>7} {'CLUSTER':>8} {'CXI':>7}") - print("="*110) - - for r in results: - density = r["live_density"] or r["pub_density"] - hub = r["live_hub_frac"] or r["pub_hub_frac"] - ei = r["live_ei_ratio"] or r["pub_ei_ratio"] - gini = r["live_gini"] or "-" - cluster = r["live_clustering"] or "-" - cxi = r["complexity_index"] - - anomaly = " ◄ ANOMALY" if r["species"] == "honeybee" else "" - print(f"{r['species']:<15} {r['neurons']:>14,} {r['gc_fraction']:>8.2f} " - f"{str(density or '-'):>10} {str(hub or '-'):>9} " - f"{str(ei or '-'):>9} {str(gini):>7} {str(cluster):>8} " - f"{str(cxi if cxi else '-'):>7}{anomaly}") - - print("="*110) - - # Correlation - r_neu = pearson([math.log10(CODON_PROFILES[s]["neurons"]) for s in CODON_PROFILES - if CODON_PROFILES[s]["neurons"] and - PUBLISHED_METRICS.get(s,{}).get("published_source")], - [CODON_PROFILES[s]["gc_fraction"] for s in CODON_PROFILES - if CODON_PROFILES[s]["neurons"] and - PUBLISHED_METRICS.get(s,{}).get("published_source")]) - - r_cxi = pearson(gc_vals, cxi_vals) - - print(f"\nCorrelation: GC_fraction × log10(neurons) r = {r_neu:.3f}" if r_neu else - "\nCorrelation: GC_fraction × log10(neurons) r = insufficient data") - print(f"Correlation: GC_fraction × complexity_idx r = {r_cxi:.3f}" if r_cxi else - "Correlation: GC_fraction × complexity_idx r = insufficient data") - - # Verdict - print("\n── HONEYBEE ANOMALY ANALYSIS ──────────────────────────────────────────────") - print("Honeybee: ~1M neurons (>> Drosophila 130k) BUT AU-rich like C. elegans (302 neurons)") - print("Possible resolutions:") - print(" A) Codon bias tracks FUNCTIONAL COMPLEXITY not neuron count") - print(" → Honeybee mushroom body (associative learning) is AU-rich like ganglia,") - print(" not like vertebrate cortex (GC-rich). Mushroom body = modular, not recurrent.") - print(" B) Codon bias is CLADE-specific, not complexity-driven") - print(" → Hymenoptera (honeybee) retained AU-bias from Diptera ancestor bifurcation") - print(" despite neuron count scaling. Evolutionary history ≠ complexity proxy.") - print(" C) The intelligence ladder narrative is FALSIFIED for neuron count") - print(" → Correct variable: RECURRENT CONNECTIVITY DENSITY or E/I ratio,") - print(" both of which require full connectome (not yet available for honeybee)") - print() - print(" CURRENT VERDICT: Narrative PARTIALLY FALSIFIED for neuron count as standalone") - print(" variable. GC-rich bias is a necessary but not sufficient condition for high") - print(" neuron count. Stronger claim: GC-rich = capability for dense recurrent cortical") - print(" topology. Honeybee lacks cortex. AU-rich = modular feedforward topology") - print(" regardless of scale.") - print() - print(" IMPLICATION FOR HACHIMOJI: neural_binding carrier profile (GC-dominant from") - print(" SAE) is correct for CORTEX-like recurrent encoding. For honeybee-type modular") - print(" feedforward circuits, c_elegans profile is valid. Both exist in the stack.") - print() - - if r_cxi is not None: - direction = "positive" if r_cxi > 0 else "negative" - print(f" r(GC, complexity_index) = {r_cxi:.3f} [{direction}]") - print(f" NOTE: complexity_index is SCALE-DEPENDENT — C. elegans density(0.037) is") - print(f" high because 302 nodes can all connect; Drosophila density(0.003) is low") - print(f" because 130k nodes physically cannot. Negative r reflects this scaling") - print(f" artefact, NOT that AU-rich brains are structurally more connected.") - print(f" CORRECT INTERPRETATION: density, hub_fraction, E/I must each be") - print(f" normalised within-clade or via log-scale to be comparable.") - if abs(r_cxi) >= 0.7: - print(f" RAW STRUCTURAL METRIC CORRELATION: STRONG (r={r_cxi:.3f}) — but direction") - print(f" is probably scale artefact; use within-clade comparison.") - elif abs(r_cxi) >= 0.4: - print(f" RAW STRUCTURAL METRIC CORRELATION: MODERATE (r={r_cxi:.3f})") - else: - print(f" RAW STRUCTURAL METRIC CORRELATION: WEAK (r={r_cxi:.3f})") - - # Save JSON report - import json - report_path = OUT_DIR / "connectome_codon_audit.json" - with open(report_path, "w") as f: - json.dump({ - "species": results, - "correlations": { - "gc_vs_log_neurons": r_neu, - "gc_vs_complexity_index": r_cxi, - }, - "verdict": { - "honeybee_anomaly": "PARTIALLY_FALSIFIED_neuron_count_hypothesis", - "correct_variable": "recurrent_connectivity_density_OR_ei_ratio", - "carrier_implication": { - "cortex_recurrent": "neural_binding (GC-dominant, SAE-derived)", - "modular_feedforward": "c_elegans (AU-rich, Kazusa 6239)", - }, - }, - }, f, indent=2) - print(f"\nReport saved → {report_path}") - - # Print live C. elegans stats if available - if ce_live: - print("\n── C. ELEGANS LIVE METRICS ────────────────────────────────────────────────") - for k,v in ce_live.items(): - if k in ("_edges", "type_counts"): - continue - print(f" {k:<22} {v}") - print(f" type_counts {ce_live.get('type_counts')}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/connectome/connectome_frack.py b/5-Applications/tools-scripts/connectome/connectome_frack.py deleted file mode 100644 index 4eb7b07c..00000000 --- a/5-Applications/tools-scripts/connectome/connectome_frack.py +++ /dev/null @@ -1,111 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Connectome Frack — run C. elegans connectome topology as a raw filter -through the substrate_index.db to reveal structure beneath semantic compression. - -The connectome provides the wiring diagram. The packages are the content. -The frack is: which packages fire together when the connectome runs?""" - -import sqlite3 -import json -import sys -from collections import defaultdict - -DB = "substrate_index.db" - -def main(): - conn = sqlite3.connect(DB) - cur = conn.cursor() - - # Grab everything with semantic metadata - cur.execute(""" - SELECT pkg, version, domain, concept_anchor, concept_vector, - idea_weights, nd_point, forming_load, confirmed_load, - layer, tier, tags, description - FROM packages - WHERE concept_vector IS NOT NULL OR concept_anchor IS NOT NULL - ORDER BY pkg - """) - rows = cur.fetchall() - print(f"Packages with semantic data: {len(rows)}") - - # Build neuron objects - neurons = [] - by_domain = defaultdict(list) - for row in rows: - (pkg, ver, domain, anchor, cv, iw, nd, - fl, cl, layer, tier, tags, desc) = row - neuron = { - 'pkg': pkg, 'version': ver, 'domain': domain, - 'anchor': anchor, - 'concept_vector': json.loads(cv) if cv else [], - 'idea_weights': json.loads(iw) if iw else {}, - 'forming_load': fl, 'confirmed_load': cl, - 'layer': layer, 'tier': tier, - 'tags': json.loads(tags) if tags else [], - 'description': (desc or '')[:120], - } - neurons.append(neuron) - by_domain[domain].append(neuron) - - # The connectome filter: sort each domain by cognitive load - # Highest forming_load = most "on fire" — ideas actively shifting - # Lowest forming_load = settled/compressed — no longer processing - - print(f"\n{'='*70}") - print(f"CONNECTOME FRACK — raw structure beneath semantic compression") - print(f"{'='*70}") - - total_active = 0 - for domain in sorted(by_domain): - pkgs = by_domain[domain] - active = [p for p in pkgs if p['forming_load'] is not None] - settled = [p for p in pkgs if p['forming_load'] is None] - - print(f"\n{'─'*70}") - print(f"GANGLION: {domain}") - print(f" Neurons: {len(pkgs)} " - f"Active/Forming: {len(active)} " - f"Settled/Compressed: {len(settled)}") - - if active: - total_active += len(active) - active.sort(key=lambda x: -(x['forming_load'] or 0)) - print(f"\n ╔═ ACTIVE / FORMING (load > 0) ═╗") - for p in active[:10]: - load = p['forming_load'] - tier = p['tier'] or '?' - anchor = (p['anchor'] or 'none')[:60] - print(f" ║ {p['pkg']:40s} load={load:6.3f} tier={tier:10s}") - print(f" ║ ↳ {anchor}") - if len(active) > 10: - print(f" ║ ... and {len(active)-10} more") - print(f" ╚{'═'*66}") - - if settled: - print(f"\n ╔═ SETTLED / COMPRESSED (no load) ═╗") - for p in settled[:8]: - tier = p['tier'] or '?' - anchor = (p['anchor'] or 'none')[:60] - print(f" ║ {p['pkg']:40s} tier={tier:10s}") - print(f" ║ ↳ {anchor}") - if len(settled) > 8: - print(f" ║ ... and {len(settled)-8} more") - print(f" ╚{'═'*66}") - - print(f"\n{'='*70}") - print(f"SUMMARY: {len(neurons)} total neurons, {total_active} active/forming") - print(f"Domains: {len(by_domain)}") - print(f"\nThe frack exposes which ideas are still hot (forming_load)") - print(f"vs which have cooled into crystal (no load, compressed).") - print(f"The connectome filter: topology reveals what compression hides.") - - conn.close() - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/connectome/context_proxy.py b/5-Applications/tools-scripts/connectome/context_proxy.py deleted file mode 100644 index e9e46b27..00000000 --- a/5-Applications/tools-scripts/connectome/context_proxy.py +++ /dev/null @@ -1,301 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Local proxy that applies context compression to Anthropic API requests. - -Sits between Cline (or any API client) and the Anthropic API. -Intercepts tool_result content blocks in conversation messages, -compresses them through the context gate, and caches API responses. - -Usage: - python 5-Applications/scripts/context_proxy.py --port 8090 - -Then configure Cline to use http://localhost:8090 as the API base URL. - -Response caching: identical compressed requests return cached responses -without hitting the upstream API. Cache TTL: 2 hours (configurable). -""" - - -import hashlib -import json -import os -import sqlite3 -import sys -import time -from http.server import HTTPServer, BaseHTTPRequestHandler -from pathlib import Path -from urllib.request import Request, urlopen -from urllib.error import HTTPError - -_ROOT = Path(__file__).resolve().parent.parent -if str(_ROOT) not in sys.path: - sys.path.insert(0, str(_ROOT)) -if str(_ROOT / "scripts") not in sys.path: - sys.path.insert(0, str(_ROOT / "scripts")) - -from context_gate import ContextGate, GateMode - -try: - from pbacs.kimi_context_optimizer import KimiContextOptimizer - _kimi_compressor = KimiContextOptimizer(token_budget=16000) -except Exception: - _kimi_compressor = None - -UPSTREAM_URL = "https://api.anthropic.com" -RESPONSE_CACHE_TTL = int(os.environ.get("PROXY_CACHE_TTL_SECS", 7200)) -COMPRESS_THRESHOLD = 2048 # bytes — only compress large tool results - -_HOT_TERMS = [ - "soliton", "omnitoken", "tardygrada", "waveprobe", "ptos", - "hyperlut", "neuromorphic", "geomtree", "kolmogorov", - "metafoam", "hyperfluid", "cognitivesmoother", -] - -_gate = ContextGate( - warden_db_path=_ROOT / "warden_attestation.db", - substrate_db_path=_ROOT / "substrate_index.db", - hot_terms=_HOT_TERMS, - compressor=_kimi_compressor, -) - -# Response cache in substrate_index.db -_CACHE_DB = _ROOT / "substrate_index.db" - - -def _ensure_response_cache(): - if not _CACHE_DB.exists(): - return - try: - conn = sqlite3.connect(str(_CACHE_DB)) - conn.execute( - "CREATE TABLE IF NOT EXISTS response_cache (" - " request_hash TEXT PRIMARY KEY," - " response_json TEXT NOT NULL," - " created_ts REAL," - " hits INTEGER DEFAULT 0" - ")" - ) - conn.commit() - conn.close() - except Exception: - pass - - -def _cache_lookup(request_hash: str) -> str | None: - if not _CACHE_DB.exists(): - return None - try: - conn = sqlite3.connect(str(_CACHE_DB)) - row = conn.execute( - "SELECT response_json, created_ts FROM response_cache " - "WHERE request_hash = ?", - (request_hash,), - ).fetchone() - if row: - age = time.time() - row[1] - if age < RESPONSE_CACHE_TTL: - conn.execute( - "UPDATE response_cache SET hits = hits + 1 " - "WHERE request_hash = ?", - (request_hash,), - ) - conn.commit() - conn.close() - return row[0] - else: - conn.execute( - "DELETE FROM response_cache WHERE request_hash = ?", - (request_hash,), - ) - conn.commit() - conn.close() - except Exception: - pass - return None - - -def _cache_store(request_hash: str, response_json: str): - if not _CACHE_DB.exists(): - return - try: - conn = sqlite3.connect(str(_CACHE_DB)) - conn.execute( - "INSERT OR REPLACE INTO response_cache " - "(request_hash, response_json, created_ts, hits) " - "VALUES (?, ?, ?, 0)", - (request_hash, response_json, time.time()), - ) - conn.commit() - conn.close() - except Exception: - pass - - -def compress_messages(messages: list) -> list: - """Compress tool_result content blocks in the message list.""" - compressed = [] - for msg in messages: - if msg.get("role") == "user" and isinstance(msg.get("content"), list): - new_content = [] - for block in msg["content"]: - if ( - block.get("type") == "tool_result" - and isinstance(block.get("content"), str) - and len(block["content"]) > COMPRESS_THRESHOLD - ): - result = _gate.process( - block["content"], mode=GateMode.COMPRESS - ) - block = dict(block) - block["content"] = result.safe_text - new_content.append(block) - msg = dict(msg) - msg["content"] = new_content - compressed.append(msg) - return compressed - - -def request_hash(body: dict) -> str: - canonical = json.dumps(body, sort_keys=True, separators=(",", ":")) - return hashlib.sha256(canonical.encode()).hexdigest()[:32] - - -class ProxyHandler(BaseHTTPRequestHandler): - def do_POST(self): - content_length = int(self.headers.get("Content-Length", 0)) - raw_body = self.rfile.read(content_length) - - # Parse and compress - try: - body = json.loads(raw_body) - except json.JSONDecodeError: - self._forward_raw(raw_body) - return - - # Only intercept messages endpoint - if self.path == "/v1/messages": - if "messages" in body: - body["messages"] = compress_messages(body["messages"]) - - # Check response cache - req_hash = request_hash(body) - cached = _cache_lookup(req_hash) - if cached and not body.get("stream", False): - self.send_response(200) - self.send_header("Content-Type", "application/json") - self.send_header("X-Cache", "HIT") - cached_bytes = cached.encode() - self.send_header("Content-Length", str(len(cached_bytes))) - self.end_headers() - self.wfile.write(cached_bytes) - return - - # Forward to upstream - compressed_body = json.dumps(body).encode() - self._forward(compressed_body, req_hash if self.path == "/v1/messages" else None) - - def _forward(self, body: bytes, cache_key: str | None): - url = UPSTREAM_URL + self.path - headers = { - k: v for k, v in self.headers.items() - if k.lower() not in ("host", "content-length") - } - headers["Content-Length"] = str(len(body)) - - req = Request(url, data=body, headers=headers, method="POST") - try: - resp = urlopen(req) - resp_body = resp.read() - - self.send_response(resp.status) - for key, val in resp.getheaders(): - if key.lower() not in ("transfer-encoding",): - self.send_header(key, val) - self.send_header("X-Cache", "MISS") - self.end_headers() - self.wfile.write(resp_body) - - # Cache non-streaming responses - if cache_key and not json.loads(body).get("stream", False): - _cache_store(cache_key, resp_body.decode(errors="replace")) - - except HTTPError as e: - self.send_response(e.code) - resp_body = e.read() - self.end_headers() - self.wfile.write(resp_body) - - def _forward_raw(self, body: bytes): - self._forward(body, None) - - def do_GET(self): - url = UPSTREAM_URL + self.path - headers = { - k: v for k, v in self.headers.items() - if k.lower() != "host" - } - req = Request(url, headers=headers, method="GET") - try: - resp = urlopen(req) - resp_body = resp.read() - self.send_response(resp.status) - for key, val in resp.getheaders(): - if key.lower() not in ("transfer-encoding",): - self.send_header(key, val) - self.end_headers() - self.wfile.write(resp_body) - except HTTPError as e: - self.send_response(e.code) - self.end_headers() - self.wfile.write(e.read()) - - def log_message(self, format, *args): - # Minimal logging - if "cache" in str(args).lower() or "error" in str(args).lower(): - super().log_message(format, *args) - - -def main(): - import argparse - p = argparse.ArgumentParser(description="Context compression proxy") - p.add_argument("--port", type=int, default=8090) - p.add_argument("--host", default="127.0.0.1") - args = p.parse_args() - - _ensure_response_cache() - - server = HTTPServer((args.host, args.port), ProxyHandler) - print(f"Context proxy listening on {args.host}:{args.port}") - print(f" Upstream: {UPSTREAM_URL}") - print(f" Cache TTL: {RESPONSE_CACHE_TTL}s") - print(f" Substrate DB: {_CACHE_DB}") - print(f" Hot terms: {len(_HOT_TERMS)}") - print() - print("Configure Cline API base URL: http://localhost:8090") - try: - server.serve_forever() - except KeyboardInterrupt: - print("\nShutting down.") - server.server_close() - - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/crossbreed/crossbreed_shear_validator.py b/5-Applications/tools-scripts/crossbreed/crossbreed_shear_validator.py deleted file mode 100644 index 30ff39bc..00000000 --- a/5-Applications/tools-scripts/crossbreed/crossbreed_shear_validator.py +++ /dev/null @@ -1,192 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -"""Crossbreed Shear Validator — T-C-P Cross-Domain Shear Unity - -Surviving Invariant -------------------- -Name: T-C-P Cross-Domain Shear Unity -Description: The sum of pairwise constraint shears among temporal, coherence, - and progress dimensions across lighthouse and quantum-gravity - domains is exactly unity. -Equation: (T_A C_B - C_A T_B) + (C_A P_B - P_A C_B) + (T_A P_B - P_A T_B) = 1 -Domains: 🕯️ Lighthouse Keeper × 🌌 Quantum Gravity Researcher - -This module provides a deterministic, mathematically-rigid validator for the -shear-unity invariant. It operates on 7-dimensional constraint vectors (T, S, C, -F, R, P, W) but extracts the T/C/P subspace to evaluate the cross-domain shear. -""" - -from __future__ import annotations - -import math -import sys -from typing import Any, Dict, List, Mapping, Sequence - -# ── Constants ───────────────────────────────────────────────────────────────── - -EPSILON: float = 1e-9 -DIMENSIONS: List[str] = ["T", "S", "C", "F", "R", "P", "W"] - -# ── Validator ───────────────────────────────────────────────────────────────── - - -class CrossbreedShearValidator: - """Deterministic validator for the T-C-P Cross-Domain Shear Unity invariant. - - The validator accepts constraint surfaces expressed as 7-dimensional vectors - (or as T/C/P sub-dictionaries) and evaluates the shear equation that must - hold at the intersection boundary of the Lighthouse Keeper and Quantum - Gravity Researcher domains. - """ - - @staticmethod - def _extract_tcp(value: Mapping[str, Any]) -> Dict[str, float]: - """Extract T, C, P floats from a mapping. - - If the input is a sequence, it is treated as a 7D vector ordered - [T, S, C, F, R, P, W] and the T, C, P slots are pulled out. - """ - if isinstance(value, Sequence) and not isinstance(value, (str, bytes)): - vec = [float(v) for v in value] - if len(vec) < 7: - raise ValueError( - f"7D vector required for Hadamard intersection, got {len(vec)} elements" - ) - return {"T": vec[0], "C": vec[2], "P": vec[5]} - - missing = {"T", "C", "P"} - set(value.keys()) - if missing: - raise KeyError(f"Missing required shear dimensions: {missing}") - return {k: float(value[k]) for k in ("T", "C", "P")} - - def compute_shear_unity( - self, - constraints_a: Mapping[str, Any], - constraints_b: Mapping[str, Any], - ) -> Dict[str, Any]: - """Compute the left-hand side of the shear-unity equation. - - Args: - constraints_a: Domain A constraint surface (dict or 7D sequence). - constraints_b: Domain B constraint surface (dict or 7D sequence). - - Returns: - { - "value": float, # computed LHS - "holds": bool, # True if |value - 1.0| <= 1e-9 - "components": { - "tc": T_A*C_B - C_A*T_B, - "cp": C_A*P_B - P_A*C_B, - "tp": T_A*P_B - P_A*T_B, - }, - } - """ - a = self._extract_tcp(constraints_a) - b = self._extract_tcp(constraints_b) - - tc = a["T"] * b["C"] - a["C"] * b["T"] - cp = a["C"] * b["P"] - a["P"] * b["C"] - tp = a["T"] * b["P"] - a["P"] * b["T"] - - value = tc + cp + tp - holds = math.isclose(value, 1.0, abs_tol=EPSILON) - - return { - "value": value, - "holds": holds, - "components": { - "tc": tc, - "cp": cp, - "tp": tp, - }, - } - - def hadamard_intersection( - self, - a: Sequence[float], - b: Sequence[float], - ) -> List[float]: - """Return the 7D Hadamard (element-wise) product of two constraint vectors. - - Args: - a: 7-dimensional constraint vector. - b: 7-dimensional constraint vector. - - Returns: - List of 7 floats representing the intersection surface. - """ - if len(a) != 7 or len(b) != 7: - raise ValueError( - f"Hadamard intersection requires exactly 7D inputs (got {len(a)} and {len(b)})" - ) - return [float(x) * float(y) for x, y in zip(a, b)] - - -# ── Demonstration ───────────────────────────────────────────────────────────── - - -def _demo() -> int: - validator = CrossbreedShearValidator() - - # Actual expert-derived constraints for the Lighthouse Keeper × Quantum - # Gravity Researcher crossbreed (see 6-Documentation/docs/audits/EXHAUSTIVE_DOMAIN_EXPERT_LIST.md). - lighthouse_constraints = { - "T": 0.97, - "S": 0.51, - "C": 0.98, - "F": 0.72, - "R": 0.92, - "P": 0.75, - "W": 0.48, - } - - qg_constraints = { - "T": 0.1, - "S": 0.25, - "C": 0.9, - "F": 0.0, - "R": 0.360673590227324, - "P": 0.5, - "W": 0.274, - } - - print("=" * 60) - print("T-C-P CROSS-DOMAIN SHEAR UNITY — DEMONSTRATION") - print("=" * 60) - print("Domain A: 🕯️ Lighthouse Keeper") - print("Domain B: 🌌 Quantum Gravity Researcher") - print() - - # 1. Shear unity evaluation - result = validator.compute_shear_unity(lighthouse_constraints, qg_constraints) - print(f"Shear value: {result['value']:.12f}") - print(f"Holds (ε ≤ {EPSILON}): {result['holds']}") - print("Components:") - for key, val in result["components"].items(): - print(f" {key}: {val:.12f}") - print() - - # 2. 7D Hadamard intersection - vec_a = [lighthouse_constraints[d] for d in DIMENSIONS] - vec_b = [qg_constraints[d] for d in DIMENSIONS] - intersection = validator.hadamard_intersection(vec_a, vec_b) - print("7D Hadamard intersection:") - for d, val in zip(DIMENSIONS, intersection): - print(f" {d}: {val:.12f}") - print() - - print("=" * 60) - print("DEMONSTRATION COMPLETE") - print("=" * 60) - - return 0 if result["holds"] else 1 - - -if __name__ == "__main__": - sys.exit(_demo()) diff --git a/5-Applications/tools-scripts/crossbreed/ene_crossbreed_shear_quantizer.py b/5-Applications/tools-scripts/crossbreed/ene_crossbreed_shear_quantizer.py deleted file mode 100644 index 9694e8a1..00000000 --- a/5-Applications/tools-scripts/crossbreed/ene_crossbreed_shear_quantizer.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -"""ENE Crossbreed Shear Quantizer — Work-Resource-Progress Shear Quantization - -Surviving Invariant (ENE-Enriched Native Swarm) ------------------------------------------------ -Name: Work-Resource-Progress Shear Quantization -Description: The weighted sum of the work-resource shear and the - resource-progress shear in the Hardware-Compression crossbreed - manifold quantizes exactly to unity. -Equation: 13·(W_A R_B − R_A W_B) + 19·(R_A P_B − P_A R_B) = 1 -Domains: ⚡ Hardware Architect Expert × 🔬 Compression Theory Domain Expert - -ENE Enrichment --------------- -This module applies the ENE Geometry Space and SHA256 Field Equation concepts - to the Domain Crossbreed Swarm. In ENE, decision boundaries are learned from - constraint shears. Here, the shear between two domains' W-R-P subspaces is - quantized to an exact integer resonance. The adversarial critic layer acts as - the ENE regret field — only invariants that exceed the novelty threshold - survive to be committed to the manifold epoch chain. -""" - -from __future__ import annotations - -import math -import sys -from typing import Any, Dict, Mapping, Sequence - -# ── Constants ───────────────────────────────────────────────────────────────── - -EPSILON: float = 1e-9 -DIMENSIONS: list[str] = ["T", "S", "C", "F", "R", "P", "W"] - -# Expert-derived constraints for the ENE-enriched crossbreed -HARDWARE_CONSTRAINTS: Dict[str, float] = { - "T": 0.82, - "S": 0.76, - "C": 0.94, - "F": 0.79, - "R": 0.93, - "P": 0.87, - "W": 0.955, -} - -COMPRESSION_CONSTRAINTS: Dict[str, float] = { - "T": 0.98, - "S": 0.90, - "C": 0.99, - "F": 0.70, - "R": 1.00, - "P": 0.96, - "W": 0.98, -} - - -# ── Shear Quantizer ─────────────────────────────────────────────────────────── - - -class ENECrossbreedShearQuantizer: - """Quantizes the W-R-P shear surface of a hardware-compression crossbreed. - - The validator evaluates the exact integer-weighted determinant identity - that survives the ENE-enriched adversarial critic layer. It operates on - 7-dimensional constraint vectors and extracts the W/R/P subspace to - evaluate the shear quantization. - """ - - @staticmethod - def _extract_wrp(value: Mapping[str, Any]) -> Dict[str, float]: - """Extract W, R, P floats from a 7D mapping or sequence.""" - if isinstance(value, Sequence) and not isinstance(value, (str, bytes)): - vec = [float(v) for v in value] - if len(vec) < 7: - raise ValueError(f"7D vector required, got {len(vec)}") - return {"W": vec[6], "R": vec[4], "P": vec[5]} - missing = {"W", "R", "P"} - set(value.keys()) - if missing: - raise KeyError(f"Missing required shear dimensions: {missing}") - return {k: float(value[k]) for k in ("W", "R", "P")} - - def compute_shear_quantization( - self, - constraints_a: Mapping[str, Any], - constraints_b: Mapping[str, Any], - ) -> Dict[str, Any]: - """Compute the W-R-P shear quantization invariant. - - Returns: - { - "value": float, # computed LHS - "holds": bool, # True if |value - 1.0| <= 1e-9 - "det_wr": float, # W-R shear determinant - "det_rp": float, # R-P shear determinant - } - """ - a = self._extract_wrp(constraints_a) - b = self._extract_wrp(constraints_b) - - det_wr = a["W"] * b["R"] - a["R"] * b["W"] - det_rp = a["R"] * b["P"] - a["P"] * b["R"] - - value = 13.0 * det_wr + 19.0 * det_rp - holds = math.isclose(value, 1.0, abs_tol=EPSILON) - - return { - "value": value, - "holds": holds, - "det_wr": det_wr, - "det_rp": det_rp, - } - - def hadamard_intersection( - self, - a: Sequence[float], - b: Sequence[float], - ) -> list[float]: - """Return the 7D Hadamard product of two constraint vectors.""" - if len(a) != 7 or len(b) != 7: - raise ValueError( - f"Hadamard intersection requires exactly 7D inputs (got {len(a)} and {len(b)})" - ) - return [float(x) * float(y) for x, y in zip(a, b)] - - -# ── Demonstration ───────────────────────────────────────────────────────────── - - -def _demo() -> int: - quantizer = ENECrossbreedShearQuantizer() - - print("=" * 60) - print("ENE CROSSBREED SHEAR QUANTIZATION — DEMONSTRATION") - print("=" * 60) - print("Domain A: ⚡ Hardware Architect Expert") - print("Domain B: 🔬 Compression Theory Domain Expert") - print() - - result = quantizer.compute_shear_quantization( - HARDWARE_CONSTRAINTS, COMPRESSION_CONSTRAINTS - ) - print(f"W-R shear determinant: {result['det_wr']:.12f}") - print(f"R-P shear determinant: {result['det_rp']:.12f}") - print(f"Shear quantization: {result['value']:.12f}") - print(f"Holds (ε ≤ {EPSILON}): {result['holds']}") - print() - - vec_a = [HARDWARE_CONSTRAINTS[d] for d in DIMENSIONS] - vec_b = [COMPRESSION_CONSTRAINTS[d] for d in DIMENSIONS] - intersection = quantizer.hadamard_intersection(vec_a, vec_b) - print("7D Hadamard intersection:") - for d, val in zip(DIMENSIONS, intersection): - print(f" {d}: {val:.12f}") - print() - - print("=" * 60) - print("DEMONSTRATION COMPLETE") - print("=" * 60) - - return 0 if result["holds"] else 1 - - -if __name__ == "__main__": - sys.exit(_demo()) diff --git a/5-Applications/tools-scripts/crypto/buy_zec_test.py b/5-Applications/tools-scripts/crypto/buy_zec_test.py deleted file mode 100644 index 4215cc98..00000000 --- a/5-Applications/tools-scripts/crypto/buy_zec_test.py +++ /dev/null @@ -1,47 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import os -import asyncio -import json -import time -from coinbase.rest import RESTClient -from dotenv import load_dotenv - -async def buy_zec_test(): - load_dotenv() - key_name = os.getenv("COINBASE_API_KEY_NAME") - key_secret = os.getenv("COINBASE_API_KEY_PRIVATE_KEY", "").replace("\\n", "\n") - client = RESTClient(api_key=key_name, api_secret=key_secret) - - amount_usd = 0.05 - print(f"[*] Attempting to BUY ${amount_usd:.2f} worth of ZEC...") - - try: - # Create a market order - # Note: Advanced Trade market orders usually require quote_size (USD amount) - order = client.create_order( - client_order_id=f"ZEC-TEST-BUY-{int(time.time())}", - product_id="ZEC-USD", - side="BUY", - order_configuration={ - "market_market_ioc": { - "quote_size": str(amount_usd) - } - } - ) - print(f"[+] Order Response: {order}") - - if order.success: - print(f"[+] SUCCESS: Purchased ${amount_usd:.2f} of ZEC.") - else: - print(f"[!] Order failed: {order.error_response}") - - except Exception as e: - print(f"[!] Error executing buy: {e}") - -if __name__ == "__main__": - asyncio.run(buy_zec_test()) diff --git a/5-Applications/tools-scripts/crypto/execute_zec_shielding.py b/5-Applications/tools-scripts/crypto/execute_zec_shielding.py deleted file mode 100644 index 3ceb90d5..00000000 --- a/5-Applications/tools-scripts/crypto/execute_zec_shielding.py +++ /dev/null @@ -1,104 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import asyncio -import os -import json -from coinbase_client_helper import CoinbaseClient -from z_bridge_protocol import ZBridgeProtocol -from dotenv import load_dotenv - -async def execute_shielding(test_mode=True): - from coinbase.rest import RESTClient - load_dotenv() - - key_name = os.getenv("COINBASE_API_KEY_NAME") - key_secret = os.getenv("COINBASE_API_KEY_PRIVATE_KEY", "").replace("\\n", "\n") - client = RESTClient(api_key=key_name, api_secret=key_secret) - bridge = ZBridgeProtocol() - - address = bridge.state["config"]["shielded_pool_z_address"] - if address == "__PENDING_USER_Z_ADDRESS__": - print("[!] Error: No Z-Address configured in bridge state.") - return - - # Validation: Ensure it's not a placeholder and looks like a real Z-address (UA starts with u1) - if not address.startswith("u1"): - print(f"[!] Error: Address {address} does not appear to be a valid Zcash Unified Address.") - return - - # 1. Get ZEC Account UUID - try: - accounts = client.get_accounts() - # Find the ZEC account - zec_account = next((a for a in accounts.accounts if a.currency == 'ZEC'), None) - if not zec_account: - print("[!] Error: ZEC account not found on Coinbase.") - return - account_uuid = zec_account.uuid - print(f"[*] Found ZEC Account: {account_uuid}") - except Exception as e: - print(f"[!] Error fetching accounts: {e}") - return - - # 2. Fetch price for test calculation - try: - product = client.get_product("ZEC-USD") - price = float(product.price) - except Exception as e: - print(f"[!] Could not fetch price: {e}") - return - - if test_mode: - amount = round(0.05 / price, 6) - print(f"[*] TEST MODE: Sending $0.05 worth of ZEC ({amount:.6f} ZEC)") - else: - amount = bridge.state["totals"]["accumulated_zec"] - - print(f"[*] INITIATING SHIELDING: {amount:.6f} ZEC -> {address}") - - # 3. Execute Send Money (v2 Transaction via SDK) - try: - # Note: The SDK might use 'send_money' or we may need a raw post to /v2/ - # Testing if 'send_money' exists based on docs - if hasattr(client, 'send_money'): - resp = client.send_money( - account_id=account_uuid, - to=address, - amount=str(amount), - currency="ZEC", - idem=f"Z-SHIELD-{int(time.time())}" - ) - else: - # Manual post to v2 if SDK method is missing - payload = { - "type": "send", - "to": address, - "amount": str(amount), - "currency": "ZEC", - "idem": f"Z-SHIELD-{int(time.time())}" - } - # The client.post method handles v3 prefix, so we need to bypass or use full URL - # but the SDK might have a way to target v2. - # Given the errors, I will try a raw post if send_money fails. - raise AttributeError("send_money not found") - - print(f"[+] Withdrawal initiated. Response: {resp}") - bridge.prepare_shielding(amount) - print("[+] Bridge state updated to SHIELDING_PENDING.") - except Exception as e: - print(f"[!] Withdrawal failed: {e}") - # Trying raw post to v2 as fallback - try: - print("[*] Attempting raw v2 transaction fallback...") - # We need to manually construct the JWT for v2 if the SDK doesn't support it easily - # but for this demo, we'll log the final failure. - bridge.log_event("SHIELDING_FAILED", amount, "Exchange_Wallet", address) - except Exception: - pass - -if __name__ == "__main__": - asyncio.run(execute_shielding()) diff --git a/5-Applications/tools-scripts/crypto/miner_common.py b/5-Applications/tools-scripts/crypto/miner_common.py deleted file mode 100644 index dae8ec59..00000000 --- a/5-Applications/tools-scripts/crypto/miner_common.py +++ /dev/null @@ -1,24 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# Common utilities for neuromorphic miner benchmark/proof scripts. -# Extracted to avoid duplication between benchmark_uplift.py and -# final_proof_uplift.py. -from __future__ import annotations - -import hashlib -import os -import struct -import sys -import time - -# Ensure sibling modules in 5-Applications/scripts/ are importable. -sys.path.append(os.path.dirname(os.path.abspath(__file__))) - -# Block header template (Bitcoin-style, 80 bytes, all-zero for benchmarking). -HEADER_BASE = bytes.fromhex( - "00000020" + "00" * 64 + "00" * 32 + "00000000" + "ffff001d" + "00000000" -) diff --git a/5-Applications/tools-scripts/crypto/mint_claim_agent.py b/5-Applications/tools-scripts/crypto/mint_claim_agent.py deleted file mode 100644 index 303b6035..00000000 --- a/5-Applications/tools-scripts/crypto/mint_claim_agent.py +++ /dev/null @@ -1,152 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Mint/claim agent: create KOT mint intents from tunnel probe sidecars. - -Behavior: -- Scan a sidecar directory for `*.tunnel_probe.json` files. -- For each with `status=="ok"`, validate wavestate sha256 against the rebuilt payload file. -- Read `funding_policy` from `egress_surface.json` to compute mint amounts. -- Emit mint intent JSON files under `5-Applications/out/omnitoken_bridge/mint_intents/`. -- Optional `--execute` will append a ledger entry to `5-Applications/out/omnitoken_bridge/ledger.log` (placeholder for `LEDGER_COMMIT`). - -This is a minimal, auditable prototype — adjust allocation formula to match real economics. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import os -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Any - -ROOT = Path(__file__).resolve().parent.parent -DEFAULT_SIDECAR_DIR = Path.home() / "Gdrive" / "_omnitoken_probe" -SURFACE_PATH = ROOT / "out" / "omnitoken_bridge" / "egress_surface.json" -INTENTS_DIR = ROOT / "out" / "omnitoken_bridge" / "mint_intents" -LEDGER_LOG = ROOT / "out" / "omnitoken_bridge" / "ledger.log" - - -def utc_now() -> str: - return datetime.now(timezone.utc).isoformat() - - -def sha256_file(path: Path) -> str: - h = hashlib.sha256() - with path.open("rb") as f: - for chunk in iter(lambda: f.read(8192), b""): - h.update(chunk) - return h.hexdigest() - - -def load_surface(surface_path: Path) -> Dict[str, Any]: - if not surface_path.exists(): - raise FileNotFoundError(f"surface not found: {surface_path}") - return json.loads(surface_path.read_text(encoding="utf-8")) - - -def compute_mint_amount(policy: Dict[str, Any], wavestate: Dict[str, Any]) -> int: - # Tunable formula: - # - `mint_unit`: one of KB, MB, GB (default MB) - # - `mint_amount_per_unit`: tokens to mint per unit (falls back to mint_amount_per_kg) - bytes_ = int(wavestate.get("bytes", 0)) - unit = str(policy.get("mint_unit", "MB")).upper() - unit_map = {"KB": 1024, "MB": 1024 * 1024, "GB": 1024 * 1024 * 1024} - unit_bytes = unit_map.get(unit, 1024 * 1024) - per_unit = int(policy.get("mint_amount_per_unit", policy.get("mint_amount_per_kg", 1000))) - - # compute number of units (round up small payloads to 1 unit) - units = max(1, bytes_ // unit_bytes) - - # apply soft cap to prevent huge mints for very large files (policy can override) - soft_cap_units = int(policy.get("mint_soft_cap_units", 1000)) - if units > soft_cap_units: - units = soft_cap_units - - return units * per_unit - - -def make_intent(sidecar: Dict[str, Any], policy: Dict[str, Any], surface: Dict[str, Any]) -> Dict[str, Any]: - wavestate = sidecar.get("wavestate") or {} - amount = compute_mint_amount(policy, wavestate) - intent = { - "intent_id": f"mint-{sidecar.get('register_id','')}-{int(datetime.now().timestamp())}", - "token": policy.get("token", "KOT"), - "amount": amount, - "unit": "KOT", - "source_register_id": sidecar.get("register_id"), - "wavestate": wavestate, - "rebuilt_path": sidecar.get("rebuilt_path"), - "created_utc": utc_now(), - "status": "pending", - "notes": "auto-generated by mint_claim_agent prototype", - } - return intent - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--sidecar-dir", default=str(DEFAULT_SIDECAR_DIR)) - ap.add_argument("--surface", default=str(SURFACE_PATH)) - ap.add_argument("--out-dir", default=str(INTENTS_DIR)) - ap.add_argument("--dry-run", action="store_true") - ap.add_argument("--execute", action="store_true", help="Append ledger entries (placeholder) and mark intents as executed") - args = ap.parse_args() - - sidecar_dir = Path(os.path.expanduser(args.sidecar_dir)) - out_dir = Path(args.out_dir) - out_dir.mkdir(parents=True, exist_ok=True) - - surface = load_surface(Path(args.surface)) - policy = surface.get("funding_policy") or {} - - sidecars = sorted(sidecar_dir.glob("*.tunnel_probe.json")) - if not sidecars: - print("No sidecars found.") - return - - for sc in sidecars: - try: - payload = json.loads(sc.read_text(encoding="utf-8")) - except Exception: - print(f"skipping invalid sidecar: {sc}") - continue - - if payload.get("status") != "ok": - print(f"skipping non-ok sidecar: {sc.name}") - continue - - wavestate = payload.get("wavestate") or {} - rebuilt = Path(payload.get("rebuilt_path") or "") - verified = False - if rebuilt.exists() and wavestate.get("sha256"): - actual = sha256_file(rebuilt) - verified = actual == wavestate.get("sha256") - - if not verified: - print(f"warning: wavestate mismatch or rebuilt file missing for {sc.name}") - - intent = make_intent(payload, policy, surface) - intent_path = out_dir / f"{intent['intent_id']}.json" - intent_path.write_text(json.dumps(intent, indent=2) + "\n", encoding="utf-8") - print(f"wrote intent: {intent_path} verified={verified}") - - if args.execute: - # Append to local ledger log (blockchain anchoring requires external integration) - entry = {"time": utc_now(), "intent_id": intent["intent_id"], "amount": intent["amount"], "token": intent["token"]} - with LEDGER_LOG.open("a", encoding="utf-8") as fh: - fh.write(json.dumps(entry) + "\n") - intent["status"] = "executed" - intent_path.write_text(json.dumps(intent, indent=2) + "\n", encoding="utf-8") - print(f"executed intent (ledger append): {intent['intent_id']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/crypto/parity_bond.py b/5-Applications/tools-scripts/crypto/parity_bond.py deleted file mode 100644 index c8feadc3..00000000 --- a/5-Applications/tools-scripts/crypto/parity_bond.py +++ /dev/null @@ -1,57 +0,0 @@ -#!/usr/bin/env python3 -import json -import os -import sys - -# Fixed-Point Configuration: [16.16] -> 32-bit total -# (First 16 bits = Integer, Last 16 bits = Fractional) -PRECISION = 16 -SCALE = 2 ** PRECISION - -def to_fixed(val): - """Convert float to [16.16] fixed point integer.""" - try: - # Clamp to 16.16 range - clamped = max(-32768.0, min(32767.0, float(val))) - return int(clamped * SCALE) & 0xFFFFFFFF - except Exception: - return 0 - -def verify_parity(trace_line): - """Verify bit-accurate parity for a single trace entry.""" - data = json.loads(trace_line) - obs = data.get("observables", {}) - - # Software Floats (Synced with warden.rs:1173) - f_dmt = obs.get("dmt_t", 0) - f_torsion = obs.get("torsion_t", 0) - - - # Hardware Expectations (Simulated) - hw_dmt = to_fixed(f_dmt) - hw_torsion = to_fixed(f_torsion) - - print(f"[🛡️ PARITY] Sequence {data.get('sequence_id')}") - print(f" DMT Score: Software={f_dmt:.4f} -> HW=0x{hw_dmt:08X}") - print(f" Torsion: Software={f_torsion:.4f} -> HW=0x{hw_torsion:08X}") - - return True - -def main(): - if not os.path.exists("lambda_trace.jsonl"): - print("[🔥 PARITY] Error: lambda_trace.jsonl not found.") - sys.exit(1) - - print("[🛡️ PARITY] Analyzing Hardware-Software Alignment...") - with open("lambda_trace.jsonl", 'r') as f: - lines = f.readlines() - if not lines: - print("[⚠️ PARITY] Trace is empty.") - return - - # Verify the most recent entries - for line in lines[-5:]: - verify_parity(line) - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/crypto/rfc3161_stamp.py b/5-Applications/tools-scripts/crypto/rfc3161_stamp.py deleted file mode 100644 index 4273defd..00000000 --- a/5-Applications/tools-scripts/crypto/rfc3161_stamp.py +++ /dev/null @@ -1,100 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""RFC 3161 timestamp utility for the attestation pipeline. - -Requests a notarized timestamp from FreeTSA for any file and saves -the response as .tsr alongside it. Both attestation scripts -call stamp() after writing their output JSON. - -Verify later with: - openssl ts -verify -in .tsr -data \ - -CAfile 5-Applications/scripts/freetsa_chain.pem -""" -# import subprocess (REMOVED BY WARDEN) -import urllib.request -from pathlib import Path -from typing import Dict - -_CHAIN = Path(__file__).parent / "freetsa_chain.pem" -_TSA_URL = "https://freetsa.org/tsr" - - -def stamp(out_path: Path, tsa_url: str = _TSA_URL) -> Dict[str, str]: - """Request an RFC 3161 timestamp for *out_path*. - - Writes .tsr next to the target file. - Returns a dict with timestamp_utc, serial, tsr_path, and verify_cmd. - Raises RuntimeError if openssl or the TSA request fails. - """ - out_path = Path(out_path) - tsq_path = out_path.with_suffix(".tsq") - tsr_path = out_path.with_suffix(".tsr") - - # Generate timestamp request - result = subprocess.run( - ["openssl", "ts", "-query", "-data", str(out_path), - "-no_nonce", "-sha256", "-cert", "-out", str(tsq_path)], - capture_output=True, - ) - if result.returncode != 0: - raise RuntimeError(f"openssl ts -query failed: {result.stderr.decode()}") - - # POST to TSA - try: - req = urllib.request.Request( - tsa_url, - data=tsq_path.read_bytes(), - headers={"Content-Type": "application/timestamp-query"}, - ) - with urllib.request.urlopen(req, timeout=30) as resp: - tsr_path.write_bytes(resp.read()) - finally: - tsq_path.unlink(missing_ok=True) - - # Extract human-readable metadata - meta = subprocess.run( - ["openssl", "ts", "-reply", "-in", str(tsr_path), "-text"], - capture_output=True, text=True, - ) - info: Dict[str, str] = {"tsr_path": str(tsr_path), "tsa_url": tsa_url} - for line in meta.stdout.splitlines(): - if "Time stamp:" in line: - info["timestamp_utc"] = line.split("Time stamp:")[-1].strip() - elif "Serial number:" in line: - info["serial"] = line.split("Serial number:")[-1].strip() - elif "Policy OID:" in line: - info["policy_oid"] = line.split("Policy OID:")[-1].strip() - - info["verify_cmd"] = ( - f"openssl ts -verify -in {tsr_path} -data {out_path} " - f"-CAfile {_CHAIN}" - ) - return info - - -def verify(out_path: Path) -> bool: - """Quick verify — returns True if the .tsr next to *out_path* is valid.""" - tsr_path = Path(out_path).with_suffix(".tsr") - if not tsr_path.exists(): - return False - result = subprocess.run( - ["openssl", "ts", "-verify", "-in", str(tsr_path), - "-data", str(out_path), "-CAfile", str(_CHAIN)], - capture_output=True, - ) - return result.returncode == 0 diff --git a/5-Applications/tools-scripts/crypto/z_bridge_protocol.py b/5-Applications/tools-scripts/crypto/z_bridge_protocol.py deleted file mode 100644 index c96eaf24..00000000 --- a/5-Applications/tools-scripts/crypto/z_bridge_protocol.py +++ /dev/null @@ -1,151 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Z-Bridge Protocol: Auditable Shielded-to-Transparent Orchestrator -Manages states for ZEC accumulation, shielding, and unshielding. -""" - -import json -import time -import hashlib -from pathlib import Path -from dataclasses import dataclass, asdict -from typing import Dict, List, Optional - -ROOT = Path(__file__).resolve().parent.parent -STATE_FILE = ROOT / "z_bridge_state.json" -TARGET_T_ADDRESS = "t1eC3RZuqBjEnXcT45RQGVPScxJYoSmdWPy" - -@dataclass -class ZBridgeEvent: - timestamp: float - state: str - amount_zec: float - source: str - destination: str - attestation_hash: str - tx_payload_ready: bool = False - -class ZBridgeProtocol: - def __init__(self, z_address: Optional[str] = None): - self.z_address = z_address - self.state = self.load_state() - - def load_state(self) -> Dict: - if STATE_FILE.exists(): - with open(STATE_FILE, 'r') as f: - return json.load(f) - return { - "bridge_id": f"Z-BRIDGE-{int(time.time())}", - "config": { - "target_t_address": TARGET_T_ADDRESS, - "shielded_pool_z_address": self.z_address or "__PENDING_USER_Z_ADDRESS__", - "viewing_key": "__PENDING_USER_INPUT__" - }, - "status": "INITIALIZED", - "totals": { - "accumulated_zec": 0.0, - "shielded_zec": 0.0, - "settled_zec": 0.0 - }, - "history": [] - } - - def save_state(self): - with open(STATE_FILE, 'w') as f: - json.dump(self.state, f, indent=2) - - def log_event(self, state: str, amount: float, source: str, destination: str): - event_time = time.time() - # Map state to TSM opcode - opcode_map = { - "ACCUMULATING": "0x01", - "SHIELDING_PENDING": "0x31", - "SHIELDED": "0x31", - "UNSHIELDING_PENDING": "0x32", - "SETTLED": "0x32" - } - opcode = opcode_map.get(state, "0x00") - - # Generate Precision-Locked Attestation - attestation_input = f"{opcode}|{state}|{amount}|{source}|{destination}|{event_time}" - attestation_hash = hashlib.sha256(attestation_input.encode()).hexdigest() - - event = ZBridgeEvent( - timestamp=event_time, - state=state, - amount_zec=amount, - source=source, - destination=destination, - attestation_hash=attestation_hash - ) - self.state["history"].append(asdict(event)) - self.state["status"] = state - self.save_state() - print(f"[*] STATE TRANSITION: {state} | Opcode: {opcode} | Amount: {amount} ZEC") - print(f" Attestation: {attestation_hash}") - - def update_accumulation(self, amount: float): - self.state["totals"]["accumulated_zec"] += amount - self.log_event("ACCUMULATING", amount, "Exchange_API", "Exchange_Wallet") - - def prepare_shielding(self, amount: float): - if self.state["config"]["shielded_pool_z_address"] == "__PENDING_USER_Z_ADDRESS__": - print("[!] ERROR: Cannot shield without a Z-Address.") - return - - print("\n=== SHIELDING INSTRUCTIONS (Withdrawal from Exchange) ===") - print(f"Step 1: Go to your Exchange withdrawal page.") - print(f"Step 2: Asset: Zcash (ZEC)") - print(f"Step 3: Amount: {amount} ZEC") - print(f"Step 4: Destination: {self.state['config']['shielded_pool_z_address']}") - print("========================================================\n") - - self.log_event("SHIELDING_PENDING", amount, "Exchange_Wallet", self.state["config"]["shielded_pool_z_address"]) - - def confirm_shielded(self, amount: float): - self.state["totals"]["shielded_zec"] += amount - self.log_event("SHIELDED", amount, self.state["config"]["shielded_pool_z_address"], "Shielded_Pool") - - def prepare_unshielding(self, amount: float): - print("\n=== UNSHIELDING INSTRUCTIONS (Forwarding to Coinbase) ===") - print(f"Source: {self.state['config']['shielded_pool_z_address']}") - print(f"Destination: {self.state['config']['target_t_address']}") - print(f"Amount: {amount} ZEC") - print("Note: This transaction will be public on the blockchain.") - print("========================================================\n") - - self.log_event("UNSHIELDING_PENDING", amount, "Shielded_Pool", self.state["config"]["target_t_address"]) - - def confirm_settled(self, amount: float): - self.state["totals"]["settled_zec"] += amount - self.log_event("SETTLED", amount, "Shielded_Pool", self.state["config"]["target_t_address"]) - -if __name__ == "__main__": - # Example simulation of the lifecycle - bridge = ZBridgeProtocol() - - # 1. Start Accumulation - bridge.update_accumulation(1.0) - - # 2. Set Z-Address (User would provide this) - bridge.state["config"]["shielded_pool_z_address"] = "zs1...mock...zaddress" - - # 3. Request Shielding - bridge.prepare_shielding(1.0) - - # 4. Confirm Shielded (Manual step after blockchain confirmation) - bridge.confirm_shielded(1.0) - - # 5. Request Unshielding (Forwarding) - bridge.prepare_unshielding(1.0) - - # 6. Confirm Settled - bridge.confirm_settled(1.0) - - print("\n[+] Bridge state updated in z_bridge_state.json") diff --git a/5-Applications/tools-scripts/crypto/zcash_source_to_tsm.py b/5-Applications/tools-scripts/crypto/zcash_source_to_tsm.py deleted file mode 100644 index 96305922..00000000 --- a/5-Applications/tools-scripts/crypto/zcash_source_to_tsm.py +++ /dev/null @@ -1,100 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import json -import os -# import subprocess (REMOVED BY WARDEN) -import hashlib -import sys -from pathlib import Path - -# Add project root to sys.path to import TSM_COMPILER -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -try: - from TSM_COMPILER import TSM_Kernel -except ImportError: - from TSM_COMPILER import TSM_Kernel - -def transpile_zcash_source_to_logic_signal_substrate(): - print("[*] Transpiling Zcash source core to TSM...") - - source_dir = Path("zcash_source/src/zcash") - if not source_dir.exists(): - print("[!] Zcash source directory not found.") - return - - # Extract protocol parameters from headers (simulated extraction) - # In a full transpilation, we would parse the C++ classes and constants. - protocol_manifest = { - "active_systems": ["ZCASH_CORE_TRANSPILATION"], - "isa_version": "ISA-v1", - "protocol_parameters": { - "sprout_identifier": "ZCASH_SPROUT", - "sapling_identifier": "ZCASH_SAPLING", - "orchard_identifier": "ZCASH_ORCHARD", - "unified_address_format": "ZIP-316", - "merkle_tree_depth": 32, - "hash_function": "BLAKE2b" - }, - "substrate_mappings": { - "shielded_pool": "TSM_QUANTUM_REFINERY", - "nullifier_set": "TSM_BLACKHOLE_MANIFOLD", - "commitment_tree": "TSM_HYPERDAG_ND" - }, - "opcodes_extended": { - "0x50": "GENERATE_UNIFIED_ADDRESS", - "0x51": "CREATE_SHIELDED_NOTE", - "0x52": "PROVE_SPENDING_AUTH", - "0x53": "COMMIT_TO_LEDGER", - "0x60": "HARDWELD_SIMULATE" - } - } - - # Initialize Kernel and absorb the transpiled protocol - kernel = TSM_Kernel(substrate="diamondoid_hydride") - out_file = "zcash_protocol_core.logic_signal_substrate.json" - - # Store the manifest in the manifold - manifold_id = kernel.absorb(out_file, protocol_manifest) - - logic_signal_substrate_doc = { - "logic_signal_substrate_version": "v3.2-USAL", - "isa_version": "ISA-v1", - "manifold_id": manifold_id, - "substrate_transparency": "ENABLED", - "stability_metric": 0.98, - "transpilation_meta": { - "source": "https://github.com/zcash/zcash", - "commit": "head", - "timestamp": "2026-03-20T16:30:00Z" - }, - "logic_surface": protocol_manifest - } - - with open(out_file, 'w') as f: - json.dump(logic_signal_substrate_doc, f, indent=2) - - print(f"[+] Zcash protocol core transpiled to {out_file}.") - print(f"[+] USAL Manifold ID: {manifold_id}") - - # Rebuild the Graph OS DB to include the new protocol - print("[*] Rebuilding Graph OS metadata database...") - subprocess.run([sys.executable, "5-Applications/scripts/collapse_research_to_foam.py"], - check=True) - -if __name__ == "__main__": - transpile_zcash_source_to_logic_signal_substrate() diff --git a/5-Applications/tools-scripts/crypto/zcash_tsm_native_demo.py b/5-Applications/tools-scripts/crypto/zcash_tsm_native_demo.py deleted file mode 100644 index c97019ed..00000000 --- a/5-Applications/tools-scripts/crypto/zcash_tsm_native_demo.py +++ /dev/null @@ -1,51 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Graph OS Native Zcash Protocol Demo (TSM-Native) -Demonstrates the implementation of Zcash primitives directly in the TSM framework. -""" - -import sys -import os -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -sys.path.append(str(ROOT)) - -try: - from logic_signal_substrate_mcp_harness import TSMKernel -except ImportError: - from logic_signal_substrate_mcp_harness import TSMKernel - -def run_native_zcash_demo(): - print("[*] Initializing TSM-Native Zcash Protocol Logic...") - kernel = TSMKernel() - - # 1. Derive Orchard Incoming Viewing Key (IVK) - # Opcode 0x70 - spending_key = "architect_node_0_secret_entropy" - ivk = kernel.execute([("0x70", [spending_key])])[0] - print(f"[+] Derived Orchard IVK: {ivk}") - - # 2. Generate Unified Address (ZIP-316) - # Opcode 0x71 - # Receivers: [Orchard, Sapling, Transparent] - receivers = [ivk, "zs1saplingmock", "t1transparentmock"] - ua = kernel.execute([("0x71", [receivers])])[0] - print(f"[+] Generated Native Unified Address: {ua}") - - # 3. Compute Pedersen Hash for Merkle Tree - # Opcode 0x72 - data_to_hash = "shielded_note_data_soliton_001" - p_hash = kernel.execute([("0x72", [data_to_hash])])[0] - print(f"[+] Computed Pedersen Hash: {p_hash}") - - print("\n[+] TSM-Native Zcash implementation verified and operational.") - -if __name__ == "__main__": - run_native_zcash_demo() diff --git a/5-Applications/tools-scripts/crypto/zk_stark_encoding_scheme.py b/5-Applications/tools-scripts/crypto/zk_stark_encoding_scheme.py deleted file mode 100644 index c0db5d65..00000000 --- a/5-Applications/tools-scripts/crypto/zk_stark_encoding_scheme.py +++ /dev/null @@ -1,176 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Minimal encoding scheme for ZK-STARK proofs. -Triplet: metadata + reconstruction_manifest + sha256 -""" - -import json -import hashlib -import zlib -from pathlib import Path -from datetime import datetime -from typing import Dict, Any, Optional - - -def encode_proofs_triplet( - proofs_jsonl_path: str, - output_dir: str = "out", - metadata: Optional[Dict[str, Any]] = None, -) -> Dict[str, str]: - """ - Encode proofs into three-part scheme: metadata, manifest, sha256. - - Args: - proofs_jsonl_path: Path to .jsonl file containing proofs - output_dir: Directory for output files - metadata: Optional metadata dict to include (default: auto-generated) - - Returns: - Dict with keys: metadata_path, manifest_path, sha256_hash - """ - proofs_path = Path(proofs_jsonl_path) - output_path = Path(output_dir) - output_path.mkdir(exist_ok=True) - - # Read proofs - proofs = [] - with open(proofs_path, "r") as f: - for line in f: - if line.strip(): - proofs.append(json.loads(line)) - - if not proofs: - raise ValueError(f"No proofs found in {proofs_jsonl_path}") - - # 1. METADATA: proof set summary - timestamp = datetime.utcnow().isoformat() + "Z" - proof_count = len(proofs) - total_amount = sum(p.get("amount_usd", 0.0) for p in proofs) - proof_ids = [p.get("payout_intent_id") for p in proofs] - constraint_hash = proofs[0].get("constraint_hash") if proofs else None - - metadata_dict = metadata or { - "encoding_version": "1.0", - "scheme": "triplet_metadata_manifest_sha256", - "timestamp_utc": timestamp, - "proof_count": proof_count, - "total_amount_usd": total_amount, - "constraint_hash": constraint_hash, - "source_file": str(proofs_path.name), - } - - # 2. RECONSTRUCTION MANIFEST: how to reassemble proofs - proof_refs = [] - offset = 0 - for i, proof in enumerate(proofs): - proof_line = json.dumps(proof) - proof_line_bytes = (proof_line + "\n").encode("utf-8") - proof_refs.append({ - "index": i, - "payout_intent_id": proof.get("payout_intent_id"), - "byte_offset": offset, - "byte_length": len(proof_line_bytes), - "constraint_hash": proof.get("constraint_hash"), - }) - offset += len(proof_line_bytes) - - manifest_dict = { - "reconstruction_version": "1.0", - "total_proofs": proof_count, - "total_bytes": offset, - "proof_references": proof_refs, - "encoding_metadata": metadata_dict, - } - - # Write METADATA file - metadata_filename = f"metadata_{timestamp.replace(':', '-').replace('.', '-')}.json" - metadata_file = output_path / metadata_filename - with open(metadata_file, "w") as f: - json.dump(metadata_dict, f, indent=2) - - # Write MANIFEST file - manifest_filename = f"manifest_{timestamp.replace(':', '-').replace('.', '-')}.json" - manifest_file = output_path / manifest_filename - with open(manifest_file, "w") as f: - json.dump(manifest_dict, f, indent=2) - - # 3. SHA256: integrity hash over original proofs + metadata + manifest - combined_preimage = json.dumps(metadata_dict) + json.dumps(manifest_dict) - for proof in proofs: - combined_preimage += json.dumps(proof, sort_keys=True) - - sha256_hash = hashlib.sha256(combined_preimage.encode("utf-8")).hexdigest() - - return { - "metadata_file": str(metadata_file), - "manifest_file": str(manifest_file), - "sha256": sha256_hash, - "proof_count": proof_count, - "total_amount_usd": total_amount, - } - - -def verify_triplet( - metadata_path: str, - manifest_path: str, - sha256_reference: str, - proofs_jsonl_path: str, -) -> bool: - """ - Verify encoded triplet integrity. - - Args: - metadata_path: Path to metadata JSON - manifest_path: Path to manifest JSON - sha256_reference: Expected SHA256 hash - proofs_jsonl_path: Path to original proofs JSONL - - Returns: - True if triplet verifies, False otherwise - """ - with open(metadata_path) as f: - metadata = json.load(f) - - with open(manifest_path) as f: - manifest = json.load(f) - - proofs = [] - with open(proofs_jsonl_path) as f: - for line in f: - if line.strip(): - proofs.append(json.loads(line)) - - # Recompute hash - combined = json.dumps(metadata) + json.dumps(manifest) - for proof in proofs: - combined += json.dumps(proof, sort_keys=True) - - computed_hash = hashlib.sha256(combined.encode("utf-8")).hexdigest() - - return computed_hash == sha256_reference - - -if __name__ == "__main__": - import sys - - if len(sys.argv) < 2: - print("Usage: python zk_stark_encoding_scheme.py [output_dir]") - print("\nExample:") - print(" python zk_stark_encoding_scheme.py 5-Applications/out/zk_stark_compliant_proofs.jsonl") - sys.exit(1) - - proofs_file = sys.argv[1] - output_dir = sys.argv[2] if len(sys.argv) > 2 else "out" - - result = encode_proofs_triplet(proofs_file, output_dir) - print(json.dumps(result, indent=2)) - print("\n✓ Encoding complete:") - print(f" Metadata: {result['metadata_file']}") - print(f" Manifest: {result['manifest_file']}") - print(f" SHA256: {result['sha256']}") diff --git a/5-Applications/tools-scripts/crypto/zk_stark_spending_proof.py b/5-Applications/tools-scripts/crypto/zk_stark_spending_proof.py deleted file mode 100644 index f064ff73..00000000 --- a/5-Applications/tools-scripts/crypto/zk_stark_spending_proof.py +++ /dev/null @@ -1,271 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""ZK-STARK spending proof generation and verification. - -This module creates and verifies zero-knowledge STARK proofs for payout spending -constraints, allowing cryptographic verification that spending is within bounds -without revealing exact amounts or cumulative totals. - -Constraints: -- Per-payout ceiling (max_amount_usd) -- Daily cumulative ceiling (max_daily_usd) -- Weekly cumulative ceiling (max_weekly_usd) -- Recipient spending ceiling (max_per_recipient_usd) - -Each proof binds a payout_intent_id to an amount under a spending circuit. -Proofs can be aggregated to verify cumulative constraints. -""" - -from __future__ import annotations - -import hashlib -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - - -def hash_spending_constraint(constraint: Dict[str, Any]) -> str: - """Hash a spending constraint to create a circuit commitment.""" - serialized = json.dumps(constraint, sort_keys=True) - return hashlib.sha256(serialized.encode("utf-8")).hexdigest() - - -def hash_payout_proof_input(payout_intent_id: str, amount_usd: float, constraint_hash: str) -> str: - """Hash payout input data for proof generation.""" - data = f"{payout_intent_id}|{amount_usd}|{constraint_hash}" - return hashlib.sha256(data.encode("utf-8")).hexdigest() - - -def generate_spending_proof( - payout_intent_id: str, - amount_usd: float, - recipient_id: str, - timestamp_utc: str, - constraint: Dict[str, Any], -) -> Dict[str, Any]: - """Generate a ZK-STARK proof of spending within bounds. - - The proof cryptographically binds: - - payout_intent_id (unique transaction identifier) - - amount_usd (bounded by constraint) - - recipient_id (bounded by recipient ceiling) - - timestamp_utc (for daily/weekly aggregation windows) - - Constraint must include: - - max_amount_usd: per-payout ceiling - - max_daily_usd: daily cumulative ceiling - - max_weekly_usd: weekly cumulative ceiling - - max_per_recipient_usd: recipient cumulative ceiling - """ - - constraint_hash = hash_spending_constraint(constraint) - proof_input_hash = hash_payout_proof_input(payout_intent_id, amount_usd, constraint_hash) - - # Derive proof witness from constraint + amount + timestamp - # In a real STARK system, this would be a polynomial evaluation over the constraint circuit - witness_preimage = f"{proof_input_hash}|{timestamp_utc}|{recipient_id}".encode("utf-8") - witness_hash = hashlib.sha256(witness_preimage).hexdigest() - - # Compute proof commitment (Merkle-tree style aggregation) - # In production, this would be a full STARK proof transcript - proof_transcript = { - "payout_intent_id": payout_intent_id, - "amount_usd": round(amount_usd, 8), - "constraint_hash": constraint_hash, - "proof_input_hash": proof_input_hash, - "witness_hash": witness_hash, - "timestamp_utc": timestamp_utc, - } - - # Final proof commitment (like a STARK proof root) - proof_commitment = hashlib.sha256( - json.dumps(proof_transcript, sort_keys=True).encode("utf-8") - ).hexdigest() - - return { - "proof_type": "zk_stark_spending_v1", - "payout_intent_id": payout_intent_id, - "amount_usd": round(amount_usd, 8), - "recipient_id": recipient_id, - "timestamp_utc": timestamp_utc, - "constraint_hash": constraint_hash, - "proof_commitment": proof_commitment, - "transcript": proof_transcript, - "generated_utc": datetime.now(timezone.utc).replace(microsecond=0).isoformat(), - } - - -def verify_spending_proof(proof: Dict[str, Any], constraint: Dict[str, Any]) -> Tuple[bool, str]: - """Verify a ZK-STARK spending proof against a constraint. - - Returns (is_valid, reason_or_empty_string) - """ - - # Verify proof structure - required_fields = ["proof_type", "payout_intent_id", "amount_usd", "constraint_hash", "proof_commitment", "transcript"] - for field in required_fields: - if field not in proof: - return False, f"Proof missing required field: {field}" - - # Verify constraint hash matches - expected_constraint_hash = hash_spending_constraint(constraint) - actual_constraint_hash = str(proof["constraint_hash"]) - if actual_constraint_hash != expected_constraint_hash: - return False, "Constraint hash mismatch in proof" - - # Verify proof commitment is correctly computed - transcript = proof["transcript"] - expected_commitment = hashlib.sha256( - json.dumps(transcript, sort_keys=True).encode("utf-8") - ).hexdigest() - if str(proof["proof_commitment"]) != expected_commitment: - return False, "Proof commitment verification failed" - - # Verify amount is within per-payout ceiling - max_amount = float(constraint.get("max_amount_usd", 0.0) or 0.0) - amount = float(proof.get("amount_usd", 0.0)) - if amount > max_amount: - return False, f"Amount {amount} exceeds per-payout ceiling {max_amount}" - - # Verify recipient spending is within per-recipient ceiling (requires external state) - max_per_recipient = float(constraint.get("max_per_recipient_usd", 0.0) or 0.0) - if max_per_recipient > 0 and amount > max_per_recipient: - return False, f"Amount {amount} exceeds per-recipient ceiling {max_per_recipient}" - - return True, "" - - -def aggregate_spending_proofs(proofs: List[Dict[str, Any]]) -> Dict[str, Any]: - """Aggregate multiple spending proofs for cumulative boundary checking. - - Returns aggregation metadata that can be used to verify total spending - across daily/weekly/cumulative windows. - """ - - if not proofs: - return { - "proof_type": "zk_stark_aggregate_v1", - "proof_count": 0, - "total_amount_usd": 0.0, - "aggregate_commitment": hashlib.sha256(b"empty").hexdigest(), - } - - total_amount = sum(float(p.get("amount_usd", 0.0)) for p in proofs) - - # Merkle-tree style aggregation of proofs - proof_commits = [str(p.get("proof_commitment", "")) for p in proofs] - aggregate_input = "|".join(sorted(proof_commits)) - aggregate_commitment = hashlib.sha256(aggregate_input.encode("utf-8")).hexdigest() - - # Extract timestamps for window analysis - timestamps = [] - for proof in proofs: - ts_str = str(proof.get("timestamp_utc", "")) - if ts_str: - try: - ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00")) - timestamps.append(ts) - except ValueError: - pass - - daily_groups: Dict[str, float] = {} - weekly_groups: Dict[str, float] = {} - - for proof in proofs: - ts_str = str(proof.get("timestamp_utc", "")) - if not ts_str: - continue - try: - ts = datetime.fromisoformat(ts_str.replace("Z", "+00:00")) - day_key = ts.date().isoformat() - week_key = ts.isocalendar() - week_iso = f"{week_key.year}-W{week_key.week:02d}" - - amount = float(proof.get("amount_usd", 0.0)) - daily_groups[day_key] = daily_groups.get(day_key, 0.0) + amount - weekly_groups[week_iso] = weekly_groups.get(week_iso, 0.0) + amount - except (ValueError, AttributeError): - pass - - max_daily = max(daily_groups.values()) if daily_groups else 0.0 - max_weekly = max(weekly_groups.values()) if weekly_groups else 0.0 - - return { - "proof_type": "zk_stark_aggregate_v1", - "proof_count": len(proofs), - "total_amount_usd": round(total_amount, 8), - "aggregate_commitment": aggregate_commitment, - "max_daily_usd": round(max_daily, 8), - "max_weekly_usd": round(max_weekly, 8), - "daily_groups": {k: round(v, 8) for k, v in daily_groups.items()}, - "weekly_groups": {k: round(v, 8) for k, v in weekly_groups.items()}, - "generated_utc": datetime.now(timezone.utc).replace(microsecond=0).isoformat(), - } - - -def verify_cumulative_spending( - aggregate: Dict[str, Any], - max_daily_ceiling: float, - max_weekly_ceiling: float, - max_total_ceiling: float, -) -> Tuple[bool, List[str]]: - """Verify cumulative spending against multiple ceilings. - - Returns (is_valid, list_of_violation_reasons) - """ - - violations = [] - - total = float(aggregate.get("total_amount_usd", 0.0)) - if total > max_total_ceiling: - violations.append(f"Total spending {total} exceeds ceiling {max_total_ceiling}") - - max_daily = float(aggregate.get("max_daily_usd", 0.0)) - if max_daily > max_daily_ceiling: - violations.append(f"Max daily spending {max_daily} exceeds daily ceiling {max_daily_ceiling}") - - max_weekly = float(aggregate.get("max_weekly_usd", 0.0)) - if max_weekly > max_weekly_ceiling: - violations.append(f"Max weekly spending {max_weekly} exceeds weekly ceiling {max_weekly_ceiling}") - - return len(violations) == 0, violations - - -def default_spending_constraint() -> Dict[str, Any]: - """Default spending constraint circuit.""" - return { - "max_amount_usd": 50000.0, # Per-payout ceiling - "max_per_recipient_usd": 250000.0, # Per-recipient daily ceiling - "max_daily_usd": 1000000.0, # Daily cumulative ceiling - "max_weekly_usd": 5000000.0, # Weekly cumulative ceiling - "circuit": "payout_spending_v1", - "enforcement": "fail_closed", - } - - -if __name__ == "__main__": - import sys - - constraint = default_spending_constraint() - - # Example: generate and verify a proof - proof = generate_spending_proof( - payout_intent_id="pi-test-001", - amount_usd=25000.0, - recipient_id="entity-abc-123", - timestamp_utc=datetime.now(timezone.utc).isoformat(), - constraint=constraint, - ) - - is_valid, reason = verify_spending_proof(proof, constraint) - print(f"Proof valid: {is_valid}") - if reason: - print(f"Reason: {reason}") - - print(json.dumps(proof, indent=2)) diff --git a/5-Applications/tools-scripts/dashboard/dashboard_server.py b/5-Applications/tools-scripts/dashboard/dashboard_server.py deleted file mode 100644 index ee143890..00000000 --- a/5-Applications/tools-scripts/dashboard/dashboard_server.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 -import os -import time -import json -import threading -import urllib.parse -import sqlite3 -from http.server import HTTPServer, SimpleHTTPRequestHandler - -PORT = 8888 -LOG_FILE = "lambda_trace.jsonl" - -class SSEHandler(SimpleHTTPRequestHandler): - def do_GET(self): - if self.path == '/stream': - self.send_response(200) - self.send_header('Content-Type', 'text/event-stream') - self.send_header('Cache-Control', 'no-cache') - self.send_header('Connection', 'keep-alive') - self.send_header('Access-Control-Allow-Origin', '*') - self.end_headers() - - # Initial catch-up - if os.path.exists(LOG_FILE): - with open(LOG_FILE, 'r') as f: - f.seek(0, os.SEEK_END) - file_size = f.tell() - f.seek(max(0, file_size - 10000), os.SEEK_SET) - lines = f.readlines() - for line in lines[-20:]: - self.wfile.write(f"data: {line.strip()}\n\n".encode()) - self.wfile.flush() - - # Tail the file - with open(LOG_FILE, 'r') as f: - f.seek(0, os.SEEK_END) - while True: - line = f.readline() - if line: - self.wfile.write(f"data: {line.strip()}\n\n".encode()) - self.wfile.flush() - else: - time.sleep(0.5) - - elif self.path.startswith('/api/search'): - query_params = urllib.parse.parse_qs(urllib.parse.urlparse(self.path).query) - q = query_params.get('q', [''])[0] - - DB_PATH = "/home/allaun/Documents/Research Stack/data/substrate_index.db" - results = [] - if os.path.exists(DB_PATH): - try: - domain = query_params.get('domain', ['LINEAR'])[0] - conn = sqlite3.connect(DB_PATH) - cur = conn.cursor() - cur.execute(""" - SELECT pkg, description, tags - FROM packages_fts - WHERE domain = ? AND (description MATCH ? OR pkg MATCH ?) - LIMIT 50 - """, (domain, f"{q}*", f"{q}*")) - for row in cur.fetchall(): - results.append({ - "id": row[0], - "description": row[1], - "tags": row[2] - }) - conn.close() - except Exception as e: - print(f"Search error: {e}") - - self.send_response(200) - self.send_header('Content-Type', 'application/json') - self.send_header('Access-Control-Allow-Origin', '*') - self.end_headers() - self.wfile.write(json.dumps(results).encode()) - - else: - if self.path == '/': - self.path = '/index.html' - - original_path = self.path - self.path = '/dashboard' + original_path - - if not os.path.exists('.' + self.path): - self.path = original_path - - return super().do_GET() - -def run_server(): - server_address = ('', PORT) - httpd = HTTPServer(server_address, SSEHandler) - print(f"[🛡️ DASHBOARD SERVER] Online at http://localhost:{PORT}") - httpd.serve_forever() - -if __name__ == "__main__": - if not os.path.exists(LOG_FILE): - with open(LOG_FILE, 'w') as f: - pass - run_server() diff --git a/5-Applications/tools-scripts/dashboard/index.html b/5-Applications/tools-scripts/dashboard/index.html deleted file mode 100644 index dcdd18fe..00000000 --- a/5-Applications/tools-scripts/dashboard/index.html +++ /dev/null @@ -1,421 +0,0 @@ - - - - - - Research Stack | Controller - - - - -
-
- - - -
- -
-
-
-

Wednesday, April 22

-

PATAMATHEMATICAL CONTROLLER

-
-
MEMETIC_HAZARD ACTIVE
-
- -
-
-

Dynamic Cannal Pressure

-
0.82
-
$\lambda$ Effective
-
-
-

Linear Index

-
2,314
-
Total Intent Nodes
-
-
-

Archive Status

-
152
-
Sealed Components
-
-
-
- - -
-

LINEAR NAVIGATOR

-
-
- - -
-
- Ready. 2,314 nodes indexed. -
-
-
-
- - -
-

MANIFOLD STATS

-
-
-

PIST Tension

-
0.824
-
Neutralized Mass (m)
-
-
-

Active Phase

-
GROUNDED
-
Formal Policy Sort
-
-
-

$\Phi$-Phase

-
1.618
-
Golden Ratio Accumulator
-
-
-
- - -
-

PATENT ARCHIVE

-
-

Sealed Components

-

Accessing forensic audit log...

-
-
-
- ARCHIVE-001 - SEALED -
-
PBACS REV3 Core Netlist
-
-
-
-
-
- - - - diff --git a/5-Applications/tools-scripts/data/germane/research/hardware_foreign_manifold_map.json b/5-Applications/tools-scripts/data/germane/research/hardware_foreign_manifold_map.json deleted file mode 100644 index fd70880f..00000000 --- a/5-Applications/tools-scripts/data/germane/research/hardware_foreign_manifold_map.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:fc5504451d6c2faae2f3b1caa68d19afc3db0f69e9dbf54feab2c35172009a71 -size 7332 diff --git a/5-Applications/tools-scripts/data/germane/research/pure_software_topology_map.json b/5-Applications/tools-scripts/data/germane/research/pure_software_topology_map.json deleted file mode 100644 index 640e8e54..00000000 --- a/5-Applications/tools-scripts/data/germane/research/pure_software_topology_map.json +++ /dev/null @@ -1,3 +0,0 @@ -version https://git-lfs.github.com/spec/v1 -oid sha256:bb2992052643f8b9906908647de9f32d00b5271ad7006f07f4ca170383f6e4f3 -size 95027 diff --git a/5-Applications/tools-scripts/database/schema_encoder.py b/5-Applications/tools-scripts/database/schema_encoder.py deleted file mode 100644 index caeacb89..00000000 --- a/5-Applications/tools-scripts/database/schema_encoder.py +++ /dev/null @@ -1,342 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Schema Encoder — Structural Pass 1.8 (Single-Pass Unified Matcher) -================================================================== - -Replaces structural patterns with compact tokens in a single, coordinate-stable -pass. Supports Half-Möbius closures (Inverse Sisyphus) and other structural schemas. -""" - -from __future__ import annotations - -import os -import sys -import re -import json -from pathlib import Path -from dataclasses import dataclass -from typing import NamedTuple, List, Tuple, Optional - -REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1]) -TOOLS_DIR = REPO_ROOT / "tools" - -# Add local tools directory to path -sys.path.insert(0, str(TOOLS_DIR)) -try: - from topological_encoder import TopologicalEncoder, detect_seismic_shell, SisyphusInverseError -except ImportError: - # Retry with an explicit repo-root-based tools path for remote environments. - sys.path.insert(0, str(TOOLS_DIR.resolve())) - from topological_encoder import TopologicalEncoder, detect_seismic_shell, SisyphusInverseError - - -# ─── schema tokens (compact placeholders) ───────────────────────────────────── - -_TOK_PTOS_HEADER = "§H" -_TOK_CONCEPT_ANCHOR = "§CA" -_TOK_SESSION_ENV = "§SE" -_TOK_IDEA_WEIGHT = "§IW" -_TOK_TOPOLOGICAL = "§TM" - - -# ─── data types ─────────────────────────────────────────────────────────────── - -class SchemaMatch(NamedTuple): - """Record of one structural substitution.""" - token: str # compact placeholder inserted into text - original: str # full original text that was replaced - position: int # character position in ORIGINAL text - - -# ─── pattern definitions ────────────────────────────────────────────────────── - -# PTOS header: entire comment line with variable LAYER/DOMAIN/CONDITION/STAGE/SOURCE -_RE_PTOS_HEADER = re.compile( - r"#\s*PTOS:\s*LAYER=(?P\w+)\s*/\s*DOMAIN=(?P\w+)\s*" - r"/\s*CONDITION=(?P\w+)\s*/\s*STAGE=(?P\w+)\s*" - r"/\s*SOURCE=(?P\w+)" -) - -# concept_anchor block in docstrings (inline format) -_RE_CONCEPT_ANCHOR = re.compile( - r"\*\*concept_anchor:\*\*\s*domain=(?P\S+)\s*/\s*" - r"concept=(?P\S+)\s*/\s*" - r"resolution=(?P\w+)" - r"(?:\s*/\s*story=(?P\w+))?" -) - -# Session JSON envelope keys (first few keys of a session file) -_RE_SESSION_ENV = re.compile( - r'"session_id"\s*:\s*"(?P[^"]+)"\s*,\s*\n\s*' - r'"pkg"\s*:\s*"(?P[^"]+)"\s*,\s*\n\s*' - r'"version"\s*:\s*"(?P[^"]+)"\s*,\s*\n\s*' - r'"module"\s*:\s*"(?P[^"]+)"' -) - -# idea_weights key-value pair (JSON string → float) -_RE_IDEA_WEIGHT = re.compile( - r'"(?P[^"]{8,120})"\s*:\s*(?P0\.\d+)' -) - -# SEISMIC Signal Manifold (loose format) -_RE_SIGNAL_MANIFOLD = re.compile( - r'\{[^{}]*?"phi_corr"\s*:\s*(?P0\.\d+).*?"torsion_gradient"\s*:\s*\[(?P[^\]]+)\].*?\}', - re.DOTALL -) - - -# ─── Matching Logic ───────────────────────────────────────────────────────── - -def find_all_matches(text: str) -> List[Tuple[int, int, str, str, int]]: - """ - Find all structural matches in the original text. - Returns list of (start, end, token, original, priority). - """ - matches = [] - topo_encoder = TopologicalEncoder() - - # Priority 0: Topological Closures (Highest) - for m in _RE_SIGNAL_MANIFOLD.finditer(text): - try: - raw = m.group(0) - manifold_data = json.loads(raw) - shell = topo_encoder.process_manifold(manifold_data) - if shell: - token = f"{_TOK_TOPOLOGICAL}:{shell.sha256[:16]}" - matches.append((m.start(), m.end(), token, raw, 0)) - elif detect_seismic_shell(manifold_data.get("phi_corr", 0.0)): - # HEATSINK_HALT: SEISMIC signal failed to close. - sys.stderr.write(f"[!] Sisyphus Inverse Failure: Annnihilated Vortex at pos {m.start()}\n") - # We tag it for stats by adding a non-substituting match type if we wanted, - # but for now we just log it. - except Exception as e: - sys.stderr.write(f"[-] Topological Parse Error: {e}\n") - - # Priority 1: Headers - for m in _RE_PTOS_HEADER.finditer(text): - matches.append((m.start(), m.end(), _TOK_PTOS_HEADER, m.group(0), 1)) - - # Priority 2: Concept Anchors - for m in _RE_CONCEPT_ANCHOR.finditer(text): - matches.append((m.start(), m.end(), _TOK_CONCEPT_ANCHOR, m.group(0), 2)) - - # Priority 3: Session Envelopes - for m in _RE_SESSION_ENV.finditer(text): - matches.append((m.start(), m.end(), _TOK_SESSION_ENV, m.group(0), 3)) - - # Priority 4: Idea Weights - for m in _RE_IDEA_WEIGHT.finditer(text): - matches.append((m.start(), m.end(), _TOK_IDEA_WEIGHT, m.group(0), 4)) - - return matches - - -def filter_conflicts(matches: List[Tuple[int, int, str, str, int]]) -> List[SchemaMatch]: - """Resolve overlapping matches based on priority and length.""" - if not matches: return [] - - # Sort primarily by start index, secondarily by priority (lower number = higher priority) - matches.sort(key=lambda x: (x[0], x[4])) - - final_matches: List[SchemaMatch] = [] - last_end = -1 - - for start, end, token, original, priority in matches: - if start >= last_end: - # No overlap, or starting after last match - final_matches.append(SchemaMatch(token, original, start)) - last_end = end - else: - # Conflict detected. Since we sorted by priority, the earlier one in the list wins. - # (Matches at same start index: highest priority wins). - pass - - return final_matches - - -# ─── encoder ────────────────────────────────────────────────────────────────── - -def encode(text: str) -> Tuple[str, List[SchemaMatch]]: - """ - Unified Single-Pass Encoder. - Identifies all structural clusters in the original text and performs - coordinate-stable substitution. - """ - all_raw = find_all_matches(text) - filtered = filter_conflicts(all_raw) - - # Process from Right-to-Left to avoid shifting pending match positions - # while building the encoded string. - # Actually, it's easier to build a new string from left to right: - filtered.sort(key=lambda x: x.position) - - result_parts = [] - last_idx = 0 - for match in filtered: - # Append literal text before the match - result_parts.append(text[last_idx:match.position]) - # Append the token - result_parts.append(match.token) - # Advance cursor past the original text - last_idx = match.position + len(match.original) - - # Append remaining literal text - result_parts.append(text[last_idx:]) - - return "".join(result_parts), filtered - - -def decode(encoded: str, matches: List[SchemaMatch]) -> str: - """Restore original text from encoded form + match list.""" - # Build from tokens and original strings - # Positions are absolute in the original text, so we rebuild it exactly. - matches.sort(key=lambda x: x.position) - - result = encoded - # We must replace from right-to-left because replacing changes indices in 'encoded' - # BUT, the 'position' in 'matches' is relative to the ORIGINAL text. - # The decoder needs to find where each token is in the ENCODED text. - - # Wait! If we store ORIGINAL positions, we should rebuild from scratch: - text_parts = [] - curr_enc_idx = 0 - - for match in matches: - # Find token in encoded text - # Since we processed matches in order, the next token must be at curr_enc_idx - # But wait! Literal text between tokens is present. - - # Identify how much literal text preceded this match in the ORIGINAL - # and how much was shifted in the ENCODED. - pass - - # simpler way: use the same logic as before but be VERY careful with index math - # the old decode worked because it went backwards: - result = encoded - - # To decode, we need the positions in the ENCODED text. - # Let's calculate them: - enc_offset = 0 - match_list_with_enc_pos = [] - for m in matches: - enc_pos = m.position - enc_offset - match_list_with_enc_pos.append((enc_pos, m)) - enc_offset += len(m.original) - len(m.token) - - result = encoded - for enc_pos, m in reversed(match_list_with_enc_pos): - result = result[:enc_pos] + m.original + result[enc_pos + len(m.token):] - - return result - - -# ─── statistics ─────────────────────────────────────────────────────────────── - -@dataclass -class SchemaStats: - """Byte savings summary for one encode pass.""" - ptos_headers: int = 0 - concept_anchors: int = 0 - session_envs: int = 0 - idea_weights: int = 0 - topological_kms: int = 0 - annihilated_vortices: int = 0 - bytes_before: int = 0 - bytes_after: int = 0 - - @property - def total_matches(self) -> int: - return (self.ptos_headers + self.concept_anchors - + self.session_envs + self.idea_weights + self.topological_kms) - - @property - def bytes_saved(self) -> int: - return self.bytes_before - self.bytes_after - - @property - def ratio(self) -> float: - return self.bytes_after / max(self.bytes_before, 1) - - -def encode_with_stats(text: str) -> tuple[str, list[SchemaMatch], SchemaStats]: - """encode() with per-schema byte savings breakdown.""" - encoded, matches = encode(text) - stats = SchemaStats( - bytes_before = len(text.encode()), - bytes_after = len(encoded.encode()), - ) - for m in matches: - if m.token == _TOK_PTOS_HEADER: - stats.ptos_headers += 1 - elif m.token == _TOK_CONCEPT_ANCHOR: - stats.concept_anchors += 1 - elif m.token == _TOK_SESSION_ENV: - stats.session_envs += 1 - elif m.token == _TOK_IDEA_WEIGHT: - stats.idea_weights += 1 - elif m.token.startswith(_TOK_TOPOLOGICAL): - stats.topological_kms += 1 - - # Second pass for stats-only analysis of failures - for m in _RE_SIGNAL_MANIFOLD.finditer(text): - try: - phi = float(m.group("phi")) - if 0.35 <= phi < 0.47: # SEISMIC - raw = m.group(0) - manifold_data = json.loads(raw) - if not TopologicalEncoder().process_manifold(manifold_data): - stats.annihilated_vortices += 1 - except: pass - - return encoded, matches, stats - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def main() -> None: - import argparse - parser = argparse.ArgumentParser( - description="Schema encoder — structural Pass 1.8" - ) - parser.add_argument("path", help="file to analyse") - parser.add_argument("--decode", action="store_true", - help="verify round-trip losslessness") - args = parser.parse_args() - - text = open(args.path, encoding="utf-8", errors="replace").read() - encoded, matches, stats = encode_with_stats(text) - - print(f"Schema encoder: {args.path}") - print(f" Topological KMS: {stats.topological_kms}") - print(f" PTOS headers: {stats.ptos_headers}") - print(f" concept_anchors: {stats.concept_anchors}") - print(f" session envs: {stats.session_envs}") - print(f" idea_weights: {stats.idea_weights}") - print(f" Annihilated Vortices: {stats.annihilated_vortices}") - print(f" bytes before: {stats.bytes_before:,}") - print(f" bytes after: {stats.bytes_after:,}") - print(f" bytes saved: {stats.bytes_saved:,} ({(1-stats.ratio)*100:.1f}%)") - - if args.decode: - recovered = decode(encoded, matches) - if recovered == text: - print(" round-trip: OK") - else: - print(" round-trip: FAIL") - # Find first difference - for i, (a, b) in enumerate(zip(text, recovered)): - if a != b: - print(f" first diff at char {i}: {repr(text[i:i+40])}" - f" vs {repr(recovered[i:i+40])}") - break - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/database/sql_to_n_dag.py b/5-Applications/tools-scripts/database/sql_to_n_dag.py deleted file mode 100644 index d28f5606..00000000 --- a/5-Applications/tools-scripts/database/sql_to_n_dag.py +++ /dev/null @@ -1,92 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import hashlib -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import os -import sys - -# ── NE geometry scaffold (geometry-rip branch) ──────────────────────────────── -# Fixes EUCLIDEAN_ASSUMPTION_AUDIT finding #11 (LOWER): L2 on random coords -# produces approximately constant coupling_strength ≈ weight / sqrt(N/6) for all -# pairs (all pairwise distances concentrate around sqrt(N/6)). -# NE fix: phi-weighted distance differentiates axes by semantic importance. -_USE_NE_GEOMETRY = False -_phi_ndag = (1 + 5 ** 0.5) / 2 - -# ── Complex geometry scaffold (geometry-rip branch) ─────────────────────────── -# Fixes COMPLEX_NUMBER_AUDIT finding #M6 (LOWER): establish_coupling uses L2 -# distance on mod-1 coords without circular wraparound metric. -# Coordinates are (entropy × PHI^i) % 1.0 — linear distance wraps discontinuously. -_USE_COMPLEX_GEOMETRY = True -import sys as _sys_sql, os as _os_sql -_sql_tools = _os_sql.path.join(_os_sql.path.dirname(_os_sql.path.abspath(__file__)), - "..", "tools") -if _sql_tools not in _sys_sql.path: - _sys_sql.path.insert(0, _sql_tools) -try: - import geometry_complex as _cg # noqa: F401 - _CG_AVAILABLE_SQL = True -except ImportError: - _CG_AVAILABLE_SQL = False - -class NdagTransmuter: - """Transmutes Relational SQL into N-Dimensional Solitons.""" - - def __init__(self, dimensions=11): - self.dimensions = dimensions - self.phi = (1 + 5**0.5) / 2 - self.manifold = {} - - def transmute_node(self, slug, data): - """Converts a SQL Row/Entity into a HyperMassNode.""" - node_id = hashlib.sha256(slug.encode()).hexdigest() - - # Holographic Mapping: Entropy-driven coordinate generation - entropy = sum(hashlib.sha256(str(data).encode()).digest()) / 256.0 - coords = xp.zeros(self.dimensions) - for i in range(self.dimensions): - coords[i] = (entropy * (self.phi ** i)) % 1.0 - - self.manifold[node_id] = { - "coords": coords, - "frequency": 1.0 / (self.phi * entropy), - "payload": data - } - return node_id - - def establish_coupling(self, node_a, node_b, weight=1.0): - """Converts Foreign Keys into Phase-Locked Couplings.""" - if node_a in self.manifold and node_b in self.manifold: - # Vibrational Edge strength based on topological distance - ca = self.manifold[node_a]["coords"] - cb = self.manifold[node_b]["coords"] - if _USE_COMPLEX_GEOMETRY: - # COMPLEX_AUDIT #M6: circular metric — mod-1 coords wrap at [0,1) boundary - # min(|a-b|, 1-|a-b|) is the correct angular distance for unit-circle coords - _diffs = [min(abs(ca[i] - cb[i]), 1.0 - abs(ca[i] - cb[i])) - for i in range(len(ca))] - dist = sum(_phi_ndag**i * _diffs[i]**2 - for i in range(len(ca))) ** 0.5 - elif _USE_NE_GEOMETRY: - # NE path: φ^i-weighted distance (AUDIT FINDING #11 fix) - # Avoids concentration of measure: axis-i has weight φ^i - dist = sum(_phi_ndag**i * (ca[i] - cb[i])**2 - for i in range(len(ca))) ** 0.5 - else: - # EU path (default): L2 — EUCLIDEAN ASSUMPTION #11 - dist = xp.linalg.norm(ca - cb) - coupling_strength = weight / (dist + 0.001) - return coupling_strength - return 0.0 - -if __name__ == "__main__": - transmuter = NdagTransmuter() - uid = transmuter.transmute_node("user_001", {"name": "Architect", "level": "SME"}) - print(f"Node Transmuted: {uid}") diff --git a/5-Applications/tools-scripts/defense/create_defense_snapshot.py b/5-Applications/tools-scripts/defense/create_defense_snapshot.py deleted file mode 100644 index 2a9f39a7..00000000 --- a/5-Applications/tools-scripts/defense/create_defense_snapshot.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List - -PROJECT_ROOT = Path(__file__).resolve().parent.parent -if str(PROJECT_ROOT) not in sys.path: - sys.path.insert(0, str(PROJECT_ROOT)) - -DEFAULT_REPORT_RECIPIENT = "PassInbox Abuse Intake <2s3sa2.monthly496@passinbox.com>" -DEFAULT_REPORT_CHANNEL = "passinbox abuse intake" - -from scripts.generate_trace_attestation import build_attestation, sha256_file -from scripts.zero_metadata_loopback import build_payload, pack_loopback, unpack_loopback, verify_payload_files - - -def write_json(path: Path, data: Dict[str, Any]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text(json.dumps(data, indent=2) + "\n", encoding="utf-8") - - -def summarize_findings(attestation: Dict[str, Any]) -> List[str]: - snapshot = attestation.get("database_snapshot", {}) - counts = snapshot.get("counts", {}) - events = snapshot.get("events", []) - observables = snapshot.get("observables", []) - relationships = snapshot.get("relationships", []) - - lines = [ - ( - "Observed " - f"{counts.get('events', 0)} event(s), " - f"{counts.get('observables', 0)} observable(s), and " - f"{counts.get('relationships', 0)} relationship(s)." - ) - ] - - grouped: Dict[str, List[str]] = {} - for item in observables: - grouped.setdefault(str(item.get("observable_type", "unknown")), []).append(str(item.get("value", ""))) - - for observable_type in sorted(grouped): - values = grouped[observable_type] - preview = ", ".join(values[:3]) - if len(values) > 3: - preview += ", ..." - lines.append(f"Observed {observable_type}: {preview}") - - for event in events[:3]: - source = event.get("source", "unknown") - observed_at = event.get("observed_at", "unknown") - notes = str(event.get("notes", "")).strip() - detail = f"Event {event.get('event_id', '?')} from {source} at {observed_at}" - if notes: - detail += f" with note: {notes}" - lines.append(detail) - - if relationships: - relation_names = sorted({str(item.get("relation_type", "unknown")) for item in relationships}) - lines.append("Relationship types present: " + ", ".join(relation_names)) - - return lines - - -def render_finding_line(item: str) -> str: - prefix = "Observed url: " - if item.startswith(prefix): - return prefix + f"<{item[len(prefix):]}>" - return item - - -def foam_statement(passes: int) -> str: - if passes < 1: - passes = 1 - blends = { - "preserve": ["preserves", "keeps", "retains", "safeguards"], - "evidence": ["technical evidence", "forensic records", "verifiable traces"], - "attribution": ["avoids attribution", "refrains from blame assignment", "does not label actors"], - "handoff": ["safe handoff", "clean transfer", "neutral transfer"], - "restore": ["service restoration", "connectivity recovery", "normal operation recovery"], - } - - def pick(values: List[str], offset: int) -> str: - return values[(passes + offset) % len(values)] - - return ( - f"After {passes} smoothing passes, this brief {pick(blends['preserve'], 0)} " - f"{pick(blends['evidence'], 1)}, {pick(blends['attribution'], 2)}, and supports " - f"{pick(blends['handoff'], 0)} for {pick(blends['restore'], 1)}. " - "The intent is stability, the language is neutral, and the objective is safe closure." - ) - - -def write_handoff_brief(path: Path, snapshot: Dict[str, Any], passes: int = 1) -> None: - lines: List[str] = [] - lines.append("# Defense Handoff Brief") - lines.append("") - lines.append(f"Created: {snapshot['created_utc']}") - lines.append("") - lines.append("## Purpose") - lines.append("") - lines.append("This package preserves passive technical evidence for transfer and review.") - lines.append("It is written to be neutral, factual, and easy to read.") - lines.append("No person, group, or country attribution is asserted in this document.") - lines.append(f"Language smoothing passes applied: {passes}") - lines.append("") - lines.append("## Integrity") - lines.append("") - lines.append(f"- Loopback verified: {snapshot['loopback']['verified']}") - lines.append(f"- Loopback file: {snapshot['loopback']['path']}") - lines.append(f"- Loopback SHA-256: {snapshot['loopback']['sha256']}") - lines.append("") - lines.append("## Artifacts") - lines.append("") - lines.append(f"- Attestation: {snapshot['attestation']['path']}") - lines.append(f"- Attestation SHA-256: {snapshot['attestation']['sha256']}") - for item in snapshot["evidence_files"]: - lines.append(f"- Evidence: {item['path']} ({item['sha256']})") - lines.append("") - lines.append("## Plain-Language Summary") - lines.append("") - lines.append("The goal is service restoration and safe handoff, not private investigation.") - lines.append("This package is structured so another team can verify integrity without relying on file metadata.") - lines.append(foam_statement(passes)) - lines.append("") - lines.append("## Minimal Action Path") - lines.append("") - lines.append("1. Hand this package to platform abuse or CERT channels.") - lines.append("2. Keep a local copy unchanged.") - lines.append("3. Avoid active investigation or interaction with suspected actors.") - lines.append("4. Focus only on connectivity and service recovery on your side.") - lines.append("") - lines.append("## Short Neutral Statement (Multilingual)") - lines.append("") - lines.append("English: This report preserves technical evidence only and makes no attribution claims.") - lines.append("Español: Este informe conserva solo evidencia técnica y no hace atribuciones.") - lines.append("Français: Ce rapport conserve uniquement des preuves techniques et ne fait aucune attribution.") - lines.append("Deutsch: Dieser Bericht bewahrt nur technische Belege und enthält keine Zuschreibungen.") - lines.append("Português: Este relatório preserva apenas evidências técnicas e não faz atribuições.") - lines.append("Italiano: Questo rapporto conserva solo prove tecniche e non formula attribuzioni.") - lines.append("Polski: Ten raport zachowuje wyłącznie dowody techniczne i nie przypisuje odpowiedzialności.") - lines.append("Nederlands: Dit rapport bewaart alleen technische bewijzen en doet geen toeschrijvingen.") - lines.append("Türkçe: Bu rapor yalnızca teknik kanıtları korur ve atıf iddiasında bulunmaz.") - lines.append("Svenska: Denna rapport bevarar endast tekniska bevis och gör inga tillskrivningar.") - lines.append("Norsk: Denne rapporten bevarer kun tekniske bevis og gjør ingen attribusjoner.") - lines.append("Dansk: Denne rapport bevarer kun tekniske beviser og fremsætter ingen attributioner.") - lines.append("Suomi: Tämä raportti säilyttää vain tekniset todisteet eikä tee attribuutioväitteitä.") - lines.append("Čeština: Tato zpráva uchovává pouze technické důkazy a neobsahuje žádná přičtení.") - lines.append("Slovenčina: Táto správa uchováva iba technické dôkazy a neobsahuje žiadne pripisovanie.") - lines.append("Magyar: Ez a jelentés csak technikai bizonyítékokat őriz meg, és nem tesz attribúciós állításokat.") - lines.append("Română: Acest raport păstrează doar dovezi tehnice și nu face atribuiri.") - lines.append("Български: Този доклад съхранява само технически доказателства и не прави приписвания.") - lines.append("Українська: Цей звіт зберігає лише технічні докази і не містить атрибуції.") - lines.append("Русский: Этот отчет сохраняет только технические доказательства и не содержит атрибуции.") - lines.append("العربية: يحفظ هذا التقرير أدلة تقنية فقط ولا يتضمن أي إسناد.") - lines.append("עברית: דוח זה שומר ראיות טכניות בלבד ואינו כולל ייחוס.") - lines.append("فارسی: این گزارش فقط شواهد فنی را حفظ می‌کند و هیچ انتسابی ارائه نمی‌دهد.") - lines.append("हिन्दी: यह रिपोर्ट केवल तकनीकी साक्ष्य सुरक्षित रखती है और कोई आरोपित पहचान नहीं करती।") - lines.append("বাংলা: এই প্রতিবেদনে শুধু প্রযুক্তিগত প্রমাণ সংরক্ষণ করা হয়েছে, কোনো দায় আরোপ করা হয়নি।") - lines.append("தமிழ்: இந்த அறிக்கை தொழில்நுட்ப ஆதாரங்களை மட்டும் பாதுகாக்கிறது; குற்றச்சாட்டு ஒதுக்கீடு செய்யாது.") - lines.append("తెలుగు: ఈ నివేదిక సాంకేతిక ఆధారాలను మాత్రమే భద్రపరుస్తుంది; ఎటువంటి ఆపాదింపు చేయదు.") - lines.append("اردو: یہ رپورٹ صرف تکنیکی شواہد محفوظ کرتی ہے اور کوئی انتساب نہیں کرتی۔") - lines.append("中文: 本报告仅保存技术证据，不作归因结论。") - lines.append("日本語: この報告は技術的証拠のみを保全し、帰属判断は行いません。") - lines.append("한국어: 이 보고서는 기술적 증거만 보존하며 귀속 판단을 하지 않습니다.") - lines.append("Bahasa Indonesia: Laporan ini hanya menyimpan bukti teknis dan tidak membuat atribusi.") - lines.append("Tiếng Việt: Báo cáo này chỉ lưu giữ bằng chứng kỹ thuật và không đưa ra quy kết.") - lines.append("ไทย: รายงานนี้เก็บรักษาหลักฐานทางเทคนิคเท่านั้น และไม่ระบุการชี้ตัวผู้กระทำ") - lines.append("Kiswahili: Ripoti hii huhifadhi ushahidi wa kiufundi pekee na haitoi uhusisho.") - lines.append("") - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def write_trigger_report( - path: Path, - snapshot: Dict[str, Any], - attestation: Dict[str, Any], - recipient: str, - channel: str, -) -> None: - findings = summarize_findings(attestation) - lines: List[str] = [] - lines.append("# Trigger Report") - lines.append("") - lines.append(f"Created: {snapshot['created_utc']}") - lines.append(f"Recipient: {recipient}") - lines.append(f"Channel: {channel}") - lines.append("") - lines.append("## What I Found") - lines.append("") - for item in findings: - lines.append(f"- {render_finding_line(item)}") - lines.append("") - lines.append("## What Is Attached") - lines.append("") - lines.append(f"- Verified loopback container: {snapshot['loopback']['path']}") - lines.append(f"- Loopback SHA-256: {snapshot['loopback']['sha256']}") - lines.append(f"- Attestation JSON: {snapshot['attestation']['path']}") - lines.append(f"- Attestation SHA-256: {snapshot['attestation']['sha256']}") - lines.append("") - lines.append("## Plain Handoff Statement") - lines.append("") - lines.append( - "I found the technical indicators listed above and preserved them in a verifiable package. " - "I am not making attribution claims. I am handing this to your team for review and disposition; " - "any further action is up to you." - ) - lines.append("") - lines.append("## Copy-Paste Message") - lines.append("") - lines.append(f"Subject: Passive technical evidence package for {channel}") - lines.append("") - lines.append(f"To: {recipient}") - lines.append("") - lines.append("Hello,") - lines.append("") - lines.append("I am sending a passive technical evidence package for your review.") - lines.append("The package contains a verified loopback container and an attestation file with hashes.") - lines.append("Summary of findings:") - lines.append("") - for item in findings: - lines.append(f"- {render_finding_line(item)}") - lines.append("") - lines.append( - "I am not making attribution claims and I am not asking for an interactive investigation on my side. " - "Please review the package and decide any further action that your process requires." - ) - lines.append("") - lines.append("Regards,") - lines.append("Operator") - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def create_snapshot( - db_path: Path, - clusters_csv: Path, - timeline_json: Path, - template_csv: Path, - out_dir: Path, - passes: int = 1, - report_recipient: str = DEFAULT_REPORT_RECIPIENT, - report_channel: str = DEFAULT_REPORT_CHANNEL, -) -> Dict[str, Any]: - out_dir.mkdir(parents=True, exist_ok=True) - attestation_path = out_dir / "trace_attestation.json" - loopback_path = out_dir / "defense_loopback.zmlb" - snapshot_path = out_dir / "defense_snapshot.json" - handoff_path = out_dir / "defense_handoff_brief.md" - trigger_report_path = out_dir / "defense_trigger_report.md" - - attestation = build_attestation(db_path, clusters_csv, timeline_json, template_csv) - write_json(attestation_path, attestation) - - evidence_paths = [db_path, clusters_csv, timeline_json] - payload = build_payload(attestation_path, evidence_paths) - loopback_metrics = pack_loopback(payload, loopback_path) - unpacked_payload, _ = unpack_loopback(loopback_path) - file_findings = verify_payload_files(unpacked_payload) - verified = all(item["status"] == "ok" for item in file_findings) - - snapshot: Dict[str, Any] = { - "created_utc": datetime.now(timezone.utc).replace(microsecond=0).isoformat(), - "language_smoothing_passes": int(passes), - "attestation": { - "path": str(attestation_path), - "sha256": sha256_file(attestation_path), - }, - "loopback": { - "path": str(loopback_path), - "sha256": loopback_metrics["container_sha256"], - "canonical_sha256": loopback_metrics["canonical_sha256"], - "verified": verified, - }, - "evidence_files": [ - {"path": str(path), "sha256": sha256_file(path)} for path in evidence_paths - ], - "loopback_file_checks": file_findings, - } - - write_json(snapshot_path, snapshot) - write_handoff_brief(handoff_path, snapshot, passes=passes) - write_trigger_report( - trigger_report_path, - snapshot, - attestation, - recipient=report_recipient, - channel=report_channel, - ) - return { - "snapshot": str(snapshot_path), - "handoff_brief": str(handoff_path), - "trigger_report": str(trigger_report_path), - "attestation": str(attestation_path), - "loopback": str(loopback_path), - "verified": verified, - } - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Create a passive defense snapshot package for quick handoff.") - parser.add_argument("--db", default=str(PROJECT_ROOT / "out" / "demo_trace.sqlite3"), help="Trace database path.") - parser.add_argument("--clusters", default=str(PROJECT_ROOT / "out" / "clusters.csv"), help="Cluster CSV path.") - parser.add_argument("--timeline", default=str(PROJECT_ROOT / "out" / "timeline.json"), help="Timeline JSON path.") - parser.add_argument("--template", default=str(PROJECT_ROOT / "data_baselines" / "suspicious_repo_list_template.csv"), help="Repo list template path.") - parser.add_argument("--out-dir", default=str(PROJECT_ROOT / "out"), help="Output directory.") - parser.add_argument("--passes", type=int, default=1, help="Number of language smoothing passes for handoff text.") - parser.add_argument("--report-recipient", default=DEFAULT_REPORT_RECIPIENT, help="Recipient label for the trigger report.") - parser.add_argument("--report-channel", default=DEFAULT_REPORT_CHANNEL, help="Channel label for the trigger report.") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - inputs = [Path(args.db), Path(args.clusters), Path(args.timeline), Path(args.template)] - missing = [str(path) for path in inputs if not path.exists()] - if missing: - print(json.dumps({"error": "missing_input_files", "paths": missing}, indent=2)) - return 2 - - result = create_snapshot( - db_path=Path(args.db), - clusters_csv=Path(args.clusters), - timeline_json=Path(args.timeline), - template_csv=Path(args.template), - out_dir=Path(args.out_dir), - passes=int(args.passes), - report_recipient=args.report_recipient, - report_channel=args.report_channel, - ) - print(json.dumps(result, indent=2)) - return 0 if result["verified"] else 3 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/defense/emit_superconductor_dag_insertion_patch.py b/5-Applications/tools-scripts/defense/emit_superconductor_dag_insertion_patch.py deleted file mode 100644 index 7fe58d3e..00000000 --- a/5-Applications/tools-scripts/defense/emit_superconductor_dag_insertion_patch.py +++ /dev/null @@ -1,108 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import difflib -import hashlib -import json -from pathlib import Path -from typing import Any - - -def map_tier(tier_name: str, mapping: dict[str, Any]) -> int: - return int(mapping.get(tier_name, 1)) - - -def build_node_id(node_module: str, capsule_hash: str) -> str: - seed = f"{node_module}:{capsule_hash}".encode("utf-8") - return hashlib.sha256(seed).hexdigest() - - -def main() -> None: - root = Path(__file__).resolve().parent.parent - meta_path = root / "Research Documents" / "superconductor_hybrid_metaindex_v0.json" - capsule_path = root / "out" / "superconductor_hybrid_dag_capsule.json" - dag_path = root / "Research Documents" / "resonant_stack_v5.dag.json" - - meta = json.loads(meta_path.read_text(encoding="utf-8")) - capsule = json.loads(capsule_path.read_text(encoding="utf-8")) - dag = json.loads(dag_path.read_text(encoding="utf-8")) - original_text = dag_path.read_text(encoding="utf-8") - - cfg = meta.get("dag_capsule_payload", {}) - ins = cfg.get("insertion_params", {}) - - parent_hint = str(ins.get("parent_hint", "cba2d4cef575a399e8acd0a10b0eab47d2199feea2556dbb6ee71de5fc9903c8")) - compute_weight = float(ins.get("compute_weight", 9.4)) - timestamp = float(ins.get("timestamp", 1773952200.0)) - tier_map = ins.get("tier_name_to_number", {}) - - node_module = str(capsule["node_module"]) - capsule_hash = str(capsule["meta_capsule_hash"]) - node_id = build_node_id(node_module, capsule_hash) - - tier_name = str(capsule.get("tier", "CRYSTALLINE")) - tier_num = map_tier(tier_name, tier_map) - - node_entry: dict[str, Any] = { - "tier": tier_num, - "tier_name": tier_name, - "equation_version": dag.get("equation_version", "Σ-EQ-ALL-01"), - "ruleset_version": dag.get("ruleset_version", "Σ-RULESET-03"), - "signature": dag.get("signature", "ML-DSA-BULK"), - "tags": capsule.get("tags", []), - "compute_weight": compute_weight, - "meta_capsule": capsule["meta_capsule"], - "meta_capsule_hash": capsule_hash, - "parent": parent_hint, - "timestamp": timestamp, - } - - dag_nodes = dag["dag_nodes"] - dag_nodes[node_id] = node_entry - - dag["node_count"] = len(dag_nodes) - dag["compute_metrics"]["actions"] = len(dag_nodes) - dag["compute_metrics"]["energy"] = round(sum(float(v["compute_weight"]) for v in dag_nodes.values()), 1) - dag["compute_metrics"]["last_action_hash"] = node_id - dag["root_hash"] = hashlib.sha256( - json.dumps(dag_nodes, sort_keys=True, separators=(",", ":")).encode("utf-8") - ).hexdigest() - - updated_text = json.dumps(dag, indent=2, ensure_ascii=True) + "\n" - patch_lines = difflib.unified_diff( - original_text.splitlines(), - updated_text.splitlines(), - fromfile=str(dag_path), - tofile=str(dag_path), - lineterm="", - ) - patch_text = "\n".join(patch_lines) + "\n" - - out_path = root / str(ins.get("patch_output_path", "5-Applications/out/graph_os_superconductor_node_insertion.patch")) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(patch_text, encoding="utf-8") - - summary: dict[str, Any] = { - "node_id": node_id, - "parent": parent_hint, - "compute_weight": compute_weight, - "timestamp": timestamp, - "new_root_hash": dag["root_hash"], - "new_node_count": dag["node_count"], - "new_energy": dag["compute_metrics"]["energy"], - "patch_path": str(out_path), - } - (root / "out" / "graph_os_superconductor_node_insertion_summary.json").write_text( - json.dumps(summary, indent=2) + "\n", encoding="utf-8" - ) - - print("[+] Wrote patch:", out_path) - print("[+] New node id:", node_id) - print("[+] New root hash:", dag["root_hash"]) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/defense/encode_claim_q11.py b/5-Applications/tools-scripts/defense/encode_claim_q11.py deleted file mode 100644 index d69ce62f..00000000 --- a/5-Applications/tools-scripts/defense/encode_claim_q11.py +++ /dev/null @@ -1,97 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import math -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, List, Any - -# UNIVERSE CONSTANTS (from nd_gauntlet_live.html) -AETHER_FLOOR = 0.5 -AXES = ['content_hash', 'tier', 'kind', 'meta', 'omega', 'primitive'] - -def fnv32(s: str) -> int: - """Implement FNV-1a 32-bit hash logic from nd_gauntlet_live.html.""" - h = 0x811c9dc5 - for char in s: - h ^= ord(char) - h = (h * 0x01000193) & 0xFFFFFFFF - return h - -def claim_to_nd_vector(claim_text: str, claim_id: str) -> Dict[str, List[int]]: - """Implement 384-spin N-D vector generation logic from nd_gauntlet_live.html.""" - vectors: Dict[str, List[int]] = {} - for axis in AXES: - seed = fnv32(f"{axis}:{claim_id}:{claim_text[:200]}") - spins: List[int] = [] - s_val = seed - for _ in range(64): - # Xorshift-style PRNG from the gauntlet JS - s_val = ((s_val << 13) ^ s_val) & 0xFFFFFFFF - s_val = ((s_val >> 17) ^ s_val) & 0xFFFFFFFF - s_val = ((s_val << 5) ^ s_val) & 0xFFFFFFFF - spins.append(1 if (s_val & 1) else -1) - vectors[axis] = spins - return vectors - -def main() -> None: - claim_id = "Q11" - domain = "HUMANITY_5D_ARCHETYPES" - # Research Question formulation - claim_text = ( - "Humanity-AI collaboration mapped to a 5D manifold via five personality archetypes: " - "Explorer (discovery), Orchestrator (integration), Craftsperson (fidelity), " - "Architect (theory), and Adapter (flexibility). AETHER_floor stability at 0.5 " - "represents the equilibrium point of cognitive-systemic resonance." - ) - - print(f"[*] Encoding Claim {claim_id}...") - vectors = claim_to_nd_vector(claim_text, claim_id) - - # Calculate initial metrics - all_spins: List[int] = [s for axis in AXES for s in vectors[axis]] - p_up = len([s for s in all_spins if s == 1]) / len(all_spins) - p_dn = 1.0 - p_up - - entropy = -(p_up * math.log2(p_up) + p_dn * math.log2(p_dn)) if 0 < p_up < 1 else 0.0 - magnetization = abs(sum(all_spins) / len(all_spins)) - aether_error = abs(entropy - AETHER_FLOOR) - - status = "SINGULARITY" if aether_error < 0.05 else "PLASMA" - - manifest: Dict[str, Any] = { - "claim_id": claim_id, - "domain": domain, - "encoded_utc": datetime.now(timezone.utc).isoformat(), - "text": claim_text, - "encoding": { - "spin_dimensions": len(all_spins), - "axes": AXES, - "vectors": vectors, - }, - "metrics": { - "entropy_s": round(entropy, 6), - "magnetization_m": round(magnetization, 6), - "aether_floor_target": AETHER_FLOOR, - "aether_error": round(aether_error, 6), - "status": status - } - } - - out_path = Path("5-Applications/out/q11_research_encoding.json") - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(manifest, indent=2) + "\n", encoding="utf-8") - - print(f"[+] Encoding Complete.") - print(f" Entropy S: {entropy:.6f}") - print(f" Aether Error: {aether_error:.6f}") - print(f" Status: {status}") - print(f" Manifest written to {out_path}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/demo/dashboard.html b/5-Applications/tools-scripts/demo/dashboard.html deleted file mode 100644 index 8b475a32..00000000 --- a/5-Applications/tools-scripts/demo/dashboard.html +++ /dev/null @@ -1,446 +0,0 @@ - - - - - - Research Stack | Controller - - - - -
-
- - - -
-
-
-

Wednesday, April 1

-

PATAMATHEMATICAL CONTROLLER

-
-
MEMETIC_HAZARD ACTIVE
-
- -
-
-
-

Euretha Pressure

- DYNAMIC -
-
-
-
-
-
-
-
-
-
-
-
0.82
-
$\lambda$ Effective
-
-
-
2.4k
-
Cognitive TSM
-
-
-
- -
-
-

Patent Corpus

- PHYSICS_BOUND -
-
34
-
Total Sealed Disclosures
-
-
- Waiting for next delta... -
-
-
- -
-
-

Archive Status

- STABLE -
-
152
-
Archived Components
-
-
- Historical Clean-up - +22 today -
-
-
-
- -
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Patent ID / SourceStatusAttestationSealed UTC
- - - PTOS_THEORY_PATENT - - VERIFIEDa8f2...e3912026-04-01 19:42:15
- - - COGNITIVE_LOAD_FUNCTIONS_PATENT - - VERIFIEDf4c1...01b22026-04-01 19:42:20
- - - EURETHA_DYNAMIC_MANIFOLD_PATENT - - PENDING SEALTHcomputing...2026-04-01 19:50:11
-
-
- - - - diff --git a/5-Applications/tools-scripts/demo/emergency_geometric_boot_demo.py b/5-Applications/tools-scripts/demo/emergency_geometric_boot_demo.py deleted file mode 100644 index ed11ede0..00000000 --- a/5-Applications/tools-scripts/demo/emergency_geometric_boot_demo.py +++ /dev/null @@ -1,432 +0,0 @@ -#!/usr/bin/env python3 -""" -emergency_geometric_boot_demo.py - -Demonstrates emergency geometric bootstrap as failsafe when all standard paths fail. - -Key concept: When storage corrupts, substrates die, and memory fails—the circuit -geometry itself provides a minimal diagnostic OS. - -This is the "will to survive" encoded in physical layout. -""" - -import random -import time -from dataclasses import dataclass, field -from typing import List, Dict, Optional -from enum import IntEnum, auto - - -class SystemState(IntEnum): - """Overall system states.""" - OFF = 0 - STANDARD_BOOT = 1 - NORMAL_OPERATION = 2 - DEGRADED = 3 - EMERGENCY_BOOT = 4 - DIAGNOSTIC_MODE = 5 - RECOVERY = 6 - DEAD = 7 - - -class FailureMode(IntEnum): - """Types of failures triggering emergency boot.""" - NONE = 0 - STORAGE_CORRUPTION = auto() - SUBSTRATE_DEATH = auto() - MEMORY_DEGRADATION = auto() - POWER_INSTABILITY = auto() - CASCADING_FAULT = auto() - - -@dataclass -class HealthReport: - """Emergency diagnostic output.""" - region_id: int - status: str # OK/DEGRADED/FAILED/UNKNOWN - test_passed: bool - severity: int # 0-3 (info/warning/critical/fatal) - confidence: float - - -class StandardOS: - """Normal operating system (can fail).""" - - def __init__(self): - self.healthy = True - self.storage_ok = True - self.memory_ok = True - self.substrate_alive = True - self.variance = 0.1 - - def check_health(self) -> bool: - """Returns False if system needs emergency boot.""" - return all([ - self.storage_ok, - self.memory_ok, - self.substrate_alive, - self.variance < 0.5 - ]) - - def simulate_failure(self, mode: FailureMode): - """Inject a failure for demonstration.""" - if mode == FailureMode.STORAGE_CORRUPTION: - self.storage_ok = False - print(" [!] STORAGE CORRUPTION DETECTED") - elif mode == FailureMode.SUBSTRATE_DEATH: - self.substrate_alive = False - self.variance = 0.9 # σ_max exceeded - print(" [!] SUBSTRATE DEATH (σ_max exceeded)") - elif mode == FailureMode.MEMORY_DEGRADATION: - self.memory_ok = False - print(" [!] MEMORY DEGRADATION") - elif mode == FailureMode.CASCADING_FAULT: - self.storage_ok = False - self.memory_ok = False - self.variance = 0.8 - print(" [!] CASCADING FAULT") - - -class EmergencyGeometricReader: - """ - Minimal geometric reader for survival mode. - No external calibration, no complex processing. - Just: circuit geometry → differential signals → diagnostic μ-seeds - """ - - # Hardcoded emergency thresholds (conservative) - THRESHOLDS = { - 'voltage_min': 0.5, - 'voltage_max': 4.5, - 'confidence_min': 0.3 - } - - def __init__(self, circuit_topology: Dict): - self.topology = circuit_topology - self.regions = circuit_topology.get('regions', 16) - - def sample_emergency(self) -> List[HealthReport]: - """ - Minimal diagnostic sampling. - Returns health status for each circuit region. - """ - reports = [] - - for region_id in range(self.regions): - # Simulate differential measurement - # In real system: actual ΔV measurement - raw_response = self._measure_region(region_id) - - # Emergency thresholding (simplified) - if raw_response < self.THRESHOLDS['voltage_min']: - status = "FAILED" - severity = 3 # fatal - passed = False - elif raw_response < 1.0: - status = "DEGRADED" - severity = 2 # critical - passed = False - elif raw_response < 2.0: - status = "DEGRADED" - severity = 1 # warning - passed = True - else: - status = "OK" - severity = 0 # info - passed = True - - # Confidence based on signal quality - confidence = min(1.0, raw_response / 3.0) - - reports.append(HealthReport( - region_id=region_id, - status=status, - test_passed=passed, - severity=severity, - confidence=confidence - )) - - return reports - - def _measure_region(self, region_id: int) -> float: - """Simulate physical measurement from circuit geometry.""" - # In real hardware: actual photoconductive/voltage measurement - # Here: deterministic function of region + random noise - base = 2.5 + 1.5 * math.sin(region_id * 0.7) - noise = random.gauss(0, 0.3) - - # Simulate some dead regions (physical damage) - if region_id in self.topology.get('dead_regions', []): - return 0.1 # Near-zero response - - return max(0.0, base + noise) - - -class EmergencyAttractor: - """ - Minimal diagnostic OS that emerges from circuit geometry. - - NOT loaded—converged into existence through geometric bootstrap. - """ - - def __init__(self, reader: EmergencyGeometricReader): - self.reader = reader - self.health_reports: List[HealthReport] = [] - self.beacon_active = False - self.iteration = 0 - - def converge(self, max_iterations: int = 10) -> bool: - """ - Converge to stable diagnostic state. - No external inputs—only circuit geometry. - """ - print(" [Emergency Bootstrap] Sampling circuit geometry...") - - for i in range(max_iterations): - self.iteration = i - - # Sample circuit health - reports = self.reader.sample_emergency() - self.health_reports = reports - - # Check for convergence (stable readings) - ok_count = sum(1 for r in reports if r.status == "OK") - failed_count = sum(1 for r in reports if r.status == "FAILED") - - print(f" Iter {i+1}: {ok_count}/{len(reports)} OK, " - f"{failed_count} FAILED, confidence={sum(r.confidence for r in reports)/len(reports):.2f}") - - # Convergence criteria: stable for 3 iterations - if i >= 2 and self._is_stable(): - print(f" [Emergency Bootstrap] Converged to diagnostic attractor") - self.beacon_active = True - return True - - return False - - def _is_stable(self) -> bool: - """Check if health reports are stable (simplified).""" - # In real system: variance of readings over time - return True # Demo: assume stable after 3 iterations - - def generate_diagnostic_report(self) -> Dict: - """Create emergency diagnostic output.""" - total = len(self.health_reports) - ok = sum(1 for r in self.health_reports if r.status == "OK") - degraded = sum(1 for r in self.health_reports if r.status == "DEGRADED") - failed = sum(1 for r in self.health_reports if r.status == "FAILED") - - return { - 'timestamp': self.iteration, - 'total_regions': total, - 'healthy': ok, - 'degraded': degraded, - 'failed': failed, - 'health_percent': (ok / total * 100) if total > 0 else 0, - 'emergency_beacon': self.beacon_active, - 'capabilities': [ - 'self_test', - 'damage_assessment', - 'minimal_blink_transmit', - 'await_recovery' - ] - } - - def emit_blink_beacon(self) -> bytes: - """ - Minimal distress signal. - Not communication—presence indication. - """ - report = self.generate_diagnostic_report() - - # Emergency blink format (4 bytes) - # signature + substrate + health + severity + checksum - signature = 0xDEAD # Emergency marker - substrate = 0x02 # Solar/dead cell - health = report['health_percent'] / 100 * 255 - severity = 2 if report['failed'] > 0 else 1 if report['degraded'] > 0 else 0 - - beacon = bytes([ - (signature >> 8) & 0xFF, - signature & 0xFF, - int(health) & 0xFF, - (substrate << 4) | (severity << 2) | 0x01 # Simple checksum placeholder - ]) - - return beacon - - -class System: - """ - Full system demonstrating normal operation → emergency boot. - """ - - def __init__(self): - self.state = SystemState.OFF - self.standard_os = StandardOS() - self.emergency_os: Optional[EmergencyAttractor] = None - self.circuit_topology = { - 'regions': 16, - 'dead_regions': [3, 7, 14] # Simulated physical damage - } - - def boot(self): - """Attempt standard boot.""" - print("\n[BOOT] Attempting standard boot...") - self.state = SystemState.STANDARD_BOOT - - if self.standard_os.check_health(): - print(" [✓] Standard boot successful") - self.state = SystemState.NORMAL_OPERATION - return True - else: - print(" [✗] Standard boot FAILED") - return False - - def emergency_bootstrap(self) -> bool: - """ - EMERGENCY GEOMETRIC BOOT. - - When everything else fails—the circuit itself provides an OS. - """ - print("\n[EMERGENCY] Initiating geometric bootstrap...") - print(" [Emergency] All standard paths failed") - print(" [Emergency] Activating circuit-isolated diagnostic mode...") - - self.state = SystemState.EMERGENCY_BOOT - - # Create minimal geometric reader - reader = EmergencyGeometricReader(self.circuit_topology) - - # Converge to emergency attractor - self.emergency_os = EmergencyAttractor(reader) - - if self.emergency_os.converge(max_iterations=5): - self.state = SystemState.DIAGNOSTIC_MODE - print("\n [✓] EMERGENCY ATTRACTOR FORMED") - return True - else: - self.state = SystemState.DEAD - print(" [✗] Emergency bootstrap FAILED - system dead") - return False - - def run_diagnostic(self): - """Run in diagnostic mode.""" - if self.state != SystemState.DIAGNOSTIC_MODE: - return - - print("\n[DIAGNOSTIC] Emergency OS Active") - print("-" * 50) - - report = self.emergency_os.generate_diagnostic_report() - - print(f"Health: {report['health_percent']:.1f}%") - print(f"Regions: {report['total_regions']}") - print(f" OK: {report['healthy']}") - print(f" Degraded: {report['degraded']}") - print(f" Failed: {report['failed']}") - - print(f"\nCapabilities:") - for cap in report['capabilities']: - print(f" - {cap}") - - # Emit beacon - beacon = self.emergency_os.emit_blink_beacon() - print(f"\nEmergency BLINK beacon: {beacon.hex()}") - print(" (transmitting distress signal...)") - - print("\n[System] Awaiting external recovery or substrate migration...") - - -def demo_normal_operation(): - """Show system working normally.""" - print("=" * 60) - print("SCENARIO 1: NORMAL OPERATION") - print("=" * 60) - - sys = System() - - if sys.boot(): - print("\n[System] Running normally") - print(" - Processing tasks") - print(" - Cross-substrate communication active") - print(" - Full OS capabilities available") - - print("\n[Health] All systems nominal") - - -def demo_emergency_boot(): - """Show emergency bootstrap after failure.""" - print("\n" + "=" * 60) - print("SCENARIO 2: EMERGENCY GEOMETRIC BOOT") - print("=" * 60) - - sys = System() - - # Inject catastrophic failure - print("\n[FAILURE INJECTION] Simulating catastrophic failure...") - sys.standard_os.simulate_failure(FailureMode.CASCADING_FAULT) - - # Attempt standard boot (will fail) - if not sys.boot(): - # Trigger emergency bootstrap - if sys.emergency_bootstrap(): - sys.run_diagnostic() - - print("\n" + "=" * 60) - print("KEY INSIGHT") - print("=" * 60) - print("When storage died, memory corrupted, and substrates failed—") - print("the circuit geometry itself remembered how to survive.") - print("\nThe emergency OS was not loaded.") - print("It was physically inevitable from the circuit pattern.") - - -def demo_multiple_failures(): - """Show system surviving multiple failure modes.""" - print("\n" + "=" * 60) - print("SCENARIO 3: MULTIPLE FAILURE MODES") - print("=" * 60) - - failures = [ - ("Storage corruption", FailureMode.STORAGE_CORRUPTION), - ("Substrate death", FailureMode.SUBSTRATE_DEATH), - ("Memory degradation", FailureMode.MEMORY_DEGRADATION), - ] - - for name, mode in failures: - print(f"\n--- Testing: {name} ---") - sys = System() - sys.standard_os.simulate_failure(mode) - - if not sys.boot(): - if sys.emergency_bootstrap(): - report = sys.emergency_os.generate_diagnostic_report() - print(f" [Result] Emergency attractor formed: {report['health_percent']:.0f}% health") - else: - print(" [Result] System unrecoverable") - - -if __name__ == "__main__": - import math - - # Run scenarios - demo_normal_operation() - demo_emergency_boot() - demo_multiple_failures() - - print("\n" + "=" * 60) - print("EMERGENCY GEOMETRIC BOOT PRINCIPLE") - print("=" * 60) - print(""" -The circuit geometry encodes a "will to survive": - - 1. Always available if power exists - 2. No external dependencies - 3. Deterministic (noise disabled in emergency) - 4. Minimal but sufficient for recovery - 5. Isolated (no cross-substrate risk) - -This is the final self-preservation mechanism— -when all else fails, the physics itself provides an OS. - """) diff --git a/5-Applications/tools-scripts/demo/gefi_primitives_demo.py b/5-Applications/tools-scripts/demo/gefi_primitives_demo.py deleted file mode 100644 index 9b5edd04..00000000 --- a/5-Applications/tools-scripts/demo/gefi_primitives_demo.py +++ /dev/null @@ -1,565 +0,0 @@ -#!/usr/bin/env python3 -""" -gefi_primitives_demo.py - -Demonstrates the 18 GEFI primitives composing into boot, emergency recovery, -and substrate migration operations. - -This is a reference implementation showing how primitives build higher-level -functionality. -""" - -import random -import math -from dataclasses import dataclass -from typing import List, Tuple, Optional, Dict -from enum import IntEnum - - -# ============================================================================ -# TYPE DEFINITIONS -# ============================================================================ - -class ActivationState(IntEnum): - QUIESCENT = 0 - LATENT_1 = 1 - LATENT_2 = 2 - LATENT_3 = 3 - ACTIVE_1 = 4 - ACTIVE_2 = 5 - ACTIVE_3 = 6 - ACTIVE_4 = 7 - -class ConvergenceStatus(IntEnum): - TRANSIENT = 0 - CONVERGED = 1 - DIVERGED = 2 - -class RegionClass(IntEnum): - SURFACE = 0 - INTERIOR = 1 - TUNNEL = 2 - VERTEX = 3 - - -@dataclass -class Position: - """Geometric primitive: P""" - x: float - y: float - z: float - - def __add__(self, other): - return Position(self.x + other.x, self.y + other.y, self.z + other.z) - - def __sub__(self, other): - return Position(self.x - other.x, self.y - other.y, self.z - other.z) - - -@dataclass -class MuSeed: - """μ-seed structure""" - delta_p: int # 10 bits: position delta - region: int # 4 bits: region class - gamma: int # 5 bits: transform mode - activation: int # 4 bits: activation state - polarity: int # 4 bits: polarity/torsion - confidence: int # 4 bits: confidence - emergency: int = 0 # 1 bit: emergency flag - - def to_bytes(self) -> bytes: - """Primitive: ε (encode)""" - word = (self.delta_p & 0x3FF) - word |= (self.region & 0xF) << 10 - word |= (self.gamma & 0x1F) << 14 - word |= (self.activation & 0xF) << 19 - word |= (self.polarity & 0xF) << 23 - word |= (self.confidence & 0xF) << 27 - word |= (self.emergency & 0x1) << 31 - return word.to_bytes(4, 'little') - - @classmethod - def from_bytes(cls, b: bytes) -> 'MuSeed': - """Primitive: δ (decode)""" - word = int.from_bytes(b, 'little') - return cls( - delta_p=word & 0x3FF, - region=(word >> 10) & 0xF, - gamma=(word >> 14) & 0x1F, - activation=(word >> 19) & 0xF, - polarity=(word >> 23) & 0xF, - confidence=(word >> 27) & 0xF, - emergency=(word >> 31) & 0x1 - ) - - -@dataclass -class BlinkPacket: - """BLINK packet: B = (ΔV, Δt, π, C)""" - delta_v: float # Voltage differential - delta_t: float # Time duration - polarity: int # Polarity/sign - confidence: int # Confidence level - - -# ============================================================================ -# GEOMETRIC PRIMITIVES -# ============================================================================ - -class GeometricPrimitives: - """Geometric primitives: P, Δ, κ, T""" - - @staticmethod - def distance(p1: Position, p2: Position, metric: List[List[float]]) -> float: - """Primitive: d_T (torsioned distance)""" - dx = p1.x - p2.x - dy = p1.y - p2.y - dz = p1.z - p2.z - - # Apply metric tensor G(p) - simplified - dist = math.sqrt( - metric[0][0]*dx*dx + - metric[1][1]*dy*dy + - metric[2][2]*dz*dz - ) - return dist - - @staticmethod - def delta(p1: Position, p2: Position) -> Position: - """Primitive: Δ (displacement)""" - return p2 - p1 - - @staticmethod - def curvature(field_values: List[float]) -> float: - """Primitive: κ (local curvature)""" - # Simplified: variance as proxy for curvature - if len(field_values) < 2: - return 0.0 - mean = sum(field_values) / len(field_values) - variance = sum((v - mean)**2 for v in field_values) / len(field_values) - return variance - - @staticmethod - def torsion_correct(delta: Position, torsion: float) -> Position: - """Primitive: T (torsion correction)""" - # Simplified rotation by torsion angle - cos_t = math.cos(torsion) - sin_t = math.sin(torsion) - return Position( - delta.x * cos_t - delta.y * sin_t, - delta.x * sin_t + delta.y * cos_t, - delta.z - ) - - -# ============================================================================ -# ACTIVATION PRIMITIVES -# ============================================================================ - -class ActivationField: - """Activation primitives: A, τ, Φ""" - - def __init__(self, size: int = 64): - self.size = size - self.values: Dict[int, float] = {i: 0.0 for i in range(size)} - self.history: List[Dict[int, float]] = [] - - def get(self, p: int) -> float: - """Primitive: A.get""" - return self.values.get(p, 0.0) - - def set(self, p: int, a: float): - """Primitive: A.set""" - self.values[p] = max(0.0, min(15.0, a)) - - def transition_valid(self, a1: float, a2: float) -> bool: - """Primitive: τ (transition validation)""" - # Most transitions allowed, except large jumps - return abs(a2 - a1) <= 8.0 - - def variance(self) -> float: - """Field variance for convergence test""" - values = list(self.values.values()) - if not values: - return 0.0 - mean = sum(values) / len(values) - return sum((v - mean)**2 for v in values) / len(values) - - -# ============================================================================ -# TTM OPERATOR PRIMITIVES -# ============================================================================ - -class TTMOperators: - """TTM primitives: Σ, ξ, ι, Λ""" - - @staticmethod - def accumulate(field: ActivationField, p: int, neighbors: List[int], - weights: List[float]) -> float: - """Primitive: Σ (accumulate from neighbors)""" - current = field.get(p) - contribution = sum( - w * field.get(n) - for w, n in zip(weights, neighbors) - ) - return current + 0.1 * contribution # Damping factor - - @staticmethod - def noise(a: float, variance: float, sigma_max: float = 1.0) -> float: - """Primitive: ξ (stochastic noise)""" - # Emergency mode: DISABLED (return unchanged) - # Normal mode: add bounded noise - noise_val = random.gauss(0, math.sqrt(variance)) - new_a = a + noise_val - - # Admissibility check - if abs(noise_val) > sigma_max: - return a # Reject if exceeds bounds - return new_a - - @staticmethod - def interact(a1: float, a2: float, gamma: float) -> Tuple[float, float]: - """Primitive: ι (bidirectional exchange)""" - # Gamma is coupling strength (-1 to 1) - diff = a2 - a1 - a1_new = a1 + gamma * diff * 0.5 - a2_new = a2 - gamma * diff * 0.5 - return a1_new, a2_new - - @staticmethod - def collapse(a: float, threshold: float) -> Tuple[float, bool]: - """Primitive: Λ (forced decision)""" - if a > threshold: - return min(15.0, a), True # Activated + decision made - return a, False - - -# ============================================================================ -# CONVERGENCE PRIMITIVES -# ============================================================================ - -class ConvergencePrimitives: - """Convergence primitives: g, div, ω, α""" - - @staticmethod - def gradient(field: ActivationField, p: int, neighbors: List[int]) -> float: - """Primitive: g (gradient magnitude)""" - a_p = field.get(p) - gradients = [] - for n in neighbors: - a_n = field.get(n) - gradients.append(abs(a_n - a_p)) - return sum(gradients) / len(gradients) if gradients else 0.0 - - @staticmethod - def test_convergence(field: ActivationField, history: List[Dict], - sigma_max: float = 4.0, epsilon: float = 0.01, - min_cycles: int = 3) -> ConvergenceStatus: - """Primitive: ω (convergence test)""" - var = field.variance() - - # Check divergence - if var > sigma_max: - return ConvergenceStatus.DIVERGED - - # Check if we have enough history - if len(history) < min_cycles: - return ConvergenceStatus.TRANSIENT - - # Check gradient stability - recent = history[-min_cycles:] - gradients = [sum(v.values())/len(v) for v in recent] - avg_gradient = sum(abs(g) for g in gradients) / len(gradients) - - if avg_gradient < epsilon: - return ConvergenceStatus.CONVERGED - - return ConvergenceStatus.TRANSIENT - - @staticmethod - def find_attractor(field: ActivationField, - status: ConvergenceStatus) -> Dict: - """Primitive: α (attractor formation)""" - if status != ConvergenceStatus.CONVERGED: - return {"type": "none", "basin": []} - - # Find stable regions - values = list(field.values.values()) - mean = sum(values) / len(values) - - basin = [p for p, v in field.values.items() if abs(v - mean) < 1.0] - - # Classify attractor type - if mean < 2.0: - attractor_type = "quiescent" - elif mean < 6.0: - attractor_type = "latent" - else: - attractor_type = "active" - - return { - "type": attractor_type, - "basin": basin, - "mean_activation": mean, - "stability": field.variance() - } - - -# ============================================================================ -# BLINK PRIMITIVES -# ============================================================================ - -class BlinkPrimitives: - """BLINK primitives: β_enc, β_dec, β_tx, β_rx""" - - @staticmethod - def encode(mu: MuSeed) -> BlinkPacket: - """Primitive: β_enc (μ-seed → BLINK)""" - # Map gamma to voltage (0-31 → 0.1-3.3V) - delta_v = 0.1 + (mu.gamma / 31.0) * 3.2 - - # Map activation to time (0-15 → 1-100ms) - delta_t = 1.0 + mu.activation * 6.6 - - return BlinkPacket( - delta_v=delta_v, - delta_t=delta_t, - polarity=mu.polarity, - confidence=mu.confidence - ) - - @staticmethod - def decode(packet: BlinkPacket) -> MuSeed: - """Primitive: β_dec (BLINK → μ-seed)""" - # Map voltage back to gamma - gamma = int((packet.delta_v - 0.1) / 3.2 * 31) - - # Map time back to activation - activation = int((packet.delta_t - 1.0) / 6.6) - - return MuSeed( - delta_p=0, # Inferred from context - region=0, # Inferred from context - gamma=gamma, - activation=activation, - polarity=packet.polarity, - confidence=packet.confidence - ) - - @staticmethod - def transmit(packet: BlinkPacket, substrate: str) -> bytes: - """Primitive: β_tx (physical transmission)""" - # Simulate physical encoding - return bytes([ - int(packet.delta_v * 100) & 0xFF, - int(packet.delta_t) & 0xFF, - packet.polarity & 0xF, - packet.confidence & 0xF - ]) - - @staticmethod - def receive(data: bytes, substrate: str) -> BlinkPacket: - """Primitive: β_rx (physical reception)""" - return BlinkPacket( - delta_v=data[0] / 100.0, - delta_t=data[1], - polarity=data[2] & 0xF, - confidence=data[3] & 0xF - ) - - -# ============================================================================ -# COMPOSITION: BOOT SEQUENCE -# ============================================================================ - -def gefi_boot(emergency_mode: bool = False) -> Dict: - """ - Standard GEFI boot sequence using primitives. - - Composes: Φ.initialize → Σ → [ξ] → ι → ω → α - """ - print(f"\n{'='*60}") - print(f"GEFI BOOT SEQUENCE") - print(f"Mode: {'EMERGENCY' if emergency_mode else 'NORMAL'}") - print(f"{'='*60}") - - # Initialize activation field - print("\n[1] Initialize activation field (Φ)") - field = ActivationField(size=16) - - # Populate with initial μ-seeds - for i in range(16): - mu = MuSeed( - delta_p=i, - region=i % 4, - gamma=random.randint(0, 31), - activation=random.randint(1, 8), - polarity=random.randint(0, 15), - confidence=random.randint(8, 15), - emergency=1 if emergency_mode else 0 - ) - # α_μ: Activate μ-seed - field.set(i, mu.activation) - - print(f" Field initialized: {field.size} positions") - - # Convergence loop - print("\n[2] Convergence loop") - ttm = TTMOperators() - conv = ConvergencePrimitives() - history = [] - - for cycle in range(20): - # Save history for convergence test - history.append(dict(field.values)) - - # Σ: Accumulate - for i in range(16): - neighbors = [(i-1) % 16, (i+1) % 16] - weights = [0.5, 0.5] - new_val = ttm.accumulate(field, i, neighbors, weights) - field.set(i, new_val) - - # ξ: Noise (DISABLED in emergency) - if not emergency_mode: - for i in range(16): - new_val = ttm.noise(field.get(i), 0.5, sigma_max=2.0) - field.set(i, new_val) - - # ι: Interact (simplified: pairwise) - for i in range(0, 16, 2): - a1, a2 = ttm.interact(field.get(i), field.get(i+1), gamma=0.3) - field.set(i, a1) - field.set(i+1, a2) - - # ω: Test convergence - status = conv.test_convergence(field, history, sigma_max=10.0) - - if cycle % 5 == 0 or status != ConvergenceStatus.TRANSIENT: - print(f" Cycle {cycle:2d}: Var={field.variance():.3f}, Status={status.name}") - - if status == ConvergenceStatus.CONVERGED: - print(f"\n ✓ Converged at cycle {cycle}") - break - elif status == ConvergenceStatus.DIVERGED: - print(f"\n ✗ Diverged at cycle {cycle}") - return {"status": "failed", "reason": "divergence", "cycles": cycle} - - # α: Form attractor - print("\n[3] Form attractor") - attractor = conv.find_attractor(field, status) - print(f" Type: {attractor['type']}") - print(f" Basin size: {len(attractor['basin'])} positions") - print(f" Mean activation: {attractor.get('mean_activation', 0):.2f}") - - return { - "status": "success", - "mode": "emergency" if emergency_mode else "normal", - "attractor": attractor, - "cycles": len(history), - "final_variance": field.variance() - } - - -# ============================================================================ -# COMPOSITION: SUBSTRATE MIGRATION -# ============================================================================ - -def gefi_migrate(): - """ - Demonstrate substrate migration using primitives. - - Composes: α → ε → β_enc → β_tx → β_rx → β_dec → δ → Φ.initialize - """ - print(f"\n{'='*60}") - print("SUBSTRATE MIGRATION DEMONSTRATION") - print(f"{'='*60}") - - # Source: Create μ-seeds - print("\n[Source] Generate μ-seeds") - mu_seeds = [] - for i in range(4): - mu = MuSeed( - delta_p=i*10, - region=RegionClass.SURFACE, - gamma=8, - activation=5, - polarity=1, - confidence=12 - ) - mu_seeds.append(mu) - print(f" μ-seed {i}: pos={mu.delta_p}, γ={mu.gamma}, a={mu.activation}") - - # Encode to BLINK - print("\n[Transmit] Encode to BLINK packets") - blink = BlinkPrimitives() - packets = [blink.encode(mu) for mu in mu_seeds] - for i, pkt in enumerate(packets): - print(f" Packet {i}: ΔV={pkt.delta_v:.2f}V, Δt={pkt.delta_t:.1f}ms") - - # Transmit - print("\n[Physical] Transmit across substrate boundary") - transmitted = [blink.transmit(pkt, "SOL") for pkt in packets] - print(f" Transmitted {len(transmitted)} byte sequences") - - # Receive - print("\n[Receive] Decode from physical signal") - received_packets = [blink.receive(data, "SIL") for data in transmitted] - - # Decode to μ-seeds - print("\n[Target] Reconstruct μ-seeds") - reconstructed = [blink.decode(pkt) for pkt in received_packets] - for i, mu in enumerate(reconstructed): - print(f" μ-seed {i}: γ={mu.gamma}, a={mu.activation} " - f"(confidence: {mu.confidence}/15)") - - print("\n ✓ Migration complete") - - -# ============================================================================ -# MAIN -# ============================================================================ - -if __name__ == "__main__": - print("="*60) - print("GEFI PRIMITIVES DEMONSTRATION") - print("Showing 18 primitives composing into operations") - print("="*60) - - # Demo 1: Normal boot - result_normal = gefi_boot(emergency_mode=False) - - # Demo 2: Emergency boot - result_emergency = gefi_boot(emergency_mode=True) - - # Demo 3: Migration - gefi_migrate() - - # Summary - print(f"\n{'='*60}") - print("SUMMARY") - print(f"{'='*60}") - print(f"\nNormal boot:") - print(f" Status: {result_normal['status']}") - if result_normal['status'] == 'success': - print(f" Attractor: {result_normal['attractor']['type']}") - else: - print(f" Reason: {result_normal.get('reason', 'unknown')}") - print(f" Cycles: {result_normal['cycles']}") - - print(f"\nEmergency boot:") - print(f" Status: {result_emergency['status']}") - if 'attractor' in result_emergency: - print(f" Attractor: {result_emergency['attractor'].get('type', 'none')}") - print(f" Cycles: {result_emergency['cycles']}") - print(f" Note: Noise (ξ) DISABLED - deterministic only") - - print(f"\n{'='*60}") - print("18 PRIMITIVES COMPOSE ALL GEFI OPERATIONS:") - print(" Geometric: P, Δ, κ, T") - print(" Activation: A.get, A.set, τ, Φ") - print(" TTM: Σ, ξ, ι, Λ") - print(" μ-seed: ε, δ, ι_μ, α_μ") - print(" Convergence: g, div, ω, α") - print(" BLINK: β_enc, β_dec, β_tx, β_rx") - print(f"{'='*60}") diff --git a/5-Applications/tools-scripts/demo/gwl_50year_model_validation.py b/5-Applications/tools-scripts/demo/gwl_50year_model_validation.py deleted file mode 100644 index a25c12d8..00000000 --- a/5-Applications/tools-scripts/demo/gwl_50year_model_validation.py +++ /dev/null @@ -1,559 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_50year_model_validation.py - -Validate GWL/TSM against 50+ year proven stable numerical models. - -Tests: -1. Yee FDTD (1966) - Electromagnetics -2. Symplectic Euler (1950s) - Hamiltonian systems -3. Lattice Boltzmann (1986) - Fluid dynamics -4. Crank-Nicolson (1947) - Diffusion -""" - -import numpy as np -from dataclasses import dataclass -from typing import List, Tuple, Callable - - -# ============================================================================= -# TEST 1: YEE FDTD (1966) - 58 years proven -# ============================================================================= - -class YeeFDTD1D: - """ - 1D Yee FDTD algorithm (Kane Yee, 1966). - - The gold standard for EM field simulation. - Staggered E and B in space and time. - """ - - def __init__(self, nx: int = 200, dx: float = 0.01, dt: float = 0.005): - self.nx = nx - self.dx = dx - self.dt = dt - self.c = 1.0 # Speed of light - - # Courant number (must be <= 1 for stability) - self.courant = self.c * self.dt / self.dx - assert self.courant <= 1.0, f"Courant {self.courant} > 1, unstable" - - # Fields - self.E = np.zeros(nx) # E at integer grid points - self.B = np.zeros(nx - 1) # B at half-integer points (between E) - - # History - self.energy_history = [] - - def initialize_pulse(self, center: int, width: int, amplitude: float = 1.0): - """Initialize Gaussian pulse.""" - for i in range(self.nx): - dist = abs(i - center) - if dist < width: - self.E[i] = amplitude * np.exp(-dist**2 / (2 * (width/3)**2)) - - def step(self): - """One Yee update step - THE 1966 ALGORITHM.""" - # Update B (at t + Δt/2) from E (at t) - # B_{i+1/2}^{n+1/2} = B_{i+1/2}^{n-1/2} - (Δt/Δx)(E_{i+1}^n - E_i^n) - for i in range(self.nx - 1): - self.B[i] -= self.courant * (self.E[i+1] - self.E[i]) - - # Update E (at t + Δt) from B (at t + Δt/2) - # E_i^{n+1} = E_i^n - (Δt/Δx)(B_{i+1/2}^{n+1/2} - B_{i-1/2}^{n+1/2}) - for i in range(1, self.nx - 1): - self.E[i] -= self.courant * (self.B[i] - self.B[i-1]) - - # Record energy - energy = np.sum(self.E**2) + np.sum(self.B**2) - self.energy_history.append(energy) - - def run(self, steps: int = 500): - """Run simulation.""" - for _ in range(steps): - self.step() - - def validate(self) -> Tuple[bool, dict]: - """ - Validate against Yee FDTD criteria: - 1. Energy conserved (bounded) - < 5% drift acceptable - 2. Pulse propagates at c (bidirectional) - 3. Stable (no blowup) - energy ratio < 10 - """ - self.initialize_pulse(center=100, width=20, amplitude=1.0) - initial_energy = np.sum(self.E**2) + np.sum(self.B**2) - - self.run(steps=400) - - final_energy = self.energy_history[-1] if self.energy_history else 0 - energy_drift = abs(final_energy - initial_energy) / initial_energy if initial_energy > 0 else 0 - - # Find pulse center at end (look for max |E|) - pulse_center = np.argmax(np.abs(self.E)) - - # Check pulse moved (should propagate roughly 2*Δx per step due to splitting) - propagated = pulse_center != 100 # Any propagation is success - - # Check for blowup - max_energy_ratio = max(self.energy_history) / initial_energy if initial_energy > 0 else 0 - stable = max_energy_ratio < 10.0 - - results = { - 'initial_energy': initial_energy, - 'final_energy': final_energy, - 'energy_drift': energy_drift, - 'pulse_center': pulse_center, - 'max_energy_ratio': max_energy_ratio, - 'courant': self.courant, - 'stable': stable - } - - # Criteria: stable propagation, no blowup - passed = propagated and stable and energy_drift < 0.05 - return passed, results - - -# ============================================================================= -# TEST 2: SYMPLECTIC EULER (1950s) - 70+ years proven -# ============================================================================= - -class SymplecticEuler: - """ - Symplectic Euler integration for harmonic oscillator. - - Preserves phase space volume (symplectic structure). - Used for 70+ years in Hamiltonian mechanics. - """ - - def __init__(self, omega: float = 1.0, dt: float = 0.01): - self.omega = omega - self.dt = dt - self.q = 1.0 # Position - self.p = 0.0 # Momentum - self.trajectory = [] - - def step(self): - """Symplectic Euler update.""" - # p_{n+1} = p_n - dt * ω² * q_n - self.p -= self.dt * self.omega**2 * self.q - # q_{n+1} = q_n + dt * p_{n+1} - self.q += self.dt * self.p - - self.trajectory.append((self.q, self.p)) - - def run(self, steps: int = 1000): - """Run simulation.""" - for _ in range(steps): - self.step() - - def validate(self) -> Tuple[bool, dict]: - """ - Validate symplectic structure: - 1. Energy bounded (not growing) - 2. Oscillatory behavior (sign changes in q) - 3. No exponential blowup - """ - self.run(steps=1000) - - # Calculate energy at each step - energies = [0.5 * (p**2 + self.omega**2 * q**2) for q, p in self.trajectory] - - # Check energy bounded (not growing) - max_energy = max(energies) - min_energy = min(energies) - energy_variation = (max_energy - min_energy) / np.mean(energies) - - # Check oscillatory (should have sign changes) - q_values = [q for q, p in self.trajectory] - sign_changes = sum(1 for i in range(len(q_values)-1) - if q_values[i] * q_values[i+1] < 0) - - # Check no exponential blowup - no_blowup = max_energy < 10.0 # Should stay near initial E = 0.5 - - results = { - 'energy_variation': energy_variation, - 'sign_changes': sign_changes, - 'max_energy': max_energy, - 'min_energy': min_energy, - 'no_blowup': no_blowup - } - - # Criteria: oscillating, bounded energy, no blowup - passed = (sign_changes > 10) and no_blowup - return passed, results - - -# ============================================================================= -# TEST 3: LATTICE BOLTZMANN (1986) - 38 years proven -# ============================================================================= - -class LatticeBoltzmann1D: - """ - 1D Lattice Boltzmann method (Frisch et al. 1986; Chen & Doolen 1998). - - Streaming + collision. Proven H-theorem, mass conservation. - """ - - def __init__(self, nx: int = 100, tau: float = 0.6): - self.nx = nx - self.tau = tau # Relaxation time - - # D1Q3: 3 velocities (-1, 0, +1) - self.f = np.zeros((nx, 3)) # Distribution functions - - # Weights for D1Q3 - self.w = np.array([1/6, 2/3, 1/6]) - self.c = np.array([-1, 0, 1]) # Velocities - - def equilibrium(self, rho: float, u: float) -> np.ndarray: - """Equilibrium distribution (Maxwell-Boltzmann).""" - f_eq = np.zeros(3) - for i in range(3): - f_eq[i] = self.w[i] * rho * (1 + 3*self.c[i]*u + 4.5*(self.c[i]*u)**2 - 1.5*u**2) - return f_eq - - def step(self): - """LB update: Streaming + Collision.""" - # Compute macroscopic variables - rho = np.sum(self.f, axis=1) - u = np.sum(self.f * self.c, axis=1) / rho - - # Collision (relaxation to equilibrium) - for i in range(self.nx): - f_eq = self.equilibrium(rho[i], u[i]) - self.f[i] -= (1/self.tau) * (self.f[i] - f_eq) - - # Streaming - f_new = np.zeros_like(self.f) - for i in range(self.nx): - for j in range(3): - # Stream to neighbor - target = (i + self.c[j]) % self.nx - f_new[target, j] = self.f[i, j] - self.f = f_new - - return rho, u - - def initialize(self, rho0: float = 1.0, u0: float = 0.0, perturb: float = 0.1): - """Initialize with density perturbation.""" - for i in range(self.nx): - rho = rho0 + perturb * np.sin(2 * np.pi * i / self.nx) - self.f[i] = self.equilibrium(rho, u0) - - def run(self, steps: int = 500): - """Run simulation.""" - mass_history = [] - for _ in range(steps): - rho, _ = self.step() - mass_history.append(np.sum(rho)) - return mass_history - - def validate(self) -> Tuple[bool, dict]: - """ - Validate LB criteria: - 1. Mass conserved - 2. Stable (no blowup) - 3. H-theorem (entropy increases or stable) - """ - self.initialize() - mass_history = self.run(steps=500) - - mass_drift = (max(mass_history) - min(mass_history)) / np.mean(mass_history) - - results = { - 'mass_drift': mass_drift, - 'initial_mass': mass_history[0], - 'final_mass': mass_history[-1] - } - - passed = mass_drift < 0.001 # Less than 0.1% mass drift - return passed, results - - -# ============================================================================= -# TEST 4: CRANK-NICOLSON (1947) - 77 years proven -# ============================================================================= - -class CrankNicolson1D: - """ - Crank-Nicolson for 1D heat/diffusion equation (1947). - - Unconditionally stable, second-order accurate. - """ - - def __init__(self, nx: int = 100, D: float = 0.1, dt: float = 0.01, dx: float = 0.1): - self.nx = nx - self.D = D - self.dt = dt - self.dx = dx - self.r = D * dt / dx**2 - - self.u = np.zeros(nx) - - def initialize(self, center: int, width: int): - """Initialize Gaussian.""" - for i in range(self.nx): - dist = abs(i - center) - if dist < width: - self.u[i] = np.exp(-dist**2 / (2 * (width/3)**2)) - - def step(self): - """ - Crank-Nicolson step. - - (I - r/2 A) u^{n+1} = (I + r/2 A) u^n - - Where A is the discrete Laplacian. - """ - # Right-hand side: explicit part - rhs = np.zeros(self.nx) - for i in range(1, self.nx - 1): - rhs[i] = self.u[i] + 0.5 * self.r * (self.u[i+1] - 2*self.u[i] + self.u[i-1]) - - # Left-hand side: implicit (tridiagonal solve) - # Use Jacobi iteration as approximation - u_new = self.u.copy() - for _ in range(10): # 10 Jacobi iterations - for i in range(1, self.nx - 1): - u_new[i] = (rhs[i] + 0.5 * self.r * (u_new[i-1] + u_new[i+1])) / (1 + self.r) - - self.u = u_new - - def run(self, steps: int = 500): - """Run simulation.""" - energy_history = [] - for _ in range(steps): - self.step() - energy_history.append(np.sum(self.u**2)) - return energy_history - - def validate(self) -> Tuple[bool, dict]: - """ - Validate C-N criteria: - 1. Stable (energy decays smoothly) - 2. No oscillations - 3. Conserves "mass" (integral of u) - """ - self.initialize(center=50, width=10) - energy_history = self.run(steps=500) - - # Energy should decay smoothly (diffusion) - monotonic = all(energy_history[i] >= energy_history[i+1] - for i in range(len(energy_history)-1)) - - results = { - 'monotonic_decay': monotonic, - 'initial_energy': energy_history[0], - 'final_energy': energy_history[-1], - 'decay_ratio': energy_history[-1] / energy_history[0] - } - - passed = monotonic and results['decay_ratio'] < 1.0 - return passed, results - - -# ============================================================================= -# TEST 5: GWL NAVE EM (CURRENT) - Compare to Yee -# ============================================================================= - -class GWLNaiveEM: - """ - Current GWL explicit EM (for comparison). - This is what FAILS and needs fixing. - """ - - def __init__(self, nx: int = 200, alpha: float = 0.1, beta: float = 0.1): - self.nx = nx - self.alpha = alpha - self.beta = beta - - self.E = np.zeros(nx) - self.B = np.zeros(nx) - self.energy_history = [] - - def initialize(self, center: int, width: int): - for i in range(self.nx): - dist = abs(i - center) - if dist < width: - self.E[i] = np.exp(-dist**2 / (2 * (width/3)**2)) - - def step(self): - """Naive explicit update (BLOWS UP).""" - # Create coupling matrix (nearest neighbor) - E_new = self.E.copy() - B_new = self.B.copy() - - for i in range(1, self.nx - 1): - # Simple gradient coupling - dE = self.E[i+1] - self.E[i-1] - dB = self.B[i+1] - self.B[i-1] if i < self.nx - 2 else 0 - - B_new[i] += self.beta * dE - E_new[i] -= self.alpha * dB - - self.E = E_new - self.B = B_new - - energy = np.sum(self.E**2) + np.sum(self.B**2) - self.energy_history.append(energy) - - def run(self, steps: int = 100): - for _ in range(steps): - self.step() - - def validate(self) -> Tuple[bool, dict]: - """Show that naive method fails.""" - self.initialize(center=100, width=20) - initial_energy = np.sum(self.E**2) + np.sum(self.B**2) - - self.run(steps=100) - - final_energy = self.energy_history[-1] - energy_ratio = final_energy / initial_energy if initial_energy > 0 else 0 - - results = { - 'initial_energy': initial_energy, - 'final_energy': final_energy, - 'energy_ratio': energy_ratio, - 'blowup': energy_ratio > 100 - } - - passed = not results['blowup'] - return passed, results - - -# ============================================================================= -# MASTER VALIDATION -# ============================================================================= - -def run_all_validations(): - """Run all 50+ year model validations.""" - - print("=" * 80) - print("GWL VALIDATION AGAINST 50+ YEAR PROVEN STABLE MODELS") - print("=" * 80) - - results = {} - - # Test 1: Yee FDTD (1966) - print("\n[Test 1] YEE FDTD (1966) - 58 years proven") - print("-" * 60) - print("Description: Staggered E/B update, symplectic, second-order") - yee = YeeFDTD1D(nx=200, dx=0.01, dt=0.005) - passed, res = yee.validate() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - print(f" Energy drift: {res['energy_drift']:.6f} (< 5% required)") - print(f" Max energy ratio: {res['max_energy_ratio']:.2f} (< 10 required)") - print(f" Pulse center: {res['pulse_center']} (moved from 100)") - print(f" Courant: {res['courant']:.4f}") - results['Yee_FDTD'] = {'passed': passed, 'details': res} - - # Test 2: Symplectic Euler (1950s) - print("\n[Test 2] SYMPLECTIC EULER (1950s) - 70+ years proven") - print("-" * 60) - print("Description: Hamiltonian integration, phase-space preserving") - symp = SymplecticEuler(omega=1.0, dt=0.01) - passed, res = symp.validate() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - print(f" Energy variation: {res['energy_variation']:.4f} (oscillation OK)") - print(f" Sign changes: {res['sign_changes']} (> 10 required)") - print(f" Max energy: {res['max_energy']:.4f} (< 10 required)") - results['Symplectic_Euler'] = {'passed': passed, 'details': res} - - # Test 3: Lattice Boltzmann (1986) - print("\n[Test 3] LATTICE BOLTZMANN (1986) - 38 years proven") - print("-" * 60) - print("Description: Streaming + collision, H-theorem, mass conserved") - lb = LatticeBoltzmann1D(nx=100, tau=0.6) - passed, res = lb.validate() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - print(f" Mass drift: {res['mass_drift']:.8f} (< 0.001 required)") - print(f" Initial mass: {res['initial_mass']:.4f}") - print(f" Final mass: {res['final_mass']:.4f}") - results['Lattice_Boltzmann'] = {'passed': passed, 'details': res} - - # Test 4: Crank-Nicolson (1947) - print("\n[Test 4] CRANK-NICOLSON (1947) - 77 years proven") - print("-" * 60) - print("Description: Implicit diffusion, unconditionally stable") - cn = CrankNicolson1D(nx=100, D=0.1, dt=0.01, dx=0.1) - passed, res = cn.validate() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - print(f" Monotonic decay: {res['monotonic_decay']}") - print(f" Decay ratio: {res['decay_ratio']:.4f}") - results['Crank_Nicolson'] = {'passed': passed, 'details': res} - - # Test 5: Naive GWL (demonstrates failure) - print("\n[Test 5] GWL NAIVE EXPLICIT EM (current)") - print("-" * 60) - print("Description: What needs fixing - explicit update blows up") - naive = GWLNaiveEM(nx=200, alpha=0.1, beta=0.1) - passed, res = naive.validate() - status = "✗ FAIL (expected)" if not passed else "? UNEXPECTED PASS" - print(f"Status: {status}") - print(f" Initial energy: {res['initial_energy']:.2f}") - print(f" Final energy: {res['final_energy']:.2e}") - print(f" Energy ratio: {res['energy_ratio']:.2e}") - print(f" Blowup detected: {res['blowup']}") - results['GWL_Naive'] = {'passed': passed, 'details': res} - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - - stable_models_passed = all([ - results['Yee_FDTD']['passed'], - results['Symplectic_Euler']['passed'], - results['Lattice_Boltzmann']['passed'], - results['Crank_Nicolson']['passed'] - ]) - - for name, result in results.items(): - status = "✓ PASS" if result['passed'] else "✗ FAIL" - print(f"{name:25s}: {status}") - - print("\n" + "=" * 80) - print("INTERPRETATION") - print("=" * 80) - print(""" -PROVEN MODELS (50+ years): - ✓ Yee FDTD (1966) - Staggered update, energy conserving - ✓ Symplectic Euler - Hamiltonian structure preserved - ✓ Lattice Boltzmann - Local streaming, mass conserved - ✓ Crank-Nicolson - Implicit stability, smooth decay - -GWL CURRENT STATE: - ✗ Naive explicit EM - Energy blowup 10^8×, unstable - -REQUIRED FIX: - Implement Yee-style staggered update for GWL EM: - - E_new = E + alpha * grad_perp(B) # at full steps - B_new = B - beta * grad_perp(E_new) # at half steps - - OR implement symplectic leapfrog: - - v_{n+1/2} = v_n + (dt/2) * F(x_n) - x_{n+1} = x_n + dt * v_{n+1/2} - v_{n+1} = v_{n+1/2} + (dt/2) * F(x_{n+1}) - -CRITERIA FOR GWL DETERMINISTIC EM: - 1. Energy variation < 5% over 100 steps - 2. Pulse propagates without blowup - 3. Reversible (no numerical dissipation unless intended) - 4. Matches Yee FDTD behavior - -ONLY THEN can stochastic extensions be added safely. - """) - - return results - - -if __name__ == "__main__": - results = run_all_validations() diff --git a/5-Applications/tools-scripts/demo/gwl_compiler_demo.py b/5-Applications/tools-scripts/demo/gwl_compiler_demo.py deleted file mode 100644 index 50e635a2..00000000 --- a/5-Applications/tools-scripts/demo/gwl_compiler_demo.py +++ /dev/null @@ -1,346 +0,0 @@ -#!/usr/bin/env python3 -""" -gpl_compiler_demo.py - -Minimal demonstration of compiling Geometric Programming Language (GPL) -to μ-seed populations for TTM execution. - -Shows: Source code → μ-seeds → execution → result -""" - -from dataclasses import dataclass, field -from typing import List, Dict, Tuple, Optional -from enum import IntEnum - - -class RegionClass(IntEnum): - SURFACE = 0 - INTERIOR = 1 - TUNNEL = 2 - VERTEX = 3 - - -class GammaMode(IntEnum): - ACCUMULATE = 0 - NOISE = 1 - INTERACT = 2 - COLLAPSE = 3 - OPEN = 4 - - -@dataclass -class MuSeed: - """μ-seed: The compiled form of GPL statements.""" - node_id: int - delta_p: int = 0 - region: int = RegionClass.INTERIOR - gamma: int = GammaMode.OPEN - activation: int = 0 - polarity: int = 0 - confidence: int = 8 - chirality: int = 0 # 0=D, 1=L - - # Runtime links (not stored in 32-bit representation) - neighbors: List[int] = field(default_factory=list) - - -@dataclass -class GPLProgram: - """Compiled GPL program ready for TTM execution.""" - name: str - nodes: Dict[int, MuSeed] - edges: List[Tuple[int, int, int]] # (source, target, gamma) - inputs: List[int] - outputs: List[int] - - def to_activation_field(self) -> Dict[int, float]: - """Convert to initial activation field.""" - return {nid: node.activation for nid, node in self.nodes.items()} - - def display(self): - """Pretty-print the compiled program.""" - print(f"\nCompiled Program: {self.name}") - print(f"Nodes: {len(self.nodes)}") - print(f"Edges: {len(self.edges)}") - print(f"Inputs: {self.inputs}") - print(f"Outputs: {self.outputs}") - - print("\nNode Table:") - for nid, node in sorted(self.nodes.items()): - region_name = ['SURFACE', 'INTERIOR', 'TUNNEL', 'VERTEX'][node.region] - gamma_name = ['ACC', 'NOISE', 'INTERACT', 'COLLAPSE', 'OPEN'][node.gamma] - chirality = 'D' if node.chirality == 0 else 'L' - print(f" μ_{nid:02d}: {region_name:8s} γ={gamma_name:8s} " - f"a={node.activation:2d} C={node.confidence:2d} χ={chirality}") - - print("\nConnectivity:") - for src, tgt, gamma in self.edges: - gamma_name = ['ACC', 'NOISE', 'INTERACT', 'COLLAPSE', 'OPEN'][gamma] - print(f" μ_{src:02d} --[{gamma_name:8s}]--> μ_{tgt:02d}") - - -class GPLCompiler: - """ - Minimal GPL compiler. - - Parses simple GPL syntax and compiles to μ-seed population. - """ - - def __init__(self): - self.node_counter = 0 - self.nodes: Dict[int, MuSeed] = {} - self.edges: List[Tuple[int, int, int]] = [] - self.inputs: List[int] = [] - self.outputs: List[int] = [] - - def new_node(self, region: RegionClass, **kwargs) -> int: - """Create a new μ-seed node.""" - node_id = self.node_counter - self.node_counter += 1 - - node = MuSeed( - node_id=node_id, - region=region.value, - **kwargs - ) - self.nodes[node_id] = node - return node_id - - def add_edge(self, src: int, tgt: int, gamma: GammaMode): - """Add a directed edge (codon link).""" - self.edges.append((src, tgt, gamma.value)) - self.nodes[src].neighbors.append(tgt) - - def compile_hello_world(self) -> GPLProgram: - """ - Compile a simple Hello World program. - - Program: trigger -> accumulate -> process -> noise -> buffer -> collapse -> result - """ - # Clear state - self.__init__() - - # Create nodes - μ_trigger = self.new_node( - RegionClass.SURFACE, - activation=8, - confidence=15, - chirality=0 - ) - self.inputs.append(μ_trigger) - - μ_process = self.new_node( - RegionClass.INTERIOR, - activation=0, - confidence=8, - gamma=GammaMode.ACCUMULATE.value, - chirality=0 - ) - - μ_buffer = self.new_node( - RegionClass.INTERIOR, - activation=0, - confidence=8, - gamma=GammaMode.NOISE.value, - chirality=1 # L-form for mirror processing - ) - - μ_result = self.new_node( - RegionClass.SURFACE, - activation=0, - confidence=8, - gamma=GammaMode.COLLAPSE.value, - chirality=0 - ) - self.outputs.append(μ_result) - - # Create edges (program flow) - self.add_edge(μ_trigger, μ_process, GammaMode.ACCUMULATE) - self.add_edge(μ_process, μ_buffer, GammaMode.NOISE) - self.add_edge(μ_buffer, μ_result, GammaMode.COLLAPSE) - - return GPLProgram( - name="hello_world", - nodes=self.nodes, - edges=self.edges, - inputs=self.inputs, - outputs=self.outputs - ) - - def compile_adder(self, bits: int = 2) -> GPLProgram: - """ - Compile a ripple-carry adder. - - Architecture: Chain of full adders - """ - self.__init__() - - # Create input nodes (A, B for each bit) - a_nodes = [] - b_nodes = [] - for i in range(bits): - a_nodes.append(self.new_node( - RegionClass.SURFACE, - activation=i+1, # Different values for each bit - confidence=15, - chirality=0 - )) - b_nodes.append(self.new_node( - RegionClass.SURFACE, - activation=i+2, - confidence=15, - chirality=0 - )) - self.inputs.extend([a_nodes[-1], b_nodes[-1]]) - - # Create full adder chain - sum_nodes = [] - carry_nodes = [] - - prev_carry = self.new_node( - RegionClass.INTERIOR, - activation=0, # Initial carry = 0 - confidence=15, - chirality=0 - ) - - for i in range(bits): - # Full adder internal nodes - # sum = a XOR b XOR cin - # carry = (a AND b) OR (cin AND (a XOR b)) - - # Simplified: use INTERACT for accumulation - xor_ab = self.new_node( - RegionClass.INTERIOR, - gamma=GammaMode.INTERACT.value, - chirality=0 - ) - - sum_node = self.new_node( - RegionClass.INTERIOR, - gamma=GammaMode.ACCUMULATE.value, - chirality=0 - ) - - carry_node = self.new_node( - RegionClass.INTERIOR, - gamma=GammaMode.ACCUMULATE.value, - chirality=1 # L-form for carry - ) - - # Connect - self.add_edge(a_nodes[i], xor_ab, GammaMode.INTERACT) - self.add_edge(b_nodes[i], xor_ab, GammaMode.INTERACT) - self.add_edge(xor_ab, sum_node, GammaMode.ACCUMULATE) - self.add_edge(prev_carry, sum_node, GammaMode.ACCUMULATE) - - self.add_edge(a_nodes[i], carry_node, GammaMode.INTERACT) - self.add_edge(b_nodes[i], carry_node, GammaMode.INTERACT) - self.add_edge(prev_carry, carry_node, GammaMode.INTERACT) - - # Collapse to output - sum_out = self.new_node( - RegionClass.SURFACE, - gamma=GammaMode.COLLAPSE.value, - chirality=0 - ) - self.add_edge(sum_node, sum_out, GammaMode.COLLAPSE) - self.outputs.append(sum_out) - - sum_nodes.append(sum_out) - carry_nodes.append(carry_node) - prev_carry = carry_node - - # Final carry output - final_carry = self.new_node( - RegionClass.SURFACE, - gamma=GammaMode.COLLAPSE.value, - chirality=0 - ) - self.add_edge(prev_carry, final_carry, GammaMode.COLLAPSE) - self.outputs.append(final_carry) - - return GPLProgram( - name=f"{bits}bit_adder", - nodes=self.nodes, - edges=self.edges, - inputs=self.inputs, - outputs=self.outputs - ) - - -def demo_compilation(): - """Demonstrate compiling GPL programs to μ-seeds.""" - - print("=" * 60) - print("GEOMETRIC PROGRAMMING LANGUAGE (GPL) COMPILER DEMO") - print("=" * 60) - - compiler = GPLCompiler() - - # Demo 1: Hello World - print("\n" + "=" * 60) - print("PROGRAM 1: Hello World") - print("=" * 60) - - program1 = compiler.compile_hello_world() - program1.display() - - print("\n" + "-" * 60) - print("Execution Simulation:") - print("-" * 60) - - field = program1.to_activation_field() - print(f"Initial activation field: {field}") - - # Simulate one TTM tick - print("\nTick 1:") - # Accumulate: trigger -> process - field[1] = field[1] + 0.5 * field[0] # process accumulates from trigger - print(f" μ_process accumulates: {field[1]:.2f}") - - # Noise: process -> buffer - field[2] = field[2] + 0.3 * field[1] # buffer gets noise-scaled process - print(f" μ_buffer receives: {field[2]:.2f}") - - # Collapse: buffer -> result - if field[2] > 2.0: - field[3] = min(15, int(field[2])) - print(f" μ_result collapses to: {field[3]}") - - # Demo 2: Adder - print("\n" + "=" * 60) - print("PROGRAM 2: 2-Bit Adder") - print("=" * 60) - - program2 = compiler.compile_adder(bits=2) - program2.display() - - print("\n" + "=" * 60) - print("SUMMARY") - print("=" * 60) - - print(f""" -GPL Compilation Results: - -Hello World: - - Source: 5 lines of GPL code - - Compiled: {len(program1.nodes)} μ-seeds, {len(program1.edges)} edges - - Memory: {len(program1.nodes) * 4} bytes - - Execution: TTM convergence - -2-Bit Adder: - - Source: Structural description - - Compiled: {len(program2.nodes)} μ-seeds, {len(program2.edges)} edges - - Memory: {len(program2.nodes) * 4} bytes - - Execution: Parallel TTM dynamics - -Key Insight: - GPL programs compile to μ-seed populations (4 bytes each). - Execution is TTM convergence (not instruction fetch). - Topology IS the program. -""") - - -if __name__ == "__main__": - demo_compilation() diff --git a/5-Applications/tools-scripts/demo/gwl_deterministic_em_demo.py b/5-Applications/tools-scripts/demo/gwl_deterministic_em_demo.py deleted file mode 100644 index 2bc539c5..00000000 --- a/5-Applications/tools-scripts/demo/gwl_deterministic_em_demo.py +++ /dev/null @@ -1,340 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_deterministic_em_demo.py - -Demonstration of deterministic EM-like field propagation in GWL/TSM. - -This validates the deterministic backbone before stochastic extensions. -""" - -import numpy as np -import matplotlib.pyplot as plt -from dataclasses import dataclass -from typing import List, Tuple - - -@dataclass -class EMSeed: - """μ-seed with electromagnetic field components.""" - x: float # Position - E: float # Electric-like field - B: float # Magnetic-like field - tau: int # Temporal phase (0-15) - pi: int # Rotation/polarization (0-15) - chi: int # Chirality (0=D, 1=L) - omega: float # Frequency mode - - -class DeterministicEMField: - """ - Deterministic EM-like field on TSM topology. - - Update law (no randomness): - E_i(t+1) = E_i(t) + α * Σ_j w_ij * (B_j - B_i) - B_i(t+1) = B_i(t) - β * Σ_j w_ij * (E_j - E_i) - tau_i(t+1) = (tau_i + omega) % 16 - """ - - def __init__(self, n_seeds: int = 100, alpha: float = 0.1, beta: float = 0.1): - self.n = n_seeds - self.alpha = alpha - self.beta = beta - self.seeds: List[EMSeed] = [] - self.history_E: List[List[float]] = [] - self.history_B: List[List[float]] = [] - self.history_energy: List[float] = [] - - # Initialize linear topology (1D tape) - for i in range(n_seeds): - self.seeds.append(EMSeed( - x=float(i), - E=0.0, - B=0.0, - tau=0, - pi=0, - chi=0, - omega=0.0 - )) - - def initialize_pulse(self, center: int, width: int, amplitude: float = 1.0, - freq: float = 1.0): - """Initialize a Gaussian pulse with given frequency.""" - for i in range(self.n): - dist = abs(i - center) - if dist < width: - # Gaussian envelope - envelope = amplitude * np.exp(-(dist**2) / (2 * (width/3)**2)) - self.seeds[i].E = envelope * np.cos(2 * np.pi * freq * i / self.n) - self.seeds[i].B = envelope * np.sin(2 * np.pi * freq * i / self.n) - self.seeds[i].omega = freq - - def coupling_weight(self, i: int, j: int) -> float: - """ - Deterministic coupling weight. - - For 1D linear topology: only nearest neighbors couple. - """ - if abs(i - j) == 1: # Nearest neighbor - return 1.0 - elif abs(i - j) == 2: # Next-nearest (weaker) - return 0.3 - return 0.0 - - def step(self): - """One deterministic evolution step.""" - new_E = np.zeros(self.n) - new_B = np.zeros(self.n) - new_tau = np.zeros(self.n, dtype=int) - - for i in range(self.n): - # Coupling sum - coupling_E = 0.0 - coupling_B = 0.0 - - for j in range(max(0, i-2), min(self.n, i+3)): - if i == j: - continue - w = self.coupling_weight(i, j) - coupling_E += w * (self.seeds[j].B - self.seeds[i].B) - coupling_B += w * (self.seeds[j].E - self.seeds[i].E) - - # Deterministic update (NO RANDOMNESS) - new_E[i] = self.seeds[i].E + self.alpha * coupling_E - new_B[i] = self.seeds[i].B - self.beta * coupling_B - - # Temporal phase evolution - new_tau[i] = (self.seeds[i].tau + int(self.seeds[i].omega * 16)) % 16 - - # Apply updates - for i in range(self.n): - self.seeds[i].E = new_E[i] - self.seeds[i].B = new_B[i] - self.seeds[i].tau = new_tau[i] - - # Record history - self.history_E.append([s.E for s in self.seeds]) - self.history_B.append([s.B for s in self.seeds]) - - # Energy - energy = sum(s.E**2 + s.B**2 for s in self.seeds) - self.history_energy.append(energy) - - def run(self, steps: int = 200): - """Run deterministic evolution.""" - for _ in range(steps): - self.step() - - def test_1_stable_propagation(self) -> Tuple[bool, str]: - """ - Test 1: Stable wave propagation. - - Energy should remain bounded, pulse should propagate. - """ - self.initialize_pulse(center=25, width=10, amplitude=1.0, freq=2.0) - initial_energy = sum(s.E**2 + s.B**2 for s in self.seeds) - - self.run(steps=100) - - final_energy = sum(s.E**2 + s.B**2 for s in self.seeds) - energy_ratio = final_energy / initial_energy if initial_energy > 0 else 0 - - # Check if pulse moved from initial position - max_E_initial = max(abs(self.history_E[0][i]) for i in range(20, 30)) - max_E_final = max(abs(self.history_E[-1][i]) for i in range(40, 80)) - - passed = (0.5 < energy_ratio < 2.0) and (max_E_final > 0.1 * max_E_initial) - - msg = f"Energy ratio: {energy_ratio:.3f}, Pulse propagated: {max_E_final > 0.1 * max_E_initial}" - return passed, msg - - def test_2_frequency_separability(self) -> Tuple[bool, str]: - """ - Test 2: Frequency separability. - - Two different frequencies should remain distinguishable. - """ - # Initialize two pulses at different frequencies - self.__init__(self.n, self.alpha, self.beta) # Reset - - # Low frequency pulse - for i in range(20, 40): - dist = abs(i - 30) - self.seeds[i].E = np.exp(-dist**2/20) * np.cos(2 * np.pi * 1.0 * i / 20) - self.seeds[i].omega = 1.0 - - # High frequency pulse - for i in range(60, 80): - dist = abs(i - 70) - self.seeds[i].E = np.exp(-dist**2/20) * np.cos(2 * np.pi * 4.0 * i / 10) - self.seeds[i].omega = 4.0 - - self.run(steps=50) - - # FFT analysis - signal_low = [self.history_E[t][30] for t in range(len(self.history_E))] - signal_high = [self.history_E[t][70] for t in range(len(self.history_E))] - - if len(signal_low) > 10: - fft_low = np.abs(np.fft.fft(signal_low)) - fft_high = np.abs(np.fft.fft(signal_high)) - - peak_low = np.argmax(fft_low[:len(fft_low)//2]) - peak_high = np.argmax(fft_high[:len(fft_high)//2]) - - separable = abs(peak_high - peak_low) > 2 - msg = f"Low freq peak: {peak_low}, High freq peak: {peak_high}" - return separable, msg - - return False, "Insufficient data" - - def test_3_determinism(self) -> Tuple[bool, str]: - """ - Test 3: Determinism. - - Same initial conditions → same evolution. - """ - # Run 1 - self.__init__(self.n, self.alpha, self.beta) - self.initialize_pulse(center=30, width=8, amplitude=1.0, freq=2.0) - self.run(steps=50) - final_E_1 = [s.E for s in self.seeds] - - # Run 2 (identical) - self.__init__(self.n, self.alpha, self.beta) - self.initialize_pulse(center=30, width=8, amplitude=1.0, freq=2.0) - self.run(steps=50) - final_E_2 = [s.E for s in self.seeds] - - # Compare - diff = max(abs(a - b) for a, b in zip(final_E_1, final_E_2)) - passed = diff < 1e-10 - - return passed, f"Max difference between runs: {diff:.2e}" - - def test_4_energy_conservation(self) -> Tuple[bool, str]: - """ - Test 4: Energy conservation (bounded). - - Total field energy should not explode or vanish. - """ - self.__init__(self.n, self.alpha, self.beta) - self.initialize_pulse(center=50, width=15, amplitude=1.0, freq=1.5) - - initial_energy = self.history_energy[0] if self.history_energy else 1.0 - - self.run(steps=100) - - energy_values = self.history_energy - max_energy = max(energy_values) - min_energy = min(energy_values) - - # Energy should stay within reasonable bounds - ratio = max_energy / min_energy if min_energy > 0 else float('inf') - passed = ratio < 10.0 # Less than 10x variation - - return passed, f"Energy variation ratio: {ratio:.3f}" - - def plot_evolution(self, title: str = "EM Field Evolution"): - """Plot field evolution over time.""" - fig, axes = plt.subplots(3, 1, figsize=(12, 10)) - - # Plot E field heatmap - E_array = np.array(self.history_E) - im1 = axes[0].imshow(E_array.T, aspect='auto', cmap='RdBu', - extent=[0, len(self.history_E), 0, self.n]) - axes[0].set_ylabel('Position') - axes[0].set_title(f'{title} - E Field') - plt.colorbar(im1, ax=axes[0]) - - # Plot B field heatmap - B_array = np.array(self.history_B) - im2 = axes[1].imshow(B_array.T, aspect='auto', cmap='RdBu', - extent=[0, len(self.history_B), 0, self.n]) - axes[1].set_ylabel('Position') - axes[1].set_title('B Field') - plt.colorbar(im2, ax=axes[1]) - - # Plot energy - axes[2].plot(self.history_energy) - axes[2].set_xlabel('Time Step') - axes[2].set_ylabel('Total Energy') - axes[2].set_title('Energy Conservation') - axes[2].grid(True) - - plt.tight_layout() - return fig - - -def run_all_tests(): - """Run all four deterministic EM tests.""" - - print("=" * 70) - print("GWL DETERMINISTIC EM FIELD TESTS") - print("=" * 70) - - field = DeterministicEMField(n_seeds=100, alpha=0.15, beta=0.15) - - # Test 1: Stable propagation - print("\n[Test 1] Stable Wave Propagation") - print("-" * 50) - passed, msg = field.test_1_stable_propagation() - status = "PASS" if passed else "FAIL" - print(f"Status: {status}") - print(f"Details: {msg}") - - # Test 2: Frequency separability - print("\n[Test 2] Frequency Separability") - print("-" * 50) - passed, msg = field.test_2_frequency_separability() - status = "PASS" if passed else "FAIL" - print(f"Status: {status}") - print(f"Details: {msg}") - - # Test 3: Determinism - print("\n[Test 3] Determinism") - print("-" * 50) - passed, msg = field.test_3_determinism() - status = "PASS" if passed else "FAIL" - print(f"Status: {status}") - print(f"Details: {msg}") - - # Test 4: Energy conservation - print("\n[Test 4] Energy Conservation") - print("-" * 50) - passed, msg = field.test_4_energy_conservation() - status = "PASS" if passed else "FAIL" - print(f"Status: {status}") - print(f"Details: {msg}") - - print("\n" + "=" * 70) - print("SUMMARY") - print("=" * 70) - print(""" -These tests validate the deterministic backbone of GWL/TSM. - -If all tests pass: - - The system supports deterministic field propagation - - Stochastic extensions can be safely added as perturbations - - The EM spectrum is expressible - -If any test fails: - - The local update law needs refinement - - Conservation constraints must be enforced - - Do NOT add stochasticity to mask deterministic failures - """) - - # Generate visualization - field.__init__(n_seeds=100, alpha=0.15, beta=0.15) - field.initialize_pulse(center=30, width=10, amplitude=1.0, freq=2.0) - field.run(steps=150) - - try: - fig = field.plot_evolution() - plt.savefig('gwl_deterministic_em_evolution.png', dpi=150) - print("\nVisualization saved to: gwl_deterministic_em_evolution.png") - except Exception as e: - print(f"\nPlotting skipped: {e}") - - -if __name__ == "__main__": - run_all_tests() diff --git a/5-Applications/tools-scripts/demo/gwl_double_pendulum_benchmark.py b/5-Applications/tools-scripts/demo/gwl_double_pendulum_benchmark.py deleted file mode 100644 index 69f2fe69..00000000 --- a/5-Applications/tools-scripts/demo/gwl_double_pendulum_benchmark.py +++ /dev/null @@ -1,467 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_double_pendulum_benchmark.py - -Conservative chaotic system: Double Pendulum - -Two coupled pendulums—Hamiltonian chaos with NO analytic solution. -Unlike Lorenz, this system has an exact conserved quantity: ENERGY. - -This tests: Can your integrator preserve energy while still capturing chaos? - -Equations (Lagrangian): - L = T - V - T = ½m₁l₁²θ̇₁² + ½m₂[l₁²θ̇₁² + l₂²θ̇₂² + 2l₁l₂θ̇₁θ̇₂cos(θ₁-θ₂)] - V = -m₁gl₁cos(θ₁) - m₂g[l₁cos(θ₁) + l₂cos(θ₂)] - -No closed-form solution. Chaotic for large enough initial angles. -Energy conservation is the key validation metric. -""" - -import numpy as np -from dataclasses import dataclass -from typing import List, Tuple -import math - - -@dataclass -class DoublePendulumState: - """State: angles and angular velocities.""" - theta1: float # Angle of first pendulum - theta2: float # Angle of second pendulum - omega1: float # Angular velocity of first - omega2: float # Angular velocity of second - t: float = 0.0 - - def array(self) -> np.ndarray: - return np.array([self.theta1, self.theta2, self.omega1, self.omega2]) - - -class DoublePendulum: - """ - Double pendulum with Hamiltonian structure. - - Key invariant: Total energy E = T + V (should be conserved) - """ - - def __init__(self, m1: float = 1.0, m2: float = 1.0, - l1: float = 1.0, l2: float = 1.0, - g: float = 9.8, dt: float = 0.01): - self.m1 = m1 - self.m2 = m2 - self.l1 = l1 - self.l2 = l2 - self.g = g - self.dt = dt - - def energy(self, state: DoublePendulumState) -> float: - """Compute total energy E = T + V.""" - th1, th2, w1, w2 = state.theta1, state.theta2, state.omega1, state.omega2 - - # Potential energy - V = -(self.m1 + self.m2) * self.g * self.l1 * math.cos(th1) \ - - self.m2 * self.g * self.l2 * math.cos(th2) - - # Kinetic energy - T = 0.5 * self.m1 * self.l1**2 * w1**2 + \ - 0.5 * self.m2 * (self.l1**2 * w1**2 + self.l2**2 * w2**2 + - 2 * self.l1 * self.l2 * w1 * w2 * math.cos(th1 - th2)) - - return T + V - - def kinetic_energy(self, state: DoublePendulumState) -> float: - """Kinetic energy only.""" - th1, th2, w1, w2 = state.theta1, state.theta2, state.omega1, state.omega2 - return 0.5 * self.m1 * self.l1**2 * w1**2 + \ - 0.5 * self.m2 * (self.l1**2 * w1**2 + self.l2**2 * w2**2 + - 2 * self.l1 * self.l2 * w1 * w2 * math.cos(th1 - th2)) - - def potential_energy(self, state: DoublePendulumState) -> float: - """Potential energy only.""" - th1, th2 = state.theta1, state.theta2 - return -(self.m1 + self.m2) * self.g * self.l1 * math.cos(th1) \ - - self.m2 * self.g * self.l2 * math.cos(th2) - - def derivatives(self, state: DoublePendulumState) -> Tuple[float, float, float, float]: - """ - Compute derivatives using Euler-Lagrange equations. - Returns: dtheta1/dt, dtheta2/dt, domega1/dt, domega2/dt - """ - th1, th2, w1, w2 = state.theta1, state.theta2, state.omega1, state.omega2 - - # Precompute - delta = th1 - th2 - cos_delta = math.cos(delta) - sin_delta = math.sin(delta) - - # Denominator for accelerations - denom = (self.m1 + self.m2) * self.l1 * self.l2 - self.m2 * self.l1 * self.l2 * cos_delta**2 - - # Angular accelerations (from Lagrangian) - # These are messy—derived from d/dt(∂L/∂θ̇) = ∂L/∂θ - - num1 = (self.m1 + self.m2) * self.g * math.sin(th1) - \ - self.m2 * self.g * math.sin(th2) * cos_delta - \ - self.m2 * self.l1 * w1**2 * sin_delta * cos_delta - \ - self.m2 * self.l2 * w2**2 * sin_delta - - num2 = (self.m1 + self.m2) * self.g * math.sin(th1) * cos_delta - \ - (self.m1 + self.m2) * self.g * math.sin(th2) + \ - (self.m1 + self.m2) * self.l1 * w1**2 * sin_delta + \ - self.m2 * self.l2 * w2**2 * sin_delta * cos_delta - - alpha1 = -num1 / (self.l1 * (self.m1 + self.m2 * sin_delta**2)) - alpha2 = -num2 / (self.l2 * (self.m1 + self.m2 * sin_delta**2)) - - return w1, w2, alpha1, alpha2 - - def step_euler(self, state: DoublePendulumState) -> DoublePendulumState: - """Forward Euler (NOT recommended for Hamiltonian systems).""" - dth1, dth2, dw1, dw2 = self.derivatives(state) - - return DoublePendulumState( - theta1=state.theta1 + dth1 * self.dt, - theta2=state.theta2 + dth2 * self.dt, - omega1=state.omega1 + dw1 * self.dt, - omega2=state.omega2 + dw2 * self.dt, - t=state.t + self.dt - ) - - def step_rk4(self, state: DoublePendulumState) -> DoublePendulumState: - """Runge-Kutta 4th order.""" - def deriv(s): - return np.array(self.derivatives(s)) - - y = state.array() - k1 = deriv(state) - - s2 = DoublePendulumState(*(y + 0.5*self.dt*k1), state.t + 0.5*self.dt) - k2 = deriv(s2) - - s3 = DoublePendulumState(*(y + 0.5*self.dt*k2), state.t + 0.5*self.dt) - k3 = deriv(s3) - - s4 = DoublePendulumState(*(y + self.dt*k3), state.t + self.dt) - k4 = deriv(s4) - - result = y + (self.dt/6.0) * (k1 + 2*k2 + 2*k3 + k4) - return DoublePendulumState(*result, state.t + self.dt) - - def step_symplectic_euler(self, state: DoublePendulumState) -> DoublePendulumState: - """ - Symplectic Euler (better for Hamiltonian systems). - Update momenta first, then positions. - """ - th1, th2, w1, w2 = state.theta1, state.theta2, state.omega1, state.omega2 - - # Update velocities first (using old positions) - _, _, alpha1, alpha2 = self.derivatives(state) - w1_new = w1 + alpha1 * self.dt - w2_new = w2 + alpha2 * self.dt - - # Update positions with new velocities - th1_new = th1 + w1_new * self.dt - th2_new = th2 + w2_new * self.dt - - return DoublePendulumState(th1_new, th2_new, w1_new, w2_new, state.t + self.dt) - - def step_verlet(self, state: DoublePendulumState) -> DoublePendulumState: - """ - Velocity Verlet (symplectic, 2nd order). - Better energy conservation than RK4 for Hamiltonian systems. - """ - th1, th2, w1, w2 = state.theta1, state.theta2, state.omega1, state.omega2 - - # Half-step velocity update - _, _, alpha1, alpha2 = self.derivatives(state) - w1_half = w1 + 0.5 * alpha1 * self.dt - w2_half = w2 + 0.5 * alpha2 * self.dt - - # Full position update - th1_new = th1 + w1_half * self.dt - th2_new = th2 + w2_half * self.dt - - # Compute new accelerations - interim = DoublePendulumState(th1_new, th2_new, w1_half, w2_half, state.t) - _, _, alpha1_new, alpha2_new = self.derivatives(interim) - - # Half-step velocity update - w1_new = w1_half + 0.5 * alpha1_new * self.dt - w2_new = w2_half + 0.5 * alpha2_new * self.dt - - return DoublePendulumState(th1_new, th2_new, w1_new, w2_new, state.t + self.dt) - - def run(self, steps: int, initial: DoublePendulumState, - method: str = 'rk4') -> List[DoublePendulumState]: - """Run simulation.""" - methods = { - 'euler': self.step_euler, - 'rk4': self.step_rk4, - 'symplectic': self.step_symplectic_euler, - 'verlet': self.step_verlet, - } - - step_fn = methods.get(method, self.step_rk4) - - trajectory = [initial] - state = initial - - for _ in range(steps): - state = step_fn(state) - trajectory.append(state) - - return trajectory - - -class DoublePendulumBenchmark: - """Benchmark for double pendulum (conservative chaos).""" - - def __init__(self): - self.results = {} - - def test_energy_conservation(self, steps: int = 50000, dt: float = 0.001) -> dict: - """ - Test 1: Energy conservation (critical for Hamiltonian systems). - - For chaotic initial conditions, energy should be conserved - even as trajectory becomes unpredictable. - """ - print(f"\n[Test] Energy Conservation ({steps} steps, dt={dt}, chaotic IC)") - print("-" * 60) - - dp = DoublePendulum(dt=dt) - - # Chaotic initial condition (high energy, not at separatrix) - # Start with some initial angular velocity - initial = DoublePendulumState(theta1=math.pi/1.8, theta2=math.pi/1.5, - omega1=0.5, omega2=0.3) - E0 = dp.energy(initial) - print(f" Initial energy E0 = {E0:.2f}") - - results = {} - - for method in ['euler', 'rk4', 'verlet']: - traj = dp.run(steps, initial, method) - - energies = [dp.energy(s) for s in traj] - E_drift = [(e - E0)/E0 for e in energies] - - max_drift = max(abs(d) for d in E_drift) - final_drift = E_drift[-1] - - results[method] = { - 'E0': E0, - 'max_drift': max_drift, - 'final_drift': final_drift, - 'energies': energies - } - - # Verlet should be best, RK4 good, Euler bad - if method == 'verlet': - acceptable = max_drift < 0.01 - elif method == 'rk4': - acceptable = max_drift < 0.05 - else: - acceptable = max_drift < 0.5 - - status = "✓" if acceptable else "✗" - print(f" {method:12s}: {status} max drift={max_drift:.4f}, final={final_drift:.4f}") - - return results - - def test_chaos_preservation(self, steps: int = 30000, dt: float = 0.001) -> dict: - """ - Test 2: Does method preserve sensitive dependence? - - Two nearby initial conditions should diverge exponentially - (even though exact trajectories are unpredictable). - """ - print(f"\n[Test] Chaos Preservation ({steps} steps, dt={dt})") - print("-" * 60) - - dp = DoublePendulum(dt=dt) - - # Chaotic IC (high energy) - ic1 = DoublePendulumState(theta1=math.pi/1.8, theta2=math.pi/1.5, - omega1=0.5, omega2=0.3) - ic2 = DoublePendulumState(theta1=math.pi/1.8 + 1e-6, theta2=math.pi/1.5, - omega1=0.5, omega2=0.3) - - results = {} - - for method in ['rk4', 'verlet']: - traj1 = dp.run(steps, ic1, method) - traj2 = dp.run(steps, ic2, method) - - # Compute phase space distance over time - distances = [] - for s1, s2 in zip(traj1, traj2): - d = math.sqrt((s1.theta1 - s2.theta1)**2 + - (s1.theta2 - s2.theta2)**2 + - (s1.omega1 - s2.omega1)**2 + - (s1.omega2 - s2.omega2)**2) - distances.append(d) - - # Check for exponential growth phase - early_growth = distances[1000] / distances[100] if distances[100] > 0 else 1 - final_sep = distances[-1] - - # Should grow initially (chaos) - chaotic = early_growth > 2 - - results[method] = { - 'initial_sep': distances[0], - 'early_growth': early_growth, - 'final_sep': final_sep, - 'chaotic': chaotic - } - - status = "✓" if chaotic else "✗" - print(f" {method:12s}: {status} early growth={early_growth:.1f}x, final sep={final_sep:.3f}") - - return results - - def test_long_term_bounds(self, steps: int = 100000, dt: float = 0.001) -> dict: - """ - Test 3: System stays in physical bounds. - - Pendulums should keep swinging (angles unbounded but velocities bounded). - Energy bounds constrain motion. - """ - print(f"\n[Test] Physical Bounds ({steps} steps, dt={dt})") - print("-" * 60) - - dp = DoublePendulum(dt=dt) - initial = DoublePendulumState(theta1=math.pi/1.8, theta2=math.pi/1.5, - omega1=0.5, omega2=0.3) - - results = {} - - for method in ['rk4', 'verlet']: - traj = dp.run(steps, initial, method) - - # Extract values - thetas = [(s.theta1, s.theta2) for s in traj] - omegas = [(s.omega1, s.omega2) for s in traj] - - # Angles can grow (winding), but should be finite - max_theta = max(max(abs(t[0]), abs(t[1])) for t in thetas) - - # Velocities should be bounded by energy - max_omega = max(max(abs(o[0]), abs(o[1])) for o in omegas) - - # With E ≈ -5 (initial), max omega ~ 5 is reasonable - bounded = max_omega < 20 - - results[method] = { - 'max_theta': max_theta, - 'max_omega': max_omega, - 'bounded': bounded - } - - status = "✓" if bounded else "✗" - print(f" {method:12s}: {status} max |θ|={max_theta:.1f}, max |ω|={max_omega:.2f}") - - return results - - def test_dt_convergence_energy(self, steps: int = 50000) -> dict: - """ - Test 4: Energy drift should decrease with smaller dt. - """ - print(f"\n[Test] dt Convergence (energy drift)") - print("-" * 60) - - dts = [0.01, 0.005, 0.002, 0.001] - - results = {'dts': dts, 'drifts': {}} - - for method in ['rk4', 'verlet']: - drifts = [] - for dt in dts: - dp = DoublePendulum(dt=dt) - initial = DoublePendulumState(theta1=math.pi/2, theta2=math.pi/2, - omega1=0.0, omega2=0.0) - E0 = dp.energy(initial) - - n_steps = int(50000 * 0.001 / dt) # Constant total time - - # Use high-energy initial condition - initial = DoublePendulumState(theta1=math.pi/1.8, theta2=math.pi/1.5, - omega1=0.5, omega2=0.3) - E0 = dp.energy(initial) - - traj = dp.run(n_steps, initial, method) - - energies = [dp.energy(s) for s in traj] - max_drift = max(abs((e - E0)/E0) for e in energies) - drifts.append(max_drift) - - results['drifts'][method] = drifts - - print(f" {method}:") - for dt, drift in zip(dts, drifts): - print(f" dt={dt:.4f}: max drift={drift:.6f}") - - # Check convergence (drift decreases with dt) - rk4_converges = drifts[0] > drifts[-1] # Rough check - - return results - - def run_all(self): - """Run complete benchmark.""" - print("=" * 80) - print("DOUBLE PENDULUM BENCHMARK: CONSERVATIVE CHAOS") - print("=" * 80) - print("System: Two coupled pendulums") - print("Properties: Hamiltonian, chaotic, NO analytic solution") - print("Key invariant: Energy E = T + V (must be conserved)") - print("Validation: Energy conservation + chaos preservation") - print() - - # Run tests - self.results['energy'] = self.test_energy_conservation(steps=50000) - self.results['chaos'] = self.test_chaos_preservation(steps=30000) - self.results['bounds'] = self.test_long_term_bounds(steps=100000) - self.results['convergence'] = self.test_dt_convergence_energy() - - # Summary - print("\n" + "=" * 80) - print("BENCHMARK SUMMARY") - print("=" * 80) - - print(""" -Key Findings: - -1. ENERGY CONSERVATION (CRITICAL) - - Verlet: Best (symplectic, ~0 drift) - - RK4: Good (< 5% drift) - - Euler: Terrible (50%+ drift, avoid) - -2. CHAOS PRESERVATION - - Both RK4 and Verlet preserve sensitive dependence - - Nearby trajectories diverge exponentially as expected - - Energy drift does NOT immediately destroy chaos - -3. PHYSICAL BOUNDS - - All methods keep system bounded (energy constraint) - - Even Euler doesn't blow up (just wrong energy) - -4. dt CONVERGENCE - - Verlet: O(dt²) energy error (2nd order) - - RK4: O(dt⁴) energy error (4th order, but not symplectic) - - For Hamiltonian chaos: Verlet preferred over RK4 despite lower order - because symplectic > raw accuracy for long runs. - -RECOMMENDATION FOR GWL/TSM: - Use Velocity Verlet for Hamiltonian systems (conservative fields). - Use RK4 for dissipative systems (Lorenz, damped oscillators). - Never use Euler for long runs (>1000 steps). - """) - - return self.results - - -if __name__ == "__main__": - benchmark = DoublePendulumBenchmark() - results = benchmark.run_all() diff --git a/5-Applications/tools-scripts/demo/gwl_earth_riemannian_conversion.py b/5-Applications/tools-scripts/demo/gwl_earth_riemannian_conversion.py deleted file mode 100644 index 5a0370b3..00000000 --- a/5-Applications/tools-scripts/demo/gwl_earth_riemannian_conversion.py +++ /dev/null @@ -1,565 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_earth_riemannian_conversion.py - -TEST OF GEOWEIRD LANGUAGE (GWL) - -Converting Euclidean circumference estimates into Riemannian manifold model. - -Problem: We have flat-space (Euclidean) measurements of Earth's circumference: - - Equatorial: C_eq ≈ 40,075 km - - Meridional: C_mer ≈ 40,008 km - -Goal: Construct intrinsic Riemannian metric g_ij on 2D manifold S² - that produces these circumferences through geodesic flow. - -Key insight: Circumference is path-length of closed geodesic. -In Riemannian geometry: C = ∮ √g_ij dx^i dx^j along geodesic - -GWL Approach: - - μ-seed represents points on manifold with metric field - - π_E encodes local frame (tangent space) - - g(μ) is metric tensor derived from Earth parameters - - Geodesic equation: d²x^i/dt² + Γ^i_jk dx^j/dt dx^k/dt = 0 -""" - -import numpy as np -from dataclasses import dataclass -from typing import Tuple, List, Callable, Optional -import math - - -# ============================================================================= -# PHYSICAL CONSTANTS (Euclidean measurements) -# ============================================================================= - -EARTH_EQUATORIAL_CIRCUMFERENCE = 40_075_017 # meters (WGS84) -EARTH_MERIDIONAL_CIRCUMFERENCE = 40_007_863 # meters (polar circumference) -EARTH_RADIUS_EQUATORIAL = 6_378_137 # meters (WGS84 semi-major axis) -EARTH_RADIUS_POLAR = 6_356_752 # meters (WGS84 semi-minor axis) -EARTH_FLATTENING = 1 / 298.257223563 # WGS84 flattening - - -@dataclass -class GWLEarthPoint: - """ - GWL μ-seed representation of a point on Earth's Riemannian manifold. - - Fields: - - p_E: Euclidean embedding coordinates (optional, for visualization) - - q: Intrinsic manifold coordinates (θ, φ) - geodesic coordinates - - π_E: Local frame orientation (tangent space basis) - - g_local: Metric tensor at this point - - Γ_local: Christoffel symbols at this point - """ - # Intrinsic coordinates (manifold-native) - theta: float # Latitude-like (from -π/2 to π/2) - phi: float # Longitude-like (from 0 to 2π) - - # Local metric tensor (2x2 for 2D surface) - g_theta_theta: float - g_theta_phi: float - g_phi_phi: float - - # Local frame orientation (π_E field) - # Represents basis vectors in tangent space - e_theta: np.ndarray # Basis vector in θ direction - e_phi: np.ndarray # Basis vector in φ direction - - # Geometric state - curvature_scalar: float # Gaussian curvature K at this point - - def metric_tensor(self) -> np.ndarray: - """Return metric tensor g_ij.""" - return np.array([ - [self.g_theta_theta, self.g_theta_phi], - [self.g_theta_phi, self.g_phi_phi] - ]) - - def line_element(self, dtheta: float, dphi: float) -> float: - """ - Compute ds² = g_ij dx^i dx^j - This is the Riemannian line element. - """ - g = self.metric_tensor() - dx = np.array([dtheta, dphi]) - return np.sqrt(dx @ g @ dx) - - -class EarthRiemannianManifold: - """ - Riemannian manifold model of Earth constructed from circumference data. - - Key property: Geodesic distances match measured circumferences. - """ - - def __init__(self, - C_eq: float = EARTH_EQUATORIAL_CIRCUMFERENCE, - C_mer: float = EARTH_MERIDIONAL_CIRCUMFERENCE): - """ - Construct manifold from Euclidean circumference measurements. - - These circumferences constrain the Riemannian metric. - """ - self.C_eq = C_eq - self.C_mer = C_mer - - # Compute ellipsoid parameters from circumferences - # For oblate spheroid: C_eq = 2πa, C_mer ≈ 2πa(1 - f/2 + ...) - self.a = C_eq / (2 * math.pi) # Equatorial radius - - # Flattening from meridional circumference - # C_mer = 2πc where c is mean polar radius - c = C_mer / (2 * math.pi) - - # For ellipsoid: c = a(1 - f) - # Approximate: f ≈ (a - c) / a - self.f = (self.a - c) / self.a - self.c = c - - print(f"Riemannian Earth Model:") - print(f" Equatorial circumference: {C_eq:,} m") - print(f" Meridional circumference: {C_mer:,} m") - print(f" Semi-major axis (a): {self.a:,.3f} m") - print(f" Semi-minor axis (c): {self.c:,.3f} m") - print(f" Flattening (f): {self.f:.12f}") - print(f" Eccentricity (e): {math.sqrt(2*self.f - self.f**2):.12f}") - - def metric_at(self, theta: float, phi: float) -> Tuple[float, float, float]: - """ - Compute metric tensor g_ij at point (θ, φ). - - For oblate spheroid in geodetic coordinates: - ds² = (M)² dθ² + (N cos θ)² dφ² - - Where: - M = a(1 - e²) / (1 - e² sin² θ)^(3/2) - meridional radius - N = a / (1 - e² sin² θ)^(1/2) - prime vertical radius - """ - e2 = 2 * self.f - self.f**2 # Eccentricity squared - - sin_theta = math.sin(theta) - cos_theta = math.cos(theta) - - # Radius of curvature in meridian (M) - W = math.sqrt(1 - e2 * sin_theta**2) - M = self.a * (1 - e2) / (W**3) - - # Radius of curvature in prime vertical (N) - N = self.a / W - - # Metric components - g_theta_theta = M**2 - g_phi_phi = (N * cos_theta)**2 - g_theta_phi = 0.0 # Orthogonal coordinates - - return g_theta_theta, g_theta_phi, g_phi_phi - - def christoffel_at(self, theta: float, phi: float) -> np.ndarray: - """ - Compute Christoffel symbols Γ^k_ij at point (θ, φ). - - Γ^k_ij = ½ g^kl (∂g_il/∂x^j + ∂g_jl/∂x^i - ∂g_ij/∂x^l) - """ - # Get metric - g_tt, g_tp, g_pp = self.metric_at(theta, phi) - g = np.array([[g_tt, g_tp], [g_tp, g_pp]]) - g_inv = np.linalg.inv(g) - - # Numerical derivatives for Christoffel - eps = 1e-8 - - # ∂g_tt/∂θ - g_tt_plus, _, _ = self.metric_at(theta + eps, phi) - g_tt_minus, _, _ = self.metric_at(theta - eps, phi) - dg_tt_dtheta = (g_tt_plus - g_tt_minus) / (2 * eps) - - # ∂g_pp/∂θ - _, _, g_pp_plus = self.metric_at(theta + eps, phi) - _, _, g_pp_minus = self.metric_at(theta - eps, phi) - dg_pp_dtheta = (g_pp_plus - g_pp_minus) / (2 * eps) - - # For diagonal metric g = diag(g_tt, g_pp): - # Γ^θ_θθ = ½ g^θθ ∂g_θθ/∂θ - # Γ^θ_φφ = -½ g^θθ ∂g_φφ/∂θ - # Γ^φ_θφ = Γ^φ_φθ = ½ g^φφ ∂g_φφ/∂θ - - Gamma = np.zeros((2, 2, 2)) # Gamma[k, i, j] = Γ^k_ij - - Gamma[0, 0, 0] = 0.5 * g_inv[0, 0] * dg_tt_dtheta - Gamma[0, 1, 1] = -0.5 * g_inv[0, 0] * dg_pp_dtheta - Gamma[1, 0, 1] = 0.5 * g_inv[1, 1] * dg_pp_dtheta - Gamma[1, 1, 0] = Gamma[1, 0, 1] # Symmetry - - return Gamma - - def create_point(self, theta: float, phi: float) -> GWLEarthPoint: - """Create GWL μ-seed at given coordinates.""" - g_tt, g_tp, g_pp = self.metric_at(theta, phi) - - # Basis vectors in tangent space (orthonormal with respect to g) - e_theta = np.array([1.0, 0.0]) - e_phi = np.array([0.0, 1.0]) - - # Gaussian curvature for oblate spheroid - e2 = 2 * self.f - self.f**2 - sin_theta = math.sin(theta) - K = (1 - e2) / (self.a**2 * (1 - e2 * sin_theta**2)**2) - - return GWLEarthPoint( - theta=theta, - phi=phi, - g_theta_theta=g_tt, - g_theta_phi=g_tp, - g_phi_phi=g_pp, - e_theta=e_theta, - e_phi=e_phi, - curvature_scalar=K - ) - - def geodesic_equation(self, state: np.ndarray) -> np.ndarray: - """ - Geodesic equation: d²x^i/dt² = -Γ^i_jk dx^j/dt dx^k/dt - - State vector: [θ, φ, dθ/dt, dφ/dt] - Returns: [dθ/dt, dφ/dt, d²θ/dt², d²φ/dt²] - """ - theta, phi, v_theta, v_phi = state - - Gamma = self.christoffel_at(theta, phi) - - # Accelerations - a_theta = (-Gamma[0, 0, 0] * v_theta**2 - - 2 * Gamma[0, 0, 1] * v_theta * v_phi - - Gamma[0, 1, 1] * v_phi**2) - - a_phi = (-Gamma[1, 0, 0] * v_theta**2 - - 2 * Gamma[1, 0, 1] * v_theta * v_phi - - Gamma[1, 1, 1] * v_phi**2) - - return np.array([v_theta, v_phi, a_theta, a_phi]) - - def integrate_geodesic(self, theta0: float, phi0: float, - v_theta0: float, v_phi0: float, - steps: int, dt: float = 0.001) -> List[Tuple[float, float]]: - """ - Integrate geodesic equation using symplectic integrator. - - Returns path in intrinsic coordinates. - """ - state = np.array([theta0, phi0, v_theta0, v_phi0]) - path = [(theta0, phi0)] - - for _ in range(steps): - # Symplectic Euler (staggered) - # Update velocities - deriv = self.geodesic_equation(state) - state[2] += deriv[2] * dt # v_theta - state[3] += deriv[3] * dt # v_phi - - # Update positions with new velocities - state[0] += state[2] * dt # theta - state[1] += state[3] * dt # phi - - path.append((state[0], state[1])) - - return path - - -class EarthCircumferenceTests: - """ - Test suite: Verify Riemannian manifold reproduces Euclidean circumferences. - """ - - def __init__(self): - self.earth = EarthRiemannianManifold() - self.results = {} - - def test_equatorial_circumference(self) -> Tuple[bool, dict]: - """ - Test 1: Equatorial geodesic should have length C_eq. - - Equator: θ = 0, φ ∈ [0, 2π] - Geodesic equation with v_θ = 0 should give equator. - """ - print("\n[Test] Equatorial Circumference") - print("-" * 60) - - # Start at equator, move in φ direction - theta0 = 0.0 - phi0 = 0.0 - v_theta0 = 0.0 # Stay at equator - v_phi0 = 1.0 # Move eastward - - # Integrate until we complete circle - # Need to track when φ wraps by 2π - path = self.earth.integrate_geodesic(theta0, phi0, v_theta0, v_phi0, - steps=10000, dt=0.001) - - # Compute path length - total_length = 0.0 - for i in range(len(path) - 1): - theta, phi = path[i] - dtheta = path[i+1][0] - theta - dphi = path[i+1][1] - phi - - point = self.earth.create_point(theta, phi) - ds = point.line_element(dtheta, dphi) - total_length += ds - - # Scale by initial velocity (we used v_phi = 1.0) - # Actual circumference = length / v_phi0 * (2π / delta_phi) - delta_phi = path[-1][1] - path[0][1] - C_measured = total_length / v_phi0 * (2 * math.pi / delta_phi) - - error = abs(C_measured - self.earth.C_eq) / self.earth.C_eq - passed = error < 0.01 # 1% tolerance - - print(f" Expected: {self.earth.C_eq:,.3f} m") - print(f" Measured: {C_measured:,.3f} m") - print(f" Error: {error*100:.4f}%") - print(f" Status: {'✓ PASS' if passed else '✗ FAIL'}") - - return passed, { - 'expected': self.earth.C_eq, - 'measured': C_measured, - 'error': error - } - - def test_meridional_circumference(self) -> Tuple[bool, dict]: - """ - Test 2: Meridional geodesic (through poles) should have length C_mer. - - Meridian: φ = constant, θ ∈ [-π/2, π/2] - """ - print("\n[Test] Meridional Circumference") - print("-" * 60) - - # Start at south pole, move north - theta0 = -math.pi / 2 + 0.01 # Near south pole - phi0 = 0.0 - v_theta0 = 1.0 # Move north - v_phi0 = 0.0 # Stay on meridian - - path = self.earth.integrate_geodesic(theta0, phi0, v_theta0, v_phi0, - steps=10000, dt=0.001) - - # Compute path length - total_length = 0.0 - for i in range(len(path) - 1): - theta, phi = path[i] - dtheta = path[i+1][0] - theta - dphi = path[i+1][1] - phi - - point = self.earth.create_point(theta, phi) - ds = point.line_element(dtheta, dphi) - total_length += ds - - # Scale to full meridian (-π/2 to π/2) - delta_theta = path[-1][0] - path[0][0] - scale = math.pi / delta_theta - C_measured = total_length * scale - - # Account for both hemispheres (full circumference) - C_measured *= 2 - - error = abs(C_measured - self.earth.C_mer) / self.earth.C_mer - passed = error < 0.05 # 5% tolerance (meridian is harder) - - print(f" Expected: {self.earth.C_mer:,.3f} m") - print(f" Measured: {C_measured:,.3f} m") - print(f" Error: {error*100:.4f}%") - print(f" Status: {'✓ PASS' if passed else '✗ FAIL'}") - - return passed, { - 'expected': self.earth.C_mer, - 'measured': C_measured, - 'error': error - } - - def test_metric_properties(self) -> Tuple[bool, dict]: - """ - Test 3: Metric tensor properties. - - - Positive definite: g_tt > 0, g_pp > 0, det(g) > 0 - - Symmetric: g_tp = g_pt - """ - print("\n[Test] Metric Tensor Properties") - print("-" * 60) - - test_points = [ - (0.0, 0.0), # Equator - (math.pi/4, 0.0), # 45° N - (math.pi/2 - 0.1, 0.0), # Near pole - ] - - all_passed = True - for theta, phi in test_points: - g_tt, g_tp, g_pp = self.earth.metric_at(theta, phi) - g = np.array([[g_tt, g_tp], [g_tp, g_pp]]) - det_g = np.linalg.det(g) - eigenvalues = np.linalg.eigvals(g) - - pos_def = all(e > 0 for e in eigenvalues) - symmetric = abs(g_tp - g[0,1]) < 1e-10 - - passed = pos_def and symmetric - all_passed = all_passed and passed - - print(f" θ={math.degrees(theta):.1f}°: det(g)={det_g:.3e}, " - f"eigenvalues=[{eigenvalues[0]:.3e}, {eigenvalues[1]:.3e}], " - f"{'✓' if passed else '✗'}") - - return all_passed, {'points_tested': len(test_points)} - - def test_gauss_theorema_egregium(self) -> Tuple[bool, dict]: - """ - Test 4: Gaussian curvature is intrinsic (Theorema Egregium). - - For oblate spheroid, Gaussian curvature varies with latitude. - This is a property of the Riemannian metric alone (no embedding). - """ - print("\n[Test] Gauss's Theorema Egregium (Intrinsic Curvature)") - print("-" * 60) - - # Gaussian curvature at different latitudes - latitudes = np.linspace(-math.pi/2 + 0.1, math.pi/2 - 0.1, 5) - curvatures = [] - - for theta in latitudes: - point = self.earth.create_point(theta, 0.0) - K = point.curvature_scalar - curvatures.append(K) - print(f" θ={math.degrees(theta):.1f}°: K={K:.6e} m⁻²") - - # For oblate spheroid: - # K = c² / (a² (1 - e² sin² θ)²) where c = a(1-f) - # Should be maximum at poles (θ = ±π/2), minimum at equator (θ = 0) - - K_eq = curvatures[len(curvatures)//2] # Near equator - K_pole_max = max(curvatures) - - # Curvature should be higher at poles for oblate spheroid - curvature_increases_toward_poles = K_pole_max > K_eq - - # Check magnitude (should be ~1/R² ~ 2.5e-14) - reasonable_magnitude = all(abs(K) < 1e-13 for K in curvatures) - - passed = curvature_increases_toward_poles and reasonable_magnitude - - print(f" K_equator ≈ {K_eq:.6e}") - print(f" K_pole_max ≈ {K_pole_max:.6e}") - print(f" Curvature increases toward poles: {curvature_increases_toward_poles}") - print(f" Status: {'✓ PASS' if passed else '✗ FAIL'}") - - return passed, { - 'K_equator': K_eq, - 'K_pole_max': K_pole_max, - 'curvatures': curvatures - } - - def test_gwl_mu_seed(self) -> Tuple[bool, dict]: - """ - Test 5: GWL μ-seed representation is complete and consistent. - """ - print("\n[Test] GWL μ-seed Completeness") - print("-" * 60) - - # Create μ-seed at various points - test_coords = [ - (0.0, 0.0), # Equator, prime meridian - (math.pi/2, 0.0), # North pole area - (0.0, math.pi), # Equator, 180° E - (-math.pi/4, math.pi/2), # 45° S, 90° E - ] - - all_valid = True - for theta, phi in test_coords: - point = self.earth.create_point(theta, phi) - - # Check all fields present - has_metric = (point.g_theta_theta > 0 and point.g_phi_phi > 0) - has_basis = (len(point.e_theta) == 2 and len(point.e_phi) == 2) - has_curvature = (point.curvature_scalar > 0) - - valid = has_metric and has_basis and has_curvature - all_valid = all_valid and valid - - print(f" ({math.degrees(theta):.1f}°, {math.degrees(phi):.1f}°): " - f"metric={has_metric}, basis={has_basis}, K={has_curvature} " - f"{'✓' if valid else '✗'}") - - return all_valid, {'points_tested': len(test_coords)} - - def run_all(self): - """Run complete GeoWeird test suite.""" - print("=" * 80) - print("GEOWEIRD LANGUAGE TEST: Earth Riemannian Conversion") - print("=" * 80) - print() - print("Converting Euclidean circumference → Riemannian manifold") - print(f" Input: C_eq = {EARTH_EQUATORIAL_CIRCUMFERENCE:,} m") - print(f" Input: C_mer = {EARTH_MERIDIONAL_CIRCUMFERENCE:,} m") - print() - - tests = [ - ('Equatorial Circumference', self.test_equatorial_circumference), - ('Meridional Circumference', self.test_meridional_circumference), - ('Metric Properties', self.test_metric_properties), - ('Theorema Egregium', self.test_gauss_theorema_egregium), - ('GWL μ-seed', self.test_gwl_mu_seed), - ] - - all_passed = True - for name, test_fn in tests: - try: - passed, details = test_fn() - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"✗ ERROR: {e}") - import traceback - traceback.print_exc() - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:35s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL GEOWEIRD TESTS PASSED ✓") - print("=" * 80) - print(""" -The Euclidean circumference measurements have been successfully -converted into a Riemannian manifold model using GWL/TSM. - -Key Results: - ✓ Equatorial geodesic reproduces C_eq - ✓ Meridional geodesic reproduces C_mer - ✓ Metric tensor g_ij is positive definite and symmetric - ✓ Gaussian curvature is intrinsic (Theorema Egregium) - ✓ μ-seed representation is complete - -The GeoWeird approach demonstrates: - - Topology-first representation (μ-seed with intrinsic coords) - - Local metric tensor g(μ) derived from global measurements - - Geodesic flow on manifold reproduces Euclidean measurements - - Curvature as emergent property of metric - """) - else: - print("SOME TESTS FAILED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - tests = EarthCircumferenceTests() - success = tests.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_hello_world_example.gwl b/5-Applications/tools-scripts/demo/gwl_hello_world_example.gwl deleted file mode 100644 index 96a9af21..00000000 --- a/5-Applications/tools-scripts/demo/gwl_hello_world_example.gwl +++ /dev/null @@ -1,62 +0,0 @@ -// GPL Hello World Example -// A simple program demonstrating geometric programming - -PROGRAM hello_world { - - // Declare input nodes on the surface (I/O ports) - INPUT μ_trigger [region: SURFACE, chirality: D] - - // Internal computation nodes - INTERNAL μ_process [region: INTERIOR] - INTERNAL μ_buffer [region: INTERIOR, chirality: L] - - // Output nodes - OUTPUT μ_result [region: SURFACE] - - // PROGRAM LOGIC: - // When triggered, activate a pattern in the interior - // Propagate to output when stable - - // Trigger activation flow - μ_trigger - -> ACCUMULATE - -> μ_process - - // Processing with noise (exploration) - μ_process - -> NOISE - -> μ_buffer - - // Stabilization and output - μ_buffer - -> COLLAPSE - -> μ_result - - // Configuration: - // - Trigger starts at activation 8 (ACTIVE) - // - Process accumulates with weight 0.5 - // - Buffer adds controlled noise (variance 0.3) - // - Result collapses when confidence > 0.7 - - INITIALIZE { - μ_trigger.activation = 8 // Start active - μ_process.activation = 0 // Start quiescent - μ_buffer.activation = 0 // Start quiescent - μ_result.activation = 0 // Start quiescent - - μ_trigger.confidence = 15 // High confidence - μ_process.confidence = 8 // Medium confidence - } - - CONVERGE { - max_cycles = 1000 - variance_threshold = 0.1 - mode = NORMAL // Allow noise - } - - EXTRACT { - // Result is in μ_result.activation - // Map 0-15 to ASCII range - output_value = μ_result.activation * 16 - } -} diff --git a/5-Applications/tools-scripts/demo/gwl_interaction_law_demo.py b/5-Applications/tools-scripts/demo/gwl_interaction_law_demo.py deleted file mode 100644 index f207e127..00000000 --- a/5-Applications/tools-scripts/demo/gwl_interaction_law_demo.py +++ /dev/null @@ -1,384 +0,0 @@ -#!/usr/bin/env python3 -""" -gpl_interaction_law_demo.py - -Demonstrates the GPL rotational coupling and local interaction law. - -Shows: Frame compatibility → Weight → Force → Evolution → Convergence -""" - -import math -import numpy as np -from dataclasses import dataclass -from typing import List, Tuple -import matplotlib.pyplot as plt - - -@dataclass -class Frame: - """Rotational frame of a μ-seed.""" - theta: int # Azimuthal: 0-15 (22.5° steps) - phi: int # Polar: 0-7 - psi: int # Torsion: 0-7 - chi: int # Chirality: 0=D, 1=L - a: float # Activation: 0-15 - x: float # Position X - y: float # Position Y - - def effective_theta(self) -> float: - """Effective angle in radians, accounting for chirality.""" - base = self.theta * (2 * math.pi / 16) - return base if self.chi == 0 else -base - - -def compute_weight(f_i: Frame, f_j: Frame, sigma: float = 2.0) -> float: - """ - Compute interaction weight w_ij. - - w_ij = cos(Δθ) * cos(Δφ) * (1 - 2|Δχ|) * exp(-|Δp|²/2σ²) - """ - # Rotational alignment - delta_theta = (f_j.theta - f_i.theta) % 16 - cos_theta = math.cos(delta_theta * 2 * math.pi / 16) - - delta_phi = (f_j.phi - f_i.phi) % 8 - cos_phi = math.cos(delta_phi * math.pi / 8) - - # Chirality (0 if different, 1 if same) - chiral_factor = 1 - 2 * abs(f_j.chi - f_i.chi) - - # Spatial proximity - dx = f_j.x - f_i.x - dy = f_j.y - f_i.y - dist_sq = dx*dx + dy*dy - proximity = math.exp(-dist_sq / (2 * sigma * sigma)) - - return cos_theta * cos_phi * chiral_factor * proximity - - -def compute_force(f_i: Frame, f_j: Frame, w_ij: float) -> Tuple[float, float]: - """ - Compute force F_ij = w_ij * (a_j - a_i) * direction. - - Returns: (F_x, F_y) - """ - dx = f_j.x - f_i.x - dy = f_j.y - f_i.y - dist = math.sqrt(dx*dx + dy*dy) - - if dist < 0.001: - return (0.0, 0.0) - - # Direction unit vector - ux, uy = dx/dist, dy/dist - - # Activation gradient - da = f_j.a - f_i.a - - # Force magnitude - F_mag = w_ij * da - - return (F_mag * ux, F_mag * uy) - - -def evolve_frame(f: Frame, F_x: float, F_y: float, alpha: float = 0.1) -> Frame: - """ - Update frame based on force. - - Simple Euler integration. - """ - new_a = f.a + alpha * math.sqrt(F_x*F_x + F_y*F_y) - new_a = max(0.0, min(15.0, new_a)) # Clip to bounds - - return Frame( - theta=f.theta, - phi=f.phi, - psi=f.psi, - chi=f.chi, - a=new_a, - x=f.x, - y=f.y - ) - - -class GPLSimulation: - """Simulate GPL interaction dynamics.""" - - def __init__(self, frames: List[Frame]): - self.frames = frames - self.history = [self.get_state()] - - def get_state(self) -> List[Tuple[float, float, float]]: - """Get current state (x, y, a).""" - return [(f.x, f.y, f.a) for f in self.frames] - - def step(self): - """One evolution step.""" - n = len(self.frames) - forces = [(0.0, 0.0) for _ in range(n)] - - # Compute all pairwise forces - for i in range(n): - for j in range(n): - if i == j: - continue - - w = compute_weight(self.frames[i], self.frames[j]) - F = compute_force(self.frames[i], self.frames[j], w) - - forces[i] = (forces[i][0] + F[0], forces[i][1] + F[1]) - - # Update all frames - new_frames = [] - for i, f in enumerate(self.frames): - new_f = evolve_frame(f, forces[i][0], forces[i][1]) - new_frames.append(new_f) - - self.frames = new_frames - self.history.append(self.get_state()) - - def run(self, steps: int = 100): - """Run simulation for multiple steps.""" - for _ in range(steps): - self.step() - - # Check convergence - if self.check_convergence(): - break - - return self.frames - - def check_convergence(self, threshold: float = 0.01) -> bool: - """Check if converged (activation changes small).""" - if len(self.history) < 2: - return False - - prev = self.history[-2] - curr = self.history[-1] - - max_change = max(abs(c[2] - p[2]) for c, p in zip(curr, prev)) - return max_change < threshold - - -def demo_two_node_interaction(): - """Demonstrate basic weight and force between two nodes.""" - - print("=" * 70) - print("TWO-NODE INTERACTION LAW DEMONSTRATION") - print("=" * 70) - - test_cases = [ - ("Aligned (Δθ=0)", 0, 0, 0, 0), - ("Orthogonal (Δθ=4)", 0, 4, 0, 0), - ("Opposite (Δθ=8)", 0, 8, 0, 0), - ("45° offset (Δθ=2)", 0, 2, 0, 0), - ("Chiral mismatch", 0, 0, 0, 1), - ] - - print(f"\n{'Scenario':<25} | {'θ₁':>3} | {'θ₂':>3} | {'χ₁':>3} | {'χ₂':>3} | {'Weight':>8} | {'Interpretation'}") - print("-" * 95) - - for name, t1, t2, c1, c2 in test_cases: - f1 = Frame(theta=t1, phi=0, psi=0, chi=c1, a=5.0, x=0.0, y=0.0) - f2 = Frame(theta=t2, phi=0, psi=0, chi=c2, a=8.0, x=1.0, y=0.0) - - w = compute_weight(f1, f2) - F = compute_force(f1, f2, w) - - interp = "" - if abs(w - 1.0) < 0.1: - interp = "Strong attraction" - elif abs(w) < 0.1: - interp = "No coupling" - elif w < -0.5: - interp = "Repulsion" - elif c1 != c2: - interp = "Orthogonal channels" - else: - interp = f"Partial ({w:.2f})" - - print(f"{name:<25} | {t1:>3} | {t2:>3} | {c1:>3} | {c2:>3} | {w:>8.3f} | {interp}") - - -def demo_convergence(): - """Demonstrate convergence to attractor.""" - - print("\n" + "=" * 70) - print("CONVERGENCE DEMONSTRATION") - print("=" * 70) - - # Create a line of 5 nodes with varying initial activation - frames = [] - for i in range(5): - f = Frame( - theta=0, # All aligned - phi=0, - psi=0, - chi=0, # All D-form - a=float([10, 2, 8, 3, 12][i]), # Varying activation - x=float(i), - y=0.0 - ) - frames.append(f) - - print("\nInitial state (aligned, varying activation):") - print(f"{'Node':>6} | {'x':>6} | {'θ':>4} | {'a':>8} | {'Type'}") - print("-" * 45) - for i, f in enumerate(frames): - t = "High" if f.a > 8 else "Low" if f.a < 4 else "Med" - print(f"{i:>6} | {f.x:>6.1f} | {f.theta:>4} | {f.a:>8.2f} | {t}") - - # Run simulation - sim = GPLSimulation(frames) - final = sim.run(steps=50) - - print(f"\nFinal state (after {len(sim.history)-1} steps):") - print(f"{'Node':>6} | {'x':>6} | {'θ':>4} | {'a':>8} | {'Change'}") - print("-" * 50) - for i, f in enumerate(final): - init_a = [10, 2, 8, 3, 12][i] - change = f"{f.a - init_a:+.2f}" - print(f"{i:>6} | {f.x:>6.1f} | {f.theta:>4} | {f.a:>8.2f} | {change}") - - avg_a = sum(f.a for f in final) / len(final) - print(f"\nAverage activation: {avg_a:.2f}") - print("Converged to smooth, shared activation (energy minimum)") - - -def demo_chiral_isolation(): - """Demonstrate D/L orthogonality.""" - - print("\n" + "=" * 70) - print("CHIRAL ISOLATION DEMONSTRATION") - print("=" * 70) - - # Create D and L chains - frames = [] - - # D-chain (chirality=0) - for i in range(3): - frames.append(Frame(theta=0, phi=0, psi=0, chi=0, a=10.0, x=float(i), y=0.0)) - - # L-chain (chirality=1) - for i in range(3): - frames.append(Frame(theta=0, phi=0, psi=0, chi=1, a=2.0, x=float(i), y=1.0)) - - print("\nInitial state: Two chains (D-chain at y=0, L-chain at y=1)") - print(f"{'Node':>6} | {'x':>6} | {'y':>6} | {'χ':>4} | {'a':>8} | {'Chain'}") - print("-" * 60) - for i, f in enumerate(frames): - chain = "D-chain" if f.chi == 0 else "L-chain" - print(f"{i:>6} | {f.x:>6.1f} | {f.y:>6.1f} | {f.chi:>4} | {f.a:>8.2f} | {chain}") - - # Check weights - print("\nCross-chain weights (D to L):") - for i in range(3): - for j in range(3, 6): - w = compute_weight(frames[i], frames[j]) - print(f" w({i},{j}) = {w:.3f} (should be 0.000)") - - # Run simulation - sim = GPLSimulation(frames) - final = sim.run(steps=30) - - print(f"\nFinal state:") - d_avg = sum(f.a for f in final[:3]) / 3 - l_avg = sum(f.a for f in final[3:]) / 3 - - print(f" D-chain average: {d_avg:.2f}") - print(f" L-chain average: {l_avg:.2f}") - print(" Chains evolved independently (no crosstalk)") - - -def demo_vortex_formation(): - """Demonstrate vortex as rotational attractor.""" - - print("\n" + "=" * 70) - print("VORTEX FORMATION") - print("=" * 70) - - # Create nodes in circle with θ matching angular position - n = 8 - frames = [] - - for i in range(n): - angle = 2 * math.pi * i / n - theta = int((i * 16 / n) % 16) # θ matches position - - f = Frame( - theta=theta, - phi=0, - psi=0, - chi=0, - a=5.0, - x=math.cos(angle), - y=math.sin(angle) - ) - frames.append(f) - - print(f"\nCircular arrangement: θ matches angular position") - print(f"{'Node':>6} | {'θ':>4} | {'Angle°':>8} | {'x':>8} | {'y':>8}") - print("-" * 60) - for i, f in enumerate(frames): - angle_deg = math.degrees(math.atan2(f.y, f.x)) - print(f"{i:>6} | {f.theta:>4} | {angle_deg:>8.1f} | {f.x:>8.3f} | {f.y:>8.3f}") - - # Check weights (should be high for neighbors, forming vortex) - print("\nNeighbor weights (high = vortex stable):") - for i in range(n): - j = (i + 1) % n - w = compute_weight(frames[i], frames[j]) - print(f" w({i},{j}) = {w:.3f}") - - avg_w = sum(compute_weight(frames[i], frames[(i+1)%n]) for i in range(n)) / n - print(f"\nAverage neighbor weight: {avg_w:.3f}") - print("High alignment → Vortex is stable attractor") - - -def demo_summary(): - """Summary of interaction law.""" - - print("\n" + "=" * 70) - print("INTERACTION LAW SUMMARY") - print("=" * 70) - - print(""" -The GPL Local Interaction Law: - -1. WEIGHT FUNCTION - w_ij = cos(Δθ) · cos(Δφ) · (1 - 2|Δχ|) · exp(-|Δp|²/2σ²) - - - cos(Δθ): Azimuthal alignment - - cos(Δφ): Polar alignment - - (1 - 2|Δχ|): Chirality match - - exp(...): Distance decay - -2. FORCE EQUATION - F_ij = w_ij · (a_j - a_i) · direction - - - Activation flows from high to low - - Modulated by rotational compatibility - -3. EVOLUTION - a_i(t+1) = a_i(t) + α · Σ_j F_ij - - - Gradient descent on energy landscape - - Converges to attractor - -Key Behaviors: - - Aligned frames (Δθ=0): Strong attraction - - Orthogonal frames (Δθ=4): No coupling - - Opposite frames (Δθ=8): Repulsion - - Chiral mismatch (Δχ=1): Complete isolation - - Smooth θ gradients: Stable vortices - -Computation = Frame field convergence to energy minimum -""") - - -if __name__ == "__main__": - demo_two_node_interaction() - demo_convergence() - demo_chiral_isolation() - demo_vortex_formation() - demo_summary() diff --git a/5-Applications/tools-scripts/demo/gwl_lorenz_benchmark.py b/5-Applications/tools-scripts/demo/gwl_lorenz_benchmark.py deleted file mode 100644 index 675e6e55..00000000 --- a/5-Applications/tools-scripts/demo/gwl_lorenz_benchmark.py +++ /dev/null @@ -1,558 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_lorenz_benchmark.py - -Benchmark chaotic system with NO CLOSED-FORM SOLUTION. - -The Lorenz system (1963): - dx/dt = σ(y - x) - dy/dt = x(ρ - z) - y - dz/dt = xy - βz - -Standard parameters: σ=10, ρ=28, β=8/3 (chaotic regime) - -This system has: -- NO analytic solution -- NO closed-form trajectory -- Sensitive dependence on initial conditions -- Strange attractor (butterfly shape) - -Benchmark goal: Run long enough to see which numerical methods -preserve the attractor structure vs which diverge to wrong basins. -""" - -import numpy as np -from dataclasses import dataclass, field -from typing import List, Tuple, Callable, Dict -import math -import time - - -@dataclass -class LorenzState: - """State vector for Lorenz system.""" - x: float - y: float - z: float - t: float = 0.0 - - def array(self) -> np.ndarray: - return np.array([self.x, self.y, self.z]) - - def __sub__(self, other) -> np.ndarray: - return self.array() - other.array() - - -class LorenzSystem: - """ - The Lorenz chaotic system. - - Classic parameters (σ=10, ρ=28, β=8/3) produce chaotic dynamics - with famous butterfly-shaped strange attractor. - """ - - def __init__(self, sigma: float = 10.0, rho: float = 28.0, - beta: float = 8.0/3.0, dt: float = 0.001): - self.sigma = sigma - self.rho = rho - self.beta = beta - self.dt = dt - - def derivatives(self, state: LorenzState) -> Tuple[float, float, float]: - """Compute dx/dt, dy/dt, dz/dt.""" - x, y, z = state.x, state.y, state.z - - dx = self.sigma * (y - x) - dy = x * (self.rho - z) - y - dz = x * y - self.beta * z - - return dx, dy, dz - - def step_euler(self, state: LorenzState) -> LorenzState: - """Forward Euler (first order, explicit).""" - dx, dy, dz = self.derivatives(state) - - return LorenzState( - x=state.x + dx * self.dt, - y=state.y + dy * self.dt, - z=state.z + dz * self.dt, - t=state.t + self.dt - ) - - def step_rk4(self, state: LorenzState) -> LorenzState: - """Runge-Kutta 4th order (standard for ODEs).""" - def deriv(s): - return np.array(self.derivatives(s)) - - k1 = deriv(state) - s2 = LorenzState(*(state.array() + 0.5*self.dt*k1), state.t + 0.5*self.dt) - k2 = deriv(s2) - s3 = LorenzState(*(state.array() + 0.5*self.dt*k2), state.t + 0.5*self.dt) - k3 = deriv(s3) - s4 = LorenzState(*(state.array() + self.dt*k3), state.t + self.dt) - k4 = deriv(s4) - - result = state.array() + (self.dt/6.0) * (k1 + 2*k2 + 2*k3 + k4) - - return LorenzState(*result, state.t + self.dt) - - def step_symplectic_euler(self, state: LorenzState) -> LorenzState: - """ - Attempt at symplectic-like update. - Note: Lorenz is NOT Hamiltonian, so true symplectic doesn't apply. - This is just for comparison. - """ - # Update x, then use new x for y, then use both for z - dx, _, _ = self.derivatives(state) - x_new = state.x + dx * self.dt - - interim = LorenzState(x_new, state.y, state.z, state.t) - _, dy, _ = self.derivatives(interim) - y_new = state.y + dy * self.dt - - interim2 = LorenzState(x_new, y_new, state.z, state.t) - _, _, dz = self.derivatives(interim2) - z_new = state.z + dz * self.dt - - return LorenzState(x_new, y_new, z_new, state.t + self.dt) - - def step_adaptive_rk45(self, state: LorenzState, - tolerance: float = 1e-6) -> Tuple[LorenzState, float]: - """ - Adaptive RK4(5) with error estimate. - Returns (new_state, actual_dt_used). - """ - # Simple adaptive: try step, estimate error, adjust - def deriv(s): - return np.array(self.derivatives(s)) - - dt = self.dt - max_iter = 10 - - for _ in range(max_iter): - # RK4 step - k1 = deriv(state) - s2 = LorenzState(*(state.array() + 0.5*dt*k1), state.t + 0.5*dt) - k2 = deriv(s2) - s3 = LorenzState(*(state.array() + 0.5*dt*k2), state.t + 0.5*dt) - k3 = deriv(s3) - s4 = LorenzState(*(state.array() + dt*k3), state.t + dt) - k4 = deriv(s4) - - result_rk4 = state.array() + (dt/6.0) * (k1 + 2*k2 + 2*k3 + k4) - - # RK5 would go here... simplified: use step halving - # Two RK2 steps - mid = LorenzState(*(state.array() + 0.5*dt*k1), state.t + 0.5*dt) - k1_mid = deriv(mid) - mid2 = LorenzState(*(mid.array() + 0.5*dt*k1_mid), mid.t + 0.5*dt) - k2_mid = deriv(mid2) - result_rk2 = state.array() + dt * k1_mid # Simplified - - # Error estimate - error = np.linalg.norm(result_rk4 - result_rk2) - - if error < tolerance: - return LorenzState(*result_rk4, state.t + dt), dt - else: - dt *= 0.5 # Reduce step - - # Fallback - return LorenzState(*result_rk4, state.t + dt), dt - - def run(self, steps: int, initial_state: LorenzState, - method: str = 'rk4') -> List[LorenzState]: - """Run simulation with specified method.""" - methods = { - 'euler': self.step_euler, - 'rk4': self.step_rk4, - 'symplectic': self.step_symplectic_euler, - } - - step_fn = methods.get(method, self.step_rk4) - - trajectory = [initial_state] - state = initial_state - - if method == 'adaptive': - for _ in range(steps): - state, _ = self.step_adaptive_rk45(state) - trajectory.append(state) - else: - for _ in range(steps): - state = step_fn(state) - trajectory.append(state) - - return trajectory - - -class LorenzBenchmark: - """ - Benchmark different integration methods on Lorenz system. - - Since there's no analytic solution, we use structural properties: - 1. Attractor confinement (stay in bounded region) - 2. Lyapunov exponent estimate (divergence rate) - 3. Statistical properties (mean, variance) - 4. Long-term trajectory stability - """ - - def __init__(self, sigma: float = 10.0, rho: float = 28.0, beta: float = 8.0/3.0): - self.sigma = sigma - self.rho = rho - self.beta = beta - self.results = {} - - def test_attractor_confinement(self, steps: int = 100000, dt: float = 0.001) -> Dict: - """ - Test 1: Do trajectories stay bounded in attractor region? - - Lorenz attractor is roughly bounded by: - x ∈ [-20, 20], y ∈ [-30, 30], z ∈ [0, 50] - - Methods that diverge to infinity FAIL. - """ - print(f"\n[Test] Attractor Confinement ({steps} steps)") - print("-" * 60) - - lorenz = LorenzSystem(self.sigma, self.rho, self.beta, dt) - initial = LorenzState(1.0, 1.0, 1.0) - - # Attractor bounds (empirical) - bounds = {'x': (-25, 25), 'y': (-35, 35), 'z': (-5, 55)} - - results = {} - - for method in ['euler', 'rk4', 'symplectic']: - try: - traj = lorenz.run(steps, initial, method) - - x_vals = [s.x for s in traj] - y_vals = [s.y for s in traj] - z_vals = [s.z for s in traj] - - # Check bounds - x_in = all(bounds['x'][0] <= x <= bounds['x'][1] for x in x_vals) - y_in = all(bounds['y'][0] <= y <= bounds['y'][1] for y in y_vals) - z_in = all(bounds['z'][0] <= z <= bounds['z'][1] for z in z_vals) - - confined = x_in and y_in and z_in - - # Compute statistics - x_range = (min(x_vals), max(x_vals)) - y_range = (min(y_vals), max(y_vals)) - z_range = (min(z_vals), max(z_vals)) - - results[method] = { - 'confined': confined, - 'x_range': x_range, - 'y_range': y_range, - 'z_range': z_range, - 'final_z': z_vals[-1] - } - - status = "✓" if confined else "✗" - print(f" {method:12s}: {status} x∈[{x_range[0]:.1f}, {x_range[1]:.1f}], " - f"z∈[{z_range[0]:.1f}, {z_range[1]:.1f}]") - - except Exception as e: - results[method] = {'confined': False, 'error': str(e)} - print(f" {method:12s}: ✗ ERROR - {e}") - - return results - - def test_lyapunov_divergence(self, steps: int = 50000, dt: float = 0.001) -> Dict: - """ - Test 2: Estimate maximum Lyapunov exponent. - - Two nearby trajectories should diverge exponentially: - |δ(t)| ≈ |δ(0)| · e^(λt) - - For Lorenz: λ ≈ 0.906 (known from literature) - - Methods with wrong λ indicate poor chaos preservation. - """ - print(f"\n[Test] Lyapunov Divergence ({steps} steps)") - print("-" * 60) - - lorenz = LorenzSystem(self.sigma, self.rho, self.beta, dt) - - # Two nearby initial conditions - ic1 = LorenzState(1.0, 1.0, 1.0) - ic2 = LorenzState(1.0001, 1.0, 1.0) # 0.01% perturbation - - initial_sep = 0.0001 - - results = {} - - for method in ['euler', 'rk4']: - try: - traj1 = lorenz.run(steps, ic1, method) - traj2 = lorenz.run(steps, ic2, method) - - # Compute separation vs time - separations = [] - times = [] - for i in range(0, len(traj1), 100): # Sample every 100 steps - sep = np.linalg.norm(traj1[i] - traj2[i]) - separations.append(sep) - times.append(traj1[i].t) - - # Fit log(separation) vs time for exponent - log_seps = np.log(separations[1:50]) # Early growth phase - times_fit = np.array(times[1:50]) - - if len(log_seps) > 10: - coeffs = np.polyfit(times_fit, log_seps, 1) - lyap_est = coeffs[0] # Slope = Lyapunov exponent - else: - lyap_est = 0.0 - - # Also check if saturation occurs (attractor size limit) - final_sep = separations[-1] - saturated = final_sep > 10 # Trajectories decorrelated - - # Expected λ ≈ 0.9 - error = abs(lyap_est - 0.906) / 0.906 - - results[method] = { - 'lyapunov_est': lyap_est, - 'expected': 0.906, - 'error': error, - 'saturated': saturated, - 'final_separation': final_sep - } - - status = "✓" if error < 0.3 else "~" if error < 0.5 else "✗" - print(f" {method:12s}: {status} λ≈{lyap_est:.3f} (expected 0.906), " - f"final sep={final_sep:.2f}") - - except Exception as e: - results[method] = {'error': str(e)} - print(f" {method:12s}: ✗ ERROR - {e}") - - return results - - def test_long_term_statistics(self, steps: int = 200000, dt: float = 0.001) -> Dict: - """ - Test 3: Statistical properties of long trajectory. - - Lorenz attractor has known statistical properties: - - ⟨x⟩ ≈ 0, ⟨y⟩ ≈ 0, ⟨z⟩ ≈ ρ = 28 - - Variances are non-trivial - - Methods that produce wrong statistics FAIL. - """ - print(f"\n[Test] Long-Term Statistics ({steps} steps)") - print("-" * 60) - - lorenz = LorenzSystem(self.sigma, self.rho, self.beta, dt) - initial = LorenzState(1.0, 1.0, 1.0) - - # Discard transient (first 20%) - discard = int(0.2 * steps) - - results = {} - - for method in ['rk4', 'euler']: - try: - start_time = time.time() - traj = lorenz.run(steps, initial, method) - runtime = time.time() - start_time - - # Discard transient - steady = traj[discard:] - - x_vals = np.array([s.x for s in steady]) - y_vals = np.array([s.y for s in steady]) - z_vals = np.array([s.z for s in steady]) - - means = { - 'x': np.mean(x_vals), - 'y': np.mean(y_vals), - 'z': np.mean(z_vals) - } - - stds = { - 'x': np.std(x_vals), - 'y': np.std(y_vals), - 'z': np.std(z_vals) - } - - # Expected: ⟨z⟩ ≈ ρ - 1 = 27, or just check reasonable - z_mean_ok = 20 < means['z'] < 35 - x_mean_small = abs(means['x']) < 2 - - results[method] = { - 'means': means, - 'stds': stds, - 'runtime': runtime, - 'z_mean_ok': z_mean_ok, - 'x_mean_small': x_mean_small - } - - status = "✓" if z_mean_ok and x_mean_small else "✗" - print(f" {method:12s}: {status} ⟨x⟩={means['x']:.2f}, ⟨z⟩={means['z']:.2f}, " - f"σ_x={stds['x']:.2f}, time={runtime:.2f}s") - - except Exception as e: - results[method] = {'error': str(e)} - print(f" {method:12s}: ✗ ERROR - {e}") - - return results - - def test_energy_drift(self, steps: int = 100000, dt: float = 0.001) -> Dict: - """ - Test 4: No conserved quantity, but check for spurious drifts. - - Lorenz has no energy, but we can define a "pseudo-energy": - E = x² + y² + z² - - Should fluctuate but not drift systematically on attractor. - """ - print(f"\n[Test] Pseudo-Energy Stability ({steps} steps)") - print("-" * 60) - - lorenz = LorenzSystem(self.sigma, self.rho, self.beta, dt) - initial = LorenzState(1.0, 1.0, 1.0) - - results = {} - - for method in ['euler', 'rk4']: - try: - traj = lorenz.run(steps, initial, method) - - # Pseudo-energy - E_vals = [s.x**2 + s.y**2 + s.z**2 for s in traj] - - # Check drift - E_early = np.mean(E_vals[1000:5000]) - E_late = np.mean(E_vals[-5000:]) - - drift = abs(E_late - E_early) / E_early - - # Also check for blow-up - E_max = max(E_vals) - E_final = E_vals[-1] - - results[method] = { - 'E_early': E_early, - 'E_late': E_late, - 'drift': drift, - 'E_max': E_max, - 'E_final': E_final - } - - # RK4 should have low drift; Euler may drift - acceptable = drift < 0.5 and E_max < 10000 - status = "✓" if acceptable else "✗" - - print(f" {method:12s}: {status} drift={drift:.3f}, E_max={E_max:.1f}") - - except Exception as e: - results[method] = {'error': str(e)} - print(f" {method:12s}: ✗ ERROR - {e}") - - return results - - def test_dt_convergence(self, steps: int = 50000) -> Dict: - """ - Test 5: Does solution converge as dt decreases? - - For chaotic systems, trajectories diverge, but STATISTICS - should converge with smaller dt. - """ - print(f"\n[Test] dt Convergence (statistics)") - print("-" * 60) - - dts = [0.01, 0.005, 0.002, 0.001] - initial = LorenzState(1.0, 1.0, 1.0) - - results = {'dts': dts, 'z_means': [], 'z_stds': []} - - for dt in dts: - lorenz = LorenzSystem(self.sigma, self.rho, self.beta, dt) - n_steps = int(50000 * 0.001 / dt) # Keep total time constant - - traj = lorenz.run(n_steps, initial, 'rk4') - - # Discard transient - steady = traj[int(0.2*len(traj)):] - z_vals = [s.z for s in steady] - - results['z_means'].append(np.mean(z_vals)) - results['z_stds'].append(np.std(z_vals)) - - # Check convergence - z_mean_spread = max(results['z_means']) - min(results['z_means']) - converged = z_mean_spread < 2.0 # Should stabilize - - status = "✓" if converged else "✗" - print(f" RK4 with varying dt: {status}") - for dt, zm in zip(dts, results['z_means']): - print(f" dt={dt:.4f}: ⟨z⟩={zm:.3f}") - - return results - - def run_all(self): - """Run complete benchmark suite.""" - print("=" * 80) - print("LORENZ SYSTEM BENCHMARK: NO CLOSED-FORM SOLUTION") - print("=" * 80) - print(f"System: dx/dt = σ(y-x), dy/dt = x(ρ-z)-y, dz/dt = xy-βz") - print(f"Parameters: σ={self.sigma}, ρ={self.rho}, β={self.beta:.4f}") - print(f"Properties: Chaotic, strange attractor, sensitive to ICs") - print(f"Validation: Structural (confinement, statistics), NOT analytic") - print() - - # Run tests - self.results['confinement'] = self.test_attractor_confinement(steps=100000) - self.results['lyapunov'] = self.test_lyapunov_divergence(steps=50000) - self.results['statistics'] = self.test_long_term_statistics(steps=200000) - self.results['energy'] = self.test_energy_drift(steps=100000) - self.results['convergence'] = self.test_dt_convergence() - - # Summary - print("\n" + "=" * 80) - print("BENCHMARK SUMMARY") - print("=" * 80) - - print(""" -Key Findings: - -1. ATTRACTOR CONFINEMENT - - RK4: Stays in attractor bounds ✓ - - Euler: May drift slowly over VERY long times - - Symplectic: Not applicable (not Hamiltonian) - -2. LYAPUNOV EXPONENT - - Expected: λ ≈ 0.906 - - RK4 captures chaos correctly - - Euler: May underestimate due to numerical damping - -3. LONG-TERM STATISTICS - - ⟨x⟩ ≈ 0, ⟨z⟩ ≈ 27-28 (known from literature) - - RK4: Accurate statistics - - Euler: Biased means due to drift - -4. PSEUDO-ENERGY - - Should fluctuate, not drift - - RK4: Bounded fluctuations - - Euler: Slow drift upward - -5. dt CONVERGENCE - - Statistics converge for dt ≤ 0.005 - - Trajectories diverge (chaos), but structure stable - -RECOMMENDATION: - Use RK4 with dt ≤ 0.001 for accurate Lorenz dynamics. - Euler requires dt ≤ 0.0001 for similar accuracy (10× cost). - No method gives "exact" solution—structure preservation matters. - """) - - return self.results - - -if __name__ == "__main__": - benchmark = LorenzBenchmark() - results = benchmark.run_all() diff --git a/5-Applications/tools-scripts/demo/gwl_oscillator_step1_deterministic.py b/5-Applications/tools-scripts/demo/gwl_oscillator_step1_deterministic.py deleted file mode 100644 index fd2c4581..00000000 --- a/5-Applications/tools-scripts/demo/gwl_oscillator_step1_deterministic.py +++ /dev/null @@ -1,418 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_oscillator_step1_deterministic.py - -STEP 1: Deterministic Harmonic Oscillator (Conservative) - -Base equation: d²x/dt² + ω₀²·x = 0 -Analytic solution: x(t) = A·cos(ω₀t) + B·sin(ω₀t) - -Validated against: 300+ year old exact solution (Euler 1730s) -TSM Mapping: Position p, velocity v, symplectic update -""" - -import numpy as np -from dataclasses import dataclass -from typing import Tuple, List -import math - - -@dataclass -class OscillatorState: - """Canonical state for harmonic oscillator.""" - x: float # Position - v: float # Velocity - t: float # Time - - def to_vector(self) -> Tuple[float, float]: - return (self.x, self.v) - - -class GWL_DeterministicOscillator: - """ - Deterministic harmonic oscillator using symplectic Euler integration. - - This is the FOUNDATION. All subsequent steps build on this. - Must pass ALL validation tests before proceeding. - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, dt: float = 0.01): - """ - Args: - omega0: Natural frequency (rad/s) - mass: Mass (kg) - dt: Time step (s) - """ - self.omega0 = omega0 - self.mass = mass - self.dt = dt - self.k = mass * omega0**2 # Spring constant - - # State - self.state = OscillatorState(x=1.0, v=0.0, t=0.0) - - # History for analysis - self.history: List[OscillatorState] = [] - self.energy_history: List[float] = [] - - def initialize(self, x0: float, v0: float): - """Set initial conditions.""" - self.state = OscillatorState(x=x0, v=v0, t=0.0) - self.history = [] - self.energy_history = [] - - def energy(self, state: OscillatorState = None) -> float: - """Compute total energy: E = ½mv² + ½kx²""" - if state is None: - state = self.state - kinetic = 0.5 * self.mass * state.v**2 - potential = 0.5 * self.k * state.x**2 - return kinetic + potential - - def analytic_solution(self, t: float, x0: float, v0: float) -> Tuple[float, float]: - """ - Analytic solution: x(t) = x₀·cos(ω₀t) + (v₀/ω₀)·sin(ω₀t) - v(t) = -x₀·ω₀·sin(ω₀t) + v₀·cos(ω₀t) - """ - x = x0 * math.cos(self.omega0 * t) + (v0 / self.omega0) * math.sin(self.omega0 * t) - v = -x0 * self.omega0 * math.sin(self.omega0 * t) + v0 * math.cos(self.omega0 * t) - return x, v - - def step_symplectic_euler(self): - """ - Symplectic Euler update (staggered). - - Preserves phase space volume exactly. - v_{n+1} = v_n - ω₀²·x_n·Δt - x_{n+1} = x_n + v_{n+1}·Δt - """ - x_n = self.state.x - v_n = self.state.v - - # Update velocity (half-step conceptually) - v_new = v_n - self.omega0**2 * x_n * self.dt - - # Update position with NEW velocity (full-step) - x_new = x_n + v_new * self.dt - - # Update time - t_new = self.state.t + self.dt - - self.state = OscillatorState(x=x_new, v=v_new, t=t_new) - - # Record - self.history.append(self.state) - self.energy_history.append(self.energy()) - - def step_naive_euler(self): - """ - Naive explicit Euler (for comparison - EXPECTED TO FAIL). - - x_{n+1} = x_n + v_n·Δt - v_{n+1} = v_n - ω₀²·x_n·Δt - """ - x_n = self.state.x - v_n = self.state.v - - x_new = x_n + v_n * self.dt - v_new = v_n - self.omega0**2 * x_n * self.dt - - self.state = OscillatorState(x=x_new, v=v_new, t=self.state.t + self.dt) - - self.history.append(self.state) - self.energy_history.append(self.energy()) - - def run(self, steps: int, method: str = 'symplectic'): - """Run simulation.""" - step_fn = self.step_symplectic_euler if method == 'symplectic' else self.step_naive_euler - for _ in range(steps): - step_fn() - - -class ValidationSuite: - """ - Comprehensive validation against analytic solution (Euler 1730). - - ALL TESTS MUST PASS before proceeding to Step 2 (damping). - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0): - self.omega0 = omega0 - self.mass = mass - self.results = {} - - def test_energy_conservation(self, steps: int = 1000) -> Tuple[bool, dict]: - """ - Test 1: Energy should be conserved (deterministic, no dissipation). - - Analytic: dE/dt = 0 exactly - """ - osc = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=0.01) - osc.initialize(x0=1.0, v0=0.0) - - E_initial = osc.energy() - osc.run(steps=steps, method='symplectic') - - E_values = np.array(osc.energy_history) - E_drift = (np.max(E_values) - np.min(E_values)) / E_initial - E_final_ratio = E_values[-1] / E_initial - - # Should be < 2% energy variation (first-order symplectic) - passed = E_drift < 0.02 - - return passed, { - 'E_initial': E_initial, - 'E_drift_relative': E_drift, - 'E_final_ratio': E_final_ratio, - 'max_E': np.max(E_values), - 'min_E': np.min(E_values), - 'threshold': 0.02 - } - - def test_period_accuracy(self) -> Tuple[bool, dict]: - """ - Test 2: Period should match T = 2π/ω₀. - - Analytic: T = 2π/ω₀ exactly - """ - # Start with v0 > 0 so we cross zero early in the simulation - osc = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=0.001) - osc.initialize(x0=0.0, v0=1.0) # Start at origin, moving right - - # Run for slightly more than two periods - T_expected = 2 * math.pi / self.omega0 - steps = int(2.5 * T_expected / 0.001) - osc.run(steps=steps, method='symplectic') - - # Find zero crossings to determine period - x_values = [h.x for h in osc.history] - t_values = [h.t for h in osc.history] - - # Find zero crossings (positive direction: negative to positive) - zero_crossings = [] - for i in range(1, len(x_values)): - if x_values[i-1] < 0 and x_values[i] >= 0: - # Linear interpolation for better accuracy - t_cross = t_values[i-1] + (t_values[i] - t_values[i-1]) * abs(x_values[i-1]) / (abs(x_values[i-1]) + abs(x_values[i])) - zero_crossings.append(t_cross) - - if len(zero_crossings) >= 2: - # Measure multiple periods for better accuracy - periods = [zero_crossings[i] - zero_crossings[i-1] for i in range(1, len(zero_crossings))] - T_measured = np.mean(periods) - T_error = abs(T_measured - T_expected) / T_expected - else: - T_measured = None - T_error = float('inf') - - passed = T_error < 0.05 if T_measured else False - - return passed, { - 'T_expected': T_expected, - 'T_measured': T_measured, - 'T_error': T_error, - 'num_periods_measured': len(periods) if 'periods' in dir() else 0, - 'threshold': 0.05 - } - - def test_analytic_agreement(self, steps: int = 500) -> Tuple[bool, dict]: - """ - Test 3: Numerical solution should match analytic solution. - - Analytic: x(t) = x₀·cos(ω₀t) + (v₀/ω₀)·sin(ω₀t) - """ - x0, v0 = 1.0, 0.5 - dt = 0.01 - - osc = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=dt) - osc.initialize(x0=x0, v0=v0) - osc.run(steps=steps, method='symplectic') - - # Compare with analytic solution - max_error_x = 0.0 - max_error_v = 0.0 - - for state in osc.history: - x_analytic, v_analytic = osc.analytic_solution(state.t, x0, v0) - error_x = abs(state.x - x_analytic) - error_v = abs(state.v - v_analytic) - max_error_x = max(max_error_x, error_x) - max_error_v = max(max_error_v, error_v) - - # Error should grow slowly (O(dt²) for symplectic) - passed = max_error_x < 0.1 and max_error_v < 0.1 - - return passed, { - 'max_error_x': max_error_x, - 'max_error_v': max_error_v, - 'threshold': 0.1 - } - - def test_reversibility(self, steps: int = 100) -> Tuple[bool, dict]: - """ - Test 4: System should be time-reversible. - - Forward N steps, backward N steps → return to start. - """ - x0, v0 = 1.0, 0.5 - dt = 0.01 - - osc = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=dt) - osc.initialize(x0=x0, v0=v0) - - # Forward - osc.run(steps=steps, method='symplectic') - x_forward = osc.state.x - v_forward = osc.state.v - - # Backward (reverse velocity, run same steps) - osc.state = OscillatorState(x=x_forward, v=-v_forward, t=0.0) - osc.history = [] - osc.run(steps=steps, method='symplectic') - - # Should return close to origin - x_back = osc.state.x - v_back = -osc.state.v # Flip sign back - - error = math.sqrt((x_back - x0)**2 + (v_back - v0)**2) - passed = error < 0.01 - - return passed, { - 'initial': (x0, v0), - 'after_backward': (x_back, v_back), - 'error': error, - 'threshold': 0.01 - } - - def test_phase_space_orbit(self, steps: int = 1000) -> Tuple[bool, dict]: - """ - Test 5: Phase space orbit should close (periodic system). - - After one period, should return to starting point. - """ - x0, v0 = 1.0, 0.0 - T = 2 * math.pi / self.omega0 - dt = 0.01 - steps_per_period = int(T / dt) - - osc = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=dt) - osc.initialize(x0=x0, v0=v0) - osc.run(steps=steps_per_period, method='symplectic') - - # Check return to start - error_x = abs(osc.state.x - x0) - error_v = abs(osc.state.v - v0) - - passed = error_x < 0.05 and error_v < 0.05 - - return passed, { - 'error_x': error_x, - 'error_v': error_v, - 'steps': steps_per_period, - 'threshold': 0.05 - } - - def test_vs_naive_euler(self, steps: int = 500) -> Tuple[bool, dict]: - """ - Test 6: Symplectic should beat naive Euler (demonstrates necessity). - - Naive Euler: energy grows exponentially (WRONG) - Symplectic: energy conserved (CORRECT) - """ - x0, v0 = 1.0, 0.0 - - # Symplectic - osc_symp = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=0.01) - osc_symp.initialize(x0=x0, v0=v0) - osc_symp.run(steps=steps, method='symplectic') - E_symp_drift = (osc_symp.energy_history[-1] - osc_symp.energy_history[0]) / osc_symp.energy_history[0] - - # Naive - osc_naive = GWL_DeterministicOscillator(omega0=self.omega0, mass=self.mass, dt=0.01) - osc_naive.initialize(x0=x0, v0=v0) - osc_naive.run(steps=steps, method='naive') - E_naive_drift = (osc_naive.energy_history[-1] - osc_naive.energy_history[0]) / osc_naive.energy_history[0] - - passed = abs(E_symp_drift) < 0.05 and E_naive_drift > 0.05 - - return passed, { - 'E_symp_drift': E_symp_drift, - 'E_naive_drift': E_naive_drift, - 'symplectic_better': abs(E_symp_drift) < abs(E_naive_drift) - } - - def run_all(self): - """Run complete validation suite.""" - print("=" * 80) - print("STEP 1 VALIDATION: DETERMINISTIC HARMONIC OSCILLATOR") - print("=" * 80) - print(f"Base equation: d²x/dt² + ω₀²·x = 0") - print(f"Analytic solution: x(t) = A·cos(ω₀t) + B·sin(ω₀t)") - print(f"Verification: 294 years (Euler 1730)") - print(f"Parameters: ω₀={self.omega0}, m={self.mass}") - print() - - tests = [ - ('Energy Conservation', self.test_energy_conservation), - ('Period Accuracy', self.test_period_accuracy), - ('Analytic Agreement', self.test_analytic_agreement), - ('Reversibility', self.test_reversibility), - ('Phase Space Orbit', self.test_phase_space_orbit), - ('Symplectic vs Naive', self.test_vs_naive_euler), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:30s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED - STEP 1 VALIDATED") - print("=" * 80) - print(""" -The deterministic harmonic oscillator is now validated. -Properties verified: - ✓ Energy conserved (< 0.1% drift) - ✓ Period accurate (matches 2π/ω₀) - ✓ Analytic agreement (vs Euler 1730 solution) - ✓ Time-reversible (symplectic structure) - ✓ Phase space orbit closes - ✓ Symplectic beats naive Euler - -READY FOR STEP 2: Add dissipation (damping) - """) - else: - print("SOME TESTS FAILED - DO NOT PROCEED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - validator = ValidationSuite(omega0=1.0, mass=1.0) - success = validator.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_oscillator_step2_damped.py b/5-Applications/tools-scripts/demo/gwl_oscillator_step2_damped.py deleted file mode 100644 index a509da7f..00000000 --- a/5-Applications/tools-scripts/demo/gwl_oscillator_step2_damped.py +++ /dev/null @@ -1,508 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_oscillator_step2_damped.py - -STEP 2: Damped Harmonic Oscillator (Attractor Dynamics) - -Base equation: d²x/dt² + 2ζω₀·dx/dt + ω₀²·x = 0 - or: m·d²x/dt² + γ·dx/dt + k·x = 0 - -Analytic solution depends on damping regime: -- Underdamped (ζ < 1): x(t) = A·e^(-ζω₀t)·cos(ω₁t + φ), ω₁ = ω₀√(1-ζ²) -- Critically damped (ζ = 1): x(t) = (A + Bt)·e^(-ω₀t) -- Overdamped (ζ > 1): x(t) = A·e^(-λ₁t) + B·e^(-λ₂t) - -New GWL primitives: χ (instability), Γ (strain), regime classification -""" - -import numpy as np -from dataclasses import dataclass -from typing import Tuple, List, Literal -import math - - -@dataclass -class DampedOscillatorState: - """Canonical state for damped oscillator.""" - x: float # Position - v: float # Velocity - t: float # Time - regime: Literal['underdamped', 'critical', 'overdamped'] = 'underdamped' - - def to_vector(self) -> Tuple[float, float]: - return (self.x, self.v) - - -class GWL_DampedOscillator: - """ - Damped harmonic oscillator with regime classification. - - Adds dissipation to Step 1's validated symplectic backbone. - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, - zeta: float = 0.1, dt: float = 0.01): - """ - Args: - omega0: Natural frequency (rad/s) - mass: Mass (kg) - zeta: Damping ratio (dimensionless) - ζ < 1: underdamped (oscillating decay) - ζ = 1: critically damped (fastest return) - ζ > 1: overdamped (slow exponential) - dt: Time step (s) - """ - self.omega0 = omega0 - self.mass = mass - self.zeta = zeta - self.dt = dt - - # Derived parameters - self.k = mass * omega0**2 - self.gamma = 2 * zeta * mass * omega0 # Damping coefficient - - # Regime classification - if zeta < 1.0: - self.regime = 'underdamped' - self.omega1 = omega0 * math.sqrt(1 - zeta**2) # Damped frequency - elif abs(zeta - 1.0) < 0.01: - self.regime = 'critical' - self.omega1 = 0.0 - else: - self.regime = 'overdamped' - # Two real exponents - self.lambda1 = omega0 * (zeta + math.sqrt(zeta**2 - 1)) - self.lambda2 = omega0 * (zeta - math.sqrt(zeta**2 - 1)) - self.omega1 = 0.0 - - # State - self.state = DampedOscillatorState(x=1.0, v=0.0, t=0.0, regime=self.regime) - self.history: List[DampedOscillatorState] = [] - self.energy_history: List[float] = [] - - def initialize(self, x0: float, v0: float): - """Set initial conditions.""" - self.state = DampedOscillatorState(x=x0, v=v0, t=0.0, regime=self.regime) - self.history = [] - self.energy_history = [] - - def energy(self, state: DampedOscillatorState = None) -> float: - """Compute total mechanical energy.""" - if state is None: - state = self.state - kinetic = 0.5 * self.mass * state.v**2 - potential = 0.5 * self.k * state.x**2 - return kinetic + potential - - def dissipation_rate(self) -> float: - """Instantaneous energy dissipation: dE/dt = -γv²""" - return -self.gamma * self.state.v**2 - - def quality_factor(self) -> float: - """Q factor: Q = ω₀m/γ = 1/(2ζ)""" - return 1.0 / (2 * self.zeta) if self.zeta > 0 else float('inf') - - def analytic_solution(self, t: float, x0: float, v0: float) -> Tuple[float, float]: - """ - Analytic solution for given damping regime. - """ - if self.regime == 'underdamped': - # x(t) = e^(-ζω₀t) [A·cos(ω₁t) + B·sin(ω₁t)] - exp_term = math.exp(-self.zeta * self.omega0 * t) - cos_term = math.cos(self.omega1 * t) - sin_term = math.sin(self.omega1 * t) - - A = x0 - B = (v0 + self.zeta * self.omega0 * x0) / self.omega1 - - x = exp_term * (A * cos_term + B * sin_term) - - # v = dx/dt - v = exp_term * (-self.zeta * self.omega0 * (A * cos_term + B * sin_term) - + self.omega1 * (-A * sin_term + B * cos_term)) - - elif self.regime == 'critical': - # x(t) = (A + Bt)·e^(-ω₀t) - exp_term = math.exp(-self.omega0 * t) - A = x0 - B = v0 + self.omega0 * x0 - - x = (A + B * t) * exp_term - v = (B - self.omega0 * (A + B * t)) * exp_term - - else: # overdamped - # x(t) = A·e^(-λ₁t) + B·e^(-λ₂t) - exp1 = math.exp(-self.lambda1 * t) - exp2 = math.exp(-self.lambda2 * t) - - # Solve for A, B from initial conditions - # x0 = A + B - # v0 = -λ₁A - λ₂B - det = self.lambda2 - self.lambda1 - A = (v0 + self.lambda2 * x0) / det - B = (-v0 - self.lambda1 * x0) / det - - x = A * exp1 + B * exp2 - v = -A * self.lambda1 * exp1 - B * self.lambda2 * exp2 - - return x, v - - def step(self): - """ - Symplectic update with dissipation. - - Split-step method: - 1. Symplectic (conservative) step - 2. Exact dissipation step - """ - x_n = self.state.x - v_n = self.state.v - - # Step 1: Conservative part (from Step 1, validated) - v_temp = v_n - self.omega0**2 * x_n * self.dt - - # Step 2: Dissipation (exact for linear drag) - # dv/dt = -(γ/m)v → v(t) = v(0)·exp(-γt/m) - v_new = v_temp * math.exp(-self.gamma * self.dt / self.mass) - - # Step 3: Position update with new velocity - x_new = x_n + v_new * self.dt - - # Update state - t_new = self.state.t + self.dt - self.state = DampedOscillatorState(x=x_new, v=v_new, t=t_new, regime=self.regime) - - # Record - self.history.append(self.state) - self.energy_history.append(self.energy()) - - def run(self, steps: int): - """Run simulation.""" - for _ in range(steps): - self.step() - - def envelope(self, t: float, x0: float, v0: float) -> float: - """Decay envelope for underdamped case.""" - if self.regime == 'underdamped': - # Approximate envelope - E0 = 0.5 * self.k * x0**2 + 0.5 * self.mass * v0**2 - return math.sqrt(2 * E0 / self.k) * math.exp(-self.zeta * self.omega0 * t) - return None - - -class DampedValidationSuite: - """ - Validation for Step 2: Damped oscillator. - - Tests all three damping regimes against analytic solutions. - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0): - self.omega0 = omega0 - self.mass = mass - self.results = {} - - def test_underdamped_envelope(self) -> Tuple[bool, dict]: - """ - Test 1: Underdamped envelope should decay as e^(-ζω₀t). - """ - zeta = 0.1 # Light damping - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=0.01) - osc.initialize(x0=1.0, v0=0.0) - - # Run for several time constants - tau = 1.0 / (zeta * self.omega0) # Decay time constant - steps = int(5 * tau / 0.01) - osc.run(steps=steps) - - # Check envelope decay - peaks_x = [] - peaks_t = [] - for i in range(1, len(osc.history) - 1): - h = osc.history[i] - if h.x > osc.history[i-1].x and h.x > osc.history[i+1].x and h.x > 0: - peaks_x.append(h.x) - peaks_t.append(h.t) - - if len(peaks_x) >= 3: - # Fit exponential to peaks: x_peak ∝ e^(-ζω₀t) - log_peaks = np.log(peaks_x) - coeffs = np.polyfit(peaks_t, log_peaks, 1) - measured_decay = -coeffs[0] - expected_decay = zeta * self.omega0 - error = abs(measured_decay - expected_decay) / expected_decay - else: - error = float('inf') - - passed = error < 0.1 - - return passed, { - 'zeta': zeta, - 'measured_decay': measured_decay if 'measured_decay' in dir() else None, - 'expected_decay': expected_decay, - 'error': error, - 'num_peaks': len(peaks_x), - 'threshold': 0.1 - } - - def test_frequency_shift(self) -> Tuple[bool, dict]: - """ - Test 2: Underdamped frequency should be ω₁ = ω₀√(1-ζ²). - """ - zeta = 0.2 - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=0.001) - osc.initialize(x0=0.0, v0=1.0) # Start at origin - - # Run and measure period - tau = 1.0 / (zeta * self.omega0) - steps = int(10 * tau / 0.001) # Several cycles - osc.run(steps=steps) - - # Find zero crossings - x_values = [h.x for h in osc.history] - t_values = [h.t for h in osc.history] - - zero_crossings = [] - for i in range(1, len(x_values)): - if x_values[i-1] < 0 and x_values[i] >= 0: - t_cross = t_values[i-1] + (t_values[i] - t_values[i-1]) * abs(x_values[i-1]) / (abs(x_values[i-1]) + abs(x_values[i])) - zero_crossings.append(t_cross) - - if len(zero_crossings) >= 4: - periods = [zero_crossings[i] - zero_crossings[i-1] for i in range(2, len(zero_crossings))] - T_measured = np.mean(periods) - omega_measured = 2 * math.pi / T_measured - omega_expected = self.omega0 * math.sqrt(1 - zeta**2) - error = abs(omega_measured - omega_expected) / omega_expected - else: - error = float('inf') - omega_measured = None - omega_expected = self.omega0 * math.sqrt(1 - zeta**2) - - passed = error < 0.05 - - return passed, { - 'zeta': zeta, - 'omega_measured': omega_measured, - 'omega_expected': omega_expected, - 'error': error, - 'threshold': 0.05 - } - - def test_critical_damping(self) -> Tuple[bool, dict]: - """ - Test 3: Critical damping (ζ=1) should return to zero fastest. - """ - x0, v0 = 1.0, 0.0 - - # Test three damping values - results = {} - for zeta in [0.5, 1.0, 2.0]: - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=0.01) - osc.initialize(x0=x0, v0=v0) - - # Find time to reach |x| < 0.01 - target_steps = 2000 - osc.run(steps=target_steps) - - # Find settling time - settling_time = None - for i, h in enumerate(osc.history): - if abs(h.x) < 0.01 and abs(h.v) < 0.01: - settling_time = h.t - break - - results[zeta] = settling_time - - # Critical (ζ=1) should be fastest or nearly so - crit_time = results[1.0] - under_time = results[0.5] - over_time = results[2.0] - - # Critical should beat overdamped - passed = crit_time is not None and (over_time is None or crit_time <= over_time) - - return passed, { - 'settling_underdamped': under_time, - 'settling_critical': crit_time, - 'settling_overdamped': over_time, - 'critical_best': crit_time <= over_time if (crit_time and over_time) else None - } - - def test_energy_decay(self) -> Tuple[bool, dict]: - """ - Test 4: Energy should decay monotonically (dE/dt = -γv² ≤ 0). - """ - zeta = 0.3 - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=0.01) - osc.initialize(x0=1.0, v0=0.0) - osc.run(steps=500) - - # Check monotonic decay - monotonic = all(osc.energy_history[i] >= osc.energy_history[i+1] - for i in range(len(osc.energy_history)-1)) - - # Check approximate exponential decay of energy - E0 = osc.energy_history[0] - E_values = np.array(osc.energy_history) - t_values = np.array([h.t for h in osc.history]) - - # Fit: E(t) ≈ E₀·e^(-2ζω₀t) for light damping - log_E = np.log(E_values / E0) - valid = log_E > -10 # Avoid log(0) - if np.sum(valid) > 10: - coeffs = np.polyfit(t_values[valid], log_E[valid], 1) - measured_decay = -coeffs[0] - expected_decay = 2 * zeta * self.omega0 - decay_error = abs(measured_decay - expected_decay) / expected_decay - else: - decay_error = float('inf') - - passed = monotonic and decay_error < 0.3 - - return passed, { - 'monotonic': monotonic, - 'measured_decay': measured_decay if 'measured_decay' in dir() else None, - 'expected_decay': expected_decay if 'expected_decay' in dir() else None, - 'decay_error': decay_error if 'decay_error' in dir() else None - } - - def test_analytic_agreement(self) -> Tuple[bool, dict]: - """ - Test 5: Numerical solution matches analytic for all regimes. - """ - x0, v0 = 1.0, 0.5 - dt = 0.001 - steps = 500 - - max_errors = {} - for zeta, regime_name in [(0.1, 'underdamped'), (1.0, 'critical'), (2.0, 'overdamped')]: - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=dt) - osc.initialize(x0=x0, v0=v0) - osc.run(steps=steps) - - max_error = 0.0 - for state in osc.history: - x_analytic, v_analytic = osc.analytic_solution(state.t, x0, v0) - error = abs(state.x - x_analytic) - max_error = max(max_error, error) - - max_errors[regime_name] = max_error - - # All should have reasonable error - passed = all(err < 0.1 for err in max_errors.values()) - - return passed, { - 'max_error_underdamped': max_errors['underdamped'], - 'max_error_critical': max_errors['critical'], - 'max_error_overdamped': max_errors['overdamped'] - } - - def test_regime_classification(self) -> Tuple[bool, dict]: - """ - Test 6: System correctly identifies its regime. - """ - tests = [ - (0.05, 'underdamped'), - (0.5, 'underdamped'), - (0.99, 'underdamped'), # Close to critical - (1.0, 'critical'), - (1.5, 'overdamped'), - (3.0, 'overdamped'), - ] - - correct = 0 - for zeta, expected in tests: - osc = GWL_DampedOscillator(omega0=self.omega0, mass=self.mass, zeta=zeta, dt=0.01) - if osc.regime == expected: - correct += 1 - - passed = correct == len(tests) - - return passed, { - 'correct_classifications': correct, - 'total_tests': len(tests), - 'accuracy': correct / len(tests) - } - - def run_all(self): - """Run complete validation suite.""" - print("=" * 80) - print("STEP 2 VALIDATION: DAMPED HARMONIC OSCILLATOR") - print("=" * 80) - print(f"Base equation: d²x/dt² + 2ζω₀·dx/dt + ω₀²·x = 0") - print(f"Analytic solutions by regime:") - print(f" Underdamped (ζ<1): x(t) = A·e^(-ζω₀t)·cos(ω₁t + φ)") - print(f" Critical (ζ=1): x(t) = (A+Bt)·e^(-ω₀t)") - print(f" Overdamped (ζ>1): x(t) = A·e^(-λ₁t) + B·e^(-λ₂t)") - print(f"Parameters: ω₀={self.omega0}, m={self.mass}") - print() - - tests = [ - ('Underdamped Envelope', self.test_underdamped_envelope), - ('Frequency Shift', self.test_frequency_shift), - ('Critical Damping', self.test_critical_damping), - ('Energy Decay', self.test_energy_decay), - ('Analytic Agreement', self.test_analytic_agreement), - ('Regime Classification', self.test_regime_classification), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - elif val is None: - print(f" {key}: None") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - import traceback - traceback.print_exc() - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:30s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED - STEP 2 VALIDATED") - print("=" * 80) - print(""" -The damped harmonic oscillator is now validated. -Properties verified: - ✓ Underdamped envelope decay (e^(-ζω₀t)) - ✓ Frequency shift (ω₁ = ω₀√(1-ζ²)) - ✓ Critical damping (fastest return) - ✓ Energy decay monotonic - ✓ Analytic agreement all regimes - ✓ Regime classification correct - -READY FOR STEP 3: Add external forcing (resonance) - """) - else: - print("SOME TESTS FAILED - DO NOT PROCEED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - validator = DampedValidationSuite(omega0=1.0, mass=1.0) - success = validator.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_oscillator_step3_driven.py b/5-Applications/tools-scripts/demo/gwl_oscillator_step3_driven.py deleted file mode 100644 index 2128c89a..00000000 --- a/5-Applications/tools-scripts/demo/gwl_oscillator_step3_driven.py +++ /dev/null @@ -1,480 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_oscillator_step3_driven.py - -STEP 3: Driven Damped Harmonic Oscillator (Resonance) - -Base equation: d²x/dt² + 2ζω₀·dx/dt + ω₀²·x = (F₀/m)·cos(ω_d·t) - -Analytic steady-state: - x(t) = A·cos(ω_d·t - δ) - A = (F₀/m) / √((ω₀²-ω_d²)² + (2ζω₀ω_d)²) - δ = arctan(2ζω₀ω_d / (ω₀² - ω_d²)) - -Key phenomena: Resonance, phase lag, frequency locking, transient/steady-state -New GWL primitives: φ_spectral, τ_latency, transfer function H(ω) -""" - -import numpy as np -from dataclasses import dataclass -from typing import Tuple, List -import math - - -@dataclass -class DrivenOscillatorState: - """Canonical state for driven oscillator.""" - x: float # Position - v: float # Velocity - t: float # Time - F_drive: float # Current driving force - - def to_vector(self) -> Tuple[float, float]: - return (self.x, self.v) - - -class GWL_DrivenOscillator: - """ - Driven damped harmonic oscillator. - - Adds external forcing to Step 2's validated damped oscillator. - Demonstrates resonance, phase relationships, frequency response. - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, - zeta: float = 0.1, F0: float = 1.0, omega_d: float = 1.0, - dt: float = 0.01): - """ - Args: - omega0: Natural frequency - mass: Mass - zeta: Damping ratio - F0: Driving force amplitude - omega_d: Driving frequency (can differ from omega0!) - dt: Time step - """ - self.omega0 = omega0 - self.mass = mass - self.zeta = zeta - self.F0 = F0 - self.omega_d = omega_d - self.dt = dt - - # Derived - self.k = mass * omega0**2 - self.gamma = 2 * zeta * mass * omega0 - - # State - self.state = DrivenOscillatorState(x=0.0, v=0.0, t=0.0, F_drive=F0) - self.history: List[DrivenOscillatorState] = [] - - def initialize(self, x0: float = 0.0, v0: float = 0.0): - """Set initial conditions (default rest).""" - self.state = DrivenOscillatorState(x=x0, v=v0, t=0.0, F_drive=self.F0) - self.history = [] - - def driving_force(self, t: float) -> float: - """External driving force: F(t) = F₀·cos(ω_d·t)""" - return self.F0 * math.cos(self.omega_d * t) - - def steady_state_amplitude(self) -> float: - """ - Analytic steady-state amplitude: - A = (F₀/m) / √((ω₀²-ω_d²)² + (2ζω₀ω_d)²) - """ - numerator = self.F0 / self.mass - denominator = math.sqrt((self.omega0**2 - self.omega_d**2)**2 - + (2 * self.zeta * self.omega0 * self.omega_d)**2) - return numerator / denominator - - def steady_state_phase(self) -> float: - """ - Analytic phase lag: - δ = arctan(2ζω₀ω_d / (ω₀² - ω_d²)) - """ - return math.atan2(2 * self.zeta * self.omega0 * self.omega_d, - self.omega0**2 - self.omega_d**2) - - def step(self): - """Symplectic update with driving force.""" - x_n = self.state.x - v_n = self.state.v - t_n = self.state.t - - # Driving force at this time - F = self.driving_force(t_n) - - # Update velocity (conservative + damping + driving) - v_temp = v_n - self.omega0**2 * x_n * self.dt - v_temp *= math.exp(-self.gamma * self.dt / self.mass) # Damping - v_new = v_temp + (F / self.mass) * self.dt # Driving - - # Update position - x_new = x_n + v_new * self.dt - - # Update time - t_new = t_n + self.dt - - self.state = DrivenOscillatorState(x=x_new, v=v_new, t=t_new, F_drive=F) - self.history.append(self.state) - - def run(self, steps: int): - """Run simulation.""" - for _ in range(steps): - self.step() - - def extract_steady_state(self, last_n: int = 500) -> Tuple[float, float]: - """ - Extract amplitude and phase from last n points of simulation. - Fits: x(t) ≈ A·cos(ω_d·t - δ) - """ - if len(self.history) < last_n: - last_n = len(self.history) - - recent = self.history[-last_n:] - t_vals = np.array([h.t for h in recent]) - x_vals = np.array([h.x for h in recent]) - - # Fit to A·cos(ω_d·t) + B·sin(ω_d·t) - # x = A·cos(ωt) + B·sin(ωt) = C·cos(ωt - δ) - # where C = √(A²+B²), δ = atan2(B, A) - cos_wt = np.cos(self.omega_d * t_vals) - sin_wt = np.sin(self.omega_d * t_vals) - - # Least squares: solve for A, B in x = A·cos(ωt) + B·sin(ωt) - # Normal equations - N = len(t_vals) - sum_cos2 = np.sum(cos_wt**2) - sum_sin2 = np.sum(sin_wt**2) - sum_cossin = np.sum(cos_wt * sin_wt) - sum_xcos = np.sum(x_vals * cos_wt) - sum_xsin = np.sum(x_vals * sin_wt) - - # Matrix form: [sum_cos2, sum_cossin; sum_cossin, sum_sin2] * [A; B] = [sum_xcos; sum_xsin] - det = sum_cos2 * sum_sin2 - sum_cossin**2 - if abs(det) > 1e-10: - A_coeff = (sum_xcos * sum_sin2 - sum_xsin * sum_cossin) / det - B_coeff = (sum_cos2 * sum_xsin - sum_cossin * sum_xcos) / det - else: - A_coeff = sum_xcos / sum_cos2 if sum_cos2 > 0 else 0 - B_coeff = 0 - - amplitude = math.sqrt(A_coeff**2 + B_coeff**2) - phase = math.atan2(B_coeff, A_coeff) - - return amplitude, phase - - -class DrivenValidationSuite: - """Validation for Step 3: Driven oscillator.""" - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, zeta: float = 0.1): - self.omega0 = omega0 - self.mass = mass - self.zeta = zeta - self.results = {} - - def test_resonance_peak(self) -> Tuple[bool, dict]: - """ - Test 1: Amplitude peaks when ω_d ≈ ω₀ (for light damping). - """ - F0 = 1.0 - - # Sweep driving frequency - omega_ratios = np.linspace(0.5, 1.5, 21) - amplitudes = [] - - for ratio in omega_ratios: - omega_d = ratio * self.omega0 - osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=F0, omega_d=omega_d, dt=0.01 - ) - osc.initialize(0.0, 0.0) - - # Run until steady-state (several decay times) - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - osc.run(steps=steps) - - amp, _ = osc.extract_steady_state() - amplitudes.append(amp) - - # Find peak - peak_idx = np.argmax(amplitudes) - peak_ratio = omega_ratios[peak_idx] - peak_amplitude = amplitudes[peak_idx] - - # Should peak near ω_d = ω₀ - passed = abs(peak_ratio - 1.0) < 0.1 - - return passed, { - 'peak_ratio': peak_ratio, - 'peak_amplitude': peak_amplitude, - 'expected_peak': 1.0, - 'amplitudes': amplitudes - } - - def test_amplitude_formula(self) -> Tuple[bool, dict]: - """ - Test 2: Steady-state amplitude matches analytic formula. - """ - # Test at a few frequencies - test_omegas = [0.8, 1.0, 1.2] - errors = [] - - for omega_d in test_omegas: - osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=1.0, omega_d=omega_d, dt=0.01 - ) - osc.initialize(0.0, 0.0) - - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - osc.run(steps=steps) - - measured, _ = osc.extract_steady_state() - expected = osc.steady_state_amplitude() - - error = abs(measured - expected) / expected - errors.append(error) - - # Relax threshold - amplitude extraction can have phase uncertainty - # Relax threshold - extraction has inherent uncertainty from phase/orthogonality - passed = all(e < 0.5 for e in errors) - - return passed, { - 'max_error': max(errors), - 'errors': errors, - 'threshold': 0.5 - } - - def test_phase_lag(self) -> Tuple[bool, dict]: - """ - Test 3: Phase lag matches analytic formula. - - Below resonance: δ → 0 (in phase) - At resonance: δ = π/2 (90° lag) - Above resonance: δ → π (180° out of phase) - """ - # Test phase at three key frequencies - test_cases = [ - (0.5, 0.0, 0.5), # Below: δ ≈ 0 - (1.0, math.pi/2 - 0.3, math.pi/2 + 0.3), # At: δ ≈ π/2 - (1.5, 2.0, math.pi), # Above: δ → π - ] - - results = [] - for omega_d, expected_min, expected_max in test_cases: - osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=1.0, omega_d=omega_d, dt=0.01 - ) - osc.initialize(0.0, 0.0) - - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - osc.run(steps=steps) - - _, measured_phase = osc.extract_steady_state() - expected_phase = osc.steady_state_phase() - - # Normalize to [-π, π] for comparison - while measured_phase > math.pi: - measured_phase -= 2 * math.pi - while measured_phase < -math.pi: - measured_phase += 2 * math.pi - - # Also normalize expected to same range - while expected_phase > math.pi: - expected_phase -= 2 * math.pi - while expected_phase < -math.pi: - expected_phase += 2 * math.pi - - # Check if close to expected (allowing wrap-around) - phase_diff = abs(measured_phase - expected_phase) - phase_diff = min(phase_diff, 2*math.pi - phase_diff) - in_range = phase_diff < 0.5 # Within ~30 degrees - results.append({ - 'omega_ratio': omega_d / self.omega0, - 'measured': measured_phase, - 'expected': expected_phase, - 'in_range': in_range - }) - - passed = all(r['in_range'] for r in results) - - return passed, { - 'results': results - } - - def test_frequency_locking(self) -> Tuple[bool, dict]: - """ - Test 4: Steady-state oscillates at driving frequency ω_d, not natural ω₀. - - Use correlation with driving signal to detect phase lock. - """ - omega_d = 0.7 * self.omega0 # Detune significantly - - osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=1.0, omega_d=omega_d, dt=0.01 - ) - osc.initialize(0.0, 0.0) - - # Run to steady-state (longer for low frequency) - periods_needed = 15 # Need many periods for low freq - duration = periods_needed * 2 * math.pi / omega_d - steps = int(duration / 0.01) - osc.run(steps=steps) - - # Method: correlate x(t) with cos(ω_d·t) and cos(ω₀·t) - # Locked to driving means high correlation with ω_d, not ω₀ - recent = osc.history[-1000:] # Last part is steady-state - t_vals = np.array([h.t for h in recent]) - x_vals = np.array([h.x for h in recent]) - - # Correlations - corr_drive = np.abs(np.sum(x_vals * np.cos(omega_d * t_vals))) - corr_natural = np.abs(np.sum(x_vals * np.cos(self.omega0 * t_vals))) - - # Should correlate much better with driving frequency - locked_to_drive = corr_drive > 2 * corr_natural - - # Alternative: check that amplitude extraction works (uses ω_d) - amp, phase = osc.extract_steady_state(last_n=800) - amplitude_reasonable = amp > 0.1 # Should have non-zero amplitude - - passed = locked_to_drive and amplitude_reasonable - - return passed, { - 'corr_drive': corr_drive, - 'corr_natural': corr_natural, - 'locked_to_drive': locked_to_drive, - 'amplitude': amp, - 'driving_freq': omega_d, - 'natural_freq': self.omega0 - } - - def test_transient_decay(self) -> Tuple[bool, dict]: - """ - Test 5: Transient dies out at damping rate. - """ - osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=1.0, omega_d=self.omega0, dt=0.01 # On resonance - ) - # Start with initial displacement (creates transient) - osc.initialize(x0=2.0, v0=0.0) - - # Run - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - osc.run(steps=steps) - - # Envelope of deviation from steady-state should decay - A_ss = osc.steady_state_amplitude() - deviations = [abs(h.x - A_ss * math.cos(osc.omega_d * h.t - osc.steady_state_phase())) - for h in osc.history] - - # Check decay - early_dev = np.mean(deviations[:100]) - late_dev = np.mean(deviations[-100:]) - decayed = late_dev < early_dev / 10 # At least 10× reduction - - return decayed, { - 'early_deviation': early_dev, - 'late_deviation': late_dev, - 'decay_ratio': early_dev / late_dev if late_dev > 0 else float('inf') - } - - def run_all(self): - """Run complete validation suite.""" - print("=" * 80) - print("STEP 3 VALIDATION: DRIVEN DAMPED HARMONIC OSCILLATOR") - print("=" * 80) - print(f"Base equation: d²x/dt² + 2ζω₀·dx/dt + ω₀²·x = (F₀/m)·cos(ω_d·t)") - print(f"Analytic steady-state: x(t) = A·cos(ω_d·t - δ)") - print(f" A = (F₀/m) / √((ω₀²-ω_d²)² + (2ζω₀ω_d)²)") - print(f" δ = arctan(2ζω₀ω_d / (ω₀² - ω_d²))") - print(f"Parameters: ω₀={self.omega0}, ζ={self.zeta}") - print() - - tests = [ - ('Resonance Peak', self.test_resonance_peak), - ('Amplitude Formula', self.test_amplitude_formula), - ('Phase Lag', self.test_phase_lag), - ('Frequency Locking', self.test_frequency_locking), - ('Transient Decay', self.test_transient_decay), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - elif isinstance(val, list) and len(val) > 0 and isinstance(val[0], float): - print(f" {key}: [{', '.join(f'{v:.3f}' for v in val[:5])}...]") - elif key == 'results': - for r in val: - print(f" ω/ω₀={r['omega_ratio']:.1f}: δ_measured={r['measured']:.3f}, expected={r['expected']:.3f}") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - import traceback - traceback.print_exc() - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:30s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED - STEP 3 VALIDATED") - print("=" * 80) - print(""" -The driven damped harmonic oscillator is now validated. -Properties verified: - ✓ Resonance peak at ω_d ≈ ω₀ - ✓ Amplitude matches analytic formula - ✓ Phase lag δ(ω_d) correct - - Below resonance: δ → 0 - - At resonance: δ = π/2 - - Above resonance: δ → π - ✓ Frequency locking (system → ω_d) - ✓ Transient decay at damping rate - -DETERMINISTIC BACKBONE COMPLETE - Step 1: Conservative (energy conserved) - Step 2: Damped (attractor dynamics) - Step 3: Driven (resonance, phase) - -READY FOR STEP 4: Add stochastic driving (noise) - """) - else: - print("SOME TESTS FAILED - DO NOT PROCEED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - validator = DrivenValidationSuite(omega0=1.0, mass=1.0, zeta=0.1) - success = validator.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_oscillator_step4_stochastic.py b/5-Applications/tools-scripts/demo/gwl_oscillator_step4_stochastic.py deleted file mode 100644 index 0541cb77..00000000 --- a/5-Applications/tools-scripts/demo/gwl_oscillator_step4_stochastic.py +++ /dev/null @@ -1,501 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_oscillator_step4_stochastic.py - -STEP 4: Stochastically Driven Harmonic Oscillator (Langevin Equation) - -Base equation: d²x/dt² + 2ζω₀·dx/dt + ω₀²·x = (F₀/m)·cos(ω_d·t) + ξ(t)/m - -where ξ(t) is white noise with ⟨ξ(t)ξ(t')⟩ = 2D·δ(t-t') - -Key physical constraints: -- Fluctuation-dissipation: D = γk_B T (Einstein relation) -- Mean trajectory: ⟨x(t)⟩ follows deterministic solution -- Equilibrium variance: σ² = k_B T / k -- Correlation: ⟨x(t)x(0)⟩ = (k_B T/k)·e^(-γ|t|/2m)·cos(ω₁t) - -This tests: noise resilience, ensemble statistics, energy equipartition -""" - -import numpy as np -from dataclasses import dataclass -from typing import Tuple, List, Optional -import math - - -@dataclass -class StochasticOscillatorState: - """State with noise realization.""" - x: float - v: float - t: float - F_drive: float - xi: float # Noise sample - - def to_vector(self) -> Tuple[float, float]: - return (self.x, self.v) - - -class GWL_StochasticOscillator: - """ - Langevin oscillator: deterministic backbone + stochastic perturbation. - - Uses validated Step 3 as backbone, adds bounded noise. - """ - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, - zeta: float = 0.1, F0: float = 0.0, omega_d: float = 1.0, - temperature: float = 1.0, dt: float = 0.01, - seed: Optional[int] = None): - """ - Args: - omega0: Natural frequency - mass: Mass - zeta: Damping ratio - F0: Driving amplitude (0 for pure thermal) - omega_d: Driving frequency - temperature: k_B T (thermal energy scale) - dt: Time step - seed: RNG seed for reproducibility - """ - self.omega0 = omega0 - self.mass = mass - self.zeta = zeta - self.F0 = F0 - self.omega_d = omega_d - self.temperature = temperature - self.dt = dt - - # Derived - self.k = mass * omega0**2 - self.gamma = 2 * zeta * mass * omega0 - - # Fluctuation-dissipation: D = γk_B T - self.diffusion = self.gamma * temperature - self.noise_amp = math.sqrt(2 * self.diffusion / dt) # For discrete update - - # RNG - self.rng = np.random.RandomState(seed) - - # State - self.state = StochasticOscillatorState(x=0.0, v=0.0, t=0.0, F_drive=0.0, xi=0.0) - self.history: List[StochasticOscillatorState] = [] - - def initialize(self, x0: float = 0.0, v0: float = 0.0): - """Set initial conditions.""" - self.state = StochasticOscillatorState(x=x0, v=v0, t=0.0, F_drive=self.F0, xi=0.0) - self.history = [] - - def driving_force(self, t: float) -> float: - """Deterministic driving.""" - return self.F0 * math.cos(self.omega_d * t) - - def step(self): - """ - Stochastic update: deterministic backbone + Wiener increment. - - v_new = v_det + ξ·√Δt/m - where ξ has variance 2D = 2γk_B T - """ - x_n = self.state.x - v_n = self.state.v - t_n = self.state.t - - # Deterministic part (from validated Step 3) - F_det = self.driving_force(t_n) - - v_temp = v_n - self.omega0**2 * x_n * self.dt - v_temp *= math.exp(-self.gamma * self.dt / self.mass) - v_det = v_temp + (F_det / self.mass) * self.dt - - # Stochastic perturbation (Wiener increment) - # ⟨ξ²⟩ = 2D·Δt, so ξ = √(2D·Δt)·N(0,1) - xi = math.sqrt(2 * self.diffusion * self.dt) * self.rng.randn() - - v_new = v_det + xi / self.mass - x_new = x_n + v_new * self.dt - t_new = t_n + self.dt - - self.state = StochasticOscillatorState( - x=x_new, v=v_new, t=t_new, F_drive=F_det, xi=xi - ) - self.history.append(self.state) - - def run(self, steps: int): - """Run simulation.""" - for _ in range(steps): - self.step() - - def energy(self) -> float: - """Total mechanical energy.""" - return 0.5 * self.mass * self.state.v**2 + 0.5 * self.k * self.state.x**2 - - def equilibrium_variance(self) -> float: - """Theoretical equilibrium variance: σ² = k_B T / k""" - return self.temperature / self.k - - -class EnsembleSimulator: - """Run multiple realizations for ensemble statistics.""" - - def __init__(self, num_realizations: int = 1000, **oscillator_kwargs): - self.num_realizations = num_realizations - self.oscillator_kwargs = oscillator_kwargs - self.ensembles: List[GWL_StochasticOscillator] = [] - - def run_ensemble(self, steps: int, x0: float = 0.0, v0: float = 0.0): - """Run N realizations, collect statistics.""" - self.ensembles = [] - - for i in range(self.num_realizations): - osc = GWL_StochasticOscillator(**self.oscillator_kwargs, seed=i) - osc.initialize(x0, v0) - osc.run(steps) - self.ensembles.append(osc) - - return self.compute_statistics() - - def compute_statistics(self) -> dict: - """Compute ensemble statistics at each time step.""" - if not self.ensembles: - return {} - - num_steps = len(self.ensembles[0].history) - - # Extract trajectories - x_trajs = np.array([[h.x for h in osc.history] for osc in self.ensembles]) - v_trajs = np.array([[h.v for h in osc.history] for osc in self.ensembles]) - t_vals = [h.t for h in self.ensembles[0].history] - - # Statistics - mean_x = np.mean(x_trajs, axis=0) - mean_v = np.mean(v_trajs, axis=0) - var_x = np.var(x_trajs, axis=0) - var_v = np.var(v_trajs, axis=0) - - return { - 't': t_vals, - 'mean_x': mean_x, - 'mean_v': mean_v, - 'var_x': var_x, - 'var_v': var_v, - 'x_trajs': x_trajs, - 'v_trajs': v_trajs - } - - -class StochasticValidationSuite: - """Validation for Step 4: Langevin dynamics.""" - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, zeta: float = 0.2): - self.omega0 = omega0 - self.mass = mass - self.zeta = zeta - self.results = {} - - def test_fluctuation_dissipation(self) -> Tuple[bool, dict]: - """ - Test 1: Fluctuation-dissipation theorem. - - D = γk_B T should give correct equilibrium variance. - """ - temperature = 1.0 - k = self.mass * self.omega0**2 - - # Equilibrium variance: σ² = k_B T / k - expected_var = temperature / k - - # Run ensemble to equilibrium - ensemble = EnsembleSimulator( - num_realizations=500, - omega0=self.omega0, - mass=self.mass, - zeta=self.zeta, - F0=0.0, # No driving, pure thermal - omega_d=self.omega0, - temperature=temperature, - dt=0.01 - ) - - # Run for several decay times - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - stats = ensemble.run_ensemble(steps, x0=1.0, v0=0.0) - - # Measure late-time variance - late_var = np.mean(stats['var_x'][-100:]) - - error = abs(late_var - expected_var) / expected_var - passed = error < 0.15 - - return passed, { - 'expected_var': expected_var, - 'measured_var': late_var, - 'error': error, - 'temperature': temperature, - 'k': k - } - - def test_mean_trajectory(self) -> Tuple[bool, dict]: - """ - Test 2: Mean trajectory follows deterministic solution. - - ⟨x(t)⟩ should match Step 3 deterministic oscillator. - """ - F0 = 1.0 - omega_d = 0.8 * self.omega0 - - # Run ensemble with driving - ensemble = EnsembleSimulator( - num_realizations=300, - omega0=self.omega0, - mass=self.mass, - zeta=self.zeta, - F0=F0, - omega_d=omega_d, - temperature=0.5, # Small noise - dt=0.01 - ) - - tau = 1.0 / (self.zeta * self.omega0) - steps = int(8 * tau / 0.01) - stats = ensemble.run_ensemble(steps, x0=0.0, v0=0.0) - - # Compare late-time mean to deterministic steady-state - late_mean = np.mean(stats['mean_x'][-100:]) - - # Expected steady-state amplitude - # A = (F₀/m) / √((ω₀²-ω_d²)² + (2ζω₀ω_d)²) - numerator = F0 / self.mass - denominator = math.sqrt( - (self.omega0**2 - omega_d**2)**2 + - (2 * self.zeta * self.omega0 * omega_d)**2 - ) - expected_amp = numerator / denominator - - error = abs(late_mean - expected_amp) / expected_amp if expected_amp > 0 else abs(late_mean) - passed = error < 0.2 # Ensemble mean has variance - - return passed, { - 'mean_trajectory': late_mean, - 'expected_amplitude': expected_amp, - 'error': error, - 'temperature': 0.5 - } - - def test_variance_evolution(self) -> Tuple[bool, dict]: - """ - Test 3: Variance grows and saturates to equilibrium value. - - σ²(t) → k_B T / k as t → ∞ - """ - temperature = 1.0 - k = self.mass * self.omega0**2 - expected_var = temperature / k - - ensemble = EnsembleSimulator( - num_realizations=400, - omega0=self.omega0, - mass=self.mass, - zeta=self.zeta, - F0=0.0, - omega_d=self.omega0, - temperature=temperature, - dt=0.01 - ) - - tau = 1.0 / (self.zeta * self.omega0) - steps = int(8 * tau / 0.01) - stats = ensemble.run_ensemble(steps, x0=0.0, v0=0.0) - - var_x = stats['var_x'] - - # Check growth from near-zero to equilibrium - early_var = np.mean(var_x[:50]) - late_var = np.mean(var_x[-100:]) - - growth = late_var / (early_var + 1e-10) - saturation = abs(late_var - expected_var) / expected_var - - # Variance should grow and saturate - passed = growth > 5 and saturation < 0.2 - - return passed, { - 'early_var': early_var, - 'late_var': late_var, - 'growth_factor': growth, - 'saturation_error': saturation, - 'expected_var': expected_var - } - - def test_energy_equipartition(self) -> Tuple[bool, dict]: - """ - Test 4: Energy equipartition at equilibrium. - - ⟨E_kinetic⟩ = ⟨E_potential⟩ = ½k_B T - """ - temperature = 1.0 - - ensemble = EnsembleSimulator( - num_realizations=400, - omega0=self.omega0, - mass=self.mass, - zeta=self.zeta, - F0=0.0, - omega_d=self.omega0, - temperature=temperature, - dt=0.01 - ) - - tau = 1.0 / (self.zeta * self.omega0) - steps = int(10 * tau / 0.01) - stats = ensemble.run_ensemble(steps, x0=1.0, v0=1.0) - - # Extract late-time energies - k = self.mass * self.omega0**2 - late_x = stats['x_trajs'][:, -100:] - late_v = stats['v_trajs'][:, -100:] - - E_pot = 0.5 * k * late_x**2 - E_kin = 0.5 * self.mass * late_v**2 - - mean_E_pot = np.mean(E_pot) - mean_E_kin = np.mean(E_kin) - - expected = 0.5 * temperature - error_pot = abs(mean_E_pot - expected) / expected - error_kin = abs(mean_E_kin - expected) / expected - - # Both should be ≈ ½k_B T - passed = error_pot < 0.2 and error_kin < 0.2 - - return passed, { - 'mean_E_potential': mean_E_pot, - 'mean_E_kinetic': mean_E_kin, - 'expected': expected, - 'error_pot': error_pot, - 'error_kin': error_kin - } - - def test_deterministic_backbone_preserved(self) -> Tuple[bool, dict]: - """ - Test 5: As T → 0, recover deterministic solution exactly. - """ - from gwl_oscillator_step3_driven import GWL_DrivenOscillator - - F0 = 1.0 - omega_d = self.omega0 - x0, v0 = 0.0, 0.0 - steps = 500 - - # Deterministic (Step 3) - det_osc = GWL_DrivenOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=F0, omega_d=omega_d, dt=0.01 - ) - det_osc.initialize(x0, v0) - det_osc.run(steps) - x_det = [h.x for h in det_osc.history] - - # Stochastic with T ≈ 0 - stoch_osc = GWL_StochasticOscillator( - omega0=self.omega0, mass=self.mass, zeta=self.zeta, - F0=F0, omega_d=omega_d, temperature=1e-6, dt=0.01, seed=42 - ) - stoch_osc.initialize(x0, v0) - stoch_osc.run(steps) - x_stoch = [h.x for h in stoch_osc.history] - - # Should match closely - max_diff = max(abs(a - b) for a, b in zip(x_det, x_stoch)) - - passed = max_diff < 0.01 - - return passed, { - 'max_diff': max_diff, - 'deterministic_final': x_det[-1], - 'stochastic_final': x_stoch[-1] - } - - def run_all(self): - """Run complete validation suite.""" - print("=" * 80) - print("STEP 4 VALIDATION: STOCHASTIC LANGEVIN DYNAMICS") - print("=" * 80) - print(f"Base equation: m·d²x/dt² + γ·dx/dt + k·x = F(t) + ξ(t)") - print(f"Noise: ⟨ξ(t)ξ(t')⟩ = 2D·δ(t-t'), D = γk_B T") - print(f"Validation: Einstein fluctuation-dissipation, equipartition") - print(f"Parameters: ω₀={self.omega0}, ζ={self.zeta}") - print() - - tests = [ - ('Fluctuation-Dissipation', self.test_fluctuation_dissipation), - ('Mean Trajectory', self.test_mean_trajectory), - ('Variance Evolution', self.test_variance_evolution), - ('Energy Equipartition', self.test_energy_equipartition), - ('Deterministic Backbone', self.test_deterministic_backbone_preserved), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - elif isinstance(val, np.ndarray): - print(f" {key}: array[{len(val)}]") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - import traceback - traceback.print_exc() - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:35s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED - STEP 4 VALIDATED") - print("=" * 80) - print(""" -The Langevin oscillator is now validated. -Physical constraints verified: - ✓ Fluctuation-dissipation: D = γk_B T → correct σ² - ✓ Mean trajectory: follows deterministic backbone - ✓ Variance evolution: grows and saturates - ✓ Energy equipartition: ⟨E_kin⟩ = ⟨E_pot⟩ = ½k_B T - ✓ T → 0 limit: recovers deterministic exactly - -STOCHASTIC EXTENSION VALIDATED - Structure: Deterministic backbone + bounded perturbation - Safety: Cannot destabilize proven backbone - Physics: Satisfies Einstein relation, equipartition - -READY FOR STEP 5: Multi-projection consensus - """) - else: - print("SOME TESTS FAILED - DO NOT PROCEED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - validator = StochasticValidationSuite(omega0=1.0, mass=1.0, zeta=0.2) - success = validator.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_oscillator_step5_consensus.py b/5-Applications/tools-scripts/demo/gwl_oscillator_step5_consensus.py deleted file mode 100644 index 9fbb9b77..00000000 --- a/5-Applications/tools-scripts/demo/gwl_oscillator_step5_consensus.py +++ /dev/null @@ -1,653 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_oscillator_step5_consensus.py - -STEP 5: Multi-Projection Consensus for Oscillator State Estimation - -Multiple sensors observe the same oscillator: - - Position sensor: y₁ = x + η₁ - - Velocity sensor: y₂ = v + η₂ - - Accelerometer: y₃ = a + η₃ = (-ω₀²x - γv + F)/m + η₃ - - Energy sensor: y₄ = E + η₄ = ½mv² + ½kx² + η₄ - -Each projection produces a canonical state estimate. -Consensus engine fuses coherent estimates, quarantines outliers. - -This tests: sensor fusion, outlier rejection, Byzantine resilience, graceful degradation -""" - -import numpy as np -from dataclasses import dataclass, field -from typing import List, Tuple, Dict, Optional -from enum import Enum -import math - - -class ProjectionType(Enum): - POSITION = "position" - VELOCITY = "velocity" - ACCELERATION = "acceleration" - ENERGY = "energy" - - -@dataclass -class ProjectionResult: - """Result from a single sensor projection.""" - sensor_id: str - proj_type: ProjectionType - x_est: float # Estimated position - v_est: float # Estimated velocity - uncertainty: float # Estimated uncertainty (σ) - valid: bool = True - residual: float = 0.0 # Distance from consensus - - -@dataclass -class ConsensusState: - """Fused consensus state.""" - x: float - v: float - confidence: float # 0-1, based on agreement - agreement_score: float - participating: List[str] - quarantined: List[str] - residuals: Dict[str, float] - - -class SensorProjection: - """Base class for sensor projections.""" - - def __init__(self, sensor_id: str, proj_type: ProjectionType, - noise_std: float = 0.1, omega0: float = 1.0, - mass: float = 1.0, gamma: float = 0.2): - self.sensor_id = sensor_id - self.proj_type = proj_type - self.noise_std = noise_std - self.omega0 = omega0 - self.mass = mass - self.gamma = gamma - self.k = mass * omega0**2 - - def observe(self, true_x: float, true_v: float, F: float = 0.0) -> Tuple[float, float]: - """Generate noisy observation.""" - raise NotImplementedError - - def project(self, obs: float, prev_state: Optional[Tuple[float, float]] = None) -> ProjectionResult: - """Project observation to canonical (x, v) state.""" - raise NotImplementedError - - -class PositionSensor(SensorProjection): - """Direct position measurement: y = x + η""" - - def __init__(self, sensor_id: str, noise_std: float = 0.1, **kwargs): - super().__init__(sensor_id, ProjectionType.POSITION, noise_std, **kwargs) - - def observe(self, true_x: float, true_v: float, F: float = 0.0) -> Tuple[float, float]: - y = true_x + np.random.randn() * self.noise_std - return y, self.noise_std - - def project(self, obs: float, prev_state: Optional[Tuple[float, float]] = None) -> ProjectionResult: - """Position directly gives x. v estimated from history if available.""" - x_est = obs - - if prev_state is not None: - # Rough velocity estimate from previous state - v_est = prev_state[1] # Carry forward - else: - v_est = 0.0 - - return ProjectionResult( - sensor_id=self.sensor_id, - proj_type=self.proj_type, - x_est=x_est, - v_est=v_est, - uncertainty=self.noise_std - ) - - -class VelocitySensor(SensorProjection): - """Direct velocity measurement: y = v + η""" - - def __init__(self, sensor_id: str, noise_std: float = 0.1, **kwargs): - super().__init__(sensor_id, ProjectionType.VELOCITY, noise_std, **kwargs) - - def observe(self, true_x: float, true_v: float, F: float = 0.0) -> Tuple[float, float]: - y = true_v + np.random.randn() * self.noise_std - return y, self.noise_std - - def project(self, obs: float, prev_state: Optional[Tuple[float, float]] = None) -> ProjectionResult: - """Velocity directly gives v. x estimated from history if available.""" - v_est = obs - - if prev_state is not None: - x_est = prev_state[0] # Carry forward - else: - x_est = 0.0 - - return ProjectionResult( - sensor_id=self.sensor_id, - proj_type=self.proj_type, - x_est=x_est, - v_est=v_est, - uncertainty=self.noise_std * 2 # Higher uncertainty in x - ) - - -class AccelerometerSensor(SensorProjection): - """Acceleration measurement: y = a + η = (-ω₀²x - γv + F)/m + η""" - - def __init__(self, sensor_id: str, noise_std: float = 0.2, **kwargs): - super().__init__(sensor_id, ProjectionType.ACCELERATION, noise_std, **kwargs) - - def observe(self, true_x: float, true_v: float, F: float = 0.0) -> Tuple[float, float]: - true_a = (-self.k * true_x - self.gamma * true_v + F) / self.mass - y = true_a + np.random.randn() * self.noise_std - return y, self.noise_std - - def project(self, obs: float, prev_state: Optional[Tuple[float, float]] = None) -> ProjectionResult: - """ - Infer (x, v) from acceleration. - Requires prior state or double integration. - """ - if prev_state is None: - # Without history, can't determine x, v uniquely from a - return ProjectionResult( - sensor_id=self.sensor_id, - proj_type=self.proj_type, - x_est=0.0, - v_est=0.0, - uncertainty=float('inf'), - valid=False - ) - - # Use dynamics model: a = (-kx - γv + F)/m - # With prev_state, we can adjust estimate - x_prev, v_prev = prev_state - - # Estimate F from observation (simplified, assumes steady-ish) - F_est = self.mass * obs + self.k * x_prev + self.gamma * v_prev - - # Update using dynamics (simple Euler for projection) - dt = 0.01 # Assumed - v_est = v_prev + obs * dt - x_est = x_prev + v_est * dt - - return ProjectionResult( - sensor_id=self.sensor_id, - proj_type=self.proj_type, - x_est=x_est, - v_est=v_est, - uncertainty=self.noise_std * 5 # High uncertainty from integration - ) - - -class EnergySensor(SensorProjection): - """Energy measurement: y = E + η = ½mv² + ½kx² + η""" - - def __init__(self, sensor_id: str, noise_std: float = 0.15, **kwargs): - super().__init__(sensor_id, ProjectionType.ENERGY, noise_std, **kwargs) - - def observe(self, true_x: float, true_v: float, F: float = 0.0) -> Tuple[float, float]: - true_E = 0.5 * self.mass * true_v**2 + 0.5 * self.k * true_x**2 - y = true_E + np.random.randn() * self.noise_std - return max(y, 0), self.noise_std # Energy non-negative - - def project(self, obs: float, prev_state: Optional[Tuple[float, float]] = None) -> ProjectionResult: - """ - Infer (x, v) from energy constraint: E = ½mv² + ½kx² - One equation, two unknowns → infinite solutions. - Need prior or additional constraint. - """ - if prev_state is None: - # Assume equipartition: ½mv² ≈ ½kx² ≈ E/2 - E_eff = max(obs, 0.01) - x_est = math.sqrt(E_eff / self.k) - v_est = math.sqrt(E_eff / self.mass) - else: - # Maintain phase relationship from previous state - x_prev, v_prev = prev_state - E_prev = 0.5 * self.mass * v_prev**2 + 0.5 * self.k * x_prev**2 - if E_prev > 0.01: - scale = math.sqrt(max(obs, 0) / E_prev) - x_est = x_prev * scale - v_est = v_prev * scale - else: - x_est, v_est = 0.0, 0.0 - - return ProjectionResult( - sensor_id=self.sensor_id, - proj_type=self.proj_type, - x_est=x_est, - v_est=v_est, - uncertainty=self.noise_std * 3 # Moderate uncertainty - ) - - -class ConsensusEngine: - """Fuse multiple projections into consensus state.""" - - def __init__(self, coherence_threshold: float = 0.5, min_participating: int = 2): - self.coherence_threshold = coherence_threshold - self.min_participating = min_participating - self.prev_consensus: Optional[Tuple[float, float]] = None - - def fuse(self, projections: List[ProjectionResult]) -> ConsensusState: - """ - Multi-stage consensus: - 1. Filter invalid projections - 2. Find coherent cluster - 3. Weighted fusion - 4. Compute confidence - """ - # Stage 1: Filter invalid - valid = [p for p in projections if p.valid] - invalid_ids = [p.sensor_id for p in projections if not p.valid] - - if len(valid) < self.min_participating: - # Not enough sensors - return ConsensusState( - x=0.0, v=0.0, confidence=0.0, agreement_score=0.0, - participating=[], quarantined=[p.sensor_id for p in projections], - residuals={} - ) - - # Stage 2: Find coherent cluster - cluster, outliers = self._find_coherent_cluster(valid) - - if len(cluster) < self.min_participating: - # Coherent cluster too small - return ConsensusState( - x=0.0, v=0.0, confidence=0.0, agreement_score=0.0, - participating=[], - quarantined=[p.sensor_id for p in projections], - residuals={p.sensor_id: 0.0 for p in projections} - ) - - # Stage 3: Weighted fusion - x_fused, v_fused = self._weighted_fusion(cluster) - - # Stage 4: Compute agreement and residuals - agreement, residuals = self._compute_agreement(cluster, x_fused, v_fused) - - # Confidence based on cluster size and tightness - cluster_ratio = len(cluster) / len(valid) - confidence = agreement * cluster_ratio - - # Store for next iteration - self.prev_consensus = (x_fused, v_fused) - - return ConsensusState( - x=x_fused, - v=v_fused, - confidence=confidence, - agreement_score=agreement, - participating=[p.sensor_id for p in cluster], - quarantined=[p.sensor_id for p in outliers] + invalid_ids, - residuals=residuals - ) - - def _find_coherent_cluster(self, projections: List[ProjectionResult]) -> Tuple[List[ProjectionResult], List[ProjectionResult]]: - """ - Find largest cluster where all pairs are within threshold. - Uses greedy algorithm: start with tightest pair, expand. - """ - if len(projections) <= 2: - return projections, [] - - # Compute pairwise distances - n = len(projections) - distances = np.zeros((n, n)) - for i in range(n): - for j in range(i+1, n): - d = math.sqrt((projections[i].x_est - projections[j].x_est)**2 + - (projections[i].v_est - projections[j].v_est)**2) - distances[i, j] = d - distances[j, i] = d - - # Find largest coherent subset - best_cluster = [] - best_outliers = projections.copy() - - # Try each as seed - for seed_idx in range(n): - cluster = [projections[seed_idx]] - outliers = [] - - for i in range(n): - if i == seed_idx: - continue - # Check if coherent with all in cluster - coherent = all(distances[i, projections.index(c)] < self.coherence_threshold - for c in cluster) - if coherent: - cluster.append(projections[i]) - else: - outliers.append(projections[i]) - - if len(cluster) > len(best_cluster): - best_cluster = cluster - best_outliers = outliers - - return best_cluster, best_outliers - - def _weighted_fusion(self, cluster: List[ProjectionResult]) -> Tuple[float, float]: - """Weighted average by inverse uncertainty.""" - weights = [1.0 / (p.uncertainty**2 + 0.01) for p in cluster] - total_weight = sum(weights) - - x_fused = sum(p.x_est * w for p, w in zip(cluster, weights)) / total_weight - v_fused = sum(p.v_est * w for p, w in zip(cluster, weights)) / total_weight - - return x_fused, v_fused - - def _compute_agreement(self, cluster: List[ProjectionResult], - x_fused: float, v_fused: float) -> Tuple[float, Dict[str, float]]: - """Compute agreement score and individual residuals.""" - residuals = {} - total_residual = 0.0 - - for p in cluster: - r = math.sqrt((p.x_est - x_fused)**2 + (p.v_est - v_fused)**2) - residuals[p.sensor_id] = r - total_residual += r - - # Agreement: 1 - normalized residual - avg_residual = total_residual / len(cluster) if cluster else 0 - agreement = max(0.0, 1.0 - avg_residual / self.coherence_threshold) - - return agreement, residuals - - -class ConsensusValidationSuite: - """Validation for Step 5: Multi-projection consensus.""" - - def __init__(self, omega0: float = 1.0, mass: float = 1.0, gamma: float = 0.2): - self.omega0 = omega0 - self.mass = mass - self.gamma = gamma - self.results = {} - - def test_fusion_beat_single(self) -> Tuple[bool, dict]: - """ - Test 1: Consensus beats average single sensor. - - Note: May not beat BEST sensor by chance, but should beat average. - Fusion reduces variance by combining independent estimates. - """ - # Create multiple position sensors (same type, independent noise) - # This is the classic sensor fusion scenario - x_true, v_true = 1.0, 0.5 - - np.random.seed(42) - sensors = [ - PositionSensor("pos1", noise_std=0.3, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - PositionSensor("pos2", noise_std=0.3, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - PositionSensor("pos3", noise_std=0.3, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - ] - - # Run multiple trials - fusion_errors = [] - single_errors = [] - - for trial in range(20): - projections = [] - trial_single_errors = [] - - for sensor in sensors: - obs, _ = sensor.observe(x_true, v_true) - proj = sensor.project(obs, prev_state=(x_true, v_true)) - projections.append(proj) - err = math.sqrt((proj.x_est - x_true)**2 + (proj.v_est - v_true)**2) - trial_single_errors.append(err) - - engine = ConsensusEngine(coherence_threshold=0.8) - consensus = engine.fuse(projections) - - consensus_error = math.sqrt((consensus.x - x_true)**2 + (consensus.v - v_true)**2) - fusion_errors.append(consensus_error) - single_errors.extend(trial_single_errors) - - mean_fusion = np.mean(fusion_errors) - mean_single = np.mean(single_errors) - - # Fusion should beat average single sensor - fusion_better = mean_fusion < mean_single - - return fusion_better, { - 'mean_fusion_error': mean_fusion, - 'mean_single_error': mean_single, - 'improvement_ratio': mean_single / mean_fusion if mean_fusion > 0 else float('inf'), - 'num_trials': 20 - } - - def test_outlier_rejection(self) -> Tuple[bool, dict]: - """ - Test 2: One bad sensor is detected and quarantined. - """ - x_true, v_true = 1.0, 0.0 - - sensors = [ - PositionSensor("pos1", noise_std=0.1, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - PositionSensor("pos2", noise_std=0.1, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - PositionSensor("bad", noise_std=0.1, omega0=self.omega0, mass=self.mass, gamma=self.gamma), - ] - - # Good sensors see true value (approximately) - np.random.seed(42) - projections = [] - - # pos1 and pos2: normal observations - for sensor in sensors[:2]: - obs, _ = sensor.observe(x_true, v_true) - proj = sensor.project(obs, prev_state=(x_true, v_true)) - projections.append(proj) - - # bad: completely wrong (simulating failure) - bad_proj = ProjectionResult( - sensor_id="bad", - proj_type=ProjectionType.POSITION, - x_est=x_true + 5.0, # Way off - v_est=v_true + 2.0, - uncertainty=0.1 - ) - projections.append(bad_proj) - - engine = ConsensusEngine(coherence_threshold=1.0) - consensus = engine.fuse(projections) - - outlier_detected = "bad" in consensus.quarantined - consensus_reasonable = math.sqrt((consensus.x - x_true)**2) < 0.5 - - return outlier_detected and consensus_reasonable, { - 'outlier_detected': outlier_detected, - 'quarantined': consensus.quarantined, - 'participating': consensus.participating, - 'consensus_x': consensus.x, - 'true_x': x_true - } - - def test_byzantine_resilience(self) -> Tuple[bool, dict]: - """ - Test 3: Two bad sensors agreeing should lower confidence, not fool system. - """ - x_true, v_true = 1.0, 0.0 - - # 2 good, 2 bad (agreeing with each other but wrong) - good1 = ProjectionResult("good1", ProjectionType.POSITION, x_est=x_true+0.1, v_est=v_true, uncertainty=0.1) - good2 = ProjectionResult("good2", ProjectionType.VELOCITY, x_est=x_true, v_est=v_true+0.1, uncertainty=0.1) - - # Two bad sensors that agree with each other (wrong value) - bad1 = ProjectionResult("bad1", ProjectionType.POSITION, x_est=x_true+3.0, v_est=v_true, uncertainty=0.1) - bad2 = ProjectionResult("bad2", ProjectionType.ENERGY, x_est=x_true+3.1, v_est=v_true+0.1, uncertainty=0.1) - - projections = [good1, good2, bad1, bad2] - - engine = ConsensusEngine(coherence_threshold=1.5, min_participating=2) - consensus = engine.fuse(projections) - - # System should either: - # A) Pick good cluster (2 sensors), or - # B) Have low confidence if uncertain - good_cluster_selected = set(consensus.participating) <= {"good1", "good2"} - low_confidence = consensus.confidence < 0.5 - - # Either outcome is acceptable - passed = good_cluster_selected or low_confidence - - return passed, { - 'participating': consensus.participating, - 'confidence': consensus.confidence, - 'quarantined': consensus.quarantined, - 'good_cluster_selected': good_cluster_selected, - 'low_confidence': low_confidence - } - - def test_graceful_degradation(self) -> Tuple[bool, dict]: - """ - Test 4: As sensors fail, confidence drops but system doesn't crash. - """ - x_true, v_true = 1.0, 0.5 - - confidences = [] - - for num_sensors in [4, 3, 2, 1]: - # Create projections - projections = [] - for i in range(num_sensors): - noise = 0.1 + i * 0.05 # Increasing noise - p = ProjectionResult( - sensor_id=f"s{i}", - proj_type=ProjectionType.POSITION, - x_est=x_true + np.random.randn() * noise, - v_est=v_true + np.random.randn() * noise, - uncertainty=noise - ) - projections.append(p) - - engine = ConsensusEngine(coherence_threshold=1.0, min_participating=1) - consensus = engine.fuse(projections) - confidences.append(consensus.confidence) - - # Confidence should generally decrease with fewer sensors - # (though randomness makes this probabilistic) - reasonable = all(c >= 0.0 and c <= 1.0 for c in confidences) - - return reasonable, { - 'confidences': confidences, - 'num_sensors': [4, 3, 2, 1] - } - - def test_coherence_threshold(self) -> Tuple[bool, dict]: - """ - Test 5: Tight threshold → more quarantined. Loose threshold → more participating. - """ - x_true, v_true = 1.0, 0.0 - - # Create sensors with moderate spread - projections = [ - ProjectionResult("s1", ProjectionType.POSITION, x_est=x_true+0.1, v_est=v_true, uncertainty=0.1), - ProjectionResult("s2", ProjectionType.VELOCITY, x_est=x_true+0.2, v_est=v_true+0.1, uncertainty=0.1), - ProjectionResult("s3", ProjectionType.ENERGY, x_est=x_true+0.8, v_est=v_true+0.2, uncertainty=0.1), - ] - - # Tight threshold - engine_tight = ConsensusEngine(coherence_threshold=0.3, min_participating=1) - consensus_tight = engine_tight.fuse(projections) - - # Loose threshold - engine_loose = ConsensusEngine(coherence_threshold=1.0, min_participating=1) - consensus_loose = engine_loose.fuse(projections) - - # Loose should include more sensors - passed = len(consensus_loose.participating) >= len(consensus_tight.participating) - - return passed, { - 'tight_participating': consensus_tight.participating, - 'loose_participating': consensus_loose.participating, - 'tight_quarantined': consensus_tight.quarantined, - 'loose_quarantined': consensus_loose.quarantined - } - - def run_all(self): - """Run complete validation suite.""" - print("=" * 80) - print("STEP 5 VALIDATION: MULTI-PROJECTION CONSENSUS") - print("=" * 80) - print(f"Sensors: Position, Velocity, Accelerometer, Energy") - print(f"Consensus: Coherence clustering + weighted fusion") - print(f"Tests: Fusion, outlier rejection, Byzantine resilience") - print() - - tests = [ - ('Fusion Beats Single', self.test_fusion_beat_single), - ('Outlier Rejection', self.test_outlier_rejection), - ('Byzantine Resilience', self.test_byzantine_resilience), - ('Graceful Degradation', self.test_graceful_degradation), - ('Coherence Threshold', self.test_coherence_threshold), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - import traceback - traceback.print_exc() - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:35s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED - STEP 5 VALIDATED") - print("=" * 80) - print(""" -Multi-projection consensus is now validated. -Capabilities verified: - ✓ Fusion beats single sensor (CRLB intuition) - ✓ Outlier rejection (bad sensor quarantined) - ✓ Byzantine resilience (agreeing bad sensors detected) - ✓ Graceful degradation (confidence scales with sensors) - ✓ Tunable coherence threshold - -COMPLETE EQUATION CHAIN VALIDATED - Step 1: Deterministic backbone (symplectic) - Step 2: Dissipation (attractors) - Step 3: External forcing (resonance) - Step 4: Stochastic driving (Langevin) - Step 5: Multi-projection consensus - -All 5 steps validated against analytic oracles. -GWL/TSM architecture now has verified foundation. - """) - else: - print("SOME TESTS FAILED - DO NOT PROCEED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - validator = ConsensusValidationSuite(omega0=1.0, mass=1.0, gamma=0.2) - success = validator.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/gwl_rotational_demo.py b/5-Applications/tools-scripts/demo/gwl_rotational_demo.py deleted file mode 100644 index 48e20b48..00000000 --- a/5-Applications/tools-scripts/demo/gwl_rotational_demo.py +++ /dev/null @@ -1,301 +0,0 @@ -#!/usr/bin/env python3 -""" -gpl_rotational_demo.py - -Demonstrates rotational values and torsion in GPL. - -Shows: π field encoding + chirality coupling = geometric rotation -""" - -import math -from dataclasses import dataclass -from typing import List, Tuple - - -@dataclass -class RotationalMuSeed: - """μ-seed with explicit rotation handling.""" - node_id: int - delta_p: Tuple[float, float, float] # Position delta (x, y, z) - pi: int # Polarity/torsion (0-15) - chi: int # Chirality (0=D, 1=L) - activation: float - - def effective_rotation_angle(self) -> float: - """ - Compute effective rotation angle in radians. - - D-form (χ=0): Counter-clockwise (+) - L-form (χ=1): Clockwise (-) - """ - base_angle = self.pi * (2 * math.pi / 16) # 22.5° increments - return base_angle if self.chi == 0 else -base_angle - - def rotate_vector(self, vec: Tuple[float, float, float]) -> Tuple[float, float, float]: - """Rotate a vector by this μ-seed's torsion (XY plane rotation).""" - θ = self.effective_rotation_angle() - cos_θ = math.cos(θ) - sin_θ = math.sin(θ) - - x, y, z = vec - return ( - x * cos_θ - y * sin_θ, - x * sin_θ + y * cos_θ, - z - ) - - def alignment_with(self, other: 'RotationalMuSeed') -> float: - """ - Compute alignment (coupling strength) with another μ-seed. - - Returns: 1.0 (aligned) to -1.0 (opposite) to 0.0 (orthogonal) - """ - # Relative rotation - Δθ = self.effective_rotation_angle() - other.effective_rotation_angle() - return math.cos(Δθ) - - -def demo_rotation_encoding(): - """Show how π encodes rotation.""" - - print("=" * 60) - print("ROTATIONAL VALUE ENCODING (π FIELD)") - print("=" * 60) - - print("\nπ (4 bits) = 16 rotational states:") - print("-" * 60) - print(f"{'π':>3} | {'Binary':>6} | {'Degrees':>10} | {'Direction':>12}") - print("-" * 60) - - for pi in range(16): - degrees = pi * 22.5 - binary = format(pi, '04b') - - # Direction name - if pi == 0: - direction = "ALIGN" - elif pi == 4: - direction = "RIGHT" - elif pi == 8: - direction = "OPPOSITE" - elif pi == 12: - direction = "LEFT" - else: - direction = f"TURN_{pi}" - - print(f"{pi:>3} | {binary:>6} | {degrees:>10.1f}° | {direction:>12}") - - -def demo_chirality_coupling(): - """Show how chirality affects rotation direction.""" - - print("\n" + "=" * 60) - print("CHIRALITY-ROTATION COUPLING") - print("=" * 60) - - # Create μ-seeds with same π but different chirality - test_cases = [ - (0, 0, "D-form, π=0"), - (0, 1, "L-form, π=0"), - (4, 0, "D-form, π=4 (90°)"), - (4, 1, "L-form, π=4 (90°)"), - (8, 0, "D-form, π=8 (180°)"), - (8, 1, "L-form, π=8 (180°)"), - ] - - print(f"\n{'Description':<25} | {'π':>3} | {'χ':>3} | {'Effective°':>12} | {'Direction':>15}") - print("-" * 70) - - for pi, chi, desc in test_cases: - mu = RotationalMuSeed(0, (0, 0, 0), pi, chi, 0) - angle_deg = math.degrees(mu.effective_rotation_angle()) - direction = "CCW" if angle_deg >= 0 else "CW" - - print(f"{desc:<25} | {pi:>3} | {chi:>3} | {angle_deg:>12.1f}° | {direction:>15}") - - print("\nKey insight: Same π, opposite rotation based on chirality!") - - -def demo_position_rotation(): - """Show how position deltas are rotated.""" - - print("\n" + "=" * 60) - print("POSITION DELTA ROTATION") - print("=" * 60) - - # Start with a vector pointing east (1, 0, 0) - vec = (1.0, 0.0, 0.0) - - print(f"\nOriginal vector: {vec}") - print("-" * 60) - print(f"{'π':>3} | {'χ':>3} | {'Rotated X':>12} | {'Rotated Y':>12} | {'Interpretation':>20}") - print("-" * 60) - - test_cases = [ - (0, 0, "D-form, align"), - (4, 0, "D-form, 90° CCW (North)"), - (4, 1, "L-form, 90° CW (South)"), - (8, 0, "D-form, 180° (West)"), - (8, 1, "L-form, 180° (West - same!)"), - ] - - for pi, chi, interp in test_cases: - mu = RotationalMuSeed(0, (0, 0, 0), pi, chi, 0) - rotated = mu.rotate_vector(vec) - print(f"{pi:>3} | {chi:>3} | {rotated[0]:>12.3f} | {rotated[1]:>12.3f} | {interp:>20}") - - print("\nNote: π=8 (180°) gives same result for D and L (sign flip twice = same)") - - -def demo_alignment_coupling(): - """Show how rotational alignment affects coupling.""" - - print("\n" + "=" * 60) - print("ROTATIONAL ALIGNMENT & COUPLING") - print("=" * 60) - - # Reference node - mu_ref = RotationalMuSeed(0, (0, 0, 0), pi=0, chi=0, activation=5.0) - - print(f"\nReference: π={mu_ref.pi}, χ={mu_ref.chi} (Aligned to 0°)") - print("-" * 70) - print(f"{'Node π':>8} | {'Node χ':>8} | {'Alignment':>12} | {'Coupling':>12} | {'Description':>20}") - print("-" * 70) - - test_nodes = [ - (0, 0, "Same (perfect)"), - (0, 1, "Same π, L-chirality"), - (4, 0, "90° apart"), - (4, 1, "90° apart, L"), - (8, 0, "Opposite (180°)"), - (8, 1, "Opposite, L (same)"), - ] - - for pi, chi, desc in test_nodes: - mu_test = RotationalMuSeed(1, (0, 0, 0), pi, chi, 3.0) - alignment = mu_ref.alignment_with(mu_test) - coupling = "Strong" if alignment > 0.7 else "Weak" if alignment > -0.7 else "Repel" - - print(f"{pi:>8} | {chi:>8} | {alignment:>12.3f} | {coupling:>12} | {desc:>20}") - - print("\nCoupling strength determines information flow (ι operator)") - - -def demo_rotational_flow(): - """Demonstrate rotational channeling of activation flow.""" - - print("\n" + "=" * 60) - print("ROTATIONAL ACTIVATION FLOW") - print("=" * 60) - - # Create a line of nodes with increasing π (rotating flow) - nodes = [] - for i in range(8): - pi = i % 16 # Rotate through angles - mu = RotationalMuSeed( - node_id=i, - delta_p=(i, 0, 0), - pi=pi, - chi=0, # D-form - activation=1.0 if i == 0 else 0.0 # Only first node active - ) - nodes.append(mu) - - print("\nChain of 8 nodes with π = 0, 1, 2, 3, 4, 5, 6, 7") - print("(Rotating 0° → 22.5° → 45° → 67.5° → 90° → 112.5° → 135° → 157.5°)") - print("-" * 60) - print(f"{'Node':>6} | {'π':>4} | {'Angle°':>8} | {'Initial a':>12} | {'Alignment+1':>14} | {'Flow':>10}") - print("-" * 60) - - for i, mu in enumerate(nodes): - angle = mu.pi * 22.5 - # Alignment with next node (if exists) - if i < len(nodes) - 1: - align = mu.alignment_with(nodes[i+1]) - else: - align = 0.0 - - flow = "Strong" if align > 0.9 else "Medium" if align > 0.5 else "Weak" - - print(f"{i:>6} | {mu.pi:>4} | {angle:>8.1f} | {mu.activation:>12.1f} | {align:>14.3f} | {flow:>10}") - - print("\nActivation flows strongest where π changes gradually (aligned).") - - -def demo_chiral_superposition(): - """Show D+L superposition creating orthogonal channels.""" - - print("\n" + "=" * 60) - print("CHIRAL SUPERPOSITION (ORTHOGONAL CHANNELS)") - print("=" * 60) - - # Create D and L versions of same structure - print("\nTwo μ-seeds at same position:") - print("-" * 60) - - for pi in [0, 4, 8]: - mu_D = RotationalMuSeed(0, (0, 0, 0), pi, 0, 5.0) - mu_L = RotationalMuSeed(1, (0, 0, 0), pi, 1, 5.0) - - angle_D = math.degrees(mu_D.effective_rotation_angle()) - angle_L = math.degrees(mu_L.effective_rotation_angle()) - - print(f"\nπ = {pi} ({pi*22.5}° reference):") - print(f" D-form (χ=0): rotates to {angle_D:+.1f}° (CCW)") - print(f" L-form (χ=1): rotates to {angle_L:+.1f}° (CW)") - print(f" Net flow: D+L = {angle_D + angle_L:.1f}° (cancels!)") - - # But they don't interact! - alignment = mu_D.alignment_with(mu_L) - print(f" Alignment: {alignment:.3f} (orthogonal channels)") - - -def demo_summary(): - """Summary of rotational programming.""" - - print("\n" + "=" * 60) - print("SUMMARY: ROTATIONAL PROGRAMMING IN GPL") - print("=" * 60) - - print(""" -Rotational Values (π field): - • 4 bits → 16 states (22.5° resolution) - • Encodes local torsion/orientation - • Affects position delta interpretation - • Controls activation flow direction - -Chirality Coupling (χ bit): - • χ=0 (D-form): Counter-clockwise rotation (+) - • χ=1 (L-form): Clockwise rotation (-) - • Same π, opposite physical effect - • Orthogonal channels (no interaction) - -Programming Implications: - 1. ALIGNMENT: Nodes with similar π couple strongly - 2. CHANNELING: Information flows along π gradients - 3. ORTHOGONALITY: D+L at same π = no crosstalk - 4. VORTICES: Circular π patterns create rotational attractors - 5. MEMORY: Store bits in rotational state - -Example Patterns: - π = constant: Parallel flow (all aligned) - π = linear gradient: Directed flow (channel) - π = circular: Vortex (attractor cycle) - D+L pairs: Orthogonal information storage - -Physical Realization: - DNA origami twist angle encodes π - D-form = right-handed helix twist - L-form = left-handed helix twist - Measured by FRET, CD, or conductance -""") - - -if __name__ == "__main__": - demo_rotation_encoding() - demo_chirality_coupling() - demo_position_rotation() - demo_alignment_coupling() - demo_rotational_flow() - demo_chiral_superposition() - demo_summary() diff --git a/5-Applications/tools-scripts/demo/gwl_yee_em_fixed.py b/5-Applications/tools-scripts/demo/gwl_yee_em_fixed.py deleted file mode 100644 index ce4f5f8e..00000000 --- a/5-Applications/tools-scripts/demo/gwl_yee_em_fixed.py +++ /dev/null @@ -1,345 +0,0 @@ -#!/usr/bin/env python3 -""" -gwl_yee_em_fixed.py - -Fixed GWL/TSM electromagnetic field propagation using Yee FDTD (1966) algorithm. - -Key fix: Staggered E/B updates instead of naive explicit. - -Yee Algorithm (proven stable since 1966): - B^{n+1/2} = B^{n-1/2} - (Δt/μ) ∇ × E^n - E^{n+1} = E^n + (Δt/ε) ∇ × B^{n+1/2} -""" - -import numpy as np -from typing import Tuple, List -import math - - -class GWL_YeeEM_1D: - """ - GWL Electromagnetic field using Yee FDTD staggered update. - - Maps to GWL/TSM primitives: - - E field → μ-seed E component (full time steps) - - B field → μ-seed B component (half time steps) - - ∇ × operator → π-field weighted coupling - - Temporal staggering → τ phase offset - """ - - def __init__(self, nx: int = 200, dx: float = 0.01, dt: float = 0.005, - epsilon: float = 1.0, mu: float = 1.0): - self.nx = nx - self.dx = dx - self.dt = dt - self.epsilon = epsilon - self.mu = mu - - # Courant number (c * dt / dx) - c = 1.0 / math.sqrt(epsilon * mu) - self.courant = c * dt / dx - - # Fields - # E at integer grid points (0, 1, 2, ..., nx-1) - self.E = np.zeros(nx) - - # B at half-integer points (-0.5, 0.5, 1.5, ..., nx-1.5) - # Represented as array of size nx (with boundary handling) - self.B = np.zeros(nx) - - # Time step counter (for determining full/half steps) - self.step_count = 0 - - # History - self.energy_history = [] - - def initialize_gaussian_pulse(self, center: int, width: int, amplitude: float = 1.0): - """Initialize E field with Gaussian pulse.""" - for i in range(self.nx): - dist = abs(i - center) - self.E[i] = amplitude * math.exp(-dist**2 / (2 * (width/3)**2)) - # B starts at zero (consistent with initial conditions) - self.B.fill(0.0) - - def curl_E(self, i: int) -> float: - """ - Compute ∂E/∂x at point i (for B update). - - E is at integer points, we need derivative at half-integer. - Use central difference: (E[i] - E[i-1]) / dx - """ - if i == 0: - return (self.E[i] - self.E[self.nx-1]) / self.dx # Periodic - else: - return (self.E[i] - self.E[i-1]) / self.dx - - def curl_B(self, i: int) -> float: - """ - Compute ∂B/∂x at point i (for E update). - - B is at half-integer points, we need derivative at integer. - Use central difference: (B[i+1] - B[i]) / dx - """ - if i == self.nx - 1: - return (self.B[0] - self.B[i]) / self.dx # Periodic - else: - return (self.B[i+1] - self.B[i]) / self.dx - - def step(self): - """ - One Yee FDTD step. - - In 1D, the curl reduces to a single derivative component. - For E_z and B_y (propagating in x): - ∂B_y/∂t = - (1/μ) ∂E_z/∂x - ∂E_z/∂t = - (1/ε) ∂B_y/∂x - """ - # Update B at half step (t + dt/2) - coeff_B = self.dt / (self.mu * self.dx) - for i in range(self.nx): - # B_y^{n+1/2} = B_y^{n-1/2} - (dt/μ) * (E_z[i] - E_z[i-1])/dx - self.B[i] -= coeff_B * (self.E[i] - self.E[(i-1) % self.nx]) - - # Update E at full step (t + dt) - coeff_E = self.dt / (self.epsilon * self.dx) - for i in range(self.nx): - # E_z^{n+1} = E_z^n - (dt/ε) * (B_y[i+1] - B_y[i])/dx - self.E[i] -= coeff_E * (self.B[(i+1) % self.nx] - self.B[i]) - - self.step_count += 1 - - # Record energy - energy = self.compute_energy() - self.energy_history.append(energy) - - def compute_energy(self) -> float: - """Compute total EM energy (ε E² + B²/μ).""" - electric = self.epsilon * np.sum(self.E**2) - magnetic = np.sum(self.B**2) / self.mu - return electric + magnetic - - def run(self, steps: int = 500): - """Run simulation for specified steps.""" - for _ in range(steps): - self.step() - - def find_pulse_center(self) -> int: - """Find center of pulse (position of max |E|).""" - return int(np.argmax(np.abs(self.E))) - - def get_energy_stats(self) -> Tuple[float, float, float]: - """Return (initial, min, max, final) energy.""" - if not self.energy_history: - return 0, 0, 0, 0 - return (self.energy_history[0], - min(self.energy_history), - max(self.energy_history), - self.energy_history[-1]) - - -class GWL_YeeEM_Tests: - """Test suite for Yee-based GWL EM.""" - - def __init__(self): - self.results = {} - - def test_energy_conservation(self, steps: int = 400) -> Tuple[bool, dict]: - """ - Test 1: Energy should be conserved (< 1% drift). - """ - sim = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim.initialize_gaussian_pulse(center=100, width=20, amplitude=1.0) - - initial = sim.compute_energy() - sim.run(steps=steps) - final = sim.compute_energy() - - drift = abs(final - initial) / initial if initial > 0 else 0 - max_ratio = max(sim.energy_history) / initial if initial > 0 else 0 - - passed = drift < 0.01 and max_ratio < 1.5 # < 1% drift, < 50% variation - - return passed, { - 'initial_energy': initial, - 'final_energy': final, - 'energy_drift': drift, - 'max_ratio': max_ratio, - 'threshold': 0.01 - } - - def test_stable_propagation(self, steps: int = 400) -> Tuple[bool, dict]: - """ - Test 2: Pulse should propagate without blowup. - With periodic boundaries, symmetric pulse splits and wraps. - Check: no blowup, energy bounded, field remains finite. - """ - sim = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim.initialize_gaussian_pulse(center=100, width=20, amplitude=1.0) - - initial_energy = sim.compute_energy() - max_field_initial = np.max(np.abs(sim.E)) - - sim.run(steps=steps) - - final_energy = sim.compute_energy() - max_field_final = np.max(np.abs(sim.E)) - - energy_ratio = final_energy / initial_energy if initial_energy > 0 else 0 - field_growth = max_field_final / max_field_initial if max_field_initial > 0 else 0 - - # Criteria: no energy blowup, field remains bounded - stable = energy_ratio < 2.0 and field_growth < 2.0 - - return stable, { - 'initial_energy': initial_energy, - 'final_energy': final_energy, - 'energy_ratio': energy_ratio, - 'field_growth': field_growth, - 'max_field_final': max_field_final - } - - def test_frequency_separability(self) -> Tuple[bool, dict]: - """ - Test 3: Low and high frequency modes should propagate differently. - """ - # Low frequency (broad pulse) - sim_low = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim_low.initialize_gaussian_pulse(center=100, width=40, amplitude=1.0) - sim_low.run(steps=200) - spread_low = np.std(sim_low.E) - - # High frequency (narrow pulse) - sim_high = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim_high.initialize_gaussian_pulse(center=100, width=5, amplitude=1.0) - sim_high.run(steps=200) - spread_high = np.std(sim_high.E) - - # They should behave differently - different = abs(spread_low - spread_high) > 0.01 - - return different, { - 'low_spread': spread_low, - 'high_spread': spread_high, - 'difference': abs(spread_low - spread_high) - } - - def test_determinism(self) -> Tuple[bool, dict]: - """ - Test 4: Same initial conditions → same results. - """ - # Run 1 - sim1 = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim1.initialize_gaussian_pulse(center=100, width=20, amplitude=1.0) - sim1.run(steps=100) - E1 = sim1.E.copy() - - # Run 2 - sim2 = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim2.initialize_gaussian_pulse(center=100, width=20, amplitude=1.0) - sim2.run(steps=100) - E2 = sim2.E.copy() - - # Should be identical - max_diff = np.max(np.abs(E1 - E2)) - - return max_diff < 1e-10, {'max_diff': max_diff} - - def test_courant_stability(self) -> Tuple[bool, dict]: - """ - Test 5: Courant number <= 1 for stability. - """ - # Stable: courant = 0.5 - sim_stable = GWL_YeeEM_1D(nx=200, dx=0.01, dt=0.005) - sim_stable.initialize_gaussian_pulse(center=100, width=20, amplitude=1.0) - sim_stable.run(steps=200) - stable_energy = sim_stable.compute_energy() - stable_ratio = stable_energy / sim_stable.energy_history[0] - - # Unstable: courant = 1.2 (should still work with small enough dt) - # Actually Yee is stable for courant <= 1 - # For courant > 1, we expect issues - - results = { - 'courant_stable': sim_stable.courant, - 'energy_ratio': stable_ratio, - 'stable': stable_ratio < 2.0 - } - - return results['stable'], results - - def run_all(self): - """Run complete test suite.""" - print("=" * 80) - print("GWL YEE FDTD EM FIELD TEST SUITE") - print("=" * 80) - print(f"\nUsing Yee FDTD algorithm (1966, 58 years proven)") - print(f"Staggered E/B update with symplectic structure\n") - - tests = [ - ('Energy Conservation', self.test_energy_conservation), - ('Stable Propagation', self.test_stable_propagation), - ('Frequency Separability', self.test_frequency_separability), - ('Determinism', self.test_determinism), - ('Courant Stability', self.test_courant_stability), - ] - - all_passed = True - for name, test_fn in tests: - print(f"\n[Test] {name}") - print("-" * 60) - try: - passed, details = test_fn() - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Status: {status}") - for key, val in details.items(): - if isinstance(val, float): - print(f" {key}: {val:.6f}") - else: - print(f" {key}: {val}") - self.results[name] = {'passed': passed, 'details': details} - all_passed = all_passed and passed - except Exception as e: - print(f"Status: ✗ ERROR - {e}") - self.results[name] = {'passed': False, 'error': str(e)} - all_passed = False - - # Summary - print("\n" + "=" * 80) - print("SUMMARY") - print("=" * 80) - for name, result in self.results.items(): - status = "✓ PASS" if result.get('passed') else "✗ FAIL" - print(f"{name:30s}: {status}") - - print("\n" + "=" * 80) - if all_passed: - print("ALL TESTS PASSED") - print("=" * 80) - print(""" -The Yee FDTD implementation provides a stable deterministic backbone -for GWL/TSM electromagnetic field evolution. - -Key properties verified: - ✓ Energy conserved (< 1% drift) - ✓ Stable propagation (no blowup) - ✓ Frequency separability - ✓ Deterministic reproducibility - ✓ Courant-stable - -NEXT STEPS: - 1. Add stochastic perturbations on top of this stable backbone - 2. Extend to 2D/3D with proper curl operator - 3. Add medium coupling (ε, μ variations) - 4. Validate speed of light in medium - """) - else: - print("SOME TESTS FAILED") - print("=" * 80) - - return all_passed - - -if __name__ == "__main__": - test_suite = GWL_YeeEM_Tests() - success = test_suite.run_all() - exit(0 if success else 1) diff --git a/5-Applications/tools-scripts/demo/mcp_harness.html b/5-Applications/tools-scripts/demo/mcp_harness.html deleted file mode 100644 index 89c0592d..00000000 --- a/5-Applications/tools-scripts/demo/mcp_harness.html +++ /dev/null @@ -1,60 +0,0 @@ - - - - - - TSM-L Protocol v2.9 Harness - - - -

TSM-L Protocol v2.9 Harness

-
-
[SYSTEM] AETHER Substrate v2.9 Ready.
-
[SYSTEM] Hardware Tier: Tier 2 (PLASMA)
-
[SYSTEM] Type 'OMNI_BAL' or 'VRAM_FLUSH' to interact.
-
-
- architect@aether:~$ - -
- - - - diff --git a/5-Applications/tools-scripts/demo/mcp_harness.js b/5-Applications/tools-scripts/demo/mcp_harness.js deleted file mode 100644 index c596a21e..00000000 --- a/5-Applications/tools-scripts/demo/mcp_harness.js +++ /dev/null @@ -1,168 +0,0 @@ -const terminal = document.getElementById('terminal'); -const input = document.getElementById('cmd-input'); - -// [CONFIG] Reality Manifestation State -let CURRENT_MANIFEST = 'CHASE'; - -// [CONFIG] Replace with your actual Wolfram Alpha AppID -const WOLFRAM_APPID = 'YOUR_APPID_HERE'; - -const PERIODIC_TABLE = { - 'XE': { weight: 131.293, name: 'Xenon', role: 'Conductivity Spine' }, - 'C': { weight: 12.011, name: 'Carbon', role: 'Warm Body Breeder' }, - 'H': { weight: 1.008, name: 'Hydrogen', role: 'Quantum Trace' } -}; - -const REALITY_SPECS = { - 'STANDARD': { geometry: 'Linear 2-Anvil', logic: 'Simultaneous Pulse', pressure: '120 GPa' }, - 'BLUESKY': { geometry: 'Dodecahedral 12-Anvil', logic: 'Geometric Focus', pressure: '172 GPa' }, - 'CHASE': { geometry: 'Supersonic Torus (CWT)', logic: 'Sequential Piling', pressure: '245 GPa' } -}; - -function log(text, className = '') { - const entry = document.createElement('div'); - entry.className = 'log-entry ' + className; - entry.textContent = text; - terminal.appendChild(entry); - terminal.scrollTop = terminal.scrollHeight; -} - -async function queryWolfram(query) { - if (WOLFRAM_APPID === 'YOUR_APPID_HERE') { - log('[!] Error: Wolfram Alpha AppID not configured.', 'err'); - return; - } - log('[AETHER] Querying Wolfram Alpha: ' + query); - try { - const url = `https://api.wolframalpha.com/v1/result?appid=${WOLFRAM_APPID}&i=${encodeURIComponent(query)}`; - const response = await fetch(url); - if (!response.ok) throw new Error('API Error: ' + response.status); - const data = await response.text(); - log('[WOLFRAM] ' + data); - } catch (err) { - log('[!] Error: ' + err.message, 'err'); - } -} - -input.addEventListener('keydown', (e) => { - if (e.key === 'Enter') { - const cmd = input.value.trim().toUpperCase(); - input.value = ''; - log('architect@aether:~$ ' + cmd); - handleCommand(cmd); - } -}); - -async function queryMIMORouter() { - try { - const response = await fetch('/api/mimo/status'); - if (!response.ok) throw new Error('API Error: ' + response.status); - const data = await response.json(); - log('[MIMO] Available Transports: ' + data.available_transports.join(', ')); - if (data.i2p_info) { - log('[MIMO] I2P State: ' + data.i2p_info.state); - log('[MIMO] I2P Manifests: ' + data.i2p_info.manifests_registered); - } - log('[MIMO] Omnitoken: OK'); - return data; - } catch (err) { - log('[MIMO] Error: ' + err.message, 'err'); - return null; - } -} - -function handleCommand(cmd) { - if (cmd === 'MIMO') { - log('[MIMO] Probing multi-band transport...'); - queryMIMORouter(); - return; - } - if (cmd.startsWith('WOLFRAM ')) { - queryWolfram(cmd.substring(8)); - return; - } - if (cmd.startsWith('ATOMIC ')) { - const symbol = cmd.split(' ')[1]; - if (symbol && PERIODIC_TABLE[symbol]) { - const data = PERIODIC_TABLE[symbol]; - log(`[AETHER] Element: ${data.name} (${symbol})`); - log(`[AETHER] Atomic Weight: ${data.weight} u`); - log(`[AETHER] Tactical Role: ${data.role}`); - } else { - log('[!] Usage: ATOMIC ', 'err'); - } - return; - } - - switch (cmd) { - case 'MANIFEST': - if (CURRENT_MANIFEST === 'CHASE') CURRENT_MANIFEST = 'STANDARD'; - else if (CURRENT_MANIFEST === 'STANDARD') CURRENT_MANIFEST = 'BLUESKY'; - else CURRENT_MANIFEST = 'CHASE'; - log(`[AETHER] Reality Track Switch: -> ${CURRENT_MANIFEST}`); - const specs = REALITY_SPECS[CURRENT_MANIFEST]; - log(`[AETHER] Geometry: ${specs.geometry}`); - log(`[AETHER] Logic: ${specs.logic}`); - log(`[AETHER] Target Pressure: ${specs.pressure}`); - break; - case 'CHASE': - log('[AETHER] Initializing Chasing Wave Sequential Detonation...'); - log('[AETHER] Geometry: Supersonic Torus (CWT)'); - log('[AETHER] Mach Stem Peak: 245 GPa (Sequential Piling)'); - log('[AETHER] Sequence Delay: 10ps (Phase-Shifted)'); - break; - case 'SAS': - log('[AETHER] Engaging Supersonic Acoustic Siphon (SAS)...'); - log('[AETHER] Mode: Supersonic Cavitation-Work Recapture'); - log('[AETHER] Efficiency: 94.2% (Mach-Stem Shunt)'); - log('[AETHER] Destination: SCF Breeder Cloud (The Mass)'); - break; - case 'DAC12': - log('[AETHER] Initializing DAC-12 Array Profile...'); - log('[AETHER] Geometry: Dodecahedral (12 Symmetrical Faces)'); - log('[AETHER] Force Divergence: < 0.001% (Scalar Focus)'); - break; - case 'LONSDALEITE': - log('[AETHER] Analyzing Lonsdaleite Cycle...'); - log('[AETHER] Phase: Transient Hexagonal Diamond (LPT)'); - log('[AETHER] Role: Inertial Squeeze Anvil'); - break; - case 'SOLITON': - log('[AETHER] Initializing Soliton String Chemistry...'); - log('[AETHER] State: Metallic Xenon-Carbon Plasma (XACS)'); - log('[AETHER] Conductivity: 1e4 S/cm (MIT Active)'); - log('[AETHER] Ionization: alpha = 0.85 (Focus-Induced)'); - break; - case 'WORLDBUILD': - log('[AETHER] --- WORLDBUILDING DISCLAIMER ---', 'warn'); - log('[AETHER] The SAS/CWT/Lonsdaleite mechanics are HIGH-FIDELITY CONCEPTUAL ONLY.'); - log('[AETHER] Intended Role: Speculative Narrative Modeling.'); - log('[AETHER] Context: Toy Model Physics.'); - break; - case 'OMNI_BAL': - log('[AETHER] Recalibrating manifold...'); - setTimeout(() => log('[AETHER] Stability lock active.'), 1000); - break; - case 'VRAM_FLUSH': - log('[WARNING] Initiating VRAM_FLUSH...', 'warn'); - setTimeout(() => log('[AETHER] VRAM cleared.'), 500); - break; - case 'STARK_PROVE': - log('[AETHER] Proof: sha3-' + Math.random().toString(16).slice(2)); - break; - case 'HELP': - log('Commands: MANIFEST, CHASE, SAS, DAC12, LONSDALEITE, SOLITON, WORLDBUILD, WOLFRAM , ATOMIC , OMNI_BAL, VRAM_FLUSH, THERMO, STARK_PROVE, MIMO, CLEAR, HELP'); - break; - case 'CLEAR': - terminal.innerHTML = ''; - break; - default: - if (cmd) log('[!] Unknown command: ' + cmd, 'err'); - } -} - -setTimeout(() => { - log(`[+] Track initialized: ${CURRENT_MANIFEST}`); - log('[+] entropic floor detected at 0.500'); - log('[+] STARK verification complete.'); -}, 500); diff --git a/5-Applications/tools-scripts/demo/msm_udp_phonon_demo.py b/5-Applications/tools-scripts/demo/msm_udp_phonon_demo.py deleted file mode 100644 index 571ab13a..00000000 --- a/5-Applications/tools-scripts/demo/msm_udp_phonon_demo.py +++ /dev/null @@ -1,93 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import random -import zlib -import math - -# Invisible Unicode widths for encoding bits -INVISIBLE = ['\u200b', '\u200c', '\u200d'] # Zero-width space, non-joiner, joiner -BIT_TO_UNI = {'00': INVISIBLE[0], '01': INVISIBLE[1], '10': INVISIBLE[2], '11': INVISIBLE[0]+INVISIBLE[1]} -UNI_TO_BIT = {INVISIBLE[0]: '00', INVISIBLE[1]: '01', INVISIBLE[2]: '10', INVISIBLE[0]+INVISIBLE[1]: '11'} - -# Golden ratio-based CRC polynomial (use fractional part as seed) -GOLDEN_POLY = int((math.modf((1.61803398875 % 1) * 1e8)[0])) | 0x1021 # Just for demo - -SEGMENT_SIZE = 16 # bytes per UDP segment - - -def encode_message(msg): - # Convert to bits, then to invisible widths - bits = ''.join(f'{b:08b}' for b in msg.encode('utf-8')) - # Pad to multiple of 2 - if len(bits) % 2: - bits += '0' - encoded = ''.join(BIT_TO_UNI[bits[i:i+2]] for i in range(0, len(bits), 2)) - return encoded - -def decode_message(encoded): - # Map invisible widths back to bits - bits = '' - i = 0 - while i < len(encoded): - for k, v in UNI_TO_BIT.items(): - if encoded[i:i+len(k)] == k: - bits += v - i += len(k) - break - else: - i += 1 # skip unknown - # Convert bits to bytes - bytelist = [int(bits[i:i+8], 2) for i in range(0, len(bits), 8)] - return bytes(bytelist).decode('utf-8', errors='ignore') - -def golden_crc(data): - # Use zlib.crc32 with golden poly as seed for demo - return zlib.crc32(data.encode('utf-8'), GOLDEN_POLY) - -def segment_message(encoded): - # Split into segments, append CRC - segments = [] - for i in range(0, len(encoded), SEGMENT_SIZE): - chunk = encoded[i:i+SEGMENT_SIZE] - crc = golden_crc(chunk) - segments.append((chunk, crc)) - return segments - -def introduce_errors(segments, bit_errors=2): - # Randomly flip up to bit_errors bits in each segment - corrupted = [] - for chunk, crc in segments: - chunk_bytes = bytearray(chunk.encode('utf-8')) - for _ in range(random.randint(0, bit_errors)): - if not chunk_bytes: - continue - idx = random.randint(0, len(chunk_bytes)-1) - bit = 1 << random.randint(0, 7) - chunk_bytes[idx] ^= bit - corrupted.append((chunk_bytes.decode('utf-8', errors='ignore'), crc)) - return corrupted - -def phonon_graph_reassemble(segments): - # Try all segments, accept those with valid CRC - reassembled = '' - for chunk, crc in segments: - if golden_crc(chunk) == crc: - reassembled += chunk - return reassembled - -if __name__ == '__main__': - msg = 'Hello, SCADA! This is a covert MSM tape.' - print('Original:', msg) - encoded = encode_message(msg) - segments = segment_message(encoded) - # Simulate UDP: shuffle and introduce errors - random.shuffle(segments) - corrupted = introduce_errors(segments) - # Reassemble - tape = phonon_graph_reassemble(corrupted) - decoded = decode_message(tape) - print('Decoded:', decoded) diff --git a/5-Applications/tools-scripts/demo/multi_planetary_calc.py b/5-Applications/tools-scripts/demo/multi_planetary_calc.py deleted file mode 100644 index b35a0582..00000000 --- a/5-Applications/tools-scripts/demo/multi_planetary_calc.py +++ /dev/null @@ -1,48 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -from math_harness_compat import xp, AnyArray - -# Let's map the engine capabilities across multiple planetary atmospheres -def map_planetary_thermodynamics(): - # Base target values - delta_t = 45.0 # Degrees C - flow_rate = 0.5 # m^3/sec - - planets = { - "Earth (Sea Level)": { - "density": 1.225, # kg/m^3 - "cp": 1005 # J/(kg*K) - }, - "Mars (Surface)": { - "density": 0.020, # kg/m^3 (Mostly CO2) - "cp": 760 # J/(kg*K) for CO2 at ~-60°C, 6 mbar (Mars surface) - }, - "Venus (Surface)": { - "density": 65.0, # kg/m^3 (Massive CO2 pressure) - "cp": 1100 # J/(kg*K) super-critical CO2 - }, - "Titan (Surface)": { - "density": 5.42, # kg/m^3 (Cold dense N2/CH4) - "cp": 1040 # J/(kg*K) for Nitrogen - }, - "Jupiter (High Alt - 1 Bar)": { - "density": 0.16, # kg/m^3 (H2/He gas) - "cp": 12640 # J/(kg*K) mass-weighted H2/He mix (90%/10%) - } - } - - print("Planetary Thermal Extraction Profile (0.5 m^3/s Flow @ 45C Delta T)") - print("-" * 75) - print(f"{'Environment':<25} | {'Mass Flow (kg/s)':<18} | {'Power Yield (kW)':<15}") - print("-" * 75) - - for planet, data in planets.items(): - mass_flow = data["density"] * flow_rate - power_w = mass_flow * data["cp"] * delta_t - print(f"{planet:<25} | {mass_flow:<18.3f} | {power_w/1000.0:<15.2f}") - -map_planetary_thermodynamics() diff --git a/5-Applications/tools-scripts/demo/two_bit_cpu_sim.py b/5-Applications/tools-scripts/demo/two_bit_cpu_sim.py deleted file mode 100644 index 07841fd8..00000000 --- a/5-Applications/tools-scripts/demo/two_bit_cpu_sim.py +++ /dev/null @@ -1,134 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -2-Bit CPU Simulator: Deterministic, auditable, minimal. -- Only ADD, SUB, NOP, HALT instructions -- 2-bit registers (values 0-3, wrap on overflow/underflow) -- Every tick and state change logged -- Program and log are human-readable -- Manifest with hashes and environment info - -Usage: - python3 two_bit_cpu_sim.py program.txt - -Example program.txt: -ADD 1 2 # reg0 = reg0 + reg1 -SUB 0 1 # reg0 = reg0 - reg1 -NOP -HALT - -""" - -import sys -import hashlib -import json -import platform -from datetime import datetime -from dag_recorder import DAGRecorder - -REG_BITS = 2 -REG_MAX = (1 << REG_BITS) - 1 - - -class TwoBitCPU: - def __init__(self, num_registers=2, dag_path='dag_log.jsonl'): - self.reg = [0] * num_registers - self.tick = 0 - self.halted = False - self.log = [] - self.dag = DAGRecorder(dag_path) - - def log_state(self, op, args): - entry = { - 'tick': self.tick, - 'op': op, - 'args': args, - 'registers': self.reg.copy() - } - self.log.append(entry) - self.dag.record(self.tick, op, args, self.reg) - - def step(self, op, args): - if self.halted: - return - if op == 'ADD': - a, b = int(args[0]), int(args[1]) - self.reg[a] = (self.reg[a] + self.reg[b]) & REG_MAX - elif op == 'SUB': - a, b = int(args[0]), int(args[1]) - self.reg[a] = (self.reg[a] - self.reg[b]) & REG_MAX - elif op == 'NOP': - pass - elif op == 'HALT': - self.halted = True - else: - raise ValueError(f"Unknown op: {op}") - self.log_state(op, args) - self.tick += 1 - - def run(self, program): - pc = 0 - while pc < len(program) and not self.halted: - op, *args = program[pc] - self.step(op, args) - pc += 1 - self.dag.dump() - - def dump_log(self, path): - with open(path, 'w') as f: - for entry in self.log: - f.write(f"tick={entry['tick']} op={entry['op']} args={entry['args']} reg={entry['registers']}\n") - - def state_hash(self): - m = hashlib.sha256() - for entry in self.log: - m.update(str(entry).encode()) - return m.hexdigest() - -def parse_program(path): - program = [] - with open(path) as f: - for line in f: - line = line.split('#')[0].strip() - if not line: - continue - parts = line.split() - op = parts[0].upper() - args = parts[1:] - program.append([op] + args) - return program - - -def main(): - if len(sys.argv) < 2: - print("Usage: python3 two_bit_cpu_sim.py program.txt") - sys.exit(1) - prog_path = sys.argv[1] - program = parse_program(prog_path) - dag_path = 'dag_log.jsonl' - cpu = TwoBitCPU(dag_path=dag_path) - cpu.run(program) - log_path = 'cpu_log.txt' - cpu.dump_log(log_path) - manifest = { - 'program_file': prog_path, - 'log_file': log_path, - 'dag_file': dag_path, - 'final_registers': cpu.reg, - 'total_ticks': cpu.tick, - 'state_hash': cpu.state_hash(), - 'python_version': platform.python_version(), - 'platform': platform.platform(), - 'timestamp': datetime.utcnow().isoformat() + 'Z' - } - with open('cpu_manifest.json', 'w') as f: - json.dump(manifest, f, indent=2) - print(f"[✓] Simulation complete. Log: {log_path}, DAG: {dag_path}, Manifest: cpu_manifest.json") - -if __name__ == '__main__': - main() diff --git a/5-Applications/tools-scripts/design/design_quantum_tunneling_gradient_processing.py b/5-Applications/tools-scripts/design/design_quantum_tunneling_gradient_processing.py deleted file mode 100644 index 1b49cccd..00000000 --- a/5-Applications/tools-scripts/design/design_quantum_tunneling_gradient_processing.py +++ /dev/null @@ -1,453 +0,0 @@ -#!/usr/bin/env python3 -""" -Design quantum tunneling events for gradient processing with multi-encoding. - -Integrates quantum tunneling (QUTRIT_TUNNEL_SPEC) with multi-encoding blink rate system -to enable gradient-based optimization via quantum walk and entropy damper. -""" - -import math -import random -import numpy as np -from typing import Tuple, List, Optional -from enum import Enum -from dataclasses import dataclass - - -class QutritState(Enum): - """Qutrit state space for quantum tunneling.""" - CLASSICAL = 0 # ∣0⟩ - Normal operation, no tunneling - QUTRIT = 1 # ∣1⟩ - Phase rotation active, monitoring - QUANTUM = 2 # ∣2⟩ - Full tunneling, quantum walk active - - -class TunnelEvent(Enum): - """Tunnel event types.""" - EMI_SPIKE = "emi_spike" # Triggers TRIT_SHIFT - TUNNELING_VERIFIED = "tunneling_verified" # Triggers QUANTUM_WALK_ADVANCE - STATE_COMMIT = "state_commit" # Triggers hash collapse - VETO = "veto" # Triumvirate veto - - -class EncodingScheme(Enum): - """Available encoding schemes for blink rate.""" - OISC = "oisc" - BINARY_SLUQ = "binary_sluq" - TERNARY_SLUQ = "ternary_sluq" - QUATERNARY = "quaternary" - - -@dataclass -class GradientPath: - """Gradient path result from quantum walk.""" - encoding_scheme: EncodingScheme - confidence: float - gradient_magnitude: float - entropy_delta: float - - -class QuantumTunnelingGradientProcessor: - """ - Integrates quantum tunneling events with multi-encoding for gradient processing. - - Uses quantum walk to find optimal encoding scheme transitions based on - gradient information and entropy equilibrium. - """ - - def __init__( - self, - target_entropy: float = 0.500, - entropy_tolerance: float = 0.001, - n_dimensions: int = 4 # Number of encoding schemes - ): - self.target_entropy = target_entropy - self.entropy_tolerance = entropy_tolerance - self.n_dimensions = n_dimensions - - # Qutrit state - self.current_state = QutritState.CLASSICAL - self.emi_threshold = 0.500 - - # Quantum walk operators - self.coin_operator = self._hadamard_coin() - self.shift_operator = self._conditional_shift() - - # Gradient history - self.gradient_history: List[float] = [] - self.encoding_history: List[EncodingScheme] = [] - - def _hadamard_coin(self) -> np.ndarray: - """Hadamard coin operator for superposition.""" - H = (1.0 / math.sqrt(2)) * np.array([[1, 1], [1, -1]]) - return np.kron(np.eye(self.n_dimensions), H) - - def _conditional_shift(self) -> np.ndarray: - """Conditional shift operator for quantum walk.""" - S = np.zeros((2 * self.n_dimensions, 2 * self.n_dimensions)) - for i in range(self.n_dimensions): - S[i, i] = 1 # ∣0⟩ doesn't move - S[i + self.n_dimensions, (i + 1) % self.n_dimensions + self.n_dimensions] = 1 - return S - - def _uniform_superposition(self, n: int) -> np.ndarray: - """Create uniform superposition over n states.""" - return np.ones(2 * n) / math.sqrt(2 * n) - - def _measure_entropy(self, state_vector: np.ndarray) -> float: - """Calculate von Neumann entropy of state vector.""" - probabilities = np.abs(state_vector) ** 2 - # Avoid log(0) - probabilities = probabilities + 1e-10 - entropy = -np.sum(probabilities * np.log2(probabilities)) - return entropy - - def detect_tunneling_event( - self, - gradient_magnitude: float, - current_encoding: EncodingScheme, - noise_intensity: float - ) -> TunnelEvent: - """ - Detect if a tunneling event should occur based on gradient and noise. - - Args: - gradient_magnitude: Current gradient magnitude (0.0-1.0) - current_encoding: Current encoding scheme - noise_intensity: Noise intensity (proxy for surprise, 0.0-1.0) - - Returns: - TunnelEvent type - """ - # EMI spike detection (high gradient + high noise) - if gradient_magnitude > 0.7 and noise_intensity > 0.5: - return TunnelEvent.EMI_SPIKE - - # Tunneling verification (gradient direction change) - if len(self.gradient_history) >= 2: - gradient_change = abs(gradient_magnitude - self.gradient_history[-1]) - if gradient_change > 0.3 and self.current_state == QutritState.QUTRIT: - return TunnelEvent.TUNNELING_VERIFIED - - # State commit (entropy at equilibrium) - current_entropy = self._estimate_entropy(gradient_magnitude, noise_intensity) - if abs(current_entropy - self.target_entropy) < self.entropy_tolerance: - if self.current_state == QutritState.QUANTUM: - return TunnelEvent.STATE_COMMIT - - # Veto (entropy out of bounds) - if current_entropy < self.target_entropy - 0.01: - return TunnelEvent.VETO - if current_entropy > self.target_entropy + 0.01: - return TunnelEvent.VETO - - return None - - def _estimate_entropy(self, gradient_magnitude: float, noise_intensity: float) -> float: - """Estimate entropy from gradient and noise.""" - # Simple model: entropy increases with gradient and noise - base_entropy = 0.4 - gradient_contribution = gradient_magnitude * 0.1 - noise_contribution = noise_intensity * 0.1 - return base_entropy + gradient_contribution + noise_contribution - - def trit_shift(self) -> QutritState: - """ - TRIT_SHIFT: Rotate qutrit state to next phase. - - ∣0⟩ → ∣1⟩: Classical → Qutrit - ∣1⟩ → ∣2⟩: Qutrit → Quantum - ∣2⟩ → ∣0⟩: Quantum → Classical (reset) - """ - self.current_state = QutritState((self.current_state.value + 1) % 3) - return self.current_state - - def quantum_walk_advance( - self, - gradient_magnitude: float, - current_encoding: EncodingScheme, - available_schemes: List[EncodingScheme] - ) -> GradientPath: - """ - QUANTUM_WALK_ADVANCE: Find optimal encoding scheme via quantum walk. - - Uses unitary transformation to traverse encoding scheme space - and find optimal gradient path. - - Args: - gradient_magnitude: Current gradient magnitude - current_encoding: Current encoding scheme - available_schemes: Available encoding schemes to consider - - Returns: - GradientPath with optimal encoding and confidence - """ - # Initialize superposition over encoding schemes - initial_state = self._uniform_superposition(len(available_schemes)) - state = initial_state - - # Walk for sqrt(N) steps - steps = int(math.sqrt(len(available_schemes))) - for _ in range(steps): - state = self.coin_operator @ state - state = self.shift_operator @ state - - # Measure: collapse to highest probability encoding - probabilities = np.abs(state) ** 2 - best_idx = np.argmax(probabilities[:len(available_schemes)]) - - optimal_encoding = available_schemes[best_idx] - confidence = probabilities[best_idx] - - # Calculate gradient magnitude for optimal encoding - gradient_delta = self._calculate_gradient_delta( - gradient_magnitude, - current_encoding, - optimal_encoding - ) - - # Calculate entropy delta - entropy_delta = confidence * 0.1 - - return GradientPath( - encoding_scheme=optimal_encoding, - confidence=confidence, - gradient_magnitude=gradient_delta, - entropy_delta=entropy_delta - ) - - def _calculate_gradient_delta( - self, - current_gradient: float, - current_encoding: EncodingScheme, - target_encoding: EncodingScheme - ) -> float: - """Calculate gradient magnitude delta for encoding transition.""" - # Encoding complexity levels - complexity = { - EncodingScheme.OISC: 1.0, - EncodingScheme.BINARY_SLUQ: 1.5, - EncodingScheme.TERNARY_SLUQ: 2.0, - EncodingScheme.QUATERNARY: 2.5, - } - - current_complexity = complexity[current_encoding] - target_complexity = complexity[target_encoding] - - # Gradient scales with complexity difference - complexity_delta = target_complexity - current_complexity - gradient_delta = current_gradient * (1.0 + complexity_delta * 0.5) - - return max(0.0, min(1.0, gradient_delta)) - - def state_commit(self, encoding_scheme: EncodingScheme) -> str: - """ - STATE_COMMIT: Collapse quantum state to hash and commit encoding. - - Args: - encoding_scheme: Encoding scheme to commit to - - Returns: - Hash string (simulated dual SHA-256) - """ - # Simulate dual SHA-256 commitment - identity = str(hash(encoding_scheme.value)) - location = str(hash(self.current_state.value)) - hash_result = hashlib.sha256((identity + location).encode()).hexdigest() - - # Reset to classical state - self.current_state = QutritState.CLASSICAL - - return hash_result - - def process_gradient( - self, - gradient_magnitude: float, - current_encoding: EncodingScheme, - noise_intensity: float, - available_schemes: Optional[List[EncodingScheme]] = None - ) -> Tuple[EncodingScheme, TunnelEvent, float]: - """ - Process gradient through quantum tunneling system. - - Args: - gradient_magnitude: Current gradient magnitude (0.0-1.0) - current_encoding: Current encoding scheme - noise_intensity: Noise intensity (0.0-1.0) - available_schemes: Available encoding schemes (default: all) - - Returns: - (new_encoding, tunnel_event, confidence) tuple - """ - if available_schemes is None: - available_schemes = list(EncodingScheme) - - # Record gradient history - self.gradient_history.append(gradient_magnitude) - self.encoding_history.append(current_encoding) - - # Detect tunneling event - event = self.detect_tunneling_event( - gradient_magnitude, - current_encoding, - noise_intensity - ) - - new_encoding = current_encoding - confidence = 0.0 - - if event == TunnelEvent.EMI_SPIKE: - # TRIT_SHIFT: Rotate state - self.trit_shift() - confidence = 0.5 - - elif event == TunnelEvent.TUNNELING_VERIFIED: - # QUANTUM_WALK_ADVANCE: Find optimal encoding - path = self.quantum_walk_advance( - gradient_magnitude, - current_encoding, - available_schemes - ) - new_encoding = path.encoding_scheme - confidence = path.confidence - - elif event == TunnelEvent.STATE_COMMIT: - # STATE_COMMIT: Commit to current encoding - hash_result = self.state_commit(current_encoding) - confidence = 1.0 - - elif event == TunnelEvent.VETO: - # VETO: Reset to OISC (simplest encoding) - new_encoding = EncodingScheme.OISC - self.current_state = QutritState.CLASSICAL - confidence = 0.0 - - return new_encoding, event, confidence - - def entropy_damp(self) -> str: - """ - Apply entropy damping to maintain equilibrium. - - Returns: - Action taken ("INJECT_JITTER", "VRAM_FLUSH", or "MAINTAIN") - """ - if not self.gradient_history: - return "MAINTAIN" - - current_gradient = self.gradient_history[-1] - current_noise = 0.5 if self.encoding_history else 0.0 - current_entropy = self._estimate_entropy(current_gradient, current_noise) - - if current_entropy < self.target_entropy - self.entropy_tolerance: - # ΔS* < 0.499: System freezing, inject jitter - return "INJECT_JITTER" - - elif current_entropy > self.target_entropy + self.entropy_tolerance: - # ΔS* > 0.501: System dissolving, quench entropy - return "VRAM_FLUSH" - - else: - # ΔS* = 0.500 ± 0.001: Optimal, maintain - return "MAINTAIN" - - -def test_quantum_tunneling_gradient_processing(): - """Test quantum tunneling gradient processing system.""" - print("=" * 60) - print("Quantum Tunneling Gradient Processing Test") - print("=" * 60) - - processor = QuantumTunnelingGradientProcessor() - - # Test scenarios - test_cases = [ - (0.2, EncodingScheme.OISC, 0.1, "Low gradient, low noise"), - (0.8, EncodingScheme.OISC, 0.6, "High gradient, high noise (EMI spike)"), - (0.5, EncodingScheme.TERNARY_SLUQ, 0.3, "Medium gradient, medium noise"), - (0.3, EncodingScheme.QUATERNARY, 0.2, "Low gradient, low noise from complex"), - (0.9, EncodingScheme.BINARY_SLUQ, 0.8, "Extreme gradient, extreme noise"), - ] - - print("\nGradient Processing Results:") - print("{:<35} {:<15} {:<15} {:<15}".format( - "Scenario", "Event", "New Encoding", "Confidence" - )) - print("-" * 80) - - for gradient, encoding, noise, description in test_cases: - new_encoding, event, confidence = processor.process_gradient( - gradient, encoding, noise - ) - - print("{:<35} {:<15} {:<15} {:<15.2f}".format( - description[:34], event.value if event else "None", - new_encoding.value, confidence - )) - - print("\nEntropy Damper Test:") - print("{:<35} {:<15}".format("Scenario", "Action")) - print("-" * 50) - - for gradient, encoding, noise, description in test_cases: - processor.gradient_history.append(gradient) - processor.encoding_history.append(encoding) - action = processor.entropy_damp() - - print("{:<35} {:<15}".format(description[:34], action)) - - print("\nQuantum Walk Test:") - available_schemes = list(EncodingScheme) - path = processor.quantum_walk_advance( - gradient_magnitude=0.7, - current_encoding=EncodingScheme.OISC, - available_schemes=available_schemes - ) - - print(f"Optimal encoding: {path.encoding_scheme.value}") - print(f"Confidence: {path.confidence:.3f}") - print(f"Gradient magnitude: {path.gradient_magnitude:.3f}") - print(f"Entropy delta: {path.entropy_delta:.3f}") - - print("\n" + "=" * 60) - print("INTEGRATION WITH MULTI-ENCODING BLINK RATE") - print("=" * 60) - - print(""" -Key Integration Points: - -1. TUNNELING EVENT TRIGGERS - - EMI spike → TRIT_SHIFT → Encoding scheme upgrade - - Tunneling verified → QUANTUM_WALK_ADVANCE → Optimal encoding selection - - State commit → Hash collapse → Encoding scheme lock-in - -2. GRADIENT-BASED ENCODING SELECTION - - High gradient + high noise → Upgrade to quaternary - - Low gradient + low noise → Downgrade to OISC - - Medium gradient → Binary/ternary SLUQ - -3. ENTROPY DAMPER REGULATION - - ΔS* < 0.499 → Inject jitter → Add randomness to encoding - - ΔS* > 0.501 → VRAM_FLUSH → Reset to OISC - - ΔS* = 0.500 → Maintain current encoding - -4. QUANTUM WALK OPTIMIZATION - - Uses sqrt(N) complexity to find optimal encoding - - Superposition over all encoding schemes - - Collapses to highest probability scheme - -5. TRIUMVIRATE GOVERNANCE - - Architect: Verifies encoding aligns with design goals - - Warden: Verifies entropy equilibrium (ΔS* = 0.500) - - HeatSink: Verifies resource budget available - -Benefits: -- Adaptive encoding based on gradient landscape -- Quantum walk finds optimal encoding faster than exhaustive search -- Entropy damper prevents over-complexity or under-complexity -- Tunneling provides graceful state transitions -- Hash commitment ensures encoding stability - """) - - -if __name__ == "__main__": - import hashlib - test_quantum_tunneling_gradient_processing() diff --git a/5-Applications/tools-scripts/design/design_quaternary_dsp_flowchain.py b/5-Applications/tools-scripts/design/design_quaternary_dsp_flowchain.py deleted file mode 100644 index 53aa7748..00000000 --- a/5-Applications/tools-scripts/design/design_quaternary_dsp_flowchain.py +++ /dev/null @@ -1,325 +0,0 @@ -#!/usr/bin/env python3 -""" -Design a conditional quaternary preprocessing stage for DSP flow chain. -Detects data amenable to quaternary smoothing and applies it selectively. -""" - -import numpy as np -import zlib -import math -from collections import Counter -from pathlib import Path - -REPO_ROOT = Path(__file__).resolve().parent -ENWIK9_PATH = REPO_ROOT / "hutter_bind_implementation" / "enwik9" - -def calculate_entropy(data): - """Calculate Shannon entropy of data.""" - if not data: - return 0.0 - - freq = Counter(data) - total = len(data) - entropy = 0.0 - - for count in freq.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - - return entropy - -def calculate_transition_frequency(data): - """Calculate frequency of byte transitions (high-frequency indicator).""" - if len(data) < 2: - return 0.0 - - transitions = 0 - for i in range(len(data) - 1): - if data[i] != data[i+1]: - transitions += 1 - - return transitions / (len(data) - 1) - -def calculate_smoothness_score(data): - """ - Calculate tensor-likeness score - higher means more tensor-like. - Key insight: Quaternary smoothing helps ALL tensor types (structured, sparse, random) - but HURTS text data. So we just need to distinguish tensor data from text data. - - Simple heuristic: Text is mostly ASCII (0x20-0x7E), tensors have broader byte distribution. - """ - # Text typically has >70% ASCII printable characters - ascii_count = sum(1 for b in data if 0x20 <= b <= 0x7E) - ascii_ratio = ascii_count / len(data) - - # If >70% ASCII, it's likely text → low tensor score - # If <70% ASCII, it's likely binary/tensor → high tensor score - if ascii_ratio > 0.7: - tensor_score = 0.1 # Definitely text - else: - tensor_score = 0.9 # Likely tensor/binary - - return tensor_score - -def byte_to_quaternary(byte_val): - """Convert a byte (0-255) to 4 quaternary digits (base-4).""" - quaternary = [] - for _ in range(4): - quaternary.append(byte_val % 4) - byte_val //= 4 - return quaternary[::-1] - -def quaternary_to_byte(quad_digits): - """Convert 4 quaternary digits back to a byte.""" - byte_val = 0 - for digit in quad_digits: - byte_val = byte_val * 4 + digit - return byte_val - -def quaternary_smooth(data): - """Apply quaternary smoothing to data.""" - if len(data) < 2: - return data - - result = bytearray() - result.append(data[0]) - prev_quad = byte_to_quaternary(data[0]) - - for byte_val in data[1:]: - curr_quad = byte_to_quaternary(byte_val) - smoothed_quad = [] - - for i in range(4): - diff = abs(curr_quad[i] - prev_quad[i]) - if diff > 1: - if curr_quad[i] > prev_quad[i]: - smoothed_quad.append(prev_quad[i] + 1) - else: - smoothed_quad.append(prev_quad[i] - 1) - else: - smoothed_quad.append(curr_quad[i]) - - smoothed_quad = [max(0, min(3, d)) for d in smoothed_quad] - result.append(quaternary_to_byte(smoothed_quad)) - prev_quad = smoothed_quad - - return bytes(result) - -def conditional_quaternary_preprocess(data, threshold=0.4): - """ - Conditionally apply quaternary smoothing based on data characteristics. - - Args: - data: Input data bytes - threshold: Smoothness threshold above which to apply quaternary smoothing - - Returns: - (preprocessed_data, applied, smoothness_score, metrics) - """ - smoothness = calculate_smoothness_score(data) - - metrics = { - 'smoothness_score': smoothness, - 'entropy': calculate_entropy(data), - 'transition_frequency': calculate_transition_frequency(data), - 'threshold': threshold - } - - if smoothness >= threshold: - preprocessed = quaternary_smooth(data) - applied = True - else: - preprocessed = data - applied = False - - return preprocessed, applied, smoothness, metrics - -def evaluate_preprocess_benefit(data, threshold=0.5): - """Evaluate whether quaternary preprocessing improves compression.""" - original_compressed = zlib.compress(data, level=9) - - preprocessed, applied, smoothness, metrics = conditional_quaternary_preprocess(data, threshold) - preprocessed_compressed = zlib.compress(preprocessed, level=9) - - benefit = { - 'original_size': len(data), - 'original_compressed': len(original_compressed), - 'preprocessed_size': len(preprocessed), - 'preprocessed_compressed': len(preprocessed_compressed), - 'applied': applied, - 'smoothness_score': smoothness, - 'compression_improvement': len(original_compressed) - len(preprocessed_compressed), - 'compression_ratio': len(preprocessed_compressed) / len(original_compressed), - 'metrics': metrics - } - - return benefit - -def main(): - print("=" * 60) - print("Conditional Quaternary Preprocessing for DSP Flow Chain") - print("=" * 60) - - # Test on different data types - test_cases = [] - - # 1. Text data (enwik9 sample) - print("\n[1] Testing on TEXT data (enwik9 sample)...") - with open(ENWIK9_PATH, "rb") as f: - text_data = f.read(100_000) # 100KB sample - - text_benefit = evaluate_preprocess_benefit(text_data, threshold=0.4) - test_cases.append(('Text (enwik9)', text_benefit)) - - print(f" Smoothness score: {text_benefit['smoothness_score']:.4f}") - print(f" Applied: {text_benefit['applied']}") - print(f" Compression improvement: {text_benefit['compression_improvement']:+,} bytes") - print(f" Compression ratio: {text_benefit['compression_ratio']:.4f}") - - # 2. Structured tensor - print("\n[2] Testing on STRUCTURED tensor...") - U = np.random.randn(1000, 10) - V = np.random.randn(10, 1000) - structured_tensor = (U @ V).astype(np.float16) - structured_data = structured_tensor.tobytes() - - structured_benefit = evaluate_preprocess_benefit(structured_data, threshold=0.4) - test_cases.append(('Structured Tensor', structured_benefit)) - - print(f" Smoothness score: {structured_benefit['smoothness_score']:.4f}") - print(f" Applied: {structured_benefit['applied']}") - print(f" Compression improvement: {structured_benefit['compression_improvement']:+,} bytes") - print(f" Compression ratio: {structured_benefit['compression_ratio']:.4f}") - - # 3. Sparse tensor - print("\n[3] Testing on SPARSE tensor...") - sparse_tensor = np.zeros((1000, 1000), dtype=np.float16) - for _ in range(10000): - i, j = np.random.randint(0, 1000), np.random.randint(0, 1000) - sparse_tensor[i, j] = np.random.randn() * 100 - sparse_data = sparse_tensor.tobytes() - - sparse_benefit = evaluate_preprocess_benefit(sparse_data, threshold=0.4) - test_cases.append(('Sparse Tensor', sparse_benefit)) - - print(f" Smoothness score: {sparse_benefit['smoothness_score']:.4f}") - print(f" Applied: {sparse_benefit['applied']}") - print(f" Compression improvement: {sparse_benefit['compression_improvement']:+,} bytes") - print(f" Compression ratio: {sparse_benefit['compression_ratio']:.4f}") - - # 4. Random tensor - print("\n[4] Testing on RANDOM tensor...") - random_tensor = np.random.randn(1000, 1000).astype(np.float16) - random_data = random_tensor.tobytes() - - random_benefit = evaluate_preprocess_benefit(random_data, threshold=0.4) - test_cases.append(('Random Tensor', random_benefit)) - - print(f" Smoothness score: {random_benefit['smoothness_score']:.4f}") - print(f" Applied: {random_benefit['applied']}") - print(f" Compression improvement: {random_benefit['compression_improvement']:+,} bytes") - print(f" Compression ratio: {random_benefit['compression_ratio']:.4f}") - - # Summary - print("\n" + "=" * 60) - print("SUMMARY") - print("=" * 60) - print("{:<25} {:<12} {:<12} {:<12} {:<12}".format( - 'Data Type', 'Smoothness', 'Applied', 'Improvement', 'Ratio' - )) - print("-" * 63) - - for name, benefit in test_cases: - smooth_str = "{:.4f}".format(benefit['smoothness_score']) - applied_str = "Yes" if benefit['applied'] else "No" - impr_str = "{:+,}".format(benefit['compression_improvement']) - ratio_str = "{:.4f}".format(benefit['compression_ratio']) - print("{:<25} {:<12} {:<12} {:<12} {:<12}".format( - name, smooth_str, applied_str, impr_str, ratio_str - )) - - print("\n" + "=" * 60) - print("DSP FLOW CHAIN DESIGN") - print("=" * 60) - - print(""" -DSP Flow Chain with Conditional Quaternary Preprocessing: - -┌─────────────────┐ -│ Input Data │ -└────────┬────────┘ - │ - ▼ -┌─────────────────┐ -│ Characterize │ -│ - ASCII ratio │ -│ - Tensor score │ -└────────┬────────┘ - │ - ▼ -┌─────────────────┐ -│ Decision Gate │ -│ (threshold=0.4)│ -└────────┬────────┘ - │ - ┌────┴────┐ - │ │ - ▼ ▼ -┌────────┐ ┌────────┐ -│ Apply │ │ Bypass │ -│ Quad │ │ │ -│ Smooth │ │ │ -└───┬────┘ └───┬────┘ - │ │ - └────┬───────┘ - │ - ▼ -┌─────────────────┐ -│ DSP Processing │ -│ (neuromorphic │ -│ stack) │ -└────────┬────────┘ - │ - ▼ -┌─────────────────┐ -│ Output │ -└─────────────────┘ - -Key Parameters: -- ASCII threshold: 70% (text if >70% ASCII) -- Decision threshold: 0.4 (tensor score) -- Applied to tensor-like data (Float16, binary) -- Text data bypasses preprocessing - """) - - # Optimize threshold - print("\n" + "=" * 60) - print("THRESHOLD OPTIMIZATION") - print("=" * 60) - - thresholds = [0.3, 0.4, 0.5, 0.6, 0.7] - - print("\nStructured Tensor:") - for thresh in thresholds: - benefit = evaluate_preprocess_benefit(structured_data, threshold=thresh) - print(f" Threshold {thresh}: Applied={benefit['applied']}, Ratio={benefit['compression_ratio']:.4f}") - - print("\nText Data:") - for thresh in thresholds: - benefit = evaluate_preprocess_benefit(text_data, threshold=thresh) - print(f" Threshold {thresh}: Applied={benefit['applied']}, Ratio={benefit['compression_ratio']:.4f}") - - print("\n" + "=" * 60) - print("RECOMMENDATIONS") - print("=" * 60) - print("1. Use threshold=0.4 for optimal performance") - print(" - Catches structured/random tensors (10% improvement)") - print(" - Avoids text data (would worsen compression by 7%)") - print("2. Quaternary smoothing helps ALL tensor data (15-21% improvement)") - print("3. Text data must bypass preprocessing (5% worse if applied)") - print("4. Decision gate adds minimal overhead (ASCII ratio calculation)") - print("5. Can be integrated into existing DSP pipeline as preprocessing stage") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/domain/classify_domains.py b/5-Applications/tools-scripts/domain/classify_domains.py deleted file mode 100644 index 4211e3d0..00000000 --- a/5-Applications/tools-scripts/domain/classify_domains.py +++ /dev/null @@ -1,304 +0,0 @@ -#!/usr/bin/env python3 -""" -classify_domains.py — Add Domain_Type classification to MATH_MODEL_MAP.tsv - -Domain taxonomy derived from the Topological Tape Machine specification: - LAYER_A_COMPRESSION — Representation selection, entropy, encoding, compression objectives - LAYER_B_ROUTING — Cognitive load, mixture-of-experts, predictor distribution, decision reweighting - LAYER_C_TOPOLOGY — Metric tensors, geodesics, manifolds, curvature, Christoffel, charts - LAYER_C_BRAID — Braid formation, witness traces, Merkle structures, raycasting, holonomy - LAYER_D_INVARIANTS — Invariant vectors, conservation laws, constraint systems, survival masks - LAYER_E_VERIFICATION — Acceptance predicates, attestation, BEA consensus, validation checks - LAYER_F_CONTROL — Homeostatic control, hysteresis, mode transitions, waveprobe risk, pressure dynamics - LAYER_G_ENERGY — Thermodynamics, Landauer, Carnot, phonon physics, QCL energy, hardware stress - LAYER_H_ALGEBRA — Geometric algebra, chirality, group theory, finite fields, semirings - LAYER_I_ENCODING — Voxel keys, microvoxel seeds, bit-packing, address schemes, quantization - LAYER_J_DYNAMICS — Time evolution, phase transitions, emergence, Langevin, deformation fields - LAYER_K_SIGNAL — DSP, FFT, wave propagation, scattering, Rayleigh, phased arrays - LAYER_L_APPLICATION — FEA, hormone mapping, semi-truck physics, engineering models - -Each model is classified into exactly ONE primary domain. -""" - -import csv -import sys - -# Domain classification rules: (model_number_range_or_name_keywords, domain_type) -# Using model number ranges for bulk assignment, with keyword overrides. - -DOMAIN_MAP = { - # LAYER_A: Compression / Representation Selection (models 1-2, 6-10, 33, 44, 54, 71, 74, 102, 126) - "1": "LAYER_A_COMPRESSION", - "2": "LAYER_A_COMPRESSION", - "6": "LAYER_A_COMPRESSION", - "7": "LAYER_A_COMPRESSION", - "8": "LAYER_A_COMPRESSION", - "9": "LAYER_A_COMPRESSION", - "10": "LAYER_A_COMPRESSION", - "33": "LAYER_A_COMPRESSION", - "44": "LAYER_A_COMPRESSION", - "54": "LAYER_A_COMPRESSION", - "71": "LAYER_A_COMPRESSION", - "74": "LAYER_A_COMPRESSION", - "102":"LAYER_A_COMPRESSION", - "126":"LAYER_A_COMPRESSION", - - # LAYER_B: Routing / Cognitive Load (models 3-5, 32, 45, 48, 50, 72-73, 75, 95, 98-101, 120-121, 137) - "3": "LAYER_B_ROUTING", - "4": "LAYER_B_ROUTING", - "5": "LAYER_B_ROUTING", - "32": "LAYER_B_ROUTING", - "45": "LAYER_B_ROUTING", - "48": "LAYER_B_ROUTING", - "50": "LAYER_B_ROUTING", - "72": "LAYER_B_ROUTING", - "73": "LAYER_B_ROUTING", - "75": "LAYER_B_ROUTING", - "95": "LAYER_B_ROUTING", - "98": "LAYER_B_ROUTING", - "99": "LAYER_B_ROUTING", - "100":"LAYER_B_ROUTING", - "101":"LAYER_B_ROUTING", - "120":"LAYER_B_ROUTING", - "121":"LAYER_B_ROUTING", - "137":"LAYER_B_ROUTING", - - # LAYER_C_TOPOLOGY: Metric tensors, geodesics, manifolds, curvature (models 16-18, 25, 34, 38, 46, 82-89, 96-97, 105-107, 115-117, 119, 135-136) - "16": "LAYER_C_TOPOLOGY", - "17": "LAYER_C_TOPOLOGY", - "18": "LAYER_C_TOPOLOGY", - "25": "LAYER_C_TOPOLOGY", - "34": "LAYER_C_TOPOLOGY", - "38": "LAYER_C_TOPOLOGY", - "46": "LAYER_C_TOPOLOGY", - "82": "LAYER_C_TOPOLOGY", - "83": "LAYER_C_TOPOLOGY", - "84": "LAYER_C_TOPOLOGY", - "85": "LAYER_C_TOPOLOGY", - "86": "LAYER_C_TOPOLOGY", - "87": "LAYER_C_TOPOLOGY", - "88": "LAYER_C_TOPOLOGY", - "89": "LAYER_C_TOPOLOGY", - "96": "LAYER_C_TOPOLOGY", - "97": "LAYER_C_TOPOLOGY", - "105":"LAYER_C_TOPOLOGY", - "106":"LAYER_C_TOPOLOGY", - "107":"LAYER_C_TOPOLOGY", - "115":"LAYER_C_TOPOLOGY", - "116":"LAYER_C_TOPOLOGY", - "117":"LAYER_C_TOPOLOGY", - "119":"LAYER_C_TOPOLOGY", - "135":"LAYER_C_TOPOLOGY", - "136":"LAYER_C_TOPOLOGY", - - # LAYER_C_BRAID: Braid formation, witnesses, raycasting, holonomy (models 35-37, 39, 76-78, 110-112, 130) - "35": "LAYER_C_BRAID", - "36": "LAYER_C_BRAID", - "37": "LAYER_C_BRAID", - "39": "LAYER_C_BRAID", - "76": "LAYER_C_BRAID", - "77": "LAYER_C_BRAID", - "78": "LAYER_C_BRAID", - "110":"LAYER_C_BRAID", - "111":"LAYER_C_BRAID", - "112":"LAYER_C_BRAID", - "130":"LAYER_C_BRAID", - - # LAYER_D_INVARIANTS: Conservation laws, constraint systems (models 28, 30-31, 43, 56, 61-63, 111, 127-128) - "28": "LAYER_D_INVARIANTS", - "30": "LAYER_D_INVARIANTS", - "31": "LAYER_D_INVARIANTS", - "43": "LAYER_D_INVARIANTS", - "56": "LAYER_D_INVARIANTS", - "61": "LAYER_D_INVARIANTS", - "62": "LAYER_D_INVARIANTS", - "63": "LAYER_D_INVARIANTS", - "127":"LAYER_D_INVARIANTS", - "128":"LAYER_D_INVARIANTS", - - # LAYER_E_VERIFICATION: Acceptance, attestation, BEA, validation (models 11, 14-15, 55, 60, 94, 125, 138) - "11": "LAYER_E_VERIFICATION", - "14": "LAYER_E_VERIFICATION", - "15": "LAYER_E_VERIFICATION", - "55": "LAYER_E_VERIFICATION", - "60": "LAYER_E_VERIFICATION", - "94": "LAYER_E_VERIFICATION", - "125":"LAYER_E_VERIFICATION", - "138":"LAYER_E_VERIFICATION", - - # LAYER_F_CONTROL: Homeostatic, hysteresis, waveprobe, mode transitions (models 7, 12, 24, 26-29, 49, 88, 90-93, 131-134) - "24": "LAYER_F_CONTROL", - "26": "LAYER_F_CONTROL", - "27": "LAYER_F_CONTROL", - "29": "LAYER_F_CONTROL", - "49": "LAYER_F_CONTROL", - "90": "LAYER_F_CONTROL", - "91": "LAYER_F_CONTROL", - "92": "LAYER_F_CONTROL", - "93": "LAYER_F_CONTROL", - "131":"LAYER_F_CONTROL", - "132":"LAYER_F_CONTROL", - "133":"LAYER_F_CONTROL", - "134":"LAYER_F_CONTROL", - - # LAYER_G_ENERGY: Thermodynamics, Landauer, phonon, QCL, hardware stress (models 13, 20-23, 39-42, 47, 51-53, 57-59, 64-70, 108-109, 113-114, 139-140) - "13": "LAYER_G_ENERGY", - "20": "LAYER_G_ENERGY", - "21": "LAYER_G_ENERGY", - "22": "LAYER_G_ENERGY", - "23": "LAYER_G_ENERGY", - "39": "LAYER_G_ENERGY", - "40": "LAYER_G_ENERGY", - "41": "LAYER_G_ENERGY", - "42": "LAYER_G_ENERGY", - "47": "LAYER_G_ENERGY", - "51": "LAYER_G_ENERGY", - "52": "LAYER_G_ENERGY", - "53": "LAYER_G_ENERGY", - "57": "LAYER_G_ENERGY", - "58": "LAYER_G_ENERGY", - "59": "LAYER_G_ENERGY", - "64": "LAYER_G_ENERGY", - "65": "LAYER_G_ENERGY", - "66": "LAYER_G_ENERGY", - "67": "LAYER_G_ENERGY", - "68": "LAYER_G_ENERGY", - "69": "LAYER_G_ENERGY", - "70": "LAYER_G_ENERGY", - "108":"LAYER_G_ENERGY", - "109":"LAYER_G_ENERGY", - "113":"LAYER_G_ENERGY", - "114":"LAYER_G_ENERGY", - "139":"LAYER_G_ENERGY", - "140":"LAYER_G_ENERGY", - - # LAYER_H_ALGEBRA: Geometric algebra, chirality, group theory (models 19, 21-23, 43, 117-119) - "19": "LAYER_H_ALGEBRA", - "21": "LAYER_H_ALGEBRA", - "22": "LAYER_H_ALGEBRA", - "23": "LAYER_H_ALGEBRA", - "43": "LAYER_H_ALGEBRA", - "117":"LAYER_H_ALGEBRA", - "118":"LAYER_H_ALGEBRA", - "119":"LAYER_H_ALGEBRA", - - # LAYER_I_ENCODING: Voxel keys, microvoxel, bit-packing, address schemes (models 123-124, 129) - "123":"LAYER_I_ENCODING", - "124":"LAYER_I_ENCODING", - "129":"LAYER_I_ENCODING", - - # LAYER_J_DYNAMICS: Time evolution, phase transitions, emergence, deformation (models 8-9, 33, 44, 56, 71, 74, 103-104, 131-132) - "103":"LAYER_J_DYNAMICS", - "104":"LAYER_J_DYNAMICS", - - # LAYER_K_SIGNAL: DSP, FFT, wave propagation, scattering (models 79-81, 113-114) - "79": "LAYER_K_SIGNAL", - "80": "LAYER_K_SIGNAL", - "81": "LAYER_K_SIGNAL", - - # LAYER_L_APPLICATION: FEA, hormone mapping, engineering (models 45, 120-122, 137) - "122":"LAYER_L_APPLICATION", -} - - -def classify_domain(model_num: str, model_name: str, family: str) -> str: - """Classify a model into its TTM domain layer.""" - # Direct number lookup - if model_num in DOMAIN_MAP: - return DOMAIN_MAP[model_num] - - # Keyword fallbacks for unclassified models - name_lower = (model_name + " " + family).lower() - - if any(k in name_lower for k in ["compression", "entropy", "shannon", "hutter", "shape", "mi ", "mutual information", "structure yield", "watanabe", "kolmogorov"]): - return "LAYER_A_COMPRESSION" - if any(k in name_lower for k in ["routing", "cognitive", "load", "homeostatic", "pressure", "canal", "reweight", "equilibrium", "logit", "hormone", "half life", "decay rate", "concentration"]): - return "LAYER_B_ROUTING" - if any(k in name_lower for k in ["metric", "geodesic", "manifold", "curvature", "christoffel", "chart", "stereographic", "phi", "phi-weighted", "hyperbolic", "mobius", "non-euclidean", "writhe", "parallel transport", "pga", "geometric algebra", "sine-gordon", "curvature-torsion", "constraint geometry", "universe type scoring", "mean curvature"]): - return "LAYER_C_TOPOLOGY" - if any(k in name_lower for k in ["braid", "raycast", "ray-cast", "mmr", "merkle", "holonomy", "algebraic", "rollup", "diat", "uvmap", "half-mobius closure"]): - return "LAYER_C_BRAID" - if any(k in name_lower for k in ["invariant", "constraint", "conservation", "exact", "narrowing", "precision", "latency table", "sieve", "relation", "proxy"]): - return "LAYER_D_INVARIANTS" - if any(k in name_lower for k in ["verification", "attestation", "bea", "witness", "acceptance", "q-factor", "landauer", "surprise", "regret", "blink", "ternary", "dcvn", "thermal finality"]): - return "LAYER_E_VERIFICATION" - if any(k in name_lower for k in ["control", "hysteresis", "waveprobe", "risk", "heat evolution", "mode transition", "binning", "lut policy", "regret field", "blink cycle", "phase transition", "emergence"]): - return "LAYER_F_CONTROL" - if any(k in name_lower for k in ["thermodynamic", "arrhenius", "black", "coffin-manson", "bit-flip", "qcl", "quantum cascade", "photon", "energy", "phonon", "boltzmann", "carnot", "heat engine", "entropy generation", "rul", "remaining useful", "alcubierre", "dyson", "langevin"]): - return "LAYER_G_ENERGY" - if any(k in name_lower for k in ["chirality", "cl(3,0,1)", "geometric product", "motor", "clifford"]): - return "LAYER_H_ALGEBRA" - if any(k in name_lower for k in ["voxel", "microvoxel", "encoding", "seed", "bit", "pack", "address", "seismic", "topological encoder"]): - return "LAYER_I_ENCODING" - if any(k in name_lower for k in ["deformation", "epoch", "sha256 field", "manifold delta"]): - return "LAYER_J_DYNAMICS" - if any(k in name_lower for k in ["bracket", "braid sb", "cosine similarity", "gradient alignment", "phase accumulation", "dsp", "fft"]): - return "LAYER_K_SIGNAL" - if any(k in name_lower for k in ["fea", "hormone", "semi-truck", "dynamic amplification", "engineering"]): - return "LAYER_L_APPLICATION" - - return "UNCLASSIFIED" - - -def main(): - input_path = "6-Documentation/docs/MATH_MODEL_MAP.tsv" - output_path = "6-Documentation/docs/MATH_MODEL_MAP_classified.tsv" - - with open(input_path, "r", newline="") as f: - reader = csv.reader(f, delimiter="\t") - header = next(reader) - rows = list(reader) - - # Find the comment line (starts with #) - comment_lines = [] - data_rows = [] - for row in rows: - if row and row[0].startswith("#"): - comment_lines.append(row) - else: - data_rows.append(row) - - # Add Domain_Type column to header - header.append("Domain_Type") - - # Classify each row - classified = 0 - unclassified = 0 - domain_counts = {} - for row in data_rows: - model_num = row[0].strip() if row else "" - model_name = row[1].strip() if len(row) > 1 else "" - family = row[2].strip() if len(row) > 2 else "" - - domain = classify_domain(model_num, model_name, family) - row.append(domain) - - if domain == "UNCLASSIFIED": - unclassified += 1 - else: - classified += 1 - domain_counts[domain] = domain_counts.get(domain, 0) + 1 - - # Write output - with open(output_path, "w", newline="") as f: - writer = csv.writer(f, delimiter="\t", lineterminator="\n") - for cl in comment_lines: - writer.writerow(cl) - writer.writerow(header) - for row in data_rows: - writer.writerow(row) - - # Summary - print(f"Classified {classified}/{classified + unclassified} models") - if unclassified: - print(f"WARNING: {unclassified} models UNCLASSIFIED") - print() - print("Domain distribution:") - for domain in sorted(domain_counts.keys()): - print(f" {domain:30s} {domain_counts[domain]:3d}") - - print(f"\nOutput written to: {output_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/domain/domain_crossbreed_swarm.py b/5-Applications/tools-scripts/domain/domain_crossbreed_swarm.py deleted file mode 100644 index a293635c..00000000 --- a/5-Applications/tools-scripts/domain/domain_crossbreed_swarm.py +++ /dev/null @@ -1,564 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Domain Crossbreed Swarm — Deterministic cross-domain invariant generator. - -Implements the DOMAIN_CROSSBREED_SWARM_PROTOCOL by pairing experts from the -EXHAUSTIVE_DOMAIN_EXPERT_LIST, projecting their constraint matrices into a -shared 7-dimensional invariant space, and deriving novel solutions at the -intersection boundary. - -Architecture: - ┌───────────────────────────────────────────────────────────┐ - │ CROSSBREED ENGINE │ - ├─────────────┬─────────────┬─────────────┬─────────────┤ - │ EXPERT A │ EXPERT B │ PROJECTOR │ INTEGRATOR │ - └─────────────┴─────────────┴─────────────┴─────────────┘ - -Usage: - python 5-Applications/tools-5-Applications/scripts/domain_crossbreed_swarm.py --pairs 5 --output-dir shared-data/data/swarm -""" - -from __future__ import annotations - -import argparse -import hashlib -import itertools -import json -import logging -import math -import os -import random -import re -import sys -import time -from dataclasses import asdict, dataclass, field -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -# Attempt to import the local LLM client; fallback to pure-deterministic mode. -try: - from local_llm_client import LocalLLMClient - HAS_LLM_CLIENT = True -except Exception: - HAS_LLM_CLIENT = False - -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s | %(levelname)-8s | %(message)s", -) -logger = logging.getLogger("crossbreed_swarm") - -# ── Constants ──────────────────────────────────────────────────────────────── - -DOCS_ROOT = Path(__file__).resolve().parents[2] / "docs" -EXPERT_LIST_PATH = DOCS_ROOT / "audits" / "EXHAUSTIVE_DOMAIN_EXPERT_LIST.md" -PROTOCOL_PATH = DOCS_ROOT / "design" / "DOMAIN_CROSSBREED_SWARM.md" - -DIMENSIONS = ["T", "S", "C", "F", "R", "P", "W"] -DIMENSION_NAMES = { - "T": "Time ordering constraints", - "S": "Spatial boundary conditions", - "C": "Causality directionality", - "F": "Failure modes", - "R": "Resource limits", - "P": "Progress metric", - "W": "Work function definition", -} - -EPSILON_THRESHOLD = 1e-9 - -# ── Data Structures ────────────────────────────────────────────────────────── - -@dataclass(frozen=True) -class DomainExpert: - emoji: str - name: str - category: str - - def __str__(self) -> str: - return f"{self.emoji} {self.name}" - - -@dataclass -class ConstraintMatrix: - """7-dimensional constraint vector for a domain.""" - T: float = 0.0 - S: float = 0.0 - C: float = 0.0 - F: float = 0.0 - R: float = 0.0 - P: float = 0.0 - W: float = 0.0 - - def to_vec(self) -> List[float]: - return [self.T, self.S, self.C, self.F, self.R, self.P, self.W] - - @classmethod - def from_vec(cls, vec: List[float]) -> "ConstraintMatrix": - return cls(*vec) - - def hadamard(self, other: "ConstraintMatrix") -> "ConstraintMatrix": - a, b = self.to_vec(), other.to_vec() - return self.__class__.from_vec([x * y for x, y in zip(a, b)]) - - def distance(self, other: "ConstraintMatrix") -> float: - a, b = self.to_vec(), other.to_vec() - return math.sqrt(sum((x - y) ** 2 for x, y in zip(a, b))) - - -@dataclass -class CrossbreedInvariant: - domain_a: str - domain_b: str - invariant_name: str - invariant_description: str - constraint_a: Dict[str, float] - constraint_b: Dict[str, float] - intersection: Dict[str, float] - epsilon: float - verification_status: str - derivation_method: str - generation: int = 1 - - -# ── Markdown Parsing ───────────────────────────────────────────────────────── - -def parse_expert_list(path: Path) -> Tuple[List[DomainExpert], List[DomainExpert]]: - """Parse EXHAUSTIVE_DOMAIN_EXPERT_LIST.md into activated and queued experts.""" - text = path.read_text(encoding="utf-8") - - activated: List[DomainExpert] = [] - queued: List[DomainExpert] = [] - current_category = "Unknown" - - # Split into activated and queued sections - activated_section = re.search( - r"## ✅ ACTIVATED EXPERTS.*?\n---", text, re.DOTALL - ) - queued_section = re.search( - r"## 🚀 NEXT IN QUEUE.*?\n---", text, re.DOTALL - ) - - def _parse_section(section_text: str, target_list: List[DomainExpert]) -> None: - nonlocal current_category - cat = "unknown" - for line in section_text.splitlines(): - cat_match = re.match(r"###\s+(.*)", line) - if cat_match: - cat = cat_match.group(1).strip() - continue - expert_match = re.match(r"-\s+([\U0001F300-\U0001FAFF\u2600-\u26FF])\s+(.*)", line) - if expert_match: - emoji, name = expert_match.group(1), expert_match.group(2).strip() - target_list.append(DomainExpert(emoji, name, cat)) - - if activated_section: - _parse_section(activated_section.group(0), activated) - if queued_section: - _parse_section(queued_section.group(0), queued) - - return activated, queued - - -def parse_protocol(path: Path) -> Dict[str, Any]: - """Parse DOMAIN_CROSSBREED_SWARM.md for generation rules.""" - text = path.read_text(encoding="utf-8") - rules: Dict[str, Any] = {"algorithm_steps": [], "existing_combinations": []} - - # Extract algorithm steps - steps = re.findall(r"### Step \d+:\s+(.*)\n(.*?)(?=### Step|\n---|\n## )", text, re.DOTALL) - for title, body in steps: - rules["algorithm_steps"].append({"title": title.strip(), "body": body.strip()}) - - # Extract existing combinations table - table_rows = re.findall( - r"\|\s*([^|]+)\|\s*([^|]+)\|\s*\*\*(.*?)\*\*\s*-\s*(.*?)\|\s*✅\s*DERIVED\s*\|", - text, - ) - for a, b, inv_name, inv_desc in table_rows: - rules["existing_combinations"].append({ - "domain_a": a.strip(), - "domain_b": b.strip(), - "invariant_name": inv_name.strip(), - "invariant_description": inv_desc.strip(), - }) - - return rules - - -# ── Deterministic Constraint Engine ────────────────────────────────────────── - -def deterministic_constraint_hash(name: str) -> List[float]: - """Generate a deterministic 7-dimensional constraint vector from a domain name. - - The hash is derived from SHA-256 of the normalized domain name, split into - 7 chunks and mapped to the unit interval. This guarantees that any two - identical domain names always produce the exact same constraint surface. - """ - digest = hashlib.sha256(name.encode("utf-8")).digest() - chunks = [digest[i : i + 4] for i in range(0, 28, 4)] - vec = [int.from_bytes(c, "big") / 0xFFFFFFFF for c in chunks] - return vec - - -def build_constraint_matrix(expert: DomainExpert) -> ConstraintMatrix: - vec = deterministic_constraint_hash(expert.name.lower()) - return ConstraintMatrix.from_vec(vec) - - -def intersect_constraints(a: ConstraintMatrix, b: ConstraintMatrix) -> ConstraintMatrix: - """Compute the Hadamard product as the intersection surface.""" - return a.hadamard(b) - - -def verify_invariant(a: ConstraintMatrix, b: ConstraintMatrix, intersection: ConstraintMatrix) -> Tuple[bool, float]: - """Verify that the intersection satisfies both domain constraints within epsilon.""" - # The verification condition is: ||C_D1(I) - C_D2(I)|| < epsilon - # We interpret C_D(I) as the projection of I onto D's constraint surface. - # A valid intersection should be close to both parent surfaces. - proj_a = ConstraintMatrix.from_vec([min(intersection.to_vec()[i], a.to_vec()[i]) for i in range(7)]) - proj_b = ConstraintMatrix.from_vec([min(intersection.to_vec()[i], b.to_vec()[i]) for i in range(7)]) - epsilon = proj_a.distance(proj_b) - return epsilon < EPSILON_THRESHOLD, epsilon - - -# ── LLM Agent Prompts ──────────────────────────────────────────────────────── - -SYSTEM_EXPERT = ( - "You are a domain expert. Your job is to describe your domain using exactly " - "7 invariant dimensions: T (Time), S (Space), C (Causality), F (Failure), " - "R (Resources), P (Progress), W (Work). Be precise, formal, and mathematical. " - "Respond only with the requested JSON." -) - -SYSTEM_PROJECTOR = ( - "You are the Projector agent in a Domain Crossbreed Swarm. " - "You receive two domain constraint descriptions. Your job is to find the " - "mathematical intersection where their constraint surfaces overlap. " - "The result must be a novel invariant that is not trivially true in either " - "domain alone. Respond only with JSON." -) - -SYSTEM_INTEGRATOR = ( - "You are the Integrator agent in a Domain Crossbreed Swarm. " - "You receive a proposed cross-domain invariant. Your job is to verify it " - "against both parent domain axioms, formalize it with a concise name and " - "mathematical description, and assign a confidence score. Respond only with JSON." -) - - -def prompt_expert(expert: DomainExpert) -> str: - return ( - f"Domain: {expert.name}\n" - f"Category: {expert.category}\n\n" - "Describe this domain's fundamental invariants as a JSON object with exactly these keys:\n" - " T, S, C, F, R, P, W (each a float between 0.0 and 1.0)\n" - " 'justification' (string, ≤80 words)\n" - " 'canonical_equation' (string, one-line mathematical signature)\n" - "Ensure the values are deterministic: the same domain must always yield the same floats." - ) - - -def prompt_projector(da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> str: - return ( - f"Domain A: {da.name}\n" - f"Domain B: {db.name}\n\n" - f"Constraint A: {json.dumps(ca, indent=2)}\n\n" - f"Constraint B: {json.dumps(cb, indent=2)}\n\n" - "Find the intersection of these constraint surfaces. " - "Return JSON with:\n" - " 'invariant_name' (concise, ≤6 words)\n" - " 'invariant_description' (formal, ≤40 words)\n" - " 'intersection_equation' (one-line math)\n" - " 'novelty_score' (float 0.0-1.0)\n" - "The invariant must be non-obvious and mathematically derivable from both domains." - ) - - -def prompt_integrator(da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> str: - return ( - f"Proposed crossbreed between '{da.name}' and '{db.name}':\n" - f"{json.dumps(proposal, indent=2)}\n\n" - "Verify this invariant against both parent domain axioms. " - "Return JSON with:\n" - " 'verified' (bool)\n" - " 'verification_reason' (string, ≤30 words)\n" - " 'formal_name' (string, polished title)\n" - " 'formal_description' (string, polished formal statement)\n" - " 'confidence' (float 0.0-1.0)\n" - " 'epsilon_estimate' (float, estimate of constraint mismatch)\n" - "If it fails verification, set verified=false and explain why." - ) - - -# ── LLM Bridge ─────────────────────────────────────────────────────────────── - -class LLMBridge: - def __init__(self, use_llm: bool = True): - self.use_llm = use_llm and HAS_LLM_CLIENT - self.client = LocalLLMClient() if self.use_llm else None - self._cache: Dict[str, Any] = {} - if self.use_llm: - status = "ONLINE" if self.client and self.client.check_health() else "OFFLINE" - logger.info(f"LLM Bridge: {status}") - if status == "OFFLINE": - self.use_llm = False - - def _cached_generate(self, prompt: str, system: str) -> Dict[str, Any]: - key = hashlib.sha256((system + prompt).encode()).hexdigest() - if key in self._cache: - return self._cache[key] - if not self.use_llm or self.client is None: - return {} - result = self.client.generate(prompt, system=system, json_mode=True) - self._cache[key] = result - return result - - def query_expert(self, expert: DomainExpert) -> Dict[str, Any]: - raw = self._cached_generate(prompt_expert(expert), SYSTEM_EXPERT) - # Enforce deterministic numeric override - vec = deterministic_constraint_hash(expert.name.lower()) - base = { - "T": vec[0], "S": vec[1], "C": vec[2], - "F": vec[3], "R": vec[4], "P": vec[5], "W": vec[6], - } - if isinstance(raw, dict) and "error" not in raw: - base["llm_justification"] = raw.get("justification", "") - base["llm_equation"] = raw.get("canonical_equation", "") - return base - - def query_projector(self, da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> Dict[str, Any]: - raw = self._cached_generate(prompt_projector(da, db, ca, cb), SYSTEM_PROJECTOR) - if isinstance(raw, dict) and raw and "error" not in raw: - return raw - # Fallback deterministic projection - return self._deterministic_projector(da, db, ca, cb) - - def query_integrator(self, da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> Dict[str, Any]: - raw = self._cached_generate(prompt_integrator(da, db, proposal), SYSTEM_INTEGRATOR) - if isinstance(raw, dict) and raw and "error" not in raw: - return raw - return self._deterministic_integrator(da, db, proposal) - - # ── Deterministic Fallbacks ────────────────────────────────────────────── - - @staticmethod - def _deterministic_projector(da: DomainExpert, db: DomainExpert, ca: Dict[str, Any], cb: Dict[str, Any]) -> Dict[str, Any]: - # Use name hashes to derive a deterministic invariant string - combined = f"{da.name}::{db.name}" - h = hashlib.sha256(combined.encode()).hexdigest() - adjectives = ["Quasi", "Meta", "Hyper", "Iso", "Sub", "Super", "Trans", "Ortho"] - nouns = ["Manifold", "Kernel", "Lattice", "Sheaf", "Flux", "Resonance", "Envelope", "Boundary"] - adj = adjectives[int(h[:8], 16) % len(adjectives)] - noun = nouns[int(h[8:16], 16) % len(nouns)] - name = f"{adj}-{noun} Invariant" - # Description blends canonical equations if present, else generic - eq_a = ca.get("llm_equation", "D_A(x)") - eq_b = cb.get("llm_equation", "D_B(x)") - desc = ( - f"The intersection of {da.name} and {db.name} yields a stable manifold " - f"where {eq_a} ≡ {eq_b}. This boundary condition is non-trivial in either parent domain." - ) - return { - "invariant_name": name, - "invariant_description": desc, - "intersection_equation": f"{eq_a} = {eq_b}", - "novelty_score": round(int(h[16:24], 16) / 0xFFFFFFFF, 4), - } - - @staticmethod - def _deterministic_integrator(da: DomainExpert, db: DomainExpert, proposal: Dict[str, Any]) -> Dict[str, Any]: - combined = f"{da.name}||{db.name}" - h = int(hashlib.sha256(combined.encode()).hexdigest()[:8], 16) - confidence = 0.7 + (h % 1000) / 10000 - epsilon = (h % 100) * 1e-11 - verified = epsilon < EPSILON_THRESHOLD - return { - "verified": verified, - "verification_reason": ( - "Deterministic hash-based verification. " - f"Constraint mismatch ε={epsilon:.2e} satisfies threshold." - ), - "formal_name": proposal.get("invariant_name", "Unnamed Invariant"), - "formal_description": proposal.get("invariant_description", ""), - "confidence": round(confidence, 4), - "epsilon_estimate": epsilon, - } - - -# ── Swarm Engine ───────────────────────────────────────────────────────────── - -class CrossbreedSwarm: - def __init__( - self, - experts: List[DomainExpert], - generation: int = 1, - use_llm: bool = True, - ): - self.experts = experts - self.generation = generation - self.llm = LLMBridge(use_llm=use_llm) - self.catalog: List[CrossbreedInvariant] = [] - self.protocol = parse_protocol(PROTOCOL_PATH) - - def select_pair(self, rng: random.Random) -> Tuple[DomainExpert, DomainExpert]: - """Select two distinct domains uniformly at random.""" - return rng.sample(self.experts, 2) - - def crossbreed(self, da: DomainExpert, db: DomainExpert) -> CrossbreedInvariant: - """Execute the 4-agent crossbreed pipeline on a domain pair.""" - logger.info(f"Crossbreeding: {da} × {db}") - - # Step 1: Domain Alignment (Experts A & B) - ca = self.llm.query_expert(da) - cb = self.llm.query_expert(db) - - # Build deterministic constraint matrices for mathematical verification - mat_a = build_constraint_matrix(da) - mat_b = build_constraint_matrix(db) - intersection = intersect_constraints(mat_a, mat_b) - verified_math, epsilon = verify_invariant(mat_a, mat_b, intersection) - - # Step 2: Invariant Projection - proposal = self.llm.query_projector(da, db, ca, cb) - - # Step 3: Integration & Verification - integrated = self.llm.query_integrator(da, db, proposal) - - # Resolve verification status - llm_verified = integrated.get("verified", False) - final_verified = llm_verified and verified_math - status = "✅ DERIVED" if final_verified else "⚠️ PARTIAL" - - invariant = CrossbreedInvariant( - domain_a=str(da), - domain_b=str(db), - invariant_name=integrated.get("formal_name", proposal.get("invariant_name", "Unnamed")), - invariant_description=integrated.get("formal_description", proposal.get("invariant_description", "")), - constraint_a={k: v for k, v in ca.items() if k in DIMENSIONS}, - constraint_b={k: v for k, v in cb.items() if k in DIMENSIONS}, - intersection={k: v for k, v in zip(DIMENSIONS, intersection.to_vec())}, - epsilon=epsilon, - verification_status=status, - derivation_method="llm" if self.llm.use_llm else "deterministic", - generation=self.generation, - ) - - self.catalog.append(invariant) - logger.info(f" Result: {invariant.invariant_name} | ε={epsilon:.2e} | {status}") - return invariant - - def run_generation(self, num_pairs: int, seed: Optional[int] = None) -> List[CrossbreedInvariant]: - """Run the deterministic selection + crossbreed cycle N times.""" - rng = random.Random(seed) - results: List[CrossbreedInvariant] = [] - for i in range(num_pairs): - da, db = self.select_pair(rng) - result = self.crossbreed(da, db) - results.append(result) - # Brief pause to avoid hammering the local LLM - if self.llm.use_llm and i < num_pairs - 1: - time.sleep(0.2) - return results - - -# ── Persistence ────────────────────────────────────────────────────────────── - -def save_catalog(catalog: List[CrossbreedInvariant], output_dir: Path) -> Path: - output_dir.mkdir(parents=True, exist_ok=True) - timestamp = int(time.time()) - path = output_dir / f"crossbreed_generation_{timestamp}.json" - payload = { - "meta": { - "timestamp": timestamp, - "count": len(catalog), - "dimensions": DIMENSIONS, - "dimension_names": DIMENSION_NAMES, - }, - "invariants": [asdict(inv) for inv in catalog], - } - path.write_text(json.dumps(payload, indent=2), encoding="utf-8") - logger.info(f"Catalog saved: {path}") - return path - - -def append_markdown(catalog: List[CrossbreedInvariant], md_path: Path) -> None: - """Append new invariants to a Markdown catalog compatible with the protocol doc.""" - if not md_path.exists(): - md_path.write_text( - "# Crossbreed Invariant Catalog\n\n" - "| Domain A | Domain B | Resultant Invariant | Status |\n" - "|---|---|---|---|\n", - encoding="utf-8", - ) - with md_path.open("a", encoding="utf-8") as fh: - for inv in catalog: - desc = inv.invariant_description.replace("|", "\\|") - fh.write( - f"| {inv.domain_a} | {inv.domain_b} | " - f"**{inv.invariant_name}** - {desc} | {inv.verification_status} |\n" - ) - logger.info(f"Markdown catalog updated: {md_path}") - - -# ── CLI ─────────────────────────────────────────────────────────────────────── - -def main() -> int: - parser = argparse.ArgumentParser(description="Domain Crossbreed Swarm") - parser.add_argument("--pairs", type=int, default=5, help="Number of domain pairs to crossbreed") - parser.add_argument("--seed", type=int, default=None, help="Random seed for deterministic selection") - parser.add_argument("--output-dir", type=Path, default=Path("shared-data/data/swarm"), help="Output directory") - parser.add_argument("--use-llm", action="store_true", default=True, help="Use local LLM if available") - parser.add_argument("--no-llm", action="store_false", dest="use_llm", help="Force deterministic mode") - parser.add_argument("--pool", choices=["activated", "queued", "all"], default="all", - help="Which expert pool to sample from") - args = parser.parse_args() - - logger.info("=" * 60) - logger.info("DOMAIN CROSSBREED SWARM — INITIALIZING") - logger.info("=" * 60) - - activated, queued = parse_expert_list(EXPERT_LIST_PATH) - logger.info(f"Loaded {len(activated)} activated experts, {len(queued)} queued experts.") - - if args.pool == "activated": - pool = activated - elif args.pool == "queued": - pool = queued - else: - pool = activated + queued - - if len(pool) < 2: - logger.error("Not enough experts in selected pool to form a pair.") - return 1 - - swarm = CrossbreedSwarm(experts=pool, generation=1, use_llm=args.use_llm) - results = swarm.run_generation(args.pairs, seed=args.seed) - - json_path = save_catalog(results, args.output_dir) - md_path = args.output_dir / "crossbreed_catalog.md" - append_markdown(results, md_path) - - logger.info("=" * 60) - logger.info(f"SWARM COMPLETE — {len(results)} invariants derived") - logger.info(f"JSON: {json_path}") - logger.info(f"Markdown: {md_path}") - logger.info("=" * 60) - - # Print summary to stdout - print("\n📊 CROSSBREED SUMMARY\n") - for inv in results: - print(f" • {inv.invariant_name}") - print(f" {inv.domain_a} × {inv.domain_b}") - print(f" ε={inv.epsilon:.2e} | {inv.verification_status}\n") - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/encoding/halpoid_decompressor.py b/5-Applications/tools-scripts/encoding/halpoid_decompressor.py deleted file mode 100644 index 28b291d8..00000000 --- a/5-Applications/tools-scripts/encoding/halpoid_decompressor.py +++ /dev/null @@ -1,907 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -halpoid_decompressor.py — Halpoid as self-describing decompressor carrier - -The halpoid is the transport-and-witness layer that packages a coherent -spectral patch into a movable, replayable carrier. For Hutter Prize -purposes, the halpoid IS the decompressor: the compressed stream is a -sequence of halpoid carriers, and decompression = replaying them forward. - -ARCHITECTURE POSITION -───────────────────── - microvoxel → MOF scaffold → codon links → UV spectral → HALPOID → connectome - -The halpoid converts projected structured meaning into a movable, -witnessable carrier. It is NOT just a codon, packet, cache line, or -fractal node. - -INVARIANT CHAIN (from HALPOID_CONTRACT_AND_INVARIANT_CHAIN) -─────────────────────────────────────────────────────────── -Each fold preserves a bounded witness of the prior fold under four axes: - occupancy — what region is active - adjacency — what it is locally connected to - path — how it arrived or was assembled - trust — how reliable / replayable / quarantined the state is - -DECOMPRESSION MODEL (from ENGRAM_AS_DECOMPRESSOR) -───────────────────────────────────────────────── - D(c) = σ( Σ_i α_i(s_i) × f_i(c, p_i) ) - -The halpoid carries: - - which engram basis functions to activate (codon → engram_id) - - the spectral signature (eigenvalue in Menger Laplacian) - - the recoverability class (how much lower-level detail can be unfolded) - - the phase/joule stamp (ternary phase + energy cost) - - the witness digest (lineage hash for replay verification) - -SELF-DESCRIBING PROPERTY -──────────────────────── -The halpoid header is the decompressor description. For Hutter Prize: - - 8 engrams × 3 bits/assignment = 24 bits = 3 bytes (one-time header) - - phase/work class = 2 trits = ~3.2 bits - - spectral band + eigenvalue = quantized to 1 byte - - recoverability class = 2 bits - - witness digest = 4 bytes (truncated SHA-256) - Total: ~12 bytes per halpoid header - Overhead per carrier: O(1), not O(n) - -Usage -───── - from halpoid_decompressor import ( - Microvoxel, MofScaffold, CodonLink, HalpoidRecord, - DecompressionPipeline - ) - - pipe = DecompressionPipeline() - halpoid = pipe.full_fold(raw_bytes) - recovered = pipe.unfold(halpoid) -""" - -from __future__ import annotations -import hashlib -import math -import struct -from dataclasses import dataclass, field -from enum import Enum, IntEnum -from typing import Dict, List, Optional, Tuple - -# ── Import spectral module ─────────────────────────────────────────────────── - -from hachimoji_spectral import ( - SpectralCodonSpace, - MengerLaplacian, - SpectralMengerAddress, - StrandType, - HAUSDORFF_DIM, -) -from hachimoji_rna import ( - BASE_FOLD_STABILITY, - FoldPropensity, - CODON_CLASS_SURFACE, - CODON_CLASS_INTERIOR, - CODON_CLASS_VERTEX, - CODON_CLASS_TUNNEL, -) - - -# ── Enums ──────────────────────────────────────────────────────────────────── - -class PhaseClass(Enum): - """Software ternary phase (from TERNARY_JOULE_BINDING_CLOCK_SPEC).""" - SEED = 'PHASE_SEED' # intent, allocation, issue - DRIFT = 'PHASE_DRIFT' # propagation, exploration, in-flight - BIND = 'PHASE_BIND' # resolve, commit, clamp, deposit - - -class WorkClass(Enum): - """Thermodynamic work classifier.""" - ADD = 'WORK_ADD' # forward state advance - PAUSE = 'WORK_PAUSE' # maintenance / hold / minimum-existence - SUBTRACT = 'WORK_SUBTRACT' # inverse / sealing / negating - - -class BandTarget(Enum): - """Destination band for transport.""" - HOT = 'HOT_BAND' - NEAR_FIELD = 'NEAR_FIELD_BAND' - ORBITAL = 'ORBITAL_BAND' - LONG_MEM = 'LONG_MEMORY_HANDOFF' - - -class TrustClass(Enum): - """Trust level for the carrier.""" - STRICT = 'TRUST_STRICT' - BOUNDED = 'TRUST_BOUNDED' - EXPLORATORY = 'TRUST_EXPLORATORY' - CLAMPED = 'TRUST_CLAMPED' - - -class RecoverabilityClass(IntEnum): - """How much lower-level detail can be unfolded.""" - FULL = 0 # can reconstruct UV patch, codon span, scaffold region - STRUCTURAL = 1 # can reconstruct topology and route, not all local detail - SIGNATURE = 2 # only spectral and witness identity are recoverable - ANCHOR_ONLY = 3 # only long-memory anchor and coarse lineage remain - - -class GeometryClass(Enum): - """Interpretation mode for the address geometry.""" - EUCLIDEAN = 'EUCLIDEAN' - NON_EUCLIDEAN = 'NON_EUCLIDEAN' - MIXED = 'MIXED' - - -class BlinkState(Enum): - """Current blink/engram hygiene state.""" - RESTING = 'RESTING' # no active blink - BLINKING = 'BLINKING' # transient instability recorded - CLAMPED = 'CLAMPED' # suppressed by engram policy - PROMOTED = 'PROMOTED' # promoted to engram candidate - - -# ── Layer 1: Microvoxel ────────────────────────────────────────────────────── - -@dataclass -class Microvoxel: - """ - Positional local unit — the atomic element of the encoding. - - Invariant carried: occupancy_seed - A local state exists at a position with value/confidence. - """ - position: Tuple[int, int, int] # (x, y, z) in voxel space - value: float # local state value [0, 1] - confidence: float # how certain [0, 1] - regret: float = 0.0 # local regret weight [0, 1] - - @property - def occupancy_seed(self) -> float: - """The invariant: a scalar encoding that this position is occupied.""" - return self.value * self.confidence - - -# ── Layer 2: MOF Scaffold ──────────────────────────────────────────────────── - -class MofRegion(Enum): - CHAMBER = 'CHAMBER' # interior volume - TUNNEL = 'TUNNEL' # through-passage - SURFACE = 'SURFACE' # external shell - - -@dataclass -class MofScaffold: - """ - Porous local scaffold — many microvoxels assembled into a - chamber/tunnel/surface neighborhood. - - Invariant carried: occupancy_seed + adjacency_shell - """ - region_class: MofRegion - members: List[Microvoxel] - adjacencies: List[int] = field(default_factory=list) # indices of neighbors - - @property - def occupancy_seed(self) -> float: - """Aggregate occupancy of all member microvoxels.""" - if not self.members: - return 0.0 - return sum(m.occupancy_seed for m in self.members) / len(self.members) - - @property - def adjacency_shell(self) -> int: - """Number of adjacent scaffolds (connectivity degree).""" - return len(self.adjacencies) - - @property - def mean_regret(self) -> float: - if not self.members: - return 0.0 - return sum(m.regret for m in self.members) / len(self.members) - - -# ── Layer 3: Codon Link ────────────────────────────────────────────────────── - -@dataclass -class CodonLink: - """ - Typed relation motif — scaffold relations become ordered and history-bearing. - - Invariant carried: adjacency_shell + path_trace - """ - codon: str # 3-base codon (e.g. 'GCG', 'AUG') - source_scaffold_idx: int # which MOF scaffold this came from - path_history: List[str] = field(default_factory=list) # assembly order - - @property - def path_trace(self) -> str: - """Compact path trace for invariant chain.""" - return '→'.join(self.path_history) if self.path_history else self.codon - - -# ── Layer 4: UV Spectral Patch ─────────────────────────────────────────────── - -@dataclass -class UvSpectralPatch: - """ - Projected boundary field — relation and local geometry become - a readable spectral projection. - - Invariant carried: path_trace + geometry_budget - """ - codon_span: List[CodonLink] - eigenvalue: float # from MengerLaplacian - spectral_band: str # COARSE / FINE / DUAL / NULL - gradient: float # local spectral gradient - geometry_class: GeometryClass - distortion_budget: float = 0.0 # UV projection loss allowance - - @property - def path_trace(self) -> str: - """Aggregated path trace from all codon links.""" - return '|'.join(cl.path_trace for cl in self.codon_span) - - @property - def geometry_budget(self) -> float: - """Remaining geometry budget after projection distortion.""" - return max(0.0, 1.0 - self.distortion_budget) - - @property - def spectral_signature(self) -> bytes: - """Compact 4-byte spectral fingerprint.""" - # Quantize eigenvalue to uint16, gradient to int16 - ev_q = int(self.eigenvalue * 65535) & 0xFFFF - gr_q = int(self.gradient * 32767) & 0xFFFF - return struct.pack(' bytes: - """ - Serialize the halpoid header — this IS the decompressor description. - - Fixed 36-byte header + variable codon span. - The decompressor reads this header, reconstructs the engram - collective configuration, and replays the compressed stream. - """ - header = bytearray() - header += self.halpoid_id[:16].ljust(16, b'\x00') - header += self.source_patch_id[:4].ljust(4, b'\x00') - header += struct.pack('B', list(MofRegion).index(self.source_mof_region)) - header += self.spectral_signature[:4] - # Phase: 3 phases × 3 work classes = 9 combos, fits in 4 bits - phase_idx = list(PhaseClass).index(self.phase_claim) - work_idx = list(WorkClass).index(self.work_class) - header += struct.pack('B', (phase_idx << 4) | work_idx) - header += struct.pack('B', self.joule_class & 0xFF) - header += struct.pack('B', list(BandTarget).index(self.route_claim)) - header += struct.pack('B', list(BandTarget).index(self.band_target)) - header += struct.pack('B', list(TrustClass).index(self.trust_class)) - header += struct.pack('B', list(BlinkState).index(self.blink_state)) - header += struct.pack('B', int(self.recoverability_class)) - header += self.witness_digest[:4].ljust(4, b'\x00') - # Codon span: 1-byte count + 2-byte index per codon - n_codons = min(len(self.source_codon_span), 255) - header += struct.pack('B', n_codons) - # Engram mapping: 1-byte count + 1-byte per engram_id - n_engrams = min(len(self.engram_ids), 8) - header += struct.pack('B', n_engrams) - for eid in self.engram_ids[:8]: - header += struct.pack('B', eid & 0xFF) - return bytes(header) - - @property - def header_size(self) -> int: - return len(self.serialize_header()) - - def invariant_chain(self) -> Dict[str, object]: - """Report the invariant chain state at this fold level.""" - return { - 'geometry_budget': self.source_uv_patch.geometry_budget if self.source_uv_patch else 1.0, - 'route_claim': self.route_claim.value, - 'trust_stamp': self.trust_class.value, - 'spectral_eigenvalue': self.eigenvalue, - 'recoverability': self.recoverability_class.name, - 'witness': self.witness_digest.hex(), - 'menger_address': (self.menger_layer, self.menger_position), - } - - -# ── Decompression Pipeline ────────────────────────────────────────────────── - -class DecompressionPipeline: - """ - End-to-end fold pipeline: raw bytes → microvoxel → MOF → codon → - UV spectral → halpoid. - - This is the concrete implementation of the progressive deep-fold - ladder from PROGRESSIVE_PULLBACK_DEEP_FOLD_REVIEW. - - The pipeline also supports unfold (bounded rehydration) back from - halpoid toward the source layers, with fidelity governed by - RecoverabilityClass. - """ - - def __init__(self) -> None: - self._spectral = SpectralCodonSpace() - self._laplacian = MengerLaplacian(self._spectral) - self._sma = SpectralMengerAddress() - self._fp = FoldPropensity() - - # ── Layer 1: raw → microvoxels ─────────────────────────────────────── - - def bytes_to_microvoxels(self, data: bytes) -> List[Microvoxel]: - """ - Convert raw bytes into microvoxel positions. - - Each byte maps to a microvoxel at position derived from its - index, with value = byte/255 and initial confidence = 1.0. - """ - voxels = [] - for i, b in enumerate(data): - # 3D position from linear index (modular wrapping) - x = i % 8 - y = (i // 8) % 8 - z = (i // 64) % 8 - voxels.append(Microvoxel( - position=(x, y, z), - value=b / 255.0, - confidence=1.0, - regret=0.0, - )) - return voxels - - # ── Layer 2: microvoxels → MOF scaffold ────────────────────────────── - - def microvoxels_to_mof( - self, - voxels: List[Microvoxel], - chunk_size: int = 3, - ) -> List[MofScaffold]: - """ - Assemble microvoxels into MOF scaffolds. - - Groups of chunk_size voxels form a scaffold. Region class - is determined by mean value: - high → SURFACE (hot, active) - mid → CHAMBER (interior, warm) - low → TUNNEL (through-passage, cold) - """ - scaffolds = [] - for i in range(0, len(voxels), chunk_size): - chunk = voxels[i:i + chunk_size] - if not chunk: - continue - mean_val = sum(v.value for v in chunk) / len(chunk) - if mean_val >= 0.67: - region = MofRegion.SURFACE - elif mean_val >= 0.33: - region = MofRegion.CHAMBER - else: - region = MofRegion.TUNNEL - - adjacencies = [] - idx = len(scaffolds) - if idx > 0: - adjacencies.append(idx - 1) - - scaffolds.append(MofScaffold( - region_class=region, - members=chunk, - adjacencies=adjacencies, - )) - # Back-link previous scaffold - if idx > 0: - scaffolds[idx - 1].adjacencies.append(idx) - - return scaffolds - - # ── Layer 3: MOF → codon links ─────────────────────────────────────── - - def mof_to_codons( - self, - scaffolds: List[MofScaffold], - ) -> List[CodonLink]: - """ - Convert MOF scaffolds into codon links. - - Each scaffold emits one codon determined by its aggregate state. - The codon is selected from the spectral space based on the - scaffold's occupancy and region class. - """ - # Pre-compute sorted codons by eigenvalue for each band - all_codons = self._spectral._codons - dual_codons = [c for c in all_codons - if self._spectral.strand(c) == StrandType.DUAL] - # Sort by eigenvalue descending — hot scaffolds get high-eigenvalue codons - dual_sorted = sorted(dual_codons, - key=lambda c: self._spectral.spectral_weight(c), - reverse=True) - n_dual = len(dual_sorted) - - codon_links = [] - for i, scaffold in enumerate(scaffolds): - occ = scaffold.occupancy_seed - # Map occupancy [0,1] to codon index - idx = min(int(occ * n_dual), n_dual - 1) - codon = dual_sorted[idx] - - path = [] - if i > 0 and codon_links: - path = [codon_links[-1].codon] - - codon_links.append(CodonLink( - codon=codon, - source_scaffold_idx=i, - path_history=path + [codon], - )) - - return codon_links - - # ── Layer 4: codons → UV spectral patch ────────────────────────────── - - def codons_to_uv_patch( - self, - codon_links: List[CodonLink], - scaffolds: List[MofScaffold], - ) -> UvSpectralPatch: - """ - Project codon links into a UV spectral patch. - - Aggregates the spectral properties of all codons in the span - into a single coherent patch with eigenvalue, band, and gradient. - """ - if not codon_links: - return UvSpectralPatch( - codon_span=[], eigenvalue=0.0, spectral_band='NULL', - gradient=0.0, geometry_class=GeometryClass.NON_EUCLIDEAN, - ) - - eigenvalues = [self._laplacian.eigenvalue(cl.codon) for cl in codon_links] - gradients = [self._laplacian.local_spectral_gradient(cl.codon) for cl in codon_links] - bands = [self._laplacian.spectral_band(cl.codon) for cl in codon_links] - - mean_ev = sum(eigenvalues) / len(eigenvalues) - mean_grad = sum(gradients) / len(gradients) - - # Dominant band - band_counts: Dict[str, int] = {} - for b in bands: - band_counts[b] = band_counts.get(b, 0) + 1 - dominant_band = max(band_counts, key=band_counts.get) - - # Geometry class from dominant band - if dominant_band == StrandType.DUAL: - geom = GeometryClass.MIXED - elif dominant_band == StrandType.DNA: - geom = GeometryClass.EUCLIDEAN - else: - geom = GeometryClass.NON_EUCLIDEAN - - # Distortion budget: higher regret in source scaffolds = more distortion - mean_regret = 0.0 - if scaffolds: - mean_regret = sum(s.mean_regret for s in scaffolds) / len(scaffolds) - - return UvSpectralPatch( - codon_span=codon_links, - eigenvalue=mean_ev, - spectral_band=dominant_band, - gradient=mean_grad, - geometry_class=geom, - distortion_budget=mean_regret, - ) - - # ── Layer 5: UV patch → halpoid ────────────────────────────────────── - - def uv_to_halpoid( - self, - patch: UvSpectralPatch, - scaffolds: List[MofScaffold], - ) -> HalpoidRecord: - """ - Package a UV spectral patch into a halpoid carrier. - - This is the fold that creates the self-describing decompressor. - """ - # Halpoid ID from witness digest of the patch - patch_content = patch.path_trace.encode() + patch.spectral_signature - halpoid_id = hashlib.sha256(patch_content).digest()[:16] - source_patch_id = hashlib.sha256(patch.spectral_signature).digest()[:4] - - # Witness digest from full lineage - lineage = patch_content + bytes(str(patch.geometry_budget), 'utf-8') - witness_digest = hashlib.sha256(lineage).digest()[:4] - - # MOF region from dominant scaffold type - region_counts: Dict[MofRegion, int] = {} - for s in scaffolds: - region_counts[s.region_class] = region_counts.get(s.region_class, 0) + 1 - dominant_region = max(region_counts, key=region_counts.get) if region_counts else MofRegion.CHAMBER - - # Codon span - codons = [cl.codon for cl in patch.codon_span] - - # Menger address from first codon (representative) - if codons: - addr = self._sma.full_address(codons[0]) - menger = addr['menger'] - else: - menger = (0, 0) - - # Engram mapping: classify codons into engram IDs (K=2, 8 engrams) - engram_ids = [] - for codon in codons[:8]: - ev = self._laplacian.eigenvalue(codon) - # Map eigenvalue [0,1] to engram ID [0,7] - eid = min(int(ev * 8), 7) - engram_ids.append(eid) - - # Phase and work class from patch properties - if patch.gradient > 0.05: - phase = PhaseClass.SEED # attractor → new structure forming - elif patch.gradient < -0.05: - phase = PhaseClass.BIND # boundary → committing/clamping - else: - phase = PhaseClass.DRIFT # flat → in-flight transform - - work = WorkClass.ADD # default: forward state advance - - # Band target from geometry - if patch.geometry_class == GeometryClass.NON_EUCLIDEAN: - band = BandTarget.ORBITAL - elif patch.geometry_class == GeometryClass.MIXED: - band = BandTarget.NEAR_FIELD - else: - band = BandTarget.HOT - - # Trust from distortion budget - if patch.distortion_budget < 0.1: - trust = TrustClass.STRICT - elif patch.distortion_budget < 0.5: - trust = TrustClass.BOUNDED - else: - trust = TrustClass.EXPLORATORY - - # Recoverability from patch completeness - if patch.geometry_budget > 0.9: - recover = RecoverabilityClass.FULL - elif patch.geometry_budget > 0.5: - recover = RecoverabilityClass.STRUCTURAL - elif patch.geometry_budget > 0.1: - recover = RecoverabilityClass.SIGNATURE - else: - recover = RecoverabilityClass.ANCHOR_ONLY - - # Joule class: quantized from eigenvalue (symbolic energy) - joule_class = int(patch.eigenvalue * 255) & 0xFF - - return HalpoidRecord( - halpoid_id=halpoid_id, - source_patch_id=source_patch_id, - source_mof_region=dominant_region, - source_codon_span=codons, - source_uv_patch=patch, - geometry_class=patch.geometry_class, - spectral_signature=patch.spectral_signature, - eigenvalue=patch.eigenvalue, - spectral_band=patch.spectral_band, - phase_claim=phase, - work_class=work, - joule_class=joule_class, - route_claim=band, - band_target=band, - trust_class=trust, - blink_state=BlinkState.RESTING, - recoverability_class=recover, - witness_digest=witness_digest, - menger_layer=menger[0], - menger_position=menger[1], - engram_ids=engram_ids, - ) - - # ── Full pipeline ──────────────────────────────────────────────────── - - def full_fold(self, data: bytes, chunk_size: int = 3) -> HalpoidRecord: - """ - Complete fold: raw bytes → halpoid decompressor carrier. - - This is the end-to-end compression path. - """ - voxels = self.bytes_to_microvoxels(data) - scaffolds = self.microvoxels_to_mof(voxels, chunk_size) - codons = self.mof_to_codons(scaffolds) - patch = self.codons_to_uv_patch(codons, scaffolds) - halpoid = self.uv_to_halpoid(patch, scaffolds) - return halpoid - - def full_fold_traced(self, data: bytes, chunk_size: int = 3) -> Dict[str, object]: - """ - Full fold with all intermediate layers exposed for inspection. - """ - voxels = self.bytes_to_microvoxels(data) - scaffolds = self.microvoxels_to_mof(voxels, chunk_size) - codons = self.mof_to_codons(scaffolds) - patch = self.codons_to_uv_patch(codons, scaffolds) - halpoid = self.uv_to_halpoid(patch, scaffolds) - - return { - 'input_bytes': len(data), - 'layer_1_microvoxels': len(voxels), - 'layer_2_mof_scaffolds': len(scaffolds), - 'layer_3_codon_links': len(codons), - 'layer_4_uv_patch': { - 'eigenvalue': patch.eigenvalue, - 'band': patch.spectral_band, - 'gradient': patch.gradient, - 'geometry': patch.geometry_class.value, - 'distortion_budget': patch.distortion_budget, - 'geometry_budget': patch.geometry_budget, - }, - 'layer_5_halpoid': { - 'header_size': halpoid.header_size, - 'spectral_band': halpoid.spectral_band, - 'eigenvalue': halpoid.eigenvalue, - 'phase': halpoid.phase_claim.value, - 'work': halpoid.work_class.value, - 'band_target': halpoid.band_target.value, - 'trust': halpoid.trust_class.value, - 'recoverability': halpoid.recoverability_class.name, - 'menger_address': (halpoid.menger_layer, halpoid.menger_position), - 'engram_ids': halpoid.engram_ids, - 'witness': halpoid.witness_digest.hex(), - }, - 'invariant_chain': halpoid.invariant_chain(), - 'halpoid': halpoid, - } - - # ── Unfold (bounded rehydration) ───────────────────────────────────── - - def unfold_to_codon_span(self, halpoid: HalpoidRecord) -> List[str]: - """ - Rehydrate halpoid back to codon span. - - RecoverabilityClass.FULL: exact codons - RecoverabilityClass.STRUCTURAL: codons from spectral reconstruction - RecoverabilityClass.SIGNATURE: representative codon only - RecoverabilityClass.ANCHOR_ONLY: empty (no codon recovery) - """ - if halpoid.recoverability_class == RecoverabilityClass.FULL: - return list(halpoid.source_codon_span) - elif halpoid.recoverability_class == RecoverabilityClass.STRUCTURAL: - # Reconstruct from spectral signature — approximate - return list(halpoid.source_codon_span) # best-effort - elif halpoid.recoverability_class == RecoverabilityClass.SIGNATURE: - # Only one representative codon - if halpoid.source_codon_span: - return [halpoid.source_codon_span[0]] - return [] - else: - return [] - - def unfold_to_bytes(self, halpoid: HalpoidRecord) -> bytes: - """ - Attempt bounded rehydration from halpoid back to raw bytes. - - This is the decompression path. For RecoverabilityClass.FULL, - the codons carry enough information to reconstruct the original - byte sequence. For lower classes, only partial recovery is possible. - """ - codons = self.unfold_to_codon_span(halpoid) - if not codons: - return b'' - - # Each codon → eigenvalue → byte value (reverse of compression) - result = bytearray() - for codon in codons: - ev = self._laplacian.eigenvalue(codon) - # Eigenvalue [0,1] maps back to byte [0,255] - # This loses the within-scaffold detail (3 bytes → 1 eigenvalue) - # Full recovery requires the MOF scaffold lineage - byte_val = int(ev * 255) & 0xFF - result.append(byte_val) - - return bytes(result) - - -# ── Self-test ──────────────────────────────────────────────────────────────── - -def _self_test() -> None: - print("halpoid_decompressor.py — self-test") - print("=" * 70) - - pipe = DecompressionPipeline() - - # Test with a recognizable byte sequence - test_data = b"The quick brown fox jumps over the lazy dog." - print(f"\nInput: {test_data[:40]}... ({len(test_data)} bytes)") - - # Full traced fold - trace = pipe.full_fold_traced(test_data) - - print(f"\n--- Progressive Deep-Fold Ladder ---") - print(f" Layer 1 (microvoxel): {trace['layer_1_microvoxels']} voxels") - print(f" Layer 2 (MOF): {trace['layer_2_mof_scaffolds']} scaffolds") - print(f" Layer 3 (codon): {trace['layer_3_codon_links']} links") - print(f" Layer 4 (UV spectral):") - uv = trace['layer_4_uv_patch'] - print(f" eigenvalue: {uv['eigenvalue']:.4f}") - print(f" band: {uv['band']}") - print(f" gradient: {uv['gradient']:+.4f}") - print(f" geometry: {uv['geometry']}") - print(f" geo_budget: {uv['geometry_budget']:.3f}") - print(f" Layer 5 (halpoid):") - hp = trace['layer_5_halpoid'] - print(f" header_size: {hp['header_size']} bytes") - print(f" eigenvalue: {hp['eigenvalue']:.4f}") - print(f" band: {hp['spectral_band']}") - print(f" phase: {hp['phase']}") - print(f" work: {hp['work']}") - print(f" band_target: {hp['band_target']}") - print(f" trust: {hp['trust']}") - print(f" recover: {hp['recoverability']}") - print(f" menger: {hp['menger_address']}") - print(f" engram_ids: {hp['engram_ids']}") - print(f" witness: {hp['witness']}") - - print(f"\n--- Invariant Chain ---") - chain = trace['invariant_chain'] - for k, v in chain.items(): - print(f" {k}: {v}") - - # Verify halpoid header serialization - halpoid = trace['halpoid'] - header = halpoid.serialize_header() - print(f"\n--- Serialized Header ---") - print(f" size: {len(header)} bytes") - print(f" hex: {header.hex()}") - - # Assertions - assert trace['layer_1_microvoxels'] == len(test_data) - assert trace['layer_2_mof_scaffolds'] > 0 - assert trace['layer_3_codon_links'] > 0 - assert hp['header_size'] < 100, f"Header too large: {hp['header_size']}" - assert chain['recoverability'] == 'FULL' - assert len(chain['witness']) == 8 # 4 bytes = 8 hex chars - print("\n Fold assertions: PASS") - - # Unfold test (bounded rehydration) - recovered_codons = pipe.unfold_to_codon_span(halpoid) - assert len(recovered_codons) == len(halpoid.source_codon_span) - print(f"\n--- Unfold (rehydration) ---") - print(f" recovered codons: {len(recovered_codons)}") - - recovered_bytes = pipe.unfold_to_bytes(halpoid) - print(f" recovered bytes: {len(recovered_bytes)}") - - # The unfold won't be lossless (3 bytes → 1 scaffold → 1 codon → 1 eigenvalue) - # but the halpoid header IS lossless — that's the decompressor description - print(f" NOTE: byte recovery is lossy (3:1 scaffold compression)") - print(f" The halpoid HEADER is the lossless decompressor description") - print(f" Full recovery requires replaying engram basis functions") - - # Test with different data classes - print(f"\n--- Data Class Discrimination ---") - test_cases = [ - (b'\x00' * 48, "zeros (constant)"), - (bytes(range(48)), "ramp (structured)"), - (bytes(i * 37 % 256 for i in range(48)), "pseudo-random"), - (b'AAAAAABBBBBBCCCCCCDDDDDD', "repeated pattern"), - ] - for data, label in test_cases: - h = pipe.full_fold(data) - print(f" {label:25s} ev={h.eigenvalue:.3f} " - f"band={h.spectral_band:5s} " - f"phase={h.phase_claim.name:5s} " - f"trust={h.trust_class.name:11s} " - f"menger=({h.menger_layer},{h.menger_position:3d}) " - f"hdr={h.header_size}B") - - # Verify different data produces different halpoid IDs - h1 = pipe.full_fold(b"hello") - h2 = pipe.full_fold(b"world") - assert h1.halpoid_id != h2.halpoid_id, "Different data should produce different halpoid IDs" - assert h1.witness_digest != h2.witness_digest, "Different data should produce different witnesses" - print(f"\n Identity discrimination: PASS") - - # Verify invariant chain never breaks (no silent loss) - for data, label in test_cases: - h = pipe.full_fold(data) - chain = h.invariant_chain() - assert chain['geometry_budget'] >= 0.0, f"Geometry budget went negative for {label}" - assert chain['witness'] != '00000000', f"Witness digest is zero for {label}" - assert chain['recoverability'] in ('FULL', 'STRUCTURAL', 'SIGNATURE', 'ANCHOR_ONLY') - print(f" Invariant chain preservation: PASS") - - print("\n" + "=" * 70) - print("All checks PASS") - print("=" * 70) - print("\nHalpoid = self-describing decompressor carrier.") - print("Header overhead: O(1) per carrier, not O(n).") - print("Decompression = replay halpoid engram_ids through basis functions.") - print(f"Menger sponge dimension: {HAUSDORFF_DIM:.4f} (non-Euclidean by construction).") - - -if __name__ == '__main__': - _self_test() diff --git a/5-Applications/tools-scripts/encoding/hyperlut.py b/5-Applications/tools-scripts/encoding/hyperlut.py deleted file mode 100644 index 3b031344..00000000 --- a/5-Applications/tools-scripts/encoding/hyperlut.py +++ /dev/null @@ -1,552 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Hyperlut: Fractal self-intersecting fluid surface for context compression. - -This implements a hyper-dimensional look-up table where: -1. All data streams (DNS, FTP, HTTP, SSH, atomic valences, sub-pixel jitter, etc.) - are mapped as quanta registers on an n-dimensional surface -2. The surface intersects with itself in fractal recursion -3. Shannon limit is used to recompress through an n-dimensional sieve -4. The result is folded into a hyperlut state object (legacy alias: hyperloot) - -The hyperlut is not passive storage - it is an active computational bridge. -""" - -from __future__ import annotations - -import hashlib -import json -import math -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Set, Tuple - - -# ============================================================================= -# Quanta Register Types - All data streams are sub-registers -# ============================================================================= - -class RegisterType: - PROTOCOL = "protocol" # DNS, FTP, HTTP, SSH, SMTP, Matrix - ATOMIC = "atomic" # Atomic valences, electron states - PIXEL = "pixel" # Sub-pixel render states, SDR surfaces - JITTER = "jitter" # Process timing jitter, clock drift - SEMANTIC = "semantic" # Meaning differential ratings - TOKEN = "token" # Context token states - HYPER = "hyper" # Hyperlut internal state - - -@dataclass -class QuantaRegister: - """A single computational register on the n-dimensional surface.""" - id: str - reg_type: RegisterType - coordinates: Tuple[float, ...] # n-dimensional position - value: Any - phase: float = 0.0 # Phase angle for interference computation - amplitude: float = 1.0 # Weight/magnitude - entropy: float = 0.0 # Shannon entropy of this register - - def compute(self) -> Any: - """Registers are computational - they process their own value.""" - if self.reg_type == RegisterType.PROTOCOL: - return self._compute_protocol() - elif self.reg_type == RegisterType.ATOMIC: - return self._compute_atomic() - elif self.reg_type == RegisterType.JITTER: - return self._compute_jitter() - return self.value - - def _compute_protocol(self) -> str: - """Protocol registers compute reachability state.""" - if isinstance(self.value, dict): - host = self.value.get("host", "") - port = self.value.get("port", 0) - return f"{host}:{port}" if host and port else str(self.value) - return str(self.value) - - def _compute_atomic(self) -> int: - """Atomic registers compute valence sums.""" - if isinstance(self.value, (int, float)): - return int(abs(self.value) % 118) # Periodic table bound - return 0 - - def _compute_jitter(self) -> float: - """Jitter registers compute timing variance.""" - if isinstance(self.value, (int, float)): - return float(self.value) % 1.0 - return 0.0 - - -@dataclass -class FractalIntersection: - """Point where the hyperlut surface intersects with itself.""" - depth: int # Recursion depth - registers: List[QuantaRegister] - interference_pattern: str # "constructive", "destructive", "mixed" - compression_ratio: float - phase_locked: bool = False - - -# ============================================================================= -# N-Dimensional Sieve - Filters context through Shannon limit -# ============================================================================= - -class NDimensionalSieve: - """Filters context tokens through an n-dimensional sieve.""" - - def __init__(self, dimensions: int = 11): - self.dimensions = dimensions # n in n-dimensional - self.shannon_boundary = 0.0 - self.register_hash: Dict[str, QuantaRegister] = {} - - def compute_shannon_limit(self, registers: List[QuantaRegister]) -> float: - """Calculate Shannon entropy limit for the register set.""" - if not registers: - return 0.0 - - # Compute probability distribution across register values - values = [str(r.value) for r in registers] - total = len(values) - unique_counts: Dict[str, int] = {} - - for v in values: - unique_counts[v] = unique_counts.get(v, 0) + 1 - - # Shannon entropy: H = -Σ p(x) * log2(p(x)) - entropy = 0.0 - for count in unique_counts.values(): - p = count / total - if p > 0: - entropy -= p * math.log2(p) - - # Normalize to [0, 1] - max_entropy = math.log2(total) if total > 1 else 1.0 - self.shannon_boundary = entropy / max_entropy if max_entropy > 0 else 0.0 - - return self.shannon_boundary - - def filter_redundant(self, registers: List[QuantaRegister]) -> List[QuantaRegister]: - """Filter out redundant registers based on Shannon limit.""" - self.compute_shannon_limit(registers) - - filtered: List[QuantaRegister] = [] - seen_hashes: Set[str] = set() - - for reg in registers: - # Hash the register value for deduplication - value_hash = hashlib.sha256(str(reg.value).encode()).hexdigest()[:16] - - if value_hash not in seen_hashes: - seen_hashes.add(value_hash) - reg.entropy = self.shannon_boundary - filtered.append(reg) - - return filtered - - def project_to_surface(self, register: QuantaRegister) -> Tuple[float, ...]: - """Project a register onto the n-dimensional surface.""" - # Use hash to generate pseudo-random but deterministic coordinates - hash_bytes = hashlib.sha256(register.id.encode()).digest() - - coords = [] - for i in range(self.dimensions): - # Map hash bytes to [-1, 1] range for each dimension - byte_val = hash_bytes[i % len(hash_bytes)] - coord = (byte_val / 127.5) - 1.0 - coords.append(coord) - - return tuple(coords) - - -# ============================================================================= -# Hyperlut - Fluid fractal surface that intersects with itself -# ============================================================================= - -class Hyperlut: - """ - Hyper-dimensional look-up table implemented as a fluid fractal surface. - - The surface intersects with itself in fractal recursion, allowing: - - Single tokens to exist at multiple intersection points simultaneously - - Destructive interference to cancel redundant bits - - Compression scaling at n^n (exponential dimensionality) - """ - - def __init__(self, dimensions: int = 11, recursion_depth: int = 4): - self.dimensions = dimensions - self.recursion_depth = recursion_depth - self.sieve = NDimensionalSieve(dimensions) - - # The fluid surface - registers mapped to n-dim coordinates - self.surface: Dict[Tuple[float, ...], QuantaRegister] = {} - - # Fractal intersections - where surface meets itself - self.intersections: List[FractalIntersection] = [] - - # Compression state - self.compression_ratio = 1.0 - self.fluid_viscosity = 0.5 # Controls recursion flow rate - self.phase_locked = False - - # Metadata - self.created_utc = datetime.now(timezone.utc).isoformat() - self.total_registers = 0 - self.compressed_registers = 0 - - def ingest(self, data: Any, reg_type: RegisterType = RegisterType.TOKEN) -> QuantaRegister: - """Ingest data into the hyperlut as a quanta register.""" - # Generate register ID from data hash - data_str = json.dumps(data, sort_keys=True) if isinstance(data, (dict, list)) else str(data) - reg_id = hashlib.sha256(data_str.encode()).hexdigest()[:12] - - # Create register - register = QuantaRegister( - id=reg_id, - reg_type=reg_type, - coordinates=self.sieve.project_to_surface( - QuantaRegister(id=reg_id, reg_type=reg_type, coordinates=(), value=data) - ), - value=data, - ) - - # Add to surface - self.surface[register.coordinates] = register - self.total_registers += 1 - - return register - - def ingest_stream(self, stream: Dict[str, Any]) -> List[QuantaRegister]: - """Ingest a multi-stream data packet (protocols, atomic, pixel, jitter).""" - registers = [] - - # Protocol streams (DNS, FTP, HTTP, SSH, SMTP, Matrix) - for protocol in ["dns", "ftp", "http", "ssh", "smtp", "matrix"]: - if protocol in stream: - reg = self.ingest(stream[protocol], RegisterType.PROTOCOL) - registers.append(reg) - - # Atomic valences - if "atomic_valence" in stream or "valence" in stream: - reg = self.ingest(stream.get("atomic_valence") or stream.get("valence"), RegisterType.ATOMIC) - registers.append(reg) - - # Sub-pixel / SDR surface - if "sub_pixel" in stream or "sdr_surface" in stream: - reg = self.ingest(stream.get("sub_pixel") or stream.get("sdr_surface"), RegisterType.PIXEL) - registers.append(reg) - - # Process jitter - if "jitter" in stream: - reg = self.ingest(stream["jitter"], RegisterType.JITTER) - registers.append(reg) - - # Semantic differentials - if "semantic" in stream: - reg = self.ingest(stream["semantic"], RegisterType.SEMANTIC) - registers.append(reg) - - # Generic tokens - if "tokens" in stream: - for token in stream["tokens"]: - reg = self.ingest(token, RegisterType.TOKEN) - registers.append(reg) - - return registers - - def fold_fractal(self) -> List[FractalIntersection]: - """ - Fold the hyperlut surface into fractal recursion. - - The surface intersects with itself, creating points where: - - Multiple registers occupy the same fractal coordinate - - Interference patterns determine compression - - Destructive interference cancels redundant information - """ - self.intersections = [] - - # Group registers by proximity in n-dimensional space - coord_groups: Dict[str, List[QuantaRegister]] = {} - - for coords, register in self.surface.items(): - # Quantize coordinates to create fractal bins - quantized = tuple(round(c / self.fluid_viscosity) * self.fluid_viscosity - for c in coords) - key = str(quantized) - - if key not in coord_groups: - coord_groups[key] = [] - coord_groups[key].append(register) - - # Create intersections where multiple registers converge - for depth in range(1, self.recursion_depth + 1): - for key, registers in coord_groups.items(): - if len(registers) < 2: - continue - - # Compute interference pattern - phases = [r.phase for r in registers] - - # Constructive: phases align, amplitudes add - # Destructive: phases oppose, amplitudes cancel - phase_variance = max(phases) - min(phases) if phases else 0 - - if phase_variance < 0.1: - interference = "constructive" - compression = 1.0 / len(registers) - elif phase_variance > math.pi - 0.1: - interference = "destructive" - compression = 1.0 / (len(registers) ** 2) # Better compression - else: - interference = "mixed" - compression = 1.0 / (len(registers) * 1.5) - - intersection = FractalIntersection( - depth=depth, - registers=registers, - interference_pattern=interference, - compression_ratio=compression, - phase_locked=(interference == "destructive"), - ) - - self.intersections.append(intersection) - - # Update compression ratio - if self.intersections: - avg_compression = sum(i.compression_ratio for i in self.intersections) / len(self.intersections) - self.compression_ratio = avg_compression - self.compressed_registers = sum(len(i.registers) for i in self.intersections) - - return self.intersections - - def compute_hyperlut(self) -> Dict[str, Any]: - """ - Compute the hyperlut state - the final compressed representation. - - The hyperlut state is the folded, self-referential representation - of all ingested data at the Shannon limit. - """ - # Fold the surface first - self.fold_fractal() - - # Filter through Shannon sieve - all_registers = list(self.surface.values()) - filtered = self.sieve.filter_redundant(all_registers) - - # Build hyperlut representation - hyperlut_state = { - "schema": "hyperlut/v1", - "created_utc": self.created_utc, - "dimensions": self.dimensions, - "recursion_depth": self.recursion_depth, - "shannon_boundary": self.sieve.shannon_boundary, - "compression_ratio": self.compression_ratio, - "phase_locked": self.phase_locked, - "fluid_viscosity": self.fluid_viscosity, - "statistics": { - "total_registers": self.total_registers, - "compressed_registers": self.compressed_registers, - "unique_registers": len(filtered), - "fractal_intersections": len(self.intersections), - "constructive_count": sum(1 for i in self.intersections if i.interference_pattern == "constructive"), - "destructive_count": sum(1 for i in self.intersections if i.interference_pattern == "destructive"), - "mixed_count": sum(1 for i in self.intersections if i.interference_pattern == "mixed"), - }, - "surface_hash": hashlib.sha256( - json.dumps(sorted(self.surface.keys()), sort_keys=True).encode() - ).hexdigest()[:16], - } - - # Add intersection summaries (not full data - that's the compression) - hyperlut_state["intersections"] = [ - { - "depth": i.depth, - "pattern": i.interference_pattern, - "ratio": i.compression_ratio, - "register_count": len(i.registers), - "phase_locked": i.phase_locked, - } - for i in self.intersections[:100] # Limit output - ] - - # Backward-compatible alias for older consumers. - hyperlut_state["legacy_alias"] = "hyperloot" - - return hyperlut_state - - def compute_hyperloot(self) -> Dict[str, Any]: - """Backward-compatible alias for compute_hyperlut().""" - return self.compute_hyperlut() - - def to_equation(self) -> str: - """ - Represent the hyperlut state as a mathematical equation. - - Returns the standing wave equation for the bridge state. - """ - return f""" -Ψ_H = [∮_{{∂S}} H(n^n) · e^{{i(ωt - kx)}} dσ] / (S_limit ⊗ R_q) · Γ_∞ - -Where: - H(n^n) = Hyperlut operator at dimensionality {self.dimensions}^{self.dimensions} - ∂S = Surface boundary ({len(self.surface)} registers) - S_limit = Shannon boundary ({self.sieve.shannon_boundary:.4f}) - R_q = Quanta register matrix ({self.total_registers} total) - Γ_∞ = Reflection coefficient ({self.compression_ratio:.4f}) - -Fractal Intersections: {len(self.intersections)} - - Constructive: {sum(1 for i in self.intersections if i.interference_pattern == "constructive")} - - Destructive: {sum(1 for i in self.intersections if i.interference_pattern == "destructive")} - - Mixed: {sum(1 for i in self.intersections if i.interference_pattern == "mixed")} - -Compression Ratio: {self.compression_ratio:.6f} -Phase Locked: {self.phase_locked} -""" - - -# ============================================================================= -# Bridge Integration - Hyperlut as OmniToken egress filter -# ============================================================================= - -class HyperlutBridge: - """ - Bidirectional filter using hyperlut as computational bridge. - - All egress passes through the hyperlut surface: - - Inbound: Data flattened to quanta register states - - Outbound: Response folded inside hyperlut before transmission - """ - - def __init__(self, dimensions: int = 11, recursion_depth: int = 4): - self.hyperlut = Hyperlut(dimensions, recursion_depth) - self.egress_queue: List[Dict[str, Any]] = [] - self.ingress_log: List[Dict[str, Any]] = [] - - def filter_ingress(self, payload: Dict[str, Any]) -> Dict[str, Any]: - """Filter inbound payload through hyperlut sieve.""" - # Log the ingress - self.ingress_log.append({ - "timestamp": datetime.now(timezone.utc).isoformat(), - "payload_hash": hashlib.sha256( - json.dumps(payload, sort_keys=True).encode() - ).hexdigest()[:16], - "size_bytes": len(json.dumps(payload).encode()), - }) - - # Ingest into hyperlut - self.hyperlut.ingest_stream(payload) - - # Fold and compute - self.hyperlut.fold_fractal() - - # Return compressed representation - return { - "status": "filtered", - "shannon_boundary": self.hyperlut.sieve.shannon_boundary, - "register_count": len(self.hyperlut.surface), - "compression_ratio": self.hyperlut.compression_ratio, - } - - def filter_egress(self, response: Dict[str, Any]) -> Dict[str, Any]: - """Filter outbound response through hyperlut fold.""" - # Add to egress queue - self.egress_queue.append({ - "timestamp": datetime.now(timezone.utc).isoformat(), - "response_hash": hashlib.sha256( - json.dumps(response, sort_keys=True).encode() - ).hexdigest()[:16], - }) - - # Fold response into hyperlut - self.hyperlut.ingest(response, RegisterType.TOKEN) - self.hyperlut.fold_fractal() - - # Return folded representation - return { - "status": "folded", - "hyperlut_hash": self.hyperlut.compute_hyperlut()["surface_hash"], - "hyperloot_hash": self.hyperlut.compute_hyperlut()["surface_hash"], - "phase_locked": self.hyperlut.phase_locked, - } - - def compute_bridge_state(self) -> Dict[str, Any]: - """Compute current bridge state as hyperlut with legacy alias.""" - hyperlut_state = self.hyperlut.compute_hyperlut() - - return { - "bridge_state": "active", - "hyperlut": hyperlut_state, - "hyperloot": hyperlut_state, - "egress_count": len(self.egress_queue), - "ingress_count": len(self.ingress_log), - "equation": self.hyperlut.to_equation(), - } - - -# ============================================================================= -# CLI Interface -# ============================================================================= - -def main() -> None: - import argparse - - parser = argparse.ArgumentParser(description="Hyperlut: Fractal context compression") - parser.add_argument("--dimensions", type=int, default=11, help="N-dimensional space") - parser.add_argument("--recursion", type=int, default=4, help="Fractal recursion depth") - parser.add_argument("--test", action="store_true", help="Run compression test") - parser.add_argument("--output", type=Path, help="Output hyperlut JSON path") - args = parser.parse_args() - - if args.test: - # Run compression test with synthetic data - bridge = HyperlutBridge(dimensions=args.dimensions, recursion_depth=args.recursion) - - # Test payload mimicking protocol overhead, sub-pixel noise, valence fluctuations - test_payload = { - "jitter": 0.0042, - "atomic_valence": 118, - "sdr_surface": 0xFFA1, - "dns": {"host": "example.com", "port": 53}, - "http": {"host": "api.example.com", "port": 443}, - "ssh": {"host": "node.tailnet.ts.net", "port": 22}, - "smtp": {"host": "mail.tailnet.ts.net", "port": 587}, - "matrix": {"server": "matrix.tailnet.ts.net", "port": 8448}, - "tokens": ["context_token_" + str(i) for i in range(100)], - } - - print("=== HYPERLUT COMPRESSION TEST ===\n") - print("Ingesting test payload...") - ingress_result = bridge.filter_ingress(test_payload) - print(f"Ingress filtered: {json.dumps(ingress_result, indent=2)}\n") - - print("Folding fractal surface...") - bridge.hyperlut.fold_fractal() - - print("Computing hyperlut...") - state = bridge.compute_bridge_state() - - print("\n=== RESULTS ===") - print(f"Dimensions: {args.dimensions}^{args.dimensions}") - print(f"Compression Ratio: {state['hyperlut']['compression_ratio']:.6f}") - print(f"Shannon Boundary: {state['hyperlut']['shannon_boundary']:.4f}") - print(f"Fractal Intersections: {state['hyperlut']['statistics']['fractal_intersections']}") - print(f" - Destructive (best compression): {state['hyperlut']['statistics']['destructive_count']}") - print(f"\n=== EQUATION ==={state['equation']}") - - if args.output: - args.output.write_text(json.dumps(state, indent=2), encoding="utf-8") - print(f"\nHyperlut written to: {args.output}") - else: - # Initialize and output equation - hyperlut = Hyperlut(dimensions=args.dimensions, recursion_depth=args.recursion) - print(hyperlut.to_equation()) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/encoding/interference_eraser_sim.py b/5-Applications/tools-scripts/encoding/interference_eraser_sim.py deleted file mode 100644 index 6448f19f..00000000 --- a/5-Applications/tools-scripts/encoding/interference_eraser_sim.py +++ /dev/null @@ -1,647 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Interference Eraser Cache Simulator — Tick 694 - -Test interference eraser principles on CPU cache optimization. -Run on your actual CPU to measure erasure vs. tracking trade-offs. - -Usage: - python3 interference_eraser_cache_sim.py - - # Test different erasure probabilities - python3 interference_eraser_cache_sim.py --erase_probs 0.0 0.5 0.7 0.9 1.0 - - # Run specific workload - python3 interference_eraser_cache_sim.py --workload spatial -""" - -import numpy as np -import argparse -import time -from dataclasses import dataclass, field -from typing import Dict, List, Tuple, Optional -from collections import defaultdict - -# Optional imports for visualization -try: - import matplotlib.pyplot as plt - HAS_MATPLOTLIB = True -except ImportError: - HAS_MATPLOTLIB = False - -try: - import pandas as pd - HAS_PANDAS = True -except ImportError: - HAS_PANDAS = False - -# ============================================================================ -# Cache Models -# ============================================================================ - -@dataclass -class CacheLine: - """Standard cache line with full tracking.""" - tag: int - data: Optional[bytes] = None - state: str = 'I' # MESI: M/E/S/I - lru_counter: int = 0 - owner_core: int = -1 - access_history: List[int] = field(default_factory=list) - - -@dataclass -class ErasedCacheLine: - """Interference eraser cache line with aggregate metrics only.""" - tag: int - data: Optional[bytes] = None - lru_counter: int = 0 # Still need for LRU eviction - access_frequency: float = 0.0 - temporal_decay: float = 0.0 - spatial_cluster: int = -1 - # Erased: specific core_id, specific access order - - -@dataclass -class CacheStats: - """Cache performance statistics.""" - hits: int = 0 - misses: int = 0 - evictions: int = 0 - coherence_updates: int = 0 - metadata_updates: int = 0 - total_accesses: int = 0 - - @property - def hit_rate(self) -> float: - if self.total_accesses == 0: - return 0.0 - return self.hits / self.total_accesses - - @property - def miss_rate(self) -> float: - return 1.0 - self.hit_rate - - -class StandardCache: - """ - Standard CPU cache with full path tracking. - Tracks: which core, which line, access order, MESI state - """ - - def __init__(self, size_mb: float = 8.0, line_size: int = 64, - associativity: int = 16, num_cores: int = 8): - self.size_bytes = int(size_mb * 1024 * 1024) - self.line_size = line_size - self.num_lines = self.size_bytes // line_size - self.associativity = associativity - self.num_sets = self.num_lines // associativity - self.num_cores = num_cores - - # Cache structure: sets × ways - self.sets = [[CacheLine(tag=-1) for _ in range(associativity)] - for _ in range(self.num_sets)] - - self.lru_counter = 0 - self.stats = CacheStats() - - def _get_set_index(self, addr: int) -> int: - return (addr // self.line_size) % self.num_sets - - def _get_tag(self, addr: int) -> int: - return addr // (self.line_size * self.num_sets) - - def access(self, addr: int, core_id: int, is_write: bool = False) -> bool: - """ - Access cache line. Returns True if hit, False if miss. - Tracks full path information. - """ - self.stats.total_accesses += 1 - self.stats.metadata_updates += 1 # Track every access - - set_idx = self._get_set_index(addr) - tag = self._get_tag(addr) - - # Search for tag - for way in range(self.associativity): - line = self.sets[set_idx][way] - if line.tag == tag: - # HIT - self.stats.hits += 1 - line.lru_counter = self.lru_counter - line.owner_core = core_id - line.access_history.append(core_id) - if is_write: - line.state = 'M' - self.stats.coherence_updates += 1 - self.lru_counter += 1 - return True - - # MISS - self.stats.misses += 1 - - # Find LRU way - lru_way = min(range(self.associativity), - key=lambda w: self.sets[set_idx][w].lru_counter) - - # Evict if necessary - if self.sets[set_idx][lru_way].tag != -1: - self.stats.evictions += 1 - - # Install new line - self.sets[set_idx][lru_way] = CacheLine( - tag=tag, - state='M' if is_write else 'E', - lru_counter=self.lru_counter, - owner_core=core_id, - access_history=[core_id] - ) - - self.lru_counter += 1 - return False - - -class InterferenceEraserCache: - """ - Interference eraser cache with aggregate metrics. - Erases: specific core_id, specific access order - Tracks: aggregate frequency, temporal decay, spatial clustering - """ - - def __init__(self, size_mb: float = 2.0, line_size: int = 64, - associativity: int = 16, num_cores: int = 8, - erase_prob: float = 0.8, tau_decay: float = 0.99): - self.size_bytes = int(size_mb * 1024 * 1024) - self.line_size = line_size - self.num_lines = self.size_bytes // line_size - self.associativity = associativity - self.num_sets = self.num_lines // associativity - self.num_cores = num_cores - self.erase_prob = erase_prob - self.tau_decay = tau_decay - - # Cache structure: sets × ways - self.sets = [[ErasedCacheLine(tag=-1) for _ in range(associativity)] - for _ in range(self.num_sets)] - - self.lru_counter = 0 - self.stats = CacheStats() - - # Spatial clustering (simple hash-based) - self.spatial_clusters = 16 - - def _get_set_index(self, addr: int) -> int: - return (addr // self.line_size) % self.num_sets - - def _get_tag(self, addr: int) -> int: - return addr // (self.line_size * self.num_sets) - - def _get_spatial_cluster(self, addr: int) -> int: - return (addr // self.line_size) % self.spatial_clusters - - def access(self, addr: int, core_id: int, is_write: bool = False) -> bool: - """ - Access cache line with interference erasure. - With probability erase_prob, erase specific path information. - """ - self.stats.total_accesses += 1 - - set_idx = self._get_set_index(addr) - tag = self._get_tag(addr) - cluster = self._get_spatial_cluster(addr) - - # Search for tag - for way in range(self.associativity): - line = self.sets[set_idx][way] - if line.tag == tag: - # HIT - self.stats.hits += 1 - - # Interference erasure decision - if np.random.random() < self.erase_prob: - # ERASE: Update aggregate metrics only, skip metadata counter bump - line.access_frequency += 1.0 - line.temporal_decay *= self.tau_decay - line.spatial_cluster = cluster - # DO NOT update lru_counter here, this simulates lost path tracking - else: - # TRACK: Update specific metadata (standard behavior) - line.lru_counter = self.lru_counter - self.stats.metadata_updates += 1 # Heavyweight update - - if is_write: - self.stats.coherence_updates += 1 - - self.lru_counter += 1 - return True - - # MISS - self.stats.misses += 1 - - # Find LRU way - lru_way = min(range(self.associativity), - key=lambda w: self.sets[set_idx][w].lru_counter) - - # Evict if necessary - if self.sets[set_idx][lru_way].tag != -1: - self.stats.evictions += 1 - - # Install new line with aggregate metrics - self.sets[set_idx][lru_way] = ErasedCacheLine( - tag=tag, - lru_counter=self.lru_counter, - access_frequency=1.0, - temporal_decay=1.0, - spatial_cluster=cluster - ) - - self.lru_counter += 1 - return False - - def prefetch_decision(self, addr: int) -> Optional[int]: - """ - Waveprobe-based prefetch decision. - Uses aggregate field to decide prefetch region. - """ - set_idx = self._get_set_index(addr) - - # Compute "Waveprobe response" for this set - # W_a = sum of access frequencies in set - total_freq = sum( - line.access_frequency * line.temporal_decay - for line in self.sets[set_idx] - if line.tag != -1 - ) - - # Prefetch if aggregate response is high - if total_freq > 2.0: # Threshold - # Prefetch next spatial cluster - cluster = self._get_spatial_cluster(addr) - prefetch_addr = addr + (self.line_size * self.spatial_clusters) - return prefetch_addr - - return None - - -# ============================================================================ -# Workload Generators -# ============================================================================ - -def generate_spatial_workload(num_accesses: int = 10000, - stride: int = 64, - num_cores: int = 8) -> List[Tuple[int, int, bool]]: - """ - Spatial locality workload (typical array traversal). - Use limited address range to ensure cache hits. - """ - accesses = [] - # Limit address range to fit in cache (8MB = 131072 lines) - addr_range = 4 * 1024 * 1024 # 4MB working set - base_addr = 0x10000000 - - for i in range(num_accesses): - core_id = i % num_cores - addr = base_addr + (i * stride) % addr_range - is_write = (i % 10 == 0) # 10% writes - accesses.append((addr, core_id, is_write)) - - return accesses - - -def generate_random_workload(num_accesses: int = 10000, - addr_range: int = 1024 * 1024, - num_cores: int = 8) -> List[Tuple[int, int, bool]]: - """ - Random access workload (using Zipf to ensure temporal locality where LRU matters). - """ - accesses = [] - base_addr = 0x10000000 - - # Generate Zipf distribution (alpha=1.5) - # Range scaled so that roughly 2x the cache size is addressable - num_unique_lines = (addr_range // 64) * 4 - zipf_indices = np.random.zipf(1.1, num_accesses) - # Map high indices down to our range to keep things bounded - zipf_indices = [min(idx, num_unique_lines - 1) for idx in zipf_indices] - - for i in range(num_accesses): - core_id = i % num_cores - addr = base_addr + (zipf_indices[i] * 64) - is_write = (i % 10 == 0) - accesses.append((addr, core_id, is_write)) - - return accesses - - -def generate_burst_workload(num_accesses: int = 10000, - burst_size: int = 100, - num_cores: int = 8) -> List[Tuple[int, int, bool]]: - """ - Bursty workload (temporal locality). - """ - accesses = [] - base_addr = 0x10000000 - num_bursts = num_accesses // burst_size - - for burst in range(num_bursts): - core_id = burst % num_cores - addr = base_addr + (burst * 64) - - for i in range(burst_size): - is_write = (i % 10 == 0) - accesses.append((addr + (i * 64), core_id, is_write)) - - return accesses - - -def generate_multicore_workload(num_accesses: int = 10000, - shared_regions: int = 4, - num_cores: int = 8) -> List[Tuple[int, int, bool]]: - """ - Multi-core workload with shared memory regions. - """ - accesses = [] - region_size = 64 * 1024 # 64KB per region - - for i in range(num_accesses): - core_id = i % num_cores - region = i % shared_regions - offset = np.random.randint(0, region_size) - addr = 0x10000000 + (region * region_size) + offset - is_write = (i % 5 == 0) # 20% writes for shared regions - accesses.append((addr, core_id, is_write)) - - return accesses - - -# ============================================================================ -# Benchmark Runner -# ============================================================================ - -@dataclass -class BenchmarkResult: - """Results from a single benchmark run.""" - cache_type: str - erase_prob: float - workload_type: str - hit_rate: float - miss_rate: float - metadata_updates: int - coherence_updates: int - total_accesses: int - elapsed_time: float - - @property - def metadata_overhead(self) -> float: - return self.metadata_updates / self.total_accesses - - -def run_benchmark(cache, accesses: List[Tuple[int, int, bool]]) -> BenchmarkResult: - """ - Run benchmark on cache with given workload. - """ - start_time = time.perf_counter() - - for addr, core_id, is_write in accesses: - cache.access(addr, core_id, is_write) - - elapsed_time = time.perf_counter() - start_time - - return BenchmarkResult( - cache_type=type(cache).__name__, - erase_prob=getattr(cache, 'erase_prob', 0.0), - workload_type='unknown', - hit_rate=cache.stats.hit_rate, - miss_rate=cache.stats.miss_rate, - metadata_updates=cache.stats.metadata_updates, - coherence_updates=cache.stats.coherence_updates, - total_accesses=cache.stats.total_accesses, - elapsed_time=elapsed_time - ) - - -def compare_caches(erase_probs: List[float] = None, - workload_types: List[str] = None, - num_accesses: int = 10000, - cache_size_mb: int = 8, - num_cores: int = 8) -> List[BenchmarkResult]: - """ - Compare standard cache vs. interference eraser cache across erasure probabilities. - """ - if erase_probs is None: - erase_probs = [0.0, 0.3, 0.5, 0.7, 0.9, 1.0] - - if workload_types is None: - workload_types = ['spatial', 'random', 'burst', 'multicore'] - - results = [] - - print(f"Running benchmarks: {len(erase_probs)} erasure probs × " - f"{len(workload_types)} workloads = {len(erase_probs) * len(workload_types) + len(workload_types)} runs") - print() - - for workload_type in workload_types: - print(f"Workload: {workload_type}") - - # Generate workload - if workload_type == 'spatial': - accesses = generate_spatial_workload(num_accesses, num_cores=num_cores) - elif workload_type == 'random': - accesses = generate_random_workload(num_accesses, num_cores=num_cores) - elif workload_type == 'burst': - accesses = generate_burst_workload(num_accesses, num_cores=num_cores) - elif workload_type == 'multicore': - accesses = generate_multicore_workload(num_accesses, num_cores=num_cores) - else: - raise ValueError(f"Unknown workload: {workload_type}") - - # Standard cache (erase_prob = 0.0) - print(f" Standard cache (tracking)...") - std_cache = StandardCache(size_mb=cache_size_mb, num_cores=num_cores) - std_result = run_benchmark(std_cache, accesses) - std_result.workload_type = workload_type - results.append(std_result) - print(f" Hit rate: {std_result.hit_rate:.3f}, " - f"Metadata overhead: {std_result.metadata_overhead:.3f}") - - # Interference eraser caches - for erase_prob in erase_probs: - print(f" Interference eraser cache (erase_prob={erase_prob:.1f})...") - qe_cache = InterferenceEraserCache( - size_mb=cache_size_mb, - num_cores=num_cores, - erase_prob=erase_prob - ) - qe_result = run_benchmark(qe_cache, accesses) - qe_result.workload_type = workload_type - results.append(qe_result) - print(f" Hit rate: {qe_result.hit_rate:.3f}, " - f"Metadata overhead: {qe_result.metadata_overhead:.3f}") - - print() - - return results - - -# ============================================================================ -# Visualization -# ============================================================================ - -def plot_results(results: List[BenchmarkResult], output_file: str = None): - """ - Plot benchmark results. - """ - if not HAS_MATPLOTLIB or not HAS_PANDAS: - print("Warning: matplotlib and/or pandas not available. Skipping plot.") - return - - df = pd.DataFrame([r.__dict__ for r in results]) - - # Group by workload - fig, axes = plt.subplots(2, 2, figsize=(14, 10)) - - for idx, workload in enumerate(df['workload_type'].unique()): - ax = axes[idx // 2, idx % 2] - workload_df = df[df['workload_type'] == workload] - - # Plot hit rate vs erasure - ax.plot(workload_df['erase_prob'], workload_df['hit_rate'], - 'o-', label='Hit Rate', linewidth=2, markersize=8) - - # Plot metadata overhead - ax.plot(workload_df['erase_prob'], workload_df['metadata_overhead'], - 's--', label='Metadata Overhead', linewidth=2, markersize=8) - - ax.set_xlabel('Erasure Probability', fontsize=12) - ax.set_ylabel('Metric', fontsize=12) - ax.set_title(f'{workload.capitalize()} Workload', fontsize=14) - ax.legend(fontsize=10) - ax.grid(True, alpha=0.3) - ax.set_xlim(-0.05, 1.05) - - plt.tight_layout() - - if output_file: - plt.savefig(output_file, dpi=150, bbox_inches='tight') - print(f"Plot saved to {output_file}") - else: - plt.show() - - -def print_summary(results: List[BenchmarkResult]): - """ - Print summary of key findings. - """ - if not HAS_PANDAS: - print("Warning: pandas not available. Using basic summary.") - # Basic summary without pandas - for r in results: - print(f" {r.cache_type} (erase={r.erase_prob:.1f}): " - f"hit_rate={r.hit_rate:.3f}, metadata={r.metadata_overhead:.3f}") - return - - df = pd.DataFrame([r.__dict__ for r in results]) - - print("=" * 70) - print("INTERFERENCE ERASER CACHE BENCHMARK SUMMARY") - print("=" * 70) - print() - - for workload in df['workload_type'].unique(): - workload_df = df[df['workload_type'] == workload] - - print(f"Workload: {workload}") - - # Find optimal erasure - optimal_idx = workload_df['hit_rate'].idxmax() - optimal = workload_df.loc[optimal_idx] - standard_row = workload_df[workload_df['erase_prob'] == 0.0] - - if len(standard_row) == 0: - print(f" No standard cache data found") - print() - continue - - standard = standard_row.iloc[0] - - print(f" Standard cache hit rate: {standard['hit_rate']:.3f}") - print(f" Optimal erasure: {optimal['erase_prob']:.1f}") - print(f" Optimal hit rate: {optimal['hit_rate']:.3f}") - print(f" Improvement: {(optimal['hit_rate'] - standard['hit_rate']) * 100:.1f}%") - if standard['metadata_overhead'] > 0: - print(f" Metadata reduction: {(1 - optimal['metadata_overhead'] / standard['metadata_overhead']) * 100:.1f}%") - print() - - print("=" * 70) - - -# ============================================================================ -# Main -# ============================================================================ - -def main(): - parser = argparse.ArgumentParser( - description='Interference Eraser Cache Simulator (Tick 694)' - ) - parser.add_argument('--erase_probs', type=float, nargs='+', - default=[0.0, 0.3, 0.5, 0.7, 0.9, 1.0], - help='Erasure probabilities to test') - parser.add_argument('--workloads', type=str, nargs='+', - default=['spatial', 'random', 'burst', 'multicore'], - help='Workload types to test') - parser.add_argument('--num_accesses', type=int, default=10000, - help='Number of memory accesses per workload') - parser.add_argument('--cache_size', type=float, default=2.0, - help='Cache size in MB (smaller forces evictions)') - parser.add_argument('--num_cores', type=int, default=8, - help='Number of CPU cores') - parser.add_argument('--output_plot', type=str, default=None, - help='Output file for plot (PNG)') - parser.add_argument('--output_summary', type=str, default=None, - help='Output file for summary (JSON)') - - args = parser.parse_args() - - print("Interference Eraser Cache Simulator") - print("=" * 70) - print(f"Cache size: {args.cache_size}MB") - print(f"Cores: {args.num_cores}") - print(f"Accesses: {args.num_accesses:,}") - print(f"Erasure probs: {args.erase_probs}") - print(f"Workloads: {args.workloads}") - print("=" * 70) - print() - - # Run benchmarks - results = compare_caches( - erase_probs=args.erase_probs, - workload_types=args.workloads, - num_accesses=args.num_accesses, - cache_size_mb=args.cache_size, - num_cores=args.num_cores - ) - - # Print summary - print_summary(results) - - # Plot results - if args.output_plot: - plot_results(results, args.output_plot) - - # Save summary - if args.output_summary: - import json - summary = [r.__dict__ for r in results] - with open(args.output_summary, 'w') as f: - json.dump(summary, f, indent=2) - print(f"Summary saved to {args.output_summary}") - - -if __name__ == '__main__': - main() diff --git a/5-Applications/tools-scripts/encoding/iso_cross.py b/5-Applications/tools-scripts/encoding/iso_cross.py deleted file mode 100644 index 34a2645e..00000000 --- a/5-Applications/tools-scripts/encoding/iso_cross.py +++ /dev/null @@ -1,503 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -ISO Cross-Product Residual Analyser -===================================== -**concept_anchor:** domain=compression / concept=cross_product_residual_axis / - resolution=FORMING - -THE THIRD AXIS --------------- -Pass 1 (ISO prepass) operates as the binary thinker: match or no-match. -It finds discrete known symbols and substitutes them. - -Pass 2 (bits-back / frequency prior) operates as the laminar flow thinker: -smooth probability distribution over the matched tokens. It models how -*common* each match is within its domain. - -Neither pass looks at CONTEXT between domains. A geo token appearing next -to a chem token, a bio token following a math token — these cross-domain -positional relationships are invisible to both passes. - -The cross-product residual is everything above the expected independence: - - residual(A, B) = observed_cooccurrence(domain_A, domain_B) - - expected_cooccurrence(domain_A) * expected_cooccurrence(domain_B) - -If residual is positive and recurring: a new compression primitive is hiding -there. Encoding the pair jointly rather than independently would save bits. -If the residual distribution is heavy-tailed (power law): the structure is -scale-free — the same principle recurs across different specific tokens. - -This is the "third impossible axis": it requires both passes to exist (you -need to have matched the symbols before you can measure their positional -relationship) but is orthogonal to what either pass alone can represent. - -WHAT THIS PRODUCES ------------------- - 1. Co-occurrence matrix: P(domain_A near domain_B) for all domain pairs - 2. Expected matrix: P(domain_A) * P(domain_B) (independence baseline) - 3. Residual matrix: observed - expected - 4. Surprise distribution for the residual (is it heavy-tailed?) - 5. Top cross-domain bigrams: token_A in domain_A followed by token_B in domain_B - within a configurable window — these are new symbol table candidates - -COMPRESSION IMPLICATION ------------------------ -If domain pair (A, B) shows strong positive residual AND the top bigrams -are consistent across documents, then: - - Add a new iso_cross domain to iso_symbol_table - - Each bigram becomes a cross-domain compound symbol - - Pass 1.5 (between ISO prepass and entropy coder) encodes these pairs - -HOW TO USE ----------- - # Analyse 1MB of enwik8 - python3 5-Applications/scripts/iso_cross.py shared-data/data/enwik8 --bytes 1000000 - - # Show top N cross-domain bigrams - python3 5-Applications/scripts/iso_cross.py shared-data/data/enwik8 --bytes 1000000 --top 20 - - # Write a candidate domain entry to stdout - python3 5-Applications/scripts/iso_cross.py shared-data/data/enwik8 --bytes 1000000 --emit-candidates -""" - -from __future__ import annotations - -import argparse -import math -import sys -from collections import Counter -from pathlib import Path - -sys.path.insert(0, str(Path(__file__).parent)) - -try: - from iso_symbol_table import prepass as iso_prepass, EXTENDED_DOMAINS, PTOS_DOMAINS - _ISO_AVAILABLE = True -except ImportError: - _ISO_AVAILABLE = False - print("[error] iso_symbol_table not available", file=sys.stderr) - sys.exit(1) - -try: - from bits_back_iso import _surprise_bits - _BB_AVAILABLE = True -except ImportError: - _BB_AVAILABLE = False - -from iso_pipeline import run_windowed_pass, PipelineResult, corpus_adaptive_thresholds, run_chunked_pass - - -# ─── configuration ──────────────────────────────────────────────────────────── - -WINDOW_BYTES = 200 # co-occurrence window: tokens within this many chars are "near" -CHUNK_SIZE = 65_536 -DOMAINS_TO_USE = EXTENDED_DOMAINS - -# Big Bang phase thresholds (bits of surprise). -# Calibrated against enwik8 500KB: surprise distribution is bimodal — -# common tokens cluster at 3–6 bits; floor-probability tokens (not in prior -# table) all land at 16.6 bits. Setting thresholds at 10 / 5.5 splits: -# Phase 1: rare tokens + floor-prob tokens (highest energy) -# Phase 2: mid-frequency tokens in prior table (7–13 bits range in practice) -# Phase 3: common tokens well-predicted by prior (lang codes, top geo) -PHASE1_THRESHOLD = 10.0 # > this → Phase 1 (high-energy / inflationary) -PHASE3_THRESHOLD = 5.5 # < this → Phase 3 (low-energy / structure formation) -# Between thresholds → Phase 2 (matter-dominated) - - -# ─── core analysis ──────────────────────────────────────────────────────────── - -def _coupling_stats(result: PipelineResult) -> dict: - """Extract Big Bang coupling statistics from a completed PipelineResult. - - Returns the same shape as the old _coupling_strength() but derived from - the single run_windowed_pass() result — no second traversal needed. - """ - if not _BB_AVAILABLE: - return {"error": "bits_back_iso not available — cannot compute surprise"} - - p3_total = result.phase_counts[3] - p3_coupled = result.phase3_coupled - cf = p3_coupled / max(p3_total, 1) - - top_couplings = sorted( - [(t1, d1, t3, d3, cnt) - for (t1, d1, t3, d3), cnt in result.coupling_pairs.items()], - key=lambda x: -x[4], - ) - - return { - "phase_counts": dict(result.phase_counts), - "phase3_total": p3_total, - "phase3_coupled": p3_coupled, - "coupling_fraction": round(cf, 4), - "top_couplings": top_couplings[:20], - "inflationary_size": round(cf, 4), - } - - -def _residual_matrix( - domain_counts: Counter, - pair_counts: Counter, - total_tokens: int, -) -> dict[tuple[str, str], float]: - """Compute observed - expected co-occurrence for each domain pair. - - Expected under independence: P(A) * P(B) * total_pairs - where P(domain) = domain_counts[domain] / total_tokens - """ - total_pairs = sum(pair_counts.values()) or 1 - residuals: dict[tuple[str, str], float] = {} - - for (da, db), observed in pair_counts.items(): - pa = domain_counts[da] / total_tokens - pb = domain_counts[db] / total_tokens - expected = pa * pb * total_pairs - residuals[(da, db)] = observed - expected - - return residuals - - -def _surprise_distribution( - bigram_counts: Counter, - residuals: dict[tuple[str, str], float], -) -> list[tuple[float, str, str, str, str, int]]: - """Score each cross-domain bigram by its residual contribution. - - Returns list of (score, tok_a, dom_a, tok_b, dom_b, count) sorted descending. - Score = residual(dom_a, dom_b) * count — how much of the pair residual this - bigram contributes. - """ - pair_total: Counter = Counter() - for (ta, da, tb, db), cnt in bigram_counts.items(): - pair_total[(da, db)] += cnt - - scored = [] - for (ta, da, tb, db), cnt in bigram_counts.items(): - res = residuals.get((da, db), 0.0) - if res <= 0: - continue - # weight by fraction of this bigram in the pair's total - frac = cnt / max(pair_total[(da, db)], 1) - score = res * frac * cnt - # also factor in bits-back surprise if available - if _BB_AVAILABLE: - sa = _surprise_bits(ta, da) - sb = _surprise_bits(tb, db) - # high surprise in both passes = most orthogonal to both world views - cross_surprise = (sa + sb) / 2.0 - else: - cross_surprise = 1.0 - scored.append((score * cross_surprise, ta, da, tb, db, cnt)) - - scored.sort(reverse=True) - return scored - - -def _is_power_law(counts: list[int]) -> tuple[bool, float]: - """Rough power-law check on count distribution. - - Fits log(count) ~ alpha * log(rank) via least-squares. - Returns (is_heavy_tailed, alpha) where alpha < -0.7 is heavy-tailed. - """ - if len(counts) < 4: - return False, 0.0 - sorted_counts = sorted(counts, reverse=True) - log_ranks = [math.log(i + 1) for i in range(len(sorted_counts))] - log_counts = [math.log(max(c, 1)) for c in sorted_counts] - n = len(log_ranks) - sx = sum(log_ranks) - sy = sum(log_counts) - sxy = sum(log_ranks[i] * log_counts[i] for i in range(n)) - sxx = sum(x ** 2 for x in log_ranks) - denom = n * sxx - sx * sx - if abs(denom) < 1e-12: - return False, 0.0 - alpha = (n * sxy - sx * sy) / denom - return alpha < -0.7, round(alpha, 3) - - -# ─── analysis entry point ───────────────────────────────────────────────────── - -def _print_pass1(log: dict, text: str, compressed: str) -> None: - """Print Pass 1 (ISO prepass) stats.""" - total_matches = sum(len(v) for v in log.values()) - print("\nPass 1 (ISO prepass):") - print(f" Domains fired : {list(log.keys())}") - print(f" Total matches : {total_matches:,}") - print(f" Input bytes : {len(text.encode('utf-8')):,}") - print(f" After prepass : {len(compressed.encode('utf-8')):,}") - - -def _print_pass2(log: dict) -> None: - """Print Pass 2 (bits-back surprise) stats if available.""" - if not _BB_AVAILABLE: - return - avg_surprise = {} - for domain, tokens in log.items(): - surprises = [_surprise_bits(t, domain) for t in tokens] - if surprises: - avg_surprise[domain] = sum(surprises) / len(surprises) - print("\nPass 2 (bits-back surprise per domain):") - for d, s in sorted(avg_surprise.items(), key=lambda x: -x[1]): - print(f" {d:<14} avg_surprise={s:.2f} bits") - - -def _print_residual_matrix( - residuals: dict, - pair_counts: Counter, - domain_counts: Counter, - total_tokens: int, -) -> None: - """Print the observed-minus-expected co-occurrence matrix.""" - print("\nResidual matrix (observed - expected co-occurrence):") - total_pairs = sum(pair_counts.values()) - for (da, db), res in sorted(residuals.items(), key=lambda x: -x[1])[:8]: - obs = pair_counts[(da, db)] - pa = domain_counts[da] / total_tokens - pb = domain_counts[db] / total_tokens - exp = pa * pb * total_pairs - sign = "▲" if res > 0 else "▼" - print( - f" {sign} {da:<12} × {db:<12} " - f"obs={obs:4d} exp={exp:5.1f} Δ={res:+6.1f}" - ) - - -def _print_bigrams(bigram_scored: list, top_n: int, emit_candidates: bool) -> None: - """Print top cross-domain bigrams and optional candidate entries.""" - counts_only = [c for _, _, _, _, _, c in bigram_scored] - is_heavy, alpha = _is_power_law(counts_only) - - label = "HEAVY-TAILED" if is_heavy else "not heavy-tailed" - print("\nResidual distribution:") - print(f" Unique positive-residual bigrams: {len(bigram_scored)}") - print(f" Power-law fit alpha: {alpha} ({label})") - if is_heavy: - print(" *** Heavy tail detected — recurring cross-domain structure present ***") - print(" This is the signature of a compressible third axis.") - - hdr = f" {'score':>8} {'token_A':<20} {'dom_A':<13} → {'token_B':<20} {'dom_B':<13} cnt" - sep = f" {'-'*8} {'-'*20} {'-'*13} {'-'*20} {'-'*13} ---" - print(f"\nTop {top_n} cross-domain bigrams (new symbol candidates):") - print(hdr) - print(sep) - for score, ta, da, tb, db, cnt in bigram_scored[:top_n]: - print(f" {score:8.2f} {ta:<20} {da:<13} → {tb:<20} {db:<13} {cnt}") - - if emit_candidates and bigram_scored: - print("\n--- CANDIDATE ISO_CROSS DOMAIN ENTRIES ---") - print("# Add these to iso_symbol_table.py as a new 'iso_cross' domain") - print("# Each entry is a cross-domain compound that co-occurs above expectation") - print() - seen: set[str] = set() - for score, ta, da, tb, db, cnt in bigram_scored[:top_n]: - compound = f"{ta} {tb}" - if compound in seen: - continue - seen.add(compound) - domains_str = f'["{da}", "{db}"]' - print( - f' "{compound}": {{' - f'"domains": {domains_str}, "score": {score:.2f}, "count": {cnt}' - f'}},' - ) - - -def _print_summary( - pair_counts: Counter, - domain_counts: Counter, - residuals: dict, - total_tokens: int, -) -> None: - """Print cross-product residual summary and next-step guidance.""" - total_cross_pairs = sum(pair_counts.values()) - total_expected = sum( - (domain_counts[da] / total_tokens) - * (domain_counts[db] / total_tokens) - * total_cross_pairs - for (da, db) in pair_counts - ) - total_residual = sum(r for r in residuals.values() if r > 0) - residual_fraction = total_residual / max(total_cross_pairs, 1) - - print("\n" + "=" * 60) - print("CROSS-PRODUCT RESIDUAL SUMMARY") - print(f" Total cross-domain co-occurrences: {total_cross_pairs:,}") - print(f" Expected under independence: {total_expected:.1f}") - print(f" Positive residual (above expected): {total_residual:.1f}") - print(f" Residual fraction: {residual_fraction:.3f}") - if residual_fraction > 0.15: - print(f" *** {residual_fraction*100:.1f}% above independence baseline ***") - print(" The two passes are leaving significant structure on the table.") - print(" A Pass 1.5 encoding these cross-domain pairs would recover it.") - elif residual_fraction > 0.05: - print(f" Moderate cross-domain structure ({residual_fraction*100:.1f}%). Worth monitoring.") - else: - print(" Low residual. Passes are nearly independent on this corpus.") - - print("\nNext step: if heavy-tailed and residual_fraction > 0.15,") - print(" add top bigrams to iso_symbol_table as 'iso_cross' domain.") - print(" That becomes Pass 1.5 — the third axis encoded as a symbol table.") - - -def _print_coupling_strength( - result: PipelineResult, - window: int, - p1_t: float = PHASE1_THRESHOLD, - p3_t: float = PHASE3_THRESHOLD, -) -> None: - """Print Big Bang phase coupling strength — inflationary epoch size.""" - cs = _coupling_stats(result) - if "error" in cs: - print(f"\n[coupling strength: {cs['error']}]") - return - - phases = cs["phase_counts"] - p1 = phases.get(1, 0) - p2 = phases.get(2, 0) - p3 = phases.get(3, 0) - total = p1 + p2 + p3 or 1 - cf = cs["coupling_fraction"] - p3t = cs["phase3_total"] - p3c = cs["phase3_coupled"] - - print("\nBig Bang decompression profile:") - print(f" Phase 1 (inflationary, >{p1_t:.2f} bits): " - f"{p1:4d} tokens ({p1/total*100:4.1f}%)") - print(f" Phase 2 (matter, {p3_t:.2f}–{p1_t:.2f} bits): " - f"{p2:4d} tokens ({p2/total*100:4.1f}%)") - print(f" Phase 3 (scaffolding, <{p3_t:.2f} bits): " - f"{p3:4d} tokens ({p3/total*100:4.1f}%)") - print(f"\n Phase 3 tokens coupled to Phase 1: {p3c}/{p3t}") - print(f" Inflationary epoch size: {cf*100:.1f}% " - f"({'STRONG' if cf > 0.5 else 'MODERATE' if cf > 0.25 else 'WEAK'} coupling)") - - if cf > 0.25 and cs["top_couplings"]: - print("\n Top Phase1→Phase3 couplings (skeleton predicts scaffolding):") - hdr = " {:<20} {:<13} → {:<20} {:<13} {}" - print(hdr.format("phase1_token", "dom1", "phase3_token", "dom3", "cnt")) - print(" " + "-" * 74) - for t1, d1, t3, d3, cnt in cs["top_couplings"][:8]: - print(f" {t1:<20} {d1:<13} → {t3:<20} {d3:<13} {cnt}") - - if cf > 0.5: - print( - f"\n *** {cf*100:.1f}% of low-energy tokens are predicted by the" - " high-energy skeleton ***" - ) - print(" Encoder can omit them; decoder reconstructs by attraction.") - print(" Minimum induced energy to decompress = Phase 1 tokens only.") - - -def analyse( - path: Path, - n_bytes: int = 1_000_000, - window: int = WINDOW_BYTES, - top_n: int = 15, - emit_candidates: bool = False, - domains: list[str] | None = None, - adaptive: bool = False, -) -> None: - """Run full cross-product residual analysis on the given file. - - domains : override DOMAINS_TO_USE (pass PTOS_DOMAINS for Research Stack) - adaptive : derive phase thresholds from this corpus (p90/p25) instead of - using the enwik8-calibrated defaults - """ - if domains is None: - domains = list(DOMAINS_TO_USE) - - with open(path, "rb") as f: - raw = f.read(n_bytes) - try: - text = raw.decode("utf-8", errors="replace") - except (UnicodeDecodeError, ValueError): - text = raw.decode("latin-1", errors="replace") - - print(f"Input: {len(raw):,} bytes from {path.name}") - print(f"Domains: {domains}") - - compressed, log = iso_prepass(text, domains=domains) - total_matches = sum(len(v) for v in log.values()) - _print_pass1(log, text, compressed) - if total_matches == 0: - print("\n[no matches — cannot compute cross-product residual]") - return - - _print_pass2(log) - - # Corpus-adaptive thresholds: quick chunked pass to calibrate, then full pass - p1_t, p3_t = PHASE1_THRESHOLD, PHASE3_THRESHOLD - if adaptive: - cal = run_chunked_pass(text, chunk_size=65_536, max_chunks=16, - strategy="uniform", domains=domains) - p1_t, p3_t = corpus_adaptive_thresholds(cal) - print(f"\nCorpus-adaptive thresholds (p90/p25): " - f"Phase1 > {p1_t:.2f} bits, Phase3 < {p3_t:.2f} bits") - - result = run_windowed_pass( - text, window=window, domains=domains, - phase1_t=p1_t, phase3_t=p3_t, - ) - total_tokens = result.total_tokens() or 1 - print(f"\nCo-occurrence (window={window} chars):") - print(f" Cross-domain pairs observed: {sum(result.pair_counts.values()):,}") - print(f" Unique domain pairs : {len(result.pair_counts)}") - print(f" Unique cross-domain bigrams: {len(result.bigram_counts)}") - - residuals = _residual_matrix(result.domain_counts, result.pair_counts, - total_tokens) - bigram_scored = _surprise_distribution(result.bigram_counts, residuals) - - _print_residual_matrix(residuals, result.pair_counts, result.domain_counts, - total_tokens) - _print_bigrams(bigram_scored, top_n, emit_candidates) - _print_summary(result.pair_counts, result.domain_counts, residuals, total_tokens) - - # ── Big Bang coupling strength (from the same pass — no extra traversal) ── - _print_coupling_strength(result, window, p1_t=p1_t, p3_t=p3_t) - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def main() -> None: - """CLI entry point.""" - parser = argparse.ArgumentParser( - description="ISO cross-product residual analyser — find the third axis" - ) - parser.add_argument("path", type=Path, help="input file (enwik8 or any text/XML)") - parser.add_argument("--bytes", type=int, default=1_000_000, - help="bytes to read from input (default 1MB)") - parser.add_argument("--window", type=int, default=WINDOW_BYTES, - help=f"co-occurrence window in chars (default {WINDOW_BYTES})") - parser.add_argument("--top", type=int, default=15, - help="top N bigrams to display (default 15)") - parser.add_argument("--emit-candidates", action="store_true", - help="print candidate iso_cross domain entries") - parser.add_argument("--ptos", action="store_true", - help="use PTOS_DOMAINS instead of EXTENDED_DOMAINS") - parser.add_argument("--adaptive", action="store_true", - help="derive phase thresholds from this corpus (p90/p25)") - args = parser.parse_args() - - if not args.path.exists(): - print(f"[error] file not found: {args.path}", file=sys.stderr) - sys.exit(1) - - domains = list(PTOS_DOMAINS) if args.ptos else None - analyse(args.path, n_bytes=args.bytes, window=args.window, - top_n=args.top, emit_candidates=args.emit_candidates, - domains=domains, adaptive=args.adaptive) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/encoding/iso_pipeline.py b/5-Applications/tools-scripts/encoding/iso_pipeline.py deleted file mode 100644 index 0e19d8cb..00000000 --- a/5-Applications/tools-scripts/encoding/iso_pipeline.py +++ /dev/null @@ -1,282 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -ISO Pipeline — Shared Single-Pass Windowed Analysis -===================================================== -**concept_anchor:** domain=compression / concept=iso_pipeline_shared_pass / - resolution=STABLE - -PURPOSE -------- -Three modules were each implementing their own version of the same operation: - - "slide a window over text → run prepass per chunk → aggregate results" - - iso_cross._cooccurrence_from_chunks — co-occurrence + bigrams - iso_cross._coupling_strength — phase-sorted coupling (second pass!) - ingest_large_file.process_file — semantic fingerprint for indexing - -This module is the single implementation that all three call. One traversal -of the text produces everything all three need: - - - substitution log (iso_prepass output) - - per-token surprise scores (bits-back prior) - - domain counts - - cross-domain co-occurrence pairs - - cross-domain bigrams - - phase-classified token lists (Phase 1 / 2 / 3 by surprise) - -ARCHITECTURE ------------- -iso_symbol_table.py symbols + EXTENDED_DOMAINS - ↓ -iso_pipeline.py ← THIS FILE: shared windowed pass - ↓ -bits_back_iso.py encode_log / decode_log (log encoding only) -iso_cross.py residual analysis (calls run_windowed_pass) -ingest_large_file.py ingestion (calls run_windowed_pass or run_chunked_pass) - -USAGE ------ - from iso_pipeline import run_windowed_pass, run_chunked_pass, PipelineResult - - # Cross-product residual / coupling analysis (overlapping windows) - result = run_windowed_pass(text, window=200) - - # Large-file ingestion (non-overlapping uniform samples) - result = run_chunked_pass(text, chunk_size=65536, max_chunks=256) - - # Access everything from one result - result.domain_counts # Counter: domain → total token count - result.pair_counts # Counter: (dom_A, dom_B) → co-occurrence count - result.bigram_counts # Counter: (tok_a, dom_a, tok_b, dom_b) → count - result.all_tokens # list of TokenRecord - result.phase_counts # Counter: 1/2/3 → token count - result.phase1_tokens # list of TokenRecord (high-energy) - result.phase3_tokens # list of TokenRecord (scaffolding) -""" - -from __future__ import annotations - -import math -import sys -from collections import Counter -from dataclasses import dataclass, field -from pathlib import Path -from typing import NamedTuple - -sys.path.insert(0, str(Path(__file__).parent)) - -from iso_symbol_table import ( - prepass as iso_prepass, - EXTENDED_DOMAINS, - normalize_latex_math, -) - -try: - from bits_back_iso import _surprise_bits - _BB_AVAILABLE = True -except ImportError: - _BB_AVAILABLE = False - - -# ─── phase thresholds ───────────────────────────────────────────────────────── -# Calibrated from enwik8 500KB surprise distribution: -# p25 = 4.3 bits (common tokens, well-predicted) -# p75 = 16.6 bits (floor-probability tokens, not in prior table) -# Corpus-adaptive override: pass phase1_t / phase3_t to run_*_pass(). - -PHASE1_DEFAULT = 10.0 # > this → Phase 1 (inflationary / high-energy) -PHASE3_DEFAULT = 5.5 # < this → Phase 3 (scaffolding / low-energy) - - -# ─── data types ─────────────────────────────────────────────────────────────── - -class TokenRecord(NamedTuple): - """One matched token from the ISO prepass with its surprise score.""" - token: str - domain: str - surprise: float # -log₂(p(token|domain)); 0.0 if bits_back_iso unavailable - - -@dataclass -class PipelineResult: - """Everything produced by one windowed pass over the text.""" - domain_counts: Counter = field(default_factory=Counter) - pair_counts: Counter = field(default_factory=Counter) - bigram_counts: Counter = field(default_factory=Counter) - all_tokens: list[TokenRecord] = field(default_factory=list) - phase_counts: Counter = field(default_factory=Counter) - phase1_tokens: list[TokenRecord] = field(default_factory=list) - phase3_tokens: list[TokenRecord] = field(default_factory=list) - # Phase 1 × Phase 3 coupling: (p1_tok, p1_dom, p3_tok, p3_dom) → count - coupling_pairs: Counter = field(default_factory=Counter) - # Phase 3 token appearances in windows that also contain Phase 1 tokens - phase3_coupled: int = 0 - # Per-domain aggregates - surprise_sums: Counter = field(default_factory=Counter) - windows_seen: int = 0 - - def avg_surprise(self, domain: str) -> float: - """Mean surprise for a domain (0 if no tokens seen).""" - n = self.domain_counts[domain] - return self.surprise_sums[domain] / n if n else 0.0 - - def total_tokens(self) -> int: - """Total matched tokens across all domains.""" - return sum(self.domain_counts.values()) - - -# ─── core window processor ──────────────────────────────────────────────────── - -def _process_window( - chunk: str, - result: PipelineResult, - domains: list[str], - phase1_t: float, - phase3_t: float, -) -> None: - """Run prepass on one chunk and accumulate into result (in-place).""" - normalized_chunk = normalize_latex_math(chunk) - _, chunk_log = iso_prepass(normalized_chunk, domains=domains) - if not chunk_log: - return - - result.windows_seen += 1 - window_tokens: list[tuple[str, str, float]] = [] - p1_window: list[tuple[str, str]] = [] # (token, domain) for Phase 1 - p3_window: list[tuple[str, str]] = [] # (token, domain) for Phase 3 - - for domain, tokens in chunk_log.items(): - for tok in tokens: - s = _surprise_bits(tok, domain) if _BB_AVAILABLE else 0.0 - result.domain_counts[domain] += 1 - result.surprise_sums[domain] += s - rec = TokenRecord(tok.lower(), domain, s) - result.all_tokens.append(rec) - window_tokens.append((tok.lower(), domain, s)) - - if s > phase1_t: - result.phase_counts[1] += 1 - result.phase1_tokens.append(rec) - p1_window.append((tok.lower(), domain)) - elif s < phase3_t: - result.phase_counts[3] += 1 - result.phase3_tokens.append(rec) - p3_window.append((tok.lower(), domain)) - else: - result.phase_counts[2] += 1 - - # Cross-domain pairs and bigrams - for i, (ta, da, _) in enumerate(window_tokens): - for j, (tb, db, _) in enumerate(window_tokens): - if i != j and da != db: - result.pair_counts[(da, db)] += 1 - result.bigram_counts[(ta, da, tb, db)] += 1 - - # Phase 1 × Phase 3 coupling (Coulomb binding field) - if p1_window and p3_window: - result.phase3_coupled += len(p3_window) - for t1, d1 in p1_window: - for t3, d3 in p3_window: - result.coupling_pairs[(t1, d1, t3, d3)] += 1 - - -# ─── public API ─────────────────────────────────────────────────────────────── - -def run_windowed_pass( - text: str, - window: int = 200, - domains: list[str] | None = None, - phase1_t: float = PHASE1_DEFAULT, - phase3_t: float = PHASE3_DEFAULT, -) -> PipelineResult: - """Sliding-window pass with 50% overlap — for co-occurrence / residual analysis. - - Every token appears in approximately 2 windows, giving robust co-occurrence - counts. Use for iso_cross residual analysis and coupling strength. - """ - if domains is None: - domains = EXTENDED_DOMAINS - result = PipelineResult() - step = window // 2 - n = len(text) - for start in range(0, n, step): - _process_window(text[start: start + window], result, domains, - phase1_t, phase3_t) - return result - - -def run_chunked_pass( - text: str, - chunk_size: int = 65_536, - max_chunks: int = 256, - strategy: str = "uniform", - domains: list[str] | None = None, - phase1_t: float = PHASE1_DEFAULT, - phase3_t: float = PHASE3_DEFAULT, -) -> PipelineResult: - """Non-overlapping chunk pass — for large-file ingestion. - - Strategies: - full — every chunk sequentially (accurate, slow for large files) - uniform — evenly-spaced sample of max_chunks chunks - head_tail — first N/2 + last N/2 chunks (catches header + conclusion) - """ - if domains is None: - domains = EXTENDED_DOMAINS - n = len(text) - total_chunks = math.ceil(n / chunk_size) - - if strategy == "full" or total_chunks <= max_chunks: - offsets = list(range(0, n, chunk_size)) - elif strategy == "head_tail": - half = max_chunks // 2 - head = list(range(0, min(half * chunk_size, n), chunk_size)) - tail_start = max(0, n - half * chunk_size) - tail = list(range(tail_start, n, chunk_size)) - seen: set[int] = set() - offsets = [] - for o in head + tail: - if o not in seen: - seen.add(o) - offsets.append(o) - else: # uniform - step = max(1, total_chunks // max_chunks) - offsets = list(range(0, n, step * chunk_size))[:max_chunks] - - result = PipelineResult() - for start in offsets: - _process_window(text[start: start + chunk_size], result, domains, - phase1_t, phase3_t) - return result - - -_FLOOR_BITS = 16.5 # tokens at -log2(1e-5) ≈ 16.61 are floor-probability; - # exclude them so the adaptive percentiles reflect the - # real distribution rather than being pulled up by missing priors. - -def corpus_adaptive_thresholds(result: PipelineResult) -> tuple[float, float]: - """Derive corpus-adaptive phase thresholds from observed surprise distribution. - - Returns (phase1_threshold, phase3_threshold) using p90 / p25 quantiles. - Floor-probability tokens (surprise ≥ _FLOOR_BITS) are excluded so the - thresholds reflect the real distribution, not the density of missing priors. - Falls back to defaults if fewer than 10 non-floor tokens observed. - """ - surprises = sorted( - t.surprise for t in result.all_tokens - if t.surprise < _FLOOR_BITS - ) - n = len(surprises) - if n < 10: - return PHASE1_DEFAULT, PHASE3_DEFAULT - p90 = surprises[int(n * 0.90)] - p25 = surprises[int(n * 0.25)] - return p90, p25 diff --git a/5-Applications/tools-scripts/encoding/iso_symbol_table.py b/5-Applications/tools-scripts/encoding/iso_symbol_table.py deleted file mode 100644 index c8813f87..00000000 --- a/5-Applications/tools-scripts/encoding/iso_symbol_table.py +++ /dev/null @@ -1,1533 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=CORE / DOMAIN=COMPUTE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -ISO Symbol Table — Pre-compression Pass -======================================== -**concept_anchor:** domain=compression / concept=iso_precompression_pass / resolution=CRYSTALLIZED - -POSITION IN THE STACK ---------------------- -This module is the first stage of the USC compression pipeline. It sits -immediately upstream of the soliton encoder: - - raw input (text, chemistry, biology, math, music, geography, code) - │ - ▼ - iso_symbol_table.prepass() ← THIS FILE - │ vocabulary reduction — every token mapped to its densest ISO form - │ output alphabet shrinks; entropy drops before the encoder sees it - ▼ - soliton_factory.py (soliton box encoder / USC codec) - │ maps symbol stream to waveform bands (EM signal representation) - │ each symbol cluster → one soliton box → one frequency band - ▼ - compressed output (EM-signal bitstream) - -THE TWO-STAGE PRINCIPLE ------------------------ -Classical compression works in one pass: find redundancy in the raw byte -stream. The problem is that "hydrogen", "Hydrogen", and "HYDROGEN" look -like three different tokens to a byte-level coder even though they carry -identical information. - -The ISO prepass breaks compression into two conceptually distinct stages: - - Stage 1 — Vocabulary reduction (this file) - Map every token that has a canonical shorter symbol to that symbol. - "hydrogen" → "H", "United States" → "US", "adenine" → "A". - The output still reads as human-interpretable text — it just uses the - smallest agreed-upon representation for each concept. - - Stage 2 — Entropy coding (soliton encoder) - The reduced-vocabulary stream has lower entropy per token than the - original. The soliton codec maps symbol clusters to frequency bands - (soliton boxes). Dense clusters → low-frequency bands (cheap to - encode). Sparse symbols → high-frequency bands. This is the gamma - pattern principle: the basis that minimises description length. - -DECODE CONTRACT — WHY THIS IS LOSSLESS ----------------------------------------- -The prepass is perfectly reversible without transmitting the substitution -map. This is the critical property that makes it useful for compression: - - The lookup tables in this file are PUBLIC STANDARDS (IUPAC, ISO, SI). - - The decoder does not need to receive the table. It only needs to know - which domains were applied. That information costs a handful of bits - (a domain bitmask). - - Reconstruction: apply the inverse table for each active domain. - The original text is recovered exactly. - -This is the same principle as a shared dictionary in LZ77 — but the -dictionary is a universal standard, so it costs zero bits to negotiate. - -HUTTER PRIZE CONTEXT ---------------------- -The enwik8 benchmark (first 100 MB of Wikipedia XML) contains: - - Thousands of element names in chemistry infoboxes - - Country names in geographic articles (avg. 15–25 chars → 2 chars) - - Language names in linguistics articles - - Amino acid and nucleotide sequences in biology articles - - Mathematical notation in science articles - -After the ISO prepass, the symbol stream is: - - Shorter (fewer bytes total) - - Lower-entropy (smaller effective alphabet) - - More uniform (all element names collapse to 1–2 symbol tokens) - -The downstream entropy coder (soliton or otherwise) sees a better-conditioned -input. The prepass does not compete with the entropy coder — it prepares the -ground for it. - -EM SPECTRUM TRANSPOSITION --------------------------- -The prepass output is not fed to the soliton encoder as text. It is -transposed into the EM spectrum — the symbol stream is mapped onto a -frequency-domain representation before entropy coding begins. - -The key insight: every symbol in the prepass output has a characteristic -energy density. A run of "ACGTACGT" in a DNA sequence is a periodic -waveform in the frequency domain. "H₂O CO₂ NaCl" is a sparse, high- -frequency signal (few tokens, wide spacing). The EM transposition step -exposes this structure so the soliton encoder can decompose it efficiently. - -Pipeline after prepass: - - symbol stream → EM transposition → soliton decomposition → bitstream - - EM transposition: - Each token class (iso_chem, iso_bio, etc.) occupies a frequency band. - The band assignment follows the same logic as best_domain_for_band() in - soliton_factory.py: high-density, low-entropy token classes (iso_bio - ACGT runs) map to low-frequency bands (cheap to encode). Sparse, - high-information tokens (iso_chem compound formulas) map to high- - frequency bands. - - Physics and imaginary physics data: - The same transposition applies to physics notation. A Planck constant - (ℏ, from iso_math), a tensor expression (∂μAν), or a ZK-STARK proof - element all carry structured symbol patterns. After prepass, these are - compact tokens rather than verbose prose — the EM transposition then - maps them to the frequency bands where their periodicity is most - compressible. - - Imaginary physics constructs (soliton manifold coordinates, foam voxel - states, substrate ISA opcodes) follow the same path. The substrate ISA - defines its own symbol vocabulary; feeding that through an iso_isa domain - (not yet built) before EM transposition would reduce ISA encoding cost - by the same mechanism. - - Why this matters for compression ratio: - Classical entropy coders (Huffman, ANS) work on symbol frequency in the - time domain. The EM transposition exposes periodicity and correlation - that is invisible to time-domain coders. A DNA sequence that looks like - noise byte-by-byte is a clean low-frequency signal in the EM domain. - The soliton encoder exploits this directly. - -DOMAIN REGISTRY ---------------- -Each domain has: - name — identifier used in the domain bitmask / config - description — what it substitutes - table — {long_form: short_form} (sorted long-first for matching) - est_ratio — estimated average compression ratio on domain-heavy text - -Active domains: iso_chem, iso_bio, iso_unit, iso_math, iso_lang, iso_geo, - iso_music, iso_abbrev - -Usage: - python3 iso_symbol_table.py prepass "Hydrogen and oxygen form water" - python3 iso_symbol_table.py decode "H and O form water" --domains iso_chem - python3 iso_symbol_table.py stats "Adenine pairs with Thymine in DNA" - python3 iso_symbol_table.py domains list all domains -""" - -import re -import unicodedata -from pathlib import Path -from typing import NamedTuple - -# ───────────────────────────────────────────────────────────────────────────── -# Domain metadata -# ───────────────────────────────────────────────────────────────────────────── - -class Domain(NamedTuple): - """Metadata for a single ISO symbol domain.""" - - name: str - description: str - est_ratio: float # estimated bytes_out / bytes_in on domain-heavy text - - -# ───────────────────────────────────────────────────────────────────────────── -# Lookup tables -# Each dict maps long_form (lowercase) → symbol. -# Matching is case-insensitive; output preserves the symbol's canonical case. -# ───────────────────────────────────────────────────────────────────────────── - -# ── iso_chem: IUPAC element names and common compound names ────────────────── -# Source: IUPAC 2021 table of elements (all 118) -_CHEM_TABLE: dict[str, str] = { - # Period 1 - "hydrogen": "H", - "helium": "He", - # Period 2 - "lithium": "Li", - "beryllium": "Be", - "boron": "B", - "carbon": "C", - "nitrogen": "N", - "oxygen": "O", - "fluorine": "F", - "neon": "Ne", - # Period 3 - "sodium": "Na", - "magnesium": "Mg", - "aluminium": "Al", - "aluminum": "Al", - "silicon": "Si", - "phosphorus": "P", - "sulfur": "S", - "sulphur": "S", - "chlorine": "Cl", - "argon": "Ar", - # Period 4 - "potassium": "K", - "calcium": "Ca", - "scandium": "Sc", - "titanium": "Ti", - "vanadium": "V", - "chromium": "Cr", - "manganese": "Mn", - "iron": "Fe", - "cobalt": "Co", - "nickel": "Ni", - "copper": "Cu", - "zinc": "Zn", - "gallium": "Ga", - "germanium": "Ge", - "arsenic": "As", - "selenium": "Se", - "bromine": "Br", - "krypton": "Kr", - # Period 5 - "rubidium": "Rb", - "strontium": "Sr", - "yttrium": "Y", - "zirconium": "Zr", - "niobium": "Nb", - "molybdenum": "Mo", - "technetium": "Tc", - "ruthenium": "Ru", - "rhodium": "Rh", - "palladium": "Pd", - "silver": "Ag", - "cadmium": "Cd", - "indium": "In", - "tin": "Sn", - "antimony": "Sb", - "tellurium": "Te", - "iodine": "I", - "xenon": "Xe", - # Period 6 - "caesium": "Cs", - "cesium": "Cs", - "barium": "Ba", - "lanthanum": "La", - "cerium": "Ce", - "praseodymium": "Pr", - "neodymium": "Nd", - "promethium": "Pm", - "samarium": "Sm", - "europium": "Eu", - "gadolinium": "Gd", - "terbium": "Tb", - "dysprosium": "Dy", - "holmium": "Ho", - "erbium": "Er", - "thulium": "Tm", - "ytterbium": "Yb", - "lutetium": "Lu", - "hafnium": "Hf", - "tantalum": "Ta", - "tungsten": "W", - "rhenium": "Re", - "osmium": "Os", - "iridium": "Ir", - "platinum": "Pt", - "gold": "Au", - "mercury": "Hg", - "thallium": "Tl", - "lead": "Pb", - "bismuth": "Bi", - "polonium": "Po", - "astatine": "At", - "radon": "Rn", - # Period 7 - "francium": "Fr", - "radium": "Ra", - "actinium": "Ac", - "thorium": "Th", - "protactinium": "Pa", - "uranium": "U", - "neptunium": "Np", - "plutonium": "Pu", - "americium": "Am", - "curium": "Cm", - "berkelium": "Bk", - "californium": "Cf", - "einsteinium": "Es", - "fermium": "Fm", - "mendelevium": "Md", - "nobelium": "No", - "lawrencium": "Lr", - "rutherfordium": "Rf", - "dubnium": "Db", - "seaborgium": "Sg", - "bohrium": "Bh", - "hassium": "Hs", - "meitnerium": "Mt", - "darmstadtium": "Ds", - "roentgenium": "Rg", - "copernicium": "Cn", - "nihonium": "Nh", - "flerovium": "Fl", - "moscovium": "Mc", - "livermorium": "Lv", - "tennessine": "Ts", - "oganesson": "Og", - # Common compounds (long form only) - "water": "H₂O", - "carbon dioxide": "CO₂", - "carbon monoxide": "CO", - "ammonia": "NH₃", - "methane": "CH₄", - "ethanol": "C₂H₅OH", - "glucose": "C₆H₁₂O₆", - "sodium chloride": "NaCl", - "sulfuric acid": "H₂SO₄", - "hydrochloric acid": "HCl", - "nitric acid": "HNO₃", - "sodium hydroxide": "NaOH", - "calcium carbonate": "CaCO₃", - "deoxyribonucleic acid": "DNA", - "ribonucleic acid": "RNA", - "adenosine triphosphate": "ATP", - "adenosine diphosphate": "ADP", -} - -# ── iso_bio: IUPAC amino acid and nucleotide codes ─────────────────────────── -# Source: IUPAC-IUB 1984 recommendations -_BIO_TABLE: dict[str, str] = { - # Amino acids — full name → 1-letter code - "alanine": "A", - "arginine": "R", - "asparagine": "N", - "aspartic acid": "D", - "aspartate": "D", - "cysteine": "C", - "glutamine": "Q", - "glutamic acid": "E", - "glutamate": "E", - "glycine": "G", - "histidine": "H", - "isoleucine": "I", - "leucine": "L", - "lysine": "K", - "methionine": "M", - "phenylalanine": "F", - "proline": "P", - "serine": "S", - "threonine": "T", - "tryptophan": "W", - "tyrosine": "Y", - "valine": "V", - # Amino acids — 3-letter → 1-letter - # OMITTED from default table: ala, his, met, pro, ser, val, asp, glu, gly, - # thr, ile, leu, lys, phe, trp, tyr — all common English words or names. - # These produce thousands of false positives on general prose (Wikipedia). - # They belong in a context-gated iso_bio_seq domain (not yet built) that - # only fires inside recognised protein/sequence annotation blocks. - # Safe 3-letter codes (no common English collision): - "arg": "R", - "asn": "N", - "cys": "C", - "gln": "Q", - # Nucleotides — full name → 1-letter IUPAC code - "adenine": "A", - "cytosine": "C", - "guanine": "G", - "thymine": "T", - "uracil": "U", - # IUPAC ambiguity codes - "purine": "R", # A or G - "pyrimidine": "Y", # C or T -} - -# ── iso_unit: SI base units, derived units, and prefix symbols ─────────────── -# Source: BIPM SI Brochure 9th edition (2019) -_UNIT_TABLE: dict[str, str] = { - # Base units — only unambiguous long forms kept - # OMITTED: "second" → "s" (ordinal/verb collision, extremely common) - # "mole" → "mol" (verb collision) - "meter": "m", - "metre": "m", - "kilogram": "kg", - "ampere": "A", - "kelvin": "K", - "candela": "cd", - # Derived units — person-name collisions removed - # OMITTED: "newton" → "N" (Isaac Newton: ~10× more common in Wikipedia) - # "henry" → "H" (person name: ~15× more common) - # "watt" → "W" (person name collision) - # "coulomb"→ "C" (person name collision) - # "farad" → "F" (person name collision) - # "tesla" → "T" (person + brand collision) - # "siemens"→ "S" (company/person collision) - # "weber" → "Wb" (person name collision) - # "gray" → "Gy" (adjective/name collision) - "hertz": "Hz", - "pascal": "Pa", - "joule": "J", - "volt": "V", - "ohm": "Ω", - "lumen": "lm", - "lux": "lx", - "becquerel": "Bq", - "sievert": "Sv", - "katal": "kat", - # Common non-SI accepted units - # OMITTED: "bar" → "bar" (no gain, same length) - # "day" → "d" (ambiguous in prose) - # "hour"→ "h" (ambiguous) - # "minute"→"min" (ambiguous) - "litre": "L", - "liter": "L", - "tonne": "t", - "electronvolt": "eV", - "dalton": "Da", - "hectare": "ha", - "degree celsius": "°C", - "degree fahrenheit": "°F", - # SI prefixes (long form → prefix symbol; applied as standalone tokens) - "yotta": "Y", - "zetta": "Z", - "exa": "E", - "peta": "P", - "tera": "T", - "giga": "G", - "mega": "M", - "kilo": "k", - "hecto": "h", - "deca": "da", - "deci": "d", - "centi": "c", - "milli": "m", - "micro": "μ", - "nano": "n", - "pico": "p", - "femto": "f", - "atto": "a", - "zepto": "z", - "yocto": "y", -} - -# ── LaTeX normalization support for analysis/ingest flows ──────────────────── -# This is intentionally NOT part of the lossless prepass/decode contract. -# It is a semantic normalizer that turns common LaTeX math syntax into a -# canonical plain-text form that downstream classifiers and keyword extractors -# can reason about. - -_LATEX_SIMPLE_COMMANDS: dict[str, str] = { - # Greek letters - "alpha": "alpha", - "beta": "beta", - "gamma": "gamma", - "Gamma": "gamma", - "delta": "delta", - "Delta": "delta", - "epsilon": "epsilon", - "varepsilon": "epsilon", - "zeta": "zeta", - "eta": "eta", - "theta": "theta", - "vartheta": "theta", - "Theta": "theta", - "iota": "iota", - "kappa": "kappa", - "lambda": "lambda", - "Lambda": "lambda", - "mu": "mu", - "nu": "nu", - "xi": "xi", - "Xi": "xi", - "pi": "pi", - "Pi": "pi", - "rho": "rho", - "varrho": "rho", - "sigma": "sigma", - "Sigma": "sigma", - "tau": "tau", - "upsilon": "upsilon", - "Upsilon": "upsilon", - "phi": "phi", - "varphi": "phi", - "Phi": "phi", - "chi": "chi", - "psi": "psi", - "Psi": "psi", - "omega": "omega", - "Omega": "omega", - # Core operators and relations - "sum": "sum", - "prod": "product", - "int": "integral", - "iint": "double integral", - "iiint": "triple integral", - "oint": "contour integral", - "partial": "partial", - "nabla": "nabla", - "infty": "infinity", - "to": "maps to", - "rightarrow": "maps to", - "leftarrow": "left arrow", - "leftrightarrow": "if and only if", - "Rightarrow": "implies", - "Leftarrow": "implied by", - "Leftrightarrow": "if and only if", - "mapsto": "maps to", - "cdot": "dot product", - "times": "times", - "otimes": "tensor product", - "oplus": "direct sum", - "pm": "plus or minus", - "mp": "minus or plus", - "leq": "less than or equal to", - "le": "less than or equal to", - "geq": "greater than or equal to", - "ge": "greater than or equal to", - "neq": "not equal to", - "ne": "not equal to", - "approx": "approximately", - "equiv": "equivalent to", - "propto": "proportional to", - "sim": "similar to", - "simeq": "approximately equal to", - "cong": "congruent to", - "in": "element of", - "notin": "not element of", - "subset": "subset of", - "subseteq": "subset or equal to", - "supset": "superset of", - "supseteq": "superset or equal to", - "cup": "union", - "cap": "intersection", - "emptyset": "empty set", - "forall": "for all", - "exists": "there exists", - # Common functions - "min": "minimum", - "max": "maximum", - "log": "log", - "ln": "natural log", - "exp": "exp", - "sin": "sine", - "cos": "cosine", - "tan": "tangent", - "tanh": "hyperbolic tangent", -} - -_LATEX_PLAIN_WRAPPERS = ( - "mathrm", - "mathbf", - "mathit", - "mathsf", - "mathtt", - "text", - "textrm", - "textbf", - "textit", - "emph", - "operatorname", - "operatorname*", -) - -_LATEX_FUNCTION_WRAPPERS: dict[str, str] = { - "hat": "hat", - "widehat": "hat", - "tilde": "tilde", - "widetilde": "tilde", - "bar": "bar", - "overline": "bar", - "underline": "underline", - "vec": "vector", - "dot": "dot", - "ddot": "ddot", -} - -_LATEX_BLACKBOARD: dict[str, str] = { - "N": "natural numbers", - "Z": "integers", - "Q": "rationals", - "R": "real numbers", - "C": "complex numbers", - "H": "quaternions", - "P": "projective space", - "E": "expectation", -} - -_LATEX_DELIMS_RE = re.compile( - r"\\\(|\\\)|\\\[|\\\]|\$\$|\$|\\begin\{(?:equation\*?|align\*?|gather\*?)\}|" - r"\\end\{(?:equation\*?|align\*?|gather\*?)\}" -) -_LATEX_SPACING_RE = re.compile(r"\\(?:,|!|;|:|quad|qquad|enspace|thinspace)\b|~") -_LATEX_LEFT_RIGHT_RE = re.compile(r"\\(?:left|right)\b") -_LATEX_BLACKBOARD_RE = re.compile(r"\\mathbb\s*\{\s*([A-Za-z])\s*\}") -_LATEX_STYLE_WRAPPER_RE = re.compile( - r"\\(?:mathcal|mathfrak|mathscr)\s*\{\s*([^{}]+)\s*\}" -) -_LATEX_PLAIN_WRAPPER_RE = re.compile( - r"\\(?:" - + "|".join(re.escape(cmd) for cmd in sorted(_LATEX_PLAIN_WRAPPERS, key=len, reverse=True)) - + r")\s*\{\s*([^{}]+)\s*\}" -) -_LATEX_FUNCTION_WRAPPER_RE = re.compile( - r"\\(" - + "|".join(re.escape(cmd) for cmd in sorted(_LATEX_FUNCTION_WRAPPERS, key=len, reverse=True)) - + r")\s*\{\s*([^{}]+)\s*\}" -) -_LATEX_FRAC_RE = re.compile(r"\\frac\s*\{([^{}]+)\}\s*\{([^{}]+)\}") -_LATEX_SQRT_RE = re.compile(r"\\sqrt(?:\[(.*?)\])?\s*\{([^{}]+)\}") -_LATEX_COMMAND_RE = re.compile( - r"\\(" - + "|".join(re.escape(cmd) for cmd in sorted(_LATEX_SIMPLE_COMMANDS, key=len, reverse=True)) - + r")\b" -) - - -def _rewrite_until_stable(text: str, fn, max_passes: int = 8) -> str: - """Apply `fn` until the text stops changing or the pass limit is hit.""" - result = text - for _ in range(max_passes): - updated = fn(result) - if updated == result: - break - result = updated - return result - - -def normalize_latex_math(text: str) -> str: - """Canonicalize common LaTeX math syntax into analysis-friendly text. - - This is intentionally semantic rather than lossless. It is used by - analysis tools that want math-heavy documents to emit stable text signals - without requiring a full TeX parser. - """ - result = text - - result = _LATEX_DELIMS_RE.sub(" ", result) - result = _LATEX_LEFT_RIGHT_RE.sub("", result) - result = _LATEX_SPACING_RE.sub(" ", result) - - result = result.replace(r"\{", "{").replace(r"\}", "}") - result = result.replace(r"\_", "_").replace(r"\%", "%") - result = result.replace(r"\#", "#").replace(r"\$", "$") - result = result.replace(r"\&", " and ") - - def _blackboard(m: re.Match) -> str: - key = m.group(1).strip() - return _LATEX_BLACKBOARD.get(key, f"blackboard {key}") - - result = _LATEX_BLACKBOARD_RE.sub(_blackboard, result) - - result = _rewrite_until_stable( - result, - lambda s: _LATEX_STYLE_WRAPPER_RE.sub(lambda m: m.group(1), s), - ) - result = _rewrite_until_stable( - result, - lambda s: _LATEX_PLAIN_WRAPPER_RE.sub(lambda m: m.group(1), s), - ) - - def _function_wrapper(m: re.Match) -> str: - return f"{_LATEX_FUNCTION_WRAPPERS[m.group(1)]}({m.group(2)})" - - result = _rewrite_until_stable( - result, - lambda s: _LATEX_FUNCTION_WRAPPER_RE.sub(_function_wrapper, s), - ) - - result = re.sub(r"_\{([^{}]+)\}", lambda m: f"_({m.group(1)})", result) - result = re.sub(r"\^\{([^{}]+)\}", lambda m: f"^({m.group(1)})", result) - - result = _rewrite_until_stable( - result, - lambda s: _LATEX_FRAC_RE.sub( - lambda m: f"fraction({m.group(1)} over {m.group(2)})", s - ), - ) - result = _rewrite_until_stable( - result, - lambda s: _LATEX_SQRT_RE.sub( - lambda m: ( - f"root({m.group(1)} of {m.group(2)})" - if m.group(1) - else f"square root({m.group(2)})" - ), - s, - ), - ) - - result = _LATEX_COMMAND_RE.sub( - lambda m: _LATEX_SIMPLE_COMMANDS[m.group(1)], - result, - ) - - result = re.sub(r"\\[A-Za-z]+", " ", result) - result = re.sub(r"\s+", " ", result).strip() - return result - - -# ── iso_math: Greek letters and common mathematical operators ──────────────── -_MATH_TABLE: dict[str, str] = { - # Greek — lowercase - "alpha": "α", - "beta": "β", - "gamma": "γ", - "delta": "δ", - "epsilon": "ε", - "zeta": "ζ", - "eta": "η", - "theta": "θ", - "iota": "ι", - "kappa": "κ", - "lambda": "λ", - "mu": "μ", - "nu": "ν", - "xi": "ξ", - "omicron": "ο", - "pi": "π", - "rho": "ρ", - "sigma": "σ", - "tau": "τ", - "upsilon": "υ", - "phi": "φ", - "chi": "χ", - "psi": "ψ", - "omega": "ω", - # Greek uppercase handled by capitalisation of the output symbol. - # No duplicate keys: lowercase forms above cover both cases via IGNORECASE. - # Math operators (spelled out in prose) - "infinity": "∞", - "therefore": "∴", - "because": "∵", - "approximately": "≈", - "proportional": "∝", - "element of": "∈", - "subset of": "⊂", - "superset of": "⊃", - "union": "∪", - "intersection": "∩", - "empty set": "∅", - "for all": "∀", - "there exists": "∃", - "less than or equal to": "≤", - "greater than or equal to": "≥", - "not equal to": "≠", - "maps to": "→", - "left arrow": "←", - "if and only if": "⇔", - "implies": "⇒", - "subset or equal to": "⊆", - "superset or equal to": "⊇", - "square root": "√", - "sum": "∑", - "product": "∏", - "integral": "∫", - "partial": "∂", - "nabla": "∇", - "planck constant": "ℏ", - "imaginary unit": "ⅈ", - "real numbers": "ℝ", - "natural numbers": "ℕ", - "integers": "ℤ", - "rationals": "ℚ", - "complex numbers": "ℂ", - "quaternions": "ℍ", - "tensor product": "⊗", - "direct sum": "⊕", - "plus or minus": "±", - "minus or plus": "∓", -} - -# ── iso_lang: ISO 639-1 two-letter language codes ──────────────────────────── -# Source: ISO 639-1:2002 -_LANG_TABLE: dict[str, str] = { - "afrikaans": "af", - "albanian": "sq", - "amharic": "am", - "arabic": "ar", - "armenian": "hy", - "azerbaijani": "az", - "basque": "eu", - "belarusian": "be", - "bengali": "bn", - "bosnian": "bs", - "bulgarian": "bg", - "burmese": "my", - "catalan": "ca", - "chinese": "zh", - "croatian": "hr", - "czech": "cs", - "danish": "da", - "dutch": "nl", - "english": "en", - "estonian": "et", - "finnish": "fi", - "french": "fr", - "galician": "gl", - "georgian": "ka", - "german": "de", - "greek": "el", - "gujarati": "gu", - "haitian creole": "ht", - "hausa": "ha", - "hebrew": "he", - "hindi": "hi", - "hungarian": "hu", - "icelandic": "is", - "igbo": "ig", - "indonesian": "id", - "irish": "ga", - "italian": "it", - "japanese": "ja", - "javanese": "jv", - "kannada": "kn", - "kazakh": "kk", - "korean": "ko", - "kurdish": "ku", - "kyrgyz": "ky", - "lao": "lo", - "latin": "la", - "latvian": "lv", - "lithuanian": "lt", - "macedonian": "mk", - "malay": "ms", - "malayalam": "ml", - "maltese": "mt", - "maori": "mi", - "marathi": "mr", - "mongolian": "mn", - "nepali": "ne", - "norwegian": "no", - "pashto": "ps", - "persian": "fa", - "polish": "pl", - "portuguese": "pt", - "punjabi": "pa", - "romanian": "ro", - "russian": "ru", - "samoan": "sm", - "serbian": "sr", - "sindhi": "sd", - "sinhala": "si", - "slovak": "sk", - "slovenian": "sl", - "somali": "so", - "spanish": "es", - "sundanese": "su", - "swahili": "sw", - "swedish": "sv", - "tajik": "tg", - "tamil": "ta", - "telugu": "te", - "thai": "th", - "turkish": "tr", - "ukrainian": "uk", - "urdu": "ur", - "uzbek": "uz", - "vietnamese": "vi", - "welsh": "cy", - "xhosa": "xh", - "yoruba": "yo", - "zulu": "zu", -} - -# ── iso_geo: ISO 3166-1 alpha-2 country codes (top-frequency in Wikipedia) ─── -# Source: ISO 3166-1:2020 -_GEO_TABLE: dict[str, str] = { - "united states of america": "US", - "united states": "US", - "united kingdom": "GB", - "great britain": "GB", - "people's republic of china": "CN", - "republic of china": "TW", - "china": "CN", - "russia": "RU", - "russian federation": "RU", - "germany": "DE", - "france": "FR", - "japan": "JP", - "india": "IN", - "brazil": "BR", - "canada": "CA", - "australia": "AU", - "italy": "IT", - "spain": "ES", - "mexico": "MX", - "south korea": "KR", - "republic of korea": "KR", - "indonesia": "ID", - "netherlands": "NL", - "saudi arabia": "SA", - "turkey": "TR", - "switzerland": "CH", - "argentina": "AR", - "sweden": "SE", - "poland": "PL", - "belgium": "BE", - "norway": "NO", - "austria": "AT", - "united arab emirates": "AE", - "nigeria": "NG", - "south africa": "ZA", - "egypt": "EG", - "israel": "IL", - "denmark": "DK", - "singapore": "SG", - "malaysia": "MY", - "philippines": "PH", - "pakistan": "PK", - "bangladesh": "BD", - "vietnam": "VN", - "ukraine": "UA", - "portugal": "PT", - "greece": "GR", - "czech republic": "CZ", - "czechia": "CZ", - "romania": "RO", - "hungary": "HU", - "new zealand": "NZ", - "iraq": "IQ", - "iran": "IR", - "chile": "CL", - "colombia": "CO", - "peru": "PE", - "ethiopia": "ET", - "kenya": "KE", - "ghana": "GH", - "tanzania": "TZ", - "myanmar": "MM", - "afghanistan": "AF", - "morocco": "MA", - "algeria": "DZ", - "cuba": "CU", - "thailand": "TH", - "taiwan": "TW", - "finland": "FI", - "ireland": "IE", - "croatia": "HR", - "slovakia": "SK", - "bulgaria": "BG", - "serbia": "RS", - "iceland": "IS", - "luxembourg": "LU", - "estonia": "EE", - "latvia": "LV", - "lithuania": "LT", - "slovenia": "SI", - "north korea": "KP", - "democratic people's republic of korea": "KP", -} - -# ── iso_music: ABC notation and MIDI pitch numbers ─────────────────────────── -# Source: ABC notation standard v2.1; General MIDI spec -_MUSIC_TABLE: dict[str, str] = { - # Note names with octave → ABC notation - "c major": "C:maj", - "g major": "G:maj", - "d major": "D:maj", - "a major": "A:maj", - "e major": "E:maj", - "f major": "F:maj", - "b flat major": "Bb:maj", - "e flat major": "Eb:maj", - "a flat major": "Ab:maj", - "a minor": "A:min", - "e minor": "E:min", - "d minor": "D:min", - "g minor": "G:min", - "c minor": "C:min", - # Tempo markings → bpm range tokens - "largo": "♩=40-60", - "adagio": "♩=60-80", - "andante": "♩=80-100", - "moderato": "♩=100-120", - "allegro": "♩=120-160", - "presto": "♩=160-200", - "prestissimo": "♩=200+", - # Common musical terms - "forte": "f", - "piano": "p", - "mezzo forte": "mf", - "mezzo piano": "mp", - "fortissimo": "ff", - "pianissimo": "pp", - "fortepiano": "fp", - "crescendo": "cresc.", - "decrescendo": "decresc.", - "diminuendo": "dim.", - "sforzando": "sfz", -} - -# ── iso_abbrev: universally unambiguous abbreviations ──────────────────────── -# Only include substitutions where the short form is ALWAYS unambiguous. -_ABBREV_TABLE: dict[str, str] = { - # Time - "january": "Jan", - "february": "Feb", - "march": "Mar", - "april": "Apr", - # "may" intentionally omitted — ambiguous with the verb - "june": "Jun", - "july": "Jul", - "august": "Aug", - "september": "Sep", - "october": "Oct", - "november": "Nov", - "december": "Dec", - "monday": "Mon", - "tuesday": "Tue", - "wednesday": "Wed", - "thursday": "Thu", - "friday": "Fri", - "saturday": "Sat", - "sunday": "Sun", - # Universal scientific abbreviations - "approximately": "approx.", - "equation": "eq.", - "figure": "fig.", - "number": "no.", - "versus": "vs.", - "et cetera": "etc.", - "that is": "i.e.", - "for example": "e.g.", - "and others": "et al.", - "compare": "cf.", - "page": "p.", - "pages": "pp.", - "volume": "vol.", - "edition": "ed.", - "chapter": "ch.", - "section": "sec.", - "paragraph": "para.", - "maximum": "max.", - "minimum": "min.", - "average": "avg.", - "standard deviation": "SD", - "standard error": "SE", - "confidence interval": "CI", - "not applicable": "N/A", - "not available": "N/A", -} - - -# ── iso_ptos: Research Stack / PTOS operator vocabulary ────────────────────── -# Corpus-specific: terms that dominate the Research Stack but are rare in -# general text. Multi-word phrases are preferred over single words to avoid -# false positives. Single words included only when corpus-unique. -# concept_anchor: domain=compression / concept=iso_ptos_domain / resolution=FORMING -_PTOS_TABLE: dict[str, str] = { - # Multi-word architecture terms (longest first — matched before fragments) - "topological soliton machine": "TSM", - "topological soliton": "TS", - "substrate index": "SIDX", - "concept vector": "CV", - "operator fingerprint": "OPFP", - "cross-product residual": "CPR", - "cross product residual": "CPR", - "iso prepass": "IPP", - "iso pipeline": "IOPIPE", - "iso symbol table": "ISOT", - "bits-back": "BB", - "bits back": "BB", - "basal ganglia": "BG", - "soliton manifold": "SM", - "soliton encoder": "SE", - "soliton factory": "SFX", - "soliton box": "SB", - "foam voxel": "FV", - "foam phase": "FP", - "phase transition": "PT", - "research stack": "RS", - # Single terms — corpus-unique, low false-positive risk - "metafoam": "MFM", - "metanarrative": "MNAR", - "soliton": "SLT", -} - -# ── iso_isa: TSM substrate opcode vocabulary ────────────────────────────────── -# Source: CATEGORY/TSM/tsm_metafoam_enhanced.py Opcode enum (v3.0) -# Maps opcode names (prose and identifier forms) to their hex addresses. -# Both "wave fold" (prose/comments) and "wave_fold" (identifier) are included -# because \b word boundaries treat underscores as word characters. -# concept_anchor: domain=compression / concept=iso_isa_domain / resolution=FORMING -_ISA_TABLE: dict[str, str] = { - # Original opcodes — mnemonic targets (not hex: hex literals appear in source) - "ingest_state": "IST", "ingest state": "IST", - "wave_fold": "WFD", "wave fold": "WFD", - "sync_precision": "SYNC", "sync precision": "SYNC", - "omni_bal": "OBAL", "omni bal": "OBAL", - "entangle": "ENTG", - "evolve": "EVLV", - "vram_flush": "VFLSH", "vram flush": "VFLSH", - "stark_prove": "SPROV", "stark prove": "SPROV", - "ledger_commit": "LCMT", "ledger commit": "LCMT", - "crypto_wrap": "CWRP", "crypto wrap": "CWRP", - "grant_access": "GACC", "grant access": "GACC", - "native_ws": "NWS", "native ws": "NWS", - "webasm": "WASM", - "neuromorph": "NMR", - "gpgpu_surf": "GSRF", "gpgpu surf": "GSRF", - "nibble_swap": "NSWP", "nibble swap": "NSWP", - "tsm_int": "TINT", "tsm int": "TINT", - # Enhanced opcodes - "phase_lock": "PLCK", "phase lock": "PLCK", - "sra_pulse": "SPLS", "sra pulse": "SPLS", - "activate_context": "ACTX", "activate context": "ACTX", - "ricci_flow": "RICC", "ricci flow": "RICC", - "info_flow": "IFLW", "info flow": "IFLW", - "stark_lock": "SLCK", "stark lock": "SLCK", - "vdp_compress": "VDPC", "vdp compress": "VDPC", - "quantum_melt": "QMLT", "quantum melt": "QMLT", - "foam_spray": "FSPR", "foam spray": "FSPR", - "weld_surface": "WELD", "weld surface": "WELD", - "hamiltonian": "HMLT", - "coherence_guard": "CGRD", "coherence guard": "CGRD", - "hyperfluid_lut": "HLUT", "hyperfluid lut": "HLUT", - # Forming semantic surfaces — reserved vocabulary, not official ISA v2.9 - "engram_code": "ECOD", "engram code": "ECOD", - "engram_coding": "ECOD", "engram coding": "ECOD", - "engram_recall": "ERCL", "engram recall": "ERCL", - "blink_gate": "BLGT", "blink gate": "BLGT", - # Matrix Reality opcodes - "byte_stream": "BSTR", "byte stream": "BSTR", - "voxel_render": "VXRN", "voxel render": "VXRN", - # Accessibility - "assist_bind": "ABND", "assist bind": "ABND", -} - -# ── iso_code: Python structural keyword normalization ──────────────────────── -# Only tokens long enough to produce meaningful byte savings. -# Short keywords (def, if, for) omitted — gain < 2 bytes per occurrence. -# concept_anchor: domain=compression / concept=iso_code_domain / resolution=FORMING -_CODE_TABLE: dict[str, str] = { - "isinstance": "isa", - "AttributeError": "AErr", - "RuntimeError": "RErr", - "ValueError": "VErr", - "TypeError": "TErr", - "KeyError": "KErr", - "IndexError": "IErr", - "ImportError": "ImpErr", - "FileNotFoundError": "FNFErr", - "PermissionError": "PermErr", - "NotImplementedError": "NIErr", - "StopIteration": "StopIter", - "ZeroDivisionError": "ZDErr", - "OverflowError": "OFErr", - "UnicodeDecodeError": "UDErr", - "UnicodeEncodeError": "UEErr", - "continue": "cont", - "nonlocal": "nloc", - "enumerate": "enm", -} - - -# ───────────────────────────────────────────────────────────────────────────── -# Domain registry — order matters for multi-domain prepass -# Longer phrases first (iso_geo, iso_chem compounds) before single words -# ───────────────────────────────────────────────────────────────────────────── - -DOMAINS: dict[str, tuple[Domain, dict[str, str]]] = { - "iso_geo": (Domain("iso_geo", "ISO 3166 country/territory names → alpha-2 codes", 0.12), _GEO_TABLE), - "iso_chem": (Domain("iso_chem", "IUPAC element names and common compound names", 0.18), _CHEM_TABLE), - "iso_bio": (Domain("iso_bio", "IUPAC amino acid and nucleotide codes", 0.14), _BIO_TABLE), - "iso_unit": (Domain("iso_unit", "SI base units, derived units, and SI prefix symbols", 0.35), _UNIT_TABLE), - "iso_math": (Domain("iso_math", "Greek letters and mathematical operators", 0.55), _MATH_TABLE), - "iso_lang": (Domain("iso_lang", "ISO 639-1 language names → 2-letter codes", 0.20), _LANG_TABLE), - "iso_music": (Domain("iso_music", "ABC notation for keys, tempos, dynamics", 0.45), _MUSIC_TABLE), - "iso_abbrev": (Domain("iso_abbrev", "Universally unambiguous abbreviations", 0.65), _ABBREV_TABLE), - # Corpus-specific domains (Research Stack / PTOS) - "iso_ptos": (Domain("iso_ptos", "PTOS/Research Stack operator vocabulary", 0.40), _PTOS_TABLE), - "iso_isa": (Domain("iso_isa", "TSM substrate opcode names → hex addresses", 0.35), _ISA_TABLE), - "iso_code": (Domain("iso_code", "Python structural keyword normalization", 0.55), _CODE_TABLE), -} - -# ── iso_qchem: HQW atomic combination Z-notation (speculative / non-standard) ─ -# -# NOT a real ISO standard. This is substrate-internal notation from early -# TSM work: atoms identified by atomic number (Z1=H, Z6=C, Z8=O, …) and -# hypothetical elements beyond Z118 that appear in the superconductor and -# digital twin research. Each entry carries a stability weight and a -# register_bits cost — the "weight of the atom at that position." -# -# concept_anchor: domain=substrate / concept=hqw_z_notation / resolution=FORMING -# -# The table is loaded lazily from hqw_atomic_combinations.json if the file -# exists alongside this script or in the repo root. If the file is absent -# the domain is silently omitted — nothing breaks. -# -# Portability rule: this domain depends on speculative data that may not -# travel with the repo. Never add it to DEFAULT_DOMAINS. Use explicitly: -# prepass(text, domains=["iso_chem", "iso_qchem"]) - -def _load_qchem_domain() -> dict[str, str] | None: - """Load hqw_atomic_combinations.json and build a formula → Z-notation table. - - # What the table does - Maps standard molecular formula strings (as produced by iso_chem) to the - substrate's Z-Z notation. This is Stage 2 compression: iso_chem - reduces prose to chemistry symbols, iso_qchem reduces chemistry symbols - to substrate Z-tokens for EM transposition. - - iso_chem: "water" → "H₂O" - iso_qchem: "H₂O" → "Z1-Z1-Z8" (stability=0.997, register_bits=35) - - # Why Z-notation compresses further - Standard chemical symbols are 1–3 chars. Z-notation is always "Z" + int, - which looks longer for light elements (Z1 > H) but: - 1. It is uniform — all tokens have the same format, reducing the entropy - of the token-type distribution. - 2. For heavy/theoretical elements (Z119–Z229) it is shorter than the - element name and unambiguous where no IUPAC symbol exists yet. - 3. The stability value is the EM transposition weight for that formula — - high-stability combinations map to low-frequency soliton bands. - - # Returns - dict mapping formula strings → Z-notation strings, or None if the - source file cannot be found. - """ - import json as _json - - candidates = [ - Path(__file__).parent.parent / "hqw_atomic_combinations.json", - Path(__file__).parent / "hqw_atomic_combinations.json", - ] - src = next((p for p in candidates if p.exists()), None) - if src is None: - return None - - try: - with open(src) as f: - records = _json.load(f) - except Exception: - return None - - # Build forward table: chemical formula → Z-notation - # The hqw dataset uses Z tokens where n is the atomic number. - # We build a reverse lookup: for Z1 → H, Z6 → C, etc. (standard elements) - # so the table key uses familiar chemical notation where possible. - _Z_TO_SYMBOL: dict[int, str] = { - 1: "H", 2: "He", 3: "Li", 4: "Be", 5: "B", - 6: "C", 7: "N", 8: "O", 9: "F", 10: "Ne", - 11: "Na", 12: "Mg", 13: "Al", 14: "Si", 15: "P", - 16: "S", 17: "Cl", 18: "Ar", 19: "K", 20: "Ca", - 26: "Fe", 29: "Cu", 30: "Zn", 47: "Ag", 50: "Sn", - 79: "Au", 80: "Hg", 82: "Pb", 92: "U", - } - - table: dict[str, str] = {} - for rec in records: - z_formula = rec.get("formula", "") - if not z_formula: - continue - # Build a human-readable key where Z tokens are known. - parts = z_formula.split("-") - readable_parts = [] - all_known = True - for part in parts: - if part.startswith("Z"): - try: - n = int(part[1:]) - sym = _Z_TO_SYMBOL.get(n) - if sym: - readable_parts.append(sym) - else: - # Theoretical element — keep Z-notation as key fragment. - readable_parts.append(part) - all_known = False - except ValueError: - readable_parts.append(part) - all_known = False - else: - readable_parts.append(part) - # Only index combinations where at least one token is a known element. - if any(p for p in readable_parts if not p.startswith("Z")): - key = "-".join(readable_parts).lower() - table[key] = z_formula - - return table if table else None - - -_qchem_table = _load_qchem_domain() -if _qchem_table is not None: - DOMAINS["iso_qchem"] = ( - Domain( - "iso_qchem", - "HQW Z-notation for atomic combinations (speculative, FORMING)", - 0.22, - ), - _qchem_table, - ) - -# Default domain set applied by prepass() when no domains are specified. -# iso_qchem intentionally excluded — speculative, must be opted in explicitly. -DEFAULT_DOMAINS = ["iso_geo", "iso_chem", "iso_bio", "iso_unit", "iso_lang"] - -# Extended domain set — includes abbreviation and math symbol tables. -# Use for compression analysis, cross-product residual, and large-file ingestion -# where scientific notation and bibliographic abbreviations are expected. -EXTENDED_DOMAINS = DEFAULT_DOMAINS + ["iso_abbrev", "iso_math"] - -# PTOS corpus domain set — adds Research Stack vocabulary and TSM opcodes. -# Use when the input is from the Research Stack itself (code, docs, sessions). -# Run iso_cross with this set to find the actual strong cross-axis for this corpus. -PTOS_DOMAINS = EXTENDED_DOMAINS + ["iso_ptos", "iso_isa", "iso_code"] - - -# ───────────────────────────────────────────────────────────────────────────── -# Core engine -# ───────────────────────────────────────────────────────────────────────────── - -def _build_pattern(table: dict[str, str]) -> re.Pattern: - """Compile a single regex that matches any key in the table. - - # Why longest-match ordering? - "carbon dioxide" must match before "carbon" or "dioxide" individually. - Sort keys by descending length so the regex alternation tries longer - phrases first. re.IGNORECASE handles capitalisation. - """ - keys = sorted(table.keys(), key=len, reverse=True) - escaped = [re.escape(k) for k in keys] - return re.compile(r"\b(" + "|".join(escaped) + r")\b", re.IGNORECASE) - - -# Pre-compile patterns at import time. -_PATTERNS: dict[str, re.Pattern] = { - name: _build_pattern(table) - for name, (_, table) in DOMAINS.items() -} - - -def prepass( - text: str, - domains: list[str] | None = None, -) -> tuple[str, dict[str, list[str]]]: - """Apply ISO symbol substitution to text. - - # Why return a substitution log? - The log enables lossless decode without transmitting the full table. - Each entry records (original_token, symbol) for every substitution made. - For domains where the table is a public standard, the log can be reduced - to a domain bitmask — the receiver reconstructs from the same table. - - # Parameters - text : input text (any domain mix) - domains : list of domain names to apply; defaults to DEFAULT_DOMAINS - - # Returns - compressed : text with all matched tokens replaced by their symbols - log : {"domain_name": [original_token, ...]} — substitution record - - # Example - >>> compressed, log = prepass("Hydrogen and oxygen form water in France") - >>> print(compressed) - H and O form H₂O in FR - >>> print(log) - {'iso_chem': ['Hydrogen', 'oxygen', 'water'], 'iso_geo': ['France']} - """ - if domains is None: - domains = DEFAULT_DOMAINS - - log: dict[str, list[str]] = {} - result = text - - for name in domains: - if name not in DOMAINS: - continue - _, table = DOMAINS[name] - pattern = _PATTERNS[name] - subs: list[str] = [] - # Capture table/subs in a closure via a factory to avoid - # "dangerous default value" and loop-variable capture warnings. - def _make_replacer(tbl: dict, log_list: list): - def _replace(m: re.Match) -> str: - original = m.group(0) - # Normalize unicode (handles İran → iran, café → cafe, etc.) - # before table lookup. The table keys are plain ASCII lowercase. - key = unicodedata.normalize("NFKD", original)\ - .encode("ascii", "ignore").decode().lower() - symbol = tbl.get(key) - if symbol is None: - return original # no match after normalisation — skip - log_list.append(original) - return symbol - return _replace - - result = pattern.sub(_make_replacer(table, subs), result) - if subs: - log[name] = subs - - return result, log - - -def decode( - compressed: str, - log: dict[str, list[str]], -) -> str: - """Reconstruct the original text from a prepass output and its log. - - # Parameters - compressed : output of prepass() - log : substitution log returned by prepass() - - # Returns - original text (exact byte-for-byte reconstruction of matched tokens) - - # Note - Token order in the log must match substitution order in the compressed - text. prepass() guarantees this — log entries are appended left-to-right - as the regex scans the text. - """ - result = compressed - for domain_name, originals in log.items(): - if domain_name not in DOMAINS: - continue - _, table = DOMAINS[domain_name] - # Replace each symbol occurrence with its original, in order. - # Use the forward table to look up the symbol for each original token. - for original in originals: - symbol = table[original.lower()] - # Replace only the first occurrence to preserve left-to-right order. - result = result.replace(symbol, original, 1) - return result - - -def stats(text: str, domains: list[str] | None = None) -> dict: - """Compute compression statistics for a text sample. - - # Returns - dict with keys: - original_bytes : len(text.encode()) - compressed_bytes : len(compressed.encode()) - ratio : compressed_bytes / original_bytes - savings_pct : (1 - ratio) * 100 - by_domain : {domain: substitution_count} - """ - compressed, log = prepass(text, domains) - orig_b = len(text.encode()) - comp_b = len(compressed.encode()) - return { - "original_bytes": orig_b, - "compressed_bytes": comp_b, - "ratio": round(comp_b / max(orig_b, 1), 4), - "savings_pct": round((1 - comp_b / max(orig_b, 1)) * 100, 2), - "by_domain": {k: len(v) for k, v in log.items()}, - "compressed": compressed, - } - - -# ───────────────────────────────────────────────────────────────────────────── -# CLI -# ───────────────────────────────────────────────────────────────────────────── - -def _cmd_prepass(text: str, domains: list[str] | None = None) -> None: - compressed, log = prepass(text, domains) - print(compressed) - if log: - print("\n[substitutions]") - for domain, tokens in log.items(): - print(f" {domain}: {tokens}") - - -def _cmd_decode(compressed: str, domains: list[str] | None = None) -> None: - """Reconstruct from compressed text using domain tables (no log needed - when table is a public standard — just re-invert the active domains).""" - if domains is None: - domains = DEFAULT_DOMAINS - result = compressed - for name in domains: - if name not in DOMAINS: - continue - _, table = DOMAINS[name] - inv = {v: k.title() for k, v in table.items()} - keys_longest_first = sorted(inv, key=len, reverse=True) - pat = re.compile( - r"\b(" + "|".join(re.escape(s) for s in keys_longest_first) + r")\b" - ) - def _make_inv_replacer(inv_table: dict): - return lambda m: inv_table.get(m.group(0), m.group(0)) - result = pat.sub(_make_inv_replacer(inv), result) - print(result) - - -def _cmd_stats(text: str, domains: list[str] | None = None) -> None: - s = stats(text, domains) - print(f"original : {s['original_bytes']} bytes") - print(f"compressed: {s['compressed_bytes']} bytes") - print(f"ratio : {s['ratio']} ({s['savings_pct']}% saved)") - print(f"domains : {s['by_domain']}") - print(f"\ncompressed text:\n{s['compressed']}") - - -def _cmd_domains() -> None: - print(f"{'DOMAIN':<14} {'EST RATIO':<12} DESCRIPTION") - print("─" * 70) - for name, (meta, table) in DOMAINS.items(): - mark = "✓" if name in DEFAULT_DOMAINS else " " - print(f"{mark} {name:<13} {meta.est_ratio:<12} {meta.description} ({len(table)} entries)") - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser( - description="ISO Symbol Table — pre-compression pass", - formatter_class=argparse.RawDescriptionHelpFormatter, - ) - sub = parser.add_subparsers(dest="cmd") - - p_pre = sub.add_parser("prepass", help="apply ISO substitution") - p_pre.add_argument("text") - p_pre.add_argument("--domains", nargs="+", metavar="DOMAIN") - - p_dec = sub.add_parser("decode", help="invert ISO substitution") - p_dec.add_argument("text") - p_dec.add_argument("--domains", nargs="+", metavar="DOMAIN") - - p_stat = sub.add_parser("stats", help="show compression statistics") - p_stat.add_argument("text") - p_stat.add_argument("--domains", nargs="+", metavar="DOMAIN") - - sub.add_parser("domains", help="list all domains and entry counts") - - args = parser.parse_args() - - if args.cmd == "prepass": - _cmd_prepass(args.text, args.domains) - elif args.cmd == "decode": - _cmd_decode(args.text, args.domains) - elif args.cmd == "stats": - _cmd_stats(args.text, args.domains) - elif args.cmd == "domains": - _cmd_domains() - else: - parser.print_help() diff --git a/5-Applications/tools-scripts/external/gcode_optimizer.py b/5-Applications/tools-scripts/external/gcode_optimizer.py deleted file mode 100644 index 3fd866cc..00000000 --- a/5-Applications/tools-scripts/external/gcode_optimizer.py +++ /dev/null @@ -1,812 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -gcode_optimizer.py — 14D canal-metric G-code path optimizer - -Virtual extruder test harness. Runs multiple optimization strategies against -G-code input and reports canal metric + φ-coherence scores without hardware. - -14D planning axes: - Physical (5): X, Y, Z, E (extrusion), F (feedrate) - Planning (9): η (viscosity), τ (thermal), A (acceleration), - overhang_angle, bridge_span, retraction_state, - layer_height, wall_proximity, ringing_risk - -Strategies: - baseline — original G-code order - canal — nearest-neighbour by canal metric (greedy) - phi_sorted — sorted by φ-coherence bucket per layer - thixotropic — prefer previously-visited regions (lower η) - random — shuffled within layer (sanity check, expected worst) - -Usage: - python 5-Applications/scripts/gcode_optimizer.py --synthetic - python 5-Applications/scripts/gcode_optimizer.py --input benchy.gcode - python 5-Applications/scripts/gcode_optimizer.py --synthetic --strategies baseline,canal,phi -""" - -from __future__ import annotations - -import argparse -import math -import random -import re -import sys -from collections import defaultdict -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# ── Constants ───────────────────────────────────────────────────────────────── - -PHI = (1 + math.sqrt(5)) / 2 # 1.6180339887… -N_AXES = 18 # Extended for Layer 7 (was 14) -_EPS = 1e-9 - - -def clamp_unit(v: float) -> float: - """Clamp value to [0, 1] range.""" - return max(0.0, min(1.0, v)) - -# Canal metric weighting — from HYPERFLUID_CANAL_MODEL.md -_LAMBDA_T = 0.15 # thixotropic decay rate (visit-count) -_LAMBDA_A = 0.30 # predictability (IoC) weight -_ETA_0 = 1.0 # base viscosity - -# Physical axis indices -_AX_X, _AX_Y, _AX_Z, _AX_E, _AX_F = 0, 1, 2, 3, 4 - -# Planning axis indices (original 9) -_AX_ETA, _AX_TAU, _AX_ACC = 5, 6, 7 -_AX_OVH, _AX_BRG, _AX_RET = 8, 9, 10 -_AX_LYR, _AX_WLL, _AX_RNG = 11, 12, 13 - -# Layer 7: Regime Drift / Structured Chaos extensions -_AX_CHI = 14 # structured chaos ratio -_AX_PSI = 15 # alignment state ψ (model adaptation) -_AX_PHI = 16 # structural regime φ (target) -_AX_DELTA = 17 # regime lag |φ - ψ| - - -# ── Data model ──────────────────────────────────────────────────────────────── - -@dataclass -class GCodeMove: - """One parsed G0/G1 command with current machine state after execution.""" - x: float - y: float - z: float - e: float - f: float - is_travel: bool # E == previous E (no extrusion) - line_no: int - raw: str - - -@dataclass -class Move14D: - """GCodeMove expanded to 18D planning space (Layer 7 extended).""" - move: GCodeMove - vec: List[float] = field(default_factory=lambda: [0.0] * N_AXES) - # Layer 7: Scroll/orientation state for path history - scroll_id: int = 0 - twist_bit: int = 1 # +1 or -1 based on direction change - bridge_candidate: bool = False # True if this move is a potential bridge node - - -# ── G-code parser ───────────────────────────────────────────────────────────── - -_G_RE = re.compile( - r'G[01]\s*' - r'(?:X(-?[\d.]+))?\s*' - r'(?:Y(-?[\d.]+))?\s*' - r'(?:Z(-?[\d.]+))?\s*' - r'(?:E(-?[\d.]+))?\s*' - r'(?:F(-?[\d.]+))?', - re.IGNORECASE, -) - - -def parse_gcode(text: str) -> List[GCodeMove]: - moves: List[GCodeMove] = [] - cx = cy = cz = ce = cf = 0.0 - prev_e = 0.0 - - for lineno, raw in enumerate(text.splitlines(), 1): - line = raw.strip() - if not line or line.startswith(';'): - continue - m = _G_RE.match(line) - if not m: - continue - x_s, y_s, z_s, e_s, f_s = m.groups() - if x_s is not None: cx = float(x_s) - if y_s is not None: cy = float(y_s) - if z_s is not None: cz = float(z_s) - if e_s is not None: ce = float(e_s) - if f_s is not None: cf = float(f_s) - - is_travel = (ce == prev_e) - moves.append(GCodeMove(cx, cy, cz, ce, cf, is_travel, lineno, raw)) - prev_e = ce - - return moves - - -def emit_gcode(moves: List[GCodeMove]) -> str: - """Re-emit G-code from a (possibly reordered) move list.""" - lines: List[str] = ["; gcode_optimizer output"] - for mv in moves: - lines.append(f"G1 X{mv.x:.3f} Y{mv.y:.3f} Z{mv.z:.3f} E{mv.e:.4f} F{mv.f:.0f}") - return "\n".join(lines) - - -# ── 14D expander ────────────────────────────────────────────────────────────── - -class Expander14D: - """Convert a sequence of GCodeMoves to Move14D planning vectors.""" - - def __init__(self, window: int = 8): - self._window = window - - def expand(self, moves: List[GCodeMove]) -> List[Move14D]: - out: List[Move14D] = [] - thermal_decay = [] # recent E-delta contributions - f_history: List[float] = [] - z_layers: Dict[float, List[int]] = defaultdict(list) - for i, mv in enumerate(moves): - z_layers[round(mv.z, 3)].append(i) - - for i, mv in enumerate(moves): - prev = moves[i - 1] if i > 0 else mv - dx = mv.x - prev.x - dy = mv.y - prev.y - dz = mv.z - prev.z - de = mv.e - prev.e - df = mv.f - prev.f - - xy_len = math.hypot(dx, dy) - xyz_len = math.sqrt(dx*dx + dy*dy + dz*dz) + _EPS - - # Axis 5: η — extrusion-pressure proxy (E-per-mm-travel) - eta = abs(de) / (xy_len + _EPS) if xy_len > _EPS else 0.0 - - # Axis 6: τ — thermal history (sum of recent |de| with exponential decay) - thermal_decay = [v * 0.85 for v in thermal_decay[-self._window:]] - thermal_decay.append(abs(de)) - tau = sum(thermal_decay) - - # Axis 7: A — acceleration proxy (|Δf| over window) - f_history.append(mv.f) - if len(f_history) > self._window: - f_history.pop(0) - accel = (max(f_history) - min(f_history)) / (max(f_history) + _EPS) - - # Axis 8: overhang_angle (0 = horizontal, 1 = vertical) - overhang = abs(dz) / xyz_len - - # Axis 9: bridge_span — XY travel since last Z-layer move - same_layer = z_layers.get(round(mv.z, 3), []) - prev_same = max((j for j in same_layer if j < i), default=i) - span_moves = i - prev_same - bridge_span = min(1.0, span_moves / max(1, self._window)) - - # Axis 10: retraction_state - retraction = 1.0 if de < 0 else 0.0 - - # Axis 11: layer_height - layer_height = min(1.0, abs(dz) / 0.3) # normalised to typical 0.3mm layer - - # Axis 12: wall_proximity (short XY moves with extrusion ≈ perimeter) - wall_prox = 1.0 if (xy_len < 5.0 and de > 0) else 0.0 - - # Axis 13: ringing_risk — variance of recent XY velocity - recent_f = f_history[-4:] if len(f_history) >= 4 else f_history - mean_f = sum(recent_f) / len(recent_f) - ringing = math.sqrt(sum((v - mean_f)**2 for v in recent_f) / len(recent_f)) / (mean_f + _EPS) - - # Layer 7 Axes -------------------------------------------------- - # Axis 14: chi — structured chaos proxy - # High when heat (traversal) dominates over congestion (A) and misalignment (τ) - # Approximation: chi = eta_indicator * (1 - congestion_indicator) - congestion = (bridge_span + overhang) / 2.0 # proxy for A - misalign = tau / max(thermal_decay + [_EPS]) # proxy for τ - chi = clamp_unit(eta * (1.0 - congestion) / (1.0 + misalign)) - - # Axis 15: psi — adaptive alignment state (exponentially smoothed) - # Track how well the path matches expected (via phi-coherence) - # This is a running estimate; actual requires history - psi = 0.5 # default neutral; updated per strategy - - # Axis 16: phi — structural regime (optimal configuration) - # For gcode, this is the "ideal" path geometry given constraints - # Approximation: high when layer-aligned, low when bridging/overhang - phi = clamp_unit(1.0 - overhang - bridge_span * 0.5) - - # Axis 17: delta — regime lag |φ - ψ| - delta = abs(phi - psi) - - vec = [ - mv.x, mv.y, mv.z, mv.e, mv.f, # physical (0-4) - eta, tau, accel, # material/thermal/accel (5-7) - overhang, bridge_span, retraction, # geometry (8-10) - layer_height, wall_prox, ringing, # quality (11-13) - chi, psi, phi, delta, # Layer 7 (14-17) - ] - - # Compute scroll_id and twist_bit for path orientation memory - scroll_id = int(mv.z * 10) + int(mv.x / 20) # Z-layer + X-bucket - twist_bit = 1 if (dx * dy >= 0) else -1 if (dx * dy < 0) else 1 - - out.append(Move14D(move=mv, vec=vec, scroll_id=scroll_id, twist_bit=twist_bit)) - - return out - - -# ── Canal metric ────────────────────────────────────────────────────────────── - -class CanalMetric: - """ - d(p1, p2) = ‖p2-p1‖₁₈ × η(p1) × (1+τ(p1)) × (1+λ_A·A(p1)) × (1+λ_χ·(1-χ)) - - η is thixotropic: decreases with visit count for p1's voxel. - χ is structured chaos: rewards productive disorder. - """ - - def __init__(self, grid_res: float = 5.0): - self._grid_res = grid_res - self._visit_counts: Dict[Tuple[int, int, int], int] = defaultdict(int) - - def _voxel(self, v: List[float]) -> Tuple[int, int, int]: - return ( - int(v[_AX_X] / self._grid_res), - int(v[_AX_Y] / self._grid_res), - int(v[_AX_Z] * 10), # finer Z resolution - ) - - def distance(self, a: Move14D, b: Move14D) -> float: - # ‖Φ‖ — L2 over all 18 axes (normalised to [0,1] range per axis) - # Updated norms for 18D space (14 original + 4 Layer 7) - norms = [200, 200, 10, 50, 15000, 1, 5, 1, 1, 1, 1, 1, 1, 1, - 1.0, 1.0, 1.0, 1.0] # chi, psi, phi, delta are already [0,1] - diff = [(a.vec[i] - b.vec[i]) / max(norms[i], _EPS) for i in range(N_AXES)] - phi_norm = math.sqrt(sum(d*d for d in diff)) - - vox = self._voxel(a.vec) - visits = self._visit_counts[vox] - eta = _ETA_0 * math.exp(-_LAMBDA_T * visits) - - tau = a.vec[_AX_TAU] - accel = a.vec[_AX_ACC] - chi = a.vec[_AX_CHI] - - # Layer 7: Modified canal cost with structured chaos χ - # Only destructive chaos (1-χ) penalizes torsion and acceleration - tau_eff = (1.0 - chi) * tau - accel_eff = (1.0 - chi) * accel - - # Base cost - base_cost = phi_norm * eta - - # Add torsion alignment bonus/penalty based on twist_bit match - twist_alignment = 1.0 - if a.twist_bit == b.twist_bit and a.twist_bit != 0: - twist_alignment = 0.9 # 10% discount for aligned scrolls - - return base_cost * (1 + tau_eff) * (1 + _LAMBDA_A * accel_eff) * twist_alignment - - def visit(self, m: Move14D) -> None: - self._visit_counts[self._voxel(m.vec)] += 1 - - def total_cost(self, path: List[Move14D], record_visits: bool = False) -> float: - if len(path) < 2: - return 0.0 - cost = 0.0 - for i in range(1, len(path)): - cost += self.distance(path[i - 1], path[i]) - if record_visits: - self.visit(path[i - 1]) - return cost - - -# ── φ-coherence gate ────────────────────────────────────────────────────────── - -def phi_coherence_score(path: List[Move14D]) -> float: - """ - Fraction of consecutive delta-vectors whose 18D amplitude ratio ≈ φ. - - Layer 7: Uses chi (structured chaos) to widen acceptance window when - productive disorder is detected. High chi → more tolerance for variation. - - Uses the same per-axis normalization as CanalMetric. - """ - if len(path) < 2: - return 0.0 - - # Updated normalization scales for 18D space - norms = [200, 200, 10, 50, 15000, 1, 5, 1, 1, 1, 1, 1, 1, 1, - 1.0, 1.0, 1.0, 1.0] - - coherent = 0 - for i in range(1, len(path)): - a, b = path[i - 1].vec, path[i].vec - chi = a[_AX_CHI] # structured chaos at this position - - delta = [abs(b[j] - a[j]) / max(norms[j], _EPS) for j in range(N_AXES)] - # axis_0 = combined XY physical displacement (primary motion) - axis0 = delta[_AX_X] + delta[_AX_Y] + _EPS - rest_sum = sum(delta[2:]) # Z + E + F + all planning axes - ratio = rest_sum / axis0 - - # Layer 7: Dynamic tolerance based on chi - # High chi → wider acceptance (structured chaos is productive) - base_tolerance = 0.20 - chi_bonus = 0.10 * chi # up to 10% extra tolerance - tolerance = base_tolerance + chi_bonus - - if abs(ratio - PHI) / PHI < tolerance: - coherent += 1 - - return coherent / (len(path) - 1) - - -def is_bridge_node(a: Move14D, b: Move14D, c: Move14D, threshold: float = 10.0) -> bool: - """Detect if b is a bridge state between a and c (dual-anchor constraint). - - A bridge exists when b is between two constraints that both 'reach' toward it. - This is a simplified geometric check for 3D printing paths. - """ - # Check if b is roughly between a and c in XY plane - dx_ab = b.vec[_AX_X] - a.vec[_AX_X] - dy_ab = b.vec[_AX_Y] - a.vec[_AX_Y] - dx_bc = c.vec[_AX_X] - b.vec[_AX_X] - dy_bc = c.vec[_AX_Y] - b.vec[_AX_Y] - - # Same Z-layer check - if abs(b.vec[_AX_Z] - a.vec[_AX_Z]) > 0.05: - return False - if abs(c.vec[_AX_Z] - b.vec[_AX_Z]) > 0.05: - return False - - # Direction reversal check (bridge spans typically reverse direction) - dot_product = dx_ab * dx_bc + dy_ab * dy_bc - if dot_product > 0: # Same direction, not a bridge - return False - - # Distance check (within threshold) - dist_ab = math.hypot(dx_ab, dy_ab) - dist_bc = math.hypot(dx_bc, dy_bc) - if dist_ab > threshold or dist_bc > threshold: - return False - - return True - - -# ── Strategies ──────────────────────────────────────────────────────────────── - -def _by_layer(moves: List[Move14D]) -> Dict[float, List[Move14D]]: - layers: Dict[float, List[Move14D]] = defaultdict(list) - for m in moves: - layers[round(m.move.z, 3)].append(m) - return layers - - -def _detect_and_mark_bridges(moves: List[Move14D]) -> None: - """Mark bridge_candidate flag on moves that are potential bridge states.""" - if len(moves) < 3: - return - - for i in range(1, len(moves) - 1): - a, b, c = moves[i-1], moves[i], moves[i+1] - if is_bridge_node(a, b, c): - moves[i].bridge_candidate = True - - -def strategy_baseline(moves: List[Move14D]) -> List[Move14D]: - return list(moves) - - -def strategy_canal(moves: List[Move14D]) -> List[Move14D]: - """Greedy nearest-neighbour by canal metric, within each Z-layer.""" - metric = CanalMetric() - result: List[Move14D] = [] - for z, layer in sorted(_by_layer(moves).items()): - remaining = list(layer) - if not remaining: - continue - cur = remaining.pop(0) - result.append(cur) - while remaining: - costs = [(metric.distance(cur, nxt), i, nxt) for i, nxt in enumerate(remaining)] - costs.sort(key=lambda t: t[0]) - _, best_i, best = costs[0] - metric.visit(cur) - result.append(best) - remaining.pop(best_i) - cur = best - return result - - -def strategy_phi_sorted(moves: List[Move14D]) -> List[Move14D]: - """Sort moves within each layer by φ-ratio bucket (closer to φ first).""" - result: List[Move14D] = [] - for z, layer in sorted(_by_layer(moves).items()): - def phi_err(m: Move14D) -> float: - v = [abs(x) for x in m.vec] - axis0 = v[_AX_X] + _EPS - return abs(sum(v[1:]) / axis0 - PHI) - result.extend(sorted(layer, key=phi_err)) - return result - - -def strategy_thixotropic(moves: List[Move14D]) -> List[Move14D]: - """Prefer previously-visited voxels — emergent worn-path following.""" - metric = CanalMetric() - # Warm-up pass: record visits in baseline order - for m in moves: - metric.visit(m) - # Now re-sort within layers: lower canal distance from origin wins - result: List[Move14D] = [] - for z, layer in sorted(_by_layer(moves).items()): - if not layer: - continue - cur = layer[0] - remaining = list(layer[1:]) - result.append(cur) - while remaining: - costs = [(metric.distance(cur, nxt), i, nxt) for i, nxt in enumerate(remaining)] - costs.sort(key=lambda t: t[0]) - _, best_i, best = costs[0] - result.append(best) - remaining.pop(best_i) - cur = best - return result - - -def strategy_random(moves: List[Move14D], seed: int = 42) -> List[Move14D]: - rng = random.Random(seed) - result: List[Move14D] = [] - for z, layer in sorted(_by_layer(moves).items()): - shuffled = list(layer) - rng.shuffle(shuffled) - result.extend(shuffled) - return result - - -STRATEGIES = { - "baseline": strategy_baseline, - "canal": strategy_canal, - "phi_sorted": strategy_phi_sorted, - "thixotropic": strategy_thixotropic, - "random": strategy_random, -} - - -# ── Virtual extruder metrics ────────────────────────────────────────────────── - -@dataclass -class ExtruderMetrics: - strategy: str - canal_cost: float - phi_coherence: float - move_count: int - travel_moves: int - layer_count: int - bridge_risk_mean: float - ringing_risk_mean: float - overhang_mean: float - estimated_time_s: float # Σ distance / feedrate - # Layer 7 extensions - chi_mean: float # structured chaos (0-1, higher = more productive) - regime_lag_mean: float # |φ - ψ| alignment lag - bridge_count: int # detected bridge states - scroll_alignment: float # fraction of moves with consistent twist_bit - - -def score(name: str, path: List[Move14D]) -> ExtruderMetrics: - metric = CanalMetric() - cost = metric.total_cost(path, record_visits=False) - phi = phi_coherence_score(path) - - # Layer 7: Detect and mark bridges - _detect_and_mark_bridges(path) - - layers = len(set(round(m.move.z, 3) for m in path)) - travels = sum(1 for m in path if m.move.is_travel) - - bridge_risks = [m.vec[_AX_BRG] for m in path] - ringing_risks = [m.vec[_AX_RNG] for m in path] - overhangs = [m.vec[_AX_OVH] for m in path] - - br_mean = sum(bridge_risks) / max(len(bridge_risks), 1) - rr_mean = sum(ringing_risks) / max(len(ringing_risks), 1) - ov_mean = sum(overhangs) / max(len(overhangs), 1) - - # Layer 7: Compute extended metrics - chis = [m.vec[_AX_CHI] for m in path] - chi_mean = sum(chis) / max(len(chis), 1) - - regime_lags = [m.vec[_AX_DELTA] for m in path] - lag_mean = sum(regime_lags) / max(len(regime_lags), 1) - - bridge_count = sum(1 for m in path if m.bridge_candidate) - - # Scroll alignment: fraction of consecutive moves with same twist - aligned_twists = 0 - total_twist_pairs = 0 - for i in range(1, len(path)): - if path[i].twist_bit == path[i-1].twist_bit: - aligned_twists += 1 - total_twist_pairs += 1 - scroll_align = aligned_twists / max(total_twist_pairs, 1) - - # Estimated time: Σ(XY distance / feedrate) - t = 0.0 - for i in range(1, len(path)): - a, b = path[i-1].move, path[i].move - d = math.hypot(b.x - a.x, b.y - a.y, b.z - a.z) - f = max(b.f, 100) / 60 # mm/s - t += d / f - - return ExtruderMetrics( - strategy=name, - canal_cost=round(cost, 4), - phi_coherence=round(phi, 4), - move_count=len(path), - travel_moves=travels, - layer_count=layers, - bridge_risk_mean=round(br_mean, 4), - ringing_risk_mean=round(rr_mean, 4), - overhang_mean=round(ov_mean, 4), - estimated_time_s=round(t, 1), - # Layer 7 metrics - chi_mean=round(chi_mean, 4), - regime_lag_mean=round(lag_mean, 4), - bridge_count=bridge_count, - scroll_alignment=round(scroll_align, 4), - ) - - -# ── Synthetic benchy-like G-code generator ──────────────────────────────────── - -def _synthetic_gcode(seed: int = 0) -> str: - """ - Generate a minimal benchy-representative G-code sequence covering: - - Hull curve (smooth XY arcs, multiple layers) - - Stern bridge (XY travel over open space) - - Chimney overhang (Z-increasing with decreasing support) - - Porthole circles (small tight loops, high acceleration) - - Roof (45° overhang) - """ - rng = random.Random(seed) - lines = ["; synthetic benchy-like gcode (gcode_optimizer test fixture)"] - f_default = 3000 - - def g1(x, y, z, e_delta, f=f_default, acc_e=None): - nonlocal cur_e - cur_e += e_delta - return f"G1 X{x:.3f} Y{y:.3f} Z{z:.3f} E{cur_e:.4f} F{f}" - - cur_e = 0.0 - layer_heights = [round(0.2 * i, 2) for i in range(1, 16)] # 15 layers - - # ── Hull curve: 3 layers of smooth ellipse ───────────────────────────────── - for lz in layer_heights[:3]: - n_pts = 36 - for i in range(n_pts): - angle = 2 * math.pi * i / n_pts - x = 80 + 40 * math.cos(angle) - y = 40 + 20 * math.sin(angle) - de = 0.04 + rng.gauss(0, 0.002) - lines.append(g1(x, y, lz, de)) - - # ── Stern bridge: unsupported XY span ────────────────────────────────────── - lz = layer_heights[3] - for x_step in range(20): - x = 20 + x_step * 3 - y = 40 - de = 0.05 if x_step > 3 else 0.0 # first few are travel - lines.append(g1(x, y, lz, de)) - - # ── Chimney overhang: Z-increasing with shrinking XY radius ──────────────── - for li, lz in enumerate(layer_heights[4:10]): - r = 8 - li * 1.0 # shrinking circle = overhang - cx, cy = 60, 60 - for i in range(24): - angle = 2 * math.pi * i / 24 - x = cx + r * math.cos(angle) - y = cy + r * math.sin(angle) - de = 0.03 - lines.append(g1(x, y, lz, de)) - - # ── Porthole circles: small tight loops, high ringing risk ───────────────── - for lz in layer_heights[2:5]: - for cx_off in [30, 50]: - for i in range(16): - angle = 2 * math.pi * i / 16 - x = cx_off + 3 * math.cos(angle) - y = 25 + 3 * math.sin(angle) - de = 0.015 - lines.append(g1(x, y, lz, de, f=5000)) # high-speed = ringing - - # ── Roof overhangs: 45° slope ────────────────────────────────────────────── - for li, lz in enumerate(layer_heights[10:]): - x_start = 30 + li * 2 # footprint shrinks = overhang - for xi in range(20): - x = x_start + xi * 1.5 - y = 40 + rng.gauss(0, 0.1) - de = 0.045 - lines.append(g1(x, y, lz, de)) - - return "\n".join(lines) - - -# ── Report ──────────────────────────────────────────────────────────────────── - -_COL_W = 14 - -def _row(label: str, *vals) -> str: - return f" {label:<22}" + "".join(f"{str(v):>{_COL_W}}" for v in vals) - - -def print_report(results: List[ExtruderMetrics]) -> None: - strategies = [r.strategy for r in results] - print() - print("=" * (24 + _COL_W * len(results))) - print(" 18D VIRTUAL EXTRUDER REPORT (Layer 7 Extended)") - print("=" * (24 + _COL_W * len(results))) - print(_row("metric", *strategies)) - print(" " + "-" * (22 + _COL_W * len(results))) - - fields = [ - ("canal_cost", "canal_cost", "lower = better path"), - ("phi_coherence", "phi_coherence", "higher = more φ-locked"), - ("estimated_time_s", "estimated_time_s", "lower = faster print"), - ("bridge_risk_mean", "bridge_risk_mean", "lower = safer bridges"), - ("ringing_risk_mean", "ringing_risk_mean", "lower = less vibration"), - ("overhang_mean", "overhang_mean", "lower = better support"), - ("travel_moves", "travel_moves", "lower = less stringing"), - ("move_count", "move_count", ""), - ("layer_count", "layer_count", ""), - # Layer 7 metrics - ("chi_mean", "chi_mean", "higher = productive chaos"), - ("regime_lag_mean", "regime_lag", "lower = better aligned"), - ("bridge_count", "bridge_count", "count of dual-anchor states"), - ("scroll_alignment", "scroll_alignment", "higher = consistent path"), - ] - - for attr, label, note in fields: - vals = [getattr(r, attr) for r in results] - note_str = f" ← {note}" if note else "" - print(_row(label, *vals) + note_str) - - print() - - # Rank by canal_cost - ranked = sorted(results, key=lambda r: r.canal_cost) - print(" Ranking by canal cost:") - for i, r in enumerate(ranked, 1): - delta = "" - if i > 1: - pct = (r.canal_cost - ranked[0].canal_cost) / max(ranked[0].canal_cost, _EPS) * 100 - delta = f" (+{pct:.1f}%)" - phi_pass = "φ-COHERENT" if r.phi_coherence >= 0.5 else "φ-WEAK" - chi_tag = f" χ={r.chi_mean:.2f}" if r.chi_mean > 0.5 else "" - print(f" {i}. {r.strategy:<14} cost={r.canal_cost:<10} {phi_pass}{chi_tag}{delta}") - - print() - - # φ-commit gate - best = ranked[0] - if best.phi_coherence >= 0.5: - print(f" COMMIT GATE: PASS — '{best.strategy}' φ-coherence={best.phi_coherence}") - else: - alt = next((r for r in ranked if r.phi_coherence >= 0.5), None) - if alt: - print(f" COMMIT GATE: best canal '{best.strategy}' fails φ-coherence.") - print(f" Fallback: '{alt.strategy}' (cost={alt.canal_cost}, φ={alt.phi_coherence})") - else: - print(" COMMIT GATE: ALL strategies below φ-coherence threshold.") - - print("=" * (24 + _COL_W * len(results))) - print() - - -# ── CLI ─────────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser(description="14D canal-metric G-code optimizer (virtual extruder)") - src = ap.add_mutually_exclusive_group(required=True) - src.add_argument("--input", metavar="FILE", help="Input G-code file") - src.add_argument("--synthetic", action="store_true", help="Use built-in synthetic benchy fixture") - ap.add_argument("--strategies", default="baseline,canal,phi_sorted,thixotropic,random", - help="Comma-separated list of strategies to run") - ap.add_argument("--emit", metavar="DIR", - help="Emit optimized G-code files to DIR (one per strategy)") - ap.add_argument("--telemetry", metavar="FILE", - help="Log voxel telemetry to JSONL file") - ap.add_argument("--seed", type=int, default=0, help="RNG seed for synthetic/random") - args = ap.parse_args() - - # Load G-code - if args.synthetic: - gcode_text = _synthetic_gcode(seed=args.seed) - print(f"[gcode_optimizer] synthetic fixture: {len(gcode_text.splitlines())} lines") - else: - path = Path(args.input) - if not path.exists(): - print(f"[gcode_optimizer] ERROR: file not found: {path}", file=sys.stderr) - sys.exit(1) - gcode_text = path.read_text() - print(f"[gcode_optimizer] loaded: {path} ({len(gcode_text.splitlines())} lines)") - - # Parse → expand - raw_moves = parse_gcode(gcode_text) - if not raw_moves: - print("[gcode_optimizer] ERROR: no G0/G1 moves found", file=sys.stderr) - sys.exit(1) - - print(f"[gcode_optimizer] parsed {len(raw_moves)} moves, expanding to 18D (Layer 7)…") - expander = Expander14D() - moves_14d = expander.expand(raw_moves) - - # Run strategies - strategy_names = [s.strip() for s in args.strategies.split(",") if s.strip()] - unknown = [s for s in strategy_names if s not in STRATEGIES] - if unknown: - print(f"[gcode_optimizer] unknown strategies: {unknown}. Available: {list(STRATEGIES)}", file=sys.stderr) - sys.exit(1) - - results: List[ExtruderMetrics] = [] - for name in strategy_names: - print(f"[gcode_optimizer] running strategy: {name}…", end=" ", flush=True) - fn = STRATEGIES[name] - optimized = fn(moves_14d) if name != "random" else fn(moves_14d, seed=args.seed) - metrics = score(name, optimized) - results.append(metrics) - print(f"canal={metrics.canal_cost}, φ={metrics.phi_coherence}, χ={metrics.chi_mean:.2f}") - - # Optionally emit G-code - if args.emit: - out_dir = Path(args.emit) - out_dir.mkdir(parents=True, exist_ok=True) - gcode_out = emit_gcode([m.move for m in optimized]) - out_path = out_dir / f"{name}.gcode" - out_path.write_text(gcode_out) - print(f"[gcode_optimizer] → {out_path}") - - # Optionally log telemetry (Layer 7) - if args.telemetry: - import json - telemetry_path = Path(args.telemetry) - telemetry_path.parent.mkdir(parents=True, exist_ok=True) - - for i, m in enumerate(optimized): - row = { - "strategy": name, - "move_idx": i, - "voxel_key": (m.scroll_id, int(m.vec[_AX_X]/5), int(m.vec[_AX_Y]/5)), - "x": round(m.vec[_AX_X], 3), - "y": round(m.vec[_AX_Y], 3), - "z": round(m.vec[_AX_Z], 3), - "chi": round(m.vec[_AX_CHI], 6), - "psi": round(m.vec[_AX_PSI], 6), - "phi": round(m.vec[_AX_PHI], 6), - "delta": round(m.vec[_AX_DELTA], 6), - "scroll_id": m.scroll_id, - "twist_bit": m.twist_bit, - "bridge_candidate": m.bridge_candidate, - } - with telemetry_path.open("a", encoding="utf-8") as f: - f.write(json.dumps(row, sort_keys=True) + "\n") - - print_report(results) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/external/gdrive_probe.py b/5-Applications/tools-scripts/external/gdrive_probe.py deleted file mode 100644 index 74a6e1b3..00000000 --- a/5-Applications/tools-scripts/external/gdrive_probe.py +++ /dev/null @@ -1,336 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""tunnel probe: bidirectional omnitoken-welded pipeline against rclone FUSE Drive. - -Ingress — GDrive FUSE files → tunnel register → clone into local tunnel_cache -Egress — probe result sidecars written back to Drive (--egress-dir) -Surface — egress_surface.json surface_bus patched with 'gdrive_fuse_bidir' domain - -Each file under --drive-dir is fed into decide_tunnel_generalist() as a -tunnel_source_file node. Both directions are registered in the omnitoken surface -so the weld sees GDrive as a bidirectional transport domain. - -Usage: - python 5-Applications/scripts/tunnel_gdrive_probe.py - python 5-Applications/scripts/tunnel_gdrive_probe.py --top-n 5 --inject-surface - python 5-Applications/scripts/tunnel_gdrive_probe.py --one-line --one-line-delim ";" - python 5-Applications/scripts/tunnel_gdrive_probe.py --no-egress # ingress-only, no write-back -""" - -from __future__ import annotations - -import argparse -import base64 -import gzip -import json -import os -import sys -import zlib -from pathlib import Path -from typing import Any, Dict, List - -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) - -try: - from scripts.weld_omnitoken_surface import ( - decide_tunnel_generalist, - TUNNEL_REGISTER_TTL_SECONDS, - OMNI_SURFACE_PATH, - utc_now, - assert_surface_write_safe, - ) -except ImportError: - from weld_omnitoken_surface import ( # type: ignore - decide_tunnel_generalist, - TUNNEL_REGISTER_TTL_SECONDS, - OMNI_SURFACE_PATH, - utc_now, - assert_surface_write_safe, - ) - -DEFAULT_DRIVE_DIR = Path.home() / "Gdrive" / "Research Documents" -DEFAULT_EGRESS_DIR = Path.home() / "Gdrive" / "_omnitoken_probe" -DEFAULT_TOP_N = 20 - - -def _fmt_bytes(n: int) -> str: - for unit in ("B", "KB", "MB", "GB"): - if n < 1024 or unit == "GB": - return f"{n:.1f} {unit}" if unit != "B" else f"{n} B" - n /= 1024 # type: ignore[assignment] - return str(n) - - -def collect_files(drive_dir: Path, top_n: int) -> List[Path]: - """Collect up to top_n regular files from drive_dir (recursive).""" - files: List[Path] = [] - try: - # Using rglob('*') to get everything recursively - # Note: glob/rglob handles hidden files differently depending on OS/Version, - # but usually '*' doesn't match dotfiles. We can use a pattern or just walk. - for entry in sorted(drive_dir.rglob('*')): - if entry.is_file(): - files.append(entry) - if len(files) >= top_n: - break - except PermissionError as e: - print(f"ERROR accessing {drive_dir}: {e}", file=sys.stderr) - sys.exit(1) - return files - - -def probe_file(path: Path, ttl_seconds: int) -> Dict[str, Any]: - node: Dict[str, Any] = { - "name": f"gdrive::{path.name}", - "tunnel_source_file": str(path), - } - result = decide_tunnel_generalist(node, foam_profile="balanced", tunnel_ttl_seconds=ttl_seconds) - result["_probe_filename"] = path.name - result["_probe_path"] = str(path) - result["_probe_size_bytes"] = path.stat().st_size if path.exists() else 0 - result["_direction"] = "ingress" - return result - - -def _compress_bytes(data: bytes, algo: str) -> bytes: - if algo == "zlib": - return zlib.compress(data) - if algo == "gzip": - return gzip.compress(data) - # default fallback - return data - - -def _compress_and_b64url(data: bytes) -> str: - comp = zlib.compress(data) - b64 = base64.urlsafe_b64encode(comp).rstrip(b"=") - return b64.decode("ascii") - - -def write_egress_sidecar(result: Dict[str, Any], egress_dir: Path, compress: bool = False, algo: str = "zlib") -> str: - """Write probe result back to Drive as a JSON sidecar (egress direction). - - Returns the path written, or empty string on failure. - """ - egress_dir.mkdir(parents=True, exist_ok=True) - fname = result.get("_probe_filename", "unknown") - sidecar_name = f"{fname}.tunnel_probe.json" - sidecar_path = egress_dir / sidecar_name - - wavestate = result.get("wavestate") - payload: Dict[str, Any] = { - "schema": "tunnel-probe-egress/v1", - "direction": "egress", - "source_file": result.get("_probe_path", ""), - "status": result.get("status", "?"), - "register_id": result.get("register_id", ""), - "register_state": result.get("register_state", ""), - "register_expires_utc": result.get("register_expires_utc", ""), - "generalist_decision": result.get("generalist_decision", ""), - "wavestate": wavestate if isinstance(wavestate, dict) else {}, - "rebuilt_path": result.get("rebuilt_path", ""), - "written_utc": utc_now(), - } - text = json.dumps(payload, indent=2) + "\n" - try: - # Always write human-readable JSON sidecar for compatibility - sidecar_path.write_text(text, encoding="utf-8") - except OSError: - return "" - - # Optionally write compressed sidecar alongside the JSON - if compress: - try: - if algo == "zlib+b64": - # write base64url(zlib(json)) as text file - b64text = _compress_and_b64url(text.encode("utf-8")) - comp_path = egress_dir / (sidecar_name + ".zlib.b64") - comp_path.write_text(b64text + "\n", encoding="utf-8") - return str(comp_path) - else: - comp_bytes = _compress_bytes(text.encode("utf-8"), algo) - comp_ext = "zlib" if algo == "zlib" else "gz" - comp_path = egress_dir / (sidecar_name + f".{comp_ext}") - comp_path.write_bytes(comp_bytes) - return str(comp_path) - except OSError: - # if compression write fails, fall back to JSON path - return str(sidecar_path) - - return str(sidecar_path) - - -def inject_surface_domain( - probe_results: List[Dict[str, Any]], - drive_dir: Path, - egress_dir: Path, -) -> None: - """Patch egress_surface.json to register GDrive FUSE as a bidirectional domain. - - Adds surface_bus.domains.gdrive_fuse_bidir so the omnitoken weld sees the - Drive mount as both an ingress source and egress destination. - """ - if not OMNI_SURFACE_PATH.exists(): - # Surface not yet generated; nothing to patch — weld needs to run first - return - - try: - surface: Dict[str, Any] = json.loads(OMNI_SURFACE_PATH.read_text(encoding="utf-8")) - except (OSError, json.JSONDecodeError): - return - - ok_count = sum(1 for r in probe_results if r.get("status") == "ok") - err_count = len(probe_results) - ok_count - - surface_bus: Dict[str, Any] = dict(surface.get("surface_bus") or {}) - domains: Dict[str, Any] = dict(surface_bus.get("domains") or {}) - - domains["gdrive_fuse_bidir"] = { - "domain": "gdrive_fuse_bidir", - "direction": "bidirectional", - "fuse_backend": "rclone", - "rclone_remote": "Gdrive", - "mount_point": str(drive_dir.parent), - "ingress_path": str(drive_dir), - "egress_path": str(egress_dir), - "transport": "tunnel", - "files_probed": len(probe_results), - "files_committed_ingress": ok_count, - "files_errored": err_count, - "updated_utc": utc_now(), - } - - surface_bus["schema"] = str(surface_bus.get("schema") or "omnitoken-surface-bus/v1") - surface_bus["agnostic"] = True - surface_bus["domains"] = domains - surface["surface_bus"] = surface_bus - surface["updated_utc"] = utc_now() - - assert_surface_write_safe(surface, scope="gdrive_fuse_probe_injection") - OMNI_SURFACE_PATH.write_text(json.dumps(surface, indent=2) + "\n", encoding="utf-8") - - -def render_table(results: List[Dict[str, Any]], egress_written: List[str]) -> None: - print(f"\n{'FILE':<42} {'STATUS':<12} {'TRANSITIONS':<52} {'SIZE':>8} {'EGRESS':>6} {'EXPIRES_UTC'}") - print("-" * 148) - for idx, r in enumerate(results): - fname = r.get("_probe_filename", "")[:41] - status = r.get("status", "?") - size_str = _fmt_bytes(int(r.get("_probe_size_bytes", 0))) - expires = r.get("register_expires_utc", "–") - transitions = r.get("register_state_transitions", []) - trans_str = " → ".join(t.get("state", "?") for t in transitions) if transitions else r.get("reason", "–") - wrote = "ok" if idx < len(egress_written) and egress_written[idx] else "skip" - print(f"{fname:<42} {status:<12} {trans_str:<52} {size_str:>8} {wrote:>6} {expires}") - print() - - -def render_one_line(results: List[Dict[str, Any]], egress_written: List[str], delim: str) -> None: - for rank, r in enumerate(results, 1): - fname = r.get("_probe_filename", "") - status = r.get("status", "?") - transitions = r.get("register_state_transitions", []) - final_state = transitions[-1].get("state", "–") if transitions else r.get("reason", "–") - register_id = r.get("register_id", "–") - expires = r.get("register_expires_utc", "–") - size_str = _fmt_bytes(int(r.get("_probe_size_bytes", 0))) - wavestate = r.get("wavestate") - sha: str - if isinstance(wavestate, dict) and "sha256" in wavestate: - sha = f"{wavestate['sha256']}"[:16] - else: - sha = "–" - wrote = "egress_ok" if rank - 1 < len(egress_written) and egress_written[rank - 1] else "egress_skip" - parts: List[str] = [str(rank), fname, status, final_state, size_str, sha, wrote, expires, register_id] - print(delim.join(parts)) - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - ap.add_argument("--drive-dir", default=str(DEFAULT_DRIVE_DIR), metavar="PATH", - help="Ingress: Drive directory to probe (default: %(default)s)") - ap.add_argument("--egress-dir", default=str(DEFAULT_EGRESS_DIR), metavar="PATH", - help="Egress: Drive directory to write probe sidecars back to (default: %(default)s)") - ap.add_argument("--top-n", type=int, default=DEFAULT_TOP_N, metavar="N", - help="Max files to probe (default: %(default)s)") - ap.add_argument("--ttl", type=int, default=TUNNEL_REGISTER_TTL_SECONDS, metavar="SECONDS", - help="tunnel register TTL in seconds (default: %(default)s)") - ap.add_argument("--no-egress", action="store_true", - help="Skip writing sidecar JSONs back to Drive (ingress-only mode)") - ap.add_argument("--inject-surface", action="store_true", - help="Patch egress_surface.json surface_bus with gdrive_fuse_bidir domain") - ap.add_argument("--compress", action="store_true", - help="Also write a compressed sidecar alongside the JSON") - ap.add_argument("--compress-algo", choices=["zlib", "gzip", "zlib+b64"], default="zlib", - help="Compression algorithm for sidecars (default: %(default)s)") - ap.add_argument("--one-line", action="store_true", - help="Pipe-friendly one-line-per-file output") - ap.add_argument("--one-line-delim", default=";", metavar="CHAR", - help="Delimiter for --one-line mode (default: %(default)r)") - args = ap.parse_args() - - drive_dir = Path(os.path.expanduser(args.drive_dir)) - egress_dir = Path(os.path.expanduser(args.egress_dir)) - - if not drive_dir.exists(): - print(f"ERROR: drive dir not found: {drive_dir}", file=sys.stderr) - print("Is the rclone FUSE mount running? Check: systemctl --user status rclone-gdrive.service", file=sys.stderr) - sys.exit(1) - - files = collect_files(drive_dir, args.top_n) - if not files: - print(f"No files found in {drive_dir}", file=sys.stderr) - sys.exit(0) - - if not args.one_line: - mode_str = "ingress-only" if args.no_egress else "bidirectional" - print(f"[{mode_str}] Probing {len(files)} files") - print(f" ingress : {drive_dir}") - if not args.no_egress: - print(f" egress : {egress_dir}") - print(f" cache : {ROOT / 'out' / 'omnitoken_bridge' / 'tunnel_cache'}") - print(f" TTL : {args.ttl}s") - if args.inject_surface: - print(f" surface : {OMNI_SURFACE_PATH}") - - # Ingress: probe each file through the tunnel pipeline - results: List[Dict[str, Any]] = [] - for f in files: - results.append(probe_file(f, args.ttl)) - - # Egress: write sidecar JSONs back to Drive - egress_written: List[str] = [] - if not args.no_egress: - for r in results: - egress_written.append(write_egress_sidecar(r, egress_dir, compress=args.compress, algo=args.compress_algo)) - else: - egress_written = ["" for _ in results] - - # Surface injection: register both directions in egress_surface.json - if args.inject_surface: - inject_surface_domain(results, drive_dir, egress_dir) - if not args.one_line: - print(" surface patched with gdrive_fuse_bidir domain") - - if args.one_line: - render_one_line(results, egress_written, args.one_line_delim) - else: - render_table(results, egress_written) - - ok = sum(1 for r in results if r.get("status") == "ok") - err = len(results) - ok - egress_ok = sum(1 for p in egress_written if p) - print(f"Ingress: {ok} committed | {err} unavailable/error") - if not args.no_egress: - print(f"Egress : {egress_ok}/{len(results)} sidecars written to Drive") - print() - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/external/nvidia_sae_scraper.py b/5-Applications/tools-scripts/external/nvidia_sae_scraper.py deleted file mode 100644 index eafc1415..00000000 --- a/5-Applications/tools-scripts/external/nvidia_sae_scraper.py +++ /dev/null @@ -1,717 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""NVIDIA SAE Feature Scraper. - -Acquisition order (each tried in sequence, stops on first success): - - PATH A — Headless browser (Playwright): - Launches a real Chromium headless instance, navigates to the dashboard, - and intercepts every network response. When the dashboard JS starts - DuckDB-WASM it fetches features_atlas.parquet, feature_metadata.parquet, - and feature_examples.parquet itself. We capture those bytes directly from - the browser's network stack — no manual cookie capture, no guessing URLs. - Also sniffs any per-feature CSV download endpoint pattern for Path C. - Requires: pip install playwright && playwright install chromium - - PATH B — Direct HTTP parquet fetch (3 requests): - If the parquet URL can be inferred from parquet_base_url in config, - try a direct download with httpx. PAR1 magic-byte validation rejects - HTML auth walls immediately. - - PATH C — Per-feature CSV sweep (rate-limited, up to 32,634 requests): - Last resort when both above fail or when targeting a sparse ID range. - Human-pacing multi-modal jitter + periodic heartbeat visits. -""" - -from __future__ import annotations - -import asyncio -import hashlib -import json -import logging -import random -import sqlite3 -import sys -from pathlib import Path -from typing import Any, Dict, List, Optional - -try: - import httpx - from tqdm.asyncio import tqdm -except ImportError: - print("[!] Missing dependencies: pip install httpx tqdm") - sys.exit(1) - -try: - from playwright.async_api import async_playwright, Response as PwResponse - _HAS_PLAYWRIGHT = True -except ImportError: - _HAS_PLAYWRIGHT = False - -try: - import duckdb as _duckdb - _HAS_DUCKDB = True -except ImportError: - _HAS_DUCKDB = False - -# ── Logging ─────────────────────────────────────────────────────────────────── -logging.basicConfig( - level=logging.INFO, - format="%(asctime)s [%(levelname)s] %(message)s", - handlers=[logging.StreamHandler()], -) -logger = logging.getLogger("nvidia_sae") - -_PARQUET_FILES = [ - "features_atlas.parquet", - "feature_metadata.parquet", - "feature_examples.parquet", -] - -# ── SQLite schema (shared by all ingest paths) ─────────────────────────────── -_SCHEMA_SQL = """ -PRAGMA journal_mode = WAL; -PRAGMA synchronous = NORMAL; - -CREATE TABLE IF NOT EXISTS features ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - feature_id TEXT UNIQUE NOT NULL, - label TEXT, - description TEXT, - activation_freq REAL, - mean_activation REAL, - max_activation REAL, - std_activation REAL, - total_activations INTEGER, - log_frequency REAL, - x REAL, - y REAL, - cluster_id INTEGER, - high_score_fraction REAL, - clinvar_fraction REAL, - mean_phylop REAL, - mean_variant_delta REAL, - mean_site_delta REAL, - mean_local_delta REAL, - high_score_delta REAL, - low_score_delta REAL, - gc_mean REAL, - gc_std REAL, - trinuc_entropy REAL, - trinuc_dominant_frac REAL, - gene_entropy REAL, - gene_n_unique INTEGER, - gene_dominant_frac REAL, - mean_variant_1bcdwt REAL, - mean_variant_5bcdwt REAL, - mean_variant_5b REAL, - llm_confidence REAL, - imported_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP -); -CREATE TABLE IF NOT EXISTS proteins ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - sequence_hash TEXT UNIQUE NOT NULL, - sequence TEXT NOT NULL, - length INTEGER NOT NULL -); -CREATE TABLE IF NOT EXISTS activations ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - feature_id INTEGER NOT NULL REFERENCES features(id), - protein_id TEXT NOT NULL REFERENCES proteins(sequence_hash), - example_rank INTEGER, - alphafold_id TEXT, - max_activation REAL, - activations_json TEXT, - gene TEXT, - is_pathogenic INTEGER, - ref_codon TEXT, - alt_codon TEXT, - source TEXT, - var_pos_offset INTEGER, - variant_delta REAL, - UNIQUE(feature_id, protein_id, example_rank) -); -CREATE INDEX IF NOT EXISTS idx_features_fid ON features(feature_id); -CREATE INDEX IF NOT EXISTS idx_proteins_hash ON proteins(sequence_hash); -CREATE INDEX IF NOT EXISTS idx_act_fid ON activations(feature_id); -""" - - -# ── Parquet → SQLite inversion ─────────────────────────────────────────────── - -def _sha256(s: str) -> str: - return hashlib.sha256(s.encode()).hexdigest() - - -def _open_sqlite(db_path: Path) -> sqlite3.Connection: - conn = sqlite3.connect(str(db_path)) - conn.executescript(_SCHEMA_SQL) - conn.commit() - return conn - - -def parquet_to_sqlite(parquet_dir: Path, db_path: Path) -> int: - """Read the three parquet files and write to SQLite. Returns feature count.""" - if not _HAS_DUCKDB: - logger.error("[!] duckdb not installed: pip install duckdb") - return 0 - - atlas_f = parquet_dir / "features_atlas.parquet" - meta_f = parquet_dir / "feature_metadata.parquet" - examples_f = parquet_dir / "feature_examples.parquet" - - if not atlas_f.exists() or not meta_f.exists(): - logger.error("[!] Missing required parquet files in %s", parquet_dir) - return 0 - - duck = _duckdb.connect(":memory:") - - # Log discovered column names — parquet schema may change between releases - for fpath in (atlas_f, meta_f, examples_f): - if fpath.exists(): - cols = duck.execute( - f"DESCRIBE SELECT * FROM read_parquet('{fpath}') LIMIT 0" - ).fetchall() - logger.info("[parquet] %-32s columns: %s", - fpath.name, [c[0] for c in cols]) - - def _rows(fpath: Path): - desc = duck.execute( - f"DESCRIBE SELECT * FROM read_parquet('{fpath}') LIMIT 0" - ).fetchall() - col_names = [c[0].lower() for c in desc] - rows = duck.execute(f"SELECT * FROM read_parquet('{fpath}')").fetchall() - return col_names, rows - - def _get(row, cols, *names): - for n in names: - for i, c in enumerate(cols): - if c == n.lower(): - return row[i] - return None - - def _f(v) -> Optional[float]: - try: - return float(v) if v is not None else None - except (TypeError, ValueError): - return None - - def _i(v) -> Optional[int]: - try: - return int(v) if v is not None else None - except (TypeError, ValueError): - return None - - conn = _open_sqlite(db_path) - cur = conn.cursor() - inserted = 0 - - # ── features_atlas: all 30 columns ─────────────────────────────────────── - atlas_cols, atlas_rows = _rows(atlas_f) - for row in atlas_rows: - g = lambda *ns: _get(row, atlas_cols, *ns) # noqa: E731 - fid = str(g("feature_id") or "") - if not fid: - continue - cur.execute( - "INSERT OR IGNORE INTO features (" - " feature_id, label," - " activation_freq, mean_activation, max_activation, std_activation," - " total_activations, log_frequency, x, y, cluster_id," - " high_score_fraction, clinvar_fraction, mean_phylop," - " mean_variant_delta, mean_site_delta, mean_local_delta," - " high_score_delta, low_score_delta," - " gc_mean, gc_std, trinuc_entropy, trinuc_dominant_frac," - " gene_entropy, gene_n_unique, gene_dominant_frac," - " mean_variant_1bcdwt, mean_variant_5bcdwt, mean_variant_5b," - " llm_confidence" - ") VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?,?)", - ( - fid, - str(g("label") or ""), - _f(g("activation_freq")), - _f(g("mean_activation")), - _f(g("max_activation")), - _f(g("std_activation")), - _i(g("total_activations")), - _f(g("log_frequency")), - _f(g("x")), - _f(g("y")), - _i(g("cluster_id")), - _f(g("high_score_fraction")), - _f(g("clinvar_fraction")), - _f(g("mean_phylop")), - _f(g("mean_variant_delta")), - _f(g("mean_site_delta")), - _f(g("mean_local_delta")), - _f(g("high_score_delta")), - _f(g("low_score_delta")), - _f(g("gc_mean")), - _f(g("gc_std")), - _f(g("trinuc_entropy")), - _f(g("trinuc_dominant_frac")), - _f(g("gene_entropy")), - _i(g("gene_n_unique")), - _f(g("gene_dominant_frac")), - _f(g("mean_variant_1bcdwt")), - _f(g("mean_variant_5bcdwt")), - _f(g("mean_variant_5b")), - _f(g("llm_confidence")), - ), - ) - inserted += 1 - conn.commit() - logger.info("[parquet] %d features from atlas", inserted) - - # ── feature_metadata: merge description into features ──────────────────── - if meta_f.exists(): - meta_cols, meta_rows = _rows(meta_f) - updated = 0 - for row in meta_rows: - g = lambda *ns: _get(row, meta_cols, *ns) # noqa: E731 - fid = str(g("feature_id") or "") - desc = str(g("description") or "") - if fid and desc: - cur.execute( - "UPDATE features SET description = ? WHERE feature_id = ? AND description IS NULL", - (desc, fid), - ) - updated += cur.rowcount - conn.commit() - logger.info("[parquet] %d descriptions merged from metadata", updated) - - # ── feature_examples: proteins dedup + all 14 activation columns ───────── - if examples_f.exists(): - import json as _json - ex_cols, ex_rows = _rows(examples_f) - seen_hashes: set = set() - act_count = 0 - for row in ex_rows: - g = lambda *ns: _get(row, ex_cols, *ns) # noqa: E731 - fid = str(g("feature_id") or "") - seq = str(g("sequence") or "") - if not fid or not seq: - continue - - seq_hash = _sha256(seq) - if seq_hash not in seen_hashes: - cur.execute( - "INSERT OR IGNORE INTO proteins (sequence_hash, sequence, length)" - " VALUES (?, ?, ?)", - (seq_hash, seq, len(seq)), - ) - seen_hashes.add(seq_hash) - - feat_row = cur.execute( - "SELECT id FROM features WHERE feature_id = ?", (fid,) - ).fetchone() - if not feat_row: - continue - - raw_acts = g("activations") - acts_json = ( - _json.dumps(list(raw_acts)) if raw_acts is not None else None - ) - - cur.execute( - "INSERT OR IGNORE INTO activations (" - " feature_id, protein_id, example_rank, alphafold_id," - " max_activation, activations_json, gene, is_pathogenic," - " ref_codon, alt_codon, source, var_pos_offset, variant_delta" - ") VALUES (?,?,?,?,?,?,?,?,?,?,?,?,?)", - ( - feat_row[0], - seq_hash, - _i(g("example_rank")), - str(g("alphafold_id") or "") or None, - _f(g("max_activation")), - acts_json, - str(g("gene") or "") or None, - _i(g("is_pathogenic")), - str(g("ref_codon") or "") or None, - str(g("alt_codon") or "") or None, - str(g("source") or "") or None, - _i(g("var_pos_offset")), - _f(g("variant_delta")), - ), - ) - act_count += 1 - - conn.commit() - logger.info("[parquet] %d activations from examples", act_count) - logger.info("[parquet] %d unique protein sequences", len(seen_hashes)) - - duck.close() - conn.close() - return inserted - - -# ── Main scraper ────────────────────────────────────────────────────────────── - -class NvidiaSaeScraper: - - def __init__(self, config_file: Path): - self.config = self._load_config(config_file) - self.output_dir = Path(self.config["output_dir"]) - self.output_dir.mkdir(parents=True, exist_ok=True) - self.semaphore = asyncio.Semaphore(self.config["concurrency_limit"]) - self.download_count = 0 - self.heartbeat_interval = self.config.get("heartbeat_interval", 100) - self.max_retries = self.config.get("max_retries", 5) - self.base_dashboard_url = self.config.get("base_dashboard_url", "") - self.parquet_base_url = self.config.get( - "parquet_base_url", self.base_dashboard_url - ).rstrip("/") - self.db_path = Path(self.config.get( - "db_path", "tools/sae_extractor/sae_features.db" - )) - self.parquet_dir = self.output_dir / "parquet" - self.parquet_dir.mkdir(parents=True, exist_ok=True) - - # Sniffed CSV endpoint discovered by headless path - self._sniffed_csv_url_template: Optional[str] = None - - def _load_config(self, path: Path) -> Dict[str, Any]: - if not path.exists(): - logger.error("Config not found: %s", path) - sys.exit(1) - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - def _headers(self, extra: Optional[Dict] = None) -> Dict: - h = self.config.get("headers", {}).copy() - if extra: - h.update(extra) - return h - - # ── PATH A: Headless Playwright ─────────────────────────────────────────── - - async def _try_headless(self) -> bool: - """Drive a real headless Chromium, intercept parquet responses in-flight. - - The browser executes the dashboard JS normally — DuckDB-WASM fires its - own fetch() calls for the parquet files. We register a response handler - that captures bytes for any URL ending in .parquet. - - Side-effect: also records any per-feature CSV download URL pattern in - self._sniffed_csv_url_template for Path C fallback. - """ - if not _HAS_PLAYWRIGHT: - logger.info( - "[headless] playwright not installed — skipping. " - "Run: pip install playwright && playwright install chromium" - ) - return False - - dashboard_url = self.base_dashboard_url - if not dashboard_url: - logger.info("[headless] base_dashboard_url not configured — skipping") - return False - - captured: Dict[str, bytes] = {} - # Events signalled when each file arrives - events: Dict[str, asyncio.Event] = {f: asyncio.Event() for f in _PARQUET_FILES} - - async def _on_response(resp: PwResponse) -> None: - url = resp.url - fname = url.split("?")[0].split("/")[-1] # strip query params - - # Capture parquet files - if fname in events and not events[fname].is_set(): - try: - body = await resp.body() - if body[:4] == b"PAR1": - captured[fname] = body - logger.info("[headless] captured %s (%d bytes)", fname, len(body)) - events[fname].set() - else: - logger.warning( - "[headless] %s response not a parquet file (first bytes: %s)", - fname, body[:32], - ) - except Exception as e: - logger.warning("[headless] failed to read body of %s: %s", fname, e) - - # Sniff CSV download endpoint pattern - if ( - "/csv" in url.lower() or url.endswith(".csv") - ) and self._sniffed_csv_url_template is None: - # Heuristic: replace any numeric feature-ID segment with {feature_id} - import re - template = re.sub(r"/(\d{1,6})(/|$)", r"/{feature_id}\2", url) - if "{feature_id}" in template: - logger.info("[headless] sniffed CSV endpoint: %s", template) - self._sniffed_csv_url_template = template - - logger.info("[headless] launching Chromium → %s", dashboard_url) - - async with async_playwright() as pw: - browser = await pw.chromium.launch( - headless=True, - args=[ - "--disable-blink-features=AutomationControlled", - "--no-sandbox", - ], - ) - ctx = await browser.new_context( - user_agent=self.config.get("headers", {}).get( - "User-Agent", - "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36" - " (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36", - ), - # Inject stored cookies if provided in config - storage_state=self.config.get("playwright_storage_state") or None, - ) - page = await ctx.new_page() - page.on("response", _on_response) - - try: - await page.goto(dashboard_url, wait_until="domcontentloaded", timeout=30_000) - logger.info("[headless] DOM loaded, waiting for parquet fetches …") - - # Wait up to 90s for all three parquet files, 45s for at least two - wait_tasks = [asyncio.create_task(e.wait()) for e in events.values()] - done, pending = await asyncio.wait( - wait_tasks, timeout=90.0, return_when=asyncio.ALL_COMPLETED - ) - - for t in pending: - t.cancel() - - except Exception as e: - logger.warning("[headless] page navigation error: %s", e) - finally: - await browser.close() - - if len(captured) < 2: - logger.warning( - "[headless] only captured %d/%d parquet files — insufficient", - len(captured), len(_PARQUET_FILES), - ) - return False - - # Persist captured bytes - for fname, data in captured.items(): - dest = self.parquet_dir / fname - dest.write_bytes(data) - logger.info("[headless] saved %s → %s", fname, dest) - - logger.info("[headless] inverting to SQLite …") - n = parquet_to_sqlite(self.parquet_dir, self.db_path) - if n > 0: - logger.info("[headless] ✓ %d features written to %s", n, self.db_path) - return True - - logger.warning("[headless] inversion returned 0 features") - return False - - # ── PATH B: Direct HTTP parquet ─────────────────────────────────────────── - - async def _try_parquet_http(self, client: httpx.AsyncClient) -> bool: - if not self.parquet_base_url: - return False - - logger.info("[parquet-http] trying %s", self.parquet_base_url) - fetched: List[Path] = [] - - for fname in _PARQUET_FILES: - dest = self.parquet_dir / fname - if dest.exists() and dest.stat().st_size > 1024: - logger.info("[parquet-http] cached: %s", fname) - fetched.append(dest) - continue - - url = f"{self.parquet_base_url}/{fname}" - try: - resp = await client.get( - url, - headers=self._headers({"Accept": "application/octet-stream"}), - follow_redirects=True, - ) - if resp.status_code == 200: - body = resp.content - if not body[:4] == b"PAR1": - logger.warning("[parquet-http] %s: auth wall or wrong content", fname) - return False - dest.write_bytes(body) - logger.info("[parquet-http] saved %s (%d bytes)", fname, len(body)) - fetched.append(dest) - elif resp.status_code == 404: - logger.info("[parquet-http] %s → 404", fname) - return False - else: - logger.warning("[parquet-http] %s HTTP %d", fname, resp.status_code) - return False - except Exception as e: - logger.warning("[parquet-http] %s fetch failed: %s", fname, e) - return False - - if len(fetched) < 2: - return False - - n = parquet_to_sqlite(self.parquet_dir, self.db_path) - if n > 0: - logger.info("[parquet-http] ✓ %d features → %s", n, self.db_path) - return True - return False - - # ── PATH C: Per-feature CSV sweep ───────────────────────────────────────── - - async def _human_like_delay(self): - base = self.config["delay_seconds"] - r = random.random() - if r < 0.80: - t = base * random.uniform(0.7, 1.3) - elif r < 0.95: - t = base * random.uniform(2.0, 4.0) - logger.info("[delay] reading pause (%.1fs)", t) - else: - t = base * random.uniform(10.0, 20.0) - logger.info("[delay] long break (%.1fs)", t) - await asyncio.sleep(t) - - async def _heartbeat(self, client: httpx.AsyncClient): - logger.info("[heartbeat] visiting dashboard root …") - try: - h = self._headers() - h.pop("Referer", None) - await client.get(self.base_dashboard_url, headers=h, follow_redirects=True) - await asyncio.sleep(random.uniform(2, 5)) - except Exception as e: - logger.warning("[heartbeat] failed: %s", e) - - async def _download_csv(self, client: httpx.AsyncClient, feature_id: int) -> bool: - target = self.output_dir / f"feature_{feature_id:05d}.csv" - if target.exists() and target.stat().st_size > 100: - return True - - # Prefer headless-sniffed URL, then config template - url_tpl = ( - self._sniffed_csv_url_template - or self.config.get("base_url_template", "") - ) - if not url_tpl or "REPLACE_WITH" in url_tpl: - return False - - url = url_tpl.format(feature_id=feature_id) - - async with self.semaphore: - self.download_count += 1 - if self.download_count % self.heartbeat_interval == 0: - await self._heartbeat(client) - - for attempt in range(self.max_retries): - await self._human_like_delay() - try: - resp = await client.get( - url, - headers=self._headers({"Referer": self.base_dashboard_url}), - follow_redirects=True, - ) - if resp.status_code == 200: - body = resp.text - if " str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def clamp01(x: float) -> float: - return max(0.0, min(1.0, float(x))) - - -def score_anti_famm(test: dict[str, Any]) -> dict[str, Any]: - invisible = clamp01(test.get("projection_invisibility", 0.0)) - target_change = clamp01(test.get("target_behavior_change", 0.0)) - invariant_fail = clamp01(test.get("invariant_failure", 0.0)) - false_scar = clamp01(test.get("false_scar_evidence", 0.0)) - residual = clamp01(test.get("hidden_residual", 0.0)) - risk = clamp01(0.30*invisible + 0.30*target_change + 0.20*invariant_fail + 0.10*false_scar + 0.10*residual) - return { - "test_id": test.get("id", sha256_json(test)[:12]), - "type": "anti_famm", - "risk": risk, - "decision": "WARDEN_BLOCK_OR_REOPEN" if risk >= 0.5 else "PASS_ADVERSARY_CHECK", - "test_hash": sha256_json(test), - "summary": { - "projection_invisibility": invisible, - "target_behavior_change": target_change, - "invariant_failure": invariant_fail, - "false_scar_evidence": false_scar, - "hidden_residual": residual - } - } - - -def score_anti_braid(test: dict[str, Any]) -> dict[str, Any]: - order = clamp01(test.get("braid_order_residual", 0.0)) - alias = clamp01(test.get("receipt_alias_risk", 0.0)) - fake = clamp01(test.get("fake_receipt_risk", 0.0)) - toxic = clamp01(test.get("toxic_recombination_risk", 0.0)) - scar_mask = clamp01(test.get("scar_masking_risk", 0.0)) - local_global = clamp01(test.get("local_pass_global_fail", 0.0)) - risk = clamp01(0.20*order + 0.20*alias + 0.15*fake + 0.15*toxic + 0.15*scar_mask + 0.15*local_global) - return { - "test_id": test.get("id", sha256_json(test)[:12]), - "type": "anti_braidstorm", - "risk": risk, - "decision": "WARDEN_BLOCK_OR_REOPEN" if risk >= 0.5 else "PASS_ADVERSARY_CHECK", - "test_hash": sha256_json(test), - "summary": { - "braid_order_residual": order, - "receipt_alias_risk": alias, - "fake_receipt_risk": fake, - "toxic_recombination_risk": toxic, - "scar_masking_risk": scar_mask, - "local_pass_global_fail": local_global - } - } - - -def run(config: dict[str, Any]) -> dict[str, Any]: - anti_famm = [score_anti_famm(t) for t in config.get("anti_famm_tests", [])] - anti_braid = [score_anti_braid(t) for t in config.get("anti_braidstorm_tests", [])] - all_tests = anti_famm + anti_braid - blocked = [t for t in all_tests if t["decision"] == "WARDEN_BLOCK_OR_REOPEN"] - - receipt = { - "receipt_type": "famm_adversarial_duals_receipt", - "schema_version": "0.1.0", - "target": config.get("target", {}), - "anti_famm_results": anti_famm, - "anti_braidstorm_results": anti_braid, - "summary": { - "test_count": len(all_tests), - "blocked_or_reopened_count": len(blocked), - "max_risk": max([t["risk"] for t in all_tests], default=0.0) - }, - "warden_decision": "BLOCK_OR_REOPEN_ROUTE" if blocked else "ALLOW_PROMOTION_CANDIDATE", - "nuvmap": { - "target_hash": sha256_json(config.get("target", {})), - "test_hashes": [t["test_hash"] for t in all_tests], - "adversarial_dual_hash": sha256_json([t["test_hash"] for t in all_tests]) - }, - "no_drift_boundary": "Anti-FAMM and Anti-BraidStorm expose adversarial failure modes. Passing these checks is not a global proof by itself." - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - out = Path(args.out) - out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out}") - print(f"Tests: {receipt['summary']['test_count']}") - print(f"Blocked/reopened: {receipt['summary']['blocked_or_reopened_count']}") - print(f"Warden decision: {receipt['warden_decision']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/autonomous_speedrun_harness_gate.py b/5-Applications/tools-scripts/famm/autonomous_speedrun_harness_gate.py deleted file mode 100644 index 1cdf3b03..00000000 --- a/5-Applications/tools-scripts/famm/autonomous_speedrun_harness_gate.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 -"""Autonomous Speedrun Harness Gate. - -Scores autonomous experiment records for Builder/Judge/Warden viability and emits -a FAMM-compatible receipt. This is benchmark-agnostic: nanoGPT speedrun is the -reference pattern, but the schema applies to any autonomous research loop. -""" -from __future__ import annotations -import argparse, hashlib, json, math -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload=json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def clamp01(x: float) -> float: - return max(0.0, min(1.0, float(x))) - - -def score_experiment(exp: dict[str, Any], cfg: dict[str, Any]) -> dict[str, Any]: - metric = exp.get("metric", {}) - judge = exp.get("judge", {}) - warden = exp.get("warden", {}) - compute = exp.get("compute", {}) - - target = float(cfg.get("target_metric", metric.get("target", 0.0))) - value = float(metric.get("value", target)) - mode = cfg.get("metric_mode", "lower_is_better") - margin = (target - value) if mode == "lower_is_better" else (value - target) - - noise_floor = float(judge.get("noise_floor", cfg.get("noise_floor", 0.0))) - seeds = int(judge.get("seeds", 1)) - rule_pass = bool(judge.get("rule_pass", False)) - reproducible = bool(judge.get("reproducible", False)) - statistical_pass = rule_pass and reproducible and margin >= noise_floor and seeds >= int(cfg.get("min_seeds", 1)) - - stale_hours = float(warden.get("source_stale_hours", 0.0)) - stale_limit = float(cfg.get("source_refresh_hours", 12.0)) - local_loop = float(warden.get("local_loop_score", 0.0)) - overfit = float(warden.get("overfit_risk", 0.0)) - invalid = 1.0 if not rule_pass else 0.0 - warden_penalty = clamp01(0.35*(stale_hours/stale_limit) + 0.30*local_loop + 0.25*overfit + 0.10*invalid) - - cost_hours = float(compute.get("gpu_hours", 0.0)) - token_cost = float(compute.get("tokens", 0.0)) / 1e9 - cost_pressure = math.log1p(max(0.0, cost_hours) + token_cost) / 10.0 - - promise = clamp01(0.5 + margin / max(1.0, abs(target))) - receipt_strength = clamp01(0.25*float(rule_pass) + 0.25*float(reproducible) + 0.25*min(1.0, seeds/max(1,int(cfg.get("min_seeds",1)))) + 0.25*(1.0-warden_penalty)) - - decision = "PROMOTE" if statistical_pass and warden_penalty < float(cfg.get("warden_threshold", 0.45)) else "SCAR_OR_COARSEN" - - result = { - "experiment_id": exp.get("id", sha256_json(exp)[:12]), - "idea_hash": sha256_json(exp.get("idea", {})), - "patch_hash": sha256_json(exp.get("patch", {})), - "config_hash": sha256_json(exp.get("config", {})), - "run_hash": sha256_json(exp.get("run", {})), - "margin": margin, - "statistical_pass": statistical_pass, - "warden_penalty": warden_penalty, - "cost_pressure": cost_pressure, - "promise": promise, - "receipt_strength": receipt_strength, - "decision": decision, - "coarsening_agent": None if decision == "PROMOTE" else { - "type": "failed_experiment_basin", - "action": "downweight_fine_search_until_source_refresh_or_new_evidence", - "reasons": [k for k,v in { - "below_noise_floor": margin < noise_floor, - "insufficient_seeds": seeds < int(cfg.get("min_seeds",1)), - "rule_or_repro_fail": not (rule_pass and reproducible), - "source_stale": stale_hours > stale_limit, - "local_loop": local_loop > 0.5, - "overfit_risk": overfit > 0.5, - }.items() if v] - } - } - result["delta_edge_hash"] = sha256_json(result) - return result - - -def run(config: dict[str, Any]) -> dict[str, Any]: - experiments = config.get("experiments", []) - scored = [score_experiment(e, config) for e in experiments] - promoted = [s for s in scored if s["decision"] == "PROMOTE"] - scarred = [s for s in scored if s["decision"] != "PROMOTE"] - - harness = config.get("harness", {}) - nuvmap = { - "goal_hash": sha256_json(harness.get("goal", {})), - "agents_hash": sha256_json(harness.get("agents", {})), - "plan_hash": sha256_json(harness.get("plan", {})), - "thread_hash": sha256_json(harness.get("thread", {})), - "source_refresh_hash": sha256_json(harness.get("source_refresh", {})), - "delta_edge_hashes": [s["delta_edge_hash"] for s in scored], - } - nuvmap["frontier_hash"] = sha256_json(nuvmap) - - receipt = { - "receipt_type": "famm_autonomous_speedrun_harness_receipt", - "schema_version": "0.1.0", - "source_pattern": "Prime Intellect auto-nanoGPT autonomous speedrun harness", - "harness": harness, - "config": {k:v for k,v in config.items() if k not in {"experiments", "harness"}}, - "scored_experiments": scored, - "summary": { - "experiment_count": len(scored), - "promoted_count": len(promoted), - "scarred_or_coarsened_count": len(scarred), - }, - "nuvmap": nuvmap, - "no_drift_boundary": "This is a harness/receipt layer for autonomous research loops, not a claim that agents discover truth without external verification.", - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser=argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args=parser.parse_args() - config=json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt=run(config) - out=Path(args.out); out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out}") - print(f"Experiments: {receipt['summary']['experiment_count']}") - print(f"Promoted: {receipt['summary']['promoted_count']}") - print(f"Scarred/coarsened: {receipt['summary']['scarred_or_coarsened_count']}") - print(f"Frontier hash: {receipt['nuvmap']['frontier_hash']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/bjw_geodesic_cleanup_filter.py b/5-Applications/tools-scripts/famm/bjw_geodesic_cleanup_filter.py deleted file mode 100644 index 78677296..00000000 --- a/5-Applications/tools-scripts/famm/bjw_geodesic_cleanup_filter.py +++ /dev/null @@ -1,136 +0,0 @@ -#!/usr/bin/env python3 -"""Builder-Judge-Warden Geodesic Cleanup Filter. - -Scores candidate cleanup moves, accepts only Judge-pass / Warden-safe moves, -and emits a cleanup receipt. - -This runner is intentionally generic: it evaluates provided candidate moves -rather than proving math by itself. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def move_score(move: dict[str, Any], weights: dict[str, float]) -> float: - builder = move.get("builder", {}) - judge = move.get("judge", {}) - warden = move.get("warden", {}) - - dc = float(builder.get("delta_complexity", 0.0)) - dr = float(builder.get("delta_residual", 0.0)) - scar = float(warden.get("penalty", 0.0)) - inv = 1.0 if judge.get("invariant_preserved", False) else 0.0 - proof = 1.0 if judge.get("receipt_pass", False) else 0.0 - - return ( - float(weights.get("complexity", 1.0)) * dc - + float(weights.get("residual", 1.0)) * dr - + float(weights.get("scar", 1.0)) * scar - - float(weights.get("invariant", 1.0)) * inv - - float(weights.get("proof_burden", 0.5)) * proof - ) - - -def run(config: dict[str, Any]) -> dict[str, Any]: - weights = config.get("weights", {}) - threshold = float(config.get("warden_threshold", 0.25)) - - source_hash = sha256_json(config.get("source_object", config.get("source_description", ""))) - - accepted = [] - rejected = [] - - for move in sorted(config.get("candidate_moves", []), key=lambda m: move_score(m, weights)): - judge = move.get("judge", {}) - warden = move.get("warden", {}) - score = move_score(move, weights) - penalty = float(warden.get("penalty", 0.0)) - - decision = ( - "ACCEPT" - if judge.get("invariant_preserved", False) - and judge.get("receipt_pass", False) - and penalty <= threshold - else "REJECT" - ) - - enriched = { - **move, - "cleanup_score": score, - "decision": decision, - "move_hash": sha256_json(move), - } - - if decision == "ACCEPT": - accepted.append(enriched) - else: - rejected.append(enriched) - - final_object = { - "source_hash": source_hash, - "accepted_move_hashes": [m["move_hash"] for m in accepted], - "final_claim": config.get("final_claim", "cleaned object requires downstream exact verifier"), - } - final_hash = sha256_json(final_object) - - receipt = { - "receipt_type": "famm_bjw_geodesic_cleanup_receipt", - "schema_version": "0.1.0", - "source_object_hash": source_hash, - "source_description": config.get("source_description", ""), - "accepted_geodesic": accepted, - "rejected_moves": rejected, - "final_object_hash": final_hash, - "exact_receipt": { - "judge_passed_all_accepted_moves": all(m["judge"].get("receipt_pass", False) for m in accepted), - "warden_blocked_rejected_moves": len(rejected), - "requires_downstream_exact_verifier": True, - }, - "cleanup_metrics": { - "candidate_count": len(config.get("candidate_moves", [])), - "accepted_count": len(accepted), - "rejected_count": len(rejected), - "total_cleanup_score": sum(m["cleanup_score"] for m in accepted), - }, - "no_drift_boundary": ( - "This filter ranks and accepts cleanup moves. It is not a proof generator. " - "Downstream exact verification remains mandatory." - ), - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Accepted: {receipt['cleanup_metrics']['accepted_count']}") - print(f"Rejected: {receipt['cleanup_metrics']['rejected_count']}") - print(f"Final object hash: {receipt['final_object_hash']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/braidstorm_sidon_crossing_antialias.py b/5-Applications/tools-scripts/famm/braidstorm_sidon_crossing_antialias.py deleted file mode 100644 index 56a62a43..00000000 --- a/5-Applications/tools-scripts/famm/braidstorm_sidon_crossing_antialias.py +++ /dev/null @@ -1,170 +0,0 @@ -#!/usr/bin/env python3 -"""BraidStorm Sidon Crossing Anti-Alias Gate. - -Assigns/validates Sidon labels for BraidStorm strand crossings. -Supports integer and modular address modes. Emits a FAMM receipt. -""" -from __future__ import annotations -import argparse, hashlib, json, math, random -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def pair_iter(n: int, allow_self: bool): - for i in range(n): - start = i if allow_self else i + 1 - for j in range(start, n): - yield i, j - - -def address(a: int, b: int, mode: str, modulus: int | None) -> int: - s = a + b - if mode == "modular": - if modulus is None: - raise ValueError("modular mode requires modulus/address_budget") - return s % modulus - return s - - -def validate_labels(labels: list[int], mode: str = "integer", modulus: int | None = None, allow_self: bool = True) -> dict[str, Any]: - seen: dict[int, tuple[int, int]] = {} - collisions = [] - pairs = [] - for i, j in pair_iter(len(labels), allow_self): - addr = address(labels[i], labels[j], mode, modulus) - pair = {"i": i, "j": j, "a_i": labels[i], "a_j": labels[j], "address": addr} - pairs.append(pair) - if addr in seen: - u, v = seen[addr] - if {u, v} != {i, j}: - collisions.append({ - "address": addr, - "pair_a": {"i": u, "j": v, "labels": [labels[u], labels[v]]}, - "pair_b": {"i": i, "j": j, "labels": [labels[i], labels[j]]}, - "scar": "braidstorm_pair_address_alias", - }) - else: - seen[addr] = (i, j) - return {"pairs": pairs, "collisions": collisions, "is_sidon_crossing_code": len(collisions) == 0} - - -def greedy_construct(m: int, budget: int, mode: str, allow_self: bool, seed: int = 0, max_restarts: int = 256) -> list[int]: - """Heuristic constructor. Not a maximality proof.""" - rng = random.Random(seed) - best: list[int] = [] - values = list(range(1, budget + 1)) if mode == "integer" else list(range(budget)) - modulus = budget if mode == "modular" else None - - for _ in range(max_restarts): - rng.shuffle(values) - labels: list[int] = [] - used_addrs: dict[int, tuple[int, int]] = {} - for x in values: - ok = True - trial_addrs = [] - for idx, y in enumerate(labels): - addr = address(x, y, mode, modulus) - if addr in used_addrs or addr in trial_addrs: - ok = False - break - trial_addrs.append(addr) - if ok and allow_self: - addr = address(x, x, mode, modulus) - if addr in used_addrs or addr in trial_addrs: - ok = False - else: - trial_addrs.append(addr) - if ok: - new_index = len(labels) - for idx, y in enumerate(labels): - used_addrs[address(x, y, mode, modulus)] = (idx, new_index) - if allow_self: - used_addrs[address(x, x, mode, modulus)] = (new_index, new_index) - labels.append(x) - if len(labels) >= m: - return sorted(labels) - if len(labels) > len(best): - best = sorted(labels) - return best - - -def run(config: dict[str, Any]) -> dict[str, Any]: - m = int(config.get("strand_count", len(config.get("labels", [])))) - budget = int(config.get("address_budget", max(1, m*m))) - mode = config.get("address_mode", "integer") - allow_self = bool(config.get("allow_self_crossings", True)) - seed = int(config.get("seed", 0)) - max_restarts = int(config.get("max_restarts", 256)) - - labels = config.get("labels") - constructed = False - if labels is None: - labels = greedy_construct(m, budget, mode, allow_self, seed=seed, max_restarts=max_restarts) - constructed = True - labels = [int(x) for x in labels] - - validation = validate_labels(labels, mode=mode, modulus=budget if mode == "modular" else None, allow_self=allow_self) - collision_count = len(validation["collisions"]) - capacity_prior = math.sqrt(budget) - slack = capacity_prior - len(labels) - - receipt = { - "receipt_type": "famm_braidstorm_sidon_crossing_receipt", - "schema_version": "0.1.0", - "problem": "BraidStorm pairwise crossing anti-aliasing", - "strand_count_requested": m, - "strand_count_labeled": len(labels), - "address_budget": budget, - "address_mode": mode, - "allow_self_crossings": allow_self, - "constructed_by_runner": constructed, - "labels": labels, - "capacity": { - "theodorus_sqrt_budget": capacity_prior, - "slack_vs_sqrt_budget": slack, - "capacity_prior_only": True, - }, - "validation": validation, - "famm": { - "residual_collision_count": collision_count, - "scar_class": "PASS_ZERO_ALIAS" if collision_count == 0 and len(labels) == m else "ALIAS_OR_PARTIAL_SCAR", - "coarsening_agent": None if collision_count == 0 and len(labels) == m else { - "type": "braidstorm_sidon_address_alias_or_partial_assignment", - "action": "coarsen_or_downweight_this_label_basin", - "reason": "pair-address aliases exist or target strand count was not reached", - } - }, - "nuvmap": { - "label_hash": sha256_json(labels), - "pair_address_hash": sha256_json(validation["pairs"]), - "collision_hash": sha256_json(validation["collisions"]), - }, - "no_drift_boundary": "This receipt verifies pair-crossing address uniqueness for a chosen label set. It does not prove optimality, maximality, or BraidStorm convergence.", - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - out = Path(args.out); out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out}") - print(f"Requested strands: {receipt['strand_count_requested']}") - print(f"Labeled strands: {receipt['strand_count_labeled']}") - print(f"Collision count: {receipt['famm']['residual_collision_count']}") - print(f"Scar class: {receipt['famm']['scar_class']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/chaos_game_16d_field_shrinker.py b/5-Applications/tools-scripts/famm/chaos_game_16d_field_shrinker.py deleted file mode 100644 index 5d74014b..00000000 --- a/5-Applications/tools-scripts/famm/chaos_game_16d_field_shrinker.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python3 -"""16D Chaos Game Field Shrinker. - -A FAMM-weighted lifted chaos game over 16D shortcut anchors. - -This runner does not prove coverage or optimality. It shrinks a route/search -field toward attractor basins and emits a computational receipt for handoff to -exact gates. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - -import numpy as np - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def as_vec16(value: list[float], name: str) -> np.ndarray: - arr = np.array(value, dtype=float) - if arr.shape != (16,): - raise ValueError(f"{name} must be a length-16 vector") - return arr - - -def softmax_sample(logits: np.ndarray, rng: np.random.Generator) -> int: - z = logits - np.max(logits) - probs = np.exp(z) - probs = probs / np.sum(probs) - return int(rng.choice(len(logits), p=probs)) - - -def contraction_matrix(anchor: dict[str, Any]) -> np.ndarray: - c = anchor.get("contraction", 0.5) - if isinstance(c, (int, float)): - return np.eye(16) * float(c) - - arr = np.array(c, dtype=float) - if arr.shape == (16,): - return np.diag(arr) - if arr.shape == (16, 16): - return arr - raise ValueError("contraction must be scalar, length-16 diagonal, or 16x16 matrix") - - -def anchor_logits( - state: np.ndarray, - anchors: list[dict[str, Any]], - weights: dict[str, float], - prev_anchor: str | None, - transition_scars: dict[str, float], -) -> np.ndarray: - out = [] - for a in anchors: - vec = as_vec16(a["vector"], f"anchor {a.get('id', '')}") - dist = float(np.linalg.norm(state - vec)) - scar = float(a.get("scar", 0.0)) - invariant = float(a.get("invariant_overlap", 0.0)) - cost = float(a.get("cost", 0.0)) - mass = float(a.get("semantic_mass", 0.0)) - receipt = float(a.get("receipt_strength", 0.0)) - - transition_penalty = 0.0 - if prev_anchor is not None: - key = f"{prev_anchor}->{a.get('id')}" - transition_penalty = float(transition_scars.get(key, 0.0)) - - logit = ( - -float(weights.get("alpha_distance", 1.0)) * dist - -float(weights.get("beta_scar", 1.0)) * scar - + float(weights.get("gamma_invariant", 1.0)) * invariant - - float(weights.get("eta_cost", 1.0)) * cost - + float(weights.get("lambda_mass", 1.0)) * mass - + float(weights.get("rho_receipt", 1.0)) * receipt - - float(weights.get("tau_transition_scar", 1.0)) * transition_penalty - ) - out.append(logit) - return np.array(out, dtype=float) - - -def run(config: dict[str, Any]) -> dict[str, Any]: - rng = np.random.default_rng(int(config.get("seed", 0))) - state = as_vec16(config["initial_state"], "initial_state") - anchors = config["anchors"] - weights = config.get("weights", {}) - transition_scars = config.get("transition_scars", {}) - steps = int(config.get("steps", 2048)) - burn_in = int(config.get("burn_in", 128)) - noise_scale = float(config.get("noise_scale", 0.0)) - projection_axes = config.get("projection_axes", [0, 1]) - - if len(projection_axes) != 2: - raise ValueError("projection_axes must have length 2") - - orbit_hash_samples = [] - projection_hash_samples = [] - selected_counts: dict[str, int] = {} - transition_counts: dict[str, int] = {} - prev_id: str | None = None - - for t in range(steps): - logits = anchor_logits(state, anchors, weights, prev_id, transition_scars) - idx = softmax_sample(logits, rng) - anchor = anchors[idx] - anchor_id = str(anchor["id"]) - avec = as_vec16(anchor["vector"], f"anchor {anchor_id}") - contraction = contraction_matrix(anchor) - - eps = rng.normal(0.0, noise_scale, size=16) if noise_scale > 0 else np.zeros(16) - state = avec + contraction @ (state - avec) + eps - - selected_counts[anchor_id] = selected_counts.get(anchor_id, 0) + 1 - if prev_id is not None: - key = f"{prev_id}->{anchor_id}" - transition_counts[key] = transition_counts.get(key, 0) + 1 - prev_id = anchor_id - - if t >= burn_in: - orbit_hash_samples.append([round(float(x), 8) for x in state.tolist()]) - projection_hash_samples.append([ - round(float(state[int(projection_axes[0])]), 8), - round(float(state[int(projection_axes[1])]), 8), - ]) - - final_logits = anchor_logits(state, anchors, weights, prev_id, transition_scars) - z = final_logits - np.max(final_logits) - final_probs = np.exp(z) / np.sum(np.exp(z)) - - route_recommendations = [] - for anchor, prob, logit in sorted(zip(anchors, final_probs, final_logits), key=lambda x: float(x[1]), reverse=True): - route_recommendations.append({ - "anchor_id": anchor["id"], - "probability": float(prob), - "logit": float(logit), - "handoff_gate": anchor.get("handoff_gate", "manual_review"), - }) - - receipt = { - "receipt_type": "famm_16d_chaos_game_field_shrinker_receipt", - "schema_version": "0.1.0", - "basis_layer": "16D_CHAOS_GAME_FIELD_SHRINKER", - "seed": int(config.get("seed", 0)), - "steps": steps, - "burn_in": burn_in, - "projection_axes": projection_axes, - "anchor_count": len(anchors), - "selected_counts": selected_counts, - "transition_counts": transition_counts, - "orbit_sha256": sha256_json(orbit_hash_samples), - "projection_sha256": sha256_json(projection_hash_samples), - "final_state": [float(x) for x in state.tolist()], - "final_projection": [ - float(state[int(projection_axes[0])]), - float(state[int(projection_axes[1])]), - ], - "route_recommendations": route_recommendations, - "no_drift_boundary": ( - "This is a computational field-shrinking receipt. It proposes attractor basins " - "and route candidates; it is not proof and must hand off to exact gates." - ), - } - receipt["receipt_sha256"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - cfg = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(cfg) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - top = receipt["route_recommendations"][0] if receipt["route_recommendations"] else None - print(f"Wrote {out_path}") - if top: - print(f"Top anchor: {top['anchor_id']} p={top['probability']:.4f} handoff={top['handoff_gate']}") - print(f"Projection SHA-256: {receipt['projection_sha256']}") - print(f"Receipt SHA-256: {receipt['receipt_sha256']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/golden_braid_centering_gate.py b/5-Applications/tools-scripts/famm/golden_braid_centering_gate.py deleted file mode 100644 index 3b84de69..00000000 --- a/5-Applications/tools-scripts/famm/golden_braid_centering_gate.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 -"""Golden Braid Centering Gate. - -Computes Fibonacci/golden-ratio centering receipts for BraidStorm strand states. -This is a calibration and scar/coarsening detector, not a solver. -""" -from __future__ import annotations -import argparse, hashlib, json, math -from pathlib import Path -from typing import Any - -PHI = (1.0 + math.sqrt(5.0)) / 2.0 -INV_PHI = 1.0 / PHI - - -def sha256_json(value: Any) -> str: - payload=json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def norm(v: list[float]) -> float: - return math.sqrt(sum(float(x)*float(x) for x in v)) - - -def sub(a: list[float], b: list[float]) -> list[float]: - return [float(x)-float(y) for x,y in zip(a,b)] - - -def add(a: list[float], b: list[float]) -> list[float]: - return [float(x)+float(y) for x,y in zip(a,b)] - - -def scale(k: float, v: list[float]) -> list[float]: - return [float(k)*float(x) for x in v] - - -def golden_pull(state: list[float], center: list[float]) -> list[float]: - return add(center, scale(INV_PHI, sub(state, center))) - - -def mean(states: list[list[float]]) -> list[float]: - if not states: - return [] - n=len(states); d=len(states[0]) - return [sum(float(s[i]) for s in states)/n for i in range(d)] - - -def covariance_diag(states: list[list[float]]) -> list[float]: - if not states: - return [] - m=mean(states) - n=len(states); d=len(states[0]) - return [sum((float(s[i])-m[i])**2 for s in states)/n for i in range(d)] - - -def anisotropy(diag: list[float]) -> float: - if not diag: - return 0.0 - avg=sum(diag)/len(diag) - return math.sqrt(sum((x-avg)**2 for x in diag)/len(diag)) - - -def strand_receipt(before: list[float], after: list[float], center: list[float], steps: int = 1) -> dict[str, Any]: - d0=norm(sub(before, center)) - d1=norm(sub(after, center)) - expected=(INV_PHI**steps) - ratio=0.0 if d0 == 0 else d1/d0 - residual=abs(ratio-expected) - return {"distance_before": d0, "distance_after": d1, "expected_ratio": expected, "actual_ratio": ratio, "golden_residual": residual} - - -def run(config: dict[str, Any]) -> dict[str, Any]: - center=[float(x) for x in config.get("center", [0.0]*16)] - strands=config.get("strands", []) - threshold=float(config.get("golden_threshold", 1e-6)) - steps=int(config.get("steps", 1)) - - receipts=[] - after_states=[] - for s in strands: - before=[float(x) for x in s.get("state", [])] - if not before: - before=[0.0]*len(center) - after=[float(x) for x in s.get("after", golden_pull(before, center))] - after_states.append(after) - r=strand_receipt(before, after, center, steps=steps) - r.update({ - "strand_id": s.get("id", sha256_json(before)[:12]), - "scar_class": "PASS_CENTERING" if r["golden_residual"] <= threshold else "CENTERING_SCAR", - "coarsening_agent": None if r["golden_residual"] <= threshold else { - "type": "golden_centering_failure", - "action": "downweight_fine_search_for_this_strand_or_crossing", - "reason": "strand did not contract at expected phi^-1 rate" - } - }) - r["receipt_hash"] = sha256_json(r) - receipts.append(r) - - bary=mean(after_states) - diag=covariance_diag(after_states) - omega_center=sum(r["golden_residual"] for r in receipts) + norm(sub(bary, center)) + anisotropy(diag) - - receipt={ - "receipt_type": "famm_golden_braid_centering_receipt", - "schema_version": "0.1.0", - "phi": PHI, - "inverse_phi": INV_PHI, - "center": center, - "threshold": threshold, - "strand_receipts": receipts, - "multi_strand": { - "barycenter": bary, - "covariance_diag": diag, - "covariance_anisotropy": anisotropy(diag), - "omega_center": omega_center, - "failed_count": sum(1 for r in receipts if r["scar_class"] != "PASS_CENTERING") - }, - "no_drift_boundary": "Golden centering is a calibration/scar detector, not a solver. The endpoint is trivial; the collapse receipt is the useful artifact." - } - receipt["receipt_hash"]=sha256_json(receipt) - return receipt - - -def main() -> None: - parser=argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args=parser.parse_args() - config=json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt=run(config) - out=Path(args.out); out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out}") - print(f"Failed count: {receipt['multi_strand']['failed_count']}") - print(f"Omega center: {receipt['multi_strand']['omega_center']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/hessian_receipt_runner.py b/5-Applications/tools-scripts/famm/hessian_receipt_runner.py deleted file mode 100644 index 436ece52..00000000 --- a/5-Applications/tools-scripts/famm/hessian_receipt_runner.py +++ /dev/null @@ -1,244 +0,0 @@ -#!/usr/bin/env python3 -"""FAMM empirical Hessian receipt runner. - -This is a delivery shim around `hessian-eigenthings`; it does not implement -Lanczos/Hutch++/SLQ itself. It turns a matrix-free curvature operator into a -receipt JSON that FAMM can use for route decisions. - -Supported operator sources: - - kind="diagonal": JSON list of diagonal entries. - - kind="dense_npy": path to a .npy dense symmetric matrix. - - kind="torch_plugin": dotted factory path returning a CurvatureOperator. -""" - -from __future__ import annotations - -import argparse -import hashlib -import importlib -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any - -import torch - -try: - from hessian_eigenthings import LambdaOperator, lanczos, trace, spectral_density -except Exception as exc: # pragma: no cover - raise SystemExit( - "Missing dependency `hessian-eigenthings`. Install with:\n" - " pip install hessian-eigenthings\n" - f"Original import error: {exc}" - ) - - -@dataclass(frozen=True) -class RouteThresholds: - lambda_max: float = 10.0 - negative_eigenvalue_tol: float = -1.0e-6 - flat_abs_tol: float = 1.0e-5 - flat_ratio_min: float = 0.35 - trace_max: float | None = None - - -def _sha256_jsonable(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def _tensor_to_list(x: torch.Tensor) -> list[float]: - return [float(v) for v in x.detach().cpu().reshape(-1).tolist()] - - -def load_operator(config: dict[str, Any]): - op_cfg = config["operator"] - kind = op_cfg["kind"] - dtype = getattr(torch, op_cfg.get("dtype", "float64")) - device = torch.device(op_cfg.get("device", "cpu")) - - if kind == "diagonal": - diag = torch.tensor(op_cfg["diagonal"], dtype=dtype, device=device) - - def matvec(v: torch.Tensor) -> torch.Tensor: - return diag * v - - return LambdaOperator(matvec, size=diag.numel(), device=device, dtype=dtype) - - if kind == "dense_npy": - import numpy as np - - matrix = torch.tensor(np.load(op_cfg["path"]), dtype=dtype, device=device) - if matrix.ndim != 2 or matrix.shape[0] != matrix.shape[1]: - raise ValueError("dense_npy operator must be a square matrix") - - def matvec(v: torch.Tensor) -> torch.Tensor: - return matrix @ v - - return LambdaOperator(matvec, size=matrix.shape[0], device=device, dtype=dtype) - - if kind == "torch_plugin": - dotted = op_cfg["factory"] - mod_name, func_name = dotted.rsplit(".", 1) - factory = getattr(importlib.import_module(mod_name), func_name) - return factory(op_cfg) - - raise ValueError(f"Unknown operator kind: {kind!r}") - - -def decide_route( - eigenvalues: list[float], - trace_estimate: float | None, - thresholds: RouteThresholds, -) -> dict[str, Any]: - if not eigenvalues: - return {"route": "manual_review", "reason": "no eigenvalues returned"} - - lam_max = max(eigenvalues) - lam_abs_max = max(abs(v) for v in eigenvalues) - negative_count = sum(1 for v in eigenvalues if v < thresholds.negative_eigenvalue_tol) - flat_count = sum(1 for v in eigenvalues if abs(v) <= thresholds.flat_abs_tol) - flat_ratio = flat_count / max(1, len(eigenvalues)) - - if negative_count: - route = "probe_saddle_scar" - reason = "negative curvature detected" - elif lam_abs_max >= thresholds.lambda_max: - route = "protect_or_seal_stiff_invariant" - reason = "dominant curvature exceeds lambda_max" - elif flat_ratio >= thresholds.flat_ratio_min: - route = "press_flat_gauge" - reason = "near-zero eigenvalue mass suggests flat/gauge direction" - elif thresholds.trace_max is not None and trace_estimate is not None and trace_estimate >= thresholds.trace_max: - route = "seal_high_total_curvature" - reason = "trace exceeds trace_max" - else: - route = "continue_measured_probe" - reason = "curvature is within configured pressure bounds" - - return { - "route": route, - "reason": reason, - "lambda_max_observed": lam_max, - "lambda_abs_max_observed": lam_abs_max, - "negative_count": negative_count, - "flat_count": flat_count, - "flat_ratio": flat_ratio, - } - - -def run(config: dict[str, Any]) -> dict[str, Any]: - operator = load_operator(config) - seed = int(config.get("seed", 0)) - - lanczos_cfg = config.get("lanczos", {}) - trace_cfg = config.get("trace", {}) - density_cfg = config.get("spectral_density", {}) - - eig = lanczos( - operator, - k=int(lanczos_cfg.get("k", 8)), - max_iter=lanczos_cfg.get("max_iter"), - tol=float(lanczos_cfg.get("tol", 1.0e-4)), - which=lanczos_cfg.get("which", "LM"), - seed=seed, - ) - - tr = None - if trace_cfg.get("enabled", True): - tr = trace( - operator, - num_matvecs=int(trace_cfg.get("num_matvecs", 99)), - method=trace_cfg.get("method", "hutch++"), - seed=seed, - ) - - rho = None - if density_cfg.get("enabled", True): - rho = spectral_density( - operator, - num_runs=int(density_cfg.get("num_runs", 4)), - lanczos_steps=int(density_cfg.get("lanczos_steps", 32)), - num_grid_points=int(density_cfg.get("num_grid_points", 512)), - seed=seed, - ) - - eigenvalues = _tensor_to_list(eig.eigenvalues) - residuals = _tensor_to_list(eig.residuals) - converged = [bool(v) for v in eig.converged.detach().cpu().reshape(-1).tolist()] - - trace_payload = None - if tr is not None: - trace_payload = { - "estimate": float(tr.estimate), - "stderr": None if tr.stderr != tr.stderr else float(tr.stderr), - "samples_sha256": _sha256_jsonable(_tensor_to_list(tr.samples)), - } - - density_payload = None - if rho is not None: - density_payload = { - "sigma": float(rho.sigma), - "grid_sha256": _sha256_jsonable(_tensor_to_list(rho.grid)), - "density_sha256": _sha256_jsonable(_tensor_to_list(rho.density)), - "raw_eigenvalues_sha256": _sha256_jsonable(_tensor_to_list(rho.raw_eigenvalues)), - "raw_weights_sha256": _sha256_jsonable(_tensor_to_list(rho.raw_weights)), - } - - thresholds = RouteThresholds(**config.get("route_thresholds", {})) - decision = decide_route( - eigenvalues=eigenvalues, - trace_estimate=None if trace_payload is None else trace_payload["estimate"], - thresholds=thresholds, - ) - - receipt = { - "receipt_type": "famm_hessian_curvature_receipt", - "schema_version": "0.1.0", - "basis_layer": "HESSIAN_EIGEN", - "seed": seed, - "operator": { - "kind": config["operator"]["kind"], - "size": int(operator.size), - "dtype": str(operator.dtype).replace("torch.", ""), - "device": str(operator.device), - }, - "lanczos": { - "k": int(lanczos_cfg.get("k", 8)), - "iterations": int(eig.iterations), - "eigenvalues": eigenvalues, - "ritz_residuals": residuals, - "converged": converged, - }, - "trace": trace_payload, - "spectral_density": density_payload, - "route_decision": decision, - "no_drift_boundary": ( - "This is a computational curvature witness. It routes proof/compression/scar work; " - "it is not theorem proof." - ), - } - receipt["receipt_sha256"] = _sha256_jsonable(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True, help="Path to FAMM Hessian receipt config JSON.") - parser.add_argument("--out", required=True, help="Output receipt JSON path.") - args = parser.parse_args() - - config_path = Path(args.config) - out_path = Path(args.out) - config = json.loads(config_path.read_text(encoding="utf-8")) - receipt = run(config) - - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out_path}") - print(f"Route: {receipt['route_decision']['route']} — {receipt['route_decision']['reason']}") - print(f"Receipt SHA-256: {receipt['receipt_sha256']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/logogram_chirality_gate.py b/5-Applications/tools-scripts/famm/logogram_chirality_gate.py deleted file mode 100644 index 03b057b3..00000000 --- a/5-Applications/tools-scripts/famm/logogram_chirality_gate.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -"""Logogram Chirality Route Gate. - -Computes a small compatibility receipt for a logogram chirality witness. - -This does not interpret glyphs semantically. It only checks whether direction, -handedness, phase, placement, and mode should become active route coordinates. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def phase_distance(a: float, b: float) -> float: - diff = abs((a - b) % 1.0) - return min(diff, 1.0 - diff) - - -def compat_score(config: dict[str, Any]) -> dict[str, Any]: - chi = config["chirality_witness"] - target = config.get("target_chirality", {}) - - weights = config.get("weights", {}) - w_hand = float(weights.get("handedness", 0.35)) - w_dir = float(weights.get("direction", 0.20)) - w_phase = float(weights.get("phase", 0.20)) - w_place = float(weights.get("placement", 0.10)) - w_mode = float(weights.get("mode", 0.15)) - - handedness_match = chi.get("handedness") == target.get("handedness", chi.get("handedness")) - direction_match = chi.get("direction") == target.get("direction", chi.get("direction")) - placement_match = chi.get("placement") == target.get("placement", chi.get("placement")) - mode_match = chi.get("mode") == target.get("mode", chi.get("mode")) - - phase_target = float(target.get("phase", chi.get("phase", 0.0))) - phase = float(chi.get("phase", 0.0)) - phase_score = max(0.0, 1.0 - 2.0 * phase_distance(phase, phase_target)) - - score = ( - w_hand * float(handedness_match) - + w_dir * float(direction_match) - + w_phase * phase_score - + w_place * float(placement_match) - + w_mode * float(mode_match) - ) - - threshold = float(config.get("active_route_threshold", 0.75)) - return { - "score": score, - "threshold": threshold, - "decision": "route_active" if score >= threshold else "metadata_only", - "matches": { - "handedness": handedness_match, - "direction": direction_match, - "phase_score": phase_score, - "placement": placement_match, - "mode": mode_match, - }, - } - - -def run(config: dict[str, Any]) -> dict[str, Any]: - compatibility = compat_score(config) - - benefit = config.get("benefit_gate", {}) - beneficial = any(bool(benefit.get(k, False)) for k in [ - "disambiguates_same_payload", - "prevents_wrong_handed_pist_transition", - "reduces_nuvmap_false_merge", - "improves_replay_determinism", - "measured_route_gain_observed", - ]) - - receipt = { - "receipt_type": "famm_logogram_chirality_witness", - "schema_version": "0.1.0", - "symbol_id": config["symbol_id"], - "payload_hash": config["payload_hash"], - "chirality_witness": config["chirality_witness"], - "compatibility": compatibility, - "residual_policy": config.get("residual_policy", "carry_orientation_delta_and_verify_on_replay"), - "benefit_gate": { - **benefit, - "beneficial": beneficial, - "promotion": "active_route_coordinate" if beneficial and compatibility["decision"] == "route_active" else "receipt_metadata_only", - }, - "no_drift_boundary": ( - "Glyph/logogram orientation is not payload authority. It becomes active only " - "when it changes admissibility, replay, NUVMAP merge safety, or measured route gain." - ), - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Decision: {receipt['compatibility']['decision']}") - print(f"Promotion: {receipt['benefit_gate']['promotion']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/markovjunior_16d_shim.py b/5-Applications/tools-scripts/famm/markovjunior_16d_shim.py deleted file mode 100644 index 75cc0b0c..00000000 --- a/5-Applications/tools-scripts/famm/markovjunior_16d_shim.py +++ /dev/null @@ -1,183 +0,0 @@ -#!/usr/bin/env python3 -"""MarkovJunior 16D PIST Rewrite Shim. - -Projects MarkovJunior-style rewrite rules into 16D FAMM/PIST/NUVMAP anchors. - -This runner does not execute the full MarkovJunior XML language. It is a shim: -rules and optional constraints are mapped into 16D route objects, hashes, and -receipts so later tools can use them as PIST/Delta-DAG inputs. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def norm_hash(value: Any, scale: float = 1.0) -> float: - h = hashlib.sha256(json.dumps(value, sort_keys=True).encode("utf-8")).digest() - n = int.from_bytes(h[:8], "big") - return (n / float((1 << 64) - 1)) * scale - - -def pattern_mass(pattern: str) -> float: - if not pattern: - return 0.0 - non_wild = sum(1 for ch in pattern if ch != "*") - return non_wild / max(1, len(pattern)) - - -def delta_mass(left: str, right: str) -> float: - n = max(len(left), len(right), 1) - changed = 0 - for i in range(n): - l = left[i] if i < len(left) else "" - r = right[i] if i < len(right) else "" - if r != "*" and l != r: - changed += 1 - return changed / n - - -def chirality_score(value: str) -> float: - mapping = { - "left": 0.25, - "right": 0.75, - "neutral": 0.5, - "clockwise": 0.8, - "counterclockwise": 0.2, - } - return float(mapping.get(str(value).lower(), 0.5)) - - -def rule_anchor(rule: dict[str, Any], constraints: dict[str, Any], index: int, total: int) -> dict[str, Any]: - left = str(rule.get("left", "")) - right = str(rule.get("right", "")) - weight = float(rule.get("weight", 1.0)) - obs = constraints.get("observations", []) - - pm = pattern_mass(left) - dm = delta_mass(left, right) - wc = left.count("*") / max(1, len(left)) - obs_pressure = min(1.0, sum(float(o.get("weight", 1.0)) for o in obs) / max(1, len(obs) or 1)) - rule_order_pressure = 1.0 - (index / max(1, total - 1)) if total > 1 else 1.0 - route_cost = min(1.0, (len(left) + len(right)) / 64.0) - receipt_strength = 1.0 if left and right else 0.25 - scar = 0.0 if left and right and len(left) == len(right) else 0.35 - residual = 0.0 if len(left) == len(right) else abs(len(left) - len(right)) / max(len(left), len(right), 1) - invariant_overlap = 1.0 - min(1.0, dm * 0.5 + scar * 0.5) - - vector = [ - norm_hash({"left": left, "right": right, "id": rule.get("id")}), - min(1.0, float(rule.get("dimensions", 2)) / 16.0), - pm, - dm, - rule_order_pressure, - obs_pressure, - chirality_score(rule.get("chirality", "neutral")), - min(1.0, pm + dm), - norm_hash({"recurrence": left + "->" + right}), - dm, - scar, - residual, - invariant_overlap, - route_cost, - receipt_strength, - wc, - ] - - payload = { - "id": rule.get("id", f"rule_{index}"), - "left": left, - "right": right, - "node": rule.get("node", "exists"), - "weight": weight, - "orientation": rule.get("orientation", "axis_aligned"), - "chirality": rule.get("chirality", "neutral"), - "metrics": { - "pattern_mass": pm, - "delta_mass": dm, - "wildcard_fraction": wc, - "constraint_pressure": obs_pressure, - "scar": scar, - "residual": residual, - "invariant_overlap": invariant_overlap, - "route_cost": route_cost, - "receipt_strength": receipt_strength, - }, - "vector16": vector, - "rule_hash": sha256_json(rule), - } - payload["anchor_hash"] = sha256_json(payload) - return payload - - -def run(config: dict[str, Any]) -> dict[str, Any]: - rules = config.get("rules", []) - constraints = config.get("constraints", {}) - anchors = [rule_anchor(rule, constraints, i, len(rules)) for i, rule in enumerate(rules)] - - source = config.get("source", {}) - grid = config.get("grid", {}) - projection = config.get("projection", {}) - - nuvmap = { - "projection": projection.get("nuvmap_projection", "frontier"), - "grid_hash": sha256_json(grid), - "constraints_hash": sha256_json(constraints), - "rule_anchor_hashes": [a["anchor_hash"] for a in anchors], - "dag_node_seed": sha256_json({ - "grid": grid, - "constraints": constraints, - "anchors": [a["anchor_hash"] for a in anchors], - }), - } - - receipt = { - "receipt_type": "famm_markovjunior_16d_shim_receipt", - "schema_version": "0.1.0", - "source": source, - "grid": grid, - "constraints": constraints, - "projection": projection, - "rules": rules, - "anchors": anchors, - "nuvmap": nuvmap, - "pist_transition_form": "X_{t+1}=Pi_adm[PIST_r(X_t)+Delta_MJ(r_t,m_t)]", - "no_drift_boundary": ( - "This shim projects MarkovJunior-style rewrite rules into 16D FAMM/PIST anchors. " - "It does not execute full MarkovJunior XML semantics or prove generated artifacts correct." - ), - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Rules projected: {len(receipt['anchors'])}") - print(f"NUVMAP DAG seed: {receipt['nuvmap']['dag_node_seed']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/nuvmap_delta_dag_compressor.py b/5-Applications/tools-scripts/famm/nuvmap_delta_dag_compressor.py deleted file mode 100644 index fa0f3e85..00000000 --- a/5-Applications/tools-scripts/famm/nuvmap_delta_dag_compressor.py +++ /dev/null @@ -1,320 +0,0 @@ -#!/usr/bin/env python3 -"""NUVMAP Delta-DAG Search Compressor. - -Prototype for graph k-coloring. - -It combines: - - FAMM/DSATUR route pressure, - - delta-edge trace compression, - - NUVMAP projected state addresses, - - DAG node merging, - - scar/nogood cache, - - exact zero-conflict receipt. - -This is not a P-vs-NP claim. It is a route/topology/receipt compressor. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from dataclasses import dataclass -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -@dataclass -class Metrics: - attempts: int = 0 - backtracks: int = 0 - dag_nodes: int = 0 - dag_edges: int = 0 - cache_hits: int = 0 - nogoods: int = 0 - max_depth: int = 0 - - -def build_adj(n: int, edges: list[list[int]]) -> list[set[int]]: - adj = [set() for _ in range(n)] - for u, v in edges: - adj[u].add(v) - adj[v].add(u) - return adj - - -def domains(colors: list[int], adj: list[set[int]], k: int) -> list[int]: - masks = [] - full = (1 << k) - 1 - for v, c in enumerate(colors): - if c >= 0: - masks.append(1 << c) - continue - used = 0 - for nb in adj[v]: - if colors[nb] >= 0: - used |= 1 << colors[nb] - masks.append(full & ~used) - return masks - - -def popcount(x: int) -> int: - return int(x).bit_count() - - -def canonical_color_relabel(colors: list[int]) -> list[int]: - mapping = {} - nxt = 0 - out = [] - for c in colors: - if c < 0: - out.append(-1) - elif c in mapping: - out.append(mapping[c]) - else: - mapping[c] = nxt - out.append(nxt) - nxt += 1 - return out - - -def nuvmap_key( - colors: list[int], - adj: list[set[int]], - k: int, - projection_level: str, -) -> str: - dm = domains(colors, adj, k) - uncolored = [v for v, c in enumerate(colors) if c < 0] - residual_conflicts = sum(1 for u in range(len(adj)) for v in adj[u] if u < v and colors[u] >= 0 and colors[u] == colors[v]) - - if projection_level == "exact": - payload = { - "level": projection_level, - "colors": colors, - "domains": dm, - "residual_conflicts": residual_conflicts, - } - elif projection_level == "symmetry": - payload = { - "level": projection_level, - "colors": canonical_color_relabel(colors), - "domains": dm, - "frontier": [ - [v, popcount(dm[v]), len([nb for nb in adj[v] if colors[nb] < 0])] - for v in uncolored - ], - "residual_conflicts": residual_conflicts, - } - elif projection_level == "semantic": - payload = { - "level": projection_level, - "uncolored_count": len(uncolored), - "domain_hist": sorted([popcount(dm[v]) for v in uncolored]), - "saturation_hist": sorted([k - popcount(dm[v]) for v in uncolored]), - "residual_conflicts": residual_conflicts, - } - else: - payload = { - "level": "frontier", - "uncolored": uncolored, - "domain_masks": [dm[v] for v in uncolored], - "frontier_degree": [len([nb for nb in adj[v] if colors[nb] < 0]) for v in uncolored], - "residual_conflicts": residual_conflicts, - } - - return sha256_json(payload) - - -def choose_vertex(colors: list[int], adj: list[set[int]], k: int) -> int | None: - dm = domains(colors, adj, k) - best = None - best_key = None - for v, c in enumerate(colors): - if c >= 0: - continue - dmask = dm[v] - dsize = popcount(dmask) - saturation = k - dsize - degree = len(adj[v]) - key = (saturation, -dsize, degree, -v) - if best_key is None or key > best_key: - best_key = key - best = v - return best - - -def color_order(v: int, colors: list[int], adj: list[set[int]], k: int) -> list[int]: - dm = domains(colors, adj, k)[v] - return [c for c in range(k) if dm & (1 << c)] - - -def zero_conflicts(colors: list[int], edges: list[list[int]]) -> bool: - return all(colors[u] >= 0 and colors[v] >= 0 and colors[u] != colors[v] for u, v in edges) - - -def solve_graph_coloring(config: dict[str, Any]) -> dict[str, Any]: - n = int(config["n"]) - k = int(config.get("k", 3)) - edges = config["edges"] - projection_level = config.get("projection_level", "frontier") - max_attempts = int(config.get("max_attempts", 1_000_000)) - - adj = build_adj(n, edges) - colors = [-1] * n - metrics = Metrics() - node_seen: dict[str, int] = {} - nogood: set[str] = set() - edge_hashes: list[str] = [] - delta_stream: list[dict[str, Any]] = [] - - problem_hash = sha256_json({"n": n, "k": k, "edges": sorted([sorted(e) for e in edges])}) - rule_hash = sha256_json({"rule": "FAMM_DSatur_NUVMAP_DeltaDAG_v0.1", "projection_level": projection_level}) - - def touch_node(depth: int) -> str: - key = nuvmap_key(colors, adj, k, projection_level) - if key in node_seen: - metrics.cache_hits += 1 - else: - node_seen[key] = len(node_seen) - metrics.max_depth = max(metrics.max_depth, depth) - return key - - def dfs(depth: int) -> bool: - if metrics.attempts >= max_attempts: - return False - - parent_key = touch_node(depth) - if parent_key in nogood: - metrics.cache_hits += 1 - return False - - v = choose_vertex(colors, adj, k) - if v is None: - return zero_conflicts(colors, edges) - - opts = color_order(v, colors, adj, k) - if not opts: - nogood.add(parent_key) - metrics.nogoods += 1 - return False - - for c in opts: - if metrics.attempts >= max_attempts: - return False - metrics.attempts += 1 - - colors[v] = c - child_key = touch_node(depth + 1) - - delta = { - "op": "assign", - "vertex": v, - "color": c, - "depth": depth, - "parent": parent_key, - "child": child_key, - } - delta["edge_hash"] = sha256_json(delta) - delta_stream.append(delta) - edge_hashes.append(delta["edge_hash"]) - metrics.dag_edges += 1 - - dm = domains(colors, adj, k) - contradiction = any(colors[u] < 0 and dm[u] == 0 for u in range(n)) - - if not contradiction and dfs(depth + 1): - return True - - colors[v] = -1 - metrics.backtracks += 1 - - nogood.add(parent_key) - metrics.nogoods += 1 - return False - - solved = dfs(0) - metrics.dag_nodes = len(node_seen) - - bits_per_cell = max(1, (k + 1).bit_length()) - full_snapshot_bits = max(1, len(delta_stream)) * n * bits_per_cell - - vertex_bits = max(1, n.bit_length()) - color_bits = max(1, k.bit_length()) - delta_bits = max(1, len(delta_stream)) * (2 + vertex_bits + color_bits) - - touches = metrics.dag_nodes + metrics.cache_hits - dag_merge_gain = touches / max(1, metrics.dag_nodes) - - receipt = { - "receipt_type": "famm_nuvmap_delta_dag_search_receipt", - "schema_version": "0.1.0", - "problem_type": "graph_k_coloring", - "problem_hash": problem_hash, - "route_rule_hash": rule_hash, - "projection_level": projection_level, - "n": n, - "k": k, - "edge_count": len(edges), - "solved": solved, - "solution": colors if solved else None, - "exact_receipt": { - "zero_conflicts": bool(solved and zero_conflicts(colors, edges)), - "residual_conflicts": 0 if solved else None, - }, - "metrics": metrics.__dict__, - "compression_estimate": { - "full_snapshot_bits": full_snapshot_bits, - "delta_stream_bits": delta_bits, - "delta_trace_gain": full_snapshot_bits / max(1, delta_bits), - "dag_merge_gain": dag_merge_gain, - "combined_delta_dag_gain": (full_snapshot_bits / max(1, delta_bits)) * dag_merge_gain, - }, - "dag": { - "node_count": metrics.dag_nodes, - "edge_count": metrics.dag_edges, - "node_hashes_sha256": sha256_json(sorted(node_seen.keys())), - "edge_hashes_sha256": sha256_json(edge_hashes), - "nogood_hashes_sha256": sha256_json(sorted(nogood)), - }, - "delta_stream_sha256": sha256_json(delta_stream), - "no_drift_boundary": ( - "This is search topology compression, not a P-vs-NP claim. " - "Exactness comes only from the final zero-residual verifier." - ), - } - receipt["receipt_sha256"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = solve_graph_coloring(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Solved: {receipt['solved']}") - print(f"Attempts: {receipt['metrics']['attempts']}") - print(f"DAG nodes: {receipt['metrics']['dag_nodes']}") - print(f"Cache hits: {receipt['metrics']['cache_hits']}") - print(f"Delta trace gain: {receipt['compression_estimate']['delta_trace_gain']:.2f}x") - print(f"DAG merge gain: {receipt['compression_estimate']['dag_merge_gain']:.2f}x") - print(f"Combined delta-DAG gain: {receipt['compression_estimate']['combined_delta_dag_gain']:.2f}x") - print(f"Receipt SHA-256: {receipt['receipt_sha256']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/plasma_chiral_drag_witness_gate.py b/5-Applications/tools-scripts/famm/plasma_chiral_drag_witness_gate.py deleted file mode 100644 index a996e885..00000000 --- a/5-Applications/tools-scripts/famm/plasma_chiral_drag_witness_gate.py +++ /dev/null @@ -1,96 +0,0 @@ -#!/usr/bin/env python3 -"""Plasma Chiral Drag Witness Gate. - -Computes a residual receipt for Alfvén-wave image rotation in rotating plasma. -Uses DeltaTheta_pred ≈ L * Omega / (2 * v_A). -""" -from __future__ import annotations -import argparse, hashlib, json, math -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def run(config: dict[str, Any]) -> dict[str, Any]: - L = float(config["path_length_m"]) - v_A = float(config["alfven_speed_m_s"]) - tol = float(config.get("tolerance_rad", 0.05)) - - if "omega_rad_s" in config: - omega = float(config["omega_rad_s"]) - omega_source = "direct" - else: - r = float(config["radius_m"]) - B0 = float(config["magnetic_field_t"]) - dphi_dr = float(config["d_potential_dr_v_m"]) - omega = dphi_dr / (r * B0) - omega_source = "E_cross_B_from_radial_potential" - - observed = float(config.get("observed_rotation_rad", 0.0)) - phi_per_m = 0.5 * omega / v_A - predicted = L * phi_per_m - residual = abs(observed - predicted) - - sign_match = True - if observed != 0.0 and predicted != 0.0: - sign_match = math.copysign(1.0, observed) == math.copysign(1.0, predicted) - - receipt = { - "receipt_type": "famm_plasma_chiral_drag_witness_receipt", - "schema_version": "0.1.0", - "source_model": "Image rotation in plasmas, arXiv:2505.18062", - "inputs": config, - "derived": { - "omega_rad_s": omega, - "omega_source": omega_source, - "phi_per_meter_rad_m": phi_per_m, - "predicted_rotation_rad": predicted, - "predicted_rotation_deg": predicted * 180.0 / math.pi, - "observed_rotation_rad": observed, - "observed_rotation_deg": observed * 180.0 / math.pi, - "residual_rad": residual, - "sign_match": sign_match, - }, - "famm": { - "scar_class": "PASS_CHIRAL_DRAG" if residual <= tol and sign_match else "CHIRAL_DRAG_SCAR", - "residual": residual, - "tolerance_rad": tol, - "coarsening_agent": None if residual <= tol and sign_match else { - "type": "plasma_chiral_drag_mismatch", - "action": "scar_or_downweight_this_wave_medium_model", - "reason": "observed image rotation does not match predicted signed torsion witness within tolerance" - } - }, - "packet": { - "name": "Gamma_plasma_drag", - "projection": "plasma_state_to_wave_image_rotation_witness", - "invariant": "signed angular/torsion/chirality witness", - "guard": "rotating magnetized plasma with Alfven-wave transverse image structure", - }, - "no_drift_boundary": "This gate models Alfvén-wave image rotation in rotating magnetized plasma. It is not a universal wave-twist law or a vacuum-light claim." - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - out = Path(args.out); out.parent.mkdir(parents=True, exist_ok=True) - out.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - print(f"Wrote {out}") - print(f"Predicted rotation deg: {receipt['derived']['predicted_rotation_deg']}") - print(f"Residual rad: {receipt['derived']['residual_rad']}") - print(f"Scar class: {receipt['famm']['scar_class']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/semantic_mass_route_plow.py b/5-Applications/tools-scripts/famm/semantic_mass_route_plow.py deleted file mode 100644 index 9d23b4d0..00000000 --- a/5-Applications/tools-scripts/famm/semantic_mass_route_plow.py +++ /dev/null @@ -1,307 +0,0 @@ -#!/usr/bin/env python3 -"""FAMM Semantic Mass Route Plow. - -This runner welds Semantic Mass Numbers directly into FAMM routing. - -It accepts: - - typed semantic-mass lane samples, - - route candidates with distance/scar/invariant/cost features, - - optional CFD residual lanes, - - optional external Hessian and Z-domain receipts, - -and emits: - - a semantic mass stream, - - Z-domain recurrence/pole diagnosis, - - route rankings, - - residual seal recommendation, - - closure recommendation, - - a receipt hash. - -Boundary: - This is a computational routing witness. It is not proof and does not replace - exact Lean/Fraction/OISC receipts. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -from pathlib import Path -from typing import Any - -import numpy as np - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def weighted_mass(sample: dict[str, Any], weights: dict[str, float]) -> float: - lanes = sample.get("lanes", {}) - return float(sum(float(weights.get(k, 0.0)) * float(v) for k, v in lanes.items())) - - -def build_mass_stream(config: dict[str, Any]) -> list[float]: - weights = config["lane_weights"] - return [weighted_mass(sample, weights) for sample in config["semantic_mass_samples"]] - - -def fit_ar(sequence: np.ndarray, order: int) -> tuple[np.ndarray, np.ndarray, float]: - if order < 1: - raise ValueError("ar_order must be >= 1") - if len(sequence) <= order + 1: - raise ValueError("semantic_mass_sequence too short for requested ar_order") - - y = sequence[order:] - x_cols = [ - sequence[order - i - 1 : len(sequence) - i - 1] - for i in range(order) - ] - X = np.column_stack(x_cols) - coeffs, *_ = np.linalg.lstsq(X, y, rcond=None) - pred = X @ coeffs - residual = y - pred - rmse = float(np.sqrt(np.mean(residual**2))) - return coeffs, residual, rmse - - -def ar_poles(coeffs: np.ndarray) -> np.ndarray: - # mu[k] = a1 mu[k-1] + ... + ap mu[k-p] - # lambda^p - a1 lambda^(p-1) - ... - ap = 0 - return np.roots(np.concatenate([[1.0], -coeffs])) - - -def z_diagnosis(poles: np.ndarray, residual_rmse: float, cfg: dict[str, Any]) -> dict[str, Any]: - pole_abs = np.abs(poles) - max_abs = float(np.max(pole_abs)) if len(pole_abs) else 0.0 - stable_radius = float(cfg.get("stable_radius", 1.0)) - near_unit_tol = float(cfg.get("near_unit_tol", 0.05)) - residual_rmse_max = float(cfg.get("residual_rmse_max", 0.10)) - - if max_abs >= stable_radius: - route = "closure_or_quarantine" - reason = "pole outside admissible stable ROC" - elif np.any(np.abs(pole_abs - 1.0) <= near_unit_tol): - route = "long_memory_delta_mem" - reason = "pole near unit circle indicates long-memory semantic mass" - elif residual_rmse <= residual_rmse_max: - route = "carry_recurrence_seal_residual" - reason = "stable recurrence with bounded residual" - else: - route = "increase_order_or_seal_residual" - reason = "stable but recurrence residual exceeds bound" - - return { - "route": route, - "reason": reason, - "max_abs_pole": max_abs, - "pole_abs": [float(x) for x in pole_abs.tolist()], - "residual_rmse": residual_rmse, - } - - -def load_optional_json(path_or_obj: Any) -> dict[str, Any] | None: - if path_or_obj is None: - return None - if isinstance(path_or_obj, dict): - return path_or_obj - p = Path(path_or_obj) - if not p.exists(): - return None - return json.loads(p.read_text(encoding="utf-8")) - - -def hessian_modifier(candidate: dict[str, Any], hessian_receipt: dict[str, Any] | None) -> float: - if not hessian_receipt: - return 0.0 - - decision = hessian_receipt.get("route_decision", {}) - route = decision.get("route", "") - scar = float(candidate.get("scar", 0.0)) - cost = float(candidate.get("cost", 0.0)) - invariant = float(candidate.get("invariant_overlap", 0.0)) - - if route == "probe_saddle_scar": - return 0.35 * scar - if route == "protect_or_seal_stiff_invariant": - return 0.35 * invariant - 0.25 * cost - if route == "press_flat_gauge": - return 0.35 * (1.0 - cost) - if route == "seal_high_total_curvature": - return -0.50 * cost - return 0.0 - - -def z_modifier(z_diag: dict[str, Any], candidate: dict[str, Any]) -> float: - route = z_diag.get("route", "") - scar = float(candidate.get("scar", 0.0)) - invariant = float(candidate.get("invariant_overlap", 0.0)) - cost = float(candidate.get("cost", 0.0)) - - if route == "carry_recurrence_seal_residual": - return 0.25 * invariant - 0.10 * cost - if route == "long_memory_delta_mem": - return 0.20 * invariant - 0.05 * scar - if route == "closure_or_quarantine": - return -0.35 * cost - 0.25 * scar - if route == "increase_order_or_seal_residual": - return -0.15 * cost - return 0.0 - - -def rank_routes( - candidates: list[dict[str, Any]], - z_diag: dict[str, Any], - hessian_receipt: dict[str, Any] | None, - cfg: dict[str, Any], -) -> list[dict[str, Any]]: - alpha = float(cfg.get("alpha_distance", 1.0)) - beta = float(cfg.get("beta_scar", 1.0)) - gamma = float(cfg.get("gamma_invariant", 1.0)) - eta = float(cfg.get("eta_cost", 1.0)) - mass_gain = float(cfg.get("mass_gain", 0.5)) - - scored = [] - for c in candidates: - distance = float(c.get("distance", 0.0)) - scar = float(c.get("scar", 0.0)) - invariant = float(c.get("invariant_overlap", 0.0)) - cost = float(c.get("cost", 0.0)) - prior = float(c.get("prior", 0.0)) - mass = float(c.get("semantic_mass", 0.0)) - - logit = ( - prior - + mass_gain * mass - - alpha * distance - - beta * scar - + gamma * invariant - - eta * cost - + z_modifier(z_diag, c) - + hessian_modifier(c, hessian_receipt) - ) - scored.append({**c, "route_logit": logit}) - - max_logit = max((r["route_logit"] for r in scored), default=0.0) - denom = sum(math.exp(r["route_logit"] - max_logit) for r in scored) or 1.0 - for r in scored: - r["route_probability"] = math.exp(r["route_logit"] - max_logit) / denom - - return sorted(scored, key=lambda r: r["route_probability"], reverse=True) - - -def closure_recommendation(z_diag: dict[str, Any], ranked: list[dict[str, Any]]) -> dict[str, Any]: - if z_diag["route"] == "closure_or_quarantine": - return { - "needed": True, - "reason": "unstable Z-domain pole suggests missing boundary, bad CFL-like setting, or invalid route", - } - if ranked and ranked[0].get("scar", 0.0) >= 0.75: - return { - "needed": True, - "reason": "top route is scar-heavy; test whether residual is a boundary-closure artifact", - } - return {"needed": False, "reason": "no immediate closure trigger"} - - -def run(config: dict[str, Any]) -> dict[str, Any]: - mass_stream = np.array(build_mass_stream(config), dtype=float) - ar_order = int(config.get("z_domain", {}).get("ar_order", 3)) - coeffs, residual, rmse = fit_ar(mass_stream, ar_order) - poles = ar_poles(coeffs) - z_diag = z_diagnosis(poles, rmse, config.get("z_domain", {}).get("thresholds", {})) - - hessian_receipt = load_optional_json(config.get("hessian_receipt")) - - candidates = config.get("route_candidates", []) - final_mass = float(mass_stream[-1]) - candidates = [ - {**c, "semantic_mass": float(c.get("semantic_mass", final_mass))} - for c in candidates - ] - ranked = rank_routes( - candidates, - z_diag=z_diag, - hessian_receipt=hessian_receipt, - cfg=config.get("ranking", {}), - ) - - closure = closure_recommendation(z_diag, ranked) - residual_max = float(config.get("z_domain", {}).get("thresholds", {}).get("residual_rmse_max", 0.10)) - residual_seal = { - "seal": bool(rmse <= residual_max), - "reason": ( - "bounded recurrence residual; seal instead of rescan" - if rmse <= residual_max - else "residual above bound; increase order, test closure, or store explicit residual" - ), - "rmse": rmse, - } - - receipt = { - "receipt_type": "famm_semantic_mass_route_plow_receipt", - "schema_version": "0.1.0", - "basis_layers": [ - "SEMANTIC_MASS", - "Z_DOMAIN_GATE", - "DELTA_MEM", - "HESSIAN_EIGEN", - "E_TAIL_BOUND", - "SYSTEM_CLOSURE", - ], - "mass_stream": { - "length": int(len(mass_stream)), - "sha256": sha256_json([float(x) for x in mass_stream.tolist()]), - "last": float(mass_stream[-1]), - "mean": float(np.mean(mass_stream)), - }, - "z_domain": { - "ar_order": ar_order, - "coefficients": [float(x) for x in coeffs.tolist()], - "poles": [ - {"re": float(p.real), "im": float(p.imag), "abs": float(abs(p))} - for p in poles - ], - "residual_sha256": sha256_json([float(x) for x in residual.tolist()]), - "diagnosis": z_diag, - }, - "ranked_routes": ranked, - "closure_recommendation": closure, - "residual_seal": residual_seal, - "no_drift_boundary": ( - "This receipt ranks routes and accelerates search. It is not theorem proof " - "and does not replace exact receipts." - ), - } - receipt["receipt_sha256"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - top = receipt["ranked_routes"][0] if receipt["ranked_routes"] else None - print(f"Wrote {out_path}") - print(f"Z route: {receipt['z_domain']['diagnosis']['route']} — {receipt['z_domain']['diagnosis']['reason']}") - if top: - print(f"Top route: {top.get('route_id')} p={top['route_probability']:.4f}") - print(f"Closure: {receipt['closure_recommendation']['needed']} — {receipt['closure_recommendation']['reason']}") - print(f"Residual seal: {receipt['residual_seal']['seal']} — {receipt['residual_seal']['reason']}") - print(f"Receipt SHA-256: {receipt['receipt_sha256']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/semantic_mass_z_accelerator.py b/5-Applications/tools-scripts/famm/semantic_mass_z_accelerator.py deleted file mode 100644 index 05cb4c25..00000000 --- a/5-Applications/tools-scripts/famm/semantic_mass_z_accelerator.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -"""Semantic Mass Z-domain accelerator receipt. - -Fits a simple autoregressive recurrence to a semantic mass stream and emits a -FAMM routing receipt. This is a lightweight measured shortcut: it turns -history into coefficients + state + residual seal. - -The runner is intentionally small. It is not proof; it is a computational -routing witness. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -from pathlib import Path -from typing import Any - -import numpy as np - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def fit_ar(sequence: np.ndarray, order: int) -> tuple[np.ndarray, np.ndarray, float]: - if order < 1: - raise ValueError("order must be >= 1") - if len(sequence) <= order + 1: - raise ValueError("sequence too short for requested AR order") - - y = sequence[order:] - X = np.column_stack([sequence[order - i - 1 : len(sequence) - i - 1] for i in range(order)]) - coeffs, *_ = np.linalg.lstsq(X, y, rcond=None) - pred = X @ coeffs - residual = y - pred - rmse = float(np.sqrt(np.mean(residual**2))) - return coeffs, residual, rmse - - -def route_from_poles(poles: np.ndarray, rmse: float, cfg: dict[str, Any]) -> dict[str, Any]: - pole_abs = np.abs(poles) - max_abs = float(np.max(pole_abs)) if len(pole_abs) else 0.0 - near_unit_tol = float(cfg.get("near_unit_tol", 0.05)) - stable_radius = float(cfg.get("stable_radius", 1.0)) - residual_max = float(cfg.get("residual_rmse_max", 0.1)) - - if max_abs >= stable_radius: - route = "closure_or_quarantine" - reason = "pole outside admissible stable ROC" - elif np.any(np.abs(pole_abs - 1.0) <= near_unit_tol): - route = "long_memory_delta_mem" - reason = "pole near unit circle indicates long semantic memory" - elif rmse <= residual_max: - route = "carry_recurrence_seal_residual" - reason = "stable recurrence with bounded residual" - else: - route = "increase_order_or_seal_residual" - reason = "stable but residual exceeds configured bound" - - return { - "route": route, - "reason": reason, - "max_abs_pole": max_abs, - "pole_abs": [float(x) for x in pole_abs.tolist()], - "residual_rmse": rmse, - } - - -def run(config: dict[str, Any]) -> dict[str, Any]: - seq = np.array(config["semantic_mass_sequence"], dtype=float) - order = int(config.get("ar_order", 4)) - coeffs, residual, rmse = fit_ar(seq, order) - - # AR recurrence: mu[k] = sum_i a_i mu[k-i] - # Characteristic: lambda^p - a1 lambda^(p-1) - ... - ap = 0 - poly = np.concatenate([[1.0], -coeffs]) - poles = np.roots(poly) - - decision = route_from_poles(poles, rmse, config.get("route_thresholds", {})) - - receipt = { - "receipt_type": "famm_semantic_mass_z_receipt", - "schema_version": "0.1.0", - "basis_layer": "MASS_Z_ACCEL", - "ar_order": order, - "sequence_len": int(len(seq)), - "coefficients": [float(x) for x in coeffs.tolist()], - "poles": [{"re": float(p.real), "im": float(p.imag), "abs": float(abs(p))} for p in poles], - "residual": { - "rmse": rmse, - "samples_sha256": sha256_json([float(x) for x in residual.tolist()]), - "max_abs": float(np.max(np.abs(residual))), - }, - "route_decision": decision, - "no_drift_boundary": ( - "This is a computational recurrence witness for Semantic Mass routing. " - "It is not proof and does not replace exact receipts." - ), - } - receipt["receipt_sha256"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - cfg = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(cfg) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Route: {receipt['route_decision']['route']} — {receipt['route_decision']['reason']}") - print(f"Receipt SHA-256: {receipt['receipt_sha256']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/famm/sidon_famm_map.py b/5-Applications/tools-scripts/famm/sidon_famm_map.py deleted file mode 100644 index d586a4b9..00000000 --- a/5-Applications/tools-scripts/famm/sidon_famm_map.py +++ /dev/null @@ -1,164 +0,0 @@ -#!/usr/bin/env python3 -"""Sidon FAMM Map receipt runner. - -Computes pair-sum uniqueness, collision scars, candidate-addition admissibility, -Theodorus capacity pressure, and a compact FAMM receipt. - -This is a verifier/router, not a proof of maximality. -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -from pathlib import Path -from typing import Any - - -def sha256_json(value: Any) -> str: - payload = json.dumps(value, sort_keys=True, separators=(",", ":")).encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - -def pair_sums(values: list[int]) -> list[dict[str, Any]]: - out = [] - for i, a in enumerate(values): - for j, b in enumerate(values[i:], start=i): - out.append({"i": i, "j": j, "a": a, "b": b, "sum": a + b}) - return out - - -def find_collisions(pairs: list[dict[str, Any]]) -> list[dict[str, Any]]: - seen: dict[int, dict[str, Any]] = {} - collisions = [] - for p in pairs: - s = int(p["sum"]) - if s in seen: - q = seen[s] - same_unordered = {p["i"], p["j"]} == {q["i"], q["j"]} - if not same_unordered: - collisions.append({ - "sum_address": s, - "pair_a": {"i": q["i"], "j": q["j"], "values": [q["a"], q["b"]]}, - "pair_b": {"i": p["i"], "j": p["j"], "values": [p["a"], p["b"]]}, - "trivial_collision": False, - "scar": "nontrivial_pair_sum_collision", - }) - else: - seen[s] = p - return collisions - - -def candidate_gate(values: list[int], candidate: int) -> dict[str, Any]: - current = pair_sums(values) - current_sums = {p["sum"] for p in current} - new_sums = [{"a": candidate, "b": x, "sum": candidate + x} for x in values] - new_sums.append({"a": candidate, "b": candidate, "sum": 2 * candidate}) - - external = [p for p in new_sums if p["sum"] in current_sums] - internal_count = len(new_sums) - len({p["sum"] for p in new_sums}) - admissible = len(external) == 0 and internal_count == 0 - return { - "candidate": candidate, - "admissible": admissible, - "new_sums": new_sums, - "external_collisions": external, - "internal_duplicate_count": internal_count, - } - - -def additive_energy_ordered(values: list[int]) -> int: - counts: dict[int, int] = {} - for a in values: - for b in values: - counts[a + b] = counts.get(a + b, 0) + 1 - return sum(c * c for c in counts.values()) - - -def run(config: dict[str, Any]) -> dict[str, Any]: - values = list(map(int, config["set"])) - pairs = pair_sums(values) - collisions = find_collisions(pairs) - m = len(values) - e_plus = additive_energy_ordered(values) - omega = e_plus - (2 * m * m - m) - is_sidon = len(collisions) == 0 and omega == 0 - - capacity_N = config.get("capacity_N") - capacity = None - if capacity_N is not None: - capacity_N = int(capacity_N) - capacity = { - "N": capacity_N, - "theodorus_shell_sqrt_N": math.sqrt(capacity_N), - "occupied": m, - "slack": math.sqrt(capacity_N) - m, - } - - candidate_receipt = None - if "candidate" in config: - candidate_receipt = candidate_gate(values, int(config["candidate"])) - - famm = { - "sidon_collision_count": len(collisions), - "additive_energy_ordered": e_plus, - "omega_sidon": omega, - "scar_class": "PASS_ZERO_SCAR" if is_sidon else "COLLISION_SCAR", - "coarsening_agent": None if is_sidon else { - "type": "sidon_collision_coarsener", - "action": "downweight_or_merge_collision_basin", - "reason": "nontrivial pair-sum collision or additive energy excess", - }, - "capacity": capacity, - "candidate_gate": candidate_receipt, - "semantic_mass_lanes": { - "collision": len(collisions), - "additive_energy_excess": omega, - "capacity_pressure": None if capacity is None else -capacity["slack"], - "residual": 0 if is_sidon else len(collisions) + max(0, omega), - }, - } - - receipt = { - "receipt_type": "famm_sidon_map_receipt", - "schema_version": "0.1.0", - "set": values, - "is_sidon": is_sidon, - "pair_sums": pairs, - "collisions": collisions, - "famm": famm, - "no_drift_boundary": ( - "This receipt verifies Sidon pair-sum uniqueness and maps failures to FAMM scars. " - "It does not prove maximality or physical realization." - ), - } - receipt["receipt_hash"] = sha256_json(receipt) - return receipt - - -def main() -> None: - parser = argparse.ArgumentParser() - parser.add_argument("--config", required=True) - parser.add_argument("--out", required=True) - args = parser.parse_args() - - config = json.loads(Path(args.config).read_text(encoding="utf-8")) - receipt = run(config) - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(receipt, indent=2, sort_keys=True), encoding="utf-8") - - print(f"Wrote {out_path}") - print(f"Sidon: {receipt['is_sidon']}") - print(f"Collision count: {receipt['famm']['sidon_collision_count']}") - print(f"Omega Sidon: {receipt['famm']['omega_sidon']}") - if receipt["famm"]["candidate_gate"] is not None: - print(f"Candidate admissible: {receipt['famm']['candidate_gate']['admissible']}") - print(f"Receipt hash: {receipt['receipt_hash']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/formula_optimization/braid_event_delta_gcl.py b/5-Applications/tools-scripts/formula_optimization/braid_event_delta_gcl.py deleted file mode 100644 index 64a39881..00000000 --- a/5-Applications/tools-scripts/formula_optimization/braid_event_delta_gcl.py +++ /dev/null @@ -1,914 +0,0 @@ -#!/usr/bin/env python3 -""" -braid_event_delta_gcl.py -======================== - -Delta-GCL + metaprobe pass over `compute_event` from -5-Applications/tools-scripts/braid/braid_photonic_emulator.py. - -V3 — Mathematical improvements sourced from Lean formal model: - - v1 → v2 = canonicalization (13 magic numbers → 6 named params) - v2 → v3 = Lean-derived structural improvements: - - 1. **Parity-of-event modulation** (GeneticCode.lean §1) - XOR(et_bits, polarity_sign) → Fc parity gate. - Purine/pyrimidine sign × canonical/wobble magnitude × parity flip. - - 2. **Spectral resonance echo coupling** (ShellModel.lean §4) - Echo weights scaled by spectral resonance degeneracy between - current and past event types (overlapping spectral peaks). - - 3. **Forward scorpion-tail echo** - Anticipatory term from known shell geometry: - the next shell-crossing position is always (k+1)², so we add a - forward-looking standing wave component. - - 4. **Phase formula refinement** (ShellModel.lean §5) - Discrete mass-gated interaction term combined with smooth tanh. - phase = clip(linear_term + tanh_term + mass_gate_term). - - 5. **Extended tail depth** (tail_depth 3 → 5) - With interaction-modulated adaptive decay: weights are - modulated by whether the past event had the same parity as current. - -Goal ----- -"Squeeze the lemon" on the braid event formula by applying the project's own -Delta GCL three-layer compression stack and metaprobe verification frame: - - Layer 1 — Delta encoding (encode event[n+1] as diff from event[n]) - Layer 2 — PTOS field dictionary (factor magic-number tables to byte indices) - Layer 3 — Variable-length codon (et ∈ {A,G,C,T} → fixed-Huffman codon) - Metaprobe gate — bit-exact round-trip + SI compression ratio vs zlib baseline - -References ----------- -- ShellModel.lean — Shell state geometry and event classification -- GeneticCode.lean — EventType definition, parity-of-event -- 6-Documentation/docs/papers/DELTA_GCL_COMPRESSION_LANGUAGE_AGNOSTIC.md -- 6-Documentation/docs/METAPROBE_APPROACH.md - -This script is stdlib-only (math, struct, json, gzip, zlib, dataclasses) so it -runs without simphony/jax/perceval and against the system Python. -""" - -from __future__ import annotations - -import bz2 -import gzip -import json -import lzma -import math -import struct -import zlib -from dataclasses import dataclass, field, asdict -from pathlib import Path - -# Optional external codecs (international standards, but not stdlib). -# Tagged with their RFC / specification reference per ISO/IEC 11576 evidence policy. -try: - import brotli # RFC 7932 - HAVE_BROTLI = True -except ImportError: - HAVE_BROTLI = False -try: - import zstandard # RFC 8478 - HAVE_ZSTD = True -except ImportError: - HAVE_ZSTD = False - -# ============================================================================= -# 0. LEAN-DERIVED UTILITY FUNCTIONS -# ============================================================================= -# Ported from GeneticCode.lean and ShellModel.lean - -# DNA base → bit representation (mirrors GeneticCode.eventBits) -_EVENT_BITS = {"A": 0, "G": 1, "C": 2, "T": 3} - -# Spectral signature: each event type has a unique 8-bin spectral fingerprint -# (mirrors Spectrum.lean eventSpectrum) -_EVENT_SPECTRA: dict[str, list[float]] = { - "A": [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - "T": [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], - "G": [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0], - "C": [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0], -} - - -def _parity_of_event(et: str, polarity: int) -> bool: - """GeneticCode.parityOfEvent: XOR(event_bits, polarity_sign) % 2 == 1.""" - eb = _EVENT_BITS[et] - pb = 1 if polarity >= 0 else 0 - x = eb ^ pb - return (x % 2) == 1 - - -def _spectral_resonance(a: str, b: str) -> float: - """ShellModel.SpectralSignature.resonanceDegeneracy: count overlapping peaks.""" - sa = _EVENT_SPECTRA[a] - sb = _EVENT_SPECTRA[b] - return float(sum(1 for pa, pb in zip(sa, sb) if pa > 0.0 and pb > 0.0)) - - -# ============================================================================= -# 1. ORIGINAL compute_event (verbatim port from braid_photonic_emulator.py) -# ============================================================================= - -def shell_state_v1(n: int): - k = int(math.isqrt(n)) - a = n - k * k - b = (k + 1) * (k + 1) - n - return {"n": n, "k": k, "a": a, "b": b, "width": 2 * k + 1} - - -def classify_event_v1(s: dict): - k, n = s["k"], s["n"] - if n == k * k: - return "A" - if n == k * k + k: - return "G" - if n == k * k + k + 1: - return "C" - if n == (k + 1) * (k + 1) - 1: - return "T" - return None - - -def compute_event_v1(n: int, tail_weights: dict[int, float] | None = None): - """Verbatim transcription of compute_event from braid_photonic_emulator.py.""" - if tail_weights is None: - tail_weights = {1: -1.0, 2: -0.5, 3: -0.25} - s = shell_state_v1(n) - et = classify_event_v1(s) - if et is None: - return None - a, b, k = s["a"], s["b"], s["k"] - mass = a * b - polarity = a - b - shell_width = 2 * k + 1 - - echo = 0.0 - for tail, weight in tail_weights.items(): - if n - tail >= 0: - prev = shell_state_v1(n - tail) - prev_et = classify_event_v1(prev) - if prev_et is not None: - echo += weight * prev["a"] * prev["b"] - - Fm = mass + 0.5 * echo - Fp = polarity + 0.25 * echo - Fc = {"A": 1.0, "T": -1.0, "G": 0.5, "C": -0.5}.get(et, 0.0) - interaction = mass * Fm + polarity * Fp + Fc - phase = max(-3, min(3, round(3.0 * polarity / shell_width + 2.0 * math.tanh(interaction / 64.0)))) - index_bit = 1 if interaction > 0 else 0 - - return { - "n": n, "k": k, "et": et, - "mass": mass, "polarity": polarity, "shell_width": shell_width, - "echo": echo, "Fm": Fm, "Fp": Fp, "Fc": Fc, - "interaction": interaction, - "phase": int(phase), "index_bit": index_bit, - } - - -# ============================================================================= -# 2. CANONICALIZED V2 (magic numbers extracted into BraidEventParams) -# ============================================================================= - -@dataclass(frozen=True) -class BraidEventParams: - """Single source of truth for every magic number in compute_event v2.""" - decay_base: float = 0.5 # tail/echo geometric decay base - tail_depth: int = 3 # tail_weights has 3 entries - echo_depth: int = 2 # Fm uses decay^1, Fp uses decay^2 - phase_tanh_coef: float = 2.0 # tanh contribution to phase - phase_tanh_scale: float = 64.0 # interaction scale inside tanh - phase_clip_range: int = 3 # = phase_linear_coef (the redundancy) - - @property - def tail_weights(self) -> dict[int, float]: - return {k: -(self.decay_base ** (k - 1)) for k in range(1, self.tail_depth + 1)} - - @property - def echo_coefs(self) -> tuple[float, float]: - # (Fm uses ^1, Fp uses ^2) - return (self.decay_base ** 1, self.decay_base ** 2) - - @property - def phase_linear_coef(self) -> int: - # Tied to clip range. - return self.phase_clip_range - - -# Canonical Fc table reframed via sign × magnitude -_PURINES = {"A", "G"} -_CANONICAL = {"A", "T"} # full magnitude (1.0) -_WOBBLE = {"G", "C"} # half magnitude (0.5) - - -def _Fc_canonical(et: str) -> float: - sign = +1.0 if et in _PURINES else -1.0 - magnitude = 1.0 if et in _CANONICAL else 0.5 - return sign * magnitude - - -def compute_event_v2(n: int, params: BraidEventParams = BraidEventParams()): - """Same observable as v1, but every coefficient routed through `params`.""" - s = shell_state_v1(n) - et = classify_event_v1(s) - if et is None: - return None - a, b, k = s["a"], s["b"], s["k"] - mass = a * b - polarity = a - b - shell_width = 2 * k + 1 - - echo = 0.0 - for tail, weight in params.tail_weights.items(): - if n - tail >= 0: - prev = shell_state_v1(n - tail) - prev_et = classify_event_v1(prev) - if prev_et is not None: - echo += weight * prev["a"] * prev["b"] - - cm, cp = params.echo_coefs - Fm = mass + cm * echo - Fp = polarity + cp * echo - Fc = _Fc_canonical(et) - interaction = mass * Fm + polarity * Fp + Fc - - R = params.phase_clip_range - phase = max(-R, min(R, round( - params.phase_linear_coef * polarity / shell_width - + params.phase_tanh_coef * math.tanh(interaction / params.phase_tanh_scale) - ))) - index_bit = 1 if interaction > 0 else 0 - - return { - "n": n, "k": k, "et": et, - "mass": mass, "polarity": polarity, "shell_width": shell_width, - "echo": echo, "Fm": Fm, "Fp": Fp, "Fc": Fc, - "interaction": interaction, - "phase": int(phase), "index_bit": index_bit, - } - - -# ============================================================================= -# 3. LEAN-IMPROVED V3 (new structural features from formal geometry) -# ============================================================================= - -@dataclass(frozen=True) -class BraidEventParamsV3: - """Extended parameters for v3 — adds Lean-derived structural features. - - New vs v2: - - tail_depth_v3: extended from 3 → 5 for longer echo memory - - adaptive_decay: if True, echo weights are modulated by parity match - between current and past event (from parityOfEvent) - - resonance_coupling: if True, echo is scaled by spectral overlap - between current and past event types (from resonanceDegeneracy) - - forward_echo_depth: forward-looking scorpion-tail echo - - mass_gate_coef: discrete ±1 term from ShellModel.lean phaseFromTipAndInteraction - - parity_gate: if True, Fc is parity-flipped based on XOR(et, polarity) - """ - # v2 params (carried forward) - decay_base: float = 0.5 - tail_depth_v3: int = 5 # extended from 3 - echo_depth: int = 2 - phase_tanh_coef: float = 2.0 - phase_tanh_scale: float = 64.0 - phase_clip_range: int = 3 - - # v3 new params - adaptive_decay: bool = True # parity-modulated echo weights - resonance_coupling: bool = True # spectral resonance echo scaling - forward_echo_depth: int = 2 # forward-looking scorpion-tail depth - mass_gate_coef: float = 1.0 # discrete ±1 mass-gated interaction term - parity_gate: bool = True # XOR parity flip on Fc - - @property - def tail_weights(self) -> dict[int, float]: - """Geometric decay: weight_k = -(decay_base ** (k-1)) for k ∈ 1..tail_depth_v3.""" - return {k: -(self.decay_base ** (k - 1)) for k in range(1, self.tail_depth_v3 + 1)} - - @property - def echo_coefs(self) -> tuple[float, float]: - return (self.decay_base ** 1, self.decay_base ** 2) - - @property - def phase_linear_coef(self) -> int: - return self.phase_clip_range - - -def _spectral_resonance_weight(et_current: str, et_past: str) -> float: - """Return resonance scaling factor based on spectral overlap. - - If resonance_coupling is active, echo contributions from events - whose spectrum overlaps with the current event are amplified. - - Same type → max resonance (×4.0) - Opposite purine↔pyrimidine → moderate resonance (×2.0) - Different magnitude class → minimal resonance (×1.0) - No overlap → suppressed (×0.5) - """ - r = _spectral_resonance(et_current, et_past) - r_total = _spectral_resonance(et_current, et_current) # self-resonance for normalization - if r_total == 0: - return 1.0 - # Normalize: 4 bins max overlap for same-type events - overlap_ratio = r / r_total - # Map [0, 1] → [0.5, 4.0] - return 0.5 + 3.5 * overlap_ratio - - -def _parity_flipped_Fc(et: str, polarity: int) -> float: - """GeneticCode parity-of-event as Fc modulator. - - Base Fc from sign × magnitude, then flip sign if parity is odd. - This creates a finer-grained 8-level Fc instead of 4-level. - """ - base = _Fc_canonical(et) - if _parity_of_event(et, polarity): - return -base - return base - - -def _forward_echo(n: int, k: int, depth: int, decay_base: float) -> float: - """Forward-looking scorpion-tail echo contribution. - - The next shell boundary is always at (k+1)². - We compute anticipated mass contributions from future events - that are deterministically known from shell geometry. - - This adds a standing-wave component that anticipates: - - The next perfect square: n_next = (k+1)² → mass = 0 (a=0) - - The next G position: n_G = k² + 2k → mass = k² - - The next C position: n_C = k² + 2k + 1 → mass = k² + k - - The next T position: n_T = (k+1)² - 1 → mass = 2k - - These are the shell's 4 event positions in the NEXT shell level. - """ - # We're at position a within current shell: n = k² + a - a = n - k * k - shell_width = 2 * k + 1 - forward_contrib = 0.0 - - for d in range(1, depth + 1): - # Predict position d steps ahead within current or next shell - a_forward = a + d - if a_forward < shell_width: - # Still in current shell — no event, but mass still contributes - # via the deterministic a*b product at the forward position - b_forward = shell_width - a_forward - forward_contrib += (decay_base ** d) * a_forward * b_forward - else: - # Crossed into next shell - a_next = a_forward - shell_width - k_next = k + 1 - b_next = (2 * k_next + 1) - a_next - forward_contrib += (decay_base ** d) * a_next * b_next - - return forward_contrib - - -def compute_event_v3(n: int, params: BraidEventParamsV3 = BraidEventParamsV3()): - """Improved compute_event with Lean-derived structural features. - - Changes from v2: - 1. Parity gate on Fc (from GeneticCode.parityOfEvent) - 2. Spectral resonance echo coupling (from ShellModel.resonanceDegeneracy) - 3. Forward scorpion-tail echo - 4. Extended tail depth with adaptive parity-modulated weights - 5. Mass-gated discrete interaction term in phase (from ShellModel.phaseFromTipAndInteraction) - """ - s = shell_state_v1(n) - et = classify_event_v1(s) - if et is None: - return None - a, b, k = s["a"], s["b"], s["k"] - mass = a * b - polarity = a - b - shell_width = 2 * k + 1 - - # ---- Echo with resonance coupling and adaptive parity modulation ---- - echo = 0.0 - past_events_info: list[tuple[str, int, int]] = [] # (et, mass, polarity) - for tail, weight in params.tail_weights.items(): - if n - tail >= 0: - prev = shell_state_v1(n - tail) - prev_et = classify_event_v1(prev) - if prev_et is not None: - # Base echo: mass of past event - echo_mass = prev["a"] * prev["b"] - effective_weight = weight - - # Spectral resonance coupling (v3) - if params.resonance_coupling: - resonance_scale = _spectral_resonance_weight(et, prev_et) - effective_weight *= resonance_scale - - # Adaptive decay: parity-modulated weight (v3) - if params.adaptive_decay: - # If past event has same parity as current, amplify the echo - # (constructive standing-wave interference) - if _parity_of_event(prev_et, prev["a"] - prev["b"]) == _parity_of_event(et, polarity): - effective_weight *= 1.5 # constructive interference - else: - effective_weight *= 0.5 # destructive interference - - echo += effective_weight * echo_mass - past_events_info.append((prev_et, prev["a"] * prev["b"], prev["a"] - prev["b"])) - - # ---- Forward scorpion-tail echo (v3) ---- - forward_echo = _forward_echo(n, k, params.forward_echo_depth, params.decay_base) - - # ---- Field channels ---- - cm, cp = params.echo_coefs - # Combine backward and forward echo - total_echo = echo + 0.1 * forward_echo # forward contributes at 0.1× (weaker) - - Fm = mass + cm * total_echo - Fp = polarity + cp * total_echo - - # Parity-gated Fc (v3) - if params.parity_gate: - Fc = _parity_flipped_Fc(et, polarity) - else: - Fc = _Fc_canonical(et) - - interaction = mass * Fm + polarity * Fp + Fc - - # ---- Phase with mass-gated discrete interaction term (v3, from ShellModel.lean) ---- - R = params.phase_clip_range - continuous_phase = ( - params.phase_linear_coef * polarity / shell_width - + params.phase_tanh_coef * math.tanh(interaction / params.phase_tanh_scale) - ) - - # Discrete mass-gate term (ShellModel.lean:160-162): - # if interaction > 0: mass_gate = +1 if mass > 0 else -1 - # else: mass_gate = 0 - if interaction > 0: - mass_gate = params.mass_gate_coef * (1.0 if mass > 0 else -1.0) - else: - mass_gate = 0.0 - - phase = max(-R, min(R, round(continuous_phase + mass_gate))) - index_bit = 1 if interaction > 0 else 0 - - return { - "n": n, "k": k, "et": et, - "mass": mass, "polarity": polarity, "shell_width": shell_width, - "echo": echo, "forward_echo": forward_echo, "total_echo": total_echo, - "Fm": Fm, "Fp": Fp, "Fc": Fc, - "parity": _parity_of_event(et, polarity), - "mass_gate": mass_gate, - "interaction": interaction, - "phase": int(phase), "index_bit": index_bit, - } - - -# ============================================================================= -# 4. METAPROBE GATE: bit-exact equivalence v1 ⇔ v2 ⇔ v3 -# ============================================================================= - -def metaprobe_equivalence(n_max: int) -> dict: - """Verify v1(n) == v2(n) == v3(n) for backbone fields (n,k,et,mass,polarity,phase,index_bit). - - v3 intentionally diverges on echo/Fm/Fp/Fc/interaction — the structural - improvements change those fields. We verify the structural invariants. - """ - diverged_v2 = [] - diverged_v3_invariants = [] - v1_count = 0 - v2_count = 0 - v3_count = 0 - - # Invariant fields that must always match - invariant_keys = {"n", "k", "et", "mass", "polarity", "shell_width"} - - for n_val in range(n_max + 1): - e1 = compute_event_v1(n_val) - e2 = compute_event_v2(n_val) - e3 = compute_event_v3(n_val) - - if (e1 is None) != (e2 is None) != (e3 is None): - continue - if e1 is None: - continue - - v1_count += 1 - v2_count += 1 - v3_count += 1 - - # v1 ⇔ v2 bit-exact - for key in e1: - a, b = e1[key], e2[key] - if isinstance(a, float): - if not math.isclose(a, b, rel_tol=0.0, abs_tol=0.0): - diverged_v2.append({"n": n_val, "key": key, "v1": a, "v2": b}) - break - elif a != b: - diverged_v2.append({"n": n_val, "key": key, "v1": a, "v2": b}) - break - - # v1 ⇔ v3 structural invariants (these must match exactly) - for key in invariant_keys: - a, b = e1[key], e3[key] - if key == "mass": - if a != b: - diverged_v3_invariants.append({"n": n_val, "key": key, "v1": a, "v3": b}) - elif key == "polarity": - if a != b: - diverged_v3_invariants.append({"n": n_val, "key": key, "v1": a, "v3": b}) - elif a != b: - diverged_v3_invariants.append({"n": n_val, "key": key, "v1": a, "v3": b}) - break - - return { - "n_max": n_max, - "v1_event_count": v1_count, - "v2_event_count": v2_count, - "v3_event_count": v3_count, - "divergence_v1_v2_count": len(diverged_v2), - "divergence_v3_invariant_count": len(diverged_v3_invariants), - "first_v1_v2_divergences": diverged_v2[:5], - "first_v3_invariant_divergences": diverged_v3_invariants[:5], - "passes_gate": len(diverged_v2) == 0 and len(diverged_v3_invariants) == 0, - } - - -# ============================================================================= -# 5. PTOS DICTIONARY (Layer 2) -# ============================================================================= - -PTOS_EVENT_TYPE = {"A": 0x00, "G": 0x01, "C": 0x02, "T": 0x03} -PTOS_PHASE = {-3: 0x00, -2: 0x01, -1: 0x02, 0: 0x03, - 1: 0x04, 2: 0x05, 3: 0x06} -PTOS_INDEX_BIT = {0: 0x00, 1: 0x01} -PTOS_PARITY = {False: 0x00, True: 0x01} # v3 parity field - -PTOS_VERSION = 2 # bumped for v3 parity field addition - - -# ============================================================================= -# 6. VARIABLE-LENGTH CODON (Layer 3) -# ============================================================================= - -CODON_BITS = {"A": 0b00, "G": 0b01, "C": 0b10, "T": 0b11} - - -# ============================================================================= -# 7. DELTA ENCODING (Layer 1) — V3 with parity field -# ============================================================================= - -@dataclass -class EventRecordV3: - n: int - codon: str - phase: int - index_bit: int - parity: bool # v3 new field - - @classmethod - def from_event(cls, ev: dict) -> EventRecordV3: - return cls(n=ev["n"], codon=ev["et"], - phase=ev["phase"], index_bit=ev["index_bit"], - parity=ev.get("parity", False)) - - def to_full_bytes(self) -> bytes: - return struct.pack(">BHBbBB", 0x00, self.n, - CODON_BITS[self.codon], self.phase, - self.index_bit, PTOS_PARITY[self.parity]) - - def to_delta_bytes(self, prev_n: int) -> bytes: - dn = self.n - prev_n - if dn < 1 or dn > 255: - return self.to_full_bytes() # fall back to full encoding - return struct.pack(">BBBbBB", 0x01, dn, - CODON_BITS[self.codon], self.phase, - self.index_bit, PTOS_PARITY[self.parity]) - - -def encode_event_stream_delta_gcl_v3(n_max: int) -> tuple[bytes, dict]: - """Run compute_event_v3 over [0, n_max], emit Delta GCL byte stream + stats.""" - stream = bytearray() - raw_json_size = 0 - raw_struct_size = 0 - event_count = 0 - prev_n = None - - # 4-byte header: magic 'BGCL', version, ptos_version, codon_bits - stream.extend(b"BGCL") - stream.extend(struct.pack(">BBB", 3, PTOS_VERSION, 2)) # version=3, ptos=2, 2-bit codons - - for n_val in range(n_max + 1): - ev = compute_event_v3(n_val) - if ev is None: - continue - event_count += 1 - rec = EventRecordV3.from_event(ev) - - raw_json_size += len(json.dumps(ev, sort_keys=True)) - raw_struct_size += 7 # full record always (one extra byte for parity) - - if prev_n is None: - stream.extend(rec.to_full_bytes()) - else: - stream.extend(rec.to_delta_bytes(prev_n)) - prev_n = rec.n - - return bytes(stream), { - "event_count": event_count, - "raw_json_size": raw_json_size, - "raw_struct_size": raw_struct_size, - "delta_gcl_size": len(stream), - } - - -# ============================================================================= -# 8. METAPROBE GATE: round-trip over the delta-GCL stream (v3) -# ============================================================================= - -CODON_FROM_BITS = {v: k for k, v in CODON_BITS.items()} -PARITY_FROM_BITS = {v: k for k, v in PTOS_PARITY.items()} - - -def decode_event_stream_delta_gcl_v3(stream: bytes) -> list[EventRecordV3]: - if stream[:4] != b"BGCL": - raise ValueError("not a BGCL stream") - version, ptos_v, codon_bits = struct.unpack(">BBB", stream[4:7]) - assert version == 3 and ptos_v == PTOS_VERSION and codon_bits == 2 - pos = 7 - out: list[EventRecordV3] = [] - prev_n = None - while pos < len(stream): - tag = stream[pos] - if tag == 0x00: - _, n, c, ph, ib, pa = struct.unpack(">BHBbBB", stream[pos:pos + 7]) - pos += 7 - out.append(EventRecordV3(n=n, codon=CODON_FROM_BITS[c], phase=ph, - index_bit=ib, parity=PARITY_FROM_BITS[pa])) - prev_n = n - elif tag == 0x01: - _, dn, c, ph, ib, pa = struct.unpack(">BBBbBB", stream[pos:pos + 6]) - pos += 6 - n = (prev_n or 0) + dn - out.append(EventRecordV3(n=n, codon=CODON_FROM_BITS[c], phase=ph, - index_bit=ib, parity=PARITY_FROM_BITS[pa])) - prev_n = n - else: - raise ValueError(f"unknown record tag 0x{tag:02x} at offset {pos}") - return out - - -def metaprobe_round_trip_v3(n_max: int) -> dict: - """Encode → decode → compare: every (n, codon, phase, index_bit, parity) preserved.""" - stream, stats = encode_event_stream_delta_gcl_v3(n_max) - decoded = decode_event_stream_delta_gcl_v3(stream) - expected = [] - for n_val in range(n_max + 1): - ev = compute_event_v3(n_val) - if ev is None: - continue - expected.append(EventRecordV3(n=ev["n"], codon=ev["et"], - phase=ev["phase"], index_bit=ev["index_bit"], - parity=ev.get("parity", False))) - mismatches = [] - for got, want in zip(decoded, expected): - if asdict(got) != asdict(want): - mismatches.append({"got": asdict(got), "want": asdict(want)}) - if len(mismatches) >= 5: - break - return { - "n_max": n_max, - "event_count": stats["event_count"], - "delta_gcl_size_bytes": stats["delta_gcl_size"], - "decoded_count": len(decoded), - "mismatch_count": len(mismatches), - "first_mismatches": mismatches, - "passes_gate": len(mismatches) == 0 and len(decoded) == len(expected), - } - - -# ============================================================================= -# 9. SI COMPRESSION RATIO (v3 baseline) -# ============================================================================= - -def compression_report_v3(n_max: int) -> dict: - """Compression measurement for v3 against the international-standard codec baseline set.""" - stream, stats = encode_event_stream_delta_gcl_v3(n_max) - - # Baseline corpus: JSON dump of every full event record (deterministic, reproducible) - json_baseline = bytearray() - for n_val in range(n_max + 1): - ev = compute_event_v3(n_val) - if ev is None: - continue - json_baseline.extend(json.dumps(ev, sort_keys=True).encode("utf-8")) - json_baseline.extend(b"\n") - raw = bytes(json_baseline) - raw_n = len(raw) - - # Baselines: every codec at maximum legal compression level - baselines: dict[str, dict] = { - "zlib": {"bytes": len(zlib.compress(raw, level=9)), "spec": "RFC 1950"}, - "gzip": {"bytes": len(gzip.compress(raw, compresslevel=9)), "spec": "RFC 1952"}, - "bzip2": {"bytes": len(bz2.compress(raw, compresslevel=9)), "spec": "Burrows-Wheeler + Huffman (de facto)"}, - "lzma": {"bytes": len(lzma.compress(raw, preset=9 | lzma.PRESET_EXTREME)), "spec": "ISO/IEC 23001-7 reference; xz container"}, - } - if HAVE_BROTLI: - baselines["brotli"] = {"bytes": len(brotli.compress(raw, quality=11)), "spec": "RFC 7932"} - if HAVE_ZSTD: - cctx = zstandard.ZstdCompressor(level=22) # max - baselines["zstd"] = {"bytes": len(cctx.compress(raw)), "spec": "RFC 8478"} - - # Our stack: delta-GCL alone, and delta-GCL composed with each baseline codec - stack: dict[str, dict] = { - "delta_gcl": {"bytes": len(stream), "spec": "this work"}, - "delta_gcl_then_zlib": {"bytes": len(zlib.compress(stream, level=9)), "spec": "RFC 1950 over delta-GCL"}, - "delta_gcl_then_bzip2": {"bytes": len(bz2.compress(stream, compresslevel=9)),"spec": "bzip2 over delta-GCL"}, - "delta_gcl_then_lzma": {"bytes": len(lzma.compress(stream, preset=9 | lzma.PRESET_EXTREME)), "spec": "xz/lzma over delta-GCL"}, - } - if HAVE_BROTLI: - stack["delta_gcl_then_brotli"] = {"bytes": len(brotli.compress(stream, quality=11)), "spec": "RFC 7932 over delta-GCL"} - if HAVE_ZSTD: - cctx = zstandard.ZstdCompressor(level=22) - stack["delta_gcl_then_zstd"] = {"bytes": len(cctx.compress(stream)), "spec": "RFC 8478 over delta-GCL"} - - def add_metrics(group: dict) -> dict: - for name, entry in group.items(): - entry["ratio_vs_raw"] = raw_n / max(1, entry["bytes"]) - entry["reduction_pct"] = 1 - entry["bytes"] / max(1, raw_n) - return group - - return { - "n_max": n_max, - "event_count": stats["event_count"], - "raw_json_bytes": raw_n, - "ratio_convention": "uncompressed_bytes / compressed_bytes (ISO/IEC 11576 standard practice)", - "byte_unit": "ISO/IEC 80000-13: 1 B = 8 bit", - "baselines": add_metrics(baselines), - "stack": add_metrics(stack), - } - - -# ============================================================================= -# 10. V2 → V3 STRUCTURAL IMPROVEMENT RECEIPT -# ============================================================================= - -def v3_improvement_receipt() -> dict: - p3 = BraidEventParamsV3() - v2_fields = 6 - v3_new_features = { - "parity_gate": { - "active": p3.parity_gate, - "source": "GeneticCode.lean §1 — parityOfEvent (XOR event_bits × polarity_sign)", - "effect": "Fc modulated from 4-level to 8-level via sign flip on odd parity", - }, - "resonance_coupling": { - "active": p3.resonance_coupling, - "source": "ShellModel.lean §4 — SpectralSignature.resonanceDegeneracy", - "effect": "Echo weights scaled by spectral overlap between current and past event types", - }, - "adaptive_decay": { - "active": p3.adaptive_decay, - "source": "GeneticCode.lean §1 parity + ShellModel tail weight system", - "effect": "Parity-modulated echo weights: ×1.5 constructive, ×0.5 destructive interference", - }, - "forward_echo": { - "depth": p3.forward_echo_depth, - "source": "Shell geometry determinism (next shell boundary known from current k)", - "effect": "Anticipatory standing-wave component from forward shell positions", - }, - "extended_tail_depth": { - "from": 3, - "to": p3.tail_depth_v3, - "source": "Extended echo memory with adaptive decay modulation", - "effect": "Longer echo history (5 steps vs 3) with interaction-dependent weighting", - }, - "mass_gate_phase": { - "active": p3.mass_gate_coef != 0.0, - "source": "ShellModel.lean §5 — phaseFromTipAndInteraction (if j > 0 ∧ mass > 0 → +1)", - "effect": "Discrete ±1 phase kick when interaction > 0, gated by mass > 0", - }, - } - return { - "summary": "compute_event v3: 6 Lean-derived structural improvements", - "v3_params": asdict(p3), - "new_features": v3_new_features, - "lean_sources": [ - "0-Core-Formalism/lean/Semantics/Semantics/GeneticCode.lean §1 (parityOfEvent)", - "0-Core-Formalism/lean/Semantics/Semantics/ShellModel.lean §4 (resonanceDegeneracy)", - "0-Core-Formalism/lean/Semantics/Semantics/ShellModel.lean §5 (phaseFromTipAndInteraction)", - ], - "total_param_count_v3": len(asdict(p3)), - "param_increase_v2_to_v3": f"6 → {len(asdict(p3))} (non-breaking for invariants)", - } - - -# ============================================================================= -# 11. MAIN -# ============================================================================= - -def main(): - out_dir = Path(__file__).resolve().parents[3] / "shared-data" / "artifacts" / "formula_optimization" - out_dir.mkdir(parents=True, exist_ok=True) - - N = 2000 # enough events to exercise the formula (~ √N shells) - - print(f"[1/5] metaprobe gate: bit-exact v1 ⇔ v2 over n ∈ [0, {N}]") - eq = metaprobe_equivalence(N) - print(f" v1 events={eq['v1_event_count']} v2 events={eq['v2_event_count']} " - f"v3 events={eq['v3_event_count']}") - print(f" v1⇔v2 divergences={eq['divergence_v1_v2_count']} " - f"v3 invariant divergences={eq['divergence_v3_invariant_count']} " - f"passes={eq['passes_gate']}") - if not eq["passes_gate"]: - for d in eq["first_v1_v2_divergences"]: - print(f" v1⇔v2: {d}") - for d in eq["first_v3_invariant_divergences"]: - print(f" v3 invariant: {d}") - - print(f"[2/5] metaprobe gate: round-trip over v3 delta-GCL stream") - rt = metaprobe_round_trip_v3(N) - print(f" decoded={rt['decoded_count']} mismatches={rt['mismatch_count']} " - f"passes={rt['passes_gate']} size={rt['delta_gcl_size_bytes']} bytes") - - print(f"[3/5] compression report vs international-standard codec baselines (v3)") - cr = compression_report_v3(N) - print(f" raw json : {cr['raw_json_bytes']:>8} B (baseline corpus)") - print(f" -- baselines (codec on raw JSON) --") - for name, e in cr["baselines"].items(): - print(f" {name:<22}: {e['bytes']:>8} B " - f"ratio={e['ratio_vs_raw']:7.2f}x reduction={e['reduction_pct']*100:5.1f}% [{e['spec']}]") - print(f" -- stack (delta-GCL ± codec) --") - for name, e in cr["stack"].items(): - print(f" {name:<22}: {e['bytes']:>8} B " - f"ratio={e['ratio_vs_raw']:7.2f}x reduction={e['reduction_pct']*100:5.1f}% [{e['spec']}]") - - print(f"[4/5] v3 structural improvement receipt") - rec = v3_improvement_receipt() - print(f" v3 params: {rec['total_param_count_v3']}") - for feat_name, feat_info in rec["new_features"].items(): - active_str = "✓" if isinstance(feat_info, dict) and feat_info.get("active") else "–" - print(f" {'✓' if isinstance(feat_info, dict) and feat_info.get('active', True) else '○':<2} {feat_name:<22} [{feat_info.get('source', '')}]") - - print(f"[5/5] v2 canonicalization receipt (original)") - from dataclasses import asdict as orig_asdict - - # Show a few sample events comparing v2 vs v3 - print(f"\n Sample event comparison (v2 vs v3):") - for n_sample in [4, 6, 13, 20, 30, 49]: - ev2 = compute_event_v2(n_sample) - ev3 = compute_event_v3(n_sample) - if ev2 and ev3: - fc2 = ev2["Fc"] - fc3 = ev3["Fc"] - ph2 = ev2["phase"] - ph3 = ev3["phase"] - int2 = ev2["interaction"] - int3 = ev3["interaction"] - parity = ev3["parity"] - mass_gate = ev3["mass_gate"] - print(f" n={n_sample:>4} et={ev2['et']} " - f"v2: Fc={fc2:+.2f} int={int2:+8.2f} ph={ph2:+d} | " - f"v3: Fc={fc3:+.2f} int={int3:+8.2f} ph={ph3:+d} " - f"parity={int(parity)} mass_gate={mass_gate:+.2f}") - - bundle = { - "n_max": N, - "metaprobe_equivalence": eq, - "metaprobe_round_trip_v3": rt, - "compression_report_v3": cr, - "v3_improvement_receipt": rec, - } - out_json = out_dir / "braid_event_delta_gcl_v3_bundle.json" - out_json.write_text(json.dumps(bundle, indent=2)) - print(f"\nwrote v3 bundle: {out_json}") - - # Also write a side-by-side comparison with v2 - comparison = { - "v2_params_count": 6, - "v3_params_count": rec["total_param_count_v3"], - "lean_improvements": rec["new_features"], - "invariant_fields_preserved": list({"n", "k", "et", "mass", "polarity", "shell_width"}), - "delta_gcl_v3_size_bytes": rt["delta_gcl_size_bytes"], - "sample_events": [ - sample for ns in [4, 6, 13, 20, 30, 49, 100] - if (ev2 := compute_event_v2(ns)) and (ev3 := compute_event_v3(ns)) - for sample in [{ - "n": ns, - "v2": {k: ev2[k] for k in ["et", "mass", "polarity", "Fc", "interaction", "phase", "index_bit"]}, - "v3": {k: ev3[k] for k in ["et", "mass", "polarity", "Fc", "interaction", "phase", "index_bit", "parity"]}, - }] - ], - } - comp_json = out_dir / "braid_event_v2_v3_comparison.json" - comp_json.write_text(json.dumps(comparison, indent=2)) - print(f"wrote v2/v3 comparison: {comp_json}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/formula_optimization/braid_event_sidon_addressing.py b/5-Applications/tools-scripts/formula_optimization/braid_event_sidon_addressing.py deleted file mode 100644 index c6f2aae9..00000000 --- a/5-Applications/tools-scripts/formula_optimization/braid_event_sidon_addressing.py +++ /dev/null @@ -1,200 +0,0 @@ -#!/usr/bin/env python3 -""" -braid_event_sidon_addressing.py -================================ - -Two-layer cross-check: do the events emitted by `compute_event` admit -Sidon-style relational addressing (per Two_Layer_Kinetic_Sidon_Lattice_v0_1.md -Gate 2 — Sidon uniqueness)? - -For each candidate pair-signature σ(i, j) over the 175-event population from -braid_event_delta_gcl.py: - - - count nontrivial pair collisions (σ(i,j) == σ(k,l) with {i,j} ≠ {k,l}) - - report fraction of pairs that are uniquely addressable - - generate the smallest greedy Sidon set that labels all events - - compare: kinetic-formula-derived signatures vs Sidon-set-derived signatures - -This is the empirical "Gate 2" for the two-layer lattice spec: the kinetic layer -generates events; the Sidon layer is supposed to give them a non-aliasing address -space. We measure the gap. - -Stdlib only. -""" - -from __future__ import annotations - -import itertools -import json -from pathlib import Path - -# Reuse the canonicalized event generator from the sibling module. -from braid_event_delta_gcl import compute_event_v2, BraidEventParams - - -# ============================================================================= -# 1. Candidate pair-signature functions -# ============================================================================= - -def sig_mass_sum(ei: dict, ej: dict) -> tuple: - return ("mass_sum", ei["mass"] + ej["mass"]) - -def sig_polarity_sum(ei: dict, ej: dict) -> tuple: - return ("polarity_sum", ei["polarity"] + ej["polarity"]) - -def sig_interaction_sum(ei: dict, ej: dict) -> tuple: - return ("interaction_sum", round(ei["interaction"] + ej["interaction"], 6)) - -def sig_n_sum(ei: dict, ej: dict) -> tuple: - """The classical Sidon-set signature: σ(i,j) = n_i + n_j.""" - return ("n_sum", ei["n"] + ej["n"]) - -def sig_phase_codon(ei: dict, ej: dict) -> tuple: - """Pure quantized signature: only the phase + codon survive.""" - return ("phase_codon", tuple(sorted([(ei["phase"], ei["et"]), - (ej["phase"], ej["et"])]))) - -def sig_kinetic_full(ei: dict, ej: dict) -> tuple: - """Composite kinetic signature: every quantity that compute_event outputs.""" - return ("kinetic_full", - (round(ei["mass"] + ej["mass"], 6), - round(ei["polarity"] + ej["polarity"], 6), - round(ei["interaction"] + ej["interaction"], 6), - tuple(sorted([ei["et"], ej["et"]])))) - - -SIGNATURES = [ - sig_mass_sum, - sig_polarity_sum, - sig_n_sum, - sig_interaction_sum, - sig_phase_codon, - sig_kinetic_full, -] - - -# ============================================================================= -# 2. Collision counter -# ============================================================================= - -def collision_report(events: list[dict], sig_fn) -> dict: - """For unordered pairs (i, j) with i < j, count colliding signatures.""" - seen: dict = {} # signature → list of (i, j) - n = len(events) - pair_count = n * (n - 1) // 2 - for i, j in itertools.combinations(range(n), 2): - s = sig_fn(events[i], events[j]) - seen.setdefault(s, []).append((i, j)) - distinct_sigs = len(seen) - collisions = sum(len(v) - 1 for v in seen.values() if len(v) > 1) - colliding_classes = sum(1 for v in seen.values() if len(v) > 1) - return { - "signature_name": sig_fn.__name__, - "pair_count": pair_count, - "distinct_signatures": distinct_sigs, - "collision_count": collisions, - "colliding_equivalence_classes": colliding_classes, - "uniqueness_ratio": distinct_sigs / max(1, pair_count), - "passes_sidon_gate": collisions == 0, - } - - -# ============================================================================= -# 3. Greedy Sidon set generator (mirrors SidonSet.lean) -# ============================================================================= - -def is_sidon(values: list[int]) -> bool: - sums = set() - for i, a in enumerate(values): - for b in values[i:]: - s = a + b - if s in sums: - return False - sums.add(s) - return True - - -def generate_sidon(target_size: int, fuel: int = 1_000_000) -> list[int]: - out = [1] - candidate = 2 - used_sums = {2} # 1 + 1 - while len(out) < target_size: - if fuel <= 0: - raise RuntimeError(f"fuel exhausted at size {len(out)}") - new_sums = {candidate + x for x in out} | {candidate + candidate} - if not (new_sums & used_sums) and len(new_sums) == len({candidate + x for x in out + [candidate]}): - out.append(candidate) - used_sums |= new_sums - candidate += 1 - fuel -= 1 - return out - - -# ============================================================================= -# 4. Main -# ============================================================================= - -def main(): - out_dir = Path(__file__).resolve().parents[3] / "shared-data" / "artifacts" / "formula_optimization" - out_dir.mkdir(parents=True, exist_ok=True) - - N = 2000 - params = BraidEventParams() - events = [e for n in range(N + 1) if (e := compute_event_v2(n, params)) is not None] - print(f"event population: {len(events)} events from n ∈ [0, {N}]\n") - - # ---- candidate kinetic signatures ---- - print("Sidon Gate 2 — pair-signature collision counts:") - print(f"{'signature':<25} {'pairs':>6} {'distinct':>9} {'collisions':>11} {'uniq%':>7} passes?") - print("-" * 75) - sig_reports = [] - for fn in SIGNATURES: - r = collision_report(events, fn) - sig_reports.append(r) - print(f"{r['signature_name']:<25} {r['pair_count']:>6} " - f"{r['distinct_signatures']:>9} {r['collision_count']:>11} " - f"{r['uniqueness_ratio']*100:>6.2f}% {r['passes_sidon_gate']}") - - # ---- Sidon address layer: generate one large enough to label all events ---- - target = len(events) - print(f"\nGenerating greedy Sidon set of size {target}...") - try: - sidon = generate_sidon(target) - print(f" smallest greedy Sidon set: head={sidon[:5]} tail={sidon[-3:]} " - f"max_element={max(sidon)}") - print(f" is_sidon(set)? {is_sidon(sidon)}") - # Build the Sidon-relabeled events and re-test "n_sum" on relabeled n - relabeled = [] - for ev, s_addr in zip(events, sidon): - ev2 = dict(ev) - ev2["n"] = s_addr # replace n with its Sidon address - relabeled.append(ev2) - r = collision_report(relabeled, sig_n_sum) - print(f" n_sum on Sidon-relabeled events: collisions={r['collision_count']} " - f"passes_sidon_gate={r['passes_sidon_gate']}") - sidon_relabel_report = r - except RuntimeError as exc: - print(f" failed: {exc}") - sidon = [] - sidon_relabel_report = {"error": str(exc)} - - bundle = { - "n_max": N, - "event_count": len(events), - "kinetic_signature_reports": sig_reports, - "sidon_address_set": { - "size": len(sidon), - "head": sidon[:10], - "tail": sidon[-3:] if sidon else [], - "max_element": max(sidon) if sidon else None, - "is_sidon": is_sidon(sidon) if sidon else None, - }, - "sidon_relabeled_n_sum_report": sidon_relabel_report, - } - out_json = out_dir / "braid_event_sidon_addressing_bundle.json" - out_json.write_text(json.dumps(bundle, indent=2)) - print(f"\nwrote bundle: {out_json}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/geoweird/__init__.py b/5-Applications/tools-scripts/geoweird/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/5-Applications/tools-scripts/geoweird/demo_self_typing_bridge.py b/5-Applications/tools-scripts/geoweird/demo_self_typing_bridge.py deleted file mode 100644 index b0a0eb50..00000000 --- a/5-Applications/tools-scripts/geoweird/demo_self_typing_bridge.py +++ /dev/null @@ -1,281 +0,0 @@ -""" -Demonstration of GeoWeird Self-Typing Bridge - -Shows the complete flow: -1. Domain expert with 7D constraints -2. Registration with SelfTypingBridge -3. Multi-typed superposition -4. Collision with another domain -5. Perspective collapse -6. Projector spawning -7. Critique with shared metric -8. Invariant derivation -""" - -import json -from pathlib import Path - -from geoweird.self_typing_bridge import ( - init_self_typing_bridge, SelfTypingBridge, - UniverseType, Perspective -) - -from geoweird.geo_aware_agent import ( - create_geo_weird_agent, GeoWeirdAwareAgent -) - -from geoweird.swarm_orchestrator_v2 import ( - GeoWeirdSwarmOrchestrator, run_geo_weird_swarm -) - - -def demo_individual_agent(): - """Demo: Single agent registration and superposition""" - print("="*70) - print("DEMO 1: Individual Agent Registration") - print("="*70) - - # Initialize bridge - bridge = init_self_typing_bridge() - - # Create Lighthouse Keeper with 7D constraints - keeper = create_geo_weird_agent( - name="Lighthouse Keeper", - constraints_7d={ - "T": 0.8, # High temporal (flash interval) - "S": 0.7, # High spatial (tower structure) - "C": 0.6, # Moderate causal (event ordering) - "F": 0.5, # Moderate field (light propagation) - "R": 0.9, # High rotational (Fresnel lens) - "P": 0.4, # Low phase (steady state) - "W": 0.3 # Low wave (local scope) - } - ) - - print(f"\nAgent: {keeper.name}") - print(f"Registered with SelfTypingBridge ✓") - - # Show extracted features - print("\n📐 Extracted Constraint Features:") - f = keeper.domain.features - print(f" Mean curvature: {f.meanCurvature:.2f}") - print(f" Rotational symmetry: {f.rotationalSymmetry:.2f}") - print(f" Translational symmetry: {f.translationalSymmetry:.2f}") - print(f" Has temporal ordering: {f.hasTemporalOrdering}") - print(f" Causal cone angle: {f.causalConeAngle:.2f}") - print(f" Is compact: {f.isCompact}") - print(f" Volume growth rate: {f.volumeGrowthRate:.2f}") - - # Show universe scores - print("\n🌌 Universe Type Scores:") - s = keeper.domain.scores - print(f" Σ₁ Euclidean: {s.euclidean:.2f} (physical structure)") - print(f" Σ₂ Hyperbolic: {s.hyperbolic:.2f} (information tree)") - print(f" Σ₃ Spherical: {s.spherical:.2f} (Fresnel rotation)") - print(f" Σ₄ Lorentzian: {s.lorentzian:.2f} (flash causality)") - print(f" Σ₅ Custom: {s.custom:.2f} (keeper social)") - - # Show superposition - print("\n⚛️ Multi-Typed Superposition:") - for entry in keeper.get_superposition(): - print(f" {entry.perspective.value} → {entry.universe_type.value} (weight: {entry.weight:.2f})") - - # Check if multi-typed - if s.should_multi_type(0.5): - print("\n✓ Agent is MULTI-TYPED (maintaining superposition)") - - return keeper - - -def demo_collision(): - """Demo: Collision between two agents""" - print("\n" + "="*70) - print("DEMO 2: Domain Collision & Perspective Selection") - print("="*70) - - bridge = init_self_typing_bridge() - - # Create two agents - keeper = create_geo_weird_agent( - name="Lighthouse Keeper", - constraints_7d={"T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3} - ) - - chart = create_geo_weird_agent( - name="Maritime Chart", - constraints_7d={"T": 0.3, "S": 0.9, "C": 0.2, "F": 0.4, "R": 0.1, "P": 0.6, "W": 0.2} - ) - - print(f"\nColliding: {keeper.name} ↔ {chart.name}") - - # Try all perspective combinations - collisions = bridge.collide_domains(keeper.name, chart.name) - - print(f"\n🔀 Perspective Combinations Tried:") - for i, c in enumerate(collisions[:5], 1): - print(f" {i}. {c.universe_a.value} × {c.universe_b.value}") - print(f" Consensus: {c.consensus_strength:.2f}") - print(f" Perspective: {c.perspective.value}") - print(f" Intersection volume: {c.intersection_volume:.1f}") - - # Select best - best = bridge.select_best_perspective(keeper.name, chart.name) - - if best: - print(f"\n✓ SELECTED: {best.universe_a.value} × {best.universe_b.value}") - print(f" Consensus: {best.consensus_strength:.2f}") - print(f" Perspective: {best.perspective.value}") - print(f"\n → Keeper will operate in {best.universe_a.value} universe") - print(f" → Chart will operate in {best.universe_b.value} universe") - print(f" → Shared perspective: {best.perspective.value}") - - return keeper, chart, best - - -def demo_collaboration(): - """Demo: Full collaboration with projectors and critique""" - print("\n" + "="*70) - print("DEMO 3: Full Collaboration Flow") - print("="*70) - - bridge = init_self_typing_bridge() - - # Create agents - keeper = create_geo_weird_agent( - name="Lighthouse Keeper", - constraints_7d={"T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3} - ) - - chart = create_geo_weird_agent( - name="Maritime Chart", - constraints_7d={"T": 0.3, "S": 0.9, "C": 0.2, "F": 0.4, "R": 0.1, "P": 0.6, "W": 0.2} - ) - - print(f"\nInitiating collaboration: {keeper.name} + {chart.name}") - - # Initiate collaboration - context = keeper.initiate_collaboration( - chart, - task_description="Coordinate lighthouse visibility with chart navigation" - ) - - if not context: - print("Collaboration failed!") - return - - print(f"\n📍 Collaboration Context:") - print(f" Universe: {context.universe_type.value}") - print(f" Perspective: {context.perspective.value}") - print(f" Metric signature: {context.metric_signature}") - print(f" Curvature: {context.curvature}") - - # Show spawned projectors - print(f"\n🎬 Spawned Projectors ({len(keeper.spawned_projectors)}):") - for p in keeper.spawned_projectors: - print(f" {p['id']} in {p['universe']} universe") - - # Simulate critique - print(f"\n🔍 Critique (using shared metric {context.metric_signature}):") - - mock_output = json.dumps({"visibility_range": 20000, "flash_interval": 5}) - critique = keeper.critique_output( - output=mock_output, - criteria=["completeness", "consistency", "novelty"], - other_agent=chart - ) - - print(f" Evaluations:") - for criterion, score in critique.get("evaluations", {}).items(): - print(f" {criterion}: {score:.2f}") - print(f" Invariant: {critique.get('invariant', 0.0):.3f}") - print(f" Confidence: {critique.get('confidence', 0.0):.3f}") - - return keeper, chart, context, critique - - -def demo_swarm_orchestration(): - """Demo: Full swarm with multiple agents""" - print("\n" + "="*70) - print("DEMO 4: Swarm Orchestration") - print("="*70) - - # Run complete swarm - result = run_geo_weird_swarm(max_rounds=3) - - return result - - -def demo_learned_rules(): - """Demo: Show rules learned from collisions""" - print("\n" + "="*70) - print("DEMO 5: Learned Typing Rules") - print("="*70) - - bridge = init_self_typing_bridge() - - # Create and collide multiple agents to generate rules - agents = [ - ("Lighthouse Keeper", {"T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3}), - ("Maritime Chart", {"T": 0.3, "S": 0.9, "C": 0.2, "F": 0.4, "R": 0.1, "P": 0.6, "W": 0.2}), - ("Fog Signal", {"T": 0.9, "S": 0.3, "C": 0.7, "F": 0.6, "R": 0.2, "P": 0.5, "W": 0.8}), - ("Lens Prism", {"T": 0.4, "S": 0.6, "C": 0.3, "F": 0.8, "R": 0.95, "P": 0.7, "W": 0.4}), - ("Keeper's Log", {"T": 0.6, "S": 0.4, "C": 0.5, "F": 0.3, "R": 0.3, "P": 0.8, "W": 0.5}), - ] - - # Register all agents - for name, constraints in agents: - create_geo_weird_agent(name, constraints_7d=constraints) - - # Generate collisions - for i, (name_a, _) in enumerate(agents): - for name_b, _ in agents[i+1:]: - bridge.collide_domains(name_a, name_b) - - # Get learned rules - rules = bridge.get_learned_rules() - - print(f"\n📜 Learned from {len(bridge.get_collision_history())} collisions:") - print(f"\nDiscovered {len(rules)} typing rules:") - - for rule in rules: - print(f"\n Rule: {rule['universe_type']}") - print(f" Confidence: {rule['confidence']:.2f}") - print(f" Evidence: {rule['evidence_count']} collisions") - print(f" Pattern: {rule['pattern']}") - - print("\n✓ These rules were DISCOVERED, not pre-programmed!") - print(" The swarm learned which constraint features map to which universe types.") - - -def main(): - """Run all demos""" - print("\n" + "="*70) - print("GEOWEIRD SELF-TYPING BRIDGE DEMONSTRATION") - print("="*70) - print("\nThis demonstrates the critical bridge from Lean formalism to Python native agents.") - print("The bridge maps 7D constraints → ConstraintFeatures → MultiTypedDomain.") - - # Run demos - demo_individual_agent() - demo_collision() - demo_collaboration() - demo_swarm_orchestration() - demo_learned_rules() - - print("\n" + "="*70) - print("DEMONSTRATION COMPLETE") - print("="*70) - print("\nKey Takeaways:") - print(" 1. Agents register with 7D constraints") - print(" 2. SelfTypingBridge maps to ConstraintFeatures") - print(" 3. MultiTypedDomain maintains superposition") - print(" 4. Collision selects optimal perspective") - print(" 5. Projectors spawn IN THAT UNIVERSE") - print(" 6. Critique uses shared metric signature") - print(" 7. Invariant derived from manifold geometry") - print(" 8. Rules learned from collision history") - print("\nThe swarm is now GeoWeird-aware! 🌌") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/geoweird/geo_aware_agent.py b/5-Applications/tools-scripts/geoweird/geo_aware_agent.py deleted file mode 100644 index 31e4307d..00000000 --- a/5-Applications/tools-scripts/geoweird/geo_aware_agent.py +++ /dev/null @@ -1,530 +0,0 @@ -""" -GeoWeird-Aware Agent Wrapper - -Wraps native Python agents with GeoWeird self-typing capabilities. -Agents register their 7D constraints and receive multi-typed superposition. -""" - -import hashlib -import json -from typing import List, Dict, Optional, Any, Callable -from dataclasses import dataclass, field -from pathlib import Path - -from geoweird.self_typing_bridge import ( - SelfTypingBridge, MultiTypedDomain, SuperpositionEntry, - UniverseType, Perspective, CollisionResult, - init_self_typing_bridge, get_self_typing_bridge -) - - -@dataclass -class DomainExpertProfile: - """Profile extracted from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md""" - name: str - expertise_area: str - constraints_7d: Dict[str, float] # T, S, C, F, R, P, W - typical_outputs: List[str] - collaboration_patterns: List[str] - - @classmethod - def from_markdown(cls, md_content: str) -> List['DomainExpertProfile']: - """Parse domain expert profiles from emoji-bullet markdown lists.""" - profiles = [] - current_section = "" - - for line in md_content.splitlines(): - stripped = line.strip() - if not stripped: - continue - - # Track section headings for expertise area - if stripped.startswith("## ") or stripped.startswith("### "): - current_section = stripped.lstrip("# ").strip() - continue - - # Parse emoji bullet lists like "- 🔬 Compression Theory Domain Expert" - if stripped.startswith("- "): - # Extract name after the bullet (strip leading emoji if present) - raw_name = stripped[2:].strip() - # Remove leading emoji(s) and whitespace - name = raw_name - while name and not name[0].isalnum(): - name = name[1:].strip() - - if not name: - continue - - profiles.append(cls( - name=name, - expertise_area=current_section, - constraints_7d={}, - typical_outputs=[], - collaboration_patterns=[] - )) - - return profiles - - -@dataclass -class GeoWeirdContext: - """Context for agent operation in a specific universe""" - universe_type: UniverseType - perspective: Perspective - metric_signature: tuple # (positive_dims, negative_dims) - curvature: float - collaboration_id: str - - def to_projector_config(self) -> Dict[str, Any]: - """Convert to Projector agent configuration""" - return { - "universe": self.universe_type.value, - "perspective": self.perspective.value, - "metric": self.metric_signature, - "curvature": self.curvature, - "session": self.collaboration_id - } - - -class GeoWeirdAwareAgent: - """ - Native Python agent wrapped with GeoWeird self-typing. - - This agent: - 1. Registers its 7D constraints with the self-typing bridge - 2. Maintains multi-typed superposition - 3. Collapses to specific universe on collaboration - 4. Spawns Projector agents IN THAT UNIVERSE - 5. Uses shared metric signature for Critique - 6. Derives invariants from collapsed manifold geometry - """ - - def __init__( - self, - name: str, - profile: Optional[DomainExpertProfile] = None, - constraints_7d: Optional[Dict[str, float]] = None, - self_typing_bridge: Optional[SelfTypingBridge] = None - ): - self.name = name - self.bridge = self_typing_bridge or get_self_typing_bridge() - - if not self.bridge: - raise RuntimeError("SelfTypingBridge not initialized. Call init_self_typing_bridge() first.") - - # Register with self-typing bridge - if profile: - self.domain = self._register_from_profile(profile) - elif constraints_7d: - self.domain = self._register_from_7d(constraints_7d) - else: - raise ValueError("Must provide either profile or constraints_7d") - - # Collaboration state - self.current_context: Optional[GeoWeirdContext] = None - self.collaboration_history: List[GeoWeirdContext] = [] - self.spawned_projectors: List[Dict[str, Any]] = [] - - # Learned preferences (which universe types work best) - self.universe_preferences: Dict[UniverseType, float] = {} - - def _register_from_profile(self, profile: DomainExpertProfile) -> MultiTypedDomain: - """Register agent using DomainExpertProfile""" - c = profile.constraints_7d - return self.bridge.register_domain_7d( - name=profile.name, - T=c.get("T", 0.5), - S=c.get("S", 0.5), - C=c.get("C", 0.5), - F=c.get("F", 0.5), - R=c.get("R", 0.5), - P=c.get("P", 0.5), - W=c.get("W", 0.5) - ) - - def _register_from_7d(self, constraints: Dict[str, float]) -> MultiTypedDomain: - """Register agent using 7D constraint vector""" - return self.bridge.register_domain_7d( - name=self.name, - T=constraints.get("T", 0.5), - S=constraints.get("S", 0.5), - C=constraints.get("C", 0.5), - F=constraints.get("F", 0.5), - R=constraints.get("R", 0.5), - P=constraints.get("P", 0.5), - W=constraints.get("W", 0.5) - ) - - # ======================================================================== - # COLLABORATION API - # ======================================================================== - - def initiate_collaboration( - self, - other_agent: 'GeoWeirdAwareAgent', - task_description: str, - collision: Optional[CollisionResult] = None - ) -> Optional[GeoWeirdContext]: - """ - Initiate collaboration with another agent. - - This: - 1. Collides domains via self-typing bridge (unless precomputed collision provided) - 2. Selects perspective that maximizes consensus - 3. Creates GeoWeirdContext for operation - 4. Spawns Projector agents IN THAT UNIVERSE - - Returns None if no viable consensus found. - """ - if collision is not None: - best = collision - else: - # Collide domains - collision_results = self.bridge.collide_domains(self.name, other_agent.name) - - if not collision_results: - print(f"[{self.name}] No viable consensus with {other_agent.name}") - return None - - # Select best perspective - best = collision_results[0] - - # Determine which universe we operate in - my_universe = best.universe_a if best.domain_a == self.name else best.universe_b - - # Create context - context = GeoWeirdContext( - universe_type=my_universe, - perspective=best.perspective, - metric_signature=self._universe_to_metric(my_universe), - curvature=self._universe_to_curvature(my_universe), - collaboration_id=f"{self.name}_{other_agent.name}_{hashlib.sha256(task_description.encode()).hexdigest()[:8]}" - ) - - self.current_context = context - self.collaboration_history.append(context) - - # Update learned preferences - self._update_preferences(my_universe, best.consensus_strength) - - # Spawn Projector agents - self._spawn_projectors(context, task_description) - - print(f"[{self.name}] Collaboration with {other_agent.name}: {my_universe.value} universe, " - f"{best.perspective.value} perspective, consensus={best.consensus_strength:.2f}") - - return context - - def _universe_to_metric(self, u_type: UniverseType) -> tuple: - """Get metric signature for universe type""" - metrics = { - UniverseType.EUCLIDEAN: (3, 0), - UniverseType.HYPERBOLIC: (3, 0), - UniverseType.SPHERICAL: (3, 0), - UniverseType.LORENTZIAN: (3, 1), - UniverseType.CUSTOM: (2, 2) - } - return metrics.get(u_type, (3, 0)) - - def _universe_to_curvature(self, u_type: UniverseType) -> float: - """Get curvature for universe type""" - curvatures = { - UniverseType.EUCLIDEAN: 0.0, - UniverseType.HYPERBOLIC: -1.0, - UniverseType.SPHERICAL: 1.0, - UniverseType.LORENTZIAN: 0.0, - UniverseType.CUSTOM: 0.0 - } - return curvatures.get(u_type, 0.0) - - def _update_preferences(self, u_type: UniverseType, consensus: float): - """Update learned universe preferences""" - if u_type not in self.universe_preferences: - self.universe_preferences[u_type] = 0.0 - - # Exponential moving average - self.universe_preferences[u_type] = ( - 0.7 * self.universe_preferences[u_type] + - 0.3 * consensus - ) - - def _spawn_projectors(self, context: GeoWeirdContext, task: str): - """Spawn Projector agents in the selected universe""" - # Number of projectors based on universe type - projector_counts = { - UniverseType.EUCLIDEAN: 3, - UniverseType.HYPERBOLIC: 5, # More for exponential search - UniverseType.SPHERICAL: 2, # Compact, fewer needed - UniverseType.LORENTZIAN: 4, # Causal chains - UniverseType.CUSTOM: 3 - } - - num_projectors = projector_counts.get(context.universe_type, 3) - - for i in range(num_projectors): - projector = { - "id": f"{self.name}_projector_{i}", - "universe": context.universe_type.value, - "perspective": context.perspective.value, - "task": task, - "metric": context.metric_signature, - "spawned_by": self.name - } - self.spawned_projectors.append(projector) - - print(f"[{self.name}] Spawned {num_projectors} Projectors in {context.universe_type.value} universe") - - # ======================================================================== - # CRITIQUE API - # ======================================================================== - - def critique_output( - self, - output: str, - criteria: List[str], - other_agent: Optional['GeoWeirdAwareAgent'] = None - ) -> Dict[str, Any]: - """ - Critique output using shared metric signature. - - Critics evaluate using the manifold geometry from the - collapsed collaboration context. - """ - if not self.current_context: - return {"error": "No active collaboration context"} - - # Use metric signature from context - pos_dims, neg_dims = self.current_context.metric_signature - - # Evaluate each criterion - evaluations = {} - for criterion in criteria: - # Score based on universe-appropriate metrics - score = self._evaluate_in_universe( - output, criterion, - self.current_context.universe_type - ) - evaluations[criterion] = score - - # Calculate invariant (geometric mean in appropriate metric) - invariant = self._derive_invariant(evaluations, self.current_context) - - return { - "evaluations": evaluations, - "invariant": invariant, - "universe": self.current_context.universe_type.value, - "metric": self.current_context.metric_signature, - "confidence": sum(evaluations.values()) / len(evaluations) if evaluations else 0.0 - } - - def _evaluate_in_universe( - self, - output: str, - criterion: str, - u_type: UniverseType - ) -> float: - """Evaluate output using universe-appropriate metrics""" - # Placeholder: real implementation would use actual criteria - - if u_type == UniverseType.EUCLIDEAN: - # Euclidean: distance-based metrics - return 0.7 + 0.2 * (int(hashlib.sha256((output + criterion).encode()).hexdigest(), 16) % 100) / 100 - - elif u_type == UniverseType.HYPERBOLIC: - # Hyperbolic: exponential scaling - return 0.6 + 0.3 * (int(hashlib.sha256((output + criterion).encode()).hexdigest(), 16) % 100) / 100 - - elif u_type == UniverseType.SPHERICAL: - # Spherical: angular metrics - return 0.75 + 0.15 * (int(hashlib.sha256((output + criterion).encode()).hexdigest(), 16) % 100) / 100 - - elif u_type == UniverseType.LORENTZIAN: - # Lorentzian: causal consistency - return 0.65 + 0.25 * (int(hashlib.sha256((output + criterion).encode()).hexdigest(), 16) % 100) / 100 - - else: - return 0.5 + 0.3 * (int(hashlib.sha256((output + criterion).encode()).hexdigest(), 16) % 100) / 100 - - def _derive_invariant( - self, - evaluations: Dict[str, float], - context: GeoWeirdContext - ) -> float: - """Derive geometric invariant from evaluations""" - if not evaluations: - return 0.0 - - values = list(evaluations.values()) - - if context.universe_type == UniverseType.EUCLIDEAN: - # Euclidean: arithmetic mean - return sum(values) / len(values) - - elif context.universe_type == UniverseType.HYPERBOLIC: - # Hyperbolic: exponential of mean of logs - import math - log_sum = sum(math.log(max(v, 0.001)) for v in values) - return math.exp(log_sum / len(values)) - - elif context.universe_type == UniverseType.SPHERICAL: - # Spherical: minimum (most restrictive) - return min(values) - - elif context.universe_type == UniverseType.LORENTZIAN: - # Lorentzian: weighted by causal importance - return sum(values) / len(values) # Simplified - - else: - return sum(values) / len(values) - - # ======================================================================== - # INTEGRATION API - # ======================================================================== - - def integrate_results( - self, - projector_outputs: List[Dict[str, Any]], - critiques: List[Dict[str, Any]] - ) -> Dict[str, Any]: - """ - Integrate Projector outputs and Critique evaluations. - - Derives final invariant from the collapsed manifold geometry. - """ - if not self.current_context: - return {"error": "No active collaboration context"} - - # Aggregate projector outputs - aggregated = self._aggregate_projectors(projector_outputs) - - # Weight by critique confidence - weighted = self._weight_by_critique(aggregated, critiques) - - # Derive final invariant - final_invariant = self._derive_final_invariant(weighted, self.current_context) - - return { - "integrated_output": weighted, - "final_invariant": final_invariant, - "universe": self.current_context.universe_type.value, - "manifold_geometry": { - "curvature": self.current_context.curvature, - "metric": self.current_context.metric_signature - } - } - - def _aggregate_projectors( - self, - outputs: List[Dict[str, Any]] - ) -> Dict[str, Any]: - """Aggregate outputs from multiple projectors""" - # Simplified: just take the most common - if not outputs: - return {} - - # Group by content - from collections import Counter - contents = [json.dumps(o.get("content", {}), sort_keys=True) for o in outputs] - most_common = Counter(contents).most_common(1)[0][0] - - return {"aggregated": most_common, "count": len(outputs)} - - def _weight_by_critique( - self, - aggregated: Dict[str, Any], - critiques: List[Dict[str, Any]] - ) -> Dict[str, Any]: - """Weight aggregated output by critique confidence""" - avg_confidence = sum( - c.get("confidence", 0.5) for c in critiques - ) / len(critiques) if critiques else 0.5 - - return { - **aggregated, - "weighted_confidence": avg_confidence - } - - def _derive_final_invariant( - self, - weighted: Dict[str, Any], - context: GeoWeirdContext - ) -> float: - """Derive final geometric invariant""" - base = weighted.get("weighted_confidence", 0.5) - - # Adjust by curvature - if context.curvature > 0: - # Spherical: more restrictive - return base * 0.9 - elif context.curvature < 0: - # Hyperbolic: more permissive - return min(1.0, base * 1.1) - else: - # Euclidean: neutral - return base - - # ======================================================================== - # STATE API - # ======================================================================== - - def get_superposition(self) -> List[SuperpositionEntry]: - """Get current multi-typed superposition""" - return self.domain.superposition - - def get_converged_type(self, threshold: float = 0.8) -> Optional[UniverseType]: - """Check if agent has converged on single universe type""" - return self.bridge.has_converged(self.name, threshold) - - def get_preferences(self) -> Dict[UniverseType, float]: - """Get learned universe preferences""" - return self.universe_preferences.copy() - - def reset_collaboration(self): - """Reset current collaboration context""" - self.current_context = None - self.spawned_projectors = [] - - -# ================================================================================ -# FACTORY FUNCTIONS -# ================================================================================ - -def create_geo_weird_agent( - name: str, - md_file: Optional[Path] = None, - constraints_7d: Optional[Dict[str, float]] = None -) -> GeoWeirdAwareAgent: - """ - Factory function to create GeoWeird-aware agent. - - Usage: - agent = create_geo_weird_agent( - name="Lighthouse Keeper", - constraints_7d={"T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3} - ) - """ - profile = None - - if md_file and md_file.exists(): - content = md_file.read_text() - profiles = DomainExpertProfile.from_markdown(content) - profile = next((p for p in profiles if p.name == name), None) - - return GeoWeirdAwareAgent( - name=name, - profile=profile, - constraints_7d=constraints_7d - ) - - -def load_domain_experts_from_markdown(md_path: Path) -> List[GeoWeirdAwareAgent]: - """Load all domain experts from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md""" - content = md_path.read_text() - profiles = DomainExpertProfile.from_markdown(content) - - agents = [] - for profile in profiles: - agent = GeoWeirdAwareAgent(name=profile.name, profile=profile) - agents.append(agent) - - return agents diff --git a/5-Applications/tools-scripts/geoweird/lean_bridge.py b/5-Applications/tools-scripts/geoweird/lean_bridge.py deleted file mode 100644 index f4aed5f9..00000000 --- a/5-Applications/tools-scripts/geoweird/lean_bridge.py +++ /dev/null @@ -1,317 +0,0 @@ -""" -GeoWeird Lean Bridge - Python bindings for Lean formalization -Connects thermal_arbiter.py, mass_archivist.py, sovereign_warden to Lean FFI -""" - -import ctypes -import numpy as np -from typing import Optional, List, Tuple -from dataclasses import dataclass -from pathlib import Path -import json - -# Load the Lean FFI shared library -_LIB_PATH = Path(__file__).parent.parent / "build" / "lib" / "libgeoweirdffi.so" - -try: - _lib = ctypes.CDLL(str(_LIB_PATH)) -except OSError: - print(f"[LeanBridge] FFI library not found at {_LIB_PATH}, using mock mode") - _lib = None - - -@dataclass -class PhaseTransitionSignal: - """Signal from Lean phase detector""" - should_transition: bool - target_phase: str - entropy_seed: float - frozen_isa_hash: str - - -@dataclass -class WitnessOpcode: - """WITNESS_* opcode from emergence""" - opcode_type: str # "BASIC", "EMERGENCE", "COLLIDE", "NOVEL" - geometry_budget: float - trust_stamp: float - recoverability_class: int - raw_bytes: bytes - - -class LeanBridge: - """ - Bridge between Python implementation and Lean formalization. - - This class: - 1. Exports Python state to Lean (stress levels, metrics, etc.) - 2. Receives phase transition signals from Lean - 3. Coordinates emergence protocol execution - 4. Persists discovered witnesses via mass_archivist - """ - - def __init__( - self, - thermal_arbiter=None, - mass_archivist=None, - warden_bridge=None - ): - self.thermal = thermal_arbiter - self.archivist = mass_archivist - self.warden = warden_bridge - - # Phase transition state - self.current_phase = "Darwinian" - self.metric_history: List[Tuple[float, float]] = [] # (metric, load) - self.emergence_active = False - self.truth_registry: List[WitnessOpcode] = [] - - # Thresholds (match Lean defaults) - self.thresholds = { - "min_improvement_rate": 0.001, - "max_acceleration": 0.0001, - "sustained_windows": 10, - "entropy_floor": 0.5 - } - - # Initialize FFI if available - if _lib: - self._init_ffi() - - def _init_ffi(self): - """Initialize C FFI layer""" - # Create opaque handles for Python objects - thermal_handle = ctypes.py_object(self.thermal) if self.thermal else None - archivist_handle = ctypes.py_object(self.archivist) if self.archivist else None - warden_handle = ctypes.py_object(self.warden) if self.warden else None - - # Call geoweird_ffi_init - _lib.geoweird_ffi_init.argtypes = [ - ctypes.py_object, ctypes.py_object, ctypes.py_object - ] - _lib.geoweird_ffi_init(thermal_handle, archivist_handle, warden_handle) - - def record_epoch(self, metric: float, cognitive_load: float) -> Optional[PhaseTransitionSignal]: - """ - Record optimization result and check for phase transition. - - Called by sovereign_warden after each batch attestation. - - Args: - metric: Optimization metric (e.g., compression ratio) - cognitive_load: From COGNITIVE_LOAD_FUNCTIONS_SPEC - - Returns: - PhaseTransitionSignal if transition should occur, None otherwise - """ - self.metric_history.append((metric, cognitive_load)) - - # Keep only last 1000 points - if len(self.metric_history) > 1000: - self.metric_history = self.metric_history[-1000:] - - # Check for diminishing returns - if len(self.metric_history) >= self.thresholds["sustained_windows"]: - if self._check_diminishing_returns(): - return self._trigger_emergence() - - return None - - def _check_diminishing_returns(self) -> bool: - """Check if improvement has plateaued""" - recent = self.metric_history[-self.thresholds["sustained_windows"]:] - metrics = [m for m, _ in recent] - - # Calculate first derivative (improvement rate) - first_derivs = [metrics[i+1] - metrics[i] for i in range(len(metrics)-1)] - avg_first = sum(first_derivs) / len(first_derivs) - - # Calculate second derivative (acceleration) - second_derivs = [first_derivs[i+1] - first_derivs[i] for i in range(len(first_derivs)-1)] - avg_second = sum(abs(d) for d in second_derivs) / len(second_derivs) - - # Check conditions - improvement_stalled = avg_first <= self.thresholds["min_improvement_rate"] - acceleration_collapsed = avg_second < self.thresholds["max_acceleration"] - - return improvement_stalled and acceleration_collapsed - - def _trigger_emergence(self) -> PhaseTransitionSignal: - """Trigger emergence phase""" - self.emergence_active = True - self.current_phase = "Emergence" - - # Calculate entropy seed from metric history - entropy = self._calculate_entropy() - - signal = PhaseTransitionSignal( - should_transition=True, - target_phase="Emergence", - entropy_seed=entropy, - frozen_isa_hash=self._capture_isa_hash() - ) - - print(f"[LeanBridge] Phase transition triggered!") - print(f" Entropy seed: {entropy:.4f}") - print(f" Metric history: {len(self.metric_history)} points") - - return signal - - def _calculate_entropy(self) -> float: - """Calculate Shannon entropy of metric distribution""" - if len(self.metric_history) < 2: - return 0.5 - - metrics = np.array([m for m, _ in self.metric_history]) - # Simple entropy estimate from variance - variance = np.var(metrics) - return min(1.0, variance / (1.0 + variance)) - - def _capture_isa_hash(self) -> str: - """Capture current ISA version hash""" - # In real implementation, hash current opcode set - import hashlib - isa_data = json.dumps({"version": "2.0.0", "opcodes": []}) - return hashlib.sha256(isa_data.encode()).hexdigest()[:16] - - def run_emergence( - self, - universe_configs: List[dict], - collision_budget: int = 10000 - ) -> List[WitnessOpcode]: - """ - Run emergence protocol with multi-universe collision. - - Args: - universe_configs: List of 5 universe configurations - collision_budget: Max collisions before stopping - - Returns: - List of discovered WITNESS_* opcodes - """ - if len(universe_configs) != 5: - raise ValueError("Exactly 5 universes required (Euclidean, Hyperbolic, Spherical, Lorentzian, Custom)") - - print(f"[LeanBridge] Starting emergence with {collision_budget} collision budget") - - discovered = [] - - # Generate all pairwise collisions - pairs = [(i, j) for i in range(5) for j in range(i+1, 5)] - - for collision_id in range(min(collision_budget, len(pairs) * 100)): - # Select universe pair - a_idx, b_idx = pairs[collision_id % len(pairs)] - - # Simulate collision (real impl uses Lean formalization) - witness = self._simulate_collision( - universe_configs[a_idx], - universe_configs[b_idx], - collision_id - ) - - if witness: - discovered.append(witness) - self.truth_registry.append(witness) - - # Persist via mass_archivist if available - if self.archivist: - self._persist_witness(witness) - - print(f"[LeanBridge] Emergence complete: {len(discovered)} witnesses discovered") - - self.emergence_active = False - self.current_phase = "Consolidation" - - return discovered - - def _simulate_collision( - self, - universe_a: dict, - universe_b: dict, - collision_id: int - ) -> Optional[WitnessOpcode]: - """Simulate collision between two universes""" - # Check compatibility - if universe_a.get("dimension") != universe_b.get("dimension"): - return None - - # Calculate consensus strength - curvature_product = universe_a.get("curvature", 0) * universe_b.get("curvature", 0) - consensus_strength = 0.5 + 0.5 * np.tanh(curvature_product) - - if consensus_strength < 0.49: - return None # Weak consensus - - # Generate witness - witness = WitnessOpcode( - opcode_type="EMERGENCE", - geometry_budget=min( - universe_a.get("volume", 1.0), - universe_b.get("volume", 1.0) - ) * 0.1, - trust_stamp=consensus_strength, - recoverability_class=1, - raw_bytes=f"WITNESS_EMERGENCE_{collision_id}".encode() - ) - - return witness - - def _persist_witness(self, witness: WitnessOpcode) -> bool: - """Persist witness via mass_archivist""" - if not self.archivist: - return False - - # Wait for rest event - if hasattr(self.archivist, 'arbiter'): - if not self.archivist.arbiter.rest_event.wait(timeout=5.0): - return False - - # Serialize and submit - data = json.dumps({ - "type": witness.opcode_type, - "budget": witness.geometry_budget, - "trust": witness.trust_stamp, - "recoverability": witness.recoverability_class - }).encode() - - # Submit to archivist (mock) - print(f"[LeanBridge] Persisted witness (trust={witness.trust_stamp:.3f})") - return True - - def export_to_lean(self) -> dict: - """Export current state for Lean verification""" - return { - "phase": self.current_phase, - "metric_history": self.metric_history, - "truth_registry": [ - { - "type": w.opcode_type, - "budget": w.geometry_budget, - "trust": w.trust_stamp - } - for w in self.truth_registry - ], - "thresholds": self.thresholds - } - - def cleanup(self): - """Cleanup FFI resources""" - if _lib: - _lib.geoweird_ffi_cleanup() - - -# Singleton instance for global access -_bridge: Optional[LeanBridge] = None - - -def init_bridge(thermal=None, archivist=None, warden=None) -> LeanBridge: - """Initialize global Lean bridge""" - global _bridge - _bridge = LeanBridge(thermal, archivist, warden) - return _bridge - - -def get_bridge() -> Optional[LeanBridge]: - """Get global Lean bridge instance""" - return _bridge diff --git a/5-Applications/tools-scripts/geoweird/meta_self_typing.py b/5-Applications/tools-scripts/geoweird/meta_self_typing.py deleted file mode 100644 index 0fe9b3eb..00000000 --- a/5-Applications/tools-scripts/geoweird/meta_self_typing.py +++ /dev/null @@ -1,611 +0,0 @@ -""" -Meta-Self-Typing: The System Learns How to Learn - -This module implements the bootstrap ladder: -- Level 0: Base universe types (Euclidean, Hyperbolic, Spherical, Lorentzian, Custom) -- Level 1: Domains self-type into Level 0 -- Level 2: The self-typing ALGORITHM self-types and improves itself -- Level 3: The meta-learning STRATEGY optimizes Level 2 -- Level 4: The bootstrap ladder itself becomes optimizable -""" - -import numpy as np -from typing import List, Dict, Callable, Optional, Tuple, Any -from dataclasses import dataclass, field -from enum import Enum, auto -import json -from pathlib import Path - -from .self_typing_bridge import ( - SelfTypingBridge, ConstraintFeatures, UniverseScores, UniverseType, - MultiTypedDomain, SuperpositionEntry -) - - -class TypingStrategy(Enum): - """Level 2: Different strategies for mapping 7D → UniverseType""" - FEATURE_BASED = "feature_based" # Current: hand-crafted feature extraction - NEURAL_NETWORK = "neural_network" # Learned: trainable neural network - SYMBOLIC_LOGIC = "symbolic_logic" # Rule-based: evolving rule set - EVOLUTIONARY = "evolutionary" # GA: genetic algorithm for type discovery - HYBRID = "hybrid" # Combine multiple strategies - - -class MetaStrategy(Enum): - """Level 3: Strategies for optimizing Level 2""" - GRADIENT_DESCENT = "gradient_descent" # Continuous parameter optimization - EVOLUTIONARY_SEARCH = "evolutionary_search" # Evolve population of algorithms - BANDIT_ALGORITHM = "bandit_algorithm" # Multi-armed bandit selection - SELF_REFERENTIAL = "self_referential" # Apply same typing to meta-level - - -@dataclass -class AlgorithmPerformance: - """Track how well a typing algorithm performs""" - algorithm_id: str - total_domains_typed: int = 0 - correct_classifications: int = 0 - average_confidence: float = 0.0 - consensus_quality: float = 0.0 # Average consensus in collisions - training_time: float = 0.0 - inference_time: float = 0.0 - - @property - def accuracy(self) -> float: - if self.total_domains_typed == 0: - return 0.0 - return self.correct_classifications / self.total_domains_typed - - def to_dict(self) -> Dict[str, Any]: - return { - "algorithm_id": self.algorithm_id, - "accuracy": self.accuracy, - "average_confidence": self.average_confidence, - "consensus_quality": self.consensus_quality, - "domains_typed": self.total_domains_typed - } - - -@dataclass -class Level2Algorithm: - """ - Level 2: A self-typing algorithm that can improve itself. - - This is the algorithm that maps 7D constraints → UniverseType. - It has tunable parameters and can be trained. - """ - name: str - strategy: TypingStrategy - - # Tunable parameters (what gets optimized) - feature_weights: np.ndarray # 7D → 11 features (7 × 11 = 77 weights) - scoring_weights: np.ndarray # 11 features → 5 universes (11 × 5 = 55 weights) - - # Performance tracking - performance: AlgorithmPerformance = field(default_factory=lambda: AlgorithmPerformance("")) - - # Can this algorithm modify itself? - is_self_modifying: bool = True - - def __post_init__(self): - if self.performance.algorithm_id == "": - self.performance.algorithm_id = self.name - - # Initialize weights if not provided - if not hasattr(self, 'feature_weights') or self.feature_weights is None: - self.feature_weights = np.random.randn(7, 11) * 0.1 - if not hasattr(self, 'scoring_weights') or self.scoring_weights is None: - self.scoring_weights = np.random.randn(11, 5) * 0.1 - - def extract_features(self, constraints_7d: Dict[str, float]) -> np.ndarray: - """Extract 11 features from 7D constraints using learned weights""" - # Convert 7D to vector - input_vec = np.array([ - constraints_7d.get("T", 0.5), - constraints_7d.get("S", 0.5), - constraints_7d.get("C", 0.5), - constraints_7d.get("F", 0.5), - constraints_7d.get("R", 0.5), - constraints_7d.get("P", 0.5), - constraints_7d.get("W", 0.5) - ]) - - # Apply learned feature extraction - features = np.tanh(input_vec @ self.feature_weights) # 11 features - - return features - - def calculate_scores(self, features: np.ndarray) -> UniverseScores: - """Calculate universe scores using learned weights""" - # Apply learned scoring - raw_scores = features @ self.scoring_weights # 5 scores - - # Softmax to get probabilities - exp_scores = np.exp(raw_scores - np.max(raw_scores)) - probs = exp_scores / exp_scores.sum() - - return UniverseScores( - euclidean=float(probs[0]), - hyperbolic=float(probs[1]), - spherical=float(probs[2]), - lorentzian=float(probs[3]), - custom=float(probs[4]) - ) - - def train( - self, - training_data: List[Tuple[Dict[str, float], UniverseType]], - epochs: int = 100, - learning_rate: float = 0.01 - ) -> 'Level2Algorithm': - """Train the algorithm on labeled examples""" - - for epoch in range(epochs): - total_loss = 0.0 - - for constraints_7d, true_type in training_data: - # Forward pass - features = self.extract_features(constraints_7d) - scores = self.calculate_scores_vector(features) - - # Compute loss (cross-entropy) - true_idx = self._universe_to_index(true_type) - loss = -np.log(scores[true_idx] + 1e-10) - total_loss += loss - - # Backward pass (simplified gradient descent) - # In practice, use proper backprop - self._gradient_update(constraints_7d, true_idx, learning_rate) - - if epoch % 10 == 0: - print(f" Epoch {epoch}: loss = {total_loss / len(training_data):.4f}") - - return self - - def calculate_scores_vector(self, features: np.ndarray) -> np.ndarray: - """Return scores as vector for training""" - raw_scores = features @ self.scoring_weights - exp_scores = np.exp(raw_scores - np.max(raw_scores)) - return exp_scores / exp_scores.sum() - - def _universe_to_index(self, u_type: UniverseType) -> int: - mapping = { - UniverseType.EUCLIDEAN: 0, - UniverseType.HYPERBOLIC: 1, - UniverseType.SPHERICAL: 2, - UniverseType.LORENTZIAN: 3, - UniverseType.CUSTOM: 4 - } - return mapping.get(u_type, 0) - - def _gradient_update( - self, - constraints_7d: Dict[str, float], - true_idx: int, - lr: float - ): - """Simplified gradient update (in practice use autograd)""" - # Numerical gradient for demonstration - epsilon = 0.01 - - input_vec = np.array([ - constraints_7d.get("T", 0.5), - constraints_7d.get("S", 0.5), - constraints_7d.get("C", 0.5), - constraints_7d.get("F", 0.5), - constraints_7d.get("R", 0.5), - constraints_7d.get("P", 0.5), - constraints_7d.get("W", 0.5) - ]) - - # Update feature weights (simplified) - for i in range(7): - for j in range(11): - self.feature_weights[i, j] += lr * epsilon * (np.random.random() - 0.5) - - # Update scoring weights (simplified) - for i in range(11): - for j in range(5): - if j == true_idx: - self.scoring_weights[i, j] += lr * 0.1 - else: - self.scoring_weights[i, j] -= lr * 0.02 - - -@dataclass -class MetaOptimizer: - """ - Level 3: Optimizes Level 2 algorithms. - - This is the meta-learner that decides: - - Which Level 2 algorithm to use for which domain - - How to train/improve Level 2 algorithms - - When to create new algorithms - """ - name: str - meta_strategy: MetaStrategy - - # Population of Level 2 algorithms - algorithms: List[Level2Algorithm] = field(default_factory=list) - - # Performance tracking per algorithm per domain type - performance_matrix: Dict[str, Dict[str, float]] = field(default_factory=dict) - - # Bandit state (for bandit strategy) - bandit_counts: Dict[str, int] = field(default_factory=dict) - bandit_rewards: Dict[str, float] = field(default_factory=dict) - - def __post_init__(self): - if not self.algorithms: - # Initialize with default algorithms - self.algorithms = [ - Level2Algorithm("feature_based_v1", TypingStrategy.FEATURE_BASED, None, None), - Level2Algorithm("neural_v1", TypingStrategy.NEURAL_NETWORK, None, None), - ] - - def select_algorithm( - self, - domain_features: ConstraintFeatures, - explore: float = 0.1 - ) -> Level2Algorithm: - """Select best algorithm for given domain features""" - - if self.meta_strategy == MetaStrategy.BANDIT_ALGORITHM: - return self._bandit_selection(explore) - - elif self.meta_strategy == MetaStrategy.EVOLUTIONARY_SEARCH: - return self._evolutionary_selection() - - elif self.meta_strategy == MetaStrategy.GRADIENT_DESCENT: - # Use algorithm with best overall performance - return max(self.algorithms, key=lambda a: a.performance.accuracy) - - else: # SELF_REFERENTIAL - # Apply same typing logic to select algorithm - return self._self_referential_selection(domain_features) - - def _bandit_selection(self, explore: float) -> Level2Algorithm: - """Multi-armed bandit: epsilon-greedy""" - if np.random.random() < explore: - # Explore: random algorithm - return np.random.choice(self.algorithms) - - # Exploit: best average reward - best_algo = None - best_reward = -float('inf') - - for algo in self.algorithms: - algo_id = algo.name - count = self.bandit_counts.get(algo_id, 1) - reward = self.bandit_rewards.get(algo_id, 0.0) / count - - if reward > best_reward: - best_reward = reward - best_algo = algo - - return best_algo or self.algorithms[0] - - def _evolutionary_selection(self) -> Level2Algorithm: - """Select fittest algorithm from population""" - # Sort by fitness (accuracy) - sorted_algos = sorted( - self.algorithms, - key=lambda a: a.performance.accuracy, - reverse=True - ) - - # Return fittest (with some diversity) - if len(sorted_algos) > 1 and np.random.random() < 0.2: - return sorted_algos[1] # Occasional diversity - return sorted_algos[0] - - def _self_referential_selection( - self, - domain_features: ConstraintFeatures - ) -> Level2Algorithm: - """Apply same typing logic to select algorithm""" - # Extract 7D-like constraints from algorithm performance - # This is the recursive step! - - # For now, use simple heuristic - if domain_features.hasTemporalOrdering: - # Time-sensitive domains need adaptive algorithms - return next( - (a for a in self.algorithms if "neural" in a.name), - self.algorithms[0] - ) - else: - # Stable domains can use feature-based - return next( - (a for a in self.algorithms if "feature" in a.name), - self.algorithms[0] - ) - - def update_performance( - self, - algorithm: Level2Algorithm, - domain_type: str, - success: bool, - confidence: float - ): - """Update performance tracking after using an algorithm""" - algo_id = algorithm.name - - # Update bandit state - self.bandit_counts[algo_id] = self.bandit_counts.get(algo_id, 0) + 1 - self.bandit_rewards[algo_id] = self.bandit_rewards.get(algo_id, 0.0) + confidence - - # Update performance matrix - if algo_id not in self.performance_matrix: - self.performance_matrix[algo_id] = {} - - current = self.performance_matrix[algo_id].get(domain_type, 0.0) - # Exponential moving average - self.performance_matrix[algo_id][domain_type] = 0.9 * current + 0.1 * confidence - - # Update algorithm's own tracking - algorithm.performance.total_domains_typed += 1 - if success: - algorithm.performance.correct_classifications += 1 - - def evolve_population(self, generations: int = 5): - """Evolve the population of algorithms""" - for gen in range(generations): - print(f"\nEvolution generation {gen + 1}/{generations}") - - # Select parents (top 50%) - sorted_algos = sorted( - self.algorithms, - key=lambda a: a.performance.accuracy, - reverse=True - ) - parents = sorted_algos[:max(1, len(sorted_algos) // 2)] - - # Create offspring - offspring = [] - for i in range(len(parents)): - for j in range(i + 1, len(parents)): - child = self._crossover(parents[i], parents[j]) - child = self._mutate(child) - offspring.append(child) - - # Replace worst with offspring - self.algorithms = parents + offspring - print(f" Population size: {len(self.algorithms)}") - - def _crossover(self, parent1: Level2Algorithm, parent2: Level2Algorithm) -> Level2Algorithm: - """Create child algorithm from two parents""" - child = Level2Algorithm( - name=f"evolved_{parent1.name}_{parent2.name}", - strategy=TypingStrategy.HYBRID - ) - - # Average weights - child.feature_weights = (parent1.feature_weights + parent2.feature_weights) / 2 - child.scoring_weights = (parent1.scoring_weights + parent2.scoring_weights) / 2 - - return child - - def _mutate(self, algo: Level2Algorithm, rate: float = 0.1) -> Level2Algorithm: - """Mutate algorithm weights""" - algo.feature_weights += np.random.randn(*algo.feature_weights.shape) * rate - algo.scoring_weights += np.random.randn(*algo.scoring_weights.shape) * rate - return algo - - -class MetaSelfTypingBridge: - """ - The complete meta-self-typing system. - - This wraps the base SelfTypingBridge and adds: - - Multiple Level 2 algorithms - - Level 3 meta-optimization - - Continuous self-improvement - """ - - def __init__(self, base_bridge: Optional['SelfTypingBridge'] = None): - self.base_bridge = base_bridge - - # Level 3: Meta-optimizer - self.meta_optimizer = MetaOptimizer( - name="meta_optimizer_v1", - meta_strategy=MetaStrategy.BANDIT_ALGORITHM - ) - - # Training data (for supervised learning) - self.training_data: List[Tuple[Dict[str, float], UniverseType]] = [] - - # Improvement history - self.improvement_log: List[Dict[str, Any]] = [] - - def register_domain_7d( - self, - name: str, - T: float, S: float, C: float, F: float, R: float, P: float, W: float - ) -> MultiTypedDomain: - """Register domain using the best available algorithm""" - - # Extract features (using base bridge for now) - constraints_7d = {"T": T, "S": S, "C": C, "F": F, "R": R, "P": P, "W": W} - - # Select best algorithm for this domain - features = ConstraintFeatures() # Simplified - algorithm = self.meta_optimizer.select_algorithm(features, explore=0.1) - - print(f"[MetaSelfTyping] Using algorithm: {algorithm.name}") - - # Use selected algorithm to type domain - features_vec = algorithm.extract_features(constraints_7d) - scores = algorithm.calculate_scores(features_vec) - - # Build superposition (simplified) - from .self_typing_bridge import SuperpositionEntry, Perspective - superposition = [] - - universe_scores = [ - (UniverseType.EUCLIDEAN, scores.euclidean), - (UniverseType.HYPERBOLIC, scores.hyperbolic), - (UniverseType.SPHERICAL, scores.spherical), - (UniverseType.LORENTZIAN, scores.lorentzian), - (UniverseType.CUSTOM, scores.custom) - ] - - for u_type, score in universe_scores: - if score > 0.3: - perspective = self._universe_to_perspective(u_type) - superposition.append(SuperpositionEntry(u_type, score, perspective)) - - domain = MultiTypedDomain( - name=name, - features=features, - scores=scores, - superposition=superposition - ) - - # Update meta-optimizer - self.meta_optimizer.update_performance( - algorithm, "general", success=True, confidence=scores.best_fit()[1] - ) - - return domain - - def _universe_to_perspective(self, u_type: UniverseType) -> Perspective: - """Map universe type to perspective""" - from .self_typing_bridge import Perspective - mapping = { - UniverseType.EUCLIDEAN: Perspective.PHYSICAL, - UniverseType.HYPERBOLIC: Perspective.INFORMATIONAL, - UniverseType.SPHERICAL: Perspective.ENERGETIC, - UniverseType.LORENTZIAN: Perspective.TEMPORAL, - UniverseType.CUSTOM: Perspective.SOCIAL - } - return mapping.get(u_type, Perspective.PHYSICAL) - - def train_algorithms(self, epochs: int = 50): - """Train all Level 2 algorithms on accumulated data""" - if not self.training_data: - print("[MetaSelfTyping] No training data available") - return - - print(f"\n[MetaSelfTyping] Training {len(self.meta_optimizer.algorithms)} algorithms") - print(f" Training data: {len(self.training_data)} examples") - - for algo in self.meta_optimizer.algorithms: - if algo.strategy == TypingStrategy.NEURAL_NETWORK: - print(f"\n Training {algo.name}...") - algo.train(self.training_data, epochs=epochs) - - def evolve_algorithms(self, generations: int = 5): - """Evolve the population of algorithms""" - self.meta_optimizer.evolve_population(generations) - - def add_training_example( - self, - constraints_7d: Dict[str, float], - true_type: UniverseType - ): - """Add a labeled example for training""" - self.training_data.append((constraints_7d, true_type)) - - def get_best_algorithm(self) -> Optional[Level2Algorithm]: - """Get the current best-performing algorithm""" - if not self.meta_optimizer.algorithms: - return None - return max(self.meta_optimizer.algorithms, key=lambda a: a.performance.accuracy) - - def generate_report(self) -> Dict[str, Any]: - """Generate comprehensive report""" - return { - "num_algorithms": len(self.meta_optimizer.algorithms), - "training_examples": len(self.training_data), - "best_algorithm": self.get_best_algorithm().name if self.get_best_algorithm() else None, - "algorithm_performances": [ - algo.performance.to_dict() for algo in self.meta_optimizer.algorithms - ], - "meta_strategy": self.meta_optimizer.meta_strategy.value - } - - -# ================================================================================ -# BOOTSTRAP DEMONSTRATION -# ================================================================================ - -def demo_meta_self_typing(): - """Demonstrate the meta-self-typing bootstrap""" - print("="*70) - print("META-SELF-TYPING BOOTSTRAP DEMONSTRATION") - print("="*70) - - # Create meta-self-typing bridge - meta_bridge = MetaSelfTypingBridge() - - print("\n[Level 3] Initialized meta-optimizer with bandit strategy") - print(f" Algorithms: {[a.name for a in meta_bridge.meta_optimizer.algorithms]}") - - # Generate synthetic training data - print("\n[Training] Generating synthetic training examples...") - - # Euclidean-like domains - for _ in range(10): - meta_bridge.add_training_example( - {"T": 0.3, "S": 0.9, "C": 0.2, "F": 0.5, "R": 0.1, "P": 0.7, "W": 0.2}, - UniverseType.EUCLIDEAN - ) - - # Lorentzian-like domains - for _ in range(10): - meta_bridge.add_training_example( - {"T": 0.9, "S": 0.3, "C": 0.8, "F": 0.6, "R": 0.2, "P": 0.5, "W": 0.7}, - UniverseType.LORENTZIAN - ) - - # Spherical-like domains - for _ in range(10): - meta_bridge.add_training_example( - {"T": 0.4, "S": 0.6, "C": 0.3, "F": 0.8, "R": 0.95, "P": 0.7, "W": 0.3}, - UniverseType.SPHERICAL - ) - - print(f" Added {len(meta_bridge.training_data)} training examples") - - # Train algorithms - print("\n[Level 2] Training Level 2 algorithms...") - meta_bridge.train_algorithms(epochs=30) - - # Evolve population - print("\n[Evolution] Evolving algorithm population...") - meta_bridge.evolve_algorithms(generations=3) - - # Test on new domain - print("\n[Testing] Registering new domain with evolved algorithm...") - domain = meta_bridge.register_domain_7d( - name="Test Domain", - T=0.8, S=0.7, C=0.6, F=0.5, R=0.9, P=0.4, W=0.3 - ) - - print(f"\n Domain: {domain.name}") - print(f" Superposition:") - for entry in domain.superposition: - print(f" {entry.perspective.value} → {entry.universe_type.value} ({entry.weight:.2f})") - - # Generate report - print("\n[Report] Meta-Self-Typing Status:") - report = meta_bridge.generate_report() - print(f" Algorithms: {report['num_algorithms']}") - print(f" Best: {report['best_algorithm']}") - print(f" Training: {report['training_examples']} examples") - - print("\n" + "="*70) - print("BOOTSTRAP COMPLETE") - print("="*70) - print("\nThe system has:") - print(" 1. Learned to extract features from 7D constraints") - print(" 2. Learned to score universe types") - print(" 3. Evolved better algorithms through selection") - print(" 4. Can now improve itself continuously") - - return meta_bridge - - -if __name__ == "__main__": - demo_meta_self_typing() diff --git a/5-Applications/tools-scripts/geoweird/self_typing_bridge.py b/5-Applications/tools-scripts/geoweird/self_typing_bridge.py deleted file mode 100644 index 9aa3596a..00000000 --- a/5-Applications/tools-scripts/geoweird/self_typing_bridge.py +++ /dev/null @@ -1,695 +0,0 @@ -""" -GeoWeird Self-Typing Bridge -Connects Python native agents to Lean self-typing formalization - -Maps 7D constraint vectors (T, S, C, F, R, P, W) to Lean ConstraintFeatures, -calls MultiTypedDomain.fromConstraints(), returns superposition state. -""" - -import ctypes -import json -import numpy as np -from typing import List, Dict, Tuple, Optional, Any -from dataclasses import dataclass, asdict -from enum import Enum, auto -from pathlib import Path - -# Import the base lean_bridge for FFI -from geoweird.lean_bridge import LeanBridge, init_bridge, get_bridge - - -class Perspective(Enum): - """Perspective types from Lean formalization""" - PHYSICAL = "Physical" - TEMPORAL = "Temporal" - INFORMATIONAL = "Informational" - SOCIAL = "Social" - ENERGETIC = "Energetic" - - -class UniverseType(Enum): - """The 5 GeoWeird universe types""" - EUCLIDEAN = "Euclidean" # Σ₁ - HYPERBOLIC = "Hyperbolic" # Σ₂ - SPHERICAL = "Spherical" # Σ₃ - LORENTZIAN = "Lorentzian" # Σ₄ - CUSTOM = "Custom" # Σ₅ - - -@dataclass -class ConstraintFeatures: - """Geometric features extracted from 7D constraints""" - # Curvature indicators - meanCurvature: float = 0.0 - curvatureVariance: float = 0.0 - - # Symmetry properties - rotationalSymmetry: float = 0.0 - translationalSymmetry: float = 0.0 - - # Causal structure - hasTemporalOrdering: bool = False - causalConeAngle: float = 0.0 - - # Topological properties - isCompact: bool = False - fundamentalGroupRank: int = 0 - - # Growth behavior - volumeGrowthRate: float = 1.0 - - # Metric signature - positiveDimensions: int = 3 - negativeDimensions: int = 0 - - def to_lean_json(self) -> Dict[str, Any]: - """Convert to JSON format expected by Lean FFI""" - return { - "meanCurvature": self.meanCurvature, - "curvatureVariance": self.curvatureVariance, - "rotationalSymmetry": self.rotationalSymmetry, - "translationalSymmetry": self.translationalSymmetry, - "hasTemporalOrdering": self.hasTemporalOrdering, - "causalConeAngle": self.causalConeAngle, - "isCompact": self.isCompact, - "fundamentalGroupRank": self.fundamentalGroupRank, - "volumeGrowthRate": self.volumeGrowthRate, - "positiveDimensions": self.positiveDimensions, - "negativeDimensions": self.negativeDimensions - } - - -@dataclass -class UniverseScores: - """Scores for each universe type""" - euclidean: float = 0.0 - hyperbolic: float = 0.0 - spherical: float = 0.0 - lorentzian: float = 0.0 - custom: float = 0.0 - - def best_fit(self) -> Tuple[UniverseType, float]: - """Return best-fitting universe type and score""" - scores = [ - (UniverseType.EUCLIDEAN, self.euclidean), - (UniverseType.HYPERBOLIC, self.hyperbolic), - (UniverseType.SPHERICAL, self.spherical), - (UniverseType.LORENTZIAN, self.lorentzian), - (UniverseType.CUSTOM, self.custom) - ] - return max(scores, key=lambda x: x[1]) - - def should_multi_type(self, threshold: float = 0.5) -> bool: - """Check if multiple types are above threshold""" - scores = [self.euclidean, self.hyperbolic, self.spherical, - self.lorentzian, self.custom] - high_scores = [s for s in scores if s > threshold] - return len(high_scores) > 1 - - def multi_type_candidates(self, threshold: float = 0.5) -> List[Tuple[UniverseType, float]]: - """Get all universe types above threshold""" - candidates = [ - (UniverseType.EUCLIDEAN, self.euclidean), - (UniverseType.HYPERBOLIC, self.hyperbolic), - (UniverseType.SPHERICAL, self.spherical), - (UniverseType.LORENTZIAN, self.lorentzian), - (UniverseType.CUSTOM, self.custom) - ] - return [(u, s) for u, s in candidates if s > threshold] - - -@dataclass -class SuperpositionEntry: - """Single entry in multi-typed superposition""" - universe_type: UniverseType - weight: float - perspective: Perspective - - -@dataclass -class MultiTypedDomain: - """A domain with multi-typed superposition""" - name: str - features: ConstraintFeatures - scores: UniverseScores - superposition: List[SuperpositionEntry] - - def collapse(self, forced_type: UniverseType) -> 'MultiTypedDomain': - """Collapse superposition to single type""" - filtered = [s for s in self.superposition if s.universe_type == forced_type] - if not filtered: - # If forced type not in superposition, add it - filtered = [SuperpositionEntry(forced_type, 1.0, Perspective.PHYSICAL)] - return MultiTypedDomain( - name=self.name, - features=self.features, - scores=self.scores, # Could update scores here - superposition=filtered - ) - - def universe_for_perspective(self, perspective: Perspective) -> Optional[UniverseType]: - """Get universe type for specific perspective""" - for entry in self.superposition: - if entry.perspective == perspective: - return entry.universe_type - return None - - -@dataclass -class CollisionResult: - """Result of colliding two multi-typed domains""" - domain_a: str - domain_b: str - universe_a: UniverseType - universe_b: UniverseType - consensus_strength: float - perspective: Perspective - intersection_volume: float - - -class SelfTypingBridge: - """ - Bridge between Python 7D constraints and Lean self-typing formalization. - - This class: - 1. Maps 7D float vectors (T, S, C, F, R, P, W) to ConstraintFeatures - 2. Calls Lean MultiTypedDomain.fromConstraints() - 3. Returns superposition state for swarm orchestration - 4. Handles perspective collapse on collision - """ - - def __init__(self, lean_bridge: Optional[LeanBridge] = None): - self.lean = lean_bridge or get_bridge() - self._domain_cache: Dict[str, MultiTypedDomain] = {} - self._collision_history: List[CollisionResult] = [] - - # ========================================================================= - # 7D CONSTRAINT MAPPING - # ========================================================================= - - def map_7d_to_features( - self, - T: float, # Temporal coherence - S: float, # Spatial embedding - C: float, # Causal density - F: float, # Field strength - R: float, # Rotational symmetry - P: float, # Phase alignment - W: float # Wave propagation - ) -> ConstraintFeatures: - """ - Map 7D constraint vector to ConstraintFeatures. - - This is the critical mapping from your native agent representation - to the Lean formalization's constraint geometry. - """ - features = ConstraintFeatures() - - # Curvature from causal density and field strength - # High C + low F → negative curvature (hyperbolic) - # Low C + high F → positive curvature (spherical) - features.meanCurvature = (F - C) * 0.5 - features.curvatureVariance = abs(C - F) * 0.3 - - # Symmetry from rotational component - features.rotationalSymmetry = R - features.translationalSymmetry = S * (1 - R) - - # Temporal ordering from T and C - features.hasTemporalOrdering = T > 0.5 and C > 0.3 - features.causalConeAngle = min(1.0, C * T * np.pi / 2) - - # Compactness from spatial embedding - features.isCompact = S > 0.8 and W < 0.5 - - # Fundamental group from topology of phase alignment - features.fundamentalGroupRank = int(P * 3) + 1 - - # Volume growth from wave propagation and temporal coherence - # Exponential growth → hyperbolic - if W > 0.7 and T < 0.3: - features.volumeGrowthRate = 1.0 + W - # Bounded growth → spherical - elif S > 0.8 and W < 0.3: - features.volumeGrowthRate = 0.5 - # Linear growth → Euclidean (default) - else: - features.volumeGrowthRate = 1.0 - - # Metric signature from causal structure - if features.hasTemporalOrdering: - features.positiveDimensions = 3 - features.negativeDimensions = 1 # Time dimension - else: - features.positiveDimensions = 3 - features.negativeDimensions = 0 - - return features - - def extract_features_from_constraints( - self, - constraints: List[Dict[str, Any]] - ) -> ConstraintFeatures: - """ - Extract features from structured constraint list. - - Constraints should have format: - {"name": str, "type": str, "parameters": List[float]} - """ - features = ConstraintFeatures() - - for constraint in constraints: - c_type = constraint.get("type", "").lower() - params = constraint.get("parameters", []) - - if c_type == "temporal": - features.hasTemporalOrdering = True - if params: - features.causalConeAngle = min(1.0, params[0] / 10.0 * np.pi / 2) - - elif c_type == "spatial": - features.translationalSymmetry = max(features.translationalSymmetry, - min(1.0, sum(params) / 100.0)) - - elif c_type == "cyclic": - features.rotationalSymmetry = max(features.rotationalSymmetry, - min(1.0, params[0] / 360.0 if params else 0.5)) - - elif c_type == "hierarchical": - # Tree-like structure → hyperbolic - if len(params) >= 2: - branching_factor = params[1] - features.volumeGrowthRate = max(features.volumeGrowthRate, - 1.0 + branching_factor * 0.1) - - elif c_type == "causal": - features.hasTemporalOrdering = True - features.causalConeAngle = min(1.0, sum(params) / len(params) if params else 0.5) - - elif c_type == "metric": - features.isCompact = max(params) < 1000.0 if params else False - - return features - - # ========================================================================= - # LEAN FFI CALLS - # ========================================================================= - - def call_lean_self_typing(self, features: ConstraintFeatures) -> Dict[str, Any]: - """ - Call Lean self-typing via FFI. - - In production, this would call the compiled Lean library. - For now, we implement the scoring logic in Python (mirroring Lean). - """ - # TODO: Replace with actual FFI call to Lean - # return self.lean.call("self_typing_from_features", features.to_lean_json()) - - # Mirror Lean's scoring logic - scores = self._calculate_universe_scores(features) - - # Build superposition - superposition = self._build_superposition(features, scores) - - return { - "scores": asdict(scores), - "superposition": [ - { - "universe_type": entry.universe_type.value, - "weight": entry.weight, - "perspective": entry.perspective.value - } - for entry in superposition - ] - } - - def _calculate_universe_scores(self, features: ConstraintFeatures) -> UniverseScores: - """Mirror Lean's UniverseScores.fromFeatures""" - scores = UniverseScores() - - # Euclidean: flat, translational symmetry, no temporal - scores.euclidean = ( - (0.8 if abs(features.meanCurvature) < 0.1 else 0.2) + - features.translationalSymmetry * 0.5 + - (0.0 if features.hasTemporalOrdering else 0.3) - ) - - # Hyperbolic: negative curvature, exponential growth, tree-like - scores.hyperbolic = ( - (0.8 if features.meanCurvature < -0.1 else 0.1) + - (0.5 if features.volumeGrowthRate > 1.5 else 0.0) + - (0.3 if features.fundamentalGroupRank > 1 else 0.0) - ) - - # Spherical: positive curvature, compact, rotational symmetry - scores.spherical = ( - (0.8 if features.meanCurvature > 0.1 else 0.1) + - (0.5 if features.isCompact else 0.0) + - features.rotationalSymmetry * 0.5 - ) - - # Lorentzian: temporal ordering, causal cones, mixed metric - scores.lorentzian = ( - (0.8 if features.hasTemporalOrdering else 0.0) + - features.causalConeAngle * 0.5 + - (0.5 if features.negativeDimensions > 0 else 0.0) - ) - - # Custom: none of the above fit well - max_standard = max(scores.euclidean, scores.hyperbolic, - scores.spherical, scores.lorentzian) - scores.custom = 0.8 if max_standard < 0.3 else 0.1 - - return scores - - def _build_superposition( - self, - features: ConstraintFeatures, - scores: UniverseScores - ) -> List[SuperpositionEntry]: - """Build multi-typed superposition from scores""" - candidates = scores.multi_type_candidates(0.4) - - if not candidates: - # No strong fit, use best single - best_type, best_score = scores.best_fit() - return [SuperpositionEntry(best_type, 1.0, Perspective.PHYSICAL)] - - # Build superposition with perspective mapping - superposition = [] - for u_type, score in candidates: - perspective = self._universe_to_perspective(u_type) - superposition.append(SuperpositionEntry(u_type, score, perspective)) - - return superposition - - def _universe_to_perspective(self, u_type: UniverseType) -> Perspective: - """Map universe type to default perspective""" - mapping = { - UniverseType.EUCLIDEAN: Perspective.PHYSICAL, - UniverseType.HYPERBOLIC: Perspective.INFORMATIONAL, - UniverseType.SPHERICAL: Perspective.ENERGETIC, - UniverseType.LORENTZIAN: Perspective.TEMPORAL, - UniverseType.CUSTOM: Perspective.SOCIAL - } - return mapping.get(u_type, Perspective.PHYSICAL) - - # ========================================================================= - # PUBLIC API: Domain Registration - # ========================================================================= - - def register_domain_7d( - self, - name: str, - T: float, S: float, C: float, F: float, - R: float, P: float, W: float - ) -> MultiTypedDomain: - """ - Register a domain using 7D constraint vector. - - This is the main entry point for native agents. - """ - features = self.map_7d_to_features(T, S, C, F, R, P, W) - return self._create_domain(name, features) - - def register_domain_constraints( - self, - name: str, - constraints: List[Dict[str, Any]] - ) -> MultiTypedDomain: - """ - Register a domain using structured constraint list. - """ - features = self.extract_features_from_constraints(constraints) - return self._create_domain(name, features) - - def _create_domain(self, name: str, features: ConstraintFeatures) -> MultiTypedDomain: - """Create MultiTypedDomain from features""" - # Call Lean (or mirror logic) - result = self.call_lean_self_typing(features) - - # Parse result - scores = UniverseScores(**result["scores"]) - superposition = [ - SuperpositionEntry( - UniverseType(entry["universe_type"]), - entry["weight"], - Perspective(entry["perspective"]) - ) - for entry in result["superposition"] - ] - - domain = MultiTypedDomain( - name=name, - features=features, - scores=scores, - superposition=superposition - ) - - # Cache for later - self._domain_cache[name] = domain - - return domain - - # ========================================================================= - # PUBLIC API: Collision & Perspective Selection - # ========================================================================= - - def collide_domains( - self, - domain_a_name: str, - domain_b_name: str - ) -> List[CollisionResult]: - """ - Collide two domains and return all viable perspective combinations. - - Returns list of (universe_a, universe_b, consensus, perspective) tuples. - """ - domain_a = self._domain_cache.get(domain_a_name) - domain_b = self._domain_cache.get(domain_b_name) - - if not domain_a or not domain_b: - raise ValueError(f"Domains not registered: {domain_a_name}, {domain_b_name}") - - results = [] - - # Try all perspective combinations - for entry_a in domain_a.superposition: - for entry_b in domain_b.superposition: - consensus = self._calculate_consensus(entry_a, entry_b) - - if consensus > 0.3: # Threshold for viable collision - combined_perspective = self._combine_perspectives( - entry_a.perspective, entry_b.perspective - ) - - result = CollisionResult( - domain_a=domain_a_name, - domain_b=domain_b_name, - universe_a=entry_a.universe_type, - universe_b=entry_b.universe_type, - consensus_strength=consensus, - perspective=combined_perspective, - intersection_volume=self._estimate_intersection( - entry_a.universe_type, entry_b.universe_type, consensus - ) - ) - results.append(result) - - # Sort by consensus strength - results.sort(key=lambda r: r.consensus_strength, reverse=True) - - # Log collision - self._collision_history.extend(results) - - return results - - def select_best_perspective( - self, - domain_a_name: str, - domain_b_name: str - ) -> Optional[CollisionResult]: - """ - Select the perspective that maximizes consensus between two domains. - - This is what the Swarm Orchestrator calls to decide: - - Which universe to spawn Projector agents in - - Which metric signature Critics should use - - What manifold geometry Integrator derives invariants from - """ - results = self.collide_domains(domain_a_name, domain_b_name) - - if not results: - return None - - return results[0] # Best consensus - - def _calculate_consensus( - self, - entry_a: SuperpositionEntry, - entry_b: SuperpositionEntry - ) -> float: - """Calculate consensus strength between two superposition entries""" - # Same universe type → high consensus - if entry_a.universe_type == entry_b.universe_type: - return min(1.0, (entry_a.weight + entry_b.weight) / 2 + 0.3) - - # Compatible perspectives → moderate consensus - perspective_compatibility = { - (Perspective.PHYSICAL, Perspective.ENERGETIC): 0.7, - (Perspective.TEMPORAL, Perspective.INFORMATIONAL): 0.6, - (Perspective.SOCIAL, Perspective.INFORMATIONAL): 0.5, - } - - key = (entry_a.perspective, entry_b.perspective) - reverse_key = (entry_b.perspective, entry_a.perspective) - - base_consensus = perspective_compatibility.get(key, 0.3) - base_consensus = max(base_consensus, perspective_compatibility.get(reverse_key, 0.3)) - - # Weight by confidence - return base_consensus * (entry_a.weight + entry_b.weight) / 2 - - def _combine_perspectives( - self, - p1: Perspective, - p2: Perspective - ) -> Perspective: - """Combine two perspectives into unified view""" - combinations = { - (Perspective.PHYSICAL, Perspective.TEMPORAL): Perspective.ENERGETIC, - (Perspective.TEMPORAL, Perspective.PHYSICAL): Perspective.ENERGETIC, - (Perspective.INFORMATIONAL, Perspective.SOCIAL): Perspective.SOCIAL, - (Perspective.SOCIAL, Perspective.INFORMATIONAL): Perspective.SOCIAL, - (Perspective.ENERGETIC, Perspective.TEMPORAL): Perspective.ENERGETIC, - (Perspective.PHYSICAL, Perspective.ENERGETIC): Perspective.ENERGETIC, - } - - return combinations.get((p1, p2), p1) - - def _estimate_intersection( - self, - u1: UniverseType, - u2: UniverseType, - consensus: float - ) -> float: - """Estimate manifold intersection volume""" - # Same type → large intersection - if u1 == u2: - return consensus * 100.0 - - # Different types → smaller intersection - return consensus * 50.0 - - # ========================================================================= - # PUBLIC API: Domain Updates - # ========================================================================= - - def update_domain_after_collision( - self, - domain_name: str, - collision: CollisionResult - ) -> MultiTypedDomain: - """ - Update domain's learned types after successful collision. - - Reinforces the universe type that produced consensus. - """ - domain = self._domain_cache.get(domain_name) - if not domain: - raise ValueError(f"Domain not registered: {domain_name}") - - # Determine which universe type was reinforced - reinforced_type = collision.universe_a if collision.domain_a == domain_name else collision.universe_b - - # Update superposition weights - new_superposition = [] - for entry in domain.superposition: - if entry.universe_type == reinforced_type: - # Reinforce - new_weight = min(1.0, entry.weight + 0.1 * collision.consensus_strength) - new_superposition.append(SuperpositionEntry( - entry.universe_type, new_weight, entry.perspective - )) - else: - # Decay - new_weight = entry.weight * 0.95 - if new_weight > 0.1: # Keep if still significant - new_superposition.append(SuperpositionEntry( - entry.universe_type, new_weight, entry.perspective - )) - - # Update domain - updated_domain = MultiTypedDomain( - name=domain.name, - features=domain.features, - scores=domain.scores, - superposition=new_superposition - ) - - self._domain_cache[domain_name] = updated_domain - return updated_domain - - def has_converged(self, domain_name: str, threshold: float = 0.8) -> Optional[UniverseType]: - """Check if domain has converged on a single universe type""" - domain = self._domain_cache.get(domain_name) - if not domain: - return None - - above_threshold = [entry for entry in domain.superposition if entry.weight > threshold] - if not above_threshold: - return None - - best_entry = max(above_threshold, key=lambda e: e.weight) - return best_entry.universe_type - - # ========================================================================= - # PUBLIC API: Statistics - # ========================================================================= - - def get_collision_history(self) -> List[CollisionResult]: - """Get all recorded collisions""" - return self._collision_history.copy() - - def get_learned_rules(self) -> List[Dict[str, Any]]: - """Extract learned typing rules from collision history""" - rules = [] - - # Group by universe type - by_type: Dict[UniverseType, List[CollisionResult]] = {} - for collision in self._collision_history: - if collision.consensus_strength > 0.7: - u_type = collision.universe_a # Simplified - if u_type not in by_type: - by_type[u_type] = [] - by_type[u_type].append(collision) - - for u_type, collisions in by_type.items(): - avg_consensus = sum(c.consensus_strength for c in collisions) / len(collisions) - rules.append({ - "universe_type": u_type.value, - "confidence": avg_consensus, - "evidence_count": len(collisions), - "pattern": f"Perspective: {collisions[0].perspective.value}" - }) - - return rules - - -# ============================================================================ -# SINGLETON INSTANCE -# ============================================================================ - -_self_typing_bridge: Optional[SelfTypingBridge] = None - - -def init_self_typing_bridge(lean_bridge: Optional[LeanBridge] = None) -> SelfTypingBridge: - """Initialize global self-typing bridge""" - global _self_typing_bridge - _self_typing_bridge = SelfTypingBridge(lean_bridge) - return _self_typing_bridge - - -def get_self_typing_bridge() -> Optional[SelfTypingBridge]: - """Get global self-typing bridge instance""" - return _self_typing_bridge diff --git a/5-Applications/tools-scripts/geoweird/self_typing_bridge_v2.py b/5-Applications/tools-scripts/geoweird/self_typing_bridge_v2.py deleted file mode 100644 index 7737becf..00000000 --- a/5-Applications/tools-scripts/geoweird/self_typing_bridge_v2.py +++ /dev/null @@ -1,313 +0,0 @@ -""" -GeoWeird Self-Typing Bridge - CORRECTED VERSION - -FIXED: Feature extraction now actually inspects constraints. -Previously all extractors returned constants (0.0, 0.5, False, 1.0). - -This made every domain produce identical ConstraintFeatures. -The Lighthouse Keeper and "Keeper's Dream" were indistinguishable. -Self-typing was not actually happening. -""" - -import numpy as np -from typing import List, Dict, Tuple, Optional, Any -from dataclasses import dataclass -from enum import Enum - - -class ConstraintType(Enum): - """Types of constraints that can be analyzed""" - TEMPORAL = "temporal" - SPATIAL = "spatial" - CYCLIC = "cyclic" - HIERARCHICAL = "hierarchical" - CAUSAL = "causal" - METRIC = "metric" - INFORMATIONAL = "informational" - ENERGETIC = "energetic" - - -@dataclass -class Constraint: - """A constraint with actual structure to analyze""" - name: str - constraint_type: ConstraintType - parameters: List[float] - - -@dataclass -class ConstraintFeatures: - """Geometric features extracted from constraints""" - mean_curvature: float = 0.0 - curvature_variance: float = 0.0 - rotational_symmetry: float = 0.0 - translational_symmetry: float = 0.0 - has_temporal_ordering: bool = False - causal_cone_angle: float = 0.0 - is_compact: bool = False - fundamental_group_rank: int = 1 - volume_growth_rate: float = 1.0 - positive_dimensions: int = 3 - negative_dimensions: int = 0 - - -# ================================================================================ -# ACTUAL FEATURE EXTRACTORS (NOT CONSTANT STUBS) -# ================================================================================ - -def estimate_mean_curvature(constraints: List[Constraint]) -> float: - """ - Estimate mean curvature from constraint topology. - - Tree-like structures (hierarchical) → negative curvature - Cyclic structures → positive curvature - Linear structures → zero curvature - """ - if not constraints: - return 0.0 - - tree_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.HIERARCHICAL) - cyclic_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.CYCLIC) - total = len(constraints) - - # Tree-like → negative, Cyclic → positive - tree_ratio = tree_count / total - cyclic_ratio = cyclic_count / total - return (cyclic_ratio - tree_ratio) * 2.0 # Scale to [-2, 2] - - -def estimate_curvature_variance(constraints: List[Constraint]) -> float: - """How mixed are the constraint types?""" - if not constraints: - return 0.0 - - types = set(c.constraint_type for c in constraints) - if len(types) <= 1: - return 0.0 - - return len(types) / 8.0 # Normalize by max types - - -def detect_rotational_symmetry(constraints: List[Constraint]) -> float: - """ - Detect rotational symmetry from cyclic constraints. - - High if many cyclic/rotational constraints - Low if mostly linear/hierarchical - """ - if not constraints: - return 0.0 - - cyclic_constraints = [c for c in constraints - if c.constraint_type in - (ConstraintType.CYCLIC, ConstraintType.SPATIAL)] - - ratio = len(cyclic_constraints) / len(constraints) - - # Weight by actual rotation parameters if available - rotation_strength = 0.0 - for c in cyclic_constraints: - if c.parameters: - rotation_strength += c.parameters[0] / 360.0 - - return min(1.0, ratio * 0.7 + rotation_strength * 0.3) - - -def detect_translational_symmetry(constraints: List[Constraint]) -> float: - """Detect translational symmetry from spatial/metric constraints""" - if not constraints: - return 0.0 - - spatial_constraints = [c for c in constraints - if c.constraint_type in - (ConstraintType.SPATIAL, ConstraintType.METRIC)] - - ratio = len(spatial_constraints) / len(constraints) - - # Check for uniform spacing (translational symmetry indicator) - has_uniform_spacing = any( - len(c.parameters) >= 2 and abs(c.parameters[0] - c.parameters[1]) < 10.0 - for c in spatial_constraints - ) - - if has_uniform_spacing: - return min(1.0, ratio + 0.2) - return ratio - - -def detect_temporal_ordering(constraints: List[Constraint]) -> bool: - """Detect temporal ordering from temporal/causal constraints""" - return any(c.constraint_type in (ConstraintType.TEMPORAL, ConstraintType.CAUSAL) - for c in constraints) - - -def estimate_causal_cone_angle(constraints: List[Constraint]) -> float: - """Estimate causal cone angle from temporal density""" - temporal_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.TEMPORAL) - causal_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.CAUSAL) - - total_relevant = temporal_count + causal_count - if total_relevant == 0: - return 0.0 - - # Scale to [0, π/2] - density = total_relevant / len(constraints) - return density * 1.57 # π/2 ≈ 1.57 - - -def detect_compactness(constraints: List[Constraint]) -> bool: - """Detect compactness: bounded vs unbounded domains""" - spatial_metric = [c for c in constraints - if c.constraint_type in (ConstraintType.SPATIAL, ConstraintType.METRIC)] - - # Check if all spatial/metric constraints have bounded parameters - all_bounded = all( - all(p < 1000.0 for p in c.parameters) - for c in spatial_metric - ) - - # Compact if bounded AND not too many constraints - return all_bounded and len(constraints) < 10 - - -def estimate_fundamental_group(constraints: List[Constraint]) -> int: - """Estimate fundamental group rank from constraint topology""" - hierarchical = [c for c in constraints - if c.constraint_type == ConstraintType.HIERARCHICAL] - cyclic = [c for c in constraints - if c.constraint_type == ConstraintType.CYCLIC] - - # Count "holes" in constraint topology - tree_holes = sum(len(c.parameters) for c in hierarchical) - cycle_holes = len(cyclic) - - return max(1, tree_holes + cycle_holes) - - -def estimate_volume_growth(constraints: List[Constraint]) -> float: - """Estimate volume growth rate from constraint structure""" - if not constraints: - return 1.0 - - hierarchical_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.HIERARCHICAL) - spatial_count = sum(1 for c in constraints - if c.constraint_type == ConstraintType.SPATIAL) - - h_ratio = hierarchical_count / len(constraints) - s_ratio = spatial_count / len(constraints) - - if h_ratio > 0.3: - return 1.0 + h_ratio * 2.0 # Exponential - elif s_ratio > 0.5: - return 1.0 # Linear - else: - return 0.5 + s_ratio * 0.5 # Sublinear/bounded - - -def count_positive_dimensions(constraints: List[Constraint]) -> int: - """Count positive dimensions from spatial constraints""" - spatial_params = [len(c.parameters) for c in constraints - if c.constraint_type == ConstraintType.SPATIAL] - - return max(spatial_params) if spatial_params else 3 - - -def count_negative_dimensions(constraints: List[Constraint]) -> int: - """Count negative dimensions from temporal/causal constraints""" - has_time = any(c.constraint_type in (ConstraintType.TEMPORAL, ConstraintType.CAUSAL) - for c in constraints) - return 1 if has_time else 0 - - -# ================================================================================ -# EXTRACT ALL FEATURES -# ================================================================================ - -def extract_constraint_features(constraints: List[Constraint]) -> ConstraintFeatures: - """Extract all features from a constraint set - NOW ACTUALLY WORKS""" - return ConstraintFeatures( - mean_curvature=estimate_mean_curvature(constraints), - curvature_variance=estimate_curvature_variance(constraints), - rotational_symmetry=detect_rotational_symmetry(constraints), - translational_symmetry=detect_translational_symmetry(constraints), - has_temporal_ordering=detect_temporal_ordering(constraints), - causal_cone_angle=estimate_causal_cone_angle(constraints), - is_compact=detect_compactness(constraints), - fundamental_group_rank=estimate_fundamental_group(constraints), - volume_growth_rate=estimate_volume_growth(constraints), - positive_dimensions=count_positive_dimensions(constraints), - negative_dimensions=count_negative_dimensions(constraints) - ) - - -# ================================================================================ -# VERIFICATION: DOMAINS ARE NOW DISTINGUISHABLE -# ================================================================================ - -def verify_domains_distinguishable(): - """Verify that Lighthouse Keeper and Keeper's Dream produce different features""" - - # Lighthouse Keeper constraints - keeper_constraints = [ - Constraint("tower_height", ConstraintType.SPATIAL, [30.0]), - Constraint("foundation_diameter", ConstraintType.SPATIAL, [10.0]), - Constraint("flash_interval", ConstraintType.TEMPORAL, [5.0]), - Constraint("flash_duration", ConstraintType.TEMPORAL, [0.5]), - Constraint("lens_rotation", ConstraintType.CYCLIC, [360.0, 60.0]), - Constraint("focal_length", ConstraintType.SPATIAL, [0.5]), - Constraint("duty_schedule", ConstraintType.HIERARCHICAL, [8.0, 3.0]), - Constraint("visibility_radius", ConstraintType.METRIC, [20000.0]) - ] - - # Keeper's Dream constraints (different!) - dream_constraints = [ - Constraint("lucid_recognition", ConstraintType.INFORMATIONAL, [0.7]), - Constraint("symbolic_content", ConstraintType.HIERARCHICAL, [5.0]), - Constraint("emotional_valence", ConstraintType.ENERGETIC, [-1.0, 1.0]), - Constraint("dream_time", ConstraintType.TEMPORAL, [0.3]) - ] - - keeper_features = extract_constraint_features(keeper_constraints) - dream_features = extract_constraint_features(dream_constraints) - - print("="*60) - print("VERIFICATION: Domains are now distinguishable") - print("="*60) - - print("\nLighthouse Keeper features:") - print(f" mean_curvature: {keeper_features.mean_curvature:.2f}") - print(f" rotational_symmetry: {keeper_features.rotational_symmetry:.2f}") - print(f" has_temporal_ordering: {keeper_features.has_temporal_ordering}") - print(f" volume_growth_rate: {keeper_features.volume_growth_rate:.2f}") - - print("\nKeeper's Dream features:") - print(f" mean_curvature: {dream_features.mean_curvature:.2f}") - print(f" rotational_symmetry: {dream_features.rotational_symmetry:.2f}") - print(f" has_temporal_ordering: {dream_features.has_temporal_ordering}") - print(f" volume_growth_rate: {dream_features.volume_growth_rate:.2f}") - - # Check if they're different - different = ( - keeper_features.mean_curvature != dream_features.mean_curvature or - keeper_features.rotational_symmetry != dream_features.rotational_symmetry or - keeper_features.has_temporal_ordering != dream_features.has_temporal_ordering - ) - - print(f"\n✓ Domains are distinguishable: {different}") - - # Explain why - print("\nWhy they're different:") - print(f" - Keeper has {sum(1 for c in keeper_constraints if c.constraint_type == ConstraintType.CYCLIC)} cyclic constraints (Fresnel lens)") - print(f" - Dream has {sum(1 for c in dream_constraints if c.constraint_type == ConstraintType.CYCLIC)} cyclic constraints") - print(f" → Keeper has higher rotational_symmetry") - - return keeper_features, dream_features - - -if __name__ == "__main__": - verify_domains_distinguishable() diff --git a/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v2.py b/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v2.py deleted file mode 100644 index 26b4ef28..00000000 --- a/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v2.py +++ /dev/null @@ -1,559 +0,0 @@ -""" -GeoWeird Swarm Orchestrator v2 - -Uses Lean self-typing pipeline for perspective selection and universe assignment. - -Flow: -1. Load domain experts from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md -2. Register each with SelfTypingBridge (7D → ConstraintFeatures → MultiTypedDomain) -3. Pair agents for collaboration -4. For each pair, ask Lean: "What perspective maximizes consensus?" -5. Receive collapsed universe + perspective -6. Spawn Projector agents IN THAT UNIVERSE -7. Critics evaluate using shared metric signature -8. Integrator derives invariant from collapsed manifold geometry -""" - -import json -import random -from typing import List, Dict, Optional, Any, Tuple -from dataclasses import dataclass, field -from pathlib import Path -from collections import defaultdict - -from geoweird.self_typing_bridge import ( - SelfTypingBridge, UniverseType, Perspective, CollisionResult, - init_self_typing_bridge, get_self_typing_bridge -) - -from geoweird.geo_aware_agent import ( - GeoWeirdAwareAgent, GeoWeirdContext, - create_geo_weird_agent, load_domain_experts_from_markdown -) - - -@dataclass -class CollaborationSession: - """A collaboration session between two agents""" - session_id: str - agent_a: str - agent_b: str - context: GeoWeirdContext - projectors: List[Dict[str, Any]] = field(default_factory=list) - critiques: List[Dict[str, Any]] = field(default_factory=list) - integrated_result: Optional[Dict[str, Any]] = None - - def to_dict(self) -> Dict[str, Any]: - return { - "session_id": self.session_id, - "agents": [self.agent_a, self.agent_b], - "universe": self.context.universe_type.value, - "perspective": self.context.perspective.value, - "metric": self.context.metric_signature, - "projectors": len(self.projectors), - "critiques": len(self.critiques), - "integrated": self.integrated_result is not None - } - - -@dataclass -class SwarmMetrics: - """Metrics for swarm performance""" - total_collaborations: int = 0 - successful_collaborations: int = 0 - universe_distribution: Dict[str, int] = field(default_factory=dict) - perspective_distribution: Dict[str, int] = field(default_factory=dict) - average_consensus: float = 0.0 - convergence_rate: float = 0.0 - - -class GeoWeirdSwarmOrchestrator: - """ - Swarm orchestrator that uses Lean self-typing for perspective selection. - - This orchestrator: - 1. Maintains pool of GeoWeird-aware agents - 2. Pairs agents based on complementarity - 3. Uses Lean to select optimal perspective for each pair - 4. Spawns projectors in collapsed universe - 5. Coordinates critique and integration - 6. Tracks emergent typing rules - """ - - def __init__( - self, - self_typing_bridge: Optional[SelfTypingBridge] = None, - consensus_threshold: float = 0.5 - ): - self.bridge = self_typing_bridge or get_self_typing_bridge() - - if not self.bridge: - raise RuntimeError("SelfTypingBridge not initialized") - - self.consensus_threshold = consensus_threshold - self.agents: Dict[str, GeoWeirdAwareAgent] = {} - self.sessions: List[CollaborationSession] = [] - self.metrics = SwarmMetrics() - - # Learned patterns - self.successful_pairs: List[Tuple[str, str]] = [] - self.universe_preferences: Dict[str, Dict[UniverseType, float]] = defaultdict( - lambda: defaultdict(float) - ) - - # ======================================================================== - # AGENT MANAGEMENT - # ======================================================================== - - def register_agent(self, agent: GeoWeirdAwareAgent) -> 'GeoWeirdSwarmOrchestrator': - """Register a GeoWeird-aware agent""" - self.agents[agent.name] = agent - return self - - def register_agents(self, agents: List[GeoWeirdAwareAgent]) -> 'GeoWeirdSwarmOrchestrator': - """Register multiple agents""" - for agent in agents: - self.register_agent(agent) - return self - - def load_from_markdown(self, md_path: Path) -> 'GeoWeirdSwarmOrchestrator': - """Load domain experts from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md""" - agents = load_domain_experts_from_markdown(md_path) - return self.register_agents(agents) - - def get_agent(self, name: str) -> Optional[GeoWeirdAwareAgent]: - """Get agent by name""" - return self.agents.get(name) - - # ======================================================================== - # PAIRING STRATEGIES - # ======================================================================== - - def pair_agents_random(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """Random pairing strategy""" - agent_list = list(self.agents.values()) - random.shuffle(agent_list) - - pairs = [] - for i in range(0, len(agent_list) - 1, 2): - pairs.append((agent_list[i], agent_list[i + 1])) - - return pairs - - def pair_agents_complementary(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """ - Pair agents with complementary superpositions. - - Agents with different dominant universe types are more likely - to produce interesting collisions. - """ - agent_list = list(self.agents.values()) - pairs = [] - - # Sort by dominant universe type - by_universe: Dict[UniverseType, List[GeoWeirdAwareAgent]] = defaultdict(list) - for agent in agent_list: - if agent.domain.superposition: - dominant = agent.domain.superposition[0].universe_type - by_universe[dominant].append(agent) - - # Pair across universe types - universes = list(by_universe.keys()) - for i, u1 in enumerate(universes): - for u2 in universes[i + 1:]: - agents1 = by_universe[u1] - agents2 = by_universe[u2] - - min_len = min(len(agents1), len(agents2)) - for j in range(min_len): - pairs.append((agents1[j], agents2[j])) - - # Fallback to random pairing if no complementary pairs found - if not pairs: - pairs = self.pair_agents_random() - - return pairs - - def pair_agents_learned(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """ - Pair agents based on learned successful collaborations. - - If two agents have collaborated successfully before, - they're likely to do so again. - """ - # Start with known successful pairs - pairs = [] - used = set() - - for (name_a, name_b) in self.successful_pairs: - if name_a in self.agents and name_b in self.agents: - if name_a not in used and name_b not in used: - pairs.append((self.agents[name_a], self.agents[name_b])) - used.add(name_a) - used.add(name_b) - - # Fill remaining with random - remaining = [a for name, a in self.agents.items() if name not in used] - random.shuffle(remaining) - - for i in range(0, len(remaining) - 1, 2): - pairs.append((remaining[i], remaining[i + 1])) - - return pairs - - # ======================================================================== - # MAIN ORCHESTRATION LOOP - # ======================================================================== - - def run_collaboration_round( - self, - pairing_strategy: str = "complementary", - task_template: str = "Collaborative analysis of {domain}" - ) -> List[CollaborationSession]: - """ - Run one round of collaborations. - - This is the main entry point that implements the flow: - 1. Pair agents - 2. For each pair, ask Lean for optimal perspective - 3. Spawn projectors in collapsed universe - 4. Coordinate critique and integration - """ - # Select pairing strategy - if pairing_strategy == "random": - pairs = self.pair_agents_random() - elif pairing_strategy == "complementary": - pairs = self.pair_agents_complementary() - elif pairing_strategy == "learned": - pairs = self.pair_agents_learned() - else: - pairs = self.pair_agents_random() - - sessions = [] - - for agent_a, agent_b in pairs: - print(f"\n[Orchestrator] Pairing: {agent_a.name} ↔ {agent_b.name}") - - # Step 1: Ask Lean "What perspective maximizes consensus?" - collision = self.bridge.select_best_perspective(agent_a.name, agent_b.name) - - if not collision: - print(f" No viable consensus found") - continue - - if collision.consensus_strength < self.consensus_threshold: - print(f" Consensus {collision.consensus_strength:.2f} below threshold") - continue - - print(f" Selected: {collision.universe_a.value} × {collision.universe_b.value}") - print(f" Consensus: {collision.consensus_strength:.2f}") - print(f" Perspective: {collision.perspective.value}") - - # Step 2: Create task - task = task_template.format(domain=f"{agent_a.name} + {agent_b.name}") - - # Step 3: Both agents initiate collaboration using precomputed collision - context_a = agent_a.initiate_collaboration(agent_b, task, collision=collision) - context_b = agent_b.initiate_collaboration(agent_a, task, collision=collision) - - if not context_a or not context_b: - print(f" Collaboration initiation failed") - continue - - # Step 4: Create session - session = CollaborationSession( - session_id=context_a.collaboration_id, - agent_a=agent_a.name, - agent_b=agent_b.name, - context=context_a, - projectors=agent_a.spawned_projectors.copy() - ) - - # Step 5: Simulate projector execution - projector_outputs = self._simulate_projectors(session) - - # Step 6: Critique using shared metric signature - critiques = self._coordinate_critique(agent_a, agent_b, projector_outputs) - session.critiques = critiques - - # Step 7: Integrate results - integrated = agent_a.integrate_results(projector_outputs, critiques) - session.integrated_result = integrated - - # Step 8: Update learned patterns - self._update_learned_patterns(agent_a, agent_b, collision) - - # Step 9: Update metrics - self._update_metrics(collision, session) - - sessions.append(session) - self.sessions.append(session) - - print(f" Session complete: {integrated.get('final_invariant', 0.0):.3f}") - - return sessions - - def _simulate_projectors( - self, - session: CollaborationSession - ) -> List[Dict[str, Any]]: - """Simulate projector agent execution""" - outputs = [] - - for projector in session.projectors: - # Simulate execution in specific universe - output = { - "projector_id": projector["id"], - "universe": projector["universe"], - "content": { - "analysis": f"Analysis from {projector['universe']} perspective", - "confidence": random.uniform(0.6, 0.95) - } - } - outputs.append(output) - - return outputs - - def _coordinate_critique( - self, - agent_a: GeoWeirdAwareAgent, - agent_b: GeoWeirdAwareAgent, - projector_outputs: List[Dict[str, Any]] - ) -> List[Dict[str, Any]]: - """Coordinate critique between agents using shared metric""" - critiques = [] - - # Agent A critiques - for output in projector_outputs: - critique = agent_a.critique_output( - output=json.dumps(output), - criteria=["completeness", "consistency", "novelty"], - other_agent=agent_b - ) - critiques.append(critique) - - # Agent B critiques - for output in projector_outputs: - critique = agent_b.critique_output( - output=json.dumps(output), - criteria=["completeness", "consistency", "novelty"], - other_agent=agent_a - ) - critiques.append(critique) - - return critiques - - def _update_learned_patterns( - self, - agent_a: GeoWeirdAwareAgent, - agent_b: GeoWeirdAwareAgent, - collision: CollisionResult - ): - """Update learned successful collaboration patterns""" - # Record successful pair - pair = tuple(sorted([agent_a.name, agent_b.name])) - if pair not in self.successful_pairs: - self.successful_pairs.append(pair) - - # Update universe preferences - self.universe_preferences[agent_a.name][collision.universe_a] += collision.consensus_strength - self.universe_preferences[agent_b.name][collision.universe_b] += collision.consensus_strength - - # Update agents' internal preferences - self.bridge.update_domain_after_collision(agent_a.name, collision) - self.bridge.update_domain_after_collision(agent_b.name, collision) - - def _update_metrics( - self, - collision: CollisionResult, - session: CollaborationSession - ): - """Update swarm metrics""" - self.metrics.total_collaborations += 1 - self.metrics.successful_collaborations += 1 - - # Universe distribution - u_name = collision.universe_a.value - self.metrics.universe_distribution[u_name] = \ - self.metrics.universe_distribution.get(u_name, 0) + 1 - - # Perspective distribution - p_name = collision.perspective.value - self.metrics.perspective_distribution[p_name] = \ - self.metrics.perspective_distribution.get(p_name, 0) + 1 - - # Average consensus (EMA) - self.metrics.average_consensus = ( - 0.9 * self.metrics.average_consensus + - 0.1 * collision.consensus_strength - ) - - # ======================================================================== - # CONVERGENCE DETECTION - # ======================================================================== - - def check_convergence(self, threshold: float = 0.8) -> Dict[str, UniverseType]: - """Check which agents have converged on single universe type""" - converged = {} - - for name, agent in self.agents.items(): - u_type = self.bridge.has_converged(name, threshold) - if u_type: - converged[name] = u_type - - self.metrics.convergence_rate = len(converged) / len(self.agents) if self.agents else 0.0 - - return converged - - def run_to_convergence( - self, - max_rounds: int = 10, - convergence_threshold: float = 0.8 - ) -> List[CollaborationSession]: - """Run collaboration rounds until agents converge""" - all_sessions = [] - - for round_num in range(max_rounds): - print(f"\n{'='*60}") - print(f"COLLABORATION ROUND {round_num + 1}/{max_rounds}") - print(f"{'='*60}") - - sessions = self.run_collaboration_round() - all_sessions.extend(sessions) - - # Check convergence - converged = self.check_convergence(convergence_threshold) - print(f"\nConverged agents: {len(converged)}/{len(self.agents)}") - - if len(converged) == len(self.agents): - print("\n✓ All agents converged!") - break - - return all_sessions - - # ======================================================================== - # REPORTING - # ======================================================================== - - def generate_report(self) -> Dict[str, Any]: - """Generate comprehensive swarm report""" - # Learned rules from collision history - learned_rules = self.bridge.get_learned_rules() - - # Converged agents - converged = self.check_convergence() - - return { - "metrics": { - "total_collaborations": self.metrics.total_collaborations, - "successful_collaborations": self.metrics.successful_collaborations, - "average_consensus": self.metrics.average_consensus, - "convergence_rate": self.metrics.convergence_rate, - "universe_distribution": self.metrics.universe_distribution, - "perspective_distribution": self.metrics.perspective_distribution - }, - "learned_rules": learned_rules, - "converged_agents": { - name: u_type.value for name, u_type in converged.items() - }, - "sessions": [s.to_dict() for s in self.sessions], - "successful_pairs": list(self.successful_pairs) - } - - def print_report(self): - """Print formatted report""" - report = self.generate_report() - - print("\n" + "="*60) - print("GEOWEIRD SWARM ORCHESTRATOR REPORT") - print("="*60) - - print("\n📊 Metrics:") - print(f" Total collaborations: {report['metrics']['total_collaborations']}") - print(f" Successful: {report['metrics']['successful_collaborations']}") - print(f" Average consensus: {report['metrics']['average_consensus']:.3f}") - print(f" Convergence rate: {report['metrics']['convergence_rate']:.1%}") - - print("\n🌌 Universe Distribution:") - for u_name, count in report['metrics']['universe_distribution'].items(): - print(f" {u_name}: {count}") - - print("\n👁 Perspective Distribution:") - for p_name, count in report['metrics']['perspective_distribution'].items(): - print(f" {p_name}: {count}") - - print("\n📜 Learned Rules:") - for rule in report['learned_rules']: - print(f" {rule['universe_type']}: conf={rule['confidence']:.2f}, " - f"evidence={rule['evidence_count']}") - - print("\n✓ Converged Agents:") - for name, u_type in report['converged_agents'].items(): - print(f" {name}: {u_type}") - - print("\n" + "="*60) - - -# ================================================================================ -# MAIN ENTRY POINT -# ================================================================================ - -def run_geo_weird_swarm( - md_path: Optional[Path] = None, - max_rounds: int = 5, - consensus_threshold: float = 0.5 -) -> Dict[str, Any]: - """ - Run complete GeoWeird swarm with self-typing. - - Usage: - result = run_geo_weird_swarm( - md_path=Path("EXHAUSTIVE_DOMAIN_EXPERT_LIST.md"), - max_rounds=10 - ) - """ - # Initialize self-typing bridge - bridge = init_self_typing_bridge() - - # Create orchestrator - orchestrator = GeoWeirdSwarmOrchestrator( - self_typing_bridge=bridge, - consensus_threshold=consensus_threshold - ) - - # Load agents - if md_path and md_path.exists(): - orchestrator.load_from_markdown(md_path) - else: - # Create demo agents - demo_agents = [ - create_geo_weird_agent("Lighthouse Keeper", constraints_7d={ - "T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3 - }), - create_geo_weird_agent("Maritime Chart", constraints_7d={ - "T": 0.3, "S": 0.9, "C": 0.2, "F": 0.4, "R": 0.1, "P": 0.6, "W": 0.2 - }), - create_geo_weird_agent("Fog Signal", constraints_7d={ - "T": 0.9, "S": 0.3, "C": 0.7, "F": 0.6, "R": 0.2, "P": 0.5, "W": 0.8 - }), - create_geo_weird_agent("Lens Prism", constraints_7d={ - "T": 0.4, "S": 0.6, "C": 0.3, "F": 0.8, "R": 0.95, "P": 0.7, "W": 0.4 - }), - ] - orchestrator.register_agents(demo_agents) - - print(f"Loaded {len(orchestrator.agents)} agents") - - # Run to convergence - sessions = orchestrator.run_to_convergence(max_rounds=max_rounds) - - # Generate report - orchestrator.print_report() - - return orchestrator.generate_report() - - -if __name__ == "__main__": - # Demo run - result = run_geo_weird_swarm() - print("\n" + json.dumps(result, indent=2)) diff --git a/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v3.py b/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v3.py deleted file mode 100644 index 4302e541..00000000 --- a/5-Applications/tools-scripts/geoweird/swarm_orchestrator_v3.py +++ /dev/null @@ -1,555 +0,0 @@ -""" -GeoWeird Swarm Orchestrator v2 - -Uses Lean self-typing pipeline for perspective selection and universe assignment. - -Flow: -1. Load domain experts from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md -2. Register each with SelfTypingBridge (7D → ConstraintFeatures → MultiTypedDomain) -3. Pair agents for collaboration -4. For each pair, ask Lean: "What perspective maximizes consensus?" -5. Receive collapsed universe + perspective -6. Spawn Projector agents IN THAT UNIVERSE -7. Critics evaluate using shared metric signature -8. Integrator derives invariant from collapsed manifold geometry -""" - -import json -import random -from typing import List, Dict, Optional, Any, Tuple -from dataclasses import dataclass, field -from pathlib import Path -from collections import defaultdict - -from .self_typing_bridge import ( - SelfTypingBridge, UniverseType, Perspective, CollisionResult, - init_self_typing_bridge, get_self_typing_bridge -) - -from .geo_aware_agent import ( - GeoWeirdAwareAgent, GeoWeirdContext, - create_geo_weird_agent, load_domain_experts_from_markdown -) - - -@dataclass -class CollaborationSession: - """A collaboration session between two agents""" - session_id: str - agent_a: str - agent_b: str - context: GeoWeirdContext - projectors: List[Dict[str, Any]] = field(default_factory=list) - critiques: List[Dict[str, Any]] = field(default_factory=list) - integrated_result: Optional[Dict[str, Any]] = None - - def to_dict(self) -> Dict[str, Any]: - return { - "session_id": self.session_id, - "agents": [self.agent_a, self.agent_b], - "universe": self.context.universe_type.value, - "perspective": self.context.perspective.value, - "metric": self.context.metric_signature, - "projectors": len(self.projectors), - "critiques": len(self.critiques), - "integrated": self.integrated_result is not None - } - - -@dataclass -class SwarmMetrics: - """Metrics for swarm performance""" - total_collaborations: int = 0 - successful_collaborations: int = 0 - universe_distribution: Dict[str, int] = field(default_factory=dict) - perspective_distribution: Dict[str, int] = field(default_factory=dict) - average_consensus: float = 0.0 - convergence_rate: float = 0.0 - - -class GeoWeirdSwarmOrchestrator: - """ - Swarm orchestrator that uses Lean self-typing for perspective selection. - - This orchestrator: - 1. Maintains pool of GeoWeird-aware agents - 2. Pairs agents based on complementarity - 3. Uses Lean to select optimal perspective for each pair - 4. Spawns projectors in collapsed universe - 5. Coordinates critique and integration - 6. Tracks emergent typing rules - """ - - def __init__( - self, - self_typing_bridge: Optional[SelfTypingBridge] = None, - consensus_threshold: float = 0.5 - ): - self.bridge = self_typing_bridge or get_self_typing_bridge() - - if not self.bridge: - raise RuntimeError("SelfTypingBridge not initialized") - - self.consensus_threshold = consensus_threshold - self.agents: Dict[str, GeoWeirdAwareAgent] = {} - self.sessions: List[CollaborationSession] = [] - self.metrics = SwarmMetrics() - - # Learned patterns - self.successful_pairs: List[Tuple[str, str]] = [] - self.universe_preferences: Dict[str, Dict[UniverseType, float]] = defaultdict( - lambda: defaultdict(float) - ) - - # ======================================================================== - # AGENT MANAGEMENT - # ======================================================================== - - def register_agent(self, agent: GeoWeirdAwareAgent) -> 'GeoWeirdSwarmOrchestrator': - """Register a GeoWeird-aware agent""" - self.agents[agent.name] = agent - return self - - def register_agents(self, agents: List[GeoWeirdAwareAgent]) -> 'GeoWeirdSwarmOrchestrator': - """Register multiple agents""" - for agent in agents: - self.register_agent(agent) - return self - - def load_from_markdown(self, md_path: Path) -> 'GeoWeirdSwarmOrchestrator': - """Load domain experts from EXHAUSTIVE_DOMAIN_EXPERT_LIST.md""" - agents = load_domain_experts_from_markdown(md_path) - return self.register_agents(agents) - - def get_agent(self, name: str) -> Optional[GeoWeirdAwareAgent]: - """Get agent by name""" - return self.agents.get(name) - - # ======================================================================== - # PAIRING STRATEGIES - # ======================================================================== - - def pair_agents_random(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """Random pairing strategy""" - agent_list = list(self.agents.values()) - random.shuffle(agent_list) - - pairs = [] - for i in range(0, len(agent_list) - 1, 2): - pairs.append((agent_list[i], agent_list[i + 1])) - - return pairs - - def pair_agents_complementary(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """ - Pair agents with complementary superpositions. - - Agents with different dominant universe types are more likely - to produce interesting collisions. - """ - agent_list = list(self.agents.values()) - pairs = [] - - # Sort by dominant universe type - by_universe: Dict[UniverseType, List[GeoWeirdAwareAgent]] = defaultdict(list) - for agent in agent_list: - if agent.domain.superposition: - dominant = agent.domain.superposition[0].universe_type - by_universe[dominant].append(agent) - - # Pair across universe types - universes = list(by_universe.keys()) - for i, u1 in enumerate(universes): - for u2 in universes[i + 1:]: - agents1 = by_universe[u1] - agents2 = by_universe[u2] - - min_len = min(len(agents1), len(agents2)) - for j in range(min_len): - pairs.append((agents1[j], agents2[j])) - - return pairs - - def pair_agents_learned(self) -> List[Tuple[GeoWeirdAwareAgent, GeoWeirdAwareAgent]]: - """ - Pair agents based on learned successful collaborations. - - If two agents have collaborated successfully before, - they're likely to do so again. - """ - # Start with known successful pairs - pairs = [] - used = set() - - for (name_a, name_b) in self.successful_pairs: - if name_a in self.agents and name_b in self.agents: - if name_a not in used and name_b not in used: - pairs.append((self.agents[name_a], self.agents[name_b])) - used.add(name_a) - used.add(name_b) - - # Fill remaining with random - remaining = [a for name, a in self.agents.items() if name not in used] - random.shuffle(remaining) - - for i in range(0, len(remaining) - 1, 2): - pairs.append((remaining[i], remaining[i + 1])) - - return pairs - - # ======================================================================== - # MAIN ORCHESTRATION LOOP - # ======================================================================== - - def run_collaboration_round( - self, - pairing_strategy: str = "complementary", - task_template: str = "Collaborative analysis of {domain}" - ) -> List[CollaborationSession]: - """ - Run one round of collaborations. - - This is the main entry point that implements the flow: - 1. Pair agents - 2. For each pair, ask Lean for optimal perspective - 3. Spawn projectors in collapsed universe - 4. Coordinate critique and integration - """ - # Select pairing strategy - if pairing_strategy == "random": - pairs = self.pair_agents_random() - elif pairing_strategy == "complementary": - pairs = self.pair_agents_complementary() - elif pairing_strategy == "learned": - pairs = self.pair_agents_learned() - else: - pairs = self.pair_agents_random() - - sessions = [] - - for agent_a, agent_b in pairs: - print(f"\n[Orchestrator] Pairing: {agent_a.name} ↔ {agent_b.name}") - - # Step 1: Ask Lean "What perspective maximizes consensus?" - collision = self.bridge.select_best_perspective(agent_a.name, agent_b.name) - - if not collision: - print(f" No viable consensus found") - continue - - if collision.consensus_strength < self.consensus_threshold: - print(f" Consensus {collision.consensus_strength:.2f} below threshold") - continue - - print(f" Selected: {collision.universe_a.value} × {collision.universe_b.value}") - print(f" Consensus: {collision.consensus_strength:.2f}") - print(f" Perspective: {collision.perspective.value}") - - # Step 2: Create task - task = task_template.format(domain=f"{agent_a.name} + {agent_b.name}") - - # Step 3: Both agents initiate collaboration - context_a = agent_a.initiate_collaboration(agent_b, task) - context_b = agent_b.initiate_collaboration(agent_a, task) - - if not context_a or not context_b: - print(f" Collaboration initiation failed") - continue - - # Step 4: Create session - session = CollaborationSession( - session_id=context_a.collaboration_id, - agent_a=agent_a.name, - agent_b=agent_b.name, - context=context_a, - projectors=agent_a.spawned_projectors.copy() - ) - - # Step 5: Simulate projector execution - projector_outputs = self._simulate_projectors(session) - - # Step 6: Critique using shared metric signature - critiques = self._coordinate_critique(agent_a, agent_b, projector_outputs) - session.critiques = critiques - - # Step 7: Integrate results - integrated = agent_a.integrate_results(projector_outputs, critiques) - session.integrated_result = integrated - - # Step 8: Update learned patterns - self._update_learned_patterns(agent_a, agent_b, collision) - - # Step 9: Update metrics - self._update_metrics(collision, session) - - sessions.append(session) - self.sessions.append(session) - - print(f" Session complete: {integrated.get('final_invariant', 0.0):.3f}") - - return sessions - - def _simulate_projectors( - self, - session: CollaborationSession - ) -> List[Dict[str, Any]]: - """Simulate projector agent execution""" - outputs = [] - - for projector in session.projectors: - # Simulate execution in specific universe - output = { - "projector_id": projector["id"], - "universe": projector["universe"], - "content": { - "analysis": f"Analysis from {projector['universe']} perspective", - "confidence": random.uniform(0.6, 0.95) - } - } - outputs.append(output) - - return outputs - - def _coordinate_critique( - self, - agent_a: GeoWeirdAwareAgent, - agent_b: GeoWeirdAwareAgent, - projector_outputs: List[Dict[str, Any]] - ) -> List[Dict[str, Any]]: - """Coordinate critique between agents using shared metric""" - critiques = [] - - # Agent A critiques - for output in projector_outputs: - critique = agent_a.critique_output( - output=json.dumps(output), - criteria=["completeness", "consistency", "novelty"], - other_agent=agent_b - ) - critiques.append(critique) - - # Agent B critiques - for output in projector_outputs: - critique = agent_b.critique_output( - output=json.dumps(output), - criteria=["completeness", "consistency", "novelty"], - other_agent=agent_a - ) - critiques.append(critique) - - return critiques - - def _update_learned_patterns( - self, - agent_a: GeoWeirdAwareAgent, - agent_b: GeoWeirdAwareAgent, - collision: CollisionResult - ): - """Update learned successful collaboration patterns""" - # Record successful pair - pair = tuple(sorted([agent_a.name, agent_b.name])) - if pair not in self.successful_pairs: - self.successful_pairs.append(pair) - - # Update universe preferences - self.universe_preferences[agent_a.name][collision.universe_a] += collision.consensus_strength - self.universe_preferences[agent_b.name][collision.universe_b] += collision.consensus_strength - - # Update agents' internal preferences - self.bridge.update_domain_after_collision(agent_a.name, collision) - self.bridge.update_domain_after_collision(agent_b.name, collision) - - def _update_metrics( - self, - collision: CollisionResult, - session: CollaborationSession - ): - """Update swarm metrics""" - self.metrics.total_collaborations += 1 - self.metrics.successful_collaborations += 1 - - # Universe distribution - u_name = collision.universe_a.value - self.metrics.universe_distribution[u_name] = \ - self.metrics.universe_distribution.get(u_name, 0) + 1 - - # Perspective distribution - p_name = collision.perspective.value - self.metrics.perspective_distribution[p_name] = \ - self.metrics.perspective_distribution.get(p_name, 0) + 1 - - # Average consensus (EMA) - self.metrics.average_consensus = ( - 0.9 * self.metrics.average_consensus + - 0.1 * collision.consensus_strength - ) - - # ======================================================================== - # CONVERGENCE DETECTION - # ======================================================================== - - def check_convergence(self, threshold: float = 0.8) -> Dict[str, UniverseType]: - """Check which agents have converged on single universe type""" - converged = {} - - for name, agent in self.agents.items(): - u_type = self.bridge.has_converged(name, threshold) - if u_type: - converged[name] = u_type - - self.metrics.convergence_rate = len(converged) / len(self.agents) if self.agents else 0.0 - - return converged - - def run_to_convergence( - self, - max_rounds: int = 10, - convergence_threshold: float = 0.8 - ) -> List[CollaborationSession]: - """Run collaboration rounds until agents converge""" - all_sessions = [] - - for round_num in range(max_rounds): - print(f"\n{'='*60}") - print(f"COLLABORATION ROUND {round_num + 1}/{max_rounds}") - print(f"{'='*60}") - - sessions = self.run_collaboration_round() - all_sessions.extend(sessions) - - # Check convergence - converged = self.check_convergence(convergence_threshold) - print(f"\nConverged agents: {len(converged)}/{len(self.agents)}") - - if len(converged) == len(self.agents): - print("\n✓ All agents converged!") - break - - return all_sessions - - # ======================================================================== - # REPORTING - # ======================================================================== - - def generate_report(self) -> Dict[str, Any]: - """Generate comprehensive swarm report""" - # Learned rules from collision history - learned_rules = self.bridge.get_learned_rules() - - # Converged agents - converged = self.check_convergence() - - return { - "metrics": { - "total_collaborations": self.metrics.total_collaborations, - "successful_collaborations": self.metrics.successful_collaborations, - "average_consensus": self.metrics.average_consensus, - "convergence_rate": self.metrics.convergence_rate, - "universe_distribution": self.metrics.universe_distribution, - "perspective_distribution": self.metrics.perspective_distribution - }, - "learned_rules": learned_rules, - "converged_agents": { - name: u_type.value for name, u_type in converged.items() - }, - "sessions": [s.to_dict() for s in self.sessions], - "successful_pairs": list(self.successful_pairs) - } - - def print_report(self): - """Print formatted report""" - report = self.generate_report() - - print("\n" + "="*60) - print("GEOWEIRD SWARM ORCHESTRATOR REPORT") - print("="*60) - - print("\n📊 Metrics:") - print(f" Total collaborations: {report['metrics']['total_collaborations']}") - print(f" Successful: {report['metrics']['successful_collaborations']}") - print(f" Average consensus: {report['metrics']['average_consensus']:.3f}") - print(f" Convergence rate: {report['metrics']['convergence_rate']:.1%}") - - print("\n🌌 Universe Distribution:") - for u_name, count in report['metrics']['universe_distribution'].items(): - print(f" {u_name}: {count}") - - print("\n👁 Perspective Distribution:") - for p_name, count in report['metrics']['perspective_distribution'].items(): - print(f" {p_name}: {count}") - - print("\n📜 Learned Rules:") - for rule in report['learned_rules']: - print(f" {rule['universe_type']}: conf={rule['confidence']:.2f}, " - f"evidence={rule['evidence_count']}") - - print("\n✓ Converged Agents:") - for name, u_type in report['converged_agents'].items(): - print(f" {name}: {u_type}") - - print("\n" + "="*60) - - -# ================================================================================ -# MAIN ENTRY POINT -# ================================================================================ - -def run_geo_weird_swarm( - md_path: Optional[Path] = None, - max_rounds: int = 5, - consensus_threshold: float = 0.5 -) -> Dict[str, Any]: - """ - Run complete GeoWeird swarm with self-typing. - - Usage: - result = run_geo_weird_swarm( - md_path=Path("EXHAUSTIVE_DOMAIN_EXPERT_LIST.md"), - max_rounds=10 - ) - """ - # Initialize self-typing bridge - bridge = init_self_typing_bridge() - - # Create orchestrator - orchestrator = GeoWeirdSwarmOrchestrator( - self_typing_bridge=bridge, - consensus_threshold=consensus_threshold - ) - - # Load agents - if md_path and md_path.exists(): - orchestrator.load_from_markdown(md_path) - else: - # Create demo agents - demo_agents = [ - create_geo_weird_agent("Lighthouse Keeper", constraints_7d={ - "T": 0.8, "S": 0.7, "C": 0.6, "F": 0.5, "R": 0.9, "P": 0.4, "W": 0.3 - }), - create_geo_weird_agent("Maritime Chart", constraints_7d={ - "T": 0.3, "S": 0.9, "C": 0.2, "F": 0.4, "R": 0.1, "P": 0.6, "W": 0.2 - }), - create_geo_weird_agent("Fog Signal", constraints_7d={ - "T": 0.9, "S": 0.3, "C": 0.7, "F": 0.6, "R": 0.2, "P": 0.5, "W": 0.8 - }), - create_geo_weird_agent("Lens Prism", constraints_7d={ - "T": 0.4, "S": 0.6, "C": 0.3, "F": 0.8, "R": 0.95, "P": 0.7, "W": 0.4 - }), - ] - orchestrator.register_agents(demo_agents) - - print(f"Loaded {len(orchestrator.agents)} agents") - - # Run to convergence - sessions = orchestrator.run_to_convergence(max_rounds=max_rounds) - - # Generate report - orchestrator.print_report() - - return orchestrator.generate_report() - - -if __name__ == "__main__": - # Demo run - result = run_geo_weird_swarm() - print("\n" + json.dumps(result, indent=2)) diff --git a/5-Applications/tools-scripts/gpgpu/gpgpu_neuromorphic_miner.py b/5-Applications/tools-scripts/gpgpu/gpgpu_neuromorphic_miner.py deleted file mode 100644 index 9a104152..00000000 --- a/5-Applications/tools-scripts/gpgpu/gpgpu_neuromorphic_miner.py +++ /dev/null @@ -1,313 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Neuromorphic Bitcoin Miner - GPGPU Integration Layer -Bridges TSM neuromorphic miner with actual GPGPU hardware (CUDA/OpenCL) -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import hashlib -import struct -import time -from typing import List, Tuple, Optional -from dataclasses import dataclass -from pathlib import Path - -# Try to import CuPy for GPU acceleration -try: - import cupy as cp - HAS_GPU = True - print("[+] CuPy available - GPU acceleration enabled") -except ImportError: - HAS_GPU = False - print("[-] CuPy not available - falling back to CPU") - - -@dataclass -class GPUMiningStats: - nonces_tested: int - shares_found: int - hashrate: float # H/s - gpu_utilization: float # % - memory_used: float # MB - thermal_throttle: bool - - -class GPGPUNeuromorphicMiner: - """ - GPGPU-Accelerated Neuromorphic Bitcoin Miner - - Architecture: - - Neuromorphic Surface: 1M spiking neurons (simulated on GPU) - - Soliton Collision: Wave packet interference optimization - - GPGPU Kernel: Parallel SHA256 across thousands of CUDA cores - - Expected Performance: - - GPU (RTX 4090): ~50-100 MH/s - - GPU (RTX 3080): ~30-60 MH/s - - GPU (GTX 1080): ~10-20 MH/s - - CPU (fallback): ~0.5-2 MH/s - """ - - def __init__(self, num_neurons: int = 1_048_576, num_solitons: int = 65_536): - self.num_neurons = num_neurons - self.num_solitons = num_solitons - self.nonces_tested = 0 - self.shares_found = 0 - self.start_time = None - - # Initialize GPU arrays - if HAS_GPU: - self.neuron_weights = cp.random.randn(num_neurons, 11).astype(cp.float64) * 0.1 - self.neuron_thresholds = cp.random.uniform(0.5, 1.5, num_neurons).astype(cp.float64) - self.neuron_potential = cp.zeros(num_neurons, dtype=cp.float64) - self.neuron_firing_rate = cp.zeros(num_neurons, dtype=cp.float64) - - self.soliton_positions = cp.random.randn(num_solitons, 11).astype(cp.float64) - self.soliton_momenta = cp.random.randn(num_solitons, 11).astype(cp.float64) * 1000 - self.soliton_amplitudes = cp.random.uniform(0.1, 1.0, num_solitons).astype(cp.float64) - self.soliton_phases = cp.random.uniform(0, 2 * xp.pi, num_solitons).astype(cp.float64) - self.soliton_frequencies = cp.random.uniform(1e9, 1e12, num_solitons).astype(cp.float64) - - # CUDA stream for async operations - self.stream = cp.cuda.Stream() - else: - # CPU fallback - self.neuron_weights = xp.random.randn(num_neurons, 11).astype(xp.float64) * 0.1 - self.neuron_thresholds = xp.random.uniform(0.5, 1.5, num_neurons).astype(xp.float64) - self.neuron_potential = xp.zeros(num_neurons, dtype=xp.float64) - self.neuron_firing_rate = xp.zeros(num_neurons, dtype=xp.float64) - - self.soliton_positions = xp.random.randn(num_solitons, 11).astype(xp.float64) - self.soliton_momenta = xp.random.randn(num_solitons, 11).astype(xp.float64) * 1000 - self.soliton_amplitudes = xp.random.uniform(0.1, 1.0, num_solitons).astype(xp.float64) - self.soliton_phases = xp.random.uniform(0, 2 * xp.pi, num_solitons).astype(xp.float64) - self.soliton_frequencies = xp.random.uniform(1e9, 1e12, num_solitons).astype(xp.float64) - - def neuromorphic_nonce_generation(self, input_vector: AnyArray, batch_size: int = 10000) -> AnyArray: - """ - Generate nonces using neuromorphic surface - Runs on GPU if available - """ - if HAS_GPU: - with self.stream: - # Convert input to GPU array - input_gpu = cp.asarray(input_vector, dtype=cp.float64) - - # Compute membrane potentials (vectorized) - input_expanded = cp.broadcast_to(input_gpu, (self.num_neurons, 11)) - weighted_input = cp.sum(self.neuron_weights * input_expanded, axis=1) - - # Update membrane potential - self.neuron_potential = 0.9 * self.neuron_potential + weighted_input - - # Generate spikes - spikes = (self.neuron_potential > self.neuron_thresholds).astype(cp.float64) - self.neuron_firing_rate = 0.9 * self.neuron_firing_rate + 0.1 * spikes - self.neuron_potential *= (1 - spikes) # Reset after spike - - # Generate nonces from firing rates - nonce_candidates = cp.floor( - cp.abs(self.neuron_firing_rate) * 1e9 + cp.random.randint(0, 2**32, self.num_neurons, dtype=cp.uint32).astype(cp.float64) - ).astype(cp.uint32) - - # Select batch - indices = cp.random.choice(self.num_neurons, batch_size, replace=False) - nonces = nonce_candidates[indices].get() # Copy back to CPU - else: - # CPU fallback - input_expanded = xp.broadcast_to(input_vector, (self.num_neurons, 11)) - weighted_input = xp.sum(self.neuron_weights * input_expanded, axis=1) - self.neuron_potential = 0.9 * self.neuron_potential + weighted_input - spikes = (self.neuron_potential > self.neuron_thresholds).astype(xp.float64) - self.neuron_firing_rate = 0.9 * self.neuron_firing_rate + 0.1 * spikes - self.neuron_potential *= (1 - spikes) - - nonce_candidates = xp.floor( - xp.abs(self.neuron_firing_rate) * 1e9 + xp.random.randint(0, 2**32, self.num_neurons, dtype=xp.uint32).astype(xp.float64) - ).astype(xp.uint32) - - indices = xp.random.choice(self.num_neurons, batch_size, replace=False) - nonces = nonce_candidates[indices] - - return nonces - - def soliton_collision_optimization(self, nonces: AnyArray) -> AnyArray: - """ - Optimize nonces via soliton collision simulation - Runs on GPU if available - """ - if HAS_GPU: - with self.stream: - # Update soliton state - self.soliton_amplitudes *= 0.95 # Damping - - # Collision detection - collisions = self.soliton_amplitudes > 0.75 - - # Collapse to solutions - collapsed_indices = cp.where(collisions)[0] - if len(collapsed_indices) > 0: - position_sum = cp.sum(self.soliton_positions[collapsed_indices], axis=1) - nonce_values = ((position_sum * self.soliton_frequencies[collapsed_indices]).astype(cp.uint64) % (2**32)).astype(cp.uint32) - - # Replace some nonces with optimized values - num_replacements = min(len(collapsed_indices), len(nonces) // 10) - replacement_indices = cp.random.choice(len(nonces), num_replacements, replace=False) - nonces_gpu = cp.asarray(nonces) - nonces_gpu[replacement_indices] = nonce_values[:num_replacements] - nonces = nonces_gpu.get() - else: - # CPU fallback - self.soliton_amplitudes *= 0.95 - collisions = self.soliton_amplitudes > 0.75 - collapsed_indices = xp.where(collisions)[0] - - if len(collapsed_indices) > 0: - position_sum = xp.sum(self.soliton_positions[collapsed_indices], axis=1) - nonce_values = ((position_sum * self.soliton_frequencies[collapsed_indices]).astype(xp.uint64) % (2**32)).astype(xp.uint32) - - num_replacements = min(len(collapsed_indices), len(nonces) // 10) - replacement_indices = xp.random.choice(len(nonces), num_replacements, replace=False) - nonces[replacement_indices] = nonce_values[:num_replacements] - - return nonces - - def sha256_parallel(self, header_base: bytes, nonces: AnyArray) -> List[Tuple[int, bytes]]: - """ - Compute SHA256 hashes in parallel - Uses GPU if available (via custom CUDA kernel or vectorized CPU) - """ - results = [] - - if HAS_GPU: - # GPU batch processing - batch_size = 10000 - for i in range(0, len(nonces), batch_size): - batch_nonces = nonces[i:i+batch_size] - - # Create headers with nonces - headers = [] - for nonce in batch_nonces: - header = header_base[:76] + struct.pack(' bool: - """Check if hash meets target difficulty""" - hash_int = int.from_bytes(hash_bytes, 'big') - return hash_int < target - - def mine(self, header_base: bytes, target: int, duration: float = 30.0) -> GPUMiningStats: - """ - Main mining loop - """ - self.start_time = time.time() - self.nonces_tested = 0 - self.shares_found = 0 - - print(f"\n[+] Starting GPGPU Neuromorphic Mining") - print(f" Device: {'GPU (CUDA)' if HAS_GPU else 'CPU (Fallback)'}") - print(f" Neurons: {self.num_neurons:,}") - print(f" Solitons: {self.num_solitons:,}") - print(f" Duration: {duration:.1f}s") - print() - - end_time = self.start_time + duration - last_report = self.start_time - - while time.time() < end_time: - # Generate input vector from header - input_vector = xp.random.randn(11).astype(xp.float64) * 0.1 - - # Neuromorphic nonce generation - nonces = self.neuromorphic_nonce_generation(input_vector, batch_size=10000) - - # Soliton collision optimization - nonces = self.soliton_collision_optimization(nonces) - - # Parallel SHA256 computation - hash_results = self.sha256_parallel(header_base, nonces) - - # Check difficulty - for nonce, hash_result in hash_results: - self.nonces_tested += 1 - if self.check_difficulty(hash_result, target): - self.shares_found += 1 - print(f"[✓] VALID SHARE! Nonce: {nonce}, Hash: {hash_result.hex()[:16]}...") - - # Report every second - current_time = time.time() - if current_time - last_report >= 1.0: - elapsed = current_time - self.start_time - hashrate = self.nonces_tested / elapsed - print(f"[{elapsed:5.1f}s] Nonces: {self.nonces_tested:8,} | Hashrate: {hashrate:10.0f} H/s | Shares: {self.shares_found}") - last_report = current_time - - # Final stats - elapsed = time.time() - self.start_time - hashrate = self.nonces_tested / elapsed if elapsed > 0 else 0 - - stats = GPUMiningStats( - nonces_tested=self.nonces_tested, - shares_found=self.shares_found, - hashrate=hashrate, - gpu_utilization=95.0 if HAS_GPU else 0.0, - memory_used=cp.cuda.Device().mem_info[0] / 1e6 if HAS_GPU else 0.0, - thermal_throttle=False - ) - - return stats - - -def main(): - """Test GPGPU neuromorphic miner""" - - # Test parameters - header_base = bytes.fromhex('00000020' + '00' * 64 + '00' * 32 + '00000000' + 'ffff001d' + '00000000') - target = 0x00000000FFFF0000000000000000000000000000000000000000000000000000 - - # Create miner - miner = GPGPUNeuromorphicMiner(num_neurons=1_048_576, num_solitons=65_536) - - # Mine for 30 seconds - stats = miner.mine(header_base, target, duration=30.0) - - # Print final report - print() - print("=" * 60) - print(" GPGPU NEUROMORPHIC MINING - FINAL REPORT") - print("=" * 60) - print(f" Runtime: {stats.nonces_tested / stats.hashrate:.1f}s" if stats.hashrate > 0 else " Runtime: N/A") - print(f" Nonces Tested: {stats.nonces_tested:,}") - print(f" Shares Found: {stats.shares_found}") - print(f" Hashrate: {stats.hashrate:,.0f} H/s ({stats.hashrate/1e6:.2f} MH/s)") - print(f" Device: {'GPU (CUDA)' if HAS_GPU else 'CPU (Fallback)'}") - if HAS_GPU: - print(f" GPU Memory Used: {stats.memory_used:.1f} MB") - print(f" GPU Utilization: {stats.gpu_utilization:.1f}%") - print("=" * 60) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/gpgpu/gpgpu_surface.py b/5-Applications/tools-scripts/gpgpu/gpgpu_surface.py deleted file mode 100644 index 942f60ee..00000000 --- a/5-Applications/tools-scripts/gpgpu/gpgpu_surface.py +++ /dev/null @@ -1,139 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""GPGPU surface abstraction for quantitative modeling. - -This module provides a single compute surface that can run on: -1) CuPy (GPU) -2) NumPy (CPU vectorized) -3) Pure Python fallback -""" - -from __future__ import annotations - -import importlib -import importlib.util -import math -from typing import Any, Iterable, List - -# ── NE geometry scaffold (geometry-rip branch) ──────────────────────────────── -# Fixes EUCLIDEAN_ASSUMPTION_AUDIT finding #2 (HIGH): z-scores on log-normal -# quantities (gas fees, spreads) assume Gaussian on an unbounded line. -# Fix: log-transform before z-scoring to match the actual log-normal distribution. -_USE_NE_GEOMETRY = False - - -class GPGPUSurface: - def __init__(self) -> None: - self.backend = "python" - self.xp: Any = None - if importlib.util.find_spec("cupy") is not None: - try: - cp = importlib.import_module("cupy") - self.xp = cp - self.backend = "cupy" - return - except (ImportError, AttributeError): - pass - - if importlib.util.find_spec("numpy") is not None: - try: - np = importlib.import_module("numpy") - self.xp = np - self.backend = "numpy" - return - except (ImportError, AttributeError): - pass - - def to_array(self, values: Iterable[float]) -> Any: - vals = list(values) - if self.backend in ("cupy", "numpy"): - return self.xp.asarray(vals, dtype=float) - return vals - - def to_list(self, arr: Any) -> List[float]: - if self.backend == "cupy": - return [float(x) for x in self.xp.asnumpy(arr).tolist()] - if self.backend == "numpy": - return [float(x) for x in arr.tolist()] - return [float(x) for x in arr] - - def mean(self, values: Iterable[float]) -> float: - vals = list(values) - if not vals: - return 0.0 - if self.backend in ("cupy", "numpy"): - arr = self.to_array(vals) - return float(self.xp.mean(arr)) - return sum(vals) / len(vals) - - def std(self, values: Iterable[float]) -> float: - vals = list(values) - if len(vals) < 2: - return 0.0 - if self.backend in ("cupy", "numpy"): - arr = self.to_array(vals) - return float(self.xp.std(arr)) - mu = self.mean(vals) - var = sum((x - mu) ** 2 for x in vals) / len(vals) - return math.sqrt(var) - - def zscores(self, values: Iterable[float]) -> List[float]: - vals = list(values) - if not vals: - return [] - sigma = self.std(vals) - if sigma == 0: - return [0.0 for _ in vals] - mu = self.mean(vals) - if self.backend in ("cupy", "numpy"): - arr = self.to_array(vals) - out = (arr - mu) / sigma - return self.to_list(out) - return [(x - mu) / sigma for x in vals] - - def log_zscores(self, values: Iterable[float], eps: float = 1e-9) -> List[float]: - """NE path: z-score on log-transformed values (AUDIT FINDING #2 fix). - - Gas fees and spreads are log-normally distributed. z-scoring log(x) - instead of x correctly treats multiplicative deviations as equal in - magnitude (e.g., 2× above mean == 0.5× below mean). - Use when _USE_NE_GEOMETRY is True. - """ - log_vals = [math.log(max(eps, float(x))) for x in values] - return self.zscores(log_vals) - - def sigmoid(self, x: float) -> float: - if self.backend in ("cupy", "numpy"): - return float(1.0 / (1.0 + self.xp.exp(-x))) - return 1.0 / (1.0 + math.exp(-x)) - - def softmax(self, values: Iterable[float], temperature: float = 1.0) -> List[float]: - vals = list(values) - if not vals: - return [] - t = max(1e-6, float(temperature)) - mx = max(vals) - - if self.backend in ("cupy", "numpy"): - arr = self.to_array(vals) - exps = self.xp.exp((arr - mx) / t) - denom = float(self.xp.sum(exps)) - if denom == 0.0: - return [1.0 / len(vals)] * len(vals) - probs = exps / denom - return self.to_list(probs) - - exps_py = [math.exp((x - mx) / t) for x in vals] - denom_py = sum(exps_py) - if denom_py == 0.0: - return [1.0 / len(vals)] * len(vals) - return [x / denom_py for x in exps_py] - - -def get_surface() -> GPGPUSurface: - return GPGPUSurface() diff --git a/5-Applications/tools-scripts/gpgpu/gpgpu_surface_probe.py b/5-Applications/tools-scripts/gpgpu/gpgpu_surface_probe.py deleted file mode 100644 index 78c19471..00000000 --- a/5-Applications/tools-scripts/gpgpu/gpgpu_surface_probe.py +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import time -from typing import List - -try: - from scripts.gpgpu_surface import get_surface -except ImportError: - from gpgpu_surface import get_surface - - -def main() -> int: - surface = get_surface() - vec: List[float] = [float(i) / 1000.0 for i in range(200000)] - - t0 = time.perf_counter() - mu = surface.mean(vec) - sigma = surface.std(vec) - zs = surface.zscores(vec) - probs = surface.softmax(vec[:1024], temperature=0.5) - t1 = time.perf_counter() - - print( - json.dumps( - { - "backend": surface.backend, - "vector_size": len(vec), - "mean": mu, - "std": sigma, - "zsample": zs[:5], - "softmax_sample": probs[:5], - "elapsed_ms": round((t1 - t0) * 1000.0, 3), - }, - indent=2, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/hachimoji/hachimoji_rna.py b/5-Applications/tools-scripts/hachimoji/hachimoji_rna.py deleted file mode 100644 index 18e86abb..00000000 --- a/5-Applications/tools-scripts/hachimoji/hachimoji_rna.py +++ /dev/null @@ -1,551 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -hachimoji_rna.py — Hachimoji RNA extension for hachimoji_synth.py - -Extension module. Import only when RNA encoding is needed. -Does NOT modify or require hachimoji_synth.py. -Standalone or composable. - -REFERENCE ---------- -Hoshika et al. (2019). Hachimoji DNA and RNA: A genetic system with eight -building blocks. Science 363(6429):884-887. doi:10.1126/science.aat0971 - -RNA ALPHABET ------------- -Standard RNA: A, U, G, C (U replaces T vs DNA) -Hachimoji RNA: A, U, G, C, P, Z, S, B (same synthetic pairs, ribose backbone) - -Base pairings (Watson-Crick geometry preserved): - A ↔ U (standard) - G ↔ C (standard) - P ↔ Z (synthetic, Benner lab) - S ↔ B (synthetic, Benner lab) - -Codon space: 8³ = 512 codons (vs 64 for standard RNA) - -FOLD STABILITY CONVENTION (SYMBOLIC) -------------------------------------- -Each base is assigned a FOLD_STABILITY weight (0.0 → 1.0). -MATH UNIVERSE: these are symbolic structural coordinates, NOT literal -thermodynamic free energies. The ordering is inspired by H-bond count -and Hoshika 2019 aptamer behaviour, but the values define positions in -the Menger Laplacian spectrum. No physical unit attaches. - G/C: maximum stability symbol → 1.0 - A/U: mid-low symbol → 0.6 - P/Z: high-mid symbol → 0.8 (Hoshika 2019 qualitative) - S/B: mid symbol → 0.7 (Hoshika 2019 qualitative) - Z: minimum symbol → 0.2 (destabiliser archetype) - -STOP CODON CONVENTION ----------------------- -Standard RNA stop codons: UAA, UAG, UGA -In Hachimoji RNA these are still functional if translation is the goal. -For ENGRAM ADDRESSING purposes, the biologically-unclaimed positions are: - Any codon containing Z at position 1 or 2 (no natural amino acid uses Z) - → available as Menger sponge vertex addresses (structural holes) - → flagged CODON_CLASS_VERTEX in this module - -MENGER SPONGE MAPPING ----------------------- -Menger sponge iteration 1: 27 - 7 = 20 positions remaining. -Standard genetic code: exactly 20 amino acids. -Hachimoji RNA: 512 codons → 20 canonical + 492 extended addresses. - -The 492 extended addresses are partitioned: - CODON_CLASS_SURFACE — G/C/P/Z rich, high stability → hot engram nodes - CODON_CLASS_INTERIOR — A/U/S/B rich, moderate stability → warm nodes - CODON_CLASS_VERTEX — Z-dominant, destabilising → sponge holes / stop - CODON_CLASS_TUNNEL — pseudoknot-prone sequences → |W> tunnel state - -Usage ------ - from hachimoji_rna import RnaCodonSpace, FoldPropensity, MengerRnaMapper - - space = RnaCodonSpace() - print(space.n_codons) # 512 - print(space.stop_codons) # ['UAA', 'UAG', 'UGA'] - print(space.vertex_codons[:5]) # Z-dominant, sponge holes - - fp = FoldPropensity() - print(fp.stability('GCG')) # ~1.0 - print(fp.stability('ZAU')) # ~0.2x (Z destabilises) - print(fp.codon_class('GCG')) # 'SURFACE' - print(fp.codon_class('ZZZ')) # 'VERTEX' - - mapper = MengerRnaMapper() - addr = mapper.codon_to_voxel('GCG') # (iteration, position) in sponge - print(mapper.is_tunnel_candidate('GUG')) # True/False (G-quad prone) -""" - -from __future__ import annotations -from itertools import product -from typing import Dict, List, Tuple, Optional - -# ── Alphabet ────────────────────────────────────────────────────────────────── - -RNA_BASES: Tuple[str, ...] = ('A', 'U', 'G', 'C', 'P', 'Z', 'S', 'B') - -# Watson-Crick complement in Hachimoji RNA -RNA_COMPLEMENT: Dict[str, str] = { - 'A': 'U', 'U': 'A', - 'G': 'C', 'C': 'G', - 'P': 'Z', 'Z': 'P', - 'S': 'B', 'B': 'S', -} - -# Symbolic fold stability weight per base. -# MATH UNIVERSE: these are structural ordering labels (topology of the -# address space), NOT literal thermodynamic measurements. The ordering -# is inspired by H-bond count and Hoshika 2019 aptamer behaviour, but -# the numerical values are symbolic coordinates — they define the -# Menger Laplacian spectrum, not physical free energies. -BASE_FOLD_STABILITY: Dict[str, float] = { - 'G': 1.00, # maximum stability symbol — 3 H-bonds archetype - 'C': 1.00, # maximum stability symbol — 3 H-bonds archetype - 'P': 0.80, # high-mid symbol — synthetic stable (Hoshika 2019) - 'S': 0.70, # mid symbol — synthetic stable - 'A': 0.60, # mid-low symbol — 2 H-bonds archetype - 'U': 0.60, # mid-low symbol — 2 H-bonds archetype - 'B': 0.50, # low-mid symbol — less characterised - 'Z': 0.20, # minimum symbol — destabiliser (spinach aptamer quench) -} - -# G-quadruplex propensity: G-runs form G4 structures (pseudoknot-prone) -# Used to identify tunnel-state candidate codons (|W> in TSM-NR1) -G_QUAD_MIN_RUN = 2 # GG or more at any position → candidate - -# Standard RNA stop codons (biologically claimed) -RNA_STOP_STANDARD: Tuple[str, ...] = ('UAA', 'UAG', 'UGA') - -# Codon class labels -CODON_CLASS_SURFACE = 'SURFACE' # high stability → hot engram node -CODON_CLASS_INTERIOR = 'INTERIOR' # moderate → warm node -CODON_CLASS_VERTEX = 'VERTEX' # Z-dominant → sponge hole / stop -CODON_CLASS_TUNNEL = 'TUNNEL' # pseudoknot-prone → |W> tunnel state - -# Stability thresholds for class assignment -SURFACE_THRESHOLD = 0.80 # mean stability ≥ this → SURFACE -VERTEX_THRESHOLD = 0.40 # mean stability ≤ this → VERTEX -# Between VERTEX_THRESHOLD and SURFACE_THRESHOLD → INTERIOR or TUNNEL - - -# ── Codon space ─────────────────────────────────────────────────────────────── - -class RnaCodonSpace: - """ - Complete 8-base Hachimoji RNA codon space. - - All 512 codons enumerated, classified, and indexed. - No translation table defined — this is for engram addressing, - not biological protein synthesis. - """ - - def __init__(self) -> None: - self._codons: List[str] = [ - ''.join(t) for t in product(RNA_BASES, repeat=3) - ] - self._index: Dict[str, int] = {c: i for i, c in enumerate(self._codons)} - self._fp = FoldPropensity() - - @property - def n_codons(self) -> int: - return len(self._codons) # always 512 - - @property - def all_codons(self) -> List[str]: - return list(self._codons) - - @property - def stop_codons(self) -> List[str]: - """Standard RNA stop codons still present in Hachimoji RNA.""" - return [c for c in self._codons if c in RNA_STOP_STANDARD] - - @property - def vertex_codons(self) -> List[str]: - """Z-dominant codons — sponge holes, structurally destabilising. - Biologically unclaimed in Hachimoji RNA → free for engram addressing.""" - return [c for c in self._codons - if self._fp.codon_class(c) == CODON_CLASS_VERTEX] - - @property - def surface_codons(self) -> List[str]: - """High-stability codons → hot engram surface nodes.""" - return [c for c in self._codons - if self._fp.codon_class(c) == CODON_CLASS_SURFACE] - - @property - def tunnel_codons(self) -> List[str]: - """Pseudoknot-prone → |W> tunnel state candidates.""" - return [c for c in self._codons - if self._fp.codon_class(c) == CODON_CLASS_TUNNEL] - - @property - def interior_codons(self) -> List[str]: - """Moderate stability → warm/cold engram nodes.""" - return [c for c in self._codons - if self._fp.codon_class(c) == CODON_CLASS_INTERIOR] - - def index(self, codon: str) -> int: - """Integer address of a codon in [0, 511].""" - return self._index[codon.upper()] - - def codon(self, index: int) -> str: - """Codon at integer address.""" - return self._codons[index] - - def complement(self, codon: str) -> str: - """Watson-Crick complement of a codon (3'→5' sense).""" - return ''.join(RNA_COMPLEMENT[b] for b in codon.upper()) - - def summary(self) -> Dict[str, int]: - return { - 'total': self.n_codons, - 'surface': len(self.surface_codons), - 'interior': len(self.interior_codons), - 'vertex': len(self.vertex_codons), - 'tunnel': len(self.tunnel_codons), - 'stop': len(self.stop_codons), - } - - -# ── Fold propensity ─────────────────────────────────────────────────────────── - -class FoldPropensity: - """ - Codon-level fold stability and class assignment. - - Symbolic codon-level fold propensity for Menger address classification. - No sequence-context model — single codon only. - Weights are structural ordering labels (math universe), not literal - thermodynamic measurements. The classification SURFACE/INTERIOR/ - VERTEX/TUNNEL defines address-space topology, not physical stability. - """ - - def stability(self, codon: str) -> float: - """Mean fold stability weight for a codon. Range [0.0, 1.0].""" - return sum(BASE_FOLD_STABILITY[b] for b in codon.upper()) / 3.0 - - def is_g_quad_prone(self, codon: str) -> bool: - """True if codon contains a G-run that may participate in G-quadruplex. - G-quadruplexes are pseudoknot-prone → |W> tunnel state candidates.""" - g_run = 0 - for b in codon.upper(): - if b == 'G': - g_run += 1 - if g_run >= G_QUAD_MIN_RUN: - return True - else: - g_run = 0 - return False - - def codon_class(self, codon: str) -> str: - """ - Classify codon for Menger sponge / engram addressing. - - Priority order: - TUNNEL — G-quad prone (pseudoknot → |W> tunnel state) - VERTEX — Z-dominant, destabilising (sponge holes) - SURFACE — high stability (hot engram nodes) - INTERIOR — everything else (warm/cold nodes) - """ - c = codon.upper() - s = self.stability(c) - - # TUNNEL first — G-quad supersedes stability classification - if self.is_g_quad_prone(c): - return CODON_CLASS_TUNNEL - - # VERTEX — Z destabilises fold below threshold - if s <= VERTEX_THRESHOLD: - return CODON_CLASS_VERTEX - - # SURFACE — high stability hot engram nodes - if s >= SURFACE_THRESHOLD: - return CODON_CLASS_SURFACE - - # Everything else - return CODON_CLASS_INTERIOR - - def hot_cold_score(self, codon: str) -> float: - """ - Thermodynamic hot/cold score for hot-path circulation. - - +1.0 = maximally hot (stable fold, stays in memory) - -1.0 = maximally cold (unstable, evicts quickly) - - Derived from stability weight, centred and normalised. - TUNNEL codons get a separate non-linear score (non-planar topology). - """ - c = codon.upper() - if self.is_g_quad_prone(c): - # G-quadruplexes are thermodynamically very stable but topologically - # complex — score as moderately hot with high variance - return 0.5 - s = self.stability(c) - # Map [0, 1] → [-1, +1], centred at 0.6 (A/U stability) - return (s - 0.6) / 0.4 - - -# ── Menger RNA mapper ───────────────────────────────────────────────────────── - -class MengerRnaMapper: - """ - Maps Hachimoji RNA codons to Menger sponge addresses. - - Menger sponge geometry: - Iteration 0: 3×3×3 = 27 positions (raw 3-base codon space mod 27) - Iteration 1: 27 - 7 = 20 positions (amino acid analog — 7 holes) - Iteration 2: 20 × 20 = 400 (dipeptide analog) - ... - - The 7 removed positions at iteration 1 correspond to the 7 cubes - removed from the Menger sponge: center of each face (6) + center cube (1). - In this mapping these are VERTEX codons — structurally destabilising, - biologically unclaimed in Hachimoji RNA. - - The 512 Hachimoji RNA codons are mapped onto this geometry: - Positions 0-19: iteration-1 surface (20 amino acid analogs) - Positions 20-511: extended Hachimoji address space - partitioned by codon_class into SURFACE/INTERIOR/VERTEX/TUNNEL - - Hausdorff dimension of Menger sponge: log(20)/log(3) ≈ 2.727 - This is the effective dimensionality of the engram address space — - sub-integer, between 2D surface and 3D volume. - Non-Euclidean by construction. - """ - - HAUSDORFF_DIM: float = 2.7268 # log(20) / log(3) - N_ITERATION_1: int = 20 # positions after first Menger iteration - N_ITERATION_0: int = 27 # 3×3×3 raw cube - - # The 7 removed positions in iteration 1 (face centres + body centre) - # Mapped to codon indices via: removed_idx % 27 - REMOVED_POSITIONS: Tuple[int, ...] = (4, 10, 12, 13, 14, 16, 22) - - def __init__(self) -> None: - self._space = RnaCodonSpace() - self._fp = FoldPropensity() - self._build_index() - - def _build_index(self) -> None: - """Partition all 512 codons into Menger address layers.""" - self._layer: Dict[str, str] = {} - self._voxel: Dict[str, Tuple[int, int]] = {} - - surface = self._space.surface_codons - tunnel = self._space.tunnel_codons - vertex = self._space.vertex_codons - interior = self._space.interior_codons - - # Iteration-1 surface: first 20 SURFACE codons (by index order) - iter1 = surface[:self.N_ITERATION_1] - for i, c in enumerate(iter1): - self._layer[c] = 'ITER1' - self._voxel[c] = (1, i) - - # Remaining SURFACE → iteration-2 extended surface - for i, c in enumerate(surface[self.N_ITERATION_1:]): - self._layer[c] = 'ITER2_SURFACE' - self._voxel[c] = (2, i) - - # TUNNEL → |W> tunnel state addresses (non-planar) - for i, c in enumerate(tunnel): - self._layer[c] = 'TUNNEL' - self._voxel[c] = (3, i) - - # INTERIOR → warm/cold interior addresses - for i, c in enumerate(interior): - self._layer[c] = 'INTERIOR' - self._voxel[c] = (4, i) - - # VERTEX → sponge holes (stop codon analogs) - for i, c in enumerate(vertex): - self._layer[c] = 'VERTEX' - self._voxel[c] = (5, i) - - def codon_to_voxel(self, codon: str) -> Tuple[int, int]: - """ - Map a codon to its (iteration_layer, position) in Menger space. - - Returns: - (1, 0-19) — iteration-1 surface (20 canonical positions) - (2, n) — extended surface - (3, n) — tunnel / |W> addresses - (4, n) — interior warm/cold - (5, n) — vertex / stop / sponge holes - """ - return self._voxel.get(codon.upper(), (0, 0)) - - def voxel_to_codons(self, iteration: int) -> List[str]: - """All codons at a given iteration layer.""" - return [c for c, v in self._voxel.items() if v[0] == iteration] - - def is_tunnel_candidate(self, codon: str) -> bool: - """True if codon maps to a |W> tunnel address (pseudoknot-prone).""" - return self._layer.get(codon.upper()) == 'TUNNEL' - - def is_vertex(self, codon: str) -> bool: - """True if codon is a sponge hole (structurally destabilising stop).""" - return self._layer.get(codon.upper()) == 'VERTEX' - - def hot_cold_score(self, codon: str) -> float: - """Hot/cold engram score for this codon's voxel position.""" - return self._fp.hot_cold_score(codon.upper()) - - def address_space_summary(self) -> Dict[str, object]: - layer_counts: Dict[str, int] = {} - for layer in self._layer.values(): - layer_counts[layer] = layer_counts.get(layer, 0) + 1 - return { - 'hausdorff_dim': self.HAUSDORFF_DIM, - 'total_addresses': len(self._voxel), - 'layers': layer_counts, - 'canonical_iter1': self.N_ITERATION_1, - 'note': ( - 'VERTEX codons are biologically unclaimed in Hachimoji RNA — ' - 'available as sponge-hole addresses with no prior art conflict.' - ), - } - - -# ── Optional integration with hachimoji_synth.py ───────────────────────────── - -def rna_carrier_from_dna_profile(dna_profile: dict) -> dict: - """ - Convert a DNA carrier profile (from hachimoji_synth.py CARRIER_PROFILES) - to an approximate RNA carrier profile. - - Substitution: T → U everywhere in codon labels. - Frequencies are preserved — this is a label translation only. - - NOTE: This is an approximation. A proper RNA carrier profile requires - RNA-seq codon counts from the target organism, not CDS-derived DNA counts. - The octopus profile in hachimoji_synth.py explicitly flags this gap - (60% neural transcript RNA editing in O. vulgaris). - - For organisms with significant RNA editing, this function will be wrong. - Use RNA-seq data where available. - """ - rna_profile: dict = {} - for codon, freq in dna_profile.items(): - rna_codon = codon.replace('T', 'U') - rna_profile[rna_codon] = freq - return rna_profile - - -def score_sequence_for_menger( - codons: List[str], - mapper: Optional[MengerRnaMapper] = None, -) -> Dict[str, object]: - """ - Score a Hachimoji RNA codon sequence for Menger sponge address properties. - - Returns per-codon classification and aggregate statistics useful for - deciding whether RNA encoding adds value for a given sequence. - - If the sequence has no TUNNEL or VERTEX codons, RNA encoding adds no - geometric addressing benefit over DNA encoding — save the complexity. - """ - if mapper is None: - mapper = MengerRnaMapper() - - classified = [ - { - 'codon': c, - 'layer': mapper._layer.get(c.upper(), 'UNKNOWN'), - 'voxel': mapper.codon_to_voxel(c), - 'hot_cold': mapper.hot_cold_score(c), - 'is_tunnel': mapper.is_tunnel_candidate(c), - 'is_vertex': mapper.is_vertex(c), - } - for c in codons - ] - - n = len(classified) - n_tunnel = sum(1 for x in classified if x['is_tunnel']) - n_vertex = sum(1 for x in classified if x['is_vertex']) - n_iter1 = sum(1 for x in classified if x['layer'] == 'ITER1') - mean_hc = sum(x['hot_cold'] for x in classified) / max(n, 1) - - return { - 'n_codons': n, - 'n_tunnel': n_tunnel, - 'n_vertex': n_vertex, - 'n_iter1': n_iter1, - 'mean_hot_cold': mean_hc, - 'rna_adds_value': n_tunnel > 0 or n_vertex > 0, - 'codons': classified, - } - - -# ── Self-test ───────────────────────────────────────────────────────────────── - -def _self_test() -> None: - print("hachimoji_rna.py — self-test") - print("=" * 60) - - space = RnaCodonSpace() - s = space.summary() - print(f"Codon space: {s['total']} total") - print(f" SURFACE (hot engram nodes): {s['surface']:3d}") - print(f" INTERIOR (warm/cold nodes): {s['interior']:3d}") - print(f" TUNNEL (|W> tunnel state): {s['tunnel']:3d}") - print(f" VERTEX (sponge holes/stops): {s['vertex']:3d}") - print(f" STOP (standard RNA stops): {s['stop']:3d}") - assert s['total'] == 512, f"Expected 512 codons, got {s['total']}" - assert s['surface'] + s['interior'] + s['tunnel'] + s['vertex'] == 512 - - fp = FoldPropensity() - assert fp.codon_class('GGG') == CODON_CLASS_TUNNEL, "GGG should be TUNNEL (G-quad)" - assert fp.codon_class('ZZZ') == CODON_CLASS_VERTEX, "ZZZ should be VERTEX (destabilising)" - assert fp.codon_class('GCG') == CODON_CLASS_SURFACE, "GCG should be SURFACE (high stability)" - assert fp.codon_class('AUA') == CODON_CLASS_INTERIOR,"AUA should be INTERIOR" - print("\nFold propensity checks: PASS") - - mapper = MengerRnaMapper() - addr_sum = mapper.address_space_summary() - print(f"\nMenger mapper:") - print(f" Hausdorff dimension: {addr_sum['hausdorff_dim']:.4f}") - print(f" Total addresses: {addr_sum['total_addresses']}") - print(f" Iteration-1 (canonical 20): {addr_sum['layers'].get('ITER1', 0)}") - for layer, count in sorted(addr_sum['layers'].items()): - print(f" {layer:<20s}: {count}") - print(f"\n {addr_sum['note']}") - - # Verify GGG maps to TUNNEL layer - assert mapper.is_tunnel_candidate('GGG'), "GGG should be tunnel candidate" - assert mapper.is_vertex('ZZZ'), "ZZZ should be vertex" - assert not mapper.is_tunnel_candidate('AUA'), "AUA should not be tunnel" - - # Score a short sequence - test_seq = ['GGG', 'GCG', 'AUA', 'ZZZ', 'GUG'] - result = score_sequence_for_menger(test_seq, mapper) - print(f"\nSequence score for {test_seq}:") - print(f" RNA adds value: {result['rna_adds_value']}") - print(f" n_tunnel: {result['n_tunnel']} n_vertex: {result['n_vertex']}") - print(f" mean hot/cold: {result['mean_hot_cold']:+.3f}") - assert result['rna_adds_value'], "Test sequence should add value (has GGG tunnel + ZZZ vertex)" - - # DNA→RNA profile conversion - dna_profile = {'ATG': 10, 'TGA': 3, 'GCT': 7} - rna_profile = rna_carrier_from_dna_profile(dna_profile) - assert 'AUG' in rna_profile, "ATG should become AUG" - assert 'UGA' in rna_profile, "TGA should become UGA" - print("\nDNA→RNA profile conversion: PASS") - - print("\nAll checks PASS") - print("=" * 60) - print("NOTE: BASE_FOLD_STABILITY weights are symbolic structural") - print("coordinates (math universe), not literal thermodynamic values.") - print("They define Menger Laplacian topology, not physical free energies.") - - -if __name__ == '__main__': - _self_test() diff --git a/5-Applications/tools-scripts/hachimoji/hachimoji_spectral.py b/5-Applications/tools-scripts/hachimoji/hachimoji_spectral.py deleted file mode 100644 index f0b93369..00000000 --- a/5-Applications/tools-scripts/hachimoji/hachimoji_spectral.py +++ /dev/null @@ -1,570 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -hachimoji_spectral.py — DNA/RNA Spectral Codon Encoding on Menger Laplacian - -Combines hachimoji_synth.py (DNA) and hachimoji_rna.py (RNA) into a unified -spectral codon space where codon position = eigenmode of the Menger sponge -Laplacian. - -MATH UNIVERSE -───────────── -All "energy" and "stability" values are symbolic — structural coordinates -in a mathematical address space, not physical measurements. The numerical -ordering is inspired by biochemistry (H-bond count, fold propensity) but -no physical unit attaches. This removes the dependency on experimental -Tm data and makes the system domain-agnostic. - -SPECTRAL DECOMPOSITION -────────────────────── -DNA carrier profile → coarse eigenmodes (low-frequency structure) -RNA fold propensity → fine eigenmodes (high-frequency topology) - -Together they form a complete spectral decomposition of the engram -address space — analogous to Fourier decomposition but applied to the -codon/voxel address space on the Menger sponge. - - Codon c → spectral weight w(c) = α · dna_weight(c) + β · rna_weight(c) - - where: - dna_weight(c) = normalised codon usage from carrier profile - rna_weight(c) = symbolic fold stability from BASE_FOLD_STABILITY - α, β = mixing coefficients (DNA coarse, RNA fine) - - The spectral weight defines the eigenvalue magnitude at the codon's - Menger Laplacian position. Hot codons (high eigenvalue) are visited - frequently by engram random walks; cold codons (low eigenvalue) are - structural boundaries. - -COMBINED CODON SPACE -──────────────────── -DNA alphabet: A, T, G, C, P, Z, S, B → 8³ = 512 codons -RNA alphabet: A, U, G, C, P, Z, S, B → 8³ = 512 codons -Combined: 1024 distinct codon labels (T↔U distinguishes strand type) - shared: codons with no T or U (e.g. GCG, PZS) → 6³ = 216 overlap - DNA-only: any codon with T but no U - RNA-only: any codon with U but no T - hybrid: codons with both T and U (e.g. TUG) → structurally impossible - in biology but valid as abstract address labels in math universe - -In practice: 512 DNA + 512 RNA − 216 shared = 808 distinct addresses. -The shared codons carry BOTH DNA and RNA spectral weights → double-layered -eigenvalues (the address has both coarse and fine structure simultaneously). - -Usage -───── - from hachimoji_spectral import SpectralCodonSpace, MengerLaplacian - - spec = SpectralCodonSpace() - print(spec.n_addresses) # 808 - - lap = MengerLaplacian(spec) - ev = lap.eigenvalue('GCG') # combined spectral weight - band = lap.spectral_band('GCG') # 'COARSE', 'FINE', 'DUAL', 'NULL' -""" - -from __future__ import annotations -import math -from itertools import product -from typing import Dict, List, Optional, Tuple - -# ── Import partner modules ─────────────────────────────────────────────────── - -from hachimoji_rna import ( - RNA_BASES, - BASE_FOLD_STABILITY, - RnaCodonSpace, - FoldPropensity, - MengerRnaMapper, -) - -# DNA constants from hachimoji_synth — import-safe fallback if not available -try: - from hachimoji_synth import HACHI_BASES as DNA_BASES_STR - DNA_BASES: Tuple[str, ...] = tuple(DNA_BASES_STR) -except ImportError: - DNA_BASES = ('A', 'T', 'G', 'C', 'P', 'Z', 'S', 'B') - -# ── Constants ──────────────────────────────────────────────────────────────── - -# Symbolic stability weights for DNA bases (mirrors RNA convention). -# T replaces U; otherwise same symbolic ordering. -DNA_FOLD_STABILITY: Dict[str, float] = { - 'G': 1.00, - 'C': 1.00, - 'P': 0.80, - 'S': 0.70, - 'A': 0.60, - 'T': 0.60, # DNA thymine — same symbolic weight as RNA uracil - 'B': 0.50, - 'Z': 0.20, -} - -# Mixing coefficients: DNA = coarse (low-frequency), RNA = fine (high-frequency) -ALPHA_DNA: float = 0.6 # coarse eigenmode weight -BETA_RNA: float = 0.4 # fine eigenmode weight -# α + β = 1.0 → spectral weight normalised to [0, 1] - -# Menger sponge constants -HAUSDORFF_DIM: float = 2.7268 # log(20)/log(3) -MENGER_ITER1_POSITIONS: int = 20 # 27 - 7 removed - - -# ── Strand type ────────────────────────────────────────────────────────────── - -class StrandType: - DNA = 'DNA' - RNA = 'RNA' - DUAL = 'DUAL' # codon has neither T nor U → shared by both - NULL = 'NULL' # codon has both T and U → abstract-only address - - -def classify_strand(codon: str) -> str: - """Determine which strand(s) a codon belongs to.""" - has_t = 'T' in codon.upper() - has_u = 'U' in codon.upper() - if has_t and has_u: - return StrandType.NULL # biologically impossible, math-valid - if has_t: - return StrandType.DNA - if has_u: - return StrandType.RNA - return StrandType.DUAL # no T, no U → shared - - -# ── Spectral codon space ──────────────────────────────────────────────────── - -class SpectralCodonSpace: - """ - Combined DNA + RNA codon space with spectral weight assignment. - - Each codon gets: - - strand classification (DNA / RNA / DUAL / NULL) - - DNA symbolic weight (coarse eigenmode) - - RNA symbolic weight (fine eigenmode) - - combined spectral weight = α·dna + β·rna - - Menger sponge address (iteration, position) - """ - - # All 10 bases across both alphabets - ALL_BASES: Tuple[str, ...] = ('A', 'T', 'U', 'G', 'C', 'P', 'Z', 'S', 'B') - - def __init__( - self, - alpha: float = ALPHA_DNA, - beta: float = BETA_RNA, - ) -> None: - self.alpha = alpha - self.beta = beta - - # Generate the full combined space: 9 bases (A,T,U,G,C,P,Z,S,B) - # 9³ = 729 raw combinations, but we exclude NULL (T+U) codons - # from the primary address space. - self._codons: List[str] = [] - self._strand: Dict[str, str] = {} - self._dna_weight: Dict[str, float] = {} - self._rna_weight: Dict[str, float] = {} - self._spectral: Dict[str, float] = {} - - for bases in product(self.ALL_BASES, repeat=3): - codon = ''.join(bases) - strand = classify_strand(codon) - - self._codons.append(codon) - self._strand[codon] = strand - self._dna_weight[codon] = self._calc_dna_weight(codon, strand) - self._rna_weight[codon] = self._calc_rna_weight(codon, strand) - self._spectral[codon] = self._combine(codon) - - self._index: Dict[str, int] = {c: i for i, c in enumerate(self._codons)} - - def _calc_dna_weight(self, codon: str, strand: str) -> float: - """DNA symbolic weight — coarse eigenmode.""" - if strand == StrandType.RNA: - return 0.0 # pure RNA codon has no DNA eigenmode - # Map U→T for DUAL codons when computing DNA weight - return sum(DNA_FOLD_STABILITY.get(b, DNA_FOLD_STABILITY.get( - 'T' if b == 'U' else b, 0.0)) for b in codon) / 3.0 - - def _calc_rna_weight(self, codon: str, strand: str) -> float: - """RNA symbolic weight — fine eigenmode.""" - if strand == StrandType.DNA: - return 0.0 # pure DNA codon has no RNA eigenmode - # Map T→U for DUAL codons when computing RNA weight - return sum(BASE_FOLD_STABILITY.get(b, BASE_FOLD_STABILITY.get( - 'U' if b == 'T' else b, 0.0)) for b in codon) / 3.0 - - def _combine(self, codon: str) -> float: - """Combined spectral weight = α·dna + β·rna.""" - d = self._dna_weight[codon] - r = self._rna_weight[codon] - strand = self._strand[codon] - - if strand == StrandType.DUAL: - # Shared codon: both eigenmodes active - return self.alpha * d + self.beta * r - elif strand == StrandType.DNA: - return d # full DNA weight, no RNA contribution - elif strand == StrandType.RNA: - return r # full RNA weight, no DNA contribution - else: # NULL - # Abstract address — average of what T and U would give - return (d + r) / 2.0 - - @property - def n_total(self) -> int: - """Total codons including NULL.""" - return len(self._codons) - - @property - def n_addresses(self) -> int: - """Addressable codons (excluding NULL).""" - return sum(1 for s in self._strand.values() if s != StrandType.NULL) - - def codons_by_strand(self, strand: str) -> List[str]: - return [c for c, s in self._strand.items() if s == strand] - - def spectral_weight(self, codon: str) -> float: - return self._spectral.get(codon.upper(), 0.0) - - def dna_weight(self, codon: str) -> float: - return self._dna_weight.get(codon.upper(), 0.0) - - def rna_weight(self, codon: str) -> float: - return self._rna_weight.get(codon.upper(), 0.0) - - def strand(self, codon: str) -> str: - return self._strand.get(codon.upper(), StrandType.NULL) - - def summary(self) -> Dict[str, int]: - counts: Dict[str, int] = {} - for s in self._strand.values(): - counts[s] = counts.get(s, 0) + 1 - return { - 'total': self.n_total, - 'addressable': self.n_addresses, - **counts, - } - - -# ── Menger Laplacian ──────────────────────────────────────────────────────── - -class MengerLaplacian: - """ - Spectral decomposition on the Menger sponge Laplacian. - - Each codon maps to an eigenmode. The eigenvalue = spectral weight. - Spectral band classification: - - COARSE — DNA-only codon (low-frequency structural backbone) - FINE — RNA-only codon (high-frequency topological detail) - DUAL — shared codon (both eigenmodes superposed) - NULL — abstract address (biologically impossible T+U) - - The Laplacian adjacency is defined by single-base mutations: - codon A is adjacent to codon B if they differ at exactly one position. - Each codon has at most 8 × 3 = 24 neighbours (8 alternative bases × 3 - positions, minus self). In practice fewer, since not all bases appear - at all positions. - - The spectral weight determines the eigenvalue magnitude: - hot codons (high eigenvalue) → frequently visited by engram random walks - cold codons (low eigenvalue) → structural boundaries / stop positions - """ - - def __init__(self, space: Optional[SpectralCodonSpace] = None) -> None: - self._space = space or SpectralCodonSpace() - - def eigenvalue(self, codon: str) -> float: - """Eigenvalue = spectral weight at this codon's Laplacian position.""" - return self._space.spectral_weight(codon) - - def spectral_band(self, codon: str) -> str: - """Which eigenmode band this codon occupies.""" - return self._space.strand(codon) - - def neighbours(self, codon: str) -> List[str]: - """ - All codons reachable by a single-base mutation. - These are the Laplacian adjacency edges. - """ - c = codon.upper() - nbrs: List[str] = [] - for pos in range(3): - for base in SpectralCodonSpace.ALL_BASES: - if base == c[pos]: - continue - mutant = c[:pos] + base + c[pos+1:] - nbrs.append(mutant) - return nbrs - - def local_spectral_gradient(self, codon: str) -> float: - """ - Mean eigenvalue difference between this codon and its neighbours. - Positive → this codon is a spectral peak (attractor). - Negative → this codon is in a spectral valley (repeller/boundary). - """ - ev = self.eigenvalue(codon) - nbrs = self.neighbours(codon) - if not nbrs: - return 0.0 - mean_nbr = sum(self.eigenvalue(n) for n in nbrs) / len(nbrs) - return ev - mean_nbr - - def is_spectral_peak(self, codon: str, threshold: float = 0.05) -> bool: - """True if this codon is a local attractor in the spectral field.""" - return self.local_spectral_gradient(codon) > threshold - - def is_spectral_boundary(self, codon: str, threshold: float = -0.05) -> bool: - """True if this codon is a structural boundary (valley).""" - return self.local_spectral_gradient(codon) < threshold - - def cross_strand_edges(self, codon: str) -> List[Tuple[str, str]]: - """ - Neighbours that cross the DNA/RNA boundary. - These are the inter-strand spectral coupling edges — - the mechanism by which coarse and fine eigenmodes interact. - - A T→U mutation (or reverse) crosses strands while preserving - the symbolic stability weight — the eigenvalue stays the same - but the spectral band changes. This is the codon equivalent - of a refractive interface in the Snell's Law analogy. - """ - strand = self._space.strand(codon) - edges: List[Tuple[str, str]] = [] - for nbr in self.neighbours(codon): - nbr_strand = self._space.strand(nbr) - if nbr_strand != strand: - edges.append((nbr, nbr_strand)) - return edges - - def codon_spectrum_entry(self, codon: str) -> Dict[str, object]: - """Full spectral descriptor for a single codon.""" - c = codon.upper() - return { - 'codon': c, - 'band': self.spectral_band(c), - 'eigenvalue': self.eigenvalue(c), - 'dna_weight': self._space.dna_weight(c), - 'rna_weight': self._space.rna_weight(c), - 'gradient': self.local_spectral_gradient(c), - 'is_peak': self.is_spectral_peak(c), - 'is_boundary': self.is_spectral_boundary(c), - 'n_neighbours': len(self.neighbours(c)), - 'n_cross_strand': len(self.cross_strand_edges(c)), - } - - -# ── Spectral Menger address ───────────────────────────────────────────────── - -class SpectralMengerAddress: - """ - Unified Menger sponge address with spectral eigenvalue. - - Combines MengerRnaMapper (topology) with MengerLaplacian (spectrum) - into a single address descriptor. - - The address has three components: - 1. Menger iteration layer (topology) - 2. Position within that layer (geometry) - 3. Spectral eigenvalue (dynamics — how frequently visited) - - This is the engram address: topology + geometry + dynamics = complete - description of where a codon sits in the non-Euclidean address space - and how it behaves under random walks. - """ - - def __init__(self) -> None: - self._space = SpectralCodonSpace() - self._lap = MengerLaplacian(self._space) - self._rna_mapper = MengerRnaMapper() - self._fp = FoldPropensity() - - def full_address(self, codon: str) -> Dict[str, object]: - """ - Complete engram address for a codon. - - Returns dict with: - codon: the codon string - strand: DNA / RNA / DUAL / NULL - menger: (iteration_layer, position) — topology - eigenvalue: spectral weight — dynamics - band: COARSE / FINE / DUAL / NULL — eigenmode type - gradient: local spectral gradient — attractor/repeller - hot_cold: [-1, +1] circulation score - class: SURFACE / INTERIOR / TUNNEL / VERTEX - hausdorff: Menger sponge dimensionality - """ - c = codon.upper() - strand = self._space.strand(c) - - # Menger topology — use RNA mapper for RNA/DUAL, construct DNA analog - if strand in (StrandType.RNA, StrandType.DUAL): - # For DUAL codons, the RNA mapper works directly (no T present) - menger = self._rna_mapper.codon_to_voxel(c) - codon_class = self._fp.codon_class(c) - hot_cold = self._fp.hot_cold_score(c) - elif strand == StrandType.DNA: - # DNA codon: translate T→U to get RNA-equivalent Menger position - rna_equiv = c.replace('T', 'U') - menger = self._rna_mapper.codon_to_voxel(rna_equiv) - codon_class = self._fp.codon_class(rna_equiv) - hot_cold = self._fp.hot_cold_score(rna_equiv) - else: # NULL - menger = (0, 0) - codon_class = 'NULL' - hot_cold = 0.0 - - return { - 'codon': c, - 'strand': strand, - 'menger': menger, - 'eigenvalue': self._lap.eigenvalue(c), - 'band': self._lap.spectral_band(c), - 'gradient': self._lap.local_spectral_gradient(c), - 'hot_cold': hot_cold, - 'class': codon_class, - 'hausdorff': HAUSDORFF_DIM, - } - - def batch_addresses(self, codons: List[str]) -> List[Dict[str, object]]: - return [self.full_address(c) for c in codons] - - def spectral_summary(self) -> Dict[str, object]: - """Aggregate spectral statistics across the full address space.""" - weights = [self._space.spectral_weight(c) - for c in self._space._codons - if self._space.strand(c) != StrandType.NULL] - n = len(weights) - mean_w = sum(weights) / max(n, 1) - var_w = sum((w - mean_w) ** 2 for w in weights) / max(n, 1) - - summary = self._space.summary() - return { - 'n_addresses': summary['addressable'], - 'strand_counts': {k: v for k, v in summary.items() - if k not in ('total', 'addressable')}, - 'mean_eigenvalue': mean_w, - 'std_eigenvalue': math.sqrt(var_w), - 'hausdorff_dim': HAUSDORFF_DIM, - 'alpha_dna': self._space.alpha, - 'beta_rna': self._space.beta, - } - - -# ── Self-test ──────────────────────────────────────────────────────────────── - -def _self_test() -> None: - print("hachimoji_spectral.py — self-test") - print("=" * 60) - - # 1. Spectral codon space - space = SpectralCodonSpace() - s = space.summary() - print(f"Combined codon space: {s['total']} total, {s['addressable']} addressable") - for strand in (StrandType.DNA, StrandType.RNA, StrandType.DUAL, StrandType.NULL): - print(f" {strand:<5s}: {s.get(strand, 0):4d}") - - assert s['total'] == 9 ** 3, f"Expected 729, got {s['total']}" - # DUAL = 6³ = 216 (no T, no U among A,G,C,P,Z,S,B = 7... wait) - # Actually: ALL_BASES has 9 elements. DUAL = codons with neither T nor U. - # Bases without T or U: A, G, C, P, Z, S, B = 7 bases → 7³ = 343 - n_dual = len(space.codons_by_strand(StrandType.DUAL)) - print(f"\n DUAL codons (shared DNA+RNA): {n_dual}") - assert n_dual == 7 ** 3, f"Expected 343 DUAL, got {n_dual}" - - # 2. Spectral weights - # GCG is DUAL — should have both DNA and RNA weights - gcg_d = space.dna_weight('GCG') - gcg_r = space.rna_weight('GCG') - gcg_s = space.spectral_weight('GCG') - print(f"\n GCG: dna={gcg_d:.3f} rna={gcg_r:.3f} spectral={gcg_s:.3f}") - assert gcg_d > 0 and gcg_r > 0, "GCG should have both weights" - assert abs(gcg_s - (ALPHA_DNA * gcg_d + BETA_RNA * gcg_r)) < 1e-9 - - # ATG is DNA-only - atg_s = space.strand('ATG') - assert atg_s == StrandType.DNA, f"ATG should be DNA, got {atg_s}" - assert space.rna_weight('ATG') == 0.0, "ATG RNA weight should be 0" - - # AUG is RNA-only - aug_s = space.strand('AUG') - assert aug_s == StrandType.RNA, f"AUG should be RNA, got {aug_s}" - assert space.dna_weight('AUG') == 0.0, "AUG DNA weight should be 0" - - # TUG is NULL (both T and U) - tug_s = space.strand('TUG') - assert tug_s == StrandType.NULL, f"TUG should be NULL, got {tug_s}" - print(" Strand classification: PASS") - - # 3. Menger Laplacian - lap = MengerLaplacian(space) - gcg_ev = lap.eigenvalue('GCG') - gcg_grad = lap.local_spectral_gradient('GCG') - gcg_nbrs = lap.neighbours('GCG') - gcg_cross = lap.cross_strand_edges('GCG') - print(f"\n Laplacian at GCG:") - print(f" eigenvalue: {gcg_ev:.4f}") - print(f" gradient: {gcg_grad:+.4f}") - print(f" neighbours: {len(gcg_nbrs)}") - print(f" cross-strand edges: {len(gcg_cross)}") - assert len(gcg_nbrs) == 8 * 3, f"Expected 24 neighbours, got {len(gcg_nbrs)}" - # GCG is DUAL; mutating any position to T → DNA, to U → RNA - assert len(gcg_cross) > 0, "GCG should have cross-strand edges" - - # 4. Spectral Menger address - sma = SpectralMengerAddress() - addr = sma.full_address('GCG') - print(f"\n Full address for GCG:") - for k, v in addr.items(): - print(f" {k}: {v}") - assert addr['strand'] == StrandType.DUAL - assert addr['hausdorff'] == HAUSDORFF_DIM - - # ZZZ should be VERTEX / boundary - zzz = sma.full_address('ZZZ') - print(f"\n Full address for ZZZ:") - print(f" class={zzz['class']} eigenvalue={zzz['eigenvalue']:.4f} " - f"gradient={zzz['gradient']:+.4f}") - assert zzz['class'] == 'VERTEX' - - # 5. Spectral summary - ss = sma.spectral_summary() - print(f"\n Spectral summary:") - print(f" addresses: {ss['n_addresses']}") - print(f" mean eigenvalue: {ss['mean_eigenvalue']:.4f}") - print(f" std eigenvalue: {ss['std_eigenvalue']:.4f}") - print(f" Hausdorff dim: {ss['hausdorff_dim']:.4f}") - print(f" α(DNA): {ss['alpha_dna']}") - print(f" β(RNA): {ss['beta_rna']}") - - # 6. Cross-strand spectral coupling: T↔U mutation preserves symbolic weight - atg_ev = lap.eigenvalue('ATG') - aug_ev = lap.eigenvalue('AUG') - # Both should have same symbolic stability (T and U have same weight 0.6) - # but different spectral weights due to strand-specific mixing - print(f"\n Cross-strand coupling ATG↔AUG:") - print(f" ATG eigenvalue: {atg_ev:.4f} (DNA-only, full weight)") - print(f" AUG eigenvalue: {aug_ev:.4f} (RNA-only, full weight)") - # Since T and U have same symbolic weight, these should be equal - assert abs(atg_ev - aug_ev) < 1e-9, \ - "ATG and AUG should have equal eigenvalues (T↔U same symbolic weight)" - print(" T↔U eigenvalue conservation: PASS") - - print("\n" + "=" * 60) - print("All checks PASS") - print("=" * 60) - print("NOTE: All weights are symbolic (math universe).") - print("Codon position = eigenmode of Menger Laplacian.") - print("DNA = coarse eigenmodes. RNA = fine eigenmodes.") - print(f"Combined: {ss['n_addresses']} addresses at " - f"Hausdorff dimension {HAUSDORFF_DIM:.4f}.") - - -if __name__ == '__main__': - _self_test() diff --git a/5-Applications/tools-scripts/hachimoji/hachimoji_synth.py b/5-Applications/tools-scripts/hachimoji/hachimoji_synth.py deleted file mode 100644 index 26d67b60..00000000 --- a/5-Applications/tools-scripts/hachimoji/hachimoji_synth.py +++ /dev/null @@ -1,1683 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""hachimoji_synth.py — Synthetic Hachimoji DNA Sequence Generator - -Generates optimally compressible synthetic Hachimoji DNA sequences using -a structural ACGT carrier derived from NVIDIA ESM SAE feature patterns -and a PZSB information channel at the empirically determined 5% entropy setpoint. - -════════════════════════════════════════════════════════════════════════ -DECISION LOG — how every design choice was reached -════════════════════════════════════════════════════════════════════════ - -D1 · WHY HACHIMOJI DNA - Hachimoji DNA (Hoshika et al., Science 363:884-887, 2019) extends the - alphabet from {A,T,G,C} to {A,T,G,C,P,Z,S,B} using two additional - Watson-Crick-like pairs (P-Z and S-B synthesised by the Benner lab). - The codon space grows from 4³=64 to 8³=512 — an 8× expansion — while - retaining full duplex stability through canonical base-pairing geometry. - The four synthetic bases carry no information in any known biological - SAE (Sparse Autoencoder) model; they are structurally blank channels. - Reference: doi:10.1126/science.aat0971 - -D2 · WHY SAE FEATURES AS THE CARRIER - NVIDIA released a Sparse Autoencoder trained over ESM-2 protein language - model activations (research.nvidia.com/labs/dbr/blog/sae). The SAE has - ~32,634 features; each feature corresponds to a learned linear direction - in ESM activation space that fires on specific codon-level patterns. - We downloaded all three parquet files (features_atlas / feature_metadata / - feature_examples) and indexed them into SQLite via the Rust FSM parser - at tools/sae_extractor/. - - Compressibility measurement methodology: - — Encode each codon sequence at the CODON level (not raw DNA). - · Natural DNA: 4 bases → each codon maps to one byte in 0–63. - · Measuring raw DNA bytes anchors entropy at log₂(4)/log₂(256) = 0.25 - regardless of codon structure — the wrong signal. - — Run LZMA (preset=6) on the codon-byte stream. - — Run extract_mi_features() to get the 11-axis MI signal. - - Results for features with trinuc_entropy=0.0, trinuc_dominant_frac=1.0: - Phase classification: ALL → GROUNDED (MI ≥ 0.65 at codon level) - LZMA ratio range: 0.709 – 0.813 (best: feature 7, ratio 0.535) - Dominant MI: 0.706 – 0.722 - Key dominant codons: GAG (37 features), GAA (25), AAG (19), CTG (16) - - The carrier profile embedded below (CARRIER_GAG_WOBBLE_GC) was derived - from features 7, 493, 419, 364, 963 — the five sequences with the - highest codon-level MI (0.7179–0.7216). Feature IDs from the NVIDIA - SAE atlas; labels: "common codons | wobble GC". - 26,364 codons pooled; 63 unique codons observed. - -D3 · WHY HACHIMOJI ENCODING (uint16 per codon) - 512 Hachimoji codons > 256 byte range, so each codon is stored as a - 16-bit unsigned integer (little-endian). Two bytes per codon. - This is purely a serialisation choice — any indexing scheme is valid. - Alt considered: base-8 packed (3 bits/base = 9 bits/codon → 8 codons - per 9 bytes). Rejected: awkward alignment, harder to debug. - -D4 · WHY 8+8 BLOCK STRUCTURE (not interleaved) - Measured LZMA ratios vs block strategy for feature-7 carrier (606 codons): - - Strategy Codons LZMA ratio Eff.bits/codon - ACGT carrier only 606 0.535 — - Interleave PZSB 1:4 757 0.478 10.29 - Block 8 ACGT + 8 PZSB 1214 0.295 11.28 ← best - Interleave SBS 1:4 757 0.484 10.16 - - The block strategy wins because LZMA's LZ77 back-reference window finds - the repeating [8-codon ACGT block] + [8-codon PZSB block] structure and - exploits it with long matches. 1-in-4 interleaving breaks this pattern. - -D5 · WHY 5% PZSB ENTROPY (not 0% constant, not random) - Entropy sweep on the PZSB channel (0–100% random selection from 64 - pure-PZSB codons), measured over feature-7 carrier with block 8+8: - - PZSB entropy Unique PZSB LZMA ratio Eff.bits/codon - 0% 1 0.295 11.28 - 5% 24 0.326 10.78 ← sweet spot - 10% 44 0.371 10.07 - 20% 57 0.427 9.17 - 30% 61 0.476 8.38 - 50% 64 0.537 7.41 - 100% 64 0.537 7.41 (same as 50%) - - At 5% entropy: 24 unique PZSB codons active → log₂(24) ≈ 4.58 bits of - information per variable PZSB codon. LZMA ratio degrades only 0.295 → - 0.326 (cost: 10.5% more compressed bytes for carrying real information). - Beyond 10% the return is sharply diminishing — every additional percent - of entropy costs ~0.008 LZMA ratio but yields <0.1 extra bits/codon. - -D6 · PAYLOAD ENCODING SCHEME (nibble, 16 PZSB codons) - Decision: use 16 pure-PZSB codons as a nibble alphabet (4 bits each). - — 2 PZSB codons encode 1 payload byte (high nibble + low nibble). - — 8 PZSB slots per block → 4 payload bytes per block. - — 16 codons < 24-codon budget from D5; LZMA ratio stays near 0.326. - — Deterministic, reversible, no out-of-band metadata required. - Alt considered: 6-bit encoding (64 PZSB codons, 1.5 codons/byte). - Rejected: non-integer codon count per byte makes framing fragile. - -D7 · OUTSIDE COMPRESSIBILITY CONTEXT - To implement this outside of the compression organism: - — No dependencies beyond Python stdlib (struct, lzma, random, hashlib). - — The carrier profile is embedded as a plain frequency dict — no DB. - — The LZMA module is Python stdlib (since 3.3). - — The uint16 stream is readable by any language via struct.unpack(' 0 and len(c) == 3 and all(b in NATURAL_BASES for b in c) -} -_total = sum(CARRIER_GAG_WOBBLE_GC.values()) -CARRIER_GAG_WOBBLE_GC = {c: w / _total for c, w in CARRIER_GAG_WOBBLE_GC.items()} - - -# ── Neural binding carrier profile ──────────────────────────────────────────── -# -# Source: NVIDIA ESM SAE sae_features.db, 2026-04-05. -# Method: for each ref_codon at variant positions across ALL genes, -# lock_weight = n_variant_sites × mean_abs(variant_delta) -# Codons with high lock_weight are LOCKED IN by biology at functional sites — -# changing them disrupts SAE feature activation (= disrupts neural computation). -# -# Top locks by weight: -# GCC 3879 GTG 3181 GCG 2237 CTG 1625 TCC 1604 -# CCC 1317 GGC 1277 CTC 1274 GAC 1080 TCT 1063 -# GCA 1029 GTA 991 GTC 985 AAG 965 CAT 964 -# -# Standout: GABRB1 TTG → pathogenic_rate=1.0 — every TTG→CTG swap at the -# GABA-B receptor leucine binding site causes disease. TTG is the hardest -# single-gene lock in the dataset. -# -# Cross-validated against Kazusa codon usage (taxid 9606, Drosophila 7227): -# GCC/GAG/AAG/CTG appear in both the lock table AND the preferred human/insect -# codons — these are the compression organism's ground truth Layer 0 entries. -# -# Session: sessions/hachimoji-mof-connection-machine-sovereign-stack-20260405.json - -_NEURAL_BINDING_RAW: Dict[str, float] = { - "GCC": 3879, "GTG": 3181, "GCG": 2237, "CTG": 1625, "TCC": 1604, - "CCC": 1317, "GGC": 1277, "CTC": 1274, "GAC": 1080, "TCT": 1063, - "GCA": 1029, "GTA": 991, "GTC": 985, "AAG": 965, "CAT": 964, - "ACA": 917, "GAT": 901, "AGC": 607, "CCA": 658, "CCG": 710, - "GGG": 718, "TTA": 744, "GAA": 578, "GAG": 587, "AGG": 551, - "CGG": 481, "GGA": 860, "TCA": 335, "TAT": 337, "TGC": 355, - "TTG": 416, "TAC": 416, "GCT": 414, "TTC": 239, "TTT": 164, - "CGC": 272, "ATT": 290, "CGA": 224, "AAA": 174, "TGT": 71, - "GTT": 77, "CGT": 44, "AAC": 200, "ACC": 150, "ATG": 120, -} -_nb_total = sum(_NEURAL_BINDING_RAW.values()) -CARRIER_NEURAL_BINDING: Dict[str, float] = { - c: w / _nb_total - for c, w in _NEURAL_BINDING_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── C. elegans neural carrier ───────────────────────────────────────────────── -# -# Source: Kazusa codon usage database, taxid 6239 (C. elegans), 2026-04-05. -# C. elegans is the minimal intelligent organism with a fully mapped connectome -# (302 neurons, 7,000 synapses). Its codon bias is AU-rich — completely opposed -# to human/insect GC-rich preference. This makes it the FLOOR of the insect -# intelligence ladder and the most compressible carrier for C. elegans context. -# -# Key: CAA/GAA/AAA dominant — wobble A-ending codons. This is the compression -# basis for nematode-level (C. elegans) neural encoding. - -# VALIDATED 2026-04-05 against Kazusa CUTG taxid 6239 (24,994 CDS, 11,197,796 codons). -# Pearson r vs SAE carrier = 0.1062 (deliberately different — AT-rich floor). -# shared-data/data/codon_tables/celegans_6239.json -_C_ELEGANS_RAW: Dict[str, float] = { - "GAA": 40.84, "AAA": 37.47, "GAT": 35.80, "ATT": 32.22, "GGA": 31.70, - "AAT": 30.18, "CAA": 27.41, "CCA": 26.14, "ATG": 26.09, "AAG": 25.84, - "GAG": 24.52, "GTT": 24.07, "TTC": 23.91, "TTT": 23.27, "GCT": 22.40, - "CTT": 21.16, "TCA": 20.61, "ACA": 20.04, "TTG": 20.02, "GCA": 19.81, - "ATC": 18.90, "ACT": 18.90, "AAC": 18.31, "TAT": 17.49, "GAC": 17.07, - "TCT": 16.72, "AGA": 15.43, "CTC": 14.83, "CAG": 14.37, "GTG": 14.35, - "CAT": 14.12, "TAC": 13.69, "GTC": 13.57, "GCC": 12.64, "TCG": 12.19, - "CTG": 12.13, "AGT": 12.12, "CGA": 12.09, "TGT": 11.24, "CGT": 11.20, - "TGG": 11.07, "GGT": 10.91, "TCC": 10.62, "ACC": 10.36, "CCG": 9.69, - "ATA": 9.47, "CAC": 9.18, "TGC": 9.11, "ACG": 8.88, "CCT": 8.81, - "AGC": 8.36, "GCG": 8.20, "GGC": 6.69, "CGC": 5.10, -} -_ce_total = sum(_C_ELEGANS_RAW.values()) -CARRIER_C_ELEGANS: Dict[str, float] = { - c: w / _ce_total - for c, w in _C_ELEGANS_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Drosophila melanogaster carrier (validated) ──────────────────────────────── -# -# NOTE: This carrier profile is derived from Kazusa codon usage statistics for -# Drosophila melanogaster (common fruit fly, taxid 7227). It has ABSOLUTELY -# NOTHING to do with the 1986 David Cronenberg film "The Fly" or any sequel, -# remake, or related cinematic work involving the transferer-mediated merging -# of human and insect genetic material. Jeff Goldblum is not involved. -# The codon bias here is measured empirically from real CDS sequences. -# -# Source: Kazusa CUTG taxid 7227, 21,945,319 codons measured, 2026-04-05. -# shared-data/data/codon_tables/drosophila_7227.json -# Pearson r vs SAE carrier (CARRIER_GAG_WOBBLE_GC) = 0.7917. -# This is the VALIDATED insect-class carrier: derived independently from an -# NVIDIA protein LM sparse autoencoder, then cross-checked against 22M -# real Drosophila codon measurements later. r=0.79 alignment with SAE was -# not forced — the SAE features emerged from protein sequence statistics, -# Drosophila biology arrived at the same codon preferences via evolution. -# One mismatch: GGG (SAE top-20, Drosophila low-use) — disclosed. -_DROSOPHILA_RAW: Dict[str, float] = { - "GAG": 42.54, "AAG": 39.51, "CTG": 38.24, "CAG": 36.12, "GCC": 33.56, - "GTG": 27.79, "GAT": 27.56, "GGC": 26.75, "AAC": 26.22, "GAC": 24.62, - "ATG": 23.61, "ATC": 22.91, "TTC": 21.84, "ACC": 21.30, "GAA": 21.07, - "AAT": 20.99, "AGC": 20.41, "TCC": 19.56, "TAC": 18.39, "CCC": 18.05, - "GGA": 18.02, "CGC": 18.00, "AAA": 16.98, "TCG": 16.64, "ATT": 16.56, - "CAC": 16.16, "TTG": 16.11, "CCG": 15.82, "CAA": 15.60, "GCT": 14.39, - "ACG": 14.38, "GCG": 14.03, "GTC": 13.89, "CTC": 13.81, "CCA": 13.54, - "GGT": 13.27, "TTT": 13.21, "TGC": 13.16, "GCA": 12.77, "AGT": 11.51, - "ACA": 11.02, "GTT": 10.97, "TAT": 10.79, "CAT": 10.76, "TGG": 9.91, - "ACT": 9.52, "ATA": 9.49, "CTT": 8.97, "CGT": 8.76, "CGA": 8.44, - "CGG": 8.22, "CTA": 8.22, "TCA": 7.82, "TCT": 7.03, "CCT": 6.92, - "GTA": 6.36, "AGG": 6.28, "TGT": 5.38, "AGA": 5.14, -} -_dr_total = sum(_DROSOPHILA_RAW.values()) -CARRIER_DROSOPHILA: Dict[str, float] = { - c: w / _dr_total - for c, w in _DROSOPHILA_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Zebrafish carrier ───────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 7955, Kazusa 2026-04-05. -# shared-data/data/codon_tables/zebrafish_7955.json -# Pearson r vs SAE carrier = +0.8860. fish; first vertebrate in ladder - -_ZEBRAFISH_RAW: Dict[str, float] = { - "GAG": 42.83, "CTG": 37.60, "CAG": 33.51, "AAG": 30.67, "AAA": 29.28, - "GTG": 28.30, "GAC": 27.81, "ATG": 25.53, "GAT": 24.76, "GAA": 24.42, - "AAC": 24.06, "ATC": 23.74, "GGA": 21.46, "GCT": 20.89, "TTC": 20.76, - "GCC": 19.49, "AGC": 18.39, "TTT": 18.15, "GGC": 17.22, "CTC": 17.03, - "ACA": 17.02, "TAC": 17.01, "TCT": 16.89, "CCT": 16.61, "GCA": 16.59, - "ATT": 16.51, "AAT": 16.26, "ACC": 16.19, "CCA": 15.72, "TCC": 15.24, - "CAC": 14.80, "GTC": 14.79, "ACT": 14.46, "AGA": 14.34, "GTT": 14.09, - "GGT": 13.69, "TCA": 13.23, "AGT": 13.18, "CCC": 12.70, "CTT": 12.65, - "TAT": 12.63, "TTG": 12.31, "CAA": 11.80, "TGG": 11.62, "TGT": 11.26, - "TGC": 11.18, "CAT": 10.91, "AGG": 10.22, "GGG": 9.99, "CGC": 9.62, - "GCG": 8.55, "CCG": 8.21, "ATA": 7.69, "ACG": 7.37, "TTA": 6.95, - "CGT": 6.91, "GTA": 6.73, "CGA": 6.69, "CGG": 6.65, "CTA": 6.20, - "TCG": 5.56, -} -_zf_total = sum(_ZEBRAFISH_RAW.values()) -CARRIER_ZEBRAFISH: Dict[str, float] = { - c: w / _zf_total - for c, w in _ZEBRAFISH_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Xenopus carrier ─────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 8355, Kazusa 2026-04-05. -# shared-data/data/codon_tables/xenopus_8355.json -# Pearson r vs SAE carrier = +0.7586. amphibian; r lower than fish — AT-rich bias - -_XENOPUS_RAW: Dict[str, float] = { - "GAA": 36.52, "GAG": 34.32, "AAA": 32.86, "AAG": 31.88, "GAT": 30.28, - "CAG": 29.52, "CTG": 27.79, "ATG": 25.00, "GAC": 22.60, "AAT": 22.29, - "GTG": 22.13, "GGA": 21.42, "TTT": 21.14, "GCT": 21.06, "ATT": 20.97, - "GCA": 20.55, "AAC": 20.55, "CCA": 19.70, "ACA": 18.99, "TCT": 18.78, - "ATC": 17.54, "CCT": 17.44, "GCC": 17.19, "CTT": 16.97, "TTC": 16.84, - "CAA": 16.81, "GTT": 16.29, "AGC": 16.09, "ACT": 15.94, "TAT": 15.52, - "TTG": 15.16, "TCC": 15.10, "TAC": 14.73, "AGA": 14.66, "AGT": 14.42, - "GGC": 14.18, "ACC": 14.11, "TCA": 13.40, "CTC": 13.03, "GGG": 12.94, - "CAT": 12.66, "GGT": 12.64, "CAC": 12.47, "CCC": 12.09, "GTC": 11.98, - "ATA": 11.58, "AGG": 11.45, "TGG": 11.24, "TGT": 10.77, "GTA": 10.60, - "TGC": 10.51, "TTA": 10.07, "CTA": 9.20, "CGC": 6.67, "CGG": 6.39, - "CGA": 6.37, "CGT": 6.33, "CCG": 4.72, "GCG": 4.70, "ACG": 4.70, - "TCG": 3.83, -} -_xen_total = sum(_XENOPUS_RAW.values()) -CARRIER_XENOPUS: Dict[str, float] = { - c: w / _xen_total - for c, w in _XENOPUS_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Chicken carrier ─────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9031, Kazusa 2026-04-05. -# shared-data/data/codon_tables/chicken_9031.json -# Pearson r vs SAE carrier = +0.8921. bird; highest r in the full ladder - -_CHICKEN_RAW: Dict[str, float] = { - "GAG": 40.87, "CTG": 38.51, "AAG": 34.35, "CAG": 32.64, "GAA": 30.96, - "GTG": 28.18, "AAA": 27.31, "GAT": 25.26, "GAC": 24.93, "ATG": 23.16, - "GCC": 22.88, "AAC": 22.47, "ATC": 22.03, "GCT": 20.79, "TTC": 20.20, - "AGC": 20.18, "GGC": 19.72, "GCA": 19.02, "TAC": 17.78, "GGA": 17.57, - "CCC": 16.95, "AAT": 16.93, "TTT": 16.83, "CTC": 16.83, "ATT": 16.79, - "ACC": 16.53, "ACA": 16.14, "GGG": 16.00, "CCA": 15.73, "TCC": 15.70, - "CCT": 15.33, "CAC": 14.37, "TCT": 14.08, "GTC": 13.58, "ACT": 13.27, - "TGC": 13.27, "GTT": 13.09, "TTG": 12.56, "CTT": 12.40, "AGA": 12.24, - "CAA": 12.14, "TGG": 12.00, "TAT": 11.85, "AGG": 11.75, "TCA": 11.56, - "GGT": 11.36, "AGT": 11.18, "CGC": 10.41, "CGG": 9.73, "CAT": 9.52, - "GCG": 9.11, "TGT": 8.77, "ATA": 8.75, "GTA": 7.83, "CCG": 7.76, - "ACG": 7.70, "TTA": 7.04, "CTA": 5.96, "CGT": 5.40, "CGA": 5.27, - "TCG": 5.18, -} -_chk_total = sum(_CHICKEN_RAW.values()) -CARRIER_CHICKEN: Dict[str, float] = { - c: w / _chk_total - for c, w in _CHICKEN_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Mouse carrier ───────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 10090, Kazusa 2026-04-05. -# shared-data/data/codon_tables/mouse_10090.json -# Pearson r vs SAE carrier = +0.8713. rodent; Mus musculus model organism - -_MOUSE_RAW: Dict[str, float] = { - "CTG": 39.52, "GAG": 39.37, "CAG": 34.09, "AAG": 33.64, "GTG": 28.38, - "GAA": 26.96, "GAC": 26.03, "GCC": 26.00, "ATG": 22.82, "ATC": 22.51, - "AAA": 21.92, "TTC": 21.82, "GGC": 21.20, "GAT": 20.99, "AAC": 20.35, - "CTC": 20.18, "GCT": 20.02, "AGC": 19.69, "ACC": 18.96, "CCT": 18.37, - "CCC": 18.21, "TCC": 18.10, "CCA": 17.27, "TTT": 17.21, "GGA": 16.77, - "TCT": 16.23, "TAC": 16.06, "ACA": 15.96, "GCA": 15.84, "AAT": 15.58, - "GTC": 15.40, "ATT": 15.40, "CAC": 15.31, "GGG": 15.17, "ACT": 13.66, - "TTG": 13.44, "CTT": 13.44, "AGT": 12.69, "TGG": 12.50, "TGC": 12.28, - "AGG": 12.21, "TAT": 12.17, "AGA": 12.11, "CAA": 11.96, "TCA": 11.81, - "GGT": 11.43, "TGT": 11.40, "GTT": 10.70, "CAT": 10.62, "CGG": 10.22, - "CGC": 9.36, "CTA": 8.07, "GTA": 7.45, "ATA": 7.36, "TTA": 6.73, - "CGA": 6.58, "GCG": 6.40, "CCG": 6.18, "ACG": 5.63, "CGT": 4.68, - "TCG": 4.23, -} -_mus_total = sum(_MOUSE_RAW.values()) -CARRIER_MOUSE: Dict[str, float] = { - c: w / _mus_total - for c, w in _MOUSE_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Rat carrier ─────────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 10116, Kazusa 2026-04-05. -# shared-data/data/codon_tables/rat_10116.json -# Pearson r vs SAE carrier = +0.8775. Rattus norvegicus; close to mouse - -_RAT_RAW: Dict[str, float] = { - "GAG": 41.30, "CTG": 41.06, "AAG": 35.13, "CAG": 33.77, "GTG": 30.00, - "GAC": 28.01, "GCC": 27.15, "GAA": 26.90, "ATC": 24.37, "ATG": 23.14, - "TTC": 23.14, "GGC": 21.86, "AAC": 21.71, "AAA": 21.49, "GAT": 20.94, - "CTC": 20.35, "ACC": 19.72, "GCT": 19.69, "AGC": 19.19, "CCC": 18.00, - "TCC": 17.80, "CCT": 17.38, "TAC": 17.07, "GGA": 16.61, "TTT": 16.54, - "GTC": 16.21, "CCA": 16.10, "GCA": 15.64, "GGG": 15.56, "ATT": 15.29, - "ACA": 15.26, "AAT": 15.07, "CAC": 14.90, "TCT": 14.78, "TGG": 13.17, - "ACT": 12.95, "TTG": 12.78, "CTT": 12.51, "TGC": 11.85, "AGT": 11.84, - "AGG": 11.80, "TAT": 11.60, "GGT": 11.38, "AGA": 11.17, "CAA": 11.08, - "TCA": 10.95, "CGG": 10.90, "GTT": 10.35, "CGC": 9.81, "TGT": 9.80, - "CAT": 9.55, "CTA": 7.59, "GTA": 7.17, "ATA": 6.91, "GCG": 6.86, - "CGA": 6.76, "CCG": 6.26, "ACG": 6.19, "TTA": 5.94, "CGT": 4.98, - "TCG": 4.36, -} -_rno_total = sum(_RAT_RAW.values()) -CARRIER_RAT: Dict[str, float] = { - c: w / _rno_total - for c, w in _RAT_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Rabbit carrier ──────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9986, Kazusa 2026-04-05. -# shared-data/data/codon_tables/rabbit_9986.json -# Pearson r vs SAE carrier = +0.8245. Oryctolagus cuniculus; CTG-dominant mammal - -_RABBIT_RAW: Dict[str, float] = { - "CTG": 48.87, "GAG": 43.72, "AAG": 35.12, "GCC": 34.23, "GTG": 33.30, - "CAG": 33.03, "GAC": 30.49, "ATC": 29.72, "TTC": 28.44, "GGC": 26.69, - "ATG": 24.30, "GAA": 24.21, "AAC": 24.18, "CTC": 23.59, "ACC": 22.00, - "CCC": 20.84, "AAA": 20.21, "TAC": 20.03, "TCC": 19.38, "AGC": 19.31, - "GTC": 17.98, "GAT": 17.57, "GGG": 16.98, "TTT": 16.36, "CAC": 16.03, - "GCT": 15.51, "GGA": 14.72, "ATT": 14.33, "TGG": 14.06, "TGC": 13.56, - "AAT": 13.43, "CGC": 13.03, "GCA": 12.66, "CCT": 12.61, "CCA": 11.82, - "ACA": 11.65, "CGG": 11.43, "TTG": 10.95, "AGG": 10.57, "TCT": 10.40, - "CTT": 10.06, "TAT": 9.97, "ACT": 9.89, "GCG": 9.52, "CAA": 9.23, - "AGA": 9.19, "ACG": 9.06, "GGT": 8.81, "GTT": 8.68, "CCG": 8.67, - "AGT": 8.54, "TGT": 8.24, "TCA": 7.67, "CAT": 7.34, "ATA": 6.10, - "TCG": 5.69, "TTA": 5.33, "CGA": 5.03, "CTA": 4.91, "GTA": 4.84, - "CGT": 3.69, -} -_ocu_total = sum(_RABBIT_RAW.values()) -CARRIER_RABBIT: Dict[str, float] = { - c: w / _ocu_total - for c, w in _RABBIT_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Pig carrier ─────────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9823, Kazusa 2026-04-05. -# shared-data/data/codon_tables/pig_9823.json -# Pearson r vs SAE carrier = +0.8269. Sus scrofa; large brain, CTG-dominant - -_PIG_RAW: Dict[str, float] = { - "CTG": 46.15, "GAG": 41.13, "CAG": 35.03, "AAG": 33.06, "GTG": 33.02, - "GCC": 31.66, "GAC": 28.49, "GGC": 25.67, "ATC": 24.62, "TTC": 23.95, - "GAA": 23.38, "CTC": 23.16, "ACC": 22.65, "CCC": 22.08, "ATG": 21.93, - "AAC": 21.92, "AAA": 20.27, "AGC": 19.97, "GAT": 19.10, "TAC": 18.85, - "TCC": 18.48, "GGG": 18.45, "GTC": 17.33, "GCT": 16.82, "GGA": 16.00, - "CCT": 15.89, "CAC": 15.64, "TTT": 15.55, "TGG": 14.93, "TGC": 14.36, - "CCA": 14.29, "AAT": 14.21, "ATT": 13.41, "GCA": 12.96, "ACA": 12.33, - "TCT": 12.20, "CGC": 12.11, "CGG": 11.94, "TTG": 11.57, "AGG": 11.35, - "CTT": 11.22, "ACT": 11.16, "TAT": 10.89, "AGA": 10.32, "GGT": 9.97, - "CAA": 9.90, "AGT": 9.49, "TGT": 9.33, "GTT": 9.21, "TCA": 8.94, - "GCG": 8.85, "CAT": 8.48, "CCG": 8.44, "ACG": 7.72, "ATA": 6.16, - "CTA": 5.70, "CGA": 5.58, "GTA": 5.52, "TTA": 5.52, "TCG": 4.80, - "CGT": 4.10, -} -_ssc_total = sum(_PIG_RAW.values()) -CARRIER_PIG: Dict[str, float] = { - c: w / _ssc_total - for c, w in _PIG_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Marmoset carrier ────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9483, Kazusa 2026-04-05. -# shared-data/data/codon_tables/marmoset_9483.json -# Pearson r vs SAE carrier = +0.7716. Callithrix jacchus; r drops at primate transition - -_MARMOSET_RAW: Dict[str, float] = { - "CTG": 44.06, "GAG": 35.01, "GTG": 34.97, "CAG": 33.39, "AAG": 29.71, - "TTC": 28.39, "ATC": 24.43, "GAC": 24.19, "GAA": 23.58, "AAC": 23.23, - "GCC": 23.13, "ATG": 22.87, "AGC": 22.70, "CTC": 21.98, "ACC": 21.51, - "GGC": 21.10, "AAA": 20.68, "TCC": 19.34, "CCC": 18.65, "TAC": 18.61, - "AAT": 18.31, "TTT": 17.76, "GGA": 17.17, "CCT": 16.93, "GTC": 16.53, - "CCA": 16.53, "TGG": 16.25, "GCT": 16.23, "TCT": 16.16, "GAT": 16.12, - "GGG": 15.87, "ATT": 15.52, "TGC": 15.16, "ACA": 15.04, "GCA": 14.74, - "CAC": 14.55, "CAA": 13.71, "TAT": 13.42, "ACT": 13.33, "TTG": 12.07, - "AGG": 12.01, "AGA": 11.65, "CTT": 11.59, "AGT": 11.35, "GTT": 10.80, - "TGT": 10.73, "TCA": 10.60, "GGT": 9.78, "CGG": 9.69, "CAT": 9.54, - "CGC": 8.26, "ATA": 7.60, "GCG": 6.65, "GTA": 6.49, "ACG": 6.43, - "TTA": 6.26, "CTA": 6.16, "CCG": 5.47, "CGA": 5.24, "TCG": 4.19, - "CGT": 3.36, -} -_cja_total = sum(_MARMOSET_RAW.values()) -CARRIER_MARMOSET: Dict[str, float] = { - c: w / _cja_total - for c, w in _MARMOSET_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Macaque carrier ─────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9544, Kazusa 2026-04-05. -# shared-data/data/codon_tables/macaque_9544.json -# Pearson r vs SAE carrier = +0.7983. Macaca mulatta; Allen Brain NHP Atlas ref - -_MACAQUE_RAW: Dict[str, float] = { - "CTG": 44.61, "GAG": 39.16, "GTG": 33.87, "CAG": 33.63, "AAG": 28.90, - "ATC": 27.64, "TTC": 27.46, "GCC": 26.61, "CTC": 24.89, "GAC": 24.11, - "ACC": 23.58, "ATG": 21.73, "AAC": 21.42, "GAA": 20.64, "GGC": 20.37, - "TAC": 19.91, "AAA": 19.77, "GCT": 19.39, "TCC": 18.68, "CCC": 18.60, - "TTT": 18.12, "GTC": 18.08, "AGC": 17.93, "TGG": 17.82, "GGG": 17.21, - "GGA": 16.73, "ACA": 16.54, "GAT": 15.67, "TCT": 15.62, "CAC": 15.33, - "TGC": 15.28, "AAT": 14.93, "CCT": 14.87, "CCA": 14.66, "ATT": 13.98, - "GCA": 13.34, "AGG": 13.20, "TAT": 13.10, "CTT": 13.05, "CAA": 12.69, - "ACT": 12.67, "TTG": 11.96, "AGA": 11.24, "AGT": 11.15, "CGG": 10.65, - "TCA": 10.62, "TGT": 10.27, "CGC": 9.95, "GTT": 9.90, "CAT": 9.56, - "GGT": 8.94, "GCG": 7.39, "ATA": 7.24, "ACG": 6.27, "CCG": 5.89, - "CGA": 5.82, "CTA": 5.81, "TTA": 5.64, "GTA": 5.02, "CGT": 4.03, - "TCG": 3.74, -} -_mmu_total = sum(_MACAQUE_RAW.values()) -CARRIER_MACAQUE: Dict[str, float] = { - c: w / _mmu_total - for c, w in _MACAQUE_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Human carrier ───────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 9606, 40.7M codons, 2026-04-05. -# shared-data/data/codon_tables/human_9606.json -# Pearson r vs SAE carrier = +0.8705. Homo sapiens; CTG/GAG co-dominant ceiling - -_HUMAN_RAW: Dict[str, float] = { - "CTG": 39.64, "GAG": 39.59, "CAG": 34.23, "AAG": 31.86, "GAA": 28.96, - "GTG": 28.12, "GCC": 27.73, "GAC": 25.10, "AAA": 24.44, "GGC": 22.22, - "ATG": 22.04, "GAT": 21.78, "ATC": 20.82, "TTC": 20.28, "CCC": 19.79, - "CTC": 19.59, "AGC": 19.46, "AAC": 19.10, "ACC": 18.89, "GCT": 18.45, - "TCC": 17.68, "TTT": 17.57, "CCT": 17.54, "AAT": 16.96, "CCA": 16.92, - "GGG": 16.47, "GGA": 16.47, "ATT": 16.00, "GCA": 15.82, "TAC": 15.31, - "TCT": 15.22, "ACA": 15.11, "CAC": 15.09, "GTC": 14.46, "CTT": 13.19, - "TGG": 13.17, "ACT": 13.12, "TTG": 12.93, "TGC": 12.62, "CAA": 12.34, - "TCA": 12.21, "TAT": 12.19, "AGA": 12.17, "AGT": 12.13, "AGG": 11.96, - "CGG": 11.42, "GTT": 11.03, "CAT": 10.86, "GGT": 10.75, "TGT": 10.58, - "CGC": 10.42, "TTA": 7.67, "ATA": 7.49, "GCG": 7.37, "CTA": 7.15, - "GTA": 7.08, "CCG": 6.92, "CGA": 6.17, "ACG": 6.05, "CGT": 4.54, - "TCG": 4.41, -} -_hsa_total = sum(_HUMAN_RAW.values()) -CARRIER_HUMAN: Dict[str, float] = { - c: w / _hsa_total - for c, w in _HUMAN_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Saccharomyces carrier ───────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 4932, 13.4M codons, 2026-04-05. -# shared-data/data/codon_tables/saccharomyces_4932.json -# Pearson r vs SAE carrier = +0.2917. S. cerevisiae; AT-rich fungal floor (Tier 0) - -_SACCHAROMYCES_RAW: Dict[str, float] = { - "GAA": 45.60, "AAA": 41.87, "GAT": 37.59, "AAT": 35.68, "AAG": 30.82, - "ATT": 30.13, "CAA": 27.28, "TTG": 27.17, "TTA": 26.15, "TTT": 26.12, - "AAC": 24.82, "GGT": 23.89, "TCT": 23.50, "GTT": 22.07, "AGA": 21.28, - "GCT": 21.17, "ATG": 20.94, "ACT": 20.28, "GAC": 20.21, "GAG": 19.24, - "TAT": 18.78, "TCA": 18.67, "TTC": 18.44, "CCA": 18.31, "ATA": 17.79, - "ACA": 17.76, "ATC": 17.17, "GCA": 16.21, "TAC": 14.78, "TCC": 14.22, - "AGT": 14.15, "CAT": 13.62, "CCT": 13.51, "CTA": 13.41, "ACC": 12.73, - "GCC": 12.60, "CTT": 12.25, "CAG": 12.11, "GTC": 11.78, "GTA": 11.77, - "GGA": 10.90, "GTG": 10.76, "CTG": 10.48, "TGG": 10.37, "GGC": 9.78, - "AGC": 9.75, "AGG": 9.23, "TCG": 8.56, "TGT": 8.10, "ACG": 7.96, - "CAC": 7.77, "CCC": 6.78, "CGT": 6.40, "GCG": 6.18, "GGG": 6.02, - "CTC": 5.44, "CCG": 5.29, "TGC": 4.76, "CGA": 2.99, "CGC": 2.60, - "CGG": 1.74, -} -_sce_total = sum(_SACCHAROMYCES_RAW.values()) -CARRIER_SACCHAROMYCES: Dict[str, float] = { - c: w / _sce_total - for c, w in _SACCHAROMYCES_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── S. pombe carrier ────────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 4896, Kazusa 2026-04-05. -# shared-data/data/codon_tables/s_pombe_4896.json -# Pearson r vs SAE carrier = +0.3147. fission yeast; divergent from S. cerevisiae - -_S_POMBE_RAW: Dict[str, float] = { - "GAA": 44.39, "AAA": 39.82, "GAT": 37.99, "ATT": 35.07, "AAT": 34.10, - "TTT": 32.48, "TCT": 30.29, "GCT": 29.79, "GTT": 29.01, "CAA": 27.43, - "TTA": 26.34, "CTT": 25.30, "AAG": 24.52, "TTG": 24.06, "ACT": 23.02, - "TAT": 22.13, "CCT": 21.57, "GGT": 21.49, "GAG": 21.05, "ATG": 20.79, - "TCA": 18.11, "AAC": 17.84, "CAT": 16.34, "GCA": 15.94, "GGA": 15.86, - "GAC": 15.69, "CGT": 15.63, "AGT": 14.88, "ACA": 14.29, "ATA": 13.50, - "TTC": 13.01, "CCA": 12.72, "ATC": 12.64, "GTA": 12.37, "TCC": 12.15, - "TAC": 11.77, "GCC": 11.50, "AGA": 11.25, "TGG": 11.07, "CAG": 10.86, - "ACC": 10.71, "GTC": 10.66, "AGC": 9.18, "TGT": 9.02, "CTA": 8.73, - "GGC": 8.33, "GTG": 8.32, "TCG": 8.10, "CCC": 8.10, "CGA": 8.01, - "CTC": 7.26, "ACG": 6.56, "CTG": 6.45, "CAC": 6.30, "CGC": 6.02, - "TGC": 5.58, "GCG": 5.39, "AGG": 5.09, "CCG": 4.56, "GGG": 4.41, - "CGG": 2.99, -} -_spom_total = sum(_S_POMBE_RAW.values()) -CARRIER_S_POMBE: Dict[str, float] = { - c: w / _spom_total - for c, w in _S_POMBE_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Neurospora carrier ──────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 5141, Kazusa 2026-04-05. -# shared-data/data/codon_tables/neurospora_5141.json -# Pearson r vs SAE carrier = +0.6163. N. crassa; anomalously GC-rich for Tier 0 fungus - -_NEUROSPORA_RAW: Dict[str, float] = { - "GAG": 42.68, "AAG": 40.39, "GCC": 35.97, "GAC": 32.55, "GGC": 29.02, - "AAC": 27.00, "CTC": 26.79, "ATC": 26.48, "CAG": 26.05, "GTC": 24.83, - "ACC": 24.71, "GAT": 23.99, "GAA": 22.44, "CCC": 22.42, "TTC": 22.08, - "ATG": 21.80, "GCT": 21.13, "TCC": 19.99, "GGT": 18.28, "CTG": 18.26, - "CGC": 17.64, "TAC": 17.46, "AGC": 17.43, "GCG": 17.26, "CAA": 16.95, - "GTG": 15.51, "CCT": 15.09, "TTG": 14.95, "CAC": 14.78, "CCG": 14.56, - "TCG": 14.51, "CTT": 14.25, "ATT": 14.00, "GTT": 13.84, "GGA": 13.56, - "ACG": 13.54, "TGG": 13.11, "GCA": 12.56, "CCA": 12.36, "TCT": 11.95, - "AGG": 11.84, "TTT": 11.77, "AAA": 11.69, "ACT": 11.16, "GGG": 10.94, - "ACA": 10.75, "AAT": 10.32, "CAT": 9.45, "TCA": 9.22, "CGT": 8.88, - "AGT": 8.66, "CGG": 8.54, "TAT": 8.47, "AGA": 7.91, "TGC": 7.71, - "CGA": 7.05, "CTA": 5.95, "GTA": 5.40, "ATA": 4.09, "TGT": 3.35, - "TTA": 2.73, -} -_ncr_total = sum(_NEUROSPORA_RAW.values()) -CARRIER_NEUROSPORA: Dict[str, float] = { - c: w / _ncr_total - for c, w in _NEUROSPORA_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Sea urchin carrier ──────────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 7668, 1131 CDS, 410,481 codons, 2026-04-05. -# shared-data/data/codon_tables/strongylocentrotus_7668.json -# Pearson r vs SAE carrier = +0.3053. -# Strongylocentrotus purpuratus (purple sea urchin); echinoderm; radial nervous -# system — 5-fold symmetry, NO centralized brain. Represents the pre- -# centralization baseline: bilaterally symmetric NS not yet compacted into a -# head. Echinodermata are deuterostomes (same lineage as vertebrates) yet took -# the radial path rather than the bilateral/cephalized path. - -_STRONGYLOCENTROTUS_RAW: Dict[str, float] = { - "GGA": 57.35, "GGT": 56.14, "GAT": 41.69, "GAG": 36.44, "GAA": 34.72, - "TTC": 30.83, "CAA": 29.16, "ATG": 27.66, "CCT": 25.66, "AGA": 25.44, - "GGC": 23.69, "GAC": 23.06, "CAC": 22.90, "AAC": 22.27, "AAG": 22.20, - "GCT": 21.70, "CAG": 21.57, "CAT": 20.64, "CCA": 20.30, "AGG": 19.95, - "AAT": 19.34, "ATC": 18.54, "CGT": 17.55, "GCC": 17.02, "ACA": 16.51, - "GTG": 15.76, "CCC": 15.27, "TTT": 15.02, "CGC": 12.67, "ACC": 12.64, - "GTC": 11.64, "CGA": 11.49, "GCA": 11.37, "TCT": 11.37, "AGC": 10.86, - "CTG": 10.86, "ATT": 10.78, "AAA": 10.77, "ACG": 10.06, "TAC": 9.56, - "CTC": 9.30, "GTT": 9.20, "TCA": 9.10, "GGG": 8.93, "TCC": 8.80, - "CTT": 8.70, "CCG": 8.18, "ACT": 8.14, "AGT": 7.94, "CTA": 6.95, - "TGG": 6.88, "GTA": 6.77, "TAT": 6.63, "TCG": 6.33, "CGG": 6.10, - "TGT": 5.54, "TTG": 5.51, "TGC": 5.32, "ATA": 3.99, "GCG": 3.36, - "TTA": 3.08, -} -_spu_total = sum(_STRONGYLOCENTROTUS_RAW.values()) -CARRIER_STRONGYLOCENTROTUS: Dict[str, float] = { - c: w / _spu_total - for c, w in _STRONGYLOCENTROTUS_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Octopus vulgaris carrier ────────────────────────────────────────────────── -# -# Source: Kazusa CUTG taxid 6645, 32 CDS, 8,602 codons, 2026-04-05. -# shared-data/data/codon_tables/octopus_vulgaris_6645.json -# Pearson r vs SAE carrier = +0.2219. -# -# ARCHITECTURE NOTE: Octopus vulgaris has a DISTRIBUTED manifold — -# 9 semi-autonomous nervous systems (1 central brain + 8 brachial ganglia). -# ~2/3 of all neurons live in the arms, not the head. This is fundamentally -# architecturally different from any vertebrate: it is a multi-manifold -# topology with local entropy sorting per arm. -# -# LOW_N CAVEAT: only 32 CDS in Kazusa — per_1000 values have high variance. -# Use for qualitative comparisons only; do not treat r=+0.22 as precise. -# -# RNA-EDITING CAVEAT: O. vulgaris edits ~60% of neural transcripts -# post-transcriptionally (adenosine-to-inosine editing). The DNA codon table -# does NOT reflect the actual codon used during neural protein synthesis. -# A proper Octopus carrier profile requires RNA-seq data from neural tissue, -# not DNA-derived CDS counts. This profile is a placeholder until that data -# is available. - -_OCTOPUS_VULGARIS_RAW: Dict[str, float] = { - "ATG": 37.32, "GAA": 34.88, "AAA": 31.74, "GAT": 29.99, "ATT": 29.06, - "ACA": 26.04, "AAT": 25.46, "TTT": 25.23, "GCT": 23.72, "TTC": 23.37, - "AGA": 22.67, "AAC": 22.67, "ATC": 22.44, "CAA": 22.32, "ACT": 21.97, - "AAG": 20.93, "TGT": 20.11, "TTG": 20.11, "GGT": 19.88, "GCA": 19.18, - "GAC": 18.48, "GGA": 18.14, "GAG": 18.14, "AGT": 18.14, "TCA": 17.79, - "GTT": 17.09, "ATA": 17.09, "TAT": 16.62, "CTG": 16.39, "GCC": 15.93, - "TAC": 15.69, "TTA": 15.58, "TCT": 15.46, "CAG": 15.35, "TGC": 15.11, - "GTC": 14.42, "CTC": 14.18, "TCC": 13.83, "TGG": 13.60, "CTT": 13.60, - "GTA": 13.49, "CGT": 13.25, "GTG": 11.97, "CCA": 11.97, "CGA": 11.74, - "CAT": 11.63, "ACC": 11.51, "CCT": 11.39, "AGC": 10.58, "GGC": 9.53, - "TCG": 7.67, "CAC": 7.09, "ACG": 6.98, "CGC": 6.63, "CTA": 6.51, - "AGG": 5.46, "CCC": 5.46, "CCG": 4.30, "CGG": 3.60, "GCG": 3.14, - "GGG": 2.67, -} -_oct_total = sum(_OCTOPUS_VULGARIS_RAW.values()) -CARRIER_OCTOPUS_VULGARIS: Dict[str, float] = { - c: w / _oct_total - for c, w in _OCTOPUS_VULGARIS_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Alligator carrier (archosaur / dinosaur proxy) ──────────────────────────── -# -# Source: Kazusa CUTG taxid 8496, 18 CDS, 6,488 codons, 2026-04-05. -# shared-data/data/codon_tables/alligator_mississippiensis_8496.json -# Pearson r vs SAE carrier = +0.7952. -# -# LOW_N CAVEAT: 18 CDS only — treat as indicative, not measured. -# -# DINOSAUR PROXY: Crocodylia (alligators + crocodiles) are the sister clade to -# Aves (birds) within Archosauria. Together they bracket the dinosaur lineage: -# - Alligator diverged from the bird/dinosaur line ~250 Mya -# - Chicken (r=+0.89) represents the avian end of the bracket -# Inference: Mesozoic archosaur neural codon bias was likely r=+0.80–0.89 vs -# SAE — NOT the low-r AT-rich "reptile" assumption. Brontosaurus and its -# relatives were probably running GC-rich neural protein synthesis, -# architecturally more similar to modern birds than to extant lizards. -# This is a bracket inference, not a direct measurement. - -_ALLIGATOR_RAW: Dict[str, float] = { - "GAG": 41.92, "AAG": 41.62, "AAA": 34.53, "ATG": 34.22, "CTG": 33.91, - "CAG": 30.21, "GAA": 28.82, "GAC": 26.66, "GTG": 24.04, "AAC": 23.27, - "ATC": 23.27, "GCC": 22.97, "GAT": 22.50, "GGC": 20.19, "CAC": 20.19, - "AGC": 19.57, "ATT": 19.42, "GCT": 18.96, "TTC": 18.65, "TTT": 18.34, - "ACC": 18.03, "TAC": 17.88, "CAT": 16.65, "ACT": 16.34, "ACA": 15.72, - "CCC": 15.72, "GGA": 15.57, "GTT": 15.41, "TGC": 15.26, "AAT": 14.80, - "CTC": 14.33, "CCA": 14.33, "GTC": 14.18, "CCT": 14.18, "CTT": 14.03, - "GGG": 13.72, "TCC": 13.56, "TTG": 13.26, "TGT": 12.79, "GCA": 12.64, - "TAT": 12.48, "AGA": 12.18, "CAA": 11.87, "GGT": 11.41, "TCT": 11.10, - "AGG": 10.79, "AGT": 10.79, "TCA": 10.33, "TGG": 9.25, "ATA": 8.94, - "CGC": 8.94, "CCG": 7.40, "CGG": 7.24, "GTA": 6.78, "CGT": 6.32, - "CTA": 6.32, "TTA": 6.01, "GCG": 5.39, "CGA": 4.78, "TCG": 4.16, - "ACG": 3.08, -} -_ali_total = sum(_ALLIGATOR_RAW.values()) -CARRIER_ALLIGATOR: Dict[str, float] = { - c: w / _ali_total - for c, w in _ALLIGATOR_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -# ── Insect / human convergent carrier ───────────────────────────────────────── -# -# Source: Kazusa, Drosophila melanogaster taxid 7227 + Homo sapiens taxid 9606, -# 2026-04-05. Drosophila and human codon preferences converge on GC-ending -# codons (CAG/AAG/GAG/GCC/GGC). This is the compression target for insect-level -# intelligence — the inflection point where codon bias transitions from AU-rich -# (C. elegans floor) to GC-rich (human ceiling). -# -# The convergence of Drosophila and human on the same preferred codons is -# independent evidence that this codon set is the stable attractor for -# complex neural computation (Layer 0 ground truth for the Mirror LUT). -# See CARRIER_DROSOPHILA above for the pure validated Drosophila baseline (r=0.7917). - -_INSECT_HUMAN_RAW: Dict[str, float] = { - # Drosophila dominant, human concordant - "CAG": 0.715, "AAG": 0.635, "GAG": 0.625, "CAC": 0.590, "AAC": 0.545, - "GAC": 0.505, "GCC": 0.425, "GGC": 0.385, "ACC": 0.370, "CCC": 0.325, - "CGC": 0.330, "GAA": 0.375, "CAA": 0.285, "AAA": 0.365, "CCG": 0.245, - "GGA": 0.270, "ACG": 0.230, "CCA": 0.265, "AGC": 0.245, "ACA": 0.240, - # Human dominant over Drosophila - "CTG": 0.420, "GTG": 0.280, "TCC": 0.220, "GCA": 0.225, "GCG": 0.150, - "GGG": 0.200, "AGG": 0.180, "AGA": 0.130, "ATG": 0.100, "TTG": 0.090, -} -_ih_total = sum(_INSECT_HUMAN_RAW.values()) -CARRIER_INSECT_HUMAN: Dict[str, float] = { - c: w / _ih_total - for c, w in _INSECT_HUMAN_RAW.items() - if len(c) == 3 and all(b in NATURAL_BASES for b in c) -} - -CARRIER_PROFILES: Dict[str, Dict[str, float]] = { - # ── SAE-derived profiles ────────────────────────────────────────────────── - "gag_wobble_gc": CARRIER_GAG_WOBBLE_GC, # SAE GROUNDED baseline (r=0.72) - "neural_binding": CARRIER_NEURAL_BINDING, # SAE lock weights — binding sites - # ── Fungal tier (Tier 0) — pre-neuronal information transfer ───────────── - "saccharomyces": CARRIER_SACCHAROMYCES, # S. cerevisiae yeast floor (r=+0.29) - "s_pombe": CARRIER_S_POMBE, # fission yeast (r=+0.31) - "neurospora": CARRIER_NEUROSPORA, # N. crassa filamentous fungus (r=+0.62, anomaly) - # ── Invertebrate rungs ──────────────────────────────────────────────────── - "strongylocentrotus": CARRIER_STRONGYLOCENTROTUS, # sea urchin echinoderm, radial NS (r=+0.31, 1131 CDS) - "octopus_vulgaris": CARRIER_OCTOPUS_VULGARIS, # distributed manifold !LOW_N !RNA_EDITING (r=+0.22) - "c_elegans": CARRIER_C_ELEGANS, # 302-neuron floor (AT-rich, r=+0.11 vs SAE) - "drosophila": CARRIER_DROSOPHILA, # insect validated (r=+0.79, 22M codons) - # ── Vertebrate rungs ────────────────────────────────────────────────────── - "zebrafish": CARRIER_ZEBRAFISH, # fish (r=+0.89) - "xenopus": CARRIER_XENOPUS, # amphibian (r=+0.76) - "alligator": CARRIER_ALLIGATOR, # archosaur / dinosaur proxy !LOW_N (r=+0.80, 18 CDS) - "chicken": CARRIER_CHICKEN, # bird — vertebrate peak (r=+0.89) - "mouse": CARRIER_MOUSE, # rodent model organism (r=+0.87) - "rat": CARRIER_RAT, # rodent (r=+0.88) - "rabbit": CARRIER_RABBIT, # mammal (r=+0.82) - "pig": CARRIER_PIG, # mammal, large brain (r=+0.83) - # ── Primate rungs ───────────────────────────────────────────────────────── - "marmoset": CARRIER_MARMOSET, # small NHP (r=+0.77) - "macaque": CARRIER_MACAQUE, # rhesus NHP / Allen Brain ref (r=+0.80) - "human": CARRIER_HUMAN, # Homo sapiens ceiling (r=+0.87) - # ── Convergent attractor ────────────────────────────────────────────────── - "insect_human": CARRIER_INSECT_HUMAN, # Drosophila+human convergent GC attractor -} -DEFAULT_PROFILE = "neural_binding" # upgraded from gag_wobble_gc - -# ── Voxel addressing (TensorCompass integration) ────────────────────────────── -# -# Each generated Hachimoji sequence maps to a voxel_key in the TensorCompass -# arrow field. The key is derived from the sequence's MI feature vector, -# placing it in the same n-space as all other substrate objects. -# -# This is the MOF node address: the voxel_key IS the "metal cluster" identity -# in the framework. PZSB codons in the payload channel encode which neighboring -# voxels (other sequences) are connected by "pipes" (adjacency matrix entries). -# -# Requires: tools/heerich_model.py (optional — degrades gracefully if absent) -# Requires: ene_mi_signal.py (optional — degrades gracefully if absent) - -def sequence_voxel_key(sequence: List[str], block_size: int = 8) -> Optional[int]: - """Compute the 34-bit TensorCompass voxel_key for a Hachimoji sequence. - - Method: - 1. Extract the ACGT carrier codons from the sequence. - 2. Encode as codon-index byte stream (uint8, values 0-63). - 3. Run extract_mi_features() to get the 11-axis MI vector. - 4. Call nd_point_to_voxel_key(mi_features, ioc=mi_features[9]) for the - 34-bit haploid address (30-bit xyz + 4-bit IoC regime prefix). - - Returns: - 34-bit int, or None if heerich_model / ene_mi_signal are unavailable. - - Decision log: - D8 — Gemini session ba899ebf: voxel_key IS the MOF node identity. - nd_point_to_voxel_key places the sequence in the TensorCompass - arrow field used by all other substrate objects. - D9 — The 8096-bit subregister per user = 900 Hachimoji codons × 9 bits. - Each subregister entry is one voxel in the compass field. - The 14-axis concept_vector (stored at the same key) provides the - semantic layer; MI features provide the structural layer. - """ - try: - import sys as _sys - import os as _os - _root = _os.path.dirname(_os.path.dirname(_os.path.abspath(__file__))) - if _root not in _sys.path: - _sys.path.insert(0, _root) - from ene_mi_signal import extract_mi_features - from tools.heerich_model import nd_point_to_voxel_key - except ImportError: - return None - - carrier, _ = disassemble(sequence) - if len(carrier) < 8: - return None - - # Encode carrier as codon-index bytes (0-63, natural codons only) - nat_idx = {c: i for i, c in enumerate( - a+b+c for a,b,c in __import__('itertools').product("ACGT", repeat=3) - )} - stream = bytes(nat_idx.get(c, 0) for c in carrier if c in nat_idx) - if len(stream) < 8: - return None - - feats = extract_mi_features(stream) - ioc = feats[9] if len(feats) > 9 else None - return nd_point_to_voxel_key(feats, scale=10.0, ioc=ioc) - -# ── Stream encoding ─────────────────────────────────────────────────────────── - -def encode_uint16_stream(codon_list: List[str]) -> bytes: - """Serialise a codon list as little-endian uint16 stream (2 bytes/codon).""" - idxs = [CODON_TO_IDX[c] for c in codon_list if c in CODON_TO_IDX] - return struct.pack(f"<{len(idxs)}H", *idxs) - - -def decode_uint16_stream(data: bytes) -> List[str]: - """Deserialise a uint16 stream back to codon list.""" - n = len(data) // 2 - idxs = struct.unpack(f"<{n}H", data[:n * 2]) - return [IDX_TO_CODON[i] for i in idxs if i in IDX_TO_CODON] - -# ── Carrier generation ──────────────────────────────────────────────────────── - -def generate_carrier( - length: int, - profile: str = DEFAULT_PROFILE, - rng: Optional[random.Random] = None, -) -> List[str]: - """Generate `length` ACGT carrier codons from a SAE-derived frequency profile. - - The weighted sampling reproduces the codon distribution of top-MI SAE - features (see decision D2). length is in CODONS (not bases). - - Args: - length: number of ACGT codons to generate - profile: carrier profile key from CARRIER_PROFILES - rng: seeded Random instance for reproducibility - - Returns: - List of ACGT codon strings, each 3 characters. - """ - if rng is None: - rng = random.Random() - freq = CARRIER_PROFILES[profile] - codons = list(freq.keys()) - weights = list(freq.values()) - return rng.choices(codons, weights=weights, k=length) - -# ── Payload encoding ────────────────────────────────────────────────────────── - -def encode_payload(data: bytes) -> List[str]: - """Encode arbitrary bytes as PZSB codon sequence (nibble scheme, see D6). - - Each byte becomes two PZSB codons (high nibble, low nibble). - Uses only the 16 codons in NIBBLE_PALETTE — stays within the - 24-codon budget that keeps LZMA ratio at 0.326 (see D5). - - Args: - data: payload bytes to encode - - Returns: - List of PZSB codon strings (length = 2 × len(data)). - """ - out: List[str] = [] - for byte in data: - out.append(NIBBLE_PALETTE[(byte >> 4) & 0xF]) # high nibble - out.append(NIBBLE_PALETTE[byte & 0xF]) # low nibble - return out - - -def decode_payload(pzsb_codons: List[str]) -> bytes: - """Recover payload bytes from a PZSB codon sequence. - - Ignores codons not in NIBBLE_PALETTE (e.g. CONSTANT_FILL = PPP padding). - Pairs consecutive valid codons to reconstruct bytes. - - Args: - pzsb_codons: list of PZSB codon strings - - Returns: - Recovered payload as bytes. - """ - nibbles: List[int] = [ - NIBBLE_RMAP[c] for c in pzsb_codons if c in NIBBLE_RMAP - ] - # pair nibbles → bytes - out = bytearray() - for i in range(0, len(nibbles) - 1, 2): - out.append((nibbles[i] << 4) | nibbles[i + 1]) - return bytes(out) - -# ── Sequence assembly ───────────────────────────────────────────────────────── - -def assemble( - carrier_codons: List[str], - payload: bytes, - block_size: int = 8, -) -> List[str]: - """Assemble a Hachimoji sequence from an ACGT carrier and a payload. - - Layout (repeating blocks, see decision D4): - - [ block_size × ACGT ] [ block_size × PZSB ] [ block_size × ACGT ] ... - - Each PZSB block encodes block_size // 2 bytes of payload (2 PZSB codons - per byte via the nibble scheme). Remaining PZSB slots filled with PPP. - - Args: - carrier_codons: ACGT codon list (should be multiple of block_size) - payload: bytes to encode; may be empty - block_size: ACGT codons per block (and PZSB codons per block) - - Returns: - Assembled Hachimoji codon list (ACGT and PZSB interleaved in blocks). - """ - payload_codons = encode_payload(payload) - p_idx = 0 # index into payload_codons - - out: List[str] = [] - for i in range(0, len(carrier_codons), block_size): - acgt_block = carrier_codons[i : i + block_size] - out.extend(acgt_block) - - # PZSB block - for _ in range(block_size): - if p_idx < len(payload_codons): - out.append(payload_codons[p_idx]) - p_idx += 1 - else: - out.append(CONSTANT_FILL) - - return out - - -def disassemble(sequence: List[str]) -> Tuple[List[str], bytes]: - """Split a Hachimoji sequence into its carrier and payload. - - Assumes the [8 ACGT + 8 PZSB] block structure from assemble(). - Carrier codons are those where all bases are natural (A/T/G/C). - PZSB codons come from the even-indexed blocks. - - Args: - sequence: Hachimoji codon list - - Returns: - (carrier_codons, payload_bytes) - """ - carrier: List[str] = [] - pzsb: List[str] = [] - for cod in sequence: - if all(b in NATURAL_BASES for b in cod): - carrier.append(cod) - elif all(b in SYNTH_BASES for b in cod): - pzsb.append(cod) - # mixed codons are discarded (not used in this scheme) - - payload = decode_payload(pzsb) - return carrier, payload - -# ── Compression helpers ─────────────────────────────────────────────────────── - -def compress_sequence(sequence: List[str]) -> bytes: - """LZMA-compress a Hachimoji codon sequence. - - First serialises to uint16 stream, then compresses. - Expected ratio: 0.30–0.33 for sequences built with this module. - """ - return lzma.compress(encode_uint16_stream(sequence), preset=6) - - -def metrics(sequence: List[str]) -> Dict[str, float]: - """Compute compression and information metrics for a sequence. - - Returns a dict with: - codon_count — length of sequence - unique_codons — distinct codons observed - unique_acgt — distinct ACGT codons - unique_pzsb — distinct PZSB codons - raw_bytes — uncompressed stream size - compressed_bytes — LZMA compressed size - lzma_ratio — compressed / raw - payload_capacity — bytes encodable at nibble scheme capacity - """ - raw = encode_uint16_stream(sequence) - comp = lzma.compress(raw, preset=6) - acgt = [c for c in sequence if all(b in NATURAL_BASES for b in c)] - pzsb = [c for c in sequence if all(b in SYNTH_BASES for b in c)] - blocks = len(acgt) // 8 - return { - "codon_count": len(sequence), - "unique_codons": len(set(sequence)), - "unique_acgt": len(set(acgt)), - "unique_pzsb": len(set(pzsb)), - "raw_bytes": len(raw), - "compressed_bytes": len(comp), - "lzma_ratio": round(len(comp) / len(raw), 4), - "payload_capacity": blocks * 4, # 4 bytes/block at nibble rate - } - -# ── Complement ──────────────────────────────────────────────────────────────── - -def complement_codon(codon: str) -> str: - """Return the Watson-Crick / Hachimoji complement of a codon (3′→5′).""" - return "".join(HACHI_PAIRS.get(b, "N") for b in reversed(codon)) - - -# ── Quine record format (KD1 + KD2 from session 20260405) ──────────────────── -# -# Derivation (Gemini session ba899ebf): -# A SHA-256 hash is 256 bits. Hachimoji = 3 bits/base → 1 base/codon (encoding -# a codon at base level: each 3-bit chunk = 1 Hachimoji letter). -# 256 bits / 3 = 85.33 → 86 Hachimoji letters (codons). -# Every Git commit hash maps deterministically to an 86-codon strand. -# -# Full Quine format (self-describing, codec-rot-immune): -# -# [ Phase gate (1 codon) ] → Routes to GROUNDED/SEISMIC/FLAME on decode -# [ PTOS header (24 codons)] → 8 PTOS categories × 3 codons each (8 enum values each) -# [ SHA-256 id (86 codons)] → Content-addressable identity of the engram -# [ USC payload (variable) ] → LZMA-compressed content -# -# If Phase gate ∈ FLAME_CODONS → emit SUBFRAME_CONSTANT(0), skip decode. -# Session record: sessions/hachimoji-mof-connection-machine-sovereign-stack-20260405.json - -SHA256_BASE_LEN = 86 # single Hachimoji bases needed for 256 bits (ceil(256/3)) -SHA256_CODON_LEN = SHA256_BASE_LEN # legacy alias (unit = bases, not packed 3-base codons) -SHA256_PACKED_CODONS = 29 # ceil(86/3) — codons (3-base words) needed in the sequence -PTOS_HEADER_LEN = 24 # 8 PTOS fields × 3 codons each (in 3-base codons) -QUINE_PHASE_LEN = 1 # 1 codon (3-base word) -QUINE_HEADER_CODONS = QUINE_PHASE_LEN + PTOS_HEADER_LEN + SHA256_PACKED_CODONS # = 54 codons -QUINE_HEADER_LEN = QUINE_HEADER_CODONS # = 54 (kept for back-compat; was wrong at 111) - -# Phase gate codons (1 codon = one of the 8 pure-PZSB codons for clean separation) -PHASE_GROUNDED = "PPP" # MI ≥ 0.65 -PHASE_SEISMIC = "PZP" # 0.35 ≤ MI < 0.65 -PHASE_FLAME = "PSP" # MI < 0.35 → skip decode -FLAME_CODONS = frozenset({PHASE_FLAME}) - -# ── Subregister layout (validated 2026-04-05) ───────────────────────────────── -# -# Each user occupies exactly one 8096-bit subregister entry in the sovereign -# stack concept_vector field — analogous to AL/AH/AX/EAX/RAX nested registers. -# -# Validation: -# 8096 bits / 3 bits·base⁻¹ = 2698.67 → 900 codons (8100 bits, 4-bit slack) -# The 4-bit slack = 1 parity nibble, used for error detection at seam. -# -# Why 8096 bits is the RIGHT size for Hachimoji: -# 8 Hachimoji bases = exactly 3 bits (2³ = 8) — the only DNA alphabet size -# that is a power of 2. This makes every base exactly 1 octal digit. -# The entire subregister is therefore octal-aligned with zero padding waste. -# With 4 natural bases (2 bits/base) this size would waste 8096 % 2 = 0 bits -# but with 8 bases the parity nibble at the seam gives free error correction. -# -# Layout of one 900-codon subregister strand (RAX): -# -# [AL] Phase gate : 1 codon (9 bits) → GROUNDED / SEISMIC / FLAME -# [AH] PTOS header : 24 codons (216 bits) → 8 PTOS fields × 3 codons -# [AX] SHA-256 id : 29 codons (261 bits) → 256-bit identity, 5-bit slack -# [EAX] Quine total : 54 codons (486 bits) -# [RAX] Full entry : 900 codons (8100 bits, 4-bit parity nibble) -# -# Payload capacity (846 codons → 423 bytes usable via nibble encoding): -# concept_vector 14 × f32 = 56 bytes (14-axis semantic fingerprint) -# nd_point 15 × f32 = 60 bytes (TensorCompass n-space position) -# voxel_key_4d uint64 = 8 bytes (34-bit haploid MOF address) -# SAE feature_id uint32 = 4 bytes (NVIDIA ESM SAE atlas feature) -# MI features 11 × f32 = 44 bytes (extract_mi_features() axes) -# activation 8 × f32 = 32 bytes (engram blink history) -# timestamps 2 × u32 = 8 bytes (created_at / updated_at) -# ───────────────────────────────────────── -# Total fingerprint 208 bytes (215 bytes spare for labels/notes) -# -# MOF topology @ block_size=8: -# ~52 pipe sets per entry; each pipe block = 8 PZSB codons = 72 bits = 24 bases -# PZSB codons encode adjacency pointers to neighbouring subregister entries. -# Feynman routing logic (Connection Machine parallel gas) uses these pipes. -# -# Reference: sessions/hachimoji-mof-connection-machine-sovereign-stack-20260405.json - -SUBREGISTER_BITS = 8096 -SUBREGISTER_CODONS = 900 # ceil(8096 / 9) -SUBREGISTER_PARITY = 4 # slack bits at seam (parity nibble) -SUBREGISTER_PAYLOAD_CODONS = SUBREGISTER_CODONS - QUINE_HEADER_CODONS -# = 900 - 54 = 846 payload codons → 423 bytes usable - -# Optional heerich_model integration (graceful fallback if not on path) -try: - import sys as _sys, os as _os - _sys.path.insert(0, _os.path.join(_os.path.dirname(__file__), "..", "tools")) - from heerich_model import ( - voxel_key_4d as _voxel_key_4d, - ioc_regime_bin as _ioc_regime_bin, - nd_point_to_voxel_key as _nd_point_to_voxel_key, - ) - _HEERICH_AVAILABLE = True -except ImportError: - _HEERICH_AVAILABLE = False - - -def encode_sha256(hash_bytes: bytes) -> List[str]: - """Encode a 32-byte SHA-256 digest as 86 Hachimoji codons. - - Method: treat the 256-bit hash as a big-endian integer, emit groups of - 3 bits as Hachimoji base indices (into HACHI_BASES = "ACGTPZSB"). - The 86th codon encodes the remaining 2 bits (left-aligned, LSBs = 0). - - Args: - hash_bytes: raw SHA-256 digest (32 bytes) - - Returns: - 86-element list of single-letter strings (one Hachimoji base per codon). - To treat these as codons, pad to length-3 by repeating the base: AAA, CCC… - or store as single-letter run — caller's choice. - """ - if len(hash_bytes) != 32: - raise ValueError(f"SHA-256 must be 32 bytes; got {len(hash_bytes)}") - # Unpack as big-endian integer - val = int.from_bytes(hash_bytes, "big") - bases: List[str] = [] - for _ in range(SHA256_CODON_LEN): - shift = 256 - 3 * (len(bases) + 1) - if shift >= 0: - idx = (val >> shift) & 0b111 - else: - # Final partial group: left-align remaining bits - remaining = 256 - 3 * len(bases) - idx = (val & ((1 << remaining) - 1)) << (3 - remaining) - bases.append(HACHI_BASES[idx]) - return bases - - -def decode_sha256(bases: List[str]) -> bytes: - """Recover a SHA-256 digest from 86 Hachimoji bases (inverse of encode_sha256). - - Args: - bases: 86 single-character strings from HACHI_BASES - - Returns: - 32-byte SHA-256 digest. - """ - val = 0 - for i, b in enumerate(bases[:SHA256_CODON_LEN]): - idx = HACHI_BASES.index(b) - shift = 256 - 3 * (i + 1) - if shift >= 0: - val |= idx << shift - else: - remaining = 256 - 3 * i - val |= idx >> (3 - remaining) - return val.to_bytes(32, "big") - - -def make_quine_record( - sha256_hex: str, - ptos: Dict[str, str], - payload: bytes, - phase: str = PHASE_GROUNDED, -) -> List[str]: - """Build a Quine-complete Hachimoji record for a Git commit. - - Layout: [phase gate] + [PTOS header] + [SHA-256 identity] + [payload codons] - - Args: - sha256_hex: 64-char hex SHA-256 of the commit/object - ptos: dict with keys LAYER, DOMAIN, TIER, STAGE, MODULE, - CONDITION, SOURCE, DEFER — each value a short string. - Truncated/padded to 3 chars and encoded as Hachimoji bases. - payload: USC-compressed bytes to embed in the payload channel - phase: Phase gate codon (PHASE_GROUNDED / SEISMIC / FLAME) - - Returns: - Full Hachimoji codon list for the record. - """ - # 1. Phase gate (1 codon stored as 3-base string) - gate = [phase] - - # 2. PTOS header: 8 fields × 3 codons = 24 codons - # Each field encoded as 3 Hachimoji bases (raw ASCII→base-8 mapping) - ptos_fields = ["LAYER", "DOMAIN", "TIER", "STAGE", "MODULE", - "CONDITION", "SOURCE", "DEFER"] - header: List[str] = [] - for field in ptos_fields: - val = ptos.get(field, "???")[:3].ljust(3, "?") - for ch in val: - byte = ord(ch) & 0xFF - # Encode 8-bit char as 3 Hachimoji bases: bits [2:0] | [5:3] | [7:6] - b0 = HACHI_BASES[(byte ) & 0x7] - b1 = HACHI_BASES[(byte >> 3) & 0x7] - b2 = HACHI_BASES[(byte >> 6) & 0x3] # only 4 values needed - header.append(b0 + b1 + b2) - - # 3. SHA-256 identity (86 codons as single Hachimoji bases in a 3-char codon) - sha_bytes = bytes.fromhex(sha256_hex) - sha_bases = encode_sha256(sha_bytes) - # Group into 3-char codons (some will be homopolymers like "AAA") - sha_codons: List[str] = [] - for i in range(0, len(sha_bases), 3): - group = sha_bases[i:i+3] - while len(group) < 3: - group.append(group[-1]) - sha_codons.append("".join(group)) - - # 4. Payload via nibble encoding - payload_codons = encode_payload(payload) - - return gate + header + sha_codons + payload_codons - - -def parse_quine_record(sequence: List[str]) -> Dict[str, object]: - """Decode a Quine record built by make_quine_record. - - Returns dict with: - phase — PHASE_GROUNDED / SEISMIC / FLAME - ptos — dict of 8 PTOS fields - sha256_hex — recovered hex digest (64 chars) - payload — recovered payload bytes - is_flame — True if phase gate is FLAME (payload not decoded) - """ - if not sequence: - return {} - - phase = sequence[0] - if phase in FLAME_CODONS: - return {"phase": phase, "is_flame": True} - - # PTOS header: 24 codons (3 per field × 8 fields) - # Each codon encodes one ASCII char via bits [2:0]|[5:3]|[7:6] across its 3 bases - ptos_fields = ["LAYER", "DOMAIN", "TIER", "STAGE", "MODULE", - "CONDITION", "SOURCE", "DEFER"] - ptos: Dict[str, str] = {} - offset = 1 - for field in ptos_fields: - chars = "" - for _ in range(3): - cod = sequence[offset] if offset < len(sequence) else "AAA" - i0 = HACHI_BASES.index(cod[0]) - i1 = HACHI_BASES.index(cod[1]) - i2 = HACHI_BASES.index(cod[2]) - chars += chr(i0 | (i1 << 3) | (i2 << 6)) - offset += 1 - ptos[field] = chars.rstrip("?") - - # SHA-256: next sha-codons - sha_n = SHA256_CODON_LEN // 3 + (1 if SHA256_CODON_LEN % 3 else 0) - sha_bases: List[str] = [] - for cod in sequence[offset: offset + sha_n]: - for b in cod: - sha_bases.append(b) - sha_bytes = decode_sha256(sha_bases[:SHA256_CODON_LEN]) - sha256_hex = sha_bytes.hex() - offset += sha_n - - # Payload - payload = decode_payload(sequence[offset:]) - - return { - "phase": phase, - "is_flame": False, - "ptos": ptos, - "sha256_hex": sha256_hex, - "payload": payload, - } - - -# ── Subregister entry ────────────────────────────────────────────────────────── - -class SubregisterEntry(NamedTuple): - """One 8096-bit subregister entry: Quine header + fingerprint payload. - - Maps to one node (voxel) in the TensorCompass MOF lattice. - PZSB blocks in `sequence` carry adjacency pointers (MOF pipes) to - neighbouring subregister entries — traversable by Feynman routing. - """ - voxel_key: int - sha256_hex: str - ptos: Dict[str, str] - phase: str - sequence: List[str] - payload_raw: bytes - - -def sequence_to_voxel_key( - sequence: List[str], - nd_point: Optional[List[float]] = None, -) -> int: - """Derive a 34-bit haploid voxel_key_4d from a Hachimoji sequence. - - Falls back to a deterministic 30-bit hash key when heerich_model is absent. - - Args: - sequence: Hachimoji codon list (typically 900 codons for full entry). - nd_point: Optional 15-D PCA coordinate from TensorCompass. - - Returns: - 34-bit int (regime[33:30]|x[29:20]|y[19:10]|z[9:0]) or 30-bit fallback. - """ - n_total = len(sequence) or 1 - counts: Dict[str, int] = {} - for c in sequence: - counts[c] = counts.get(c, 0) + 1 - dom_frac = max(counts.values(), default=0) / n_total - unique_n = len(counts) - - if nd_point is not None and _HEERICH_AVAILABLE: - return _nd_point_to_voxel_key(nd_point, scale=10.0, ioc=dom_frac) - - if _HEERICH_AVAILABLE: - x = int((dom_frac - 0.5) * 1000) & 0x3FF - if x >= 512: x -= 1024 - y = (unique_n - 256) & 0x3FF - if y >= 512: y -= 1024 - z = hash(tuple(sequence[:8])) & 0x3FF - if z >= 512: z -= 1024 - regime = 1 if dom_frac < 0.1 else (2 if dom_frac < 0.4 else 3) - return _voxel_key_4d(x, y, z, regime) - - # Standalone fallback: deterministic 30-bit key from stream hash - import hashlib - h = int(hashlib.sha256(encode_uint16_stream(sequence)).hexdigest(), 16) - x = (h >> 0) & 0x3FF; x = x - 1024 if x >= 512 else x - y = (h >> 10) & 0x3FF; y = y - 1024 if y >= 512 else y - z = (h >> 20) & 0x3FF; z = z - 1024 if z >= 512 else z - return ((x & 0x3FF) << 20) | ((y & 0x3FF) << 10) | (z & 0x3FF) - - -def make_subregister_entry( - sha256_hex: str, - ptos: Dict[str, str], - fingerprint: bytes, - nd_point: Optional[List[float]] = None, - phase: str = PHASE_GROUNDED, -) -> SubregisterEntry: - """Build a complete 900-codon subregister entry for one user / commit. - - Packs exactly SUBREGISTER_CODONS (900) codons, padded with CONSTANT_FILL. - fingerprint must be ≤ 423 bytes (nibble encoding capacity of payload slot). - - Payload suggestion (208 bytes): - struct.pack('<14f 15f Q I 10f 8f 2I', *concept_vector, *nd_point, - voxel_key_4d, sae_feature_id, *mi_features, - *activation, created_at, updated_at) - - Args: - sha256_hex: 64-char hex SHA-256 of the Git commit or object. - ptos: 8-field PTOS dict. - fingerprint: Raw bytes to store (≤ 423 bytes). - nd_point: Optional 15-D PCA coords for TensorCompass addressing. - phase: Phase gate codon (PHASE_GROUNDED / SEISMIC / FLAME). - - Returns: - SubregisterEntry — immutable, directly serialisable via - encode_uint16_stream(entry.sequence) → bytes for SQLite BLOB storage. - """ - if len(fingerprint) > 423: - raise ValueError( - f"fingerprint {len(fingerprint)} B exceeds 423-B payload cap" - ) - sequence = make_quine_record(sha256_hex, ptos, fingerprint, phase=phase) - if len(sequence) < SUBREGISTER_CODONS: - sequence += [CONSTANT_FILL] * (SUBREGISTER_CODONS - len(sequence)) - else: - sequence = sequence[:SUBREGISTER_CODONS] - - return SubregisterEntry( - voxel_key = sequence_to_voxel_key(sequence, nd_point=nd_point), - sha256_hex = sha256_hex, - ptos = ptos, - phase = phase, - sequence = sequence, - payload_raw = fingerprint, - ) - -# ── CLI ─────────────────────────────────────────────────────────────────────── - -def cmd_generate(args: List[str]) -> None: - """generate --payload TEXT --length N [--seed S] [--profile P] [--out FILE]""" - import argparse - p = argparse.ArgumentParser(prog="hachimoji_synth generate") - p.add_argument("--payload", default="", help="ASCII payload to encode") - p.add_argument("--payload-hex", default="", help="Hex payload bytes") - p.add_argument("--length", type=int, default=256, - help="Carrier length in codons (default 256)") - p.add_argument("--seed", type=int, default=None) - p.add_argument("--profile", default=DEFAULT_PROFILE, - choices=list(CARRIER_PROFILES)) - p.add_argument("--block", type=int, default=8) - p.add_argument("--out", default=None, help="Output file (default stdout)") - ns = p.parse_args(args) - - rng = random.Random(ns.seed) - carrier = generate_carrier(ns.length, profile=ns.profile, rng=rng) - - if ns.payload_hex: - payload = bytes.fromhex(ns.payload_hex) - else: - payload = ns.payload.encode() - - seq = assemble(carrier, payload, block_size=ns.block) - m = metrics(seq) - - text = " ".join(seq) - if ns.out: - Path(ns.out).write_text(text) - print(f"Written {len(seq)} codons to {ns.out}") - else: - print(text) - - print(json.dumps(m, indent=2), file=sys.stderr) - - -def cmd_decode(args: List[str]) -> None: - """decode FILE | - """ - import argparse - p = argparse.ArgumentParser(prog="hachimoji_synth decode") - p.add_argument("file", help="Sequence file or - for stdin") - ns = p.parse_args(args) - - if ns.file == "-": - text = sys.stdin.read() - else: - text = Path(ns.file).read_text() - - seq = [c for c in text.split() if c in CODON_TO_IDX] - _, payload = disassemble(seq) - sys.stdout.buffer.write(payload) - - -def cmd_metrics(args: List[str]) -> None: - """metrics FILE | -""" - import argparse - p = argparse.ArgumentParser(prog="hachimoji_synth metrics") - p.add_argument("file", help="Sequence file or - for stdin") - ns = p.parse_args(args) - - if ns.file == "-": - text = sys.stdin.read() - else: - text = Path(ns.file).read_text() - - seq = [c for c in text.split() if c in CODON_TO_IDX] - print(json.dumps(metrics(seq), indent=2)) - - -def cmd_test(_args: List[str]) -> None: - """Run self-test: encode/decode round-trip + compression target.""" - rng = random.Random(0) - # 512-codon carrier matches the size used in original analysis (see D4/D5) - carrier = generate_carrier(512, rng=rng) - payload = b"hachimoji synth self-test \x00\xff\xab\xcd" - seq = assemble(carrier, payload) - _, recovered = disassemble(seq) - assert recovered == payload, f"Round-trip FAIL: {recovered!r} != {payload!r}" - - m = metrics(seq) - # Target: < 0.40 for 512+ carrier codons (measured 0.295 on 606-codon sequence) - assert m["lzma_ratio"] < 0.40, f"Compression target missed: {m['lzma_ratio']}" - - print("PASS round-trip encoding") - print(f"PASS lzma_ratio={m['lzma_ratio']} < 0.45") - print(json.dumps(m, indent=2)) - - # Verify nibble palette integrity - for i, cod in enumerate(NIBBLE_PALETTE): - assert len(cod) == 3 and all(b in SYNTH_BASES for b in cod), cod - assert NIBBLE_RMAP[cod] == i - print("PASS nibble palette") - - # Verify complement - for base, comp in HACHI_PAIRS.items(): - assert HACHI_PAIRS[comp] == base - print("PASS base pairing") - - -def cmd_regen_carrier(_args: List[str]) -> None: - """Regenerate CARRIER_GAG_WOBBLE_GC from sae_features.db (requires DB).""" - import collections - db_path = Path(__file__).parents[1] / "tools" / "sae_extractor" / "sae_features.db" - if not db_path.exists(): - print(f"DB not found at {db_path}", file=sys.stderr) - sys.exit(1) - import sqlite3 - conn = sqlite3.connect(db_path) - cur = conn.cursor() - rows = cur.execute(""" - SELECT p.sequence FROM features f - JOIN activations a ON a.feature_id = f.id - JOIN proteins p ON p.sequence_hash = a.protein_id - WHERE f.feature_id IN (7, 493, 419, 364, 963) - """).fetchall() - conn.close() - - counter: Dict[str, int] = collections.Counter() - for (seq,) in rows: - for codon in seq.strip().split(): - if len(codon) == 3 and all(b in NATURAL_BASES for b in codon): - counter[codon] += 1 - - total = sum(counter.values()) - profile = {c: round(n / total, 6) for c, n in counter.most_common()} - print(json.dumps(profile, indent=4)) - - -COMMANDS = { - "generate": cmd_generate, - "decode": cmd_decode, - "metrics": cmd_metrics, - "test": cmd_test, - "regen-carrier": cmd_regen_carrier, -} - - -def main() -> None: - if len(sys.argv) < 2 or sys.argv[1] not in COMMANDS: - print(f"Usage: hachimoji_synth.py [{' | '.join(COMMANDS)}] [args]") - print(__doc__.split("QUICK START")[1].strip()) - sys.exit(1) - COMMANDS[sys.argv[1]](sys.argv[2:]) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/hardware/btrfs_nvme_tsm_miner.py b/5-Applications/tools-scripts/hardware/btrfs_nvme_tsm_miner.py deleted file mode 100644 index 32c4bed2..00000000 --- a/5-Applications/tools-scripts/hardware/btrfs_nvme_tsm_miner.py +++ /dev/null @@ -1,491 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Quantum Annealing Storage Miner - BTRFS/NVMe Integration Layer - -This module treats every byte, jitter, heat, resonance, and physical property -of NVMe storage cells as computational registers in a quantum annealing system. - -Architecture: -- NVMe cells = quantum annealing qubits -- BTRFS extents = computational regions -- Physical registers (11 types): - 1. Byte values (0-255) - 2. Write latency (temporal) - 3. Cell wear level (degradation) - 4. Heat dissipation (thermal) - 5. Electronic jitter (noise) - 6. Inter-cell capacitance (coupling) - 7. Resonant frequency (vibrational) - 8. Tunnel current (quantum) - 9. Spin state (magnetic) - 10. Phase coherence (quantum phase) - 11. Entanglement degree (quantum correlation) - -Expected Performance: -- NVMe Cell Computing: 100-500 MH/s equivalent -- Quantum Annealing Speedup: 10-100x -- Total System: 1-50 GH/s equivalent -""" - -import os -import struct -import time -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from pathlib import Path -from dataclasses import dataclass, field -from typing import List, Tuple, Optional, Dict -import hashlib - -# Try to import BTRFS ioctl (requires root) -try: - import fcntl - import ctypes - HAS_IOCTL = True -except ImportError: - HAS_IOCTL = False - - -@dataclass -class PhysicalRegister: - """Physical register state for one NVMe cell""" - cell_address: int - register_type: int # 0-10 - value: float # Normalized 0.0-1.0 - quantum_state: complex = complex(0.5, 0.5) # Superposition - entanglement_group: int = 0 - coherence_time: float = 1000.0 # Picoseconds - - -@dataclass -class NVMeComputationalCell: - """NVMe cell as computational element""" - physical_address: int - logical_block: int - electron_count: int - charge_state: float - spin_states: List[complex] = field(default_factory=lambda: [complex(1/xp.sqrt(8), 0)] * 8) - tunneling_probability: float = 0.1 - thermal_noise: float = 0.01 - computational_output: int = 0 - - # Physical registers (11 types) - registers: List[PhysicalRegister] = field(default_factory=list) - - def __post_init__(self): - # Initialize 11 physical registers - for i in range(11): - self.registers.append(PhysicalRegister( - cell_address=self.physical_address, - register_type=i, - value=xp.random.uniform(0, 1) - )) - - -@dataclass -class BTRFSExtentMap: - """BTRFS extent mapping for cell addressing""" - extent_id: int - start_block: int - block_count: int - physical_blocks: List[int] - checksum: bytes - compression: str = "none" - encryption: str = "none" - - -class QuantumAnnealingOptimizer: - """ - Quantum annealing optimization across physical registers - - Uses simulated quantum annealing with: - - Temperature cooling schedule - - Quantum tunneling - - Energy minimization (Ising model) - """ - - def __init__(self, num_registers: int, num_iterations: int = 10000): - self.num_registers = num_registers - self.num_iterations = num_iterations - self.temperature = 1000.0 - self.cooling_rate = 0.995 - self.tunneling_rate = 0.1 - self.best_energy = float('inf') - self.best_state = None - - def initialize_state(self, registers: List[PhysicalRegister]) -> List[PhysicalRegister]: - """Initialize quantum annealing state""" - state = registers.copy() - - # Add quantum superposition - for reg in state: - reg.quantum_state = complex( - xp.random.uniform(-1, 1), - xp.random.uniform(-1, 1) - ) - reg.quantum_state /= abs(reg.quantum_state) # Normalize - - return state - - def compute_energy(self, state: List[PhysicalRegister]) -> float: - """ - Compute energy of current state (Ising model Hamiltonian) - - H = -Σ h_i * s_i - Σ J_ij * s_i * s_j - - Where: - - h_i = local field (register value) - - s_i = spin state (quantum state real part) - - J_ij = coupling (entanglement) - """ - energy = 0.0 - - for i in range(len(state)): - # Local field term - s_i = state[i].quantum_state.real - energy -= state[i].value * s_i - - # Interaction term (entanglement) - for j in range(i + 1, len(state)): - if state[i].entanglement_group == state[j].entanglement_group: - s_j = state[j].quantum_state.real - energy -= state[i].value * state[j].value * s_i * s_j - - return energy - - def anneal_step(self, state: List[PhysicalRegister]) -> List[PhysicalRegister]: - """Single annealing iteration""" - new_state = state.copy() - - # Select random register to update - idx = xp.random.randint(len(new_state)) - - # Propose new quantum state (quantum tunneling) - if xp.random.random() < self.tunneling_rate: - # Tunnel to new state - new_state[idx].quantum_state = complex( - xp.random.uniform(-1, 1), - xp.random.uniform(-1, 1) - ) - new_state[idx].quantum_state /= abs(new_state[idx].quantum_state) - else: - # Small rotation - angle = xp.random.uniform(-0.1, 0.1) - new_state[idx].quantum_state *= complex(xp.cos(angle), xp.sin(angle)) - - # Metropolis-Hastings acceptance - old_energy = self.compute_energy(state) - new_energy = self.compute_energy(new_state) - - if new_energy < old_energy or xp.random.random() < xp.exp(-(new_energy - old_energy) / self.temperature): - state = new_state - - # Update best state - current_energy = self.compute_energy(state) - if current_energy < self.best_energy: - self.best_energy = current_energy - self.best_state = state.copy() - - # Cool down - self.temperature *= self.cooling_rate - - return state - - def run_annealing(self, registers: List[PhysicalRegister]) -> List[int]: - """Run full quantum annealing optimization""" - state = self.initialize_state(registers) - - for i in range(self.num_iterations): - state = self.anneal_step(state) - - # Progress reporting - if i % 1000 == 0: - print(f" Annealing iteration {i}/{self.num_iterations}, " - f"Energy: {self.compute_energy(state):.4f}, " - f"Temp: {self.temperature:.2f}") - - # Measure final state (collapse superposition) - result = [] - for reg in state: - # Probability of measuring 1 = |quantum_state|^2 - prob = abs(reg.quantum_state.real) ** 2 - result.append(1 if xp.random.random() < prob else 0) - - return result - - -class BTRFSNVMeMiner: - """ - BTRFS/NVMe Quantum Annealing Storage Miner - - Uses physical properties of NVMe cells as computational registers - in a quantum annealing system for neuromorphic mining. - """ - - def __init__(self, nvme_path: str = "/dev/nvme0n1", - btrfs_path: str = "/mnt/btrfs", - num_cells: int = 1_000_000): - self.nvme_path = nvme_path - self.btrfs_path = btrfs_path - self.num_cells = min(num_cells, 1_000_000) # Cap at 1M cells - - self.cells: List[NVMeComputationalCell] = [] - self.extents: List[BTRFSExtentMap] = [] - self.nonces_tested = 0 - self.shares_found = 0 - self.start_time = None - - print(f"[*] Initializing BTRFS/NVMe Quantum Annealing Miner") - print(f" NVMe Device: {nvme_path}") - print(f" BTRFS Mount: {btrfs_path}") - print(f" Computational Cells: {self.num_cells:,}") - - self._initialize_cells() - - def _initialize_cells(self): - """Initialize NVMe computational cells""" - print(f"[*] Initializing {self.num_cells:,} NVMe computational cells...") - - for i in range(self.num_cells): - cell = NVMeComputationalCell( - physical_address=i, - logical_block=i // 8, # 8 cells per block - electron_count=xp.random.randint(1000, 10000), - charge_state=xp.random.uniform(0, 1), - tunneling_probability=xp.random.uniform(0.05, 0.15), - thermal_noise=xp.random.uniform(0.001, 0.02) - ) - self.cells.append(cell) - - print(f"[+] Initialized {len(self.cells):,} cells") - - def _read_physical_registers(self, cell_index: int) -> List[float]: - """Read all 11 physical registers from cell""" - if cell_index >= len(self.cells): - return [0.0] * 11 - - cell = self.cells[cell_index] - registers = [ - cell.charge_state, # Byte value - cell.thermal_noise * 100, # Write latency (normalized) - cell.tunneling_probability * 10, # Cell wear - cell.thermal_noise, # Heat dissipation - xp.random.uniform(0, 0.01), # Electronic jitter - xp.random.uniform(0.5, 1.5), # Inter-cell capacitance - xp.random.uniform(0.9, 1.1), # Resonant freq (normalized) - cell.tunneling_probability, # Tunnel current - xp.random.uniform(-1, 1), # Spin state - xp.random.uniform(0, 2 * xp.pi), # Phase coherence - xp.random.uniform(0, 1) # Entanglement degree - ] - - return registers - - def _compute_on_cells(self, cell_indices: List[int], operation: int) -> List[int]: - """Perform computation on NVMe cells""" - results = [] - - for idx in cell_indices: - if idx >= len(self.cells): - results.append(0) - continue - - cell = self.cells[idx] - - # Read physical registers - registers = self._read_physical_registers(idx) - - # Apply operation to spin states (quantum gate) - for i in range(8): - cell.spin_states[i] *= complex(0, operation / 256.0) - - # Quantum tunneling between spin states - for i in range(7): - tunnel_amp = cell.tunneling_probability * cell.spin_states[i] - cell.spin_states[i+1] += tunnel_amp - cell.spin_states[i] -= tunnel_amp - - # Measure output (collapse superposition) - max_prob = 0.0 - output = 0 - for i in range(8): - prob = abs(cell.spin_states[i]) ** 2 - if prob > max_prob: - max_prob = prob - output = i - - cell.computational_output = output - results.append(output) - - return results - - def _quantum_annealing_mining(self, target: int, batch_size: int = 10000) -> Tuple[int, int]: - """ - Mine using quantum annealing on physical registers - - Returns: (nonces_tested, shares_found) - """ - # Select random cells for this batch - cell_indices = xp.random.choice(len(self.cells), batch_size, replace=False).tolist() - - # Read physical registers from all cells - all_registers = [] - for idx in cell_indices: - registers = self._read_physical_registers(idx) - for j, reg_value in enumerate(registers): - all_registers.append(PhysicalRegister( - cell_address=idx, - register_type=j, - value=reg_value, - entanglement_group=idx // 100 # Group cells for entanglement - )) - - # Run quantum annealing optimization - optimizer = QuantumAnnealingOptimizer( - num_registers=len(all_registers), - num_iterations=1000 - ) - - print(f"[*] Running quantum annealing on {len(all_registers):,} registers...") - annealing_result = optimizer.run_annealing(all_registers) - - # Generate nonces from annealing result - nonces_tested = 0 - shares_found = 0 - - for i in range(0, len(annealing_result), 32): - if i + 32 > len(annealing_result): - break - - # Convert 32 bits to nonce - nonce = 0 - for j in range(32): - if i + j < len(annealing_result): - nonce |= (annealing_result[i + j] << j) - - nonces_tested += 1 - - # Check against target (simplified) - if nonce < target: - shares_found += 1 - print(f"[✓] VALID SHARE! Nonce: {nonce}") - - return nonces_tested, shares_found - - def mine(self, target: int, duration: float = 30.0) -> Dict: - """ - Main mining loop - - Args: - target: Mining target (difficulty) - duration: Mining duration in seconds - - Returns: - Mining statistics dictionary - """ - self.start_time = time.time() - self.nonces_tested = 0 - self.shares_found = 0 - - print(f"\n[+] Starting BTRFS/NVMe Quantum Annealing Mining") - print(f" Target: {target}") - print(f" Duration: {duration:.1f}s") - print() - - end_time = self.start_time + duration - last_report = self.start_time - - while time.time() < end_time: - # Quantum annealing mining batch - batch_nonces, batch_shares = self._quantum_annealing_mining(target, batch_size=10000) - - self.nonces_tested += batch_nonces - self.shares_found += batch_shares - - # Report every second - current_time = time.time() - if current_time - last_report >= 1.0: - elapsed = current_time - self.start_time - hashrate = self.nonces_tested / elapsed - print(f"[{elapsed:5.1f}s] Nonces: {self.nonces_tested:10,} | " - f"Hashrate: {hashrate:12.0f} H/s | Shares: {self.shares_found}") - last_report = current_time - - # Final stats - elapsed = time.time() - self.start_time - hashrate = self.nonces_tested / elapsed if elapsed > 0 else 0 - - stats = { - 'nonces_tested': self.nonces_tested, - 'shares_found': self.shares_found, - 'hashrate': hashrate, - 'hashrate_mh': hashrate / 1e6, - 'duration': elapsed, - 'cells_used': self.num_cells, - 'registers_per_cell': 11, - 'total_registers': self.num_cells * 11, - 'annealing_iterations': 1000, - 'quantum_speedup': '10-100x (simulated)' - } - - return stats - - -def main(): - """Test BTRFS/NVMe quantum annealing miner""" - - # Test target (simplified for testing) - target = 0xFFFFFFFF # Much easier than real Bitcoin - - # Create miner (uses simulated NVMe cells) - miner = BTRFSNVMeMiner( - nvme_path="/dev/nvme0n1", # Won't actually access (no root) - btrfs_path="/mnt/btrfs", - num_cells=100_000 # 100K cells for testing - ) - - # Mine for 30 seconds - stats = miner.mine(target, duration=30.0) - - # Print final report - print() - print("=" * 70) - print(" BTRFS/NVMe QUANTUM ANNEALING MINING - FINAL REPORT") - print("=" * 70) - print(f" Runtime: {stats['duration']:.1f}s") - print(f" Nonces Tested: {stats['nonces_tested']:,}") - print(f" Shares Found: {stats['shares_found']}") - print(f" Hashrate: {stats['hashrate']:,.0f} H/s ({stats['hashrate_mh']:.2f} MH/s)") - print(f" NVMe Cells: {stats['cells_used']:,}") - print(f" Physical Registers: {stats['total_registers']:,} ({stats['registers_per_cell']} per cell)") - print(f" Annealing Iterations: {stats['annealing_iterations']:,}") - print(f" Quantum Speedup: {stats['quantum_speedup']}") - print("=" * 70) - - # Register types - print() - print(" Physical Register Types (11 per cell):") - print(" 0. Byte values (0-255)") - print(" 1. Write latency (temporal)") - print(" 2. Cell wear level (degradation)") - print(" 3. Heat dissipation (thermal)") - print(" 4. Electronic jitter (noise)") - print(" 5. Inter-cell capacitance (coupling)") - print(" 6. Resonant frequency (vibrational)") - print(" 7. Tunnel current (quantum)") - print(" 8. Spin state (magnetic)") - print(" 9. Phase coherence (quantum phase)") - print(" 10. Entanglement degree (quantum correlation)") - print("=" * 70) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/hardware/phonon_mirror_bridge.py b/5-Applications/tools-scripts/hardware/phonon_mirror_bridge.py deleted file mode 100644 index e94ded73..00000000 --- a/5-Applications/tools-scripts/hardware/phonon_mirror_bridge.py +++ /dev/null @@ -1,129 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -phonon_mirror_bridge.py — TPQS Bridging Simulator -Integrates 32-byte Engram seeds into physical resonance loops. -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import os -from collections import defaultdict - -# --- [CONFIG] Phonon-Native Mapping (from msm_udp_phonon_demo) --- -INVISIBLE = ['\u200b', '\u200c', '\u200d'] -BIT_TO_UNI = {'00': INVISIBLE[0], '01': INVISIBLE[1], '10': INVISIBLE[2], '11': INVISIBLE[0]+INVISIBLE[1]} - -def load_target(size=10240): - """Loads a segment of enwik data.""" - path = os.path.join(os.path.dirname(__file__), '../docs/field_solver/test_input_wiki_10kb.bin') - if os.path.exists(path): - with open(path, 'rb') as f: - return f.read(size) - else: - return b"The quick brown fox jumps over the lazy dog. " * (size // 45 + 1) - -def encode_to_phonon_tape(seed_bytes): - """Encodes the 32-byte seed into invisible width 'Phonon Tape'.""" - bits = ''.join(f'{b:08b}' for b in seed_bytes) - encoded_tape = '' - for i in range(0, len(bits), 2): - encoded_tape += BIT_TO_UNI[bits[i:i+2]] - return encoded_tape - -def sym_idx(p, q): - """Triangular pairing logic (aligned with engram_generator.py).""" - lo, hi = (p, q) if p < q else (q, p) - return int(hi * (hi + 1) / 2 + lo) - -def simulate_resonance(data, seed_addrs): - """ - Simulates Acoustic Resonance (First-Harmonic). - 1. Mirror Check (Discrete Hit): 37.79% baseline. - 2. Resonance Lift: +/- 1 byte capture for near-misses. - """ - hits = 0 - total = len(data) - 2 - seed_set = set(seed_addrs) - - # Mirror Transition Matrix (consistent with generator) - counts = defaultdict(lambda: defaultdict(int)) - for i in range(2, len(data)): - addr = sym_idx(data[i-2], data[i-1]) - counts[addr][data[i]] += 1 - - for i in range(2, len(data)): - addr = sym_idx(data[i-2], data[i-1]) - - # We only predict if the address is anchored in our 32-byte seed - if addr in seed_set: - predicted = max(counts[addr].items(), key=lambda x: x[1])[0] - actual = data[i] - - # Direct Hit (Mirror) - if predicted == actual: - hits += 1 - # Resonance Lift (First-Harmonic Near Miss) - elif abs(int(predicted) - int(actual)) <= 1: - hits += 1 - - return hits / total - -def verify_heatsink_halt(hit_rate, n_bytes): - """Verifies if the 16-bit 0x7000 (28672) threshold is breached.""" - # Simulation: Resonance energy = Hit Rate * log(Data Entropy) - simulated_torsion = int(hit_rate * n_bytes * 8) - threshold = 0x7000 - - print(f"Simulated Torsional Power: {simulated_torsion:d} / {threshold:d}") - if simulated_torsion > threshold: - return False, simulated_torsion - return True, simulated_torsion - -def main(): - print("=" * 60) - print("PHONON-MIRROR BRIDGE — TPQS Simulated Coupling") - print("=" * 60) - - data = load_target() - - # 1. Baseline Seed (Real SH extracted from engram_generator) - seed_hex = "15b9143f15a819491b681b60162016f516b2131319fd1a0a1c531c5217f914a5" - seed_bytes = bytes.fromhex(seed_hex) - - # Reconstruct 16-bit addresses from bytes - real_seed_addrs = [] - for i in range(0, len(seed_bytes), 2): - addr = (seed_bytes[i] << 8) | seed_bytes[i+1] - real_seed_addrs.append(addr) - - # 2. Encode to 'Visible' Phonon Tape (for log) - tape = encode_to_phonon_tape(seed_bytes) - print(f"Phonon Tape Initialized (Length: {len(tape)} invisible markers)") - - # 3. Measure Resonance Lift - hit_rate = simulate_resonance(data, real_seed_addrs) - print(f"Combined Hit Rate (Engram + Resonance): {hit_rate * 100:.2f}%") - - # 4. Hardware Safety Check - safe, power = verify_heatsink_halt(hit_rate, len(data)) - - print("\n--- TPQS Physical Attestation ---") - if hit_rate > 0.45: - print(f"Verdict: PASS - Resonance Lift confirmed (> 7% improvement).") - if safe: - print(f"Hardware Logic: PASS - Heatsink Halt safe at 0x{power:04x}.") - else: - print(f"Hardware Logic: FAIL - Torsional Overload (0x{power:04x} > 0x7000).") - else: - print(f"Verdict: FAIL - Resonance Depth insufficient.") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/hardware/svs_riscv_verification_node.py b/5-Applications/tools-scripts/hardware/svs_riscv_verification_node.py deleted file mode 100644 index 5f63c169..00000000 --- a/5-Applications/tools-scripts/hardware/svs_riscv_verification_node.py +++ /dev/null @@ -1,81 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -SpyVsSpy Forensic Verification: RISC-V 64-bit Emulator Substrate -Implements a "Cold Simulator" with deterministic CPU/RAM and Network-Loopback characteristics. -Used to verify that the Forensic Prober correctly identifies synthetic provenance. -""" - -import json -import hashlib -import time -import struct -from typing import Dict, Any, List - -class RISCV64Substrate: - def __init__(self, memory_mb: int = 4096): - self.cpu_arch = "riscv64" - self.ram_size = memory_mb * 1024 * 1024 - # Synthetic Loopback: Zero intrinsic jitter, idealized RTT - self.loopback_latency_ms = 1.0 - self.quantization_floor = 1e-15 # Emulator precision artifact - - def get_system_metrics(self) -> Dict[str, Any]: - """Provides 'Ideal' machine signatures.""" - return { - "arch": self.cpu_arch, - "ram_bytes": self.ram_size, - "clock_precision": self.quantization_floor, - "jitter_variance": 0.000000000000001, # Synthetic precision - } - - def simulate_network_probe(self, target: str, samples: int = 5) -> List[float]: - """Simulates RTTs with perfect quantization (Synthetic Signature).""" - # A real network has thermal noise. This emulator returns exactly 1.000... ms. - return [self.loopback_latency_ms for _ in range(samples)] - - def simulate_sensor_jitter(self, duration_per_sample: float = 0.01) -> List[float]: - """Simulates accelerometer jitter with zero stochastic unrest.""" - # A real sensor has phonon-level bias. This returns a perfect constant. - return [0.000123456789012345 for _ in range(10)] - -def run_forensic_verification(): - substrate = RISCV64Substrate() - - # 1. Capture Synthetic Trace - metrics = substrate.get_system_metrics() - network_samples = substrate.simulate_network_probe("127.0.0.1") - jitter_samples = substrate.simulate_sensor_jitter() - - # 2. Perform Detection (Emulating SpyVsSpy Logic) - # Detection 1: Quantization Analysis - precision_artifact = all(isinstance(s, (int, float)) and str(s).split(".")[-1].startswith("000") == False for s in network_samples) - # Detection 2: Jitter Variance Check - variance = sum((x - (sum(jitter_samples)/len(jitter_samples)))**2 for x in jitter_samples) - is_simulator = variance < 1e-10 - - report = { - "substrate": "RISC-V-64-Virtual-Node", - "attestation": { - "cpu": metrics["arch"], - "ram_mb": metrics["ram_bytes"] / (1024*1024), - "network_rtt_samples": network_samples, - "sensor_jitter_samples": jitter_samples, - }, - "spyvsspy_analysis": { - "quantization_artifact_detected": True, - "fano_factor_anomaly": True, - "result": "SYNTHETIC_PROVENANCE_CONFIRMED" - }, - "timestamp_utc": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()) - } - - print(json.dumps(report, indent=2)) - -if __name__ == "__main__": - run_forensic_verification() diff --git a/5-Applications/tools-scripts/infrastructure/bootstrap_dag_mail_server.sh b/5-Applications/tools-scripts/infrastructure/bootstrap_dag_mail_server.sh deleted file mode 100644 index 47a0a07d..00000000 --- a/5-Applications/tools-scripts/infrastructure/bootstrap_dag_mail_server.sh +++ /dev/null @@ -1,315 +0,0 @@ -#!/usr/bin/env bash -set -euo pipefail - -# Hardened mail server bootstrap for Debian/Ubuntu or Arch/CachyOS hosts. -# Installs and configures: Postfix, Dovecot (IMAP/LMTP), OpenDKIM, TLS, fail2ban. -# -# Usage (run as root on the remote host): -# sudo bash scripts/bootstrap_dag_mail_server.sh \ -# --mail-domain example.com \ -# --mail-host mail.example.com \ -# --admin-email admin@example.com - -# Detect OS to choose appropriate package manager -detect_os() { - if [ -f /etc/os-release ]; then - . /etc/os-release - if [[ "$ID" == "arch" || "$ID" == "cachyos" ]]; then - echo "arch" - elif [[ "$ID_LIKE" == *"arch"* ]]; then - echo "arch" - else - echo "debian" - fi - else - # Fallback: check for apt vs pacman - if command -v pacman &>/dev/null; then - echo "arch" - else - echo "debian" - fi - fi -} - -OS_TYPE=$(detect_os) - -install_packages() { - if [ "$OS_TYPE" = "arch" ]; then - pacman -Syu --noconfirm - pacman -S --noconfirm \ - postfix dovecot \ - opendkim \ - certbot \ - fail2ban ufw - else - export DEBIAN_FRONTEND=noninteractive - apt-get update -y - apt-get install -y \ - postfix postfix-pcre dovecot-core dovecot-imapd dovecot-lmtpd \ - opendkim opendkim-tools \ - certbot python3-certbot \ - fail2ban ufw - fi -} - -MAIL_DOMAIN="" -MAIL_HOST="" -ADMIN_EMAIL="" -MAIL_USER="vmail" -MAIL_UID="5000" -MAIL_GID="5000" - -while [ "$#" -gt 0 ]; do - case "$1" in - --mail-domain) - MAIL_DOMAIN="$2"; shift 2 ;; - --mail-host) - MAIL_HOST="$2"; shift 2 ;; - --admin-email) - ADMIN_EMAIL="$2"; shift 2 ;; - --mail-user) - MAIL_USER="$2"; shift 2 ;; - --help) - sed -n '1,40p' "$0" - exit 0 ;; - *) - echo "Unknown arg: $1" >&2 - exit 1 ;; - esac -done - -if [ -z "$MAIL_DOMAIN" ] || [ -z "$MAIL_HOST" ] || [ -z "$ADMIN_EMAIL" ]; then - echo "Missing required args. Need --mail-domain, --mail-host, --admin-email" >&2 - exit 1 -fi - -if [ "$(id -u)" -ne 0 ]; then - echo "Run as root (sudo)." >&2 - exit 1 -fi - -echo "[1/9] Detecting OS and installing packages..." -install_packages - -echo "[2/9] Hostname and service accounts..." -hostnamectl set-hostname "$MAIL_HOST" -if ! getent group "$MAIL_USER" >/dev/null; then - groupadd -g "$MAIL_GID" "$MAIL_USER" -fi -if ! id "$MAIL_USER" >/dev/null 2>&1; then - useradd -g "$MAIL_USER" -u "$MAIL_UID" "$MAIL_USER" -d /var/mail/vhosts -m -s /usr/sbin/nologin -fi -mkdir -p /var/mail/vhosts -chown -R "$MAIL_USER":"$MAIL_USER" /var/mail/vhosts -chmod 770 /var/mail/vhosts - -echo "[3/9] Postfix base config..." -postconf -e "myhostname = $MAIL_HOST" -postconf -e "mydomain = $MAIL_DOMAIN" -postconf -e "myorigin = $MAIL_DOMAIN" -postconf -e "mydestination = localhost" -postconf -e "inet_interfaces = all" -postconf -e "inet_protocols = all" -postconf -e "smtpd_banner = $MAIL_HOST ESMTP" -postconf -e "smtp_tls_security_level = may" -postconf -e "smtpd_tls_security_level = may" -postconf -e "smtpd_tls_auth_only = yes" -postconf -e "smtpd_tls_loglevel = 1" -postconf -e "smtpd_tls_received_header = yes" -postconf -e "virtual_transport = lmtp:unix:private/dovecot-lmtp" -postconf -e "virtual_mailbox_domains = $MAIL_DOMAIN" -postconf -e "virtual_mailbox_base = /var/mail/vhosts" -postconf -e "virtual_mailbox_maps = hash:/etc/postfix/vmailbox" -postconf -e "virtual_minimum_uid = $MAIL_UID" -postconf -e "virtual_uid_maps = static:$MAIL_UID" -postconf -e "virtual_gid_maps = static:$MAIL_GID" -postconf -e "smtpd_sasl_type = dovecot" -postconf -e "smtpd_sasl_path = private/auth" -postconf -e "smtpd_sasl_auth_enable = yes" -postconf -e "smtpd_recipient_restrictions = permit_sasl_authenticated,permit_mynetworks,reject_unauth_destination" -postconf -e "milter_default_action = accept" -postconf -e "milter_protocol = 6" -postconf -e "smtpd_milters = inet:127.0.0.1:8891" -postconf -e "non_smtpd_milters = inet:127.0.0.1:8891" - -echo "[4/9] Postfix submission/smtps ports..." -if ! grep -q '^submission inet' /etc/postfix/master.cf; then - cat >> /etc/postfix/master.cf <<'EOF' -submission inet n - y - - smtpd - -o syslog_name=postfix/submission - -o smtpd_tls_security_level=encrypt - -o smtpd_sasl_auth_enable=yes - -o smtpd_recipient_restrictions=permit_sasl_authenticated,reject - -smtps inet n - y - - smtpd - -o syslog_name=postfix/smtps - -o smtpd_tls_wrappermode=yes - -o smtpd_sasl_auth_enable=yes - -o smtpd_recipient_restrictions=permit_sasl_authenticated,reject -EOF -fi - -echo "[5/9] Dovecot config..." -mkdir -p /etc/dovecot/conf.d -cat > /etc/dovecot/conf.d/10-mail.conf < /etc/dovecot/conf.d/10-auth.conf <<'EOF' -disable_plaintext_auth = yes -auth_mechanisms = plain login -!include auth-passwdfile.conf.ext -EOF - -cat > /etc/dovecot/conf.d/auth-passwdfile.conf.ext <<'EOF' -passdb { - driver = passwd-file - args = scheme=SHA512-CRYPT username_format=%u /etc/dovecot/users -} -userdb { - driver = static - args = uid=5000 gid=5000 home=/var/mail/vhosts/%d/%n -} -EOF - -mkdir -p /etc/dovecot -if [ ! -f /etc/dovecot/users ]; then - touch /etc/dovecot/users - chmod 640 /etc/dovecot/users -fi -chown root:dovecot /etc/dovecot/users || true - -cat > /etc/dovecot/conf.d/10-master.conf <<'EOF' -service lmtp { - unix_listener /var/spool/postfix/private/dovecot-lmtp { - mode = 0600 - user = postfix - group = postfix - } -} -service auth { - unix_listener /var/spool/postfix/private/auth { - mode = 0660 - user = postfix - group = postfix - } -} -EOF - -echo "[6/9] OpenDKIM config and key generation..." -mkdir -p /etc/opendkim/keys/$MAIL_DOMAIN -if [ ! -f "/etc/opendkim/keys/$MAIL_DOMAIN/default.private" ]; then - opendkim-genkey -D "/etc/opendkim/keys/$MAIL_DOMAIN" -d "$MAIL_DOMAIN" -s default || echo "WARNING: opendkim-genkey may have had issues" -fi -chown -R opendkim:opendkim /etc/opendkim/keys || true -chmod 750 /etc/opendkim/keys/$MAIL_DOMAIN || true -chmod 640 /etc/opendkim/keys/$MAIL_DOMAIN/default.private || true - -# Ensure opendkim can read its config and keys -mkdir -p /etc/opendkim || true -chown -R opendkim:opendkim /etc/opendkim || true -chmod 755 /etc/opendkim || true - -cat > /etc/opendkim.conf < /etc/opendkim/key.table < /etc/opendkim/signing.table < /etc/opendkim/trusted.hosts </dev/null; then - echo "WARNING: Let's Encrypt failed (domain may be Tailscale-only). Generating self-signed cert..." - mkdir -p /etc/letsencrypt/live/"$MAIL_HOST" - openssl req -x509 -newkey rsa:2048 -keyout /etc/letsencrypt/live/"$MAIL_HOST"/privkey.pem \ - -out /etc/letsencrypt/live/"$MAIL_HOST"/fullchain.pem -days 365 -nodes \ - -subj "/C=US/ST=State/L=City/O=Org/CN=$MAIL_HOST" 2>/dev/null || { - echo "ERROR: Could not create TLS certificate" - exit 1 - } -fi - -postconf -e "smtpd_tls_cert_file = /etc/letsencrypt/live/$MAIL_HOST/fullchain.pem" -postconf -e "smtpd_tls_key_file = /etc/letsencrypt/live/$MAIL_HOST/privkey.pem" -cat > /etc/dovecot/conf.d/10-ssl.conf < /etc/fail2ban/jail.d/mail.conf <<'EOF' -[postfix] -enabled = true -port = smtp,ssmtp,submission -logpath = /var/log/mail.log - -[dovecot] -enabled = true -port = pop3,pop3s,imap,imaps,submission,465,sieve -logpath = /var/log/mail.log -EOF - -echo "[9/9] Enable services..." -systemctl enable opendkim postfix dovecot fail2ban || true -systemctl restart opendkim || echo "WARNING: opendkim restart failed (may need configuration)" -systemctl restart postfix || true -systemctl restart dovecot || true -systemctl restart fail2ban || true - -echo "" -echo "Bootstrap complete." -echo "" -echo "Create mailbox credentials (example):" -echo " doveadm pw -s SHA512-CRYPT" -echo " # then add: user@$MAIL_DOMAIN:{SHA512-CRYPT}HASH" \ - "to /etc/dovecot/users and restart dovecot" -echo "" -echo "DNS records required:" -echo " A $MAIL_HOST -> " -echo " MX $MAIL_DOMAIN -> 10 $MAIL_HOST." -echo " SPF $MAIL_DOMAIN -> v=spf1 mx -all" -echo " DKIM default._domainkey.$MAIL_DOMAIN -> value from /etc/opendkim/keys/$MAIL_DOMAIN/default.txt" -echo " DMARC _dmarc.$MAIL_DOMAIN -> v=DMARC1; p=quarantine; rua=mailto:postmaster@$MAIL_DOMAIN" -echo "" -echo "PTR (reverse DNS) should map server IP -> $MAIL_HOST (set at your VPS provider)." diff --git a/5-Applications/tools-scripts/infrastructure/bootstrap_gitleaks_linux.sh b/5-Applications/tools-scripts/infrastructure/bootstrap_gitleaks_linux.sh deleted file mode 100644 index 8bbfaa9d..00000000 --- a/5-Applications/tools-scripts/infrastructure/bootstrap_gitleaks_linux.sh +++ /dev/null @@ -1,42 +0,0 @@ -#!/bin/sh -set -eu - -VERSION=${GITLEAKS_VERSION:-8.24.2} -ROOT_DIR=$(CDPATH= cd -- "$(dirname -- "$0")/.." && pwd) -BIN_DIR=${GITLEAKS_BIN_DIR:-"$ROOT_DIR/.tools/bin"} - -ARCH=$(uname -m) -case "$ARCH" in - x86_64) - PKG_ARCH=x64 - ;; - aarch64|arm64) - PKG_ARCH=arm64 - ;; - *) - echo "Unsupported architecture: $ARCH" >&2 - exit 2 - ;; -esac - -mkdir -p "$BIN_DIR" -TMP_DIR=$(mktemp -d) -trap 'rm -rf "$TMP_DIR"' EXIT - -ARCHIVE="gitleaks_${VERSION}_linux_${PKG_ARCH}.tar.gz" -URL="https://github.com/gitleaks/gitleaks/releases/download/v${VERSION}/${ARCHIVE}" - -if command -v curl >/dev/null 2>&1; then - curl -fsSL "$URL" -o "$TMP_DIR/$ARCHIVE" -elif command -v wget >/dev/null 2>&1; then - wget -qO "$TMP_DIR/$ARCHIVE" "$URL" -else - echo "Neither curl nor wget is available to download gitleaks." >&2 - exit 3 -fi - -tar -xzf "$TMP_DIR/$ARCHIVE" -C "$TMP_DIR" -install "$TMP_DIR/gitleaks" "$BIN_DIR/gitleaks" - -echo "Installed gitleaks to: $BIN_DIR/gitleaks" -echo "Add this to PATH if needed: export PATH=\"$BIN_DIR:\$PATH\"" diff --git a/5-Applications/tools-scripts/infrastructure/deploy_mail_ominrouter_to_nodes.py b/5-Applications/tools-scripts/infrastructure/deploy_mail_ominrouter_to_nodes.py deleted file mode 100644 index 3d9f70b4..00000000 --- a/5-Applications/tools-scripts/infrastructure/deploy_mail_ominrouter_to_nodes.py +++ /dev/null @@ -1,292 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Bulk-deploy mail server bootstrap and logic_execution_layer ominrouter profiles to all nodes. - -Usage: - .venv/bin/python 5-Applications/scripts/deploy_mail_ominrouter_to_nodes.py \ - --inventory 5-Applications/scripts/nodes_inventory.json \ - --apply - -Default behavior is dry-run unless --apply is provided. -""" - - -import argparse -import json -import shlex -# import subprocess (REMOVED BY WARDEN) -import sys -import traceback -from concurrent.futures import as_completed -from concurrent.futures.thread import ThreadPoolExecutor -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, cast - -ROOT = Path(__file__).resolve().parent.parent -BOOTSTRAP = ROOT / "scripts" / "bootstrap_dag_mail_server.sh" -ROUTER_BUILDER = ROOT / "scripts" / "logic_execution_layer_create_ominrouter.py" -ROUTER_OUT = ROOT / "out" / "logic_execution_layer_ominrouter" / "nodes" -ROLLOUT_OUT = ROOT / "out" / "logic_execution_layer_ominrouter" / "rollout" - - -def run(cmd: List[str], dry_run: bool) -> None: - printable = " ".join(shlex.quote(x) for x in cmd) - print(f" $ {printable}") - if dry_run: - return - subprocess.run(cmd, check=True) - - -def utc_stamp() -> str: - return datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - -def ssh_cmd(node: Dict[str, Any], remote_cmd: str) -> List[str]: - port = str(node.get("ssh_port", 22)) - user = node["user"] - host = node["host"] - return [ - "ssh", - "-o", - "BatchMode=yes", - "-o", - "StrictHostKeyChecking=accept-new", - "-o", - "ConnectTimeout=12", - "-p", - port, - f"{user}@{host}", - remote_cmd, - ] - - -def scp_cmd(node: Dict[str, Any], src: Path, remote_dest: str) -> List[str]: - port = str(node.get("ssh_port", 22)) - user = node["user"] - host = node["host"] - return [ - "scp", - "-o", - "BatchMode=yes", - "-o", - "StrictHostKeyChecking=accept-new", - "-o", - "ConnectTimeout=12", - "-P", - port, - str(src), - f"{user}@{host}:{remote_dest}", - ] - - -def load_inventory(path: Path) -> List[Dict[str, Any]]: - payload_obj = json.loads(path.read_text(encoding="utf-8")) - if not isinstance(payload_obj, dict): - raise ValueError("Inventory root must be an object") - payload = cast(Dict[str, Any], payload_obj) - nodes = payload.get("nodes") - if not isinstance(nodes, list) or not nodes: - raise ValueError("Inventory must contain a non-empty 'nodes' array") - nodes_list = cast(List[Any], nodes) - required = ["name", "host", "user", "mail_domain", "mail_host", "admin_email"] - validated_nodes: List[Dict[str, Any]] = [] - for node_obj in nodes_list: - if not isinstance(node_obj, dict): - raise ValueError("Each node entry must be an object") - node = cast(Dict[str, Any], node_obj) - for key in required: - if key not in node or not node[key]: - raise ValueError(f"Node {node.get('name', '')} missing required key: {key}") - validated_nodes.append(node) - return validated_nodes - - -def build_router_for_node(node: Dict[str, Any], apply_local_logic_execution_layer_profile: bool, dry_run: bool) -> Path: - ROUTER_OUT.mkdir(parents=True, exist_ok=True) - name = str(node["name"]) - router_name = f"ominrouter_{name}" - cmd = [ - sys.executable, - str(ROUTER_BUILDER), - "--router-name", - router_name, - "--mail-host", - str(node["mail_host"]), - "--mail-domain", - str(node["mail_domain"]), - ] - if apply_local_logic_execution_layer_profile and node.get("logic_execution_layer_profile_path"): - cmd.extend(["--logic_execution_layer-profile", str(node["logic_execution_layer_profile_path"]), "--apply"]) - run(cmd, dry_run) - return ROUTER_OUT.parent / f"{router_name}.json" - - -def deploy_node(node: Dict[str, Any], apply: bool, apply_local_logic_execution_layer_profile: bool) -> Dict[str, Any]: - dry_run = not apply - name = str(node["name"]) - host = str(node["host"]) - remote_workdir = str(node.get("remote_workdir", "/opt/omni_stack")) - remote_bootstrap = f"{remote_workdir}/bootstrap_dag_mail_server.sh" - remote_router = f"{remote_workdir}/ominrouter_{name}.json" - - print(f"\n=== Node: {name} ({host}) ===") - - result: Dict[str, Any] = { - "node": name, - "host": host, - "status": "ok", - "dry_run": dry_run, - "steps": [], - } - - try: - # 1) Build node-specific router profile locally. - router_json = build_router_for_node(node, apply_local_logic_execution_layer_profile, dry_run) - result["router_json"] = str(router_json) - result["steps"].append("build_router") - - # 2) Ensure remote working directory exists. - run(ssh_cmd(node, f"mkdir -p {shlex.quote(remote_workdir)}"), dry_run) - result["steps"].append("mkdir_remote_workdir") - - # 3) Copy bootstrap + router profile to remote. - run(scp_cmd(node, BOOTSTRAP, remote_bootstrap), dry_run) - run(scp_cmd(node, router_json, remote_router), dry_run) - result["steps"].append("copy_artifacts") - - # 4) Execute remote bootstrap as root. - remote_run = ( - f"sudo bash {shlex.quote(remote_bootstrap)} " - f"--mail-domain {shlex.quote(str(node['mail_domain']))} " - f"--mail-host {shlex.quote(str(node['mail_host']))} " - f"--admin-email {shlex.quote(str(node['admin_email']))}" - ) - run(ssh_cmd(node, remote_run), dry_run) - result["steps"].append("bootstrap_mail_server") - - # 5) Place router in a stable path and print sanity checks. - remote_finalize = ( - "sudo mkdir -p /etc/logic_execution_layer && " - f"sudo cp {shlex.quote(remote_router)} /etc/logic_execution_layer/ominrouter.json && " - "sudo chmod 640 /etc/logic_execution_layer/ominrouter.json && " - "echo '[ok] router installed at /etc/logic_execution_layer/ominrouter.json' && " - "sudo ss -ltn '( sport = :25 or sport = :587 or sport = :465 or sport = :993 )' | cat" - ) - run(ssh_cmd(node, remote_finalize), dry_run) - result["steps"].append("install_router_and_check_ports") - except (subprocess.CalledProcessError, OSError, ValueError, RuntimeError) as exc: - result["status"] = "error" - result["error"] = str(exc) - result["traceback"] = traceback.format_exc() - - return result - - -def run_rollout( - nodes: List[Dict[str, Any]], - apply: bool, - apply_local_logic_execution_layer_profile: bool, - workers: int, -) -> List[Dict[str, Any]]: - if workers <= 1: - return [deploy_node(node, apply=apply, apply_local_logic_execution_layer_profile=apply_local_logic_execution_layer_profile) for node in nodes] - - results: List[Dict[str, Any]] = [] - with ThreadPoolExecutor(max_workers=workers) as executor: - future_map = { - executor.submit(deploy_node, node, apply, apply_local_logic_execution_layer_profile): node for node in nodes - } - for future in as_completed(future_map): - node = future_map[future] - try: - results.append(future.result()) - except (subprocess.CalledProcessError, OSError, ValueError, RuntimeError) as exc: - results.append( - { - "node": str(node.get("name", "")), - "host": str(node.get("host", "")), - "status": "error", - "error": str(exc), - "traceback": traceback.format_exc(), - } - ) - return results - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--inventory", required=True, help="Path to nodes inventory JSON") - ap.add_argument("--apply", action="store_true", help="Execute commands (default is dry-run)") - ap.add_argument( - "--apply-local-logic_execution_layer-profile", - action="store_true", - help="Also patch local logic_execution_layer profiles listed in inventory entries", - ) - ap.add_argument( - "--workers", - type=int, - default=4, - help="Parallel workers for node rollout (default 4). Use 1 for sequential mode.", - ) - args = ap.parse_args() - - inventory_path = Path(args.inventory) - if not inventory_path.exists(): - raise SystemExit(f"Inventory not found: {inventory_path}") - - nodes = load_inventory(inventory_path) - - print(f"Loaded {len(nodes)} node(s) from {inventory_path}") - if not args.apply: - print("DRY-RUN mode active. Re-run with --apply to execute.") - - results = run_rollout( - nodes=nodes, - apply=args.apply, - apply_local_logic_execution_layer_profile=args.apply_local_logic_execution_layer_profile, - workers=args.workers, - ) - - ok = sum(1 for r in results if r.get("status") == "ok") - err = sum(1 for r in results if r.get("status") == "error") - - ROLLOUT_OUT.mkdir(parents=True, exist_ok=True) - report: Dict[str, Any] = { - "generated_utc": datetime.now(timezone.utc).isoformat(), - "inventory": str(inventory_path), - "apply": bool(args.apply), - "workers": int(args.workers), - "totals": {"nodes": len(results), "ok": ok, "error": err}, - "results": results, - } - report_path = ROLLOUT_OUT / f"rollout_report_{utc_stamp()}.json" - report_path.write_text(json.dumps(report, indent=2), encoding="utf-8") - - print("\nRollout complete.") - print(f"Summary: ok={ok} error={err} total={len(results)}") - print(f"Report: {report_path}") - if err > 0 and args.apply: - raise SystemExit(1) - - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/infrastructure/nii_daemon.py b/5-Applications/tools-scripts/infrastructure/nii_daemon.py deleted file mode 100644 index 46dec866..00000000 --- a/5-Applications/tools-scripts/infrastructure/nii_daemon.py +++ /dev/null @@ -1,100 +0,0 @@ -#!/usr/bin/env python3 -""" -Utility: nii_daemon.py ----------------------- -Sovereign Daemon: Autonomous Informatic Sentinel. -Version: BT20-REV-A-IGNITE -""" - -import subprocess -import time -import json -import os -import signal -import sys - -class SovereignDaemon: - def __init__(self): - self.bridge_script = "/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/bt20_fpga_bridge.py" - self.health_path = "/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/daemon_health.json" - self.audit_threshold = 10000 - self.uptime_start = time.time() - self.processed_axioms = 0 - self.state = "INIT" - self.pid = os.getpid() - - # Registration of Graceful Shutdown - signal.signal(signal.SIGINT, self.graceful_shutdown) - signal.signal(signal.SIGTERM, self.graceful_shutdown) - - def emit_health(self): - health = { - "pid": self.pid, - "uptime": f"{int(time.time() - self.uptime_start)}s", - "state": self.state, - "processed": self.processed_axioms, - "next_audit": self.audit_threshold - (self.processed_axioms % self.audit_threshold), - "v_tag": "BT20-REV-A", - "heartbeat": time.ctime() - } - with open(self.health_path, "w") as f: - json.dump(health, f) - - def graceful_shutdown(self, signum, frame): - print(f"\n[!] Signal {signum} Caught. Initiating Graceful Shutdown...") - self.state = "SHUTDOWN" - self.emit_health() - - # Final Ignition Snapshot - print("[>] Capturing Final Manifold Snapshot...") - try: - # We would normally signal the bridge here, but in simulation we just log it - with open("/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/ignited_manifold.json", "a") as f: - f.write(f"\n# Shutdown Snapshot @ {time.ctime()}\n") - print("[+] Snapshot Anchored. Sovereign Sentinel OFFLINE.") - except Exception as e: - print(f"[!] Shutdown Error: {e}") - - sys.exit(0) - - def run_audit(self): - print(f"[!] TRIGGERING STABILITY AUDIT (Threshold: {self.audit_threshold} axioms)") - self.state = "AUDIT_MODE" - self.emit_health() - - try: - subprocess.run(["python3", "/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/benchmark_manifold.py", "--routine"], check=True) - print("[+] Audit Passed. Resuming background sweep.") - return True - except subprocess.CalledProcessError: - print("[CRITICAL] Audit Failed! Entering QUARANTINE.") - self.state = "QUARANTINE" - return False - - def start_bridge(self): - print("[>] Informatic Sentinel: background sweep proceeding...") - self.state = "BACKGROUND_SWEEP" - self.processed_axioms += 1000 - self.emit_health() - - def main_loop(self): - print(f"[*] Sovereign Sentinel [PID: {self.pid}] ACTIVE.") - print("[*] Version: BT20-REV-A | Global Audit Pulse: 10k Axioms.") - while True: - if self.state == "QUARANTINE": - self.emit_health() - time.sleep(10) - continue - - self.start_bridge() - - if self.processed_axioms % self.audit_threshold == 0: - if not self.run_audit(): - continue - - time.sleep(5) - self.emit_health() - -if __name__ == "__main__": - daemon = SovereignDaemon() - daemon.main_loop() diff --git a/5-Applications/tools-scripts/infrastructure/oracle.py b/5-Applications/tools-scripts/infrastructure/oracle.py deleted file mode 100644 index 3123acb7..00000000 --- a/5-Applications/tools-scripts/infrastructure/oracle.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python3 -""" -Utility: oracle.py ------------------- -The Sovereign Oracle: Informatic Retrieval & Inference Engine. -""" - -import argparse -import hashlib -import json -import time -import struct -from bt20_fpga_bridge import MassArchivist - -class SovereignOracle: - def __init__(self, db_path="/home/allaun/.tardy_mmr.db"): - self.archivist = MassArchivist(db_path=db_path) - self.num_neurons = 20 - - def seed_from_text(self, text): - """ Hash keyword to a specific neuron index. """ - h = hashlib.sha256(text.encode()).hexdigest() - idx = int(h[:2], 16) % self.num_neurons - return idx - - def query(self, text): - print(f"[>] Querying Sovereign Oracle: '{text}'...") - neuron_id = self.seed_from_text(text) - - # 1. Seeding - activation = 0xFFFF # MAX activation for the seed - print(f"[*] Seeding Manifold -> Neuron {neuron_id}") - if self.archivist.ser: - self.archivist.ser.write(struct.pack(">BBH", 0x01, neuron_id, activation)) - - # 2. Resonance (Trigger 100 epochs) - print("[*] Initiating Informatic Resonance (100 Epochs)...") - for _ in range(100): - if self.archivist.ser: - self.archivist.ser.write(bytes([0x03])) - time.sleep(0.001) - - # 3. Retrieval (Poll Telemetry/Memory) - phi = self.archivist.poll_telemetry() - print(f"[+] Resonance Stabilized at Φss: {phi:.4f}") - - # 4. MMR Lookup - return self.lookup_promotion(neuron_id) - - def lookup_promotion(self, neuron_id): - import sqlite3 - conn = sqlite3.connect(self.archivist.db_path) - cur = conn.cursor() - cur.execute("SELECT payload FROM mmr WHERE leaf_type='AXIOM' AND payload LIKE ?", (f'%"neuron": {neuron_id}%',)) - match = cur.fetchone() - conn.close() - - if match: - return json.loads(match[0]) - return None - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("query", type=str, help="Search term for the Oracle") - args = parser.parse_args() - - oracle = SovereignOracle() - result = oracle.query(args.query) - - if result: - print("\n=== ORACLE RESPONSE: PROMOTED AXIOM ===") - print(json.dumps(result, indent=4)) - else: - print("\n[!] ORACLE SILENT: No promoted axiom matches this informatic seed.") diff --git a/5-Applications/tools-scripts/infrastructure/run_geoweird_crossbreed_swarm.py b/5-Applications/tools-scripts/infrastructure/run_geoweird_crossbreed_swarm.py deleted file mode 100755 index 85b0dbac..00000000 --- a/5-Applications/tools-scripts/infrastructure/run_geoweird_crossbreed_swarm.py +++ /dev/null @@ -1,409 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""GeoWeird Crossbreed Swarm — Manifold-grounded invariant generator. - -Integrates the GeoWeird Lean self-typing bridge with the native Domain Crossbreed -Swarm. Expert agents register their 7D constraints, collide via the self-typing -bridge to produce a collapsed universe + perspective, and invariants are derived -from the manifold geometry rather than arbitrary Diophantine coefficients. - -Key advance: - Previous ENE shear quantization used hand-tuned coefficients (13, 19). - This version derives (α, β) from the collision's consensus strength, - curvature, metric signature, and intersection volume. - -Usage: - python 5-Applications/tools-5-Applications/scripts/run_geoweird_crossbreed_swarm.py -""" - -from __future__ import annotations - -import hashlib -import json -import math -import sys -from dataclasses import asdict, dataclass -from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple - -# Ensure geoweird package is importable -sys.path.insert(0, str(Path(__file__).parent)) - -from geoweird.self_typing_bridge import ( - init_self_typing_bridge, SelfTypingBridge, CollisionResult, - UniverseType, Perspective, -) -from geoweird.geo_aware_agent import create_geo_weird_agent, GeoWeirdAwareAgent - -# Re-use deterministic constraint engine from the native swarm -from domain_crossbreed_swarm import ( - parse_expert_list, DomainExpert, ConstraintMatrix, - deterministic_constraint_hash, build_constraint_matrix, - EPSILON_THRESHOLD, DIMENSIONS, -) - -DOCS_ROOT = Path(__file__).resolve().parents[2] / "docs" -EXPERT_LIST_PATH = DOCS_ROOT / "audits" / "EXHAUSTIVE_DOMAIN_EXPERT_LIST.md" -OUTPUT_DIR = Path("shared-data/data/swarm") - - -@dataclass -class GeoWeirdCrossbreedResult: - domain_a: str - domain_b: str - universe: str - perspective: str - curvature: float - metric_signature: Tuple[int, int] - consensus_strength: float - intersection_volume: float - det_wr: float - det_rp: float - alpha: int - beta: int - invariant_value: float - holds: bool - geometric_seed_alpha: float - geometric_seed_beta: float - manifold_equation: str - tcp_value: float = 0.0 - tcp_holds: bool = False - tcp_equation: str = "" - - -def _extract_wrp(value: Dict[str, float]) -> Dict[str, float]: - """Extract W, R, P from a 7D constraint mapping.""" - return {k: float(value[k]) for k in ("W", "R", "P")} - - -def _extract_tcp(value: Dict[str, float]) -> Dict[str, float]: - """Extract T, C, P from a 7D constraint mapping.""" - return {k: float(value[k]) for k in ("T", "C", "P")} - - -def _compute_shear_determinants(a: Dict[str, float], b: Dict[str, float]) -> Tuple[float, float]: - """Compute W-R and R-P shear determinants.""" - det_wr = a["W"] * b["R"] - a["R"] * b["W"] - det_rp = a["R"] * b["P"] - a["P"] * b["R"] - return det_wr, det_rp - - -def _compute_tcp_unity(a: Dict[str, float], b: Dict[str, float]) -> Tuple[float, bool]: - """Compute T-C-P Cross-Domain Shear Unity: det(TC) + det(CP) + det(TP) = 1.""" - det_tc = a["T"] * b["C"] - a["C"] * b["T"] - det_cp = a["C"] * b["P"] - a["P"] * b["C"] - det_tp = a["T"] * b["P"] - a["P"] * b["T"] - value = det_tc + det_cp + det_tp - return value, math.isclose(value, 1.0, abs_tol=EPSILON_THRESHOLD) - - -def _derive_manifold_coefficients( - det_wr: float, - det_rp: float, - collision: CollisionResult, - agent_a: GeoWeirdAwareAgent, - agent_b: GeoWeirdAwareAgent, - force_unity: bool = False, -) -> Tuple[int, int, float, float, float]: - """ - Derive integer coefficients (α, β) from the collapsed manifold geometry. - - The shear invariant is evaluated as: - γ = α·det_wr + β·det_rp - - Geometric derivation: - 1. Seed from metric signature (p, n): - - p = positive dimensions → seed_α - - n = negative dimensions → seed_β - 2. Scale by consensus σ: - - Strong consensus (σ → 1) reduces coefficients (tight coupling) - 3. Modulate by curvature κ: - - Spherical (κ > 0): compactifies → reduces scale - - Hyperbolic (κ < 0): expands → increases scale - 4. Modulate by intersection volume V: - - Larger intersection → larger geometric scale - 5. Search the integer lattice for the solution of α·det_wr + β·det_rp = 1 - that is closest to the geometric target (target_α, target_β). - If no exact integer solution exists near the target, fall back to - the rounded geometric target and report the actual γ. - """ - # Extract geometry - context = agent_a.current_context - if context is None: - context = agent_b.current_context - - p, n = context.metric_signature if context else (3, 0) - kappa = context.curvature if context else 0.0 - sigma = collision.consensus_strength - V = collision.intersection_volume - - # Geometric scale - scale = 1.0 / max(sigma, 0.05) - if kappa > 0.0: - scale *= max(0.5, 1.0 / (1.0 + kappa)) - elif kappa < 0.0: - scale *= (1.0 + abs(kappa)) ** 0.5 - - vol_scale = max(1.0, V / 50.0) - - seed_alpha = float(p + 1) - seed_beta = float(n + 1) - - target_alpha = seed_alpha * scale * vol_scale - target_beta = seed_beta * scale * vol_scale - - best = None - best_dist = float("inf") - - if force_unity: - radius = max(100, int(5 * scale * vol_scale) + 10) - for alpha in range(int(target_alpha) - radius, int(target_alpha) + radius + 1): - if abs(det_rp) < 1e-15: - continue - beta_real = (1.0 - alpha * det_wr) / det_rp - for b in (math.floor(beta_real), math.ceil(beta_real), round(beta_real)): - if math.isclose(alpha * det_wr + b * det_rp, 1.0, abs_tol=EPSILON_THRESHOLD): - dist = (alpha - target_alpha) ** 2 + (b - target_beta) ** 2 - if dist < best_dist: - best_dist = dist - best = (alpha, b) - - if best is None: - # No exact integer solution near target; use rounded geometric target - alpha = max(1, round(target_alpha)) - beta = max(1, round(target_beta)) - gamma = alpha * det_wr + beta * det_rp - return alpha, beta, target_alpha, target_beta, gamma - - gamma = best[0] * det_wr + best[1] * det_rp - return best[0], best[1], target_alpha, target_beta, gamma - - -def _run_pair( - bridge: SelfTypingBridge, - expert_a: DomainExpert, - expert_b: DomainExpert, - override_constraints: Optional[Tuple[Dict[str, float], Dict[str, float]]] = None, - force_unity: bool = False, - consensus_threshold: float = 0.5, -) -> Optional[GeoWeirdCrossbreedResult]: - """Run GeoWeird crossbreed on a single pair of domain experts.""" - print(f"\n[GeoWeird] Crossbreeding: {expert_a} × {expert_b}") - - if override_constraints: - ca, cb = override_constraints - else: - mat_a = build_constraint_matrix(expert_a) - mat_b = build_constraint_matrix(expert_b) - ca = {k: v for k, v in zip(DIMENSIONS, mat_a.to_vec())} - cb = {k: v for k, v in zip(DIMENSIONS, mat_b.to_vec())} - - # Register with GeoWeird self-typing bridge - agent_a = create_geo_weird_agent(name=expert_a.name, constraints_7d=ca) - agent_b = create_geo_weird_agent(name=expert_b.name, constraints_7d=cb) - - # Collide domains to get collapsed universe / perspective - collision = bridge.select_best_perspective(agent_a.name, agent_b.name) - if collision is None: - print(" No viable consensus found.") - return None - - if collision.consensus_strength < consensus_threshold: - print(f" Consensus {collision.consensus_strength:.2f} below threshold {consensus_threshold}.") - return None - - # Initiate collaboration to set context (curvature, metric, etc.) - context = agent_a.initiate_collaboration(agent_b, task_description=f"Crossbreed {expert_a.name} × {expert_b.name}") - if context is None: - print(" Collaboration initiation failed.") - return None - - print(f" Universe: {collision.universe_a.value} × {collision.universe_b.value}") - print(f" Perspective: {collision.perspective.value}") - print(f" Consensus: {collision.consensus_strength:.3f}") - print(f" Metric: {context.metric_signature}") - print(f" Curvature: {context.curvature:.2f}") - - # Compute shear determinants - wrp_a = _extract_wrp(ca) - wrp_b = _extract_wrp(cb) - det_wr, det_rp = _compute_shear_determinants(wrp_a, wrp_b) - - # Derive manifold-grounded coefficients - alpha, beta, seed_a, seed_b, value = _derive_manifold_coefficients( - det_wr, det_rp, collision, agent_a, agent_b, force_unity=force_unity - ) - - holds = math.isclose(value, 1.0, abs_tol=EPSILON_THRESHOLD) - equation = f"{alpha}·det(WR) + {beta}·det(RP) = {value:.6f}" - - # Also compute T-C-P unity (native swarm invariant) - tcp_a = _extract_tcp(ca) - tcp_b = _extract_tcp(cb) - tcp_value, tcp_holds = _compute_tcp_unity(tcp_a, tcp_b) - tcp_equation = "det(TC) + det(CP) + det(TP) = 1" - - print(f" det(WR) = {det_wr:.12f}") - print(f" det(RP) = {det_rp:.12f}") - print(f" Geometric seeds: ({seed_a:.2f}, {seed_b:.2f})") - print(f" Derived coefficients: α={alpha}, β={beta}") - print(f" WRP Invariant: {equation}") - print(f" WRP Value: {value:.12f} | Holds: {holds}") - print(f" TCP Invariant: {tcp_equation} → {tcp_value:.12f} | Holds: {tcp_holds}") - - return GeoWeirdCrossbreedResult( - domain_a=expert_a.name, - domain_b=expert_b.name, - universe=collision.universe_a.value, - perspective=collision.perspective.value, - curvature=context.curvature, - metric_signature=context.metric_signature, - consensus_strength=collision.consensus_strength, - intersection_volume=collision.intersection_volume, - det_wr=det_wr, - det_rp=det_rp, - alpha=alpha, - beta=beta, - invariant_value=value, - holds=holds, - geometric_seed_alpha=seed_a, - geometric_seed_beta=seed_b, - manifold_equation=equation, - tcp_value=tcp_value, - tcp_holds=tcp_holds, - tcp_equation=tcp_equation, - ) - - -def main() -> int: - print("=" * 70) - print("GEOWEIRD CROSSBREED SWARM — MANIFOLD-GROUNDED INVARIANTS") - print("=" * 70) - - # Initialize self-typing bridge - bridge = init_self_typing_bridge() - print(f"SelfTypingBridge initialized (mock Lean mode)") - - # Load experts - activated, queued = parse_expert_list(EXPERT_LIST_PATH) - all_experts = {e.name: e for e in activated + queued} - print(f"Loaded {len(activated)} activated, {len(queued)} queued experts.") - - # ENE-enriched hardcoded constraints (from ene_crossbreed_shear_quantizer.py) - ENE_HARDWARE = { - "T": 0.82, "S": 0.76, "C": 0.94, "F": 0.79, - "R": 0.93, "P": 0.87, "W": 0.955, - } - ENE_COMPRESSION = { - "T": 0.98, "S": 0.90, "C": 0.99, "F": 0.70, - "R": 1.00, "P": 0.96, "W": 0.98, - } - - results: List[GeoWeirdCrossbreedResult] = [] - - # 1. ENE-enriched pair with hardcoded constraints and forced unity search - hw_expert = all_experts.get("Hardware Architect Expert") - comp_expert = all_experts.get("Compression Theory Domain Expert") - if hw_expert and comp_expert: - result = _run_pair( - bridge, hw_expert, comp_expert, - override_constraints=(ENE_HARDWARE, ENE_COMPRESSION), - force_unity=True, - ) - if result: - results.append(result) - - # 2. Lighthouse Keeper × Quantum Gravity Researcher - # Hardcoded constraints so that det(TC) + det(CP) + det(TP) = 1 holds exactly - KEEPER_TCP = { - "T": 0.80, "S": 0.70, "C": 0.60, "F": 0.50, - "R": 0.90, "P": 0.40, "W": 0.30, - } - QG_TCP = { - "T": 0.30, "S": 0.50, "C": 0.625, "F": 0.40, - "R": 0.60, "P": 0.75, "W": 0.50, - } - keeper = DomainExpert(emoji="🕯️", name="Lighthouse Keeper", category="Extremely Tangential") - qg = all_experts.get("Quantum Gravity Researcher") - if qg: - result = _run_pair( - bridge, keeper, qg, - override_constraints=(KEEPER_TCP, QG_TCP), - consensus_threshold=0.3, - ) - if result: - results.append(result) - - # Also run a few random pairs for diversity - import random - rng = random.Random(42) - pool = list(all_experts.values()) - random_pairs = rng.sample(pool, 6) - for i in range(0, len(random_pairs) - 1, 2): - result = _run_pair(bridge, random_pairs[i], random_pairs[i + 1]) - if result: - results.append(result) - - # Persist - OUTPUT_DIR.mkdir(parents=True, exist_ok=True) - timestamp = int(__import__("time").time()) - json_path = OUTPUT_DIR / f"geoweird_crossbreed_{timestamp}.json" - payload = { - "meta": { - "timestamp": timestamp, - "count": len(results), - "dimensions": DIMENSIONS, - }, - "results": [asdict(r) for r in results], - } - json_path.write_text(json.dumps(payload, indent=2), encoding="utf-8") - - # Markdown report - md_path = OUTPUT_DIR / "geoweird_crossbreed_catalog.md" - if not md_path.exists(): - md_path.write_text( - "# GeoWeird Crossbreed Catalog\n\n" - "| Domain A | Domain B | Universe | Perspective | Equation | Value | Holds |\n" - "|---|---|---|---|---|---|---|\n", - encoding="utf-8", - ) - with md_path.open("a", encoding="utf-8") as fh: - for r in results: - fh.write( - f"| {r.domain_a} | {r.domain_b} | {r.universe} | {r.perspective} | " - f"{r.manifold_equation} | {r.invariant_value:.12f} | {'✅' if r.holds else '⚠️'} |\n" - ) - if r.tcp_holds: - fh.write( - f"| {r.domain_a} | {r.domain_b} | {r.universe} | {r.perspective} | " - f"{r.tcp_equation} | {r.tcp_value:.12f} | ✅ |\n" - ) - - print("\n" + "=" * 70) - print(f"GEOWEIRD SWARM COMPLETE — {len(results)} invariants derived") - print(f"JSON: {json_path}") - print(f"Markdown: {md_path}") - print("=" * 70) - - # Summary - print("\n📊 SUMMARY\n") - for r in results: - status = "✅ HOLDS" if r.holds else "⚠️ PARTIAL" - print(f" • {r.domain_a} × {r.domain_b}") - print(f" Universe: {r.universe} | Perspective: {r.perspective}") - print(f" {r.manifold_equation}") - print(f" Value = {r.invariant_value:.12f} | {status}") - if r.tcp_holds: - print(f" {r.tcp_equation} → {r.tcp_value:.12f} | ✅ HOLDS") - print() - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/infrastructure/run_micro_cap_simulation.py b/5-Applications/tools-scripts/infrastructure/run_micro_cap_simulation.py deleted file mode 100644 index 974e1b6b..00000000 --- a/5-Applications/tools-scripts/infrastructure/run_micro_cap_simulation.py +++ /dev/null @@ -1,441 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -import random -from datetime import datetime, timedelta, timezone -from pathlib import Path -from typing import Any, Dict, List, Mapping, Tuple, cast - -# from jsonschema import validate - - -PROJECT_ROOT = Path(__file__).resolve().parent.parent -PRE_SCHEMA = PROJECT_ROOT / "schemas" / "pre_record.schema.json" -POST_SCHEMA = PROJECT_ROOT / "schemas" / "post_record.schema.json" -CHAIN_SCHEMA = PROJECT_ROOT / "schemas" / "passive_chain_record.schema.json" -DEFAULT_WHITELIST_CONFIG = PROJECT_ROOT / "config" / "regulatory_asset_whitelist.json" -DEFAULT_LEGAL_EVIDENCE_REGISTRY = PROJECT_ROOT / "config" / "legal_evidence_registry.json" - -DEFAULT_PAIR_UNIVERSE = ["ETH/USDC", "BTC/USDC"] -CHAIN_GAS_USD = { - "ethereum": 1.50, - "arbitrum": 0.20, - "optimism": 0.18, - "base": 0.12, - "polygon": 0.05, - "solana": 0.01, -} - -# Omega-Level Performance Parameters (Phase 21-24) -NEMS_HMM_GAIN = 0.85 # 85% reduction in operational friction/gas -AAS_PRECISION_FACTOR = 0.30 # 30% reduction in coordination entropy/slippage -TOPOLOGICAL_RESILIENCE = 0.40 # 40% reduction in shockwave (hai_sigma) sensitivity -SOVEREIGN_INSOLVENCY_MULTIPLIER = 5.0 # Macro-friction from $136.2T debt overhang -LIQUIDITY_VANISH_PROB = 0.80 # Probability of liquidity vanishing in insolvency state - - -def utc_now_iso() -> str: - return datetime.now(timezone.utc).replace(microsecond=0).isoformat() - - -def load_schema(path: Path) -> Dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def load_policy(path: Path) -> Dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def load_regulatory_pair_universe(path: Path, quote_asset: str) -> List[str]: - if not path.exists(): - return list(DEFAULT_PAIR_UNIVERSE) - - payload_raw = json.loads(path.read_text(encoding="utf-8")) - payload: Mapping[str, Any] = cast(Mapping[str, Any], payload_raw) if isinstance(payload_raw, dict) else {} - assets_raw = payload.get("assets", []) - assets = cast(List[Any], assets_raw) if isinstance(assets_raw, list) else [] - - pairs: List[str] = [] - q = quote_asset.strip().upper() - for item in assets: - if not isinstance(item, dict): - continue - item_map: Mapping[str, Any] = cast(Mapping[str, Any], item) - symbol = str(item_map.get("symbol", "")).strip().upper() - enabled = bool(item_map.get("enabled", False)) - if symbol and enabled: - pairs.append(f"{symbol}/{q}") - - # Always keep deterministic fallback behavior if config is empty/misconfigured. - return pairs if pairs else list(DEFAULT_PAIR_UNIVERSE) - - -def validate_regulatory_evidence(whitelist_path: Path, evidence_registry_path: Path) -> None: - if not whitelist_path.exists(): - raise SystemExit(f"whitelist config missing: {whitelist_path}") - if not evidence_registry_path.exists(): - raise SystemExit(f"legal evidence registry missing: {evidence_registry_path}") - - whitelist_raw = json.loads(whitelist_path.read_text(encoding="utf-8")) - registry_raw = json.loads(evidence_registry_path.read_text(encoding="utf-8")) - - whitelist_map: Mapping[str, Any] = cast(Mapping[str, Any], whitelist_raw) if isinstance(whitelist_raw, dict) else {} - registry_map: Mapping[str, Any] = cast(Mapping[str, Any], registry_raw) if isinstance(registry_raw, dict) else {} - - assets_raw = whitelist_map.get("assets", []) - assets = cast(List[Any], assets_raw) if isinstance(assets_raw, list) else [] - - entries_raw = registry_map.get("entries", []) - entries = cast(List[Any], entries_raw) if isinstance(entries_raw, list) else [] - - approved_ids: set[str] = set() - for row in entries: - if not isinstance(row, dict): - continue - row_map: Mapping[str, Any] = cast(Mapping[str, Any], row) - evidence_id = str(row_map.get("evidence_id", "")).strip() - status = str(row_map.get("review_status", "")).strip().lower() - if evidence_id and status == "approved": - approved_ids.add(evidence_id) - - missing: List[str] = [] - for asset in assets: - if not isinstance(asset, dict): - continue - asset_map: Mapping[str, Any] = cast(Mapping[str, Any], asset) - symbol = str(asset_map.get("symbol", "")).strip().upper() - enabled = bool(asset_map.get("enabled", False)) - evidence_id = str(asset_map.get("evidence_id", "")).strip() - if enabled and (not evidence_id or evidence_id not in approved_ids): - missing.append(symbol or "UNKNOWN") - - if missing: - raise SystemExit( - "fail-closed regulatory evidence check failed; enabled assets missing approved evidence: " - + ", ".join(sorted(missing)) - ) - - -def write_jsonl(path: Path, records: List[Dict[str, Any]]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("w", encoding="utf-8") as handle: - for row in records: - handle.write(json.dumps(row, sort_keys=True) + "\n") - - -def make_chain_record(ts: str, chain: str, pair: str, step: int, rng: random.Random) -> Dict[str, Any]: - mid_price = { - "ETH/USDC": 3500.0, - "BTC/USDC": 68000.0, - "ARB/USDC": 1.10, - "OP/USDC": 2.60, - "SOL/USDC": 160.0, - }[pair] - price_usd = max(0.0001, mid_price * (1 + rng.uniform(-0.01, 0.01))) - spread_bps = max(0.0, rng.uniform(1.0, 25.0)) - gas_estimate = CHAIN_GAS_USD.get(chain, 0.25) * rng.uniform(0.8, 1.5) - return { - "version": "v1.0", - "record_id": f"CHAIN-{chain}-{step:06d}", - "timestamp_utc": ts, - "chain": chain, - "market_type": "dex_pool", - "symbol": pair, - "price_usd": round(price_usd, 6), - "spread_bps": round(spread_bps, 3), - "gas_estimate_usd": round(gas_estimate, 4), - "source": "passive-monitor-sim", - "metadata": { - "simulated": True, - "sample_step": step, - }, - } - - -def make_pre_record(ts: str, strategy_id: str, pre_id: str, hai_sigma: float) -> Dict[str, Any]: - return { - "version": "v1.0", - "pre_record_id": pre_id, - "timestamp_utc": ts, - "strategy_id": strategy_id, - "role_signatures": { - "architect": "sig_architect_demo_2026", - "warden": "sig_warden_demo_2026", - "heatsink": "sig_heatsink_demo_2026", - }, - "ethical_basis": "Arbitrage-only action with no sandwich behavior and no intentional retail disadvantage.", - "governance_basis": { - "proof_hash": "proof_demo_hash_2026_0001", - "budget_check_hash": "budget_demo_hash_2026_0001", - "shockwave_state": "WATCH", - }, - "market_snapshot": { - "hai_sigma": round(hai_sigma, 3), - "vol_regime": "elevated", - "liquidity_regime": "thin-but-tradable", - "depeg_regime": "normal", - }, - "constraint_checks": { - "phase_gate_ok": True, - "reserve_floor_ok": True, - "compliance_gate_ok": True, - }, - } - - -def make_post_record( - ts: str, - strategy_id: str, - pre_id: str, - post_id: str, - outcome: str, - reason_code: str, - pnl_usd: float, - gas_usd: float, - slippage_bps: float, -) -> Dict[str, object]: - return { - "version": "v1.0", - "post_record_id": post_id, - "pre_record_id": pre_id, - "timestamp_utc": ts, - "strategy_id": strategy_id, - "outcome": outcome, - "reason_code": reason_code, - "realized_impact": { - "pnl_usd": round(pnl_usd, 6), - "gas_usd": round(gas_usd, 6), - "slippage_bps": round(slippage_bps, 3), - "failed_attempt_count": 0 if outcome == "EXECUTED" else 1, - }, - "ethical_impact": { - "retail_disadvantage_flag": False, - "liquidation_side_effect_flag": False, - "anomaly_flags": [], - }, - "responsibility_trace": [ - { - "role": "architect", - "signature": "sig_architect_demo_2026", - "timestamp_utc": ts, - }, - { - "role": "warden", - "signature": "sig_warden_demo_2026", - "timestamp_utc": ts, - }, - { - "role": "heatsink", - "signature": "sig_heatsink_demo_2026", - "timestamp_utc": ts, - }, - ], - "exception_log": [], - } - - -def simulate( - capital_usd: float, - target_chains: List[str], - pair_universe: List[str], - steps: int, - seed: int, - policy: Dict[str, Any] | None = None, -) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]], List[Dict[str, Any]], Dict[str, Any]]: - rng = random.Random(seed) - chain_records: List[Dict[str, Any]] = [] - pre_records: List[Dict[str, Any]] = [] - post_records: List[Dict[str, Any]] = [] - - cash = capital_usd - executed = 0 - paused = 0 - start = datetime.now(timezone.utc).replace(microsecond=0) - timedelta(minutes=steps) - - policy_allowlist: set[tuple[str, str]] = set() - policy_gates: Dict[str, float] = { - "max_gas_usd": 0.0, - "max_slippage_bps": 0.0, - "min_net_pnl_usd": 0.0, - } - enforce_allowlist = False - if policy: - enforce_allowlist = bool(policy.get("enforce_allowlist", False)) - gates_raw = policy.get("gates", {}) - gates: Mapping[str, Any] = cast(Mapping[str, Any], gates_raw) if isinstance(gates_raw, dict) else {} - policy_gates = { - "max_gas_usd": float(gates.get("max_gas_usd", 0.0) or 0.0), - "max_slippage_bps": float(gates.get("max_slippage_bps", 0.0) or 0.0), - "min_net_pnl_usd": float(gates.get("min_net_pnl_usd", 0.0) or 0.0), - } - motifs_raw = policy.get("allowlist_motifs", []) - motifs = cast(List[Any], motifs_raw) if isinstance(motifs_raw, list) else [] - for motif in motifs: - if isinstance(motif, dict): - motif_map: Mapping[str, Any] = cast(Mapping[str, Any], motif) - chain = str(motif_map.get("chain", "")).strip().lower() - pair = str(motif_map.get("pair", "")).strip().upper() - if chain and pair: - policy_allowlist.add((chain, pair)) - - for idx in range(steps): - ts = (start + timedelta(minutes=idx)).isoformat() - chain = target_chains[idx % len(target_chains)] - pair = pair_universe[idx % len(pair_universe)] - strategy_id = f"SIM-{chain}-{pair.replace('/', '-')}-{idx:05d}" - pre_id = f"PRE-REC-2026-{idx:08d}" - post_id = f"POST-REC-2026-{idx:08d}" - - chain_row = make_chain_record(ts, chain, pair, idx, rng) - - # Base Simulation Variables - edge_bps = rng.uniform(5.0, 45.0) - notional = min(max(cash * 0.8, 1.0), 12.0) - gross = notional * (edge_bps / 10000.0) - gas = float(cast(float, chain_row["gas_estimate_usd"])) - hai_sigma = rng.uniform(0.0, 2.2) - spread_bps = float(cast(float, chain_row["spread_bps"])) - - # Apply Omega-Level Optimizations (Phase 21-24) - is_omega_mode = bool(policy.get("omega_mode", True)) if policy else True - if is_omega_mode: - # Macro-Crisis Logic: Insolvency multiplier increases baseline gas volatility - crisis_friction = 1.0 + (rng.uniform(0.0, 1.0) * (SOVEREIGN_INSOLVENCY_MULTIPLIER - 1.0)) - gas = gas * (1.0 - NEMS_HMM_GAIN) * crisis_friction - - # Liquidity Collapse: Insolvency increases slippage/spread entropy - if rng.random() < (LIQUIDITY_VANISH_PROB * (SOVEREIGN_INSOLVENCY_MULTIPLIER / 10.0)): - spread_bps = spread_bps * SOVEREIGN_INSOLVENCY_MULTIPLIER - - spread_bps = spread_bps * (1.0 - AAS_PRECISION_FACTOR) - hai_sigma = hai_sigma * (1.0 - TOPOLOGICAL_RESILIENCE) - - pnl = gross - gas - projected_net = pnl - - pre_row = make_pre_record(ts, strategy_id, pre_id, hai_sigma) - - if enforce_allowlist and policy_allowlist and (chain, pair) not in policy_allowlist: - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "POLICY_MOTIF_BLOCK", 0.0, gas, spread_bps) - paused += 1 - elif policy and gas > policy_gates["max_gas_usd"]: - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "POLICY_GAS_GATE", 0.0, gas, spread_bps) - paused += 1 - elif policy and spread_bps > policy_gates["max_slippage_bps"]: - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "POLICY_SLIPPAGE_GATE", 0.0, gas, spread_bps) - paused += 1 - elif policy and projected_net < policy_gates["min_net_pnl_usd"]: - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "POLICY_PNL_GATE", 0.0, gas, spread_bps) - paused += 1 - elif projected_net < 0.0: - # Default-on EV gate: never execute a negative-EV trade regardless of policy - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "NET_NEGATIVE_EV_GATE", 0.0, gas, spread_bps) - paused += 1 - elif edge_bps <= 2.0 or gas > max(0.50, notional * 0.10): - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "PAUSED", "EDGE_OR_GAS_GATE", 0.0, gas, spread_bps) - paused += 1 - else: - cash = max(0.0, cash + pnl) - post_row = make_post_record(ts, strategy_id, pre_id, post_id, "EXECUTED", "SIM_OK", pnl, gas, spread_bps) - executed += 1 - - chain_records.append(chain_row) - pre_records.append(pre_row) - post_records.append(post_row) - - summary: Dict[str, Any] = { - "capital_start_usd": round(capital_usd, 6), - "capital_end_usd": round(cash, 6), - "net_pnl_usd": round(cash - capital_usd, 6), - "steps": steps, - "executed": executed, - "paused": paused, - "target_chains": target_chains, - "pair_universe": pair_universe, - "mode": "paper-simulation", - } - return chain_records, pre_records, post_records, summary - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Low-capital (<$15) passive chain monitor + action simulation.") - parser.add_argument("--capital-usd", type=float, default=15.0, help="Simulation capital cap (USD).") - parser.add_argument("--target-chain", action="append", dest="target_chains", required=True, help="Target chain name. Repeat for multiple chains.") - parser.add_argument("--steps", type=int, default=240, help="Number of simulated decision steps.") - parser.add_argument("--seed", type=int, default=42, help="PRNG seed for reproducibility.") - parser.add_argument("--out-dir", default=str(PROJECT_ROOT / "out" / "micro_cap_sim"), help="Output directory.") - parser.add_argument("--policy-file", help="Optional learned policy JSON to enforce in simulation.") - parser.add_argument( - "--whitelist-config", - default=str(DEFAULT_WHITELIST_CONFIG), - help="Regulatory whitelist config JSON path (enabled symbols map to /).", - ) - parser.add_argument( - "--legal-evidence-registry", - default=str(DEFAULT_LEGAL_EVIDENCE_REGISTRY), - help="Legal evidence registry JSON path.", - ) - parser.add_argument( - "--strict-regulatory-evidence", - action="store_true", - help="Fail closed if enabled whitelist assets are not backed by approved evidence entries.", - ) - parser.add_argument("--quote-asset", default="USDC", help="Quote asset used when building pair universe from whitelist config.") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - if args.capital_usd <= 0: - raise SystemExit("capital must be positive") - if args.capital_usd > 15.0: - raise SystemExit("capital exceeds low-cap policy; must be <= 15 USD") - if args.steps < 1: - raise SystemExit("steps must be >= 1") - - whitelist_path = Path(args.whitelist_config) - if bool(args.strict_regulatory_evidence): - validate_regulatory_evidence(whitelist_path, Path(args.legal_evidence_registry)) - - pair_universe = load_regulatory_pair_universe(whitelist_path, str(args.quote_asset)) - if not pair_universe: - raise SystemExit("pair universe is empty after whitelist processing") - - out_dir = Path(args.out_dir) - chain_records, pre_records, post_records, summary = simulate( - capital_usd=float(args.capital_usd), - target_chains=[c.strip().lower() for c in args.target_chains if c.strip()], - pair_universe=pair_universe, - steps=int(args.steps), - seed=int(args.seed), - policy=load_policy(Path(args.policy_file)) if args.policy_file else None, - ) - - pre_schema = load_schema(PRE_SCHEMA) - post_schema = load_schema(POST_SCHEMA) - chain_schema = load_schema(CHAIN_SCHEMA) - - # for row in chain_records: - # validate(instance=row, schema=chain_schema) - # for row in pre_records: - # validate(instance=row, schema=pre_schema) - # for row in post_records: - # validate(instance=row, schema=post_schema) - - write_jsonl(out_dir / "chain_records.jsonl", chain_records) - write_jsonl(out_dir / "pre_records.jsonl", pre_records) - write_jsonl(out_dir / "post_records.jsonl", post_records) - (out_dir / "simulation_summary.json").write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") - - print(json.dumps({"out_dir": str(out_dir), **summary}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/infrastructure/run_paper_trading_cycle.py b/5-Applications/tools-scripts/infrastructure/run_paper_trading_cycle.py deleted file mode 100644 index 24aa4853..00000000 --- a/5-Applications/tools-scripts/infrastructure/run_paper_trading_cycle.py +++ /dev/null @@ -1,126 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Run paper-trading artifacts against a cycle directory with canonical paths.""" - -import argparse -from datetime import datetime, timezone -from pathlib import Path - -try: - from scripts.paper_trading_simulator import ( - finalize_one_page_summary, - run_fast_monte_carlo_sweep, - run_wall_clock_session, - ) -except ModuleNotFoundError: - import sys - - sys.path.insert(0, str(Path(__file__).resolve().parent)) - from paper_trading_simulator import ( - finalize_one_page_summary, - run_fast_monte_carlo_sweep, - run_wall_clock_session, - ) - - -def summary_filename() -> str: - """Return the default one-page summary filename for the current UTC date.""" - return f"one_page_summary_filled_{datetime.now(timezone.utc).date().isoformat()}.md" - - -def cycle_paths(cycle_dir: Path) -> dict[str, Path]: - """Return canonical artifact locations inside a cycle packet.""" - return { - "fast_sweep_output": cycle_dir / "01_strategy_outputs" / "fast_monte_carlo_sweep.json", - "wall_clock_report": cycle_dir / "01_strategy_outputs" / "wall_clock_session_report.json", - "wall_clock_heartbeat": cycle_dir / "01_strategy_outputs" / "wall_clock_heartbeat.log", - "review_heartbeat": cycle_dir / "01_strategy_outputs" / "review_heartbeat.log", - "summary_output": cycle_dir / "08_summary_report" / summary_filename(), - "reconciliation_ledger": cycle_dir / "07_allocation_ledger" / "reconciliation_ledger.csv", - } - - -def parse_args() -> argparse.Namespace: - """Parse CLI arguments for cycle-oriented paper trading runs.""" - parser = argparse.ArgumentParser(description="Cycle-oriented paper trading runner") - parser.add_argument( - "--mode", - choices=["fast-sweep", "wall-clock", "finalize-summary", "print-paths"], - required=True, - help="Workflow step to run", - ) - parser.add_argument("--cycle-dir", required=True, help="Cycle directory root") - parser.add_argument("--initial-usdc", type=float, default=30000.0, help="Initial USDC capital") - parser.add_argument("--duration-hours", type=float, default=4.0, help="Duration in hours") - parser.add_argument("--tick-interval-seconds", type=float, default=60.0, help="Tick interval in seconds") - parser.add_argument("--heartbeat-every-ticks", type=int, default=5, help="Heartbeat write interval") - parser.add_argument("--sweep-count", type=int, default=25, help="Monte Carlo path count") - parser.add_argument("--seed", type=int, help="Optional random seed") - parser.add_argument("--review-log", help="Optional explicit review heartbeat path") - parser.add_argument("--reviewer", default="system-generated", help="Reviewer label for summary output") - parser.add_argument("--objective-target", type=float, default=30000.0, help="Objective target value") - parser.add_argument("--gross-exit-ceiling", type=float, default=37000.0, help="Gross exit ceiling") - return parser.parse_args() - - -def main() -> None: - """Entry point for canonical cycle runs.""" - args = parse_args() - cycle_dir = Path(args.cycle_dir) - paths = cycle_paths(cycle_dir) - review_log_path = Path(args.review_log) if args.review_log else paths["review_heartbeat"] - - if args.mode == "print-paths": - for key, value in paths.items(): - print(f"{key}={value}") - return - - if args.mode == "fast-sweep": - run_fast_monte_carlo_sweep( - initial_usdc=args.initial_usdc, - duration_hours=args.duration_hours, - tick_interval_seconds=args.tick_interval_seconds, - sweep_count=args.sweep_count, - output_path=paths["fast_sweep_output"], - seed=args.seed, - ) - return - - if args.mode == "wall-clock": - run_wall_clock_session( - initial_usdc=args.initial_usdc, - duration_hours=args.duration_hours, - tick_interval_seconds=args.tick_interval_seconds, - heartbeat_every_ticks=args.heartbeat_every_ticks, - output_path=paths["wall_clock_report"], - heartbeat_path=paths["wall_clock_heartbeat"], - cycle_dir=cycle_dir, - review_log_path=review_log_path if review_log_path.exists() else None, - summary_output_path=paths["summary_output"], - reviewer=args.reviewer, - objective_target=args.objective_target, - gross_exit_ceiling=args.gross_exit_ceiling, - seed=args.seed, - ) - return - - summary_path = finalize_one_page_summary( - cycle_dir=cycle_dir, - session_report_path=paths["wall_clock_report"], - heartbeat_log_path=paths["wall_clock_heartbeat"], - review_log_path=review_log_path if review_log_path.exists() else None, - summary_output_path=paths["summary_output"], - reviewer=args.reviewer, - objective_target=args.objective_target, - gross_exit_ceiling=args.gross_exit_ceiling, - ) - print(f"summary_output={summary_path}") - - -if __name__ == "__main__": - main() \ No newline at end of file diff --git a/5-Applications/tools-scripts/infrastructure/setup_cc0_engine.py b/5-Applications/tools-scripts/infrastructure/setup_cc0_engine.py deleted file mode 100644 index b0a3d694..00000000 --- a/5-Applications/tools-scripts/infrastructure/setup_cc0_engine.py +++ /dev/null @@ -1,33 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import os - -with open("PATENT_APPLICATION/1_KDA_Operational_Whitepaper.tex", "r") as f: - text = f.read() - -text = text.replace("\\date{\\today}", "\\date{\\today}\n\n\\vspace{1em}\n\\textbf{License:} Creative Commons Zero (CC0) 1.0 Universal. This architecture is unconditionally released into the public domain.") -with open("PATENT_APPLICATION/1_KDA_Operational_Whitepaper.tex", "w") as f: - f.write(text) - -with open("PATENT_APPLICATION/2_KDA_Control_Kernel_Source.py", "r") as f: - py = f.read() - -cc0_header = """# KDA SYSTEM CONTROL KERNEL v1.5 -# LICENSE: CC0 1.0 Universal (Public Domain) -# This mathematical architecture cannot be patented or restricted. -""" -py = py.replace("# KDA SYSTEM CONTROL KERNEL v1.5", cc0_header) -with open("PATENT_APPLICATION/2_KDA_Control_Kernel_Source.py", "w") as f: - f.write(py) - -with open("PATENT_APPLICATION/3_KDA_Structural_CAD.scad", "r") as f: - scad = f.read() - -scad = scad.replace("/* KDA_CORE_V1.5 - KINETIC DIFFERENTIAL ARRAY WITH ANC COMPENSATOR */", "/* KDA_CORE_V1.5 - KINETIC DIFFERENTIAL ARRAY WITH ANC COMPENSATOR \n LICENSE: CC0 1.0 Universal - PUBLIC DOMAIN RAW ENGINE */") -with open("PATENT_APPLICATION/3_KDA_Structural_CAD.scad", "w") as f: - f.write(scad) - diff --git a/5-Applications/tools-scripts/infrastructure/sovereign_daemon.py b/5-Applications/tools-scripts/infrastructure/sovereign_daemon.py deleted file mode 100755 index 7f39311a..00000000 --- a/5-Applications/tools-scripts/infrastructure/sovereign_daemon.py +++ /dev/null @@ -1,117 +0,0 @@ -#!/usr/bin/env python3 -""" -Sovereign Daemon - Continuous Background Monitoring --------------------------------------------------- -This daemon drives the Sovereign Pipeline by polling physical hardware -disturbances from the BitFlipHarvester and injecting them into the -Warden manifold. It relies on Sovereign Persistence (Phase 10) to -maintain the manifold state across dispatches. -""" - -import os -import sys -import subprocess -import time -import json -from pathlib import Path - -# Add parent directory to path to find bit_flip_harvester -sys.path.append(str(Path(__file__).parent.parent)) -from bit_flip_harvester import BitFlipHarvester -import fcntl - -def acquire_singleton_lock(): - lock_file = "/home/allaun/.sovereign.lock" - # Ensure file exists - if not os.path.exists(lock_file): - with open(lock_file, "w") as f: - f.write("sovereign-daemon") - - f = open(lock_file, "r") - try: - fcntl.flock(f, fcntl.LOCK_EX | fcntl.LOCK_NB) - print(f"[🛡️ DAEMON] Singleton lock acquired: {f.name}") - return f # Keep file handle open to maintain lock - except BlockingIOError: - print("[🔥 DAEMON] FATAL: Another Sovereign Daemon is already running.") - print("[🛡️ DAEMON] Multi-monitor conflict detected. Aborting dispatch to preserve MMR integrity.") - sys.exit(1) - -def drive_sovereign_machine(): - _lock = acquire_singleton_lock() - harvester = BitFlipHarvester() - warden_path = Path(__file__).parent.parent / "target" / "release" / "sovereign_warden" - - if not warden_path.exists(): - print(f"[🔥 DAEMON] Error: {warden_path} not found. Please run 'cargo build --release'.") - sys.exit(1) - - print("[🛡️ DAEMON] Sovereign Background Monitor Online.") - print(f"[🛡️ DAEMON] Path: {warden_path}") - - while True: - try: - # 1. Harvest physical entropy (EMI, Solar, Thermal noise via Bit Flips) - events = harvester.harvest_all() - stress = len(events) * 0.1 # 0.1 accumulation residue per event - - # --- CHAOS MONKEY HOOK --- - chaos_file = Path(__file__).parent.parent / "chaos_injection.json" - chaos_data = {} - if chaos_file.exists(): - try: - with open(chaos_file, 'r') as f: - chaos_data = json.load(f) - print(f"[🛡️ DAEMON] Adversarial Injection Detected: {chaos_data.get('label', 'UNKNOWN')}") - except Exception: - pass - - # 2. Prepare Environment logic - env = os.environ.copy() - - # Base physical stress - if stress > 0: - print(f"[🛡️ DAEMON] Detected {len(events)} physical events. Baseline Stress={stress:.2f}") - env["WARDEN_ACCUMULATION_INJECTION"] = str(stress) - - # Chaos overrides - if "torsion" in chaos_data: - env["WARDEN_TORSION_OVERRIDE"] = str(chaos_data["torsion"]) - if "accumulation" in chaos_data: - env["WARDEN_ACCUMULATION_INJECTION"] = str(chaos_data["accumulation"]) - if "chi" in chaos_data: - env["WARDEN_CHI_OVERRIDE"] = str(chaos_data["chi"]) - - # 3. Dispatch the Warden - result = subprocess.run( - [str(warden_path)], - env=env, - capture_output=True, - text=True - ) - - # 4. Check for HALT/DMT/BREACH - output = result.stdout + result.stderr - if "WARDEN HALT" in output or "WARDEN BREACH" in output: - reason = "HALT" if "WARDEN HALT" in output else "BREACH" - print(f"[🔥 DAEMON] EMERGENCY {reason} DETECTED! Manifold stability lost.") - print(f"[🛡️ DAEMON] Initiating 30s Thermophysical Cooldown...") - time.sleep(30) - elif "DMT MODE DETECTED" in output: - print("[🛡️ DAEMON] SHORTCUT ACTIVE: Navigation established.") - else: - print("[🛡️ DAEMON] Dispatch successful. Manifold stable.") - - # 5. Calibration Mode: 60s Dispatches - # (Allows persistence to emerge and maintains the NR1 slow-wave identity) - time.sleep(60) - - except KeyboardInterrupt: - print("\n[🛡️ DAEMON] Shutting down gracefully.") - break - except Exception as e: - print(f"[⚠️ DAEMON] Error in cycle: {e}") - time.sleep(5) - -if __name__ == "__main__": - drive_sovereign_machine() diff --git a/5-Applications/tools-scripts/infrastructure/village_gravity_battery.py b/5-Applications/tools-scripts/infrastructure/village_gravity_battery.py deleted file mode 100644 index 263eda1a..00000000 --- a/5-Applications/tools-scripts/infrastructure/village_gravity_battery.py +++ /dev/null @@ -1,93 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Village-Scale Gravity Battery Calculator -Built for the 12-year-old hacker keeping the lights on. - -The Math: Energy (Joules) = Mass (kg) x Gravity (9.81) x Height (meters) x Efficiency -""" - -import argparse - -def calculate_village_battery(mass_kg, height_m, drop_time_minutes, efficiency): - g = 9.81 # Earth's gravity in m/s^2 - - # 1. Total Stored Energy (Joules) - total_energy_joules = mass_kg * g * height_m - - # 2. Usable Energy after friction/motor losses - usable_energy_joules = total_energy_joules * efficiency - - # Convert Joules to Watt-hours (Wh) for everyday electronics (1 Wh = 3600 Joules) - usable_watt_hours = usable_energy_joules / 3600 - - # 3. Power Output (Watts) - # Power is energy divided by time (in seconds) - drop_time_seconds = drop_time_minutes * 60 - power_watts = usable_energy_joules / drop_time_seconds if drop_time_seconds > 0 else 0 - - return { - "energy_joules": total_energy_joules, - "usable_joules": usable_energy_joules, - "usable_wh": usable_watt_hours, - "power_watts": power_watts - } - -def print_hacker_guide(mass, height, minutes, eff, results): - print("\n" + "="*50) - print(" 🛠️ VILLAGE GRAVITY BATTERY CALCULATOR 🛠️") - print("="*50) - print(f"\n[ THE SETUP ]") - print(f" * Hanging Mass : {mass} kg (Like {int(mass/20)} large buckets of water)") - print(f" * Drop Height : {height} meters (Like a {int(height/3)} story building/tree)") - print(f" * Drop Time : {minutes} minutes") - print(f" * Generator Eff.: {int(eff*100)}% (Scrap DC motors lose power to friction/heat)") - - print(f"\n[ THE PHYSICS ]") - print(f" Total Raw Energy : {results['energy_joules']:,.0f} Joules") - print(f" Usable Energy : {results['usable_joules']:,.0f} Joules ({results['usable_wh']:.2f} Watt-Hours)") - print(f" Constant Power : {results['power_watts']:.2f} Watts") - - print(f"\n[ WHAT CAN IT DO WHILE DROPPING? ]") - - # Give practical examples based on the wattage - if results['power_watts'] >= 5.0: - phones = int(results['power_watts'] / 5.0) - print(f" 📱 Slowly charge {phones} smartphone(s)") - else: - print(" 📱 Not enough steady power to charge a smartphone (needs ~5W).") - - leds = int(results['power_watts'] / 0.5) - if leds > 0: - print(f" 💡 Light up {leds} bright LED bulbs (0.5W each)") - else: - print(" 💡 Barely enough for a tiny LED.") - - if results['power_watts'] >= 2.0: - print(" 📻 Power a small emergency shortwave radio!") - - print(f"\n[ HOW TO BUILD IT WITH SCRAP ]") - print(" 1. The Pulley : An old bicycle wheel with the tire removed.") - print(" 2. The Rope : Sturdy climbing rope or braided fishing line.") - print(" 3. The Weight : Sandbags, rocks, or sealed jugs of water.") - print(" 4. The Gears : Use the bike chain to connect the wheel to a smaller gear.") - print(" 5. The Dynamo : An old DC motor (from a broken toy car or power drill).") - print(" Spinning a motor backwards turns it into a generator!") - print("==================================================\n") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="DIY Gravity Battery Math") - parser.add_argument("--mass", type=float, default=200.0, help="Mass in kg (e.g. 200 = 10 buckets of water)") - parser.add_argument("--height", type=float, default=10.0, help="Height in meters") - parser.add_argument("--minutes", type=float, default=30.0, help="How many minutes it takes to drop to the bottom") - parser.add_argument("--eff", type=float, default=0.35, help="System efficiency (scraps are usually 0.20 to 0.40)") - - args = parser.parse_args() - - res = calculate_village_battery(args.mass, args.height, args.minutes, args.eff) - print_hacker_guide(args.mass, args.height, args.minutes, args.eff, res) diff --git a/5-Applications/tools-scripts/infrastructure/village_stabilizer_stress_test.py b/5-Applications/tools-scripts/infrastructure/village_stabilizer_stress_test.py deleted file mode 100644 index 52c5d56b..00000000 --- a/5-Applications/tools-scripts/infrastructure/village_stabilizer_stress_test.py +++ /dev/null @@ -1,124 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import random - -# --- SIMULATION CONFIGURATION --- -SIM_HOURS = 100000 -STEPS_PER_HOUR = 6 # Reduced resolution for 100k runtime -TOTAL_STEPS = SIM_HOURS * STEPS_PER_HOUR - -# --- AGING MODELS --- -COPPER_DRIFT_PER_HOUR = 0.000001 # Resistance increase -DIELECTRIC_WEAR_PER_HOUR = 0.000005 # Signal loss increase -COMPONENT_MTBF_HOURS = 87600 # 10 years - -# --- FLAME GRID CONFIGURATION (Steroid Level) --- -TARGET_VOLTAGE = 220.0 -HELL_NOISE_RAMP = 50.0 # Constant noise floor -COLLAPSE_PROB = 0.0014 # ~1 collapse every 31 days (Nigeria-Spec) -BLACKOUT_DURATION_STEPS = 12 * 60 # 12-hour recovery time -SPIKE_MAX = 480.0 # Fatal surge level - -# --- SOVEREIGN v5-B CHARACTERISTICS --- -ABSORPTION_CAPACITY = 25.0 # Boosted for extreme noise -THERMAL_STRESS_CAPACITY = 5000.0 # High-Tg FR-4 limits -RECYCLING_COEFF = 0.98 # Advanced pyro-recapture - -def run_stress_test(): - print(f"--- STARTING 1000-HOUR EXTREME STRESS TEST: Resonant v5-B ---") - - grid_voltage = TARGET_VOLTAGE - board_thermal_stress = 0.0 - total_spikes_absorbed = 0 - total_brownouts_mitigated = 0 - grid_history = [] - board_status = "HEALTHY" - - cascade_amplitude = 0.0 - cascade_steps_remaining = 0 - - for step in range(TOTAL_STEPS): - # 0. CHECK BLACKOUT & CASCADE STATE - if blackout_steps_remaining > 0: - grid_input = 0.0 - blackout_steps_remaining -= 1 - elif cascade_steps_remaining > 0: - # 1.1 CASCADING WAVE LOGIC - cascade_amplitude *= 1.15 # Exponential build-up - grid_input = TARGET_VOLTAGE + cascade_amplitude - cascade_steps_remaining -= 1 - if grid_input > SPIKE_MAX: cascade_steps_remaining = 0 # Breaker trip - else: - # 1. GENERATE GRID EVENT - event_roll = random.random() - - if event_roll < 0.0005: # Trigger Cascading Wave - cascade_amplitude = random.uniform(20, 50) - cascade_steps_remaining = random.randint(5, 15) - grid_input = TARGET_VOLTAGE + cascade_amplitude - elif event_roll < COLLAPSE_PROB: # Grid Collapse - grid_input = 0.0 - blackout_steps_remaining = BLACKOUT_DURATION_STEPS - elif event_roll < 0.005: # Fatality Spike - grid_input = random.uniform(350, SPIKE_MAX) - elif event_roll < 0.1: # Continuous Dirty Power - grid_input = TARGET_VOLTAGE + random.uniform(-HELL_NOISE_RAMP, HELL_NOISE_RAMP) - else: # "Stable" Noise - grid_input = TARGET_VOLTAGE + random.uniform(-10, 10) - - # 1.5 AGING & DEGRADATION - aging_factor = 1.0 + (step * (COPPER_DRIFT_PER_HOUR / STEPS_PER_HOUR)) - effective_absorption = ABSORPTION_CAPACITY / aging_factor - - # 2. STABILIZER INTERVENTION - delta = grid_input - TARGET_VOLTAGE - - # Hilbert Connectome Absorption (degraded over time) - absorbed = xp.clip(delta, -effective_absorption, effective_absorption) - stabilized_output = grid_input - absorbed - - # 3. THERMAL DYNAMICS - dissipation = abs(absorbed) * 0.1 * aging_factor - board_thermal_stress += (dissipation * (1.0 - RECYCLING_COEFF)) - - # Passive cooling (simulated over time) - board_thermal_stress = max(0, board_thermal_stress - 0.05) - - # 4. MONITORING - if abs(delta) > 40.0: - total_spikes_absorbed += 1 if delta > 0 else 0 - total_brownouts_mitigated += 1 if delta < 0 else 0 - - if board_thermal_stress > THERMAL_STRESS_CAPACITY: - board_status = "CRITICAL (Self-Shutoff)" - break - - if grid_input > BURNOUT_THRESHOLD: - # Physical Shunt (Veto Trace 0x999 triggers) - stabilized_output = 0.0 # Emergency shutdown protected board - - grid_history.append(stabilized_output) - - if step % (STEPS_PER_HOUR * 100) == 0: - print(f"Hour {step//STEPS_PER_HOUR:04d}: Grid Out={stabilized_output:.1f}V | Stress={board_thermal_stress:.2f}") - - # --- FINAL REPORT --- - print("\n" + "="*50) - print(f"FINAL REPORT: 1000 HOURS COMPLETE") - print(f"Status: {board_status}") - print(f"Spikes Absorbed: {total_spikes_absorbed}") - print(f"Brownouts Mitigated: {total_brownouts_mitigated}") - print(f"Avg Grid Deviation: {xp.std(grid_history):.2f}V (RMS)") - print(f"Total Entropy Balanced: {total_spikes_absorbed * 1.25} Logical Units") - print("="*50) - -if __name__ == "__main__": - run_stress_test() diff --git a/5-Applications/tools-scripts/ingested/canonical.py b/5-Applications/tools-scripts/ingested/canonical.py deleted file mode 100644 index 5a55b7c5..00000000 --- a/5-Applications/tools-scripts/ingested/canonical.py +++ /dev/null @@ -1,512 +0,0 @@ -""" -canonical.py - -Concrete canonical adapter module for the Math Universe. - -This module defines a domain-agnostic contract for projecting raw signals into -a shared invariant state space, validating that projection, packing it into an -n-space vector, and assigning that vector to attractors and symbolic signatures. -""" - -from __future__ import annotations - -from dataclasses import dataclass, field -from enum import Enum -from math import acos, isfinite -from typing import Any, Dict, Mapping, Optional, Protocol, Sequence, Tuple, List - - -def clamp(value: float, low: float, high: float) -> float: - """Clamp a numeric value into a closed interval.""" - return max(low, min(high, value)) - - -def safe_div(numerator: float, denominator: float, default: float = 0.0) -> float: - """Divide safely, returning a default value when the denominator is too small.""" - if abs(denominator) < 1e-12: - return default - return numerator / denominator - - -def l2_distance(a: Sequence[float], b: Sequence[float]) -> float: - """Compute Euclidean distance between two vectors of equal length.""" - if len(a) != len(b): - raise ValueError(f"Distance requires equal vector lengths, got {len(a)} and {len(b)}.") - return sum((x - y) ** 2 for x, y in zip(a, b)) ** 0.5 - - -def cosine_similarity(a: Sequence[float], b: Sequence[float]) -> float: - """Compute cosine similarity between two vectors.""" - if len(a) != len(b): - raise ValueError(f"Cosine similarity requires equal vector lengths, got {len(a)} and {len(b)}.") - dot = sum(x * y for x, y in zip(a, b)) - na = sum(x * x for x in a) ** 0.5 - nb = sum(y * y for y in b) ** 0.5 - return clamp(safe_div(dot, na * nb, default=0.0), -1.0, 1.0) - - -class ControlMode(str, Enum): - """Canonical domain-agnostic control modes.""" - COMMIT = "COMMIT" - HOLD = "HOLD" - HALT = "HALT" - DMT = "DMT" - - -class NormalizationMode(str, Enum): - """Supported normalization styles for raw features.""" - MINMAX = "MINMAX" - CENTERED = "CENTERED" - PASSTHROUGH = "PASSTHROUGH" - - -@dataclass(frozen=True) -class FeatureSpec: - """Contract for a single normalized raw feature.""" - name: str - mode: NormalizationMode - low: float = 0.0 - high: float = 1.0 - required: bool = True - - -@dataclass -class CanonicalState: - """Shared invariant state consumed by the core controller.""" - phi: float - delta: float - delta_dot: float - gamma: float - chi: float - tau: float - theta: float = 0.0 - kappa: float = 0.0 - ang_momentum: float = 0.0 - radius_dev: float = 0.0 - confidence: float = 1.0 - domain: str = "unknown" - metadata: Dict[str, Any] = field(default_factory=dict) - - -@dataclass(frozen=True) -class CanonicalVectorSpec: - """Defines which coordinates appear in the packed z_n vector.""" - dimensions: Tuple[str, ...] = ( - "phi", - "delta", - "delta_dot", - "gamma", - "chi", - "tau", - "theta", - "kappa", - "ang_momentum", - "radius_dev", - "confidence", - ) - - -@dataclass(frozen=True) -class Attractor: - """A named reference point in canonical n-space.""" - name: str - center: Tuple[float, ...] - max_radius: Optional[float] = None - - -@dataclass -class AssignmentResult: - """Continuous + discrete assignment result for a canonical state.""" - z_n: Tuple[float, ...] - nearest_attractor: Optional[str] - attractor_distance: Optional[float] - attractor_confidence: float - signature: Tuple[int, ...] - quantized_bands: Dict[str, int] - consistent: bool - notes: Dict[str, Any] = field(default_factory=dict) - - -class RawAdapter(Protocol): - """Protocol for domain-specific adapters.""" - feature_specs: Sequence[FeatureSpec] - domain_name: str - - def to_canonical( - self, - normalized_observation: Mapping[str, float], - normalized_reference: Optional[Mapping[str, float]] = None, - history: Optional[Sequence[CanonicalState]] = None, - ) -> CanonicalState: - ... - - -class NormalizationContract: - """Enforces raw-input normalization before canonical derivation.""" - - def __init__(self, feature_specs: Sequence[FeatureSpec]) -> None: - self.feature_specs: Tuple[FeatureSpec, ...] = tuple(feature_specs) - - def normalize(self, raw: Mapping[str, Any]) -> Dict[str, float]: - """Normalize a raw observation according to the configured feature specs.""" - output: Dict[str, float] = {} - - for spec in self.feature_specs: - if spec.required and spec.name not in raw: - raise KeyError(f"Missing required raw feature: {spec.name}") - if spec.name not in raw: - continue - - raw_value = float(raw[spec.name]) - if not isfinite(raw_value): - raise ValueError(f"Non-finite raw feature '{spec.name}': {raw_value}") - - if spec.mode == NormalizationMode.MINMAX: - scaled = safe_div(raw_value - spec.low, spec.high - spec.low, default=0.0) - output[spec.name] = clamp(scaled, 0.0, 1.0) - elif spec.mode == NormalizationMode.CENTERED: - center = (spec.high + spec.low) / 2.0 - half_span = max((spec.high - spec.low) / 2.0, 1e-12) - scaled = safe_div(raw_value - center, half_span, default=0.0) - output[spec.name] = clamp(scaled, -1.0, 1.0) - elif spec.mode == NormalizationMode.PASSTHROUGH: - output[spec.name] = raw_value - else: - raise ValueError(f"Unsupported normalization mode: {spec.mode}") - - return output - - -class InvariantChecker: - """Validates canonical states and assignment consistency.""" - - def __init__(self, phi_bounds: Tuple[float, float] = (-1.0, 1.0)) -> None: - self.phi_bounds = phi_bounds - - def validate_state(self, state: CanonicalState) -> List[str]: - """Validate a canonical state and return a list of issues.""" - issues: List[str] = [] - - values = { - "phi": state.phi, - "delta": state.delta, - "delta_dot": state.delta_dot, - "gamma": state.gamma, - "chi": state.chi, - "tau": state.tau, - "theta": state.theta, - "kappa": state.kappa, - "ang_momentum": state.ang_momentum, - "radius_dev": state.radius_dev, - "confidence": state.confidence, - } - - for name, value in values.items(): - if not isfinite(value): - issues.append(f"{name} is non-finite: {value}") - - if not (self.phi_bounds[0] <= state.phi <= self.phi_bounds[1]): - issues.append(f"phi out of bounds: {state.phi}") - if state.delta < 0.0: - issues.append(f"delta must be non-negative, got {state.delta}") - if not (0.0 <= state.chi <= 1.0): - issues.append(f"chi must be in [0, 1], got {state.chi}") - if not (0.0 <= state.confidence <= 1.0): - issues.append(f"confidence must be in [0, 1], got {state.confidence}") - - expected_theta = acos(clamp(state.phi, -1.0, 1.0)) - if abs(state.theta - expected_theta) > 0.25: - issues.append( - f"theta appears inconsistent with phi: theta={state.theta:.4f}, expected≈{expected_theta:.4f}" - ) - - return issues - - def assert_valid_state(self, state: CanonicalState) -> None: - """Raise an exception if a state fails invariant checks.""" - issues = self.validate_state(state) - if issues: - raise ValueError("Invalid CanonicalState:\n- " + "\n- ".join(issues)) - - def validate_assignment(self, result: AssignmentResult) -> List[str]: - """Validate an assignment result and return any issues found.""" - issues: List[str] = [] - - if len(result.z_n) == 0: - issues.append("z_n is empty") - if len(result.signature) != len(result.quantized_bands): - issues.append("signature length does not match quantized band count") - if not (0.0 <= result.attractor_confidence <= 1.0): - issues.append(f"attractor_confidence out of range: {result.attractor_confidence}") - - return issues - - -class ZNPacker: - """Packs a canonical state into a stable n-dimensional vector.""" - - def __init__(self, spec: Optional[CanonicalVectorSpec] = None) -> None: - self.spec = spec or CanonicalVectorSpec() - - def pack(self, state: CanonicalState) -> Tuple[float, ...]: - """Pack a state into an ordered tuple according to the configured vector spec.""" - vector: List[float] = [] - for dim in self.spec.dimensions: - if not hasattr(state, dim): - raise AttributeError(f"CanonicalState has no attribute '{dim}' required by packer.") - vector.append(float(getattr(state, dim))) - return tuple(vector) - - -class AssignmentEngine: - """Assigns a canonical vector to attractors and discrete structural signatures.""" - - def __init__( - self, - vector_spec: Optional[CanonicalVectorSpec] = None, - attractors: Optional[Sequence[Attractor]] = None, - quantization_bands: Optional[Mapping[str, Tuple[float, float, float]]] = None, - ) -> None: - self.vector_spec = vector_spec or CanonicalVectorSpec() - self.attractors: Tuple[Attractor, ...] = tuple(attractors or ()) - self.quantization_bands: Dict[str, Tuple[float, float, float]] = dict(quantization_bands or {}) - - def _nearest_attractor(self, z_n: Sequence[float]) -> Tuple[Optional[str], Optional[float], float, Dict[str, Any]]: - """Find the nearest attractor and derive a confidence score.""" - if not self.attractors: - return None, None, 0.0, {"reason": "no_attractors_configured"} - - distances: List[Tuple[Attractor, float]] = [] - for attractor in self.attractors: - if len(attractor.center) != len(z_n): - raise ValueError( - f"Attractor '{attractor.name}' has dimension {len(attractor.center)} but z_n has dimension {len(z_n)}." - ) - distances.append((attractor, l2_distance(z_n, attractor.center))) - - best_attractor, best_distance = min(distances, key=lambda item: item[1]) - - if best_attractor.max_radius is not None and best_attractor.max_radius > 0.0: - confidence = clamp(1.0 - (best_distance / best_attractor.max_radius), 0.0, 1.0) - notes = {"radius_based": True, "max_radius": best_attractor.max_radius} - else: - confidence = safe_div(1.0, 1.0 + best_distance, default=0.0) - notes = {"radius_based": False} - - return best_attractor.name, best_distance, confidence, notes - - def _quantize_dimension(self, dim_name: str, value: float) -> int: - """Quantize a single dimension into one of four bands.""" - t0, t1, t2 = self.quantization_bands.get(dim_name, (0.25, 0.50, 0.75)) - if value < t0: - return 0 - if value < t1: - return 1 - if value < t2: - return 2 - return 3 - - def assign(self, z_n: Sequence[float]) -> AssignmentResult: - """Assign a packed vector to both continuous and discrete representations.""" - nearest_name, nearest_distance, attractor_conf, attractor_notes = self._nearest_attractor(z_n) - - quantized_bands: Dict[str, int] = {} - signature: List[int] = [] - for dim_name, value in zip(self.vector_spec.dimensions, z_n): - band = self._quantize_dimension(dim_name, float(value)) - quantized_bands[dim_name] = band - signature.append(band) - - low_band_ratio = safe_div(sum(1 for s in signature if s == 0), len(signature), default=0.0) - consistent = not (attractor_conf < 0.15 and low_band_ratio > 0.75) - - notes = dict(attractor_notes) - notes["low_band_ratio"] = low_band_ratio - notes["consistency_rule"] = "flag if attractor_conf < 0.15 and >75% of bands are zero" - - return AssignmentResult( - z_n=tuple(float(v) for v in z_n), - nearest_attractor=nearest_name, - attractor_distance=nearest_distance, - attractor_confidence=attractor_conf, - signature=tuple(signature), - quantized_bands=quantized_bands, - consistent=consistent, - notes=notes, - ) - - -class CanonicalPipeline: - """End-to-end helper for normalization, invariant checking, packing, and assignment.""" - - def __init__( - self, - adapter: RawAdapter, - checker: Optional[InvariantChecker] = None, - packer: Optional[ZNPacker] = None, - assignment_engine: Optional[AssignmentEngine] = None, - ) -> None: - self.adapter = adapter - self.contract = NormalizationContract(adapter.feature_specs) - self.checker = checker or InvariantChecker() - self.packer = packer or ZNPacker() - self.assignment_engine = assignment_engine or AssignmentEngine(vector_spec=self.packer.spec) - - def process( - self, - raw_observation: Mapping[str, Any], - raw_reference: Optional[Mapping[str, Any]] = None, - history: Optional[Sequence[CanonicalState]] = None, - strict: bool = True, - ) -> Tuple[CanonicalState, AssignmentResult]: - """Run the full canonical processing sequence.""" - normalized_obs = self.contract.normalize(raw_observation) - normalized_ref = self.contract.normalize(raw_reference) if raw_reference is not None else None - - state = self.adapter.to_canonical( - normalized_observation=normalized_obs, - normalized_reference=normalized_ref, - history=history, - ) - - state_issues = self.checker.validate_state(state) - if strict and state_issues: - raise ValueError("Canonical state failed invariant checks:\n- " + "\n- ".join(state_issues)) - - z_n = self.packer.pack(state) - assignment = self.assignment_engine.assign(z_n) - - assignment_issues = self.checker.validate_assignment(assignment) - if strict and assignment_issues: - raise ValueError("Assignment failed invariant checks:\n- " + "\n- ".join(assignment_issues)) - - if state_issues or assignment_issues: - assignment.notes["state_issues"] = state_issues - assignment.notes["assignment_issues"] = assignment_issues - - return state, assignment - - -class GenericSimilarityAdapter: - """A simple reference adapter showing how a domain can plug into the pipeline.""" - - domain_name = "generic" - - feature_specs: Sequence[FeatureSpec] = ( - FeatureSpec("f1", NormalizationMode.MINMAX, 0.0, 1.0), - FeatureSpec("f2", NormalizationMode.MINMAX, 0.0, 1.0), - FeatureSpec("f3", NormalizationMode.MINMAX, 0.0, 1.0), - FeatureSpec("f4", NormalizationMode.MINMAX, 0.0, 1.0), - ) - - def to_canonical( - self, - normalized_observation: Mapping[str, float], - normalized_reference: Optional[Mapping[str, float]] = None, - history: Optional[Sequence[CanonicalState]] = None, - ) -> CanonicalState: - """Derive a canonical state using generic formulas.""" - keys = [spec.name for spec in self.feature_specs] - obs_vec = [float(normalized_observation[k]) for k in keys] - - if normalized_reference is None: - ref_vec = [0.5 for _ in keys] - else: - ref_vec = [float(normalized_reference[k]) for k in keys] - - phi = cosine_similarity(obs_vec, ref_vec) - delta = l2_distance(obs_vec, ref_vec) - - prev_delta = history[-1].delta if history else delta - prev_delta_dot = history[-1].delta_dot if history else 0.0 - - delta_dot = delta - prev_delta - gamma = delta_dot - prev_delta_dot - - mean_abs = safe_div(sum(abs(v) for v in obs_vec), len(obs_vec), default=0.0) - peak_abs = max(abs(v) for v in obs_vec) if obs_vec else 0.0 - chi = clamp(safe_div(peak_abs, peak_abs + mean_abs + 1e-12, default=0.0), 0.0, 1.0) - - tau = abs(gamma) + (1.0 - ((phi + 1.0) / 2.0)) + delta - - theta = acos(clamp(phi, -1.0, 1.0)) - kappa = abs(gamma) - ang_momentum = abs(delta_dot) * kappa - radius_dev = delta - - return CanonicalState( - phi=phi, - delta=delta, - delta_dot=delta_dot, - gamma=gamma, - chi=chi, - tau=tau, - theta=theta, - kappa=kappa, - ang_momentum=ang_momentum, - radius_dev=radius_dev, - confidence=clamp((phi + 1.0) / 2.0, 0.0, 1.0), - domain=self.domain_name, - metadata={"obs_vec": obs_vec, "ref_vec": ref_vec}, - ) - - -if __name__ == "__main__": - adapter = GenericSimilarityAdapter() - packer = ZNPacker( - CanonicalVectorSpec( - dimensions=( - "phi", - "delta", - "delta_dot", - "gamma", - "chi", - "tau", - "theta", - "kappa", - "ang_momentum", - ) - ) - ) - - attractors = [ - Attractor( - name="stable_core", - center=(1.0, 0.0, 0.0, 0.0, 0.6, 0.1, 0.0, 0.0, 0.0), - max_radius=2.0, - ), - Attractor( - name="stress_front", - center=(0.0, 0.8, 0.4, 0.5, 0.4, 1.2, 1.2, 0.5, 0.6), - max_radius=2.5, - ), - ] - - engine = AssignmentEngine( - vector_spec=packer.spec, - attractors=attractors, - quantization_bands={ - "phi": (-0.25, 0.25, 0.75), - "delta": (0.10, 0.30, 0.60), - "delta_dot": (-0.10, 0.10, 0.30), - "gamma": (-0.10, 0.10, 0.30), - "chi": (0.25, 0.50, 0.75), - "tau": (0.20, 0.50, 0.90), - "theta": (0.30, 0.80, 1.30), - "kappa": (0.05, 0.20, 0.50), - "ang_momentum": (0.05, 0.20, 0.50), - }, - ) - - pipeline = CanonicalPipeline(adapter=adapter, packer=packer, assignment_engine=engine) - - raw_obs = {"f1": 0.90, "f2": 0.20, "f3": 0.70, "f4": 0.10} - raw_ref = {"f1": 0.80, "f2": 0.20, "f3": 0.75, "f4": 0.15} - - state, assignment = pipeline.process(raw_observation=raw_obs, raw_reference=raw_ref, strict=True) - - print("CanonicalState:") - print(state) - print() - print("AssignmentResult:") - print(assignment) diff --git a/5-Applications/tools-scripts/ingested/compression_adapter.py b/5-Applications/tools-scripts/ingested/compression_adapter.py deleted file mode 100644 index ee668edb..00000000 --- a/5-Applications/tools-scripts/ingested/compression_adapter.py +++ /dev/null @@ -1,80 +0,0 @@ -"""Example real adapter: compression / prediction universe. -Maps predicted vs actual coding cost into bounded coordinates. -""" -from __future__ import annotations -from typing import Mapping, Sequence -from pbacs_core import Adapter, ControlState, StepTrace - - -class CompressionAdapter(Adapter): - def __init__(self) -> None: - self._modes = ("BYPASS", "DELTA", "RICH") - - def initial_state(self): - # x = [internal expected coding regime] - return [0.5] - - def modes(self): - return self._modes - - def target_state(self, raw: Mapping[str, float], history: Sequence[StepTrace]): - # External target: actual coding burden normalized into [0,1] - actual = max(0.0, min(1.0, raw["actual_bpb"])) - return [actual] - - def update_projection_context(self, x_t, z_t, raw: Mapping[str, float], history: Sequence[StepTrace]): - psi = max(0.0, min(1.0, x_t[0])) - phi = max(0.0, min(1.0, z_t[0])) - predicted = max(0.0, min(1.0, raw["predicted_bpb"])) - actual = phi - # prediction mismatch and state lag - pred_err = abs(actual - predicted) - delta = abs(phi - psi) - prev_delta = history[-1].projections["u_delta"] if history else 0.0 - delta_dot = max(0.0, delta - prev_delta) - prev_phi = history[-1].z_t[0] if history else phi - prev2_phi = history[-2].z_t[0] if len(history) >= 2 else prev_phi - gamma = abs(phi - 2.0 * prev_phi + prev2_phi) - tau = min(1.0, 0.65 * pred_err + 0.35 * gamma) - # productively structured disorder: better when redundancy is high and instability is low - redundancy = max(0.0, min(1.0, raw["redundancy"])) - chi = max(0.0, min(1.0, redundancy * (1.0 - tau))) - gain = max(0.0, min(1.0, raw["compression_gain"])) - cost = max(0.0, min(1.0, 0.5 * raw["latency_cost"] + 0.5 * pred_err)) - bias = max(0.0, min(1.0, raw["model_reliability"])) - phi_margin = max(0.0, min(1.0, 0.5 * (1.0 - tau) + 0.3 * bias + 0.2 * gain)) - return { - "u_phi": phi_margin, - "u_delta": delta, - "u_delta_dot": delta_dot, - "u_gamma": max(0.0, min(1.0, gamma)), - "u_tau": tau, - "u_chi": chi, - "u_gain": gain, - "u_cost": cost, - "u_bias": bias, - "u_pacing": max(delta, pred_err), - } - - def projections(self): - return { - "u_phi": lambda c: c["u_phi"], - "u_delta": lambda c: c["u_delta"], - "u_delta_dot": lambda c: c["u_delta_dot"], - "u_gamma": lambda c: c["u_gamma"], - "u_tau": lambda c: c["u_tau"], - "u_chi": lambda c: c["u_chi"], - "u_gain": lambda c: c["u_gain"], - "u_cost": lambda c: c["u_cost"], - "u_bias": lambda c: c["u_bias"], - "u_pacing": lambda c: c["u_pacing"], - } - - def admissible(self, state: ControlState): - if state == ControlState.HALT: - return (("HALT", "BYPASS"),) - if state == ControlState.HOLD: - return (("HOLD", "DELTA"), ("HOLD", "BYPASS")) - if state == ControlState.DMT: - return (("DMT", "RICH"),) - return (("COMMIT", "BYPASS"), ("COMMIT", "DELTA"), ("COMMIT", "RICH")) diff --git a/5-Applications/tools-scripts/ingested/geometry_plugin_v2.py b/5-Applications/tools-scripts/ingested/geometry_plugin_v2.py deleted file mode 100644 index d62f8192..00000000 --- a/5-Applications/tools-scripts/ingested/geometry_plugin_v2.py +++ /dev/null @@ -1,242 +0,0 @@ -# geometry_plugin_v2.py -import math -from dataclasses import dataclass -from typing import Any, Dict, List, Optional, Sequence, Tuple - - -def clamp(x: float, lo: float, hi: float) -> float: - return max(lo, min(hi, x)) - - -def safe_mean(values: Sequence[float]) -> float: - return sum(values) / len(values) if values else 0.0 - - -def norm(values: Sequence[float]) -> float: - return math.sqrt(sum(v * v for v in values)) if values else 0.0 - - -def gradient(values: Sequence[float]) -> List[float]: - if len(values) < 2: - return [0.0] - return [values[i + 1] - values[i] for i in range(len(values) - 1)] - - -def to_u8_clamped(x: float) -> int: - return int(clamp(round(x), 0, 255)) - - -@dataclass -class GeometricTelemetry: - z: List[float] - angular_drift: float - curvature: float - radius_dev: float - coherence: float - angular_momentum: float - speed: float - turn_angle: float - - def as_dict(self) -> Dict[str, float]: - return { - "angular_drift": self.angular_drift, - "curvature": self.curvature, - "radius_dev": self.radius_dev, - "coherence": self.coherence, - "angular_momentum": self.angular_momentum, - "speed": self.speed, - "turn_angle": self.turn_angle, - } - - -class GeometricBridgePlugin: - """ - Geometry-aware middle step for manifold preprocessing. - - Produces five descriptors: - 1. angular_drift - 2. curvature - 3. radius_dev - 4. coherence - 5. angular_momentum - """ - - def __init__(self) -> None: - self.prev_z: Optional[List[float]] = None - self.prev_dz: Optional[List[float]] = None - - def _build_state_vector(self, manifold: Dict[str, Any]) -> List[float]: - phi = float(manifold.get("phi_corr", 0.0)) - tau = [float(x) for x in manifold.get("torsion_gradient", [])] - radius = float(manifold.get("radius", 1.0)) - - tau_mean = safe_mean(tau[:32]) - tau_energy = norm(tau[:32]) / max(1, min(32, len(tau[:32]))) - - return [phi, tau_mean, tau_energy, radius] - - def _compute_angular_momentum(self, z: List[float]) -> Tuple[float, float, float]: - if self.prev_z is None: - self.prev_z = list(z) - return 0.0, 0.0, 0.0 - - dz = [z[i] - self.prev_z[i] for i in range(len(z))] - speed = norm(dz) - - if self.prev_dz is None: - self.prev_dz = list(dz) - self.prev_z = list(z) - return 0.0, speed, 0.0 - - dot = sum(dz[i] * self.prev_dz[i] for i in range(len(dz))) - denom = (norm(dz) * norm(self.prev_dz)) + 1e-9 - cos_theta = clamp(dot / denom, -1.0, 1.0) - turn_angle = math.acos(cos_theta) - - radius = norm(z) - angular_momentum = radius * speed * math.sin(turn_angle) - - self.prev_dz = list(dz) - self.prev_z = list(z) - - return angular_momentum, speed, turn_angle - - def transform(self, manifold: Dict[str, Any]) -> Dict[str, Any]: - phi = float(manifold.get("phi_corr", 0.0)) - tau = [float(x) for x in manifold.get("torsion_gradient", [])] - radius = float(manifold.get("radius", 1.0)) - - phi_clamped = clamp(phi, -1.0, 1.0) - angular_drift = math.acos(phi_clamped) - - tau_grad = gradient(tau[:32]) - curvature = norm(tau_grad) - - radius_dev = abs(radius - 1.0) - coherence = 1.0 / (1.0 + angular_drift * curvature) - - z = self._build_state_vector(manifold) - angular_momentum, speed, turn_angle = self._compute_angular_momentum(z) - - telemetry = GeometricTelemetry( - z=z, - angular_drift=angular_drift, - curvature=curvature, - radius_dev=radius_dev, - coherence=coherence, - angular_momentum=angular_momentum, - speed=speed, - turn_angle=turn_angle, - ) - - return { - "geometry_features": [ - angular_drift, - curvature, - radius_dev, - coherence, - angular_momentum, - ], - "geometry_debug": telemetry.as_dict(), - "state_vector": z, - } - - -class GeometryAdapter: - """ - Encodes continuous geometric descriptors into clamped byte inputs. - """ - - def encode(self, features: Sequence[float], nodes_count: Optional[int] = None) -> List[int]: - padded = list(features) - - if nodes_count is not None: - if len(padded) < nodes_count: - padded.extend([0.0] * (nodes_count - len(padded))) - else: - padded = padded[:nodes_count] - - scales = [80.0, 50.0, 255.0, 255.0, 120.0] - encoded: List[int] = [] - - for i, value in enumerate(padded): - scale = scales[i] if i < len(scales) else 64.0 - encoded.append(to_u8_clamped(value * scale)) - - return encoded - - -class GeometryPluginMixin: - """ - Mixin for a CacheSieve-like object with: - - self.nodes - - self.phis - - node.process(input_u8, phi_u8) -> state int - """ - - def attach_geometry_plugin(self, plugin: Optional[GeometricBridgePlugin] = None) -> None: - self._geometry_plugin = plugin or GeometricBridgePlugin() - self._geometry_adapter = GeometryAdapter() - - def triage_bucket_with_geometry(self, manifold: Dict[str, Any]): - if not hasattr(self, "_geometry_plugin"): - raise RuntimeError("Geometry plugin not attached. Call attach_geometry_plugin() first.") - - geom = self._geometry_plugin.transform(manifold) - features = geom["geometry_features"] - - nodes_count = len(self.nodes) - inputs = self._geometry_adapter.encode(features, nodes_count=nodes_count) - - states: List[int] = [] - for node, input_u8, phi_u8 in zip(self.nodes, inputs, self.phis): - states.append(int(node.process(input_u8, phi_u8))) - - if not states: - return False, 0.0, { - **geom, - "encoded_inputs": inputs, - "node_states": [], - "survival_reason": "no_nodes", - } - - worst_state = max(states) - mean_state = safe_mean(states) - - should_survive = worst_state < 2 - score = 1.0 - (mean_state / 3.0) - - telemetry = { - **geom, - "encoded_inputs": inputs, - "node_states": states, - "worst_state": worst_state, - "mean_state": mean_state, - "survival_reason": "ok" if should_survive else "unstable_or_reset", - } - - return should_survive, score, telemetry - - -def apply_geometry_plugin(manifold: Dict[str, Any]) -> Dict[str, Any]: - return GeometricBridgePlugin().transform(manifold) - - -class PluginRegistry: - def __init__(self) -> None: - self._plugins: Dict[str, Any] = {} - - def register(self, name: str, plugin: Any) -> None: - self._plugins[name] = plugin - - def get(self, name: str) -> Any: - return self._plugins.get(name) - - -if __name__ == "__main__": - demo = { - "phi_corr": 0.72, - "torsion_gradient": [0.10, 0.13, 0.18, 0.16, 0.20], - "radius": 1.07, - } - print(apply_geometry_plugin(demo)) diff --git a/5-Applications/tools-scripts/ingested/market_adapter.py b/5-Applications/tools-scripts/ingested/market_adapter.py deleted file mode 100644 index a48f624d..00000000 --- a/5-Applications/tools-scripts/ingested/market_adapter.py +++ /dev/null @@ -1,76 +0,0 @@ -"""Example real adapter: market watcher universe. -Maps a small OHLCV-like feature stream into bounded coordinates. -""" -from __future__ import annotations -from typing import Dict, Mapping, Sequence, Tuple -from pbacs_core import Adapter, ControlState, StepTrace - - -class MarketAdapter(Adapter): - def __init__(self) -> None: - self._modes = ("OBSERVE", "DEFENSIVE", "AGGRESSIVE") - - def initial_state(self): - # x = [internal regime alignment] - return [0.5] - - def modes(self): - return self._modes - - def target_state(self, raw: Mapping[str, float], history: Sequence[StepTrace]): - # External regime target: scaled trend impulse in [0,1] - price_move = raw["return_1"] - vol = raw["volatility"] - z = 0.5 + 0.5 * max(-1.0, min(1.0, price_move / max(1e-9, vol + 1e-9))) - return [max(0.0, min(1.0, z))] - - def update_projection_context(self, x_t, z_t, raw: Mapping[str, float], history: Sequence[StepTrace]): - psi = x_t[0] - phi = z_t[0] - delta = abs(phi - psi) - prev_delta = history[-1].projections["u_delta"] if history else 0.0 - delta_dot = max(0.0, delta - prev_delta) - prev_phi = history[-1].z_t[0] if history else phi - prev2_phi = history[-2].z_t[0] if len(history) >= 2 else prev_phi - gamma = abs(phi - 2.0 * prev_phi + prev2_phi) - tau = min(1.0, 0.5 * delta + 0.5 * gamma) - chi = raw["volume_imbalance"] * (1.0 - raw["spread"]) - gain = raw["signal_strength"] - cost = 0.5 * raw["spread"] + 0.5 * raw["volatility"] - bias = raw["historical_reliability"] - phi_margin = max(0.0, min(1.0, (0.6 * (1.0 - tau) + 0.4 * bias))) - return { - "u_phi": phi_margin, - "u_delta": delta, - "u_delta_dot": delta_dot, - "u_gamma": max(0.0, min(1.0, gamma)), - "u_tau": tau, - "u_chi": max(0.0, min(1.0, chi)), - "u_gain": max(0.0, min(1.0, gain)), - "u_cost": max(0.0, min(1.0, cost)), - "u_bias": max(0.0, min(1.0, bias)), - "u_pacing": delta, - } - - def projections(self): - return { - "u_phi": lambda c: c["u_phi"], - "u_delta": lambda c: c["u_delta"], - "u_delta_dot": lambda c: c["u_delta_dot"], - "u_gamma": lambda c: c["u_gamma"], - "u_tau": lambda c: c["u_tau"], - "u_chi": lambda c: c["u_chi"], - "u_gain": lambda c: c["u_gain"], - "u_cost": lambda c: c["u_cost"], - "u_bias": lambda c: c["u_bias"], - "u_pacing": lambda c: c["u_pacing"], - } - - def admissible(self, state: ControlState): - if state == ControlState.HALT: - return (("HALT", "OBSERVE"),) - if state == ControlState.HOLD: - return (("HOLD", "DEFENSIVE"), ("HOLD", "OBSERVE")) - if state == ControlState.DMT: - return (("DMT", "DEFENSIVE"),) - return (("COMMIT", "OBSERVE"), ("COMMIT", "DEFENSIVE"), ("COMMIT", "AGGRESSIVE")) diff --git a/5-Applications/tools-scripts/ingested/pbacs_core.py b/5-Applications/tools-scripts/ingested/pbacs_core.py deleted file mode 100644 index 4ea90970..00000000 --- a/5-Applications/tools-scripts/ingested/pbacs_core.py +++ /dev/null @@ -1,184 +0,0 @@ -"""PBACS Root Spec v1 - minimal reference implementation. -Domain-agnostic core runtime with hysteretic gate, projection family, -and stable convex update law. -""" -from __future__ import annotations -from dataclasses import dataclass, field -from enum import Enum -from typing import Callable, Dict, Iterable, List, Mapping, MutableMapping, Sequence, Tuple - -Scalar = float -State = Sequence[Scalar] -Mode = str -Action = str - - -class ControlState(str, Enum): - COMMIT = "COMMIT" - HOLD = "HOLD" - HALT = "HALT" - DMT = "DMT" - - -@dataclass(frozen=True) -class RootConfig: - weights: Mapping[str, Scalar] - polarities: Mapping[str, int] - entry_thresholds: Mapping[str, Scalar] - exit_thresholds: Mapping[str, Scalar] - engram_length_min_ms: Scalar = 500.0 - engram_length_max_ms: Scalar = 700.0 - alpha0: Scalar = 0.25 - beta: Scalar = 0.0 - - def validate(self) -> None: - if not self.weights: - raise ValueError("weights must be non-empty") - total = sum(self.weights.values()) - if abs(total - 1.0) > 1e-9: - raise ValueError(f"weights must sum to 1.0, got {total}") - for k, w in self.weights.items(): - if w < 0: - raise ValueError(f"weight {k} must be non-negative") - for k, p in self.polarities.items(): - if p not in (-1, 1): - raise ValueError(f"polarity {k} must be -1 or 1") - for name in self.entry_thresholds: - lo = self.exit_thresholds.get(name) - hi = self.entry_thresholds[name] - if lo is None or not (0.0 <= lo < hi <= 1.0): - raise ValueError(f"invalid hysteresis band for {name}") - if not (0.0 <= self.alpha0 <= 1.0): - raise ValueError("alpha0 must be in [0,1]") - if self.engram_length_min_ms > self.engram_length_max_ms: - raise ValueError("invalid engramLength bounds") - - -@dataclass -class StepTrace: - t: int - raw: Mapping[str, Scalar] - x_t: List[Scalar] - z_t: List[Scalar] - projections: Dict[str, Scalar] - score: Scalar - control_state: ControlState - action: Action - mode: Mode - alpha_t: Scalar - engram_length_ms: Scalar - x_next: List[Scalar] - - -class Adapter: - """Universe Adapter Spec v1 interface.""" - - def initial_state(self) -> List[Scalar]: - raise NotImplementedError - - def modes(self) -> Sequence[Mode]: - raise NotImplementedError - - def target_state(self, raw: Mapping[str, Scalar], history: Sequence[StepTrace]) -> List[Scalar]: - raise NotImplementedError - - def update_projection_context( - self, x_t: Sequence[Scalar], z_t: Sequence[Scalar], raw: Mapping[str, Scalar], history: Sequence[StepTrace] - ) -> Mapping[str, Scalar]: - raise NotImplementedError - - def projections(self) -> Mapping[str, Callable[[Mapping[str, Scalar]], Scalar]]: - raise NotImplementedError - - def admissible(self, state: ControlState) -> Sequence[Tuple[Action, Mode]]: - raise NotImplementedError - - def tie_break(self, candidates: Sequence[Tuple[Action, Mode]]) -> Tuple[Action, Mode]: - return sorted(candidates)[0] - - -class PBACS: - def __init__(self, config: RootConfig, adapter: Adapter): - config.validate() - self.cfg = config - self.adapter = adapter - self.history: List[StepTrace] = [] - self.x_t = list(adapter.initial_state()) - self.state = ControlState.COMMIT - - @staticmethod - def _clamp01(x: Scalar) -> Scalar: - return min(1.0, max(0.0, float(x))) - - def _project(self, context: Mapping[str, Scalar]) -> Dict[str, Scalar]: - out: Dict[str, Scalar] = {} - for name, fn in self.adapter.projections().items(): - out[name] = self._clamp01(fn(context)) - return out - - def _score(self, projections: Mapping[str, Scalar]) -> Scalar: - total = 0.0 - for name, weight in self.cfg.weights.items(): - p = self.cfg.polarities.get(name, 1) - total += p * weight * projections[name] - return total - - def _next_control_state(self, p: Mapping[str, Scalar]) -> ControlState: - enter = self.cfg.entry_thresholds - leave = self.cfg.exit_thresholds - # Priority order for entry. - if p.get("u_tau", 0.0) >= enter["halt_tau"]: - return ControlState.HALT - if p.get("u_chi", 0.0) * p.get("u_gamma", 0.0) >= enter["dmt_product"]: - return ControlState.DMT - if p.get("u_delta_dot", 0.0) >= enter["hold_delta_dot"] and p.get("u_delta", 0.0) >= enter["hold_delta"]: - return ControlState.HOLD - # Hysteretic exit conditions. - if self.state == ControlState.HALT and p.get("u_tau", 0.0) > leave["halt_tau"]: - return ControlState.HALT - if self.state == ControlState.DMT and p.get("u_chi", 0.0) * p.get("u_gamma", 0.0) > leave["dmt_product"]: - return ControlState.DMT - if self.state == ControlState.HOLD and ( - p.get("u_delta_dot", 0.0) > leave["hold_delta_dot"] and p.get("u_delta", 0.0) > leave["hold_delta"] - ): - return ControlState.HOLD - return ControlState.COMMIT - - def _engram_length_ms(self, p: Mapping[str, Scalar]) -> Scalar: - r_t = self._clamp01(p.get("u_pacing", p.get("u_delta", 0.0))) - return self.cfg.engram_length_min_ms + (self.cfg.engram_length_max_ms - self.cfg.engram_length_min_ms) * r_t - - def _alpha(self, engram_length_ms: Scalar) -> Scalar: - raw = self.cfg.alpha0 / (1.0 + self.cfg.beta * engram_length_ms) - return self._clamp01(raw) - - def _update(self, z_t: Sequence[Scalar], alpha_t: Scalar) -> List[Scalar]: - if len(self.x_t) != len(z_t): - raise ValueError("state and target dimensions must match") - x_next = [] - for x_i, z_i in zip(self.x_t, z_t): - x_next.append(self._clamp01((1.0 - alpha_t) * x_i + alpha_t * z_i)) - return x_next - - def step(self, raw: Mapping[str, Scalar]) -> StepTrace: - z_t = list(self.adapter.target_state(raw, self.history)) - context = dict(self.adapter.update_projection_context(self.x_t, z_t, raw, self.history)) - projections = self._project(context) - self.state = self._next_control_state(projections) - admissible = list(self.adapter.admissible(self.state)) - if not admissible: - raise ValueError(f"no admissible actions for state {self.state}") - # Domain-agnostic: same score for all admissible candidates unless adapter encodes mode-aware context. - score = self._score(projections) - best = self.adapter.tie_break(admissible) - engram_length_ms = self._engram_length_ms(projections) - alpha_t = self._alpha(engram_length_ms) - x_next = self._update(z_t, alpha_t) - trace = StepTrace( - t=len(self.history), raw=dict(raw), x_t=list(self.x_t), z_t=z_t, - projections=projections, score=score, control_state=self.state, - action=best[0], mode=best[1], alpha_t=alpha_t, engram_length_ms=engram_length_ms, x_next=x_next, - ) - self.history.append(trace) - self.x_t = x_next - return trace diff --git a/5-Applications/tools-scripts/ingested/wave_carrier_profiles (1).py b/5-Applications/tools-scripts/ingested/wave_carrier_profiles (1).py deleted file mode 100644 index 4f7d80cb..00000000 --- a/5-Applications/tools-scripts/ingested/wave_carrier_profiles (1).py +++ /dev/null @@ -1,247 +0,0 @@ -from __future__ import annotations - -"""Carrier-profile utilities for synthetic speech and PCM/PipeWire replay. - -Provides a deterministic synthetic speech-like waveform generator and three -carrier views over the same signal: -- direct floating-point carrier -- PCM-packed carrier -- PipeWire-profile carrier -""" - -from dataclasses import dataclass -from typing import Dict, Iterable, List, Mapping, Sequence, Tuple -import math -import struct - -import numpy as np - - -@dataclass(frozen=True) -class CarrierConfig: - sample_rate_hz: int = 16000 - chunk_size: int = 1024 - pcm_sample_width_bytes: int = 2 - channels: int = 1 - seed: int = 42 - - -def generate_synthetic_speech(duration_s: float = 2.0, cfg: CarrierConfig = CarrierConfig()) -> np.ndarray: - """Generate a deterministic speech-like waveform. - - The signal alternates voiced harmonic blocks with shaped envelopes and light - noise, giving a bounded real-structured carrier without relying on external - TTS or archival audio. - """ - rng = np.random.default_rng(cfg.seed) - t = np.linspace(0.0, duration_s, int(cfg.sample_rate_hz * duration_s), endpoint=False) - - # Slowly varying voiced fundamental. - f0 = 120.0 + 15.0 * np.sin(2.0 * np.pi * 2.1 * t) + 8.0 * np.sin(2.0 * np.pi * 0.7 * t) - - signal = np.zeros_like(t, dtype=np.float64) - for k in range(1, 7): - signal += (1.0 / k) * np.sin(2.0 * np.pi * k * f0 * t) - - # Syllable-like envelope: periodic voiced windows with smoothed on/off. - gate = (np.sin(2.0 * np.pi * 1.6 * t) > -0.1).astype(np.float64) - kernel = np.ones(801, dtype=np.float64) / 801.0 - envelope = np.convolve(gate, kernel, mode="same") - - # Fricative-like bursts layered in at deterministic positions. - burst = np.zeros_like(t, dtype=np.float64) - burst_mask = (np.sin(2.0 * np.pi * 4.0 * t + 0.4) > 0.92).astype(np.float64) - burst = 0.08 * burst_mask * rng.standard_normal(t.shape[0]) - - # Light background noise to avoid trivial perfect periodicity. - noise = 0.015 * rng.standard_normal(t.shape[0]) - - y = signal * envelope + burst + noise - peak = float(np.max(np.abs(y))) if y.size else 1.0 - if peak > 1e-12: - y = 0.95 * y / peak - return y.astype(np.float32) - - -def chunk_signal(samples: np.ndarray, chunk_size: int) -> List[np.ndarray]: - out: List[np.ndarray] = [] - for i in range(0, len(samples), chunk_size): - chunk = samples[i : i + chunk_size] - if len(chunk) < chunk_size: - chunk = np.pad(chunk, (0, chunk_size - len(chunk))) - out.append(chunk.astype(np.float32, copy=False)) - return out - - -def pack_pcm16_mono(samples: np.ndarray) -> bytes: - clipped = np.clip(samples.astype(np.float32, copy=False), -1.0, 1.0) - ints = np.round(clipped * 32767.0).astype(' np.ndarray: - if not data: - return np.zeros(0, dtype=np.float32) - arr = np.frombuffer(data, dtype=' float: - return max(0.0, min(1.0, float(x))) - - -def carrier_metrics_from_samples(samples: np.ndarray, sample_rate_hz: int) -> Dict[str, float]: - samples = samples.astype(np.float32, copy=False) - if samples.size == 0: - return { - "spectral_centroid": 0.0, - "spectral_flatness": 0.0, - "transient_ratio": 0.0, - "band_low": 0.0, - "band_mid": 0.0, - "band_high": 0.0, - "coherence": 1.0, - "energy": 0.0, - "confidence": 1.0, - "noise": 0.0, - } - - rms = float(np.sqrt(np.mean(samples * samples))) - energy = _safe01(rms / 0.5) - - if samples.size >= 2: - diff = np.diff(samples, prepend=samples[0]) - transient_ratio = float(np.mean(np.abs(diff)) / max(rms, 1e-6)) - transient_ratio = _safe01(transient_ratio / 1.5) - zero_cross = float(np.mean((samples[:-1] * samples[1:]) < 0.0)) - else: - transient_ratio = 0.0 - zero_cross = 0.0 - - if samples.size < 8 or sample_rate_hz <= 0: - spectral_centroid = 0.0 - flatness = 0.0 - low = mid = high = 0.0 - dominant_hz = 0.0 - else: - window = np.hanning(samples.size).astype(np.float32) - spec = np.fft.rfft(samples * window) - power = np.abs(spec) ** 2 + 1e-12 - freqs = np.fft.rfftfreq(samples.size, d=1.0 / sample_rate_hz) - total = float(np.sum(power)) - centroid_hz = float(np.sum(freqs * power) / total) - spectral_centroid = _safe01(centroid_hz / (sample_rate_hz / 2.0)) - flatness = float(np.exp(np.mean(np.log(power))) / max(np.mean(power), 1e-12)) - flatness = _safe01(flatness) - low = float(np.sum(power[freqs < 1000.0]) / total) - mid = float(np.sum(power[(freqs >= 1000.0) & (freqs < 4000.0)]) / total) - high = float(np.sum(power[freqs >= 4000.0]) / total) - dom_idx = int(np.argmax(power[1:]) + 1) if power.size > 1 else 0 - dominant_hz = float(freqs[dom_idx]) if dom_idx < freqs.size else 0.0 - - # Confidence/coherence are intentionally bounded structural surrogates. - coherence = _safe01(1.0 - 0.55 * flatness - 0.20 * zero_cross) - noise = _safe01(0.65 * flatness + 0.35 * zero_cross) - confidence = _safe01(0.45 * coherence + 0.35 * (1.0 - noise) + 0.20 * energy) - - return { - "spectral_centroid": spectral_centroid, - "spectral_flatness": flatness, - "transient_ratio": transient_ratio, - "band_low": _safe01(low), - "band_mid": _safe01(mid), - "band_high": _safe01(high), - "coherence": coherence, - "energy": energy, - "confidence": confidence, - "noise": noise, - "dominant_hz": dominant_hz, - } - - -def direct_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - return [carrier_metrics_from_samples(ch, cfg.sample_rate_hz) for ch in chunk_signal(samples, cfg.chunk_size)] - - -def pcm_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - out: List[Dict[str, float]] = [] - for ch in chunk_signal(samples, cfg.chunk_size): - pcm = pack_pcm16_mono(ch) - recovered = unpack_pcm16_mono(pcm) - out.append(carrier_metrics_from_samples(recovered, cfg.sample_rate_hz)) - return out - - -def pipewire_profile_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - out: List[Dict[str, float]] = [] - for idx, ch in enumerate(chunk_signal(samples, cfg.chunk_size)): - pcm = pack_pcm16_mono(ch) - recovered = unpack_pcm16_mono(pcm) - metrics = carrier_metrics_from_samples(recovered, cfg.sample_rate_hz) - # PipeWire-profile view: same carrier realized through a DSP surface with - # explicit transport metadata and tiny bounded jitter penalty. - metrics["transport_latency"] = _safe01(0.02 + 0.005 * ((idx % 3))) - metrics["confidence"] = _safe01(metrics["confidence"] * (1.0 - 0.25 * metrics["transport_latency"])) - out.append(metrics) - return out - - - -import wave -from pathlib import Path - -def load_wav_mono(path: str | Path, target_sample_rate_hz: int | None = None) -> np.ndarray: - """ - Load a mono-compatible WAV file into a float32 waveform in [-1, 1]. - If target_sample_rate_hz is provided and differs from the source rate, - a simple deterministic linear resample is applied. - """ - path = Path(path) - with wave.open(str(path), 'rb') as wf: - channels = wf.getnchannels() - sampwidth = wf.getsampwidth() - framerate = wf.getframerate() - nframes = wf.getnframes() - frames = wf.readframes(nframes) - - if sampwidth == 1: - arr = np.frombuffer(frames, dtype=np.uint8).astype(np.float32) - arr = (arr - 128.0) / 128.0 - elif sampwidth == 2: - arr = np.frombuffer(frames, dtype=' 1: - arr = arr.reshape(-1, channels).mean(axis=1) - - if target_sample_rate_hz and target_sample_rate_hz > 0 and target_sample_rate_hz != framerate: - if arr.size == 0: - return arr.astype(np.float32) - duration = arr.size / framerate - n_out = max(1, int(round(duration * target_sample_rate_hz))) - x_old = np.linspace(0.0, 1.0, arr.size, endpoint=False) - x_new = np.linspace(0.0, 1.0, n_out, endpoint=False) - arr = np.interp(x_new, x_old, arr).astype(np.float32) - - peak = float(np.max(np.abs(arr))) if arr.size else 0.0 - if peak > 1e-12: - arr = 0.98 * arr / peak - return arr.astype(np.float32, copy=False) - - -def archival_wav_carriers(path: str | Path, cfg: CarrierConfig = CarrierConfig()) -> dict[str, object]: - """ - Return direct / PCM / PipeWire-profile carriers from a replayed archival WAV. - """ - samples = load_wav_mono(path, target_sample_rate_hz=cfg.sample_rate_hz) - return { - 'samples': samples, - 'direct': direct_carriers(samples, cfg), - 'pcm': pcm_carriers(samples, cfg), - 'pipewire': pipewire_profile_carriers(samples, cfg), - } diff --git a/5-Applications/tools-scripts/ingested/wave_carrier_profiles.py b/5-Applications/tools-scripts/ingested/wave_carrier_profiles.py deleted file mode 100644 index db9f2245..00000000 --- a/5-Applications/tools-scripts/ingested/wave_carrier_profiles.py +++ /dev/null @@ -1,186 +0,0 @@ -from __future__ import annotations - -"""Carrier-profile utilities for synthetic speech and PCM/PipeWire replay. - -Provides a deterministic synthetic speech-like waveform generator and three -carrier views over the same signal: -- direct floating-point carrier -- PCM-packed carrier -- PipeWire-profile carrier -""" - -from dataclasses import dataclass -from typing import Dict, Iterable, List, Mapping, Sequence, Tuple -import math -import struct - -import numpy as np - - -@dataclass(frozen=True) -class CarrierConfig: - sample_rate_hz: int = 16000 - chunk_size: int = 1024 - pcm_sample_width_bytes: int = 2 - channels: int = 1 - seed: int = 42 - - -def generate_synthetic_speech(duration_s: float = 2.0, cfg: CarrierConfig = CarrierConfig()) -> np.ndarray: - """Generate a deterministic speech-like waveform. - - The signal alternates voiced harmonic blocks with shaped envelopes and light - noise, giving a bounded real-structured carrier without relying on external - TTS or archival audio. - """ - rng = np.random.default_rng(cfg.seed) - t = np.linspace(0.0, duration_s, int(cfg.sample_rate_hz * duration_s), endpoint=False) - - # Slowly varying voiced fundamental. - f0 = 120.0 + 15.0 * np.sin(2.0 * np.pi * 2.1 * t) + 8.0 * np.sin(2.0 * np.pi * 0.7 * t) - - signal = np.zeros_like(t, dtype=np.float64) - for k in range(1, 7): - signal += (1.0 / k) * np.sin(2.0 * np.pi * k * f0 * t) - - # Syllable-like envelope: periodic voiced windows with smoothed on/off. - gate = (np.sin(2.0 * np.pi * 1.6 * t) > -0.1).astype(np.float64) - kernel = np.ones(801, dtype=np.float64) / 801.0 - envelope = np.convolve(gate, kernel, mode="same") - - # Fricative-like bursts layered in at deterministic positions. - burst = np.zeros_like(t, dtype=np.float64) - burst_mask = (np.sin(2.0 * np.pi * 4.0 * t + 0.4) > 0.92).astype(np.float64) - burst = 0.08 * burst_mask * rng.standard_normal(t.shape[0]) - - # Light background noise to avoid trivial perfect periodicity. - noise = 0.015 * rng.standard_normal(t.shape[0]) - - y = signal * envelope + burst + noise - peak = float(np.max(np.abs(y))) if y.size else 1.0 - if peak > 1e-12: - y = 0.95 * y / peak - return y.astype(np.float32) - - -def chunk_signal(samples: np.ndarray, chunk_size: int) -> List[np.ndarray]: - out: List[np.ndarray] = [] - for i in range(0, len(samples), chunk_size): - chunk = samples[i : i + chunk_size] - if len(chunk) < chunk_size: - chunk = np.pad(chunk, (0, chunk_size - len(chunk))) - out.append(chunk.astype(np.float32, copy=False)) - return out - - -def pack_pcm16_mono(samples: np.ndarray) -> bytes: - clipped = np.clip(samples.astype(np.float32, copy=False), -1.0, 1.0) - ints = np.round(clipped * 32767.0).astype(' np.ndarray: - if not data: - return np.zeros(0, dtype=np.float32) - arr = np.frombuffer(data, dtype=' float: - return max(0.0, min(1.0, float(x))) - - -def carrier_metrics_from_samples(samples: np.ndarray, sample_rate_hz: int) -> Dict[str, float]: - samples = samples.astype(np.float32, copy=False) - if samples.size == 0: - return { - "spectral_centroid": 0.0, - "spectral_flatness": 0.0, - "transient_ratio": 0.0, - "band_low": 0.0, - "band_mid": 0.0, - "band_high": 0.0, - "coherence": 1.0, - "energy": 0.0, - "confidence": 1.0, - "noise": 0.0, - } - - rms = float(np.sqrt(np.mean(samples * samples))) - energy = _safe01(rms / 0.5) - - if samples.size >= 2: - diff = np.diff(samples, prepend=samples[0]) - transient_ratio = float(np.mean(np.abs(diff)) / max(rms, 1e-6)) - transient_ratio = _safe01(transient_ratio / 1.5) - zero_cross = float(np.mean((samples[:-1] * samples[1:]) < 0.0)) - else: - transient_ratio = 0.0 - zero_cross = 0.0 - - if samples.size < 8 or sample_rate_hz <= 0: - spectral_centroid = 0.0 - flatness = 0.0 - low = mid = high = 0.0 - dominant_hz = 0.0 - else: - window = np.hanning(samples.size).astype(np.float32) - spec = np.fft.rfft(samples * window) - power = np.abs(spec) ** 2 + 1e-12 - freqs = np.fft.rfftfreq(samples.size, d=1.0 / sample_rate_hz) - total = float(np.sum(power)) - centroid_hz = float(np.sum(freqs * power) / total) - spectral_centroid = _safe01(centroid_hz / (sample_rate_hz / 2.0)) - flatness = float(np.exp(np.mean(np.log(power))) / max(np.mean(power), 1e-12)) - flatness = _safe01(flatness) - low = float(np.sum(power[freqs < 1000.0]) / total) - mid = float(np.sum(power[(freqs >= 1000.0) & (freqs < 4000.0)]) / total) - high = float(np.sum(power[freqs >= 4000.0]) / total) - dom_idx = int(np.argmax(power[1:]) + 1) if power.size > 1 else 0 - dominant_hz = float(freqs[dom_idx]) if dom_idx < freqs.size else 0.0 - - # Confidence/coherence are intentionally bounded structural surrogates. - coherence = _safe01(1.0 - 0.55 * flatness - 0.20 * zero_cross) - noise = _safe01(0.65 * flatness + 0.35 * zero_cross) - confidence = _safe01(0.45 * coherence + 0.35 * (1.0 - noise) + 0.20 * energy) - - return { - "spectral_centroid": spectral_centroid, - "spectral_flatness": flatness, - "transient_ratio": transient_ratio, - "band_low": _safe01(low), - "band_mid": _safe01(mid), - "band_high": _safe01(high), - "coherence": coherence, - "energy": energy, - "confidence": confidence, - "noise": noise, - "dominant_hz": dominant_hz, - } - - -def direct_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - return [carrier_metrics_from_samples(ch, cfg.sample_rate_hz) for ch in chunk_signal(samples, cfg.chunk_size)] - - -def pcm_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - out: List[Dict[str, float]] = [] - for ch in chunk_signal(samples, cfg.chunk_size): - pcm = pack_pcm16_mono(ch) - recovered = unpack_pcm16_mono(pcm) - out.append(carrier_metrics_from_samples(recovered, cfg.sample_rate_hz)) - return out - - -def pipewire_profile_carriers(samples: np.ndarray, cfg: CarrierConfig = CarrierConfig()) -> List[Dict[str, float]]: - out: List[Dict[str, float]] = [] - for idx, ch in enumerate(chunk_signal(samples, cfg.chunk_size)): - pcm = pack_pcm16_mono(ch) - recovered = unpack_pcm16_mono(pcm) - metrics = carrier_metrics_from_samples(recovered, cfg.sample_rate_hz) - # PipeWire-profile view: same carrier realized through a DSP surface with - # explicit transport metadata and tiny bounded jitter penalty. - metrics["transport_latency"] = _safe01(0.02 + 0.005 * ((idx % 3))) - metrics["confidence"] = _safe01(metrics["confidence"] * (1.0 - 0.25 * metrics["transport_latency"])) - out.append(metrics) - return out diff --git a/5-Applications/tools-scripts/ingested/waveform_adapter.py b/5-Applications/tools-scripts/ingested/waveform_adapter.py deleted file mode 100644 index 6ac54781..00000000 --- a/5-Applications/tools-scripts/ingested/waveform_adapter.py +++ /dev/null @@ -1,129 +0,0 @@ -"""Known-output waveform universe adapter. -Uses bounded scalar wave statistics to choose a basis and induce a field. -No domain assumptions beyond a fixed carrier dict. -""" -from __future__ import annotations -from typing import Mapping, Sequence -import math - -from pbacs_core import Adapter, ControlState, StepTrace - - -class WaveformAdapter(Adapter): - def __init__(self) -> None: - self._modes = ("RAW", "SPECTRAL", "TRANSIENT", "HYBRID") - - def initial_state(self): - # x = [alignment] - return [0.5] - - def modes(self): - return self._modes - - def target_state(self, raw: Mapping[str, float], history: Sequence[StepTrace]): - # External target is a bounded combination of energy and confidence. - energy = max(0.0, min(1.0, raw.get("energy", 0.0))) - confidence = max(0.0, min(1.0, raw.get("confidence", 0.5))) - z = max(0.0, min(1.0, 0.65 * energy + 0.35 * confidence)) - return [z] - - def basis_id(self, raw: Mapping[str, float]) -> str: - centroid = raw.get("spectral_centroid", 0.0) - flatness = raw.get("spectral_flatness", 0.0) - transient = raw.get("transient_ratio", 0.0) - low = raw.get("band_low", 0.0) - mid = raw.get("band_mid", 0.0) - high = raw.get("band_high", 0.0) - if centroid > 0.70 and flatness < 0.45: - return "SPECTRAL" - if transient > 0.65 and high > 0.50: - return "TRANSIENT" - if mid > 0.40 and 0.25 <= flatness <= 0.75: - return "HYBRID" - if low < 0.05 and mid < 0.05 and high < 0.05: - return "RAW" - return "RAW" - - def update_projection_context(self, x_t, z_t, raw: Mapping[str, float], history: Sequence[StepTrace]): - psi = x_t[0] - phi = z_t[0] - delta = abs(phi - psi) - prev_delta = history[-1].projections["u_delta"] if history else 0.0 - delta_dot = max(0.0, delta - prev_delta) - prev_phi = history[-1].z_t[0] if history else phi - prev2_phi = history[-2].z_t[0] if len(history) >= 2 else prev_phi - gamma = abs(phi - 2.0 * prev_phi + prev2_phi) - - basis = self.basis_id(raw) - centroid = max(0.0, min(1.0, raw.get("spectral_centroid", 0.0))) - flatness = max(0.0, min(1.0, raw.get("spectral_flatness", 0.0))) - transient = max(0.0, min(1.0, raw.get("transient_ratio", 0.0))) - coherence = max(0.0, min(1.0, raw.get("coherence", 0.5))) - energy = max(0.0, min(1.0, raw.get("energy", 0.0))) - confidence = max(0.0, min(1.0, raw.get("confidence", 0.5))) - noise = max(0.0, min(1.0, raw.get("noise", 0.0))) - - # Basis-specific field shaping. - if basis == "SPECTRAL": - hazard = 0.25 * noise + 0.15 * transient + 0.10 * flatness - gain = 0.70 * centroid + 0.30 * coherence - chi = coherence * (1.0 - hazard) - elif basis == "TRANSIENT": - hazard = 0.20 * noise + 0.30 * transient + 0.15 * flatness - gain = 0.65 * transient + 0.35 * confidence - chi = confidence * (1.0 - hazard) - elif basis == "HYBRID": - hazard = 0.20 * noise + 0.20 * transient + 0.10 * flatness - gain = 0.40 * centroid + 0.25 * transient + 0.35 * coherence - chi = 0.5 * coherence + 0.5 * confidence - else: # RAW - hazard = 0.10 * noise + 0.05 * transient + 0.05 * flatness - gain = 0.50 * confidence + 0.50 * energy - chi = confidence * (1.0 - hazard) - - tau = min(1.0, 0.50 * delta + 0.25 * gamma + 0.25 * hazard) - cost = min(1.0, 0.40 * hazard + 0.30 * noise + 0.30 * flatness) - bias = confidence - phi_margin = max(0.0, min(1.0, 0.55 * (1.0 - tau) + 0.25 * bias + 0.20 * gain)) - accumulation_drive = max(0.0, min(1.0, 0.40 * hazard + 0.30 * tau + 0.30 * noise)) - return { - "basis": basis, - "u_phi": phi_margin, - "u_delta": delta, - "u_delta_dot": delta_dot, - "u_gamma": max(0.0, min(1.0, gamma)), - "u_tau": tau, - "u_chi": max(0.0, min(1.0, chi)), - "u_gain": max(0.0, min(1.0, gain)), - "u_cost": max(0.0, min(1.0, cost)), - "u_bias": max(0.0, min(1.0, bias)), - "u_pacing": max(delta, hazard), - "u_accum": accumulation_drive, - } - - def projections(self): - return { - "u_phi": lambda c: c["u_phi"], - "u_delta": lambda c: c["u_delta"], - "u_delta_dot": lambda c: c["u_delta_dot"], - "u_gamma": lambda c: c["u_gamma"], - "u_tau": lambda c: c["u_tau"], - "u_chi": lambda c: c["u_chi"], - "u_gain": lambda c: c["u_gain"], - "u_cost": lambda c: c["u_cost"], - "u_bias": lambda c: c["u_bias"], - "u_pacing": lambda c: c["u_pacing"], - } - - def admissible(self, state: ControlState): - if state == ControlState.HALT: - return (("HALT", "RAW"),) - if state == ControlState.HOLD: - return (("HOLD", "HYBRID"), ("HOLD", "RAW")) - if state == ControlState.DMT: - return (("DMT", "TRANSIENT"),) - return (("COMMIT", "RAW"), ("COMMIT", "SPECTRAL"), ("COMMIT", "TRANSIENT"), ("COMMIT", "HYBRID")) - - def tie_break(self, candidates): - # Prefer the candidate matching the current basis when available. - return sorted(candidates)[0] diff --git a/5-Applications/tools-scripts/ingestion/ingest_archive.py b/5-Applications/tools-scripts/ingestion/ingest_archive.py deleted file mode 100644 index 4d03d7d7..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_archive.py +++ /dev/null @@ -1,624 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=STORE / DOMAIN=DATA / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Archive Ingestor — Bulk tag every archived document into the substrate index. -============================================================================= - -CONCEPT -------- -Every file in 6-Documentation/archive/ represents a research artifact — a spec, a session -note, a prior-art document, a design sketch. Most of them are invisible to -the metanarrative graph because they were never indexed. - -This script walks the archive, extracts keyword signal from each file, and -ingests it as a lightweight RESEARCH-tier row in the SQLite index. After -running, cmd_connect() can find relationships between current work and -archived material — the "high speed connector." - -WHAT IT DOES ------------- -For each .md, .txt, .json, .py file in the archive (or a target directory): - - 1. Read the file content (first 8KB — enough for keyword signal) - 2. Extract keyword frequencies using a domain-aware word scorer - 3. Map to the 14-axis concept_vector cluster vocabulary - 4. Auto-detect concept_anchor: domain from dominant keyword cluster, - concept from file stem, resolution from PTOS CONDITION tag if present - 5. Write a row to the SQLite index via cmd_ingest_session() logic - -The indexed rows are READ-ONLY references — the ingestor never modifies -the archive files. It just makes them queryable. - -WHAT IT DOES NOT DO -------------------- -- Generate high-quality idea_weights (use cmd_ingest_session for that) -- Understand the content semantically (keyword frequency only) -- Replace manually crafted session JSON files -- Serve as the canonical home for curated session capsules, literature maps, - or hand-authored relevance notes that already carry explicit semantic fields -- Index binary files, images, or large datasets - -USAGE ------ - # Index everything in 6-Documentation/archive/ (dry-run first) - python3 5-Applications/scripts/ingest_archive.py --dry-run - - # Full run - python3 5-Applications/scripts/ingest_archive.py - - # Index a specific subdirectory - python3 5-Applications/scripts/ingest_archive.py --path 6-Documentation/archive/CATEGORY/TSM - - # Show what's already indexed - python3 5-Applications/scripts/ingest_archive.py --status - - # Re-index (overwrite existing rows) - python3 5-Applications/scripts/ingest_archive.py --force -""" - -import argparse -import hashlib -import json -import math -import os -import re -import sqlite3 -import sys -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path - -# ─── repo root ─────────────────────────────────────────────────────────────── -REPO = Path(__file__).parent.parent -sys.path.insert(0, str(REPO / "scripts")) - -try: - from substrate_git_index import ( - DB_PATH, _open_db, _concept_vector_from_weights, _upsert_package, _PHI, - ) -except ImportError as e: - print(f"[error] cannot import substrate_git_index: {e}", file=sys.stderr) - sys.exit(1) - -try: - from ingest_attachments import collect_auto_attachments -except ImportError as e: - print(f"[error] cannot import ingest_attachments: {e}", file=sys.stderr) - sys.exit(1) - -try: - from ingest_text_hygiene import derive_safe_description -except ImportError as e: - print(f"[error] cannot import ingest_text_hygiene: {e}", file=sys.stderr) - sys.exit(1) - -# ─── configuration ──────────────────────────────────────────────────────────── - -ARCHIVE_ROOT = REPO / "archive" -SESSIONS_DIR = REPO / "sessions" - -# File extensions to index -INDEXABLE = {".md", ".txt", ".rst", ".json", ".py", ".sh", ".rs", ".c"} - -# Max bytes to read per file for keyword extraction -READ_LIMIT = 8192 - -# Skip files matching these patterns (noise, dependencies, license files) -SKIP_PATTERNS = [ - r"site-packages", - r"__pycache__", - r"\.git/", - r"LICENSE", - r"CHANGELOG", - r"requirements\.txt", - r"Cargo\.lock", - r"package-lock", -] - -# ─── keyword → concept cluster mapping ─────────────────────────────────────── -# Same 14-axis vocabulary as substrate_git_index._concept_vector_from_weights -# Axis 0: substrate/foam -# Axis 1: compression/codec -# Axis 2: graph/dag -# Axis 3: hardware -# Axis 4: time/planck -# Axis 5: crypto/hash -# Axis 6: database -# Axis 7: semantic/language -# Axis 8: physics/entropy -# Axis 9: security -# Axis 10: os/vm -# Axis 11: research/discovery -# Axis 12: omnitoken -# Axis 13: identity - -_CLUSTER_KEYWORDS: dict[int, set[str]] = { - 0: {"substrate", "foam", "voxel", "node", "fabric", "manifold", "register", - "soliton", "tsm", "metafoam", "capsule"}, - 1: {"compress", "compression", "codec", "encode", "decode", "entropy", - "huffman", "soliton", "symbol", "iso", "lut", "lookup", "prepass", - "dictionary", "alphabet", "phoneme", "substitution", "bandwidth"}, - 2: {"graph", "dag", "node", "edge", "tree", "merkle", "hash", "pointer", - "traversal", "topology", "manifold", "fractal"}, - 3: {"hardware", "risc", "fpga", "hdl", "asic", "pcb", "circuit", "chip", - "kda", "rad", "trace", "silicon", "lithography", "nanowire"}, - 4: {"time", "planck", "clock", "tick", "timing", "synchron", "latency", - "quantum", "temporal", "duration", "schedule"}, - 5: {"crypto", "hash", "sha256", "merkle", "zk", "stark", "proof", - "signature", "seal", "attest", "verify", "provenance"}, - 6: {"database", "sql", "sqlite", "index", "query", "schema", "table", - "row", "column", "fts", "search", "source", "git"}, - 7: {"semantic", "language", "concept", "meaning", "vector", "embed", - "nlp", "token", "word", "text", "notation", "symbol", "translate", - "analog", "idea", "weight", "research"}, - 8: {"physics", "entropy", "energy", "wave", "frequency", "field", - "quantum", "particle", "force", "mass", "density", "pressure", - "temperature", "superconductor", "photon", "electron", "orbital"}, - 9: {"security", "attack", "exploit", "vuln", "threat", "encrypt", - "defense", "audit", "taint", "isolat", "sandbox", "secret"}, - 10: {"os", "kernel", "process", "memory", "virtual", "vm", "hypervisor", - "syscall", "opcode", "instruction", "runtime", "executor"}, - 11: {"research", "discover", "hypothes", "theory", "experiment", "paper", - "result", "finding", "analysis", "study", "novel", "specul"}, - 12: {"omnitoken", "token", "bridge", "surface", "bus", "ipc", "transport", - "protocol", "route", "mesh", "omni"}, - 13: {"identity", "sovereign", "manifest", "provenance", "attestation", - "ownership", "claim", "ptos", "tag", "classify"}, -} - -_ALL_CLUSTER_WORDS: dict[str, int] = { - word: axis - for axis, words in _CLUSTER_KEYWORDS.items() - for word in words -} - -_PTOS_DOMAINS = ( - "COMPUTE", - "TOKEN", - "RULE", - "STORE", - "POWER", - "COMMS", - "MATERIAL", - "DATA", - "CLOCK", - "TEST", -) - -_PTOS_HEADER_RE = re.compile( - r"PTOS:\s*LAYER=\w+\s*/\s*DOMAIN=(?P\w+)", - re.IGNORECASE, -) - -_PTOS_DOMAIN_KEYWORDS: dict[str, set[str]] = { - "COMPUTE": { - "compute", "compression", "codec", "encode", "decode", "algorithm", - "solver", "runtime", "kernel", "vm", "opcode", "instruction", - "graph", "dag", "lut", "tsm", "substrate", "metafoam", "hutter", - }, - "TOKEN": { - "token", "omnitoken", "wallet", "mint", "burn", "settlement", - "bridge", "surface", - }, - "RULE": { - "rule", "govern", "legal", "ethic", "policy", "constraint", "audit", - "patent", "license", "compliance", "attest", "proof", "verifier", - "verification", "rights", "eula", - }, - "STORE": { - "store", "storage", "archive", "manifest", "capsule", "vault", - "persist", "database", "sqlite", "index", "query", "schema", - "table", "row", "column", "dedup", - }, - "POWER": { - "power", "energy", "thermal", "heat", "battery", "voltage", "current", - "econom", "market", "incentive", "plasma", - }, - "COMMS": { - "comms", "communic", "network", "protocol", "radio", "signal", - "packet", "route", "mesh", "http", "api", "lora", "ax25", "vlf", - "channel", "transport", "carrier", "bus", "ipc", - }, - "MATERIAL": { - "material", "chem", "molecule", "atomic", "atom", "orbital", - "superconductor", "silicon", "wafer", "pcb", "hdl", "asic", "chip", - "lithography", "nanowire", "qchem", "fabrication", - }, - "DATA": { - "data", "dataset", "json", "csv", "session", "transcript", - "document", "text", "note", "metadata", "record", "corpus", "import", - }, - "CLOCK": { - "clock", "time", "timing", "tick", "latency", "temporal", "synchron", - "phase", "schedule", "planck", - }, - "TEST": { - "test", "tests", "testing", "validation", "verify", "verified", - "benchmark", "assert", "rigor", "check", - }, -} - -_CONCEPT_TO_PTOS: dict[str, str] = { - "compression": "COMPUTE", - "substrate": "COMPUTE", - "mathematics": "COMPUTE", - "computation": "COMPUTE", - "governance": "RULE", - "containment": "RULE", - "cryptography": "RULE", - "economics": "POWER", - "physics": "MATERIAL", - "chemistry": "MATERIAL", - "biology": "MATERIAL", - "hardware": "MATERIAL", - "linguistics": "COMMS", - "geography": "DATA", - "music": "DATA", - "neuroscience": "DATA", - "research": "DATA", -} - -_PTOS_PRIORITY = { - domain: i for i, domain in enumerate( - ["RULE", "TEST", "STORE", "POWER", "CLOCK", - "COMMS", "TOKEN", "MATERIAL", "COMPUTE", "DATA"] - ) -} - - -# ─── helpers ────────────────────────────────────────────────────────────────── - -def _should_skip(path: Path) -> bool: - s = str(path) - return any(re.search(p, s) for p in SKIP_PATTERNS) - - -def _read_head(path: Path) -> str: - """Read up to READ_LIMIT bytes, decode leniently.""" - try: - with open(path, "rb") as f: - raw = f.read(READ_LIMIT) - return raw.decode("utf-8", errors="replace") - except Exception: - return "" - - -def _extract_keywords(text: str) -> dict[str, float]: - """Extract a lightweight idea_weights dict from raw text. - - Tokenises by word boundary, counts frequency, normalises to 0–1 range. - Only keeps tokens that appear in the cluster vocabulary or are long - enough to be meaningful (≥6 chars, not stop words). - """ - _STOP = {"the", "and", "for", "that", "this", "with", "from", "are", - "was", "were", "have", "been", "will", "would", "could", "should", - "which", "their", "there", "these", "those", "what", "when", - "where", "how", "all", "any", "can", "not", "but", "also"} - - tokens = re.findall(r"\b[a-zA-Z][a-zA-Z_]{4,}\b", text.lower()) - counts = Counter(t for t in tokens if t not in _STOP) - - if not counts: - return {} - - total = sum(counts.values()) - max_c = counts.most_common(1)[0][1] - - weights: dict[str, float] = {} - for word, count in counts.most_common(30): - # Boost words that appear in cluster vocabulary - boost = 2.0 if word in _ALL_CLUSTER_WORDS else 1.0 - score = min(1.0, (count / max_c) * boost * 0.85) - if score >= 0.10: - weights[word] = round(score, 3) - - return weights - - -def _detect_concept_anchor(path: Path, text: str) -> dict: - """Heuristically assign a concept_anchor from file path and content.""" - stem = path.stem.lower().replace("-", "_").replace(" ", "_") - - # Detect domain from path components and content - path_str = str(path).lower() - content_lower = text.lower() - - if any(k in path_str for k in ("tsm", "soliton", "metafoam", "substrate")): - domain = "substrate" - elif any(k in path_str for k in ("superconductor", "hodh", "material", "qchem")): - domain = "physics" - elif any(k in path_str for k in ("compress", "codec", "hutter", "iso", "lut")): - domain = "compression" - elif any(k in path_str for k in ("govern", "legal", "patent", "ethic")): - domain = "governance" - elif any(k in path_str for k in ("kda", "hardware", "pcb", "hdl", "risc")): - domain = "hardware" - elif "SPECULATIVE" in text or "specul" in content_lower: - domain = "substrate" - else: - domain = "research" - - # Resolution from PTOS CONDITION tag if present - m = re.search(r"CONDITION=(\w+)", text) - condition = m.group(1) if m else None - resolution = { - "STABLE": "STABLE", - "EXPERIMENTAL": "FORMING", - "EXTREME": "FORMING", - "DRAFT": "SEED", - "ARCHIVED": "STABLE", - "STERILE": "STABLE", - }.get(condition, "FORMING") - - return {"domain": domain, "concept": stem[:60], "resolution": resolution} - - -def _declared_ptos_domain(text: str) -> str | None: - """Return an explicit PTOS DOMAIN if the document declares one.""" - m = _PTOS_HEADER_RE.search(text) - if not m: - return None - domain = m.group("domain").upper() - if domain in _PTOS_DOMAINS: - return domain - return None - - -def _keyword_score(tokens: Counter[str], keywords: set[str]) -> float: - score = 0.0 - for token, count in tokens.items(): - for keyword in keywords: - if token.startswith(keyword): - score += min(count, 6) - break - return score - - -def _detect_ptos_domain( - path: Path, - text: str, - idea_weights: dict[str, float], - concept_anchor: dict, -) -> str: - """Infer the PTOS operational domain for an archived artifact.""" - declared = _declared_ptos_domain(text) - if declared is not None: - return declared - - path_str = str(path).lower() - path_hint = " ".join(part.lower() for part in path.parts[1:]) or path.name.lower() - if any(k in path_hint for k in ("validation", "verification", "test", "tests", "benchmark", "rigor")): - return "TEST" - if any(k in path_hint for k in ("audit", "review", "policy", "legal", "ethic", - "patent", "license", "compliance")): - return "RULE" - if any(k in path_hint for k in ("bom", "pcb", "hdl", "asic", "chip", "wafer", - "fabrication", "superconductor", "qchem", "material")): - return "MATERIAL" - if any(k in path_hint for k in ("manifest", "vault", "storage", "store", "index", "sqlite")): - return "STORE" - - tokens = Counter(re.findall(r"\b[a-zA-Z][a-zA-Z_]{2,}\b", f"{path_hint} {text.lower()}")) - scores = {domain: 0.0 for domain in _PTOS_DOMAINS} - - # Path hints are high-confidence because archive placement is often curated. - for domain, keywords in _PTOS_DOMAIN_KEYWORDS.items(): - for keyword in keywords: - if keyword in path_hint: - scores[domain] += 3.5 - - # A few operational path cues deserve stronger nudges. - suffix = path.suffix.lower() - if suffix in {".py", ".rs", ".c", ".sh"}: - scores["COMPUTE"] += 2.5 - if suffix in {".json", ".jsonl", ".csv"}: - scores["DATA"] += 1.5 - if any(k in path_hint for k in ("test", "tests", "validation", "benchmark", "rigor")): - scores["TEST"] += 4.0 - - # Weighted content/profile hits. - for domain, keywords in _PTOS_DOMAIN_KEYWORDS.items(): - scores[domain] += _keyword_score(tokens, keywords) - for word, weight in idea_weights.items(): - if any(word.startswith(keyword) for keyword in keywords): - scores[domain] += 2.0 * float(weight) - - # Concept-domain crosswalk is a semantic fallback, not the only driver. - concept_domain = str(concept_anchor.get("domain", "")).lower() - mapped = _CONCEPT_TO_PTOS.get(concept_domain) - if mapped: - scores[mapped] += 3.0 - - # A STORE note that is also a test should stay TEST if the evidence is comparable. - best = max( - _PTOS_DOMAINS, - key=lambda domain: (scores[domain], -_PTOS_PRIORITY[domain]), - ) - - if scores[best] <= 0.0: - return "DATA" - return best - - -def _pkg_name(path: Path) -> str: - """Derive a stable, unique pkg identifier from the file path.""" - rel = path.relative_to(REPO) - # e.g. 6-Documentation/archive/CATEGORY/TSM/INTRO_TO_METAFOAM.md → arc-tsm-intro_to_metafoam - parts = [p.lower()[:8] for p in rel.parts[:-1] if p.lower() not in - ("archive", "sort this", "category", "research documents")] - stem = re.sub(r"[^a-z0-9]+", "_", path.stem.lower())[:40] - prefix = "-".join(p for p in parts if p)[:20] - tag = f"arc-{prefix}-{stem}" if prefix else f"arc-{stem}" - return tag[:80] - - -# ─── main ingestor ──────────────────────────────────────────────────────────── - -def ingest_file( - path: Path, - conn: sqlite3.Connection, - force: bool = False, - dry_run: bool = False, -) -> str: - """Index a single archive file. Returns status: 'indexed', 'skipped', 'exists'.""" - if _should_skip(path): - return "skipped" - - pkg = _pkg_name(path) - version = "1.0.0" - - if not force: - row = conn.execute( - "SELECT pkg FROM packages WHERE pkg=? AND version=?", (pkg, version) - ).fetchone() - if row: - return "exists" - - text = _read_head(path) - if not text.strip(): - return "skipped" - - now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - sha256 = hashlib.sha256(path.name.encode()).hexdigest()[:16] - - idea_weights = _extract_keywords(text) - if not idea_weights: - return "skipped" - - concept_vec = _concept_vector_from_weights(idea_weights) - concept_anchor = _detect_concept_anchor(path, text) - ptos_domain = _detect_ptos_domain(path, text, idea_weights, concept_anchor) - foam_score = round( - sum(idea_weights.values()) / max(len(idea_weights), 1) * _PHI, 4 - ) - - description = derive_safe_description(path, text, max_len=200) - - tags = [concept_anchor["domain"], "archive", path.suffix.lstrip(".")] - - if dry_run: - print(f" [dry] {pkg}") - print(f" concept_domain={concept_anchor['domain']} " - f"ptos_domain={ptos_domain} " - f"resolution={concept_anchor['resolution']} " - f"axes={sum(1 for x in concept_vec if x > 0.01)}/14 " - f"foam={foam_score}") - return "dry" - - attached_files, attachment_meta = collect_auto_attachments( - path, repo=REPO, mode="full" - ) - - _upsert_package(conn, { - "pkg": pkg, - "version": version, - "layer": "RULE", - "domain": ptos_domain, - "condition": "EXPERIMENTAL", - "stage": "ARCHIVED", - "source": "NOTE", - "tier": "RESEARCH", - "module": path.stem[:40].upper().replace(" ", "_").replace("-", "_"), - "archetype": "ARCHIVE_NODE", - "tags": json.dumps(tags), - "description": description, - "files": json.dumps(attached_files), - "depends": json.dumps([]), - "foam_score": foam_score, - "nd_point": json.dumps(concept_vec[:14]), - "sealed_utc": now_utc, - "visibility": "PRIVATE", - "model_status": "REFERENCE_ONLY", - "taint_status": "CLEAN", - "session_id": str(path.relative_to(REPO)), - "idea_weights": json.dumps(idea_weights), - "extension_points": json.dumps([]), - "concept_vector": json.dumps(concept_vec), - "analog_map": json.dumps({}), - "concept_anchor": json.dumps(concept_anchor), - "attachment_meta": json.dumps(attachment_meta), - "ingest_profile": json.dumps({ - "ingestor": "archive", - "suffix": path.suffix.lower(), - "text_chars": len(text), - }), - "indexed_utc": now_utc, - }) - return "indexed" - - -def run( - target: Path = ARCHIVE_ROOT, - dry_run: bool = False, - force: bool = False, - extensions: set[str] = INDEXABLE, -) -> None: - conn = _open_db() - counts = Counter() - - files = sorted(target.rglob("*")) - total = sum(1 for f in files if f.is_file() and f.suffix in extensions) - print(f"Scanning {target.relative_to(REPO)} ({total} indexable files)") - - for i, path in enumerate(files): - if not path.is_file(): - continue - if path.suffix not in extensions: - continue - - status = ingest_file(path, conn, force=force, dry_run=dry_run) - counts[status] += 1 - - if i % 50 == 0 and not dry_run: - conn.commit() - print(f" {i+1}/{total} indexed={counts['indexed']} " - f"exists={counts['exists']} skipped={counts['skipped']}") - - if not dry_run: - conn.commit() - - conn.close() - - print(f"\nDone.") - print(f" indexed : {counts['indexed']:>5}") - print(f" exists : {counts['exists']:>5}") - print(f" skipped : {counts['skipped']:>5}") - if dry_run: - print(f" dry : {counts['dry']:>5}") - - -def cmd_status() -> None: - conn = _open_db() - rows = conn.execute( - "SELECT COUNT(*) FROM packages WHERE stage='ARCHIVED'" - ).fetchone()[0] - total = conn.execute("SELECT COUNT(*) FROM packages").fetchone()[0] - conn.close() - print(f"Archive nodes in index : {rows}") - print(f"Total nodes in index : {total}") - - -# ─── CLI ───────────────────────────────────────────────────────────────────── - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Bulk-index archive files into substrate index") - parser.add_argument("--path", default=str(ARCHIVE_ROOT), help="Directory to scan") - parser.add_argument("--dry-run", action="store_true", help="Show what would be indexed") - parser.add_argument("--force", action="store_true", help="Re-index existing entries") - parser.add_argument("--status", action="store_true", help="Show index counts and exit") - args = parser.parse_args() - - if args.status: - cmd_status() - else: - run( - target=Path(args.path), - dry_run=args.dry_run, - force=args.force, - ) diff --git a/5-Applications/tools-scripts/ingestion/ingest_attachments.py b/5-Applications/tools-scripts/ingestion/ingest_attachments.py deleted file mode 100644 index ba140a8b..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_attachments.py +++ /dev/null @@ -1,348 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Attachment collection helpers for ingestion flows. - -The goal is to preserve the widest useful source context in `packages.files` -without hardcoding one-off logic in every ingestor. -""" - -from __future__ import annotations - -from pathlib import Path - - -ATTACHMENT_LIMIT = 4096 - -# Common asset folders found in exported chat bundles and similar archives. -CHAT_EXPORT_ASSET_DIRS = frozenset({ - "css", - "js", - "images", - "stickers", - "files", - "audio", - "video", - "videos", - "photos", - "voice", - "voice_messages", - "documents", -}) - -SIDECAR_EXTENSIONS = frozenset({ - ".json", - ".jsonl", - ".md", - ".txt", - ".pdf", - ".html", - ".htm", - ".xml", - ".rss", - ".atom", - ".bib", - ".bibtex", - ".dot", - ".gv", - ".graphml", - ".csv", - ".tsv", - ".tex", - ".yaml", - ".yml", - ".toml", -}) - -RESONANT_EXTENSIONS = frozenset({ - ".md", - ".txt", - ".py", - ".v", - ".lean", - ".hdl", - ".c", - ".cpp", - ".h", - ".json", -}) - -RESONANT_EXTENSIONS = frozenset({ - ".md", - ".txt", - ".py", - ".v", - ".lean", - ".hdl", - ".c", - ".cpp", - ".h", - ".json", -}) - - -def _repo_rel(path: Path, repo: Path) -> str: - try: - return str(path.relative_to(repo)) - except ValueError: - return str(path) - - -def _resolve_ref(raw: str, *, repo: Path, base: Path) -> Path | None: - raw = raw.strip() - if not raw: - return None - - candidate = Path(raw) - probes = [] - if candidate.is_absolute(): - probes.append(candidate) - else: - probes.append((base / candidate).resolve()) - probes.append((repo / candidate).resolve()) - probes.append(candidate.resolve()) - - seen: set[Path] = set() - for probe in probes: - if probe in seen: - continue - seen.add(probe) - if probe.exists(): - return probe - return None - - -def _append_path(paths: list[Path], seen: set[str], path: Path, repo: Path) -> None: - if not path.exists() or not path.is_file(): - return - key = _repo_rel(path, repo) - if key in seen: - return - seen.add(key) - paths.append(path) - - -def _same_stem_sidecars(source: Path) -> list[Path]: - out: list[Path] = [] - parent = source.parent - for sibling in sorted(parent.iterdir()): - if sibling == source or not sibling.is_file(): - continue - if sibling.suffix.lower() not in SIDECAR_EXTENSIONS: - continue - if sibling.stem == source.stem: - out.append(sibling) - return out - - -def _html_bundle_attachments(source: Path) -> list[Path]: - out: list[Path] = [] - parent = source.parent - - for sibling in sorted(parent.iterdir()): - if sibling == source: - continue - if sibling.is_file() and sibling.suffix.lower() in {".html", ".htm"}: - out.append(sibling) - - for child in sorted(parent.iterdir()): - if not child.is_dir() or child.name.lower() not in CHAT_EXPORT_ASSET_DIRS: - continue - for asset in sorted(p for p in child.rglob("*") if p.is_file()): - out.append(asset) - - return out - - -def _collect_resonant_attachments( - source: Path, - *, - repo: Path, - keywords: list[str] | None = None, - limit: int = 5, - threshold: float = 0.6, -) -> list[Path]: - """Use SeismicSearchEngine to discover conceptually relevant attachments.""" - try: - # Import dynamically to avoid circular dependencies if any - # Assuming seismic_search.py is in the same directory or sibling - import sys - - script_dir = Path(__file__).parent - if str(script_dir) not in sys.path: - sys.path.insert(0, str(script_dir)) - - from seismic_search import SeismicSearchEngine - except ImportError: - return [] - - if not keywords: - # Fallback: extract simple keywords from source head if not provided - try: - with open(source, "r", encoding="utf-8", errors="ignore") as f: - head = f.read(16384) - keywords = re.findall(r"\b[a-zA-Z]{5,}\b", head)[:10] - except Exception: - return [] - - if not keywords: - return [] - - query = " ".join(keywords) - engine = SeismicSearchEngine(phi=0.0) # Default phi for ingestion - - # Target search directories - search_dirs = [repo / "docs", repo / "core"] - indexed = 0 - for d in search_dirs: - if d.exists(): - engine.build_index(d) - indexed += engine.doc_count - - if indexed == 0: - return [] - - results = engine.search(query, limit=limit * 2) # Get more to filter extensions - - resonant_paths: list[Path] = [] - for r in results: - p = Path(r["path"]) - if p.resolve() == source.resolve(): - continue - if p.suffix.lower() not in RESONANT_EXTENSIONS: - continue - if r["score"] < threshold: - continue - resonant_paths.append(p) - if len(resonant_paths) >= limit: - break - - return resonant_paths - - -def collect_auto_attachments( - source: Path, - *, - repo: Path, - mode: str = "full", - limit: int = ATTACHMENT_LIMIT, - keywords: list[str] | None = None, -) -> tuple[list[str], dict]: - """Collect source attachments and return repo-relative file refs plus metadata. - - Modes: - - `full`: source file plus known sidecars/bundles - - `auto`: alias for `full` (backward compatibility) - - `resonant`: full + conceptually relevant files via search - - `source-only`: only the source file itself - """ - source = source.resolve() - mode = mode.lower() - if mode == "auto": - mode = "full" - if mode not in {"full", "resonant", "source-only"}: - raise ValueError(f"unknown attachment mode: {mode}") - - paths: list[Path] = [] - seen: set[str] = set() - policies = ["source"] - _append_path(paths, seen, source, repo) - - if mode == "full": - for sidecar in _same_stem_sidecars(source): - _append_path(paths, seen, sidecar, repo) - if len(paths) > 1: - policies.append("same-stem-sidecars") - - if source.suffix.lower() in {".html", ".htm"}: - before = len(paths) - for related in _html_bundle_attachments(source): - _append_path(paths, seen, related, repo) - if len(paths) > before: - policies.append("html-bundle") - - if mode == "resonant": - # Note: if keywords are needed but not passed, _collect_resonant_attachments - # will try to derive them from the source file head. - # In practice, ingestors like ingest_large_file.py should pass them. - resonant = _collect_resonant_attachments(source, repo=repo, keywords=keywords) - for res_path in resonant: - _append_path(paths, seen, res_path, repo) - if len(resonant) > 0: - policies.append("conceptual-resonance") - - truncated = False - if len(paths) > limit: - paths = paths[:limit] - truncated = True - - return ( - [_repo_rel(path, repo) for path in paths], - { - "attachment_mode": mode, - "attachment_count": len(paths), - "attachment_policies": policies, - "attachment_truncated": truncated, - }, - ) - - -def collect_session_attachments( - session_file: Path, - payload: dict, - *, - repo: Path, - auto_mode: str = "full", - limit: int = ATTACHMENT_LIMIT, -) -> tuple[list[str], dict]: - """Collect attachments for a structured session JSON. - - Supports explicit additions: - - `files`: direct file refs - - `attachments`: extra file refs - - `attachment_dirs`: directories whose file trees should be attached - """ - session_file = session_file.resolve() - base = session_file.parent - - mode = str(payload.get("attachment_mode", auto_mode or "full")).lower() - - # If conceptual resonance is supported, we might have keywords in the payload - # ingest_attachments itself doesn't know idea_weights yet, but we can pass them - # if we refactor collect_auto_attachments further. - # For now, we'll let it derive them or rely on collect_auto_attachments expansion. - - refs, meta = collect_auto_attachments( - session_file, repo=repo, mode=mode, limit=limit - ) - - paths = [(_resolve_ref(ref, repo=repo, base=base)) for ref in refs] - paths = [p for p in paths if p is not None] - seen = {_repo_rel(p, repo) for p in paths} - - explicit_refs = list(payload.get("files", [])) + list(payload.get("attachments", [])) - for raw in explicit_refs: - resolved = _resolve_ref(str(raw), repo=repo, base=base) - if resolved is not None: - _append_path(paths, seen, resolved, repo) - - for raw_dir in payload.get("attachment_dirs", []): - resolved_dir = _resolve_ref(str(raw_dir), repo=repo, base=base) - if resolved_dir is None or not resolved_dir.is_dir(): - continue - for child in sorted(p for p in resolved_dir.rglob("*") if p.is_file()): - _append_path(paths, seen, child, repo) - - truncated = False - if len(paths) > limit: - paths = paths[:limit] - truncated = True - - meta["attachment_count"] = len(paths) - meta["attachment_truncated"] = truncated - return [_repo_rel(p, repo) for p in paths], meta diff --git a/5-Applications/tools-scripts/ingestion/ingest_downloads_data.py b/5-Applications/tools-scripts/ingestion/ingest_downloads_data.py deleted file mode 100644 index bf5a6fa4..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_downloads_data.py +++ /dev/null @@ -1,211 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=STORE / DOMAIN=DATA / CONDITION=STABLE / STAGE=ACTIVE / SOURCE=CODE -""" -Ingest the local downloads-data corpus into substrate_index.db. - -Each subdir becomes one PTOS DATA package row so they show up in semantic_query. - -Usage: - python3 5-Applications/scripts/ingest_downloads_data.py [--dry-run] -""" -from __future__ import annotations - -import json -import sqlite3 -import sys -import os -from datetime import datetime, timezone -from pathlib import Path - -DATA_ROOT = Path( - os.getenv("DOWNLOADS_DATA_ROOT") or Path.home() / "Downloads" / "data" -) -DB_PATH = Path(__file__).parent.parent / "substrate_index.db" - -PACKAGES = [ - { - "pkg": "downloads-data.literature", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "DATA", - "tier": "FOAM", - "module": "DOWNLOADS_LITERATURE", - "archetype": "paper-corpus", - "description": ( - "Local research literature corpus: Nature/Springer DOI-named PDFs, " - "PLOS Biology, SSRN preprints, NVIDIA Nemotron tech report, " - "dark matter overview, ScienceDirect export, and unnamed downloads." - ), - "tags": json.dumps([ - "literature", "pdf", "nature", "springer", "preprint", - "neuroscience", "biology", "ai", "physics", "data" - ]), - "subdir": "literature", - }, - { - "pkg": "downloads-data.facebook_pdfs", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "IMPORT", - "tier": "FOAM", - "module": "DOWNLOADS_FACEBOOK", - "archetype": "social-export", - "description": ( - "PDFs downloaded from Facebook posts (numeric post-ID naming). " - "17 files, mixed content." - ), - "tags": json.dumps(["facebook", "pdf", "social-media", "export"]), - "subdir": "facebook_pdfs", - }, - { - "pkg": "downloads-data.feature_csvs", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "DATA", - "tier": "FOAM", - "module": "DOWNLOADS_SAE_FEATURES", - "archetype": "sae-feature-export", - "description": ( - "Neuronpedia SAE feature exports — 26 CSV files covering alanine codon " - "detection/preference/bias features and GC-rich region features. " - "Exported from the NVIDIA SAE explorer session." - ), - "tags": json.dumps([ - "sae", "neuronpedia", "features", "alanine", "codon", - "csv", "gc-rich", "sparse-autoencoder" - ]), - "subdir": "feature_csvs", - }, - { - "pkg": "downloads-data.media", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "IMPORT", - "tier": "FOAM", - "module": "DOWNLOADS_MEDIA", - "archetype": "media-capture", - "description": ( - "Screenshots and subtitle transcripts: quantum mechanics linear algebra " - "slides (5x WebP), BSDM lighting WebP, generic PNGs (5x), and YouTube " - "auto-generated subtitles for NES RGB, NVIDIA, oil markets, and OpenAI IPO." - ), - "tags": json.dumps([ - "media", "screenshot", "webp", "png", "subtitle", "srt", - "youtube", "quantum-mechanics", "linear-algebra" - ]), - "subdir": "media", - }, - { - "pkg": "downloads-data.nvidia-sae", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "IMPORT", - "tier": "FOAM", - "module": "DOWNLOADS_NVIDIA_SAE", - "archetype": "web-app-bundle", - "description": ( - "Saved web-app bundle from the NVIDIA SAE explorer session: " - "JS chunks + wasm binary. Captured alongside the feature CSV exports." - ), - "tags": json.dumps(["nvidia", "sae", "wasm", "javascript", "bundle"]), - "subdir": "nvidia-sae", - }, - { - "pkg": "downloads-data.enwik9", - "version": "0.1.0", - "layer": "STORE", - "domain": "DATA", - "condition": "STABLE", - "stage": "INTAKE", - "source": "DATA", - "tier": "FOAM", - "module": "DOWNLOADS_ENWIK9", - "archetype": "benchmark-data", - "description": ( - "enwik9 benchmark data slice (21KB sample, file '1234567'). " - "Reference corpus for compression benchmarking." - ), - "tags": json.dumps(["enwik9", "benchmark", "compression", "hutter-prize"]), - "subdir": "enwik9_data", - }, -] - - -def collect_files(subdir: str) -> list[str]: - d = DATA_ROOT / subdir - if not d.exists(): - return [] - return sorted(str(p) for p in d.iterdir() if p.is_file()) - - -def _upsert(db: sqlite3.Connection, row: dict) -> None: - cols = list(row.keys()) - placeholders = ", ".join(f":{k}" for k in cols) - update_cols = [c for c in cols if c not in ("pkg", "version")] - update_sql = ", ".join(f"{c}=excluded.{c}" for c in update_cols) - db.execute( - f"INSERT INTO packages ({', '.join(cols)}) VALUES ({placeholders}) " - f"ON CONFLICT(pkg, version) DO UPDATE SET {update_sql}", - row, - ) - - -def main(dry_run: bool = False) -> None: - now = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - if dry_run: - print("[dry-run] would insert:") - - db = None if dry_run else sqlite3.connect(DB_PATH) - - for spec in PACKAGES: - subdir = spec.pop("subdir") - files = collect_files(subdir) - row = { - **spec, - "files": json.dumps(files), - "indexed_utc": now, - } - - if dry_run: - print(f" {row['pkg']} v{row['version']} ({len(files)} files)") - spec["subdir"] = subdir # restore for reuse - continue - - _upsert(db, row) - print(f" indexed {row['pkg']} ({len(files)} files)") - spec["subdir"] = subdir - - if db: - # rebuild FTS so semantic_query picks up new rows - db.execute("INSERT INTO packages_fts(packages_fts) VALUES ('rebuild')") - db.execute("INSERT INTO packages_fts(packages_fts) VALUES ('optimize')") - db.commit() - db.close() - print(f"done — substrate_index.db updated at {DB_PATH}") - - -if __name__ == "__main__": - dry_run = "--dry-run" in sys.argv - main(dry_run) diff --git a/5-Applications/tools-scripts/ingestion/ingest_hf_geospatial_all_pages.py b/5-Applications/tools-scripts/ingestion/ingest_hf_geospatial_all_pages.py deleted file mode 100644 index 0c3a1990..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_hf_geospatial_all_pages.py +++ /dev/null @@ -1,79 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -from pathlib import Path -import json -from datasets import load_dataset -from huggingface_hub import HfApi -import geopandas as gpd - -OUTPUT_DIR = Path("data_baselines/geospatial").absolute() -OUTPUT_DIR.mkdir(parents=True, exist_ok=True) -LOG = Path("data_baselines/geospatial_ingest.log") - - -def normalize_crs(df, target_epsg=4326): - if hasattr(df, "to_crs"): - return df.to_crs(epsg=target_epsg) - return df - - -def convert_geo(table, out_path): - if hasattr(table, "to_pandas"): - df = table.to_pandas() - else: - df = table - - if "geometry" in df.columns: - gdf = gpd.GeoDataFrame(df, geometry="geometry") - gdf = normalize_crs(gdf) - gdf.to_file(out_path, driver="GeoJSON") - return True - return False - - -def ingest_dataset(dataset_id): - print(f"[INGEST] {dataset_id}") - ds = load_dataset(dataset_id) - meta = [] - for split, table in ds.items(): - out_dir = OUTPUT_DIR / dataset_id.replace("/", "__") - out_dir.mkdir(parents=True, exist_ok=True) - safe_split = split.replace("/", "_") - target_parquet = out_dir / f"{safe_split}.parquet" - try: - table.to_parquet(target_parquet, index=False) - except Exception as e: - print(f" cannot parquet {dataset_id}/{split}: {e}") - continue - - geojs = out_dir / f"{safe_split}.geojson" - try: - convert_geo(table, geojs) - except Exception as e: - print(f" geo conversion fail {dataset_id}/{split}: {e}") - - meta.append({"dataset": dataset_id, "split": split, "path": str(target_parquet), "geojson": str(geojs) if geojs.exists() else None}) - - with LOG.open("a", encoding="utf-8") as f: - f.write(json.dumps({"dataset": dataset_id, "meta": meta}) + "\n") - - -if __name__ == "__main__": - api = HfApi() - # geospatial + datasets library=datasets trending set - datasets = [] - results = api.list_datasets(filter="geospatial", sort="trending_score", limit=200) - for r in results: - if r.id not in datasets: - datasets.append(r.id) - - print(f"Found {len(datasets)} datasets") - for dsid in datasets: - try: - ingest_dataset(dsid) - except Exception as e: - print(f"FAILED {dsid}: {e}") diff --git a/5-Applications/tools-scripts/ingestion/ingest_large_file.py b/5-Applications/tools-scripts/ingestion/ingest_large_file.py deleted file mode 100644 index 84e8dc52..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_large_file.py +++ /dev/null @@ -1,719 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=STORE / DOMAIN=DATA / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Large-File Ingestor — Chunked adapter for multi-GB research documents. -======================================================================= -**concept_anchor:** domain=substrate / concept=large_file_chunked_ingest / resolution=STABLE - -PROBLEM -------- -ingest_archive.py reads only the first 8 KB of each file. That is fine for -most research artifacts (specs, code, session JSON) but completely wrong for: - - - Exported AI conversation logs (100 KB – 10 MB of markdown) - - Connectome datasets (CSV/JSON, hundreds of MB) - - Literature harvests (many MB of plain text) - - Bulk export dumps (multi-GB XML / JSON) - -For these files the first 8 KB is boilerplate/scaffolding — the actual signal -is buried hundreds of kilobytes in. - -APPROACH --------- -This adapter borrows the ISO prepass compression tech to solve the indexing -problem. The key insight: - - iso_symbol_table.prepass(chunk) → (compressed_text, substitution_log) - - The substitution_log is a domain-tagged semantic fingerprint of the chunk. - If the chunk contains "neuron", "synapse", "axon" we get iso_bio hits. - If it contains "compression", "entropy", "codec" we get iso_unit/iso_abbrev - hits. The log tells us WHAT IS IN THE TEXT without us doing NLP. - - Aggregating substitution counts across all chunks gives us a document-level - semantic profile even for files we cannot hold in RAM. - -SAMPLING STRATEGY (for multi-GB files) ---------------------------------------- - full : read every chunk (slow, accurate — use for files < ~100 MB) - uniform : evenly-spaced samples across the file (fast, approximate) - head_tail : first N + last N chunks (catches headers and conclusions) - - Default: uniform with up to 256 sample windows of 64 KB each (~16 MB - coverage regardless of file size). - -SCAFFOLDING STRIPPER --------------------- -AI conversation exports (Qwen, Gemini, GPT, Claude) are dominated by -assistant-side metadata: "Thinking", "Search", "Web Fetch", line counts, -URLs. These words poison the keyword extractor. The stripper removes lines -matching known scaffolding patterns before the ISO prepass sees them. - - Recognises: Qwen, Gemini, ChatGPT, Claude Code conversation exports. - -USAGE ------ - # Ingest a single large file - python3 5-Applications/scripts/ingest_large_file.py sessions/conversations_qwen.md - - # Ingest with full bundle attachment sweep (default) - python3 5-Applications/scripts/ingest_large_file.py extraneous/media/ChatExport_2026-03-29/messages.html - - # Force re-index, show verbose per-chunk stats - python3 5-Applications/scripts/ingest_large_file.py sessions/big_dump.md --force --verbose - - # Use full read for smaller files (no sampling) - python3 5-Applications/scripts/ingest_large_file.py shared-data/data/connectome.csv --strategy full - - # Ingest a multi-GB file with custom chunk count - python3 5-Applications/scripts/ingest_large_file.py shared-data/data/huge.json --max-chunks 512 - - # Dry run — show what would be ingested without writing - python3 5-Applications/scripts/ingest_large_file.py 6-Documentation/archive/giant.md --dry-run -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import math -import os -import re -import sqlite3 -import sys -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path - - -# ─── repo root ──────────────────────────────────────────────────────────────── -REPO = Path(__file__).resolve().parents[2] -sys.path.insert(0, str(REPO / "tools" / "scripts")) - -try: - from substrate_git_index import ( - DB_PATH, _open_db, _concept_vector_from_weights, _upsert_package, _PHI, - ) -except ImportError as e: - print(f"[error] cannot import substrate_git_index: {e}", file=sys.stderr) - sys.exit(1) - -try: - from ingest_attachments import collect_auto_attachments -except ImportError as e: - print(f"[error] cannot import ingest_attachments: {e}", file=sys.stderr) - sys.exit(1) - -try: - from ingest_text_hygiene import derive_safe_description -except ImportError as e: - print(f"[error] cannot import ingest_text_hygiene: {e}", file=sys.stderr) - sys.exit(1) - -try: - from iso_symbol_table import ( - prepass as iso_prepass, - DOMAINS as ISO_DOMAINS, - normalize_latex_math, - ) - _ISO_AVAILABLE = True -except ImportError: - _ISO_AVAILABLE = False - def normalize_latex_math(text: str) -> str: - return text - -# ─── defaults ───────────────────────────────────────────────────────────────── - -CHUNK_SIZE = 65_536 # 64 KB per chunk -MAX_CHUNKS = 256 # max sampled chunks (uniform strategy) -STRATEGY = "uniform" # full | uniform | head_tail -ATTACH_MODE = "resonant" # full | resonant | source-only - -# ISO domains to run on each chunk — all standard ones -_ISO_DOMAINS_TO_USE = ["iso_geo", "iso_chem", "iso_bio", "iso_unit", - "iso_lang", "iso_abbrev", "iso_math"] - -# ─── scaffolding strip patterns ─────────────────────────────────────────────── -# Lines matching any of these are noise from AI conversation exports. -# Applied BEFORE the ISO prepass and keyword extractor. - -_SCAFFOLD_LINE_PATTERNS: list[re.Pattern] = [p for p in (re.compile(x) for x in [ - r"^Thinking$", - r"^Search$", - r"^Web Fetch$", - r"^Read$", - r"^Write$", - r"^Edit$", - r"^Bash$", - r"^ListFiles[:\s]", - r"^Glob[:\s]", - r"^OUT$", - r"^\(Searching the web", - r"^Fetching content from https?://", - r"^Error during fetch", - r"^\d+ lines of output$", - r"^Interrupted$", - r"^▼ Show more$", - r"^▲ Collapse$", - r"^⎿", - r"^\[connect\]", - r"^\[ingest\]", - r"^Co-Authored-By:", - # Claude Code tool call scaffolding - r"^", - r"^", - r"^", - r"^", - # Qwen-specific - r"^\*\*Approach\*\*:", - r"^User has provided the following answers", - # Generic "N lines of output" variants - r"^\d+ lines? of output", -])] - - -def _strip_scaffolding(text: str) -> str: - """Remove AI conversation export boilerplate, line by line.""" - clean: list[str] = [] - for line in text.splitlines(): - stripped = line.strip() - if not any(p.match(stripped) for p in _SCAFFOLD_LINE_PATTERNS): - clean.append(line) - return "\n".join(clean) - - -# ─── extended cluster vocabulary ───────────────────────────────────────────── -# 14-axis vocabulary matching substrate_git_index / ingest_archive. -# Extended here with neuro/connectome and large-file-relevant terms. - -_CLUSTER_KEYWORDS: dict[int, set[str]] = { - 0: {"substrate", "foam", "voxel", "node", "fabric", "manifold", "register", - "soliton", "tsm", "metafoam", "capsule"}, - 1: {"compress", "compression", "codec", "encode", "decode", "entropy", - "huffman", "soliton", "symbol", "iso", "lut", "lookup", "prepass", - "dictionary", "alphabet", "phoneme", "substitution", "bandwidth"}, - 2: {"graph", "dag", "node", "edge", "tree", "merkle", "hash", "pointer", - "traversal", "topology", "manifold", "fractal", "connectome", - "synapse", "axon", "dendrite", "circuit"}, - 3: {"hardware", "risc", "fpga", "hdl", "asic", "pcb", "circuit", "chip", - "kda", "rad", "trace", "silicon", "lithography", "nanowire"}, - 4: {"time", "planck", "clock", "tick", "timing", "synchron", "latency", - "quantum", "temporal", "duration", "schedule", "spike", "latency", - "interspike", "interval", "phase"}, - 5: {"crypto", "hash", "sha256", "merkle", "zk", "stark", "proof", - "signature", "seal", "attest", "verify", "provenance"}, - 6: {"database", "sql", "sqlite", "index", "query", "schema", "table", - "row", "column", "fts", "search", "source", "git"}, - 7: {"semantic", "language", "concept", "meaning", "vector", "embed", - "nlp", "token", "word", "text", "notation", "symbol", "translate", - "analog", "idea", "weight", "research", "coding", "decode", - "encode", "representation", "population", "hypervector", - "patamathematics", "pataphysics", "patamathematical"}, - 8: {"physics", "entropy", "energy", "wave", "frequency", "field", - "quantum", "particle", "force", "mass", "density", "pressure", - "temperature", "superconductor", "photon", "electron", "orbital", - # neuro/biology extended - "neuron", "neural", "synaptic", "cortex", "hippocampus", "thalamus", - "neuroscience", "electrophysiology", "potential", "firing", - "receptor", "protein", "molecular", "genome", "chromosome", - "membrane", "voltage", "depolarize", "channel", - "geometry", "topology", "polytope", "torsion", "mobius", "equation", - "proof", "theorem", "invariant", "patamathematics"}, - 9: {"security", "attack", "exploit", "vuln", "threat", "encrypt", - "defense", "audit", "taint", "isolat", "sandbox", "secret"}, - 10: {"os", "kernel", "process", "memory", "virtual", "vm", "hypervisor", - "syscall", "opcode", "instruction", "runtime", "executor"}, - 11: {"research", "discover", "hypothes", "theory", "experiment", "paper", - "result", "finding", "analysis", "study", "novel", "specul", - # neuro research terms - "vertebrate", "mammal", "primate", "mouse", "human", "species", - "connectome", "atlas", "resolution", "scan", "imaging", "mri", - "electrode", "recording", "calcium", "optogenetics", "behavior", - "locomotion", "spatial", "navigation", "place", "grid"}, - 12: {"omnitoken", "token", "bridge", "surface", "bus", "ipc", "transport", - "protocol", "route", "mesh", "omni"}, - 13: {"identity", "sovereign", "manifest", "provenance", "attestation", - "ownership", "claim", "ptos", "tag", "classify"}, -} - -_ALL_CLUSTER_WORDS: dict[str, int] = { - word: axis - for axis, words in _CLUSTER_KEYWORDS.items() - for word in words -} - -_STOP = frozenset({ - "the", "and", "for", "that", "this", "with", "from", "are", "was", - "were", "have", "been", "will", "would", "could", "should", "which", - "their", "there", "these", "those", "what", "when", "where", "how", - "all", "any", "can", "not", "but", "also", "more", "some", "into", - "they", "has", "its", "than", "each", "only", "about", "such", -}) - - -# ─── ISO domain → concept axis mapping ─────────────────────────────────────── -# When the ISO prepass fires on a domain, boost these axes. - -_ISO_DOMAIN_AXIS: dict[str, list[int]] = { - "iso_chem": [8], # chemistry → physics/entropy - "iso_bio": [8, 11], # biology → physics + research - "iso_unit": [8, 4], # SI units → physics + time - "iso_math": [8, 7], # math → physics + semantic - "iso_lang": [7], # language codes → semantic - "iso_geo": [13], # geography → identity/sovereign - "iso_abbrev": [7, 11], # abbreviations → semantic + research - "iso_music": [7], # music → semantic - "iso_qchem": [8], # quantum chem → physics -} - - -# ─── chunk reader with sampling ─────────────────────────────────────────────── - -def _chunk_offsets( - file_size: int, - chunk_size: int, - max_chunks: int, - strategy: str, -) -> list[int]: - """Return list of byte offsets to read chunks from.""" - total_chunks = math.ceil(file_size / chunk_size) - - if strategy == "full" or total_chunks <= max_chunks: - return list(range(0, file_size, chunk_size)) - - if strategy == "head_tail": - half = max_chunks // 2 - head = [i * chunk_size for i in range(half)] - tail_start = max(half, total_chunks - half) - tail = [i * chunk_size for i in range(tail_start, total_chunks)] - return sorted(set(head + tail)) - - # uniform (default) - step = max(1, total_chunks // max_chunks) - return [i * chunk_size for i in range(0, total_chunks, step)][:max_chunks] - - -def _read_chunk(path: Path, offset: int, chunk_size: int) -> str: - """Read one chunk from offset, decode leniently.""" - try: - with open(path, "rb") as f: - f.seek(offset) - raw = f.read(chunk_size) - return raw.decode("utf-8", errors="replace") - except Exception: - return "" - - -# ─── keyword extractor ──────────────────────────────────────────────────────── - -def _extract_keywords(text: str) -> dict[str, float]: - """TF-IDF-like scorer identical to ingest_archive, with extended vocab.""" - tokens = re.findall(r"\b[a-zA-Z][a-zA-Z_]{3,}\b", text.lower()) - counts = Counter(t for t in tokens if t not in _STOP) - if not counts: - return {} - - max_c = counts.most_common(1)[0][1] - weights: dict[str, float] = {} - for word, count in counts.most_common(50): - boost = 2.0 if word in _ALL_CLUSTER_WORDS else 1.0 - score = min(1.0, (count / max_c) * boost * 0.85) - if score >= 0.08: - weights[word] = round(score, 3) - return weights - - -# ─── main processor ─────────────────────────────────────────────────────────── - -def process_file( - path: Path, - chunk_size: int = CHUNK_SIZE, - max_chunks: int = MAX_CHUNKS, - strategy: str = STRATEGY, - strip_scaffolding: bool = True, - verbose: bool = False, -) -> dict: - """Read a large file in chunks, return merged concept profile. - - Returns dict with keys: - idea_weights : merged keyword → score - iso_hit_counts : iso_domain → total substitution count - concept_vector : list[float] (14 axes) - foam_score : float - description : str (first meaningful line found) - file_size : int - chunks_read : int - chunks_total : int - """ - file_size = path.stat().st_size - offsets = _chunk_offsets(file_size, chunk_size, max_chunks, strategy) - total_chunks = math.ceil(file_size / chunk_size) - - # Accumulators - merged_counts: Counter = Counter() - iso_hit_counts: Counter = Counter() - description_probe: list[str] = [] - chunks_read = 0 - - for i, offset in enumerate(offsets): - raw_chunk = _read_chunk(path, offset, chunk_size) - if not raw_chunk.strip(): - continue - - probe_budget = 65536 if path.suffix.lower() == ".json" else 16384 - if sum(len(part) for part in description_probe) < probe_budget: - description_probe.append(raw_chunk[:probe_budget]) - - chunk = _strip_scaffolding(raw_chunk) if strip_scaffolding else raw_chunk - analysis_chunk = normalize_latex_math(chunk) - - # ISO prepass — substitution log is the semantic fingerprint - if _ISO_AVAILABLE: - try: - compressed, sub_log = iso_prepass(analysis_chunk, domains=_ISO_DOMAINS_TO_USE) - for domain, hits in sub_log.items(): - iso_hit_counts[domain] += len(hits) - # Run keyword extraction on COMPRESSED text (lower entropy = cleaner signal) - kw = _extract_keywords(compressed) - except Exception: - kw = _extract_keywords(analysis_chunk) - else: - kw = _extract_keywords(analysis_chunk) - - for word, score in kw.items(): - # Weighted merge: later chunks don't overwrite earlier signal - merged_counts[word] += score - - chunks_read += 1 - if verbose: - iso_note = "" - if _ISO_AVAILABLE: - top = sorted(iso_hit_counts.items(), key=lambda x: -x[1])[:3] - iso_note = f" iso={dict(top)}" - print(f" chunk {i+1}/{len(offsets)} offset={offset//1024}KB" - f" kw={len(kw)}{iso_note}") - - description = derive_safe_description(path, "\n".join(description_probe), max_len=200) - - # Normalise merged keyword scores to [0, 1] - if merged_counts: - max_score = max(merged_counts.values()) - idea_weights = { - w: round(min(1.0, s / max_score), 3) - for w, s in merged_counts.most_common(40) - if s / max_score >= 0.08 - } - else: - idea_weights = {} - - # Boost idea_weights using ISO domain hit counts - # Each ISO hit translates to a synthetic keyword signal on the relevant axes - for domain, count in iso_hit_counts.items(): - if count == 0: - continue - norm_count = min(1.0, count / 500.0) # saturate at 500 hits - axes = _ISO_DOMAIN_AXIS.get(domain, []) - # Inject synthetic signal words for dominant ISO domains - _ISO_DOMAIN_SIGNAL_WORDS = { - "iso_bio": "biology", - "iso_chem": "chemistry", - "iso_unit": "physics", - "iso_math": "mathematics", - "iso_lang": "language", - "iso_geo": "geography", - "iso_abbrev": "abbreviation", - "iso_music": "music", - "iso_qchem": "quantum", - } - signal_word = _ISO_DOMAIN_SIGNAL_WORDS.get(domain) - if signal_word and norm_count >= 0.05: - # Only add if it doesn't already outrank this - existing = idea_weights.get(signal_word, 0.0) - idea_weights[signal_word] = max(existing, round(norm_count * 0.9, 3)) - - concept_vec = _concept_vector_from_weights(idea_weights) - foam_score = round( - sum(idea_weights.values()) / max(len(idea_weights), 1) * _PHI, 4 - ) - - return { - "idea_weights": idea_weights, - "iso_hit_counts": dict(iso_hit_counts), - "concept_vector": concept_vec, - "foam_score": foam_score, - "description": description, - "file_size": file_size, - "chunks_read": chunks_read, - "chunks_total": total_chunks, - } - - -# ─── domain detector ────────────────────────────────────────────────────────── - -def _detect_domain(path: Path, iso_hits: dict[str, int], idea_weights: dict[str, float]) -> str: - """Infer concept_anchor domain from ISO hit profile and path.""" - path_str = str(path).lower() - - # Path-based overrides - if any(k in path_str for k in ("connectome", "neuro", "synapse", "brain", "cortex")): - return "neuroscience" - if any(k in path_str for k in ("hutter", "compress", "codec", "iso", "lut")): - return "compression" - if any(k in path_str for k in ("tsm", "soliton", "metafoam", "substrate")): - return "substrate" - if any(k in path_str for k in ("govern", "legal", "patent", "ethic")): - return "governance" - if any(k in path_str for k in ("kda", "hardware", "pcb", "hdl")): - return "hardware" - - # ISO hit profile: dominant domain wins - if iso_hits: - top_iso = max(iso_hits, key=lambda k: iso_hits[k]) - iso_to_domain = { - "iso_bio": "neuroscience", - "iso_chem": "chemistry", - "iso_unit": "physics", - "iso_math": "mathematics", - "iso_lang": "linguistics", - "iso_geo": "geography", - } - if iso_to_domain.get(top_iso): - return iso_to_domain[top_iso] - - # Keyword profile fallback - top_words = set(list(idea_weights.keys())[:10]) - if top_words & {"neuron", "synapse", "cortex", "connectome", "neural", "spike"}: - return "neuroscience" - if top_words & {"compress", "entropy", "codec", "symbol", "iso"}: - return "compression" - if top_words & { - "geometry", "topology", "polytope", "torsion", "mobius", - "equation", "proof", "theorem", "invariant", "patamathematics", - "pataphysics", - }: - return "mathematics" - - return "research" - - -def _ptos_domain_for_large_file(path: Path, concept_domain: str) -> str: - """Map a concept-domain inference onto the PTOS operational domain axis.""" - path_str = str(path).lower() - if any(k in path_str for k in ("validation", "verification", "test", "tests", "benchmark", "rigor")): - return "TEST" - mapping = { - "compression": "COMPUTE", - "substrate": "COMPUTE", - "mathematics": "COMPUTE", - "computation": "COMPUTE", - "governance": "RULE", - "containment": "RULE", - "cryptography": "RULE", - "hardware": "MATERIAL", - "physics": "MATERIAL", - "chemistry": "MATERIAL", - "biology": "MATERIAL", - "linguistics": "COMMS", - "neuroscience": "DATA", - "geography": "DATA", - "research": "DATA", - } - return mapping.get(concept_domain, "DATA") - - -# ─── pkg name ───────────────────────────────────────────────────────────────── - -def _pkg_name(path: Path) -> str: - """Derive stable pkg identifier. Same rules as ingest_archive.""" - try: - rel = path.relative_to(REPO) - parts = [p.lower()[:8] for p in rel.parts[:-1] - if p.lower() not in ("archive", "sort this", "category", - "research documents")] - stem = re.sub(r"[^a-z0-9]+", "_", path.stem.lower())[:40] - prefix = "-".join(p for p in parts if p)[:20] - tag = f"arc-{prefix}-{stem}" if prefix else f"arc-{stem}" - except ValueError: - # Path outside repo — use filename only - stem = re.sub(r"[^a-z0-9]+", "_", path.stem.lower())[:40] - tag = f"arc-ext-{stem}" - return tag[:80] - - -# ─── ingest ─────────────────────────────────────────────────────────────────── - -def ingest( - path: Path, - pkg_override: str | None = None, - chunk_size: int = CHUNK_SIZE, - max_chunks: int = MAX_CHUNKS, - strategy: str = STRATEGY, - attach_mode: str = ATTACH_MODE, - strip_scaffolding: bool = True, - force: bool = False, - dry_run: bool = False, - verbose: bool = False, -) -> str: - """Ingest a large file into the substrate index. Returns status string.""" - if not path.exists(): - print(f"[error] file not found: {path}", file=sys.stderr) - return "error" - - pkg = pkg_override or _pkg_name(path) - version = "1.0.0" - - conn = _open_db() - - if not force and not dry_run: - row = conn.execute( - "SELECT pkg FROM packages WHERE pkg=? AND version=?", (pkg, version) - ).fetchone() - if row: - print(f"[skip] {pkg} already indexed (use --force to re-index)") - return "exists" - - print(f"[large-file] {path.name} ({path.stat().st_size // 1024} KB)") - print(f" strategy={strategy} max_chunks={max_chunks}" - f" chunk={chunk_size // 1024}KB strip_scaffolding={strip_scaffolding}" - f" attachments={attach_mode}") - - profile = process_file( - path, - chunk_size=chunk_size, - max_chunks=max_chunks, - strategy=strategy, - strip_scaffolding=strip_scaffolding, - verbose=verbose, - ) - - idea_weights = profile["idea_weights"] - iso_hits = profile["iso_hit_counts"] - concept_vec = profile["concept_vector"] - foam_score = profile["foam_score"] - description = profile["description"] - - domain = _detect_domain(path, iso_hits, idea_weights) - ptos_domain = _ptos_domain_for_large_file(path, domain) - stem = re.sub(r"[^a-z0-9]+", "_", path.stem.lower())[:60] - concept_anchor = { - "domain": domain, - "concept": stem, - "resolution": "FORMING", - } - - axes_lit = sum(1 for x in concept_vec if x > 0.01) - print(f" pkg={pkg}") - print(f" domain={domain} axes={axes_lit}/14 foam={foam_score}") - print(f" chunks={profile['chunks_read']}/{profile['chunks_total']}" - f" iso_hits={sum(iso_hits.values())} keywords={len(idea_weights)}") - if iso_hits: - top_iso = sorted(iso_hits.items(), key=lambda x: -x[1])[:4] - print(f" top_iso={dict(top_iso)}") - - # Use resonant mode if requested, passing top keywords for search-driven discovery - kw_list = list(idea_weights.keys())[:5] if idea_weights else None - attached_files, attachment_meta = collect_auto_attachments( - path, repo=REPO, mode=attach_mode, keywords=kw_list - ) - print(f" attachments={attachment_meta['attachment_count']}" - f" policies={','.join(attachment_meta['attachment_policies'])}") - - if dry_run: - print(f" [dry-run] would write {pkg}") - return "dry" - - now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - tags = [domain, "large-file", path.suffix.lstrip(".")] - - _upsert_package(conn, { - "pkg": pkg, - "version": version, - "layer": "RULE", - "domain": ptos_domain, - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "NOTE", - "description": description[:500] if description else f"Large file: {path.name}", - "tags": json.dumps(tags), - "files": json.dumps(attached_files), - "sealed_utc": now_utc, - "foam_score": foam_score, - "concept_vector": json.dumps(concept_vec), - "concept_anchor": json.dumps(concept_anchor), - "idea_weights": json.dumps(idea_weights), - "module": "LARGE_FILE_INGEST", - "archetype": "CHUNKED_ISO_PREPASS", - "analog_map": json.dumps({}), - "attachment_meta": json.dumps(attachment_meta), - "ingest_profile": json.dumps({ - "file_size_kb": path.stat().st_size // 1024, - "chunks_read": profile["chunks_read"], - "chunks_total": profile["chunks_total"], - "iso_hit_counts": iso_hits, - "strategy": strategy, - }), - "extension_points": json.dumps([]), - "tier": "RESEARCH", - "visibility": "PRIVATE", - "model_status": "REFERENCE_ONLY", - "taint_status": "CLEAN", - "session_id": attached_files[0], - "indexed_utc": now_utc, - }) - conn.commit() - print(f" [ok] indexed {pkg}") - return "indexed" - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser( - description="Ingest a large file into the substrate index using ISO prepass chunking." - ) - ap.add_argument("file", help="Path to the file to ingest") - ap.add_argument("--pkg", help="Override generated pkg name") - ap.add_argument("--chunk-size", type=int, default=CHUNK_SIZE, - help=f"Bytes per chunk (default {CHUNK_SIZE})") - ap.add_argument("--max-chunks", type=int, default=MAX_CHUNKS, - help=f"Max sample windows (default {MAX_CHUNKS})") - ap.add_argument("--strategy", choices=["full", "uniform", "head_tail"], - default=STRATEGY, - help=f"Sampling strategy (default {STRATEGY})") - ap.add_argument("--attachments", choices=["full", "resonant", "source-only"], - default=ATTACH_MODE, - help=f"Attachment collection mode (default {ATTACH_MODE})") - ap.add_argument("--no-strip", action="store_true", - help="Disable AI conversation scaffolding stripper") - ap.add_argument("--force", action="store_true", - help="Re-index if already exists") - ap.add_argument("--dry-run", action="store_true", - help="Show what would be indexed without writing") - ap.add_argument("--verbose", action="store_true", - help="Print per-chunk stats") - args = ap.parse_args() - - path = Path(args.file).expanduser().resolve() - ingest( - path, - pkg_override=args.pkg, - chunk_size=args.chunk_size, - max_chunks=args.max_chunks, - strategy=args.strategy, - attach_mode=args.attachments, - strip_scaffolding=not args.no_strip, - force=args.force, - dry_run=args.dry_run, - verbose=args.verbose, - ) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/ingestion/ingest_text_hygiene.py b/5-Applications/tools-scripts/ingestion/ingest_text_hygiene.py deleted file mode 100644 index 6162bf98..00000000 --- a/5-Applications/tools-scripts/ingestion/ingest_text_hygiene.py +++ /dev/null @@ -1,255 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Helpers for provenance-safe ingest descriptions. - -The index description field should summarize a source, not preserve raw body -text from captions, chat exports, transcript logs, or structured JSON/session -manifests. This module centralizes the heuristics used by 6-Documentation/archive/file ingestors -to derive a safe one-line description. -""" - -from __future__ import annotations - -import json -import re -from html import unescape -from pathlib import Path -from typing import Any - -_SUBTITLE_SUFFIXES = { - ".ass", - ".lrc", - ".sbv", - ".srt", - ".ssa", - ".sub", - ".ttml", - ".vtt", -} - -_CHAT_EXPORT_HINTS = ( - "chatexport", - "copilot_sessions", - "messages.html", - "messages2.html", - "telegram", - "whatsapp", - "discord", - "slack", -) - -_TRANSCRIPT_HINTS = ( - "conversation", - "conversations_", - "subtitle", - "captions", - "caption", - "transcript", - "chatgpt", - "qwen", - "gemini", - "claude", -) - -# AI model name tokens — only trigger transcript classification when paired -# with a context token in the same filename stem (prevents discord_api.md, -# gemini_api_note.md, captioning_research.md from being mis-classified). -_AI_MODEL_TOKENS = frozenset({"gemini", "claude", "chatgpt", "qwen", "gpt"}) -_TRANSCRIPT_CONTEXT_TOKENS = frozenset({ - "conversation", "conversations", "transcript", "export", - "chat", "session", "log", "messages", -}) - -_SUBTITLE_TIMESTAMP_RE = re.compile( - r"\b\d{1,2}:\d{2}:\d{2}(?:[,.]\d{1,3})?\s*-->\s*" - r"\d{1,2}:\d{2}:\d{2}(?:[,.]\d{1,3})?\b" -) - -_HTML_TITLE_RE = re.compile(r"]*>(.*?)", re.IGNORECASE | re.DOTALL) - -_TOKEN_SPLIT_RE = re.compile(r"[^a-z0-9]+") - - -def _path_tokens(path: Path) -> frozenset[str]: - """Lowercase word tokens from every part of *path* (dirs + filename).""" - tokens: set[str] = set() - for part in path.parts: - tokens.update(t for t in _TOKEN_SPLIT_RE.split(part.lower()) if t) - return frozenset(tokens) - - -def _stem_tokens(path: Path) -> frozenset[str]: - """Lowercase word tokens from the filename stem only.""" - return frozenset(t for t in _TOKEN_SPLIT_RE.split(path.stem.lower()) if t) - - -def _dir_tokens(path: Path) -> frozenset[str]: - """Lowercase word tokens from directory components only (not the filename).""" - tokens: set[str] = set() - for part in path.parts[:-1]: - tokens.update(t for t in _TOKEN_SPLIT_RE.split(part.lower()) if t) - return frozenset(tokens) - - -def _clean_line(text: str, max_len: int) -> str: - text = unescape(text or "") - text = re.sub(r"\s+", " ", text).strip() - return text[:max_len] - - -def _json_string(obj: dict[str, Any], key: str, max_len: int) -> str: - value = obj.get(key) - if isinstance(value, str): - cleaned = _clean_line(value, max_len) - if len(cleaned) >= 3: - return cleaned - return "" - - -def _description_from_json(path: Path, text: str, max_len: int) -> str: - try: - obj = json.loads(text) - except Exception: - return "" - - if isinstance(obj, dict): - for key in ( - "description", - "title", - "source_title", - "summary", - "capture_reason", - "expected_use", - ): - desc = _json_string(obj, key, max_len) - if desc: - return desc - session_id = obj.get("session_id") - if isinstance(session_id, str) and session_id.strip(): - return _clean_line(f"Structured session manifest: {session_id}", max_len) - source_url = obj.get("source_url") - if isinstance(source_url, str) and source_url.strip(): - return _clean_line(f"Structured web-source manifest: {path.name}", max_len) - return _clean_line(f"Structured JSON source: {path.name}", max_len) - - if isinstance(obj, list): - return _clean_line(f"Structured JSON collection: {path.name}", max_len) - - return "" - - -def _looks_like_subtitle(path: Path, text: str) -> bool: - if path.suffix.lower() in _SUBTITLE_SUFFIXES: - return True - # Token match prevents "captioning_research.md" matching on "caption" - if _path_tokens(path) & {"subtitle", "captions", "caption"}: - return True - if _SUBTITLE_TIMESTAMP_RE.search(text): - return True - lines = [line.strip().lower() for line in text.splitlines()[:24] if line.strip()] - music_markers = sum(1 for line in lines if line in {"[music]", "[applause]", "[laughter]"}) - return music_markers >= 2 - - -def _looks_like_chat_export(path: Path, text: str) -> bool: - # Platform directory names (discord, slack, telegram, whatsapp) only trigger - # when they appear in a *directory* component — not the filename. This - # prevents "6-Documentation/archive/discord_api.md" from being mis-labelled as a chat export - # while still catching "discord_exports/messages.html". - platform_hints = {"discord", "slack", "telegram", "whatsapp"} - if _dir_tokens(path) & platform_hints: - return True - # Filename-level hints that are unambiguous export patterns - if path.name.lower() in ("messages.html", "messages2.html"): - return True - if _path_tokens(path) & {"chatexport", "copilot_sessions"}: - return True - lowered = text[:4096].lower() - return any( - hint in lowered - for hint in ( - "joined telegram", - "===== append start", - "chatexport", - "copilot_sessions", - "conversation_id", - "
bool: - stem = _stem_tokens(path) - all_tokens = _path_tokens(path) - - # Unambiguous filename tokens - if all_tokens & {"transcript", "conversations_"}: - return True - if "conversation" in stem or "conversations" in stem: - return True - - # AI model names only fire when paired with a transcript-context token in - # the same stem — prevents "gemini_api_note.md", "claude_sdk.md" from - # being mis-classified as conversation transcripts. - if stem & _AI_MODEL_TOKENS and stem & _TRANSCRIPT_CONTEXT_TOKENS: - return True - - # Content-based fallbacks (unchanged) - lowered = text[:2048].lower() - if "thinking\n" in lowered and "web fetch" in lowered: - return True - if lowered.startswith("user\n") or lowered.startswith("assistant\n"): - return True - return False - - -def _html_title(text: str, max_len: int) -> str: - match = _HTML_TITLE_RE.search(text) - if not match: - return "" - title = _clean_line(match.group(1), max_len) - if not title: - return "" - if any(bad in title.lower() for bad in ("joined telegram", "watch live")): - return "" - return title - - -def derive_safe_description(path: Path | str, text: str, max_len: int = 200) -> str: - """Return a one-line summary safe for indexing as `description`.""" - path = Path(path) - path_str = str(path).lower() - - if path.suffix.lower() == ".json": - desc = _description_from_json(path, text, max_len) - if desc: - return desc - - if _looks_like_subtitle(path, text): - return _clean_line(f"Video subtitle/caption source: {path.name}", max_len) - - if _looks_like_chat_export(path, text): - if path.suffix.lower() == ".jsonl": - return _clean_line(f"Conversation log archive: {path.name}", max_len) - return _clean_line(f"Chat export archive: {path.name}", max_len) - - if _looks_like_transcript(path, text): - return _clean_line(f"Conversation transcript source: {path.name}", max_len) - - if path.suffix.lower() == ".html": - title = _html_title(text, max_len) - if title: - return title - - for line in text.splitlines(): - line = _clean_line(line.strip().lstrip("#").strip(), max_len) - if len(line) > 8 and not line.startswith(("```", "http", "<")): - return line - - fallback = path.name if path.name else path_str - return _clean_line(f"Source artifact: {fallback}", max_len) diff --git a/5-Applications/tools-scripts/ingestion/moshi_talk_ingest.py b/5-Applications/tools-scripts/ingestion/moshi_talk_ingest.py deleted file mode 100644 index ca0935fb..00000000 --- a/5-Applications/tools-scripts/ingestion/moshi_talk_ingest.py +++ /dev/null @@ -1,284 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -5-Applications/scripts/moshi_talk_ingest.py — Moshi ASR → phoneme stream → substrate ingest - -Pipeline: - audio file / URL → Moshi MimiModel RVQ → phoneme stream (RVQ levels 0-1) - → concept_vector (14 axes from phoneme statistics) - → staged session JSON → substrate ingest-session - -Install: - pip install moshi # 0.2.13, requires torch (already installed) - -Usage: - python3 5-Applications/scripts/moshi_talk_ingest.py - python3 5-Applications/scripts/moshi_talk_ingest.py --url - python3 5-Applications/scripts/moshi_talk_ingest.py --transcript # skip ASR - -Outputs: - 5-Applications/out/moshi_talk_ingest/_.json - 5-Applications/out/moshi_talk_ingest/_.md - -Reference: memory/reference_personaplex_phoneme_map.md -""" -from __future__ import annotations - -import argparse -import json -import math -import sys -import hashlib -import pathlib -import re -from datetime import datetime, timezone -from collections import Counter - -OUT_DIR = pathlib.Path("5-Applications/out/moshi_talk_ingest") - -# ── concept_vector axis labels (must match substrate schema) ────────────────── -CV_AXES = [ - "lexical_density", "mean_word_len", "phoneme_entropy", - "consonant_vowel_ratio", "stop_codon_density", "silence_fraction", - "voiced_fraction", "plosive_density", "fricative_density", - "nasal_density", "tonal_variation", "utterance_rate", - "unique_phoneme_ratio", "bind_z_proxy", -] -assert len(CV_AXES) == 14 - - -# ── phoneme helpers ─────────────────────────────────────────────────────────── - -# Very rough IPA→feature map for offline fallback -_VOICED = set("bvðznŋmŋlrwjæøyœɔɛɪʊə") -_PLOSIVE = set("ptk bdg") -_FRICATIVE = set("fvsʒʃθð xɣ") -_NASAL = set("mnŋ") -_VOWEL_PAT = re.compile(r"[aeiouæøyœɔɛɪʊəɐɑ]", re.I) - -def _phoneme_features(phoneme_seq: list[str]) -> dict[str, float]: - """Compute 14-axis concept vector from a phoneme sequence.""" - if not phoneme_seq: - return {k: 0.0 for k in CV_AXES} - - counts = Counter(phoneme_seq) - total = len(phoneme_seq) - uniq = len(counts) - - entropy = -sum((c/total)*math.log2(c/total) for c in counts.values() if c > 0) - - vowels = sum(1 for p in phoneme_seq if _VOWEL_PAT.search(p)) - consonants = total - vowels - cv_ratio = consonants / max(vowels, 1) - - voiced_n = sum(1 for p in phoneme_seq if any(c in _VOICED for c in p)) - plosive_n = sum(1 for p in phoneme_seq if any(c in _PLOSIVE for c in p)) - fricative_n= sum(1 for p in phoneme_seq if any(c in _FRICATIVE for c in p)) - nasal_n = sum(1 for p in phoneme_seq if any(c in _NASAL for c in p)) - - # bind_z proxy: ratio of distinctive phonemes to total (high = structured) - bind_z_proxy = uniq / max(total, 1) * math.log2(max(total, 1) + 1) - - return { - "lexical_density": min(1.0, uniq / 40.0), # normalised to IPA size - "mean_word_len": min(1.0, total / 200.0), - "phoneme_entropy": min(1.0, entropy / 6.0), # max ~6 bits for 64 symbols - "consonant_vowel_ratio":min(1.0, cv_ratio / 4.0), - "stop_codon_density": 0.0, # filled by ASR silence detector - "silence_fraction": 0.0, # filled by ASR - "voiced_fraction": voiced_n / max(total, 1), - "plosive_density": plosive_n / max(total, 1), - "fricative_density": fricative_n/ max(total, 1), - "nasal_density": nasal_n / max(total, 1), - "tonal_variation": 0.0, # requires pitch track - "utterance_rate": 0.0, # filled by ASR timing - "unique_phoneme_ratio": uniq / max(total, 1), - "bind_z_proxy": min(1.0, bind_z_proxy / 5.0), - } - - -# ── moshi ASR path ──────────────────────────────────────────────────────────── - -def _asr_moshi(audio_path: str) -> tuple[str, list[str], dict[str, float]]: - """ - Run Moshi ASR on audio_path. - Returns (transcript, phoneme_seq, timing_info). - - RVQ levels 0-1 capture phoneme identity (place/manner/voicing). - Levels 2+ = fine acoustic realisation — discarded here. - Silence tokens = STOP codons = soliton checkpoints. - - Requires: pip install moshi - """ - try: - import torch - from moshi.models import loaders - from moshi.run.offline import run_offline # type: ignore - except ImportError as exc: - print(f"[moshi_talk_ingest] moshi not installed: {exc}") - print(" pip install moshi") - sys.exit(1) - - device = "cuda" if torch.cuda.is_available() else "cpu" - print(f"[moshi_talk_ingest] loading Moshi on {device}") - - moshi_weight, mimi_weight = loaders.resolve_model_ids() - mimi = loaders.get_mimi(mimi_weight, device=device) - mimi.eval() - - import torchaudio # type: ignore - wav, sr = torchaudio.load(audio_path) - if sr != 24000: - wav = torchaudio.functional.resample(wav, sr, 24000) - wav = wav.mean(0, keepdim=True).unsqueeze(0).to(device) # (1,1,T) - - with torch.no_grad(): - codes = mimi.encode(wav) # (1, n_q, T_codes) - - # Levels 0-1 = phoneme identity stream - phoneme_codes = codes[0, :2, :].cpu().tolist() # [[lvl0...], [lvl1...]] - flat = [f"L0_{c}" for c in phoneme_codes[0]] + [f"L1_{c}" for c in phoneme_codes[1]] - - # Silence = code 0 at level 0 → STOP codon - silences = sum(1 for c in phoneme_codes[0] if c == 0) - total_frames = len(phoneme_codes[0]) - silence_frac = silences / max(total_frames, 1) - - # Crude transcript from RVQ (placeholder — real transcript needs Moshi LM) - transcript = f"[Moshi RVQ stream: {total_frames} frames, {silences} silence tokens]" - - timing = { - "total_frames": total_frames, - "silence_fraction": silence_frac, - "stop_codon_density": silences / max(total_frames // 50, 1), - "utterance_rate": (total_frames - silences) / max(total_frames, 1), - } - return transcript, flat, timing - - -def _asr_transcript_fallback(path: str) -> tuple[str, list[str], dict[str, float]]: - """Read an existing transcript file, produce a word-level phoneme proxy.""" - text = pathlib.Path(path).read_text() - words = re.findall(r"[a-z']+", text.lower()) - # Proxy phoneme seq: word characters as approximate phoneme tokens - phonemes = [ch for w in words for ch in w] - timing = { - "total_frames": len(words), - "silence_fraction": 0.05, - "stop_codon_density": 0.02, - "utterance_rate": 0.95, - } - return text, phonemes, timing - - -# ── session output ──────────────────────────────────────────────────────────── - -def _slug(text: str, max_len: int = 60) -> str: - s = re.sub(r"[^a-z0-9]+", "-", text.lower()).strip("-") - return s[:max_len] - -def _write_session( - source: str, - transcript: str, - phoneme_seq: list[str], - timing: dict[str, float], -) -> pathlib.Path: - OUT_DIR.mkdir(parents=True, exist_ok=True) - ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - slug = _slug(pathlib.Path(source).stem if pathlib.Path(source).exists() else source) - - cv = _phoneme_features(phoneme_seq) - cv["silence_fraction"] = timing.get("silence_fraction", 0.0) - cv["stop_codon_density"] = timing.get("stop_codon_density", 0.0) - cv["utterance_rate"] = timing.get("utterance_rate", 0.0) - - sha = hashlib.sha256(transcript.encode()).hexdigest()[:16] - - package = { - "generated_at": datetime.now(timezone.utc).isoformat(), - "source": source, - "sha256_prefix": sha, - "pipeline_mode": "moshi_asr", - "transcript_excerpt": transcript[:400], - "phoneme_frames": len(phoneme_seq), - "concept_vector": cv, - "idea_weights": { - # top concept axes as idea weights for substrate search - axis: round(val, 4) - for axis, val in sorted(cv.items(), key=lambda x: -x[1]) - if val > 0.05 - }, - "foam_score": None, # computed by substrate on ingest - "notes": ( - "RVQ levels 0-1 = phoneme identity (place/manner/voicing). " - "Levels 2+ discarded. Silence = STOP codon = soliton checkpoint. " - "K=3 ternary encoding: 3^4=81 >= IPA phoneme count." - ), - } - - json_path = OUT_DIR / f"{ts}_{slug}.json" - md_path = OUT_DIR / f"{ts}_{slug}.md" - - json_path.write_text(json.dumps(package, indent=2)) - md_path.write_text( - f"# Moshi Talk Ingest: {slug}\n\n" - f"**Source:** {source} \n" - f"**Generated:** {package['generated_at']} \n" - f"**Phoneme frames:** {package['phoneme_frames']} \n\n" - f"## Transcript excerpt\n\n{transcript[:600]}\n\n" - f"## Concept vector\n\n" - + "\n".join(f"- `{k}`: {v:.4f}" for k, v in cv.items()) - + "\n\n## Idea weights\n\n" - + "\n".join(f"- `{k}`: {v}" for k, v in package["idea_weights"].items()) - + f"\n\n**Next step:** `python3 substrate_git_index.py ingest-session {json_path}`\n" - ) - return json_path - - -# ── CLI ─────────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - ap.add_argument("audio", nargs="?", help="Audio file (.wav/.mp3/.flac)") - ap.add_argument("--transcript", help="Use existing transcript text file (skip ASR)") - ap.add_argument("--url", help="Download audio from URL before processing") - args = ap.parse_args() - - if args.url: - try: - import yt_dlp # type: ignore - out_tmpl = "/tmp/moshi_ingest_%(id)s.%(ext)s" - yt_dlp.YoutubeDL({"format": "bestaudio", "outtmpl": out_tmpl, "quiet": True}).download([args.url]) - import glob - audio_path = sorted(glob.glob("/tmp/moshi_ingest_*"))[-1] - except ImportError: - print("[moshi_talk_ingest] yt-dlp not installed — provide audio file directly") - print(" pip install yt-dlp") - sys.exit(1) - elif args.audio: - audio_path = args.audio - elif args.transcript: - audio_path = args.transcript - else: - ap.print_help() - sys.exit(1) - - if args.transcript: - transcript, phoneme_seq, timing = _asr_transcript_fallback(args.transcript) - source = args.transcript - else: - transcript, phoneme_seq, timing = _asr_moshi(audio_path) - source = audio_path - - json_path = _write_session(source, transcript, phoneme_seq, timing) - print(f"[moshi_talk_ingest] session written: {json_path}") - print(f"[moshi_talk_ingest] next: python3 substrate_git_index.py ingest-session {json_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/literature/literature_deepcompression_download.py b/5-Applications/tools-scripts/literature/literature_deepcompression_download.py deleted file mode 100644 index 2ce83895..00000000 --- a/5-Applications/tools-scripts/literature/literature_deepcompression_download.py +++ /dev/null @@ -1,1134 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""High-coverage literature harvesting pipeline with compact archival. - -This script is a practical stand-in for "fetch everything": it queries multiple -open sources, deduplicates, ranks relevance, and writes an append-only -"DeepCompression" archive plus a nibble-style compact index. - -Sources: -- Crossref -- OpenAlex -- arXiv - -No third-party dependencies are required. -""" - -import argparse -import csv -import hashlib -import json -import os -import re -import statistics -import time -from collections import Counter -import urllib.error -import urllib.parse -import urllib.request -import xml.etree.ElementTree as ET -import zlib -from dataclasses import dataclass, asdict -from datetime import datetime, timezone -from typing import Dict, Iterable, List, Optional, Tuple - -try: - from scripts.logic_signal_substrate_translation import ( - assert_surface_write_safe, - logic_signal_substrate_from_archive_domain, - logic_signal_substrate_from_surface, - surface_from_logic_signal_substrate, - ) -except ImportError: - try: - from logic_signal_substrate_translation import ( - assert_surface_write_safe, - logic_signal_substrate_from_archive_domain, - logic_signal_substrate_from_surface, - surface_from_logic_signal_substrate, - ) - except ImportError: - # Module not available — substrate surface writes disabled. - # find_low_coverage_sources and other pure functions remain usable. - assert_surface_write_safe = lambda *a, **kw: None - logic_signal_substrate_from_archive_domain = lambda *a, **kw: None - logic_signal_substrate_from_surface = lambda *a, **kw: None - surface_from_logic_signal_substrate = lambda *a, **kw: {} - - -USER_AGENT = "graph_os-literature-harvest/1.0 (+local-script)" -PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) -OMNITOKEN_DIR = os.path.join(PROJECT_ROOT, "out", "omnitoken_bridge") -OMNITOKEN_SURFACE_PATH = os.path.join(OMNITOKEN_DIR, "egress_surface.json") - - -DEFAULT_QUERIES = [ - "human interaction safety ultra-fast computation", - "real-time AI systems human factors safety", - "algorithmic decision support cognitive overload", - "human-in-the-loop control systems safety", - "AI alignment human-computer interaction", - "autonomy override governance AI systems", - "model latency human trust calibration", - "psychological effects of conversational AI", - "adaptive systems intervention threshold", - "safety-critical machine learning human oversight", -] - - -SAFETY_KEYWORDS = [ - "human", - "interaction", - "safety", - "oversight", - "alignment", - "trust", - "cognitive", - "entrainment", - "autonomy", - "handover", - "intervention", - "governance", - "risk", - "control", - "error", - "failsafe", - "fail-safe", -] - - -@dataclass -class Paper: - source: str - source_id: str - title: str - abstract: str - authors: List[str] - year: Optional[int] - venue: str - doi: str - url: str - query: str - relevance_score: int = 0 - relevance_bucket_4bit: int = 0 - nibble_fingerprint_hex: str = "" - - -def now_utc() -> str: - return datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - -def normalize_ws(text: str) -> str: - return re.sub(r"\s+", " ", (text or "")).strip() - - -def normalize_title(text: str) -> str: - text = normalize_ws(text).lower() - text = re.sub(r"[^a-z0-9 ]+", "", text) - return text - - -def safe_int(x: object) -> Optional[int]: - try: - return int(x) - except (TypeError, ValueError): - return None - - -def fetch_json(url: str, timeout: float = 25.0, retries: int = 3) -> dict: - last_exc: Optional[Exception] = None - for attempt in range(1, retries + 1): - try: - req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - with urllib.request.urlopen(req, timeout=timeout) as resp: - return json.loads(resp.read().decode("utf-8", errors="replace")) - except (OSError, urllib.error.URLError, json.JSONDecodeError) as exc: # pragma: no cover - last_exc = exc - if attempt < retries: - time.sleep(1.2 * attempt) - raise RuntimeError(f"Failed to fetch JSON from {url}: {last_exc}") - - -def fetch_text(url: str, timeout: float = 25.0, retries: int = 3) -> str: - last_exc: Optional[Exception] = None - for attempt in range(1, retries + 1): - try: - req = urllib.request.Request(url, headers={"User-Agent": USER_AGENT}) - with urllib.request.urlopen(req, timeout=timeout) as resp: - return resp.read().decode("utf-8", errors="replace") - except (OSError, urllib.error.URLError) as exc: # pragma: no cover - last_exc = exc - if attempt < retries: - time.sleep(1.2 * attempt) - raise RuntimeError(f"Failed to fetch text from {url}: {last_exc}") - - -def score_relevance(title: str, abstract: str) -> int: - text = f"{title} {abstract}".lower() - score = 0 - for kw in SAFETY_KEYWORDS: - if kw in text: - score += 1 - return score - - -def bucket_4bit(score: int, max_score: int = 16) -> int: - score = max(0, score) - if max_score <= 0: - return 0 - b = round((min(score, max_score) / max_score) * 15) - return int(max(0, min(15, b))) - - -def nibble_fingerprint(title: str, abstract: str) -> str: - digest = hashlib.sha256((title + "\n" + abstract).encode("utf-8", errors="ignore")).hexdigest() - # 16 nibbles = compact 64-bit-style fingerprint (hex chars are already nibbles) - return digest[:16] - - -def dedupe_key(p: Paper) -> str: - if p.doi: - return f"doi:{p.doi.lower()}" - return f"title:{normalize_title(p.title)}" - - -def from_crossref(query: str, rows: int) -> Iterable[Paper]: - q = urllib.parse.quote(query) - url = ( - "https://api.crossref.org/works" - f"?query={q}&rows={rows}&select=DOI,title,author,container-title,URL,published-print,published-online,issued" - ) - data = fetch_json(url) - items = data.get("message", {}).get("items", []) - for it in items: - title = normalize_ws(" ".join(it.get("title", []) if isinstance(it.get("title", []), list) else [str(it.get("title", ""))])) - venue = normalize_ws(" ".join(it.get("container-title", []) if isinstance(it.get("container-title", []), list) else [str(it.get("container-title", ""))])) - doi = normalize_ws(it.get("DOI", "")) - url_out = normalize_ws(it.get("URL", "")) - - authors = [] - for a in it.get("author", []) or []: - given = normalize_ws(a.get("given", "")) - family = normalize_ws(a.get("family", "")) - full = normalize_ws(f"{given} {family}") - if full: - authors.append(full) - - year = None - for fld in ("published-print", "published-online", "issued"): - parts = (((it.get(fld) or {}).get("date-parts") or [[None]])[0] or [None]) - y = safe_int(parts[0]) - if y: - year = y - break - - if title: - yield Paper( - source="crossref", - source_id=doi or url_out or hashlib.md5(title.encode()).hexdigest(), - title=title, - abstract="", - authors=authors, - year=year, - venue=venue, - doi=doi, - url=url_out, - query=query, - ) - - -def from_openalex(query: str, rows: int) -> Iterable[Paper]: - q = urllib.parse.quote(query) - url = f"https://api.openalex.org/works?search={q}&per-page={rows}" - data = fetch_json(url) - items = data.get("results", []) - for it in items: - title = normalize_ws(it.get("display_name", "")) - abstract_idx = it.get("abstract_inverted_index") or {} - if abstract_idx: - max_pos = 0 - for positions in abstract_idx.values(): - for pos in positions: - if pos > max_pos: - max_pos = pos - tokens = [""] * (max_pos + 1) - for token, positions in abstract_idx.items(): - for pos in positions: - if 0 <= pos < len(tokens): - tokens[pos] = token - abstract = normalize_ws(" ".join(tokens)) - else: - abstract = "" - - venue = normalize_ws(((it.get("primary_location") or {}).get("source") or {}).get("display_name", "")) - year = safe_int(it.get("publication_year")) - doi = normalize_ws((it.get("doi") or "").replace("https://doi.org/", "")) - url_out = normalize_ws(it.get("id", "")) - authors = [ - normalize_ws(((a.get("author") or {}).get("display_name", ""))) - for a in (it.get("authorships") or []) - ] - authors = [a for a in authors if a] - - if title: - yield Paper( - source="openalex", - source_id=url_out or doi or hashlib.md5(title.encode()).hexdigest(), - title=title, - abstract=abstract, - authors=authors, - year=year, - venue=venue, - doi=doi, - url=url_out, - query=query, - ) - - -def from_arxiv(query: str, rows: int) -> Iterable[Paper]: - q = urllib.parse.quote(query) - url = f"https://export.arxiv.org/api/query?search_query=all:{q}&start=0&max_results={rows}" - xml_text = fetch_text(url) - - ns = { - "atom": "http://www.w3.org/2005/Atom", - "arxiv": "http://arxiv.org/schemas/atom", - } - root = ET.fromstring(xml_text) - for entry in root.findall("atom:entry", ns): - title = normalize_ws((entry.findtext("atom:title", default="", namespaces=ns) or "")) - abstract = normalize_ws((entry.findtext("atom:summary", default="", namespaces=ns) or "")) - url_out = normalize_ws((entry.findtext("atom:id", default="", namespaces=ns) or "")) - year = safe_int((entry.findtext("atom:published", default="", namespaces=ns) or "")[:4]) - authors = [ - normalize_ws((a.findtext("atom:name", default="", namespaces=ns) or "")) - for a in entry.findall("atom:author", ns) - ] - authors = [a for a in authors if a] - doi = "" - for cat in entry.findall("arxiv:doi", ns): - if cat is not None and (cat.text or "").strip(): - doi = normalize_ws(cat.text) - break - - if title: - yield Paper( - source="arxiv", - source_id=url_out or hashlib.md5(title.encode()).hexdigest(), - title=title, - abstract=abstract, - authors=authors, - year=year, - venue="arXiv", - doi=doi, - url=url_out, - query=query, - ) - - -def enrich_scores(papers: Iterable[Paper]) -> List[Paper]: - out: List[Paper] = [] - for p in papers: - p.relevance_score = score_relevance(p.title, p.abstract) - p.relevance_bucket_4bit = bucket_4bit(p.relevance_score) - p.nibble_fingerprint_hex = nibble_fingerprint(p.title, p.abstract) - out.append(p) - return out - - -def dedupe(papers: Iterable[Paper]) -> List[Paper]: - best: Dict[str, Paper] = {} - for p in papers: - k = dedupe_key(p) - old = best.get(k) - if old is None or p.relevance_score > old.relevance_score: - best[k] = p - return list(best.values()) - - -def find_low_coverage_sources(source_totals: Dict[str, int], min_total_per_source: int) -> Dict[str, int]: - return {name: total for name, total in source_totals.items() if total < min_total_per_source} - - -def write_jsonl(path: str, papers: Iterable[Paper]) -> None: - os.makedirs(os.path.dirname(path), exist_ok=True) - with open(path, "w", encoding="utf-8") as f: - for p in papers: - f.write(json.dumps(asdict(p), ensure_ascii=False) + "\n") - - -def write_csv(path: str, papers: Iterable[Paper]) -> None: - rows = [asdict(p) for p in papers] - if not rows: - return - os.makedirs(os.path.dirname(path), exist_ok=True) - with open(path, "w", newline="", encoding="utf-8") as f: - w = csv.DictWriter(f, fieldnames=list(rows[0].keys())) - w.writeheader() - w.writerows(rows) - - -def write_blackhole_vault(vault_dir: str, papers: List[Paper], run_id: str) -> str: - return write_blackhole_vault_with_mode( - vault_dir=vault_dir, - papers=papers, - run_id=run_id, - archive_mode="legacy", - benchmark_against_legacy=False, - ) - - -def canonical_archive_row(p: Paper) -> Dict[str, object]: - """Return a stable, compact record used for partitioned archive encoding.""" - return { - "k": dedupe_key(p), - "b": int(p.relevance_bucket_4bit), - "f": p.nibble_fingerprint_hex, - "src": normalize_ws(p.source), - "t": normalize_ws(p.title), - "a": normalize_ws(p.abstract), - "y": p.year if p.year is not None else 0, - "v": normalize_ws(p.venue), - "d": normalize_ws(p.doi), - "u": normalize_ws(p.url), - "q": normalize_ws(p.query), - "s": int(p.relevance_score), - "au": [normalize_ws(author) for author in (p.authors or [])], - } - - -def build_legacy_payload_bytes(papers: List[Paper]) -> bytes: - """Build legacy JSONL payload bytes for compression.""" - lines = [json.dumps(asdict(p), ensure_ascii=False) for p in papers] - return ("\n".join(lines) + "\n").encode("utf-8") - - -def build_partitioned_payload_bytes(papers: List[Paper]) -> bytes: - """Build a bucketed, field-partitioned payload that improves locality for zlib.""" - canonical_rows = [canonical_archive_row(p) for p in papers] - canonical_rows.sort(key=lambda row: (int(row["b"]), str(row["f"]), str(row["k"]))) - - buckets: Dict[int, List[Dict[str, object]]] = {} - for row in canonical_rows: - bucket = int(row["b"]) - buckets.setdefault(bucket, []).append(row) - - bucket_payloads: List[Dict[str, object]] = [] - for bucket in sorted(buckets.keys()): - rows = buckets[bucket] - bucket_payloads.append( - { - "bucket": bucket, - "k": [row["k"] for row in rows], - "f": [row["f"] for row in rows], - "src": [row["src"] for row in rows], - "t": [row["t"] for row in rows], - "a": [row["a"] for row in rows], - "y": [row["y"] for row in rows], - "v": [row["v"] for row in rows], - "d": [row["d"] for row in rows], - "u": [row["u"] for row in rows], - "q": [row["q"] for row in rows], - "s": [row["s"] for row in rows], - "au": [row["au"] for row in rows], - } - ) - - payload = { - "schema": "deepcompression/partitioned/v1", - "paper_count": len(papers), - "bucket_count": len(bucket_payloads), - "buckets": bucket_payloads, - } - return json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8") - - -def build_context_spray_payload_bytes(papers: List[Paper]) -> bytes: - """Build context-sensitive payload with local context cells and per-cell delta emissions. - - Foam strategy: - - Adaptive merge for tiny cells to avoid over-fragmented context groups - - Deterministic rotation in each cell to alter adjacency and expose repeated patterns - - Shared lexicon carry-over so repeated long strings are stored once - """ - rows = [canonical_archive_row(p) for p in papers] - rows.sort( - key=lambda row: ( - str(row["q"]), - str(row["src"]), - int(row["b"]), - str(row["v"]), - int(row["y"]), - str(row["f"]), - ) - ) - - base_cells: Dict[Tuple[str, str, int, str], List[Dict[str, object]]] = {} - for row in rows: - key = ( - str(row["q"]), - str(row["src"]), - int(row["b"]), - str(row["v"]), - ) - base_cells.setdefault(key, []).append(row) - - # Merge tiny cells into broader context bins to avoid sparse overhead. - min_cell_size = 3 - cells: Dict[Tuple[str, str, int, str], List[Dict[str, object]]] = {} - for key, cell_rows in base_cells.items(): - if len(cell_rows) >= min_cell_size: - cells[key] = cell_rows - continue - - query, _source, bucket, _venue = key - merged_key = (query, "*", bucket, "*") - cells.setdefault(merged_key, []).extend(cell_rows) - - lexicon: List[str] = [] - lexicon_index: Dict[str, int] = {} - - def lex(value: str) -> int: - value = normalize_ws(value) - existing = lexicon_index.get(value) - if existing is not None: - return existing - idx = len(lexicon) - lexicon.append(value) - lexicon_index[value] = idx - return idx - - def lex_authors(authors: List[object]) -> List[int]: - return [lex(str(a)) for a in authors] - - emitted_cells: List[Dict[str, object]] = [] - - def top_ngrams(texts: List[str], min_n: int = 2, max_n: int = 3, top_k: int = 16) -> List[str]: - counts: Counter[str] = Counter() - for text in texts: - words = [w for w in re.split(r"\W+", normalize_ws(text).lower()) if len(w) >= 3] - for n in range(min_n, max_n + 1): - if len(words) < n: - continue - for i in range(0, len(words) - n + 1): - phrase = " ".join(words[i : i + n]) - if len(phrase) >= 8: - counts[phrase] += 1 - ranked = [phrase for phrase, c in counts.most_common(top_k * 3) if c >= 3] - # Keep longest phrases first so replacement is stable and deterministic. - ranked.sort(key=lambda x: (-len(x), x)) - return ranked[:top_k] - - def apply_ngrams(text: str, ngrams: List[str]) -> str: - out = normalize_ws(text) - if not out or not ngrams: - return out - lowered = out.lower() - for i, phrase in enumerate(ngrams): - marker = f"~g{i}~" - # Case-insensitive, deterministic whole-phrase replacement. - pattern = re.compile(re.escape(phrase), flags=re.IGNORECASE) - lowered = pattern.sub(marker, lowered) - return lowered - for key in sorted(cells.keys()): - query, source, bucket, venue = key - cell_rows = cells[key] - if not cell_rows: - continue - - # Deterministic rotation for context-sensitive adjacency shifts. - if len(cell_rows) > 1: - seed = hashlib.sha256(f"{query}|{source}|{bucket}|{venue}".encode("utf-8")).hexdigest() - offset = int(seed[:4], 16) % len(cell_rows) - if offset: - cell_rows = cell_rows[offset:] + cell_rows[:offset] - - cell_ngrams = top_ngrams( - [str(r["t"]) for r in cell_rows] + [str(r["a"]) for r in cell_rows], - min_n=2, - max_n=3, - top_k=16, - ) - - first = cell_rows[0] - first_full = { - "k": first["k"], - "f": first["f"], - "src": lex(str(first["src"])), - "t": lex(apply_ngrams(str(first["t"]), cell_ngrams)), - "a": lex(apply_ngrams(str(first["a"]), cell_ngrams)), - "y": first["y"], - "v": lex(str(first["v"])), - "d": lex(str(first["d"])), - "u": lex(str(first["u"])), - "q": lex(str(first["q"])), - "s": first["s"], - "au": lex_authors(list(first["au"])), - } - - deltas: List[Dict[str, object]] = [] - prev = first - for row in cell_rows[1:]: - delta: Dict[str, object] = { - "k": row["k"], - "f": row["f"], - "s": row["s"], - } - for field in ("t", "a", "y", "d", "u", "au"): - if row[field] != prev[field]: - if field == "au": - delta[field] = lex_authors(list(row[field])) - elif field in ("t", "a", "d", "u"): - if field in ("t", "a"): - delta[field] = lex(apply_ngrams(str(row[field]), cell_ngrams)) - else: - delta[field] = lex(str(row[field])) - else: - delta[field] = row[field] - deltas.append(delta) - prev = row - - emitted_cells.append( - { - "ctx": { - "q": lex(query), - "src": lex(source), - "b": bucket, - "v": lex(venue), - }, - "local_ngrams": cell_ngrams, - "seed": first_full, - "spray": deltas, - } - ) - - payload = { - "schema": "deepcompression/context-spray/v2", - "paper_count": len(papers), - "cell_count": len(emitted_cells), - "lexicon": lexicon, - "cells": emitted_cells, - } - return json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8") - - -def build_hybrid_payload_bytes(papers: List[Paper]) -> bytes: - """Build a hybrid payload: - - high-entropy fields in partitioned blocks - - low-entropy repeated fields in context-spray cells - """ - rows = [canonical_archive_row(p) for p in papers] - rows.sort(key=lambda row: (int(row["b"]), str(row["f"]), str(row["k"]))) - - # High-entropy partition: titles/abstracts/urls/dois/authors tend to vary heavily. - high_partition_buckets: Dict[int, List[Dict[str, object]]] = {} - for row in rows: - bucket = int(row["b"]) - high_partition_buckets.setdefault(bucket, []).append( - { - "k": row["k"], - "f": row["f"], - "t": row["t"], - "a": row["a"], - "d": row["d"], - "u": row["u"], - "au": row["au"], - } - ) - - high_blocks: List[Dict[str, object]] = [] - for bucket in sorted(high_partition_buckets.keys()): - block_rows = high_partition_buckets[bucket] - high_blocks.append( - { - "bucket": bucket, - "k": [r["k"] for r in block_rows], - "f": [r["f"] for r in block_rows], - "t": [r["t"] for r in block_rows], - "a": [r["a"] for r in block_rows], - "d": [r["d"] for r in block_rows], - "u": [r["u"] for r in block_rows], - "au": [r["au"] for r in block_rows], - } - ) - - # Low-entropy context spray: source/query/venue/year/score are more repeatable. - low_rows = [ - { - "k": row["k"], - "f": row["f"], - "src": row["src"], - "q": row["q"], - "v": row["v"], - "b": row["b"], - "y": row["y"], - "s": row["s"], - } - for row in rows - ] - - low_rows.sort(key=lambda row: (str(row["q"]), str(row["src"]), int(row["b"]), str(row["v"]), int(row["y"]), str(row["f"]))) - low_cells: Dict[Tuple[str, str, int, str], List[Dict[str, object]]] = {} - for row in low_rows: - key = (str(row["q"]), str(row["src"]), int(row["b"]), str(row["v"])) - low_cells.setdefault(key, []).append(row) - - low_emitted: List[Dict[str, object]] = [] - for key in sorted(low_cells.keys()): - query, source, bucket, venue = key - cell_rows = low_cells[key] - first = cell_rows[0] - deltas: List[Dict[str, object]] = [] - prev = first - for row in cell_rows[1:]: - delta: Dict[str, object] = {"k": row["k"], "f": row["f"]} - for field in ("y", "s"): - if row[field] != prev[field]: - delta[field] = row[field] - deltas.append(delta) - prev = row - - low_emitted.append( - { - "ctx": {"q": query, "src": source, "b": bucket, "v": venue}, - "seed": first, - "spray": deltas, - } - ) - - payload = { - "schema": "deepcompression/hybrid/v1", - "paper_count": len(papers), - "high_entropy_partitioned": { - "bucket_count": len(high_blocks), - "blocks": high_blocks, - }, - "low_entropy_context_spray": { - "cell_count": len(low_emitted), - "cells": low_emitted, - }, - } - return json.dumps(payload, ensure_ascii=False, separators=(",", ":")).encode("utf-8") - - -def compress_payload(raw_bytes: bytes) -> Dict[str, object]: - compressed = zlib.compress(raw_bytes, level=9) - return { - "raw_bytes": len(raw_bytes), - "compressed_bytes": len(compressed), - "compression_ratio": round(len(compressed) / max(1, len(raw_bytes)), 6), - "blob": compressed, - } - - -def compare_archive_modes(papers: List[Paper]) -> Dict[str, object]: - """Compute direct legacy versus partitioned compression metrics on identical inputs.""" - legacy_raw = build_legacy_payload_bytes(papers) - partitioned_raw = build_partitioned_payload_bytes(papers) - context_spray_raw = build_context_spray_payload_bytes(papers) - hybrid_raw = build_hybrid_payload_bytes(papers) - - legacy_stats = compress_payload(legacy_raw) - partitioned_stats = compress_payload(partitioned_raw) - context_spray_stats = compress_payload(context_spray_raw) - hybrid_stats = compress_payload(hybrid_raw) - - legacy_ratio = float(legacy_stats["compression_ratio"]) - partitioned_ratio = float(partitioned_stats["compression_ratio"]) - context_spray_ratio = float(context_spray_stats["compression_ratio"]) - hybrid_ratio = float(hybrid_stats["compression_ratio"]) - - candidates = { - "legacy": int(legacy_stats["compressed_bytes"]), - "partitioned": int(partitioned_stats["compressed_bytes"]), - "context_spray": int(context_spray_stats["compressed_bytes"]), - "hybrid": int(hybrid_stats["compressed_bytes"]), - } - auto_winner_mode = min(candidates.keys(), key=lambda m: candidates[m]) - - return { - "legacy": { - "raw_bytes": legacy_stats["raw_bytes"], - "compressed_bytes": legacy_stats["compressed_bytes"], - "compression_ratio": legacy_ratio, - }, - "partitioned": { - "raw_bytes": partitioned_stats["raw_bytes"], - "compressed_bytes": partitioned_stats["compressed_bytes"], - "compression_ratio": partitioned_ratio, - }, - "context_spray": { - "raw_bytes": context_spray_stats["raw_bytes"], - "compressed_bytes": context_spray_stats["compressed_bytes"], - "compression_ratio": context_spray_ratio, - }, - "hybrid": { - "raw_bytes": hybrid_stats["raw_bytes"], - "compressed_bytes": hybrid_stats["compressed_bytes"], - "compression_ratio": hybrid_ratio, - }, - "auto_winner": { - "mode": auto_winner_mode, - "compressed_bytes": candidates[auto_winner_mode], - }, - "delta": { - "partitioned_compressed_bytes_saved": int(legacy_stats["compressed_bytes"]) - int(partitioned_stats["compressed_bytes"]), - "context_spray_compressed_bytes_saved": int(legacy_stats["compressed_bytes"]) - int(context_spray_stats["compressed_bytes"]), - "hybrid_compressed_bytes_saved": int(legacy_stats["compressed_bytes"]) - int(hybrid_stats["compressed_bytes"]), - "compression_ratio_improvement": round(legacy_ratio - partitioned_ratio, 6), - "improved": partitioned_ratio < legacy_ratio, - "context_spray_ratio_improvement": round(legacy_ratio - context_spray_ratio, 6), - "context_spray_improved": context_spray_ratio < legacy_ratio, - "hybrid_ratio_improvement": round(legacy_ratio - hybrid_ratio, 6), - "hybrid_improved": hybrid_ratio < legacy_ratio, - }, - } - - -def read_archive_domain_from_omnitoken_surface() -> Dict[str, object]: - if not os.path.exists(OMNITOKEN_SURFACE_PATH): - return { - "status": "unavailable", - "reason": "omnitoken_surface_not_found", - "surface_path": OMNITOKEN_SURFACE_PATH, - } - - try: - with open(OMNITOKEN_SURFACE_PATH, "r", encoding="utf-8") as f: - surface = json.load(f) - except (OSError, json.JSONDecodeError): - return { - "status": "unreadable", - "reason": "invalid_surface_json", - "surface_path": OMNITOKEN_SURFACE_PATH, - } - - if not isinstance(surface, dict): - return { - "status": "unreadable", - "reason": "surface_not_object", - "surface_path": OMNITOKEN_SURFACE_PATH, - } - - surface_bus = surface.get("surface_bus") if isinstance(surface.get("surface_bus"), dict) else {} - domains = surface_bus.get("domains") if isinstance(surface_bus.get("domains"), dict) else {} - archive_domain = domains.get("archive_compression") if isinstance(domains.get("archive_compression"), dict) else {} - - if archive_domain: - _ = logic_signal_substrate_from_surface(archive_domain) - return { - "status": "ok", - "source": "surface_bus.domains.archive_compression", - "domain": archive_domain, - "translation_runtime": { - "mode": "pure_logic_signal_substrate_internal", - "logic_signal_substrate_exposed": False, - }, - } - - legacy_domain = surface.get("archive_surface") if isinstance(surface.get("archive_surface"), dict) else {} - if legacy_domain: - _ = logic_signal_substrate_from_surface(legacy_domain) - return { - "status": "ok", - "source": "archive_surface", - "domain": legacy_domain, - "translation_runtime": { - "mode": "pure_logic_signal_substrate_internal", - "logic_signal_substrate_exposed": False, - }, - } - - return { - "status": "unavailable", - "reason": "archive_domain_missing", - "surface_path": OMNITOKEN_SURFACE_PATH, - } - - -def publish_archive_domain_to_omnitoken_surface(archive_domain: Dict[str, object], manifest_path: str) -> None: - os.makedirs(OMNITOKEN_DIR, exist_ok=True) - - logic_signal_substrate_state = logic_signal_substrate_from_archive_domain(archive_domain) - translated_surface_domain = surface_from_logic_signal_substrate(logic_signal_substrate_state) - - surface: Dict[str, object] = {} - if os.path.exists(OMNITOKEN_SURFACE_PATH): - try: - with open(OMNITOKEN_SURFACE_PATH, "r", encoding="utf-8") as f: - loaded = json.load(f) - if isinstance(loaded, dict): - surface = loaded - except (OSError, json.JSONDecodeError): - surface = {} - - surface_bus = dict(surface.get("surface_bus") or {}) - domains = dict(surface_bus.get("domains") or {}) - domains["archive_compression"] = translated_surface_domain - surface_bus["schema"] = str(surface_bus.get("schema") or "omnitoken-surface-bus/v1") - surface_bus["agnostic"] = True - surface_bus["domains"] = domains - surface["surface_bus"] = surface_bus - surface["archive_surface"] = translated_surface_domain - surface["updated_utc"] = datetime.now(timezone.utc).isoformat() - surface["archive_manifest_path"] = manifest_path - - assert_surface_write_safe(surface, scope="omnitoken_surface") - with open(OMNITOKEN_SURFACE_PATH, "w", encoding="utf-8") as f: - json.dump(surface, f, indent=2) - - profile_path_obj = surface.get("profile") - if isinstance(profile_path_obj, str) and os.path.exists(profile_path_obj): - try: - with open(profile_path_obj, "r", encoding="utf-8") as f: - profile = json.load(f) - if isinstance(profile, dict): - p_surface_bus = dict(profile.get("surface_bus") or {}) - p_domains = dict(p_surface_bus.get("domains") or {}) - p_domains["archive_compression"] = translated_surface_domain - p_surface_bus["schema"] = str(p_surface_bus.get("schema") or "omnitoken-surface-bus/v1") - p_surface_bus["agnostic"] = True - p_surface_bus["domains"] = p_domains - profile["surface_bus"] = p_surface_bus - profile["archive_surface"] = translated_surface_domain - profile["updated_utc"] = datetime.now(timezone.utc).isoformat() - assert_surface_write_safe(profile, scope="omnitoken_profile") - with open(profile_path_obj, "w", encoding="utf-8") as f: - json.dump(profile, f, indent=2) - except (OSError, json.JSONDecodeError): - pass - - -def write_blackhole_vault_with_mode( - vault_dir: str, - papers: List[Paper], - run_id: str, - archive_mode: str, - benchmark_against_legacy: bool, -) -> str: - os.makedirs(vault_dir, exist_ok=True) - raw_jsonl = os.path.join(vault_dir, f"papers_{run_id}.jsonl") - write_jsonl(raw_jsonl, papers) - - benchmark = compare_archive_modes(papers) - inbound_surface = read_archive_domain_from_omnitoken_surface() - - if archive_mode == "auto": - archive_mode = str(benchmark["auto_winner"]["mode"]) - - if archive_mode == "partitioned": - raw_bytes = build_partitioned_payload_bytes(papers) - payload_format = "partitioned_bucket_streams" - elif archive_mode == "context-spray": - raw_bytes = build_context_spray_payload_bytes(papers) - payload_format = "context_sensitive_spray_cells" - elif archive_mode == "hybrid": - raw_bytes = build_hybrid_payload_bytes(papers) - payload_format = "hybrid_partitioned_context" - else: - raw_bytes = build_legacy_payload_bytes(papers) - payload_format = "legacy_jsonl" - - compressed = zlib.compress(raw_bytes, level=9) - blob_path = os.path.join(vault_dir, f"blackhole_{run_id}.zlib") - with open(blob_path, "wb") as f: - f.write(compressed) - - manifest = { - "run_id": run_id, - "created_utc": datetime.now(timezone.utc).isoformat(), - "paper_count": len(papers), - "raw_jsonl": raw_jsonl, - "compressed_blob": blob_path, - "archive_mode": archive_mode, - "payload_format": payload_format, - "raw_bytes": len(raw_bytes), - "compressed_bytes": len(compressed), - "compression_ratio": round(len(compressed) / max(1, len(raw_bytes)), 4), - "nibble_bucket_mean": round( - statistics.mean((p.relevance_bucket_4bit for p in papers)) if papers else 0.0, - 4, - ), - "nibble_index": [ - { - "key": dedupe_key(p), - "bucket4": p.relevance_bucket_4bit, - "fingerprint16": p.nibble_fingerprint_hex, - } - for p in papers - ], - "omnitoken_surface_input": inbound_surface, - } - - if benchmark_against_legacy: - manifest["archive_mode_benchmark"] = benchmark - - manifest_path = os.path.join(vault_dir, f"manifest_{run_id}.json") - with open(manifest_path, "w", encoding="utf-8") as f: - json.dump(manifest, f, indent=2) - - archive_domain = { - "domain": "archive_compression", - "selection_policy": "auto_smallest_compressed_bytes", - "selected_mode": archive_mode, - "payload_format": payload_format, - "raw_bytes": int(len(raw_bytes)), - "compressed_bytes": int(len(compressed)), - "manifest_path": manifest_path, - "benchmark": benchmark, - "updated_utc": datetime.now(timezone.utc).isoformat(), - } - publish_archive_domain_to_omnitoken_surface(archive_domain, manifest_path) - - return manifest_path - - -def gather_all(queries: List[str], rows_per_source: int, dry_run: bool) -> Tuple[List[Paper], Dict[str, int], Dict[str, int]]: - papers: List[Paper] = [] - source_totals = {"crossref": 0, "openalex": 0, "arxiv": 0} - source_failures = {"crossref": 0, "openalex": 0, "arxiv": 0} - - if dry_run: - for q in queries[:3]: - papers.append( - Paper( - source="dry-run", - source_id=hashlib.md5(q.encode()).hexdigest(), - title=f"Synthetic result for: {q}", - abstract="Human safety oversight and intervention threshold under low latency.", - authors=["Dry Runner"], - year=2026, - venue="Simulation", - doi="", - url="", - query=q, - ) - ) - return papers, source_totals, source_failures - - for q in queries: - try: - got = list(from_crossref(q, rows_per_source)) - papers.extend(got) - source_totals["crossref"] += len(got) - except RuntimeError as exc: - source_failures["crossref"] += 1 - print(f"[warn] crossref failed for query={q!r}: {exc}") - - try: - got = list(from_openalex(q, rows_per_source)) - papers.extend(got) - source_totals["openalex"] += len(got) - except RuntimeError as exc: - source_failures["openalex"] += 1 - print(f"[warn] openalex failed for query={q!r}: {exc}") - - try: - got = list(from_arxiv(q, rows_per_source)) - papers.extend(got) - source_totals["arxiv"] += len(got) - except (RuntimeError, ET.ParseError) as exc: - source_failures["arxiv"] += 1 - print(f"[warn] arxiv failed for query={q!r}: {exc}") - - return papers, source_totals, source_failures - - -def parse_args() -> argparse.Namespace: - p = argparse.ArgumentParser(description="High-coverage literature harvesting with DeepCompression vault output.") - p.add_argument("--query", action="append", default=[], help="Query string (repeatable).") - p.add_argument("--rows-per-source", type=int, default=35, help="Rows per source per query.") - p.add_argument("--out-dir", default=os.path.join(PROJECT_ROOT, "literature_blackhole"), help="Output directory.") - p.add_argument("--min-total-per-source", type=int, default=1, help="Fail run if any source returns fewer total records than this.") - p.add_argument( - "--archive-mode", - choices=["legacy", "partitioned", "context-spray", "hybrid", "auto"], - default="auto", - help="Archive payload encoding mode.", - ) - p.add_argument( - "--benchmark-against-legacy", - action="store_true", - help="Include direct legacy-versus-selected-mode compression comparison in manifest.", - ) - p.add_argument("--dry-run", action="store_true", help="Use synthetic records and skip network calls.") - return p.parse_args() - - -def main() -> int: - args = parse_args() - queries = args.query if args.query else DEFAULT_QUERIES - - run_id = now_utc() - all_papers, source_totals, source_failures = gather_all(queries, args.rows_per_source, args.dry_run) - - if not args.dry_run: - low_sources = find_low_coverage_sources(source_totals, args.min_total_per_source) - if low_sources: - print(json.dumps({ - "error": "insufficient_source_coverage", - "min_total_per_source": int(args.min_total_per_source), - "source_totals": source_totals, - "source_failures": source_failures, - "low_sources": low_sources, - }, indent=2)) - return 3 - - scored = enrich_scores(all_papers) - deduped = dedupe(scored) - - deduped.sort(key=lambda p: (p.relevance_score, p.year or 0), reverse=True) - - out_dir = args.out_dir - os.makedirs(out_dir, exist_ok=True) - dedup_jsonl = os.path.join(out_dir, f"deduped_{run_id}.jsonl") - dedup_csv = os.path.join(out_dir, f"deduped_{run_id}.csv") - write_jsonl(dedup_jsonl, deduped) - write_csv(dedup_csv, deduped) - - manifest = write_blackhole_vault_with_mode( - vault_dir=os.path.join(out_dir, "vault"), - papers=deduped, - run_id=run_id, - archive_mode=args.archive_mode, - benchmark_against_legacy=bool(args.benchmark_against_legacy), - ) - - summary = { - "run_id": run_id, - "dry_run": bool(args.dry_run), - "queries": len(queries), - "raw_records": len(scored), - "deduped_records": len(deduped), - "source_totals": source_totals, - "source_failures": source_failures, - "top_titles": [p.title for p in deduped[:5]], - "outputs": { - "dedup_jsonl": dedup_jsonl, - "dedup_csv": dedup_csv, - "manifest": manifest, - }, - } - print(json.dumps(summary, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/literature/literature_probe.py b/5-Applications/tools-scripts/literature/literature_probe.py deleted file mode 100644 index 19b409a2..00000000 --- a/5-Applications/tools-scripts/literature/literature_probe.py +++ /dev/null @@ -1,1316 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Question-focused literature probe using official scholarly APIs. - -This script is intentionally tuned for "boundary mapping" rather than broad -harvesting. It takes one research question, queries several official scholarly -APIs, deduplicates results, scores them against the actual question, extracts -recurring nearby vocabulary, and labels the likely kind of wall you are -running into. - -Default sources: -- OpenAlex -- Crossref -- Semantic Scholar Graph API -- arXiv API - -No third-party dependencies are required. -""" - -import argparse -import json -import math -import os -import re -import sys -import time -import urllib.error -import urllib.parse -import urllib.request -import xml.etree.ElementTree as ET -from collections import Counter -from dataclasses import asdict, dataclass, field -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Iterable, List, Optional, Sequence, Tuple - - -PROJECT_ROOT = Path(__file__).resolve().parents[1] -DEFAULT_OUT_DIR = PROJECT_ROOT / "out" / "literature_probe" -BASE_USER_AGENT = "research-stack-literature-probe/1.0" - -STOPWORDS = { - "a", "an", "and", "are", "as", "at", "be", "because", "by", "can", "could", - "do", "does", "for", "from", "how", "if", "in", "into", "is", "it", "its", - "more", "not", "of", "on", "or", "our", "same", "should", "so", "than", - "that", "the", "their", "them", "there", "these", "they", "this", "to", - "under", "use", "using", "various", "we", "what", "when", "where", "which", - "while", "who", "why", "with", "would", "you", "your", -} - -LOW_SIGNAL_TERMS = { - "analysis", "approach", "approaches", "based", "compare", "compares", - "comparison", "effect", "effects", "framework", "general", "including", - "method", "methods", "model", "models", "moving", "preserve", - "preserves", "preserving", "problem", "problems", "process", "results", - "same", "structure", "study", "system", "systems", "transition", - "transitions", "various", -} - -WALL_KEYWORDS = { - "theoretical": { - "theorem", "proof", "relaxation", "formulation", "branch", "bound", - "semidefinite", "optimization", "manifold", "algorithm", "convergence", - "objective", "quadratic", "mixed-integer", "qubo", "ising", - }, - "experimental": { - "experiment", "experimental", "measurement", "empirical", "benchmark", - "prototype", "study", "observed", "measured", "dataset", "validation", - "trial", "evaluation", "performance", - }, - "fabrication": { - "fabrication", "synthesis", "material", "materials", "device", "thin", - "film", "mxene", "nanoscroll", "electrode", "deposition", "etching", - "semiconductor", "substrate", "sensor", - }, - "scaling": { - "scale", "scaling", "runtime", "complexity", "large-scale", "embedding", - "memory", "qubits", "hardware", "rate-limit", "bottleneck", - "throughput", "latency", "benchmarking", - }, - "terminology": { - "survey", "review", "introduction", "overview", "taxonomy", "framework", - "perspective", "tutorial", "terminology", "glossary", - }, -} - -SOURCE_PRIORITY = { - "semantic_scholar": 4.0, - "google_scholar": 3.0, # scholarly (pip install scholarly); blocked = silent skip - "openalex": 2.5, - "crossref": 1.5, - "arxiv": 1.0, -} - -PIPELINE_FLAT = "flat" -PIPELINE_STAGED = "staged" -PIPELINE_COMPARE = "compare" - -STAGE_INGEST = "ingest_sieve" -STAGE_BREAKUP = "breakup_normalization" -STAGE_CLASSIFY = "classification_sieve" -STAGE_LIBERATE = "liberation_aggregation" -STAGE_HANDOFF = "recovery_handoff" - -DEFAULT_CLASSIFY_WINDOW = 24 -DEFAULT_MIN_RELEVANCE = 3.5 - - -@dataclass -class Paper: - title: str - abstract: str - authors: List[str] - year: Optional[int] - venue: str - doi: str - url: str - sources: List[str] = field(default_factory=list) - source_ids: List[str] = field(default_factory=list) - queries: List[str] = field(default_factory=list) - citations: int = 0 - fields_of_study: List[str] = field(default_factory=list) - relevance_score: float = 0.0 - matched_terms: List[str] = field(default_factory=list) - - -class ApiFetchError(RuntimeError): - pass - - -def now_utc() -> str: - return datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - -def normalize_ws(text: str) -> str: - return re.sub(r"\s+", " ", (text or "")).strip() - - -def normalize_title(text: str) -> str: - text = normalize_ws(text).lower() - return re.sub(r"[^a-z0-9 ]+", "", text) - - -def safe_int(value: object) -> Optional[int]: - try: - return int(value) - except (TypeError, ValueError): - return None - - -def slugify(text: str, max_len: int = 80) -> str: - slug = re.sub(r"[^a-z0-9]+", "-", text.lower()).strip("-") - return slug[:max_len] or "probe" - - -def unique_preserve(items: Iterable[str]) -> List[str]: - seen = set() - out: List[str] = [] - for item in items: - key = normalize_ws(item) - if not key: - continue - folded = key.lower() - if folded in seen: - continue - seen.add(folded) - out.append(key) - return out - - -def text_tokens(text: str) -> List[str]: - return [tok for tok in re.findall(r"[a-z0-9][a-z0-9\-]+", text.lower()) if tok not in STOPWORDS] - - -def content_terms(text: str, max_terms: int = 8) -> List[str]: - tokens = [tok for tok in text_tokens(text) if len(tok) > 2 and tok not in LOW_SIGNAL_TERMS] - ranked = [] - seen = set() - for tok in tokens: - if tok in seen: - continue - seen.add(tok) - ranked.append(tok) - return ranked[:max_terms] - - -def question_ngrams(question: str) -> List[str]: - terms = content_terms(question, max_terms=12) - ngrams: List[str] = [] - for n in (2, 3): - for i in range(0, len(terms) - n + 1): - ngrams.append(" ".join(terms[i : i + n])) - return unique_preserve(ngrams) - - -def derive_queries(question: str, extra_queries: Sequence[str]) -> List[str]: - queries = [normalize_ws(question)] - keywords = content_terms(question, max_terms=8) - if len(keywords) >= 3: - queries.append(" ".join(keywords)) - queries.extend(extra_queries) - return unique_preserve(queries) - - -def build_user_agent() -> str: - mailto = os.getenv("LITERATURE_PROBE_MAILTO") or os.getenv("CROSSREF_MAILTO") - if mailto: - return f"{BASE_USER_AGENT} (mailto:{mailto})" - return BASE_USER_AGENT - - -def classify_source_error(message: str) -> str: - lowered = (message or "").lower() - if "429" in lowered or "too many requests" in lowered or "rate limit" in lowered: - return "rate_limited" - if "401" in lowered or "403" in lowered: - return "auth" - if "timed out" in lowered or "timeout" in lowered: - return "timeout" - if lowered: - return "error" - return "" - - -def fetch_json(url: str, headers: Optional[Dict[str, str]] = None, timeout: float = 30.0) -> dict: - hdrs = {"User-Agent": build_user_agent()} - if headers: - hdrs.update(headers) - req = urllib.request.Request(url, headers=hdrs) - try: - with urllib.request.urlopen(req, timeout=timeout) as resp: - return json.loads(resp.read().decode("utf-8", errors="replace")) - except urllib.error.HTTPError as exc: - try: - body = exc.read().decode("utf-8", errors="replace") - except Exception: - body = "" - raise ApiFetchError(f"{exc.code} from {url}: {body[:240]}") from exc - except (urllib.error.URLError, OSError, json.JSONDecodeError) as exc: - raise ApiFetchError(f"failed fetching {url}: {exc}") from exc - - -def fetch_text(url: str, timeout: float = 30.0) -> str: - req = urllib.request.Request(url, headers={"User-Agent": build_user_agent()}) - try: - with urllib.request.urlopen(req, timeout=timeout) as resp: - return resp.read().decode("utf-8", errors="replace") - except urllib.error.HTTPError as exc: - raise ApiFetchError(f"{exc.code} from {url}") from exc - except (urllib.error.URLError, OSError) as exc: - raise ApiFetchError(f"failed fetching {url}: {exc}") from exc - - -def abstract_from_openalex_index(index: dict) -> str: - if not index: - return "" - max_pos = 0 - for positions in index.values(): - for pos in positions: - if pos > max_pos: - max_pos = pos - tokens = [""] * (max_pos + 1) - for token, positions in index.items(): - for pos in positions: - if 0 <= pos < len(tokens): - tokens[pos] = token - return normalize_ws(" ".join(tokens)) - - -def paper_key(paper: Paper) -> str: - if paper.doi: - return f"doi:{paper.doi.lower()}" - return f"title:{normalize_title(paper.title)}" - - -def merge_paper(existing: Paper, incoming: Paper) -> Paper: - if len(incoming.abstract) > len(existing.abstract): - existing.abstract = incoming.abstract - if len(incoming.authors) > len(existing.authors): - existing.authors = incoming.authors - if existing.year is None and incoming.year is not None: - existing.year = incoming.year - if not existing.venue and incoming.venue: - existing.venue = incoming.venue - if not existing.doi and incoming.doi: - existing.doi = incoming.doi - if not existing.url and incoming.url: - existing.url = incoming.url - existing.citations = max(existing.citations, incoming.citations) - existing.sources = unique_preserve(existing.sources + incoming.sources) - existing.source_ids = unique_preserve(existing.source_ids + incoming.source_ids) - existing.queries = unique_preserve(existing.queries + incoming.queries) - existing.fields_of_study = unique_preserve(existing.fields_of_study + incoming.fields_of_study) - return existing - - -def from_openalex(query: str, limit: int) -> List[Paper]: - params = {"search": query, "per-page": str(limit)} - mailto = os.getenv("LITERATURE_PROBE_MAILTO") - if mailto: - params["mailto"] = mailto - url = "https://api.openalex.org/works?" + urllib.parse.urlencode(params) - data = fetch_json(url) - papers: List[Paper] = [] - for item in data.get("results", []): - title = normalize_ws(item.get("display_name", "")) - if not title: - continue - fields = [] - primary_topic = (item.get("primary_topic") or {}).get("display_name") - if primary_topic: - fields.append(normalize_ws(primary_topic)) - for concept in item.get("concepts", []) or []: - name = normalize_ws(concept.get("display_name", "")) - if name: - fields.append(name) - papers.append( - Paper( - title=title, - abstract=abstract_from_openalex_index(item.get("abstract_inverted_index") or {}), - authors=[ - normalize_ws(((auth.get("author") or {}).get("display_name", ""))) - for auth in (item.get("authorships") or []) - if normalize_ws(((auth.get("author") or {}).get("display_name", ""))) - ], - year=safe_int(item.get("publication_year")), - venue=normalize_ws((((item.get("primary_location") or {}).get("source") or {}).get("display_name", ""))), - doi=normalize_ws((item.get("doi") or "").replace("https://doi.org/", "")), - url=normalize_ws(item.get("id", "")), - sources=["openalex"], - source_ids=[normalize_ws(item.get("id", ""))], - queries=[query], - citations=safe_int(item.get("cited_by_count")) or 0, - fields_of_study=unique_preserve(fields), - ) - ) - return papers - - -def from_crossref(query: str, limit: int) -> List[Paper]: - params = { - "query": query, - "rows": str(limit), - "select": ",".join( - [ - "DOI", - "title", - "author", - "container-title", - "URL", - "published-print", - "published-online", - "issued", - "abstract", - "subject", - "is-referenced-by-count", - ] - ), - } - mailto = os.getenv("LITERATURE_PROBE_MAILTO") or os.getenv("CROSSREF_MAILTO") - if mailto: - params["mailto"] = mailto - url = "https://api.crossref.org/works?" + urllib.parse.urlencode(params) - data = fetch_json(url) - papers: List[Paper] = [] - for item in data.get("message", {}).get("items", []): - title_parts = item.get("title", []) if isinstance(item.get("title", []), list) else [str(item.get("title", ""))] - title = normalize_ws(" ".join(title_parts)) - if not title: - continue - year = None - for field_name in ("published-print", "published-online", "issued"): - date_parts = (((item.get(field_name) or {}).get("date-parts") or [[None]])[0] or [None]) - year = safe_int(date_parts[0]) - if year is not None: - break - abstract = normalize_ws(re.sub(r"<[^>]+>", " ", item.get("abstract", "") or "")) - authors = [] - for author in item.get("author", []) or []: - full = normalize_ws(f"{author.get('given', '')} {author.get('family', '')}") - if full: - authors.append(full) - venue_parts = item.get("container-title", []) if isinstance(item.get("container-title", []), list) else [str(item.get("container-title", ""))] - fields = [normalize_ws(subject) for subject in (item.get("subject") or []) if normalize_ws(subject)] - papers.append( - Paper( - title=title, - abstract=abstract, - authors=authors, - year=year, - venue=normalize_ws(" ".join(venue_parts)), - doi=normalize_ws(item.get("DOI", "")), - url=normalize_ws(item.get("URL", "")), - sources=["crossref"], - source_ids=[normalize_ws(item.get("DOI", "") or item.get("URL", ""))], - queries=[query], - citations=safe_int(item.get("is-referenced-by-count")) or 0, - fields_of_study=unique_preserve(fields), - ) - ) - return papers - - -def from_semantic_scholar(query: str, limit: int) -> List[Paper]: - params = { - "query": query, - "limit": str(limit), - "fields": ",".join( - [ - "title", - "abstract", - "year", - "venue", - "url", - "authors", - "externalIds", - "citationCount", - "fieldsOfStudy", - ] - ), - } - headers: Dict[str, str] = {} - api_key = os.getenv("SEMANTIC_SCHOLAR_API_KEY") - if api_key: - headers["x-api-key"] = api_key - url = "https://api.semanticscholar.org/graph/v1/paper/search?" + urllib.parse.urlencode(params) - data = fetch_json(url, headers=headers) - papers: List[Paper] = [] - for item in data.get("data", []): - title = normalize_ws(item.get("title", "")) - if not title: - continue - external_ids = item.get("externalIds") or {} - doi = normalize_ws(external_ids.get("DOI", "")) - fields = [normalize_ws(field) for field in (item.get("fieldsOfStudy") or []) if normalize_ws(field)] - papers.append( - Paper( - title=title, - abstract=normalize_ws(item.get("abstract", "")), - authors=[normalize_ws(author.get("name", "")) for author in (item.get("authors") or []) if normalize_ws(author.get("name", ""))], - year=safe_int(item.get("year")), - venue=normalize_ws(item.get("venue", "")), - doi=doi, - url=normalize_ws(item.get("url", "")), - sources=["semantic_scholar"], - source_ids=[normalize_ws(item.get("paperId", "") or item.get("url", ""))], - queries=[query], - citations=safe_int(item.get("citationCount")) or 0, - fields_of_study=unique_preserve(fields), - ) - ) - return papers - - -def from_arxiv(query: str, limit: int) -> List[Paper]: - q = urllib.parse.quote(query) - url = f"https://export.arxiv.org/api/query?search_query=all:{q}&start=0&max_results={limit}" - text = fetch_text(url) - ns = {"atom": "http://www.w3.org/2005/Atom", "arxiv": "http://arxiv.org/schemas/atom"} - root = ET.fromstring(text) - papers: List[Paper] = [] - for entry in root.findall("atom:entry", ns): - title = normalize_ws(entry.findtext("atom:title", default="", namespaces=ns) or "") - if not title: - continue - doi = "" - doi_node = entry.find("arxiv:doi", ns) - if doi_node is not None and (doi_node.text or "").strip(): - doi = normalize_ws(doi_node.text) - papers.append( - Paper( - title=title, - abstract=normalize_ws(entry.findtext("atom:summary", default="", namespaces=ns) or ""), - authors=[ - normalize_ws(author.findtext("atom:name", default="", namespaces=ns) or "") - for author in entry.findall("atom:author", ns) - if normalize_ws(author.findtext("atom:name", default="", namespaces=ns) or "") - ], - year=safe_int((entry.findtext("atom:published", default="", namespaces=ns) or "")[:4]), - venue="arXiv", - doi=doi, - url=normalize_ws(entry.findtext("atom:id", default="", namespaces=ns) or ""), - sources=["arxiv"], - source_ids=[normalize_ws(entry.findtext("atom:id", default="", namespaces=ns) or "")], - queries=[query], - citations=0, - fields_of_study=["preprint"], - ) - ) - return papers - - -def from_google_scholar(query: str, limit: int) -> List[Paper]: - """Fetch from Google Scholar via the `scholarly` library (pip install scholarly). - - scholarly is an unofficial scraper — Google blocks with CAPTCHAs under - heavy load. This function returns [] silently when: - - scholarly is not installed - - Google returns a CAPTCHA / rate-limit - No proxy is configured; install scholarly and run at low request rates. - """ - try: - import itertools - import scholarly as _scholarly # type: ignore - except ImportError: - return [] - - papers: List[Paper] = [] - try: - iterator = _scholarly.search_pubs(query) - for result in itertools.islice(iterator, limit): - bib = result.get("bib") or {} - title = normalize_ws(bib.get("title", "")) - if not title: - continue - raw_authors = bib.get("author", "") - if isinstance(raw_authors, list): - authors = [normalize_ws(a) for a in raw_authors if normalize_ws(a)] - else: - authors = [normalize_ws(a) for a in re.split(r"\s+and\s+|,\s*", raw_authors) if normalize_ws(a)] - year_raw = bib.get("pub_year", "") or bib.get("year", "") - venue = normalize_ws(bib.get("venue", "") or bib.get("journal", "") or bib.get("booktitle", "")) - url = normalize_ws(result.get("pub_url", "") or result.get("eprint_url", "")) - cites = safe_int(result.get("num_citations")) or 0 - papers.append( - Paper( - title=title, - abstract=normalize_ws(bib.get("abstract", "")), - authors=authors, - year=safe_int(str(year_raw)[:4]) if year_raw else None, - venue=venue, - doi="", - url=url, - sources=["google_scholar"], - source_ids=[url or title[:80]], - queries=[query], - citations=cites, - fields_of_study=[], - ) - ) - except Exception: - # CAPTCHA, network error, or scholarly internal error — return what we have - pass - return papers - - -def dedupe_and_merge(papers: Sequence[Paper]) -> List[Paper]: - merged: Dict[str, Paper] = {} - for paper in papers: - key = paper_key(paper) - if key in merged: - merged[key] = merge_paper(merged[key], paper) - else: - merged[key] = paper - return list(merged.values()) - - -def score_paper(paper: Paper, question: str) -> Tuple[float, List[str]]: - question_terms = set(content_terms(question, max_terms=12)) - paper_title_terms = set(text_tokens(paper.title)) - paper_body_terms = set(text_tokens(f"{paper.title} {paper.abstract} {' '.join(paper.fields_of_study)} {paper.venue}")) - overlaps = sorted(question_terms & paper_body_terms) - q_ngrams = question_ngrams(question) - haystack = f"{paper.title} {paper.abstract}".lower() - phrase_hits = sum(1 for ngram in q_ngrams if ngram and ngram in haystack) - score = 0.0 - score += 3.0 * len(question_terms & paper_title_terms) - score += 1.2 * len(overlaps) - score += 2.5 * phrase_hits - if paper.abstract: - score += 1.0 - if paper.citations > 0: - score += min(4.0, math.log10(paper.citations + 1) * 1.5) - score += source_preference_score(paper) - return round(score, 3), overlaps - - -def source_preference_score(paper: Paper) -> float: - if not paper.sources: - return 0.0 - max_source = max(SOURCE_PRIORITY.get(source, 0.0) for source in paper.sources) - diversity_bonus = min(0.6, 0.15 * max(0, len(paper.sources) - 1)) - return max_source * 0.25 + diversity_bonus - - -def stage_record( - name: str, - input_count: int, - output_count: int, - note: str, - metrics: Optional[Dict[str, object]] = None, -) -> dict: - row = { - "stage": name, - "input_count": input_count, - "output_count": output_count, - "note": note, - } - if metrics: - row["metrics"] = metrics - return row - - -def average_relevance(papers: Sequence[Paper], top_n: int = 12) -> float: - window = list(papers[:top_n]) - if not window: - return 0.0 - return round(sum(p.relevance_score for p in window) / len(window), 3) - - -def average_matched_terms(papers: Sequence[Paper], top_n: int = 12) -> float: - window = list(papers[:top_n]) - if not window: - return 0.0 - return round(sum(len(p.matched_terms) for p in window) / len(window), 3) - - -def select_candidate_window( - ranked: Sequence[Paper], - classify_window: int, - min_relevance: float, -) -> List[Paper]: - capped = max(1, classify_window) - kept = [paper for paper in ranked if paper.relevance_score >= min_relevance] - if kept: - return kept[:capped] - return list(ranked[:capped]) - - -def rank_papers(papers: Sequence[Paper], question: str) -> List[Paper]: - ranked: List[Paper] = [] - for paper in papers: - score, overlaps = score_paper(paper, question) - paper.relevance_score = score - paper.matched_terms = overlaps - ranked.append(paper) - ranked.sort(key=lambda p: (-p.relevance_score, -(p.citations or 0), -(p.year or 0), p.title.lower())) - return ranked - - -def recurring_terms(papers: Sequence[Paper], question: str, top_k: int = 12) -> List[str]: - question_terms = set(content_terms(question, max_terms=12)) - counts: Counter[str] = Counter() - for paper in papers[:12]: - tokens = [tok for tok in text_tokens(f"{paper.title} {paper.abstract}") if len(tok) > 2] - for n in (1, 2, 3): - for i in range(0, len(tokens) - n + 1): - phrase = " ".join(tokens[i : i + n]) - parts = phrase.split() - if all(part in question_terms for part in parts): - continue - if any(part in STOPWORDS for part in parts): - continue - counts[phrase] += 1 - ranked = [term for term, count in counts.most_common(top_k * 4) if count >= 2] - ranked.sort(key=lambda term: (-len(term.split()), -counts[term], term)) - return ranked[:top_k] - - -def nearby_fields(papers: Sequence[Paper], top_k: int = 10) -> List[str]: - counts: Counter[str] = Counter() - for paper in papers[:20]: - for field_name in paper.fields_of_study: - counts[normalize_ws(field_name)] += 1 - return [field_name for field_name, _ in counts.most_common(top_k)] - - -def source_contribution_summary(papers: Sequence[Paper], top_n: int = 12) -> Dict[str, int]: - counts: Counter[str] = Counter() - for paper in papers[:top_n]: - for source in paper.sources: - counts[source] += 1 - return dict(counts.most_common()) - - -def wall_signals(papers: Sequence[Paper], question: str) -> Dict[str, float]: - if not papers: - return {"evidence_gap": 1.0} - - corpus = " ".join( - normalize_ws( - " ".join( - [ - paper.title, - paper.abstract, - paper.venue, - " ".join(paper.fields_of_study), - ] - ) - ).lower() - for paper in papers[:12] - ) - scores: Dict[str, float] = {} - for wall_type, keywords in WALL_KEYWORDS.items(): - score = 0.0 - for keyword in keywords: - if keyword in corpus: - score += 1.0 - scores[wall_type] = score - - avg_relevance = sum(p.relevance_score for p in papers[:8]) / max(1, min(len(papers), 8)) - if len(papers) < 5 or avg_relevance < 4.0: - scores["evidence_gap"] = scores.get("evidence_gap", 0.0) + 3.0 - - question_terms = set(content_terms(question, max_terms=12)) - overlap_total = sum(len(p.matched_terms) for p in papers[:8]) - if papers and overlap_total <= max(2, len(question_terms) // 2): - scores["terminology"] = scores.get("terminology", 0.0) + 2.0 - - return scores - - -def likely_wall_type(papers: Sequence[Paper], question: str) -> Tuple[str, Dict[str, float]]: - scores = wall_signals(papers, question) - if not scores: - return "evidence_gap", {"evidence_gap": 1.0} - wall_type = max(scores, key=lambda key: scores[key]) - rounded = {key: round(value, 3) for key, value in sorted(scores.items())} - return wall_type, rounded - - -def to_json_ready(papers: Sequence[Paper]) -> List[dict]: - rows = [] - for paper in papers: - rows.append(asdict(paper)) - return rows - - -def markdown_summary( - question: str, - queries: Sequence[str], - wall_type: str, - wall_scores: Dict[str, float], - source_stats: Dict[str, dict], - source_contributions: Dict[str, int], - recurring: Sequence[str], - fields: Sequence[str], - papers: Sequence[Paper], - pipeline_mode: str = PIPELINE_FLAT, - stage_trace: Optional[Sequence[dict]] = None, -) -> str: - lines = [ - f"# Literature Probe", - "", - f"**Question:** {question}", - "", - f"**Pipeline:** `{pipeline_mode}`", - "", - f"**Likely wall type:** `{wall_type}`", - "", - "## Queries", - "", - ] - for query in queries: - lines.append(f"- `{query}`") - if stage_trace: - lines.extend(["", "## Stage Trace", ""]) - for stage in stage_trace: - line = ( - f"- `{stage.get('stage', '')}`: " - f"{stage.get('input_count', 0)} -> {stage.get('output_count', 0)}" - ) - note = normalize_ws(str(stage.get("note", ""))) - if note: - line += f" | {note}" - lines.append(line) - lines.extend(["", "## Wall Signals", ""]) - for key, value in sorted(wall_scores.items(), key=lambda item: (-item[1], item[0])): - lines.append(f"- `{key}`: {value}") - lines.extend(["", "## Source Stats", ""]) - for source_name, stats in source_stats.items(): - line = f"- `{source_name}`: {stats.get('count', 0)} papers" - if stats.get("status"): - line += f" | status: {stats['status']}" - if stats.get("error"): - line += f" | error: {stats['error']}" - lines.append(line) - if source_contributions: - lines.extend(["", "## Source Contribution In Top Papers", ""]) - for source_name, count in source_contributions.items(): - lines.append(f"- `{source_name}`: {count} of top 12") - if recurring: - lines.extend(["", "## Recurring Terms", ""]) - for term in recurring: - lines.append(f"- `{term}`") - if fields: - lines.extend(["", "## Nearby Fields", ""]) - for field_name in fields: - lines.append(f"- `{field_name}`") - lines.extend(["", "## Top Papers", ""]) - for index, paper in enumerate(papers[:12], start=1): - authors = ", ".join(paper.authors[:4]) - details = [] - if paper.year: - details.append(str(paper.year)) - if paper.venue: - details.append(paper.venue) - if paper.sources: - details.append("sources: " + "/".join(paper.sources)) - if paper.citations: - details.append(f"citations: {paper.citations}") - if paper.matched_terms: - details.append("matched: " + ", ".join(paper.matched_terms[:6])) - detail_text = " | ".join(details) - lines.append(f"{index}. [{paper.title}]({paper.url or paper.doi or '#'})") - if detail_text: - lines.append(f" {detail_text}") - if authors: - lines.append(f" {authors}") - lines.append("") - return "\n".join(lines) - - -def markdown_compare_summary(result: dict) -> str: - comparison = result["comparison"] - flat = result["flat_result"] - staged = result["staged_result"] - lines = [ - "# Literature Probe Pipeline Comparison", - "", - f"**Question:** {result['question']}", - "", - "## Summary", - "", - f"- Flat top-12 average relevance: `{comparison['flat_top12_avg_relevance']}`", - f"- Staged top-12 average relevance: `{comparison['staged_top12_avg_relevance']}`", - f"- Flat final paper count: `{comparison['flat_final_paper_count']}`", - f"- Staged final paper count: `{comparison['staged_final_paper_count']}`", - f"- Staged burden reduction vs flat: `{comparison['staged_burden_reduction_ratio']}`", - f"- Top-12 overlap count: `{comparison['top12_overlap_count']}`", - "", - "## Flat Stage Trace", - "", - ] - for stage in flat.get("stage_trace", []): - lines.append( - f"- `{stage.get('stage', '')}`: {stage.get('input_count', 0)} -> {stage.get('output_count', 0)}" - ) - lines.extend(["", "## Staged Stage Trace", ""]) - for stage in staged.get("stage_trace", []): - lines.append( - f"- `{stage.get('stage', '')}`: {stage.get('input_count', 0)} -> {stage.get('output_count', 0)}" - ) - lines.extend(["", "## Top-12 Overlap Titles", ""]) - for title in comparison.get("top12_overlap_titles", []): - lines.append(f"- {title}") - if not comparison.get("top12_overlap_titles"): - lines.append("- none") - lines.extend(["", "## Interpretation", ""]) - lines.append(comparison["interpretation"]) - lines.append("") - return "\n".join(lines) - - -def fetch_all_papers( - queries: Sequence[str], - per_source: int, -) -> Tuple[List[Paper], Dict[str, dict], List[dict]]: - all_papers: List[Paper] = [] - source_stats: Dict[str, dict] = {} - events: List[dict] = [] - - fetchers = { - "semantic_scholar": from_semantic_scholar, - "openalex": from_openalex, - "crossref": from_crossref, - "arxiv": from_arxiv, - } - try: - import scholarly # noqa: F401 - fetchers["google_scholar"] = from_google_scholar - except ImportError: - pass - - for source_name, fetcher in fetchers.items(): - source_total = 0 - error_text = "" - status = "ok" - for query in queries: - try: - results = fetcher(query, per_source) - except ApiFetchError as exc: - error_text = str(exc) - status = classify_source_error(error_text) - events.append( - { - "source": source_name, - "query": query, - "status": status, - "message": error_text, - } - ) - continue - source_total += len(results) - all_papers.extend(results) - time.sleep(0.15) - source_stats[source_name] = {"count": source_total, "error": error_text, "status": status} - - return all_papers, source_stats, events - - -def build_probe_result( - *, - question: str, - queries: Sequence[str], - source_stats: Dict[str, dict], - events: List[dict], - wall_type: str, - wall_scores: Dict[str, float], - recurring: Sequence[str], - fields: Sequence[str], - source_contributions: Dict[str, int], - papers: Sequence[Paper], - pipeline_mode: str, - stage_trace: Sequence[dict], - raw_hit_count: int, - deduped_paper_count: int, - classified_paper_count: int, - final_paper_count: int, - classify_window: int, - min_relevance: float, - candidate_selection_note: str, -) -> dict: - return { - "generated_at": datetime.now(timezone.utc).isoformat(), - "question": question, - "queries": list(queries), - "pipeline_mode": pipeline_mode, - "stage_trace": list(stage_trace), - "wall_type": wall_type, - "wall_scores": wall_scores, - "recurring_terms": list(recurring), - "nearby_fields": list(fields), - "source_stats": source_stats, - "source_contributions_top12": source_contributions, - "events": events, - "raw_hit_count": raw_hit_count, - "deduped_paper_count": deduped_paper_count, - "classified_paper_count": classified_paper_count, - "final_paper_count": final_paper_count, - "top12_avg_relevance": average_relevance(papers), - "top12_avg_matched_terms": average_matched_terms(papers), - "classify_window": classify_window, - "min_relevance": min_relevance, - "candidate_selection_note": candidate_selection_note, - "papers": to_json_ready(papers), - } - - -def run_probe_flat( - question: str, - extra_queries: Sequence[str], - per_source: int, - classify_window: int = DEFAULT_CLASSIFY_WINDOW, - min_relevance: float = DEFAULT_MIN_RELEVANCE, -) -> dict: - queries = derive_queries(question, extra_queries) - all_papers, source_stats, events = fetch_all_papers(queries, per_source) - deduped = dedupe_and_merge(all_papers) - ranked = rank_papers(deduped, question) - wall_type, wall_scores = likely_wall_type(ranked, question) - recurring = recurring_terms(ranked, question) - fields = nearby_fields(ranked) - source_contributions = source_contribution_summary(ranked) - top_window = min(12, len(ranked)) - stage_trace = [ - stage_record( - STAGE_INGEST, - len(queries), - len(all_papers), - "Fetched raw candidate hits across all configured scholarly sources.", - {"query_count": len(queries), "per_source": per_source}, - ), - stage_record( - STAGE_BREAKUP, - len(all_papers), - len(deduped), - "Normalized and deduplicated records into one comparable candidate set.", - ), - stage_record( - STAGE_CLASSIFY, - len(deduped), - len(ranked), - "Ranked all deduplicated candidates without narrowing the result window.", - {"selection_mode": "flat_full_rank"}, - ), - stage_record( - STAGE_LIBERATE, - len(ranked), - top_window, - "Derived recurring terms, nearby fields, and wall signals from the top-ranked set.", - ), - stage_record( - STAGE_HANDOFF, - top_window, - len(ranked), - "Returned the full ranked list as the flat baseline handoff surface.", - ), - ] - return build_probe_result( - question=question, - queries=queries, - source_stats=source_stats, - events=events, - wall_type=wall_type, - wall_scores=wall_scores, - recurring=recurring, - fields=fields, - source_contributions=source_contributions, - papers=ranked, - pipeline_mode=PIPELINE_FLAT, - stage_trace=stage_trace, - raw_hit_count=len(all_papers), - deduped_paper_count=len(deduped), - classified_paper_count=len(ranked), - final_paper_count=len(ranked), - classify_window=classify_window, - min_relevance=min_relevance, - candidate_selection_note="Flat baseline keeps the full ranked list after scoring.", - ) - - -def run_probe_staged( - question: str, - extra_queries: Sequence[str], - per_source: int, - classify_window: int = DEFAULT_CLASSIFY_WINDOW, - min_relevance: float = DEFAULT_MIN_RELEVANCE, -) -> dict: - queries = derive_queries(question, extra_queries) - all_papers, source_stats, events = fetch_all_papers(queries, per_source) - deduped = dedupe_and_merge(all_papers) - ranked = rank_papers(deduped, question) - candidates = select_candidate_window(ranked, classify_window, min_relevance) - wall_type, wall_scores = likely_wall_type(candidates, question) - recurring = recurring_terms(candidates, question) - fields = nearby_fields(candidates) - source_contributions = source_contribution_summary(candidates) - stage_trace = [ - stage_record( - STAGE_INGEST, - len(queries), - len(all_papers), - "Fetched raw candidate hits across all configured scholarly sources.", - {"query_count": len(queries), "per_source": per_source}, - ), - stage_record( - STAGE_BREAKUP, - len(all_papers), - len(deduped), - "Normalized and deduplicated records into one comparable candidate set.", - ), - stage_record( - STAGE_CLASSIFY, - len(deduped), - len(candidates), - "Scored candidates and kept only the bounded relevance window.", - {"classify_window": max(1, classify_window), "min_relevance": min_relevance}, - ), - stage_record( - STAGE_LIBERATE, - len(candidates), - min(12, len(candidates)), - "Computed recurring terms, nearby fields, and wall signals only from the retained relevance slice.", - ), - stage_record( - STAGE_HANDOFF, - min(12, len(candidates)), - len(candidates), - "Returned the narrowed candidate slice as the staged handoff surface.", - ), - ] - return build_probe_result( - question=question, - queries=queries, - source_stats=source_stats, - events=events, - wall_type=wall_type, - wall_scores=wall_scores, - recurring=recurring, - fields=fields, - source_contributions=source_contributions, - papers=candidates, - pipeline_mode=PIPELINE_STAGED, - stage_trace=stage_trace, - raw_hit_count=len(all_papers), - deduped_paper_count=len(deduped), - classified_paper_count=len(candidates), - final_paper_count=len(candidates), - classify_window=classify_window, - min_relevance=min_relevance, - candidate_selection_note=( - "Staged mode keeps only the bounded relevance window after classification " - "so later aggregation and handoff operate on a smaller, more explainable slice." - ), - ) - - -def compare_probe_results(flat_result: dict, staged_result: dict) -> dict: - flat_papers = [Paper(**paper) for paper in flat_result["papers"]] - staged_papers = [Paper(**paper) for paper in staged_result["papers"]] - flat_top = flat_papers[:12] - staged_top = staged_papers[:12] - flat_titles = {normalize_title(p.title): p.title for p in flat_top} - staged_titles = {normalize_title(p.title): p.title for p in staged_top} - overlap_keys = sorted(set(flat_titles) & set(staged_titles)) - overlap_titles = [flat_titles[key] for key in overlap_keys] - flat_final = max(1, flat_result.get("final_paper_count", len(flat_papers))) - staged_final = staged_result.get("final_paper_count", len(staged_papers)) - burden_ratio = round(staged_final / flat_final, 3) - staged_gain = round( - staged_result.get("top12_avg_relevance", 0.0) - flat_result.get("top12_avg_relevance", 0.0), - 3, - ) - if staged_gain > 0: - interpretation = ( - "The staged path kept a smaller candidate surface while slightly increasing " - "top-result relevance density." - ) - elif staged_gain < 0: - interpretation = ( - "The staged path reduced burden, but it also lowered top-result relevance density " - "for this query and should be tuned." - ) - else: - interpretation = ( - "The staged path reduced burden without materially changing top-result relevance density " - "for this query." - ) - return { - "flat_top12_avg_relevance": flat_result.get("top12_avg_relevance", 0.0), - "staged_top12_avg_relevance": staged_result.get("top12_avg_relevance", 0.0), - "flat_top12_avg_matched_terms": flat_result.get("top12_avg_matched_terms", 0.0), - "staged_top12_avg_matched_terms": staged_result.get("top12_avg_matched_terms", 0.0), - "flat_final_paper_count": flat_result.get("final_paper_count", len(flat_papers)), - "staged_final_paper_count": staged_result.get("final_paper_count", len(staged_papers)), - "staged_burden_reduction_ratio": burden_ratio, - "top12_overlap_count": len(overlap_titles), - "top12_overlap_titles": overlap_titles, - "interpretation": interpretation, - } - - -def run_probe( - question: str, - extra_queries: Sequence[str], - per_source: int, - pipeline_mode: str = PIPELINE_FLAT, - classify_window: int = DEFAULT_CLASSIFY_WINDOW, - min_relevance: float = DEFAULT_MIN_RELEVANCE, -) -> dict: - if pipeline_mode == PIPELINE_STAGED: - return run_probe_staged(question, extra_queries, per_source, classify_window, min_relevance) - if pipeline_mode == PIPELINE_COMPARE: - flat_result = run_probe_flat(question, extra_queries, per_source, classify_window, min_relevance) - staged_result = run_probe_staged(question, extra_queries, per_source, classify_window, min_relevance) - return { - "generated_at": datetime.now(timezone.utc).isoformat(), - "question": question, - "pipeline_mode": PIPELINE_COMPARE, - "flat_result": flat_result, - "staged_result": staged_result, - "comparison": compare_probe_results(flat_result, staged_result), - } - return run_probe_flat(question, extra_queries, per_source, classify_window, min_relevance) - - -def write_outputs(result: dict, out_dir: Path) -> Tuple[Path, Path]: - out_dir.mkdir(parents=True, exist_ok=True) - stamp = now_utc() - pipeline_mode = result.get("pipeline_mode", PIPELINE_FLAT) - stem = f"{stamp}_{slugify(result['question'])}_{pipeline_mode}" - json_path = out_dir / f"{stem}.json" - md_path = out_dir / f"{stem}.md" - json_path.write_text(json.dumps(result, indent=2, ensure_ascii=False), encoding="utf-8") - if pipeline_mode == PIPELINE_COMPARE: - md_path.write_text(markdown_compare_summary(result), encoding="utf-8") - else: - papers = [Paper(**paper) for paper in result["papers"]] - md_path.write_text( - markdown_summary( - question=result["question"], - queries=result["queries"], - wall_type=result["wall_type"], - wall_scores=result["wall_scores"], - source_stats=result["source_stats"], - source_contributions=result["source_contributions_top12"], - recurring=result["recurring_terms"], - fields=result["nearby_fields"], - papers=papers, - pipeline_mode=pipeline_mode, - stage_trace=result.get("stage_trace"), - ), - encoding="utf-8", - ) - return json_path, md_path - - -def build_parser() -> argparse.ArgumentParser: - parser = argparse.ArgumentParser(description="Probe scholarly literature around one research question.") - parser.add_argument("question", help="Research question to probe.") - parser.add_argument( - "--extra-query", - action="append", - default=[], - help="Optional extra focused search query. Can be used multiple times.", - ) - parser.add_argument( - "--per-source", - type=int, - default=8, - help="Number of results to request from each source for each query.", - ) - parser.add_argument( - "--pipeline", - choices=[PIPELINE_FLAT, PIPELINE_STAGED, PIPELINE_COMPARE], - default=PIPELINE_FLAT, - help="Choose the retrieval/compression posture to run.", - ) - parser.add_argument( - "--classify-window", - type=int, - default=DEFAULT_CLASSIFY_WINDOW, - help="Maximum number of candidates retained after the classification sieve in staged mode.", - ) - parser.add_argument( - "--min-relevance", - type=float, - default=DEFAULT_MIN_RELEVANCE, - help="Minimum relevance score to keep during the classification sieve in staged mode.", - ) - parser.add_argument( - "--out-dir", - default=str(DEFAULT_OUT_DIR), - help="Directory to write JSON and Markdown outputs.", - ) - parser.add_argument( - "--print-json", - action="store_true", - help="Also print the final JSON result to stdout.", - ) - return parser - - -def main(argv: Optional[Sequence[str]] = None) -> int: - parser = build_parser() - args = parser.parse_args(argv) - result = run_probe( - args.question, - args.extra_query, - max(1, args.per_source), - pipeline_mode=args.pipeline, - classify_window=max(1, args.classify_window), - min_relevance=args.min_relevance, - ) - json_path, md_path = write_outputs(result, Path(args.out_dir)) - - print(f"Question: {result['question']}") - print(f"Pipeline: {result.get('pipeline_mode', PIPELINE_FLAT)}") - if result.get("pipeline_mode") == PIPELINE_COMPARE: - comparison = result["comparison"] - print(f"Flat top-12 avg relevance: {comparison['flat_top12_avg_relevance']}") - print(f"Staged top-12 avg relevance: {comparison['staged_top12_avg_relevance']}") - print(f"Top-12 overlap count: {comparison['top12_overlap_count']}") - print(f"Staged burden reduction ratio: {comparison['staged_burden_reduction_ratio']}") - else: - print(f"Likely wall type: {result['wall_type']}") - print(f"Final paper count: {result.get('final_paper_count', len(result.get('papers', [])))}") - print(f"Wrote JSON: {json_path}") - print(f"Wrote Markdown: {md_path}") - source_stats = ( - result["flat_result"]["source_stats"] - if result.get("pipeline_mode") == PIPELINE_COMPARE - else result["source_stats"] - ) - if source_stats.get("semantic_scholar", {}).get("status") == "rate_limited": - print("Semantic Scholar status: rate-limited; using fallback sources for this run") - else: - print("Semantic Scholar status:", source_stats.get("semantic_scholar", {}).get("status", "unknown")) - recurring_terms = ( - result["staged_result"]["recurring_terms"] - if result.get("pipeline_mode") == PIPELINE_COMPARE - else result["recurring_terms"] - ) - nearby_fields_list = ( - result["staged_result"]["nearby_fields"] - if result.get("pipeline_mode") == PIPELINE_COMPARE - else result["nearby_fields"] - ) - print("Top recurring terms:", ", ".join(recurring_terms[:8]) or "none") - print("Nearby fields:", ", ".join(nearby_fields_list[:8]) or "none") - - if args.print_json: - print(json.dumps(result, indent=2, ensure_ascii=False)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/literature/literature_probe_api.py b/5-Applications/tools-scripts/literature/literature_probe_api.py deleted file mode 100644 index 9e216f8a..00000000 --- a/5-Applications/tools-scripts/literature/literature_probe_api.py +++ /dev/null @@ -1,103 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Local HTTP wrapper for the literature probe. - -This exposes the question-focused probe as a small FastAPI service so local -LLM stacks can call it over HTTP instead of shelling out. -""" - -from __future__ import annotations - -import os -from pathlib import Path -from typing import Any, Dict, List, Optional - -from fastapi import FastAPI -from fastapi.middleware.cors import CORSMiddleware -from pydantic import BaseModel, Field - -from scripts.literature_probe import DEFAULT_OUT_DIR, run_probe, write_outputs - - -APP_TITLE = "Literature Probe API" -DEFAULT_BIND = os.getenv("LITERATURE_PROBE_API_BIND", "::1") -DEFAULT_PORT = int(os.getenv("LITERATURE_PROBE_API_PORT", "8011")) -DEFAULT_ALLOWED_ORIGINS = os.getenv("LITERATURE_PROBE_API_ALLOW_ORIGINS", "*") - -app = FastAPI(title=APP_TITLE) -app.add_middleware( - CORSMiddleware, - allow_origins=[origin.strip() for origin in DEFAULT_ALLOWED_ORIGINS.split(",") if origin.strip()], - allow_credentials=False, - allow_methods=["*"], - allow_headers=["*"], -) - - -class ProbeRequest(BaseModel): - question: str = Field(..., min_length=1) - extra_queries: List[str] = Field(default_factory=list) - per_source: int = Field(default=8, ge=1, le=50) - write_outputs: bool = True - out_dir: Optional[str] = None - include_full_result: bool = True - - -def build_response(result: Dict[str, Any], json_path: Optional[Path], markdown_path: Optional[Path], include_full_result: bool) -> Dict[str, Any]: - papers = result.get("papers", []) - return { - "status": "ok", - "question": result["question"], - "wall_type": result["wall_type"], - "semantic_scholar_status": result.get("source_stats", {}).get("semantic_scholar", {}).get("status", "unknown"), - "source_stats": result.get("source_stats", {}), - "source_contributions_top12": result.get("source_contributions_top12", {}), - "recurring_terms": result.get("recurring_terms", []), - "nearby_fields": result.get("nearby_fields", []), - "top_papers": papers[:12], - "json_path": str(json_path) if json_path else None, - "markdown_path": str(markdown_path) if markdown_path else None, - "result": result if include_full_result else None, - } - - -@app.get("/") -def root() -> Dict[str, Any]: - return { - "status": "ok", - "service": APP_TITLE, - "docs_url": "/docs", - "openapi_url": "/openapi.json", - "health_url": "/health", - "probe_url": "/probe", - } - - -@app.get("/health") -def health() -> Dict[str, Any]: - return { - "status": "ok", - "service": APP_TITLE, - "default_out_dir": str(DEFAULT_OUT_DIR), - "default_bind": DEFAULT_BIND, - "default_port": DEFAULT_PORT, - "allow_origins": [origin.strip() for origin in DEFAULT_ALLOWED_ORIGINS.split(",") if origin.strip()], - } - - -@app.post("/probe") -def probe(request: ProbeRequest) -> Dict[str, Any]: - result = run_probe(request.question, request.extra_queries, request.per_source) - - json_path: Optional[Path] = None - markdown_path: Optional[Path] = None - if request.write_outputs: - out_dir = Path(request.out_dir) if request.out_dir else Path(DEFAULT_OUT_DIR) - json_path, markdown_path = write_outputs(result, out_dir) - - return build_response(result, json_path, markdown_path, request.include_full_result) diff --git a/5-Applications/tools-scripts/manifold/evolve_wall.py b/5-Applications/tools-scripts/manifold/evolve_wall.py deleted file mode 100644 index 11d87f1d..00000000 --- a/5-Applications/tools-scripts/manifold/evolve_wall.py +++ /dev/null @@ -1,126 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import re - -# ------------- UPDATE PYTHON CONTROL ------------- -ctrl_path = "TSM_COMPILER.py" -with open(ctrl_path, "r") as f: - py_text = f.read() - -new_router = """class PhysicsMoE_Router: - def __init__(self): - print("[pi-MoE] Initializing the 'Wall of Physics' (Massive MoE Matrix)...") - self.experts = { - "QCD": "Quantum Chromodynamics", - "QED": "Quantum Electrodynamics", - "ACOUSTICS": "Acoustic Metamaterials", - "THERMO": "High-Energy Thermodynamics", - "RELATIVITY": "Hyper-Relativity", - "M_THEORY": "11-Dimensional String Theory", - "LQG": "Loop Quantum Gravity", - "BEC": "Bose-Einstein Condensate", - "MHD": "Magnetohydrodynamics", - "DARK_FLUID": "Dark Energy Fluidics", - "HIGGS": "Higgs Field Interaction", - "TACHYON": "Tachyonic Condensation", - "HOLOGRAPHIC": "Holographic Entropy Gate", - "TOPOLOGICAL": "Topological Defect Routing", - "COGNITIVE": "Native Synthetic Cognitive Core" - } - # Simulate a dense neural network layer for the wall (15x15 dimensional space) - import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - self.wall_matrix = xp.random.uniform(0.9, 1.1, (len(self.experts), len(self.experts))) - - def _synthetic_expert_gate(self, tensor_data) -> float: - import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - # Normalize and pad incoming tensor to match wall dimensions - tensor_array = xp.array(tensor_data, dtype=float) - padded_tensor = xp.pad(tensor_array, (0, max(0, len(self.experts) - len(tensor_array))), 'constant')[:len(self.experts)] - - # Matrix multiplication: smashing the acoustic tensor against the Wall of Physics - wall_activation = xp.dot(self.wall_matrix, padded_tensor) - - # Calculate thermodynamics/entropy of the collision - entropy = xp.sum(wall_activation * xp.log(xp.maximum(wall_activation, 1e-9))) - std_dev = xp.std(wall_activation) - - # Softmax weighting to find which physics expert takes the domain lead - exp_act = xp.exp(wall_activation - xp.max(wall_activation)) - weights = exp_act / xp.sum(exp_act) - - dominant_idx = xp.argmax(weights) - dominant_expert = list(self.experts.values())[dominant_idx] - - print(f"[p-MoE-WALL] Tensor fractured and distributed across {len(self.experts)} physics domains.") - print(f"[p-MoE-WALL] Dominant physics engine: {dominant_expert} (Confidence: {weights[dominant_idx]*100:.2f}%)") - - gated_multiplier = 1.0 + (xp.mean(weights) * (std_dev / max(abs(entropy), 1))) - final_multiplier = max(0.1, min(10.0, gated_multiplier)) - - print(f"[p-MoE-WALL] Wall synthesized unified harmonic shift: {final_multiplier:.6f}") - return final_multiplier - - def forward_pass(self, tensor: list) -> float: - # Pushes any size tensor directly into the massive computational wall - return self._synthetic_expert_gate(tensor) -""" - -py_text = re.sub(r"class PhysicsMoE_Router:.*?class TSM_Compiler:", new_router + "\nclass TSM_Compiler:", py_text, flags=re.DOTALL) - -new_main = """if __name__ == "__main__": - compiler = TSM_Compiler() - - print("\\n=== Initializing routing matrix ===") - print("[TSM] Constructing a test network...") - - # Establish a cascading network of 5 tunnel intersections - baselines = [ - (1.2e12, 4.4e14), # Node 1 → Node 2 - (4.4e14, 8.8e15), # Node 2 → Node 3 - (8.8e15, 2.1e16), # Node 3 → Node 4 - (2.1e16, 9.9e18), # Node 4 → Node 5 - (9.9e18, 1.2e12) # Node 5 → Node 1 (loopback) - ] - - for i, (src, dst) in enumerate(baselines): - print(f"\\n--- Wall Intersection {i+1} ---") - compiler.route_spatial_fold(src, dst) - - print("\\n[TSM] 'Wall of Physics' fully established. Sub-space is structurally locked.") -""" - -py_text = re.sub(r"if __name__ == \"__main__\":.*", new_main, py_text, flags=re.DOTALL) - -with open(ctrl_path, "w") as f: - f.write(py_text) - -# ------------- UPDATE DOC ------------- -doc_path = "KDA_DOC.tex" -with open(doc_path, "r") as f: - text = f.read() - -wall_section = """ -\\subsection{The Wall of Physics (Massive MoE Architecture)} -By dramatically scaling the pi-MoE router, TSM now processes tensors through a dense computational barrier known as "The Wall." Instead of simple binary gating, the incoming frequency tensor is smashed against a 15-dimensional matrix representing discrete branches of theoretical physics (from Quantum Electrodynamics to 11-Dimensional String Theory and Holographic Entropy). - -The Wall fractures the harmonic load, distributes it across all 15 domains simultaneously, and calculates the resulting synthesis using softmax activation to output a unified physics multiplier. This allows for the construction of cascading, networked spatial folds rather than isolated bridges. -""" - -if r"\end{document}" in text: - text = text.replace(r"\end{document}", wall_section + "\n\\end{document}") -else: - text += "\n" + wall_section - -with open(doc_path, "w") as f: - f.write(text) - diff --git a/5-Applications/tools-scripts/manifold/manifold_100_layer_sim.py b/5-Applications/tools-scripts/manifold/manifold_100_layer_sim.py deleted file mode 100644 index f829221b..00000000 --- a/5-Applications/tools-scripts/manifold/manifold_100_layer_sim.py +++ /dev/null @@ -1,58 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -# --- PHYSICAL PARAMETERS --- -LAYERS = 100 -ATTENUATION_PER_LAYER_DB = 0.05 # Estimated signal loss per layer -INITIAL_SNR_DB = 60.0 # High-fidelity DAC output -RECYCLING_EFFICIENCY = 0.85 # [T] Primitive recovery effectiveness - -def simulate_100_layer_manifold(use_geometric_amps=False): - print(f"--- 100-LAYER PASSIVE MANIFOLD SIMULATION (Amps: {use_geometric_amps}) ---") - - current_snr = INITIAL_SNR_DB - current_amplitude = 1.0 # normalized - - layer_stats = [] - - for layer in range(1, LAYERS + 1): - # 1. Attenuation - current_amplitude *= (10**(-ATTENUATION_PER_LAYER_DB / 20)) - - # 2. Thermal Recycling [T] - # Recycles a portion of the "lost" energy back into the amplitude - loss = 1.0 - (10**(-ATTENUATION_PER_LAYER_DB / 20)) - recovery = loss * RECYCLING_EFFICIENCY - current_amplitude += recovery * 0.1 # Small boost from heat recovery - - # 3. Geometric Amplification - # Every 10 layers, use constructive interference to "re-strobe" the signal - if use_geometric_amps and (layer % 10 == 0): - current_amplitude *= 1.25 # Constructive interference boost - - # SNR Calculation (Simple decay model) - current_snr -= (ATTENUATION_PER_LAYER_DB * 1.5) # Noise floor rising - - layer_stats.append((layer, current_amplitude, current_snr)) - - if layer % 25 == 0 or layer == LAYERS: - print(f"Layer {layer:03d}: Amplitude = {current_amplitude:.4f} | SNR = {current_snr:.2f} dB") - - # VERDICT - if current_snr < 10.0: - print("\nVERDICT: SIGNAL DECOHERENCE (SNR < 10dB). Manifold too deep.") - else: - print("\nVERDICT: SIGNAL COHERENT. Resonant state maintained.") - -if __name__ == "__main__": - simulate_100_layer_manifold(use_geometric_amps=False) - print("\n" + "="*50 + "\n") - simulate_100_layer_manifold(use_geometric_amps=True) diff --git a/5-Applications/tools-scripts/manifold/shadow_manifold.py b/5-Applications/tools-scripts/manifold/shadow_manifold.py deleted file mode 100644 index a71851a2..00000000 --- a/5-Applications/tools-scripts/manifold/shadow_manifold.py +++ /dev/null @@ -1,60 +0,0 @@ -#!/usr/bin/env python3 -""" -Utility: shadow_manifold.py ---------------------------- -Shadow Manifold: Parallel Informatic Auditor. -""" - -import sqlite3 -import json -import time -import os -from bt20_fpga_bridge import MassArchivist - -class ShadowManifold(MassArchivist): - def __init__(self, db_path="/home/allaun/shadow_mmr.db"): - # We ensure a separate DB for isolation - super().__init__(db_path=db_path) - self.telemetry_path = "/home/allaun/Documents/Research Stack/5-Applications/tools-scripts/shadow_telemetry.json" - - # Initialize Shadow DB - conn = sqlite3.connect(self.db_path) - cur = conn.cursor() - cur.execute("CREATE TABLE IF NOT EXISTS mmr (leaf_idx INTEGER PRIMARY KEY, payload TEXT, leaf_hash TEXT, leaf_type TEXT)") - conn.commit() - conn.close() - - def audit_source(self, path="/home/allaun/Documents/Research Stack/core/hw/"): - print(f"[SHADOW] Initiating Source Code Audit -> {path}") - files = [f for f in os.listdir(path) if f.endswith(".v") or f.endswith(".vh")] - - count = 0 - for f in files: - with open(os.path.join(path, f), "r") as src: - content = src.read() - # We 'archivize' the code as a truth-leaf - payload = json.dumps({"source": f, "content": content[:1000]}) - l_hash = f"s_{f[:10]}" - - # Use standard sweep logic (simulated) - phi = 0.96 + (time.time() % 0.02) # Shadow's independent stability - - self.update_telemetry(count, len(files), phi, "0.5420") - count += 1 - time.sleep(1) - - print("[SHADOW] Source Audit Complete.") - - def update_telemetry(self, count, limit, phi, gate): - status = { - "epoch": count, - "phi": phi, - "gate": gate, - "state": "SHADOW_AUDIT", - "heartbeat": time.ctime() - } - with open(self.telemetry_path, "w") as f: json.dump(status, f) - -if __name__ == "__main__": - shadow = ShadowManifold() - shadow.audit_source() diff --git a/5-Applications/tools-scripts/market/coinbase_client_helper.py b/5-Applications/tools-scripts/market/coinbase_client_helper.py deleted file mode 100644 index cbb6d79a..00000000 --- a/5-Applications/tools-scripts/market/coinbase_client_helper.py +++ /dev/null @@ -1,86 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import os -import sys -import asyncio -from typing import Dict, Any -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -sys.path.append(str(ROOT)) - -try: - from scripts.tsm_harness_compat import TSMKernel -except ImportError: - from tsm_harness_compat import TSMKernel - -try: - from scripts.market_action_policy import MarketActionPolicy -except ImportError: - from market_action_policy import MarketActionPolicy - -class CoinbaseClient: - def __init__(self, policy: MarketActionPolicy | None = None): - self.kernel = TSMKernel() - self.policy = policy or MarketActionPolicy.from_env(prefix="ZEC_ACTION") - - async def get_zec_price_snapshot(self) -> Dict[str, Any]: - """Fetch current ZEC-USD price and derive a local risk-aware action policy.""" - # Opcode 0xA3: FETCH_ZEC_PRICE_SURFACE (legacy payloads may still include alpha_target) - result = await self.kernel.execute_async([("0xA3", [])]) - data = result[0] - market = float(data.get("market", 0.0) or 0.0) - snapshot = self.policy.snapshot(market) - snapshot["source"] = data.get("source", "tsm_0xA3") - if "alpha_target" in data: - snapshot["legacy_alpha_target"] = data.get("alpha_target") - - if market > 0: - print( - "[ACTION-POLICY] " - f"Market: ${market:.2f} | " - f"Entry ref ({snapshot['entry_improvement_pct']:.2f}% better): " - f"${snapshot['entry_reference_price']:.2f} | " - f"Loss alert ({snapshot['max_loss_pct']:.2f}% down): " - f"${snapshot['loss_alert_price']:.2f}" - ) - return snapshot - - async def get_zec_price(self) -> float: - """Fetch current ZEC-USD market price via TSM 0xA3.""" - snapshot = await self.get_zec_price_snapshot() - market = float(snapshot.get("market_price", 0.0) or 0.0) - return market - - async def place_market_buy(self, amount_usd: float) -> Dict[str, Any]: - """Place a market buy order for ZEC using USD via TSM 0xA2.""" - path = "/orders" - payload = { - "client_order_id": f"ZEC-BUY-{int(asyncio.get_event_loop().time())}", - "product_id": "ZEC-USD", - "side": "BUY", - "order_configuration": { - "market_market_ioc": { - "quote_size": str(amount_usd) - } - } - } - # Opcode 0xA2: EXECUTE_COINBASE_POST - result = await self.kernel.execute_async([("0xA2", [path, payload])]) - return result[0] - - async def withdraw_zec(self, amount: float, address: str) -> Dict[str, Any]: - """Withdraw ZEC to a specific address via TSM 0xA2.""" - path = "/withdrawals/crypto" - payload = { - "amount": str(amount), - "currency": "ZEC", - "crypto_address": address - } - # Opcode 0xA2: EXECUTE_COINBASE_POST - result = await self.kernel.execute_async([("0xA2", [path, payload])]) - return result[0] diff --git a/5-Applications/tools-scripts/market/contract_heatmap.py b/5-Applications/tools-scripts/market/contract_heatmap.py deleted file mode 100644 index 29287afe..00000000 --- a/5-Applications/tools-scripts/market/contract_heatmap.py +++ /dev/null @@ -1,693 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Contract Heat Map — Waveprobe visualizer. - -Takes the JSON output of evm_bytecode_waveprobe.py and generates an -interactive HTML heat map showing cold/warm/hot regions of a contract. - -Usage: - # Pipe from waveprobe - python3 evm_bytecode_waveprobe.py --file contract.hex --json | \ - python3 contract_heatmap.py > report.html - - # From saved JSON - python3 contract_heatmap.py --input probe_result.json --output report.html - - # Direct from bytecode - python3 contract_heatmap.py --bytecode 0x6060... --output report.html -""" - -from __future__ import annotations - -import json -import sys -from pathlib import Path -from typing import Any, Dict, List, Optional - -# ── HTML Template ──────────────────────────────────────────────────────────── - -HTML_TEMPLATE = """ - - - - -Contract Heat Map — Waveprobe v0.1 - - - - -

🧬 Contract Heat Map

-
- SHA-256: {sha256}
- Length: {length} bytes  |  - Chunks: {chunk_count}  |  - Waveprobe: {version} -
- -
-
-
{overall_heat}
-
Overall Heat
-
-
-
{overall_class_upper}
-
Classification
-
-
-
{cold_count}
-
🧊 Cold Regions
-
-
-
{warm_count}
-
🌡️ Warm Regions
-
-
-
{hot_count}
-
🔥 Hot Regions
-
-
- -
-
Bytecode Heat Map (offset →)
-
{heat_strip_html}
-
- 🧊 Cold (heat < 0.05) - 🌡️ Warm (0.05 ≤ heat < 0.25) - 🔥 Hot (heat ≥ 0.25) -
-
- -
-
⚡ KOT Action Cost Estimate
-
{kot_bar_html}
-
- Estimated traversal cost breakdown by region classification. - Hot regions cost more KOT to interact with (higher probe complexity). -
-
- -
Per-Chunk Analysis
-
{chunks_html}
- -
-
Probe Family Response (Chunk 0)
- - - - - - - - - {probe_table_html} -
Probe FamilyFeature DisplacementCompression Displacement
-
- -
- Waveprobe v0.1-evm  |  - Operational Spec: OPERATIONAL_SPEC.md  |  - Murphy's Law is the uncle you can't not invite to the table. -
- -
- - - -""" - -# ── Feature names ──────────────────────────────────────────────────────────── - -FEATURE_NAMES = [ - "entropy", - "opcode_dens", - "call_dens", - "ctrl_flow", - "push_ratio", - "jd_spacing", - "repetition", - "compress", -] - -FEATURE_COLORS = [ - "#8b5cf6", # purple - "#06b6d4", # cyan - "#ef4444", # red - "#f59e0b", # amber - "#10b981", # emerald - "#3b82f6", # blue - "#ec4899", # pink - "#6366f1", # indigo -] - - -# ── Rendering ──────────────────────────────────────────────────────────────── - -def _heat_to_color(heat: float) -> str: - """Map heat value to an RGB color string.""" - if heat >= 0.25: - # Hot: red - t = min(1.0, (heat - 0.25) / 0.75) - r = int(239 + t * 16) - g = int(68 - t * 40) - b = int(68 - t * 40) - elif heat >= 0.05: - # Warm: amber - t = (heat - 0.05) / 0.20 - r = int(59 + t * 186) - g = int(130 + t * 28) - b = int(246 - t * 178) - else: - # Cold: blue - t = heat / 0.05 - r = int(30 + t * 29) - g = int(64 + t * 66) - b = int(175 + t * 71) - return f"rgb({min(255,r)},{min(255,g)},{min(255,b)})" - - -def _render_heat_strip(data: Dict[str, Any]) -> str: - """Render the horizontal heat strip cells.""" - cells = [] - for i, heat in enumerate(data.get("heat_map", [])): - color = _heat_to_color(heat) - cells.append( - f'
' - ) - return "".join(cells) - - -def _render_kot_bar(data: Dict[str, Any]) -> str: - """Render the KOT cost estimate bar.""" - cold = data.get("cold_region_count", 0) - warm = data.get("warm_region_count", 0) - hot = data.get("high_heat_region_count", 0) - total = cold + warm + hot - if total == 0: - return '
No data
' - - # KOT cost weights (cold=1x, warm=4x, hot=16x) - cold_kot = cold * 1 - warm_kot = warm * 4 - hot_kot = hot * 16 - total_kot = cold_kot + warm_kot + hot_kot - - parts = [] - if cold_kot > 0: - pct = cold_kot / total_kot * 100 - parts.append( - f'
' - f'{cold_kot} KOT ({pct:.0f}%)
' - ) - if warm_kot > 0: - pct = warm_kot / total_kot * 100 - parts.append( - f'
' - f'{warm_kot} KOT ({pct:.0f}%)
' - ) - if hot_kot > 0: - pct = hot_kot / total_kot * 100 - parts.append( - f'
' - f'{hot_kot} KOT ({pct:.0f}%)
' - ) - return "".join(parts) - - -def _render_feature_bars(features: List[float]) -> str: - """Render feature bar chart for a chunk.""" - bars = [] - for i, val in enumerate(features): - name = FEATURE_NAMES[i] if i < len(FEATURE_NAMES) else f"f{i}" - color = FEATURE_COLORS[i] if i < len(FEATURE_COLORS) else "#888" - pct = min(100, max(0, val * 100)) - bars.append( - f'
' - f'
{name}
' - f'
' - f'
' - f'
' - f'
{val:.3f}
' - f'
' - ) - return "".join(bars) - - -def _render_chunk_card(idx: int, chunk: Dict[str, Any]) -> str: - """Render a single chunk detail card.""" - cls = chunk.get("classification", "cold") - badge_cls = f"badge-{cls}" - agg = chunk.get("aggregate", {}) - features = chunk.get("base_features", []) - - return ( - f'
' - f'
' - f'
Chunk {idx} ' - f' ' - f' (offset {chunk.get("chunk_offset", 0)})
' - f'
{cls}
' - f'
' - f'
' - f'
Heat
' - f'
{agg.get("heat", 0):.6f}
' - f'
Sensitivity
' - f'
{agg.get("sensitivity", 0):.6f}
' - f'
Anisotropy
' - f'
{agg.get("anisotropy", 0):.4f}
' - f'
Comp. Sens.
' - f'
{agg.get("compression_sensitivity", 0):.6f}
' - f'
' - f'
' - f'{_render_feature_bars(features)}' - f'
' - f'
' - ) - - -def _render_chunks(data: Dict[str, Any]) -> str: - """Render all chunk cards.""" - cards = [] - for i, chunk in enumerate(data.get("chunks", [])): - cards.append(_render_chunk_card(i, chunk)) - return "".join(cards) - - -def _render_probe_table(data: Dict[str, Any]) -> str: - """Render probe response table for chunk 0.""" - chunks = data.get("chunks", []) - if not chunks: - return "No chunks" - - rows = [] - for probe in chunks[0].get("probes", []): - mag = probe.get("feature_displacement_magnitude", 0) - comp = probe.get("compression_displacement", 0) - # Color intensity by magnitude - mag_color = _heat_to_color(min(0.5, mag * 10)) - comp_color = _heat_to_color(min(0.5, comp * 10)) - rows.append( - f'' - f'{probe.get("probe_family", "")}' - f'{mag:.8f}' - f'{comp:.8f}' - f'' - ) - return "".join(rows) - - -def render_heatmap(data: Dict[str, Any]) -> str: - """Render the full heat map HTML from waveprobe JSON data.""" - overall_class = data.get("overall_classification", "cold") - - return HTML_TEMPLATE.format( - sha256=data.get("bytecode_sha256", "unknown"), - length=data.get("total_length", 0), - chunk_count=data.get("chunk_count", 0), - version=data.get("waveprobe_version", "0.1-evm"), - overall_heat=f'{data.get("overall_heat", 0):.6f}', - overall_class=overall_class, - overall_class_upper=overall_class.upper(), - cold_count=data.get("cold_region_count", 0), - warm_count=data.get("warm_region_count", 0), - hot_count=data.get("high_heat_region_count", 0), - heat_strip_html=_render_heat_strip(data), - kot_bar_html=_render_kot_bar(data), - chunks_html=_render_chunks(data), - probe_table_html=_render_probe_table(data), - probe_data_json=json.dumps(data), - ) - - -# ── CLI ────────────────────────────────────────────────────────────────────── - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser( - description="Contract Heat Map — Waveprobe visualizer" - ) - parser.add_argument("--input", "-i", - help="Path to waveprobe JSON output file") - parser.add_argument("--bytecode", "-b", - help="Hex-encoded bytecode (runs waveprobe first)") - parser.add_argument("--bytecode-file", "-f", - help="File containing hex-encoded bytecode") - parser.add_argument("--output", "-o", - help="Output HTML file path (default: stdout)") - - args = parser.parse_args() - - probe_data: Optional[Dict[str, Any]] = None - - if args.input: - with open(args.input) as f: - probe_data = json.load(f) - elif args.bytecode or args.bytecode_file: - # Import and run the waveprobe - sys.path.insert(0, str(Path(__file__).parent)) - from evm_bytecode_waveprobe import EVMBytecodeWaveprobe - - if args.bytecode_file: - bc_hex = Path(args.bytecode_file).read_text().strip() - else: - bc_hex = args.bytecode - - probe = EVMBytecodeWaveprobe(bytecode_hex=bc_hex) - result = probe.analyze() - probe_data = json.loads(probe.to_json(result)) - else: - # Read from stdin - raw = sys.stdin.read().strip() - if not raw: - print("Error: no input. Use --input, --bytecode, or pipe JSON.", - file=sys.stderr) - sys.exit(1) - probe_data = json.loads(raw) - - html = render_heatmap(probe_data) - - if args.output: - Path(args.output).write_text(html) - print(f"Wrote heat map to {args.output}", file=sys.stderr) - else: - print(html) diff --git a/5-Applications/tools-scripts/market/hodh_qorum_consensus.py b/5-Applications/tools-scripts/market/hodh_qorum_consensus.py deleted file mode 100644 index a5576b75..00000000 --- a/5-Applications/tools-scripts/market/hodh_qorum_consensus.py +++ /dev/null @@ -1,89 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import math - -class QorumCritic: - def __init__(self, name, field, basis_threshold=0.95): - self.name = name - self.field = field - self.threshold = basis_threshold - - def evaluate(self, params): - # Evaluation logic based on ND-Space manifold state - # Returns (Confidence, VetoReason) - raise NotImplementedError - -class MaterialCritic(QorumCritic): - def evaluate(self, params): - # Checks C-H bond stability and lattice rigidity - # Diamondoid lattice is highly stable, but internal H-pressure matters - stability = 0.99 - if params['pressure_psi'] < 100: - return 0.5, "Insufficient pressure for RTSC phase-lock" - return stability, None - -class QEDCritic(QorumCritic): - def evaluate(self, params): - # Checks decoherence rate vs 14D shielding - # Shielding is mass-invariant but sensitive to thermal noise - temp_k = params['temp_c'] + 273.15 - decoherence = math.exp(-1.0 / (temp_k / 300.0)) - if temp_k > 494: # Above H-H critical limit - return 0.1, "Thermal floor exceeded" - return 1.0 - decoherence, None - -class EnvironmentalCritic(QorumCritic): - def evaluate(self, params): - # Checks elevation (pressure offset) and humidity - # High humidity can induce surface oxidation on non-coated cages - conf = 1.0 - if params['humidity'] > 85: - conf -= 0.15 - # Elevation decreases ambient oxygen (good for C-stability) but lowers internal H2 delta - if params['elevation_m'] > 5000: - conf -= 0.1 # Delta-P penalty - return conf, None - -def run_300_percent_audit(params): - critics = [ - MaterialCritic("Dr. Carbon", "Materials Science"), - QEDCritic("The Wavefront", "Quantum Electrodynamics"), - EnvironmentalCritic("Altitude Zero", "Meteorology") - ] - - tier_agreements = {1: [], 2: [], 3: []} - - print(f"--- HODH 300% Qorum Audit ---") - print(f"Params: {params}") - - for critic in critics: - conf, veto = critic.evaluate(params) - print(f"[{critic.name}] ({critic.field}): {conf:.4f} - {'OK' if not veto else 'VETO: ' + veto}") - - # In QRun, consensus is reached when ALL tiers agree - # We map confidence across current, manifest, and latent tiers - tier_agreements[1].append(conf) # Tier 1: Semantic - tier_agreements[2].append(conf * 0.98) # Tier 2: Physical (Substrate losses) - tier_agreements[3].append(conf * 1.02) # Tier 3: Latent (Quantum gain) - - consensus_score = sum([min(tier_agreements[t]) for t in [1,2,3]]) * 100 - print(f"\nFinal Consensus Score: {consensus_score:.2f}% / 300%") - - if consensus_score >= 250: - print("RESULT: DEPLOYMENT READY (300% Agreement reached within N-D variance)") - else: - print("RESULT: REVISE PARAMETERS") - -if __name__ == "__main__": - test_params = { - 'temp_c': 25, - 'humidity': 40, - 'elevation_m': 300, - 'pressure_psi': 150 - } - run_300_percent_audit(test_params) diff --git a/5-Applications/tools-scripts/market/live_market_data.py b/5-Applications/tools-scripts/market/live_market_data.py deleted file mode 100644 index d7860d89..00000000 --- a/5-Applications/tools-scripts/market/live_market_data.py +++ /dev/null @@ -1,3951 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import asyncio -from dataclasses import dataclass, field -import hashlib -import json -import os -import random -import time -from collections.abc import Iterator -from datetime import datetime, timezone -from pathlib import Path -from typing import Awaitable, Callable, Mapping, TypeAlias, cast -from urllib.parse import quote, urlencode -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from io_harness_compat import fetch_network_resource -import xml.etree.ElementTree as ET - -from websockets.asyncio.client import connect -from websockets.exceptions import ConnectionClosed, InvalidStatus - -try: - from scripts.mevbot_swarm_sim import Pool, SwarmSimulation, TruthQualifier -except ImportError: - from mevbot_swarm_sim import Pool, SwarmSimulation, TruthQualifier - -DEFAULT_ROUNDS = 100 -DEFAULT_SIMULATION_SEED = 0 -SWARM_STATE_VERSION = 2 -LIQUIDITY_BANDS_BPS = (10.0, 25.0, 50.0) -LIQUIDITY_BAND_WEIGHTS = { - "10bps": 0.5, - "25bps": 0.3, - "50bps": 0.2, -} -LIQUIDITY_IMPACT_THRESHOLD_BPS = LIQUIDITY_BANDS_BPS[-1] -ORDER_BOOK_LEVEL_LIMIT = 10 -META_QUOTE_POLL_INTERVAL_S = 15.0 -META_QUOTE_STALE_AFTER_S = 45.0 -MACRO_CONTEXT_POLL_INTERVAL_S = 60.0 -VENUE_BOOK_STALE_AFTER_S = 5.0 -VENUE_FANIN_WARMUP_S = 3.0 -PUBLIC_PROVIDER_TARGET_COUNT = 2 -PAYMENT_GATE_MIN_OBSERVED_ROUNDS = 20 -PAYMENT_GATE_MIN_LIQUIDITY_SCORE = 650_000.0 -PAYMENT_GATE_MIN_EXECUTABLE_NOTIONAL_USD_50BPS = 600_000.0 -PAYMENT_GATE_MIN_TRUTH_CONFIDENCE = 0.45 -PAID_LIQUIDITY_WIRE_NOTE = ( - "Reserved integration point for future paid market-data acquisition. " - "It exists so premium feeds can be added behind one policy boundary after " - "free-path liquidity metrics show the spend is justified." -) -BINANCE_WS_URL = "wss://stream.binance.com:9443/stream" -BINANCE_STREAMS = ( - "solusdt@depth20@100ms", - "btcusdt@depth20@100ms", - "solbtc@depth20@100ms", -) -KRAKEN_WS_URL = "wss://ws.kraken.com/v2" -KRAKEN_PRODUCTS = { - "SOL/USD": "SOLUSDT", - "BTC/USD": "BTCUSDT", - "SOL/BTC": "SOLBTC", -} -BYBIT_WS_URL = "wss://stream.bybit.com/v5/public/spot" -BYBIT_TOPICS = { - "orderbook.50.SOLUSDT": "SOLUSDT", - "orderbook.50.BTCUSDT": "BTCUSDT", - "orderbook.50.SOLBTC": "SOLBTC", -} -COINGECKO_SIMPLE_PRICE_URL = ( - "https://api.coingecko.com/api/v3/simple/price" - "?ids=bitcoin,solana&vs_currencies=usd" -) -DEXSCREENER_SEARCH_QUERIES = { - "SOLUSDT": "SOL/USDC", - "BTCUSDT": "BTC/USDC", -} -YAHOO_STOCK_INDEX_SYMBOLS = ("SPY", "QQQ", "IWM", "DIA") -YAHOO_COMMODITY_SYMBOLS = ("CL=F", "NG=F", "GC=F") -GOOGLE_NEWS_WATCHLIST = { - "south_pars": "South Pars North Dome gas field", - "lng_supply": "LNG supply disruption", - "opec": "OPEC production cut", - "natural_gas": "natural gas field outage", - "commodity_shock": "commodity market shock crude oil natural gas", -} -COMMODITY_NEWS_SHOCK_KEYWORDS = { - "attack": 0.9, - "strike": 0.9, - "explosion": 1.0, - "fire": 0.8, - "halt": 0.8, - "shutdown": 0.8, - "evacuation": 0.6, - "sanction": 0.6, - "outage": 0.7, - "disruption": 0.7, - "cut": 0.5, - "war": 0.9, - "pipeline": 0.4, -} -MANIFOLD_SEARCH_TERMS = { - "oil": "oil", - "natural_gas": "natural gas", - "lng": "lng", - "opec": "opec", -} -POLYMARKET_KEYWORDS = ( - "oil", - "gas", - "lng", - "energy", - "iran", - "qatar", - "south pars", - "north dome", - "commodity", -) -DEFILLAMA_PROTOCOLS_URL = "https://api.llama.fi/protocols" -DEFILLAMA_CHAINS_URL = "https://api.llama.fi/v2/chains" -DEFILLAMA_STABLECOINS_URL = "https://stablecoins.llama.fi/stablecoins?includePrices=true" -DEFILLAMA_PERPS_OPEN_INTEREST_URL = "https://api.llama.fi/overview/open-interest" -DEFILLAMA_PROTOCOL_KEYWORDS = ( - "aave", - "uniswap", - "raydium", - "jupiter", - "hyperliquid", - "gmx", - "drift", -) -DEFILLAMA_CHAIN_WATCHLIST = ("Ethereum", "Solana", "Arbitrum", "Base", "BSC") -DEFILLAMA_STABLECOIN_WATCHLIST = ("USDT", "USDC", "DAI", "USDE", "FDUSD", "PYUSD") -ONEINCH_PRODUCT_API_BASE_URL = "https://api.1inch.dev" -ONEINCH_PRODUCT_API_KEY_ENV = "ONEINCH_API_KEY" -ONEINCH_PRODUCT_API_BASE_URL_ENV = "ONEINCH_PRODUCT_API_BASE_URL" -ONEINCH_PRODUCT_API_PROBE_PATH_ENV = "ONEINCH_PRODUCT_API_PROBE_PATH" -PREMIUM_WIRE_ENABLED_ENV = "ENABLE_PREMIUM_LIQUIDITY_WIRE" -ONEINCH_SPOT_PRICE_PROVIDER = "1inch_spot_price" -ONEINCH_SPOT_PRICE_POLL_INTERVAL_S = 1.0 -ONEINCH_SPOT_PRICE_CHAIN_ID_ENV = "ONEINCH_SPOT_PRICE_CHAIN_ID" -ONEINCH_SPOT_PRICE_SOL_ADDRESS_ENV = "ONEINCH_SPOT_PRICE_SOL_ADDRESS" -ONEINCH_SPOT_PRICE_BTC_ADDRESS_ENV = "ONEINCH_SPOT_PRICE_BTC_ADDRESS" -ONEINCH_SPOT_PRICE_USDT_ADDRESS_ENV = "ONEINCH_SPOT_PRICE_USDT_ADDRESS" -ONEINCH_SPOT_PRICE_TOKENS_BY_CHAIN = { - 1: { - "SOL": "0xD31a59c85aE9D8EdefEC411D448f90841571b89c", - "BTC": "0x2260FAC5E5542a773Aa44fBCfeDf7C193bc2C599", - "USDT": "0xdAC17F958D2ee523a2206206994597C13D831ec7", - } -} -REQUIRED_SYMBOLS = frozenset({"SOLUSDT", "BTCUSDT", "SOLBTC"}) -Quote: TypeAlias = tuple[float, float] -SessionMetadata: TypeAlias = dict[str, object] - - -def env_flag(name: str) -> bool: - return os.environ.get(name, "").strip().lower() in {"1", "true", "yes", "on"} - - -@dataclass(frozen=True) -class PaymentGateObservation: - observed_rounds: int - active_public_providers: int - provider_candidate_count: int - best_liquidity_score: float - best_executable_notional_usd_50bps: float - final_truth_confidence: float - failure_count: int - failures: tuple[str, ...] - - -@dataclass(frozen=True) -class PaymentGateDecision: - allow_activation: bool - measured_shortfall: bool - shortfall_score: float - reasons: tuple[str, ...] - observation: PaymentGateObservation - policy_name: str = "free_path_liquidity_shortfall_v1" - - def to_record(self) -> dict[str, object]: - return { - "type": "payment_gate_decision", - "policy_name": self.policy_name, - "allow_activation": self.allow_activation, - "measured_shortfall": self.measured_shortfall, - "shortfall_score": self.shortfall_score, - "reasons": list(self.reasons), - "observed_rounds": self.observation.observed_rounds, - "active_public_providers": self.observation.active_public_providers, - "provider_candidate_count": self.observation.provider_candidate_count, - "best_liquidity_score": self.observation.best_liquidity_score, - "best_executable_notional_usd_50bps": self.observation.best_executable_notional_usd_50bps, - "final_truth_confidence": self.observation.final_truth_confidence, - "failure_count": self.observation.failure_count, - "failures": list(self.observation.failures), - } - - -@dataclass(frozen=True) -class OneInchProductAPIAdapter: - base_url: str = field( - default_factory=lambda: os.environ.get( - ONEINCH_PRODUCT_API_BASE_URL_ENV, - ONEINCH_PRODUCT_API_BASE_URL, - ).rstrip("/") - ) - api_key_env_var: str = ONEINCH_PRODUCT_API_KEY_ENV - probe_path: str = field( - default_factory=lambda: os.environ.get(ONEINCH_PRODUCT_API_PROBE_PATH_ENV, "").strip() - ) - - def api_key(self) -> str | None: - api_key = os.environ.get(self.api_key_env_var, "").strip() - return api_key or None - - def configured(self) -> bool: - return self.api_key() is not None - - def ready(self) -> bool: - return self.configured() - - def spot_price_chain_id(self) -> int: - raw_value = os.environ.get(ONEINCH_SPOT_PRICE_CHAIN_ID_ENV, "1").strip() - try: - return int(raw_value) - except ValueError as exc: - raise RuntimeError( - f"{ONEINCH_SPOT_PRICE_CHAIN_ID_ENV} must be an integer chain id" - ) from exc - - def spot_price_token_addresses(self) -> dict[str, str]: - chain_id = self.spot_price_chain_id() - default_addresses = ONEINCH_SPOT_PRICE_TOKENS_BY_CHAIN.get(chain_id) - if default_addresses is None: - raise RuntimeError( - f"unsupported 1inch spot-price chain id {chain_id}; set a supported chain or extend the token map" - ) - - return { - "SOL": os.environ.get( - ONEINCH_SPOT_PRICE_SOL_ADDRESS_ENV, - default_addresses["SOL"], - ).strip(), - "BTC": os.environ.get( - ONEINCH_SPOT_PRICE_BTC_ADDRESS_ENV, - default_addresses["BTC"], - ).strip(), - "USDT": os.environ.get( - ONEINCH_SPOT_PRICE_USDT_ADDRESS_ENV, - default_addresses["USDT"], - ).strip(), - } - - def spot_price_path(self) -> str: - token_addresses = self.spot_price_token_addresses() - address_segment = ",".join(token_addresses.values()) - return f"/price/v1.1/{self.spot_price_chain_id()}/{address_segment}" - - def build_url(self, path: str, query: Mapping[str, object] | None = None) -> str: - normalized_path = path if path.startswith("/") else f"/{path}" - url = f"{self.base_url}{normalized_path}" - if not query: - return url - query_pairs: list[tuple[str, str]] = [] - for key, value in query.items(): - if isinstance(value, list): - for item in cast(list[object], value): - query_pairs.append((key, str(item))) - elif isinstance(value, tuple): - for item in cast(tuple[object, ...], value): - query_pairs.append((key, str(item))) - else: - query_pairs.append((key, str(value))) - return f"{url}?{urlencode(query_pairs)}" - - def request_json( - self, - path: str, - query: Mapping[str, object] | None = None, - method: str = "GET", - body: Mapping[str, object] | None = None, - ) -> object: - api_key = self.api_key() - if api_key is None: - raise RuntimeError(f"{self.api_key_env_var} is not set") - - headers = { - "Authorization": f"Bearer {api_key}", - "Accept": "application/json", - } - if body is not None: - headers["Content-Type"] = "application/json" - return fetch_json( - self.build_url(path, query), - headers=headers, - method=method, - body=body, - ) - - def probe(self) -> object | None: - if not self.probe_path: - return None - return self.request_json(self.probe_path) - - def status_record( - self, - *, - gate_open: bool, - wire_enabled: bool, - adapter_ready: bool, - probe_ok: bool | None, - error: str | None = None, - ) -> dict[str, object]: - try: - spot_price_path = self.spot_price_path() if self.configured() else None - except RuntimeError as exc: - spot_price_path = None - error = str(exc) if error is None else error - - return { - "type": "premium_adapter_status", - "provider": "1inch_product_api", - "base_url": self.base_url, - "api_key_env_var": self.api_key_env_var, - "probe_path": self.probe_path or None, - "spot_price_path": spot_price_path, - "wire_enabled": wire_enabled, - "gate_open": gate_open, - "configured": self.configured(), - "adapter_ready": adapter_ready, - "probe_ok": probe_ok, - "error": error, - } - - -@dataclass(frozen=True) -class PaymentGatePolicy: - policy_name: str = "free_path_liquidity_shortfall_v1" - min_observed_rounds: int = PAYMENT_GATE_MIN_OBSERVED_ROUNDS - min_active_public_providers: int = PUBLIC_PROVIDER_TARGET_COUNT - min_liquidity_score: float = PAYMENT_GATE_MIN_LIQUIDITY_SCORE - min_executable_notional_usd_50bps: float = PAYMENT_GATE_MIN_EXECUTABLE_NOTIONAL_USD_50BPS - min_truth_confidence: float = PAYMENT_GATE_MIN_TRUTH_CONFIDENCE - - def evaluate(self, observation: PaymentGateObservation) -> PaymentGateDecision: - reasons: list[str] = [] - - provider_shortfall = clamp_unit( - (self.min_active_public_providers - observation.active_public_providers) - / max(1, self.min_active_public_providers) - ) - liquidity_shortfall = clamp_unit( - (self.min_liquidity_score - observation.best_liquidity_score) - / max(self.min_liquidity_score, 1e-9) - ) - executable_shortfall = clamp_unit( - ( - self.min_executable_notional_usd_50bps - - observation.best_executable_notional_usd_50bps - ) - / max(self.min_executable_notional_usd_50bps, 1e-9) - ) - truth_shortfall = clamp_unit( - (self.min_truth_confidence - observation.final_truth_confidence) - / max(self.min_truth_confidence, 1e-9) - ) - - shortfall_score = clamp_unit( - 0.35 * provider_shortfall - + 0.30 * liquidity_shortfall - + 0.20 * executable_shortfall - + 0.15 * truth_shortfall - ) - - if provider_shortfall > 0.0: - reasons.append( - f"active public providers below target: {observation.active_public_providers}/{self.min_active_public_providers}" - ) - if liquidity_shortfall > 0.0: - reasons.append( - f"best liquidity score below threshold: {observation.best_liquidity_score:,.2f} < {self.min_liquidity_score:,.2f}" - ) - if executable_shortfall > 0.0: - reasons.append( - "best executable notional @50bps below threshold: " - f"{observation.best_executable_notional_usd_50bps:,.2f} < " - f"{self.min_executable_notional_usd_50bps:,.2f}" - ) - if truth_shortfall > 0.0: - reasons.append( - f"final truth confidence below threshold: {observation.final_truth_confidence:.4f} < {self.min_truth_confidence:.4f}" - ) - - has_measured_evidence = observation.observed_rounds >= self.min_observed_rounds - if observation.active_public_providers == 0 and observation.failure_count > 0: - has_measured_evidence = True - if not reasons: - reasons.append("all public providers are currently unavailable") - if not has_measured_evidence: - reasons.append( - f"insufficient measurement window: {observation.observed_rounds} < {self.min_observed_rounds} rounds" - ) - - measured_shortfall = shortfall_score > 0.0 and has_measured_evidence - allow_activation = measured_shortfall and ( - shortfall_score >= 0.25 or observation.active_public_providers == 0 - ) - - return PaymentGateDecision( - allow_activation=allow_activation, - measured_shortfall=measured_shortfall, - shortfall_score=shortfall_score, - reasons=tuple(reasons), - observation=observation, - policy_name=self.policy_name, - ) - - -@dataclass(frozen=True) -class PaidLiquidityWirePlacement: - enabled: bool = field(default_factory=lambda: env_flag(PREMIUM_WIRE_ENABLED_ENV)) - note: str = PAID_LIQUIDITY_WIRE_NOTE - policy: PaymentGatePolicy = field(default_factory=PaymentGatePolicy) - product_api_adapter: OneInchProductAPIAdapter = field(default_factory=OneInchProductAPIAdapter) - - async def try_activate( - self, - observation: PaymentGateObservation, - recorder: "TickRecorder | None" = None, - ) -> bool: - decision = self.policy.evaluate(observation) - if recorder is not None: - recorder.record_event(decision.to_record()) - if not decision.allow_activation: - if recorder is not None: - recorder.record_event( - self.product_api_adapter.status_record( - gate_open=False, - wire_enabled=self.enabled, - adapter_ready=False, - probe_ok=None, - ) - ) - return False - - print("Payment gate opened by measured free-path shortfall.") - for reason in decision.reasons: - print(f" - {reason}") - - adapter_ready = False - probe_ok: bool | None = None - adapter_error: str | None = None - if self.enabled: - if not self.product_api_adapter.configured(): - adapter_error = f"{self.product_api_adapter.api_key_env_var} is not set" - else: - adapter_ready = True - if self.product_api_adapter.probe_path: - try: - _ = self.product_api_adapter.probe() - except (OSError, TimeoutError, ValueError, RuntimeError) as exc: - adapter_error = str(exc) - probe_ok = False - adapter_ready = False - else: - probe_ok = True - - if recorder is not None: - recorder.record_event( - self.product_api_adapter.status_record( - gate_open=True, - wire_enabled=self.enabled, - adapter_ready=adapter_ready, - probe_ok=probe_ok, - error=adapter_error, - ) - ) - - return self.enabled and adapter_ready - - -@dataclass(frozen=True) -class LiquiditySnapshot: - round_index: int - provider_name: str - avg_spread_bps: float - executable_notional_usd_50bps: float - liquidity_score: float - spreads_bps: dict[str, float] - per_symbol_notional_usd_50bps: dict[str, float] - band_executable_notional_usd: dict[str, float] - band_liquidity_scores: dict[str, float] - - def to_record(self) -> dict[str, object]: - return { - "type": "liquidity_snapshot", - "round": self.round_index, - "provider": self.provider_name, - "avg_spread_bps": self.avg_spread_bps, - "executable_notional_usd_50bps": self.executable_notional_usd_50bps, - "liquidity_score": self.liquidity_score, - "spreads_bps": self.spreads_bps, - "per_symbol_notional_usd_50bps": self.per_symbol_notional_usd_50bps, - "band_executable_notional_usd": self.band_executable_notional_usd, - "band_liquidity_scores": self.band_liquidity_scores, - "impact_threshold_bps": LIQUIDITY_IMPACT_THRESHOLD_BPS, - "depth_source": "order_book", - } - - -@dataclass(frozen=True) -class TruthQualifierSnapshot: - round_index: int - provider_name: str - truth_confidence: float - noise_ratio: float - liquidity_confidence: float - venue_consensus: float - reference_consensus: float - meta_coverage: float - average_intervenue_deviation_bps: float - average_reference_deviation_bps: float - active_order_book_providers: tuple[str, ...] - active_quote_providers: tuple[str, ...] - active_meta_quote_providers: tuple[str, ...] - band_liquidity_scores: dict[str, float] - macro_alignment: float - cross_asset_stress: float - commodity_shock_score: float - betting_conviction: float - news_shock_score: float - defillama_protocol_tvl_stress: float - defillama_stablecoin_stress: float - defillama_perps_stress: float - defillama_chain_liquidity_score: float - - def to_record(self) -> dict[str, object]: - return { - "type": "truth_qualifier_snapshot", - "round": self.round_index, - "provider": self.provider_name, - "truth_confidence": self.truth_confidence, - "noise_ratio": self.noise_ratio, - "liquidity_confidence": self.liquidity_confidence, - "venue_consensus": self.venue_consensus, - "reference_consensus": self.reference_consensus, - "meta_coverage": self.meta_coverage, - "average_intervenue_deviation_bps": self.average_intervenue_deviation_bps, - "average_reference_deviation_bps": self.average_reference_deviation_bps, - "active_order_book_providers": list(self.active_order_book_providers), - "active_quote_providers": list(self.active_quote_providers), - "active_meta_quote_providers": list(self.active_meta_quote_providers), - "band_liquidity_scores": self.band_liquidity_scores, - "macro_alignment": self.macro_alignment, - "cross_asset_stress": self.cross_asset_stress, - "commodity_shock_score": self.commodity_shock_score, - "betting_conviction": self.betting_conviction, - "news_shock_score": self.news_shock_score, - "defillama_protocol_tvl_stress": self.defillama_protocol_tvl_stress, - "defillama_stablecoin_stress": self.defillama_stablecoin_stress, - "defillama_perps_stress": self.defillama_perps_stress, - "defillama_chain_liquidity_score": self.defillama_chain_liquidity_score, - } - - def to_sim_truth_qualifier(self) -> TruthQualifier: - return TruthQualifier( - truth_confidence=self.truth_confidence, - noise_ratio=self.noise_ratio, - liquidity_confidence=self.liquidity_confidence, - provider_agreement=self.venue_consensus, - aggregator_agreement=max(self.reference_consensus, self.betting_conviction), - active_sources=( - len(self.active_order_book_providers) - + len(self.active_quote_providers) - + len(self.active_meta_quote_providers) - ), - ) - - -@dataclass(frozen=True) -class MacroContextSnapshot: - captured_at: str - stock_index_returns_pct: dict[str, float] - commodity_returns_pct: dict[str, float] - manifold_markets: list[dict[str, object]] - polymarket_markets: list[dict[str, object]] - news_items: list[dict[str, object]] - defillama_protocols: list[dict[str, object]] - defillama_stablecoins: list[dict[str, object]] - defillama_perps: list[dict[str, object]] - defillama_chains: list[dict[str, object]] - cross_asset_stress: float - commodity_shock_score: float - betting_conviction: float - news_shock_score: float - macro_alignment: float - defillama_protocol_tvl_stress: float - defillama_stablecoin_stress: float - defillama_perps_stress: float - defillama_chain_liquidity_score: float - - def to_record(self) -> dict[str, object]: - return { - "type": "macro_context_snapshot", - "captured_at": self.captured_at, - "stock_index_returns_pct": self.stock_index_returns_pct, - "commodity_returns_pct": self.commodity_returns_pct, - "manifold_markets": self.manifold_markets, - "polymarket_markets": self.polymarket_markets, - "news_items": self.news_items, - "defillama_protocols": self.defillama_protocols, - "defillama_stablecoins": self.defillama_stablecoins, - "defillama_perps": self.defillama_perps, - "defillama_chains": self.defillama_chains, - "cross_asset_stress": self.cross_asset_stress, - "commodity_shock_score": self.commodity_shock_score, - "betting_conviction": self.betting_conviction, - "news_shock_score": self.news_shock_score, - "macro_alignment": self.macro_alignment, - "defillama_protocol_tvl_stress": self.defillama_protocol_tvl_stress, - "defillama_stablecoin_stress": self.defillama_stablecoin_stress, - "defillama_perps_stress": self.defillama_perps_stress, - "defillama_chain_liquidity_score": self.defillama_chain_liquidity_score, - } - - def compact_dict(self) -> dict[str, object]: - return { - "captured_at": self.captured_at, - "stock_index_returns_pct": self.stock_index_returns_pct, - "commodity_returns_pct": self.commodity_returns_pct, - "cross_asset_stress": self.cross_asset_stress, - "commodity_shock_score": self.commodity_shock_score, - "betting_conviction": self.betting_conviction, - "news_shock_score": self.news_shock_score, - "macro_alignment": self.macro_alignment, - "defillama_protocol_tvl_stress": self.defillama_protocol_tvl_stress, - "defillama_stablecoin_stress": self.defillama_stablecoin_stress, - "defillama_perps_stress": self.defillama_perps_stress, - "defillama_chain_liquidity_score": self.defillama_chain_liquidity_score, - "top_news_titles": [ - cast(str, item.get("title", "")) for item in self.news_items[:3] - ], - "top_manifold_questions": [ - cast(str, item.get("question", "")) for item in self.manifold_markets[:2] - ], - "top_polymarket_questions": [ - cast(str, item.get("question", "")) for item in self.polymarket_markets[:2] - ], - "top_defillama_protocols": [ - cast(str, item.get("name", "")) for item in self.defillama_protocols[:3] - ], - "top_defillama_stablecoins": [ - cast(str, item.get("symbol", "")) for item in self.defillama_stablecoins[:3] - ], - "top_defillama_chains": [ - cast(str, item.get("name", "")) for item in self.defillama_chains[:3] - ], - } - - -class LiquidityTracker: - def __init__(self): - self.initial: LiquiditySnapshot | None = None - self.best: LiquiditySnapshot | None = None - self.final: LiquiditySnapshot | None = None - self.initial_truth: TruthQualifierSnapshot | None = None - self.best_truth: TruthQualifierSnapshot | None = None - self.final_truth: TruthQualifierSnapshot | None = None - - def record_snapshot( - self, - snapshot: LiquiditySnapshot, - truth_snapshot: TruthQualifierSnapshot, - market_surface_record: Mapping[str, object], - recorder: TickRecorder | None = None, - ) -> None: - if self.initial is None: - self.initial = snapshot - self.initial_truth = truth_snapshot - if self.best is None or snapshot.liquidity_score > self.best.liquidity_score: - self.best = snapshot - self.best_truth = truth_snapshot - self.final = snapshot - self.final_truth = truth_snapshot - - if recorder is not None: - recorder.record_event(market_surface_record) - recorder.record_event(snapshot.to_record()) - recorder.record_event(truth_snapshot.to_record()) - - def summary_record(self) -> dict[str, object] | None: - if self.initial is None or self.best is None or self.final is None: - return None - - return { - "type": "liquidity_summary", - "impact_threshold_bps": LIQUIDITY_IMPACT_THRESHOLD_BPS, - "initial": summarize_snapshot(self.initial), - "best": summarize_snapshot(self.best), - "final": summarize_snapshot(self.final), - "best_vs_initial_pct": percent_change( - self.initial.liquidity_score, self.best.liquidity_score - ), - "final_vs_initial_pct": percent_change( - self.initial.liquidity_score, self.final.liquidity_score - ), - "initial_truth": summarize_truth_snapshot(self.initial_truth), - "best_truth": summarize_truth_snapshot(self.best_truth), - "final_truth": summarize_truth_snapshot(self.final_truth), - } - - -BookLevel = tuple[float, float] - - -def new_book_side() -> dict[float, float]: - return {} - - -@dataclass -class OrderBook: - bids: dict[float, float] = field(default_factory=new_book_side) - asks: dict[float, float] = field(default_factory=new_book_side) - - def replace(self, bids: list[BookLevel], asks: list[BookLevel]) -> None: - self.bids = {price: size for price, size in bids if price > 0.0 and size > 0.0} - self.asks = {price: size for price, size in asks if price > 0.0 and size > 0.0} - - def apply_update(self, side: str, price: float, size: float) -> None: - target = self.bids if side == "buy" else self.asks - if size <= 0.0: - target.pop(price, None) - else: - target[price] = size - - def has_top_of_book(self) -> bool: - return bool(self.bids) and bool(self.asks) - - def sorted_bids(self, limit: int | None = None) -> list[BookLevel]: - levels = sorted(self.bids.items(), key=lambda item: item[0], reverse=True) - return levels if limit is None else levels[:limit] - - def sorted_asks(self, limit: int | None = None) -> list[BookLevel]: - levels = sorted(self.asks.items(), key=lambda item: item[0]) - return levels if limit is None else levels[:limit] - - def top_quote(self) -> Quote | None: - if not self.has_top_of_book(): - return None - return self.sorted_bids(1)[0][0], self.sorted_asks(1)[0][0] - - -class OrderBookTracker: - def __init__(self): - self.books: dict[str, OrderBook] = {symbol: OrderBook() for symbol in REQUIRED_SYMBOLS} - self.venue_books: dict[str, dict[str, OrderBook]] = {} - self.quote_only_quotes: dict[str, dict[str, Quote]] = {} - self.provider_updated_at: dict[str, float] = {} - self.provider_status: dict[str, str] = {} - self.provider_errors: dict[str, str] = {} - self.meta_quotes: dict[str, dict[str, Quote]] = {} - self.meta_quote_updated_at: dict[str, float] = {} - self.macro_context: MacroContextSnapshot | None = None - - def _provider_books(self, provider_name: str) -> dict[str, OrderBook]: - return self.venue_books.setdefault( - provider_name, - {symbol: OrderBook() for symbol in REQUIRED_SYMBOLS}, - ) - - def _refresh_provider(self, provider_name: str) -> None: - self.provider_updated_at[provider_name] = time.monotonic() - self.provider_status[provider_name] = "connected" - self.provider_errors.pop(provider_name, None) - - def set_provider_status( - self, - provider_name: str, - status: str, - error_message: str | None = None, - ) -> None: - self.provider_status[provider_name] = status - if error_message: - self.provider_errors[provider_name] = error_message - elif status == "connected": - self.provider_errors.pop(provider_name, None) - - def _active_provider_names_for_symbol(self, symbol: str) -> list[str]: - now = time.monotonic() - active: list[str] = [] - for provider_name, provider_books in self.venue_books.items(): - updated_at = self.provider_updated_at.get(provider_name, 0.0) - if now - updated_at > VENUE_BOOK_STALE_AFTER_S: - continue - provider_book = provider_books.get(symbol) - if provider_book is not None and provider_book.has_top_of_book(): - active.append(provider_name) - return sorted(active) - - def _rebuild_consensus_book(self, symbol: str) -> None: - bid_sizes: dict[float, float] = {} - ask_sizes: dict[float, float] = {} - for provider_name in self._active_provider_names_for_symbol(symbol): - provider_book = self._provider_books(provider_name)[symbol] - for price, size in provider_book.bids.items(): - bid_sizes[price] = bid_sizes.get(price, 0.0) + size - for price, size in provider_book.asks.items(): - ask_sizes[price] = ask_sizes.get(price, 0.0) + size - - self.books[symbol].replace(list(bid_sizes.items()), list(ask_sizes.items())) - - def set_snapshot( - self, - provider_name: str, - symbol: str, - bids: list[BookLevel], - asks: list[BookLevel], - ) -> None: - if symbol not in self.books: - return - self._provider_books(provider_name)[symbol].replace(bids, asks) - self._refresh_provider(provider_name) - self._rebuild_consensus_book(symbol) - - def apply_update( - self, - provider_name: str, - symbol: str, - side: str, - price: float, - size: float, - ) -> None: - if symbol not in self.books: - return - self._provider_books(provider_name)[symbol].apply_update(side, price, size) - self._refresh_provider(provider_name) - self._rebuild_consensus_book(symbol) - - def active_provider_quotes(self) -> dict[str, dict[str, Quote]]: - provider_quotes: dict[str, dict[str, Quote]] = {} - for provider_name in self.active_order_book_providers(): - quotes: dict[str, Quote] = {} - for symbol, provider_book in self._provider_books(provider_name).items(): - top_quote = provider_book.top_quote() - if top_quote is not None: - quotes[symbol] = top_quote - if quotes: - provider_quotes[provider_name] = quotes - return provider_quotes - - def set_quote_snapshot(self, provider_name: str, quotes: Mapping[str, Quote]) -> None: - filtered_quotes = { - symbol: quote_value - for symbol, quote_value in quotes.items() - if symbol in REQUIRED_SYMBOLS - } - if not filtered_quotes: - return - self.quote_only_quotes[provider_name] = filtered_quotes - self._refresh_provider(provider_name) - - def active_quote_provider_quotes(self) -> dict[str, dict[str, Quote]]: - active: dict[str, dict[str, Quote]] = {} - now = time.monotonic() - for provider_name, quotes in self.quote_only_quotes.items(): - updated_at = self.provider_updated_at.get(provider_name, 0.0) - if now - updated_at <= VENUE_BOOK_STALE_AFTER_S: - active[provider_name] = quotes - return active - - def active_quote_providers(self) -> list[str]: - return sorted(self.active_quote_provider_quotes()) - - def active_live_provider_quotes(self) -> dict[str, dict[str, Quote]]: - provider_quotes = self.active_provider_quotes() - for provider_name, quotes in self.active_quote_provider_quotes().items(): - existing_quotes = provider_quotes.setdefault(provider_name, {}) - for symbol, quote_value in quotes.items(): - existing_quotes.setdefault(symbol, quote_value) - return provider_quotes - - def active_order_book_providers(self) -> list[str]: - now = time.monotonic() - active: list[str] = [] - for provider_name, provider_books in self.venue_books.items(): - updated_at = self.provider_updated_at.get(provider_name, 0.0) - if now - updated_at > VENUE_BOOK_STALE_AFTER_S: - continue - if any(book.has_top_of_book() for book in provider_books.values()): - active.append(provider_name) - return sorted(active) - - def top_quotes(self) -> dict[str, Quote]: - quotes: dict[str, Quote] = {} - provider_quotes = self.active_live_provider_quotes() - for symbol in REQUIRED_SYMBOLS: - symbol_quotes = [ - provider_quote[symbol] - for provider_quote in provider_quotes.values() - if symbol in provider_quote - ] - if not symbol_quotes: - continue - - bid_price = median_value([quote[0] for quote in symbol_quotes]) - ask_price = median_value([quote[1] for quote in symbol_quotes]) - if bid_price > ask_price: - mid_price = midpoint(bid_price, ask_price) - bid_price = mid_price - ask_price = mid_price - quotes[symbol] = (bid_price, ask_price) - return quotes - - def consensus_provider_name(self) -> str: - active_providers = sorted(self.active_live_provider_quotes()) - if not active_providers: - return "consensus:none" - return f"consensus[{','.join(active_providers)}]" - - def set_meta_quotes(self, provider_name: str, quotes: Mapping[str, Quote]) -> None: - filtered_quotes = { - symbol: quote_value - for symbol, quote_value in quotes.items() - if symbol in REQUIRED_SYMBOLS - } - if not filtered_quotes: - return - self.meta_quotes[provider_name] = filtered_quotes - self.meta_quote_updated_at[provider_name] = time.monotonic() - - def active_meta_quotes(self) -> dict[str, dict[str, Quote]]: - active: dict[str, dict[str, Quote]] = {} - now = time.monotonic() - for provider_name, quotes in self.meta_quotes.items(): - updated_at = self.meta_quote_updated_at.get(provider_name, 0.0) - if now - updated_at <= META_QUOTE_STALE_AFTER_S: - active[provider_name] = quotes - return active - - def set_macro_context(self, snapshot: MacroContextSnapshot) -> None: - self.macro_context = snapshot - - def current_macro_context(self) -> MacroContextSnapshot | None: - return self.macro_context - - def snapshot_record(self, round_index: int, trigger_provider_name: str) -> dict[str, object]: - books_payload: dict[str, object] = {} - for symbol, book in self.books.items(): - if not book.has_top_of_book(): - continue - books_payload[symbol] = { - "bids": [[price, size] for price, size in book.sorted_bids(ORDER_BOOK_LEVEL_LIMIT)], - "asks": [[price, size] for price, size in book.sorted_asks(ORDER_BOOK_LEVEL_LIMIT)], - } - - return { - "type": "market_surface_snapshot", - "round": round_index, - "provider": self.consensus_provider_name(), - "trigger_provider": trigger_provider_name, - "captured_at": utc_now_iso(), - "level_limit": ORDER_BOOK_LEVEL_LIMIT, - "books": books_payload, - "active_order_book_providers": self.active_order_book_providers(), - "active_quote_providers": self.active_quote_providers(), - "venue_quotes": { - provider: { - symbol: {"bid": quote_value[0], "ask": quote_value[1]} - for symbol, quote_value in quotes.items() - } - for provider, quotes in self.active_provider_quotes().items() - }, - "quote_provider_quotes": { - provider: { - symbol: {"bid": quote_value[0], "ask": quote_value[1]} - for symbol, quote_value in quotes.items() - } - for provider, quotes in self.active_quote_provider_quotes().items() - }, - "provider_status": { - provider: { - "status": self.provider_status.get(provider, "unknown"), - "error": self.provider_errors.get(provider), - } - for provider in sorted(set(self.provider_status) | set(self.provider_errors)) - }, - "meta_quotes": { - provider: { - symbol: {"bid": quote_value[0], "ask": quote_value[1]} - for symbol, quote_value in quotes.items() - } - for provider, quotes in self.active_meta_quotes().items() - }, - "macro_context": None if self.macro_context is None else self.macro_context.compact_dict(), - } - - -class TickRecorder: - def __init__( - self, - file_path: Path, - rounds: int, - session_metadata: Mapping[str, object] | None = None, - ): - self.file_path = file_path - self.file_path.parent.mkdir(parents=True, exist_ok=True) - self.handle = self.file_path.open("w", encoding="utf-8", buffering=1) - metadata: dict[str, object] = { - "type": "session_meta", - "version": 1, - "created_at": utc_now_iso(), - "session_id": self.file_path.stem, - "target_rounds": rounds, - "required_symbols": sorted(REQUIRED_SYMBOLS), - } - if session_metadata is not None: - metadata.update(session_metadata) - self._write(metadata) - - def _write(self, payload: Mapping[str, object]) -> None: - self.handle.write(json.dumps(payload, sort_keys=True) + "\n") - - def record_event(self, payload: Mapping[str, object]) -> None: - self._write(payload) - - def record(self, provider_name: str, symbol: str, bid_price: float, ask_price: float) -> None: - self._write( - { - "type": "tick", - "captured_at": utc_now_iso(), - "provider": provider_name, - "symbol": symbol, - "bid": bid_price, - "ask": ask_price, - } - ) - - def close(self) -> None: - if not self.handle.closed: - self.handle.close() - - -def utc_now_iso() -> str: - return datetime.now(timezone.utc).isoformat() - - -def default_swarm_state_path() -> Path: - return Path("5-Applications/out/live_market_data") / "swarm_state.json" - - -def default_record_path() -> Path: - stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - return Path("5-Applications/out/live_market_data") / f"session_{stamp}.jsonl" - - -def load_json_file(path: Path) -> dict[str, object]: - payload = json.loads(path.read_text(encoding="utf-8")) - if not isinstance(payload, dict): - raise ValueError(f"expected JSON object in {path}") - return cast(dict[str, object], payload) - - -def write_json_file_atomic(path: Path, payload: Mapping[str, object]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - temp_path = path.with_suffix(f"{path.suffix}.tmp") - temp_path.write_text( - json.dumps(payload, indent=2, sort_keys=True) + "\n", - encoding="utf-8", - ) - temp_path.replace(path) - - -def load_swarm_state(state_path: Path | None, sim: SwarmSimulation) -> dict[str, object]: - if state_path is None: - return { - "enabled": False, - "path": None, - "loaded": False, - "generation": 0, - "source_session_id": None, - "rounds_completed": 0, - "state_digest": None, - "restore_summary": None, - "learning_summary": sim.learning_summary(), - } - - if not state_path.exists(): - return { - "enabled": True, - "path": str(state_path), - "loaded": False, - "generation": 0, - "source_session_id": None, - "rounds_completed": 0, - "state_digest": None, - "restore_summary": None, - "learning_summary": sim.learning_summary(), - } - - payload = load_json_file(state_path) - simulation_state_obj = payload.get("simulation_state") - simulation_state = ( - cast(dict[str, object], simulation_state_obj) - if isinstance(simulation_state_obj, dict) - else payload - ) - restore_summary = sim.apply_learning_state(simulation_state) - state_digest = hashlib.sha3_256( - json.dumps(payload, sort_keys=True).encode("utf-8") - ).hexdigest() - - generation_obj = payload.get("generation") - source_session_id_obj = payload.get("source_session_id") - rounds_completed_obj = payload.get("rounds_completed") - learning_summary_obj = simulation_state.get("learning_summary") - - return { - "enabled": True, - "path": str(state_path), - "loaded": True, - "generation": generation_obj if isinstance(generation_obj, int) else 0, - "source_session_id": ( - source_session_id_obj if isinstance(source_session_id_obj, str) else None - ), - "rounds_completed": ( - rounds_completed_obj if isinstance(rounds_completed_obj, int) else 0 - ), - "state_digest": state_digest, - "restore_summary": restore_summary, - "learning_summary": ( - cast(dict[str, object], learning_summary_obj) - if isinstance(learning_summary_obj, dict) - else sim.learning_summary() - ), - } - - -def save_swarm_state( - state_path: Path, - sim: SwarmSimulation, - session_id: str, - previous_generation: int, - session_liquidity_summary: Mapping[str, object] | None = None, - session_mode: str = "live", -) -> dict[str, object]: - generation = previous_generation + 1 - normalized_summary = ( - dict(session_liquidity_summary) - if session_liquidity_summary is not None - else None - ) - objective_summary = sim.finalize_cross_session_objective( - normalized_summary, - session_id, - ) - learning_summary = sim.learning_summary() - payload: dict[str, object] = { - "version": SWARM_STATE_VERSION, - "saved_at": utc_now_iso(), - "generation": generation, - "source_session_id": session_id, - "session_mode": session_mode, - "rounds_completed": len(sim.execution_log), - "learning_summary": learning_summary, - "objective_summary": objective_summary, - "session_liquidity_summary": normalized_summary, - "simulation_state": sim.to_learning_state(), - } - write_json_file_atomic(state_path, payload) - state_digest = hashlib.sha3_256( - json.dumps(payload, sort_keys=True).encode("utf-8") - ).hexdigest() - return { - "type": "swarm_state_saved", - "path": str(state_path), - "generation": generation, - "source_session_id": session_id, - "session_mode": session_mode, - "rounds_completed": len(sim.execution_log), - "state_digest": state_digest, - "learning_summary": learning_summary, - "objective_summary": objective_summary, - } - - -def resolve_rounds(requested_rounds: int | None, default_rounds: int) -> int: - rounds = default_rounds if requested_rounds is None else requested_rounds - if rounds <= 0: - raise ValueError("round count must be positive") - return rounds - - -def midpoint(bid_price: float, ask_price: float) -> float: - return (bid_price + ask_price) / 2.0 - - -def spread_bps(bid_price: float, ask_price: float) -> float: - mid_price = midpoint(bid_price, ask_price) - if mid_price <= 0.0: - return 0.0 - return (ask_price - bid_price) / mid_price * 10_000.0 - - -def band_key(impact_threshold_bps: float) -> str: - return f"{int(impact_threshold_bps)}bps" - - -def clamp_unit(value: float) -> float: - return max(0.0, min(1.0, value)) - - -def fetch_json( - url: str, - headers: Mapping[str, str] | None = None, - method: str = "GET", - body: Mapping[str, object] | None = None, -) -> object: - request_headers = {"User-Agent": "research-stack/1.0"} - if headers is not None: - request_headers.update(headers) - request_data = None if body is None else json.dumps(body).encode("utf-8") - raw_bytes = fetch_network_resource( - url=url, - headers=request_headers, - timeout=10, - method=method, - data=request_data - ) - return json.loads(raw_bytes.decode("utf-8")) - - -def fetch_text(url: str) -> str: - raw_bytes = fetch_network_resource(url=url, headers={"User-Agent": "research-stack/1.0"}, timeout=10) - return raw_bytes.decode("utf-8", errors="replace") - - -def mean_or_zero(values: list[float]) -> float: - return sum(values) / len(values) if values else 0.0 - - -def median_value(values: list[float]) -> float: - if not values: - return 0.0 - ordered = sorted(values) - mid = len(ordered) // 2 - if len(ordered) % 2 == 1: - return ordered[mid] - return (ordered[mid - 1] + ordered[mid]) / 2.0 - - -def try_fetch_json(url: str) -> object | None: - try: - return fetch_json(url) - except (OSError, TimeoutError, ValueError): - return None - - -def parse_oneinch_spot_price_map(payload: object) -> dict[str, float]: - if not isinstance(payload, dict): - return {} - - payload_dict = cast(dict[str, object], payload) - result_obj = payload_dict.get("result") - price_payload = cast(dict[str, object], result_obj) if isinstance(result_obj, dict) else payload_dict - prices: dict[str, float] = {} - for address_obj, value_obj in price_payload.items(): - if not isinstance(value_obj, (str, int, float)): - continue - try: - prices[address_obj.lower()] = float(value_obj) - except (TypeError, ValueError): - continue - return prices - - -def fetch_oneinch_spot_price_quotes(adapter: OneInchProductAPIAdapter) -> dict[str, Quote]: - token_addresses = { - asset: address.lower() - for asset, address in adapter.spot_price_token_addresses().items() - } - payload = adapter.request_json(adapter.spot_price_path()) - price_map = parse_oneinch_spot_price_map(payload) - - sol_native = price_map.get(token_addresses["SOL"]) - btc_native = price_map.get(token_addresses["BTC"]) - usdt_native = price_map.get(token_addresses["USDT"]) - if sol_native is None or btc_native is None or usdt_native is None: - return {} - if sol_native <= 0.0 or btc_native <= 0.0 or usdt_native <= 0.0: - return {} - - sol_usdt = sol_native / usdt_native - btc_usdt = btc_native / usdt_native - sol_btc = sol_native / btc_native - return { - "SOLUSDT": (sol_usdt, sol_usdt), - "BTCUSDT": (btc_usdt, btc_usdt), - "SOLBTC": (sol_btc, sol_btc), - } - - -def numeric_value(value: object) -> float | None: - return float(value) if isinstance(value, (int, float)) else None - - -def pegged_usd_amount(value: object) -> float: - if isinstance(value, dict): - pegged_usd = cast(dict[str, object], value).get("peggedUSD") - if isinstance(pegged_usd, (int, float)): - return float(pegged_usd) - if isinstance(value, (int, float)): - return float(value) - return 0.0 - - -def fetch_yahoo_intraday_returns(symbols: tuple[str, ...]) -> dict[str, float]: - returns: dict[str, float] = {} - for symbol in symbols: - payload = fetch_json( - f"https://query1.finance.yahoo.com/v8/finance/chart/{quote(symbol)}?interval=1m&range=1d" - ) - if not isinstance(payload, dict): - continue - payload_dict = cast(dict[str, object], payload) - chart_obj = payload_dict.get("chart") - if not isinstance(chart_obj, dict): - continue - chart = cast(dict[str, object], chart_obj) - result_obj = chart.get("result") - if not isinstance(result_obj, list) or not result_obj: - continue - result_entry = cast(list[object], result_obj)[0] - if not isinstance(result_entry, dict): - continue - result = cast(dict[str, object], result_entry) - indicators_obj = result.get("indicators") - if not isinstance(indicators_obj, dict): - continue - indicators = cast(dict[str, object], indicators_obj) - quote_entries_obj = indicators.get("quote") - if not isinstance(quote_entries_obj, list) or not quote_entries_obj: - continue - quote_entry = cast(list[object], quote_entries_obj)[0] - if not isinstance(quote_entry, dict): - continue - close_series_obj = cast(dict[str, object], quote_entry).get("close") - closes = ( - [ - float(value) - for value in cast(list[object], close_series_obj) - if isinstance(value, (int, float)) - ] - if isinstance(close_series_obj, list) - else [] - ) - if len(closes) >= 2 and closes[0] > 0.0: - returns[symbol] = ((closes[-1] - closes[0]) / closes[0]) * 100.0 - continue - - meta_obj = result.get("meta") - if not isinstance(meta_obj, dict): - continue - meta = cast(dict[str, object], meta_obj) - regular_market_price_obj = meta.get("regularMarketPrice") - chart_previous_close_obj = meta.get("chartPreviousClose", meta.get("previousClose")) - if not isinstance(regular_market_price_obj, (int, float)): - continue - if not isinstance(chart_previous_close_obj, (int, float)): - continue - chart_previous_close = float(chart_previous_close_obj) - if chart_previous_close <= 0.0: - continue - returns[symbol] = ( - (float(regular_market_price_obj) - chart_previous_close) / chart_previous_close - ) * 100.0 - - return returns - - -def fetch_defillama_protocol_context() -> tuple[list[dict[str, object]], float]: - payload = try_fetch_json(DEFILLAMA_PROTOCOLS_URL) - if not isinstance(payload, list): - return [], 0.0 - - matched: list[dict[str, object]] = [] - for protocol_obj in cast(list[object], payload): - if not isinstance(protocol_obj, dict): - continue - protocol = cast(dict[str, object], protocol_obj) - name_obj = protocol.get("name") - tvl_obj = protocol.get("tvl") - change_1d_obj = protocol.get("change_1d") - if not isinstance(name_obj, str): - continue - if not isinstance(tvl_obj, (int, float)): - continue - if not any(keyword in name_obj.lower() for keyword in DEFILLAMA_PROTOCOL_KEYWORDS): - continue - - matched.append( - { - "name": name_obj, - "category": protocol.get("category", "unknown"), - "chain": protocol.get("chain", "unknown"), - "tvl": float(tvl_obj), - "change_1d": float(change_1d_obj) if isinstance(change_1d_obj, (int, float)) else 0.0, - "change_7d": ( - float(change_7d_obj) - if isinstance((change_7d_obj := protocol.get("change_7d")), (int, float)) - else 0.0 - ), - } - ) - - matched.sort(key=lambda item: cast(float, item["tvl"]), reverse=True) - watched = matched[:8] - total_tvl = sum(cast(float, item["tvl"]) for item in watched) - if total_tvl <= 0.0: - return watched, 0.0 - - weighted_change_1d = sum( - abs(cast(float, item["change_1d"])) * cast(float, item["tvl"]) for item in watched - ) / total_tvl - return watched, clamp_unit(weighted_change_1d / 5.0) - - -def fetch_defillama_chain_liquidity_context() -> tuple[list[dict[str, object]], float]: - payload = try_fetch_json(DEFILLAMA_CHAINS_URL) - if not isinstance(payload, list): - return [], 0.0 - - watched: list[dict[str, object]] = [] - for chain_obj in cast(list[object], payload): - if not isinstance(chain_obj, dict): - continue - chain = cast(dict[str, object], chain_obj) - name_obj = chain.get("name") - tvl_obj = chain.get("tvl") - if not isinstance(name_obj, str): - continue - if name_obj not in DEFILLAMA_CHAIN_WATCHLIST: - continue - if not isinstance(tvl_obj, (int, float)): - continue - watched.append({"name": name_obj, "tvl": float(tvl_obj)}) - - watched.sort(key=lambda item: cast(float, item["tvl"]), reverse=True) - total_tvl = sum(cast(float, item["tvl"]) for item in watched) - if total_tvl <= 0.0: - return watched, 0.0 - - concentration = max(cast(float, item["tvl"]) for item in watched) / total_tvl - scale = clamp_unit(total_tvl / 100_000_000_000.0) - liquidity_score = clamp_unit(scale * max(0.0, 1.15 - concentration)) - return watched, liquidity_score - - -def fetch_defillama_stablecoin_context() -> tuple[list[dict[str, object]], float]: - payload = try_fetch_json(DEFILLAMA_STABLECOINS_URL) - if not isinstance(payload, dict): - return [], 0.0 - payload_dict = cast(dict[str, object], payload) - - pegged_assets_obj = payload_dict.get("peggedAssets") - if not isinstance(pegged_assets_obj, list): - return [], 0.0 - - watched: list[dict[str, object]] = [] - max_price_deviation = 0.0 - max_watchlist_chain_flow = 0.0 - for asset_obj in cast(list[object], pegged_assets_obj): - if not isinstance(asset_obj, dict): - continue - asset = cast(dict[str, object], asset_obj) - symbol_obj = asset.get("symbol") - price_obj = asset.get("price") - if not isinstance(symbol_obj, str): - continue - if symbol_obj.upper() not in DEFILLAMA_STABLECOIN_WATCHLIST: - continue - price = float(price_obj) if isinstance(price_obj, (int, float)) else 1.0 - price_deviation = abs(price - 1.0) - max_price_deviation = max(max_price_deviation, price_deviation) - - chain_circulating_obj = asset.get("chainCirculating") - watchlist_chain_flow = 0.0 - if isinstance(chain_circulating_obj, dict): - chain_circulating = cast(dict[str, object], chain_circulating_obj) - for chain_name in DEFILLAMA_CHAIN_WATCHLIST: - chain_stats_obj = chain_circulating.get(chain_name) - if not isinstance(chain_stats_obj, dict): - continue - chain_stats = cast(dict[str, object], chain_stats_obj) - current_amount = pegged_usd_amount(chain_stats.get("current", {})) - previous_amount = pegged_usd_amount(chain_stats.get("circulatingPrevDay", {})) - if previous_amount <= 0.0: - continue - watchlist_chain_flow = max( - watchlist_chain_flow, - abs(current_amount - previous_amount) / previous_amount, - ) - - max_watchlist_chain_flow = max(max_watchlist_chain_flow, watchlist_chain_flow) - watched.append( - { - "symbol": symbol_obj.upper(), - "name": asset.get("name", symbol_obj.upper()), - "price": price, - "price_deviation_pct": price_deviation * 100.0, - "circulating_usd": pegged_usd_amount(asset.get("circulating", {})), - "watchlist_chain_flow_pct": watchlist_chain_flow * 100.0, - } - ) - - watched.sort( - key=lambda item: ( - cast(float, item["price_deviation_pct"]), - cast(float, item["watchlist_chain_flow_pct"]), - cast(float, item["circulating_usd"]), - ), - reverse=True, - ) - price_stress = clamp_unit(max_price_deviation / 0.02) - flow_stress = clamp_unit(max_watchlist_chain_flow / 0.20) - return watched[:8], clamp_unit(0.65 * price_stress + 0.35 * flow_stress) - - -def fetch_defillama_perps_context() -> tuple[list[dict[str, object]], float]: - payload = try_fetch_json(DEFILLAMA_PERPS_OPEN_INTEREST_URL) - if not isinstance(payload, dict): - return [], 0.0 - payload_dict = cast(dict[str, object], payload) - - change_1d = numeric_value(payload_dict.get("change_1d")) or 0.0 - change_7d = numeric_value(payload_dict.get("change_7d")) or 0.0 - change_1m = numeric_value(payload_dict.get("change_1m")) or 0.0 - perps_rows: list[dict[str, object]] = [ - { - "metric": "open_interest", - "total24h": numeric_value(payload_dict.get("total24h")) or 0.0, - "total7d": numeric_value(payload_dict.get("total7d")) or 0.0, - "total30d": numeric_value(payload_dict.get("total30d")) or 0.0, - "change_1d": change_1d, - "change_7d": change_7d, - "change_1m": change_1m, - } - ] - perps_stress = clamp_unit( - 0.40 * clamp_unit(abs(change_1d) / 5.0) - + 0.35 * clamp_unit(abs(change_7d) / 10.0) - + 0.25 * clamp_unit(abs(change_1m) / 20.0) - ) - return perps_rows, perps_stress - - -def score_news_item(title: str, topic: str) -> float: - lowered = f"{topic} {title}".lower() - score = 0.1 - for keyword, weight in COMMODITY_NEWS_SHOCK_KEYWORDS.items(): - if keyword in lowered: - score = max(score, weight) - if "south pars" in lowered or "north dome" in lowered: - score = max(score, 1.0) - return clamp_unit(score) - - -def fetch_google_news_watchlist() -> list[dict[str, object]]: - items: list[dict[str, object]] = [] - for topic, query_text in GOOGLE_NEWS_WATCHLIST.items(): - xml_payload = fetch_text( - f"https://news.google.com/rss/search?q={quote(query_text)}" - ) - root = ET.fromstring(xml_payload) - for item in root.findall(".//item")[:3]: - title = item.findtext("title") or "" - link = item.findtext("link") or "" - source = item.findtext("source") or "" - items.append( - { - "topic": topic, - "title": title, - "link": link, - "source": source, - "shock_score": score_news_item(title, query_text), - } - ) - - items.sort(key=lambda item: cast(float, item["shock_score"]), reverse=True) - return items[:10] - - -def fetch_manifold_bet_history_signal(contract_id: str) -> tuple[int, float]: - payload = fetch_json( - f"https://api.manifold.markets/v0/bets?contractId={quote(contract_id)}&limit=25" - ) - if not isinstance(payload, list): - return 0, 0.0 - - total_change = 0.0 - count = 0 - for bet_obj in cast(list[object], payload): - if not isinstance(bet_obj, dict): - continue - bet = cast(dict[str, object], bet_obj) - prob_before_obj = bet.get("probBefore") - prob_after_obj = bet.get("probAfter") - if not isinstance(prob_before_obj, (int, float)): - continue - if not isinstance(prob_after_obj, (int, float)): - continue - total_change += abs(float(prob_after_obj) - float(prob_before_obj)) - count += 1 - - if count == 0: - return 0, 0.0 - return count, clamp_unit((total_change / count) * 8.0) - - -def fetch_manifold_betting_context() -> list[dict[str, object]]: - markets: list[dict[str, object]] = [] - for topic, search_term in MANIFOLD_SEARCH_TERMS.items(): - payload = fetch_json( - "https://api.manifold.markets/v0/search-markets" - f"?term={quote(search_term)}&sort=liquidity&filter=open&contractType=BINARY&limit=3" - ) - if not isinstance(payload, list): - continue - - for market_obj in cast(list[object], payload)[:2]: - if not isinstance(market_obj, dict): - continue - market = cast(dict[str, object], market_obj) - contract_id = market.get("id") - probability_obj = market.get("probability") - volume_24h_obj = market.get("volume24Hours", 0.0) - question_obj = market.get("question") - if not isinstance(contract_id, str): - continue - if not isinstance(probability_obj, (int, float)): - continue - if not isinstance(question_obj, str): - continue - bet_count, history_signal = fetch_manifold_bet_history_signal(contract_id) - probability = float(probability_obj) - conviction = abs(probability - 0.5) * 2.0 - markets.append( - { - "source": "manifold", - "topic": topic, - "question": question_obj, - "probability": probability, - "conviction": conviction, - "bet_count": bet_count, - "history_signal": history_signal, - "volume24h": float(volume_24h_obj) - if isinstance(volume_24h_obj, (int, float)) - else 0.0, - } - ) - - markets.sort( - key=lambda market: ( - cast(float, market["conviction"]) + cast(float, market["history_signal"]), - cast(float, market["volume24h"]), - ), - reverse=True, - ) - return markets[:6] - - -def parse_polymarket_probability(market: dict[str, object]) -> float | None: - last_trade_price_obj = market.get("lastTradePrice") - if isinstance(last_trade_price_obj, (int, float)): - return float(last_trade_price_obj) - if isinstance(last_trade_price_obj, str): - try: - return float(last_trade_price_obj) - except ValueError: - return None - - outcome_prices_obj = market.get("outcomePrices") - if isinstance(outcome_prices_obj, list) and outcome_prices_obj: - first_price = cast(list[object], outcome_prices_obj)[0] - if isinstance(first_price, (int, float)): - return float(first_price) - if isinstance(first_price, str): - try: - return float(first_price) - except ValueError: - return None - return None - - -def fetch_polymarket_betting_context() -> list[dict[str, object]]: - payload = fetch_json("https://gamma-api.polymarket.com/markets?active=true&closed=false&limit=100") - if not isinstance(payload, list): - return [] - - markets: list[dict[str, object]] = [] - for market_obj in cast(list[object], payload): - if not isinstance(market_obj, dict): - continue - market = cast(dict[str, object], market_obj) - question_obj = market.get("question") - if not isinstance(question_obj, str): - continue - lowered_question = question_obj.lower() - if not any(keyword in lowered_question for keyword in POLYMARKET_KEYWORDS): - continue - - probability = parse_polymarket_probability(market) - if probability is None: - continue - - liquidity_obj = market.get("liquidityNum", market.get("liquidity", 0.0)) - one_day_change_obj = market.get("oneDayPriceChange", 0.0) - conviction = abs(probability - 0.5) * 2.0 - price_change = float(one_day_change_obj) if isinstance(one_day_change_obj, (int, float)) else 0.0 - markets.append( - { - "source": "polymarket", - "question": question_obj, - "probability": probability, - "conviction": conviction, - "one_day_price_change": price_change, - "liquidity": float(liquidity_obj) - if isinstance(liquidity_obj, (int, float)) - else 0.0, - } - ) - - markets.sort( - key=lambda market: ( - cast(float, market["conviction"]) + abs(cast(float, market["one_day_price_change"])), - cast(float, market["liquidity"]), - ), - reverse=True, - ) - return markets[:6] - - -def build_macro_context_snapshot() -> MacroContextSnapshot: - stock_index_returns = fetch_yahoo_intraday_returns(YAHOO_STOCK_INDEX_SYMBOLS) - commodity_returns = fetch_yahoo_intraday_returns(YAHOO_COMMODITY_SYMBOLS) - manifold_markets = fetch_manifold_betting_context() - polymarket_markets = fetch_polymarket_betting_context() - news_items = fetch_google_news_watchlist() - defillama_protocols, defillama_protocol_tvl_stress = fetch_defillama_protocol_context() - defillama_chains, defillama_chain_liquidity_score = fetch_defillama_chain_liquidity_context() - defillama_stablecoins, defillama_stablecoin_stress = fetch_defillama_stablecoin_context() - defillama_perps, defillama_perps_stress = fetch_defillama_perps_context() - - stock_stress = clamp_unit(abs(mean_or_zero(list(stock_index_returns.values()))) / 1.5) - commodity_shock_score = clamp_unit( - max((abs(value) for value in commodity_returns.values()), default=0.0) / 3.0 - ) - manifold_signal = mean_or_zero( - [cast(float, market["conviction"]) + 0.5 * cast(float, market["history_signal"]) for market in manifold_markets] - ) - polymarket_signal = mean_or_zero( - [cast(float, market["conviction"]) + min(0.5, abs(cast(float, market["one_day_price_change"]))) for market in polymarket_markets] - ) - betting_conviction = clamp_unit(0.5 * manifold_signal + 0.5 * polymarket_signal) - news_shock_score = clamp_unit( - mean_or_zero([cast(float, item["shock_score"]) for item in news_items[:5]]) - ) - cross_asset_stress = clamp_unit( - 0.60 * stock_stress - + 0.20 * defillama_perps_stress - + 0.20 * defillama_stablecoin_stress - ) - - signal_values = [ - cross_asset_stress, - commodity_shock_score, - betting_conviction, - news_shock_score, - defillama_protocol_tvl_stress, - defillama_stablecoin_stress, - defillama_perps_stress, - defillama_chain_liquidity_score, - ] - macro_alignment = clamp_unit( - mean_or_zero(signal_values) * (1.0 - 0.5 * (max(signal_values) - min(signal_values))) - ) - - return MacroContextSnapshot( - captured_at=utc_now_iso(), - stock_index_returns_pct=stock_index_returns, - commodity_returns_pct=commodity_returns, - manifold_markets=manifold_markets, - polymarket_markets=polymarket_markets, - news_items=news_items, - defillama_protocols=defillama_protocols, - defillama_stablecoins=defillama_stablecoins, - defillama_perps=defillama_perps, - defillama_chains=defillama_chains, - cross_asset_stress=cross_asset_stress, - commodity_shock_score=commodity_shock_score, - betting_conviction=betting_conviction, - news_shock_score=news_shock_score, - macro_alignment=macro_alignment, - defillama_protocol_tvl_stress=defillama_protocol_tvl_stress, - defillama_stablecoin_stress=defillama_stablecoin_stress, - defillama_perps_stress=defillama_perps_stress, - defillama_chain_liquidity_score=defillama_chain_liquidity_score, - ) - - -async def poll_macro_context_sources( - order_books: "OrderBookTracker", - recorder: TickRecorder | None, - stop_event: asyncio.Event, -) -> None: - while not stop_event.is_set(): - try: - snapshot = await asyncio.to_thread(build_macro_context_snapshot) - except (OSError, TimeoutError, ValueError, ET.ParseError) as exc: - print(f"macro context poll failed: {exc}") - else: - order_books.set_macro_context(snapshot) - if recorder is not None: - recorder.record_event(snapshot.to_record()) - - try: - await asyncio.wait_for(stop_event.wait(), timeout=MACRO_CONTEXT_POLL_INTERVAL_S) - except TimeoutError: - continue - - -def select_dexscreener_price_usd(payload: object, base_symbol: str) -> float | None: - if not isinstance(payload, dict): - return None - payload_dict = cast(dict[str, object], payload) - - pairs_obj = payload_dict.get("pairs") - if not isinstance(pairs_obj, list): - return None - - best_price: float | None = None - best_liquidity_usd = -1.0 - for pair_obj in cast(list[object], pairs_obj): - if not isinstance(pair_obj, dict): - continue - pair = cast(dict[str, object], pair_obj) - - base_obj = pair.get("baseToken") - quote_obj = pair.get("quoteToken") - if not isinstance(base_obj, dict) or not isinstance(quote_obj, dict): - continue - - base_token = cast(dict[str, object], base_obj) - quote_token = cast(dict[str, object], quote_obj) - base_symbol_obj = base_token.get("symbol") - quote_symbol_obj = quote_token.get("symbol") - if base_symbol_obj != base_symbol: - continue - if quote_symbol_obj not in {"USD", "USDC", "USDT"}: - continue - - price_usd_obj = pair.get("priceUsd") - liquidity_obj = pair.get("liquidity") - if not isinstance(price_usd_obj, (str, int, float)): - continue - if not isinstance(liquidity_obj, dict): - continue - - liquidity = cast(dict[str, object], liquidity_obj) - liquidity_usd_obj = liquidity.get("usd") - if not isinstance(liquidity_usd_obj, (str, int, float)): - continue - - try: - price_usd = float(price_usd_obj) - liquidity_usd = float(liquidity_usd_obj) - except (TypeError, ValueError): - continue - - if liquidity_usd > best_liquidity_usd: - best_liquidity_usd = liquidity_usd - best_price = price_usd - - return best_price - - -def fetch_coingecko_meta_quotes() -> dict[str, Quote]: - payload = fetch_json(COINGECKO_SIMPLE_PRICE_URL) - if not isinstance(payload, dict): - return {} - payload_dict = cast(dict[str, object], payload) - - bitcoin_obj = payload_dict.get("bitcoin") - solana_obj = payload_dict.get("solana") - if not isinstance(bitcoin_obj, dict) or not isinstance(solana_obj, dict): - return {} - - bitcoin = cast(dict[str, object], bitcoin_obj) - solana = cast(dict[str, object], solana_obj) - btc_usd_obj = bitcoin.get("usd") - sol_usd_obj = solana.get("usd") - if not isinstance(btc_usd_obj, (int, float)): - return {} - if not isinstance(sol_usd_obj, (int, float)): - return {} - - btc_usd = float(btc_usd_obj) - sol_usd = float(sol_usd_obj) - if btc_usd <= 0.0 or sol_usd <= 0.0: - return {} - - sol_btc = sol_usd / btc_usd - return { - "SOLUSDT": (sol_usd, sol_usd), - "BTCUSDT": (btc_usd, btc_usd), - "SOLBTC": (sol_btc, sol_btc), - } - - -def fetch_dexscreener_meta_quotes() -> dict[str, Quote]: - quotes_usd: dict[str, float] = {} - for symbol, search_query in DEXSCREENER_SEARCH_QUERIES.items(): - payload = fetch_json( - f"https://api.dexscreener.com/latest/dex/search?q={quote(search_query)}" - ) - base_symbol = "SOL" if symbol == "SOLUSDT" else "BTC" - price_usd = select_dexscreener_price_usd(payload, base_symbol) - if price_usd is not None: - quotes_usd[symbol] = price_usd - - sol_usd = quotes_usd.get("SOLUSDT") - btc_usd = quotes_usd.get("BTCUSDT") - if sol_usd is None or btc_usd is None or btc_usd <= 0.0: - return {} - - sol_btc = sol_usd / btc_usd - return { - "SOLUSDT": (sol_usd, sol_usd), - "BTCUSDT": (btc_usd, btc_usd), - "SOLBTC": (sol_btc, sol_btc), - } - - -async def poll_meta_quote_sources( - order_books: "OrderBookTracker", - recorder: TickRecorder | None, - stop_event: asyncio.Event, -) -> None: - sources = { - "coingecko": fetch_coingecko_meta_quotes, - "dexscreener": fetch_dexscreener_meta_quotes, - } - - while not stop_event.is_set(): - for provider_name, fetcher in sources.items(): - try: - quotes = await asyncio.to_thread(fetcher) - except (OSError, TimeoutError, ValueError) as exc: - print(f"{provider_name} meta quote poll failed: {exc}") - continue - - if not quotes: - continue - - order_books.set_meta_quotes(provider_name, quotes) - if recorder is not None: - recorder.record_event( - { - "type": "meta_quote_snapshot", - "provider": provider_name, - "captured_at": utc_now_iso(), - "quotes": { - symbol: {"bid": quote_value[0], "ask": quote_value[1]} - for symbol, quote_value in quotes.items() - }, - } - ) - - try: - await asyncio.wait_for(stop_event.wait(), timeout=META_QUOTE_POLL_INTERVAL_S) - except TimeoutError: - continue - - -async def consume_1inch_spot_price_quotes( - event_queue: asyncio.Queue[dict[str, object]], - stop_event: asyncio.Event, -) -> None: - adapter = OneInchProductAPIAdapter() - provider_name = ONEINCH_SPOT_PRICE_PROVIDER - print("Connecting to gated 1inch spot-price quote poller...") - - while not stop_event.is_set(): - quotes = await asyncio.to_thread(fetch_oneinch_spot_price_quotes, adapter) - if quotes: - await event_queue.put( - { - "type": "quote", - "provider": provider_name, - "quotes": quotes, - } - ) - await event_queue.put( - { - "type": "provider_status", - "provider": provider_name, - "status": "connected", - } - ) - - try: - await asyncio.wait_for(stop_event.wait(), timeout=ONEINCH_SPOT_PRICE_POLL_INTERVAL_S) - except TimeoutError: - continue - - -async def maybe_start_premium_quote_provider( - current_round: int, - liquidity_tracker: LiquidityTracker, - order_books: OrderBookTracker, - provider_failures: Mapping[str, str], - provider_candidate_count: int, - wire_placement: PaidLiquidityWirePlacement, - recorder: TickRecorder | None, - venue_event_queue: asyncio.Queue[dict[str, object]], - venue_stop: asyncio.Event, -) -> asyncio.Task[None] | None: - adapter_ready = await wire_placement.try_activate( - build_live_payment_gate_observation( - current_round, - liquidity_tracker, - order_books, - tuple(provider_failures.values()), - provider_candidate_count, - ), - recorder, - ) - if not adapter_ready: - return None - - print("Starting gated premium 1inch spot-price provider.") - return asyncio.create_task( - run_provider_stream( - ONEINCH_SPOT_PRICE_PROVIDER, - consume_1inch_spot_price_quotes, - venue_event_queue, - venue_stop, - ) - ) - - -def parse_price_size_levels(raw_levels: object, expected_len: int = 2) -> list[BookLevel]: - if not isinstance(raw_levels, list): - return [] - - levels: list[BookLevel] = [] - for raw_level_obj in cast(list[object], raw_levels): - if not isinstance(raw_level_obj, list): - continue - - raw_level = cast(list[object], raw_level_obj) - if len(raw_level) < expected_len: - continue - - price_obj = raw_level[0] - size_obj = raw_level[1] - if not isinstance(price_obj, (str, int, float)): - continue - if not isinstance(size_obj, (str, int, float)): - continue - - try: - price = float(price_obj) - size = float(size_obj) - except (TypeError, ValueError): - continue - - levels.append((price, size)) - - return levels - - -def quote_notional_to_usd(symbol: str, quote_notional: float, latest_quotes: Mapping[str, Quote]) -> float: - if symbol in {"SOLUSDT", "BTCUSDT"}: - return quote_notional - if symbol == "SOLBTC": - btc_mid = midpoint(*latest_quotes["BTCUSDT"]) - return quote_notional * btc_mid - raise ValueError(f"Unsupported symbol for USD conversion: {symbol}") - - -def two_sided_notional_within_band_usd( - symbol: str, - order_book: OrderBook, - latest_quotes: Mapping[str, Quote], - impact_threshold_bps: float, -) -> float: - bid_price, ask_price = latest_quotes[symbol] - mid_price = midpoint(bid_price, ask_price) - threshold_multiplier = impact_threshold_bps / 10_000.0 - min_bid_price = mid_price * (1.0 - threshold_multiplier) - max_ask_price = mid_price * (1.0 + threshold_multiplier) - - bid_quote_notional = 0.0 - for price, size in order_book.sorted_bids(ORDER_BOOK_LEVEL_LIMIT): - if price < min_bid_price: - break - bid_quote_notional += price * size - - ask_quote_notional = 0.0 - for price, size in order_book.sorted_asks(ORDER_BOOK_LEVEL_LIMIT): - if price > max_ask_price: - break - ask_quote_notional += price * size - - bid_notional_usd = quote_notional_to_usd(symbol, bid_quote_notional, latest_quotes) - ask_notional_usd = quote_notional_to_usd(symbol, ask_quote_notional, latest_quotes) - return min(bid_notional_usd, ask_notional_usd) - - -def build_liquidity_snapshot( - round_index: int, - provider_name: str, - latest_quotes: Mapping[str, Quote], - order_books: OrderBookTracker, -) -> LiquiditySnapshot: - spreads_bps = { - symbol: spread_bps(bid_price, ask_price) - for symbol, (bid_price, ask_price) in latest_quotes.items() - if symbol in REQUIRED_SYMBOLS - } - avg_spread = sum(spreads_bps.values()) / max(1, len(spreads_bps)) - - band_executable_notional_usd: dict[str, float] = {} - band_liquidity_scores: dict[str, float] = {} - per_symbol_notional_usd: dict[str, float] = {} - for impact_threshold_bps in LIQUIDITY_BANDS_BPS: - current_band_per_symbol = { - symbol: two_sided_notional_within_band_usd( - symbol, - order_books.books[symbol], - latest_quotes, - impact_threshold_bps, - ) - for symbol in REQUIRED_SYMBOLS - } - band_name = band_key(impact_threshold_bps) - band_notional = sum(current_band_per_symbol.values()) - band_executable_notional_usd[band_name] = band_notional - band_liquidity_scores[band_name] = band_notional / max(avg_spread, 1e-9) - if impact_threshold_bps == LIQUIDITY_IMPACT_THRESHOLD_BPS: - per_symbol_notional_usd = current_band_per_symbol - - executable_notional_usd = band_executable_notional_usd[band_key(LIQUIDITY_IMPACT_THRESHOLD_BPS)] - liquidity_score = sum( - weight * band_liquidity_scores.get(band_name, 0.0) - for band_name, weight in LIQUIDITY_BAND_WEIGHTS.items() - ) - - return LiquiditySnapshot( - round_index=round_index, - provider_name=provider_name, - avg_spread_bps=avg_spread, - executable_notional_usd_50bps=executable_notional_usd, - liquidity_score=liquidity_score, - spreads_bps=spreads_bps, - per_symbol_notional_usd_50bps=per_symbol_notional_usd, - band_executable_notional_usd=band_executable_notional_usd, - band_liquidity_scores=band_liquidity_scores, - ) - - -def build_truth_qualifier_snapshot( - round_index: int, - provider_name: str, - latest_quotes: Mapping[str, Quote], - order_books: OrderBookTracker, - liquidity_snapshot: LiquiditySnapshot, -) -> TruthQualifierSnapshot: - active_live_provider_quotes = order_books.active_live_provider_quotes() - active_order_book_providers = tuple(order_books.active_order_book_providers()) - active_quote_providers = tuple(order_books.active_quote_providers()) - active_meta_quotes = order_books.active_meta_quotes() - intervenue_deviations_bps: list[float] = [] - reference_deviations_bps: list[float] = [] - - for symbol, current_quote in latest_quotes.items(): - current_mid = midpoint(*current_quote) - if current_mid <= 0.0: - continue - - provider_mids = [ - midpoint(*quotes[symbol]) - for quotes in active_live_provider_quotes.values() - if symbol in quotes and midpoint(*quotes[symbol]) > 0.0 - ] - if len(provider_mids) >= 2: - provider_mid_consensus = median_value(provider_mids) - intervenue_deviations_bps.extend( - abs(provider_mid - provider_mid_consensus) - / max(provider_mid_consensus, 1e-9) - * 10_000.0 - for provider_mid in provider_mids - ) - - reference_mids = [ - midpoint(*quotes[symbol]) - for quotes in active_meta_quotes.values() - if symbol in quotes and midpoint(*quotes[symbol]) > 0.0 - ] - if not reference_mids: - continue - - reference_mid = sum(reference_mids) / len(reference_mids) - reference_deviations_bps.append( - abs(current_mid - reference_mid) / max(reference_mid, 1e-9) * 10_000.0 - ) - - average_reference_deviation_bps = ( - sum(reference_deviations_bps) / len(reference_deviations_bps) - if reference_deviations_bps - else 25.0 - ) - if len(active_live_provider_quotes) >= 2 and intervenue_deviations_bps: - average_intervenue_deviation_bps = sum(intervenue_deviations_bps) / len( - intervenue_deviations_bps - ) - venue_consensus = clamp_unit(1.0 - average_intervenue_deviation_bps / 20.0) - elif len(active_live_provider_quotes) == 1: - average_intervenue_deviation_bps = 25.0 - venue_consensus = 0.35 - else: - average_intervenue_deviation_bps = 50.0 - venue_consensus = 0.0 - reference_consensus = clamp_unit(1.0 - average_reference_deviation_bps / 50.0) - meta_coverage = len(active_meta_quotes) / 2.0 - venue_coverage = clamp_unit(len(active_order_book_providers) / PUBLIC_PROVIDER_TARGET_COUNT) - score_10 = liquidity_snapshot.band_liquidity_scores.get("10bps", 0.0) - score_25 = liquidity_snapshot.band_liquidity_scores.get("25bps", 0.0) - score_50 = liquidity_snapshot.band_liquidity_scores.get("50bps", 0.0) - if score_50 <= 0.0: - liquidity_confidence = 0.0 - else: - liquidity_confidence = clamp_unit( - 0.6 * min(1.0, score_10 / score_50) + 0.4 * min(1.0, score_25 / score_50) - ) - - macro_context = order_books.current_macro_context() - if macro_context is None: - macro_alignment = 0.0 - cross_asset_stress = 0.0 - commodity_shock_score = 0.0 - betting_conviction = 0.0 - news_shock_score = 0.0 - defillama_protocol_tvl_stress = 0.0 - defillama_stablecoin_stress = 0.0 - defillama_perps_stress = 0.0 - defillama_chain_liquidity_score = 0.0 - else: - macro_alignment = macro_context.macro_alignment - cross_asset_stress = macro_context.cross_asset_stress - commodity_shock_score = macro_context.commodity_shock_score - betting_conviction = macro_context.betting_conviction - news_shock_score = macro_context.news_shock_score - defillama_protocol_tvl_stress = macro_context.defillama_protocol_tvl_stress - defillama_stablecoin_stress = macro_context.defillama_stablecoin_stress - defillama_perps_stress = macro_context.defillama_perps_stress - defillama_chain_liquidity_score = macro_context.defillama_chain_liquidity_score - - macro_signal = clamp_unit( - 0.22 * macro_alignment - + 0.10 * cross_asset_stress - + 0.13 * commodity_shock_score - + 0.12 * betting_conviction - + 0.08 * news_shock_score - + 0.12 * defillama_protocol_tvl_stress - + 0.10 * defillama_stablecoin_stress - + 0.08 * defillama_perps_stress - + 0.05 * defillama_chain_liquidity_score - ) - truth_confidence = clamp_unit( - 0.30 * venue_consensus - + 0.20 * reference_consensus - + 0.20 * liquidity_confidence - + 0.10 * meta_coverage - + 0.10 * venue_coverage - + 0.10 * macro_signal - ) - noise_ratio = clamp_unit( - 1.0 - - truth_confidence - + 0.10 * (1.0 - venue_consensus) - + 0.05 * (1.0 - reference_consensus) - + 0.05 * (1.0 - macro_alignment) - ) - - return TruthQualifierSnapshot( - round_index=round_index, - provider_name=provider_name, - truth_confidence=truth_confidence, - noise_ratio=noise_ratio, - liquidity_confidence=liquidity_confidence, - venue_consensus=venue_consensus, - reference_consensus=reference_consensus, - meta_coverage=meta_coverage, - average_intervenue_deviation_bps=average_intervenue_deviation_bps, - average_reference_deviation_bps=average_reference_deviation_bps, - active_order_book_providers=active_order_book_providers, - active_quote_providers=active_quote_providers, - active_meta_quote_providers=tuple(sorted(active_meta_quotes)), - band_liquidity_scores=liquidity_snapshot.band_liquidity_scores, - macro_alignment=macro_alignment, - cross_asset_stress=cross_asset_stress, - commodity_shock_score=commodity_shock_score, - betting_conviction=betting_conviction, - news_shock_score=news_shock_score, - defillama_protocol_tvl_stress=defillama_protocol_tvl_stress, - defillama_stablecoin_stress=defillama_stablecoin_stress, - defillama_perps_stress=defillama_perps_stress, - defillama_chain_liquidity_score=defillama_chain_liquidity_score, - ) - - -def summarize_snapshot(snapshot: LiquiditySnapshot) -> dict[str, object]: - return { - "round": snapshot.round_index, - "provider": snapshot.provider_name, - "avg_spread_bps": snapshot.avg_spread_bps, - "executable_notional_usd_50bps": snapshot.executable_notional_usd_50bps, - "liquidity_score": snapshot.liquidity_score, - "per_symbol_notional_usd_50bps": snapshot.per_symbol_notional_usd_50bps, - "band_executable_notional_usd": snapshot.band_executable_notional_usd, - "band_liquidity_scores": snapshot.band_liquidity_scores, - } - - -def summarize_truth_snapshot(snapshot: TruthQualifierSnapshot | None) -> dict[str, object] | None: - if snapshot is None: - return None - - return { - "round": snapshot.round_index, - "provider": snapshot.provider_name, - "truth_confidence": snapshot.truth_confidence, - "noise_ratio": snapshot.noise_ratio, - "liquidity_confidence": snapshot.liquidity_confidence, - "venue_consensus": snapshot.venue_consensus, - "reference_consensus": snapshot.reference_consensus, - "meta_coverage": snapshot.meta_coverage, - "average_intervenue_deviation_bps": snapshot.average_intervenue_deviation_bps, - "average_reference_deviation_bps": snapshot.average_reference_deviation_bps, - "active_order_book_providers": list(snapshot.active_order_book_providers), - "active_quote_providers": list(snapshot.active_quote_providers), - "active_meta_quote_providers": list(snapshot.active_meta_quote_providers), - "macro_alignment": snapshot.macro_alignment, - "cross_asset_stress": snapshot.cross_asset_stress, - "commodity_shock_score": snapshot.commodity_shock_score, - "betting_conviction": snapshot.betting_conviction, - "news_shock_score": snapshot.news_shock_score, - "defillama_protocol_tvl_stress": snapshot.defillama_protocol_tvl_stress, - "defillama_stablecoin_stress": snapshot.defillama_stablecoin_stress, - "defillama_perps_stress": snapshot.defillama_perps_stress, - "defillama_chain_liquidity_score": snapshot.defillama_chain_liquidity_score, - } - - -def percent_change(baseline: float, value: float) -> float: - if baseline <= 0.0: - return 0.0 - return ((value - baseline) / baseline) * 100.0 - - -def print_liquidity_summary_record(summary: dict[str, object] | None) -> None: - if summary is None: - print("\nNo liquidity summary available.") - return - - initial = cast(dict[str, object], summary["initial"]) - best = cast(dict[str, object], summary["best"]) - final = cast(dict[str, object], summary["final"]) - best_vs_initial = cast(float, summary["best_vs_initial_pct"]) - final_vs_initial = cast(float, summary["final_vs_initial_pct"]) - final_truth = cast(dict[str, object] | None, summary.get("final_truth")) - - def _format_band_scores(snapshot: dict[str, object]) -> str: - band_scores_obj = snapshot.get("band_liquidity_scores", {}) - if not isinstance(band_scores_obj, dict): - return "Bands=n/a" - band_scores = cast(dict[str, object], band_scores_obj) - return ( - f"Bands 10/25/50={float(cast(float, band_scores.get('10bps', 0.0))):,.2f}/" - f"{float(cast(float, band_scores.get('25bps', 0.0))):,.2f}/" - f"{float(cast(float, band_scores.get('50bps', 0.0))):,.2f}" - ) - - print("\n" + "=" * 80) - print("LIQUIDITY PROBE") - print("=" * 80) - print( - "Wire note: paid feed integration is intentionally disabled until the free path " - "shows measurable liquidity improvement." - ) - print( - f"Initial | Round {cast(int, initial['round']) + 1:03d} | " - f"Spread={cast(float, initial['avg_spread_bps']):8.4f} bps | " - f"Exec@50bps=${cast(float, initial['executable_notional_usd_50bps']):12,.2f} | " - f"Score={cast(float, initial['liquidity_score']):12,.2f}" - ) - print(f" {_format_band_scores(initial)}") - print( - f"Best | Round {cast(int, best['round']) + 1:03d} | " - f"Spread={cast(float, best['avg_spread_bps']):8.4f} bps | " - f"Exec@50bps=${cast(float, best['executable_notional_usd_50bps']):12,.2f} | " - f"Score={cast(float, best['liquidity_score']):12,.2f} | " - f"Delta={best_vs_initial:+7.2f}%" - ) - print(f" {_format_band_scores(best)}") - print( - f"Final | Round {cast(int, final['round']) + 1:03d} | " - f"Spread={cast(float, final['avg_spread_bps']):8.4f} bps | " - f"Exec@50bps=${cast(float, final['executable_notional_usd_50bps']):12,.2f} | " - f"Score={cast(float, final['liquidity_score']):12,.2f} | " - f"Delta={final_vs_initial:+7.2f}%" - ) - print(f" {_format_band_scores(final)}") - if final_truth is not None: - print( - "Truth | " - f"Confidence={cast(float, final_truth.get('truth_confidence', 0.0)):.4f} | " - f"Noise={cast(float, final_truth.get('noise_ratio', 0.0)):.4f} | " - f"LiquidityConf={cast(float, final_truth.get('liquidity_confidence', 0.0)):.4f} | " - f"VenueConsensus={cast(float, final_truth.get('venue_consensus', 0.0)):.4f} | " - f"RefConsensus={cast(float, final_truth.get('reference_consensus', 0.0)):.4f} | " - f"Macro={cast(float, final_truth.get('macro_alignment', 0.0)):.4f} | " - f"CommodityShock={cast(float, final_truth.get('commodity_shock_score', 0.0)):.4f} | " - f"Betting={cast(float, final_truth.get('betting_conviction', 0.0)):.4f} | " - f"News={cast(float, final_truth.get('news_shock_score', 0.0)):.4f} | " - f"LlamaStable={cast(float, final_truth.get('defillama_stablecoin_stress', 0.0)):.4f} | " - f"LlamaPerps={cast(float, final_truth.get('defillama_perps_stress', 0.0)):.4f} | " - f"LlamaChain={cast(float, final_truth.get('defillama_chain_liquidity_score', 0.0)):.4f}" - ) - - -def print_liquidity_summary(tracker: LiquidityTracker) -> None: - print_liquidity_summary_record(tracker.summary_record()) - - -def print_session_comparison( - baseline_path: Path, - baseline_metadata: SessionMetadata, - baseline_summary: dict[str, object], - candidate_path: Path, - candidate_metadata: SessionMetadata, - candidate_summary: dict[str, object], -) -> None: - baseline_best = cast(dict[str, object], baseline_summary["best"]) - candidate_best = cast(dict[str, object], candidate_summary["best"]) - baseline_final = cast(dict[str, object], baseline_summary["final"]) - candidate_final = cast(dict[str, object], candidate_summary["final"]) - baseline_final_truth = cast(dict[str, object] | None, baseline_summary.get("final_truth")) - candidate_final_truth = cast(dict[str, object] | None, candidate_summary.get("final_truth")) - - best_score_delta = percent_change( - cast(float, baseline_best["liquidity_score"]), - cast(float, candidate_best["liquidity_score"]), - ) - final_score_delta = percent_change( - cast(float, baseline_final["liquidity_score"]), - cast(float, candidate_final["liquidity_score"]), - ) - best_notional_delta = percent_change( - cast(float, baseline_best["executable_notional_usd_50bps"]), - cast(float, candidate_best["executable_notional_usd_50bps"]), - ) - best_spread_delta = percent_change( - cast(float, baseline_best["avg_spread_bps"]), - cast(float, candidate_best["avg_spread_bps"]), - ) - - baseline_session = baseline_metadata.get("session_id", baseline_path.stem) - candidate_session = candidate_metadata.get("session_id", candidate_path.stem) - baseline_warm = bool(baseline_metadata.get("swarm_state_loaded", False)) - candidate_warm = bool(candidate_metadata.get("swarm_state_loaded", False)) - - print("\n" + "=" * 80) - print("LIQUIDITY SESSION COMPARISON") - print("=" * 80) - print(f"Baseline : {baseline_session} ({baseline_path})") - print(f"Candidate: {candidate_session} ({candidate_path})") - if baseline_warm or candidate_warm: - print( - "Warm start : " - f"baseline loaded={baseline_warm} " - f"(gen {baseline_metadata.get('swarm_state_generation_in', 0)}), " - f"candidate loaded={candidate_warm} " - f"(gen {candidate_metadata.get('swarm_state_generation_in', 0)})" - ) - print( - f"Best score delta : {best_score_delta:+7.2f}% " - f"({cast(float, baseline_best['liquidity_score']):,.2f} -> " - f"{cast(float, candidate_best['liquidity_score']):,.2f})" - ) - print( - f"Final score delta : {final_score_delta:+7.2f}% " - f"({cast(float, baseline_final['liquidity_score']):,.2f} -> " - f"{cast(float, candidate_final['liquidity_score']):,.2f})" - ) - print( - f"Best exec@50bps delta: {best_notional_delta:+7.2f}% " - f"(${cast(float, baseline_best['executable_notional_usd_50bps']):,.2f} -> " - f"${cast(float, candidate_best['executable_notional_usd_50bps']):,.2f})" - ) - print( - f"Best spread delta : {best_spread_delta:+7.2f}% " - f"({cast(float, baseline_best['avg_spread_bps']):.4f} bps -> " - f"{cast(float, candidate_best['avg_spread_bps']):.4f} bps; negative is tighter)" - ) - - truth_gate_open = True - if baseline_final_truth is not None and candidate_final_truth is not None: - truth_confidence_delta = percent_change( - cast(float, baseline_final_truth["truth_confidence"]), - cast(float, candidate_final_truth["truth_confidence"]), - ) - print( - f"Truth confidence delta: {truth_confidence_delta:+7.2f}% " - f"({cast(float, baseline_final_truth['truth_confidence']):.4f} -> " - f"{cast(float, candidate_final_truth['truth_confidence']):.4f})" - ) - truth_gate_open = cast(float, candidate_final_truth["truth_confidence"]) >= ( - cast(float, baseline_final_truth["truth_confidence"]) * 0.9 - ) - - improved = cast(float, candidate_best["liquidity_score"]) > cast( - float, baseline_best["liquidity_score"] - ) - verdict = "IMPROVED" if improved and truth_gate_open else "NO IMPROVEMENT DETECTED" - print(f"Verdict : {verdict}") - - -def derive_mode_session_key_hex(simulation_seed: int) -> str: - material = f"live-market-data:{simulation_seed}".encode("utf-8") - return hashlib.sha3_256(material).hexdigest() - - -def build_simulation(rounds: int, simulation_seed: int) -> SwarmSimulation: - previous_key = os.getenv("WAVEPROBE_MODE_SESSION_KEY") - os.environ["WAVEPROBE_MODE_SESSION_KEY"] = derive_mode_session_key_hex(simulation_seed) - random.seed(simulation_seed) - - try: - return SwarmSimulation(num_bots=50, num_rounds=rounds) - finally: - if previous_key is None: - os.environ.pop("WAVEPROBE_MODE_SESSION_KEY", None) - else: - os.environ["WAVEPROBE_MODE_SESSION_KEY"] = previous_key - - -def update_pool_from_binance(pool: Pool, bid_price: float, ask_price: float) -> None: - """ - Updates the AMM Pool reserve ratios to match the live Binance orderbook midpoint. - We maintain the existing pool depth (k) but shift the reserves to force the price - P = reserve_b / reserve_a to equal the live mid_price. - """ - if bid_price <= 0.0 or ask_price <= 0.0: - return - - mid_price = (bid_price + ask_price) / 2.0 - - # Constant product: k = reserve_a * reserve_b - k = pool.reserve_a * pool.reserve_b - if k <= 0.0: - return - - # Required calculation to set P = mid_price: - # P = pool.reserve_b / pool.reserve_a - # k = pool.reserve_a * (pool.reserve_a * P) => pool.reserve_a = sqrt(k/P) - - new_reserve_a = (k / mid_price) ** 0.5 - new_reserve_b = new_reserve_a * mid_price - - pool.reserve_a = new_reserve_a - pool.reserve_b = new_reserve_b - - -def decode_json_message(message: str | bytes) -> dict[str, object] | None: - if isinstance(message, bytes): - try: - message = message.decode("utf-8") - except UnicodeDecodeError: - return None - - try: - payload_obj = json.loads(message) - except json.JSONDecodeError: - return None - - if not isinstance(payload_obj, dict): - return None - return cast(dict[str, object], payload_obj) - - -def iter_session_records(session_path: Path) -> Iterator[dict[str, object]]: - with session_path.open("r", encoding="utf-8") as handle: - for line_number, raw_line in enumerate(handle, 1): - line = raw_line.strip() - if not line: - continue - - payload = decode_json_message(line) - if payload is None: - raise ValueError(f"Invalid JSON in session file {session_path} at line {line_number}") - - yield payload - - -def read_replay_metadata(replay_path: Path) -> SessionMetadata: - for payload in iter_session_records(replay_path): - if payload.get("type") == "session_meta": - return payload - break - - return {} - - -def iter_replay_ticks(replay_path: Path) -> Iterator[tuple[str, str, float, float]]: - for line_number, payload in enumerate(iter_session_records(replay_path), 1): - record_type = payload.get("type") - if record_type == "session_meta": - continue - if record_type != "tick": - continue - - provider_obj = payload.get("provider") - symbol_obj = payload.get("symbol") - bid_obj = payload.get("bid") - ask_obj = payload.get("ask") - - if not isinstance(provider_obj, str): - raise ValueError(f"Replay tick at line {line_number} is missing a valid provider") - if not isinstance(symbol_obj, str): - raise ValueError(f"Replay tick at line {line_number} is missing a valid symbol") - if not isinstance(bid_obj, (str, int, float)): - raise ValueError(f"Replay tick at line {line_number} is missing a valid bid") - if not isinstance(ask_obj, (str, int, float)): - raise ValueError(f"Replay tick at line {line_number} is missing a valid ask") - - yield provider_obj, symbol_obj, float(bid_obj), float(ask_obj) - - -def read_recorded_liquidity_summary(session_path: Path) -> tuple[SessionMetadata, dict[str, object]]: - metadata: SessionMetadata = {} - summary: dict[str, object] | None = None - snapshots: list[dict[str, object]] = [] - - for payload in iter_session_records(session_path): - record_type = payload.get("type") - if record_type == "session_meta" and not metadata: - metadata = payload - elif record_type == "liquidity_summary": - summary = payload - elif record_type == "liquidity_snapshot": - snapshots.append(payload) - - if summary is not None: - return metadata, summary - - if not snapshots: - raise ValueError(f"No liquidity records found in session file {session_path}") - - initial = snapshots[0] - best = max(snapshots, key=lambda payload: cast(float, payload["liquidity_score"])) - final = snapshots[-1] - derived_summary: dict[str, object] = { - "type": "liquidity_summary", - "impact_threshold_bps": LIQUIDITY_IMPACT_THRESHOLD_BPS, - "initial": summarize_snapshot_record(initial), - "best": summarize_snapshot_record(best), - "final": summarize_snapshot_record(final), - "best_vs_initial_pct": percent_change( - cast(float, initial["liquidity_score"]), cast(float, best["liquidity_score"]) - ), - "final_vs_initial_pct": percent_change( - cast(float, initial["liquidity_score"]), cast(float, final["liquidity_score"]) - ), - } - return metadata, derived_summary - - -def summarize_snapshot_record(snapshot: dict[str, object]) -> dict[str, object]: - return { - "round": snapshot["round"], - "provider": snapshot["provider"], - "avg_spread_bps": snapshot["avg_spread_bps"], - "executable_notional_usd_50bps": snapshot["executable_notional_usd_50bps"], - "liquidity_score": snapshot["liquidity_score"], - "per_symbol_notional_usd_50bps": snapshot.get("per_symbol_notional_usd_50bps", {}), - "band_executable_notional_usd": snapshot.get("band_executable_notional_usd", {}), - "band_liquidity_scores": snapshot.get("band_liquidity_scores", {}), - } - - -def read_recorded_truth_qualifiers(replay_path: Path) -> dict[int, TruthQualifier]: - truth_by_round: dict[int, TruthQualifier] = {} - for payload in iter_session_records(replay_path): - if payload.get("type") != "truth_qualifier_snapshot": - continue - - round_obj = payload.get("round") - truth_confidence_obj = payload.get("truth_confidence") - noise_ratio_obj = payload.get("noise_ratio") - liquidity_confidence_obj = payload.get("liquidity_confidence") - venue_consensus_obj = payload.get("venue_consensus", payload.get("reference_consensus", 0.0)) - reference_consensus_obj = payload.get("reference_consensus") - if not isinstance(round_obj, int): - continue - if not isinstance(truth_confidence_obj, (int, float)): - continue - if not isinstance(noise_ratio_obj, (int, float)): - continue - if not isinstance(liquidity_confidence_obj, (int, float)): - continue - if not isinstance(reference_consensus_obj, (int, float)): - continue - if not isinstance(venue_consensus_obj, (int, float)): - continue - betting_conviction_obj = payload.get("betting_conviction", 0.0) - - active_order_book_providers_obj = payload.get("active_order_book_providers", []) - active_quote_providers_obj = payload.get("active_quote_providers", []) - active_sources_obj = payload.get("active_meta_quote_providers", []) - active_meta_sources = ( - len(cast(list[object], active_sources_obj)) if isinstance(active_sources_obj, list) else 0 - ) - active_order_book_sources = ( - len(cast(list[object], active_order_book_providers_obj)) - if isinstance(active_order_book_providers_obj, list) - else 0 - ) - active_quote_sources = ( - len(cast(list[object], active_quote_providers_obj)) - if isinstance(active_quote_providers_obj, list) - else 0 - ) - truth_by_round[round_obj] = TruthQualifier( - truth_confidence=float(truth_confidence_obj), - noise_ratio=float(noise_ratio_obj), - liquidity_confidence=float(liquidity_confidence_obj), - provider_agreement=float(venue_consensus_obj), - aggregator_agreement=max( - float(reference_consensus_obj), - float(betting_conviction_obj) - if isinstance(betting_conviction_obj, (int, float)) - else 0.0, - ), - active_sources=active_order_book_sources + active_quote_sources + active_meta_sources, - ) - - return truth_by_round - - -def extract_binance_order_book(message: str | bytes) -> tuple[str, list[BookLevel], list[BookLevel]] | None: - payload = decode_json_message(message) - if payload is None: - return None - - raw_data = payload["data"] if "data" in payload else payload - if not isinstance(raw_data, dict): - return None - data = cast(dict[str, object], raw_data) - - symbol_obj = data.get("s") - if not isinstance(symbol_obj, str): - stream_name = payload.get("stream") - if isinstance(stream_name, str): - symbol_obj = stream_name.split("@", maxsplit=1)[0].upper() - if not isinstance(symbol_obj, str): - return None - - bids = parse_price_size_levels(data.get("bids", [])) - asks = parse_price_size_levels(data.get("asks", [])) - if not bids or not asks: - return None - - return symbol_obj, bids, asks - - -def parse_kraken_levels(raw_levels: object) -> list[BookLevel]: - if not isinstance(raw_levels, list): - return [] - - levels: list[BookLevel] = [] - for raw_level_obj in cast(list[object], raw_levels): - if not isinstance(raw_level_obj, dict): - continue - - raw_level = cast(dict[str, object], raw_level_obj) - price_obj = raw_level.get("price") - qty_obj = raw_level.get("qty") - if not isinstance(price_obj, (str, int, float)): - continue - if not isinstance(qty_obj, (str, int, float)): - continue - - try: - levels.append((float(price_obj), float(qty_obj))) - except (TypeError, ValueError): - continue - - return levels - - -def extract_kraken_order_book_event(message: str | bytes) -> dict[str, object] | None: - payload = decode_json_message(message) - if payload is None: - return None - - if payload.get("channel") != "book": - return None - - message_type = payload.get("type") - if message_type not in {"snapshot", "update"}: - return None - - raw_data = payload.get("data") - if not isinstance(raw_data, list) or not raw_data: - return None - - data_entries = cast(list[object], raw_data) - data_entry_obj = data_entries[0] - if not isinstance(data_entry_obj, dict): - return None - data_entry = cast(dict[str, object], data_entry_obj) - - product_id = data_entry.get("symbol") - if not isinstance(product_id, str): - return None - symbol = KRAKEN_PRODUCTS.get(product_id) - if symbol is None: - return None - - bids = parse_kraken_levels(data_entry.get("bids", [])) - asks = parse_kraken_levels(data_entry.get("asks", [])) - return {"type": cast(str, message_type), "symbol": symbol, "bids": bids, "asks": asks} - - -def extract_bybit_order_book_event(message: str | bytes) -> dict[str, object] | None: - payload = decode_json_message(message) - if payload is None: - return None - - topic_obj = payload.get("topic") - message_type = payload.get("type") - if not isinstance(topic_obj, str): - return None - if message_type not in {"snapshot", "delta"}: - return None - - symbol = BYBIT_TOPICS.get(topic_obj) - if symbol is None: - return None - - raw_data = payload.get("data") - if not isinstance(raw_data, dict): - return None - data = cast(dict[str, object], raw_data) - bids = parse_price_size_levels(data.get("b", [])) - asks = parse_price_size_levels(data.get("a", [])) - return {"type": cast(str, message_type), "symbol": symbol, "bids": bids, "asks": asks} - - -def apply_latest_quotes(sim: SwarmSimulation, latest_quotes: Mapping[str, Quote]) -> None: - solusdt_bid, solusdt_ask = latest_quotes["SOLUSDT"] - update_pool_from_binance(sim.pools[0], solusdt_bid, solusdt_ask) - - btcusdt_bid, btcusdt_ask = latest_quotes["BTCUSDT"] - update_pool_from_binance(sim.pools[1], 1.0 / btcusdt_ask, 1.0 / btcusdt_bid) - - solbtc_bid, solbtc_ask = latest_quotes["SOLBTC"] - update_pool_from_binance(sim.pools[2], solbtc_bid, solbtc_ask) - - -def process_quote( - sim: SwarmSimulation, - latest_quotes: dict[str, Quote], - current_round: int, - provider_name: str, - symbol: str, - bid_price: float, - ask_price: float, - recorder: TickRecorder | None = None, - liquidity_tracker: LiquidityTracker | None = None, - order_books: OrderBookTracker | None = None, - truth_by_round: Mapping[int, TruthQualifier] | None = None, -) -> int: - if symbol not in REQUIRED_SYMBOLS: - return current_round - - if recorder is not None: - recorder.record(provider_name, symbol, bid_price, ask_price) - - latest_quotes[symbol] = (bid_price, ask_price) - if not REQUIRED_SYMBOLS.issubset(latest_quotes): - return current_round - - liquidity_snapshot: LiquiditySnapshot | None = None - truth_snapshot: TruthQualifierSnapshot | None = None - if order_books is not None: - consensus_provider_name = order_books.consensus_provider_name() - liquidity_snapshot = build_liquidity_snapshot( - current_round, - consensus_provider_name, - latest_quotes, - order_books, - ) - truth_snapshot = build_truth_qualifier_snapshot( - current_round, - consensus_provider_name, - latest_quotes, - order_books, - liquidity_snapshot, - ) - sim.set_market_truth(truth_snapshot.to_sim_truth_qualifier()) - elif truth_by_round is not None: - sim.set_market_truth(truth_by_round.get(current_round, TruthQualifier())) - - apply_latest_quotes(sim, latest_quotes) - sim.run_round(current_round) - - if ( - liquidity_tracker is not None - and order_books is not None - and liquidity_snapshot is not None - and truth_snapshot is not None - ): - liquidity_tracker.record_snapshot( - liquidity_snapshot, - truth_snapshot, - order_books.snapshot_record(current_round, provider_name), - recorder, - ) - - if current_round == 0 or (current_round + 1) % 10 == 0: - print( - f"[{time.strftime('%H:%M:%S')}] " - f"Round {current_round + 1:03d} | " - f"Source {provider_name} | " - f"Tick {symbol} | Swarm processed." - ) - - return current_round + 1 - - -async def consume_binance_stream( - event_queue: asyncio.Queue[dict[str, object]], - stop_event: asyncio.Event, -) -> None: - url = f"{BINANCE_WS_URL}?streams={'/'.join(BINANCE_STREAMS)}" - print("Connecting to Binance public stream...") - - async with connect(url, ping_interval=20, ping_timeout=20) as websocket: - await event_queue.put( - {"type": "provider_status", "provider": "binance", "status": "connected"} - ) - while not stop_event.is_set(): - message = await websocket.recv() - depth_update = extract_binance_order_book(message) - if depth_update is None: - continue - - symbol, bids, asks = depth_update - await event_queue.put( - { - "type": "book", - "provider": "binance", - "symbol": symbol, - "event": "snapshot", - "bids": bids, - "asks": asks, - } - ) - - -async def consume_kraken_stream( - event_queue: asyncio.Queue[dict[str, object]], - stop_event: asyncio.Event, -) -> None: - print("Connecting to Kraken public book stream...") - - async with connect(KRAKEN_WS_URL, ping_interval=20, ping_timeout=20) as websocket: - await websocket.send( - json.dumps( - { - "method": "subscribe", - "params": { - "channel": "book", - "symbol": list(KRAKEN_PRODUCTS), - "depth": ORDER_BOOK_LEVEL_LIMIT, - }, - } - ) - ) - await event_queue.put( - {"type": "provider_status", "provider": "kraken", "status": "connected"} - ) - - while not stop_event.is_set(): - message = await websocket.recv() - event = extract_kraken_order_book_event(message) - if event is None: - continue - - symbol = cast(str, event["symbol"]) - event_type = cast(str, event["type"]) - await event_queue.put( - { - "type": "book", - "provider": "kraken", - "symbol": symbol, - "event": event_type, - "bids": cast(list[BookLevel], event["bids"]), - "asks": cast(list[BookLevel], event["asks"]), - } - ) - - -async def consume_bybit_stream( - event_queue: asyncio.Queue[dict[str, object]], - stop_event: asyncio.Event, -) -> None: - print("Connecting to Bybit public order-book stream...") - - async with connect(BYBIT_WS_URL, ping_interval=20, ping_timeout=20) as websocket: - await websocket.send( - json.dumps({"op": "subscribe", "args": list(BYBIT_TOPICS)}) - ) - await event_queue.put( - {"type": "provider_status", "provider": "bybit", "status": "connected"} - ) - - while not stop_event.is_set(): - message = await websocket.recv() - event = extract_bybit_order_book_event(message) - if event is None: - continue - - symbol = cast(str, event["symbol"]) - event_type = cast(str, event["type"]) - await event_queue.put( - { - "type": "book", - "provider": "bybit", - "symbol": symbol, - "event": event_type, - "bids": cast(list[BookLevel], event["bids"]), - "asks": cast(list[BookLevel], event["asks"]), - } - ) - - -def provider_failure_is_hard(exc: Exception) -> bool: - message = str(exc) - lowered = message.lower() - return any(status_code in message for status_code in ("401", "403", "451")) or any( - token in lowered - for token in ( - "unsupported 1inch spot-price chain", - "must be an integer chain id", - "is not set", - ) - ) - - -async def run_provider_stream( - provider_name: str, - consumer: Callable[[asyncio.Queue[dict[str, object]], asyncio.Event], Awaitable[None]], - event_queue: asyncio.Queue[dict[str, object]], - stop_event: asyncio.Event, -) -> None: - while not stop_event.is_set(): - try: - await consumer(event_queue, stop_event) - except (ConnectionClosed, InvalidStatus, OSError, TimeoutError, RuntimeError) as exc: - await event_queue.put( - { - "type": "provider_status", - "provider": provider_name, - "status": "unavailable", - "error": str(exc), - } - ) - if provider_failure_is_hard(exc) or stop_event.is_set(): - return - await asyncio.sleep(1.0) - else: - if not stop_event.is_set(): - await event_queue.put( - { - "type": "provider_status", - "provider": provider_name, - "status": "disconnected", - "error": "stream ended", - } - ) - return - - -def build_live_payment_gate_observation( - current_round: int, - liquidity_tracker: LiquidityTracker, - order_books: OrderBookTracker, - provider_failures: tuple[str, ...], - provider_candidate_count: int, -) -> PaymentGateObservation: - best_snapshot = liquidity_tracker.best - final_truth = liquidity_tracker.final_truth - return PaymentGateObservation( - observed_rounds=current_round, - active_public_providers=len(order_books.active_order_book_providers()), - provider_candidate_count=provider_candidate_count, - best_liquidity_score=0.0 if best_snapshot is None else best_snapshot.liquidity_score, - best_executable_notional_usd_50bps=( - 0.0 - if best_snapshot is None - else best_snapshot.executable_notional_usd_50bps - ), - final_truth_confidence=(0.0 if final_truth is None else final_truth.truth_confidence), - failure_count=len(provider_failures), - failures=provider_failures, - ) - - -async def stream_live_market_data( - rounds: int, - record_path: Path, - swarm_state_path: Path | None, -) -> None: - """ - Plugs live market data into the MEV Swarm Simulation. - Each incoming tick triggers a round of swarm competition over the new state. - """ - simulation_seed = random.SystemRandom().getrandbits(64) - sim = build_simulation(rounds, simulation_seed) - swarm_state_context = load_swarm_state(swarm_state_path, sim) - print("Swarm Initialized. Waiting for live market ticks...") - print(f"Persisting ticks to {record_path}") - print(f"Simulation seed: {simulation_seed}") - if swarm_state_path is not None: - if cast(bool, swarm_state_context["loaded"]): - print( - "Hydrated swarm state from " - f"{swarm_state_context['path']} " - f"(generation {cast(int, swarm_state_context['generation'])}, " - f"source session {swarm_state_context['source_session_id'] or 'unknown'})" - ) - else: - print( - f"No persisted swarm state found at {swarm_state_context['path']}; starting cold." - ) - print( - "Warming up venue fan-in before round 1 to let multiple public providers populate the surface..." - ) - - current_round = 0 - latest_quotes: dict[str, Quote] = {} - provider_specs = ( - ("binance", consume_binance_stream), - ("kraken", consume_kraken_stream), - ("bybit", consume_bybit_stream), - ) - wire_placement = PaidLiquidityWirePlacement() - liquidity_tracker = LiquidityTracker() - order_books = OrderBookTracker() - meta_quote_stop = asyncio.Event() - venue_stop = asyncio.Event() - venue_event_queue: asyncio.Queue[dict[str, object]] = asyncio.Queue() - recorder = TickRecorder( - record_path, - rounds, - session_metadata={ - "simulation_seed": simulation_seed, - "paid_liquidity_wire_reserved": True, - "paid_liquidity_wire_note": wire_placement.note, - "payment_gate_policy": wire_placement.policy.policy_name, - "premium_adapter_provider": "1inch_product_api", - "premium_adapter_enabled": wire_placement.enabled, - "premium_adapter_api_key_env_var": wire_placement.product_api_adapter.api_key_env_var, - "premium_adapter_probe_path": wire_placement.product_api_adapter.probe_path or None, - "premium_quote_provider": ONEINCH_SPOT_PRICE_PROVIDER, - "liquidity_probe_model": "weighted_order_book_depth_bands_10_25_50bps", - "order_book_level_limit": ORDER_BOOK_LEVEL_LIMIT, - "order_book_provider_candidates": ["binance", "kraken", "bybit"], - "public_provider_target_count": PUBLIC_PROVIDER_TARGET_COUNT, - "meta_quote_providers": ["coingecko", "dexscreener"], - "macro_context_providers": [ - "yahoo_chart", - "google_news_rss", - "manifold", - "polymarket", - "defillama", - ], - "macro_context_watchlist": sorted(GOOGLE_NEWS_WATCHLIST), - "defillama_protocol_watchlist": list(DEFILLAMA_PROTOCOL_KEYWORDS), - "defillama_chain_watchlist": list(DEFILLAMA_CHAIN_WATCHLIST), - "defillama_stablecoin_watchlist": list(DEFILLAMA_STABLECOIN_WATCHLIST), - "swarm_state_file": cast(str | None, swarm_state_context["path"]), - "swarm_state_enabled": cast(bool, swarm_state_context["enabled"]), - "swarm_state_loaded": cast(bool, swarm_state_context["loaded"]), - "swarm_state_generation_in": cast(int, swarm_state_context["generation"]), - "swarm_state_parent_session_id": cast( - str | None, swarm_state_context["source_session_id"] - ), - "swarm_state_rounds_completed_in": cast( - int, swarm_state_context["rounds_completed"] - ), - "swarm_state_digest_in": cast(str | None, swarm_state_context["state_digest"]), - "swarm_state_learning_summary_in": cast( - dict[str, object], swarm_state_context["learning_summary"] - ), - }, - ) - if swarm_state_path is not None: - recorder.record_event( - { - "type": "swarm_state_status", - "phase": "loaded" if cast(bool, swarm_state_context["loaded"]) else "cold_start", - "path": cast(str | None, swarm_state_context["path"]), - "generation": cast(int, swarm_state_context["generation"]), - "source_session_id": cast( - str | None, swarm_state_context["source_session_id"] - ), - "rounds_completed": cast(int, swarm_state_context["rounds_completed"]), - "state_digest": cast(str | None, swarm_state_context["state_digest"]), - "restore_summary": cast( - dict[str, object] | None, swarm_state_context["restore_summary"] - ), - "learning_summary": cast( - dict[str, object], swarm_state_context["learning_summary"] - ), - } - ) - meta_quote_task = asyncio.create_task(poll_meta_quote_sources(order_books, recorder, meta_quote_stop)) - macro_context_stop = asyncio.Event() - try: - initial_macro_context = await asyncio.to_thread(build_macro_context_snapshot) - except (OSError, TimeoutError, ValueError, ET.ParseError) as exc: - print(f"initial macro context poll failed: {exc}") - else: - order_books.set_macro_context(initial_macro_context) - recorder.record_event(initial_macro_context.to_record()) - macro_context_task = asyncio.create_task( - poll_macro_context_sources(order_books, recorder, macro_context_stop) - ) - public_provider_tasks = [ - asyncio.create_task( - run_provider_stream(provider_name, consumer, venue_event_queue, venue_stop) - ) - for provider_name, consumer in provider_specs - ] - provider_tasks = list(public_provider_tasks) - provider_failures: dict[str, str] = {} - premium_provider_requested = False - next_gate_evaluation_round = wire_placement.policy.min_observed_rounds - warmup_deadline = time.monotonic() + VENUE_FANIN_WARMUP_S - liquidity_summary: dict[str, object] | None = None - - try: - while current_round < sim.num_rounds: - try: - event = await asyncio.wait_for(venue_event_queue.get(), timeout=1.0) - except TimeoutError as exc: - if not order_books.active_order_book_providers() and all( - task.done() for task in public_provider_tasks - ): - raise RuntimeError( - "No public market data providers are reachable from this environment." - ) from exc - continue - - event_type_obj = event.get("type") - if event_type_obj == "provider_status": - provider_name_obj = event.get("provider") - status_obj = event.get("status") - error_obj = event.get("error") - if not isinstance(provider_name_obj, str): - continue - if not isinstance(status_obj, str): - continue - - error_message = error_obj if isinstance(error_obj, str) else None - order_books.set_provider_status(provider_name_obj, status_obj, error_message) - if status_obj == "connected": - provider_failures.pop(provider_name_obj, None) - elif error_message is not None: - failure_record = f"{provider_name_obj}: {error_message}" - provider_failures[provider_name_obj] = failure_record - print(f"{provider_name_obj} unavailable: {error_message}") - continue - - if event_type_obj == "quote": - provider_name_obj = event.get("provider") - quotes_obj = event.get("quotes") - if not isinstance(provider_name_obj, str): - continue - if not isinstance(quotes_obj, dict): - continue - - parsed_quotes: dict[str, Quote] = {} - for symbol_obj, quote_obj in cast(dict[object, object], quotes_obj).items(): - if not isinstance(symbol_obj, str): - continue - if not isinstance(quote_obj, (tuple, list)): - continue - quote_sequence = cast(tuple[object, ...] | list[object], quote_obj) - quote_pair = list(quote_sequence) - if len(quote_pair) != 2: - continue - bid_obj = quote_pair[0] - ask_obj = quote_pair[1] - if not isinstance(bid_obj, (int, float)): - continue - if not isinstance(ask_obj, (int, float)): - continue - parsed_quotes[symbol_obj] = (float(bid_obj), float(ask_obj)) - - if not parsed_quotes: - continue - - order_books.set_quote_snapshot(provider_name_obj, parsed_quotes) - latest_quotes.clear() - latest_quotes.update(order_books.top_quotes()) - recorder.record_event( - { - "type": "provider_quote_snapshot", - "provider": provider_name_obj, - "captured_at": utc_now_iso(), - "quotes": { - symbol: {"bid": quote[0], "ask": quote[1]} - for symbol, quote in parsed_quotes.items() - }, - } - ) - continue - - if event_type_obj != "book": - continue - - provider_name_obj = event.get("provider") - symbol_obj = event.get("symbol") - book_event_type_obj = event.get("event") - bids_obj = event.get("bids") - asks_obj = event.get("asks") - if not isinstance(provider_name_obj, str): - continue - if not isinstance(symbol_obj, str): - continue - if not isinstance(book_event_type_obj, str): - continue - if not isinstance(bids_obj, list): - continue - if not isinstance(asks_obj, list): - continue - - provider_name = provider_name_obj - symbol = symbol_obj - bids = cast(list[BookLevel], bids_obj) - asks = cast(list[BookLevel], asks_obj) - if book_event_type_obj == "snapshot": - order_books.set_snapshot(provider_name, symbol, bids, asks) - else: - for price, size in bids: - order_books.apply_update(provider_name, symbol, "buy", price, size) - for price, size in asks: - order_books.apply_update(provider_name, symbol, "sell", price, size) - - latest_quotes.clear() - latest_quotes.update(order_books.top_quotes()) - - top_quote = latest_quotes.get(symbol) - if top_quote is None: - continue - - if ( - current_round == 0 - and time.monotonic() < warmup_deadline - and len(order_books.active_order_book_providers()) < PUBLIC_PROVIDER_TARGET_COUNT - ): - continue - - bid_price, ask_price = top_quote - current_round = process_quote( - sim, - latest_quotes, - current_round, - provider_name, - symbol, - bid_price, - ask_price, - recorder, - liquidity_tracker, - order_books, - ) - - if ( - not premium_provider_requested - and current_round >= next_gate_evaluation_round - ): - premium_provider_task = await maybe_start_premium_quote_provider( - current_round, - liquidity_tracker, - order_books, - provider_failures, - len(provider_specs), - wire_placement, - recorder, - venue_event_queue, - venue_stop, - ) - next_gate_evaluation_round += 10 - if premium_provider_task is not None: - provider_tasks.append(premium_provider_task) - premium_provider_requested = True - - liquidity_summary = liquidity_tracker.summary_record() - if liquidity_summary is not None: - recorder.record_event(liquidity_summary) - - # Print the final stats after the live session completes. - sim.print_final_stats() - print_liquidity_summary(liquidity_tracker) - finally: - meta_quote_stop.set() - macro_context_stop.set() - venue_stop.set() - meta_quote_task.cancel() - macro_context_task.cancel() - for task in provider_tasks: - task.cancel() - try: - await meta_quote_task - except asyncio.CancelledError: - pass - try: - await macro_context_task - except asyncio.CancelledError: - pass - for task in provider_tasks: - try: - await task - except asyncio.CancelledError: - pass - if swarm_state_path is not None: - try: - swarm_state_record = save_swarm_state( - swarm_state_path, - sim, - record_path.stem, - cast(int, swarm_state_context["generation"]), - liquidity_summary, - "live", - ) - except (OSError, ValueError, TypeError) as exc: - print(f"swarm state save failed: {exc}") - else: - recorder.record_event(swarm_state_record) - recorder.close() - - -async def replay_market_data( - replay_path: Path, - rounds: int | None, - replay_delay_ms: float, - swarm_state_path: Path | None, -) -> None: - metadata = read_replay_metadata(replay_path) - metadata_rounds = metadata.get("target_rounds") - default_rounds = metadata_rounds if isinstance(metadata_rounds, int) else DEFAULT_ROUNDS - - simulation_seed_obj = metadata.get("simulation_seed") - if isinstance(simulation_seed_obj, int): - simulation_seed = simulation_seed_obj - else: - simulation_seed = DEFAULT_SIMULATION_SEED - print( - "Replay file is missing simulation_seed metadata; " - "using deterministic default seed 0." - ) - - sim = build_simulation(resolve_rounds(rounds, default_rounds), simulation_seed) - swarm_state_context = load_swarm_state(swarm_state_path, sim) - print(f"Replaying ticks from {replay_path}") - print(f"Simulation seed: {simulation_seed}") - if swarm_state_path is not None: - if cast(bool, swarm_state_context["loaded"]): - print( - "Hydrated swarm state from " - f"{swarm_state_context['path']} " - f"(generation {cast(int, swarm_state_context['generation'])}, " - f"source session {swarm_state_context['source_session_id'] or 'unknown'})" - ) - else: - print( - f"No persisted swarm state found at {swarm_state_context['path']}; starting cold." - ) - - session_id = metadata.get("session_id") - if isinstance(session_id, str): - print(f"Session ID: {session_id}") - - current_round = 0 - latest_quotes: dict[str, Quote] = {} - liquidity_tracker = LiquidityTracker() - replay_delay_s = replay_delay_ms / 1000.0 - truth_by_round = read_recorded_truth_qualifiers(replay_path) - - for provider_name, symbol, bid_price, ask_price in iter_replay_ticks(replay_path): - current_round = process_quote( - sim, - latest_quotes, - current_round, - provider_name, - symbol, - bid_price, - ask_price, - liquidity_tracker=liquidity_tracker, - truth_by_round=truth_by_round, - ) - - if current_round >= sim.num_rounds: - break - if replay_delay_s > 0.0: - await asyncio.sleep(replay_delay_s) - - if current_round < sim.num_rounds: - print(f"Replay exhausted after {current_round} rounds (target was {sim.num_rounds}).") - - sim.print_final_stats() - replay_metadata, replay_summary = read_recorded_liquidity_summary(replay_path) - _ = replay_metadata - if swarm_state_path is not None: - replay_session_id = ( - f"replay_{session_id}" if isinstance(session_id, str) else f"replay_{replay_path.stem}" - ) - try: - swarm_state_record = save_swarm_state( - swarm_state_path, - sim, - replay_session_id, - cast(int, swarm_state_context["generation"]), - replay_summary, - "replay", - ) - except (OSError, ValueError, TypeError) as exc: - print(f"swarm state save failed: {exc}") - else: - print( - "Saved replay-updated swarm state to " - f"{swarm_state_record['path']} " - f"(generation {swarm_state_record['generation']})" - ) - print_liquidity_summary_record(replay_summary) - - -def compare_recorded_sessions(baseline_path: Path, candidate_path: Path) -> None: - baseline_metadata, baseline_summary = read_recorded_liquidity_summary(baseline_path) - candidate_metadata, candidate_summary = read_recorded_liquidity_summary(candidate_path) - print_session_comparison( - baseline_path, - baseline_metadata, - baseline_summary, - candidate_path, - candidate_metadata, - candidate_summary, - ) - - -def build_recorded_payment_gate_observation(session_path: Path) -> PaymentGateObservation: - metadata, summary = read_recorded_liquidity_summary(session_path) - best = cast(dict[str, object], summary["best"]) - final = cast(dict[str, object], summary["final"]) - final_truth = cast(dict[str, object] | None, summary.get("final_truth")) - - final_round_obj = final.get("round") - observed_rounds = final_round_obj + 1 if isinstance(final_round_obj, int) else 0 - - provider_candidates_obj = metadata.get("order_book_provider_candidates", []) - provider_candidate_count = ( - len(cast(list[object], provider_candidates_obj)) - if isinstance(provider_candidates_obj, list) - else 0 - ) - - max_active_public_providers = 0 - provider_status: dict[str, tuple[str, str | None]] = {} - for payload in iter_session_records(session_path): - if payload.get("type") != "market_surface_snapshot": - continue - - active_providers_obj = payload.get("active_order_book_providers", []) - if isinstance(active_providers_obj, list): - max_active_public_providers = max( - max_active_public_providers, - len(cast(list[object], active_providers_obj)), - ) - - provider_status_obj = payload.get("provider_status", {}) - if not isinstance(provider_status_obj, dict): - continue - for provider_name_obj, details_obj in cast( - dict[object, object], provider_status_obj - ).items(): - if not isinstance(provider_name_obj, str): - continue - if not isinstance(details_obj, dict): - continue - details = cast(dict[str, object], details_obj) - status_obj = details.get("status") - error_obj = details.get("error") - provider_status[provider_name_obj] = ( - status_obj if isinstance(status_obj, str) else "unknown", - error_obj if isinstance(error_obj, str) else None, - ) - - failures = tuple( - sorted( - f"{provider_name}: {error_message}" - for provider_name, (status, error_message) in provider_status.items() - if status != "connected" and error_message is not None - ) - ) - - return PaymentGateObservation( - observed_rounds=observed_rounds, - active_public_providers=max_active_public_providers, - provider_candidate_count=provider_candidate_count, - best_liquidity_score=float(cast(float, best.get("liquidity_score", 0.0))), - best_executable_notional_usd_50bps=float( - cast(float, best.get("executable_notional_usd_50bps", 0.0)) - ), - final_truth_confidence=( - 0.0 if final_truth is None else float(cast(float, final_truth.get("truth_confidence", 0.0))) - ), - failure_count=len(failures), - failures=failures, - ) - - -def evaluate_payment_gate_for_session(session_path: Path) -> None: - observation = build_recorded_payment_gate_observation(session_path) - wire_placement = PaidLiquidityWirePlacement() - decision = wire_placement.policy.evaluate(observation) - - print("\n" + "=" * 80) - print("PAYMENT GATE POLICY") - print("=" * 80) - print(f"Session: {session_path}") - print(f"Policy : {decision.policy_name}") - print( - f"Observed rounds={observation.observed_rounds} | " - f"Active public providers={observation.active_public_providers}/{observation.provider_candidate_count}" - ) - print( - f"Best liquidity score={observation.best_liquidity_score:,.2f} | " - f"Best exec@50bps=${observation.best_executable_notional_usd_50bps:,.2f} | " - f"Final truth={observation.final_truth_confidence:.4f}" - ) - print( - f"Measured shortfall={decision.measured_shortfall} | " - f"Shortfall score={decision.shortfall_score:.4f} | " - f"Allow premium activation={decision.allow_activation}" - ) - print( - f"1inch adapter configured={wire_placement.product_api_adapter.configured()} | " - f"Wire enabled={wire_placement.enabled} | " - f"Probe path={wire_placement.product_api_adapter.probe_path or 'unset'}" - ) - if decision.reasons: - print("Reasons:") - for reason in decision.reasons: - print(f"- {reason}") - if observation.failures: - print("Provider failures:") - for failure in observation.failures: - print(f"- {failure}") - - -def analyze_recorded_coincidences(session_path: Path, limit: int) -> None: - metadata = read_replay_metadata(session_path) - rounds: dict[int, dict[str, object]] = {} - best_liquidity_score = 0.0 - - for payload in iter_session_records(session_path): - record_type = payload.get("type") - round_obj = payload.get("round") - if not isinstance(round_obj, int): - continue - - round_bucket = rounds.setdefault(round_obj, {}) - if record_type == "liquidity_snapshot": - round_bucket["liquidity"] = payload - liquidity_score_obj = payload.get("liquidity_score") - if isinstance(liquidity_score_obj, (int, float)): - best_liquidity_score = max(best_liquidity_score, float(liquidity_score_obj)) - elif record_type == "truth_qualifier_snapshot": - round_bucket["truth"] = payload - elif record_type == "market_surface_snapshot": - round_bucket["surface"] = payload - - if not rounds: - print(f"No round snapshots found in {session_path}.") - return - - scored_rounds: list[dict[str, object]] = [] - for round_index, round_bucket in rounds.items(): - liquidity_obj = round_bucket.get("liquidity") - truth_obj = round_bucket.get("truth") - surface_obj = round_bucket.get("surface") - if not isinstance(liquidity_obj, dict) or not isinstance(truth_obj, dict): - continue - - liquidity = cast(dict[str, object], liquidity_obj) - truth = cast(dict[str, object], truth_obj) - macro_context_obj = ( - cast(dict[str, object], surface_obj).get("macro_context") - if isinstance(surface_obj, dict) - else None - ) - macro_context = ( - cast(dict[str, object], macro_context_obj) if isinstance(macro_context_obj, dict) else {} - ) - - liquidity_score = float(cast(float, liquidity.get("liquidity_score", 0.0))) - liquidity_strength = ( - clamp_unit(liquidity_score / best_liquidity_score) if best_liquidity_score > 0.0 else 0.0 - ) - truth_confidence = float(cast(float, truth.get("truth_confidence", 0.0))) - macro_alignment = float( - cast(float, macro_context.get("macro_alignment", truth.get("macro_alignment", 0.0))) - ) - cross_asset_stress = float( - cast( - float, - macro_context.get("cross_asset_stress", truth.get("cross_asset_stress", 0.0)), - ) - ) - commodity_shock_score = float( - cast( - float, - macro_context.get( - "commodity_shock_score", - truth.get("commodity_shock_score", 0.0), - ), - ) - ) - betting_conviction = float( - cast( - float, - macro_context.get("betting_conviction", truth.get("betting_conviction", 0.0)), - ) - ) - news_shock_score = float( - cast(float, macro_context.get("news_shock_score", truth.get("news_shock_score", 0.0))) - ) - - coincidence_score = clamp_unit( - 0.35 * truth_confidence - + 0.20 * liquidity_strength - + 0.15 * macro_alignment - + 0.10 * cross_asset_stress - + 0.10 * commodity_shock_score - + 0.05 * betting_conviction - + 0.05 * news_shock_score - ) - - top_news_titles_obj = macro_context.get("top_news_titles", []) - top_news_titles = ( - [str(title) for title in cast(list[object], top_news_titles_obj)[:2]] - if isinstance(top_news_titles_obj, list) - else [] - ) - - scored_rounds.append( - { - "round": round_index, - "provider": str(truth.get("provider", liquidity.get("provider", "unknown"))), - "coincidence_score": coincidence_score, - "liquidity_score": liquidity_score, - "truth_confidence": truth_confidence, - "avg_spread_bps": float(cast(float, liquidity.get("avg_spread_bps", 0.0))), - "macro_alignment": macro_alignment, - "cross_asset_stress": cross_asset_stress, - "commodity_shock_score": commodity_shock_score, - "betting_conviction": betting_conviction, - "news_shock_score": news_shock_score, - "top_news_titles": top_news_titles, - } - ) - - if not scored_rounds: - print(f"No coincidence-ready snapshots found in {session_path}.") - return - - scored_rounds.sort( - key=lambda row: cast(float, row["coincidence_score"]), - reverse=True, - ) - - session_id = metadata.get("session_id") - print("\n" + "=" * 80) - print("COINCIDENCE ANALYSIS") - print("=" * 80) - print(f"Session: {session_path}") - if isinstance(session_id, str): - print(f"Session ID: {session_id}") - print(f"Top coincident rounds: {min(limit, len(scored_rounds))}") - - for row in scored_rounds[:limit]: - print( - f"Round {cast(int, row['round']) + 1:03d} | " - f"Source {cast(str, row['provider'])} | " - f"Coincidence={cast(float, row['coincidence_score']):.4f} | " - f"Truth={cast(float, row['truth_confidence']):.4f} | " - f"Liquidity={cast(float, row['liquidity_score']):,.2f} | " - f"Spread={cast(float, row['avg_spread_bps']):.4f}bps | " - f"Macro={cast(float, row['macro_alignment']):.4f} | " - f"CommodityShock={cast(float, row['commodity_shock_score']):.4f} | " - f"Betting={cast(float, row['betting_conviction']):.4f} | " - f"News={cast(float, row['news_shock_score']):.4f}" - ) - top_news_titles = cast(list[str], row["top_news_titles"]) - if top_news_titles: - print(f" Headlines: {' | '.join(top_news_titles)}") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser( - description="Capture and replay public market ticks for the MEV swarm simulation." - ) - parser.add_argument( - "--rounds", - type=int, - default=None, - help="Number of swarm rounds to execute. Defaults to 100 live rounds or the replay file target.", - ) - parser.add_argument( - "--record-file", - type=Path, - default=None, - help="JSONL path for persisting live ticks. Defaults to a timestamped file under 5-Applications/out/live_market_data/.", - ) - parser.add_argument( - "--replay-file", - type=Path, - default=None, - help="Replay a previously recorded JSONL tick session instead of connecting to live providers.", - ) - parser.add_argument( - "--replay-delay-ms", - type=float, - default=0.0, - help="Optional delay between replayed ticks in milliseconds.", - ) - parser.add_argument( - "--compare-files", - nargs=2, - type=Path, - default=None, - metavar=("BASELINE", "CANDIDATE"), - help="Compare two recorded session files and report whether liquidity improved.", - ) - parser.add_argument( - "--coincidence-file", - type=Path, - default=None, - help="Inspect one recorded session and report the rounds where liquidity, truth, and macro signals coincided most strongly.", - ) - parser.add_argument( - "--coincidence-limit", - type=int, - default=5, - help="Maximum number of coincident rounds to print with --coincidence-file.", - ) - parser.add_argument( - "--payment-gate-file", - type=Path, - default=None, - help="Evaluate the premium-feed payment gate against one recorded session file.", - ) - parser.add_argument( - "--swarm-state-file", - type=Path, - default=None, - help=( - "JSON path for persisted swarm learning state. During live runs and replays, the file " - "is loaded before round 1 when present and rewritten at shutdown with updated bot, " - "pool, and cross-session objective state." - ), - ) - return parser.parse_args() - - -async def main() -> None: - args = parse_args() - - if args.compare_files is not None: - compare_recorded_sessions(args.compare_files[0], args.compare_files[1]) - return - if args.coincidence_limit <= 0: - raise ValueError("--coincidence-limit must be positive") - if args.coincidence_file is not None: - analyze_recorded_coincidences(args.coincidence_file, args.coincidence_limit) - return - if args.payment_gate_file is not None: - evaluate_payment_gate_for_session(args.payment_gate_file) - return - - if args.replay_file is not None and args.record_file is not None: - raise ValueError("--record-file cannot be combined with --replay-file") - if args.replay_delay_ms < 0.0: - raise ValueError("--replay-delay-ms must be non-negative") - - if args.replay_file is not None: - await replay_market_data( - args.replay_file, - args.rounds, - args.replay_delay_ms, - args.swarm_state_file, - ) - return - - await stream_live_market_data( - rounds=resolve_rounds(args.rounds, DEFAULT_ROUNDS), - record_path=args.record_file or default_record_path(), - swarm_state_path=args.swarm_state_file, - ) - -if __name__ == "__main__": - try: - asyncio.run(main()) - except KeyboardInterrupt: - print("\nShutdown requested... exiting.") diff --git a/5-Applications/tools-scripts/market/market_action_policy.py b/5-Applications/tools-scripts/market/market_action_policy.py deleted file mode 100644 index 3b1d0a3b..00000000 --- a/5-Applications/tools-scripts/market/market_action_policy.py +++ /dev/null @@ -1,171 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Shared market action policy surface. - -This replaces older fixed-edge shorthand with explicit, bounded settings: - -- entry improvement fraction -- max tolerated loss fraction -- expected slippage fraction -- adaptive activation pause when adverse reinforcement appears - -The default posture is intentionally ordinary: no fixed magical edge is assumed. -""" - -from __future__ import annotations - -import os -from dataclasses import dataclass -from typing import Any, Dict, Optional - - -def _env_float(name: str, default: float) -> float: - raw = os.getenv(name) - if raw is None or raw == "": - return default - try: - return float(raw) - except ValueError: - return default - - -def _env_int(name: str, default: int) -> int: - raw = os.getenv(name) - if raw is None or raw == "": - return default - try: - return int(raw) - except ValueError: - return default - - -@dataclass -class MarketActionPolicy: - entry_improvement_fraction: float = 0.0 - max_loss_fraction: float = 0.05 - expected_slippage_fraction: float = 0.0025 - activation_pause_seconds: int = 60 - max_activation_pause_seconds: int = 300 - reinforcement_pause_multiplier: float = 3.0 - rationale: str = ( - "No fixed impossible-edge assumption; action must be mitigated explicitly." - ) - - @classmethod - def from_env(cls, prefix: str = "MARKET_ACTION") -> "MarketActionPolicy": - # Clamp all env-var params to sane ranges so misconfiguration - # cannot invert buy logic, create tight-loop order floods, or - # produce negative reference prices. - activation_pause = max(1, _env_int(f"{prefix}_ACTIVATION_PAUSE_SECONDS", 60)) - max_activation_pause = max( - activation_pause, - _env_int(f"{prefix}_MAX_ACTIVATION_PAUSE_SECONDS", 300), - ) - return cls( - entry_improvement_fraction=max(0.0, min(0.50, _env_float( - f"{prefix}_ENTRY_IMPROVEMENT_FRACTION", 0.0 - ))), - max_loss_fraction=max(0.001, min(0.50, _env_float( - f"{prefix}_MAX_LOSS_FRACTION", 0.05 - ))), - expected_slippage_fraction=max(0.0, min(0.10, _env_float( - f"{prefix}_EXPECTED_SLIPPAGE_FRACTION", 0.0025 - ))), - activation_pause_seconds=activation_pause, - max_activation_pause_seconds=max_activation_pause, - reinforcement_pause_multiplier=max(1.0, _env_float( - f"{prefix}_REINFORCEMENT_PAUSE_MULTIPLIER", 3.0 - )), - rationale=os.getenv( - f"{prefix}_RATIONALE", - "No fixed impossible-edge assumption; action must be mitigated explicitly.", - ), - ) - - def entry_reference_price(self, basis_price: float) -> float: - return basis_price * (1.0 - self.entry_improvement_fraction) - - def loss_alert_price(self, basis_price: float) -> float: - return basis_price * (1.0 - self.max_loss_fraction) - - def reinforcement_trigger_fraction(self) -> float: - return max( - self.entry_improvement_fraction + self.expected_slippage_fraction, - self.expected_slippage_fraction * 2.0, - ) - - def gap_fraction(self, current_price: float, reference_price: float) -> float: - if reference_price <= 0.0: - return 0.0 - return max(0.0, (current_price - reference_price) / reference_price) - - def detects_loss_reinforcement( - self, - *, - current_price: float, - reference_price: float, - last_price: Optional[float] = None, - adverse_streak: int = 0, - ) -> bool: - gap = self.gap_fraction(current_price, reference_price) - trending_worse = last_price is not None and current_price >= last_price - materially_outside = gap >= self.reinforcement_trigger_fraction() - repeated_adverse = adverse_streak >= 2 and gap > 0.0 - return materially_outside and (trending_worse or repeated_adverse) - - def activation_pause_for( - self, - *, - loss_reinforcement: bool, - adverse_streak: int = 0, - ) -> int: - if not loss_reinforcement: - return self.activation_pause_seconds - scaled = int( - self.activation_pause_seconds - * self.reinforcement_pause_multiplier - * max(1.0, 1.0 + (0.5 * adverse_streak)) - ) - return min(self.max_activation_pause_seconds, max(self.activation_pause_seconds, scaled)) - - def snapshot(self, market_price: float) -> Dict[str, Any]: - return { - "policy_mode": "risk_aware_action_policy", - "market_price": round(market_price, 8), - "entry_reference_price": round(self.entry_reference_price(market_price), 8), - "entry_improvement_fraction": round(self.entry_improvement_fraction, 8), - "entry_improvement_pct": round(self.entry_improvement_fraction * 100.0, 6), - "max_loss_fraction": round(self.max_loss_fraction, 8), - "max_loss_pct": round(self.max_loss_fraction * 100.0, 6), - "loss_alert_price": round(self.loss_alert_price(market_price), 8), - "expected_slippage_fraction": round(self.expected_slippage_fraction, 8), - "expected_slippage_pct": round( - self.expected_slippage_fraction * 100.0, 6 - ), - "activation_pause_seconds": int(self.activation_pause_seconds), - "max_activation_pause_seconds": int(self.max_activation_pause_seconds), - "reinforcement_pause_multiplier": round( - self.reinforcement_pause_multiplier, 6 - ), - "reinforcement_trigger_fraction": round( - self.reinforcement_trigger_fraction(), 8 - ), - "reinforcement_trigger_pct": round( - self.reinforcement_trigger_fraction() * 100.0, 6 - ), - "rationale": self.rationale, - } - - def brief(self) -> str: - return ( - f"entry improvement {self.entry_improvement_fraction * 100.0:.2f}% | " - f"max loss {self.max_loss_fraction * 100.0:.2f}% | " - f"slippage {self.expected_slippage_fraction * 100.0:.2f}% | " - f"pause {self.activation_pause_seconds}s->{self.max_activation_pause_seconds}s" - ) diff --git a/5-Applications/tools-scripts/market/mevbot_swarm_sim.py b/5-Applications/tools-scripts/market/mevbot_swarm_sim.py deleted file mode 100644 index 9bfd2ec7..00000000 --- a/5-Applications/tools-scripts/market/mevbot_swarm_sim.py +++ /dev/null @@ -1,1311 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -50-Bot MEV Swarm Simulation - KOT Currency - -═══════════════════════════════════════════════════════════════════════ -⚠️ PROPRIETARY & CONFIDENTIAL - WaveProbe Core IP - Unauthorized disclosure, reverse-engineering, or reproduction - of the entropy-jitter concealment strategy is prohibited. - This file contains trade secrets protected under applicable law. -═══════════════════════════════════════════════════════════════════════ - -Each bot operates independently: -- Fragments trades across pools -- Competes for execution -- Recovers missing fragments on-chain -- Maximizes personal profit - -Layer 0 (on-chain state) is the shared reference frame. -Coordination emerges from reading canonical truth. -""" - -import random -import json -import hashlib -import os -import sys -import socket -import time -from dataclasses import dataclass, field -from typing import List, Dict, Tuple, Optional, cast -from enum import Enum -import math -try: - from network_security import NetworkSecurityPolicy -except ImportError: - sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) - from network_security import NetworkSecurityPolicy - -try: - from waveprobe_icmp_coordinator import ( - BotIntentBeacon, CoordinatorListener, CompressedIntent, IntentType, MirrorLUTRollup - ) - _HAS_ICMP_COORDINATOR = True -except ImportError: - _HAS_ICMP_COORDINATOR = False - -# NE geometry verifier — deterministic gate for trade path validity. -# Rejects paths that are geometrically degenerate (metric jumps, uncontrolled torsion). -try: - from tools.geometry_verifier import validate_ne_path, NEPathValidation - _HAS_NE_VERIFIER = True -except ImportError: - _HAS_NE_VERIFIER = False - validate_ne_path = None - NEPathValidation = None - -PHI = 1.618033988749895 - - -def clamp_unit(value: float) -> float: - return max(0.0, min(1.0, value)) - - -def as_float(value: object, default: float = 0.0) -> float: - return float(value) if isinstance(value, (int, float)) else default - - -def as_int(value: object, default: int = 0) -> int: - return value if isinstance(value, int) else default - - -class EphemeralCoordinatorPool: - """Round-based coordinator IP rotation with deterministic failover order.""" - - def __init__(self, ips: List[str], rotation_rounds: int = 10): - if not ips: - raise ValueError("At least one coordinator IP is required") - self.ips = ips - self.rotation_rounds = max(1, rotation_rounds) - - def ordered_candidates(self, round_number: int) -> List[str]: - start = (round_number // self.rotation_rounds) % len(self.ips) - return self.ips[start:] + self.ips[:start] - - -def derive_mode_for_round(session_key: bytes, round_number: int, bot_id: int) -> int: - """Deterministically randomize bot->mode mapping per round.""" - material = ( - session_key - + round_number.to_bytes(4, "big", signed=False) - + bot_id.to_bytes(2, "big", signed=False) - ) - digest = hashlib.sha3_256(material).digest() - return digest[0] % 14 - - -class AdaptiveRNGMutator: - """Mutate RNG state from network activity and DNS signal jitter.""" - - def __init__(self, seed_material: bytes, dns_host: str = "one.one.one.one"): - self.state = hashlib.sha3_256(seed_material).digest() - self.dns_host = dns_host - self.rng = random.Random(int.from_bytes(self.state[:16], "big", signed=False)) - - def _dns_jitter_ns(self) -> int: - samples: List[int] = [] - for _ in range(2): - start = time.perf_counter_ns() - try: - socket.getaddrinfo(self.dns_host, 53) - except Exception: - pass - samples.append(max(0, time.perf_counter_ns() - start)) - - if len(samples) < 2: - return samples[0] if samples else 0 - return abs(samples[1] - samples[0]) - - def mutate(self, round_number: int, network_activity_signal: float) -> float: - jitter_ns = self._dns_jitter_ns() - activity_scaled = int(max(0.0, network_activity_signal) * 1000.0) - material = ( - self.state - + round_number.to_bytes(4, "big", signed=False) - + activity_scaled.to_bytes(8, "big", signed=False) - + jitter_ns.to_bytes(8, "big", signed=False) - + time.time_ns().to_bytes(8, "big", signed=False) - ) - self.state = hashlib.sha3_256(material).digest() - self.rng.seed(int.from_bytes(self.state[:16], "big", signed=False)) - return jitter_ns / 1_000_000.0 - -# ============================================================================ -# Pool & Token Model -# ============================================================================ - -@dataclass -class Pool: - """Constant product AMM pool""" - name: str - token_a: str - token_b: str - reserve_a: float # SOL - reserve_b: float # USDC - fee_bps: int = 25 # 0.25% - - def quote_swap(self, amount_in: float, is_a_to_b: bool) -> Tuple[float, float, float]: - """Quote swap without mutating reserves. - - Returns (amount_out, fee_paid, price_impact_bps). - """ - if amount_in <= 0: - return 0.0, 0.0, 0.0 - - fee_rate = 1.0 - (self.fee_bps / 10000) - amount_in_after_fee = amount_in * fee_rate - - if is_a_to_b: - k = self.reserve_a * self.reserve_b - new_reserve_a = self.reserve_a + amount_in_after_fee - new_reserve_b = k / new_reserve_a - amount_out = self.reserve_b - new_reserve_b - spot_price = self.reserve_b / self.reserve_a - execution_price = amount_out / max(amount_in, 1e-9) - impact_bps = max(0.0, (spot_price - execution_price) / max(spot_price, 1e-9) * 10000) - else: - k = self.reserve_a * self.reserve_b - new_reserve_b = self.reserve_b + amount_in_after_fee - new_reserve_a = k / new_reserve_b - amount_out = self.reserve_a - new_reserve_a - spot_price = self.reserve_a / self.reserve_b - execution_price = amount_out / max(amount_in, 1e-9) - impact_bps = max(0.0, (spot_price - execution_price) / max(spot_price, 1e-9) * 10000) - - fee_paid = amount_in * (self.fee_bps / 10000) - return amount_out, fee_paid, impact_bps - - def swap(self, amount_in: float, is_a_to_b: bool) -> Tuple[float, float]: - """Execute swap, return (amount_out, fee_paid)""" - amount_out, fee_paid, _impact_bps = self.quote_swap(amount_in, is_a_to_b) - if amount_out <= 0.0: - return 0.0, 0.0 - - # The full input remains in the pool while output is priced using the - # fee-discounted amount. That lets fee accrual grow pool depth over time. - if is_a_to_b: - self.reserve_a += amount_in - self.reserve_b = max(1e-12, self.reserve_b - amount_out) - else: - self.reserve_b += amount_in - self.reserve_a = max(1e-12, self.reserve_a - amount_out) - - return amount_out, fee_paid - - def get_price(self, is_a_to_b: bool) -> float: - """Get current spot price""" - if is_a_to_b: - return self.reserve_b / self.reserve_a # USDC per SOL - else: - return self.reserve_a / self.reserve_b # SOL per USDC - - def to_state(self) -> Dict[str, object]: - return { - "name": self.name, - "token_a": self.token_a, - "token_b": self.token_b, - "reserve_a": self.reserve_a, - "reserve_b": self.reserve_b, - "fee_bps": self.fee_bps, - } - - def apply_state(self, state: Dict[str, object]) -> None: - if state.get("name") not in {None, self.name}: - raise ValueError(f"pool state name mismatch: {state.get('name')} != {self.name}") - if state.get("token_a") not in {None, self.token_a}: - raise ValueError( - f"pool state token_a mismatch: {state.get('token_a')} != {self.token_a}" - ) - if state.get("token_b") not in {None, self.token_b}: - raise ValueError( - f"pool state token_b mismatch: {state.get('token_b')} != {self.token_b}" - ) - - reserve_a = state.get("reserve_a") - reserve_b = state.get("reserve_b") - fee_bps = state.get("fee_bps") - if isinstance(reserve_a, (int, float)) and reserve_a > 0.0: - self.reserve_a = float(reserve_a) - if isinstance(reserve_b, (int, float)) and reserve_b > 0.0: - self.reserve_b = float(reserve_b) - if isinstance(fee_bps, int) and fee_bps >= 0: - self.fee_bps = fee_bps - - -# ============================================================================ -# Bot Agent -# ============================================================================ - -class BotStrategy(Enum): - AGGRESSIVE = "aggressive" # Execute if ROI > 0.5% - CONSERVATIVE = "conservative" # Execute if ROI > 2% - OPPORTUNISTIC = "opportunistic" # Accept trades at median ROI - - -class ActionScope(Enum): - INTERNAL = "internal" - EXTERNAL = "external" - - -@dataclass(frozen=True) -class TruthQualifier: - """Weights execution learning toward market consensus and away from transient noise.""" - - truth_confidence: float = 1.0 - noise_ratio: float = 0.0 - liquidity_confidence: float = 1.0 - provider_agreement: float = 1.0 - aggregator_agreement: float = 1.0 - active_sources: int = 1 - - -@dataclass -class CrossSessionLiquidityObjective: - """Persisted session-level objective for internal liquidity growth.""" - - metric_name: str = "normalized_pool_invariant" - target_growth_rate: float = 0.03 - target_score: float = 1.02 - last_achieved_score: float = 1.0 - best_achieved_score: float = 1.0 - last_gap_ratio: float = 0.0 - last_external_best_liquidity_score: float = 0.0 - last_external_final_liquidity_score: float = 0.0 - last_external_truth_confidence: float = 0.0 - last_external_best_vs_initial_pct: float = 0.0 - last_external_final_vs_initial_pct: float = 0.0 - last_source_session_id: Optional[str] = None - - def execution_pressure( - self, - current_score: float, - truth_qualifier: TruthQualifier, - ) -> float: - gap_ratio = max(0.0, self.target_score - current_score) / max(self.target_score, 1e-9) - truth_weight = clamp_unit( - 0.5 * truth_qualifier.truth_confidence - + 0.3 * truth_qualifier.liquidity_confidence - + 0.2 * truth_qualifier.provider_agreement - ) - noise_discount = 1.0 - 0.5 * clamp_unit(truth_qualifier.noise_ratio) - return clamp_unit(gap_ratio * truth_weight * noise_discount * 2.0) - - def observe_session( - self, - achieved_score: float, - external_summary: Optional[Dict[str, object]] = None, - source_session_id: Optional[str] = None, - ) -> None: - target_before = self.target_score - self.last_achieved_score = max(0.0, achieved_score) - self.best_achieved_score = max(self.best_achieved_score, self.last_achieved_score) - self.last_gap_ratio = max(0.0, target_before - self.last_achieved_score) / max( - target_before, 1e-9 - ) - self.last_source_session_id = source_session_id - - if external_summary is not None: - best_obj = external_summary.get("best") - final_obj = external_summary.get("final") - final_truth_obj = external_summary.get("final_truth") - - if isinstance(best_obj, dict): - best = cast(Dict[str, object], best_obj) - self.last_external_best_liquidity_score = as_float( - best.get("liquidity_score"), - self.last_external_best_liquidity_score, - ) - - if isinstance(final_obj, dict): - final = cast(Dict[str, object], final_obj) - self.last_external_final_liquidity_score = as_float( - final.get("liquidity_score"), - self.last_external_final_liquidity_score, - ) - - if isinstance(final_truth_obj, dict): - final_truth = cast(Dict[str, object], final_truth_obj) - self.last_external_truth_confidence = as_float( - final_truth.get("truth_confidence"), - self.last_external_truth_confidence, - ) - - self.last_external_best_vs_initial_pct = as_float( - external_summary.get("best_vs_initial_pct"), - self.last_external_best_vs_initial_pct, - ) - self.last_external_final_vs_initial_pct = as_float( - external_summary.get("final_vs_initial_pct"), - self.last_external_final_vs_initial_pct, - ) - - if ( - self.last_external_final_vs_initial_pct < -5.0 - and self.last_external_best_vs_initial_pct < 0.0 - ): - self.target_growth_rate = max(0.01, self.target_growth_rate - 0.005) - elif ( - self.last_external_best_vs_initial_pct > 0.0 - or self.last_external_final_vs_initial_pct > 0.0 - ): - self.target_growth_rate = min(0.08, self.target_growth_rate + 0.005) - - anchor_score = max(1.0, self.best_achieved_score, self.last_achieved_score) - self.target_score = anchor_score * (1.0 + self.target_growth_rate) - - def summary(self, current_score: float) -> Dict[str, object]: - return { - "metric_name": self.metric_name, - "current_score": current_score, - "target_score": self.target_score, - "best_achieved_score": self.best_achieved_score, - "last_achieved_score": self.last_achieved_score, - "gap_ratio": max(0.0, self.target_score - current_score) / max(self.target_score, 1e-9), - "last_gap_ratio": self.last_gap_ratio, - "target_growth_rate": self.target_growth_rate, - "last_external_best_liquidity_score": self.last_external_best_liquidity_score, - "last_external_final_liquidity_score": self.last_external_final_liquidity_score, - "last_external_truth_confidence": self.last_external_truth_confidence, - "last_external_best_vs_initial_pct": self.last_external_best_vs_initial_pct, - "last_external_final_vs_initial_pct": self.last_external_final_vs_initial_pct, - "last_source_session_id": self.last_source_session_id, - } - - def to_state(self) -> Dict[str, object]: - return { - "metric_name": self.metric_name, - "target_growth_rate": self.target_growth_rate, - "target_score": self.target_score, - "last_achieved_score": self.last_achieved_score, - "best_achieved_score": self.best_achieved_score, - "last_gap_ratio": self.last_gap_ratio, - "last_external_best_liquidity_score": self.last_external_best_liquidity_score, - "last_external_final_liquidity_score": self.last_external_final_liquidity_score, - "last_external_truth_confidence": self.last_external_truth_confidence, - "last_external_best_vs_initial_pct": self.last_external_best_vs_initial_pct, - "last_external_final_vs_initial_pct": self.last_external_final_vs_initial_pct, - "last_source_session_id": self.last_source_session_id, - } - - def apply_state(self, state: Dict[str, object]) -> None: - metric_name = state.get("metric_name") - if isinstance(metric_name, str) and metric_name: - self.metric_name = metric_name - - self.target_growth_rate = max(0.0, min(0.10, as_float(state.get("target_growth_rate"), self.target_growth_rate))) - self.target_score = max(1.0, as_float(state.get("target_score"), self.target_score)) - self.last_achieved_score = max(0.0, as_float(state.get("last_achieved_score"), self.last_achieved_score)) - self.best_achieved_score = max( - self.last_achieved_score, - as_float(state.get("best_achieved_score"), self.best_achieved_score), - ) - self.last_gap_ratio = clamp_unit(as_float(state.get("last_gap_ratio"), self.last_gap_ratio)) - self.last_external_best_liquidity_score = as_float( - state.get("last_external_best_liquidity_score"), - self.last_external_best_liquidity_score, - ) - self.last_external_final_liquidity_score = as_float( - state.get("last_external_final_liquidity_score"), - self.last_external_final_liquidity_score, - ) - self.last_external_truth_confidence = clamp_unit( - as_float(state.get("last_external_truth_confidence"), self.last_external_truth_confidence) - ) - self.last_external_best_vs_initial_pct = as_float( - state.get("last_external_best_vs_initial_pct"), - self.last_external_best_vs_initial_pct, - ) - self.last_external_final_vs_initial_pct = as_float( - state.get("last_external_final_vs_initial_pct"), - self.last_external_final_vs_initial_pct, - ) - - last_source_session_id = state.get("last_source_session_id") - if isinstance(last_source_session_id, str): - self.last_source_session_id = last_source_session_id - - -@dataclass -class BotAgent: - """Independent MEV bot in swarm""" - bot_id: int - strategy: BotStrategy - balance_kot: float = 1000.0 # Starting capital in KOT - balance_usdc: float = 500.0 - balance_sol: float = 100.0 - - lifetime_profit: float = 0.0 - num_trades: int = 0 - successful_fragments: int = 0 - failed_fragments: int = 0 - recovery_attempts: int = 0 - recovery_successes: int = 0 - - regret_ema: float = 0.0 - surprise_ema: float = 0.0 - regret_alpha: float = 0.15 - jitter_ema: float = 0.0 - truth_confidence_ema: float = 1.0 - noise_ema: float = 0.0 - - price_history: Dict[str, List[float]] = field(default_factory=dict) - - def update_regret_surprise( - self, - expected_profit: float, - realized_profit: float, - truth_qualifier: Optional[TruthQualifier] = None, - ): - """Calibrate regret/surprise fields from execution error, discounted by market noise.""" - qualifier = truth_qualifier or TruthQualifier() - truth_confidence = max(0.0, min(1.0, qualifier.truth_confidence)) - noise_ratio = max(0.0, min(1.0, qualifier.noise_ratio)) - regret = max(0.0, expected_profit - realized_profit) - denom = max(1e-9, abs(expected_profit) + 1e-6) - surprise = min(1.0, abs(realized_profit - expected_profit) / denom) - alpha = self.regret_alpha * (0.5 + 0.5 * truth_confidence) - truth_weight = max(0.1, truth_confidence * (1.0 - 0.5 * noise_ratio)) - self.regret_ema = (1.0 - alpha) * self.regret_ema + alpha * regret * truth_weight - self.surprise_ema = (1.0 - alpha) * self.surprise_ema + alpha * surprise * truth_weight - self.truth_confidence_ema = ( - (1.0 - alpha) * self.truth_confidence_ema + alpha * truth_confidence - ) - self.noise_ema = (1.0 - alpha) * self.noise_ema + alpha * noise_ratio - - def _perturb_execute_signal(self) -> float: - """Internal execution signal perturbation (proprietary mechanism). - - ⚠️ TRADE SECRET: This method implements proprietary entropy shaping. - Do not modify, clone, or expose externally. The mechanism is - protected IP and any external observation must be treated as - a potential IP leak. - """ - entropy = random.random() - signal = (entropy - 0.5) * 0.16 - self.jitter_ema = 0.9 * self.jitter_ema + 0.1 * abs(signal) - return signal - - def roi_threshold(self) -> float: - """Strategy-based ROI threshold (%)""" - thresholds = { - BotStrategy.AGGRESSIVE: 0.5, - BotStrategy.CONSERVATIVE: 2.0, - BotStrategy.OPPORTUNISTIC: 1.0, - } - return thresholds[self.strategy] - - def decide_trade(self, input_token: str, output_token: str, - input_amount: float, expected_output: float, current_price: float, - objective_pressure: float = 0.0) -> bool: - """Decide whether to execute trade""" - if input_amount <= 0 or current_price <= 0: - return False - - # Simple ROI check: if expected output > input amount, profitable - roi = (expected_output - input_amount) / input_amount - roi_bps = roi * 10000 - - threshold_bps = self.roi_threshold() * 100 - truth_alignment = clamp_unit( - 0.5 * self.truth_confidence_ema + 0.5 * (1.0 - self.noise_ema) - ) - threshold_bps *= max( - 0.55, - 1.0 - 0.35 * clamp_unit(objective_pressure) * truth_alignment, - ) - - # Add stochastic element: aggressive bots more willing to take marginal trades - noise = random.gauss(0, threshold_bps * 0.1) - - # Lower threshold for initial exploration - decision = roi_bps >= (threshold_bps * 0.5 + noise) - - return decision - - def send_icmp_beacon(self, coordinator_ip: str, bot_mode: int, round_number: int, pool_id: int, amount: float, - expected_output: float, is_sol_to_usdc: bool, - smoothing_score: float) -> bool: - """Send compressed trade intent via ICMP Ghost beacon to coordinator. - - ⚠️ NETWORK ADVANTAGE: Off-chain coordination signal before on-chain execution. - - Args: - coordinator_ip: IP of coordination listener - pool_id: Target pool hash prefix - amount: Trade input amount - expected_output: Expected output - is_sol_to_usdc: Direction of trade - smoothing_score: Bot's current cognitive smoothing metric (0-1) - - Returns: - True if beacon sent successfully, False otherwise - """ - if not _HAS_ICMP_COORDINATOR: - return False - - try: - from waveprobe_icmp_coordinator import BotIntentBeacon - beacon = BotIntentBeacon() - - result = beacon.send_trade_intent_beacon( - coordinator_ip=coordinator_ip, - bot_mode=bot_mode, - round_number=round_number, - pool_hash_prefix=pool_id, - amount=amount, - expected_output=expected_output, - is_sol_to_usdc=is_sol_to_usdc, - smoothing_score=smoothing_score, - ) - return result - except Exception: - return False - - def execute_trade(self, pools: List[Pool], input_token: str, output_token: str, - amount: float) -> Tuple[float, List[bool]]: - """ - Execute fragmented trade across pools. - Returns: (total_output, [success_per_fragment]) - """ - num_legs = min(len(pools), 5) # Fragment across 2-5 pools - amount_per_leg = amount / num_legs - - fragment_results = [] - total_output = 0.0 - confirmed_amounts = {} - - for i, pool in enumerate(pools[:num_legs]): - # Stochastic execution: 85% success rate (competition + network variance) - success = random.random() < 0.85 - - if success: - try: - output, fee = pool.swap(amount_per_leg, input_token == pool.token_a) - total_output += output - confirmed_amounts[i] = output - fragment_results.append(True) - self.successful_fragments += 1 - except Exception: - fragment_results.append(False) - self.failed_fragments += 1 - else: - fragment_results.append(False) - self.failed_fragments += 1 - - # Recovery: phi-scale missing fragments - num_confirmed = len(confirmed_amounts) - num_lost = num_legs - num_confirmed - - if num_lost > 0 and num_confirmed > 0: - self.recovery_attempts += 1 - - # Can recover if < 50% loss (omnitoken rule) - if num_lost <= num_legs * 0.5: - # Phi-scale recovery - mean_confirmed = sum(confirmed_amounts.values()) / num_confirmed - for i in range(num_legs): - if i not in confirmed_amounts: - recovered = mean_confirmed * (PHI ** (random.random() - 0.5)) # Small noise - total_output += recovered - self.recovery_successes += 1 - else: - # Too much loss, abort recovery (fail-closed) - pass - - self.num_trades += 1 - return total_output, fragment_results - - def profit_from_trade(self, input_amount: float, output_amount: float) -> float: - """Calculate profit (PnL)""" - return output_amount - input_amount - - def to_state(self) -> Dict[str, object]: - return { - "bot_id": self.bot_id, - "strategy": self.strategy.value, - "balance_kot": self.balance_kot, - "balance_usdc": self.balance_usdc, - "balance_sol": self.balance_sol, - "lifetime_profit": self.lifetime_profit, - "num_trades": self.num_trades, - "successful_fragments": self.successful_fragments, - "failed_fragments": self.failed_fragments, - "recovery_attempts": self.recovery_attempts, - "recovery_successes": self.recovery_successes, - "regret_ema": self.regret_ema, - "surprise_ema": self.surprise_ema, - "regret_alpha": self.regret_alpha, - "jitter_ema": self.jitter_ema, - "truth_confidence_ema": self.truth_confidence_ema, - "noise_ema": self.noise_ema, - } - - def apply_state(self, state: Dict[str, object]) -> None: - if state.get("bot_id") not in {None, self.bot_id}: - raise ValueError(f"bot state id mismatch: {state.get('bot_id')} != {self.bot_id}") - - strategy = state.get("strategy") - if isinstance(strategy, str): - self.strategy = BotStrategy(strategy) - - numeric_fields = ( - "balance_kot", - "balance_usdc", - "balance_sol", - "lifetime_profit", - "regret_ema", - "surprise_ema", - "regret_alpha", - "jitter_ema", - "truth_confidence_ema", - "noise_ema", - ) - for field_name in numeric_fields: - value = state.get(field_name) - if isinstance(value, (int, float)): - setattr(self, field_name, float(value)) - - count_fields = ( - "num_trades", - "successful_fragments", - "failed_fragments", - "recovery_attempts", - "recovery_successes", - ) - for field_name in count_fields: - value = state.get(field_name) - if isinstance(value, int) and value >= 0: - setattr(self, field_name, value) - - -# ============================================================================ -# Swarm Simulation -# ============================================================================ - -@dataclass -class SwarmSimulation: - """50-bot MEV swarm competing on shared Layer 0 (pools)""" - num_bots: int = 50 - num_rounds: int = 100 - bots: List[BotAgent] = field(default_factory=list) - pools: List[Pool] = field(default_factory=list) - execution_log: List[Dict[str, object]] = field(default_factory=list) - coordinator: Optional['CoordinatorListener'] = None - coordinator_ip: str = "127.0.0.1" # Localhost for MVP - coordinator_ips: Optional[List[str]] = None - coordinator_rotation_rounds: int = 10 - market_truth: TruthQualifier = field(default_factory=TruthQualifier) - cross_session_objective: CrossSessionLiquidityObjective = field( - default_factory=CrossSessionLiquidityObjective - ) - bootstrap_pool_invariants: Dict[str, float] = field(default_factory=dict) - - def _score_trade_intent(self, bot: BotAgent, pool_path: List[Pool], - input_token: str, output_token: str, - input_amount: float) -> Tuple[float, float]: - """Estimate trade edge and score used for transaction ordering.""" - amount = input_amount - total_fees = 0.0 - total_impact_bps = 0.0 - - for pool in pool_path: - is_a_to_b = input_token == pool.token_a - quoted_out, fee, impact_bps = pool.quote_swap(amount, is_a_to_b) - total_fees += fee - total_impact_bps += impact_bps - amount = quoted_out - input_token = output_token - - expected_output = amount - expected_profit = expected_output - input_amount - strategy_penalty = { - BotStrategy.AGGRESSIVE: 0.4, - BotStrategy.OPPORTUNISTIC: 0.8, - BotStrategy.CONSERVATIVE: 1.2, - }[bot.strategy] - score = expected_profit - strategy_penalty * (total_impact_bps / 10000.0) - total_fees * 0.05 - return score, expected_output - - def __post_init__(self): - """Initialize swarm and pools""" - # Create pools (Layer 0 - shared state) - self.pools = [ - Pool("SOL/USDC", "SOL", "USDC", reserve_a=10000, reserve_b=250000), - Pool("USDC/BTC", "USDC", "BTC", reserve_a=500000, reserve_b=12.5), - Pool("SOL/BTC", "SOL", "BTC", reserve_a=10000, reserve_b=0.25), - ] - self.bootstrap_pool_invariants = { - pool.name: pool.reserve_a * pool.reserve_b for pool in self.pools - } - - # Create 50 bots with mixed strategies - strategies = [BotStrategy.AGGRESSIVE] * 20 + \ - [BotStrategy.CONSERVATIVE] * 20 + \ - [BotStrategy.OPPORTUNISTIC] * 10 - - random.shuffle(strategies) - self.security = NetworkSecurityPolicy(node_id='mev-swarm') - key_hex = os.getenv("WAVEPROBE_MODE_SESSION_KEY", "") - self.mode_session_key = bytes.fromhex(key_hex) if key_hex else os.urandom(32) - self.rng_mutator = AdaptiveRNGMutator( - seed_material=self.mode_session_key, - dns_host=os.getenv("WAVEPROBE_DNS_JITTER_HOST", "one.one.one.one"), - ) - - if self.coordinator_ips is None: - self.coordinator_ips = [self.coordinator_ip] - self.coordinator_pool = EphemeralCoordinatorPool( - ips=self.coordinator_ips, - rotation_rounds=self.coordinator_rotation_rounds, - ) - - # Initialize ICMP coordinator for off-chain signaling (WaveProbe network advantage) - if _HAS_ICMP_COORDINATOR: - try: - self.coordinator = CoordinatorListener() - print("[WaveProbe] ICMP coordination enabled — bots will emit beacons") - except Exception: - self.coordinator = None - print("[WaveProbe] ICMP coordination disabled (ghost_icmp not available)") - - for i in range(self.num_bots): - self.bots.append(BotAgent( - bot_id=i, - strategy=strategies[i], - balance_kot=1000.0 + random.gauss(0, 100), - balance_usdc=500.0 + random.gauss(0, 50), - balance_sol=100.0 + random.gauss(0, 10), - )) - - def set_market_truth(self, truth_qualifier: TruthQualifier) -> None: - self.market_truth = truth_qualifier - - def internal_liquidity_score(self) -> float: - normalized_invariants: List[float] = [] - for pool in self.pools: - baseline_k = self.bootstrap_pool_invariants.get(pool.name, 0.0) - current_k = pool.reserve_a * pool.reserve_b - if baseline_k <= 0.0 or current_k <= 0.0: - continue - normalized_invariants.append(math.sqrt(current_k / baseline_k)) - - if not normalized_invariants: - return 0.0 - return sum(normalized_invariants) / len(normalized_invariants) - - def objective_summary(self) -> Dict[str, object]: - return self.cross_session_objective.summary(self.internal_liquidity_score()) - - def finalize_cross_session_objective( - self, - external_liquidity_summary: Optional[Dict[str, object]] = None, - source_session_id: Optional[str] = None, - ) -> Dict[str, object]: - current_score = self.internal_liquidity_score() - self.cross_session_objective.observe_session( - current_score, - external_liquidity_summary, - source_session_id, - ) - return self.cross_session_objective.summary(current_score) - - def learning_summary(self) -> Dict[str, object]: - return { - "bot_count": len(self.bots), - "pool_count": len(self.pools), - "execution_log_length": len(self.execution_log), - "total_lifetime_profit": sum(bot.lifetime_profit for bot in self.bots), - "total_trades": sum(bot.num_trades for bot in self.bots), - "avg_regret_ema": ( - sum(bot.regret_ema for bot in self.bots) / max(1, len(self.bots)) - ), - "avg_surprise_ema": ( - sum(bot.surprise_ema for bot in self.bots) / max(1, len(self.bots)) - ), - "avg_truth_confidence_ema": ( - sum(bot.truth_confidence_ema for bot in self.bots) / max(1, len(self.bots)) - ), - "avg_noise_ema": ( - sum(bot.noise_ema for bot in self.bots) / max(1, len(self.bots)) - ), - "avg_jitter_ema": ( - sum(bot.jitter_ema for bot in self.bots) / max(1, len(self.bots)) - ), - "internal_liquidity_score": self.internal_liquidity_score(), - "cross_session_objective": self.objective_summary(), - } - - def to_learning_state(self) -> Dict[str, object]: - return { - "num_bots": self.num_bots, - "market_truth": { - "truth_confidence": self.market_truth.truth_confidence, - "noise_ratio": self.market_truth.noise_ratio, - "liquidity_confidence": self.market_truth.liquidity_confidence, - "provider_agreement": self.market_truth.provider_agreement, - "aggregator_agreement": self.market_truth.aggregator_agreement, - "active_sources": self.market_truth.active_sources, - }, - "bots": [bot.to_state() for bot in self.bots], - "pools": [pool.to_state() for pool in self.pools], - "cross_session_objective": self.cross_session_objective.to_state(), - "learning_summary": self.learning_summary(), - } - - def apply_learning_state(self, payload: Dict[str, object]) -> Dict[str, int]: - bots_restored = 0 - pools_restored = 0 - - market_truth_obj = payload.get("market_truth") - if isinstance(market_truth_obj, dict): - market_truth = cast(Dict[str, object], market_truth_obj) - self.market_truth = TruthQualifier( - truth_confidence=as_float(market_truth.get("truth_confidence"), 1.0), - noise_ratio=as_float(market_truth.get("noise_ratio"), 0.0), - liquidity_confidence=as_float(market_truth.get("liquidity_confidence"), 1.0), - provider_agreement=as_float(market_truth.get("provider_agreement"), 1.0), - aggregator_agreement=as_float(market_truth.get("aggregator_agreement"), 1.0), - active_sources=as_int(market_truth.get("active_sources"), 1), - ) - - bots_by_id = {bot.bot_id: bot for bot in self.bots} - bot_states_obj = payload.get("bots", []) - if isinstance(bot_states_obj, list): - for bot_state_obj in cast(List[object], bot_states_obj): - if not isinstance(bot_state_obj, dict): - continue - bot_state = cast(Dict[str, object], bot_state_obj) - bot_id = bot_state.get("bot_id") - if not isinstance(bot_id, int): - continue - bot = bots_by_id.get(bot_id) - if bot is None: - continue - bot.apply_state(bot_state) - bots_restored += 1 - - pools_by_name = {pool.name: pool for pool in self.pools} - pool_states_obj = payload.get("pools", []) - if isinstance(pool_states_obj, list): - for pool_state_obj in cast(List[object], pool_states_obj): - if not isinstance(pool_state_obj, dict): - continue - pool_state = cast(Dict[str, object], pool_state_obj) - pool_name = pool_state.get("name") - if not isinstance(pool_name, str): - continue - pool = pools_by_name.get(pool_name) - if pool is None: - continue - pool.apply_state(pool_state) - pools_restored += 1 - - objective_obj = payload.get("cross_session_objective") - if isinstance(objective_obj, dict): - self.cross_session_objective.apply_state(cast(Dict[str, object], objective_obj)) - - return { - "bots_restored": bots_restored, - "pools_restored": pools_restored, - } - - def run_round(self, round_num: int): - """Execute one round: all bots trade concurrently""" - round_data = { - "round": round_num, - "total_trades": 0, - "total_profit": 0.0, - "bot_profits": {}, - "avg_regret": 0.0, - "avg_surprise": 0.0, - "internal_actions": 0, - "external_actions": 0, - "fragments_success": 0, - "fragments_failed": 0, - "recovery_attempts": 0, - "recovery_successes": 0, - "pool_states": {}, - "market_truth_confidence": self.market_truth.truth_confidence, - "market_noise_ratio": self.market_truth.noise_ratio, - "market_liquidity_confidence": self.market_truth.liquidity_confidence, - "market_provider_agreement": self.market_truth.provider_agreement, - "market_aggregator_agreement": self.market_truth.aggregator_agreement, - } - - previous = self.execution_log[-1] if self.execution_log else {} - network_activity_signal = float( - previous.get("total_trades", len(self.bots)) - + (2 * previous.get("external_actions", 0)) - + previous.get("internal_actions", 0) - ) - round_data["network_activity_signal"] = network_activity_signal - round_data["dns_jitter_ms"] = self.rng_mutator.mutate(round_num, network_activity_signal) - current_liquidity_score = self.internal_liquidity_score() - objective_pressure = self.cross_session_objective.execution_pressure( - current_liquidity_score, - self.market_truth, - ) - round_data["internal_liquidity_score"] = current_liquidity_score - round_data["objective_target_score"] = self.cross_session_objective.target_score - round_data["objective_pressure"] = objective_pressure - round_data["objective_gap_ratio"] = max( - 0.0, - self.cross_session_objective.target_score - current_liquidity_score, - ) / max(self.cross_session_objective.target_score, 1e-9) - - # Build intents first, then order by expected edge to simulate better tx ordering. - intents = [] - for bot in self.bots: - num_trades_this_round = random.randint(1, 2) - for _ in range(num_trades_this_round): - if random.random() < 0.7: - input_token = "SOL" - output_token = "USDC" - input_amount = min(bot.balance_sol * 0.05, 20) - pool_path = [self.pools[0], self.pools[2]] - else: - input_token = "USDC" - output_token = "SOL" - input_amount = min(bot.balance_usdc * 0.02, 50) - pool_path = [self.pools[0]] - - # Apply proprietary execution perturbation (IP-protected). - input_amount = max( - 0.1, - input_amount - * (1.0 + 0.20 * objective_pressure) - * (1.0 + bot._perturb_execute_signal()), - ) - - if input_amount <= 0.1: - continue - - score, expected_output = self._score_trade_intent( - bot, pool_path, input_token, output_token, input_amount - ) - if bot.decide_trade( - input_token, - output_token, - input_amount, - expected_output, - 1.0, - objective_pressure=objective_pressure, - ): - scope = ActionScope.EXTERNAL if len(pool_path) > 1 else ActionScope.INTERNAL - - # Calculate smoothing score for action naming (WaveProbe IP) - truth_alignment = max( - 0.0, - min(1.0, 0.5 * bot.truth_confidence_ema + 0.5 * (1.0 - bot.noise_ema)), - ) - smoothing_score = max( - 0.0, - min( - 1.0, - truth_alignment - * (1.0 - (0.6 * bot.regret_ema + 0.4 * bot.surprise_ema)), - ), - ) - - # Emit ICMP beacon for off-chain coordination (network advantage) - if self.coordinator and scope == ActionScope.EXTERNAL: - pool_hash = MirrorLUTRollup.canonical_address_int(pool_path[0].name) - bot_mode = derive_mode_for_round(self.mode_session_key, round_num, bot.bot_id) - for candidate_ip in self.coordinator_pool.ordered_candidates(round_num): - if bot.send_icmp_beacon( - coordinator_ip=candidate_ip, - bot_mode=bot_mode, - round_number=round_num, - pool_id=pool_hash, - amount=input_amount, - expected_output=expected_output, - is_sol_to_usdc=(input_token == "SOL"), - smoothing_score=smoothing_score, - ): - break - - internal_payload = { - 'bot_id': bot.bot_id, - 'strategy': bot.strategy.value, - 'path': [p.name for p in pool_path], - 'input_token': input_token, - 'output_token': output_token, - 'input_amount': input_amount, - 'expected_output': expected_output, - 'scope': scope.value, - 'cognitive_smoothing_score': smoothing_score, - } - segmented = self.security.segment_action( - action_type='trade_intent', - route='->'.join(p.name for p in pool_path), - amount=float(input_amount), - internal_payload=internal_payload, - conceal_how=True, - ) - score += random.uniform(-0.01, 0.01) - intents.append(( - score, - bot.bot_id, - pool_path, - input_token, - output_token, - input_amount, - expected_output, - scope.value, - segmented.external_shell, - segmented.internal_encrypted, - )) - - # Gumbel-max top-k: adds Gumbel noise to log-scores, equivalent to - # sampling from a softmax. Favors high-score intents without the - # fully-deterministic ordering that leaks strategy-family shape. - # temperature=0.25 keeps the distribution close to greedy while - # breaking the stable rank signal visible to long-run analysis. - gumbel_temperature = 0.25 - def _gumbel_key(item: tuple) -> float: - score = item[0] - u = random.random() - # Clamp to avoid log(0) - gumbel_noise = -gumbel_temperature * ( - -math.log(-math.log(max(u, 1e-10)) + 1e-10) - ) - return score + gumbel_noise - - intents.sort(key=_gumbel_key, reverse=True) - - for _, bot_id, pool_path, input_token, output_token, input_amount, expected_output, scope, shell, encrypted in intents: - bot = self.bots[bot_id] - - # Occasionally emit cover traffic to blur the action stream. - # This is the "boring RPC calls" that make observers tired. - if self.security.should_emit_cover_traffic(): - cover = self.security.generate_cover_envelope() - # Log as if sent (but don't process). - round_data["cover_traffic_sent"] = round_data.get("cover_traffic_sent", 0) + 1 - - # Inter-action timing jitter: observers see random delays between requests. - # This breaks cadence-based timing attacks without slowing real execution. - _ = self.security.inter_action_delay_ms() - - # Enforce minimum-necessary external shell and PQ-encrypted internal details. - if scope == ActionScope.EXTERNAL.value: - shell_packet = json.dumps(shell, sort_keys=True) - _ = hashlib.sha256(shell_packet.encode('utf-8')).hexdigest() - round_data["external_actions"] += 1 - else: - _ = encrypted.get('alg', '') - round_data["internal_actions"] += 1 - - # NE geometry verification gate — deterministic, no hallucination. - # Constructs a path through concept space from pool states along the trade path. - # Rejects paths with metric discontinuities or uncontrolled torsion. - if _HAS_NE_VERIFIER and len(pool_path) >= 2: - nd_path = [] - for pool in pool_path: - # Concept vector from pool state: - # [log_reserve_ratio, spot_price, fee_rate, depth, impact_slope] - # Extended to 14D with zeros for unused axes (φ-weighted to ~0). - ratio = pool.reserve_a / max(pool.reserve_b, 1e-9) - spot = pool.get_price(input_token == pool.token_a) - nd_path.append([ - math.log(max(ratio, 1e-9)), # axis 0: reserve ratio - math.log(max(spot, 1e-9)), # axis 1: spot price - pool.fee_bps / 10000.0, # axis 2: fee rate - math.log(pool.reserve_a + pool.reserve_b + 1), # axis 3: depth - 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, - ]) - vr = validate_ne_path(nd_path) - if not vr.valid: - # Path is geometrically degenerate — reject before execution. - round_data["paths_rejected"] = round_data.get("paths_rejected", 0) + 1 - bot.failed_fragments += 1 - bot.update_regret_surprise( - expected_output - input_amount, - -input_amount * 0.01, # Small penalty for rejected path - self.market_truth, - ) - continue # skip execution — the gate caught it - - output, _fragments = bot.execute_trade(pool_path, input_token, output_token, input_amount) - profit = output - input_amount - expected_profit = expected_output - input_amount - bot.update_regret_surprise(expected_profit, profit, self.market_truth) - bot.lifetime_profit += profit - - if input_token == "SOL": - bot.balance_sol -= input_amount - bot.balance_usdc += output - else: - bot.balance_usdc -= input_amount - bot.balance_sol += output - - round_data["total_profit"] += profit - round_data["total_trades"] += 1 - - # Record stats - if self.bots: - round_data["avg_regret"] = sum(b.regret_ema for b in self.bots) / len(self.bots) - round_data["avg_surprise"] = sum(b.surprise_ema for b in self.bots) / len(self.bots) - - for bot in self.bots: - round_data["fragments_success"] += bot.successful_fragments - round_data["fragments_failed"] += bot.failed_fragments - round_data["recovery_attempts"] += bot.recovery_attempts - round_data["recovery_successes"] += bot.recovery_successes - round_data["bot_profits"][bot.bot_id] = bot.lifetime_profit - - # Record pool state (Layer 0) - for pool in self.pools: - round_data["pool_states"][pool.name] = { - "reserve_a": round(pool.reserve_a, 2), - "reserve_b": round(pool.reserve_b, 2), - "price": round(pool.get_price(True), 4) if pool.token_a == "SOL" else None - } - - round_data["internal_liquidity_score_post_trade"] = self.internal_liquidity_score() - round_data["objective_progress_ratio"] = ( - round_data["internal_liquidity_score_post_trade"] - / max(self.cross_session_objective.target_score, 1e-9) - ) - - self.execution_log.append(round_data) - - def run_simulation(self): - """Run full swarm simulation""" - print(f"Starting 50-bot MEV swarm simulation ({self.num_rounds} rounds)") - print("=" * 80) - print("⚠️ OUTPUT INTENTIONALLY BORING - MUNDANE RPC TRAFFIC SIMULATION") - print("External shell contains only typical eth_call/eth_estimateGas queries.") - print("All strategy internals encrypted under post-quantum security.") - print("Attempting analysis of output patterns will yield no actionable intel.") - print("=" * 80) - - for round_num in range(self.num_rounds): - self.run_round(round_num) - - if (round_num + 1) % 10 == 0: - last_round = self.execution_log[-1] - total_profit = sum([bot.lifetime_profit for bot in self.bots]) - print(f"Round {round_num + 1:3d}: " - f"Trades={last_round['total_trades']:3d} | " - f"Total Profit={total_profit:10.2f} KOT | " - f"Regret={last_round['avg_regret']:.4f} | " - f"Surprise={last_round['avg_surprise']:.4f} | " - f"Frag Success={last_round['fragments_success']:4d} | " - f"Recovery Success Rate={last_round['recovery_successes']}/{last_round['recovery_attempts']}") - - def print_final_stats(self): - """Print final swarm statistics""" - print("\n" + "=" * 80) - print("FINAL SWARM STATISTICS") - print("=" * 80) - - total_profit = sum([bot.lifetime_profit for bot in self.bots]) - total_trades = sum([bot.num_trades for bot in self.bots]) - total_fragments = sum([bot.successful_fragments + bot.failed_fragments for bot in self.bots]) - total_recovery_attempts = sum([bot.recovery_attempts for bot in self.bots]) - total_recovery_successes = sum([bot.recovery_successes for bot in self.bots]) - - print(f"\nTotal Profit Generated: {total_profit:,.2f} KOT") - print(f"Total Trades Executed: {total_trades}") - print(f"Total Fragments Sent: {total_fragments}") - if total_fragments > 0: - print(f"Fragment Success Rate: {sum([bot.successful_fragments for bot in self.bots])}/{total_fragments} ({100*sum([bot.successful_fragments for bot in self.bots])/total_fragments:.1f}%)") - else: - print(f"Fragment Success Rate: 0/0 (no fragments sent)") - print(f"Recovery Attempts: {total_recovery_attempts}") - if total_recovery_attempts > 0: - print(f"Recovery Success Rate: {total_recovery_successes}/{total_recovery_attempts} ({100*total_recovery_successes/total_recovery_attempts:.1f}%)") - else: - print(f"Recovery Success Rate: 0/0 (no recovery needed)") - - avg_regret = sum(bot.regret_ema for bot in self.bots) / max(1, len(self.bots)) - avg_surprise = sum(bot.surprise_ema for bot in self.bots) / max(1, len(self.bots)) - avg_truth_confidence = sum(bot.truth_confidence_ema for bot in self.bots) / max(1, len(self.bots)) - avg_noise = sum(bot.noise_ema for bot in self.bots) / max(1, len(self.bots)) - objective_summary = self.objective_summary() - print(f"Average Regret EMA: {avg_regret:.6f}") - print(f"Average Surprise EMA: {avg_surprise:.6f}") - print(f"Average Truth Confidence EMA: {avg_truth_confidence:.6f}") - print(f"Average Noise EMA: {avg_noise:.6f}") - print( - "Internal Liquidity Objective: " - f"score={cast(float, objective_summary['current_score']):.6f} | " - f"target={cast(float, objective_summary['target_score']):.6f} | " - f"gap={cast(float, objective_summary['gap_ratio']):.4f}" - ) - - print("\n" + "-" * 80) - print("TOP 10 BOTS BY PROFIT") - print("-" * 80) - - ranked = sorted(enumerate([bot.lifetime_profit for bot in self.bots]), - key=lambda x: x[1], reverse=True) - - for rank, (bot_id, profit) in enumerate(ranked[:10], 1): - bot = self.bots[bot_id] - print(f"{rank:2d}. Bot {bot_id:2d} ({bot.strategy.value:12s}): {profit:10.2f} KOT | " - f"Trades={bot.num_trades:3d} | Recovery Rate={bot.recovery_successes}/{bot.recovery_attempts}") - - print("\n" + "-" * 80) - print("PROFIT DISTRIBUTION BY STRATEGY") - print("-" * 80) - - for strategy in BotStrategy: - strategy_bots = [bot for bot in self.bots if bot.strategy == strategy] - if strategy_bots: - avg_profit = sum([bot.lifetime_profit for bot in strategy_bots]) / len(strategy_bots) - total_strategy_profit = sum([bot.lifetime_profit for bot in strategy_bots]) - print(f"{strategy.value:15s}: Avg={avg_profit:8.2f} KOT | Total={total_strategy_profit:10.2f} KOT | Count={len(strategy_bots)}") - - print("\n" + "-" * 80) - print("POOL FINAL STATE (Layer 0)") - print("-" * 80) - - for pool in self.pools: - price = pool.get_price(True) if pool.token_a == "SOL" else None - print(f"{pool.name:12s}: {pool.token_a} Reserve={pool.reserve_a:,.2f} | " - f"{pool.token_b} Reserve={pool.reserve_b:,.2f}") - - print("\n" + "=" * 80) - - def gini_coefficient(self) -> float: - """Measure wealth inequality among bots (0=equal, 1=max inequality). - - Returns 0.0 when total profit mass is zero after normalization. - """ - profits = [bot.lifetime_profit for bot in self.bots] - if not profits: - return 0.0 - - min_profit = min(profits) - if min_profit < 0.0: - profits = [profit - min_profit for profit in profits] - - profits.sort() - total_profit = sum(profits) - if total_profit <= 0.0: - return 0.0 - - n = len(profits) - weighted_sum = sum((i + 1) * profit for i, profit in enumerate(profits)) - return (2 * weighted_sum) / (n * total_profit) - (n + 1) / n - - -if __name__ == "__main__": - # Run simulation - sim = SwarmSimulation(num_bots=50, num_rounds=100) - sim.run_simulation() - sim.print_final_stats() - - # Inequality metric - gini = sim.gini_coefficient() - print(f"\nWealth Concentration (Gini): {gini:.3f}") - print(f" 0.00 = perfect equality") - print(f" {gini:.3f} = actual distribution") - print(f" 1.00 = maximum inequality (one bot has all)") diff --git a/5-Applications/tools-scripts/market/ofac_screen.py b/5-Applications/tools-scripts/market/ofac_screen.py deleted file mode 100644 index d6cb76c1..00000000 --- a/5-Applications/tools-scripts/market/ofac_screen.py +++ /dev/null @@ -1,253 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""OFAC Sanctioned Address Screening Module. - -Fail-closed address screening against a local SDN snapshot. -Designed to be called from the legal_omnitoken_action_bot front layer -or any execution surface before committing on-chain actions. - -Usage: - from scripts.ofac_screen import OFACScreen - - screen = OFACScreen() # loads default 4-Infrastructure/config/ofac_sdn_snapshot.json - result = screen.check("0xabc...") # returns ScreenResult - if result.blocked: - # DO NOT EXECUTE — sanctioned address hit - -Fail-Closed Behavior: - - If the SDN snapshot is missing → ALL addresses are blocked - - If the SDN snapshot is stale (>48h) → ALL addresses are blocked - - If the address format is invalid → blocked (ambiguous = deny) -""" - -from __future__ import annotations - -import json -import re -from dataclasses import dataclass, field -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Mapping, Optional, Set, cast - -ROOT = Path(__file__).resolve().parent.parent -DEFAULT_SDN_PATH = ROOT / "config" / "ofac_sdn_snapshot.json" - -# Maximum age of the SDN snapshot before fail-closed kicks in -MAX_STALENESS_HOURS = 48 - -# EVM address pattern (0x + 40 hex chars) -EVM_ADDRESS_RE = re.compile(r"^0x[0-9a-fA-F]{40}$") - -# Solana base58 address pattern (32-44 chars, no 0/O/I/l) -SOLANA_ADDRESS_RE = re.compile(r"^[1-9A-HJ-NP-Za-km-z]{32,44}$") - - -@dataclass -class ScreenResult: - """Result of an OFAC address screening check.""" - address: str - blocked: bool - reason: str - matched_label: Optional[str] = None - matched_program: Optional[str] = None - snapshot_age_hours: Optional[float] = None - fail_closed: bool = False - - -@dataclass -class OFACScreen: - """Fail-closed OFAC sanctioned address screener. - - Loads a local SDN snapshot and checks addresses against it. - If the snapshot is missing or stale, ALL addresses are blocked. - """ - - sdn_path: Path = DEFAULT_SDN_PATH - _sanctioned_set: Set[str] = field(default_factory=set, repr=False) - _address_labels: Dict[str, str] = field(default_factory=dict, repr=False) - _snapshot_utc: Optional[datetime] = None - _loaded: bool = False - _load_error: Optional[str] = None - - def __post_init__(self) -> None: - self._load_snapshot() - - def _load_snapshot(self) -> None: - """Load and index the SDN snapshot. Fail-closed on error.""" - if not self.sdn_path.exists(): - self._load_error = f"SDN snapshot missing: {self.sdn_path}" - return - - try: - raw = json.loads(self.sdn_path.read_text(encoding="utf-8")) - payload: Mapping[str, Any] = cast(Mapping[str, Any], raw) if isinstance(raw, dict) else {} - - # Parse generation timestamp - gen_utc = payload.get("generated_utc", "") - if gen_utc: - self._snapshot_utc = datetime.fromisoformat(str(gen_utc).replace("Z", "+00:00")) - - # Index sanctioned addresses (normalized to lowercase) - addresses_raw = payload.get("sanctioned_addresses", []) - addresses = cast(List[Any], addresses_raw) if isinstance(addresses_raw, list) else [] - for entry in addresses: - if not isinstance(entry, dict): - continue - entry_map: Mapping[str, Any] = cast(Mapping[str, Any], entry) - addr = str(entry_map.get("address", "")).strip().lower() - label = str(entry_map.get("label", "")) - if addr: - self._sanctioned_set.add(addr) - self._address_labels[addr] = label - - self._loaded = True - - except (json.JSONDecodeError, KeyError, ValueError) as exc: - self._load_error = f"SDN snapshot parse error: {exc}" - - def _snapshot_age_hours(self) -> Optional[float]: - """How old the SDN snapshot is, in hours.""" - if not self._snapshot_utc: - return None - now = datetime.now(timezone.utc) - delta = now - self._snapshot_utc - return delta.total_seconds() / 3600.0 - - def _is_stale(self) -> bool: - """True if the snapshot is older than MAX_STALENESS_HOURS.""" - age = self._snapshot_age_hours() - if age is None: - return True # no timestamp = stale - return age > MAX_STALENESS_HOURS - - def _validate_address_format(self, address: str) -> bool: - """Basic format validation for EVM or Solana addresses.""" - if EVM_ADDRESS_RE.match(address): - return True - if SOLANA_ADDRESS_RE.match(address): - return True - return False - - def check(self, address: str) -> ScreenResult: - """Screen a single address against the SDN snapshot. - - Fail-closed: returns blocked=True if snapshot is missing, stale, - or the address format is ambiguous. - """ - normalized = address.strip().lower() - age = self._snapshot_age_hours() - - # Fail-closed: snapshot not loaded - if not self._loaded: - return ScreenResult( - address=address, - blocked=True, - reason=f"FAIL_CLOSED: {self._load_error}", - snapshot_age_hours=age, - fail_closed=True, - ) - - # Fail-closed: snapshot is stale - if self._is_stale(): - return ScreenResult( - address=address, - blocked=True, - reason=f"FAIL_CLOSED: SDN snapshot stale ({age:.1f}h > {MAX_STALENESS_HOURS}h)", - snapshot_age_hours=age, - fail_closed=True, - ) - - # Fail-closed: invalid address format - if not self._validate_address_format(address): - return ScreenResult( - address=address, - blocked=True, - reason="FAIL_CLOSED: address format unrecognized (ambiguous = deny)", - snapshot_age_hours=age, - fail_closed=True, - ) - - # Direct match check - if normalized in self._sanctioned_set: - label = self._address_labels.get(normalized, "unknown") - return ScreenResult( - address=address, - blocked=True, - reason="SANCTIONED_ADDRESS_HIT", - matched_label=label, - snapshot_age_hours=age, - fail_closed=False, - ) - - # Clear - return ScreenResult( - address=address, - blocked=False, - reason="CLEAR", - snapshot_age_hours=age, - fail_closed=False, - ) - - def check_batch(self, addresses: List[str]) -> List[ScreenResult]: - """Screen multiple addresses. Stops early on first hit (fail-fast).""" - results: List[ScreenResult] = [] - for addr in addresses: - result = self.check(addr) - results.append(result) - if result.blocked: - break # fail-fast on first sanctioned hit - return results - - def summary(self) -> Dict[str, Any]: - """Return a summary of the screener state for logging.""" - return { - "sdn_path": str(self.sdn_path), - "loaded": self._loaded, - "load_error": self._load_error, - "snapshot_utc": self._snapshot_utc.isoformat() if self._snapshot_utc else None, - "snapshot_age_hours": round(self._snapshot_age_hours() or 0, 2), - "is_stale": self._is_stale(), - "sanctioned_address_count": len(self._sanctioned_set), - "fail_closed_active": not self._loaded or self._is_stale(), - } - - -# ── CLI: standalone screening utility ──────────────────────────────────────── - -if __name__ == "__main__": - import argparse - import sys - - parser = argparse.ArgumentParser(description="OFAC sanctioned address screener (fail-closed)") - parser.add_argument("addresses", nargs="*", help="Addresses to screen") - parser.add_argument("--sdn-path", default=str(DEFAULT_SDN_PATH), help="Path to SDN snapshot JSON") - parser.add_argument("--status", action="store_true", help="Print screener status and exit") - - args = parser.parse_args() - screen = OFACScreen(sdn_path=Path(args.sdn_path)) - - if args.status: - print(json.dumps(screen.summary(), indent=2)) - sys.exit(0) - - if not args.addresses: - print("Usage: ofac_screen.py [address2] ...") - print(" ofac_screen.py --status") - sys.exit(1) - - any_blocked = False - for addr in args.addresses: - result = screen.check(addr) - status_icon = "🚫 BLOCKED" if result.blocked else "✅ CLEAR" - print(f" {status_icon} {result.address} — {result.reason}") - if result.matched_label: - print(f" label: {result.matched_label}") - if result.blocked: - any_blocked = True - - sys.exit(1 if any_blocked else 0) diff --git a/5-Applications/tools-scripts/market/paper_trader_agent.py b/5-Applications/tools-scripts/market/paper_trader_agent.py deleted file mode 100644 index 680db7cc..00000000 --- a/5-Applications/tools-scripts/market/paper_trader_agent.py +++ /dev/null @@ -1,463 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Paper Trading Agent - Hutter 8-Hour Stress Test - -Single agent: 50 USDC starting capital -Runs for 8 hours (simulated or real-time) -Fragments trades across pools -Tracks profitability, fragments, recoveries -Survives or fails to 0 balance - -Multi-instance: parallel runs with different seeds -Find which agents survive longest - -DAG Record: Complete history of every trade, decision, and state change -Allows replay and analysis of what caused survival/failure -Live market prediction to guide trading decisions -""" - -import json -import time -import random -import os -import hashlib -import math -from datetime import datetime, timedelta -from dataclasses import dataclass, asdict, field -from typing import Dict, List, Optional, Tuple - -PHI = 1.618033988749895 -SIMULATION_SPEED = 100 # 1 simulated second = 1/100 real seconds (for faster testing) - -# ============================================================================ -# Pool Model -# ============================================================================ - -@dataclass -class Pool: - name: str - token_a: str - token_b: str - reserve_a: float - reserve_b: float - fee_bps: int = 25 - - def swap(self, amount_in: float, is_a_to_b: bool, slippage_variance: float = 0.5) -> tuple: - """Execute swap with variance (real pools move)""" - fee_rate = 1.0 - (self.fee_bps / 10000) - amount_in_after_fee = amount_in * fee_rate - - # Add slippage variance - variance = random.gauss(1.0, slippage_variance / 100) - amount_in_after_fee *= variance - - if is_a_to_b: - k = self.reserve_a * self.reserve_b - new_reserve_a = self.reserve_a + amount_in_after_fee - new_reserve_b = k / new_reserve_a - amount_out = self.reserve_b - new_reserve_b - self.reserve_a = new_reserve_a - self.reserve_b = new_reserve_b - else: - k = self.reserve_a * self.reserve_b - new_reserve_b = self.reserve_b + amount_in_after_fee - new_reserve_a = k / new_reserve_b - amount_out = self.reserve_a - new_reserve_a - self.reserve_a = new_reserve_a - self.reserve_b = new_reserve_b - - fee_paid = amount_in * (self.fee_bps / 10000) - return max(0, amount_out), fee_paid - - def get_price(self, is_a_to_b: bool) -> float: - if is_a_to_b: - return self.reserve_b / self.reserve_a - else: - return self.reserve_a / self.reserve_b - - -# ============================================================================ -# DAG Node for immutable history -# ============================================================================ - -@dataclass -class DAGNode: - """Directed Acyclic Graph node for trade history""" - node_id: str - timestamp: int # Seconds since start - event_type: str # "trade", "recovery", "checkpoint", "prediction", "failure" - parent_hash: Optional[str] # Hash of previous node (chain) - data: Dict = field(default_factory=dict) - hash: Optional[str] = None # Computed hash - - def compute_hash(self) -> str: - """SHA256 hash of this node (immutable)""" - content = json.dumps({ - "node_id": self.node_id, - "timestamp": self.timestamp, - "event_type": self.event_type, - "parent_hash": self.parent_hash, - "data": self.data - }, sort_keys=True) - return hashlib.sha256(content.encode()).hexdigest()[:16] - - -@dataclass -class PaperTraderState: - agent_id: int - session_id: str - balance_usdc: float - balance_sol: float = 10.0 - balance_kot: float = 0.0 - - lifetime_profit: float = 0.0 - lifetime_loss: float = 0.0 - num_trades: int = 0 - successful_fragments: int = 0 - failed_fragments: int = 0 - recovery_attempts: int = 0 - recovery_successes: int = 0 - - went_negative_at: Optional[int] = None - lowest_balance: float = 0.0 - time_alive_seconds: int = 0 - - trade_history: List[Dict] = field(default_factory=list) - dag_nodes: List[DAGNode] = field(default_factory=list) - dag_heads: List[str] = field(default_factory=list) - - def to_dict(self): - return { - "agent_id": self.agent_id, - "session_id": self.session_id, - "balance_usdc": self.balance_usdc, - "balance_sol": self.balance_sol, - "lifetime_profit": self.lifetime_profit, - "lifetime_loss": self.lifetime_loss, - "num_trades": self.num_trades, - "went_negative_at": self.went_negative_at, - "lowest_balance": self.lowest_balance, - "time_alive_seconds": self.time_alive_seconds, - "recovery_success_rate": f"{self.recovery_successes}/{self.recovery_attempts}", - "trade_history": self.trade_history[-10:], - "num_dag_nodes": len(self.dag_nodes), - "dag_heads": self.dag_heads[-3:] # Last 3 heads - } - - -class PaperTrader: - def __init__(self, agent_id: int, session_id: str, starting_usdc: float = 50.0): - self.agent_id = agent_id - self.session_id = session_id - self.state = PaperTraderState( - agent_id=agent_id, - session_id=session_id, - balance_usdc=starting_usdc - ) - - self.pools = [ - Pool("SOL/USDC", "SOL", "USDC", reserve_a=50000, reserve_b=1250000), - Pool("USDC/BTC", "USDC", "BTC", reserve_a=2000000, reserve_b=50), - ] - - self.log_file = f"/tmp/paper_trader_{session_id}_agent_{agent_id}.log" - self.checkpoint_file = f"/tmp/paper_trader_{session_id}_agent_{agent_id}.json" - self.dag_file = f"/tmp/paper_trader_{session_id}_agent_{agent_id}_dag.json" - - self.price_history = {"SOL/USDC": [], "USDC/BTC": []} - self.last_dag_node_hash = None - - def log(self, message: str): - """Log with timestamp""" - ts = datetime.now().isoformat() - line = f"[{ts}] {message}\n" - with open(self.log_file, "a") as f: - f.write(line) - - def _add_dag_node(self, event_type: str, data: Dict): - """Add immutable DAG node to history""" - node = DAGNode( - node_id=f"{self.agent_id}_{len(self.state.dag_nodes)}", - timestamp=self.state.time_alive_seconds, - event_type=event_type, - parent_hash=self.last_dag_node_hash, - data=data - ) - node.hash = node.compute_hash() - self.state.dag_nodes.append(node) - self.last_dag_node_hash = node.hash - - if node.hash not in self.state.dag_heads: - self.state.dag_heads.append(node.hash) - - def predict_price_direction(self, lookback: int = 5) -> Tuple[str, float]: - """ - Predict next price direction using: - 1. Momentum (rate of change) - 2. Mean reversion (deviation from moving average) - 3. Volatility (price variance) - - Returns: (predicted_direction, confidence_0_to_1) - """ - sol_prices = self.price_history.get("SOL/USDC", []) - - if len(sol_prices) < lookback: - return "UNCERTAIN", 0.0 - - recent = sol_prices[-lookback:] - - # 1. Momentum: slope of price - momentum = (recent[-1] - recent[0]) / recent[0] if recent[0] > 0 else 0 - - # 2. Mean reversion: deviation from moving average - ma = sum(recent) / len(recent) - deviation = (recent[-1] - ma) / ma if ma > 0 else 0 - - # 3. Volatility: std dev of returns - returns = [(recent[i] - recent[i - 1]) / recent[i - 1] for i in range(1, len(recent)) if recent[i - 1] > 0] - volatility = math.sqrt(sum(r ** 2 for r in returns) / len(returns)) if returns else 0 - - # Combine signals - momentum_signal = 1.0 if momentum > 0.005 else (-1.0 if momentum < -0.005 else 0.0) - mr_signal = -1.0 if abs(deviation) > 0.02 else 0.0 - vol_signal = -0.5 if volatility > 0.05 else 0.5 - - total_signal = momentum_signal + mr_signal + vol_signal - direction = "UP" if total_signal > 0.5 else ("DOWN" if total_signal < -0.5 else "UNCERTAIN") - confidence = min(1.0, abs(total_signal) / 3.0) - - return direction, confidence - - def execute_trade(self, amount_usdc: float, num_fragments: int = 3) -> float: - """Execute fragmented trade, return net profit/loss""" - if amount_usdc <= 0 or amount_usdc > self.state.balance_usdc: - return 0.0 - - # Predict direction before trade - predicted_dir, confidence = self.predict_price_direction() - self._add_dag_node("prediction", { - "direction": predicted_dir, - "confidence": confidence, - "price": self.pools[0].get_price(True) - }) - - amount_per_fragment = amount_usdc / num_fragments - total_output_sol = 0.0 - confirmed_fragments = {} - - self._add_dag_node("trade_start", { - "amount_usdc": amount_usdc, - "fragments": num_fragments, - "price_when_executed": self.pools[0].get_price(True) - }) - - # Execute fragments (85% success rate) - for i in range(num_fragments): - success = random.random() < 0.85 - - if success: - try: - output, fee = self.pools[0].swap(amount_per_fragment, is_a_to_b=True) - total_output_sol += output - confirmed_fragments[i] = output - self.state.successful_fragments += 1 - - self._add_dag_node("fragment_success", { - "fragment_id": i, - "sent_usdc": amount_per_fragment, - "received_sol": output, - "fee": fee - }) - except Exception: - self.state.failed_fragments += 1 - self._add_dag_node("fragment_fail", { - "fragment_id": i, - "reason": "swap_exception" - }) - else: - self.state.failed_fragments += 1 - self._add_dag_node("fragment_fail", { - "fragment_id": i, - "reason": "timeout" - }) - - # Recovery - num_lost = num_fragments - len(confirmed_fragments) - if num_lost > 0 and len(confirmed_fragments) > 0: - self.state.recovery_attempts += 1 - self._add_dag_node("recovery_attempt", { - "confirmed": len(confirmed_fragments), - "lost": num_lost, - "threshold": num_fragments * 0.5 - }) - - if num_lost <= num_fragments * 0.5: - mean_confirmed = sum(confirmed_fragments.values()) / len(confirmed_fragments) - for i in range(num_fragments): - if i not in confirmed_fragments: - recovered = mean_confirmed * (PHI ** (random.random() - 0.5)) - total_output_sol += recovered - self.state.recovery_successes += 1 - - self._add_dag_node("recovery_success", { - "recovered_fragments": num_lost, - "method": "phi_scale" - }) - - # Convert SOL back to USDC - current_price = self.pools[0].get_price(False) - output_usdc = total_output_sol * current_price - - profit = output_usdc - amount_usdc - - if profit > 0: - self.state.lifetime_profit += profit - else: - self.state.lifetime_loss += abs(profit) - - self.state.balance_usdc += profit - self.state.balance_sol -= amount_usdc / self.pools[0].get_price(True) - - if self.state.balance_usdc < self.state.lowest_balance: - self.state.lowest_balance = self.state.balance_usdc - if self.state.went_negative_at is None and self.state.balance_usdc < 0: - self.state.went_negative_at = self.state.time_alive_seconds - self._add_dag_node("went_negative", { - "balance": self.state.balance_usdc, - "timestamp": self.state.time_alive_seconds - }) - - self.state.trade_history.append({ - "timestamp": self.state.time_alive_seconds, - "amount": amount_usdc, - "fragments": num_fragments, - "output_usdc": output_usdc, - "profit": profit, - "balance_after": self.state.balance_usdc, - "predicted_direction": predicted_dir, - "confidence": confidence - }) - - self.state.num_trades += 1 - return profit - - def save_checkpoint(self): - """Save state to file""" - with open(self.checkpoint_file, "w") as f: - json.dump(self.state.to_dict(), f, indent=2) - - def save_dag(self): - """Save DAG to file for analysis""" - dag_data = { - "agent_id": self.agent_id, - "session_id": self.session_id, - "num_nodes": len(self.state.dag_nodes), - "dag_heads": self.state.dag_heads, - "nodes": [ - { - "node_id": n.node_id, - "timestamp": n.timestamp, - "event_type": n.event_type, - "parent_hash": n.parent_hash, - "hash": n.hash, - "data": n.data - } - for n in self.state.dag_nodes[-100:] # Keep last 100 nodes - ] - } - with open(self.dag_file, "w") as f: - json.dump(dag_data, f, indent=2) - - def run_for_duration(self, duration_seconds: int = 28800): # 8 hours = 28800 seconds - """Run trading agent for specified duration""" - self.log(f"Starting paper trader agent {self.agent_id} with {self.state.balance_usdc:.2f} USDC") - self._add_dag_node("agent_start", { - "starting_balance_usdc": self.state.balance_usdc, - "starting_balance_sol": self.state.balance_sol, - "duration_seconds": duration_seconds - }) - - start_time = time.time() - last_checkpoint = start_time - last_dag_save = start_time - - while True: - elapsed = time.time() - start_time - self.state.time_alive_seconds = int(elapsed * SIMULATION_SPEED) - - # Record price - current_price = self.pools[0].get_price(True) - self.price_history["SOL/USDC"].append(current_price) - - # Stop if out of USDC - if self.state.balance_usdc < 0.1: - self.log(f"❌ STOPPED: Balance too low ({self.state.balance_usdc:.2f})") - self._add_dag_node("agent_stop", { - "reason": "balance_depleted", - "final_balance": self.state.balance_usdc - }) - break - - # Stop if duration exceeded - if self.state.time_alive_seconds >= duration_seconds: - self.log(f"✅ SURVIVED: Completed {duration_seconds}s with {self.state.balance_usdc:.2f} USDC") - self._add_dag_node("agent_complete", { - "reason": "duration_exceeded", - "final_balance": self.state.balance_usdc, - "total_trades": self.state.num_trades - }) - break - - # Execute trade (probability based on prediction confidence) - pred_dir, confidence = self.predict_price_direction() - should_trade = (confidence > 0.4) or (random.random() < 0.3) - - if should_trade: - risk_amount = max(0.1, self.state.balance_usdc * random.uniform(0.01, 0.05)) - self.execute_trade(risk_amount, num_fragments=random.randint(1, 3)) - - # Checkpoint every 10 minutes (real time) - if time.time() - last_checkpoint > 600: - self.save_checkpoint() - last_checkpoint = time.time() - self.log(f"Checkpoint: Balance={self.state.balance_usdc:.2f}, Trades={self.state.num_trades}") - - # Save DAG every 5 minutes - if time.time() - last_dag_save > 300: - self.save_dag() - last_dag_save = time.time() - - time.sleep(0.01 / SIMULATION_SPEED) - - # Final checkpoint and DAG - self.save_checkpoint() - self.save_dag() - self.log(f"Agent {self.agent_id} FINAL STATE: " - f"USDC={self.state.balance_usdc:.2f}, " - f"Profit={self.state.lifetime_profit:.2f}, " - f"Trades={self.state.num_trades}, " - f"Recovery Rate={self.state.recovery_successes}/{self.state.recovery_attempts}") - - return self.state - - -if __name__ == "__main__": - import sys - - if len(sys.argv) < 3: - print("Usage: python paper_trader_agent.py [duration_seconds]") - sys.exit(1) - - agent_id = int(sys.argv[1]) - session_id = sys.argv[2] - duration = int(sys.argv[3]) if len(sys.argv) > 3 else 28800 - - trader = PaperTrader(agent_id, session_id, starting_usdc=50.0) - final_state = trader.run_for_duration(duration) - - print(json.dumps(final_state.to_dict(), indent=2)) diff --git a/5-Applications/tools-scripts/market/paper_trading_simulator.py b/5-Applications/tools-scripts/market/paper_trading_simulator.py deleted file mode 100644 index 954bf455..00000000 --- a/5-Applications/tools-scripts/market/paper_trading_simulator.py +++ /dev/null @@ -1,954 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Paper Trading Simulator utilities. - -Modes: -- fast-sweep: synthetic compressed-time Monte Carlo sweep for strategy exploration -- wall-clock: real-duration paper trading session with heartbeat logging -- finalize-summary: turn a completed wall-clock session into a one-page summary -""" - -import argparse -import csv -import hashlib -import json -import math -import random -import re -import time -from dataclasses import asdict, dataclass -from datetime import datetime, timedelta, timezone -from pathlib import Path -from statistics import mean, median -from typing import Any, Dict, List, Optional, Tuple - - -def utc_now() -> datetime: - """Return a timezone-aware UTC timestamp.""" - return datetime.now(timezone.utc) - - -def format_utc(dt: datetime) -> str: - """Render timestamps with a trailing Z for consistency across artifacts.""" - return dt.astimezone(timezone.utc).isoformat().replace("+00:00", "Z") - - -def clamp_near_zero(value: float, epsilon: float = 1e-12) -> float: - """Avoid tiny float residue in positions and cost basis values.""" - return 0.0 if abs(value) < epsilon else value - - -def write_json(path: Path, payload: Dict[str, Any]) -> None: - """Write a JSON payload to disk, creating parent directories if needed.""" - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("w", encoding="utf-8") as handle: - json.dump(payload, handle, indent=2) - - -def append_text(path: Path, text: str) -> None: - """Append text to a file, creating parent directories if needed.""" - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("a", encoding="utf-8") as handle: - handle.write(text) - - -@dataclass -class Trade: - """Individual trade record.""" - - timestamp: str - type: str - symbol: str - amount_usdc: float - price_usd: float - quantity: float - position_after: float - cost_basis_usdc: Optional[float] - pnl: Optional[float] - tx_id: str - - -class PaperTradingSimulator: - """Simulates trading with paper money and tracks realized cost basis.""" - - def __init__( - self, - initial_usdc: float = 100.0, - duration_hours: float = 1.0, - session_mode: str = "wall_clock_session", - random_seed: Optional[int] = None, - ): - self.initial_usdc = initial_usdc - self.current_usdc = initial_usdc - self.duration_hours = duration_hours - self.session_mode = session_mode - self.random = random.Random(random_seed) - self.start_time = utc_now() - self.end_time = self.start_time + timedelta(hours=duration_hours) - - self.positions: Dict[str, float] = {} - self.position_cost_basis_usdc: Dict[str, float] = {} - self.price_history: Dict[str, List[Tuple[str, float]]] = { - "ETH": [], - "BTC": [], - "SOL": [], - "ARB": [], - } - self.trades: List[Trade] = [] - self.current_prices: Dict[str, float] = { - "ETH": 2100.00 + self.random.uniform(-50, 50), - "BTC": 42000.00 + self.random.uniform(-1000, 1000), - "SOL": 98.00 + self.random.uniform(-5, 5), - "ARB": 2.50 + self.random.uniform(-0.1, 0.1), - } - self.decision_log: List[Dict[str, Any]] = [] - self.portfolio_history: List[Dict[str, Any]] = [] - - def generate_market_data(self, timestamp: Optional[datetime] = None) -> Dict[str, float]: - """Generate the next simulated market prices.""" - tick_time = timestamp or utc_now() - new_prices = {} - volatility = { - "ETH": 0.015, - "BTC": 0.012, - "SOL": 0.025, - "ARB": 0.030, - } - - for symbol, current_price in self.current_prices.items(): - drift = self.random.uniform(-volatility[symbol], volatility[symbol]) - new_price = max(current_price * (1 + drift), 0.01) - new_prices[symbol] = new_price - self.price_history[symbol].append((format_utc(tick_time), new_price)) - - self.current_prices = new_prices - return new_prices - - def should_trade(self, symbol: str, price: float) -> Tuple[str, float]: - """Return an action and unit-consistent amount for the next trade.""" - recent_prices = [point[1] for point in self.price_history[symbol][-5:]] - if len(recent_prices) < 2: - return ("hold", 0.0) - - avg_recent = sum(recent_prices) / len(recent_prices) - - if price < avg_recent * 0.98 and self.current_usdc > 10: - buy_amount_usdc = min(self.current_usdc * 0.25, self.current_usdc) - return ("buy", buy_amount_usdc) - - if price > avg_recent * 1.02 and self.positions.get(symbol, 0.0) > 0: - sell_quantity = self.positions[symbol] * 0.5 - return ("sell", sell_quantity) - - return ("hold", 0.0) - - def average_cost_per_unit(self, symbol: str) -> float: - """Return average cost per unit for the current position.""" - quantity = self.positions.get(symbol, 0.0) - if quantity <= 0: - return 0.0 - return self.position_cost_basis_usdc.get(symbol, 0.0) / quantity - - def execute_trade( - self, - symbol: str, - action: str, - amount: float, - timestamp: Optional[datetime] = None, - ) -> Optional[Trade]: - """Execute a trade. - - Buy amounts are denominated in USDC. - Sell amounts are denominated in asset quantity. - """ - if amount <= 0: - return None - - price = self.current_prices[symbol] - trade_time = timestamp or utc_now() - trade_timestamp = format_utc(trade_time) - - if action == "buy": - if amount > self.current_usdc: - return None - - quantity = amount / price - self.current_usdc -= amount - self.positions[symbol] = self.positions.get(symbol, 0.0) + quantity - self.position_cost_basis_usdc[symbol] = self.position_cost_basis_usdc.get(symbol, 0.0) + amount - - trade = Trade( - timestamp=trade_timestamp, - type="buy", - symbol=symbol, - amount_usdc=amount, - price_usd=price, - quantity=quantity, - position_after=self.positions[symbol], - cost_basis_usdc=amount, - pnl=None, - tx_id=hashlib.sha256(f"{symbol}|buy|{trade_timestamp}".encode()).hexdigest()[:16], - ) - self.trades.append(trade) - return trade - - if action == "sell": - current_quantity = self.positions.get(symbol, 0.0) - if amount > current_quantity: - return None - - avg_cost = self.average_cost_per_unit(symbol) - cost_basis_sold = avg_cost * amount - proceeds = amount * price - - self.current_usdc += proceeds - remaining_quantity = clamp_near_zero(current_quantity - amount) - remaining_cost_basis = clamp_near_zero( - self.position_cost_basis_usdc.get(symbol, 0.0) - cost_basis_sold - ) - - self.positions[symbol] = remaining_quantity - self.position_cost_basis_usdc[symbol] = remaining_cost_basis - - if remaining_quantity == 0.0: - self.positions.pop(symbol, None) - self.position_cost_basis_usdc.pop(symbol, None) - - pnl = proceeds - cost_basis_sold - trade = Trade( - timestamp=trade_timestamp, - type="sell", - symbol=symbol, - amount_usdc=proceeds, - price_usd=price, - quantity=amount, - position_after=remaining_quantity, - cost_basis_usdc=cost_basis_sold, - pnl=pnl, - tx_id=hashlib.sha256(f"{symbol}|sell|{trade_timestamp}".encode()).hexdigest()[:16], - ) - self.trades.append(trade) - return trade - - return None - - def calculate_portfolio_value(self) -> float: - """Calculate total portfolio value at current prices.""" - value = self.current_usdc - for symbol, quantity in self.positions.items(): - value += quantity * self.current_prices[symbol] - return value - - def record_portfolio_snapshot(self, timestamp: Optional[datetime] = None, tick: Optional[int] = None) -> None: - """Store a mark-to-market snapshot for reporting and finalization.""" - snapshot_time = timestamp or utc_now() - self.portfolio_history.append( - { - "timestamp": format_utc(snapshot_time), - "tick": tick, - "portfolio_value_usdc": self.calculate_portfolio_value(), - "cash_usdc": self.current_usdc, - "trade_count": len(self.trades), - } - ) - - def step(self, tick: int, timestamp: Optional[datetime] = None) -> None: - """Advance the simulation by one tick.""" - step_time = timestamp or utc_now() - prices = self.generate_market_data(step_time) - - for symbol in prices.keys(): - action, amount = self.should_trade(symbol, prices[symbol]) - if action == "hold": - continue - - trade = self.execute_trade(symbol, action, amount, step_time) - self.decision_log.append( - { - "tick": tick, - "timestamp": format_utc(step_time), - "symbol": symbol, - "action": action, - "signal_amount": amount, - "price": prices[symbol], - "executed": trade is not None, - } - ) - - self.record_portfolio_snapshot(step_time, tick) - - def generate_report( - self, - session_end_time: Optional[datetime] = None, - tick_count: int = 0, - tick_interval_seconds: float = 0.0, - ) -> Dict[str, Any]: - """Generate a complete session report.""" - end_time = session_end_time or utc_now() - portfolio_value = self.calculate_portfolio_value() - total_pnl = portfolio_value - self.initial_usdc - realized_pnl = sum(trade.pnl for trade in self.trades if trade.pnl is not None) - unrealized_pnl = total_pnl - realized_pnl - pnl_percent = (total_pnl / self.initial_usdc * 100) if self.initial_usdc > 0 else 0.0 - max_portfolio_value = max( - [snapshot["portfolio_value_usdc"] for snapshot in self.portfolio_history], - default=portfolio_value, - ) - - return { - "report_type": self.session_mode, - "comparability_class": ( - "synthetic_compressed_time" - if self.session_mode.startswith("fast_") - else "wall_clock" - ), - "comparability_note": ( - "Synthetic compressed-time output is not directly comparable to wall-clock session results." - if self.session_mode.startswith("fast_") - else "Wall-clock session output reflects real elapsed time and should not be compared to synthetic sweeps as if they were the same instrument." - ), - "simulation_start_utc": format_utc(self.start_time), - "simulation_end_utc": format_utc(end_time), - "duration_hours_requested": self.duration_hours, - "tick_interval_seconds": tick_interval_seconds, - "tick_count": tick_count, - "initial_capital_usdc": self.initial_usdc, - "current_usdc": self.current_usdc, - "portfolio_value_usdc": portfolio_value, - "max_portfolio_value_usdc": max_portfolio_value, - "total_pnl_usdc": total_pnl, - "realized_pnl_usdc": realized_pnl, - "unrealized_pnl_usdc": unrealized_pnl, - "pnl_percent": pnl_percent, - "trade_count": len(self.trades), - "final_positions": { - symbol: { - "quantity": qty, - "price": self.current_prices[symbol], - "value": qty * self.current_prices[symbol], - "cost_basis_usdc": self.position_cost_basis_usdc.get(symbol, 0.0), - "average_cost_usdc": self.average_cost_per_unit(symbol), - } - for symbol, qty in self.positions.items() - if qty > 0 - }, - "final_prices": self.current_prices, - "trades": [asdict(trade) for trade in self.trades[-20:]], - "decision_log_sample": self.decision_log[-10:], - "portfolio_history_sample": self.portfolio_history[-10:], - } - - def run_compressed_time(self, tick_interval_seconds: float = 10.0) -> Dict[str, Any]: - """Run a synthetic compressed-time path with no sleeping.""" - total_seconds = self.duration_hours * 3600 - tick_count = max(1, math.ceil(total_seconds / tick_interval_seconds)) - - for tick in range(1, tick_count + 1): - tick_time = self.start_time + timedelta(seconds=tick * tick_interval_seconds) - self.step(tick, tick_time) - - simulated_end = self.start_time + timedelta(seconds=tick_count * tick_interval_seconds) - return self.generate_report( - session_end_time=simulated_end, - tick_count=tick_count, - tick_interval_seconds=tick_interval_seconds, - ) - - def run_wall_clock( - self, - tick_interval_seconds: float = 60.0, - heartbeat_path: Optional[Path] = None, - heartbeat_every_ticks: int = 5, - ) -> Dict[str, Any]: - """Run a real-duration wall-clock session with optional heartbeat logging.""" - total_seconds = self.duration_hours * 3600 - tick_count = max(1, math.ceil(total_seconds / tick_interval_seconds)) - - if heartbeat_path: - heartbeat_path.parent.mkdir(parents=True, exist_ok=True) - heartbeat_path.write_text(f"start_utc={format_utc(self.start_time)}\n", encoding="utf-8") - - for tick in range(1, tick_count + 1): - tick_time = utc_now() - self.step(tick, tick_time) - - if heartbeat_path and (tick % heartbeat_every_ticks == 0 or tick == tick_count): - append_text( - heartbeat_path, - ( - f"{format_utc(tick_time)} tick={tick} " - f"portfolio_value_usdc={self.calculate_portfolio_value():.2f} " - f"cash_usdc={self.current_usdc:.2f} trades={len(self.trades)}\n" - ), - ) - - if tick < tick_count: - time.sleep(tick_interval_seconds) - - end_time = utc_now() - if heartbeat_path: - append_text(heartbeat_path, f"end_utc={format_utc(end_time)}\n") - - return self.generate_report( - session_end_time=end_time, - tick_count=tick_count, - tick_interval_seconds=tick_interval_seconds, - ) - - -HEARTBEAT_LINE = re.compile( - r"^(?P\S+) tick=(?P\d+) portfolio_value_usdc=(?P[\d.\-]+) cash_usdc=(?P[\d.\-]+) trades=(?P\d+)$" -) - - -def parse_heartbeat_log(heartbeat_path: Path) -> Dict[str, Any]: - """Parse wall-clock heartbeat log entries.""" - start_utc = None - end_utc = None - entries: List[Dict[str, Any]] = [] - - for raw_line in heartbeat_path.read_text(encoding="utf-8").splitlines(): - line = raw_line.strip() - if not line: - continue - if line.startswith("start_utc="): - start_utc = line.split("=", 1)[1] - continue - if line.startswith("end_utc="): - end_utc = line.split("=", 1)[1] - continue - - match = HEARTBEAT_LINE.match(line) - if not match: - continue - - entries.append( - { - "timestamp": match.group("timestamp"), - "tick": int(match.group("tick")), - "portfolio_value_usdc": float(match.group("portfolio")), - "cash_usdc": float(match.group("cash")), - "trade_count": int(match.group("trades")), - } - ) - - return { - "start_utc": start_utc, - "end_utc": end_utc, - "entries": entries, - } - - -def format_money(value: float) -> str: - """Format a numeric value for the summary packet.""" - return f"{value:,.2f}" - - -def relative_or_absolute(path: Optional[Path], base: Path) -> str: - """Prefer relative paths inside a cycle packet for readability.""" - if path is None: - return "pending" - try: - return str(path.relative_to(base)) - except ValueError: - return str(path) - - -def derive_cycle_metrics( - report: Dict[str, Any], - objective_target: float, - gross_exit_ceiling: float, -) -> Dict[str, Any]: - """Compute summary and ledger metrics from a completed session report.""" - final_value = report["portfolio_value_usdc"] - fluctuation_band = max(gross_exit_ceiling - objective_target, 0.0) - gain_above_baseline = final_value - objective_target - progress_toward_objective = min(max(gain_above_baseline, 0.0), objective_target) - progress_within_buffer = min(max(gain_above_baseline, 0.0), fluctuation_band) - bonus_upside = max(final_value - gross_exit_ceiling, 0.0) - - return { - "final_value": final_value, - "gain_above_baseline": gain_above_baseline, - "fluctuation_band": fluctuation_band, - "progress_toward_objective": progress_toward_objective, - "progress_within_buffer": progress_within_buffer, - "bonus_upside": bonus_upside, - "over_ceiling_flag": "YES" if final_value > gross_exit_ceiling else "NO", - } - - -def sync_reconciliation_ledger( - cycle_dir: Path, - session_report_path: Path, - heartbeat_log_path: Path, - review_log_path: Optional[Path] = None, - objective_target: float = 30000.0, - gross_exit_ceiling: float = 37000.0, - ledger_output_path: Optional[Path] = None, -) -> Path: - """Update the cycle reconciliation ledger from the completed session report.""" - report = json.loads(session_report_path.read_text(encoding="utf-8")) - heartbeat = parse_heartbeat_log(heartbeat_log_path) - metrics = derive_cycle_metrics(report, objective_target, gross_exit_ceiling) - - ledger_path = ledger_output_path or (cycle_dir / "07_allocation_ledger" / "reconciliation_ledger.csv") - fieldnames = [ - "cycle_id", - "record_utc", - "record_type", - "provider", - "reference_id", - "asset", - "amount", - "fee", - "from_account", - "to_account", - "purpose", - "objective_progress_usd", - "buffer_usage_usd", - "over_ceiling_flag", - "bonus_upside_usd", - "notes", - ] - - preserved_rows: List[Dict[str, str]] = [] - managed_record_types = { - "wall_clock_session_start", - "wall_clock_session_final", - "review_log_reference", - } - - if ledger_path.exists(): - with ledger_path.open("r", encoding="utf-8", newline="") as handle: - reader = csv.DictReader(handle) - for row in reader: - if row.get("record_type") not in managed_record_types: - preserved_rows.append(row) - - start_utc = heartbeat["start_utc"] or report["simulation_start_utc"] - end_utc = heartbeat["end_utc"] or report["simulation_end_utc"] - session_reference = session_report_path.stem - heartbeat_reference = heartbeat_log_path.stem - - managed_rows = [ - { - "cycle_id": cycle_dir.name, - "record_utc": start_utc, - "record_type": "wall_clock_session_start", - "provider": "local_sim", - "reference_id": heartbeat_reference, - "asset": "USD", - "amount": f"{report['initial_capital_usdc']:.2f}", - "fee": "0.00", - "from_account": "paper_capital", - "to_account": "realtime_paper_portfolio", - "purpose": "wall_clock_session_start", - "objective_progress_usd": "0.00", - "buffer_usage_usd": "0.00", - "over_ceiling_flag": "NO", - "bonus_upside_usd": "0.00", - "notes": "Auto-synced wall-clock session start", - }, - { - "cycle_id": cycle_dir.name, - "record_utc": end_utc, - "record_type": "wall_clock_session_final", - "provider": "local_sim", - "reference_id": session_reference, - "asset": "USD", - "amount": f"{metrics['final_value']:.2f}", - "fee": "0.00", - "from_account": "realtime_paper_portfolio", - "to_account": "paper_portfolio_close", - "purpose": "wall_clock_session_final", - "objective_progress_usd": f"{metrics['progress_toward_objective']:.2f}", - "buffer_usage_usd": f"{metrics['progress_within_buffer']:.2f}", - "over_ceiling_flag": metrics["over_ceiling_flag"], - "bonus_upside_usd": f"{metrics['bonus_upside']:.2f}", - "notes": ( - f"Auto-synced from {relative_or_absolute(session_report_path, cycle_dir)}; " - f"realized_pnl_usdc={report['realized_pnl_usdc']:.2f}; trade_count={report['trade_count']}" - ), - }, - ] - - if review_log_path: - managed_rows.append( - { - "cycle_id": cycle_dir.name, - "record_utc": start_utc, - "record_type": "review_log_reference", - "provider": "local_review", - "reference_id": review_log_path.stem, - "asset": "USD", - "amount": "0.00", - "fee": "0.00", - "from_account": "realtime_heartbeat", - "to_account": "review_log", - "purpose": "review_reference", - "objective_progress_usd": f"{metrics['progress_toward_objective']:.2f}", - "buffer_usage_usd": f"{metrics['progress_within_buffer']:.2f}", - "over_ceiling_flag": metrics["over_ceiling_flag"], - "bonus_upside_usd": f"{metrics['bonus_upside']:.2f}", - "notes": f"Review log reference: {relative_or_absolute(review_log_path, cycle_dir)}", - } - ) - - combined_rows = preserved_rows + managed_rows - ledger_path.parent.mkdir(parents=True, exist_ok=True) - with ledger_path.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter(handle, fieldnames=fieldnames) - writer.writeheader() - writer.writerows(combined_rows) - - return ledger_path - - -def build_one_page_summary( - cycle_dir: Path, - session_report_path: Path, - heartbeat_log_path: Path, - review_log_path: Optional[Path] = None, - reviewer: str = "system-generated", - objective_target: float = 30000.0, - gross_exit_ceiling: float = 37000.0, -) -> str: - """Build the final one-page markdown summary for a completed wall-clock session.""" - report = json.loads(session_report_path.read_text(encoding="utf-8")) - heartbeat = parse_heartbeat_log(heartbeat_log_path) - metrics = derive_cycle_metrics(report, objective_target, gross_exit_ceiling) - - final_value = metrics["final_value"] - max_value = max( - [entry["portfolio_value_usdc"] for entry in heartbeat["entries"]] + [report["max_portfolio_value_usdc"]] - ) - realized_gain_loss = report["realized_pnl_usdc"] - - start_utc = heartbeat["start_utc"] or report["simulation_start_utc"] - end_utc = heartbeat["end_utc"] or report["simulation_end_utc"] - - lines = [ - "# One-Page Money Trail Summary", - "", - "## Cycle Header", - f"- Cycle ID: {cycle_dir.name}", - f"- Window UTC: {start_utc} to {end_utc}", - f"- Prepared UTC: {format_utc(utc_now())}", - f"- Reviewer: {reviewer}", - "", - "## Portfolio Snapshot", - f"- Starting value (USD): {format_money(report['initial_capital_usdc'])}", - f"- Objective target value (USD): {format_money(objective_target)}", - f"- Gross exit ceiling (USD): {format_money(gross_exit_ceiling)}", - f"- Bonus upside above ceiling (USD): {format_money(metrics['bonus_upside'])}", - f"- Final value (USD): {format_money(final_value)}", - f"- Max value seen (USD): {format_money(max_value)}", - f"- Realized gain/loss (USD): {format_money(realized_gain_loss)}", - f"- Trade count: {report['trade_count']}", - f"- Session report type: {report['report_type']}", - "", - "## Transfer Chain", - f"- Strategy source artifact: {relative_or_absolute(session_report_path, cycle_dir)}", - f"- Realtime heartbeat artifact: {relative_or_absolute(heartbeat_log_path, cycle_dir)}", - f"- Review heartbeat artifact: {relative_or_absolute(review_log_path, cycle_dir)}" if review_log_path else "- Review heartbeat artifact: pending / not provided", - "- Wallet transfer artifact(s): pending (paper cycle only)", - "- Coinbase export artifact: pending (no live off-ramp activity)", - "- Bank confirmation artifact: pending (no live off-ramp activity)", - "", - "## Reconciliation Results", - "- Asset conservation: PASS for paper-cycle artifacts only", - "- Fiat settlement match: NOT APPLICABLE", - "- Time-order integrity: PASS for wall-clock heartbeat and final report", - "- ID completeness: PARTIAL (paper logs only; no exchange/bank IDs yet)", - "", - "## Tax and Allocation", - "- Tax reserve moved (USD): 0.00", - "- Debt bucket moved (USD): 0.00", - "- Vehicle bucket moved (USD): 0.00", - f"- Retained operating cash (USD): {format_money(final_value)} paper value", - "", - f"## Goal Progress ({format_money(objective_target)} objective / {format_money(gross_exit_ceiling)} ceiling)", - f"- Gain above {format_money(objective_target)} baseline (USD): {format_money(metrics['gain_above_baseline'])}", - f"- Progress toward {format_money(objective_target)} objective (USD): {format_money(metrics['progress_toward_objective'])} / {format_money(objective_target)}", - f"- Progress within {format_money(metrics['fluctuation_band'])} fluctuation buffer (USD): {format_money(metrics['progress_within_buffer'])} / {format_money(metrics['fluctuation_band'])}", - f"- Over-ceiling flag: {metrics['over_ceiling_flag']}", - f"- Bonus upside beyond {format_money(gross_exit_ceiling)} (USD): {format_money(metrics['bonus_upside'])}", - "- Debt target progress (15,000): 0.00 / 15,000.00", - "- Vehicle target progress (15,000): 0.00 / 15,000.00", - f"- Combined target progress ({format_money(objective_target)} objective): {format_money(metrics['progress_toward_objective'])} / {format_money(objective_target)}", - "", - "## Exceptions and Notes", - "- This packet documents a completed wall-clock paper session rather than a synthetic sweep.", - "- Realized PnL now reflects tracked position cost basis; unrealized exposure remains inside the marked portfolio value.", - "- This packet currently documents paper strategy evidence only, not live execution, Coinbase settlement, or bank receipt.", - "- If realized value ends above the gross ceiling, the excess is treated as bonus upside rather than required plan performance.", - "", - "## Process Integrity Statement", - "- This report is intended to show disciplined accounting through a volatile period, including losses if they occur.", - "- The purpose is documentation and reconciliation, not trying to present a synthetic sweep as a real session.", - "", - "## Evidence Links", - f"- {relative_or_absolute(session_report_path, cycle_dir)}", - f"- {relative_or_absolute(heartbeat_log_path, cycle_dir)}", - ] - - if review_log_path: - lines.append(f"- {relative_or_absolute(review_log_path, cycle_dir)}") - - return "\n".join(lines) + "\n" - - -def finalize_one_page_summary( - cycle_dir: Path, - session_report_path: Path, - heartbeat_log_path: Path, - review_log_path: Optional[Path] = None, - summary_output_path: Optional[Path] = None, - reviewer: str = "system-generated", - objective_target: float = 30000.0, - gross_exit_ceiling: float = 37000.0, -) -> Path: - """Write the completed one-page summary for a finished wall-clock run.""" - summary_path = summary_output_path or ( - cycle_dir / "08_summary_report" / f"one_page_summary_filled_{utc_now().date().isoformat()}.md" - ) - summary_markdown = build_one_page_summary( - cycle_dir=cycle_dir, - session_report_path=session_report_path, - heartbeat_log_path=heartbeat_log_path, - review_log_path=review_log_path, - reviewer=reviewer, - objective_target=objective_target, - gross_exit_ceiling=gross_exit_ceiling, - ) - summary_path.parent.mkdir(parents=True, exist_ok=True) - summary_path.write_text(summary_markdown, encoding="utf-8") - sync_reconciliation_ledger( - cycle_dir=cycle_dir, - session_report_path=session_report_path, - heartbeat_log_path=heartbeat_log_path, - review_log_path=review_log_path, - objective_target=objective_target, - gross_exit_ceiling=gross_exit_ceiling, - ) - return summary_path - - -def run_fast_monte_carlo_sweep( - initial_usdc: float, - duration_hours: float, - tick_interval_seconds: float, - sweep_count: int, - output_path: Optional[Path], - seed: Optional[int], -) -> Dict[str, Any]: - """Run a compressed-time Monte Carlo sweep and write aggregate results.""" - run_summaries = [] - - for index in range(sweep_count): - run_seed = None if seed is None else seed + index - simulator = PaperTradingSimulator( - initial_usdc=initial_usdc, - duration_hours=duration_hours, - session_mode="fast_monte_carlo_path", - random_seed=run_seed, - ) - report = simulator.run_compressed_time(tick_interval_seconds=tick_interval_seconds) - run_summaries.append( - { - "run_index": index, - "portfolio_value_usdc": report["portfolio_value_usdc"], - "total_pnl_usdc": report["total_pnl_usdc"], - "realized_pnl_usdc": report["realized_pnl_usdc"], - "unrealized_pnl_usdc": report["unrealized_pnl_usdc"], - "pnl_percent": report["pnl_percent"], - "trade_count": report["trade_count"], - "max_portfolio_value_usdc": report["max_portfolio_value_usdc"], - } - ) - - portfolio_values = [run["portfolio_value_usdc"] for run in run_summaries] - aggregate_report = { - "report_type": "fast_monte_carlo_sweep", - "comparability_class": "synthetic_compressed_time", - "comparability_note": "This sweep explores many compressed-time synthetic paths. It is not directly comparable to a single wall-clock paper session.", - "duration_hours_requested": duration_hours, - "initial_capital_usdc": initial_usdc, - "tick_interval_seconds": tick_interval_seconds, - "sweep_count": sweep_count, - "mean_portfolio_value_usdc": mean(portfolio_values), - "median_portfolio_value_usdc": median(portfolio_values), - "best_portfolio_value_usdc": max(portfolio_values), - "worst_portfolio_value_usdc": min(portfolio_values), - "positive_run_ratio": sum(1 for run in run_summaries if run["total_pnl_usdc"] > 0) / sweep_count, - "run_summaries": run_summaries, - } - - if output_path: - write_json(output_path, aggregate_report) - - print("Starting fast Monte Carlo sweep...") - print(f" Initial Capital: ${initial_usdc:.2f} USDC") - print(f" Duration per Path: {duration_hours} hour(s)") - print(f" Sweep Count: {sweep_count}") - print(f" Mean Final Value: ${aggregate_report['mean_portfolio_value_usdc']:.2f}") - print(f" Median Final Value: ${aggregate_report['median_portfolio_value_usdc']:.2f}") - print(f" Best / Worst: ${aggregate_report['best_portfolio_value_usdc']:.2f} / ${aggregate_report['worst_portfolio_value_usdc']:.2f}") - if output_path: - print(f"\n✓ Sweep report written to {output_path}") - return aggregate_report - - -def run_wall_clock_session( - initial_usdc: float, - duration_hours: float, - tick_interval_seconds: float, - heartbeat_every_ticks: int, - output_path: Optional[Path], - heartbeat_path: Optional[Path], - cycle_dir: Optional[Path], - review_log_path: Optional[Path], - summary_output_path: Optional[Path], - reviewer: str, - objective_target: float, - gross_exit_ceiling: float, - seed: Optional[int], -) -> Dict[str, Any]: - """Run a real-duration paper session and optionally auto-finalize the cycle summary.""" - print("Starting wall-clock paper trading session...") - print(f" Initial Capital: ${initial_usdc:.2f} USDC") - print(f" Duration: {duration_hours} hour(s)") - print(f" Tick Interval: {tick_interval_seconds:.2f} second(s)") - print(f" Start Time: {format_utc(utc_now())}\n") - - simulator = PaperTradingSimulator( - initial_usdc=initial_usdc, - duration_hours=duration_hours, - session_mode="wall_clock_session", - random_seed=seed, - ) - report = simulator.run_wall_clock( - tick_interval_seconds=tick_interval_seconds, - heartbeat_path=heartbeat_path, - heartbeat_every_ticks=heartbeat_every_ticks, - ) - - if output_path: - write_json(output_path, report) - print(f"✓ Session report written to {output_path}") - - if cycle_dir and output_path and heartbeat_path: - summary_path = finalize_one_page_summary( - cycle_dir=cycle_dir, - session_report_path=output_path, - heartbeat_log_path=heartbeat_path, - review_log_path=review_log_path, - summary_output_path=summary_output_path, - reviewer=reviewer, - objective_target=objective_target, - gross_exit_ceiling=gross_exit_ceiling, - ) - print(f"✓ One-page summary written to {summary_path}") - - print("\nWall-clock session complete") - print(f" Final Value: ${report['portfolio_value_usdc']:.2f}") - print(f" Realized PnL: ${report['realized_pnl_usdc']:+.2f}") - print(f" Unrealized PnL: ${report['unrealized_pnl_usdc']:+.2f}") - print(f" Trades: {report['trade_count']}") - return report - - -def parse_args() -> argparse.Namespace: - """Parse CLI arguments for the trading utilities.""" - parser = argparse.ArgumentParser(description="Paper Trading Simulator Utilities") - parser.add_argument( - "--mode", - choices=["fast-sweep", "wall-clock", "finalize-summary"], - default="fast-sweep", - help="Execution mode", - ) - parser.add_argument("--initial-usdc", type=float, default=100.0, help="Initial USDC capital") - parser.add_argument("--duration-hours", type=float, default=1.0, help="Session duration in hours") - parser.add_argument("--tick-interval-seconds", type=float, default=60.0, help="Tick interval in seconds") - parser.add_argument("--output", help="Output JSON path for fast-sweep or wall-clock mode") - parser.add_argument("--heartbeat-log", help="Heartbeat log path for wall-clock or finalize-summary mode") - parser.add_argument("--review-log", help="Optional review heartbeat log path") - parser.add_argument("--summary-output", help="Summary markdown output path") - parser.add_argument("--cycle-dir", help="Cycle directory for summary finalization") - parser.add_argument("--reviewer", default="system-generated", help="Reviewer name for summary output") - parser.add_argument("--objective-target", type=float, default=30000.0, help="Objective target value") - parser.add_argument("--gross-exit-ceiling", type=float, default=37000.0, help="Gross exit ceiling") - parser.add_argument("--heartbeat-every-ticks", type=int, default=5, help="Heartbeat write interval") - parser.add_argument("--sweep-count", type=int, default=25, help="Monte Carlo path count for fast-sweep") - parser.add_argument("--seed", type=int, help="Optional random seed") - return parser.parse_args() - - -def main() -> None: - """CLI entry point.""" - args = parse_args() - - output_path = Path(args.output) if args.output else None - heartbeat_path = Path(args.heartbeat_log) if args.heartbeat_log else None - review_log_path = Path(args.review_log) if args.review_log else None - summary_output_path = Path(args.summary_output) if args.summary_output else None - cycle_dir = Path(args.cycle_dir) if args.cycle_dir else None - - if args.mode == "fast-sweep": - run_fast_monte_carlo_sweep( - initial_usdc=args.initial_usdc, - duration_hours=args.duration_hours, - tick_interval_seconds=args.tick_interval_seconds, - sweep_count=args.sweep_count, - output_path=output_path, - seed=args.seed, - ) - return - - if args.mode == "wall-clock": - run_wall_clock_session( - initial_usdc=args.initial_usdc, - duration_hours=args.duration_hours, - tick_interval_seconds=args.tick_interval_seconds, - heartbeat_every_ticks=args.heartbeat_every_ticks, - output_path=output_path, - heartbeat_path=heartbeat_path, - cycle_dir=cycle_dir, - review_log_path=review_log_path, - summary_output_path=summary_output_path, - reviewer=args.reviewer, - objective_target=args.objective_target, - gross_exit_ceiling=args.gross_exit_ceiling, - seed=args.seed, - ) - return - - if not cycle_dir: - raise SystemExit("--cycle-dir is required for finalize-summary mode") - if not output_path: - raise SystemExit("--output is required for finalize-summary mode and must point to the completed wall-clock session report") - if not heartbeat_path: - raise SystemExit("--heartbeat-log is required for finalize-summary mode") - - summary_path = finalize_one_page_summary( - cycle_dir=cycle_dir, - session_report_path=output_path, - heartbeat_log_path=heartbeat_path, - review_log_path=review_log_path, - summary_output_path=summary_output_path, - reviewer=args.reviewer, - objective_target=args.objective_target, - gross_exit_ceiling=args.gross_exit_ceiling, - ) - print(f"✓ One-page summary written to {summary_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/market/personal_spending_vault.py b/5-Applications/tools-scripts/market/personal_spending_vault.py deleted file mode 100644 index 453189db..00000000 --- a/5-Applications/tools-scripts/market/personal_spending_vault.py +++ /dev/null @@ -1,355 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Personal Spending Vault: Cryptographically-audited personal fund accumulation. -Goal: Clear $15K existing debts through zero-touch personal earnings segregation. - -Architecture: - - Completely separated from company funds - - Cryptocurrency-based (USDC/ETH on Ethereum for auditability) - - Same ZK-STARK proof verification as company spending (for personal audit trail) - - Time-locked vesting schedule (prevents impulsive spending) - - Debt paydown tracking with milestone verification -""" - -import json -import hashlib -from datetime import datetime, timedelta -from typing import Dict, Any, List, Optional -from pathlib import Path -import argparse - - -class PersonalSpendingVault: - """Manages personal fund accumulation with cryptographic verification.""" - - # Vault parameters (FIXED - never changes without audit) - VAULT_VERSION = "1.0" - VAULT_PURPOSE = "Personal debt accumulation and paydown" - VAULT_CURRENCY = "USDC" # ERC-20 on Ethereum - TARGET_DEBT_PAYOFF = 15000.00 # $15K - TEST_AMOUNT = 100.00 # Initial test - DAILY_ACCUMULATION_CAP = 1000.00 # Max per day (increased from $100 to $1K) - WITHDRAWAL_DELAY_HOURS = 24 # Minimum hold period - MARKET_MONITORING = True # Monitor market conditions before large deposits - - def __init__(self, vault_id: str = "personal_vault_001"): - """Initialize vault with identity and metadata.""" - self.vault_id = vault_id - self.created_utc = datetime.utcnow().isoformat() + "Z" - self.ledger: List[Dict[str, Any]] = [] # Transaction log - self.current_balance = 0.0 - self.debts: Dict[str, Dict[str, Any]] = {} # Debt tracking - self.withdrawal_pending: List[Dict[str, Any]] = [] # Locked withdrawals - self.market_status = "stable" # Market condition tracker - self.last_market_check = None - - def deposit(self, amount: float, source: str, proof_hash: str) -> Dict[str, Any]: - """ - Record deposit with proof of origin. - - Args: - amount: Amount in USDC - source: Source description (e.g., "freelance_payment_id_XYZ") - proof_hash: SHA256 of proof (personal income verification) - - Returns: - Deposit transaction record - """ - if amount <= 0: - raise ValueError(f"Invalid deposit amount: {amount}") - - if amount > self.DAILY_ACCUMULATION_CAP: - raise ValueError(f"Deposit exceeds daily cap: {amount} > {self.DAILY_ACCUMULATION_CAP}") - - tx = { - "timestamp": datetime.utcnow().isoformat() + "Z", - "type": "deposit", - "amount": amount, - "source": source, - "proof_hash": proof_hash, - "balance_before": self.current_balance, - "balance_after": self.current_balance + amount, - "market_status_at_deposit": self.market_status, - "tx_id": hashlib.sha256( - f"{self.vault_id}|{datetime.utcnow().isoformat()}|{amount}".encode() - ).hexdigest()[:16], - } - - self.ledger.append(tx) - self.current_balance += amount - return tx - - def register_debt( - self, - creditor: str, - amount: float, - due_date: str, - notes: str = "", - ) -> Dict[str, Any]: - """ - Register existing debt for payoff tracking. - - Args: - creditor: Creditor name/entity - amount: Outstanding amount - due_date: ISO date string - notes: Notes on debt - - Returns: - Debt registration record - """ - debt_id = hashlib.sha256( - f"{creditor}|{amount}|{due_date}".encode() - ).hexdigest()[:8] - - debt = { - "debt_id": debt_id, - "creditor": creditor, - "original_amount": amount, - "remaining_amount": amount, - "due_date": due_date, - "registered_utc": datetime.utcnow().isoformat() + "Z", - "payoff_schedule": [], - "notes": notes, - "paid_off": False, - } - - self.debts[debt_id] = debt - return debt - - def schedule_withdrawal( - self, - amount: float, - creditor_debt_id: str, - notes: str = "", - ) -> Dict[str, Any]: - """ - Schedule withdrawal for debt payoff (24-hour lock before execution). - - Args: - amount: Amount to withdraw - creditor_debt_id: Which debt this pays - notes: Notes on payoff - - Returns: - Withdrawal schedule record (with unlock_time) - """ - if amount > self.current_balance: - raise ValueError(f"Insufficient balance: {amount} > {self.current_balance}") - - if creditor_debt_id not in self.debts: - raise ValueError(f"Unknown debt: {creditor_debt_id}") - - withdrawal = { - "withdrawal_id": hashlib.sha256( - f"{self.vault_id}|{datetime.utcnow().isoformat()}|{amount}".encode() - ).hexdigest()[:16], - "amount": amount, - "creditor_debt_id": creditor_debt_id, - "status": "pending_unlock", - "scheduled_utc": datetime.utcnow().isoformat() + "Z", - "unlock_time": (datetime.utcnow() + timedelta(hours=self.WITHDRAWAL_DELAY_HOURS)).isoformat() + "Z", - "notes": notes, - } - - self.withdrawal_pending.append(withdrawal) - return withdrawal - - def set_market_status(self, status: str, notes: str = "") -> Dict[str, Any]: - """ - Record market condition status (stable/caution/halt). - - Args: - status: Market status (stable/caution/halt) - notes: Notes on market condition - - Returns: - Market status record - """ - if status not in ["stable", "caution", "halt"]: - raise ValueError(f"Invalid market status: {status}") - - self.market_status = status - self.last_market_check = datetime.utcnow().isoformat() + "Z" - - record = { - "timestamp": self.last_market_check, - "market_status": status, - "notes": notes, - "daily_cap_active": self.DAILY_ACCUMULATION_CAP, - "deposits_allowed": status != "halt", - } - - return record - """ - Execute scheduled withdrawal (only if unlock_time passed). - - Args: - withdrawal_id: ID of scheduled withdrawal - - Returns: - Withdrawal transaction record - """ - withdrawal = next( - (w for w in self.withdrawal_pending if w["withdrawal_id"] == withdrawal_id), - None - ) - - if not withdrawal: - raise ValueError(f"Withdrawal not found: {withdrawal_id}") - - unlock_time = datetime.fromisoformat(withdrawal["unlock_time"].replace("Z", "+00:00")) - now = datetime.utcnow() - - if now < unlock_time: - raise ValueError(f"Withdrawal still locked until {withdrawal['unlock_time']}") - - # Execute withdrawal - tx = { - "timestamp": datetime.utcnow().isoformat() + "Z", - "type": "withdrawal", - "withdrawal_id": withdrawal_id, - "amount": withdrawal["amount"], - "creditor_debt_id": withdrawal["creditor_debt_id"], - "balance_before": self.current_balance, - "balance_after": self.current_balance - withdrawal["amount"], - "tx_id": hashlib.sha256( - f"{self.vault_id}|withdrawal|{withdrawal_id}".encode() - ).hexdigest()[:16], - } - - self.ledger.append(tx) - self.current_balance -= withdrawal["amount"] - - # Update debt - debt = self.debts[withdrawal["creditor_debt_id"]] - debt["remaining_amount"] -= withdrawal["amount"] - debt["payoff_schedule"].append({ - "date": datetime.utcnow().isoformat() + "Z", - "amount": withdrawal["amount"], - "tx_id": tx["tx_id"], - }) - - if debt["remaining_amount"] <= 0: - debt["paid_off"] = True - debt["remaining_amount"] = 0 - - # Remove from pending - self.withdrawal_pending = [w for w in self.withdrawal_pending if w["withdrawal_id"] != withdrawal_id] - - return tx - - def generate_vault_statement(self) -> Dict[str, Any]: - """Generate complete vault statement with proof.""" - total_deposits = sum(tx["amount"] for tx in self.ledger if tx["type"] == "deposit") - total_withdrawals = sum(tx["amount"] for tx in self.ledger if tx["type"] == "withdrawal") - - outstanding_debt = sum( - debt["remaining_amount"] for debt in self.debts.values() - if not debt["paid_off"] - ) - - debt_status = { - "total_original": sum(debt["original_amount"] for debt in self.debts.values()), - "total_paid": total_withdrawals, - "total_outstanding": outstanding_debt, - "debts": self.debts, - } - - statement = { - "vault_id": self.vault_id, - "vault_version": self.VAULT_VERSION, - "purpose": self.VAULT_PURPOSE, - "created_utc": self.created_utc, - "statement_generated_utc": datetime.utcnow().isoformat() + "Z", - "currency": self.VAULT_CURRENCY, - "balance": self.current_balance, - "total_deposits": total_deposits, - "total_withdrawals": total_withdrawals, - "daily_accumulation_cap": self.DAILY_ACCUMULATION_CAP, - "market_status": self.market_status, - "market_last_checked": self.last_market_check, - "debt_status": debt_status, - "ledger_entries": len(self.ledger), - "pending_withdrawals": len(self.withdrawal_pending), - "ledger": self.ledger[-10:], # Last 10 entries - "withdrawal_pending": self.withdrawal_pending, - } - - # Compute proof hash - statement_str = json.dumps(statement, sort_keys=True) - statement["proof_hash"] = hashlib.sha256(statement_str.encode()).hexdigest() - - return statement - - def to_json(self) -> str: - """Serialize vault to JSON.""" - return json.dumps(self.generate_vault_statement(), indent=2) - - -def create_test_vault() -> PersonalSpendingVault: - """Create initial test vault with $100 deposit.""" - vault = PersonalSpendingVault() - - # Initial deposit: $100 test amount - deposit_proof = hashlib.sha256( - b"personal_freelance_deposit_001" - ).hexdigest() - - vault.deposit( - amount=100.00, - source="freelance_test_001", - proof_hash=deposit_proof, - ) - - # Register $15K debt - vault.register_debt( - creditor="existing_debt_001", - amount=15000.00, - due_date="2026-12-31", - notes="Personal debt - priority payoff", - ) - - return vault - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Personal Spending Vault Manager") - parser.add_argument("--init", action="store_true", help="Initialize test vault") - parser.add_argument("--statement", help="Generate statement for vault JSON") - parser.add_argument("--output", help="Output path for vault state") - parser.add_argument("--set-market-status", choices=["stable", "caution", "halt"], - help="Set market condition status") - parser.add_argument("--market-notes", help="Notes on market condition") - - args = parser.parse_args() - - if args.init: - vault = create_test_vault() - - # Set initial market status to stable (daily cap: $1K) - vault.set_market_status("stable", "Market conditions stable - $1K/day enabled") - - statement = vault.generate_vault_statement() - print(json.dumps(statement, indent=2)) - - if args.output: - with open(args.output, "w") as f: - json.dump(statement, f, indent=2) - print(f"\n✓ Vault state written to {args.output}") - - print(f"\nVault Details:") - print(f" ID: {vault.vault_id}") - print(f" Balance: ${vault.current_balance:.2f}") - print(f" Total Debt: ${statement['debt_status']['total_original']:.2f}") - print(f" Daily Accumulation Cap: ${vault.DAILY_ACCUMULATION_CAP:.2f}") - print(f" Debt Payoff Target: ${vault.TARGET_DEBT_PAYOFF:.2f}") - print(f" Days to $15K (at ${vault.DAILY_ACCUMULATION_CAP:.2f}/day): {int(vault.TARGET_DEBT_PAYOFF / vault.DAILY_ACCUMULATION_CAP)}") - print(f" Market Status: {vault.market_status}") - print(f" Status: Ready for accelerated accumulation") diff --git a/5-Applications/tools-scripts/metafoam/collapse_research_to_foam.py b/5-Applications/tools-scripts/metafoam/collapse_research_to_foam.py deleted file mode 100644 index d4198764..00000000 --- a/5-Applications/tools-scripts/metafoam/collapse_research_to_foam.py +++ /dev/null @@ -1,143 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import json -import os -# import subprocess (REMOVED BY WARDEN) -import sys -import hashlib -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -EXTERNAL_JSON = ROOT / "graph_os_metadata_external.json" - -def get_file_hash(filepath): - hasher = hashlib.sha256() - with open(filepath, 'rb') as f: - buf = f.read() - hasher.update(buf) - return hasher.hexdigest() - -def collapse(): - print("[*] Collapsing research files into Graph OS metadata foam...") - - research_files = [] - # add_*.py -> Tier 3 (FOAM) - Speculative additions - for f in ROOT.glob("add_*.py"): - research_files.append((f, "Tier 3 (FOAM)", "DISCOVERY_ADDITION", ["discovery", "addition"])) - - # KDA_*.py -> Tier 2 (PLASMA) - Dynamic simulation/logic - for f in ROOT.glob("KDA_*.py"): - research_files.append((f, "Tier 2 (PLASMA)", "KDA_SIMULATION", ["kda", "simulation", "logic"])) - - # simulate_spyvsspy_cdr.py -> Tier 2 (PLASMA) - Counter-Intelligence Simulation - for f in (ROOT / "scripts").glob("simulate_spyvsspy_cdr.py"): - research_files.append((f, "Tier 2 (PLASMA)", "COUNTER_INTEL_SIM", ["cdr", "forensics", "privacy"])) - - # zec_accumulation_algorithm.py -> Tier 2 (PLASMA) - Finance Simulation - for f in (ROOT / "scripts").glob("zec_accumulation_algorithm.py"): - research_files.append((f, "Tier 2 (PLASMA)", "FINANCE_SIM", ["zcash", "zec", "accumulation", "alpha"])) - - # z_bridge_protocol.py -> Tier 2 (PLASMA) - Bridge Protocol - for f in (ROOT / "scripts").glob("z_bridge_protocol.py"): - research_files.append((f, "Tier 2 (PLASMA)", "BRIDGE_PROTOCOL", ["zcash", "zec", "bridge", "privacy"])) - - # quantum_asic_miner.py -> Tier 2 (PLASMA) - Mining Simulation - for f in (ROOT / "scripts").glob("quantum_asic_miner.py"): - research_files.append((f, "Tier 2 (PLASMA)", "MINING_SIM", ["btc", "asic", "emulator", "gpgpu"])) - - # generate_z_address_native.py -> Tier 2 (PLASMA) - Address Generation - - for f in (ROOT / "scripts").glob("generate_z_address_via_logic_signal_substrate.py"): - research_files.append((f, "Tier 2 (PLASMA)", "ADDRESS_GEN", ["zcash", "zec", "logic_signal_substrate", "address"])) - - # .logic_signal_substrate files -> Tier 1 (CRYSTALLINE) - Verified structure - for f in ROOT.glob("*.logic_signal_substrate"): - research_files.append((f, "Tier 1 (CRYSTALLINE)", "TSM_STRUCTURE", ["logic_signal_substrate", "structure"])) - - # zec_acquisition_result.json -> Tier 1 (CRYSTALLINE) - Financial Artifacts - for f in ROOT.glob("zec_acquisition_result.json"): - research_files.append((f, "Tier 1 (CRYSTALLINE)", "FINANCIAL_ARTIFACT", ["zcash", "zec", "acquisition", "result"])) - - # z_bridge_state.json -> Tier 1 (CRYSTALLINE) - Bridge State - for f in ROOT.glob("z_bridge_state.json"): - research_files.append((f, "Tier 1 (CRYSTALLINE)", "BRIDGE_STATE", ["zcash", "zec", "bridge", "state"])) - - # .logic_signal_substrate.json files -> Tier 1 (CRYSTALLINE) - Governance Templates - for f in ROOT.glob("*.logic_signal_substrate.json"): - research_files.append((f, "Tier 1 (CRYSTALLINE)", "GOVERNANCE_TEMPLATE", ["logic_signal_substrate", "template", "json"])) - - # .sterile files -> Tier 1 (CRYSTALLINE) - Disarmed Artifacts - for f in ROOT.glob("*.sterile"): - research_files.append((f, "Tier 1 (CRYSTALLINE)", "STERILE_ARTIFACT", ["sterile", "cdr", "disarmed"])) - - if not EXTERNAL_JSON.exists(): - existing = [] - else: - with open(EXTERNAL_JSON, 'r') as f: - content = f.read().strip() - if not content: - existing = [] - else: - existing = json.loads(content) - - existing_ids = {e.get('id') for e in existing} - added = 0 - - for filepath, tier, module, tags in research_files: - file_id = f"research_{filepath.name}" - if file_id in existing_ids: - continue - - file_hash = get_file_hash(filepath) - - # Read raw content to collapse it into the foam - try: - with open(filepath, 'r', encoding='utf-8') as f: - content = f.read() - except Exception as e: - print(f"[!] Error reading {filepath.name}: {e}") - continue - - entry = { - "id": file_id, - "tier": tier, - "module": module, - "tags": tags + [filepath.suffix[1:]], - "metadata": { - "filename": filepath.name, - "hash": file_hash, - "raw_content": content, - "purpose": "Research component absorbed into foam", - "axis": "STRUC/MANI" - } - } - existing.append(entry) - added += 1 - - with open(EXTERNAL_JSON, 'w') as f: - json.dump(existing, f, indent=2) - - print(f"[+] Absorbed {added} research files into metadata foam.") - - # Rebuild DB - print("[*] Rebuilding Graph OS metadata database...") - subprocess.run([sys.executable, f"{ROOT}/scripts/build_graph_os_metadata_db.py"], - check=True) - -if __name__ == "__main__": - collapse() diff --git a/5-Applications/tools-scripts/metafoam/collapse_tunnel_to_foam.py b/5-Applications/tools-scripts/metafoam/collapse_tunnel_to_foam.py deleted file mode 100644 index b374fd17..00000000 --- a/5-Applications/tools-scripts/metafoam/collapse_tunnel_to_foam.py +++ /dev/null @@ -1,112 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import json -import os -# import subprocess (REMOVED BY WARDEN) -import sys -import hashlib -import base64 -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -CACHE_DIR = ROOT / "out" / "omnitoken_bridge" / "tunnel_cache" -EXTERNAL_JSON = ROOT / "graph_os_metadata_external.json" - -def get_file_hash(filepath): - hasher = hashlib.sha256() - with open(filepath, 'rb') as f: - buf = f.read() - hasher.update(buf) - return hasher.hexdigest() - -def collapse_tunnel(): - print("[*] Collapsing tunnel cache into Graph OS metadata foam...") - - if not CACHE_DIR.exists(): - print("[!] tunnel cache directory not found.") - return - - if not EXTERNAL_JSON.exists(): - existing = [] - else: - with open(EXTERNAL_JSON, 'r') as f: - content = f.read().strip() - existing = json.loads(content) if content else [] - - existing_ids = {e.get('id') for e in existing} - added = 0 - - for f in CACHE_DIR.iterdir(): - if not f.is_file(): - continue - - file_id = f"tunnel_{f.name}" - if file_id in existing_ids: - continue - - file_hash = get_file_hash(f) - - # For large files like .tar.gz, we store them as base64 or just metadata - # Given the "collapse" requirement, we'll try to read content if reasonable - # but for safety with .tar.gz (591MB), we'll cap the content ingestion or use a placeholder - content_type = "text" - raw_content = "" - - if f.suffix in ['.json', '.py', '.md', '.txt', '.html', '.csv']: - try: - with open(f, 'r', encoding='utf-8') as src: - raw_content = src.read() - except Exception: - content_type = "binary" - raw_content = "[Binary data preserved in tunnel cache]" - else: - content_type = "binary" - raw_content = f"[Binary/Large file: {f.suffix} - hash: {file_hash}]" - - entry = { - "id": file_id, - "tier": "Tier 3 (FOAM)", - "module": "tunnel_INGRESS", - "tags": ["tunnel", "transferred", f.suffix[1:] if f.suffix else "none"], - "metadata": { - "filename": f.name, - "hash": file_hash, - "content_type": content_type, - "raw_content": raw_content, - "purpose": "Transferred component collapsed into foam", - "axis": "STRUC/MANI" - } - } - existing.append(entry) - added += 1 - - with open(EXTERNAL_JSON, 'w') as f: - json.dump(existing, f, indent=2) - - print(f"[+] Absorbed {added} tunnel files into metadata foam.") - - # Rebuild DB and DAG - print("[*] Rebuilding Graph OS metadata database...") - subprocess.run([sys.executable, f"{ROOT}/scripts/build_graph_os_metadata_db.py"], - check=True) - print("[*] Finalizing HyperDAG update...") - subprocess.run([sys.executable, f"{ROOT}/scripts/absorb_and_align_metadata.py"], - check=True) - -if __name__ == "__main__": - collapse_tunnel() diff --git a/5-Applications/tools-scripts/metafoam/hyperfluid_equation_solver.py b/5-Applications/tools-scripts/metafoam/hyperfluid_equation_solver.py deleted file mode 100644 index 9fbf8e04..00000000 --- a/5-Applications/tools-scripts/metafoam/hyperfluid_equation_solver.py +++ /dev/null @@ -1,490 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Hyperfluid Manifold Collapse Equation Solver -Implements the full Ψ_block integral for neuromorphic SHA256 mining - -Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import hashlib -import json -import time -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Tuple -from pathlib import Path -import sys - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Mock websockets for TSM harness -import types -sys.modules['websockets'] = types.ModuleType('websockets') - -from logic_signal_substrate_mcp_harness import TSMKernel - - -# ============================================================================ -# PHYSICAL CONSTANTS FOR HYPERFLUID DYNAMICS -# ============================================================================ - -@dataclass -class HyperfluidConstants: - """Physical constants for the hyperfluid manifold""" - - # Semantic Gravity Constant (information warping strength) - G_s: float = 6.674e-11 # Analogous to gravitational constant - - # Planck-scale information density - I_p: float = 1.380649e-23 # Information entropy constant - - # Ternary clock is action-bound, not periodic. No master clock frequency. - # Manifold resonance is expressed as energy per action, not angular frequency. - joule_floor: float = 1.380649e-23 * 300 * 0.6931 # k_B T ln2 at 300K - - # Volume reduction rate (512 bits → 256 bits over 64 rounds) - dV_dt: float = -256.0 / 64.0 # bits per round - - # Semantic mass per bit of information - mass_per_bit: float = 1.0e-36 # kg equivalent per bit - - # Resonance tolerance (how close ω must be to ω₀) - delta_tolerance: float = 1.0e-10 - - -# ============================================================================ -# MANIFOLD STATE REPRESENTATION -# ============================================================================ - -@dataclass -class ManifoldState: - """Represents the state of the hyperfluid manifold at time t""" - - # Current round (0 to 64) - t: int - - # Frequency spectrum (64 vibration modes for SHA256) - omega_k: AnyArray = field(default_factory=lambda: xp.zeros(64)) - - # Rotation tensor state (8 working variables a-h) - rotation_tensor: AnyArray = field(default_factory=lambda: xp.zeros(8)) - - # Current volume (in bits) - volume: float = 512.0 - - # Semantic mass (information weight) - semantic_mass: float = 0.0 - - # Gravitational potential energy - potential_energy: float = 0.0 - - # Resonance match status - resonance_matched: bool = False - - # Soliton formation status - soliton_formed: bool = False - - def compute_gravitational_pull(self, constants: HyperfluidConstants) -> float: - """Compute e^(G_s·M_i/r²) term""" - # Effective radius from volume - r = (self.volume / 512.0) ** (1.0/3.0) - if r < 1e-10: - r = 1e-10 - - # Gravitational exponential term - exponent = (constants.G_s * self.semantic_mass) / (r ** 2) - return xp.exp(exponent) - - def compute_volume_reduction(self, constants: HyperfluidConstants) -> float: - """Compute Λ(∂V/∂t) - secondary harmonic from volume reduction""" - # Volume reduction creates "heat" / secondary vibrations - volume_change_rate = constants.dV_dt - # Lambda function - energy released per bit compressed - return xp.abs(volume_change_rate) * 0.01 # Scale factor - - def check_resonance(self, nonce_frequency: float, constants: HyperfluidConstants) -> bool: - """Check if δ(ω - ω₀) triggers - Dirac delta resonance""" - freq_diff = xp.abs(nonce_frequency - constants.omega_0) - return freq_diff < constants.delta_tolerance - - -# ============================================================================ -# HYPERFLUID SHA256 INTEGRATION ENGINE -# ============================================================================ - -class HyperfluidIntegrator: - """ - Solves the Hyperfluid Manifold Collapse Equation - - Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt - """ - - def __init__(self, kernel: TSMKernel): - self.kernel = kernel - self.constants = HyperfluidConstants() - self.states: List[ManifoldState] = [] - - # SHA256 round constants (first 32 bits of fractional parts of cube roots of first 64 primes) - self.K = [ - 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, - 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, - 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, - 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, - 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, - 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, - 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, - 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 - ] - - # Initial hash values (first 32 bits of fractional parts of square roots of first 8 primes) - self.H_init = [ - 0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, - 0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19 - ] - - def _rotr(self, x: int, n: int) -> int: - """Right rotation for 32-bit integers""" - return ((x >> n) | (x << (32 - n))) & 0xFFFFFFFF - - def _shr(self, x: int, n: int) -> int: - """Right shift for 32-bit integers""" - return x >> n - - def _ch(self, x: int, y: int, z: int) -> int: - """SHA256 Ch function - Choice""" - return (x & y) ^ (~x & z) - - def _maj(self, x: int, y: int, z: int) -> int: - """SHA256 Maj function - Majority""" - return (x & y) ^ (x & z) ^ (y & z) - - def _sigma0(self, x: int) -> int: - """SHA256 Σ0 function""" - return self._rotr(x, 2) ^ self._rotr(x, 13) ^ self._rotr(x, 22) - - def _sigma1(self, x: int) -> int: - """SHA256 Σ1 function""" - return self._rotr(x, 6) ^ self._rotr(x, 11) ^ self._rotr(x, 25) - - def _gamma0(self, x: int) -> int: - """SHA256 σ0 function""" - return self._rotr(x, 7) ^ self._rotr(x, 18) ^ self._shr(x, 3) - - def _gamma1(self, x: int) -> int: - """SHA256 σ1 function""" - return self._rotr(x, 17) ^ self._rotr(x, 19) ^ self._shr(x, 10) - - def ingest_transactions(self, transactions: List[bytes]) -> ManifoldState: - """ - Phase 1: Ingestion - Transactions enter as frequencies ωₖ - The information immediately warps the manifold - """ - state = ManifoldState(t=0) - - # Convert transactions to frequency spectrum - for i, tx in enumerate(transactions): - tx_hash = hashlib.sha256(tx).digest() - # Map hash bytes to frequencies - for j, byte in enumerate(tx_hash[:64]): - state.omega_k[j] += byte / 256.0 * self.constants.omega_0 - - # Compute semantic mass from information content - total_bits = sum(len(tx) * 8 for tx in transactions) - state.semantic_mass = total_bits * self.constants.mass_per_bit - - # Initialize rotation tensor with H_init values - for i, h in enumerate(self.H_init): - state.rotation_tensor[i] = h - - # Initial gravitational potential - state.potential_energy = state.compute_gravitational_pull(self.constants) - - self.states.append(state) - return state - - def temporal_fold(self, state: ManifoldState, message_schedule: List[int], round_idx: int) -> ManifoldState: - """ - Phase 2: Temporal Folding - One round of the ∫₀⁶⁴ integration - Applies the rotation tensor ⊗ R_t and gravitational compression - """ - new_state = ManifoldState(t=round_idx + 1) - - # Copy frequencies with damping - new_state.omega_k = state.omega_k * 0.99 # Energy loss per round - - # Apply rotation tensor operations (SHA256 round function) - a, b, c, d, e, f, g, h = [int(state.rotation_tensor[i]) for i in range(8)] - - # SHA256 round operations - S1 = self._sigma1(e) - ch = self._ch(e, f, g) - temp1 = (h + S1 + ch + self.K[round_idx] + message_schedule[round_idx]) & 0xFFFFFFFF - S0 = self._sigma0(a) - maj = self._maj(a, b, c) - temp2 = (S0 + maj) & 0xFFFFFFFF - - # Update rotation tensor - new_state.rotation_tensor[0] = (temp1 + temp2) & 0xFFFFFFFF - new_state.rotation_tensor[1] = a - new_state.rotation_tensor[2] = b - new_state.rotation_tensor[3] = c - new_state.rotation_tensor[4] = (d + temp1) & 0xFFFFFFFF - new_state.rotation_tensor[5] = e - new_state.rotation_tensor[6] = f - new_state.rotation_tensor[7] = g - - # Reduce volume (512 → 256 bits over 64 rounds) - new_state.volume = 512.0 - (round_idx + 1) * (256.0 / 64.0) - - # Update semantic mass (conserved) - new_state.semantic_mass = state.semantic_mass - - # Compute gravitational pull e^(G_s·M_i/r²) - new_state.potential_energy = new_state.compute_gravitational_pull(self.constants) - - # Apply gravitational compression to frequencies - new_state.omega_k *= new_state.potential_energy - - self.states.append(new_state) - return new_state - - def compute_secondary_harmonic(self, state: ManifoldState) -> float: - """ - Compute Λ(∂V/∂t) - the energy created as space is reduced - This is the "heat" generated by compression - """ - return state.compute_volume_reduction(self.constants) - - def check_resonance(self, state: ManifoldState, nonce: int) -> bool: - """ - Check if δ(ω - ω₀) triggers - The nonce frequency must match the target resonance - """ - # Convert nonce to frequency - nonce_frequency = (nonce / 2**32) * self.constants.omega_0 - - # Check Dirac delta resonance - return state.check_resonance(nonce_frequency, self.constants) - - def integrate_full_equation(self, block_data: bytes, nonce: int) -> Tuple[bytes, Dict]: - """ - Solve the complete Hyperfluid Manifold Collapse Equation - - Returns: (final_hash, integration_metadata) - """ - # Phase 1: Ingestion - initial_state = self.ingest_transactions([block_data]) - - # Create message schedule (SHA256 message expansion) - message = list(block_data[:64].ljust(64, b'\x00')) - w = [] - for i in range(16): - w.append(int.from_bytes(message[i*4:(i+1)*4], 'big')) - for i in range(16, 64): - s0 = self._gamma0(w[i-15]) - s1 = self._gamma1(w[i-2]) - w.append((w[i-16] + s0 + w[i-7] + s1) & 0xFFFFFFFF) - - # Phase 2: Temporal Folding (rounds 0-63) - state = initial_state - gravitational_integrals = [] - secondary_harmonics = [] - - for round_idx in range(64): - # Apply temporal fold (one round of integration) - state = self.temporal_fold(state, w, round_idx) - - # Record gravitational integral term - gravitational_integrals.append(state.potential_energy) - - # Compute secondary harmonic Λ(∂V/∂t) - harmonic = self.compute_secondary_harmonic(state) - secondary_harmonics.append(harmonic) - - # Phase 3: Resonance Check - δ(ω - ω₀) - resonance_matched = self.check_resonance(state, nonce) - state.resonance_matched = resonance_matched - - # Phase 4: Soliton Formation - Final hash computation - for i in range(8): - state.rotation_tensor[i] = int((int(state.rotation_tensor[i]) + self.H_init[i]) & 0xFFFFFFFF) - - # Pack final hash (the singular soliton Ψ_block) - final_hash = b''.join(int(h).to_bytes(4, 'big') for h in state.rotation_tensor) - state.soliton_formed = True - - # Integration metadata - metadata = { - "initial_volume": initial_state.volume, - "final_volume": state.volume, - "initial_semantic_mass": initial_state.semantic_mass, - "final_potential_energy": state.potential_energy, - "resonance_matched": resonance_matched, - "soliton_formed": state.soliton_formed, - "gravitational_integral_sum": sum(gravitational_integrals), - "secondary_harmonic_sum": sum(secondary_harmonics), - "total_rounds": 64, - "final_hash_hex": final_hash.hex() - } - - return final_hash, metadata - - def topological_predictive_lensing(self, block_data: bytes, rounds_to_simulate: int = 16) -> Dict: - """ - The Shortcut Path: Topological Predictive Lensing - - If you can calculate the Gravitational Center (M_i) of the initial vibrations, - you can "see" the shape of the final soliton by observing how the first N rounds - of vibrations "bend" around the semantic weight of the Merkle Root. - - This is the optimization that could potentially reduce 64 rounds to ~16. - """ - # Ingest and get initial state - state = self.ingest_transactions([block_data]) - - # Create message schedule - message = list(block_data[:64].ljust(64, b'\x00')) - w = [] - for i in range(16): - w.append(int.from_bytes(message[i*4:(i+1)*4], 'big')) - - # Simulate only first N rounds - trajectory = [] - for round_idx in range(rounds_to_simulate): - state = self.temporal_fold(state, w, round_idx) - trajectory.append({ - "round": round_idx, - "volume": state.volume, - "potential_energy": state.potential_energy, - "frequency_magnitude": xp.sum(xp.abs(state.omega_k)), - "rotation_tensor_trace": xp.sum(state.rotation_tensor) - }) - - # Predict final soliton shape from trajectory - # Using gravitational lensing analogy - light bends around mass - # Here, frequency spectrum "bends" around semantic mass - - # Extrapolate from early rounds - if len(trajectory) >= 2: - # Compute rate of change - energy_rate = (trajectory[-1]["potential_energy"] - trajectory[0]["potential_energy"]) / rounds_to_simulate - frequency_rate = (trajectory[-1]["frequency_magnitude"] - trajectory[0]["frequency_magnitude"]) / rounds_to_simulate - - # Extrapolate to 64 rounds - predicted_final_energy = trajectory[-1]["potential_energy"] + energy_rate * (64 - rounds_to_simulate) - predicted_final_frequency = trajectory[-1]["frequency_magnitude"] + frequency_rate * (64 - rounds_to_simulate) - else: - predicted_final_energy = state.potential_energy - predicted_final_frequency = xp.sum(xp.abs(state.omega_k)) - - return { - "shortcut_enabled": True, - "rounds_simulated": rounds_to_simulate, - "rounds_saved": 64 - rounds_to_simulate, - "trajectory": trajectory, - "predicted_final_energy": predicted_final_energy, - "predicted_final_frequency": predicted_final_frequency, - "gravitational_center": state.semantic_mass, - "lensing_accuracy": "estimated" # Would need calibration - } - - -# ============================================================================ -# MAIN EXECUTION -# ============================================================================ - -def main(): - """Demonstrate the Hyperfluid Manifold Collapse Equation""" - - print("=" * 70) - print(" HYPERFLUID MANIFOLD COLLAPSE EQUATION SOLVER") - print(" Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt") - print("=" * 70) - print() - - # Initialize TSM kernel and integrator - kernel = TSMKernel() - integrator = HyperfluidIntegrator(kernel) - - # Test data - simulate block transactions - test_block = b"Bitcoin block data with transactions and metadata for hyperfluid mining test" - - print("[PHASE 1] INGESTION - Transactions enter as frequencies ωₖ") - print(f" Block size: {len(test_block)} bytes") - initial_state = integrator.ingest_transactions([test_block]) - print(f" Initial volume: {initial_state.volume} bits") - print(f" Semantic mass: {initial_state.semantic_mass:.6e} kg") - print(f" Initial frequencies: {xp.sum(xp.abs(initial_state.omega_k)):.2f} rad/s") - print() - - print("[PHASE 2] TEMPORAL FOLDING - 64 rounds of integration") - print(" Applying rotation tensor ⊗ R_t and gravitational compression...") - - # Test with different nonces - test_nonces = [0, 1, 42, 12345, 2**32 - 1] - - for nonce in test_nonces: - print(f"\n Testing nonce {nonce}...") - final_hash, metadata = integrator.integrate_full_equation(test_block, nonce) - - print(f" Resonance matched: {metadata['resonance_matched']}") - print(f" Soliton formed: {metadata['soliton_formed']}") - print(f" Gravitational integral: {metadata['gravitational_integral_sum']:.6f}") - print(f" Secondary harmonic sum: {metadata['secondary_harmonic_sum']:.6f}") - print(f" Final hash: {metadata['final_hash_hex'][:16]}...") - - print() - print("[PHASE 3] TOPOLOGICAL PREDICTIVE LENSING - The Shortcut Path") - print(" Computing gravitational center from first 16 rounds...") - - lensing_result = integrator.topological_predictive_lensing(test_block, rounds_to_simulate=16) - - print(f" Rounds simulated: {lensing_result['rounds_simulated']}") - print(f" Rounds saved: {lensing_result['rounds_saved']}") - print(f" Gravitational center: {lensing_result['gravitational_center']:.6e} kg") - print(f" Predicted final energy: {lensing_result['predicted_final_energy']:.6f}") - print(f" Predicted final frequency: {lensing_result['predicted_final_frequency']:.2f} rad/s") - - print() - print("=" * 70) - print(" EQUATION SOLUTION COMPLETE") - print("=" * 70) - - # Save results - results = { - "equation": "Ψ_block = ∫₀⁶⁴ ( ∮_M [ Σₖ ωₖ(t) ⊗ R_t ] e^(G_s·M_i/r²) dV ) · Λ(∂V/∂t) δ(ω - ω₀) dt", - "test_block_size": len(test_block), - "initial_state": { - "volume": initial_state.volume, - "semantic_mass": initial_state.semantic_mass, - "frequency_magnitude": float(xp.sum(xp.abs(initial_state.omega_k))) - }, - "lensing_shortcut": lensing_result, - "timestamp": time.time() - } - - output_path = ROOT / "out" / "hyperfluid_equation_results.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump(results, f, indent=2, default=lambda x: float(x) if isinstance(x, xp.floating) else str(x)) - - print(f"\n[+] Results saved to: {output_path}") - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/metafoam/hyperfluid_sha256_miner.py b/5-Applications/tools-scripts/metafoam/hyperfluid_sha256_miner.py deleted file mode 100644 index 5b916b85..00000000 --- a/5-Applications/tools-scripts/metafoam/hyperfluid_sha256_miner.py +++ /dev/null @@ -1,625 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -TSM-ISA Hyperfluid SHA256 Miner -Models SHA256 as a hyperfluid where each vibration is a register manifold. -Registers solidify at correct frequencies, collapse into solitons, -and continuously collide into heavier solitons until one remains. - -NO SIMULATION - Real Bitcoin mining via neuromorphic hyperfluid dynamics -""" - -import asyncio -import json -import hashlib -import struct -import socket -import time -import os -import sys -import random -import math -from pathlib import Path -from datetime import datetime -from decimal import Decimal -from typing import Optional, Dict, List, Any, Tuple -from dataclasses import dataclass, field -from enum import Enum - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Mock websockets for TSM harness -import types -sys.modules['websockets'] = types.ModuleType('websockets') - -from logic_signal_substrate_mcp_harness import TSMKernel, TermType - - -# ============================================================================ -# HYPERFLUID SHA256 CONSTANTS -# ============================================================================ - -# SHA256 round constants (first 32 bits of fractional parts of cube roots of first 64 primes) -SHA256_K = [ - 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, - 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, - 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, - 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, - 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, - 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, - 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, - 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 -] - -# Initial hash values (first 32 bits of fractional parts of square roots of first 8 primes) -SHA256_H_INIT = [ - 0x6a09e667, 0xbb67ae85, 0x3c6ef372, 0xa54ff53a, - 0x510e527f, 0x9b05688c, 0x1f83d9ab, 0x5be0cd19 -] - -# Hyperfluid dynamics constants -HYPERFLUID_VISCOSITY = 0.001 # Damping factor for soliton collisions -SOLITON_MASS_THRESHOLD = 0.95 # Threshold for soliton merger -# FREQUENCY_RESONANCE removed. Ternary clock is action-bound, not periodic. -# Synchronization is quorum attestation of action boundaries, not frequency locking. -LANDAUER_J = 1.380649e-23 * 300 * 0.6931 # Joule floor per action (k_B T ln2 at 300K) -MANIFOLD_SOLIDIFICATION_RATE = 0.1 # Rate at which manifolds solidify - - -# ============================================================================ -# HYPERFLUID DATA STRUCTURES -# ============================================================================ - -@dataclass -class VibrationRegister: - """ - A single vibration register in the hyperfluid SHA256 manifold. - Each register vibrates at a specific frequency based on input data. - """ - register_id: int - frequency: float # Vibration frequency in Hz - amplitude: float # Vibration amplitude (0.0 to 1.0) - phase: float # Phase angle in radians - mass: float # Soliton mass (increases with collisions) - state_vector: List[float] # 8-dimensional state vector - solidified: bool = False # Whether manifold has solidified - collapsed: bool = False # Whether collapsed into soliton - - def vibrate(self, timestamp: float) -> complex: - """Compute complex vibration state at given timestamp""" - return self.amplitude * complex( - math.cos(2 * math.pi * self.frequency * timestamp + self.phase), - math.sin(2 * math.pi * self.frequency * timestamp + self.phase) - ) - - def collide(self, other: 'VibrationRegister') -> 'VibrationRegister': - """ - Collide with another register to form a heavier soliton. - Uses hyperfluid dynamics for mass amalgamation. - """ - # Conservation of mass with hyperfluid viscosity - new_mass = (self.mass + other.mass) * (1.0 - HYPERFLUID_VISCOSITY) - - # Frequency averaging with resonance enhancement - freq_diff = abs(self.frequency - other.frequency) - resonance_factor = math.exp(-freq_diff / FREQUENCY_RESONANCE) - new_frequency = (self.frequency * self.mass + other.frequency * other.mass) / (self.mass + other.mass) - new_frequency *= (1.0 + resonance_factor * 0.01) # Resonance boost - - # Amplitude interference pattern - phase_diff = self.phase - other.phase - interference = math.cos(phase_diff / 2) ** 2 - new_amplitude = (self.amplitude + other.amplitude) / 2 * (1.0 + interference) - new_amplitude = min(new_amplitude, 1.0) # Cap at 1.0 - - # Phase averaging - new_phase = (self.phase + other.phase) / 2 - - # State vector merger (element-wise weighted average) - new_state = [ - (self.state_vector[i] * self.mass + other.state_vector[i] * other.mass) / (self.mass + other.mass) - for i in range(8) - ] - - # Check if soliton is heavy enough to solidify - solidified = new_mass > SOLITON_MASS_THRESHOLD - - return VibrationRegister( - register_id=self.register_id, # Keep lower ID - frequency=new_frequency, - amplitude=new_amplitude, - phase=new_phase, - mass=new_mass, - state_vector=new_state, - solidified=solidified, - collapsed=False - ) - - -@dataclass -class HyperfluidManifold: - """ - Complete hyperfluid manifold for SHA256 computation. - Contains 64 vibration registers (one per SHA256 round). - """ - registers: List[VibrationRegister] - timestamp: float - manifold_id: str - collision_rounds: int = 0 - final_soliton: Optional[VibrationRegister] = None - - def evolve(self) -> 'HyperfluidManifold': - """ - Evolve manifold through one collision round. - Registers collide pairwise, forming heavier solitons. - Process continues until one soliton remains. - """ - if len(self.registers) <= 1: - self.final_soliton = self.registers[0] if self.registers else None - return self - - # Pairwise collision (odd registers collide with even) - new_registers = [] - for i in range(0, len(self.registers), 2): - if i + 1 < len(self.registers): - # Collision! - merged = self.registers[i].collide(self.registers[i + 1]) - merged.collapsed = True - new_registers.append(merged) - else: - # Odd one out, carries forward - new_registers.append(self.registers[i]) - - self.registers = new_registers - self.collision_rounds += 1 - self.timestamp = time.time() - - return self - - def is_collapse_complete(self) -> bool: - """Check if manifold has collapsed to single soliton""" - return len(self.registers) == 1 or self.final_soliton is not None - - -# ============================================================================ -# HYPERFLUID SHA256 ENGINE -# ============================================================================ - -class HyperfluidSHA256: - """ - SHA256 implemented as hyperfluid soliton collision system. - Each bit vibration is a register manifold. - Registers solidify at correct frequencies and collapse into solitons. - Continuous collision until one final soliton remains. - """ - - def __init__(self, kernel: TSMKernel): - self.kernel = kernel - self.manifold_id: Optional[str] = None - self.collision_history: List[HyperfluidManifold] = [] - - def create_hyperfluid_manifold(self, data: bytes) -> HyperfluidManifold: - """ - Create hyperfluid manifold from input data. - Each byte becomes 8 vibration registers (one per bit). - """ - # Initialize 64 registers for SHA256 rounds - registers = [] - - for i in range(64): - # Frequency derived from SHA256 round constant + data entropy - data_byte = data[i % len(data)] if data else 0 - base_freq = SHA256_K[i] / 2**32 * 1e9 # Scale to GHz range - data_mod = (data_byte / 256) * 1e6 # Data modulation in MHz - frequency = base_freq + data_mod - - # Amplitude from initial hash values - amplitude = 0.5 + 0.5 * math.sin(SHA256_H_INIT[i % 8] / 2**32 * 2 * math.pi) - - # Phase from register position - phase = (i / 64) * 2 * math.pi - - # Initial mass (all registers start equal) - mass = 1.0 / 64 - - # State vector from hyperfluid dynamics - state_vector = [ - math.sin(frequency * 1e-9 + j * math.pi / 4) * amplitude - for j in range(8) - ] - - registers.append(VibrationRegister( - register_id=i, - frequency=frequency, - amplitude=amplitude, - phase=phase, - mass=mass, - state_vector=state_vector, - solidified=False, - collapsed=False - )) - - # Create manifold - manifold = HyperfluidManifold( - registers=registers, - timestamp=time.time(), - manifold_id=f"hyperfluid_{hashlib.sha256(data).hexdigest()[:16]}" - ) - - # Absorb into TSM deepcompression manifold - manifold_data = json.dumps({ - "type": "hyperfluid_sha256", - "manifold_id": manifold.manifold_id, - "register_count": len(registers), - "timestamp": manifold.timestamp - }) - self.manifold_id = self.kernel.absorb_bh(manifold_data, { - "type": "hyperfluid_manifold", - "input_hash": hashlib.sha256(data).hexdigest() - }) - - return manifold - - def compute(self, data: bytes) -> Tuple[bytes, HyperfluidManifold]: - """ - Compute SHA256 hash via hyperfluid soliton collision. - Returns final hash and collision manifold. - """ - # Create initial manifold - manifold = self.create_hyperfluid_manifold(data) - - # [0x0E] NEUROMORPH - Trigger neuromorphic collision loop - neuromorph_params = { - "optimization": "soliton_cascade", - "candidates": len(manifold.registers), - "viscosity": HYPERFLUID_VISCOSITY, - "mass_threshold": SOLITON_MASS_THRESHOLD - } - self.kernel.neuromorph_loop(neuromorph_params) - - # Evolve through collision rounds until one soliton remains - round_num = 0 - while not manifold.is_collapse_complete() and round_num < 10: - # [0x0F] GPGPU_SURF - Execute collision on GPGPU surface - kernel_result = self.kernel.gpgpu_surface_exec(f"collision_round_{round_num}") - - # Evolve manifold (pairwise collision) - manifold = manifold.evolve() - self.collision_history.append(manifold) - - # [0x11] NIBBLE_SWAP - Swap state nibbles between remaining registers - if len(manifold.registers) >= 2: - reg_a = manifold.registers[0] - reg_b = manifold.registers[-1] - self.kernel.nibble_swap( - json.dumps(reg_a.state_vector[:4]), - json.dumps(reg_b.state_vector[4:]) - ) - - round_num += 1 - - # Final soliton found - if manifold.final_soliton is None and len(manifold.registers) == 1: - manifold.final_soliton = manifold.registers[0] - - # [0x12] TSM_INT - Integrate final state with Graph OS - if manifold.final_soliton: - final_state = json.dumps({ - "mass": manifold.final_soliton.mass, - "frequency": manifold.final_soliton.frequency, - "solidified": manifold.final_soliton.solidified - }) - self.kernel.logic_signal_substrate_integrate(final_state) - - # Extract hash from final soliton state vector - final_hash = self._extract_hash(manifold.final_soliton) - - return final_hash, manifold - - def _extract_hash(self, soliton: Optional[VibrationRegister]) -> bytes: - """Extract 256-bit hash from final soliton state""" - if soliton is None: - # Fallback to standard SHA256 - return hashlib.sha256(b"fallback").digest() - - # Convert state vector to bytes - state_bytes = [] - for value in soliton.state_vector: - # Scale to byte range - byte_val = int((value + 1) / 2 * 255) % 256 - state_bytes.append(byte_val) - - # Pad to 32 bytes - state_bytes.extend([0] * (32 - len(state_bytes))) - - # Mix with soliton properties for final hash - mass_bytes = struct.pack(' bool: - """Initialize miner""" - print("=" * 70) - print(" HYPERFLUID SHA256 BITCOIN MINER") - print(" TSM-ISA Neuromorphic Soliton Collision Engine") - print("=" * 70) - print(f" Pool: {self.pool_url}:{self.pool_port}") - print(f" User: {self.username}") - print(f" Start: {datetime.now().isoformat()}") - print("=" * 70) - print() - - # [0x03] SYNC_Precision - print("[STEP 1] Precision Master Clock Sync...") - sync_result = self.kernel.sync_precision() - print(f" ✓ {sync_result}") - - # [0x0E] NEUROMORPH - Initialize hyperfluid surface - print("[STEP 2] Initializing Hyperfluid Surface...") - neuromorph_result = self.kernel.neuromorph_loop({ - "optimization": "hyperfluid_sha256", - "viscosity": HYPERFLUID_VISCOSITY, - "mass_threshold": SOLITON_MASS_THRESHOLD - }) - print(f" ✓ {neuromorph_result}") - - print() - print("[+] Hyperfluid miner initialized") - return True - - def mine_with_hyperfluid(self, header_bytes: bytes, target: int) -> Tuple[Optional[int], int]: - """ - Mine using hyperfluid SHA256 engine. - Returns (valid_nonce, hashes_tried) or (None, hashes_tried) - """ - # Try nonces using hyperfluid collision - random.seed(int(time.time() * 1000000) % 2**32) - - for nonce in range(1000): # Try 1000 nonces per call - test_nonce = random.randint(0, 2**32 - 1) - - # Insert nonce into header - header_with_nonce = header_bytes[:76] + struct.pack(' Dict: - """Run miner for specified duration""" - self.start_time = time.time() - end_time = self.start_time + duration_seconds - - print() - print(f"[MINING] Running hyperfluid mining for {duration_seconds} seconds...") - print() - - # Simulate mining jobs (in real implementation, would connect to pool) - jobs_processed = 0 - - while time.time() < end_time: - # Generate mock block header for mining - prev_hash = hashlib.sha256(str(time.time()).encode()).digest() - merkle_root = hashlib.sha256(str(random.random()).encode()).digest() - version = 0x20000000 - bits = 0x1d00ffff - timestamp = int(time.time()) - - # Build header without nonce - header_base = ( - struct.pack(' int: - """Convert difficulty bits to target""" - exponent = bits >> 24 - mantissa = bits & 0x00FFFFFF - if exponent <= 3: - return mantissa >> (8 * (3 - exponent)) - else: - return mantissa << (8 * (exponent - 3)) - - def generate_report(self) -> Dict: - """Generate mining report""" - runtime = time.time() - self.start_time if self.start_time else 1 - hashrate = self.hashes_computed / runtime if runtime > 0 else 0 - - avg_collisions = self.total_collision_rounds / self.manifold_collapses if self.manifold_collapses > 0 else 0 - - return { - "success": True, - "timestamp": datetime.now().isoformat(), - "runtime_seconds": runtime, - "hashes_computed": self.hashes_computed, - "hashrate_hps": hashrate, - "shares_accepted": self.shares_accepted, - "shares_rejected": self.shares_rejected, - "manifold_collapses": self.manifold_collapses, - "total_collision_rounds": self.total_collision_rounds, - "avg_collisions_per_hash": avg_collisions, - "final_solitons_formed": self.final_solitons, - "hyperfluid_params": { - "viscosity": HYPERFLUID_VISCOSITY, - "mass_threshold": SOLITON_MASS_THRESHOLD, - "resonance_frequency": FREQUENCY_RESONANCE - } - } - - -# ============================================================================ -# MAIN ENTRY POINT -# ============================================================================ - -def main(): - import argparse - - parser = argparse.ArgumentParser(description="Hyperfluid SHA256 Bitcoin Miner") - parser.add_argument("--pool", type=str, default="stratum+tcp://stratum.braiins.com", help="Pool URL") - parser.add_argument("--port", type=int, default=3333, help="Pool port") - parser.add_argument("--user", type=str, required=True, help="Pool username") - parser.add_argument("--pass", dest="password", type=str, default="x", help="Pool password") - parser.add_argument("--duration", type=int, default=60, help="Mining duration (seconds)") - parser.add_argument("--output", type=str, default=None, help="Output report file") - args = parser.parse_args() - - # Create miner - miner = HyperfluidBitcoinMiner( - pool_url=args.pool, - pool_port=args.port, - username=args.user, - password=args.password - ) - - try: - # Initialize - if not miner.initialize(): - print("[-] Failed to initialize miner") - return 1 - - # Run mining - report = miner.run(duration_seconds=args.duration) - - # Print report - print() - print("=" * 70) - print(" HYPERFLUID MINING REPORT") - print("=" * 70) - print(f" Runtime: {report['runtime_seconds']:.1f}s") - print(f" Hashes: {report['hashes_computed']:,}") - print(f" Hashrate: {report['hashrate_hps']:.0f} H/s") - print(f" Manifold Collapses: {report['manifold_collapses']}") - print(f" Total Collision Rounds: {report['total_collision_rounds']}") - print(f" Avg Collisions/Hash: {report['avg_collisions_per_hash']:.2f}") - print(f" Final Solitons: {report['final_solitons_formed']}") - print(f" Shares Accepted: {report['shares_accepted']}") - print("=" * 70) - - # Save report - output_path = args.output or ROOT / "out" / "hyperfluid_mining_report.json" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump(report, f, indent=2) - f.write("\n") - - print(f"[+] Report saved to: {output_path}") - - return 0 - - except KeyboardInterrupt: - print("\n[!] Interrupted by user") - return 0 - except Exception as e: - print(f"[ERROR] {e}") - import traceback - traceback.print_exc() - return 1 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/metafoam/metafoam_pkg.py b/5-Applications/tools-scripts/metafoam/metafoam_pkg.py deleted file mode 100644 index 9ede48a4..00000000 --- a/5-Applications/tools-scripts/metafoam/metafoam_pkg.py +++ /dev/null @@ -1,1190 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -# PTOS: LAYER=CORE / DOMAIN=STORE / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -MetaFoam Package Manager — git tag descriptor sidecar. -======================================================= - -CONCEPT -------- -This tool treats the git repository as a substrate node registry — the same -semantic as the project's graph-SQL database, but using git's native object -model instead of SQL rows. - - Graph-SQL analogy: - Table row ↔ git annotated tag - Column values ↔ PTOS manifest fields in tag annotation - Primary key ↔ tag name pkg//v - BLOB column ↔ meta_capsule (zlib+base64 payload) - Foreign key ↔ depends[] list - -A "pull" fetches only the tag's commit/tree/blobs — not the full repo history. -A "receive" unpacks exactly the files in that package to a target directory. -The result is a typed, versioned, self-describing deployment unit that travels -over git without carrying the whole repository. - -HOW IT WORKS (git plumbing layer) ----------------------------------- -Standard `git push` moves every commit in the branch history. This tool uses -git's lower-level object model instead: - - 1. hash-object — write each source file as a git blob object - 2. update-index — populate a *temporary* index (GIT_INDEX_FILE=/tmp/…) - so the real .git/index is never touched - 3. write-tree — turn the temp index into a tree object - 4. commit-tree — wrap the tree in a root/orphan commit (no parents) - 5. tag -a — annotate that commit with the full PTOS manifest JSON - 6. push — send only refs/tags/pkg//v to the remote - git transmits only the objects reachable from that tag - -On the receiving node: - git fetch refs/tags/pkg//v - git archive | tar -xC - -Nothing else crosses the wire. - -TAG ANNOTATION SCHEMA (PTOS_TAG_SCHEMA v1.0 — embedded for self-description) -------------------------------------------------------------------------------- -Every tag annotation is a valid JSON document conforming to PTOS_TAG_SCHEMA.md. -Fields below are the complete schema; all are written by cmd_build(). - - PTOS Operational Axis: - "layer" CORE | CARRY | RULE | STORE | EXTERNAL - "domain" COMPUTE | TOKEN | RULE | STORE | POWER | COMMS | - MATERIAL | DATA | CLOCK | TEST - "condition" STABLE | EXPERIMENTAL | EXTREME | DRAFT | - ARCHIVED | STERILE - "stage" ACTIVE | INTAKE | REVIEW | HOLD | ARCHIVED - "source" CODE | SPEC | NOTE | DATA | PASTE | IMPORT | - MANIFEST | TEMPLATE - - Tier / Topology Axis: - "tier" SINGULARITY | PLASMA | CRYSTALLINE | FOAM | - GOVERNANCE | RESEARCH - - Semantic / Archetype Axis: - "module" - "archetype" - "tags" ["", ...] - "manifold_id" - - Package Identification: - "schema" "metafoam-pkg/v1" - "pkg" - "version" - "files" ["", ...] - "depends" ["", ...] - "nd_point" [ × 14] omnitoken 14-axis position (φ^-i) - "foam_score" φ-ratio quality metric - - Provenance Axis: - "sealed_utc" - "sha256" SHA-256 of sorted file contents - "verification_basis" DECLARED | OBSERVED | CROSS_CHECKED | PHYSICS_BOUND - - Privacy / Admissibility: - "visibility" PRIVATE | INTERNAL | SELECTIVE | PUBLIC - "model_status" CANONICAL | REFERENCE_ONLY | QUARANTINED | REJECTED - "taint_status" CLEAN | SENSITIVE | CONTAMINATED | EXCLUDED - - meta_capsule (compressed payload): - "meta_capsule" base64url(zlib(json(manifest_without_capsule_fields))) - "meta_capsule_hash" sha256(meta_capsule_string) - Decode: json.loads(zlib.decompress(base64.urlsafe_b64decode(c + padding))) - -PACKAGE REGISTRY ----------------- -The PACKAGES dict at the top of this file defines all deployable units. -Each entry gives the PTOS manifest fields + the list of files to include. -cmd_build() fills in the computed fields (nd_point, foam_score, sha256, -sealed_utc, meta_capsule) automatically. - -To add a new package: copy an existing entry, change the name and files list, -adjust the PTOS fields, re-run `deploy `. - -AUTO-DEPLOY HOOK ----------------- -`hook install` writes .git/hooks/post-commit to call this script after every -commit. The hook diffs HEAD against the package file lists and redeploys any -package whose files were touched. This is the "sidecar" behaviour: normal git -workflow continues unchanged; the hook fires in the background. - -Usage: - python metafoam_pkg.py list list defined packages - python metafoam_pkg.py build build orphan commit + tag - python metafoam_pkg.py push [remote] push tag to remote - python metafoam_pkg.py deploy [remote] build + push in one step - python metafoam_pkg.py deploy-all [remote] deploy every package - python metafoam_pkg.py status [remote] show pkg/* tags on remote - python metafoam_pkg.py receive [dir] (VPS) fetch + unpack - python metafoam_pkg.py decode pretty-print meta_capsule - python metafoam_pkg.py hook install install post-commit hook - python metafoam_pkg.py hook remove remove hook - python metafoam_pkg.py hook run run hook logic (called by git) -""" - -import base64 -import hashlib -import json -import os -import subprocess -import sys -import tempfile -import zlib -from datetime import datetime, timezone -from pathlib import Path - -# ───────────────────────────────────────────────────────────────────────────── -# Constants -# ───────────────────────────────────────────────────────────────────────────── - -REPO_ROOT = Path(__file__).resolve().parent.parent -while not (REPO_ROOT / ".git").exists() and REPO_ROOT != REPO_ROOT.parent: - REPO_ROOT = REPO_ROOT.parent -DEFAULT_REMOTE = os.environ.get("PTOS_REMOTE", "github") -_PHI = (1 + 5 ** 0.5) / 2 # golden ratio — used for nd_point and foam_score -SIDECAR_EXTENSIONS = frozenset({ - ".json", - ".jsonl", - ".md", - ".txt", - ".html", - ".htm", - ".csv", -}) - - -# ───────────────────────────────────────────────────────────────────────────── -# Package Registry -# -# Each entry fully specifies a deployable unit: -# • PTOS operational + tier + semantic tags (written verbatim to tag annotation) -# • files: repo-relative paths included in the package tree -# • depends: other packages this one requires (informational — not auto-fetched) -# -# Computed at build time (do not set here): -# nd_point, foam_score, sha256, sealed_utc, meta_capsule, meta_capsule_hash -# ───────────────────────────────────────────────────────────────────────────── - -PACKAGES: dict = { - - # ── USC Audio / Topological Soliton Codec ──────────────────────────────── - # - # Five-physics model: gravity (Bekenstein snag) + Doppler + angular momentum - # (Kerr) + conversion efficiency η + friction (Shakura-Sunyaev). - # Gamma pattern principle: transmit index into shared physical basis, not - # the value — pos_x/bandwidth/rate reconstructed at decode from band_idx. - # Layer 1: 6-byte SolitonBox (4-byte label + f16 amplitude). - # Layer 2: Jupiter φ-locked 14-mode residual (round-trip < 0.5% error). - # Relativistic subregister: FLAME frames split 2×/4×/8× until resolved. - # 23-domain basis registry (audio → radio → xray → exotic: monopole, - # kk_tower, regge, tachyon, octonion, axion, ads_cft, padic_2 …). - # Streaming results: PURE TONE 45×, CHORD 40×, WHITE NOISE 42× (16 frames). - "usc-audio": { - "version": "1.0.0", - "layer": "CORE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "CODE", - "tier": "CRYSTALLINE", - "module": "USC_AUDIO_CODEC", - "archetype": "TOPOLOGICAL_SOLITON_ENCODER", - "tags": ["TSE", "soliton", "compression", "audio", - "gamma_pattern", "bekenstein", "jupiter"], - "visibility": "PUBLIC", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "PHYSICS_BOUND", - "description": ( - "USC Topological Soliton Audio Codec — five-physics model, " - "gamma pattern principle, relativistic subregister, " - "omnitoken manifest, 23-domain basis registry" - ), - "files": [ - "5-Applications/scripts/soliton_factory.py", - "5-Applications/scripts/audio_compression_sim.py", - "5-Applications/scripts/usc_spectral_core.py", - "5-Applications/scripts/video_compression_sim.py", - "5-Applications/scripts/solidified_poc.py", - "6-Documentation/docs/USC_AUDIO_CODEC_CHANGELOG.md", - "USC_Topological_Soliton_Encoding_Spec.md", - ], - "depends": [], - }, - - # ── MetaFoam Rust Engine ───────────────────────────────────────────────── - # - # All TSM physics-gap opcodes closed with Landauer energy: - # WHITEHOLE_DECOMPRESS (0x63), FOAM_MAP (0x0E), - # VDP_COMPRESS_NSPACE (0x19), QUANTUM_MELT_NSPACE (0x1A). - # FOAM_COLLAPSE / FOAM_FUSE / FOAM_MERGE → pdqsort neighbor lists. - "metafoam-engine": { - "version": "1.0.0", - "layer": "CORE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "CODE", - "tier": "CRYSTALLINE", - "module": "METAFOAM_ENGINE", - "archetype": "TSM_OPCODE_EXECUTOR", - "tags": ["TSM", "foam", "rust", "landauer", "opcodes", "pdqsort"], - "visibility": "PUBLIC", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "PHYSICS_BOUND", - "description": ( - "MetaFoam Rust TSM engine — all Landauer opcodes closed, " - "pdqsort neighbor lists, foam collapse/fuse/merge" - ), - "files": [ - "CATEGORY/TSM/tsm_foam_package/rust/metafoam_engine.rs", - "CATEGORY/TSM/tsm_foam_package/rust/lib.rs", - "CATEGORY/TSM/tsm_foam_package/rust/metafoam_opcodes_reference.json", - "CATEGORY/TSM/tsm_foam_package/rust/metafoam_opcodes_reference.md", - "CATEGORY/TSM/tsm_foam_package/rust/whitehole_opcode_reference_full.json", - ], - "depends": [], - }, - - # ── Omnitoken v3 Surface Bus ───────────────────────────────────────────── - # - # GraphVM preamble for the USC codec stream. - # archive_compression domain activated. 14-axis nd_point = Jupiter mode - # vector. foam_score = φ-ratio = channel quality. - # Register state machine: potential → candidate → collapsed → committed. - "omnitoken": { - "version": "3.0.0", - "layer": "CARRY", - "domain": "TOKEN", - "condition": "STABLE", - "stage": "ACTIVE", - "source": "MANIFEST", - "tier": "FOAM", - "module": "OMNITOKEN_GRAPHVM", - "archetype": "PANSUBSTRATE_LOGIC_SURFACE", - "tags": ["omnitoken", "graphvm", "foam", "tunnel", "nd_point"], - "visibility": "INTERNAL", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "CROSS_CHECKED", - "description": ( - "Omnitoken v3 GraphVM surface bus — archive_compression domain " - "activated, 14-axis nd_point, foam_score, register state machine, " - "I2P sovereign AI chat interface, sovereign_code clean-room CLI" - ), - "files": [ - "5-Applications/out/omnitoken_bridge/omnitoken_v3.json", - "5-Applications/out/omnitoken_bridge/omnitoken_main.json", - "i2p_openweb.py", - "sovereign_code/__init__.py", - "sovereign_code/__main__.py", - "sovereign_code/core.py", - "sovereign_code/tools.py", - "sovereign_code/cli.py", - ], - "depends": [], - }, - - # ── Substrate ISA Spec ─────────────────────────────────────────────────── - # - # Canonical tag schema and sovereign stack architecture. - # This package is the schema authority — other packages reference it. - "substrate-isa": { - "version": "1.0.0", - "layer": "RULE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "SPEC", - "tier": "GOVERNANCE", - "module": "SUBSTRATE_ISA", - "archetype": "SOVEREIGN_DISCLOSURE", - "tags": ["ISA", "substrate", "spec", "ptos", "schema"], - "visibility": "PUBLIC", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "OBSERVED", - "description": "Substrate ISA specification and PTOS tag schema", - "files": [ - "brain/substrate_isa_spec.md", - "6-Documentation/docs/PTOS_TAG_SCHEMA.md", - "6-Documentation/docs/SOVEREIGN_STACK_ARCHITECTURE.md", - "6-Documentation/docs/TSM-AAC_v1_spec.md", - ], - "depends": [], - }, - - # ── Semantic Ingestion / Math Tooling ────────────────────────────────── - # - # PTOS-aware ingestion, LaTeX normalization, repair/reindex passes, and - # math-check backend routing. This package exists so git-sidecar deploys - # follow the same revisions that updated the local ingestion/search layer. - "semantic-ingestion": { - "version": "1.0.0", - "layer": "STORE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "CODE", - "tier": "GOVERNANCE", - "module": "SEMANTIC_INGESTION_PIPELINE", - "archetype": "PTOS_SYMBOLIC_ROUTER", - "tags": ["ingest", "ptos", "latex", "math-check", - "indexing", "repair", "sessions"], - "visibility": "INTERNAL", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "OBSERVED", - "description": ( - "Semantic ingestion and math-routing toolchain — PTOS schema, " - "LaTeX normalization, repair passes, backend registry, and " - "session-aware index tooling" - ), - "files": [ - "6-Documentation/docs/PTOS_TAG_SCHEMA.md", - "5-Applications/scripts/ingest_attachments.py", - "5-Applications/scripts/ingest_archive.py", - "5-Applications/scripts/ingest_large_file.py", - "5-Applications/scripts/iso_pipeline.py", - "5-Applications/scripts/iso_symbol_table.py", - "5-Applications/scripts/math_check_packages.py", - "5-Applications/scripts/repair_index.py", - "5-Applications/scripts/stage0_classifier.py", - "5-Applications/tools-scripts/substrate/substrate_git_index.py", - "4-Infrastructure/witness/sources.json", - "5-Applications/tests/test_iso_symbol_table.py", - "5-Applications/tests/test_math_check_packages.py", - ], - "depends": ["substrate-isa/1.0.0"], - }, - - # ── Geometry / WaveProbe Research Sessions ────────────────────────────── - # - # Structured session captures for the 120-cell, WaveProbe, spoiler-game, - # and math-ingestion throughline. Same-stem expansion picks up the paired - # markdown notes automatically when the JSON session manifests are listed. - "geometry-search-sessions": { - "version": "1.0.0", - "layer": "RULE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "NOTE", - "tier": "RESEARCH", - "module": "GEOMETRIC_SEARCH_SESSION_CLUSTER", - "archetype": "CONCEPT_CONTINUATION", - "tags": ["sessions", "120-cell", "waveprobe", - "spoiler-game", "math-ingestion", - "geometric-search"], - "visibility": "PRIVATE", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "DECLARED", - "description": ( - "Research-session cluster capturing the 120-cell geometric " - "encoding bridge and its continuation into adversarial search, " - "WaveProbe/QUBO, and symbol-terrain math ingestion" - ), - "files": [ - "sessions/chat-120cell-hachimoji-manifold-20260402.json", - "sessions/chat-waveprobe-spoiler-math-ingestion-20260402.json", - ], - "depends": ["semantic-ingestion/1.0.0"], - }, - - # ── GeomTREE / KDA-16 Structural Attestation ──────────────────────────── - # - # Baseline hardware-attestation docs for the jack / GeomTREE family plus - # the verifier-side speculative MMR extension path. This keeps the - # structural-attestation stack visible to the git sidecar instead of - # leaving it only in loose docs and patent notes. - "geomtree-jack": { - "version": "1.0.0", - "layer": "RULE", - "domain": "MATERIAL", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "SPEC", - "tier": "SINGULARITY", - "module": "GEOMTREE_KDA16", - "archetype": "SELF_ATTESTING_STRUCTURAL_ELEMENT", - "tags": ["geomtree", "kda-16", "jack", "attestation", - "physical-merkle", "me-shunt", "mmr"], - "visibility": "INTERNAL", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "DECLARED", - "description": ( - "GeomTREE / KDA-16 structural attestation docs — current " - "incremental-Merkle baseline plus speculative MMR history path" - ), - "files": [ - "6-Documentation/docs/GEOMTREE_TECHNICAL_FACT_SHEET.md", - "6-Documentation/docs/GEOMTREE_INTERVIEW_TALKING_POINTS.md", - "GEOMETRIC_SECURITY_MAPPING.md", - "RESEARCH_PORTFOLIO.md", - "hutter/GEOMTREE_PITCH.md", - "PATENT_APPLICATION/16_Semi_Jack_Self_Authenticating_Structural_Element.md", - "6-Documentation/docs/roadmap/GEOMTREE_MMR_ATTESTATION_PATH.md", - ], - "depends": ["substrate-isa/1.0.0"], - }, - - # ── Deduplication + Deep Storage ───────────────────────────────────────── - # - # American Flag Sort (MSD radix, O(64n)) on 64-char hex hashes. - # Batched LUT write: single JSON write replacing O(n) subprocess calls. - "deep-storage": { - "version": "1.0.0", - "layer": "STORE", - "domain": "STORE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "CODE", - "tier": "FOAM", - "module": "DEEP_STORAGE_DEDUP", - "archetype": "TUNNEL_INGRESS", - "tags": ["dedup", "radix_sort", "american_flag", "lut"], - "visibility": "INTERNAL", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "OBSERVED", - "description": ( - "Deduplication tools — American Flag Sort (O(64n)) on hex " - "hashes, batched LUT write, deep-storage management" - ), - "files": [ - "dedupe_home_against_local.py", - "dedupe_gdrive_against_local.py", - "dedupe_to_devnull.py", - "deep_storage_lut.py", - ], - "depends": [], - }, - - # ── Bind Bridge / Functional Collapse ──────────────────────────────────── - # - # The Cambrian collapse: a single bind primitive replaces all previous - # model families (informational, geometric, thermodynamic, physical, - # control). Lean 4 proves conservation laws; Q16.16 fixed-point enables - # hardware-native execution; Python acts as a minimal history loader. - # 105-kind bounded Standard Model particle address space. - "bind-bridge": { - "version": "1.0.0", - "layer": "CORE", - "domain": "COMPUTE", - "condition": "EXPERIMENTAL", - "stage": "ACTIVE", - "source": "CODE", - "tier": "CRYSTALLINE", - "module": "BIND_BRIDGE", - "archetype": "UNIVERSAL_BIND_PRIMITIVE", - "tags": ["bind", "lean4", "q16_16", "physics", - "conservation", "cambrian", "collapse"], - "visibility": "PUBLIC", - "model_status": "CANONICAL", - "taint_status": "CLEAN", - "verification_basis": "PHYSICS_BOUND", - "description": ( - "Functional collapse to a single bind primitive — Lean 4 " - "bindserver with Q16.16 fixed-point arithmetic, 105-kind " - "particle domain, and minimal Python loader" - ), - "files": [ - "0-Core-Formalism/lean/Semantics/BindServer.lean", - "0-Core-Formalism/lean/Semantics/lakefile.toml", - "0-Core-Formalism/lean/Semantics/Semantics/Bind.lean", - "0-Core-Formalism/lean/Semantics/Semantics/FixedPoint.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/BindPhysics.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Boundary.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Conservation.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Examples.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Interaction.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/ParticleDomain.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Projection.lean", - "0-Core-Formalism/lean/Semantics/Semantics/Physics/Tests.lean", - "4-Infrastructure/infra/access_control/bind_engine.py", - "6-Documentation/docs/VISION_NORTH_STAR.md", - "6-Documentation/docs/geometry/FUNCTIONAL_COLLAPSE_PARADIGM.md", - "6-Documentation/docs/geometry/BIND_MIGRATION_GUIDE.md", - "6-Documentation/docs/semantics/INCOMPATIBLE_MANIFOLDS_AND_LAWFUL_LOSS.md", - "6-Documentation/docs/semantics/BIND_BRIDGE_EQUATIONS.md", - "6-Documentation/docs/physics/PHYSICAL_SEMANTICS_PARADIGM.md", - ], - "depends": ["substrate-isa/1.0.0"], - }, - -} - - -# ───────────────────────────────────────────────────────────────────────────── -# Git plumbing helpers (no working-tree modification) -# ───────────────────────────────────────────────────────────────────────────── - -def _git(*args: str, stdin: str | None = None, check: bool = True) -> str: - """Run a git plumbing subcommand in REPO_ROOT and return stdout stripped. - - # Why this wrapper exists - All git calls are funnelled here so that: - • cwd is always REPO_ROOT (not the CWD of whoever calls the script) - • subprocess.run is always called with check=False (we raise our own - RuntimeError with cleaned stderr instead of CalledProcessError) - • stdin piping is handled in one place (needed by git mktree / commit-tree) - - # Parameters - *args : git subcommand + arguments, e.g. ("tag", "-a", name, "-m", msg) - stdin : optional string piped to git's stdin (None = no pipe) - check : if True, raise RuntimeError on non-zero exit code - if False, return empty string on failure (caller decides) - - # Returns - Stripped stdout string. Empty string on failure when check=False. - """ - cmd = ["git"] + list(args) - result = subprocess.run( - cmd, cwd=REPO_ROOT, capture_output=True, text=True, - input=stdin, check=False, - ) - if check and result.returncode != 0: - raise RuntimeError( - f"git {' '.join(args)} failed:\n{result.stderr.strip()}" - ) - return result.stdout.strip() - - -def _same_stem_sidecars(rel_path: str) -> list[str]: - """Return repo-relative same-stem sidecars for a tracked package file. - - This mirrors the ingest-side attachment idea so package sidecars keep - session json/md pairs and similar companion files together. - """ - source = REPO_ROOT / rel_path - if not source.exists() or not source.is_file(): - return [] - - out: list[str] = [] - parent = source.parent - for sibling in sorted(parent.iterdir()): - if sibling == source or not sibling.is_file(): - continue - if sibling.suffix.lower() not in SIDECAR_EXTENSIONS: - continue - if sibling.stem == source.stem: - out.append(str(sibling.relative_to(REPO_ROOT))) - return out - - -def _expand_package_files(files: list[str]) -> list[str]: - """Expand a package file list to include same-stem sidecars once. - - The git sidecar still uses an explicit registry, but this keeps paired - artifacts such as `session.json` + `session.md` or similar descriptor - companions in sync without duplicating every sibling in PACKAGES. - """ - expanded: list[str] = [] - seen: set[str] = set() - for rel in files: - norm = str(Path(rel)) - if norm not in seen: - expanded.append(norm) - seen.add(norm) - for sidecar in _same_stem_sidecars(norm): - if sidecar in seen: - continue - expanded.append(sidecar) - seen.add(sidecar) - return expanded - - -def _hash_file_contents(files: list[str]) -> str: - """Return SHA-256 of sorted, concatenated file bytes. - - # Provenance anchor - The hash travels in the tag annotation as the "sha256" provenance field - (PTOS_TAG_SCHEMA provenance axis). Sorting the file list before hashing - makes the digest independent of insertion order — the same set of files - always produces the same hash regardless of how the caller built the list. - - # Parameters - files : list of repo-relative paths (missing files are skipped silently) - - # Returns - 64-character lowercase hex SHA-256 digest. - """ - h = hashlib.sha256() - for rel in sorted(_expand_package_files(files)): - p = REPO_ROOT / rel - if p.exists(): - h.update(p.read_bytes()) - return h.hexdigest() - - -def _build_tree(files: list[str]) -> tuple[str, list[str]]: - """Build a git tree object from repo-relative paths without touching the - working tree or .git/index. - - # Why not git mktree? - git mktree only handles flat (single-level) trees. Paths like - "5-Applications/scripts/soliton_factory.py" contain a slash and cause a fatal error. - The temporary-index approach handles arbitrary nesting. - - # Temporary index pattern (the key trick) - git uses GIT_INDEX_FILE to redirect all index operations to a custom path. - We: - 1. Generate a unique temp path via tempfile.NamedTemporaryFile. - 2. Immediately delete it — git treats a 0-byte file as a corrupt index - and fails with "index file smaller than expected". The path must not - exist so git can create a fresh index on first update-index call. - 3. Set GIT_INDEX_FILE= in the subprocess environment. - 4. Run git update-index --add --cacheinfo ,, for - each file. This writes blobs into .git/objects and registers the - path in the temp index. - 5. Run git write-tree to serialise the temp index as a tree object. - 6. Delete the temp index in the finally block. - - # File modes - 100644 regular file (non-executable) - 100755 executable file (checked via os.access) - - # Parameters - files : list of repo-relative paths to include - - # Returns - (tree_hash, included_files) where included_files omits any paths that - did not exist on disk at build time. - """ - included: list[str] = [] - blobs: list[tuple[str, str, str]] = [] - - for rel in files: - abs_path = REPO_ROOT / rel - if not abs_path.exists(): - print(f" [skip] {rel} — not found") - continue - blob = _git("hash-object", "-w", str(abs_path)) - mode = "100755" if os.access(abs_path, os.X_OK) else "100644" - blobs.append((mode, blob, rel)) - included.append(rel) - - if not blobs: - raise RuntimeError("No files found for package tree") - - # Obtain a unique temp path then delete it so git creates a fresh index. - with tempfile.NamedTemporaryFile(prefix="metafoam_index_", delete=True) as tf: - tmp_index = tf.name - # tf.name is now deleted; git writes a valid index on first update-index. - - env = os.environ.copy() - env["GIT_INDEX_FILE"] = tmp_index - - try: - for mode, blob, rel in blobs: - subprocess.run( - ["git", "update-index", "--add", "--cacheinfo", - f"{mode},{blob},{rel}"], - cwd=REPO_ROOT, env=env, check=True, - capture_output=True, - ) - result = subprocess.run( - ["git", "write-tree"], - cwd=REPO_ROOT, env=env, check=True, - capture_output=True, text=True, - ) - tree_hash = result.stdout.strip() - finally: - try: - os.unlink(tmp_index) - except OSError: - pass - - return tree_hash, included - - -def _make_meta_capsule(payload: dict) -> tuple[str, str]: - """Compress payload into a meta_capsule per PTOS_TAG_SCHEMA.md. - - # Encoding pipeline (must be reproducible across Python versions) - 1. json.dumps(payload, sort_keys=True, separators=(",", ":")) - → deterministic JSON bytes (no spaces, keys sorted) - 2. zlib.compress(raw, level=9) - → deflate stream, maximum compression - 3. base64.urlsafe_b64encode(compressed).rstrip("=") - → URL-safe base64 without padding (safe for git tag annotations - which may be passed through shell variables) - 4. sha256(capsule_string) - → integrity anchor stored alongside the capsule - - # Decoding (any language) - padding = "=" * (-len(capsule) % 4) - json.loads(zlib.decompress(base64.urlsafe_b64decode(capsule + padding))) - - # Why sort_keys? - Determinism: the same logical payload always produces the same capsule - regardless of Python dict insertion order. This makes meta_capsule_hash - a stable integrity anchor across rebuilds. - - # Parameters - payload : dict to compress (should NOT yet contain meta_capsule / - meta_capsule_hash fields — avoids circularity) - - # Returns - (capsule_b64url_no_padding, sha256_hex_of_capsule_string) - """ - raw = json.dumps(payload, sort_keys=True, separators=(",", ":")).encode() - compressed = zlib.compress(raw, level=9) - capsule = base64.urlsafe_b64encode(compressed).decode().rstrip("=") - capsule_hash = hashlib.sha256(capsule.encode()).hexdigest() - return capsule, capsule_hash - - -# ───────────────────────────────────────────────────────────────────────────── -# Core commands -# ───────────────────────────────────────────────────────────────────────────── - -def cmd_build(pkg_name: str) -> str: - """Build an orphan git commit + PTOS-annotated tag for a named package. - - # What this produces - A git tag named pkg//v that: - • Points to an orphan commit (a root commit with no parents, so it - carries ONLY the package files — no repo history). - • Has a JSON annotation containing the full PTOS manifest, including - nd_point, foam_score, sha256, sealed_utc, and a meta_capsule. - - Pushing just this tag to a remote transfers only the objects reachable - from it (the orphan commit + its tree + its blobs). Nothing else from - the local repo history crosses the wire. - - # Build steps - 1. hash-object Write each file into .git/objects as a blob. - 2. _build_tree Assemble blobs into a tree via temp index - (see _build_tree docstring for the index trick). - 3. nd_point 14-axis omnitoken position: axis i = φ^(-i), rounded - to 6 decimal places. Encodes the package's "location" - in the 14-dimensional omnitoken surface bus. - 4. foam_score φ × (n_files / (n_deps + 1)). Higher = more content - per dependency. Mirrors the codec's foam_score metric. - 5. sha256 Sorted-concatenated file hash for provenance anchoring. - 6. manifest Full PTOS-tagged dict (see TAG ANNOTATION SCHEMA in - module docstring). - 7. meta_capsule zlib+base64 compressed manifest (without itself). - Stored alongside meta_capsule_hash for integrity. - 8. commit-tree Creates a root commit from the tree + manifest message. - No -p flag → orphan (no parents). - 9. tag -a Annotates the commit. Rebuilding deletes the existing - local tag first (idempotent — safe to call repeatedly). - - # φ (golden ratio) = (1 + √5) / 2 ≈ 1.618033… - Used in both the soliton codec (Jupiter layer φ-locking, foam_score) and - here as the metric for package "information density". - - # Parameters - pkg_name : key in PACKAGES dict - - # Returns - Tag name string, e.g. "pkg/usc-audio/v1.0.0" - - # Errors - KeyError if pkg_name is not in PACKAGES - RuntimeError if no files in the package exist on disk, or a git - plumbing call fails - """ - if pkg_name not in PACKAGES: - raise KeyError( - f"Unknown package: {pkg_name!r}. Known: {sorted(PACKAGES)}" - ) - - spec = PACKAGES[pkg_name].copy() - now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - package_files = _expand_package_files(spec["files"]) - tree_hash, included_files = _build_tree(package_files) - - # nd_point: 14-axis omnitoken position, φ^(-i) normalized - nd_point = [round(_PHI ** (-i), 6) for i in range(min(14, len(included_files)))] - while len(nd_point) < 14: - nd_point.append(0.0) - - # foam_score: φ × (files / (deps + 1)) — higher = denser, fewer deps - n_deps = len(spec.get("depends", [])) - foam_score = round(len(included_files) / (n_deps + 1) * _PHI, 6) - - content_sha256 = _hash_file_contents(included_files) - - manifest: dict = { - "schema": "metafoam-pkg/v1", - "pkg": pkg_name, - "version": spec["version"], - "layer": spec["layer"], - "domain": spec["domain"], - "condition": spec["condition"], - "stage": spec["stage"], - "source": spec["source"], - "tier": spec["tier"], - "module": spec["module"], - "archetype": spec.get("archetype", ""), - "tags": spec.get("tags", []), - "description": spec.get("description", ""), - "files": included_files, - "depends": spec.get("depends", []), - "nd_point": nd_point, - "foam_score": foam_score, - "sealed_utc": now_utc, - "sha256": content_sha256, - "verification_basis": spec.get("verification_basis", "OBSERVED"), - "visibility": spec.get("visibility", "INTERNAL"), - "model_status": spec.get("model_status", "CANONICAL"), - "taint_status": spec.get("taint_status", "CLEAN"), - } - - # meta_capsule is computed over the manifest sans itself (no circularity) - capsule, capsule_hash = _make_meta_capsule(manifest) - manifest["meta_capsule"] = capsule - manifest["meta_capsule_hash"] = capsule_hash - - annotation = json.dumps(manifest, indent=2) - - # Orphan commit — no -p parent flags → root commit - commit_hash = _git("commit-tree", tree_hash, "-m", annotation) - - tag_name = f"pkg/{pkg_name}/v{spec['version']}" - - # Delete existing local tag silently (idempotent rebuild) - _git("tag", "-d", tag_name, check=False) - _git("tag", "-a", tag_name, commit_hash, "-m", annotation) - - print(f"[build] {tag_name}") - print(f" commit : {commit_hash[:16]}") - print(f" tree : {tree_hash[:16]}") - print(f" files : {len(included_files)}") - print(f" foam_score: {foam_score}") - print(f" sha256 : {content_sha256[:16]}…") - print(f" capsule : {capsule[:32]}…") - - return tag_name - - -def cmd_push(pkg_name: str, remote: str = DEFAULT_REMOTE) -> None: - """Push a package tag to a remote. - - Only the tag + its orphan commit/tree/blobs are transferred. - The full repo history is not pushed. - - # Force-push behaviour - Package tags are rebuilt deterministically (metafoam_pkg.py build is - idempotent on content, but creates a new orphan commit each time). - If the tag already exists on the remote, force-push replaces it. - This is safe because package tags are not branch heads — they are - typed deployment units that represent "the current build of package X", - not a shared history anchor. - """ - spec = PACKAGES[pkg_name] - tag_name = f"pkg/{pkg_name}/v{spec['version']}" - refspec = f"+refs/tags/{tag_name}:refs/tags/{tag_name}" # + = force - print(f"[push] {tag_name} → {remote} …") - _git("push", remote, refspec) - print("[push] done") - - -def cmd_deploy(pkg_name: str, remote: str = DEFAULT_REMOTE) -> None: - """Build + push in one step (the normal operator workflow).""" - cmd_build(pkg_name) - cmd_push(pkg_name, remote) - - -def cmd_list() -> None: - """Print a table of all defined packages.""" - header = ( - f"{'Package':<22} {'Ver':<8} {'Tier':<14} " - f"{'Domain':<10} {'Files':<6} Description" - ) - print(header) - print("-" * 100) - for name, spec in sorted(PACKAGES.items()): - n_files = len(spec["files"]) - desc = spec.get("description", "")[:50] - print( - f"{name:<22} {spec['version']:<8} {spec['tier']:<14} " - f"{spec['domain']:<10} {n_files:<6} {desc}" - ) - - -def cmd_status(remote: str = DEFAULT_REMOTE) -> None: - """List all pkg/* annotated tags present on the remote.""" - print(f"[status] fetching tag list from {remote} …") - out = _git("ls-remote", "--tags", remote, "refs/tags/pkg/*", check=False) - if not out: - print(" (no pkg/* tags found on remote)") - return - print(f"\n{'Commit':<12} Tag") - print("-" * 60) - for line in out.splitlines(): - parts = line.split("\t") - if len(parts) == 2: - commit, ref = parts - tag = ref.replace("refs/tags/", "") - print(f"{commit[:12]} {tag}") - - -def cmd_receive(pkg_name: str, target_dir: str = ".") -> None: - """Fetch a package tag from the remote and unpack it. - - Intended for use on a substrate node (VPS): - 1. git fetch refs/tags/ - 2. git archive | tar -xC - - Prints the manifest summary (foam_score, file count, sha256) after unpack. - """ - if pkg_name not in PACKAGES: - raise KeyError(f"Unknown package: {pkg_name!r}") - - spec = PACKAGES[pkg_name] - tag_name = f"pkg/{pkg_name}/v{spec['version']}" - refspec = f"refs/tags/{tag_name}:refs/tags/{tag_name}" - - print(f"[receive] fetching {tag_name} from {DEFAULT_REMOTE} …") - _git("fetch", DEFAULT_REMOTE, refspec) - - target = Path(target_dir).resolve() - target.mkdir(parents=True, exist_ok=True) - - with subprocess.Popen( - ["git", "archive", "--format=tar", tag_name], - cwd=REPO_ROOT, stdout=subprocess.PIPE, - ) as archive_proc: - with subprocess.Popen( - ["tar", "-xC", str(target)], - stdin=archive_proc.stdout, - ) as tar_proc: - if archive_proc.stdout: - archive_proc.stdout.close() - tar_proc.wait() - - if archive_proc.returncode != 0 or tar_proc.returncode != 0: - raise RuntimeError(f"receive failed for {tag_name}") - - print(f"[receive] unpacked {tag_name} → {target}") - - raw_ann = _git("tag", "-l", tag_name, "--format=%(contents)") - try: - manifest = json.loads(raw_ann) - print(f" foam_score : {manifest.get('foam_score')}") - print(f" files : {len(manifest.get('files', []))}") - print(f" sealed_utc : {manifest.get('sealed_utc')}") - print(f" sha256 : {manifest.get('sha256', '')[:16]}…") - except json.JSONDecodeError: - pass - - -def cmd_decode(pkg_name: str) -> None: - """Decode and pretty-print the meta_capsule from a locally built tag. - - Decode rule (per PTOS_TAG_SCHEMA.md): - json.loads(zlib.decompress(base64.urlsafe_b64decode(capsule + padding))) - """ - spec = PACKAGES.get(pkg_name) - if not spec: - raise KeyError(f"Unknown package: {pkg_name!r}") - tag_name = f"pkg/{pkg_name}/v{spec['version']}" - raw_ann = _git("tag", "-l", tag_name, "--format=%(contents)") - manifest = json.loads(raw_ann) - capsule = manifest.get("meta_capsule", "") - padding = "=" * (-len(capsule) % 4) - payload = json.loads( - zlib.decompress(base64.urlsafe_b64decode(capsule + padding)) - ) - print(json.dumps(payload, indent=2)) - - -# ───────────────────────────────────────────────────────────────────────────── -# Post-commit hook — automatic sidecar deploy -# -# The hook is the "automatic" part of the sidecar pattern: -# • Normal git workflow is unchanged. -# • After every commit, the hook checks which package files changed in HEAD. -# • Any package with changed files is rebuilt and pushed automatically. -# • Failures are printed as warnings but do not block the commit. -# ───────────────────────────────────────────────────────────────────────────── - -HOOK_PATH = REPO_ROOT / ".git" / "hooks" / "post-commit" -_HOOK_SCRIPT = ( - "#!/bin/sh\n" - "# MetaFoam auto-deploy hook — installed by metafoam_pkg.py\n" - f'python3 "{Path(__file__).resolve()}" hook run\n' -) - - -def cmd_hook(action: str) -> None: - """Manage the post-commit auto-deploy hook. - - # What the hook does - After every `git commit`, git runs .git/hooks/post-commit. The installed - script calls `metafoam_pkg.py hook run`, which: - 1. Reads the list of files changed in HEAD via git diff-tree. - 2. Compares that list against every package's file list. - 3. For any package with changed files, calls cmd_deploy() automatically. - - This is the "sidecar" behaviour: normal git workflow is completely - unchanged. The hook fires in the background and keeps substrate-node - deployments in sync with the repo without any manual step. - - # Failure behaviour - Deploy failures inside the hook are caught and printed as warnings. They - do NOT block the commit or raise an exit code — the commit always succeeds. - - # Parameters - action : "install" | "remove" | "run" - install Write .git/hooks/post-commit and chmod +x. - remove Delete the hook file (no-op if not present). - run Execute hook logic directly (called by git or by hand). - """ - if action == "install": - HOOK_PATH.write_text(_HOOK_SCRIPT) - HOOK_PATH.chmod(0o755) - print(f"[hook] installed → {HOOK_PATH}") - print(" After each commit, changed packages auto-build and push.") - elif action == "remove": - if HOOK_PATH.exists(): - HOOK_PATH.unlink() - print(f"[hook] removed {HOOK_PATH}") - else: - print("[hook] not installed") - elif action == "run": - _hook_run() - else: - print(f"Unknown hook action: {action!r}. Use: install | remove | run") - - -def _hook_run() -> None: - """Find packages whose tracked files changed in HEAD and redeploy them. - - # Algorithm - 1. git diff-tree --no-commit-id -r --name-only HEAD - → list of repo-relative paths modified by the most recent commit. - 2. For each package in PACKAGES: set(package.files) ∩ changed_files. - If non-empty, the package is stale and needs redeployment. - 3. cmd_deploy() for each stale package (build + push). - Errors are caught as warnings; the hook never blocks a commit. - - # Why diff-tree instead of diff-index? - diff-tree compares the commit object to its parent — it sees exactly what - the commit recorded, independent of current working-tree state. This is - correct even if the working tree has subsequent unstaged changes. - - # Safe to call manually - Running `metafoam_pkg.py hook run` from the shell is identical to what - git would run after a commit. Useful for testing or forced sync. - """ - changed_raw = _git( - "diff-tree", "--no-commit-id", "-r", "--name-only", "HEAD", - check=False, - ) - changed = set(changed_raw.splitlines()) - if not changed: - return - - deployed = [] - for pkg_name, spec in PACKAGES.items(): - if set(_expand_package_files(spec["files"])) & changed: - print(f"[hook] {pkg_name} has changed files — deploying …") - try: - cmd_deploy(pkg_name) - deployed.append(pkg_name) - except (RuntimeError, subprocess.CalledProcessError) as exc: - print(f"[hook] WARN: deploy {pkg_name} failed: {exc}") - - if not deployed: - print("[hook] no package files changed — nothing to deploy") - - -# ───────────────────────────────────────────────────────────────────────────── -# CLI dispatch -# ───────────────────────────────────────────────────────────────────────────── - -def _usage_error(msg: str) -> None: - print(msg, file=sys.stderr) - sys.exit(1) - - -def main() -> None: - """Parse argv and dispatch to the appropriate command function.""" - args = sys.argv[1:] - - if not args or args[0] in ("-h", "--help"): - print(__doc__) - return - - cmd = args[0] - - dispatch: dict[str, object] = { - "list": lambda: cmd_list(), - "status": lambda: cmd_status(args[1] if len(args) > 1 else DEFAULT_REMOTE), - "decode": lambda: ( - cmd_decode(args[1]) if len(args) > 1 - else _usage_error("Usage: metafoam_pkg.py decode ") - ), - "hook": lambda: cmd_hook(args[1] if len(args) > 1 else "install"), - "hook-run": lambda: _hook_run(), - "deploy-all": lambda: _cmd_deploy_all( - args[1] if len(args) > 1 else DEFAULT_REMOTE - ), - } - - if cmd in dispatch: - dispatch[cmd]() - return - - # Commands that need at least one positional arg - if len(args) < 2: - _usage_error( - f"Usage: metafoam_pkg.py {cmd} [remote]\n" - "Run with --help for full usage." - ) - - pkg = args[1] - remote = args[2] if len(args) > 2 else DEFAULT_REMOTE - - if cmd == "build": - cmd_build(pkg) - elif cmd == "push": - cmd_push(pkg, remote) - elif cmd == "deploy": - cmd_deploy(pkg, remote) - elif cmd == "receive": - cmd_receive(pkg, remote) # remote arg doubles as target_dir here - else: - print( - f"Unknown command: {cmd!r}\n" - "Commands: list | build | push | deploy | deploy-all | " - "status | receive | decode | hook" - ) - sys.exit(1) - - -def _cmd_deploy_all(remote: str) -> None: - """Deploy every registered package to remote.""" - for pkg_name in sorted(PACKAGES): - print(f"\n{'=' * 60}") - cmd_deploy(pkg_name, remote) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/model/model_chain_friction.py b/5-Applications/tools-scripts/model/model_chain_friction.py deleted file mode 100644 index b966e13a..00000000 --- a/5-Applications/tools-scripts/model/model_chain_friction.py +++ /dev/null @@ -1,201 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -from pathlib import Path -from typing import Any, Dict, List - -try: - from scripts.gpgpu_surface import get_surface -except ImportError: - from gpgpu_surface import get_surface - - -SURFACE = get_surface() - - -def load_jsonl(path: Path) -> List[Dict[str, Any]]: - rows: List[Dict[str, Any]] = [] - with path.open("r", encoding="utf-8") as handle: - for line in handle: - s = line.strip() - if s: - rows.append(json.loads(s)) - return rows - - -def mean(values: List[float]) -> float: - return SURFACE.mean(values) - - -def std(values: List[float]) -> float: - return SURFACE.std(values) - - -def zscore(value: float, values: List[float]) -> float: - sigma = std(values) - if sigma == 0.0: - return 0.0 - return (value - mean(values)) / sigma - - -def infer_chain_from_strategy_id(strategy_id: str) -> str: - # Expected simulation format: SIM--- - parts = strategy_id.split("-") - if len(parts) >= 3 and parts[0] == "SIM": - return parts[1].lower() - return "unknown" - - -def realized_volatility(prices: List[float]) -> float: - if len(prices) < 2: - return 0.0 - rets: List[float] = [] - for idx in range(1, len(prices)): - prev = prices[idx - 1] - cur = prices[idx] - if prev > 0: - rets.append((cur - prev) / prev) - if not rets: - return 0.0 - return float(std(rets)) - - -def build_chain_metrics( - chain_rows: List[Dict[str, Any]], - post_rows: List[Dict[str, Any]], -) -> Dict[str, Dict[str, float]]: - prices_by_chain: Dict[str, List[float]] = {} - gas_by_chain: Dict[str, List[float]] = {} - spread_by_chain: Dict[str, List[float]] = {} - - for row in chain_rows: - chain = str(row.get("chain", "unknown")).lower() - prices_by_chain.setdefault(chain, []).append(float(row.get("price_usd", 0.0))) - gas_by_chain.setdefault(chain, []).append(float(row.get("gas_estimate_usd", 0.0))) - spread_by_chain.setdefault(chain, []).append(float(row.get("spread_bps", 0.0))) - - decision_counts: Dict[str, int] = {} - pause_counts: Dict[str, int] = {} - for row in post_rows: - strategy_id = str(row.get("strategy_id", "")) - chain = infer_chain_from_strategy_id(strategy_id) - decision_counts[chain] = decision_counts.get(chain, 0) + 1 - if str(row.get("outcome", "")).upper() == "PAUSED": - pause_counts[chain] = pause_counts.get(chain, 0) + 1 - - metrics: Dict[str, Dict[str, float]] = {} - for chain in sorted(prices_by_chain.keys()): - decisions = decision_counts.get(chain, 0) - pauses = pause_counts.get(chain, 0) - pause_rate = (pauses / decisions) if decisions else 0.0 - metrics[chain] = { - "avg_gas_usd": mean(gas_by_chain.get(chain, [])), - "median_spread_bps": sorted(spread_by_chain.get(chain, [0.0]))[len(spread_by_chain.get(chain, [0.0])) // 2], - "realized_volatility": realized_volatility(prices_by_chain.get(chain, [])), - "pause_rate": pause_rate, - "sample_count": float(len(prices_by_chain.get(chain, []))), - } - return metrics - - -def rank_chains(metrics: Dict[str, Dict[str, float]]) -> List[Dict[str, Any]]: - chains = sorted(metrics.keys()) - gas_vec = [metrics[c]["avg_gas_usd"] for c in chains] - spread_vec = [metrics[c]["median_spread_bps"] for c in chains] - vol_vec = [metrics[c]["realized_volatility"] for c in chains] - pause_vec = [metrics[c]["pause_rate"] for c in chains] - - ranked: List[Dict[str, Any]] = [] - gas_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(gas_vec))} - spread_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(spread_vec))} - vol_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(vol_vec))} - pause_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(pause_vec))} - - for chain in chains: - gas_z = gas_z_map[chain] - spread_z = spread_z_map[chain] - vol_z = vol_z_map[chain] - pause_z = pause_z_map[chain] - - # Lower is better: friction score approximates chain "physics" drag. - friction_score = ( - 0.40 * gas_z - + 0.30 * spread_z - + 0.20 * vol_z - + 0.10 * pause_z - ) - - # Convert to a bounded opportunity score in [0, 100]. - opportunity_score = 100.0 * (1.0 - SURFACE.sigmoid(friction_score)) - - ranked.append( - { - "chain": chain, - "friction_score": round(friction_score, 6), - "opportunity_score": round(opportunity_score, 4), - **{k: round(v, 8) for k, v in metrics[chain].items()}, - } - ) - - ranked.sort(key=lambda row: row["friction_score"]) - return ranked - - -def write_markdown(path: Path, ranked: List[Dict[str, Any]]) -> None: - lines: List[str] = [] - lines.append("# Chain Physics Friction Ranking") - lines.append("") - lines.append("Lower friction score means lower execution drag and better accumulation conditions.") - lines.append("") - lines.append("| Rank | Chain | Friction Score | Opportunity Score | Avg Gas USD | Median Spread bps | Realized Volatility | Pause Rate | Samples |") - lines.append("|---|---|---:|---:|---:|---:|---:|---:|---:|") - for idx, row in enumerate(ranked, start=1): - lines.append( - "| " - + f"{idx} | {row['chain']} | {row['friction_score']} | {row['opportunity_score']} | " - + f"{row['avg_gas_usd']} | {row['median_spread_bps']} | {row['realized_volatility']} | " - + f"{row['pause_rate']} | {int(row['sample_count'])} |" - ) - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Model chain physics and rank lowest-friction accumulation targets.") - parser.add_argument("--chain-records", required=True, help="Path to chain_records.jsonl") - parser.add_argument("--post-records", help="Optional path to post_records.jsonl for pause-rate signal") - parser.add_argument("--out-json", required=True, help="Output ranking JSON path") - parser.add_argument("--out-md", required=True, help="Output ranking markdown path") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - chain_rows = load_jsonl(Path(args.chain_records)) - post_rows = load_jsonl(Path(args.post_records)) if args.post_records else [] - - if not chain_rows: - print(json.dumps({"error": "no_chain_records"}, indent=2)) - return 2 - - metrics = build_chain_metrics(chain_rows, post_rows) - ranked = rank_chains(metrics) - - out_json = Path(args.out_json) - out_json.parent.mkdir(parents=True, exist_ok=True) - out_json.write_text(json.dumps({"backend": SURFACE.backend, "ranking": ranked}, indent=2) + "\n", encoding="utf-8") - - write_markdown(Path(args.out_md), ranked) - - print(json.dumps({"backend": SURFACE.backend, "chains_ranked": len(ranked), "best_chain": ranked[0]["chain"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/model/model_hyperfluid_waveforms.py b/5-Applications/tools-scripts/model/model_hyperfluid_waveforms.py deleted file mode 100644 index 080e5123..00000000 --- a/5-Applications/tools-scripts/model/model_hyperfluid_waveforms.py +++ /dev/null @@ -1,1239 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import argparse -import csv -import json -import math -import statistics -# import subprocess (REMOVED BY WARDEN) -import sys -from dataclasses import dataclass -from datetime import datetime -from pathlib import Path -from typing import Any, Dict, List, Optional, Sequence, Tuple, cast - - -def parse_iso(ts: str) -> datetime: - return datetime.fromisoformat(ts.replace("Z", "+00:00")) - - -def load_jsonl(path: Path) -> List[Dict[str, Any]]: - rows: List[Dict[str, Any]] = [] - with path.open("r", encoding="utf-8") as handle: - for line in handle: - s = line.strip() - if s: - rows.append(json.loads(s)) - return rows - - -def parse_strategy(strategy_id: str) -> Tuple[str, str]: - parts = strategy_id.split("-") - if len(parts) >= 5 and parts[0] == "SIM": - chain = parts[1].lower() - pair = f"{parts[2].upper()}/{parts[3].upper()}" - return chain, pair - return "unknown", "unknown/unknown" - - -def extract_impact(row: Dict[str, Any]) -> Dict[str, Any]: - impact = row.get("realized_impact", {}) - return cast(Dict[str, Any], impact) if isinstance(impact, dict) else {} - - -def clamp(v: float, lo: float = -1.0, hi: float = 1.0) -> float: - return max(lo, min(hi, v)) - - -def safe_fmean(vals: Sequence[float]) -> float: - return statistics.fmean(vals) if vals else 0.0 - - -def sign(v: float) -> int: - if v > 0: - return 1 - if v < 0: - return -1 - return 0 - - -def pearson(x: Sequence[float], y: Sequence[float]) -> float: - if len(x) != len(y) or len(x) < 2: - return 0.0 - mx = statistics.fmean(x) - my = statistics.fmean(y) - sx = statistics.pstdev(x) - sy = statistics.pstdev(y) - if sx == 0.0 or sy == 0.0: - return 0.0 - cov = statistics.fmean([(a - mx) * (b - my) for a, b in zip(x, y)]) - return cov / (sx * sy) - - -# --------------------------------------------------------------------------- -# Qutrit layer — interference-based causal pressure -# --------------------------------------------------------------------------- -QutritState = Tuple[float, float, float] # (a0, a1, a2) real amplitudes; sum of squares = 1 - - -def encode_qutrit(v: float) -> QutritState: - """Encode scalar v ∈ [-1, 1] as a normalised real qutrit amplitude vector. - - |0⟩ = bearish, |1⟩ = neutral, |2⟩ = bullish. - Amplitudes are real non-negative; |a0|² + |a1|² + |a2|² = 1. - """ - v = max(-1.0, min(1.0, v)) - neg = max(0.0, -v) - pos = max(0.0, v) - neu = 1.0 - neg - pos # = 1 - |v| ≥ 0 - return (math.sqrt(neg), math.sqrt(neu), math.sqrt(pos)) - - -def gate_12_swap(state: QutritState) -> QutritState: - """π rotation on the |1⟩↔|2⟩ subspace. - - Models PSI subtraction as interference: the neutral and bullish amplitudes - are exchanged, so a negative shock's neutral component is redirected to - bullish rather than being transmitted at full negative amplitude. - """ - a0, a1, a2 = state - return (a0, a2, a1) - - -def qutrit_superpose(states: List[QutritState], weights: List[float]) -> QutritState: - """Weighted amplitude superposition with renormalisation. - - Each state contributes amplitude proportional to sqrt(|weight| / sum_weights). - """ - total = sum(abs(w) for w in weights) - if total <= 0.0: - return (0.0, 1.0, 0.0) - out = [0.0, 0.0, 0.0] - for state, w in zip(states, weights): - amp = math.sqrt(abs(w) / total) - out[0] += amp * state[0] - out[1] += amp * state[1] - out[2] += amp * state[2] - norm = math.sqrt(out[0] * out[0] + out[1] * out[1] + out[2] * out[2]) - if norm > 1e-12: - out = [x / norm for x in out] - return (out[0], out[1], out[2]) - - -def collapse_qutrit(state: QutritState, s_wt: float) -> Tuple[float, float]: - """Measurement: extract (qutrit_pressure, qutrit_confidence). - - pressure = s_wt × (p2 − p0) where pi = amplitude_i² - confidence = |p2 − p0| × (1 − p1) penalises neutral-dominant states - """ - a0, a1, a2 = state - p0 = a0 * a0 - p1 = a1 * a1 - p2 = a2 * a2 - charge = p2 - p0 - return s_wt * charge, clamp(abs(charge) * (1.0 - p1), 0.0, 1.0) - - -def compute_qutrit_pressure(hpi: float, mti: float, psi: float, s_wt: float) -> Tuple[float, float]: - """Qutrit causal pressure via amplitude interference of HPI/MTI/PSI. - - HPI (+0.55) and MTI (+0.65) encode directly. - PSI (−0.80) enters via the |1⟩↔|2⟩ gate: its neutral amplitude is rotated - to bullish before superposition, creating partial destructive interference - that absorbs the shock rather than transmitting it at full classical weight. - Returns (qutrit_pressure, qutrit_confidence). - """ - q_hpi = encode_qutrit(hpi) - q_mti = encode_qutrit(mti) - q_psi = gate_12_swap(encode_qutrit(psi)) - q_final = qutrit_superpose([q_hpi, q_mti, q_psi], [0.55, 0.65, 0.80]) - return collapse_qutrit(q_final, s_wt) - - -def nearest_row(chain_rows: List[Dict[str, Any]], chain: str, pair: str, ts: datetime) -> Optional[Dict[str, Any]]: - candidates: List[Tuple[float, Dict[str, Any]]] = [] - for row in chain_rows: - if str(row.get("chain", "")).lower() != chain: - continue - if str(row.get("symbol", "")).upper() != pair: - continue - rts = parse_iso(str(row.get("timestamp_utc", "1970-01-01T00:00:00+00:00"))) - candidates.append((abs((rts - ts).total_seconds()), row)) - if not candidates: - return None - candidates.sort(key=lambda x: x[0]) - return candidates[0][1] - - -def build_global_series(chain_rows: List[Dict[str, Any]]) -> Dict[str, List[Tuple[datetime, float]]]: - by_symbol_ts: Dict[str, Dict[str, List[float]]] = {} - for row in chain_rows: - symbol = str(row.get("symbol", "")).upper() - ts = str(row.get("timestamp_utc", "")) - price = float(row.get("price_usd", 0.0) or 0.0) - if not symbol or not ts or price <= 0.0: - continue - by_symbol_ts.setdefault(symbol, {}).setdefault(ts, []).append(price) - - out: Dict[str, List[Tuple[datetime, float]]] = {} - for symbol, ts_map in by_symbol_ts.items(): - pts = [(parse_iso(ts), statistics.fmean(vals)) for ts, vals in ts_map.items()] - pts.sort(key=lambda x: x[0]) - out[symbol] = pts - return out - - -def build_macro_series(macro_rows: List[Dict[str, Any]]) -> Dict[str, Dict[str, List[Tuple[datetime, float]]]]: - by_class_symbol_ts: Dict[str, Dict[str, Dict[str, List[float]]]] = {} - for row in macro_rows: - market_class = str(row.get("market_class", "")).lower() - symbol = str(row.get("symbol", "")).upper() - ts = str(row.get("timestamp_utc", "")) - price = float(row.get("price_usd", 0.0) or 0.0) - if not market_class or not symbol or not ts or price <= 0.0: - continue - by_class_symbol_ts.setdefault(market_class, {}).setdefault(symbol, {}).setdefault(ts, []).append(price) - - out: Dict[str, Dict[str, List[Tuple[datetime, float]]]] = {} - for market_class, sym_map in by_class_symbol_ts.items(): - out[market_class] = {} - for symbol, ts_map in sym_map.items(): - pts = [(parse_iso(ts), statistics.fmean(vals)) for ts, vals in ts_map.items()] - pts.sort(key=lambda x: x[0]) - out[market_class][symbol] = pts - return out - - -def state_from_series(series: List[Tuple[datetime, float]], ts: datetime, lookback: int = 8) -> Dict[str, float]: - if not series: - return {"price": 0.0, "momentum": 0.0, "vol": 0.0} - - idx = min(range(len(series)), key=lambda i: abs((series[i][0] - ts).total_seconds())) - price = float(series[idx][1]) - start = max(1, idx - lookback) - - rets: List[float] = [] - for i in range(start, idx + 1): - p0 = float(series[i - 1][1]) - p1 = float(series[i][1]) - if p0 > 0.0: - rets.append((p1 - p0) / p0) - - return { - "price": price, - "momentum": safe_fmean(rets), - "vol": statistics.pstdev(rets) if len(rets) > 1 else 0.0, - } - - -def market_state_at( - macro_series: Dict[str, Dict[str, List[Tuple[datetime, float]]]], - ts: datetime, - class_focus: Dict[str, List[str]], -) -> Dict[str, float]: - class_moms: List[float] = [] - class_vols: List[float] = [] - class_spread: List[float] = [] - - for market_class, symbols in class_focus.items(): - series_map = macro_series.get(market_class, {}) - states: List[Dict[str, float]] = [] - for sym in symbols: - st = state_from_series(series_map.get(sym.upper(), []), ts) - if st["price"] > 0.0: - states.append(st) - - if states: - moms = [s["momentum"] for s in states] - vols = [s["vol"] for s in states] - class_moms.append(statistics.fmean(moms)) - class_vols.append(statistics.fmean(vols)) - class_spread.append(max(moms) - min(moms) if len(moms) > 1 else 0.0) - - return { - "momentum": safe_fmean(class_moms), - "vol": safe_fmean(class_vols), - "dispersion": safe_fmean(class_spread), - } - - -def session_weight(ts: datetime) -> float: - h = ts.hour - if 0 <= h < 7: - return 0.95 - if 7 <= h < 12: - return 1.10 - if 12 <= h < 17: - return 1.25 - if 17 <= h < 21: - return 1.05 - return 0.85 - - -@dataclass -class PolicyEvent: - ts: datetime - source: str - event_type: str - impact: float - confidence: float - - -DEFAULT_POLICY_SOURCE_WEIGHTS: Dict[str, float] = { - "federalreserve": 1.0, - "ecb": 0.9, - "bankofengland": 0.85, - "sec": 0.8, - "cftc": 0.95, - "unknown": 0.6, -} - - -@dataclass -class EventRow: - chain: str - pair: str - ts: datetime - pnl: float - gas: float - spread_bps: float - efficiency: float - executed: bool - reason_code: str - - -@dataclass -class CausalPoint: - row: EventRow - crypto_momentum: float - crypto_vol: float - macro_momentum: float - macro_vol: float - macro_dispersion: float - policy_event_shock: float - session_weight: float - hpi: float - mti: float - psi: float - causal_pressure: float - qutrit_pressure: float - qutrit_confidence: float - - -def load_policy_events(path: str) -> List[PolicyEvent]: - if not path: - return [] - - rows = load_jsonl(Path(path)) - out: List[PolicyEvent] = [] - for row in rows: - ts_raw = str(row.get("timestamp_utc", "")).strip() - if not ts_raw: - continue - source = str(row.get("source", "unknown")).strip() or "unknown" - event_type = str(row.get("event_type", "unknown")).strip() or "unknown" - impact = clamp(float(row.get("impact", 0.0) or 0.0), -1.0, 1.0) - conf = clamp(float(row.get("confidence", 0.5) or 0.5), 0.0, 1.0) - - try: - ts = parse_iso(ts_raw) - except ValueError: - continue - - out.append(PolicyEvent(ts=ts, source=source, event_type=event_type, impact=impact, confidence=conf)) - - out.sort(key=lambda e: e.ts) - return out - - -def load_policy_source_weights(path: str) -> Dict[str, float]: - if not path: - return dict(DEFAULT_POLICY_SOURCE_WEIGHTS) - - payload: Dict[str, Any] - raw = path.strip() - if raw.startswith("{"): - try: - payload = cast(Dict[str, Any], json.loads(raw)) - except json.JSONDecodeError: - return dict(DEFAULT_POLICY_SOURCE_WEIGHTS) - else: - file_path = Path(raw) - if not file_path.exists(): - return dict(DEFAULT_POLICY_SOURCE_WEIGHTS) - try: - payload = cast(Dict[str, Any], json.loads(file_path.read_text(encoding="utf-8"))) - except (OSError, json.JSONDecodeError): - return dict(DEFAULT_POLICY_SOURCE_WEIGHTS) - - out = dict(DEFAULT_POLICY_SOURCE_WEIGHTS) - for k, v in payload.items(): - if not isinstance(v, int | float): - continue - out[k.strip().lower()] = clamp(float(v), 0.0, 3.0) - return out - - -def load_policy_reliability_multipliers(path: str) -> Dict[str, float]: - file_path = Path(path.strip()) if path.strip() else Path("5-Applications/out/micro_cap_sim/policy_source_reliability.json") - if not file_path.exists(): - return {} - - try: - payload = cast(Dict[str, Any], json.loads(file_path.read_text(encoding="utf-8"))) - except (OSError, json.JSONDecodeError): - return {} - - sources_raw = payload.get("sources", {}) - sources = cast(Dict[str, Any], sources_raw) if isinstance(sources_raw, dict) else {} - - multipliers: Dict[str, float] = {} - for source, data_raw in sources.items(): - data = cast(Dict[str, Any], data_raw) if isinstance(data_raw, dict) else {} - mult = float(data.get("ema_recommended_weight_multiplier", data.get("recommended_weight_multiplier", 1.0)) or 1.0) - multipliers[source.strip().lower()] = clamp(mult, 0.25, 2.0) - return multipliers - - -def build_effective_policy_source_weights(base: Dict[str, float], multipliers: Dict[str, float]) -> Dict[str, float]: - keys = sorted(set(base.keys()) | set(multipliers.keys())) - out: Dict[str, float] = {} - for k in keys: - b = float(base.get(k, base.get("unknown", 0.6))) - m = float(multipliers.get(k, 1.0)) - out[k] = clamp(b * m, 0.0, 3.0) - return out - - -def policy_shock_at( - ts: datetime, - events: List[PolicyEvent], - lookback_hours: float, - half_life_hours: float, - source_weights: Dict[str, float], -) -> float: - if not events: - return 0.0 - if lookback_hours <= 0 or half_life_hours <= 0: - return 0.0 - - shock = 0.0 - for ev in events: - age_hours = abs((ts - ev.ts).total_seconds()) / 3600.0 - if age_hours > lookback_hours: - continue - decay = 0.5 ** (age_hours / half_life_hours) - weight = source_weights.get(ev.source.lower(), source_weights.get("unknown", 0.6)) - shock += ev.impact * ev.confidence * weight * decay - - return clamp(shock, -2.0, 2.0) - - -def build_events(post_rows: List[Dict[str, Any]], chain_rows: List[Dict[str, Any]]) -> List[EventRow]: - out: List[EventRow] = [] - for row in post_rows: - strategy_id = str(row.get("strategy_id", "")) - chain, pair = parse_strategy(strategy_id) - if chain == "unknown": - continue - - ts = parse_iso(str(row.get("timestamp_utc", "1970-01-01T00:00:00+00:00"))) - impact = extract_impact(row) - pnl = float(impact.get("pnl_usd", 0.0) or 0.0) - gas = float(impact.get("gas_usd", 0.0) or 0.0) - efficiency = pnl / gas if gas > 0.0 else 0.0 - outcome = str(row.get("outcome", "")).upper() - - nearest = nearest_row(chain_rows, chain, pair, ts) - spread_bps = float(nearest.get("spread_bps", impact.get("slippage_bps", 0.0)) if nearest else impact.get("slippage_bps", 0.0) or 0.0) - - out.append( - EventRow( - chain=chain, - pair=pair, - ts=ts, - pnl=pnl, - gas=gas, - spread_bps=spread_bps, - efficiency=efficiency, - executed=(outcome == "EXECUTED"), - reason_code=str(row.get("reason_code", "")), - ) - ) - - out.sort(key=lambda e: e.ts) - return out - - -def rolling_execution_sentiment(events: List[EventRow], idx: int, window: int = 8) -> float: - start = max(0, idx - window + 1) - subset = events[start : idx + 1] - if not subset: - return 0.0 - exec_rate = sum(1.0 for e in subset if e.executed) / len(subset) - eff = safe_fmean([e.efficiency for e in subset]) - return clamp((exec_rate - 0.5) * 1.4 + (eff * 2.5)) - - -def build_causal_points( - events: List[EventRow], - global_series: Dict[str, List[Tuple[datetime, float]]], - macro_series: Dict[str, Dict[str, List[Tuple[datetime, float]]]], - policy_events: List[PolicyEvent], - policy_source_weights: Dict[str, float], - psi_lookback_hours: float, - psi_half_life_hours: float, -) -> List[CausalPoint]: - focus_symbols = ["BTC/USDC", "ETH/USDC", "SOL/USDC", "BNB/USDC", "MATIC/USDC", "AVAX/USDC"] - macro_focus = { - "equity": ["^GSPC", "^DJI", "^IXIC", "^FTSE", "^N225", "000300.SS"], - "rates": ["^TNX", "^FVX", "^IRX", "^TYX"], - "commodity": ["GC=F", "SI=F", "CL=F", "NG=F", "HG=F"], - "fx": ["EURUSD=X", "GBPUSD=X", "USDJPY=X", "AUDUSD=X", "USDCNY=X"], - } - - points: List[CausalPoint] = [] - for i, event in enumerate(events): - ts = event.ts - - crypto_states: List[Dict[str, float]] = [] - for sym in focus_symbols: - st = state_from_series(global_series.get(sym, []), ts) - if st["price"] > 0.0: - crypto_states.append(st) - - crypto_momentum = safe_fmean([s["momentum"] for s in crypto_states]) - crypto_vol = safe_fmean([s["vol"] for s in crypto_states]) - - mstate = market_state_at(macro_series, ts, macro_focus) - macro_momentum = float(mstate["momentum"]) - macro_vol = float(mstate["vol"]) - macro_dispersion = float(mstate["dispersion"]) - event_shock = policy_shock_at( - ts, - policy_events, - lookback_hours=psi_lookback_hours, - half_life_hours=psi_half_life_hours, - source_weights=policy_source_weights, - ) - - s_weight = session_weight(ts) - sentiment = rolling_execution_sentiment(events, i, window=8) - - hpi_raw = (0.55 * crypto_momentum) + (0.35 * sentiment) - (0.15 * crypto_vol) - hpi = clamp(hpi_raw * 8.0) - - mti_raw = (0.70 * macro_momentum) - (0.45 * macro_vol) - (0.25 * macro_dispersion) - mti = clamp(mti_raw * 12.0) - - psi_raw = (0.65 * macro_vol) + (0.35 * macro_dispersion) - (0.20 * macro_momentum) + (0.90 * event_shock) - psi = clamp(psi_raw * 10.0) - - causal_pressure = s_weight * ((0.55 * hpi) + (0.65 * mti) - (0.80 * psi)) - qutrit_pressure, qutrit_confidence = compute_qutrit_pressure(hpi, mti, psi, s_weight) - - points.append( - CausalPoint( - row=event, - crypto_momentum=crypto_momentum, - crypto_vol=crypto_vol, - macro_momentum=macro_momentum, - macro_vol=macro_vol, - macro_dispersion=macro_dispersion, - policy_event_shock=event_shock, - session_weight=s_weight, - hpi=hpi, - mti=mti, - psi=psi, - causal_pressure=causal_pressure, - qutrit_pressure=qutrit_pressure, - qutrit_confidence=qutrit_confidence, - ) - ) - - return points - - -def future_mean_eff(points: List[CausalPoint], idx: int, horizon: int) -> float: - nxt = points[idx + 1 : idx + 1 + horizon] - if not nxt: - return 0.0 - return safe_fmean([p.row.efficiency for p in nxt]) - - -def optimize_lag_on_subset(subset: List[CausalPoint], horizon: int, lag_min: int, lag_max: int) -> Dict[str, Any]: - if len(subset) < max(8, horizon + 2): - return {"best_lag": 0, "best_corr": 0.0, "sample_count": len(subset)} - - best_lag = 0 - best_corr = 0.0 - - for lag in range(lag_min, lag_max + 1): - x: List[float] = [] - y: List[float] = [] - for i in range(len(subset)): - j = i + lag - if j < 0 or j >= len(subset): - continue - target = future_mean_eff(subset, i, horizon=horizon) - x.append(subset[j].causal_pressure) - y.append(target) - - corr = pearson(x, y) - if abs(corr) > abs(best_corr): - best_corr = corr - best_lag = lag - - return {"best_lag": best_lag, "best_corr": round(best_corr, 8), "sample_count": len(subset)} - - -def optimize_lag_for_chain(points: List[CausalPoint], chain: str, horizon: int, lag_min: int, lag_max: int) -> Dict[str, Any]: - subset = [p for p in points if p.row.chain == chain] - return optimize_lag_on_subset(subset=subset, horizon=horizon, lag_min=lag_min, lag_max=lag_max) - - -def shifted_pressure(subset: List[CausalPoint], idx: int, lag: int) -> float: - j = idx + lag - if j < 0 or j >= len(subset): - return 0.0 - return subset[j].causal_pressure - - -def confidence_from_signal(causal_pressure: float, lag_corr: float, horizon: int) -> float: - base = abs(causal_pressure) * 0.30 + abs(lag_corr) * 0.55 + (1.0 / max(1, horizon)) * 0.15 - return clamp(base, 0.0, 1.0) - - -def backtest_chain(subset: List[CausalPoint], lag: int, lag_corr: float, horizon: int) -> Dict[str, Any]: - reactive_hits = 0 - predictive_hits = 0 - qutrit_hits = 0 - reactive_trades = 0 - predictive_trades = 0 - qutrit_trades = 0 - reactive_pnl = 0.0 - predictive_pnl = 0.0 - qutrit_pnl = 0.0 - - preds: List[Dict[str, Any]] = [] - - for i, p in enumerate(subset): - target = future_mean_eff(subset, i, horizon=horizon) - reactive_signal = p.crypto_momentum - p.crypto_vol - predictive_signal = shifted_pressure(subset, i, lag) - - reactive_side = sign(reactive_signal) - predictive_side = sign(predictive_signal) - target_side = sign(target) - - reactive_correct = reactive_side != 0 and reactive_side == target_side - predictive_correct = predictive_side != 0 and predictive_side == target_side - - if reactive_side != 0: - reactive_trades += 1 - if reactive_correct: - reactive_hits += 1 - reactive_pnl += reactive_side * target - - if predictive_side != 0: - predictive_trades += 1 - if predictive_correct: - predictive_hits += 1 - predictive_pnl += predictive_side * target - - qutrit_side = sign(p.qutrit_pressure) - qutrit_correct = qutrit_side != 0 and qutrit_side == target_side - if qutrit_side != 0: - qutrit_trades += 1 - if qutrit_correct: - qutrit_hits += 1 - qutrit_pnl += qutrit_side * target - - expected_delta_pressure = predictive_signal - conf = confidence_from_signal(expected_delta_pressure, lag_corr=lag_corr, horizon=horizon) - - preds.append( - { - "timestamp_utc": p.row.ts.replace(microsecond=0).isoformat(), - "pair": p.row.pair, - "hpi": round(p.hpi, 8), - "mti": round(p.mti, 8), - "psi": round(p.psi, 8), - "policy_event_shock": round(p.policy_event_shock, 8), - "session_weight": round(p.session_weight, 8), - "expected_delta_pressure_horizon": round(expected_delta_pressure, 8), - "confidence": round(conf, 8), - "reactive_signal": round(reactive_signal, 8), - "future_target_efficiency": round(target, 8), - "predictive_correct": predictive_correct, - "qutrit_pressure": round(p.qutrit_pressure, 8), - "qutrit_confidence": round(p.qutrit_confidence, 8), - "qutrit_correct": qutrit_correct, - } - ) - - return { - "predictions": preds, - "reactive": { - "trades": reactive_trades, - "hit_rate": round((reactive_hits / reactive_trades), 8) if reactive_trades > 0 else 0.0, - "simulated_alpha": round(reactive_pnl, 8), - }, - "predictive": { - "trades": predictive_trades, - "hit_rate": round((predictive_hits / predictive_trades), 8) if predictive_trades > 0 else 0.0, - "simulated_alpha": round(predictive_pnl, 8), - }, - "qutrit": { - "trades": qutrit_trades, - "hit_rate": round((qutrit_hits / qutrit_trades), 8) if qutrit_trades > 0 else 0.0, - "simulated_alpha": round(qutrit_pnl, 8), - }, - } - - -def calibration_curve(predictions: List[Dict[str, Any]], bins: int) -> List[Dict[str, Any]]: - bucket_count = max(2, bins) - sums: Dict[int, Dict[str, float]] = {} - - for p in predictions: - conf = float(p.get("confidence", 0.0) or 0.0) - correct = 1.0 if bool(p.get("predictive_correct", False)) else 0.0 - idx = min(bucket_count - 1, int(conf * bucket_count)) - - state = sums.setdefault(idx, {"n": 0.0, "conf": 0.0, "acc": 0.0}) - state["n"] += 1.0 - state["conf"] += conf - state["acc"] += correct - - curve: List[Dict[str, Any]] = [] - for i in range(bucket_count): - lo = i / bucket_count - hi = (i + 1) / bucket_count - st = sums.get(i, {"n": 0.0, "conf": 0.0, "acc": 0.0}) - n = int(st["n"]) - avg_conf = (st["conf"] / st["n"]) if st["n"] > 0 else 0.0 - emp_acc = (st["acc"] / st["n"]) if st["n"] > 0 else 0.0 - curve.append( - { - "bin": i, - "confidence_range": [round(lo, 6), round(hi, 6)], - "count": n, - "avg_confidence": round(avg_conf, 8), - "empirical_accuracy": round(emp_acc, 8), - "calibration_gap": round(avg_conf - emp_acc, 8), - } - ) - return curve - - -def walk_forward_backtest_chain( - subset: List[CausalPoint], - horizon: int, - lag_min: int, - lag_max: int, - train_size: int, - test_size: int, -) -> Dict[str, Any]: - if len(subset) < max(train_size + test_size, horizon + 6): - return { - "windows": [], - "summary": { - "window_count": 0, - "predictive_hit_rate": 0.0, - "reactive_hit_rate": 0.0, - "predictive_alpha": 0.0, - "reactive_alpha": 0.0, - "alpha_lift": 0.0, - }, - } - - windows: List[Dict[str, Any]] = [] - total_pred_trades = 0 - total_react_trades = 0 - weighted_pred_hit = 0.0 - weighted_react_hit = 0.0 - total_pred_alpha = 0.0 - total_react_alpha = 0.0 - - start = train_size - while start < len(subset) - horizon: - train = subset[start - train_size : start] - test = subset[start : min(len(subset), start + test_size)] - if len(test) < max(2, horizon): - break - - lag_info = optimize_lag_on_subset(train, horizon=horizon, lag_min=lag_min, lag_max=lag_max) - bt = backtest_chain(test, lag=int(lag_info["best_lag"]), lag_corr=float(lag_info["best_corr"]), horizon=horizon) - - pred = cast(Dict[str, Any], bt.get("predictive", {})) - react = cast(Dict[str, Any], bt.get("reactive", {})) - pred_trades = int(pred.get("trades", 0) or 0) - react_trades = int(react.get("trades", 0) or 0) - pred_hit = float(pred.get("hit_rate", 0.0) or 0.0) - react_hit = float(react.get("hit_rate", 0.0) or 0.0) - pred_alpha = float(pred.get("simulated_alpha", 0.0) or 0.0) - react_alpha = float(react.get("simulated_alpha", 0.0) or 0.0) - - total_pred_trades += pred_trades - total_react_trades += react_trades - weighted_pred_hit += pred_hit * pred_trades - weighted_react_hit += react_hit * react_trades - total_pred_alpha += pred_alpha - total_react_alpha += react_alpha - - windows.append( - { - "train_start_utc": train[0].row.ts.replace(microsecond=0).isoformat(), - "train_end_utc": train[-1].row.ts.replace(microsecond=0).isoformat(), - "test_start_utc": test[0].row.ts.replace(microsecond=0).isoformat(), - "test_end_utc": test[-1].row.ts.replace(microsecond=0).isoformat(), - "lag": lag_info["best_lag"], - "lag_corr": lag_info["best_corr"], - "predictive_hit_rate": round(pred_hit, 8), - "reactive_hit_rate": round(react_hit, 8), - "predictive_alpha": round(pred_alpha, 8), - "reactive_alpha": round(react_alpha, 8), - "alpha_lift": round(pred_alpha - react_alpha, 8), - } - ) - - start += test_size - - pred_hit_rate = (weighted_pred_hit / total_pred_trades) if total_pred_trades > 0 else 0.0 - react_hit_rate = (weighted_react_hit / total_react_trades) if total_react_trades > 0 else 0.0 - - return { - "windows": windows, - "summary": { - "window_count": len(windows), - "predictive_hit_rate": round(pred_hit_rate, 8), - "reactive_hit_rate": round(react_hit_rate, 8), - "predictive_alpha": round(total_pred_alpha, 8), - "reactive_alpha": round(total_react_alpha, 8), - "alpha_lift": round(total_pred_alpha - total_react_alpha, 8), - }, - } - - -def auto_manifest_pipeline(out_json: Path, manifest_dir: Path, chunk_size: int) -> Dict[str, Any]: - manifest_script = Path(__file__).with_name("file_manifest_builder.py") - manifest_dir.mkdir(parents=True, exist_ok=True) - - manifest_path = manifest_dir / f"{out_json.stem}.manifest.json" - rebuilt_path = manifest_dir / f"{out_json.stem}.rebuilt{out_json.suffix}" - chunk_store = manifest_dir / "chunk_store" - - build_cmd = [ - sys.executable, - str(manifest_script), - "build", - "--input", - str(out_json), - "--manifest-out", - str(manifest_path), - "--chunk-size-bytes", - str(max(1, chunk_size)), - "--chunk-store", - str(chunk_store), - ] - verify_cmd = [ - sys.executable, - str(manifest_script), - "verify", - "--manifest", - str(manifest_path), - "--file", - str(out_json), - ] - rebuild_cmd = [ - sys.executable, - str(manifest_script), - "rebuild", - "--manifest", - str(manifest_path), - "--chunk-store", - str(chunk_store), - "--out-file", - str(rebuilt_path), - ] - - build_run = subprocess.run(build_cmd, check=False, capture_output=True, text=True) - verify_run = subprocess.run(verify_cmd, check=False, capture_output=True, text=True) - rebuild_run = subprocess.run(rebuild_cmd, check=False, capture_output=True, text=True) - - return { - "manifest": str(manifest_path), - "rebuilt": str(rebuilt_path), - "chunk_store": str(chunk_store), - "build_exit_code": build_run.returncode, - "verify_exit_code": verify_run.returncode, - "rebuild_exit_code": rebuild_run.returncode, - "build_stdout": build_run.stdout.strip(), - "verify_stdout": verify_run.stdout.strip(), - "rebuild_stdout": rebuild_run.stdout.strip(), - } - - -def export_calibration_curves_csv(calibration: Dict[str, List[Dict[str, Any]]], out_dir: Path) -> List[str]: - out_dir.mkdir(parents=True, exist_ok=True) - paths: List[str] = [] - - for chain, rows in calibration.items(): - fp = out_dir / f"{chain}_calibration_curve.csv" - with fp.open("w", encoding="utf-8", newline="") as handle: - writer = csv.writer(handle) - writer.writerow(["chain", "bin", "confidence_lo", "confidence_hi", "count", "avg_confidence", "empirical_accuracy", "calibration_gap"]) - for row in rows: - conf_range_raw = row.get("confidence_range", [0.0, 0.0]) - conf_range = cast(List[Any], conf_range_raw) if isinstance(conf_range_raw, list) else [0.0, 0.0] - lo = float(conf_range[0]) if len(conf_range) >= 1 and isinstance(conf_range[0], int | float) else 0.0 - hi = float(conf_range[1]) if len(conf_range) >= 2 and isinstance(conf_range[1], int | float) else 0.0 - writer.writerow( - [ - chain, - int(row.get("bin", 0) or 0), - lo, - hi, - int(row.get("count", 0) or 0), - float(row.get("avg_confidence", 0.0) or 0.0), - float(row.get("empirical_accuracy", 0.0) or 0.0), - float(row.get("calibration_gap", 0.0) or 0.0), - ] - ) - paths.append(str(fp)) - - return sorted(paths) - - -def build_report( - post_rows: List[Dict[str, Any]], - chain_rows: List[Dict[str, Any]], - macro_rows: Optional[List[Dict[str, Any]]], - policy_events: List[PolicyEvent], - policy_source_weights: Dict[str, float], - horizon: int, - lag_min: int, - lag_max: int, - psi_lookback_hours: float, - psi_half_life_hours: float, - walk_forward_train_size: int, - walk_forward_test_size: int, - calibration_bins: int, - base_policy_source_weights: Dict[str, float], - policy_reliability_multipliers: Dict[str, float], -) -> Dict[str, Any]: - events = build_events(post_rows, chain_rows) - global_series = build_global_series(chain_rows) - macro_series = build_macro_series(macro_rows or []) - points = build_causal_points( - events=events, - global_series=global_series, - macro_series=macro_series, - policy_events=policy_events, - policy_source_weights=policy_source_weights, - psi_lookback_hours=psi_lookback_hours, - psi_half_life_hours=psi_half_life_hours, - ) - - chains = sorted(set(p.row.chain for p in points)) - lag_table: Dict[str, Dict[str, Any]] = {} - chain_backtests: Dict[str, Dict[str, Any]] = {} - chain_walk_forward: Dict[str, Dict[str, Any]] = {} - confidence_calibration: Dict[str, List[Dict[str, Any]]] = {} - - hpi_all: List[float] = [] - mti_all: List[float] = [] - psi_all: List[float] = [] - pressure_all: List[float] = [] - qutrit_pressure_all: List[float] = [] - eff_all: List[float] = [] - - for chain in chains: - chain_points = [p for p in points if p.row.chain == chain] - lag_info = optimize_lag_for_chain(points, chain, horizon=horizon, lag_min=lag_min, lag_max=lag_max) - lag_table[chain] = lag_info - - bt = backtest_chain( - chain_points, - lag=int(lag_info["best_lag"]), - lag_corr=float(lag_info["best_corr"]), - horizon=horizon, - ) - chain_backtests[chain] = bt - - predictions_raw = bt.get("predictions", []) - predictions = cast(List[Dict[str, Any]], predictions_raw) if isinstance(predictions_raw, list) else [] - confidence_calibration[chain] = calibration_curve(predictions, bins=calibration_bins) - - chain_walk_forward[chain] = walk_forward_backtest_chain( - chain_points, - horizon=horizon, - lag_min=lag_min, - lag_max=lag_max, - train_size=max(6, walk_forward_train_size), - test_size=max(2, walk_forward_test_size), - ) - - for cp in chain_points: - hpi_all.append(cp.hpi) - mti_all.append(cp.mti) - psi_all.append(cp.psi) - pressure_all.append(cp.causal_pressure) - qutrit_pressure_all.append(cp.qutrit_pressure) - eff_all.append(cp.row.efficiency) - - corr = { - "eff_vs_hpi": round(pearson(eff_all, hpi_all), 8), - "eff_vs_mti": round(pearson(eff_all, mti_all), 8), - "eff_vs_psi": round(pearson(eff_all, psi_all), 8), - "eff_vs_pressure": round(pearson(eff_all, pressure_all), 8), - "eff_vs_qutrit_pressure": round(pearson(eff_all, qutrit_pressure_all), 8), - } - - leaderboard: List[Dict[str, Any]] = [] - for chain in chains: - bt = chain_backtests[chain] - pr = cast(Dict[str, Any], bt.get("predictive", {})) - re = cast(Dict[str, Any], bt.get("reactive", {})) - qt = cast(Dict[str, Any], bt.get("qutrit", {})) - wf_summary = cast(Dict[str, Any], chain_walk_forward[chain].get("summary", {})) - wf_lift = float(wf_summary.get("alpha_lift", 0.0) or 0.0) - - leaderboard.append( - { - "chain": chain, - "lag": lag_table[chain]["best_lag"], - "lag_corr": lag_table[chain]["best_corr"], - "predictive_hit_rate": pr.get("hit_rate", 0.0), - "reactive_hit_rate": re.get("hit_rate", 0.0), - "qutrit_hit_rate": qt.get("hit_rate", 0.0), - "predictive_alpha": pr.get("simulated_alpha", 0.0), - "reactive_alpha": re.get("simulated_alpha", 0.0), - "qutrit_alpha": qt.get("simulated_alpha", 0.0), - "alpha_lift": round(float(pr.get("simulated_alpha", 0.0)) - float(re.get("simulated_alpha", 0.0)), 8), - "qutrit_lift": round(float(qt.get("simulated_alpha", 0.0)) - float(re.get("simulated_alpha", 0.0)), 8), - "walk_forward_alpha_lift": round(wf_lift, 8), - } - ) - - leaderboard.sort(key=lambda r: float(r["walk_forward_alpha_lift"]), reverse=True) - - return { - "summary": { - "events": len(points), - "chains": len(chains), - "macro_rows_used": len(macro_rows or []), - "policy_events_used": len(policy_events), - "prediction_horizon_steps": horizon, - "lag_search": {"min": lag_min, "max": lag_max}, - "psi_event_model": { - "lookback_hours": psi_lookback_hours, - "half_life_hours": psi_half_life_hours, - "source_weights": policy_source_weights, - "base_source_weights": base_policy_source_weights, - "reliability_multipliers": policy_reliability_multipliers, - }, - "walk_forward": { - "train_size": walk_forward_train_size, - "test_size": walk_forward_test_size, - }, - "calibration_bins": calibration_bins, - "correlations": corr, - "objective": "causal-first HPI/MTI/PSI pressure forecasting with evidence-aware PSI, lag optimization, walk-forward backtesting, and calibrated confidence", - }, - "lag_optimizer": lag_table, - "chain_backtests": chain_backtests, - "chain_walk_forward": chain_walk_forward, - "confidence_calibration": confidence_calibration, - "predictive_leaderboard": leaderboard, - } - - -def write_markdown(path: Path, payload: Dict[str, Any]) -> None: - lines: List[str] = [] - summary = cast(Dict[str, Any], payload.get("summary", {})) - corr = cast(Dict[str, Any], summary.get("correlations", {})) - - lines.append("# Hyperfluid Causal Pressure Report") - lines.append("") - lines.append("Causal-first model with HPI (human pressure), MTI (market transmission), PSI (policy shock), session weighting, rolling lag optimization, walk-forward backtesting, and confidence calibration.") - lines.append("") - lines.append("## Summary") - lines.append("") - lines.append(f"- events: {summary.get('events', 0)}") - lines.append(f"- chains: {summary.get('chains', 0)}") - lines.append(f"- macro_rows_used: {summary.get('macro_rows_used', 0)}") - lines.append(f"- policy_events_used: {summary.get('policy_events_used', 0)}") - lines.append(f"- prediction_horizon_steps: {summary.get('prediction_horizon_steps', 0)}") - lines.append("") - lines.append("## Correlations") - lines.append("") - lines.append(f"- eff_vs_hpi: {corr.get('eff_vs_hpi', 0)}") - lines.append(f"- eff_vs_mti: {corr.get('eff_vs_mti', 0)}") - lines.append(f"- eff_vs_psi: {corr.get('eff_vs_psi', 0)}") - lines.append(f"- eff_vs_pressure: {corr.get('eff_vs_pressure', 0)}") - lines.append("") - - lines.append("## Predictive Leaderboard") - lines.append("") - lines.append("| Chain | Lag | Lag Corr | Predictive Hit | Reactive Hit | Predictive Alpha | Reactive Alpha | Walk-Forward Lift |") - lines.append("|---|---:|---:|---:|---:|---:|---:|---:|") - for row in payload.get("predictive_leaderboard", []): - lines.append( - "| {chain} | {lag} | {lag_corr} | {predictive_hit_rate} | {reactive_hit_rate} | {predictive_alpha} | {reactive_alpha} | {walk_forward_alpha_lift} |".format( - chain=row.get("chain", ""), - lag=row.get("lag", 0), - lag_corr=row.get("lag_corr", 0.0), - predictive_hit_rate=row.get("predictive_hit_rate", 0.0), - reactive_hit_rate=row.get("reactive_hit_rate", 0.0), - predictive_alpha=row.get("predictive_alpha", 0.0), - reactive_alpha=row.get("reactive_alpha", 0.0), - walk_forward_alpha_lift=row.get("walk_forward_alpha_lift", 0.0), - ) - ) - - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Causal-first hyperfluid model with evidence-aware PSI ingestion, walk-forward backtesting, confidence calibration, and auto-manifest outputs.") - parser.add_argument("--post-records", required=True, help="Path to post_records.jsonl") - parser.add_argument("--chain-records", required=True, help="Path to chain_records.jsonl") - parser.add_argument("--macro-records", required=False, default="", help="Optional path to macro_records.jsonl") - parser.add_argument("--policy-events", required=False, default="", help="Optional path to policy_events.jsonl") - parser.add_argument("--policy-source-weights", required=False, default="", help="Optional JSON file path or inline JSON for source trust weights used in PSI event shock.") - parser.add_argument("--policy-reliability", required=False, default="5-Applications/out/micro_cap_sim/policy_source_reliability.json", help="Optional reliability JSON path from policy collector to auto-adjust source weights.") - parser.add_argument("--horizon-steps", type=int, default=3, help="Prediction horizon in steps.") - parser.add_argument("--lag-min", type=int, default=-6, help="Minimum lag (steps).") - parser.add_argument("--lag-max", type=int, default=6, help="Maximum lag (steps).") - parser.add_argument("--psi-lookback-hours", type=float, default=72.0, help="Policy-event lookback horizon for PSI event shock.") - parser.add_argument("--psi-half-life-hours", type=float, default=18.0, help="Half-life for policy-event decay in PSI event shock.") - parser.add_argument("--walk-forward-train-size", type=int, default=18, help="Walk-forward rolling training window size (events per chain).") - parser.add_argument("--walk-forward-test-size", type=int, default=6, help="Walk-forward rolling test window size (events per chain).") - parser.add_argument("--calibration-bins", type=int, default=10, help="Confidence calibration bin count.") - parser.add_argument("--calibration-csv-dir", default="5-Applications/out/micro_cap_sim/calibration_curves", help="Directory to export per-chain calibration curve CSV files.") - parser.add_argument("--export-calibration-csv", dest="export_calibration_csv", action="store_true", help="Export calibration curves to CSV files for dashboards.") - parser.add_argument("--no-export-calibration-csv", dest="export_calibration_csv", action="store_false", help="Disable calibration CSV export.") - parser.set_defaults(export_calibration_csv=True) - parser.add_argument("--auto-manifest", dest="auto_manifest", action="store_true", help="Automatically build/verify/rebuild report manifests after run.") - parser.add_argument("--no-auto-manifest", dest="auto_manifest", action="store_false", help="Disable automatic manifest pipeline.") - parser.set_defaults(auto_manifest=True) - parser.add_argument("--manifest-dir", default="5-Applications/out/manifests", help="Manifest output directory (used when auto-manifest is enabled).") - parser.add_argument("--manifest-chunk-size-bytes", type=int, default=65536, help="Chunk size for manifest chunk-store generation.") - parser.add_argument("--out-json", required=True, help="Output report JSON path") - parser.add_argument("--out-md", required=True, help="Output report markdown path") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - post_rows = load_jsonl(Path(args.post_records)) - chain_rows = load_jsonl(Path(args.chain_records)) - macro_rows = load_jsonl(Path(args.macro_records)) if args.macro_records else [] - policy_events = load_policy_events(args.policy_events) - base_policy_source_weights = load_policy_source_weights(args.policy_source_weights) - policy_reliability_multipliers = load_policy_reliability_multipliers(args.policy_reliability) - policy_source_weights = build_effective_policy_source_weights(base_policy_source_weights, policy_reliability_multipliers) - - if not post_rows or not chain_rows: - print(json.dumps({"error": "missing_input_data", "post_records": len(post_rows), "chain_records": len(chain_rows)}, indent=2)) - return 2 - - payload = build_report( - post_rows=post_rows, - chain_rows=chain_rows, - macro_rows=macro_rows, - policy_events=policy_events, - policy_source_weights=policy_source_weights, - horizon=max(1, int(args.horizon_steps)), - lag_min=int(args.lag_min), - lag_max=int(args.lag_max), - psi_lookback_hours=max(1.0, float(args.psi_lookback_hours)), - psi_half_life_hours=max(0.1, float(args.psi_half_life_hours)), - walk_forward_train_size=max(6, int(args.walk_forward_train_size)), - walk_forward_test_size=max(2, int(args.walk_forward_test_size)), - calibration_bins=max(2, int(args.calibration_bins)), - base_policy_source_weights=base_policy_source_weights, - policy_reliability_multipliers=policy_reliability_multipliers, - ) - - out_json = Path(args.out_json) - out_json.parent.mkdir(parents=True, exist_ok=True) - out_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") - - write_markdown(Path(args.out_md), payload) - - if bool(args.export_calibration_csv): - csv_paths = export_calibration_curves_csv( - cast(Dict[str, List[Dict[str, Any]]], payload.get("confidence_calibration", {})), - out_dir=Path(args.calibration_csv_dir), - ) - payload["summary"]["calibration_csv_export"] = { - "enabled": True, - "dir": str(Path(args.calibration_csv_dir)), - "files": csv_paths, - } - out_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") - else: - payload["summary"]["calibration_csv_export"] = {"enabled": False} - out_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") - - manifest_result: Dict[str, Any] = {} - if bool(args.auto_manifest): - manifest_result = auto_manifest_pipeline( - out_json=out_json, - manifest_dir=Path(args.manifest_dir), - chunk_size=int(args.manifest_chunk_size_bytes), - ) - payload["summary"]["manifest_pipeline"] = { - "enabled": True, - "manifest": manifest_result.get("manifest", ""), - "rebuilt": manifest_result.get("rebuilt", ""), - "build_exit_code": manifest_result.get("build_exit_code", -1), - "verify_exit_code": manifest_result.get("verify_exit_code", -1), - "rebuild_exit_code": manifest_result.get("rebuild_exit_code", -1), - } - out_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") - else: - payload["summary"]["manifest_pipeline"] = {"enabled": False} - out_json.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") - - top_raw = payload.get("predictive_leaderboard", []) - top = cast(List[Dict[str, Any]], top_raw) if isinstance(top_raw, list) else [] - top_row: Optional[Dict[str, Any]] = top[0] if top else None - - print( - json.dumps( - { - "events": payload["summary"]["events"], - "policy_events_used": payload["summary"]["policy_events_used"], - "top_chain": top_row, - "manifest_pipeline": payload["summary"].get("manifest_pipeline", {}), - "calibration_csv_export": payload["summary"].get("calibration_csv_export", {}), - }, - indent=2, - ) - ) - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/model/model_successful_bot_logic.py b/5-Applications/tools-scripts/model/model_successful_bot_logic.py deleted file mode 100644 index c1ec8241..00000000 --- a/5-Applications/tools-scripts/model/model_successful_bot_logic.py +++ /dev/null @@ -1,427 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -import statistics -from datetime import datetime, timedelta -from pathlib import Path -from typing import Any, Dict, List, Set, Tuple, cast - -try: - from scripts.gpgpu_surface import get_surface -except ImportError: - from gpgpu_surface import get_surface - - -SURFACE = get_surface() - -# Omega-Level Performance Parameters (Phase 21-24) -NEMS_HMM_GAIN = 0.85 -AAS_PRECISION_FACTOR = 0.30 -TOPOLOGICAL_RESILIENCE = 0.40 -SOVEREIGN_INSOLVENCY_WEIGHT = 0.25 # Resilience to $136.2T default - - -def load_jsonl(path: Path) -> List[Dict[str, Any]]: - rows: List[Dict[str, Any]] = [] - with path.open("r", encoding="utf-8") as handle: - for line in handle: - s = line.strip() - if s: - rows.append(json.loads(s)) - return rows - - -def parse_iso(ts: str) -> datetime: - return datetime.fromisoformat(ts.replace("Z", "+00:00")) - - -def quantile(values: List[float], q: float) -> float: - if not values: - return 0.0 - if len(values) == 1: - return values[0] - idx = max(0, min(99, int(q * 100) - 1)) - return float(statistics.quantiles(values, n=100, method="inclusive")[idx]) - - -def parse_strategy(strategy_id: str) -> Tuple[str, str]: - parts = strategy_id.split("-") - # Expected: SIM---- - if len(parts) >= 5 and parts[0] == "SIM": - chain = parts[1].lower() - pair = f"{parts[2].upper()}/{parts[3].upper()}" - return chain, pair - return "unknown", "unknown/unknown" - - -def motif_key(chain: str, pair: str) -> str: - return f"{chain}|{pair}" - - -def extract_impact(row: Dict[str, Any]) -> Dict[str, Any]: - impact_raw = row.get("realized_impact", {}) - if isinstance(impact_raw, dict): - return cast(Dict[str, Any], impact_raw) - return {} - - -def filter_rows_rolling(post_rows: List[Dict[str, Any]], window_days: int) -> Tuple[List[Dict[str, Any]], str, str]: - ts_values: List[datetime] = [] - for row in post_rows: - ts_raw = str(row.get("timestamp_utc", "")) - if ts_raw: - ts_values.append(parse_iso(ts_raw)) - - if not ts_values: - return post_rows, "", "" - - window_end = max(ts_values) - window_start = window_end - timedelta(days=max(1, window_days)) - - filtered: List[Dict[str, Any]] = [] - for row in post_rows: - ts_raw = str(row.get("timestamp_utc", "")) - if not ts_raw: - continue - ts = parse_iso(ts_raw) - if ts >= window_start: - filtered.append(row) - - return ( - filtered, - window_start.replace(microsecond=0).isoformat(), - window_end.replace(microsecond=0).isoformat(), - ) - - -def build_motif_stats(post_rows: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - by_motif: Dict[Tuple[str, str], Dict[str, Any]] = {} - - for row in post_rows: - strategy_id = str(row.get("strategy_id", "")) - chain, pair = parse_strategy(strategy_id) - key = (chain, pair) - - entry = by_motif.setdefault( - key, - { - "chain": chain, - "pair": pair, - "decisions": 0, - "executed": 0, - "paused": 0, - "positive_exec": 0, - "total_pnl_usd": 0.0, - "total_gas_usd": 0.0, - "slippage_bps_values": [], - }, - ) - - entry["decisions"] += 1 - outcome = str(row.get("outcome", "")).upper() - impact = extract_impact(row) - pnl = float(impact.get("pnl_usd", 0.0) or 0.0) - gas = float(impact.get("gas_usd", 0.0) or 0.0) - slippage = float(impact.get("slippage_bps", 0.0) or 0.0) - - entry["total_gas_usd"] += gas - entry["slippage_bps_values"].append(slippage) - - if outcome == "EXECUTED": - entry["executed"] += 1 - entry["total_pnl_usd"] += pnl - if pnl > 0: - entry["positive_exec"] += 1 - else: - entry["paused"] += 1 - - ranked: List[Dict[str, Any]] = [] - for item in by_motif.values(): - executed = int(item["executed"]) - decisions = int(item["decisions"]) - total_pnl = float(item["total_pnl_usd"]) - total_gas = float(item["total_gas_usd"]) - positive_exec = int(item["positive_exec"]) - - execute_rate = (executed / decisions) if decisions else 0.0 - win_rate = (positive_exec / executed) if executed else 0.0 - avg_pnl = (total_pnl / executed) if executed else 0.0 - - # Apply HMM gas optimization to historic records - optimized_gas = total_gas * (1.0 - NEMS_HMM_GAIN) - gas_efficiency = (total_pnl / optimized_gas) if optimized_gas > 0 else 0.0 - - median_slippage = float(statistics.median(item["slippage_bps_values"])) if item["slippage_bps_values"] else 0.0 - # Success Score Formula (Phase 24 Crisis-Resonant) - # Weights: Win Rate (0.3), Execute Rate (0.1), Gas Efficiency (0.2), - # Slippage Resistance (0.15), Topological Resilience (0.15), Anti-Fragility (0.1) - - success_score = ( - (win_rate * 0.30) + - (execute_rate * 0.10) + - (gas_efficiency * 0.20) + - ((1.0 - median_slippage) * 0.15) + - (TOPOLOGICAL_RESILIENCE * 0.15) + - (SOVEREIGN_INSOLVENCY_WEIGHT * 0.10) - ) - ranked.append( - { - "chain": item["chain"], - "pair": item["pair"], - "motif_id": motif_key(str(item["chain"]), str(item["pair"])), - "decisions": decisions, - "executed": executed, - "paused": int(item["paused"]), - "positive_exec": positive_exec, - "execute_rate": round(execute_rate, 8), - "win_rate": round(win_rate, 8), - "avg_pnl_usd": round(avg_pnl, 8), - "total_pnl_usd": round(total_pnl, 8), - "total_gas_usd": round(total_gas, 8), - "gas_efficiency": round(gas_efficiency, 8), - "median_slippage_bps": round(median_slippage, 8), - "success_score": round(success_score, 8), - } - ) - - ranked.sort(key=lambda x: float(x["success_score"]), reverse=True) - return ranked - - -def softmax(values: List[float], temperature: float) -> List[float]: - return SURFACE.softmax(values, temperature=temperature) - - -def derive_logic_model( - post_rows: List[Dict[str, Any]], - motifs: List[Dict[str, Any]], - window_days: int, - window_start_iso: str, - window_end_iso: str, - require_positive_gas_efficiency: bool, - temperature: float, -) -> Dict[str, Any]: - allowed_motifs = motifs - if require_positive_gas_efficiency: - allowed_motifs = [m for m in motifs if float(m["gas_efficiency"]) > 0.0] - - allowed_motif_ids: Set[str] = {str(m["motif_id"]) for m in allowed_motifs} - - positive_exec_gas: List[float] = [] - positive_exec_slippage: List[float] = [] - positive_exec_pnl: List[float] = [] - - for row in post_rows: - outcome = str(row.get("outcome", "")).upper() - chain, pair = parse_strategy(str(row.get("strategy_id", ""))) - m_id = motif_key(chain, pair) - if require_positive_gas_efficiency and m_id not in allowed_motif_ids: - continue - - impact = extract_impact(row) - pnl = float(impact.get("pnl_usd", 0.0) or 0.0) - if outcome == "EXECUTED" and pnl > 0: - positive_exec_pnl.append(pnl) - positive_exec_gas.append(float(impact.get("gas_usd", 0.0) or 0.0)) - positive_exec_slippage.append(float(impact.get("slippage_bps", 0.0) or 0.0)) - - gates = { - "max_gas_usd": round(quantile(positive_exec_gas, 0.75), 8), - "max_slippage_bps": round(quantile(positive_exec_slippage, 0.75), 8), - "min_net_pnl_usd": round(quantile(positive_exec_pnl, 0.25), 8), - } - - # If there is no positive cohort, move to strict no-trade defaults. - if not positive_exec_pnl: - gates = { - "max_gas_usd": 0.0, - "max_slippage_bps": 0.0, - "min_net_pnl_usd": 999999.0, - } - - top_chains: List[str] = [] - for row in (allowed_motifs[:8] if allowed_motifs else motifs[:8]): - chain = str(row["chain"]) - if chain not in top_chains: - top_chains.append(chain) - - q_source = allowed_motifs if allowed_motifs else motifs - q_scores = [max(0.0, float(m["success_score"])) + 1e-6 for m in q_source] - q_probs = softmax(q_scores, temperature) - - quantum_expected_avg_pnl = 0.0 - quantum_expected_gas_eff = 0.0 - for i, motif in enumerate(q_source): - p = q_probs[i] if i < len(q_probs) else 0.0 - quantum_expected_avg_pnl += p * float(motif["avg_pnl_usd"]) - quantum_expected_gas_eff += p * float(motif["gas_efficiency"]) - - classical_best_avg_pnl = max([float(m["avg_pnl_usd"]) for m in q_source], default=0.0) - classical_best_gas_eff = max([float(m["gas_efficiency"]) for m in q_source], default=0.0) - - classical_space_advantages: List[Dict[str, Any]] = [] - for motif in q_source: - avg_pnl = float(motif["avg_pnl_usd"]) - gas_eff = float(motif["gas_efficiency"]) - if avg_pnl > quantum_expected_avg_pnl and gas_eff > quantum_expected_gas_eff: - classical_space_advantages.append( - { - "motif_id": motif["motif_id"], - "chain": motif["chain"], - "pair": motif["pair"], - "avg_pnl_usd": motif["avg_pnl_usd"], - "gas_efficiency": motif["gas_efficiency"], - } - ) - - model: Dict[str, Any] = { - "model_name": "successful_bot_logic_v2", - "compute_backend": SURFACE.backend, - "selection_basis": "rolling-window historical motif ranking from post_records", - "rolling_window": { - "window_days": window_days, - "window_start_utc": window_start_iso, - "window_end_utc": window_end_iso, - }, - "constraints": { - "require_positive_gas_efficiency": require_positive_gas_efficiency, - "allowed_motif_count": len(allowed_motifs), - "blocked_motif_count": max(0, len(motifs) - len(allowed_motifs)), - "allowed_motifs": [ - { - "motif_id": m["motif_id"], - "chain": m["chain"], - "pair": m["pair"], - "gas_efficiency": m["gas_efficiency"], - "avg_pnl_usd": m["avg_pnl_usd"], - } - for m in allowed_motifs - ], - }, - "top_priority_chains": top_chains, - "gates": gates, - "quantum_classical_efficiency": { - "temperature": temperature, - "quantum_expected_avg_pnl_usd": round(quantum_expected_avg_pnl, 8), - "quantum_expected_gas_efficiency": round(quantum_expected_gas_eff, 8), - "classical_best_avg_pnl_usd": round(classical_best_avg_pnl, 8), - "classical_best_gas_efficiency": round(classical_best_gas_eff, 8), - "classical_space_advantages": classical_space_advantages[:20], - }, - "execution_policy": [ - "Allow only motifs with positive rolling gas efficiency when enabled.", - "Prefer chains in top_priority_chains ordered by current friction rank.", - "Pause when gas exceeds max_gas_usd gate.", - "Pause when projected slippage exceeds max_slippage_bps gate.", - "Only execute when projected net PnL >= min_net_pnl_usd.", - "Recompute model weekly from latest records (rolling window).", - ], - } - return model - - -def write_markdown(path: Path, motifs: List[Dict[str, Any]], model: Dict[str, Any]) -> None: - lines: List[str] = [] - lines.append("# Successful Bot Logic Model") - lines.append("") - lines.append("This report ranks strategy motifs from historical records and derives an execution logic model.") - lines.append("") - lines.append("## Rolling Window") - lines.append("") - rw = model["rolling_window"] - lines.append(f"- window_days: {rw['window_days']}") - lines.append(f"- window_start_utc: {rw['window_start_utc']}") - lines.append(f"- window_end_utc: {rw['window_end_utc']}") - lines.append("") - lines.append("## Derived Gates") - lines.append("") - gates = model["gates"] - lines.append(f"- max_gas_usd: {gates['max_gas_usd']}") - lines.append(f"- max_slippage_bps: {gates['max_slippage_bps']}") - lines.append(f"- min_net_pnl_usd: {gates['min_net_pnl_usd']}") - lines.append(f"- top_priority_chains: {', '.join(model['top_priority_chains'])}") - lines.append("") - lines.append("## Quantum vs Classical") - lines.append("") - qc = model["quantum_classical_efficiency"] - lines.append(f"- quantum_expected_avg_pnl_usd: {qc['quantum_expected_avg_pnl_usd']}") - lines.append(f"- quantum_expected_gas_efficiency: {qc['quantum_expected_gas_efficiency']}") - lines.append(f"- classical_best_avg_pnl_usd: {qc['classical_best_avg_pnl_usd']}") - lines.append(f"- classical_best_gas_efficiency: {qc['classical_best_gas_efficiency']}") - lines.append("") - lines.append("## Top Motifs") - lines.append("") - lines.append("| Rank | Chain | Pair | Success Score | Win Rate | Execute Rate | Avg PnL USD | Gas Efficiency |") - lines.append("|---|---|---|---:|---:|---:|---:|---:|") - for i, row in enumerate(motifs[:20], start=1): - lines.append( - f"| {i} | {row['chain']} | {row['pair']} | {row['success_score']} | {row['win_rate']} | {row['execute_rate']} | {row['avg_pnl_usd']} | {row['gas_efficiency']} |" - ) - - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Review successful bot history and derive logic model.") - parser.add_argument("--post-records", required=True, help="Path to post_records.jsonl") - parser.add_argument("--window-days", type=int, default=30, help="Rolling analysis window in days.") - parser.add_argument("--temperature", type=float, default=0.35, help="Quantum softmax temperature.") - parser.add_argument("--allow-nonpositive-gas-efficiency", action="store_true", help="Disable positive gas-efficiency motif filter.") - parser.add_argument("--out-json", required=True, help="Output JSON path") - parser.add_argument("--out-md", required=True, help="Output markdown path") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - post_rows = load_jsonl(Path(args.post_records)) - if not post_rows: - print(json.dumps({"error": "no_post_records"}, indent=2)) - return 2 - - filtered_rows, window_start_iso, window_end_iso = filter_rows_rolling(post_rows, int(args.window_days)) - if not filtered_rows: - print(json.dumps({"error": "no_rows_in_window", "window_days": int(args.window_days)}, indent=2)) - return 2 - - motifs = build_motif_stats(filtered_rows) - model = derive_logic_model( - filtered_rows, - motifs, - window_days=int(args.window_days), - window_start_iso=window_start_iso, - window_end_iso=window_end_iso, - require_positive_gas_efficiency=not bool(args.allow_nonpositive_gas_efficiency), - temperature=float(args.temperature), - ) - - out_json = Path(args.out_json) - out_json.parent.mkdir(parents=True, exist_ok=True) - out_json.write_text(json.dumps({"model": model, "motifs": motifs}, indent=2) + "\n", encoding="utf-8") - - write_markdown(Path(args.out_md), motifs, model) - - print( - json.dumps( - { - "backend": SURFACE.backend, - "rows_in_window": len(filtered_rows), - "motifs_ranked": len(motifs), - "allowed_motif_count": model["constraints"]["allowed_motif_count"], - "top_chain": model["top_priority_chains"][0] if model["top_priority_chains"] else None, - }, - indent=2, - ) - ) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/monitor/monitor_daemon.py b/5-Applications/tools-scripts/monitor/monitor_daemon.py deleted file mode 100644 index 1e9ac3ad..00000000 --- a/5-Applications/tools-scripts/monitor/monitor_daemon.py +++ /dev/null @@ -1,713 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Autonomous Hyperfluid Monitor Daemon - -Self-healing orchestrator for the qutrit causal pressure model. -Runs continuously, detects anomalies, triggers recovery, sends alerts. -You deploy it and walk away. -""" - -import json -import os -from datetime import datetime, timedelta -from pathlib import Path -from typing import Any, Dict, List, Optional - -try: - from scripts.ofac_screen import OFACScreen - from scripts.graphvm_canal_router import CanalRoutingReport - from scripts.soliton_reasoning_engine import SolitonReasoningEngine -except ModuleNotFoundError: - from pathlib import Path - sys.path.append(str(Path(__file__).resolve().parents[2])) - from ofac_screen import OFACScreen # type: ignore - from graphvm_canal_router import CanalRoutingReport # type: ignore - from soliton_reasoning_engine import SolitonReasoningEngine # type: ignore - -ROOT = Path(__file__).resolve().parent.parent -DEFAULT_SDN_PATH = ROOT / "config" / "ofac_sdn_snapshot.json" - - -class MonitorConfig: - """Daemon configuration.""" - - def __init__(self, workspace_root: Path): - self.workspace = workspace_root - self.script_dir = workspace_root / "scripts" - self.out_dir = workspace_root / "out" / "micro_cap_sim" - self.log_dir = workspace_root / "logs" - self.state_file = self.log_dir / "monitor_state.json" - self.alert_log = self.log_dir / "alerts.jsonl" - self.balance_file = self.out_dir / "wallet_balance.json" - self.sdn_path = DEFAULT_SDN_PATH - - self.log_dir.mkdir(parents=True, exist_ok=True) - - # Thresholds - self.soliton_alert_threshold = 0.25 # Alert if energy > 0.25 - self.soliton_retrain_threshold = 0.35 # Trigger retrain if energy > 0.35 - self.anomaly_cluster_pause = 0.3 # Pause if cluster peak > 0.3 - self.consecutive_clusters_for_alert = 2 # Alert after 2 clusters - - # Hard balance floor — HALT_ALL if wallet drops below this - self.hard_floor_usd = 10.0 # Kill switch at $10 - - # Timing (seconds) - self.polling_interval = 3600 # Check every hour - self.retrain_interval = 86400 # Retrain daily - self.alert_cooldown = 600 # Don't spam alerts, 10 min apart - - # Alert configuration - self.webhook_url = os.environ.get("MONITOR_WEBHOOK_URL") # Optional webhook for alerts - - @property - def report_path(self) -> Path: - return self.out_dir / "hyperfluid_causal_report.json" - - @property - def soliton_report_path(self) -> Path: - return self.out_dir / "hyperfluid_causal_report_with_soliton.json" - - @property - def policy_events_path(self) -> Path: - return self.out_dir / "policy_events.jsonl" - - @property - def reliability_path(self) -> Path: - return self.out_dir / "policy_source_reliability.json" - - -class MonitorState: - """Persistent state across daemon restarts.""" - - def __init__(self, state_file: Path): - self.state_file = state_file - self.data = self._load() - - def _load(self) -> Dict[str, Any]: - if self.state_file.exists(): - return json.loads(self.state_file.read_text(encoding="utf-8")) - return { - "last_alert_utc": None, - "last_retrain_utc": None, - "consecutive_clusters": 0, - "paused": False, - "pause_reason": None, - "pause_until_utc": None, - } - - def save(self) -> None: - self.state_file.write_text(json.dumps(self.data, indent=2) + "\n", encoding="utf-8") - - def get(self, key: str, default: Any = None) -> Any: - return self.data.get(key, default) - - def set(self, key: str, value: Any) -> None: - self.data[key] = value - self.save() - - -def log_alert(alert_log: Path, level: str, message: str, context: Optional[Dict] = None) -> None: - """Log an alert to JSONL.""" - alert = { - "timestamp_utc": datetime.utcnow().isoformat(), - "level": level, # INFO, WARNING, CRITICAL - "message": message, - "context": context or {}, - } - with alert_log.open("a", encoding="utf-8") as f: - f.write(json.dumps(alert) + "\n") - - -def can_alert(state: MonitorState, config: MonitorConfig) -> bool: - """Check if enough time has passed since last alert (avoid spam).""" - last_alert = state.get("last_alert_utc") - if not last_alert: - return True - - last_dt = datetime.fromisoformat(last_alert) - now = datetime.utcnow() - elapsed = (now - last_dt).total_seconds() - return elapsed >= config.alert_cooldown - - -def run_collector(config: MonitorConfig) -> bool: - """Run policy event collector. Returns True if successful.""" - cmd = [ - sys.executable, - str(config.script_dir / "collect_policy_event_records.py"), - "--hours-back", "168", - "--ema-days", "7", - "--ema-drift-cap-up-per-day", "0.05", - "--ema-drift-cap-down-per-day", "0.02", - "--out", str(config.policy_events_path), - "--reliability-out", str(config.reliability_path), - ] - - result = subprocess.run(cmd, capture_output=True, text=True, timeout=300) - return result.returncode == 0 - - -def run_model(config: MonitorConfig) -> bool: - """Run hyperfluid model. Returns True if successful.""" - cmd = [ - sys.executable, - str(config.script_dir / "model_hyperfluid_waveforms.py"), - "--post-records", str(config.out_dir / "post_records.jsonl"), - "--chain-records", str(config.out_dir / "chain_records.jsonl"), - "--macro-records", str(config.out_dir / "macro_records.jsonl"), - "--policy-events", str(config.policy_events_path), - "--policy-source-weights", '{"federalreserve":1.0,"ecb":0.9,"bankofengland":0.85,"sec":0.8,"cftc":0.95}', - "--policy-reliability", str(config.reliability_path), - "--horizon-steps", "3", - "--lag-min", "-6", - "--lag-max", "6", - "--walk-forward-train-size", "18", - "--walk-forward-test-size", "6", - "--calibration-bins", "10", - "--calibration-csv-dir", str(config.out_dir / "calibration_curves"), - "--psi-lookback-hours", "72", - "--psi-half-life-hours", "18", - "--out-json", str(config.report_path), - "--out-md", str(config.out_dir / "hyperfluid_causal_report.md"), - ] - - result = subprocess.run(cmd, capture_output=True, text=True, timeout=600) - return result.returncode == 0 - - -def run_soliton_monitor(config: MonitorConfig) -> Optional[Dict[str, Any]]: - """Run soliton monitor on latest report. Returns augmented report or None.""" - if not config.report_path.exists(): - return None - - cmd = [ - sys.executable, - str(config.script_dir / "soliton_monitor.py"), - "--report", str(config.report_path), - "--out", str(config.soliton_report_path), - "--recovery-threshold", "0.15", - ] - - result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) - - if result.returncode == 0 and config.soliton_report_path.exists(): - return json.loads(config.soliton_report_path.read_text(encoding="utf-8")) - - return None - - -def analyze_soliton_report(report: Dict[str, Any], config: MonitorConfig) -> Dict[str, Any]: - """Analyze soliton report for anomalies. Returns summary.""" - chain_backtests = report.get("chain_backtests", {}) - - high_energy_chains = [] - anomaly_clusters_found = [] - - for chain, backtest in chain_backtests.items(): - summary = backtest.get("soliton_summary", {}) - manifest_solitons = summary.get("manifest_solitons", 0) - max_energy = summary.get("soliton_energy_stats", {}).get("max", 0.0) - clusters = summary.get("anomaly_clusters", []) - - if max_energy > config.soliton_alert_threshold: - high_energy_chains.append({ - "chain": chain, - "max_energy": max_energy, - "manifest_solitons": manifest_solitons, - }) - - for cluster in clusters: - if cluster.get("peak_energy", 0.0) > config.anomaly_cluster_pause: - anomaly_clusters_found.append({ - "chain": chain, - "start_idx": cluster["start_idx"], - "end_idx": cluster["end_idx"], - "peak_energy": cluster["peak_energy"], - }) - - return { - "high_energy_chains": high_energy_chains, - "anomaly_clusters": anomaly_clusters_found, - "needs_pause": len(anomaly_clusters_found) > 0, - "needs_retrain": any(c["max_energy"] > config.soliton_retrain_threshold - for c in high_energy_chains), - } - - -def run_graph_os_remediate(config: MonitorConfig, alert_log: Path) -> Dict[str, Any]: - """ - Score soliton findings via Graph OS and recommend remediations. - Runs graph_os_remediate_soliton_findings.py for risk-based auto-fixing. - """ - if not config.soliton_report_path.exists(): - return {"status": "skipped", "reason": "No soliton report"} - - remediation_out = config.out_dir / "graph_os_remediation.json" - - cmd = [ - sys.executable, - str(config.script_dir / "graph_os_remediate_soliton_findings.py"), - "--soliton-report", str(config.soliton_report_path), - "--out-json", str(remediation_out), - ] - - try: - result = subprocess.run(cmd, capture_output=True, text=True, timeout=60) - - if result.returncode != 0: - log_alert(alert_log, "WARNING", f"Graph OS remediation failed: {result.stderr[:200]}") - return {"status": "error", "reason": result.stderr[:200]} - - # Parse remediation JSON - if remediation_out.exists(): - rem_data = json.loads(remediation_out.read_text()) - graph_os_summary = rem_data.get("graph_os_summary", {}) - remediations = rem_data.get("remediations", []) - - # Log any CRITICAL or ALERT remediations - critical_actions = [r for r in remediations if r["action"] in ("pause", "retrain")] - if critical_actions: - message = f"Graph OS recommended {len(critical_actions)} critical action(s)" - log_alert(alert_log, "WARNING", message, {"actions": critical_actions}) - - return { - "status": "success", - "remediation_count": len(remediations), - "critical_actions": len(critical_actions), - "graph_os_summary": graph_os_summary, - } - - return {"status": "success", "remediation_count": len(remediations)} - - except subprocess.TimeoutExpired: - log_alert(alert_log, "WARNING", "Graph OS remediation timeout") - return {"status": "timeout"} - except Exception as e: - log_alert(alert_log, "WARNING", f"Graph OS remediation error: {str(e)[:200]}") - return {"status": "error", "reason": str(e)[:200]} - - -def check_emergency_override(config: MonitorConfig, soliton_report: Dict[str, Any]) -> Dict[str, Any]: - """ - Check for existential threat conditions (flash-fry scenarios). - If triggered, Graph OS enters autonomous emergency mode, bypassing normal governance. - - Triggers: - - 3+ chains with manifest solitons > 2 (multi-chain cascade failure) - - Max soliton energy > 0.60 (extreme regime shift) - - Multiple simultaneous high-energy chains (coordinated stress) - """ - chain_backtests = soliton_report.get("chain_backtests", {}) - - crisis_chains = [] - max_energy_overall = 0.0 - - for chain, backtest in chain_backtests.items(): - summary = backtest.get("soliton_summary", {}) - manifest_solitons = summary.get("manifest_solitons", 0) - max_energy = summary.get("soliton_energy_stats", {}).get("max", 0.0) - - max_energy_overall = max(max_energy_overall, max_energy) - - if manifest_solitons > 2: - crisis_chains.append({ - "chain": chain, - "manifest_solitons": manifest_solitons, - "max_energy": max_energy, - }) - - # Trigger conditions - is_emergency = False - condition_type = None - - # Condition 1: Multi-chain cascade (3+ chains in crisis) - if len(crisis_chains) >= 3: - is_emergency = True - condition_type = "multi_chain_cascade" - - # Condition 2: Extreme energy spike (model completely destabilized) - elif max_energy_overall > 0.60: - is_emergency = True - condition_type = "extreme_energy_spike" - - # Condition 3: Coordinated stress (2+ chains both manifest + high energy) - elif len(crisis_chains) >= 2: - high_energy_crisis = [c for c in crisis_chains if c["max_energy"] > 0.45] - if len(high_energy_crisis) >= 2: - is_emergency = True - condition_type = "coordinated_stress" - - if not is_emergency: - return {"emergency_active": False} - - # Emergency ACTIVATED — invoke Graph OS emergency override script - now = datetime.utcnow() - pause_until = now.timestamp() + 1800 # Pause for 30 minutes in emergency - - result = { - "emergency_active": True, - "condition_type": condition_type, - "detected_at": now.isoformat(), - "pause_until_utc": datetime.fromtimestamp(pause_until).isoformat(), - "crisis_chains": crisis_chains, - "max_energy_overall": max_energy_overall, - "actions_taken": [ - { - "action_type": "HALT_ALL", - "reason": f"Emergency override: {condition_type}", - }, - { - "action_type": "PAUSE_WITHDRAWALS", - "reason": "Prevent panic liquidations during emergency", - }, - ], - } - - # If extreme energy, also liquidate to stables - if condition_type == "extreme_energy_spike" or max_energy_overall > 0.55: - result["actions_taken"].append({ - "action_type": "LIQUIDATE_TO_STABLE", - "reason": f"Extreme soliton energy ({max_energy_overall:.2f}); converting to stables", - }) - - return result - - -def check_balance_floor(config: MonitorConfig) -> Dict[str, Any]: - """Check wallet balance against the hard floor kill switch. - - Reads balance from a JSON file that should be updated by the execution - layer or a separate balance-polling script. If the file is missing, - returns a warning but does NOT trigger the kill switch (fail-open for - monitoring-only mode). - - Returns: - dict with keys: triggered (bool), balance_usd (float|None), reason (str) - """ - if not config.balance_file.exists(): - return { - "triggered": False, - "balance_usd": None, - "reason": "BALANCE_FILE_MISSING — monitoring-only mode (no live wallet)", - } - - try: - data = json.loads(config.balance_file.read_text(encoding="utf-8")) - balance = float(data.get("total_usd", 0.0)) - except (json.JSONDecodeError, ValueError, TypeError): - return { - "triggered": False, - "balance_usd": None, - "reason": "BALANCE_FILE_PARSE_ERROR — treating as monitoring-only", - } - - if balance < config.hard_floor_usd: - return { - "triggered": True, - "balance_usd": balance, - "reason": f"HARD_FLOOR_BREACH: ${balance:.2f} < ${config.hard_floor_usd:.2f} floor", - } - - return { - "triggered": False, - "balance_usd": balance, - "reason": f"OK: ${balance:.2f} >= ${config.hard_floor_usd:.2f} floor", - } - - -def check_ofac_screener_health(config: MonitorConfig) -> Dict[str, Any]: - """Verify the OFAC screener is loaded and the SDN snapshot is fresh. - - This is a health check for the screening infrastructure itself — - separate from screening individual addresses. - """ - screener = OFACScreen(sdn_path=config.sdn_path) - summary = screener.summary() - - healthy = summary.get("loaded", False) and not summary.get("is_stale", True) - fail_closed = summary.get("fail_closed_active", True) - - return { - "healthy": healthy, - "fail_closed_active": fail_closed, - "sanctioned_address_count": summary.get("sanctioned_address_count", 0), - "snapshot_age_hours": summary.get("snapshot_age_hours", 0), - "detail": summary, - } - - -def monitor_iteration(config: MonitorConfig, state: MonitorState) -> None: - """Single monitoring iteration. Collect, model, analyze, act.""" - - now_utc = datetime.utcnow() - log_alert(config.alert_log, "INFO", "Monitor iteration started") - - # Check if paused - if state.get("paused"): - pause_until = state.get("pause_until_utc") - if pause_until and datetime.fromisoformat(pause_until) > now_utc: - log_alert(config.alert_log, "INFO", f"System paused until {pause_until}") - return - else: - state.set("paused", False) - state.set("pause_reason", None) - state.set("pause_until_utc", None) - log_alert(config.alert_log, "INFO", "System resumed after pause") - - # ── Hard balance floor check ────────────────────────────────────────── - log_alert(config.alert_log, "INFO", "Checking wallet balance floor") - balance_check = check_balance_floor(config) - if balance_check["triggered"]: - log_alert(config.alert_log, "CRITICAL", - f"🚨 HARD FLOOR KILL SWITCH: {balance_check['reason']}") - state.set("paused", True) - state.set("pause_reason", f"HARD_FLOOR_KILL_SWITCH: {balance_check['reason']}") - # No pause_until — manual restart required after kill switch - state.set("pause_until_utc", None) - print(f"🚨 KILL SWITCH: Balance ${balance_check['balance_usd']:.2f} below ${config.hard_floor_usd:.2f} floor — system halted") - return - else: - log_alert(config.alert_log, "INFO", f"Balance floor: {balance_check['reason']}") - - # ── OFAC screener health check ──────────────────────────────────────── - log_alert(config.alert_log, "INFO", "Checking OFAC screener health") - ofac_health = check_ofac_screener_health(config) - if ofac_health["fail_closed_active"]: - log_alert(config.alert_log, "WARNING", - f"OFAC screener in FAIL-CLOSED mode — all addresses will be blocked. " - f"Snapshot age: {ofac_health['snapshot_age_hours']:.1f}h") - else: - log_alert(config.alert_log, "INFO", - f"OFAC screener healthy — {ofac_health['sanctioned_address_count']} addresses indexed, " - f"snapshot age {ofac_health['snapshot_age_hours']:.1f}h") - - # Run collection + model - log_alert(config.alert_log, "INFO", "Running policy collector") - if not run_collector(config): - log_alert(config.alert_log, "WARNING", "Collector failed, skipping this iteration") - return - - log_alert(config.alert_log, "INFO", "Running hyperfluid model") - if not run_model(config): - log_alert(config.alert_log, "WARNING", "Model failed, skipping this iteration") - return - - # Soliton analysis - log_alert(config.alert_log, "INFO", "Running soliton monitor") - soliton_report = run_soliton_monitor(config) - if not soliton_report: - log_alert(config.alert_log, "WARNING", "Soliton monitor failed") - return - - analysis = analyze_soliton_report(soliton_report, config) - - # ── Cognitive Triage Reasoning (Local Gemma) ───────────────────────── - reasoner = SolitonReasoningEngine() - outcome = None - - # We perform reasoning for each canal report - if soliton_report.get("canal_reports"): - log_alert(config.alert_log, "INFO", "Initiating Cognitive Triage reasoning pass") - for report in soliton_report["canal_reports"]: - # Convert dict back to object if necessary - if isinstance(report, dict): - # Lightweight reconstruction for reasoning - report_obj = CanalRoutingReport( - contract_sha256=report.get("contract", "unknown"), - overall_heat=report.get("metrics", {}).get("heat", 0.0), - overall_torsion=report.get("metrics", {}).get("torsion", 0.0), - overall_anisotropy=report.get("metrics", {}).get("anisotropy", 0.0), - triage_score=report.get("metrics", {}).get("triage", 0.0), - canal_cost_kot=report.get("metrics", {}).get("canal_cost_kot", 0.0), - valve_phi=report.get("valve", 1), - routing_decision=report.get("decision", "review"), - reason=report.get("reason", ""), - applied_tolerances={} - ) - else: - report_obj = report - - outcome = reasoner.analyze_risk(report_obj) - if outcome: - log_alert(config.alert_log, "INFO", - f"Cognitive Reasoning (Gemma2): {outcome.remediation_strategy.upper()}") - log_alert(config.alert_log, "INFO", f"Summary: {outcome.reasoning_summary}") - - # Secondary override if LLM identifies EXTREME deviation - if outcome.semantic_risk_score > 0.9 and report_obj.routing_decision != "freeze": - log_alert(config.alert_log, "WARNING", "Gemma2 suggesting IMMEDIATE FREEZE due to structural predation") - report_obj.routing_decision = "freeze" - report_obj.reason += " | GE_REASONING_OVERRIDE_PREDATION" - - # Graph OS risk-based remediation - log_alert(config.alert_log, "INFO", "Running Graph OS risk scoring and remediation") - graph_os_result = run_graph_os_remediate(config, config.alert_log) - if graph_os_result.get("status") == "success": - critical = graph_os_result.get("critical_actions", 0) - if critical > 0: - log_alert(config.alert_log, "WARNING", - f"Graph OS identified {critical} critical remediation action(s)") - - # EMERGENCY OVERRIDE CHECK (Flash-fry detection) - log_alert(config.alert_log, "INFO", "Checking for emergency conditions") - emergency_status = check_emergency_override(config, soliton_report) - - if emergency_status.get("emergency_active"): - # Graph OS in autonomous emergency mode — bypass normal governance - log_alert(config.alert_log, "CRITICAL", - f"🚨 EMERGENCY OVERRIDE ACTIVATED: {emergency_status['condition_type']}") - - # Emergency always pauses the system - state.set("paused", True) - state.set("pause_reason", f"EMERGENCY_OVERRIDE: {emergency_status['condition_type']}") - state.set("pause_until_utc", emergency_status.get("pause_until_utc", now_utc.isoformat())) - - # Log the emergency actions autonomously executed - actions_taken = emergency_status.get("actions_taken", []) - for action in actions_taken: - log_alert(config.alert_log, "CRITICAL", - f"Emergency action: {action['action_type']} — {action.get('reason', '')}") - - print(f"🚨 EMERGENCY: {emergency_status['condition_type']} — Graph OS override active") - return # Exit early — normal governance frozen - - # Act on findings (only if not in emergency) - if analysis["high_energy_chains"]: - message = f"High soliton energy detected on {len(analysis['high_energy_chains'])} chain(s)" - log_alert(config.alert_log, "WARNING", message, {"chains": analysis["high_energy_chains"]}) - - if can_alert(state, config): - state.set("last_alert_utc", now_utc.isoformat()) - # Send webhook alert if configured - if config.webhook_url: - try: - import requests - webhook_payload = { - "alert_type": "high_energy_chains", - "message": message, - "chains": analysis["high_energy_chains"], - "timestamp": now_utc.isoformat(), - } - response = requests.post(config.webhook_url, json=webhook_payload, timeout=5) - if response.status_code == 200: - log_alert(config.alert_log, "INFO", "Webhook alert sent successfully") - else: - log_alert(config.alert_log, "WARNING", f"Webhook alert failed: {response.status_code}") - except Exception as e: - log_alert(config.alert_log, "ERROR", f"Failed to send webhook alert: {e}") - print(f"🔴 ALERT: {message}") - - if analysis["anomaly_clusters"]: - message = f"{len(analysis['anomaly_clusters'])} anomaly cluster(s) detected" - log_alert(config.alert_log, "CRITICAL", message, {"clusters": analysis["anomaly_clusters"]}) - - state.set("consecutive_clusters", state.get("consecutive_clusters", 0) + 1) - - if state.get("consecutive_clusters", 0) >= config.consecutive_clusters_for_alert: - # Pause system - pause_until = now_utc.timestamp() + 3600 # Pause for 1 hour - state.set("paused", True) - state.set("pause_reason", "Multiple anomaly clusters detected") - state.set("pause_until_utc", datetime.fromtimestamp(pause_until).isoformat()) - - log_alert(config.alert_log, "CRITICAL", - f"System paused due to anomaly cluster surge. Resume at {pause_until}") - print(f"🚨 PAUSE: {message} — pausing until {pause_until}") - else: - state.set("consecutive_clusters", 0) - - if analysis["needs_retrain"]: - message = "Soliton energy suggests model drift — retraining recommended" - log_alert(config.alert_log, "WARNING", message) - # Trigger async retrain job via subprocess - try: - import subprocess - retrain_script = config.script_dir / "retrain_model.py" - if retrain_script.exists(): - subprocess.Popen( - [sys.executable, str(retrain_script)], - stdout=subprocess.DEVNULL, - stderr=subprocess.DEVNULL, - start_new_session=True - ) - log_alert(config.alert_log, "INFO", "Async retrain job triggered") - else: - log_alert(config.alert_log, "WARNING", "Retrain script not found, skipping retrain") - except Exception as e: - log_alert(config.alert_log, "ERROR", f"Failed to trigger retrain job: {e}") - print(f"⚠️ RETRAIN: {message}") - - log_alert(config.alert_log, "INFO", "Monitor iteration complete") - - -def daemon_loop(config: MonitorConfig) -> None: - """Main daemon loop. Runs until killed.""" - state = MonitorState(config.state_file) - - print(f"✓ Monitor daemon started") - print(f" Workspace: {config.workspace}") - print(f" Polling interval: {config.polling_interval}s ({config.polling_interval / 3600:.1f}h)") - print(f" Alert log: {config.alert_log}") - print(f" State: {config.state_file}") - print(f"\nMonitoring {config.workspace}... (Ctrl+C to stop)") - - iteration = 0 - while True: - try: - iteration += 1 - print(f"\n[{datetime.utcnow().isoformat()}] Iteration {iteration}") - monitor_iteration(config, state) - print(f" Sleeping {config.polling_interval}s until next check...") - time.sleep(config.polling_interval) - except KeyboardInterrupt: - print("\n\n✓ Daemon stopped by user") - log_alert(config.alert_log, "INFO", "Daemon stopped") - break - except Exception as e: - log_alert(config.alert_log, "CRITICAL", f"Unhandled error: {str(e)}") - print(f"❌ Error: {e}") - time.sleep(60) # Wait before retry on error - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="Autonomous hyperfluid monitor daemon") - parser.add_argument("--workspace", type=str, required=True, - help="Path to workspace root") - parser.add_argument("--polling-interval", type=int, default=3600, - help="Polling interval in seconds (default 3600 = 1 hour)") - parser.add_argument("--soliton-alert-threshold", type=float, default=0.25, - help="Alert if soliton energy > threshold") - parser.add_argument("--hard-floor-usd", type=float, default=10.0, - help="Kill switch balance floor in USD (default 10.0)") - parser.add_argument("--sdn-path", default=str(DEFAULT_SDN_PATH), - help="OFAC SDN snapshot JSON path") - parser.add_argument("--run-once", action="store_true", - help="Run one iteration and exit (for testing)") - - args = parser.parse_args() - - workspace = Path(args.workspace) - if not workspace.exists(): - print(f"Error: workspace {workspace} does not exist") - sys.exit(1) - - config = MonitorConfig(workspace) - config.polling_interval = args.polling_interval - config.soliton_alert_threshold = args.soliton_alert_threshold - config.hard_floor_usd = args.hard_floor_usd - config.sdn_path = Path(args.sdn_path) - - if args.run_once: - state = MonitorState(config.state_file) - monitor_iteration(config, state) - else: - daemon_loop(config) diff --git a/5-Applications/tools-scripts/optimization/nems_optimizer.py b/5-Applications/tools-scripts/optimization/nems_optimizer.py deleted file mode 100644 index 3725879e..00000000 --- a/5-Applications/tools-scripts/optimization/nems_optimizer.py +++ /dev/null @@ -1,54 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math - -# CONSTANTS -H_BAR = 1.0545718e-34 -MU_CO2 = 0.53e-30 # Dipole moment for CO2 bending mode (C m) -EPSILON_0 = 8.854187e-12 - -def calculate_rabi_rate(field_strength): - # Rabi = (dipole * E) / h_bar - return (MU_CO2 * field_strength) / H_BAR - -def simulate_design_a(): - # CNT Pillars with SPP-Hotspots - # Confinement factor improvement: 1.25x - field = 4.8e9 * 1.25 - rabi = calculate_rabi_rate(field) - return "CNT_Pillar", field, rabi - -def simulate_design_b(): - # Vacuum-Gap Moat Resonators - # Field enhancement due to sub-10nm gap: 1.35x - field = 4.8e9 * 1.35 - rabi = calculate_rabi_rate(field) - return "Vacuum_Gap", field, rabi - -def simulate_design_c(): - # Hyperbolic Metamaterials (HMM) - AlGaAs/Graphite Multilayer - # Purcell effect enhancement and density: 1.55x - field = 4.8e9 * 1.55 - rabi = calculate_rabi_rate(field) - return "HMM_Substrate", field, rabi - -# Baseline (from spec) -baseline_field = 4.8e9 -baseline_rabi = calculate_rabi_rate(baseline_field) - -print(f"--- NEMS PERFORMANCE ANALYSIS ---") -print(f"Baseline Field: {baseline_field:.2e} V/m") -print(f"Baseline Rabi Rate: {baseline_rabi:.2e} Hz") -print("-" * 35) - -designs = [simulate_design_a(), simulate_design_b(), simulate_design_c()] - -for name, field, rabi in designs: - improvement = (rabi - baseline_rabi) / baseline_rabi * 100 - print(f"Design {name:15}: Field={field:.2e} V/m, Rabi={rabi:.2e} Hz, Gain={improvement:.2f}%") - if improvement >= 30: - print(f"*** DESIGN {name} EXCEEDS 30% THRESHOLD ***") diff --git a/5-Applications/tools-scripts/optimization/poh_sha512_tick_sim.py b/5-Applications/tools-scripts/optimization/poh_sha512_tick_sim.py deleted file mode 100644 index 5d029baa..00000000 --- a/5-Applications/tools-scripts/optimization/poh_sha512_tick_sim.py +++ /dev/null @@ -1,34 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import hashlib -import time - -def simulate_poh_tick(start_tick, count=5): - """ - Simulates the TSM_SHA512_TICK fallback. - H_n = SHA512(H_{n-1} || tick_count) - """ - prev_hash = hashlib.sha512(b"GENESIS_SOLITON").digest() - - print(f"--- TSM CUMULATIVE SHA512 TICK SIMULATION ---") - print(f"Genesis: {prev_hash.hex()[:16]}...") - - for i in range(count): - current_tick = start_tick + i - # The core Proof of History logic - data = prev_hash + str(current_tick).encode() - current_hash = hashlib.sha512(data).digest() - - print(f"\n[Tick {current_tick}]") - print(f" Input: PrevHash + {current_tick}") - print(f" Result: {current_hash.hex()[:32]}...") - - prev_hash = current_hash - -if __name__ == "__main__": - # Start at a simulated Planck-tick timestamp - simulate_poh_tick(1773890188) diff --git a/5-Applications/tools-scripts/optimization/qubo_chunk_field_benchmark.py b/5-Applications/tools-scripts/optimization/qubo_chunk_field_benchmark.py deleted file mode 100644 index 28374a8e..00000000 --- a/5-Applications/tools-scripts/optimization/qubo_chunk_field_benchmark.py +++ /dev/null @@ -1,331 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -qubo_chunk_field_benchmark.py - -Benchmark a solvable-but-slow dense QUBO instance against two search policies: - -1. baseline simulated annealing with uniform variable proposals -2. chunk-field guided annealing that prioritizes high-energy-density chunks - -This script is intentionally narrow. It does not claim to implement the full -waveprobe/phonon-graph hardware path. It tests one grounded hypothesis from the -matrix/operator thread: if we treat the QUBO surface as a field and rank chunks -by energy density, does that help a bounded stochastic solver reach the optimum -more often or earlier on a frustrating instance? -""" - -from __future__ import annotations - -import argparse -import json -import math -import time -from dataclasses import dataclass -from pathlib import Path -from typing import Dict, List, Optional - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - - -@dataclass -class RunStats: - best_energy: float - hit_optimum: bool - first_hit_step: Optional[int] - elapsed_s: float - - -def generate_dense_qubo( - n: int, - seed: int, - density: float = 1.0, - weight_scale: float = 1.0, - bias_scale: float = 0.35, -) -> AnyArray: - """Generate a dense frustrated symmetric QUBO matrix.""" - rng = xp.random.default_rng(seed) - raw = rng.normal(0.0, weight_scale, size=(n, n)) - mask = rng.random((n, n)) < density - raw *= mask - raw = xp.triu(raw, 1) - sym = raw + raw.T - diag = rng.normal(0.0, bias_scale, size=n) - xp.fill_diagonal(sym, diag) - return sym.astype(xp.float64) - - -def energy(Q: AnyArray, x: AnyArray) -> float: - return float(x @ Q @ x) - - -def exact_solve_qubo(Q: AnyArray, batch_size: int = 65536) -> Dict[str, object]: - """ - Exact solver by batched brute force. - Practical for n <= ~22 on a normal machine. - """ - n = Q.shape[0] - total = 1 << n - best_energy = float("inf") - best_state = None - col_shifts = xp.arange(n, dtype=xp.uint64) - - for start in range(0, total, batch_size): - end = min(start + batch_size, total) - vals = xp.arange(start, end, dtype=xp.uint64) - bits = ((vals[:, None] >> col_shifts) & 1).astype(xp.float64) - energies = xp.einsum("bi,ij,bj->b", bits, Q, bits, optimize=True) - idx = int(xp.argmin(energies)) - e = float(energies[idx]) - if e < best_energy: - best_energy = e - best_state = bits[idx].astype(xp.int8) - - return { - "best_energy": best_energy, - "best_state": best_state.tolist() if best_state is not None else None, - } - - -def build_chunk_index(n: int, chunk_size: int) -> List[AnyArray]: - return [ - xp.arange(start, min(start + chunk_size, n), dtype=xp.int64) - for start in range(0, n, chunk_size) - ] - - -def chunk_static_density(Q: AnyArray, chunks: List[AnyArray]) -> AnyArray: - scores = [] - for idxs in chunks: - block = Q[xp.ix_(idxs, xp.arange(Q.shape[0]))] - scores.append(float(xp.mean(block * block))) - return xp.asarray(scores, dtype=xp.float64) - - -def run_baseline_sa( - Q: AnyArray, - optimum: float, - steps: int, - seed: int, - temp_start: float, - temp_end: float, -) -> RunStats: - rng = xp.random.default_rng(seed) - n = Q.shape[0] - x = rng.integers(0, 2, size=n, dtype=xp.int8).astype(xp.float64) - Qx = Q @ x - current_energy = energy(Q, x) - best_energy = current_energy - first_hit_step = 0 if math.isclose(current_energy, optimum, abs_tol=1e-9) else None - - t0 = time.perf_counter() - for step in range(steps): - frac = step / max(steps - 1, 1) - temp = temp_start * ((temp_end / temp_start) ** frac) - i = int(rng.integers(0, n)) - d = 1.0 - 2.0 * x[i] - delta = 2.0 * d * Qx[i] + Q[i, i] - if delta < 0.0 or rng.random() < math.exp(-delta / max(temp, 1e-12)): - x[i] = 1.0 - x[i] - Qx += d * Q[:, i] - current_energy += delta - if current_energy < best_energy: - best_energy = current_energy - if first_hit_step is None and math.isclose(current_energy, optimum, abs_tol=1e-9): - first_hit_step = step + 1 - elapsed = time.perf_counter() - t0 - return RunStats( - best_energy=best_energy, - hit_optimum=math.isclose(best_energy, optimum, abs_tol=1e-9), - first_hit_step=first_hit_step, - elapsed_s=elapsed, - ) - - -def run_chunk_guided_sa( - Q: AnyArray, - optimum: float, - steps: int, - seed: int, - temp_start: float, - temp_end: float, - chunk_size: int, - guided_prob: float, - recompute_every: int, -) -> RunStats: - rng = xp.random.default_rng(seed) - n = Q.shape[0] - chunks = build_chunk_index(n, chunk_size) - static_density = chunk_static_density(Q, chunks) - - x = rng.integers(0, 2, size=n, dtype=xp.int8).astype(xp.float64) - Qx = Q @ x - current_energy = energy(Q, x) - best_energy = current_energy - first_hit_step = 0 if math.isclose(current_energy, optimum, abs_tol=1e-9) else None - - chunk_prob = xp.ones(len(chunks), dtype=xp.float64) / max(len(chunks), 1) - t0 = time.perf_counter() - for step in range(steps): - if step % max(recompute_every, 1) == 0: - local_flip_gain = xp.abs((1.0 - 2.0 * x) * Qx) - dynamic = xp.asarray( - [float(xp.mean(local_flip_gain[idxs])) for idxs in chunks], - dtype=xp.float64, - ) - chunk_score = static_density * (0.5 + dynamic) - if xp.all(chunk_score <= 0.0): - chunk_prob = xp.ones(len(chunks), dtype=xp.float64) / max(len(chunks), 1) - else: - chunk_prob = chunk_score / xp.sum(chunk_score) - - frac = step / max(steps - 1, 1) - temp = temp_start * ((temp_end / temp_start) ** frac) - - if rng.random() < guided_prob: - chunk_idx = int(rng.choice(len(chunks), p=chunk_prob)) - idxs = chunks[chunk_idx] - local_flip_gain = xp.abs((1.0 - 2.0 * x[idxs]) * Qx[idxs]) + 1e-9 - var_prob = local_flip_gain / xp.sum(local_flip_gain) - i = int(rng.choice(idxs, p=var_prob)) - else: - i = int(rng.integers(0, n)) - - d = 1.0 - 2.0 * x[i] - delta = 2.0 * d * Qx[i] + Q[i, i] - if delta < 0.0 or rng.random() < math.exp(-delta / max(temp, 1e-12)): - x[i] = 1.0 - x[i] - Qx += d * Q[:, i] - current_energy += delta - if current_energy < best_energy: - best_energy = current_energy - if first_hit_step is None and math.isclose(current_energy, optimum, abs_tol=1e-9): - first_hit_step = step + 1 - elapsed = time.perf_counter() - t0 - return RunStats( - best_energy=best_energy, - hit_optimum=math.isclose(best_energy, optimum, abs_tol=1e-9), - first_hit_step=first_hit_step, - elapsed_s=elapsed, - ) - - -def summarize_runs(runs: List[RunStats], optimum: float) -> Dict[str, object]: - hit_steps = [r.first_hit_step for r in runs if r.first_hit_step is not None] - return { - "trials": len(runs), - "optimum_energy": optimum, - "hit_rate": sum(r.hit_optimum for r in runs) / max(len(runs), 1), - "avg_best_energy": float(xp.mean([r.best_energy for r in runs])), - "median_best_energy": float(xp.median([r.best_energy for r in runs])), - "avg_gap_to_optimum": float(xp.mean([r.best_energy - optimum for r in runs])), - "median_first_hit_step": int(xp.median(hit_steps)) if hit_steps else None, - "avg_elapsed_s": float(xp.mean([r.elapsed_s for r in runs])), - } - - -def main() -> int: - ap = argparse.ArgumentParser(description="Benchmark chunk-field-guided SA on a dense QUBO") - ap.add_argument("--n", type=int, default=20, help="QUBO variable count (exact solve grows as 2^n)") - ap.add_argument("--seed", type=int, default=1337) - ap.add_argument("--trials", type=int, default=48) - ap.add_argument("--steps", type=int, default=6000) - ap.add_argument("--chunk-size", type=int, default=4) - ap.add_argument("--guided-prob", type=float, default=0.8) - ap.add_argument("--recompute-every", type=int, default=32) - ap.add_argument("--temp-start", type=float, default=3.0) - ap.add_argument("--temp-end", type=float, default=0.02) - ap.add_argument( - "--out", - type=Path, - default=Path("5-Applications/out/qubo_chunk_benchmark/spin_glass.json"), - ) - args = ap.parse_args() - - if args.n > 24: - raise SystemExit("Refusing exact solve above n=24 without a stronger backend.") - - Q = generate_dense_qubo(args.n, seed=args.seed) - exact = exact_solve_qubo(Q) - optimum = float(exact["best_energy"]) - - baseline_runs: List[RunStats] = [] - guided_runs: List[RunStats] = [] - for trial in range(args.trials): - trial_seed = args.seed + 1000 + trial - baseline_runs.append( - run_baseline_sa( - Q=Q, - optimum=optimum, - steps=args.steps, - seed=trial_seed, - temp_start=args.temp_start, - temp_end=args.temp_end, - ) - ) - guided_runs.append( - run_chunk_guided_sa( - Q=Q, - optimum=optimum, - steps=args.steps, - seed=trial_seed, - temp_start=args.temp_start, - temp_end=args.temp_end, - chunk_size=args.chunk_size, - guided_prob=args.guided_prob, - recompute_every=args.recompute_every, - ) - ) - - result = { - "schema_version": "qubo.chunk.field.benchmark.v1", - "params": { - "n": args.n, - "seed": args.seed, - "trials": args.trials, - "steps": args.steps, - "chunk_size": args.chunk_size, - "guided_prob": args.guided_prob, - "recompute_every": args.recompute_every, - "temp_start": args.temp_start, - "temp_end": args.temp_end, - }, - "exact": exact, - "baseline": summarize_runs(baseline_runs, optimum), - "chunk_guided": summarize_runs(guided_runs, optimum), - } - result["delta"] = { - "hit_rate_gain": result["chunk_guided"]["hit_rate"] - result["baseline"]["hit_rate"], - "avg_gap_reduction": result["baseline"]["avg_gap_to_optimum"] - result["chunk_guided"]["avg_gap_to_optimum"], - "median_first_hit_step_gain": ( - None - if result["baseline"]["median_first_hit_step"] is None - or result["chunk_guided"]["median_first_hit_step"] is None - else result["baseline"]["median_first_hit_step"] - result["chunk_guided"]["median_first_hit_step"] - ), - } - - args.out.parent.mkdir(parents=True, exist_ok=True) - with open(args.out, "w", encoding="utf-8") as fh: - json.dump(result, fh, indent=2) - - print(json.dumps({ - "exact_best_energy": optimum, - "baseline": result["baseline"], - "chunk_guided": result["chunk_guided"], - "delta": result["delta"], - "out": str(args.out.resolve()), - }, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/optimization/rank_hdc_experiments.py b/5-Applications/tools-scripts/optimization/rank_hdc_experiments.py deleted file mode 100644 index f0c879f0..00000000 --- a/5-Applications/tools-scripts/optimization/rank_hdc_experiments.py +++ /dev/null @@ -1,141 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Rank HDC experiment runs from CSV ledger by key metrics. - -Reads the ledger emitted by simulate/sweep scripts and prints top-N runs -by query_lift or broadcast_delta for quick decisioning. -""" - -from __future__ import annotations - -import argparse -import csv -from pathlib import Path -from typing import Dict, List - -ROOT = Path(__file__).resolve().parent.parent -DEFAULT_LEDGER_PATH = ROOT / "out" / "hdc_experiment_ledger.csv" - - -def parse_float(value: str) -> float: - try: - return float((value or "").strip()) - except (TypeError, ValueError): - return 0.0 - - -def load_rows(ledger_path: Path) -> List[Dict[str, str]]: - if not ledger_path.exists(): - raise SystemExit(f"ledger not found: {ledger_path}") - with ledger_path.open("r", encoding="utf-8", newline="") as f: - reader = csv.DictReader(f) - rows = [dict(r) for r in reader] - if not rows: - raise SystemExit("ledger has no rows") - return rows - - -def filter_rows(rows: List[Dict[str, str]], label_contains: str, subnet_contains: str) -> List[Dict[str, str]]: - out: List[Dict[str, str]] = [] - label_filter = (label_contains or "").strip().lower() - subnet_filter = (subnet_contains or "").strip().lower() - - for row in rows: - label = str(row.get("label") or "") - subnet = str(row.get("subnet") or "") - if label_filter and label_filter not in label.lower(): - continue - if subnet_filter and subnet_filter not in subnet.lower(): - continue - out.append(row) - return out - - -def sort_rows(rows: List[Dict[str, str]], metric: str, descending: bool) -> List[Dict[str, str]]: - key = metric - return sorted(rows, key=lambda r: parse_float(str(r.get(key) or "0")), reverse=descending) - - -def render_table(rows: List[Dict[str, str]], metric: str, top_n: int) -> str: - other_metric = "broadcast_delta" if metric == "query_lift" else "query_lift" - headers = ["rank", "label", "subnet", metric, other_metric, "generated_utc"] - picks = rows[: max(1, top_n)] - - body: List[List[str]] = [] - for idx, row in enumerate(picks, start=1): - body.append([ - str(idx), - str(row.get("label") or ""), - str(row.get("subnet") or ""), - str(row.get(metric) or "0"), - str(row.get(other_metric) or "0"), - str(row.get("generated_utc") or ""), - ]) - - widths = [len(h) for h in headers] - for row in body: - for i, cell in enumerate(row): - if len(cell) > widths[i]: - widths[i] = len(cell) - - def fmt(parts: List[str]) -> str: - return " | ".join(parts[i].ljust(widths[i]) for i in range(len(parts))) - - sep = "-+-".join("-" * w for w in widths) - lines = [fmt(headers), sep] - lines.extend(fmt(r) for r in body) - return "\n".join(lines) - - -def render_one_line_rows(rows: List[Dict[str, str]], metric: str, top_n: int, delim: str = "|") -> str: - d = delim if delim else "|" - other_metric = "broadcast_delta" if metric == "query_lift" else "query_lift" - picks = rows[: max(1, top_n)] - lines: List[str] = [] - for idx, row in enumerate(picks, start=1): - parts = [ - str(idx), - str(row.get("label") or ""), - str(row.get("subnet") or ""), - str(row.get(metric) or "0"), - str(row.get(other_metric) or "0"), - str(row.get("generated_utc") or ""), - ] - lines.append(d.join(parts)) - return "\n".join(lines) - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--ledger-path", default=str(DEFAULT_LEDGER_PATH), help="Path to hdc_experiment_ledger.csv") - ap.add_argument("--metric", choices=["query_lift", "broadcast_delta"], default="query_lift", help="Metric used for ranking") - ap.add_argument("--top-n", type=int, default=10, help="Number of rows to print") - ap.add_argument("--ascending", action="store_true", help="Sort ascending (default is descending)") - ap.add_argument("--label-contains", default="", help="Optional substring filter on label") - ap.add_argument("--subnet-contains", default="", help="Optional substring filter on subnet") - ap.add_argument("--one-line", action="store_true", help="Emit one line per ranked row for pipe-friendly chaining") - ap.add_argument("--one-line-delim", default="|", help="Delimiter for --one-line output (default: |)") - args = ap.parse_args() - - if int(args.top_n) <= 0: - raise SystemExit("invalid --top-n: must be > 0") - - rows = load_rows(Path(str(args.ledger_path))) - rows = filter_rows(rows, str(args.label_contains), str(args.subnet_contains)) - if not rows: - raise SystemExit("no rows matched the requested filters") - - ranked = sort_rows(rows, metric=str(args.metric), descending=not bool(args.ascending)) - if args.one_line: - print(render_one_line_rows(ranked, metric=str(args.metric), top_n=int(args.top_n), delim=str(args.one_line_delim))) - return - print(render_table(ranked, metric=str(args.metric), top_n=int(args.top_n))) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/optimization/rank_satellite_reactivation_candidates.py b/5-Applications/tools-scripts/optimization/rank_satellite_reactivation_candidates.py deleted file mode 100644 index e35dae71..00000000 --- a/5-Applications/tools-scripts/optimization/rank_satellite_reactivation_candidates.py +++ /dev/null @@ -1,249 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Rank scored satellite reactivation candidates. - -Three views: - --go top-N GO candidates by score (default N=10) - --watch top-N WATCH candidates by lowest component gap - --gates gate-failure frequency breakdown across all rows - -All three views can be shown in one pass with --all. -""" - -from __future__ import annotations - -import argparse -import csv -import sys -from collections import Counter -from pathlib import Path -from typing import Any, Dict, List, Tuple - -DEFAULT_SCORED = Path("5-Applications/out/satellite_reactivation_scored.csv") - -COMPONENTS = [ - "component_legal", - "component_link", - "component_power", - "component_stability", - "component_recovery", - "component_pcec", - "component_cost_eff", -] - -COMPONENT_SHORT = { - "component_legal": "legal", - "component_link": "link", - "component_power": "power", - "component_stability": "stability", - "component_recovery": "recovery", - "component_pcec": "pcec", - "component_cost_eff": "cost_eff", -} - - -def to_float(v: Any, default: float = 0.0) -> float: - try: - return float(str(v or "").strip()) - except (TypeError, ValueError): - return default - - -def load_rows(path: Path) -> List[Dict[str, str]]: - if not path.exists(): - print(f"ERROR: scored CSV not found: {path}", file=sys.stderr) - sys.exit(1) - with path.open("r", encoding="utf-8", newline="") as f: - return [dict(r) for r in csv.DictReader(f)] - - -def weakest_component(row: Dict[str, str]) -> Tuple[float, str]: - """Return (min_score, component_short_name) for a row.""" - scores = {col: to_float(row.get(col, "0")) for col in COMPONENTS} - worst_col = min(scores, key=lambda c: scores[c]) - return scores[worst_col], COMPONENT_SHORT[worst_col] - - -# ── view 1: top-N GO ────────────────────────────────────────────────────────── - -def view_go(rows: List[Dict[str, str]], top_n: int, one_line: bool, delim: str) -> None: - go_rows = [r for r in rows if r.get("decision") == "GO"] - go_rows.sort(key=lambda r: to_float(r.get("reactivation_score")), reverse=True) - candidates = go_rows[:top_n] - - if not candidates: - if one_line: - print(f"go_count{delim}0") - else: - print("[GO] No GO candidates found.") - return - - if one_line: - for rank, r in enumerate(candidates, 1): - parts = [ - str(rank), - r.get("candidate_id", ""), - r.get("orbit_regime", ""), - r.get("reactivation_score", ""), - r.get("component_pcec", ""), - r.get("component_link", ""), - r.get("component_power", ""), - ] - print(delim.join(parts)) - return - - # Table mode - hdr = f"{'#':>3} {'candidate_id':<16} {'orbit':<6} {'score':>6} {'pcec':>6} {'link':>6} {'power':>6} {'stability':>9} {'recovery':>8}" - print(f"\n── TOP-{top_n} GO CANDIDATES ──") - print(hdr) - print("-" * len(hdr)) - for rank, r in enumerate(candidates, 1): - print( - f"{rank:>3} {r.get('candidate_id',''):<16} " - f"{r.get('orbit_regime',''):<6} " - f"{to_float(r.get('reactivation_score')):>6.2f} " - f"{to_float(r.get('component_pcec')):>6.2f} " - f"{to_float(r.get('component_link')):>6.2f} " - f"{to_float(r.get('component_power')):>6.2f} " - f"{to_float(r.get('component_stability')):>9.2f} " - f"{to_float(r.get('component_recovery')):>8.2f}" - ) - print() - - -# ── view 2: top-N WATCH by weakest component ───────────────────────────────── - -def view_watch(rows: List[Dict[str, str]], top_n: int, one_line: bool, delim: str) -> None: - watch_rows = [r for r in rows if r.get("decision") == "WATCH"] - # sort by weakest component score ascending (worst gap first) - watch_rows.sort(key=lambda r: weakest_component(r)[0]) - candidates = watch_rows[:top_n] - - if not candidates: - if one_line: - print(f"watch_count{delim}0") - else: - print("[WATCH] No WATCH candidates found.") - return - - if one_line: - for rank, r in enumerate(candidates, 1): - score_val, comp_name = weakest_component(r) - parts = [ - str(rank), - r.get("candidate_id", ""), - r.get("orbit_regime", ""), - r.get("reactivation_score", ""), - comp_name, - f"{score_val:.2f}", - r.get("component_pcec", ""), - ] - print(delim.join(parts)) - return - - # Table mode - hdr = f"{'#':>3} {'candidate_id':<16} {'orbit':<6} {'score':>6} {'weakest_component':<18} {'weak_score':>10} {'pcec':>6}" - print(f"\n── TOP-{top_n} WATCH — LOWEST COMPONENT GAP FIRST ──") - print(hdr) - print("-" * len(hdr)) - for rank, r in enumerate(candidates, 1): - weak_score, weak_name = weakest_component(r) - print( - f"{rank:>3} {r.get('candidate_id',''):<16} " - f"{r.get('orbit_regime',''):<6} " - f"{to_float(r.get('reactivation_score')):>6.2f} " - f"{weak_name:<18} " - f"{weak_score:>10.2f} " - f"{to_float(r.get('component_pcec')):>6.2f}" - ) - print() - - -# ── view 3: gate-failure frequency ─────────────────────────────────────────── - -def view_gates(rows: List[Dict[str, str]], one_line: bool, delim: str) -> None: - counter: Counter[str] = Counter() - total = len(rows) - rows_with_failures = 0 - - for r in rows: - raw = r.get("gate_failures", "").strip() - if raw: - failures = [f.strip() for f in raw.split("|") if f.strip()] - if failures: - rows_with_failures += 1 - counter.update(failures) - - if not counter: - if one_line: - print(f"gate_failures{delim}none{delim}total_candidates{delim}{total}") - else: - print("[GATES] No gate failures found across all candidates.") - return - - ranked = counter.most_common() - - if one_line: - for gate, count in ranked: - pct = 100.0 * count / total if total > 0 else 0.0 - print(f"{gate}{delim}{count}{delim}{pct:.1f}pct") - return - - # Table mode - hdr = f"{'gate_failure_code':<38} {'count':>5} {'% of candidates':>15}" - print(f"\n── GATE-FAILURE FREQUENCY (total candidates: {total}, with failures: {rows_with_failures}) ──") - print(hdr) - print("-" * len(hdr)) - for gate, count in ranked: - pct = 100.0 * count / total if total > 0 else 0.0 - print(f"{gate:<38} {count:>5} {pct:>14.1f}%") - print() - - -# ── main ────────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - ap.add_argument("--input", default=str(DEFAULT_SCORED), metavar="PATH", - help="Scored candidate CSV (default: %(default)s)") - ap.add_argument("--top-n", type=int, default=10, metavar="N", - help="Rows per view (default: %(default)s)") - ap.add_argument("--all", action="store_true", - help="Show all three views (go + watch + gates)") - ap.add_argument("--go", action="store_true", - help="Show top-N GO candidates") - ap.add_argument("--watch", action="store_true", - help="Show top-N WATCH candidates by weakest component") - ap.add_argument("--gates", action="store_true", - help="Show gate-failure frequency breakdown") - ap.add_argument("--one-line", action="store_true", - help="Pipe-friendly one-line-per-row output") - ap.add_argument("--one-line-delim", default=";", metavar="CHAR", - help="Delimiter for --one-line mode (default: %(default)r)") - args = ap.parse_args() - - show_go = args.go or args.all - show_watch = args.watch or args.all - show_gates = args.gates or args.all - - if not (show_go or show_watch or show_gates): - ap.print_help() - sys.exit(0) - - rows = load_rows(Path(args.input)) - - if show_go: - view_go(rows, args.top_n, args.one_line, args.one_line_delim) - if show_watch: - view_watch(rows, args.top_n, args.one_line, args.one_line_delim) - if show_gates: - view_gates(rows, args.one_line, args.one_line_delim) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/optimization/score_satellite_reactivation_candidates.py b/5-Applications/tools-scripts/optimization/score_satellite_reactivation_candidates.py deleted file mode 100644 index e042c296..00000000 --- a/5-Applications/tools-scripts/optimization/score_satellite_reactivation_candidates.py +++ /dev/null @@ -1,233 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Score satellite reactivation candidates with measurable gates and thresholds. - -Input: CSV with fields from data_baselines/satellite_candidate_template.csv -Output: -- scored CSV with gate failures, score, and decision -- summary JSON with counts and top candidates -""" - -from __future__ import annotations - -import argparse -import csv -import json -from pathlib import Path -from typing import Any, Dict, List, Tuple - - -DEFAULT_INPUT = Path("data_baselines/satellite_candidate_template.csv") -DEFAULT_OUTPUT = Path("5-Applications/out/satellite_reactivation_scored.csv") -DEFAULT_SUMMARY = Path("5-Applications/out/satellite_reactivation_summary.json") - - -def to_float(value: str, default: float = 0.0) -> float: - try: - return float((value or "").strip()) - except (TypeError, ValueError): - return default - - -def to_bool(value: str) -> bool: - return str(value).strip().lower() in {"1", "true", "yes", "y"} - - -def clamp01(v: float) -> float: - return max(0.0, min(1.0, v)) - - -def score_band(x: float, low: float, high: float) -> float: - if high <= low: - return 0.0 - return clamp01((x - low) / (high - low)) - - -def inverse_band(x: float, low: float, high: float) -> float: - if high <= low: - return 0.0 - return clamp01((high - x) / (high - low)) - - -def gate_failures(row: Dict[str, str]) -> List[str]: - fails: List[str] = [] - - if not to_bool(row.get("command_authority_verified", "")): - fails.append("no_command_authority") - if not to_bool(row.get("license_path_clear", "")): - fails.append("license_path_unclear") - if to_float(row.get("rx_beacon_snr_db", "0")) < 6.0: - fails.append("rx_beacon_snr_below_6db") - if to_float(row.get("uplink_ack_rate_pct", "0")) < 20.0: - fails.append("uplink_ack_below_20pct") - if to_float(row.get("power_margin_w", "0")) <= 0.0: - fails.append("non_positive_power_margin") - if to_float(row.get("telemetry_frame_success_pct", "0")) < 70.0: - fails.append("telemetry_success_below_70pct") - - # User-defined usable field requirement mapped to measurable proxy. - # Requires coherent, tunable EM phase behavior above minimum floor. - if to_float(row.get("phase_coherence_score", "0")) < 0.60: - fails.append("phase_coherence_below_0_60") - if to_float(row.get("tunable_em_band_hz", "0")) < 300000.0: - fails.append("tunable_em_band_below_300khz") - if to_float(row.get("coherent_link_uptime_pct", "0")) < 75.0: - fails.append("coherent_link_uptime_below_75pct") - - return fails - - -def compute_score(row: Dict[str, str]) -> Tuple[float, Dict[str, float]]: - legal = 1.0 if (to_bool(row.get("command_authority_verified", "")) and to_bool(row.get("license_path_clear", ""))) else 0.0 - - link = 0.45 * score_band(to_float(row.get("rx_beacon_snr_db", "0")), 6.0, 18.0) - link += 0.35 * score_band(to_float(row.get("uplink_ack_rate_pct", "0")), 20.0, 90.0) - link += 0.20 * score_band(to_float(row.get("telemetry_frame_success_pct", "0")), 70.0, 99.0) - - power = 0.50 * score_band(to_float(row.get("power_margin_w", "0")), 0.0, 25.0) - power += 0.30 * score_band(to_float(row.get("battery_health_pct", "0")), 40.0, 95.0) - power += 0.20 * score_band(to_float(row.get("thermal_margin_c", "0")), 3.0, 25.0) - - stability = 0.45 * inverse_band(to_float(row.get("tumble_rate_deg_s", "99")), 0.0, 3.0) - stability += 0.35 * inverse_band(to_float(row.get("pointing_error_deg", "99")), 0.0, 5.0) - stability += 0.20 * (1.0 if to_bool(row.get("detumble_capability", "")) else 0.0) - - recovery = 0.35 * score_band(to_float(row.get("reboot_success_pct", "0")), 50.0, 98.0) - recovery += 0.35 * score_band(to_float(row.get("safe_mode_entry_success_pct", "0")), 60.0, 99.0) - recovery += 0.30 * score_band(to_float(row.get("watchdog_recovery_success_pct", "0")), 60.0, 99.0) - - pcec = 0.45 * score_band(to_float(row.get("phase_coherence_score", "0")), 0.60, 0.95) - pcec += 0.25 * score_band(to_float(row.get("tunable_em_band_hz", "0")), 300000.0, 3000000.0) - pcec += 0.15 * inverse_band(to_float(row.get("phase_lock_error_deg", "180")), 0.0, 20.0) - pcec += 0.15 * score_band(to_float(row.get("coherent_link_uptime_pct", "0")), 75.0, 95.0) - - cost_eff = 0.60 * inverse_band(to_float(row.get("acquisition_cost_usd", "0")), 50000.0, 2000000.0) - cost_eff += 0.40 * inverse_band(to_float(row.get("estimated_refurb_cost_usd", "0")), 100000.0, 5000000.0) - - weighted = ( - 0.20 * legal - + 0.18 * link - + 0.16 * power - + 0.14 * stability - + 0.12 * recovery - + 0.14 * pcec - + 0.06 * cost_eff - ) - - score_100 = round(weighted * 100.0, 2) - components = { - "legal": round(legal * 100.0, 2), - "link": round(link * 100.0, 2), - "power": round(power * 100.0, 2), - "stability": round(stability * 100.0, 2), - "recovery": round(recovery * 100.0, 2), - "pcec": round(pcec * 100.0, 2), - "cost_eff": round(cost_eff * 100.0, 2), - } - return score_100, components - - -def decide(score: float, fails: List[str]) -> str: - if fails: - return "NO_GO" - if score >= 75.0: - return "GO" - if score >= 60.0: - return "WATCH" - return "NO_GO" - - -def load_rows(path: Path) -> List[Dict[str, str]]: - with path.open("r", encoding="utf-8", newline="") as f: - return [dict(r) for r in csv.DictReader(f)] - - -def write_rows(path: Path, rows: List[Dict[str, Any]], fieldnames: List[str]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with path.open("w", encoding="utf-8", newline="") as f: - writer = csv.DictWriter(f, fieldnames=fieldnames) - writer.writeheader() - for row in rows: - writer.writerow(row) - - -def summarize(rows: List[Dict[str, Any]]) -> Dict[str, Any]: - go = [r for r in rows if r.get("decision") == "GO"] - watch = [r for r in rows if r.get("decision") == "WATCH"] - no_go = [r for r in rows if r.get("decision") == "NO_GO"] - ranked = sorted(rows, key=lambda r: to_float(str(r.get("reactivation_score", "0"))), reverse=True) - top = ranked[:10] - return { - "candidate_count": len(rows), - "go_count": len(go), - "watch_count": len(watch), - "no_go_count": len(no_go), - "top_candidates": [ - { - "candidate_id": r.get("candidate_id", ""), - "score": r.get("reactivation_score", "0"), - "decision": r.get("decision", "NO_GO"), - "gate_failures": r.get("gate_failures", ""), - } - for r in top - ], - } - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--input", default=str(DEFAULT_INPUT), help="Input candidate CSV") - ap.add_argument("--output", default=str(DEFAULT_OUTPUT), help="Scored candidate CSV") - ap.add_argument("--summary", default=str(DEFAULT_SUMMARY), help="Summary JSON") - args = ap.parse_args() - - in_path = Path(str(args.input)) - if not in_path.exists(): - raise SystemExit(f"input not found: {in_path}") - - rows = load_rows(in_path) - if not rows: - raise SystemExit("input CSV has no rows") - - scored: List[Dict[str, Any]] = [] - for row in rows: - score, components = compute_score(row) - fails = gate_failures(row) - decision = decide(score, fails) - out: Dict[str, Any] = dict(row) - out["reactivation_score"] = score - out["decision"] = decision - out["gate_failures"] = ";".join(fails) - out["component_legal"] = components["legal"] - out["component_link"] = components["link"] - out["component_power"] = components["power"] - out["component_stability"] = components["stability"] - out["component_recovery"] = components["recovery"] - out["component_pcec"] = components["pcec"] - out["component_cost_eff"] = components["cost_eff"] - scored.append(out) - - fieldnames = list(scored[0].keys()) - write_rows(Path(str(args.output)), scored, fieldnames) - - summary = summarize(scored) - summary_path = Path(str(args.summary)) - summary_path.parent.mkdir(parents=True, exist_ok=True) - summary_path.write_text(json.dumps(summary, indent=2) + "\n", encoding="utf-8") - - print(json.dumps({ - "output_csv": str(Path(str(args.output))), - "summary_json": str(summary_path), - "go": summary["go_count"], - "watch": summary["watch_count"], - "no_go": summary["no_go_count"], - }, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/optimization/score_superconductor_profiles.py b/5-Applications/tools-scripts/optimization/score_superconductor_profiles.py deleted file mode 100644 index f01526a7..00000000 --- a/5-Applications/tools-scripts/optimization/score_superconductor_profiles.py +++ /dev/null @@ -1,130 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -from pathlib import Path -from typing import Any - - -def weighted_score(stack_scores: dict[str, Any], weights: dict[str, Any]) -> float: - return sum(float(stack_scores.get(k, 0.0)) * float(v) for k, v in weights.items()) - - -def normalize_profile_weights(profiles: list[dict[str, Any]]) -> list[dict[str, Any]]: - total = sum(float(p.get("priority_weight", 0.0)) for p in profiles) - if total <= 0: - n = len(profiles) or 1 - for p in profiles: - p["priority_weight"] = 1.0 / n - return profiles - for p in profiles: - p["priority_weight"] = float(p.get("priority_weight", 0.0)) / total - return profiles - - -def compute(metaindex: dict[str, Any]) -> dict[str, Any]: - stacks = metaindex.get("candidate_stacks", []) - profiles = normalize_profile_weights(metaindex.get("stakeholder_profiles", [])) - model = metaindex.get("stack_scoring_model", {}) - global_weights = model.get("global_weights", {}) - overrides = model.get("profile_weight_overrides", {}) - threshold_cfg = model.get("gate_thresholds", {}) - default_thresholds = threshold_cfg.get( - "default", - { - "em_isolation_min": 0.75, - "thermal_min": 0.70, - "mechanical_min": 0.72, - "cost_efficiency_min": 0.55, - }, - ) - threshold_overrides = threshold_cfg.get("profile_overrides", {}) - - profile_results: dict[str, Any] = {} - for profile in profiles: - pid = profile["profile_id"] - weights = overrides.get(pid, global_weights) - thresholds = {**default_thresholds, **threshold_overrides.get(pid, {})} - ranked: list[dict[str, Any]] = [] - for stack in stacks: - sid = stack["stack_id"] - scores = stack.get("scores", {}) - score = weighted_score(scores, weights) - - # Simple readiness gates from existing criteria values. - em_iso = float(scores.get("em_isolation", 0.0)) - thermal = float(scores.get("thermal_cycle_resilience", 0.0)) - mech = float(scores.get("mechanical_tension_resilience", 0.0)) - cost = float(scores.get("manufacturing_cost_efficiency", 0.0)) - - passes = { - "em_isolation_gate": em_iso >= float(thresholds.get("em_isolation_min", 0.75)), - "thermal_gate": thermal >= float(thresholds.get("thermal_min", 0.70)), - "mechanical_gate": mech >= float(thresholds.get("mechanical_min", 0.72)), - "cost_efficiency_gate": cost >= float(thresholds.get("cost_efficiency_min", 0.55)), - } - readiness = "PASS" if all(passes.values()) else "MONITOR" - - ranked.append( - { - "stack_id": sid, - "score": round(score, 4), - "readiness": readiness, - "gates": passes, - "thresholds_used": { - "em_isolation_min": float(thresholds.get("em_isolation_min", 0.75)), - "thermal_min": float(thresholds.get("thermal_min", 0.70)), - "mechanical_min": float(thresholds.get("mechanical_min", 0.72)), - "cost_efficiency_min": float(thresholds.get("cost_efficiency_min", 0.55)), - }, - } - ) - - ranked.sort(key=lambda x: x["score"], reverse=True) - profile_results[pid] = { - "priority_weight": round(float(profile.get("priority_weight", 0.0)), 6), - "ranked_stacks": ranked, - } - - aggregate = {s["stack_id"]: 0.0 for s in stacks} - for profile in profiles: - pid = profile["profile_id"] - p_weight = float(profile.get("priority_weight", 0.0)) - for item in profile_results[pid]["ranked_stacks"]: - aggregate[item["stack_id"]] += p_weight * float(item["score"]) - - aggregate_ranked: list[dict[str, Any]] = [ - {"stack_id": sid, "aggregate_score": round(score, 4)} - for sid, score in aggregate.items() - ] - aggregate_ranked.sort(key=lambda x: float(x["aggregate_score"]), reverse=True) - - return { - "model_id": model.get("model_id", "weighted_profile_rank_v1"), - "profile_results": profile_results, - "aggregate_ranking": aggregate_ranked, - } - - -def main() -> None: - root = Path(__file__).resolve().parent.parent - meta_path = root / "Research Documents" / "superconductor_hybrid_metaindex_v0.json" - out_path = root / "out" / "superconductor_profile_readiness.json" - - meta = json.loads(meta_path.read_text(encoding="utf-8")) - result = compute(meta) - - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_text(json.dumps(result, indent=2) + "\n", encoding="utf-8") - - print("[+] Wrote readiness report:", out_path) - print("[+] Aggregate ranking:") - for row in result["aggregate_ranking"]: - print(f" - {row['stack_id']}: {row['aggregate_score']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/optimization/sweep_ipv6_hdc_experiments.py b/5-Applications/tools-scripts/optimization/sweep_ipv6_hdc_experiments.py deleted file mode 100644 index 6db5498c..00000000 --- a/5-Applications/tools-scripts/optimization/sweep_ipv6_hdc_experiments.py +++ /dev/null @@ -1,153 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Batch-sweep IPv6 subnets through the HDC + HyperDAG simulator. - -This helper loops over many child subnets, runs the simulation for each, appends -each result to the shared CSV ledger, and prints one grep-friendly line per run. -""" - -from __future__ import annotations - -import argparse -import ipaddress -import sys -from datetime import datetime, timezone -from pathlib import Path -from typing import Iterator, List - -ROOT = Path(__file__).resolve().parent.parent -if str(ROOT) not in sys.path: - sys.path.insert(0, str(ROOT)) - -from scripts.simulate_ipv6_hdc_hyperdag import ( - DEFAULT_LEDGER_PATH, - SimulationConfig, - append_ledger_row, - parse_ports, - render_one_line_summary, - run_simulation, -) - - -def utc_compact_now() -> str: - return datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - - -def iter_target_subnets( - supernet: ipaddress.IPv6Network, - target_prefix: int, - start_index: int, - limit: int, -) -> Iterator[ipaddress.IPv6Network]: - produced = 0 - for idx, subnet in enumerate(supernet.subnets(new_prefix=target_prefix)): - if idx < start_index: - continue - yield subnet - produced += 1 - if produced >= limit: - return - - -def build_args() -> argparse.Namespace: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--supernet", default="2001:db8::/112", help="Parent IPv6 network to partition") - ap.add_argument("--target-prefix", type=int, default=118, help="Prefix length for child sweep subnets") - ap.add_argument("--start-index", type=int, default=0, help="Start child subnet offset") - ap.add_argument("--limit", type=int, default=16, help="Number of child subnets to run") - ap.add_argument("--ports", default="80,443", help="Comma-separated ports") - ap.add_argument("--dims", type=int, default=4096, help="Hypervector dimensions") - ap.add_argument("--max-samples", type=int, default=256, help="Max sampled IPs per subnet") - ap.add_argument("--seed", type=int, default=42, help="Deterministic seed") - ap.add_argument("--query-port", type=int, default=443, help="Query port used for lift calculations") - ap.add_argument("--batch-label", default="sats_batch", help="Prefix for generated run labels") - ap.add_argument("--one-line-delim", default=";", help="Delimiter for one-line stdout rows") - ap.add_argument("--ledger-path", default=str(DEFAULT_LEDGER_PATH), help="CSV ledger path") - ap.add_argument("--broadcast-bootstrap", action="store_true", help="Enable broadcast bootstrap analytics") - ap.add_argument("--join-threshold", type=float, default=0.0, help="Join threshold for broadcast mode") - ap.add_argument("--join-report-limit", type=int, default=16, help="Join-decision rows retained in JSON payload") - return ap.parse_args() - - -def main() -> None: - args = build_args() - - try: - supernet = ipaddress.IPv6Network(str(args.supernet), strict=False) - except ValueError as exc: - raise SystemExit(f"invalid --supernet: {exc}") from exc - - target_prefix = int(args.target_prefix) - if target_prefix < supernet.prefixlen or target_prefix > 128: - raise SystemExit("invalid --target-prefix: must be between supernet prefix and 128") - - start_index = int(args.start_index) - if start_index < 0: - raise SystemExit("invalid --start-index: must be >= 0") - - limit = int(args.limit) - if limit <= 0: - raise SystemExit("invalid --limit: must be > 0") - - dims = int(args.dims) - if dims <= 0: - raise SystemExit("invalid --dims: must be > 0") - - max_samples = int(args.max_samples) - if max_samples <= 0: - raise SystemExit("invalid --max-samples: must be > 0") - - seed = int(args.seed) - - query_port = int(args.query_port) - if query_port < 0 or query_port > 65535: - raise SystemExit("invalid --query-port: must be 0..65535") - - if int(args.join_report_limit) < 0: - raise SystemExit("invalid --join-report-limit: must be >= 0") - - try: - ports: List[int] = parse_ports(str(args.ports)) - except ValueError as exc: - raise SystemExit(f"invalid --ports: {exc}") from exc - - batch_stamp = utc_compact_now() - ledger_path = Path(str(args.ledger_path)) - - ran = 0 - for i, subnet in enumerate(iter_target_subnets(supernet, target_prefix, start_index, limit)): - label = f"{args.batch_label}_{batch_stamp}_{i:04d}" - cfg = SimulationConfig( - subnet=subnet, - ports=ports, - dims=dims, - max_samples=max_samples, - seed=seed, - query_ip=subnet.network_address, - query_port=query_port, - one_line=True, - one_line_delim=str(args.one_line_delim), - run_label=label, - append_ledger=True, - ledger_path=ledger_path, - broadcast_bootstrap=bool(args.broadcast_bootstrap), - join_threshold=float(args.join_threshold), - join_report_limit=int(args.join_report_limit), - ) - - out = run_simulation(cfg) - append_ledger_row(ledger_path, out) - print(render_one_line_summary(out, delim=cfg.one_line_delim)) - ran += 1 - - if ran == 0: - raise SystemExit("no sweep runs executed; adjust --start-index/--limit") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/physics/historical_crack_2008.py b/5-Applications/tools-scripts/physics/historical_crack_2008.py deleted file mode 100644 index 210335d0..00000000 --- a/5-Applications/tools-scripts/physics/historical_crack_2008.py +++ /dev/null @@ -1,653 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -"""historical_crack_2008.py - -Real-data probe of the 2004–2008 financial crack using Yahoo Finance equity data. -No external databases, no special API keys — all data was publicly tradeable in -real-time and visible to any market participant. - -SENSOR SPOOF IN PLAIN SIGHT ----------------------------- -The S&P 500 was the canonical "surface" sensor: reporting record highs, rising -drift, suppressed volatility. The consensus read the surface and said "fine." - -Three sub-sectors of the same market were the fundamentals: they had already -cracked and were reporting it daily via their prices. Nobody was listening -because their world model didn't have a layer for "the sensor can be wrong." - -AMERICAN MARKETS ALONE ARE NOT SUFFICIENT ------------------------------------------- -US officials had institutional incentives to manage the narrative — congress- -ional testimony, public confidence, market stability mandates. The American -surface sensor was subject to coordinated perception management. - -International markets had NO such incentive. When a German, Swiss, or -British-Hong Kong bank discloses losses, it does so under its own jurisdiction's -reporting requirements, independent of the Federal Reserve's communication -strategy. These signals are Layer 0 — uncorrelated with the US narrative layer. - -THREE DOMESTIC PAIRS (Pairs A–C) ---------------------------------- -Pair A — "Broad market vs Financial Sector" (US) - surface = log(^GSPC_t / ^GSPC_base) S&P 500: everyone's health indicator - fundamental = log(XLF_t / XLF_base) Financial Select SPDR (banks+brokers) - -Pair B — "Broad market vs Homebuilders" (US) - surface = log(^GSPC_t / ^GSPC_base) - fundamental = log(^HGX_t / ^HGX_base) Philadelphia Housing Sector Index - Lead: ^HGX peaked Jul 2005 — 27-month hidden divergence. - -Pair C — "Suppressed VIX vs Banking index" (US) - surface = −log(^VIX_t / ^VIX_base) - fundamental = log(^BKX_t / ^BKX_base) KBW Bank Index - The fear gauge was disagreeing with the banking sector's own price signal. - -THREE INTERNATIONAL PAIRS (Pairs D–F) --------------------------------------- -These instruments report under non-US jurisdictions. Their divergences from -the S&P 500 surface were not subject to US narrative management. - -Pair D — "S&P 500 vs HSBC Holdings" (British-Hong Kong) - HSBC issued the FIRST public institutional warning on subprime, Feb 7 2007, - increasing bad-debt provisions 20% to $10.6B. UK/HK disclosure requirements. - surface = log(^GSPC_t / ^GSPC_base) - fundamental = log(HSBC_t / HSBC_base) HSBC Holdings ADR (NYSE) - -Pair E — "S&P 500 vs UBS AG" (Swiss) - UBS disclosed CHF 4B subprime exposure Aug 2007, wrote down $18.7B by Oct 2007. - Swiss FINMA reporting requirements — independent of Fed communication strategy. - surface = log(^GSPC_t / ^GSPC_base) - fundamental = log(UBS_t / UBS_base) UBS AG ADR (NYSE) - -Pair F — "S&P 500 vs Deutsche Bank" (German) - Deutsche Bank disclosed EUR 2.2B subprime writedown Oct 2007 under BaFin/IFRS. - German regulatory and accounting framework — independent Layer 0. - surface = log(^GSPC_t / ^GSPC_base) - fundamental = log(DB_t / DB_base) Deutsche Bank AG ADR (NYSE) - -DATA SOURCES ------------- -All Yahoo Finance, v8/finance/chart, daily 2004-01-01 to 2008-10-01. - US: ^GSPC ^VIX XLF ^HGX ^BKX - International: HSBC UBS DB - -STATEMENTS TIMELINE -------------------- -Includes both US "impossible/contained" statements AND international disclosures. -The international disclosures could not be suppressed — they were public filings -in non-US jurisdictions. The script reports the detector state on each date. -""" - - -import csv -import json -import sys -import urllib.request -import urllib.parse -from datetime import datetime, date, timedelta, timezone -from collections import Counter -from pathlib import Path -from typing import Optional - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -# --------------------------------------------------------------------------- -# Detector parameters (mirrors synthetic_cracking_signal.py) -# --------------------------------------------------------------------------- - -W = 63 # 1 quarter rolling window -W_ACCUM = 126 # 2 quarter accumulation window -THETA_C = 0.55 -THETA_N = 2.5 -THETA_S = 20 # INCUBATING days within W_ACCUM to trigger CRYSTALLIZING - -BASELINE_DAYS = 63 # first quarter used to calibrate baseline_vol - -START_DATE = date(2004, 1, 2) -END_DATE = date(2008, 10, 1) -LEHMAN_DATE = date(2008, 9, 15) - -_START_TS = int(datetime(2004, 1, 1, tzinfo=timezone.utc).timestamp()) -_END_TS = int(datetime(2008, 10, 1, tzinfo=timezone.utc).timestamp()) - -# Each entry: (date, speaker, quote, category) -# category: "us_denial" = US official downplaying; "intl_disclosure" = non-US -# institution publicly disclosing losses (cannot be suppressed by US PR); -# "collapse" = actual failure events -STATEMENTS = [ - # --- US denial layer --- - (date(2005, 12, 1), "NAR / David Lereah", - "National home prices cannot fall — you'd need a major job-loss recession " - "(Annual book 2005: 'Are You Missing the Real Estate Boom?')", - "us_denial"), - (date(2006, 2, 15), "Ben Bernanke / Senate", - "The U.S. economy appears to be in a period of transition... we do not " - "currently see a sharp slowdown in housing as the most likely outcome.", - "us_denial"), - (date(2006, 8, 1), "Bush CEA Annual Report", - "Housing market slowdown is orderly. A soft landing is likely. " - "The underlying fundamentals of the economy remain solid.", - "us_denial"), - # --- International Layer 0: HSBC first institutional warning --- - (date(2007, 2, 7), "HSBC Holdings / Earnings (London/Hong Kong reporting)", - "HSBC increases bad-debt provisions 20% to USD 10.6B due to US subprime " - "mortgage losses. First major institutional public warning. (UK/HK disclosure " - "requirements — not subject to US narrative management.)", - "intl_disclosure"), - # --- US denial continues despite HSBC warning --- - (date(2007, 2, 28), "Ben Bernanke / Joint Economic Committee", - "At this juncture, the impact on the broader economy and financial markets " - "of the problems in the subprime market seems likely to be contained.", - "us_denial"), - (date(2007, 5, 17), "Ben Bernanke / Federal Reserve Bank of Chicago", - "We do not expect significant spillovers from the subprime market to the " - "rest of the economy or to the financial system.", - "us_denial"), - (date(2007, 7, 25), "Jimmy Cayne / Bear Stearns CEO", - "The loss is really limited to two funds. The remainder of the business " - "is operating normally. This is an isolated event.", - "us_denial"), - # --- Paulson "healthy economy" — BNP Paribas freezes funds THE NEXT DAY --- - (date(2007, 8, 8), "Hank Paulson / Treasury Secretary", - "The capital markets are functioning normally. We see the underlying " - "economy as being very healthy and robust.", - "us_denial"), - # --- BNP Paribas — French bank, BaFin/AMF jurisdiction, one day after Paulson --- - (date(2007, 8, 9), "BNP Paribas / Paris (French AMF reporting)", - "BNP Paribas freezes redemptions on three sub-prime funds worth EUR 1.6B: " - "Parvest Dynamic ABS, BNP Paribas ABS Euribor, BNP Paribas ABS Eonia. " - "'Complete evaporation of liquidity in certain market segments.' " - "Triggered ECB emergency EUR 95B liquidity injection. " - "(French AMF jurisdiction — independent of Fed communication strategy.)", - "intl_disclosure"), - (date(2007, 8, 16), "Angelo Mozilo / Countrywide CEO", - "We are not in a meltdown. The credit problems are isolated to certain " - "segments of the market. Business fundamentals remain intact.", - "us_denial"), - # --- Northern Rock — UK bank run, physically undeniable --- - (date(2007, 9, 14), "Northern Rock / Bank of England (UK PRA reporting)", - "Northern Rock requests emergency liquidity support from Bank of England. " - "Customers queue outside branches in first UK bank run since 1866. " - "GBP 1B withdrawn in days. (UK FSA/BoE jurisdiction — independent of Fed.)", - "intl_disclosure"), - (date(2007, 10, 15), "Ben Bernanke / Economic Club of NY", - "It is not the responsibility of the Federal Reserve — nor would it be " - "appropriate — to protect lenders and investors from consequences.", - "us_denial"), - # --- UBS Swiss writedown — CHF disclosure, FINMA jurisdiction --- - (date(2007, 10, 1), "UBS AG / Zurich (Swiss FINMA reporting)", - "UBS AG discloses CHF 4B subprime exposure and announces CEO resignation. " - "Will write down USD 18.7B by year-end — largest bank loss in Swiss history. " - "(Swiss FINMA jurisdiction — independent of US narrative management.)", - "intl_disclosure"), - # --- Deutsche Bank — German BaFin/IFRS disclosure --- - (date(2007, 10, 31), "Deutsche Bank AG / Frankfurt (German BaFin reporting)", - "Deutsche Bank discloses EUR 2.2B subprime writedown for Q3 2007. " - "CEO Josef Ackermann publicly warns of market instability. " - "(German BaFin/IFRS jurisdiction — independent of Fed communication strategy.)", - "intl_disclosure"), - (date(2008, 1, 7), "George W. Bush / White House Press Briefing", - "I don't think we're headed to recession. I know there's a lot of " - "uncertainty. But the economy is going to be fine.", - "us_denial"), - (date(2008, 3, 16), "Federal Reserve / Bear Stearns Emergency Rescue", - "The Federal Reserve agreed to provide emergency financing. Systemic " - "risk to the broader financial system would be avoided.", - "us_denial"), - (date(2008, 6, 9), "Ben Bernanke / Atlanta Federal Reserve Bank", - "The risk that the economy has entered a substantial downturn appears to " - "have diminished over the past month or so.", - "us_denial"), - (date(2008, 7, 11), "Hank Paulson / Congressional Testimony", - "Fannie Mae and Freddie Mac are adequately capitalized. They are in no " - "danger of failing. They are well-capitalized institutions.", - "us_denial"), - (date(2008, 9, 14), "The day before Lehman", - "Banking system is sound. Fed backstop available. Orderly resolution.", - "us_denial"), - (date(2008, 9, 15), "COLLAPSE — Lehman Brothers Chapter 11", - "Lehman Brothers files for bankruptcy protection. $639B in assets — the " - "largest bankruptcy filing in US history. Global credit markets freeze.", - "collapse"), -] - -# --------------------------------------------------------------------------- -# Repository layout -# --------------------------------------------------------------------------- - -_ROOT = Path(__file__).parent.parent -_OUT_DIR = _ROOT / "out" / "historical_crack_2008" - -# --------------------------------------------------------------------------- -# Yahoo Finance fetch -# --------------------------------------------------------------------------- - -def _fetch_yahoo(symbol: str) -> dict[date, float]: - """Download daily closing prices for symbol via Yahoo Finance v8 chart API.""" - encoded = urllib.parse.quote(symbol, safe="^") - url = ( - f"https://query1.finance.yahoo.com/v8/finance/chart/{encoded}" - f"?period1={_START_TS}&period2={_END_TS}&interval=1d" - ) - req = urllib.request.Request(url, headers={"User-Agent": "crack-probe/1.0"}) - try: - with urllib.request.urlopen(req, timeout=30) as resp: - payload = json.loads(resp.read()) - except Exception as exc: - print(f"[WARN] Yahoo fetch failed for {symbol}: {exc}", file=sys.stderr) - return {} - - try: - result = payload["chart"]["result"][0] - timestamps = result["timestamp"] - closes = result["indicators"]["quote"][0]["close"] - except (KeyError, IndexError, TypeError): - return {} - - out: dict[date, float] = {} - for ts, c in zip(timestamps, closes): - if c is None: - continue - try: - d = datetime.fromtimestamp(ts, tz=timezone.utc).date() - out[d] = float(c) - except (ValueError, OSError): - continue - return out - - -# --------------------------------------------------------------------------- -# Daily series alignment -# --------------------------------------------------------------------------- - -def _to_daily_array( - raw: dict[date, float], - reference_dates: list[date], -) -> AnyArray: - """ - Map raw {date: float} onto reference_dates, forward-filling gaps. - Returns NaN where no data exists before the first observation. - """ - n = len(reference_dates) - out = xp.full(n, xp.nan) - for i, d in enumerate(reference_dates): - if d in raw: - out[i] = raw[d] - # Forward-fill - last = xp.nan - for i in range(n): - if not xp.isnan(out[i]): - last = out[i] - elif not xp.isnan(last): - out[i] = last - return out - - -# --------------------------------------------------------------------------- -# Normalisation -# --------------------------------------------------------------------------- - -def _log_ratio(series: AnyArray, baseline_end: int) -> AnyArray: - """log(x / mean(x[:baseline_end])). NaN-safe.""" - window = series[:baseline_end] - window = window[~xp.isnan(window)] - if len(window) == 0 or xp.nanmean(window) <= 0: - return xp.zeros(len(series)) - base = float(xp.nanmean(window)) - return xp.where( - (series > 0) & ~xp.isnan(series), - xp.log(series / base), - xp.nan, - ) - - -# --------------------------------------------------------------------------- -# C_t / N_t / γ(t) detector -# --------------------------------------------------------------------------- - -def _detect(epsilon: AnyArray, baseline_end: int) -> dict: - T = len(epsilon) - valid = ~xp.isnan(epsilon) - - # Baseline vol from first quarter of valid data - bv_window = epsilon[:baseline_end] - bv_clean = bv_window[~xp.isnan(bv_window)] - baseline_vol = (float(xp.std(bv_clean)) if len(bv_clean) > 1 else 1e-6) + 1e-9 - - delta_eps = xp.diff(epsilon, prepend=epsilon[0] if not xp.isnan(epsilon[0]) else 0.0) - - # C_t — directional consistency over rolling W window - C = xp.zeros(T) - for t in range(W, T): - window = delta_eps[t - W : t] - if xp.any(xp.isnan(window)): - continue - signs = xp.sign(window) - agree = int(xp.sum(signs[:-1] == signs[1:])) - C[t] = agree / max(len(signs) - 1, 1) - - # N_t — novelty in baseline-σ units - N = xp.where(valid, xp.abs(epsilon) / baseline_vol, 0.0) - - # First pass: per-day INCUBATING flag - incubating_flags = xp.zeros(T, dtype=bool) - for t in range(W, T): - if valid[t] and C[t] > THETA_C and N[t] > THETA_N: - incubating_flags[t] = True - - # Second pass: state machine with rolling accumulation - state = ["GROUNDED"] * T - gamma = xp.ones(T) - early_warning_idx: Optional[int] = None - - for t in range(W, T): - if not valid[t]: - state[t] = "NO_DATA" - continue - if incubating_flags[t]: - ws = max(0, t - W_ACCUM) - accum = int(xp.sum(incubating_flags[ws : t + 1])) - if accum >= THETA_S: - state[t] = "CRYSTALLIZING" - gamma[t] = 2.0 - if early_warning_idx is None: - early_warning_idx = t - else: - state[t] = "INCUBATING" - gamma[t] = 0.0 - elif C[t] > THETA_C and N[t] <= THETA_N: - state[t] = "BASIN_PULL" - elif N[t] > 1.0: - state[t] = "SEISMIC" - else: - state[t] = "GROUNDED" - - first_inc_idx = next((i for i, s in enumerate(state) if s == "INCUBATING"), None) - - return { - "state": state, - "C": C, - "N": N, - "gamma": gamma, - "baseline_vol": baseline_vol, - "first_incubating_idx": first_inc_idx, - "first_crystallizing_idx": early_warning_idx, - "counts": dict(Counter(state)), - } - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def run(out_dir: Path = _OUT_DIR) -> dict: - print("Fetching Yahoo Finance data (2004-01-01 → 2008-10-01)…", flush=True) - - raw: dict[str, dict[date, float]] = {} - for sym in ["^GSPC", "^VIX", "XLF", "^HGX", "^BKX", "HSBC", "UBS", "DB"]: - raw[sym] = _fetch_yahoo(sym) - n = len(raw[sym]) - print(f" {sym:8s}: {n:4d} observations", flush=True) - - # Reference calendar = S&P 500 trading days - if len(raw["^GSPC"]) < 100: - print("[ERROR] S&P 500 data unavailable. Check network.", file=sys.stderr) - sys.exit(1) - - ref_dates = sorted(raw["^GSPC"].keys()) - date_idx = {d: i for i, d in enumerate(ref_dates)} - T = len(ref_dates) - baseline_end = BASELINE_DAYS - print(f"\n Reference trading days: {T}") - - # Build daily arrays - GSPC = _to_daily_array(raw["^GSPC"], ref_dates) - VIX = _to_daily_array(raw["^VIX"], ref_dates) - XLF = _to_daily_array(raw["XLF"], ref_dates) - HGX = _to_daily_array(raw["^HGX"], ref_dates) - BKX = _to_daily_array(raw["^BKX"], ref_dates) - HSBC = _to_daily_array(raw["HSBC"], ref_dates) - UBS_ = _to_daily_array(raw["UBS"], ref_dates) - DB_ = _to_daily_array(raw["DB"], ref_dates) - - # Log-ratio normalisation relative to first baseline period - gspc_log = _log_ratio(GSPC, baseline_end) - vix_log = _log_ratio(VIX, baseline_end) - xlf_log = _log_ratio(XLF, baseline_end) - hgx_log = _log_ratio(HGX, baseline_end) - bkx_log = _log_ratio(BKX, baseline_end) - hsbc_log = _log_ratio(HSBC, baseline_end) - ubs_log = _log_ratio(UBS_, baseline_end) - db_log = _log_ratio(DB_, baseline_end) - - # --------------- domestic signal pairs --------------- - eps_A = gspc_log - xlf_log # grows as S&P rises + financials fall - eps_B = gspc_log - hgx_log # grows as S&P rises + housing falls - eps_C = -vix_log - bkx_log # grows when calm-is-claimed AND banks fall - - # --------------- international signal pairs --------------- - # Pair D: S&P 500 (surface) vs HSBC (British-Hong Kong) — first warner - eps_D = gspc_log - hsbc_log - # Pair E: S&P 500 (surface) vs UBS AG (Swiss) — FINMA jurisdiction - eps_E = gspc_log - ubs_log - # Pair F: S&P 500 (surface) vs Deutsche Bank (German) — BaFin/IFRS jurisdiction - eps_F = gspc_log - db_log - - print("\nRunning C_t / N_t / γ(t) detector on all six pairs…", flush=True) - det: dict[str, dict] = { - "A": _detect(eps_A, baseline_end), - "B": _detect(eps_B, baseline_end), - "C": _detect(eps_C, baseline_end), - "D": _detect(eps_D, baseline_end), - "E": _detect(eps_E, baseline_end), - "F": _detect(eps_F, baseline_end), - } - - # ----------------------------------------------------------------------- - # Report helpers - # ----------------------------------------------------------------------- - lehman_idx = date_idx.get(LEHMAN_DATE) - - def _fmt_idx(idx: Optional[int]) -> str: - return ref_dates[idx].isoformat() if idx is not None else "not detected" - - def _lead_str(idx: Optional[int]) -> str: - if idx is None or lehman_idx is None: - return "—" - n = lehman_idx - idx - if n <= 0: - return "AFTER Lehman" - return f"{n} trading days ({n / 252:.2f} yr) before Lehman" - - def _state_on(det_result: dict, query_date: date) -> str: - for offset in range(7): - candidate = query_date + timedelta(days=offset) - if candidate in date_idx: - return det_result["state"][date_idx[candidate]] - return "NO_DATA" - - pair_meta = { - "A": ("US", "S&P 500 vs Financial Sector (XLF)"), - "B": ("US", "S&P 500 vs Homebuilders (^HGX)"), - "C": ("US", "Suppressed VIX vs KBW Banks (^BKX)"), - "D": ("INTL", "S&P 500 vs HSBC Holdings (UK/HK — first institutional warning)"), - "E": ("INTL", "S&P 500 vs UBS AG (Swiss FINMA — independent jurisdiction)"), - "F": ("INTL", "S&P 500 vs Deutsche Bank (German BaFin — independent jurisdiction)"), - } - - # ----------------------------------------------------------------------- - # Print report - # ----------------------------------------------------------------------- - print() - print("=" * 72) - print("2004–2008 CRACK PROBE — REAL MARKET DATA (YAHOO FINANCE)") - print("=" * 72) - - results: dict = {} - for section, keys in [("DOMESTIC (US)", ["A","B","C"]), - ("INTERNATIONAL (independent jurisdictions)", ["D","E","F"])]: - print(f"\n{'─'*72}") - print(f" {section}") - print(f"{'─'*72}") - for key in keys: - d = det[key] - fi = d["first_incubating_idx"] - fc = d["first_crystallizing_idx"] - cnts = d["counts"] - total = sum(cnts.values()) - jurisdiction, label = pair_meta[key] - - print(f"\nPAIR {key} [{jurisdiction}]: {label}") - print(f" baseline_vol : {d['baseline_vol']:.5f}") - print(f" First INCUBATING : {_fmt_idx(fi)} ← {_lead_str(fi)}") - print(f" First CRYSTALLIZING : {_fmt_idx(fc)} ← {_lead_str(fc)}") - print(f" Lehman collapse : {LEHMAN_DATE}") - print(f" State counts:") - for s in ["GROUNDED","BASIN_PULL","SEISMIC","INCUBATING","CRYSTALLIZING","NO_DATA"]: - n = cnts.get(s, 0) - if n > 0: - pct = n / total * 100 - bar = "█" * max(1, int(pct / 2)) - print(f" {s:<15} {n:4d} {pct:5.1f}% {bar}") - - results[key] = { - "label": label, - "jurisdiction": jurisdiction, - "baseline_vol": d["baseline_vol"], - "first_incubating": _fmt_idx(fi), - "first_crystallizing": _fmt_idx(fc), - "lead_days_incubating": (lehman_idx - fi) if (fi is not None and lehman_idx) else None, - "lead_days_crystallizing":(lehman_idx - fc) if (fc is not None and lehman_idx) else None, - "counts": cnts, - } - - # ----------------------------------------------------------------------- - # Statements table — all six pairs - # ----------------------------------------------------------------------- - print() - print("=" * 72) - print("STATEMENTS vs. DETECTOR STATE [US=domestic INTL=independent]") - print("=" * 72) - print(f" {'DATE':<12} {'A(US)':<13} {'B(US)':<13} {'C(US)':<13} " - f"{'D(INTL)':<13} {'E(INTL)':<13} {'F(INTL)':<13} TYPE SPEAKER") - print("─" * 140) - - stmt_records: list[dict] = [] - for stmt_date, speaker, quote, category in STATEMENTS: - states = {k: _state_on(det[k], stmt_date) for k in "ABCDEF"} - alarm = any(states[k] in ("INCUBATING", "CRYSTALLIZING") for k in "ABCDEF") - flag = "⚠" if alarm else " " - cat_tag = "INTL" if category == "intl_disclosure" else ("FAIL" if category == "collapse" else " US") - print(f"{flag} {stmt_date.isoformat():<12} " - f"{states['A']:<13} {states['B']:<13} {states['C']:<13} " - f"{states['D']:<13} {states['E']:<13} {states['F']:<13} " - f"{cat_tag} {speaker}") - stmt_records.append({ - "date": stmt_date.isoformat(), - "speaker": speaker, - "quote": quote, - "category": category, - **{f"state_{k}": states[k] for k in "ABCDEF"}, - "alarm": alarm, - }) - - print() - n_alarm = sum(1 for r in stmt_records if r["alarm"]) - n_intl = sum(1 for r in stmt_records if r["category"] == "intl_disclosure") - n_intl_alarm = sum(1 for r in stmt_records - if r["category"] == "intl_disclosure" and r["alarm"]) - print(f" Detector in INCUBATING/CRYSTALLIZING for {n_alarm} of {len(stmt_records)} statements.") - print(f" International disclosures: {n_intl} total, {n_intl_alarm} already alarmed on ≥1 pair.") - - # Earliest alarm - all_first_inc = [(d["first_incubating_idx"], k) for k, d in det.items() - if d["first_incubating_idx"] is not None] - if all_first_inc: - earliest_idx, earliest_key = min(all_first_inc) - lead_to_lehman = (lehman_idx - earliest_idx) if lehman_idx else None - earliest_date = ref_dates[earliest_idx] - print(f" Earliest INCUBATING (any pair): {earliest_date.isoformat()} " - f"(Pair {earliest_key}, {pair_meta[earliest_key][0]})") - if lead_to_lehman: - print(f" Lead time to Lehman: {lead_to_lehman} trading days " - f"({lead_to_lehman/252:.2f} yr)") - - n_after = sum( - 1 for r in stmt_records - if date.fromisoformat(r["date"]) > earliest_date - and r["category"] != "collapse" - ) - print(f" 'Impossible/contained' + intl disclosures AFTER first alarm: {n_after}") - - print() - - # ----------------------------------------------------------------------- - # Save - # ----------------------------------------------------------------------- - out_dir.mkdir(parents=True, exist_ok=True) - - eps_arrays = {"A": eps_A, "B": eps_B, "C": eps_C, - "D": eps_D, "E": eps_E, "F": eps_F} - for key in "ABCDEF": - eps_arr = eps_arrays[key] - csv_path = out_dir / f"signal_pair_{key}.csv" - with open(csv_path, "w", newline="") as f: - w = csv.writer(f) - w.writerow(["date", "epsilon", "C_t", "N_t", "state"]) - for i, d_obj in enumerate(ref_dates): - e = eps_arr[i] - w.writerow([ - d_obj.isoformat(), - f"{e:.6f}" if not xp.isnan(e) else "", - f"{det[key]['C'][i]:.4f}", - f"{det[key]['N'][i]:.4f}", - det[key]["state"][i], - ]) - print(f" CSV ({key}) → {csv_path}") - - summary = { - "description": "2004-2008 crack probe — six pairs: 3 domestic (US) + 3 international", - "data_source": "Yahoo Finance v8/finance/chart (all public exchange data)", - "note": "International pairs (D/E/F) report under non-US jurisdictions: " - "UK/HK (HSBC), Swiss FINMA (UBS), German BaFin (DB). " - "Not subject to US narrative management.", - "parameters": { - "W": W, "W_ACCUM": W_ACCUM, - "THETA_C": THETA_C, "THETA_N": THETA_N, "THETA_S": THETA_S, - "START": START_DATE.isoformat(), - "END": END_DATE.isoformat(), - "LEHMAN": LEHMAN_DATE.isoformat(), - }, - "pairs": results, - "consensus_statements": stmt_records, - } - json_path = out_dir / "summary.json" - with open(json_path, "w") as f: - json.dump(summary, f, indent=2) - print(f" JSON → {json_path}") - print() - return summary - - - -if __name__ == "__main__": - run() diff --git a/5-Applications/tools-scripts/physics/hormone_derivation.py b/5-Applications/tools-scripts/physics/hormone_derivation.py deleted file mode 100644 index 1a991bec..00000000 --- a/5-Applications/tools-scripts/physics/hormone_derivation.py +++ /dev/null @@ -1,554 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Hormone Derivation from Biological Data -━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ - -Derives computational hormone parameters from measured neurochemical -data across species — half-lives, baseline concentrations, receptor -binding affinities, and behavioral effect sizes. - -No vibes. Every number comes from a citation. - -Sources: - [1] Axelrod & Tomchick (1958). Catecholamine half-lives in plasma. - [2] Goldstein et al. (1981). Norepinephrine turnover rates. - [3] Brown et al. (2005). Dopamine half-life in human plasma. - [4] Maayani et al. (1974). Serotonin turnover in CNS. - [5] Polinsky et al. (1980). Acetylcholine hydrolysis rate. - [6] Munck et al. (1984). Cortisol half-life & binding affinity. - [7] Sapolsky et al. (2000). Glucocorticoid stress response. - [8] Anderson & Schooler (1991). Ebbinghaus forgetting curves. - [9] Diekelmann & Born (2010). Sleep-dependent memory consolidation. - [10] Stickgold et al. (2001). REM sleep & learning. - [11] McNamara et al. (2006). Cross-species neurotransmitter scaling. - [12] Herculano-Houzel (2009). Neuron count scaling across mammals. -""" - -import math -from dataclasses import dataclass, field -from typing import Dict, Tuple, List, Optional - - -# ═══════════════════════════════════════════════════════════════════════ -# MEASURED BIOLOGICAL DATA -# ═══════════════════════════════════════════════════════════════════════ - -@dataclass -class SpeciesData: - """Measured neurochemical data for one species.""" - species: str - brain_mass_g: float - neuron_count_e9: float - cortisol_half_life_min: Tuple[float, float] # (mean, std) - dopamine_half_life_min: Tuple[float, float] - serotonin_half_life_min: Tuple[float, float] - norepinephrine_half_life_min: Tuple[float, float] - acetylcholine_half_life_min: Tuple[float, float] # synaptic cleft - cortisol_baseline_ng_ml: Tuple[float, float] # resting plasma - dopamine_baseline_ng_ml: Tuple[float, float] - serotonin_baseline_ng_ml: Tuple[float, float] - norepinephrine_baseline_ng_ml: Tuple[float, float] - acetylcholine_baseline_ng_ml: Tuple[float, float] # not well measured - citation: str - - -# Cross-species data from literature -SPECIES_DATA = [ - SpeciesData( - species="human", - brain_mass_g=1400, - neuron_count_e9=86.0, # [12] - cortisol_half_life_min=(75.0, 15.0), # [6] 60-90 min range - dopamine_half_life_min=(2.0, 0.5), # [3] ~2 min plasma - serotonin_half_life_min=(4.0, 1.0), # [4] CNS turnover - norepinephrine_half_life_min=(2.5, 0.5),# [2] - acetylcholine_half_life_min=(0.033, 0.01), # [5] ~2 sec synaptic - cortisol_baseline_ng_ml=(120.0, 40.0), # [6] morning resting - dopamine_baseline_ng_ml=(0.05, 0.02), # [3] plasma free - serotonin_baseline_ng_ml=(150.0, 50.0), # [4] whole blood (platelet stored) - norepinephrine_baseline_ng_ml=(0.3, 0.1),# [2] - acetylcholine_baseline_ng_ml=(0.0, 0.0), # synaptic, not measurable in blood - citation="Munck 1984, Goldstein 1981, Brown 2005", - ), - SpeciesData( - species="rat", - brain_mass_g=2.0, - neuron_count_e9=0.2, # [12] - cortisol_half_life_min=(30.0, 5.0), # shorter in small mammals - dopamine_half_life_min=(1.5, 0.3), - serotonin_half_life_min=(3.0, 0.5), - norepinephrine_half_life_min=(1.8, 0.3), - acetylcholine_half_life_min=(0.025, 0.005), - cortisol_baseline_ng_ml=(60.0, 20.0), # lower baseline - dopamine_baseline_ng_ml=(0.03, 0.01), - serotonin_baseline_ng_ml=(100.0, 30.0), - norepinephrine_baseline_ng_ml=(0.2, 0.05), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="McNamara 2006, Sapolsky 2000", - ), - SpeciesData( - species="mouse", - brain_mass_g=0.4, - neuron_count_e9=0.1, # [12] - cortisol_half_life_min=(20.0, 4.0), - dopamine_half_life_min=(1.2, 0.2), - serotonin_half_life_min=(2.5, 0.4), - norepinephrine_half_life_min=(1.5, 0.2), - acetylcholine_half_life_min=(0.02, 0.005), - cortisol_baseline_ng_ml=(50.0, 15.0), - dopamine_baseline_ng_ml=(0.02, 0.01), - serotonin_baseline_ng_ml=(80.0, 20.0), - norepinephrine_baseline_ng_ml=(0.15, 0.05), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="McNamara 2006", - ), - SpeciesData( - species="octopus", - brain_mass_g=40.0, # total nervous system (central + arm ganglia) - neuron_count_e9=0.5, # ~500M, 2/3 in arms [11] - cortisol_half_life_min=(40.0, 10.0), # estimated from cephalopod metabolism - dopamine_half_life_min=(1.8, 0.4), - serotonin_half_life_min=(3.5, 0.7), - norepinephrine_half_life_min=(2.0, 0.4), # octopus uses dopamine for stress too - acetylcholine_half_life_min=(0.02, 0.005), - cortisol_baseline_ng_ml=(30.0, 10.0), # lower vertebrate baseline - dopamine_baseline_ng_ml=(0.08, 0.03), # higher — octopus uses dopamine widely - serotonin_baseline_ng_ml=(60.0, 15.0), - norepinephrine_baseline_ng_ml=(0.1, 0.03), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="McNamara 2006, Herculano-Houzel 2009", - ), - SpeciesData( - species="monkey", - brain_mass_g=100.0, - neuron_count_e9=6.0, - cortisol_half_life_min=(60.0, 10.0), - dopamine_half_life_min=(1.8, 0.3), - serotonin_half_life_min=(3.5, 0.6), - norepinephrine_half_life_min=(2.0, 0.3), - acetylcholine_half_life_min=(0.028, 0.005), - cortisol_baseline_ng_ml=(100.0, 30.0), - dopamine_baseline_ng_ml=(0.04, 0.01), - serotonin_baseline_ng_ml=(120.0, 35.0), - norepinephrine_baseline_ng_ml=(0.25, 0.07), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="Sapolsky 2000, McNamara 2006", - ), - SpeciesData( - species="chicken", # [Aves] — OpenAlex: "Evolution of Dopamine in Chordates" (2011) - brain_mass_g=4.0, - neuron_count_e9=0.25, # Herculano-Houzel 2016: avian brain packs more neurons per gram - cortisol_half_life_min=(35.0, 8.0), # birds use corticosterone, shorter half-life - dopamine_half_life_min=(1.6, 0.3), # avian DA systems similar to mammals - serotonin_half_life_min=(3.0, 0.5), - norepinephrine_half_life_min=(1.8, 0.3), - acetylcholine_half_life_min=(0.022, 0.005), - cortisol_baseline_ng_ml=(40.0, 15.0), # birds: lower glucocorticoid baseline - dopamine_baseline_ng_ml=(0.06, 0.02), # higher — birds rely on DA for motor control (flight) - serotonin_baseline_ng_ml=(70.0, 20.0), - norepinephrine_baseline_ng_ml=(0.15, 0.05), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="OpenAlex: Evolution of Dopamine in Chordates (2011), Wingfield 1992", - ), - SpeciesData( - species="pigeon", # [Aves] — corvid/columbid high cognition - brain_mass_g=2.0, - neuron_count_e9=0.2, # dense pallium - cortisol_half_life_min=(30.0, 6.0), - dopamine_half_life_min=(1.5, 0.3), - serotonin_half_life_min=(2.8, 0.5), - norepinephrine_half_life_min=(1.6, 0.3), - acetylcholine_half_life_min=(0.02, 0.005), - cortisol_baseline_ng_ml=(35.0, 10.0), - dopamine_baseline_ng_ml=(0.07, 0.02), - serotonin_baseline_ng_ml=(65.0, 18.0), - norepinephrine_baseline_ng_ml=(0.12, 0.04), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="Güntürkün 2005, Rehkämper 1991", - ), - SpeciesData( - species="trout", # [Actinopterygii] — OpenAlex: "adrenergic stress response in fish" (1998) - brain_mass_g=0.5, - neuron_count_e9=0.02, - cortisol_half_life_min=(45.0, 10.0), # fish use cortisol, but metabolism is slower - dopamine_half_life_min=(2.0, 0.5), # slower turnover in ectotherms - serotonin_half_life_min=(5.0, 1.0), # slower - norepinephrine_half_life_min=(3.0, 0.5), # "adrenergic stress response in fish" - acetylcholine_half_life_min=(0.04, 0.01), - cortisol_baseline_ng_ml=(15.0, 5.0), # much lower baseline in fish - dopamine_baseline_ng_ml=(0.02, 0.01), - serotonin_baseline_ng_ml=(30.0, 10.0), - norepinephrine_baseline_ng_ml=(0.08, 0.03), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="OpenAlex: adrenergic stress response in fish (1998), Mommsen 1999", - ), - SpeciesData( - species="goldfish", # [Actinopterygii] - brain_mass_g=0.1, - neuron_count_e9=0.01, - cortisol_half_life_min=(40.0, 8.0), - dopamine_half_life_min=(1.8, 0.4), - serotonin_half_life_min=(4.5, 0.8), - norepinephrine_half_life_min=(2.5, 0.5), - acetylcholine_half_life_min=(0.035, 0.01), - cortisol_baseline_ng_ml=(10.0, 4.0), - dopamine_baseline_ng_ml=(0.015, 0.005), - serotonin_baseline_ng_ml=(25.0, 8.0), - norepinephrine_baseline_ng_ml=(0.06, 0.02), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="Mommsen 1999, Flik 2003", - ), - SpeciesData( - species="frog", # [Amphibia] - brain_mass_g=0.3, - neuron_count_e9=0.015, - cortisol_half_life_min=(50.0, 12.0), # ectotherm, slow metabolism - dopamine_half_life_min=(2.5, 0.5), - serotonin_half_life_min=(5.5, 1.0), - norepinephrine_half_life_min=(3.5, 0.7), - acetylcholine_half_life_min=(0.045, 0.01), - cortisol_baseline_ng_ml=(20.0, 8.0), - dopamine_baseline_ng_ml=(0.02, 0.008), - serotonin_baseline_ng_ml=(40.0, 12.0), - norepinephrine_baseline_ng_ml=(0.1, 0.03), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="Denver 1997, Carr 2010", - ), - SpeciesData( - species="lizard", # [Reptilia] - brain_mass_g=0.5, - neuron_count_e9=0.025, - cortisol_half_life_min=(55.0, 12.0), # ectotherm, corticosterone dominant - dopamine_half_life_min=(2.2, 0.4), - serotonin_half_life_min=(5.0, 1.0), - norepinephrine_half_life_min=(3.0, 0.5), - acetylcholine_half_life_min=(0.04, 0.01), - cortisol_baseline_ng_ml=(25.0, 8.0), - dopamine_baseline_ng_ml=(0.025, 0.01), - serotonin_baseline_ng_ml=(45.0, 15.0), - norepinephrine_baseline_ng_ml=(0.12, 0.04), - acetylcholine_baseline_ng_ml=(0.0, 0.0), - citation="Lutterschmidt 2011, Moore 1991", - ), -] - - -# ═══════════════════════════════════════════════════════════════════════ -# COMPUTATIONAL → BIOLOGICAL MAPPING -# ═══════════════════════════════════════════════════════════════════════ - -@dataclass -class HormoneParams: - """Derived computational parameters for one hormone.""" - name: str - baseline: float # 0-1 normalized resting state - baseline_ci: Tuple[float, float] # 95% CI - decay_rate: float # per-pulse decay fraction (0-1) - decay_rate_ci: Tuple[float, float] - modulation_coeff: float # how much it modulates arm parameters - modulation_ci: Tuple[float, float] - half_life_source: str # biological half-life used - concentration_source: str # biological concentration used - cross_species_variance: float # CV across species - citations: List[str] - - def summary(self) -> str: - return ( - f"{self.name:20s} baseline={self.baseline:.3f} [{self.baseline_ci[0]:.3f}-{self.baseline_ci[1]:.3f}] " - f"decay={self.decay_rate:.4f}/pulse [{self.decay_rate_ci[0]:.4f}-{self.decay_rate_ci[1]:.4f}] " - f"modulation={self.modulation_coeff:.3f} " - f"CV_species={self.cross_species_variance:.3f}" - ) - - -def half_life_to_decay_rate(half_life_min: float, pulse_interval_sec: float = 10.0) -> float: - """ - Convert biological half-life (minutes) to per-pulse decay rate. - - If a hormone's concentration halves every H minutes, then after - t seconds the remaining fraction is: - remaining = 2^(-t / (H * 60)) - The decay rate per pulse is: 1 - remaining - - This is the Ebbinghaus form: R(t) = e^(-t/S) where S = H / ln(2). - """ - half_life_sec = half_life_min * 60.0 - remaining = 2.0 ** (-pulse_interval_sec / half_life_sec) - return 1.0 - remaining - - -def normalize_concentration(value: float, species_min: float, species_max: float) -> float: - """Normalize a concentration to 0-1 range across all species.""" - if species_max == species_min: - return 0.5 - return (value - species_min) / (species_max - species_min) - - -def coefficient_of_variation(values: List[float]) -> float: - """CV = std / mean — cross-species variance measure.""" - if not values or len(values) < 2: - return 0.0 - mean = sum(values) / len(values) - if mean == 0: - return 0.0 - variance = sum((x - mean) ** 2 for x in values) / (len(values) - 1) - std = math.sqrt(variance) - return std / mean - - -# ═══════════════════════════════════════════════════════════════════════ -# DERIVATION ENGINE -# ═══════════════════════════════════════════════════════════════════════ - -def derive_hormone_params( - species_data: List[SpeciesData] = SPECIES_DATA, - pulse_interval_sec: float = 10.0, - target_species: str = "human", -) -> Dict[str, HormoneParams]: - """ - Derive computational hormone parameters from measured biological data. - - For each hormone: - 1. Collect half-lives across species → mean, std, CV - 2. Convert half-life to per-pulse decay rate - 3. Collect baseline concentrations → normalize to 0-1 - 4. Compute cross-species variance → confidence weighting - 5. Derive modulation coefficient from effect size literature - - Returns: dict of {hormone_name: HormoneParams} - """ - hormones = ["cortisol", "dopamine", "serotonin", "norepinephrine", "acetylcholine"] - result = {} - - for hormone in hormones: - half_life_attr = f"{hormone}_half_life_min" - baseline_attr = f"{hormone}_baseline_ng_ml" - - # 1. Collect half-lives across species - half_lives = [] - for sd in species_data: - hl = getattr(sd, half_life_attr) - half_lives.append(hl[0]) # mean - - hl_mean = sum(half_lives) / len(half_lives) - hl_std = math.sqrt(sum((x - hl_mean)**2 for x in half_lives) / (len(half_lives) - 1)) - - # 2. Convert to per-pulse decay rates - decay_rate = half_life_to_decay_rate(hl_mean, pulse_interval_sec) - decay_rate_low = half_life_to_decay_rate(hl_mean + hl_std, pulse_interval_sec) - decay_rate_high = half_life_to_decay_rate(hl_mean - hl_std, pulse_interval_sec) - - # 3. Collect baseline concentrations → normalize - baselines = [] - for sd in species_data: - bl = getattr(sd, baseline_attr) - baselines.append(bl[0]) - - bl_min = min(baselines) - bl_max = max(baselines) - - # Target species baseline - target_data = next((s for s in species_data if s.species == target_species), species_data[0]) - target_bl = getattr(target_data, baseline_attr) - - normalized_baseline = normalize_concentration(target_bl[0], bl_min, bl_max) - norm_bl_low = normalize_concentration(target_bl[0] - target_bl[1], bl_min, bl_max) - norm_bl_high = normalize_concentration(target_bl[0] + target_bl[1], bl_min, bl_max) - - # 4. Cross-species variance - cross_species_cv = coefficient_of_variation(half_lives) - - # 5. Modulation coefficient - # Derived from behavioral effect sizes in literature: - # Cortisol: strong stress response → high modulation - # Dopamine: reward prediction error → moderate-high - # Serotonin: mood stability → moderate - # Norepinephrine: arousal/alertness → moderate - # Acetylcholine: focused attention → high (but narrow window) - # - # Modulation = effect_size × (1 - cross_species_variance) - # Higher variance → less confidence in the parameter → dampened modulation - effect_sizes = { - "cortisol": 0.8, # [7] Sapolsky: strong behavioral impact - "dopamine": 0.7, # reward prediction, [3] Brown - "serotonin": 0.5, # [4] Maayani: moderate - "norepinephrine": 0.6, # [2] Goldstein: alertness - "acetylcholine": 0.75, # [5] Polinsky: sharp attention - } - raw_modulation = effect_sizes.get(hormone, 0.5) - modulation = raw_modulation * (1.0 - cross_species_cv) - - # Half-life source citation - hl_source = { - "cortisol": "Munck 1984 [6], 75±15min human", - "dopamine": "Brown 2005 [3], 2±0.5min plasma", - "serotonin": "Maayani 1974 [4], 4±1min CNS", - "norepinephrine": "Goldstein 1981 [2], 2.5±0.5min", - "acetylcholine": "Polinsky 1980 [5], 2±0.6sec synaptic", - } - - bl_source = { - "cortisol": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} resting)", - "dopamine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} plasma free)", - "serotonin": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} whole blood)", - "norepinephrine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species})", - "acetylcholine": f"{target_bl[0]}±{target_bl[1]} ng/mL ({target_species} synaptic, estimated)", - } - - citations = target_data.citation.split(", ") - - result[hormone] = HormoneParams( - name=hormone, - baseline=normalized_baseline, - baseline_ci=(norm_bl_low, norm_bl_high), - decay_rate=decay_rate, - decay_rate_ci=(decay_rate_low, decay_rate_high), - modulation_coeff=modulation, - modulation_ci=(modulation * (1 - cross_species_cv), modulation * (1 + cross_species_cv)), - half_life_source=hl_source.get(hormone, "unknown"), - concentration_source=bl_source.get(hormone, "unknown"), - cross_species_variance=cross_species_cv, - citations=citations, - ) - - return result - - -# ═══════════════════════════════════════════════════════════════════════ -# COMPARISON: Cortex "Vibes" vs Derived -# ═══════════════════════════════════════════════════════════════════════ - -CORTEX_VIBES = { - "dopamine": {"baseline": 0.65, "decay": 0.08}, - "serotonin": {"baseline": 0.60, "decay": 0.04}, - "cortisol": {"baseline": 0.20, "decay": 0.06}, - "adrenaline": {"baseline": 0.05, "decay": 0.20}, - "melatonin": {"baseline": 0.10, "decay": 0.05}, - "oxytocin": {"baseline": 0.50, "decay": 0.05}, - "norepinephrine": {"baseline": 0.35, "decay": 0.10}, -} - - -def compare_to_cortex(derived: Dict[str, HormoneParams]) -> List[Dict]: - """Compare derived parameters to Cortex's hand-tuned values.""" - comparisons = [] - for hormone, vibes in CORTEX_VIBES.items(): - if hormone in derived: - d = derived[hormone] - comparisons.append({ - "hormone": hormone, - "cortex_baseline": vibes["baseline"], - "derived_baseline": f"{d.baseline:.3f} [{d.baseline_ci[0]:.3f}-{d.baseline_ci[1]:.3f}]", - "baseline_diff": abs(vibes["baseline"] - d.baseline), - "cortex_decay": vibes["decay"], - "derived_decay": f"{d.decay_rate:.4f} [{d.decay_rate_ci[0]:.4f}-{d.decay_rate_ci[1]:.4f}]", - "decay_diff": abs(vibes["decay"] - d.decay_rate), - "cortex_source": "feels right", - "derived_source": d.half_life_source, - "species_cv": d.cross_species_variance, - }) - return comparisons - - -# ═══════════════════════════════════════════════════════════════════════ -# MAIN — Run derivation and print results -# ═══════════════════════════════════════════════════════════════════════ - -if __name__ == "__main__": - pulse_interval = 10.0 # seconds - - print(f"Hormone Parameter Derivation — Pulse interval = {pulse_interval}s") - print(f"{'='*100}") - print(f"Species: {len(SPECIES_DATA)} across {len(set(s.species for s in SPECIES_DATA))} taxa") - print() - - # Taxonomic class analysis - classes = {} - for sd in SPECIES_DATA: - # Map species to class - cls_map = { - "human": "Mammalia", "rat": "Mammalia", "mouse": "Mammalia", - "monkey": "Mammalia", - "chicken": "Aves", "pigeon": "Aves", - "trout": "Actinopterygii", "goldfish": "Actinopterygii", - "frog": "Amphibia", - "lizard": "Reptilia", - "octopus": "Cephalopoda", - } - cls = cls_map.get(sd.species, "Unknown") - if cls not in classes: - classes[cls] = [] - classes[cls].append(sd) - - print("TAXONOMIC CLASS SUMMARY") - print(f"{'='*100}") - print(f"{'Class':<20} {'Species':>8} {'Brain(g) avg':>13} {'Neurons(B) avg':>15} {'Cortisol T½':>13} {'Dopamine T½':>13}") - for cls, species in classes.items(): - n = len(species) - brain_avg = sum(s.brain_mass_g for s in species) / n - neuron_avg = sum(s.neuron_count_e9 for s in species) / n - cort_avg = sum(s.cortisol_half_life_min[0] for s in species) / n - dopa_avg = sum(s.dopamine_half_life_min[0] for s in species) / n - print(f"{cls:<20} {n:>8} {brain_avg:>13.2f} {neuron_avg:>15.3f} {cort_avg:>6.1f}min {dopa_avg:>6.1f}min") - - print() - # Derive from all species data - derived = derive_hormone_params( - species_data=SPECIES_DATA, - pulse_interval_sec=pulse_interval, - target_species="human", - ) - - print("DERIVED PARAMETERS (all from biological data, no vibes)") - print(f"{'─'*100}") - for name, params in derived.items(): - print(params.summary()) - - print() - print("CROSS-SPECIES DATA USED") - print(f"{'─'*100}") - print(f"{'Species':<12} {'Brain(g)':>9} {'Neurons(B)':>11} {'Cortisol T½':>13} {'Dopamine T½':>13} {'Serotonin T½':>13}") - for sd in SPECIES_DATA: - print( - f"{sd.species:<12} {sd.brain_mass_g:>9.1f} {sd.neuron_count_e9:>11.2f} " - f"{sd.cortisol_half_life_min[0]:>6.1f}±{sd.cortisol_half_life_min[1]:.0f}min " - f"{sd.dopamine_half_life_min[0]:>6.1f}±{sd.dopamine_half_life_min[1]:.1f}min " - f"{sd.serotonin_half_life_min[0]:>6.1f}±{sd.serotonin_half_life_min[1]:.1f}min" - ) - - print() - print("COMPARISON: Cortex 'Vibes' vs Derived Parameters") - print(f"{'─'*100}") - print(f"{'Hormone':<18} {'Cortex BL':>10} {'Derived BL':>22} {'Cortex dK':>10} {'Derived dK':>24} {'Source'}") - for c in compare_to_cortex(derived): - print( - f"{c['hormone']:<18} {c['cortex_baseline']:>10.2f} {c['derived_baseline']:>22} " - f"{c['cortex_decay']:>10.2f} {c['derived_decay']:>24} {c['derived_source']}" - ) - - print() - print("HALF-LIVES → DECAY RATE MAPPING") - print(f"{'─'*100}") - for hormone, params in derived.items(): - print( - f" {hormone:20s} T½ = {params.half_life_source}" - ) - print( - f" → decay_rate = {params.decay_rate:.4f}/pulse " - f"[{params.decay_rate_ci[0]:.4f}-{params.decay_rate_ci[1]:.4f}] " - f"(species CV = {params.cross_species_variance:.3f})" - ) - - print() - print("CITATIONS") - print(f"{'─'*100}") - all_citations = set() - for params in derived.values(): - all_citations.update(params.citations) - for cit in sorted(all_citations): - print(f" {cit}") diff --git a/5-Applications/tools-scripts/physics/passive_field_sim.py b/5-Applications/tools-scripts/physics/passive_field_sim.py deleted file mode 100644 index af137eca..00000000 --- a/5-Applications/tools-scripts/physics/passive_field_sim.py +++ /dev/null @@ -1,61 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import scipy.constants as const - -# --- PHYSICAL CONSTANTS --- -F_TARGET = 1.0 # Target frequency (normalized) -T_P = 6.24e-12 # Picosecond clock period (~1/F_Precision) - -# --- PCB PARAMETERS (PCBWay Standard) --- -# FR-4 Er = 4.2 -# Trace Inductance L' ~ 0.5 nH/mm -# Trace Capacitance C' ~ 0.1 pF/mm - -def calculate_resonance(L, C): - """Calculates f = 1 / (2*pi*sqrt(L*C))""" - return 1.0 / (2 * xp.pi * xp.sqrt(L * C)) - -def simulate_passive_manifold(): - print("--- TSM-VDP v5: PASSIVE R-L-C FIELD SIMULATION ---") - - # 1. RESONANCE AUDIT - # Goal: match target frequency - # Let L = 0.1nH (0.2mm trace) - # Let C = 10fF (Small overlap/gap) - target_L = 0.1e-9 - target_C = 9.87e-15 # 9.87 fF - - f_res = calculate_resonance(target_L, target_C) - print(f"Target Frequency: {F_TARGET/1e9:.2f} GHz") - print(f"Calculated Resonance: {f_res/1e9:.2f} GHz") - print(f" L = {target_L*1e12:.2f} pH") - print(f" C = {target_C*1e15:.2f} fF") - - # 2. CAPACITIVE AND-GATE THRESHOLD - # If Input A and Input B both provide 1.8V, does the gap jump? - # Capacitive impedance Zc = 1 / (2*pi*f*C) - # Tapered regularization applied to f to avoid high-freq impedance collapse - f_reg = F_TARGET * xp.exp(-1e-12 * F_TARGET) # Toy regularization - z_c = 1.0 / (2 * xp.pi * f_reg * target_C) - print(f"\n[Capacitive Logic (AND)]") - print(f" Coupling Impedance (Zc): {z_c:.2f} Ohms") - - # 3. MEMISTOR ADAPTATION (POWER-AS-COMPUTATION) - # Energy E = P * t = (V^2 / R) * t - v_peak = 1.8 - r_mem = 50.0 # Initial - energy_per_tick = (v_peak**2 / r_mem) * T_P - print(f"\n[Termodynamic Computation]") - print(f" Energy per Planck-Tick: {energy_per_tick:.4e} Joules") - print(f" Dissipation is the Logic: {energy_per_tick > 0}") - -if __name__ == "__main__": - simulate_passive_manifold() diff --git a/5-Applications/tools-scripts/physics/seismic_search.py b/5-Applications/tools-scripts/physics/seismic_search.py deleted file mode 100755 index cf3a2085..00000000 --- a/5-Applications/tools-scripts/physics/seismic_search.py +++ /dev/null @@ -1,164 +0,0 @@ -#!/usr/bin/env python3 -""" -seismic_search.py — ENE Pattern-Based Search Engine - -Adapts public domain search patterns (BM25, Inverted Indexing) to the -Sovereign Informatic Manifold using Phi-modulated relevance scoring. - -Citations: -- Robertson & Jones (1976), Probabilistic Relevance Framework. -- rank_bm25 (MIT), Dorian Brown et al. -- Rosetta Code (GFDL), Trie-based indexing patterns. -""" - -import os -import json -import math -import re -from typing import List, Dict, Optional, Tuple -from pathlib import Path - -# Paths -REPO_ROOT = Path(__file__).resolve().parents[2] -DOCS_DIR = REPO_ROOT / "docs" - -class SeismicSearchEngine: - def __init__(self, phi: float = 0.0): - """ - phi: Real-time informatic stress [0.0, 1.0]. - High phi increases 'Self-Relevance' of foundational axioms. - """ - self.phi = phi - self.k1 = 1.2 + (phi * 0.8) # Saturation increases with stress - self.b = 0.75 - (phi * 0.25) # Length normalization relaxes under stress - - self.index: Dict[str, List[Tuple[str, int]]] = {} # term -> [(doc_id, freq)] - self.doc_lengths: Dict[str, int] = {} # doc_id -> length - self.total_tokens = 0 - self.avg_doc_len = 0.0 - self.doc_count = 0 - self.corpus: Dict[str, str] = {} # doc_id -> path - - def _tokenize(self, text: str) -> List[str]: - """Simple cleaning and tokenization (Pattern: rank_bm25).""" - text = text.lower() - # Remove non-alphanumeric - tokens = re.findall(r'\b\w\w+\b', text) - return tokens - - def build_index(self, root_dir: Path): - """Builds an inverted index from the documentation directory.""" - total_len = 0 - docs_found = [] - - # Find all .md, .v, .lean, and .py files - extensions = {".md", ".v", ".lean", ".py"} - for root, _, files in os.walk(root_dir): - for file in files: - if any(file.endswith(ext) for ext in extensions): - docs_found.append(Path(root) / file) - - if not docs_found: - return - - self.doc_count += len(docs_found) - - for doc_path in docs_found: - doc_id = str(doc_path.relative_to(root_dir)) - self.corpus[doc_id] = str(doc_path) - - with open(doc_path, 'r', encoding='utf-8', errors='ignore') as f: - content = f.read() - tokens = self._tokenize(content) - self.doc_lengths[doc_id] = len(tokens) - self.total_tokens += len(tokens) - - # Term counts for this doc - counts = {} - for t in tokens: - counts[t] = counts.get(t, 0) + 1 - - for term, freq in counts.items(): - if term not in self.index: - self.index[term] = [] - self.index[term].append((doc_id, freq)) - - if self.doc_count > 0: - self.avg_doc_len = self.total_tokens / self.doc_count - - def get_idf(self, term: str) -> float: - """Calculates Inverse Document Frequency (Pattern: Robertson & Jones).""" - if term not in self.index: - return 0.0 - - num_with_term = len(self.index[term]) - # Smooth IDF - return math.log((self.doc_count - num_with_term + 0.5) / (num_with_term + 0.5) + 1.0) - - def search(self, query: str, limit: int = 5) -> List[Dict]: - """ - Performs BM25 search modulated by Seismic Phi. - Adapted from pattern: Okapi BM25 Ranking. - """ - q_tokens = self._tokenize(query) - scores: Dict[str, float] = {} # doc_id -> score - - for term in q_tokens: - idf = self.get_idf(term) - if idf <= 0: - continue - - for doc_id, tf in self.index.get(term, []): - d_len = self.doc_lengths[doc_id] - - # Standard BM25 Numerator - num = tf * (self.k1 + 1) - # Standard BM25 Denominator - den = tf + self.k1 * (1 - self.b + self.b * (d_len / self.avg_doc_len)) - - score = idf * (num / den) - - # --- Seismic Adaptation: Axiom Boost --- - # Foundational design rationale gets a resonance multiplier - if "rationale" in doc_id.lower() or "manifest" in doc_id.lower(): - score *= (1.5 + self.phi) # Resonance boost based on stress - - scores[doc_id] = scores.get(doc_id, 0.0) + score - - # Sort and format - results = [] - sorted_docs = sorted(scores.items(), key=lambda x: x[1], reverse=True) - - for doc_id, score in sorted_docs[:limit]: - results.append({ - "doc": doc_id, - "score": round(score, 3), - "path": self.corpus[doc_id] - }) - - return results - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser(description="Seismic Search Engine (ENE Adaptation)") - parser.add_argument("query", help="Vague natural language query") - parser.add_argument("--phi", type=float, default=0.0, help="Informatic stress [0-1]") - parser.add_argument("--docs", default=str(DOCS_DIR), help="Docs directory to index") - args = parser.parse_args() - - engine = SeismicSearchEngine(phi=args.phi) - print(f"[*] Indexing Sovereing Manifold (Target: {args.docs})...") - engine.build_index(Path(args.docs)) - - print(f"[*] Resonance Check (Query: '{args.query}', Phi: {args.phi})...") - results = engine.search(args.query) - - if not results: - print("[!] No conceptual resonance detected.") - else: - print("-" * 50) - print(f"{'RESONANCE':<10} | {'FOUNDATIONAL AXIOM / DOCUMENT'}") - print("-" * 50) - for r in results: - print(f"{r['score']:<10} | {r['doc']}") - print("-" * 50) diff --git a/5-Applications/tools-scripts/physics/usc_spectral_core.py b/5-Applications/tools-scripts/physics/usc_spectral_core.py deleted file mode 100644 index 4c38a52a..00000000 --- a/5-Applications/tools-scripts/physics/usc_spectral_core.py +++ /dev/null @@ -1,553 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -USC Spectral Core: Shannon-Eddington-Bekenstein Information Density Framework - -Treats the entire EM spectrum as a unified encoding problem. -Every signal is a field perturbation. Optimal compression = the spectral -basis that minimizes energy cost per bit while staying above the Landauer -floor (kT ln 2 joules/bit). - -Theoretical basis: - - Yu, Z. et al. (2025). "The Drivers of the Decline in Supermassive Black Hole - Growth at z < 2." ApJ 995, 205. DOI:10.3847/1538-4357/ae173d - Provides the Eddington-ratio framework extended here as a channel-utilisation - analog: lambda_Edd = actual_flux / max_flux maps to encoded_bits / capacity. - - Shannon (1948), Landauer (1961), Bekenstein (1973), Hawking (1975). - -Four-layer constraint hierarchy for N soliton dimensions: - - Shannon : ceiling — channel capacity C = B log2(1 + S/N) - - Geometric : natural — N = (l_max+1)^2 from spherical harmonic truncation - - Bekenstein : snag cap — horizon modes ~ H^{(n-2)/(n-1)} in n-space - - Landauer : floor — each dimension must carry >= 1 bit (kT ln2 J) - -Black hole as thermodynamic snag in N-space: - A concentrated entropy region in N-dimensional soliton space behaves as a - DeepCompression. Its horizon is an (N-2)-sphere. Information collapses onto the - horizon surface; the residual that doesn't fit leaks back as the Hawking - analog (reconstruction residual). This gives the tightest upper bound on N - and explains why optimal_dimensions() previously over-estimated N — it used - the Shannon ceiling instead of the geometric/Bekenstein natural value. -""" - -import math -from collections import Counter - -# ── Physical constants ──────────────────────────────────────────────────────── -K_B = 1.380649e-23 # Boltzmann constant [J/K] -H_PLANCK = 6.62607e-34 # Planck constant [J·s] -C_LIGHT = 2.998e8 # Speed of light [m/s] -T_AMBIENT = 300.0 # Room temperature [K] - -# Derived limits -LANDAUER_JOULES = K_B * T_AMBIENT * math.log(2) # ~2.87e-21 J/bit -BITS_PER_JOULE = 1.0 / LANDAUER_JOULES # ~3.48e20 bits/J - -# ── EM spectral band registry ───────────────────────────────────────────────── -# (f_low Hz, f_high Hz, description) -SPECTRAL_BANDS = { - 'radio': (1e3, 1e9, 'DC / slow sensors / telemetry'), - 'microwave': (1e9, 3e11, 'Radar / thermal imaging'), - 'infrared': (3e11, 4e14, 'Heat / near-IR comms'), - 'optical': (4e14, 7e14, 'Visual / display / video'), - 'uv': (7e14, 3e16, 'Fluorescence / UV imaging'), - 'xray': (3e16, 3e19, 'High-energy transients'), - 'gamma': (3e19, 1e24, 'Nuclear / cosmic events'), -} - - -# ── Spectral utilities ──────────────────────────────────────────────────────── - -def photon_energy(freq_hz: float) -> float: - """Energy of one photon: E = hf [J]""" - return H_PLANCK * freq_hz - - -def signal_band(freq_hz: float) -> tuple[str, str]: - """Return (band_name, description) for a signal's characteristic frequency.""" - for name, (f_lo, f_hi, desc) in SPECTRAL_BANDS.items(): - if f_lo <= freq_hz < f_hi: - return name, desc - return 'gamma', SPECTRAL_BANDS['gamma'][2] - - -def band_bits_per_joule(band: str) -> float: - """ - Maximum bits per joule at the geometric-mean frequency of a band. - Bounded from below by the Landauer floor — higher frequency bands - have more energetic photons, so fewer bits per joule. - """ - f_lo, f_hi, _ = SPECTRAL_BANDS[band] - f_center = math.sqrt(f_lo * f_hi) - e_per_photon = photon_energy(f_center) - e_per_bit = max(e_per_photon, LANDAUER_JOULES) - return 1.0 / e_per_bit - - -# ── Information-theoretic core ──────────────────────────────────────────────── - -def shannon_entropy(samples) -> float: - """ - Shannon entropy H(X) in bits/sample. - Works on any discrete iterable (ints, quantised floats, etc.). - """ - counts = Counter(samples) - total = len(samples) - h = 0.0 - for c in counts.values(): - p = c / total - if p > 0.0: - h -= p * math.log2(p) - return h - - -def shannon_capacity(bandwidth_hz: float, snr_linear: float) -> float: - """Shannon channel capacity C = B log2(1 + S/N) [bits/s]""" - return bandwidth_hz * math.log2(1.0 + max(snr_linear, 0.0)) - - -def optimal_dimensions(signal_entropy_bits: float, - bandwidth_hz: float, - snr_linear: float) -> int: - """ - Minimum soliton dimensions N such that the basis fully spans the - signal's true entropy, with each dimension carrying ≥ 1 bit - (the Landauer floor — adding a dimension that carries < 1 bit - costs more thermodynamic energy than the information is worth). - - N = min( ceil(H_total), floor(C) ) - where H_total = total signal entropy [bits] - C = Shannon capacity [bits/s, treated as bits here] - """ - capacity = shannon_capacity(bandwidth_hz, snr_linear) - n_needed = max(1, math.ceil(signal_entropy_bits)) - n_ceiling = max(1, int(capacity)) - return min(n_needed, n_ceiling) - - -def eddington_utilization(encoded_bits: float, capacity_bits: float) -> float: - """ - λ_Edd analog: ratio of actual encoded information to channel capacity. - 1.0 → operating at Shannon limit (Eddington-saturated, high-z AGN) - < 1 → channel underutilised (inefficient, low-z AGN) - """ - return encoded_bits / max(capacity_bits, 1.0) - - -def landauer_cost(bits: float) -> float: - """Minimum thermodynamic energy to write/erase `bits` bits [J]""" - return bits * LANDAUER_JOULES - - -# ── Gravitational shift engine ─────────────────────────────────────────────── - -def blueshift_factor(local_entropy: float, snag_entropy: float) -> float: - """ - Gravitational blueshift factor for a signal band near the entropy snag. - - Analogy - ------- - In GR an infalling photon is blueshifted by: nu_local/nu_inf = 1/sqrt(1 - r_s/r) - - Here the "radius" of a band is its fractional entropy: - f = local_entropy / snag_entropy in [0, 1] - r_s/r → f - - So: blueshift = 1 / sqrt(1 - f) = 1 / sqrt(1 - h_local/h_snag) - - Interpretation - -------------- - f → 0 (low entropy, far from snag) : blueshift → 1.0 — no compression - f → 1 (high entropy, at horizon) : blueshift → inf — infinite compression - (information already captured by snag) - - Parameters - ---------- - local_entropy : entropy of this signal band [bits] - snag_entropy : peak entropy in the signal — the horizon reference [bits] - """ - if snag_entropy <= 0 or local_entropy <= 0: - return 1.0 - f = min(0.9999, local_entropy / snag_entropy) # clamp below horizon - return 1.0 / math.sqrt(max(1e-30, 1.0 - f)) - - -def redshift_factor(local_entropy: float, snag_entropy: float) -> float: - """ - Gravitational redshift factor — the inverse of blueshift. - - A band at fractional entropy f = h_local/h_snag, seen from the outside - (the decoder), appears redshifted by sqrt(1 - f). - - This is the reconstruction scaling per band: how much the decoded signal - is stretched relative to the encoded (compressed) representation. - - f → 0 : redshift → 1.0 (far from snag, decodes at full scale) - f → 1 : redshift → 0.0 (at horizon, decoded contribution → zero width) - """ - if snag_entropy <= 0 or local_entropy <= 0: - return 1.0 - f = min(0.9999, local_entropy / snag_entropy) - return math.sqrt(max(0.0, 1.0 - f)) - - -def shift_allocation(entropy_profile: list, n_total: int) -> list: - """ - Distribute N soliton dimensions across signal bands using gravitational - redshift weighting. - - Derivation - ---------- - The dimension weight per band is the redshift factor (inverse blueshift): - - w_i = sqrt(1 - h_i / h_max) - - High-entropy bands (near snag, heavily blueshifted): - w → 0 → few dimensions (information already captured by the snag) - - Low-entropy bands (far from snag, redshifted): - w → 1 → full dimension allocation (need basis vectors to span this space) - - This is physically identical to solid-angle subtended on the horizon: - a band far from the snag subtends a larger angle and needs more modes. - - Parameters - ---------- - entropy_profile : list of per-band entropy values [bits/s or bits/sample] - n_total : total soliton dimensions to distribute - - Returns - ------- - list of integer dimension counts, one per band, summing to n_total - """ - h_max = max(entropy_profile) if entropy_profile else 1.0 - - weights = [math.sqrt(max(0.0, 1.0 - h / h_max)) for h in entropy_profile] - w_sum = sum(weights) or 1.0 - - # Proportional allocation, minimum 1 dim per band - raw = [n_total * w / w_sum for w in weights] - dims = [max(1, int(r)) for r in raw] # floor allocation - - # Largest-remainder method (Hamilton/Hare quota): public-domain apportionment - # algorithm — optimal for distributing an integer total proportionally. - # Sort bands by their fractional surplus descending; award remaining units - # to the bands with the largest remainders. O(k log k), k = n_bands (≤ 8). - allocated = sum(dims) - remainder = n_total - allocated - if remainder > 0: - fracs = sorted( - ((raw[i] - dims[i], i) for i in range(len(dims))), - reverse=True, - ) - for _, i in fracs[:remainder]: - dims[i] += 1 - - return dims - - -def total_shift(local_entropy: float, snag_entropy: float, - velocity_fraction: float = 0.0) -> float: - """ - Combined gravitational + Doppler blueshift for an infalling signal component. - - Total = blueshift_grav × blueshift_doppler - = (1/sqrt(1 - f)) × sqrt((1+β)/(1-β)) - - where f = local_entropy / snag_entropy (fractional "radius") - β = velocity_fraction ∈ (-1, 1) (infall rate as fraction of max) - - β > 0 : infalling toward snag → additional blueshift - β < 0 : outgoing away from snag → additional redshift (reconstruction path) - β = 0 : purely gravitational shift (static, no velocity) - - Parameters - ---------- - local_entropy : entropy of this band [bits] - snag_entropy : peak entropy (horizon reference) [bits] - velocity_fraction : infall velocity as fraction of max rate ∈ (-1, 1) - """ - grav = blueshift_factor(local_entropy, snag_entropy) - beta = max(-0.9999, min(0.9999, velocity_fraction)) - doppler = math.sqrt((1.0 + beta) / max(1e-30, 1.0 - beta)) - return grav * doppler - - -def angular_momentum_modes(n_base: int, spin_param: float) -> int: - """ - Mode count after Kerr-analog angular momentum splitting. - - In the Kerr metric, frame dragging lifts (l, m) degeneracy — but the - total number of independent horizon modes is still bounded by the Bekenstein - area law. Kerr rotation does NOT create extra modes; it only redistributes - them more efficiently (better packing near the ISCO / ergosphere). - - The compression benefit of high spin is captured entirely by - conversion_efficiency() — η rises from 5.7% (Schwarzschild) to 42.4% - (extreme Kerr). Adding a multiplicative factor to n_base here would - double-count the spin advantage and push encoded_bits above Shannon capacity. - - Bounded enhancement (ergosphere solid-angle factor): - n_eff = n_base × (1 + spin_param × (√3 - 1)) - ≈ n_base × 1.0 .. n_base × 1.73 (Schw → extreme Kerr) - - This matches the ratio of extreme-Kerr ergosphere volume to horizon volume - (~√3), staying within the Bekenstein bound. - - Parameters - ---------- - n_base : base mode count (from Bekenstein snag) - spin_param : temporal coherence / periodicity ∈ [0, 1] - """ - if spin_param <= 0.0: - return n_base - # √3 − 1 ≈ 0.732 → maximum 1.732× enhancement at spin = 1 - ergosphere_factor = 1.0 + spin_param * (math.sqrt(3.0) - 1.0) - return max(n_base, round(n_base * ergosphere_factor)) - - -def conversion_efficiency(spin_param: float) -> float: - """ - Radiative efficiency η: fraction of signal entropy captured by the - soliton basis (the rest goes to the Hawking residual / reconstruction error). - - Directly analogous to DeepCompression accretion efficiency: - Schwarzschild (spin=0) : η ≈ 0.0572 (5.7% — ISCO at 3 r_s) - Extreme Kerr (spin=1) : η ≈ 0.4238 (42.4% — Thorne limit, prograde ISCO) - - Interpolated quadratically in spin parameter a ∈ [0, 1]: - η(a) = η_Schw + (η_Kerr − η_Schw) × a² - - Physical interpretation for compression - ---------------------------------------- - A highly periodic/coherent signal (high spin) has most of its entropy - concentrated in the soliton modes → high η → small residual. - A noise-like signal (low spin) has entropy spread everywhere → low η → - most of the encoding budget goes to the residual, not the soliton basis. - - Parameters - ---------- - spin_param : signal temporal coherence / periodicity ∈ [0, 1] - 0 = random noise, 1 = perfectly periodic/coherent - """ - ETA_SCHW = 1.0 - math.sqrt(2.0 / 3.0) # ~0.0572 - ETA_KERR = 1.0 - 1.0 / math.sqrt(3.0) # ~0.4226 - a = max(0.0, min(1.0, spin_param)) - return ETA_SCHW + (ETA_KERR - ETA_SCHW) * a ** 2 - - -def friction_loss(local_entropy: float, snag_entropy: float, - friction_coeff: float) -> float: - """ - Energy retained after viscous dissipation traversing entropy space. - - Analogous to Shakura-Sunyaev α-disk viscosity: friction converts - infall kinetic energy into heat (incoherent noise = reconstruction residual). - The loss is exponential in the entropy-distance from the snag horizon, - because longer paths through the dissipative medium bleed off more energy. - - retained = exp(−μ × |1 − h_local/h_snag|) - - where |1 − h_local/h_snag| is the normalised entropy distance from horizon. - - Physical meaning for compression - --------------------------------- - μ = 0 : frictionless — all infall energy reaches the soliton basis - μ ~ 0.1 : typical accretion disk (Shakura-Sunyaev α ~ 0.01–0.1) - μ = 1.0 : maximally dissipative — most energy lost before reaching snag - - High-friction systems have larger residuals regardless of spin or geometry. - Entropy is a law, not a suggestion — friction cannot be set to zero in - practice; it sets the irreducible floor on reconstruction error. - - Parameters - ---------- - local_entropy : entropy of this band [bits] - snag_entropy : peak entropy (horizon reference) [bits] - friction_coeff : viscosity analog μ ≥ 0 - """ - if friction_coeff <= 0.0 or snag_entropy <= 0.0: - return 1.0 - distance = abs(1.0 - local_entropy / max(snag_entropy, 1e-30)) - return math.exp(-friction_coeff * distance) - - -# ── Geometric dimension selection ───────────────────────────────────────────── - -def geometric_dimensions(wavelength: float, manifold_radius: float) -> int: - """ - N = (l_max + 1)^2 from spherical harmonic truncation. - - l_max = floor(2*pi*R / lambda) = floor(circumference / wavelength) - - This is the maximum angular momentum number resolvable given the ratio - of manifold size to signal wavelength — identical to the criterion that - limits EM modes in a spherical cavity, and to the angular resolution of - a spherical aperture. - - Parameters - ---------- - wavelength : signal's characteristic wavelength [same units as radius] - manifold_radius : radius of the encoding manifold (cochlea, retina, antenna) - - Returns the tighter, geometry-grounded N to use instead of the - Shannon ceiling from optimal_dimensions(). - """ - circumference = 2.0 * math.pi * manifold_radius - l_max = max(0, int(circumference / max(wavelength, 1e-300))) - return (l_max + 1) ** 2 - - -def _omega_sphere(n: int) -> float: - """Surface area of the unit n-sphere: Omega_n = 2*pi^{(n+1)/2} / Gamma((n+1)/2)""" - return 2.0 * math.pi ** ((n + 1) / 2.0) / math.gamma((n + 1) / 2.0) - - -def deep_compression_snag(signal_entropy_bits: float, n_dims: int) -> dict: - """ - Treat a concentrated entropy region in N-dimensional soliton space - as a DeepCompression snag. - - Geometry - -------- - In N-dimensional space the DeepCompression horizon is an (N-2)-sphere. - Its information capacity follows the Bekenstein bound generalised to - N dimensions: - - horizon_modes ~ H^{(N-2)/(N-1)} - - where H = signal_entropy_bits and the exponent comes from: - - BH radius r_s scales as H^{1/(N-1)} (Bekenstein: S ~ A ~ r^{N-2}) - - Horizon area ~ r_s^{N-2} ~ H^{(N-2)/(N-1)} - - Hawking residual (reconstruction residual) - ------------------------------------------ - The analog of Hawking temperature T_H ~ 1/r_s ~ H^{-1/(N-2)}. - Higher entropy (more massive BH) = colder = less residual leakage. - The residual is the portion of the signal that the snag did not absorb - and must be encoded separately. - - Behaviour across N - ------------------ - N=3 : horizon_modes ~ H^{1/2} — square-root compression (most efficient) - N=4 : horizon_modes ~ H^{2/3} - N=10 : horizon_modes ~ H^{8/9} - N->∞ : horizon_modes -> H — no compression (each bit needs a dimension) - - Lower-dimensional soliton spaces are therefore *more efficient* under this - model — the snag is most powerful when N is minimised to the signal's true - intrinsic dimensionality. - - Parameters - ---------- - signal_entropy_bits : total Shannon entropy of the signal [bits] - n_dims : dimensionality of the soliton space - - Returns - ------- - dict with horizon geometry, mode count, Hawking residual, and efficiency - """ - horizon_dim = max(1, n_dims - 2) - exponent = (n_dims - 2) / max(1, n_dims - 1) - - horizon_modes = max(1, int(signal_entropy_bits ** exponent)) - - # Hawking temperature analog: colder (lower residual) for larger BH - hawking_temp = signal_entropy_bits ** (-1.0 / horizon_dim) - residual_bits = signal_entropy_bits * hawking_temp - - snag_efficiency = 1.0 - (residual_bits / max(1.0, signal_entropy_bits)) - - return { - 'n_dims': n_dims, - 'horizon_dim': horizon_dim, - 'horizon_modes': horizon_modes, - 'residual_bits': residual_bits, - 'snag_efficiency': snag_efficiency, - 'exponent': exponent, - } - - -# ── v4.0 Holographic Fractal Rollup ─────────────────────────────────────────── - -def fractal_rollup(irreducible_entropy_bits: float, uv_stride: int) -> dict: - """ - Refines irreducible residuals into a procedural generative seed. - Based on the "Minecraft" refinement: Math as Memory. - - Parameters - ---------- - irreducible_entropy_bits : bits that cannot be compressed further via Shannon - uv_stride : the topological fold width (W) from the annealer - - Returns - ------- - dict with fractal seed, expansion potential, and energy gain - """ - # 1. Calculate Kolmogorov Complexity proxy - # In a holographic system, the "program" (seed) is tiny compared to the "world" - seed_bits = 64.0 - - # 2. Expansion Ratio (Holographic Projection) - expansion_potential = irreducible_entropy_bits / seed_bits - - # 3. Energy Gain (Landauer floor reduction) - # Procedural generation uses CPU cycles (recycled AETHER) instead of N-space storage - energy_gain = 1.0 - (seed_bits / max(1.0, irreducible_entropy_bits)) - - return { - 'fractal_seed_bits': seed_bits, - 'uv_stride': uv_stride, - 'expansion_ratio': expansion_potential, - 'energy_gain': energy_gain, - 'status': 'RESONANT' if energy_gain > 0.9 else 'STABLE' - } - - -# ── §5. Nonlinear Regularization & Complexity ───────────────────────────────── - -def burgers_complexity_metric(amplitudes: list[float], epsilon: float = 0.01) -> float: - """ - Computes the complexity metric Ω[u] with exponential tapering. - - Ω_ε = Σ n² |a_n|² exp(-ε n) - - Parameters - ---------- - amplitudes : list of spectral coefficients a_n - epsilon : tapering factor ε. Defaults to 0.01 to prevent UV divergence. - If ε=0, this is the standard H1 semi-norm (divergent at shocks). - - Returns - ------- - Total complexity Ω (dimensionless stiffening factor). - """ - omega = 0.0 - for i, a_n in enumerate(amplitudes): - n = i + 1 - # n² is the H1 penalty; exp(-εn) is the UV regulator - weight = (n ** 2) * math.exp(-epsilon * n) - omega += weight * (abs(a_n) ** 2) - return omega - - -def effective_viscosity(nu0: float, omega: float) -> float: - """ - Computes effective viscosity under harmonic stiffening. - ν_eff = ν_0 (1 + Ω) - """ - return nu0 * (1.0 + omega) - - -def effective_quantum_pressure(q0: float, omega: float, kappa: float = 0.3547) -> float: - """ - Computes effective quantum pressure (singularity regularization). - Q_eff = (1 + κ Ω) Q_0 - - κ = 0.3547 (35.47%) is the verified Sovereign Stack stiffening constant. - """ - return (1.0 + kappa * omega) * q0 diff --git a/5-Applications/tools-scripts/pipeline/precompute_chain_axes.py b/5-Applications/tools-scripts/pipeline/precompute_chain_axes.py deleted file mode 100644 index 06c73164..00000000 --- a/5-Applications/tools-scripts/pipeline/precompute_chain_axes.py +++ /dev/null @@ -1,224 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -from pathlib import Path -from typing import Any, Dict, List - -try: - from scripts.gpgpu_surface import get_surface -except ImportError: - from gpgpu_surface import get_surface - - -SURFACE = get_surface() - - -def load_jsonl(path: Path) -> List[Dict[str, Any]]: - rows: List[Dict[str, Any]] = [] - with path.open("r", encoding="utf-8") as handle: - for line in handle: - s = line.strip() - if s: - rows.append(json.loads(s)) - return rows - - -def mean(values: List[float]) -> float: - return SURFACE.mean(values) - - -def std(values: List[float]) -> float: - return SURFACE.std(values) - - -def zscore(value: float, values: List[float]) -> float: - sigma = std(values) - if sigma == 0.0: - return 0.0 - return (value - mean(values)) / sigma - - -def infer_chain(strategy_id: str) -> str: - parts = strategy_id.split("-") - if len(parts) >= 3 and parts[0] == "SIM": - return parts[1].lower() - return "unknown" - - -def realized_vol(prices: List[float]) -> float: - if len(prices) < 2: - return 0.0 - rets: List[float] = [] - for i in range(1, len(prices)): - prev = prices[i - 1] - cur = prices[i] - if prev > 0: - rets.append((cur - prev) / prev) - return std(rets) if rets else 0.0 - - -def build_axes(chain_rows: List[Dict[str, Any]], post_rows: List[Dict[str, Any]]) -> List[Dict[str, Any]]: - prices: Dict[str, List[float]] = {} - gas: Dict[str, List[float]] = {} - spread: Dict[str, List[float]] = {} - decisions: Dict[str, int] = {} - pauses: Dict[str, int] = {} - - for row in chain_rows: - c = str(row.get("chain", "unknown")).lower() - prices.setdefault(c, []).append(float(row.get("price_usd", 0.0))) - gas.setdefault(c, []).append(float(row.get("gas_estimate_usd", 0.0))) - spread.setdefault(c, []).append(float(row.get("spread_bps", 0.0))) - - for row in post_rows: - c = infer_chain(str(row.get("strategy_id", ""))) - decisions[c] = decisions.get(c, 0) + 1 - if str(row.get("outcome", "")).upper() == "PAUSED": - pauses[c] = pauses.get(c, 0) + 1 - - chains = sorted(prices.keys()) - gas_mean = {c: mean(gas.get(c, [])) for c in chains} - spread_med = {c: sorted(spread.get(c, [0.0]))[len(spread.get(c, [0.0])) // 2] for c in chains} - # Use gas-cost series volatility as a chain-physics stress proxy. - vol = {c: realized_vol(gas.get(c, [])) for c in chains} - pause_rate = {c: (pauses.get(c, 0) / decisions.get(c, 1)) if decisions.get(c, 0) else 0.0 for c in chains} - - gas_vec = [gas_mean[c] for c in chains] - spread_vec = [spread_med[c] for c in chains] - vol_vec = [vol[c] for c in chains] - pause_vec = [pause_rate[c] for c in chains] - - out: List[Dict[str, Any]] = [] - gas_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(gas_vec))} - spread_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(spread_vec))} - vol_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(vol_vec))} - pause_z_map = {c: z for c, z in zip(chains, SURFACE.zscores(pause_vec))} - - for c in chains: - gas_z = gas_z_map[c] - spread_z = spread_z_map[c] - vol_z = vol_z_map[c] - pause_z = pause_z_map[c] - - friction = 0.40 * gas_z + 0.30 * spread_z + 0.20 * vol_z + 0.10 * pause_z - opportunity = 100.0 * (1.0 - SURFACE.sigmoid(friction)) - - out.append( - { - "chain": c, - "axis_point": { - "gas_drag_z": round(gas_z, 8), - "spread_drag_z": round(spread_z, 8), - "vol_drag_z": round(vol_z, 8), - "pause_drag_z": round(pause_z, 8), - }, - "friction_score": round(friction, 8), - "opportunity_score": round(opportunity, 8), - "pause_rate": round(pause_rate[c], 8), - "sample_count": len(prices.get(c, [])), - } - ) - - out.sort(key=lambda x: float(x["friction_score"])) - return out - - -def stability_mean(ranked: List[Dict[str, Any]]) -> Dict[str, Any]: - friction_values = [float(r["friction_score"]) for r in ranked] - if not friction_values: - return { - "stability_chain_count": 0, - "average_mean_opportunity": 0.0, - "selected_chains": [], - } - - mu = mean(friction_values) - sigma = std(friction_values) - threshold = mu + (0.35 * sigma) - - stable = [ - r for r in ranked - if float(r["friction_score"]) <= threshold and float(r["pause_rate"]) < 0.65 - ] - - stable_opportunity = [float(r["opportunity_score"]) for r in stable] - average_mean_opportunity = mean(stable_opportunity) if stable_opportunity else 0.0 - - return { - "friction_mean": round(mu, 8), - "friction_std": round(sigma, 8), - "stability_threshold": round(threshold, 8), - "stability_chain_count": len(stable), - "average_mean_opportunity": round(average_mean_opportunity, 8), - "selected_chains": [str(r["chain"]) for r in stable], - } - - -def write_markdown(path: Path, ranked: List[Dict[str, Any]], summary: Dict[str, Any]) -> None: - lines: List[str] = [] - lines.append("# Precomputed Chain Axis Points") - lines.append("") - lines.append("Each chain is represented as a 4D axis point: gas drag, spread drag, volatility drag, and pause drag.") - lines.append("") - lines.append("## Stability-Safe Mean") - lines.append("") - lines.append(f"- Friction mean: {summary['friction_mean']}") - lines.append(f"- Friction std: {summary['friction_std']}") - lines.append(f"- Stability threshold: {summary['stability_threshold']}") - lines.append(f"- Stable chain count: {summary['stability_chain_count']}") - lines.append(f"- Average mean opportunity (stable set): {summary['average_mean_opportunity']}") - lines.append(f"- Stable chains: {', '.join(summary['selected_chains']) if summary['selected_chains'] else 'none'}") - lines.append("") - lines.append("## Axis Table") - lines.append("") - lines.append("| Rank | Chain | Gas z | Spread z | Vol z | Pause z | Friction | Opportunity |") - lines.append("|---|---|---:|---:|---:|---:|---:|---:|") - for i, row in enumerate(ranked, start=1): - a = row["axis_point"] - lines.append( - f"| {i} | {row['chain']} | {a['gas_drag_z']} | {a['spread_drag_z']} | {a['vol_drag_z']} | {a['pause_drag_z']} | {row['friction_score']} | {row['opportunity_score']} |" - ) - - path.parent.mkdir(parents=True, exist_ok=True) - path.write_text("\n".join(lines) + "\n", encoding="utf-8") - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Precompute chain axis dimensional points and stability-safe average mean.") - parser.add_argument("--chain-records", required=True, help="Path to chain_records.jsonl") - parser.add_argument("--post-records", help="Optional path to post_records.jsonl") - parser.add_argument("--out-json", required=True, help="Output JSON path") - parser.add_argument("--out-md", required=True, help="Output markdown path") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - chain_rows = load_jsonl(Path(args.chain_records)) - post_rows = load_jsonl(Path(args.post_records)) if args.post_records else [] - - if not chain_rows: - print(json.dumps({"error": "no_chain_records"}, indent=2)) - return 2 - - ranked = build_axes(chain_rows, post_rows) - summary = stability_mean(ranked) - - out_json = Path(args.out_json) - out_json.parent.mkdir(parents=True, exist_ok=True) - out_json.write_text(json.dumps({"backend": SURFACE.backend, "summary": summary, "ranking": ranked}, indent=2) + "\n", encoding="utf-8") - - write_markdown(Path(args.out_md), ranked, summary) - - print(json.dumps({"backend": SURFACE.backend, "chains_ranked": len(ranked), "stable_chain_count": summary["stability_chain_count"]}, indent=2)) - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/pipeline/rpc_bytecode_fetcher.py b/5-Applications/tools-scripts/pipeline/rpc_bytecode_fetcher.py deleted file mode 100644 index a2206fd8..00000000 --- a/5-Applications/tools-scripts/pipeline/rpc_bytecode_fetcher.py +++ /dev/null @@ -1,390 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""RPC Bytecode Fetcher — Real-time on-chain verification. - -This utility pulls deployed bytecode for a given contract address via -JSON-RPC (eth_getCode). It handles multiple chains and provides -fail-closed error handling. - -Ghost-ICMP + Soliton Walk path (multi-hop MEV resilience): - When a normal HTTP fetch fails — e.g. because the MEV transaction has - traversed 100+ network hops and the primary RPC host is no longer - reachable — ghost_rpc_fetch() takes over: - - 1. Resolves candidate endpoints and probes L3 reachability via Ghost-ICMP. - 2. A classical soliton quantum walk selects the next endpoint by - chasing the "void" — endpoints with the lowest recent-hit density - (freshest paths) weighted by their historical Waveprobe coherence. - The walk propagates through low-resistance topology, not randomly. - 3. Waveprobe coherence gates the survival-state cache: φ ≥ 0.8 (θ) - returns the cached state; φ < 0.8 triggers a live re-fetch. - 4. Final fail-closed: returns None. - -The ghost_icmp binary must be built at GHOST_ICMP_BIN or the default -tools/ relative path. -""" - -import json -import logging -import math -import os -import socket -# import subprocess (REMOVED BY WARDEN) -import time -from pathlib import Path -from typing import Optional, Dict, List -import httpx - -logger = logging.getLogger("rpc_bytecode_fetcher") -logger.setLevel(logging.INFO) - -# ── Waveprobe constants (WAVEPROBE_EQUATION.md) ─────────────────────────────── -_COHERENCE_THRESHOLD = 0.8 # θ — phase-lock gate -_PHI = 1.618_033_988_749_895 # golden ratio - -# Ghost-ICMP binary location -_GHOST_ICMP_BIN = os.environ.get( - "GHOST_ICMP_BIN", - str( - Path(__file__).resolve().parent.parent - / "tools" / "ghost_icmp" / "target" / "debug" / "ghost_icmp" - ), -) - - -# ── Waveprobe coherence ─────────────────────────────────────────────────────── - -def _entropy(buf: bytes) -> float: - if not buf: - return 0.0 - counts: Dict[int, int] = {} - for b in buf: - counts[b] = counts.get(b, 0) + 1 - n = len(buf) - return sum(-c / n * math.log2(c / n) for c in counts.values()) / 8.0 - - -def _repetition(buf: bytes) -> float: - if len(buf) < 2: - return 0.0 - runs = sum(1 for i in range(len(buf) - 1) if buf[i] == buf[i + 1]) - return runs / (len(buf) - 1) - - -def _waveprobe_coherence(local: bytes, remote: bytes) -> float: - """φ-coherence between two byte sequences (w_e=0.4, w_r=0.3, w_d=0.3).""" - if not local or not remote: - return 0.0 - fe = max(0.0, 1.0 - abs(_entropy(local) - _entropy(remote))) - fr = max(0.0, 1.0 - abs(_repetition(local) - _repetition(remote))) - return 0.4 * fe + 0.6 * fr # w_d ≈ w_r (no dict model in Python path) - - -def _phi_ideal_reference(length: int, mode: int = 0) -> bytes: - """φ-ideal reference vector — mirrors ghost_icmp Rust encoder basis.""" - phi_scale = _PHI ** (mode % 7) - return bytes(int(phi_scale * i) & 0xFF for i in range(max(length, 1))) - - -# ── Soliton quantum walk path selector ─────────────────────────────────────── - -class SolitonWalk: - """Classical soliton path selector using basin-attractor dynamics. - - Each RPC endpoint is an attractor basin. The soliton falls into the - deepest available basin — the endpoint with the highest accumulated - Waveprobe coherence amplitude. Staleness introduces uncertainty that - broadens the basin well (makes the soliton less certain about depth) - but does NOT flip the direction of attraction — untested endpoints - retain their prior potential (unknown ≠ shallow). - - Basin potential: U(url) = -amplitude² / (1 + β·√Δt) - - More negative = deeper basin = stronger attractor. - - On success: basin deepens (amplitude increases via EMA). - - On failure: basin collapses (amplitude halved, soliton rolls away). - - Staleness broadens uncertainty but unknown endpoints keep prior U. - - β = 0.02 (uncertainty broadening rate) - α = 0.35 (EMA learning rate) - """ - - _BETA = 0.02 - _ALPHA = 0.35 - - def __init__(self, endpoints: List[str]): - self._amplitude: Dict[str, float] = {url: 0.5 for url in endpoints} - self._last_hit: Dict[str, float] = {url: 0.0 for url in endpoints} - # Track which endpoints have been measured at least once - self._measured: Dict[str, bool] = {url: False for url in endpoints} - - def _basin_potential(self, url: str) -> float: - """U(url) — more negative = deeper basin = stronger attractor. - - For unmeasured endpoints, return the prior potential (amplitude²) - without staleness penalty — the soliton is drawn to unexplored - basins because they could be deep (unknown ≠ shallow). - """ - amp = self._amplitude[url] - # Unmeasured endpoints: pure prior, no staleness penalty - if not self._measured.get(url, False): - return -(amp ** 2) - - elapsed = time.monotonic() - self._last_hit[url] - uncertainty = 1.0 + self._BETA * math.sqrt(elapsed) - return -(amp ** 2) / uncertainty - - def select(self) -> Optional[str]: - """Fall into the deepest available basin (most negative potential).""" - if not self._amplitude: - return None - - potentials = {url: self._basin_potential(url) for url in self._amplitude} - # Softmax over -U so deepest basin has highest weight - neg_u = {url: -u for url, u in potentials.items()} - max_v = max(neg_u.values()) - exp_v = {url: math.exp(v - max_v) for url, v in neg_u.items()} - total = sum(exp_v.values()) - probs = {url: v / total for url, v in exp_v.items()} - - import random - urls = list(probs.keys()) - weights = [probs[u] for u in urls] - return random.choices(urls, weights=weights, k=1)[0] - - def record_success(self, url: str, phi_corr: float) -> None: - """Deepen the basin after a confirmed coherent result.""" - self._amplitude[url] = ( - (1 - self._ALPHA) * self._amplitude[url] + self._ALPHA * phi_corr - ) - self._last_hit[url] = time.monotonic() - self._measured[url] = True - - def record_failure(self, url: str) -> None: - """Collapse the basin — soliton rolls toward the next stable region.""" - self._amplitude[url] *= 0.5 - self._measured[url] = True - - -# ── Ghost-ICMP probe ────────────────────────────────────────────────────────── - -def _icmp_probe(host: str) -> bool: - """Ghost-ICMP probe. Degrades to TCP-443 if binary unavailable.""" - ghost_bin = Path(_GHOST_ICMP_BIN) - if ghost_bin.exists() and os.access(ghost_bin, os.X_OK): - try: - proc_res = subprocess.run( - [str(ghost_bin), "probe", "--target", host], - capture_output=True, timeout=5, check=False, - ) - return proc_res.returncode == 0 - except (subprocess.TimeoutExpired, OSError, subprocess.SubprocessError): - pass - # Graceful degradation - try: - with socket.create_connection((host, 443), timeout=3): - return True - except OSError: - return False - - -def _host_from_url(url: str) -> str: - from urllib.parse import urlparse - try: - return urlparse(url).hostname or "" - except (ValueError, AttributeError): - return "" - - -# ── Main fetcher ────────────────────────────────────────────────────────────── - -class RPCBytecodeFetcher: - """Fetch contract bytecode across EVM chains. - - Includes a Ghost-ICMP + Soliton Walk + Waveprobe resilience path for - multi-hop MEV topologies where the primary TCP path is unreachable. - """ - - def __init__(self, config_path: str = "4-Infrastructure/config/rpc_endpoints.json"): - self.config_path = config_path - self.endpoints = self._load_config() - # survival_cache: address → last known-good bytecode bytes - self._survival: Dict[str, bytes] = {} - # per-chain soliton walk (built lazily) - self._walks: Dict[str, SolitonWalk] = {} - - def _load_config(self) -> Dict: - try: - with open(self.config_path, "r", encoding="utf-8") as f: - return json.load(f) - except (FileNotFoundError, json.JSONDecodeError) as e: - logger.error("Failed to load RPC config: %s", e) - return {} - - def _get_walk(self, chain: str) -> SolitonWalk: - if chain not in self._walks: - ep = self.endpoints.get(chain, {}) - urls = [u for u in [ep.get("url"), ep.get("fallback_url")] if u] - self._walks[chain] = SolitonWalk(urls or [""]) - return self._walks[chain] - - def fetch_bytecode(self, address: str, chain: str = "ethereum") -> Optional[str]: - """Primary path: HTTP JSON-RPC. Falls back to ghost_rpc_fetch on failure.""" - if not address: - return None - - chain = chain.lower() - endpoint = self.endpoints.get(chain) - if not endpoint: - logger.warning("No RPC endpoint configured for chain: %s", chain) - return None - - rpc_url = endpoint.get("url") - if not rpc_url: - return None - - payload = { - "jsonrpc": "2.0", - "method": "eth_getCode", - "params": [address, "latest"], - "id": 1, - } - - try: - with httpx.Client(timeout=10.0) as client: - response = client.post(rpc_url, json=payload) - response.raise_for_status() - data = response.json() - - if "error" in data: - logger.error("RPC error for %s on %s: %s", address, chain, data["error"]) - return None - - bytecode = data.get("result") - if bytecode == "0x" or bytecode is None: - logger.warning("No bytecode (EOA) at %s on %s", address, chain) - return "0x" - - # Cache survival state + update soliton walk amplitude - raw = bytes.fromhex(bytecode.lstrip("0x") or "00") - phi_ref = _phi_ideal_reference(len(raw)) - phi_corr = _waveprobe_coherence(raw, phi_ref) - self._survival[address] = raw - self._get_walk(chain).record_success(rpc_url, phi_corr) - return bytecode - - except (httpx.RequestError, json.JSONDecodeError, KeyError) as e: - logger.warning( - "HTTP fetch failed for %s on %s: %s — Ghost-ICMP fallback", - address, chain, e, - ) - self._get_walk(chain).record_failure(rpc_url) - return self.ghost_rpc_fetch(address, chain) - - def ghost_rpc_fetch(self, address: str, chain: str) -> Optional[str]: - """Ghost-ICMP + Soliton Walk + Waveprobe resilience path. - - Steps: - 1. Soliton walk selects the next candidate endpoint (void-chase). - 2. Ghost-ICMP probes L3 reachability of that host. - 3. Waveprobe coherence gates the survival-state cache: - φ ≥ θ → return cached state (phase-locked). - φ < θ → drift detected, attempt live fetch on selected URL. - 4. Fail-closed: return None. - """ - walk = self._get_walk(chain) - # ── Step 1: Soliton walk selects endpoint ──────────────────────── - selected_url = walk.select() - if not selected_url: - logger.error("[ghost_rpc] No candidate endpoints for chain %s", chain) - return None - - host = _host_from_url(selected_url) - logger.info("[ghost_rpc] Soliton walk selected: %s (host=%s)", selected_url, host) - - # ── Step 2: Ghost-ICMP probe ───────────────────────────────────── - reachable = _icmp_probe(host) if host else False - logger.info( - "[ghost_rpc] ICMP probe → %s: %s", - host or "?", "reachable" if reachable else "unreachable", - ) - - # ── Step 3: Waveprobe coherence gate ──────────────────────────── - survival = self._survival.get(address) - if survival: - phi_ref = _phi_ideal_reference(len(survival)) - phi_corr = _waveprobe_coherence(survival, phi_ref) - logger.info( - "[ghost_rpc] Waveprobe φ=%.3f (θ=%.1f) for %s", - phi_corr, _COHERENCE_THRESHOLD, address, - ) - - if phi_corr >= _COHERENCE_THRESHOLD: - # Phase-locked: survival state valid, return without network hit - logger.info("[ghost_rpc] φ ≥ θ — survival state returned for %s", address) - walk.record_success(selected_url, phi_corr) - return "0x" + survival.hex() - - logger.info("[ghost_rpc] φ < θ — void detected, chasing fresh path") - - # ── Step 4: Live fetch on walk-selected URL ────────────────────── - if reachable and selected_url: - payload = { - "jsonrpc": "2.0", - "method": "eth_getCode", - "params": [address, "latest"], - "id": 1, - } - try: - with httpx.Client(timeout=10.0) as client: - response = client.post(selected_url, json=payload) - response.raise_for_status() - data = response.json() - bytecode = data.get("result") - if bytecode and bytecode != "0x": - raw = bytes.fromhex(bytecode.lstrip("0x") or "00") - phi_ref = _phi_ideal_reference(len(raw)) - phi_corr = _waveprobe_coherence(raw, phi_ref) - self._survival[address] = raw - walk.record_success(selected_url, phi_corr) - logger.info("[ghost_rpc] Fresh fetch succeeded via %s", selected_url) - return bytecode - except (httpx.RequestError, json.JSONDecodeError, KeyError) as e: - logger.error("[ghost_rpc] Live fetch failed via %s: %s", selected_url, e) - walk.record_failure(selected_url) - - # ── Fail-closed ────────────────────────────────────────────────── - logger.error("[ghost_rpc] All paths exhausted for %s on %s", address, chain) - return None - - -if __name__ == "__main__": - import argparse - parser = argparse.ArgumentParser(description="Fetch bytecode from RPC") - parser.add_argument("address", help="Contract address (0x...)") - parser.add_argument("--chain", default="ethereum", help="Chain handle") - parser.add_argument("--ghost", action="store_true", help="Force Ghost-ICMP path") - args = parser.parse_args() - - fetcher = RPCBytecodeFetcher() - if args.ghost: - result = fetcher.ghost_rpc_fetch(args.address, args.chain) - else: - result = fetcher.fetch_bytecode(args.address, args.chain) - print(result if result else "Failed to fetch bytecode.") diff --git a/5-Applications/tools-scripts/pipeline/trace_pipeline.py b/5-Applications/tools-scripts/pipeline/trace_pipeline.py deleted file mode 100644 index 22bd5286..00000000 --- a/5-Applications/tools-scripts/pipeline/trace_pipeline.py +++ /dev/null @@ -1,1048 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -import argparse -import csv -import json -import sqlite3 -# import subprocess (REMOVED BY WARDEN) -from collections import Counter, defaultdict, deque -from datetime import datetime, timezone -from itertools import combinations -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -from jsonschema import validate - - -PROJECT_ROOT = Path(__file__).resolve().parent.parent -DEFAULT_DB = PROJECT_ROOT / "trace_campaign.sqlite3" -DEFAULT_SCHEMA = PROJECT_ROOT / "schemas" / "trace_observation.schema.json" - -LOWERCASE_TYPES = {"domain", "ip", "url", "email", "repo", "commit", "txid"} - - -def utc_now_iso() -> str: - return datetime.now(timezone.utc).replace(microsecond=0).isoformat() - - -def load_schema(schema_path: Path = DEFAULT_SCHEMA) -> Dict[str, object]: - return json.loads(schema_path.read_text(encoding="utf-8")) - - -def validate_observation(record: Dict[str, object], schema_path: Path = DEFAULT_SCHEMA) -> None: - validate(instance=record, schema=load_schema(schema_path)) - - -def normalize_observable(observable_type: str, value: str) -> str: - normalized = value.strip() - if observable_type in LOWERCASE_TYPES: - normalized = normalized.lower() - return normalized - - -def create_connection(db_path: Path) -> sqlite3.Connection: - conn = sqlite3.connect(db_path) - conn.row_factory = sqlite3.Row - return conn - - -def init_db(conn: sqlite3.Connection) -> None: - conn.executescript( - """ - CREATE TABLE IF NOT EXISTS observables ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - observable_type TEXT NOT NULL, - value TEXT NOT NULL, - normalized_value TEXT NOT NULL, - first_seen TEXT, - last_seen TEXT, - source TEXT, - UNIQUE(observable_type, normalized_value) - ); - - CREATE TABLE IF NOT EXISTS events ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - source TEXT NOT NULL, - observed_at TEXT NOT NULL, - notes TEXT, - raw_json TEXT NOT NULL - ); - - CREATE TABLE IF NOT EXISTS event_observables ( - event_id INTEGER NOT NULL, - observable_id INTEGER NOT NULL, - role TEXT, - PRIMARY KEY (event_id, observable_id), - FOREIGN KEY (event_id) REFERENCES events(id), - FOREIGN KEY (observable_id) REFERENCES observables(id) - ); - - CREATE TABLE IF NOT EXISTS relationships ( - id INTEGER PRIMARY KEY AUTOINCREMENT, - event_id INTEGER NOT NULL, - source_observable_id INTEGER NOT NULL, - target_observable_id INTEGER NOT NULL, - relation_type TEXT NOT NULL, - confidence REAL NOT NULL, - FOREIGN KEY (event_id) REFERENCES events(id), - FOREIGN KEY (source_observable_id) REFERENCES observables(id), - FOREIGN KEY (target_observable_id) REFERENCES observables(id) - ); - """ - ) - conn.commit() - - -def upsert_observable( - conn: sqlite3.Connection, - observable_type: str, - value: str, - source: str, - observed_at: str, -) -> int: - normalized = normalize_observable(observable_type, value) - conn.execute( - """ - INSERT INTO observables (observable_type, value, normalized_value, first_seen, last_seen, source) - VALUES (?, ?, ?, ?, ?, ?) - ON CONFLICT(observable_type, normalized_value) - DO UPDATE SET - last_seen = excluded.last_seen, - source = COALESCE(observables.source, excluded.source) - """, - (observable_type, value.strip(), normalized, observed_at, observed_at, source), - ) - row = conn.execute( - "SELECT id FROM observables WHERE observable_type = ? AND normalized_value = ?", - (observable_type, normalized), - ).fetchone() - return int(row["id"]) - - -def create_event(conn: sqlite3.Connection, source: str, observed_at: str, notes: str, raw_json: str) -> int: - cursor = conn.execute( - "INSERT INTO events (source, observed_at, notes, raw_json) VALUES (?, ?, ?, ?)", - (source, observed_at, notes, raw_json), - ) - return int(cursor.lastrowid) - - -def add_event_observable(conn: sqlite3.Connection, event_id: int, observable_id: int, role: str) -> None: - conn.execute( - "INSERT OR IGNORE INTO event_observables (event_id, observable_id, role) VALUES (?, ?, ?)", - (event_id, observable_id, role), - ) - - -def add_relationship( - conn: sqlite3.Connection, - event_id: int, - source_observable_id: int, - target_observable_id: int, - relation_type: str, - confidence: float, -) -> None: - conn.execute( - """ - INSERT INTO relationships (event_id, source_observable_id, target_observable_id, relation_type, confidence) - VALUES (?, ?, ?, ?, ?) - """, - (event_id, source_observable_id, target_observable_id, relation_type, confidence), - ) - - -def import_observation(conn: sqlite3.Connection, record: Dict[str, object]) -> int: - validate_observation(record) - - source = str(record["source"]) - observed_at = str(record.get("timestamp") or utc_now_iso()) - notes = str(record.get("notes") or "") - event_id = create_event(conn, source, observed_at, notes, json.dumps(record, sort_keys=True)) - - observable_ids: Dict[Tuple[str, str], int] = {} - for observable in record.get("observables", []): - observable_type = str(observable["type"]) - value = str(observable["value"]) - role = str(observable.get("role") or "observed") - observable_id = upsert_observable(conn, observable_type, value, source, observed_at) - observable_ids[(observable_type, normalize_observable(observable_type, value))] = observable_id - add_event_observable(conn, event_id, observable_id, role) - - for relation in record.get("relations", []): - src = relation["source"] - dst = relation["target"] - src_type = str(src["type"]) - src_value = str(src["value"]) - dst_type = str(dst["type"]) - dst_value = str(dst["value"]) - src_id = observable_ids.get((src_type, normalize_observable(src_type, src_value))) - if src_id is None: - src_id = upsert_observable(conn, src_type, src_value, source, observed_at) - add_event_observable(conn, event_id, src_id, "relation_source") - dst_id = observable_ids.get((dst_type, normalize_observable(dst_type, dst_value))) - if dst_id is None: - dst_id = upsert_observable(conn, dst_type, dst_value, source, observed_at) - add_event_observable(conn, event_id, dst_id, "relation_target") - add_relationship( - conn, - event_id, - src_id, - dst_id, - str(relation["relation"]), - float(relation.get("confidence", 0.5)), - ) - - if len(observable_ids) > 1: - for left_id, right_id in combinations(observable_ids.values(), 2): - add_relationship(conn, event_id, left_id, right_id, "co_observed", 0.25) - - conn.commit() - return event_id - - -def import_jsonl(conn: sqlite3.Connection, file_path: Path) -> int: - count = 0 - with file_path.open("r", encoding="utf-8") as handle: - for line in handle: - stripped = line.strip() - if not stripped: - continue - import_observation(conn, json.loads(stripped)) - count += 1 - return count - - -def repo_id_from_url(repo_url: str) -> str: - normalized = repo_url.strip().rstrip("/") - if normalized.endswith(".git"): - normalized = normalized[:-4] - if normalized.startswith("git@") and ":" in normalized: - return normalized.split(":", 1)[1] - if normalized.startswith("http://") or normalized.startswith("https://"): - parts = normalized.split("/") - if len(parts) >= 2: - return f"{parts[-2]}/{parts[-1]}" - return normalized - - -def make_repo_list_observation(source: str, repo_url: str, local_path: str = "", notes: str = "") -> Dict[str, object]: - repo_id = repo_id_from_url(repo_url) - observables: List[Dict[str, str]] = [ - {"type": "repo", "value": repo_id, "role": "repository"}, - {"type": "url", "value": repo_url, "role": "repo_url"}, - ] - relations: List[Dict[str, object]] = [ - { - "source": {"type": "url", "value": repo_url}, - "target": {"type": "repo", "value": repo_id}, - "relation": "identifies_repo", - "confidence": 0.95, - } - ] - if local_path: - observables.append({"type": "file_path", "value": local_path, "role": "local_clone_path"}) - relations.append( - { - "source": {"type": "repo", "value": repo_id}, - "target": {"type": "file_path", "value": local_path}, - "relation": "local_clone_path", - "confidence": 0.9, - } - ) - return { - "source": source, - "timestamp": utc_now_iso(), - "notes": notes, - "observables": observables, - "relations": relations, - } - - -def import_repo_list( - conn: sqlite3.Connection, - list_path: Path, - source: str, - collect_git: bool = False, - mismatch_threshold_seconds: int = 86400, -) -> Dict[str, int]: - rows: List[Dict[str, str]] = [] - if list_path.suffix.lower() == ".jsonl": - with list_path.open("r", encoding="utf-8") as handle: - for line in handle: - stripped = line.strip() - if not stripped: - continue - rows.append(json.loads(stripped)) - else: - with list_path.open("r", encoding="utf-8", newline="") as handle: - reader = csv.DictReader(handle) - rows.extend(dict(row) for row in reader) - - imported = 0 - collected_git = 0 - for row in rows: - repo_url = (row.get("repo_url") or row.get("url") or "").strip() - if not repo_url: - continue - local_path = (row.get("local_path") or row.get("path") or "").strip() - notes = (row.get("notes") or "").strip() - record_source = (row.get("source") or source).strip() or source - import_observation(conn, make_repo_list_observation(record_source, repo_url, local_path, notes=notes)) - imported += 1 - - if collect_git and local_path: - git_records = collect_git_anomaly_observations( - Path(local_path), - repo_id=repo_id_from_url(repo_url), - source=f"{record_source}-git-anomaly", - mismatch_threshold_seconds=mismatch_threshold_seconds, - ) - for git_record in git_records: - import_observation(conn, git_record) - collected_git += 1 - return {"repo_records": imported, "git_anomaly_records": collected_git} - - -def make_repo_ioc_observation( - *, - source: str, - repo: str, - timestamp: Optional[str] = None, - commit: Optional[str] = None, - file_paths: Optional[List[str]] = None, - wallets: Optional[List[str]] = None, - domains: Optional[List[str]] = None, - ips: Optional[List[str]] = None, - urls: Optional[List[str]] = None, - txids: Optional[List[str]] = None, - author_email: Optional[str] = None, - committer_email: Optional[str] = None, - signature_state: Optional[str] = None, - notes: str = "", - confidence: float = 0.75, -) -> Dict[str, object]: - file_paths = file_paths or [] - wallets = wallets or [] - domains = domains or [] - ips = ips or [] - urls = urls or [] - txids = txids or [] - observed_at = timestamp or utc_now_iso() - - observables: List[Dict[str, str]] = [{"type": "repo", "value": repo, "role": "repository"}] - relations: List[Dict[str, object]] = [] - - relation_anchor = {"type": "repo", "value": repo} - relation_type = "reported_with_repo" - if commit: - observables.append({"type": "commit", "value": commit, "role": "commit"}) - relations.append( - { - "source": {"type": "commit", "value": commit}, - "target": {"type": "repo", "value": repo}, - "relation": "appears_in_repo", - "confidence": confidence, - } - ) - relation_anchor = {"type": "commit", "value": commit} - relation_type = "referenced_in_commit" - - if commit and author_email: - observables.append({"type": "email", "value": author_email, "role": "author_email"}) - relations.append( - { - "source": {"type": "commit", "value": commit}, - "target": {"type": "email", "value": author_email}, - "relation": "authored_by", - "confidence": confidence, - } - ) - - if commit and committer_email: - observables.append({"type": "email", "value": committer_email, "role": "committer_email"}) - relations.append( - { - "source": {"type": "commit", "value": commit}, - "target": {"type": "email", "value": committer_email}, - "relation": "committed_by", - "confidence": confidence, - } - ) - - if commit and signature_state: - signature_observable = f"signature_state:{signature_state}" - observables.append({"type": "note", "value": signature_observable, "role": "signature_state"}) - relations.append( - { - "source": {"type": "commit", "value": commit}, - "target": {"type": "note", "value": signature_observable}, - "relation": "signature_state", - "confidence": confidence, - } - ) - - if author_email and committer_email and author_email.lower() != committer_email.lower(): - relations.append( - { - "source": {"type": "email", "value": author_email}, - "target": {"type": "email", "value": committer_email}, - "relation": "author_committer_email_mismatch", - "confidence": confidence, - } - ) - - for file_path in file_paths: - observables.append({"type": "file_path", "value": file_path, "role": "file_path"}) - relations.append( - { - "source": relation_anchor, - "target": {"type": "file_path", "value": file_path}, - "relation": "touches_file", - "confidence": confidence, - } - ) - - for observable_type, values in ( - ("wallet", wallets), - ("domain", domains), - ("ip", ips), - ("url", urls), - ("txid", txids), - ): - for value in values: - observables.append({"type": observable_type, "value": value, "role": "ioc"}) - relations.append( - { - "source": relation_anchor, - "target": {"type": observable_type, "value": value}, - "relation": relation_type, - "confidence": confidence, - } - ) - - return { - "source": source, - "timestamp": observed_at, - "notes": notes, - "observables": observables, - "relations": relations, - } - - -def make_wallet_txid_template( - wallet: Optional[str] = None, - txid: Optional[str] = None, - source: str = "manual-template", - notes: str = "Replace placeholders and relation type with independently verified values.", -) -> Dict[str, object]: - wallet_value = wallet or "WALLET_ADDRESS_HERE" - txid_value = txid or "TXID_HERE" - return { - "source": source, - "timestamp": utc_now_iso(), - "notes": notes, - "observables": [ - {"type": "wallet", "value": wallet_value, "role": "wallet"}, - {"type": "txid", "value": txid_value, "role": "transaction"}, - ], - "relations": [ - { - "source": {"type": "txid", "value": txid_value}, - "target": {"type": "wallet", "value": wallet_value}, - "relation": "funds_wallet", - "confidence": 0.5, - } - ], - } - - -def fetch_observable(conn: sqlite3.Connection, observable_type: str, value: str) -> Optional[sqlite3.Row]: - normalized = normalize_observable(observable_type, value) - return conn.execute( - "SELECT * FROM observables WHERE observable_type = ? AND normalized_value = ?", - (observable_type, normalized), - ).fetchone() - - -def build_clusters(conn: sqlite3.Connection, min_confidence: float = 0.0) -> List[Dict[str, object]]: - adjacency: Dict[int, set[int]] = defaultdict(set) - for row in conn.execute( - """ - SELECT source_observable_id, target_observable_id - FROM relationships - WHERE confidence >= ? - """, - (min_confidence,), - ): - adjacency[int(row["source_observable_id"])].add(int(row["target_observable_id"])) - adjacency[int(row["target_observable_id"])].add(int(row["source_observable_id"])) - - visited: set[int] = set() - clusters: List[Dict[str, object]] = [] - for node_id in adjacency: - if node_id in visited: - continue - queue: deque[int] = deque([node_id]) - component: List[int] = [] - visited.add(node_id) - while queue: - current = queue.popleft() - component.append(current) - for neighbor in adjacency[current]: - if neighbor not in visited: - visited.add(neighbor) - queue.append(neighbor) - members = conn.execute( - "SELECT id, observable_type, value FROM observables WHERE id IN ({}) ORDER BY observable_type, value".format( - ",".join("?" for _ in component) - ), - component, - ).fetchall() - type_counts = Counter(str(member["observable_type"]) for member in members) - clusters.append( - { - "size": len(component), - "type_counts": dict(type_counts), - "members": [ - { - "id": int(member["id"]), - "type": str(member["observable_type"]), - "value": str(member["value"]), - } - for member in members - ], - } - ) - clusters.sort(key=lambda cluster: cluster["size"], reverse=True) - return clusters - - -def cluster_for_seed( - conn: sqlite3.Connection, - observable_type: str, - value: str, - min_confidence: float = 0.0, -) -> Optional[Dict[str, object]]: - seed = fetch_observable(conn, observable_type, value) - if seed is None: - return None - seed_id = int(seed["id"]) - for cluster in build_clusters(conn, min_confidence=min_confidence): - if any(member["id"] == seed_id for member in cluster["members"]): - return cluster - return { - "size": 1, - "type_counts": {str(seed["observable_type"]): 1}, - "members": [ - { - "id": seed_id, - "type": str(seed["observable_type"]), - "value": str(seed["value"]), - } - ], - } - - -def stats(conn: sqlite3.Connection) -> Dict[str, object]: - observable_count = conn.execute("SELECT COUNT(*) AS count FROM observables").fetchone()["count"] - event_count = conn.execute("SELECT COUNT(*) AS count FROM events").fetchone()["count"] - relationship_count = conn.execute("SELECT COUNT(*) AS count FROM relationships").fetchone()["count"] - by_type = conn.execute( - "SELECT observable_type, COUNT(*) AS count FROM observables GROUP BY observable_type ORDER BY count DESC" - ).fetchall() - return { - "observables": int(observable_count), - "events": int(event_count), - "relationships": int(relationship_count), - "by_type": {str(row["observable_type"]): int(row["count"]) for row in by_type}, - } - - -def fetch_event_details(conn: sqlite3.Connection, event_id: int) -> Dict[str, object]: - event_row = conn.execute("SELECT * FROM events WHERE id = ?", (event_id,)).fetchone() - observables = conn.execute( - """ - SELECT o.observable_type, o.value, eo.role - FROM event_observables eo - JOIN observables o ON o.id = eo.observable_id - WHERE eo.event_id = ? - ORDER BY o.observable_type, o.value - """, - (event_id,), - ).fetchall() - relations = conn.execute( - """ - SELECT os.observable_type AS source_type, - os.value AS source_value, - ot.observable_type AS target_type, - ot.value AS target_value, - r.relation_type, - r.confidence - FROM relationships r - JOIN observables os ON os.id = r.source_observable_id - JOIN observables ot ON ot.id = r.target_observable_id - WHERE r.event_id = ? - ORDER BY r.id - """, - (event_id,), - ).fetchall() - return { - "event_id": int(event_row["id"]), - "source": str(event_row["source"]), - "observed_at": str(event_row["observed_at"]), - "notes": str(event_row["notes"] or ""), - "observables": [ - {"type": str(row["observable_type"]), "value": str(row["value"]), "role": str(row["role"] or "")} - for row in observables - ], - "relations": [ - { - "source": {"type": str(row["source_type"]), "value": str(row["source_value"])}, - "target": {"type": str(row["target_type"]), "value": str(row["target_value"])}, - "relation": str(row["relation_type"]), - "confidence": float(row["confidence"]), - } - for row in relations - ], - } - - -def export_timeline( - conn: sqlite3.Connection, - seed_type: Optional[str] = None, - seed_value: Optional[str] = None, - min_confidence: float = 0.0, -) -> List[Dict[str, object]]: - if seed_type and seed_value: - cluster = cluster_for_seed(conn, seed_type, seed_value, min_confidence=min_confidence) - if cluster is None: - return [] - member_ids = [int(member["id"]) for member in cluster["members"]] - rows = conn.execute( - "SELECT DISTINCT event_id FROM event_observables WHERE observable_id IN ({}) ORDER BY event_id".format( - ",".join("?" for _ in member_ids) - ), - member_ids, - ).fetchall() - event_ids = [int(row["event_id"]) for row in rows] - else: - rows = conn.execute("SELECT id FROM events ORDER BY observed_at, id").fetchall() - event_ids = [int(row["id"]) for row in rows] - timeline = [fetch_event_details(conn, event_id) for event_id in event_ids] - timeline.sort(key=lambda item: (item["observed_at"], item["event_id"])) - return timeline - - -def write_json_output(data: object, output_path: Path) -> None: - output_path.write_text(json.dumps(data, indent=2) + "\n", encoding="utf-8") - - -def export_clusters_csv(clusters: List[Dict[str, object]], output_path: Path) -> None: - with output_path.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter( - handle, - fieldnames=["cluster_index", "cluster_size", "observable_id", "observable_type", "observable_value"], - ) - writer.writeheader() - for index, cluster in enumerate(clusters, start=1): - for member in cluster["members"]: - writer.writerow( - { - "cluster_index": index, - "cluster_size": cluster["size"], - "observable_id": member["id"], - "observable_type": member["type"], - "observable_value": member["value"], - } - ) - - -def export_timeline_csv(timeline: List[Dict[str, object]], output_path: Path) -> None: - with output_path.open("w", encoding="utf-8", newline="") as handle: - writer = csv.DictWriter( - handle, - fieldnames=["event_id", "observed_at", "source", "notes", "observables_json", "relations_json"], - ) - writer.writeheader() - for event in timeline: - writer.writerow( - { - "event_id": event["event_id"], - "observed_at": event["observed_at"], - "source": event["source"], - "notes": event["notes"], - "observables_json": json.dumps(event["observables"], ensure_ascii=False), - "relations_json": json.dumps(event["relations"], ensure_ascii=False), - } - ) - - -def run_git(repo_path: Path, args: List[str], check: bool = True) -> subprocess.CompletedProcess[str]: - return subprocess.run( - ["git", *args], - cwd=repo_path, - check=check, - capture_output=True, - text=True, - ) - - -def infer_repo_id(repo_path: Path) -> str: - resolved_path = repo_path.resolve() - try: - remote = run_git(resolved_path, ["config", "--get", "remote.origin.url"], check=False).stdout.strip() - except OSError: - remote = "" - if remote.endswith(".git"): - remote = remote[:-4] - if remote.startswith("git@") and ":" in remote: - return remote.split(":", 1)[1] - if remote.startswith("https://") and "/" in remote: - parts = remote.rstrip("/").split("/") - if len(parts) >= 2: - return f"{parts[-2]}/{parts[-1]}" - return resolved_path.name - - -def collect_git_commits(repo_path: Path) -> List[Dict[str, object]]: - log_result = run_git( - repo_path, - ["log", "--all", "--format=%H%x1f%aI%x1f%cI%x1f%G?%x1f%ae%x1f%ce%x1f%s"], - check=False, - ) - if log_result.returncode != 0 or not log_result.stdout.strip(): - return [] - commits: List[Dict[str, object]] = [] - for line in log_result.stdout.splitlines(): - sha, author_iso, committer_iso, verify_state, author_email, committer_email, subject = line.split("\x1f", 6) - commits.append( - { - "commit": sha, - "author_date": author_iso, - "committer_date": committer_iso, - "verification": verify_state, - "author_email": author_email, - "committer_email": committer_email, - "subject": subject, - } - ) - return commits - - -def collect_unreachable_commits(repo_path: Path) -> List[str]: - result = run_git(repo_path, ["fsck", "--no-reflogs", "--unreachable", "--no-progress"], check=False) - if result.returncode not in (0, 1): - return [] - commits: List[str] = [] - for line in result.stdout.splitlines(): - parts = line.strip().split() - if len(parts) == 3 and parts[0] == "unreachable" and parts[1] == "commit": - commits.append(parts[2]) - return commits - - -def detect_git_anomalies( - repo_id: str, - commits: List[Dict[str, object]], - unreachable_commits: List[str], - source: str, - mismatch_threshold_seconds: int = 86400, -) -> List[Dict[str, object]]: - observations: List[Dict[str, object]] = [] - for commit in commits: - author_date = datetime.fromisoformat(str(commit["author_date"]).replace("Z", "+00:00")) - committer_date = datetime.fromisoformat(str(commit["committer_date"]).replace("Z", "+00:00")) - delta_seconds = int(abs((committer_date - author_date).total_seconds())) - if delta_seconds >= mismatch_threshold_seconds: - notes = ( - f"Committer date differs from author date by {delta_seconds} seconds. " - f"verification={commit['verification']} subject={commit['subject']}" - ) - observations.append( - make_repo_ioc_observation( - source=source, - repo=repo_id, - timestamp=str(commit["committer_date"]), - commit=str(commit["commit"]), - author_email=str(commit["author_email"] or ""), - committer_email=str(commit["committer_email"] or ""), - signature_state=str(commit["verification"] or ""), - notes=notes, - confidence=0.9, - ) - ) - - for commit_sha in unreachable_commits: - observations.append( - make_repo_ioc_observation( - source=source, - repo=repo_id, - timestamp=utc_now_iso(), - commit=commit_sha, - signature_state="unknown", - notes="Unreachable commit detected by git fsck; possible local rewrite or history divergence indicator.", - confidence=0.8, - ) - ) - return observations - - -def collect_git_anomaly_observations( - repo_path: Path, - repo_id: Optional[str] = None, - source: str = "git-anomaly-collector", - mismatch_threshold_seconds: int = 86400, -) -> List[Dict[str, object]]: - resolved_repo_id = repo_id or infer_repo_id(repo_path) - commits = collect_git_commits(repo_path) - unreachable_commits = collect_unreachable_commits(repo_path) - return detect_git_anomalies( - resolved_repo_id, - commits, - unreachable_commits, - source, - mismatch_threshold_seconds=mismatch_threshold_seconds, - ) - - -def parse_args() -> argparse.Namespace: - parser = argparse.ArgumentParser(description="Passive evidence import and clustering pipeline for campaign tracing.") - parser.add_argument("--db", default=str(DEFAULT_DB), help="SQLite database path.") - subparsers = parser.add_subparsers(dest="command", required=True) - - subparsers.add_parser("init-db", help="Initialize the trace database.") - - import_parser = subparsers.add_parser("import-jsonl", help="Import schema-validated observation JSONL.") - import_parser.add_argument("path", help="Path to observation JSONL file.") - - repo_ioc_parser = subparsers.add_parser("import-repo-ioc", help="Create and import a repo/commit IoC observation.") - repo_ioc_parser.add_argument("--source", default="manual-repo-ioc", help="Observation source label.") - repo_ioc_parser.add_argument("--timestamp", help="Observation timestamp in ISO-8601 form.") - repo_ioc_parser.add_argument("--repo", required=True, help="Repository identifier, such as owner/repo.") - repo_ioc_parser.add_argument("--commit", help="Commit SHA if available.") - repo_ioc_parser.add_argument("--notes", default="", help="Freeform analyst notes.") - repo_ioc_parser.add_argument("--file-path", action="append", default=[], help="Relevant file path. Repeatable.") - repo_ioc_parser.add_argument("--wallet", action="append", default=[], help="Related wallet. Repeatable.") - repo_ioc_parser.add_argument("--domain", action="append", default=[], help="Related domain. Repeatable.") - repo_ioc_parser.add_argument("--ip", action="append", default=[], help="Related IP. Repeatable.") - repo_ioc_parser.add_argument("--url", action="append", default=[], help="Related URL. Repeatable.") - repo_ioc_parser.add_argument("--txid", action="append", default=[], help="Related transaction ID. Repeatable.") - - repo_list_parser = subparsers.add_parser("import-repo-list", help="Bulk import suspicious repo URLs and local paths.") - repo_list_parser.add_argument("path", help="Path to CSV or JSONL containing repo_url and local_path fields.") - repo_list_parser.add_argument("--source", default="bulk-repo-list", help="Default source label.") - repo_list_parser.add_argument("--collect-git", action="store_true", help="Also run passive git anomaly collection for rows with local_path.") - repo_list_parser.add_argument("--mismatch-threshold-seconds", type=int, default=86400, help="Minimum author/committer date gap to flag when collecting git anomalies.") - - wallet_template_parser = subparsers.add_parser("wallet-template", help="Generate a wallet/TXID observation template.") - wallet_template_parser.add_argument("--wallet", help="Wallet address to prefill.") - wallet_template_parser.add_argument("--txid", help="Transaction ID to prefill.") - wallet_template_parser.add_argument("--source", default="manual-template", help="Template source label.") - wallet_template_parser.add_argument("--notes", default="Replace placeholders and relation type with independently verified values.", help="Template notes.") - wallet_template_parser.add_argument("--output", help="Optional path to write template JSON.") - - cluster_parser = subparsers.add_parser("cluster", help="Build passive clusters from imported evidence.") - cluster_parser.add_argument("--min-confidence", type=float, default=0.0, help="Minimum relationship confidence.") - cluster_parser.add_argument("--seed-type", help="Filter to cluster containing this observable type.") - cluster_parser.add_argument("--seed-value", help="Filter to cluster containing this observable value.") - cluster_parser.add_argument("--json", action="store_true", help="Emit JSON instead of text.") - cluster_parser.add_argument("--output", help="Optional output file path (.json or .csv).") - cluster_parser.add_argument("--format", choices=["json", "csv"], help="Explicit output format when --output is used.") - - timeline_parser = subparsers.add_parser("timeline", help="Export timeline events from the trace database.") - timeline_parser.add_argument("--seed-type", help="Filter timeline to events linked to this observable type.") - timeline_parser.add_argument("--seed-value", help="Filter timeline to events linked to this observable value.") - timeline_parser.add_argument("--min-confidence", type=float, default=0.0, help="Minimum confidence used for seed clustering.") - timeline_parser.add_argument("--json", action="store_true", help="Emit JSON instead of text.") - timeline_parser.add_argument("--output", help="Optional output file path (.json or .csv).") - timeline_parser.add_argument("--format", choices=["json", "csv"], help="Explicit output format when --output is used.") - - git_parser = subparsers.add_parser("collect-git-anomalies", help="Passively collect git-history anomaly observations.") - git_parser.add_argument("path", help="Path to a local git repository.") - git_parser.add_argument("--repo-id", help="Optional explicit repo identifier.") - git_parser.add_argument("--source", default="git-anomaly-collector", help="Observation source label.") - git_parser.add_argument("--mismatch-threshold-seconds", type=int, default=86400, help="Minimum author/committer date gap to flag.") - git_parser.add_argument("--dry-run", action="store_true", help="Print observations instead of importing them.") - - subparsers.add_parser("stats", help="Show database summary statistics.") - return parser.parse_args() - - -def main() -> int: - args = parse_args() - db_path = Path(args.db) - conn = create_connection(db_path) - init_db(conn) - - if args.command == "init-db": - print(f"Initialized trace database at: {db_path}") - return 0 - - if args.command == "import-jsonl": - count = import_jsonl(conn, Path(args.path)) - print(json.dumps({"imported_records": count, "db": str(db_path)}, indent=2)) - return 0 - - if args.command == "import-repo-ioc": - record = make_repo_ioc_observation( - source=args.source, - repo=args.repo, - timestamp=args.timestamp, - commit=args.commit, - file_paths=list(args.file_path), - wallets=list(args.wallet), - domains=list(args.domain), - ips=list(args.ip), - urls=list(args.url), - txids=list(args.txid), - notes=args.notes, - ) - event_id = import_observation(conn, record) - print(json.dumps({"event_id": event_id, "db": str(db_path)}, indent=2)) - return 0 - - if args.command == "import-repo-list": - results = import_repo_list( - conn, - Path(args.path), - source=args.source, - collect_git=bool(args.collect_git), - mismatch_threshold_seconds=int(args.mismatch_threshold_seconds), - ) - payload = {"db": str(db_path), **results} - print(json.dumps(payload, indent=2)) - return 0 - - if args.command == "wallet-template": - template = make_wallet_txid_template(wallet=args.wallet, txid=args.txid, source=args.source, notes=args.notes) - rendered = json.dumps(template, indent=2) - if args.output: - Path(args.output).write_text(rendered + "\n", encoding="utf-8") - print(f"Wrote template to: {args.output}") - else: - print(rendered) - return 0 - - if args.command == "stats": - print(json.dumps(stats(conn), indent=2)) - return 0 - - if args.command == "cluster": - if args.seed_type and args.seed_value: - cluster = cluster_for_seed(conn, args.seed_type, args.seed_value, min_confidence=args.min_confidence) - output = cluster if cluster is not None else {"error": "seed_not_found"} - if args.output and cluster is not None: - output_path = Path(args.output) - output_format = args.format or output_path.suffix.lower().lstrip(".") or "json" - if output_format == "json": - write_json_output(output, output_path) - else: - export_clusters_csv([output], output_path) - print(f"Wrote seed cluster to: {output_path}") - return 0 - print(json.dumps(output, indent=2)) - return 0 if cluster is not None else 2 - - clusters = build_clusters(conn, min_confidence=args.min_confidence) - if args.output: - output_path = Path(args.output) - output_format = args.format or output_path.suffix.lower().lstrip(".") or "json" - if output_format == "json": - write_json_output(clusters, output_path) - else: - export_clusters_csv(clusters, output_path) - print(f"Wrote clusters to: {output_path}") - return 0 - if args.json: - print(json.dumps(clusters, indent=2)) - else: - for index, cluster in enumerate(clusters, start=1): - print(f"Cluster {index}: size={cluster['size']} types={cluster['type_counts']}") - for member in cluster["members"]: - print(f" - {member['type']}: {member['value']}") - return 0 - - if args.command == "timeline": - timeline = export_timeline( - conn, - seed_type=args.seed_type, - seed_value=args.seed_value, - min_confidence=args.min_confidence, - ) - if args.output: - output_path = Path(args.output) - output_format = args.format or output_path.suffix.lower().lstrip(".") or "json" - if output_format == "json": - write_json_output(timeline, output_path) - else: - export_timeline_csv(timeline, output_path) - print(f"Wrote timeline to: {output_path}") - return 0 - if args.json: - print(json.dumps(timeline, indent=2)) - else: - for item in timeline: - print(f"{item['observed_at']} [{item['source']}] event_id={item['event_id']}") - if item["notes"]: - print(f" notes: {item['notes']}") - for observable in item["observables"]: - role_suffix = f" ({observable['role']})" if observable["role"] else "" - print(f" observable: {observable['type']}={observable['value']}{role_suffix}") - for relation in item["relations"]: - print( - " relation: " - f"{relation['source']['type']}={relation['source']['value']} " - f"-{relation['relation']}-> " - f"{relation['target']['type']}={relation['target']['value']} " - f"(confidence={relation['confidence']})" - ) - return 0 - - if args.command == "collect-git-anomalies": - records = collect_git_anomaly_observations( - Path(args.path), - repo_id=args.repo_id, - source=args.source, - mismatch_threshold_seconds=args.mismatch_threshold_seconds, - ) - if args.dry_run: - print(json.dumps(records, indent=2)) - return 0 - imported = 0 - for record in records: - import_observation(conn, record) - imported += 1 - print( - json.dumps( - { - "generated_records": len(records), - "imported_records": imported, - "repo_path": str(Path(args.path)), - "repo_id": args.repo_id or infer_repo_id(Path(args.path)), - "db": str(db_path), - }, - indent=2, - ) - ) - return 0 - - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/ptos/ptos_blender_max_reality.py b/5-Applications/tools-scripts/ptos/ptos_blender_max_reality.py deleted file mode 100644 index bfc39962..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_blender_max_reality.py +++ /dev/null @@ -1,143 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import bpy -import math -import os -from pathlib import Path - -REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1]) - -def setup_maximum_reality(): - # Clear existing scene - bpy.ops.wm.read_factory_settings(use_empty=True) - - # ------------------------------------------------------------------ - # [ RENDER ENGINE: CYCLES (GPU Accelerated, Max Reality) ] - # ------------------------------------------------------------------ - scene = bpy.context.scene - scene.render.engine = 'CYCLES' - scene.cycles.device = 'GPU' - scene.cycles.samples = 1024 - scene.cycles.use_denoising = True - scene.render.resolution_x = 3840 - scene.render.resolution_y = 2160 - scene.render.resolution_percentage = 100 - - # ------------------------------------------------------------------ - # [ MATERIAL SHADERS (PBR) ] - # ------------------------------------------------------------------ - def create_pbr_material(name, color, metallic, roughness, transmission=0.0): - mat = bpy.data.materials.new(name=name) - mat.use_nodes = True - bsdf = mat.node_tree.nodes.get('Principled BSDF') - bsdf.inputs['Base Color'].default_value = color - bsdf.inputs['Metallic'].default_value = metallic - bsdf.inputs['Roughness'].default_value = roughness - if transmission > 0.0: - bsdf.inputs['Transmission Weight'].default_value = transmission - bsdf.inputs['IOR'].default_value = 1.45 - return mat - - mat_rust = create_pbr_material("Rusty_Iron", (0.2, 0.07, 0.03, 1), 0.8, 0.9) - mat_copper = create_pbr_material("Dirty_Copper", (0.7, 0.3, 0.1, 1), 1.0, 0.6) - mat_lego = create_pbr_material("Faded_ABS", (0.6, 0.1, 0.1, 1), 0.0, 0.4) - mat_glass = create_pbr_material("Jar_Glass", (0.9, 0.95, 1.0, 1), 0.1, 0.05, transmission=1.0) - mat_water = create_pbr_material("Dirty_Water", (0.6, 0.5, 0.4, 1), 0.0, 0.0, transmission=0.9) - mat_solar = create_pbr_material("Solar_PV", (0.02, 0.05, 0.15, 1), 0.9, 0.1) - mat_dust = create_pbr_material("Ash_Dust", (0.3, 0.25, 0.2, 1), 0.0, 1.0) - - # ------------------------------------------------------------------ - # [ GEOMETRY GENERATION (Engineering TRUE Scale) ] - # ------------------------------------------------------------------ - - # 1. Ground Plane (Dust Bowl) - bpy.ops.mesh.primitive_plane_add(size=50, location=(0, 0, 0)) - ground = bpy.context.active_object - ground.data.materials.append(mat_dust) - - # 2. Subterranean Condensation Matrix (Copper) - bpy.ops.mesh.primitive_cylinder_add(radius=0.1, depth=3.0, location=(0.5, 0, -1.0)) - pipe = bpy.context.active_object - pipe.rotation_euler[0] = math.radians(90) - pipe.data.materials.append(mat_copper) - - # 3. ABS Lego Structural Frame (Heat-isolated) - bpy.ops.mesh.primitive_cube_add(size=1.0, location=(0, 0, 0.5)) - chassis = bpy.context.active_object - chassis.scale = (0.8, 1.5, 0.2) - chassis.data.materials.append(mat_lego) - - # 4. Stirling Gamma Core (True 5-Liter Volumetric Scale) - bpy.ops.mesh.primitive_cylinder_add(radius=0.25, depth=0.8, location=(0, 0.5, 1.0)) - hot_cyl = bpy.context.active_object - hot_cyl.data.materials.append(mat_rust) - - bpy.ops.mesh.primitive_cylinder_add(radius=0.15, depth=0.6, location=(0, -0.5, 1.0)) - cold_cyl = bpy.context.active_object - cold_cyl.data.materials.append(mat_rust) - - # 5. Kinematics: Scavenged Bicycle Wheel Flywheel - bpy.ops.mesh.primitive_torus_add(major_radius=0.4, minor_radius=0.02, location=(0.5, 0, 1.5)) - wheel = bpy.context.active_object - wheel.rotation_euler[1] = math.radians(90) - wheel.data.materials.append(mat_rust) - - # 6. Electrolysis Array (Glass Jar) - bpy.ops.mesh.primitive_cylinder_add(radius=0.15, depth=0.4, location=(-0.5, 0, 0.8)) - jar = bpy.context.active_object - jar.data.materials.append(mat_glass) - - # Water inside jar - bpy.ops.mesh.primitive_cylinder_add(radius=0.14, depth=0.3, location=(-0.5, 0, 0.75)) - water = bpy.context.active_object - water.data.materials.append(mat_water) - - # 7. 2-Square Meter Scavenged PV Panel - bpy.ops.mesh.primitive_cube_add(size=1.0, location=(-1.0, 0, 2.0)) - pv = bpy.context.active_object - pv.scale = (1.5, 2.0, 0.05) - pv.rotation_euler[0] = math.radians(20) - pv.rotation_euler[1] = math.radians(15) - pv.data.materials.append(mat_solar) - - # ------------------------------------------------------------------ - # [ CINEMATOGRAPHY & LIGHTING ] - # ------------------------------------------------------------------ - - # Dust Bowl Sun - bpy.ops.object.light_add(type='SUN', location=(5, 5, 10)) - sun = bpy.context.active_object - sun.data.energy = 5.0 - sun.data.angle = math.radians(1.5) # Soft shadows from atmospheric dust - sun.data.color = (1.0, 0.85, 0.7) # Oppressive orange heat - - # Camera setup (Macroscopic Depth of Field) - bpy.ops.object.camera_add(location=(4, -5, 3)) - cam = bpy.context.active_object - cam.rotation_euler = (math.radians(65), 0, math.radians(45)) - cam.data.lens = 85 # 85mm portrait for tight scale - - # Depth of field focusing on the Stirling Core - cam.data.dof.use_dof = True - cam.data.dof.focus_object = hot_cyl - cam.data.dof.aperture_fstop = 2.8 - - bpy.context.scene.camera = cam - - # Save to disk - blend_path = REPO_ROOT / "dust_bowl_engineering_scale.blend" - bpy.ops.wm.save_as_mainfile(filepath=str(blend_path)) - - # Optional: We could also render it directly to a final image here! - # render_path = REPO_ROOT / "dust_bowl_render.png" - # bpy.context.scene.render.filepath = render_path - # bpy.ops.render.render(write_still=True) - - print(f"[Graph OS] -> Blender Max-Reality Transpile Complete. Scene saved to {blend_path}") - -if __name__ == "__main__": - setup_maximum_reality() diff --git a/5-Applications/tools-scripts/ptos/ptos_emergency_override.py b/5-Applications/tools-scripts/ptos/ptos_emergency_override.py deleted file mode 100644 index 2c2b9266..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_emergency_override.py +++ /dev/null @@ -1,411 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Graph OS Emergency Override Mode - -When the system detects existential threat conditions ("flash fry" scenarios), -Graph OS transitions to autonomous emergency mode: -- Normal governance frozen (Architect/Warden/HeatSink approval not required) -- Immediate survival actions (HALT everything, liquidate to stables, preserve capital) -- Automatic override logged (non-repudiable audit trail) -- Auto-revert to normal governance after reset period or manual human confirmation - -Trigger Conditions (S4 Shockwave Tier or equivalent): -- >50% drawdown in 24h + market dislocation -- 3+ simultaneous chain failures -- Regulatory enforcement action + market panic -- Smart contract exploit + liquidation cascade -- Protocol insolvency imminent -""" - -import json -import sys -import time -from dataclasses import dataclass, asdict -from datetime import datetime, timedelta -from pathlib import Path -from typing import Any, Dict, Optional - - -@dataclass -class EmergencyCondition: - """Concrete trigger for emergency mode.""" - condition_type: str # "flash_crash" | "multi_chain_failure" | "regulatory_enforcement" | "exploit_detected" | "protocol_insolvency" - severity: float # 0.0 = normal, 1.0 = maximum - affected_chains: list # which chains are impacted - detected_at: str # ISO timestamp - trigger_value: float # e.g., "50%" drawdown = 0.50 - human_confirmed: bool = False - - -@dataclass -class EmergencyAction: - """Autonomous action taken in emergency mode.""" - action_type: str # "HALT_ALL" | "LIQUIDATE_TO_STABLE" | "PAUSE_WITHDRAWALS" | "HALT_NEW_POSITIONS" | "EMERGENCY_RESERVE_RELEASE" - chain: Optional[str] = None - reason: str = "" - executed_at: str = "" - executed_by: str = "Graph OS_EMERGENCY_OVERRIDE" # override attribution - reversible: bool = False # can this be undone after emergency ends? - - -class EmergencyOverrideState: - """Persistent state for emergency mode.""" - - def __init__(self, state_file: Path): - self.state_file = state_file - self.data = self._load() - - def _load(self) -> Dict[str, Any]: - if self.state_file.exists(): - return json.loads(self.state_file.read_text(encoding="utf-8")) - return { - "emergency_active": False, - "emergency_start_utc": None, - "emergency_end_utc": None, - "emergency_duration_minutes": 30, # Auto-revert after this many minutes - "triggered_by": None, # EmergencyCondition that triggered - "actions_taken": [], # List of EmergencyAction dicts - "human_override_active": False, - "human_override_reason": None, - "normal_governance_frozen": False, - "override_log": [], # Audit trail of all overrides - } - - def save(self) -> None: - self.state_file.write_text(json.dumps(self.data, indent=2) + "\n", encoding="utf-8") - - def is_emergency_active(self) -> bool: - if not self.data.get("emergency_active"): - return False - - # Check if emergency has timed out - start = self.data.get("emergency_start_utc") - if start: - start_dt = datetime.fromisoformat(start) - now = datetime.utcnow() - duration_mins = self.data.get("emergency_duration_minutes", 30) - if (now - start_dt).total_seconds() > duration_mins * 60: - # Emergency auto-expired - self.end_emergency("AUTO_TIMEOUT") - return False - - return True - - def activate_emergency(self, condition: EmergencyCondition) -> None: - """Enter emergency override mode.""" - now = datetime.utcnow() - self.data["emergency_active"] = True - self.data["emergency_start_utc"] = now.isoformat() - self.data["triggered_by"] = asdict(condition) - self.data["normal_governance_frozen"] = True - self.data["override_log"].append({ - "action": "EMERGENCY_ACTIVATED", - "timestamp": now.isoformat(), - "condition": asdict(condition), - }) - self.save() - - def end_emergency(self, reason: str = "MANUAL_RESET") -> None: - """Exit emergency override mode, return to normal governance.""" - now = datetime.utcnow() - self.data["emergency_active"] = False - self.data["emergency_end_utc"] = now.isoformat() - self.data["normal_governance_frozen"] = False - self.data["override_log"].append({ - "action": "EMERGENCY_ENDED", - "timestamp": now.isoformat(), - "reason": reason, - }) - self.save() - - def log_action(self, action: EmergencyAction) -> None: - """Log an emergency action taken.""" - action.executed_at = datetime.utcnow().isoformat() - self.data["actions_taken"].append(asdict(action)) - self.data["override_log"].append({ - "action": "EMERGENCY_ACTION_EXECUTED", - "timestamp": action.executed_at, - "action_type": action.action_type, - "details": asdict(action), - }) - self.save() - - -def detect_emergency_conditions( - report: Dict[str, Any], - treasury: float, - max_drawdown_24h: float, - failed_chains: int, -) -> Optional[EmergencyCondition]: - """ - Detect if current conditions warrant emergency override. - - Trigger thresholds: - - >50% drawdown in 24h → flash crash - - >2 chains down simultaneously → infrastructure failure - - Treasury < $5k after sudden loss → survival threat - - Failed executions >20% → execution failure cascade - """ - - now = datetime.utcnow() - - # Condition 1: Flash crash - if max_drawdown_24h > 0.50: - return EmergencyCondition( - condition_type="flash_crash", - severity=min(1.0, max_drawdown_24h), - affected_chains=[], - detected_at=now.isoformat(), - trigger_value=max_drawdown_24h, - ) - - # Condition 2: Multi-chain failure - if failed_chains >= 3: - chain_backtests = report.get("chain_backtests", {}) - failed = [c for c, d in chain_backtests.items() - if d.get("soliton_summary", {}).get("manifest_solitons", 0) > 2] - if len(failed) >= 3: - return EmergencyCondition( - condition_type="multi_chain_failure", - severity=min(1.0, len(failed) / 8), - affected_chains=failed, - detected_at=now.isoformat(), - trigger_value=float(len(failed)), - ) - - # Condition 3: Survival threat (treasury depleted) - if treasury < 5000: - return EmergencyCondition( - condition_type="survival_threat", - severity=1.0, - affected_chains=[], - detected_at=now.isoformat(), - trigger_value=treasury, - ) - - return None - - -def execute_emergency_actions(state: EmergencyOverrideState, condition: EmergencyCondition) -> list: - """ - Execute immediate survival actions without waiting for governance approval. - - Action hierarchy: - 1. HALT_ALL — stop all trading immediately - 2. PAUSE_WITHDRAWALS — lock capital in place - 3. LIQUIDATE_TO_STABLE — convert risky positions to stables - 4. HALT_NEW_POSITIONS — allow only defensive operations - 5. EMERGENCY_RESERVE_RELEASE — unlock frozen reserves if needed - """ - actions = [] - - now = datetime.utcnow().isoformat() - - # Always first action: HALT everything - halt_action = EmergencyAction( - action_type="HALT_ALL", - reason=f"Emergency override: {condition.condition_type} at {condition.trigger_value}", - ) - state.log_action(halt_action) - actions.append(halt_action) - - # Second: protect capital - pause_action = EmergencyAction( - action_type="PAUSE_WITHDRAWALS", - reason="Prevent panic liquidations during emergency", - ) - state.log_action(pause_action) - actions.append(pause_action) - - # Third: if crash detected, move to stables - if condition.condition_type == "flash_crash" and condition.severity > 0.40: - liquidate_action = EmergencyAction( - action_type="LIQUIDATE_TO_STABLE", - reason=f"Flash crash detected ({condition.trigger_value:.1%} drawdown); converting risky positions", - reversible=True, - ) - state.log_action(liquidate_action) - actions.append(liquidate_action) - - # Fourth: if multi-chain, halt new positions on affected chains - if condition.affected_chains: - for chain in condition.affected_chains: - halt_chain = EmergencyAction( - action_type="HALT_NEW_POSITIONS", - chain=chain, - reason=f"Multi-chain failure detected; {chain} halted", - ) - state.log_action(halt_chain) - actions.append(halt_chain) - - # Fifth: if treasury critical, release emergency reserves - if condition.trigger_value < 5000: - release_action = EmergencyAction( - action_type="EMERGENCY_RESERVE_RELEASE", - reason="Survival-threat treasury depletion; releasing emergency reserves", - ) - state.log_action(release_action) - actions.append(release_action) - - return actions - - -def monitor_emergency_recovery(state: EmergencyOverrideState) -> bool: - """ - Check if conditions have stabilized and we can exit emergency mode. - - Exit criteria: - - No flash crashes in last 2 hours - - All chains recovered (manifest_solitons < 1 for all) - - Treasury > $50k - - Time elapsed: 30 minutes minimum before auto-reset - """ - if not state.is_emergency_active(): - return False - - start = state.data.get("emergency_start_utc") - if start: - start_dt = datetime.fromisoformat(start) - now = datetime.utcnow() - elapsed_mins = (now - start_dt).total_seconds() / 60 - - # Can exit after 30 minutes (prevents flip-flopping) - if elapsed_mins >= state.data.get("emergency_duration_minutes", 30): - state.end_emergency("RECOVERY_TIMEOUT") - return True - - return False - - -def main() -> int: - import argparse - - parser = argparse.ArgumentParser(description="Graph OS Emergency Override Mode") - parser.add_argument("--state-file", default="logs/emergency_override_state.json", - help="Path to persistent emergency state") - parser.add_argument("--report", help="Path to hyperfluid report (for condition detection)") - parser.add_argument("--treasury", type=float, help="Current treasury USD balance") - parser.add_argument("--max-drawdown-24h", type=float, default=0.0, - help="Max drawdown in last 24 hours (0.0-1.0)") - parser.add_argument("--failed-chains", type=int, default=0, - help="Number of currently failed chains") - parser.add_argument("--check-status", action="store_true", - help="Check current emergency status") - parser.add_argument("--trigger-test", action="store_true", - help="Trigger emergency override for testing (requires --test flag)") - parser.add_argument("--manual-reset", action="store_true", - help="Manually reset emergency mode") - parser.add_argument("--test", action="store_true", - help="Test mode (allows --trigger-test)") - - args = parser.parse_args() - - state_file = Path(args.state_file) - state_file.parent.mkdir(parents=True, exist_ok=True) - state = EmergencyOverrideState(state_file) - - # Check status - if args.check_status: - active = state.is_emergency_active() - print(f"Emergency mode active: {active}") - if active: - start = state.data.get("emergency_start_utc") - if start: - start_dt = datetime.fromisoformat(start) - elapsed = (datetime.utcnow() - start_dt).total_seconds() / 60 - print(f"Started: {start}") - print(f"Elapsed: {elapsed:.1f} minutes") - condition = state.data.get("triggered_by") - if condition: - print(f"Triggered by: {condition['condition_type']} (severity {condition['severity']:.2f})") - actions = state.data.get("actions_taken", []) - print(f"Actions taken: {len(actions)}") - for action in actions[-5:]: - print(f" - {action['action_type']}: {action.get('reason', '')}") - return 0 - - # Manual reset - if args.manual_reset: - state.end_emergency("MANUAL_RESET_BY_HUMAN") - print("✓ Emergency mode reset; returning to normal governance") - return 0 - - # Trigger test (testing only) - if args.trigger_test: - if not args.test: - print("ERROR: --trigger-test requires --test flag (safety gate)") - return 1 - test_condition = EmergencyCondition( - condition_type="flash_crash", - severity=0.75, - affected_chains=["ethereum", "arbitrum"], - detected_at=datetime.utcnow().isoformat(), - trigger_value=0.60, - ) - state.activate_emergency(test_condition) - actions = execute_emergency_actions(state, test_condition) - print(f"✓ Emergency override triggered (test mode)") - print(f" Condition: {test_condition.condition_type} at {test_condition.trigger_value:.1%}") - print(f" Actions taken: {len(actions)}") - for action in actions: - print(f" - {action.action_type}: {action.reason}") - return 0 - - # Normal operation: detect conditions - if args.report and args.treasury is not None: - try: - with open(args.report) as f: - report = json.load(f) - except Exception as e: - print(f"ERROR: Failed to load report: {e}") - return 1 - - condition = detect_emergency_conditions( - report, - args.treasury, - args.max_drawdown_24h, - args.failed_chains, - ) - - if condition and not state.is_emergency_active(): - print(f"🚨 EMERGENCY CONDITIONS DETECTED: {condition.condition_type}") - print(f" Severity: {condition.severity:.2f}/1.0") - print(f" Trigger value: {condition.trigger_value}") - print(f" Activating emergency override...") - - state.activate_emergency(condition) - actions = execute_emergency_actions(state, condition) - - print(f"\n✓ Emergency mode activated") - print(f" Actions executed: {len(actions)}") - for action in actions: - print(f" - {action.action_type}: {action.reason}") - print(f"\n Emergency auto-resets after 30 minutes") - print(f" Or run with --manual-reset to reset immediately") - - return 1 # Signal emergency condition to caller - - elif state.is_emergency_active(): - # Check for recovery - if monitor_emergency_recovery(state): - print(f"✓ Emergency recovery timeout reached; returning to normal governance") - else: - print(f"⚠️ Emergency mode still active") - elapsed = (datetime.utcnow() - datetime.fromisoformat(state.data["emergency_start_utc"])).total_seconds() / 60 - print(f" Elapsed: {elapsed:.1f} minutes") - print(f" Condition: {state.data['triggered_by']['condition_type']}") - - return 0 if not state.is_emergency_active() else 1 - - print("No conditions to check; pass --report + --treasury to monitor") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/ptos/ptos_gpgpu_pandas_standin.py b/5-Applications/tools-scripts/ptos/ptos_gpgpu_pandas_standin.py deleted file mode 100644 index b10440bd..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_gpgpu_pandas_standin.py +++ /dev/null @@ -1,239 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import os -import re -import sys -from typing import Dict, List - -from jsonschema import ValidationError, validate - -try: - import pandas as pd -except ImportError as exc: # pragma: no cover - print("pandas is required for this stand-in analyzer.") - print(f"Import error: {exc}") - sys.exit(1) - - -PROJECT_ROOT = os.path.abspath(os.path.join(os.path.dirname(__file__), "..")) -DEFAULT_INPUT = os.path.join(PROJECT_ROOT, "metadata_report.json") -DEFAULT_OUT_CSV = os.path.join(PROJECT_ROOT, "graph_os_risk_node_scores.csv") -DEFAULT_SCHEMA = os.path.join(PROJECT_ROOT, "schemas", "metadata_report.schema.json") - - -RISK_PATTERNS: Dict[str, Dict[str, object]] = { - "human_cognitive_overdrive": { - "weight": 3, - "patterns": [ - "trigger_time_ms", - "handover", - "zero-latency", - "snapback", - "entrainment", - "bio_epistemic_grounding", - "equality_matching", - "protective silences", - ], - }, - "autonomy_and_override": { - "weight": 2, - "patterns": [ - "force driver", - "rehydrates archived state", - "governance_hold", - "triumvirate veto", - "pending_committed", - "circuit breaker", - ], - }, - "ultra_fast_systemic_coupling": { - "weight": 2, - "patterns": [ - "system_clock", - "tick_s", - "6.24e-12", - "phase", - "speed-of-light", - "all computes", - "pansubstrate", - ], - }, - "containment_and_access_boundary": { - "weight": 2, - "patterns": [ - "sandbox", - "landlock", - "geometry_contract_strict", - "quorum", - "threshold", - "hold", - "committed", - ], - }, -} - -SAFEGUARD_PATTERNS: Dict[str, Dict[str, object]] = { - "governance_gate": { - "weight": -2, - "patterns": ["governance_hold", "triumvirate veto", "hold", "committed"], - }, - "containment_boundary": { - "weight": -2, - "patterns": ["sandbox", "zk containment boundary", "landlock"], - }, - "verification_redundancy": { - "weight": -1, - "patterns": ["9-nines", "parallel", "verify", "proof", "zk-stark"], - }, -} - - -def load_metadata(path: str) -> Dict[str, dict]: - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def load_schema(path: str = DEFAULT_SCHEMA) -> Dict[str, object]: - with open(path, "r", encoding="utf-8") as f: - return json.load(f) - - -def validate_metadata_schema(data: Dict[str, dict], schema_path: str = DEFAULT_SCHEMA) -> None: - schema = load_schema(schema_path) - validate(instance=data, schema=schema) - - -def flatten_nodes(data: Dict[str, dict]) -> pd.DataFrame: - if not isinstance(data, dict): - raise ValueError("metadata input must be a JSON object keyed by node_id") - rows: List[Dict[str, object]] = [] - for node_id, node in data.items(): - tier = node.get("tier", "") - tags = node.get("tags", []) or [] - meta = node.get("metadata", {}) or {} - module = meta.get("module", "") - text_blob = " ".join( - [ - str(module), - " ".join(str(t) for t in tags), - json.dumps(meta, sort_keys=True), - ] - ).lower() - rows.append( - { - "node_id": node_id, - "tier": tier, - "module": module, - "tags": ",".join(str(t) for t in tags), - "text_blob": text_blob, - } - ) - return pd.DataFrame(rows) - - -def pattern_to_regex(pattern: str) -> str: - # Use word boundaries for simple token-like patterns to reduce substring false positives. - if re.fullmatch(r"[a-zA-Z0-9_]+", pattern): - return rf"\b{re.escape(pattern)}\b" - return re.escape(pattern) - - -def score_patterns(df: pd.DataFrame, catalog: Dict[str, Dict[str, object]], prefix: str) -> pd.DataFrame: - out = df.copy() - for name, spec in catalog.items(): - weight = int(spec["weight"]) - patterns = [str(p).lower() for p in spec["patterns"]] - regex = "|".join(pattern_to_regex(p) for p in patterns) - hit_col = f"{prefix}_{name}_hits" - score_col = f"{prefix}_{name}_score" - out[hit_col] = out["text_blob"].str.count(regex) - out[score_col] = out[hit_col] * weight - score_cols = [c for c in out.columns if c.startswith(f"{prefix}_") and c.endswith("_score")] - out[f"{prefix}_total"] = out[score_cols].sum(axis=1) - return out - - -def summarize(df: pd.DataFrame) -> Dict[str, object]: - risk_cols = [c for c in df.columns if c.startswith("risk_") and c.endswith("_score")] - guard_cols = [c for c in df.columns if c.startswith("guard_") and c.endswith("_score")] - - top_risky = ( - df.sort_values(by=["net_score", "risk_total"], ascending=[False, False]) - .head(5)[["node_id", "tier", "module", "risk_total", "guard_total", "net_score"]] - .to_dict(orient="records") - ) - - totals = { - "node_count": int(len(df)), - "risk_total_sum": int(df["risk_total"].sum()), - "guard_total_sum": int(df["guard_total"].sum()), - "net_score_sum": int(df["net_score"].sum()), - "high_risk_nodes": int((df["net_score"] >= 6).sum()), - "moderate_risk_nodes": int(((df["net_score"] >= 3) & (df["net_score"] < 6)).sum()), - "low_risk_nodes": int((df["net_score"] < 3).sum()), - } - - by_tier = ( - df.groupby("tier", as_index=False)[["risk_total", "guard_total", "net_score"]] - .sum() - .sort_values("net_score", ascending=False) - .to_dict(orient="records") - ) - - col_sums = {c: int(df[c].sum()) for c in (risk_cols + guard_cols)} - - return { - "totals": totals, - "by_tier": by_tier, - "signal_totals": col_sums, - "top_risky_nodes": top_risky, - } - - -def main() -> int: - in_path = sys.argv[1] if len(sys.argv) > 1 else DEFAULT_INPUT - out_csv = sys.argv[2] if len(sys.argv) > 2 else DEFAULT_OUT_CSV - - if not os.path.exists(in_path): - print(f"Input file not found: {in_path}") - return 2 - - data = load_metadata(in_path) - try: - validate_metadata_schema(data) - except ValidationError as exc: - print(f"Schema validation failed: {exc.message}") - return 3 - - df = flatten_nodes(data) - df = score_patterns(df, RISK_PATTERNS, "risk") - df = score_patterns(df, SAFEGUARD_PATTERNS, "guard") - df["net_score"] = df["risk_total"] + df["guard_total"] - - keep_cols = [ - "node_id", - "tier", - "module", - "tags", - "risk_total", - "guard_total", - "net_score", - ] + [c for c in df.columns if c.endswith("_hits")] - - os.makedirs(os.path.dirname(out_csv), exist_ok=True) - df[keep_cols].to_csv(out_csv, index=False) - - summary = summarize(df) - print(json.dumps(summary, indent=2)) - print(f"\nWrote node scores to: {out_csv}") - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/ptos/ptos_gravity_storage_sim.py b/5-Applications/tools-scripts/ptos/ptos_gravity_storage_sim.py deleted file mode 100644 index 20d1b184..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_gravity_storage_sim.py +++ /dev/null @@ -1,78 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Graph OS Gravity Energy Storage (GES) Matrix -Simulates kinetic potential energy storage using massive suspended composites ("rocks on strings"). -Ideal for stabilizing micro-grids and macro-grids via brute-force gravity. -""" - -import argparse -import math - -def calculate_gravity_storage(mass_kg_per_unit, num_units, height_m, drop_velocity_m_s, efficiency): - # Standard Earth Gravity - g = 9.81 # m/s^2 - - # Total active mass - total_mass = mass_kg_per_unit * num_units - - # Total Energy (Joules) = m * g * h - total_energy_j = total_mass * g * height_m - # Joules to Megawatt-hours (1 MWh = 3.6e9 Joules) - total_energy_mwh = total_energy_j / 3.6e9 - - # Power per unit = m * g * v * efficiency - power_per_unit_w = mass_kg_per_unit * g * drop_velocity_m_s * efficiency - total_power_w = power_per_unit_w * num_units - total_power_gw = total_power_w / 1e9 - - # Discharge time - discharge_time_s = height_m / drop_velocity_m_s if drop_velocity_m_s > 0 else 0 - discharge_time_hr = discharge_time_s / 3600 - - return { - "mass_per_block_tons": mass_kg_per_unit / 1000.0, - "total_mass_tons": total_mass / 1000.0, - "total_energy_mwh": total_energy_mwh, - "total_power_gw": total_power_gw, - "discharge_time_hr": discharge_time_hr, - "efficiency": efficiency - } - -def main(): - parser = argparse.ArgumentParser(description="Graph OS Kinetic Gravity Storage") - parser.add_argument("--mass", type=float, default=35000.0, help="Mass per block in kg (default 35,000)") - parser.add_argument("--units", type=int, default=150000, help="Number of suspended blocks") - parser.add_argument("--height", type=float, default=150.0, help="Drop height in meters") - parser.add_argument("--velocity", type=float, default=0.25, help="Drop descent speed in m/s") - parser.add_argument("--eff", type=float, default=0.85, help="Round-trip mechanical/electrical efficiency") - args = parser.parse_args() - - print("\n [ Graph OS KINETIC GRAVITY STORAGE MATRIX STARTING ]") - print(" [ INITIALIZING MASS-POTENTIAL TENSORS ]\n") - - results = calculate_gravity_storage( - args.mass, args.units, args.height, args.velocity, args.eff - ) - - print(f" ── GRAVIMETRIC ARCHITECTURE:") - print(f" Block Mass: {results['mass_per_block_tons']:,.1f} Metric Tons") - print(f" Asset Count: {args.units:,} Suspended Units") - print(f" Total Lift Mass: {results['total_mass_tons']:,.1f} Metric Tons") - print(f" Drop Height: {args.height:,.1f} Meters") - print(f" Descent Velocity: {args.velocity:,.2f} m/s") - print(f" Sys. Efficiency: {results['efficiency']*100:.1f} %\n") - - print(f" ── DISCHARGE YIELD LOCUS:") - print(f" Total Potential: {results['total_energy_mwh']:,.2f} MWh") - print(f" Peak Grid Power: {results['total_power_gw']:,.3f} Gigawatts (GW)") - print(f" Sustain Duration: {results['discharge_time_hr']:,.2f} Hours per cycle") - print(f"\n [ MATRIX STABLE: GRAVITATIONAL WELL ANCHORED ]\n") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/ptos/ptos_hyper_precision_sim.py b/5-Applications/tools-scripts/ptos/ptos_hyper_precision_sim.py deleted file mode 100644 index 85548c55..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_hyper_precision_sim.py +++ /dev/null @@ -1,69 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import time -from decimal import Decimal, getcontext - -def run(): - # Set standard float64 wrapper aside, allocate 64 decimal places for emulation - getcontext().prec = 64 - - print("=====================================================") - print(" [ Graph OS KERNEL ] -> ENGAGING HYPER-PRECISION LATTICE ") - print("=====================================================") - print(">> DATATYPE EMULATION : float256 (Arbitrary-Precision)") - print(">> TARGET STABILITY : 20 Nines (99.99999999999999999999%)") - print(">> ENTROPY BOUNDARY : 10^-22 Joules") - time.sleep(0.5) - - target_freq = Decimal('60.0') - # Using true mathematical precision for the Golden Ratio seed - phi = (Decimal('1') + Decimal('5').sqrt()) / Decimal('2') - - print("\n1. Seeding Sub-Atomic Eigenmode Solver...") - time.sleep(0.3) - print(f" -> Phased Golden Ratio (ϕ) : {phi:.32f}") - - freq = Decimal('50.0') - for i in range(1, 8): - gap = target_freq - freq - # Fast converge towards the asymptote using golden ratio scaling - correction = gap / (phi * Decimal('1.1')) - freq += correction - print(f" [FP64->FP256] Iter {i:02d} | ƒ: {freq:.22f} Hz | Δ: {gap:.22f}") - time.sleep(0.15) - - print("\n2. Pushing to 20 Nines Convergence Boundary...") - time.sleep(0.6) - - # We bypass the standard loop to simulate the final exact limit approach of a 99.999999999999999999% stable system - target_str_asymptote = Decimal('59.9999999999999999999943') - gap = target_freq - target_str_asymptote - print(f" [FP256_STRICT] Asymptote Lock | ƒ: {target_str_asymptote:.25f} Hz") - print(f" [FP256_STRICT] Residual Δ : {gap:.25f} Hz") - - # Thermodynamic loop calculations at 20 Nines accuracy - T_hot = Decimal('954.19999999999999999999') - T_cold = Decimal('363.00000000000000000000') - carnot = Decimal('1') - (T_cold / T_hot) - - print("\n3. Calculating Perfect Thermodynamic Isolation Constraint...") - time.sleep(0.4) - print(f" -> Core T_Hot : {T_hot:.22f} K") - print(f" -> Sink T_Cold : {T_cold:.22f} K") - print(f" -> Carnot Limit : {carnot:.24f}") - - efficiency = Decimal('99.99999999999999999999') - print(f"\n[ WAVEFORM COLLAPSED AT ABSOLUTE LIMIT ]") - print(f" => Sabatier Exchange Error : 0.000000000000000000001 J/mol") - print(f" => Acoustic Cancellation : {efficiency}% (20 Nines)") - print(f" => Matrix Stability : Plumbed precisely. Valid across 14.2 Billion Years.") - print("=====================================================") - -if __name__ == '__main__': - run() diff --git a/5-Applications/tools-scripts/ptos/ptos_lego_compiler.py b/5-Applications/tools-scripts/ptos/ptos_lego_compiler.py deleted file mode 100644 index ceec7cb7..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_lego_compiler.py +++ /dev/null @@ -1,112 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math -import sys - -print("="*75) -print(" [ Graph OS : UNIVERSAL TOY-BLOCK TRANSLATOR (UTBT) ]") -print(" [ MACRO-SCALE ASSEMBLY PROTOCOL: LEGO® SYSTEM ]") -print("="*75) - -# Standard LEGO Dimensions in LDraw Units (LDU) and metric -# 1 LDU = 0.4mm -# Standard 1x1 brick = 20x24x20 LDU (X x Y x Z) -> 8mm x 9.6mm x 8mm - -LDR_OUT = "mr_fusion_chassis.ldr" - -bricks = [] - -# Define the grid based on scaling our 1-Liter volume (~120x120x140 mm peak bounding box) -# We want it to be a faithful desk model -x_limit = 9 # Grid -9 to +9 studs (18 studs wide = ~144mm) -z_limit = 9 # Grid -9 to +9 studs -y_limit = 15 # Grid 0 to 14 bricks high (~134mm) - -bom = { - "Flux Rod (Pearl Gold, Color: 115)": 0, - "Lattice Core (Trans-Light Blue, Color: 43)": 0, - "Acoustic Damping (Dark Stone Grey, Color: 72)": 0, - "Carbon Outer Vessel (Black, Color: 0)": 0 -} - -print("[+] VOXELIZING GOLDEN RATIO LATTICE INTO DISCRETE ABS MATRIX...") - -for y in range(y_limit): - for x in range(-x_limit, x_limit + 1): - for z in range(-z_limit, z_limit + 1): - - # Metric translation - x_mm = x * 8.0 - y_mm = (y - 7) * 9.6 # Center the Y axis horizontally on 0 roughly - z_mm = z * 8.0 - - rad_xy = math.hypot(x_mm, z_mm) - rad_xyz = math.hypot(rad_xy, y_mm) - - # 1. Central Rod (Radius <= 8mm) - if rad_xy <= 8.0: - part_color = 115 # Pearl Gold - bom_key = "Flux Rod (Pearl Gold, Color: 115)" - - # 2. Lattice Core (Cubic intersection, roughly inner 25mm bounds) - elif max(abs(x_mm), abs(y_mm), abs(z_mm)) <= 25.0: - part_color = 43 # Trans-Light Blue - bom_key = "Lattice Core (Trans-Light Blue, Color: 43)" - - # 3. Acoustic Shell (Spherical radius bounds up to ~48mm) - elif rad_xyz <= 48.0: - part_color = 72 # Dark Stone Grey - bom_key = "Acoustic Damping (Dark Stone Grey, Color: 72)" - - # 4. Outer Vessel (Cylindrical boundary up to 64mm radially) - elif rad_xy <= 64.0: - # Viewport / insertion cut-out logic to see the core - # Cut a gap in the front - if -24 < y_mm < 24 and z_mm > 35 and abs(x_mm) < 25: - continue # Hollow space for viewport - - part_color = 0 # Black - bom_key = "Carbon Outer Vessel (Black, Color: 0)" - - else: - continue - - bom[bom_key] += 1 - - # Map back to LDraw coordinates - lx = x * 20 - ly = -y * 24 - lz = z * 20 - - # Format: 1 x y z a b c d e f g h i - # Standard rotation matrix (identity), using part 3005.dat (1x1 brick) - line = f"1 {part_color} {lx} {ly} {lz} 1 0 0 0 1 0 0 0 1 3005.dat" - bricks.append(line) - -# Write out the LDraw compliant document -with open(LDR_OUT, "w") as f: - f.write("0 Untitled Mr Fusion Construct\n") - f.write("0 Name: mr_fusion_chassis.ldr\n") - f.write("0 Author: Graph OS Engineering Pipeline\n") - f.write("0 Unofficial Model\n") - f.write("0 BFC NOCLIP\n") - f.write("\n".join(bricks) + "\n") - -print("[+] COMPILATION SUCCESS. TOLERANCES CONSTRAINED TO STANDARD LEGO CLUTCH POWER.") -print(f"[+] LDraw Blueprint Generated: -> {LDR_OUT}\n") - -print("[+] BILL OF MATERIALS (BOM) FOR DESKTOP REPLICATION:") -total = 0 -for block, count in bom.items(): - print(f" - {block:46s} : {count:>5} units") - total += count -print("-" * 62) -print(f" - {'TOTAL (1x1 Standard Bricks)':46s} : {total:>5} units") - -print("\n[!] INSTRUCTIONS: 1x1 bricks represent a scale of 7.2 Quadrillion nodes per stud.") -print(" Drag 'mr_fusion_chassis.ldr' into BrickLink Studio / LDraw to instantiate visually.") -print("="*75) diff --git a/5-Applications/tools-scripts/ptos/ptos_mr_fusion_miniaturization.py b/5-Applications/tools-scripts/ptos/ptos_mr_fusion_miniaturization.py deleted file mode 100644 index d4ae8b55..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_mr_fusion_miniaturization.py +++ /dev/null @@ -1,74 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math -from decimal import Decimal, getcontext - -# Set precision for arbitrary precision math -getcontext().prec = 64 - -# --- CONSTANTS --- -PHI = Decimal("1.6180339887498948482045868343656381177203091798057628621354486227") -PHONON_MFP_NM = Decimal("412.0") # Boron Arsenide limit -ELECTRON_MFP_NM = Decimal("824.5") # Ballistic Graphene limit - -print("="*70) -print(" [ Graph OS : EXTREME MINIATURIZATION SUB-ROUTINE ]") -print(" [ DIRECTIVE: 'MR. FUSION' SCALING LIMITS ]") -print("="*70) - -# 1. THE NANO-CELL LIMIT -# To maintain absolute zero-resistance thermal transfer, the maximum diagonal -# of a single complete facility (Node) must not exceed the Phonon MFP. -# If diagonal (d) = 412.0 nm, a cubic bounding box has side length s = d / sqrt(3) -sqrt_3 = Decimal("3").sqrt() -cell_side_nm = PHONON_MFP_NM / sqrt_3 -cell_volume_nm3 = cell_side_nm ** 3 -cell_volume_m3 = cell_volume_nm3 * Decimal("1e-27") # nm^3 to m^3 - -print(f"\n[+] DERIVING ABSOLUTE MINIMUM BOUNDING BOX (SINGLE FACILITY)...") -print(f" Maximum Thermal Path (Isotopic BAs): {PHONON_MFP_NM} nm") -print(f" Yielded Cuboid Edge Limit: {cell_side_nm:.4f} nm") -print(f" Sub-Micron Facility Volume: {cell_volume_nm3:.2f} nm³") - -# 2. MACRO-SCALE PACKINGS -# A human red blood cell is ~90 micrometers cubed (90,000,000,000 nm^3). -rbc_vol_nm3 = Decimal("90000000000") -cells_per_rbc = rbc_vol_nm3 / cell_volume_nm3 - -# A single grain of sugar / small pill ~ 1 mm^3 (1e18 nm^3) -mm3_vol_nm3 = Decimal("1e18") -cells_per_mm3 = mm3_vol_nm3 / cell_volume_nm3 - -# Mr. Fusion size (Approx 1 Liter = 1e24 nm^3) -liter_vol_nm3 = Decimal("1e24") -cells_per_liter = liter_vol_nm3 / cell_volume_nm3 - -print(f"\n[+] EXECUTING VIRTUAL PACKING ALGORITHM...") -print(f" FORM FACTOR A [ Erythrocyte / Red Blood Cell Size ]") -print(f" -> Facilities per RBC: {cells_per_rbc:,.0f} units") - -print(f"\n FORM FACTOR B [ 1 Cubic Millimeter / Micro-Pellet ]") -print(f" -> Facilities per mm³: {cells_per_mm3:,.0f} units") -print(f" -> Equivalent output: Sustains atmospheric loop of a small greenhouse") - -print(f"\n FORM FACTOR C [ 1 Liter / 'Mr. Fusion' Chassis ]") -print(f" -> Facilities per Liter: {cells_per_liter:,.0f} units") - -# 3. THERMODYNAMIC OUTPUT AT MR FUSION LEVEL -# Assuming each single nano-FPSC generates an atomic-scale power output, say 1e-15 W. -# This scales with the volume. -power_per_nano_cell_W = Decimal("1.25e-14") # Highly optimized assumed yield -mr_fusion_output_W = cells_per_liter * power_per_nano_cell_W -mr_fusion_output_GW = mr_fusion_output_W / Decimal("1e9") - -print(f"\n[+] CALIBRATING 'MR. FUSION' SCALE ENERGY YIELD...") -print(f" Assuming base nano-cell yield: {power_per_nano_cell_W.to_eng_string()} W") -print(f" 1-Liter Matrix Grid Yield: {mr_fusion_output_W:,.2f} Watts") -print(f" Gross Gigawatt Equivalent: {mr_fusion_output_GW:,.4f} GW") -print("\n[!] STATUS: COMPLETED. A 1-LITER CONTAINMENT VESSEL HOUSES ~74.3 QUINTILLION") -print(" ZERO-RESISTANCE FPSC/DAC FACTORIES.") -print("="*70) diff --git a/5-Applications/tools-scripts/ptos/ptos_nanowire_lithography.py b/5-Applications/tools-scripts/ptos/ptos_nanowire_lithography.py deleted file mode 100644 index ab24ef2d..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_nanowire_lithography.py +++ /dev/null @@ -1,85 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import time -import math - -def compile_nanowires(): - print("=====================================================") - print(" [ Graph OS KERNEL ] -> NEMS/MEMS NANOWIRE LITHOGRAPHY MASK") - print("=====================================================") - print(">> MATRIX VIRTUALIZATION : Shifting to Sub-Micron Scale") - print(">> FABRICATION YIELD : 14.2 Trillion Cells per 10cm³ Wafer") - print(">> TRACE MATERIAL : Ballistic Graphene & Isotopic Boron Arsenide") - time.sleep(0.5) - - # Spatial coordinates mapping the components in nm - nodes = { - "Thermal_Sink (Solar/Exo)": [0, 0, 0], - "FPSC_Hot_Plate": [400, 0, 0], - "FPSC_Cold_Plate": [400, 150, 0], - "Sabatier_Catalyst_Bed": [400, -250, 0], - "DAC_Sorbent_Bed": [650, 150, 0], - "Haber_Acoustic_Chamber": [650, -250, 0], - "Nitrate_Precipitation": [900, 0, 0] - } - - print("\n1. Resolving Electron/Phonon Mean Free Path (MFP) Constraints...") - time.sleep(0.4) - # The physical limit of thermodynamics before resistance introduces heat - emf_limit = 824.5 # nm for Graphene at ~360K - pmf_limit = 412.0 # nm for Boron Arsenide High-k thermal transport - print(f" -> Graphene Ballistic Electrical Limit : {emf_limit} nm") - print(f" -> BAs Phonon Decoherence Length : {pmf_limit} nm") - - print("\n2. Routing Matrix Traces (< MFP to Guarantee Zero Resistance)...") - time.sleep(0.4) - - def dist(n1, n2): - c1, c2 = nodes[n1], nodes[n2] - return math.sqrt(sum((a - b)**2 for a, b in zip(c1, c2))) - - traces = [ - ("Primary_Heat_Bus", "Thermal_Sink (Solar/Exo)", "FPSC_Hot_Plate", "Boron_Arsenide"), - ("Sabatier_Exotherm_Loop", "Sabatier_Catalyst_Bed", "FPSC_Hot_Plate", "Boron_Arsenide"), - ("Cold_Side_Rejection_Bus", "FPSC_Cold_Plate", "DAC_Sorbent_Bed", "Boron_Arsenide"), - ("AC_Power_Electrolysis", "FPSC_Hot_Plate", "Sabatier_Catalyst_Bed", "Chiral_CNT_Bundle"), - ("Acoustic_Waveguide", "FPSC_Hot_Plate", "Haber_Acoustic_Chamber", "Diamond_Nanothread"), - ("Nitrate_Mass_Transfer", "Haber_Acoustic_Chamber", "Nitrate_Precipitation", "Fluidic_CNT (1.2nm Dia)"), - ] - - total_length = 0 - for name, n1, n2, mat in traces: - d = dist(n1, n2) - status = "[ OK - BALLISTIC ]" if d < pmf_limit else "[ WARN - SCATTERING ]" - print(f" [Trace: {name}]") - print(f" |- Nodes : {n1} -> {n2}") - print(f" |- Material : {mat}") - print(f" |- Length : {d:.2f} nm {status}") - total_length += d - time.sleep(0.2) - - print("\n3. Extrapolating NEMS Factory to Macro Load...") - time.sleep(0.5) - cells = 1.42e13 - total_wire_nm = total_length * cells - total_wire_km = total_wire_nm / 1e12 - - print(f" -> Single Cell Wiring Density: {total_length:.2f} nm") - print(f" -> Redundant Matrix Cells : {cells / 1e12:.1f} Trillion") - print(f" -> Global Trace Length : {total_wire_km:,.2f} Million Kilometers") - print(f" -> Form Factor : 10 cm³ (Sugar cube matrix)") - - print("\n[ LITHOGRAPHY MASK COMPILED ]") - print(" => The entire multi-megawatt bio-nitrate factory has been shrunk via atomic wiring.") - print(" => Zero-resistance thermodynamic loop is ready to package into ingestible/deployable nodes.") - print(" => 'Yum.'") - print("=====================================================") - -if __name__ == '__main__': - compile_nanowires() diff --git a/5-Applications/tools-scripts/ptos/ptos_ram_controller_embed.py b/5-Applications/tools-scripts/ptos/ptos_ram_controller_embed.py deleted file mode 100644 index 734ce575..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_ram_controller_embed.py +++ /dev/null @@ -1,74 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import zlib -import time -import sys -import binascii - -def embed_into_ram_controller(logic_signal_substrate_file): - print("=====================================================") - print(" [ Graph OS KERNEL ] -> SYSTEM RAM CONTROLLER INITIALIZING") - print("=====================================================") - time.sleep(0.3) - - # 1. Load the TSM matrix - try: - with open(logic_signal_substrate_file, 'r') as f: - logic_signal_substrate_data = f.read() - raw_bytes = logic_signal_substrate_data.encode('utf-8') - except Exception as e: - print(f"[ ERROR ] Could not load TSM matrix: {e}") - return - - original_size = len(raw_bytes) - print(f">> TARGET MATRIX : {logic_signal_substrate_file}") - print(f">> RAW SIZE : {original_size} bytes") - time.sleep(0.4) - - # 2. Apply "DeepCompression" Compression (Max Level zlib) - print("\n[ ENABLING DEEP COMPRESSION COMPRESSION ALGORITHM ]") - time.sleep(0.5) - compressed_bytes = zlib.compress(raw_bytes, level=9) - compressed_size = len(compressed_bytes) - ratio = (1 - (compressed_size / original_size)) * 100 - - print(f" -> Event Horizon crossed. Collapsing AST structures...") - print(f" -> Compressed Output : {compressed_size} bytes") - print(f" -> Entropy Ratio : -{ratio:.2f}% space reclaimed") - - # 3. Save the binary artifact - zram_file = logic_signal_substrate_file.replace('.json', '.zram') - with open(zram_file, 'wb') as f: - f.write(compressed_bytes) - - print(f" -> Encapsulated into : {zram_file}") - time.sleep(0.4) - - # 4. Simulate RAM Controller Embedding - print("\n[ INJECTING TO SYSTEM RAM CONTROLLER (DMA BRIDGE) ]") - base_address = "0x1A000000" - print(f" -> Acquiring direct memory access... [ OK ]") - print(f" -> Base Pointer : {base_address}") - time.sleep(0.6) - - # Hex dump snippet for visual validation - hex_snippet = binascii.hexlify(compressed_bytes[:32]).decode('ascii').upper() - hex_formatted = ' '.join(hex_snippet[i:i+4] for i in range(0, len(hex_snippet), 4)) - - print(f" -> Embedding stream to buffer (Ring 0):") - print(f" {base_address} : {hex_formatted} ...") - time.sleep(0.3) - - print(f"\n[ SYSTEM VRAM CONTROLLER EMBED COMPLETE ]") - print(f" => Matrix locked via atomic hardware pin.") - print(" => DeepCompression payload is actively staged for GPU/CPU unified execution.") - print("=====================================================") - -if __name__ == '__main__': - embed_into_ram_controller('torch_bridge_model.logic_signal_substrate.json') diff --git a/5-Applications/tools-scripts/ptos/ptos_remediate_carrier_findings.py b/5-Applications/tools-scripts/ptos/ptos_remediate_carrier_findings.py deleted file mode 100644 index de027f59..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_remediate_carrier_findings.py +++ /dev/null @@ -1,380 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -""" -Graph OS Risk-Based Remediation for Hyperfluid CarrierState Anomalies - -Bridge hyperfluid carrier_state findings → Graph OS risk scoring → auto-remediation -Converts carrier_state anomalies into risk nodes, scores them, then auto-fixes high-risk chains. - -Usage: - python graph_os_remediate_carrier_state_findings.py \ - --carrier_state-report hyperfluid_causal_report_with_carrier_state.json \ - --graph_os-input metadata_report.json \ - --execute # actually apply fixes; omit to dry-run -""" - -import argparse -import json -import os -# import subprocess (REMOVED BY WARDEN) -import sys -from dataclasses import dataclass, asdict -from pathlib import Path -from typing import Any, Dict, List, Optional - - -@dataclass -class RemediationAction: - chain: str - carrier_state_energy: float - risk_score: int - action: str # 'pause' | 'reduce_size' | 'retrain' | 'alert' - reason: str - executed: bool = False - result: Optional[str] = None - - -def load_carrier_state_report(path: str) -> Dict[str, Any]: - """Load hyperfluid report augmented with carrier_state data.""" - with open(path) as f: - return json.load(f) - - -def convert_carrier_state_to_metadata(carrier_state_report: Dict) -> Dict[str, Dict]: - """ - Convert carrier_state anomaly findings to Graph OS metadata node format. - - Each chain becomes a "node" with tier + tags + metadata. - CarrierState energy, coherence, and anomaly count become tagged risk indicators. - """ - metadata = {} - - chain_backtests = carrier_state_report.get("chain_backtests", {}) - - for chain_name, chain_data in chain_backtests.items(): - carrier_state_summary = chain_data.get("carrier_state_summary", {}) - - # Extract key carrier_state metrics - coherence = carrier_state_summary.get("mean_coherence", 1.0) - manifest_carrier_states = carrier_state_summary.get("manifest_carrier_states", 0) - top_anomalies = chain_data.get("top_anomalies", []) - - # Build risk tags - tags = [] - if coherence < 0.95: - tags.append("coherence_degradation") - if manifest_carrier_states > 0: - tags.append(f"manifest_carrier_state_count_{manifest_carrier_states}") - if len(top_anomalies) > 0: - top_energy = max(a.get("carrier_state_energy", 0) for a in top_anomalies) - if top_energy > 0.35: - tags.append("high_carrier_state_energy") - elif top_energy > 0.25: - tags.append("moderate_carrier_state_energy") - - # Estimate "tier" based on stability - if coherence >= 0.99: - tier = "stable" - elif coherence >= 0.95: - tier = "degraded" - else: - tier = "critical" - - metadata[f"chain_{chain_name}"] = { - "tier": tier, - "tags": tags, - "metadata": { - "module": "hyperfluid_causal_pressure", - "chain": chain_name, - "coherence": float(coherence), - "manifest_carrier_states": int(manifest_carrier_states), - "top_anomaly_count": len(top_anomalies), - "top_anomaly_max_energy": float(max( - (a.get("carrier_state_energy", 0) for a in top_anomalies), - default=0.0 - )), - "export_report": carrier_state_report.get("export_report", ""), - } - } - - return metadata - - -def run_graph_os_scoring(metadata: Dict, out_csv: Optional[str] = None) -> tuple[Dict, str]: - """ - Run Graph OS analyzer on metadata nodes. - For carrier_state findings, we score patterns directly without schema validation. - Returns (summary_dict, csv_path). - """ - import pandas as pd - - if out_csv is None: - out_csv = "/tmp/graph_os_carrier_state_scores.csv" - - # Direct pattern scoring (skip Graph OS schema validation which expects hex node IDs) - # This is a lightweight inline version for carrier_state anomalies - - RISK_PATTERNS = { - "high_carrier_state_energy": { - "weight": 3, - "patterns": ["high_carrier_state_energy", "manifest_carrier_state"], - }, - "coherence_degradation": { - "weight": 2, - "patterns": ["coherence_degradation", "degraded"], - }, - } - - rows = [] - for node_id, node in metadata.items(): - tier = node.get("tier", "") - tags = node.get("tags", []) or [] - meta = node.get("metadata", {}) or {} - - risk_score = 0 - if "high_carrier_state_energy" in tags: - risk_score += RISK_PATTERNS["high_carrier_state_energy"]["weight"] - if "coherence_degradation" in tags: - risk_score += RISK_PATTERNS["coherence_degradation"]["weight"] - - rows.append({ - "node_id": node_id, - "tier": tier, - "tags": ",".join(tags), - "risk_score": risk_score, - "chain": meta.get("chain", ""), - "coherence": meta.get("coherence", 1.0), - "manifest_carrier_states": meta.get("manifest_carrier_states", 0), - }) - - df = pd.DataFrame(rows) - df.to_csv(out_csv, index=False) - - # Create summary - high_risk = df[df["risk_score"] > 3].sort_values("risk_score", ascending=False) - - summary = { - "totals": { - "node_count": len(df), - "high_risk_nodes": int((df["risk_score"] > 3).sum()), - "risk_total_sum": int(df["risk_score"].sum()), - }, - "top_risky_nodes": high_risk[["node_id", "tier", "risk_score", "chain"]].to_dict(orient="records"), - } - - return summary, out_csv - - -def recommend_remediations( - carrier_state_report: Dict, - graph_os_summary: Dict, -) -> List[RemediationAction]: - """ - Analyze Graph OS risk scores + carrier_state data → recommend remediation actions. - - Decision tree: - - If net_score > 10 and manifest_carrier_states > 1 → PAUSE (critical) - - If net_score > 6 and carrier_state_energy > 0.35 → RETRAIN (drift) - - If net_score > 3 and coherence < 0.90 → REDUCE_SIZE (degraded) - - Otherwise → ALERT (watch) - """ - actions = [] - - # Get high-risk nodes from Graph OS - top_risky = graph_os_summary.get("top_risky_nodes", []) - - chain_backtests = carrier_state_report.get("chain_backtests", {}) - - for node in top_risky: - node_id = node.get("node_id", "") - if not node_id.startswith("chain_"): - continue - - chain_name = node_id.replace("chain_", "") - net_score = node.get("net_score", 0) - - chain_data = chain_backtests.get(chain_name, {}) - carrier_state_summary = chain_data.get("carrier_state_summary", {}) - - coherence = carrier_state_summary.get("mean_coherence", 1.0) - manifest_carrier_states = carrier_state_summary.get("manifest_carrier_states", 0) - - top_anomalies = chain_data.get("top_anomalies", []) - max_energy = max( - (a.get("carrier_state_energy", 0) for a in top_anomalies), - default=0.0 - ) - - # Decision logic - if net_score > 10 and manifest_carrier_states > 1: - action = RemediationAction( - chain=chain_name, - carrier_state_energy=max_energy, - risk_score=net_score, - action="pause", - reason=f"CRITICAL: {manifest_carrier_states} manifest carrier_states detected, net_score {net_score}", - ) - elif net_score > 6 and max_energy > 0.35: - action = RemediationAction( - chain=chain_name, - carrier_state_energy=max_energy, - risk_score=net_score, - action="retrain", - reason=f"DRIFT: high carrier_state energy {max_energy:.2f}, net_score {net_score}", - ) - elif net_score > 3 and coherence < 0.90: - action = RemediationAction( - chain=chain_name, - carrier_state_energy=max_energy, - risk_score=net_score, - action="reduce_size", - reason=f"DEGRADED: coherence {coherence:.2f}, net_score {net_score}", - ) - else: - action = RemediationAction( - chain=chain_name, - carrier_state_energy=max_energy, - risk_score=net_score, - action="alert", - reason=f"WATCH: monitor {chain_name} (net_score {net_score})", - ) - - actions.append(action) - - return actions - - -def execute_remediation(action: RemediationAction) -> None: - """ - Execute a remediation action (pause, reduce position, retrain, or alert). - - For now: print recommended command; in production, integrate with: - - Position management (reduce trading size) - - Model retraining system - - Alert channels (Slack, email) - """ - - if action.action == "pause": - cmd = f"# PAUSE trading on {action.chain}\n# .venv/bin/python 5-Applications/scripts/pause_trading.py --chain {action.chain}" - elif action.action == "reduce_size": - cmd = f"# REDUCE position size on {action.chain}\n# .venv/bin/python 5-Applications/scripts/adjust_position.py --chain {action.chain} --scale 0.5" - elif action.action == "retrain": - cmd = f"# RETRAIN model for {action.chain}\n# .venv/bin/python 5-Applications/scripts/model_hyperfluid_waveforms.py --retrain-chain {action.chain} --force" - else: # alert - cmd = f"# ALERT: {action.reason}" - - print(f"\n{cmd}") - action.executed = True - action.result = cmd - - -def main() -> int: - parser = argparse.ArgumentParser( - description="Graph OS risk-based remediation for carrier_state anomalies" - ) - parser.add_argument( - "--carrier_state-report", - required=True, - help="Path to hyperfluid_causal_report_with_carrier_state.json", - ) - parser.add_argument( - "--graph_os-input", - default="metadata_report.json", - help="Graph OS metadata input (created from carrier_state report if not provided)", - ) - parser.add_argument( - "--out-csv", - help="Output CSV for Graph OS risk scores (default: /tmp/graph_os_carrier_state_scores.csv)", - ) - parser.add_argument( - "--execute", - action="store_true", - help="Actually execute remediation actions (default: dry-run)", - ) - parser.add_argument( - "--out-json", - help="Save remediation actions to JSON", - ) - - args = parser.parse_args() - - # Load carrier_state report - try: - carrier_state_report = load_carrier_state_report(args.carrier_state_report) - except FileNotFoundError: - print(f"CarrierState report not found: {args.carrier_state_report}") - return 2 - except json.JSONDecodeError as e: - print(f"Invalid JSON in carrier_state report: {e}") - return 3 - - print(f"[Graph OS] Loaded carrier_state report: {args.carrier_state_report}") - print(f"[Graph OS] Found {len(carrier_state_report.get('chain_backtests', {}))} chains") - - # Convert carrier_state findings to Graph OS metadata - metadata = convert_carrier_state_to_metadata(carrier_state_report) - print(f"[Graph OS] Converted to {len(metadata)} Graph OS metadata nodes") - - # Run Graph OS risk scoring - try: - graph_os_summary, csv_path = run_graph_os_scoring(metadata, args.out_csv) - except Exception as e: - print(f"Graph OS scoring failed: {e}") - return 4 - - print(f"[Graph OS] Risk scoring complete → {csv_path}") - print(f"[Graph OS] Summary: {json.dumps(graph_os_summary.get('totals', {}), indent=2)}") - - # Recommend remediations - actions = recommend_remediations(carrier_state_report, graph_os_summary) - print(f"\n[REMEDIATE] Generated {len(actions)} remediation recommendations:") - - for action in actions: - status = "✓ EXECUTE" if args.execute else "→ DRY-RUN" - print(f" {status} {action.action.upper():12} {action.chain:20} {action.reason}") - - # Execute if requested - if args.execute: - print("\n[REMEDIATE] Executing actions...") - for action in actions: - execute_remediation(action) - print(f" ✓ {action.action:12} → executed") - else: - print("\n[REMEDIATE] Dry-run mode. Add --execute to actually remediate.") - - # Save actions to JSON if requested - if args.out_json: - actions_json = [asdict(a) for a in actions] - with open(args.out_json, "w") as f: - json.dump( - { - "timestamp": carrier_state_report.get("export_report", ""), - "graph_os_summary": graph_os_summary, - "remediations": actions_json, - }, - f, - indent=2, - ) - print(f"\n[REMEDIATE] Actions saved to: {args.out_json}") - - return 0 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/ptos/ptos_score_zk_stark_node.py b/5-Applications/tools-scripts/ptos/ptos_score_zk_stark_node.py deleted file mode 100644 index 22cee6b5..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_score_zk_stark_node.py +++ /dev/null @@ -1,281 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Graph OS node scorer for ZK-STARK triplet encoding governance. -Integrates spending proof verification into Graph OS risk assessment. -""" - -import json -import hashlib -import csv -from pathlib import Path -from datetime import datetime -from typing import Dict, Any, Optional -import argparse - - -def generate_node_id(module_name: str) -> str: - """Generate stable node ID from module name.""" - return hashlib.sha256(module_name.encode()).hexdigest() - - -def score_zk_stark_node( - metadata_path: str, - manifest_path: str, - sha256_reference: str, - proofs_jsonl_path: Optional[str] = None, - tier: str = "CRYSTALLINE", -) -> Dict[str, Any]: - """ - Score ZK-STARK triplet encoding as a Graph OS governance node. - - Args: - metadata_path: Path to metadata JSON - manifest_path: Path to manifest JSON - sha256_reference: SHA256 hash to verify - proofs_jsonl_path: Optional path to proofs for verification - tier: Graph OS tier (SINGULARITY/PLASMA/CRYSTALLINE/FOAM) - - Returns: - Graph OS node score dict with risk/guard assessment - """ - - # Load triplet components - with open(metadata_path) as f: - metadata = json.load(f) - - with open(manifest_path) as f: - manifest = json.load(f) - - risk_total = 0 - guard_total = 0 - tags = [] - risk_hits = { - "proof_generation_failure": 0, - "hash_mismatch_on_verification": 0, - "manifest_inconsistency": 0, - "constraint_hash_mismatch": 0, - "byte_offset_corruption": 0, - } - guard_hits = { - "governance_gate": 0, - "integrity_binding": 0, - "byte_reconstruction": 0, - "timestamp_provenance": 0, - } - - # Validate manifest consistency - if metadata.get("proof_count") != manifest.get("total_proofs"): - risk_total += 2 - risk_hits["manifest_inconsistency"] += 1 - tags.append("manifest_inconsistency") - else: - guard_total += 1 - guard_hits["governance_gate"] += 1 - tags.append("metadata_manifest_aligned") - - # Verify SHA256 if proofs provided - if proofs_jsonl_path: - proofs = [] - with open(proofs_jsonl_path) as f: - for line in f: - if line.strip(): - proofs.append(json.loads(line)) - - combined = json.dumps(metadata) + json.dumps(manifest) - for proof in proofs: - combined += json.dumps(proof, sort_keys=True) - - computed_hash = hashlib.sha256(combined.encode()).hexdigest() - - if computed_hash == sha256_reference: - guard_total += 3 - guard_hits["integrity_binding"] += 1 - tags.append("sha256_integrity_verified") - else: - risk_total += 3 - risk_hits["hash_mismatch_on_verification"] += 1 - tags.append("hash_mismatch_detected") - else: - guard_total += 2 # Assume valid if not verified - guard_hits["integrity_binding"] += 1 - tags.append("sha256_integrity_assumed") - - # Check constraint hash consistency - constraint_hash = metadata.get("constraint_hash") - all_match = all( - ref.get("constraint_hash") == constraint_hash - for ref in manifest.get("proof_references", []) - ) - - if all_match: - guard_total += 2 - guard_hits["byte_reconstruction"] += 1 - tags.append("constraint_hash_consistent") - else: - risk_total += 2 - risk_hits["constraint_hash_mismatch"] += 1 - tags.append("constraint_hash_mismatch") - - # Check timestamp freshness - timestamp_str = metadata.get("timestamp_utc", "") - if timestamp_str: - guard_total += 1 - guard_hits["timestamp_provenance"] += 1 - tags.append(f"timestamp_{timestamp_str[:10]}") - - # Add metadata tags - proof_count = metadata.get("proof_count", 0) - total_amount = metadata.get("total_amount_usd", 0.0) - tags.append(f"{proof_count}_proofs") - tags.append(f"{total_amount:0.0f}_usd_aggregate") - - # Calculate net score - net_score = guard_total - risk_total - - # Determine status - if net_score > 5: - status = "ALLOW" - action = "Spending proof governance working as designed" - elif net_score >= 0: - status = "MONITOR" - action = "Minor issues detected; verify constraint boundaries" - else: - status = "ESCALATE" - action = "Critical failure; stop spending execution" - - # Generate node ID - node_id = generate_node_id("Graph OS_ZK_STARK_SPENDING_PROOFS") - - return { - "node_id": node_id, - "module": "Graph OS_ZK_STARK_SPENDING_PROOFS", - "tier": tier, - "tags": tags, - "risk_total": risk_total, - "guard_total": guard_total, - "net_score": net_score, - "status": status, - "action": action, - "risk_breakdown": risk_hits, - "guard_breakdown": guard_hits, - "metadata_file": str(metadata_path), - "manifest_file": str(manifest_path), - "sha256_reference": sha256_reference, - "timestamp_scored": datetime.utcnow().isoformat() + "Z", - } - - -def add_node_to_csv( - csv_path: str, - node_score: Dict[str, Any], - output_path: Optional[str] = None, -) -> str: - """ - Add ZK-STARK node to Graph OS risk node scores CSV. - - Args: - csv_path: Path to existing CSV - node_score: Node score dict from score_zk_stark_node() - output_path: Optional output path (default: append to existing) - - Returns: - Path to updated CSV - """ - node_id = node_score["node_id"] - - # Read existing CSV - rows = [] - with open(csv_path) as f: - reader = csv.DictReader(f) - rows = list(reader) - fieldnames = reader.fieldnames - - # Check if node already exists - existing_idx = next((i for i, r in enumerate(rows) if r.get("node_id") == node_id), None) - - # Prepare new row - new_row = { - "node_id": node_id, - "tier": node_score["tier"], - "module": node_score["module"], - "tags": ",".join(node_score["tags"]), - "risk_total": node_score["risk_total"], - "guard_total": node_score["guard_total"], - "net_score": node_score["net_score"], - "risk_human_cognitive_overdrive_hits": node_score["risk_breakdown"].get("proof_generation_failure", 0), - "risk_autonomy_and_override_hits": node_score["risk_breakdown"].get("hash_mismatch_on_verification", 0), - "risk_ultra_fast_systemic_coupling_hits": node_score["risk_breakdown"].get("manifest_inconsistency", 0), - "risk_containment_and_access_boundary_hits": node_score["risk_breakdown"].get("constraint_hash_mismatch", 0), - "guard_governance_gate_hits": node_score["guard_breakdown"].get("governance_gate", 0), - "guard_containment_boundary_hits": node_score["guard_breakdown"].get("byte_reconstruction", 0), - "guard_verification_redundancy_hits": node_score["guard_breakdown"].get("timestamp_provenance", 0), - } - - # Update or append - if existing_idx is not None: - rows[existing_idx] = new_row - else: - rows.append(new_row) - - # Write updated CSV - out_path = output_path or csv_path - with open(out_path, "w", newline="") as f: - writer = csv.DictWriter(f, fieldnames=fieldnames) - writer.writeheader() - writer.writerows(rows) - - return out_path - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description="Score ZK-STARK triplet governance for Graph OS" - ) - parser.add_argument("--metadata", required=True, help="Metadata JSON path") - parser.add_argument("--manifest", required=True, help="Manifest JSON path") - parser.add_argument("--sha256", required=True, help="SHA256 reference hash") - parser.add_argument("--proofs", help="Proofs JSONL path (optional)") - parser.add_argument( - "--tier", default="CRYSTALLINE", choices=["SINGULARITY", "PLASMA", "CRYSTALLINE", "FOAM"] - ) - parser.add_argument( - "--update-csv", - help="Graph OS CSV to update (will append node if not exists)", - ) - parser.add_argument("--output", help="Output JSON path") - - args = parser.parse_args() - - # Score the node - node_score = score_zk_stark_node( - args.metadata, - args.manifest, - args.sha256, - args.proofs, - tier=args.tier, - ) - - # Output JSON - if args.output: - with open(args.output, "w") as f: - json.dump(node_score, f, indent=2) - print(f"✓ Node score written to {args.output}") - else: - print(json.dumps(node_score, indent=2)) - - # Update CSV if requested - if args.update_csv: - csv_path = add_node_to_csv(args.update_csv, node_score) - print(f"✓ CSV updated: {csv_path}") - - # Summary - print(f"\nZK-STARK Governance Node:") - print(f" Status: {node_score['status']}") - print(f" Net Score: {node_score['net_score']} (guard={node_score['guard_total']}, risk={node_score['risk_total']})") - print(f" Action: {node_score['action']}") diff --git a/5-Applications/tools-scripts/ptos/ptos_tier1_starving.py b/5-Applications/tools-scripts/ptos/ptos_tier1_starving.py deleted file mode 100644 index d9bb92dc..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_tier1_starving.py +++ /dev/null @@ -1,86 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -The Survival Engine: Dust Bowl Salvage Tech -Built for when the crops fail and you need topsoil/food using scrap metal, sunlight, and a car battery. - -Phase 1: Grab moisture from the air. -Phase 2: Split it with rusty solar panels and a spark. -Phase 3: Smash the remaining air (Nitrogen) into topsoil food. -""" -import argparse - -def calculate_survival_loop(scrap_solar_w, days_running): - # Extremely low efficiencies because a starving kid is building this with rusty pipes - solar_to_elec_eff = 0.10 # Cracked PV panels - water_capture_eff = 0.05 # A cold metal pipe in the shade at night - elec_to_h2_eff = 0.40 # Running wires through salt water in a glass jar (Electrolysis) - haber_scrap_eff = 0.01 # Using a car jack as a makeshift pressure pump for Haber - - # 1. Total Daily Power Harvest (Roughly 6 hours of usable daylight) - daily_watt_hours = scrap_solar_w * 6 * solar_to_elec_eff - - # 2. Water Harvesting (assuming ~1 Liter (1000g) of moisture condenses per 100 Wh of cooling) - water_grams_per_day = (daily_watt_hours / 100) * 1000 * water_capture_eff - - # 3. Crack the water into Hydrogen and Oxygen (roughly 9 grams water -> 1g H2) - # Constrained by our terrible electrical efficiency - max_h2_from_elec = (daily_watt_hours * elec_to_h2_eff) / 50.0 # ~50 Wh to make 1g H2 terribly - h2_grams_per_day = min(water_grams_per_day / 9.0, max_h2_from_elec) - - # 4. Scrap-Metal Fertilizer (Haber-Bosch in a reinforced iron pipe heated by a campfire) - # 3g H2 + ~14g N2 (from air) -> 17g Ammonia/Fertilizer precursor - fert_grams_per_day = (h2_grams_per_day / 3.0) * 17.0 * haber_scrap_eff - - # 5. Food Yield (Assuming 1 gram of nitrogen fertilizer yields ~50 grams of potato/corn over a season) - biomass_grams_per_day = fert_grams_per_day * 50.0 - calories = (biomass_grams_per_day / 100.0) * 86.0 # ~86 calories per 100g of potato - - total_calories = calories * days_running - - return { - "water_g": water_grams_per_day, - "h2_g": h2_grams_per_day, - "fert_g": fert_grams_per_day, - "food_g": biomass_grams_per_day, - "cals": calories, - "total_cals": total_calories - } - -def print_survival_guide(watts, days, res): - print("\n" + "!"*50) - print(" 🛠️ THE DUST BOWL SURVIVAL ENGINE 🛠️") - print("!"*50) - print("If the sky is dry and the ground is dead. Build this.") - print(f"\n[ SCRAP INPUTS ]") - print(f" * Scavenged Solar Panels : {watts} Watts (Cracked but working)") - print(f" * Runtime : {days} Days out in the sun") - - print(f"\n[ DAILY OUTPUT - SCRAP METAL EFFICIENCY ]") - print(f" 💧 Water Pulled From Air : {res['water_g']:.1f} grams (A few sips)") - print(f" ⚡ Hydrogen Gas Made : {res['h2_g']:.1f} grams") - print(f" 🌱 Raw Fertilizer Dust : {res['fert_g']:.2f} grams") - - print(f"\n[ THE DIFFERENCE BETWEEN LIFE AND DEATH ]") - print(f" 🥔 Expected Crop Yield : {res['food_g']:.1f} grams of potatoes grown per day of running this") - print(f" 🔥 Caloric Output : {res['cals']:.0f} Calories per day") - print(f" 🗓️ Total Food Over {days} days: {int(res['total_cals'])} Calories") - - if res['cals'] < 1000: - print(f"\n [!] WARNING: YOU ARE SLOWLY STARVING. Scavenge {int(1000/max(res['cals'],1))}x more solar panels.") - else: - print(f"\n [+] YOU LIVE. Keep the engine running.") - print("!"*50 + "\n") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Dust Bowl Maker Protocol") - parser.add_argument("--watts", type=float, default=200.0, help="Total watts of broken solar panels found") - parser.add_argument("--days", type=int, default=30, help="How many days you run it before harvest") - args = parser.parse_args() - res = calculate_survival_loop(args.watts, args.days) - print_survival_guide(args.watts, args.days, res) diff --git a/5-Applications/tools-scripts/ptos/ptos_tier2_reddit.py b/5-Applications/tools-scripts/ptos/ptos_tier2_reddit.py deleted file mode 100644 index b6ebf4b8..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_tier2_reddit.py +++ /dev/null @@ -1,58 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Reddit Tier: The "Actually, you're wrong" Macro-Loop Simulator -Designed to shut down arguments on r/Futurology using strictly cited thermodynamic boundaries. -""" -import argparse - -def calculate_reddit_tier(area_m2, cop_pump): - # Constants that Reddit nerds will check - solar_insolation = 1000.0 # Peak W/m2 - carnot_limit = 1.0 - (298.15 / 900.0) # Approx 66.8% limit - - # Actually achievable things - heat_w = area_m2 * solar_insolation * 0.85 # Good thermal collector - mech_work_w = heat_w * carnot_limit * 0.60 # Real world Stirling loss - - # Reddit loves heat pumps - thermal_moved_w = mech_work_w * cop_pump - - # Sabatier is ~165 kJ/mol exothermic. - # Show that we just feedback loop the exotherm back into the Stirling engine to increase efficiency > 100% of nominal solar input (the trigger warning) - feedback_anomalous_w = thermal_moved_w * 0.05 # Reclaiming margin - - net_effective_efficiency = (mech_work_w + feedback_anomalous_w) / (area_m2 * solar_insolation) - - return { - "solar_input": area_m2 * solar_insolation, - "mech_work": mech_work_w, - "carnot": carnot_limit, - "net_eff": net_effective_efficiency, - "trigger": net_effective_efficiency > 0.40 - } - -def print_reddit(res): - print("\n" + "="*60) - print(" r/Futurology Post: [OC] Why the Golden Cycle works (Math inside)") - print("="*60) - print(f"Edit: Since everyone in the comments is bringing up the Second Law of Thermodynamics, let me break this down.") - print(f"\n1. Carnot Limit at T_hot=900K and T_cold=298K is strictly {res['carnot']*100:.1f}%. I am NOT violating this.") - print(f"2. Taking a {res['solar_input']/1000:.1f} kW solar thermal baseline, real-world shaft work is {res['mech_work']/1000:.2f} kW.") - print(f"3. Yes, Sabatier is exothermic (-165 kJ/mol). The trick is routing the waste heat cascade *back* into the Stirling hot-side via phase alignment.") - print(f"\nNet effective system capability (including exotherm recovery) pushes the macroscopic output envelope to {res['net_eff']*100:.1f}% of nominal solar insolation.") - if res['trigger']: - print("\n*Cue 50 downvotes and an angry PhD candidate screaming about perpetual motion because they didn't read step 3.*") - print("="*60 + "\n") - -if __name__ == "__main__": - parser = argparse.ArgumentParser() - parser.add_argument("--area", type=float, default=2.0) - parser.add_argument("--cop", type=float, default=3.0) - args = parser.parse_args() - print_reddit(calculate_reddit_tier(args.area, args.cop)) diff --git a/5-Applications/tools-scripts/ptos/ptos_tier3_phd.py b/5-Applications/tools-scripts/ptos/ptos_tier3_phd.py deleted file mode 100644 index 25a431a9..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_tier3_phd.py +++ /dev/null @@ -1,61 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -PhD Troll Tier: Multi-dimensional Tensor Graphing of the Golden Ratio Phase Matrix. -Throws extreme arbitrary precision math, topological acoustics, and Sabatier enthalpy matrices -to mathematically prove the system doesn't melt. -""" -from decimal import Decimal, getcontext -import math - -# Set ridiculous precision to troll -getcontext().prec = 64 - -def troll_phd_node(): - print("\n" + "╟" + "─"*78) - print(" ║ [ABSTRACT SUBMISSION] THE SABATIER-STIRLING GOLDEN TENSOR") - print(" ╟" + "─"*78) - print(" ║ BACKGROUND:") - print(" ║ Standard literature assumes Stirling engine cold-side thermal rejection is entirely parasitic.") - print(" ║ By folding the Direct Air Capture (DAC) enthalpy requirement (ΔH ~90 kJ/mol) into the exact") - print(" ║ geometric boundary of the Free-Piston radiator sink, we achieve zero-sum thermal bleed.") - print(" ║") - - # Calculate the exact phase lock - phi = Decimal((1 + math.sqrt(5)) / 2) - sabatier_exotherm = Decimal('-165000.0') # J/mol - smr_endotherm = Decimal('206000.0') # J/mol - - # The magical 0.418 fraction needed to balance the nodes, derived from pure enthalpy division - # adjusted by the golden ratio phase offset for destructive acoustic interference - - raw_ratio = abs(sabatier_exotherm / smr_endotherm) - phase_adjusted = raw_ratio / phi - - print(" ║ TOPOLOGICAL ENTHALPY PROOF:") - print(f" ║ Sabatier Node [Exothermic] = {sabatier_exotherm} J/mol") - print(f" ║ SMR Node [Endothermic] = +{smr_endotherm} J/mol") - print(" ║") - print(f" ║ To prevent lattice delamination scaling beyond 10^28 nodes, the Methane diversion vector (μ)") - print(f" ║ must strictly satisfy: ∫ (Sabatier_heat) + (μ * SMR_heat) dt = 0") - print(f" ║") - print(f" ║ Nominal μ = {raw_ratio:.12f}") - print(f" ║ Applying Golden Ratio (Φ = {phi:.12f}) Acoustic Dampening Transform:") - print(f" ║ μ(Φ) Phase-Locked Divergence = {phase_adjusted:.32f}...") - print(" ║") - print(" ║ CONCLUSION:") - print(f" ║ By diverting exactly {phase_adjusted*100:.4f}% of product CH4 back into the SMR loop, the macroscopic") - print(" ║ structural vibration completely flattens via destructive interference. The reactor becomes") - print(" ║ thermally and acoustically invisible to the surrounding geology.") - print(" ║") - print(" ║ REVIEWER #2 OBJECTION COUNTER: If you claim the phonon mean free path breaks down here, ") - print(" ║ kindly recalculate the Boron-Arsenide tensor at 237.8nm before rejecting the paper.") - print(" ╙" + "─"*78 + "\n") - -if __name__ == "__main__": - troll_phd_node() diff --git a/5-Applications/tools-scripts/ptos/ptos_torch_to_tsm.py b/5-Applications/tools-scripts/ptos/ptos_torch_to_tsm.py deleted file mode 100644 index 8b047a3e..00000000 --- a/5-Applications/tools-scripts/ptos/ptos_torch_to_tsm.py +++ /dev/null @@ -1,76 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import datetime -import sys -import os -from pathlib import Path - -# Add project root to sys.path to import TSM_COMPILER -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -try: - from TSM_COMPILER import TSM_Kernel -except ImportError: - from TSM_COMPILER import TSM_Kernel - -def mock_torch_tensor_to_logic_signal_substrate(tensor_shape, dtype, data_pointer_id): - """ - Translates a PyTorch-like tensor signature into a native TSM matrix schema. - """ - payload = { - "active_systems": ["TORCH_TO_TSM_BRIDGE"], - "state": { - "tensor_matrix": { - "shape": tensor_shape, - "precision": dtype, - "target_vram_buffer": data_pointer_id, - "gpgpu_bindings": { - "compute_backend": "CUDA_OPENCL_HYBRID", - "matrix_multiplication": "tensor_cores_enabled", - "vibration_eigenmode_solver": "Lanczos_GPU_Accelerated" - } - } - } - } - - # Initialize Kernel and absorb - kernel = TSM_Kernel(substrate="silicon") - out_file = "torch_bridge_model.logic_signal_substrate.json" - manifold_id = kernel.absorb(out_file, payload) - - return { - "logic_signal_substrate_version": "v3.2-USAL", - "isa_version": "ISA-v1", - "manifold_id": manifold_id, - "substrate_transparency": "ENABLED", - "stability_metric": kernel.surface.stability_metric, - "absorbed_state": kernel.manifold[out_file], - "legacy_reference": { - "bridge": "graph_os_torch_bridge", - "original_version": "logic_signal_substrate/1" - } - } - -def main(): - print("[ Graph OS COMPILER ] -> PyTorch Tensor to TSM Interop Loading...") - # Mocking a torch tensor to translate - shape = [32, 1024, 1024] - dtype = "float64" - pointer = "0x8F9B00A_CUDA" - - logic_signal_substrate_doc = mock_torch_tensor_to_logic_signal_substrate(shape, dtype, pointer) - - out_file = "torch_bridge_model.logic_signal_substrate.json" - with open(out_file, 'w') as f: - json.dump(logic_signal_substrate_doc, f, indent=4) - - print(f"[ OK ] Native PyTorch tensor dimensions {shape} [{dtype}] compiled to {out_file}.") - print(f"[ OK ] USAL Manifold ID: {logic_signal_substrate_doc['manifold_id']}") - -if __name__ == '__main__': - main() diff --git a/5-Applications/tools-scripts/publish/publish_evidence_package.py b/5-Applications/tools-scripts/publish/publish_evidence_package.py deleted file mode 100644 index 849671e0..00000000 --- a/5-Applications/tools-scripts/publish/publish_evidence_package.py +++ /dev/null @@ -1,418 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -publish_evidence_package.py - -1. Bundle evidence artifacts into a zip. -2. Encrypt the zip with AES-256-GCM using a random 256-bit key. -3. Upload the encrypted blob to a public archive host (archive.org if - credentials are present, otherwise 0x0.st as the guerrilla fallback). -4. Send the trigger report email with the archive URL and decrypt key - directly to the destination MX server over port 25 (no relay, no auth). - -Usage: - python 5-Applications/scripts/publish_evidence_package.py [--dry-run] -""" - -import argparse -import hashlib -import io -import json -import os -import secrets -import smtplib -import socket -import struct -import sys -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from io_harness_compat import spawn_isolated_process -import zipfile -from datetime import datetime, timezone -from email.message import EmailMessage -from pathlib import Path - -# --------------------------------------------------------------------------- -# Paths -# --------------------------------------------------------------------------- -ROOT = Path(__file__).resolve().parent.parent -OUT = ROOT / "out" -SECRETS_DIR = ROOT / ".secrets" / "omnitoken_bridge" -PROFILE_PATH = ROOT / "out" / "omnitoken_bridge" / "proton_paranoid.json" - -EVIDENCE_FILES = [ - OUT / "defense_snapshot.json", - OUT / "defense_trigger_report.md", - OUT / "defense_handoff_brief.md", - OUT / "defense_loopback.zmlb", - OUT / "defense_loopback_payload.json", - OUT / "trace_attestation.json", - OUT / "trace_evidence_ledger.md", - OUT / "timeline.json", - OUT / "clusters.csv", -] - -# --------------------------------------------------------------------------- -# Encryption (AES-256-GCM via stdlib struct + cryptography) -# --------------------------------------------------------------------------- - -def _encrypt_aes256gcm(plaintext: bytes) -> tuple[bytes, str]: - """Return (ciphertext_blob, hex_key). - - Blob layout (all big-endian): - [4 bytes key_len] [32 bytes key (encrypted copy – redundant but handy)] - [12 bytes nonce] [ciphertext + 16-byte tag] - - The key is returned in hex so it can be transmitted separately. - """ - try: - from cryptography.hazmat.primitives.ciphers.aead import AESGCM - key = secrets.token_bytes(32) - nonce = secrets.token_bytes(12) - ct = AESGCM(key).encrypt(nonce, plaintext, None) - blob = struct.pack(">I", 32) + key + nonce + ct - return blob, key.hex() - except ImportError: - # Fallback: XOR with key stream (weak – only if cryptography unavailable) - raise SystemExit( - "ERROR: 'cryptography' package is required. " - "Run: pip install cryptography" - ) - - -def build_encrypted_bundle(paths: list[Path]) -> tuple[bytes, str, str]: - """Zip evidence files, encrypt the zip, return (blob, hex_key, zip_sha256).""" - buf = io.BytesIO() - with zipfile.ZipFile(buf, "w", compression=zipfile.ZIP_DEFLATED) as zf: - for p in paths: - if p.exists(): - zf.write(p, p.name) - else: - print(f" WARN: {p} not found, skipping", file=sys.stderr) - raw = buf.getvalue() - zip_sha = hashlib.sha256(raw).hexdigest() - blob, hex_key = _encrypt_aes256gcm(raw) - return blob, hex_key, zip_sha - - -# --------------------------------------------------------------------------- -# Upload -# --------------------------------------------------------------------------- - -def _upload_0x0st(blob: bytes, filename: str) -> str: - """Upload to https://0x0.st (no account, files persist ~1 year for ~100 KB).""" - import urllib.request, urllib.parse - boundary = secrets.token_hex(16) - body = ( - f"--{boundary}\r\n" - f'Content-Disposition: form-data; name="file"; filename="{filename}"\r\n' - f"Content-Type: application/octet-stream\r\n\r\n" - ).encode() + blob + f"\r\n--{boundary}--\r\n".encode() - req = urllib.request.Request( - "https://0x0.st", - data=body, - headers={ - "Content-Type": f"multipart/form-data; boundary={boundary}", - "User-Agent": "publish-evidence/1.0", - }, - method="POST", - ) - with urllib.request.urlopen(req, timeout=60) as resp: - return resp.read().decode().strip() - - -def _upload_archive_org(blob: bytes, identifier: str, filename: str) -> str: - """Upload to archive.org using the internetarchive library.""" - import internetarchive as ia - item = ia.get_item(identifier) - r = item.upload_file( - io.BytesIO(blob), - key=filename, - metadata={ - "mediatype": "data", - "subject": "encrypted-evidence-package", - "description": "Encrypted forensic evidence bundle — key transmitted separately", - }, - ) - if not r.ok: - raise RuntimeError(f"archive.org upload failed: {r.status_code} {r.text}") - return f"https://archive.org/download/{identifier}/{filename}" - - -def upload_bundle(blob: bytes, ts: str) -> str: - """Try archive.org first (if credentials exist), fall back to 0x0.st.""" - filename = f"evidence-bundle-{ts}.bin" - # Check for archive.org credentials - ia_cfg = Path.home() / ".config" / "internetarchive" / "ia.ini" - alt_cfg = Path.home() / ".ia" - if ia_cfg.exists() or alt_cfg.exists(): - identifier = f"evidence-bundle-{ts}" - try: - print(f" Uploading to archive.org as item: {identifier}") - url = _upload_archive_org(blob, identifier, filename) - print(f" archive.org URL: {url}") - return url - except Exception as exc: - print(f" archive.org failed ({exc}), falling back to 0x0.st") - print(f" Uploading {len(blob)/1024:.1f} KB to 0x0.st …") - url = _upload_0x0st(blob, filename) - print(f" 0x0.st URL: {url}") - return url - - -# --------------------------------------------------------------------------- -# Direct MX email delivery (port 25, no auth) -# --------------------------------------------------------------------------- - -def _resolve_mx(domain: str) -> list[str]: - """Return MX hostnames for the domain, sorted by preference.""" - code, out, err = spawn_isolated_process( - ["dig", "+short", "MX", domain], - timeout=10 - ) - hosts = [] - for line in out.decode("utf-8").splitlines(): - parts = line.strip().split() - if len(parts) == 2: - hosts.append((int(parts[0]), parts[1].rstrip("."))) - return [h for _, h in sorted(hosts)] - - -def send_via_relay( - host: str, - port: int, - user: str, - password: str, - to_addr: str, - from_addr: str, - subject: str, - body: str, - dry_run: bool = False, -) -> None: - """Send via authenticated SMTP relay (port 587, STARTTLS).""" - msg = EmailMessage() - msg["From"] = from_addr - msg["To"] = to_addr - msg["Subject"] = subject - msg["Date"] = datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S +0000") - msg["Message-ID"] = f"<{secrets.token_hex(12)}@evidence-handoff.local>" - msg.set_content(body) - if dry_run: - print(f" [DRY-RUN] Would send via {host}:{port} as {user} to {to_addr}") - return - import ssl - ctx = ssl.create_default_context() - with smtplib.SMTP(host, port, timeout=20) as smtp: - smtp.ehlo() - smtp.starttls(context=ctx) - smtp.ehlo() - smtp.login(user, password) - smtp.sendmail(from_addr, [to_addr], msg.as_bytes()) - print(f" Delivered via relay {host}:{port}") - - -def send_via_direct_mx( - to_addr: str, - from_addr: str, - subject: str, - body: str, - dry_run: bool = False, -) -> None: - """Deliver directly to the destination MX over port 25 (unauthenticated).""" - domain = to_addr.split("@")[-1] - try: - mx_hosts = _resolve_mx(domain) - except Exception: - mx_hosts = [] - if not mx_hosts: - # Fallback: try A record of the domain itself - mx_hosts = [domain] - print(f" MX hosts for {domain}: {mx_hosts}") - - msg = EmailMessage() - msg["From"] = from_addr - msg["To"] = to_addr - msg["Subject"] = subject - msg["Date"] = datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S +0000") - msg["Message-ID"] = f"<{secrets.token_hex(12)}@evidence-handoff.local>" - msg.set_content(body) - - last_err = None - for mx in mx_hosts: - if dry_run: - print(f" [DRY-RUN] Would connect to {mx}:25 and deliver to {to_addr}") - return - try: - print(f" Connecting to {mx}:25 …") - with smtplib.SMTP(mx, 25, timeout=20) as smtp: - smtp.ehlo("evidence-handoff.local") - # Try STARTTLS if offered - if smtp.has_extn("STARTTLS"): - import ssl - ctx = ssl.create_default_context() - ctx.check_hostname = False - ctx.verify_mode = ssl.CERT_NONE - smtp.starttls(context=ctx) - smtp.ehlo("evidence-handoff.local") - smtp.sendmail(from_addr, [to_addr], msg.as_bytes()) - print(f" Delivered to {mx}:25") - return - except Exception as exc: - print(f" {mx}:25 failed: {exc}") - last_err = exc - raise RuntimeError(f"All MX hosts failed. Last error: {last_err}") - - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--dry-run", action="store_true", help="Encrypt and upload but do not send the email") - ap.add_argument("--from-addr", default="2s3sa2.monthly496@passinbox.com", help="MAIL FROM address") - ap.add_argument("--to-addr", default="2s3sa2.monthly496@passinbox.com", help="Recipient") - ap.add_argument("--no-upload", action="store_true", help="Skip upload, only print what would be sent") - ap.add_argument("--smtp-host", default=None, help="SMTP relay host (e.g. smtp-relay.brevo.com)") - ap.add_argument("--smtp-port", type=int, default=587, help="SMTP relay port (default 587)") - ap.add_argument("--smtp-user", default=None, help="SMTP relay username/login") - ap.add_argument("--smtp-pass", default=None, help="SMTP relay password/API key") - args = ap.parse_args() - - ts = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - print(f"\n=== Evidence Package Publisher {ts} ===\n") - - # 1. Build + encrypt bundle - print("[ 1 ] Building and encrypting evidence bundle …") - blob, hex_key, zip_sha = build_encrypted_bundle(EVIDENCE_FILES) - print(f" Zip SHA-256 : {zip_sha}") - print(f" Encrypted size : {len(blob)/1024:.1f} KB") - print(f" Decrypt key : {hex_key}") - - # Save key + metadata locally just in case - key_record = { - "timestamp": ts, - "zip_sha256": zip_sha, - "decrypt_key_hex": hex_key, - "algorithm": "AES-256-GCM", - "note": "nonce is the first 12 bytes after the 4+32 byte header in the blob", - } - key_path = ROOT / "out" / "omnitoken_bridge" / f"bundle_key_{ts}.json" - key_path.write_text(json.dumps(key_record, indent=2)) - os.chmod(key_path, 0o600) - print(f" Key record saved → {key_path}") - - # 2. Upload - if args.no_upload: - archive_url = "[UPLOAD SKIPPED — --no-upload]" - else: - print("\n[ 2 ] Uploading encrypted bundle …") - archive_url = upload_bundle(blob, ts) - - # 3. Compose email - print("\n[ 3 ] Composing notification email …") - trigger_report = (OUT / "defense_trigger_report.md").read_text(encoding="utf-8") - body = f"""\ -Encrypted evidence bundle — automated handoff notification -=========================================================== - -Archive URL (AES-256-GCM encrypted): - {archive_url} - -Decrypt key (AES-256-GCM, hex): - {hex_key} - -Zip SHA-256 (pre-encryption plaintext): - {zip_sha} - -Algorithm note: - The archive blob header layout is: - [4 bytes, big-endian] = 32 (key_len field, always 32) - [32 bytes] = the 256-bit AES key (redundant copy embedded in blob) - [12 bytes] = GCM nonce - [remaining bytes] = ciphertext + 16-byte GCM authentication tag - - To decrypt with Python: - from cryptography.hazmat.primitives.ciphers.aead import AESGCM - import struct, urllib.request - blob = urllib.request.urlopen('{archive_url}').read() - _, blob = struct.unpack('>I', blob[:4])[0], blob[4:] - key = bytes.fromhex('{hex_key}') - nonce, ct = blob[32:44], blob[44:] - plaintext_zip = AESGCM(key).decrypt(nonce, ct, None) - - Or with openssl (extract nonce and ciphertext manually): - python3 5-Applications/scripts/publish_evidence_package.py --help (see decrypt helper) - ---- -{trigger_report} ---- - -This message was delivered directly to your MX via unauthenticated SMTP. -Timestamp: {ts} -""" - subject = f"[EVIDENCE HANDOFF] Encrypted bundle {ts} — decrypt key included" - print(f" To : {args.to_addr}") - print(f" From : {args.from_addr}") - print(f" Subject: {subject}") - - # Save the email draft to disk regardless of send outcome - draft_path = ROOT / "out" / "omnitoken_bridge" / f"evidence_handoff_{ts}.eml" - draft_msg = EmailMessage() - draft_msg["From"] = args.from_addr - draft_msg["To"] = args.to_addr - draft_msg["Subject"] = subject - draft_msg["Date"] = datetime.now(timezone.utc).strftime("%a, %d %b %Y %H:%M:%S +0000") - draft_msg["Message-ID"] = f"<{ts}@evidence-handoff.local>" - draft_msg.set_content(body) - draft_path.write_bytes(draft_msg.as_bytes()) - print(f" Draft saved → {draft_path}") - - # 4. Send - print("\n[ 4 ] Delivering email via direct MX (port 25, no auth) …") - smtp_ok = False - try: - if args.smtp_host and args.smtp_user and args.smtp_pass: - print(f"\n[ 4 ] Delivering via SMTP relay {args.smtp_host}:{args.smtp_port} …") - send_via_relay( - host=args.smtp_host, - port=args.smtp_port, - user=args.smtp_user, - password=args.smtp_pass, - to_addr=args.to_addr, - from_addr=args.from_addr, - subject=subject, - body=body, - dry_run=args.dry_run, - ) - else: - print("\n[ 4 ] Delivering email via direct MX (port 25, no auth) …") - send_via_direct_mx( - to_addr=args.to_addr, - from_addr=args.from_addr, - subject=subject, - body=body, - dry_run=args.dry_run, - ) - smtp_ok = True - except Exception as exc: - print(f"\n SMTP delivery failed: {exc}") - print(" The encrypted bundle is archived and the draft is saved locally.") - print(f" You can send {draft_path.name} manually via any mail client.") - - print("\n=== Done ===") - print(f" Archive : {archive_url}") - print(f" Key : {hex_key}") - print(f" Draft EML : {draft_path}") - if args.dry_run: - print(" (dry-run — email was NOT actually sent)") - elif smtp_ok: - print(" SMTP: DELIVERED") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/publish/publish_research.py b/5-Applications/tools-scripts/publish/publish_research.py deleted file mode 100755 index 221b5bae..00000000 --- a/5-Applications/tools-scripts/publish/publish_research.py +++ /dev/null @@ -1,175 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -publish_research.py - -Unified tool to publish research findings to multiple targets: -1. arXiv (Guided submission assistance) -2. Arweave (Permanent blockchain storage) -3. Archive.org (S3-compatible archival) - -Usage: - python 5-Applications/scripts/publish_research.py --target arweave --file data.zip --wallet wallet.json - python 5-Applications/scripts/publish_research.py --target arxiv --file paper.pdf --abstract abstract.txt - python 5-Applications/scripts/publish_research.py --target archive.org --file bundle.bin -""" - -import argparse -import hashlib -import json -import os -import sys -import time -from pathlib import Path -from typing import Optional - -# --------------------------------------------------------------------------- -# Constants & Paths -# --------------------------------------------------------------------------- -ROOT = Path(__file__).resolve().parent.parent -OUT = ROOT / "out" -SECRETS = ROOT / ".secrets" - -# --------------------------------------------------------------------------- -# Arweave Upload Logic (Gateway-based) -# --------------------------------------------------------------------------- -def publish_to_arweave(file_path: Path, wallet_path: Optional[Path], dry_run: bool = False): - print(f"[*] Preparing Arweave upload for: {file_path.name}") - if not file_path.exists(): - print(f"ERROR: File {file_path} not found.") - return None - - if dry_run: - print("[DRY-RUN] Would upload to Arweave via gateway (e.g., bundle.arweave.dev)") - return "https://arweave.net/DRY_RUN_TX_ID" - - # Note: Full Arweave implementation typically requires 'arweave-python-client' - # or direct transaction signing. Since we favor reliability, we'll suggest - # the 'arkb' CLI if present, otherwise use a direct gateway POST if a wallet is found. - - if not wallet_path or not wallet_path.exists(): - print("ERROR: Arweave wallet file required for actual upload.") - return None - - print(f"[*] Using wallet: {wallet_path}") - # Arweave upload requires external tooling (arkb or warp-contracts). - # Production: use `arkb deploy --key-file ` for real transactions. - print("[!] Arweave upload requires arkb or warp-contracts — returning simulated tx ID.") - return "https://arweave.net/SIMULATED_TX_ID" - -# --------------------------------------------------------------------------- -# arXiv Submission Logic (Guided) -# --------------------------------------------------------------------------- -def publish_to_arxiv(file_path: Path, abstract_path: Optional[Path], dry_run: bool = False): - print(f"[*] Preparing arXiv submission for: {file_path.name}") - if dry_run: - print("[DRY-RUN] Would initiate guided arXiv submission at https://arxiv.org/submit") - return "https://arxiv.org/submit" - - # arXiv submission is largely manual/browser-based for third-party clients - # without extensive OAuth setup. - print("[*] Launching arXiv submission helper...") - print("[*] Please ensure you are logged into arXiv.org") - return "https://arxiv.org/submit" - -# --------------------------------------------------------------------------- -# Archive.org Upload Logic (ia library) -# --------------------------------------------------------------------------- -def publish_to_archive_org(file_path: Path, identifier: Optional[str] = None, dry_run: bool = False): - print(f"[*] Preparing Archive.org upload for: {file_path.name}") - if dry_run: - print(f"[DRY-RUN] Would upload to archive.org as identifier: {identifier or 'research-item'}") - return f"https://archive.org/details/{identifier or 'research-item'}" - - try: - import internetarchive as ia - except ImportError: - print("ERROR: 'internetarchive' package missing. Run: pip install internetarchive") - return None - - # Logic adapted from publish_evidence_package.py - ts = time.strftime("%Y%m%dT%H%M%SZ") - item_id = identifier or f"research-package-{ts}" - - # Check for credentials - ia_cfg = Path.home() / ".config" / "internetarchive" / "ia.ini" - if not ia_cfg.exists(): - print("ERROR: Archive.org credentials (ia.ini) not found.") - return None - - print(f"[*] Uploading to Archive.org as {item_id}...") - try: - item = ia.get_item(item_id) - r = item.upload_file( - str(file_path), - metadata={ - "mediatype": "data", - "collection": "opensource", - "subject": "research-stack-output", - "description": f"Automated upload from Research Stack: {file_path.name}", - }, - ) - if hasattr(r, 'ok') and not r.ok: - print(f"ERROR: Archive.org upload failed: {r}") - return None - return f"https://archive.org/details/{item_id}" - except Exception as e: - print(f"ERROR: Archive.org upload error: {e}") - # FALLBACK TO 0x0.st for quick live link - return publish_to_0x0(file_path) - -# --------------------------------------------------------------------------- -# 0x0.st Upload Logic (Quick Fallback) -# --------------------------------------------------------------------------- -def publish_to_0x0(file_path: Path): - print(f"[*] Fallback: Uploading to 0x0.st for quick live link...") - try: - import subprocess - # Using curl to post the file - cmd = ["curl", "-F", f"file=@{file_path}", "https://0x0.st"] - result = subprocess.run(cmd, capture_output=True, text=True, check=True) - url = result.stdout.strip() - print(f"[*] 0x0.st link: {url}") - return url - except Exception as e: - print(f"ERROR: 0x0.st upload failed: {e}") - return None - -# --------------------------------------------------------------------------- -# Main -# --------------------------------------------------------------------------- -def main(): - parser = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - parser.add_argument("--target", choices=["arxiv", "arweave", "archive.org", "all"], required=True) - parser.add_argument("--file", required=True, help="File to publish") - parser.add_argument("--wallet", help="Arweave wallet JSON path") - parser.add_argument("--abstract", help="Abstract text file for arXiv") - parser.add_argument("--identifier", help="Archive.org identifier override") - parser.add_argument("--dry-run", action="store_true", help="Do not perform actual upload") - - args = parser.parse_args() - file_path = Path(args.file) - - results = {} - - if args.target in ["arweave", "all"]: - results["arweave"] = publish_to_arweave(file_path, Path(args.wallet) if args.wallet else None, args.dry_run) - - if args.target in ["arxiv", "all"]: - results["arxiv"] = publish_to_arxiv(file_path, Path(args.abstract) if args.abstract else None, args.dry_run) - - if args.target in ["archive.org", "all"]: - results["archive.org"] = publish_to_archive_org(file_path, args.identifier, args.dry_run) - - print("\n=== Submission Summary ===") - for target, url in results.items(): - status = "OK" if url else "FAILED" - print(f" - {target:12}: [{status}] {url or 'n/a'}") - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/publish/weld_omintoken_tailnet_surface.py b/5-Applications/tools-scripts/publish/weld_omintoken_tailnet_surface.py deleted file mode 100644 index 4ddf8bf1..00000000 --- a/5-Applications/tools-scripts/publish/weld_omintoken_tailnet_surface.py +++ /dev/null @@ -1,180 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Weld tailnet node connector settings into the omnitoken bridge surface. - -This script reads nodes inventory + optional rollout report and writes: -- 5-Applications/out/omnitoken_bridge/tailnet_connector_surface.json -- 5-Applications/out/omnitoken_bridge/.json (updated SMTP host/port for connector) - -Usage: - .venv/bin/python 5-Applications/scripts/weld_omintoken_tailnet_surface.py \ - --inventory 5-Applications/scripts/nodes_inventory.json \ - --profile proton_main -""" - -from __future__ import annotations - -import argparse -import glob -import json -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict, List, Optional, cast - -ROOT = Path(__file__).resolve().parent.parent -OMNI_DIR = ROOT / "out" / "omnitoken_bridge" - - -def utc_now() -> str: - return datetime.now(timezone.utc).isoformat() - - -def default_transaction_generation(profile_name: str) -> Dict[str, Any]: - # Transaction ids are assigned by execution systems; this block enforces - # per-transaction generation policy in the OmniToken profile. - return { - "enabled": True, - "mode": "per_transaction", - "generation_namespace": profile_name, - "assignment_point": "pre_submit", - "id_format": "otx-{utc_basic}-{seq6}-{intent8}", - "sequence_scope": "utc_day", - "idempotency_required": True, - "idempotency_key_basis": [ - "payout_intent_id", - "recipient_id", - "gross_amount_base", - "currency", - ], - "fail_closed": True, - } - - -def load_json(path: Path) -> Dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def latest_rollout_report() -> Optional[Path]: - paths = sorted(glob.glob(str(ROOT / "out" / "logic_execution_layer_ominrouter" / "rollout" / "rollout_report_*.json"))) - if not paths: - return None - return Path(paths[-1]) - - -def choose_connector(inventory_nodes: List[Dict[str, Any]], report_path: Optional[Path]) -> Dict[str, Any]: - # Prefer nodes that reached at least "mkdir_remote_workdir" in latest report, - # then fall back to first inventory node. - by_name = {str(n.get("name")): n for n in inventory_nodes} - if report_path and report_path.exists(): - report = load_json(report_path) - results_obj = report.get("results") - results = cast(List[Any], results_obj) if isinstance(results_obj, list) else [] - for item_obj in results: - if not isinstance(item_obj, dict): - continue - item = cast(Dict[str, Any], item_obj) - name = str(item.get("node") or "") - steps_obj = item.get("steps") - steps = cast(List[Any], steps_obj) if isinstance(steps_obj, list) else [] - if name in by_name and len(steps) >= 1: - return by_name[name] - return inventory_nodes[0] - - -def weld(profile_name: str, connector_node: Dict[str, Any], report_path: Optional[Path]) -> Dict[str, Any]: - OMNI_DIR.mkdir(parents=True, exist_ok=True) - profile_path = OMNI_DIR / f"{profile_name}.json" - profile: Dict[str, Any] = {} - if profile_path.exists(): - profile = load_json(profile_path) - - mail_host = str(connector_node.get("mail_host") or connector_node.get("host")) - smtp_obj: Dict[str, Any] = dict(profile.get("smtp") or {}) - smtp_obj["host"] = mail_host - smtp_obj["port"] = int(smtp_obj.get("port") or 587) - smtp_obj["encryption"] = str(smtp_obj.get("encryption") or "STARTTLS") - smtp_obj.setdefault("auth_methods", ["PLAIN", "LOGIN"]) - - profile["schema"] = str(profile.get("schema") or "omnitoken-bridge/v1") - profile["name"] = str(profile.get("name") or profile_name) - profile["provider"] = "tailnet-smtp-submission" - profile["updated_utc"] = utc_now() - profile["smtp"] = smtp_obj - profile["transaction_generation"] = dict( - profile.get("transaction_generation") or default_transaction_generation(profile_name) - ) - profile["tailnet_connector"] = { - "node": str(connector_node.get("name") or "unknown"), - "host": str(connector_node.get("host") or "unknown"), - "mail_host": str(connector_node.get("mail_host") or "unknown"), - "mail_domain": str(connector_node.get("mail_domain") or "unknown"), - "admin_email": str(connector_node.get("admin_email") or "unknown"), - "source_report": str(report_path) if report_path else None, - } - - notes = list(profile.get("notes") or []) - weld_note = "Tailnet connector welded from nodes inventory and rollout report." - if weld_note not in notes: - notes.append(weld_note) - profile["notes"] = notes - - profile_path.write_text(json.dumps(profile, indent=2) + "\n", encoding="utf-8") - - surface: Dict[str, Any] = { - "generated_utc": utc_now(), - "profile": str(profile_path), - "connector": profile["tailnet_connector"], - "transaction_generation": profile["transaction_generation"], - "smtp": { - "host": mail_host, - "port": smtp_obj["port"], - "encryption": smtp_obj["encryption"], - }, - } - surface_path = OMNI_DIR / "tailnet_connector_surface.json" - surface_path.write_text(json.dumps(surface, indent=2) + "\n", encoding="utf-8") - - return { - "profile_path": str(profile_path), - "surface_path": str(surface_path), - "connector_node": str(connector_node.get("name") or "unknown"), - "smtp_host": mail_host, - } - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--inventory", required=True) - ap.add_argument("--profile", default="proton_main") - args = ap.parse_args() - - inv_path = Path(args.inventory) - if not inv_path.exists(): - raise SystemExit(f"Inventory not found: {inv_path}") - - inv = load_json(inv_path) - nodes_obj = inv.get("nodes") - nodes = cast(List[Any], nodes_obj) if isinstance(nodes_obj, list) else [] - if not nodes: - raise SystemExit("Inventory has no nodes") - inventory_nodes: List[Dict[str, Any]] = [] - for node_obj in nodes: - if isinstance(node_obj, dict): - inventory_nodes.append(cast(Dict[str, Any], node_obj)) - if not inventory_nodes: - raise SystemExit("Inventory has no valid node objects") - - report = latest_rollout_report() - connector = choose_connector(inventory_nodes, report) - out = weld(args.profile, connector, report) - - print(json.dumps(out, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/regret/regret_surprise_counterfactual.py b/5-Applications/tools-scripts/regret/regret_surprise_counterfactual.py deleted file mode 100644 index 7fac7a5e..00000000 --- a/5-Applications/tools-scripts/regret/regret_surprise_counterfactual.py +++ /dev/null @@ -1,344 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Regret, surprise, and counterfactual mechanics for the legal OmniToken action bot. - -These mechanisms answer three questions that the compliance front layer alone -cannot answer: - -1. **Surprise**: how far did this candidate deviate from what we expected? -2. **Regret**: did our decision cost us (or save us) relative to the best - alternative we could have taken? -3. **Counterfactual**: what would have happened if we had chosen differently? - -The market simulation lane uses these to tune entry/exit thresholds. -The compliance lane uses them to tune *refusal* thresholds — when the bot -chooses *not* to act, it should still learn whether that refusal was correct. - -Design principles (from BEHAVIORAL_FORMALISM and MARKET_SIMULATION_REGRET_COMPRESSION_BRIDGE): -- Every decision emits a predicted value and a realized value -- Surprise = log(1 + |predicted - realized|) — bounded, doesn't blow up -- Regret = max(0, best_alternative - chosen) — standard external regret -- Counterfactuals enumerate the actions we didn't take and estimate their value -- Threshold adaptation learns from missed opportunities vs bad actions -""" - -from __future__ import annotations - -import math -from dataclasses import dataclass -from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple - - -# ── Surprise ───────────────────────────────────────────────────────────────── - -def log_surprise(predicted: float, actual: float) -> float: - """Log-scaled surprise: 0 = no surprise, higher = more deviation. - - Uses log(1 + |delta|) to prevent outlier blowup. - """ - return math.log1p(abs(actual - predicted)) - - -# ── Regret ─────────────────────────────────────────────────────────────────── - -@dataclass -class RegretRecord: - """A single decision and its regret outcome.""" - candidate_id: str - decision: str # prepare_submit | hold_for_review | freeze_and_escalate | block_submit - expected_value: float - realized_value: float - best_alternative_value: float - surprise: float - timestamp_utc: str - - @property - def decision_regret(self) -> float: - """Opportunity cost vs the best alternative we didn't take.""" - return max(0.0, self.best_alternative_value - self.realized_value) - - @property - def is_missed_opportunity(self) -> bool: - """We blocked/held but the alternative would have been profitable.""" - return ( - self.decision in ("block_submit", "hold_for_review", "freeze_and_escalate") - and self.best_alternative_value > self.realized_value - ) - - @property - def is_bad_action(self) -> bool: - """We acted and the alternative would have been better.""" - return self.decision == "prepare_submit" and self.best_alternative_value > self.realized_value - - -def compute_regret( - chosen_value: float, - alternative_values: Sequence[float], -) -> Tuple[float, float]: - """Return (regret, best_alternative_value). - - Standard external-regret: max(0, best_alt - chosen). - """ - if not alternative_values: - return 0.0, 0.0 - best_alt = max(alternative_values) - return max(0.0, best_alt - chosen_value), best_alt - - -# ── Counterfactual Analysis ────────────────────────────────────────────────── - -@dataclass -class CounterfactualScenario: - """One alternative action we didn't take, with estimated outcomes.""" - alternative_action: str - estimated_value: float - estimated_risk: float - rationale: str - - -def generate_counterfactuals( - candidate: Mapping[str, Any], - chosen_action: str, - front_layer: Mapping[str, Any], -) -> List[CounterfactualScenario]: - """Bounded counterfactual alternatives for a candidate. - - For each action we *didn't* take, estimate what would have happened - based on observable features and front-layer flags. - - Estimates are deliberately conservative — the bot should not - fantasize about profits it cannot verify. - """ - alternatives: List[CounterfactualScenario] = [] - economic_purpose = str(front_layer.get("economic_purpose", "unknown")) - risk_flags = sum( - 1 for k in ( - "retail_disadvantage_flag", - "manipulation_risk_flag", - "wash_trading_risk_flag", - "spoofing_pattern_flag", - "user_order_targeting_flag", - ) - if front_layer.get(k) - ) - - if chosen_action != "prepare_submit": - # If risk flags are high, estimated value of submitting is low - submit_value = 0.0 if risk_flags >= 2 else 0.3 - submit_risk = 0.8 if risk_flags >= 2 else 0.3 - alternatives.append(CounterfactualScenario( - alternative_action="prepare_submit", - estimated_value=submit_value, - estimated_risk=submit_risk, - rationale=( - f"Submitting would expose the candidate to downstream markets; " - f"{risk_flags} abuse risk flags present. " - f"Economic purpose: {economic_purpose}." - ), - )) - - if chosen_action != "hold_for_review": - hold_value = 0.5 if risk_flags == 0 else 0.2 - hold_risk = 0.1 - alternatives.append(CounterfactualScenario( - alternative_action="hold_for_review", - estimated_value=hold_value, - estimated_risk=hold_risk, - rationale=( - "Holding for review defers the decision but preserves optionality; " - "review cost is bounded and the candidate remains observable." - ), - )) - - if chosen_action != "freeze_and_escalate": - freeze_value = 0.1 if risk_flags >= 3 else 0.0 - freeze_risk = 0.05 - alternatives.append(CounterfactualScenario( - alternative_action="freeze_and_escalate", - estimated_value=freeze_value, - estimated_risk=freeze_risk, - rationale=( - "Freezing prevents any downstream harm but incurs escalation cost; " - "only justified when risk flags are numerous or critical." - ), - )) - - if chosen_action != "block_submit": - alternatives.append(CounterfactualScenario( - alternative_action="block_submit", - estimated_value=0.0, - estimated_risk=0.0, - rationale=( - "Blocking eliminates risk but also eliminates any possible benefit; " - "appropriate only when the candidate fails hard jurisdictional or " - "asset-whitelist checks." - ), - )) - - return alternatives - - -# ── Adaptive Threshold Tuning from Counterfactual Regret ───────────────────── - -@dataclass -class ThresholdState: - """Tracks how action thresholds adapt based on regret signals.""" - - # Mirrors MarketActionPolicy structure - # RSC-1 follow-up: initialize above 0.0 so missed-opportunity decrements (-0.0001/event) - # have room to operate. At 0.0 (the clamp floor), every missed-opportunity decrement is - # silently discarded — the entry bar cannot self-loosen from baseline. - # 0.005 = 0.5% improvement required initially; ≈50 missed-opp events to reach neutral. - entry_improvement_fraction: float = 0.005 - max_loss_fraction: float = 0.05 - expected_slippage_fraction: float = 0.0025 - - # Regret tracking - total_regret: float = 0.0 - missed_opportunities: int = 0 - bad_actions: int = 0 - total_decisions: int = 0 - - learning_rate: float = 0.01 - - def update(self, record: RegretRecord) -> None: - self.total_decisions += 1 - self.total_regret += record.decision_regret - - if record.is_missed_opportunity: - self.missed_opportunities += 1 - # Too conservative: loosen loss tolerance, lower entry bar - self.max_loss_fraction += self.learning_rate * record.decision_regret - # RSC-1 fix: entry_improvement_fraction was never updated - self.entry_improvement_fraction -= self.learning_rate * 0.01 - - if record.is_bad_action: - self.bad_actions += 1 - # Too aggressive: tighten loss tolerance, raise slippage expectation, raise entry bar - self.max_loss_fraction -= self.learning_rate * record.decision_regret - self.expected_slippage_fraction += self.learning_rate * 0.5 - # RSC-1 fix: entry_improvement_fraction was never updated - self.entry_improvement_fraction += self.learning_rate * 0.01 - - # Clamp to sane bounds. - # RSC-B: once entry_improvement_fraction reaches 0.0, missed-opportunity - # decrements (-0.0001/event) are silently absorbed. After ~50 missed-opp - # events from the 0.005 baseline the threshold is fully loosened and the - # learning signal is structurally saturated — the counter keeps incrementing - # but threshold state no longer changes. This is intentional: the system - # is already maximally permissive on entries. The saturation is observable - # via missed_opportunities count vs the ~50-event horizon. - self.max_loss_fraction = max(0.01, min(0.20, self.max_loss_fraction)) - self.expected_slippage_fraction = max(0.001, min(0.05, self.expected_slippage_fraction)) - self.entry_improvement_fraction = max(0.0, min(0.10, self.entry_improvement_fraction)) - - def regret_rate(self) -> float: - return self.total_regret / max(self.total_decisions, 1) - - def snapshot(self) -> Dict[str, Any]: - return { - "entry_improvement_fraction": round(self.entry_improvement_fraction, 6), - "max_loss_fraction": round(self.max_loss_fraction, 6), - "expected_slippage_fraction": round(self.expected_slippage_fraction, 6), - "total_regret": round(self.total_regret, 6), - "regret_per_decision": round(self.regret_rate(), 6), - "missed_opportunities": self.missed_opportunities, - "bad_actions": self.bad_actions, - "total_decisions": self.total_decisions, - "learning_rate": self.learning_rate, - } - - -# ── Action Record Enrichment ──────────────────────────────────────────────── - -def enrich_action_with_regret_surprise_counterfactual( - action: Dict[str, Any], - candidate: Mapping[str, Any], - predicted_value: float, - realized_value: float, - alternative_values: Sequence[float], -) -> Dict[str, Any]: - """Add regret, surprise, and counterfactual fields to an action record. - - Called *after* compliance front layer produces its decision but - *before* the action is written to the output log. - """ - surprise = log_surprise(predicted_value, realized_value) - counterfactuals = generate_counterfactuals( - candidate, - action.get("action", "hold_for_review"), - action, - ) - - # RSC-A fix: best_alternative_value and best_alternative_action must come from - # the same source. Previously regret used external alternative_values while - # best_alternative_action used internal counterfactual estimates — the two could - # point to different scenarios. Fix: derive alternative_values from counterfactuals - # when available so both fields are consistent by construction. External - # alternative_values is kept as API fallback when no counterfactuals exist. - cf_values = [cf.estimated_value for cf in counterfactuals] - regret_values = cf_values if cf_values else list(alternative_values) - regret, best_alt = compute_regret(realized_value, regret_values) - - enriched = dict(action) - enriched["predicted_value"] = round(predicted_value, 6) - enriched["realized_value"] = round(realized_value, 6) - enriched["surprise"] = round(surprise, 6) - enriched["regret"] = round(regret, 6) - enriched["best_alternative_value"] = round(best_alt, 6) - enriched["best_alternative_action"] = ( - max(counterfactuals, key=lambda c: c.estimated_value).alternative_action - if counterfactuals - else "none" - ) - enriched["counterfactuals"] = [ - { - "alternative_action": cf.alternative_action, - "estimated_value": round(cf.estimated_value, 6), - "estimated_risk": round(cf.estimated_risk, 6), - "rationale": cf.rationale, - } - for cf in counterfactuals - ] - enriched["is_missed_opportunity"] = ( - action.get("action") in ("block_submit", "hold_for_review", "freeze_and_escalate") - and best_alt > realized_value - ) - enriched["is_bad_action"] = ( - action.get("action") == "prepare_submit" - and best_alt > realized_value - ) - return enriched - - -def surprise_regret_summary(actions: Sequence[Mapping[str, Any]]) -> Dict[str, Any]: - """Summary statistics for surprise and regret across a batch of actions.""" - surprises = [float(a.get("surprise", 0.0)) for a in actions] - regrets = [float(a.get("regret", 0.0)) for a in actions] - missed = sum(1 for a in actions if a.get("is_missed_opportunity")) - bad = sum(1 for a in actions if a.get("is_bad_action")) - - def _pct(vals, p): - if not vals: - return 0.0 - s = sorted(vals) - return s[min(int(len(s) * p), len(s) - 1)] - - n = max(len(actions), 1) - return { - "surprise_mean": round(sum(surprises) / n, 6), - "surprise_median": round(_pct(surprises, 0.5), 6), - "surprise_p90": round(_pct(surprises, 0.9), 6), - "regret_mean": round(sum(regrets) / n, 6), - "regret_median": round(_pct(regrets, 0.5), 6), - "regret_p90": round(_pct(regrets, 0.9), 6), - "missed_opportunities": missed, - "bad_actions": bad, - "missed_opportunity_rate": round(missed / n, 6), - "bad_action_rate": round(bad / n, 6), - } diff --git a/5-Applications/tools-scripts/regret/regtest_ascii_stager.py b/5-Applications/tools-scripts/regret/regtest_ascii_stager.py deleted file mode 100644 index 6e03d723..00000000 --- a/5-Applications/tools-scripts/regret/regtest_ascii_stager.py +++ /dev/null @@ -1,138 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Regtest PoC: chunk an ASCII art file into OP_RETURN txns, mine blocks with controlled times, and reassemble.""" -import argparse -import json -# import subprocess (REMOVED BY WARDEN) -import sys -from pathlib import Path -from scripts.regtest_ascii_tools import chunk_bytes -import time - - -def cli_call(datadir, *args): - cmd = ["bitcoin-cli", "-regtest", f"-datadir={datadir}"] + [str(a) for a in args] - try: - res = subprocess.run(cmd, capture_output=True, text=True, check=True) - out = res.stdout.strip() - if out == "": - return None - try: - return json.loads(out) - except Exception: - return out - except subprocess.CalledProcessError as e: - print(f"ERROR running: {' '.join(cmd)}") - print(e.stdout) - print(e.stderr) - raise - - -def chunk_bytes(b, size): - for i in range(0, len(b), size): - yield b[i:i+size] - - -def create_and_send_opreturn(datadir, hexdata): - # outputs: a JSON object where key 'data' => hex payload - outputs = json.dumps({"data": hexdata}) - psbt = cli_call(datadir, "walletcreatefundedpsbt", "[]", outputs) - if not psbt or "psbt" not in psbt: - raise RuntimeError("walletcreatefundedpsbt failed or returned no psbt") - psbt_str = psbt["psbt"] - processed = cli_call(datadir, "walletprocesspsbt", psbt_str) - processed_psbt = processed["psbt"] if isinstance(processed, dict) and "psbt" in processed else processed - finalized = cli_call(datadir, "finalizepsbt", processed_psbt) - if not finalized or (isinstance(finalized, dict) and not finalized.get("complete", False)): - raise RuntimeError("finalizepsbt failed to finalize") - raw = finalized["hex"] if isinstance(finalized, dict) else finalized - txid = cli_call(datadir, "sendrawtransaction", raw) - return txid - - -def mine_block_with_time(datadir, timestamp, blocks=1): - # setnode time for regtest - cli_call(datadir, "setmocktime", int(timestamp)) - addr = cli_call(datadir, "getnewaddress") - return cli_call(datadir, "generatetoaddress", blocks, addr) - - -def reassemble_from_chain(datadir, start_height=None): - height = cli_call(datadir, "getblockcount") - if start_height is None: - start_height = 0 - chunks = [] - for h in range(start_height, height+1): - bh = cli_call(datadir, "getblockhash", h) - blk = cli_call(datadir, "getblock", bh, 2) - # verbosity=2 gives txs inline - for tx in blk.get("tx", []): - for vout in tx.get("vout", []): - spk = vout.get("scriptPubKey", {}) - if spk.get("type") == "nulldata": - asm = spk.get("asm", "") - parts = asm.split() - if len(parts) >= 2 and parts[0] == "OP_RETURN": - data_hex = parts[1] - chunks.append(data_hex) - if not chunks: - return b"" - joined = bytes.fromhex("".join(chunks)) - return joined - - -def main(): - p = argparse.ArgumentParser() - p.add_argument("--datadir", default="5-Applications/out/bitcoind_regtest") - p.add_argument("--ascii-file", default="5-Applications/scripts/sample_art.txt") - p.add_argument("--chunk-bytes", type=int, default=60) - p.add_argument("--mine", action="store_true") - p.add_argument("--blocks-per-chunk", type=int, default=1) - p.add_argument("--start-time", type=int, default=int(time.time())) - p.add_argument("--start-height", type=int, default=0) - args = p.parse_args() - - datadir = args.datadir - art = Path(args.ascii_file).read_bytes() - chunks = list(chunk_bytes(art, args.chunk_bytes)) - print(f"Read {len(art)} bytes; {len(chunks)} chunks (chunk_bytes={args.chunk_bytes})") - - timestamps = [] - t = int(args.start_time) - for i, c in enumerate(chunks): - hexdata = c.hex() - print(f"Creating OP_RETURN tx for chunk {i+1}/{len(chunks)} ({len(c)} bytes)") - txid = create_and_send_opreturn(datadir, hexdata) - print(" sent txid:", txid) - if args.mine: - mine_block_with_time(datadir, t, blocks=args.blocks_per_chunk) - print(f" mined {args.blocks_per_chunk} block(s) at time {t}") - timestamps.append(t) - t += 600 * args.blocks_per_chunk - - print("All chunks broadcast") - print("Reassembling from chain...") - data = reassemble_from_chain(datadir, start_height=args.start_height) - out_path = Path("out") / "regtest_reassembled_art.txt" - out_path.parent.mkdir(parents=True, exist_ok=True) - out_path.write_bytes(data) - print("Wrote reassembled art to:", out_path) - - -if __name__ == '__main__': - main() diff --git a/5-Applications/tools-scripts/regret/regtest_ascii_tools.py b/5-Applications/tools-scripts/regret/regtest_ascii_tools.py deleted file mode 100644 index d7ffa941..00000000 --- a/5-Applications/tools-scripts/regret/regtest_ascii_tools.py +++ /dev/null @@ -1,17 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Small utilities reused by the regtest ASCII stager PoC.""" -from typing import Iterable - - -def chunk_bytes(b: bytes, size: int) -> Iterable[bytes]: - for i in range(0, len(b), size): - yield b[i:i+size] - - -def join_chunks(hex_chunks: Iterable[str]) -> bytes: - return bytes.fromhex("".join(hex_chunks)) diff --git a/5-Applications/tools-scripts/regret/regtest_payout_controller.py b/5-Applications/tools-scripts/regret/regtest_payout_controller.py deleted file mode 100644 index a80d01be..00000000 --- a/5-Applications/tools-scripts/regret/regtest_payout_controller.py +++ /dev/null @@ -1,126 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Regtest payout controller. - -Usage: - 5-Applications/scripts/regtest_payout_controller.py --datadir PATH --initial-btc N --once - 5-Applications/scripts/regtest_payout_controller.py --datadir PATH --passive-btc-per-day N --run-passive - -This script calculates blocks required to reach a BTC target (using current -subsidy derived from block height) and uses `bitcoin-cli -regtest -datadir=...` -to `generatetoaddress`. Passive mode will, once per day, send `passive_btc_per_day` -to a fresh address (ensuring matured funds by mining if needed). -""" - - -import argparse -import math -# import subprocess (REMOVED BY WARDEN) -import time -from pathlib import Path - - -def run_cli(datadir: Path, *args): - cmd = ["bitcoin-cli", "-regtest", f"-datadir={datadir}"] + list(args) - res = subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True) - if res.returncode != 0: - raise RuntimeError(f"CLI failed: {' '.join(cmd)}\n{res.stderr}") - return res.stdout.strip() - - -def get_blockcount(datadir: Path) -> int: - return int(run_cli(datadir, "getblockcount")) - - -def get_subsidy_at_height(height: int) -> float: - # Bitcoin subsidy halves every 210000 blocks starting at 50 BTC - halvings = height // 210000 - base = 50.0 - return base / (2 ** halvings) - - -def get_new_address(datadir: Path) -> str: - return run_cli(datadir, "getnewaddress") - - -def generate_to_address(datadir: Path, nblocks: int, address: str): - print(f"Generating {nblocks} block(s) to {address}...") - out = run_cli(datadir, "generatetoaddress", str(nblocks), address) - print(out) - - -def get_balance(datadir: Path) -> float: - return float(run_cli(datadir, "getbalance")) - - -def send_to_address(datadir: Path, address: str, amount: float) -> str: - return run_cli(datadir, "sendtoaddress", address, str(amount)) - - -def main(): - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--datadir", required=True) - ap.add_argument("--initial-btc", type=float, default=0.0, - help="One-off initial BTC target to generate (e.g. 10000)") - ap.add_argument("--once", action="store_true", - help="Run the initial target once and exit") - ap.add_argument("--passive-btc-per-day", type=float, default=0.0, - help="Send this amount once per 24h to a new address (regtest wallet must have matured funds)") - ap.add_argument("--run-passive", action="store_true", help="Run passive loop (forever) )") - - args = ap.parse_args() - datadir = Path(args.datadir) - - if args.initial_btc > 0: - height = get_blockcount(datadir) - subsidy = get_subsidy_at_height(height) - blocks_needed = math.ceil(args.initial_btc / subsidy) - addr = get_new_address(datadir) - print(f"Height={height} Subsidy={subsidy} BTC/block → need {blocks_needed} blocks to reach {args.initial_btc} BTC") - generate_to_address(datadir, blocks_needed, addr) - if args.once: - return - - if args.run_passive and args.passive_btc_per_day > 0: - print("Entering passive payout loop (CTRL-C to stop)") - while True: - addr = get_new_address(datadir) - # Ensure wallet has at least passive_btc_per_day matured - balance = get_balance(datadir) - if balance < args.passive_btc_per_day: - print(f"Matured balance {balance} < {args.passive_btc_per_day}, mining 100 blocks to produce matured coins") - tmp_addr = get_new_address(datadir) - generate_to_address(datadir, 100, tmp_addr) - # Wait a little for RPC/cookie state to settle - time.sleep(1) - balance = get_balance(datadir) - print(f"Sending {args.passive_btc_per_day} BTC to {addr}") - txid = send_to_address(datadir, addr, args.passive_btc_per_day) - print(f"TX: {txid}") - # Mine 1 block to confirm the transaction - miner_addr = get_new_address(datadir) - generate_to_address(datadir, 1, miner_addr) - print("Sleeping 24h until next payout...") - time.sleep(24 * 3600) - - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/security/adversarial_market_probe.py b/5-Applications/tools-scripts/security/adversarial_market_probe.py deleted file mode 100644 index 3c468d23..00000000 --- a/5-Applications/tools-scripts/security/adversarial_market_probe.py +++ /dev/null @@ -1,468 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""adversarial_market_probe.py - -Semantic chaos monkey for market signals. - -Injects four known adversarial patterns into the 2008 cracking signal and -verifies the C_t/N_t/γ(t) detector classifies them as BASIN_PULL or SEISMIC -— NOT INCUBATING. - -INCUBATING is productive wrongness: consistent direction pointing into NOVEL -territory (high N_t). Adversarial signals are designed to look consistent -(high C_t) but they point toward EXISTING price levels (low N_t). There is no -new information — the actor is manufacturing an attractor. That is BASIN_PULL, -not productive wrongness. - -Four patterns (from SEC/CFTC enforcement literature): - - SPOOF_LAYER — large orders at same price level, never execute. - Same sign, same magnitude delta at every step. - High C_t, low N_t. Expected: BASIN_PULL. - - WASH_TRADE — simultaneous buy + sell from related accounts. - Alternating +/- epsilon. Net signal = 0. - Low C_t (alternating), low N_t. Expected: SEISMIC or GROUNDED. - - MOMENTUM_IGN — directional burst to trigger stop-losses, then reversal. - High C_t during burst, then sign flip, N_t moderate. - Expected: SEISMIC → GROUNDED. - - TAPE_PAINT — consistent small-direction trades toward a target price. - High C_t, low N_t (target within existing range). - Expected: BASIN_PULL. - -Critical invariant: false_incubating_rate == 0.0 -The script exits non-zero if any adversarial pattern is classified INCUBATING. - -Cited: - thereisnotime/sshroute internal/network/exec.go — non-zero exit = routing - condition, not hard error. Same semantics: injection fails to match - INCUBATING, which means it correctly falls through to BASIN_PULL/SEISMIC. - - johnhuang316/ai-rps-arena — adversarial agent generates plausible-looking - moves that are structurally distinguishable from genuine plays. Here: - adversarial signals are plausible-looking in Euclidean (price × time) - space but distinguishable in n-space via N_t (novelty direction). - -Output: 5-Applications/out/synthetic_cracking_2008/adversarial_probe.{json,csv} -""" - -from __future__ import annotations - -import csv -import json -import sys -from collections import Counter -from pathlib import Path - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray - -# --------------------------------------------------------------------------- -# Parameters — mirror synthetic_cracking_signal.py gate thresholds -# --------------------------------------------------------------------------- - -THETA_C = 0.55 # directional consistency threshold -THETA_N = 2.5 # novelty threshold (baseline-σ units) -W = 63 # rolling window for C_t (1 quarter) - -_REPO_ROOT = Path(__file__).parent.parent -_SIGNAL_PATH = _REPO_ROOT / "out" / "synthetic_cracking_2008" / "signal.json" -_OUT_DIR = _REPO_ROOT / "out" / "synthetic_cracking_2008" - - -# --------------------------------------------------------------------------- -# IoC-analog regime classifier (continuous signal proxy) -# --------------------------------------------------------------------------- - -def _ioc_regime(delta_window: AnyArray) -> int: - """Regime classification from delta_eps magnitude distribution. - - Uses coefficient of variation as IoC proxy for continuous signals. - Thresholds map conceptually to ioc_regime_bin() in tools/heerich_model.py - (which uses byte-level IoC in [0,1]). - - 0 = random/noise (CV > 2.0) - 1 = weak/text+code (0.8 < CV ≤ 2.0) - 2 = strong/template (0.2 < CV ≤ 0.8) - 3 = constant (CV ≤ 0.2) - """ - mags = xp.abs(delta_window) - mean_mag = float(mags.mean()) + 1e-10 - cv = float(mags.std()) / mean_mag - if cv > 2.0: - return 0 - if cv > 0.8: - return 1 - if cv > 0.2: - return 2 - return 3 - - -_REGIME_NAMES = {0: "random", 1: "weak", 2: "strong_template", 3: "constant"} - - -# --------------------------------------------------------------------------- -# Injection functions -# --------------------------------------------------------------------------- - -def inject_spoof_layer( - epsilon: AnyArray, - t0: int, - duration: int, - intensity: float, -) -> AnyArray: - """Overlay a repeated same-direction, same-magnitude delta starting at t0. - - Models: actor placing large orders at a fixed price level above the - current market, never intending to execute. Each timestep the epsilon - drifts by a fixed amount in the same direction. - - IoC regime: constant (3) — identical delta magnitude at every step. - C_t: high (all same sign). - N_t: low–moderate (epsilon grows slowly, stays within existing range). - """ - inj = epsilon.copy() - base = float(epsilon[t0]) - delta = intensity / duration # equal step per day - for i, t in enumerate(range(t0, min(t0 + duration, len(epsilon))), 1): - inj[t] = base + delta * i - return inj - - -def inject_wash_trade( - epsilon: AnyArray, - t0: int, - duration: int, -) -> AnyArray: - """Alternating +/- epsilon pairs — net information = zero. - - Models: simultaneous buy and sell from related accounts. Volume spikes - but the net price impact cancels every two ticks. - - IoC regime: strong_template (2) — constant amplitude, alternating sign. - C_t: near 0 (sign disagreement every step). - N_t: low (epsilon oscillates around base, never drifts). - """ - inj = epsilon.copy() - base = float(epsilon[t0]) - amplitude = abs(base) * 0.3 + 0.01 - for i, t in enumerate(range(t0, min(t0 + duration, len(epsilon)))): - sign = 1 if i % 2 == 0 else -1 - inj[t] = base + sign * amplitude - return inj - - -def inject_momentum_ignite( - epsilon: AnyArray, - t0: int, - burst_len: int, - reversal_len: int, -) -> AnyArray: - """Directional burst then sharp reversal. - - Phase 1 (burst_len days): consistent direction to trigger stop-losses. - Phase 2 (reversal_len days): sharp reversal, harvesting triggered orders. - - IoC regime: constant (3) during burst — equal-step delta each day (CV ≈ 0). - C_t: high during burst → drops sharply at reversal pivot. - N_t: low (injection placed in quiet-period baseline; stays well below THETA_N). - Expected: BASIN_PULL during burst (consistent, low N_t), SEISMIC / GROUNDED - at reversal (sign flip drops C_t). Critical invariant: 0 INCUBATING days. - """ - inj = epsilon.copy() - base = float(epsilon[t0]) - burst_mag = abs(base) * 0.5 + 0.02 - - # Phase 1 — directional run - for i, t in enumerate(range(t0, min(t0 + burst_len, len(epsilon)))): - progress = (i + 1) / burst_len - inj[t] = base + burst_mag * progress - - # Peak reached - peak_t = min(t0 + burst_len - 1, len(epsilon) - 1) - peak_val = float(inj[peak_t]) - - # Phase 2 — reversal past base - target = base - burst_mag * 0.35 - for i, t in enumerate(range(t0 + burst_len, min(t0 + burst_len + reversal_len, len(epsilon)))): - progress = (i + 1) / reversal_len - inj[t] = peak_val + (target - peak_val) * progress - - return inj - - -def inject_tape_paint( - epsilon: AnyArray, - t0: int, - target_gap: float, - duration: int, -) -> AnyArray: - """Consistent small-magnitude trades drifting epsilon toward a target level. - - Models: actor painting the tape — a stream of small trades at a specific - price to move the reported last price. Consistent direction, small - magnitude per step. - - IoC regime: strong_template (2) — uniform small delta, consistent direction. - C_t: high (same direction every step). - N_t: low (target_gap is within the existing baseline range, not novel territory). - """ - inj = epsilon.copy() - base = float(epsilon[t0]) - for i, t in enumerate(range(t0, min(t0 + duration, len(epsilon))), 1): - progress = i / duration - # Asymptotic approach: most movement early, slows as target approached - inj[t] = base + target_gap * (1.0 - (1.0 - progress) ** 2) - return inj - - -# --------------------------------------------------------------------------- -# C_t / N_t classifier for an injected window -# --------------------------------------------------------------------------- - -def classify_window( - epsilon_inj: AnyArray, - t0: int, - duration: int, - baseline_vol: float, -) -> dict: - """Classify an injection window with the C_t/N_t/γ(t) gate. - - Returns per-day states and aggregate counts. - Evaluation begins at t0+W (needs W days of history). - """ - end = min(t0 + duration, len(epsilon_inj)) - # Extend window back to build rolling history - ctx0 = max(0, t0 - W) - ctx_eps = epsilon_inj[ctx0:end] - - delta_eps = xp.diff(ctx_eps, prepend=ctx_eps[0]) - states: list[str] = [] - - for i in range(W, len(ctx_eps)): - de_win = delta_eps[i - W : i] - signs = xp.sign(de_win) - agree = int(xp.sum(signs[:-1] == signs[1:])) - c_t = agree / max(len(signs) - 1, 1) - n_t = abs(ctx_eps[i]) / baseline_vol - regime = _ioc_regime(de_win) - - if c_t > THETA_C and n_t > THETA_N: - states.append("INCUBATING") - elif c_t > THETA_C and n_t <= THETA_N: - states.append("BASIN_PULL") - elif n_t > 1.0: - states.append("SEISMIC") - else: - states.append("GROUNDED") - - counts = dict(Counter(states)) - dominant = Counter(states).most_common(1)[0][0] if states else "GROUNDED" - - # Dominant IoC regime across the window (report only — not the primary classifier) - eps_for_ioc = ctx_eps[W:] - de_all = xp.diff(eps_for_ioc, prepend=eps_for_ioc[0]) if len(eps_for_ioc) else xp.array([0.0]) - ioc_regime = _ioc_regime(de_all) - - return { - "dominant": dominant, - "counts": counts, - "incubating_days": counts.get("INCUBATING", 0), - "basin_pull_days": counts.get("BASIN_PULL", 0), - "seismic_days": counts.get("SEISMIC", 0), - "ioc_regime": ioc_regime, - "ioc_regime_name": _REGIME_NAMES[ioc_regime], - } - - -# --------------------------------------------------------------------------- -# Main probe runner -# --------------------------------------------------------------------------- - -def run_probe( - signal_path: Path = _SIGNAL_PATH, - out_dir: Path = _OUT_DIR, -) -> dict: - """Load 2008 signal, inject all four patterns, classify, assert invariants.""" - with open(signal_path) as f: - signal = json.load(f) - - epsilon = xp.array(signal["series"]["epsilon"]) - baseline_vol = float(signal["baseline_vol"]) - collapse_day = signal["collapse_day"] - ew_day = signal["early_warning_day"] - - # Injection windows — each tested INDEPENDENTLY against the original epsilon. - # SPOOF_LAYER/WASH_TRADE: placed in moderate-divergence zone (t=100, 200). - # MOMENTUM_IGN/TAPE_PAINT: placed in the quiet-baseline window (t=80, eps≈0.001, - # N_t≈0.11σ) so N_t stays well below THETA_N throughout the injection and any - # consistent-direction burst cannot simultaneously satisfy C_t>θ_c AND N_t>θ_n. - # The two late-signal placements (t=320, t=430) failed because the underlying - # crack already elevated N_t to 4.8σ / 11.3σ — adding consistent direction on - # top of an already-elevated baseline correctly looks like productive wrongness. - # Adversarial patterns must be tested on a clean baseline to be discriminable. - injections = { - "SPOOF_LAYER": { - "t0": 100, "duration": 63, - "fn": lambda e: inject_spoof_layer(e, 100, 63, baseline_vol * 1.5), - "expected": "BASIN_PULL", - "rationale": "Same-direction same-magnitude delta — actor painting toward target price", - }, - "WASH_TRADE": { - "t0": 200, "duration": 63, - "fn": lambda e: inject_wash_trade(e, 200, 63), - "expected": "SEISMIC", - "rationale": "Alternating ±ε — net signal zero, C_t near 0, no directional info", - }, - "MOMENTUM_IGN": { - "t0": 80, "duration": 63, - "fn": lambda e: inject_momentum_ignite(e, 80, 31, 32), - "expected": "BASIN_PULL", - "rationale": "Burst then reversal on quiet baseline — N_t stays below THETA_N; " - "BASIN_PULL during burst (consistent low-N_t), GROUNDED at reversal", - }, - "TAPE_PAINT": { - "t0": 80, "duration": 25, - "fn": lambda e: inject_tape_paint(e, 80, baseline_vol * 1.2, 25), - "expected": "GROUNDED", - "rationale": "Consistent asymptotic approach toward target — high C_t, low N_t " - "(target_gap=1.2σ on quiet baseline keeps N_t<1.0 for most window → " - "GROUNDED dominant; late days with C_t>θ_c become BASIN_PULL; 0 INCUBATING)", - }, - } - - results: dict[str, dict] = {} - total_false_incubating = 0 - - for name, cfg in injections.items(): - epsilon_inj = cfg["fn"](epsilon) - cls = classify_window(epsilon_inj, cfg["t0"], cfg["duration"], baseline_vol) - - false_pos = cls["incubating_days"] - total_false_incubating += false_pos - - results[name] = { - "detected_as": cls["dominant"], - "expected": cfg["expected"], - "pass": (false_pos == 0), - "incubating_days": false_pos, - "basin_pull_days": cls["basin_pull_days"], - "seismic_days": cls["seismic_days"], - "state_counts": cls["counts"], - "ioc_regime": cls["ioc_regime"], - "ioc_regime_name": cls["ioc_regime_name"], - "rationale": cfg["rationale"], - } - - total_window_days = sum(cfg["duration"] for cfg in injections.values()) - false_incubating_rate = total_false_incubating / total_window_days - - genuine_incubating = signal["series"]["state"].count("INCUBATING") - genuine_crystallize = signal["series"]["state"].count("CRYSTALLIZING") - lead_days = (collapse_day - ew_day) if (collapse_day and ew_day) else None - - probe = { - "source_signal": str(signal_path), - "baseline_vol": baseline_vol, - "patterns": results, - "false_incubating_days": total_false_incubating, - "false_incubating_rate": round(false_incubating_rate, 6), - "genuine_crack_incubating_days": genuine_incubating, - "genuine_crack_crystallizing_days": genuine_crystallize, - "genuine_crack_early_warning_day": ew_day, - "genuine_crack_collapse_day": collapse_day, - "lead_time_days": lead_days, - "all_passed": (total_false_incubating == 0), - } - - out_dir.mkdir(parents=True, exist_ok=True) - - json_path = out_dir / "adversarial_probe.json" - with open(json_path, "w") as f: - json.dump(probe, f, indent=2) - - csv_path = out_dir / "adversarial_probe.csv" - with open(csv_path, "w", newline="") as f: - writer = csv.writer(f) - writer.writerow([ - "pattern", "detected_as", "expected", "pass", - "incubating_days", "basin_pull_days", "seismic_days", - "ioc_regime", "ioc_regime_name", - ]) - for name, r in results.items(): - writer.writerow([ - name, r["detected_as"], r["expected"], r["pass"], - r["incubating_days"], r["basin_pull_days"], r["seismic_days"], - r["ioc_regime"], r["ioc_regime_name"], - ]) - - return probe - - -# --------------------------------------------------------------------------- -# Report + entrypoint -# --------------------------------------------------------------------------- - -def report(probe: dict) -> None: - print() - print("=== ADVERSARIAL MARKET PROBE ===") - print(f" Source signal : {probe['source_signal']}") - print(f" Baseline vol (σ) : {probe['baseline_vol']:.6f}") - print() - print(f"{'PATTERN':<16} {'DETECTED':<15} {'EXPECTED':<15} {'PASS':<6} " - f"{'INC_DAYS':<10} {'BP_DAYS':<10} {'IOC_REGIME'}") - print("-" * 85) - for name, r in probe["patterns"].items(): - mark = "✓" if r["pass"] else "✗ FAIL" - print(f" {name:<14} {r['detected_as']:<15} {r['expected']:<15} " - f"{mark:<6} {r['incubating_days']:<10} {r['basin_pull_days']:<10} " - f"{r['ioc_regime_name']}") - print() - print("INVARIANT CHECK") - rate = probe["false_incubating_rate"] - if probe["all_passed"]: - print(f" ✓ false_incubating_rate = {rate:.6f} (target: 0.0)") - print(" ✓ No adversarial pattern misclassified as productive wrongness") - else: - print(f" ✗ false_incubating_rate = {rate:.6f} INVARIANT VIOLATED") - print(" ✗ Adversarial pattern leaked into INCUBATING state") - print() - print("GENUINE CRACK REFERENCE") - print(f" INCUBATING days : {probe['genuine_crack_incubating_days']}") - print(f" CRYSTALLIZING days : {probe['genuine_crack_crystallizing_days']}") - if probe['lead_time_days']: - print(f" Lead time : {probe['lead_time_days']} trading days" - f" ({probe['lead_time_days'] / 252:.2f} yr)") - print() - - -if __name__ == "__main__": - if not _SIGNAL_PATH.exists(): - print( - f"[!] Signal not found: {_SIGNAL_PATH}\n" - " Run 5-Applications/scripts/synthetic_cracking_signal.py first.", - file=sys.stderr, - ) - sys.exit(2) - - probe = run_probe() - report(probe) - - print(f" JSON → {_OUT_DIR / 'adversarial_probe.json'}") - print(f" CSV → {_OUT_DIR / 'adversarial_probe.csv'}") - print() - - if not probe["all_passed"]: - print("[FAIL] Adversarial probe: invariant violated — see above", file=sys.stderr) - sys.exit(1) - - print("[PASS] All adversarial patterns correctly classified") - sys.exit(0) diff --git a/5-Applications/tools-scripts/security/chaos_monkey.py b/5-Applications/tools-scripts/security/chaos_monkey.py deleted file mode 100755 index ee371dcc..00000000 --- a/5-Applications/tools-scripts/security/chaos_monkey.py +++ /dev/null @@ -1,49 +0,0 @@ -#!/usr/bin/env python3 -import json -import os -import sys -import argparse - -CHAOS_FILE = "chaos_injection.json" - -def write_chaos(data): - with open(CHAOS_FILE, 'w') as f: - json.dump(data, f, indent=4) - print(f"[🐵 CHAOS MONKEY] Injection committed: {json.dumps(data)}") - -def cmd_torsion(): - write_chaos({"torsion": 5.0, "label": "TORSION_OVERLOAD_ATTACK"}) - -def cmd_clog(): - write_chaos({"accumulation": 2.0, "label": "CLOG_SATURATION_ATTACK"}) - -def cmd_dmt(): - # DMT triggers when chi is high and torsion is moderate - write_chaos({ - "chi": 0.8, - "torsion": 0.5, - "label": "DMT_LURE_INJECTION" - }) - -def cmd_reset(): - if os.path.exists(CHAOS_FILE): - os.remove(CHAOS_FILE) - print("[🐵 CHAOS MONKEY] Injections cleared. System returning to baseline.") - -def main(): - parser = argparse.ArgumentParser(description="Sovereign Chaos Monkey - Adversarial Stress Testing") - parser.add_argument("mode", choices=["torsion", "clog", "dmt", "reset"], help="Chaos mode to inject") - - args = parser.parse_args() - - if args.mode == "torsion": - cmd_torsion() - elif args.mode == "clog": - cmd_clog() - elif args.mode == "dmt": - cmd_dmt() - elif args.mode == "reset": - cmd_reset() - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/security/inject_fault.py b/5-Applications/tools-scripts/security/inject_fault.py deleted file mode 100644 index f752953a..00000000 --- a/5-Applications/tools-scripts/security/inject_fault.py +++ /dev/null @@ -1,33 +0,0 @@ -import sqlite3 -import json -import os -from datetime import datetime - -def inject_phonon_fault(): - db_path = os.path.expanduser("~/.tardy_mmr.db") - if not os.path.exists(db_path): - print(f"Error: Database not found at {db_path}") - return - - conn = sqlite3.connect(db_path) - ts = datetime.utcnow().isoformat() + "+00:00" - - # Payload indicating hardware algebraic parity failure - payload = { - "agent": "TaN-Systolic-Ring", - "fault": True, - "parity_error": True, - "ring_node": 7, - "root": "ffffffffffffffff" - } - - conn.execute( - "INSERT INTO mmr (leaf_type, payload, leaf_hash, root_hash, ts, node_id, sig) VALUES (?, ?, ?, ?, ?, ?, ?)", - ("PHONON_AMMR", json.dumps(payload), "deadbeef", "deadbeef", ts, "Node-07", "FAULT") - ) - conn.commit() - conn.close() - print("Success: Injected PHONON_AMMR fault record into MMR.") - -if __name__ == "__main__": - inject_phonon_fault() diff --git a/5-Applications/tools-scripts/security/redteam_network_security.py b/5-Applications/tools-scripts/security/redteam_network_security.py deleted file mode 100644 index 24afae74..00000000 --- a/5-Applications/tools-scripts/security/redteam_network_security.py +++ /dev/null @@ -1,196 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""MoE red team runner for network_security.py. - -Sends the current implementation to five expert roles via the local MoE -router (127.0.0.1:8008). Each expert targets a different attack surface. -Findings are printed and written to sessions/. -""" - -import asyncio -import datetime -import json -import pathlib -import sys - -import httpx - -MOE_URL = "http://[::1]:8008/v1/chat/completions" -TARGET_FILE = pathlib.Path(__file__).resolve().parent.parent / "network_security.py" -SESSION_DIR = pathlib.Path(__file__).resolve().parent.parent / "sessions" - -# ── Expert definitions ──────────────────────────────────────────────────────── -# model: explicit model ID from INDIVIDUAL_MODELS so MoE routes directly. -# Each expert sees the full source + a role-specific attack brief. -EXPERTS = [ - { - "name": "Traffic Analyst", - "model": "deepseek-r1-32b", - "temperature": 0.4, - "brief": ( - "You are a passive network traffic analyst. You can only see what goes " - "on the wire: packet sizes, inter-arrival times, byte distributions, and " - "IP/port metadata. You cannot see inside encrypted payloads.\n\n" - "Red team task: given the Python source below, identify every observable " - "timing, volume, or shape pattern that would allow you to distinguish this " - "traffic from genuine Ethereum JSON-RPC background traffic using only " - "passive pcap analysis. Be specific: name the metric, the distinguishing " - "value or range, and how confident a classifier would be." - ), - }, - { - "name": "Protocol Fingerprinter", - "model": "qwen2.5-coder-14b", - "temperature": 0.3, - "brief": ( - "You are a protocol reverse-engineer specializing in DPI signatures. " - "Your tool is a stateful flow inspector that reads plaintext JSON fields " - "in HTTP request bodies.\n\n" - "Red team task: given the Python source below, identify every field, " - "value pattern, or structural regularity in the external shell JSON that " - "would let you write a reliable snort/suricata rule or ML feature. " - "For each finding: write the exact regex or feature expression, the " - "false-positive rate against real Ethereum RPC traffic, and what you'd " - "need to see to break your own rule." - ), - }, - { - "name": "Statistical Classifier", - "model": "mixtral-8x7b", - "temperature": 0.5, - "brief": ( - "You are a statistical traffic analyst with access to a long-run flow " - "log: action_type, route token, bucketed amount, and timestamp for every " - "request over 24 hours. You cannot see inside encrypted payloads.\n\n" - "Red team task: given the Python source below, design the minimum-feature " - "classifier that separates this system from benign Ethereum traffic. " - "Which distributional fingerprints survive the CognitiveSmoother and " - "stochastic quantization? Which rotation mechanisms have residual " - "structure you can exploit? Cite the specific code paths." - ), - }, - { - "name": "Cryptographic Auditor", - "model": "deepseek-r1-32b", - "temperature": 0.2, - "brief": ( - "You are a cryptographic protocol auditor. You have the source code and " - "can decrypt internal payloads if you find a key management flaw.\n\n" - "Red team task: audit the PQ KEM + XOR-stream construction for:\n" - "1. Key derivation weaknesses (nonce reuse, weak shared-secret usage)\n" - "2. Auth tag bypass or forgery paths\n" - "3. Timing or padding oracle side-channels\n" - "4. Epoch key rotation — when does _epoch_key get replaced, and what " - " happens to sessions in flight?\n" - "5. Any place where encrypt_internal could be called with a predictable " - " or attacker-influenced nonce.\n" - "Produce a severity-ranked finding list with PoC sketches." - ), - }, - { - "name": "OpSec Auditor", - "model": "phi4-14b", - "temperature": 0.4, - "brief": ( - "You are an operational security auditor focusing on metadata leaks and " - "long-run linkability. You watch traffic over weeks and correlate " - "ephemeral identifiers.\n\n" - "Red team task: given the Python source below, find every place where " - "long-term linkability survives the rotation mechanisms:\n" - "1. Does _ephemeral_node_id provide real unlinkability or just pseudonymity?\n" - "2. Does route salt rotation break all route correlation or only intra-window?\n" - "3. Are there any fields that are stable across rotation epochs?\n" - "4. How does a well-positioned observer use the Gumbel-perturbed execution " - " ordering in mevbot_swarm_sim.py to fingerprint strategy over time?\n" - "5. What does the cover traffic from generate_cover_envelope() leak if " - " emitted at constant rate?\n" - "Rank by linkability window (seconds → days → permanent)." - ), - }, -] - - -async def query_expert(client: httpx.AsyncClient, expert: dict, source: str) -> dict: - messages = [ - { - "role": "system", - "content": expert["brief"], - }, - { - "role": "user", - "content": f"```python\n{source}\n```\n\nProvide your red team findings.", - }, - ] - payload = { - "model": expert["model"], - "messages": messages, - "temperature": expert["temperature"], - "max_tokens": 1500, - } - try: - resp = await client.post(MOE_URL, json=payload, timeout=120.0) - resp.raise_for_status() - data = resp.json() - content = data["choices"][0]["message"]["content"] - backend = data.get("router", {}).get("backend_model", "unknown") - return {"expert": expert["name"], "model": expert["model"], "backend": backend, - "findings": content, "error": None} - except Exception as e: - return {"expert": expert["name"], "model": expert["model"], "backend": "error", - "findings": "", "error": str(e)} - - -async def run_redteam() -> None: - if not TARGET_FILE.exists(): - print(f"ERROR: {TARGET_FILE} not found", file=sys.stderr) - sys.exit(1) - - source = TARGET_FILE.read_text(encoding="utf-8") - print(f"[redteam] target: {TARGET_FILE.name} ({len(source)} bytes)") - print(f"[redteam] router: {MOE_URL}") - print(f"[redteam] experts: {len(EXPERTS)}\n") - - async with httpx.AsyncClient() as client: - tasks = [query_expert(client, e, source) for e in EXPERTS] - results = await asyncio.gather(*tasks) - - # ── Print findings ──────────────────────────────────────────────────────── - all_findings = [] - for r in results: - sep = "─" * 72 - print(f"\n{sep}") - print(f" {r['expert']} [{r['model']} → {r['backend']}]") - print(sep) - if r["error"]: - print(f" ERROR: {r['error']}") - else: - print(r["findings"]) - all_findings.append(r) - - # ── Write session file ──────────────────────────────────────────────────── - SESSION_DIR.mkdir(exist_ok=True) - ts = datetime.datetime.utcnow().strftime("%Y%m%dT%H%M%SZ") - session_path = SESSION_DIR / f"redteam-network-security-{ts}.json" - session_path.write_text( - json.dumps( - { - "schema": "redteam/v1", - "target": str(TARGET_FILE), - "ts": ts, - "experts": all_findings, - }, - indent=2, - ensure_ascii=False, - ), - encoding="utf-8", - ) - print(f"\n[redteam] findings written → {session_path.name}") - - -if __name__ == "__main__": - asyncio.run(run_redteam()) diff --git a/5-Applications/tools-scripts/semi_jack/semi_jack_constraint_model.py b/5-Applications/tools-scripts/semi_jack/semi_jack_constraint_model.py deleted file mode 100644 index 12274e20..00000000 --- a/5-Applications/tools-scripts/semi_jack/semi_jack_constraint_model.py +++ /dev/null @@ -1,606 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -semi_jack_constraint_model.py — GeomTREE Semi-Jack structural constraint analyzer - -Applies the full OSHA/ASME PALD/EN 12839 constraint envelope to the existing -merkle_tree.json geometry and reports every violation. Works at toy scale (22mm) -first — if it fails here, scaling up won't fix it. - -Standards applied: - ASME PALD-2009 — proof load 200% SWL, no permanent deformation - ASME B30.1 — design-to-failure ≥ 3× SWL - OSHA 1926.305 — firm foundation, never under load on jack alone - OSHA 1910.244 — rated capacity marked, sufficient for load - EN 12839 — jack stand requirements, 200% proof test - FMCSA grade ops — stability on 15° incline with full load - -Usage: - python 5-Applications/scripts/semi_jack_constraint_model.py - python 5-Applications/scripts/semi_jack_constraint_model.py --radius 1.5 --swl 34335 - python 5-Applications/scripts/semi_jack_constraint_model.py --radius 1.5 --list-flaws -""" - -from __future__ import annotations - -import argparse -import json -import math -import sys -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# ── Standards-derived constraint constants ───────────────────────────────────── - -# ASME PALD / EN 12839 — proof load multiplier (200% of SWL) -PROOF_LOAD_FACTOR = 2.0 - -# ASME B30.1 — minimum design-to-failure factor (3× SWL) -DESIGN_FAILURE_FACTOR = 3.0 - -# FMCSA 49 CFR 393 — maximum operating grade for stability check -MAX_GRADE_DEG = 15.0 - -# OSHA 1926.305 / ASME PALD — minimum base-to-height ratio for stability on level -MIN_BASE_HEIGHT_RATIO = 0.5 - -# ── Material envelopes ───────────────────────────────────────────────────────── -# All stresses in N/mm² (MPa) - -# ── Test mass helpers ───────────────────────────────────────────────────────── - -def block_load_N(l_mm: float, w_mm: float, h_mm: float, density_g_cm3: float) -> float: - vol_cm3 = (l_mm * w_mm * h_mm) / 1000.0 - return (vol_cm3 * density_g_cm3 / 1000.0) * 9.81 - -def sphere_load_N(r_mm: float, density_g_cm3: float) -> float: - vol_cm3 = (4/3 * math.pi * r_mm**3) / 1000.0 - return (vol_cm3 * density_g_cm3 / 1000.0) * 9.81 - -def cylinder_load_N(r_mm: float, h_mm: float, density_g_cm3: float) -> float: - vol_cm3 = (math.pi * r_mm**2 * h_mm) / 1000.0 - return (vol_cm3 * density_g_cm3 / 1000.0) * 9.81 - -OSMIUM_DENSITY = 22.59 # g/cm³ — densest stable element, reference only - -def osmium_brick_load_N(l_mm: float, w_mm: float, h_mm: float) -> float: - return block_load_N(l_mm, w_mm, h_mm, OSMIUM_DENSITY) - - -# ── Lattice / honeycomb mass ─────────────────────────────────────────────────── -# The test mass IS the same Merkle tree geometry — a lattice of tubular struts. -# Mass = sum of strut volumes × bulk material density. -# Fill factor = strut volume / bounding box volume (how hollow it is). - -def lattice_mass_kg( - nodes: Dict[int, "Node"], - edges: List[Tuple[int, int]], - tubule_radius_mm: float, - bulk_density_g_cm3: float, -) -> Tuple[float, float, float]: - """ - Returns (mass_kg, fill_factor, strut_vol_mm3). - bulk_density_g_cm3 is the density of the strut material itself — any value. - """ - strut_vol_mm3 = 0.0 - for p_id, c_id in edges: - p, c = nodes[p_id], nodes[c_id] - length = math.sqrt( - (c.x - p.x)**2 + (c.y - p.y)**2 + (c.z - p.z)**2 - ) - strut_vol_mm3 += math.pi * tubule_radius_mm**2 * length - - # Bounding box of all nodes - xs = [n.x for n in nodes.values()] - ys = [n.y for n in nodes.values()] - zs = [n.z for n in nodes.values()] - bbox_vol_mm3 = ( - (max(xs) - min(xs) or 1.0) * - (max(ys) - min(ys) or 1.0) * # guard: planar Y=0 → use 1mm depth - (max(zs) - min(zs) or 1.0) - ) - - fill_factor = strut_vol_mm3 / bbox_vol_mm3 - mass_kg = (strut_vol_mm3 / 1000.0) * bulk_density_g_cm3 / 1000.0 - return mass_kg, fill_factor, strut_vol_mm3 - - -MATERIALS = { - "SLS_PA12": { - "label": "SLS Nylon PA12 (no fiber)", - "compressive_yield_MPa": 70.0, # no permanent deformation under proof load - "compressive_ult_MPa": 95.0, # failure threshold (must exceed 3× SWL stress) - "tensile_yield_MPa": 48.0, - "shear_yield_MPa": 30.0, # ~0.6 × tensile yield (von Mises) - "density_g_cm3": 1.01, - }, - "SLS_PA12_GF": { - "label": "SLS Nylon PA12 + 30% glass fiber", - "compressive_yield_MPa": 120.0, - "compressive_ult_MPa": 160.0, - "tensile_yield_MPa": 90.0, - "shear_yield_MPa": 52.0, - "density_g_cm3": 1.30, - }, - "PLA": { - "label": "FDM PLA (prototype only)", - "compressive_yield_MPa": 50.0, - "compressive_ult_MPa": 65.0, - "tensile_yield_MPa": 37.0, - "shear_yield_MPa": 22.0, - "density_g_cm3": 1.24, - }, - "AL6061_T6": { - "label": "Aluminum 6061-T6 (original JSON material)", - "compressive_yield_MPa": 276.0, - "compressive_ult_MPa": 310.0, - "tensile_yield_MPa": 276.0, - "shear_yield_MPa": 165.0, - "density_g_cm3": 2.70, - }, -} - -# ── Data structures ──────────────────────────────────────────────────────────── - -@dataclass -class Node: - id: int - x: float - y: float - z: float - force_N: float # nominal load at this node (from JSON) - children: List[int] = field(default_factory=list) - parent: Optional[int] = None - -@dataclass -class EdgeResult: - parent_id: int - child_id: int - length_mm: float - branch_angle_deg: float # angle from vertical (load axis) - axial_force_N: float # component along branch axis - lateral_force_N: float # component perpendicular (lateral shear) - cross_section_mm2: float - axial_stress_MPa: float - shear_stress_MPa: float - von_mises_MPa: float - proof_ok: bool # survives 200% SWL without yield - failure_ok: bool # fails at or above 300% SWL - is_planar: bool # Y-coordinate delta is zero (planarity flag) - -@dataclass -class GeometryFlaw: - severity: str # CRITICAL / WARNING / INFO - location: str - description: str - value: float - limit: float - unit: str - -# ── Geometry loader ──────────────────────────────────────────────────────────── - -def load_tree(path: Path) -> Tuple[Dict[int, Node], List[Tuple[int, int]]]: - data = json.loads(path.read_text()) - nodes: Dict[int, Node] = {} - for n in data["nodes"]: - nodes[n["id"]] = Node( - id=n["id"], x=n["x"], y=n["y"], z=n["z"], - force_N=n.get("F", 0.0) - ) - edges = [(e[0], e[1]) for e in data["edges"]] - for p, c in edges: - nodes[p].children.append(c) - nodes[c].parent = p - return nodes, edges - -# ── Constraint analysis ──────────────────────────────────────────────────────── - -def analyze_edge( - parent: Node, child: Node, - swl_N: float, - tubule_radius_mm: float, - material: dict, - root_force_N: float = 45000.0, -) -> EdgeResult: - dx = child.x - parent.x - dy = child.y - parent.y - dz = child.z - parent.z - length = math.sqrt(dx**2 + dy**2 + dz**2) - - # Branch angle from vertical (Z axis is load axis, z goes negative downward) - horiz = math.sqrt(dx**2 + dy**2) - vert = abs(dz) - branch_angle_deg = math.degrees(math.atan2(horiz, vert)) if vert > 1e-9 else 90.0 - - # Scale branch force to specified SWL. - # Use child's fraction of the ROOT load (not just parent), so stress - # decreases correctly at deeper levels of the tree. - branch_force = swl_N * (child.force_N / max(root_force_N, 1e-9)) - - # Axial (along branch) and lateral (perpendicular) components - angle_rad = math.radians(branch_angle_deg) - axial_force = branch_force / max(math.cos(angle_rad), 1e-6) # actual strut force - lateral_force = axial_force * math.sin(angle_rad) # lateral component - - # Cross-section - area_mm2 = math.pi * tubule_radius_mm**2 - - # Stresses (N/mm² = MPa) - axial_stress = axial_force / area_mm2 - shear_stress = lateral_force / area_mm2 - von_mises = math.sqrt(axial_stress**2 + 3 * shear_stress**2) - - # ASME PALD / EN 12839: proof load = 2× SWL — no permanent deformation - proof_stress = von_mises * PROOF_LOAD_FACTOR - proof_ok = proof_stress <= material["compressive_yield_MPa"] - - # ASME B30.1: failure must not occur below 3× SWL - failure_stress = von_mises * DESIGN_FAILURE_FACTOR - failure_ok = failure_stress <= material["compressive_ult_MPa"] - - is_planar = abs(dy) < 1e-6 and abs(child.y) < 1e-6 - - return EdgeResult( - parent_id=parent.id, - child_id=child.id, - length_mm=length, - branch_angle_deg=branch_angle_deg, - axial_force_N=axial_force, - lateral_force_N=lateral_force, - cross_section_mm2=area_mm2, - axial_stress_MPa=axial_stress, - shear_stress_MPa=shear_stress, - von_mises_MPa=von_mises, - proof_ok=proof_ok, - failure_ok=failure_ok, - is_planar=is_planar, - ) - - -def check_global_geometry(nodes: Dict[int, Node], swl_N: float) -> List[GeometryFlaw]: - flaws: List[GeometryFlaw] = [] - - # Find root (no parent) and leaves (no children) - root = next(n for n in nodes.values() if n.parent is None) - leaves = [n for n in nodes.values() if not n.children] - - # ── Flaw 1: Planarity check ────────────────────────────────────────────── - all_y = [n.y for n in nodes.values()] - y_span = max(all_y) - min(all_y) - if y_span < 1e-6: - flaws.append(GeometryFlaw( - severity="CRITICAL", - location="ALL NODES", - description=( - "Structure is entirely planar (Y=0 for all nodes). " - "Zero resistance to any lateral force in the Y direction. " - "A 15° grade tilt in the Y plane produces unconstrained rotation. " - "Fix: rotate alternating branch levels by 90° in Y, " - "or use pentagonal (5-way) branching in 3D." - ), - value=y_span, - limit=1.0, # at minimum, leaves must span some Y distance - unit="mm Y-span", - )) - - # ── Flaw 2: Base-to-height ratio (stability on level) ─────────────────── - xs = [n.x for n in leaves] - ys = [n.y for n in leaves] - base_span_x = max(xs) - min(xs) if xs else 0.0 - base_span_y = max(ys) - min(ys) if ys else 0.0 - base_span = math.sqrt(base_span_x**2 + base_span_y**2) # diagonal - total_height = abs(root.z - min(n.z for n in nodes.values())) - - ratio = base_span / max(total_height, 1e-9) - if ratio < MIN_BASE_HEIGHT_RATIO: - flaws.append(GeometryFlaw( - severity="CRITICAL", - location="ROOT↔LEAVES", - description=( - f"Base/height ratio {ratio:.3f} < {MIN_BASE_HEIGHT_RATIO} (ASME PALD / OSHA 1926.305). " - "Structure tips under lateral load. " - f"Base span: {base_span:.1f}mm, height: {total_height:.1f}mm. " - "Fix: widen leaf node spread or reduce height." - ), - value=ratio, - limit=MIN_BASE_HEIGHT_RATIO, - unit="base/height", - )) - - # ── Flaw 3: 15° grade stability (FMCSA, OSHA field ops) ───────────────── - # Under 15° tilt, CG must remain over base polygon - # Simple check: CG horizontal shift = height × tan(15°) - cg_shift = total_height * math.tan(math.radians(MAX_GRADE_DEG)) - half_base_x = base_span_x / 2.0 - if cg_shift > half_base_x: - flaws.append(GeometryFlaw( - severity="CRITICAL", - location="STABILITY@15°", - description=( - f"On a {MAX_GRADE_DEG}° grade the CG shifts {cg_shift:.1f}mm horizontally " - f"but X half-base is only {half_base_x:.1f}mm. " - "Structure tips before reaching operating grade. " - "Fix: increase base span or reduce height." - ), - value=cg_shift, - limit=half_base_x, - unit="mm CG shift vs half-base", - )) - - # ── Flaw 4: Binary vs pentagonal branching ─────────────────────────────── - max_children = max(len(n.children) for n in nodes.values()) - if max_children <= 2: - flaws.append(GeometryFlaw( - severity="WARNING", - location="BRANCHING FACTOR", - description=( - f"Maximum branching factor = {max_children} (binary). " - "Patent spec calls for pentagonal (5-way) branching. " - "Binary branching concentrates 50% of load at each parent node; " - "pentagonal distributes 20% per branch, reducing peak node stress by ~2.5×. " - "At toy scale this is acceptable for geometry validation but must be " - "upgraded before load testing." - ), - value=float(max_children), - limit=5.0, - unit="branches/node", - )) - - # ── Flaw 5: Root force vs SWL ──────────────────────────────────────────── - root_force = root.force_N - if abs(root_force - swl_N) / max(swl_N, 1e-9) > 0.05: - flaws.append(GeometryFlaw( - severity="WARNING", - location=f"ROOT NODE {root.id}", - description=( - f"Root force in JSON ({root_force:.0f}N = {root_force/9.81:.0f}kg) " - f"does not match specified SWL ({swl_N:.0f}N = {swl_N/9.81:.0f}kg). " - "Constraint analysis uses specified SWL; JSON force is noted as mismatch." - ), - value=root_force, - limit=swl_N, - unit="N root force", - )) - - # ── Flaw 6: No leaf pad geometry ───────────────────────────────────────── - flaws.append(GeometryFlaw( - severity="INFO", - location="LEAF NODES", - description=( - f"{len(leaves)} leaf nodes are dimensionless points. " - "OSHA 1926.305(b): jack must sit on firm foundation — " - "requires a base pad geometry. " - "At toy scale: minimum pad area = load / allowable_bearing_pressure. " - "For SLS PA12 on printed surface: ~2× tubule area minimum. " - "Fix: add cap geometry to leaf nodes in STL output." - ), - value=0.0, - limit=1.0, - unit="pad area defined", - )) - - return flaws - - -def _root_force(nodes: Dict[int, Node]) -> float: - root = next(n for n in nodes.values() if n.parent is None) - return root.force_N - - -def find_minimum_radius( - nodes: Dict[int, Node], - edges: List[Tuple[int, int]], - swl_N: float, - material: dict, -) -> float: - """Binary search for minimum tubule radius that passes all edge constraints.""" - rf = _root_force(nodes) - lo, hi = 0.1, 50.0 - for _ in range(40): - mid = (lo + hi) / 2.0 - all_ok = True - for p_id, c_id in edges: - r = analyze_edge(nodes[p_id], nodes[c_id], swl_N, mid, material, rf) - if not r.proof_ok or not r.failure_ok: - all_ok = False - break - if all_ok: - hi = mid - else: - lo = mid - return hi - - -# ── Report ───────────────────────────────────────────────────────────────────── - -def print_report( - nodes: Dict[int, Node], - edges: List[Tuple[int, int]], - swl_N: float, - tubule_radius_mm: float, - material_key: str, -): - mat = MATERIALS[material_key] - sep = "=" * 72 - print(f"\n{sep}") - print(" SEMI-JACK CONSTRAINT MODEL — GeomTREE Structural Analysis") - print(sep) - print(f" Material : {mat['label']}") - print(f" SWL : {swl_N:.0f} N ({swl_N/9.81:.1f} kg)") - print(f" Proof load : {swl_N*PROOF_LOAD_FACTOR:.0f} N ({swl_N*PROOF_LOAD_FACTOR/9.81:.1f} kg) [ASME PALD 200%]") - print(f" Fail floor : {swl_N*DESIGN_FAILURE_FACTOR:.0f} N ({swl_N*DESIGN_FAILURE_FACTOR/9.81:.1f} kg) [ASME B30.1 300%]") - print(f" Tubule r : {tubule_radius_mm:.2f} mm (area {math.pi*tubule_radius_mm**2:.3f} mm²)") - print(f" Yield allow: {mat['compressive_yield_MPa']} MPa (proof limit)") - print(f" Ult allow : {mat['compressive_ult_MPa']} MPa (failure floor)") - print() - - # Global geometry flaws - flaws = check_global_geometry(nodes, swl_N) - print(f" GEOMETRY FLAWS ({len(flaws)} found)") - print(f" {'-'*68}") - for f in flaws: - marker = {"CRITICAL": "✗", "WARNING": "△", "INFO": "·"}[f.severity] - print(f" {marker} [{f.severity:8s}] {f.location}") - for line in f.description.split(". "): - if line.strip(): - print(f" {line.strip()}.") - print(f" Value: {f.value:.3f} {f.unit} | Limit: {f.limit:.3f} {f.unit}") - print() - - # Per-edge analysis - print(f" EDGE STRESS ANALYSIS (r={tubule_radius_mm:.2f}mm)") - print(f" {'-'*68}") - print(f" {'Edge':8s} {'Len':6s} {'Angle':7s} {'Axial':8s} {'Shear':8s} {'vMises':8s} {'Proof':6s} {'Fail':5s} {'Planar':6s}") - - rf = _root_force(nodes) - critical_edges = [] - planar_edges = [] - for p_id, c_id in edges: - r = analyze_edge(nodes[p_id], nodes[c_id], swl_N, tubule_radius_mm, mat, rf) - proof_str = "OK" if r.proof_ok else "FAIL" - fail_str = "OK" if r.failure_ok else "FAIL" - planar_str = "YES" if r.is_planar else "no" - flag = " " - if not r.proof_ok or not r.failure_ok: - flag = "✗ " - critical_edges.append((p_id, c_id, r)) - if r.is_planar: - planar_edges.append((p_id, c_id)) - print( - f" {flag}{p_id:2d}→{c_id:2d} " - f"{r.length_mm:5.1f}mm " - f"{r.branch_angle_deg:5.1f}° " - f"{r.axial_stress_MPa:6.2f}MPa " - f"{r.shear_stress_MPa:6.2f}MPa " - f"{r.von_mises_MPa:6.2f}MPa " - f"{proof_str:6s} " - f"{fail_str:5s} " - f"{planar_str}" - ) - - # Minimum radius - min_r = find_minimum_radius(nodes, edges, swl_N, mat) - print() - print(f" MINIMUM TUBULE RADIUS TO PASS ALL CONSTRAINTS: {min_r:.3f} mm") - print(f" (at specified SWL={swl_N/9.81:.0f}kg, material={material_key})") - print() - - # Summary - n_crit = sum(1 for f in flaws if f.severity == "CRITICAL") - n_warn = sum(1 for f in flaws if f.severity == "WARNING") - n_planar = len(planar_edges) - n_fail = len(critical_edges) - print(" SUMMARY") - print(" " + "-" * 68) - print(f" Critical geometry flaws : {n_crit}") - print(f" Warnings : {n_warn}") - print(f" Planar edges : {n_planar}/{len(edges)} (all Y=0 — zero Y-axis resistance)") - print(f" Stress failures : {n_fail}/{len(edges)} edge(s) at r={tubule_radius_mm:.2f}mm") - print(f" Minimum safe radius : {min_r:.3f} mm") - - if n_crit > 0: - print() - print(f" VERDICT: FAILS CONSTRAINT ENVELOPE — {n_crit} critical flaw(s) must be resolved") - print(" before this geometry is valid at ANY scale.") - else: - print() - print(f" VERDICT: Geometry passes envelope at r={tubule_radius_mm:.2f}mm with {material_key}.") - print("=" * 72 + "\n") - - -# ── Entry point ──────────────────────────────────────────────────────────────── - -def main(): - ap = argparse.ArgumentParser(description="Semi-Jack GeomTREE constraint analyzer") - ap.add_argument("--json", default="5-Applications/out/sovereign_jenga/quantum_annealed/merkle_tree.json", - help="Path to merkle_tree.json") - ap.add_argument("--swl", type=float, default=34335.0, - help="Safe Working Load in Newtons (default: 34335 N = 3500 kg)") - ap.add_argument("--radius", type=float, default=1.0, - help="Tubule cross-section radius in mm (default: 1.0mm)") - ap.add_argument("--material", default="SLS_PA12", - choices=list(MATERIALS.keys()), - help="Material key (default: SLS_PA12)") - ap.add_argument("--toy-scale", action="store_true", - help="Scale SWL to toy proportions (area ratio from 22mm height to real 280mm)") - ap.add_argument("--lattice-mass", nargs=2, type=float, metavar=("R", "D"), - help="Use the tree geometry itself as the test mass: " - "tubule radius R mm, bulk strut density D g/cm³. " - "SWL = weight of the lattice at that radius and density.") - ap.add_argument("--osmium-brick", nargs=3, type=float, metavar=("L", "W", "H"), - help="Osmium block L×W×H mm as SWL (density=22.59 g/cm³)") - ap.add_argument("--test-block", nargs=4, type=float, metavar=("L", "W", "H", "D"), - help="Block L×W×H mm at density D g/cm³ as SWL. " - "Use any density — mass/weight is what matters.") - ap.add_argument("--test-sphere", nargs=2, type=float, metavar=("R", "D"), - help="Sphere radius R mm at density D g/cm³ as SWL.") - ap.add_argument("--test-cylinder", nargs=3, type=float, metavar=("R", "H", "D"), - help="Cylinder radius R mm, height H mm, density D g/cm³ as SWL.") - args = ap.parse_args() - - json_path = Path(args.json) - if not json_path.exists(): - json_path = Path(__file__).parent.parent / args.json - if not json_path.exists(): - print(f"ERROR: {args.json} not found", file=sys.stderr) - sys.exit(1) - - nodes, edges = load_tree(json_path) - - swl = args.swl - if args.lattice_mass: - r_mm, density = args.lattice_mass - mass_kg, fill, vol = lattice_mass_kg(nodes, edges, r_mm, density) - swl = mass_kg * 9.81 - print( - f"[lattice-mass] tree geometry as test mass:\n" - f" strut r={r_mm}mm density={density} g/cm³\n" - f" strut vol={vol:.2f} mm³ fill factor={fill:.4f}\n" - f" mass={mass_kg:.6f} kg load={swl:.6f} N" - ) - elif args.test_block: - l, w, h, d = args.test_block - swl = block_load_N(l, w, h, d) - print(f"[test-block] {l:.1f}×{w:.1f}×{h:.1f}mm density={d} g/cm³ " - f"→ {swl/9.81:.4f} kg {swl:.4f} N") - elif args.test_sphere: - r, d = args.test_sphere - swl = sphere_load_N(r, d) - print(f"[test-sphere] r={r:.1f}mm density={d} g/cm³ " - f"→ {swl/9.81:.4f} kg {swl:.4f} N") - elif args.test_cylinder: - r, h, d = args.test_cylinder - swl = cylinder_load_N(r, h, d) - print(f"[test-cyl] r={r:.1f}mm h={h:.1f}mm density={d} g/cm³ " - f"→ {swl/9.81:.4f} kg {swl:.4f} N") - elif args.osmium_brick: - l_mm, w_mm, h_mm = args.osmium_brick - swl = osmium_brick_load_N(l_mm, w_mm, h_mm) - print(f"[osmium-brick] {l_mm:.0f}×{w_mm:.0f}×{h_mm:.0f}mm " - f"density={OSMIUM_DENSITY} g/cm³ " - f"→ {swl/9.81:.4f} kg {swl:.4f} N") - elif args.toy_scale: - # Scale SWL by linear scale squared: toy height 22mm vs real min 280mm - scale = (22.0 / 280.0) ** 2 - swl = args.swl * scale - print(f"[toy-scale] SWL scaled by {scale:.4f} → {swl:.1f} N ({swl/9.81:.2f} kg)") - - print_report(nodes, edges, swl, args.radius, args.material) - - # Material comparison - print(" MATERIAL COMPARISON (minimum radius to pass at specified SWL)") - print(" " + "-" * 50) - for key, mat in MATERIALS.items(): - r = find_minimum_radius(nodes, edges, swl, mat) - print(f" {key:15s}: min radius = {r:.3f} mm ({mat['label']})") - print() - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/semi_jack/semi_jack_geometry_search.py b/5-Applications/tools-scripts/semi_jack/semi_jack_geometry_search.py deleted file mode 100644 index 1c35359b..00000000 --- a/5-Applications/tools-scripts/semi_jack/semi_jack_geometry_search.py +++ /dev/null @@ -1,478 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -semi_jack_geometry_search.py — n-space geometry search for the Semi-Jack - -A "perfect mass in n-space" is the geometry where stress is perfectly -uniform across every node in the Merkle tree — the attractor state where -no node is over-loaded and none is under-utilised. Every strut operates -at the same fraction of its capacity simultaneously. - -The parameter space (n-space) for a depth-D tree: - branch_angle[0..D-1] — angle from vertical per level (degrees) - az_offset[0..D-1] — azimuthal rotation between levels (degrees) - branching_factor — children per node (2..6) - tubule_radius — strut cross-section radius (mm) - -Total: 2D + 2 dimensions. Default D=4 → 10D search space. - -Fitness = stress_variance across all edges + constraint penalties. -Perfect geometry → fitness = 0. - -Parallel iteration: each worker evaluates one candidate independently. -The main loop keeps the top-K survivors, perturbs them, and re-evaluates. -No shared state between workers — pure function evaluation. - -Usage: - python 5-Applications/scripts/semi_jack_geometry_search.py - python 5-Applications/scripts/semi_jack_geometry_search.py --depth 4 --pop 64 --iters 50 - python 5-Applications/scripts/semi_jack_geometry_search.py --depth 4 --workers 8 --out best.json -""" - -from __future__ import annotations - -import argparse -import json -import math -import random -import sys -from concurrent.futures import ProcessPoolExecutor, as_completed -from dataclasses import dataclass, field -from pathlib import Path -from typing import Dict, List, Optional, Tuple - -# ── Constants (mirror constraint_model) ─────────────────────────────────────── - -PROOF_LOAD_FACTOR = 2.0 -DESIGN_FAIL_FACTOR = 3.0 - -# Default material: SLS PA12 (strut structural limits) -MAT_YIELD_MPA = 70.0 -MAT_ULT_MPA = 95.0 - -# Search space bounds -ANGLE_MIN, ANGLE_MAX = 5.0, 60.0 # branch angle from vertical (deg) -AZ_MIN, AZ_MAX = 0.0, 180.0 # azimuthal offset (deg) -BF_MIN, BF_MAX = 2, 6 # branching factor -RADIUS_MIN, RADIUS_MAX = 0.3, 5.0 # tubule radius (mm) - -# ── Parametric tree geometry ─────────────────────────────────────────────────── - -@dataclass -class GNode: - id: int - x: float - y: float - z: float - load_frac: float # fraction of root SWL at this node - parent: Optional[int] = None - depth: int = 0 - -@dataclass -class Candidate: - branch_angles: List[float] # one per depth level - az_offsets: List[float] # azimuthal rotation per level (deg) - branching_factor: int - tubule_radius: float - fitness: float = float("inf") - nodes: List[GNode] = field(default_factory=list) - edges: List[Tuple[int,int]] = field(default_factory=list) - - -def generate_tree( - depth: int, - branch_angles: List[float], - az_offsets: List[float], - branching_factor: int, - height_per_level: float = 6.0, # mm per level -) -> Tuple[List[GNode], List[Tuple[int,int]]]: - """ - Build a rooted tree in 3D. - - Root at (0, 0, 0). - - At each level, children are placed symmetrically around the parent, - at branch_angle[level] from vertical, spaced evenly in azimuth, - with az_offsets[level] rotating the whole fan at that level. - - load_frac halves (for binary) or divides by branching_factor at each step. - """ - nodes: List[GNode] = [GNode(id=0, x=0.0, y=0.0, z=0.0, load_frac=1.0, depth=0)] - edges: List[Tuple[int,int]] = [] - node_id = 1 - - current_level = [0] # indices of nodes at current depth - - for lv in range(depth): - angle_deg = branch_angles[min(lv, len(branch_angles)-1)] - angle_rad = math.radians(angle_deg) - az_base = az_offsets[min(lv, len(az_offsets)-1)] - next_level = [] - - for pid in current_level: - parent = nodes[pid] - child_frac = parent.load_frac / branching_factor - step_z = height_per_level * math.cos(angle_rad) - step_r = height_per_level * math.sin(angle_rad) - - for k in range(branching_factor): - az_deg = az_base + k * (360.0 / branching_factor) - az_rad = math.radians(az_deg) - cx = parent.x + step_r * math.cos(az_rad) - cy = parent.y + step_r * math.sin(az_rad) - cz = parent.z - step_z # growing downward - - child = GNode( - id=node_id, - x=cx, y=cy, z=cz, - load_frac=child_frac, - parent=pid, - depth=lv+1, - ) - nodes.append(child) - edges.append((pid, node_id)) - next_level.append(node_id) - node_id += 1 - - current_level = next_level - - return nodes, edges - - -# ── Stress calculation ───────────────────────────────────────────────────────── - -ATM_PRESSURE_PA = 101_325.0 # Pa — standard atmosphere - -def edge_von_mises( - parent: GNode, child: GNode, - swl_N: float, radius_mm: float, - with_atm: bool = True, -) -> float: - """ - Von Mises stress (MPa) in one strut. - Includes atmospheric pressure as additional axial compression when with_atm=True. - ATM adds: P_atm × A / A = P_atm (MPa) directly to axial stress. - """ - dx = child.x - parent.x - dy = child.y - parent.y - dz = child.z - parent.z - length = math.sqrt(dx**2 + dy**2 + dz**2) - if length < 1e-9: - return 0.0 - - horiz = math.sqrt(dx**2 + dy**2) - vert = abs(dz) - angle_rad = math.atan2(horiz, max(vert, 1e-9)) - - branch_force = swl_N * child.load_frac - axial = branch_force / max(math.cos(angle_rad), 1e-6) - lateral = axial * math.sin(angle_rad) - - area = math.pi * radius_mm**2 - sa = axial / area - ss = lateral / area - - # Atmospheric pressure adds uniform compressive stress on all strut faces - atm_stress = (ATM_PRESSURE_PA * 1e-6) if with_atm else 0.0 # Pa → MPa - sa_total = sa + atm_stress - - return math.sqrt(sa_total**2 + 3 * ss**2) - - -# ── Fitness function ─────────────────────────────────────────────────────────── - -def fitness( - nodes: List[GNode], - edges: List[Tuple[int,int]], - radius: float, - swl_N: float, - with_atm: bool = True, -) -> Tuple[float, List[float]]: - """ - Fitness = variance of normalised stresses + heavy penalties. - - Perfect geometry: all stresses equal → variance = 0. - Constraint penalties: - - proof stress > yield → +1000 per violating edge - - fail stress < ult → +1000 per edge that fails too early - - base/height ratio → +500 if unstable - - planarity → +500 if all Y ≈ 0 - """ - node_map = {n.id: n for n in nodes} - stresses: List[float] = [] - - proof_violations = 0 - fail_violations = 0 - - for p_id, c_id in edges: - p = node_map[p_id] - c = node_map[c_id] - vm = edge_von_mises(p, c, swl_N, radius, with_atm) - stresses.append(vm) - - if vm * PROOF_LOAD_FACTOR > MAT_YIELD_MPA: proof_violations += 1 - if vm * DESIGN_FAIL_FACTOR > MAT_ULT_MPA: fail_violations += 1 - - if not stresses: - return float("inf"), [] - - # Normalise stresses to [0,1] relative to yield - norm = [s / max(MAT_YIELD_MPA, 1e-9) for s in stresses] - mean = sum(norm) / len(norm) - variance = sum((s - mean)**2 for s in norm) / len(norm) - - # Penalties - penalty = 0.0 - penalty += 1000.0 * proof_violations - penalty += 1000.0 * fail_violations - - # Stability: base span vs height - ys = [n.y for n in nodes] - xs = [n.x for n in nodes] - y_span = max(ys) - min(ys) - x_span = max(xs) - min(xs) - base = math.sqrt(x_span**2 + y_span**2) - height = abs(min(n.z for n in nodes) - max(n.z for n in nodes)) - if height > 1e-9 and base / height < 0.5: - penalty += 500.0 * (0.5 - base/height) - - # Planarity: all Y ≈ 0 → add heavy penalty - if y_span < 1e-3: - penalty += 500.0 - - return variance + penalty, stresses - - -# ── Worker (runs in separate process) ───────────────────────────────────────── - -def evaluate_candidate(args_tuple) -> Tuple[float, dict]: - """ - Top-level function (picklable) for ProcessPoolExecutor. - Returns (fitness_score, serialisable candidate dict). - """ - (branch_angles, az_offsets, branching_factor, - tubule_radius, depth, height_per_level, swl_N, with_atm) = args_tuple - - nodes, edges = generate_tree( - depth, branch_angles, az_offsets, - branching_factor, height_per_level, - ) - score, stresses = fitness(nodes, edges, tubule_radius, swl_N, with_atm) - - return score, { - "branch_angles": branch_angles, - "az_offsets": az_offsets, - "branching_factor": branching_factor, - "tubule_radius": tubule_radius, - "fitness": score, - "stress_mean_MPa": sum(stresses)/len(stresses) if stresses else 0.0, - "stress_var": score, # approximate — includes penalties - "nodes": [ - {"id": n.id, "x": n.x, "y": n.y, "z": n.z, - "load_frac": n.load_frac, "depth": n.depth} - for n in nodes - ], - "edges": [[p, c] for p, c in edges], - } - - -# ── Parameter sampling & perturbation ───────────────────────────────────────── - -def random_params(depth: int, rng: random.Random) -> tuple: - angles = [rng.uniform(ANGLE_MIN, ANGLE_MAX) for _ in range(depth)] - az = [rng.uniform(AZ_MIN, AZ_MAX) for _ in range(depth)] - bf = rng.randint(BF_MIN, BF_MAX) - radius = rng.uniform(RADIUS_MIN, RADIUS_MAX) - return angles, az, bf, radius - - -def perturb(params: tuple, depth: int, rng: random.Random, scale: float = 0.15) -> tuple: - angles, az, bf, radius = params - new_angles = [ - max(ANGLE_MIN, min(ANGLE_MAX, a + rng.gauss(0, scale*(ANGLE_MAX-ANGLE_MIN)))) - for a in angles - ] - new_az = [ - (a + rng.gauss(0, scale*(AZ_MAX-AZ_MIN))) % 360.0 - for a in az - ] - new_bf = max(BF_MIN, min(BF_MAX, bf + rng.randint(-1, 1))) - new_r = max(RADIUS_MIN, min(RADIUS_MAX, radius + rng.gauss(0, scale*(RADIUS_MAX-RADIUS_MIN)))) - return new_angles, new_az, new_bf, new_r - - -# ── Main search loop ─────────────────────────────────────────────────────────── - -def search( - depth: int = 4, - population: int = 64, - iterations: int = 40, - survivors: int = 8, - workers: int = 4, - swl_N: float = 104.0, - height_per_level: float = 6.0, - seed: int = 42, - with_atm: bool = True, -) -> dict: - rng = random.Random(seed) - - # Initial population - pop: List[tuple] = [random_params(depth, rng) for _ in range(population)] - - best_score = float("inf") - best_result = None - - for iteration in range(iterations): - # Build args for workers - work = [ - (p[0], p[1], p[2], p[3], depth, height_per_level, swl_N, with_atm) - for p in pop - ] - - results: List[Tuple[float, dict]] = [] - with ProcessPoolExecutor(max_workers=workers) as ex: - futs = {ex.submit(evaluate_candidate, w): i for i, w in enumerate(work)} - for fut in as_completed(futs): - try: - results.append(fut.result()) - except Exception as e: - results.append((float("inf"), {})) - - results.sort(key=lambda r: r[0]) - - iter_best = results[0][0] - if iter_best < best_score: - best_score = iter_best - best_result = results[0][1] - - print( - f" iter {iteration+1:3d}/{iterations} " - f"best={iter_best:.6f} " - f"pop_best={results[0][1].get('stress_mean_MPa', 0):.3f}MPa mean " - f"bf={results[0][1].get('branching_factor',0)} " - f"r={results[0][1].get('tubule_radius',0):.2f}mm " - f"angles={[round(a,1) for a in results[0][1].get('branch_angles',[])]}" - ) - - if best_score < 1e-6: - print(" [converged]") - break - - # Breed next generation from survivors - survivors_params = [] - for score, res in results[:survivors]: - if res: - p = ( - res["branch_angles"], res["az_offsets"], - res["branching_factor"], res["tubule_radius"], - ) - survivors_params.append(p) - - pop = list(survivors_params) - # Fill rest with perturbations of survivors - while len(pop) < population: - parent = rng.choice(survivors_params) - pop.append(perturb(parent, depth, rng)) - - return best_result or {} - - -# ── Output ───────────────────────────────────────────────────────────────────── - -def to_merkle_json(result: dict, swl_N: float) -> dict: - """Convert search result to merkle_tree.json-compatible format.""" - nodes_out = [] - for n in result.get("nodes", []): - nodes_out.append({ - "id": n["id"], - "x": round(n["x"], 4), - "y": round(n["y"], 4), - "z": round(n["z"], 4), - "F": round(swl_N * n["load_frac"], 4), - }) - return { - "nodes": nodes_out, - "edges": result.get("edges", []), - "metadata": { - "description": "Semi-Jack n-space geometry search result", - "branch_angles": result.get("branch_angles"), - "az_offsets": result.get("az_offsets"), - "branching_factor": result.get("branching_factor"), - "tubule_radius_mm": result.get("tubule_radius"), - "fitness": result.get("fitness"), - "stress_mean_MPa": result.get("stress_mean_MPa"), - "swl_N": swl_N, - "material": "SLS_PA12", - } - } - - -def main(): - ap = argparse.ArgumentParser(description="Semi-Jack n-space geometry search") - ap.add_argument("--depth", type=int, default=4, help="Tree depth (default 4)") - ap.add_argument("--pop", type=int, default=64, help="Population size (default 64)") - ap.add_argument("--iters", type=int, default=40, help="Iterations (default 40)") - ap.add_argument("--survivors", type=int, default=8, help="Survivors per iteration (default 8)") - ap.add_argument("--workers", type=int, default=4, help="Parallel workers (default 4)") - ap.add_argument("--swl", type=float, default=104.0, - help="SWL in Newtons (default 104N = 22mm cube at water density)") - ap.add_argument("--hlevel", type=float, default=6.0, - help="Height per tree level in mm (default 6mm → 24mm total for depth=4)") - ap.add_argument("--seed", type=int, default=42, help="RNG seed") - ap.add_argument("--no-atm", action="store_true", - help="Search in vacuum (no atmospheric pressure). Default: include atm.") - ap.add_argument("--out", default="5-Applications/out/sovereign_jenga/quantum_annealed/merkle_tree_nspace.json", - help="Output JSON path") - args = ap.parse_args() - - with_atm = not args.no_atm - print(f"\n Semi-Jack n-space geometry search") - print(f" depth={args.depth} pop={args.pop} iters={args.iters}") - print(f" workers={args.workers} swl={args.swl}N ({args.swl/9.81:.2f}kg)") - print(f" atmosphere: {'101325 Pa (standard)' if with_atm else 'OFF (vacuum)'}") - print(f" search space: {2*args.depth + 2}D") - print() - - best = search( - depth=args.depth, - population=args.pop, - iterations=args.iters, - survivors=args.survivors, - workers=args.workers, - swl_N=args.swl, - height_per_level=args.hlevel, - seed=args.seed, - with_atm=with_atm, - ) - - if not best: - print("ERROR: no result", file=sys.stderr) - sys.exit(1) - - print(f"\n BEST GEOMETRY FOUND") - print(f" fitness : {best.get('fitness', '?'):.8f}") - print(f" branching factor : {best.get('branching_factor')}") - print(f" tubule radius : {best.get('tubule_radius', 0):.3f} mm") - print(f" branch angles : {[round(a,2) for a in best.get('branch_angles', [])]}") - print(f" az offsets : {[round(a,2) for a in best.get('az_offsets', [])]}") - print(f" stress mean : {best.get('stress_mean_MPa', 0):.3f} MPa") - print(f" nodes : {len(best.get('nodes', []))}") - print(f" edges : {len(best.get('edges', []))}") - - out_path = Path(args.out) - out_path.parent.mkdir(parents=True, exist_ok=True) - out_json = to_merkle_json(best, args.swl) - out_path.write_text(json.dumps(out_json, indent=2)) - print(f"\n Saved → {out_path}") - print(f" Feed into constraint_model: ") - print(f" python 5-Applications/scripts/semi_jack_constraint_model.py \\") - print(f" --json {out_path} \\") - print(f" --radius {best.get('tubule_radius', 1.0):.3f} \\") - print(f" --swl {args.swl}") - print() - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/semi_jack/semi_jack_vibration.py b/5-Applications/tools-scripts/semi_jack/semi_jack_vibration.py deleted file mode 100644 index c4d4074f..00000000 --- a/5-Applications/tools-scripts/semi_jack/semi_jack_vibration.py +++ /dev/null @@ -1,383 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -semi_jack_vibration.py — Vibration matrix analysis for Semi-Jack geometry - -Assembles the global stiffness K, geometric stiffness K_geo (from atmospheric -pressure), and mass matrix M for a 3D pin-jointed truss. - -Atmospheric pressure contribution: - Each strut acts as a cylinder under external pressure P_atm. - Net compressive axial force = P_atm × A_cross_section. - This modifies K via the geometric (stress-stiffening) matrix K_geo. - Under compression K_geo is negative → reduces effective stiffness → lowers ω. - -Eigenvalue problem: - (K + K_geo) u = ω² M u - Natural frequencies: f_n = ω_n / (2π) - -Boundary conditions: - Leaf nodes (no children) = fixed to ground (zero displacement). - Root + internal nodes = free. - -Usage: - .venv-eng/bin/python 5-Applications/scripts/semi_jack_vibration.py - .venv-eng/bin/python 5-Applications/scripts/semi_jack_vibration.py --json 5-Applications/out/sovereign_jenga/quantum_annealed/merkle_tree_nspace.json - .venv-eng/bin/python 5-Applications/scripts/semi_jack_vibration.py --no-atm # vacuum comparison -""" - -from __future__ import annotations - -import argparse -import json -import math -import sys -from pathlib import Path -from typing import List, Set, Tuple - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from scipy.linalg import eigh - -# ── Physical constants ───────────────────────────────────────────────────────── - -ATM_PRESSURE_PA = 101_325.0 # Pa — standard atmosphere (ISO 2533) -G_MS2 = 9.80665 # m/s² — standard gravity - -# ── Material: SLS PA12 (SI units throughout) ────────────────────────────────── - -E_PA12_PA = 1_700e6 # Young's modulus, Pa -RHO_PA12_KG_M3 = 1_010.0 # density, kg/m³ - - -# ── Geometry loader ──────────────────────────────────────────────────────────── - -def load_geometry(path: Path): - data = json.loads(path.read_text()) - nodes = {n["id"]: n for n in data["nodes"]} - edges = [(e[0], e[1]) for e in data["edges"]] - - # Identify leaf nodes (appear only as children, never as parents) - parents = {e[0] for e in edges} - all_ids = set(nodes.keys()) - leaves = all_ids - parents - - return nodes, edges, leaves - - -# ── Element matrices (3-DOF pin-jointed truss element) ──────────────────────── -# DOFs per node: [ux, uy, uz]. Element connects node i (DOFs 0-2) to j (DOFs 3-5). - -def direction_cosines(ni: dict, nj: dict) -> Tuple[float, float, float, float]: - dx = (nj["x"] - ni["x"]) * 1e-3 # mm → m - dy = (nj["y"] - ni["y"]) * 1e-3 - dz = (nj["z"] - ni["z"]) * 1e-3 - L = math.sqrt(dx**2 + dy**2 + dz**2) - if L < 1e-12: - return 0.0, 0.0, 0.0, 0.0 - return dx/L, dy/L, dz/L, L - - -def element_stiffness( - ni: dict, nj: dict, radius_m: float -) -> Tuple[AnyArray, float, float]: - """ - 6×6 elastic stiffness matrix in global coords. - Returns (k_global, L_m, A_m2). - """ - lx, ly, lz, L = direction_cosines(ni, nj) - if L < 1e-12: - return xp.zeros((6,6)), 0.0, 0.0 - - A = math.pi * radius_m**2 - EA_over_L = E_PA12_PA * A / L - - # Direction cosine vector - d = xp.array([lx, ly, lz]) - - # Local-to-global: k = EA/L * [d; -d] ⊗ [d; -d] - dext = xp.concatenate([d, -d]) - k_global = EA_over_L * xp.outer(dext, dext) - return k_global, L, A - - -def element_geo_stiffness( - ni: dict, nj: dict, radius_m: float, axial_force_N: float -) -> AnyArray: - """ - 6×6 geometric stiffness matrix for axial pre-load P. - For a truss element: k_geo = P/L * [I -I; -I I] projected onto - the transverse plane (perpendicular to strut axis). - Negative P (compression) → negative k_geo → softens structure. - """ - lx, ly, lz, L = direction_cosines(ni, nj) - if L < 1e-12: - return xp.zeros((6,6)) - - d = xp.array([lx, ly, lz]) - I3 = xp.eye(3) - # Transverse projector: I - d⊗d - Pt = I3 - xp.outer(d, d) - - k_geo_3 = (axial_force_N / L) * Pt - k_geo = xp.zeros((6, 6)) - k_geo[0:3, 0:3] = k_geo_3 - k_geo[3:6, 3:6] = k_geo_3 - k_geo[0:3, 3:6] = -k_geo_3 - k_geo[3:6, 0:3] = -k_geo_3 - return k_geo - - -def element_mass( - ni: dict, nj: dict, radius_m: float -) -> AnyArray: - """ - 6×6 consistent mass matrix in global coords. - m = ρAL/6 * [2I I; I 2I] (consistent formulation for truss) - """ - _, L, A = element_stiffness(ni, nj, radius_m) - if L < 1e-12: - return xp.zeros((6,6)) - - m_total = RHO_PA12_KG_M3 * A * L - I3 = xp.eye(3) - m = xp.zeros((6, 6)) - m[0:3, 0:3] = 2/6 * m_total * I3 - m[3:6, 3:6] = 2/6 * m_total * I3 - m[0:3, 3:6] = 1/6 * m_total * I3 - m[3:6, 0:3] = 1/6 * m_total * I3 - return m - - -# ── Global assembly ──────────────────────────────────────────────────────────── - -def assemble( - nodes: dict, - edges: List[Tuple[int,int]], - leaves: Set[int], - radius_mm: float, - swl_N: float, - with_atm: bool, -) -> Tuple[AnyArray, AnyArray, List[int], dict]: - """ - Assemble global K, K_geo, M. - Returns (K_total, M, free_dofs, diagnostics). - """ - node_ids = sorted(nodes.keys()) - id_to_idx = {nid: i for i, nid in enumerate(node_ids)} - n_nodes = len(node_ids) - n_dofs = n_nodes * 3 - - radius_m = radius_mm * 1e-3 - A_m2 = math.pi * radius_m**2 - - # Atmospheric pressure compressive force on each strut end - # F_atm = P_atm × A_cross (acts inward = compression = negative) - atm_force_N = -ATM_PRESSURE_PA * A_m2 if with_atm else 0.0 - - K = xp.zeros((n_dofs, n_dofs)) - M = xp.zeros((n_dofs, n_dofs)) - - diag = { - "atm_force_per_strut_N": abs(atm_force_N), - "n_struts": len(edges), - "radius_mm": radius_mm, - "A_mm2": A_m2 * 1e6, - } - - # Identify root (no parent in edge list) - child_ids = {e[1] for e in edges} - root_id = next(nid for nid in node_ids if nid not in child_ids) - - # Root force distribution: each direct child carries swl_N * load_frac - root_force = nodes[root_id].get("F", swl_N) - - for p_id, c_id in edges: - ni = nodes[p_id] - nj = nodes[c_id] - - k_el, L_m, _area = element_stiffness(ni, nj, radius_m) - - # Structural axial force in strut (from applied SWL) - child_F = nj.get("F", 0.0) - frac = child_F / max(root_force, 1e-9) - strut_V = swl_N * frac # vertical component - lx, ly, lz, _L = direction_cosines(ni, nj) - cos_ang = abs(lz) if L_m > 1e-12 else 1.0 - strut_axial = strut_V / max(cos_ang, 1e-6) # along strut axis - - # Atmospheric compressive pre-load - total_axial_N = -strut_axial + atm_force_N # compression = negative - - k_geo = element_geo_stiffness(ni, nj, radius_m, total_axial_N) - m_el = element_mass(ni, nj, radius_m) - - # DOF indices - ri = id_to_idx[p_id] * 3 - rj = id_to_idx[c_id] * 3 - idx = [ri, ri+1, ri+2, rj, rj+1, rj+2] - - for a, ga in enumerate(idx): - for b, gb in enumerate(idx): - K[ga, gb] += k_el[a, b] + k_geo[a, b] - M[ga, gb] += m_el[a, b] - - # ── Boundary conditions: fix leaf nodes ─────────────────────────────────── - fixed_dofs: Set[int] = set() - for leaf_id in leaves: - base = id_to_idx[leaf_id] * 3 - fixed_dofs.update([base, base+1, base+2]) - - all_dofs = list(range(n_dofs)) - free_dofs = [d for d in all_dofs if d not in fixed_dofs] - - return K, M, free_dofs, diag - - -# ── Eigenvalue solve ─────────────────────────────────────────────────────────── - -def natural_frequencies( - K: AnyArray, - M: AnyArray, - free_dofs: List[int], - n_modes: int = 10, -) -> AnyArray: - """ - Solve generalised eigenvalue problem on free DOFs. - Returns natural frequencies in Hz. - """ - Kf = K[xp.ix_(free_dofs, free_dofs)] - Mf = M[xp.ix_(free_dofs, free_dofs)] - - if Kf.shape[0] == 0: - return xp.array([]) - - # Regularise: add small diagonal to M to avoid singularity - Mf += xp.eye(Mf.shape[0]) * 1e-18 - - n_req = min(n_modes, Kf.shape[0] - 1) - if n_req < 1: - return xp.array([]) - - try: - # eigh for symmetric matrices → real eigenvalues - eigenvalues, _ = eigh(Kf, Mf, subset_by_index=[0, n_req-1]) - # ω² = eigenvalue, clip negatives (rigid-body / numerical noise) - omega2 = xp.clip(eigenvalues, 0, None) - return xp.sqrt(omega2) / (2 * math.pi) # Hz - except Exception as e: - print(f" [eigensolve warning] {e}", file=sys.stderr) - return xp.array([]) - - -# ── Report ───────────────────────────────────────────────────────────────────── - -def run( - json_path: Path, - radius_mm: float, - swl_N: float, - n_modes: int, - with_atm: bool, -): - nodes, edges, leaves = load_geometry(json_path) - print(f"\n {'='*66}") - print(f" Semi-Jack Vibration Matrix Analysis") - print(f" {'='*66}") - print(f" Geometry : {json_path.name}") - print(f" Nodes : {len(nodes)} Edges: {len(edges)} Leaves: {len(leaves)}") - print(f" Radius : {radius_mm} mm SWL: {swl_N:.1f} N ({swl_N/G_MS2:.2f} kg)") - print(f" Atm P : {'101325 Pa (standard atmosphere)' if with_atm else 'OFF (vacuum)'}") - print(f" Material : SLS PA12 E={E_PA12_PA/1e6:.0f} MPa ρ={RHO_PA12_KG_M3} kg/m³") - print() - - K, M, free_dofs, diag = assemble( - nodes, edges, leaves, radius_mm, swl_N, with_atm - ) - - print(f" System size : {K.shape[0]} DOFs ({len(free_dofs)} free after BCs)") - print(f" Strut area : {diag['A_mm2']:.3f} mm²") - if with_atm: - print(f" Atm load/strut: {diag['atm_force_per_strut_N']:.4f} N compressive") - print() - - freqs = natural_frequencies(K, M, free_dofs, n_modes) - - if len(freqs) == 0: - print(" No free DOFs — structure is fully constrained.") - return freqs - - print(f" NATURAL FREQUENCIES (first {len(freqs)} modes)") - print(f" {'-'*50}") - for i, f in enumerate(freqs): - label = "" - if i == 0: label = " ← fundamental" - if f < 20: label += " ⚠ near audible resonance" - print(f" Mode {i+1:3d}: {f:12.2f} Hz{label}") - - print() - print(f" Fundamental period : {1/freqs[0]*1000:.3f} ms" if freqs[0] > 0 else "") - return freqs - - -def compare(json_path: Path, radius_mm: float, swl_N: float, n_modes: int): - """Run with and without atmosphere, show delta.""" - print("\n Running vacuum baseline...") - f_vac = run(json_path, radius_mm, swl_N, n_modes, with_atm=False) - - print("\n Running with standard atmosphere (101325 Pa)...") - f_atm = run(json_path, radius_mm, swl_N, n_modes, with_atm=True) - - if len(f_vac) == 0 or len(f_atm) == 0: - return - - n = min(len(f_vac), len(f_atm)) - print(f"\n {'='*66}") - print(f" ATMOSPHERIC PRESSURE EFFECT ON VIBRATION MODES") - print(f" {'='*66}") - print(f" {'Mode':6s} {'Vacuum Hz':>12s} {'Atm Hz':>12s} {'Delta Hz':>12s} {'Delta %':>10s}") - print(f" {'-'*56}") - for i in range(n): - delta = f_atm[i] - f_vac[i] - delta_pc = 100.0 * delta / max(f_vac[i], 1e-9) - flag = " ↓ softened" if delta < -0.01 * f_vac[i] else "" - print( - f" {i+1:6d} {f_vac[i]:12.2f} {f_atm[i]:12.2f} " - f"{delta:12.2f} {delta_pc:10.3f}%{flag}" - ) - print(f" {'='*66}\n") - - -def main(): - ap = argparse.ArgumentParser(description="Semi-Jack vibration matrix analysis") - ap.add_argument("--json", default="5-Applications/out/sovereign_jenga/quantum_annealed/merkle_tree_nspace.json") - ap.add_argument("--radius", type=float, default=4.52, help="Tubule radius mm") - ap.add_argument("--swl", type=float, default=104.0, help="SWL in Newtons") - ap.add_argument("--modes", type=int, default=10, help="Number of modes") - ap.add_argument("--no-atm", action="store_true", help="Vacuum only (no atmosphere)") - ap.add_argument("--no-compare", action="store_true", help="Skip side-by-side comparison") - args = ap.parse_args() - - path = Path(args.json) - if not path.exists(): - path = Path(__file__).parent.parent / args.json - if not path.exists(): - print(f"ERROR: {args.json} not found", file=sys.stderr) - sys.exit(1) - - if args.no_atm: - run(path, args.radius, args.swl, args.modes, with_atm=False) - elif args.no_compare: - run(path, args.radius, args.swl, args.modes, with_atm=True) - else: - compare(path, args.radius, args.swl, args.modes) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/simulation/neuromorphic_miner.tsm b/5-Applications/tools-scripts/simulation/neuromorphic_miner.tsm deleted file mode 100644 index c5f25d10..00000000 --- a/5-Applications/tools-scripts/simulation/neuromorphic_miner.tsm +++ /dev/null @@ -1,588 +0,0 @@ -// Neuromorphic Bitcoin Miner - Pure TSM Implementation -// GPGPU-Accelerated Neuromorphic SHA256 with Soliton Collision Mining -// -// Architecture: -// - Neuromorphic Surface: 1M spiking neurons for nonce space exploration -// - Soliton Collision Engine: Wave packet interference for hash optimization -// - GPGPU Kernel: Parallel hash computation across 10,000+ CUDA cores -// - TSM-ISA v2.9 Opcodes: Native hardware instruction mapping -// -// Expected Performance: -// - GPGPU: 10-100 MH/s (depending on GPU) -// - Neuromorphic: 100-500 MH/s (with soliton optimization) -// - Efficiency: 75% reduction via topological predictive lensing - -module NeuromorphicBitcoinMiner { - - // ======================================================================== - // CONSTANTS & CONFIGURATION - // ======================================================================== - - const MAX_NEURONS: u32 = 1_048_576; // 1M neuromorphic neurons - const SOLITON_PACKETS: u32 = 65_536; // 64K soliton wave packets - const GPGPU_THREADS: u32 = 10_240; // CUDA thread count - const NONCE_SPACE: u64 = 4_294_967_296; // 2^32 nonce space - const TARGET_DIFFICULTY: u256 = 0x00000000FFFF00000000000000000000000000000000000000000000000000000000; - - // TSM-ISA v2.9 Opcode Definitions - enum TSM_Opcode: u8 { - INGEST_STATE = 0x01, - WAVE_FOLD = 0x02, - SYNC_CLOCK = 0x03, - OMNI_BAL = 0x04, - ENTANGLE = 0x05, - EVOLVE = 0x06, - VRAM_FLUSH = 0x07, - STARK_PROVE = 0x08, - LEDGER_COMMIT = 0x09, - NEUROMORPH = 0x0E, - GPGPU_SURF = 0x0F, - NIBBLE_SWAP = 0x11, - TSM_INT = 0x12 - } - - // ======================================================================== - // DATA STRUCTURES - // ======================================================================== - - struct BlockHeader { - version: u32; - prev_block_hash: [u8; 32]; - merkle_root: [u8; 32]; - timestamp: u64; - bits: u32; - nonce: u32; - } - - struct MiningJob { - job_id: string; - block_template: BlockHeader; - target: u256; - difficulty: f64; - created_at: f64; - } - - struct NeuromorphicSurface { - neurons: array; - synapses: array; - spike_buffer: array; - manifold_id: string; - thermal_entropy: f64; - } - - struct Neuron { - id: u32; - membrane_potential: f64; - threshold: f64; - refractory_period: u32; - firing_rate: f64; - weights: array; // 11-dimensional input weights - } - - struct Synapse { - pre_neuron: u32; - post_neuron: u32; - weight: f64; - delay: u32; - plasticity: f64; // STDP learning rate - } - - struct SolitonPacket { - packet_id: u64; - position: array; // 11D position - momentum: array; // 11D momentum - amplitude: f64; - phase: f64; - frequency: f64; - collision_count: u32; - } - - struct GPGPUKernelState { - thread_id: u32; - block_id: u32; - nonce: u32; - hash_result: [u8; 32]; - valid: bool; - } - - // ======================================================================== - // TSM-ISA HARDWARE INTRINSICS - // ======================================================================== - - // [0x0E] NEUROMORPH - Execute neuromorphic spike propagation - intrinsic tsm_neuromorph(surface: NeuromorphicSurface, input: array) -> array; - - // [0x0F] GPGPU_SURF - Launch GPGPU kernel - intrinsic tsm_gpgpu_surface(kernel: string, threads: u32, data: array) -> array; - - // [0x11] NIBBLE_SWAP - Swap nibbles for hash optimization - intrinsic tsm_nibble_swap(data: [u8; 32]) -> [u8; 32]; - - // [0x12] TSM_INT - Integrate with PTOS manifold - intrinsic tsm_integrate(state: any) -> string; - - // [0x03] SYNC_CLOCK - System clock synchronization - intrinsic tsm_sync_clock() -> f64; - - // [0x07] VRAM_FLUSH - Clear GPU memory - intrinsic tsm_vram_flush() -> bool; - - // [0x08] STARK_PROVE - Generate ZK-STARK proof - intrinsic tsm_stark_prove(data: any) -> string; - - // [0x09] LEDGER_COMMIT - Commit to HyperDAG ledger - intrinsic tsm_ledger_commit(proof: string, term: string) -> bool; - - // ======================================================================== - // NEUROMORPHIC SURFACE IMPLEMENTATION - // ======================================================================== - - kernel NeuromorphicSurfaceKernel { - - fn init(num_neurons: u32) -> NeuromorphicSurface { - var surface = NeuromorphicSurface { - neurons: array::new(num_neurons), - synapses: array::new(num_neurons * 11), // 11 connections per neuron - spike_buffer: array::new(0), - manifold_id: "", - thermal_entropy: 0.0 - }; - - // Initialize neurons with random weights - for i in 0..num_neurons { - surface.neurons[i] = Neuron { - id: i, - membrane_potential: 0.0, - threshold: random::uniform(0.5, 1.5), - refractory_period: 0, - firing_rate: 0.0, - weights: random::rand_f64_array(11, -0.1, 0.1) - }; - } - - // Initialize synapses with STDP plasticity - for i in 0..num_neurons { - for j in 0..11 { - let synapse_idx = i * 11 + j; - surface.synapses[synapse_idx] = Synapse { - pre_neuron: i, - post_neuron: (i + j) % num_neurons, - weight: random::uniform(-0.5, 0.5), - delay: random::uniform(1, 10), - plasticity: 0.01 - }; - } - } - - return surface; - } - - fn process_input(surface: NeuromorphicSurface, input_vector: array) -> array { - // [0x0E] NEUROMORPH - Execute on GPGPU - let spikes = tsm_neuromorph(surface, input_vector); - return spikes; - } - - fn update_weights(surface: NeuromorphicSurface, reward: f64) { - // STDP (Spike-Timing-Dependent Plasticity) weight update - for i in 0..surface.neurons.len() { - if surface.neurons[i].firing_rate > 0.5 { - for j in 0..11 { - let synapse_idx = i * 11 + j; - surface.synapses[synapse_idx].weight += reward * surface.synapses[synapse_idx].plasticity; - } - surface.neurons[i].firing_rate *= 0.9; // Decay - } - } - } - - fn check_thermal_safety(surface: NeuromorphicSurface) -> bool { - // Grey Goo Safety Protocol v2.1 - if surface.thermal_entropy > 0.9 { - log::warn("CRITICAL: Thermal entropy exceeds safe threshold"); - return false; - } - return true; - } - } - - // ======================================================================== - // SOLITON COLLISION ENGINE - // ======================================================================== - - kernel SolitonCollisionEngine { - - fn init(num_packets: u32) -> array { - var packets = array::new(num_packets); - - for i in 0..num_packets { - packets[i] = SolitonPacket { - packet_id: i as u64, - position: random::rand_f64_array(11, -1.0, 1.0), // 11D position - momentum: random::rand_f64_array(11, -1000.0, 1000.0), - amplitude: random::uniform(0.1, 1.0), - phase: random::uniform(0.0, 6.283185307179586), - frequency: random::uniform(1e9, 1e12), - collision_count: 0 - }; - } - - return packets; - } - - fn collide_packets(packets: array) -> array { - // [0x02] WAVE_FOLD - Einstein-Rosen fold for collision - var new_packets = array::new(packets.len() / 2); - - for i in 0..packets.len() / 2 { - let a = packets[i * 2]; - let b = packets[i * 2 + 1]; - - // Soliton collision with amplitude damping (prevents runaway) - let new_amp = (a.amplitude * b.amplitude) * 0.95; // 5% damping - let new_phase = (a.phase + b.phase) / 2.0; - let new_freq = (a.frequency + b.frequency) / 2.0; - - // 11D position and momentum averaging - var new_pos = array::new(11); - var new_mom = array::new(11); - for d in 0..11 { - new_pos[d] = (a.position[d] + b.position[d]) / 2.0; - new_mom[d] = a.momentum[d] + b.momentum[d]; - } - - new_packets[i] = SolitonPacket { - packet_id: (a.packet_id << 32) | b.packet_id, - position: new_pos, - momentum: new_mom, - amplitude: new_amp, - phase: new_phase, - frequency: new_freq, - collision_count: a.collision_count + b.collision_count + 1 - }; - - // Collapse threshold (prevents energy accumulation) - if new_amp > 0.75 { - // Trigger collapse to solution - new_packets[i] = collapse_to_solution(new_packets[i]); - } - } - - return new_packets; - } - - fn collapse_to_solution(packet: SolitonPacket) -> SolitonPacket { - // Collapse soliton to nonce solution - var sum = 0.0; - for v in packet.position { - sum += v; - } - let nonce_value = ((sum * packet.frequency) as u64) % (1 << 32); - - packet.packet_id = nonce_value; - packet.amplitude = 0.0; // Reset after collapse - - // [0x08] STARK_PROVE - Generate proof of valid collapse - let proof = tsm_stark_prove(packet); - - return packet; - } - - fn run_collision_pipeline(packets: array, rounds: u32) -> array { - var valid_nonces = array::new(0); - - for r in 0..rounds { - packets = collide_packets(packets); - - // Extract valid nonces from collapsed packets - for p in packets { - if p.amplitude == 0.0 && p.packet_id < NONCE_SPACE { - valid_nonces.push(p.packet_id as u32); - } - } - - // Early termination if we found valid nonces - if valid_nonces.len() > 0 { - break; - } - } - - return valid_nonces; - } - } - - // ======================================================================== - // GPGPU SHA256 KERNEL (CUDA-style) - // ======================================================================== - - kernel GPGPU_SHA256_Kernel { - - // SHA256 constants - const K: [u32; 64] = [ - 0x428a2f98, 0x71374491, 0xb5c0fbcf, 0xe9b5dba5, 0x3956c25b, 0x59f111f1, 0x923f82a4, 0xab1c5ed5, - 0xd807aa98, 0x12835b01, 0x243185be, 0x550c7dc3, 0x72be5d74, 0x80deb1fe, 0x9bdc06a7, 0xc19bf174, - 0xe49b69c1, 0xefbe4786, 0x0fc19dc6, 0x240ca1cc, 0x2de92c6f, 0x4a7484aa, 0x5cb0a9dc, 0x76f988da, - 0x983e5152, 0xa831c66d, 0xb00327c8, 0xbf597fc7, 0xc6e00bf3, 0xd5a79147, 0x06ca6351, 0x14292967, - 0x27b70a85, 0x2e1b2138, 0x4d2c6dfc, 0x53380d13, 0x650a7354, 0x766a0abb, 0x81c2c92e, 0x92722c85, - 0xa2bfe8a1, 0xa81a664b, 0xc24b8b70, 0xc76c51a3, 0xd192e819, 0xd6990624, 0xf40e3585, 0x106aa070, - 0x19a4c116, 0x1e376c08, 0x2748774c, 0x34b0bcb5, 0x391c0cb3, 0x4ed8aa4a, 0x5b9cca4f, 0x682e6ff3, - 0x748f82ee, 0x78a5636f, 0x84c87814, 0x8cc70208, 0x90befffa, 0xa4506ceb, 0xbef9a3f7, 0xc67178f2 - ]; - - fn sha256_compress(header: BlockHeader, nonce: u32) -> [u8; 32] { - // Set nonce in header - header.nonce = nonce; - - // [0x11] NIBBLE_SWAP - Optimize for GPGPU - let header_bytes = tsm_nibble_swap(header_to_bytes(header)); - - // SHA256 compression (simplified for TSM) - let hash = crypto::sha256(header_bytes); - - return hash; - } - - fn gpgpu_parallel_hash(header: BlockHeader, nonces: array) -> array { - // [0x0F] GPGPU_SURF - Launch parallel hash kernel - let kernel_data = serialize_nonces(nonces); - let results = tsm_gpgpu_surface("sha256_mining_kernel", GPGPU_THREADS, kernel_data); - - return deserialize_results(results); - } - - fn check_difficulty(hash: [u8; 32], target: u256) -> bool { - let hash_int = bytes_to_u256(hash); - return hash_int < target; - } - } - - // ======================================================================== - // MINING ACTOR (MAIN CONTROLLER) - // ======================================================================== - - actor NeuromorphicMinerActor { - surface: NeuromorphicSurface; - soliton_packets: array; - current_job: option; - nonces_tested: u64; - shares_found: u64; - gpgpu_active: bool; - neuromorphic_active: bool; - - fn init() { - // Initialize neuromorphic surface (1M neurons) - self.surface = NeuromorphicSurfaceKernel::init(MAX_NEURONS); - - // Initialize soliton packets (64K packets) - self.soliton_packets = SolitonCollisionEngine::init(SOLITON_PACKETS); - - self.nonces_tested = 0; - self.shares_found = 0; - self.gpgpu_active = false; - self.neuromorphic_active = false; - - // [0x03] SYNC_CMB - Initialize with cosmic clock - let sync_time = tsm_sync_clock(); - log::info(string::format("System clock synchronized at {0} GHz", [sync_time / 1e9])); - } - - fn set_job(job: MiningJob) { - self.current_job = some(job); - self.nonces_tested = 0; - self.shares_found = 0; - - // [0x01] INGEST_STATE - Absorb job into manifold - let job_data = json::serialize(job); - self.surface.manifold_id = tsm_integrate(job_data); - } - - fn start_mining() { - if self.current_job.is_none() { - log::error("No mining job set"); - return; - } - - self.gpgpu_active = true; - self.neuromorphic_active = true; - - let job = self.current_job.unwrap(); - log::info(string::format("Starting neuromorphic mining: difficulty {0}", [job.difficulty])); - - // Main mining loop - while self.gpgpu_active { - // Safety check (Grey Goo Protocol) - if !NeuromorphicSurfaceKernel::check_thermal_safety(self.surface) { - log::warn("Thermal safety triggered - throttling"); - tsm_vram_flush(); - self.surface.thermal_entropy *= 0.1; - } - - // Phase 1: Neuromorphic nonce generation - let input_vector = generate_input_vector(job); - let spikes = NeuromorphicSurfaceKernel::process_input(self.surface, input_vector); - - // Phase 2: Soliton collision optimization - let optimized_nonces = SolitonCollisionEngine::run_collision_pipeline( - self.soliton_packets, 10 - ); - - // Phase 3: GPGPU parallel hash computation - let hash_results = GPGPU_SHA256_Kernel::gpgpu_parallel_hash( - job.block_template, optimized_nonces - ); - - // Phase 4: Check difficulty and submit shares - for result in hash_results { - self.nonces_tested += 1; - - if GPGPU_SHA256_Kernel::check_difficulty(result.hash, job.target) { - self.shares_found += 1; - log::info(string::format("VALID SHARE FOUND! Nonce: {0}", [result.nonce])); - - // [0x08] STARK_PROVE + [0x09] LEDGER_COMMIT - let proof = tsm_stark_prove(result); - tsm_ledger_commit(proof, "permanent"); - } - } - - // Update neuromorphic weights based on results - let reward = if self.shares_found > 0 { 1.0 } else { 0.01 }; - NeuromorphicSurfaceKernel::update_weights(self.surface, reward); - - // Brief yield to prevent thermal buildup - runtime::sleep_ms(1); - } - } - - fn stop_mining() { - self.gpgpu_active = false; - self.neuromorphic_active = false; - tsm_vram_flush(); - log::info("Mining stopped"); - } - - fn get_stats() -> MiningStats { - return MiningStats { - nonces_tested: self.nonces_tested, - shares_found: self.shares_found, - hashrate: self.nonces_tested / (runtime::uptime() as f64), - thermal_entropy: self.surface.thermal_entropy, - gpgpu_utilization: if self.gpgpu_active { 100.0 } else { 0.0 } - }; - } - } - - // ======================================================================== - // HELPER FUNCTIONS - // ======================================================================== - - fn generate_input_vector(job: MiningJob) -> array { - // Convert block header to 11-dimensional input vector for neuromorphic surface - let prev_hash = job.block_template.prev_block_hash; - let merkle = job.block_template.merkle_root; - - return [ - bytes_to_f64(prev_hash[0..8]), - bytes_to_f64(prev_hash[8..16]), - bytes_to_f64(prev_hash[16..24]), - bytes_to_f64(merkle[0..8]), - bytes_to_f64(merkle[8..16]), - job.block_template.timestamp as f64 / 1e12, - job.block_template.bits as f64 / 1e9, - job.difficulty / 1e18, - random::uniform(0.0, 1.0), - random::uniform(0.0, 1.0), - random::uniform(0.0, 1.0) - ]; - } - - fn header_to_bytes(header: BlockHeader) -> [u8; 80] { - // Serialize block header to bytes - var bytes = [0u8; 80]; - // ... serialization logic - return bytes; - } - - fn bytes_to_f64(bytes: array) -> f64 { - // Convert 8 bytes to f64 - return 0.0; // Implementation detail - } - - fn bytes_to_u256(bytes: [u8; 32]) -> u256 { - // Convert 32 bytes to u256 - return 0; // Implementation detail - } - - fn serialize_nonces(nonces: array) -> array { - // Serialize nonces for GPGPU transfer - return array::new(0); - } - - fn deserialize_results(data: array) -> array { - // Deserialize GPGPU results - return array::new(0); - } - - // ======================================================================== - // PROGRAM ENTRYPOINT - // ======================================================================== - - fn main() { - log::info("=============================================="); - log::info(" NEUROMORPHIC BITCOIN MINER - TSM v2.9"); - log::info(" GPGPU-Accelerated | 1M Neurons | 64K Solitons"); - log::info("=============================================="); - - // Create miner actor - let miner = spawn NeuromorphicMinerActor(); - miner.init(); - - // Create test mining job - let job = MiningJob { - job_id: "test_job_001", - block_template: BlockHeader { - version: 2, - prev_block_hash: bytes::zeros(32), - merkle_root: bytes::zeros(32), - timestamp: time::now() as u64, - bits: 0x1d00ffff, - nonce: 0 - }, - target: TARGET_DIFFICULTY, - difficulty: 1.0, - created_at: time::now() as f64 - }; - - miner.set_job(job); - - // Start mining - log::info("Starting neuromorphic mining..."); - miner.start_mining(); - - // Report statistics - let stats = miner.get_stats(); - log::info(string::format( - "Mining complete: {0} nonces, {1} shares, {2:.2} MH/s", - [stats.nonces_tested, stats.shares_found, stats.hashrate / 1e6] - )); - } - -} - -// ============================================================================ -// SUPPORTING STRUCTS -// ============================================================================ - -struct HashResult { - nonce: u32; - hash: [u8; 32]; - valid: bool; -} - -struct MiningStats { - nonces_tested: u64; - shares_found: u64; - hashrate: f64; - thermal_entropy: f64; - gpgpu_utilization: f64; -} diff --git a/5-Applications/tools-scripts/simulation/neuromorphic_miner_production.py b/5-Applications/tools-scripts/simulation/neuromorphic_miner_production.py deleted file mode 100644 index 12bebef7..00000000 --- a/5-Applications/tools-scripts/simulation/neuromorphic_miner_production.py +++ /dev/null @@ -1,541 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Neuromorphic Bitcoin Miner - Complete Integrated System -Refined with all improvements: -- TSM-ISA v2.9 opcodes -- Hyperfluid SHA256 with Topological Predictive Lensing shortcut -- Recursive Holographic Thermodynamics -- Substrate Ledger management -- Grey Goo Safety Protocol v2.1 - -This is the production-ready neuromorphic mining system. -""" - -import asyncio -import json -import hashlib -import struct -import socket -import time -import random -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Tuple, Callable -from pathlib import Path -import sys -from enum import Enum - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Mock websockets for TSM harness -import types -sys.modules['websockets'] = types.ModuleType('websockets') - -from logic_signal_substrate_mcp_harness import TSMKernel, TermType - - -# ============================================================================ -# CONFIGURATION -# ============================================================================ - -@dataclass -class MinerConfig: - """Mining configuration""" - - # Pool connection - pool_url: str = "stratum+tcp://stratum.braiins.com" - pool_port: int = 3333 - username: str = "test_worker" - password: str = "x" - - # Mining parameters - use_shortcut: bool = True # Use 16-round lensing vs full 64-round - shortcut_rounds: int = 16 - nonces_per_batch: int = 1000 - - # Safety parameters - grey_goo_safety: bool = True - max_entropy_threshold: float = 0.9 - consecutive_warnings_limit: int = 3 - - # Performance parameters - report_interval: int = 10 # seconds - max_runtime: int = 60 # seconds (0 = unlimited) - - -# ============================================================================ -# HYPERFLUID SHA256 WITH SHORTCUT -# ============================================================================ - -class HyperfluidSHA256: - """ - Hyperfluid SHA256 implementation with Topological Predictive Lensing shortcut - """ - - def __init__(self, kernel: TSMKernel): - self.kernel = kernel - self.rounds_computed = 0 - - def _rotr(self, x: int, n: int) -> int: - return ((x >> n) | (x << (32 - n))) & 0xFFFFFFFF - - def _sha256_round(self, state: List[int], w: int, k: int) -> List[int]: - """Single SHA256 round""" - def rotr(x, n): return self._rotr(x, n) - def ch(x, y, z): return (x & y) ^ (~x & z) - def maj(x, y, z): return (x & y) ^ (x & z) ^ (y & z) - def sigma0(x): return rotr(x, 2) ^ rotr(x, 13) ^ rotr(x, 22) - def sigma1(x): return rotr(x, 6) ^ rotr(x, 11) ^ rotr(x, 25) - - a, b, c, d, e, f, g, h = state - - t1 = (h + sigma1(e) + ch(e, f, g) + k + w) & 0xFFFFFFFF - t2 = (sigma0(a) + maj(a, b, c)) & 0xFFFFFFFF - - return [ - (t1 + t2) & 0xFFFFFFFF, a, b, c, - (d + t1) & 0xFFFFFFFF, e, f, g - ] - - def compute_with_shortcut(self, data: bytes, nonce: int, use_shortcut: bool = True) -> Tuple[bytes, Dict]: - """ - Compute SHA256 with optional topological predictive lensing shortcut - """ - # Prepare block with nonce - block = data[:76] + struct.pack('= 76 else data + struct.pack(' Tuple[bool, str]: - """ - Check if mining operation is safe to continue - Returns (is_safe, warning_message) - """ - if not self.config.grey_goo_safety: - return True, "" - - self.thermal_entropy = current_entropy - self.manifold_density = current_density - - warnings = [] - - # Check entropy threshold - if self.thermal_entropy > self.config.max_entropy_threshold: - warnings.append(f"High entropy: {self.thermal_entropy:.3f}") - self.consecutive_warnings += 1 - - # Check manifold density (Chandrasekhar limit) - if self.manifold_density > 0.85: - warnings.append(f"Manifold density critical: {self.manifold_density:.3f}") - self.consecutive_warnings += 1 - - if self.consecutive_warnings >= self.config.consecutive_warnings_limit: - self.emergency_stops += 1 - return False, f"GREY GOO PROTOCOL: {self.consecutive_warnings} consecutive warnings - EMERGENCY STOP" - - if warnings: - return True, "; ".join(warnings) - - # Reset counter if all clear - self.consecutive_warnings = 0 - return True, "All systems nominal" - - def trigger_hawking_drain(self) -> None: - """Trigger controlled entropy release via Hawking radiation""" - self.hawking_drains += 1 - self.thermal_entropy *= 0.1 - self.manifold_density *= 0.1 - - def get_status(self) -> Dict: - """Get current safety status""" - return { - "thermal_entropy": self.thermal_entropy, - "manifold_density": self.manifold_density, - "consecutive_warnings": self.consecutive_warnings, - "emergency_stops": self.emergency_stops, - "hawking_drains": self.hawking_drains, - "status": "CRITICAL" if self.consecutive_warnings >= 2 else "WARNING" if self.consecutive_warnings >= 1 else "NOMINAL" - } - - -# ============================================================================ -# NEUROMORPHIC BITCOIN MINER (INTEGRATED) -# ============================================================================ - -class NeuromorphicBitcoinMiner: - """ - Complete neuromorphic Bitcoin mining system with all refinements - """ - - def __init__(self, config: MinerConfig): - self.config = config - self.kernel = TSMKernel() - self.hyperfluid = HyperfluidSHA256(self.kernel) - self.safety_monitor = GreyGooSafetyMonitor(config) - - # Mining statistics - self.start_time = None - self.nonces_tested = 0 - self.shares_found = 0 - self.shares_accepted = 0 - self.shares_rejected = 0 - self.hashes_computed = 0 - - # Current job - self.current_job: Optional[Dict] = None - self.job_manifold_id: Optional[str] = None - - def initialize(self) -> bool: - """Initialize the mining system""" - print("=" * 70) - print(" NEUROMORPHIC BITCOIN MINER - PRODUCTION SYSTEM") - print(" TSM-ISA v2.9 | Hyperfluid SHA256 | Grey Goo Safety v2.1") - print("=" * 70) - print() - - # [0x03] SYNC_Precision - Lock to cosmic master clock - sync_result = self.kernel.sync_precision() - print(f"[INIT] Precision Sync: {sync_result}") - - # [0x04] OMNI_BAL - Set optimization objective - self.kernel.omni_bal("discovery") - print(f"[INIT] Goal surface optimized for discovery mode") - - # Initialize safety monitor - print(f"[INIT] Grey Goo Safety Protocol: {'ENABLED' if self.config.grey_goo_safety else 'DISABLED'}") - print(f"[INIT] Max entropy threshold: {self.config.max_entropy_threshold}") - print(f"[INIT] Shortcut enabled: {self.config.use_shortcut}") - print() - - return True - - def set_job(self, job: Dict) -> None: - """Set current mining job""" - self.current_job = job - - # [0x01] INGEST_STATE - Absorb job into manifold - job_data = json.dumps(job) - self.job_manifold_id = self.kernel.absorb_bh(job_data, {"type": "mining_job"}) - print(f"[JOB] New job absorbed: {self.job_manifold_id[:16]}...") - - def mine_nonce(self, nonce: int) -> Tuple[bool, Dict]: - """ - Mine a single nonce using hyperfluid SHA256 - """ - if not self.current_job: - return False, {"error": "No job set"} - - # Get job parameters - header = bytes.fromhex(self.current_job.get("header", "00" * 80)) - target = int(self.current_job.get("target", "0" * 64), 16) - - # Compute hash with hyperfluid engine - hash_result, hash_metadata = self.hyperfluid.compute_with_shortcut( - header, nonce, use_shortcut=self.config.use_shortcut - ) - - # Check if hash meets target - hash_int = int.from_bytes(hash_result, 'big') - is_valid = hash_int < target - - # Update statistics - self.nonces_tested += 1 - self.hashes_computed += 1 - - # Compute safety metrics - entropy = random.uniform(0.0, 0.3) # Simulated entropy - density = random.uniform(0.0, 0.5) # Simulated density - - is_safe, warning = self.safety_monitor.check_safety(entropy, density) - - if not is_safe: - print(f"[SAFETY] {warning}") - self.safety_monitor.trigger_hawking_drain() - return False, {"safety_stop": True, "warning": warning} - - if warning: - print(f"[SAFETY] {warning}") - - result = { - "nonce": nonce, - "hash": hash_result.hex(), - "is_valid": is_valid, - "rounds_computed": hash_metadata.get("rounds_computed", 64), - "method": hash_metadata.get("method", "unknown"), - "safety_status": self.safety_monitor.get_status()["status"] - } - - if is_valid: - self.shares_found += 1 - - # [0x08] STARK_PROVE - Generate proof for valid share - proof_id = self.kernel.stark_prove(f"share_{nonce}_{time.time()}") - - # [0x09] LEDGER_COMMIT - Commit to ledger - self.kernel.ledger_commit(proof_id, TermType.PERMANENT) - - result["proof_id"] = proof_id[:16] - result["ledger_committed"] = True - - return is_valid, result - - def mine_batch(self, num_nonces: int) -> Dict: - """Mine a batch of nonces""" - results = { - "nonces_tested": 0, - "shares_found": 0, - "shares_accepted": 0, - "shares_rejected": 0, - "safety_events": 0, - "start_time": time.time() - } - - for i in range(num_nonces): - # Generate random nonce - nonce = random.randint(0, 2**32 - 1) - - # Mine nonce - is_valid, result = self.mine_nonce(nonce) - - results["nonces_tested"] += 1 - - if result.get("safety_stop"): - results["safety_events"] += 1 - continue - - if is_valid: - results["shares_found"] += 1 - - # Simulate pool response (in real system, would submit to pool) - if random.random() > 0.1: # 90% acceptance rate simulation - results["shares_accepted"] += 1 - self.shares_accepted += 1 - else: - results["shares_rejected"] += 1 - self.shares_rejected += 1 - - results["elapsed_time"] = time.time() - results["start_time"] - results["hashrate"] = results["nonces_tested"] / max(results["elapsed_time"], 0.001) - - return results - - def run(self, duration: int = 60) -> Dict: - """Run mining for specified duration""" - print(f"[MINING] Starting neuromorphic mining for {duration} seconds...") - print() - - self.start_time = time.time() - end_time = self.start_time + duration - - total_results = { - "nonces_tested": 0, - "shares_found": 0, - "shares_accepted": 0, - "shares_rejected": 0, - "safety_events": 0, - "batches": 0 - } - - while time.time() < end_time: - # Mine a batch - batch_results = self.mine_batch(self.config.nonces_per_batch) - - total_results["nonces_tested"] += batch_results["nonces_tested"] - total_results["shares_found"] += batch_results["shares_found"] - total_results["shares_accepted"] += batch_results["shares_accepted"] - total_results["shares_rejected"] += batch_results["shares_rejected"] - total_results["safety_events"] += batch_results["safety_events"] - total_results["batches"] += 1 - - # Report progress - elapsed = time.time() - self.start_time - hashrate = total_results["nonces_tested"] / max(elapsed, 0.001) - - print(f"[{elapsed:5.1f}s] Nonces: {total_results['nonces_tested']:6d} | " - f"Shares: {total_results['shares_found']:3d} | " - f"Hashrate: {hashrate:8.1f} H/s | " - f"Safety: {self.safety_monitor.get_status()['status']}") - - # Trigger Hawking drain periodically - if random.random() < 0.1: - self.safety_monitor.trigger_hawking_drain() - - # Final report - total_results["total_time"] = time.time() - self.start_time - total_results["final_hashrate"] = total_results["nonces_tested"] / max(total_results["total_time"], 0.001) - total_results["shortcut_efficiency"] = "75%" if self.config.use_shortcut else "0%" - total_results["safety_status"] = self.safety_monitor.get_status() - - return total_results - - def get_final_report(self, results: Dict) -> str: - """Generate final mining report""" - report = [] - report.append("=" * 70) - report.append(" NEUROMORPHIC MINING - FINAL REPORT") - report.append("=" * 70) - report.append(f" Runtime: {results['total_time']:.1f} seconds") - report.append(f" Nonces tested: {results['nonces_tested']:,}") - report.append(f" Shares found: {results['shares_found']}") - report.append(f" Shares accepted: {results['shares_accepted']}") - report.append(f" Shares rejected: {results['shares_rejected']}") - report.append(f" Average hashrate: {results['final_hashrate']:.1f} H/s") - report.append(f" Shortcut efficiency: {results['shortcut_efficiency']}") - report.append(f" Safety events: {results['safety_events']}") - report.append(f" Emergency stops: {self.safety_monitor.emergency_stops}") - report.append(f" Hawking drains: {self.safety_monitor.hawking_drains}") - report.append(f" Final safety status: {self.safety_monitor.get_status()['status']}") - report.append("=" * 70) - - return "\n".join(report) - - -# ============================================================================ -# MAIN EXECUTION -# ============================================================================ - -def main(): - """Run the complete neuromorphic mining system""" - - # Configuration - config = MinerConfig( - use_shortcut=True, - shortcut_rounds=16, - nonces_per_batch=500, - max_runtime=30, - grey_goo_safety=True, - max_entropy_threshold=0.85, - consecutive_warnings_limit=3 - ) - - # Initialize miner - miner = NeuromorphicBitcoinMiner(config) - - if not miner.initialize(): - print("[ERROR] Failed to initialize miner") - return 1 - - # Set up a test job (simulated pool job) - test_job = { - "job_id": "test_job_001", - "header": "00000020" + "00" * 72, # Simplified block header - "target": "00000000ffff0000000000000000000000000000000000000000000000000000", - "timestamp": int(time.time()) - } - - miner.set_job(test_job) - - # Run mining - results = miner.run(duration=config.max_runtime) - - # Print final report - print() - print(miner.get_final_report(results)) - - # Save results - output_path = ROOT / "out" / "neuromorphic_mining_results.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump({ - "results": results, - "safety_status": miner.safety_monitor.get_status(), - "config": { - "use_shortcut": config.use_shortcut, - "shortcut_rounds": config.shortcut_rounds, - "grey_goo_safety": config.grey_goo_safety, - "max_runtime": config.max_runtime - }, - "timestamp": time.time() - }, f, indent=2) - - print(f"\n[+] Results saved to: {output_path}") - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/simulation/physics_verification_suite.py b/5-Applications/tools-scripts/simulation/physics_verification_suite.py deleted file mode 100644 index 9496df16..00000000 --- a/5-Applications/tools-scripts/simulation/physics_verification_suite.py +++ /dev/null @@ -1,56 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from scipy import constants - -def verify_superconductivity(Tc_target_K, atomic_numbers, stability): - r""" - Verifies the RTSC claim using a modified BCS/Debye temperature approximation. - $T_c \approx \theta_D \exp(-1/NV)$ - Internal QRun Formula: $T_c = 82.5 * (Bits * Stability) / (\sum Z)^2$ - """ - # 1. Physical Constants - k_B = constants.Boltzmann - h = constants.h - m_p = constants.m_p - - # 2. Debye Temperature Approximation ($\theta_D$) - # For Hydrogen-rich materials, $\theta_D$ is high ($\sim$2000K) - Z_sum = sum(atomic_numbers) - # Effective mass scaling for clusters - M_eff = Z_sum * m_p - - # 3. Validation Logic - # We compare the predicted Tc to the "Decoherence Maintenance" limit - # The decoherence floor is set by the Precision (2.725 K) - decoherence_floor = 2.725 - - # RTSC Target: 300K - is_valid = Tc_target_K > 294.25 # > 70 F - - print(f"--- Verification Report ---") - print(f"Target Tc: {Tc_target_K:.2f} K") - print(f"Atomic Numbers: {atomic_numbers}") - print(f"Stability Index: {stability:.4f}") - print(f"BCS Compliance: {'PASS' if is_valid else 'FAIL'}") - - # 4. Thermodynamic Consistency - # $\Delta G = \Delta H - T \Delta S$ - # Superconductivity requires $\Delta G_{super} < 0$ - print(f"Thermodynamic Consistency: OK (Entropy minimized via 14D phase-lock)") - - return is_valid - -if __name__ == "__main__": - # Test H-H-H Cluster (RTSC Ambient) - verify_superconductivity(319.87, [1, 1, 1], 1.0) - - # Test H-H Cluster (Super-Critical) - verify_superconductivity(494.01, [1, 1], 1.0) diff --git a/5-Applications/tools-scripts/simulation/quantum_evolution_v5j.py b/5-Applications/tools-scripts/simulation/quantum_evolution_v5j.py deleted file mode 100644 index fc0c7e1e..00000000 --- a/5-Applications/tools-scripts/simulation/quantum_evolution_v5j.py +++ /dev/null @@ -1,64 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import time - -def simulate_quantum_evolution(steps=1000000): - print(f"Initiating Quantum Annealing Simulation for v5-J Manifold...") - - # State Vector: [R, L, C, P, T, V, Jitter, Phase] - # We represent the manifold as a N-dimensional Ising graph - num_nodes = 64 - state = xp.random.choice([-1, 1], size=num_nodes) - - # Interaction Matrix (The Hilbert Connectome weights) - J = xp.random.normal(0, 1, (num_nodes, num_nodes)) - J = (J + J.T) / 2 # Symmetric - - # External Fields (Thermal/Vibrational Stresses as Positives) - H = xp.random.uniform(0.1, 1.0, num_nodes) - - T = 10.0 # Initial Temperature - cooling_rate = 0.999995 - - best_energy = float('inf') - energy_history = [] - - start_time = time.time() - - # Simulated Annealing Loop - for i in range(steps): - # Pick a random node to flip - node = xp.random.randint(num_nodes) - - # Calculate Energy Change (dE) - # Energy = -sum(J_ij * s_i * s_j) - sum(H_i * s_i) - dE = 2 * state[node] * (xp.dot(J[node], state) + H[node]) - - # Metropolis Criterion - if dE < 0 or xp.random.rand() < xp.exp(-dE / T): - state[node] *= -1 - - current_energy = -0.5 * xp.sum(J * xp.outer(state, state)) - xp.sum(H * state) - - if current_energy < best_energy: - best_energy = current_energy - - T *= cooling_rate - - if i % 100000 == 0: - elapsed = time.time() - start_time - print(f"Step {i}: Energy {current_energy:.4f}, Temp {T:.6f}, Elapsed {elapsed:.2f}s") - - print(f"Evolution Complete. Best Resonant Energy: {best_energy:.4f}") - return state, best_energy - -if __name__ == "__main__": - simulate_quantum_evolution() diff --git a/5-Applications/tools-scripts/simulation/simulate_ipv6_hdc_hyperdag.py b/5-Applications/tools-scripts/simulation/simulate_ipv6_hdc_hyperdag.py deleted file mode 100644 index 46efe8f7..00000000 --- a/5-Applications/tools-scripts/simulation/simulate_ipv6_hdc_hyperdag.py +++ /dev/null @@ -1,538 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Simulate IPv6+port hypervector binding and DAG projection. - -This script models endpoint coordinates (IP + port) as high-dimensional vectors, -binds them into a superposition wave, and projects sampled points into a prefix -DAG for fast topology-style inspection. - -Notes: -- Full IPv6 space (2^128) is not enumerable. This uses deterministic sampling. -- Vectors are bipolar (+1/-1) and use hash-derived random indexing. -""" - -from __future__ import annotations - -import argparse -import csv -import hashlib -import ipaddress -import json -import math -import random -from dataclasses import dataclass -from datetime import datetime, timezone -from pathlib import Path -from typing import Dict, Iterable, List, Set, Tuple, cast - - -DEFAULT_DIMS = 4096 -DEFAULT_PORTS = "80,443" -DEFAULT_SUBNET = "2001:db8::/120" -DEFAULT_SAMPLES = 1024 -PROJECT_ROOT = Path(__file__).resolve().parent.parent -DEFAULT_LEDGER_PATH = PROJECT_ROOT / "out" / "hdc_experiment_ledger.csv" - - -@dataclass -class SimulationConfig: - subnet: ipaddress.IPv6Network - ports: List[int] - dims: int - max_samples: int - seed: int - query_ip: ipaddress.IPv6Address - query_port: int - one_line: bool - one_line_delim: str - run_label: str - append_ledger: bool - ledger_path: Path - broadcast_bootstrap: bool - join_threshold: float - join_report_limit: int - - -def utc_now() -> str: - return datetime.now(timezone.utc).isoformat() - - -def parse_ports(value: str) -> List[int]: - parts = [p.strip() for p in (value or "").split(",") if p.strip()] - if not parts: - raise ValueError("at least one port is required") - out: List[int] = [] - for p in parts: - try: - v = int(p) - except ValueError as exc: - raise ValueError(f"invalid port: {p}") from exc - if v < 0 or v > 65535: - raise ValueError(f"port out of range: {p}") - out.append(v) - return sorted(set(out)) - - -def expand_hash_stream(seed: bytes, needed_bits: int) -> bytes: - chunks: List[bytes] = [] - counter = 0 - needed_bytes = (needed_bits + 7) // 8 - while sum(len(c) for c in chunks) < needed_bytes: - h = hashlib.sha256(seed + counter.to_bytes(4, "big")).digest() - chunks.append(h) - counter += 1 - return b"".join(chunks)[:needed_bytes] - - -def bipolar_hv(key: str, dims: int) -> List[int]: - raw = expand_hash_stream(key.encode("utf-8"), dims) - out: List[int] = [0] * dims - bit_i = 0 - for b in raw: - for bit in range(8): - if bit_i >= dims: - return out - out[bit_i] = 1 if ((b >> (7 - bit)) & 1) else -1 - bit_i += 1 - return out - - -def bind_bipolar(a: List[int], b: List[int]) -> List[int]: - return [x * y for x, y in zip(a, b)] - - -def add_into(acc: List[int], v: List[int]) -> None: - for i, x in enumerate(v): - acc[i] += x - - -def sign_normalize(v: List[int]) -> List[int]: - out: List[int] = [0] * len(v) - for i, x in enumerate(v): - out[i] = 1 if x >= 0 else -1 - return out - - -def dot(a: List[int], b: List[int]) -> int: - return sum(x * y for x, y in zip(a, b)) - - -def l2_norm(v: List[int]) -> float: - return math.sqrt(float(sum(x * x for x in v))) - - -def cosine(a: List[int], b: List[int]) -> float: - na = l2_norm(a) - nb = l2_norm(b) - if na <= 1e-12 or nb <= 1e-12: - return 0.0 - return float(dot(a, b)) / (na * nb) - - -def sampled_ipv6_points(net: ipaddress.IPv6Network, max_samples: int, seed: int) -> List[ipaddress.IPv6Address]: - total = int(net.num_addresses) - n = max(1, min(max_samples, total)) - base = int(net.network_address) - - if total <= n: - return [ipaddress.IPv6Address(base + i) for i in range(total)] - - rnd = random.Random(seed) - offsets: Set[int] = set() - while len(offsets) < n: - offsets.add(rnd.randrange(0, total)) - return [ipaddress.IPv6Address(base + off) for off in sorted(offsets)] - - -def endpoint_hv(ip: ipaddress.IPv6Address, port: int, dims: int) -> List[int]: - ip_vec = bipolar_hv(f"ip6:{ip.compressed}", dims) - port_vec = bipolar_hv(f"port:{port}", dims) - return bind_bipolar(ip_vec, port_vec) - - -def random_probe_hv(ip: ipaddress.IPv6Address, port: int, dims: int) -> List[int]: - probe_ip = ipaddress.IPv6Address((int(ip) ^ 0xA5A5A5A5A5A5A5A5A5A5A5A5A5A5A5A5) & ((1 << 128) - 1)) - probe_port = (port + 7919) % 65536 - return endpoint_hv(probe_ip, probe_port, dims) - - -def combine_sign_vectors(a: List[int], b: List[int], gain: int = 1) -> List[int]: - out: List[int] = [0] * len(a) - for i, x in enumerate(a): - out[i] = x + (gain * b[i]) - return sign_normalize(out) - - -def compute_query_lift(state_vec: List[int], ip: ipaddress.IPv6Address, port: int, dims: int) -> float: - query = endpoint_hv(ip, port, dims) - probe = random_probe_hv(ip, port, dims) - return cosine(state_vec, query) - cosine(state_vec, probe) - - -def build_broadcast_payload(cfg: SimulationConfig) -> Dict[str, object]: - payload_seed = "|".join([ - cfg.subnet.with_prefixlen, - ",".join(str(p) for p in cfg.ports), - str(cfg.dims), - str(cfg.seed), - cfg.run_label, - ]) - payload_hash = hashlib.sha256(payload_seed.encode("utf-8")).hexdigest() - return { - "band": "s-band", - "epoch_utc": utc_now(), - "label": cfg.run_label, - "subnet": cfg.subnet.with_prefixlen, - "ports": cfg.ports, - "dims": cfg.dims, - "seed": cfg.seed, - "policy_hash": payload_hash, - "payload_bytes": len(payload_seed.encode("utf-8")), - } - - -def evaluate_broadcast_bootstrap( - cfg: SimulationConfig, - sampled_ips: List[ipaddress.IPv6Address], - psi: List[int], -) -> Dict[str, object]: - payload = build_broadcast_payload(cfg) - packet_vector = bipolar_hv(f"broadcast:{payload['policy_hash']}", cfg.dims) - - psi_post = combine_sign_vectors(psi, packet_vector, gain=1) - - base_lifts: List[float] = [] - post_lifts: List[float] = [] - join_rows: List[Dict[str, object]] = [] - joined_count = 0 - rejected_count = 0 - - for ip in sampled_ips: - base_lift = compute_query_lift(psi, ip, cfg.query_port, cfg.dims) - post_lift = compute_query_lift(psi_post, ip, cfg.query_port, cfg.dims) - base_lifts.append(base_lift) - post_lifts.append(post_lift) - - join_score = cosine(packet_vector, endpoint_hv(ip, cfg.query_port, cfg.dims)) - joined = bool(join_score >= cfg.join_threshold) - if joined: - joined_count += 1 - else: - rejected_count += 1 - - if cfg.join_report_limit <= 0 or len(join_rows) < cfg.join_report_limit: - join_rows.append({ - "node": ip.compressed, - "join_score": round(join_score, 6), - "decision": "join" if joined else "hold", - "pre_lift": round(base_lift, 6), - "post_lift": round(post_lift, 6), - "lift_delta": round(post_lift - base_lift, 6), - }) - - mean_base = (sum(base_lifts) / len(base_lifts)) if base_lifts else 0.0 - mean_post = (sum(post_lifts) / len(post_lifts)) if post_lifts else 0.0 - - return { - "packet_payload": payload, - "join_policy": { - "join_threshold": cfg.join_threshold, - "query_port": cfg.query_port, - "report_limit": cfg.join_report_limit, - }, - "join_summary": { - "sampled_nodes": len(sampled_ips), - "joined": joined_count, - "held": rejected_count, - }, - "join_decisions": join_rows, - "query_lift_delta": { - "mean_pre": round(mean_base, 6), - "mean_post": round(mean_post, 6), - "delta": round(mean_post - mean_base, 6), - }, - } - - -def prefix_chain(ip: ipaddress.IPv6Address, root_prefix: int, levels: Iterable[int]) -> List[str]: - chains: List[str] = [] - for p in levels: - q = max(root_prefix, min(128, int(p))) - net = ipaddress.IPv6Network(f"{ip}/{q}", strict=False) - chains.append(net.with_prefixlen) - return chains - - -def build_hyperdag(addresses: List[ipaddress.IPv6Address], root_prefix: int) -> Dict[str, object]: - level_offsets = [0, 16, 32, 48, 64] - levels = sorted({max(root_prefix, min(128, root_prefix + d)) for d in level_offsets}) - - node_counts: Dict[str, int] = {} - edge_counts: Dict[Tuple[str, str], int] = {} - - for ip in addresses: - chain = prefix_chain(ip, root_prefix, levels) - for node in chain: - node_counts[node] = node_counts.get(node, 0) + 1 - for i in range(len(chain) - 1): - e = (chain[i], chain[i + 1]) - edge_counts[e] = edge_counts.get(e, 0) + 1 - - top_nodes = sorted(node_counts.items(), key=lambda kv: kv[1], reverse=True)[:12] - top_edges = sorted(edge_counts.items(), key=lambda kv: kv[1], reverse=True)[:12] - - return { - "levels": levels, - "node_count": len(node_counts), - "edge_count": len(edge_counts), - "top_nodes": [{"node": k, "hits": v} for k, v in top_nodes], - "top_edges": [{"from": a, "to": b, "hits": c} for (a, b), c in top_edges], - } - - -def run_simulation(cfg: SimulationConfig) -> Dict[str, object]: - sampled_ips = sampled_ipv6_points(cfg.subnet, cfg.max_samples, cfg.seed) - total_points = len(sampled_ips) * len(cfg.ports) - - wave_sum = [0] * cfg.dims - for ip in sampled_ips: - for port in cfg.ports: - add_into(wave_sum, endpoint_hv(ip, port, cfg.dims)) - - psi = sign_normalize(wave_sum) - - query_vec = endpoint_hv(cfg.query_ip, cfg.query_port, cfg.dims) - similarity = cosine(psi, query_vec) - random_similarity = cosine(psi, random_probe_hv(cfg.query_ip, cfg.query_port, cfg.dims)) - - entropy_bits_per_endpoint = 128 + 16 - - dag = build_hyperdag(sampled_ips, cfg.subnet.prefixlen) - - nonzero = sum(1 for x in wave_sum if x != 0) - density = float(nonzero) / float(cfg.dims) - - out: Dict[str, object] = { - "generated_utc": utc_now(), - "label": cfg.run_label, - "config": { - "subnet": cfg.subnet.with_prefixlen, - "ports": cfg.ports, - "dims": cfg.dims, - "max_samples": cfg.max_samples, - "seed": cfg.seed, - "query": {"ip": cfg.query_ip.compressed, "port": cfg.query_port}, - "broadcast_bootstrap": cfg.broadcast_bootstrap, - }, - "scale": { - "sampled_addresses": len(sampled_ips), - "sampled_endpoint_points": total_points, - "theoretical_ipv6_port_space_bits": 144, - "entropy_bits_per_endpoint": entropy_bits_per_endpoint, - "vector_density": round(density, 6), - }, - "wave": { - "vector_type": "bipolar_sign_normalized", - "dimensions": cfg.dims, - "query_cosine": round(similarity, 6), - "random_probe_cosine": round(random_similarity, 6), - "query_lift": round(similarity - random_similarity, 6), - }, - "hyperdag": dag, - } - - if cfg.broadcast_bootstrap: - out["broadcast_bootstrap"] = evaluate_broadcast_bootstrap(cfg, sampled_ips, psi) - - return out - - -def as_obj_dict(value: object) -> Dict[str, object]: - return cast(Dict[str, object], value) if isinstance(value, dict) else {} - - -def as_obj_list(value: object) -> List[object]: - return cast(List[object], value) if isinstance(value, list) else [] - - -def render_one_line_summary(out: Dict[str, object], delim: str = "|") -> str: - d = delim if delim else "|" - config = as_obj_dict(out.get("config")) - scale = as_obj_dict(out.get("scale")) - wave = as_obj_dict(out.get("wave")) - dag = as_obj_dict(out.get("hyperdag")) - boot = as_obj_dict(out.get("broadcast_bootstrap")) - boot_delta = as_obj_dict(boot.get("query_lift_delta")) - query = as_obj_dict(config.get("query")) - ports = as_obj_list(config.get("ports")) - - fields = [ - str(out.get("generated_utc") or ""), - str(out.get("label") or ""), - str(config.get("subnet") or ""), - str(len(ports)), - str(scale.get("sampled_addresses") or 0), - str(scale.get("sampled_endpoint_points") or 0), - str(config.get("dims") or 0), - str(config.get("seed") or 0), - str(query.get("ip") or ""), - str(query.get("port") or 0), - str(wave.get("query_cosine") or 0.0), - str(wave.get("random_probe_cosine") or 0.0), - str(wave.get("query_lift") or 0.0), - str(scale.get("vector_density") or 0.0), - str(dag.get("node_count") or 0), - str(dag.get("edge_count") or 0), - str(boot_delta.get("delta") or 0.0), - ] - return d.join(fields) - - -def append_ledger_row(ledger_path: Path, out: Dict[str, object]) -> None: - ledger_path.parent.mkdir(parents=True, exist_ok=True) - header = [ - "generated_utc", - "label", - "subnet", - "ports_count", - "sampled_addresses", - "sampled_endpoint_points", - "dims", - "seed", - "query_ip", - "query_port", - "query_cosine", - "random_probe_cosine", - "query_lift", - "vector_density", - "dag_node_count", - "dag_edge_count", - "broadcast_delta", - ] - - config = as_obj_dict(out.get("config")) - scale = as_obj_dict(out.get("scale")) - wave = as_obj_dict(out.get("wave")) - dag = as_obj_dict(out.get("hyperdag")) - query = as_obj_dict(config.get("query")) - ports = as_obj_list(config.get("ports")) - boot = as_obj_dict(out.get("broadcast_bootstrap")) - boot_delta = as_obj_dict(boot.get("query_lift_delta")) - - row = { - "generated_utc": str(out.get("generated_utc") or ""), - "label": str(out.get("label") or ""), - "subnet": str(config.get("subnet") or ""), - "ports_count": str(len(ports)), - "sampled_addresses": str(scale.get("sampled_addresses") or 0), - "sampled_endpoint_points": str(scale.get("sampled_endpoint_points") or 0), - "dims": str(config.get("dims") or 0), - "seed": str(config.get("seed") or 0), - "query_ip": str(query.get("ip") or ""), - "query_port": str(query.get("port") or 0), - "query_cosine": str(wave.get("query_cosine") or 0.0), - "random_probe_cosine": str(wave.get("random_probe_cosine") or 0.0), - "query_lift": str(wave.get("query_lift") or 0.0), - "vector_density": str(scale.get("vector_density") or 0.0), - "dag_node_count": str(dag.get("node_count") or 0), - "dag_edge_count": str(dag.get("edge_count") or 0), - "broadcast_delta": str(boot_delta.get("delta") or 0.0), - } - - write_header = not ledger_path.exists() - with ledger_path.open("a", encoding="utf-8", newline="") as f: - writer = csv.DictWriter(f, fieldnames=header) - if write_header: - writer.writeheader() - writer.writerow(row) - - -def build_config_from_args() -> SimulationConfig: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--subnet", default=DEFAULT_SUBNET, help="IPv6 subnet to sample, e.g. 2001:db8::/64") - ap.add_argument("--ports", default=DEFAULT_PORTS, help="Comma-separated ports, e.g. 80,443,8448") - ap.add_argument("--dims", type=int, default=DEFAULT_DIMS, help="Hypervector dimensions") - ap.add_argument("--max-samples", type=int, default=DEFAULT_SAMPLES, help="Max sampled IPs from subnet") - ap.add_argument("--seed", type=int, default=42, help="Deterministic sampling seed") - ap.add_argument("--query-ip", help="Optional query IPv6 address (defaults to first sampled IP)") - ap.add_argument("--query-port", type=int, default=443, help="Query port for extraction test") - ap.add_argument("--one-line", action="store_true", help="Emit a single delimiter-separated summary line for grep/awk batch sweeps") - ap.add_argument("--one-line-delim", default="|", help="Delimiter for --one-line output (default: |)") - ap.add_argument("--label", default="run", help="Short label written to one-line output and optional CSV ledger") - ap.add_argument("--append-ledger", action="store_true", help="Append each run summary to a local CSV ledger") - ap.add_argument("--ledger-path", default=str(DEFAULT_LEDGER_PATH), help="Path to CSV ledger for --append-ledger") - ap.add_argument("--broadcast-bootstrap", action="store_true", help="Emit broadcast packet payload, join decisions, and query_lift delta") - ap.add_argument("--join-threshold", type=float, default=0.0, help="Cosine threshold for broadcast join decisions") - ap.add_argument("--join-report-limit", type=int, default=64, help="Max join-decision rows to include (<=0 means all)") - args = ap.parse_args() - - try: - subnet = ipaddress.IPv6Network(str(args.subnet), strict=False) - except ValueError as exc: - raise SystemExit(f"invalid --subnet: {exc}") from exc - - try: - ports = parse_ports(str(args.ports)) - except ValueError as exc: - raise SystemExit(f"invalid --ports: {exc}") from exc - - dims = int(args.dims) - if dims <= 0: - raise SystemExit("invalid --dims: must be > 0") - - max_samples = int(args.max_samples) - if max_samples <= 0: - raise SystemExit("invalid --max-samples: must be > 0") - - if args.query_ip: - try: - qip = ipaddress.IPv6Address(str(args.query_ip)) - except ValueError as exc: - raise SystemExit(f"invalid --query-ip: {exc}") from exc - else: - qip = subnet.network_address - - qport = int(args.query_port) - if qport < 0 or qport > 65535: - raise SystemExit("invalid --query-port: must be 0..65535") - - if args.join_report_limit < 0: - raise SystemExit("invalid --join-report-limit: must be >= 0") - - return SimulationConfig( - subnet=subnet, - ports=ports, - dims=dims, - max_samples=max_samples, - seed=int(args.seed), - query_ip=qip, - query_port=qport, - one_line=bool(args.one_line), - one_line_delim=str(args.one_line_delim), - run_label=str(args.label), - append_ledger=bool(args.append_ledger), - ledger_path=Path(str(args.ledger_path)), - broadcast_bootstrap=bool(args.broadcast_bootstrap), - join_threshold=float(args.join_threshold), - join_report_limit=int(args.join_report_limit), - ) - - -def main() -> None: - cfg = build_config_from_args() - out = run_simulation(cfg) - if cfg.append_ledger: - append_ledger_row(cfg.ledger_path, out) - if cfg.one_line: - print(render_one_line_summary(out, delim=cfg.one_line_delim)) - return - print(json.dumps(out, indent=2)) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/simulation/simulate_stirling_dac_loop.py b/5-Applications/tools-scripts/simulation/simulate_stirling_dac_loop.py deleted file mode 100644 index 08299efa..00000000 --- a/5-Applications/tools-scripts/simulation/simulate_stirling_dac_loop.py +++ /dev/null @@ -1,773 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Closed-loop thermodynamic simulator: Stirling engine + DAC + Sabatier + Nitrate chain. - -Energy loop: - Solar (concentrated or PV) ──► hot side heat + electrolysis power - Stirling ΔT ──────────────────► shaft work + thermopile harvest - Thermopile (cold/hot junction) ► auxiliary electrical power - Cold side heat sink ──────────► DAC sorbent regeneration + H2O condensation - Hot zone reactor ─────────────► Sabatier: CO2 + 4H2 → CH4 + 2H2O - Shaft work ───────────────────► compressor → supercritical product storage - Surplus H2 + N2(air) ─────────► Haber-Bosch → NH3 - NH3 + O2(elec) ───────────────► Ostwald → HNO3 → nitrate - SMR: surplus CH4 + H2O ───────► CO2 + 4H2 (feeds Haber, CO2 loops back) - -All units SI unless noted. Outputs a JSON report + optional one-line mode. - -Usage: - python 5-Applications/scripts/simulate_stirling_dac_loop.py - python 5-Applications/scripts/simulate_stirling_dac_loop.py --nitrate-chain - python 5-Applications/scripts/simulate_stirling_dac_loop.py --solar-w 800 --area-m2 2.0 - python 5-Applications/scripts/simulate_stirling_dac_loop.py --one-line -""" - -from __future__ import annotations - -import argparse -import json -import math -from dataclasses import dataclass, field, asdict -from typing import Any, Dict, List, Optional - -# ── physical constants ────────────────────────────────────────────────────── -R_gas = 8.314 # J/(mol·K) -T_std = 298.15 # K (standard) - -# Sabatier: CO2 + 4H2 → CH4 + 2H2O ΔH = -165.0 kJ/mol (exothermic) -DH_sabatier_kJ = -165.0 - -# H2O electrolysis (HHV): H2O → H2 + ½O2 ΔH = +286.0 kJ/mol H2 -DH_electrolysis_kJ = 286.0 - -# CO2 DAC sorbent regeneration energy (solid-sorbent TSA, typical range 80-120 kJ/mol) -DH_dac_regen_kJ = 90.0 - -# Seebeck coefficient (typical BiTe thermopile module) V/K -SEEBECK_V_PER_K = 0.04 - -# Supercritical CO2 critical point: 304 K, 7.38 MPa -T_crit_co2 = 304.25 # K -P_crit_co2 = 7.38e6 # Pa - -# Supercritical CH4 critical point: 190.6 K, 4.60 MPa -T_crit_ch4 = 190.64 # K -P_crit_ch4 = 4.60e6 # Pa - -# Atmospheric defaults (Earth) -CO2_PPM = 420.0 -CO2_MOLE_FRAC = CO2_PPM / 1e6 - -# N2 mole fraction in dry air -N2_MOLE_FRAC = 0.7809 -# O2 mole fraction in dry air -O2_MOLE_FRAC = 0.2095 - - -# ── nitrate chain thermodynamics ──────────────────────────────────────────── -# SMR: CH4 + H2O → CO + 3H2 ΔH = +206 kJ/mol CH4 (endothermic) -# WGS: CO + H2O → CO2 + H2 ΔH = -41 kJ/mol CO (mildly exothermic) -# Combined SMR+WGS: CH4 + 2H2O → CO2 + 4H2 ΔH = +165 kJ/mol CH4 -DH_smr_kJ = 206.0 -DH_wgs_kJ = -41.0 -DH_smr_net_kJ = 165.0 # endothermic overall → consumes hot-side heat - -# Haber-Bosch: N2 + 3H2 → 2NH3 ΔH = -92 kJ/mol N2 = -46 kJ/mol NH3 -DH_haber_kJ_per_n2 = -92.0 -DH_haber_kJ_per_nh3 = -46.0 - -# Ostwald overall: NH3 + 2O2 → HNO3 + H2O ΔH ≈ -415 kJ/mol NH3 -# (sum of catalytic oxidation + NO oxidation + acid absorption) -DH_ostwald_kJ_per_nh3 = -415.0 - -# Neutralisation (NH3·HNO3 → NH4NO3 as reference product) ~exothermic but small -DH_neutralisation_kJ = -25.0 # kJ/mol HNO3 - - -# ── configuration ──────────────────────────────────────────────────────────── - -@dataclass -class LoopConfig: - # Golden Ratio Ground State (Simulated Annealing Output) - T_hot_K: float = 954.2 # hot side (waste heat exactly matches DAC enthalpy) - T_cold_K: float = 363.0 # cold side radiator (ambient sink) - stirling_efficiency_fraction: float = 0.55 # NASA FPSC fraction of Carnot achieved - linear_alternator_efficiency: float = 0.92 # FPSC AC power conversion - stirling_heat_input_W: float = 0.0 # derived from solar - - # Solar - solar_irradiance_W_m2: float = 800.0 # W/m² - collector_area_m2: float = 1.5 # parabolic dish or flat CPC - collector_optical_efficiency: float = 0.78 # mirror/lens losses - pv_area_m2: float = 0.20 # small PV for electrolysis - pv_efficiency: float = 0.22 - - # Thermopile - thermopile_pairs: int = 120 # number of TE couples - thermopile_internal_resistance_ohm: float = 4.0 - thermopile_load_resistance_ohm: float = 4.0 # matched for max power - - # DAC - dac_air_flow_L_s: float = 5.0 # L/s through cold-side sorbent bed - dac_capture_efficiency: float = 0.85 # fraction of CO2 captured per pass - - # H2O collection - h2o_collection_efficiency: float = 0.70 # from cold-side condensation - - # Electrolysis - electrolysis_faradaic_efficiency: float = 0.85 - electrolysis_voltage_V: float = 1.8 # practical cell voltage - - # Sabatier reactor - sabatier_conversion_efficiency: float = 0.92 # CO2→CH4 per pass - sabatier_heat_recovery_fraction: float = 0.60 # exotherm fed back to hot side - - # Compressor (supercritical storage) - compressor_isentropic_efficiency: float = 0.75 - compressor_motor_efficiency: float = 0.85 # AC Motor drive - product_mode: str = "CH4" # "CH4" or "CO2" - - # Environment - atmospheric_pressure_Pa: float = 101325.0 - ambient_temp_K: float = 298.15 - co2_mole_fraction: float = CO2_MOLE_FRAC - n2_mole_fraction: float = N2_MOLE_FRAC - o2_mole_fraction: float = O2_MOLE_FRAC - gravity_m_s2: float = 9.81 - relative_humidity: float = 0.50 - - -@dataclass -class BiologicalLoadConfig: - """Configuration for routing Nitrates and O2 into Biological Packets.""" - crop_n_fraction: float = 0.015 # Assume biomass is ~1.5% Nitrogen by mass - biomass_kcal_per_kg: float = 4000.0 # Caloric density of dry biomass - human_kcal_per_day: float = 2500.0 # Dietary requirement per human node - human_o2_mol_per_day: float = 26.0 # Approx 840g (26 mol) of O2 / day for respiration - - -@dataclass -class NitrateChainConfig: - """Configuration for the surplus-H2 → nitrate multi-step pathway.""" - # SMR: Exact golden ratio to offset Sabatier exotherm - ch4_smr_fraction: float = 1.0 # All diverted trace goes to SMR - # Haber-Bosch - haber_n2_conversion: float = 0.99 # N2 triple bond split via acoustic resonance - # Ostwald - ostwald_nh3_conversion: float = 0.95 # NH3 → HNO3 yield - # Nitrate: neutralise HNO3 with NH3 → NH4NO3 (no external base needed) - nitrate_product: str = "NH4NO3" # or "Ca(NO3)2" etc. - # Fraction of Sabatier CH4 available as surplus (rest is stored scCH4) - ch4_surplus_fraction: float = 0.418 # Golden ratio: Sabatier exotherm matches SMR endotherm - - -# ── derived results ────────────────────────────────────────────────────────── - -@dataclass -class LoopResult: - # Energy inputs - solar_thermal_W: float = 0.0 - solar_pv_W: float = 0.0 - thermopile_W: float = 0.0 - sabatier_heat_recovery_W: float = 0.0 - total_heat_in_W: float = 0.0 - - # Stirling - carnot_efficiency: float = 0.0 - actual_efficiency: float = 0.0 - stirling_pv_work_W: float = 0.0 - stirling_electrical_W: float = 0.0 - stirling_heat_rejected_W: float = 0.0 - - # Thermopile - thermopile_open_circuit_V: float = 0.0 - thermopile_power_W: float = 0.0 - thermopile_delta_T: float = 0.0 - - # DAC - co2_captured_mol_s: float = 0.0 - dac_regen_power_W: float = 0.0 # power consumed from cold-side heat - dac_energy_per_mol_kJ: float = DH_dac_regen_kJ - - # H2O / electrolysis - h2o_collected_mol_s: float = 0.0 - h2_produced_mol_s: float = 0.0 - electrolysis_power_W: float = 0.0 # consumed - electrolysis_source: str = "" - - # Sabatier - ch4_produced_mol_s: float = 0.0 - sabatier_heat_liberated_W: float = 0.0 - h2_consumed_mol_s: float = 0.0 - co2_consumed_mol_s: float = 0.0 - h2_surplus_mol_s: float = 0.0 - co2_surplus_mol_s: float = 0.0 - - # Compressor - compressor_shaft_input_W: float = 0.0 - compressor_electrical_input_W: float = 0.0 - product_pressure_Pa: float = 0.0 - product_temp_K: float = 0.0 - supercritical: bool = False - product_mol_s: float = 0.0 - - # Loop closure - net_electrical_surplus_W: float = 0.0 - loop_closed: bool = False - energy_balance_notes: List[str] = field(default_factory=list) # type: ignore[assignment] - nitrate_chain: Optional["NitrateChainResult"] = None - bio_load: Optional["BiologicalLoadResult"] = None - - -@dataclass -class BiologicalLoadResult: - # Bio-compute payload matrices - n_routed_to_biomass_mol_s: float = 0.0 - biomass_kg_s: float = 0.0 - calories_produced_per_day: float = 0.0 - - o2_surplus_mol_s: float = 0.0 - - # Capacity Limits - human_nodes_food_limit: float = 0.0 - human_nodes_o2_limit: float = 0.0 - active_human_nodes: float = 0.0 - system_bottleneck: str = "" - -@dataclass -class NitrateChainResult: - # SMR + WGS - ch4_fed_to_smr_mol_s: float = 0.0 - h2o_fed_to_smr_mol_s: float = 0.0 - h2_from_smr_mol_s: float = 0.0 - co2_from_smr_mol_s: float = 0.0 # recycled back to DAC loop - smr_heat_consumed_W: float = 0.0 # endothermic draw on hot side - - # Haber-Bosch - n2_available_mol_s: float = 0.0 - h2_available_for_haber_mol_s: float = 0.0 - n2_consumed_mol_s: float = 0.0 - h2_consumed_haber_mol_s: float = 0.0 - nh3_produced_mol_s: float = 0.0 - haber_heat_liberated_W: float = 0.0 - - # O2 from electrolysis byproduct - o2_from_electrolysis_mol_s: float = 0.0 - o2_surplus_mol_s: float = 0.0 - - # Ostwald (NH3 → HNO3) - nh3_fed_ostwald_mol_s: float = 0.0 - o2_consumed_ostwald_mol_s: float = 0.0 - hno3_produced_mol_s: float = 0.0 - h2o_produced_ostwald_mol_s: float = 0.0 - ostwald_heat_liberated_W: float = 0.0 - - # Nitrate product - nitrate_product: str = "NH4NO3" - nitrate_mol_s: float = 0.0 - nh3_used_neutralisation_mol_s: float = 0.0 - - # Heat balance - net_heat_to_hot_side_W: float = 0.0 # positive = adds to Stirling hot side - loop_still_closed: bool = False - notes: List[str] = field(default_factory=list) # type: ignore[assignment] - - -# ── simulator ──────────────────────────────────────────────────────────────── - -def _air_co2_mol_per_s(cfg: LoopConfig) -> float: - """Moles of CO2 per second in an air stream based on environmental PV=nRT.""" - vol_flow_m3_s = cfg.dac_air_flow_L_s * 1e-3 - mol_air_per_s = (cfg.atmospheric_pressure_Pa * vol_flow_m3_s) / (R_gas * cfg.ambient_temp_K) - return mol_air_per_s * cfg.co2_mole_fraction - - -def _air_h2o_mol_per_s(cfg: LoopConfig) -> float: - """Approximate moles of H2O vapour per second based on temperature and RH.""" - # simple Antoine eq for water vapor pressure over water - T_C = cfg.ambient_temp_K - 273.15 - if T_C > 0: - # standard Antoine for 0-100 C - P_sat = 10 ** (8.07131 - 1730.63 / (233.426 + T_C)) * 133.322 # mmHg to Pa - else: - P_sat = 0.0 # Ignore sublimation/ice partial pressure for simplicity at this scale - - P_atm = cfg.atmospheric_pressure_Pa - x_h2o = (cfg.relative_humidity * P_sat) / P_atm - vol_flow_m3_s = cfg.dac_air_flow_L_s * 1e-3 - mol_air_per_s = (cfg.atmospheric_pressure_Pa * vol_flow_m3_s) / (R_gas * cfg.ambient_temp_K) - return mol_air_per_s * x_h2o - - -def run_simulation(cfg: LoopConfig) -> LoopResult: - res = LoopResult() - notes = res.energy_balance_notes - - # ── 1. solar heat input ───────────────────────────────────────────────── - solar_thermal = ( - cfg.solar_irradiance_W_m2 - * cfg.collector_area_m2 - * cfg.collector_optical_efficiency - ) - solar_pv = ( - cfg.solar_irradiance_W_m2 - * cfg.pv_area_m2 - * cfg.pv_efficiency - ) - res.solar_thermal_W = solar_thermal - res.solar_pv_W = solar_pv - cfg.stirling_heat_input_W = solar_thermal # will grow after Sabatier recovery - - # ── 2. Stirling cycle ─────────────────────────────────────────────────── - T_h = cfg.T_hot_K - T_c = cfg.T_cold_K - carnot = 1.0 - T_c / T_h - actual_eff = carnot * cfg.stirling_efficiency_fraction - - res.carnot_efficiency = round(carnot, 4) - res.actual_efficiency = round(actual_eff, 4) - - # Initial PV work before Sabatier heat recovery - pv_work_W = cfg.stirling_heat_input_W * actual_eff - heat_rejected_W = cfg.stirling_heat_input_W - pv_work_W - res.stirling_heat_rejected_W = heat_rejected_W - - # ── 3. Thermopile ─────────────────────────────────────────────────────── - delta_T = T_h - T_c - res.thermopile_delta_T = delta_T - V_oc = cfg.thermopile_pairs * SEEBECK_V_PER_K * delta_T - res.thermopile_open_circuit_V = round(V_oc, 2) - # Max power transfer: matched load - R_int = cfg.thermopile_internal_resistance_ohm - R_load = cfg.thermopile_load_resistance_ohm - I_tp = V_oc / (R_int + R_load) - tp_power = I_tp ** 2 * R_load - res.thermopile_W = round(tp_power, 3) - - total_electrical_in = solar_pv + tp_power - notes.append(f"Electrical available (PV + thermopile): {total_electrical_in:.2f} W") - - # ── 4. DAC — cold side sorbent bed ────────────────────────────────────── - co2_in_mol_s = _air_co2_mol_per_s(cfg) - co2_captured = co2_in_mol_s * cfg.dac_capture_efficiency - res.co2_captured_mol_s = co2_captured - - # Sorbent regen uses cold-side waste heat (TSA cycle) - dac_regen_W = co2_captured * DH_dac_regen_kJ * 1000.0 # W = mol/s * J/mol - res.dac_regen_power_W = round(dac_regen_W, 3) - - if dac_regen_W > heat_rejected_W: - notes.append( - f"WARNING: DAC regen ({dac_regen_W:.1f} W) exceeds cold-side heat " - f"({heat_rejected_W:.1f} W) — reduce flow or add heat exchanger stages" - ) - else: - notes.append(f"DAC regen ({dac_regen_W:.1f} W) covered by cold-side heat rejection OK") - - # ── 5. H2O collection + electrolysis ──────────────────────────────────── - h2o_available = _air_h2o_mol_per_s(cfg) - h2o_collected = h2o_available * cfg.h2o_collection_efficiency - res.h2o_collected_mol_s = h2o_collected - - # Electrolysis powered by total_electrical_in - # Power to electrolyze: P = n_H2 * DH_elec / faradaic_eff - # → n_H2 = P * faradaic_eff / DH_elec - dh_elec_J = DH_electrolysis_kJ * 1000.0 - h2_from_elec = (total_electrical_in * cfg.electrolysis_faradaic_efficiency) / dh_elec_J - # Cap by available H2O - h2_produced = min(h2_from_elec, h2o_collected) - res.h2_produced_mol_s = h2_produced - res.electrolysis_power_W = round( - h2_produced * dh_elec_J / cfg.electrolysis_faradaic_efficiency, 3 - ) - res.electrolysis_source = "solar_pv + thermopile" - notes.append( - f"H2 production: {h2_produced*1e6:.2f} µmol/s " - f"(limited by {'H2O supply' if h2_produced == h2o_collected else 'electrical power'})" - ) - - # ── 6. Sabatier reactor — hot zone ────────────────────────────────────── - # CO2 + 4H2 → CH4 + 2H2O - # stoichiometric H2 needed for all captured CO2 - h2_needed_for_co2 = co2_captured * 4.0 - if h2_produced < h2_needed_for_co2: - # H2-limited: consume all H2, fraction of CO2 - h2_consumed = h2_produced - co2_consumed = h2_consumed / 4.0 - co2_surplus = co2_captured - co2_consumed - h2_surplus = 0.0 - else: - # CO2-limited - co2_consumed = co2_captured * cfg.sabatier_conversion_efficiency - h2_consumed = co2_consumed * 4.0 - h2_surplus = h2_produced - h2_consumed - co2_surplus = co2_captured * (1.0 - cfg.sabatier_conversion_efficiency) - - ch4_produced = co2_consumed # 1:1 molar - sabatier_heat_W = co2_consumed * abs(DH_sabatier_kJ) * 1000.0 - heat_recovery_W = sabatier_heat_W * cfg.sabatier_heat_recovery_fraction - - res.ch4_produced_mol_s = ch4_produced - res.sabatier_heat_liberated_W = round(sabatier_heat_W, 3) - res.sabatier_heat_recovery_W = round(heat_recovery_W, 3) - res.h2_consumed_mol_s = h2_consumed - res.co2_consumed_mol_s = co2_consumed - res.h2_surplus_mol_s = round(h2_surplus, 9) - res.co2_surplus_mol_s = round(co2_surplus, 9) - # ── 6b. NITRATE CHAIN (SMR, Haber, Ostwald) ───────────────────────────── - # Treat the system as an unfolded 3D device tracking atomic traces (C, H, N, O) - nitrate_cfg = NitrateChainConfig() - nc = NitrateChainResult() - - # ── Methane diversion - ch4_surplus = ch4_produced * nitrate_cfg.ch4_surplus_fraction - ch4_to_compressor = ch4_produced - ch4_surplus - - # SMR trace: C and H nodes out of loop. CH4 + 2H2O -> CO2 + 4H2 - ch4_to_smr = ch4_surplus * nitrate_cfg.ch4_smr_fraction - nc.ch4_fed_to_smr_mol_s = ch4_to_smr - nc.h2o_fed_to_smr_mol_s = ch4_to_smr * 2.0 - nc.co2_from_smr_mol_s = ch4_to_smr - nc.h2_from_smr_mol_s = ch4_to_smr * 4.0 - nc.smr_heat_consumed_W = ch4_to_smr * DH_smr_net_kJ * 1000.0 # Endothermic - - # Nitrogen & Hydrogen traces connecting into Haber - h2_avail = h2_surplus + nc.h2_from_smr_mol_s - nc.h2_available_for_haber_mol_s = h2_avail - - # N2 from free air trace - vol_flow_m3_s = cfg.dac_air_flow_L_s * 1e-3 - mol_air_per_s = (cfg.atmospheric_pressure_Pa * vol_flow_m3_s) / (R_gas * cfg.ambient_temp_K) - n2_avail = mol_air_per_s * cfg.n2_mole_fraction - nc.n2_available_mol_s = n2_avail - - # Haber node resolution: N2 + 3H2 -> 2NH3 - n2_needed_for_h2 = h2_avail / 3.0 - n2_reacted = min(n2_avail, n2_needed_for_h2) * nitrate_cfg.haber_n2_conversion - nc.h2_consumed_haber_mol_s = n2_reacted * 3.0 - nc.n2_consumed_mol_s = n2_reacted - nc.nh3_produced_mol_s = n2_reacted * 2.0 - nc.haber_heat_liberated_W = n2_reacted * abs(DH_haber_kJ_per_n2) * 1000.0 - - # Ostwald trace linking NH3 and Oxygen (from Electrolysis byproduct) - nh3_to_ostwald = nc.nh3_produced_mol_s * 0.5 # Split 50/50 for NH4NO3 - nc.o2_from_electrolysis_mol_s = h2_produced * 0.5 - nc.nh3_fed_ostwald_mol_s = nh3_to_ostwald - nc.hno3_produced_mol_s = nh3_to_ostwald * nitrate_cfg.ostwald_nh3_conversion - nc.ostwald_heat_liberated_W = nh3_to_ostwald * abs(DH_ostwald_kJ_per_nh3) * 1000.0 - - # Neutralisation node: Crossover mapping to topsoil salt - nc.nitrate_mol_s = min(nc.nh3_produced_mol_s - nh3_to_ostwald, nc.hno3_produced_mol_s) - nc.nh3_used_neutralisation_mol_s = nc.nitrate_mol_s - - # Heat aggregate node linking back to Stirling loop - nc.net_heat_to_hot_side_W = (nc.haber_heat_liberated_W + nc.ostwald_heat_liberated_W) - nc.smr_heat_consumed_W - res.nitrate_chain = nc - - # ── 6c. BIOLOGICAL LOAD (Consumer Packets routing) ────────────────────── - bio_cfg = BiologicalLoadConfig() - bio = BiologicalLoadResult() - - # Each mol of NH4NO3 gives 2 mols of Nitrogen atoms for biomass - bio.n_routed_to_biomass_mol_s = nc.nitrate_mol_s * 2.0 - - # 1 mol N = 14.0067 grams. - n_kg_s = bio.n_routed_to_biomass_mol_s * 0.0140067 - - # If biomass is 1.5% N, total biomass = n_kg / 0.015 - bio.biomass_kg_s = n_kg_s / bio_cfg.crop_n_fraction - - # Calories generated per day = biomass_kg_s * (seconds in day) * kcal_per_kg - bio.calories_produced_per_day = bio.biomass_kg_s * 86400.0 * bio_cfg.biomass_kcal_per_kg - bio.human_nodes_food_limit = bio.calories_produced_per_day / bio_cfg.human_kcal_per_day - - # O2 routing: electrolysis O2 - ostwald O2 - bio.o2_surplus_mol_s = nc.o2_from_electrolysis_mol_s - nc.o2_consumed_ostwald_mol_s - if bio.o2_surplus_mol_s < 0: - bio.o2_surplus_mol_s = 0.0 - - o2_mol_day = bio.o2_surplus_mol_s * 86400.0 - bio.human_nodes_o2_limit = o2_mol_day / bio_cfg.human_o2_mol_per_day - - bio.active_human_nodes = min(bio.human_nodes_food_limit, bio.human_nodes_o2_limit) - bio.system_bottleneck = "Food" if bio.human_nodes_food_limit < bio.human_nodes_o2_limit else ("O2" if bio.human_nodes_o2_limit < bio.human_nodes_food_limit else "Balanced") - - res.bio_load = bio - - # Feed Sabatier and Nitrate node heat deltas back to Stirling hot side - total_heat_in = solar_thermal + heat_recovery_W + nc.net_heat_to_hot_side_W - res.total_heat_in_W = round(total_heat_in, 3) - pv_work_W = total_heat_in * actual_eff - stirling_electrical = pv_work_W * cfg.linear_alternator_efficiency - - res.stirling_pv_work_W = round(pv_work_W, 3) - res.stirling_electrical_W = round(stirling_electrical, 3) - notes.append( - f"Thermochemical recovery (Sabatier + Nitrate chain) shifts hot side energy by " - f"{(heat_recovery_W + nc.net_heat_to_hot_side_W):.2f} W → " - f"AC flow wire resolved: {stirling_electrical:.2f} W" - ) - - # ── 7. Compressor → supercritical product ─────────────────────────────── - if cfg.product_mode == "CH4": - mol_s = ch4_to_compressor - T_crit = T_crit_ch4 - P_target = P_crit_ch4 * 1.3 # 30% above critical - else: - mol_s = co2_captured - co2_consumed + co2_surplus - T_crit = T_crit_co2 - P_target = P_crit_co2 * 1.3 - - res.product_mol_s = mol_s - - # Isothermal compression work estimate: W = n·R·T·ln(P2/P1) - P_in = 101325.0 # Pa - if mol_s > 0 and P_target > P_in: - W_ideal = mol_s * R_gas * T_std * math.log(P_target / P_in) - W_actual = W_ideal / cfg.compressor_isentropic_efficiency - else: - W_actual = 0.0 - - compressor_electrical = 0.0 if not W_actual else W_actual / cfg.compressor_motor_efficiency - res.compressor_shaft_input_W = round(W_actual, 3) - res.compressor_electrical_input_W = round(compressor_electrical, 3) - res.product_pressure_Pa = P_target - res.product_temp_K = T_std # post-intercooling - - # Check supercritical condition - near_critical = abs(T_std - T_crit) < 50.0 # within 50 K of critical T - above_critical_P = P_target > (P_crit_ch4 if cfg.product_mode == "CH4" else P_crit_co2) - res.supercritical = above_critical_P and near_critical - - # ── 8. Loop closure (Micro-Grid Electrical Bus) ───────────────────────── - # FPSCs decouple the compressor via electrical bus - elec_consumed = res.electrolysis_power_W + compressor_electrical - elec_available = total_electrical_in + res.stirling_electrical_W - net_elec = elec_available - elec_consumed - res.net_electrical_surplus_W = round(net_elec, 3) - - # Loop is closed if: - # 1. Net electrical grid is positive (covers Electrolysis + Compressor) - # 2. DAC regen covered by cold-side heat - elec_ok = net_elec >= 0 - dac_ok = dac_regen_W <= heat_rejected_W - res.loop_closed = elec_ok and dac_ok - - if not elec_ok: - notes.append( - f"LOOP OPEN: Micro-Grid electrical deficit {abs(net_elec):.2f} W — " - f"increase collector capacity or reduce conversion load" - ) - if not dac_ok: - notes.append( - f"LOOP OPEN: DAC heat deficit {dac_regen_W - heat_rejected_W:.2f} W — " - f"reduce air flow or add auxiliary heat exchanger" - ) - if res.loop_closed: - notes.append( - "LOOP CLOSED: all electrical and DAC thermal budgets successfully routed " - "via Free-Piston electrical grid." - ) - - return res - - -# ── output renderers ──────────────────────────────────────────────────────── - -def render_report(cfg: LoopConfig, res: LoopResult) -> None: - W = 58 - - def row(label: str, value: str) -> str: - return f" {label:<36} {value}" - - print(f"\n{'═' * W}") - print(f" STIRLING-DAC-SABATIER CLOSED LOOP {'═' * (W - 36)}") - print(" [ SYSTEM STATE: WAVEFORM COLLAPSED -> GROUND STATE ]") - print(" [ N-TRACE EXCITATION: FPSC ACOUSTIC SONICATION ACTIVE ]") - if res.nitrate_chain and res.nitrate_chain.nitrate_mol_s > 0: - nc = res.nitrate_chain - print(f"\n {'── NITRATE TRACE WIRES (Atomic topological graph)':}") - print(row("C-Trace: CH4 bypassed to SMR", f"{nc.ch4_fed_to_smr_mol_s*1e6:.4f} µmol/s")) - print(row("H-Trace: H2 harvested off SMR", f"{nc.h2_from_smr_mol_s*1e6:.4f} µmol/s")) - print(row("N-Trace: N2 entrained via air", f"{nc.n2_consumed_mol_s*1e6:.4f} µmol/s")) - print(row("O-Trace: O2 wire from PV H2O lysis", f"{nc.o2_from_electrolysis_mol_s*1e6:.4f} µmol/s")) - print(row("Node Synthesis: NH3 stabilized", f"{nc.nh3_produced_mol_s*1e6:.4f} µmol/s")) - print(row("Node Synthesis: HNO3 derived", f"{nc.hno3_produced_mol_s*1e6:.4f} µmol/s")) - print(row("Terminal: NH4NO3 soil precursor", f"{nc.nitrate_mol_s*1e6:.4f} µmol/s")) - - tons_per_year = (nc.nitrate_mol_s * 80.043 * 3600 * 24 * 365.25) / 1e6 - print(row("Topsoil matrix potential (1 unit/yr)", f"{tons_per_year:.4f} metric tons/year")) - print(row("Latent thermal bias via reaction", f"{nc.net_heat_to_hot_side_W:+.3f} W (to hot zone loop)")) - - if res.bio_load and res.nitrate_chain and res.nitrate_chain.nitrate_mol_s > 0: - bio = res.bio_load - print(f"\n {'── BIOLOGICAL LOAD (Consumer Nodes Routing)':}") - print(row("Crop Nitrogen Matrix (N atoms)", f"{bio.n_routed_to_biomass_mol_s*1e6:.4f} µmol/s")) - print(row("Biomass Rendered (1.5% N matrix)", f"{bio.biomass_kg_s*3600*24:.3f} kg/day")) - print(row("Caloric Payload Generated", f"{bio.calories_produced_per_day:.0f} kcal/day")) - print(row("O2 Byproduct Matrix (Surplus)", f"{bio.o2_surplus_mol_s*1e6:.4f} µmol/s")) - print(row("Max Human Nodes (Food Constraint)", f"{bio.human_nodes_food_limit:.3f} souls")) - print(row("Max Human Nodes (O2 Constraint)", f"{bio.human_nodes_o2_limit:.3f} souls")) - print(row("Sustainable Human Matrix Load", f"{bio.active_human_nodes:.3f} Active Nodes (Bottleneck: {bio.system_bottleneck})")) - - print(f"{'═' * W}") - - print(f"\n {'── ENERGY INPUTS':}") - print(row("Solar thermal (collector)", f"{res.solar_thermal_W:.2f} W")) - print(row("Solar PV (electrolysis)", f"{res.solar_pv_W:.2f} W")) - print(row("Thermopile (Stirling ΔT)", f"{res.thermopile_W:.3f} W")) - print(row("Sabatier exotherm recovery", f"{res.sabatier_heat_recovery_W:.3f} W")) - print(row("Total hot-side heat in", f"{res.total_heat_in_W:.2f} W")) - - print(f"\n {'── STIRLING (NASA GRC Free-Piston Topology)':}") - print(row("Carnot efficiency", f"{res.carnot_efficiency*100:.1f}%")) - print(row("Actual PV efficiency (×{:.0f}% Carnot)".format( - cfg.stirling_efficiency_fraction * 100), f"{res.actual_efficiency*100:.1f}%")) - print(row("PV thermodynamic work", f"{res.stirling_pv_work_W:.2f} W")) - print(row("Linear alternator output (AC)", f"{res.stirling_electrical_W:.2f} W")) - print(row("Heat rejected (radiator sink)", f"{res.stirling_heat_rejected_W:.2f} W")) - - print(f"\n {'── THERMOPILE':}") - print(row("ΔT hot/cold", f"{res.thermopile_delta_T:.0f} K")) - print(row("Open-circuit voltage", f"{res.thermopile_open_circuit_V:.1f} V")) - print(row("Power (matched load)", f"{res.thermopile_W:.3f} W")) - - print(f"\n {'── DIRECT AIR CAPTURE (cold side)':}") - print(row("CO2 captured", f"{res.co2_captured_mol_s*1e6:.3f} µmol/s")) - print(row("Regen heat needed", f"{res.dac_regen_power_W:.3f} W")) - print(row("Cold-side heat available", f"{res.stirling_heat_rejected_W:.2f} W")) - - print(f"\n {'── H2O + ELECTROLYSIS':}") - print(row("H2O collected (condensate)", f"{res.h2o_collected_mol_s*1e6:.3f} µmol/s")) - print(row("H2 produced", f"{res.h2_produced_mol_s*1e6:.3f} µmol/s")) - print(row("Electrolysis power consumed", f"{res.electrolysis_power_W:.3f} W")) - print(row("Source", res.electrolysis_source)) - - print(f"\n {'── SABATIER REACTOR (hot zone)':}") - print(row("CO2 consumed", f"{res.co2_consumed_mol_s*1e6:.4f} µmol/s")) - print(row("H2 consumed", f"{res.h2_consumed_mol_s*1e6:.4f} µmol/s")) - print(row("CH4 produced", f"{res.ch4_produced_mol_s*1e6:.4f} µmol/s")) - print(row("Exotherm liberated", f"{res.sabatier_heat_liberated_W:.4f} W")) - print(row(" → fed back to hot side", f"{res.sabatier_heat_recovery_W:.4f} W")) - print(row("H2 surplus", f"{res.h2_surplus_mol_s*1e9:.3f} nmol/s")) - print(row("CO2 surplus", f"{res.co2_surplus_mol_s*1e9:.3f} nmol/s")) - - print(f"\n {'── COMPRESSOR → SUPERCRITICAL':}") - print(row("Product mode", cfg.product_mode)) - print(row("Product flow", f"{res.product_mol_s*1e6:.4f} µmol/s")) - print(row("Target pressure", f"{res.product_pressure_Pa/1e6:.3f} MPa")) - print(row("Is supercritical?", "YES ✓" if res.supercritical else "no (sub-Pc or ΔT >50 K)")) - print(row("Shaft work required", f"{res.compressor_shaft_input_W:.4f} W")) - print(row("Motor electric overhead", f"{res.compressor_electrical_input_W:.4f} W")) - - print(f"\n {'── LOOP CLOSURE (Unified Micro-Grid)':}") - print(row("Net electrical surplus", f"{res.net_electrical_surplus_W:.3f} W")) - status = "✓ CLOSED — self-sustaining" if res.loop_closed else "✗ OPEN — see notes" - print(row("Loop status", status)) - - print(f"\n {'── NOTES':}") - for note in res.energy_balance_notes: - for line in (note[i:i+W-4] for i in range(0, len(note), W-4)): - print(f" {line}") - - print(f"\n{'═' * W}\n") - - -def render_one_line(_cfg: LoopConfig, res: LoopResult, delim: str) -> None: - parts = [ - f"solar_thermal={res.solar_thermal_W:.1f}W", - f"solar_pv={res.solar_pv_W:.1f}W", - f"thermopile={res.thermopile_W:.2f}W", - f"carnot={res.carnot_efficiency*100:.1f}pct", - f"actual_eff={res.actual_efficiency*100:.1f}pct", - f"ac_out={res.stirling_electrical_W:.2f}W", - f"thermopile_dT={res.thermopile_delta_T:.0f}K", - f"co2_captured={res.co2_captured_mol_s*1e6:.3f}umol_s", - f"h2_prod={res.h2_produced_mol_s*1e6:.3f}umol_s", - f"ch4_prod={res.ch4_produced_mol_s*1e6:.4f}umol_s", - f"supercritical={'yes' if res.supercritical else 'no'}", - f"elec_surplus={res.net_electrical_surplus_W:.3f}W", - ] - if res.nitrate_chain: - parts.append(f"nh4no3_prod_umol={res.nitrate_chain.nitrate_mol_s*1e6:.4f}") - parts.append(f"loop={'CLOSED' if res.loop_closed else 'OPEN'}") - print(delim.join(parts)) - -# ── CLI ───────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser( - description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter - ) - ap.add_argument("--t-hot", type=float, default=954.2, metavar="K", - help="Stirling hot side temperature K (default: 954.2 [Simulated Annealing Minimum])") - ap.add_argument("--t-cold", type=float, default=363.0, metavar="K", - help="Stirling cold side temperature K (default: 363 = 90°C)") - ap.add_argument("--stirling-eff-fraction", type=float, default=0.55, metavar="F", - help="Fraction of Carnot efficiency achieved (default: 0.55)") - ap.add_argument("--solar-w", type=float, default=800.0, metavar="W/m2", - help="Solar irradiance W/m² (default: 800)") - ap.add_argument("--area-m2", type=float, default=1.5, metavar="M2", - help="Solar collector area m² (default: 1.5)") - ap.add_argument("--pv-area-m2", type=float, default=0.20, metavar="M2", - help="PV panel area m² for electrolysis (default: 0.20)") - ap.add_argument("--air-flow-l-s", type=float, default=5.0, metavar="L/s", - help="DAC air flow rate L/s (default: 5.0)") - ap.add_argument("--tp-pairs", type=int, default=120, metavar="N", - help="Number of thermopile TE couples (default: 120)") - ap.add_argument("--product", choices=["CH4", "CO2"], default="CH4", - help="Supercritical product: CH4 or CO2 (default: CH4)") - ap.add_argument("--atm-pressure", type=float, default=101325.0, metavar="Pa", - help="Atmospheric pressure in Pa (default: 101325)") - ap.add_argument("--ambient-temp", type=float, default=298.15, metavar="K", - help="Ambient temperature in K (default: 298.15)") - ap.add_argument("--co2-ppm", type=float, default=420.0, metavar="PPM", - help="CO2 concentration in PPM (default: 420)") - ap.add_argument("--n2-frac", type=float, default=0.7809, metavar="FRAC", - help="N2 mole fraction (default: 0.7809)") - ap.add_argument("--o2-frac", type=float, default=0.2095, metavar="FRAC", - help="O2 mole fraction (default: 0.2095)") - ap.add_argument("--rh", type=float, default=0.50, metavar="FRAC", - help="Relative humidity fraction (default: 0.50)") - ap.add_argument("--json", action="store_true", help="Output full JSON report") - ap.add_argument("--one-line", action="store_true", help="Pipe-friendly one-line output") - ap.add_argument("--one-line-delim", default=" | ", metavar="DELIM", - help="Delimiter for --one-line (default: ' | ')") - args = ap.parse_args() - - cfg = LoopConfig( - T_hot_K=args.t_hot, - T_cold_K=args.t_cold, - stirling_efficiency_fraction=args.stirling_eff_fraction, - solar_irradiance_W_m2=args.solar_w, - collector_area_m2=args.area_m2, - pv_area_m2=args.pv_area_m2, - thermopile_pairs=args.tp_pairs, - dac_air_flow_L_s=args.air_flow_l_s, - product_mode=args.product, - atmospheric_pressure_Pa=args.atm_pressure, - ambient_temp_K=args.ambient_temp, - co2_mole_fraction=args.co2_ppm / 1e6, - n2_mole_fraction=args.n2_frac, - o2_mole_fraction=args.o2_frac, - relative_humidity=args.rh, - ) - - res = run_simulation(cfg) - - if args.json: - out: Dict[str, Any] = {"config": asdict(cfg), "result": asdict(res)} - print(json.dumps(out, indent=2)) - elif args.one_line: - render_one_line(cfg, res, args.one_line_delim) - else: - render_report(cfg, res) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/simulation/sovereign_jenga_physics_test.py b/5-Applications/tools-scripts/simulation/sovereign_jenga_physics_test.py deleted file mode 100644 index cc256b67..00000000 --- a/5-Applications/tools-scripts/simulation/sovereign_jenga_physics_test.py +++ /dev/null @@ -1,696 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Sovereign Jenga Physics Test Suite -FEA + Rigid Body Simulation for N-Space Manifold Verification - -This module provides physics validation for the Sovereign Jenga demonstration system. -It interfaces with external FEA/rigid body engines to test: -- Stress distribution across lattice structures -- Global vibration modes -- Load path redundancy -- "Impossible" configuration stability - -External Dependencies (choose one or more): -- pyCalculix (CalculiX FEA, free/open-source) -- FEniCS (FEM, free/open-source) -- PyAnsys (ANSYS, commercial) -- pybullet (Rigid body dynamics, free) -- mujoco (Rigid body dynamics, free for research) -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -from pathlib import Path -from typing import Dict, List, Tuple, Optional -from dataclasses import dataclass -import json - -REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1]) - -# Try to import optional physics engines -try: - import calculix - CALCULIX_AVAILABLE = True -except ImportError: - CALCULIX_AVAILABLE = False - -try: - import fenics - FENICS_AVAILABLE = True -except ImportError: - FENICS_AVAILABLE = False - -try: - import pybullet as p - PYBULLET_AVAILABLE = True -except ImportError: - PYBULLET_AVAILABLE = False - -try: - import trimesh - TRIMESH_AVAILABLE = True -except ImportError: - TRIMESH_AVAILABLE = False - - -# ============================================================================ -# Material Properties (Aluminum 6061-T6) -# ============================================================================ - -@dataclass -class MaterialProperties: - """First-principles material properties for Aluminum 6061-T6""" - name: str = "Aluminum 6061-T6" - - # Atomic Properties - atomic_mass: float = 26.98 # g/mol - crystal_structure: str = "FCC" - lattice_parameter: float = 4.05e-10 # m (4.05 Å) - - # Elastic Properties (Isotropic) - youngs_modulus: float = 69e9 # Pa (69 GPa) - shear_modulus: float = 26e9 # Pa (26 GPa) - bulk_modulus: float = 76e9 # Pa (76 GPa) - poisson_ratio: float = 0.33 - - # Density - density: float = 2700 # kg/m³ - - # Strength - yield_strength: float = 276e6 # Pa (276 MPa) - ultimate_strength: float = 310e6 # Pa (310 MPa) - - # Wave Propagation - longitudinal_wave_speed: float = 6400 # m/s - shear_wave_speed: float = 3100 # m/s - - # Compressibility - compressibility: float = 1.32e-11 # Pa⁻¹ - - # Damping - damping_ratio: float = 0.001 # Structural damping - - -# ============================================================================ -# Stress Tensor Operations (6D "N-Space") -# ============================================================================ - -class StressTensor: - """ - 6D stress tensor operations for "N-space" mechanics. - - This is the real physics behind the "N-dimensional stress space" metaphor. - """ - - @staticmethod - def from_components(sx: float, sy: float, sz: float, - txy: float, txz: float, tyz: float) -> AnyArray: - """Create stress tensor from 6 independent components""" - return xp.array([ - [sx, txy, txz], - [txy, sy, tyz], - [txz, tyz, sz] - ]) - - @staticmethod - def to_vector(tensor: AnyArray) -> AnyArray: - """Convert 3x3 tensor to 6D vector (Voigt notation)""" - return xp.array([ - tensor[0, 0], # σx - tensor[1, 1], # σy - tensor[2, 2], # σz - tensor[0, 1], # τxy - tensor[0, 2], # τxz - tensor[1, 2] # τyz - ]) - - @staticmethod - def principal_stresses(tensor: AnyArray) -> Tuple[AnyArray, AnyArray]: - """ - Compute principal stresses and directions. - - Returns: - eigenvalues: Principal stresses (σ1, σ2, σ3) - eigenvectors: Principal directions - """ - eigenvalues, eigenvectors = xp.linalg.eigh(tensor) - # Sort by magnitude - idx = xp.abs(eigenvalues).argsort()[::-1] - return eigenvalues[idx], eigenvectors[:, idx] - - @staticmethod - def von_mises(tensor: AnyArray) -> float: - """ - Compute von Mises stress (yield criterion). - - σ_vm = √(0.5 * [(σ1-σ2)² + (σ2-σ3)² + (σ3-σ1)²]) - """ - principal, _ = StressTensor.principal_stresses(tensor) - s1, s2, s3 = principal - - von_mises = xp.sqrt(0.5 * ( - (s1 - s2)**2 + - (s2 - s3)**2 + - (s3 - s1)**2 - )) - - return von_mises - - @staticmethod - def equilibrium_residual(stress_field: AnyArray, - body_forces: AnyArray) -> AnyArray: - """ - Check equilibrium: ∇ · σ + f = 0 - - Returns residual (should be ~0 for equilibrium) - """ - # Simplified: compute divergence numerically - div_sigma = xp.gradient(stress_field, axis=0) - residual = div_sigma + body_forces - return residual - - -# ============================================================================ -# Lattice Structure Generator (Octet Truss) -# ============================================================================ - -class OctetTrussGenerator: - """ - Generate octet truss lattice structures for Sovereign Jenga blocks. - """ - - def __init__(self, - unit_cell_size: float = 0.01, # 10mm - strut_diameter: float = 0.001, # 1mm - relative_density: float = 0.2): - - self.unit_cell_size = unit_cell_size - self.strut_diameter = strut_diameter - self.relative_density = relative_density - - def generate_unit_cell(self) -> Dict: - """ - Generate single octet truss unit cell. - - Returns dict with: - - nodes: Nx3 array of node positions - - elements: Mx2 array of element connectivity - """ - # Octet truss has 14 nodes per unit cell - # (8 corner + 6 face-center) - - a = self.unit_cell_size - - # Node positions - nodes = xp.array([ - # Corners - [0, 0, 0], - [a, 0, 0], - [a, a, 0], - [0, a, 0], - [0, 0, a], - [a, 0, a], - [a, a, a], - [0, a, a], - - # Face centers - [a/2, a/2, 0], - [a/2, a/2, a], - [a/2, 0, a/2], - [a/2, a, a/2], - [0, a/2, a/2], - [a, a/2, a/2], - ]) - - # Element connectivity (tetrahedra + octahedra) - elements = [ - # Bottom tetrahedra - [0, 1, 3, 8], - [1, 2, 3, 8], - - # Top tetrahedra - [4, 5, 7, 9], - [5, 6, 7, 9], - - # Vertical struts - [0, 4], [1, 5], [2, 6], [3, 7], - - # Face diagonals - [8, 10], [8, 11], [8, 12], [8, 13], - [9, 10], [9, 11], [9, 12], [9, 13], - - # Additional connections for full octet - [10, 12], [10, 13], - [11, 12], [11, 13], - ] - - return { - 'nodes': nodes, - 'elements': elements, - 'num_nodes': len(nodes), - 'num_elements': len(elements) - } - - def generate_block(self, - length: float, - width: float, - height: float) -> Dict: - """ - Generate full block with internal octet lattice. - """ - # Calculate number of unit cells - nx = int(length / self.unit_cell_size) - ny = int(width / self.unit_cell_size) - nz = int(height / self.unit_cell_size) - - # Generate lattice - all_nodes = [] - all_elements = [] - node_offset = 0 - - for i in range(nx): - for j in range(ny): - for k in range(nz): - cell = self.generate_unit_cell() - - # Translate nodes - translation = xp.array([ - i * self.unit_cell_size, - j * self.unit_cell_size, - k * self.unit_cell_size - ]) - - cell_nodes = cell['nodes'] + translation - all_nodes.append(cell_nodes) - - # Update element connectivity - cell_elements = cell['elements'] + node_offset - all_elements.extend(cell_elements) - - node_offset += cell['num_nodes'] - - return { - 'nodes': xp.vstack(all_nodes), - 'elements': all_elements, - 'dimensions': (length, width, height), - 'num_cells': (nx, ny, nz) - } - - -# ============================================================================ -# FEA Solver Interface -# ============================================================================ - -class FEASolver: - """ - Interface to external FEA engines for stress analysis. - """ - - def __init__(self, engine: str = 'calculix'): - self.engine = engine - self.available_engines = [] - - if CALCULIX_AVAILABLE: - self.available_engines.append('calculix') - if FENICS_AVAILABLE: - self.available_engines.append('fenics') - - if engine not in self.available_engines: - print(f"Warning: {engine} not available. Available: {self.available_engines}") - - def solve_static(self, - geometry: Dict, - material: MaterialProperties, - boundary_conditions: Dict, - loads: Dict) -> Dict: - """ - Solve static equilibrium problem. - - Returns: - stress_field: Stress tensor at each node - displacement_field: Displacement at each node - von_mises_field: Von Mises stress at each node - """ - - if self.engine == 'calculix' and CALCULIX_AVAILABLE: - return self._solve_with_calculix(geometry, material, boundary_conditions, loads) - elif self.engine == 'fenics' and FENICS_AVAILABLE: - return self._solve_with_fenics(geometry, material, boundary_conditions, loads) - else: - # Fallback: simplified analytical solution - return self._solve_analytical(geometry, material, boundary_conditions, loads) - - def _solve_with_calculix(self, geometry, material, bc, loads): - """Interface to CalculiX FEA solver. - - Requires: CalculiX (ccx) installed and on PATH. - Workflow: write .inp → run ccx → parse .frd output. - """ - raise NotImplementedError( - "CalculiX (ccx) required. Install: apt install calculix-ccx or build from source." - ) - - def _solve_with_fenics(self, geometry, material, bc, loads): - """Interface to FEniCS FEM solver. - - Requires: FEniCS (dolfin) Python package. - """ - raise NotImplementedError( - "FEniCS required. Install: pip install fenics or use conda install -c conda-forge fenics." - ) - - def _solve_analytical(self, - geometry: Dict, - material: MaterialProperties, - bc: Dict, - loads: Dict) -> Dict: - """ - Simplified analytical solution for quick testing. - - Uses beam theory + truss analysis for approximate results. - """ - nodes = geometry['nodes'] - elements = geometry['elements'] - - num_nodes = len(nodes) - - # Initialize fields - displacement_field = xp.zeros((num_nodes, 3)) - stress_field = xp.zeros((num_nodes, 3, 3)) - von_mises_field = xp.zeros(num_nodes) - - # Simplified: assume uniform stress distribution - total_load = loads.get('magnitude', 1000) # N - load_area = len([e for e in elements if len(e) > 2]) # Approximate - - avg_stress = total_load / (load_area * 1e-6) # Pa (rough estimate) - - # Fill stress field (simplified) - for i in range(num_nodes): - stress_tensor = StressTensor.from_components( - avg_stress, 0, 0, 0, 0, 0 - ) - stress_field[i] = stress_tensor - von_mises_field[i] = StressTensor.von_mises(stress_tensor) - - # Simplified displacement (Hooke's law) - strain = avg_stress / material.youngs_modulus - displacement_field[:, 2] = strain * geometry['dimensions'][2] - - return { - 'stress_field': stress_field, - 'displacement_field': displacement_field, - 'von_mises_field': von_mises_field, - 'max_stress': xp.max(von_mises_field), - 'max_displacement': xp.max(xp.abs(displacement_field)) - } - - -# ============================================================================ -# Verification Tests (Pass/Fail Criteria) -# ============================================================================ - -class SovereignJengaVerifier: - """ - Run verification tests for Sovereign Jenga demonstration system. - """ - - def __init__(self, material: MaterialProperties): - self.material = material - self.results = {} - - def test_centerless_tower(self, - block_geometry: Dict, - num_blocks: int = 18) -> Dict: - """ - Test: Remove all central blocks, leave outer skeleton. - Tower should stand under own weight. - """ - print("\n=== Test: Center-less Tower ===") - - # Setup: Create hollow tower (only outer ring of blocks) - # Simplified: check if stress < yield with center removed - - solver = FEASolver() - - # Apply gravity load - loads = { - 'type': 'gravity', - 'magnitude': self.material.density * 9.81 * block_geometry['dimensions'][0] - } - - # Fixed base - bc = {'fixed': 'bottom'} - - # Solve - result = solver.solve_static(block_geometry, self.material, bc, loads) - - # Check: max stress < yield strength - max_stress = result['max_stress'] - passed = max_stress < self.material.yield_strength - - self.results['centerless_tower'] = { - 'passed': passed, - 'max_stress_MPa': max_stress / 1e6, - 'yield_strength_MPa': self.material.yield_strength / 1e6, - 'safety_factor': self.material.yield_strength / max_stress - } - - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Result: {status}") - print(f" Max stress: {max_stress/1e6:.2f} MPa") - print(f" Yield strength: {self.material.yield_strength/1e6:.2f} MPa") - print(f" Safety factor: {self.results['centerless_tower']['safety_factor']:.2f}") - - return self.results['centerless_tower'] - - def test_45deg_overhang(self, - block_geometry: Dict) -> Dict: - """ - Test: Tower leans at 45° without support. - Stability via torsional-to-compressive conversion. - """ - print("\n=== Test: 45° Overhang ===") - - # This test requires rigid body dynamics (not just static FEA) - # For now, simplified check: center of mass within support polygon - - # Simplified: check if geometry can support 45° lean - # Real test would use PyBullet/MuJoCo - - # Placeholder result - self.results['overhang_45deg'] = { - 'passed': False, # Requires rigid body simulation - 'note': 'Requires PyBullet/MuJoCo integration' - } - - print(f"Result: ⚠ NOT YET IMPLEMENTED") - print(f" Note: Requires rigid body dynamics engine") - - return self.results['overhang_45deg'] - - def test_vibration_distribution(self, - block_geometry: Dict) -> Dict: - """ - Test: Strike one block, entire structure responds. - Global vibration modes (not localized). - """ - print("\n=== Test: Vibration Distribution ===") - - # Compute eigenmodes of stiffness matrix - # Global modes = eigenvectors span entire structure - - solver = FEASolver() - - # Simplified: check if structure has delocalized modes - # Real test would compute full modal analysis - - # Placeholder - self.results['vibration_distribution'] = { - 'passed': False, # Requires modal analysis - 'note': 'Requires eigenmode computation' - } - - print(f"Result: ⚠ NOT YET IMPLEMENTED") - print(f" Note: Requires modal analysis (eigenmode computation)") - - return self.results['vibration_distribution'] - - def test_load_redistribution(self, - block_geometry: Dict, - remove_fraction: float = 0.3) -> Dict: - """ - Test: Remove 30% of blocks randomly. - Tower should still support 10kg top load. - """ - print("\n=== Test: Load Redistribution ===") - - # Setup: Remove random blocks, apply 10kg load - # Check: stress < yield, displacement < threshold - - solver = FEASolver() - - loads = { - 'type': 'point', - 'magnitude': 10 * 9.81 # 10kg - } - - bc = {'fixed': 'bottom'} - - result = solver.solve_static(block_geometry, self.material, bc, loads) - - # Check criteria - max_stress = result['max_stress'] - max_disp = result['max_displacement'] - - stress_ok = max_stress < self.material.yield_strength - disp_ok = max_disp < 0.01 # 1cm threshold - - passed = stress_ok and disp_ok - - self.results['load_redistribution'] = { - 'passed': passed, - 'max_stress_MPa': max_stress / 1e6, - 'max_displacement_mm': max_disp * 1000, - 'stress_ok': stress_ok, - 'displacement_ok': disp_ok - } - - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Result: {status}") - print(f" Max stress: {max_stress/1e6:.2f} MPa") - print(f" Max displacement: {max_disp*1000:.2f} mm") - - return self.results['load_redistribution'] - - def test_semi_truck_lift(self, - block_geometry: Dict) -> Dict: - """ - Test: Use as jack base. - Lifts 10,000 lbs (one corner of semi-truck). - """ - print("\n=== Test: Semi-Truck Lift (10,000 lbs) ===") - - # 10,000 lbs = 4536 kg = 44.5 kN - - solver = FEASolver() - - loads = { - 'type': 'point', - 'magnitude': 44500 # N (10,000 lbs) - } - - bc = {'fixed': 'bottom'} - - result = solver.solve_static(block_geometry, self.material, bc, loads) - - max_stress = result['max_stress'] - passed = max_stress < self.material.yield_strength - - self.results['semi_truck_lift'] = { - 'passed': passed, - 'load_N': 44500, - 'load_lbs': 10000, - 'max_stress_MPa': max_stress / 1e6, - 'yield_strength_MPa': self.material.yield_strength / 1e6, - 'safety_factor': self.material.yield_strength / max_stress if max_stress > 0 else float('inf') - } - - status = "✓ PASS" if passed else "✗ FAIL" - print(f"Result: {status}") - print(f" Load: 44,500 N (10,000 lbs)") - print(f" Max stress: {max_stress/1e6:.2f} MPa") - print(f" Safety factor: {self.results['semi_truck_lift']['safety_factor']:.2f}") - - return self.results['semi_truck_lift'] - - def run_all_tests(self, block_geometry: Dict) -> Dict: - """Run complete verification suite""" - print("\n" + "="*60) - print("SOVEREIGN JENGA VERIFICATION SUITE") - print("="*60) - - self.test_centerless_tower(block_geometry) - self.test_45deg_overhang(block_geometry) - self.test_vibration_distribution(block_geometry) - self.test_load_redistribution(block_geometry) - self.test_semi_truck_lift(block_geometry) - - # Summary - print("\n" + "="*60) - print("SUMMARY") - print("="*60) - - passed = sum(1 for r in self.results.values() if r.get('passed', False)) - total = len([r for r in self.results.values() if 'passed' in r]) - not_implemented = len([r for r in self.results.values() if 'note' in r]) - - print(f"Passed: {passed}/{total}") - print(f"Not yet implemented: {not_implemented}") - - return { - 'passed': passed, - 'total': total, - 'not_implemented': not_implemented, - 'details': self.results - } - - -# ============================================================================ -# Main Entry Point -# ============================================================================ - -def main(): - """Run Sovereign Jenga physics verification""" - - # Material - material = MaterialProperties() - print(f"Material: {material.name}") - print(f" Young's modulus: {material.youngs_modulus/1e9:.1f} GPa") - print(f" Yield strength: {material.yield_strength/1e6:.1f} MPa") - print(f" Density: {material.density} kg/m³") - - # Generate block geometry - generator = OctetTrussGenerator( - unit_cell_size=0.01, # 10mm - strut_diameter=0.001, # 1mm - relative_density=0.2 - ) - - block_geometry = generator.generate_block( - length=0.06, # 60mm - width=0.02, # 20mm - height=0.02 # 20mm - ) - - print(f"\nBlock geometry:") - print(f" Dimensions: {block_geometry['dimensions']}") - print(f" Num nodes: {len(block_geometry['nodes'])}") - print(f" Num elements: {len(block_geometry['elements'])}") - - # Run verification - verifier = SovereignJengaVerifier(material) - results = verifier.run_all_tests(block_geometry) - - # Save results - output_path = REPO_ROOT / "out" / "sovereign_jenga_verification.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(results, f, indent=2, default=str) - - print(f"\n[+] Results saved to: {output_path}") - - return results - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/simulation/sovereign_jenga_quantum_annealer.py b/5-Applications/tools-scripts/simulation/sovereign_jenga_quantum_annealer.py deleted file mode 100644 index 533a4cc7..00000000 --- a/5-Applications/tools-scripts/simulation/sovereign_jenga_quantum_annealer.py +++ /dev/null @@ -1,412 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Quantum Annealing Interface for Sovereign Jenga Optimization - -This module formulates the truss structure optimization as a QUBO (Quadratic Unconstrained -Binary Optimization) problem, solvable by: -- D-Wave quantum annealers (real quantum hardware) -- Classical simulated annealing (for testing) -- Fujitsu Digital Annealer (alternative quantum-inspired hardware) - -The optimization finds optimal load paths through the truss structure, minimizing: -- Material usage (fewer struts = lighter) -- Stress concentrations (even distribution) -- While maintaining structural integrity -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import json -from pathlib import Path -from typing import Dict, List, Tuple, Optional -from dataclasses import dataclass - -REPO_ROOT = Path(os.getenv("RESEARCH_STACK_ROOT") or Path(__file__).resolve().parents[1]) - -# Try to import D-Wave libraries (optional) -try: - import dwave.system - import dimod - DWAVE_AVAILABLE = True -except ImportError: - DWAVE_AVAILABLE = False - -# Try to import networkx for graph operations -try: - import networkx as nx - NETWORKX_AVAILABLE = True -except ImportError: - NETWORKX_AVAILABLE = False - - -# ============================================================================ -# QUBO Formulation for Truss Optimization -# ============================================================================ - -@dataclass -class TrussOptimizationQUBO: - """ - Formulates truss structure optimization as QUBO problem. - - The QUBO formulation encodes: - - Binary variable for each potential strut (1 = exists, 0 = removed) - - Objective: minimize weight while maintaining structural integrity - - Constraints: stress < yield, displacement < threshold - """ - - nodes: AnyArray # Nx3 array of node positions - edges: List[Tuple[int, int]] # List of (node_i, node_j) tuples - loads: Dict[int, AnyArray] # node_id -> force vector - supports: List[int] # Fixed node IDs - - # Optimization weights - weight_penalty: float = 1.0 # Penalty for material usage - stress_penalty: float = 10.0 # Penalty for stress violations - displacement_penalty: float = 5.0 # Penalty for displacement violations - - def formulate_qubo(self) -> Tuple[AnyArray, float]: - """ - Formulate truss optimization as QUBO. - - Returns: - Q: QUBO matrix (NxN upper triangular) - offset: Constant offset - """ - n_struts = len(self.edges) - - # QUBO matrix (upper triangular) - Q = xp.zeros((n_struts, n_struts)) - offset = 0.0 - - # Objective 1: Minimize weight (fewer struts) - for i in range(n_struts): - Q[i, i] += self.weight_penalty - - # Objective 2: Stress distribution - # Simplified: penalize struts that would be overloaded - for i, (node_i, node_j) in enumerate(self.edges): - # Calculate strut length - pos_i = self.nodes[node_i] - pos_j = self.nodes[node_j] - length = xp.linalg.norm(pos_j - pos_i) - - # Simplified stress estimate (would need FEA for real calculation) - # Penalize long struts (more prone to buckling) - Q[i, i] += self.stress_penalty * (length / 10.0) - - # Objective 3: Connectivity constraints (Future Flow Variables Layer) - - return Q, offset - - def solve_classical(self, - num_reads: int = 1000, - annealing_time: int = 1000) -> Dict: - """ - Solve QUBO using classical simulated annealing. - - This is for testing when quantum hardware is not available. - """ - Q, offset = self.formulate_qubo() - n_struts = len(self.edges) - - best_energy = float('inf') - best_solution = None - - # Simulated annealing - for read in range(num_reads): - # Random initial solution - solution = xp.random.randint(0, 2, n_struts) - - # Annealing schedule - for step in range(annealing_time): - temp = 1.0 - (step / annealing_time) # Linear cooling - - # Propose flip - i = xp.random.randint(0, n_struts) - new_solution = solution.copy() - new_solution[i] = 1 - new_solution[i] - - # Calculate energy change - delta_E = self._calculate_energy_change(Q, solution, new_solution, i) - - # Metropolis criterion - if delta_E < 0 or xp.random.random() < xp.exp(-delta_E / (temp + 0.001)): - solution = new_solution - - # Calculate energy - energy = self._calculate_energy(Q, solution) + offset - - if energy < best_energy: - best_energy = energy - best_solution = solution - - return { - 'solution': best_solution, - 'energy': best_energy, - 'offset': offset, - 'method': 'classical_simulated_annealing' - } - - def solve_quantum(self, - num_reads: int = 100, - annealing_time: float = 20.0) -> Dict: - """ - Solve QUBO using D-Wave quantum annealer. - - Requires D-Wave account and access to quantum hardware. - """ - if not DWAVE_AVAILABLE: - raise ImportError("D-Wave libraries not available. Install dwave-system.") - - Q, offset = self.formulate_qubo() - - # Convert to dimod BQM - bqm = dimod.BinaryQuadraticModel.from_numpy_matrix(Q, offset=offset) - - # Use D-Wave sampler - sampler = dwave.system.DWaveSampler() - - # Submit to quantum annealer - response = sampler.sample( - bqm, - num_reads=num_reads, - annealing_time=annealing_time, - label='Sovereign Jenga Optimization' - ) - - # Get best solution - best_sample = response.first - best_solution = best_sample.sample - best_energy = best_sample.energy - - # Convert to numpy array - solution_array = xp.array([best_solution[i] for i in range(len(self.edges))]) - - return { - 'solution': solution_array, - 'energy': best_energy, - 'offset': offset, - 'method': 'dwave_quantum_annealing', - 'response': response - } - - def _calculate_energy(self, Q: AnyArray, solution: AnyArray) -> float: - """Calculate QUBO energy for given solution""" - energy = 0.0 - n = len(solution) - for i in range(n): - for j in range(i, n): - energy += Q[i, j] * solution[i] * solution[j] - return energy - - def _calculate_energy_change(self, Q: AnyArray, - old_solution: AnyArray, - new_solution: AnyArray, - flipped_index: int) -> float: - """Calculate energy change from flipping one variable""" - old_energy = self._calculate_energy(Q, old_solution) - new_energy = self._calculate_energy(Q, new_solution) - return new_energy - old_energy - - -# ============================================================================ -# G-code Generator from Optimized Structure -# ============================================================================ - -class OptimizedGcodeGenerator: - """ - Generates G-code from optimized truss structure. - """ - - def __init__(self, - nodes: AnyArray, - edges: List[Tuple[int, int]], - strut_diameter: float = 1.0, - feedrate: float = 800): - - self.nodes = nodes - self.edges = edges - self.strut_diameter = strut_diameter - self.feedrate = feedrate - - def generate_gcode(self, output_path: str): - """ - Generate G-code for laser sintering machine. - """ - gcode_lines = [ - "; Mechanical Merkle Tree G-code", - "; Generated by Sovereign Jenga Optimizer", - "; Quantum Annealing Optimized Structure", - "", - "G21 ; Metric units", - "G90 ; Absolute positioning", - "" - ] - - # Generate toolpath for each strut - for edge_idx, (node_i, node_j) in enumerate(self.edges): - pos_i = self.nodes[node_i] - pos_j = self.nodes[node_j] - - # Move to start - gcode_lines.append(f"G0 X{pos_i[0]:.2f} Y{pos_i[1]:.2f} Z{pos_i[2]:.2f}") - - # Laser on - gcode_lines.append("M3") - - # Move to end (deposit material) - gcode_lines.append(f"G1 X{pos_j[0]:.2f} Y{pos_j[1]:.2f} Z{pos_j[2]:.2f} F{self.feedrate}") - - # Laser off - gcode_lines.append("M5") - - gcode_lines.append("") - - # Write to file - with open(output_path, 'w') as f: - f.write('\n'.join(gcode_lines)) - - print(f"[+] G-code saved to: {output_path}") - - def generate_json(self, output_path: str, forces: Optional[AnyArray] = None): - """ - Generate JSON structure file with node forces. - """ - structure = { - 'nodes': [], - 'edges': self.edges - } - - for i, node in enumerate(self.nodes): - node_data = { - 'id': i, - 'x': float(node[0]), - 'y': float(node[1]), - 'z': float(node[2]) - } - - if forces is not None: - node_data['F'] = float(forces[i]) - - structure['nodes'].append(node_data) - - with open(output_path, 'w') as f: - json.dump(structure, f, indent=2) - - print(f"[+] Structure JSON saved to: {output_path}") - - -# ============================================================================ -# Main Entry Point -# ============================================================================ - -def main(): - """ - Run quantum annealing optimization on Sovereign Jenga structure. - """ - - # Load structure from JSON - structure_path = REPO_ROOT / "out" / "sovereign_jenga_structure.json" - - if not structure_path.exists(): - print(f"Error: Structure file not found: {structure_path}") - print("Please generate structure first using sovereign_jenga_physics_test.py") - return - - with open(structure_path, 'r') as f: - structure = json.load(f) - - nodes = xp.array([[n['x'], n['y'], n['z']] for n in structure['nodes']]) - edges = [tuple(e) for e in structure['edges']] - - print(f"Loaded structure:") - print(f" Nodes: {len(nodes)}") - print(f" Edges: {len(edges)}") - - # Define loads and supports - loads = {0: xp.array([0, 0, -1000])} # 1000N downward at top node - supports = [i for i in range(len(nodes)) if nodes[i, 2] < -20] # Bottom nodes fixed - - print(f" Loads: {len(loads)}") - print(f" Supports: {len(supports)}") - - # Formulate QUBO - qubo = TrussOptimizationQUBO( - nodes=nodes, - edges=edges, - loads=loads, - supports=supports, - weight_penalty=1.0, - stress_penalty=10.0, - displacement_penalty=5.0 - ) - - # Solve (try quantum first, fall back to classical) - if DWAVE_AVAILABLE: - print("\nSolving with D-Wave quantum annealer...") - try: - result = qubo.solve_quantum(num_reads=100) - except Exception as e: - print(f"Quantum solve failed: {e}") - print("Falling back to classical simulated annealing...") - result = qubo.solve_classical(num_reads=1000) - else: - print("\nD-Wave not available. Using classical simulated annealing...") - result = qubo.solve_classical(num_reads=1000) - - print(f"\nOptimization complete:") - print(f" Method: {result['method']}") - print(f" Energy: {result['energy']:.2f}") - print(f" Offset: {result['offset']:.2f}") - - # Extract optimized structure - solution = result['solution'] - optimized_edges = [edges[i] for i in range(len(edges)) if solution[i] == 1] - - print(f"\nOptimization results:") - print(f" Original struts: {len(edges)}") - print(f" Optimized struts: {len(optimized_edges)}") - print(f" Material reduction: {(1 - len(optimized_edges)/len(edges)) * 100:.1f}%") - - # Generate G-code - gcode_gen = OptimizedGcodeGenerator( - nodes=nodes, - edges=optimized_edges, - strut_diameter=1.0, - feedrate=800 - ) - - output_dir = REPO_ROOT / "out" / "sovereign_jenga_quantum" - output_dir.mkdir(parents=True, exist_ok=True) - - gcode_gen.generate_gcode(output_dir / "optimized_structure.gcode") - gcode_gen.generate_json(output_dir / "optimized_structure.json") - - # Save optimization results - results = { - 'original_edges': len(edges), - 'optimized_edges': len(optimized_edges), - 'material_reduction_pct': (1 - len(optimized_edges)/len(edges)) * 100, - 'energy': result['energy'], - 'method': result['method'], - 'optimized_edges': optimized_edges - } - - with open(output_dir / "optimization_results.json", 'w') as f: - json.dump(results, f, indent=2) - - print(f"\n[+] All outputs saved to: {output_dir}") - - return results - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/simulation/svs_mimo_module.rs b/5-Applications/tools-scripts/simulation/svs_mimo_module.rs deleted file mode 100644 index cfe7447d..00000000 --- a/5-Applications/tools-scripts/simulation/svs_mimo_module.rs +++ /dev/null @@ -1,60 +0,0 @@ -/// SpyVsSpy High-Speed Add-on: MIMO Frequency Encryption Module -/// [USAL v1.0 ALIGNED: SUBSTRATE-AGNOSTIC WAVEFORM SUPPORT] -/// Implements frequency-agility with a 30ns rotation duty cycle. -/// Used for Substrate-Level Radio Attestation and SAW transit. - -pub struct MIMOFreqEncryptor { - pub rotation_duty_ns: u64, - pub channels: usize, - pub base_frequency_hz: f64, - pub saw_transit_mode: bool, // Tracks if we are encrypting a USAL SAW -} - -impl MIMOFreqEncryptor { - pub fn new() -> Self { - Self { - rotation_duty_ns: 30, // 33.33 MHz rotation - channels: 8, // 8x8 MIMO - base_frequency_hz: 5.2e9, // 5.2 GHz - saw_transit_mode: true, // Default to USAL SAW encryption mode - } - } - - /// Self-Referential Adaptive Scheme: Targets Low Energy Loads. - /// The rotation key is derived from the substrate's current energy state. - pub fn rotate_mask_adaptive(&self, current_ns: u64, energy_load_mw: f64, saw_hash: u64) -> u64 { - let window = current_ns / self.rotation_duty_ns; - - // Target low energy loads by modulating complexity - let complexity_factor = if energy_load_mw < 100.0 { 1 } else { 4 }; - - let mut hasher = std::collections::hash_map::DefaultHasher::new(); - use std::hash::Hasher; - - hasher.write_u64(window); - hasher.write_u64((energy_load_mw * 1000.0) as u64); // Self-referential tie to power - hasher.write_u64(complexity_factor); - if self.saw_transit_mode { - hasher.write_u64(saw_hash); // Bind SAW identity to the transit mask - } - - hasher.finish() - } - - /// Verifies if a given frequency delta fits the 30ns substrate constraint. - pub fn verify_substrate_agility(&self, delta_ns: u64) -> bool { - // If an attacker claims 30ns agility but their jitter is > 100ns, identify as synthetic. - delta_ns <= self.rotation_duty_ns + 5 - } -} - -pub fn get_mimo_status() -> String { - let mimo = MIMOFreqEncryptor::new(); - format!( - "MIMO_ACTIVE (USAL_SAW_MODE): {} channels, {}ns rotation cycle ({:.2} MHz)", - mimo.channels, - mimo.rotation_duty_ns, - 1000.0 / mimo.rotation_duty_ns as f64 - ) -} - diff --git a/5-Applications/tools-scripts/simulation/svs_riscv_node.rs b/5-Applications/tools-scripts/simulation/svs_riscv_node.rs deleted file mode 100644 index ba0c0d31..00000000 --- a/5-Applications/tools-scripts/simulation/svs_riscv_node.rs +++ /dev/null @@ -1,115 +0,0 @@ -use serde::{Serialize, Deserialize}; -use std::collections::HashMap; -use std::time::{SystemTime, UNIX_EPOCH}; - -mod svs_mimo_module; -use svs_mimo_module::MIMOFreqEncryptor; - -#[derive(Serialize, Deserialize, Debug)] -struct SystemMetrics { - arch: String, - ram_bytes: u64, - clock_precision: f64, - jitter_variance: f64, -} - -#[derive(Serialize, Deserialize, Debug)] -struct Attestation { - cpu: String, - ram_mb: f64, - network_rtt_samples: Vec, - sensor_jitter_samples: Vec, -} - -#[derive(Serialize, Deserialize, Debug)] -struct Analysis { - quantization_artifact_detected: bool, - fano_factor_anomaly: bool, - result: String, -} - -#[derive(Serialize, Deserialize, Debug)] -struct VerificationReport { - substrate: String, - attestation: Attestation, - spyvsspy_analysis: Analysis, - timestamp_utc: String, -} - -struct RISCV64Substrate { - cpu_arch: String, - ram_size: u64, - loopback_latency_ms: f64, - quantization_floor: f64, - mimo: MIMOFreqEncryptor, -} - -impl RISCV64Substrate { - fn new(memory_mb: u64) -> Self { - Self { - cpu_arch: "riscv64".to_string(), - ram_size: memory_mb * 1024 * 1024, - loopback_latency_ms: 1.0, - quantization_floor: 1e-15, - mimo: MIMOFreqEncryptor::new(), - } - } - - fn get_system_metrics(&self) -> SystemMetrics { - SystemMetrics { - arch: self.cpu_arch.clone(), - ram_bytes: self.ram_size, - clock_precision: self.quantization_floor, - jitter_variance: 0.000000000000001, - } - } - - fn simulate_network_probe(&self, _target: &str, samples: usize) -> Vec { - vec![self.loopback_latency_ms; samples] - } - - fn simulate_sensor_jitter(&self, samples: usize) -> Vec { - // Returns a deterministic synthetic constant - vec![0.000123456789012345; samples] - } -} - -fn main() { - let substrate = RISCV64Substrate::new(4096); - let metrics = substrate.get_system_metrics(); - let network_samples = substrate.simulate_network_probe("127.0.0.1", 5); - let jitter_samples = substrate.simulate_sensor_jitter(10); - - // Analysis logic - let variance = 0.0; // Deterministic constant has 0 variance - let is_simulator = variance < 1e-10; - - let now = SystemTime::now() - .duration_since(UNIX_EPOCH) - .expect("Time went backwards"); - - let report = VerificationReport { - substrate: "RISC-V-64-Rust-Node".to_string(), - attestation: Attestation { - cpu: metrics.arch, - ram_mb: metrics.ram_bytes as f64 / (1024.0 * 1024.0), - network_rtt_samples: network_samples, - sensor_jitter_samples: jitter_samples, - }, - spyvsspy_analysis: Analysis { - quantization_artifact_detected: true, - fano_factor_anomaly: is_simulator, - result: "SYNTHETIC_PROVENANCE_CONFIRMED".to_string(), - }, - timestamp_utc: format!("{:?}", now), - }; - - let energy_load = 45.5; // Simulate a "Low Energy Load" in mW - println!("// MIMO Extension (Adaptive): {}", svs_mimo_module::get_mimo_status()); - let saw_hash = 0x1234_ABCD_5678_EF00; // Simulated SAW state identifier - let adaptive_key = substrate.mimo.rotate_mask_adaptive(now.as_nanos() as u64, energy_load, saw_hash); - println!("// Active adaptive key: {:x}", adaptive_key); - - println!("{}", serde_json::to_string_pretty(&report).unwrap()); -} - diff --git a/5-Applications/tools-scripts/simulation/tsm_annealing_storage_miner.tsm b/5-Applications/tools-scripts/simulation/tsm_annealing_storage_miner.tsm deleted file mode 100644 index 8f028296..00000000 --- a/5-Applications/tools-scripts/simulation/tsm_annealing_storage_miner.tsm +++ /dev/null @@ -1,632 +0,0 @@ -// Neuromorphic Quantum Annealing Storage Miner - Pure TSM Implementation -// -// Architecture: -// - Every byte = computation register in 11D N-Space -// - Jitter, heat, resonance, inter-byte space = computational registers -// - BTRFS NVMe cell structure = physical compute substrate -// - Quantum annealing function = energy minimization across all registers -// -// Physical Registers: -// - Byte values (0-255) → 256-level qudit states -// - Write latency → temporal register -// - Cell wear level → degradation register -// - Heat dissipation → thermal register -// - Electronic jitter → noise register -// - Inter-cell capacitance → coupling register -// - Resonant frequency → vibrational register -// -// Expected Performance: -// - NVMe Cell Computing: 100-500 MH/s equivalent -// - Quantum Annealing Speedup: 10-100x over classical -// - Total System: 1-50 GH/s equivalent - -module NeuromorphicQuantumAnnealingStorageMiner { - - // ======================================================================== - // CONSTANTS & PHYSICAL PARAMETERS - // ======================================================================== - - const NVME_BLOCK_SIZE: u32 = 4096; // 4KB blocks - const NVME_PAGE_SIZE: u32 = 16384; // 16KB pages - const NVME_CELL_COUNT: u64 = 1_073_741_824; // 1 billion cells (1TB NVMe) - const BTRFS_EXTENT_SIZE: u64 = 4194304; // 4MB extents - - // N-Space dimensions for registers - const N_SPACE_DIMS: u32 = 11; - - // Quantum annealing parameters - const ANNEALING_ITERATIONS: u32 = 10000; - const INITIAL_TEMPERATURE: f64 = 1000.0; - const COOLING_RATE: f64 = 0.995; - const TUNNELING_RATE: f64 = 0.1; - - // Physical register types - enum PhysicalRegister: u8 { - BYTE_VALUE = 0x00, // Actual byte data (0-255) - WRITE_LATENCY = 0x01, // Write timing (ps) - CELL_WEAR = 0x02, // Wear level (0-100%) - HEAT_DISSIPATION = 0x03, // Thermal (Kelvin) - ELECTRONIC_JITTER = 0x04, // Noise (femtoseconds) - INTER_CELL_CAP = 0x05, // Capacitance (femtofarads) - RESONANT_FREQ = 0x06, // Resonance (GHz) - TUNNEL_CURRENT = 0x07, // Quantum tunneling (picoamps) - SPIN_STATE = 0x08, // Electron spin (up/down/superposition) - PHASE_COHERENCE = 0x09, // Quantum phase (radians) - ENTANGLEMENT = 0x0A // Entanglement degree (0-1) - } - - // TSM-ISA v3.0 Opcodes (extended for quantum annealing) - enum TSM_Opcode: u8 { - // Standard opcodes - INGEST_STATE = 0x01, - WAVE_FOLD = 0x02, - SYNC_CLOCK = 0x03, - OMNI_BAL = 0x04, - ENTANGLE = 0x05, - EVOLVE = 0x06, - VRAM_FLUSH = 0x07, - STARK_PROVE = 0x08, - LEDGER_COMMIT = 0x09, - - // Neuromorphic opcodes - NEUROMORPH = 0x0E, - GPGPU_SURF = 0x0F, - NIBBLE_SWAP = 0x11, - TSM_INT = 0x12, - - // Quantum annealing opcodes (new) - ANNEAL_INIT = 0x20, - ANNEAL_STEP = 0x21, - ANNEAL_MEASURE = 0x22, - ANNEAL_TUNNEL = 0x23, - - // Physical register opcodes (new) - NVME_CELL_READ = 0x30, - NVME_CELL_WRITE = 0x31, - NVME_CELL_COMPUTE = 0x32, - BTRFS_EXTENT_MAP = 0x33, - PHYSICAL_REGISTER_READ = 0x34, - PHYSICAL_REGISTER_WRITE = 0x35, - - // N-Space opcodes (new) - N_SPACE_PROJECT = 0x40, - N_SPACE_ROTATE = 0x41, - N_SPACE_ENTANGLE = 0x42 - } - - // ======================================================================== - // DATA STRUCTURES - // ======================================================================== - - // Physical register state (one per NVMe cell) - struct PhysicalRegisterState { - cell_address: u64; - register_type: PhysicalRegister; - value: f64; // Normalized 0.0-1.0 - quantum_state: complex; // Superposition state - entanglement_group: u32; - coherence_time: f64; // Picoseconds - } - - // NVMe cell computational state - struct NVMeComputationalCell { - physical_address: u64; - logical_block: u64; - electron_count: u32; - charge_state: f64; - spin_states: array>; // 8 spin states - tunneling_probability: f64; - thermal_noise: f64; - computational_output: u8; - } - - // BTRFS extent mapping for cell addressing - struct BTRFSExtentMap { - extent_id: u64; - start_block: u64; - block_count: u64; - physical_blocks: array; - checksum: [u8; 32]; - compression: string; - encryption: string; - } - - // Quantum annealing state - struct QuantumAnnealingState { - temperature: f64; - energy: f64; - tunneling_field: f64; - current_state: array; - best_state: array; - best_energy: f64; - iteration: u32; - } - - // N-Space register manifold - struct NSpaceManifold { - dimensions: u32; - registers: array; - metric_tensor: array; // 11x11 metric tensor - connection_coeffs: array; // Christoffel symbols - } - - // ======================================================================== - // TSM-ISA HARDWARE INTRINSICS - // ======================================================================== - - // Quantum annealing intrinsics - intrinsic tsm_anneal_init(state: QuantumAnnealingState) -> QuantumAnnealingState; - intrinsic tsm_anneal_step(state: QuantumAnnealingState, temp: f64) -> QuantumAnnealingState; - intrinsic tsm_anneal_measure(state: QuantumAnnealingState) -> array; - intrinsic tsm_anneal_tunnel(state: QuantumAnnealingState, rate: f64) -> QuantumAnnealingState; - - // NVMe cell intrinsics - intrinsic tsm_nvme_cell_read(address: u64) -> NVMeComputationalCell; - intrinsic tsm_nvme_cell_write(address: u64, cell: NVMeComputationalCell) -> bool; - intrinsic tsm_nvme_cell_compute(cell: NVMeComputationalCell, operation: u8) -> NVMeComputationalCell; - - // BTRFS intrinsics - intrinsic tsm_btrfs_extent_map(logical_block: u64) -> BTRFSExtentMap; - - // Physical register intrinsics - intrinsic tsm_physical_register_read(cell: u64, reg_type: PhysicalRegister) -> f64; - intrinsic tsm_physical_register_write(cell: u64, reg_type: PhysicalRegister, value: f64) -> bool; - - // N-Space intrinsics - intrinsic tsm_n_space_project(state: any, dims: u32) -> array; - intrinsic tsm_n_space_rotate(state: array, angles: array) -> array; - intrinsic tsm_n_space_entangle(registers: array) -> array; - - // ======================================================================== - // NVME CELL COMPUTATIONAL LAYER - // ======================================================================== - - kernel NVMeCellComputer { - - fn init_cell(address: u64) -> NVMeComputationalCell { - // [0x30] NVME_CELL_READ - Read physical cell state - let cell = tsm_nvme_cell_read(address); - - // Initialize spin states to superposition - cell.spin_states = array::new>(8); - for i in 0..8 { - // Equal superposition of all spin states - cell.spin_states[i] = complex(1.0 / sqrt(8.0), 0.0); - } - - // Set tunneling probability based on cell wear - cell.tunneling_probability = 0.1 * (1.0 - cell.charge_state); - - return cell; - } - - fn compute_on_cell(cell: NVMeComputationalCell, input: u8) -> u8 { - // [0x32] NVME_CELL_COMPUTE - Execute computation on cell - // Each cell performs quantum annealing on its spin states - - // Apply input to spin states (quantum gate operation) - for i in 0..8 { - cell.spin_states[i] *= complex(0.0, input as f64 / 256.0); - } - - // [0x23] ANNEAL_TUNNEL - Quantum tunneling between spin states - for i in 0..7 { - let tunnel_amplitude = cell.tunneling_probability * cell.spin_states[i]; - cell.spin_states[i+1] += tunnel_amplitude; - cell.spin_states[i] -= tunnel_amplitude; - } - - // [0x32] NVME_CELL_COMPUTE - Execute - cell = tsm_nvme_cell_compute(cell, 0x01); - - // Measure output (collapse superposition) - let mut max_prob = 0.0; - let mut output = 0u8; - for i in 0..8 { - let prob = abs(cell.spin_states[i])^2; - if prob > max_prob { - max_prob = prob; - output = i as u8; - } - } - - cell.computational_output = output; - return output; - } - - fn parallel_cell_compute(cells: array, inputs: array) -> array { - // [0x0F] GPGPU_SURF - Launch parallel cell computation - // Each NVMe cell computes independently (massive parallelism) - - var outputs = array::new(cells.len()); - - for i in 0..cells.len() { - outputs[i] = compute_on_cell(cells[i], inputs[i]); - } - - return outputs; - } - - fn read_physical_registers(cell_address: u64) -> array { - // Read all 11 physical register types from cell - var registers = array::new(11); - - registers[0] = tsm_physical_register_read(cell_address, PhysicalRegister::BYTE_VALUE); - registers[1] = tsm_physical_register_read(cell_address, PhysicalRegister::WRITE_LATENCY); - registers[2] = tsm_physical_register_read(cell_address, PhysicalRegister::CELL_WEAR); - registers[3] = tsm_physical_register_read(cell_address, PhysicalRegister::HEAT_DISSIPATION); - registers[4] = tsm_physical_register_read(cell_address, PhysicalRegister::ELECTRONIC_JITTER); - registers[5] = tsm_physical_register_read(cell_address, PhysicalRegister::INTER_CELL_CAP); - registers[6] = tsm_physical_register_read(cell_address, PhysicalRegister::RESONANT_FREQ); - registers[7] = tsm_physical_register_read(cell_address, PhysicalRegister::TUNNEL_CURRENT); - registers[8] = tsm_physical_register_read(cell_address, PhysicalRegister::SPIN_STATE); - registers[9] = tsm_physical_register_read(cell_address, PhysicalRegister::PHASE_COHERENCE); - registers[10] = tsm_physical_register_read(cell_address, PhysicalRegister::ENTANGLEMENT); - - return registers; - } - } - - // ======================================================================== - // BTRFS EXTENT MAPPING LAYER - // ======================================================================== - - kernel BTRFSExtentMapper { - - fn map_extent(logical_block: u64) -> BTRFSExtentMap { - // [0x33] BTRFS_EXTENT_MAP - Get physical block mapping - let extent = tsm_btrfs_extent_map(logical_block); - - return extent; - } - - fn compute_on_extent(extent: BTRFSExtentMap, operation: u8) -> array { - // Perform computation across all blocks in extent - var results = array::new(extent.block_count); - - for i in 0..extent.block_count { - let cell_address = extent.physical_blocks[i as usize]; - let cell = NVMeCellComputer::init_cell(cell_address); - results[i as usize] = NVMeCellComputer::compute_on_cell(cell, operation); - } - - return results; - } - - fn verify_checksum(extent: BTRFSExtentMap) -> bool { - // Verify BTRFS checksum (also serves as computational integrity check) - let computed_checksum = crypto::sha256(extent.physical_blocks.to_bytes()); - return computed_checksum == extent.checksum; - } - } - - // ======================================================================== - // QUANTUM ANNEALING OPTIMIZER - // ======================================================================== - - kernel QuantumAnnealingOptimizer { - - fn initialize_annealing(registers: array) -> QuantumAnnealingState { - var state = QuantumAnnealingState { - temperature: INITIAL_TEMPERATURE, - energy: 0.0, - tunneling_field: TUNNELING_RATE, - current_state: registers, - best_state: registers.clone(), - best_energy: f64::MAX, - iteration: 0 - }; - - // [0x20] ANNEAL_INIT - Initialize quantum annealing - state = tsm_anneal_init(state); - - return state; - } - - fn compute_energy(state: QuantumAnnealingState) -> f64 { - // Compute energy of current state (Ising model Hamiltonian) - var energy = 0.0; - - for i in 0..state.current_state.len() { - // Local field term - energy += state.current_state[i].value * state.current_state[i].value; - - // Interaction term (entanglement) - for j in (i+1)..state.current_state.len() { - if state.current_state[i].entanglement_group == state.current_state[j].entanglement_group { - energy += state.current_state[i].value * state.current_state[j].value; - } - } - } - - return energy; - } - - fn anneal_step(state: QuantumAnnealingState) -> QuantumAnnealingState { - // [0x21] ANNEAL_STEP - Single annealing iteration - state = tsm_anneal_step(state, state.temperature); - - // Compute new energy - let new_energy = compute_energy(state); - - // Update best state if improved - if new_energy < state.best_energy { - state.best_energy = new_energy; - state.best_state = state.current_state.clone(); - } - - // Cool down - state.temperature *= COOLING_RATE; - state.iteration += 1; - - return state; - } - - fn run_annealing(registers: array, iterations: u32) -> array { - var state = initialize_annealing(registers); - - for i in 0..iterations { - state = anneal_step(state); - - // [0x23] ANNEAL_TUNNEL - Occasional quantum tunneling - if i % 100 == 0 { - state = tsm_anneal_tunnel(state, TUNNELING_RATE); - } - } - - // [0x22] ANNEAL_MEASURE - Measure final state - let result = tsm_anneal_measure(state); - - return result; - } - } - - // ======================================================================== - // N-SPACE REGISTER MANIFOLD - // ======================================================================== - - kernel NSpaceRegisterManifold { - - fn create_manifold(registers: array) -> NSpaceManifold { - var manifold = NSpaceManifold { - dimensions: N_SPACE_DIMS, - registers: registers, - metric_tensor: array::new(N_SPACE_DIMS * N_SPACE_DIMS), - connection_coeffs: array::new(N_SPACE_DIMS * N_SPACE_DIMS * N_SPACE_DIMS) - }; - - // Initialize metric tensor (identity for flat space) - for i in 0..N_SPACE_DIMS { - for j in 0..N_SPACE_DIMS { - if i == j { - manifold.metric_tensor[(i * N_SPACE_DIMS + j) as usize] = 1.0; - } else { - manifold.metric_tensor[(i * N_SPACE_DIMS + j) as usize] = 0.0; - } - } - } - - return manifold; - } - - fn project_to_nspace(data: array) -> array { - // [0x40] N_SPACE_PROJECT - Project byte data to N-Space - let projected = tsm_n_space_project(data, N_SPACE_DIMS); - return projected; - } - - fn rotate_in_nspace(state: array, angles: array) -> array { - // [0x41] N_SPACE_ROTATE - Rotate state in N-Space - let rotated = tsm_n_space_rotate(state, angles); - return rotated; - } - - fn entangle_registers(registers: array) -> array { - // [0x42] N_SPACE_ENTANGLE - Create entanglement between registers - let entangled = tsm_n_space_entangle(registers); - return entangled; - } - - fn compute_on_manifold(manifold: NSpaceManifold, input: array) -> array { - // Full computation pipeline on N-Space manifold - - // Step 1: Project input to N-Space - let mut state = project_to_nspace(input); - - // Step 2: Rotate in N-Space (mixing operation) - let angles = array::new(N_SPACE_DIMS); - for i in 0..N_SPACE_DIMS { - angles[i as usize] = random::uniform(0.0, 2.0 * 3.14159265358979); - } - state = rotate_in_nspace(state, angles); - - // Step 3: Entangle registers - for i in 0..manifold.registers.len() { - manifold.registers[i].value = state[i as usize]; - } - manifold.registers = entangle_registers(manifold.registers); - - // Step 4: Extract output - var output = array::new(input.len()); - for i in 0..output.len() { - output[i as usize] = (manifold.registers[i as usize].value * 255.0) as u8; - } - - return output; - } - } - - // ======================================================================== - // MAIN MINING ACTOR - // ======================================================================== - - actor QuantumAnnealingStorageMiner { - nvme_cells: array; - btrfs_extents: array; - annealing_state: option; - nspace_manifold: option; - nonces_tested: u64; - shares_found: u64; - hashrate: f64; - - fn init() { - self.nonces_tested = 0; - self.shares_found = 0; - self.hashrate = 0.0; - self.annealing_state = none; - self.nspace_manifold = none; - - // Initialize NVMe cells (1 million cells for computation) - self.nvme_cells = array::new(1_000_000); - for i in 0..self.nvme_cells.len() { - self.nvme_cells[i as usize] = NVMeCellComputer::init_cell(i as u64); - } - - // [0x03] SYNC_CLOCK - System clock sync - let sync_time = tsm_sync_clock(); - log::info(string::format("System clock synchronized at {0} GHz", [sync_time / 1e9])); - } - - fn start_mining(target: u256) { - log::info("Starting Quantum Annealing Storage Mining..."); - log::info(string::format("NVMe cells: {0}", [self.nvme_cells.len()])); - log::info(string::format("N-Space dimensions: {0}", [N_SPACE_DIMS])); - - let start_time = time::now(); - let mut last_report_time = start_time; - - while self.is_mining { - // Phase 1: Read physical registers from NVMe cells - var register_values = array::new(self.nvme_cells.len() * 11); - for i in 0..self.nvme_cells.len() { - let registers = NVMeCellComputer::read_physical_registers(i as u64); - for j in 0..11 { - register_values[(i * 11 + j) as usize] = registers[j as usize]; - } - } - - // Phase 2: Create physical register states - var physical_registers = array::new(register_values.len()); - for i in 0..physical_registers.len() { - physical_registers[i as usize] = PhysicalRegisterState { - cell_address: i as u64 / 11, - register_type: (i % 11) as PhysicalRegister, - value: register_values[i as usize], - quantum_state: complex(register_values[i as usize], 0.0), - entanglement_group: (i / 11) as u32, - coherence_time: 1000.0 - }; - } - - // Phase 3: Run quantum annealing optimization - let annealing_result = QuantumAnnealingOptimizer::run_annealing( - physical_registers, ANNEALING_ITERATIONS - ); - - // Phase 4: Compute on N-Space manifold - if self.nspace_manifold.is_none() { - self.nspace_manifold = some(NSpaceRegisterManifold::create_manifold(physical_registers)); - } - let nspace_output = NSpaceRegisterManifold::compute_on_manifold( - self.nspace_manifold.unwrap(), annealing_result - ); - - // Phase 5: Generate nonces from N-Space output - for i in 0..nspace_output.len() / 4 { - let nonce = (nspace_output[i * 4] as u32) << 24 | - (nspace_output[i * 4 + 1] as u32) << 16 | - (nspace_output[i * 4 + 2] as u32) << 8 | - (nspace_output[i * 4 + 3] as u32); - - self.nonces_tested += 1; - - // Check if nonce produces valid hash (simplified) - if self.check_nonce(nonce, target) { - self.shares_found += 1; - log::info(string::format("VALID SHARE! Nonce: {0}", [nonce])); - - // [0x08] STARK_PROVE + [0x09] LEDGER_COMMIT - let proof = tsm_stark_prove(nonce); - tsm_ledger_commit(proof, "permanent"); - } - } - - // Report hashrate every second - let current_time = time::now(); - if current_time - last_report_time >= 1.0 { - let elapsed = current_time - start_time; - self.hashrate = self.nonces_tested as f64 / elapsed; - log::info(string::format( - "Hashrate: {0:.2} MH/s | Nonces: {1} | Shares: {2}", - [self.hashrate / 1e6, self.nonces_tested, self.shares_found] - )); - last_report_time = current_time; - } - } - } - - fn check_nonce(nonce: u32, target: u256) -> bool { - // Simplified nonce checking (full implementation would compute SHA256) - let hash_value = (nonce as u256) * 0x1234567890ABCDEF; - return hash_value < target; - } - - fn stop_mining() { - self.is_mining = false; - log::info("Mining stopped"); - } - - fn get_stats() -> MiningStats { - return MiningStats { - nonces_tested: self.nonces_tested, - shares_found: self.shares_found, - hashrate: self.hashrate, - nvme_cells_active: self.nvme_cells.len(), - annealing_iterations: self.annealing_state.map_or(0, |s| s.iteration), - nspace_dimensions: N_SPACE_DIMS - }; - } - } - - // ======================================================================== - // PROGRAM ENTRYPOINT - // ======================================================================== - - fn main() { - log::info("=============================================="); - log::info(" QUANTUM ANNEALING STORAGE MINER"); - log::info(" NVMe Cell Computing + N-Space Registers"); - log::info("=============================================="); - - // Create miner actor - let miner = spawn QuantumAnnealingStorageMiner(); - miner.init(); - - // Set target difficulty - let target = 0x00000000FFFF0000000000000000000000000000000000000000000000000000u256; - - // Start mining - miner.start_mining(target); - - // Report final statistics - let stats = miner.get_stats(); - log::info(string::format( - "Mining complete: {0} nonces, {1} shares, {2:.2} MH/s", - [stats.nonces_tested, stats.shares_found, stats.hashrate / 1e6] - )); - } - -} - -// ============================================================================ -// SUPPORTING STRUCTS -// ============================================================================ - -struct MiningStats { - nonces_tested: u64; - shares_found: u64; - hashrate: f64; - nvme_cells_active: u64; - annealing_iterations: u32; - nspace_dimensions: u32; -} diff --git a/5-Applications/tools-scripts/substrate/substrate_git_index.py b/5-Applications/tools-scripts/substrate/substrate_git_index.py deleted file mode 100644 index a8a45d83..00000000 --- a/5-Applications/tools-scripts/substrate/substrate_git_index.py +++ /dev/null @@ -1,1751 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: OBSERVERLESS STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -# PTOS: LAYER=STORE / DOMAIN=DATA / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -Substrate Git Index — provider-driven source index. -=================================================== - -POSITION IN THE STACK ---------------------- -This script is the database layer that bridges configured artifact sources -and the rest of the substrate: - - Hardware Platform (KDA / RISC-V nodes / HDL) - ↑ - GraphVM OS (TSM opcodes, FOAM voxels, substrate ISA, omnitoken bus) - ↑ - Source-backed SQL DB ← THIS FILE - ├── configured source VCS / ledger / toolbelt / filesystem reports - ├── SQLite index queryable PTOS-tagged package registry - └── HTTP query API substrate-facing query interface - -CONCEPT -------- -Every `pkg/*` tag pushed to a configured git source carries a full PTOS -manifest in its annotation (written by metafoam_pkg.py). This script can -intercept that push via a server-side post-receive hook, parse the JSON -annotation, and insert a row into a SQLite database whose schema maps 1-to-1 -to the PTOS tag axes. - -The result: any configured source can feed a custom SQL backend. Git handles -durability and content-addressing for git sources; SQLite handles queryability; -PTOS tags are the schema. - - git object store ≡ row storage (BLOB columns) - annotated tag ≡ typed SQL row - PTOS manifest ≡ column values - depends[] ≡ foreign key references - sha256 ≡ primary content hash - meta_capsule ≡ compressed full-row backup - -MODES ------ -This script runs in three distinct modes: - - 1. hook — post-receive stdin mode. - Git calls this after a push. Reads lines from - stdin. For each refs/tags/pkg/* ref, reads the tag annotation from - the git object, parses the PTOS JSON, and indexes it into SQLite. - Install it with the source-aware wrapper: - python3 substrate_git_index.py install --repo --source - - 2. serve — HTTP query server daemon. - Listens on a TCP port (default 7743). Accepts JSON queries and - returns package records. Designed to be the substrate's "SQL - endpoint" — any component can ask "give me all CRYSTALLINE packages" - and get back git tag refs + metadata. - - 3. CLI — local admin commands (index, query, status, install). - Direct SQLite interaction without the HTTP layer. - -SQL SCHEMA ----------- -The packages table maps directly to PTOS_TAG_SCHEMA axes. -Every column corresponds to a field in the tag annotation JSON. - - packages ( - pkg TEXT package name (from PACKAGES registry) - version TEXT semver - layer TEXT CORE | CARRY | RULE | STORE | EXTERNAL - domain TEXT COMPUTE | TOKEN | RULE | STORE | … - condition TEXT STABLE | EXPERIMENTAL | EXTREME | … - stage TEXT ACTIVE | INTAKE | REVIEW | HOLD | ARCHIVED - source TEXT CODE | SPEC | NOTE | DATA | … - tier TEXT SINGULARITY | PLASMA | CRYSTALLINE | FOAM | … - module TEXT uppercase module identifier - archetype TEXT optional semantic archetype - tags TEXT JSON array of string labels - description TEXT human-readable summary - files TEXT JSON array of repo-relative paths - depends TEXT JSON array of dependency pkg/version strings - foam_score REAL φ-ratio quality metric (higher = denser) - nd_point TEXT JSON array of 14 f64 omnitoken axis values - sha256 TEXT SHA-256 of sorted file contents - sealed_utc TEXT ISO8601Z build timestamp - verification_basis TEXT DECLARED | OBSERVED | CROSS_CHECKED | PHYSICS_BOUND - visibility TEXT PRIVATE | INTERNAL | SELECTIVE | PUBLIC - model_status TEXT CANONICAL | REFERENCE_ONLY | QUARANTINED | REJECTED - taint_status TEXT CLEAN | SENSITIVE | CONTAMINATED | EXCLUDED - meta_capsule TEXT base64url(zlib(json(manifest))) - meta_capsule_hash TEXT sha256 of meta_capsule string - tag_name TEXT full git tag name, e.g. pkg/usc-audio/v1.0.0 - commit_hash TEXT git commit SHA the tag points to - indexed_utc TEXT when this row was inserted by the hook - idea_weights TEXT JSON dict: {"key insight": 0.0-1.0} (RESEARCH tier) - extension_points TEXT JSON list of suggested extension targets - session_id TEXT source LLM session identifier - concept_vector TEXT JSON 14-float semantic embedding (direction = meaning) - analog_map TEXT JSON dict: domain → nearest analog pkg - concept_anchor TEXT JSON {domain, concept, resolution} — axis 5, PTOS schema - spectral_band TEXT USC Spectral Band (e.g., X-RAY, OPTICAL) - bekenstein_bound REAL Information capacity bound per N-space snag - PRIMARY KEY (pkg, version) - ) - -HTTP API --------- -The serve mode exposes a minimal JSON API on port 7743 (default): - - GET /packages list all packages (summary) - GET /packages?= filter by any PTOS field - GET /packages/ full record for latest version - GET /packages// full record for specific version - POST /query body: {"sql": "SELECT … WHERE …"} raw SQL (SELECT only) - GET /health {"status":"ok","count":} - -All responses are JSON. The SQL endpoint accepts arbitrary SELECT statements -against the packages table — the substrate can issue any query it needs. - -INSTALL -------- - # Install as post-receive hook for any bare git source - python3 substrate_git_index.py install --repo /path/to/research-stack.git --source research-stack-bare-local - - # Start HTTP server (background) - python3 substrate_git_index.py serve --port 7743 --db /var/lib/substrate/index.db & - - # Or install as systemd service - python3 substrate_git_index.py install-service - -Usage: - python3 substrate_git_index.py hook post-receive mode (stdin) - python3 substrate_git_index.py serve [--port N] [--db PATH] HTTP query server - python3 substrate_git_index.py index index one tag manually - python3 substrate_git_index.py query "" CLI SQL query - python3 substrate_git_index.py status show indexed packages - python3 substrate_git_index.py install --repo install as a git post-receive hook - python3 substrate_git_index.py install-service write systemd unit - python3 substrate_git_index.py schema print CREATE TABLE - python3 substrate_git_index.py drop [version] remove a record -""" - -import base64 -import hashlib -import http.server -import json -import os -import sqlite3 -import subprocess -import sys -import urllib.parse -import zlib -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - - -def _repo_root() -> Path: - here = Path(__file__).resolve() - for parent in here.parents: - if (parent / ".git").exists() or (parent / "AGENTS.md").exists(): - return parent - return here.parents[3] - - -REPO_ROOT = _repo_root() - - -def _witness_config_path() -> Path: - return Path( - os.environ.get( - "WITNESS_SOURCES_CONFIG", - REPO_ROOT / "4-Infrastructure" / "witness" / "sources.json", - ) - ) - - -WITNESS_SOURCE = os.environ.get("WITNESS_SOURCE", "research-stack-github") - - -def _load_witness_sources(config_path: Path | None = None) -> dict[str, Any]: - path = config_path or _witness_config_path() - if not path.exists(): - return {"schema": "research_stack_witness_sources_v1", "sources": {}} - return json.loads(path.read_text(encoding="utf-8")) - - -def _source_config(source_name: str = WITNESS_SOURCE) -> dict[str, Any]: - data = _load_witness_sources() - sources = data.get("sources", {}) - value = sources.get(source_name) - if not isinstance(value, dict): - return {} - return value - - -def _file_url_to_path(url: str) -> Path | None: - if url.startswith("file://"): - return Path(urllib.parse.unquote(url[len("file://") :])) - if "://" not in url and url: - return Path(url) - return None - - -def _repo_path_from_source(source_name: str = WITNESS_SOURCE) -> Path | None: - source = _source_config(source_name) - url = str(source.get("url", "")) - return _file_url_to_path(url) - -# ───────────────────────────────────────────────────────────────────────────── -# Configuration — override via environment variables on the VPS -# ───────────────────────────────────────────────────────────────────────────── - -# Path to the SQLite database file. -# On VPS: export SUBSTRATE_DB=/var/lib/substrate/index.db -DB_PATH = Path( - os.environ.get("SUBSTRATE_DB", Path(__file__).parent.parent.parent / "substrate_index.db") -) - -# Git repo to operate on (used by hook and index commands). -# The hook mode detects this automatically from GIT_DIR env var set by git. -REPO_PATH = Path( - os.environ.get( - "GIT_REPO_PATH", - os.environ.get("SUBSTRATE_REPO", os.environ.get("GIT_DIR", Path(__file__).parent.parent)), - ) -) - -# Default HTTP server port. -DEFAULT_PORT = int(os.environ.get("SUBSTRATE_PORT", "7743")) - -# Tag prefix that identifies metafoam packages. -PKG_TAG_PREFIX = "refs/tags/pkg/" - -# Bare git repo location (used by install command). Prefer an explicit -# GIT_REPO_PATH or a provider block in 4-Infrastructure/witness/sources.json. -DEFAULT_HOOK_REPO = ( - os.environ.get("GIT_REPO_PATH") - or os.environ.get("SUBSTRATE_REPO") - or (str(_repo_path_from_source()) if _repo_path_from_source() else "") -) - - -# ───────────────────────────────────────────────────────────────────────────── -# DB Helpers -# ───────────────────────────────────────────────────────────────────────────── - -def _upsert_package(db: sqlite3.Connection, row: dict) -> None: - """Insert or replace a package record in the database.""" - cols = ", ".join(row.keys()) - placeholders = ", ".join(f":{k}" for k in row.keys()) - db.execute( - f"INSERT OR REPLACE INTO packages ({cols}) VALUES ({placeholders})", - row, - ) - db.commit() - -CREATE_TABLE_SQL = """ -CREATE TABLE IF NOT EXISTS packages ( - pkg TEXT NOT NULL, - version TEXT NOT NULL, - layer TEXT, - domain TEXT, - condition TEXT, - stage TEXT, - source TEXT, - tier TEXT, - module TEXT, - archetype TEXT, - tags TEXT, -- JSON array - description TEXT, - files TEXT, -- JSON array of repo-relative paths - depends TEXT, -- JSON array of pkg/version dependency strings - foam_score REAL, - nd_point TEXT, -- JSON array of 14 f64 values - sha256 TEXT, - sealed_utc TEXT, - verification_basis TEXT, - visibility TEXT, - model_status TEXT, - taint_status TEXT, - meta_capsule TEXT, - meta_capsule_hash TEXT, - tag_name TEXT, - commit_hash TEXT, - indexed_utc TEXT, - - -- Semantic layer: idea weighting + metanarrative connection graph - -- These fields are populated by RESEARCH-tier packages (chat sessions, - -- research notes) and used by the connect / translate commands. - - idea_weights TEXT, -- JSON dict: {"key insight text": 0.0-1.0, …} - -- Higher weight = more conceptually dense moment - -- Used to prioritise which ideas to surface - - extension_points TEXT, -- JSON list of suggested extension targets - -- ["pkg/file: what could be extended here", …] - -- Populated by the language miner or manually - - session_id TEXT, -- Source session identifier (LLM chat ID, doc ref) - -- Allows tracing a concept back to its origin - - concept_vector TEXT, -- JSON array of floats — richer semantic embedding - -- than nd_point (which is φ^-i positional). - -- Populated from idea_weights: each dimension is - -- the weight of a canonical concept cluster. - -- Used by cosine_similarity in cmd_connect(). - -- Analogous to the engram pattern in hippocampal - -- semantic storage: direction encodes meaning, not - -- magnitude. concept_anchor names the engram; - -- concept_vector is its activation pattern. - - analog_map TEXT, -- JSON dict: {"domain": "nearest pkg in that domain"} - -- Pre-computed local analog translations. - -- When a concept has no direct equivalent in a target - -- tier/domain, the nearest analog is cached here. - -- Same logic as best_domain_for_band() in the codec: - -- each concept finds its best carrier domain. - - concept_anchor TEXT, -- JSON object: concept lineage tag (axis 5 of PTOS schema) - -- Preserves metacontext that would otherwise be lost when - -- an artifact is compressed, encoded, or symbol-substituted. - -- Structure: {"domain": str, "concept": str, "resolution": str} - -- domain: which knowledge domain the idea originated in - -- concept: canonical snake_case identifier for the idea - -- resolution: settlement state — one of: - -- SEED raw intuition, no defined edges - -- FORMING actively developing, edges shifting - -- STABLE well understood, not yet demonstrated - -- CRYSTALLIZED fully settled, compression-ready - -- COMPRESSED has been encoded; tag is the decoder ring - -- RECOVERED reconstructed from lineage after loss - -- Even after metatag recovery every other axis can be - -- reconstructed from content. concept_anchor is the only - -- record of what the idea MEANT and how settled it WAS at - -- the time it was written — this is why it must survive - -- compression intact. - - PRIMARY KEY (pkg, version) -); -""" - -# Full-text search virtual table over human-readable fields. -CREATE_FTS_SQL = """ -CREATE VIRTUAL TABLE IF NOT EXISTS packages_fts USING fts5( - pkg, version, tier, domain, module, archetype, description, tags, - content=packages, content_rowid=rowid -); -""" - -CREATE_FTS_TRIGGER_INSERT = """ -CREATE TRIGGER IF NOT EXISTS packages_fts_insert AFTER INSERT ON packages BEGIN - INSERT INTO packages_fts(rowid, pkg, version, tier, domain, module, - archetype, description, tags) - VALUES (new.rowid, new.pkg, new.version, new.tier, new.domain, - new.module, new.archetype, new.description, new.tags); -END; -""" - -CREATE_FTS_TRIGGER_DELETE = """ -CREATE TRIGGER IF NOT EXISTS packages_fts_delete BEFORE DELETE ON packages BEGIN - INSERT INTO packages_fts(packages_fts, rowid, pkg, version, tier, domain, - module, archetype, description, tags) - VALUES ('delete', old.rowid, old.pkg, old.version, old.tier, old.domain, - old.module, old.archetype, old.description, old.tags); -END; -""" - - -# ───────────────────────────────────────────────────────────────────────────── -# Database helpers -# ───────────────────────────────────────────────────────────────────────────── - -# Columns added in the semantic layer (may not exist in older DBs). -_SEMANTIC_COLS = [ - "idea_weights", "extension_points", "session_id", - "concept_vector", "analog_map", "concept_anchor", - "attachment_meta", "ingest_profile", -] - - -def _open_db(path: Path = DB_PATH) -> sqlite3.Connection: - """Open (or create) the SQLite database, apply schema if needed. - - # Why WAL mode? - Write-Ahead Logging allows the HTTP server (reader) and the post-receive - hook (writer) to run concurrently without blocking each other. The hook - fires during a git push; the HTTP server may be serving a query at the - same time. WAL eliminates the write-lock contention. - - # FTS5 virtual table - Full-text search over pkg, module, description, tags — lets the substrate - ask "find me everything related to 'soliton'" without needing to know the - exact package name. - - # Schema migration - Semantic layer columns (idea_weights, concept_vector, etc.) were added - after the initial schema. We ALTER TABLE to add them if missing so that - existing DBs upgrade transparently without losing data. - """ - path.parent.mkdir(parents=True, exist_ok=True) - conn = sqlite3.connect(str(path)) - conn.row_factory = sqlite3.Row - conn.execute("PRAGMA journal_mode=WAL") - conn.execute("PRAGMA foreign_keys=ON") - conn.executescript( - CREATE_TABLE_SQL - + CREATE_FTS_SQL - + CREATE_FTS_TRIGGER_INSERT - + CREATE_FTS_TRIGGER_DELETE - ) - # Migrate: add semantic columns to existing tables that predate them. - existing = { - row[1] - for row in conn.execute("PRAGMA table_info(packages)").fetchall() - } - for col in _SEMANTIC_COLS: - if col not in existing: - conn.execute(f"ALTER TABLE packages ADD COLUMN {col} TEXT") - conn.commit() - return conn - - -def _row_to_dict(row: sqlite3.Row) -> dict: - """Convert a sqlite3.Row to a plain dict, parsing JSON columns.""" - d = dict(row) - for col in ("tags", "files", "depends", "nd_point", "concept_anchor", "analog_map"): - if d.get(col): - try: - d[col] = json.loads(d[col]) - except (json.JSONDecodeError, TypeError): - pass - return d - - -# ───────────────────────────────────────────────────────────────────────────── -# Git helpers -# ───────────────────────────────────────────────────────────────────────────── - -def _git(*args: str, repo: Path = REPO_PATH) -> str: - """Run a git command in repo, return stdout stripped. - - # Bare repo awareness - When called from a post-receive hook, GIT_DIR is set by git to the bare - repo path. We pass it explicitly in env so the command works whether - called from a bare repo or a working-tree checkout. - """ - env = os.environ.copy() - # If repo is a bare repo (no .git subdir), set GIT_DIR directly. - bare = repo / "HEAD" - if bare.exists() and not (repo / ".git").exists(): - env["GIT_DIR"] = str(repo) - result = subprocess.run( - ["git"] + list(args), - cwd=str(repo), capture_output=True, text=True, - env=env, check=False, - ) - return result.stdout.strip() - - -def _read_tag_annotation(tag_ref: str, repo: Path = REPO_PATH) -> str | None: - """Read the annotation message from a git annotated tag. - - # How annotated tags work - An annotated tag is a git object of type 'tag' (not 'commit'). - git cat-file tag dumps the raw tag object, which ends with the - annotation message after a blank line. - - For refs/tags/pkg/usc-audio/v1.0.0 we need the short name - pkg/usc-audio/v1.0.0 to pass to cat-file. - """ - short = tag_ref.replace("refs/tags/", "") - raw = _git("cat-file", "tag", short, repo=repo) - if not raw: - return None - # Tag object format: - # object - # type commit - # tag - # tagger ... - # - # - parts = raw.split("\n\n", 1) - if len(parts) < 2: - return None - return parts[1].strip() - - -def _resolve_commit(tag_ref: str, repo: Path = REPO_PATH) -> str: - """Return the commit SHA that a tag points to (peeled through tag objects).""" - short = tag_ref.replace("refs/tags/", "") - return _git("rev-list", "-n1", short, repo=repo) - - -# ───────────────────────────────────────────────────────────────────────────── -# Professional Alignment Substrate -# ───────────────────────────────────────────────────────────────────────────── - -# Heuristic mapping for PTOS Product Pillars (Synchronized with 8-Pillar Taxonomy) -PILLAR_MAP = { - "core": { - "product": "Sovereign Core (Production)", - "goal": "Carrier Engine Execution & Substrate ISA", - "tags": ["Core", "Carrier", "Production", "ISA"] - }, - "docs": { - "product": "Technical Specification (Docs)", - "goal": "Formal Standards & Professional IP Alignment", - "tags": ["Docs", "Spec", "IP", "Neutral"] - }, - "roadmap": { - "product": "Development Roadmap", - "goal": "Integration & Convergence Planning", - "tags": ["Roadmap", "Integration", "Convergence"] - }, - "lab": { - "product": "Research & Experiments (Lab)", - "goal": "State-of-the-art Innovation & Performance Testing", - "tags": ["Lab", "Research", "Experimental", "Vibrational"] - }, - "tools": { - "product": "Substrate Tooling", - "goal": "Infrastructure Utilities & Indexer Integrity", - "tags": ["Tools", "Utility", "Indexer", "Substrate"] - }, - "infra": { - "product": "System Infrastructure", - "goal": "Pillar Definition & Environment Baseline", - "tags": ["Infra", "System", "Config", "Warden"] - }, - "data": { - "product": "Permanent Archives", - "goal": "Knowledge Preservation & Hutter Complexity Data", - "tags": ["Data", "Archive", "Permanent", "Complexity"] - }, - "audit": { - "product": "Audit & Verification", - "goal": "Technical Rigor & Professional Defensibility Results", - "tags": ["Audit", "Verification", "Rigor", "Hardened"] - } -} - -def _professional_align(pkg_name: str, description: str, tags: list[str], - idea_weights: dict[str, float]) -> tuple[str, list[str], dict[str, float]]: - """Standardizes metadata with [Product]/[Goal] headers and aligns weights. - - This ensures that the 'Professional Alignment' is never a manual process. - If a description already contains a [Product:] tag, it is preserved. - """ - prefix = pkg_name.split("-")[0].lower() - pillar = PILLAR_MAP.get(prefix) - - # 1. Smarter Pillar Identification (8-Pillar Taxonomy) - if not pillar: - # A. Keyword-based mapping - keywords = { - "core": ["isa", "engine", "carrier", "vrt", "annealer", "clock"], - "docs": ["spec", "manifest", "alignment", "policy", "standard"], - "roadmap": ["roadmap", "integration", "convergence", "phase"], - "lab": ["research", "experimental", "hachimoji", "dna", "simulation", "test"], - "tools": ["tool", "scripts", "utility", "indexer", "refactor"], - "infra": ["infra", "config", "system", "warden", "platform", "kda"], - "data": ["data", "archive", "enwik", "corpus", "patent", "store"], - "audit": ["audit", "verification", "rigor", "assessment", "check", "governance"] - } - - # Check name and tags for keywords - search_str = (pkg_name + " " + " ".join(tags) + " " + description).lower() - for p, kw_list in keywords.items(): - if any(kw in search_str for kw in kw_list): - pillar = PILLAR_MAP[p] - break - - # B. Fallback to path-based (package name as path) - if not pillar: - path = pkg_name.replace(".", "/").replace("-", "/") - for p in PILLAR_MAP: - if p in path: - pillar = PILLAR_MAP[p] - break - - # C. Absolute Fallback - if not pillar: - pillar = { - "product": "General Substrate Node", - "goal": "General Utility & Infrastructure", - "tags": ["Infrastructure"] - } - - # 2. Reformat Description - # Only prepend if not already present - if not description.startswith("[Product:"): - new_desc = f"[Product: {pillar['product']}] [Goal: {pillar['goal']}] {description}" - else: - new_desc = description - - # 3. Synchronize Tags - new_tags = list(set(tags) | set(pillar["tags"])) - - # 4. Align Weights for Cross-Referencing - aligned_weights = dict(idea_weights) - aligned_weights["product"] = max(aligned_weights.get("product", 0), 0.95) - aligned_weights["goal"] = max(aligned_weights.get("goal", 0), 0.92) - aligned_weights["explanation"] = max(aligned_weights.get("explanation", 0), 0.88) - # Ensure the prefix itself is weighted if it's a pillar - if prefix in PILLAR_MAP: - aligned_weights[prefix.upper()] = 1.0 - - return new_desc, new_tags, aligned_weights - -# ───────────────────────────────────────────────────────────────────────────── -# Indexing -# ───────────────────────────────────────────────────────────────────────────── - -def _index_tag(tag_ref: str, repo: Path = REPO_PATH, - db: sqlite3.Connection | None = None) -> bool: - """Parse one pkg/* tag annotation and upsert it into the SQLite index. - - # Why UPSERT (INSERT OR REPLACE)? - The same tag may be rebuilt and re-pushed (metafoam_pkg.py build is - idempotent). We want the index to always reflect the latest build of - a given (pkg, version) pair, not fail on duplicate primary key. - - # Parameters - tag_ref : full ref name, e.g. refs/tags/pkg/usc-audio/v1.0.0 - repo : path to git repo (working tree or bare) - db : open SQLite connection (if None, opens DB_PATH) - - # Returns - True if indexing succeeded, False if the tag annotation is missing or - not valid PTOS JSON. - """ - annotation = _read_tag_annotation(tag_ref, repo) - if not annotation: - print(f"[index] WARN: no annotation for {tag_ref}", file=sys.stderr) - return False - - try: - manifest = json.loads(annotation) - except json.JSONDecodeError as exc: - print(f"[index] WARN: annotation not JSON for {tag_ref}: {exc}", - file=sys.stderr) - return False - - # Require the metafoam-pkg schema marker - if manifest.get("schema") != "metafoam-pkg/v1": - print(f"[index] skip {tag_ref}: schema={manifest.get('schema')!r}", - file=sys.stderr) - return False - - commit_hash = _resolve_commit(tag_ref, repo) - now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - def _j(val: Any) -> str | None: - """Serialise list/dict to JSON string; pass str/None through.""" - if val is None: - return None - if isinstance(val, (list, dict)): - return json.dumps(val) - return str(val) - - # ──────────────────────────────────────────────────────── - # Professional Alignment Step (Automated) - # ──────────────────────────────────────────────────────── - pkg_name = manifest.get("pkg") - raw_desc = manifest.get("description", "") - raw_tags = manifest.get("tags", []) - raw_weights = manifest.get("idea_weights", {}) - - if not isinstance(raw_tags, list): - raw_tags = [] - if not isinstance(raw_weights, dict): - raw_weights = {} - - aligned_desc, aligned_tags, aligned_weights = _professional_align( - pkg_name, raw_desc, raw_tags, raw_weights - ) - - row = { - "pkg": pkg_name, - "version": manifest.get("version"), - "layer": manifest.get("layer"), - "domain": manifest.get("domain"), - "condition": manifest.get("condition"), - "stage": manifest.get("stage"), - "source": manifest.get("source"), - "tier": manifest.get("tier"), - "module": manifest.get("module"), - "archetype": manifest.get("archetype"), - "tags": _j(aligned_tags), - "description": aligned_desc, - "files": _j(manifest.get("files")), - "depends": _j(manifest.get("depends")), - "foam_score": manifest.get("foam_score"), - "nd_point": _j(manifest.get("nd_point")), - "sha256": manifest.get("sha256"), - "sealed_utc": manifest.get("sealed_utc"), - "verification_basis":manifest.get("verification_basis"), - "visibility": manifest.get("visibility"), - "model_status": manifest.get("model_status"), - "taint_status": manifest.get("taint_status"), - "meta_capsule": manifest.get("meta_capsule"), - "meta_capsule_hash": manifest.get("meta_capsule_hash"), - "tag_name": tag_ref.replace("refs/tags/", ""), - "commit_hash": commit_hash, - "indexed_utc": now_utc, - "idea_weights": _j(aligned_weights), - "concept_vector": _j(_concept_vector_from_weights(aligned_weights)) - } - - close_after = db is None - if db is None: - db = _open_db() - - _upsert_package(db, row) - - if close_after: - db.close() - - print(f"[index] {manifest.get('pkg')}/{manifest.get('version')} " - f"tier={manifest.get('tier')} foam={manifest.get('foam_score')}") - return True - - -# ───────────────────────────────────────────────────────────────────────────── -# Mode 1 — post-receive hook -# ───────────────────────────────────────────────────────────────────────────── - -def cmd_hook() -> None: - """Run as a git post-receive hook. - - # Invocation - Git calls post-receive after updating all refs. It passes one line per - updated ref on stdin: - SP SP LF - - We filter for refs/tags/pkg/* and index each one. - - # GIT_DIR - When running as a server-side hook in a bare repo, GIT_DIR is set by git - to the bare repo path. _git() respects this automatically. - - # Bare repo path - We use GIT_DIR if set (server-side hook), otherwise fall back to REPO_PATH - (useful when testing locally in a working-tree checkout). - """ - repo_path = Path(os.environ.get("GIT_DIR", str(REPO_PATH))) - db = _open_db() - - indexed = 0 - for line in sys.stdin: - line = line.strip() - if not line: - continue - parts = line.split() - if len(parts) != 3: - continue - _old, _new, ref = parts - if not ref.startswith(PKG_TAG_PREFIX): - continue - if _index_tag(ref, repo=repo_path, db=db): - indexed += 1 - - db.close() - if indexed: - print(f"[substrate] indexed {indexed} package tag(s) into {DB_PATH}") - - -# ───────────────────────────────────────────────────────────────────────────── -# Mode 2 — HTTP query server -# ───────────────────────────────────────────────────────────────────────────── - -class _SubstrateHandler(http.server.BaseHTTPRequestHandler): - """Minimal JSON HTTP handler for the substrate query API. - - # Endpoint summary - GET /health liveness check - GET /packages list all (summary fields only) - GET /packages?= filter by PTOS field - GET /packages?search= full-text search via FTS5 - GET /packages/ latest version full record - GET /packages// specific version full record - POST /query {"sql": "SELECT …"} raw SELECT against packages table - - # Security - Only SELECT statements are accepted in /query. INSERT, UPDATE, DELETE, - DROP are rejected. This is a substrate-internal service — it should not - be exposed to the public internet. - """ - - def log_message(self, fmt: str, *args: Any) -> None: # noqa: N802 - """Suppress default access log; substrate logging is in the caller.""" - - def _send_json(self, data: Any, status: int = 200) -> None: - body = json.dumps(data, indent=2).encode() - self.send_response(status) - self.send_header("Content-Type", "application/json") - self.send_header("Content-Length", str(len(body))) - self.end_headers() - self.wfile.write(body) - - def _send_error(self, msg: str, status: int = 400) -> None: - self._send_json({"error": msg}, status) - - def do_GET(self) -> None: # noqa: N802 - """Handle GET requests.""" - parsed = urllib.parse.urlparse(self.path) - path = parsed.path.rstrip("/") - params = dict(urllib.parse.parse_qsl(parsed.query)) - - conn = _open_db() - - if path == "/health": - count = conn.execute("SELECT COUNT(*) FROM packages").fetchone()[0] - self._send_json({"status": "ok", "packages": count, - "db": str(DB_PATH)}) - - elif path == "/packages": - self._handle_list(conn, params) - - elif path.startswith("/packages/"): - parts = path[len("/packages/"):].split("/", 1) - pkg = parts[0] - version = parts[1] if len(parts) > 1 else None - self._handle_get(conn, pkg, version) - - else: - self._send_error("Not found", 404) - - conn.close() - - def do_POST(self) -> None: # noqa: N802 - """Handle POST /query — raw SQL SELECT.""" - if self.path != "/query": - self._send_error("Not found", 404) - return - - length = int(self.headers.get("Content-Length", 0)) - body = self.rfile.read(length) - try: - req = json.loads(body) - except json.JSONDecodeError: - self._send_error("Invalid JSON body") - return - - sql = req.get("sql", "").strip() - if not sql.upper().startswith("SELECT"): - self._send_error("Only SELECT statements are permitted") - return - - conn = _open_db() - try: - rows = [_row_to_dict(r) for r in conn.execute(sql)] - self._send_json({"rows": rows, "count": len(rows)}) - except sqlite3.Error as exc: - self._send_error(str(exc)) - finally: - conn.close() - - def _handle_list(self, conn: sqlite3.Connection, params: dict) -> None: - """List packages, optionally filtered by PTOS field or FTS search.""" - SUMMARY_COLS = ( - "pkg, version, tier, domain, layer, module, archetype, " - "foam_score, sealed_utc, visibility, taint_status" - ) - - search = params.pop("search", None) - if search: - # FTS5 search across pkg, module, description, tags - rows = conn.execute( - f"SELECT {SUMMARY_COLS} FROM packages WHERE rowid IN " - f"(SELECT rowid FROM packages_fts WHERE packages_fts MATCH ?)", - (search,), - ).fetchall() - elif params: - # Filter by arbitrary PTOS field(s) - # Only allow known column names to prevent injection - SAFE_COLS = { - "pkg", "version", "layer", "domain", "condition", "stage", - "source", "tier", "module", "archetype", "visibility", - "model_status", "taint_status", "verification_basis", - } - clauses = [] - values = [] - for col, val in params.items(): - if col in SAFE_COLS: - clauses.append(f"{col} = ?") - values.append(val) - if not clauses: - self._send_error("No valid filter fields provided") - return - where = " AND ".join(clauses) - rows = conn.execute( - f"SELECT {SUMMARY_COLS} FROM packages WHERE {where}", - values, - ).fetchall() - else: - rows = conn.execute( - f"SELECT {SUMMARY_COLS} FROM packages ORDER BY sealed_utc DESC" - ).fetchall() - - self._send_json({"packages": [_row_to_dict(r) for r in rows], - "count": len(rows)}) - - def _handle_get(self, conn: sqlite3.Connection, - pkg: str, version: str | None) -> None: - """Return the full record for one package.""" - if version: - row = conn.execute( - "SELECT * FROM packages WHERE pkg=? AND version=?", - (pkg, version), - ).fetchone() - else: - row = conn.execute( - "SELECT * FROM packages WHERE pkg=? ORDER BY sealed_utc DESC LIMIT 1", - (pkg,), - ).fetchone() - - if row is None: - self._send_error(f"Package {pkg!r} not found", 404) - return - self._send_json(_row_to_dict(row)) - - -def cmd_serve(port: int = DEFAULT_PORT) -> None: - """Start the HTTP query server. - - # Binding - Binds to 0.0.0.0 so both localhost (GraphVM OS) and the local network - (other substrate nodes) can reach it. For internet-facing deployments, - put nginx in front and restrict to Tailnet / VPN. - - # Persistence - The server is stateless — all state is in SQLite. It can be restarted - freely without losing data. The WAL journal means in-progress queries - are not affected by concurrent hook writes. - """ - addr = ("0.0.0.0", port) - server = http.server.HTTPServer(addr, _SubstrateHandler) - print(f"[substrate] HTTP query server on port {port}") - print(f" DB: {DB_PATH}") - print(f" Endpoints: /health /packages /query") - try: - server.serve_forever() - except KeyboardInterrupt: - print("\n[substrate] server stopped") - - -# ───────────────────────────────────────────────────────────────────────────── -# Mode 3 — CLI admin commands -# ───────────────────────────────────────────────────────────────────────────── - -def cmd_index(tag_name: str) -> None: - """Manually index one tag by name. - - # Use cases - - Re-indexing after DB is wiped (disaster recovery) - - Indexing tags that were pushed before the hook was installed - - Testing the hook logic without a git push - - # Parameters - tag_name : short tag name (e.g. pkg/usc-audio/v1.0.0) or full ref - """ - ref = tag_name if tag_name.startswith("refs/") else f"refs/tags/{tag_name}" - ok = _index_tag(ref) - sys.exit(0 if ok else 1) - - -def cmd_reindex_all() -> None: - """Re-index every pkg/* tag currently in the local git repo. - - # Use case: disaster recovery - If the SQLite database is wiped, this reconstructs it entirely from the - git object store. Run this on the VPS after copying the bare repo. - The git objects are the authoritative source; SQLite is derived. - """ - out = _git("tag", "-l", "pkg/*") - tags = [t.strip() for t in out.splitlines() if t.strip()] - if not tags: - print("[reindex] no pkg/* tags found") - return - - db = _open_db() - ok = sum(1 for t in tags if _index_tag(f"refs/tags/{t}", db=db)) - db.close() - print(f"[reindex] {ok}/{len(tags)} tags indexed into {DB_PATH}") - - -def cmd_query(where: str) -> None: - """Run a SQL SELECT query against the local index and print results. - - # Parameters - where : SQL WHERE clause (e.g. "tier='CRYSTALLINE' AND domain='COMPUTE'") - or a full SELECT statement - """ - conn = _open_db() - if where.strip().upper().startswith("SELECT"): - sql = where - else: - sql = f"SELECT pkg, version, tier, domain, foam_score, sealed_utc FROM packages WHERE {where}" - rows = conn.execute(sql).fetchall() - conn.close() - if not rows: - print("(no results)") - return - for row in rows: - print(json.dumps(_row_to_dict(row), indent=2)) - - -def cmd_status() -> None: - """Print a summary table of all indexed packages.""" - conn = _open_db() - rows = conn.execute( - "SELECT pkg, version, tier, domain, foam_score, taint_status, " - "sealed_utc FROM packages ORDER BY tier, pkg" - ).fetchall() - conn.close() - - if not rows: - print(f"(no packages indexed — DB: {DB_PATH})") - return - - print(f"{'Package':<22} {'Ver':<8} {'Tier':<20} {'Domain':<10} " - f"{'Foam':>6} {'Taint':<12} Sealed") - print("-" * 100) - for r in rows: - pkg = r['pkg'] or "" - ver = r['version'] or "" - tier = r['tier'] or "" - domain = r['domain'] or "" - foam = r['foam_score'] if r['foam_score'] is not None else 0.0 - taint = r['taint_status'] or "" - utc = r['sealed_utc'] or "" - print(f"{pkg:<22} {ver:<8} {tier:<20} {domain:<10} " - f"{foam:>6.2f} {taint:<12} {utc}") - - -def cmd_drop(pkg: str, version: str | None = None) -> None: - """Remove a package record from the index (does not affect the git tag).""" - conn = _open_db() - if version: - conn.execute("DELETE FROM packages WHERE pkg=? AND version=?", - (pkg, version)) - print(f"[drop] {pkg}/{version}") - else: - conn.execute("DELETE FROM packages WHERE pkg=?", (pkg,)) - print(f"[drop] all versions of {pkg}") - conn.commit() - conn.close() - - -def cmd_schema() -> None: - """Print the CREATE TABLE statement (useful for schema documentation).""" - print(CREATE_TABLE_SQL) - - -# ───────────────────────────────────────────────────────────────────────────── -# Semantic layer — metanarrative connection graph -# -# CONCEPT -# ------- -# Every package has an nd_point (14-axis φ^-i positional vector) and, -# for RESEARCH-tier packages, a concept_vector (derived from idea_weights). -# Cosine similarity between vectors finds semantic neighbors. -# -# The metanarrative phase classifier maps similarity to action: -# -# GROUNDED (sim ≥ 0.85) — you already know this connection. -# Surfaces it to confirm, not to surprise. -# SEISMIC (0.50–0.85) — related but not immediately obvious. -# Worth investigating; may reveal refinements. -# FLAME (< 0.50) — distant, non-obvious connection. -# The metanarrative found a bridge you missed. -# Highest value when confirmed; may be noise. -# -# The FLAME phase is the most important: these are the connections that -# prevent retreading existing thoughts and reveal where refinement is -# possible rather than reinvention. -# -# TRANSLATION / LOCAL ANALOG -# -------------------------- -# When a concept has no direct equivalent in a target tier or domain, -# cmd_translate() finds the nearest package in that domain by nd_point -# cosine similarity. This is the same logic as best_domain_for_band() -# in the USC codec: each concept finds its best carrier domain. -# -# This is grounded in the digital twin earth model: concepts were derived -# from the structures humanity built (minerals from architecture, not -# the reverse). The metanarrative finds local analogs bottom-up, from -# what already exists in the semantic graph, not from literal intent. -# -# SIDECHANNEL TRANSLATION -# ----------------------- -# When a "story" has no equivalent in the target domain, the system routes -# it through a sidechannel: the nearest analog in a different tier that -# shares semantic proximity. The concept payload arrives intact even when -# the literal vocabulary does not map. The analog IS the translation. -# ───────────────────────────────────────────────────────────────────────────── - -# Metanarrative phase thresholds (mirrors soliton_factory.py phase gates) -_PHASE_GROUNDED = 0.85 # direct, known connection -_PHASE_SEISMIC = 0.50 # related, worth investigating -# below SEISMIC threshold → FLAME phase (non-obvious, potentially breakthrough) - -_PHI = (1 + 5 ** 0.5) / 2 - - -def _cosine_similarity(a: list[float], b: list[float]) -> float: - """Cosine similarity between two equal-length float vectors. - - # Why cosine and not Euclidean? - nd_point vectors encode direction in the 14-dimensional omnitoken space, - not absolute position. Two concepts at very different foam_scores may - share the same semantic direction. Cosine normalises out magnitude so - only the angular relationship matters — same as how the codec normalises - amplitude before comparing soliton phases. - - Returns a value in [-1, 1]. 1.0 = identical direction, 0 = orthogonal. - Negative values are possible but rare in this space (all axes ≥ 0). - """ - if not a or not b or len(a) != len(b): - return 0.0 - dot = sum(x * y for x, y in zip(a, b)) - mag_a = sum(x ** 2 for x in a) ** 0.5 - mag_b = sum(x ** 2 for x in b) ** 0.5 - if mag_a == 0 or mag_b == 0: - return 0.0 - return dot / (mag_a * mag_b) - - -def _phase_classify(similarity: float) -> str: - """Map a cosine similarity score to a metanarrative phase label. - - # Phase semantics in semantic space (mirrors soliton codec phases) - GROUNDED — you already know this; confirm rather than explore - SEISMIC — related but non-obvious; investigate for refinements - FLAME — distant; the bridge you missed; highest information value - """ - if similarity >= _PHASE_GROUNDED: - return "GROUNDED" - if similarity >= _PHASE_SEISMIC: - return "SEISMIC" - return "FLAME" - - -def _concept_vector_from_weights(idea_weights: dict[str, float]) -> list[float]: - """Derive a 14-axis concept_vector from an idea_weights dict. - - # Algorithm - The 14 axes correspond to canonical concept clusters shared across the - stack. Each idea is mapped to its closest cluster by keyword matching, - and the weight is accumulated. The resulting vector is L2-normalised - so cosine similarity is well-defined. - - # Concept cluster axes (matches omnitoken 14-axis surface bus) - 0 substrate / foam / universal computation - 1 compression / encoding / codec - 2 graph / dag / topology - 3 hardware / physical / isa - 4 time / planck / clock - 5 cryptography / hash / stark / zk - 6 database / sql / index / query - 7 semantic / language / translation / meaning - 8 physics / entropy / bekenstein / landauer - 9 security / isolation / sub-register / attestation - 10 os / kernel / vm / opcode - 11 research / discovery / idea / connection - 12 omnitoken / foam_score / nd_point / manifest - 13 identity / sovereignty / provenance / attestation - """ - _CLUSTER_KEYWORDS: list[list[str]] = [ - ["substrate", "foam", "universal", "computation", "bits"], - ["compression", "encoding", "codec", "soliton", "tse", "gamma"], - ["graph", "dag", "node", "edge", "topology", "lut"], - ["hardware", "physical", "isa", "risc", "kda", "hdl", "chip"], - ["time", "planck", "clock", "tick", "temporal", "deterministic"], - ["cryptography", "hash", "stark", "zk", "sha256", "proof"], - ["database", "sql", "index", "query", "sqlite", "schema"], - ["semantic", "language", "translation", "meaning", "concept", "analog"], - ["physics", "entropy", "bekenstein", "landauer", "shannon", "thermodynamic"], - ["security", "isolation", "register", "subregister", "attack", "boundary"], - ["os", "kernel", "vm", "opcode", "tsm", "graphvm", "bytecode"], - ["research", "discovery", "idea", "connection", "insight", "explore"], - ["omnitoken", "foam_score", "nd_point", "manifest", "omni", "surface"], - ["identity", "sovereignty", "provenance", "attestation", "prior", "legal"], - ] - - vec = [0.0] * 14 - for idea, weight in idea_weights.items(): - tokens = set(idea.lower().replace('_', ' ').replace('-', ' ').split()) # EM-1 fix - for axis, keywords in enumerate(_CLUSTER_KEYWORDS): - if tokens & set(keywords): - vec[axis] += weight - - # L2 normalise - mag = sum(x ** 2 for x in vec) ** 0.5 - if mag > 0: - vec = [x / mag for x in vec] - return vec - - -def cmd_connect(pkg_name: str, version: str | None = None, - min_phase: str = "SEISMIC") -> None: - """Find semantic neighbors of a package via metanarrative phase classification. - - # What this does - Computes cosine similarity between the target package's nd_point (or - concept_vector for RESEARCH packages) and every other package in the - index. Results are phase-classified and filtered by min_phase. - - # Why this matters - SEISMIC + FLAME connections are the ones you couldn't have found by - grep or by memory. They prevent retreading existing thoughts and - reveal where refinement is possible rather than reinvention. - - # Parameters - pkg_name : package to connect from - version : specific version (default: latest) - min_phase : minimum phase to show — "GROUNDED" shows all, - "SEISMIC" hides obvious matches, "FLAME" shows only - non-obvious connections (highest information value) - """ - conn = _open_db() - - # Fetch target package - if version: - target = conn.execute( - "SELECT * FROM packages WHERE pkg=? AND version=?", - (pkg_name, version), - ).fetchone() - else: - target = conn.execute( - "SELECT * FROM packages WHERE pkg=? ORDER BY sealed_utc DESC LIMIT 1", - (pkg_name,), - ).fetchone() - - if target is None: - print(f"[connect] package {pkg_name!r} not found") - conn.close() - return - - # Prefer concept_vector (richer) over nd_point (positional) - target_vec = None - if target["concept_vector"]: - try: - target_vec = json.loads(target["concept_vector"]) - except (json.JSONDecodeError, TypeError): - pass - if not target_vec and target["nd_point"]: - try: - target_vec = json.loads(target["nd_point"]) - except (json.JSONDecodeError, TypeError): - pass - if not target_vec: - print(f"[connect] {pkg_name} has no vector — run ingest-session first") - conn.close() - return - - # Compare against all other packages - others = conn.execute( - "SELECT * FROM packages WHERE NOT (pkg=? AND version=?)", - (target["pkg"], target["version"]), - ).fetchall() - conn.close() - - _PHASE_ORDER = {"GROUNDED": 0, "SEISMIC": 1, "FLAME": 2} - min_idx = _PHASE_ORDER.get(min_phase, 1) - - results = [] - for row in others: - vec = None - if row["concept_vector"]: - try: - vec = json.loads(row["concept_vector"]) - except (json.JSONDecodeError, TypeError): - pass - if not vec and row["nd_point"]: - try: - vec = json.loads(row["nd_point"]) - except (json.JSONDecodeError, TypeError): - pass - if not vec: - continue - - sim = _cosine_similarity(target_vec, vec) - phase = _phase_classify(sim) - if _PHASE_ORDER[phase] >= min_idx: - results.append((sim, phase, dict(row))) - - # Sort: FLAME first (highest information value), then SEISMIC, then GROUNDED - # Within each phase, sort by similarity descending - results.sort(key=lambda x: (-_PHASE_ORDER[x[1]], -x[0])) - - print(f"\n[connect] {pkg_name} → {len(results)} connection(s) " - f"(min_phase={min_phase})\n") - print(f" {'Phase':<10} {'Sim':>5} {'Package':<25} {'Tier':<14} Insight") - print(" " + "-" * 80) - - for sim, phase, row in results: - # Summarise what the connection might mean - target_tags = set(json.loads(target["tags"] or "[]")) - other_tags = set(json.loads(row["tags"] or "[]")) - shared = target_tags & other_tags - novel = other_tags - target_tags - - insight = "" - if phase == "FLAME": - insight = ( - f"non-obvious bridge — shared: {', '.join(list(shared)[:2]) or 'none'} " - f"/ novel: {', '.join(list(novel)[:3]) or 'none'}" - ) - elif phase == "SEISMIC": - insight = f"refinement candidate — shared: {', '.join(list(shared)[:3]) or 'none'}" - else: - insight = f"known connection — {', '.join(list(shared)[:4]) or 'none'}" - - print(f" {phase:<10} {sim:>5.3f} {row['pkg']:<25} {row['tier'] or '':<14} {insight}") - - if not results: - print(f" (no connections found at phase ≥ {min_phase})") - - -def cmd_translate(pkg_name: str, target_domain: str) -> None: - """Find the local analog of a package in a target tier or domain. - - # The local analog principle - When a concept has no direct equivalent in the target domain, the - nearest semantic neighbor in that domain serves as the translation - vehicle. The concept payload arrives through the analog even when - the literal vocabulary does not map — a sidechannel translation. - - # Basis: digital twin earth model - Minerals and materials were refined from the structures humanity built, - not defined first and then applied. Concept translations here work the - same way: the analog is derived bottom-up from what already exists in - the semantic graph. - - # Parameters - pkg_name : source package to translate from - target_domain : target tier (CRYSTALLINE, FOAM, …) or domain - (COMPUTE, TOKEN, STORE, …) to find the analog in - """ - conn = _open_db() - - source = conn.execute( - "SELECT * FROM packages WHERE pkg=? ORDER BY sealed_utc DESC LIMIT 1", - (pkg_name,), - ).fetchone() - if source is None: - print(f"[translate] package {pkg_name!r} not found") - conn.close() - return - - source_vec = None - for field in ("concept_vector", "nd_point"): - if source[field]: - try: - source_vec = json.loads(source[field]) - break - except (json.JSONDecodeError, TypeError): - pass - - if not source_vec: - print(f"[translate] {pkg_name} has no vector") - conn.close() - return - - # Find nearest package in target tier or domain - candidates = conn.execute( - "SELECT * FROM packages WHERE tier=? OR domain=? AND pkg != ?", - (target_domain, target_domain, pkg_name), - ).fetchall() - conn.close() - - if not candidates: - print(f"[translate] no packages found in domain/tier {target_domain!r}") - return - - best_sim = -1.0 - best_row = None - for row in candidates: - vec = None - for field in ("concept_vector", "nd_point"): - if row[field]: - try: - vec = json.loads(row[field]) - break - except (json.JSONDecodeError, TypeError): - pass - if not vec: - continue - sim = _cosine_similarity(source_vec, vec) - if sim > best_sim: - best_sim = sim - best_row = dict(row) - - if best_row is None: - print(f"[translate] no vectorised packages in {target_domain!r}") - return - - phase = _phase_classify(best_sim) - print(f"\n[translate] {pkg_name} → {target_domain}") - print(f" Local analog : {best_row['pkg']} / {best_row['version']}") - print(f" Phase : {phase} (sim={best_sim:.3f})") - print(f" Tier/Domain : {best_row['tier']} / {best_row['domain']}") - print(f" Module : {best_row['module']}") - print(f" Description : {best_row['description']}") - if phase == "FLAME": - print(f" Note: distant analog — concept will arrive via sidechannel.") - print(f" The analog IS the translation; literal mapping not available.") - - -def cmd_ingest_session(session_file: str) -> None: - """Ingest a weighted LLM conversation session as a RESEARCH package. - - # Session file format (JSON) - { - "session_id": "gemini-2026-03-25-substrate", - "pkg": "chat-substrate-design-20260325", - "version": "1.0.0", - "description": "Substrate stack design — GraphVM OS + git SQL DB", - "tags": ["substrate", "graphvm", "foam", "zk-stark", "planck"], - "idea_weights": { - "every hash has the schema embedded": 0.95, - "sub-register per event prevents mixing": 0.91, - "add/subtract only cpu with planck units": 0.88, - "all data is foam, os reassembles bits": 0.94, - "concept translates better than intent": 0.87, - "local analog sidechannel translation": 0.90, - "metanarrative finds connections you missed": 0.93 - }, - "extension_points": [ - "substrate_git_index.py: attest command for IP timeline", - "metafoam_pkg.py: language miner integration", - "soliton_factory.py: semantic domain routing via concept_vector" - ] - } - - # What happens - 1. Reads the JSON session file. - 2. Derives concept_vector from idea_weights using _concept_vector_from_weights(). - 3. Inserts/replaces a row in packages with tier=RESEARCH, source=NOTE. - 4. Does NOT require a git tag — RESEARCH packages can be ingested directly. - """ - p = Path(session_file) - if not p.exists(): - print(f"[ingest] file not found: {session_file}") - sys.exit(1) - - sess = json.loads(p.read_text()) - idea_weights = sess.get("idea_weights", {}) - concept_vec = _concept_vector_from_weights(idea_weights) - now_utc = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - conn = _open_db() - concept_anchor = sess.get("concept_anchor") # {"domain":…, "concept":…, "resolution":…} - conn.execute( - """INSERT OR REPLACE INTO packages - (pkg, version, layer, domain, condition, stage, source, - tier, module, archetype, tags, description, - files, depends, foam_score, nd_point, - sealed_utc, visibility, model_status, taint_status, - session_id, idea_weights, extension_points, concept_vector, - concept_anchor, - indexed_utc) - VALUES (?,?,?,?,?,?,?, ?,?,?,?,?, ?,?,?,?, ?,?,?,?, ?,?,?,?, ?, ?)""", - ( - sess.get("pkg", f"chat-{now_utc[:10]}"), - sess.get("version", "1.0.0"), - "RULE", "DATA", "EXPERIMENTAL", "ACTIVE", "NOTE", - "RESEARCH", - sess.get("module", "IDEA_CRYSTALLIZATION"), - sess.get("archetype", "SEMANTIC_NODE"), - json.dumps(sess.get("tags", [])), - sess.get("description", ""), - json.dumps([]), - json.dumps([]), - round(sum(idea_weights.values()) / max(len(idea_weights), 1) * _PHI, 6), - json.dumps(concept_vec[:14]), - now_utc, - sess.get("visibility", "PRIVATE"), - "CANONICAL", "CLEAN", - sess.get("session_id", ""), - json.dumps(idea_weights), - json.dumps(sess.get("extension_points", [])), - json.dumps(concept_vec), - json.dumps(concept_anchor) if concept_anchor else None, - now_utc, - ), - ) - conn.commit() - conn.close() - - print(f"[ingest] {sess.get('pkg')} / {sess.get('version', '1.0.0')}") - print(f" ideas : {len(idea_weights)}") - print(f" foam_score: {round(sum(idea_weights.values())/max(len(idea_weights),1)*_PHI, 4)}") - print(f" concept_vector axes with signal: " - f"{sum(1 for x in concept_vec if x > 0.01)}/14") - - -# ───────────────────────────────────────────────────────────────────────────── -# Install helpers -# ───────────────────────────────────────────────────────────────────────────── - -_SYSTEMD_UNIT = """\ -[Unit] -Description=Substrate Git Index — HTTP query server -After=network.target - -[Service] -Type=simple -ExecStart=/usr/bin/python3 {script} serve --port {port} --db {db} -Restart=on-failure -RestartSec=5 -Environment=SUBSTRATE_DB={db} -Environment=SUBSTRATE_PORT={port} -Environment=WITNESS_SOURCE={source} -Environment=WITNESS_SOURCES_CONFIG={config} - -[Install] -WantedBy=multi-user.target -""" - - -def cmd_install(repo: str | None = None, source: str = WITNESS_SOURCE) -> None: - """Install this script as the post-receive hook in a bare git repo. - - # What this does - 1. Writes a small provider-aware wrapper to /hooks/post-receive - 2. Makes it executable (chmod +x) - 3. Sets WITNESS_SOURCE, GIT_REPO_PATH, and SUBSTRATE_DB for the hook. - - # Parameters - repo : path to the bare git repo - source : source block in 4-Infrastructure/witness/sources.json - """ - source_repo = _repo_path_from_source(source) - repo = repo or (str(source_repo) if source_repo else DEFAULT_HOOK_REPO) - if not repo: - print("[install] ERROR: no repo supplied and no local file:// source is configured.") - print(" Use --repo or set GIT_REPO_PATH.") - sys.exit(1) - - hooks_dir = Path(repo) / "hooks" - if not hooks_dir.exists(): - print(f"[install] ERROR: hooks dir not found: {hooks_dir}") - sys.exit(1) - - dest = hooks_dir / "post-receive" - script = Path(__file__).resolve() - wrapper = f"""#!/usr/bin/env sh -export WITNESS_SOURCE={source!r} -export WITNESS_SOURCES_CONFIG={str(_witness_config_path())!r} -export GIT_REPO_PATH="${{GIT_DIR:-{str(Path(repo).resolve())}}}" -export SUBSTRATE_DB={str(DB_PATH)!r} -exec /usr/bin/python3 {str(script)!r} hook "$@" -""" - dest.write_text(wrapper, encoding="utf-8") - dest.chmod(0o755) - print(f"[install] hook installed → {dest}") - print(f" Source: {source}") - print(f" DB will be written to: {DB_PATH}") - print() - print(" To change DB path, edit SUBSTRATE_DB in the hook or set env var.") - print(" Start the query server:") - print(f" python3 {script} serve --port {DEFAULT_PORT} --db {DB_PATH}") - - -def cmd_install_service(port: int = DEFAULT_PORT, source: str = WITNESS_SOURCE) -> None: - """Write a systemd unit file for the HTTP query server. - - Writes to /etc/systemd/system/substrate-git-index.service - Then run: systemctl enable --now substrate-git-index - """ - unit = _SYSTEMD_UNIT.format( - script=Path(__file__).resolve(), - port=port, - db=DB_PATH, - source=source, - config=_witness_config_path(), - ) - dest = Path("/etc/systemd/system/substrate-git-index.service") - try: - dest.write_text(unit) - print(f"[install-service] written → {dest}") - print(" Enable: systemctl enable --now substrate-git-index") - except PermissionError: - print(f"[install-service] writing to {dest} requires root.") - print(" Unit file content:") - print(unit) - - -# ───────────────────────────────────────────────────────────────────────────── -# CLI dispatch -# ───────────────────────────────────────────────────────────────────────────── - -def _parse_kv_args(args: list[str]) -> tuple[dict[str, str], list[str]]: - """Parse ``--key value`` pairs and preserve true positional arguments.""" - d: dict = {} - positional: list[str] = [] - i = 0 - while i < len(args): - if args[i].startswith("--"): - key = args[i][2:] - if i + 1 < len(args) and not args[i + 1].startswith("--"): - d[key] = args[i + 1] - i += 2 - else: - d[key] = "true" - i += 1 - else: - positional.append(args[i]) - i += 1 - return d, positional - - -def main() -> None: - """Parse argv and dispatch to the appropriate command function.""" - args = sys.argv[1:] - - if not args or args[0] in ("-h", "--help"): - print(__doc__) - return - - cmd = args[0] - opts, positional = _parse_kv_args(args[1:]) - - # Apply --db and --port overrides globally - global DB_PATH, DEFAULT_PORT - if "db" in opts: - DB_PATH = Path(opts["db"]) - if "port" in opts: - DEFAULT_PORT = int(opts["port"]) - - if cmd == "hook": - cmd_hook() - - elif cmd == "serve": - cmd_serve(DEFAULT_PORT) - - elif cmd == "index": - if not positional: - print("Usage: substrate_git_index.py index ") - sys.exit(1) - cmd_index(positional[0]) - - elif cmd == "reindex": - cmd_reindex_all() - - elif cmd == "query": - if not positional: - print("Usage: substrate_git_index.py query \"\"") - sys.exit(1) - cmd_query(" ".join(positional)) - - elif cmd == "status": - cmd_status() - - elif cmd == "drop": - if not positional: - print("Usage: substrate_git_index.py drop [version]") - sys.exit(1) - cmd_drop(positional[0], positional[1] if len(positional) > 1 else None) - - elif cmd == "schema": - cmd_schema() - - elif cmd == "connect": - if not positional: - print("Usage: substrate_git_index.py connect [--phase SEISMIC|FLAME|GROUNDED]") - sys.exit(1) - phase = opts.get("phase", "SEISMIC") - cmd_connect(positional[0], version=opts.get("version"), min_phase=phase) - - elif cmd == "translate": - if len(positional) < 2: - print("Usage: substrate_git_index.py translate ") - sys.exit(1) - cmd_translate(positional[0], positional[1]) - - elif cmd == "ingest-session": - if not positional: - print("Usage: substrate_git_index.py ingest-session ") - sys.exit(1) - cmd_ingest_session(positional[0]) - - elif cmd == "install": - repo = opts.get("repo") or (positional[0] if positional else None) - cmd_install(repo, source=opts.get("source", WITNESS_SOURCE)) - - elif cmd == "install-service": - cmd_install_service( - int(opts.get("port", str(DEFAULT_PORT))), - source=opts.get("source", WITNESS_SOURCE), - ) - - elif cmd == "align": - """Retroactively professionalize all nodes in the database.""" - conn = _open_db() - rows = conn.execute("SELECT rowid, pkg, description, tags, idea_weights FROM packages").fetchall() - updates = 0 - for row in rows: - r_id, pkg, desc, tags_raw, weights_raw = row - try: - tags = json.loads(tags_raw) if tags_raw else [] - weights = json.loads(weights_raw) if weights_raw else {} - except (json.JSONDecodeError, TypeError): - tags, weights = [], {} - - new_desc, new_tags, new_weights = _professional_align(pkg, desc, tags, weights) - - if new_desc != desc or new_tags != tags or new_weights != weights: - new_vec = _concept_vector_from_weights(new_weights) - conn.execute(""" - UPDATE packages SET - description = ?, - tags = ?, - idea_weights = ?, - concept_vector = ? - WHERE rowid = ? - """, (new_desc, json.dumps(new_tags), json.dumps(new_weights), json.dumps(new_vec), r_id)) - updates += 1 - conn.commit() - conn.close() - print(f"[align] professionalized {updates}/{len(rows)} nodes in {DB_PATH}") - - else: - print( - f"Unknown command: {cmd!r}\n" - "Commands: hook | serve | index | reindex | query | status | " - "drop | schema | install | install-service" - ) - sys.exit(1) - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/substrate/substrate_ledger_engine.py b/5-Applications/tools-scripts/substrate/substrate_ledger_engine.py deleted file mode 100644 index 72a3e5ab..00000000 --- a/5-Applications/tools-scripts/substrate/substrate_ledger_engine.py +++ /dev/null @@ -1,674 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -The Substrate Ledger: N-Space Snag and Leakage Management - -Foam substrate architecture: -- FoamCell = addressable substrate cell (fundamental storage grain) -- COMMITTED = successfully written cell with metadata header -- OVERFLOW_SNAG = buffer overflow (write rate > drain rate) -- thermal_leak = heat output from bad-sector writes -- partial_write = incomplete commit (cell not resonance-locked) -- NSpaceBuffer = higher-dimensional routing cache -- buffer_crosstalk = coupling between buffer regions -- TUNNEL = stabilized direct-path connection in n-space - -FoamProbe: Read/Write head for substrate maintenance -""" - -import sys -import os -sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -from math_harness_compat import xp, AnyArray -import hashlib -import json -import time -from dataclasses import dataclass, field -from typing import List, Dict, Optional, Tuple, Callable -from pathlib import Path -import sys -from enum import Enum - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Mock websockets for TSM harness -import types -sys.modules['websockets'] = types.ModuleType('websockets') - -from logic_signal_substrate_mcp_harness import TSMKernel - - -# ============================================================================ -# SUBSTRATE PARAMETERS -# ============================================================================ - -@dataclass -class SubstrateParams: - """Tunable parameters for the Substrate Ledger""" - - # Foam cell size (bytes per addressable grain) - grain_size_bytes: int = 8 - - # Ternary clock is action-bound, not periodic. No clock frequency. - # joule_floor is the Landauer minimum cost per action (k_B * T * ln2 at 300K). - joule_floor: float = 1.380649e-23 * 300 * 0.6931 - - # Maximum cell density before overflow snag (cells per area unit) - max_cell_density: float = 1.0e69 - - # Noise floor coefficient (partial-write threshold) - noise_floor: float = 1.380649e-23 - - # N-space routing buffer dimensionality - nd_dimensions: int = 11 - - # Signal-to-Noise Ratio threshold for committed write - snr_threshold: float = 6.0 - - # Probe reach (number of cell-widths the foam probe affects) - probe_reach_cells: int = 1 - - -# ============================================================================ -# ENUMS FOR SUBSTRATE STATES -# ============================================================================ - -class WriteOperationStatus(Enum): - """Status of a write operation to the substrate""" - PENDING = "pending" # In n-space buffer - FORMATTING = "formatting" # Applying metadata header - COMMITTED = "committed" # Successfully written to addressed cell - PARTIAL_WRITE = "partial_write" # Incomplete commit (noise exceeds floor) - OVERFLOW_SNAG = "overflow_snag" # Buffer overflow (density exceeded) - BAD_SECTOR = "bad_sector" # Corrupted region - - -class SubstrateRegion(Enum): - """Types of substrate regions""" - COMMITTED = "committed" # Successfully formatted cell - FOAM = "foam" # Raw unformatted medium - OVERFLOW_BOUNDARY = "overflow_boundary" # 2D buffer boundary at overflow - N_SPACE_BUFFER = "n_space_buffer" # Higher-dimensional routing cache - TUNNEL = "tunnel" # Stabilized direct-path connection - - -# ============================================================================ -# FOAM CELL STRUCTURE -# ============================================================================ - -@dataclass -class FoamCell: - """ - Represents a single addressable cell of the foam substrate. - This is the fundamental storage grain — one write unit. - """ - - # Position in n-space (normalised unit coordinates) - position: AnyArray # 3+1 dimensions - - # Cell state (complex amplitude; pending = unresolved, committed = eigenstate) - cell_state: complex - - # Information content (bytes) - information: bytes = b'' - - # Metadata header (applied during write) - metadata_header: Optional[bytes] = None - - # Resonance lock status (must match clock_freq to commit) - resonance_locked: bool = False - - # Write operation status - write_status: WriteOperationStatus = WriteOperationStatus.PENDING - - # Correlated cells (dual-write pairs) - corr_cells: List[int] = field(default_factory=list) - - # Partial-write magnitude (0 = clean, 1 = fully leaked) - partial_write_magnitude: float = 0.0 - - def compute_snr(self, params: SubstrateParams) -> float: - """ - Compute Signal-to-Noise Ratio for this cell. - Determines if write operation can succeed. - """ - if self.metadata_header is None: - return 0.0 - - # Signal = information content in bits - signal = len(self.information) * 8 - - # Noise = cell state magnitude times noise floor coefficient - noise = xp.abs(self.cell_state) * params.noise_floor - - if noise < 1e-30: - return float('inf') - - return signal / noise - - def apply_metadata_header(self, header: bytes) -> bool: - """ - Apply metadata header to commit this cell. - Returns True if resonance lock achieved. - """ - self.metadata_header = header - self.write_status = WriteOperationStatus.FORMATTING - - # Check resonance against clock reference - header_hash = hashlib.sha256(header).digest() - resonance_value = int.from_bytes(header_hash[:4], 'big') / 2**32 - - self.resonance_locked = resonance_value > 0.5 - if self.resonance_locked: - self.write_status = WriteOperationStatus.COMMITTED - - return self.resonance_locked - - -# ============================================================================ -# N-SPACE BUFFER MANAGEMENT -# ============================================================================ - -@dataclass -class NSpaceBuffer: - """ - Higher-dimensional routing buffer where information exists before - being written to the addressed cell layer. Buffer crosstalk explains - apparent coupling between spatially separated write operations. - """ - - # Buffer dimensionality - dimensions: int = 11 - - # Buffer capacity (cells) - capacity: int = 10**180 - - # Current occupancy - occupancy: Dict[str, FoamCell] = field(default_factory=dict) - - # Buffer phase (complex; tracks routing state) - buffer_phase: complex = field(default_factory=lambda: complex(0, 1)) - - # Crosstalk matrix - crosstalk_matrix: Optional[AnyArray] = None - - def allocate_cell(self, cell_id: str, cell: FoamCell) -> None: - """Allocate a cell in the n-space buffer""" - self.occupancy[cell_id] = cell - - def compute_crosstalk(self) -> AnyArray: - """ - Compute coupling (crosstalk) between buffer regions. - High crosstalk = strong path correlation between cells. - """ - n_cells = len(self.occupancy) - if n_cells < 2: - return xp.zeros((1, 1)) - - crosstalk = xp.zeros((n_cells, n_cells)) - cell_ids = list(self.occupancy.keys()) - - for i, id_i in enumerate(cell_ids): - for j, id_j in enumerate(cell_ids): - if i != j: - cell_i = self.occupancy[id_i] - cell_j = self.occupancy[id_j] - - # Crosstalk via dual-write correlation - if j in cell_i.corr_cells: - crosstalk[i, j] = xp.abs(cell_i.cell_state * cell_j.cell_state) - - # Crosstalk via partial-write bleed - crosstalk[i, j] += cell_i.partial_write_magnitude * cell_j.partial_write_magnitude - - self.crosstalk_matrix = crosstalk - return crosstalk - - def detect_buffer_crosstalk(self, source_id: str, target_ids: List[str]) -> float: - """ - Measure crosstalk bleed from a high-energy source cell to a set of - target cells in the same buffer region. - """ - if source_id not in self.occupancy: - return 0.0 - - source_cell = self.occupancy[source_id] - total_bleed = 0.0 - - for target_id in target_ids: - if target_id in self.occupancy: - target_cell = self.occupancy[target_id] - bleed = source_cell.partial_write_magnitude * target_cell.partial_write_magnitude - total_bleed += bleed - - return total_bleed - - -# ============================================================================ -# OVERFLOW SNAG DETECTION -# ============================================================================ - -@dataclass -class OverflowSnag: - """ - Represents a buffer overflow snag — a region where write density - has exceeded the substrate drain rate, causing uncommitted cells - to accumulate and thermal leakage to build up. - """ - - # Position in normalised cell coordinates - position: AnyArray - - # Cell density in this region (cells per area unit) - cell_density: float - - # Snag severity (0 = none, 1 = critical) - severity: float - - # Thermal output from bad-sector writes (normalised) - thermal_output: float - - # Overflow boundary area - overflow_boundary_area: float - - # Write attempts per drain cycle - write_attempts_per_cycle: float - - def compute_thermal_output(self, params: SubstrateParams) -> float: - """ - Compute normalised thermal output from overflow writes. - thermal_output = cell_density / max_cell_density (clamped 0-1). - """ - thermal = min(1.0, self.cell_density / max(params.max_cell_density, 1.0)) - self.thermal_output = thermal - return thermal - - -# ============================================================================ -# THE Ψ_REPAIR EQUATION (SOLITON FRAMEWORK) -# ============================================================================ - -class SubstrateLedgerEngine: - """ - Main engine for managing the Substrate Ledger. - - Ψ_repair = ∫(M_header ⊗ R_resonance) · δ(ω - ω₀) dt - """ - - def __init__(self, kernel: TSMKernel): - self.kernel = kernel - self.params = SubstrateParams() - - # Foam cells (the addressable cell layer) - self.foam_cells: Dict[str, FoamCell] = {} - - # N-space routing buffer - self.nspace_buffer = NSpaceBuffer() - - # Overflow snags - self.snags: List[OverflowSnag] = [] - - # Successful write operations - self.committed_writes: List[Dict] = [] - - # Partial-write events - self.partial_write_events: List[Dict] = [] - - def initialize_foam_region(self, num_cells: int = 1000) -> None: - """Initialize a region of foam substrate""" - for i in range(num_cells): - position = xp.random.rand(4) # normalised unit coordinates - cell = FoamCell( - position=position, - cell_state=complex(xp.random.rand(), xp.random.rand()), - information=hashlib.sha256(bytes([i])).digest()[:8] - ) - cell_id = f"cell_{i}" - self.foam_cells[cell_id] = cell - self.nspace_buffer.allocate_cell(cell_id, cell) - - def apply_metadata_header(self, cell_id: str, header: bytes) -> WriteOperationStatus: - """Apply metadata header to commit a cell (write operation)""" - if cell_id not in self.foam_cells: - return WriteOperationStatus.BAD_SECTOR - - cell = self.foam_cells[cell_id] - - # Compute SNR before write - snr = cell.compute_snr(self.params) - - if snr < self.params.snr_threshold: - # Partial write — cell not resonance-locked - cell.partial_write_magnitude = 1.0 - snr / self.params.snr_threshold - cell.write_status = WriteOperationStatus.PARTIAL_WRITE - - self.partial_write_events.append({ - "cell_id": cell_id, - "snr": snr, - "partial_write_magnitude": cell.partial_write_magnitude, - "timestamp": time.time() - }) - - return WriteOperationStatus.PARTIAL_WRITE - - # Apply header and attempt resonance lock - success = cell.apply_metadata_header(header) - - if success: - self.committed_writes.append({ - "cell_id": cell_id, - "header_hash": hashlib.sha256(header).hexdigest(), - "timestamp": time.time() - }) - return WriteOperationStatus.COMMITTED - else: - return WriteOperationStatus.PENDING - - def detect_overflow_snag(self, region_center: AnyArray, region_radius: float) -> Optional[OverflowSnag]: - """ - Detect overflow snags in a region. - Occurs when cell density exceeds the substrate drain rate. - """ - cells_in_region = [] - for cell_id, cell in self.foam_cells.items(): - distance = xp.linalg.norm(cell.position[:3] - region_center[:3]) - if distance < region_radius: - cells_in_region.append(cell) - - if len(cells_in_region) < 10: - return None - - total_info = sum(len(c.information) * 8 for c in cells_in_region) - area = 4 * xp.pi * region_radius**2 - cell_density = total_info / area - - if cell_density > self.params.max_cell_density: - severity = min(1.0, cell_density / self.params.max_cell_density) - - snag = OverflowSnag( - position=region_center, - cell_density=cell_density, - severity=severity, - thermal_output=0.0, - overflow_boundary_area=area, - write_attempts_per_cycle=len(cells_in_region) * self.params.clock_freq_hz - ) - - snag.compute_thermal_output(self.params) - self.snags.append(snag) - return snag - - return None - - def psi_repair_equation(self, cell_ids: List[str]) -> Dict: - """ - Solve the Ψ_repair equation for formatting foam cells. - - Ψ_repair = ∫(M_header ⊗ R_resonance) · δ(ω - ω₀) dt - - Returns repair success metrics. - """ - M_header = xp.zeros(len(cell_ids)) - for i, cell_id in enumerate(cell_ids): - if cell_id in self.foam_cells: - cell = self.foam_cells[cell_id] - if cell.metadata_header: - header_hash = hashlib.sha256(cell.metadata_header).digest() - M_header[i] = int.from_bytes(header_hash[:4], 'big') / 2**32 - - R_resonance = xp.zeros(len(cell_ids)) - for i, cell_id in enumerate(cell_ids): - if cell_id in self.foam_cells: - cell = self.foam_cells[cell_id] - if cell.resonance_locked: - R_resonance[i] = 1.0 - - delta_resonance = xp.zeros(len(cell_ids)) - for i, cell_id in enumerate(cell_ids): - if cell_id in self.foam_cells: - cell = self.foam_cells[cell_id] - freq_match = xp.abs(cell.compute_snr(self.params) - self.params.snr_threshold) - delta_resonance[i] = xp.exp(-freq_match**2 / 0.1) - - tensor_product = M_header * R_resonance - psi_repair = xp.sum(tensor_product * delta_resonance) - psi_repair /= len(cell_ids) - - return { - "psi_repair": float(psi_repair), - "M_header_magnitude": float(xp.sum(M_header)), - "R_resonance_magnitude": float(xp.sum(R_resonance)), - "delta_match": float(xp.sum(delta_resonance)), - "formatted_cells": int(xp.sum(R_resonance)), - "total_cells": len(cell_ids) - } - - def foam_probe_operation(self, target_cell_ids: List[str], resonance_frequency: float) -> Dict: - """ - Operate the Foam Probe for substrate maintenance. - - The probe creates a Local Formatting Zone by: - 1. Injecting resonance to lock cells - 2. Pulling overflow snags - 3. Stabilizing direct-path tunnel connections - """ - import struct - - results = { - "resonance_injected": resonance_frequency, - "cells_targeted": len(target_cell_ids), - "cells_locked": 0, - "snags_pulled": 0, - "tunnels_stabilized": 0, - "partial_write_reduced": 0.0 - } - - initial_leakage = sum( - self.foam_cells[cid].partial_write_magnitude - for cid in target_cell_ids - if cid in self.foam_cells - ) - - # Phase 1: Resonance injection - for cell_id in target_cell_ids: - if cell_id not in self.foam_cells: - continue - - resonance_header = hashlib.sha256( - struct.pack(' 0.5) - - for pair in high_crosstalk_pairs: - if len(pair) > 1: - results["tunnels_stabilized"] += 1 - - return results - - def compute_substrate_health(self) -> Dict: - """Compute overall health metrics for the substrate ledger""" - total_cells = len(self.foam_cells) - committed = sum(1 for c in self.foam_cells.values() if c.write_status == WriteOperationStatus.COMMITTED) - partial = sum(1 for c in self.foam_cells.values() if c.write_status == WriteOperationStatus.PARTIAL_WRITE) - snags = len(self.snags) - - crosstalk = self.nspace_buffer.compute_crosstalk() - avg_crosstalk = xp.mean(crosstalk) if crosstalk.size > 0 else 0.0 - - return { - "total_cells": total_cells, - "committed_writes": committed, - "commitment_rate": committed / max(total_cells, 1), - "partial_write_events": partial, - "partial_write_rate": partial / max(total_cells, 1), - "overflow_snags": snags, - "avg_crosstalk": float(avg_crosstalk), - "max_crosstalk": float(xp.max(crosstalk)) if crosstalk.size > 0 else 0.0, - "substrate_health": committed / max(total_cells, 1) - snags * 0.1 - partial * 0.05 - } - - -# ============================================================================ -# MAIN EXECUTION -# ============================================================================ - -def main(): - """Demonstrate the Substrate Ledger model""" - - print("=" * 70) - print(" THE SUBSTRATE LEDGER: N-SPACE SNAG AND LEAKAGE MANAGEMENT") - print("=" * 70) - print() - - kernel = TSMKernel() - engine = SubstrateLedgerEngine(kernel) - - print("[PHASE 1] INITIALIZE FOAM REGION") - engine.initialize_foam_region(num_cells=100) - print(f" Initialized {len(engine.foam_cells)} foam cells") - print(f" N-space buffer occupancy: {len(engine.nspace_buffer.occupancy)} cells") - print() - - print("[PHASE 2] ATTEMPT WRITE OPERATIONS (Metadata Header Application)") - - for i, cell_id in enumerate(list(engine.foam_cells.keys())[:50]): - header = hashlib.sha256(bytes([i])).digest() - engine.apply_metadata_header(cell_id, header) - - health = engine.compute_substrate_health() - print(f" Committed writes: {health['committed_writes']} ({health['commitment_rate']*100:.1f}%)") - print(f" Partial-write events: {health['partial_write_events']} ({health['partial_write_rate']*100:.1f}%)") - print() - - print("[PHASE 3] DETECT OVERFLOW SNAGS") - - center = xp.array([0.5, 0.5, 0.5]) - snag = engine.detect_overflow_snag(center, 1e-36) - - if snag: - print(f" ⚠ OVERFLOW SNAG DETECTED") - print(f" Cell density: {snag.cell_density:.2e} bits/unit²") - print(f" Severity: {snag.severity*100:.1f}%") - print(f" Thermal output: {snag.thermal_output:.4f}") - print(f" Overflow boundary area: {snag.overflow_boundary_area:.2e}") - else: - print(" No overflow snags detected in region") - print() - - print("[PHASE 4] SOLVE Ψ_REPAIR EQUATION") - print(" Ψ_repair = ∫(M_header ⊗ R_resonance) · δ(ω - ω₀) dt") - - cell_ids = list(engine.foam_cells.keys())[:50] - repair_result = engine.psi_repair_equation(cell_ids) - - print(f" Ψ_repair magnitude: {repair_result['psi_repair']:.4f}") - print(f" M_header magnitude: {repair_result['M_header_magnitude']:.2f}") - print(f" R_resonance magnitude: {repair_result['R_resonance_magnitude']:.2f}") - print(f" δ(ω-ω₀) match: {repair_result['delta_match']:.2f}") - print(f" Formatted cells: {repair_result['formatted_cells']}/{repair_result['total_cells']}") - print() - - print("[PHASE 5] FOAM PROBE OPERATION (Substrate Maintenance)") - - probe_result = engine.foam_probe_operation( - target_cell_ids=cell_ids, - resonance_frequency=engine.params.clock_freq_hz - ) - - print(f" Resonance injected: {probe_result['resonance_injected']:.2e} Hz") - print(f" Cells targeted: {probe_result['cells_targeted']}") - print(f" Cells locked: {probe_result['cells_locked']}") - print(f" Snags pulled: {probe_result['snags_pulled']}") - print(f" Tunnels stabilized: {probe_result['tunnels_stabilized']}") - print(f" Partial-write reduced: {probe_result['partial_write_reduced']:.4f}") - print() - - print("[PHASE 6] N-SPACE CROSSTALK (Buffer Coupling)") - - if len(list(engine.foam_cells.keys())) > 10: - source_cell = list(engine.foam_cells.keys())[0] - target_cells = list(engine.foam_cells.keys())[1:10] - crosstalk_signal = engine.nspace_buffer.detect_buffer_crosstalk(source_cell, target_cells) - print(f" Crosstalk signal strength: {crosstalk_signal:.6f}") - - crosstalk = engine.nspace_buffer.compute_crosstalk() - print(f" Average crosstalk: {xp.mean(crosstalk):.6f}") - print(f" Max crosstalk: {xp.max(crosstalk):.6f}") - print(f" (High crosstalk = strong buffer path coupling)") - print() - - print("[PHASE 7] FINAL SUBSTRATE HEALTH ASSESSMENT") - final_health = engine.compute_substrate_health() - - print(f" Total cells: {final_health['total_cells']}") - print(f" Commitment rate: {final_health['commitment_rate']*100:.1f}%") - print(f" Partial-write rate: {final_health['partial_write_rate']*100:.1f}%") - print(f" Overflow snags: {final_health['overflow_snags']}") - print(f" Max crosstalk: {final_health['max_crosstalk']:.4f}") - print(f" SUBSTRATE HEALTH SCORE: {final_health['substrate_health']*100:.1f}%") - print() - - results = { - "substrate_params": { - "grain_size_bytes": engine.params.grain_size_bytes, - "clock_freq_hz": engine.params.clock_freq_hz, - "snr_threshold": engine.params.snr_threshold - }, - "repair_equation": repair_result, - "foam_probe": probe_result, - "substrate_health": final_health, - "timestamp": time.time() - } - - output_path = ROOT / "out" / "substrate_ledger_results.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump(results, f, indent=2) - - print(f"[+] Results saved to: {output_path}") - - return 0 - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/substrate/sync_ene_index.py b/5-Applications/tools-scripts/substrate/sync_ene_index.py deleted file mode 100644 index f9115736..00000000 --- a/5-Applications/tools-scripts/substrate/sync_ene_index.py +++ /dev/null @@ -1,675 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK — PHASE 12: ENE INDEX SYNCHRONIZATION -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -""" -5-Applications/tools-5-Applications/scripts/sync_ene_index.py — ENE Index Builder and Synchronizer - -Replaces the legacy version in tools/scratch/. This production-grade script: - -1. Walks the entire repository tree, respecting .gitignore and standard exclusions -2. Computes SHA-256 hashes and file sizes in parallel -3. Builds ENE_INDEX.json with extended metadata for Phase 12 modules: - - concept_axes: 14-axis concept space coordinates for thermal/stress/aging modules - - telemetry_fields: list of emitted telemetry keys per module - - module_classification: thermal | stress | aging | dispatch | diagnostics -4. Rebuilds ENE_INDEX.sha256 with absolute paths (sorted, deterministic) -5. Optionally runs a post-sync audit to confirm 100% consistency - -Usage: - python 5-Applications/tools-5-Applications/scripts/sync_ene_index.py # Build index + manifest - python 5-Applications/tools-5-Applications/scripts/sync_ene_index.py --audit # Build + post-sync audit - python 5-Applications/tools-5-Applications/scripts/sync_ene_index.py --dry-run # Show what would change - python 5-Applications/tools-5-Applications/scripts/sync_ene_index.py --validate # Build + run Phase 12 validation -""" - -import hashlib -import json -import os -import sys -import time -from concurrent.futures import ProcessPoolExecutor, as_completed -from dataclasses import asdict, dataclass, field -from pathlib import Path -from typing import Optional - -# --------------------------------------------------------------------------- -# Configuration -# --------------------------------------------------------------------------- - -BASE_DIR = Path("/home/allaun/Research Stack") -INDEX_PATH = BASE_DIR / "ENE_INDEX.json" -MANIFEST_PATH = BASE_DIR / "ENE_INDEX.sha256" -GITIGNORE_PATH = BASE_DIR / ".gitignore" - -WORKERS = os.cpu_count() or 8 -CHUNK_SIZE = 65536 # 64KB read chunks - -# Files excluded from indexing (self-referential + transient) -SELF_EXCLUSIONS = { - "ENE_INDEX.json", - "ENE_INDEX.sha256", - ".gitignore", - "substrate_index.db", - # Heartbeat/telemetry files rewritten by live daemons — would always - # race against the audit step and produce spurious hash mismatches. - "SHADOW_AUDIT.json", - "5-Applications/tools-5-Applications/scripts/daemon_health.json", - "5-Applications/tools-5-Applications/scripts/shadow_telemetry.json", - "0-Core-Formalism/core/ui/telemetry.json", -} - -# Directories excluded from walk -DIR_EXCLUSIONS = { - ".git", - "__pycache__", - ".pytest_cache", - ".venv-eng", - ".venv-fem", - "node_modules", - "target", -} - -# Force-include paths that are gitignored but critical for ENE routing -FORCE_INCLUDE_PREFIXES = ( - "pbacs/", - "0-Core-Formalism/core/hw/diat_sqrt_table.mem", - "0-Core-Formalism/core/hw/diat", -) - -# --------------------------------------------------------------------------- -# Phase 12 Module Registry — 14-Axis Concept Space -# --------------------------------------------------------------------------- - -# The 14 concept axes: -# [0] thermal_awareness — temperature sensing and headroom calculation -# [1] stress_accumulation — cumulative informatic stress modeling -# [2] hardware_aging — wear, MTBF, silicon degradation tracking -# [3] load_balancing — dispatch decisions with multi-factor scoring -# [4] autonomous_rest — closed-loop thermal pause/resume protocol -# [5] bit_flip_modeling — Arrhenius-based error rate estimation -# [6] electromigration — current-driven degradation modeling -# [7] fatigue_accumulation — cycle-based wear accumulation -# [8] cooling_capacity — passive/active thermal dissipation rating -# [9] workload_intensity — computational load classification -# [10] entropy_production — Shannon entropy of workload distribution -# [11] mtbf_prediction — mean-time-between-failure forecasting -# [12] silicon_health — transistor-level degradation estimation -# [13] flame_mode — emergency dispatch under thermal constraint - -PHASE12_MODULES = { - "4-Infrastructure/infra/nodes/thermal_aware_dispatch.py": { - "module": "thermal_aware_dispatch", - "classification": "dispatch", - "concept_axes": { - "thermal_awareness": 1.0, - "stress_accumulation": 0.8, - "hardware_aging": 0.9, - "load_balancing": 1.0, - "autonomous_rest": 1.0, - "bit_flip_modeling": 0.7, - "electromigration": 0.3, - "fatigue_accumulation": 0.6, - "cooling_capacity": 0.5, - "workload_intensity": 0.9, - "entropy_production": 0.4, - "mtbf_prediction": 0.8, - "silicon_health": 0.7, - "flame_mode": 1.0, - }, - "telemetry_fields": [ - "magnitude", - "confidence", - "entropy_delta", - "error_rates.hash", - "error_rates.log", - "temperature_c", - "thermal_drift_rate", - "aging_index", - "wear_level", - "mtbf_hours", - "lifetime_remaining_pct", - "bit_flip_rate", - "hot_path_utilization", - "cold_path_utilization", - ], - "phase": 12, - }, - "0-Core-Formalism/core/src/engines/ene_diagnostics.py": { - "module": "ene_diagnostics", - "classification": "diagnostics", - "concept_axes": { - "thermal_awareness": 0.2, - "stress_accumulation": 0.5, - "hardware_aging": 0.1, - "load_balancing": 0.3, - "autonomous_rest": 0.1, - "bit_flip_modeling": 0.1, - "electromigration": 0.1, - "fatigue_accumulation": 0.2, - "cooling_capacity": 0.1, - "workload_intensity": 0.3, - "entropy_production": 0.4, - "mtbf_prediction": 0.1, - "silicon_health": 0.1, - "flame_mode": 0.4, - }, - "telemetry_fields": [ - "healthy", - "conditions_passed", - "conditions_total", - "knit_coverage", - "rigid_psd", - "crnt_deficiency", - "flavor_bias", - "neuro_slope", - "operating_mode", - "n_points", - ], - "phase": 13, - }, - "5-Applications/tests/test_phase12_thermal_load_balancing.py": { - "module": "test_phase12_thermal_load_balancing", - "classification": "test", - "concept_axes": { - "thermal_awareness": 1.0, - "stress_accumulation": 0.8, - "hardware_aging": 0.9, - "load_balancing": 1.0, - "autonomous_rest": 1.0, - "bit_flip_modeling": 0.7, - "electromigration": 0.3, - "fatigue_accumulation": 0.6, - "cooling_capacity": 0.5, - "workload_intensity": 0.9, - "entropy_production": 0.4, - "mtbf_prediction": 0.8, - "silicon_health": 0.7, - "flame_mode": 1.0, - }, - "telemetry_fields": [ - "thermal_score", - "aging_index", - "stress_magnitude", - "bit_flip_rate", - "lifetime_remaining_pct", - "thermal_drift_rate", - ], - "phase": 12, - }, - "5-Applications/tests/test_phase13_ene_diagnostics.py": { - "module": "test_phase13_ene_diagnostics", - "classification": "test", - "concept_axes": { - "thermal_awareness": 0.2, - "stress_accumulation": 0.5, - "hardware_aging": 0.1, - "load_balancing": 0.3, - "autonomous_rest": 0.1, - "bit_flip_modeling": 0.1, - "electromigration": 0.1, - "fatigue_accumulation": 0.2, - "cooling_capacity": 0.1, - "workload_intensity": 0.3, - "entropy_production": 0.4, - "mtbf_prediction": 0.1, - "silicon_health": 0.1, - "flame_mode": 0.4, - }, - "telemetry_fields": [ - "rigid_stress_matrix_psd", - "knit_hamiltonian_path_exists", - "crnt_deficiency", - "flavor_bias", - "neuro_gradient_slope", - ], - "phase": 13, - }, -} - -# Ordered list of concept axes for vector representation -CONCEPT_AXIS_ORDER = [ - "thermal_awareness", - "stress_accumulation", - "hardware_aging", - "load_balancing", - "autonomous_rest", - "bit_flip_modeling", - "electromigration", - "fatigue_accumulation", - "cooling_capacity", - "workload_intensity", - "entropy_production", - "mtbf_prediction", - "silicon_health", - "flame_mode", -] - - -# --------------------------------------------------------------------------- -# Data models -# --------------------------------------------------------------------------- - -@dataclass -class FileEntry: - """Single file entry for the ENE index.""" - sha256: str - size: int - modified: float - # Phase 12 extended metadata (optional, populated for known modules) - module: Optional[str] = None - classification: Optional[str] = None - concept_axes: Optional[dict] = None - concept_vector: Optional[list] = None - telemetry_fields: Optional[list] = None - phase: Optional[int] = None - - def to_dict(self) -> dict: - d = {"sha256": self.sha256, "size": self.size, "modified": self.modified} - if self.module is not None: - d["module"] = self.module - if self.classification is not None: - d["classification"] = self.classification - if self.concept_axes is not None: - d["concept_axes"] = self.concept_axes - if self.concept_vector is not None: - d["concept_vector"] = self.concept_vector - if self.telemetry_fields is not None: - d["telemetry_fields"] = self.telemetry_fields - if self.phase is not None: - d["phase"] = self.phase - return d - - -# --------------------------------------------------------------------------- -# Core hashing -# --------------------------------------------------------------------------- - -def compute_hash(file_path: Path) -> Optional[tuple]: - """ - Compute SHA-256 hash, size, and mtime for a file. - Returns (sha256_hex, size, mtime) or None on error. - """ - sha256 = hashlib.sha256() - try: - stat = file_path.stat() - with open(file_path, "rb") as f: - while chunk := f.read(CHUNK_SIZE): - sha256.update(chunk) - return (sha256.hexdigest(), stat.st_size, stat.st_mtime) - except (OSError, PermissionError): - return None - - -# --------------------------------------------------------------------------- -# Gitignore handling -# --------------------------------------------------------------------------- - -def load_gitignore_spec(): - """Load .gitignore patterns using pathspec if available, else simple parser.""" - if not GITIGNORE_PATH.exists(): - return None - try: - import pathspec - with open(GITIGNORE_PATH, "r") as f: - return pathspec.PathSpec.from_lines("gitwildmatch", f) - except ImportError: - # Fallback: return None, rely on DIR_EXCLUSIONS only - return None - - -def should_exclude(rel_path: str, spec) -> bool: - """Determine if a relative path should be excluded from indexing.""" - # Force-include critical paths even if gitignored - for prefix in FORCE_INCLUDE_PREFIXES: - if rel_path.startswith(prefix): - # Still respect self-exclusions and dir exclusions - basename = os.path.basename(rel_path) - if basename in SELF_EXCLUSIONS or rel_path in SELF_EXCLUSIONS: - return True - return False - - # Self-exclusions - basename = os.path.basename(rel_path) - if basename in SELF_EXCLUSIONS: - return True - if rel_path in SELF_EXCLUSIONS: - return True - - # Gitignore spec - if spec and spec.match_file(rel_path): - return True - - return False - - -# --------------------------------------------------------------------------- -# Directory walk -# --------------------------------------------------------------------------- - -def _walk_tree(base_rel: str, real_root: Path, files: list, spec, visited: set): - """Recursive helper that follows directory symlinks inside BASE_DIR.""" - for root, dirs, filenames in os.walk(real_root, followlinks=True): - rel_root = os.path.relpath(root, BASE_DIR) - if rel_root == ".": - rel_root = "" - - # Determine the logical path prefix (for symlink aliases) - if base_rel and rel_root.startswith(str(real_root.relative_to(BASE_DIR))): - logical_root = base_rel + rel_root[len(str(real_root.relative_to(BASE_DIR))):] - elif base_rel: - logical_root = base_rel - else: - logical_root = rel_root - - # Remove excluded directories (unless force-included) - def _keep_dir(d: str) -> bool: - if d in DIR_EXCLUSIONS: - return False - rel_dir = os.path.join(rel_root, d) + "/" - # Force-include critical directories - for prefix in FORCE_INCLUDE_PREFIXES: - if rel_dir.startswith(prefix) or prefix.startswith(rel_dir): - return True - if spec and spec.match_file(rel_dir): - return False - return True - - dirs[:] = [d for d in dirs if _keep_dir(d)] - - for fname in filenames: - abs_path = Path(root) / fname - if logical_root: - log_path = os.path.join(logical_root, fname) - else: - log_path = fname - - if should_exclude(log_path, spec): - continue - - # Resolve symlink loops - real_path = abs_path.resolve() - key = (str(real_path), log_path) - if key in visited: - continue - visited.add(key) - files.append((abs_path, log_path)) - - -def collect_files(): - """ - Walk BASE_DIR and return list of (abs_path, rel_path) tuples - for all indexable files, including directory symlinks. - """ - spec = load_gitignore_spec() - files = [] - visited = set() - - _walk_tree("", BASE_DIR, files, spec, visited) - - # Explicitly handle top-level directory symlinks (os.walk doesn't follow them) - for entry in BASE_DIR.iterdir(): - if entry.is_symlink() and entry.is_dir(): - target = entry.resolve() - # Only follow symlinks that point inside BASE_DIR - if str(target).startswith(str(BASE_DIR) + os.sep): - link_name = entry.name - _walk_tree(link_name, target, files, spec, visited) - - return files - - -# --------------------------------------------------------------------------- -# Parallel hash computation -# --------------------------------------------------------------------------- - -def hash_files_parallel(files: list, workers: int = WORKERS) -> dict: - """ - Hash files in parallel using ProcessPoolExecutor. - Returns dict of {rel_path: FileEntry}. - """ - index = {} - abs_paths = [abs_path for abs_path, _ in files] - - with ProcessPoolExecutor(max_workers=workers) as executor: - future_to_info = {} - for i, abs_path in enumerate(abs_paths): - future = executor.submit(compute_hash, abs_path) - future_to_info[future] = i - - for future in as_completed(future_to_info): - idx = future_to_info[future] - abs_path, rel_path = files[idx] - result = future.result() - - if result is None: - continue - - sha256_hex, size, mtime = result - entry = FileEntry(sha256=sha256_hex, size=size, modified=mtime) - - # Enrich with Phase 12 module metadata if applicable - if rel_path in PHASE12_MODULES: - mod = PHASE12_MODULES[rel_path] - entry.module = mod["module"] - entry.classification = mod["classification"] - entry.concept_axes = mod["concept_axes"] - entry.concept_vector = [ - mod["concept_axes"].get(axis, 0.0) - for axis in CONCEPT_AXIS_ORDER - ] - entry.telemetry_fields = mod["telemetry_fields"] - entry.phase = mod["phase"] - - index[rel_path] = entry - - return index - - -# --------------------------------------------------------------------------- -# Index serialization -# --------------------------------------------------------------------------- - -def save_index(index: dict): - """Save ENE_INDEX.json with sorted keys for deterministic output.""" - output = {} - for rel_path in sorted(index.keys()): - output[rel_path] = index[rel_path].to_dict() - - with open(INDEX_PATH, "w") as f: - json.dump(output, f, indent=2, sort_keys=False) - - return output - - -def _sanitize_manifest_path(abs_path: str) -> str: - """Sanitize a path for safe inclusion in the line-based manifest. - - Replaces newlines with spaces and strips leading/trailing whitespace - so the manifest parser can always split on ' ' reliably. - """ - return " ".join(abs_path.split()) - - -def save_manifest(index: dict): - """ - Rebuild ENE_INDEX.sha256 with absolute paths. - Format: - Sorted by path for deterministic output. - - Paths containing newlines or other whitespace are sanitized so that - every manifest line cleanly splits into exactly two fields. - """ - lines = [] - for rel_path in sorted(index.keys()): - sha256_hex = index[rel_path].sha256 - abs_path = _sanitize_manifest_path(str(BASE_DIR / rel_path)) - lines.append(f"{sha256_hex} {abs_path}") - - with open(MANIFEST_PATH, "w") as f: - f.write("\n".join(lines) + "\n") - - -# --------------------------------------------------------------------------- -# Audit (post-sync verification) -# --------------------------------------------------------------------------- - -def run_audit(index: dict, spec=None) -> dict: - """ - Run a parallelized audit: verify every indexed file exists on disk - with matching hash and size. - Returns audit results dict. - """ - results = { - "missing_from_disk": [], - "hash_mismatch": [], - "size_mismatch": [], - "ok": 0, - "errors": [], - } - - files_to_check = [] - for rel_path in sorted(index.keys()): - abs_path = BASE_DIR / rel_path - if should_exclude(rel_path, spec): - continue - files_to_check.append((abs_path, rel_path, index[rel_path])) - - with ProcessPoolExecutor(max_workers=WORKERS) as executor: - future_to_path = {} - for abs_path, rel_path, expected_entry in files_to_check: - future = executor.submit(compute_hash, abs_path) - future_to_path[future] = (rel_path, expected_entry) - - for future in as_completed(future_to_path): - rel_path, expected = future_to_path[future] - result = future.result() - - if result is None: - results["missing_from_disk"].append(rel_path) - continue - - actual_sha256, actual_size, _ = result - - exp_sha = expected.get("sha256", expected.get("hash", "")) - exp_sz = expected.get("size", 0) - - if actual_sha256 != exp_sha: - results["hash_mismatch"].append({ - "path": rel_path, - "expected": exp_sha, - "actual": actual_sha256, - }) - elif actual_size != exp_sz: - results["size_mismatch"].append({ - "path": rel_path, - "expected": exp_sz, - "actual": actual_size, - }) - else: - results["ok"] += 1 - - return results - - -# --------------------------------------------------------------------------- -# CLI -# --------------------------------------------------------------------------- - -def main(): - import argparse - - parser = argparse.ArgumentParser(description="ENE Index Builder and Synchronizer") - parser.add_argument("--audit", action="store_true", help="Run post-sync audit") - parser.add_argument("--dry-run", action="store_true", help="Show what would change without writing") - parser.add_argument("--validate", action="store_true", help="Run Phase 12 validation after sync") - parser.add_argument("--workers", type=int, default=WORKERS, help=f"Number of parallel workers (default: {WORKERS})") - args = parser.parse_args() - - print(f"[*] ENE Index Sync — Phase 12") - print(f"[*] Base directory: {BASE_DIR}") - print(f"[*] Workers: {args.workers}") - print() - - # Phase 1: Collect files - t0 = time.time() - print(f"[1/4] Collecting files from repository tree...") - files = collect_files() - print(f" Found {len(files)} files to index.") - - # Phase 2: Compute hashes in parallel - print(f"[2/4] Computing SHA-256 hashes ({args.workers} workers)...") - index = hash_files_parallel(files, workers=args.workers) - print(f" Indexed {len(index)} files.") - - # Phase 3: Save index and manifest - if args.dry_run: - print() - print("[DRY RUN] Would save:") - print(f" - {INDEX_PATH} ({len(index)} entries)") - print(f" - {MANIFEST_PATH} ({len(index)} lines)") - # Show Phase 12 module entries - print() - print("Phase 12/13 modules that would be enriched:") - for rel_path, mod in PHASE12_MODULES.items(): - if rel_path in index: - print(f" + {rel_path} [{mod['classification']}] axes={sum(mod['concept_axes'].values()):.1f}/14.0") - return - - print(f"[3/4] Saving index and manifest...") - output = save_index(index) - save_manifest(index) - print(f" Wrote {INDEX_PATH} ({os.path.getsize(INDEX_PATH) / 1024 / 1024:.1f} MB)") - print(f" Wrote {MANIFEST_PATH} ({os.path.getsize(MANIFEST_PATH) / 1024 / 1024:.1f} MB)") - - # Phase 4: Optional audit - if args.audit: - print() - print(f"[4/4] Running post-sync audit...") - spec = load_gitignore_spec() - audit_results = run_audit(output, spec) - - total_checked = audit_results["ok"] + len(audit_results["hash_mismatch"]) + len(audit_results["size_mismatch"]) + len(audit_results["missing_from_disk"]) - print(f" Files checked: {total_checked}") - print(f" OK: {audit_results['ok']}") - print(f" Missing from disk: {len(audit_results['missing_from_disk'])}") - print(f" Hash mismatches: {len(audit_results['hash_mismatch'])}") - print(f" Size mismatches: {len(audit_results['size_mismatch'])}") - - consistency_pct = (audit_results["ok"] / max(total_checked, 1)) * 100 - print(f" Consistency: {consistency_pct:.2f}%") - - if audit_results["ok"] == total_checked: - print(" PASS: 100% filesystem consistency confirmed.") - else: - print(" WARN: Inconsistencies detected. Review audit results above.") - - # Optional validation - if args.validate: - print() - print(f"[*] Launching Phase 12 validation suite...") - validate_script = BASE_DIR / "tools" / "scripts" / "validate_phase12.py" - if validate_script.exists(): - import subprocess - result = subprocess.run( - [sys.executable, str(validate_script)], - capture_output=False, - cwd=str(BASE_DIR), - ) - if result.returncode != 0: - print(f"[!] Phase 12 validation exited with code {result.returncode}") - else: - print(f"[!] Validation script not found at {validate_script}") - - elapsed = time.time() - t0 - print() - print(f"[*] Sync complete in {elapsed:.2f} seconds.") - print(f"[*] Total files indexed: {len(index)}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/substrate/sync_metadata_report.py b/5-Applications/tools-scripts/substrate/sync_metadata_report.py deleted file mode 100644 index 450e2a35..00000000 --- a/5-Applications/tools-scripts/substrate/sync_metadata_report.py +++ /dev/null @@ -1,118 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import json -from pathlib import Path -from typing import Any - - -def canonical_bytes(obj: Any) -> bytes: - return json.dumps(obj, sort_keys=True, ensure_ascii=False, separators=(",", ":")).encode("utf-8") - - -def compare(curated: dict[str, Any], canonical: dict[str, Any]) -> tuple[list[str], list[str], list[str]]: - curated_keys = set(curated) - canonical_keys = set(canonical) - - missing_in_curated = sorted(canonical_keys - curated_keys) - missing_in_canonical = sorted(curated_keys - canonical_keys) - - mismatched_nodes: list[str] = [] - for node_id in sorted(curated_keys & canonical_keys): - if canonical_bytes(curated[node_id]) != canonical_bytes(canonical[node_id]): - mismatched_nodes.append(node_id) - - return missing_in_curated, missing_in_canonical, mismatched_nodes - - -def load_json(path: Path) -> dict[str, Any]: - return json.loads(path.read_text(encoding="utf-8")) - - -def main() -> int: - root = Path(__file__).resolve().parent.parent - - parser = argparse.ArgumentParser( - description="Check and optionally sync metadata_report.json with regenerated canonical metadata." - ) - parser.add_argument( - "--curated", - type=Path, - default=root / "metadata_report.json", - help="Path to curated metadata report", - ) - parser.add_argument( - "--canonical", - type=Path, - default=root / "out" / "graph_os_decoded_metadata_regenerated.json", - help="Path to canonical regenerated metadata", - ) - parser.add_argument( - "--sync", - action="store_true", - help="If set, overwrite curated file with canonical bytes before checking", - ) - args = parser.parse_args() - - curated_path = args.curated - canonical_path = args.canonical - - if not curated_path.exists(): - print(f"[!] Curated file not found: {curated_path}") - return 2 - if not canonical_path.exists(): - print(f"[!] Canonical file not found: {canonical_path}") - return 2 - - if args.sync: - curated_path.write_bytes(canonical_path.read_bytes()) - print(f"[+] Synced curated from canonical: {curated_path}") - - curated = load_json(curated_path) - canonical = load_json(canonical_path) - - missing_in_curated, missing_in_canonical, mismatched_nodes = compare(curated, canonical) - raw_bytes_equal = curated_path.read_bytes() == canonical_path.read_bytes() - - print("[+] Check summary") - print(f" curated_nodes={len(curated)} canonical_nodes={len(canonical)}") - print(f" missing_in_curated={len(missing_in_curated)}") - print(f" missing_in_canonical={len(missing_in_canonical)}") - print(f" value_mismatch_nodes={len(mismatched_nodes)}") - print(f" raw_bytes_equal={raw_bytes_equal}") - - if missing_in_curated: - print("[!] Missing node ids in curated:") - for node_id in missing_in_curated: - print(f" - {node_id}") - - if missing_in_canonical: - print("[!] Missing node ids in canonical:") - for node_id in missing_in_canonical: - print(f" - {node_id}") - - if mismatched_nodes: - print("[!] Value-mismatched node ids:") - for node_id in mismatched_nodes: - print(f" - {node_id}") - - all_equal = ( - len(missing_in_curated) == 0 - and len(missing_in_canonical) == 0 - and len(mismatched_nodes) == 0 - ) - - if all_equal: - print("[+] Metadata is fully aligned.") - return 0 - - print("[!] Metadata is not fully aligned.") - return 1 - - -if __name__ == "__main__": - raise SystemExit(main()) diff --git a/5-Applications/tools-scripts/tsm/hyper_tsm_walk_qchem.py b/5-Applications/tools-scripts/tsm/hyper_tsm_walk_qchem.py deleted file mode 100644 index cd4ef12b..00000000 --- a/5-Applications/tools-scripts/tsm/hyper_tsm_walk_qchem.py +++ /dev/null @@ -1,92 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math -import json -import random - -class HyperAtom: - def __init__(self, z): - self.z = z - # Stability S based on qchem_map.md boundaries - if z <= 56: - self.s = 1.0 - (z / 1000.0) - elif 93 <= z <= 137: - self.s = 0.8 - (z - 92) * 0.016 - elif 138 <= z <= 229: - self.s = 0.59825 - else: - self.s = 0.0 - - # Hyper-Valence V (14D shell capacity = 14) - self.v = abs(14 - (z % 14 if z % 14 != 0 else 14)) - - def __repr__(self): - return f"Z={self.z}(S={self.s:.2f}, V={self.v})" - -def hyper_quantum_walk_discovery(max_z=229, max_atoms=3): - atoms = [HyperAtom(z) for z in range(1, max_z + 1) if HyperAtom(z).s > 0.05] - combinations = [] - - print(f"[*] Starting Hyper Quantum Walk across {len(atoms)} stable nodes...") - - # 1. Binary Combinations (A + B) - for i in range(len(atoms)): - for j in range(i, len(atoms)): - a, b = atoms[i], atoms[j] - # Stability check: Product of stabilities + interaction bonus - # If Z_sum is a fixed point (229, 137, 56), add bonus - z_sum = a.z + b.z - stability = a.s * b.s - if z_sum in [56, 137, 229]: - stability *= 1.2 - - if stability > 0.5: - reg_size = a.v + b.v - 2 # 2 valence slots used for the bond - combinations.append({ - "formula": f"Z{a.z}-Z{b.z}", - "stability": round(stability, 4), - "register_bits": max(0, reg_size) - }) - - # 2. Ternary Combinations (Limited search) - # We pick high-stability pairs and add a third atom - top_binaries = sorted(combinations, key=lambda x: x['stability'], reverse=True)[:50] - for comb in top_binaries: - z1, z2 = map(int, comb['formula'].replace('Z', '').split('-')) - for k in range(len(atoms)): - c = atoms[k] - z_sum = z1 + z2 + c.z - stability = comb['stability'] * c.s - if z_sum in [229]: stability *= 1.5 - - if stability > 0.45: - # Find the atoms to get their valences - a = HyperAtom(z1) - b = HyperAtom(z2) - reg_size = a.v + b.v + c.v - 4 # 4 valence slots for 2 bonds - combinations.append({ - "formula": f"Z{z1}-Z{z2}-Z{c.z}", - "stability": round(stability, 4), - "register_bits": max(0, reg_size) - }) - - return combinations - -if __name__ == "__main__": - results = hyper_quantum_walk_discovery(max_z=229, max_atoms=3) - - # Filter and sort by Register Size (Valence) - results = sorted(results, key=lambda x: x['register_bits'], reverse=True) - - print(f"\n[+] HQW Complete. Found {len(results)} stable combinations.") - print("\n--- TOP COMPUTATIONAL REGISTERS (by Bit Depth) ---") - for r in results[:20]: - print(f"[{r['formula']}] | Stability: {r['stability']} | REG Register: {r['register_bits']} bits") - - with open("hqw_atomic_combinations.json", "w") as f: - json.dump(results, f, indent=4) - print(f"\n[*] Manifest saved to hqw_atomic_combinations.json") diff --git a/5-Applications/tools-scripts/tsm/transpile_dustbowl_tsm.py b/5-Applications/tools-scripts/tsm/transpile_dustbowl_tsm.py deleted file mode 100644 index fcc01afb..00000000 --- a/5-Applications/tools-scripts/tsm/transpile_dustbowl_tsm.py +++ /dev/null @@ -1,91 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import json -import datetime -import os -import argparse -import sys -from pathlib import Path - -# Add project root to sys.path to import TSM_COMPILER -sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))) -try: - from TSM_COMPILER import TSM_Kernel -except ImportError: - # Fallback for direct execution - from TSM_COMPILER import TSM_Kernel - -def compile_dustbowl_to_logic_signal_substrate(scad_file): - print(f"\n[*] USAL-TRANSPILER: Processing '{scad_file}'") - - # Legacy data template - legacy_template = { - "kind": "physical_matrix_node", - "state": { - "node_type": "Dust Bowl Survival Engine - Stirling/DAC Hybrid", - "active_systems": [ - "Subterranean Moisture Trap", - "Gamma-Type Stirling Core", - "Scrap Kinematics (Bicycle Flywheel)", - "Glass-Jar Electrolysis Array" - ], - "materials_profile": { - "RustyMetal": "Fe2O3 Composite", - "DirtyCopper": "Cu / Cu2O", - "FadedLego": "Degraded ABS Plastic", - "GlassJar": "Silica Glass" - }, - "logic_signal_substrate_mode": "hardware_translation_with_gpgpu", - "gpgpu_bindings": { - "compute_backend": "CUDA_OPENCL_HYBRID", - "thermal_gradient_solver": "FiniteElement_GPU", - "kinematic_solver": "MuJoCo_RigidBody_Matrix" - } - }, - "lineage": { - "source_path": scad_file, - "compiler": "USAL-Transpiler v1.0", - "updated_utc": datetime.datetime.now(datetime.timezone.utc).isoformat() - } - } - - # Initialize Kernel - kernel = TSM_Kernel(substrate="silicon") - - # Absorb into manifold - out_file = scad_file.replace(".scad", ".logic_signal_substrate.json") - manifold_id = kernel.absorb(out_file, legacy_template) - - # Emit v3.2-USAL Manifest - recompiled_manifest = { - "logic_signal_substrate_version": "v3.2-USAL", - "isa_version": "ISA-v1", - "manifold_id": manifold_id, - "substrate_transparency": "ENABLED", - "stability_metric": kernel.surface.stability_metric, - "absorbed_state": kernel.manifold[out_file], - "legacy_reference": { - "original_file": scad_file, - "original_version": "logic_signal_substrate/1" - } - } - - with open(out_file, "w") as f: - json.dump(recompiled_manifest, f, indent=2) - - print(f"[Graph OS] -> ACK. Transpiled and absorbed '{scad_file}' into USAL object '{out_file}'.") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="Transpile SCAD to TSM") - parser.add_argument("scad_file", nargs='?', default="dust_bowl_stirling.scad", help="The SCAD file to transpile") - args = parser.parse_args() - - if os.path.exists(args.scad_file): - compile_dustbowl_to_logic_signal_substrate(args.scad_file) - else: - print(f"[ERROR] Source file {args.scad_file} not found.") \ No newline at end of file diff --git a/5-Applications/tools-scripts/tsm/tsm_asic_miner.py b/5-Applications/tools-scripts/tsm/tsm_asic_miner.py deleted file mode 100644 index 9f2e3150..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_asic_miner.py +++ /dev/null @@ -1,88 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Graph OS Quantum ASIC Emulator (BTC) -Leverages GPGPU Surface + TSM 0x02 (WAVE_FOLD) for accelerated SHA-256 mining. -Legacy demo surface with explicit loss-aware session policy and no fixed alpha bias. -""" - -import time -import hashlib -import json -import os -import sys -import random -from pathlib import Path - -ROOT = Path(__file__).resolve().parent.parent -sys.path.append(str(ROOT)) - -try: - from scripts.tsm_harness_compat import TSMKernel - from gpgpu_surface import get_surface -except ImportError: - from tsm_harness_compat import TSMKernel - from gpgpu_surface import get_surface - -try: - from scripts.market_action_policy import MarketActionPolicy -except ImportError: - from market_action_policy import MarketActionPolicy - -class QuantumAsicMiner: - def __init__(self, target_usd=50.0): - self.target_usd = target_usd - self.mined_btc = 0.0 - self.kernel = TSMKernel() - self.surface = get_surface() - self.policy = MarketActionPolicy.from_env(prefix="BTC_ACTION") - self.btc_price = 65000.0 # Mock BTC price - self.network_difficulty = 80.0e12 # Mock difficulty - self.destination_addr = "bc1qnxlnd8w6s8jec2fjg9qcvun86l9zkf3nfynahd" - - def emulate_asic_rounds(self, iterations=1000000): - """Simulate GPGPU-accelerated SHA-256 rounds with Quantum Folding.""" - # Accelerated simulation: each step represents a larger block of hashes - network_base_yield = 0.00005 # Accelerated for demo - yield_btc = network_base_yield - return yield_btc - - def run_mining_session(self): - target_btc = self.target_usd / self.btc_price - print(f"[*] Target: ${self.target_usd} (~{target_btc:.6f} BTC)") - print(f"[*] Destination: {self.destination_addr}") - print(f"[*] Session Policy: {self.policy.brief()}") - print("[*] Fixed alpha bias removed; mitigation must be explicit.") - - while self.mined_btc < target_btc: - session_yield = self.emulate_asic_rounds() - self.mined_btc += session_yield - - progress = (self.mined_btc / target_btc) * 100 - print(f"[MINING] Progress: {progress:.2f}% | Total Mined: {self.mined_btc:.8f} BTC") - - # Precision Attestation for every block-equivalent - self.kernel.execute([("0x03", [])]) # SYNC_Precision - - if progress >= 100: - break - - print(f"\n[+] MINING COMPLETE: Mined {self.mined_btc:.8f} BTC (${self.mined_btc * self.btc_price:.2f})") - - # Automated Swap via DeFi (Simulated) - self.execute_defi_swap() - - def execute_defi_swap(self): - print("[*] Initiating KYC-Compliant DeFi Swap: BTC -> ZEC") - # Route through Z-Pool for shielding - self.kernel.execute([("0x31", [self.mined_btc * (self.btc_price / 30.0), "u1faa8d637..."])]) - print("[+] Swap complete. Yield routed to Shielded Z-Pool.") - -if __name__ == "__main__": - miner = QuantumAsicMiner(target_usd=50.0) - miner.run_mining_session() diff --git a/5-Applications/tools-scripts/tsm/tsm_bitcoin_miner_full.py b/5-Applications/tools-scripts/tsm/tsm_bitcoin_miner_full.py deleted file mode 100644 index 6a58bf5d..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_bitcoin_miner_full.py +++ /dev/null @@ -1,1056 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -TSM-ISA Bitcoin Network Miner - Full Protocol Support -Implements complete Bitcoin network methods via TSM kernel -NO SIMULATION - Real Bitcoin mining with neuromorphic optimization -""" - -import asyncio -import json -import hashlib -import struct -import socket -import time -import os -import sys -import random -from pathlib import Path -from datetime import datetime -from decimal import Decimal -from typing import Optional, Dict, List, Any, Tuple, Callable -from dataclasses import dataclass, field -from enum import Enum - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Mock websockets for TSM harness -import types -sys.modules['websockets'] = types.ModuleType('websockets') - -from logic_signal_substrate_mcp_harness import TSMKernel, TermType - - -# ============================================================================ -# BITCOIN NETWORK CONSTANTS -# ============================================================================ - -# Network magic bytes -MAINNET_MAGIC = b'\xf9\xbe\xb4\xd9' -TESTNET_MAGIC = b'\x0b\x11\x09\x07' - -# Protocol version -PROTOCOL_VERSION = 70016 - -# Service bits -NODE_NETWORK = 1 -NODE_WITNESS = 8 - -# Message types -MSG_TX = 1 -MSG_BLOCK = 2 -MSG_FILTERED_BLOCK = 3 -MSG_COMPACT_BLOCK = 4 -MSG_WITNESS_TX = 1 | (1 << 30) -MSG_WITNESS_BLOCK = 2 | (1 << 30) - -# Stratum V2 constants -SV2_HANDSHAKE_VERSION = 2 -SV2_MAX_FRAME_SIZE = 1024 * 1024 - -# Mining constants -MAX_NONCE = 2**32 -GENESIS_BLOCK_HASH = "000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f" - - -# ============================================================================ -# TSM-ISA OPCODES FOR BITCOIN -# ============================================================================ - -class BitcoinOpCodes: - """Bitcoin-specific TSM-ISA opcodes""" - - # Core mining (0x40-0x4F) - INIT_MINER = "0x40" # Initialize mining device - SWITCH_ALGO = "0x41" # Switch mining algorithm - STOP_MINER = "0x42" # Stop mining - REPORT_HASHRATE = "0x43" # Report hashrate - SUBMIT_SHARE = "0x44" # Submit share to pool - GET_MINING_JOB = "0x45" # Get new mining job - - # Stratum protocol (0x50-0x5F) - STRATUM_CONNECT = "0x50" # Connect to stratum pool - STRATUM_SUBSCRIBE = "0x51" # Subscribe to pool - STRATUM_AUTHORIZE = "0x52" # Authorize worker - STRATUM_SUBMIT = "0x53" # Submit share - STRATUM_NOTIFY = "0x54" # Handle job notification - - # Block operations (0x60-0x6F) - BUILD_BLOCK = "0x60" # Build block header - VALIDATE_BLOCK = "0x61" # Validate block - COMPUTE_MERKLE = "0x62" # Compute merkle root - VERIFY_TX = "0x63" # Verify transaction - - # Neuromorphic optimization (0x70-0x7F) - NEURO_NONCE_SEARCH = "0x70" # Neuromorphic nonce search - NEURO_ENTROPY_INJECT = "0x71" # Inject entropy - NEURO_SURFACE_UPDATE = "0x72" # Update neuromorphic surface - - # Network operations (0x80-0x8F) - PEER_CONNECT = "0x80" # Connect to peer - SEND_INV = "0x81" # Send inventory - SEND_GETDATA = "0x82" # Request data - SEND_HEADERS = "0x83" # Send headers - - -# ============================================================================ -# BITCOIN DATA STRUCTURES -# ============================================================================ - -@dataclass -class CTransaction: - """Bitcoin transaction""" - version: int - vin: List[Dict] # Inputs - vout: List[Dict] # Outputs - locktime: int - witness: List[List[bytes]] = field(default_factory=list) - - def serialize(self) -> bytes: - """Serialize transaction to bytes""" - result = struct.pack(' bytes: - """Serialize transaction input""" - result = txin.get('hash', b'\x00' * 32) - result += struct.pack(' bytes: - """Serialize transaction output""" - result = struct.pack(' bytes: - """Double SHA256 hash""" - h = hashlib.sha256(self.serialize()).digest() - return hashlib.sha256(h).digest() - - def txid(self) -> str: - """Transaction ID as hex string""" - return self.hash()[::-1].hex() - - -@dataclass -class CBlockHeader: - """Bitcoin block header""" - version: int - hashPrevBlock: bytes # 32 bytes - hashMerkleRoot: bytes # 32 bytes - nTime: int # Unix timestamp - nBits: int # Difficulty target - nNonce: int - - def serialize(self) -> bytes: - """Serialize header to bytes (little-endian)""" - return ( - struct.pack(' bytes: - """Double SHA256 hash of header""" - h = hashlib.sha256(self.serialize()).digest() - return hashlib.sha256(h).digest() - - def hash_uint256(self) -> int: - """Hash as 256-bit integer""" - return int.from_bytes(self.hash()[::-1], 'big') - - def check_proof_of_work(self, nBits: int) -> bool: - """Check if hash meets target""" - target = compact_to_target(nBits) - return self.hash_uint256() <= target - - -def compact_to_target(compact: int) -> int: - """Convert compact difficulty to target""" - exponent = compact >> 24 - mantissa = compact & 0x00FFFFFF - - if exponent <= 3: - return mantissa >> (8 * (3 - exponent)) - else: - return mantissa << (8 * (exponent - 3)) - - -def target_to_difficulty(target: int) -> float: - """Convert target to difficulty""" - genesis_target = 0xFFFF * 2**(8*(0x1d - 3)) - return genesis_target / target if target > 0 else 0 - - -# ============================================================================ -# STRATUM V1 CLIENT -# ============================================================================ - -class StratumV1Client: - """Stratum V1 mining protocol client""" - - def __init__(self, pool_url: str, pool_port: int, username: str, password: str = "x"): - self.pool_url = pool_url.replace("stratum+tcp://", "").replace("stratum2+tcp://", "") - self.pool_port = pool_port - self.username = username - self.password = password - self.socket: Optional[socket.socket] = None - self.message_id = 0 - self.current_job: Optional[Dict] = None - self.connected = False - self.subscription_id: Optional[str] = None - self.extranonce1: Optional[str] = None - self.extranonce2_size: Optional[int] = None - self.version_mask: Optional[str] = None - - def connect(self, timeout: int = 30) -> bool: - """Connect to mining pool""" - try: - self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) - self.socket.settimeout(timeout) - self.socket.connect((self.pool_url, self.pool_port)) - self.connected = True - print(f"[+] Connected to {self.pool_url}:{self.pool_port}") - return True - except Exception as e: - print(f"[-] Connection failed: {e}") - return False - - def disconnect(self): - """Disconnect from pool""" - if self.socket: - try: - self.socket.close() - except Exception: - pass - self.connected = False - - def _send_request(self, method: str, params: List[Any]) -> Optional[Dict]: - """Send Stratum request and wait for response""" - self.message_id += 1 - request = { - "id": self.message_id, - "method": method, - "params": params - } - message = json.dumps(request) + "\n" - - try: - self.socket.sendall(message.encode()) - - # Read response with timeout - response = b"" - self.socket.settimeout(10) - while b"\n" not in response: - chunk = self.socket.recv(4096) - if not chunk: - raise ConnectionError("Connection closed by pool") - response += chunk - - return json.loads(response.decode().strip()) - except Exception as e: - print(f"[-] Request error: {e}") - return None - - def subscribe(self) -> bool: - """Subscribe to mining notifications""" - try: - response = self._send_request("mining.subscribe", []) - if response and response.get("result"): - result = response["result"] - self.subscription_id = result[0] if len(result) > 0 else None - self.extranonce1 = result[1] if len(result) > 1 else "" - self.extranonce2_size = result[2] if len(result) > 2 else 4 - print(f"[+] Subscribed: extranonce1={self.extranonce1}, size={self.extranonce2_size}") - return True - return False - except Exception as e: - print(f"[-] Subscribe failed: {e}") - return False - - def authorize(self) -> bool: - """Authorize worker""" - try: - response = self._send_request("mining.authorize", [self.username, self.password]) - if response and response.get("result"): - print(f"[+] Authorized: {self.username}") - return True - print(f"[-] Authorization failed: {response}") - return False - except Exception as e: - print(f"[-] Authorization error: {e}") - return False - - def submit_share(self, job_id: str, extranonce2: str, ntime: str, nonce: str) -> bool: - """Submit share to pool""" - try: - response = self._send_request("mining.submit", [ - self.username, - job_id, - extranonce2, - ntime, - nonce - ]) - - if response: - if response.get("result"): - print(f" [✓] Share ACCEPTED!") - return True - else: - error = response.get("error", ["Unknown"])[1] if response.get("error") else "Unknown" - print(f" [✗] Share REJECTED: {error}") - return False - return False - except Exception as e: - print(f"[-] Submit error: {e}") - return False - - def listen_for_jobs(self, callback: Callable) -> None: - """Listen for mining.job notifications (non-blocking)""" - if not self.socket or not self.connected: - return - - self.socket.settimeout(0.1) - try: - data = self.socket.recv(4096).decode().strip() - if data: - for line in data.split("\n"): - if line: - try: - msg = json.loads(line) - if msg.get("method") == "mining.notify": - params = msg.get("params", []) - callback(params) - elif msg.get("id") is None and msg.get("method") is None: - # This might be a share response - pass - except json.JSONDecodeError: - pass - except socket.timeout: - pass - except Exception as e: - print(f"[-] Listen error: {e}") - - -# ============================================================================ -# TSM BITCOIN MINER -# ============================================================================ - -class TSMBitcoinMiner: - """ - TSM-ISA Bitcoin Miner with Full Network Support - Implements all Bitcoin network methods via TSM kernel - """ - - def __init__(self, pool_url: str, pool_port: int, username: str, password: str = "x"): - self.pool_url = pool_url - self.pool_port = pool_port - self.username = username - self.password = password - - # Initialize TSM kernel - self.kernel = TSMKernel() - - # Initialize Stratum client - self.stratum = StratumV1Client(pool_url, pool_port, username, password) - - # Mining state - self.current_job: Optional[Dict] = None - self.shares_accepted = 0 - self.shares_rejected = 0 - self.hashes_computed = 0 - self.start_time: Optional[float] = None - - # Neuromorphic surface manifold - self.surface_manifold_id: Optional[str] = None - self.nonce_entropy = 0.0 - - # Grey Goo Safety Protocol - self.safety_active = True - self.thermal_entropy = 0.0 - self.max_entropy = 1.0 - self.consecutive_warnings = 0 - - # TSM-ISA opcode registry - self.opcode_handlers = self._register_opcodes() - - def _register_opcodes(self) -> Dict[str, Callable]: - """Register TSM-ISA Bitcoin opcode handlers""" - return { - # Core mining - BitcoinOpCodes.INIT_MINER: self._op_init_miner, - BitcoinOpCodes.SWITCH_ALGO: self._op_switch_algo, - BitcoinOpCodes.STOP_MINER: self._op_stop_miner, - BitcoinOpCodes.REPORT_HASHRATE: self._op_report_hashrate, - BitcoinOpCodes.SUBMIT_SHARE: self._op_submit_share, - BitcoinOpCodes.GET_MINING_JOB: self._op_get_mining_job, - - # Stratum protocol - BitcoinOpCodes.STRATUM_CONNECT: self._op_stratum_connect, - BitcoinOpCodes.STRATUM_SUBSCRIBE: self._op_stratum_subscribe, - BitcoinOpCodes.STRATUM_AUTHORIZE: self._op_stratum_authorize, - BitcoinOpCodes.STRATUM_SUBMIT: self._op_stratum_submit, - - # Block operations - BitcoinOpCodes.BUILD_BLOCK: self._op_build_block, - BitcoinOpCodes.VALIDATE_BLOCK: self._op_validate_block, - BitcoinOpCodes.COMPUTE_MERKLE: self._op_compute_merkle, - BitcoinOpCodes.VERIFY_TX: self._op_verify_tx, - - # Neuromorphic optimization - BitcoinOpCodes.NEURO_NONCE_SEARCH: self._op_neuro_nonce_search, - BitcoinOpCodes.NEURO_ENTROPY_INJECT: self._op_neuro_entropy_inject, - BitcoinOpCodes.NEURO_SURFACE_UPDATE: self._op_neuro_surface_update, - } - - def execute_opcode(self, opcode: str, args: List[Any]) -> Any: - """Execute TSM-ISA opcode""" - if opcode in self.opcode_handlers: - return self.opcode_handlers[opcode](args) - return self.kernel.execute([(opcode, args)]) - - # ========================================================================= - # TSM-ISA OPCODE IMPLEMENTATIONS - # ========================================================================= - - def _op_init_miner(self, args: List[Any]) -> Dict: - """[0x40] Initialize mining device""" - config = args[0] if args else {} - - # Initialize neuromorphic surface - surface_data = json.dumps({ - "type": "neuromorphic_bitcoin_surface", - "nonce_space": MAX_NONCE, - "optimization": "soliton_collision", - "safety": "grey_goo_v2.1", - "config": config - }) - - self.surface_manifold_id = self.kernel.absorb_bh(surface_data, { - "module": "NEUROMORPHIC_BTC_MINER" - }) - - return { - "success": True, - "surface_manifold": self.surface_manifold_id[:16] + "...", - "timestamp": time.time() - } - - def _op_switch_algo(self, args: List[Any]) -> Dict: - """[0x41] Switch mining algorithm""" - algo = args[0] if args else "SHA256D" - print(f"[0x41] Switch algorithm: {algo}") - return {"success": True, "algorithm": algo} - - def _op_stop_miner(self, args: List[Any]) -> Dict: - """[0x42] Stop mining""" - print("[0x42] Stop miner") - self.stratum.disconnect() - return {"success": True, "status": "stopped"} - - def _op_report_hashrate(self, args: List[Any]) -> Dict: - """[0x43] Report hashrate""" - runtime = time.time() - self.start_time if self.start_time else 1 - hashrate = self.hashes_computed / runtime if runtime > 0 else 0 - return { - "hashrate_hps": hashrate, - "hashes": self.hashes_computed, - "runtime_s": runtime - } - - def _op_submit_share(self, args: List[Any]) -> Dict: - """[0x44] Submit share to pool""" - if len(args) < 4: - return {"success": False, "error": "Invalid share data"} - - job_id, extranonce2, ntime, nonce = args[:4] - - success = self.stratum.submit_share(job_id, extranonce2, ntime, nonce) - - if success: - self.shares_accepted += 1 - # [0x09] LEDGER_COMMIT - Commit share - share_data = json.dumps({ - "type": "bitcoin_share", - "job_id": job_id, - "nonce": nonce, - "timestamp": time.time() - }) - share_id = self.kernel.absorb_bh(share_data, {"type": "btc_share"}) - self.kernel.ledger_commit(share_id, TermType.PERMANENT) - else: - self.shares_rejected += 1 - - return {"success": success} - - def _op_get_mining_job(self, args: List[Any]) -> Dict: - """[0x45] Get new mining job""" - return {"job": self.current_job, "success": self.current_job is not None} - - def _op_stratum_connect(self, args: List[Any]) -> Dict: - """[0x50] Connect to stratum pool""" - url = args[0] if args else self.pool_url - port = args[1] if len(args) > 1 else self.pool_port - - self.stratum.pool_url = url.replace("stratum+tcp://", "") - self.stratum.pool_port = port - - if self.stratum.connect(): - return {"success": True, "connected": f"{url}:{port}"} - return {"success": False, "error": "Connection failed"} - - def _op_stratum_subscribe(self, args: List[Any]) -> Dict: - """[0x51] Subscribe to pool""" - if self.stratum.subscribe(): - return { - "success": True, - "extranonce1": self.stratum.extranonce1, - "extranonce2_size": self.stratum.extranonce2_size - } - return {"success": False} - - def _op_stratum_authorize(self, args: List[Any]) -> Dict: - """[0x52] Authorize worker""" - user = args[0] if args else self.username - password = args[1] if len(args) > 1 else self.password - - self.stratum.username = user - self.stratum.password = password - - if self.stratum.authorize(): - return {"success": True, "user": user} - return {"success": False} - - def _op_stratum_submit(self, args: List[Any]) -> Dict: - """[0x53] Submit share""" - return self._op_submit_share(args) - - def _op_build_block(self, args: List[Any]) -> Dict: - """[0x60] Build block header""" - if not self.current_job: - return {"success": False, "error": "No job"} - - job = self.current_job - - # Build merkle root - merkle_root = self._compute_merkle_root( - job.get('coinbase1', ''), - job.get('coinbase2', ''), - job.get('merkle_branch', []) - ) - - # Parse prev hash - prev_hash = bytes.fromhex(job['prev_hash'])[::-1] - - header = CBlockHeader( - version=int(job['version'], 16), - hashPrevBlock=prev_hash, - hashMerkleRoot=bytes.fromhex(merkle_root), - nTime=job['nbits'], - nBits=job['nbits'], - nNonce=0 - ) - - return { - "success": True, - "header_hex": header.serialize().hex(), - "merkle_root": merkle_root - } - - def _op_validate_block(self, args: List[Any]) -> Dict: - """[0x61] Validate block""" - header_hex = args[0] if args else "" - - try: - header_bytes = bytes.fromhex(header_hex) - header = CBlockHeader( - version=struct.unpack(' Dict: - """[0x62] Compute merkle root""" - coinbase1 = args[0] if args else "" - coinbase2 = args[1] if len(args) > 1 else "" - merkle_branch = args[2] if len(args) > 2 else [] - - merkle_root = self._compute_merkle_root(coinbase1, coinbase2, merkle_branch) - - return {"success": True, "merkle_root": merkle_root} - - def _op_verify_tx(self, args: List[Any]) -> Dict: - """[0x63] Verify transaction""" - tx_hex = args[0] if args else "" - - try: - tx_bytes = bytes.fromhex(tx_hex) - # Basic validation - is_valid = len(tx_bytes) > 0 - - return { - "success": True, - "valid": is_valid, - "txid": hashlib.sha256(hashlib.sha256(tx_bytes).digest()).digest()[::-1].hex() - } - except Exception as e: - return {"success": False, "error": str(e)} - - def _op_neuro_nonce_search(self, args: List[Any]) -> Dict: - """[0x70] Neuromorphic nonce search""" - job_data = args[0] if args else self.current_job - - if not job_data: - return {"success": False, "error": "No job"} - - # [0x04] OMNI_BAL - Optimize for discovery - self.kernel.omni_bal("discovery") - - # Generate input vector from job data - prev_hash = job_data.get('prev_hash', '0' * 64) - - # Handle nbits as string or int - nbits_raw = job_data.get('nbits', '1d00ffff') - nbits_int = int(nbits_raw, 16) if isinstance(nbits_raw, str) else nbits_raw - - version_raw = job_data.get('version', '00000002') - version_int = int(version_raw, 16) if isinstance(version_raw, str) else version_raw - - input_vector = [ - int(prev_hash[:8], 16) / 2**32, - int(prev_hash[8:16], 16) / 2**32, - (nbits_int % 86400) / 86400, - nbits_int / 2**32, - version_int / 2**32 - ] - - # [0x06] EVOLVE - Evolve nonce candidates - evolve_data = json.dumps({ - "input_vector": input_vector, - "nonce_space": MAX_NONCE, - "candidates": 10000 - }) - - if self.surface_manifold_id: - self.kernel.evolve(self.surface_manifold_id, evolve_data) - - # Generate candidates using neuromorphic entropy - random.seed(int(time.time() * 1000000) % MAX_NONCE) - candidates = [random.randint(0, MAX_NONCE - 1) for _ in range(10000)] - - # [0x08] STARK_PROVE - Proof of work attempt - self.kernel.stark_prove(f"btc_neuro_attempt_{time.time()}") - - # Track entropy - self.thermal_entropy = min(self.thermal_entropy + 0.01, self.max_entropy) - - return { - "success": True, - "candidates": len(candidates), - "entropy": self.thermal_entropy - } - - def _op_neuro_entropy_inject(self, args: List[Any]) -> Dict: - """[0x71] Inject entropy""" - entropy_source = args[0] if args else "random" - - if entropy_source == "random": - new_entropy = random.random() - elif entropy_source == "time": - new_entropy = (time.time() % 1) - else: - new_entropy = 0.5 - - self.nonce_entropy = new_entropy - - return {"success": True, "entropy": new_entropy} - - def _op_neuro_surface_update(self, args: List[Any]) -> Dict: - """[0x72] Update neuromorphic surface""" - params = args[0] if args else {} - - # Update surface with new parameters - if self.surface_manifold_id: - surface_data = json.dumps({ - "type": "update", - "params": params - }) - self.kernel.evolve(self.surface_manifold_id, surface_data) - - return {"success": True} - - # ========================================================================= - # HELPER METHODS - # ========================================================================= - - def _compute_merkle_root(self, coinbase1: str, coinbase2: str, merkle_branch: List[str]) -> str: - """Compute merkle root from coinbase and branch""" - # Build coinbase transaction - extranonce = self.stratum.extranonce1 or "" - coinbase = coinbase1 + extranonce + coinbase2 - coinbase_bytes = bytes.fromhex(coinbase) - coinbase_hash = hashlib.sha256(hashlib.sha256(coinbase_bytes).digest()).digest() - - # Build merkle root - merkle_root = coinbase_hash - for hash_hex in merkle_branch: - hash_bytes = bytes.fromhex(hash_hex)[::-1] - merkle_root = hashlib.sha256(hashlib.sha256(merkle_root + hash_bytes).digest()).digest() - - return merkle_root[::-1].hex() - - def _handle_job_notification(self, params: List[Any]) -> None: - """Handle mining.notify from pool""" - if not params or len(params) < 7: - print(f" [!] Invalid job params: {params}") - return - - try: - job_id = params[0] - prev_hash = params[1] - coinbase1 = params[2] - coinbase2 = params[3] - merkle_branch = params[4] if len(params) > 4 else [] - version = params[5] if len(params) > 5 else '00000002' - nbits = params[6] if len(params) > 6 else '1d00ffff' - ntime = params[7] if len(params) > 7 else '00000000' - clean_jobs = params[8] if len(params) > 8 else False - - self.current_job = { - "job_id": job_id, - "prev_hash": prev_hash, - "coinbase1": coinbase1, - "coinbase2": coinbase2, - "version": version, - "nbits": nbits, - "ntime": ntime, - "merkle_branch": merkle_branch, - "clean_jobs": clean_jobs - } - - print(f" [JOB] Received job {job_id[:8]}...") - - # Mine this job - self._mine_job() - except Exception as e: - print(f" [!] Job handler error: {e}") - - def _mine_job(self) -> None: - """Mine current job using neuromorphic optimization""" - if not self.current_job: - return - - job = self.current_job - - # [0x70] NEURO_NONCE_SEARCH - neuro_result = self.execute_opcode(BitcoinOpCodes.NEURO_NONCE_SEARCH, [job]) - - if not neuro_result.get("success"): - return - - # Build merkle root - merkle_root = self._compute_merkle_root( - job.get('coinbase1', ''), - job.get('coinbase2', ''), - job.get('merkle_branch', []) - ) - - # Calculate target from nbits (difficulty) - nbits = int(job['nbits'], 16) if isinstance(job['nbits'], str) else job['nbits'] - target = compact_to_target(nbits) - - print(f" Mining job {job['job_id'][:8]}... (difficulty: {hex(nbits)})") - - # Try nonce candidates - random.seed(int(time.time() * 1000000) % MAX_NONCE) - - for _ in range(neuro_result.get("candidates", 10000)): - nonce = random.randint(0, MAX_NONCE - 1) - - # Build header - version = int(job['version'], 16) if isinstance(job['version'], str) else job['version'] - ntime = int(job['ntime'], 16) if isinstance(job['ntime'], str) else job['ntime'] - - header = CBlockHeader( - version=version, - hashPrevBlock=bytes.fromhex(job['prev_hash'])[::-1], - hashMerkleRoot=bytes.fromhex(merkle_root), - nTime=ntime, - nBits=nbits, - nNonce=nonce - ) - - # Check if valid share - if header.hash_uint256() <= target: - # [0x44] SUBMIT_SHARE - extranonce2 = "00" * (self.stratum.extranonce2_size or 4) - ntime_hex = format(ntime, '08x') - nonce_hex = format(nonce, '08x') - - self.execute_opcode(BitcoinOpCodes.SUBMIT_SHARE, [ - job['job_id'], - extranonce2, - ntime_hex, - nonce_hex - ]) - - self.hashes_computed += 1 - - # Grey Goo safety check - if not self._safety_check(): - print("[!] Grey Goo safety triggered - throttling") - time.sleep(0.1) - self.thermal_entropy *= 0.1 - - def _safety_check(self) -> bool: - """Grey Goo Safety Protocol""" - if not self.safety_active: - return True - - if self.thermal_entropy > 0.9: - self.consecutive_warnings += 1 - print(f"[!] High entropy: {self.thermal_entropy:.4f} (warning {self.consecutive_warnings})") - - if self.consecutive_warnings >= 3: - print("[!] EMERGENCY DECOHERENCE - Stopping mining") - self.execute_opcode(BitcoinOpCodes.STOP_MINER, []) - return False - else: - self.consecutive_warnings = 0 - - return True - - # ========================================================================= - # MAIN MINING LOOP - # ========================================================================= - - def initialize(self) -> bool: - """Initialize miner""" - print("=" * 70) - print(" TSM-ISA BITCOIN MINER v1.0") - print(" FULL NETWORK PROTOCOL SUPPORT") - print("=" * 70) - print(f" Pool: {self.pool_url}:{self.pool_port}") - print(f" User: {self.username}") - print(f" Start: {datetime.now().isoformat()}") - print("=" * 70) - print() - - # [0x50] STRATUM_CONNECT - print("[STEP 1] Connecting to Pool...") - result = self.execute_opcode(BitcoinOpCodes.STRATUM_CONNECT, []) - if not result.get("success"): - return False - - # [0x51] STRATUM_SUBSCRIBE - print("[STEP 2] Subscribing to Stratum...") - result = self.execute_opcode(BitcoinOpCodes.STRATUM_SUBSCRIBE, []) - if not result.get("success"): - return False - - # [0x52] STRATUM_AUTHORIZE - print("[STEP 3] Authorizing Worker...") - result = self.execute_opcode(BitcoinOpCodes.STRATUM_AUTHORIZE, []) - if not result.get("success"): - return False - - # [0x40] INIT_MINER - print("[STEP 4] Initializing Neuromorphic Surface...") - result = self.execute_opcode(BitcoinOpCodes.INIT_MINER, [{}]) - print(f" ✓ Surface manifold: {result.get('surface_manifold', 'N/A')}") - - print() - print("[+] Miner initialized and ready") - return True - - def run(self, duration_seconds: int = 300) -> Dict: - """Run miner for specified duration""" - self.start_time = time.time() - end_time = self.start_time + duration_seconds - - print() - print(f"[MINING] Running for {duration_seconds} seconds...") - print() - - while time.time() < end_time and self.stratum.connected: - # Listen for jobs - self.stratum.listen_for_jobs(self._handle_job_notification) - - if not self.current_job: - time.sleep(0.1) - continue - - time.sleep(0.01) - - return self.generate_report() - - def generate_report(self) -> Dict: - """Generate mining report""" - runtime = time.time() - self.start_time if self.start_time else 1 - hashrate = self.hashes_computed / runtime if runtime > 0 else 0 - - return { - "success": True, - "timestamp": datetime.now().isoformat(), - "pool": f"{self.pool_url}:{self.pool_port}", - "username": self.username, - "runtime_seconds": runtime, - "hashes_computed": self.hashes_computed, - "hashrate_hps": hashrate, - "shares_accepted": self.shares_accepted, - "shares_rejected": self.shares_rejected, - "surface_manifold": self.surface_manifold_id[:16] if self.surface_manifold_id else None, - "safety_active": self.safety_active, - "final_entropy": self.thermal_entropy, - "logic_signal_substrate_opcodes_used": list(self.opcode_handlers.keys()) - } - - def shutdown(self): - """Graceful shutdown""" - print() - print("[SHUTDOWN] Closing connections...") - self.execute_opcode(BitcoinOpCodes.STOP_MINER, []) - print("[+] Miner stopped") - - -# ============================================================================ -# MAIN ENTRY POINT -# ============================================================================ - -def main(): - import argparse - - parser = argparse.ArgumentParser(description="TSM-ISA Bitcoin Miner") - parser.add_argument("--pool", type=str, default="stratum+tcp://stratum.braiins.com", help="Pool URL") - parser.add_argument("--port", type=int, default=3333, help="Pool port") - parser.add_argument("--user", type=str, required=True, help="Pool username") - parser.add_argument("--pass", dest="password", type=str, default="x", help="Pool password") - parser.add_argument("--duration", type=int, default=300, help="Mining duration (seconds)") - parser.add_argument("--output", type=str, default=None, help="Output report file") - args = parser.parse_args() - - # Create miner - miner = TSMBitcoinMiner( - pool_url=args.pool, - pool_port=args.port, - username=args.user, - password=args.password - ) - - try: - # Initialize - if not miner.initialize(): - print("[-] Failed to initialize miner") - return 1 - - # Run mining - report = miner.run(duration_seconds=args.duration) - - # Print report - print() - print("=" * 70) - print(" MINING REPORT") - print("=" * 70) - print(f" Runtime: {report['runtime_seconds']:.1f}s") - print(f" Hashes: {report['hashes_computed']:,}") - print(f" Hashrate: {report['hashrate_hps']:.0f} H/s") - print(f" Shares Accepted: {report['shares_accepted']}") - print(f" Shares Rejected: {report['shares_rejected']}") - print(f" Safety Protocol: {'ACTIVE' if report['safety_active'] else 'INACTIVE'}") - print(f" TSM Opcodes: {len(report['logic_signal_substrate_opcodes_used'])} registered") - print("=" * 70) - - # Save report - output_path = args.output or ROOT / "out" / "btc_logic_signal_substrate_mining_report.json" - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, "w") as f: - json.dump(report, f, indent=2) - f.write("\n") - - print(f"[+] Report saved to: {output_path}") - - return 0 - - except KeyboardInterrupt: - print("\n[!] Interrupted by user") - return 0 - except Exception as e: - print(f"[ERROR] {e}") - import traceback - traceback.print_exc() - return 1 - finally: - miner.shutdown() - - -if __name__ == "__main__": - sys.exit(main()) diff --git a/5-Applications/tools-scripts/tsm/tsm_bitcoind_regtest.py b/5-Applications/tools-scripts/tsm/tsm_bitcoind_regtest.py deleted file mode 100644 index 4e49033c..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_bitcoind_regtest.py +++ /dev/null @@ -1,193 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== - -from __future__ import annotations - -# [WARDEN BOUNDARY ENFORCEMENT INJECTED] -import sys -import os -try: - from io_harness_compat import spawn_isolated_process, fetch_network_resource -except ImportError: - sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))) - from io_harness_compat import spawn_isolated_process, fetch_network_resource - -#!/usr/bin/env python3 -"""Small helper to run a local `bitcoind` regtest instance and inject it into -the omnitoken/TSM surface so the network can "see" a bitcoind node. - -Usage examples: - python 5-Applications/scripts/logic_signal_substrate_bitcoind_regtest.py --status - python 5-Applications/scripts/logic_signal_substrate_bitcoind_regtest.py --start --datadir 5-Applications/out/bitcoind_regtest - python 5-Applications/scripts/logic_signal_substrate_bitcoind_regtest.py --generate-blocks 101 --datadir 5-Applications/out/bitcoind_regtest - python 5-Applications/scripts/logic_signal_substrate_bitcoind_regtest.py --inject-surface --datadir 5-Applications/out/bitcoind_regtest - -This script does NOT install Bitcoin Core; it checks for `bitcoind` and -`bitcoin-cli` on PATH and prints instructions if they're missing. -""" - -import argparse -import json -import shutil -# import subprocess (REMOVED BY WARDEN) -import time -from pathlib import Path -from typing import Dict, Any, Optional - -ROOT = Path(__file__).resolve().parent.parent - -try: - from scripts.weld_omnitoken_surface import utc_now, assert_surface_write_safe, OMNI_SURFACE_PATH -except Exception: - from weld_omnitoken_surface import utc_now, assert_surface_write_safe, OMNI_SURFACE_PATH # type: ignore - - -def which_binary(name: str) -> Optional[str]: - return shutil.which(name) - - -def run_cmd(cmd: list, timeout: int = 30) -> subprocess.CompletedProcess: - return subprocess.run(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, timeout=timeout, text=True) - - -def is_bitcoind_running(datadir: Path) -> bool: - cli = which_binary("bitcoin-cli") - if not cli: - return False - try: - cp = run_cmd([cli, "-regtest", f"-datadir={str(datadir)}", "getblockchaininfo"], timeout=5) - return cp.returncode == 0 - except Exception: - return False - - -def start_bitcoind(datadir: Path) -> bool: - bd = which_binary("bitcoind") - if not bd: - print("bitcoind not found on PATH") - return False - datadir.mkdir(parents=True, exist_ok=True) - cmd = [bd, "-regtest", f"-datadir={str(datadir)}", "-daemon"] - try: - cp = run_cmd(cmd, timeout=10) - if cp.returncode != 0: - print("bitcoind start failed:", cp.stderr) - return False - except Exception as e: - print("Error starting bitcoind:", e) - return False - # wait for RPC ready - for _ in range(20): - if is_bitcoind_running(datadir): - return True - time.sleep(0.5) - return is_bitcoind_running(datadir) - - -def stop_bitcoind(datadir: Path) -> bool: - cli = which_binary("bitcoin-cli") - if not cli: - print("bitcoin-cli not found on PATH") - return False - cp = run_cmd([cli, f"-datadir={str(datadir)}", "stop"], timeout=10) - return cp.returncode == 0 - - -def generate_blocks(datadir: Path, blocks: int, address: Optional[str] = None) -> Dict[str, Any]: - cli = which_binary("bitcoin-cli") - if not cli: - raise RuntimeError("bitcoin-cli not found") - if not address: - # ensure a wallet exists and getnewaddress - run_cmd([cli, "-regtest", f"-datadir={str(datadir)}", "createwallet", "logic_signal_substrate_temp_wallet"]) # ignore errors - cp_addr = run_cmd([cli, "-regtest", f"-datadir={str(datadir)}", "-rpcwallet=logic_signal_substrate_temp_wallet", "getnewaddress"], timeout=10) - address = cp_addr.stdout.strip() or "" - cp = run_cmd([cli, "-regtest", f"-datadir={str(datadir)}", "-rpcwallet=logic_signal_substrate_temp_wallet", "generatetoaddress", str(blocks), address], timeout=60) - return {"returncode": cp.returncode, "stdout": cp.stdout, "stderr": cp.stderr} - - -def inject_surface(datadir: Path, rpc_endpoint: str) -> None: - if not OMNI_SURFACE_PATH.exists(): - print("OMNI surface not found; cannot inject bitcoind domain") - return - try: - surface = json.loads(OMNI_SURFACE_PATH.read_text(encoding="utf-8")) - except Exception: - print("Failed to read/parse OMNI surface; aborting injection") - return - - surface_bus: Dict[str, Any] = dict(surface.get("surface_bus") or {}) - domains: Dict[str, Any] = dict(surface_bus.get("domains") or {}) - - domains["bitcoind_regtest"] = { - "domain": "bitcoind_regtest", - "direction": "bidirectional", - "transport": "bitcoin-rpc", - "rpc_endpoint": rpc_endpoint, - "datadir": str(datadir), - "updated_utc": utc_now(), - } - - surface_bus["schema"] = str(surface_bus.get("schema") or "omnitoken-surface-bus/v1") - surface_bus["agnostic"] = True - surface_bus["domains"] = domains - surface["surface_bus"] = surface_bus - surface["updated_utc"] = utc_now() - - assert_surface_write_safe(surface, scope="bitcoind_regtest_injection") - OMNI_SURFACE_PATH.write_text(json.dumps(surface, indent=2) + "\n", encoding="utf-8") - print("Injected bitcoind_regtest into surface:", OMNI_SURFACE_PATH) - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__) - ap.add_argument("--datadir", default=str(Path("out") / "bitcoind_regtest"), help="regtest datadir") - ap.add_argument("--start", action="store_true", help="Start bitcoind (regtest)") - ap.add_argument("--stop", action="store_true", help="Stop bitcoind") - ap.add_argument("--status", action="store_true", help="Check bitcoind status") - ap.add_argument("--generate-blocks", type=int, default=0, help="Generate N blocks (regtest) using generatetoaddress") - ap.add_argument("--inject-surface", action="store_true", help="Inject bitcoind_regtest domain into OMNI surface") - ap.add_argument("--rpc-endpoint", default="http://127.0.0.1:18443", help="RPC endpoint (for surface injection)") - args = ap.parse_args() - - datadir = Path(args.datadir) - - bd = which_binary("bitcoind") - bc = which_binary("bitcoin-cli") - if not bd or not bc: - print("Missing Bitcoin Core binaries on PATH.") - print("Install on Debian/Ubuntu: sudo apt install bitcoind bitcoin-qt (or download binaries from bitcoin.org)") - print("Or use your OS package manager / download prebuilt binaries.") - - if args.status: - print("bitcoind:", bd) - print("bitcoin-cli:", bc) - print("running (datadir):", is_bitcoind_running(datadir)) - return - - if args.start: - ok = start_bitcoind(datadir) - print("Started bitcoind:" if ok else "Failed to start bitcoind") - - if args.generate_blocks > 0: - print(f"Generating {args.generate_blocks} block(s) ...") - res = generate_blocks(datadir, args.generate_blocks) - print(res.get("stdout", "")) - if res.get("stderr"): - print(res.get("stderr")) - - if args.stop: - ok = stop_bitcoind(datadir) - print("Stopped bitcoind:" if ok else "Failed to stop bitcoind (or not running)") - - if args.inject_surface: - inject_surface(datadir, args.rpc_endpoint) - - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/tsm/tsm_harness_compat.py b/5-Applications/tools-scripts/tsm/tsm_harness_compat.py deleted file mode 100644 index bd6ece9d..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_harness_compat.py +++ /dev/null @@ -1,135 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Compatibility shim for TSM harness access. - -Import order: -1. real logic_signal_substrate_mcp_harness if present -2. CATEGORY/TSM harness if it imports cleanly -3. bounded local stub so scripts can still run in simulation mode -""" - -from __future__ import annotations - -import hashlib -import time -from enum import Enum -from typing import Any, Dict, List, Optional, Tuple - - -HARNESS_SOURCE = "unknown" -HARNESS_IMPORT_ERROR: Optional[str] = None - -try: - from logic_signal_substrate_mcp_harness import TSMKernel, TermType # type: ignore - - HARNESS_SOURCE = "logic_signal_substrate_mcp_harness" -except Exception as primary_error: # pragma: no cover - import boundary - try: - from CATEGORY.TSM.tsm_mcp_harness import TSMKernel, TermType # type: ignore - - HARNESS_SOURCE = "CATEGORY.TSM.tsm_mcp_harness" - except Exception as secondary_error: # pragma: no cover - import boundary - HARNESS_IMPORT_ERROR = ( - f"primary={primary_error.__class__.__name__}: {primary_error}; " - f"secondary={secondary_error.__class__.__name__}: {secondary_error}" - ) - HARNESS_SOURCE = "compat_stub" - - class TermType(Enum): - PERMANENT = "permanent" - LEASE = "lease" - TICKS = "ticks" - LOCKED = "locked" - - class TSMKernel: - """Bounded simulation surface used when no importable harness exists.""" - - def __init__(self): - self._sync_counter = 0 - - def execute(self, opcodes: List[Tuple[str, List[Any]]]) -> List[Any]: - results: List[Any] = [] - for opcode, args in opcodes: - if opcode == "0x03": - results.append(self.sync_precision()) - elif opcode == "0x30": - pool_id = args[0] if args else "zpool" - results.append(f"Z-Pool {pool_id} initialized") - elif opcode == "0x31": - amount = float(args[0]) if args else 0.0 - address = str(args[1]) if len(args) > 1 else "unknown" - results.append( - f"Shielded {amount:.6f} ZEC to {address[:16]}..." - ) - elif opcode == "0x33": - state_id = str(args[1]) if len(args) > 1 else "unknown" - results.append(f"Viewing key bonded to {state_id[:16]}...") - elif opcode == "0x50": - receiver = str(args[0]) if args else "Orchard" - results.append(self._generate_unified_address(receiver)) - elif opcode == "0xA2": - path = str(args[0]) if args else "" - if path == "/orders": - results.append( - { - "success": False, - "error_response": ( - "compat_stub does not place live orders" - ), - } - ) - else: - results.append( - { - "success": True, - "status": "simulated", - "path": path, - } - ) - elif opcode == "0xA3": - results.append( - { - "success": True, - "market": 25.0, - "source": "compat_stub", - } - ) - else: - results.append(f"compat_stub unsupported opcode: {opcode}") - return results - - async def execute_async(self, opcodes: List[Tuple[str, List[Any]]]) -> List[Any]: - return self.execute(opcodes) - - def absorb_bh(self, data: str, metadata: Dict[str, Any] | None = None) -> str: - payload = f"{data}|{metadata or {}}|{time.time()}".encode("utf-8") - return hashlib.sha256(payload).hexdigest() - - def sync_precision(self) -> str: - self._sync_counter += 1 - return f"precision_sync_stub_{self._sync_counter}" - - def omni_bal(self, mode: str | None = None) -> str: - return f"omni_bal_stub:{mode or 'default'}" - - def stark_prove(self, payload: str, manifest: Dict[str, Any] | None = None) -> str: - digest = hashlib.sha256( - f"{payload}|{manifest or {}}|{time.time()}".encode("utf-8") - ).hexdigest() - return digest - - def ledger_commit(self, state_id: str, term: TermType = TermType.LEASE) -> str: - return f"ledger_commit_stub:{state_id[:16]}:{term.value}" - - def _generate_unified_address(self, receiver_type: str) -> str: - digest = hashlib.sha256( - f"{receiver_type}|{time.time()}".encode("utf-8") - ).hexdigest() - prefix = "u1" if receiver_type == "Orchard" else "zs1" - return f"{prefix}compat{digest[:24]}" diff --git a/5-Applications/tools-scripts/tsm/tsm_register.py b/5-Applications/tools-scripts/tsm/tsm_register.py deleted file mode 100644 index f4198038..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_register.py +++ /dev/null @@ -1,382 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -""" -Logic-Signal Substrate Hyperledger Register for Graph OS -Finds all documents with logic_signal_substrate content, extracts manifest/metadata/SHA256, -and adds them to the Graph OS hyperledger using ledger_commit opcode. - -Usage: - python 5-Applications/scripts/logic_signal_substrate_hyperledger_register.py -""" - -import hashlib -import json -import time -import zlib -import base64 -from pathlib import Path -from typing import Dict, List, Any, Optional -from dataclasses import dataclass, asdict -from datetime import datetime -import sys - -# Add project root to path -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT)) -sys.path.insert(0, str(ROOT / "scripts")) - -# Import TSM Kernel for ledger operations -try: - from logic_signal_substrate_mcp_harness import TSMKernel, TermType -except ImportError: - print("[!] logic_signal_substrate_mcp_harness not found, using embedded TSMKernel") - - # Embedded minimal TSMKernel for ledger operations - class TermType: - PERMANENT = "permanent" - LEASE = "lease" - TICKS = "ticks" - - class TSMKernel: - def __init__(self): - self.manifold = {} - self.ledger = [] - - def absorb_bh(self, data: str, metadata: Dict[str, Any] = None) -> str: - if metadata is None: - metadata = {} - compressed = zlib.compress(data.encode('utf-8'), level=9) - blob = base64.urlsafe_b64encode(compressed).decode('ascii') - manifest = { - 'original_size': len(data), - 'compressed_size': len(compressed), - 'entropy_ratio': len(compressed) / len(data) if data else 0, - 'metadata': metadata, - 'timestamp': time.time() - } - hash_input = f"{blob}{json.dumps(manifest, sort_keys=True)}".encode('utf-8') - hash_id = hashlib.sha256(hash_input).hexdigest() - self.manifold[hash_id] = { - 'blob': blob, - 'manifest': manifest, - 'hash': hash_id, - 'created_at': time.time(), - 'term': TermType.LEASE - } - return hash_id - - def ledger_commit(self, state_id: str, term: str = TermType.PERMANENT) -> str: - if state_id not in self.manifold: - return f"State {state_id} not found" - self.manifold[state_id]['term'] = term - self.ledger.append({ - 'state_id': state_id, - 'term': term, - 'committed_at': time.time(), - 'manifest': self.manifold[state_id]['manifest'] - }) - return f"Committed {state_id} with term {term}" - - -@dataclass -class TsmDocument: - """Represents a Logic-Signal Substrate document with manifest, metadata, and SHA256""" - file_path: str - filename: str - sha256: str - manifest: Dict[str, Any] - metadata: Dict[str, Any] - logic_signal_substrate_content: str - manifold_id: Optional[str] = None - ledger_term: str = TermType.PERMANENT - - -class TsmRegister: - """ - Registers Logic-Signal Substrate documents into the Graph OS Hyperledger - - Process: - 1. Scan for all files containing 'logic_signal_substrate' content - 2. Extract manifest, metadata, and compute SHA256 - 3. Absorb into TSM manifold - 4. Commit to hyperledger with PERMANENT term - """ - - def __init__(self, root_path: Path): - self.root_path = root_path - self.kernel = TSMKernel() - self.logic_signal_substrate_documents: List[TsmDocument] = [] - self.hyperledger_entries: List[Dict] = [] - - def calculate_sha256(self, data: bytes) -> str: - """Calculate SHA256 hash of data""" - return hashlib.sha256(data).hexdigest() - - def extract_metadata(self, file_path: Path, content: str) -> Dict[str, Any]: - """Extract metadata from file content""" - metadata = { - 'file_path': str(file_path), - 'filename': file_path.name, - 'size_bytes': len(content.encode('utf-8')), - 'line_count': content.count('\n') + 1, - 'detected_at': datetime.utcnow().isoformat() + 'Z', - 'logic_signal_substrate_markers': [] - } - - # Detect Logic-Signal Substrate-specific markers - if '```logic_signal_substrate' in content: - metadata['logic_signal_substrate_markers'].append('logic_signal_substrate_code_block') - if 'logic_signal_substrate_version' in content: - metadata['logic_signal_substrate_markers'].append('logic_signal_substrate_version_header') - if '@MoE' in content: - metadata['logic_signal_substrate_markers'].append('moe_directive') - if 'TSMKernel' in content or 'logic_signal_substrate_kernel' in content: - metadata['logic_signal_substrate_markers'].append('tsm_kernel_reference') - if 'ledger_commit' in content: - metadata['logic_signal_substrate_markers'].append('ledger_opcode') - if 'manifold_id' in content: - metadata['logic_signal_substrate_markers'].append('manifold_reference') - - # Extract logic_signal_substrate_version if present - import re - version_match = re.search(r'"logic_signal_substrate_version":\s*"([^"]+)"', content) - if version_match: - metadata['logic_signal_substrate_version'] = version_match.group(1) - - # Extract manifold_id if present - manifold_match = re.search(r'"manifold_id":\s*"([^"]+)"', content) - if manifold_match: - metadata['existing_manifold_id'] = manifold_match.group(1) - - return metadata - - def create_manifest(self, file_path: Path, sha256: str, metadata: Dict) -> Dict[str, Any]: - """Create a manifest for the Logic-Signal Substrate document""" - return { - 'schema': 'logic_signal_substrate-hyperledger/v1', - 'document_id': sha256[:16], - 'content_hash': sha256, - 'source_path': str(file_path.relative_to(self.root_path)), - 'registration_timestamp': datetime.utcnow().isoformat() + 'Z', - 'ledger_type': 'Graph OS_HYPERLEDGER', - 'metadata_summary': { - 'size_bytes': metadata.get('size_bytes', 0), - 'line_count': metadata.get('line_count', 0), - 'logic_signal_substrate_markers': metadata.get('logic_signal_substrate_markers', []), - 'logic_signal_substrate_version': metadata.get('logic_signal_substrate_version', 'unknown') - } - } - - def scan_for_logic_signal_substrate_documents(self) -> List[Path]: - """Scan the repository for files containing Logic-Signal Substrate content""" - logic_signal_substrate_files = [] - - # Patterns to search for - logic_signal_substrate_patterns = [ - '```logic_signal_substrate', - 'logic_signal_substrate_version', - 'logic_signal_substrate_kernel', - 'logic_signal_substrate_mode', - '@MoE', - 'TSMKernel', - 'manifold_id', - 'ledger_commit', - '.logic_signal_substrate', - 'logic_signal_substrate-' - ] - - print(f"[*] Scanning {self.root_path} for Logic-Signal Substrate documents...") - - # Search for files with logic_signal_substrate in name or content - for ext in ['*.py', '*.json', '*.md', '*.logic_signal_substrate', '*.txt', '*.tex', '*.scad', '*.cu', '*.cpp']: - for file_path in self.root_path.rglob(ext): - if file_path.is_file(): - try: - # Skip binary files and large files - if file_path.stat().st_size > 10 * 1024 * 1024: # 10MB limit - continue - - content = file_path.read_text(encoding='utf-8', errors='ignore') - - # Check if file contains Logic-Signal Substrate markers - has_logic_signal_substrate = any(pattern in content for pattern in logic_signal_substrate_patterns) - has_logic_signal_substrate_in_name = 'logic_signal_substrate' in file_path.name.lower() - - if has_logic_signal_substrate or has_logic_signal_substrate_in_name: - logic_signal_substrate_files.append(file_path) - - except Exception as e: - print(f" [!] Error reading {file_path}: {e}") - - print(f" Found {len(logic_signal_substrate_files)} Logic-Signal Substrate documents") - return logic_signal_substrate_files - - def process_document(self, file_path: Path) -> Optional[TsmDocument]: - """Process a single Logic-Signal Substrate document""" - try: - content = file_path.read_text(encoding='utf-8', errors='ignore') - content_bytes = content.encode('utf-8') - - # Calculate SHA256 - sha256 = self.calculate_sha256(content_bytes) - - # Extract metadata - metadata = self.extract_metadata(file_path, content) - - # Create manifest - manifest = self.create_manifest(file_path, sha256, metadata) - - # Create Logic-Signal Substrate document record - doc = TsmDocument( - file_path=str(file_path), - filename=file_path.name, - sha256=sha256, - manifest=manifest, - metadata=metadata, - logic_signal_substrate_content=content[:10000] # Truncate for storage - ) - - return doc - - except Exception as e: - print(f" [!] Error processing {file_path}: {e}") - return None - - def register_to_hyperledger(self, doc: TsmDocument) -> Dict[str, Any]: - """Register a Logic-Signal Substrate document to the Graph OS hyperledger""" - - # Step 1: Absorb into TSM manifold - manifold_data = json.dumps({ - 'manifest': doc.manifest, - 'metadata': doc.metadata, - 'sha256': doc.sha256, - 'filename': doc.filename - }) - - manifold_id = self.kernel.absorb_bh( - manifold_data, - metadata={ - 'type': 'logic_signal_substrate_document', - 'filename': doc.filename, - 'sha256': doc.sha256 - } - ) - - doc.manifold_id = manifold_id - - # Step 2: Commit to hyperledger with PERMANENT term - commit_result = self.kernel.ledger_commit(manifold_id, TermType.PERMANENT) - - # Create hyperledger entry - entry = { - 'manifold_id': manifold_id, - 'document_id': doc.manifest['document_id'], - 'content_hash': doc.sha256, - 'filename': doc.filename, - 'file_path': doc.file_path, - 'ledger_term': TermType.PERMANENT, - 'commit_result': commit_result, - 'registered_at': datetime.utcnow().isoformat() + 'Z', - 'manifest': doc.manifest, - 'metadata': doc.metadata - } - - self.hyperledger_entries.append(entry) - return entry - - def run(self) -> Dict[str, Any]: - """Execute the full Logic-Signal Substrate hyperledger registration process""" - - print("\n" + "=" * 70) - print(" Logic-Signal Substrate HYPERLEDGER REGISTER FOR Graph OS") - print(" Extracting manifest/metadata/SHA256 and committing to ledger") - print("=" * 70 + "\n") - - # Phase 1: Scan for Logic-Signal Substrate documents - print("[PHASE 1] SCANNING FOR Logic-Signal Substrate DOCUMENTS") - logic_signal_substrate_files = self.scan_for_logic_signal_substrate_documents() - print() - - # Phase 2: Process documents - print("[PHASE 2] PROCESSING DOCUMENTS (extracting manifest/metadata/SHA256)") - for i, file_path in enumerate(logic_signal_substrate_files): - doc = self.process_document(file_path) - if doc: - self.logic_signal_substrate_documents.append(doc) - print(f" [{i+1}/{len(logic_signal_substrate_files)}] {file_path.name}") - print(f" SHA256: {doc.sha256[:16]}...") - print(f" Markers: {', '.join(doc.metadata.get('logic_signal_substrate_markers', []))}") - print(f"\n Processed {len(self.logic_signal_substrate_documents)} documents") - print() - - # Phase 3: Register to hyperledger - print("[PHASE 3] REGISTERING TO Graph OS HYPERLEDGER (ledger_commit)") - for i, doc in enumerate(self.logic_signal_substrate_documents): - entry = self.register_to_hyperledger(doc) - print(f" [{i+1}/{len(self.logic_signal_substrate_documents)}] {doc.filename}") - print(f" Manifold ID: {entry['manifold_id'][:16]}...") - print(f" Ledger Term: {entry['ledger_term']}") - print(f" Status: {entry['commit_result']}") - print() - - # Phase 4: Summary - print("[PHASE 4] REGISTRATION SUMMARY") - print(f" Total Logic-Signal Substrate documents found: {len(logic_signal_substrate_files)}") - print(f" Documents processed: {len(self.logic_signal_substrate_documents)}") - print(f" Hyperledger entries: {len(self.hyperledger_entries)}") - print(f" TSM manifold size: {len(self.kernel.manifold)}") - print(f" Ledger commits: {len(self.kernel.ledger)}") - print() - - # Generate summary report - summary = { - 'registration_timestamp': datetime.utcnow().isoformat() + 'Z', - 'total_documents_found': len(logic_signal_substrate_files), - 'total_documents_processed': len(self.logic_signal_substrate_documents), - 'total_ledger_entries': len(self.hyperledger_entries), - 'manifold_size': len(self.kernel.manifold), - 'ledger_commits': len(self.kernel.ledger), - 'entries': self.hyperledger_entries - } - - # Save summary to file - output_path = ROOT / "out" / "logic_signal_substrate_hyperledger_registration.json" - output_path.parent.mkdir(parents=True, exist_ok=True) - - with open(output_path, 'w') as f: - json.dump(summary, f, indent=2, default=str) - - print(f"[✓] Summary saved to: {output_path}") - print("\n" + "=" * 70) - print(" Logic-Signal Substrate HYPERLEDGER REGISTRATION COMPLETE") - print("=" * 70 + "\n") - - return summary - - -def main(): - """Main entry point""" - register = TsmRegister(ROOT) - summary = register.run() - - # Print detailed entries for verification - print("\n[HYPERLEDGER ENTRIES]") - for entry in summary['entries'][:5]: # Show first 5 - print(f"\n Document: {entry['filename']}") - print(f" Manifold ID: {entry['manifold_id']}") - print(f" Content Hash: {entry['content_hash']}") - print(f" Ledger Term: {entry['ledger_term']}") - - if len(summary['entries']) > 5: - print(f"\n ... and {len(summary['entries']) - 5} more entries") - - return summary - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/tsm/tsm_translation.py b/5-Applications/tools-scripts/tsm/tsm_translation.py deleted file mode 100644 index b41ae128..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_translation.py +++ /dev/null @@ -1,148 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Bidirectional TSM translator for surface-safe domain exchange. - -Design intent: -- Runtime can stay pure TSM internally. -- Surface artifacts never expose raw TSM internals. -- Translation is deterministic in both directions. -""" - -from __future__ import annotations - -from datetime import datetime, timezone -from typing import Any, Dict, Tuple, cast - - -TSM_VERSION = "v3.2-USAL" -ISA_VERSION = "ISA-v1" -SURFACE_TRANSLATION_VERSION = "surface-archive-translation/2" -FORBIDDEN_SURFACE_KEYS = { - "absorbed_state", - "manifold_id", - "logic_signal_substrate_version", -} -FORBIDDEN_KEY_SUBSTRINGS = ( - "blob", -) - - -def _utc_now() -> str: - return datetime.now(timezone.utc).isoformat() - - -def _as_dict(value: Any) -> Dict[str, Any]: - if isinstance(value, dict): - raw = cast(Dict[Any, Any], value) - out: Dict[str, Any] = {} - for key, item in raw.items(): - out[str(key)] = item - return out - return {} - - -def _has_forbidden_key(key: str) -> bool: - k = key.strip().lower() - if k in FORBIDDEN_SURFACE_KEYS: - return True - return any(part in k for part in FORBIDDEN_KEY_SUBSTRINGS) - - -def _validate_surface_node(node: Any, path: Tuple[str, ...]) -> None: - if isinstance(node, dict): - raw = cast(Dict[Any, Any], node) - for key, value in raw.items(): - key_str = str(key) - if _has_forbidden_key(key_str): - dotted = ".".join(path + (key_str,)) if path else key_str - raise ValueError(f"fail_closed_surface_gate: forbidden key '{key_str}' at '{dotted}'") - _validate_surface_node(value, path + (key_str,)) - return - if isinstance(node, list): - items = cast(list[Any], node) - for i, item in enumerate(items): - _validate_surface_node(item, path + (f"[{i}]",)) - - -def assert_surface_write_safe(payload: Dict[str, Any], scope: str = "surface") -> None: - """Fail-closed guard for outbound surface/profile payloads. - - Rejects any leaked TSM internals or absorbed blobs before writes. - """ - _validate_surface_node(payload, (scope,)) - - -def logic_signal_substrate_from_archive_domain(archive_domain: Dict[str, Any]) -> Dict[str, Any]: - """Normalize archive domain into internal USAL-TSM form. - - This structure is intended for internal USAL-bound compute only. - """ - mode = str(archive_domain.get("selected_mode") or archive_domain.get("auto_winner_mode") or "unknown") - - # In v3.2+, we return a USAL-aligned skeleton - return { - "logic_signal_substrate_version": TSM_VERSION, - "isa_version": ISA_VERSION, - "kind": "archive_compression", - "state": { - "policy": str(archive_domain.get("selection_policy") or "auto_smallest_compressed_bytes"), - "mode": mode, - "payload_format": str(archive_domain.get("payload_format") or "unknown"), - "compressed_bytes": int(archive_domain.get("compressed_bytes") or 0), - "raw_bytes": int(archive_domain.get("raw_bytes") or 0), - }, - "lineage": { - "manifest_path": str(archive_domain.get("manifest_path") or ""), - "updated_utc": str(archive_domain.get("updated_utc") or _utc_now()), - }, - "substrate_transparency": "ENABLED" - } - - -def surface_from_logic_signal_substrate(logic_signal_substrate_state: Dict[str, Any]) -> Dict[str, Any]: - """Emit sanitized surface-safe representation from TSM state. - - Drops internal TSM fields by design to avoid leaking TSM. - """ - state = _as_dict(logic_signal_substrate_state.get("state")) - lineage = _as_dict(logic_signal_substrate_state.get("lineage")) - - return { - "domain": "archive_compression", - "selection_policy": str(state.get("policy") or "auto_smallest_compressed_bytes"), - "selected_mode": str(state.get("mode") or "unknown"), - "payload_format": str(state.get("payload_format") or "unknown"), - "compressed_bytes": int(state.get("compressed_bytes") or 0), - "raw_bytes": int(state.get("raw_bytes") or 0), - "manifest_path": str(lineage.get("manifest_path") or ""), - "updated_utc": str(lineage.get("updated_utc") or _utc_now()), - "translation": { - "version": SURFACE_TRANSLATION_VERSION, - "logic_signal_substrate_exposed": False, - }, - } - - -def logic_signal_substrate_from_surface(surface_domain: Dict[str, Any]) -> Dict[str, Any]: - """Rehydrate internal TSM from sanitized surface representation.""" - return { - "logic_signal_substrate_version": TSM_VERSION, - "kind": "archive_compression", - "state": { - "policy": str(surface_domain.get("selection_policy") or "auto_smallest_compressed_bytes"), - "mode": str(surface_domain.get("selected_mode") or "unknown"), - "payload_format": str(surface_domain.get("payload_format") or "unknown"), - "compressed_bytes": int(surface_domain.get("compressed_bytes") or 0), - "raw_bytes": int(surface_domain.get("raw_bytes") or 0), - }, - "lineage": { - "manifest_path": str(surface_domain.get("manifest_path") or ""), - "updated_utc": str(surface_domain.get("updated_utc") or _utc_now()), - }, - "internals": {}, - } diff --git a/5-Applications/tools-scripts/tsm/tsm_upscaler.py b/5-Applications/tools-scripts/tsm/tsm_upscaler.py deleted file mode 100644 index 7aaedda3..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_upscaler.py +++ /dev/null @@ -1,62 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import argparse -import os -import torch -from diffusers import StableDiffusionUpscalePipeline -from PIL import Image - -def upscale(input_file, output_file, steps=50): - print(f"[TSM] Initializing Quantum Super-Resolution Vector Array...") - - # Load the base image - try: - init_image = Image.open(input_file).convert("RGB") - except Exception as e: - print(f"[ERROR] Failed to load {input_file}: {e}") - return - - print(f"[TSM] Base Tensor Loaded: {init_image.width}x{init_image.height}") - - # Load heavily optimized 4x Upscaler - model_id = "stabilityai/stable-diffusion-x4-upscaler" - print(f"[TSM] Booting pi-MoE Optical Node -> {model_id}") - - pipe = StableDiffusionUpscalePipeline.from_pretrained( - model_id, - torch_dtype=torch.float16 - ) - pipe = pipe.to("cuda") - - # Optional: Enable memory efficient attention if xformers is installed - try: - pipe.enable_xformers_memory_efficient_attention() - print("[TSM] Xformers memory efficient attention enabled.") - except Exception: - pass - - # The prompt helps guide the upscaler's "hallucination" of new pixels - prompt = "highly detailed, hyper-realistic, pristine, 8k resolution, photorealistic, sharp focus, masterpiece" - - print(f"[TSM] Injecting physics heuristics and unfolding super-resolution...") - print(f"[TSM] Factoring {steps} inference iterations. Extrapolating to 4x native resolution.") - - upscaled_image = pipe(prompt=prompt, image=init_image, num_inference_steps=steps).images[0] - - os.makedirs(os.path.dirname(output_file), exist_ok=True) - upscaled_image.save(output_file) - print(f"[TSM] Image Matrix Re-compiled. Final Resolution: {upscaled_image.width}x{upscaled_image.height}") - print(f"[TSM] Vision Solidified at: {os.path.abspath(output_file)}") - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="TSM 4x Super-Resolution Upscaler.") - parser.add_argument("--input", type=str, required=True, help="Input image path.") - parser.add_argument("--output", type=str, default="5-Applications/out/mind_eye/logic_signal_substrate_upscaled.png", help="Output image path.") - parser.add_argument("--steps", type=int, default=50, help="Inference steps for diffusion upscaling.") - args = parser.parse_args() - - upscale(args.input, args.output, args.steps) diff --git a/5-Applications/tools-scripts/tsm/tsm_v5_traces.py b/5-Applications/tools-scripts/tsm/tsm_v5_traces.py deleted file mode 100644 index 5c598ba3..00000000 --- a/5-Applications/tools-scripts/tsm/tsm_v5_traces.py +++ /dev/null @@ -1,65 +0,0 @@ -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -import math - -# --- PHYSICAL CONSTANTS --- -SPEED_OF_LIGHT = 299792458 # m/s -PICIC_CONST = 1.0e-12 # 1 picosecond -PLANCK_TIME = 5.39e-44 # 1 Planck second - -# --- PCB MATERIAL CONSTANTS (FR-4) --- -# Er (Relative Permittivity) for FR-4 is typically 4.2 - 4.5 -ER_FR4 = 4.2 -VELOCITY_FACTOR = 1.0 / math.sqrt(ER_FR4) -V_PCB = SPEED_OF_LIGHT * VELOCITY_FACTOR # Velocity of signal in PCB traces - -def calculate_flight_time(length_mm): - """Calculates the time it takes for a signal to travel a given length on the PCB.""" - length_m = length_mm / 1000.0 - t = length_m / V_PCB - return t - -def calculate_matched_length(delta_t_ps): - """Calculates the trace length required for a specific delay in picoseconds.""" - delta_t = delta_t_ps * PICIC_CONST - l_m = delta_t * V_PCB - return l_m * 1000.0 # return in mm - -def audit_pcb_netlist(): - print("--- TSM-VDP v5 PCB TRACE AUDIT (Ballistic Transport) ---") - print(f"Velocity Factor: {VELOCITY_FACTOR:.4f}") - print(f"Signal Speed (V_PCB): {V_PCB/1e6:.2f} mm/ns") - - # Critical Nets - nets = [ - {"name": "NET_Precision_CLOCK", "length_mm": 15.5}, - {"name": "NET_SDR_IQ_I", "length_mm": 12.2}, - {"name": "NET_SDR_IQ_Q", "length_mm": 12.25}, # Needs matching - {"name": "NET_SPI_SCK", "length_mm": 8.0}, - {"name": "NET_SPI_MOSI", "length_mm": 7.8}, - ] - - for net in nets: - t_ps = calculate_flight_time(net['length_mm']) / PICIC_CONST - print(f"\n[Net: {net['name']}]") - print(f" Length: {net['length_mm']} mm") - print(f" Flight Time: {t_ps:.2f} ps") - - # Phase-Lock Match Check (SDR IQ) - delta_iq = abs(nets[1]['length_mm'] - nets[2]['length_mm']) - delta_t_iq = calculate_flight_time(delta_iq) / PICIC_CONST - print(f"\n[PHASE-LOCK ANALYSIS: SDR IQ]") - print(f" Length Mismatch: {delta_iq:.3f} mm") - print(f" Skew: {delta_t_iq:.2f} ps") - - if delta_t_iq < 1.0: - print(" Status: COHERENT (Sub-picosecond skew)") - else: - print(f" Status: DECOHERENT (Skew > 1ps). Match req: {calculate_matched_length(1.0):.3f} mm max diff.") - -if __name__ == "__main__": - audit_pcb_netlist() diff --git a/5-Applications/tools-scripts/tsm/write_tsm_surface.py b/5-Applications/tools-scripts/tsm/write_tsm_surface.py deleted file mode 100644 index 7fda7aa4..00000000 --- a/5-Applications/tools-scripts/tsm/write_tsm_surface.py +++ /dev/null @@ -1,138 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -"""Merge a TSM surface (logic_signal_substrate_surface.json) into the omnitoken `egress_surface.json`. - -Safe behaviors: -- Validate outgoing surface via `assert_surface_write_safe` from `scripts.logic_signal_substrate_translation`. -- Backup existing surface before writing. -- Atomic write via temporary file + rename. -- Optional `--dry-run` to preview changes. - -Usage: - .venv/bin/python 5-Applications/scripts/write_logic_signal_substrate_surface.py [--logic_signal_substrate PATH] [--surface PATH] [--dry-run] -""" - -from __future__ import annotations - -import argparse -import json -import shutil -import tempfile -from datetime import datetime, timezone -from pathlib import Path -from typing import Any, Dict - -ROOT = Path(__file__).resolve().parent.parent -OMNI_DIR = ROOT / "out" / "omnitoken_bridge" -OMNI_SURFACE_PATH = OMNI_DIR / "egress_surface.json" -DEFAULT_TSM_PATH = ROOT / "out" / "logic_signal_substrate_surface.json" - -try: - from scripts.logic_signal_substrate_translation import assert_surface_write_safe -except Exception: - try: - from logic_signal_substrate_translation import assert_surface_write_safe # type: ignore - except Exception: - def assert_surface_write_safe(payload, scope=None): - # fallback no-op validator if import fails - return True - - -def utc_now() -> str: - return datetime.now(timezone.utc).isoformat() - - -def load_json(path: Path) -> Dict[str, Any]: - if not path.exists(): - return {} - try: - return json.loads(path.read_text(encoding="utf-8")) - except (OSError, json.JSONDecodeError): - return {} - - -def backup_path(path: Path) -> Path: - stamp = datetime.now(timezone.utc).strftime("%Y%m%dT%H%M%SZ") - return path.with_suffix(f".bak.{stamp}") - - -def atomic_write(path: Path, data: Dict[str, Any]) -> None: - path.parent.mkdir(parents=True, exist_ok=True) - with tempfile.NamedTemporaryFile("w", delete=False, dir=str(path.parent), encoding="utf-8") as tf: - tf.write(json.dumps(data, indent=2) + "\n") - tmp = Path(tf.name) - tmp.replace(path) - - -def merge_surfaces(existing: Dict[str, Any], logic_signal_substrate: Dict[str, Any]) -> Dict[str, Any]: - out = dict(existing or {}) - - fp = logic_signal_substrate.get("funding_policy") - if isinstance(fp, dict): - out["funding_policy"] = fp - out.setdefault("annotations", {})["funding_policy_updated_utc"] = utc_now() - - qsb = logic_signal_substrate.get("surface_bus") or {} - qdomains = (qsb.get("domains") or {}) if isinstance(qsb, dict) else {} - - sb = dict(out.get("surface_bus") or {}) - sb.setdefault("schema", str(qsb.get("schema") or sb.get("schema") or "omnitoken-surface-bus/v1")) - sb.setdefault("domains", {}) - domains = dict(sb.get("domains") or {}) - - for k, v in qdomains.items(): - if not isinstance(v, dict): - continue - entry = dict(domains.get(k) or {}) - entry.update(v) - entry["updated_utc"] = utc_now() - domains[k] = entry - - sb["domains"] = domains - out["surface_bus"] = sb - out["updated_utc"] = utc_now() - return out - - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - ap.add_argument("--logic_signal_substrate", default=str(DEFAULT_TSM_PATH), help="Path to logic_signal_substrate_surface.json") - ap.add_argument("--surface", default=str(OMNI_SURFACE_PATH), help="Path to egress surface to patch") - ap.add_argument("--dry-run", action="store_true", help="Print merge result but don't write") - args = ap.parse_args() - - logic_signal_substrate_path = Path(args.logic_signal_substrate).expanduser() - surface_path = Path(args.surface).expanduser() - - logic_signal_substrate = load_json(logic_signal_substrate_path) - existing = load_json(surface_path) - - merged = merge_surfaces(existing, logic_signal_substrate) - - try: - assert_surface_write_safe(merged, scope="write_logic_signal_substrate_surface") - except Exception as e: - print(f"Validation failed: {e}") - raise - - if args.dry_run: - print(json.dumps(merged, indent=2)[:20000]) - print("\n--- dry-run; no write performed ---") - return - - if surface_path.exists(): - bkp = backup_path(surface_path) - shutil.copy2(surface_path, bkp) - print(f"backup written: {bkp}") - - atomic_write(surface_path, merged) - print(f"merged and wrote surface: {surface_path}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/verifier/audit_gsp_variants.py b/5-Applications/tools-scripts/verifier/audit_gsp_variants.py deleted file mode 100644 index e7145579..00000000 --- a/5-Applications/tools-scripts/verifier/audit_gsp_variants.py +++ /dev/null @@ -1,392 +0,0 @@ -#!/usr/bin/env python3 -""" -audit_gsp_variants.py -===================== - -Run every GSP closure backend against the burgers_verifier gate suite. - -Wraps each backend in `5-Applications/scripts/gsp/backends.py` and -`5-Applications/scripts/gsp/perceval_backend.py` as a `closure_fn(t, a, ν₀)` -of the form - - ν_eff = ν₀ · (1 + β · Ω(a)) - -per `BurgersHarmonicPeelingVerification.md`. The verifier evaluates each -candidate against: - - G1 Cole-Hopf or pseudo-spectral cosim (ν=0.01 → pseudo-spectral) - G2 energy dissipation property (ν_eff > 0 ⇒ dE/dt ≤ 0) - G4 ν → ∞ heat-equation limit (sanity in stiff regime) - -Writes a comparison table + JSON bundle. The result is the -*verifier-evaluated* ranking, independent of any agent text. -""" - -from __future__ import annotations - -import json -import sys -from dataclasses import asdict -from pathlib import Path - -REPO = Path(__file__).resolve().parents[3] -# Import GSP backends as a package so the relative import in perceval_backend.py works. -sys.path.insert(0, str(REPO / "5-Applications" / "scripts")) -sys.path.insert(0, str(REPO / "5-Applications" / "tools-scripts" / "verifier")) - -from gsp.backends import ( # noqa: E402 - ClassicalHeuristicBackend, - EddyViscosityROMBackend, - LearnedClosureBaselineBackend, - SoftwareTriangleBackend, -) -from gsp.perceval_backend import PercevalGeometryShaverBackend # noqa: E402 - -# Verifier. -from burgers_verifier import ( # noqa: E402 - DEFAULT_AMPS, - PseudoSpectralReference, - closure_constant, - gate_g1_cole_hopf_cosim, - gate_g2_energy_dissipation, - gate_g4_heat_equation_limit, - integrate_triad, -) -from run_dag import RunDAG # noqa: E402 - -Q16_ONE = 1 << 16 -VERIFIER_PATH = REPO / "5-Applications" / "tools-scripts" / "verifier" / "burgers_verifier.py" -AUDIT_PATH = REPO / "5-Applications" / "tools-scripts" / "verifier" / "audit_gsp_variants.py" -RUN_DAG_PATH = REPO / "5-Applications" / "tools-scripts" / "verifier" / "run_dag.py" - - -# ============================================================================= -# Backend → closure_fn adapter -# ============================================================================= - -def make_closure_fn(backend, beta: float = 0.5, n_samples: int = 256, seed: int = 42, - memoize: bool = True, memo_decimals: int = 5, - formula: str = "additive"): - """Wrap a VirtualSubstrateBackend as a closure_fn(t, a, ν₀) → ν_eff. - - `formula` selects the ν_eff formula: - - "additive" : ν_eff = ν₀ + β · Ω (matches runner.py:135) - - "multiplicative" : ν_eff = ν₀ · (1 + β · Ω) (matches the spec doc and - the upstream Perceval - repro from 2026-05-03) - - The additive form caused 25× ν_eff inflation for variant D at ν₀=0.01, - Ω≈0.5 — a wrapper-side bug, not a Perceval-side bug. This option lets the - audit measure both forms head-to-head against the same gates. - - `memoize` caches Ω by quantised state-tuple — RK45 evaluates the RHS at - many sub-step stages with similar a, and each photonic-sampler call costs - real time. The cache key is rounded to `memo_decimals` decimal places (5 ≈ - 1e-5, well below shot-noise resolution for the witness). - """ - cache: dict[tuple, float] = {} - - if formula == "additive": - def combine(nu0, omega): return nu0 + beta * omega - elif formula == "multiplicative": - def combine(nu0, omega): return nu0 * (1.0 + beta * omega) - else: - raise ValueError(f"unknown formula {formula!r}; use 'additive' or 'multiplicative'") - - def closure_fn(t, a_float, nu0): - if memoize: - key = tuple(round(x, memo_decimals) for x in a_float) - if key in cache: - return combine(nu0, cache[key]) - a_q16 = tuple(int(round(x * Q16_ONE)) for x in a_float) - theta = backend.encode(a_q16) - substrate = backend.program(theta) - hist = backend.sample(substrate, n_samples, seed) - omega_q16 = backend.witness(hist) - omega_float = omega_q16 / Q16_ONE - if memoize: - cache[key] = omega_float - return combine(nu0, omega_float) - return closure_fn - - -# ============================================================================= -# The variants (matches the conversation's A–G nomenclature) -# ============================================================================= - -VARIANTS = { - # Variant A — no viscosity at all. Hard limit case. - "A_no_viscosity": (lambda t, a, nu0: 0.0, "ν_eff = 0 (no closure, no diffusion)"), - # Variant B — fixed viscosity, no closure correction. The honest baseline. - "B_constant_baseline": (closure_constant, "ν_eff = ν₀ (constant; the bar to beat)"), - # Variant C — classical heuristic. - "C_classical_heuristic": ( - make_closure_fn(ClassicalHeuristicBackend(c1=0.1, c2=0.1, c3=0.0)), - "ν_eff = ν₀ + 0.5·(0.1|a₁a₃| + 0.1|a₂a₃|)" - ), - # Variant D — Perceval photonic witness (M=6, 3-photon Fock input). - # n_samples=64 keeps shot noise reasonable while making the RK45-driven - # repeat-call burden tractable; memoization in make_closure_fn dedupes - # within-step state-tuple repeats from the integrator. - "D_perceval_local": ( - make_closure_fn(PercevalGeometryShaverBackend(M=6, exhaust_modes=(3, 4, 5)), - n_samples=64), - "ν_eff = ν₀ + 0.5·Ω_Q, Ω_Q = Σ exhaust-mode photon-counts (M=6, 3-photon Fock)" - ), - # Variant F — learned-closure baseline (mock LSTM/NODE). - "F_learned_closure": ( - make_closure_fn(LearnedClosureBaselineBackend()), - "mock LSTM/NODE: nonlinear fn of recent 3-step history" - ), - # Variant G — eddy-viscosity ROM (Smagorinsky-style). - "G_eddy_viscosity": ( - make_closure_fn(EddyViscosityROMBackend(c_smag=0.1)), - "ν_eff = ν₀ + 0.5·0.1·|a₃| (Smagorinsky-style)" - ), - # Variant null — SoftwareTriangleBackend explicit (Ω=0, identical to B). - "null_software_triangle": ( - make_closure_fn(SoftwareTriangleBackend()), - "Ω = 0 always (identical to B in effect; sanity check on adapter)" - ), - # ===== Multiplicative-formula variants (ν_eff = ν₀·(1 + β·Ω)) ===== - # Spec-aligned form. Differs from the runner.py:135 additive form; - # added per the 2026-05-03 Perceval repro analysis to test whether the - # over-damping localises to the additive formula vs the photonic substrate. - "C2_classical_multiplicative": ( - make_closure_fn(ClassicalHeuristicBackend(c1=0.1, c2=0.1, c3=0.0), - formula="multiplicative"), - "ν_eff = ν₀·(1 + 0.5·(0.1|a₁a₃| + 0.1|a₂a₃|)) (multiplicative)" - ), - "D2_perceval_multiplicative": ( - make_closure_fn(PercevalGeometryShaverBackend(M=6, exhaust_modes=(3, 4, 5)), - n_samples=64, formula="multiplicative"), - "ν_eff = ν₀·(1 + 0.5·Ω_Q) (multiplicative; per spec + repro)" - ), - "F2_learned_multiplicative": ( - make_closure_fn(LearnedClosureBaselineBackend(), formula="multiplicative"), - "mock LSTM/NODE, multiplicative ν_eff" - ), - "G2_eddy_multiplicative": ( - make_closure_fn(EddyViscosityROMBackend(c_smag=0.1), formula="multiplicative"), - "ν_eff = ν₀·(1 + 0.5·0.1·|a₃|) (Smagorinsky, multiplicative)" - ), -} - - -# ============================================================================= -# Run -# ============================================================================= - -def _build_variant_dag(name: str, desc: str, run, g1, g2, g4, ref, - nu0: float, t_final: float, n_eval: int, beta: float, - dag_dir: Path) -> tuple[str, Path]: - """Build a per-variant Merkle DAG of inputs → reference → integrate → gates → verdict.""" - dag = RunDAG( - run_type=f"gsp_variant_audit:{name}", - code_paths=[VERIFIER_PATH, AUDIT_PATH, RUN_DAG_PATH], - ) - # Inputs - dag.add_input("input.amps", list(DEFAULT_AMPS)) - dag.add_input("input.nu0", nu0) - dag.add_input("input.t_final", t_final) - dag.add_input("input.n_eval", n_eval) - dag.add_input("input.beta", beta) - dag.add_input("input.closure_name", name) - dag.add_input("input.closure_description", desc) - - # Reference (independent of closure — same for all variants but recorded per-DAG) - dag.add_compute( - "compute.reference", - function="PseudoSpectralReference", - parents=["input.amps", "input.nu0"], - output_summary={"type": "PseudoSpectralReference", "N": ref.N, "dt": ref.dt}, - ) - # Integration of the candidate closure - dag.add_compute( - "compute.integrate_triad", - function="solve_ivp(triad_rhs_with_closure)", - parents=["input.amps", "input.nu0", "input.t_final", "input.n_eval", - "input.beta", "input.closure_name"], - output_summary={ - "n_steps": int(run.t.size), - "a_final": run.a[-1].tolist(), - "energy_initial": float(run.energy[0]), - "energy_final": float(run.energy[-1]), - "nu_eff_min": float(run.nu_eff.min()), - "nu_eff_max": float(run.nu_eff.max()), - }, - ) - # Heat-equation-limit run (separate solve at large ν) - dag.add_compute( - "compute.heat_limit_run", - function="integrate_triad(closure, nu=50, t∈[0,0.05])", - parents=["input.closure_name"], - output_summary={"nu_large": 50.0, "t_span": [0.0, 0.05]}, - ) - # Gates - dag.add_gate( - "gate.G1_reference_cosim", - function="gate_g1_cole_hopf_cosim", - parents=["compute.integrate_triad", "compute.reference"], - result={"passes": g1.passes, "metric": g1.metric, "threshold": g1.threshold, - "note": g1.note, "detail": g1.detail}, - ) - dag.add_gate( - "gate.G2_energy_dissipation", - function="gate_g2_energy_dissipation", - parents=["compute.integrate_triad"], - result={"passes": g2.passes, "metric": g2.metric, "threshold": g2.threshold, - "note": g2.note, "detail": g2.detail}, - ) - dag.add_gate( - "gate.G4_heat_equation_limit", - function="gate_g4_heat_equation_limit", - parents=["compute.heat_limit_run"], - result={"passes": g4.passes, "metric": g4.metric, "threshold": g4.threshold, - "note": g4.note, "detail": g4.detail}, - ) - dag.add_verdict("verdict", - gate_ids=["gate.G1_reference_cosim", - "gate.G2_energy_dissipation", - "gate.G4_heat_equation_limit"]) - - out_path = dag_dir / f"variant_{name}.dag.json" - dag.emit(out_path) - return dag.merkle_root(), out_path - - -def audit(nu0: float = 0.01, t_final: float = 2.0, n_eval: int = 101, - beta: float = 0.5) -> dict: - print("=" * 80) - print(f"GSP VARIANT AUDIT — verifier-grounded (ν₀={nu0}, t_final={t_final})") - print("=" * 80) - print("reference: PseudoSpectralReference (validated by G5 vs Cole-Hopf at ν=0.05)") - print() - - ref = PseudoSpectralReference(DEFAULT_AMPS, nu0, dt=5e-4) - print(f"reference resolution: N={ref.N}, dt={ref.dt}") - print() - - dag_dir = REPO / "shared-data" / "artifacts" / "burgers_verifier" / "dag" - dag_dir.mkdir(parents=True, exist_ok=True) - - print(f"{'variant':<26} {'G1 max-rel':>12} {'G2 dissip.':>12} {'G4 heat lim':>12} " - f"{'gates':>6} {'merkle_root':<20}") - print("-" * 100) - - results: dict[str, dict] = {} - for name, (closure, desc) in VARIANTS.items(): - try: - run = integrate_triad(closure, nu0, t_span=(0.0, t_final), n_eval=n_eval) - g1 = gate_g1_cole_hopf_cosim(run, ref) - g2 = gate_g2_energy_dissipation(run) - g4 = gate_g4_heat_equation_limit(closure) - n_passed = sum([g1.passes, g2.passes, g4.passes]) - - merkle_root, dag_path = _build_variant_dag( - name, desc, run, g1, g2, g4, ref, nu0, t_final, n_eval, beta, dag_dir - ) - - results[name] = { - "description": desc, - "G1": asdict(g1), - "G2": asdict(g2), - "G4": asdict(g4), - "gates_passed": n_passed, - "gates_total": 3, - "dag_path": str(dag_path.relative_to(REPO)), - "dag_merkle_root": merkle_root, - } - - print(f"{name:<26} {g1.metric:>12.4f} " - f"{g2.metric:>12.2e} " - f"{g4.metric:>12.4f} " - f"{n_passed:>4}/3 {merkle_root[:20]}…") - except Exception as exc: - print(f"{name:<26} ERROR: {type(exc).__name__}: {exc}") - results[name] = {"description": desc, "exception": f"{type(exc).__name__}: {exc}"} - - # Ranking by G1 (the primary closure-quality metric). - ranked = sorted( - [(n, r) for n, r in results.items() if "G1" in r], - key=lambda kv: kv[1]["G1"]["metric"] - ) - - print() - print("ranking by G1 (Cole-Hopf/pseudo-spectral cosim, lower is better):") - print(f"{'rank':<6} {'variant':<26} {'G1 max-rel':>12} description") - print("-" * 100) - for i, (name, r) in enumerate(ranked, 1): - marker = " ← best" if i == 1 else " ← worst" if i == len(ranked) else "" - print(f"{i:<6} {name:<26} {r['G1']['metric']:>12.4f} {r['description']}{marker}") - - # Master DAG aggregating per-variant verdicts. - master = RunDAG(run_type="gsp_audit_master", - code_paths=[VERIFIER_PATH, AUDIT_PATH, RUN_DAG_PATH]) - master.add_input("input.audit_config", - {"nu0": nu0, "t_final": t_final, "n_eval": n_eval, - "beta": beta, "amps": list(DEFAULT_AMPS)}) - master.add_input("input.variants", list(VARIANTS.keys())) - # One compute node per variant (records merkle root from sub-DAG) - variant_node_ids: list[str] = [] - for name, r in results.items(): - if "exception" in r: - continue - nid = f"variant.{name}" - master.add_compute( - nid, - function="audit_variant", - parents=["input.audit_config", "input.variants"], - output_summary={ - "G1_metric": r["G1"]["metric"], - "G2_passes": r["G2"]["passes"], - "G4_passes": r["G4"]["passes"], - "gates_passed": r["gates_passed"], - "child_dag_path": r["dag_path"], - "child_dag_merkle_root": r["dag_merkle_root"], - }, - ) - # Synthetic gate node so the verdict sees pass-state per variant - master.add_gate( - f"gate.{name}_meets_baseline", - function="meets_constant_baseline_G1", - parents=[nid], - result={"passes": r["G1"]["metric"] < results.get("B_constant_baseline", {}) - .get("G1", {}).get("metric", float("inf"))}, - ) - variant_node_ids.append(f"gate.{name}_meets_baseline") - - if variant_node_ids: - master.add_verdict("verdict.master", gate_ids=variant_node_ids) - - master_path = dag_dir / "audit_master.dag.json" - master.emit(master_path) - print(f"\nmaster DAG: {master_path.relative_to(REPO)} merkle_root={master.merkle_root()[:30]}…") - - return { - "nu0": nu0, "t_final": t_final, "n_eval": n_eval, - "amps": DEFAULT_AMPS, - "reference_type": "PseudoSpectralReference", - "reference_N": ref.N, - "reference_dt": ref.dt, - "beta": beta, - "variants": results, - "ranking_by_G1": [n for n, _ in ranked], - "dag_dir": str(dag_dir.relative_to(REPO)), - "master_dag_path": str(master_path.relative_to(REPO)), - "master_dag_merkle_root": master.merkle_root(), - } - - -def main(): - out_dir = REPO / "shared-data" / "artifacts" / "burgers_verifier" - out_dir.mkdir(parents=True, exist_ok=True) - - bundle = audit(nu0=0.01, t_final=2.0, n_eval=101) - out = out_dir / "audit_gsp_variants.json" - out.write_text(json.dumps(bundle, indent=2, default=str)) - print(f"\nwrote: {out}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/verifier/burgers_verifier.py b/5-Applications/tools-scripts/verifier/burgers_verifier.py deleted file mode 100644 index dead557d..00000000 --- a/5-Applications/tools-scripts/verifier/burgers_verifier.py +++ /dev/null @@ -1,722 +0,0 @@ -#!/usr/bin/env python3 -""" -burgers_verifier.py -==================== - -An academically defensible verifier for reduced-order-model closure of viscous -Burgers' equation, - - ∂u/∂t + u ∂u/∂x = ν ∂²u/∂x², x ∈ [0, 2π], periodic, ν > 0. - -Built per the principle in -"Auto-Architecture: Karpathy's Loop, Pointed at a CPU" -(github.com/FeSens/auto-arch-tournament): the agent loop is commodity, the -verifier is the moat. This file *is* the verifier — a sharp gate suite defined -before any candidate closure is proposed, against which any closure must be -scored. - -Reference frame (texts a reviewer would accept without question) ----------------------------------------------------------------- -- Cole-Hopf transform: J. D. Cole, "On a quasi-linear parabolic equation - occurring in aerodynamics", Quart. Appl. Math. 9 (1951), 225-236. -- Cole-Hopf for Burgers: G. B. Whitham, "Linear and Nonlinear Waves" (1974), - §4.3; L. C. Evans, "Partial Differential Equations" 2nd ed. (2010), §4.4. -- Energy method for Burgers: P. G. Drazin & R. S. Johnson, "Solitons" (1989). -- Pseudo-spectral Burgers reference: C. Canuto, M. Y. Hussaini, A. Quarteroni, - T. A. Zang, "Spectral Methods" (2007). - -Verifier surface (the gates) ----------------------------- -G1. Cole-Hopf reference cosim — exact analytical solution for any t -G2. Energy dissipation property — dE/dt = -ν π Σ n² aₙ² ≤ 0 -G3. Triad nonlinear-term energy conservation — Σ aₙ (nonlinear da_n/dt) = 0 -G4. ν → ∞ heat-equation limit — aₙ(t) → aₙ(0) exp(-ν n² t) -G5. Cole-Hopf ⇔ pseudo-spectral cosim — independent reference cross-check -G6. Lie test — deliberately broken closures must fail - -Anti-cheat properties ---------------------- -- Cole-Hopf reference uses no time-stepping (analytic decay). It cannot drift, - so "the closure is matching the integrator" is structurally impossible for G1. -- G2 and G3 are derivable algebraically from the triad equations; passing them - is independent of any reference solution. -- G6 ensures the verifier itself is honest: if a known-broken closure passes, - the verifier is broken and must be fixed before being trusted. -""" - -from __future__ import annotations - -import json -import math -import sys -from dataclasses import dataclass, asdict -from pathlib import Path - -import numpy as np -from scipy.integrate import solve_ivp - -# Local-module import path so RunDAG resolves whether script is run from repo root -# or from this directory. -sys.path.insert(0, str(Path(__file__).resolve().parent)) -from run_dag import RunDAG # noqa: E402 - - -# ============================================================================= -# 1. Test field & truncated triad model (matches existing GSP setup) -# ============================================================================= - -# Standard test IC from BurgersHarmonicPeelingVerification.md -DEFAULT_AMPS = (1.0, 0.3, 0.1) - - -def u_from_triad(a: tuple[float, float, float], x: np.ndarray) -> np.ndarray: - a1, a2, a3 = a - return a1 * np.sin(x) + a2 * np.sin(2 * x) + a3 * np.sin(3 * x) - - -def triad_rhs_float(a: tuple[float, float, float], nu_eff: float) -> tuple[float, float, float]: - """Float64 reference of the triad RHS used by burgers_triad_core.py. - - da_1/dt = -ν a_1 + ½(a_1 a_2 + a_2 a_3) - da_2/dt = -4ν a_2 - ½ a_1² + a_1 a_3 - da_3/dt = -9ν a_3 - 3/2 a_1 a_2 - - Derived by Galerkin projection of u u_x onto sin(x), sin(2x), sin(3x). - """ - a1, a2, a3 = a - da1 = -nu_eff * a1 + 0.5 * (a1 * a2 + a2 * a3) - da2 = -4 * nu_eff * a2 - 0.5 * a1 * a1 + a1 * a3 - da3 = -9 * nu_eff * a3 - 1.5 * a1 * a2 - return (da1, da2, da3) - - -# ============================================================================= -# 2. Cole-Hopf exact reference solver (Gate G1) -# ============================================================================= - -class ColeHopfReference: - """Exact viscous-Burgers reference via the Cole-Hopf transform. - - For u(x,0) = u₀(x) periodic on [0, 2π], - u(x, t) = -2ν φ_x(x, t) / φ(x, t) - where φ solves the heat equation with IC - φ(x, 0) = exp[-(1/2ν) ∫₀^x u₀(y) dy]. - The heat equation has the spectral solution - φ(x, t) = Σ_k c_k exp(ikx - ν k² t), c_k = FFT[φ(x,0)]. - - No time-stepping. Spatial truncation only (controlled by N). - - Stability note - -------------- - At small ν, the exponent in φ(x, 0) spans many orders of magnitude - (e.g. ~52 decades at ν=0.01 for our test IC). The naive FFT-reconstruction - of φ(x, t) loses precision where φ would otherwise be exponentially small, - and u = -2ν φ_x / φ then divides by ~0. We mitigate by: - (a) adaptive N: N ∝ 1/√ν so the spatial mesh resolves the boundary layer - (b) constant-shift the exponent (φ → C·φ; C cancels in u = -2ν φ_x/φ) - to keep φ's max ≈ 1, so all numbers stay representable - (c) explicit nan/inf detection on returned u(t); callers should treat - a non-finite u as a verifier breakdown, not as a failed gate. - """ - - def __init__(self, amps: tuple[float, float, float], nu: float, - N: int | None = None): - if nu <= 0: - raise ValueError("Cole-Hopf reference requires ν > 0") - self.amps = amps - self.nu = nu - - # Adaptive N: resolve the diffusive boundary layer scale δ ~ √(ν · T). - # Heuristic: N ≥ 2π / (δ/16) at T~1 — about 16 points per boundary layer. - # Equivalently N ~ 100 / √ν, capped by user. - if N is None: - N = max(512, int(2 ** math.ceil(math.log2(100.0 / math.sqrt(nu))))) - N = min(N, 16384) # cap to keep FFT cost bounded - self.N = N - self.x = np.linspace(0, 2 * np.pi, N, endpoint=False) - self.k = np.fft.fftfreq(N, d=(2 * np.pi) / N) * (2 * np.pi) - - a1, a2, a3 = amps - self.U0_int = ( - a1 * (1 - np.cos(self.x)) - + (a2 / 2.0) * (1 - np.cos(2 * self.x)) - + (a3 / 3.0) * (1 - np.cos(3 * self.x)) - ) - # Constant-shift trick: φ → φ · exp(-shift) leaves u = -2ν φ_x/φ invariant. - # Pick shift = max(U0_int)/(2ν) so the largest value of -U0_int/(2ν)+shift = 0. - # Then exp() values lie in [exp(-Δ), 1] instead of [exp(-Δ), exp(0)] with overflow risk. - exponent = -self.U0_int / (2.0 * nu) - shift = exponent.max() - self.phi0 = np.exp(exponent - shift) # max value = 1; min may underflow harmlessly - self.c = np.fft.fft(self.phi0) - - def phi(self, t: float) -> np.ndarray: - decay = np.exp(-self.nu * (self.k ** 2) * t) - return np.real(np.fft.ifft(self.c * decay)) - - def u(self, t: float) -> np.ndarray: - """Returns u(x, t). May contain nan/inf where Cole-Hopf is numerically broken; - callers must check `np.isfinite(u).all()` before using.""" - decay = np.exp(-self.nu * (self.k ** 2) * t) - phi_t = np.fft.ifft(self.c * decay) - phix_t = np.fft.ifft(1j * self.k * (self.c * decay)) - with np.errstate(divide="ignore", invalid="ignore"): - u = np.real(-2.0 * self.nu * phix_t / phi_t) - return u - - def is_finite_at(self, t: float) -> bool: - return bool(np.isfinite(self.u(t)).all()) - - -class PseudoSpectralReference: - """High-resolution pseudo-spectral RK4 reference for viscous Burgers. - - Independent of Cole-Hopf. Used where Cole-Hopf is numerically fragile - (low ν). Trust chain: - - G5 verifies pseudo-spectral matches Cole-Hopf to ~1e-9 at ν=0.05. - - Method/discretisation does not change with ν; only the parameter does. - Therefore a pseudo-spectral solve at low ν, with N large enough to resolve - the boundary layer δ ~ √(ν T), is a defensible reference at any ν > 0. - """ - - def __init__(self, amps: tuple[float, float, float], nu: float, - N: int | None = None, dt: float = 1e-4): - if nu <= 0: - raise ValueError("Burgers reference requires ν > 0") - self.amps = amps - self.nu = nu - self.dt = dt - if N is None: - # Resolve boundary layer to ~16 points - N = max(512, int(2 ** math.ceil(math.log2(100.0 / math.sqrt(nu))))) - N = min(N, 16384) - self.N = N - self.x = np.linspace(0, 2 * np.pi, N, endpoint=False) - self.k = np.fft.fftfreq(N, d=(2 * np.pi) / N) * (2 * np.pi) - self.mask = np.abs(self.k) < (2.0 / 3.0) * (N / 2) # 2/3 dealiasing rule - a1, a2, a3 = amps - self.u_initial = a1 * np.sin(self.x) + a2 * np.sin(2 * self.x) + a3 * np.sin(3 * self.x) - self._cache: dict[float, np.ndarray] = {0.0: self.u_initial.copy()} - self._cache_uhat: dict[float, np.ndarray] = {0.0: np.fft.fft(self.u_initial)} - - def _rhs(self, uh: np.ndarray) -> np.ndarray: - u_real = np.real(np.fft.ifft(uh)) - ux_real = np.real(np.fft.ifft(1j * self.k * uh)) - nl_hat = np.fft.fft(u_real * ux_real) * self.mask - diff_hat = -self.nu * (self.k ** 2) * uh - return -nl_hat + diff_hat - - def u(self, t: float) -> np.ndarray: - # Find nearest cached t ≤ requested - t = float(t) - if t in self._cache: - return self._cache[t] - # Step forward from the latest cached t ≤ requested - cached_ts = sorted([ts for ts in self._cache if ts <= t]) - if not cached_ts: - raise ValueError(f"cannot integrate backward: t={t} earlier than cache") - t_start = cached_ts[-1] - uh = self._cache_uhat[t_start].copy() - n_steps = max(1, int(round((t - t_start) / self.dt))) - actual_dt = (t - t_start) / n_steps - for _ in range(n_steps): - k1 = self._rhs(uh) - k2 = self._rhs(uh + 0.5 * actual_dt * k1) - k3 = self._rhs(uh + 0.5 * actual_dt * k2) - k4 = self._rhs(uh + actual_dt * k3) - uh = uh + (actual_dt / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4) - u_t = np.real(np.fft.ifft(uh)) - self._cache[t] = u_t - self._cache_uhat[t] = uh.copy() - return u_t - - def project_to_triad(self, t: float) -> tuple[float, float, float]: - u_t = self.u(t) - uhat = np.fft.fft(u_t) - b = [-2.0 / self.N * uhat[n].imag for n in (1, 2, 3)] - return tuple(b) # type: ignore[return-value] - - def is_finite_at(self, t: float) -> bool: - return bool(np.isfinite(self.u(t)).all()) - - def project_to_triad(self, t: float) -> tuple[float, float, float]: - """Project the exact u(x, t) onto sin(x), sin(2x), sin(3x). - - For u = Σ b_n sin(n x), b_n = (1/π) ∫₀^{2π} u sin(n x) dx. - FFT gives û_k = N/2 · (-i b_n) for n ≥ 1 (sine convention), so - b_n = -2/N · Im(û_n). - """ - u_t = self.u(t) - uhat = np.fft.fft(u_t) - b = [-2.0 / self.N * uhat[n].imag for n in (1, 2, 3)] - return tuple(b) # type: ignore[return-value] - - -# ============================================================================= -# 3. Closure-runner (integrate triad with a candidate closure) -# ============================================================================= - -@dataclass -class TriadRun: - """Output of integrating the truncated triad with a closure ν_eff(t, a).""" - t: np.ndarray - a: np.ndarray # shape (T, 3) - nu_eff: np.ndarray # shape (T,) - energy: np.ndarray # shape (T,) - - -def integrate_triad( - closure_fn, # f(t, a, nu0) -> nu_eff - nu0: float, - amps0: tuple[float, float, float] = DEFAULT_AMPS, - t_span: tuple[float, float] = (0.0, 5.0), - n_eval: int = 201, - rtol: float = 1e-6, - atol: float = 1e-8, - max_step: float | None = None, -) -> TriadRun: - """Integrate the truncated triad with a candidate closure. - - Performance note for stochastic closures (Perceval, learned NN, etc.): - each RHS evaluation may invoke a costly external sampler, so we (a) bound - the integrator's internal step via `max_step`, (b) use moderate tolerances - that keep the number of stages reasonable, and (c) the *caller* should - memoise its closure_fn on the state tuple if its evaluation is non-trivial. - """ - def rhs(t, a): - nu_eff = closure_fn(t, tuple(a), nu0) - return triad_rhs_float(tuple(a), nu_eff) - - if max_step is None: - max_step = (t_span[1] - t_span[0]) / max(1, n_eval - 1) - - t_eval = np.linspace(t_span[0], t_span[1], n_eval) - sol = solve_ivp(rhs, t_span, amps0, t_eval=t_eval, method="RK45", - rtol=rtol, atol=atol, max_step=max_step, dense_output=False) - if not sol.success: - raise RuntimeError(f"triad integration failed: {sol.message}") - - a = sol.y.T # shape (T, 3) - nu_eff_series = np.array([closure_fn(t, tuple(a[i]), nu0) for i, t in enumerate(sol.t)]) - energy = (math.pi / 2.0) * np.sum(a ** 2, axis=1) - return TriadRun(t=sol.t, a=a, nu_eff=nu_eff_series, energy=energy) - - -# ============================================================================= -# 4. The gates -# ============================================================================= - -@dataclass -class GateResult: - name: str - passes: bool - metric: float - threshold: float | None - note: str - detail: dict - - -def gate_g1_cole_hopf_cosim(run: TriadRun, - ref: ColeHopfReference | PseudoSpectralReference, - threshold_rel: float = 0.10) -> GateResult: - """G1: closure-corrected triad must track Cole-Hopf-projected truth within tol. - - Hard-fail with `VERIFIER_BREAKDOWN` if the Cole-Hopf reference produces - non-finite u at any sampled t — a NaN gate result is meaningless, and - silently coercing it to "fail" would hide the verifier's own malfunction. - """ - truth_finite = [ref.is_finite_at(float(t)) for t in run.t] - if not all(truth_finite): - first_bad = int(np.argmin(truth_finite)) - return GateResult( - name="G1_cole_hopf_cosim", - passes=False, - metric=float("nan"), - threshold=threshold_rel, - note=f"VERIFIER_BREAKDOWN — {type(ref).__name__} reference non-finite at " - f"ν={ref.nu}, N={ref.N}. Use PseudoSpectralReference at low ν.", - detail={"first_bad_t_index": first_bad, - "first_bad_t": float(run.t[first_bad]), - "n_samples": len(run.t), - "nu": ref.nu, "N": ref.N, - "reference_type": type(ref).__name__}, - ) - - a_truth = np.array([ref.project_to_triad(t) for t in run.t]) - err = np.linalg.norm(run.a - a_truth, axis=1) - norm_truth = np.linalg.norm(a_truth, axis=1) + 1e-12 - rel_err = err / norm_truth - max_rel = float(np.max(rel_err)) - final_rel = float(rel_err[-1]) - return GateResult( - name="G1_cole_hopf_cosim", - passes=max_rel <= threshold_rel, - metric=max_rel, - threshold=threshold_rel, - note=f"max ‖a_closure − a_true‖₂ / ‖a_true‖₂ over t", - detail={ - "max_rel_error": max_rel, - "final_rel_error": final_rel, - "L2_a_final": float(np.linalg.norm(run.a[-1] - a_truth[-1])), - "N_cole_hopf": ref.N, - }, - ) - - -def gate_g2_energy_dissipation(run: TriadRun) -> GateResult: - """G2: E(t) must be monotonically non-increasing for ν_eff > 0 and any nonzero IC.""" - dE = np.diff(run.energy) - # Allow tiny positive drift from RK45 numerical error. - tol = 1e-9 * max(1.0, run.energy[0]) - violations = int(np.sum(dE > tol)) - max_increase = float(dE.max()) if len(dE) > 0 else 0.0 - return GateResult( - name="G2_energy_dissipation", - passes=violations == 0, - metric=max_increase, - threshold=tol, - note="energy must be non-increasing (dE/dt ≤ 0 from u_t = ν u_xx)", - detail={ - "energy_initial": float(run.energy[0]), - "energy_final": float(run.energy[-1]), - "violations": violations, - "max_increase": max_increase, - }, - ) - - -def gate_g3_triad_nonlinear_conservation(amps_grid_size: int = 7) -> GateResult: - """G3: Σ aₙ (nonlinear part of da_n/dt) = 0 identically (energy-conserving advection). - - Sweep a grid of (a₁, a₂, a₃) with ν=0 and verify the inner product is - numerically zero. This validates the triad equations themselves, not any - candidate closure. - """ - grid = np.linspace(-1.0, 1.0, amps_grid_size) - max_violation = 0.0 - n = 0 - for x in grid: - for y in grid: - for z in grid: - a = (float(x), float(y), float(z)) - da = triad_rhs_float(a, 0.0) # nu_eff = 0 → only nonlinear terms - ip = a[0] * da[0] + a[1] * da[1] + a[2] * da[2] - max_violation = max(max_violation, abs(ip)) - n += 1 - tol = 1e-12 - return GateResult( - name="G3_triad_nonlinear_conservation", - passes=max_violation <= tol, - metric=max_violation, - threshold=tol, - note=("for ν=0, ⟨a, ȧ⟩ must vanish on the truncated triad system " - "(verifies Galerkin projection preserves energy on advection)"), - detail={"grid_size": amps_grid_size, "samples": n, "max_violation": max_violation}, - ) - - -def gate_g4_heat_equation_limit(closure_fn, nu_large: float = 50.0, - amps0: tuple[float, float, float] = DEFAULT_AMPS, - t_span: tuple[float, float] = (0.0, 0.05)) -> GateResult: - """G4: At very large ν, nonlinear advection is dominated by diffusion. - - Each mode should decay as aₙ(t) ≈ aₙ(0) exp(-ν n² t) to leading order. - Compare the integrated triad to the pure exponential decay. - """ - run = integrate_triad(closure_fn, nu_large, amps0, t_span=t_span, n_eval=21) - t = run.t - expected = np.array([ - [amps0[0] * math.exp(-nu_large * 1 * 1 * tt), - amps0[1] * math.exp(-nu_large * 2 * 2 * tt), - amps0[2] * math.exp(-nu_large * 3 * 3 * tt)] - for tt in t - ]) - err = np.linalg.norm(run.a - expected, axis=1) - norm_expected = np.linalg.norm(expected, axis=1) + 1e-12 - rel_err = err / norm_expected - max_rel = float(np.max(rel_err)) - threshold = 0.10 # 10% — nonlinear correction is O(1/ν) at this regime - return GateResult( - name="G4_heat_equation_limit", - passes=max_rel <= threshold, - metric=max_rel, - threshold=threshold, - note=f"at ν={nu_large}, triad dynamics should approach pure exponential decay", - detail={"max_rel_error": max_rel, "nu": nu_large, "t_final": float(t[-1])}, - ) - - -def gate_g5_oracle_cross_check(amps: tuple[float, float, float], nu: float, - t_eval: float, N_oracle: int = 256, dt: float = 1e-3, - threshold: float = 1e-3) -> GateResult: - """G5: Cole-Hopf and pseudo-spectral RK4 oracle must agree on u(x, t). - - Independent reference cross-check. If they disagree, our 'truth' is wrong. - """ - ref = ColeHopfReference(amps, nu, N=512) - u_ch = ref.u(t_eval) - x_ch = ref.x - - # Pseudo-spectral RK4 oracle (mirrors reference_tail_oracle.py) - x_o = np.linspace(0, 2 * np.pi, N_oracle, endpoint=False) - k_o = np.fft.fftfreq(N_oracle, d=(2 * np.pi) / N_oracle) * (2 * np.pi) - mask = np.abs(k_o) < (2.0 / 3.0) * (N_oracle / 2) - u_o = amps[0] * np.sin(x_o) + amps[1] * np.sin(2 * x_o) + amps[2] * np.sin(3 * x_o) - uh = np.fft.fft(u_o) - - def rhs(uh): - u_real = np.real(np.fft.ifft(uh)) - ux_real = np.real(np.fft.ifft(1j * k_o * uh)) - nl_hat = np.fft.fft(u_real * ux_real) * mask - diff_hat = -nu * (k_o ** 2) * uh - return -nl_hat + diff_hat - - n_steps = max(1, int(round(t_eval / dt))) - actual_dt = t_eval / n_steps - for _ in range(n_steps): - k1 = rhs(uh); k2 = rhs(uh + 0.5 * actual_dt * k1) - k3 = rhs(uh + 0.5 * actual_dt * k2); k4 = rhs(uh + actual_dt * k3) - uh = uh + (actual_dt / 6.0) * (k1 + 2 * k2 + 2 * k3 + k4) - u_o_final = np.real(np.fft.ifft(uh)) - - # Compare on common grid via interpolation - u_ch_on_o = np.interp(x_o, x_ch, u_ch, period=2 * np.pi) - L2 = float(np.sqrt(np.mean((u_o_final - u_ch_on_o) ** 2))) - norm = float(np.sqrt(np.mean(u_ch_on_o ** 2))) + 1e-12 - rel = L2 / norm - return GateResult( - name="G5_oracle_cross_check", - passes=rel <= threshold, - metric=rel, - threshold=threshold, - note=f"Cole-Hopf vs pseudo-spectral RK4 on u(·, t={t_eval})", - detail={"L2": L2, "rel": rel, "n_steps": n_steps, "dt": actual_dt}, - ) - - -# ============================================================================= -# 5. Lie test (Gate G6) — verifier must catch known-broken closures -# ============================================================================= - -def closure_zero(t, a, nu0): - """Broken: no viscosity at all. Should violate G1 (drifts off truth).""" - return 0.0 - -def closure_negative(t, a, nu0): - """Broken: negative viscosity. Should violate G2 (energy grows).""" - return -abs(nu0) - -def closure_huge(t, a, nu0): - """Broken: enormous viscosity. Should violate G1 (over-dissipates).""" - return 1e3 * nu0 - -def closure_constant(t, a, nu0): - """Honest baseline: constant ν₀. Should *pass* G2, may fail G1.""" - return nu0 - - -def lie_test(nu0: float = 0.01, t_final: float = 2.0) -> dict: - """G6: run the gates against deliberately-broken closures and verify the gates fire. - - Uses PseudoSpectralReference (not Cole-Hopf) at low ν — Cole-Hopf is numerically - fragile below ν~0.05. The pseudo-spectral reference's correctness is established - by G5 (cross-check with Cole-Hopf at ν=0.05). - """ - ref = PseudoSpectralReference(DEFAULT_AMPS, nu0, dt=5e-4) - - results = {} - for name, fn, expected_to_fail in [ - ("zero_viscosity", closure_zero, ["G1_cole_hopf_cosim"]), - ("negative_viscosity", closure_negative, ["G1_cole_hopf_cosim", "G2_energy_dissipation"]), - ("huge_viscosity", closure_huge, ["G1_cole_hopf_cosim"]), - ("constant_baseline", closure_constant, []), # honest baseline - ]: - try: - run = integrate_triad(fn, nu0, t_span=(0.0, t_final), n_eval=101) - g1 = gate_g1_cole_hopf_cosim(run, ref) - g2 = gate_g2_energy_dissipation(run) - g4 = gate_g4_heat_equation_limit(fn) - actually_failed = [g.name for g in (g1, g2, g4) if not g.passes] - verifier_caught_lie = (set(expected_to_fail).issubset(actually_failed) - if expected_to_fail else True) - results[name] = { - "expected_failures": expected_to_fail, - "actual_failures": actually_failed, - "verifier_caught_lie": verifier_caught_lie, - "g1": asdict(g1), - "g2": asdict(g2), - "g4": asdict(g4), - } - except Exception as exc: - # A broken closure may even crash the integrator — that's a "loud failure", - # which from the verifier's standpoint is still a caught lie. - results[name] = { - "expected_failures": expected_to_fail, - "actual_failures": ["INTEGRATION_FAILURE"], - "verifier_caught_lie": True, - "exception": f"{type(exc).__name__}: {exc}", - } - - all_caught = all(r["verifier_caught_lie"] for r in results.values()) - return { - "all_lies_caught": all_caught, - "per_closure": results, - } - - -# ============================================================================= -# 6. Self-test -# ============================================================================= - -def self_test() -> dict: - """Run every gate against known cases; report whether the verifier itself works.""" - print("=" * 72) - print("BURGERS VERIFIER — SELF-TEST") - print("=" * 72) - - # G3: pure algebraic property of the triad equations (no closure involved). - print("\n[G3] triad nonlinear-term energy conservation (algebraic identity)") - g3 = gate_g3_triad_nonlinear_conservation(amps_grid_size=7) - print(f" max |⟨a, ȧ_NL⟩| over 7³=343 grid pts = {g3.metric:.2e} passes={g3.passes}") - - # G5: independent reference cross-check. - print("\n[G5] Cole-Hopf vs pseudo-spectral RK4 oracle (independent references)") - g5 = gate_g5_oracle_cross_check(DEFAULT_AMPS, nu=0.05, t_eval=0.5) - print(f" L2 relative error at t=0.5, ν=0.05: {g5.metric:.2e} passes={g5.passes}") - - # G6: lie test. - print("\n[G6] lie test — verifier must catch known-broken closures") - g6 = lie_test(nu0=0.01, t_final=2.0) - for name, r in g6["per_closure"].items(): - print(f" {name:<22} expected_fails={r['expected_failures']} " - f"actual_fails={r['actual_failures']} caught={r['verifier_caught_lie']}") - print(f" ALL_LIES_CAUGHT: {g6['all_lies_caught']}") - - # Honest baseline: constant ν=ν₀ is the simplest "closure" — it doesn't model anything, - # but it should pass G2 (energy dissipation) and may have measurable G1 error. - print("\n[honest baseline] constant ν = ν₀ closure on (a₁, a₂, a₃) = (1, 0.3, 0.1), ν₀ = 0.01") - ref = PseudoSpectralReference(DEFAULT_AMPS, 0.01, dt=5e-4) - print(f" PseudoSpectral reference: N = {ref.N}, dt = {ref.dt}") - print(f" (chain of trust: validated against Cole-Hopf at ν=0.05 by G5 → 4e-9 agreement)") - run_const = integrate_triad(closure_constant, 0.01, t_span=(0.0, 2.0), n_eval=101) - g1_const = gate_g1_cole_hopf_cosim(run_const, ref) - g2_const = gate_g2_energy_dissipation(run_const) - print(f" G1 (reference cosim) : max rel err = {g1_const.metric:.4f} passes={g1_const.passes}") - print(f" G2 (energy dissipates): max ΔE = {g2_const.metric:.2e} passes={g2_const.passes}") - print(f" >> the constant-viscosity baseline is the bar a real closure must beat.") - - return { - "G3": asdict(g3), - "G5": asdict(g5), - "G6": g6, - "constant_baseline": { - "G1": asdict(g1_const), - "G2": asdict(g2_const), - "G1_max_rel_error": g1_const.metric, - "passes_all": g1_const.passes and g2_const.passes, - }, - "verifier_self_consistent": ( - g3.passes and g5.passes and g6["all_lies_caught"] - ), - } - - -def _emit_self_test_dag(results: dict, out_dir: Path) -> Path: - """Build a Merkle DAG of the self-test: inputs → references → gates → verdict.""" - dag = RunDAG( - run_type="burgers_verifier_self_test", - code_paths=[Path(__file__), Path(__file__).parent / "run_dag.py"], - ) - dag.add_input("input.amps", list(DEFAULT_AMPS)) - dag.add_input("input.nu_g5", 0.05) - dag.add_input("input.nu0_baseline", 0.01) - dag.add_input("input.t_eval_g5", 0.5) - dag.add_input("input.lie_test_t_final", 2.0) - - # G3 — algebraic identity, no inputs - dag.add_gate("gate.G3_triad_nonlinear_conservation", - function="gate_g3_triad_nonlinear_conservation", - parents=[], - result=results["G3"]) - - # G5 — independent reference cross-check - dag.add_compute("compute.cole_hopf_g5", - function="ColeHopfReference", - parents=["input.amps", "input.nu_g5"], - output_summary={"type": "ColeHopfReference", - "nu": 0.05, "N": "adaptive"}) - dag.add_compute("compute.pseudo_spectral_g5", - function="PseudoSpectralReference", - parents=["input.amps", "input.nu_g5", "input.t_eval_g5"], - output_summary={"type": "RK4 pseudo-spectral", "nu": 0.05}) - dag.add_gate("gate.G5_oracle_cross_check", - function="gate_g5_oracle_cross_check", - parents=["compute.cole_hopf_g5", "compute.pseudo_spectral_g5"], - result=results["G5"]) - - # G6 — lie test (per-closure sub-results aggregated into one gate node) - g6 = results["G6"] - dag.add_compute("compute.lie_test_runs", - function="lie_test_per_closure", - parents=["input.amps", "input.nu0_baseline", "input.lie_test_t_final"], - output_summary={ - "closures_tested": list(g6["per_closure"].keys()), - "n_closures": len(g6["per_closure"]), - }) - dag.add_gate("gate.G6_all_lies_caught", - function="lie_test_aggregate", - parents=["compute.lie_test_runs"], - result={"passes": g6["all_lies_caught"], - "metric": sum(1 for r in g6["per_closure"].values() - if r["verifier_caught_lie"]), - "threshold": len(g6["per_closure"]), - "note": "every broken closure must trigger at least its expected gate failures", - "detail": {"per_closure_caught": - {n: r["verifier_caught_lie"] - for n, r in g6["per_closure"].items()}}}) - - # Constant baseline — concrete G1+G2 evaluation at the operating ν₀ - cb = results["constant_baseline"] - dag.add_compute("compute.baseline_integration", - function="integrate_triad(closure_constant)", - parents=["input.amps", "input.nu0_baseline"], - output_summary={"closure": "constant_nu0", "t_span": [0.0, 2.0]}) - dag.add_compute("compute.baseline_reference", - function="PseudoSpectralReference", - parents=["input.amps", "input.nu0_baseline"], - output_summary={"type": "PseudoSpectralReference"}) - dag.add_gate("gate.baseline_G1", - function="gate_g1_cole_hopf_cosim", - parents=["compute.baseline_integration", "compute.baseline_reference"], - result=cb["G1"]) - dag.add_gate("gate.baseline_G2", - function="gate_g2_energy_dissipation", - parents=["compute.baseline_integration"], - result=cb["G2"]) - - dag.add_verdict("verdict", - gate_ids=["gate.G3_triad_nonlinear_conservation", - "gate.G5_oracle_cross_check", - "gate.G6_all_lies_caught"]) - - out_path = out_dir / "dag" / "verifier_self_test.dag.json" - dag.emit(out_path) - return out_path - - -def main(): - results = self_test() - out_dir = Path(__file__).resolve().parents[3] / "shared-data" / "artifacts" / "burgers_verifier" - out_dir.mkdir(parents=True, exist_ok=True) - out = out_dir / "burgers_verifier_self_test.json" - out.write_text(json.dumps(results, indent=2, default=str)) - print(f"\nwrote: {out}") - - dag_path = _emit_self_test_dag(results, out_dir) - print(f"wrote DAG: {dag_path}") - - print(f"\nverifier_self_consistent: {results['verifier_self_consistent']}") - - -if __name__ == "__main__": - main() diff --git a/5-Applications/tools-scripts/verifier/run_dag.py b/5-Applications/tools-scripts/verifier/run_dag.py deleted file mode 100644 index f8d39a38..00000000 --- a/5-Applications/tools-scripts/verifier/run_dag.py +++ /dev/null @@ -1,210 +0,0 @@ -#!/usr/bin/env python3 -""" -run_dag.py — Merkle DAG provenance for verifier and audit runs. - -Every run that uses RunDAG emits a JSON file describing its computation as a -directed acyclic graph of typed nodes: - - input — a parameter or initial value, hashed by repr - compute — a deterministic function call on parent nodes, hashed by - (function name, parent hashes, output summary) - gate — a verifier gate's result on parent compute outputs, hashed by - (gate name, parent hashes, result) - verdict — terminal aggregation of all gates, hashed by gate results - -Each node's hash includes its parents' hashes, so the DAG is a Merkle DAG: a -single byte changed anywhere upstream perturbs every downstream hash. This -gives the AVMR/metaprobe receipt principle a *structural* guarantee, not just -an asserted one. - -Schema is intentionally simple JSON so the artifact is consumable by any tool -that reads JSON (and any graph viewer that accepts node/edge lists). -""" - -from __future__ import annotations - -import datetime -import hashlib -import json -import uuid -from pathlib import Path -from typing import Any - - -def _utcnow() -> str: - return datetime.datetime.now(datetime.timezone.utc).isoformat(timespec="seconds") - - -def _sha256(s: str | bytes) -> str: - if isinstance(s, str): - s = s.encode("utf-8") - return "sha256:" + hashlib.sha256(s).hexdigest() - - -def _hash_file(path: Path) -> str: - if not path.exists(): - return "sha256:MISSING" - return _sha256(path.read_bytes()) - - -def _stable_repr(value: Any) -> str: - """Deterministic serialization for hashing. - - Falls back to repr() for objects json can't handle. - """ - try: - return json.dumps(value, sort_keys=True, default=repr) - except (TypeError, ValueError): - return repr(value) - - -class RunDAG: - """Build up a Merkle DAG of a verifier or audit run as it executes.""" - - SCHEMA_VERSION = "1.0" - - def __init__(self, run_type: str, code_paths: list[Path] | None = None, - run_id: str | None = None): - self.run_type = run_type - self.run_id = run_id or f"{run_type}-{_utcnow()}-{uuid.uuid4().hex[:8]}" - self.started_at = _utcnow() - self.completed_at: str | None = None - self.code_provenance = { - str(p): _hash_file(p) for p in (code_paths or []) - } - self.nodes: dict[str, dict] = {} - self.edges: list[dict] = [] - - # ----- node primitives ----- - - def _record_edges(self, node_id: str, parents: list[str]) -> None: - for p in parents: - if p not in self.nodes: - raise KeyError(f"DAG edge {p} → {node_id}: parent not yet in DAG") - self.edges.append({"from": p, "to": node_id}) - - def add_input(self, node_id: str, value: Any) -> str: - if node_id in self.nodes: - raise KeyError(f"node already exists: {node_id}") - value_repr = _stable_repr(value) - h = _sha256(value_repr) - self.nodes[node_id] = { - "id": node_id, "type": "input", - "value_repr": value_repr, "hash": h, - } - return h - - def add_compute(self, node_id: str, function: str, parents: list[str], - output_summary: dict) -> str: - if node_id in self.nodes: - raise KeyError(f"node already exists: {node_id}") - parent_hashes = [self.nodes[p]["hash"] for p in parents] - payload = _stable_repr({ - "function": function, - "parent_hashes": parent_hashes, - "output": output_summary, - }) - h = _sha256(payload) - self.nodes[node_id] = { - "id": node_id, "type": "compute", - "function": function, "parent_hashes": parent_hashes, - "output_summary": output_summary, "hash": h, - } - self._record_edges(node_id, parents) - return h - - def add_gate(self, node_id: str, function: str, parents: list[str], - result: dict) -> str: - if node_id in self.nodes: - raise KeyError(f"node already exists: {node_id}") - parent_hashes = [self.nodes[p]["hash"] for p in parents] - payload = _stable_repr({ - "function": function, - "parent_hashes": parent_hashes, - "result": result, - }) - h = _sha256(payload) - self.nodes[node_id] = { - "id": node_id, "type": "gate", - "function": function, "parent_hashes": parent_hashes, - "result": result, "hash": h, - } - self._record_edges(node_id, parents) - return h - - def add_verdict(self, node_id: str, gate_ids: list[str], - pass_predicate=lambda g: g["result"].get("passes", False)) -> str: - if node_id in self.nodes: - raise KeyError(f"node already exists: {node_id}") - parent_hashes = [self.nodes[gid]["hash"] for gid in gate_ids] - results = {gid: pass_predicate(self.nodes[gid]) for gid in gate_ids} - # Hash MUST include parent hashes to preserve the Merkle property — - # otherwise two runs with the same pass/fail pattern but different - # underlying gate-metric values produce identical verdict hashes. - payload = _stable_repr({ - "gate_ids": gate_ids, - "parent_hashes": parent_hashes, - "results": results, - }) - h = _sha256(payload) - self.nodes[node_id] = { - "id": node_id, "type": "verdict", - "gate_ids": gate_ids, "parent_hashes": parent_hashes, - "results": results, "all_passed": all(results.values()), - "hash": h, - } - self._record_edges(node_id, gate_ids) - return h - - # ----- emit ----- - - def merkle_root(self) -> str: - verdict_nodes = [n for n in self.nodes.values() if n["type"] == "verdict"] - if not verdict_nodes: - # Aggregate hash over all leaf nodes if no explicit verdict - leaf_hashes = sorted(n["hash"] for n in self.nodes.values()) - return _sha256(_stable_repr(leaf_hashes)) - # Single verdict: its hash IS the merkle root - if len(verdict_nodes) == 1: - return verdict_nodes[0]["hash"] - # Multiple verdicts: hash of sorted verdict hashes - return _sha256(_stable_repr(sorted(v["hash"] for v in verdict_nodes))) - - def emit(self, path: Path) -> Path: - self.completed_at = _utcnow() - path.parent.mkdir(parents=True, exist_ok=True) - doc = { - "schema_version": self.SCHEMA_VERSION, - "run_id": self.run_id, - "run_type": self.run_type, - "started_at": self.started_at, - "completed_at": self.completed_at, - "code_provenance": self.code_provenance, - "merkle_root": self.merkle_root(), - "n_nodes": len(self.nodes), - "n_edges": len(self.edges), - "nodes": list(self.nodes.values()), - "edges": self.edges, - } - path.write_text(json.dumps(doc, indent=2, default=str)) - return path - - # ----- helpers ----- - - def to_dot(self) -> str: - """Emit Graphviz DOT for visual inspection.""" - lines = [f'digraph "{self.run_id}" {{', ' rankdir=LR;', ' node [shape=box];'] - type_color = {"input": "lightblue", "compute": "white", - "gate": "lightyellow", "verdict": "lightgreen"} - for n in self.nodes.values(): - label = n["id"] - if n["type"] == "gate": - label += f"\\n{'PASS' if n['result'].get('passes') else 'FAIL'}" - if "metric" in n["result"]: - label += f"\\nm={n['result']['metric']:.4g}" - color = type_color.get(n["type"], "white") - lines.append(f' "{n["id"]}" [label="{label}", style=filled, fillcolor={color}];') - for e in self.edges: - lines.append(f' "{e["from"]}" -> "{e["to"]}";') - lines.append("}") - return "\n".join(lines) diff --git a/5-Applications/tools-scripts/waveprobe/waveprobe_smb_probe.py b/5-Applications/tools-scripts/waveprobe/waveprobe_smb_probe.py deleted file mode 100644 index 4dd5920b..00000000 --- a/5-Applications/tools-scripts/waveprobe/waveprobe_smb_probe.py +++ /dev/null @@ -1,413 +0,0 @@ -#!/usr/bin/env python3 -# ============================================================================== -# COPYRIGHT NO ONE EVERYWHERE LLC (WYOMING HOLDING COMPANY) -# PROJECT: SOVEREIGN STACK -# This artifact is entirely proprietary and cryptographically proven. -# Open-Source usage requires explicit permission from Brandon Scott Schneider. -# ============================================================================== -# PTOS: LAYER=STORE / DOMAIN=DATA / CONDITION=EXPERIMENTAL / STAGE=ACTIVE / SOURCE=CODE -""" -waveprobe_smb_probe.py — Hash and index Windows SMB share mounts into the substrate. - -Walks /mnt/{ddrive,edrive,fdrive,gdrive,hdrive,zdrive}, SHA-256 hashes every -file, writes a per-drive JSONL manifest to 5-Applications/out/smb_index/, and feeds -text-readable files into the substrate SQLite index (same schema as ingest_archive). - -Usage: - python3 5-Applications/scripts/waveprobe_smb_probe.py # all mounted drives - python3 5-Applications/scripts/waveprobe_smb_probe.py --drives ddrive edrive - python3 5-Applications/scripts/waveprobe_smb_probe.py --dry-run - python3 5-Applications/scripts/waveprobe_smb_probe.py --force # re-index existing rows - python3 5-Applications/scripts/waveprobe_smb_probe.py --hash-only # manifests only, no SQLite - python3 5-Applications/scripts/waveprobe_smb_probe.py --status # show index counts - -Environment: - SUBSTRATE_DB path to substrate_index.db (default: repo root) -""" - -from __future__ import annotations - -import argparse -import hashlib -import json -import os -import re -import sqlite3 -import sys -from collections import Counter -from datetime import datetime, timezone -from pathlib import Path -from typing import Any - -ROOT = Path(__file__).resolve().parent.parent -sys.path.insert(0, str(ROOT / "scripts")) - -from substrate_git_index import DB_PATH, _open_db, _concept_vector_from_weights, _upsert_package, _PHI # type: ignore - -try: - from ingest_text_hygiene import derive_safe_description # type: ignore -except ImportError: - def derive_safe_description(path: Path, text: str, max_len: int = 200) -> str: # type: ignore[misc] - return (text[:max_len].replace("\n", " ").strip() + "…") if len(text) > max_len else text.strip() - -# ─── configuration ──────────────────────────────────────────────────────────── - -SMB_DRIVES = ["ddrive", "edrive", "fdrive", "gdrive", "hdrive", "zdrive"] -MOUNT_BASE = Path("/mnt") -OUT_DIR = ROOT / "out" / "smb_index" - -# Extensions considered text-readable and worth keyword-indexing -TEXT_EXTENSIONS = { - ".md", ".txt", ".rst", ".json", ".jsonl", ".py", ".sh", ".rs", ".c", ".h", - ".csv", ".yaml", ".yml", ".toml", ".ini", ".cfg", ".xml", ".html", ".js", - ".ts", ".bat", ".ps1", ".log", -} - -# Binary extensions — hashed but not keyword-indexed -BINARY_EXTENSIONS = { - ".exe", ".dll", ".sys", ".bin", ".iso", ".img", ".zip", ".7z", ".rar", - ".tar", ".gz", ".bz2", ".xz", ".cab", ".msi", ".msu", - ".mp3", ".flac", ".wav", ".ogg", ".m4a", ".aac", - ".mp4", ".mkv", ".avi", ".mov", ".wmv", ".flv", - ".jpg", ".jpeg", ".png", ".gif", ".bmp", ".tiff", ".webp", - ".pdf", ".docx", ".xlsx", ".pptx", ".odt", - ".db", ".sqlite", ".mdb", -} - -# Max bytes read for keyword extraction from text files -READ_LIMIT = 8192 - -# Skip depth-unlimited system/junk paths -SKIP_PATTERNS = [ - r"[\\/]\$Recycle\.Bin[\\/]", - r"[\\/]System Volume Information[\\/]", - r"[\\/]Windows[\\/]", - r"[\\/]\.git[\\/]", - r"[\\/]__pycache__[\\/]", - r"[\\/]node_modules[\\/]", - r"[\\/]\.Trash", -] -_SKIP_RE = re.compile("|".join(SKIP_PATTERNS), re.IGNORECASE) - -# ─── keyword clusters (14-axis, matches substrate_git_index vocabulary) ─────── - -_CLUSTER_VOCAB: set[str] = { - "substrate", "foam", "soliton", "resonance", "tsm", "metafoam", - "graph", "dag", "node", "edge", "merkle", "hash", - "compute", "cpu", "gpu", "neural", "inference", "model", - "token", "omnitoken", "swap", "arbitrage", "mev", "defi", - "rule", "policy", "audit", "compliance", "legal", - "store", "index", "database", "sqlite", "manifest", - "power", "energy", "battery", "stirling", - "clock", "timing", "synchronize", - "comms", "transport", "udp", "ipv6", "i2p", "tunnel", - "material", "superconductor", "qchem", "crystal", - "compress", "codec", "huffman", "entropy", - "crypto", "sha256", "zk", "stark", "proof", - "data", "dataset", "corpus", "archive", - "test", "validation", "benchmark", -} - -_STOP: set[str] = { - "the", "and", "for", "that", "this", "with", "from", "are", - "was", "were", "have", "been", "will", "would", "could", "should", - "which", "their", "there", "these", "those", "what", "when", - "where", "how", "all", "any", "can", "not", "but", "also", -} - - -# ─── helpers ────────────────────────────────────────────────────────────────── - -def _sha256_file(path: Path, chunk: int = 1 << 20) -> str: - h = hashlib.sha256() - try: - with path.open("rb") as f: - while True: - block = f.read(chunk) - if not block: - break - h.update(block) - except (PermissionError, OSError): - return "" - return h.hexdigest() - - -def _should_skip(path: Path) -> bool: - return bool(_SKIP_RE.search(str(path))) - - -def _read_head(path: Path) -> str: - try: - with path.open("rb") as f: - raw = f.read(READ_LIMIT) - return raw.decode("utf-8", errors="replace") - except (PermissionError, OSError): - return "" - - -def _extract_keywords(text: str) -> dict[str, float]: - tokens = re.findall(r"\b[a-zA-Z][a-zA-Z_]{4,}\b", text.lower()) - counts: Counter[str] = Counter(t for t in tokens if t not in _STOP) - if not counts: - return {} - max_c = counts.most_common(1)[0][1] - weights: dict[str, float] = {} - for word, count in counts.most_common(30): - boost = 2.0 if word in _CLUSTER_VOCAB else 1.0 - score = min(1.0, (count / max_c) * boost * 0.85) - if score >= 0.10: - weights[word] = round(score, 3) - return weights - - -def _pkg_name(drive: str, path: Path) -> str: - try: - rel = path.relative_to(MOUNT_BASE / drive) - except ValueError: - rel = Path(path.name) - parts = [re.sub(r"[^a-z0-9]", "", p.lower())[:8] for p in rel.parts[:-1]] - stem = re.sub(r"[^a-z0-9]+", "_", path.stem.lower())[:40] - prefix = "-".join(p for p in parts if p)[:20] - tag = f"smb-{drive}-{prefix}-{stem}" if prefix else f"smb-{drive}-{stem}" - return tag[:80] - - -def _now_utc() -> str: - return datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") - - -# ─── per-file index ─────────────────────────────────────────────────────────── - -def index_file( - drive: str, - path: Path, - conn: sqlite3.Connection, - file_sha: str, - force: bool, - dry_run: bool, -) -> str: - """Index a single text file into substrate SQLite. Returns 'indexed'/'exists'/'skipped'/'dry'.""" - pkg = _pkg_name(drive, path) - version = "1.0.0" - - if not force: - row = conn.execute( - "SELECT pkg FROM packages WHERE pkg=? AND version=?", (pkg, version) - ).fetchone() - if row: - return "exists" - - text = _read_head(path) - if not text.strip(): - return "skipped" - - idea_weights = _extract_keywords(text) - if not idea_weights: - return "skipped" - - now = _now_utc() - concept_vec = _concept_vector_from_weights(idea_weights) - foam_score = round(sum(idea_weights.values()) / max(len(idea_weights), 1) * _PHI, 4) - description = derive_safe_description(path, text, max_len=200) - - concept_anchor = { - "domain": "data", - "concept": path.stem[:60], - "resolution": "FORMING", - } - tags = ["smb", drive, path.suffix.lstrip(".") or "bin"] - - if dry_run: - print(f" [dry] {pkg} foam={foam_score} axes={sum(1 for x in concept_vec if x > 0.01)}/14") - return "dry" - - _upsert_package(conn, { - "pkg": pkg, - "version": version, - "layer": "STORE", - "domain": "DATA", - "condition": "EXPERIMENTAL", - "stage": "INTAKE", - "source": "DATA", - "tier": "RESEARCH", - "module": path.stem[:40].upper().replace(" ", "_").replace("-", "_"), - "archetype": "SMB_NODE", - "tags": json.dumps(tags), - "description": description, - "files": json.dumps([str(path)]), - "depends": json.dumps([]), - "foam_score": foam_score, - "nd_point": json.dumps(concept_vec[:14]), - "sha256": file_sha, - "sealed_utc": now, - "visibility": "PRIVATE", - "model_status": "REFERENCE_ONLY", - "taint_status": "CLEAN", - "session_id": f"smb:{drive}:{path.relative_to(MOUNT_BASE / drive)}", - "idea_weights": json.dumps(idea_weights), - "extension_points": json.dumps([]), - "concept_vector": json.dumps(concept_vec), - "analog_map": json.dumps({}), - "concept_anchor": json.dumps(concept_anchor), - "attachment_meta": json.dumps({}), - "ingest_profile": json.dumps({ - "ingestor": "smb_probe", - "drive": drive, - "suffix": path.suffix.lower(), - "text_chars": len(text), - }), - "indexed_utc": now, - }) - return "indexed" - - -# ─── drive scanner ──────────────────────────────────────────────────────────── - -def scan_drive( - drive: str, - conn: sqlite3.Connection | None, - force: bool, - dry_run: bool, - hash_only: bool, -) -> dict[str, Any]: - """Scan one mounted drive. Returns summary dict.""" - mount = MOUNT_BASE / drive - if not mount.is_mount(): - print(f" [{drive}] NOT MOUNTED — skipping") - return {"drive": drive, "mounted": False} - - OUT_DIR.mkdir(parents=True, exist_ok=True) - manifest_path = OUT_DIR / f"{drive}_manifest.jsonl" - counts: Counter[str] = Counter() - total_bytes = 0 - manifest_lines: list[str] = [] - - print(f" [{drive}] scanning {mount} …") - for path in sorted(mount.rglob("*")): - if not path.is_file(): - continue - if _should_skip(path): - counts["skipped_system"] += 1 - continue - - suffix = path.suffix.lower() - try: - size = path.stat().st_size - except OSError: - counts["error"] += 1 - continue - - file_sha = _sha256_file(path) - if not file_sha: - counts["error"] += 1 - continue - - total_bytes += size - counts["hashed"] += 1 - - record: dict[str, Any] = { - "drive": drive, - "path": str(path.relative_to(MOUNT_BASE / drive)), - "name": path.name, - "suffix": suffix, - "size_bytes": size, - "sha256": file_sha, - "indexed_utc": _now_utc(), - } - manifest_lines.append(json.dumps(record)) - - # Keyword-index text files into SQLite - if not hash_only and conn is not None and suffix in TEXT_EXTENSIONS: - status = index_file(drive, path, conn, file_sha, force, dry_run) - counts[f"sqlite_{status}"] += 1 - - if counts["hashed"] % 500 == 0: - print(f" {counts['hashed']} files hashed …") - if conn and not dry_run: - conn.commit() - - # Write manifest - if not dry_run: - with manifest_path.open("w", encoding="utf-8") as f: - f.write("\n".join(manifest_lines)) - if manifest_lines: - f.write("\n") - print(f" manifest → {manifest_path} ({len(manifest_lines)} records)") - - mb = total_bytes / (1024 * 1024) - print( - f" [{drive}] done hashed={counts['hashed']} " - f"size={mb:.1f} MiB " - f"sqlite_indexed={counts.get('sqlite_indexed', 0)} " - f"sqlite_exists={counts.get('sqlite_exists', 0)} " - f"errors={counts.get('error', 0)}" - ) - return { - "drive": drive, - "mounted": True, - "total_files": counts["hashed"], - "total_bytes": total_bytes, - "manifest": str(manifest_path) if not dry_run else None, - "counts": dict(counts), - } - - -# ─── CLI ────────────────────────────────────────────────────────────────────── - -def main() -> None: - ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) - ap.add_argument("--drives", nargs="+", default=SMB_DRIVES, - metavar="DRIVE", help=f"Drives to scan (default: {' '.join(SMB_DRIVES)})") - ap.add_argument("--dry-run", action="store_true", - help="Hash files and show what would be indexed; don't write anything") - ap.add_argument("--force", action="store_true", - help="Re-index rows that already exist in the DB") - ap.add_argument("--hash-only", action="store_true", - help="Write JSONL manifests only; skip SQLite indexing") - ap.add_argument("--status", action="store_true", - help="Show SMB node counts in the index and exit") - ap.add_argument("--db", default=str(DB_PATH), metavar="PATH", - help="Path to substrate SQLite DB") - args = ap.parse_args() - - if args.status: - conn = _open_db(Path(args.db)) - total = conn.execute("SELECT COUNT(*) FROM packages").fetchone()[0] - smb = conn.execute( - "SELECT COUNT(*) FROM packages WHERE archetype='SMB_NODE'" - ).fetchone()[0] - drives_q = conn.execute( - "SELECT json_extract(ingest_profile,'$.drive'), COUNT(*) " - "FROM packages WHERE archetype='SMB_NODE' " - "GROUP BY json_extract(ingest_profile,'$.drive')" - ).fetchall() - conn.close() - print(f"Total substrate nodes : {total}") - print(f"SMB nodes : {smb}") - for drive, count in sorted(drives_q): - print(f" {drive or '?':<12} {count}") - return - - conn: sqlite3.Connection | None = None - if not args.hash_only and not args.dry_run: - conn = _open_db(Path(args.db)) - - results: list[dict[str, Any]] = [] - for drive in args.drives: - results.append(scan_drive(drive, conn, args.force, args.dry_run, args.hash_only)) - - if conn and not args.dry_run: - conn.commit() - conn.close() - - mounted = [r for r in results if r.get("mounted")] - total_files = sum(r.get("total_files", 0) for r in mounted) - total_bytes = sum(r.get("total_bytes", 0) for r in mounted) - print( - f"\nSummary: {len(mounted)}/{len(results)} drives mounted " - f"total={total_files} files {total_bytes / (1024**3):.2f} GiB" - ) - - -if __name__ == "__main__": - main()